@article {pmid40656455,
year = {2025},
author = {Komosar, M and Tamburro, G and Graichen, U and Comani, S and Haueisen, J},
title = {Combination of spatial and temporal de-noising and artifact reduction techniques in multi-channel dry EEG.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1576954},
pmid = {40656455},
issn = {1662-4548},
abstract = {INTRODUCTION: Dry electroencephalography (EEG) allows for recording cortical activity in ecological scenarios with a high channel count, but it is often more prone to artifacts as compared to gel-based EEG. Spatial harmonic analysis (SPHARA) and ICA-based methods (Fingerprint and ARCI) have been separately used in previous studies for dry EEG de-noising and physiological artifact reduction. Here, we investigate if the combination of these techniques further improves EEG signal quality. For this purpose, we also introduced an improved version of SPHARA.<br><br>METHODS: Dry 64-channel EEG was recorded from 11 healthy volunteers during a motor performance paradigm (left and right hand, feet, and tongue movements). EEG signals were denoised separately using Fingerprint + ARCI, SPHARA, a combination of these two methods, and a combination of these two methods including an improved SPHARA version. The improved version of SPHARA includes an additional zeroing of artifactual jumps in single channels before application of SPHARA. The EEG signal quality after application of each denoising method was calculated by means of standard deviation (SD), signal to noise ratio (SNR), and root mean square deviation (RMSD), and a generalized linear mixed effects (GLME) model was used to identify significant changes of these parameters and quantify the changes in the EEG signal quality.<br><br>RESULTS: The grand average values of SD improved from 9.76 (reference preprocessed EEG) to 8.28, 7.91, 6.72, and 6.15 &#x3bc;V for Fingerprint + ARCI, SPHARA, Fingerprint + ARCI + SPHARA, and Fingerprint + ARCI + improved SPHARA, respectively. Similarly, the RMSD values improved from 4.65 to 4.82, 6.32, and 6.90 &#x3bc;V, and the SNR values changed from 2.31 to 1.55, 4.08, and 5.56 dB.<br><br>DISCUSSION: Our results demonstrate the different performance aspects of Fingerprint + ARCI and SPHARA, artifact reduction and de-noising techniques that complement each other. We also demonstrated that a combination of these techniques yields superior performance in the reduction of artifacts and noise in dry EEG recordings, which can be extended to infant EEG and adult MEG applications.},
}
@article {pmid40655558,
year = {2025},
author = {Alonso-Vázquez, D and Mendoza-Montoya, O and Caraza, R and Martinez, HR and Antelis, JM},
title = {From pronounced to imagined: improving speech decoding with multi-condition EEG data.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1583428},
pmid = {40655558},
issn = {1662-5196},
abstract = {INTRODUCTION: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feedback. Here, we investigated whether incorporating EEG data from overt (pronounced) speech could enhance imagined speech classification.<br><br>METHODS: Our approach systematically compares four classification scenarios by modifying the training dataset: intra-subject (using only imagined speech, combining overt and imagined speech, and using only overt speech) and multi-subject (combining overt speech data from different participants with the imagined speech of the target participant). We implemented all scenarios using the convolutional neural network EEGNet. To this end, twenty-four healthy participants pronounced and imagined five Spanish words.<br><br>RESULTS: In binary word-pair classifications, combining overt and imagined speech data in the intra-subject scenario led to accuracy improvements of 3%-5.17% in four out of 10 word pairs, compared to training with imagined speech only. Although the highest individual accuracy (95%) was achieved with imagined speech alone, the inclusion of overt speech data allowed more participants to surpass 70% accuracy, increasing from 10 (imagined only) to 15 participants. In the intra-subject multi-class scenario, combining overt and imagined speech did not yield statistically significant improvements over using imagined speech exclusively.<br><br>DISCUSSION: Finally, we observed that features such as word length, phonological complexity, and frequency of use contributed to higher discriminability between certain imagined word pairs. These findings suggest that incorporating overt speech data can improve imagined speech decoding in individualized models, offering a feasible strategy to support the early adoption of brain-computer interfaces before speech deterioration occurs in individuals with motor neuron diseases.},
}
@article {pmid40654838,
year = {2025},
author = {Lin, X and Zhang, X and Wang, Z and Chen, J and Lee, J and Lee, AJ and Yang, H and Remy, A and Shen, H and He, Y and Zhao, H and Zhang, X and Wang, W and Aljović, A and Vlassak, JJ and Lu, N and Liu, J},
title = {Plastic-elastomer heterostructure for robust flexible brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.29.651325},
pmid = {40654838},
issn = {2692-8205},
abstract = {Electronics for neural signal recording must be robust across multiple and deep brain regions while preserving tissue-level flexibility to ensure stable tracking over months or years. However, existing electronics cannot simultaneously achieve robustness and tissue-level flexibility, limiting their potential for customizable and scalable neuroscience research and clinical applications. Here, we introduce FlexiSoft, an electronic platform based on a plastic-elastomer heterostructure that uniquely integrates mechanical robustness and tissue-level flexibility. Compared to conventional flexible electronics of similar thickness, the FlexiSoft platform demonstrates an order-of- magnitude improvement in both mechanical robustness (critical energy release rate) and flexibility (flexural rigidity). Leveraging these mechanical advantages, we developed FlexiSoft probe for robust implantation, demonstrated by its ability to withstand repeated insertion and removal, as well as to reach centimeter-scale depths comparable to those in the human brain. The platform enables long-term recording from the same neurons across the hippocampus (HPC) and primary motor cortex (M1) during a months-long motor learning task, thereby revealing long-term dynamic changes in neuronal firing patterns. Additionally, FlexiSoft's unique robustness and flexibility enable curved implantation routes, opening new directions of customizable implantation pathways. In summary, we present FlexiSoft as a novel, robust, and tissue-level flexible heterostructure electronics platform that advances flexible brain-computer interfaces (BCIs) with strong translational potential for neuroscience and clinical applications.},
}
@article {pmid40653584,
year = {2025},
author = {Monteiro, RV and Amarante, JEV and Bona, VS and Lins, RBE and Lopes, GC and Blackburn, M and Swanson, C and Latorre, JA and De Souza, GM},
title = {Microshear bond strength of conventional and bioactive restorative materials to irradiated and non-irradiated dentin: an in vitro study.},
journal = {Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer},
volume = {33},
number = {8},
pages = {688},
pmid = {40653584},
issn = {1433-7339},
mesh = {Humans ; *Dentin/radiation effects/ultrastructure ; *Composite Resins/chemistry ; *Dental Bonding/methods ; Materials Testing ; In Vitro Techniques ; Shear Strength ; Microscopy, Electron, Scanning ; Resin Cements/chemistry ; Dentin-Bonding Agents/chemistry ; Surface Properties ; Time Factors ; },
abstract = {PURPOSE: To evaluate the effect of conventional and bioactive restorative materials on bond strength to control (non-irradiated) and irradiated dentin.<br><br>METHODS: Human dentin fragments (240) were polished and divided into non-irradiated dentin (NI; n&#x2009;=&#x2009;120) and irradiated dentin (ID; n&#x2009;=&#x2009;120). ID specimens received 70&#xa0;Gy irradiation (2&#xa0;Gy/fraction, 5&#xa0;days/week for 7&#xa0;weeks). All dentin surfaces were bonded to restorative materials with Scotchbond universal adhesive in self-etching mode. Microshear bond strength cylinders were built on the bonded surface according to the restorative material (4 subgroups, n&#x2009;=&#x2009;30): conventional resin composite (CC-Filtek Z250) and three bioactive restorative composites (BCI-Activa BioActive-Restorative; BCII-Beautiful II; BCIII-Predicta Bulk). Specimens were stored in water at 37&#xa0;&#xb0;C for 24&#xa0;h or 30&#xa0;days and subjected to microshear bond strength test. The data was analyzed using two-way ANOVA and Tukey's post-hoc test (&#x237a;&#x2009;<&#x2009;0.05). The morphological surface of both NI and ID dentin (n&#x2009;=&#x2009;3) was analyzed using Scanning Electron Microscopy (SEM).<br><br>RESULTS: Two-way ANOVA revealed a significant effect of the Time/Radiation (p&#x2009;<&#x2009;0.001). Restorative material (p&#x2009;=&#x2009;0.191) and the interaction Time/Radiation*Restorative material (p&#x2009;=&#x2009;0.169) were not significant. Irradiation decreased the bond strength of CC specimens at both 24&#xa0;h (p&#x2009;<&#x2009;0.001) and 30&#xa0;days (p&#x2009;<&#x2009;0.001). None of the bioactive materials were significantly affected by irradiation and storage time. The SEM analysis revealed morphological changes in the ID specimens.<br><br>CONCLUSION: Ionizing radiation-induced morphological changes in the dentin surface. These changes negatively affected the conventional resin composite bond strengths to dentin. However, these morphological alterations did not affect the bond strength of the bioactive restorative materials.},
}
@article {pmid40649800,
year = {2025},
author = {Jaszczuk, P and Bratelj, D and Capone, C and Rudnick, M and Pötzel, T and Verma, RK and Fiechter, M},
title = {Advances in Neuromodulation and Digital Brain-Spinal Cord Interfaces for Spinal Cord Injury.},
journal = {International journal of molecular sciences},
volume = {26},
number = {13},
pages = {},
pmid = {40649800},
issn = {1422-0067},
mesh = {Humans ; *Spinal Cord Injuries/therapy/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Spinal Cord Stimulation/methods ; Spinal Cord/physiopathology ; Animals ; },
abstract = {Spinal cord injury (SCI) results in a significant loss of motor, sensory, and autonomic function, imposing substantial biosocial and economic burdens. Traditional approaches, such as stem cell therapy and immune modulation, have faced translational challenges, whereas neuromodulation and digital brain-spinal cord interfaces combining brain-computer interface (BCI) technology and epidural spinal cord stimulation (ESCS) to create brain-spine interfaces (BSIs) offer promising alternatives by leveraging residual neural pathways to restore physiological function. This review examines recent advancements in neuromodulation, focusing on the future translation of clinical trial data to clinical practice. We address key considerations, including scalability, patient selection, surgical techniques, postoperative rehabilitation, and ethical implications. By integrating interdisciplinary collaboration, standardized protocols, and patient-centered design, neuromodulation has the potential to revolutionize SCI rehabilitation, reducing long-term disability and enhancing quality of life globally.},
}
@article {pmid40648603,
year = {2025},
author = {Bonanno, M and Saracino, B and Ciancarelli, I and Panza, G and Manuli, A and Morone, G and Calabrò, RS},
title = {Assistive Technologies for Individuals with a Disability from a Neurological Condition: A Narrative Review on the Multimodal Integration.},
journal = {Healthcare (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {40648603},
issn = {2227-9032},
abstract = {BACKGROUND/OBJECTIVES: Neurological disorders often result in a broad spectrum of disabilities that impact mobility, communication, cognition, and sensory processing, leading to significant limitations in independence and quality of life. Assistive technologies (ATs) offer tools to compensate for these impairments, support daily living, and improve quality of life. The World Health Organization encourages the adoption and diffusion of effective assistive technology (AT). This narrative review aims to explore the integration, benefits, and challenges of assistive technologies in individuals with neurological disabilities, focusing on their role across mobility, communication, cognitive, and sensory domains.<br><br>METHODS: A narrative approach was adopted by reviewing relevant studies published between 2014 and 2024. Literature was sourced from PubMed and Scopus using specific keyword combinations related to assistive technology and neurological disorders.<br><br>RESULTS: Findings highlight the potential of ATs, ranging from traditional aids to intelligent systems like brain-computer interfaces and AI-driven devices, to enhance autonomy, communication, and quality of life. However, significant barriers remain, including usability issues, training requirements, accessibility disparities, limited user involvement in design, and a low diffusion of a health technology assessment approach.<br><br>CONCLUSIONS: Future directions emphasize the need for multidimensional, user-centered solutions that integrate personalization through machine learning and artificial intelligence to ensure long-term adoption and efficacy. For instance, combining brain-computer interfaces (BCIs) with virtual reality (VR) using machine learning algorithms could help monitor cognitive load in real time. Similarly, ATs driven by artificial intelligence technology could be useful to dynamically respond to users' physiological and behavioral data to optimize support in daily tasks.},
}
@article {pmid40648390,
year = {2025},
author = {Jezierska, K and Turoń-Skrzypińska, A and Rotter, I and Syroka, A and Łukowiak, M and Rawojć, K and Rawojć, P and Rył, A},
title = {Latency and Amplitude of Cortical Activation in Interactive vs. Passive Tasks: An fNIRS Study Using the NefroBall System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648390},
issn = {1424-8220},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Movement/physiology ; *Prefrontal Cortex/physiology ; *Spectroscopy, Near-Infrared/methods ; *Visual Cortex/physiology ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) allows non-invasive assessment of cortical activity during naturalistic tasks. This study aimed to compare cortical activation dynamics-specifically the latency (tmax) and amplitude (ΔoxyHb) of oxygenated haemoglobin changes-in passive observation and an interactive task using the Nefroball system. A total of 117 healthy adults performed two tasks involving rhythmic hand movements: a passive protocol and an interactive game-controlled condition. fNIRS recorded signals from the visual, parietal, motor, and prefrontal cortices of the left hemisphere. The Mann-Whitney test revealed significantly shorter tmax in all areas during the interactive task, suggesting faster recruitment of cortical networks. ΔoxyHb amplitude was significantly higher only in the visual cortex during the interactive task, indicating increased visual processing demand. No significant ΔoxyHb differences were observed in the motor, prefrontal, or parietal cortices. Weak but significant positive correlations were found between tmax and ΔoxyHb in the motor and prefrontal regions, but only in the passive condition. These findings support the notion that interactive tasks elicit faster, though not necessarily stronger, cortical responses. The results have potential implications for designing rehabilitation protocols and brain-computer interfaces involving visual-motor integration.},
}
@article {pmid40648293,
year = {2025},
author = {Zhang, J and Zhao, D and Li, Y and Ming, G and Pei, W},
title = {Four-Dimensional Adjustable Electroencephalography Cap for Solid-Gel Electrode.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
pmid = {40648293},
issn = {1424-8220},
support = {62401325//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; Adult ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Male ; Head ; Equipment Design ; Female ; },
abstract = {Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user's head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain-computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life.},
}
@article {pmid40648241,
year = {2025},
author = {Carìa, A},
title = {Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133987},
pmid = {40648241},
issn = {1424-8220},
support = {no number available//5xMille Unitn/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Language ; Artificial Intelligence ; *Communication ; Brain/physiology ; Deep Learning ; Electroencephalography ; Large Language Models ; },
abstract = {Integration of advanced artificial intelligence with neurotechnology offers transformative potential for assistive communication. This perspective article examines the emerging convergence between non-invasive brain-computer interface (BCI) spellers and large language models (LLMs), with a focus on predictive communication for individuals with motor or language impairments. First, I will review the evolution of language models-from early rule-based systems to contemporary deep learning architectures-and their role in enhancing predictive writing. Second, I will survey existing implementations of BCI spellers that incorporate language modeling and highlight recent pilot studies exploring the integration of LLMs into BCI. Third, I will examine how, despite advancements in typing speed, accuracy, and user adaptability, the fusion of LLMs and BCI spellers still faces key challenges such as real-time processing, robustness to noise, and the integration of neural decoding outputs with probabilistic language generation frameworks. Finally, I will discuss how fully integrating LLMs with BCI technology could substantially improve the speed and usability of BCI-mediated communication, offering a path toward more intuitive, adaptive, and effective neurotechnological solutions for both clinical and non-clinical users.},
}
@article {pmid40648159,
year = {2025},
author = {Ionita, S and Coman, DA},
title = {Narrowband Theta Investigations for Detecting Cognitive Mental Load.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {13},
pages = {},
doi = {10.3390/s25133902},
pmid = {40648159},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Theta Rhythm/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; },
abstract = {The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function-specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified.},
}
@article {pmid40646750,
year = {2025},
author = {Li, S and Tang, Z and Li, M and Yang, L and Shang, Z},
title = {Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region.},
journal = {Animals : an open access journal from MDPI},
volume = {15},
number = {13},
pages = {},
pmid = {40646750},
issn = {2076-2615},
support = {62301496//the National Natural Science Foundation of China/ ; GZC20232447//the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; 252102311095//the Key Scientific and Technological Projects of Henan Province/ ; 252102210008//the Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.},
}
@article {pmid40645213,
year = {2025},
author = {Meng, L and Zhao, H and Dong, M and Wang, Q and Shi, Y and Wang, D and Zhu, X and Xu, R and Ming, D},
title = {Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeeca},
pmid = {40645213},
issn = {1741-2552},
abstract = {OBJECTIVE: The flexibility of cognitive resource allocation is deteriorated due to aging and neurological degenerative diseases, such as Parkinson's disease (PD). Dual task performance reflects a subject's ability to allocate cognitive resources, and the investigation of cortical activation changes during dual tasking can provide a deep insight into the reallocation of neural resources. However, the cortical changes induced by increased cognitive task difficulty during dual tasking with changes in behavioral outcomes have not been explored in PD and older adults.<br><br>APPROACH: We designed a novel dual task paradigm comprising of balance maintenance and visual working memory (VWM) task to assess cognitive-motor interaction. Nineteen early-stage PD, 13 age-matched older adults (OA) and 15 young adults (YA) completed 4 blocks of 25 trials each for two VWM difficulty levels (2 squares and 4 squares). Behavioral performance, postural stability, and 32-channel EEG were recorded. One-way ANOVA was used to examine group and task effects while Spearman's correlation analysis assessed associations between EEG changes and behavioral performance.<br><br>MAIN RESULTS: Both PD and OA groups exhibited significantly longer reaction time, reduced postural stability, prolonged P300 latency and less alpha event related desynchronization (ERD) enhancement in response to the increased VWM task difficulty. Moreover, PD patients demonstrated significantly alpha ERD reduction at FC3, C3 and P4 in the 500-700ms compared to the OAs. The ERD changes at the central and parietal regions were found to be significantly related to postural stability and clinical scores, respectively.<br><br>SIGNIFICANCE: The results provide novel evidence that cortical EEG responses during dual tasking may reflect deficits in attention resource reallocation and reduced cognitive flexibility in PD and OA groups. These observed cortical changes with increasing cognitive task difficulty are correlated with postural instability, highlighting their potential as neurophysiological biomarkers for dual-task dysfunction.},
}
@article {pmid40645212,
year = {2025},
author = {Li, Y and Zhao, Z and Liu, J and Peng, Y and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based speech imagery decoding by dynamic hypergraph learning within projected and selected feature subspaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeec8},
pmid = {40645212},
issn = {1741-2552},
abstract = {OBJECT: Speech imagery is a nascent paradigm that is receiving widespread attention in current Brain-Computer Interface (BCI) research. By collecting the electroencephalogram (EEG) data generated when imagining the pronunciation of a sentence or word in human mind, machine learning methods are used to decode the intention that the subject wants to express. Among existing decoding methods, graph is often used as an effective tool to model the data structure; however, in the field of BCI research, the correlations between EEG samples may not be fully characterized by simple pairwise relationships. Therefore, this paper attempts to employ a more effective data structure to model EEG data.<br><br>APPROACH: In this paper, we introduce hypergraph to describe the high-order correlations between samples by viewing feature vectors extracted from each sample as vertices and then connecting them through hyperedges. We also dynamically update the weights of hyperedges, the weights of vertices and the structure of the hypergraph in two transformed subspaces, i.e., projected and feature-weighted subspaces. Accordingly, two dynamic hypergraph learning models, i.e., dynamic hypergraph semi-supervised learning within projected subspace (DHSLP) and dynamic hypergraph semi-supervised learning within selected feature subspace (DHSLF), are proposed for speech imagery decoding.<br><br>MAIN RESULTS: To validate the proposed models, we performed a series of experiments on two EEG datasets. The obtained results demonstrated that both DHSLP and DHSLF have statistically significant improvements in decoding imagined speech intentions to existing studies. Specifically, DHSLP achieved accuracies of 78.40% and 66.64% on the two datasets, while DHSLF achieved accuracies of 71.07% and 63.94%.<br><br>SIGNIFICANCE: Our study indicates the effectiveness of the learned hypergraphs in characterizing the underlying semantic information of imagined contents; besides, interpretable results on quantitatively exploring the discriminative EEG channels in speech imagery decoding are obtained, which lay the foundation for further exploration of the physiological mechanisms during speech imagery.},
}
@article {pmid40644990,
year = {2025},
author = {Cai, G and Chen, Y and Yang, B and Yang, Y and Ma, T and Wang, Y},
title = {CGNet: A Complex-valued Graph Network for jointly learning amplitude-phase information in EEG-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107795},
doi = {10.1016/j.neunet.2025.107795},
pmid = {40644990},
issn = {1879-2782},
abstract = {The synergy between amplitude and phase in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provides comprehensive and essential insights into neural oscillatory processes. However, constrained by real-valued computation paradigms, most deep learning methods have to process amplitude and phase independently, neglecting their crucial interaction mechanisms. To address this issue, we construct a Complex-valued Graph Network (CGNet) to capture comprehensive information from EEG signals, where both amplitude and phase information are encoded into the complex-valued representation. Specifically, we design a two-scale complex-valued convolutional network to learn local spatio-temporal information, develop a spatial attention module to enhance spatial information learning, and formulate a dynamic graph convolution to capture global temporal dependencies. Furthermore, we extend CGNet to Filter-Band CGNet (FBCGNet), enhancing the model's adaptability to broadband EEG data. Applied to motor imagery and execution BCI tasks, CGNet achieves state-of-the-art classification performance while maintaining computational efficiency comparable to other advanced algorithms. Notably, FBCGNet further improves CGNet's performance. Visualization results show that CGNet can identify the key spatio-temporal information consistent with paradigm principles. In addition, compared with using amplitude or phase alone, CGNet can capture more comprehensive task-related neural activities, thereby showing higher classification performance. CGNet is a promising tool for mining amplitude-phase information and offering more comprehensive neural decoding in EEG-based BCIs.},
}
@article {pmid40644885,
year = {2025},
author = {Al-Hadithy, SS and Abdalkafor, AS and Al-Khateeb, B},
title = {Emotion recognition in EEG Signals: Deep and machine learning approaches, challenges, and future directions.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110713},
doi = {10.1016/j.compbiomed.2025.110713},
pmid = {40644885},
issn = {1879-0534},
abstract = {A crucial part of brain-computer interfaces is the use of electroencephalogram (EEG) signals for human emotion identification, which analyzes patterns of brain activity to determine the emotional state. This field of study is becoming increasingly important for developing advanced applications that enhance brain machine interaction and improve brain health assessment systems. However, EEG signal analysis faces significant challenges due to their subject-specific nature, high noise levels, and the scarcity of high-quality labeled data, which collectively limit model generalizability and complicate signal analysis. Traditional approaches have employed handcrafted features with Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests (RF) for EEG feature extraction and classification. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable automatic feature learning from raw data to extract temporal, spatial, and spectral properties. The study employs a literature review approach and the analysis of the popular datasets (e.g., DEAP, SEED, AMIGOS). Despite technological advances, the fundamental challenges of noisy subject variability, and limited labeled data persist, requiring future research to focus on improving model robustness, scalability, and interpretability while addressing current limitations.},
}
@article {pmid40644284,
year = {2025},
author = {Petrich, LC and Neumann, S and Pilarski, PM and Fyshe, A},
title = {Neural Network Sparsity in Brain-Body-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1-8},
doi = {10.1109/ICORR66766.2025.11062950},
pmid = {40644284},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-body-machine interfaces acquire, process, and translate brain signals for individuals with severe motor impairments to communicate and control the assistive technology that supports their daily life activities. Electroencephalography (EEG) is a standard approach for acquiring such brain signals due to its low cost and high temporal resolution. EEG signals can be thought of as a proxy for the user's intent. One established method for translating this intent into inferences and actions are neural networks. However, densely connected neural networks can be computationally expensive-a problem for real-time, deployed brain-body-machine interface systems. In this paper we investigate the use of sparsity in neural networks for EEG-based motor classification, with the goal of reducing the number of neuronal connections without sacrificing a system's performance. We compare two sparsity-inducing algorithms, weight pruning and sparse evolutionary training, with a dense neural network under three experimental conditions. Overall, our results show that sparse neural networks can achieve higher performance accuracy and generalization than their densely-connected counterparts for an EEG-based classification task. We found that sparse evolutionary training achieves the highest and most stable performance across all experiments. Introducing sparsity into the network is a viable option for efficient EEG-based control, with promising applications in a range of related rehabilitation and assistive technologies. This brings us closer to helping individuals with severe motor impairments reclaim independence through more computationally realizable methods of interacting with their technology and the world around them.},
}
@article {pmid40644274,
year = {2025},
author = {Patarini, F and Maronati, C and Manuello, J and Cuturi, LF and Monti, M and Savina, G and Ferrari, E and Iarrobino, I and Iani, C and Rubichi, S and Ciaramidaro, A and Astolfi, L and Cavallo, A and Toppi, J},
title = {Handling Kinematic Features in an Action Observation Task to Optimize a Brain Computer Interface-Based Rehabilitation Training.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1078-1082},
doi = {10.1109/ICORR66766.2025.11062958},
pmid = {40644274},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Electroencephalography ; *Stroke Rehabilitation ; Male ; Female ; Adult ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) technology promotes neuroplasticity mechanisms which favor the functional motor recovery in stroke survivors. Patients' residual motor abilities determine the intention, which, once detected by the BCI is fed back via an effector. The majority of studies aimed at optimizing the feedback branch, but not enough attention has been posed to supporting patient in the movement intention that should be detected by the BCI system. The inclusion of a visual motor priming (observed action before a task) in a BCI could promote the retrieval of movements from the patient's own impaired motor repertoire. None of the motor priming proposed until so far have been tailored to the patients' residual motor ability, although it is well known that the human brain recognizes movements closer from a kinematic perspective to its own repertoire more easily. The aim of this study was to investigate how individual motor style in an action observation task would affect the observer's cortical excitability. EEG signals were recorded from 10 individuals during an action observation task where different levels of motor distance between the observer and the agent were modulated. EEG-based group spectral activations shown an involvement of bilateral parietal areas in beta band in case of more unpredictable kinematics. The results would open the way for the design of a kinematic-based visual motor priming to be embedded in a BCI system for post-stroke rehabilitation.},
}
@article {pmid40644240,
year = {2025},
author = {Gonzalez-Cely, AX and Soekadar, SR and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {76-81},
doi = {10.1109/ICORR66766.2025.11063181},
pmid = {40644240},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Male ; Adult ; Electroencephalography ; *Exercise Test ; *Imagination/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Lower-limb rehabilitation traditionally relies on physical therapy, but motor imagery(MI)-based brain- computer interfaces (BCIs) promise to facilitate neuroplasticity and adaptation by closing the perception-action cycle. Here, we present a BCI system based on kinesthetic MI that enables treadmill velocity control, establishing a closed-loop feedback mechanism. The system was tested in a healthy participant translating mu (8-12 Hz) and high-beta (18-24 Hz) rhythm modulation into treadmill velocity control commands. Feature extraction techniques, including power spectral density (PSD) and Riemannian geometry (RG), were used for MI- and resting state classification. Additionally, Logistic Regression (LR), k-nearest neighbors, support vector machine, and Linear Discriminant Analysis (LDA) were employed and optimized for accuracy. The results showed increased mu and highbeta activation modulation at the vertex. The online RG+LDA classifier achieving an average accuracy of 72%, while the pseudo-online RG+LR reached 95%. The study's novelty lies in combining kinesthetic MI with treadmill control and employing RG for feature extraction, demonstrating its potential to enhance cortical modulation during rehabilitation. Future work will have to validate the system in poststroke patients for clinical applicability.},
}
@article {pmid40644193,
year = {2025},
author = {Mannan, MMN and Lloyd, DG and Pizzolato, C},
title = {Optimising Continuous Control of Real-Time Brain-Computer Interfaces Through Trial Length and Feedback Update Interval Selection.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {284-288},
doi = {10.1109/ICORR66766.2025.11063010},
pmid = {40644193},
issn = {1945-7901},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer promising potential to aid neurorehabilitation by transforming motor imagery (MI) signals into control commands, bypassing damaged neural pathways to support motor recovery. However, a key challenge in BCI research is achieving an effective balance between classification accuracy and real-time responsiveness, as both are critical for enhancing user embodiment and control for neurorehabilitation outcomes. This study investigates the impact of trial length and feedback update interval (FUI) on classification accuracy in an MI-based BCI system. Using EEG data from five subjects across 50 sessions, we evaluated classification performance across various trial length (1-5 seconds) and FUI (0.2-1 second) configurations. Results revealed that both trial length and FUI significantly influenced classification accuracy, with longer trial length (4-5 seconds) and FUI (0.4-1 seconds) yielding the highest accuracy. However, post-hoc analyses indicated a saturation effect, with no significant differences in the accuracy for these parameters. These findings underscore the importance of balancing signal stability with responsiveness for optimal BCI performance, providing insights into parameter settings that can enhance BCI usability in neurorehabilitation. Future work may explore adaptive approaches to dynamically adjust these parameters based on real-time requirements, potentially offering a more responsive and efficient BCI for clinical rehabilitation.},
}
@article {pmid40644184,
year = {2025},
author = {Koellner, J and Wimpff, M and Gizzi, L and Vujaklija, I and Yang, B},
title = {Exploring Cortical Responses to Blood Flow Restriction through Deep Learning.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {546-552},
doi = {10.1109/ICORR66766.2025.11063023},
pmid = {40644184},
issn = {1945-7901},
mesh = {Humans ; *Deep Learning ; Magnetoencephalography ; Male ; Adult ; Female ; Brain-Computer Interfaces ; *Cerebral Cortex/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Resistance Training ; },
abstract = {Blood flow restriction (BFR) training, which combines low-intensity resistance exercises with restricted blood flow, is effective in promoting muscle hypertrophy and strength. However, its impact on cortical activity remains largely unexplored, presenting an opportunity to investigate neural mechanisms using brain-computer interfaces (BCIs). Deep learning (DL)-based BCIs, with their large capacity for decoding complex brain signals, offer a promising avenue for such exploration. This study utilized magnetoencephalography (MEG) to analyze cortical responses in six subjects across three conditions-before, during, and after BFR. After preprocessing steps, such as data standardization and Euclidean-space alignment to optimize performance, the BaseNet architecture was utilized to classify the data. The models were tested using within-subject, cross-subject, and cross-time data splits. The results revealed classification accuracy well above 90% for individual subjects, indicating that cortical responses to BFR are detectable on a personal level. However, cross-subject models achieved only chance-level accuracy (33%), highlighting significant variability between individuals. Cross-time models showed better performance, with accuracy exceeding 50%. These findings suggest that while BFR elicits distinct cortical activity patterns, these responses are highly individualized, presenting challenges for generalization.},
}
@article {pmid40644160,
year = {2025},
author = {Toppi, J and Savina, G and Colamarino, E and De Seta, V and Patarini, F and Cincotti, F and Pichiorri, F and Mattia, D},
title = {Hybrid Brain Computer Interface-Based Rehabilitation Addressing Post-Stroke Maladaptive Movement Patterns.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {431-436},
doi = {10.1109/ICORR66766.2025.11062988},
pmid = {40644160},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; Aged ; Movement/physiology ; Adult ; Stroke/physiopathology ; Electromyography ; },
abstract = {Hybrid Brain-Computer Interfaces (hBCI) integrate brain and muscle signals to enhance motor rehabilitation of stroke survivors, by closing the loop between the lesioned brain and the paretic limb. To date, little attention has been devoted to their potential efficacy in managing the maladaptive movement patterns that afflict post-stroke motor outcome (unwanted abnormal co-contrations, spasticity). This study proposes a comparison of Cortico-Muscular Coherence (CMC) patterns assessed in stroke patients before and after a 1-month rehabilitation intervention based on a hBCI-controlled Functional Electrical Stimulation (FES) treatment, which included a module to monitor non-physiological movement patterns. Results demonstrated the efficacy of this type of assistive technology for post-stroke rehabilitation, addressing patient-tailored interventions able to reduce the maladaptive mechanisms.},
}
@article {pmid40644144,
year = {2025},
author = {Bastos-Filho, T and Gonzalez-Cely, AX and Mehrpour, S and Souza, F and Villa-Parra, AC and Cabral, F},
title = {Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1203-1208},
doi = {10.1109/ICORR66766.2025.11063073},
pmid = {40644144},
issn = {1945-7901},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Neurofeedback/methods ; Male ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Chronic Disease ; Electromyography ; Middle Aged ; Stroke/physiopathology ; },
abstract = {This work presents the application of a rehabilitation protocol using a novel Non-Invasive Brain Stimulation (NIBS) technique, called cerebrospinal Direct Current Stimulation (csDCS), together with the use of a Brain-Computer Interface (BCI) based on Motor Imagery (MI) with Neurofeedback (NFB), and applying Functional Electrical Stimulation (FES) plus the use of a pedal exerciser. This protocol uses the concept of Alternating Treatment Design (ATD), in which a chronic post-stroke subject is submitted to these techniques to recover his left hand and leg movements. The rehabilitation progress was verified through metrics, such as Fugl Meyer Assessment (FMA), Functional Independence Measure (FIM), Ashworth Scale, Muscle Strength Grading (MSG), and surface Electromyography (sEMG). Results from these metrics include a 41% gain in hand function recovery, a 5% gain in performance in motor and cognitive/social domains, and a 50% improvement in both wrist extensor muscle strength and finger extensor muscle strength. In addition, there was a 17% gain of Maximum Voluntary Contraction (MVC) for the tibialis anterior muscle of the patient's left leg. On the other hand, there was a worsening in some values of EMG, probably due to the participant having received application of botulinum toxin in his hand.},
}
@article {pmid40644135,
year = {2025},
author = {Sun, Q and Merino, EC and Yang, L and Faes, A and Van Hulle, MM},
title = {On the Impact of Proprioception in EEG Representations and Decoding During Human-Hand Exoskeleton Interaction.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {186-192},
doi = {10.1109/ICORR66766.2025.11063039},
pmid = {40644135},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Proprioception/physiology ; Male ; *Exoskeleton Device ; Female ; Adult ; Brain-Computer Interfaces ; Young Adult ; *Hand/physiology ; Movement/physiology ; Fingers/physiology ; },
abstract = {Controlling a hand exoskeleton based on electroencephalogram (EEG)-based brain-computer interfacing (BCI) holds promise for human motor augmentation and neurore-habilitation. To achieve natural control, a critical step is to understand the impact of proprioception provided by the exoskeleton during interaction. In this study, we aim to approach the goal by quantifying EEG representations and BCI performance. We monitored 25 healthy subjects' full-scalp EEG while performing different finger movement tasks with a cable-driven hand exoskeleton. Each task involves three movement modalities, i.e., imagined (IM), passive (PM), and congruent imagined and passive (IPM) finger flexion. We found that alpha (8 - 13 Hz) and beta (13 - 30 Hz) band desynchronization in the sensorimotor area was significantly stronger for PM and IPM tasks compared to IM, with no significant difference between PM and IPM. Using machine learning models, we achieved a high accuracy in classifying exoskeleton-assisted movements from the rest condition (IPM vs. REST: 0.80 ± 0.07, PM vs. REST: 0.72 ± 0.10), with the IPM modality returning the highest accuracy. However, distinguishing between IPM and PM yielded only 0.61 ± 0.09, significantly lower than the condition of intention detection without the exoskeleton (IM vs. REST: 0.73 ± 0.08). Our findings suggest that sensorimotor EEG activity can track proprioceptive feedback induced by the hand exoskeleton. While this feedback is pronounced and distinguishable, detecting motor intention during exoskeleton movement remains highly challenging. This highlights the need for advanced decoders and control strategies for the future development of continuous BCI-actuated hand exoskeletons.},
}
@article {pmid40644105,
year = {2025},
author = {Shevchenko, O and Yeremeieva, S and Laschowski, B},
title = {Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {222-227},
doi = {10.1109/ICORR66766.2025.11063033},
pmid = {40644105},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Humans ; *Algorithms ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.},
}
@article {pmid40644100,
year = {2025},
author = {Feng, Z and Kakkos, I and Matsopoulos, GK and Guan, C and Sun, Y},
title = {Explaining E/MEG Source Imaging and Beyond: An Updated Review.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3588350},
pmid = {40644100},
issn = {2168-2208},
abstract = {E/MEG source imaging (ESI) provides noninvasive measurements of brain activity with high spatial and temporal resolution. In particular, the wearability and portability of EEG make it an attractive area of research not only in the biomedical communities especially when considering the wide applications prospects including brain-computer interface (BCI), neuromarketing, neuroergonomics, etc. Although there are already some valuable and impressive reviews on ESI, these reviews introduce the ESI models in a relatively isolated way and lack the recent advances in ESI. In this work, we aim to: 1) provide a timely in-depth review of the widely-explored/state-of-the art ESI models including their underlying neurophysiological assumptions and mathematical derivations; 2) list the primary applications of ESI and highlight the crucial steps regarding its implementations; 3) discuss the challenges in ESI and suggest several future research prospects; 4) demonstrate practical usage and implementation details of various representative ESI models along with open-source dataset/codes (link). As a rapidly expanding field, the development of ESI is continuously growing and evolving to embrace new technologies. We believe the widespread applications of ESI is happening, and it will dramatically expand our understanding of brain dynamics.},
}
@article {pmid40644042,
year = {2025},
author = {Kim, M and Jo, S and Cho, H and Ye, S and Kim, Y and Park, HS},
title = {Development of Multimodal EEG-EMG Human Machine Interface for Hand-Wrist Rehabilitation: A Preliminary Study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2025},
number = {},
pages = {1564-1569},
doi = {10.1109/ICORR66766.2025.11063079},
pmid = {40644042},
issn = {1945-7901},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Electromyography/methods/instrumentation ; *Wrist/physiology ; Male ; Adult ; Wearable Electronic Devices ; Stroke Rehabilitation ; *Robotics/instrumentation ; Female ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {Patients with neurological disorders, such as stroke, often undergo upper limb motor impairments, severely limiting their ability to perform activities of daily living (ADL). Wearable robots have been developed to provide intensive and precise repetitive training for upper limb rehabilitation. Effective rehabilitation requires aligning robotic assistance with patient movement intention to promote brain plasticity. Additionally, robotic assistance must accommodate the complex, coordinated upper limb motions required for ADL tasks, including not only isolated hand movements but also integrated hand and wrist actions. This paper presents a multimodal human-machine interface (HMI) for integrated hand-wrist rehabilitation using both EEG and EMG signals. A three-degrees-of-freedom (3-DOF) soft wearable robot, combining a robotic hand glove and forearm skin brace, was designed to assist coordinated hand and wrist movements during reaching and grasping. EEG signals classified rest and grasp states using a Riemannian geometry approach, while EMG signals from three forearm muscles detected reaching onset to trigger the wrist adjustment. Preliminary tests with four healthy participants demonstrated 85% accuracy in EEG-based classification and sufficient EMG amplitude for motion onset detection. Future studies will expand participant testing to improve system robustness and evaluate its effectiveness for stroke rehabilitation.},
}
@article {pmid40640801,
year = {2025},
author = {Gao, W and Yan, Z and Zhou, H and Xie, Y and Wang, H and Yang, J and Yu, J and Ni, C and Liu, P and Xie, M and Huang, L and Ye, Z},
title = {Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {498},
pmid = {40640801},
issn = {1477-3155},
support = {National Innovation Platform Development Program (No. 2020021105012440), the National Natural Science Foundation of China (No. 82172524, 81974355)//Zhewei Ye/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electrodes, Implanted ; Animals ; *Biocompatible Materials/chemistry ; Brain/physiology ; },
abstract = {Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research. Despite significant advancements over the years, conventional neural electrode interfaces remain insufficient for fully achieving these objectives, particularly in the context of long-term implantation. The primary limitation stems from the poor biocompatibility and mechanical mismatch between the interfacing electrodes and neural tissues, which induce a local immune response and scar tissue formation, thus decreasing the performance and useful lifespan. Therefore, neural interfaces should ideally exhibit appropriate stiffness and minimal foreign body reactions to mitigate neuroinflammation and enhance recording quality. This review provides an exhaustive analysis of the current understanding of the critical failure modes that may impact the performance of implantable neural electrodes. Additionally, this study provides a comprehensive overview of the current research on coating materials and design strategies for implanted neural interfaces and discusses the primary challenges currently facing long-term implantation of neural electrodes. Finally, we present our perspective and propose possible future research directions to improve implantable neural interfaces for BCIs.},
}
@article {pmid40640486,
year = {2025},
author = {Pierrieau, E and Dussard, C and Plantey-Veux, A and Guerrini, C and Lau, B and Pillette, L and George, N and Jeunet-Kelway, C},
title = {Changes in cortical beta power predict motor control flexibility, not vigor.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1041},
pmid = {40640486},
issn = {2399-3642},
support = {ANR-20-CE37-0012//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {Humans ; Male ; Female ; *Beta Rhythm/physiology ; Adult ; Electroencephalography ; Young Adult ; *Motor Cortex/physiology ; Neurofeedback ; Brain-Computer Interfaces ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {The amplitude of beta-band activity (β power; 13-30 Hz) over motor cortical regions is used to assess and decode movement in clinical settings and brain-computer interfaces, as β power is often assumed to predict the strength of the brain's motor output, or "vigor". However, recent conflicting evidence challenges this assumption and underscores the need to clarify the relationship between β power and movement. In this study, sixty participants were trained to self-regulate β power using electroencephalography-based neurofeedback before performing different motor tasks. Results show that β power modulations can impact different motor variables, or the same variables in opposite directions, depending on task constraints. Importantly, downregulation of β power is associated with better task performance regardless of whether performance implied increasing or decreasing motor vigor. These findings demonstrate that β power should be interpreted as a measure of motor flexibility, which underlies adaptation to environmental constraints, rather than vigor.},
}
@article {pmid40638250,
year = {2025},
author = {Zhang, X and Ma, D and Wang, J and Su, N and Guo, J},
title = {Structures and Molecular Mechanisms of Insect Odorant and Gustatory Receptors.},
journal = {Physiology (Bethesda, Md.)},
volume = {},
number = {},
pages = {},
doi = {10.1152/physiol.00011.2025},
pmid = {40638250},
issn = {1548-9221},
support = {2020YFA0908501//Ministry of Science and Technology of China/ ; 32371204//National Science Foundation of China/ ; LD25C050004//Zhejiang Provincial Natural Science Foundation/ ; //Foundamental Research Funds for the Central Universities/ ; //Ministry of Education Frontier Science Center for Brain Science &amp; Brain-Machine Integration/ ; //K.C. Wong Education Foundation/ ; },
abstract = {Insects rely on chemoreceptors in sensory neurons to detect and discriminate various chemicals in constantly changing environments. Among the chemoreceptors, odorant receptors (ORs) and gustatory receptors (GRs) play essential roles in sensing different odorant and tastant molecules, thereby regulating insects' physiology and behaviors such as feeding, mating, and alarming. ORs and GRs are evolutionarily related seven-transmembrane helical proteins that constitute a large family of tetrameric ion channels. In recent years, great progress has been made in the structures and molecular mechanisms of insect ORs and GRs. In this review, we summarize the available structures of insect ORs and GRs, analyze their diverse ligand recognition modes, and examine their conserved ligand activation mechanisms. These structural analyses will not only enhance our understanding of molecular basis of insect ORs and GRs but also provide critical insights for the future discovery of repellents and attractants.},
}
@article {pmid40636103,
year = {2025},
author = {Qi, R and Lin, Y and Liu, S and Cao, X and Xie, M and Yu, C and Sun, H and Gao, L and Li, X},
title = {Vocal taking turns is premature at birth and improved by the postnatal phonetic environment in marmosets.},
journal = {National science review},
volume = {12},
number = {7},
pages = {nwaf162},
pmid = {40636103},
issn = {2053-714X},
abstract = {Precisely time-controlled vocal antiphony is crucial for the social communication of arboreal marmosets. However, it remains unclear when this antiphony is formed and how postnatal acoustic environments affect its development. In the present study, we systematically recorded the emitted calls of infant marmosets in an antiphonal calling scenario from postnatal day one (P1) to postnatal 10 weeks (W10). We found that infant marmosets emit most types of adult calls and engage in turn-taking as early as in P1. In addition, parent-reared infants emitted more antiphonal phee calls than hand-reared marmosets in W10. Call transitions in parent-reared W10 animals mainly occurred between phee calls or from phee calls to other call types. In contrast, P1 and hand-reared W10 marmosets displayed call transitions among various types of calls. These findings suggest that the antiphony in marmosets emerges on P1 but remains immature, and the antiphony skills can be improved by development environments, especially by the vocal feedback from parents.},
}
@article {pmid40634126,
year = {2025},
author = {Abbagnano, E and Pascual-Valdunciel, A and Zicher, B and Ibáñez, J and Farina, D},
title = {Projection of Cortical Beta Band Oscillations to a Motor Neuron Pool Across the Full Range of Recruitment.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0453-25.2025},
pmid = {40634126},
issn = {1529-2401},
abstract = {Cortical beta band oscillations (13-30 Hz) are associated with sensorimotor control, but their precise role remains unclear. Evidence suggests that for low-threshold motor neurons, these oscillations are conveyed to muscles via the fastest corticospinal fibers. However, their transmission to motor neurons of different sizes may vary due to differences in the relative strength of corticospinal and reticulospinal projections across the motor neuron pool. Consequently, it remains uncertain whether corticospinal beta transmission follows similar pathways and maintains consistent strength across the entire motor neuron pool. To investigate this, we examined beta activity in motor neurons innervating the tibialis anterior muscle across the full range of recruitment thresholds in a study involving 12 participants of both sexes. We characterized beta activity at both the cortical and motor unit levels while participants performed contractions from mild to submaximal levels. Corticomuscular coherence remained unchanged across contraction forces after normalizing for the net motor unit spike rate, suggesting that beta oscillations are transmitted with similar strength to motor neurons, regardless of size. To further explore beta transmission, we estimated corticospinal delays using the cumulant density function, identifying peak correlations between cortical and muscular activity. Once compensated for variable peripheral axonal propagation delay across motor neurons, the corticospinal delay remained stable, and its value (approximately 14 ms) indicated projections through the fastest corticospinal fibers for all motor neurons. These findings demonstrate that corticospinal beta band transmission is determined by the fastest pathway connecting in the corticospinal tract, projecting similarly across the entire motor neuron pool.Significance Statement Beta band oscillations (13-30 Hz) play a key role in sensorimotor control, yet their precise transmission to motor neurons remains unclear. This study demonstrates that beta oscillations are transmitted similarly across the entire motor neuron pool, regardless of recruitment threshold. By examining corticomuscular coherence and corticospinal delays during voluntary contractions, we show that beta activity is consistently relayed to motor neurons via the fastest corticospinal fibers. These findings provide evidence that beta band activity is not preferentially directed toward specific subsets of motor neurons but is instead a global signal influencing motor output. This insight advances our understanding of how the central nervous system regulates movement and may have implications for neurorehabilitation and brain-machine interfaces.},
}
@article {pmid40632037,
year = {2025},
author = {Kumar, R and Soni, A and Ahmed, T and Beniwal, K},
title = {Experiences and Well-Being of Early-Career Trauma Nurses in India: A Mixed Methods Study.},
journal = {Journal of trauma nursing : the official journal of the Society of Trauma Nurses},
volume = {32},
number = {4},
pages = {189-200},
pmid = {40632037},
issn = {1078-7496},
mesh = {Humans ; India ; Adult ; Female ; Male ; *Quality of Life/psychology ; Surveys and Questionnaires ; *Trauma Nursing ; Job Satisfaction ; Resilience, Psychological ; *Nursing Staff, Hospital/psychology ; Trauma Centers ; Burnout, Professional/psychology ; },
abstract = {BACKGROUND: Trauma nursing is fast-paced and high-pressure work that can affect nurses' physical and mental health. However, these effects remain unexplored among novice trauma nurses in a newly established trauma center in India.<br><br>OBJECTIVE: To examine relationships between professional quality of life, sleep disturbances, anxiety, and resilience and to explore the experiences of novice trauma nurses in a newly established trauma center in India.<br><br>METHODS: This sequential mixed-methods study was conducted between April and June 2024 in a newly established trauma center in India. A purposive sample of 80 nurses was surveyed using a demographic questionnaire, the Brief Resilience Scale, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, and the Professional Quality of Life Scale. Nine nurses were interviewed to explore their trauma experiences. The analysis included descriptive and inferential statistics, bootstrap-based mediation testing, and thematic content analysis.<br><br>RESULTS: A total of 80 nurses completed the survey (response rate: 67.8%) with a mean age of 27.7&#xa0;years (standard deviation [SD]&#xa0;=&#xa0;2.89) and average years of trauma experience of 2.08&#xa0;years (SD&#xa0;=&#xa0;1.93). Higher compassion satisfaction correlated with fewer sleep disturbances (r =&#xa0;-.23, p&#xa0;=&#xa0;.037). Burnout positively correlated with anxiety (r =&#xa0;.24, p =&#xa0;.033) and sleep disturbances (r =&#xa0;.34, p =&#xa0;.023), and negatively with nurses' resilience (r =&#xa0;-.12, p&#xa0;=&#xa0;.049). Professional quality of life significantly correlated with resilience (r =&#xa0;.18, p&#xa0;=&#xa0;.048). Resilience mediated the relationship between anxiety and both burnout (&#x3b2;&#xa0;=&#xa0;.24, bootstrap confidence interval [BCI]&#xa0;=&#xa0;0.04, 0.46, p =&#xa0;.041) and secondary traumatic stress (&#x3b2;&#xa0;=&#xa0;.24, BCI&#xa0;=&#xa0;0.03, 0.52, p&#xa0;=&#xa0;.029). Qualitative analysis revealed three major themes: personal and professional adaptation to trauma life, adverse physical and psychological issues, and challenges faced in trauma care.<br><br>CONCLUSION: Our findings highlight the adverse impact of trauma nursing on sleep, resilience, anxiety, and professional quality of life among novice nurses in a newly established Level I trauma center in India. Targeted interventions are required to enhance resilience and mitigate the impact of caring for trauma patients.},
}
@article {pmid40631920,
year = {2025},
author = {Wang, X and Jun, F and Lin, C and Wang, X},
title = {Psychedelics and the Gut Microbiome: Unraveling the Interplay and Therapeutic Implications.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00418},
pmid = {40631920},
issn = {1948-7193},
abstract = {Classic psychedelics and the gut microbiome interact bidirectionally through mechanisms involving 5-HT2A receptor signaling, neuroplasticity, and microbial metabolism. This viewpoint highlights how psychedelics may reshape microbiota and how microbes influence psychedelic efficacy, proposing microbiome-informed strategies─such as probiotics or dietary interventions─to personalize and enhance psychedelic-based mental health therapies.},
}
@article {pmid40631106,
year = {2025},
author = {Ding, Y and Dunn, SLS and Sakon, JJ and Duan, C and Zhang, Y and Berger, JI and Rhone, AE and Nourski, KV and Kawasaki, H and Howard, MA and Roychowdhury, VP and Fried, I},
title = {Reading specific memories from human neurons before and after sleep.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662486},
pmid = {40631106},
issn = {2692-8205},
abstract = {The ability to retrieve a single episode encountered just once is a hallmark of human intelligence and episodic memory [1]. Yet, decoding a specific memory from neuronal activity in the human brain remains a formidable challenge. Here, we develop a transformer neural network model [2, 3] trained on neuronal spikes from intracranial microelectrodes recorded during a single viewing of an audiovisual episode. Combining spikes throughout the brain via cross-channel attention [4], capable of discovering neural patterns spread across brain regions and timescales, individual participant models predict memory retrieval of specific concepts such as persons or places. Brain regions differentially contribute to memory decoding before and after sleep. Models trained using only medial temporal lobe (MTL) spikes significantly decode concepts before but not after sleep, while models trained using only frontal cortex (FC) spikes decode concepts after but not before sleep. These findings suggest a system-wide distribution of information across neural populations that transforms over wake/sleep cycles [5]. Such decoding of internally generated memories suggests a path towards brain-computer interfaces to treat episodic memory disorders through enhancement or muting of specific memories.},
}
@article {pmid40631097,
year = {2025},
author = {Lee, W and Scherschligt, X and Nishimoto, M and Rouse, AG},
title = {Neural trajectories improve motor precision.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.01.662682},
pmid = {40631097},
issn = {2692-8205},
abstract = {Populations of neurons in motor cortex signal voluntary movement. Most classic neural encoding models and current brain-computer interface decoders assume individual neurons sum together along a neural dimension to represent movement features such as velocity or force. However, large population neural analyses continue to identify trajectories of neural activity evolving with time that traverse multiple dimensions. Explanations for these neural trajectories typically focus on how cortical circuits processes learn, organize, and implement movements. However, descriptions of how these neural trajectories might improve performance, and specifically motor precision, are lacking. In this study, we proposed and tested a computational model that highlights the role of neural trajectories, through the selective co-activation and selective timing of firing rates across the neural populations, for improving motor precision. Our model uses experimental results from a center-out reaching task as inspiration to create several physiologically realistic models for the neural encoding of movement. Using a recurrent neural network to simulate how a downstream population of neurons might receive such information, like the spinal cord and motor units, we show that movements are more accurate when neural information specific to the phase and/or amplitude of movement are incorporated across time instead of an instantaneous, velocity-only tuning model. Our finding suggests that precise motor control arises from spatiotemporal recruitment of neural populations that create distinct neural trajectories. We anticipate our results will significantly impact not only how neural encoding of movement in motor cortex is described but also future understating for how brain networks communicate information for planning and executing movements. Our model also provides potential inspiration for how to incorporate selective activation across a neural population to improve future brain-computer interfaces.},
}
@article {pmid40505654,
year = {2025},
author = {Becker, B},
title = {Will our social brain inherently shape and be shaped by interactions with AI?.},
journal = {Neuron},
volume = {113},
number = {13},
pages = {2037-2041},
doi = {10.1016/j.neuron.2025.04.034},
pmid = {40505654},
issn = {1097-4199},
mesh = {Humans ; *Brain/physiology ; *Artificial Intelligence ; *Social Behavior ; *Interpersonal Relations ; *Social Interaction ; Animals ; *Brain-Computer Interfaces ; },
abstract = {Social-specific brain circuits enable rapid understanding and affiliation in interpersonal interactions. These evolutionarily and experience-shaped mechanisms will influence-and be influenced by-interactions with conversational AI agents (chatbots, avatars). This NeuroView explores fundamental circuits, computations, and societal implications.},
}
@article {pmid40630938,
year = {2025},
author = {Liu, L and Wang, F and Chen, X and Liu, L and Wang, Y and Bei, J and Lei, L and Zhao, Z and Tang, C},
title = {Designing Multifunctional Microneedles in Biomedical Engineering: Materials, Methods, and Applications.},
journal = {International journal of nanomedicine},
volume = {20},
number = {},
pages = {8693-8728},
pmid = {40630938},
issn = {1178-2013},
mesh = {*Needles ; Humans ; *Drug Delivery Systems/instrumentation/methods ; Tissue Engineering/methods ; *Biomedical Engineering/methods/instrumentation ; Animals ; Biocompatible Materials/chemistry ; Equipment Design ; *Microinjections/instrumentation ; Brain-Computer Interfaces ; },
abstract = {This review focuses on the emerging technology of multifunctional microneedles (MNs) within the biomedical engineering (BME) field, highlighting their potential in drug delivery, diagnostics, and therapeutics. Previous studies have explored MNs in various applications; however, their diverse functionalities across different material types and advanced application domains have been rarely comprehensively explored. This review bridges this gap by providing insights into the application of MNs in materials science, drug delivery, diagnostic monitoring, and tissue engineering. The unique properties and skin effects of various inorganic (eg, silicon, metals) and organic materials (eg, polysaccharides, polymers, proteins) used in MNs are examined. The analysis emphasizes the advantages of different MN materials, ie, their biocompatibility, degradation rates, and application specificity. In addition, the preparation processes and application scenarios of each MN type, such as minimally invasive drug delivery in transdermal applications and their benefits in tissue engineering for promoting repair, regeneration, and precise delivery of cells and growth factors in tissues like skin, cartilage, muscle, bone, and nerves, are discussed. Furthermore, this review explores the innovative use of MNs in brain-computer interfaces-an area not yet thoroughly examined. This novel application offers significant opportunities in neuroscience and clinical practice. Overall, this review provides valuable insights into the current research landscape and unexplored areas of MNs, contributing to future advancements in BME.},
}
@article {pmid40630584,
year = {2025},
author = {Hahn, NV and Stein, E and , and Donoghue, JP and Simeral, JD and Hochberg, LR and Willett, FR},
title = {Long-term performance of intracortical microelectrode arrays in 14 BrainGate clinical trial participants.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.07.02.25330310},
pmid = {40630584},
abstract = {Brain-computer interfaces have enabled people with paralysis to control computer cursors, operate prosthetic limbs, and communicate through handwriting, speech, and typing. Most high-performance demonstrations have used silicon microelectrode "Utah" arrays to record brain activity at single neuron resolution. However, reports so far have typically been limited to one or two individuals, with no systematic assessment of the longevity, decoding accuracy, and day-to-day stability properties of chronically implanted Utah arrays. Here, we present a comprehensive evaluation of 20 years of neural data from the BrainGate and BrainGate2 pilot clinical trials. This dataset spans 2,319 recording sessions and 20 arrays from the first 14 participants in these trials. On average, arrays successfully recorded neural spiking waveforms on 35.6% of electrodes, with only a 7% decline over the study enrollment period (up to 7.6 years, with a mean of 2.8 years). We assessed movement intention decoding performance using a "decoding signal-to-noise ratio" (dSNR) metric, and found that 11 of 14 arrays provided meaningful movement decoding throughout study enrollment (dSNR > 1). Three arrays reached a peak dSNR greater than 4.5, approaching that achieved during able-bodied computer mouse control (6.29). We also found that dSNR increases logarithmically with the number of electrodes, providing a pathway for scaling performance. Longevity and reliability of Utah array recordings in this study were better than in prior nonhuman primate studies. However, achieving peak performance consistently will require addressing unknown sources of variability.},
}
@article {pmid40629288,
year = {2025},
author = {Paret, C and Jindrová, M and Kleindienst, N and Eck, J and Breman, H and Lührs, M and Barth, B and Ethofer, T and Fallgatter, AJ and Goebel, R and Hoell, A and Lockhofen, D and Reinhold, AS and Maier, S and Matthies, S and Mulert, C and Schönholz, C and van Elst, LT and Schmahl, C},
title = {A randomised controlled trial of amygdala fMRI-neurofeedback versus sham-feedback in borderline-personality disorder - systematic literature review and introduction to the BrainSTEADy trial.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {687},
pmid = {40629288},
issn = {1471-244X},
support = {PA 3107/4-1//Deutsche Forschungsgemeinschaft/ ; SCHM 1526/26-1//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Adult ; Female ; Humans ; Male ; *Amygdala/physiopathology/diagnostic imaging ; *Borderline Personality Disorder/therapy/physiopathology/diagnostic imaging ; Emotional Regulation ; *Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; },
abstract = {BACKGROUND: Individuals with Borderline-Personality Disorder (BPD) experience intensive, unstable negative emotions. Hyperactivity of the amygdala is assumed to drive exaggerated emotional responses in BPD. Functional Magnetic Resonance Imaging (fMRI)-based neurofeedback is an endogenous neuromodulation method intended to address the imbalance of neural circuits and thus holds the potential as a treatment for BPD. Many original articles and meta-analyses show that fMRI-neurofeedback can improve psychiatric symptoms. In contrast, there is a lack of publications that aggregate and evaluate data of the safety of the treatment. Furthermore, evidence on the efficacy of fMRI-neurofeedback for the treatment of BPD is limited. Preliminary evidence suggests that downregulation of amygdala hyperactivation through fMRI-neurofeedback can ameliorate emotion dysregulation. To test this assumption, BrainSTEADy (Brain Signal Training to Enhance Affect Down-regulation), a multi-center clinical trial, is conducted. First, we present a systematic literature review evaluating the safety of fMRI-neurofeedback and assessing clinical performance in BPD. Second, we describe the study protocol of BrainSTEADy.<br><br>METHODS: Literature research: From 2,609 screened paper abstracts, 758 were identified as potentially relevant. Twenty studies reported adverse events or undesirable side effects. Two papers provided relevant data for the assessment of clinical performance in BPD. BrainSTEADy study protocol: During four sessions, patients will receive graphical fMRI-neurofeedback from their right amygdala or sham-feedback while viewing images with aversive content. The primary endpoint, 'negative affect intensity', will be assessed after the last neurofeedback session using Ecological Momentary Assessment (EMA). Secondary endpoints will be assessed after the last neurofeedback session, at 3-month and at 6-month follow-up. This trial is a multi-center, patient- and investigator-blind, randomized, parallel-group superiority study with a planned interim-analysis once half of the recruitment target is met (N&#x2009;=&#x2009;82).<br><br>DISCUSSION: As suggested by literature review, fMRI-neurofeedback is a safe treatment for patients, although future studies should systematically assess and report adverse events. Although fMRI-neurofeedback showed promising effects in BPD, current evidence is limited and calls for a randomized controlled trial such as BrainSTEADy, which aims to test whether amygdala-fMRI-neurofeedback specifically reduces emotion instability in BPD beyond nonspecific benefit. Endpoint measures encompassing EMA, clinical interviews, psychological questionnaires, quality of life, and neuroimaging will enable a comprehensive analysis of effects and mechanisms of neurofeedback treatment.<br><br>TRIAL REGISTRATION: The study protocol was first posted 2024/10/04 on ClinicalTrials.gov and received the ID NCT06626789.},
}
@article {pmid40629037,
year = {2025},
author = {Wood, H},
title = {Brain-computer interface restores naturalistic speech to a man with ALS.},
journal = {Nature reviews. Neurology},
volume = {},
number = {},
pages = {},
pmid = {40629037},
issn = {1759-4766},
}
@article {pmid40628758,
year = {2025},
author = {Mathiyazhagan, S and Devasena, MSG},
title = {Motor imagery EEG signal classification using novel deep learning algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24539},
pmid = {40628758},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; Algorithms ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Wavelet Analysis ; *Imagination/physiology ; },
abstract = {Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. To begin, the preprocessing stage of the proposed approach includes an innovative hybrid approach that combines empirical mode decomposition (EMD) for extracting intrinsic signal modes. In addition to that, continuous wavelet transform (CWT) is used for multi-resolution analysis. For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. To attain enhanced performance the parameters of the classifier network are optimized using the Far and near optimization (FNO) algorithm. This combined approach provides superior classification accuracy and adaptability to diverse conditions in EEG signal analysis. The evaluations of the proposed approach were conducted using benchmark BCI competition IV Dataset 2a and Physionet dataset. On the BCI dataset, the proposed approach achieves 95.7% accuracy, 96.2% recall, 95.9% precision, and 97.5% specificity. In addition, it delivers 94.1% accuracy, 94.0% recall, 93.6% precision, and 95.0% specificity on the PhysioNet dataset. With better results, the proposed model attained superior performance compared to existing methods such as CNN, LSTM, and BiLSTM algorithms.},
}
@article {pmid40628277,
year = {2025},
author = {Afdideh, F and Shamsollahi, MB},
title = {Subject-specific feature extraction approach for a three-class motor imagery-based brain-computer interface enabling navigation in a virtual environment: open-access framework.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/aded19},
pmid = {40628277},
issn = {2057-1976},
abstract = {Brain-Computer Interface (BCI) is a system that aids individuals with disabilities to establish a novel communication channel between the brain and computer. Among various electrophysiological sources that can drive a BCI system, Motor Imagery (MI) facilitates more natural communication for users with motor disabilities, whereas electroencephalogram (EEG) is considered the most practical brain imaging modality. However, subject training is a critical aspect of such a type of BCI. One possible solution to address this challenge is to leverage the Virtual Reality (VR) technology. This study proposes a VR in MI- and EEG-based BCI (MI-EEG-BCI-VR) framework wherein users navigate a Virtual Environment (VE) following cue-based training, and employing a subject-specific feature extraction approach. The assigned task involves performing the left hand, right hand, and feet movement imagination to navigate from the start station to the end station as quickly as possible. The generated brain signals are collected using three bipolar EEG channels only. The proposed open-access MATLAB-based MI-EEG-BCI-VR framework was validated with eight healthy participants. One participant demonstrated satisfactory performance in navigating the VE. Notably, it achieved the highest performance of 82.28 5.11% for MI and 97.72 4.55% for Motor Execution (ME) after just a single training session. .},
}
@article {pmid40628276,
year = {2025},
author = {Fedosov, N and Medvedeva, D and Shevtsov, O and Ossadtchi, A},
title = {A reliable and reproducible real-time access to sensorimotor rhythm with a small number of optically pumped magnetometers.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aded35},
pmid = {40628276},
issn = {1741-2552},
abstract = {\textbf{Objective.}
Recent advances in biomagnetic sensing have led to the development of compact, wearable devices capable of detecting weak magnetic fields generated by biological activity. Optically pumped magnetometers (OPMs) have shown significant promise in functional neuroimaging. Brain rhythms play a crucial role in diagnostics, cognitive research, and neurointerfaces. Here we demonstrate that a small number of OPMs can reliably capture sensorimotor rhythms (SMR). \textbf{Approach.}
We conducted real-movement and motor-imagery experiments with nine participants in two distinct magnetically shielded rooms (MSR), each equipped with different ambient field suppression systems. We used only 3 OPMs located above the sensorimotor region and standard common-spatial-patterns (CSP) based processing to decode the real and imaginary movement intentions of our participants. We evaluated reproducibility of the CSP components' spectral profiles and assessed the decoding accuracy deterioration with reduction of OPM's count. We also assessed the influence of the magnetic field orientation on the decoding accuracy and implemented a real-time motor imagery BCI solution. \textbf{Main Results.}
Under optimal conditions, OPM sensors deliver informative signals suitable for practical motor imagery brain-computer interface (BCI) applications. Those subjects who participated in the experiments in both MSRs exhibit highly reproducible SMR spectral patterns across two different magnetically shielded environments. The magnetic field components with radial orientation yield higher decoding accuracy than their tangential counterparts. In some subjects we observed more than 80 \% of binary decoding accuracy using a single OPM sensor. Finally we demonstrate real-time performance of our system along with clearly pronounced and behaviorally relevant fluctuations of the SMR power. \textbf{Significance.}
For the first time, we demonstrated reliable and reproducible tracking of sensorimotor rhythm components using a small number of contactless OPM sensors during real movements and motor imagery. Our findings pave the way for more efficient post-stroke neurorehabilitation by enabling motor imagery-based BCI solutions to accelerate functional recovery.},
}
@article {pmid40627787,
year = {2025},
author = {Li, Y and Zhang, J},
title = {Utilizing statistical analysis for motion imagination classification in brain-computer interface systems.},
journal = {PloS one},
volume = {20},
number = {7},
pages = {e0327121},
doi = {10.1371/journal.pone.0327121},
pmid = {40627787},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; *Motion ; *Brain/physiology ; Male ; },
abstract = {In this study, we introduce a novel Field-Agnostic Riemannian-Kernel Alignment (FARKA) method to advance the classification of motion imagination in Brain-Computer Interface (BCI) systems. BCI systems enable direct control of external devices through brain activity, bypassing peripheral nerves and muscles. Among various BCI technologies, electroencephalography (EEG) based on non-intrusive cortical potential signals stands out due to its high temporal resolution and non-invasive nature. EEG-based BCI technology encodes human brain intentions into cortical potentials, which are recorded and decoded into control commands. This technology is crucial for applications in motion rehabilitation, training optimization, and motion control. The proposed FARKA method combines Riemannian Alignment for sample alignment, Riemannian Tangent Space for spatial representation extraction, and Knowledge Kernel Adaptation to learn field-agnostic kernel matrices. Our approach addresses the limitations of current methods by enhancing classification performance and efficiency in inter-individual MI tasks. Experimental results on three public EEG datasets demonstrate the superior performance of FARKA compared to existing methods.},
}
@article {pmid40627473,
year = {2025},
author = {Zhao, Z and Cao, Y and Yu, H and Yu, H and Huang, J},
title = {CNNViT-MILF-a: A Novel Architecture Leveraging the Synergy of CNN and ViT for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3587026},
pmid = {40627473},
issn = {2168-2208},
abstract = {Accurate motor imagery (MI) classification in EEG-based brain-computer interfaces (BCIs) is essential for applications in engineering, medicine, and artificial intelligence. Due to the limitations of single-model approaches, hybrid model architectures have emerged as a promising direction. In particular, convolutional neural networks (CNNs) and vision transformers (ViTs) demonstrate strong complementary capabilities, leading to enhanced performance. This study proposes a series of novel models, termed as CNNViT-MI, to explore the synergy of CNNs and ViTs for MI classification. Specifically, five fusion strategies were defined: parallel integration, sequential integration, hierarchical integration, early fusion, and late fusion. Based on these strategies, eight candidate models were developed. Experiments were conducted on four datasets: BCI competition IV dataset 2a, BCI competition IV dataset 2b, high gamma dataset, and a self-collected MI-GS dataset. The results demonstrate that CNNViT-MILF-a achieves the best performance among all candidates by leveraging ViT as the backbone for global feature extraction and incorporating CNN-based local representations through a late fusion strategy. Compared to the best-performing state-ofthe-art (SOTA) methods, mean accuracy was improved by 2.27%, 2.31%, 0.74%, and 2.50% on the respective datasets, confirming the model's effectiveness and broad applicability, other metrics showed similar improvements. In addition, significance analysis, ablation studies, and visualization analysis were conducted, and corresponding clinical integration and rehabilitation protocols were developed to support practical use in healthcare.},
}
@article {pmid40627471,
year = {2025},
author = {Chen, X and Fu, Z and Zhang, P and Chen, X and Huang, J},
title = {Intracortical Brain-Machine Interfaces with High-Performance Neural Decoding through Efficient Transfer Meta-learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3586870},
pmid = {40627471},
issn = {1558-2531},
abstract = {Implantable brain-machine interfaces (iBMIs) have emerged as a groundbreaking neural technology for restoring motor function and enabling direct neural communication pathways. Despite their therapeutic potential in neurological rehabilitation, the critical challenge of neural decoder calibration persists, particularly in the context of transfer learning. Traditional calibration approaches assume the availability of extensive neural recordings, which is often impractical in clinical settings due to patient fatigue and neural signal variability. Furthermore, the inherent constraints of implanted neural processors-including limited computational capacity and power consumption requirements-demand streamlined processing algorithms. To address these clinical and technical challenges, we developed DMM-WcycleGAN (Dimensionality Reduction Model-Agnostic Meta-Learning based Wasserstein Cycle Generative Adversarial Networks), a novel neural decoding framework that integrates meta-learning principles with optimal transfer learning strategies. This innovative approach enables efficient decoder calibration using minimal neural data while implementing dimensionality reduction techniques to optimize computational efficiency in implanted devices. In vivo experiments with non-human primates demonstrated DMM-WcycleGAN's superior performance in mitigating neural signal distribution shifts between historical and current recordings, achieving a 3% enhancement in neural decoding accuracy using only ten calibration trials while reducing the calibration duration by over 70%, thus significantly improving the clinical viability of iBMI systems.},
}
@article {pmid40626564,
year = {2025},
author = {Hu, Z and Luo, K and Liu, Y},
title = {Classification of motor imagery based on multi-scale feature extraction and fusion-residual temporal convolutional network.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2528892},
pmid = {40626564},
issn = {1476-8259},
abstract = {Brain-computer interface (BIC) decodes electroencephalogram (EEG) signals to realize the interaction between brain and external devices. However, traditional methods show limited performance in motor imagery electroencephalogram (MI-EEG) classification. In this paper, we introduce a multi-scale temporal convolutional network (MS-TCNet) that employs parallel multi-scale convolutions for spatiotemporal feature extraction, efficient channel attention (ECA) for channel weights optimization, and fusion-residual temporal convolution (FR-TCN) for high-level temporal feature capture. Experimental results show that MS-TCNet achieved remarkable decoding accuracies of 87.85% and 92.85% on the BCI IV-2a and BCI IV-2b datasets, respectively. The proposed MS-TCNet surpasses existing baseline models across various performance metrics, demonstrating its effectiveness in advancing MI-EEG decoding.},
}
@article {pmid40624803,
year = {2025},
author = {Li, S and Gao, S and Hu, Y and Xu, J and Sheng, W},
title = {Brain-Computer Interfaces in Spinal Cord Injury: A Promising Therapeutic Strategy.},
journal = {The European journal of neuroscience},
volume = {62},
number = {1},
pages = {e70183},
doi = {10.1111/ejn.70183},
pmid = {40624803},
issn = {1460-9568},
support = {2023TSYCLJ0031//Program of Technological Leading Talent of Tianshan Talent/ ; 2023YFY-QKMS-06//Youth Foundation of Research and Development/ ; 2021D01D18//Key Program of Natural Science Foundation of Xinjiang Uygur Autonomous Region/ ; 82360257//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Neurological Rehabilitation/methods ; Animals ; },
abstract = {The current treatment regimen for spinal cord injury (SCI), a neurological disorder with a high incidence of disability, is based on early surgical decompression and administration of pharmacological agents. However, the efficacy of such an approach remains limited, and most patients have sensory and functional deficits below the level of injury, which seriously affects their quality of life. This necessitates further exploration into effective treatment modalities. In recent years, considerable advancements have been made in developing and utilizing brain-computer interfaces (BCI), which facilitate neurorehabilitation and enhance motor function by transforming brain signals into diverse forms of output commands. BCI-assisted systems provide alternative means of rehabilitative exercise or limb movement in patients with SCI, including electrical stimulation and exoskeleton robots. BCI shows great potential in the rehabilitation of patients with SCI. This review summarizes the current research status and limitations of BCI for SCI to provide novel insights into the concept of multimodal rehabilitation and treatment of SCI and facilitate BCI's future development.},
}
@article {pmid40624755,
year = {2025},
author = {Barios, JA and Vales, Y and Catalán, JM and Blanco-Ivorra, A and Martínez-Pascual, D and García-Aracil, N},
title = {Post-Movement Beta Rebound for Longitudinal Monitoring of Motor Rehabilitation in Stroke Patients Using an Exoskeleton-Assisted Paradigm.},
journal = {International journal of neural systems},
volume = {35},
number = {9},
pages = {2550044},
doi = {10.1142/S0129065725500443},
pmid = {40624755},
issn = {1793-6462},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Middle Aged ; Female ; Aged ; *Stroke/physiopathology/diagnosis ; *Exoskeleton Device ; *Sensorimotor Cortex/physiopathology ; *Beta Rhythm/physiology ; Electroencephalography ; Adult ; Longitudinal Studies ; *Motor Activity/physiology ; Brain-Computer Interfaces ; Movement/physiology ; },
abstract = {Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers - such as post-movement beta rebound (PMBR) - offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.},
}
@article {pmid40622874,
year = {2025},
author = {Annett, EG and Shook, JR and Giordano, J},
title = {Super Soldiers or Social Burden? Ethical Exploration of the Benefits and Costs of Military Bioenhancement.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/21507740.2025.2519457},
pmid = {40622874},
issn = {2150-7759},
abstract = {Biotechnological enhancements for military personnel arouse scrutiny, beyond the ethics of experimental research and due care during operational service, to the eventual return to a civilian life. Reversal of enhancements-by withdrawal, extraction, deactivation, modification, destruction, etc.-will be just as experimental and consequential. Super soldiering may not smoothly transition to ordinary habilitation and lifestyle. Complete reversions of dramatic augmentations, such as prosthetics or brain-computer interfacing, could be more damaging to the person than the initial installation. Partial reversions would be just as perplexing, as discharged personnel retain workable technology to prevent disability while other careers next beckon for a suitably empowered individual. Either way, all such biotechnological enhancements must be treated as ethical and social experiments having both positive and negative potential outcomes. Life stages of technologically modified military personnel require special ethical consideration beyond the lifecycle of the technology itself. The post-enhancement veteran is a largely unexplored area, and we propose that these civilian "supra-soldiers" will become a cohort of increasing interest, requiring continued care and ethical support. To that end, we suggest a system of guidelines to ensure ethically sound support for those who serve, and have served, in national defense.},
}
@article {pmid40622660,
year = {2025},
author = {Kong, L and Zhu, B and Zhuang, Y and Lai, J and Hu, S},
title = {Viewing Psychiatric Disorders Through Viruses: Simple Architecture, Burgeoning Implications.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40622660},
issn = {1995-8218},
abstract = {A growing interest in the comprehensive pathogenic mechanisms of psychiatric disorders from the perspective of the microbiome has been witnessed in recent decades; the intrinsic link between microbiota and brain function through the microbiota-gut-brain axis or other pathways has gradually been realized. However, little research has focused on viruses-entities characterized by smaller dimensions, simpler structures, greater diversity, and more intricate interactions with their surrounding milieu compared to bacteria. To date, alterations in several populations of bacteriophages and viruses have been documented in both mouse models and patients with psychiatric disorders, including schizophrenia, major depressive disorder, autism spectrum disorder, and Alzheimer's disease, accompanied by metabolic disruptions that may directly or indirectly impact brain function. In addition, eukaryotic virus infection-mediated brain dysfunction provides insights into the psychiatric pathology involving viruses. Efforts towards virus-based diagnostic and therapeutic approaches have primarily been documented. However, limitations due to the lack of large-scale cohort studies, reliability, clinical applicability, and the unclear role of viruses in microbiota interactions pose a challenge for future studies. Nevertheless, it is conceivable that investigations into viruses herald a new era in the field of precise psychiatry.},
}
@article {pmid40621214,
year = {2025},
author = {Kwon, J and Min, BK},
title = {Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545726},
pmid = {40621214},
issn = {1662-5161},
abstract = {We investigated whether the phase-lag types of cross-frequency coupled alternating current stimulation (CFC-tACS), a non-invasive technique aimed at enhancing cognitive functions, could be decoded using task-based electroencephalographic (EEG) signals. EEG recordings were obtained from 21 healthy individuals engaged in a modified Sternberg task. CFC-tACS was administered online for 6 s during the middle of the retention period with either a 45° or 180° phase lag between the central executive network and the default mode network. To decode different phase-lag tACS conditions, we trained a modified EEGNet using task-based EEG signals before and after the online tACS application. When utilizing parietal EEG signals, the model achieved a decoding accuracy of 81.73%. Feature maps predominantly displayed EEG beta activity in the parietal region, suggesting that the model heavily weighted the beta band, indicative of top-down cognitive control influenced by tACS phase-lag type. Thus, EEG signals can decode online stimulation types, and task-related EEG spectral characteristics may indicate neuromodulatory activity during brain stimulation. This study could advance communicative strategies in brain-machine interfacing (BMI)-neuromodulation within a closed-loop system.},
}
@article {pmid40620352,
year = {2025},
author = {Ying, A and Lv, J and Huang, J and Wang, T and Si, P and Zhang, J and Zuo, G and Xu, J},
title = {A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1591398},
pmid = {40620352},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery (MI)-based brain-computer interfaces (BCI) offers promising applications in rehabilitation. Traditional force-based MI-BCI paradigms generally require subjects to imagine constant force during static or dynamic state. It is challenging to meet the demands of dynamic interaction with force intensity variation in MI-BCI systems.<br><br>METHODS: To address this gap, we designed a novel MI paradigm inspired by daily life, where subjects imagined variations in force intensity during dynamic unilateral upper-limb movements. In a single trial, the subjects were required to complete one of three combinations of force intensity variations: large-to-small, large-to-medium, or medium-to-small. During the execution of this paradigm, electroencephalography (EEG) features exhibit dynamic coupling, with subtle variations in intensity, timing, frequency coverage, and spatial distribution, as the force intensity imagined by the subjects changed. To recognize these fine-grained features, we propose a feature fusion network with a spatial-temporal-enhanced strategy and an information reconstruction (FN-SSIR) algorithm. This model combines a multi-scale spatial-temporal convolution module with a spatial-temporal-enhanced strategy, a convolutional auto-encoder for information reconstruction, and a long short-term memory with self-attention, enabling the comprehensive extraction and fusion of EEG features across fine-grained time-frequency variations and dynamic spatial-temporal patterns.<br><br>RESULTS: The proposed FN-SSIR achieved a classification accuracy of 86.7% &#xb1; 6.6% on our force variation MI dataset, and 78.4% &#xb1; 13.0% on the BCI Competition IV 2a dataset.<br><br>DISCUSSION: These findings highlight the potential of this paradigm and algorithm for advancing MI-BCI systems in rehabilitation training based on dynamic force interactions.},
}
@article {pmid40620639,
year = {2023},
author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and , and Varol, E and Hurwitz, C and Paninski, L},
title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.},
journal = {Advances in neural information processing systems},
volume = {36},
number = {},
pages = {77604-77631},
pmid = {40620639},
issn = {1049-5258},
support = {U19 NS123716/NS/NINDS NIH HHS/United States ; },
abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.},
}
@article {pmid40619564,
year = {2025},
author = {Beressa, G and Feyissa, GT and Murimi, M and Muhammed, AH and Abdulkadir, A and Jema, AT and Alenko, A and Kebede, A and Lencha, B and Sahiledengle, B and Solomon, D and Atlaw, D and Gomora, D and Zenbaba, D and Dibaba, D and Nigussie, E and Nugusu, F and Desta, F and Ejigu, N and Wake, SK and Girma, S and Jidha, TD and Yazew, T and Tadesse, TM and Elala, T and Tekalegn, Y and Belachew, T},
title = {Nutritional status and associated factors among school age children in Southeast Ethiopia using a bayesian analysis approach.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {24141},
pmid = {40619564},
issn = {2045-2322},
mesh = {Humans ; Child ; Ethiopia/epidemiology ; *Nutritional Status ; Bayes Theorem ; Adolescent ; Male ; Female ; Cross-Sectional Studies ; *Growth Disorders/epidemiology ; Prevalence ; Body Mass Index ; *Thinness/epidemiology ; Malnutrition/epidemiology ; Schools ; },
abstract = {Undernutrition among school-age children is a major public health concern in sub-Saharan Africa. This study aimed to assess the nutritional status and associated factors among school-age children in the hard-to-reach pastoral communities in Southeast Ethiopia. We conducted a school-based cross-sectional study among 395 randomly selected schoolchildren aged 7-14 years in pastoral communities in Bale Zone. We employed a hybrid of multistage sampling and systematic random sampling to select the respondents. We used the Z scores of height for age (HAZ) and body mass index for age (BAZ) based on the World Health Organization (WHO) guidance to classify nutritional status of the school-age children. We conducted a Bayesian linear regression analysis estimation using Markov chain Monte Carlo (MCMC). We calculated the mean, along with a 95% Bayesian credible interval (BCI), to identify factors associated with nutritional status. The overall prevalence of stunting and thinness among school-age children 7-14 years was 26.6% (95% CI: 21.8, 31.4%) and 28.9% (95% CI: 24.3, 33.2%), respectively. The mean and SD of HAZ and BAZ scores were -0.82 (2.13) and -0.87 (1.73), respectively. A unit increment in the age of the child and a unit increment in dietary diversity score were associated with an increment in HAZ scores by 0.122 and 0.120 units, respectively. Travelling to school for more than 30 min and more (compared to travelling less than 30 min) and being a child of a literate father (compared to being a child of an illiterate father) were associated with a decrement in the mean HAZ scores by 0.81 and 0.675 units, respectively. Children who come from rich families had BAZ scores, which are about 0.50 units higher when compared to those children coming from poor families. The high burden of stunting and thinning among the hard-to-reach pastoral communities underscores the importance of strengthening nutrition intervention programs such as school feeding and multisectoral collaboration and economic empowerment to improve accessibility of diversified food among school-age children in the hard-to-reach pastoral communities. Younger school children, children from poor families and children who have less access to school and diverse diets should be prioritised during school based nutritional interventions.},
}
@article {pmid40616172,
year = {2025},
author = {Ji, X and Zhang, J and Chen, D and Qin, Q and Huang, F},
title = {Research on transcranial magnetic stimulation for stroke rehabilitation: a visual analysis based on CiteSpace.},
journal = {European journal of medical research},
volume = {30},
number = {1},
pages = {575},
pmid = {40616172},
issn = {2047-783X},
support = {No.CRSI2022CZ-17//China Rehabilitation Research Center under the Central Public Welfare Scientific Research Institute Basic Research Business Fund Project/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; *Stroke/therapy ; Bibliometrics ; },
abstract = {OBJECTIVE: This study aimed to analyze recent research and emerging trends in transcranial magnetic stimulation (TMS) for stroke rehabilitation.<br><br>METHODS: We employed bibliometric methods to retrieve relevant Chinese and English literature on TMS for stroke rehabilitation from China National Knowledge Infrastructure (CNKI) and Web of Science Core Collection (WOSCC) respectively, including publications up to April 10, 2025. CiteSpace 6.4.R1 was utilized to generate knowledge maps, focusing on authors, institutions, countries, and keywords.<br><br>RESULTS: We identified 1301 publications since the inception of the database through April 10, 2025, including 797 articles in Chinese and 504 articles in English. The number of articles available in both languages increased over time. Fudan University and University of Manchester were the institutions with the most outputs. Co-occurrence and clustering keyword analyses revealed similarities between Chinese and English terms, with key research areas include the role of TMS in motor cortex areas, post-stroke cognitive impairment (PSCI), and dysphagia, and TMS has been integrated with other therapeutic approaches for stroke patients.<br><br>CONCLUSION: TMS, a noninvasive brain stimulation technique, has been applied to improve stroke patients' functional outcomes and daily living skills. Future investigations should integrate TMS with cutting-edge technologies including artificial intelligence and brain&#x2012;computer interfaces to uncover its full potential in restoring neural function in stroke survivors.},
}
@article {pmid40615688,
year = {2025},
author = {Li, Y and Wang, YJ and Su, C and Deng, F and Pan, Y},
title = {Bidirectional information flow in cooperative learning reflects emergent leadership.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1000},
pmid = {40615688},
issn = {2399-3642},
support = {Nos. 62207025, 62337001//National Natural Science Foundation of China (National Science Foundation of China)/ ; No. LMS25C090002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Leadership ; Male ; Female ; *Learning/physiology ; *Cooperative Behavior ; Adult ; Spectroscopy, Near-Infrared ; Young Adult ; *Brain/physiology ; },
abstract = {Advances in social neuroscience have shown that one of the fundamental characteristics of cooperative learning is synchronization between learners' brains. However, the directionality of this synchronization, and the role of emergent leadership (i.e., a group leader emerges naturally), in cooperative learning remain unclear. Here, we investigated the directionality and dynamics of information flow by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning and Granger causality analysis (GCA). Through a 6 min dyadic cooperative learning task, we observed that dyads' utterance score increased over time and remained stable at the end of interaction, suggesting successful cooperative learning. At the neural level, we found a stronger leader-to-follower Granger causality in the left middle temporal gyrus, alongside a more pronounced follower-to-leader causality in the left sensorimotor cortex. Moreover, we found that information transfer in both directions increased and peaked around the first half of time into the task, followed by a decline. These temporally similar yet spatially dissociable patterns of directional information flow suggest a hierarchical organization of bidirectional communication during cooperative learning with emergent leadership.},
}
@article {pmid40615618,
year = {2025},
author = {Hobbs, FDR and Dorward, J and Hayward, G and Yu, LM and Saville, BR and Butler, CC and , },
title = {The PRINCIPLE randomised controlled open label platform trial of hydroxychloroquine for treating COVID19 in community based patients at high risk.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23850},
pmid = {40615618},
issn = {2045-2322},
support = {CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; CV220-074//UK Research and Innovation/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; MC_PC_19079//National Institute for Health and Care Research/ ; },
mesh = {Humans ; *Hydroxychloroquine/therapeutic use/adverse effects/administration &amp; dosage ; *COVID-19 Drug Treatment ; Female ; Male ; Aged ; Middle Aged ; United Kingdom/epidemiology ; SARS-CoV-2 ; COVID-19/virology ; *Antiviral Agents/therapeutic use ; Hospitalization/statistics &amp; numerical data ; Prospective Studies ; Aged, 80 and over ; Treatment Outcome ; },
abstract = {Early on in the COVID-19 pandemic, we aimed to assess the effectiveness of hydroxychloroquine on reducing the need for hospital admission in patients in the community at higher risk of complications from COVID-19 syndromic illness (testing was largely unavailable at the time, hence not microbiologically confirmed SARS-CoV-2 infection), as part of the national open-label, multi-arm, prospective, adaptive platform, randomised clinical trial in community care in the United Kingdom (UK). People aged 65 and over, or aged 50 and over with comorbidities, and who had been unwell for up to 14 days with suspected COVID-19 were randomised to usual care with the addition of hydroxychloroquine, 200 mg twice a day for seven days, or usual care without hydroxychloroquine (control). Participants were recruited based on symptoms and approximately 5% had confirmed SARS-COV2 infection. The primary outcome while hydroxychloroquine was in the trial was hospital admission or death related to suspected COVID-19 infection within 28 days from randomisation. First recruitment was on April 2, 2020, and the hydroxychloroquine arm was suspended by the UK Medicines Regulator on May 22, 2020. 207 were randomised to hydroxychloroquine and 206 to usual care, and 190 and 194 contributed to the primary analysis results presented, respectively. There was no swab result available within 28 days of randomisation for 39% in both groups: 107 (54%) in the hydroxychloroquine group and 111 (55%) in the usual care group tested negative for SARS-Cov-2, and 13 (7%) and 11 (5%) tested positive. 13 participants, (seven (3·7%) in the usual care plus hydroxychloroquine and six (3.1%) in the usual care group were hospitalized (odds ratio 1·04 [95% BCI 0·36 to 3.00], probability of superiority 0·47). There was one serious adverse event, in the usual care group. More people receiving hydroxychloroquine reported nausea. We found no evidence from this treatment arm of the PRINCIPLE trial, stopped early and therefore under-powered for reasons external to the trial, that hydroxychloroquine reduced hospital admission or death in people with suspected, but mostly unconfirmed COVID-19.},
}
@article {pmid40615558,
year = {2025},
author = {Xi, C and Lu, B and Guo, X and Qin, Z and Yan, C and Hu, S},
title = {Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40615558},
issn = {1476-5578},
abstract = {Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous findings of specific brain network connectivity patterns have been inconsistent, and the clinical utility of FCs for predicting treatment outcomes in bipolar depression was underexplored. To identify robust neuro-biomarkers of bipolar depression, a connectome-based analysis was conducted on resting-state functional MRI (rs-fMRI) data of 580 bipolar depression patients and 116 healthy controls (HCs). A subsample of 148 patients underwent a 4-week quetiapine treatment with post-treatment clinical assessment. Adopting machine learning, a predictive model based on pre-treatment brain connectome was then constructed to predict treatment response and identify the efficacy-specific networks. Distinct brain network connectivity patterns were observed in bipolar depression compared to HCs. Elevated intra-network connectivity was identified within the default mode network (DMN), sensorimotor network (SMN), and subcortical network (SC); and as to the inter-network connectivity, increased FCs were between the DMN, SMN and frontoparietal (FPN), ventral attention network (VAN), and decreased FCs were between the SC and cortical networks, especially the DMN and FPN. And the global network topology analyses revealed decreased global efficiency and increased characteristic path length in BD compared to HC. Further, the support vector regression model successfully predicted the efficacy of quetiapine treatment, as indicated by a high correspondence between predicted and actual HAMD reduction ratio values (r(df=147)=0.4493, p = 2*10[-4]). The identified efficacy-specific networks primarily encompassed FCs between the SMN and SC, and between the FPN, DMN, and VAN. These identified networks further predicted treatment response with r = 0.3940 in the subsequent validation with an independent cohort (n = 43). These findings presented the characteristic aberrant patterns of brain network connectome in bipolar depression and demonstrated the predictive potential of pre-treatment network connectome for quetiapine response. Promisingly, the identified connectivity networks may serve as functional targets for future precise treatments for bipolar depression.},
}
@article {pmid40614757,
year = {2025},
author = {Mishler, JH and Yun, R and Perlmutter, S and Rao, RPN and Fetz, EE},
title = {Manipulation of neuronal activity by an artificial spiking neural network implemented on a closed-loop brain-computer interface in non-human primates.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adec1c},
pmid = {40614757},
issn = {1741-2552},
abstract = {OBJECTIVE: Closed-loop brain-computer interfaces (clBCIs) can be used to bridge, modulate, or repair damaged connections within the brain to restore functional deficits. Towards this goal, we demonstrate that small artificial spiking neural networks (SNNs) can be bidirectionally interfaced with single neurons (SNs) in the neocortex of non-human primates (NHPs) to create artificial connections between the SNs to manipulate their activity in predictable ways.<br><br>APPROACH: Spikes from a small group of SNs were recorded from primary motor cortex of two awake NHPs during rest. The SNs were then interfaced with a small network of integrate-and-fire units (IFUs) that were programmed on a custom clBCI. Spikes from the SNs evoked excitatory and/or inhibitory postsynaptic potentials (EPSPs/IPSPs) in the IFUs, which themselves spiked when their membrane potentials exceeded a predetermined threshold. Spikes from the IFUs triggered single pulses of intracortical microstimulation (ICMS) to modulate the activity of the cortical SNs.<br><br>MAIN RESULTS: We show that the altered closed-loop dynamics within the cortex depends on several factors including the connectivity between the SNs and IFUs, as well as the precise timing of the ICMS. We additionally show that the closed-loop dynamics can reliably be modeled from open-loop measurements.<br><br>SIGNIFICANCE: Our results demonstrate a new type of hybrid biological-artificial neural system based on a clBCI that interfaces SNs in the brain with artificial IFUs to modulate biological activity in the brain. Our model of the closed-loop dynamics may be leveraged in the future to develop training algorithms that shape the closed-loop dynamics of networks in the brain to correct aberrant neural activity and rehabilitate damaged neural circuits.},
}
@article {pmid40614457,
year = {2025},
author = {Yan, H and Wang, Z and Li, J},
title = {MSC-transformer-based 3D-attention with knowledge distillation for multi-action classification of separate lower limbs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107806},
doi = {10.1016/j.neunet.2025.107806},
pmid = {40614457},
issn = {1879-2782},
abstract = {Deep learning has been extensively applied to motor imagery (MI) classification using electroencephalogram (EEG). However, most existing deep learning models do not extract features from EEG using dimension-specific attention mechanisms based on the characteristics of each dimension (e.g., spatial dimension), while effectively integrate local and global features. Furthermore, implicit information generated by the models has been ignored, leading to underutilization of essential information of EEG. Although MI classification has been relatively thoroughly investigated, the exploration of classification including real movement (RM) and motor observation (MO) is very limited, especially for separate lower limbs. To address the above problems and limitations, we proposed a multi-scale separable convolutional Transformer-based filter-spatial-temporal attention model (MSC-T3AM) to classify multiple lower limb actions. In MSC-T3AM, spatial attention, filter and temporal attention modules are embedded to allocate appropriate attention to each dimension. Multi-scale separable convolutions (MSC) are separately applied after the projections of query, key, and value in self-attention module to improve computational efficiency and classification performance. Furthermore, knowledge distillation (KD) was utilized to help model learn suitable probability distribution. The comparison results demonstrated that MSC-T3AM with online KD achieved best performance in classification accuracy, exhibiting an elevation of 2 %-19 % compared to a few counterpart models. The visualization of features extracted by MSC-T3AM with online KD reiterated the superiority of the proposed model. The ablation results showed that filter and temporal attention modules contributed most for performance improvement (improved by 2.8 %), followed by spatial attention module (1.2 %) and MSC module (1 %). Our study also suggested that online KD was better than offline KD and the case without KD. The code of MSC-T3AM is available at: https://github.com/BICN001/MSC-T3AM.},
}
@article {pmid40611671,
year = {2025},
author = {Alemu, RZ and Blakeman, A and Fung, AL and Hazen, M and Negandhi, J and Papsin, BC and Cushing, SL and Gordon, KA},
title = {Children With Bilateral Cochlear Implants Show Emerging Spatial Hearing of Stationary and Moving Sound.},
journal = {Trends in hearing},
volume = {29},
number = {},
pages = {23312165251356333},
doi = {10.1177/23312165251356333},
pmid = {40611671},
issn = {2331-2165},
mesh = {Humans ; *Sound Localization ; *Cochlear Implants ; Child ; Male ; Female ; *Cochlear Implantation/instrumentation ; Auditory Threshold ; Speech Perception ; Adolescent ; Cues ; Acoustic Stimulation ; *Persons with Hearing Disabilities/rehabilitation/psychology ; Case-Control Studies ; Eye Movements ; Noise/adverse effects ; Head Movements ; *Hearing Loss, Bilateral/physiopathology/rehabilitation/psychology ; },
abstract = {Spatial hearing in children with bilateral cochlear implants (BCIs) was assessed by: (a) comparing localization of stationary and moving sound, (b) investigating the relationship between sound localization and sensitivity to interaural level and timing differences (ILDs/ITDs), (c) evaluating effects of aural preference on sound localization, and (d) exploring head and eye (gaze) movements during sound localization. Children with BCIs (n = 42, MAge = 12.3 years) with limited duration of auditory deprivation and peers with typical hearing (controls; n = 37, MAge = 12.9 years) localized stationary and moving sound with unrestricted head and eye movements. Sensitivity to binaural cues was measured by a lateralization task to ILDs and ITDs. Spatial separation effects were measured by spondee-word recognition thresholds (SNR thresholds) when noise was presented in front (colocated/0°) or with 90° of left/right separation. BCI users had good speech reception thresholds (SRTs) in quiet but higher SRTs in noise than controls. Spatial separation of noise from speech revealed a greater advantage for the right ear across groups. BCI users showed increased errors localizing stationary sound and detecting moving sound direction compared to controls. Decreased ITD sensitivity occurred with poorer localization of stationary sound in BCI users. Gaze movements in BCI users were more random than controls for stationary and moving sounds. BCIs support symmetric hearing in children with limited duration of auditory deprivation and promote spatial hearing which is albeit impaired. Spatial hearing was thus considered to be "emerging." Remaining challenges may reflect disruptions in ITD sensitivity and ineffective gaze movements.},
}
@article {pmid40611622,
year = {2025},
author = {Dahò, M and Monzani, D},
title = {The multifaceted nature of inner speech: Phenomenology, neural correlates, and implications for aphasia and psychopathology.},
journal = {Cognitive neuropsychology},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/02643294.2025.2527983},
pmid = {40611622},
issn = {1464-0627},
abstract = {This narrative review explores the phenomenon of inner speech - mental speech without visible articulation - and its implications for cognitive science and clinical practice. Despite its importance, the many neural mechanisms underlying inner speech remain unclear. We propose classifying inner speech into monologic, dialogal, elicited, and spontaneous forms, and discuss related phenomenological and neural correlates theories. A literature review on PubMed (1990-2024) identified 83 studies. Dialogal forms recruit Theory of Mind networks, compared to monologic forms. Task-elicited inner speech activates the left inferior frontal gyrus more strongly, while spontaneous inner speech engages Heschl's gyrus, suggesting auditory involvement. Evidence regarding aphasia suggests inner speech may be partially preserved even when overt speech is impaired, offering a potential route for rehabilitation. Future research should also address the emotional aspects of inner speech, its role in psychopathology, and its developmental trajectory. Such studies may improve interventions for disorders related to dysfunctional inner speech.Abbreviation: ACC: anterior cingulate cortex; ALE: activation likelihood estimation; AVH: auditory verbal hallucination; BMI: brain-machine interface; CD: corollary discharge; ConDialInt: consciousness-dialogue-intentionality; DES: descriptive experience sampling; DTI: diffusion tensor imaging; dPMC: dorsal premotor cortex; dmPFC: dorsomedial prefrontal cortex; IFG: inferior frontal gyrus; M1: primary motor cortex; MedFG: medial frontal gyrus; MFG: middle frontal gyrus; MTG: middle temporal gyrus; MRI: magnetic resonance imaging; preSMA: presupplementary motor area; PrG: precentral gyrus; SMA: supplementary motor area; SMG: supramarginal gyrus; SPC: superior parietal cortex; SPL: superior parietal lobule; STG: superior temporal gyrus; STS: superior temporal sulcus; TVA: temporal vocal areas; ToM: theory of mind; vmPFC: ventromedial prefrontal cortex.},
}
@article {pmid40611619,
year = {2025},
author = {Ponomarev, T and Vasilyev, A and Novikova, E and Pokidko, A and Zaitseva, N and Zaitsev, D and Kaplan, A},
title = {Brain mechanisms of (dis)agreement: ERP evidence from binary choice responses.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {7},
pages = {},
doi = {10.1093/cercor/bhaf167},
pmid = {40611619},
issn = {1460-2199},
support = {121032300070-1//Lomonosov Moscow State University/ ; },
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; Electroencephalography ; *Brain/physiology ; Young Adult ; Adult ; *Choice Behavior/physiology ; Brain-Computer Interfaces ; *Decision Making/physiology ; },
abstract = {Agreement and disagreement are essential brain processes that enable effective communication and decision-making. However, a clear neurophysiological framework explaining their organization is still lacking. The present study aimed to identify EEG correlates of implicit agreement and disagreement, developing a novel experimental paradigm to model these internal responses. Participants were tasked with mentally responding to binary ("yes" or "no") questions and evaluating the accuracy of a computer system's attempts to "guess" their responses. Event-related potentials (ERP) revealed distinct patterns associated with agreement and disagreement in two key contexts: when participants read the final word of a question and when they observed the computer's "guess." Disagreement, compared to agreement, elicited larger ERP amplitudes, specifically an enhanced N400 component in the first context and increased feedback-related negativity in the second. Considering the associations of these ERP components with cognitive processes, this research offers robust evidence linking agreement and disagreement to the brain's effort in reconciling personal beliefs and expectations with new information. Furthermore, the experimental framework and findings provide a foundation for the development of brain-computer interfaces (BCIs) capable of detecting "yes" and "no" commands based on their intrinsic EEG predictors, offering promising applications in assistive technologies and neural communication systems.},
}
@article {pmid40611612,
year = {2025},
author = {Saeed, S and Wang, H and Jia, M and Liu, TT and Xu, L and Zhang, X and Hu, SH},
title = {The spectrum of overlapping anti-NMDAR encephalitis and demyelinating syndromes: a systematic review of presentation, diagnosis, management, and outcomes.},
journal = {Annals of medicine},
volume = {57},
number = {1},
pages = {2517813},
doi = {10.1080/07853890.2025.2517813},
pmid = {40611612},
issn = {1365-2060},
mesh = {Humans ; *Anti-N-Methyl-D-Aspartate Receptor Encephalitis/diagnosis/therapy/complications/immunology ; *Demyelinating Diseases/diagnosis/therapy/immunology/complications ; Autoantibodies ; Treatment Outcome ; },
abstract = {BACKGROUND: Anti-NMDAR encephalitis frequently overlaps with demyelinating diseases (MOGAD, NMOSD, MS), creating complex syndromes with diverse presentations and challenging management.<br><br>METHODS: Systematic search of databases including MEDLINE, Google Scholar, Embase, Scopus, Cochrane Library, and Web of Science up to March 2024 for studies on co-existing anti-NMDAR encephalitis and demyelinating syndromes. Data extracted on clinical characteristics, diagnostics, treatments, and outcomes.<br><br>RESULTS: Twenty-five studies identified 256 patients (16.2%) with co-existing Anti-NMDAR encephalitis and demyelinating syndromes, primarily MOGAD (94.5%), with fewer cases involving NMOSD or MS. The Anti-NMDAR&#xa0;+&#xa0;MOGAD subgroup exhibited seizures (51-72.7%), psychiatric symptoms (45.5-71.4%), cognitive dysfunction (30.6%), and movement disorders (30.6%). All patients had CSF anti-NMDAR antibodies, with MOG (60%) or AQP4 (25%) antibodies. Use of standardized, cell-based assays and adherence to established criteria are essential to avoid false positives, particularly for MOG. MRI abnormalities were seen in 75% of patients. First-line immunotherapies were effective in 70% of cases; 80% of refractory cases responded to second-line therapies.<br><br>CONCLUSIONS: Anti-NMDAR encephalitis overlapping with demyelinating diseases is challenging. Tailored treatments based on detailed immune profiles are key to better outcomes.},
}
@article {pmid40611081,
year = {2025},
author = {Wei, Y and Xu, Y and Chen, W and Zheng, J and Chen, H and Chen, S},
title = {Can heart rate variability demonstrate the effects and the levels of mindfulness? A repeated-measures study on experienced and novice mindfulness practitioners.},
journal = {BMC complementary medicine and therapies},
volume = {25},
number = {1},
pages = {231},
pmid = {40611081},
issn = {2662-7671},
mesh = {Humans ; *Heart Rate/physiology ; *Mindfulness ; Male ; Female ; Adult ; Young Adult ; Middle Aged ; Meditation ; },
abstract = {BACKGROUND: Heart rate variability (HRV) is a potential biomarker that might demonstrate the effects of mindfulness, but it might be influenced by practice experiences. This study wanted to elucidate the possibility of using HRV metrics to reveal the effects of mindfulness and examine its variation between novice and experienced mindfulness practitioners.<br><br>METHODS: Forty-six participants (20 experienced practitioners, 26 novices) were enrolled to practice 14-day mindfulness training. HRV data were collected during three phases (20&#xa0;min baseline, T1; 20&#xa0;min mindfulness, T2; 20&#xa0;min post-mindfulness, T3) using Holter monitoring. The linear mixed model was conducted to explore the effects of group and time based on standardized data.<br><br>RESULTS: The experienced group had higher full-scale scores of FFMQ both in the pre-test (t&#x2009;=&#x2009;-3.34, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.002) and the post-test (t&#x2009;=&#x2009;-2.35, df&#x2009;=&#x2009;44, p&#x2009;=&#x2009;0.025). Both groups showed significant changes in HRV indices (e.g., RMSSD, SDNN, LnHF) from T1 to T2 or T3 (p&#x2009;<&#x2009;0.05). In the experienced group, significant fluctuations (p&#x2009;<&#x2009;0.05) were observed at T2, followed by recovery at T3, in SD1/SD2, Sample Entropy, normalized High Frequency (HFn), DFA_&#x3b1;1, and DFA_&#x3b1;2. In contrast, the novice participants only showed monotonic changes in SD1/SD2 and DFA_&#x3b1;1.<br><br>CONCLUSIONS: This study revealed significant HRV changes during mindfulness practice, with distinct patterns observed between novice and experienced practitioners.},
}
@article {pmid40609489,
year = {2025},
author = {Cui, H and Hu, D and Yang, T and Huang, C and Yang, Z and Dong, S},
title = {Humidity sensors based on surface-functionalized tunable photonic crystal grating.},
journal = {Talanta},
volume = {296},
number = {},
pages = {128521},
doi = {10.1016/j.talanta.2025.128521},
pmid = {40609489},
issn = {1873-3573},
abstract = {Photonic crystal (PC)-based humidity sensors detect changes in humidity using periodic structural color variations and have significant potential in the humidity detection field. However, current technologies typically rely on observing these structural color changes with the human eye. The human eye has limited color discrimination, thus resulting in insufficient detection accuracy. Meanwhile, viewing angles and ambient lighting can also disrupt observations. Here, we propose a humidity sensor based on surface-functionalized tunable PC grating. The tunable PC grating consists of a 600 nm polystyrene (PS) microsphere PC and a humidity-sensitive hydrogel. As ambient humidity increases, the hydrophilic amide groups (-CONH2) inside the hydrogel interact with the hydrogen bonds between water molecules and triggers hydrogel swelling, exerts interfacial stress on the PS microsphere lattice, thus expanding the lattice spacing of the PS microspheres and causing a red shift in the reflected wavelength. Integrating the surface-functionalized tunable PC grating into a Czerny-Turner (C-T) optical system enables us to directly translate humidity into precise spectral shifts, overcoming the limitations of human eye-based observations. Experimental results demonstrate a strong linear response over the range of 24-94 % relative humidity (RH), as well as excellent repeatability and long-term stability. We provide an innovative solution for high-precision optical humidity sensing.},
}
@article {pmid40609413,
year = {2025},
author = {Wang, Y and Gao, Y and He, R and Gao, Y and Xu, Z and Wang, C and Liu, F},
title = {Global ocean surface pCO2 retrieval and the influence of mesoscale eddies on its performance.},
journal = {The Science of the total environment},
volume = {993},
number = {},
pages = {179856},
doi = {10.1016/j.scitotenv.2025.179856},
pmid = {40609413},
issn = {1879-1026},
abstract = {CO2 exchange at air-sea interface is crucial for global carbon cycle. Uncertainties in CO2 flux quantification are constrained by ocean surface partial pressure of CO2 (pCO2) variations. While regional pCO2 retrieval algorithms exist, the impact of mesoscale eddies on accuracy remains understudies. We improve the global ocean surface pCO2 retrieval algorithm using XGBoost, incorporating sea surface temperature (SST), chlorophyll-a (Chl-a), sea surface salinity (SSS), mixed layer depth (MLD), and sea surface height (SSH), achieving high performance (R[2]= 0.95, RMSE = 10.52 μatm) at daily resolution. The SHAP method and the sequential feature removal method were used to assesses the individual impacts. The results reveal that SSH significantly enhances model accuracy, increasing R[2] by ∼10% and decreasing RMSE by ∼38%. Regional evaluations show better performance in the Atlantic, with overestimation (underestimation) at ocean gyre fronts (interiors). The models perform better in summer, while in winter, more overestimation is observed in the North Pacific. The future prediction in global field shows excellent spatiotemporal extrapolation performance. The results verify mesoscale dynamics significantly impact the retrieval accuracy in energetic regions. Relative error normalized quantities were calculated for cyclonic and anticyclonic eddies in eddy-active regions to analyze the influence of energetic mesoscale dynamic, suggesting that regional and seasonal variations in errors are linked to differences in eddy-induced nutrient flux and baroclinic instabilities.},
}
@article {pmid40609325,
year = {2025},
author = {Ren, X and Zhou, C and Jiang, Y and Zhao, J and Tina, X and Xu, N and Fu, M and Ni, P and Li, T and Zhang, X},
title = {Generation of an induced pluripotent stem cell line (HZSMHCi002-A) from a patient with neuronal intranuclear inclusion disease carrying GGC repeat expansion in the NOTCH2NLC gene.},
journal = {Stem cell research},
volume = {87},
number = {},
pages = {103761},
doi = {10.1016/j.scr.2025.103761},
pmid = {40609325},
issn = {1876-7753},
abstract = {The NOTCH2NLC gene contains a GGC repeat expansion in its 5' untranslated region. This expansion is associated with neuronal intranuclear inclusion disease (NIID). NIID is a rare neurodegenerative disorder. Its clinical features include cognitive decline, paroxysmal symptoms, and autonomic dysfunction. We generated an induced pluripotent stem cell (iPSC) line from a female patient's PBMCs carrying a high GGC repeat expansion in NOTCH2NLC. The iPSC line displayed typical pluripotent morphology. It expressed key pluripotency markers and demonstrated differentiation potential in teratoma assays. This cell line serves as a useful model for studying disease mechanisms and developing therapeutic strategies.},
}
@article {pmid40609285,
year = {2025},
author = {Xu, JJ and Chen, YL and Yu, H and Chen, DF and Li, HF and Wu, ZY},
title = {Genetic and Clinical Features of SLC2A1-Related Paroxysmal Exercise-Induced Dyskinesia.},
journal = {Pediatric neurology},
volume = {170},
number = {},
pages = {31-37},
doi = {10.1016/j.pediatrneurol.2025.06.006},
pmid = {40609285},
issn = {1873-5150},
abstract = {BACKGROUND: Paroxysmal exercise-induced dyskinesia (PED) is a rare movement disorder characterized by choreoathetosis and dystonia triggered by sustained exercise, commonly affecting the lower extremities. PED is an autosomal dominant disorder genetically linked to mutations in the SLC2A1 gene. The transmembrane protein Glut1, encoded by the SLC2A1 gene, can transport glucose from blood to the brain. This study aimed to characterize the genetic and clinical features of SLC2A1-related PED.<br><br>METHODS: We reported two Chinese PED families presenting with involuntary movements after prolonged exercise. Whole-exome sequencing was performed on two probands, and cosegregation analysis was subsequently carried out in available family members. Additionally, we summarized and analyzed the genetic and clinical features of SLC2A1-related PED by retrieving information from the literature.<br><br>RESULTS: Genetic testing identified two missense mutations in SLC2A1 in these families, including a known disease-causing mutation, c.997C>T (p.R333W), and a novel mutation, c.823G>C (p.A275P). Upon review of the literature, mutations in certain regions of the Glut1 protein, particularly in transmembrane segments 3, 4, 5, 7, and 8, together with the intracellular domain, were more frequently seen in PED. Among the various types of epilepsy, absence seizures were the most common in patients with PED. Furthermore, familial PED had a later onset and a higher cerebrospinal fluid/blood glucose ratio. Patients with missense mutations exhibited a later onset than those with truncated mutations.<br><br>CONCLUSIONS: Our study identified a new disease-causing mutation and, through an extensive literature review, provided a detailed genetic and clinical description of PED associated with SLC2A1 mutations.},
}
@article {pmid40608885,
year = {2025},
author = {Yang, Z and Si, X and Jin, W and Huang, D and Zang, Y and Yin, S and Ming, D},
title = {SEEG Emotion Recognition Based on Transformer Network With Channel Selection and Explainability.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3585528},
pmid = {40608885},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology for emotion recognition holds significant potential for future applications in the treatment of refractory emotional disorders. Stereo-electroencephalography (SEEG), being less invasive, can precisely record neural activities originating from the cortex and the deep structures of the brain. Thus, it has broad application prospects in constructing emotion recognition BCI. In this study, SEEG data from nine subjects were collected to construct an emotion dataset, and a Spatial Transformer-based Hybrid Network (STHN) was proposed for SEEG emotion recognition. The triple-classification accuracy of STHN reached 83.56%, outperforming the baseline methods such as EEGNet, TSception, and the deep convolution neural network. Moreover, STHN can assign weights to each SEEG channel and select those channels that contribute more significantly to emotion recognition. It was found that when using the top 30% weighted SEEG channels as model inputs, the accuracy did not decrease significantly. Most of the channels with higher weights were located in brain regions strongly associated with emotions, such as the frontal lobe, the temporal lobe, and the hippocampus. This indicates that STHN is not merely a "black-box" model but possesses a degree of explainability. To the best of our knowledge, this is the first study to develop an SEEG emotion recognition algorithm, which is expected to play a crucial role in the monitoring and treatment of patients with refractory emotional disorders in the future.},
}
@article {pmid40608881,
year = {2025},
author = {Yu, X and Yu, X},
title = {Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.},
journal = {IEEE transactions on cybernetics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TCYB.2025.3580726},
pmid = {40608881},
issn = {2168-2275},
abstract = {Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.},
}
@article {pmid40606836,
year = {2025},
author = {Cantillo-Negrete, J and Rodríguez-García, ME and Carrillo-Mora, P and Arias-Carrión, O and Ortega-Robles, E and Galicia-Alvarado, MA and Valdés-Cristerna, R and Ramirez-Nava, AG and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Pacheco-Gallegos, MDR and Marín-Arriaga, N and Carino-Escobar, RI},
title = {The ReHand-BCI trial: a randomized controlled trial of a brain-computer interface for upper extremity stroke neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1579988},
pmid = {40606836},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interfaces (BCI) are a promising complementary therapy for stroke rehabilitation due to the close-loop feedback that can be provided with these systems, but more evidence is needed regarding their clinical and neuroplasticity effects.<br><br>METHODS: A randomized controlled trial was performed using the ReHand-BCI system that provides feedback with a robotic hand orthosis. The experimental group (EG) used the ReHand-BCI, while sham-BCI was given to the control group (CG). Both groups performed 30 therapy sessions, with primary outcomes being the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Secondary outcomes were hemispheric dominance, measured with electroencephalography and functional magnetic resonance imaging, white matter integrity via diffusion tensor imaging, and corticospinal tract integrity and excitability, measured with transcranial magnetic stimulation.<br><br>RESULTS: At post-treatment, patients in both groups had significantly different FMA-UE scores (EG: baseline&#x202f;=&#x202f;24.5[20, 36], post-treatment 28[23, 43], CG: baseline&#x202f;=&#x202f;26[16, 37.5], post-treatment&#x202f;=&#x202f;34[17.3, 46.5]), while only the EG had significantly different ARAT scores at post-treatment (EG: baseline&#x202f;=&#x202f;8.5[5, 26], post-treatment&#x202f;=&#x202f;20[7, 36], CG: baseline&#x202f;=&#x202f;3[1.8, 30.5], post-treatment&#x202f;=&#x202f;15[2.5, 40.8]). In addition, across the intervention, the EG showed trends of more pronounced ipsilesional cortical activity and higher ipsilesional corticospinal tract integrity, although these differences were not statistically different compared to the control group, likely due to the study's sample size.<br><br>CONCLUSION: To the authors' knowledge, this is the first clinical trial that has assessed such a wide range of physiological effects across a long BCI intervention, implying that a more pronounced ipsilesional hemispheric dominance is associated with upper extremity motor recovery. Therefore, the study brings light into the neuroplasticity effects of a closed-loop BCI-based neurorehabilitation intervention in stroke.<br><br>CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/, identifier NCT04724824.},
}
@article {pmid40606655,
year = {2025},
author = {Jacob, JE and Chandrasekharan, S},
title = {Editorial: Advanced EEG analysis techniques for neurological disorders.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1637890},
doi = {10.3389/fninf.2025.1637890},
pmid = {40606655},
issn = {1662-5196},
}
@article {pmid40605914,
year = {2025},
author = {Yang, L and Zhu, W},
title = {Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {106},
pmid = {40605914},
issn = {1871-4080},
abstract = {Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.},
}
@article {pmid40603471,
year = {2025},
author = {Liao, W and Liu, H and Wang, W},
title = {Advancing BCI with a transformer-based model for motor imagery classification.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {23380},
pmid = {40603471},
issn = {2045-2322},
support = {2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; 2020AAA0105800//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Machine Learning ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise. This paper introduces EEGEncoder, a deep learning framework that employs modified transformers and Temporal Convolutional Networks (TCNs) to surmount these limitations. We propose a novel fusion architecture, named Dual-Stream Temporal-Spatial Block (DSTS), to capture temporal and spatial features, improving the accuracy of Motor Imagery classification task. Additionally, we use multiple parallel structures to enhance the model's performance. When tested on the BCI Competition IV-2a dataset, our proposed model achieved an average accuracy of 86.46% for subject dependent and average 74.48% for subject independent.},
}
@article {pmid40603333,
year = {2025},
author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD},
title = {Correction: A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1132},
doi = {10.1038/s41597-025-05466-y},
pmid = {40603333},
issn = {2052-4463},
}
@article {pmid40602422,
year = {2025},
author = {Jin, J and Liang, W and Xu, R and Chen, W and Xu, R and Wang, X and Cichocki, A},
title = {A transformer-based network with second-order pooling for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeae8},
pmid = {40602422},
issn = {1741-2552},
abstract = {OBJECTIVE: Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks (CNNs), have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.<br><br>APPROACH: To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.<br><br>MAIN RESULTS: SecTNet is evaluated on two publicly available EEG datasets, namely BCI Competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI Competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.<br><br>SIGNIFICANCE: These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.},
}
@article {pmid40602419,
year = {2025},
author = {Li, L and Wei, B},
title = {A two-stage EEG zero-shot classification algorithm guided by class reconstruction.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adeaea},
pmid = {40602419},
issn = {1741-2552},
abstract = {Researchers have long been dedicated to decoding human visual representations from neural signals. These studies are crucial in uncovering the mechanisms of visual processing in the human brain. Electroencephalogram(EEG) signals have garnered widespread attention recently due to their noninvasive nature and low cost. EEG classification is one of the most popular topics in brain-computer interface(BCI) research. However, most traditional EEG classification algorithms are difficult to generalize to unseen classes that were not involved in the training phase. The main objective of this work is to improve the performance of these EEG classification algorithms for unseen classes. In this work, we propose a two-stage zero-shot EEG classification algorithm guided by class reconstruction. The method is specifically designed with a two-stage training strategy based on class reconstruction. This structure and training strategy enable the model to thoroughly learn the relations and distinctions among EEG embeddings of different classes. The Contrastive Language-Image Pre-training(CLIP) model has a well-aligned latent space and powerful cross-modality generalization ability. The method bridges the modality gap between EEG, images, and text using CLIP features. It significantly improves the model's performance in unseen classes. We conducted experiments on the ImageStimulus-EEG dataset to evaluate the performance of the proposed method. Meanwhile, it was compared with the state-of-the-art model and the baseline model. The experimental results demonstrate that our model achieves superior performance in among Top-1, Top-3, and Top-5 classification accuracy for a 50-way zero-shot classification task, reaching 17.77%, 38.76% and 54.75%, respectively. These results further validate the effectiveness of the proposed method in EEG zero-shot classification.},
}
@article {pmid40602315,
year = {2025},
author = {Del Pup, F and Zanola, A and Tshimanga, LF and Bertoldo, A and Finos, L and Atzori, M},
title = {The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110608},
doi = {10.1016/j.compbiomed.2025.110608},
pmid = {40602315},
issn = {1879-0534},
abstract = {Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal properties (e.g., biometric). Such variability in model evaluation leads to incomparable studies and, increasingly, overestimated performance claims, which are detrimental to the field. Nevertheless, no comprehensive guidelines for proper data partitioning and cross-validation exist in the domain, nor is there a quantitative evaluation of the impact of different approaches on model accuracy, reliability, and generalizability. To assist researchers in identifying optimal experimental strategies, this paper thoroughly investigates the role of data partitioning and cross-validation in evaluating EEG deep learning models. Five cross-validation settings are compared across three supervised cross-subject classification tasks (brain-computer interfaces, Parkinson's, and Alzheimer's disease classification) and four established architectures of increasing complexity (ShallowConvNet, EEGNet, DeepConvNet, and Temporal-based ResNet). The comparison of over 100,000 trained models underscores, first, the importance of using subject-based cross-validation strategies for evaluating EEG deep learning architectures, except when within-subject analyses are acceptable (e.g., BCI). Second, it highlights the greater reliability of nested approaches (e.g., N-LNSO) compared to non-nested counterparts, which are prone to data leakage and favor larger models overfitting to validation data. In conclusion, this work provides EEG deep learning researchers with an analysis of data partitioning and cross-validation and offers guidelines to avoid data leakage, currently undermining the domain with potentially overestimated performance claims.},
}
@article {pmid40602314,
year = {2025},
author = {Huang, S and Wei, Q},
title = {A deep learning model combining convolutional neural networks and a selective kernel mechanism for SSVEP-Based BCIs.},
journal = {Computers in biology and medicine},
volume = {196},
number = {Pt A},
pages = {110691},
doi = {10.1016/j.compbiomed.2025.110691},
pmid = {40602314},
issn = {1879-0534},
abstract = {Existing deep learning methods for brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) face several challenges, such as overfitting when training data are insufficient, and the difficulty of effectively capturing global temporal features due to limited receptive fields. To address these challenges, we propose a novel deep learning model, FBCNN-TKS, which extracts harmonic components from SSVEP signals using a filter bank technique, followed by feature extraction through convolutional neural networks (CNNs) and a temporal kernel selection (TKS) module, and finally the weighted sum of cross-entropy loss and center loss is used as the objective function for model optimization. The key innovation of our approach lies in the introduction of the TKS module, which significantly enhances feature extraction capability by providing a broader receptive field. Additionally, dilated and grouped convolutions are used in TKS module to reduce the number of model parameters, minimizing the risk of overfitting and improving classification accuracy. Experimental results manifest that FBCNN-TKS outperforms state-of-the-art methods in terms of classification accuracy and information transfer rate (ITR). Specifically, FBCNN-TKS achieved the highest ITRs of 251.54 bpm and 203.47 bpm with the highest accuracies of 83.10 % and 72.98 % on public datasets Benchmark and BETA respectively at the data length of 0.4s, exhibiting superior performance. The FBCNN-TKS model bears big potential for the development of high-performance SSVEP-BCI character spelling systems.},
}
@article {pmid40601454,
year = {2025},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Chen, X and Liu, J and Zeng, LL and Hu, D},
title = {DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3581991},
pmid = {40601454},
issn = {2162-2388},
abstract = {Electroencephalography (EEG) plays a crucial role in neuroscience research and clinical practice, but it remains limited by nonuniform data, noise, and difficulty in labeling. To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG. First, we propose a novel brain region topological heterogeneity (BRTH) division method to partition the nonuniform data into fixed patches based on neuroscientific priors. Second, we design a denoised pseudo-label generator (DPLG), which utilizes a denoising reconstruction pretext task to enable the learning of generalizable representations from massive unlabeled EEG, suppressing the influence of noise and artifacts. Furthermore, we utilize an asymmetric autoencoder with self-attention as the backbone in the proposed DMAE-EEG, which captures long-range spatiotemporal dependencies and interactions from unlabeled EEG data across 14 public datasets. The proposed DMAE-EEG is validated on both generative (signal quality enhancement) and discriminative tasks (motion intention recognition). In the quality enhancement, DMAE-EEG outperforms existing statistical methods with normalized mean squared error (nMSE) reduction of 27.78%-50.00% under corruption levels of 25%, 50%, and 75%, respectively. In motion intention recognition, DMAE-EEG achieves a relative improvement of 2.71%-6.14% in intrasession classification balanced accuracy across 2-6 class motor execution and imagery tasks, outperforming state-of-the-art methods. Overall, the results suggest that the pretraining framework DMAE-EEG can capture generalizable spatiotemporal representations from massive unlabeled EEG and enhance the knowledge transferability across sessions, subjects, and tasks in various downstream scenarios, advancing EEG-aided diagnosis and brain-computer communication and control, and other clinical practice.},
}
@article {pmid40601441,
year = {2025},
author = {Si, X and Han, Y and Li, S and Zhang, S and Ming, D},
title = {The cortical spatial responses and decoding of emotion imagery towards a novel fNIRS-based affective BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3584765},
pmid = {40601441},
issn = {1558-0210},
abstract = {Functional near-infrared spectroscopy (fNIRS), with its non-invasive and high spatial resolution, holds promise in developing novel affective brain-computer interface (BCI). Similar to motor imagery BCI, emotion imagery BCI could recognize internal emotions and convey them to the external world. This holds clinical value for expressing emotions in patients with neurological impairments and serves as a proactive emotion regulation method. However, the fNIRS features of emotion imagery for affective BCI and the discriminability of different emotion categories remain unclear. Here, this study designed a novel emotion verbal imagery paradigm (imagining descriptions of happy or sad scenes). First, task-related hemodynamic responses were analyzed from 17 subjects. Then, statistical analyses were then conducted to reveal the significant cortical spatial response patterns. Additionally, decoding experiments and model interpretability are employed to assist in validating the feasibility of the emotion imagery BCI. Results showed: (1) Happy imagery recruited frontoparietal regions, such as the left dorsal secondary motor cortex, ventral secondary motor cortex, and inferior parietal lobe. (2) Sad imagery mainly recruited the right dorsolateral prefrontal cortex. (3) The left dorsal sensorimotor cortex exhibited selective responsiveness to happy imagery and sad imagery. (4) The classification results of the emotion imagery task exceeded the random level. (5) Emotional categories activation responses showed significant similarity with the hemodynamic responses of the imagination tasks. Taken together, by proposing the emotion imagery fNIRS paradigm, this work could shed light on the development of feature non-invasive BCI.},
}
@article {pmid40600191,
year = {2025},
author = {Ma Thi, C and Nguyen The, HA and Nguyen Minh, K and Vu Thanh, L and Nguyen Dinh, H and Huynh Thi, NY and Ha Thi, TH and Hoang Tien, TN and Au Dao, DT and Nguyen Hoang, KL and Huynh Kha, V and Le Hoang, TL},
title = {UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1580931},
pmid = {40600191},
issn = {1662-4548},
}
@article {pmid40598468,
year = {2025},
author = {Chen, Y and Zhao, N and Zhang, J and Wu, X and Huang, J and Xu, X and Cai, F and Chen, S and Xu, L and Yan, W and Hong, Y and Wang, Y and Ling, H and Ji, J and Chen, G and Gu, H and Zhang, J and Wu, Q},
title = {Molecular signatures of invasive and non-invasive pituitary adenomas: a comprehensive analysis of DNA methylation and gene expression.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {373},
pmid = {40598468},
issn = {1741-7015},
mesh = {Humans ; *DNA Methylation ; *Pituitary Neoplasms/genetics/pathology ; *Adenoma/genetics/pathology ; Male ; Female ; Middle Aged ; Adult ; *Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics ; Neoplasm Invasiveness ; Gene Expression Profiling ; },
abstract = {BACKGROUND: Pituitary adenomas (PAs) are benign tumors in the pituitary gland. However, 30-40% of these tumors are invasive, complicating diagnosis and treatment. Invasive pituitary adenomas (IPAs) often respond poorly to conventional therapies, emphasizing the need for better diagnostic and therapeutic strategies. Understanding DNA methylation patterns in IPAs may reveal new biomarkers and therapeutic targets, leading to more effective management of this challenging disease.<br><br>METHODS: Reduced representation bisulfite sequencing (RRBS) and RNA sequencing (RNA-seq) were performed on 129 samples from the Second Affiliated Hospital of Zhejiang University, including 69 tissue samples from invasive and non-invasive pituitary adenomas (NPA) and 60 blood samples from IPA, NPA and healthy individuals. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were identified in tissues. Pearson correlation analysis was used to identify associations between DNA methylation status and gene expression, as well as the effect of methylation on gene expression at different sites. Blood samples were analyzed to detect DMRs and DEGs, correlating with tissue-derived findings. Finally, ROC analysis and a random forest model were used to identify biomarkers for discriminating invasive from non-invasive phenotypes.<br><br>RESULTS: We identified 347 DMRs between IPA and NPA, of which 63% (219/347) were hypomethylated. Additionally, 543 mRNAs showed differential expression, with 350 upregulated and 193 downregulated. 17 genes demonstrated concurrent aberrant methylation and expression, primarily within introns, promoters, and CpG islands (CGIs). Notably, only protein tyrosine phosphatase receptor type T (PTPRT) exhibited a remarkably high correlation (r&#x2009;=&#x2009;0.81) between its DNA methylation levels and mRNA expression levels. This correlation was observed within the intronic region/opensea of the gene's CGIs. Plasma sample analysis revealed 852 DMRs between IPA and NPA, with 52% (447/852) being hypomethylated. Three tumor tissue-derived blood biomarkers (MIR4535, SLC8A1-AS1, and TTC34) accurately discriminated between IPA and NPA patients with a combined AUC of 0.980. These markers also differentiated NPA from healthy controls, though with different methylation patterns.<br><br>CONCLUSIONS: The relationship between DNA methylation and gene expression is complex. Plasma-based DNA methylation markers can effectively discriminate between IPA and NPA, as well as between NPA and healthy individuals (N group).},
}
@article {pmid40598460,
year = {2025},
author = {Lu, R and Pang, Z and Gao, T and He, Z and Hu, Y and Zhuang, J and Zhang, Q and Gao, Z},
title = {Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {380},
pmid = {40598460},
issn = {1741-7015},
support = {82372570//the National Science Foundation of China/ ; 82372570//the National Science Foundation of China/ ; 82271422//the National Science Foundation of China/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 23Y11900900//Medical Innovation Research Project funded by Shanghai Science and Technology Commission/ ; 22ZR1479000//Shanghai Natural Science Foundation/ ; 20234Y0043//Shanghai Municipal Health Commission/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Aged ; *Feedback, Sensory/physiology ; Chronic Disease ; Magnetic Resonance Imaging ; Adult ; Neuronal Plasticity ; },
abstract = {BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehabilitation therapies rarely achieve substantial functional recovery. Sensory feedback during movement plays a pivotal role in driving neuroplasticity. This study introduces a novel multi-modal sensory feedback brain-computer interface (Multi-FDBK-BCI) system that integrates proprioceptive, tactile, and visual stimuli into motor imagery-based training. We aimed to explore the potential therapeutic efficacy and elucidate its neural mechanisms underlying motor recovery.<br><br>METHODS: Thirty-nine chronic stroke patients were randomized to either the Multi-FDBK-BCI group (n = 20) or the conventional motor imagery therapy group (n = 19). Motor recovery was assessed using the Fugl-Meyer Assessment (primary outcome), Motor Status Scale, Action Research Arm Test, and surface electromyography. Functional MRI was used to examine brain activation patterns during upper limb tasks, while Granger causality analysis and machine learning evaluated inter-regional connectivity changes and their predictive value for recovery.<br><br>RESULTS: Multi-FDBK-BCI training led to significantly greater motor recovery compared to conventional therapy. Functional MRI revealed enhanced activation of high-order transmodal networks-including the default mode, dorsal/ventral attention, and frontoparietal networks-during paralyzed limb movement, with activation strength positively correlated with motor improvement. Granger causality analysis identified a distinct information flow pattern: signals from the lesioned motor cortex were relayed through transmodal networks to the intact motor cortex, promoting interhemispheric communication. These functional connectivity changes not only supported motor recovery but also served as robust predictors of therapeutic outcomes.<br><br>CONCLUSIONS: Our findings highlight the Multi-FDBK-BCI system as a promising strategy for chronic stroke rehabilitation, leveraging activity-dependent neuroplasticity within high-order transmodal networks. This multi-modal approach holds significant potential for patients with limited recovery options and sheds new light on the neural drivers of motor restoration, warranting further investigation in clinical neurorehabilitation.<br><br>TRIAL REGISTRATION: All data used in the present study were obtained from a research trial registered with the ClinicalTrials.gov database (ChiCTR-ONC-17010739, registered 26 February 2017, starting from 10 January 2017).},
}
@article {pmid40596215,
year = {2025},
author = {Rabbani, M and Sabith, NUS and Parida, A and Iqbal, I and Mamun, SM and Khan, RA and Ahmed, F and Ahamed, SI},
title = {EEG based real time classification of consecutive two eye blinks for brain computer interface applications.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21007},
pmid = {40596215},
issn = {2045-2322},
mesh = {Humans ; *Blinking/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Support Vector Machine ; Neural Networks, Computer ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of brain-computer interfaces (BCI). This study introduces a novel real-time EEG-based framework for classifying three blink states: no blink, single blink, and two consecutive blinks in one model. EEG data were collected from ten healthy participants using an 8-channel wearable headset under controlled blinking conditions. The data were preprocessed and analyzed using four feature extraction techniques: basic statistical, time-domain, amplitude-driven, and frequency-domain methods. The most significant features were selected to develop three machine learning models: XGBoost, support vector machine (SVM), and neural network (NN). We achieved the highest accuracy of 89.0% for classifying multiple-eye blink detection. To further enhance the model's capacity and suitability for real-life BCI applications, we trained and employed the You Only Look Once (YOLO) model, achieving a recall of 98.67%, a precision of 95.39%, and mAP50 of 99.5%, demonstrating its superior accuracy and robustness in classifying two consecutive eye blinks. In conclusion, this study will be the first groundwork and open a new dimension in EEG-based BCI research by classifying multiple-eye blink detection.},
}
@article {pmid40595635,
year = {2025},
author = {Liu, L and Gao, Z and Niu, X and Yu, H and Xin, X and Gu, Y and Ma, G and Gu, Y and Liu, Y and Fang, S and Marquardt, T and Wang, L},
title = {SEMA3B switches axon-axon to axon-glia interactions required for unmyelinated axon envelopment and integrity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5433},
pmid = {40595635},
issn = {2041-1723},
support = {32100758//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Axons/metabolism ; *Semaphorins/metabolism/genetics ; Mice ; Schwann Cells/metabolism ; Hyperalgesia/metabolism ; Male ; Mice, Inbred C57BL ; *Nerve Fibers, Unmyelinated/metabolism ; Peripheral Nerve Injuries/metabolism ; Endocytosis ; *Neuroglia/metabolism ; Cell Communication ; },
abstract = {During peripheral nerve (PN) development, unmyelinated axons (nmAs) tightly fasciculate before being separated and enveloped by non-myelinating Schwann cells (nmSCs), glial cells essential for maintaining nmA integrity. How such a switch from axon-axon to axon-glia interactions is achieved remains poorly understood. Here, we find that inactivating SC-derived SEMA3B or its axonal receptor components in mice leads to incomplete nmA separation and envelopment by nmSCs, eliciting hyperalgesia and allodynia. Conversely, increasing SEMA3B levels in SCs accelerates nmA separation and envelopment. SEMA3B transiently promotes nmA defasciculation accompanied by cell adhesion molecule (CAM) endocytosis, subsequently facilitating nmA-nmSC association. Restoring SEMA3B expression following PN injury promotes nmA-nmSC re-association and alleviates hyperalgesia and allodynia. We propose that SEMA3B-induced CAM turnover facilitates a switch from axon-axon to axon-glia interactions promoting nmA envelopment by nmSCs, which may be exploitable for alleviating PN injury-induced pain by accelerating the restoration of nmA integrity.},
}
@article {pmid40594904,
year = {2025},
author = {Sayem, M and Rafi, MA and Mishu, ID and Mahmud, Z},
title = {Comprehensive genomic analysis reveals virulence and antibiotic resistance genes in a multidrug-resistant Bacillus cereus isolated from hospital wastewater in Bangladesh.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22915},
pmid = {40594904},
issn = {2045-2322},
mesh = {*Bacillus cereus/genetics/pathogenicity/isolation &amp; purification/drug effects ; *Wastewater/microbiology ; Bangladesh ; *Drug Resistance, Multiple, Bacterial/genetics ; Phylogeny ; Hospitals ; Virulence/genetics ; Genome, Bacterial ; Whole Genome Sequencing ; Genomics/methods ; Anti-Bacterial Agents/pharmacology ; Virulence Factors/genetics ; Humans ; },
abstract = {Hospital wastewater represents a significant reservoir for antimicrobial-resistant bacteria, including multidrug-resistant (MDR) Bacillus cereus, a pathogen of growing concern due to its potential impact on public health and environmental safety. This study characterizes the genomic features, antimicrobial resistance (AMR) mechanisms, and virulence potential of Bacillus cereus MBC, isolated from hospital wastewater in Dhaka, Bangladesh. Using whole-genome sequencing (WGS) and advanced bioinformatics, we analyzed the isolate's taxonomy, phylogenetics, functional annotation, and biosynthetic potential. The genome, spanning 5.6 Mb with a GC content of 34.84%, contained 5,881 protein-coding sequences, including 1,424 hypothetical proteins, and 28 genes associated with AMR. Phylogenetic analysis revealed a close genetic relationship with Bacillus cereus ATCC 14579, sharing virulence factors such as hemolysin BL (HBL), non-hemolytic enterotoxin (NHE), and cytotoxin K (CytK), all contributing to its pathogenicity. The ability to form biofilms further enhances the strain's persistence and resistance in hospital environments. AMR profiling identified genes conferring resistance to beta-lactams (e.g., BcI, BcII, BcIII), tetracyclines (tetB(P)), glycopeptides (vanY), and fosfomycin, highlighting the bacterium's capacity to resist a wide array of antibiotics. Functional annotation revealed metabolic pathways involved in iron acquisition and the biosynthesis of siderophores such as petrobactin and bacillibactin, reinforcing the bacterium's adaptability in nutrient-limited environments. Mobile genetic elements, including prophages, CRISPR-Cas systems, and transposable elements, suggest significant horizontal gene transfer (HGT), enhancing genetic plasticity and resistance spread. Pangenomic analysis, involving 125 B. cereus strains, revealed a high degree of genetic diversity and close relationships with strains from clinical, food, and agricultural environments, emphasizing the overlap between clinical and environmental reservoirs of resistance. The strain's isolation from hospital wastewater underscores the complex interplay between environmental contaminants and bacterial evolution, which fosters MDR traits. Our findings underscore the urgent need for enhanced genomic surveillance and wastewater management strategies to mitigate the spread of MDR B. cereus and AMR genes in hospital environments.},
}
@article {pmid40594760,
year = {2025},
author = {Kanna, RK and Shoran, P and Yadav, M and Ahmed, MN and Burje, S and Shukla, G and Sinha, A and Hussain, MR and Khalid, S},
title = {Improving EEG based brain computer interface emotion detection with EKO ALSTM model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {20727},
pmid = {40594760},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Algorithms ; *Brain/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; },
abstract = {Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscular or muscle channels. Various techniques for automatic emotion identification based on body language, speech, or facial expressions are nowadays in use. However, the monitoring of exterior emotions, which are easily manipulated, limits the applicability of these procedures. EEG-based emotion detection research might yield significant benefits for enhancing BCI application performance and user experience. To overcome these issues, this study proposed a novel EKO-ALSTM for emotion detection in EEG-based brain-computer interfaces. The proposed study comprises EEG-based signals that record the electrical activity of the brain connected to various emotional states, which are gathered as real-time acquired EEG signals for emotion detection. The data was pre-processed using a bandpass filter to remove unwanted frequency noise for the obtained data. Then, feature extraction is performed using DWT from pre-processed data. Specifically, the proposed approach is implemented using Python software. The proposed system and existing algorithms are compared using a variety of evaluation criteria, including specificity, F1 score, accuracy, recall or sensitivity, and positive predictive values or precision. The results demonstrated that the proposed method achieved better performance in EEG-based BCI emotion detection with an accuracy of 97.93%, a positive predictive value of 96.24%, a sensitivity of 97.81%, and a specificity of 97.75%. This study emphasizes that innovative approaches have significantly increased the accuracy of emotion identification when applied to EEG-based emotion recognition systems. Additionally, the findings suggest that integrating advanced machine learning techniques can further enhance the effectiveness and reliability of these systems in real-world applications, paving the way for more responsive and intuitive BCI technologies.},
}
@article {pmid40594416,
year = {2025},
author = {Wechakarn, P and Klomchitcharoen, S and Jatupornpoonsub, T and Jirakittayakorn, N and Puttanawarut, C and Likitsuntonwong, W and Chaimongkolnukul, K and Wongsawat, Y},
title = {Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {22166},
pmid = {40594416},
issn = {2045-2322},
mesh = {Animals ; *Brain Injuries, Traumatic/surgery/mortality ; *Stereotaxic Techniques ; Disease Models, Animal ; Rats ; *Neurosurgical Procedures/methods/instrumentation ; Male ; Operative Time ; Rats, Sprague-Dawley ; Mice ; },
abstract = {Controlled cortical impact (CCI) is the most widely used mechanical model of traumatic brain injury (TBI) in rodent brains. This neurosurgical procedure generally involves the use of a stereotaxic system, which requires reaching a specific brain region with the most accurate position possible. In this study, a modified stereotaxic system for TBI induction was developed to evaluate preclinical research in rodents for conducting neural stimulation experiments by using an implanted electrode to assist in rehabilitation after severe TBI. The proposed model aims to reduce animal mortality during surgery and alleviate the negative side effects potentially caused by prolonged anesthesia drug usage. Isoflurane is applied as an anesthetic drug before stereotaxic surgery in rodents, which promotes hypothermia in the animal body. The result showed notable improvement in rodent survival after applying an active warming pad system to prevent hypothermia. Compared with the conventional stereotaxic system, the modified CCI device with a mounted 3D-printed header significantly improved performance in the surgical procedure, decreasing the total operation time by 21.7%, especially in the Bregma‒Lambda measurement. These findings indicate the tangible capability of our modified stereotaxic system, which allows surgeons to perform stereotaxic surgery faster and lowers the risk of intraoperative mortality.},
}
@article {pmid40594365,
year = {2025},
author = {Hadi-Saleh, Z and Mosleh, M and Al-Shahe, MA and Mosleh, M},
title = {Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {21202},
pmid = {40594365},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Movement/physiology ; Brain/physiology ; },
abstract = {The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement intentions. This technology has many applications in the fields of medicine, rehabilitation, mind-controlled computers and assistive technologies. Despite significant progress in EEG-based BCI systems, there are challenges such as signal noise, low decoding accuracy, instability and changeability of signals, etc. To address these limitations, this article presents a new approach to classify MI from EEG signals with the help of synergistic Hilbert-Huang Transform(HHT) as pre-processing, Permutation Conditional Mutual Information Common Space Pattern (PCMICSP) as features and optimized back propagation neural network(BPNN) based on Honey Badger Algorithm(HBA) as classifier. Using the ergodicity of the HBA, along with chaotic mechanisms and global convergence, this approach encodes and optimizes the weights and thresholds of a BPNN. Initially, a comprehensive optimal solution is obtained through the honey badger algorithm. Subsequently, this solution is further refined to reach a more precise optimal state by introducing chaotic disturbances. The proposed method efficiency was confirmed through experimental analysis on a set of data of benchmark that is generally accessible of EEGMMIDB (imagery database or motor movement of EEG). Our experimental analysis outcome showed that mechanism development is important. Now, two EEG signal levels were taken into consideration: the first being an epileptic and the other being non-epileptic. The presented technique generated a max accuracy of 89.82% in comparison with other methods.},
}
@article {pmid40590757,
year = {2025},
author = {Chang, T and Cho, SI and Chai, JY and Min, KD},
title = {Implications of predator species richness in terms of zoonotic spillover transmission of filovirus diseases in Africa.},
journal = {Transactions of the Royal Society of Tropical Medicine and Hygiene},
volume = {},
number = {},
pages = {},
doi = {10.1093/trstmh/traf065},
pmid = {40590757},
issn = {1878-3503},
support = {NRF-2021R1C1C2012611//National Research Foundation of Korea/ ; },
abstract = {BACKGROUND: A rich biodiversity of predators has been suggested to suppress the risk of zoonotic spillover by regulating prey abundance and behavior. We evaluated the association between predator species richness and spillover events of Ebolavirus and Marburgvirus in Africa.<br><br>METHODS: Historical records of filovirus outbreaks, along with ecological, geographical and socioeconomic factors, were considered in this environmental study. We used the maximum entropy approach (Maxent modeling) and stacked species distribution models to estimate predator species richness. Logistic regression analyses accounting for spatiotemporal autocorrelations were conducted to assess the association between predator species richness and spillover risk, adjusting for potential confounders.<br><br>RESULTS: Higher species richness of certain predators-the order Strigiformes and the family Colubridae-was associated with lower risks of Ebolavirus spillover, but not with Marburgvirus spillover. The third quartile (OR=0.02, 95% Bayesian credible interval [BCI]=0.00-0.84) and fourth quartile (OR=0.07, 95% BCI=0.00-0.42) of Strigiformes species richness, as well as the third quartile (OR=0.15, 95% BCI=0.01-0.73) and fourth quartile (OR=0.53, 95% BCI=0.03-0.85) of Colubridae species richness, were significantly associated with reduced odds of Ebolavirus index cases.<br><br>CONCLUSION: These findings support a possible role for predator species richness in suppressing zoonotic spillover.},
}
@article {pmid40590380,
year = {2025},
author = {Kushwaha, N and Mishra, N and Lalawat, RS and Padhy, PK and Gupta, VK},
title = {Automated posture adjustment system for immobilized patients using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2025.2523322},
pmid = {40590380},
issn = {1476-8259},
abstract = {This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed approach follows a structured five-step process, ensuring accurate and efficient classification. The dataset collected using the MindRove EEG device captures brain activity during four motor imagery tasks: Leftward, Rightward, Upward, and Zeroth. Pre-processing involved filtering, followed by feature extraction using a Convolutional Recurrent Denoising Autoencoder (CRDAE) model. After that Classification is performed using artificial intelligence (AI) models, including Gated Recurrent Unit (GRU) with Attention, Temporal Transformer (TT), Bidirectional Long Short-Term Memory with attention mechanisms (Bi-LSTM with AM), and proposed Graph Transformer All Attention (GTAA). The GTAA model demonstrates superior performance, achieving the highest classification accuracy among the evaluated models. Additionally, the proposed system validated against the BCI Competition IV 2a datasets and ten-fold subject cross-validation, demonstrating its reliability and efficiency for real-time BCI applications. This study underscores the potential of integrating advanced AI techniques with EEG signal measurement and instrumentation for practical implementations.},
}
@article {pmid40590025,
year = {2025},
author = {Deuel, TA and Wenlock, J and McGovern, A and Rosenthal, J and Pampin, J},
title = {Musical auditory feedback BCI: clinical pilot study of the Encephalophone.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1592640},
pmid = {40590025},
issn = {1662-5161},
abstract = {INTRODUCTION: Therapeutic strategies for patients with severe acquired motor disability are relatively limited and show variable efficacy. Innovative technologies such as brain-computer interfaces (BCIs) have been developed recently that might benefit certain types of patients.<br><br>METHODS: Here, we tested a previously described auditory BCI, the Encephalophone, which may offer new options to improve quality of life and function. Eleven subjects with acquired moderate to severe motor disability, who had lost their ability to express themselves musically, were enrolled and 10 completed a clinical pilot study of the hands-free Encephalophone brain-computer interface (BCI). Subjects were briefly instructed on the use of the Encephalophone BCI, which uses EEG measured motor imagery to allow users to generate musical notes in real time without requiring movement. Subjects then underwent a pitch-matching task, a measure of accuracy, to attempt to match a given target pitch 3 times within 10&#x202f;s. They were allowed free play, where they could improvise music over a backing track. After 2-3 songs - approximately 10&#x202f;min - of freely improvised playing, subjects repeated the pitch-matching task. There were 3 sessions of testing and free play per subject, within 2&#x202f;weeks, with at least 1&#x202f;day separating sessions.<br><br>RESULTS: All subjects, on average, improved their pitch-matching accuracy by 15.6 percentage points and increased their number of hits by 58.7% over the 3 sessions, with all subjects scoring accuracy percentages significantly above random probability (19.05%). A subjective self-reporting survey of ratings of such factors as a feeling of expressing oneself, enjoyment, discomfort, and feeling of control showed a generally favorable response.<br><br>DISCUSSION: We suggest that this training approach using an auditory BCI may provide an innovative solution to challenges in recovery from motor disability.<br><br>CLINICAL TRIAL REGISTRATION: https://research.providence.org/clinical-research, Swedish Health Services #: STUDY2017000301.},
}
@article {pmid40588550,
year = {2025},
author = {Tian, Y and Li, H and Ye, W and Yuan, X and Guo, X and Guo, F},
title = {Temperature-dependent modulation of light-induced circadian responses in Drosophila melanogaster.},
journal = {The EMBO journal},
volume = {},
number = {},
pages = {},
pmid = {40588550},
issn = {1460-2075},
support = {32171008//the National Natural Science Foundation of China/ ; 32471210//the National Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2025ZFJH01-01//the Fundamental Research Funds for the Central Universities/ ; 226-2024-00133//the Fundamental Research Funds for the Central Universities/ ; },
abstract = {Animals entrain their circadian rhythms to multiple external signals, such as light and temperature, which are integrated in master clock neurons to adjust circadian phases. However, the precise mechanisms underlying this process remain unclear. Here, we use in vivo two-photon calcium imaging while precisely controlling temperature to investigate how the Drosophila melanogaster circadian clock integrates light and temperature inputs in circadian neurons. We show that light responses modulate the circadian clock in central pacemaker neurons, with temperature acting as a fine-tuning mechanism to achieve optimal adaptation. Our results suggest that temperature-sensitive dorsal clock neurons DN1as regulate the light-induced firing of s-LNv circadian pacemaker neurons and release of the neuropeptide PDF through inhibitory glutamatergic signaling. Specifically, higher temperatures suppress s-LNv firing upon light exposure, while lower temperatures enhance this response. Behavioral analyses further indicate that lower temperatures accelerate phase adjustment, whereas higher temperatures decelerate them in response to new light-dark cycles. This novel mechanism of temperature-dependent modulation of circadian phase adjustment provides new insights into the adaptive strategies of animals for survival in fluctuating environments.},
}
@article {pmid40588517,
year = {2025},
author = {Ding, Y and Udompanyawit, C and Zhang, Y and He, B},
title = {EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5401},
pmid = {40588517},
issn = {2041-1723},
support = {NS124564, NS131069, NS127849, NS096761//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Robotics/methods/instrumentation ; *Fingers/physiology ; Male ; Adult ; Female ; *Hand/physiology ; Young Adult ; Movement/physiology ; Brain/physiology ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) connect human thoughts to external devices, offering the potential to enhance life quality for individuals with motor impairments and general population. Noninvasive BCIs are accessible to a wide audience but currently face challenges, including unintuitive mappings and imprecise control. In this study, we present a real-time noninvasive robotic control system using movement execution (ME) and motor imagery (MI) of individual finger movements to drive robotic finger motions. The proposed system advances state-of-the-art electroencephalography (EEG)-BCI technology by decoding brain signals for intended finger movements into corresponding robotic motions. In a study involving 21 able-bodied experienced BCI users, we achieved real-time decoding accuracies of 80.56% for two-finger MI tasks and 60.61% for three-finger tasks. Brain signal decoding was facilitated using a deep neural network, with fine-tuning enhancing BCI performance. Our findings demonstrate the feasibility of naturalistic noninvasive robotic hand control at the individuated finger level.},
}
@article {pmid40588007,
year = {2025},
author = {Mahoney, TB and Grayden, DB and John, SE},
title = {Sub-scalp EEG for sensorimotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade9f1},
pmid = {40588007},
issn = {1741-2552},
abstract = {To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.},
}
@article {pmid40587936,
year = {2025},
author = {Vadivelan D, S and Sethuramalingam, P},
title = {A hybrid approach for EEG motor imagery classification using adaptive margin disparity and knowledge transfer in convolutional neural networks.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110675},
doi = {10.1016/j.compbiomed.2025.110675},
pmid = {40587936},
issn = {1879-0534},
abstract = {- Motor Imagery (MI) using Electroencephalography (EEG) is essential in Brain-Computer Interface (BCI) technology, enabling interaction with external devices by interpreting brain signals. Recent advancements in Convolutional Neural Networks (CNNs) have significantly improved EEG classification tasks; however, traditional CNN-based methods rely on fixed convolution modes and kernel sizes, limiting their ability to capture diverse temporal and spatial features from one-dimensional EEG-MI signals. This paper introduces the Adaptive Margin Disparity with Knowledge Transfer 2D Model (AMD-KT2D), a novel framework designed to enhance EEG-MI classification. The process begins by transforming EEG-MI signals into 2D time-frequency representations using the Optimized Short-Time Fourier Transform (OptSTFT), which optimizes windowing functions and time-frequency resolution to preserve dynamic temporal and spatial features. The AMD-KT2D framework integrates a guide-learner architecture where Improved ResNet50 (IResNet50), pre-trained on a large-scale dataset, extracts high-level spatial-temporal features, while a Customized 2D Convolutional Neural Network (C2DCNN) captures multi-scale features. To ensure feature alignment and knowledge transfer, the Adaptive Margin Disparity Discrepancy (AMDD) loss function minimizes domain disparity, facilitating multi-scale feature learning in C2DCNN. The optimized learner model then classifies EEG-MI images into left and right-hand movement motor imagery classes. Experimental results on the real-world EEG-MI dataset collected using the Emotiv Epoc Flex system demonstrated that AMD-KT2D achieved a classification accuracy of 96.75 % for subject-dependent and 92.17 % for subject-independent, showcasing its effectiveness in leveraging domain adaptation, knowledge transfer, and multi-scale feature learning for advanced EEG-based BCI applications.},
}
@article {pmid40587626,
year = {2025},
author = {Li, Z and Huang, Z and Li, J and Tang, Y and Li, J and Ding, X},
title = {Shear-Aligned Flexible Polarized Fluorescent Antennas for Wearable Visible Light Communications.},
journal = {ACS applied materials &amp; interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c06121},
pmid = {40587626},
issn = {1944-8252},
abstract = {Wearable visible light communication systems face fundamental limitations in dense multi-input multioutput configurations due to signal crosstalk between channels. Here, we demonstrate shear-aligned flexible polarized fluorescent antennas (FPFAs) fabricated through a scalable thermally assisted brush-coating induction (BCI) process. By systematically investigating the synergistic effects of ″coffee-ring″ phenomena and shear forces on halloysite nanotube alignment, we reveal the underlying physical mechanism enabling the formation of highly ordered structures with an orientation degree of 0.89. We encapsulate these structures in a sandwich configuration that maintains polarization performance while exhibiting mechanical stability, with parallel fracture strength 4.25 times higher than conventional designs. When integrated with quantum dot fluorescent conversion layers, these FPFAs achieve a 4.95-fold improvement in signal-to-noise ratio (SNR) compared to traditional receivers across wide viewing angles, even under extreme bending conditions. The resulting wearable communication system maintains 85.1% transmission accuracy at distances up to 9 m under ambient lighting, a 935% improvement over conventional approaches, with superior resilience to environmental disturbances including rain and fog. This work establishes an effective strategy for polarization multiplexing in wearable optical communications, with applications spanning healthcare monitoring, secure communications, and augmented reality interfaces in dynamic environments.},
}
@article {pmid40586134,
year = {2025},
author = {Zhang, Q and Liu, B and Wang, Z and Zhou, J and Yang, X and Zhou, Q and Zhao, Y and Li, S and Zhou, J and Wang, C},
title = {Training-Free Regulation of Grasping by Intracortical Tactile Feedback Designed via S1-M1 Communication.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e03011},
doi = {10.1002/advs.202503011},
pmid = {40586134},
issn = {2198-3844},
support = {2021ZD0201600//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201604//STI 2030-Major Projects, Ministry of Science and Technology of the People's Republic of China/ ; 82327810//National Major Scientific Instruments and Equipments Development Project of National Natural Science Foundation of China/ ; },
abstract = {Tactile feedback is essential for grip force control when operating a neuroprosthesis. Due to limited knowledge of cortical sensorimotor coordination, artificial feedback is mostly counterintuitive, requiring training to be associated with grasping behaviors. The current study investigates sensorimotor communication by recording neural activities from the primary sensory cortex (S1) and the primary motor cortex (M1) while macaques grasp targets of various textures and loads. Intracortical micro-stimulation is also delivered to S1 to validate the intervention of sensorimotor communication in grasping. The findings identify an S1→M1 functional pathway through which tactile information is transferred. The pathway is shared by both natural and artificial neural propagations. Moreover, it is demonstrated that sensory and motor decoding of neural activities in M1, as well as the actual grip force, are modulated by stimulation designed via S1→M1 communication, without prior training. The work provides a biomimetic strategy to design intuitive haptic feedback for brain-machine interfaces utilizing the S1→M1 pathway.},
}
@article {pmid40585760,
year = {2025},
author = {Zheng, Q and Wu, Y and Zhu, J and Feng, K and Bai, Y and Li, G and Ni, G},
title = {Applications and Challenges of Auditory Brain-Computer Interfaces in Objective Auditory Assessments for Pediatric Cochlear Implants.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {3},
pages = {20240078},
pmid = {40585760},
issn = {2766-2098},
abstract = {Cochlear implants (CI) are the premier intervention for individuals with severe to profound hearing impairment. Worldwide, an estimated 600,000 individuals have enhanced their hearing through cochlear implantation, with nearly half being children. The evaluations after implantation are crucial for appropriate clinical interventions and care. Current clinical practice lacks methods to assess the recovery of advanced auditory functions in cochlear-implanted children. Yet, recent advancements in electroencephalographic (EEG) techniques show promise in accurately evaluating auditory rehabilitation in this demographic. This review elucidates the evolution of brain-computer interface (BCI) technology for auditory assessment, focusing primarily on its application in pediatric cochlear implant recipients. Emphasis is placed on promising clinical biomarkers for auditory rehabilitation and the neural adaptability accompanying cortical adjustments after implantation. Additionally, we discuss emerging challenges and prospects in applying BCI technology to these children.},
}
@article {pmid40584823,
year = {2025},
author = {Jiang, M and Pan, X and Wang, X and Luo, Q},
title = {Will the embedded semantic radicals be activated when recognizing Chinese phonograms?.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1550536},
pmid = {40584823},
issn = {1662-5161},
abstract = {INTRODUCTION: A majority of Chinese characters are phonograms composed of phonetic and semantic radicals that serve different functions. While radical processing in character recognition has drawn significant interest, there is inconsistency regarding the semantic activation of embedded semantic radicals, and little is known about the duration of such sub-lexical semantic activation.<br><br>METHODS: Using a priming character decision task and a between-subjects design, this study examined whether semantic radicals embedded in SP phonograms (semantic radicals on the left and phonetic radicals on the right) can be automatically activated and how long such activation persists. We manipulated semantic relatedness between embedded radicals and target characters, prime frequency, and stimulus onset asynchronies (SOAs).<br><br>RESULTS: Facilitatory effects were observed on targets preceded by low-frequency primes at an SOA of 500 ms. No significant priming effects were found at SOAs of 100 ms or 1000 ms, regardless of prime frequency.<br><br>DISCUSSION: These findings suggest that sub-lexical semantic activation can occur and remain robust at 500 ms but may dissipate before 1000 ms. The study contributes valuable evidence for the automaticity and time course of embedded semantic radical processing in Chinese phonogram recognition, thereby enhancing our understanding of sub-lexical semantic processing in logographic writing systemse.},
}
@article {pmid40584523,
year = {2025},
author = {Shen, Y and Jiang, L and Lai, J and Hu, J and Liang, F and Zhang, X and Ma, F},
title = {A comprehensive review of rehabilitation approaches for traumatic brain injury: efficacy and outcomes.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1608645},
pmid = {40584523},
issn = {1664-2295},
abstract = {Traumatic Brain Injury (TBI), particularly in moderate-to-severe cases, remains a leading cause of long-term disability worldwide, affecting over 64 million individuals annually. Its complex and multifactorial nature demands an integrated, multidisciplinary rehabilitation approach to address the diverse physical, cognitive, behavioral, and psychosocial impairments that follow injury. We conducted a structured literature search using PubMed, Scopus, and Web of Science databases for suitable studies. This comprehensive review critically examines key rehabilitation strategies for TBI, including neuropsychological assessments, cognitive and neuroplasticity-based interventions, psychosocial support, and community reintegration through occupational therapy. The review emphasizes emerging technological innovations such as virtual reality, robotics, brain-computer interfaces, and tele-rehabilitation, which are expanding access to care and enhancing recovery outcomes. Furthermore, it also explores regenerative approaches, such as stem cell therapies and nanotechnology, highlighting their future potential in neurorehabilitation. Special attention is given to the importance of rigorous outcome evaluation, including standardized functional measures, neuropsychological testing, and advanced statistical methodologies to assess treatment efficacy and clinical significance. Patient-centered care is emphasized as a core element-rehabilitation plans are tailored to each individual's cognitive profile, functional needs, and life goals. Studies show this approach leads to better outcomes in executive functioning, emotional wellbeing, and community reintegration. It identifies gaps in current research, such as the lack of longitudinal studies, predictors of individualized treatment success, cost-benefit evaluations, and strategies to manage comorbidities like PTSD. Thus, combining conventional and technology-assisted rehabilitation-guided by patient-centered strategies-can enhance recovery in moderate-to-severe TBI. Future research should focus on long-term effectiveness, cost-efficiency, and scalable personalized care models.},
}
@article {pmid40584436,
year = {2025},
author = {Zamani, S and Sadeghi, J and Kamalabadi-Farahani, M and Aghayan, SN and Arabpour, Z and Djalilian, AR and Salehi, M},
title = {Comparison of cellular, mechanical, and optical properties of different polymers for corneal tissue engineering.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {8},
pages = {1082-1099},
pmid = {40584436},
issn = {2008-3866},
abstract = {OBJECTIVES: The invention of corneal tissue engineering is essential for vision due to the lack of effective treatments and donated corneas. Finding the right polymer is crucial for reducing inflammation, ensuring biocompatibility, and mimicking natural cornea properties.<br><br>MATERIALS AND METHODS: In this study, solvent casting and physical crosslinking (freeze-thaw cycles) were used to fabricate polymeric scaffolds of Polyvinyl alcohol, alginate, gelatin, carboxymethyl chitosan, carboxymethyl cellulose, polyacrylic acid, polyvinyl pyrrolidone, and their combinations. The mechanical evaluation of scaffolds for tension and suture ability was conducted. Biodegradability, swelling, water vapor, bacterial permeability, anti-inflammatory properties, blood compatibility, Blood Clotting Index (BCI), pH alterations, and cell compatibility with human Mesenchymal Stem cells (MSCs) were investigated with MTT. The hydrophilicity of the samples and the ability to adhere to surfaces were also compared with the contact angle and adhesive test, respectively. Finally, quantitative and qualitative analysis was used to check the transparency of the samples.<br><br>RESULTS: The mechanical strength of polyvinyl alcohol and polyvinyl pyrrolidone samples was highest, showing good suture ability. All samples had blood compatibility below 5% and cell compatibility above 75%. Polyvinyl alcohol was the most transparent at around 93%. Carboxymethyl chitosan effectively inhibited bacterial permeability, while its anti-inflammatory potential showed no significant difference.<br><br>CONCLUSION: This study aims to choose the best polymer composition for corneal tissue engineering. The selection depends on the study's goals, like mechanical strength or transparency. Comparing polymers across different dimensions provides better insight for polymer selection.},
}
@article {pmid40584269,
year = {2025},
author = {Ji, D and Yu, H and Xiao, X and Huang, Y and Zhou, X and Xu, M and Jung, TP and Ming, D},
title = {A user-friendly BCI encoding by high frequency single-frequency-SDMA SSaVEF using MEG.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {101},
pmid = {40584269},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) delivers high spatial resolution and superior detection performance for high-frequency signals compared to Electroencephalography (EEG). Therefore, researchers can leverage MEG for high-frequency steady-state asymmetric visual evoked potential (SSaVEP). Current SSaVEP encoding typically uses low-frequency stimulation with relatively large stimulus areas, hindering the applicability of this encoding method in user-friendly brain-computer interface (BCI) systems. This study introduces an ultra critical flicker frequency (ultra-CFF) single-frequency-SDMA steady-state asymmetric visual evoked field (SSaVEF) encoding powered by MEG and presents an eight-command SSaVEF-BCI system. The BCI system features a 60 Hz SSVEF visual stimulus landmark and eight visual targets spaced 45° apart. Ten participants took part in the offline experiments, during which data from 41 channels in the occipital region were collected. This study analyzed the spatiotemporal characteristics, frequency-space characteristics, signal-to-noise ratio, and other features of the SSaVEF signals. We also evaluated the system's performance using the multi-DCPM algorithm. Using the multi-DCPM algorithm, the system achieved an impressive average classification accuracy of 81.65% with 4-s length data. With a data length of 1 s, the system achieved an average Information Transfer Rate (ITR) of 32.05 bits/min, with the highest individual ITR reached an astonishing 64.45 bits/min. This study represents the exploration of a high-frequency spatial encoding SSVEF-BCI system based on MEG. The results demonstrate MEG's feasibility and potential of applying MEG in such BCI systems, providing both theoretical and practical value for the further development and implementation of future BCI systems.},
}
@article {pmid40584164,
year = {2025},
author = {Sharma, MK and Chaudhary, S and Shenoy, S},
title = {Development and testing of range of motion driven motor unit recruitment device for knee rehabilitation: A randomized controlled trial.},
journal = {MethodsX},
volume = {14},
number = {},
pages = {103382},
pmid = {40584164},
issn = {2215-0161},
abstract = {Existing research on neuromuscular electrical stimulation (NMES) identifies two primary control approaches: therapist-operated systems and participant-controlled systems. Therapist-operated NMES devices typically employ switches and potentiometers for control, whereas participant-controlled systems offer diverse input methods, including switches, buttons, joysticks, electromyography electrodes, voice-activated commands, and sip-and-puff devices. A critical limitation of current NMES technology lies in its failure to mimic the body's natural muscle recruitment process during electrical stimulation, resulting in premature fatigue and diminished user engagement. A particularly significant drawback is the absence of joint range-of-motion dependency observed during voluntary movements and active involvement of participant. This limitation prevents precise control over spatial and temporal parameters, such as modulating motor unit recruitment relative to joint position, during neuromuscular rehabilitation. Furthermore, existing devices cannot accurately reproduce the co-contraction dynamics and reciprocal activation patterns seen in synergistic, agonist, and antagonist muscle groups during natural movement. Addressing these challenges requires developing innovative NMES technology capable of activating the neuromuscular system while replicating natural voluntary recruitment patterns. Such advancements would not only improve muscle strengthening outcomes but also enhance participant adherence through more effective cortical and peripheral neuromuscular engagement.•Development of neuromuscular electrical stimulation (NMES) device to replicate natural neuromuscular activation patterns through bio-inspired stimulation protocols.•Engineered to mitigate existing limitations of conventional NMES systems, optimizing therapeutic applications for neuromuscular re-education and functional recovery.•Integrates muscle synergy principles, enabling synchronized synergistic, agonist and antagonist activation for enhanced cortical and peripheral neuromuscular engagement and optimize functional rehabilitation outcomes.•Advances rehabilitation strategies by combining dual focus on muscular reconditioning and neural adaptation for holistic recovery.•Demonstrates potential to amplify strength gains while fostering neuroplasticity, supporting long-term functional recovery in neuromuscular rehabilitation.},
}
@article {pmid40581689,
year = {2025},
author = {Olza, A and Soto, D and Santana, R},
title = {Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {17},
pmid = {40581689},
issn = {2198-4018},
support = {IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; IT1504-22//IKUR strategy/ ; KK-2023/00090//Elkartek/ ; KK-2023/00090//Elkartek/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2019-105494GB-I00//Project grant/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; PID2022-137442NB-I00//BERC by Spanish Ministry of Science and Innovation/ ; CEX2020-001010-S//Severo Ochoa programme/ ; CEX2020-001010-S//Severo Ochoa programme/ ; },
abstract = {In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.},
}
@article {pmid40581220,
year = {2025},
author = {Alsamri, J and Alamgeer, M and Alamri, MZ and Ghaleb, M and Asklany, SA and Almansour, H and Alsafari, S and Alghamdi, EA},
title = {Longitudinal EEG-based assessment of neuroplasticity and adaptive responses to transcranial focused ultrasound stimulation.},
journal = {Journal of neuroscience methods},
volume = {422},
number = {},
pages = {110521},
doi = {10.1016/j.jneumeth.2025.110521},
pmid = {40581220},
issn = {1872-678X},
abstract = {BACKGROUND: An emerging non-invasive neuromodulation technique named Transcranial-focused ultrasound stimulation (tFUS) offered several advantages than the conventional methods in terms of high spatial precision and penetration depth. In neurological disorders, this emerging method have gained a lot of attention, because of has the potential for therapeutic modulation of brain activity. Then, lack of standardized, Real-Time (RT) assessment protocols will result in unclear comprehension regarding the way the repeated tFUS applications may impacts the neuroplasticity and adaptive brain responses in a long-term. Here, the short-term and long-term neuroplastic modifications were effectively identified by the the longitudinal integration of EEG biomarkers with tFUS stimulation sessions. An adaptive modulation strategies customized for individual neural responses are also facilitated by this hypothesis.<br><br>NEW METHODS: To integrate the tFUS with high-resolution electroencephalogram (EEG) monitoring in many sessions, Integrated Longitudinal Evaluation Protocol (ILEP) model was suggested in this study. To extract amplitude, latency, spectral dynamics, and connectivity features from evoked potentials, pre-, during-, and post-stimulation EEG signals were identified by the protocol. Then, for monitoring neuroadaptive trajectories over time, the intrgration of the statistical modeling and neural network (NN)-based pattern recognition was employed, and it will assist in analysing those features. For the purpose of differentiating the short-term oscillatory effects from long-term neuroplastic shifts, the following ways will helps in processing the EEG signals: time-frequency decomposition, event-related potential (ERP) analysis, and machine learning (ML) classifiers. Here, the subject-specific response patterns and temporal evolution of brain dynamics were effectively detected by the application of the Deep learning (DL) models.<br><br>RESULTS ANALYSIS: After the tFUS, both the short-term and long-term modifications in brain activity were effectively detected by the application of ILEP, and it was demonstrated by the outcomes of the simulation and empirical data. Here, the location-specific, session-dependent EEG modifications are consistent with the adaptive neuroplastic processes, and it was revealed by the outcomes of the simulation. Then, accurate neuroadaptive signals were separated from noise and temporary conditions, and it was facilitated by the potential of the model.<br><br>A dynamic, session-over-session monitoring of brain responses was facilitated by the ILEP model. But static images was offered by those conventional methods. With an integration of closed-loop feedback and advanced neural modelling, the suggested model executes better than the conventional methods. This suggested model also facilitates in offering a customized neuromodulation therapies.<br><br>CONCLUSION: For monitoring the neuroplastic modifications induced by tFUS,this suggested ILEP model becomes an effective, sacalable. So, this suggested model facilitates an adaptive assessment model for that tracking, and it was demonstrated in this study. The future, RT, closed-loop neuromodulation systems in therapeutic and cognitive enhancement contexts may get benefits from the integration of EEG feedback mechanisms in the suggested model.},
}
@article {pmid40579374,
year = {2025},
author = {Yang, C and Zhang, L and Liu, J and Li, K and Li, S and Yang, Z and Bishop, JR and Deng, W and Yao, L and Lui, S and Gong, Q},
title = {More Severe Brain Network Hierarchy Disorganization in Treatment-Naive Deficit Compared to Non-deficit Schizophrenia and Underlying Neurotransmitter Associations.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae231},
pmid = {40579374},
issn = {1745-1701},
support = {82102007//National Natural Science Foundation of China/ ; 82120108014//National Natural Science Foundation of China/ ; 82071908//National Natural Science Foundation of China/ ; 82202110//National Natural Science Foundation of China/ ; 2022YFC2009901//National Key Research and Development Program of China/ ; 2022YFC2009900//National Key Research and Development Program of China/ ; 2021JDTD0002//Sichuan Science and Technology Program/ ; 2022-YF09-00062-SN//Chengdu Science and Technology Office, major technology application demonstration project/ ; 2022-GH03-00017-HZ//Chengdu Science and Technology Office, major technology application demonstration project/ ; ZYGD23003//West China Hospital, Sichuan University/ ; ZYAI24010//West China Hospital, Sichuan University/ ; ZYGX2022YGRH008//Fundamental Research Funds for the Central Universities/ ; GZB20240493//Postdoctoral Fellowship Program of CPSF/ ; T2019069//Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars/ ; },
abstract = {BACKGROUND AND HYPOTHESIS: Deficit schizophrenia (DS) represents a distinct entity characterized by primary and enduring negative symptoms, yet the neurobiological differences between DS and non-DS (NDS) remain undetermined. Using a gradient-based approach, we hypothesize that DS and NDS will exhibit convergent and divergent brain functional hierarchy patterns, each with a specific underlying neurotransmitter architecture.<br><br>STUDY DESIGN: Resting-state functional magnetic resonance imaging images were acquired from 44 treatment-naive DS, 55 treatment-naive NDS, and 60 matched healthy controls (HCs). Gradient metrics were calculated using the BrainSpace toolbox. The spatial correlation between gradient abnormalities in DS or NDS and density maps of 10 neurotransmitters derived by the JuSpace toolbox was analyzed to link the neuroimaging to underlying neurotransmitter information.<br><br>STUDY RESULTS: Both DS and NDS exhibited compressed gradient patterns compared to HC, suggesting reduced network differentiation, with more severe disorganization in DS. The ventral attention network was associated with depression symptoms in DS, whereas the visual network was related to total, general, and paranoid symptom scores in NDS. Moreover, spatial correlation of neurotransmitter analysis revealed that the gradient alterations of DS were primarily related to the serotonergic system while those of NDS were predominantly associated with the dopamine system.<br><br>CONCLUSIONS: The study suggests that independent from the potential effects of antipsychotic medication, DS and NDS are characterized by different neuropathology in brain hierarchy patterns, potentially linked to neurochemical metabolic distinction. Our findings support the hypothesis that DS is a distinct subtype versus NDS from neurodevelopmental perspective.},
}
@article {pmid40578761,
year = {2025},
author = {Brands, R and Fuchs, L and Seyffer, JM and Bajcinca, N and Bartsch, J and Peuker, UA and Schmidt, V and Thommes, M},
title = {Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets.},
journal = {Journal of pharmaceutical sciences},
volume = {},
number = {},
pages = {103889},
doi = {10.1016/j.xphs.2025.103889},
pmid = {40578761},
issn = {1520-6017},
abstract = {The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm[3]. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting.},
}
@article {pmid40578508,
year = {2025},
author = {Metin, S and Altan, H and Tercan, E and Dedeoglu, BG and Gurdal, H},
title = {DUSP1 protein's impact on breast cancer: Anticancer response and sensitivity to cisplatin.},
journal = {Biochimica et biophysica acta. Gene regulatory mechanisms},
volume = {},
number = {},
pages = {195103},
doi = {10.1016/j.bbagrm.2025.195103},
pmid = {40578508},
issn = {1876-4320},
abstract = {Dual-Specificity Phosphatase 1 (DUSP1) modulates the activity of members of the Mitogen-Activated Protein Kinase (MAPK) family, including p38, JNK, and ERK1/2, which affects various cellular functions in cancer. Moreover, DUSP1 is known to influence the outcomes of cancer chemotherapy. This study aimed to reduce DUSP1 protein expression using CRISPR/Cas9 and siRNA and assess its effects on cell proliferation, migration, and tumor growth potential in triple-negative breast cancer (TNBC) cells. We examined the expression levels of p38, JNK, and ERK1/2, along with their phosphorylated forms, and investigated DUSP1's influence to cisplatin sensitivity. Our findings revealed that the downregulation of DUSP1 expression inhibited the proliferation, migration, and tumor growth potential of TNBC cells. Additionally, BCI, an inhibitor of DUSP1/6, demonstrated anti-proliferative effects on these cells. Decreasing the expression of DUSP1 increased the phosphorylation ratio of p38 and JNK, but not ERK1/2. Moreover, the anticancer response induced by cisplatin was enhanced by reducing DUSP1 expression or by treating the cells with BCI. Notably, cisplatin treatment increased p38 phosphorylation, which was significantly augmented by reduced DUSP1 expression. We also demonstrated that the DUSP1 inhibition-induced anticancer response in these cells predominantly relied on p38 activity. These findings contribute to a better understanding of the role of DUSP1 in breast cancer and offer insights into potential therapeutic strategies targeting DUSP1 to enhance the efficacy of cisplatin treatment. Our study highlights that decreased DUSP1 protein expression and activity mediates an anticancer response and increases the sensitivity of MDA-MB231 cells to cisplatin by regulating p38.},
}
@article {pmid40578406,
year = {2025},
author = {Cao, Y and Chen, Z and Yin, Y and Kang, X and Zhang, Y and Xu, Z and Yang, X and Yang, B and He, Q and Yan, H and Luo, P},
title = {Autophagy-dependent hepatocyte apoptosis mediates gilteritinib-induced hepatotoxicity.},
journal = {Toxicology letters},
volume = {410},
number = {},
pages = {189-196},
doi = {10.1016/j.toxlet.2025.06.018},
pmid = {40578406},
issn = {1879-3169},
abstract = {Gilteritinib, a dual FLT3/AXL inhibitor, is clinically effective for relapsed/refractory FLT3-mutated acute myeloid leukemia (AML) but is limited by severe hepatotoxicity. This study investigates the molecular mechanisms underlying gilteritinib-induced liver injury, focusing on the interplay between autophagy and apoptosis. In vitro and in vivo models, including human hepatocyte HL-7702 cells and C57BL/6 J mice, were employed. Gilteritinib treatment significantly upregulated autophagy markers (LC3-II) and induced autophagosome formation, as confirmed by western blot, TEM, and mCherry-GFP-LC3 reporter assays. Concurrently, apoptosis markers (cleaved-PARP, cleaved-Caspase3, Annexin V/PI staining) increased dose- and time-dependently. Pharmacological inhibition of autophagy with autophagy inhibitor 3-methyladenine (3-MA, 5 mM) or gene silence of Atg7 attenuated apoptosis, mitochondrial membrane potential loss, and ROS overproduction, while autophagy induction by Torin1 (100 nM) exacerbated hepatocyte death. In vivo, gilteritinib-treated mice exhibited elevated serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) levels, alongside histopathological damage, all of which were mitigated in Atg7-deficient mice. These findings demonstrate that gilteritinib triggers excessive autophagy, which drives hepatocyte apoptosis and liver injury. Targeting autophagy pathways, represents a potential therapeutic strategy to alleviate gilteritinib-induced hepatotoxicity, enabling safer clinical use of this vital AML therapy. This study elucidates a critical autophagy-apoptosis axis in drug-induced liver injury and provides actionable insights for managing adverse effects of targeted cancer therapies.},
}
@article {pmid40578388,
year = {2025},
author = {Lin, Z and Jiang, X and Dai, C and Jia, F},
title = {Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade917},
pmid = {40578388},
issn = {1741-2552},
abstract = {OBJECTIVE: Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.<br><br>APPROACH: We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when&#xD;the data is split into multiple batches and used sequentially.<br><br>MAIN RESULTS: The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (r) of 0.466 with only 0.5K FLOPs per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a > 2&#xd7; decoding precision on noisy signals compared with all state-of-the-art deep neural networks. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.<br><br>SIGNIFICANCE: In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.},
}
@article {pmid40578216,
year = {2025},
author = {Zhang, H and Wang, H and An, J and Zheng, S and Wu, D},
title = {A lightweight spiking neural network for EEG-based motor imagery classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {191},
number = {},
pages = {107741},
doi = {10.1016/j.neunet.2025.107741},
pmid = {40578216},
issn = {1879-2782},
abstract = {Spiking neural networks (SNNs) aim to simulate the human brain neural network, using sparse spike event streams for effective and energy-efficient spatio-temporal signal processing. This paper proposes a lightweight SNN model for electroencephalogram (EEG) based motor imagery (MI) classification, a classical brain-computer interface paradigm. The model has three desirable characteristics: (1) it has a brain-inspired architecture; (2) it is energy efficient; and, (3) it is dataset agnostic. Within-subject and cross-subject experiments on three public datasets demonstrated the superiority of our SNN model over four classical convolutional neural network based models in EEG based MI classification.},
}
@article {pmid40576544,
year = {2025},
author = {Wu, Y and Bao, K and Liang, J and Li, Z and Shi, Y and Tang, R and Xu, K and Wei, M and Chen, Z and Jian, J and Luo, Y and Tang, Y and Deng, Q and Dai, H and Sun, C and Zhang, W and Lin, H and Zhang, K and Li, L},
title = {Photonic Interfaces: an Innovative Wearable Sensing Solution for Continuous Monitoring of Human Motion and Physiological Signals.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2500727},
doi = {10.1002/smtd.202500727},
pmid = {40576544},
issn = {2366-9608},
support = {10300000H062401/001//Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China/ ; 2024SDXHDX0005//"Pioneer" and "Leading Goose" Key Research and Development Program of Zhejiang Province/ ; 12104375//National Natural Science Foundation of China/ ; 62175202//National Natural Science Foundation of China/ ; 2024C03150//Key Research and Development Program of Zhejiang Province/ ; 2023GD003/110500Y0022303//Key Project of Westlake Institute for Optoelectronics/ ; },
abstract = {Flexible integrated photonic sensors are gaining prominence in intelligent wearable sensing due to their compact size, exceptional sensitivity, rapid response, robust immunity to electromagnetic interference, and the capability to enable parallel sensing through optical multiplexing. However, integrating these sensors for practical applications, such as monitoring human motions and physiological activities together, remains a significant challenge. Herein, it is presented an innovative fully packaged integrated photonic wearable sensor, which features a delicately designed flexible necklace-shaped microring resonator (MRR), along with a pair of grating couplers (GCs) coupled to a fiber array (FA). The necklace-shaped MRR is engineered to minimize waveguide sidewall-induced scattering loss, with a measured intrinsic quality factor (Qint) of 1.68 × 10[5], ensuring highly sensitive and precise signal monitoring. GCs and FA enhance the seamless wearability of devices while maintaining superior sensitivity to monitor various human motions and physiological signs. These are further classified signals using machine learning algorithms, achieving an accuracy rate of 97%. This integrated photonic wearable sensor shows promise for human-machine interfaces, touch-responsive wearable monitors, and artificial skin, especially in environments susceptible to electromagnetic interference, such as intensive care units (ICUs) and spacecraft. This work significantly advances the field of smart wearable technology.},
}
@article {pmid40575493,
year = {2025},
author = {Mehta, D},
title = {Brain-Computer Interface tool use and the Contemplation Conundrum: a blueprint of mental action, agency, and control.},
journal = {Oxford open neuroscience},
volume = {4},
number = {},
pages = {kvaf002},
pmid = {40575493},
issn = {2753-149X},
abstract = {This paper approaches the role of intentional action in brain-computer interface (BCI) tool use to allow for an ethical discourse regarding the development and usage of neurotechnology. The exploration of mental actions and user control in BCI tool use brings us closer to understanding the philosophical underpinnings of intentions and agency for BCI-mediated actions. The author presents that under some theories of intentional action, certain BCI-mediated overt movements qualify as both voluntary and unintentional. This plausibly magnifies the ethical considerations surrounding BCI tool use. This problem is referred by the author as the contemplation conundrum. Thus, the paper proposes research scope for the neural correlates of intention formation and the neural correlates of imagination aimed at clarifying implementational control and safeguarding privacy of thought in BCI tool use.},
}
@article {pmid40574626,
year = {2025},
author = {Kaszás, A and Meszéna, D and Fiáth, R and Slézia, A and Ulbert, I and Katona, G},
title = {Capturing the Electrical Activity of all Cortical Neurons: Are Solutions Within Reach?.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06225},
doi = {10.1002/advs.202506225},
pmid = {40574626},
issn = {2198-3844},
support = {TKP2021-EGA-42//Thematic Programme of Excellence/ ; NAP2022-I-2/2022//Hungarian Brain Research Program/ ; RRF-2.3.1-21-2022-00015//Pharmaceutical Research and Development Laboratory Project/ ; HUN-REN-HAZAHIVO-2023//Hungarian Research Network/ ; KSZF-174/2023//Hungarian Research Network/ ; 2019-2.1.7-ERA-NET-2021-00023//ERA-NET/ ; //Bolyai J&#xe1;nos Scholarship of the Hungarian Academy of Sciences/ ; 150574//National Research, Development and Innovation Office/ ; PD143582//National Research, Development and Innovation Office/ ; },
abstract = {Recent advancements in high-density implantable intracortical electrode technology have significantly improved neural interfaces for both research and clinical applications. However, a significant challenge persists: scaling up these devices to achieve recording of nearly all single-unit activity across large brain volumes. This critical review explores recent progress in neural electrode design, focusing on the challenges of achieving scalable solutions for this ambitious goal. The physical and technical constraints of both rigid and flexible probes are addressed, highlighting the limitations imposed by shank stiffness, mechanical tissue damage, and foreign body response. It is identified that the physics of inserting the electrodes into the brain tissue poses a fundamental constraint, which inherently restricts achievable electrode density. Biohybrid strategies, integrating biological and synthetic components, have shown promise, but they have yet to overcome the major challenges necessary to achieve a scalable functional interface. It is concluded that, given the current limitations of available techniques, there is a pressing need to explore fundamentally novel approaches to realize the vision of recording the electrical activity of every cortical neuron within the brain.},
}
@article {pmid40573719,
year = {2025},
author = {Safarov, F and Kutlimuratov, A and Khojamuratova, U and Abdusalomov, A and Cho, YI},
title = {Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123832},
pmid = {40573719},
issn = {1424-8220},
support = {20022362//Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2024/ ; 2410003714//Establishment of standardization basis for BCI and AI Interoperability/ ; },
mesh = {Humans ; *Emotions/physiology ; *Facial Recognition/physiology ; *Facial Expression ; Algorithms ; Deep Learning ; *Pattern Recognition, Automated/methods ; Face/physiology ; *Automated Facial Recognition/methods ; Neural Networks, Computer ; Convolutional Neural Networks ; },
abstract = {Facial emotion recognition (FER) is vital for improving human-machine interactions, serving as the foundation for AI systems that integrate cognitive and emotional intelligence. This helps bridge the gap between mechanical processes and human emotions, enhancing machine engagement with humans. Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. By integrating these features into a specially modified AlexNet architecture, our approach not only classifies facial emotions more accurately but also demonstrates significant improvements in performance and adaptability under various operational conditions. To validate the effectiveness of our proposed model, we conducted evaluations using the FER2013 and RAF-DB benchmark datasets, where it achieved impressive accuracies of 98.10% and 93.34% for the two datasets, with standard deviations of 1.63% and 3.62%, respectively. On the FER-2013 dataset, the model attained a precision of 98.2%, a recall of 97.9%, and an F1-score of 98.0%. Meanwhile, for the other dataset, it achieved a precision of 93.54%, a recall of 93.12%, and an F1-score of 93.34%. These results underscore the model's robustness and its capability to deliver high-precision emotion recognition, making it an ideal solution for deployment in environments where hardware limitations are a critical concern.},
}
@article {pmid40573479,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {12},
pages = {},
doi = {10.3390/s25123592},
pmid = {40573479},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; none//THERS/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Male ; Adult ; *Photic Stimulation/methods ; Electroencephalography/methods ; Female ; Young Adult ; },
abstract = {The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain-computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation-an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen's d: 0.49-0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods.},
}
@article {pmid40571414,
year = {2025},
author = {Yang, L and Li, M and Yang, L and Wang, Z and Shang, Z},
title = {Hippocampal LFP responses during pigeon homing flight in outdoors.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0185-25.2025},
pmid = {40571414},
issn = {1529-2401},
abstract = {The hippocampal formation (HF) plays a key role in avian spatial navigation. Previous studies suggest that the HF may serve different functions at various stages in pigeons' long-distance outdoor homing flight. However, it remains unclear whether the HF exhibits specific neural responses during these stages. In this study, we employed a wearable bimodal data recording system to simultaneously capture flight trajectories and hippocampal local field potential (LFP) signals of pigeons (either sex) during outdoor homing navigation. Our results revealed significant differences in hippocampal neural responses across the initial decision-making (DM) and en route navigation (ER) stages. Specifically, elevated LFP power in theta (4-12 Hz) and beta (12-30 Hz) bands was detected during the DM stage compared to the ER stage, while the high gamma (60-120 Hz) band exhibited the opposite pattern. In addition, we examined typical theta-beta phase-amplitude coupling (PAC) during the ER stage. Additionally, stage-specific hippocampal responses remained consistent across release sites. Notably, the difference in hippocampal responses across stages diminished along with the accumulation of homing experience. These results offer new insights into the role of the avian HF in homing flight navigation and suggest parallels between avian and mammalian hippocampal mechanisms in spatial learning.Significance Statement It remains unclear whether the hippocampal formation (HF) exhibits specific neural responses during various stages in the long-distance outdoor navigation of pigeons. By recording hippocampal local field potentials (LFPs) and positional data during natural outdoor flights, we reveal distinct neural response patterns that differentiate between initial decision-making and sustained navigation stages. We detected band-specific power and coupling responses between different navigation stages, consistent across multiple release sites. Additionally, we found that the LFP responses differences across stages gradually diminish along with the accumulation of the homing experience. Our study offers new insights into the role of the avian HF in outdoor homing flight.},
}
@article {pmid40567082,
year = {2025},
author = {Lachkar, S and Ibrahimi, A and Boualaoui, I and Sayegh, HE and Nouini, Y},
title = {Botulinum toxin A in idiopathic overactive bladder: a narrative review of 5410 cases.},
journal = {The Canadian journal of urology},
volume = {32},
number = {3},
pages = {145-165},
pmid = {40567082},
issn = {1488-5581},
mesh = {Humans ; *Urinary Bladder, Overactive/drug therapy ; *Botulinum Toxins, Type A/therapeutic use/adverse effects ; *Neuromuscular Agents/therapeutic use/adverse effects ; Treatment Outcome ; },
abstract = {INTRODUCTION: When conservative treatments fail, botulinum toxin A (BoNT-A) is an option for refractory idiopathic overactive bladder (OAB). This review evaluates the efficacy, safety, and predictive factors for BoNT-A in this situation.<br><br>MATERIALS AND METHODS: A literature search up to January 2025 was performed using PubMed, Google Scholar, and Embase to assess efficacy, safety, and predictors of adverse events (AE) related to BoNT-A. The risk of bias was assessed using the Risk of Bias 2 (RoB 2) tool for randomized studies and the Critical Appraisal Skills Programme (CASP) checklist for cohort studies. The quality of the review was evaluated based on the Oxford criteria, following the Strengthening the Assessment of Narrative Review Articles (SANRA) guidelines, and by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews.<br><br>RESULTS: 31 studies were included, involving 5410 patients. BoNT-A improves OAB symptoms even after reinjections. Higher doses do not enhance efficacy but increase AE. AE includes high post-void residual (PVR), clean intermittent self-catheterization (CISC), and Urinary Tract Infection (UTI). Predictors of CISC include age, male gender, hysterectomy, &#x2265;3 vaginal deliveries, mixed incontinence, prior mid-urethral sling (MUS), high PVR, low Pressure at Pdet at First Micturition (PIP1) in women, low Bladder Compliance Index (BCI) in men, and high Bladder Outlet Obstruction Index (BOOI). Diabetes and heart failure increase PVR. UTIs are more frequent in women and men with benign prostatic hyperplasia, with CISC increasing the risk fivefold. Severe complications are rare. Predictors of poor response include male gender, high BOOI, low urinary flow, and diabetes.<br><br>DISCUSSION: BoNT-A is effective for OAB, especially for incontinence. AE is dose-dependent and limits treatment adherence. Their link with poor response remains unclear.<br><br>CONCLUSION: BoNT-A effectively treats refractory idiopathic OAB, improving symptoms and quality of life with repeated injections.},
}
@article {pmid40566931,
year = {2025},
author = {Lin, K and Chen, J and Pan, J and Wang, R and Wu, S and Wen, W and Li, Y and Wang, L and Yuan, F},
title = {Electro-Acupuncture to Treat Disorder of Consciousness (AcuDoc): Study Protocol for a Randomized Sham-Controlled Trial.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70637},
pmid = {40566931},
issn = {2162-3279},
support = {//Health Commission of Guangzhou City/ ; //NATCM's Project of High-level Construction of Key TCM Disciplines/ ; //Guangzhou Municipal Science and Technology Bureau/ ; //2023A04J0473/ ; },
mesh = {Humans ; *Electroacupuncture/methods ; Adult ; *Brain Injuries, Traumatic/complications/therapy/physiopathology ; *Consciousness Disorders/therapy/etiology/physiopathology ; Female ; Male ; Young Adult ; Middle Aged ; Randomized Controlled Trials as Topic ; Electroencephalography ; },
abstract = {BACKGROUND: Treatment of disorders of consciousness (DOC) remains a clinical challenge. Electroacupuncture (EA) was shown to have the potential to promote the recovery of consciousness. This trial aims to explore the therapeutic effects and mechanisms of EA in patients with DOC due to traumatic brain injury (TBI) through a multimodal approach.<br><br>METHODS: A total of 50 adult patients with DOC due to TBI and 25 healthy subjects will be enrolled in the study. Patients enrolled in the study will be assigned to the EA group or the sham-EA group through stratified randomization. All patients receive behavioral assessments (CRS-R and brain-computer interface), neurophysiological evaluations (EEG, somatosensory evoked potentials, brainstem auditory evoked potentials), and neuroimaging evaluations (rs-fMRI, amide proton transfer, intravoxel incoherent motion, neurite orientation dispersion and density imaging) before and after the 14-day EA or sham-EA treatment. Each healthy subject will receive a set of neurophysiological and neuroimaging examinations but no treatments. The practitioner administering EA and sham-EA is the only one aware of the grouping results. In the sham-EA group, sham-acupoints, sham-acupuncture, and sham-wire are utilized. The primary outcome measurement is the change in CRS-R score after 14 days of treatment compared with the baseline CRS-R score.<br><br>DISCUSSION: The AcuDoc trial will be the first randomized sham-controlled study to investigate the clinical benefits of EA in patients with DOC. This trial will elucidate the role of EA in the treatment of DOC due to TBI and provide evidence of its therapeutic mechanisms.},
}
@article {pmid40566772,
year = {2025},
author = {Zhang, T and Chen, J and Lu, Y and Xu, D and Yan, S and Ouyang, Z},
title = {[The analysis of invention patents in the field of artificial intelligent medical devices].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {504-511},
doi = {10.7507/1001-5515.202407044},
pmid = {40566772},
issn = {1001-5515},
mesh = {*Artificial Intelligence ; *Patents as Topic ; Humans ; *Inventions ; China ; Brain-Computer Interfaces ; Telemedicine ; *Equipment and Supplies ; Robotics ; Algorithms ; },
abstract = {The emergence of new-generation artificial intelligence technology has brought numerous innovations to the healthcare field, including telemedicine and intelligent care. However, the artificial intelligent medical device sector still faces significant challenges, such as data privacy protection and algorithm reliability. This study, based on invention patent analysis, revealed the technological innovation trends in the field of artificial intelligent medical devices from aspects such as patent application time trends, hot topics, regional distribution, and innovation players. The results showed that global invention patent applications had remained active, with technological innovations primarily focused on medical image processing, physiological signal processing, surgical robots, brain-computer interfaces, and intelligent physiological parameter monitoring technologies. The United States and China led the world in the number of invention patent applications. Major international medical device giants, such as Philips, Siemens, General Electric, and Medtronic, were at the forefront of global technological innovation, with significant advantages in patent application volumes and international market presence. Chinese universities and research institutes, such as Zhejiang University, Tianjin University, and the Shenzhen Institute of Advanced Technology, had demonstrated notable technological innovation, with a relatively high number of patent applications. However, their overseas market expansion remained limited. This study provides a comprehensive overview of the technological innovation trends in the artificial intelligent medical device field and offers valuable information support for industry development from an informatics perspective.},
}
@article {pmid40566769,
year = {2025},
author = {Wu, H and Chen, S and Jia, J},
title = {[Research progress on brain mechanism of brain-computer interface technology in the upper limb motor function rehabilitation in stroke patients].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {480-487},
doi = {10.7507/1001-5515.202404015},
pmid = {40566769},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Upper Extremity/physiopathology ; *Brain/physiopathology ; Electroencephalography ; Stroke/physiopathology ; },
abstract = {Stroke causes abnormality of brain physiological function and limb motor function. Brain-computer interface (BCI) connects the patient's active consciousness to an external device, so as to enhance limb motor function. Previous studies have preliminarily confirmed the efficacy of BCI rehabilitation training in improving upper limb motor function after stroke, but the brain mechanism behind it is still unclear. This paper aims to review on the brain mechanism of upper limb motor dysfunction in stroke patients and the improvement of brain function in those receiving BCI training, aiming to further explore the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function after stroke. The results of this study show that in the fields of imaging and electrophysiology, abnormal activity and connectivity have been found in stroke patients. And BCI training for stroke patients can improve their upper limb motor function by increasing the activity and connectivity of one hemisphere of the brain and restoring the balance between the bilateral hemispheres of the brain. This article summarizes the brain mechanism of BCI in promoting the rehabilitation of upper limb motor function in stroke in both imaging and electrophysiology, and provides a reference for the clinical application and scientific research of BCI in stroke rehabilitation in the future.},
}
@article {pmid40566768,
year = {2025},
author = {Liu, X and Yang, B and Gan, A and Zhang, J},
title = {[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {473-479},
doi = {10.7507/1001-5515.202503048},
pmid = {40566768},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Speech/physiology ; Algorithms ; Male ; Adult ; Imagination ; },
abstract = {Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.},
}
@article {pmid40566767,
year = {2025},
author = {Li, X and Cao, X and Wang, J and Zhu, W and Huang, Y and Wan, F and Hu, Y},
title = {[Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {464-472},
doi = {10.7507/1001-5515.202310069},
pmid = {40566767},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; },
abstract = {Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.},
}
@article {pmid40566766,
year = {2025},
author = {Zhu, Y and Ji, Z and Li, S and Wang, H and Fu, Y and Wang, H},
title = {[A portable steady-state visual evoked potential brain-computer interface system for smart healthcare].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {455-463},
doi = {10.7507/1001-5515.202412051},
pmid = {40566766},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Adult ; Male ; },
abstract = {This paper realized a portable brain-computer interface (BCI) system tailored for smart healthcare. Through the decoding of steady-state visual evoked potential (SSVEP), this system can rapidly and accurately identify the intentions of subjects, thereby meeting the practical demands of daily medical scenarios. Firstly, an SSVEP stimulation interface and an electroencephalogram (EEG) signal acquisition software were designed, which enable the system to execute multi-target and multi-task operations while also incorporating data visualization functionality. Secondly, the EEG signals recorded from the occipital region were decomposed into eight sub-frequency bands using filter bank canonical correlation analysis (FBCCA). Subsequently, the similarity between each sub-band signal and the reference signals was computed to achieve efficient SSVEP decoding. Finally, 15 subjects were recruited to participate in the online evaluation of the system. The experimental results indicated that in real-world scenarios, the system achieved an average accuracy of 85.19% in identifying the intentions of the subjects, and an information transfer rate (ITR) of 37.52 bit/min. This system was awarded third prize in the Visual BCI Innovation Application Development competition at the 2024 World Robot Contest, validating its effectiveness. In conclusion, this study has developed a portable, multifunctional SSVEP online decoding system, providing an effective approach for human-computer interaction in smart healthcare.},
}
@article {pmid40566765,
year = {2025},
author = {Chai, X and Wang, N and Song, J and Yang, Y},
title = {[Detection of motor intention in patients with consciousness disorder based on electroencephalogram and functional near infrared spectroscopy combined with motor brain-computer interface paradigm].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {447-454},
doi = {10.7507/1001-5515.202502027},
pmid = {40566765},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *Electroencephalography/methods ; *Consciousness Disorders/physiopathology/diagnosis ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/physiopathology/diagnosis ; },
abstract = {Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks (P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.},
}
@article {pmid40566764,
year = {2025},
author = {Pan, J and Zhang, Z and Zhang, Y and Wang, F and Xiao, J},
title = {[Brain-computer interface technology and its applications for patients with disorders of consciousness].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {438-446},
doi = {10.7507/1001-5515.202410061},
pmid = {40566764},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Consciousness Disorders/diagnosis/rehabilitation/physiopathology ; Electroencephalography ; Brain/physiopathology ; Consciousness ; },
abstract = {With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.},
}
@article {pmid40566763,
year = {2025},
author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Gong, A and Fu, Y},
title = {[Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {3},
pages = {431-437},
doi = {10.7507/1001-5515.202407097},
pmid = {40566763},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; },
abstract = {The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.},
}
@article {pmid40566538,
year = {2025},
author = {Pilipović, K and Janković, T and Rajič Bumber, J and Belančić, A and Mršić-Pelčić, J},
title = {Traumatic Brain Injury: Novel Experimental Approaches and Treatment Possibilities.},
journal = {Life (Basel, Switzerland)},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/life15060884},
pmid = {40566538},
issn = {2075-1729},
support = {UIP-2017-05-9517//Croatian Science Foundation/ ; uniri-iskusni-biomed-23-56//University of Rijeka/ ; uniri-mladi-biomed-23-38//University of Rijeka/ ; uniri-iskusni-biomed-23-82//University of Rijeka/ ; },
abstract = {Traumatic brain injury (TBI) remains a critical global health issue with limited effective treatments. Traditional care of TBI patients focuses on stabilization and symptom management without regenerating damaged brain tissue. In this review, we analyze the current state of treatment of TBI, with focus on novel therapeutic approaches aimed at reducing secondary brain injury and promoting recovery. There are few innovative strategies that break away from the traditional, biological target-focused treatment approaches. Precision medicine includes personalized treatments based on biomarkers, genetics, advanced imaging, and artificial intelligence tools for prognosis and monitoring. Stem cell therapies are used to repair tissue, regulate immune responses, and support neural regeneration, with ongoing development in gene-enhanced approaches. Nanomedicine uses nanomaterials for targeted drug delivery, neuroprotection, and diagnostics by crossing the blood-brain barrier. Brain-machine interfaces enable brain-device communication to restore lost motor or neurological functions, while virtual rehabilitation and neuromodulation use virtual and augmented reality as well as brain stimulation techniques to improve rehabilitation outcomes. While these approaches show great potential, most are still in development and require more clinical testing to confirm safety and effectiveness. The future of TBI therapy looks promising, with innovative strategies likely to transform care.},
}
@article {pmid40564460,
year = {2025},
author = {Liu, Z and Fan, K and Gu, Q and Ruan, Y},
title = {Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060645},
pmid = {40564460},
issn = {2306-5354},
abstract = {The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain-computer interface technology. This paper addresses the challenge of recognizing motor imagery EEG signals with few channels, which is essential for portable and real-time applications. A novel framework is proposed that applies a continuous wavelet transform to convert time-domain EEG signals into two-dimensional time-frequency representations. These images are then concatenated into channel-dependent multilayer EEG time-frequency representations (CDML-EEG-TFR), incorporating multidimensional information of time, frequency, and channels, allowing for a more comprehensive and enriched brain representation under the constraint of few channels. By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. Moreover, the transfer learning strategy effectively addresses the issue of data sparsity in the context of a few channels. Our approach enhances the classification accuracy of motor imagery EEG signals in few-channel scenarios. Experimental results on the BCI Competition IV 2b dataset show a significant improvement in classification accuracy, reaching 80.21%. This study highlights the potential of CDML-EEG-TFR and the EfficientNet-based transfer learning strategy in few-channel EEG signal classification, laying a foundation for practical applications and further research in medical and sports fields.},
}
@article {pmid40564444,
year = {2025},
author = {Garcia-Palencia, O and Fernandez, J and Shim, V and Kasabov, NK and Wang, A and The Alzheimer's Disease Neuroimaging Initiative, },
title = {Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060628},
pmid = {40564444},
issn = {2306-5354},
support = {Project 22-UOA-120, 23-UOA-055-CSG//Health Research Council of New Zealand and Royal Society Catalyst/ ; 23-UOA-055-CSG//University of Auckland/ ; },
abstract = {Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain's biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain-computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives.},
}
@article {pmid40564430,
year = {2025},
author = {Darvishi, H and Mohammadi, A and Maghami, MH and Sadeghi, M and Sawan, M},
title = {EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {6},
pages = {},
doi = {10.3390/bioengineering12060614},
pmid = {40564430},
issn = {2306-5354},
abstract = {Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, offering potential solutions for motor-impaired individuals. While traditional BCI studies often focus solely on amplitude variations or inter-channel connectivity, movement-related brain activity is inherently dynamic, involving interactions across regions and frequency bands. We propose that combining amplitude-based (filter bank common spatial patterns, FBCSP) and phase-based connectivity features (phase-locking value, PLV) improves decoding accuracy. EEG signals from ten healthy subjects were recorded during arm movements, with electromyography (EMG) as ground truth. After preprocessing (resampling, normalization, bandpass filtering), FBCSP and multi-lag PLV features were fused, and the ReliefF algorithm selected the most informative subset. A feedforward neural network achieved average metrics of: Pearson correlation 0.829 ± 0.077, R-squared value 0.675 ± 0.126, and root mean square error (RMSE) 0.579 ± 0.098 in predicting EMG amplitudes indicative of arm movement angles. Analysis highlighted contributions from both FBCSP and PLV, particularly in the 4-8 Hz and 24-28 Hz bands. This fusion approach, augmented by data-driven feature selection, significantly enhances movement decoding accuracy, advancing robust neuroprosthetic control systems.},
}
@article {pmid40563754,
year = {2025},
author = {Gkintoni, E and Vassilopoulos, SP and Nikolaou, G and Vantarakis, A},
title = {Neurotechnological Approaches to Cognitive Rehabilitation in Mild Cognitive Impairment: A Systematic Review of Neuromodulation, EEG, Virtual Reality, and Emerging AI Applications.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060582},
pmid = {40563754},
issn = {2076-3425},
abstract = {Background/Objectives: Mild Cognitive Impairment (MCI) represents a clinical syndrome characterized by cognitive decline greater than expected for an individual's age and education level but not severe enough to significantly interfere with daily activities, with variable trajectories that may remain stable, progress to dementia, or occasionally revert to normal cognition. This systematic review examines neurotechnological approaches to cognitive rehabilitation in MCI populations, including neuromodulation, electroencephalography (EEG), virtual reality (VR), cognitive training, physical exercise, and artificial intelligence (AI) applications. Methods: A systematic review following PRISMA guidelines was conducted on 34 empirical studies published between 2014 and 2024. Studies were identified through comprehensive database searches and included if they employed neurotechnological interventions targeting cognitive outcomes in individuals with MCI. Results: Evidence indicates promising outcomes across multiple intervention types. Neuromodulation techniques showed beneficial effects on memory and executive function. EEG analyses identified characteristic neurophysiological markers of MCI with potential for early detection and monitoring. Virtual reality enhanced assessment sensitivity and rehabilitation engagement through ecologically valid environments. Cognitive training demonstrated the most excellent efficacy with multi-domain, adaptive approaches. Physical exercise interventions yielded improvements through multiple neurobiological pathways. Emerging AI applications showed potential for personalized assessment and intervention through predictive modeling and adaptive algorithms. Conclusions: Neurotechnological approaches offer promising avenues for MCI rehabilitation, with the most substantial evidence for integrated interventions targeting multiple mechanisms. Neurophysiological monitoring provides valuable biomarkers for diagnosis and treatment response. Future research should focus on more extensive clinical trials, standardized protocols, and accessible implementation models to translate these technological advances into clinical practice.},
}
@article {pmid40563743,
year = {2025},
author = {Mróz, K and Jonak, K},
title = {Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060571},
pmid = {40563743},
issn = {2076-3425},
support = {FD-20/II-3/999//Lublin University of Technology/ ; },
abstract = {Background: Recent advancements in machine learning (ML) have significantly influenced the analysis of brain signals, particularly electroencephalography (EEG), enhancing the detection of complex neural patterns. ML enables large-scale data processing, offering novel opportunities for diagnosing and treating mental disorders. However, challenges such as data variability, noise, and model interpretability remain significant. This study reviews the current limitations of EEG-based anxiety detection and explores the potential of advanced AI models, including transformers and VAE-D2GAN, to improve diagnostic accuracy and real-time monitoring. Methods: The paper presents the application of ML algorithms, with a focus on convolutional neural networks (CNN) and recurrent neural networks (RNN), in identifying biomarkers of anxiety disorders and predicting therapy responses. Additionally, it discusses the role of brain-computer interfaces (BCIs) in assisting individuals with disabilities by enabling device control through brain activity. Results: Experimental EEG research on BCI applications was conducted, focusing on motor imagery-based brain activity. Findings indicate that successive training sessions improve signal classification accuracy, emphasizing the need for personalized and adaptive EEG analysis methods. Challenges in BCI usability and technological constraints in EEG processing are also addressed. Conclusions: By integrating ML with EEG analysis, this study highlights the potential for future healthcare applications, including neurorehabilitation, anxiety disorder therapy, and predictive clinical models. Future research should focus on optimizing ML algorithms, enhancing personalization, and addressing ethical concerns related to patient privacy.},
}
@article {pmid40563723,
year = {2025},
author = {Fodor, MA and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Streamlining cVEP Paradigms: Effects of a Minimized Electrode Montage on Brain-Computer Interface Performance.},
journal = {Brain sciences},
volume = {15},
number = {6},
pages = {},
doi = {10.3390/brainsci15060549},
pmid = {40563723},
issn = {2076-3425},
support = {101118964//This project has received funding from the European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie grant agreement No 101118964./ ; },
abstract = {(1) Background: Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals, offering potential applications in assistive technology and neurorehabilitation. Code-modulated visual evoked potential (cVEP)-based BCIs employ code-pattern-based stimulation to evoke neural responses, which can then be classified to infer user intent. While increasing the number of EEG electrodes across the visual cortex enhances classification accuracy, it simultaneously reduces user comfort and increases setup complexity, duration, and hardware costs. (2) Methods: This online BCI study, involving thirty-eight able-bodied participants, investigated how reducing the electrode count from 16 to 6 affected performance. Three experimental conditions were tested: a baseline 16-electrode configuration, a reduced 6-electrode setup without retraining, and a reduced 6-electrode setup with retraining. (3) Results: Our results indicate that, on average, performance declines with fewer electrodes; nonetheless, retraining restored near-baseline mean Information Transfer Rate (ITR) and accuracy for those participants for whom the system remained functional. The results reveal that for a substantial number of participants, the classification pipeline fails after electrode removal, highlighting individual differences in the cVEP response characteristics or inherent limitations of the classification approach. (4) Conclusions: Ultimately, this suggests that minimal cVEP-BCI electrode setups capable of reliably functioning across all users might only be feasible through other, more flexible classification methods that can account for individual differences. These findings aim to serve as a guideline for what is currently achievable with this common cVEP paradigm and to highlight where future research should focus in order to move closer to a practical and user-friendly system.},
}
@article {pmid40562060,
year = {2025},
author = {Xu, F and Liu, Y and Li, Y and Zhang, C and Han, Z and He, T and Xiao, X and Chao, F and Leng, J and Xu, M},
title = {Research on coding and decoding algorithm of binocular brain-controlled unmanned vehicle.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade829},
pmid = {40562060},
issn = {1741-2552},
abstract = {With the rapid development of Brain-Computer Interface (BCI) technology, Steady-State Visual Evoked Potential (SSVEP) has emerged as an effective method for high-efficiency information transmission. However, traditional single-frequency stimulation methods face limitations in command set scalability and visual comfort. To address these issues, we propose a novel binocular SSVEP stimulation paradigm for brain-controlled unmanned vehicles. This system uses a checkerboard and phase encoding for stimulus presentation, encoding a single target with two frequencies to expand the command set. The frequencies are set between 30-35 Hz to enhance visual comfort. By leveraging polarized light technology, each eye receives distinct frequencies, suppressing intermodulation components and reducing the stimulated area for each eye. We also introduce an improved Filter Bank Dual-frequency Task-Discriminant Component Analysis (FBD-TDCA) algorithm. Experimental results show that, in a 15-command simulation, only six frequencies successfully encoded all commands, achieving comparable performance to traditional single-frequency paradigms. A 12-target brain-controlled unmanned vehicle online simulation with 12 participants further validated the proposed paradigm and algorithm. In the binocular stimulation paradigm, the average Information Transfer Rate (ITR) reached 154.67±19.69 bits/min in online experiments, with offline training yielding an ITR of 170.7±31.2 bits/min. This novel stimulation paradigm not only supports large-scale target sets for BCI systems but also improves visual comfort, offering stability and feasibility for practical brain-controlled applications. .},
}
@article {pmid40561510,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.},
journal = {JMIR formative research},
volume = {9},
number = {},
pages = {e60859},
doi = {10.2196/60859},
pmid = {40561510},
issn = {2561-326X},
mesh = {Humans ; *Brain-Computer Interfaces/psychology ; *Social Media/statistics &amp; numerical data ; Natural Language Processing ; *Public Opinion ; Male ; Emotions ; Female ; Adult ; },
abstract = {BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.<br><br>OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.<br><br>METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users' demographics by matching predefined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.<br><br>RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; &#x3c4;=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the "broadcasting" group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the "scientific" group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.<br><br>CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.},
}
@article {pmid40561478,
year = {2025},
author = {Chen, CS and Chang, SH and Liu, CW and Pan, TM},
title = {Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.},
journal = {JMIR medical informatics},
volume = {13},
number = {},
pages = {e72027},
doi = {10.2196/72027},
pmid = {40561478},
issn = {2291-9694},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Adult ; },
abstract = {BACKGROUND: Electroencephalography (EEG) has been widely used to measure brain activity, but its potential to generate accurate images from neural signals remains a challenge. Most EEG-decoding research has focused on tasks such as motor imagery, emotion recognition, and brain wave classification, which involve EEG signal analysis and classification. Some studies have explored the correlation between EEG and images, primarily focusing on EEG-image pair classification or transformation. However, EEG-based image generation remains underexplored.<br><br>OBJECTIVE: The primary goal of this study was to extend EEG-based classification to image generation, addressing the limitations of previous methods and unlocking the full potential of EEG for image synthesis. To achieve more meaningful EEG-to-image generation, we developed a novel framework, Neural-Cognitive Multimodal EEG-Informed Image (NECOMIMI), which was specifically designed to generate images directly from EEG signals.<br><br>METHODS: We developed a 2-stage NECOMIMI method, which integrated the novel Neural Encoding Representation Vectorizer (NERV) EEG encoder that we designed with a diffusion-based generative model. The Category-Based Assessment Table (CAT) score was introduced to evaluate the semantic quality of EEG-generated images. In addition, the ThingsEEG dataset was used to validate and benchmark the CAT score, providing a standardized measure for assessing EEG-to-image generation performance.<br><br>RESULTS: The NERV EEG encoder achieved state-of-the-art performance in several zero-shot classification tasks, with an average accuracy of 94.8% (SD 1.7%) in the 2-way task and 86.8% (SD 3.4%) in the 4-way task, outperforming models such as Natural Image Contrast EEG, Multimodal Similarity-Keeping Contrastive Learning, and Adaptive Thinking Mapper ShallowNet. This highlighted its superiority as a feature extraction tool for EEG signals. In a 1-stage image generation framework, EEG embeddings often resulted in abstract or generalized images such as landscapes instead of specific objects. Our proposed 2-stage NECOMIMI architecture effectively extracted semantic information from noisy EEG signals, showing its ability to capture and represent underlying concepts derived from brain wave activity. We further conducted a perturbation study to test whether the model overly depended on visual cortex EEG signals for scene-based image generation. The perturbation of visual cortex EEG channels led to a notable increase in Fr&#xe9;chet inception distance scores, suggesting that our model relied heavily on posterior brain signals to generate semantically coherent images.<br><br>CONCLUSIONS: NECOMIMI demonstrated the potential of EEG-to-image generation, revealing the challenges of translating noisy EEG data into accurate visual representations. The novel NERV EEG encoder for multimodal contrastive learning reached state-of-the-art performance both on n-way zero-shot and EEG-informed image generation. The introduction of the CAT score provided a new evaluation metric, paving the way for future research to refine generative models. In addition, this study highlighted the significant clinical potential of EEG-to-image generation, particularly in enhancing brain-machine interface systems and improving quality of life for individuals with motor impairments.},
}
@article {pmid40554057,
year = {2025},
author = {Park, S and Ha, J and Kim, L},
title = {Improving single-trial detection of error-related potentials by considering the effect of heartbeat-evoked potentials in a motor imagery-based brain-computer interface.},
journal = {Computers in biology and medicine},
volume = {195},
number = {},
pages = {110563},
doi = {10.1016/j.compbiomed.2025.110563},
pmid = {40554057},
issn = {1879-0534},
abstract = {OBJECTIVE: This study aimed to determine the effect of heartbeat-evoked potentials (HEPs) on changes in the error-related potential (ErrP) epoch and classification performance in single trials, specifically distinguishing between correct and error conditions in a three-class motor imagery-based brain-computer interface.<br><br>METHODS: Eleven individuals participated in this study, with 10 participants assigned to the offline group and 10 to the real-time group. The experiment consisted of 360 motor imagery trials, involving both correct and erroneous feedback. The ErrP trial was categorized into three conditions based on whether the heartbeat was within the ErrP epoch time window or not: (1) including heartbeat trials (ErrPIHB), (2) excluding heartbeat trials (ErrPEHB), and (3) total trials (ErrPT).<br><br>RESULTS: A small negativity was observed at approximately 200&#xa0;ms, followed by a subsequent positivity at approximately 300&#xa0;ms. The prominent amplitudes at approximately 200 and 300&#xa0;ms in the ErrPEHB condition notably differed from those in the ErrPT and ErrPIHB conditions, showing the highest classification accuracy. In the offline experiment dataset of 10 participants, the ErrPEHB condition demonstrated the highest classification accuracy (0.89). This was higher by 0.07 and 0.11 compared to the ErrPT (0.82) and ErrPIHB (0.78) conditions, respectively. For real-time analysis, the novel ErrP paradigm proposed in this study achieved a classification accuracy of 0.89 for 10 participants, a 0.05 increase compared with that of the conventional ErrP paradigm.<br><br>CONCLUSION AND SIGNIFICANCE: These findings can contribute to obtaining pure or clear ErrP epochs associated with the target response and significantly improve classification performance.},
}
@article {pmid40553977,
year = {2025},
author = {Jiang, H and Qi, H and Tang, A and Hu, S and Lai, J},
title = {Start the Engine of Neuroregeneration: A Mechanistic and Strategic Overview of Direct Astrocyte-to-Neuron Reprogramming.},
journal = {Ageing research reviews},
volume = {},
number = {},
pages = {102808},
doi = {10.1016/j.arr.2025.102808},
pmid = {40553977},
issn = {1872-9649},
abstract = {The decline of adult neurogenesis and neuronal function during aging underlies the onset and progression of neurodegenerative diseases such as Alzheimer's disease. Conventional therapies, including neurotransmitter modulators and antibodies targeting pathogenic proteins, offer only symptomatic improvement. As the most abundant glial cells in the brain, astrocytes outnumber neurons nearly fivefold. However, their proliferative and transdifferentiation potential renders them ideal candidates for in situ neuronal replacement. Direct astrocyte-to-neuron reprogramming offers a promising regenerative approach to restore damaged neural circuits. Herein, we propose a "car start-up" model to conceptualize this process, emphasizing the need to inhibit non-neuronal fate pathways (release the handbrake), suppress transcriptional repressors (release the footbrake), and activate neuron-specific gene expression (step on the gas). Additionally, overcoming metabolic barriers in the cytoplasm is essential for successful lineage conversion. Viral or non-viral vectors deliver reprogramming factors, while small molecules serve as metabolic and epigenetic fuel to boost efficiency. In summary, we review the current evidence supporting direct astrocyte-to-neuron reprogramming as a viable regenerative strategy in the aging brain. We also highlight the conceptual "car start-up" model as a useful framework to dissect the molecular logic of lineage conversion and emphasize its promising therapeutic potential for combating neurodegenerative diseases.},
}
@article {pmid40553738,
year = {2025},
author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W},
title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119714},
doi = {10.1016/j.jad.2025.119714},
pmid = {40553738},
issn = {1573-2517},
abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.<br><br>METHODS: A total of 1988 participants aged 60&#x202f;years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.<br><br>RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR]&#x202f;=&#x202f;1.21, 95&#x202f;% confidence interval [CI]&#x202f;=&#x202f;1.05-1.39), stroke (HR&#x202f;=&#x202f;1.77, 95%CI&#x202f;=&#x202f;1.20-2.63), stomach or other digestive disease (HR&#x202f;=&#x202f;1.28, 95%CI&#x202f;=&#x202f;1.11-1.48), arthritis or rheumatism (HR&#x202f;=&#x202f;1.41, 95%CI&#x202f;=&#x202f;1.24-1.60), chronic lung diseases (HR&#x202f;=&#x202f;1.25, 95%CI&#x202f;=&#x202f;1.03-1.52) and kidney disease (HR&#x202f;=&#x202f;1.38, 95%CI&#x202f;=&#x202f;1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13&#x202f;% (adjusted HR&#x202f;=&#x202f;1.13, 95&#x202f;% CI&#x202f;=&#x202f;1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR&#x202f;=&#x202f;1.47, 95&#x202f;% CI&#x202f;=&#x202f;1.22-1.77) and respiratory pattern (HR&#x202f;=&#x202f;1.47, 95&#x202f;% CI&#x202f;=&#x202f;1.07-2.04) showing higher DS risk compared to the relatively healthy group.<br><br>CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.},
}
@article {pmid40551292,
year = {2025},
author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H},
title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {6},
pages = {e70270},
pmid = {40551292},
issn = {1552-5279},
support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; },
abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.<br><br>METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.<br><br>RESULTS: Increased brain conductivity was associated with A&#x3b2; and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.<br><br>DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.<br><br>HIGHLIGHTS: Brain tissue conductivity correlates with A&#x3b2; and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.},
}
@article {pmid40550006,
year = {2025},
author = {Liu, Y and Fan, P and Pan, Y and Ping, J},
title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.},
journal = {Chemical reviews},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.chemrev.5c00115},
pmid = {40550006},
issn = {1520-6890},
abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.},
}
@article {pmid40549688,
year = {2025},
author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W},
title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {220},
pages = {},
doi = {10.3791/67931},
pmid = {40549688},
issn = {1940-087X},
mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; },
abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.},
}
@article {pmid40549518,
year = {2025},
author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M},
title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3579332},
pmid = {40549518},
issn = {2162-2388},
abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.},
}
@article {pmid40548156,
year = {2025},
author = {Tzimourta, KD},
title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.},
journal = {Cureus},
volume = {17},
number = {6},
pages = {e85897},
pmid = {40548156},
issn = {2168-8184},
abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.},
}
@article {pmid40546334,
year = {2025},
author = {Cruz, MV and Jamal, S and Sethuraman, SC},
title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {16},
pmid = {40546334},
issn = {2228-7477},
abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.},
}
@article {pmid40545006,
year = {2025},
author = {Feng, J and Jia, W and Li, Z},
title = {Electroencephalography: A Valuable Tool for Assessing Motor Impairment and Recovery Post-Stroke.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110518},
doi = {10.1016/j.jneumeth.2025.110518},
pmid = {40545006},
issn = {1872-678X},
abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.},
}
@article {pmid40544658,
year = {2025},
author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.},
journal = {Ultrasonics},
volume = {155},
number = {},
pages = {107734},
doi = {10.1016/j.ultras.2025.107734},
pmid = {40544658},
issn = {1874-9968},
abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.<br><br>OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.<br><br>RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90&#xa0;%, P = 0.001) in KA mice treated with LIPU&#xa0;+&#xa0;SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.<br><br>CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.},
}
@article {pmid40542951,
year = {2025},
author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y},
title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40542951},
issn = {1995-8218},
abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.},
}
@article {pmid40541755,
year = {2025},
author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K},
title = {Shared and distinct neural signatures of feature and spatial attention.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121296},
doi = {10.1016/j.neuroimage.2025.121296},
pmid = {40541755},
issn = {1095-9572},
abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.},
}
@article {pmid40502202,
year = {2025},
author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {Encoding of speech modes and loudness in ventral precentral gyrus.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40502202},
issn = {2692-8205},
abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.},
}
@article {pmid40541523,
year = {2025},
author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z},
title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.06.004},
pmid = {40541523},
issn = {1879-2022},
abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.},
}
@article {pmid40538971,
year = {2025},
author = {Miao, Y and Li, K and Zhao, W and Zhang, Y},
title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {94},
pmid = {40538971},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.},
}
@article {pmid40538970,
year = {2025},
author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R},
title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {95},
pmid = {40538970},
issn = {1871-4080},
abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.},
}
@article {pmid40536865,
year = {2025},
author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J},
title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3579528},
pmid = {40536865},
issn = {1558-2531},
abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.<br><br>METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.<br><br>RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.<br><br>CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.<br><br>SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.},
}
@article {pmid40536747,
year = {2025},
author = {Fei, SW and Chen, JL and Hu, YB},
title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40536747},
issn = {2662-4737},
abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.},
}
@article {pmid40536356,
year = {2025},
author = {Rizzo, M and Dawson, JD},
title = {AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.},
journal = {Annals of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/ana.27225},
pmid = {40536356},
issn = {1531-8249},
support = {//University of Nebraska Foundation/ ; U54GM115458//National Institute of General Medical Sciences/ ; R01AG017177//National Institute on Aging/ ; },
abstract = {Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025.},
}
@article {pmid40535306,
year = {2025},
author = {Xavier Fidêncio, A and Grün, F and Klaes, C and Iossifidis, I},
title = {Hybrid brain-computer interface using error-related potential and reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1569411},
pmid = {40535306},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.},
}
@article {pmid40535187,
year = {2025},
author = {Bao, Y and Zhou, H and Geng, F and Hu, Y},
title = {The relation between game disorder and interruption during game is mediated by game craving.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1579016},
pmid = {40535187},
issn = {1664-1078},
abstract = {The burgeoning user base and potential negative effects of excessive involvement in gaming, particularly Internet Gaming Disorder (IGD), demand significant attention. While existing research has explored the susceptibility of individuals with IGD to game-related stimuli, the question of why it is challenging for these individuals to disengage from gaming remains under-explored. Drawing parallels with the concept of interruption, we hypothesize that negative emotions triggered during gaming interruptions would drive individuals' craving for the game and compelling them to continue playing, reinforcing the IGD cycle. In this study, 42 male 'League of Legends' players, aged 19 to 29, experienced controlled interruptions every 3 min during gaming and non-gaming control tasks. Our findings demonstrate that interruptions during gaming elicited significantly higher levels of anger and anxiety compared to the control tasks. Further, we found a positive correlation between the severity of IGD symptoms and the intensity of anger and anxiety induced by gaming interruptions. Additionally, our analysis suggests that craving partially mediates the relationship between anger arousal during gaming interruptions and IGD severity. These findings provide new insights into how emotional responses to gaming interruptions contribute to IGD, offering a novel perspective for future research and potential treatment approaches.},
}
@article {pmid40534746,
year = {2025},
author = {Zheng, K and Guo, L and Liang, W and Liu, P},
title = {Comparison of the effects of transcranial direct current stimulation combined with different rehabilitation interventions on motor function in people suffering from stroke-related symptoms: a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1586685},
pmid = {40534746},
issn = {1664-2295},
abstract = {BACKGROUND: This study employs network meta-analysis to assess the efficacy of transcranial direct current stimulation (tDCS) combined with different rehabilitation approaches in enhancing motor function in people suffering from stroke-related symptoms (PSSS). The objective is to determine the most effective tDCS-based rehabilitation approach and offer valuable evidence to guide clinical decision-making.<br><br>METHODS: This study included randomized controlled trials (RCTs) published before September 23, 2024. We conducted a systematic search across eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (SinoMed), Wanfang, and VIP. Network meta-analysis (NMA) was conducted utilizing R Studio and Stata 15.0 for data analysis.<br><br>RESULTS: A total of 74 RCTs were included in this study, encompassing 4,335 PSSS and 11 intervention strategies. The NMA revealed that brain-computer interface therapy (BCIT) in combination with tDCS [surface under the cumulative ranking curve (SUCRA)&#x202f;=&#x202f;88.34%] was the most effective tDCS-based intervention for improving the Fugl-Meyer Assessment for Upper Extremity score in PSSS. Mirror therapy (MT) in combination with tDCS (SUCRA&#x202f;=&#x202f;85.96%) was identified as the optimal intervention for enhancing the Action Research Arm Test score in PSSS. MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;84.29%) was the best approach for improving the Fugl-Meyer Assessment for Lower Extremity score. Additionally, acupuncture and moxibustion (AM) in combination with tDCS (SUCRA&#x202f;=&#x202f;77.16%) was the most effective intervention for increasing the Berg Balance Scale score in PSSS. The two-dimensional clustering analysis showed that MT&#x202f;+&#x202f;tDCS (SUCRA&#x202f;=&#x202f;75.83%/85.96%) was the optimal tDCS-based rehabilitation strategy for treating upper limb motor dysfunction in PSSS, while AM+tDCS (SUCRA&#x202f;=&#x202f;76.94%/77.16%) was the best tDCS-based rehabilitation strategy for improving lower limb motor dysfunction in PSSS.<br><br>CONCLUSION: BCIT+tDCS was identified as the optimal tDCS-based rehabilitation strategy for improving upper limb motor ability in PSSS, MT&#x202f;+&#x202f;tDCS was the most effective intervention for enhancing arm mobility, MT&#x202f;+&#x202f;tDCS was the best protocol for improving lower limb motor ability, while AM+tDCS was the best strategy for improving balance ability. Furthermore, MT&#x202f;+&#x202f;tDCS was the optimal tDCS-based rehabilitation approach for treating upper limb motor dysfunction, whereas AM+tDCS was the most effective strategy for addressing lower limb motor dysfunction in PSSS. Future studies may focus on investigating the therapeutic effects of MT combined with tDCS on Berg Balance Scale score in PSSS, as well as the effects of AM combined with tDCS on Action Research Arm Test score, in order to further explore the therapeutic potential of these two intervention strategies.<br><br>https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621998, Identifier PROSPERO CRD42024621998.},
}
@article {pmid40534671,
year = {2025},
author = {Zhai, H and Wang, H and Li, H and Wang, X},
title = {The Intersection of Psychedelics and Sleep: Exploring the Impacts on Sleep Architecture, Dream States, and Therapeutic Implications.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {6},
pages = {1832-1836},
pmid = {40534671},
issn = {2575-9108},
abstract = {The interplay between psychedelics, such as psilocybin, lysergic acid diethylamide (LSD) and dimethyltryptamine (DMT), and sleep is an emerging area, but their impact on sleep remains relatively underexplored. This viewpoint provides a perspective on how psychedelics may alter sleep phases, dreaming, and their potential therapeutic applications for sleep disorders.},
}
@article {pmid40533772,
year = {2025},
author = {Lv, S and Ran, X and Xia, M and Zhang, Y and Pang, T and Zhou, X and Zhao, Z and Yu, Y and Gao, Z},
title = {Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {137},
pmid = {40533772},
issn = {1743-0003},
support = {221100310500//the Major Science and Technology Projects of Henan Province/ ; 82302298//the National Natural Science Foundation of China/ ; 82201709//the National Natural Science Foundation of China/ ; 24IRTSTHN042//Innovative Research Team (in Science and Technology) in University of Henan Province/ ; XTkf01//the Open Project Program of Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder/ ; 242102521012//International Science and Technology Cooperation Project of Henan Province/ ; 242102310055//the Science and Technology Research Project of Henan Province/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; *Hand/physiopathology ; *Stroke Rehabilitation/methods ; *Functional Laterality/physiology ; Support Vector Machine ; Adult ; },
abstract = {BACKGROUND: Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients.<br><br>METHODS: This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms.<br><br>RESULTS: Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B&#x2192;A, D&#x2192;A and D&#x2192;C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients.<br><br>CONCLUSION: Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.},
}
@article {pmid40532880,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119667},
doi = {10.1016/j.jad.2025.119667},
pmid = {40532880},
issn = {1573-2517},
abstract = {BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning models to evaluate the diagnostic potential of cytokine and cognitive assessments.<br><br>METHODS: This study involved 64 early-onset SCZ patients, 53 adolescents with MDD, and 33 healthy controls. The plasma concentrations of 44 cytokines were measured using the LUMINEX multiplex assay. Cognitive function was tested with the Cambridge Neuropsychological Test Automated Battery. Random Forest and Extreme Gradient Boosting models were used for classification, with their effectiveness evaluated via ROC curve analysis.<br><br>RESULTS: SCZ patients exhibited significantly elevated levels of CCL11, IL-2 and IL-13, while MDD patients displayed increased CXCL2 and G-CSF levels but decreased CCL20 and CCL11 levels. SCZ patients showed significant cognitive impairments compared to healthy controls. Elevated CCL11 were associated with poorer memory accuracy, and higher G-CSF were linked to worse executive function. The XGBoost model was more sensitive in classifying MDD than the Random Forest model, but both struggled to differentiate SCZ patients from healthy controls due to low specificity.<br><br>CONCLUSION: Early-onset SCZ and adolescent MDD patients showed unique peripheral cytokine profiles, with SCZ patients experiencing significant cognitive deficits. The cytokine CCL11 was found to have a significant association with cognitive dysfunction. Cytokine levels and cognitive assessments may serve as potential biomarkers for the diagnosis of MDD.},
}
@article {pmid40530005,
year = {2025},
author = {Shao, W and Meng, W and Zuo, J and Li, X and Ming, D},
title = {Opportunities and Challenges of Brain-on-a-Chip Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0287},
pmid = {40530005},
issn = {2692-7632},
abstract = {The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.},
}
@article {pmid40529543,
year = {2025},
author = {Faber, J and Tsytsarev, V and Pais-Vieira, M and Aksenova, T},
title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies, volume II.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619232},
pmid = {40529543},
issn = {1662-5161},
}
@article {pmid40527877,
year = {2025},
author = {Moreira, JPC and Carvalho, VR and Mendes, EMAM and Fallah, A and Sejnowski, TJ and Lainscsek, C and Comstock, L},
title = {An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1017},
pmid = {40527877},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Speech ; *Brain-Computer Interfaces ; Phonetics ; Transcranial Magnetic Stimulation ; Adult ; Male ; Female ; },
abstract = {Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.},
}
@article {pmid40527666,
year = {2025},
author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M},
title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.},
journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jht.2025.04.009},
pmid = {40527666},
issn = {1545-004X},
abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.<br><br>PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.<br><br>STUDY DESIGN: Scoping review.<br><br>METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.<br><br>RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.<br><br>CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.},
}
@article {pmid40527337,
year = {2025},
author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and DeMatteo, JC and Lam, JLW and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA},
title = {Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade568},
pmid = {40527337},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. Approach. One adult male rhesus macque was implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios. Main Results. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization.<br><br>SIGNIFICANCE: The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.&#xD.},
}
@article {pmid40527331,
year = {2025},
author = {Xin, H and Li, H and Qi, H},
title = {A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade56a},
pmid = {40527331},
issn = {1741-2552},
abstract = {This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom (2-DOF) control through overt and covert visual selective attention. Methods: In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets, and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space. Results: EEG analysis demonstrated that overt and covert targets elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 96%, and 91% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP-based selective attention and validate the effectiveness of the proposed paradigm. Conclusion: This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP-based BCIs, which have primarily been limited to single-degree-of-freedom applications. .},
}
@article {pmid40526548,
year = {2025},
author = {Li, C and Cao, Z and Pan, Y and Zhu, P and Li, P and Li, F and Chen, H and Lu, BL and Wan, F and Yao, D and Xu, P},
title = {EEG-Based Emotion Monitoring and Regulation System by Learning the Discriminative Brain Network Manifold.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3576182},
pmid = {40526548},
issn = {2162-2388},
abstract = {Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ($p \lt 0.001$) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.},
}
@article {pmid40526539,
year = {2025},
author = {Jia, T and Long, H and Ji, L and Guan, X},
title = {EEG-based Spatial-Channel Interaction Attention Neural Networks for Detecting Empathy in Motor Collaboration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580617},
pmid = {40526539},
issn = {2168-2208},
abstract = {Embodied intelligence and humanoid robots aim to mimic interpersonal interactions to achieve affective human-robot interaction (HRI). A major challenge in advancing HRI lies in effectively emulating interpersonal affective interactions and evaluating the resulting artificial empathy. To address these challenges, we propose SpatialChannel Interaction Attention Neural Networks (SCIANN)-a novel EEG-based architecture that combines topological brain activation and connectivity patterns to decode empathy in motor collaboration. A private EEG dataset from a collaborative brain-computer interface motor control experiment and a public EEG dataset from a dyadic perceptual crossing experiment were used for evaluating SCIANN's performance with comparisons with five baseline models. Results showed that SCIANN outperformed the state-of-the-art baseline models. In the private dataset, SCIANN reached an accuracy of 100% both in inter-subject and cross-subject tests for detecting whether empathy is induced or not. For classifying 4-class empathy, it achieved an accuracy of 98.3% in the inter-subject test, and 48.1% in the cross-subject test. In the public dataset, SCIANN reached a classification accuracy of 92.2% in inter-subject and 91.7% in cross-subject tests for detecting whether empathy is induced or not. Feature visualization results revealed that contributing EEG channel importance features and channel interaction features aligned with established neurophysiological findings. These results collectively demonstrate SCIANN's potential as a robust, generalizable framework for artificial empathy assessment in HRI applications.},
}
@article {pmid40526534,
year = {2025},
author = {Luo, T and Zhang, J and Qiu, Y and Zhang, L and Hu, Y and Yu, Z and Liang, Z},
title = {M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580612},
pmid = {40526534},
issn = {2168-2208},
abstract = {Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG's non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6.67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.},
}
@article {pmid40524963,
year = {2025},
author = {Zhao, W and Lu, H and Zhang, B and Zheng, X and Wang, W and Zhou, H},
title = {TCANet: a temporal convolutional attention network for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {91},
pmid = {40524963},
issn = {1871-4080},
abstract = {Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.},
}
@article {pmid40523119,
year = {2025},
author = {Kamitani, Y and Tanaka, M and Shirakawa, K},
title = {Visual Image Reconstruction from Brain Activity via Latent Representation.},
journal = {Annual review of vision science},
volume = {},
number = {},
pages = {},
doi = {10.1146/annurev-vision-110423-023616},
pmid = {40523119},
issn = {2374-4650},
abstract = {Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.},
}
@article {pmid40522886,
year = {2025},
author = {Nam, J and Shin, H and You, C and Baeg, E and Kim, JG and Yang, S and Han, MR},
title = {Cortical Stimulation-Based Transcriptome Shifts on Parkinson's Disease Animal Model.},
journal = {ASN neuro},
volume = {17},
number = {1},
pages = {2513881},
doi = {10.1080/17590914.2025.2513881},
pmid = {40522886},
issn = {1759-0914},
mesh = {Animals ; *Transcriptome/physiology ; Male ; Disease Models, Animal ; *Motor Cortex/metabolism ; *Parkinson Disease/genetics/therapy/metabolism ; Mice ; Mice, Inbred C57BL ; },
abstract = {Parkinson's disease is the second most prevalent neurodegenerative disorder and is characterized by the degeneration of dopaminergic neurons. Significant improvements in gait balance, particularly in step length and velocity, were observed with less invasive wireless cortical stimulation. Transcriptome sequencing was performed to demonstrate the cellular mechanism, specifically targeting the primary motor cortex, where stimulation was applied. Our findings indicated that 38 differentially expressed genes (DEGs), initially downregulated following Parkinson's disease induction, were subsequently restored to normal levels after cortical stimulation. These 38 DEGs are potential targets for the treatment of motor disorders in Parkinson's disease. These genes are implicated in crucial processes, such as astrocyte-mediated blood vessel development and microglia-mediated phagocytosis of damaged motor neurons, suggesting their significant roles in improving behavioral disorders. Moreover, these biomarkers not only facilitate the rapid and accurate diagnosis of Parkinson's disease but also assist in precision medicine approaches.},
}
@article {pmid40522806,
year = {2025},
author = {Wang, P and Qi, Y and Pan, G},
title = {Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3577222},
pmid = {40522806},
issn = {1558-2531},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.<br><br>METHODS: In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.<br><br>RESULTS: Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.<br><br>CONCLUSION: PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.<br><br>SIGNIFICANCE: Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.},
}
@article {pmid40522801,
year = {2025},
author = {Yao, Y and Swaef, W and Geirnaert, S and Bertrand, A},
title = {EEG-Based Decoding of Selective Visual Attention in Superimposed Videos.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580261},
pmid = {40522801},
issn = {2168-2208},
abstract = {Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.},
}
@article {pmid40522765,
year = {2026},
author = {Kinfe, T and Brenner, S and Etminan, N},
title = {Brain-computer interfaces re-shape functional neurosurgery.},
journal = {Neural regeneration research},
volume = {21},
number = {3},
pages = {1122-1123},
doi = {10.4103/NRR.NRR-D-24-01336},
pmid = {40522765},
issn = {1673-5374},
}
@article {pmid40522539,
year = {2025},
author = {Ullah, A and Bookwalter, J and Sant, H and Azapagic, A and Shea, J and Berlet, R and Jha, N and Bailes, J and Gale, BK},
title = {An Osmosis-driven 3D-printed brain implant for drug delivery.},
journal = {Biomedical microdevices},
volume = {27},
number = {3},
pages = {29},
pmid = {40522539},
issn = {1572-8781},
mesh = {*Printing, Three-Dimensional ; *Osmosis ; *Drug Delivery Systems/instrumentation ; *Brain/surgery/metabolism ; *Brain Neoplasms/drug therapy ; Humans ; *Prostheses and Implants ; Glioblastoma/drug therapy ; },
abstract = {Glioblastoma is a highly malignant brain tumor with limited survival rates due to challenges in complete surgical excision, high recurrence (> 90%), and the inefficacy of systemic drug delivery. Significant efforts have been made to develop drug-loaded brain implants, catheters, and wafers aimed at enhancing survival rates by suppressing tumor recurrence. However, these devices often fail due to clogging, reflux, and the inability to be fully implanted intracranially. Furthermore, a lack of tissue penetration, diffusion distance, and duration of therapy have limited effectiveness of these implants. To address existing challenges, this study reports an osmosis-driven, 3D-printed brain implant with the potential for precise device customization to meet therapeutic needs, while negating systemic toxicity. It is capable of being loaded with two distinct therapeutic agents and implanted directly into the tumor resection cavity during surgery. The device features dual reservoirs, osmotic membranes, and precision-engineered needles for anchoring the device in the resection cavity and perfusing. Further, the device was characterized in vitro using 0.2% agarose gel as a brain tissue analog, with food dye as a drug analog and sodium chloride serving as an osmogen. A design of experiment approach was implemented to investigate various parameters, including membrane pore size, osmogen concentration, needle length, and their effects on release rates. The results demonstrated that the optimized implant achieves flow rates of 2.5 ± 0.1 µl/Hr and diffusion distance of up to 15.5 ± 0.4 mm, using 25 nm pore osmotic membranes with 25.3% osmogen concentration, aligning with model predictions.},
}
@article {pmid40520823,
year = {2025},
author = {Ivan Brown, A and MacDuffie, KE and Goering, S and Klein, E},
title = {The "wheels that keep me goin'": invisible forms of support for brain pioneers.},
journal = {Neuroethics},
volume = {18},
number = {1},
pages = {},
pmid = {40520823},
issn = {1874-5490},
abstract = {Research participants in long-term, first-in-human trials of implantable neural devices (i.e., brain pioneers) are critical to the success of the emerging field of neurotechnology. How these participants fare in studies can make or break a research program. Yet, their ability to enroll, participate, and seamlessly exit studies relies on both the support of family/caregivers and care from researchers that is often hidden from view. The present study offers an initial exploration of the different kinds of support that play a role in neural device trials from the perspectives of brain pioneers and their support partners (spouses, paid caregivers, parents, etc.). Using a mixed methods approach (semi-structured, open-ended interviews and a survey) with interpretive grounded theory, we present narratives from a study of six pioneers -- four in brain-computer interface (BCI) trials, and two in deep brain stimulation (DBS) trials -- and five support partners, about their experiences of being supported and supporting participants in implantable neural device studies. Our findings indicate the substantial amount of work involved on the part of pioneers - and some support partners - to make these studies successful. A central finding of the study is that non-logistical forms of support - social, emotional, and epistemic support - play a role, alongside more widely acknowledged forms of support, such as transportation and physical and clinical care. We argue that developing a better understanding of the kinds of support that enable neurotechnology studies to go well can help bridge the gap between abstract ethical principles of caring for subjects and on-the-ground practice.},
}
@article {pmid40520820,
year = {2025},
author = {Aubinet, C and Gillet, A and Regnier, A},
title = {Disorders of Consciousness, Language and Communication Following Severe Brain Injury.},
journal = {Psychologica Belgica},
volume = {65},
number = {1},
pages = {169-188},
pmid = {40520820},
issn = {2054-670X},
abstract = {Patients with severe brain injuries and disorders of consciousness (DoC) represent a complex clinical population in terms of diagnosis, prognosis, and management, including critical ethical considerations. Behavioral assessment scales remain the primary tools for evaluating the level of consciousness of these patients following a coma; however, they heavily depend on language and communication abilities. This reliance can lead to underestimating residual consciousness in cases where language impairments go undetected. Accordingly, the latest international guidelines on DoC diagnosis have highlighted aphasia as a significant confounding factor that must be addressed. On the other hand, accurately assessing residual language abilities is essential for better characterizing the patient's cognitive profile. This, in turn, enables neuropsychologists and speech-language therapists to tailor and plan effective rehabilitation programs. This review examines the current literature on language function and communication skills in patients with DoC, detailing the latest tools for assessing and managing language and consciousness in individuals with severe brain injuries. We explore the critical role of language function in evaluating residual consciousness, particularly in DoC behavioral diagnoses and in identifying covert consciousness through neuroimaging passive or active paradigms. Furthermore, we discuss how therapies aimed at recovering consciousness-such as pharmacological treatments, electromagnetic therapies, sensory or cognitive stimulation, and communication aids like brain-computer interfaces-may also impact or rely on language function and communication abilities. Further research is needed to refine methodologies and better understand the interplay between language processing, communication and levels of consciousness.},
}
@article {pmid40519866,
year = {2025},
author = {Wang, K and Ren, S and Jia, Y and Yan, X and Wang, L and Fan, Y},
title = {Neuromorphic chips for biomedical engineering.},
journal = {Mechanobiology in medicine},
volume = {3},
number = {3},
pages = {100133},
pmid = {40519866},
issn = {2949-9070},
abstract = {The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.},
}
@article {pmid40519178,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {Response to letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521399},
pmid = {40519178},
issn = {1745-2422},
}
@article {pmid40519177,
year = {2025},
author = {Cordero, DA},
title = {Letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521393},
pmid = {40519177},
issn = {1745-2422},
}
@article {pmid40514107,
year = {2025},
author = {Zuo, M and Chen, X and Sui, L},
title = {A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.},
journal = {Medical engineering &amp; physics},
volume = {141},
number = {},
pages = {104363},
doi = {10.1016/j.medengphy.2025.104363},
pmid = {40514107},
issn = {1873-4030},
mesh = {*Virtual Reality ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Brain-Computer Interfaces ; Female ; Young Adult ; Attention ; *Neural Networks, Computer ; },
abstract = {Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats. This study investigates EEG classification in response to 2D and 3D VR stimuli to deepen our understanding of the neural mechanisms driving VR interactions, with implications for brain-computer interfaces (BCIs). We introduce STA-EEGNet, an innovative model that enhances EEGNet by incorporating spatial-temporal attention (STA), improving EEG signal classification from VR environments. A one-way analysis of variance (ANOVA) was utilized to optimize channel selection, enhancing model accuracy. Comparative experiments showed that STA-EEGNet surpassed traditional EEGNet, achieving a peak accuracy of 99.78 % with channel selection. These findings highlight the benefits of spatial-temporal attention and optimal channel selection in classifying VR-evoked EEG data. This study underscores the importance of integrating spatial-temporal attention with compact convolutional neural networks like EEGNet, not only improving EEG signal classification but also advancing neural decoding and optimizing BCI applications.},
}
@article {pmid40513959,
year = {2025},
author = {Yang, Q and Guo, W and Wang, L and Zhang, Y and Tian, Y and Ming, D and Xiao, X and Yang, J},
title = {Effects of Fstl1 on neuroinflammation and microglia activation in lipopolysaccharide-induced acute depression-like mice.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115696},
doi = {10.1016/j.bbr.2025.115696},
pmid = {40513959},
issn = {1872-7549},
abstract = {Depression is the most prevalent psychiatric illness, and its pathogenesis is associated with neuroinflammation. Follistatinlike protein 1 (FSTL1), a novel inflammatory protein, participates in the pathogenesis of diseases related to neuroinflammation. Therefore, we aimed to investigate the effect of FSTL1 in the pathogenesis of depression mediated using neuroinflammation-mediated models. Our results showed that lipopolysaccharide (LPS) administration could induce despair-like behavior and increase proinflammatory cytokine levels in both male and female mice. Then, a significant positive correlation between hippocampal Fstl1 mRNA expression, microglial activation and desperate-like behaviors was observed in male mice. Moreover, knockdown FSTL1 significantly reduced microglial activation and the expression of proinflammatory cytokines, while overexpression of Fstl1 in hippocampus could exacerbate the activation of microglial under the LPS-induced condition in male mice. Mechanically, knockdown Fstl1 inhibited LPS-induced activation of BV2 microglia and reduced the production of proinflammatory cytokines, thereby protecting the survival of HT22 neurons. In conclusion, our results implied that Fstl1 may modulate despair-like behaviors through regulation of microglial activation and neuronal viability, which would lay the experimental and theoretical foundation for the neuroinflammatory mechanisms underlying depression.},
}
@article {pmid40513226,
year = {2025},
author = {Vasilyev, AN and Svirin, EP and Dubynin, IA and Butorina, AV and Nuzhdin, YO and Ossadtchi, AE and Stroganova, TA and Shishkin, SL},
title = {Intentionally versus spontaneously prolonged Gaze: A MEG study of active gaze-based interaction.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {189},
number = {},
pages = {76-96},
doi = {10.1016/j.cortex.2025.05.010},
pmid = {40513226},
issn = {1973-8102},
abstract = {Eye fixations are increasingly employed to control computers through gaze-sensitive interfaces, yet the brain mechanisms supporting this non-visual use of gaze remain poorly understood. In this study, we employed 306-channel magnetoencephalography (MEG) to find out what is specific to brain activity when gaze is used voluntarily for control. MEG was recorded while participants played a video game controlled by their eye movements. Each move required object selection by fixating it for at least 500 msec. Gaze dwells were classified as intentional if followed by a confirmation gaze on a designated location and as spontaneous otherwise. We identified both induced oscillatory and sustained phase-locked MEG activity differentiating intentional and spontaneous gaze dwells. Induced power analysis revealed prominent alpha-beta band synchronization (8-30 Hz) localized in the frontal cortex, with location broadly consistent with the frontal eye fields. This synchronization began 500-750 msec before intentional fixation onset and peaked shortly after it, suggesting proactive inhibition of saccadic activity. Sustained evoked responses further distinguished the two conditions, showing gradually rising cortical activation with a maximum at 200 msec post-onset in the inferior temporal cortex during intentional fixations, likely indicative of focused attentional engagement on spatial targets. These findings illuminate the neural dynamics underlying intentional gaze control, shedding light on the roles of proactive inhibitory mechanisms and attentional processes in voluntary behavior. By leveraging a naturalistic gaze-based interaction paradigm, this study offers a novel framework for investigating voluntary control under free behavior conditions and holds potential applications for enhancing hybrid eye-brain-computer interfaces.},
}
@article {pmid40512634,
year = {2025},
author = {Yan, T and Ming, Z and Huang, Y and Liu, Z and Chen, Q and Zhang, D and Liu, M and Suo, D and Zhang, J and Liu, S},
title = {Enhanced Brain-Controlled Mobile Robot based on SE-VEP Paradigm with Single Stimulus.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3579373},
pmid = {40512634},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).},
}
@article {pmid40511861,
year = {2025},
author = {Ramirez, P},
title = {Alternative ways to access AAC technologies.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/07434618.2025.2513902},
pmid = {40511861},
issn = {1477-3848},
abstract = {More than 21 years ago, I had a car accident that led to a brain stem stroke, leaving me paralyzed and unable to speak. I was desperate to communicate. One day, my sister wrote down the alphabet and pointed to each letter accordingly. I nodded, yes or no, and she wrote my message down. Later, I used a laser light with a letter board and then a laptop with a head pointer. More recently, I started using a gyroscopic air mouse. During outings, I use the laser and the letter board. They are easy to carry and use. Plus, I can communicate in English and Spanish which is very important because my family does not speak English. I am currently enrolled in a clinical trial at the University of California, San Francisco to investigate brain computer interface to control a robotic arm and communicate. They placed an implant in the surface of my brain; the implant connects to a computer system that collects brain signals and translates neural activity from my sensorimotor cortex into intended speech and motor actions. This type of research is needed to enhance communication and improve lives.},
}
@article {pmid40510263,
year = {2025},
author = {Amande, TJ and Kaszyk, V and Brown, F},
title = {Identification of OqxB Efflux Pump and Tigecycline Resistance Gene Cluster tmexC3D2-toprJ3 in Multidrug-Resistant Pseudomonas Stutzeri Isolate G3.},
journal = {Infection and drug resistance},
volume = {18},
number = {},
pages = {2889-2899},
pmid = {40510263},
issn = {1178-6973},
abstract = {PURPOSE: To identify antibiotic resistance genes (ARGs) and understand the molecular basis of multidrug resistance in P. stutzeri isolate G3.<br><br>METHODS: Whole-genome sequencing of isolate G3 was conducted at 30X coverage using Illumina NovaSeq 6000. Reads were trimmed using Trimmomatic and assessed using a combination of scripts that incorporated Samtools, BedTools, and bwa-mem. De novo assembly was performed using SPAdes, and assembly metrics were evaluated using QUAST. The assembled genome was uploaded to a Type Strain Genome Server (TYGS) for taxonomic identification. Genome annotation was performed using the KBase and Proksee software using PROKKA. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD).<br><br>RESULTS: P. stutzeri isolate G3 demonstrated resistance to most antibiotics tested, including meropenem (10&#xa0;&#xb5;g), ciprofloxacin (5&#xa0;&#xb5;g), gentamicin (10&#xa0;&#xb5;g), and tetracycline (30&#xa0;&#xb5;g). The ARGs identified were PmpM, AdeF, rsmA, vgb(A), BcI, cipA, OCH-2, and tet(45). A tigecycline-resistant gene cluster, tmexC3D2-toprJ3, was found in NODE_84, while the oqxB gene, encoding a resistance-nodulation-division (RND) efflux pump, was in NODE_309. Phylogenetic analysis showed OqxB clustered with Pseudomonas species, distinct from Klebsiella and Enterobacter. Comparative analysis of oqxB revealed P. stutzeri isolate G3 shared 78-100% identity with Pseudomonas aeruginosa strain 1334/14 in key components of the multidrug efflux system, including the transcriptional regulator MexT, periplasmic adaptor subunit MexE, and permease subunit MexF.<br><br>CONCLUSION: Our findings offer new insights into the reservoir of ARGs in the draft genome of Pseudomonas stutzeri isolate G3, including the tmexC3D2-toprJ3 and oqxB genes, highlighting its genomic plasticity and public health significance. This adaptability enables P. stutzeri to thrive in clinical environments, despite its natural habitat association. This study advances our understanding of the molecular mechanisms driving resistance in P. stutzeri and offers valuable insights to inform strategies for combating the spread of antimicrobial resistance in clinical and environmental settings.},
}
@article {pmid40510210,
year = {2025},
author = {Gazerani, P},
title = {A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1587296},
pmid = {40510210},
issn = {1664-2295},
abstract = {OBJECTIVE: The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.<br><br>METHODS: Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.<br><br>CONCLUSION: A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.},
}
@article {pmid40506548,
year = {2025},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Miller, LM and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice-synthesis neuroprosthesis.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40506548},
issn = {1476-4687},
abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech, such as prosody and immediately hearing one's own voice. Here we demonstrate a brain-to-voice neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real time to change intonation and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}
@article {pmid40506484,
year = {2025},
author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D},
title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {992},
pmid = {40506484},
issn = {2052-4463},
mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; },
abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.},
}
@article {pmid40505916,
year = {2025},
author = {Bikiaris, RE and Matschek, NI and Koumentakou, I and Niti, A and Kyzas, GZ},
title = {Synergistic effects of arginine and tannic acid on chitosan matrices: An approach for hemostatic sponge development.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {145105},
doi = {10.1016/j.ijbiomac.2025.145105},
pmid = {40505916},
issn = {1879-0003},
abstract = {This study presents the development of a novel multifunctional hydrogel biocomposite sponge designed to address the complexities of wound healing, including rapid hemostasis, infection prevention, and tissue regeneration. Recognizing the limitations of conventional wound dressings that lack multifunctionality, this study introduces a 3D chitosan/tannic acid (CS/TA) hydrogel. After testing three chitosan/tannic acid (CS/TA) ratios, CS/TA-1 (1:0.16), CS/TA-2 (1:0.25), and CS/TA-3 (1:0.34), the most effective formulation, CS/TA-2, was enhanced with sodium alginate (SA) and arginine (Arg) for optimal performance. Arginine, with its guanidinium functional group, served as a green crosslinker through physical interactions, enhancing the sponge's mechanical strength while also improving its hemostatic performance and biocompatibility, promoting cellular interactions. Its inclusion significantly amplified antioxidant activity (>90 %), mitigating oxidative stress and contributing to enhanced therapeutic outcomes. Ionic crosslinking and freeze-drying created a porous, absorbent sponge with high water retention and compression resilience. SEM confirmed the sponge's interconnected porosity, enabling cell infiltration and nutrient exchange. Blood Clotting Index (BCI) assessments demonstrated the hemostatic effectiveness of CS/TA/SA/Arg-3, with 25 % BCI at 5 min and 20 % at 15 min, along with excellent hemocompatibility, achieving a 2.08 % hemolysis rate. These results suggest the hydrogel sponge's potential for effective wound management in emergencies and clinical applications.},
}
@article {pmid40503091,
year = {2025},
author = {Li, Q and Pan, Y},
title = {Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf013},
pmid = {40503091},
issn = {2634-4416},
abstract = {Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards "naturalistic" approaches, with keywords such as "face-to-face" and "real-world" gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.},
}
@article {pmid40502712,
year = {2025},
author = {Chen, H and Zhang, M and Ye, T and Wolpert, MA and Ding, N},
title = {Low-frequency cortical activity reflects context-dependent parsing of word sequences.},
journal = {iScience},
volume = {28},
number = {6},
pages = {112650},
pmid = {40502712},
issn = {2589-0042},
abstract = {During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context-the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.},
}
@article {pmid40501187,
year = {2025},
author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y},
title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.},
journal = {Social neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/17470919.2025.2517068},
pmid = {40501187},
issn = {1747-0927},
abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.},
}
@article {pmid40499369,
year = {2025},
author = {Banovoth, RS and K V, K},
title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.},
journal = {Computers in biology and medicine},
volume = {194},
number = {},
pages = {110397},
doi = {10.1016/j.compbiomed.2025.110397},
pmid = {40499369},
issn = {1879-0534},
abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.},
}
@article {pmid40499342,
year = {2025},
author = {Silveira, I and Varandas, R and Gamboa, H},
title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108863},
doi = {10.1016/j.cmpb.2025.108863},
pmid = {40499342},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.<br><br>METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.<br><br>RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.<br><br>CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.},
}
@article {pmid40498623,
year = {2025},
author = {Lian, Q and Wang, Y and Qi, Y},
title = {Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3578627},
pmid = {40498623},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.},
}
@article {pmid40496741,
year = {2025},
author = {Tiwari, N and Anwar, S and Bhattacharjee, V},
title = {EEG dataset for natural image recognition through visual stimuli.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111639},
pmid = {40496741},
issn = {2352-3409},
abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.},
}
@article {pmid40496017,
year = {2025},
author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ},
title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.},
journal = {Frontiers in cellular and infection microbiology},
volume = {15},
number = {},
pages = {1560700},
pmid = {40496017},
issn = {2235-2988},
mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; },
abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.<br><br>METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.<br><br>RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.<br><br>CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.},
}
@article {pmid40495523,
year = {2025},
author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X},
title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.},
journal = {The American journal of psychiatry},
volume = {},
number = {},
pages = {appiajp20240475},
doi = {10.1176/appi.ajp.20240475},
pmid = {40495523},
issn = {1535-7228},
abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.<br><br>METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.<br><br>RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.<br><br>CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.},
}
@article {pmid40495436,
year = {2025},
author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA},
title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70589},
doi = {10.1002/brb3.70589},
pmid = {40495436},
issn = {2162-3279},
support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.<br><br>METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.<br><br>RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.<br><br>CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.},
}
@article {pmid40494420,
year = {2025},
author = {Spinelli, R and Sanchís, I and Siano, A},
title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.},
journal = {Bioorganic &amp; medicinal chemistry letters},
volume = {},
number = {},
pages = {130305},
doi = {10.1016/j.bmcl.2025.130305},
pmid = {40494420},
issn = {1464-3405},
abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.},
}
@article {pmid40494387,
year = {2025},
author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ},
title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade344},
pmid = {40494387},
issn = {1741-2552},
abstract = {OBJECTIVE: Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.<br><br>APPROACH: The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.<br><br>RESULTS: The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.<br><br>SIGNIFICANCE: These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.},
}
@article {pmid40494367,
year = {2025},
author = {Thielen, J},
title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.},
journal = {Biomedical physics &amp; engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ade316},
pmid = {40494367},
issn = {2057-1976},
abstract = {This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency. Approach. In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied. Main Results. Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort. Significance. This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.},
}
@article {pmid40493465,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R},
title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3578235},
pmid = {40493465},
issn = {1558-2531},
abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.},
}
@article {pmid40493186,
year = {2025},
author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J},
title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {24},
pages = {e2418920122},
doi = {10.1073/pnas.2418920122},
pmid = {40493186},
issn = {1091-6490},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; },
abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.},
}
@article {pmid40490658,
year = {2025},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR},
title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.},
journal = {Behavior research methods},
volume = {57},
number = {7},
pages = {196},
pmid = {40490658},
issn = {1554-3528},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.},
}
@article {pmid40490007,
year = {2025},
author = {Chen, Y and Peng, Y and Tang, J and Camilleri, TA and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade290},
pmid = {40490007},
issn = {1741-2552},
abstract = {As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e., depression, anxiety) are detected, which are considered as the two basic functions of aBCI system. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems. Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders. Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information','outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI. Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI system in practical deployment.},
}
@article {pmid40490003,
year = {2025},
author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I},
title = {Speech imagery brain-computer interfaces: a systematic literature review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade28e},
pmid = {40490003},
issn = {1741-2552},
abstract = {Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines. \textit{Approach}
. We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode Speech Imagery from neural activity. \textit{Main results}
. Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6\% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions. \textit{Significance}
Speech Imagery is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.},
}
@article {pmid40489280,
year = {2025},
author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z},
title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577813},
pmid = {40489280},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.},
}
@article {pmid40485770,
year = {2025},
author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A},
title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1491494},
pmid = {40485770},
issn = {2296-9144},
abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.},
}
@article {pmid40398443,
year = {2025},
author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T},
title = {CNNs improve decoding of selective attention to speech in cochlear implant users.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addb7b},
pmid = {40398443},
issn = {1741-2552},
mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; },
abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.},
}
@article {pmid40484831,
year = {2025},
author = {Zhao, JZ},
title = {[A historical review and future outlook of neurosurgery in China].},
journal = {Zhonghua yi xue za zhi},
volume = {105},
number = {21},
pages = {1679-1685},
doi = {10.3760/cma.j.cn112137-20250325-00727},
pmid = {40484831},
issn = {0376-2491},
mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; },
abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.},
}
@article {pmid40483841,
year = {2025},
author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A},
title = {Supervised factor selection in tensor decomposition of EEG signal.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108866},
doi = {10.1016/j.cmpb.2025.108866},
pmid = {40483841},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.<br><br>METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.<br><br>RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.<br><br>CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.},
}
@article {pmid40483616,
year = {2025},
author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X},
title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2514132},
pmid = {40483616},
issn = {1476-8259},
abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.},
}
@article {pmid40482972,
year = {2025},
author = {M V, H and K, K and B, SB},
title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115652},
doi = {10.1016/j.bbr.2025.115652},
pmid = {40482972},
issn = {1872-7549},
abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.<br><br>NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.<br><br>RESULTS: The proposed model achieved average classification accuracies of 83.83% &#xb1; 5.94 and 64.58% &#xb1; 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.<br><br>CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.},
}
@article {pmid40481499,
year = {2025},
author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G},
title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {422},
pmid = {40481499},
issn = {1477-3155},
support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.<br><br>RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.<br><br>CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.<br><br>GRAPHICAL ABSTRACT: [Image: see text]<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.},
}
@article {pmid40481295,
year = {2025},
author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H},
title = {Expectation violation enhances short-term source memory.},
journal = {Psychonomic bulletin &amp; review},
volume = {},
number = {},
pages = {},
pmid = {40481295},
issn = {1531-5320},
abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.},
}
@article {pmid40481078,
year = {2025},
author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X},
title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {19938},
pmid = {40481078},
issn = {2045-2322},
support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; },
mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; },
abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.},
}
@article {pmid40481044,
year = {2025},
author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J},
title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {953},
pmid = {40481044},
issn = {2052-4463},
support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; },
abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.},
}
@article {pmid40480870,
year = {2025},
author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V},
title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110502},
doi = {10.1016/j.jneumeth.2025.110502},
pmid = {40480870},
issn = {1872-678X},
}
@article {pmid40480308,
year = {2025},
author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y},
title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115330},
doi = {10.1016/j.expneurol.2025.115330},
pmid = {40480308},
issn = {1090-2430},
abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.<br><br>OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.<br><br>METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.<br><br>RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.<br><br>CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.},
}
@article {pmid40457516,
year = {2025},
author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D},
title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e202505038},
doi = {10.1002/anie.202505038},
pmid = {40457516},
issn = {1521-3773},
support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; },
abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.},
}
@article {pmid40480249,
year = {2025},
author = {Pritchard, M and Campelo, F and Goldingay, H},
title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade1f9},
pmid = {40480249},
issn = {1741-2552},
abstract = {Objective: Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG &amp; EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings. Approach: We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection &amp; Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature. Main results: EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems. Significance: To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies, enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.},
}
@article {pmid40479831,
year = {2025},
author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J},
title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2025.2.JNS243025},
pmid = {40479831},
issn = {1933-0693},
abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.<br><br>METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.<br><br>RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.<br><br>CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.},
}
@article {pmid40478867,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Tan, Y},
title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0324848},
doi = {10.1371/journal.pone.0324848},
pmid = {40478867},
issn = {1932-6203},
mesh = {*Phonetics ; Humans ; Male ; *Language ; Semantics ; Female ; China ; Adult ; Animals ; Young Adult ; East Asian People ; },
abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.},
}
@article {pmid40478707,
year = {2025},
author = {Li, H and Zhang, H and Chen, Y},
title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577611},
pmid = {40478707},
issn = {2168-2208},
abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.},
}
@article {pmid40476694,
year = {2025},
author = {Nazareth, G},
title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-2},
doi = {10.1080/07434618.2025.2508491},
pmid = {40476694},
issn = {1477-3848},
abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 24. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.},
}
@article {pmid40475558,
year = {2025},
author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD},
title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.05.15.653674},
pmid = {40475558},
issn = {2692-8205},
abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models- available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r <.35) and within-person emotional states (r =.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.},
}
@article {pmid40477046,
year = {2020},
author = {Amoo-Adare, EA},
title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.},
journal = {Postdigital science and education},
volume = {2},
number = {3},
pages = {606-613},
pmid = {40477046},
issn = {2524-4868},
}
@article {pmid40472937,
year = {2025},
author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ},
title = {Clonorchiasis in China: geospatial modelling of the population infected or at risk, based on national surveillance.},
journal = {The Journal of infection},
volume = {},
number = {},
pages = {106528},
doi = {10.1016/j.jinf.2025.106528},
pmid = {40472937},
issn = {1532-2742},
abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and chemotherapy need hinders the control.<br><br>METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 &#xd7; 5km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.<br><br>RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.<br><br>CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.<br><br>Environmental and socioeconomic data are open access (Table S1 in Supplementary Information). Epidemiological and behavioral data are not publicly available but are available on reasonable request after reviewed by the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research).},
}
@article {pmid40472336,
year = {2025},
author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J},
title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0319031},
pmid = {40472336},
issn = {1932-6203},
mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; },
abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.},
}
@article {pmid40471721,
year = {2025},
author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G},
title = {Applying SSVEP BCI on Dynamic Background.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3576984},
pmid = {40471721},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.},
}
@article {pmid40471491,
year = {2025},
author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y},
title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40471491},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; },
abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.},
}
@article {pmid40470749,
year = {2025},
author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K},
title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e17408},
doi = {10.1002/advs.202417408},
pmid = {40470749},
issn = {2198-3844},
support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; },
abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.},
}
@article {pmid40469097,
year = {2025},
author = {Wang, Z and Wang, Y},
title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1599960},
pmid = {40469097},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.},
}
@article {pmid40469096,
year = {2025},
author = {Li, P and Yu, D and Cheng, L and Wang, K},
title = {Influence of attentional state on EEG-based motor imagery of lower limb.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1545492},
pmid = {40469096},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery (MI) has emerged as a promising technique for enhancing motor skill acquisition and facilitating neural adaptation training. Attention plays a key role in regulating the neural mechanisms underlying MI. This study aims to investigate how attentional states modulate EEG-based lower-limb motor imagery performance by influencing event-related desynchronization (ERD) and the alpha modulation index (AMI) and to develop a real-time attention monitoring method based on the theta/beta ratio (TBR).<br><br>METHODS: Fourteen healthy right-handed subjects (aged 21-23) performed right-leg MI tasks, while their attentional states were modulated via a key-press paradigm. EEG signals were recorded using a 32-channel system and preprocessed with independent component analysis (ICA) to remove artifacts. Attentional states were quantified using both the traditional offline AMI and the real-time TBR index, with time-frequency analysis applied to assess ERD and its relationship with attention.<br><br>RESULTS: The results indicated a significant increase in ERD during high attentional states compared to low attentional states, with AMI values showing a strong positive correlation with ERD (r&#x202f;=&#x202f;0.9641, p&#x202f;<&#x202f;0.01). Cluster-based permutation testing confirmed that this &#x3b1;-band ERD difference was significant (corrected p&#x202f;<&#x202f;0.05). Moreover, the TBR index proved to be an effective real-time metric, decreasing significantly under focused attention. Offline paired t-tests showed a significant TBR reduction [t (13)&#x202f;=&#x202f;5.12, p&#x202f;=&#x202f;2.4&#x202f;&#xd7;&#x202f;10[-5]], and online analyses further validated second-by-second discrimination (Bonferroni-corrected p&#x202f;<&#x202f;0.01). These findings confirm the feasibility and efficacy of TBR for real-time attention monitoring and suggest that enhanced attentional focus during lower-limb MI can improve signal quality and overall performance.<br><br>CONCLUSION: This study reveals that attentional states significantly influence the neural efficiency of lower-limb motor imagery by modulating ERD/AMI and demonstrates that the TBR can serve as a real-time indicator of attention, providing a novel tool for optimizing attentional processes in motor skill training.},
}
@article {pmid40468342,
year = {2025},
author = {Wang, M and Zhou, H and Zhang, X and Chen, Q and Tong, Q and Han, Q and Zhao, X and Wang, D and Lai, J and He, H and Zhang, S and Hu, S},
title = {Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial.},
journal = {BMC medicine},
volume = {23},
number = {1},
pages = {334},
pmid = {40468342},
issn = {1741-7015},
support = {52407261, 82201675//National Natural Science Foundation of China/ ; 52407261, 82201675//National Natural Science Foundation of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; No. JNL-2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; 226-2022-00193, 226-2022-00002, 2023ZFJH01-01, 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Double-Blind Method ; Female ; Male ; *Bipolar Disorder/therapy/complications/psychology ; *Diffusion Tensor Imaging/methods ; Adult ; *Transcranial Magnetic Stimulation/methods ; *Cognitive Dysfunction/therapy/etiology/diagnostic imaging ; Middle Aged ; *Transcranial Direct Current Stimulation/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes.<br><br>METHODS: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal&#xa0;anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3&#xa0;weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3).<br><br>RESULTS: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed.<br><br>CONCLUSIONS: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05964777.},
}
@article {pmid40467567,
year = {2025},
author = {Pang, J and Xu, J and Chen, L and Teng, H and Su, C and Zhang, Z and Gao, L and Zhang, R and Liu, G and Chen, Y and He, J and Pang, Y and Li, H},
title = {Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {188},
pmid = {40467567},
issn = {2158-3188},
support = {222102310205//Science and Technology Department of Henan Province (Henan Provincial Department of Science and Technology)/ ; 62103377//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnostic imaging/pathology/genetics/blood ; Female ; Male ; Adult ; *Cerebellum/diagnostic imaging/physiopathology/pathology ; Magnetic Resonance Imaging ; *Inflammation/blood ; Interleukin-6/blood ; Gray Matter/diagnostic imaging/pathology ; C-Reactive Protein/metabolism/analysis ; Middle Aged ; Prefrontal Cortex/diagnostic imaging/physiopathology ; Case-Control Studies ; Young Adult ; },
abstract = {A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.},
}
@article {pmid40465456,
year = {2025},
author = {Li, C and Hasegawa, I and Tanigawa, H},
title = {Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.},
journal = {STAR protocols},
volume = {6},
number = {2},
pages = {103870},
doi = {10.1016/j.xpro.2025.103870},
pmid = {40465456},
issn = {2666-1667},
abstract = {Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al.[1].},
}
@article {pmid40463690,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {40463690},
issn = {2331-8422},
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}
@article {pmid40462746,
year = {2025},
author = {Song, J and Chai, X and Zhang, X and Lv, Z and Wan, F and Yang, Y and Shan, X and Liu, J},
title = {HEGNet: EEG and EMG fusion decoding method in motor imagery and actual movement.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/10255842.2025.2512877},
pmid = {40462746},
issn = {1476-8259},
abstract = {The widespread adoption od brain-computer interface (BCI) has been hindered by the limited classification accuracy of electroencephalography (EEG) signals alone. This study proposes a novel BCI model, HEGNet, that addresses this challenge by fusing EEG and electromyography (EMG) signals. HEGNet incorporates an EMG feature extraction component to mitigate the inherent instability and low signal-to-noise ratio limitations of relying solely on EEG data. Additionally, HEGNet employs a feature fusion module to dynamically adjust the focus on EEG and EMG features, thereby enhancing its overall robustness. These findings suggest that EMG information can serve as a valuable supplement to EEG data.},
}
@article {pmid40031188,
year = {2025},
author = {Yu, H and Zeng, F and Liu, D and Wang, J and Liu, J},
title = {Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {6},
pages = {4147-4160},
doi = {10.1109/JBHI.2025.3530922},
pmid = {40031188},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Acupuncture Therapy/methods ; Male ; Adult ; *Brain-Computer Interfaces ; Female ; *Signal Processing, Computer-Assisted ; *Deep Learning ; Young Adult ; *Brain/physiology ; Neural Networks, Computer ; },
abstract = {Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain responses is still unclear. We proposed a deep learning framework using electroencephalographic activity of stimulated subjects to decode the needling processes of various acupuncture manipulations performed on Zusanli acupoint. Contrastive representation learning integrated with domain adaptation strategy was applied to estimate 3D hand postures and hand joint motion trajectories of acupuncturist with video recordings, by which finite dimensional representations of behavior manifolds for needling operations were inferred. Distinct transition dynamics of behavior manifold were observed for acupuncture with lifting-thrusting and twisting-rotating manipulations. Moreover, latent neural manifolds of acupuncture evoked EEG signals were estimated in low dimensional state space of brain activities with unsupervised manifold learning, which can reliably represent acupuncture stimulations. Furthermore, a nonlinear decoder based on neural networks was designed to transform neural manifolds to behavior manifolds and further predict acupuncture manipulation as well as needling process. Experimental results demonstrated a high performance of the proposed decoding framework for four types of acupuncture manipulations with a precision of 92.42%. The EEG decoder provides an acupuncture-brain interface linking somatosensory stimulations with neural representations, an effective scheme for revealing clinical efficacy of acupuncture treatment.},
}
@article {pmid40461535,
year = {2025},
author = {He, X and Chen, J and Zhong, Y and Cen, P and Shen, L and Huang, F and Wang, J and Jin, C and Zhou, R and Zhang, X and Wang, A and Fan, J and Wu, S and Tu, M and Qin, X and Luo, X and Zhou, Y and Peng, J and Zhou, Y and Civelek, AC and Tian, M and Zhang, H},
title = {Forebrain neural progenitors effectively integrate into host brain circuits and improve neural function after ischemic stroke.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5132},
pmid = {40461535},
issn = {2041-1723},
support = {82030049, 32027802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82102095//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302262//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82302267//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82394433//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY23H180005//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Neural Stem Cells/transplantation/metabolism/cytology ; Rats ; Humans ; Forkhead Transcription Factors/metabolism/genetics ; *Prosencephalon/cytology ; Nerve Tissue Proteins/metabolism/genetics ; Neurons/metabolism/cytology ; *Ischemic Stroke/therapy/physiopathology/diagnostic imaging ; Induced Pluripotent Stem Cells/cytology/transplantation/metabolism ; Cell Differentiation ; Male ; Stem Cell Transplantation/methods ; Recovery of Function ; Rats, Sprague-Dawley ; Neurogenesis ; Disease Models, Animal ; *Stroke/therapy ; Positron-Emission Tomography ; Synapses ; },
abstract = {Human cortical neural progenitor cell transplantation holds significant potential in cortical stroke treatment by replacing lost cortical neurons and repairing damaged brain circuits. However, commonly utilized human cortical neural progenitors are limited in yield a substantial proportion of diverse cortical neurons and require an extended period to achieve functional maturation and synaptic integration, thereby potentially diminishing the optimal therapeutic benefits of cell transplantation for cortical stroke. Here, we generated forkhead box G1 (FOXG1)-positive forebrain progenitors from human inducible pluripotent stem cells, which can differentiate into diverse and balanced cortical neurons including upper- and deep-layer excitatory and inhibitory neurons, achieving early functional maturation simultaneously in vitro. Furthermore, these FOXG1 forebrain progenitor cells demonstrate robust cortical neuronal differentiation, rapid functional maturation and efficient synaptic integration after transplantation into the sensory cortex of stroke-injured adult rats. Notably, we have successfully utilized the non-invasive [18]F-SynVesT-1 PET imaging technique to assess alterations in synapse count before and after transplantation therapy of FOXG1 progenitors in vivo. Moreover, the transplanted FOXG1 progenitors improve sensory and motor function recovery following stroke. These findings provide systematic and compelling evidence for the suitability of these FOXG1 progenitors for neuronal replacement in ischemic cortical stroke.},
}
@article {pmid40460359,
year = {2025},
author = {Chen, Z and Zhang, Y and Ding, J and Li, Z and Tian, Y and Zeng, M and Wu, X and Su, B and Jiang, J and Wu, C and Wei, D and Sun, J and Lim, CT and Fan, H},
title = {Hydrogel-Based Multifunctional Deep Brain Probe for Neural Sensing, Manipulation, and Therapy.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c03865},
pmid = {40460359},
issn = {1936-086X},
abstract = {Implantable deep brain probes (DBPs) constitute a vital component of brain-machine interfaces, facilitating direct interaction between neural tissues and the external environment. Most multifunctional DBPs used for neural system sensing and modulation are currently fabricated through thermal tapering of polymeric materials. However, this approach faces a fundamental challenge in selecting materials that simultaneously accommodate the thermal stretching process and yet match the modulus of brain tissues. Here, we introduce a kind of multifunctional hydrogel-based fiber (HybF) designed for neural sensing, on-demand deep brain manipulation, and photodynamic therapy, and was achieved by integrating ion chelation/dechelation effects with templating methods throughout the entire wet-spinning process. With a low bending stiffness of approximately 0.3 N/m and a high conductivity of about 97 S/m at 1 kHz, HybF facilitates a high-quality signal recording (SNR ∼10) while minimizing immune rejection. It also effectively mediates deep brain optogenetic stimulation, successfully manipulating the behavior of hippocampal neurons in hSyn-ChrimsonR-tdTomato SD rats. Importantly, by leveraging HybF, this study explores the use of a spatiotemporally controllable photodynamic strategy in antiepilepsy, in which the high-amplitude abnormal electrical discharges were instantaneously eliminated without affecting normal cognitive/memory abilities. The above innovative approach established a distinct paradigm for deep brain manipulation and degenerative disease treatment, providing interesting insights into brain circuits and bioelectronic devices.},
}
@article {pmid40459463,
year = {2025},
author = {Savitz, BL and Dean, YE and Popa, NK and Cornely, RM and Byers, V and Gutama, BW and Abbott, EN and Torres-Guzman, R and Alter, N and Stehr, JD and Hill, JB and Elmaraghi, S},
title = {Targeted Muscle Reinnervation and Regenerative Peripheral Nerve Interface for Myoelectric Prosthesis Control: The State of Evidence.},
journal = {Annals of plastic surgery},
volume = {94},
number = {6S Suppl 4},
pages = {S572-S576},
doi = {10.1097/SAP.0000000000004273},
pmid = {40459463},
issn = {1536-3708},
mesh = {Humans ; *Artificial Limbs ; Electromyography ; *Nerve Regeneration/physiology ; *Muscle, Skeletal/innervation ; *Peripheral Nerves/physiology/surgery ; *Amputation Stumps/innervation ; Phantom Limb/prevention &amp; control ; *Amputation, Surgical/rehabilitation ; },
abstract = {Prosthetic rehabilitation after amputation poses significant challenges, often due to functional limitations, residual limb pain (RLP), and phantom limb pain (PLP). These issues not only affect physical health but also mental well-being and quality of life. In this review, we describe targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) and explore their clinical role in the evolution of myoelectric prosthetic control as well as postamputation pain and neuroma management. Early myoelectric prostheses, which detected electrical potentials from muscles to control prosthetic limbs, faced limitations such as inconsistent signal acquisition and complex control modes. Novel microsurgical techniques at the turn of the century such as TMR and RPNI significantly advanced myoelectric prosthetic control. TMR involves reinnervating denervated muscles with residual nerves to create electromyography (EMG) potentials and prevent painful neuromas. Similarly, RPNI relies on small muscle grafts to amplify EMG signals and distinguish from stochastic noise for refined prosthetic control. Techniques like TMR and RPNI not only improved prosthetic function, but also significantly reduced postamputation pain, making them critical in improving amputees' quality of life. Modern myoelectric prostheses evolved with advancements in microprocessor and sensor technologies, enhancing their functionality and user experience. Today, researchers have developed more intuitive and reliable prosthetic control by utilizing pattern recognition software and machine learning algorithms that may supersede reliance on surgically amplifying EMG signals. Future developments in brain-computer interfaces and machine learning hold promise for even greater advancements in prosthetic technology, emphasizing the importance of continued innovation in this field.},
}
@article {pmid40459258,
year = {2025},
author = {Seibert, B and Caceres, CJ and Gay, LC and Shetty, N and Faccin, FC and Carnaccini, S and Walters, MS and Marr, LC and Lowen, AC and Rajao, DS and Perez, DR},
title = {Air-liquid interface model for influenza aerosol exposure in vitro.},
journal = {Journal of virology},
volume = {},
number = {},
pages = {e0061925},
doi = {10.1128/jvi.00619-25},
pmid = {40459258},
issn = {1098-5514},
abstract = {UNLABELLED: Airborne transmission is an essential mode of infection and spread of influenza viruses among humans. However, most studies use liquid inoculum for virus infection. To better replicate natural airborne infections in vitro, we generated a calm-aerosol settling chamber system designed to examine the aerosol infectivity of influenza viruses in different cell types. Aerosol inoculation was characterized for multiple influenza A virus (FLUAV) subtypes, including pandemic 2009 H1N1, seasonal swine H3N2, and avian H9N2, using this exposure system. While each FLUAV strain displayed high infectivity within MDCK cells via liquid inoculation, differences in infectivity were observed during airborne inoculation. This was further observed in recently developed immortalized differentiated human airway epithelial cells (BCi-NS1.1) cultured in an air-liquid interface. The airborne infectious dose 50 for each virus was based on the exposure dose per well. Our findings indicate that this system has the potential to enhance our understanding of the factors influencing influenza transmission via the airborne route. This could be invaluable for conducting risk assessments, potentially reducing the reliance on extensive and costly in vivo animal studies.<br><br>IMPORTANCE: This study presents a significant advancement in influenza research by developing a novel in vitro system to assess aerosol infectivity, a crucial aspect of influenza transmission. The system's ability to differentiate between mammalian-adapted and avian-adapted influenza viruses based on their aerosol infectivity offers a valuable tool for pre-screening the pandemic potential of different strains. This could potentially streamline the risk assessment process and inform public health preparedness strategies. Moreover, the system's capacity to examine aerosol infectivity in human airway epithelial cells provides a more relevant model for studying virus-host interactions in natural airborne infections. Overall, this study provides an accessible platform for investigating aerosol infectivity, which could significantly contribute to our understanding of influenza transmission and pandemic preparedness.},
}
@article {pmid40459142,
year = {2025},
author = {Gao, J and Jiang, D and Wang, H and Wang, X},
title = {Opioid Enantiomers: Exploring the Complex Interplay of Stereochemistry, Pharmacodynamics, and Therapeutic Potential.},
journal = {Journal of medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.jmedchem.5c00136},
pmid = {40459142},
issn = {1520-4804},
abstract = {Opioids have been essential in pain management, particularly when other analgesics prove insufficient, but their use is complicated by risks of addiction, tolerance, and a range of adverse effects. These challenges are further exacerbated by the presence of opioid enantiomers that interact in a variety of ways with biological systems. This Perspective provides a comprehensive exploration of opioid enantiomers, focusing on their synthesis, pharmacodynamics, and potential therapeutic applications beyond traditional pain management. It highlights the complexity of synthesizing morphine enantiomers and additional challenges in producing the less-studied (+)-morphine. The Perspective also examines structure-activity relationship studies on (+)-opioid compounds, revealing their potential in modulating neuroinflammatory responses through non-opioid pathways and suggesting new therapeutic avenues for conditions like neuropathic pain and drug addiction. Furthermore, it discusses the differential safety profiles of opioid enantiomers, emphasizing the need for future research to advance precision medicine in opioid therapy, ultimately aiming to develop safer and more effective pain management strategies.},
}
@article {pmid40458259,
year = {2025},
author = {Zhang, W and Wang, T and Qin, C and Xu, B and Hu, H and Wang, T and Shen, Y},
title = {Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {13},
number = {},
pages = {1591316},
pmid = {40458259},
issn = {2296-4185},
abstract = {INTRODUCTION: The application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.<br><br>METHODS: This study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.<br><br>RESULTS: Experimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.<br><br>DISCUSSION: The vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.},
}
@article {pmid40457127,
year = {2025},
author = {Xiao, Z and She, Q and Fang, F and Meng, M and Zhang, Y},
title = {Auxiliary classifier adversarial networks with maximum subdomain discrepancy for EEG-based emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40457127},
issn = {1741-0444},
support = {62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; ZY2024025//Wenzhou Institute of Biomaterials and Engineering/ ; },
abstract = {Domain adaptation (DA) is considered to be effective solutions for unsupervised emotion recognition cross-session and cross-subject tasks based on electroencephalogram (EEG). However, the cross-domain shifts caused by individual differences and sessions differences seriously limit the generalization ability of existing models. Moreover, existing models often overlook the discrepancies among task-specific subdomains. In this study, we propose the auxiliary classifier adversarial networks (ACAN) to tackle these two key issues by aligning global domains and subdomains and maximizing subdomain discrepancies to enhance model effectiveness. Specifically, to address cross-domain discrepancies, we deploy a domain alignment module in the feature space to reduce inter-domain and inter-subdomain discrepancies. Meanwhile, to maximum subdomain discrepancies, the auxiliary adversarial classifier is introduced to generate distinguishable subdomain features by promoting adversarial learning between feature extractor and auxiliary classifier. System experiment results on three benchmark databases (SEED, SEED-IV, and DEAP) validate the model's effectiveness and superiority in cross-session and cross-subject experiments. The method proposed in this study outperforms other state-of-the-art DA, that effectively address domain shifts in multiple emotion recognition tasks, and promote the development of brain-computer interfaces.},
}
@article {pmid40456926,
year = {2025},
author = {Slutzky, MW and Vansteensel, MJ and Herff, C and Gaunt, RA},
title = {A brain-computer interface working definition.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40456926},
issn = {2157-846X},
}
@article {pmid40456256,
year = {2025},
author = {Tangermann, M and Chevallier, S and Dold, M and Guetschel, P and Kobler, R and Papadopoulo, T and Thielen, J},
title = {Learning from Small Datasets - Review of Workshop 6 of the 10th International BCI Meeting 2023.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf80},
pmid = {40456256},
issn = {1741-2552},
abstract = {In brain-computer interfacing (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models. Minimizing the calibration can be crucial for enhancing the usability of a BCI application with patients, increasing the acceptance by healthy users, facilitating a fast adaptation during non-stationary recordings, or transferring between sessions. At the 10th International BCI Meeting in 2023, our workshop addressed the latest proposed techniques to train classification or regression machine learning models with small datasets. We explored methodologies from both traditional machine learning as well as deep learning. In addition to talks and discussions, we introduced Python toolboxes for all presented methods and for the benchmarking of classification models. This review provides a comprehensive overview of the workshop's content and discusses the insights that were obtained.},
}
@article {pmid40456243,
year = {2025},
author = {Galiotta, V and Caracci, V and Toppi, J and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Riccio, A},
title = {P300-based Brain-Computer Interface for communication in Assistive Technology centres: influence of users' profile on BCI access.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf7f},
pmid = {40456243},
issn = {1741-2552},
abstract = {Assistive technology (AT) refers to any product that enables people to live independently and with dignity and to participate in activities of daily life. A Brain-Computer Interface (BCI) is an AT that provides an alternative output, based on neurophysiological signals, to control an external device. The aim of the study is to screen patients accessing an AT-center to investigate their eligibility for BCI access and the factors influencing the BCI control. Approach. Thirty-five users and 11 healthy subjects were included in the study. Participants were required to operate a P300-speller BCI. We evaluated the influence of clinical diagnosis, socio-demographic factors, level of dependence and disability of users, neuropsychological profile on BCI performance. Main results. The 7.1% of the users controlled the system with a mean accuracy of 93.6±8.0%, while 8 users had an online accuracy below 70%. We found that the neuropsychological profile significantly affected online accuracy and ITR. Significance. The percentage of users who had an accuracy considered functional for communication is an encouraging data in terms of BCI effectiveness. The results regarding accuracy and factors influencing (and not influencing) it, are a contribution to the introduction of BCIs in the AT-centers, considering the BCI for communication both as an AT and as an additional input to provide multimodal access to AT.},
}
@article {pmid40456242,
year = {2025},
author = {Schmid, PR and Sweeney-Reed, CM and Dürschmid, S and Reichert, C},
title = {Stimulus predictability has little impact on decoding of covert visual spatial attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf81},
pmid = {40456242},
issn = {1741-2552},
abstract = {Brain-computer interfaces (BCI) that are aimed at supporting completely locked-in patients require independence from eye movements. Since visual spatial attention (VSA) shifts precede eye movements, they can be used for non-invasive, gaze-independent BCI control. In VSA tasks, stimuli locations and presentation onsets are commonly unpredictable. In this study we investigated the impact of predictability of potential target stimuli on the decoding accuracy of a BCI. Approach. We presented visual stimuli simultaneously to the left and right visual fields while participants shifted attention to a target stimulus. Using canonical correlation analysis, we decoded the direction of attention under different combinations of temporal and spatial predictability and compared the performance. Main results. We found no variation in decoding accuracies with spatial predictability. In addition, jittered timing did not alter the decoding accuracy compared to a constant stimulus onset asynchrony (SOA). Finally, reducing the SOA enabled faster BCI communication without accuracy loss. Using time-resolved decoding and interpretable models, we show that a later positive difference wave (between 300 ms and 350 ms post-stimulus onset) at occipital sites, rather than the N2pc, primarily contributes to decoding the target receiving attention. Significance. Our results demonstrate that stimulus predictability has no beneficial impact on decoding accuracy, but the paradigm proved robust to alterations in various stimulus parameters, making VSA a promising cognitive process for use in non-invasive, gaze-independent BCI-based communication. .},
}
@article {pmid40456241,
year = {2025},
author = {Pang, Z and Zhang, R and Li, M and Li, Z and Cui, H and Chen, X},
title = {SSVEP-based BCI using ultra-low-frequency and high-frequency peripheral flickers.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addf82},
pmid = {40456241},
issn = {1741-2552},
abstract = {OBJECTIVE: Existing steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems predominantly employ a flicker frequency range of 8-20 Hz, which often induces visual fatigue in users, thereby compromising system performance. Considering that, this study introduces an innovative paradigm to enhance the user experience of SSVEP-based BCIs while maintaining the performance.<br><br>APPROACH: The system encodes 12 targets by integrating ultra-low-frequency (2.00-3.32 Hz) and high-frequency (34.00-35.32 Hz) flickers with peripheral stimulation, and task-related component analysis (TRCA) is employed for SSVEP signal identification.<br><br>MAIN RESULTS: The feasibility of the ultra-low-frequency peripheral stimulation paradigm was validated through online experiments, achieving an average accuracy of 89.03 &#xb1; 9.95% and an information transfer rate (ITR) of 66.74 &#xb1; 15.44 bits/min. For the high-frequency peripheral stimulation paradigm, only the stimulation frequency changed, the paradigm, the signal processing algorithm and the step of frequency and phase were unchanged. The online experiments demonstrated an average accuracy of 93.55 &#xb1; 3.02% and an ITR of 51.88 &#xb1; 3.74 bits/min.<br><br>SIGNIFICANCE: The performance of the proposed system has reached a relatively high level among the current user-friendly SSVEP-based BCI systems. This study successfully innovates the paradigm for SSVEP-based BCIs, offering new insights into the development of user-friendly systems that balance high performance and user comfort.},
}
@article {pmid40456131,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Correction: Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e78147},
doi = {10.2196/78147},
pmid = {40456131},
issn = {1438-8871},
abstract = {[This corrects the article DOI: 10.2196/71741.].},
}
@article {pmid40456094,
year = {2025},
author = {Arpaia, P and Esposito, A and Galdieri, F and Natalizio, A},
title = {Acquisition delay of wireless EEG instruments in time-sensitive applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3575695},
pmid = {40456094},
issn = {1558-0210},
abstract = {The aim of this study is to characterize the acquisition delay in wireless EEG instruments and evaluate its impact on the detection of time-locked neural phenomena, such as P300 and movement-related cortical potentials (MRCP). Accurate timing is critical for both research and clinical applications, especially for real-time brain-computer interfaces (BCI). A measurement setup was thus developed to assess acquisition delays and their uncertainty. Delays were measured at both the start and stop of a reference signal generation to investigate the consistency and reliability of the devices. BCI experiments were also performed to evaluate the impact of the measured delay on the detection of the time-locked phenomena. Statistical tests confirmed significant differences in delays across devices and configurations (e.g., from few tens to a hundred ms). These delays directly impacted P300 and MRCP detection, raising concerns about potential misclassification. Nonetheless, the correction of the measured acquisition delay proved beneficial, especially with regard to the P300 latency measured through low-cost instrumentation.},
}
@article {pmid40456080,
year = {2025},
author = {Jin, L and Song, Y and Zhao, H and Cao, J and Cheung, VCK and Liao, WH},
title = {Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3576088},
pmid = {40456080},
issn = {2168-2208},
abstract = {Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results of 90.17% on SEED, 88.38% on PhysioNet, and 77.02% on the OpenBMI dataset in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.},
}
@article {pmid40455568,
year = {2025},
author = {Chen, Y and Fan, Z and Shi, N and Cheng, B and Huang, C and Liu, X and Gao, X and Liu, R},
title = {MXene-Based Microneedle Electrode for Brain-Computer Interface in Diverse Scenarios.},
journal = {ACS applied materials &amp; interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.5c03798},
pmid = {40455568},
issn = {1944-8252},
abstract = {In this study, we introduce a brain-computer interface (BCI) framework incorporating MXene microneedle EEG electrodes, tailored for versatile deployment. The dry electrodes, configured as 1 mm[2] microneedles, underwent meticulous processing to establish a cohesive integration with the MXene conductive material. The microneedle architecture facilitates epidermal penetration, yielding low contact impedance, enabling the recording of spontaneous EEG and induced brain activity, and ensuring high precision in steady-state visual evoked potential (SSVEP) speller. Simultaneously, the microneedle electrode demonstrates commendable biological compatibility and superior nuclear magnetic resonance compatibility. It exhibits minimal artifact generation and manifests no heating-related adaptations in nuclear magnetic environments. The inherent microneedle electrode structure endows it with robust anti-interference capabilities. In vibrational environments, the SSVEP text input accuracy of the microneedle electrode remains comparable to that of gel electrodes, maintaining consistent impedance and delivering high-fidelity EEG acquisition during real-motion scenarios. The microneedle electrode devised in this study serves as a reliable signal acquisition tool, thereby advancing the development of BCI systems tailored for practical usage scenarios.},
}
@article {pmid40454682,
year = {2025},
author = {Pitt, KM and Mikuls, A and Ousley, CL and Boster, JB and Mahmoudi, M and McCarthy, J and Burnison, J},
title = {Considering whether brain-computer interfaces have prospective potential to support children who have the physical abilities for touch-based AAC access: a forum manuscript.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/07434618.2025.2495897},
pmid = {40454682},
issn = {1477-3848},
abstract = {Augmentative and alternative communication (AAC) may help address communication challenges for both those with developmental disabilities (DD) and intellectual and developmental disabilities (IDD). This forum manuscript explores the possibility of various future applications of brain-computer interface technology for AAC control (BCI-AAC) by children who have the physical abilities to utilize touch-based AAC access. Due to the early status of BCI-AAC research, the forum focuses on those with DD, though considerations for those with IDD are also discussed. Departing from the prevalent focus on severe speech and physical impairments (SSPI), this work shifts the spotlight toward children who may employ touch selection for AAC access, exploring the challenges and prospective possibilities within this population. Applying the International Classification of Functioning, Disability, and Health (ICF) framework, we explore potential BCI-AAC considerations across Activities and Participation, Functions and Structures, Environmental Factors, and Personal Factors. Proposing prospective BCI-AAC strategies, such as leveraging brain activity for functional intent recognition and emotion detection, this paper is designed to fuel discussion on tailoring AAC interventions to the diverse profiles of children with DD and IDD. Acknowledging the significant hurdles faced by BCI-AAC technology, we support the inclusive consideration of individuals in BCI-AAC development. While not seeking to lay a definitive roadmap, this forum aims to serve as a catalyst for future interdisciplinary dialogues, including those who use AAC and their support network, laying the groundwork for considering diverse BCI-AAC applications in children.},
}
@article {pmid40450930,
year = {2025},
author = {Fan, C and Song, Y and Mao, X},
title = {A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {190},
number = {},
pages = {107684},
doi = {10.1016/j.neunet.2025.107684},
pmid = {40450930},
issn = {1879-2782},
abstract = {In order to enhance the decoding ability of brain states and evaluate the functional connection changes of relevant nodes in brain regions during motor imagery (MI), this paper proposes a brain functional network construction method which fuses edge features and node features. And we use deep learning methods to realize MI classification of left and right hand grasping tasks. Firstly, we use phase locking value (PLV) to extract edge features and input a weighted PLV to enhanced common space pattern (ECSP) to extract node features. Then, we fuse edge features and node features to construct a novel brain functional network. Finally, we construct an attention and multi-scale feature convolutional neural network (AMSF-CNN) to validate our method. The performance indicators of the brain functional network on the SHU_Dataset in the corresponding brain region will increase and be higher than those in the contralateral brain region when imagining one hand grasping. The average accuracy of our method reaches 79.65 %, which has a 25.85 % improvement compared to the accuracy provided by SHU_Dataset. By comparing with other methods on SHU_Dataset and BCI IV 2a Dataset, the average accuracies achieved by our method outperform other references. Therefore, our method provides theoretical support for exploring the working mechanism of the human brain during MI.},
}
@article {pmid40450863,
year = {2025},
author = {Niu, X and Zhang, J and Peng, Y and Kong, Y and Li, Y and Han, Y and Shi, L and Zheng, G},
title = {Extraction and analysis of abnormal EEG features in children with amblyopia.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {175},
number = {},
pages = {2110765},
doi = {10.1016/j.clinph.2025.2110765},
pmid = {40450863},
issn = {1872-8952},
abstract = {OBJECTIVE: Early and accurate diagnosis of amblyopia is crucial for the healthy development of children. Existing clinical diagnostic methods rely on patient cooperation, which can easily lead to misdiagnosis. The commonly used features derived from visual evoked potentials (VEP) only provided limited information for characterizing the whole brain, highlighting the need for integrating additional data sources, such as brain network metrics, to achieve a more comprehensive understanding.<br><br>METHODS: We extracted 488 features from 64-channel EEG data recorded from thirty amblyopic children. The features mainly derived from a weighted functional brain network based on coherence across different frequency bands. Feature selection and linear classification techniques were employed to assess their effectiveness in distinguishing amblyopia from normal children.<br><br>RESULTS: Abnormal EEG features were distributed not only in the occipital lobe but also in non-visual regions, with a higher prevalence in the alpha and beta bands. Their decoding performance surpassed traditional VEP features, and their combination achieved the highest accuracy (89.00%). Moreover, features beyond the occipital lobe exhibited limited decoding performance when considered individually, yet they still have an obvious contribution.<br><br>CONCLUSIONS: The study identified novel abnormal EEG features associated with amblyopia and demonstrated the potential of multi-channel EEG recordings to assist in the diagnosis of amblyopia.<br><br>SIGNIFICANCE: The study suggests amblyopia may impair more abilities beyond visual cognition and further provides objective biomarkers for diagnosing amblyopia, which is essential for effective treatment.},
}
@article {pmid40450806,
year = {2025},
author = {Wosnick, N and Dörfer, T and Turner, M and Nicholls, C and Richardson, M and Génier, I and Hauser-Davis, RA},
title = {Assessing the potential physiological impacts of urban development around lemon shark (Negaprion brevirostris) nurseries: effects on neonate and juvenile health.},
journal = {Marine pollution bulletin},
volume = {218},
number = {},
pages = {118233},
doi = {10.1016/j.marpolbul.2025.118233},
pmid = {40450806},
issn = {1879-3363},
abstract = {Urbanization driven by population growth, development and tourism increasingly threatens even remote areas, potentially impacting biodiversity. This is particularly concerning given the ecological and economic importance of biodiversity, especially for island nations, where ecotourism plays a crucial role in the economy. This study examines urban-driven degradation effects on the nurseries of lemon sharks, a predator with strong site fidelity to its birthing and nursery areas. Six sites in South Eleuthera, The Bahamas, were assessed, analyzing proxies indicative of body condition (triglycerides/cholesterol ratio, body condition index) and energetic stress markers (glucose, β-hydroxybutyrate, triglycerides, total cholesterol) in neonates and juveniles compared across nurseries relative to degradation scores. While TAG/CHOL and BCI were not significantly different between nurseries, energetic markers were overall higher in more degraded nurseries. Moreover, total urban score was a significant predictor for glucose, β-hydroxybutyrate, and triglyceride ciruclating concentrations. These findings, coupled with prior studies carried out in Bimini, suggest that urban development around lemon shark nurseries in The Bahamas may negatively impact shark health. Cooperative monitoring, community initiatives for mangrove preservation, and stronger urbanization laws are required to mitigate these impacts. As urbanization and environmental degradation are universal threats to mangroves worldwide, this approach can be adapted to study urbanization impacts on other species in regions such as Southeast Asia, the Caribbean, the Pacific Islands, and the coasts of Africa and South America, which face similar urban encroachment, habitat degradation, and biodiversity loss challenges.},
}
@article {pmid40450046,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {Human voices communicating trustworthy intent: A demographically diverse speech audio dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {921},
pmid = {40450046},
issn = {2052-4463},
mesh = {Humans ; Adult ; Middle Aged ; Female ; Male ; *Voice ; Young Adult ; Adolescent ; *Speech ; *Trust ; },
abstract = {The multi-disciplinary field of voice perception and trustworthiness lacks accessible and diverse speech audio datasets representing diverse speaker demographics, including age, ethnicity, and sex. Existing datasets primarily feature white, younger adult speakers, limiting generalisability. This paper introduces a novel open-access speech audio dataset with 1,152 utterances from 96 untrained speakers, across white, black and south Asian backgrounds, divided into younger (N = 60, ages 18-45) and older (N = 36, ages 60+) adults. Each speaker recorded both, their natural speech patterns (i.e. "neutral" or no intent), and their attempt to convey their trustworthy intent as they perceive it during speech production. Our dataset is described and evaluated through classification methods between neutral and trustworthy speech. Specifically, extracted acoustic and voice quality features were analysed using linear and non-linear classification models, achieving accuracies of around 70%. This dataset aims to close a crucial gap in the existing literature and provide additional research opportunities that can contribute to the generalisability and applicability of future research results in this field.},
}
@article {pmid40448829,
year = {2025},
author = {Marques, LM and Strauss, A and Castellani, A and Barbosa, S and Simis, M and Fregni, F and Battistella, L},
title = {Dynamics of sensorimotor-related brain oscillations: EEG insights from healthy individuals in varied upper limb movement conditions.},
journal = {Experimental brain research},
volume = {243},
number = {7},
pages = {160},
pmid = {40448829},
issn = {1432-1106},
support = {#21/05897-5//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #21/12790-2//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #20/08512-4//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; #17/12943-8//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; Cross-Sectional Studies ; Electroencephalography ; *Sensorimotor Cortex/physiology ; *Upper Extremity/physiology ; *Brain Waves/physiology ; Movement/physiology ; *Motor Activity/physiology ; Brain-Computer Interfaces ; *Psychomotor Performance/physiology ; *Cortical Synchronization/physiology ; Imagination/physiology ; Middle Aged ; },
abstract = {Event-related desynchronization (ERD) and event-related synchronization (ERS) are critical neurophysiological phenomena associated with motor execution and inhibitory processes. Their utility spans neurophysiological biomarker research and Brain-Computer Interface (BCI) development. However, standardized frameworks for analyzing ERD and ERS oscillations across motor tasks and frequency ranges remain scarce. This study conducted a cross-sectional analysis of 76 healthy participants from the DEFINE cohort to explore ERD and ERS variations across four motor-related tasks (Motor Execution, Motor Imagery, Active Observation, and Passive Observation) and six frequency bands (Delta, Theta, Low Alpha, High Alpha, Low Beta, and High Beta) using C3 electrode activity. Repeated measures ANOVA revealed task-sensitive ERD and ERS power modulations, with oscillatory responses spanning the 1-30 Hz spectrum. Beta activity exhibited pronounced differences between tasks, highlighting its relevance in motor control, while other bands showed distinct task-dependent variations. These findings underscore the variability in ERD/ERS patterns across different tasks and frequency bands, reinforcing the importance of further research into standardized analytical frameworks. By refining ERD/ERS analyses, our study contributes to developing reference frameworks that can enhance clinical and Brain-Computer Interface (BCI) applications.},
}
@article {pmid40448287,
year = {2025},
author = {Cao, L and Zheng, Q and Wu, Y and Liu, H and Guo, M and Bai, Y and Ni, G},
title = {A dual-modality study on the neural features of cochlear implant simulated tone and consonant perception.},
journal = {Annals of the New York Academy of Sciences},
volume = {},
number = {},
pages = {},
doi = {10.1111/nyas.15380},
pmid = {40448287},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; 824B2056//National Natural Science Foundation of China/ ; },
abstract = {Accurately perceiving lexical tones and consonants is critical for understanding speech in tonal languages. Cochlear implant (CI) users exhibit reduced phonetic perception due to spectral loss in CI encoding, yet the underlying neural mechanisms remain unclear. This study combined electroencephalography and functional near-infrared spectroscopy (fNIRS) to investigate the neural processing mechanisms of CI-simulated channelized speech in 26 normal-hearing adults during the processing of tones (T1-T4) and consonants ("ba," "da," "ga," "za"). Results showed that the N1 amplitude in auditory evoked potentials was significantly lower for channelized speech than a natural human voice (NH), particularly for T2 and T4 tones, indicating a weaker perception of channelized speech. Functional connectivity analysis revealed that an NH exhibited significantly higher synchrony in the δ and θ frequency bands than channelized speech, which was more pronounced in the right temporal lobe. This finding was also observed with "za" consonants. fNIRS results showed stronger right temporal lobe activation for channelized speech, suggesting that the brain requires greater auditory effort to process channelized speech. Combining both modalities revealed neural compensatory mechanisms underlying channelized speech-manifesting as "low-efficiency perception with high cognitive load." This study provides potential biomarkers for CI rehabilitation assessment and a foundation for future research.},
}
@article {pmid40446349,
year = {2025},
author = {Wang, S and Chen, G and Xie, J and Yang, R and Wang, X and Shan, Q and Liu, W and Zhao, D and Wang, F and Li, K and Zhang, Q and Guo, Y},
title = {Development and validation of a predictive model for poor initial outcomes after Gamma Knife radiosurgery for trigeminal neuralgia: a prognostic correlative analysis.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-12},
doi = {10.3171/2025.2.JNS242655},
pmid = {40446349},
issn = {1933-0693},
abstract = {OBJECTIVE: The present study aimed to develop a reliable predictive model for identifying preoperative predictors of poor initial outcomes in patients with primary trigeminal neuralgia (PTN) treated with Gamma Knife radiosurgery (GKRS) and further elucidate the clinical significance of these predictors in initial outcomes and long-term pain recurrence.<br><br>METHODS: A total of 217 PTN patients were divided into a training set (n = 167) and a validation set (n = 50). The initial outcomes of GKRS treatment were assessed based on the Barrow Neurological Institute pain intensity scale. A predictive model was developed through multivariate regression and validated with repeated sampling. The differences in predictors of long-term pain recurrence were assessed using Kaplan-Meier analysis. The association between predictors was tested using chi-square tests, and subgroup analyses were performed to compare initial outcomes and long-term pain recurrence between two clinically significant correlates.<br><br>RESULTS: The training and validation sets showed areas under the curve of 0.85 and 0.88, respectively. Calibration curves and decision curve analysis indicated significant clinical benefits in both sets. Independent risk factors for poor initial outcomes included hyperglycemia, absence of neurovascular contact, carbamazepine insensitivity, and atypical pain (trigeminal neuralgia type 2 [TN2]). Carbamazepine insensitivity was moderately associated with TN2 and predicted long-term pain recurrence. Patients with both phenotypes had significantly worse initial outcomes compared with other subgroups (adjusted p = 0.0125).<br><br>CONCLUSIONS: Patients with both TN2 and carbamazepine insensitivity have the poorest initial treatment outcomes and face an increased risk of recurrence. Furthermore, this predictive model is highly accurate and useful, offering a comprehensive method of identifying PTN patients likely to experience poor initial outcomes based on clinical characteristics and imaging perspectives. The authors believe that the nomogram presented in this model enables clinicians to calculate multiple variables and predict the probability of adverse events.},
}
@article {pmid40446280,
year = {2025},
author = {Liang, X and Ding, Y and Yuan, Z and Han, Y and Zhou, Y and Jiang, J and Xie, Z and Fei, P and Sun, Y and Jia, P and Gu, G and Zhong, Z and Chen, F and Si, G and Gong, Z},
title = {Mechanics of Soft-Body Rolling Motion without External Torque.},
journal = {Physical review letters},
volume = {134},
number = {19},
pages = {198401},
doi = {10.1103/PhysRevLett.134.198401},
pmid = {40446280},
issn = {1079-7114},
mesh = {Animals ; Robotics ; Larva/physiology ; *Models, Biological ; Biomechanical Phenomena ; *Drosophila/physiology ; Muscle Contraction/physiology ; Torque ; },
abstract = {The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.},
}
@article {pmid40443843,
year = {2025},
author = {Yang, H and Li, T and Zhao, L and Wei, Y and Chen, X and Pan, J and Fu, Y},
title = {Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1554266},
pmid = {40443843},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a novel human-computer interaction technology, and its rapid development has led to a growing demand for skilled BCI professionals, culminating in the emergence of the BCI major. Despite its significance, there is limited literature addressing the curriculum design for this emerging major. This paper seeks to bridge this gap by proposing and discussing a curricular framework for the BCI major, based on the inherently multidisciplinary nature of BCI research and development. The paper begins by elucidating the primary factors behind the emergence of the BCI major, the increasing demand for both medical and non-medical applications of BCI, and the corresponding need for specialized talent. It then delves into the multidisciplinary nature of BCI research and offers principles for curriculum design to address this nature. Based on these principles, the paper provides detailed suggestions for structuring a BCI curriculum. Finally, it discusses the challenges confronting the development of the BCI major, including the lack of consensus and international collaboration in the construction of the BCI major, as well as the inadequacy or lack of teaching materials. Future work needs to improve the curriculum design of the BCI major from a competency-oriented perspective. It is expected that this paper will provide a reference for the curriculum design and construction of the BCI major.},
}
@article {pmid40442937,
year = {2025},
author = {Wang, F and Wang, L and Zhu, X and Lu, Y and Du, X},
title = {Neuron-Inspired Ferroelectric Bioelectronics for Adaptive Biointerfacing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2416698},
doi = {10.1002/adma.202416698},
pmid = {40442937},
issn = {1521-4095},
support = {B2302045//Shenzhen Medical Research Fund/ ; 52022102//National Natural Science Foundation of China/ ; 52261160380//National Natural Science Foundation of China/ ; 32471042//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2017YFA0701303//National Key R&amp;D Program of China/ ; Y2023100//Youth Innovation Promotion Association of CAS/ ; RCJC20221008092729033//Fundamental Research Program of Shenzhen/ ; JCYJ20220818101800001//Fundamental Research Program of Shenzhen/ ; 2024A1515010645//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
abstract = {Implantable bioelectronics, which are essential to neuroscience studies, neurological disorder treatment, and brain-machine interfaces, have become indispensable communication bridges between biological systems and the external world through sensing, monitoring, or manipulating bioelectrical signals. However, conventional implantable bioelectronic devices face key challenges in adaptive interfacing with neural tissues due to their lack of neuron-preferred properties and neuron-similar behaviors. Here, innovative neuron-inspired ferroelectric bioelectronics (FerroE) are reported that consists of biocompatible polydopamine-modified barium titanate nanoparticles, ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, and cellular-scale micropyramid array structures, imparting adaptive interfacing with neural systems. These FerroE not only achieve neuron-preferred flexible and topographical properties, but also offer neuron-similar behaviors including highly efficient and stable light-induced polarization change, superior capability of producing electric signals, and seamless integration and adaptive communication with neurons. Moreover, the FerroE allows for adaptive interfacing with both peripheral and central neural networks of mice, enabling regulation of their heart rate and motion behavior in a wireless, non-genetic, and non-contact manner. Notably, the FerroE demonstrates unprecedented structural and functional stability and negligible immune response even after 3 months of implantation in vivo. Such bioinspired FerroE are opening new opportunities for next-generation brain-machine interfaces, tissue engineering materials, and biomedical devices.},
}
@article {pmid40442546,
year = {2025},
author = {Wang, L and Li, T and Li, X and Liu, F and Feng, C},
title = {Mapping trait justice sensitivity in the Brain: Whole-brain resting-state functional connectivity as a predictor of other-oriented not self-oriented justice sensitivity.},
journal = {Cognitive, affective &amp; behavioral neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40442546},
issn = {1531-135X},
support = {2024B0303390003//Research Center for Brain Cognition and Human Development, Guangdong, China/ ; 32020103008//National Natural Science Foundation of China/ ; 32271126//National Natural Science Foundation of China/ ; 81922036//National Natural Science Foundation of China/ ; },
abstract = {Justice sensitivity (JS) reflects personal concern and commitment to the principle of justice, showing considerable heterogeneity among the general population. Despite a growing interest in the behavioral characteristics of JS over the past decades, the neurobiological substrates underlying trait JS are not well comprehended. We addressed this issue by employing a machine learning approach to decode the trait JS, encompassing its various orientations, from whole-brain resting-state functional connectivity. We demonstrated that the machine-learning model could decode the individual trait of other-oriented JS but not self-oriented JS from resting-state functional connectivity across multiple neural systems, including functional connectivity between and within parietal lobe and motor cortex as well as their connectivity with other brain systems. Key nodes that contributed to the prediction model included the parietal, motor, temporal, and subcortical regions that have been linked to other-oriented JS. Additionally, the machine learning model can distinctly distinguish between the distinct roles associated with other-oriented JS, including observer, perpetrator, and beneficiary, with key brain regions in the predictive networks exhibiting both similarities and disparities. These findings remained robust using different validation procedures. Collectively, these results support the separation between other-oriented JS and self-oriented JS, while also highlighting the distinct intrinsic neural correlates among the three roles of other-oriented JS: observer, perpetrator, and beneficiary.},
}
@article {pmid40442206,
year = {2025},
author = {Akama, T and Zhang, Z and Li, P and Hongo, K and Minamikawa, S and Polouliakh, N},
title = {Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18869},
pmid = {40442206},
issn = {2045-2322},
mesh = {*Music ; Humans ; *Neural Networks, Computer ; Electroencephalography ; *Brain/physiology ; Male ; *Auditory Perception/physiology ; Female ; Adult ; Acoustic Stimulation ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a significant improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.},
}
@article {pmid40442062,
year = {2025},
author = {Shah, NP and Avansino, D and Kamdar, F and Nicolas, C and Kapitonava, A and Vargas-Irwin, C and Hochberg, LR and Pandarinath, C and Shenoy, KV and Willett, FR and Henderson, JM},
title = {Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {5008},
pmid = {40442062},
issn = {2041-1723},
support = {Milton Safenowtiz Postdoctoral Scholarship//Amyotrophic Lateral Sclerosis Association (ALS Association)/ ; },
mesh = {Humans ; *Fingers/physiology ; *Motor Cortex/physiology/physiopathology ; Male ; Movement/physiology ; Adult ; Quadriplegia/physiopathology ; },
abstract = {How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.},
}
@article {pmid40441574,
year = {2025},
author = {Rahman, MH and Mondal, MIH},
title = {Investigation of neem-oil-loaded PVA/chitosan biocomposite film for hydrophobic dressing, rapid hemostasis and wound healing applications.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {144712},
doi = {10.1016/j.ijbiomac.2025.144712},
pmid = {40441574},
issn = {1879-0003},
abstract = {The present work aims to develop a hydrophobic dressing with a blood-repellent surface that achieves fast clotting without blood loss, having antibacterial properties, clot self-detachment, and superior wound healing activity. For these reasons, a novel approach was applied by producing a hydrophobic film made of PVA, chitosan, and neem seed oil (NSO). The film had the necessary hydrophobicity, mechanical strength, stability and was able to transmit water vapor to be suitable for the wound skin surface and demonstrated faster blood clotting (BCI = 91.44 % in 5 min and 85.22 % in 10 min). The proportion of red blood cells (2.78 %) and platelets (17.33 %) attached to the film proved its excellent hemostatic activity. It was anti-adhesive, created spontaneous clot detachment, and exhibited antibacterial properties at the wound site, as evidenced by in vivo testing. Moreover, in vivo testing and histopathological findings showed enhanced wound healing activity, greater re-epithelialization, and decreased granulation tissue. Additionally, the film's eco-friendliness was evaluated using a soil burial degradation test, and the results show that it deteriorated into the soil but did so slowly because of its hydrophobic property. Thus, PVA/CS/NSO composite film may be a green biomedical material for hemostasis and wound healing.},
}
@article {pmid40440260,
year = {2025},
author = {Tong, B and Li, G and Bu, X and Wang, Y and Yu, X},
title = {A deep learning-based algorithm for the detection of personal protective equipment.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322115},
pmid = {40440260},
issn = {1932-6203},
mesh = {*Personal Protective Equipment ; *Deep Learning ; Humans ; *Algorithms ; Neural Networks, Computer ; Construction Industry ; },
abstract = {Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in existing models. To address this issue, this paper proposes an improved model based on YOLOv8n.By enriching feature diversity and enhancing the model's adaptability to geometric transformations, the detection accuracy is improved.A Multi-Scale Group Convolution Module (MSGP) was designed to extract multi-level features using different convolution kernels. A Multi-Scale Feature Diffusion Pyramid Network (MFDPN) was developed, which aggregates multi-scale features through the Multiscale Feature Focus (MFF) module and diffuses them across scales, providing each scale with detailed contextual information. A customized Task Alignment Module was introduced to integrate interactive features, optimizing both classification and localization tasks. The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. The proposed MFD-YOLO(1.5) model achieves significant progress in detecting personal protective equipment on construction sites, with a substantial reduction in parameter count, making it suitable for deployment on resource-constrained edge devices.},
}
@article {pmid40438784,
year = {2025},
author = {Du, Y and Yang, X and Wang, M and Lv, Q and Zhou, H and Sang, G},
title = {Longitudinal changes in children with autism spectrum disorder receiving applied behavior analysis or early start denver model interventions over six months.},
journal = {Frontiers in pediatrics},
volume = {13},
number = {},
pages = {1546001},
pmid = {40438784},
issn = {2296-2360},
abstract = {BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication difficulties, restricted interests, repetitive behaviors, and sensory abnormalities. The rising prevalence of ASD presents a significant public health concern, with no pharmacological treatments available for its core symptoms. Therefore, early and effective behavioral interventions are crucial to improving developmental outcomes for children with ASD. Current interventions primarily focus on educational rehabilitation methods, including Applied behavior Analysis (ABA) and the Early Start Denver Model (ESDM).<br><br>OBJECTIVE: This study aims to examine the developmental changes in children with ASD following six months of ABA therapy or ESDM intervention.<br><br>METHODS: From December 2021 to December 2023, 30 children receiving ABA therapy at the Zhejiang Rehabilitation Medical Center (40&#x2005;min/session, 4 sessions/day, 5 days/week), while another 30 children undergoing ESDM training at Hangzhou Children's Hospital (2&#x2005;h of one-on-one sessions and 0.5&#x2005;h of group sessions/day, 5 days/week). Both groups participated in their respective interventions for six months. Pre- and post-treatment assessments were conducted using the Psycho-educational Profile-Third Edition (PEP-3).<br><br>RESULTS: Both groups showed significant improvements in PEP-3 scores post-treatment, including cognitive verbal/pre-verbal, expressive language, receptive language, social reciprocity, small muscles, imitation, emotional expression, and verbal and nonverbal behavioral characteristics.<br><br>CONCLUSION: Both ABA and ESDM interventions were associated with comprehensive improvements in children with ASD over a six-month period.},
}
@article {pmid40438090,
year = {2025},
author = {Ding, W and Liu, A and Chen, X and Xie, C and Wang, K and Chen, X},
title = {Reducing calibration efforts of SSVEP-BCIs by shallow fine-tuning-based transfer learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {81},
pmid = {40438090},
issn = {1871-4080},
abstract = {The utilization of transfer learning (TL), particularly through pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has substantially reduced the calibration efforts. However, commonly employed fine-tuning approaches, including end-to-end fine-tuning and last-layer fine-tuning, require data from target subjects that encompass all categories (stimuli), resulting in a time-consuming data collection process, especially in systems with numerous categories. To address this challenge, this study introduces a straightforward yet effective ShallOw Fine-Tuning (SOFT) method to substantially reduce the number of calibration categories needed for model fine-tuning, thereby further mitigating the calibration efforts for target subjects. Specifically, SOFT involves freezing the parameters of the deeper layers while updating those of the shallow layers during fine-tuning. Freezing the parameters of deeper layers preserves the model's ability to recognize semantic and high-level features across all categories, as established during pre-training. Moreover, data from different categories exhibit similar individual-specific low-level features in SSVEP-BCIs. Consequently, updating the parameters of shallow layers-responsible for processing low-level features-with data solely from partial categories enables the fine-tuned model to efficiently capture the individual-related features shared by all categories. The effectiveness of SOFT is validated using two public datasets. Comparative analysis with commonly used end-to-end and last-layer fine-tuning methods reveals that SOFT achieves higher classification accuracy while requiring fewer calibration categories. The proposed SOFT method further decreases the calibration efforts for target subjects by reducing the calibration category requirements, thereby improving the feasibility of SSVEP-BCIs for real-world applications.},
}
@article {pmid40437332,
year = {2025},
author = {Cherukuri, SB and Ramakrishnan, S},
title = {Eye-blink artifact removal in single-channel electroencephalogram using K-means and Savitzky Golay-singular Spectrum Analysis hybrid technique.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40437332},
issn = {2662-4737},
abstract = {Electroencephalogram (EEG) acquisition systems are used to record the neural condition of humans for diagnosing various neural problems. The eye-blink or Electrooculogram (EOG) artifact caused by eye-lid movements, influences the EEG signal measurements and interferes with the diagnosis. The complete removal of eye-blink artifact while preserving the EEG content is a challenging task that needs highly efficient denoising methods, particularly from Single-Channel EEG which is widely used for Out-Of-Hospital (OOH) neurological patients and for Brain-Computer-Interface (BCI) applications. When compared to multi-channel EEG systems, Single-channel EEG system suffers certain difficulties such as lack of spatial information, redundancy, etc. This paper proposes an innovative hybrid method combining K-Means clustering and Savitzky Golay-Singular Spectrum Analysis (SG-SSA) methods for effective eye-blink artifact removal from single channel EEG. The eye-blink artifact is extracted and then subtracted from the noisy EEG signal, so that the EEG content available in the eye-blink periods are preserved. Through extensive experiments with synthetic as well as real time EEG, we show that our proposed method outperforms the other contemporary methods from literature. Our proposed hybrid approach achieves a significant reduction in Mean Absolute Error (MAE) and Relative Root Mean Square Error (RRMSE) than the Fourier-Bessel Series Expansion based Empirical Wavelet Transform (FBSE-EWT), SSA combined with independent component analysis (SSA-ICA) and Ensemble Empirical Mode Decomposition combined with ICA (EEMD-ICA), proposed in recent literature.},
}
@article {pmid40436265,
year = {2025},
author = {Chopra, M and Kumar, H},
title = {Navigating the complexities of spinal cord injury: an overview of pathology, treatment strategies and clinical trials.},
journal = {Drug discovery today},
volume = {},
number = {},
pages = {104387},
doi = {10.1016/j.drudis.2025.104387},
pmid = {40436265},
issn = {1878-5832},
abstract = {Spinal cord injury (SCI) is a debilitating neurological condition characterized by sensory and motor deficits. It significantly affects patient quality of life and poses a substantial socioeconomic burden. The complex and multifaceted pathophysiology of SCI complicates its effective treatment. Following the primary mechanical insult, a secondary injury cascade disrupts the microenvironment at the injury site, exacerbating the tissue damage. Despite extensive research, no fully effective treatment is currently available. This review explores current pharmacological and non-pharmacological treatment strategies at the preclinical and clinical stages, providing insights into promising interventions. The findings highlight potential avenues for future research aimed at improving SCI management.},
}
@article {pmid40434889,
year = {2025},
author = {Ruszala, BM and Schieber, MH},
title = {Injecting information in the cortical reach-to-grasp network is effective in ventral but not dorsal nodes.},
journal = {Cell reports},
volume = {44},
number = {5},
pages = {115664},
doi = {10.1016/j.celrep.2025.115664},
pmid = {40434889},
issn = {2211-1247},
abstract = {Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.},
}
@article {pmid40434816,
year = {2025},
author = {Liu, L},
title = {Did you see it?.},
journal = {eLife},
volume = {14},
number = {},
pages = {},
pmid = {40434816},
issn = {2050-084X},
mesh = {Humans ; *Brain/physiology ; *Consciousness/physiology ; },
abstract = {Cautious reporting choices can artificially enhance how well analyses of brain activity reflect conscious and unconscious experiences, making distinguishing between the two more challenging.},
}
@article {pmid40434551,
year = {2025},
author = {Sokhadze, E},
title = {Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {40434551},
issn = {1573-3270},
abstract = {Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.},
}
@article {pmid40433677,
year = {2025},
author = {He, J and Zhou, G and Sun, B and Yan, L and Lang, X and Yang, Y and Hao, H},
title = {Graphene quantum dots induced performance enhancement in memristors.},
journal = {Nanoscale},
volume = {},
number = {},
pages = {},
doi = {10.1039/d5nr00597c},
pmid = {40433677},
issn = {2040-3372},
abstract = {With the rapid development of information technology, the demand for miniaturization, integration, and intelligence of electronic devices is growing rapidly. As a key device in the non-von Neumann architecture, memristors can perform computations while storing data, enhancing computational efficiency and reducing power consumption. Memristors have become pivotal in driving the advancement of artificial intelligence (AI) and Internet of Things technologies. Combining the electronic properties of graphene with the size effects of quantum dots, graphene quantum dot (GQD)-based memristors exhibit potential applications in constructing brain-inspired neuromorphic computing systems and achieving AI hardware acceleration, making them a focal point of research interest. This review provides an overview of the preparation, mechanism, and application of GQD-based memristors. Initially, the structure, properties, and synthesis methods of GQDs are introduced in detail. Subsequently, the memristive mechanisms of GQD-based memristors are presented from three perspectives: the metal conductive filament mechanism, the electron trapping and detrapping mechanism, and the oxygen vacancy conductive filament mechanism. Furthermore, the different application scenarios of GQD-based memristors in both digital and analog types are summarized, encompassing information storage, brain-like artificial synapses, visual perception systems, and brain-machine interfaces. Finally, the challenges and future development prospects of GQD-based memristors are discussed.},
}
@article {pmid40431969,
year = {2025},
author = {You, Z and Guo, Y and Zhang, X and Zhao, Y},
title = {Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103178},
pmid = {40431969},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.},
}
@article {pmid40431893,
year = {2025},
author = {Sasatake, Y and Matsushita, K},
title = {EEG Baseline Analysis for Effective Extraction of P300 Event-Related Potentials for Welfare Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25103102},
pmid = {40431893},
issn = {1424-8220},
support = {JPMJSP2125//JST SPRING/ ; Not Applicable//THERS Make New Standards Program for the Next Generation Researchers/ ; },
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Enabling individuals with complete paralysis to operate devices voluntarily requires an effective interface; EEG-based P300 event-related potential (ERP) interfaces are considered a promising approach. P300 is an EEG peak generated in response to specific sensory stimuli recognized by an individual. Accurate detection of this peak necessitates a stable pre-stimulus baseline EEG signal, which serves as the reference for baseline correction. Previous studies have commonly employed either a single-time-point amplitude (e.g., at 100 ms before stimulus onset) or a time-range-averaged amplitude over a specified pre-stimulus period (e.g., 0-200 ms) as a baseline correction method, assuming these provide the most stable EEG reference. However, in assistive P300 interfaces, continuous visual stimuli at 400 ms intervals are typically used to efficiently evoke P300 peaks. Since stimuli are presented before the EEG stabilizes, it remains unclear whether conventional neuroscience baseline correction methods are suitable for such applications. To address this, the present study conducted a P300 induction experiment based on continuous 400 ms interval visual stimuli. Using EEG data recorded from 0 to 1000 ms before each visual stimulus (sampled at 1 ms intervals), we applied three baseline correction methods-single-time-point amplitude, time-range-averaged amplitude, and multi-time-point amplitude-to determine the most effective EEG reference and evaluate the impact on P300 detection performance. The results showed that baseline correction using an amplitude at a single point in time is unstable when the basic EEG rhythm and low-frequency noise remain, while time-range-averaged baseline correction using the 0-200 ms pre-stimulus period led to relatively effective P300 detection. However, it was also found that using only one value averaged over the amplitude from 0 to 200 ms did not result in an accurate EEG reference potential, resulting in an error. Finally, this study confirmed that the multi-time-point baseline correction method, through which the amplitude state from 0 to 200 ms before the visual stimulus is comprehensively evaluated, may be the most effective method for P300 determination.},
}
@article {pmid40431780,
year = {2025},
author = {Polo-Hortigüela, C and Ortiz, M and Soriano-Segura, P and Iáñez, E and Azorín, JM},
title = {Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {10},
pages = {},
doi = {10.3390/s25102987},
pmid = {40431780},
issn = {1424-8220},
support = {PID2021-124111OB-C31//MICIU /AEI/10.13039/501100011033 and by ERDF, EU/ ; PRE2022-103336//MICIU/AEI/10.13039 501100011033/ ; //Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI), Generalitat Valenciana and European Union/ ; //Project "Neurokit" funded by Centro Internacional para la Investigaci&#xf3;n del Envejecimiento de la Fundaci&#xf3;n de la Comunitat Valenciana (ICAR)/ ; 101118964//European Union's research and innovation programme under the Marie Sk&#x142;odowska-Curie/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain-Computer Interfaces ; Movement/physiology ; Male ; *Ankle/physiology ; *Exoskeleton Device ; Adult ; Biomechanical Phenomena ; Foot/physiology ; Female ; Wearable Electronic Devices ; Fourier Analysis ; },
abstract = {Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain-machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert-Huang (HHT), and Chirplet (CT) methods. The 8-20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies.},
}
@article {pmid40428890,
year = {2025},
author = {Endzelytė, E and Petruševičienė, D and Kubilius, R and Mingaila, S and Rapolienė, J and Rimdeikienė, I},
title = {Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {61},
number = {5},
pages = {},
doi = {10.3390/medicina61050932},
pmid = {40428890},
issn = {1648-9144},
mesh = {Humans ; Male ; Female ; *Brain-Computer Interfaces/standards/trends ; *Occupational Therapy/methods/standards ; *Stroke Rehabilitation/methods/standards ; Middle Aged ; Aged ; Activities of Daily Living/psychology ; Upper Extremity/physiopathology ; Adult ; Stroke/complications ; },
abstract = {Background and Objectives: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. By bypassing peripheral nerves and muscles, BCIs enable individuals with severe motor impairments to communicate their intentions directly through control signals derived from brain activity, opening new pathways for recovery and improving the quality of life. The aim of this study was to explore the beneficial effects of BCI system-based interventions on upper limb motor function and performance of activities of daily living (ADL) in stroke patients. We hypothesized that integrating BCI into occupational therapy would result in measurable improvements in hand strength, dexterity, independence in daily activities, and cognitive function compared to baseline. Materials and Methods: An observational study was conducted on 56 patients with subacute stroke. All patients received standard medical care and rehabilitation for 54 days, as part of the comprehensive treatment protocol. Patients underwent BCI training 2-3 times a week instead of some occupational therapy sessions, with each patient completing 15 sessions of BCI-based recoveriX treatment during rehabilitation. The occupational therapy program included bilateral exercises, grip-strengthening activities, fine motor/coordination tasks, tactile discrimination exercises, proprioceptive training, and mirror therapy to enhance motor recovery through visual feedback. Participants received ADL-related training aimed at improving their functional independence in everyday activities. Routine occupational therapy was provided five times a week for 50 min per session. Upper extremity function was evaluated using the Box and Block Test (BBT), Nine-Hole Peg Test (9HPT), and dynamometry to assess gross manual dexterity, fine motor skills, and grip strength. Independence in daily living was assessed using the Functional Independence Measure (FIM). Results: Statistically significant improvements were observed across all the outcome measures (p < 0.001). The strength of the stroke-affected hand improved from 5.0 kg to 6.7 kg, and that of the unaffected hand improved from 29.7 kg to 40.0 kg. Functional independence increased notably, with the FIM scores rising from 43.0 to 83.5. Cognitive function also improved, with MMSE scores increasing from 22.0 to 26.0. The effect sizes ranged from moderate to large, indicating clinically meaningful benefits. Conclusions: This study suggests that BCI-based occupational therapy interventions effectively improve upper extremity motor function and daily functions and have a positive impact on the cognition of patients with subacute stroke.},
}
@article {pmid40428702,
year = {2025},
author = {Ma, X and Miao, T and Xie, F and Zhang, J and Zheng, L and Liu, X and Hai, H},
title = {Development of Wearable Wireless Multichannel f-NIRS System to Evaluate Activities.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050576},
pmid = {40428702},
issn = {2072-666X},
abstract = {Functional near-infrared spectroscopy is a noninvasive neuroimaging technique that uses optical signals to monitor subtle changes in hemoglobin concentrations within the superficial tissue of the human body. This technology has widespread applications in long-term brain-computer interface monitoring within both traditional medical domains and, increasingly, domestic settings. The popularity of this approach lies in the fact that new single-channel brain oxygen sensors can be used in a variety of scenarios. Given the diverse sensor structure requirements across applications and numerous approaches to data acquisition, the accurate extraction of comprehensive brain activity information requires a multichannel near-infrared system. This study proposes a novel distributed multichannel near-infrared system that integrates two near-infrared light emissions at differing wavelengths (660 nm, 850 nm) with a photoelectric receiver. This substantially improves the accuracy of regional signal sampling. Through a basic long-time mental arithmetic paradigm, we demonstrate that the accompanying algorithm supports offline analysis and is sufficiently versatile for diverse scenarios relevant to the system's functionality.},
}
@article {pmid40428683,
year = {2025},
author = {Hong, S},
title = {Wireless Optogenetic Microsystems Accelerate Artificial Intelligence-Neuroscience Coevolution Through Embedded Closed-Loop System.},
journal = {Micromachines},
volume = {16},
number = {5},
pages = {},
doi = {10.3390/mi16050557},
pmid = {40428683},
issn = {2072-666X},
support = {N/A//Hongik University/ ; },
abstract = {Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AI-integrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration.},
}
@article {pmid40428114,
year = {2025},
author = {Zheng, Y and Wu, S and Chen, J and Yao, Q and Zheng, S},
title = {Cross-Subject Motor Imagery Electroencephalogram Decoding with Domain Generalization.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {5},
pages = {},
doi = {10.3390/bioengineering12050495},
pmid = {40428114},
issn = {2306-5354},
abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals in the brain-computer interface (BCI) can assist patients in accelerating motor function recovery. To realize the implementation of plug-and-play functionality for MI-BCI applications, cross-subject models are employed to alleviate time-consuming calibration and avoid additional model training for target subjects by utilizing EEG data from source subjects. However, the diversity in data distribution among subjects limits the model's robustness. In this study, we investigate a cross-subject MI-EEG decoding model with domain generalization based on a deep learning neural network that extracts domain-invariant features from source subjects. Firstly, a knowledge distillation framework is adopted to obtain the internally invariant representations based on spectral features fusion. Then, the correlation alignment approach aligns mutually invariant representations between each pair of sub-source domains. In addition, we use distance regularization on two kinds of invariant features to enhance generalizable information. To assess the effectiveness of our approach, experiments are conducted on the BCI Competition IV 2a and the Korean University dataset. The results demonstrate that the proposed model achieves 8.93% and 4.4% accuracy improvements on two datasets, respectively, compared with current state-of-the-art models, confirming that the proposed approach can effectively extract invariant features from source subjects and generalize to the unseen target distribution, hence paving the way for effective implementation of the plug-and-play functionality in MI-BCI applications.},
}
@article {pmid40426690,
year = {2025},
author = {Taha, BN and Baykara, M and Alakuş, TB},
title = {Neurophysiological Approaches to Lie Detection: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050519},
pmid = {40426690},
issn = {2076-3425},
abstract = {Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. Methods: This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. Results: CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. Conclusions: EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.},
}
@article {pmid40426631,
year = {2025},
author = {Mao, Q and Zhu, H and Yan, W and Zhao, Y and Hei, X and Luo, J},
title = {MCL-SWT: Mirror Contrastive Learning with Sliding Window Transformer for Subject-Independent EEG Recognition.},
journal = {Brain sciences},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/brainsci15050460},
pmid = {40426631},
issn = {2076-3425},
support = {23JK0556//the Scientific Research Program Founded by Shaanxi Provincial Education Department of China/ ; 61906152, 62376213 and U21A20524//the National Natural Science Foundation of China/ ; },
abstract = {Background: In brain-computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low sensitivity to spatial dynamics of neural activity and difficulty balancing high temporal resolution features with manageable computational complexity. The overarching objective is to address these critical issues. Methods: We introduce Mirror Contrastive Learning with Sliding Window Transformer (MCL-SWT). Inspired by left/right hand motor imagery inducing event-related desynchronization (ERD) in the contralateral sensorimotor cortex, we develop a mirror contrastive loss function. It segregates feature spaces of EEG signals from contralateral ERD locations while curtailing variability in signals sharing similar ERD locations. The Sliding Window Transformer computes self-attention scores over high temporal resolution features, enabling efficient capture of global temporal dependencies. Results: Evaluated on benchmark datasets for subject-independent MI EEG recognition, MCL-SWT achieves classification accuracies of 66.48% and 75.62%, outperforming State-of-the-Art models by 2.82% and 2.17%, respectively. Ablation studies validate the efficacy of both the mirror contrastive loss and sliding window mechanism. Conclusions: These findings underscore MCL-SWT's potential as a robust, interpretable framework for subject-independent EEG recognition. By addressing existing challenges, MCL-SWT could significantly advance BCI technology development.},
}
@article {pmid40426214,
year = {2025},
author = {Li, K and Liang, H and Qiu, J and Zhang, X and Cai, B and Wang, D and Zhang, D and Lin, B and Han, H and Yang, G and Zhu, Z},
title = {Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {118},
pmid = {40426214},
issn = {1743-0003},
support = {2024XHSZ-Y08//Zhejiang Health Information Association Research Program/ ; 82401786//National Natural Science Foundation of China/ ; 82201637//National Natural Science Foundation of China/ ; 2024KY246//Zhejiang Provincial Medical and Health Technology Project/ ; BMI2400025//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; 2024C03150//Key R&amp;D Program of Zhejiang Province/ ; J-202402//Qiushi Youth Program from Scientific Research Cultivation Foundation/ ; },
mesh = {*Brain/physiology/cytology ; Humans ; Microscopy, Fluorescence/methods ; Animals ; *Single-Cell Analysis/methods ; *Neurons/physiology ; Optogenetics ; *Brain Mapping/methods ; },
abstract = {As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.},
}
@article {pmid40425805,
year = {2025},
author = {Liu, CW and Wang, YM and Chen, SY and Lu, LY and Liang, TY and Fang, KC and Chen, P and Lee, IC and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK},
title = {The cerebellum shapes motions by encoding motor frequencies with precision and cross-individual uniformity.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {40425805},
issn = {2157-846X},
support = {NTUMC 110C101-011//NTU | College of Medicine, National Taiwan University (College of Medicine, National Taiwan University)/ ; NSC-145-11//National Taiwan University Hospital (NTUH)/ ; 113-UN0013//National Taiwan University Hospital (NTUH)/ ; 108-039//National Taiwan University Hospital (NTUH)/ ; 112-UN0024//National Taiwan University Hospital (NTUH)/ ; 113-E0001//National Taiwan University Hospital (NTUH)/ ; AS-TM-112-01-02//Academia Sinica/ ; NHRI-EX113-11303NI//National Health Research Institutes (NHRI)/ ; 109-2326-B-002-013-MY4//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 107-2321-B-002-020//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2321-002-059-MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 110-2321-B-002-012//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 111-2628-B-002-036//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 112-2628-B-002-011//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 113-2628-B-002-002//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; R01NS118179//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS104423//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01NS124854//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; },
abstract = {Understanding brain behaviour encoding or designing neuroprosthetics requires identifying precise, consistent neural algorithms across individuals. However, cerebral microstructures and activities are individually variable, posing challenges for identifying precise codes. Here, despite cerebral variability, we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in vivo electrophysiology and optogenetics in mice, we confirm that deep cerebellar neurons encode frequencies using populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism is consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validate the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for a brain-computer interface for motor control.},
}
@article {pmid40425792,
year = {2025},
author = {Chen, ZP and Zhao, X and Wang, S and Cai, R and Liu, Q and Ye, H and Wang, MJ and Peng, SY and Xue, WX and Zhang, YX and Li, W and Tang, H and Huang, T and Zhang, Q and Li, L and Gao, L and Zhou, H and Hang, C and Zhu, JN and Li, X and Liu, X and Cong, Q and Yan, C},
title = {GABA-dependent microglial elimination of inhibitory synapses underlies neuronal hyperexcitability in epilepsy.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40425792},
issn = {1546-1726},
support = {82373856//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900824//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071097//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82471481//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200778//National Natural Science Foundation of China (National Science Foundation of China)/ ; 020813005031//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; 2019M651779//Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)/ ; },
abstract = {Neuronal hyperexcitability is a common pathophysiological feature of many neurological diseases. Neuron-glia interactions underlie this process but the detailed mechanisms remain unclear. Here, we reveal a critical role of microglia-mediated selective elimination of inhibitory synapses in driving neuronal hyperexcitability. In epileptic mice of both sexes, hyperactive inhibitory neurons directly activate surveilling microglia via GABAergic signaling. In response, these activated microglia preferentially phagocytose inhibitory synapses, disrupting the balance between excitatory and inhibitory synaptic transmission and amplifying network excitability. This feedback mechanism depends on both GABA-GABAB receptor-mediated microglial activation and complement C3-C3aR-mediated microglial engulfment of inhibitory synapses, as pharmacological or genetic blockage of both pathways effectively prevents inhibitory synapse loss and ameliorates seizure symptoms in mice. Additionally, putative cell-cell interaction analyses of brain tissues from males and females with temporal lobe epilepsy reveal that inhibitory neurons induce microglial phagocytic states and inhibitory synapse loss. Our findings demonstrate that inhibitory neurons can directly instruct microglial states to control inhibitory synaptic transmission through a feedback mechanism, leading to the development of neuronal hyperexcitability in temporal lobe epilepsy.},
}
@article {pmid40425030,
year = {2025},
author = {Dehgan, A and Abdelhedi, H and Hadid, V and Rish, I and Jerbi, K},
title = {Artificial neural networks for magnetoencephalography: A review of an emerging field.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd4a},
pmid = {40425030},
issn = {1741-2552},
abstract = {Objective: Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive processes with an unparalleled combination of high temporal and spatial precision. While MEG data analytics have traditionally relied on advanced signal processing and mathematical and statistical tools, the recent surge in Artificial Intelligence (AI) has led to the growing use of Machine Learning (ML) methods for MEG data classification. An emerging trend in this field is the use of Artificial Neural Networks (ANNs) to address various MEG-related tasks. This review aims to provide a comprehensive overview of the state of the art in this area.Approach: This topical review included studies that applied ANNs to MEG data. Studies were sourced from PubMed, Google Scholar, arXiv, and bioRxiv using targeted search queries. The included studies were categorized into three groups: Classification, Modeling, and Other. Key findings and trends were summarized to provide a comprehensive assessment of the field.Main Results:The review identified 119 relevant studies, with 69 focused on Classification, 16 on Modeling, and 34 in the Other category. Classification studies addressed tasks such as brain decoding, clinical diagnostics, and BCI implementations, often achieving high predictive accuracy. Modeling studies explored the alignment between ANN activations and brain processes, offering insights into the neural representations captured by these networks. The Other category demonstrated innovative uses of ANNs for artifact correction, preprocessing, and neural source localization.Significance: By establishing a detailed portrait of the current state of the field, this review highlights the strengths and current limitations of ANNs in MEG research. It also provides practical recommendations for future work, offering a helpful reference for seasoned researchers and newcomers interested in using ANNs to explore the complex dynamics of the human brain with MEG.},
}
@article {pmid40425024,
year = {2025},
author = {Wolpaw, JR},
title = {Making brain-computer interfaces as reliable as muscles.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd47},
pmid = {40425024},
issn = {1741-2552},
abstract = {While BCIs can restore basic communication to people lacking muscle control, they cannot yet restore actions that require the extremely high reliability of natural (i.e., muscle-based) actions. Most BCI research focuses on neural engineering; it seeks to improve the measurement and analysis of brain signals. But neural engineering alone cannot make BCIs reliable. A BCI does not simply decode brain activity; it enables its user to acquire a skill that is produced not by nerves and muscles but rather by the BCI. Thus, BCI research should focus also on neuroscience; it should seek to develop BCI skills that emulate natural skills. A natural skill is produced by a network of neurons and synapses that may extend from cortex to spinal cord. This network has been given the name heksor, from the ancient Greek word hexis. A heksor changes through life; it modifies itself as needed to maintain the key features of its skill, the attributes that make the skill satisfactory. Heksors overlap; they share neurons and synapses. Through their concurrent changes, heksors keep neuronal and synaptic properties in a negotiated equilibrium that enables each to produce its skill satisfactorily. A BCI-based skill is produced by a synthetic heksor, a network of neurons, synapses, and software that produces a BCI-based skill and should change as needed to maintain the skill's key features. A synthetic heksor shares neurons and synapses with natural heksors. Like natural heksors, it can benefit from multimodal sensory feedback, using signals from multiple brain areas, and maintaining the skill's key features rather than all its details. A synthetic heksor also needs successful co-adaptation between its CNS and software components and successful integration into the negotiated equilibrium that heksors establish and maintain. With due attention to both neural engineering and neuroscience, BCIs could become as reliable as muscles.},
}
@article {pmid40425023,
year = {2025},
author = {Wu, D},
title = {Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addd49},
pmid = {40425023},
issn = {1741-2552},
abstract = {Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.},
}
@article {pmid40424668,
year = {2025},
author = {Sawyer, A and Brannigan, J and Spielman, L and , and Putrino, D and Fry, A},
title = {Development of a novel clinical outcome assessment: digital instrumental activities of daily living.},
journal = {EBioMedicine},
volume = {116},
number = {},
pages = {105732},
doi = {10.1016/j.ebiom.2025.105732},
pmid = {40424668},
issn = {2352-3964},
abstract = {BACKGROUND: Digital technology is integral to activities of daily living, particularly instrumental activities of daily living (IADLs). However, tools that accommodate digital performance of IADLs are lacking. The aim of this study was to develop a novel Digital IADL Scale.<br><br>METHODS: The multi-stage methodology included: (i) deductive item generation via a systematic review and assignment to domains using a Delphi process, (ii) inductive item generation via a survey of individuals with lived experience (IWLE) of severe paralysis, (iii) item refinement via item rating surveys of content experts and IWLE, and (iv) focus group discussions with key opinion leaders.<br><br>FINDINGS: The systematic review identified 1250 IADL items from validated IADL measures, of which 353 met criteria. Deduplication reduced the deductive item set to 77, of which 42 remained following the Delphi process. IWLE generated 152 items, of which 132 met criteria. Deduplication reduced the inductive item set to 41. The combined item pool was reduced to 69 following the item rating surveys. Following focus group feedback, a list of nine domains, containing 37 items, and suggested response scale options are presented.<br><br>INTERPRETATION: We describe the initial development of a scale to assess functional independence within IADLs that may be completed digitally, which will be submitted to further validation.<br><br>FUNDING: Support for this project was provided in kind by the Abilities Research Center. No formal funding was received.},
}
@article {pmid40423756,
year = {2025},
author = {Moeller, A and Andres Porras, JM},
title = {Human enhancement, past and present.},
journal = {Monash bioethics review},
volume = {},
number = {},
pages = {},
pmid = {40423756},
issn = {1836-6716},
abstract = {One important role the medical humanities might and should play relates to public education. In this instance, we mean helping persons to think about their own aims or purposes as potential receivers of enhancement interventions, and similarly helping to inform the developers of said interventions. This article argues that, in the light of real and speculative applications of emerging biotechnologies and artificial intelligence aimed at human enhancement-including germline genetic engineering, the linking of the human brain with an artificial general intelligence by way of a brain-computer interface, and various interventions directed toward life extension-historians would do well to consider the following three practices as they participate in the medical humanities and the shared task of public education: (1) Taking under scrutiny a broad swath of topics and timeframes as it relates to past efforts aimed at human enhancement; (2) Focusing on past engagement with enhancement efforts and their perceived relation to the pursuit of living well; and (3) Entering into debates on enhancement as equal participants. In support of these assertions, this article takes efforts directed towards the prolongation of life in medieval Europe as an illustrative example. It also highlights continuities and discontinuities between past and present justifications for human enhancement, and addresses how similarities and differences can shape and challenge contemporary bioethical arguments.},
}
@article {pmid40423554,
year = {2025},
author = {Brackman, KN and Taychert, MT and Serrell, EC and Gralnek, D and Manakas, C and Knoedler, M and Antar, A and Allen, GO and Grimes, MD},
title = {Clinical Outcomes of HoLEP in Patients with Diminished Bladder Contractility.},
journal = {Urology practice},
volume = {},
number = {},
pages = {101097UPJ0000000000000840},
doi = {10.1097/UPJ.0000000000000840},
pmid = {40423554},
issn = {2352-0787},
abstract = {INTRODUCTION: Bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) is common in aging men and can be treated with holmium laser enucleation of the prostate (HoLEP). However, diminished bladder contractility (DC) is also highly prevalent (9-48%) and can be clinically indistinguishable from BOO without urodynamics. While HoLEP effectively treats BPH/BOO, clinical outcomes data for DC patients are limited and mixed. We aim to compare the prevalence and risk factors for catheter dependence among patients with and without DC post-HoLEP.<br><br>METHODS: A retrospective cohort study was conducted on 179 patients with preoperative urodynamics who underwent HoLEP between June 2018 and December 2023. Diminished contractility was defined as Bladder Contractility Index (BCI) < 100. Statistical analyses included univariate and multivariate logistic regression.<br><br>RESULTS: Among 179 patients 103 (57.5%) had DC (BCI <100). Post HoLEP all normal contractility (NC) patients were voiding while 7.8% of DC patients were catheter dependent (p = 0.01) at mean follow up of 28 months. Preoperative BCI was associated with post HoLEP catheter dependence (OR = 0.97, 95% CI 0.95-1.00, p = 0.046). Postoperative international prostate symptom scores were significantly higher in DC compared to NC groups despite similar preoperative scores.<br><br>CONCLUSIONS: HoLEP rendered 95.5% (171/179) of patients catheter free. However, DC patients were more likely to require catheterization postoperatively and reported worse urinary symptoms compared to NC patients. Our results support obtaining urodynamics when there is clinical concern for DC, as this may guide shared decision-making prior to pursuing HoLEP.},
}
@article {pmid40422053,
year = {2025},
author = {Avital, N and Shulkin, N and Malka, D},
title = {Automatic Calculation of Average Power in Electroencephalography Signals for Enhanced Detection of Brain Activity and Behavioral Patterns.},
journal = {Biosensors},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/bios15050314},
pmid = {40422053},
issn = {2079-6374},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Precise analysis of electroencephalogram (EEG) signals is critical for advancing the understanding of neurological conditions and mapping brain activity. However, accurately visualizing brain regions and behavioral patterns from neural signals remains a significant challenge. The present study proposes a novel methodology for the automated calculation of the average power of EEG signals, with a particular focus on the beta frequency band which is known for its pronounced activity during cognitive tasks such as 2D content engagement. An optimization algorithm is employed to determine the most appropriate digital filter type and order for EEG signal processing, thereby enhancing both signal clarity and interpretability. To validate the proposed methodology, an experiment was conducted with 22 students, during which EEG data were recorded while participants engaged in cognitive tasks. The collected data were processed using MATLAB (version R2023a) and the EEGLAB toolbox (version 2022.1) to evaluate various filters, including finite impulse response (FIR) and infinite impulse response (IIR) Butterworth and IIR Chebyshev filters with a 0.5% passband ripple. Results indicate that the IIR Chebyshev filter, configured with a 0.5% passband ripple and a fourth-order design, outperformed the alternatives by effectively reducing average power while preserving signal fidelity. This optimized filtering approach significantly improves the accuracy of neural signal visualizations, thereby facilitating the creation of detailed brain activity maps. By refining the analysis of EEG signals, the proposed method enhances the detection of specific neural behaviors and deepens the understanding of functional brain regions. Moreover, it bolsters the reliability of real-time brain activity monitoring, potentially advancing neurological diagnostics and insights into cognitive processes. These findings suggest that the technique holds considerable promise for future applications in brain-computer interfaces and advanced neurological assessments, offering a valuable tool for both clinical practice and research exploration.},
}
@article {pmid40421845,
year = {2025},
author = {Pizzolante, S and Covelli, E and Filippi, C and Barbara, M},
title = {Percutaneous Bone Implant Surgery: A MIPS Modified Technique.},
journal = {The Laryngoscope},
volume = {},
number = {},
pages = {},
doi = {10.1002/lary.32192},
pmid = {40421845},
issn = {1531-4995},
abstract = {Since their introduction, passive percutaneous hearing aids have undergone substantial evolution, including changes in implant production, improvements in the sound processor, and simplification of surgical implantation techniques. The latest innovation comes from the minimally invasive technique proposed for the PONTO system (MIPS), which does not involve the creation of a mucoperiosteal flap in order to leave the surrounding soft tissue and vascular microcirculation intact. This study proposes a modified surgical technique compared to the one proposed for the PONTO system in order to overcome some steps of the traditional surgical technique for the placement of the Baha Connect prosthesis. Our technique does not involve any incision but the exposure of the periosteum using a skin punch and subsequent drilling without the use of any protective cannula. The described procedure allows one to overcome some steps of the traditional surgical technique and, consequently, also some post-operative complications. Moreover, a minimally invasive procedure can help reduce surgical time and the invasiveness of the application.},
}
@article {pmid40420994,
year = {2025},
author = {Esteves, D and Valente, M and Bendor, SE and Andrade, A and Vourvopoulos, A},
title = {Identifying EEG biomarkers of sense of embodiment in virtual reality: insights from spatio-spectral features.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1572851},
pmid = {40420994},
issn = {2673-6195},
abstract = {The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one's own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification. This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion. Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe's role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.},
}
@article {pmid40420178,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Qu, D},
title = {Semantic radicals' semantic attachment to their composed phonograms.},
journal = {BMC psychology},
volume = {13},
number = {1},
pages = {559},
pmid = {40420178},
issn = {2050-7283},
support = {20BYY095//National Social Science Fund of China/ ; 2019YBYY131//Chongqing Social Science Planning Fund/ ; 22SKGH236//Humanities and Social Sciences Research Project Fund of Chongqing Municipal Education Commission/ ; },
mesh = {Humans ; *Semantics ; Female ; Male ; Young Adult ; Reaction Time ; Adult ; Decision Making ; *Reading ; },
abstract = {In Chinese character processing studies, it is widely accepted that semantic radicals, whether character or non-character ones, can undergo semantic activation. However, there is a notable absence of studies dedicated to understanding the nature and operation of the semantic radicals' semantic information. To address this gap, the present study employed a masked semantic priming paradigm combined with a part-of-speech decision task and a lexical decision task across three experiments. Experiment 1 was designed to examine the semantic autonomy and the semantic attachment of semantic radicals in transparent phonograms. Experiment 2 sought to further investigate the degree of semantic autonomy of semantic radicals in opaque phonograms. Experiment 3 was crafted to further probe into the presence of semantic attachment of semantic radicals in pseudo-characters. Results showed significant priming effects in both transparent and opaque phonogram conditions, with faster reaction times and higher accuracy for semantically related prime-target pairs. However, no such priming effect was observed in the pseudo-character condition, indicating that semantic radicals are not activated in non-lexical contexts. These findings suggest that semantic radicals were semantically activated when embedded in both transparent and opaque phonograms, but not when planted in pseudo-characters. The plausible account put forward is that semantic radicals stand on pars with their composed phonograms in possessing their own semantic information, but the former is semantically strongly attached to the latter, such that it cannot live without the latter's semantic company.},
}
@article {pmid40419791,
year = {2025},
author = {Chen, Y and Ding, K and Zheng, S and Gao, S and Xu, X and Wu, H and Zhou, F and Wang, Y and Xu, J and Wang, C and Ling, C and Xu, J and Wang, L and Wu, Q and Giamas, G and Chen, G and Zhang, J and Yi, C and Ji, J},
title = {Post-translational modifications in DNA damage repair: mechanisms underlying temozolomide resistance in glioblastoma.},
journal = {Oncogene},
volume = {},
number = {},
pages = {},
pmid = {40419791},
issn = {1476-5594},
support = {82203035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82403931//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Temozolomide (TMZ) resistance is one of the critical factors contributing to the poor prognosis of glioblastoma (GBM). As a first-line chemotherapeutic agent for GBM, TMZ exerts its cytotoxic effects through DNA alkylation. However, its therapeutic efficacy is significantly compromised by enhanced DNA damage repair (DDR) mechanisms in GBM cells. Although several DDR-targeting drugs have been developed, their clinical outcomes remain suboptimal. Post-translational modifications (PTMs) in GBM cells play a pivotal role in maintaining the genomic stability of DDR mechanisms, including methylguanine-DNA methyltransferase-mediated repair, DNA mismatch repair dysfunction, base excision repair, and double-strand break repair. This review focuses on elucidating the regulatory roles of PTMs in the intrinsic mechanisms underlying TMZ resistance in GBM. Furthermore, we explore the feasibility of enhancing TMZ-induced cytotoxicity by targeting PTM-related enzymatic to disrupt key steps in PTM-mediated DDR pathways. By integrating current preclinical insights and clinical challenges, this work highlights the potential of modulating PTM-driven networks as a novel therapeutic strategy to overcome TMZ resistance and improve treatment outcomes for GBM patients.},
}
@article {pmid40419502,
year = {2025},
author = {Rajabi, N and Zanettin, I and Ribeiro, AH and Vasco, M and Björkman, M and Lundström, JN and Kragic, D},
title = {Exploring the feasibility of olfactory brain-computer interfaces.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18404},
pmid = {40419502},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Odorants/analysis ; Male ; Adult ; Female ; *Smell/physiology ; Feasibility Studies ; Neural Networks, Computer ; Young Adult ; *Olfactory Perception/physiology ; *Brain/physiology ; },
abstract = {In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.},
}
@article {pmid40419488,
year = {2025},
author = {Wang, D and Xue, H and Xia, L and Li, Z and Zhao, Y and Fan, X and Sun, K and Wang, H and Hamalainen, T and Zhang, C and Cong, F and Li, Y and Song, F and Lin, J},
title = {A tough semi-dry hydrogel electrode with anti-bacterial properties for long-term repeatable non-invasive EEG acquisition.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {105},
pmid = {40419488},
issn = {2055-7434},
support = {2022 ZD0210700//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Non-invasive brain-computer interfaces (NI-BCIs) have garnered significant attention due to their safety and wide range of applications. However, developing non-invasive electroencephalogram (EEG) electrodes that are highly sensitive, comfortable to wear, and reusable has been challenging due to the limitations of conventional electrodes. Here, we introduce a simple method for fabricating semi-dry hydrogel EEG electrodes with antibacterial properties, enabling long-term, repeatable acquisition of EEG. By utilizing N-acryloyl glycinamide and hydroxypropyltrimethyl ammonium chloride chitosan, we have prepared electrodes that not only possess good mechanical properties (compression modulus 65 kPa) and anti-fatigue properties but also exhibit superior antibacterial properties. These electrodes effectively inhibit the growth of both Gram-negative (E. coli) and Gram-positive (S. epidermidis) bacteria. Furthermore, the hydrogel maintains stable water retention properties, resulting in an average contact impedance of <400 Ω measured over 12 h, and an ionic conductivity of 0.39 mS cm[-1]. Cytotoxicity and skin irritation tests have confirmed the high biocompatibility of the hydrogel electrodes. In an N170 event-related potential (ERP) test on human volunteers, we successfully captured the expected ERP signal waveform and a high signal-to-noise ratio (20.02 dB), comparable to that of conventional wet electrodes. Moreover, contact impedance on the scalps remained below 100 kΩ for 12 h, while wet electrodes became unable to detect signals after 7-8 h due to dehydration. In summary, our hydrogel electrodes are capable of detecting ERPs over extended periods in an easy-to-use manner with antibacterial properties. This reduces the risk of bacterial infection associated with prolonged reuse and expands the potential of NI-BCIs in daily life.},
}
@article {pmid40419083,
year = {2025},
author = {Chen, L and Zhang, L and Wang, Z and Li, Q and Gu, B and Ming, D},
title = {Task-related reconfiguration patterns of frontoparietal network during motor imagery.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.05.035},
pmid = {40419083},
issn = {1873-7544},
abstract = {Motor imagery (MI) is closely associated with the frontoparietal network that includes prefrontal and posterior parietal regions. Studying task-related network reconfiguration after brain shifts from the resting state to the MI task is an important way to understand the brain's response process. However, how the brain modulates functional connectivity of the frontoparietal network when it shifts to MI has not been thoroughly studied. In this study, we attempted to characterize the frontoparietal network reconfiguration patterns as the brain transitioned to motor imagery tasks. We performed the analysis using EEG signals from 52 healthy subjects during left- and right-hand MI tasks. The results indicated distinct reconfiguration patterns in the frontoparietal network across four typical brain wave rhythms (theta (4 ∼ 7 Hz), alpha (8 ∼ 13 Hz), beta (14 ∼ 30 Hz), and gamma (31 ∼ 45 Hz)). Meanwhile, there was a significant positive correlation between the frontoparietal network reconfiguration and the event-related desynchronization of alpha and beta rhythms in the sensorimotor cortex. We further found that subjects with better MI-BCI performance exhibited greater reconfiguration of the frontoparietal network in alpha and beta rhythms. These findings implied that MI was accompanied by a shift in information interaction between brain regions, which might contribute to understanding the neural mechanisms of MI.},
}
@article {pmid40418615,
year = {2025},
author = {Song, Y and Wang, Y and He, H and Gao, X},
title = {Recognizing Natural Images From EEG With Language-Guided Contrastive Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3562743},
pmid = {40418615},
issn = {2162-2388},
abstract = {Electroencephalography (EEG), known for its convenient noninvasive acquisition but moderate signal-to-noise ratio, has recently gained much attention due to the potential to decode image information. However, previous works have not delivered sufficient evidence of this task, primarily limited by performance and biological plausibility. In this work, we first introduce a self-supervised framework to demonstrate the feasibility of recognizing images from EEG signals. Contrastive learning is leveraged to align the representations of EEG responses with image stimuli. Then, language descriptions of the stimuli generated by large language models (LLMs) help guide learning core semantic information. With the framework, we attain significantly above-chance results on the THINGS-EEG2 dataset, achieving a top-1 accuracy of 19.7% and a top-5 accuracy of 51.5% in challenging 200-way zero-shot tasks. Furthermore, we conduct thorough experiments to resolve the human visual responses with EEG from temporal, spatial, spectral, and semantic perspectives. These results provide evidence of feasibility and plausibility regarding EEG-based image recognition, substantiated by comparative studies with the THINGS-Magnetoencephalography (MEG) dataset. The findings offer valuable insights for neural decoding and real-world applications of brain-computer interfaces (BCIs), such as health care and robot control. The code is available at https://github.com/eeyhsong/NICE-LLM.},
}
@article {pmid40416647,
year = {2025},
author = {Teng, Y and Song, L and Shi, J and Lv, Q and Hou, S and Ramakrishna, S},
title = {Advancing electrospinning towards the future of biomaterials in biomedical engineering.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbaf034},
pmid = {40416647},
issn = {2056-3418},
abstract = {Biomaterial is a material designed to take a form that can direct, through interactions with living systems, the course of any therapeutic or diagnostic procedure. Growing demand for improved and affordable healthcare treatments and unmet clinical needs seek further advancement of biomaterials. Over the past 25 years, the electrospinning method has been innovated to enhance biomaterials at nanometer and micrometer length scales for diverse healthcare applications. Recent developments include intelligent (smart) biomaterials and sustainable biomaterials. Intelligent materials can sense, adapt to and respond to external stimuli, autonomously adjusting to enhance functionality and performance. Sustainable biomaterials possess several key characteristics, including renewability, a low carbon footprint, circularity, durability, biocompatibility, biodegradability and others. Herein, advances in electrospun biomaterials, encompassing process innovations, working principles and the effects of process variables, are presented succinctly. The potential of electrospun intelligent biomaterials and sustainable biomaterials in specific biomedical applications, including tissue engineering, regenerative medicine, drug delivery systems, brain-computer interfaces, biosensors, personal protective equipment and wearable devices, is explored. More effective healthcare demands further advancements in electrospun biomaterials. In the future, the distinctive characteristics of intelligent biomaterials and sustainable biomaterials, integrated with various emerging technologies (such as AI and data transmission), will enable physicians to conduct remote diagnosis and treatment. This advancement significantly enhances telemedicine capabilities for more accurate disease prediction and management.},
}
@article {pmid40416500,
year = {2025},
author = {Mokienko, OA},
title = {The Potential of Near-Infrared Spectroscopy as a Therapeutic Tool Following a Stroke (Review).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {17},
number = {2},
pages = {73-83},
pmid = {40416500},
issn = {2309-995X},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy/diagnosis/diagnostic imaging ; },
abstract = {The advancement of novel technologies for the rehabilitation of post-stroke patients represents a significant challenge for a range of interdisciplinary fields. Near-infrared spectroscopy (NIRS) is an optical neuroimaging technique based on recording local hemodynamic changes at the cerebral cortex level. The technology is typically employed in post-stroke patients for diagnostic purposes, including the assessment of neuroplastic processes accompanying therapy, the study of hemispheric asymmetry, and the examination of functional brain networks. However, functional NIRS can also be used for therapeutic purposes, including the provision of biofeedback during rehabilitation tasks, as well as the navigation method during transcranial stimulation. The effectiveness of therapeutic NIRS application in stroke patients remains insufficiently studied, despite existing scientific evidence confirming its promising potential as a treatment method. The review examines the published literature on the therapeutic applications of NIRS after stroke, evaluating its potential role in the rehabilitation process. The paper describes NIRS features, advantages, and disadvantages, determining its position among other neuroimaging technologies; analyzes the findings of neurophysiological studies, which justified the clinical trials of NIRS technology; and evaluates the results of the studies on the therapeutic use of NIRS in post-stroke patients. Two potential applications of NIRS for therapeutic purposes following a stroke were suggested: the first was to provide real-time feedback during movement training (motor or ideomotor ones, including that in brain-computer interface circuits), and the second was to facilitate navigation during transcranial stimulation. Based on a comprehensive literature review, there were proposed and justified further research lines and development in this field.},
}
@article {pmid40414967,
year = {2025},
author = {Qian, L and Jia, C and Wang, J and Shi, L and Wang, Z and Wang, S},
title = {The dynamics of stimulus selection in the nucleus isthmi pars magnocellularis of avian midbrain network.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {18260},
pmid = {40414967},
issn = {2045-2322},
support = {2024M752934//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Mesencephalon/physiology ; Neurons/physiology ; Photic Stimulation ; *Nerve Net/physiology ; Birds/physiology ; },
abstract = {The nucleus isthmi pars magnocellularis (Imc) serves as a critical node in the avian midbrain network for encoding stimulus salience and selection. While reciprocal inhibitory projections among Imc neurons (inhibitory loop) are known to govern stimulus selection, existing studies have predominantly focused on stimulus selection under stimuli of constant relative intensity. However, animals typically encounter complex and changeable visual scenes. Thus, how Imc neurons represent stimulus selection under varying relative stimulus intensities remains unclear. Here, we examined the dynamics of stimulus selection by in vivo recording of Imc neurons' responses to spatiotemporally successive visual stimuli divided into two segments: the previous stimulus and the post stimulus. Our data demonstrate that Imc neurons can encode sensory memory of the previous stimulus, which modulates competition and salience representation in the post stimulus. This history-dependent modulation is also manifested in persistent neural activity after stimulus cessation. We identified, through neural tracing, focal inactivation, and computational modeling experiments, projections from the nucleus isthmi pars parvocellularis (Ipc) to "shepherd's crook" (Shc) neurons, which could be either direct or indirect. These projections enhance Imc neurons' responses and persistent neural activity after stimulus cessation. This connectivity supports a Shc-Ipc-Shc excitatory loop in the midbrain network. The coexistence of excitatory and inhibitory loops provides a neural substrate for continuous attractor network models, a proposed framework for neural information representation. This study also offers a potential explanation for how animals maintain short-term attention to targets in complex and changeable environments.},
}
@article {pmid40414233,
year = {2025},
author = {Paton, NI and Cousins, C and Sari, IP and Burhan, E and Ng, NK and Dalay, VB and Suresh, C and Kusmiati, T and Chew, KL and Balanag, VM and Lu, Q and Ruslami, R and Djaharuddin, I and Sugiri, JJR and Veto, RS and Sekaggya-Wiltshire, C and Avihingsanon, A and Saini, JK and Papineni, P and Nunn, AJ and Crook, AM and , },
title = {Efficacy and safety of 8-week regimens for the treatment of rifampicin-susceptible pulmonary tuberculosis (TRUNCATE-TB): a prespecified exploratory analysis of a multi-arm, multi-stage, open-label, randomised controlled trial.},
journal = {The Lancet. Infectious diseases},
volume = {},
number = {},
pages = {},
doi = {10.1016/S1473-3099(25)00151-3},
pmid = {40414233},
issn = {1474-4457},
abstract = {BACKGROUND: WHO recommends a 2-month optimal duration for new drug regimens for rifampicin-susceptible tuberculosis. We aimed to investigate the efficacy and safety of the 8-week regimens that were assessed as part of the TRUNCATE management strategy of the TRUNCATE-TB trial.<br><br>METHODS: TRUNCATE-TB was a multi-arm, multi-stage, open-label, randomised controlled trial in which participants aged 18-65 years with rifampicin-susceptible pulmonary tuberculosis were randomly assigned via a web-based system, using permuted blocks, to 24-week standard treatment (rifampicin, isoniazid, pyrazinamide, and ethambutol) or the TRUNCATE management strategy comprising initial 8-week treatment, then post-treatment monitoring and re-treatment where needed. The four 8-week regimens comprised five drugs, modified from standard treatment: high-dose rifampicin and linezolid, or high-dose rifampicin and clofazimine, or bedaquiline and linezolid, all given with isoniazid, pyrazinamide, and ethambutol; and rifapentine, linezolid, and levofloxacin, given with isoniazid and pyrazinamide. Here, we report the efficacy (proportion with unfavourable outcome; and difference from standard treatment, assessed via Bayesian methods) and safety of the 8-week regimens, assessed in the intention-to-treat population. This prespecified exploratory analysis is distinct from the previously reported 96-week outcome of the strategy in which the regimens were deployed. This trial is registered with ClinicalTrials.gov (NCT03474198).<br><br>FINDINGS: Between March 21, 2018, and March 26, 2020, 675 participants (674 in the intention-to-treat population) were enrolled and randomly assigned to the standard treatment group or one of the four 8-week regimen groups. Two 8-week regimens progressed to full enrolment. An unfavourable outcome (mainly relapse) occurred in seven (4%) of 181 participants on standard treatment; 46 (25%) of 184 on the high-dose rifampicin and linezolid-containing regimen (adjusted difference 21&#xb7;0%, 95% Bayesian credible interval [BCI] 14&#xb7;3-28&#xb7;1); and 26 (14%) of 189 on the bedaquiline and linezolid-containing regimen (adjusted difference 9&#xb7;3% [4&#xb7;3-14&#xb7;9]). Grade 3-4 adverse events occurred in 24 (14%) of 181 participants on standard treatment, 20 (11%) of 184 on the rifampicin-linezolid regimen, and 22 (12%) of 189 on the bedaquiline-linezolid regimen.<br><br>INTERPRETATION: Efficacy was worse with 8-week regimens, although the difference from standard treatment varied between regimens. Even the best 8-week regimen (bedaquiline-linezolid) should only be used as part of a management strategy involving post-treatment monitoring and re-treatment if necessary.<br><br>FUNDING: Singapore National Medical Research Council; UK Department of Health and Social Care; UK Foreign, Commonwealth, and Development Office; UK Medical Research Council; Wellcome Trust; and UK Research and Innovation Medical Research Council.},
}
@article {pmid40411529,
year = {2025},
author = {Sun, Y and Guan, M and Chen, X and Feng, F and He, R and Huang, L and Tong, X and Zhou, H and Liu, X and Ming, D},
title = {Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.},
journal = {Epilepsia},
volume = {},
number = {},
pages = {},
doi = {10.1111/epi.18463},
pmid = {40411529},
issn = {1528-1167},
support = {020/0903065111//Tianjin University Innovation Fund/ ; 2021YFF1200602//National Key Technologies Research and Development Program/ ; c02022088//National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; },
abstract = {OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.<br><br>METHODS: We proposed a novel deep learning framework, U-IEDNet, for detecting IEDs in multichannel EEG. The U-IEDNet framework employs convolutional layers and bidirectional gated recurrent units as a temporal encoder to extract temporal features from single-channel EEG, followed by the use of transformer networks as a spatial encoder to fuse multichannel features and extract interchannel interaction information. Transposed convolutional layers form a temporal decoder, creating a U-shaped architecture with the encoder. This upsamples features to estimate the probability of each EEG sampling point falling within the IED range, enabling segmentation of IEDs from background activity. Two datasets, a public database with 370 patient recordings and our own annotated database with 43 patient recordings, were used for model establishment and validation.<br><br>RESULTS: The results showed prominent advantage compared with other methods. U-IEDNet achieved a recall of .916, precision of .911, F1-score of .912, and false positive rate (FPR) of .030 on the public database. The classification performance in our own annotated database achieved a recall of .905, a precision of .902, an F1-score of .903, and an FPR of .072. The segmentation performance had a recall of .903, a precision of .916, and an F1-score of .909. Additionally, this study analyzes attention weights in the transformer network based on brain network theory to elucidate the spatial feature fusion process, enhancing the interpretability of the IED detection model.<br><br>SIGNIFICANCE: In this paper, we aim to present an artificial intelligence-based toolbox for IED detection, which may facilitate epilepsy diagnosis at the bedside in the future. U-IEDNet demonstrates great potential to improve the accuracy and efficiency of IED detection in multichannel EEG recordings.},
}
@article {pmid40409524,
year = {2025},
author = {Li, Y and Pan, Y and Zhao, D},
title = {Understanding the neurobiology and computational mechanisms of social conformity: implications for psychiatric disorders.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2025.05.011},
pmid = {40409524},
issn = {1873-2402},
abstract = {Social conformity and psychiatric disorders share overlapping brain regions and neural pathways, arousing our interest in uncovering their potentially shared underlying neural and computational mechanisms. Critically, the dynamics of group behavior may either mitigate or exacerbate mental health conditions, highlighting the need to bridge social neuroscience and psychiatry. Our work examines how aberrant neurobiological circuits and computations influence social conformity. We propose a hierarchical computational framework, based on dynamical systems and active inference, to facilitate the interpretation of the multi-layered interplay among processes that drive social conformity. We underscore the significant implications of this hierarchical computational framework for guiding future research on psychiatry, particularly with respect to the clinical translation of interventions such as targeted pharmacotherapy and neurostimulation techniques. The interdisciplinary efforts hold the potential to propel the fields of social and clinical neuroscience forward, fostering the emergence of more efficacious and individualized therapeutic approaches tailored to psychiatric disorders characterized by aberrant social behaviors.},
}
@article {pmid40408764,
year = {2025},
author = {Jing, S and Dai, Z and Liu, X and Yang, X and Cheng, J and Chen, T and Feng, Z and Liu, X and Dong, F and Xin, Y and Han, Z and Hu, H and Su, X and Wang, C},
title = {Effectiveness of Neurofeedback-Assisted and Conventional 6-Week Web-Based Mindfulness Interventions on Mental Health of Chinese Nursing Students: Randomized Controlled Trial.},
journal = {Journal of medical Internet research},
volume = {27},
number = {},
pages = {e71741},
doi = {10.2196/71741},
pmid = {40408764},
issn = {1438-8871},
mesh = {Humans ; *Students, Nursing/psychology ; *Mindfulness/methods ; Female ; Male ; *Mental Health ; China ; *Neurofeedback/methods ; Adult ; Young Adult ; Anxiety/therapy ; *Internet ; Depression/therapy ; *Internet-Based Intervention ; East Asian People ; },
abstract = {BACKGROUND: Nursing students experience disproportionately high rates of mental health challenges, underscoring the urgent need for innovative, scalable interventions. Web-based mindfulness programs, and more recently, neurofeedback-enhanced approaches, present potentially promising avenues for addressing this critical issue.<br><br>OBJECTIVE: This study aimed to explore the effectiveness of the neurofeedback-assisted online mindfulness intervention (NAOM) and the conventional online mindfulness intervention (COM) in reducing mental health symptoms among Chinese nursing students.<br><br>METHODS: A 3-armed randomized controlled trial was conducted among 147 nursing students in Beijing, China, using a 6-week web-based mindfulness program. Participants received NAOM, COM, or general mental health education across 6 weeks. Electroencephalogram and validated tools such as the Patient Health Questionnaire and the Generalized Anxiety Disorder Questionnaire were used to primarily assess symptoms of depression and anxiety at baseline, immediately after the intervention, and at 1 and 3 months after the intervention. Generalized estimating equations were used to evaluate the effects of intervention and time.<br><br>RESULTS: A total of 155 participants enrolled in the study, and 147 finished all assessments. Significant reductions in the symptoms of depression, anxiety, and fatigue were observed in the NAOM (mean difference [MD]=-3.330, Cohen d=0.926, P<.001; MD=-3.468, Cohen d=1.091, P<.001; MD=-2.620, Cohen d=0.743, P<.001, respectively) and the COM (MD=-1.875, Cohen d=0.490, P=.03; MD=-1.750, Cohen d=0.486, P=.02; MD=-2.229, Cohen d=0.629, P=.01, respectively) groups compared with the control group at postintervention assessment. Moreover, the NAOM group showed significantly better effects than the COM group in alleviating depressive symptoms (MD=-1.455; Cohen d=0.492; P=.04) and anxiety symptoms (MD=-1.718; Cohen d=0.670; P=.04) and improving the level of mindfulness (MD=-3.765; Cohen d=1.245; P<.001) at the postintervention assessment. However, no significant difference except for the anxiety symptoms was observed across the 3 groups at the 1- and 3-month follow-ups.<br><br>CONCLUSIONS: This 6-week web-based mindfulness intervention, both conventional and neurofeedback-assisted, effectively alleviated mental health problems in the short term among nursing students. The addition of neurofeedback demonstrated greater short-term benefits; however, but these effects were not sustained over the long term. Future research should focus on long-term interventions using a more robust methodological approach.<br><br>TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2400080314; https://www.chictr.org.cn/bin/project/edit?pid=211845.},
}
@article {pmid40408491,
year = {2025},
author = {Zhou, H and Wu, J and Li, J and Pan, Z and Lu, J and Shen, M and Wang, T and Hu, Y and Gao, Z},
title = {Event cache: An independent component in working memory.},
journal = {Science advances},
volume = {11},
number = {21},
pages = {eadt3063},
pmid = {40408491},
issn = {2375-2548},
mesh = {*Memory, Short-Term/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Female ; Adult ; Young Adult ; Brain Mapping ; *Cerebellum/physiology ; Brain/physiology ; },
abstract = {Working memory (WM) has been a major focus of cognitive science and neuroscience for the past 50 years. While most WM research has centered on the mechanisms of objects, there has been a lack of investigation into the cognitive and neural mechanisms of events, which are the building blocks of our experience. Using confirmatory factor analysis, psychophysical experiments, and resting-state and task functional magnetic resonance imaging methods, our study demonstrated that events have an independent storage space within WM, named as event cache, with distinct neural correlates compared to object storage in WM. We found the cerebellar network to be the most essential network for event cache, with the left cerebellum Crus I being particularly involved in encoding and maintaining events. Our findings shed critical light on the neuropsychological mechanism of WM by revealing event cache as an independent component of WM and encourage the reconsideration of theoretical models for WM.},
}
@article {pmid40408214,
year = {2025},
author = {Chen, W and Li, Y and Zheng, N and Shi, W},
title = {DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3573042},
pmid = {40408214},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculography (EOG), electromyography (EMG), and electrocardiography (ECG), which degrade signal quality and affect subsequent analysis. Effective EEG denoising is critical for enhancing the performance of EEG-based applications, including disease diagnosis and brain-computer interfaces (BCIs). While recent deep learning (DL) approaches have shown promise in this area, they often struggle to efficiently model the temporal dependencies inherent in EEG signals, as well as to capture local contextual information simultaneously. In this work, we introduce DenoiseMamba, a novel deep learning-based EEG denoising model. The model incorporates the ConvSSD module, which integrates convolutional neural networks (CNNs) with structured state-space duality (SSD) mechanisms. This allows DenoiseMamba to capture both local and global spatiotemporal features, resulting in more effective artifact suppression. Extensive experiments on three semi-simulated datasets demonstrate that DenoiseMamba outperforms existing methods in EEG reconstruction accuracy, effectively eliminating myoelectric, electrooculographic, and electrocardiographic artifacts while preserving critical EEG signal details.},
}
@article {pmid40408213,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {MTSNet: Convolution-based Transformer Network with Multi-scale Temporal-Spectral Feature Fusion for SSVEP Signal Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3573410},
pmid = {40408213},
issn = {2168-2208},
abstract = {Improving the decoding performance of steady-state visual evoked (SSVEP) signals is crucial for the practical application of SSVEP-based brain-computer interface (BCI) systems. Although numerous methods have achieved impressive results in decoding SSVEP signals, most of them focus only on the temporal or spectral domain information or concatenate them directly, which may ignore the complementary relationship between different features. To address this issue, we propose a dual-branch convolution-based Transformer network with multi-scale temporal-spectral feature fusion, termed MTSNet, to improve the decoding performance of SSVEP signals. Specifically, the temporal branch extracts temporal features from the SSVEP signals using the multi-level convolution-based Transformer (Convformer) that can adapt to the dynamic fluctuations of SSVEP signals. In parallel, the spectral branch takes the complex spectrum converted from temporal signals by the zero-padding fast Fourier transform as input and uses the Convformer to extract spectral features. These extracted temporal and spectral features are then integrated by the multi-scale feature fusion module to obtain comprehensive features with different scale information, thereby enhancing the interactions between the features and improving the effectiveness and robustness. Extensive experimental results on two widely used public SSVEP datasets, Benchmark and BETA, show that the proposed MTSNet significantly outperforms the state-of-the-art calibration-free methods in terms of accuracy and ITR. The superior performance demonstrates the effectiveness of our method in decoding SSVEP signals, which may facilitate the practical application of SSVEP-based BCI systems.},
}
@article {pmid40408200,
year = {2025},
author = {Ingolfsson, TM and Kartsch, V and Benini, L and Cossettini, A},
title = {A Wearable Ultra-Low-Power System for EEG-based Speech-Imagery Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2025.3573027},
pmid = {40408200},
issn = {1940-9990},
abstract = {Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling assistive communication for individuals with speech impairments or to enhance privacy. However, existing EEG-based speech imagery systems remain impractical for use outside specialized laboratories due to their reliance on high-channel-count and resource-intensive machine learning models running on external computing platforms. In this work, we present the first end-to-end demonstration of EEG-based speech imagery decoding on a low-channel, ultra-low-power wearable device. Building on our previous work on vowel imagery, we introduce an extended framework leveraging the BioGAP platform and VOWELNET, a lightweight neural network optimized for embedded speech imagery classification. In particular, we demonstrate state-of- the-art accuracy in the classification of an expanded vocabulary comprising vowels, commands, and rest states (13 classes) with a subject-specific training approach, achieving up to 50.0% for one subject (42.8% average) in multi-class classification. We deploy our model on an embedded biosignal acquisition and processing platform (BioGAP), based on the GAP9 processor, for real-time inference with minimal power consumption (25.93 mW). Our system achieves continuous execution for more than 21 hours on a small LiPo battery while maintaining classification latencies of 40.9 ms. Finally, we also explore the benefits of applying Continual Learning techniques to progressively improve the system's performance throughout its operational lifetime, and we demonstrate that electrodes located on the temporal area contribute the most to the overall accuracy. This work marks a significant step toward practical, real-time, and unobtrusive speech imagery BCIs, unlocking new opportunities for covert communication and assistive technologies.},
}
@article {pmid40407663,
year = {2025},
author = {Astefanei, O and Martu, C and Cozma, S and Radulescu, L},
title = {Cochlear and Bone Conduction Implants in Asymmetric Hearing Loss and Single-Sided Deafness: Effects on Localization, Speech in Noise, and Quality of Life.},
journal = {Audiology research},
volume = {15},
number = {3},
pages = {},
doi = {10.3390/audiolres15030049},
pmid = {40407663},
issn = {2039-4330},
abstract = {BACKGROUND: Single-sided deafness (SSD) and asymmetric hearing loss (AHL) impair spatial hearing and speech perception, often reducing quality of life. Cochlear implants (CIs) and bone conduction implants (BCIs) are rehabilitation options used in SSD and AHL to improve auditory perception and support functional integration in daily life.<br><br>OBJECTIVE: We aimed to evaluate hearing outcomes after auditory implantation in SSD and AHL patients, focusing on localization accuracy, speech-in-noise understanding, tinnitus relief, and perceived benefit.<br><br>METHODS: In this longitudinal observational study, 37 patients (adults and children) received a CI or a BCI according to clinical indications. Outcomes included localization and spatial speech-in-noise assessment, tinnitus ratings, and SSQ12 scores. Statistical analyses used parametric and non-parametric tests (p < 0.05).<br><br>RESULTS: In adult CI users, localization error significantly decreased from 81.9&#xb0; &#xb1; 15.8&#xb0; to 43.7&#xb0; &#xb1; 13.5&#xb0; (p < 0.001). In children, regardless of the implant type (CI or BCI), localization error improved from 74.3&#xb0; to 44.8&#xb0;, indicating a consistent spatial benefit. In adult BCI users, localization error decreased from 74.6&#xb0; to 69.2&#xb0;, but the improvement did not reach statistical significance. Tinnitus severity, measured on a 10-point VAS scale, decreased significantly in CI users (mean reduction: 2.8 &#xb1; 2.0, p < 0.001), while changes in BCI users were small and of limited clinical relevance. SSQ12B/C scores improved in all adult groups, with the largest gains observed in spatial hearing for CI users (2.1 &#xb1; 1.2) and in speech understanding for BCI users (1.6 &#xb1; 0.9); children reported high benefits across all domains. Head shadow yielded the most consistent benefit across all groups (up to 4.9 dB in adult CI users, 3.8 dB in adult BCI users, and 4.6 dB in children). Although binaural effects were smaller in BCI users, positive gains were observed, especially in pediatric cases. Correlation analysis showed that daily device use positively predicted SSQ12 improvement (r = 0.57) and tinnitus relief (r = 0.42), while longer deafness duration was associated with poorer localization outcomes (r = -0.48).<br><br>CONCLUSIONS: CIs and BCIs provide measurable benefits in SSD and AHL rehabilitation. Outcomes vary with age, device, and deafness duration, underscoring the need for early intervention and consistent auditory input.},
}
@article {pmid40406801,
year = {2025},
author = {Moumdjian, RA},
title = {Bioethics of neurotechnologies: a field in effervescence.},
journal = {Neurological research},
volume = {},
number = {},
pages = {1-4},
doi = {10.1080/01616412.2025.2499896},
pmid = {40406801},
issn = {1743-1328},
abstract = {Brain-Computer Interface (BCI) comprises a device that detects brain signals conveying specific intentions and translates them into executable outputs by a machine. It enables neurologically impaired patients to regain some control over their environment, thereby aiding in their rehabilitation. Some authors argue that 'the use of BCI is the greatest ethical challenge that neuroscience faces today. Ethical issues highlighted in the literature include safety, justice, privacy, security, and the balance of risks and benefits.},
}
@article {pmid40403087,
year = {2025},
author = {Essam, AA and Ibrahim, A and Seif Al-Nasr, A and El-Saqa, M and Mohamed, S and Anwar, A and Eldeib, A and Akcakaya, M and Khalaf, A},
title = {Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0311075},
pmid = {40403087},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Ultrasonography, Doppler, Transcranial/methods ; Male ; Adult ; Female ; Young Adult ; Bayes Theorem ; *Brain/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) provide alternative means of communication and control for individuals with severe motor or speech impairments. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to advance the state of the art in multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) by introducing advanced analysis approaches that enhance system performance. Our EEG-fTCD BCIs employ two distinct paradigms to infer user intent: motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. In the MI paradigm, we introduce the use of Filter Bank Common Spatial Pattern (FBCSP) for the first time in an EEG-fTCD BCI, while in the flickering MR/WG paradigm, we extend FBCSP application to non-motor imagery tasks. Additionally, we extract previously unexplored time-series features from the envelope of fTCD signals, leveraging richer information from cerebral blood flow dynamics. Furthermore, we employ a Bayesian fusion framework that allows EEG and fTCD to contribute unequally to decision-making. The multimodal EEG-fTCD system achieved high classification accuracies across tasks in both paradigms. In the MI paradigm, accuracies of 94.53%, 94.9%, and 96.29% were achieved for left arm MI vs. baseline, right arm MI vs. baseline, and right arm MI vs. left arm MI, respectively - outperforming EEG-only accuracy by 3.87%, 3.80%, and 5.81%, respectively. In the MR/WG paradigm, the system achieved 95.27%, 85.93%, and 96.97% for MR vs. baseline, WG vs. baseline, and MR vs. WG, respectively, showing accuracy improvements of 2.28%, 4.95%, and 1.56%, respectively compared to EEG-only results. Overall, the proposed analysis approach improved classification accuracy for 5 out of 6 binary classification problems within the MI and MR/WG paradigms, with gains ranging from 0.64% to 9% compared to our previous EEG-fTCD studies. Additionally, our results demonstrate that EEG-fTCD BCIs with the proposed analysis techniques outperform multimodal EEG-fNIRS BCIs in both accuracy and speed, improving classification performance by 2.7% to 24.7% and reducing trial durations by 2-38 seconds. These findings highlight the potential of the proposed approach to advance assistive technologies and improve patient quality of life.},
}
@article {pmid40402697,
year = {2025},
author = {Chaisaen, R and Autthasan, P and Ditthapron, A and Wilaiprasitporn, T},
title = {AlphaGrad: Normalized Gradient Descent for Adaptive Multi-loss Functions in EEG-based Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3572197},
pmid = {40402697},
issn = {2168-2208},
abstract = {In this study, we propose AlphaGrad, a novel adaptive loss blending strategy for optimizing multi-task learning (MTL) models in motor imagery (MI)-based electroencephalography (EEG) classification. AlphaGrad is the first method to automatically adjust multi-loss functions with differing metric scales, including mean square error, cross-entropy, and deep metric learning, within the context of MI-EEG. We evaluate AlphaGrad using two state-of-the-art MTL-based neural networks, MIN2Net and FBMSNet, across four benchmark datasets. Experimental results show that AlphaGrad consistently outperforms existing strategies such as AdaMT, GradApprox, and fixed-weight baselines in classification accuracy and training stability. Compared to baseline static weighting, AlphaGrad achieves over 10% accuracy improvement on subject-independent MI tasks when evaluated on the largest benchmark dataset. Furthermore, AlphaGrad demonstrates robust adaptability across various EEG paradigms-including steady-state visually evoked potential (SSVEP) and event-related potential (ERP), making it broadly applicable to brain-computer interface (BCI) systems. We also provide gradient trajectory visualizations highlighting AlphaGrad's ability to maintain training stability and avoid local minima. These findings underscore AlphaGrad's promise as a general-purpose solution for adaptive multi-loss optimization in biomedical time-series learning.},
}
@article {pmid40401160,
year = {2025},
author = {Zhao, L},
title = {Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf007},
pmid = {40401160},
issn = {2634-4416},
abstract = {Functional magnetic resonance imaging (fMRI) provides a powerful tool for studying brain function by capturing neural activity in a non-invasive manner. Mapping brain function from fMRI data enables researchers to investigate the spatial and temporal dynamics of neural processes, providing insights into how the brain responds to various tasks and stimuli. In this review, we explore the evolution of deep learning-based methods for brain function mapping using fMRI. We begin by discussing various network architectures such as convolutional neural networks, recurrent neural networks, and transformers. We further examine supervised, unsupervised, and self-supervised learning paradigms for fMRI-based brain function mapping, highlighting the strengths and limitations of each approach. Additionally, we discuss emerging trends such as fMRI embedding, brain foundation models, and brain-inspired artificial intelligence, emphasizing their potential to revolutionize brain function mapping. Finally, we delve into the real-world applications and prospective impact of these advancements, particularly in the diagnosis of neural disorders, neuroscientific research, and brain-computer interfaces for decoding brain activity. This review aims to provide a comprehensive overview of current techniques and future directions in the field of deep learning and fMRI-based brain function mapping.},
}
@article {pmid40401149,
year = {2025},
author = {Leong, F and Micera, S and Shokur, S},
title = {Optimization frameworks for bespoke sensory encoding in neuroprosthetics.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {020901},
pmid = {40401149},
issn = {2473-2877},
abstract = {Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain-machine interface with sensory feedback would provide the user with sensation about movement, pressure, curvature, texture, etc. Despite advances in neural interfaces that allow for complex stimulation patterns (e.g., multisite stimulation or the possibility of targeting a precise neural ensemble), a key question remains: How can we best exploit the potential of these technologies? The increasing number of electrodes coupled with more parameters being explored leads to an exponential increase in the number of possible combinations, making a brute-force approach, such as systematic search, impractical. This Perspective outlines three different optimization frameworks-namely, the explicit, physiological, and self-optimized methods-allowing one to potentially converge faster toward effective parameters. Although our focus will be on the somatosensory system, these frameworks are flexible and applicable to various sensory systems (e.g., vision) and stimulator types.},
}
@article {pmid40399603,
year = {2025},
author = {Wang, Y and Fukuma, R and Seymour, B and Yang, H and Kishima, H and Yanagisawa, T},
title = {Neurofeedback modulation of insula activity via MEG-based brain-machine interface: a double-blind randomized controlled crossover trial.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {770},
pmid = {40399603},
issn = {2399-3642},
support = {JP19dm0307008//Japan Agency for Medical Research and Development (AMED)/ ; 19dm0207070h//Japan Agency for Medical Research and Development (AMED)/ ; JP24wm0625517//Japan Agency for Medical Research and Development (AMED)/ ; JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; JP20H05705//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 22H04998//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 214251/Z/18/Z//Wellcome Trust (Wellcome)/ ; EP/W03509X/1//DH | National Institute for Health Research (NIHR)/ ; 203316//DH | National Institute for Health Research (NIHR)/ ; },
mesh = {Humans ; *Neurofeedback/methods ; *Magnetoencephalography/methods ; Double-Blind Method ; Male ; Cross-Over Studies ; *Brain-Computer Interfaces ; Female ; Adult ; Young Adult ; *Insular Cortex/physiology ; Pain Threshold/physiology ; *Cerebral Cortex/physiology ; },
abstract = {Insula activity has often been linked to pain perception, making it a potential target for therapeutic neuromodulation strategies such as neurofeedback. However, it is not known whether insula activity is under cognitive control and, if so, whether this activity is consequently causally related to pain. Here, we conducted a double-blind randomized controlled crossover trial to test the modulation of insula activity and pain thresholds using neurofeedback training. Nineteen healthy subjects underwent neurofeedback training for upmodulation and downmodulation of right insula activity using our magnetoencephalography (MEG)-based brain-machine interface. We observed significant differences in insula activity between the upmodulation and downmodulation training sessions. Furthermore, resting-state insula activity significantly decreased following downmodulation training compared to following upmodulation training. Compared with upmodulation training, downmodulation training was also associated with increased pain thresholds, albeit with no significant interaction effect. These findings show that humans can cognitively modulate insula activity as a potential route to develop therapeutic MEG neurofeedback systems for clinical testing. However, the present findings do not provide direct evidence of a causal link between modulation of insula activity and changes in pain thresholds.},
}
@article {pmid39818881,
year = {2025},
author = {Valle, G and Alamri, AH and Downey, JE and Lienkämper, R and Jordan, PM and Sobinov, AR and Endsley, LJ and Prasad, D and Boninger, ML and Collinger, JL and Warnke, PC and Hatsopoulos, NG and Miller, LE and Gaunt, RA and Greenspon, CM and Bensmaia, SJ},
title = {Tactile edges and motion via patterned microstimulation of the human somatosensory cortex.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {315-322},
pmid = {39818881},
issn = {1095-9203},
support = {R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; Bionics ; Cervical Cord/injuries ; *Deep Brain Stimulation/methods ; Hand/physiology ; *Somatosensory Cortex/physiology ; *Touch/physiology ; *Touch Perception/physiology ; Trauma, Nervous System/physiopathology/therapy ; *Brain-Computer Interfaces ; },
abstract = {Intracortical microstimulation (ICMS) of somatosensory cortex evokes tactile sensations whose properties can be systematically manipulated by varying stimulation parameters. However, ICMS currently provides an imperfect sense of touch, limiting manual dexterity and tactile experience. Leveraging our understanding of how tactile features are encoded in the primary somatosensory cortex (S1), we sought to inform individuals with paralysis about local geometry and apparent motion of objects on their skin. We simultaneously delivered ICMS through electrodes with spatially patterned projected fields (PFs), evoking sensations of edges. We then created complex PFs that encode arbitrary tactile shapes and skin indentation patterns. By delivering spatiotemporally patterned ICMS, we evoked sensation of motion across the skin, the speed and direction of which could be controlled. Thus, we improved individuals' tactile experience and use of brain-controlled bionic hands.},
}
@article {pmid40398442,
year = {2025},
author = {Peterson, V and Spagnolo, V and Galván, CM and Nieto, N and Spies, R and Milone, DH},
title = {Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addb7a},
pmid = {40398442},
issn = {1741-2552},
abstract = {OBJECTIVE: Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.<br><br>APPROACH: Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.<br><br>MAIN RESULTS: Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.<br><br>SIGNIFICANCE: This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.},
}
@article {pmid40398440,
year = {2025},
author = {Van der Eerden, JHM and Liu, PC and Villalobos, J and Yanagisawa, T and Grayden, DB and John, SE},
title = {Decoding cortical responses from visual input using an endovascular brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/addb7c},
pmid = {40398440},
issn = {1741-2552},
abstract = {Implantable neural interfaces enable recording of high-quality brain signals that can improve our understanding of brain function. This work examined the feasibility of using a minimally invasive endovascular neural interface (ENI) to record interpretable cortical activity from the visual cortex. Approach: A sheep model (n = 5) was used to record and decode visually evoked potentials from the cortex both with an ENI and a subdural electrode grid. Sets of distinct experimental visual stimuli were presented to attempt decoding from the recorded cortical potentials, using perceptual categories of colour, contrast, movement direction orientation, spatial frequency and temporal frequency. Decoding performances are presented as accuracy scores from K-fold cross-validation of a stratified random forest classification model. The study compared the signal quality and decoding performance between the ENI and electrocorticography electrodes. Main results: Recordings from the ENI array resulted in lower decoding performances than the electrocorticography array, but the classification scores were significantly above chance in the stimuli categories of colour, contrast, direction and temporal frequency. This study is the first report of visually evoked neural activity using a minimally-invasive ENI. Significance: Overall, the results show that implantable macro-electrodes yield sufficient neural signal definition to discern primary visual percepts, using both endo-vascular and intracranial surgical placements.},
}
@article {pmid40398391,
year = {2025},
author = {Huang, W and Shu, N},
title = {AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders.},
journal = {Cell reports. Medicine},
volume = {6},
number = {5},
pages = {102132},
doi = {10.1016/j.xcrm.2025.102132},
pmid = {40398391},
issn = {2666-3791},
mesh = {Humans ; *Precision Medicine/methods ; *Multimodal Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Artificial Intelligence ; *Neuroimaging/methods ; },
abstract = {Neuropsychiatric disorders have complex pathological mechanism, pronounced clinical heterogeneity, and a prolonged preclinical phase, which presents a challenge for early diagnosis and development of precise intervention strategies. With the development of large-scale multimodal neuroimaging datasets and advancement of artificial intelligence (AI) algorithms, the integration of multimodal imaging with AI techniques has emerged as a pivotal avenue for early detection and tailoring individualized treatment for neuropsychiatric disorders. To support these advances, in this review, we outline multimodal neuroimaging techniques, AI methods, and strategies for multimodal data fusion. We highlight applications of multimodal AI based on neuroimaging data in precision medicine for neuropsychiatric disorders, discussing challenges in clinical adoption, their emerging solutions, and future directions.},
}
@article {pmid40398228,
year = {2025},
author = {Xiao, S and Huang, X and He, X and Chen, Z and Li, X and Wei, X and Liu, Q and Dong, H and Zeng, X and Bai, W},
title = {Interactions between curcumin and fish-/bovine-derived (type I and II) collagens: Preparation of nanoparticle and their application in Pickering emulsions.},
journal = {Food chemistry},
volume = {487},
number = {},
pages = {144781},
doi = {10.1016/j.foodchem.2025.144781},
pmid = {40398228},
issn = {1873-7072},
abstract = {This study aims to elucidate the interaction mechanisms between curcumin (Cur) and four collagen subtypes (fish type I [FCI], bovine type I [BCI], fish type II [FCII], bovine type II [BCII]), with parallel characterization of the structural and functional attributes of their derived nanoparticles. Type I Collagen/Cur nanoparticles exhibited superior solution stability compared to type II. Cur binding significantly enhanced the surface hydrophobicity, absolute ζ potential, and surface tension of collagen, while reduced dynamic interfacial tension. The binding type of Cur to collagen was static, and binding process was enthalpy-driven exothermic reaction. Molecular dynamics simulations revealed that hydrophobic interactions, hydrogen bonds, and electrostatic forces dominated the binding process. The binding affinity followed the order: FCI/Cur > BCI/Cur > FCII/Cur > BCII/Cur. The binding sites of Cur to type I collagen and type II collagen were around Ser129-Glu135 and Asn179-Ser183 residues. Collagen/Cur nanoparticle stabilized emulsions and improved oxidative stability and storage modulus.},
}
@article {pmid40395924,
year = {2025},
author = {Russell, M and Hincks, S and Wang, L and Babar, A and Chen, Z and White, Z and Jacob, RJK},
title = {Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1550629},
pmid = {40395924},
issn = {2673-6195},
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has proven in recent time to be a reliable workload-detection tool, usable in real-time implicit Brain-Computer Interfaces. But what can be done in terms of application of neural measurements of the prefrontal cortex beyond mental workload? We trained and tested a first prototype example of a memory prosthesis leveraging a real-time implicit fNIRS-based BCI interface intended to present information appropriate to a user's current brain state from moment to moment. Our prototype implementation used data from two tasks designed to interface with different brain networks: a creative visualization task intended to engage the Default Mode Network (DMN), and a complex knowledge-worker task to engage the Dorsolateral Prefrontal Cortex (DLPFC). Performance of 71% from leave-one-out cross-validation across participants indicates that such tasks are differentiable, which is promising for the development of future applied fNIRS-based BCI systems. Further, analyses within lateral and medial left prefrontal areas indicates promising approaches for future classification.},
}
@article {pmid40395688,
year = {2025},
author = {Tibermacine, IE and Russo, S and Citeroni, F and Mancini, G and Rabehi, A and Alharbi, AH and El-Kenawy, EM and Napoli, C},
title = {Adversarial denoising of EEG signals: a comparative analysis of standard GAN and WGAN-GP approaches.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1583342},
pmid = {40395688},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalography (EEG) signals frequently contain substantial noise and interference, which can obscure clinically and scientifically relevant features. Traditional denoising approaches, such as linear filtering or wavelet thresholding, often struggle with nonlinear or time-varying artifacts. In response, the present study explores a Generative Adversarial Network (GAN) framework to enhance EEG signal quality, focusing on two variants: a conventional GAN model and a Wasserstein GAN with Gradient Penalty (WGAN-GP).<br><br>METHODS: Data were obtained from two distinct EEG datasets: a "healthy" set of 64-channel recordings collected during various motor/imagery tasks, and an "unhealthy" set of 18-channel recordings from individuals with orthopedic impairments. Both datasets underwent comprehensive preprocessing, including band-pass filtering (8-30 Hz), channel standardization, and artifact trimming. The training stage involved adversarial learning, in which a generator sought to reconstruct clean EEG signals while a discriminator (or critic in the case of WGAN-GP) attempted to distinguish between real and generated signals. The model evaluation was conducted using quantitative metrics such as signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), correlation coefficient, mutual information, and dynamic time warping (DTW) distance.<br><br>RESULTS: Experimental findings indicate that adversarial learning substantially improves EEG signal fidelity across multiple quantitative metrics. Specifically, WGAN-GP achieved an SNR of up to 14.47 dB (compared to 12.37 dB for the standard GAN) and exhibited greater training stability, as evidenced by consistently lower relative root mean squared error (RRMSE) values. In contrast, the conventional GAN model excelled in preserving finer signal details, reflected in a PSNR of 19.28 dB and a correlation coefficient exceeding 0.90 in several recordings. Both adversarial frameworks outperformed classical wavelet-based thresholding and linear filtering methods, demonstrating superior adaptability to nonlinear distortions and dynamic interference patterns in EEG time-series data.<br><br>DISCUSSION: By systematically comparing standard GAN and WGAN-GP architectures, this study highlights a practical trade-off between aggressive noise suppression and high-fidelity signal reconstruction. The demonstrated improvements in signal quality underscore the promise of adversarially trained models for applications ranging from basic neuroscience research to real-time brain-computer interfaces (BCIs) in clinical or consumer-grade settings. The results further suggest that GAN-based frameworks can be easily scaled to next-generation wireless networks and complex electrophysiological datasets, offering robust and dynamic solutions to long-standing challenges in EEG denoising.},
}
@article {pmid40395354,
year = {2025},
author = {Zargarian, SS and Rinoldi, C and Ziai, Y and Zakrzewska, A and Fiorelli, R and Gazińska, M and Marinelli, M and Majkowska, M and Hottowy, P and Mindur, B and Czajkowski, R and Kublik, E and Nakielski, P and Lanzi, M and Kaczmarek, L and Pierini, F},
title = {Chronic Probing of Deep Brain Neuronal Activity Using Nanofibrous Smart Conducting Hydrogel-Based Brain-Machine Interface Probes.},
journal = {Small science},
volume = {5},
number = {5},
pages = {2400463},
pmid = {40395354},
issn = {2688-4046},
abstract = {The mechanical mismatch between microelectrode of brain-machine interfaces (BMIs) and soft brain tissue during electrophysiological investigations leads to inflammation, glial scarring, and compromising performance. Herein, a nanostructured, stimuli-responsive, conductive, and semi-interpenetrating polymer network hydrogel-based coated BMIs probe is introduced. The system interface is composed of a cross-linkable poly(N-isopropylacrylamide)-based copolymer and regioregular poly[3-(6-methoxyhexyl)thiophene] fabricated via electrospinning and integrated into a neural probe. The coating's nanofibrous architecture offers a rapid swelling response and faster shape recovery compared to bulk hydrogels. Moreover, the smart coating becomes more conductive at physiological temperatures, which improves signal transmission efficiency and enhances its stability during chronic use. Indeed, detecting acute neuronal deep brain signals in a mouse model demonstrates that the developed probe can record high-quality signals and action potentials, favorably modulating impedance and capacitance. Evaluation of in vivo neuronal activity and biocompatibility in chronic configuration shows the successful recording of deep brain signals and a lack of substantial inflammatory response in the long-term. The development of conducting fibrous hydrogel bio-interface demonstrates its potential to overcome the limitations of current neural probes, highlighting its promising properties as a candidate for long-term, high-quality detection of neuronal activities for deep brain applications such as BMIs.},
}
@article {pmid40395337,
year = {2025},
author = {Yao, J and Zhou, Z and Tong, Q and Li, L and Wei, J and Lu, J and Hu, S and Bao, A and He, H},
title = {Magnetic resonance imaging of postmortem human brain specimens: methodological considerations and prospects in psychoradiology.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf012},
pmid = {40395337},
issn = {2634-4416},
abstract = {Ex vivo magnetic resonance imaging (MRI) has revolutionized psychoradiological research by enabling detailed structural and pathological assessments of the brain in conditions ranging from psychiatric disorders to neurodegenerative diseases. By providing high-resolution images of postmortem brain tissue, ex vivo MRI overcomes several limitations inherent in in vivo imaging, offering unparalleled insights into the underlying pathophysiology of mental disorders. This review critically summarizes the state-of-the-art ex vivo MRI methodologies for neuroanatomical mapping and pathological characterization in psychoradiology, while also establishing standardized specimen processing protocols. Furthermore, we explore the prospects of application in ex vivo MRI in schizophrenia, major depressive disorder and bipolar disorder, highlighting its role in understanding neuroanatomical alterations, disease progression, and the validation of in vivo neuroimaging biomarkers.},
}
@article {pmid40395088,
year = {2025},
author = {Zhang, S and Gu, J and Yang, Y and Li, J and Ni, L},
title = {Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.},
journal = {Brain and behavior},
volume = {15},
number = {5},
pages = {e70451},
doi = {10.1002/brb3.70451},
pmid = {40395088},
issn = {2162-3279},
support = {2020Z388//Jiangsu Postdoctoral Research Foundation/ ; //Top Talent Support Program for young and middle-aged people of the Wuxi Health Committee/ ; M202033//Wuxi Health Commission Scientific Research Project/ ; 24CC00903//Beijing Academy of Science and Technology Think Tank Research Project/ ; ZYYB05//Wuxi Administration of Traditional Chinese Medicine/ ; },
mesh = {*Bibliometrics ; Humans ; *Biomedical Research/trends ; *Neurosciences/trends ; *Brain/physiology ; United States ; China ; },
abstract = {BACKGROUND: Brain science research is considered the crown jewel of 21st-century scientific research; the United States, the United Kingdom, and Japan have elevated brain science research to a national strategic level. This study employs bibliometric analysis and knowledge graph visualization to map global trends, research hotspots, and collaborative networks in brain science, providing insights into the field's evolving landscape and future directions.<br><br>METHODS: We analyzed 13,590 articles (1990-2023) from the Web of Science Core Collection using CiteSpace and VOSviewer. Metrics included publication volume, co-authorship networks, citation patterns, keyword co-occurrence, and burst detection. Analytical tools such as VOSviewer, CiteSpace, and online bibliometric platforms were employed to facilitate this investigation.<br><br>RESULTS: The United States, China, and Germany dominated research output, with China's publications rising from sixth to second globally post-2016, driven by national initiatives like the China Brain Project. However, China exhibited limited international collaboration compared to the United States and European Union. Key journals included Human Brain Mapping and Journal of Neural Engineering, while emergent themes centered on "task analysis," "deep learning," and "brain-computer interfaces" (BCIs). Research clusters revealed three focal areas: (1) Brain Exploration (e.g., fMRI, diffusion tensor imaging), (2) Brain Protection (e.g., stroke rehabilitation, amyotrophic lateral sclerosis therapies), and (3) Brain Creation (e.g., neuromorphic computing, BCIs integrated with AR/VR). Despite China's high output, its influence lagged in highly cited scholars, reflecting a "quantity-over-quality" challenge.<br><br>CONCLUSION: Brain science research is in a golden period of development. This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in brain science. It reveals current research frontiers and crucial directions, offering a strategic roadmap for researchers and policymakers to navigate countries when planning research layouts.},
}
@article {pmid40395013,
year = {2025},
author = {Pyo, YW and Kim, H and Park, HG},
title = {Graphene-Integrated Ultrathin Neural Probe for Multiregional Cortical Recordings.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c03145},
pmid = {40395013},
issn = {1936-086X},
abstract = {Electrophysiological measurement techniques are essential for understanding the functions of the central and peripheral nervous systems. Specifically, noninvasive neural probes, such as surface electrode arrays, provide stable electrophysiological recordings without eliciting an immunological response. However, the ability to capture complex interactions across multiple brain regions is limited by their localized recording site. Here, we present the "large-area NeuroWeb (LNW)", an ultrathin, minimally invasive neural probe designed for extensive cortical recording and stimulation. LNW consists of four recording areas, each containing 16-channel platinum electrodes interconnected by graphene networks. In vivo experiments of the mouse brain exhibit stable, high-quality single-unit spike recordings for up to 7 days post-surgery. Simultaneous high-resolution neural activity recordings are performed across left/right somatosensory cortex and cerebellum, simplifying the experimental procedure by eliminating the necessity for multiple synchronized probes, thus reducing tissue displacement and inflammation. Furthermore, whisker and electrical stimulations demonstrate that the LNW has precise and bidirectional connections with neurons for reliable, region-specific signal acquisition and activation. These findings highlight the capability of LNW to facilitate comprehensive and accurate mapping of neuronal dynamics, thereby advancing brain-machine interfaces and neural prostheses.},
}
@article {pmid40393988,
year = {2025},
author = {Garro, F and Fenoglio, E and Ceroni, I and Forsiuk, I and Canepa, M and Mozzon, M and Bruschi, A and Zippo, F and Laffranchi, M and De Michieli, L and Buccelli, S and Chiappalone, M and Semprini, M},
title = {An EEG-EMG dataset from a standardized reaching task for biomarker research in upper limb assessment.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {831},
pmid = {40393988},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Upper Extremity/physiology ; *Electromyography ; Biomarkers ; Adult ; Movement ; },
abstract = {This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a reaching task with and without the assistance of an upper-limb exoskeleton. It was designed to explore electrophysiological biomarkers for assessing assistive technologies. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks. A custom-designed touch panel was built to standardize and simulate natural upper-limb movements relevant to daily activities. The dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. To provide an overview of data quality, we present subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency- domain decomposition for EMG. Beyond providing a methodology for evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.},
}
@article {pmid40393212,
year = {2025},
author = {Lu, Q and Yi, M and Jiang, J},
title = {Bioelectronic nose for ultratrace odor detection via brain-computer interface with olfactory bulb electrode arrays.},
journal = {Biosensors &amp; bioelectronics},
volume = {285},
number = {},
pages = {117585},
doi = {10.1016/j.bios.2025.117585},
pmid = {40393212},
issn = {1873-4235},
abstract = {Rapid and accurate detection of hazardous volatile compounds is crucial for public health and environmental safety. While conventional methods, including electronic noses, typically exhibit detection thresholds in the parts-per-million (ppm) range, many harmful substances pose risks at parts-per-billion (ppb) concentrations or lower. To address this challenge, we leverage the exceptional sensitivity of the mammalian olfactory system, specifically that of Rattus norvegicus (lab rat), which has evolved to detect and discriminate a vast array of odors at extremely low concentrations. In this study, we developed a novel bio-hybrid system that integrates behavioral training with in vivo electrophysiological recordings from the olfactory bulb (OB). Rats were operantly conditioned to recognize target odors, namely TNT (2,4,6-trinitrotoluene), TNP (2,4,6-trinitrophenol), and chlorine gas (Cl2), at ppb levels. Concurrent with behavioral testing, we recorded neural activity from both the dorsal and ventral OB using a customdesigned, multi-channel electrode array optimized for the rat OB's cytoarchitecture. Electrophysiological data were decoded using a Support Vector Machine algorithm, achieving a mean accuracy of over 90 % in classifying odor identity at ppb concentrations based on OB activity patterns. These results demonstrate the feasibility of utilizing a brain-computer interface with the olfactory system to achieve ultratrace detection of hazardous substances. This bio-hybrid approach offers significantly enhanced sensitivity compared to existing electronic nose technologies, paving the way for highly effective environmental and biomedical sensing applications.},
}
@article {pmid40390719,
year = {2025},
author = {Leung, ES and Mofatteh, M},
title = {Investigating the Feasibility and Safety of Osseointegration With Neural Interfaces for Advanced Prosthetic Control.},
journal = {Cureus},
volume = {17},
number = {4},
pages = {e82567},
pmid = {40390719},
issn = {2168-8184},
abstract = {Osseointegrated neural interfaces (ONI), particularly in conjunction with peripheral nerve interfaces (PNIs), have emerged as a promising advancement for intuitive neuroprosthetics. PNIs can decode neural signals and allow precise prosthetic movement control and bidirectional communication for haptic feedback, while osseointegration can address limitations of traditional socket-based prosthetics, such as poor stability, limited dexterity, and lack of sensory feedback. This review explores advancements in ONIs, including screw-fit and press-fit systems and their integration with PNIs for bidirectional communication. ONIs integrated with PNIs (OIPNIs) have shown improvements in signal fidelity, motor control, and sensory feedback compared to popular surface electromyography (sEMG) systems. Additionally, emerging technologies such as hybrid electrode designs (e.g., cuff and sieve electrode (CASE)) and regenerative peripheral nerve interfaces (RPNIs) show potential for enhancing selectivity and reducing complications such as micromotion and scarring. Despite these advances, challenges remain, including infection risk, electrode degradation, and variability in long-term signal stability. Osseointegration combined with advanced neural interfaces represents a transformative approach to prosthetic control, offering more natural and intuitive movement with sensory feedback. Further research is needed to address long-term biocompatibility, reduce surgical invasiveness, and explore emerging technologies such as machine learning for personalized ONI designs. The findings of this review underscore the potential of ONIs to enhance embodiment and quality of life for amputees and highlight current pitfalls and possible areas of improvement and future research.},
}
@article {pmid40389429,
year = {2025},
author = {Karpowicz, BM and Ali, YH and Wimalasena, LN and Sedler, AR and Keshtkaran, MR and Bodkin, K and Ma, X and Rubin, DB and Williams, ZM and Cash, SS and Hochberg, LR and Miller, LE and Pandarinath, C},
title = {Stabilizing brain-computer interfaces through alignment of latent dynamics.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4662},
pmid = {40389429},
issn = {2041-1723},
mesh = {*Brain-Computer Interfaces ; Animals ; *Motor Cortex/physiology ; Macaca mulatta ; Neural Networks, Computer ; Male ; Movement/physiology ; Humans ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high-performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks- to months-long timescales without any supervised recalibration.},
}
@article {pmid40387950,
year = {2025},
author = {Wu, P and Zhu, J and He, Q and Wang, Z and Shi, L},
title = {Visual numerical cognition in pigeons: conformity to the Weber-Fechner law.},
journal = {Animal cognition},
volume = {28},
number = {1},
pages = {39},
pmid = {40387950},
issn = {1435-9456},
mesh = {Animals ; *Columbidae/physiology ; *Cognition ; *Visual Perception ; Male ; },
abstract = {As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber-Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber-Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution.},
}
@article {pmid40382989,
year = {2025},
author = {Wang, T and Dai, Q and Xiong, W},
title = {Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {189},
number = {},
pages = {107573},
doi = {10.1016/j.neunet.2025.107573},
pmid = {40382989},
issn = {1879-2782},
abstract = {In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules.},
}
@article {pmid40382679,
year = {2025},
author = {Covelli, E and Filippi, C and Lazzerini, F and Tromboni, E and Tarentini, S and Pizzolante, S and Forli, F and Berrettini, S and Bruschini, L},
title = {Traditional and adaptive speech audiometry in single-sided deaf (SSD) subjects rehabilitated by bone conductive implants (BCI), quality of life and long-term utilization.},
journal = {Acta oto-laryngologica},
volume = {},
number = {},
pages = {1-7},
doi = {10.1080/00016489.2025.2504032},
pmid = {40382679},
issn = {1651-2251},
abstract = {BACKGROUND: Single-sided deafness (SSD) encompasses the presence of a profoundly deaf ear with a normal, contralateral one. Patients with SSD may have difficulty with speech intelligibility in noise and localizing sounds.<br><br>AIMS/OBJECTIVES: This retrospective study aims to evaluate the long-term effectiveness of bone conduction implant (BCI) in a group of patients with SSD.<br><br>MATERIAL AND METHODS: Audiologic benefit was assessed through conventional speech audiometry and adaptive Matrix test. Impact on quality of life was evaluated with the Glasgow Benefit Inventory (GBI) questionnaire. BCI usage data were also obtained from each subject.<br><br>RESULTS: Thirty-two patients were included. No statistically significant improvements were found at standard audiometric tests using BCI, but at Matrix test the mean SRT is reached at S/N -1.16&#x2009;dB without BCI and -2.07 with BCI with a statistically significant difference (p&#x2009;=&#x2009;0.026). The mean GBI score was 25.12, ranging from -8.3 to 47.2. Ten subjects (31%) discontinued the BCI use overtime.<br><br>CONCLUSIONS AND SIGNIFICANCE: Benefit assessment of BCI in SSD recipients can be difficult. Adaptive audiometric test could be useful. Quality of life measures seem to suggest potential 'beyond-auditory' benefits. SSD recipients can be inconsistent users of BCI.},
}
@article {pmid40382338,
year = {2025},
author = {Zhou, S and Zhu, Y and Du, A and Niu, S and Du, Y and Yang, Y and Chen, W and Du, S and Sun, L and Liu, Y and Wu, H and Lou, H and Li, XM and Duan, S and Yang, H},
title = {A midbrain circuit mechanism for noise-induced negative valence coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4610},
pmid = {40382338},
issn = {2041-1723},
support = {LR24C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Ventral Tegmental Area/physiology/cytology ; *Noise ; Mice ; *Inferior Colliculi/physiology/cytology ; GABAergic Neurons/physiology/metabolism ; Male ; Mice, Inbred C57BL ; Acoustic Stimulation ; *Emotions/physiology ; Geniculate Bodies/physiology ; *Mesencephalon/physiology ; Auditory Pathways/physiology ; Optogenetics ; Auditory Perception/physiology ; Female ; Dopamine/metabolism ; Neurons/physiology ; },
abstract = {Unpleasant sounds elicit a range of negative emotional reactions, yet the underlying neural mechanisms remain largely unknown. Here we show that glutamatergic neurons in the central inferior colliculus (CIC[glu]) relay noise information to GABAergic neurons in the ventral tegmental area (VTA[GABA]) via the cuneiform nucleus (CnF), encoding negative emotions in mice. In contrast, the CIC[glu]→medial geniculate (MG) canonical auditory pathway processes salient stimuli. By combining viral tracing, calcium imaging, and optrode recording, we demonstrate that the CnF acts downstream of CIC[glu] to convey negative valence to the mesolimbic dopamine system by activating VTA[GABA] neurons. Optogenetic or chemogenetic inhibition of any connection within the CIC[glu]→CnF[glu] → VTA[GABA] circuit, or direct excitation of the mesolimbic dopamine (DA) system is sufficient to alleviate noise-induced negative emotion perception. Our findings highlight the significance of the CIC[glu]→CnF[glu] → VTA[GABA] circuit in coping with acoustic stressors.},
}
@article {pmid40381460,
year = {2025},
author = {Qi, G and Zhao, S and Yu, J and Li, P and Guan, W},
title = {Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver's EEG cognitive data.},
journal = {Accident; analysis and prevention},
volume = {219},
number = {},
pages = {108102},
doi = {10.1016/j.aap.2025.108102},
pmid = {40381460},
issn = {1879-2057},
abstract = {Without human participation in driving operations, the adoption of autonomous driving (AD) technology greatly enhances driving safety by reducing human errors. Even though AD can handle common scenarios properly, some exceptions still call for the human takeover with AD failing to engage due to the incomprehensible or intensely conflict situations that rarely occur. To help AD understand and recognize the disengagement scenarios effectively, this paper incorporates the human electroencephalogram (EEG) cognitive data into modeling and proposes a transfer learning framework to let AD absorb the integrative knowledge from the manual driving (MD). Several disengagement scenarios are designed using a driving simulator and EEG data are collected from both "drivers" in MD and "supervisors" in AD. A conditional maximum mean discrepancy (CMMD) function is introduced to identify the common brain activity characteristics, allowing the recognition model to be transferred from the cognitively demanding domain of MD to the less demanding domain of AD. The results indicate that the proposed model can achieve an 80 % recognition rate for typical disengagement scenarios, such as static obstacles, intersection conflict and vehicle cut-in, using only 30 % of AD training labels. The transferable common feature space from EEG data improves the recognition accuracy by 21.2 % compared with the model only using AD domain data. By accurately recognizing the type of disengagement scenarios, the AD system can activate appropriate safety mechanisms or provide more explicit takeover prompts, which could effectively reduce the risk of accidents due to delayed or incorrect takeovers.},
}
@article {pmid40380329,
year = {2025},
author = {Chen, W and Chen, H and Jiang, W and Chen, C and Xu, M and Ruan, H and Chen, H and Yu, Z and Chen, S},
title = {Heart rate variability and heart rate asymmetry in adolescents with major depressive disorder during nocturnal sleep period.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {497},
pmid = {40380329},
issn = {1471-244X},
support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology ; *Heart Rate/physiology ; Adolescent ; Male ; Female ; Electrocardiography ; *Sleep/physiology ; Case-Control Studies ; },
abstract = {BACKGROUND: Although reduced heart rate variability (HRV) has been observed in adolescents with major depressive disorder (MDD), substantial between-study heterogeneity and conflicting outcomes exist. Moreover, few studies have investigated heart rate asymmetry (HRA) features despite the high sensitivity of nonlinear indices to heart rate fluctuations. This study aimed to investigate the variations in HRV measures, especially the nonlinear features of HRA, among adolescents with MDD during the nocturnal sleep period.<br><br>METHODS: Adolescents with MDD and healthy controls completed the clinical assessment of depressive symptom severity and sleep quality followed by a three-night sleep electrocardiogram (ECG) monitoring. Traditional time-domain and frequency-domain HRV measures, nonlinear HRA measures, and the prevalence of different HRA forms and HRA compensation were calculated.<br><br>RESULTS: A total of 61 participants with 154 nocturnal ECG time series were available for analysis. Vagally-mediated HRV measures, such as RMSSD, PNN50, and HF, as well as C1d were statistically lower in clinically depressed adolescents compared with healthy controls, whereas C2d was significantly higher. A substantial decrease in the prevalence of short-term HRA, long-term HRA, and the corresponding compensation effect were also observed. In contrast to the medium to large effect sizes observed in traditional HRV indices, nonlinear HRA features showed extremely large effect sizes in discriminating MDD (C1d: Cohen's d= -&#x2009;1.38; C2d: Cohen's d&#x2009;=&#x2009;1.11), and exhibited a statistical correlation with the severity of depression (C1d: rho = -&#x2009;0.269; C2d: rho&#x2009;=&#x2009;0.243). Moreover, there were no significant differences in the distributions of nocturnal HRA measures collected over various nights.<br><br>CONCLUSION: Adolescents with MDD suffered a significant decrease in vagal tone compared to healthy controls, and the features focusing on the directionality of heart rate variations may provide further information on cardiac autonomic activity associated with depression.},
}
@article {pmid40379686,
year = {2025},
author = {Bom, MS and Brak, AMA and Raemaekers, M and Ramsey, NF and Vansteensel, MJ and Branco, MP},
title = {Large-scale fMRI dataset for the design of motor-based Brain-Computer Interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {804},
pmid = {40379686},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Child ; Adolescent ; Aged ; Adult ; Aged, 80 and over ; Middle Aged ; Young Adult ; Male ; Female ; },
abstract = {Functional Magnetic Resonance Imaging (fMRI) data is commonly used to map sensorimotor cortical organization and to localise electrode target sites for implanted Brain-Computer Interfaces (BCIs). Functional data recorded during motor and somatosensory tasks from both adults and children specifically designed to map and localise BCI target areas throughout the lifespan is rare. Here, we describe a large-scale dataset collected from 155 human participants while they performed motor and somatosensory tasks involving the fingers, hands, arms, feet, legs, and mouth region. The dataset includes data from both adults and children (age range: 6-89 years) performing a set of standardized tasks. This dataset is particularly relevant to study developmental patterns in motor representation on the cortical surface and for the design of paediatric motor-based implanted BCIs.},
}
@article {pmid40378852,
year = {2025},
author = {Ding, W and Liu, A and Cheng, L and Chen, X},
title = {Data augmentation using masked principal component representation for deep learning-based SSVEP-BCIs.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add9d1},
pmid = {40378852},
issn = {1741-2552},
abstract = {OBJECTIVE: Data augmentation has been demonstrated to improve the classification accuracy of deep learning (DL) models in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), particularly when dealing with limited electroencephalography (EEG) data. However, current data augmentation methods often rely on signal-level manipulations, which may result in significant distortion of EEG signals. To overcome this limitation, this study proposes a component-level data augmentation method called Masked Principal Component Representation (MPCR).<br><br>APPROACH: MPCR utilizes a principal component-based reconstruction approach, integrating a random masking strategy applied to principal component representations. Specifically, certain principal components are randomly selected and set to zero, thereby introducing random perturbations in the reconstructed samples. Furthermore, reconstructing samples via linear combinations of the remaining components effectively preserves the primary inherent structure of EEG signals. By expanding the input space covered by training samples, MPCR helps the trained model learn more robust features. To validate the effectiveness of MPCR, experiments are performed on two widely utilized public datasets.<br><br>MAIN RESULTS: Experimental results indicate that MPCR substantially enhances classification accuracy across diverse DL models. Additionally, compared to various state-of-the-art data augmentation approaches, MPCR demonstrates both greater performance and high compatibility.<br><br>SIGNIFICANCE: This study proposes a simple yet effective component-level data augmentation method, contributing valuable insights for advancing data augmentation methods in EEG-based BCIs.},
}
@article {pmid40377015,
year = {2025},
author = {Yang, T and Zhang, D and Huang, H and Liu, F and Wu, J and Ma, X and Liu, S and Huang, M and Zhou, YD and Shen, Y},
title = {Astrocytic mGluR5-dependent calcium hyperactivity promotes amyloid-β pathology and cognitive impairment.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf186},
pmid = {40377015},
issn = {1460-2156},
abstract = {Astrocytic dysfunction is a crucial factor for the pathogenesis of Alzheimer's disease. Metabotropic glutamate receptor 5 (mGluR5) is ubiquitously expressed in the brain and is a key molecule that regulates synaptic transmission and plasticity. It has been shown that mGluR5 is elevated in astrocytes in Alzheimer's disease. However, it remains elusive how astrocytic mGluR5 contributes to the pathogenesis of Alzheimer's disease. Here, we first quantified a high expression level of astrocytic mGluR5 in the hippocampus of Alzheimer's disease brains and demonstrated that the expression of astrocytic mGluR5 was positively correlated with Alzheimer's disease progression in both humans and mice. Upregulating astrocytic mGluR5 in the CA1 area at an early stage accelerated, whereas downregulating these receptors rescued, Aβ pathology and cognitive impairment in Alzheimer's disease mice. Moreover, the activation of mGluR5 led to calcium hyperactivity in astrocytes, causing Aβ pathology progression due to dysregulated Aβ uptake and degradation in astrocytes. Importantly, attenuating astrocytic calcium hyperactivity in the hippocampal CA1 area in the prodromal phase ameliorated Aβ pathology and cognitive defects in Alzheimer's disease mice. Our findings thus reveal a fundamental contribution of astrocytic mGluR5 in presymptomatic Alzheimer's disease that may serve as a potential diagnostic and therapeutic target for early Alzheimer's disease pathogenesis.},
}
@article {pmid40374051,
year = {2025},
author = {Wang, M and Wang, Y and Yang, Y},
title = {Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds.},
journal = {NeuroImage},
volume = {314},
number = {},
pages = {121243},
doi = {10.1016/j.neuroimage.2025.121243},
pmid = {40374051},
issn = {1095-9572},
abstract = {Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions.},
}
@article {pmid40373768,
year = {2025},
author = {Luo, T and Liu, C and Cheng, T and Zhao, GQ and Huang, Y and Luan, JY and Guo, J and Liu, X and Wang, YF and Dong, Y and Xiao, Y and He, E and Sun, RZ and Chen, X and Chen, J and Ma, J and Megason, S and Ji, J and Xu, PF},
title = {Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers.},
journal = {Cell stem cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.stem.2025.04.011},
pmid = {40373768},
issn = {1875-9777},
abstract = {Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion ("ORDER"), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy.},
}
@article {pmid40372852,
year = {2025},
author = {Lo, YT and Maggi, A and Wu, K and Zhong, H and Choi, W and Nguyen, TD and Abedi, A and Agyeman, K and Sakellaridi, S and Edgerton, VR and Kreydin, E and Lee, D and Sideris, C and Liu, CY and Christopoulos, VN},
title = {Exploring the Feasibility of Bidirectional Spinal Cord Machine Interface through Sensing and Stimulation of Axonal Bundles.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3570324},
pmid = {40372852},
issn = {1558-0210},
abstract = {Spinal cord injury (SCI) patients experience long-term deficits in motor and sensory functions. While brain-machine interface (BMI) has shown great promise for restoring neurological functions after SCI, spinal cord-machine interface (SCMI) offers unique advantages, such as more defined somatotopy and the compact organization of neural elements in the spinal cord. In the current study, we aim to demonstrate the feasibility of sensing and evoking compound action potentials (CAPs) via electrode implantation in spinal cord axonal bundles, an essential prerequisite for advancing toward SCMI development. To do so, we designed microelectrode arrays (MEA) optimized for recording and stimulation in the spinal cord. For sensory mapping, the MEAs were inserted into the lumbar dorsal column (i.e., the fasciculus gracilis) to determine somatotopic representations corresponding to tactile stimulation across lower body regions and assess proprioceptive signals with varying hip positions. For stimulations, at the L3 level, we delivered electrical pulses both rostrally, along ascending afferent tracts (dorsal column), and caudally, down descending corticospinal tract. We successfully captured axonal CAPs from the dorsal columns with high spatial precision that corresponded to known dermatomal somatotopy. Proprioceptive changes produced by abduction at the hip resulted in modulation of discharge frequency in the dorsal column axons. We demonstrated that stimulation pulses emitted by a caudally placed electrode could be propagated up the ascending fibers and be intercepted by a rostrally placed electrode array along the same axonal tracts. We also confirmed that electrical pulses can be directed down descending corticospinal tracts resulting in specific activations of lower limb muscles. These findings set a critical groundwork for developing closed-loop, bidirectional SCMI systems capable of sensing and modulating spinal cord activity.},
}
@article {pmid40371570,
year = {2025},
author = {Ding, P and Tan, L and Pan, H and Gong, A and Nan, W and Fu, Y},
title = {The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.},
journal = {Brain connectivity},
volume = {},
number = {},
pages = {},
doi = {10.1089/brain.2024.0084},
pmid = {40371570},
issn = {2158-0022},
abstract = {Objectives: Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (PTSD). However, existing research lacks comprehensive methodologies and experimental designs. There are concerns in the field regarding the effectiveness and mechanistic interpretability of NF, prompting this study to conduct a systematic analysis of primary NF techniques and research outcomes in PTSD modulation. The study aims to explore reasons behind these concerns and propose directions for addressing them. Methods: A search conducted in the Web of Science database up to December 1, 2023, yielded 111 English articles, of which 80 were excluded based on predetermined criteria irrelevant to this study. The remaining 31 original studies were included in the literature review. A checklist was developed to assess the robustness and credibility of these 31 studies. Subsequently, these original studies were classified into electroencephalogram-based NF (EEG-NF) and functional magnetic resonance imaging-based NF (fMRI-NF) based on BCI type. Data regarding target brain regions, target signals, modulation protocols, control group types, assessment methods, data processing strategies, and reported outcomes were extracted and synthesized. Consensus theories from existing research and directions for future improvements in related studies were distilled. Results: Analysis of all included studies revealed that the average sample size of PTSD patients in EEG and fMRI NF studies was 17.4 (SD 7.13) and 14.6 (SD 6.37), respectively. Due to sample and neurofeedback training protocol constraints, 93% of EEG-NF studies and 87.5% of fMRI-NF studies used traditional statistical methods, with minimal utilization of basic machine learning (ML) methods and no studies utilizing deep learning (DL) methods. Apart from approximately 25% of fMRI NF studies supporting exploratory psychoregulatory strategies, the remaining EEG and fMRI studies lacked explicit NF modulation guidance. Only 13% of studies evaluated NF effectiveness methods involving signal classification, decoding during the NF process, and lacking in process monitoring and assessment means. Conclusion: In summary, NF holds promise as an adjunctive intervention technique for PTSD, potentially aiding in symptom alleviation for PTSD patients. However, improvements are necessary in the process evaluation mechanisms for PTSD-NF, clarity in NF modulation guidance, and development of ML/DL methods suitable for PTSD-NF with small sample sizes. To address these challenges, it is crucial to adopt more rigorous methodologies for monitoring NF, and future research should focus on the integration of advanced data analysis techniques to enhance the effectiveness and precision of PTSD-NF interventions. Impact Statement The implications of this study are to address the limited application of Neurofeedback training (NFT) in post-traumatic stress disorder (PTSD) research, where a significant portion of the approaches, foundational research, and conclusions lack consensus. There is a notable absence of retrospective statistical analyses on NFT interventions for PTSD. This study provides a comprehensive statistical analysis and discussion of existing research, offering valuable insights for future studies. The findings hold significance for researchers, clinicians, and practitioners in the field, providing a foundation for informed, evidence-based interventions for PTSD treatment.},
}
@article {pmid40370566,
year = {2025},
author = {Hong, W and Ma, H and Yang, Z and Wang, J and Peng, B and Wang, L and Du, Y and Yang, L and Zhang, L and Li, Z and Huang, H and Zhu, D and Yang, B and He, Q and Wang, J and Weng, Q},
title = {Optineurin restrains CCR7 degradation to guide type II collagen-stimulated dendritic cell migration in rheumatoid arthritis.},
journal = {Acta pharmaceutica Sinica. B},
volume = {15},
number = {3},
pages = {1626-1642},
pmid = {40370566},
issn = {2211-3835},
abstract = {Dendritic cells (DCs) serve as the primary antigen-presenting cells in autoimmune diseases, like rheumatoid arthritis (RA), and exhibit distinct signaling profiles due to antigenic diversity. Type II collagen (CII) has been recognized as an RA-specific antigen; however, little is known about CII-stimulated DCs, limiting the development of RA-specific therapeutic interventions. In this study, we show that CII-stimulated DCs display a preferential gene expression profile associated with migration, offering a new perspective for targeting DC migration in RA treatment. Then, saikosaponin D (SSD) was identified as a compound capable of blocking CII-induced DC migration and effectively ameliorating arthritis. Optineurin (OPTN) is further revealed as a potential SSD target, with Optn deletion impairing CII-pulsed DC migration without affecting maturation. Function analyses uncover that OPTN prevents the proteasomal transport and ubiquitin-dependent degradation of C-C chemokine receptor 7 (CCR7), a pivotal chemokine receptor in DC migration. Optn-deficient DCs exhibit reduced CCR7 expression, leading to slower migration in CII-surrounded environment, thus alleviating arthritis progression. Our findings underscore the significance of antigen-specific DC activation in RA and suggest OPTN is a crucial regulator of CII-specific DC migration. OPTN emerges as a promising drug target for RA, potentially offering significant value for the therapeutic management of RA.},
}
@article {pmid40369268,
year = {2025},
author = {Pan, S and Cai, Y and Liu, R and Jiang, S and Zhao, H and Jiang, J and Lin, Z and Liu, Q and Lu, H and Liang, S and Fan, W and Chen, X and Wu, Y and Wang, F and Chen, Z and Hu, R and Yang, L},
title = {Targeting 5-HT to Alleviate Dose-Limiting Neurotoxicity in Nab-Paclitaxel-Based Chemotherapy.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40369268},
issn = {1995-8218},
abstract = {Chemotherapy-induced peripheral neurotoxicity (CIPN) is a severe dose-limiting adverse event of chemotherapy. Presently, the mechanism underlying the induction of CIPN remains unclear, and no effective treatment is available. In this study, through metabolomics analyses, we found that nab-paclitaxel therapy markedly increased serum serotonin [5-hydroxtryptamine (5-HT)] levels in both cancer patients and mice compared to the respective controls. Furthermore, nab-paclitaxel-treated enterochromaffin (EC) cells showed increased 5-HT synthesis, and serotonin-treated Schwann cells showed damage, as indicated by the activation of CREB3L3/MMP3/FAS signaling. Venlafaxine, an inhibitor of serotonin and norepinephrine reuptake, was found to protect against nerve injury by suppressing the activation of CREB3L3/MMP3/FAS signaling in Schwann cells. Remarkably, venlafaxine was found to significantly alleviate nab-paclitaxel-induced CIPN in patients without affecting the clinical efficacy of chemotherapy. In summary, our study reveals that EC cell-derived 5-HT plays a critical role in nab-paclitaxel-related neurotoxic lesions, and venlafaxine co-administration represents a novel approach to treating chronic cumulative neurotoxicity commonly reported in nab-paclitaxel-based chemotherapy.},
}
@article {pmid40368962,
year = {2025},
author = {Dias, C and Sousa, T and Cruz, A and Costa, D and Mouga, S and Castelhano, J and Pires, G and Castelo-Branco, M},
title = {A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {16671},
pmid = {40368962},
issn = {2045-2322},
support = {10.54499/UI/BD/150832/2021, https://doi.org/10.54499/UI/BD/150832/2021//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; CEEC: 2021.01469.CEECIND//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PTDC/EEI-AUT/30935/2017;//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; UIDB/4950/2020, https://doi.org/10.54499/UIDB/04950/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e a Tecnologia/ ; PT/FB/BL-2018-306//Funda&#xe7;&#xe3;o Bial/ ; CAIXA Impulse 2024//'la Caixa' Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Autistic Disorder/physiopathology ; *Theta Rhythm/physiology ; Female ; Adult ; *Executive Function/physiology ; Electroencephalography ; Young Adult ; Memory, Short-Term/physiology ; *Frontal Lobe/physiopathology ; },
abstract = {Midfrontal theta oscillations have been linked to executive function, yet their role in autism-where this function is often compromised-remains unclear. We hypothesized that preparatory increases in theta power may help normalize performance in autism. To test this, we used a challenging interactive executive function task designed to impose a high working memory load and require constant error monitoring. An electroencephalogram (EEG)-based brain-computer interface (BCI) was used to maximize cognitive load and engagement. Neural activity from autistic and non-autistic adults was compared while participants were asked to mentally reverse pseudowords (engaging working memory) and write them using the BCI, which provided real-time performance feedback (maximizing error monitoring). The study focused on theta power modulation during the preparatory (pre-response) and feedback (post-response) periods but also explored the role of posterior alpha oscillations. Results showed similar task performance between groups, but distinct recruitment of brain resources, particularly during the preparatory period. The finding of an increased preparatory theta in autism favors the hypothesis of compensatory recruitment of cognitive control and attentional mechanisms to achieve accurate results.},
}
@article {pmid40367961,
year = {2025},
author = {Partovi, A and Grayden, DB and Burkitt, AN},
title = {POC-CSP: A Novel Parameterised and Orthogonally-Constrained Neural Network layer for learning Common Spatial Patterns (CSP) in EEG signals.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add8bc},
pmid = {40367961},
issn = {1741-2552},
abstract = {OBJECTIVE: Common Spatial Patterns (CSP) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal was to transform CSP into a trainable machine learning model that can learn from data, be regularized, and be integrated into end-to-end classification networks.<br><br>APPROACH: We developed a novel Parameterised and Orthogonally-Constrained Neural Network layer for learning Common Spatial Patterns (POC-CSP) that maintains CSP's mathematical properties while allowing trainable weights. The layer uses parameterization based on Lie Group theory to convert constrained optimization into unconstrained optimization, enabling integration with standard neural network training methods. We evaluated the approach on two public motor imagery datasets, focusing on both subject-specific and multi-subject paradigms.<br><br>MAIN RESULTS: POC-CSP outperformed both conventional CSP and existing neural network implementations in subject-specific classification tasks. In a novel multi-subject paradigm, POC-CSP achieved superior generalization. When fine-tuned with just 50% of a new subject's data, POC-CSP achieved 0.95 average accuracy across subjects, substantially outperforming subject-specific models trained with more data.<br><br>SIGNIFICANCE: These findings demonstrate that combining CSP's proven effectiveness with neural networks' flexibility can significantly improve EEG signal processing performance. The ability to generalize across subjects and achieve high accuracy with minimal subject-specific training data makes POC-CSP particularly valuable for practical brain-computer interface applications, where collecting large amounts of training data from each new user is often impractical or unfeasible.},
}
@article {pmid40367953,
year = {2025},
author = {Xu, X and Drougard, N and Roy, RN},
title = {Does topological data analysis work for EEG-based brain-computer interfaces?.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add8bd},
pmid = {40367953},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are systems that establish a direct communication pathway with machines through brain activity only, recorded for example via electroencephalography (EEG). Topological data analysis (TDA) extracts topological features of the shape of the data and showed promising results in various applications. However, the work evaluating TDA systematically on EEG-based BCI is rare. Our study aims to fill this gap.<br><br>APPROACH: The hypothesis is that the&#xD;topology of the EEG dynamics is different under different mental states so that the&#xD;topological features are discriminant. By adopting a dynamical system point of view,&#xD;the non-stationary nature of EEG is respected. In practice, topological information is&#xD;encoded by the persistence diagram. To turn it into a feature vector, some classical&#xD;vector- and function-based representations are used. Each feature vector is then&#xD;classified by several basic linear and non-linear classifiers.<br><br>MAIN RESULTS: A benchmark&#xD;comparing TDA with the gold standard methods was established on 3 publicly available&#xD;datasets (2 active BCI datasets based on motor-imagery, 1 passive BCI dataset for&#xD;mental workload estimation). TDA had significantly lower performance in intra-&#xD;subject classification, yet comparable and sometimes higher performance in inter-&#xD;subject classification. The persistence consistently outperformed all other topological&#xD;features. We explained theoretically the link between persistence and spectral power&#xD;and demonstrated it experimentally.<br><br>SIGNIFICANCE: To our knowledge, this is the first&#xD;study that evaluates TDA in both intra- and inter-subject classification on various&#xD;types of datasets. Insights on the connection between persistence and classical EEG&#xD;features are also given for the first time.},
}
@article {pmid40367199,
year = {2025},
author = {Yashinski, M},
title = {Neuroprosthesis converts brain activity to speech.},
journal = {Science robotics},
volume = {10},
number = {102},
pages = {eady7192},
doi = {10.1126/scirobotics.ady7192},
pmid = {40367199},
issn = {2470-9476},
mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Brain/physiology ; *Neural Prostheses ; },
abstract = {A neuroprosthesis decodes short bits of neural activity and synthesizes speech synchronously with a user's vocal intent.},
}
@article {pmid40366622,
year = {2025},
author = {Yang, L and Li, H and Wang, X},
title = {Psilocybin and Obsessive-Compulsive Disorder: Exploring New Therapeutic Horizons.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40366622},
issn = {1995-8218},
}
@article {pmid40366280,
year = {2025},
author = {Liu, M and Chang, S and Chen, M and Li, P and Roe, AW and Hu, JM},
title = {How shape information is coded by V4 cortical response of Macaque Monkey.},
journal = {Journal of neurophysiology},
volume = {},
number = {},
pages = {},
doi = {10.1152/jn.00520.2024},
pmid = {40366280},
issn = {1522-1598},
support = {32471052//MOST | National Natural Science Foundation of China (NSFC)/ ; 32100802//MOST | National Natural Science Foundation of China (NSFC)/ ; },
abstract = {Previous neural recording studies have shown that monkey V4 can process the shape information across populations of neurons. The responses recorded from each single neuron make it possible to retrieve shape information. However, these studies did not fully characterize the spatial distribution of activity in the cortex. There are multiple types of functional columns (orientation, curvature) in V4; how do these structures respond to different shapes? Here, with intrinsic optical imaging, we explored the cortical responses of V4 to contours (straight and curved) and shapes (circle and square). We found that in V4, the response of neurons to different shapes is highly dependent on the compositional features contained in the shape. A specific local network would have a higher response magnitude to its corresponding shape than other shapes. Meanwhile, the cortical response of V4 exhibits a tolerance to the shift of stimulus location. Our results suggest that two essential cortical response features in V4 are the specificity of the activated response pattern in the cortex and tolerance to the stimulus location variance. These features can help decode shape information from imaging results.},
}
@article {pmid40364497,
year = {2025},
author = {Kim, JH and Nam, H and Won, D and Im, CH},
title = {Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.},
journal = {Experimental neurobiology},
volume = {},
number = {},
pages = {},
doi = {10.5607/en25011},
pmid = {40364497},
issn = {1226-2560},
abstract = {Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx, and DANN, with three representative deep learning architectures (ShallowFBCSPNet, EEGNet, and TSception) to enable improved subject-independent EEG-based emotion recognition. The performances of the DG-integrated deep learning models were quantitatively evaluated using two emotional EEG datasets collected by the authors. Data from each subject were treated as distinct domains in each model. Binary classification tasks were conducted to identify the valence or arousal state of each participant based on a ten-fold cross-validation strategy. The results indicated that the application of DG methods consistently enhanced classification accuracy across datasets. In one dataset, TSception combined with VREx achieved the highest performance for both valence and arousal classifications. In the other dataset, TSception with VREx still yielded the highest valence classification accuracy, while TSception combined with GroupDRO showed the best arousal classification performance among the twelve models, slightly outperforming TSception with VREx. These findings underscore the potential of DG approaches to mitigate distributional shifts caused by intersubject and intersession variabilities to implement robust subject-independent EEG-based emotion recognition systems.},
}
@article {pmid40363359,
year = {2025},
author = {Deng, X and Huo, H and Ai, L and Xu, D and Li, C},
title = {A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
doi = {10.3390/s25092922},
pmid = {40363359},
issn = {1424-8220},
support = {No. XTZW2024-KF02//Chongqing Key Laboratory of Germplasm Innovation 755 and Utilization of Native Plants under Grant/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Signal-To-Noise Ratio ; Movement/physiology ; Deep Learning ; Algorithms ; },
abstract = {Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite the significant clinical and application value of MI-BCI technology, accurately decoding high-dimensional and low signal-to-noise ratio (SNR) electroencephalography (EEG) signals remains challenging. Moreover, traditional deep learning approaches exhibit limitations in processing EEG signals, particularly in capturing the intrinsic correlations between electrode channels and long-distance temporal dependencies. To address these challenges, this research introduces a novel end-to-end decoding network that integrates convolutional neural networks (CNNs) and a Swin Transformer, aiming at enhancing the classification accuracy of the MI paradigm in EEG signals. This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. Experimental results show that on the BCI Competition IV-2a dataset, the proposed method achieves 83.99% classification accuracy, which is significantly better than the existing deep learning methods. This finding underscores the efficacy of combining a CNN and Swin Transformer in a 3D data space for processing high-dimensional, low-SNR EEG signals, offering a new perspective for the future development of MI-BCI. Future research could further explore the applicability of this method across various BCI tasks and its potential clinical implementations.},
}
@article {pmid40363182,
year = {2025},
author = {Hougaard, BI and Knoche, H and Kristensen, MS and Jochumsen, M},
title = {Experience of Virtual Help in a Simulated BCI Stroke Rehabilitation Serious Game and How to Measure It.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {9},
pages = {},
doi = {10.3390/s25092742},
pmid = {40363182},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Video Games ; Aged ; Adult ; *Stroke/physiopathology ; User-Computer Interface ; },
abstract = {Designers of digital rehabilitation experiences can accommodate error-prone input devices like brain-computer interfaces (BCIs) by incorporating virtual help mechanisms to adjust the difficulty, but it is unclear on what grounds users are willing to accept such help. To study users' experience of virtual help mechanisms, we used three help mechanisms in a blink-controlled game simulating a BCI-based stroke rehabilitation exercise. A mixed-method, simulated BCI study was used to evaluate game help by 19 stroke patients who rated their frustration and perceived control when experiencing moderately high input recognition. None of the help mechanisms affected ratings of frustration, which were low throughout the study, but two mechanisms affected patients' perceived control ratings positively and negatively. Patient ratings were best explained by the amount of positive feedback, including game help, which increased perceived control ratings by 8% and decreased frustration ratings by 3%. The qualitative analysis revealed appeal, interference, self-blame, and prominence as deciding experiential factors of help, but it was unclear how they affected frustration and perceived control ratings. Building upon the results, we redesigned and tested self-reported measures of help quantity, help appeal, irritation, and pacing with game-savvy adults in a follow-up study using the same game. Help quantity appeared larger when game help shielded players from negative feedback, but this did not necessarily appeal to them. Future studies should validate or control for the constructs of perceived help quantity and appeal.},
}
@article {pmid40361409,
year = {2025},
author = {Marín-Liébana, S and Llor, P and Serrano-García, L and Fernández-Murga, ML and Comes-Raga, A and Torregrosa, D and Pérez-García, JM and Cortés, J and Llombart-Cussac, A},
title = {Gene Expression Signatures for Guiding Initial Therapy in ER+/HER2- Early Breast Cancer.},
journal = {Cancers},
volume = {17},
number = {9},
pages = {},
doi = {10.3390/cancers17091482},
pmid = {40361409},
issn = {2072-6694},
abstract = {In triple-negative (TNBC) and human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients, neoadjuvant systemic therapy is the standard recommendation for tumors larger than 2 cm. Monitoring the response to primary systemic therapy allows for the assessment of treatment effects, the need for breast-conserving surgery (BCS), and the achievement of pathological complete responses (pCRs). In estrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer, the benefit of neoadjuvant strategies is controversial, as they have shown lower tumor downstaging and pCR rates compared to other breast cancers. In recent decades, several gene expression assays have been developed to tailor adjuvant treatments in ER+/HER2- early breast cancer (EBC) to identify the patients that will benefit the most from adjuvant chemotherapy (CT) and those at low risk who could be spared from undergoing CT. It is still a challenge to identify patients who will benefit from neoadjuvant systemic treatment (CT or endocrine therapy (ET)). Here, we review the published data on the most common gene expression signatures (MammaPrint (MP), BluePrint (BP), Oncotype Dx, PAM50, the Breast Cancer Index (BCI), and EndoPredict (EP)) and their ability to predict the response to neoadjuvant treatment, as well as the possibility of using them on core needle biopsies. Additionally, we review the changes in the gene expression signatures after neoadjuvant treatment, and the ongoing clinical trials related to the utility of gene expression signatures in the neoadjuvant setting.},
}
@article {pmid40360495,
year = {2025},
author = {Li, J and Mo, D and Hu, J and Wang, S and Gong, J and Huang, Y and Li, Z and Yuan, Z and Xu, M},
title = {PEDOT:PSS-based bioelectronics for brain monitoring and modulation.},
journal = {Microsystems &amp; nanoengineering},
volume = {11},
number = {1},
pages = {87},
pmid = {40360495},
issn = {2055-7434},
support = {30802-110690303//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; 2021ZD0204300//National Science Foundation of China | Major Research Plan/ ; MYRGGRG2023-00038-FHS//Universidade de Macau (University of Macau)/ ; 28709-312200502501//Beijing Normal University (BNU)/ ; },
abstract = {The growing demand for advanced neural interfaces that enable precise brain monitoring and modulation has catalyzed significant research into flexible, biocompatible, and highly conductive materials. PEDOT:PSS-based bioelectronic materials exhibit high conductivity, mechanical flexibility, and biocompatibility, making them particularly suitable for integration into neural devices for brain science research. These materials facilitate high-resolution neural activity monitoring and provide precise electrical stimulation across diverse modalities. This review comprehensively examines recent advances in the development of PEDOT:PSS-based bioelectrodes for brain monitoring and modulation, with a focus on strategies to enhance their conductivity, biocompatibility, and long-term stability. Furthermore, it highlights the integration of multifunctional neural interfaces that enable synchronous stimulation-recording architectures, hybrid electro-optical stimulation modalities, and multimodal brain activity monitoring. These integrations enable fundamentally advancing the precision and clinical translatability of brain-computer interfaces. By addressing critical challenges related to efficacy, integration, safety, and clinical translation, this review identifies key opportunities for advancing next-generation neural devices. The insights presented are vital for guiding future research directions in the field and fostering the development of cutting-edge bioelectronic technologies for neuroscience and clinical applications.},
}
@article {pmid40360243,
year = {2025},
author = {Schurzig, D and Iseke, R and Maier, H and Prenzler, NK and Lenarz, T and Ghoncheh, M},
title = {Clinical Evidence on the Influence of Implant Position onto Maximum Output with the Bonebridge Bone Conduction Implant.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {},
number = {},
pages = {},
doi = {10.1097/MAO.0000000000004533},
pmid = {40360243},
issn = {1537-4505},
abstract = {HYPOTHESIS: In bone conduction implantation, the position of the implant influences the audiological benefit of the patient.<br><br>BACKGROUND: One way of treating hearing loss is the implantation of bone conduction implants (BCIs), which effectively transmit vibrations through the skull bone to the cochlea given that the implant transducer is securely fixated. Laboratory research on the efficacy of bone conduction sound transmission found that a closer proximity of the transducer to the ipsilateral cochlea yields significantly higher cochlear promontory vibrations and hence, higher stimulation efficacy. Up to now, this finding has not been reproduced using clinical data such as the functional or effective gain.<br><br>METHODS: The present, retrospective study was conducted on a cohort of 28 BCI patients to correlate the implantation site of the BC transducer, derived from clinical postoperative imaging and defined in a standardized coordinate system, with maximum output values that are exclusively based on a novel calculation method only employing clinical audiological data.<br><br>RESULTS: It could be shown that the efficacy of BCI stimulation is in fact correlated with the transducer distance to the cochlea, and that this correlation is frequency dependent. Furthermore, the longitudinal distance of the transducer and the ipsilateral external auditory canal is negatively correlated with the maximal output while the sagittal distance is not.<br><br>CONCLUSION: The present study is hence the first one to clinically demonstrate the significance of BCI placement for maximizing patient benefit, which should be considered during the preoperative planning of bone conduction implantation.},
}
@article {pmid40359554,
year = {2025},
author = {Liu, MY and Fang, MZ and Zhang, BH and Dang, CX and Zhang, YS and Wu, L and Liu, B and Li, Z},
title = {Bibliometric analysis of brain-computer interface research in spinal cord injury: current landscape and future directions.},
journal = {International journal of surgery (London, England)},
volume = {},
number = {},
pages = {},
doi = {10.1097/JS9.0000000000002475},
pmid = {40359554},
issn = {1743-9159},
}
@article {pmid40358727,
year = {2025},
author = {Aamir, A and Siddiqui, M},
title = {Integration of brain-computer interfaces with sacral nerve stimulation: a vision for closed-loop, volitional control of bladder function in neurogenic patients through real-time cortical signal modulation and peripheral neuro-stimulation.},
journal = {World journal of urology},
volume = {43},
number = {1},
pages = {301},
pmid = {40358727},
issn = {1433-8726},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; *Urinary Bladder, Neurogenic/therapy/physiopathology ; *Lumbosacral Plexus ; },
abstract = {Sacral nerve stimulation (SNS) and brain-computer interfaces (BCI) are emerging neuromodulation therapies that offer innovative solutions for chronic neurological disorders. SNS, primarily used in the management of conditions such as urinary incontinence and chronic pelvic pain, demonstrates significant therapeutic potential. In contrast, BCIs are rapidly advancing in their ability to restore lost motor functions and improve the quality of life of patients with severe neurological impairments, such as spinal cord injury and stroke. The integration of SNS and BCI technologies presents a promising avenue for enhancing neuromodulation outcomes by leveraging the potential of both systems. This article explores the combined operation of SNS and BCI, addressing current challenges, future directions, and the potential for these combined therapies to revolutionise the field of functional neuromodulation.},
}
@article {pmid40358723,
year = {2025},
author = {Bénard, A and Maliia, DM and Yochum, M and Köksal-Ersöz, E and Houvenaghel, JF and Wendling, F and Sauleau, P and Benquet, P},
title = {Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.},
journal = {Brain topography},
volume = {38},
number = {4},
pages = {43},
pmid = {40358723},
issn = {1573-6792},
support = {855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; 855109//HORIZON EUROPE Reforming and enhancing the European Research and Innovation system/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Models, Neurological ; Computer Simulation ; *Rest/physiology ; Adult ; Scalp/physiology ; Male ; *Brain Waves/physiology ; Female ; },
abstract = {Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associated with various brain diseases. However, understanding the cellular origins of scalp EEG signals and their spatiotemporal changes during the resting state (RS) in humans remains challenging, as cellular-level recordings are typically restricted to animal models. The objective of this study was to simulate individual-specific spatiotemporal features of RS EEG and measure the degree of similarity between real and simulated EEG. Using a physiologically grounded whole-brain computational model (based on known neuronal subtypes and their structural and functional connectivity) that simulates interregional cortical circuitry activity, realistic individual EEG recordings during RS of three healthy subjects were created. The model included interconnected neural mass modules simulating activities of different neuronal subtypes, including pyramidal cells and four types of GABAergic interneurons. High-definition EEG and source localization were used to delineate the cortical extent of alpha and beta-gamma rhythms. To evaluate the realism of the simulated EEG, we developed a similarity index based on cross-correlation analysis in the frequency domain across various bipolar channels respecting standard longitudinal montage. Alpha oscillations were produced by strengthening the somatostatin-pyramidal loop in posterior regions, while beta-gamma oscillations were generated by increasing the excitability of parvalbumin-interneurons on pyramidal neurons in anterior regions. The generation of realistic individual RS EEG rhythms represents a significant advance for research fields requiring data augmentation, including brain-computer interfaces and artificial intelligence training.},
}
@article {pmid40356632,
year = {2025},
author = {Liao, XY and Jiang, YE and Xu, RJ and Qian, TT and Liu, SL and Che, Y},
title = {A bibliometric analysis of electroencephalogram research in stroke: current trends and future directions.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1539736},
pmid = {40356632},
issn = {1664-2295},
abstract = {BACKGROUND: Electroencephalography (EEG) has become an indispensable tool in stroke research for real-time monitoring of neural activity, prognosis prediction, and rehabilitation support. In recent decades, EEG applications in stroke research have expanded, particularly in areas like brain-computer interfaces (BCI) and neurofeedback for motor recovery. However, a comprehensive analysis of research trends in this domain is currently unavailable.<br><br>METHODS: The study collected data from the Web of Science Core Collection database, selecting publications related to stroke and EEG from 2005 to 2024. Visual analysis tools such as VOSviewer and CiteSpace were utilized to build knowledge maps of the research field, analyzing the distribution of publications, authors, institutions, journals, and collaboration networks. Additionally, co-occurrence, clustering, and burst detection of keywords were analyzed in detail.<br><br>RESULTS: A total of 2,931 publications were identified, indicating a consistent increase in EEG research in stroke, with significant growth post-2017. The United States, China, and Germany emerged as the leading contributors, with high collaboration networks among Western institutions. Key research areas included signal processing advancements, EEG applications in seizure risk and consciousness disorder assessment, and EEG-driven rehabilitation techniques. Notably, recent studies have focused on integrating EEG with machine learning and multimodal data for more precise functional evaluations.<br><br>CONCLUSION: The findings reveal that EEG has evolved from a diagnostic tool to a therapeutic support platform in the context of stroke care. The advent of deep learning and multimodal integration has positioned EEG for expanded applications in personalized rehabilitation. It is recommended that future studies prioritize interdisciplinary collaboration and standardized EEG methodologies in order to facilitate clinical adoption and enhance translational potential in stroke management.},
}
@article {pmid40356046,
year = {2025},
author = {Chen, S and Hu, J and Zhang, D and Li, Z and Zheng, Z and Gui, S and He, N},
title = {Preparation and hemostatic evaluation of carboxymethyl chitosan/gelatin/clinodiside a composite sponges.},
journal = {Journal of biomaterials science. Polymer edition},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/09205063.2025.2499285},
pmid = {40356046},
issn = {1568-5624},
abstract = {In trauma resuscitation, rapid hemostasis is a top priority for rescuing patients from the risk of hemorrhagic shock and infection. Traditional hemostatic materials are not effective for hemostasis and have some limitations. We added "Duanxue Liu" saponin A (Clinodiside A) to a hemostatic sponge based on carboxymethyl chitosan (CMCS) and gelatin to improve its hemostatic effect. Clinodiside A has hemostatic, anti-inflammatory and antibacterial effects, and its preparation into a sponge can help to improve the coagulation ability, prolong the action time of the drug, increase the bioavailability and improve the stability of the drug. The results showed that the prepared hemostatic sponge had a honeycomb porous structure, strong shape recovery ability, good water absorption and porosity, low hemolysis rate and no obvious cytotoxicity. The results of in vitro coagulation test showed that the coagulation time of GOC, GOC-1, GOC-2 and GOC-3 sponges was shorter than that of the control group, and the BCI index was much lower than that of the commercially available sponges. In the tail-breaking experiment of SD rats, GOC-3 showed the lowest blood loss of 0.2549 g and the hemostasis time of 55 s. In the experiment of rabbit ear artery, GOC-3 showed the lowest blood loss of 98.75 mg and the hemostasis time of 95 s. This indicates that the Clinique A hemostatic sponges have highly efficient hemostatic properties. Therefore, the prepared CMCS/Gel/Clinodiside A sponge has a good application prospect as a hemostatic dressing.},
}
@article {pmid40355527,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {Author Correction: A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {4381},
doi = {10.1038/s41467-025-59792-1},
pmid = {40355527},
issn = {2041-1723},
}
@article {pmid40355001,
year = {2025},
author = {Ognard, J and El Hajj, G and Verma, O and Ghozy, S and Kadirvel, R and Kallmes, DF and Brinjikji, W},
title = {Advances in endovascular brain computer interface: Systematic review and future implications.},
journal = {Journal of neuroscience methods},
volume = {420},
number = {},
pages = {110471},
doi = {10.1016/j.jneumeth.2025.110471},
pmid = {40355001},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) translate neural activity into real-world commands. While traditional invasive BCIs necessitate craniotomy, endovascular BCIs offer a minimally invasive alternative using the venous system for electrode placement.<br><br>NEW METHOD: This systematic review evaluates the technical feasibility, safety, and clinical outcomes of endovascular BCIs, discussing their future implications. A systematic review was conducted per PRISMA guidelines. The search spanned PubMed, Web of Science, and Scopus databases using keywords related to neural interfaces and endovascular approaches. Studies were included if they reported on endovascular BCIs in preclinical or clinical settings. Dual independent screening and extraction focused on electrode material, recording capabilities, safety parameters, and clinical efficacy.<br><br>RESULTS: From 1385 initial publications, 26 met the inclusion criteria. Seventeen studies investigated the Stentrode device. Among the 24 preclinical studies, 16 used ovine or rodent models, and 9 addressed engineering or simulation aspects. Two clinical studies reported six ALS patients successfully using an endovascular BCI for digital communication. Preclinical data established the endovascular ovine model, demonstrating stable neural recordings and vascular changes with long-term implantation. Key challenges include thrombosis risk, long-term electrode stability, and anatomical variability.<br><br>Endovascular BCI reduced invasiveness, improved safety profiles, with comparable neural recording fidelity to invasive methods, and promising preliminary clinical outcomes in severely paralyzed patients.<br><br>CONCLUSIONS: Early results are promising, but clinical data remain scarce. Further research is needed to optimize signal processing, enhance electrode biocompatibility, and refine endovascular procedures for broader clinical applications.},
}
@article {pmid40354812,
year = {2025},
author = {Poli, R and Mercimek, ACC and Cinel, C},
title = {Novel Sequential BCI Speller based on ERPs and Event-Related Slow Cortical Potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add772},
pmid = {40354812},
issn = {1741-2552},
abstract = {One of the most effective Brain-Computer Interfaces (BCI) spellers, Donchin and Farwell's matrix speller, uses visual stimulus presentation and the oddball effect, eliciting P300 event-related potentials to rare and randomly presented stimuli of interest. Although proposed almost 4 decades ago, most BCI spellers still rely on this principle and the original matrix speller design although some of the issues that affect oddball spellers have progressively been addressed over the years with significant, but very incremental, performance improvements. Farwell and Donchin seminal paper suggested the future possibility of abandoning the oddball paradigm, for a regular/periodic presentation pattern which they predicted might produce a Contingent Negative Variation (CNV) thus improve speller performance. However, this has never been investigated. Building on our past research on a BCI for cursor control which adopted a periodic stimulation protocol, here we explore whether a periodic presentation pattern could be a viable alternative to the oddball paradigm in a BCI speller. Approach. We tested the periodic presentation principle in a BCI speller where 36 letters are organised around a circle and are highlighted sequentially, and compared it to the original matrix speller at two stimulus presentation rates. Main Results. Our periodic speller produces not only clear P300s, but also equally clear CNVs, as postulated by Farwell and Donchin, as well as other Slow Cortical Potentials (SCPs). At the higher stimulation rate, this leads to significantly higher AUC, classification accuracy, ITR and utility w.r.t. Donchin's speller. Significance. Our findings suggest that periodic stimulation can not only produce clear P300s but also a variety of event-related SCPs, leading to significant performance improvements over Donchin's paradigm. This work opens new avenues for BCI spelling where ERPs are combined with naturally-triggered (rather than trained) SCPs, that will hopefully result in more efficient communication systems for individuals with severe motor impairments.},
}
@article {pmid40354807,
year = {2025},
author = {Ladouce, S and Torre-Tresols, JJ and Le Goff, K and Dehais, F},
title = {EEG-based assessment of long-term vigilance and lapses of attention using a user-centered frequency-tagging approach.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add771},
pmid = {40354807},
issn = {1741-2552},
abstract = {Sustaining vigilance over extended periods is crucial for many critical operations but remains challenging due to the cognitive resources required. Fatigue and other factors contribute to fluctuations in vigilance, causing attentional focus to drift from task-relevant information. Such lapses of attention, common in prolonged tasks, lead to decreased performance and missed critical information, with potentially serious consequences. Identifying physiological markers that predict inattention is key to developing preventive strategies. Approach: Previous research has established electroencephalography (EEG) responses to periodic visual stimuli, known as steady-state visual evoked potentials (SSVEP), as sensitive markers of attention. In this study, we evaluated a minimally intrusive SSVEP-based approach for tracking vigilance in healthy participants (N = 16) during two sessions of a 45-minute sustained visual attention task (Mackworth's clock task). A 14 Hz frequency-tagging flicker was either superimposed on the task or absent. Main results: Results revealed that SSVEP responses were lower prior to lapses of attention, while other spectral EEG markers, such as frontal theta and parietal alpha activity, did not reliably distinguish between detected and missed attention probes. Importantly, the flicker did not affect task performance or participant experience. Significance: This non-intrusive frequency-tagging method provides a continuous measure of vigilance, effectively detecting attention lapses in prolonged tasks. It holds promise for integration into passive brain-computer interfaces, offering a practical solution for real-time vigilance monitoring in high-stakes settings like air traffic control or driving. .},
}
@article {pmid40353311,
year = {2025},
author = {Shah, AM},
title = {Unlocking Naturalistic Speech With Brain-Computer Interface.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.15021},
pmid = {40353311},
issn = {1525-1594},
abstract = {Novel speech brain-computer interface poses the ability to decode detected neural signals in nearly real time. This decreases brain-to-voice latency and has the opportunity to restore naturalistic communication. Trial Registration: ClinicalTrials.gov: NCT03698149.},
}
@article {pmid40352906,
year = {2025},
author = {Besharat, A and Samadzadehaghdam, N and Ghadiri, T},
title = {A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1544452},
doi = {10.3389/fnins.2025.1544452},
pmid = {40352906},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) rely on the brain's response to visual stimuli. However, accurately recognizing target frequencies using training-based methods remains challenging due to the time-consuming calibration sessions required by subject-specific training methods.<br><br>METHOD: To address this limitation, this study proposes a novel hybrid method called Hybrid task-related component and canonical correlation analysis (H-TRCCA). In the training phase, four spatial filters are derived using canonical correlation analysis (CCA) to maximize the correlation between the training data and reference signals. Additionally, a spatial filter is also computed using task-related component analysis (TRCA). In the test phase, correlation coefficients obtained from the CCA method are clustered using the k-means++ clustering algorithm. The cluster with the highest average correlation identifies the candidate stimuli. Finally, for each candidate, the correlation values are summed and combined with the TRCA-based correlation coefficients.<br><br>RESULTS: The H-TRCCA algorithm was validated using two publicly available benchmark datasets. Experimental results using only two training trials per frequency with 1s data length showed that H-TRCCA achieved average accuracies of 91.44% for Dataset I and 80.46% for Dataset II. Additionally, it achieved maximum average information transfer rates of 188.36 bits/min and 139.96 bits/min for Dataset I and II, respectively.<br><br>DISCUSSION: Remarkably H-TRCCA achieves comparable performance to other methods that require five trials, utilizing only two or three training trials. The proposed H-TRCCA method outperforms state-of-the-art techniques, showing superior performance and robustness with limited calibration data.},
}
@article {pmid40352453,
year = {2025},
author = {Li, Y and Mei, Z and Liu, Z and Li, J and Sun, G and Ong, MEH and Chen, J and Fan, H and Cao, C},
title = {Cardiometabolic multimorbidity and the risk of sudden cardiac death among geriatric community dwellers using longitudinal EHR-derived data.},
journal = {Frontiers in endocrinology},
volume = {16},
number = {},
pages = {1515495},
doi = {10.3389/fendo.2025.1515495},
pmid = {40352453},
issn = {1664-2392},
mesh = {Humans ; Aged ; Male ; Female ; *Multimorbidity ; *Electronic Health Records/statistics &amp; numerical data ; Retrospective Studies ; Aged, 80 and over ; China/epidemiology ; *Death, Sudden, Cardiac/epidemiology/etiology ; *Independent Living/statistics &amp; numerical data ; Risk Factors ; Longitudinal Studies ; *Cardiovascular Diseases/epidemiology ; Prevalence ; *Metabolic Diseases/epidemiology ; },
abstract = {BACKGROUND: Cardiometabolic multimorbidity (CMM) has increased globally in recent years, especially among geriatric community dwellers. However, it is currently unclear how SCD risk is impacted by CMM in older adults. This study aimed to examine the associations between CMM and SCD among geriatric community dwellers in a province of China.<br><br>METHODS: This study was a retrospective, population-based cohort design based on electronic health records (EHRs) of geriatric community dwellers (&#x2265;65 years old) in four towns of Tianjin, China. 55,130 older adults were included in our study. Older adults were categorized into different CMM patterns according to the cardiometabolic disease (CMD) status at baseline. The count of CMDs was also entered as a continuous variable to examine the potential additive effect of CMM on SCD. Cox proportional hazard models were used to evaluate associations between CMM and SCD. The results are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs).<br><br>RESULTS: The prevalence of CMM was approximately 25.3% in geriatric community dwellers. Among participants with CMM, hypertension and diabetes was the most prevalent combination (9,379, 17.0%). The highest crude mortality rates for SCD were 7.5 (2.9, 19.1) per 1000 person-years in older adults with hypertension, coronary heart disease, diabetes and stroke (HR, 4.496; 95% CI, 1.696, 11.917), followed by those with hypertension, coronary heart disease, and stroke (HR, 3.290; 95% CI, 1.056, 10.255). The risks of SCD were significantly increased with increasing numbers of CMDs (HR, 1.787; 95% CI, 1.606, 1.987). The demographic, risk factors, serum measures and ECG-adjusted HR for SCD was 1.488 (1.327, 1.668) for geriatric community dwellers with an increasing number of CMDs.<br><br>CONCLUSION: The risk of SCD varied by the pattern of CMM, and increased with increasing number of CMM among geriatric community dwellers.},
}
@article {pmid40351848,
year = {2025},
author = {Jin, X and Yuan, Y and Chang, C and Wu, X and Tan, X and Liu, Z},
title = {Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251341163},
pmid = {40351848},
issn = {2055-2076},
abstract = {OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating issues related to the shortage and unequal distribution of healthcare resources. The purpose of this study is to identify key factors affecting the service quality of telemedicine platforms in China, with the dual objectives of advancing patient wellbeing and informing evidence-based service innovations for industry stakeholders.<br><br>METHODS: To quantitatively assess the impact of these key factors on health and wellbeing from the perspective of healthcare consumers, a total of 25,499 valid online reviews were collected from telemedicine platforms. To establish a service quality evaluation framework, this study proposes a novel approach that combines the Servqual quality assessment model with a CNN-BiLSTM deep learning model enhanced by an attention mechanism.<br><br>RESULTS: Analysis of the full sample shows that healthcare consumers are most concerned about the quality of services provided by telemedicine platforms, with the most important being the professional competence of doctors, a critical factor for promoting consumer health and wellbeing. The proposed hybrid deep learning approach demonstrates superior performance in sentiment classification accuracy, outperforming conventional methods by 11.11 percentage points. This methodological innovation enables more precise identification of consumer sentiment patterns across service dimensions.<br><br>CONCLUSION: The novel quality assessment framework introduced here provides actionable insights for advancing telemedicine platforms, driving progress toward precision healthcare and consumer-centric wellbeing. Furthermore, it enables healthcare consumers to select telemedicine services aligned with their personalized needs.},
}
@article {pmid40351570,
year = {2025},
author = {Sercek, I and Sampathila, N and Tasci, I and Ekmekyapar, T and Tasci, B and Barua, PD and Baygin, M and Dogan, S and Tuncer, T and Tan, RS and Acharya, UR},
title = {A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer's disease detection.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {71},
pmid = {40351570},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.},
}
@article {pmid40350042,
year = {2025},
author = {Xie, C and Wang, L and Yang, J and Guo, J},
title = {A subject transfer neural network fuses Generator and Euclidean Alignment for EEG-based motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110483},
doi = {10.1016/j.jneumeth.2025.110483},
pmid = {40350042},
issn = {1872-678X},
abstract = {BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devices indirectly through cognitive processes. Although it has great development prospects, the significant difference in EEG signals among individuals hinders users from further utilizing the BCI system.<br><br>NEW METHOD: Addressing this difference and improving BCI classification accuracy remain key challenges. In this paper, we propose a transfer learning model based on deep learning to transfer the data distribution from the source domain to the target domain, named a subject transfer neural network combining the Generator with Euclidean alignment (ST-GENN). It consists of three parts: 1) Align the original EEG signals in the Euclidean space; 2) Send the aligned data to the Generator to obtain the transferred features; 3) Utilize the Convolution-attention-temporal (CAT) classifier to classify the transferred features.<br><br>RESULTS: The model is validated on BCI competition IV 2a, BCI competition IV 2b and SHU datasets to evaluate its classification performance, and the results are 82.85%, 86.28% and 67.2% for the three datasets, respectively.<br><br>The results have been shown to be robust to subject variability, with the average accuracy of the proposed method outperforming baseline algorithms by ranging from 2.03% to 15.43% on the 2a dataset, from 0.86% to 10.16% on the 2b dataset and from 3.3% to 17.9% on the SHU dataset.<br><br>The advantage of our model lies in its ability to effectively transfer the experience and knowledge of the source domain data to the target domain, thus bridging the gap between them. Our method can improve the practicability of MI-BCI systems.},
}
@article {pmid40349743,
year = {2025},
author = {Dong, T and Lee, HH and Zang, H and Lee, H and Tian, Q and Wan, L and Fan, Q and Huang, SY},
title = {In Vivo Cortical Microstructure Mapping Using High-Gradient Diffusion MRI Accounting for Intercompartmental Water Exchange Effects.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121258},
doi = {10.1016/j.neuroimage.2025.121258},
pmid = {40349743},
issn = {1095-9572},
abstract = {In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves cortical microstructure mapping of the in vivo human brain with the evaluation of water exchange effects. This approach potentially provides a more appropriate description of in vivo cortical microstructure for improving data interpretation in future neurobiological studies.},
}
@article {pmid40348851,
year = {2025},
author = {Sun, G and Yu, C and Cai, R and Li, M and Fan, L and Sun, H and Lyu, C and Lin, Y and Gao, L and Wang, KH and Li, X},
title = {Neural representation of self-initiated locomotion in the secondary motor cortex of mice across different environmental contexts.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {725},
pmid = {40348851},
issn = {2399-3642},
support = {32371074//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170991//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Locomotion/physiology ; Mice ; Male ; *Neurons/physiology ; Mice, Inbred C57BL ; Environment ; },
abstract = {The secondary motor cortex (M2) plays an important role in the adaptive control of locomotor behaviors. However, it is unclear how M2 neurons encode the same type of locomotor control variables in different environmental contexts. Here we image the neuronal activity in M2 with a miniscope while mice are moving freely in each of three environments: a Y-maze, a running-wheel, and an open-field. These animals show distinct locomotor patterns in different environmental contexts. Surprisingly, a large population of M2 neurons are active before starting and after ceasing locomotion, while maintaining decreased neural activity during locomotion. Furthermore, the majority of these neurons are consistently engaged across various contexts, suggesting egocentric voluntary control functions. In contrast, the smaller populations of locomotion-activated M2 neurons are mostly context-specific, suggesting exocentric navigation functions. Thus, our results demonstrate that M2 neurons encode motor control variables for self-initiated locomotor behaviors in both context-dependent and context-independent manners.},
}
@article {pmid40347675,
year = {2025},
author = {Chen, L and Liu, Y and Wang, Z and Zhang, L and Cheng, S and Ming, D},
title = {Using non-invasive brain stimulation to modulate performance in visuomotor rotation adaptation: A scoping review.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {187},
number = {},
pages = {144-158},
doi = {10.1016/j.cortex.2025.04.010},
pmid = {40347675},
issn = {1973-8102},
abstract = {As research on the visuomotor rotation (VMR) adaptation expands its scope from behavioral science to encompass neuropsychological perspectives, an increasing number of studies have employed non-invasive brain stimulation (NIBS) techniques to explore the specific contributions of different neural structures to VMR adaptation. Despite early studies suggesting that cerebellar stimulation influenced the rate of adaptation and that stimulating primary motor cortex led to an enhanced retention of newly learned adaptation, subsequent studies could not always achieve consistent results. To probe this inconsistency, we systematically comb through past studies and extract numerous details, including paradigm designs, context settings, and modulation protocols in this scoping review. In summary, the paradigm design primarily serves two purposes: to dissociate implicit and explicit adaptation and to assess the retention of motor memory, whilst context settings such as apparatus, movement-related parameters and the information provided for subjects may complicate the modulated neuropsychological processes. We also conclude key NIBS parameters such as target regions and timing in stimulation protocols. Furthermore, we recognize the potential of neurophysiological biomarkers to support future VMR studies that incorporate NIBS and advocate for the use of several newly emerging NIBS techniques to enrich the field.},
}
@article {pmid40347660,
year = {2025},
author = {Chen, X and Chen, Y and McNamara, TP},
title = {Processing spatial cue conflict in navigation: Distance estimation.},
journal = {Cognitive psychology},
volume = {158},
number = {},
pages = {101734},
doi = {10.1016/j.cogpsych.2025.101734},
pmid = {40347660},
issn = {1095-5623},
abstract = {Spatial navigation involves the use of various cues. This study examined how cue conflict influences navigation by contrasting landmarks and optic flow. Participants estimated spatial distances under different levels of cue conflict: minimal conflict, large conflict, and large conflict with explicit awareness of landmark instability. Whereas increased cue conflict alone had little behavioral impact, adding explicit awareness reduced reliance on landmarks and impaired the precision of spatial localization based on them. To understand the underlying mechanisms, we tested two cognitive models: a Bayesian causal inference (BCI) model and a non-Bayesian sensory disparity model. The BCI model provided a better fit to the data, revealing two independent mechanisms for reduced landmark reliance: increased sensory noise for unstable landmarks and lower weighting of unstable landmarks when landmarks and optic flow were judged to originate from different causes. Surprisingly, increased cue conflict did not decrease the prior belief in a common cause, even when explicit awareness of landmark instability was imposed. Additionally, cue weighting in the same-cause judgment was determined by bottom-up sensory reliability, while in the different-cause judgment, it correlated with participants' subjective evaluation of cue quality, suggesting a top-down metacognitive influence. The BCI model further identified key factors contributing to suboptimal cue combination in minimal cue conflicts, including the prior belief in a common cause and prior knowledge of the target location. Together, these findings provide critical insights into how navigators resolve conflicting spatial cues and highlight the utility of the BCI model in dissecting cue interaction mechanisms in navigation.},
}
@article {pmid40342556,
year = {2025},
author = {Haseeb, M and Nadeem, R and Sultana, N and Naseer, N and Nazeer, H and Dehais, F},
title = {Monitoring pilots' mental workload in real flight conditions using multinomial logistic regression with a ridge estimator.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1441801},
doi = {10.3389/frobt.2025.1441801},
pmid = {40342556},
issn = {2296-9144},
abstract = {Piloting an aircraft is a cognitive task that requires continuous verbal, visual, and auditory attentions (e.g., Air Traffic Control Communication). An increase or decrease in mental workload from a specific level can alter auditory and visual attention, resulting in pilot errors. The objective of this research is to monitor pilots' mental workload using advanced machine learning techniques to achieve improved accuracy compared to previous studies. Electroencephalogram (EEG) data were recorded from 22 pilots operating under visual flight rules (VFR) conditions using a six dry-electrode Enobio Neuroelectrics system, and the Riemannian artifact subspace reconstruction (rASR) filter was used for data cleaning. An information gain (IG) attribute evaluator was used to select 25 optimal features out of 72 power spectral and statistical extracted features. In this study, 15 classifiers were used for classification. Multinomial logistic regression with a ridge estimator was selected, achieving a significant mean accuracy of 84.6% on the dataset from 17 subjects. Data were initially collected from 22 subjects, but 5 were excluded due to data synchronization issues. This work has several limitations, such as the author did not counter balance the order of scenario, could not control all the variables such as wind conditions, and workload was not stationary in each leg of the flight pattern. This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight scenarios.},
}
@article {pmid40341884,
year = {2025},
author = {Bertheau, MAK and Boetzel, C and Herrmann, CS},
title = {Event-related potentials reveal incongruent behavior of autonomous vehicles in the moral machine dilemma.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {16048},
pmid = {40341884},
issn = {2045-2322},
mesh = {Humans ; Male ; Female ; *Evoked Potentials/physiology ; *Morals ; Electroencephalography ; Adult ; Young Adult ; *Decision Making/physiology ; *Artificial Intelligence ; },
abstract = {We investigated event-related potentials (ERPs) in the context of autonomous vehicles (AVs)-specifically in ambiguous, morally challenging traffic situations. In our study, participants (n = 34) observed a putative artificial intelligence (AI) making decisions in a dilemma situation involving an AV, expanding on the Moral Machine (MM) experiment. Additionally to the original MM experiment, we incorporated electroencephalography recordings. We were able to replicate most of the behavioral findings of the original MM: In case of an unavoidable traffic accident, participants consistently favored sparing pedestrians over passengers, more characters over fewer characters, and humans over pets. Beyond that, in the ERP we observed an increased P3 (322-422 ms), and late positive potential (LPP) (500-900 MS) amplitude in fronto-central regions when the putative AI's decision on a moral dilemma was incongruent to the participants' decision. As P3, and LPP are associated with the processing of stimulus significance, our findings suggest that these ERP components could potentially be used to identify critical, or unacceptable situations during human-AI interactions involving moral decision-making. This might be useful in brain computer interfaces research when, classifying single-trial ERP components, to dynamically adopt an AV's behavior.},
}
@article {pmid40341243,
year = {2025},
author = {Lore, S and Poganik, JR and Atala, A and Church, G and Gladyshev, VN and Scheibye-Knudsen, M and Verdin, E},
title = {Replacement as an aging intervention.},
journal = {Nature aging},
volume = {},
number = {},
pages = {},
pmid = {40341243},
issn = {2662-8465},
support = {1U01AI180158-01//U.S. Department of Health &amp; Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; 1U01AI180158-01//U.S. Department of Health &amp; Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)/ ; },
abstract = {Substantial progress in aging research continues to deepen our understanding of the fundamental mechanisms of aging, yet there is a lack of interventions conclusively shown to attenuate the processes of aging in humans. By contrast, replacement interventions such as joint replacements, pacemaker devices and transplant therapies have a long history of restoring function in injury or disease contexts. Here, we consider biological and synthetic replacement-based strategies as aging interventions. We discuss innovations in tissue engineering, such as the use of scaffolds or bioprinting to generate functional tissues, methods for enhancing donor-recipient compatibility through genetic engineering and recent progress in both cell therapies and xenotransplantation strategies. We explore synthetic approaches including prostheses, external devices and brain-machine interfaces. Additionally, we evaluate the evidence from heterochronic parabiosis experiments in mice and donor-recipient age-mismatched transplants to consider whether systemic benefits could result from personalized replacement approaches. Finally, we outline key challenges and future directions required to advance replacement therapies as viable, scalable and ethical interventions for aging.},
}
@article {pmid40340020,
year = {2025},
author = {Yu, X and Jian, Z and Dang, L and Zhang, X and He, P and Xiong, X and Feng, Y and Rehman, AU},
title = {Chemogenetic modulation in stroke recovery: A promising stroke therapy Approach.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.brs.2025.05.003},
pmid = {40340020},
issn = {1876-4754},
abstract = {Stroke remains a leading cause of long-term disability and mortality worldwide, necessitating novel therapeutic strategies to enhance recovery. Traditional rehabilitation approaches, including physical therapy and pharmacological interventions, often provide limited functional improvement. Neuromodulation has emerged as a promising strategy to promote post-stroke recovery by enhancing neuroplasticity and functional reorganization. Among various neuromodulatory techniques, chemogenetics, particularly Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), offers precise, cell-type-specific, and temporally controlled modulation of neuronal and glial activity. This review explores the mechanisms and therapeutic potential of chemogenetic modulation in stroke recovery. Preclinical studies have demonstrated that activation of excitatory DREADDs (hM3Dq) in neurons located within the peri-infarct area or contralateral M1 has been shown to enhance neuroplasticity, facilitate axonal sprouting, and lead to improved behavioral recovery following stroke. Conversely, stimulation of inhibitory DREADDs (hM4Di) suppresses stroke-induced excitotoxicity, mitigates peri-infarct spreading depolarizations (PIDs), and modulates neuroinflammatory responses. By targeting specific neuronal and glial populations, chemogenetics enables phase-specific interventions-early inhibition to minimize damage during the acute phase and late excitation to promote plasticity during the recovery phase. Despite its advantages over traditional neuromodulation techniques, such as optogenetics and deep brain stimulation, several challenges remain before chemogenetics can be translated into clinical applications. These include optimizing viral vector delivery, improving ligand specificity, minimizing off-target effects, and ensuring long-term receptor stability. Furthermore, integrating chemogenetics with existing stroke rehabilitation strategies, including brain-computer interfaces and physical therapy, may enhance functional recovery by facilitating adaptive neuroplasticity. Future research should focus on refining chemogenetic tools to enable clinical application. By offering a highly selective, reversible, and minimally invasive approach, chemogenetics holds great potential for revolutionizing post-stroke therapy and advancing personalized neuromodulation strategies.},
}
@article {pmid40338888,
year = {2025},
author = {Faisal, M and Khosa, I and Waris, A and Gilani, SO and Khan, MJ and Hazzazi, F and Ijaz, MA},
title = {Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.},
journal = {PloS one},
volume = {20},
number = {5},
pages = {e0322580},
doi = {10.1371/journal.pone.0322580},
pmid = {40338888},
issn = {1932-6203},
mesh = {Humans ; *Electromyography/methods ; *Upper Extremity/physiology ; Male ; Adult ; Female ; Movement/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Neurological disorders, such as stroke, spinal cord injury, and amyotrophic lateral sclerosis, result in significant motor function impairments, affecting millions of individuals worldwide. To address the need for innovative and effective interventions, this study investigates the efficacy of electromyography (EMG) decoding in improving motor function outcomes. While existing literature has extensively explored classifier selection and feature set optimization, the choice of preprocessing technique, particularly time-domain windowing techniques, remains understudied posing a significant knowledge gap. This study presents upper limb movement classification by providing a comprehensive comparison of eight time-domain windowing techniques. For this purpose, the EMG data from volunteers is recorded involving fifteen distinct movements of fingers. The rectangular window technique among others emerged as the most effective, achieving a classification accuracy of 99.98% while employing 40 time-domain features and a L-SVM classifier, among other classifiers. This optimal combination has implications for the development of more accurate and reliable myoelectric control systems. The achieved high classification accuracy demonstrates the feasibility of using surface EMG signals for accurate upper limb movement classification. The study's results have the potential to improve the accuracy and reliability of prosthetic limbs and wearable sensors and inform the development of personalized rehabilitation programs. The findings can contribute to the advancement of human-computer interaction and brain-computer interface technologies.},
}
@article {pmid40338479,
year = {2025},
author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA},
title = {Predicting fixations and gaze location from EEG.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40338479},
issn = {1741-0444},
support = {CHIST-ERA-20-BCI-001//HORIZON EUROPE Framework Programme/ ; 101071147//HORIZON EUROPE European Innovation Council/ ; PCI2021-122036-2A//Agencia Estatal de Investigaci&#xf3;n/ ; },
abstract = {Brain signals carry cognitive information that can be relevant in downstream tasks, but what about eye-gaze? Although this can be estimated with eye-trackers, it can be very convenient in practice to do it without extra equipment. We consider the challenging tasks of fixation prediction and gaze estimation from electroencephalography (EEG) using deep learning models. We argue that there are three critical design criteria when designing neural architectures for EEG: (1) the spatial and temporal dimensions of the data, (2) the local vs global nature of the data processing, and (3) the overall structure and order with which the steps (1) and (2) are orchestrated. We propose two model architectures, based on Transformers and LSTMs, with different variants in this large design space, and compare them with recent state-of-the-art (SOTA) approaches under two constraints: reduced EEG signal length and reduced set of EEG channels. Our Transformer-based model outperforms the LSTM-only model, but it turns out to be more sensitive with short signal lengths and with less number of channels. Interestingly, our results are similar or slightly better than SOTA, and the models are trained from scratch (i.e., without pre-training or fine-tuning). Our findings provide useful insights for advancing in eye-from-EEG tasks.},
}
@article {pmid40336015,
year = {2025},
author = {Yang, P and Zhang, X and Song, H and Zhang, X},
title = {An investigation into mental illness and its comorbidities from the perspective of supervenience physicalism.},
journal = {Philosophy, ethics, and humanities in medicine : PEHM},
volume = {20},
number = {1},
pages = {10},
pmid = {40336015},
issn = {1747-5341},
abstract = {The exploration into the origin of human spirituality has always been a hot spot with many unsolved questions in the philosophy of mind, and issues concerning mental illness and its comorbidities are still unclear. In the 1970s, Donald Davidson first proposed anomalous monism with the supervenience concept, a theory that both insists on physicalism and transcends traditional reductionism. This theory provides solid and accessible proof for perceiving the mind-body relationship of spiritual origin in a non-reductionist approach. This paper develops arguments in two aspects. First, three principles of anomalous monism are employed to explore the origin of mental illness. Second, the comorbidity of mental illness is explained with the help of the supervenience theory.},
}
@article {pmid40335704,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Publisher Correction: Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41586-025-09086-9},
pmid = {40335704},
issn = {1476-4687},
}
@article {pmid40334964,
year = {2025},
author = {Dong, L and Qi, Y and Luan, M and Liu, Q and Wang, M and Tian, C and Zheng, Y},
title = {A multi-channel implantable micro-magnetic stimulator for synergistic magnetic neuromodulation.},
journal = {Brain research},
volume = {},
number = {},
pages = {149679},
doi = {10.1016/j.brainres.2025.149679},
pmid = {40334964},
issn = {1872-6240},
abstract = {Micro-magnetic stimulation (μMS) is an emerging technology in magnetic neuromodulation. However, for larger brain structures with complex neural pathways, such as deep brain neural clusters, traditional implantable single-point μMS devices are immobile and incapable of multi-regional magnetic modulation. While multi-channel μMS can effectively address this limitation, its large size, difficulty in implantation, and unclear synergistic modulation patterns restrict its application. To tackle these challenges, this study designs a 4 × 4 array micro-coil structure targeted at the deep hippocampal region of the mouse brain. Numerical simulations were performed to analyze the coupling coefficients among the micro-coils and the distribution of the electromagnetic field in the structure, indicating that, with optimized parameters, the effective magnetic stimulation threshold can be achieved. Based on this, a multi-channel μMS device was fabricated, solving key issues such as waterproofing, biocompatibility, and dual-brain-region implantation of both stimulation and recording electrodes. A multi-point synergistic magnetic stimulation protocol was developed. After determining the synergistic magnetic stimulation parameters and effective target positions through in vitro experiments, real-time monitoring of calcium signal changes in the CA1 region of the hippocampus in mice during synergistic magnetic stimulation was performed. The results demonstrate that synergistic magnetic stimulation significantly enhances synaptic plasticity and calcium signal activity. This validates the feasibility of the implantable multi-channel micro-magnetic stimulator.},
}
@article {pmid40334847,
year = {2025},
author = {Yuan, J and Pan, H and Sun, Y and Wang, Y and Jia, J},
title = {Neural responses to global and local visual information processing provide neural signatures of ADHD symptoms.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {},
number = {},
pages = {112582},
doi = {10.1016/j.ijpsycho.2025.112582},
pmid = {40334847},
issn = {1872-7697},
abstract = {Individuals with ADHD are thought to exhibit a reduced "global bias" in perceptual processing. This bias, found in typically developed individuals, characterizes the tendency to prioritize global over local information processing. However, the relationship between specific ADHD symptoms and global or local processing remains unclear. This study addresses this gap by employing an ensemble perception task with a large sample (N = 465). EEG recordings allowed for the isolation of neural responses to individual and global stimuli using linear regression modeling. The adult ADHD self-report scale was used to assess ADHD symptoms. The results showed a significant association between ensemble perception and early responses to global stimuli. Furthermore, inattention symptoms were associated with early responses to global stimuli, suggesting a reduced global prioritization in individuals with higher inattention scores. Moreover, inattention symptom was associated with later responses to local stimuli, as shown by attenuated neural responses to local stimuli in individuals with more severe symptoms. These findings provide insights that ADHD includes deficits in both global and local processing, challenging earlier theories that focused solely on global processing impairments.},
}
@article {pmid40334819,
year = {2025},
author = {Yu, H and Zhou, X and Ru, Q and Anthony, A and Cameron, M and Liu, Y and Klann, IP and Guo, H and Lin, J and Wang, D and Chang, D},
title = {The modulatory effects of persimmon leaf extract on sleep-related neurotransmitters and its potential hypnotic effects.},
journal = {Fitoterapia},
volume = {},
number = {},
pages = {106576},
doi = {10.1016/j.fitote.2025.106576},
pmid = {40334819},
issn = {1873-6971},
abstract = {PURPOSE: Persimmon leaf is a traditional herbal medicine with diverse therapeutic applications. This study aimed to explore the effect of persimmon leaf extract (PLE) on the modulation of neurotransmitters involved in sleep regulation and its overall impact on sleep latency and duration.<br><br>METHODS: The key components of PLE were identified by ultra performance liquid chromatography. The modulatory effects of PLE in sleep and wakefulness-related neurotransmitters were studied in human neuroblastoma SH-SY5Y cells. PLE was also investigated in pentobarbital sodium-induced sleep and para-chlorophenylalanine (PCPA)-induced insomnia models in mice and rats.<br><br>RESULTS: PLE induced chloride influx and increased the intracellular production of gamma-aminobutyric acid (GABA), a neurotransmitter crucial for sleep regulation, in SH-SY5Y cells. Furthermore, PLE influenced the cellular expressions of serotonin, dopamine, and adenosine. It increased monoamine oxidase enzyme-A activity and reduced serotonin levels and its metabolites. It induced dopamine biosynthesis and degradation pathways. In the pentobarbital-induced sleep experiment, PLE significantly prolonged total sleep duration and reduced sleep latency in a dose-dependent manner. In the PCPA-induced insomnia model, PLE consistently increased GABA production, and lowered dopamine expression.<br><br>CONCLUSION: PLE exhibited modulatory effects on sleep-related neurotransmitters in vitro, which may also contribute to its hypnotic effects by extending the sleep duration and shortening sleeping latency in vivo.},
}
@article {pmid40334321,
year = {2025},
author = {Chen, X and Jia, T and Wu, D},
title = {Data alignment based adversarial defense benchmark for EEG-based BCIs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107516},
doi = {10.1016/j.neunet.2025.107516},
pmid = {40334321},
issn = {1879-2782},
abstract = {Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). While most studies have focused on enhancing the accuracy of EEG-based BCIs, more attention should be given to their security. Recent findings reveal that EEG-based BCIs are vulnerable to adversarial attacks. To address this, we present the first adversarial defense benchmark based on data alignment, aiming to enhance both the accuracy and robustness of EEG-based BCIs. This study evaluates nine adversarial defense approaches (including five defense strategies) across five EEG datasets (covering three paradigms), three neural networks, and four experimental scenarios. Our results show for the first time that integrating data augmentation, data alignment, and robust training can further improve both the accuracy and robustness of BCIs compared to using only one or two of them. Furthermore, we provide insights into the characteristics of various adversarial defense approaches based on EEG data alignment, offering valuable guidance for developing more accurate and secure EEG-based BCIs.},
}
@article {pmid40332078,
year = {2025},
author = {Park, BS and Yang, HR and Kang, H and Kim, KK and Kim, YT and Yang, S and Kim, JG},
title = {α2-Adrenergic Receptors in Hypothalamic Dopaminergic Neurons: Impact on Food Intake and Energy Expenditure.},
journal = {International journal of molecular sciences},
volume = {26},
number = {8},
pages = {},
doi = {10.3390/ijms26083590},
pmid = {40332078},
issn = {1422-0067},
support = {NRF-2021R1C1C2005067//National Research Foundation of Korea/ ; Research Grant (2020)//Incheon National University/ ; },
mesh = {Animals ; *Energy Metabolism/drug effects ; *Hypothalamus/metabolism/cytology/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Receptors, Adrenergic, alpha-2/metabolism/genetics ; Mice ; Male ; *Eating/drug effects ; Mice, Inbred C57BL ; },
abstract = {The adrenergic system plays an active role in modulating synaptic transmission in hypothalamic neurocircuitry. While α2-adrenergic receptors are widely distributed in various organs and are involved in various physiological functions, their specific role in the regulation of energy metabolism in the brain remains incompletely understood. Herein, we investigated the functions of α2-adrenergic receptors in the hypothalamus on energy metabolism in mice. Our study confirmed the expression of α2-adrenergic receptors in hypothalamic dopaminergic neurons and assessed metabolic phenotypes, including food intake and energy expenditure, after treatment with guanabenz, an α2-adrenergic receptor agonist. Guanabenz treatment significantly increased food intake (0.25 ± 0.03 g vs. 0.98 ± 0.05 g, p < 0.001) and body weight (-0.1 ± 0.04 g vs. 0.33 ± 0.03 g, p < 0.001) within 6 h post-treatment. Furthermore, guanabenz markedly elevated energy expenditure parameters, including respiratory exchange ratio (RER, 1.017 ± 0.007 vs. 1.113 ± 0.03, p < 0.01) and carbon dioxide production (1.512 ± 0.018 mL/min vs. 1.635 ± 0.036 mL/min, p < 0.05), compared to vehicle-treated controls. Furthermore, using chemogenetic techniques, we demonstrated that the altered metabolic phenotypes induced by guanabenz treatment were effectively reversed by inhibiting the activity of dopaminergic neurons in the hypothalamic arcuate nucleus (ARC) using a chemogenetic technique. Our findings suggest functional connectivity between hypothalamic α2-adrenergic receptor signals and dopaminergic neurons in metabolic controls.},
}
@article {pmid40330423,
year = {2009},
author = {Mollazadeh, M and Aggarwal, V and Thakor, NV and Law, AJ and Davidson, A and Schieber, MH},
title = {Coherency between Spike and LFP Activity in M1 during Hand Movements.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2009},
number = {},
pages = {506-509},
pmid = {40330423},
issn = {1948-3546},
abstract = {Local field potentials (LFP) represent the dendritic activity of a population of cells near the recording electrode. However, how LFP activity is related to single unit activity, and if it provides any additional information has not been well studied. Previously we have shown that temporal spectral modulation of LFP activity can be used to decode dexterous movements of the hand. Here, we analyze simultaneous spike and LFP recordings from M1 cortex in a rhesus monkey performing fine hand movements. Using multitaper spectral analysis, we found that both LFP and spiking activity show an increase in power in the <12 Hz and 70-200 Hz (high gamma) ranges, but, were significantly coherent only during the pre-movement time at low frequencies (<12 Hz). Furthermore, using either LFP or spiking activity, we were able to decode amongst three different hand grasps with high accuracy (99% using 97 spikes and 70% using 8 LFP channels). However, while spikes were better in decoding movement types, LFPs performed much better (94% success) than spikes (77%) when differentiating between rest and movement. We also found that combining spike and LFP activity can improve decoding performance when fewer spikes are considered, as may be the case when single unit recordings degrade over time (71% using 40 spikes and 76% using 8 LFPs, vs 88% using 40 spikes + 8 LFPs). Thus, the relative stability of LFP activity can help augment single-unit activity for the chronic operation of a multimodal BMI.},
}
@article {pmid40329250,
year = {2025},
author = {Karimi, J and Cherono, A and Alegana, V and Mutua, M and Kiarie, H and Muthee, R and Temmerman, M and Gichangi, P},
title = {Geographic inequalities, and social-demographic determinants of reproductive, maternal and child health at sub-national levels in Kenya.},
journal = {BMC public health},
volume = {25},
number = {1},
pages = {1656},
pmid = {40329250},
issn = {1471-2458},
support = {211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; 211208/WT_/Wellcome Trust/United Kingdom ; },
abstract = {BACKGROUND: Global initiatives have emphasized tracking indicators to monitor progress, particularly in countries with the highest maternal and child mortality. Routine data can be used to monitor indicators for improved target setting at national and subnational levels. Our objective was to assess the geographic inequalities in estimates of reproductive, maternal and child health indicators from routine data at the subnational level in Kenya.<br><br>METHODS: Monthly data from 47 counties clustered in 8 regions, from January 2018 to December 2021 were assembled from the District Health Information Software version 2 (DHIS2) in Kenya. This included women of reproductive age receiving family planning commodities, pregnant women completing four antenatal care visits, deliveries conducted by skilled birth attendants, fully immunized children at 1&#xa0;year and number of maternal deaths at health facilities, from which five indicators were constructed with denominators. A hierarchical Bayesian model was used to generate estimates of the five indicators at the at sub-national levels(counties and sub counties), adjusting for four determinants of health. A reproductive, maternal, and child health (RMCH) index was generated from the 5 indicators to compare overall performance across the continuum of care in reproductive, maternal and child health across the different counties.<br><br>RESULTS: The DHIS2 data quality for the selected 5 indicators was acceptable with detection of less than 3% outliers for the Facility Maternal Mortality Ratio (FMMR) and less than 1% for the other indicators. Overall, counties in the north-eastern, eastern and coastal regions had the lowest RMCH index due to low service coverage and high FMMR. Full immunization coverage at 1&#xa0;year (FIC) had the highest estimate (79.3%, BCI: 77.8-80.5%), while Women of Reproductive age receiving FP commodities had the lowest estimate (38.6%, BCI: 38.2-38.9%). FMMR was estimated at 105.4, (BCI 67.3-177.1)Health facility density was an important determinant in estimating all five indicators. Maternal education was positively correlated with higher FIC coverage, while wealthier sub counties had higher FMMR.<br><br>CONCLUSIONS: Tracking of RMCH indicators revealed geographical inequalities at the County and subcounty level, often masked by national-level estimates. These findings underscore the value of routine monitoring indicators as a potential for evidence-based sub-national planning and precision targeting of interventions to marginalized populations.},
}
@article {pmid40327905,
year = {2025},
author = {Sarankumar, R and Ramkumar, M and Karthik, V and Muthuvel, SK},
title = {Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.},
journal = {Biochemical and biophysical research communications},
volume = {768},
number = {},
pages = {151856},
doi = {10.1016/j.bbrc.2025.151856},
pmid = {40327905},
issn = {1090-2104},
abstract = {Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient. This research aims to improve HER2 status prediction accuracy by proposing an Enhanced Hybrid Model with Optimized Attention Network (EHMOA-net) for histopathology image analysis. The methodology involves patch segmentation using an Encoder-Decoder-based hybrid weights alignment with Multi-Dilated U-net (EDMDU) model applied to the TCGA dataset, followed by preprocessing through enhanced Macenko stain normalization for segmented patches and images from the BCI dataset. Improved non-subsampled shearlet transform is utilized for feature extraction, and the Hybrid Enhanced Rough k-means clustering and Fuzzy C-Means (HERFCM) algorithm is employed to cluster neighboring image patches based on similar features. Finally, HER2 prediction is performed using nested graph neural networks integrated with a visual attention network. The proposed method, implemented in Python, achieves an accuracy of 97.85 %, surpassing existing techniques. These findings demonstrate the effectiveness of EHMOA-net in improving HER2 prediction accuracy and its potential utility in clinical applications.},
}
@article {pmid40327498,
year = {2025},
author = {Xu, Y and Wang, X and Li, J and Zhang, X and Li, F and Gao, Q and Fu, C and Leng, Y},
title = {A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3567392},
pmid = {40327498},
issn = {1558-0210},
abstract = {The anthropomorphic grasping capability of prosthetic hands is critical for enhancing user experience and functional efficiency. Existing prosthetic hands relying on brain-computer interfaces (BCI) and electromyography (EMG) face limitations in achieving natural grasping due to insufficient gesture adaptability and intent recognition. While vision systems enhance object perception, they lack dynamic human-like gesture control during grasping. To address these challenges, we propose a vision-powered prosthetic hand system that integrates two innovations. Spatial Geometry-based Gesture Mapping (SG-GM) dynamically models finger joint angles as polynomial functions of hand-object distance, derived from geometric features of human grasping sequences. These functions enable continuous anthropomorphic gesture transitions, mimicking natural hand movements. Motion Trajectory Regressionbased Grasping Intent Estimation (MTR-GIE) predicts user intent in multi-object environments by regressing wrist trajectories and spatially segmenting candidate objects. Experiments with eight daily objects demonstrated high anthropomorphism (similarity coefficient R[2]=0.911, root mean squared error RMSE=2.47°), rapid execution (3.0710.41 s), and robust success rates (95.43% single-object; 88.75% multi-object). The MTR-GIE achieved 94.35% intent estimation accuracy under varying object spacing. This work pioneers vision-driven dynamic gesture synthesis for prosthetics, eliminating dependency on invasive sensors and advancing real-world usability.},
}
@article {pmid40326979,
year = {2026},
author = {Du, X and Wang, Y and Wang, X and Tian, X and Jing, W},
title = {Neural circuit mechanisms of epilepsy: Maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels.},
journal = {Neural regeneration research},
volume = {21},
number = {2},
pages = {455-465},
doi = {10.4103/NRR.NRR-D-24-00537},
pmid = {40326979},
issn = {1673-5374},
abstract = {Epilepsy, a common neurological disorder, is characterized by recurrent seizures that can lead to cognitive, psychological, and neurobiological consequences. The pathogenesis of epilepsy involves neuronal dysfunction at the molecular, cellular, and neural circuit levels. Abnormal molecular signaling pathways or dysfunction of specific cell types can lead to epilepsy by disrupting the normal functioning of neural circuits. The continuous emergence of new technologies and the rapid advancement of existing ones have facilitated the discovery and comprehensive understanding of the neural circuit mechanisms underlying epilepsy. Therefore, this review aims to investigate the current understanding of the neural circuit mechanisms in epilepsy based on various technologies, including electroencephalography, magnetic resonance imaging, optogenetics, chemogenetics, deep brain stimulation, and brain-computer interfaces. Additionally, this review discusses these mechanisms from three perspectives: structural, synaptic, and transmitter circuits. The findings reveal that the neural circuit mechanisms of epilepsy encompass information transmission among different structures, interactions within the same structure, and the maintenance of homeostasis at the cellular, synaptic, and neurotransmitter levels. These findings offer new insights for investigating the pathophysiological mechanisms of epilepsy and enhancing its clinical diagnosis and treatment.},
}
@article {pmid40323825,
year = {2025},
author = {Kim, H and Chang, WK and Kim, WS and Jang, JH and Lee, YA and Vermehren, M and Peekhaus, N and Colucci, A and Angerhöfer, C and Hömberg, V and Soekadar, SR and Paik, NJ},
title = {Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {218},
pages = {},
doi = {10.3791/67601},
pmid = {40323825},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Stroke Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Male ; Female ; Middle Aged ; Electroencephalography/methods ; Activities of Daily Living ; Aged ; Adult ; Electrooculography/methods ; },
abstract = {This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes electroencephalogram (EEG) and electrooculogram (EOG) signals to help users assist upper limb function in everyday tasks while interacting with a robotic hand. We evaluated the effectiveness of this BCI-robot system using the Berlin Bimanual Test for Stroke (BeBiTS), a set of 10 daily living tasks involving both hands. Eight stroke patients participated in this study, but only four participants could adapt to the BCI robot system training and perform the postBeBiTS. Notably, when the preBeBiTS score for each item was four or less, the BCI robot system showed greater assistive effectiveness in the postBeBiTS assessment. Furthermore, our current robotic hand does not assist with arm and wrist functions, limiting its use in tasks requiring complex hand movements. More participants are required to confirm the training effectiveness of the BCI-robot system, and future research should consider using robots that can assist with a broader range of upper limb functions. This study aimed to determine the BCI-robot system's ability to assist patients in performing daily living activities.},
}
@article {pmid40321898,
year = {2025},
author = {Zhang, S and Song, Y and Lv, S and Jing, L and Wang, M and Liu, Y and Xu, W and Jiao, P and Zhang, S and Wang, M and Liu, J and Wu, Y and Cai, X},
title = {Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0249},
pmid = {40321898},
issn = {2692-7632},
abstract = {Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity in primates enhances our understanding of neural mechanisms, facilitates the study of major brain diseases, enables brain-computer interactions, and supports advancements in artificial intelligence. Traditional imaging methods such as magnetic resonance imaging, positron emission computed tomography, and scalp electroencephalogram are limited in spatial resolution. They cannot accurately capture deep brain signals from individual neurons. With the progress of microelectromechanical systems and other micromachining technologies, single-neuron level detection and stimulation technology in rodents based on microelectrodes has made important progress. However, compared with rodents, human and nonhuman primates have larger brain volume that needs deeper implantation depth, and the test object has higher safety and device preparation requirements. Therefore, high-resolution devices suitable for long-term detection in the brains of primates are urgently needed. This paper reviewed electrode array devices used for electrophysiological and electrochemical detections in primates' deep brains. The research progress of neural recording and stimulation technologies was introduced from the perspective of electrode type and device structures, and their potential value in neuroscience research and clinical disease treatments was discussed. Finally, it is speculated that future electrodes will have a lot of room for development in terms of flexibility, high resolution, deep brain, and high throughput. The improvements in electrode forms and preparation process will expand our understanding of deep brain neural activities, and bring new opportunities and challenges for the further development of neuroscience.},
}
@article {pmid40321282,
year = {2025},
author = {Forenzo, D and Zhang, Y and Wittenberg, GF and He, B},
title = {Continuous Reaching and Grasping with a BCI Controlled Robotic Arm in Healthy and Stroke-Affected Individuals.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.16.25325551},
pmid = {40321282},
abstract = {Recent advancements in signal processing techniques have enabled non-invasive Brain-Computer Interfaces (BCIs) to control assistive devices, like robotic arms, directly with users' EEG signals. However, the applications of these systems are currently limited by the low signal-to-noise ratio and spatial resolution of EEG from which brain intention is decoded. In this study, we propose a motor-imagery (MI) paradigm, inspired by the mechanisms of a computer mouse, that adds an additional "click" signal to an established 2D movement BCI paradigm. The additional output signal increases the degrees of freedom of the BCI system and may enable more complex tasks. We evaluated this paradigm using deep learning (DL) based signal processing on both healthy subjects and stroke-survivors in online BCI tasks derived from two potential applications: clicking on virtual targets and moving physical objects with a robotic arm in a continuous reach-and-grasp task. The results show that subjects were able to control both movement and clicking simultaneously to grab, move, and place up to an average of 7 cups in a 5-minute run using the robotic arm. The proposed paradigm provides an additional degree of freedom to EEG BCIs, and improves upon existing systems by enabling continuous control of reach-and-grasp tasks instead of selecting from a discrete list of predetermined actions. The tasks studied in these experiments show BCIs may be used to control computer cursors or robotic arms for complex real-world or clinical applications in the near future, potentially improving the lives of both healthy individuals and motor-impaired patients.},
}
@article {pmid40318536,
year = {2025},
author = {Tang, E and Li, J and Liu, H and Peng, C and Zhou, D and Hu, S and Chen, H},
title = {Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia.},
journal = {Journal of psychiatric research},
volume = {186},
number = {},
pages = {434-444},
doi = {10.1016/j.jpsychires.2025.04.030},
pmid = {40318536},
issn = {1879-1379},
abstract = {People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, ηp[2] = .092; task two: p = .021, ηp[2] = .074; task three: p = .015, ηp[2] = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = -.344, p = .040) or higher medication dosages (r = -.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication.},
}
@article {pmid40317785,
year = {2025},
author = {Stieglitz, T and Bersch, I and Mrachacz-Kersting, N and Pasluosta, C},
title = {Differences and Commonalities of Electrical Stimulation Paradigms After Central Paralysis and Amputation.},
journal = {Artificial organs},
volume = {},
number = {},
pages = {},
doi = {10.1111/aor.15017},
pmid = {40317785},
issn = {1525-1594},
abstract = {BACKGROUND: Patients with spinal cord injury (SCI) or with severe brain stroke suffer from life-lasting functional and sensory impairments. Other traumatic injuries such as limb loss after an accident or disease also affect motor function and sensory feedback and impair quality of life in those individuals. Invasive and non-invasive functional electrical stimulation (FES) is a well-established method to partially restore function and sensory feedback of paralyzed and phantom limbs. It is also a supporting technology for the rehabilitation of the neuromuscular system and for complementing assistive devices.<br><br>METHODS: This work reviews the current state-of-the-art of FES as a technology for restoring function and supporting rehabilitation therapy and assistive devices.<br><br>RESULTS: Electrodes, electrical stimulation, use of brain signals for rehabilitation and control, and sensory feedback are covered as parts of the whole. A perspective is given on how clinical and research protocols developed for patients with SCI and brain injuries can be translated to the treatment of patients with an amputation and vice versa. We further elaborate on how motor learning strategies with quantitative electrophysiological and kinematic measurements may help caregivers in the rehabilitation process. Insights from practitioners (collected during a workshop of the IFESS 2025) have been integrated to summarize common needs, open questions, and challenges.<br><br>CONCLUSIONS: The information from the literature and from practitioners was integrated to propose the next steps towards establishing common guidelines and measures of FES in clinical practice towards evidence-driven treatment and objective assessments.},
}
@article {pmid40317558,
year = {2025},
author = {Du, X and Shen, F and Yu, C and Wang, Y and Yu, J and Yao, L and Liu, N and Zhuang, S},
title = {SMYD3 as an Epigenetic Regulator of Renal Tubular Cell Survival and Regeneration Following Acute Kidney Injury in Mice.},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
volume = {39},
number = {9},
pages = {e70533},
doi = {10.1096/fj.202500089R},
pmid = {40317558},
issn = {1530-6860},
support = {82370698//MOST | National Natural Science Foundation of China (NSFC)/ ; 82070700//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; *Acute Kidney Injury/metabolism/pathology/genetics ; Mice ; *Histone-Lysine N-Methyltransferase/metabolism/genetics/antagonists &amp; inhibitors ; *Epigenesis, Genetic ; *Kidney Tubules/metabolism/pathology ; *Regeneration ; Male ; Cell Survival ; Mice, Inbred C57BL ; Apoptosis ; Cell Proliferation ; Reperfusion Injury/metabolism/pathology ; Histones/metabolism ; },
abstract = {The protein SET and MYND-Domain Containing 3 (SMYD3) is a methyltransferase that modifies various non-histone and histone proteins, linking it to tumorigenesis and cyst formation. However, its role in acute kidney injury (AKI) remains unclear. This study investigates the role and mechanism of AKI using a murine model of ischemia-reperfusion (IR)-induced AKI. After IR injury, SMYD3 and H3K4me3 levels increased in the kidneys, correlating with renal dysfunction, tubular cell injury, and apoptosis. Administration of BCI-121, a selective SMYD3 inhibitor, exacerbated IR-induced tubular cell injury and apoptosis, leading to more severe renal dysfunction and pathological changes. Pharmacological inhibition of SMYD3 also impaired the dedifferentiation and proliferation of renal tubular cells, key regenerative processes in injured kidneys, as evidenced by decreased expression of vimentin, snail, proliferating cell nuclear antigen (PCNA), cyclin D1, and retinoblastoma protein (RB). Additionally, SMYD3 inhibition reduced phosphorylation of the epithelial growth factor receptor (EGFR) and AKT, as well as EGFR expression in damaged kidneys. Finally, both BCI-121 and SMYD3 siRNA reduced EGF-induced expression of vimentin, snail, cyclin D1, PCNA, and EGFR, along with phosphorylation of RB and AKT in cultured renal tubular cells. Chip assay indicated that SMYD3 and H3K4me3 are enriched at the promoter of EGFR and SMYD3 inhibition blocked this response. These data suggest that SMYD3 plays an important role as an epigenetic regulator of renal tubular cell survival and regenerative pathways following kidney injury. Targeting SMYD3 or its epigenetic effects could offer therapeutic potential for enhancing kidney regeneration in AKI and related renal diseases.},
}
@article {pmid40315903,
year = {2025},
author = {Chueh, SY and Chen, Y and Subramanian, N and Goolsby, B and Navarro, P and Oweiss, K},
title = {Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add37b},
pmid = {40315903},
issn = {1741-2552},
abstract = {Brain Computer Interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales. Here, we propose a meta plasticity model of BCI learning and skill consolidation at the single cell and population levels comprised of three elements: a) behavioral time scale synaptic plasticity (BTSP), b) intrinsic plasticity (IP) and c) synaptic scaling (SS) operating at time scales from seconds to minutes to hours and days. Notably, the model is able to explain representational drift - a frequent and widespread phenomenon that adversely affects BCI control and continued use. Approach: We developed an all-optical approach to characterize IP, BTSP and SS with single cell resolution in awake mice using fluorescent two photon (2P) GCaMP7s imaging and optogenetic stimulation of the soma targeted ChRmineKv2.1. We further trained mice on a one-dimensional (1D) BCI control task and systematically characterized within session (seconds to minutes) learning as well as across sessions (days and weeks) with different neural ensembles. Main results: On the time scale of seconds, substantial BTSP could be induced and was followed by significant IP over minutes. Over the time scale of days and weeks, these changes could predict BCI control proficiency, suggesting that BTSP and IP might be complemented by SS to stabilize and consolidate BCI control. Significance: Our results provide early experimental support for a meta plasticity model of continual BCI learning and skill consolidation. The model predictions may be used to design and calibrate neural decoders with complete autonomy while considering the temporal and spatial scales of plasticity mechanisms. With the power of modern-day machine learning (ML) and artificial Intelligence (AI), fully autonomous neural decoding and adaptation in BCIs might be achieved with minimal to no human intervention. .},
}
@article {pmid40315795,
year = {2025},
author = {Chen, TY and Lien, KH and Yeh, KT and Tu, JC and Wai-Yee Ho, V and Chan, KC},
title = {Bonebridge BCI 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {193},
number = {},
pages = {112370},
doi = {10.1016/j.ijporl.2025.112370},
pmid = {40315795},
issn = {1872-8464},
abstract = {OBJECTIVE: To evaluate the safety and efficacy of Bonebridge bone conduction implant (BCI) 602 implantation in syndromic and non-syndromic patients with bilateral microtia and aural atresia (AA).<br><br>METHODS: This retrospective study included 15 patients (3 syndromic, 12 non-syndromic) with bilateral microtia and AA who underwent BCI 602 implantation at a tertiary medical center between January 2022 and June 2024. Intraoperative and postoperative complications were recorded, with a minimum follow-up of six months. Audiological outcomes, including functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were analyzed.<br><br>RESULTS: No intraoperative complications occurred in any cases. One minor postoperative complication (6.7&#xa0;%) was reported in a non-syndromic patient during follow-up. The mean unaided and aided sound field threshold pure tone averages were 60.3&#xa0;&#xb1;&#xa0;8.7&#xa0;dB HL and 23.8&#xa0;&#xb1;&#xa0;3.9&#xa0;dB HL, respectively, yielding an FHG of 36.6&#xa0;&#xb1;&#xa0;9.3&#xa0;dB HL (p&#xa0;<&#xa0;0.05). SRT improved from 57.0&#xa0;&#xb1;&#xa0;5.9&#xa0;dB HL to 27.0&#xa0;&#xb1;&#xa0;6.5&#xa0;dB HL in quiet and from 0.3&#xa0;&#xb1;&#xa0;8.5&#xa0;dB SNR to -10.7&#xa0;&#xb1;&#xa0;4.2&#xa0;dB SNR in noise. WRS increased from 45.1&#xa0;&#xb1;&#xa0;20.7&#xa0;% to 89.9&#xa0;&#xb1;&#xa0;5.6&#xa0;% in quiet and from 40.9&#xa0;&#xb1;&#xa0;20.9&#xa0;% to 80.9&#xa0;&#xb1;&#xa0;13.8&#xa0;% in noise (p&#xa0;<&#xa0;0.05). Improvements in FHG, SRT, and WRS were comparable between syndromic and non-syndromic groups (p&#xa0;>&#xa0;0.05).<br><br>CONCLUSIONS: The Bonebridge BCI 602 is a safe and effective option for hearing restoration in both syndromic and non-syndromic patients with bilateral microtia and AA. Its compact design enhances surgical safety and minimizes risks to critical structures, particularly in syndromic patients with complex temporal bone anatomy.},
}
@article {pmid40315092,
year = {2025},
author = {Zeng, F and Wen, X and Tang, H and Hu, G and Hou, W and Zhang, X},
title = {Age-related Changes in Action Observation EEG Response and its Effect on BCI Performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3566371},
pmid = {40315092},
issn = {1558-0210},
abstract = {Action observation-based brain-computer interface (AO-BCI) can simultaneously elicit steady-state motion visual evoked potential in the occipital region and sensorimotor rhythm in the sensorimotor region, demonstrating substantial potential in neuro-rehabilitation applications. While current AO-BCI research primarily focuses on the younger population, this study conducted a comparative investigation of age-related differences in EEG response to the AO-BCI by enrolling 18 older and 18 younger subjects. We employed task discriminant component analysis (TDCA) to decode observed actions and performed comprehensive analyses of prefrontal EEG responses, i.e. approximate entropy (ApEn), sample entropy (SamEn), and rhythm power ratios (RPR), and the whole-brain functional network. Regression analyses were subsequently conducted to analyze the effects on the classification accuracy. Results revealed significantly diminished TDCA accuracy in older subjects (77.01% ± 14.67%) compared to younger subjects (87.22% ± 15.22%). Age-dependent EEG responses emerged across multiple dimensions: 1) Prefrontal ApEn, SamEn, and RPR exhibited distinct aging patterns; 2) Brain network analysis uncovered significant intergroup differences in α and β band connectivity strength; 3) θ band network topology demonstrated reduced prefrontal nodal degree along with enhanced global efficiency in older subjects. Regression analysis identified a robust inverse relationship between the β/θ RPR during stimulation and overall accuracy. And the β/θ RPR and the β band ApEn might be the main factor that causing individual differences in the identification accuracy in older and younger subjects, respectively. This study provides novel insights into age-related neuro-mechanisms in AO-BCI, establishing quantitative relationships between specific EEG features and BCI performance. These findings would offer guidelines for optimizing AO-BCI in rehabilitation.},
}
@article {pmid40313536,
year = {2025},
author = {Xiong, W and Ma, L and Li, H},
title = {Synthesizing intelligible utterances from EEG of imagined speech.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1565848},
pmid = {40313536},
issn = {1662-4548},
abstract = {Decoding natural language directly from neural activity is of great interest to people with limited communication means. Being a non-invasive and convenient approach, the electroencephalogram (EEG) offers better portability and wider application potentiality. We present an EEG-to-speech system (ETS) that synthesizes audible, and highly understandable language by EEG of imagined speech. Our ETS applies a specially designed X-shape deep neural network (DNN) to realize an End-to-End correspondence between imagined speech EEG and the speech. The system innovatively incorporates dynamic time warping into the network's training process, using actual speech EEG data as a bridge to temporally align imagined speech EEG signals with speech signals. The ETS performance was evaluated on 13 participants who pretraining four Chinese disyllabic words. These words cover all four tones and 40% of the phonemes in Chinese. Our synthesized utterances' average accuracy across all subjects was 91.23%, the average MOS value was 3.50, and the best accuracy was 99% with an MOS value of 3.99. Furthermore, a partial feedback mechanism for DNN and spectral subtraction-based speech enhancement are introduced to improve the quality of generated speech. Our findings prove that non-invasive approaches can be a significant step in regaining verbal language ability.},
}
@article {pmid40313458,
year = {2025},
author = {Xu, Y and Yu, B and Chen, X and Peng, A and Tao, Q and He, Y and Wang, Y and Li, XM},
title = {DSCT: a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing.},
journal = {National science review},
volume = {12},
number = {5},
pages = {nwaf030},
pmid = {40313458},
issn = {2053-714X},
abstract = {Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT)-an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.},
}
@article {pmid40313281,
year = {2025},
author = {Lee, J and Park, H and Spencer, A and Gong, X and DeNardo, M and Vashahi, F and Pollet, F and Norris, S and Hinton, H and El Fakiri, M and Mehrotra, A and Huang, R and Bar, J and Swann, J and Affonseca, D and Armitage, O and Garry, R and Grumbles, E and Murali, A and Tasserie, J and Fragoso, C and Albouy, R and Couturier, CP and Paulk, AC and Coughlin, B and Cash, SS and Costine-Bartell, B and Baskin, B and Stinson, T and Moradi Chameh, H and Movahed, M and Bazrgar, B and Falby, M and Zhang, D and Valiante, TA and Francis, A and Candanedo, C and Bermudez, R and Liu, J and Ye, T and Le Floch, P},
title = {Clinical translation of ultrasoft Fleuron probes for stable, high-density, and bidirectional brain interfaces.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.24.25326126},
pmid = {40313281},
abstract = {Building brain foundation models to capture the underpinning neural dynamics of human behavior requires large functional neural datasets for training, which current implantable Brain-Computer Interfaces (iBCIs) cannot achieve due to the instability of rigid materials in the brain. How can we realize high-density neural recordings with wide brain region access at single-neuron resolution, while maintaining the long-term stability required? In this study, we present a novel approach to overcome these trade-offs, by introducting Fleuron, a family of ultrasoft, ultra-low-k dielectric materials compatible with thin-film scalable microfabrication techniques. We successfully integrate up to 1,024 sites within a single minimally-invasive Fleuron depth electrode. The combination of the novel implant material and geometry enables single-unit level recordings for 18 months in rodent models, and achieves a large number of units detected per electrode across animals. 128-site Fleuron probes, that cover 8x larger tissue volume than state-of-the-art polyimide counterparts, can track over 100 single-units over months. Stability in neural recordings correlates with reduced glial encapsulation compared to polyimide controls up to 9-month post-implantation. Fleuron probes are integrated with a low-power, mixed-signal ASIC to achieve over 1,000 channels electronic interfaces and can be safely implanted in depth using minimally-invasive surgical techniques via a burr hole approach without requiring specialized robotics. Fleuron probes further create a unique contrast in clinical 3T MRI, allowing for post-operative position confirmation. Large-animal and ex vivo human tissue studies confirm safety and functionality in larger brains. Finally, Fleuron probes are used for the first time ever intraoperatively during planned resection surgeries, confirming in-human usability, and demonstrating the potential of the technology for clincical translation in iBCIs.},
}
@article {pmid40313239,
year = {2025},
author = {Sun, S and Li, C and Xie, X and Wan, X and Liu, T and Li, D and Duan, D and Yu, H and Wen, D},
title = {Digital therapeutics for cognitive impairments associated with schizophrenia: our opinion.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1535309},
pmid = {40313239},
issn = {1664-0640},
}
@article {pmid40311414,
year = {2025},
author = {Pattanayak, S and Dash, P and Satpathi, S and Sahoo, AK and Das, NR and Nayak, B and Sahoo, SK},
title = {Additive manufacturing of 316 L stainless steel orthopedic implant with improved in vitro hemocompatibility and hydrophilicity for osteoinduction in Wistar rat model.},
journal = {Biomaterials advances},
volume = {175},
number = {},
pages = {214322},
doi = {10.1016/j.bioadv.2025.214322},
pmid = {40311414},
issn = {2772-9508},
abstract = {Long-term implantation is still challenging for 316 L stainless steel (SS) due to low hydrophilicity and borderline corrosion, which further advances a coating to induce osteoinduction and prevent metallic ions leaching. Here, arc-based direct energy deposition technology is introduced to fabricate 316 L SS via additive manufacturing (AM). The AM 316 L SS are subjected to metallurgical, mechanical, chemical, in vitro and in vivo analyses for their possible orthopedic applications. Compared to commercially available 316 L SS implant, the AM implants encompass γ-austenite phases with δ-ferrite structures that induce pinning dislocations, improve resistance to crack propagation and enhance mechanical performances. The evolution of δ-ferrite structures with higher inter-layer dwell times promotes Cr and Mo content, improving passive layer thickness and thereby enhancing the corrosion resistance, which prevents the release of toxic ions into the bloodstream and cellular metabolism. Additionally, improved BCI with less adherence and activation of platelets on the AM deposits indicates uninterrupted blood flow along the site of implantation and improved thrombo-resistance. The reduction in contact angle (highly hydrophilic) promotes the adsorption of body fluid and proteinaceous materials that boost the adhesion, proliferation, and cytoplasmic extension of cells (from in vitro), marrow spaces, collagen fibers, and tissue adherences (from in vivo). The AM implants do not show any acute toxicity in blood profiles and vital organs (liver and kidney) after long-term implantation in Wistar rats. These peculiarities highlight the hemocompatibility and osteointegration capabilities of AM implants with a faster bone regeneration rate.},
}
@article {pmid40309860,
year = {2025},
author = {Gong, J and Liu, H and Duan, F and Che, Y and Yan, Z},
title = {Research on Adaptive Discriminating Method of Brain-Computer Interface for Motor Imagination.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040412},
pmid = {40309860},
issn = {2076-3425},
support = {MKF202203//Open Foundation of Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University/ ; },
abstract = {(1) Background: Brain-computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual's imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability.},
}
@article {pmid40309849,
year = {2025},
author = {Omer, K and Ferracuti, F and Freddi, A and Iarlori, S and Vella, F and Monteriù, A},
title = {Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040359},
pmid = {40309849},
issn = {2076-3425},
abstract = {BACKGROUND/OBJECTIVES: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.<br><br>METHODS: The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.<br><br>RESULTS: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.<br><br>CONCLUSIONS: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.},
}
@article {pmid40309789,
year = {2025},
author = {Khan, S and Kallis, L and Mee, H and El Hadwe, S and Barone, D and Hutchinson, P and Kolias, A},
title = {Invasive Brain-Computer Interface for Communication: A Scoping Review.},
journal = {Brain sciences},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/brainsci15040336},
pmid = {40309789},
issn = {2076-3425},
abstract = {BACKGROUND: The rapid expansion of the brain-computer interface for patients with neurological deficits has garnered significant interest, and for patients, it provides an additional route where conventional rehabilitation has its limits. This has particularly been the case for patients who lose the ability to communicate. Circumventing neural injuries by recording from the intact cortex and subcortex has the potential to allow patients to communicate and restore self-expression. Discoveries over the last 10-15 years have been possible through advancements in technology, neuroscience, and computing. By examining studies involving intracranial brain-computer interfaces that aim to restore communication, we aimed to explore the advances made and explore where the technology is heading.<br><br>METHODS: For this scoping review, we systematically searched PubMed and OVID Embase. After processing the articles, the search yielded 41 articles that we included in this review.<br><br>RESULTS: The articles predominantly assessed patients who had either suffered from amyotrophic lateral sclerosis, cervical cord injury, or brainstem stroke, resulting in tetraplegia and, in some cases, difficulty speaking. Of the intracranial implants, ten had ALS, six had brainstem stroke, and thirteen had a spinal cord injury. Stereoelectroencephalography was also used, but the results, whilst promising, are still in their infancy. Studies involving patients who were moving cursors on a screen could improve the speed of movement by optimising the interface and utilising better decoding methods. In recent years, intracortical devices have been successfully used for accurate speech-to-text and speech-to-audio decoding in patients who are unable to speak.<br><br>CONCLUSIONS: Here, we summarise the progress made by BCIs used for communication. Speech decoding directly from the cortex can provide a novel therapeutic method to restore full, embodied communication to patients suffering from tetraplegia who otherwise cannot communicate.},
}
@article {pmid40307763,
year = {2025},
author = {Zhang, L and Guan, X and Xue, H and Liu, X and Zhang, B and Liu, S and Ming, D},
title = {Sex-specific patterns in social visual attention among individuals with autistic traits.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {440},
pmid = {40307763},
issn = {1471-244X},
support = {Grant Nos. 23JCZDJC01030//Natural Science Foundation of Tianjin (Key Program)/ ; 2022YGZD02//Tianjin Education Commission Research Program Project/ ; Grant Nos. 81925020//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Autism is a neurodevelopmental condition more prevalent in males, with sex differences emerging in both prevalence and core symptoms. However, most studies investigating behavioral and cognitive features of autism tend to include more male samples, leading to a male-biased understanding. The sex imbalance limits the specificity of these features, especially in female individuals with autism. Hence, it is necessary to explore sex-related differences in behavioral-cognitive traits linked to autism in the general population.<br><br>METHODS: In this study, we designed a dynamic emotion-discrimination task to investigate sex differences in attention to emotional stimuli among the general population with autistic traits. Behavioral and eye movement data were recorded during the task, and the Autism-Spectrum Quotient (AQ) was used to assess autistic traits. Qualitative and quantitative methods were used to analyze gaze patterns in male and female groups. Additionally, correlation analyses were conducted to examine the relationship between AQ scores and proportion of fixation time in both groups.<br><br>RESULTS: Significant sex differences in attention to the eye regions of faces were observed, with females focusing more on the eyes than males. Correlation analyses revealed that, in males, lower eye-looking was associated with higher levels of autistic traits, whereas no such association was found in females.<br><br>CONCLUSIONS: Overall, these results reveal that attention patterns to emotional faces differed between females and males, and autistic traits predicted the trend of eye-looking in males. These findings suggest that sex-related stratification in social attention should be considered in clinical&#xa0;contexts.},
}
@article {pmid40307561,
year = {2025},
author = {, and Ferrante, O and Gorska-Klimowska, U and Henin, S and Hirschhorn, R and Khalaf, A and Lepauvre, A and Liu, L and Richter, D and Vidal, Y and Bonacchi, N and Brown, T and Sripad, P and Armendariz, M and Bendtz, K and Ghafari, T and Hetenyi, D and Jeschke, J and Kozma, C and Mazumder, DR and Montenegro, S and Seedat, A and Sharafeldin, A and Yang, S and Baillet, S and Chalmers, DJ and Cichy, RM and Fallon, F and Panagiotaropoulos, TI and Blumenfeld, H and de Lange, FP and Devore, S and Jensen, O and Kreiman, G and Luo, H and Boly, M and Dehaene, S and Koch, C and Tononi, G and Pitts, M and Mudrik, L and Melloni, L},
title = {Adversarial testing of global neuronal workspace and integrated information theories of consciousness.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40307561},
issn = {1476-4687},
abstract = {Different theories explain how subjective experience arises from brain activity[1,2]. These theories have independently accrued evidence, but have not been directly compared[3]. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)[4,5] and global neuronal workspace theory (GNWT)[6-10] via a theory-neutral consortium[11-13]. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof[12]. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here[14-17]. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.},
}
@article {pmid40306303,
year = {2025},
author = {Dinh, TH and Singh, AK and Doan, QM and Linh Trung, N and Nguyen, DN and Lin, CT},
title = {An EEG signal smoothing algorithm using upscale and downscale representation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add297},
pmid = {40306303},
issn = {1741-2552},
abstract = {OBJECTIVE: Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal's features is important in EEG signal analysis and brain-computer interface (BCI). This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict processing.<br><br>APPROACH: Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal.<br><br>MAIN RESULTS: Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR &#x2264; 5 dB), where our fitting error is only 86.4%-90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based cognitive conflict processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific cognitive conflict peaks, i.e. the prediction error negativity (PEN) and error-related positive potential (Pe), can be easily observed at multiple line-width levels, while the insignificant ones are eliminated.<br><br>SIGNIFICANCE: These results demonstrated not only the advance of the proposed approach but also its impact on classification accuracy enhancement.},
}
@article {pmid40305243,
year = {2025},
author = {Yang, X and Li, Y and Zhang, J and Tian, H and Li, S and Pan, G},
title = {EvoMoE: Evolutionary Mixture-of-Experts for SSVEP-EEG classification with User-Independent Training.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3565882},
pmid = {40305243},
issn = {2168-2208},
abstract = {The analysis of EEG data in BCI systems captures unique individual characteristics, presenting diverse patterns that deviate from conventional identical distribution assumptions. Therefore, applying AI models directly to brain data becomes challenging due to the non-identical distribution issue. Meanwhile, as user numbers in BCI systems rise, scalable models are crucial to handle the growing data volume. Moreover, the limited availability of individual data necessitates the use of collective data for training, requiring models with strong generalization capabilities. To address these challenges, we propose Evolutionary Mixture of Experts (EvoMoE), a framework leveraging a set of diverse experts to model data from individuals. Users with similar distributions are grouped together, allowing experts to handle EEG data with different distribution types. The gating network of EvoMoE selects experts that closely match the distribution of the current sample, effectively tackling non-identical distribution issues. When encountering an unrecognized distribution, a new expert is introduced to accommodate the new data pattern, ensuring model adaptability. Evaluations on two 40-category BCI Speller datasets demonstrate significant performance improvements over state-of-the-art methods. On the BETA dataset, our online EvoMoE achieves 13.06% increase in accuracy and a 27.24-point increase in high information transfer rate (ITR) compared to the online UI method. The Bench dataset shows 3.64% increase in accuracy and a 10.42-point increase in ITR. These qualities make it a promising solution for practical BCI implementation, while setting the stage for the development of comprehensive biological big models.},
}
@article {pmid40302943,
year = {2025},
author = {Wu, X and Ye, Y and Sun, M and Mei, Y and Ji, B and Wang, M and Song, E},
title = {Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0192},
pmid = {40302943},
issn = {2692-7632},
abstract = {Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare.},
}
@article {pmid40302941,
year = {2025},
author = {Feng, C and Cao, L and Wu, D and Zhang, E and Wang, T and Jiang, X and Chen, J and Wu, H and Lin, S and Hou, Q and Zhu, J and Yang, J and Sawan, M and Zhang, Y},
title = {Acoustic Inspired Brain-to-Sentence Decoder for Logosyllabic Language.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0257},
pmid = {40302941},
issn = {2692-7632},
abstract = {Recent advances in brain-computer interfaces (BCIs) have demonstrated the potential to decode language from brain activity into sound or text, which has predominantly focused on alphabetic languages, such as English. However, logosyllabic languages, such as Mandarin Chinese, present marked challenges for establishing decoders that cover all characters, due to its unique syllable structures, extended character sets (e.g., over 50,000 characters for Mandarin Chinese), and complex mappings between characters and syllables, thus hindering practical applications. Here, we leverage the acoustic features of Mandarin Chinese syllables, constructing prediction models for syllable components (initials, tones, and finals), and decode speech-related stereoelectroencephalography (sEEG) signals into coherent Chinese sentences. The results demonstrate a high sentence-level offline decoding performance with a median character accuracy of 71.00% over the full spectrum of characters in the best participant. We also verified that incorporating acoustic-related features into the design of prediction models substantially enhances the accuracy of initials, tones, and finals. Moreover, our findings revealed that effective speech decoding also involves subcortical structures like the thalamus in addition to traditional language-related brain regions. Overall, we established a brain-to-sentence decoder for logosyllabic languages over full character set with a large intracranial electroencephalography dataset.},
}
@article {pmid40301357,
year = {2025},
author = {Ni, H and Yang, Y and Zhang, F and Sun, Y and Zheng, Y and Zhu, J and Xu, K},
title = {Dataset of long-term multi-site LFP activity with spontaneous chronic seizures in temporal lobe epilepsy rats.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {709},
pmid = {40301357},
issn = {2052-4463},
support = {82272112//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {The characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous seizures and interictal fragments in the chronic period. The LFP data were saved in MATLAB, stored in the Neurodata Without Borders format, and published on the DANDI Archive. We validated the dataset technically through specific signal analysis. In addition, we provided MATLAB codes for basic analyses of this dataset, including power spectral analysis, seizure onset pattern identification, and interictal spike detection. This dataset could reveal how the electrophysiological and epileptic network properties of the brain of rats with chronic TLE changed during epilepsy development, thus help inform the design of adaptive neuromodulation for epilepsy.},
}
@article {pmid40301272,
year = {2025},
author = {Tan, Q and Jia, O and Anderson, BA and Jia, K and Gong, M},
title = {Reward history alters priority map based on spatial relationship, but not absolute location.},
journal = {Psychonomic bulletin &amp; review},
volume = {},
number = {},
pages = {},
pmid = {40301272},
issn = {1531-5320},
abstract = {Attention is rapidly directed to stimuli associated with rewards in past experience, independent of current task goals and physical salience of stimuli. However, despite the robust attentional priority given to reward-associated features, studies often indicate negligible priority toward previously rewarded locations. Here, we propose a relational account of value-driven attention, a mechanism that relies on spatial relationship between items to achieve value-guided selections. In three experiments (N = 124), participants were trained to associate specific locations with rewards (e.g., high-reward: top-left; low-reward: top-right). They then performed an orientation-discrimination task where the target's absolute location (top-left or top-right) or spatial relationship ("left of" or "right of") had previously predicted reward. Performance was superior when the target's spatial relationship matched high-reward than low-reward, irrespective of absolute locations. Conversely, the impact of reward was absent when the target matched the absolute location but not the spatial relationship associated with high reward. Our findings challenge the default assumption of location specificity in value-driven attention, demonstrating a generalizable mechanism that humans adopted to integrate value and spatial information into priority maps for adaptive behavior.},
}
@article {pmid40300857,
year = {2025},
author = {Zhang, W and Pan, X and Wang, L and Li, W and Dai, X and Zheng, M and Guo, H and Chen, X and Xu, Y and Wu, H and He, Q and Yang, B and Ding, L},
title = {Selective BCL-2 inhibitor triggers STING-dependent antitumor immunity via inducing mtDNA release.},
journal = {Journal for immunotherapy of cancer},
volume = {13},
number = {4},
pages = {},
doi = {10.1136/jitc-2024-010889},
pmid = {40300857},
issn = {2051-1426},
abstract = {BACKGROUND: The stimulator of interferon genes (STING) signaling pathway has been demonstrated to propagate the cancer-immunity cycle and remodel the tumor microenvironment and has emerged as an appealing target for cancer immunotherapy. Interest in STING agonist development has increased, and the candidates hold significant promise; however, most are still in the early stages of human clinical trials. We found that ABT-199 activated the STING pathway to enhance the immunotherapeutic effect, and provided a ready-to-use small molecule drug for STING signaling activation.<br><br>METHODS: Phosphorylation of STING, TBK1, and IRF3, as well as activation of the interferon-I (IFN-I) signaling pathway, were detected following ABT-199 treatment in various colorectal cancer cells. C57BL/6J and BALB/c mice with subcutaneous tumors were employed to evaluate the in vivo therapeutic effects of the ABT-199 and anti-PD-L1 combination. Flow cytometry and ELISA were employed to analyze the level and activity of tumor-infiltrating T lymphocytes. Immunofluorescence and quantitative real-time PCR were conducted to assess the source and accumulation of double stranded DNA (dsDNA) in the cytoplasm. Chemical cross-linking assay, co-immunoprecipitation, and CRISPR/Cas9-mediated knockout were performed to investigate the molecular mechanism underlying ABT-199-induced voltage-dependent anion channel protein 1 (VDAC1) oligomerization and mitochondrial DNA (mtDNA) release.<br><br>RESULTS: ABT-199 significantly activated the STING signaling pathway in various colorectal cancer cells, which was evidenced by increased phosphorylation of TBK1 and IRF3, and upregulation of C-C motif chemokine ligand 5 (CCL5), C-X-C motif chemokine ligand 10 (CXCL10), and interferon beta transcription. By promoting chemokine expression and cytotoxic T-cell infiltration, ABT-199 promoted antitumor immunity and synergized with anti-PD-L1 therapy to improve antitumor efficacy. ABT-199 induced mtDNA accumulation in the cytoplasm and triggered STING signaling via the canonical pathway. cGAS or STING-KO models significantly abolished both STING signaling activation and the antitumor efficacy of ABT-199. Mechanically, ABT-199 promoted VDAC1 oligomerization by disturbing the binding between BCL-2 and VDAC1, thereby facilitating mtDNA release into the cytoplasm. ABT-199-triggered STING signaling was attenuated when VADC1 was knocked out. Consistently, the antitumor effect of ABT-199 in vivo was abolished in the absence of VDAC1.<br><br>CONCLUSIONS: Our results identify a ready-to-use small molecule compound for STING activation, reveal the underlying molecular mechanism through which ABT-199 activates the STING signaling pathway, and provide a theoretical basis for the use of ABT-199 in cancer immunotherapy.},
}
@article {pmid40297854,
year = {2025},
author = {Liu, X and Jin, X and Yun, L and Chen, Z},
title = {Prefrontal cortex activity during binocular color fusion and rivalry: an fNIRS study.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1527434},
pmid = {40297854},
issn = {1664-2295},
abstract = {INTRODUCTION: Understanding how the brain processes color information from both the left and right eyes is a significant topic in neuroscience. Binocular color fusion and rivalry, which involve advanced cognitive functions in the prefrontal cortex (PFC), provide a unique perspective for exploring brain activity.<br><br>METHODS: This study used functional near-infrared spectroscopy (fNIRS) to examine PFC activity during binocular color fusion and rivalry conditions. The study included two fNIRS experiments: Experiment 1 employed long-duration (90 s) stimulation to assess brain functional connectivity, while Experiment 2 used short-duration (10 s) repeated stimulation (eight trials), analyzed with a generalized linear model to evaluate brain activation levels. Statistical tests were then conducted to compare the differences in brain functional connectivity strength and activation levels.<br><br>RESULTS: The results indicated that functional connectivity strength was significantly higher during the color fusion condition than the color rivalry condition, and the color rivalry condition was stronger than the Mid-Gray field condition. Additionally, brain activation levels during binocular color fusion were significantly greater, with significant differences concentrated in channel (CH) 12, CH13, and CH14. CH12 is located in the dorsolateral prefrontal cortex, while CH13 and CH14 are in the frontal eye fields, areas associated with higher cognitive functions and visual attention.<br><br>DISCUSSION: These findings suggest that binocular color fusion requires stronger brain integration and higher brain activation levels. Overall, this study demonstrates that color fusion is more cognitively challenging than color rivalry, engaging more attention and executive functions. These results provide theoretical support for the development of color-based brain-computer interfaces and offer new insights into future research on the brain's color-visual information processing mechanisms.},
}
@article {pmid40297442,
year = {2025},
author = {Padmaja, GKR and Bhagat, NA and Balasubramani, PP},
title = {Assessing the utility of Fronto-Parietal and Cingulo-Opercular networks in predicting the trial success of brain-machine interfaces for upper extremity stroke rehabilitation.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.08.25325026},
pmid = {40297442},
abstract = {For stroke participants undergoing motor rehabilitation, brain-machine/computer interfaces (BMI/BCI) can potentially improve the efficacy of robotic or exoskeleton-based therapies by ensuring patient engagement and active participation, through monitoring of motor intent. In such interventions, exploring the network-level understanding of the source space, in terms of various cognitive dimensions such as executive control versus reward processing is fruitful in both improving the existing therapy protocols as well as understanding the subject-level differences. This contrasts to traditional approaches that predominantly investigate rehabilitation from resting state data. Moreover, conventional BMIs used for stroke rehabilitation barely accommodate people suffering from moderate to severe cognitive impairments. In this first-of-the-kind study, we explore the cognitive dimensions of a BMI trial by probing the networks that are core to the BMI performance and propose a network connectivity-based measurement with the potential to characterize the cognitive impairments in patients for closed-loop intervention. Specifically, we tease apart the extent of cognitive evaluation versus executive control aspects of impairments in these patients, by measuring the activation power of a major cognitive evaluation network-the Cingulo-Opercular Network (CON) and a major executive control circuit-the Fronto-Parietal network (FPN), and the connectivity between FPN-CON. We test our hypothesis in a previously collected dataset of electroencephalography (EEG) and structural imaging performed on stroke patients with upper limb impairments, while they underwent an exoskeleton-based BMI intervention for about 12 sessions over 4 weeks. Our logistic regression modeling results suggest that the connectivity between FPN and CON networks and their source powers predict trial failure accurately to about 84.2%. In the future, we aim to integrate these observations into a closed-loop design to adaptively control the cognitive difficulty and passively increase the subject's motivation and attention factor for effective BMI learning.},
}
@article {pmid40296528,
year = {2025},
author = {Yang, C and Wang, H and Wang, K and Cao, Z and Ren, F and Zhou, G and Chen, Y and Sun, B},
title = {Silk Fibroin-Based Biomemristors for Bionic Artificial Intelligence Robot Applications.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.5c02480},
pmid = {40296528},
issn = {1936-086X},
abstract = {In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.},
}
@article {pmid40295498,
year = {2025},
author = {Fang, Z and Sims, CR},
title = {Humans learn generalizable representations through efficient coding.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3989},
pmid = {40295498},
issn = {2041-1723},
support = {2024M761999//China Postdoctoral Science Foundation/ ; },
abstract = {Reinforcement learning theory explains human behavior as driven by the goal of maximizing reward. Conventional approaches, however, offer limited insights into how people generalize from past experiences to new situations. Here, we propose refining the classical reinforcement learning framework by incorporating an efficient coding principle, which emphasizes maximizing reward using the simplest necessary representations. This refined framework predicts that intelligent agents, constrained by simpler representations, will inevitably: 1) distill environmental stimuli into fewer, abstract internal states, and 2) detect and utilize rewarding environmental features. Consequently, complex stimuli are mapped to compact representations, forming the foundation for generalization. We tested this idea in two experiments that examined human generalization. Our findings reveal that while conventional models fall short in generalization, models incorporating efficient coding achieve human-level performance. We argue that the classical RL objective, augmented with efficient coding, represents a more comprehensive computational framework for understanding human behavior in both learning and generalization.},
}
@article {pmid40294568,
year = {2025},
author = {Pan, L and Wang, K and Huang, Y and Sun, X and Meng, J and Yi, W and Xu, M and Jung, TP and Ming, D},
title = {Enhancing motor imagery EEG classification with a Riemannian geometry-based spatial filtering (RSF) method.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {188},
number = {},
pages = {107511},
doi = {10.1016/j.neunet.2025.107511},
pmid = {40294568},
issn = {1879-2782},
abstract = {Motor imagery (MI) refers to the mental simulation of movements without physical execution, and it can be captured using electroencephalography (EEG). This area has garnered significant research interest due to its substantial potential in brain-computer interface (BCI) applications, especially for individuals with physical disabilities. However, accurate classification of MI EEG signals remains a major challenge due to their non-stationary nature, low signal-to-noise ratio, and sensitivity to both external and physiological noise. Traditional classification methods, such as common spatial pattern (CSP), often assume that the data is stationary and Gaussian, which limits their applicability in real-world scenarios where these assumptions do not hold. These challenges highlight the need for more robust methods to improve classification accuracy in MI-BCI systems. To address these issues, this study introduces a Riemannian geometry-based spatial filtering (RSF) method that projects EEG signals into a lower-dimensional subspace, maximizing the Riemannian distance between covariance matrices from different classes. By leveraging the inherent geometric properties of EEG data, RSF enhances the discriminative power of the features while maintaining robustness against noise. The performance of RSF was evaluated in combination with ten commonly used MI decoding algorithms, including CSP with linear discriminant analysis (CSP-LDA), Filter Bank CSP (FBCSP), Minimum Distance to Riemannian Mean (MDM), Tangent Space Mapping (TSM), EEGNet, ShallowConvNet (sCNN), DeepConvNet (dCNN), FBCNet, Graph-CSPNet, and LMDA-Net, using six publicly available MI-BCI datasets. The results demonstrate that RSF significantly improves classification accuracy and reduces computational time, particularly for deep learning models with high computational complexity. These findings underscore the potential of RSF as an effective spatial filtering approach for MI EEG classification, providing new insights and opportunities for the development of robust MI-BCI systems. The code for this research is available at https://github.com/PLC-TJU/RSF.},
}
@article {pmid40292964,
year = {2025},
author = {Kasawala, E and Mouli, S},
title = {Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061802},
pmid = {40292964},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; Male ; Adult ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; },
abstract = {In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.},
}
@article {pmid40292808,
year = {2025},
author = {Gao, D and Wang, Y and Fu, P and Qiu, J and Li, H},
title = {Modeling and Parameter Analysis of Basic Single Channel Neuron Mass Model for SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {6},
pages = {},
doi = {10.3390/s25061706},
pmid = {40292808},
issn = {1424-8220},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; Brain-Computer Interfaces ; *Neurons/physiology ; *Models, Neurological ; Visual Cortex/physiology ; Electroencephalography ; Photic Stimulation ; },
abstract = {While steady-state visual evoked potentials (SSVEPs) are widely used in brain-computer interfaces (BCIs) due to their robustness to rhythmic visual stimuli, their generation mechanisms remain poorly understood. Challenges such as experimental complexity, inter-subject variability, and limited physiological interpretability hinder the development of efficient BCI systems. This study employed a single-channel neural mass model (NMM) of V1 cortical dynamics to investigate the biophysical underpinnings of SSVEP generation. By systematically varying synaptic gain, time constants, and external input parameters, we simulated δ/α/γ band oscillations and analyzed their generation principles. The model demonstrates that synaptic gain controls oscillation amplitude and harmonic content, and time constants determine signal decay kinetics and frequency precision, while input variance modulates harmonic stability. Our results reveal how V1 circuitry generates frequency-locked SSVEP responses through excitatory-inhibitory interactions and dynamic filtering mechanisms. This computational framework successfully reproduces fundamental SSVEP characteristics without requiring multi-subject experimental data, offering new insights into the physiological basis of SSVEP-based brain-computer interfaces.},
}
@article {pmid40292646,
year = {2025},
author = {Jeon, Y and Kim, M and Song, KH},
title = {Development of Hydrogels Fabricated via Stereolithography for Bioengineering Applications.},
journal = {Polymers},
volume = {17},
number = {6},
pages = {},
doi = {10.3390/polym17060765},
pmid = {40292646},
issn = {2073-4360},
support = {Incheon National University (International Cooperative) Research Grant in 2020//Incheon National University/ ; },
abstract = {The architectures of hydrogels fabricated with stereolithography (SLA) 3D printing systems have played various roles in bioengineering applications. Typically, the SLA systems successively illuminated light to a layer of photo-crosslinkable hydrogel precursors for the fabrication of hydrogels. These SLA systems can be classified into point-scanning types and digital micromirror device (DMD) types. The point-scanning types form layers of hydrogels by scanning the precursors with a focused light, while DMD types illuminate 2D light patterns to the precursors to form each hydrogel layer at once. Overall, SLA systems were cost-effective and allowed the fabrication of hydrogels with good shape fidelity and uniform mechanical properties. As a result, hydrogel constructs fabricated with the SLA 3D printing systems were used to regenerate tissues and develop lab-on-a-chip devices and native tissue-like models.},
}
@article {pmid40292025,
year = {2025},
author = {Pawlak, WA and Howard, N},
title = {Neuromorphic algorithms for brain implants: a review.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1570104},
pmid = {40292025},
issn = {1662-4548},
abstract = {Neuromorphic computing technologies are about to change modern computing, yet most work thus far has emphasized hardware development. This review focuses on the latest progress in algorithmic advances specifically for potential use in brain implants. We discuss current algorithms and emerging neurocomputational models that, when implemented on neuromorphic hardware, could match or surpass traditional methods in efficiency. Our aim is to inspire the creation and deployment of models that not only enhance computational performance for implants but also serve broader fields like medical diagnostics and robotics inspiring next generations of neural implants.},
}
@article {pmid40290410,
year = {2025},
author = {Ng, JY},
title = {Exploring the intersection of brain-computer interfaces and traditional, complementary, and integrative medicine.},
journal = {Integrative medicine research},
volume = {14},
number = {2},
pages = {101142},
pmid = {40290410},
issn = {2213-4220},
abstract = {Brain-computer interfaces (BCIs) represent a transformative innovation in healthcare, enabling direct communication between the brain and external devices. This educational article explores the potential intersection of BCIs and traditional, complementary, and integrative medicine (TCIM). BCIs have shown promise in enhancing mind-body practices such as meditation, while their integration with energy-based therapies may offer novel insights and measurable outcomes. Emerging advancements, including artificial intelligence-enhanced BCIs, hold potential for improving personalization and expanding the therapeutic efficacy of TCIM interventions. Despite these opportunities, integrating BCIs with TCIM presents considerable ethical, cultural, and practical challenges. Concerns related to informed consent, cultural sensitivity, data privacy, accessibility, and regulatory frameworks must be addressed to ensure responsible implementation. Interdisciplinary collaboration among relevant stakeholders, including TCIM and conventional practitioners, researchers, and policymakers among other relevant stakeholders is crucial for developing integrative healthcare models that balance innovation with patient safety and respect for diverse healing traditions. Future directions include expanding evidence bases to validate TCIM practices through BCI-enhanced research, fostering equitable access to neurotechnological advancements, and promoting global ethical guidelines to navigate complex sociocultural dynamics. BCIs have the potential to revolutionize TCIM, offering novel solutions for complex health challenges and fostering a more inclusive, integrative approach to healthcare, provided that they are utilized responsibly and ethically.},
}
@article {pmid40289727,
year = {2025},
author = {Lin, X and Zhang, X and Chen, J and Liu, J},
title = {Material Selection and Device Design of Scalable Flexible Brain-Computer Interfaces: A Balance Between Electrical and Mechanical Performance.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2413938},
doi = {10.1002/adma.202413938},
pmid = {40289727},
issn = {1521-4095},
support = {DMR-2011754//Directorate for Engineering/ ; },
abstract = {Brain-computer interfaces (BCIs) hold the potential to revolutionize brain function restoration, enhance human capability, and advance our understanding of cognitive mechanisms by directly linking neural signals with hardware. However, the mechanical mismatch between conventional rigid BCIs and soft brain tissue limits long-term interface stability. Next-generation BCIs must achieve long-term biocompatibility while maintaining high performance, enabling the integration of millions of sensors within tissue-level flexible and soft, stable neural interfaces. Lithographic fabrication techniques provide scalable thin-film flexible electronics, but traditional electronic materials often fail to meet the unique requirements of BCIs. This review examines the selection of materials and device design for flexible BCIs, starting with an analysis of intrinsic material properties-Young's modulus, electrical conductivity and dielectric constant. It then explores the integration of material selection with electrode design to optimize electrical circuits and assess key mechanical factors. Next, the correlation between electrical and mechanical performance is analyzed to guide material selection and device design. Finally, recent advances in neural probes are reviewed, highlighting improvements in signal quality, recording stability, and scalability. This review focuses on scalable, lithography-based BCIs, aiming to identify optimal materials and designs for long-term, reliable neural recordings.},
}
@article {pmid40289349,
year = {2025},
author = {Pitt, KM and Boster, JB},
title = {Identifying P300 brain-computer interface training strategies for AAC in children: a focus group study.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/07434618.2025.2495912},
pmid = {40289349},
issn = {1477-3848},
abstract = {The integration of Brain-Computer Interface (BCI) technology into Augmentative and Alternative Communication (AAC) systems introduces new complexities in training, particularly for children with diverse cognitive, sensory, motor, and linguistic abilities. Effective AAC training is crucial for enabling individuals to achieve personal goals and enhance social participation. This study aimed to explore potential training strategies for children using P300 based BCI-AAC systems through focus group discussions with experts in AAC and BCI technologies. Participants identified six key themes for effective training: (1) Scaffolding-developing adaptive systems tailored to each child's developmental level, including preteaching, visual display adaptations, and gamification; (2) Verbal Instructions-emphasizing the use of clear, simple language and spoken prompts; (3) Feedback-incorporating immediate feedback and biofeedback methods to reinforce learning; (4) Positioning-ensuring proper trunk stability and addressing electrode placement; (5) Modeling and Physical Supports-using physical cues and demonstrating BCI-AAC use; and (6) Considerations for Visual Impairment-accommodating cortical visual impairment (CVI) with suitable stimuli and environmental adjustments. These insights offer an initial foundation for identifying P300 BCI-AAC training strategies for children. Further systematic research with end users, support networks, and professionals is needed to validate, refine, and expand interventions that support diverse communication needs.},
}
@article {pmid40289107,
year = {2025},
author = {Zhi, Y and Guo, Y and Li, S and He, X and Wei, H and Laster, K and Wu, Q and Zhao, D and Xie, J and Ruan, S and Lemoine, NR and Li, H and Dong, Z and Liu, K},
title = {FBL promotes hepatocellular carcinoma tumorigenesis and progression by recruiting YY1 to enhance CAD gene expression.},
journal = {Cell death &amp; disease},
volume = {16},
number = {1},
pages = {348},
pmid = {40289107},
issn = {2041-4889},
abstract = {Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Accumulating evidence suggests that epigenetic dysregulation contributes to the initiation and progression of HCC. We aimed to investigate key epigenetic regulators that contribute to tumorigenesis and progression, providing a theoretical basis for targeted therapy for HCC. We performed a comprehensive epigenetic analysis of differentially expressed genes in LIHC from the TCGA database. We identified fibrillarin (FBL), an rRNA 2'-O-methyltransferase, as an essential contributor to HCC. A series of in vitro and in vivo biological experiments were performed to investigate the potential mechanisms of FBL. FBL knockdown suppressed the proliferation of HCC cells. In vivo studies using cell-derived xenograft (CDX), patient-derived xenograft (PDX), and diethylnitrosamine (DEN)-induced HCC models in Fbl liver-specific knockout mice demonstrated the critical role of FBL in HCC carcinogenesis and progression. Mechanistically, FBL regulates the expression of CAD in HCC cells by recruiting YY1 to the CAD promoter region. We also revealed that fludarabine phosphate is a novel inhibitor of FBL and can inhibit HCC growth in vitro and in vivo. The antitumor activity of lenvatinib has been shown to be synergistically enhanced by fludarabine phosphate. Our study highlights the cancer-promoting role of the FBL-YY1-CAD axis in HCC and identifies fludarabine phosphate as a novel inhibitor of FBL. A schematic diagram depicting the FBL-YY1-CAD signaling pathway and its regulatory role in HCC progression.},
}
@article {pmid40288968,
year = {2025},
author = {Pan, L and Sun, X and Wang, K and Cao, Y and Xu, M and Ming, D},
title = {[Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {2},
pages = {272-279},
doi = {10.7507/1001-5515.202411035},
pmid = {40288968},
issn = {1001-5515},
abstract = {Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.},
}
@article {pmid40288455,
year = {2025},
author = {Tang, Y and Tang, Z and Zhou, Y and Luo, Y and Wen, X and Yang, Z and Jiang, T and Luo, N},
title = {A systematic review of resting-state functional-MRI studies in the diagnosis, comorbidity and treatment of postpartum depression.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.04.142},
pmid = {40288455},
issn = {1573-2517},
abstract = {BACKGROUND: Postpartum depression (PPD) is a common and serious mental health problem that affects many new mothers and their families worldwide. In recent years, there has been an increasing number of studies using magnetic resonance techniques (MRI), particularly functional MRI (fMRI), to explore the neuroimaging biomarker of this disease.<br><br>METHODS: PubMed database was used to search for English literature focusing on resting-state fMRI and PPD published up to June 2024.<br><br>RESULTS: After screening, 17 studies were finally identified, among which all 17 studies reported abnormal regions or connectivity compared to health controls (HC), 4 studies reported results considering the differences between PPD and PPD with anxiety (PPD-A), and 2 studies reported biomarkers for the treatment of PPD. The existing studies indicate that PPD is characterized by functional impairments in multiple brain regions, especially the medial prefrontal cortex (MPFC), precentral gyrus and cerebellum. Abnormal functional connectivity has been widely reported in the dorsomedial prefrontal cortex (dmPFC), anterior cingulate cortex (ACC) and the orbitofrontal cortex (OFC). However, none of the four comorbidity studies identified overlapping discriminative biomarkers between PPD and PPD-A. Additionally, the two treatment-related studies consistently reported functional improvements in the amygdala after effective treatment.<br><br>CONCLUSION: The affected brain regions were highly overlapped with major depressive disorder (MDD), suggesting that PPD may be categorized as a potential subtype of MDD. Considering the negative effects of medication on PPD, future efforts should focus on developing non-pharmacological therapies, such as transcranial magnetic stimulation (TMS) and acupuncture, to support women with PPD in overcoming this unique and important phase.},
}
@article {pmid40288311,
year = {2025},
author = {Li, L and Jiang, C},
title = {Electrodeposited coatings for neural electrodes: A review.},
journal = {Biosensors &amp; bioelectronics},
volume = {282},
number = {},
pages = {117492},
doi = {10.1016/j.bios.2025.117492},
pmid = {40288311},
issn = {1873-4235},
abstract = {Neural electrodes play a pivotal role in ensuring safe stimulation and high-quality recording for various bioelectronics such as neuromodulation devices and brain-computer interfaces. With the miniaturization of electrodes and the increasing demand for multi-functionality, the incorporation of coating materials via electrodeposition to enhance electrodes performance emerges as a highly effective strategy. These coatings not only substantially improve the stimulation and recording performance of electrodes but also introduce additional functionalities. This review began by outlining the application scenarios and critical requirements of neural electrodes. It then delved into the deposition principles and key influencing factors. Furthermore, the advancements in the electrochemical performance and adhesion stability of these coatings were reviewed. Ultimately, the latest innovative works in the electrodeposited coating applications were highlighted, and future perspectives were summarized.},
}
@article {pmid40287824,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {The potential power of neuralink - how brain-machine interfaces can revolutionize medicine.},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2498457},
pmid = {40287824},
issn = {1745-2422},
}
@article {pmid40287725,
year = {2025},
author = {Zhang, X and Xie, L and Liu, W and Liang, S and Huang, L and Wang, M and Tian, L and Zhang, L and Liang, Z and Li, H and Huang, G},
title = {Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {97},
pmid = {40287725},
issn = {1743-0003},
support = {62201356//National Natural Science Foundation of China/ ; 62276169//National Natural Science Foundation of China/ ; 62271326//National Natural Science Foundation of China/ ; 2023SHIBS0003//Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions/ ; JCYJ20210324134401004//Shenzhen Science and Technology Innovation Program/ ; JCYJ20241202124222027//Shenzhen Science and Technology Innovation Program/ ; C2401028//Shenzhen Medical Research Foundation/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Exoskeleton Device ; Movement/physiology ; Adult ; Aged ; Algorithms ; Stroke/physiopathology ; Evoked Potentials/physiology ; Motor Cortex/physiopathology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.<br><br>METHODS: EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.<br><br>RESULTS: Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 &#xb1; 4.51 &#xa0;&#x3bc;V; ipsilateral: - 4.33 &#xb1; 3.69 &#xa0;&#x3bc;V). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.<br><br>CONCLUSIONS: These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.},
}
@article {pmid40281719,
year = {2025},
author = {Wan, X and Liu, Z and Yao, Y and Wan Hasan, WZ and Liu, T and Duan, D and Xie, X and Wen, D},
title = {Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040359},
pmid = {40281719},
issn = {2306-5354},
support = {62206014//National Natural Science Foundation of China/ ; 62276022//National Natural Science Foundation of China/ ; 2023YFF1203702//National Key Research and Development Program of China/ ; },
abstract = {Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.},
}
@article {pmid40281692,
year = {2025},
author = {Acuña Luna, KP and Hernandez-Rios, ER and Valencia, V and Trenado, C and Peñaloza, C},
title = {Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive-Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
doi = {10.3390/bioengineering12040331},
pmid = {40281692},
issn = {2306-5354},
abstract = {This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.},
}
@article {pmid40281628,
year = {2025},
author = {Atkinson, C and Lombardi, L and Lang, M and Keesey, R and Hawthorn, R and Seitz, Z and Leuthardt, EC and Brunner, P and Seáñez, I},
title = {Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {95},
pmid = {40281628},
issn = {1743-0003},
support = {K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K01-NS127936/NS/NINDS NIH HHS/United States ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; K12-HD073945//National Institute of Child Health and Human Development/ ; U24-NS109103/NH/NIH HHS/United States ; U24-NS109103/NH/NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; Male ; Female ; Adult ; Electroencephalography ; *Spinal Cord Stimulation/methods ; *Brain-Computer Interfaces ; Middle Aged ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Movement/physiology ; Young Adult ; *Transcutaneous Electric Nerve Stimulation/methods ; Sensorimotor Cortex/physiology ; Discriminant Analysis ; },
abstract = {Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). However, its efficacy is limited to areas with remaining sensorimotor function. Spinal cord stimulation (SCS) creates a temporary prosthetic effect that may allow further rehabilitation-induced recovery in individuals without remaining sensorimotor function, thereby extending the therapeutic reach of motor rehabilitation to individuals with more severe injuries. In this work, we report our first steps in developing a non-invasive brain-spine interface (BSI) based on electroencephalography (EEG) and transcutaneous spinal cord stimulation (tSCS). The objective of this study was to identify EEG-based neural correlates of lower limb movement in the sensorimotor cortex of unimpaired individuals (N = 17) and to quantify the performance of a linear discriminant analysis (LDA) decoder in detecting movement onset from these neural correlates. Our results show that initiation of knee extension was associated with event-related desynchronization in the central-medial cortical regions at frequency bands between 4 and 44 Hz. Our neural decoder using µ (8-12 Hz), low β (16-20 Hz), and high β (24-28 Hz) frequency bands achieved an average area under the curve (AUC) of 0.83 ± 0.06 s.d. (n = 7) during a cued movement task offline. Generalization to imagery and uncued movement tasks served as positive controls to verify robustness against movement artifacts and cue-related confounds, respectively. With the addition of real-time decoder-modulated tSCS, the neural decoder performed with an average AUC of 0.81 ± 0.05 s.d. (n = 9) on cued movement and 0.68 ± 0.12 s.d. (n = 9) on uncued movement. Our results suggest that the decrease in decoder performance in uncued movement may be due to differences in underlying cortical strategies between conditions. Furthermore, we explore alternative applications of the BSI system by testing neural decoders trained on uncued movement and imagery tasks. By developing a non-invasive BSI, tSCS can be timed to be delivered only during voluntary effort, which may have implications for improving rehabilitation.},
}
@article {pmid40280929,
year = {2025},
author = {Bai, Y and Tang, Q and Zhao, R and Liu, H and Zhang, S and Guo, M and Guo, M and Wang, J and Wang, C and Xing, M and Ni, G and Ming, D},
title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {701},
pmid = {40280929},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Reading ; *Semantics ; China ; *Language ; *Natural Language Processing ; East Asian People ; },
abstract = {Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400-450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models.},
}
@article {pmid40280532,
year = {2025},
author = {Gazerani, P},
title = {The neuroplastic brain: current breakthroughs and emerging frontiers.},
journal = {Brain research},
volume = {},
number = {},
pages = {149643},
doi = {10.1016/j.brainres.2025.149643},
pmid = {40280532},
issn = {1872-6240},
abstract = {Neuroplasticity, the brain's capacity to reorganize itself by forming new neural connections, is central to modern neuroscience. Once believed to occur only during early development, research now shows that plasticity continues throughout the lifespan, supporting learning, memory, and recovery from injury or disease. Substantial progress has been made in understanding the mechanisms underlying neuroplasticity and their therapeutic applications. This overview article examines synaptic plasticity, structural remodeling, neurogenesis, and functional reorganization, highlighting both adaptive (beneficial) and maladaptive (harmful) processes across different life stages. Recent strategies to harness neuroplasticity, ranging from pharmacological agents and lifestyle interventions to cutting-edge technologies like brain-computer interfaces (BCIs) and targeted neuromodulation are evaluated in light of current empirical evidence. Contradictory findings in the literature are addressed, and methodological limitations that hamper widespread clinical adoption are discussed. The ethical and societal implications of deploying novel neuroplasticity-based interventions, including issues of equitable access, data privacy, and the blurred line between treatment and enhancement, are then explored in a structured manner. By integrating mechanistic insights, empirical data, and ethical considerations, the aim is to provide a comprehensive and balanced perspective for researchers, clinicians, and policymakers working to optimize brain health across diverse populations.},
}
@article {pmid40280369,
year = {2025},
author = {Zhang, Y and Gao, Y and Zhou, J and Zhang, Z and Feng, M and Liu, Y},
title = {Advances in Brain-Computer Interface Controlled Functional Electrical Stimulation for Upper Limb Recovery After Stroke.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111354},
doi = {10.1016/j.brainresbull.2025.111354},
pmid = {40280369},
issn = {1873-2747},
abstract = {Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.},
}
@article {pmid40280291,
year = {2025},
author = {Han, MJ and Oh, Y and Ann, Y and Kang, S and Baeg, E and Hong, SJ and Sohn, H and Kim, SG},
title = {Whole-brain effective connectivity of the sensorimotor system using 7T fMRI with electrical microstimulation in non-human primates.},
journal = {Progress in neurobiology},
volume = {},
number = {},
pages = {102760},
doi = {10.1016/j.pneurobio.2025.102760},
pmid = {40280291},
issn = {1873-5118},
abstract = {The sensorimotor system is a crucial interface between the brain and the environment, and it is endowed with multiple computational mechanisms that enable efficient behaviors. For example, predictive processing via an efference copy of a motor command has been proposed as one of the key computations used to compensate for the sensory consequence of movement. However, the neural pathways underlying this process remain unclear, particularly regarding whether the M1-to-S1 pathway plays a dominant role in predictive processing and how its influence compares to that of other pathways. In this study, we present a causally inferable input-output map of the sensorimotor effective connectivity that we made by combining ultrahigh-field functional MRI, electrical microstimulation of the S1/M1 cortex, and dynamic causal modeling for the whole sensorimotor network in anesthetized primates. We investigated how motor signals from M1 are transmitted to S1 at the circuit level, either via direct cortico-cortical projections or indirectly via subcortical structures such as the thalamus. Across different stimulation conditions, we observed a robust asymmetric connectivity from M1 to S1 that was also the most prominent output from M1. In the thalamus, we identified distinct activations: M1 stimulation showed connections to the anterior part of ventral thalamic nuclei, whereas S1 was linked to the more posterior regions of the ventral thalamic nuclei. These findings suggest that the cortico-cortical projection from M1 to S1, rather than the cortico-thalamic loop, plays a dominant role in transmitting movement-related information. Together, our detailed dissection of the sensorimotor circuitry underscores the importance of M1-to-S1 connectivity in sensorimotor coordination.},
}
@article {pmid40280150,
year = {2025},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, S and Brandman, D},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add0e5},
pmid = {40280150},
issn = {1741-2552},
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. Approach. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. Main results. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. Significance. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click. (BrainGate2 ClinicalTrials.gov ID NCT00912041).},
}
@article {pmid40280132,
year = {2025},
author = {Zhou, N and Chen, J and Hu, M and Wen, N and Cai, W and Li, P and Zhao, L and Meng, Y and Zhao, D and Yang, X and Liu, S and Huang, F and Zhao, C and Feng, X and Jiang, Z and Xie, E and Pan, H and Cen, Z and Chen, X and Luo, W and Tang, B and Min, J and Wang, F and Yang, J and Xu, H},
title = {SLC7A11 is an unconventional H[+] transporter in lysosomes.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.004},
pmid = {40280132},
issn = {1097-4172},
abstract = {Lysosomes maintain an acidic pH of 4.5-5.0, optimal for macromolecular degradation. Whereas proton influx is produced by a V-type H[+] ATPase, proton efflux is mediated by a fast H[+] leak through TMEM175 channels, as well as an unidentified slow pathway. A candidate screen on an orphan lysosome membrane protein (OLMP) library enabled us to discover that SLC7A11, the protein target of the ferroptosis-inducing compound erastin, mediates a slow lysosomal H[+] leak through downward flux of cystine and glutamate, two H[+] equivalents with uniquely large but opposite concentration gradients across lysosomal membranes. SLC7A11 deficiency or inhibition caused lysosomal over-acidification, reduced degradation, accumulation of storage materials, and ferroptosis, as well as facilitated α-synuclein aggregation in neurons. Correction of abnormal lysosomal acidity restored lysosome homeostasis and prevented ferroptosis. These studies have revealed an unconventional H[+] transport conduit that is integral to lysosomal flux of protonatable metabolites to regulate lysosome function, ferroptosis, and Parkinson's disease (PD) pathology.},
}
@article {pmid40280131,
year = {2025},
author = {Xin, Q and Wang, J and Zheng, J and Tan, Y and Jia, X and Ni, Z and Xu, Z and Feng, J and Wu, Z and Li, Y and Li, XM and Ma, H and Hu, H},
title = {Neuron-astrocyte coupling in lateral habenula mediates depressive-like behaviors.},
journal = {Cell},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cell.2025.04.010},
pmid = {40280131},
issn = {1097-4172},
abstract = {The lateral habenula (LHb) neurons and astrocytes have been strongly implicated in depression etiology, but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely moving mice, we discover that stress induces a most rapid astrocytic calcium rise and a bimodal neuronal response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/adenosine, LHb astrocytes mediate the second-wave LHb neuronal activation and norepinephrine (NE) release. Activation or inhibition of LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors, respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.},
}
@article {pmid40280000,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {86},
number = {},
pages = {103713},
doi = {10.1016/j.scr.2025.103713},
pmid = {40280000},
issn = {1876-7753},
abstract = {Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo.},
}
@article {pmid40279233,
year = {2025},
author = {Aung, HW and Jiao Li, J and An, Y and Su, SW},
title = {A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3558171},
pmid = {40279233},
issn = {2162-2388},
abstract = {Brain-computer interfaces (BCIs) rely on accurately decoding electroencephalography (EEG) motor imagery (MI) signals for effective device control. Graph neural networks (GNNs) outperform convolutional neural networks (CNNs) in this regard, by leveraging the spatial relationships between EEG electrodes through adjacency matrices. The EEG graph lottery ticket framework, EEG_GLT-Net, featuring the state-of-the-art (SOTA) EEG_GLT adjacency matrix method, has notably enhanced EEG MI signal classification, evidenced by an average accuracy of 83.95% across 20 subjects on the PhysioNet dataset. This significantly exceeds the 76.10% accuracy rate achieved using the Pearson correlation coefficient (PCC) method in the same framework. In this research, we advance the field by applying a reinforcement learning (RL) approach to the classification of EEG MI signals. Our innovative method empowers the RL agent, enabling not only the classification of EEG MI data points with higher accuracy but effective identification of EEG MI data points that are less distinct. We present the EEG_RL-Net, an enhancement of the EEG_GLT-Net framework, which incorporates the trained EEG_GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39% alongside the RL-centric dueling deep Q network (Dueling DQN) block. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 ms. This model illustrates the transformative effect of the RL in EEG MI time point classification.},
}
@article {pmid40277624,
year = {2025},
author = {Zhang, W and Tang, X and Dang, X and Wang, M},
title = {A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
doi = {10.3390/biomimetics10040225},
pmid = {40277624},
issn = {2313-7673},
abstract = {Transfer learning is the act of using the data or knowledge in a problem to help solve different but related problems. In a brain computer interface (BCI), it is important to deal with individual differences between topics and/or tasks. A kind of capsule decision neural network (CDNN) based on transfer learning is proposed. In order to solve the problem of feature distortion caused by EEG feature extraction algorithm, a deep capsule decision network was constructed. The architecture includes multiple primary capsules to form a hidden layer, and the connection between the advanced capsule and the primary capsule is determined by the neural decision routing algorithm. Unlike the dynamic routing algorithm that iteratively calculates the similarity between primary capsules and advanced capsules, the neural decision network computes the relationship between each capsule in the deep and shallow hidden layers in a probabilistic manner. At the same time, the distribution of the EEG covariance matrix is aligned in Riemann space, and the regional adaptive method is further introduced to improve the independent decoding ability of the capsule decision neural network for the subject's EEG signals. Experiments on two motor imagery EEG datasets show that CDNN outperforms several of the most advanced transfer learning methods.},
}
@article {pmid40277574,
year = {2025},
author = {Jiao, P and Jia, Q and Li, S and Shan, J and Xu, W and Wang, Y and Liu, Y and Wang, M and Song, Y and Zhang, Y and Yu, Y and Wang, M and Cai, X},
title = {Distinct Neural Activities in Hippocampal Subregions Revealed Using a High-Performance Wireless Microsystem with PtNPs/PEDOT:PSS-Enhanced Microelectrode Arrays.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/bios15040262},
pmid = {40277574},
issn = {2079-6374},
support = {2022YFC2402501//the National Key Research and Development Program of China/ ; 2022YFC2402503//the National Key Research and Development Program of China/ ; 2022YFB3205602//the National Key Research and Development Program of China/ ; T2293730//the National Natural Science Foundation of China/ ; T2293731//the National Natural Science Foundation of China/ ; 61960206012//the National Natural Science Foundation of China/ ; 62121003//the National Natural Science Foundation of China/ ; 62333020//the National Natural Science Foundation of China/ ; 62171434//the National Natural Science Foundation of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation 2030/ ; PTYQ2024BJ0009//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; },
mesh = {Animals ; Microelectrodes ; *Wireless Technology ; Rats ; *Hippocampus/physiology ; Platinum/chemistry ; Polymers/chemistry ; Polystyrenes/chemistry ; Metal Nanoparticles/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Biosensing Techniques ; Rats, Sprague-Dawley ; Male ; },
abstract = {Wireless microsystems for neural signal recording have emerged as a solution to overcome the limitations of tethered systems, which restrict the mobility of subjects and introduce noise interference. However, existing microsystems often face data throughput, signal processing, and long-distance wireless transmission challenges. This study presents a high-performance wireless microsystem capable of 32-channel, 30 kHz real-time recording, featuring Field Programmable Gate Array (FPGA)-based signal processing to reduce transmission load. The microsystem is integrated with platinum nanoparticles/poly (3,4-ethylenedioxythiophene) polystyrene sulfonate-enhanced microelectrode arrays for improved signal quality. A custom NeuroWireless platform was developed for seamless data reception and storage. Experimental validation in rats demonstrated the microsystem's ability to detect spikes and local field potentials from the hippocampal CA1 and CA2 subregions. Comparative analysis of the neural signals revealed distinct activity patterns between these subregions. The wireless microsystem achieves high accuracy and throughput over distances up to 30 m, demonstrating its resilience and potential for neuroscience research. This work provides a compact, adaptable solution for multi-channel neural signal detection and offers a foundation for future applications in brain-computer interfaces.},
}
@article {pmid40277527,
year = {2025},
author = {Tavakolidakhrabadi, A and Stark, M and Küenzi, A and Carrara, S and Bessire, C},
title = {Optimized Microfluidic Biosensor for Sensitive C-Reactive Protein Detection.},
journal = {Biosensors},
volume = {15},
number = {4},
pages = {},
doi = {10.3390/bios15040214},
pmid = {40277527},
issn = {2079-6374},
support = {52116.1 IP-LS//Innosuisse - Swiss Innovation Agency/ ; },
mesh = {*C-Reactive Protein/analysis ; *Biosensing Techniques ; Metal Nanoparticles/chemistry ; Gold/chemistry ; Immunoassay ; Humans ; Point-of-Care Testing ; Microfluidics ; },
abstract = {Lateral flow immunoassays (LFIAs) were integrated into microfluidic chips and tested to enhance point-of-care testing (POCT), with the aim of improving sensitivity and expanding the range of CRP detection. The microfluidic approach improves upon traditional methods by precisely controlling fluid speed, thus enhancing sensitivity and accuracy in CRP measurements. The microfluidic approach also enables a one-step detection system, eliminating the need for buffer solution steps and reducing the nitrocellulose (NC) pad area to just the detection test line. This approach minimizes the non-specific binding of conjugated antibodies to unwanted areas of the NC pad, eliminating the need to block those areas, which enhances the sensitivity of detection. The gold nanoparticle method detects CRP in the high-sensitivity range of 1-10 μg/mL, which is suitable for chronic disease monitoring. To broaden the CRP detection range, including infection levels beyond 10 μg/mL, fluorescent labels were introduced, extending the measuring range from 1 to 70 μg/mL. Experimental results demonstrate that integrating microfluidic technology significantly enhances operational efficiency by precisely controlling the flow rate and optimizing the mixing efficiency while reducing fabrication resources by eliminating the need for separate pads, making these methods suitable for resource-limited settings. Microfluidics also provides greater control over fluid dynamics compared to traditional LFIA methods, which contributes to enhanced detection sensitivity even with lower sample volumes and no buffer solution, helping to enhance the usability of POCT. These findings highlight the potential to develop accessible, accurate, and cost-effective diagnostic tools essential for timely medical interventions at the POC.},
}
@article {pmid40275590,
year = {2025},
author = {Haghani Dogahe, M and Mahan, MA and Zhang, M and Bashiri Aliabadi, S and Rouhafza, A and Karimzadhagh, S and Feizkhah, A and Monsef, A and Habibi Roudkenar, M},
title = {Advancing Prosthetic Hand Capabilities Through Biomimicry and Neural Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683251331593},
doi = {10.1177/15459683251331593},
pmid = {40275590},
issn = {1552-6844},
abstract = {Background and ObjectivesProsthetic hand development is undergoing a transformative phase, blending biomimicry and neural interface technologies to redefine functionality and sensory feedback. This article explores the symbiotic relationship between biomimetic design principles and neural interface technology (NIT) in advancing prosthetic hand capabilities.MethodsDrawing inspiration from biological systems, researchers aim to replicate the intricate movements and capabilities of the human hand through innovative prosthetic designs. Central to this endeavor is NIT, facilitating seamless communication between artificial devices and the human nervous system. Recent advances in fabrication methods have propelled brain-computer interfaces, enabling precise control of prosthetic hands by decoding neural activity.ResultsAnatomical complexities of the human hand underscore the importance of understanding biomechanics, neuroanatomy, and control mechanisms for crafting effective prosthetic solutions. Furthermore, achieving the goal of a fully functional cyborg hand necessitates a multidisciplinary approach and biomimetic design to replicate the body's inherent capabilities. By incorporating the expertise of clinicians, tissue engineers, bioengineers, electronic and data scientists, the next generation of the implantable devices is not only anatomically and biomechanically accurate but also offer intuitive control, sensory feedback, and proprioception, thereby pushing the boundaries of current prosthetic technology.ConclusionBy integrating machine learning algorithms, biomechatronic principles, and advanced surgical techniques, prosthetic hands can achieve real-time control while restoring tactile sensation and proprioception. This manuscript contributes novel approaches to prosthetic hand development, with potential implications for enhancing the functionality, durability, and safety of the prosthetic limb.},
}
@article {pmid40274133,
year = {2025},
author = {Meng, L and Wang, D and Ma, J and Shi, Y and Zhao, H and Wang, Y and Shi, Q and Zhu, X and Ming, D},
title = {Unraveling Parkinson's disease motor subtypes: A deep learning approach based on spatiotemporal dynamics of EEG microstates.},
journal = {Neurobiology of disease},
volume = {},
number = {},
pages = {106915},
doi = {10.1016/j.nbd.2025.106915},
pmid = {40274133},
issn = {1095-953X},
abstract = {BACKGROUND: Despite prior studies on early-stage PD brain connectivity and temporal patterns, differences between tremor-dominant (TD) and postural instability/gait difficulty (PIGD) motor subtypes, remain poorly understood. Our study aims to understand the contribution of altered brain network dynamics to heterogeneous motor phenotypes in Parkinson's disease (PD) for improving personalized treatment.<br><br>METHODS: Electroencephalography (EEG) microstate dynamics were firstly used to capture spatiotemporal brain network changes. A deep learning model was developed to classify PD motor subtypes where spatial variability and electrode location data were incorporated into the analysis.<br><br>RESULTS: Compared to healthy individuals, both PD-TD and PD-PIGD patients showed increased local segregation of brain regions. The PD-PIGD subtype had more severe and extensive disorganization in microstate A dynamics, suggesting greater disruption in auditory and motor-related networks. Incorporating spatial information significantly improved the accuracy of subtype classification, with an AUC of 0.972, indicating that EEG microstate dynamic spatial patterns reflect distinct PD motor pathologies. The increased spatial variability in the PD-PIGD group was more closely associated with motor impairments.<br><br>CONCLUSIONS: This study presents a novel framework for differentiating PD motor subtypes and emphasizes dynamic brain network features as potential markers for understanding motor symptom variability in PD, which may contribute to the development of personalized treatment strategies.<br><br>TRIAL REGISTRATION: ChiCTR2300067657.},
}
@article {pmid40273947,
year = {2025},
author = {Ahmadi, H and Mesin, L},
title = {Universal semantic feature extraction from EEG signals: A task-independent framework.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/add08f},
pmid = {40273947},
issn = {1741-2552},
abstract = {Extracting universal, task-independent semantic features from electroencephalography (EEG) signals remains an open challenge. Traditional approaches are often task-specific, limiting their generalization across different EEG paradigms. This study aims to develop a robust, unsupervised framework for learning high-level, task-independent neural representations. Approach: We propose a novel framework integrating convolutional neural networks (CNNs), AutoEncoders, and Transformers to extract both low-level spatiotemporal patterns and high-level semantic features from EEG signals. The model is trained in an unsupervised manner to ensure adaptability across diverse EEG paradigms, including motor imagery (MI), steady-state visually evoked potentials (SSVEP), and event-related potentials (ERP, specifically P300). Extensive analyses, including clustering, correlation, and ablation studies, are conducted to validate the quality and interpretability of the extracted features. Main Results: Our method achieves state-of-the-art performance, with average classification accuracies of 83.50% and 84.84% on MI datasets (BCICIV 2a and BCICIV 2b), 98.41% and 99.66% on SSVEP datasets (Lee2019-SSVEP and Nakanishi2015), and an average AUC of 91.80% across eight ERP datasets. t-SNE and clustering analyses reveal that the extracted features exhibit enhanced separability and structure compared to raw EEG data. Correlation studies confirm the framework's ability to balance universal and subject-specific features, while ablation results highlight the near-optimality of the selected model configuration. Significance: This work establishes a universal framework for task-independent semantic feature extraction from EEG signals, bridging the gap between conventional feature engineering and modern deep learning (DL) methods. By providing robust, generalizable representations across diverse EEG paradigms, this approach lays the foundation for advanced brain-computer interface (BCI) appli- cations, cross-task EEG analysis, and future developments in semantic EEG processing.},
}
@article {pmid40271395,
year = {2025},
author = {Han, CH and Kim, SU and Lim, KS and Jung, YJ and Lee, S and Kim, SH and Hwang, HJ},
title = {Evaluation of a silicone-based flexible dry electrode for measuring human bioelectrical signals.},
journal = {Biomedical engineering letters},
volume = {15},
number = {3},
pages = {563-574},
doi = {10.1007/s13534-025-00471-x},
pmid = {40271395},
issn = {2093-985X},
abstract = {The development of conductive polymer-based dry electrodes with high conductivity is promising for practical applications in daily life due to their biocompatibility, flexibility, lightweight, and comfort. The objective of this study is to demonstrate the feasibility of using a novel silicone-based dry electrode for measuring various bioelectrical signals.The silicone-based electrode, manufactured using an optimized polymer matrix, combines high conductivity with flexibility, ensuring superior wearability and reliable bioelectrical signal monitoring. To evaluate its performance, we compared its impedance and flexibility with those of a commercial electrode. Additionally, its compatibility for measuring biological signals was assessed through performance comparisons across various bioelectrical signals. Fourteen healthy participants performed three experimental paradigms: (1) eyes closed and open to measure alpha electroencephalography (EEG) as well as resting-state electrocardiography (ECG), (2) eye blinking to measure electrooculography (EOG), and (3) wrist movement to measure electromyography (EMG). All bioelectrical signals were measured simultaneously using both the silicone-based dry electrode and a commercial dry electrode. The performance of the silicone-based dry electrode was evaluated by comparing the signal quality of both electrodes. The silicone-based dry electrode exhibited lower electrical impedance (39.43 kΩ on average, p = 0.0058) and greater flexibility (Young's modulus: silicone 1.51 ± 0.10 MPa vs. commercial 2.46 ± 0.38 MPa) compared to the commercial dry electrode. Overall, there were minimal differences in signal quality between the two electrodes: i) EEG (α power SNR: silicone 1.39 ± 0.34 vs. commercial 1.36 ± 0.29), ii) ECG (R-peak recall: 99.20 ± 2.50%, correlation coefficient: 0.96 ± 0.08), iii) EOG (eye blink recall: 100.00%, correlation coefficient: 0.98 ± 0.03), and iv) EMG (no significant difference in SNR values). These findings indicate that the developed electrode not only ensures superior flexibility but also maintains compatible electrical properties for measuring various bioelectrical signals.},
}
@article {pmid40270987,
year = {2025},
author = {Jiang, Z and Hu, K and Qu, J and Bian, Z and Yu, D and Zhou, J},
title = {Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1559335},
doi = {10.3389/fninf.2025.1559335},
pmid = {40270987},
issn = {1662-5196},
abstract = {INTRODUCTION: Motor imagery electroencephalographic (MI-EEG) signal recognition is used in various brain-computer interface (BCI) systems. In most existing BCI systems, this identification relies on classification algorithms. However, generally, a large amount of subject-specific labeled training data is required to reliably calibrate the classification algorithm for each new subject. To address this challenge, an effective strategy is to integrate transfer learning into the construction of intelligent models, allowing knowledge to be transferred from the source domain to enhance the performance of models trained in the target domain. Although transfer learning has been implemented in EEG signal recognition, many existing methods are designed specifically for certain intelligent models, limiting their application and generalization.<br><br>METHODS: To broaden application and generalization, an extended-LSR-based inductive transfer learning method is proposed to facilitate transfer learning across various classical intelligent models, including neural networks, Takagi-SugenoKang (TSK) fuzzy systems, and kernel methods.<br><br>RESULTS AND DISCUSSION: The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.},
}
@article {pmid40270567,
year = {2025},
author = {Hammond, L and Rowley, D and Tuck, C and Floreani, ED and Wieler, A and Kim, VS and Bahari, H and Andersen, J and Kirton, A and Kinney-Lang, E},
title = {BCI move: exploring pediatric BCI-controlled power mobility.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1456692},
doi = {10.3389/fnhum.2025.1456692},
pmid = {40270567},
issn = {1662-5161},
abstract = {INTRODUCTION: Children and young people (CYP) with severe physical disabilities often experience barriers to independent mobility, placing them at risk for developmental impairments and restricting their independence and participation. Pilot work suggests that brain-computer interface (BCIs) could enable powered mobility control for children with motor disabilities. We explored how severely disabled CYP could use BCI to achieve individualized, functional power mobility goals and acquire power mobility skills. We also explored the practicality of pediatric BCI-enabled power mobility.<br><br>METHODS: Nine CYP aged 7-17 years with severe physical disabilities and their caregivers participated in up to 12 BCI-enabled power mobility training sessions focused on a personalized power mobility goal. Goal achievement was assessed using the Canadian Occupational Performance Measure (COPM) and Goal Attainment Scaling (GAS). The Assessment for Learning Powered Mobility (ALP) was used to measure session-by-session power mobility skill acquisition. BCI set-up and calibration metrics, perceived workload, and participant engagement were also reported.<br><br>RESULTS: Significant improvements in COPM performance (Z&#x202f;=&#x202f;-2.869, adjusted p&#x202f;=&#x202f;0.012) and satisfaction scores (Z&#x202f;=&#x202f;-2.809, adjusted p&#x202f;=&#x202f;0.015) and GAS T scores (Z&#x202f;=&#x202f;-2.805, p&#x202f;=&#x202f;0.005) were observed following the intervention. ALP scores displayed a small but significant increase over time (R [2]&#x202f;=&#x202f;0.07-0.19; adjusted p&#x202f;=&#x202f;<0.001-0.039), with 7/9 participants achieving increased overall ALP scores following the intervention. Setup and calibration times were practical although calibration consistency was highly variable. Participants reported moderate workload with no significant change over time (R [2]&#x202f;=&#x202f;0.00-0.13; adjusted p&#x202f;=&#x202f;0.006-1.000), although there was a trend towards increased frustration over time(R [2]&#x202f;=&#x202f;0.13; adjusted p&#x202f;=&#x202f;0.006).<br><br>DISCUSSION: Participants were highly engaged throughout the intervention. BCI-enabled power mobility appears to help CYP with severe physical disabilities achieve personalized power mobility goals and acquire power mobility skills. BCI-enabled power mobility training also appears to be practical, but BCI performance optimization and skill acquisition may be needed to translate this technology into clinical use.},
}
@article {pmid39711704,
year = {2025},
author = {Deng, G and Niu, M and Luo, Y and Rao, S and Xie, J and Yu, Z and Liu, W and Zhao, S and Pan, G and Li, X and Deng, W and Guo, W and Li, T and Jiang, H},
title = {A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39711704},
abstract = {Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain gaps. Traditional scoring is labor-intensive, while deep learning models often fail to generalize across datasets. Here, we present LPSGM, a unified and flexible large polysomnography (PSG) model designed to enhance cross-center generalization in sleep staging and enable fine-tuning for disease diagnosis. Trained on 220,500 hours of PSG data from 16 public datasets, LPSGM integrates domain-adaptive learning and supports variable-channel configurations, achieving performance comparable to models trained directly on target-center data. In a prospective clinical study, LPSGM matches expert-level accuracy with lower variability. When fine-tuned, it attains 88.01% accuracy in narcolepsy detection and 100% in depression detection. These results establish LPSGM as a scalable, plug-and-play solution for automated PSG analysis, bridging the gap between sleep staging and clinical deployment.},
}
@article {pmid40269846,
year = {2025},
author = {Mansour, S and Giles, J and Nair, KPS and Marshall, R and Ali, A and Arvaneh, M},
title = {A clinical trial evaluating feasibility and acceptability of a brain-computer interface for telerehabilitation in patients with stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {91},
pmid = {40269846},
issn = {1743-0003},
support = {MC-PC-19051//UK Medical Research Council/ ; },
abstract = {BACKGROUND: We have created a groundbreaking telerehabilitation system known as Tele BCI-FES. This innovative system merges brain-computer interface (BCI) and functional electrical stimulation (FES) technologies to rehabilitate upper limb function following a stroke. Our system pioneers the concept of allowing patients to undergo BCI therapy from the comfort of their homes, while ensuring supervised therapy and real-time adjustment capabilities. In this paper, we introduce our single-arm clinical trial, which evaluates the feasibility and acceptance of this proposed system as a telerehabilitation solution for upper extremity recovery in stroke survivors.<br><br>METHOD: The study involved eight chronic patients with stroke and their caregivers who were recruited to attend nine home-based Tele BCI-FES sessions (three sessions per week) while receiving remote support from the research team. The primary outcomes of this study were recruitment and retention rates, as well as participants perception on the adoption of technology. The secondary outcomes involved assessing improvements in upper extremity function using the Fugl-Meyer Assessment for Upper Extremity (FMA_UE) and the Leeds Arm Spasticity Impact Scale.<br><br>RESULTS: Seven chronic patients with stroke completed the home-based Tele BCI-FES sessions, with high retention (87.5%) and recruitment rates (86.7%). Although participants provided mixed feedback on setup ease, they found the system progressively easier to use, and the setup process became more efficient with continued sessions. Participants suggested modifications to enhance user experience. Following the intervention, a significant increase in FMA_UE scores was observed, with an average improvement of 3.83 points (p = 0.032). The observed improvement of 3.83 points in the FMA-UE score approaches the reported Minimal clinically important difference of 4.25 points for patients with chronic stroke.<br><br>CONCLUSION: This study serves as a proof of concept, showcasing the feasibility and acceptability of the proposed Tele BCI-FES system for rehabilitating the upper extremities of stroke survivors. While some participants demonstrated significant improvements in FMA-UE scores, these findings are not generalizable, as they were derived from a small-scale feasibility study. The results should be interpreted cautiously within the study's specific context. Additionally, the intervention was not compared to other therapeutic approaches, limiting conclusions regarding its relative effectiveness. To further validate the efficacy of the proposed Tele BCI-FES system, it is essential to conduct additional research with larger sample sizes and extended rehabilitation sessions. Moreover, future studies should include comparisons with other therapeutic approaches to better evaluate the relative effectiveness of this intervention. Trial registration This clinical study is registered at clinicaltrials.gov https://clinicaltrials.gov/study/NCT05215522 under the study identifier (NCT05215522) and registered with the ISRCTN registry https://doi.org/10.1186/ISRCTN42991002 (ISRCTN42991002).},
}
@article {pmid40264507,
year = {2025},
author = {Decker, J and Daeglau, M and Zich, C and Kranczioch, C},
title = {Nature documentaries vs. quiet rest: no evidence for an impact on event-related desynchronization during motor imagery and neurofeedback.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539172},
pmid = {40264507},
issn = {1662-5161},
abstract = {Motor imagery (MI) in combination with neurofeedback (NF) has emerged as a promising approach in motor neurorehabilitation, facilitating brain activity modulation and promoting motor learning. Although MI-NF has been demonstrated to enhance motor performance and cortical plasticity, its efficacy varies considerably across individuals. Various context factors have been identified as influencing neurophysiological outcomes in motor execution and MI, however, their specific impact on event-related desynchronization (ERD), a key neurophysiological marker in NF, remains insufficiently understood. Previous research suggested that declarative interference following MI-NF may serve as a context factor hindering the progression of ERD. Yet, no significant changes in ERD within the mu and beta (8-30 Hz) frequency bands were observed across blocks in either a declarative interference or a control condition. This raises the question of whether the absence of ERD modulation could be attributed to the break task that was common to both declarative interference and control condition: watching nature documentaries immediately after MI blocks. To investigate this, we conducted a follow-up study replicating the original methodology while collecting new data. We compared NF-MI-ERD between groups with and without nature documentaries as a post-MI condition. Participants completed three sessions of kinesthetic MI-NF training involving a finger-tapping task over two consecutive days, with quiet rest as the post-MI condition (group quiet rest). 64-channel EEG data were analyzed from 17 healthy participants (8 females, 18-35 years, M and SD: 25.2 ± 4.2 years). Data were compared to a previously recorded dataset (group documentaries), in which 17 participants (10 females, 23-32 years, M and SD: 25.8 ± 2.5 years) watched nature documentaries after MI blocks. The results showed no significant main effects for blocks or group, though a session-by-group interaction was observed. Post-hoc tests, however, did not reveal significant differences in ERD development between the groups across individual blocks. These findings do not provide evidence that nature documentaries used as a post-MI condition negatively affect across-block development of NF-MI-ERD. This study highlights the importance of exploring additional context factors in MI-NF training to better understand their influence on ERD development.},
}
@article {pmid40263649,
year = {2025},
author = {Li, X and Dai, P and Yuan, Y},
title = {[Perioperative safety assessment and complications follow-up of simultaneous bilateral cochlear implantation in young infants].},
journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery},
volume = {39},
number = {5},
pages = {413-418;424},
doi = {10.13201/j.issn.2096-7993.2025.05.004},
pmid = {40263649},
issn = {2096-7993},
mesh = {Humans ; *Cochlear Implantation/adverse effects/methods ; Infant ; *Postoperative Complications ; *Hearing Loss, Sensorineural/surgery ; Follow-Up Studies ; Male ; Perioperative Period ; Female ; Cochlear Implants ; },
abstract = {Objective:To evaluate the perioperative safety and long-term complications of simultaneous bilateral cochlear implantation（BCI） in young infants, providing reference data for clinical BCI in young children. Methods:Seventy-four infants aged 6-23 months with congenital severe to profound sensorineural hearing loss who were candidates for cochlear implantation at the Department of Otolaryngology, Chinese PLA General Hospital between August 2018 and August 2019 were consecutively enrolled. Parents made the decision to implant either unilaterally or bilaterally. Participants were divided into unilateral cochlear implantation（UCI） group（before and after 12 months of age） and simultaneous BCI group（before and after 12 months of age）. Safety indicators, including perioperative risk variables, complications, and other postoperative adverse events were monitored, with complications followed up for 5-6 years. Comparisons were made between the BCI and UCI, as well as between implantation before and after 12 months of age regarding perioperative safety and long-term complications. Results:A total of 40 BCI patients（23 before 12 months, 17 after 12 months） and 34 UCI patients（20 before 12 months, 14 after 12 months） were included in the study. Regarding perioperative risk variables, the BCI group showed significantly longer anesthesia duration, operative time, and greater blood loss compared to the UCI group, though less than twice that of the UCI group; no anesthetic complications occurred in either group; and there was no significant difference in postoperative hospital stay between the groups. Regarding surgical complications during the 5-year follow-up period, the BCI group experienced 7 complications（2 major, 5 minor）, while the UCI group had 7 complications（1 major, 6 minor）, with no statistical differences between groups. Regarding other postoperative adverse events, the BCI group demonstrated significantly higher total adverse event rates than the UCI group（80.0% vs 38.2%）, with higher rates of moderate to severe anemia（60.0% vs 20.6%） and lower mean hemoglobin levels[（92.35±12.14） g/L vs（102.39±13.09） g/L]. No significant differences were found in postoperative fever rates（50.0% vs 52.9%） or C-reactive protein levels between groups. Within the BCI group, patients implanted before 12 months indicated notably higher rates of total adverse events（91.3% vs 64.7%）, high fever（26.1% vs 0）, and moderate to severe anemia（78.3% vs 35.3%） compared to those implanted after 12 months. Conclusion:Simultaneous BCI in young children under 2 years of age demonstrates controllable overall risks. Compared to UCI, while it shows no increase in anesthetic or surgical complications, it presents higher perioperative risks and adverse event rates, especially in patients implanted before 12 months of age, warranting special attention from medical staff.},
}
@article {pmid40262425,
year = {2025},
author = {Bao, X and Feng, X and Huang, H and Li, M and Chen, D and Wang, Z and Li, J and Huang, Q and Cai, Y and Li, Y},
title = {Day-night hyperarousal in tinnitus patients.},
journal = {Sleep medicine},
volume = {131},
number = {},
pages = {106519},
doi = {10.1016/j.sleep.2025.106519},
pmid = {40262425},
issn = {1878-5506},
abstract = {Tinnitus, which affects 12-30 % of the population, is associated with sleep disturbances and daytime dysfunction, yet the neural mechanisms that link wake-up states remain unclear. This study investigated electroencephalographic (EEG) characteristics of 51 tinnitus patients and 51 controls across wakefulness (eyes-open, eyes-closed, mental arithmetic) and sleep stages (N1, N2, N3, REM) to clarify day-night pathological mechanisms. The key findings showed persistent hyperarousal in tinnitus: wakefulness revealed enhanced gamma power (30-45 Hz) in eyes-closed and task states, while sleep demonstrated elevated gamma/beta power across all stages accompanied by reduced delta/theta power in deep sleep (N2/N3).). An analysis of sleep structure indicates impaired stability in maintaining the N2 stage among tinnitus patients, corroborating a reduction in N3 duration and an increased proportion of the N2 stage. From the wake states to the sleep stages, group × state interactions for the delta/theta power suggest an impaired state regulation capacity in tinnitus patients. Correlation clustering further revealed aberrant integration of wake-related gamma/beta activity into non-rapid eye movement sleep, indicating neuroplastic overgeneralization of wake hyperarousal into sleep. These results extend the so-called loss-of-inhibition theory to sleep, proposing that deficient low-frequency oscillations fail to suppress hyperarousal, impairing sleep-dependent neuroplasticity, and perpetuating daytime symptoms. Furthermore, this study establishes sleep as a critical therapeutic target to interrupt the 24-h dysfunctional cycle of tinnitus.},
}
@article {pmid40262392,
year = {2025},
author = {Li, X and Dong, X and Wang, J and Mao, H and Tu, X and Li, W and He, J and Li, Q and Zhang, P},
title = {Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces.},
journal = {Computers in biology and medicine},
volume = {192},
number = {Pt A},
pages = {110231},
doi = {10.1016/j.compbiomed.2025.110231},
pmid = {40262392},
issn = {1879-0534},
abstract = {Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of the iBMI decoder to maintain decoding performance, which requires collecting and labeling a large amount of new data. To address this challenge and minimize the time needed for decoder recalibration, we proposed an active learning domain adversarial neural network (AL-DANN). This model leveraged a substantial volume of historical data alongside a small amount of current data (four samples per category) to calibrate the decoder. By incorporating domain adversarial and active learning strategies, the model effectively transferred knowledge from historical data to new data, reducing the demand for new samples. We validated the proposed method using neural signals recorded from three monkeys performing different movements in a classification task or a regression task. The results showed that the AL-DANN outperformed existing state-of-the-art methods. Impressively, it required only four new samples per category for decoder recalibration, leading to a recalibration time reduction of over 80 %. To our knowledge, this is the first study to incorporate deep transfer learning into iBMI decoder calibration, highlighting the significant potential of applying deep learning technologies in iBMIs.},
}
@article {pmid40262075,
year = {2025},
author = {Shao, WW and Shao, Q and Xu, HH and Qiao, GJ and Wang, RX and Ma, ZY and Meng, WW and Yang, ZB and Zang, YL and Li, XH},
title = {Repetitive training enhances the pattern recognition capability of cultured neural networks.},
journal = {PLoS computational biology},
volume = {21},
number = {4},
pages = {e1013043},
doi = {10.1371/journal.pcbi.1013043},
pmid = {40262075},
issn = {1553-7358},
abstract = {Cultured neural networks in vitro have demonstrated the biocomputing capability to recognize patterns. However, the underlying mechanisms behind information processing and pattern recognition remain less understood. Here, we developed an in vitro neural network integrated with microelectrode arrays (MEAs) to explore the network's classification capability and elucidate the mechanisms underlying this classification. After applying different stimulation patterns using MEAs, the network exhibited structural alterations and distinct electrical responses that recognized various stimulation patterns. Alongside the reshaping of network structures, repeated training increased recognition accuracy for each stimulation pattern. Additionally, it was reported for the first time that spontaneous networks after stimulation are more closely related to the structures of evoked networks. This work provides new insights into the structural changes underlying information processing and contributes to our understanding of how cultured neural networks respond to different patterns.},
}
@article {pmid40261790,
year = {2025},
author = {Luo, J and Liu, Q and Tai, P and Li, G and Li, Y},
title = {A Multi-level Integrated EEG-Channel Selection Method Based on the Lateralization Index.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3563416},
pmid = {40261790},
issn = {1558-0210},
abstract = {The importance of optimizing channel selection for portable brain-computer interface (BCI) technology is increasingly recognized. Effective channel selection reduces computational load and enhances user experience by making BCI systems more comfortable and easier to use. A significant challenge lies in reducing the number of electrodes without compromising decoding accuracy. Although some methods have been proposed in previous studies, these often increase computational load and overlook the importance of channel selection across different subjects. Therefore, we propose a novel Multi-level Integrated EEG-Channel Selection method based on the Lateralization Index (MLI-ECS-LI). This method leverages the lateralization index in selecting important channels and can achieve the channel selection for the cross-tasks and the cross-subjects scenarios. To evaluate the effectiveness of the proposed method, the time and frequency domain features from selected channels were extracted. Three widely used classifiers, Least Squares Support Vector Machine (LSSVM), Random Forest (RF), and Support Vector Machine (SVM) were used to classify movement types based on these features. Compared to the conventional condition (C1-C6), the average decoding accuracies across 21 healthy subjects demonstrated an improved performance of 6.6%, 4.9%, 6.9% (LSSVM); 3.8%, 2.8%, 4.5%(RF); and 7.6%, 5.6%, 9.2%(SVM) via using the channels selected from the conditions of the single task, the cross-tasks, and the cross-subjects scenarios, respectively. These results demonstrated the potential of the proposed method in improving the utility of the portable Motor Imagery Brain-Computer Interface (MI-BCI) and effectiveness in practical applications.},
}
@article {pmid40260641,
year = {2025},
author = {Xu, Y and Li, YL and Yu, G and Ou, Z and Yao, S and Li, Y and Huang, Y and Chen, J and Ding, Q},
title = {Effect of Brain Computer Interface Training on Frontoparietal Network Function for Young People: A Functional Near-Infrared Spectroscopy Study.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {4},
pages = {e70400},
pmid = {40260641},
issn = {1755-5949},
support = {2024A04J3082//Guangzhou Science and Technology Program/ ; A2024500//Guangdong Medical Research Foundation/ ; 82102678//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared ; Male ; Female ; *Parietal Lobe/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Young Adult ; *Frontal Lobe/physiology/diagnostic imaging ; Adult ; Attention/physiology ; *Nerve Net/physiology/diagnostic imaging ; Executive Function/physiology ; },
abstract = {AIMS: Inattention in young people is one of the main reasons for their declining learning ability. Frontoparietal networks (FPNs) are associated with attention and executive function. Brain computer interface (BCI) training has been applied in neurorehabilitation, but there is a lack of research on its application to cognition. This study aimed to investigate the effect of BCI on the attention network in healthy young adults.<br><br>METHODS: Twenty-seven healthy people performed BCI training for 5 consecutive days. An attention network test (ANT) was performed at baseline and immediately after the fifth day of training and included simultaneous functional near-infrared spectroscopy recording.<br><br>RESULTS: BCI performance improved significantly after BCI training (p&#x2009;=&#x2009;0.005). The efficiencies of the alerting and executive control networks were enhanced after BCI training (p&#x2009;=&#x2009;0.032 and 0.003, respectively). The functional connectivity in the bilateral prefrontal cortices and the right posterior parietal cortex increased significantly after BCI training (p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: Our findings suggested that repetitive BCI training could improve attention and induce lasting neuroplastic changes in FPNs. It might be a promising rehabilitative strategy for clinical populations with attention deficits. The right PPC may also be an effective target for neuromodulation in diseases with attention deficits.},
}
@article {pmid40260139,
year = {2025},
author = {Zhang, T and Wang, N and Chai, X and He, Q and Cao, T and Yuan, L and Lan, Q and Yang, Y and Zhao, J},
title = {Evaluation of pressure-induced pain in patients with disorders of consciousness based on functional near infrared spectroscopy.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542691},
pmid = {40260139},
issn = {1664-2295},
abstract = {OBJECTIVE: This study aimed to investigate the brain's hemodynamic responses (HRO) and functional connectivity in patients with disorders of consciousness (DoC) in response to acute pressure pain stimulation using near-infrared spectroscopy (NIRS).<br><br>METHODS: Patients diagnosed with DoC underwent pressure stimulation while brain activity was measured using NIRS. Changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations were monitored across several regions of interest (ROIs), including the primary somatosensory cortex (PSC), primary motor cortex (PMC), dorsolateral prefrontal cortex (dPFC), somatosensory association cortex (SAC), temporal gyrus (TG), and frontopolar area (FPA). Functional connectivity was assessed during pre-stimulation, stimulation, and post-stimulation phases.<br><br>RESULTS: No significant changes in HbO or HbR concentrations were observed during the stimulation vs. baseline or stimulation vs. post-stimulation comparisons, indicating minimal activation of the targeted brain regions in response to the pressure stimulus. However, functional connectivity between key regions, particularly the PSC, PMC, and dPFC, showed significant enhancement during the stimulation phase (r > 0.9, p < 0.001), suggesting greater coordination among sensory, motor, and cognitive regions. These changes in connectivity were not accompanied by significant activation in pain-related brain areas.<br><br>CONCLUSION: Although pain-induced brain activation was minimal in patients with DoC, enhanced functional connectivity during pain stimulation suggests that the brain continues to process pain information through coordinated activity between regions. The findings highlight the importance of assessing functional connectivity as a potential method for evaluating pain processing in patients with DoC.},
}
@article {pmid40257892,
year = {2025},
author = {Hashemi, M and Depannemaecker, D and Saggio, M and Triebkorn, P and Rabuffo, G and Fousek, J and Ziaeemehr, A and Sip, V and Athanasiadis, A and Breyton, M and Woodman, M and Wang, H and Petkoski, S and Sorrentino, P and Jirsa, V},
title = {Principles and Operation of Virtual Brain Twins.},
journal = {IEEE reviews in biomedical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/RBME.2025.3562951},
pmid = {40257892},
issn = {1941-1189},
abstract = {Current clinical methods often overlook individual variability by relying on population-wide trials, while mechanismbased trials remain underutilized in neuroscience due to the brain's complexity. This situation may change through the use of a Virtual Brain Twin (VBT), which is a personalized digital replica of an individual's brain, integrating structural and functional brain data into advanced computational models and inference algorithms. By bridging the gap between molecular mechanisms, whole-brain dynamics, and imaging data, VBTs enhance the understanding of (patho)physiological mechanisms, advancing insights into both healthy and disordered brain function. Central to VBT is the network modeling that couples mesoscopic representation of neuronal activity through white matter connectivity, enabling the simulation of brain dynamics at a network level. This transformative approach provides interpretable predictive capabilities, supporting clinicians in personalizing treatments and optimizing interventions. This Review outlines the key components of VBT development, covering the conceptual, mathematical, technical, and clinical aspects. We describe the stages of VBT construction-from anatomical coupling and modeling to simulation and Bayesian inference-and demonstrate their applications in resting-state, healthy aging, multiple sclerosis, and epilepsy. Finally, we discuss potential extensions to other neurological disorders, such as Parkinson's disease, and explore future applications in consciousness research and brain-computer interfaces, paving the way for advancements in personalized medicine and brainmachine integration.},
}
@article {pmid40257874,
year = {2025},
author = {Li, S and Liu, G and Feng, F and Chang, Z and Li, W and Duan, F},
title = {An Interventional Brain-Computer Interface for Long-Term EEG Collection and Motion Classification of a Quadruped Mammal.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562922},
pmid = {40257874},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCI) acquire electroencephalogram (EEG) signals to effectively address postoperative motor dysfunction in stroke patients by discerning their motor intentions during significant movements. Traditionally, noninvasive BCIs have been constrained by limitations in their usage environments; whereas, invasive BCIs damage neural permanently. Therefore, we proposed a novel interventional BCI, in which electrodes are implanted along the veins into the brain to acquire intracerebral EEG signals without an open craniotomy. We collect EEG signals from the primary motor cortex in the superior sagittal sinus of sheep during three different significant movements: laying down; standing; and walking. The first three month data are used to train the neural network, and The fourth month of data were used to validate. The deep learning model achieved an 86% accuracy rate in classifying motion states in validation. Furthermore, the results of the power spectral density (PSD) show that the signal power in the main frequency band did not decrease over a period of five months, which demonstrates that the interventional BCI has the ability to effectively capture EEG signals over long periods of time.},
}
@article {pmid40257872,
year = {2025},
author = {Huang, S and Liu, Y and Wang, Z and Wu, W and Guo, J and Xu, W and Ming, D},
title = {Enhanced Brain Functional Interaction Following BCI-guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562700},
pmid = {40257872},
issn = {1558-0210},
abstract = {Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, the neuroplasticity effects of BCI-actuated SRF (BCI-SRF) training based on the "six finger" motor imagery paradigm are still unclear. This study recruited 20 healthy right-handed participants and randomly assigned them to either a BCI-SRF training group or a sham SRF training group. During the testing phase before and after 4 weeks of training, all participants were tested for SRF-finger opposition sequence behavior, resting state fMRI (rs-fMRI), and task-based fMRI (tb-fMRI). The results showed that compared with the Sham group, the BCI-SRF group improved the accuracy rate of the SRF-finger opposition sequence by 132%. The activation analysis of tb-fMRI before and after training revealed a significant increase in left middle frontal gyrus only in the BCI-SRF group. In addition, the BCI-SRF group showed an increase in FC between the right primary motor cortex and left cerebellum inferior lobe, as well as between the left middle frontal gyrus and the right precuneus lobe after training, while there was no significant change in the Sham group. In addition, only the BCI-SRF group showed a significant increase in clustering coefficients after training. Moreover, the increase in the clustering coefficients of the two groups is positively correlated with the improvement of the accuracy of the SRF-finger opposition sequences. These results indicate that the integration of BCI and SRF significantly regulates the functional interaction between motor learning and cognitive imagery brain regions, enhances the integration and processing ability of brain networks for local information, and improves human-machine interaction behavioral performance.},
}
@article {pmid40254808,
year = {2025},
author = {Wang, H and Wang, X},
title = {Exploring the Role of Psychedelics in Modulating Ego and Treating Neuropsychiatric Disorders.},
journal = {ACS chemical neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1021/acschemneuro.5c00247},
pmid = {40254808},
issn = {1948-7193},
abstract = {This viewpoint explores the therapeutic potential of psychedelics in treating neuropsychiatric disorders, particularly through the modulation of brain entropy and the experience of ego dissolution. Psychedelics disrupt rigid neural patterns, facilitating enhanced connectivity and fostering profound emotional breakthroughs that may alleviate symptoms of disorders like depression, anxiety, PTSD, and addiction. Despite their promising potential, the clinical application of psychedelics presents significant challenges, including the need for careful patient screening, managing adverse experiences, and addressing ethical considerations, all of which are essential for their safe integration into therapy.},
}
@article {pmid40254540,
year = {2025},
author = {Fu, Q and Tong, L and Zhang, H and Xu, H},
title = {Multimodal Imaging Diagnosis of Apical Ventricular Aneurysm With Thrombosis Resulting From Blunt Myocardial Injury: A Case Report.},
journal = {Journal of clinical ultrasound : JCU},
volume = {},
number = {},
pages = {},
doi = {10.1002/jcu.24026},
pmid = {40254540},
issn = {1097-0096},
support = {20210101260JC//the Science and Technology Development Program of the Jilin Province/ ; },
abstract = {This article presents the case of a male patient who sustained blunt myocardial injury following a traffic accident. A series of diagnostic imaging procedures were conducted on the patient, including electrocardiography, echocardiography, computed tomography angiography, and cardiac magnetic resonance imaging, which demonstrated edema in a portion of the myocardium and the formation of a ventricular aneurysm with thrombus in the left ventricular apex. After 6 months and 1 year, echocardiography demonstrated no detection of thrombus, but the apical left ventricular aneurysm was not significantly different from the anterior film, leading to a final clinical diagnosis of blunt cardiac injury (BCI).},
}
@article {pmid40253420,
year = {2025},
author = {Wang, B and Zhang, X and Zhang, L and Kong, XZ},
title = {A naturalistic fMRI dataset in response to public speaking.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {659},
pmid = {40253420},
issn = {2052-4463},
support = {32171031//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; Female ; Male ; *Speech ; Young Adult ; Adult ; *Brain/physiology ; *Communication ; },
abstract = {Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.},
}
@article {pmid40253415,
year = {2025},
author = {He, T and Wei, M and Wang, R and Wang, R and Du, S and Cai, S and Tao, W and Li, H},
title = {VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {657},
pmid = {40253415},
issn = {2052-4463},
support = {62271432//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Speech ; *Language ; Brain-Computer Interfaces ; },
abstract = {Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.},
}
@article {pmid40253381,
year = {2025},
author = {Xue, S and Jin, B and Jiang, J and Guo, L and Zhou, J and Wang, C and Liu, J},
title = {A multi-subject and multi-session EEG dataset for modelling human visual object recognition.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {663},
pmid = {40253381},
issn = {2052-4463},
support = {U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; U21B2043, 62206279//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Brain-Computer Interfaces ; Machine Learning ; *Visual Perception ; *Pattern Recognition, Visual ; Algorithms ; },
abstract = {We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.},
}
@article {pmid40252874,
year = {2025},
author = {Ye, Z and Lv, C and Zhou, H and Bao, Y and Hong, T and He, Q and Hu, Y},
title = {Neural substrates of attack event prediction in video games: the role of ventral posterior cingulate cortex and theory of mind network.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121228},
doi = {10.1016/j.neuroimage.2025.121228},
pmid = {40252874},
issn = {1095-9572},
abstract = {Action anticipation, the ability to observe actions and predict the intent of others, plays a crucial role in social interaction and fields such as electronic sports. However, the neural mechanisms underlying the inference of purpose from action observation remain unclear. In this study, we conducted an fMRI experiment using video game combat scenarios to investigate the neural correlates of action anticipation and its relationship with task performance. The results showed that the higher level of ability to infer the purpose from action observation during experiment associates with higher level of proficiency in real world electric gaming competition. The action anticipation task activates visual streams, fronto-parietal network, and the ventral posterior cingulate cortex (vPCC), a key hub in the theory of mind network. The strength of vPCC activation during action anticipation, but not movement direction judgment, was positively correlated with gaming proficiency. Finite impulse response analysis revealed distinct dynamic response profiles in the vPCC compared to other theory of mind regions. These findings suggest that theory of mind ability may be an important factor influencing individual competitive performance, with the vPCC serving as a core neural substrate for inferring purpose from action observation.},
}
@article {pmid40250541,
year = {2025},
author = {Liu, Y and Wang, M and Rao, H},
title = {Common Neural Activations of Creativity and Exploration: A Meta-analysis of Task-based fMRI Studies.},
journal = {Neuroscience and biobehavioral reviews},
volume = {},
number = {},
pages = {106158},
doi = {10.1016/j.neubiorev.2025.106158},
pmid = {40250541},
issn = {1873-7528},
abstract = {Creativity is a common, complex, and multifaceted cognitive activity with significant implications for technological progress, social development, and human survival. Understanding the neurocognitive mechanisms underlying creative thought is essential for fostering individual creativity. While previous studies have demonstrated that exploratory behavior positively influences creative performance, few studies investigated the relationship between creativity and exploration at the neural level. To address this gap, we conducted a quantitative meta-analysis comprising 80 creativity experiments (1,850 subjects) and 23 exploration experiments (646 subjects) to examine potential shared neural activations between creativity and exploration. Furthermore, we analyzed the neural similarities and differences among three forms of creative thinking-divergent thinking (DT), convergent thinking (CT), and artistic creativity-and their relationship with exploration. The conjunction analysis of creativity and exploration revealed significant activations in the bilateral IFJ and left preSMA. Further conjunction analyses revealed that both CT and artistic creativity exhibited common neural activations with exploration, with CT co-activating the left IFJ and artistic creativity co-activating both the right IFJ and left preSMA, while DT did not. Additionally, the conjunction analyses across the three forms of creativity did not identify shared neural activations. Further functional decoding analyses of the overlapping brain regions associated with CT and exploration, as well as artistic creativity and exploration, revealed correlations with inhibitory control mechanisms. These results enhance our understanding of the role of exploration in the creative thinking process and provide valuable insights for developing strategies to foster innovative thinking.},
}
@article {pmid40249697,
year = {2025},
author = {Cao, B and Tsai, CL and Zhou, N and Do, T and Lin, CT},
title = {A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3562217},
pmid = {40249697},
issn = {1558-0210},
abstract = {Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application. However, unlike conventional computer screen-based SSVEP (CS-SSVEP), which benefits from stable experimental environments, AR-based SSVEP (AR-SSVEP) systems are susceptible to the interference of real-world environment and device instability. Particularly, the performance of AR-SSVEP significantly declines as the target frequency increases. Therefore, our study introduced a 3D paradigm that combines flicker frequency with rotation patterns as stimuli, enabling expansion of target sets without additional frequencies. In the proposed design, in addition to the conventional frequency-based SSVEP feature, bio-marker elicited by visual perception of rotation was investigated. An experimental comparison between this novel 3D paradigm and a traditional 2D approach, which increases targets by adding frequencies, reveals significant advantages. The 12-class 3D paradigm achieved an accuracy of 76.5% and an information transfer rate (ITR) of 70.42 bits/min using 1-second EEG segments. In contrast, the 2D paradigm exhibited a lower performance with 72.07% accuracy and 62.28 bits/min ITR. The result underscores the 3D paradigm's superiority in enhancing the practical applications of SSVEP-based BCIs in AR settings, especially with shorter time windows, by effectively expanding target recognition without compromising system efficiency.},
}
@article {pmid40247883,
year = {2025},
author = {Uszko, JM and Schroeder, JC and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Morphological control of cuprate superconductors using sea sponges as templates.},
journal = {RSC advances},
volume = {15},
number = {14},
pages = {11189-11193},
pmid = {40247883},
issn = {2046-2069},
abstract = {Functional porous superconducting sponges, consisting of YBa2Cu3O6+δ (YBCO) and Bi2Sr2CaCu2O8+δ (BSCCO), were created by biotemplating with natural sea sponges. Naturally occurring calcium in the spongin fibers was utilized to dope YBCO and to form BSCCO without adding any external calcium source. The sample morphology was confirmed with scanning electron microscopy, and the sample composition was confirmed with energy-dispersive X-ray spectroscopy, powder electron diffraction and high-resolution transmission electron microscopy. The YBCO sponge exhibited a critical temperature (T c) of approximately 70 K, and the BSCCO sponge showed a T c of 77 K. This proof-of-concept study demonstrates the feasibility of using sea sponges as a greener, more sustainable template for superconductor synthesis.},
}
@article {pmid40247859,
year = {2025},
author = {Kuo, YT and Wang, HL and Chen, BW and Wang, CF and Lo, YC and Lin, SH and Chen, PC and Chen, YY},
title = {Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces.},
journal = {APL bioengineering},
volume = {9},
number = {2},
pages = {026106},
pmid = {40247859},
issn = {2473-2877},
abstract = {Neural signal degradation poses a significant challenge in maintaining stable performance when decoding motor tasks using multiunit activity (MUA) and local field potential (LFP) signals in the implantable brain machine interface (iBMI) applications. Effective methods for imputing degraded or missing signals are essential to restore neural signal integrity, thereby improving decoding accuracy and system robustness over long-term recordings with fluctuating signal quality. This study introduces a confidence-weighted Bayesian linear regression (CW-BLR) approach to impute neural signals affected by degradation, enhancing the robustness and consistency of decoding. The performance of CW-BLR was compared to traditional methods-mean imputation (Mean-imp) and Gaussian-mixture-model-based expectation-maximization (GMM-EM)-using a kernel-sliced inverse regression (kSIR) decoder to evaluate decoding outcomes. Four Wistar rats were trained to perform a forelimb-reaching task while neural activity (MUA and LFPs) was recorded over 27 days. CW-BLR imputed signals degraded during days 8-27. Decoding performance was evaluated using kSIR and compared with Mean-imp and GMM-EM. CW-BLR demonstrated superior performance by effectively preserving both temporal and spatial dependencies within the neural signals. CW-BLR-imputed data significantly improved decoding accuracy over traditional imputation methods, with the kSIR decoder showing consistently higher performance, particularly in maintaining signal quality from the degraded period. CW-BLR offers a robust and effective imputation framework for iBMI applications, addressing signal degradation challenges and maintaining accurate decoding over prolonged recordings. By utilizing confidence-based quality metrics, CW-BLR surpasses traditional methods, providing stable neural decoding across fluctuating signal quality scenarios.},
}
@article {pmid40246195,
year = {2025},
author = {Isis Yonza, AK and Tao, L and Zhang, X and Postnov, D and Kucharz, K and Lind, B and Asiminas, A and Han, A and Sonego, V and Kim, K and Cai, C},
title = {Spatially and temporally mismatched blood flow and neuronal activity by high-intensity intracortical microstimulation.},
journal = {Brain stimulation},
volume = {18},
number = {3},
pages = {885-896},
doi = {10.1016/j.brs.2025.04.015},
pmid = {40246195},
issn = {1876-4754},
abstract = {INTRODUCTION: Intracortial microstimulation (ICMS) is widely used in neuroprosthetic brain-machine interfacing, particularly in restoring lost sensory and motor functions. Spiking neuronal activity requires increased cerebral blood flow to meet local metabolic demands, a process conventionally denoted as neurovascular coupling (NVC). However, it is unknown precisely how and to what extent ICMS elicits NVC and how the neuronal and blood flow responses to ICMS correlate. Suboptimal NVC by ICMS may compromise oxygen and energy delivery to the activated neurons thus impair neuroprosthetic functionality.<br><br>MATERIAL AND METHOD: We used wide-field imaging (WFI), laser speckle imaging (LSI) and two-photon microscopy (TPM) to study living, transgenic mice expressing calcium (Ca[2+]) fluorescent indicators in either neurons or vascular mural cells (VMC), as well as to measure vascular inner lumen diameters.<br><br>RESULT: By testing a range of stimulation amplitudes and examining cortical tissue responses at different distances from the stimulating electrode tip, we found that high stimulation intensities (&#x2265;50&#xa0;&#x3bc;A) elicited a spatial and temporal neurovascular decoupling in regions most adjacent to electrode tip (<200&#xa0;&#x3bc;m), with significantly delayed onset times of blood flow responses to ICMS and compromised maximum blood flow increases. We attribute these effects respectively to delayed Ca[2+] signalling and decreased Ca[2+] sensitivity in VMCs.<br><br>CONCLUSION: Our study offers new insights into ICMS-associated neuronal and vascular physiology with potentially critical implications towards the optimal design of ICMS in neuroprosthetic therapies: low intensities preserve NVC; high intensities disrupt NVC responses and potentially precipitate blood supply deficits.},
}
@article {pmid40246009,
year = {2025},
author = {Ren, J and Wang, Y and Wang, Y and Zhang, Y and Xing, M and Deng, S and Tong, S and Wang, L and Zheng, C and Yang, J and Ni, G and Ming, D},
title = {Dynamic changes of hippocampal dendritic spines in Alzheimer's disease mice among the different stages.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115266},
doi = {10.1016/j.expneurol.2025.115266},
pmid = {40246009},
issn = {1090-2430},
abstract = {Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) peptides and a progressive decline in cognitive function. Hippocampus as a crucial brain area for learning and memory, is also adversely affected by AD's pathology. The accumulation of Aβ is often associated with the loss of dendritic spines of the hippocampus. However, the dynamic alterations in dendritic spines throughout AD progression are not fully understood. To investigate it, we conducted in-vivo imaging in two mouse models representing the early and late stages of AD pathology: young mice injected with Aβ1-42 oligomers and APP/PS1 transgenic mice. In the early-stage AD model, imaging was conducted at third- and fifth- weeks post-injection. In the late-stage AD model, a four-month imaging began at 14 months old. The imaging results showed spine elimination in both models. Notably, acute Aβ exposure was linked to heightened spine loss on secondary dendrites, while in the late stage the primary effect was on tertiary dendrites. Concurrently, with the metabolism of Aβ, cognition recovered to some extent by five weeks post Aβ1-42 exposure. These findings suggested that dendritic spine plasticity was impaired during the development of AD, as evidenced by increasing spine loss at different levels. However, the cognitive recovery observed in early-stage AD model mice may indicate a compensatory structural reorganization, highlighting the potential of early intervention to mitigate disease progression. Our results provide novel insights into the neurotoxic effects of Aβ1-42 and may contribute to the development of therapeutic strategies for AD.},
}
@article {pmid40245876,
year = {2025},
author = {Zhao, D and Dong, G and Pei, W and Gao, X and Wang, Y},
title = {Comparisons of stimulus paradigms for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adce32},
pmid = {40245876},
issn = {1741-2552},
abstract = {Objective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.Main results.The online information transfer rates for the three stimulus paradigms were 53.77 bits min[-1], 51.41 ± 3.55 bits min[-1], and 52.07 ± 3.09 bits min[-1], respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.Significance.These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.},
}
@article {pmid40245253,
year = {2025},
author = {Singh, K and Lin, CC and Huang, WH and Lei, WL and Chiueh, H and Wang, YH and Chang, PH and Lin, RZ and Huang, WC},
title = {Ultrabioconformal, Self-Healable, and Antioxidized Polydopamine-Inspired Nanowire Hydrogels Enable Resolving Power in Forehead and Ear Electroencephalograms for Brain Function Assessment.},
journal = {ACS applied materials &amp; interfaces},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsami.4c23013},
pmid = {40245253},
issn = {1944-8252},
abstract = {Continuous brain function monitoring by high-performance electroencephalogram (EEG) suggests a high impact for advancing precision personalized medication of neurodevelopmental or neurodegenerative disorders. Forehead and ear EEGs are nonhairy recording strategies that allow the recording of brain activity using only a few electrodes. However, they require well-designed electrodes that are easy and comfortable to carry while simultaneously performing durable high-quality EEG acquisition. Herein, we propose a new ultrabiocompliant EEG sensor that enables seamless contact to surfaces of both earhole and forehead, while permitting prolonged and high-quality EEG signal identification. Bioinspired polydopamine/platinum-silver nanowires, called PDA-Ag@Pt NWs, are synthesized with noticeable performances in electrical conductivity, antioxidation ability, cytocompatibility, and adhesion. PDA-Ag@Pt NWs can promote synchronic gelation and interlinks within polydopamine-polyacrylamide (PDA-PAM) hydrogels, in turn leading to the one-step formation of a nanowire/hydrogel matrix, called PDA-PAM/NW, as an electrode patch in the presence of adhesive and self-healing capabilities. Combined with a self-designed signal processor, a portable electrophysiological signal recording system was realized. The PDA-PAM/NW electrode patch outperformed commercial electrodes in terms of reliability and resolution for electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) recording. In addition, through brain cognitive assessment by frontal- and ear-EEG recording, the ultrathin design and comfortable adhesion of PDA-PAM/NW electrodes make participants comfortable over time, subsequently providing the identification of the brain activity in high resolution. This work underscores the potential of the ultrabiocompliant and durable patch in the development of comfy, long-lasting, and high-performance wearable brain-machine interfaces for the revolution in neuroscience.},
}
@article {pmid40245060,
year = {2025},
author = {Akhter, J and Nazeer, H and Naseer, N and Naeem, R and Kallu, KD and Lee, J and Ko, SY},
title = {Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0314447},
pmid = {40245060},
issn = {1932-6203},
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Deep Learning ; *Brain-Computer Interfaces ; Male ; Adult ; Algorithms ; Female ; Neural Networks, Computer ; Young Adult ; Hand Strength/physiology ; },
abstract = {The functional near-infrared spectroscopy-based brain-computer interface (fNIRS-BCI) systems recognize patterns in brain signals and generate control commands, thereby enabling individuals with motor disabilities to regain autonomy. In this study hand gripping data is acquired using fNIRS neuroimaging system, preprocessing is performed using nirsLAB and features extraction is performed using deep learning (DL) Algorithms. For feature extraction and classification stack and fft methods are proposed. Convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional long-short-term memory (Bi-LSTM) are employed to extract features. The stack method classifies these features using a stack model and the fft method enhances features by applying fast Fourier transformation which is followed by classification using a stack model. The proposed methods are applied to fNIRS signals from twenty participants engaged in a two-class hand-gripping motor activity. The classification performance of the proposed methods is compared with conventional CNN, LSTM, and Bi-LSTM algorithms and one another. The proposed fft and stack methods yield 90.11% and 87.00% classification accuracies respectively, which are significantly higher than those achieved by CNN (85.16%), LSTM (79.46%), and Bi-LSTM (81.88%) conventional algorithms. The results show that the proposed stack and fft methods can be effectively used for the classification of the two and three-class problems in fNIRS-BCI applications.},
}
@article {pmid40244939,
year = {2025},
author = {Noel, JP and Bockbrader, M and Bertoni, T and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A},
title = {Neuronal responses in the human primary motor cortex coincide with the subjective onset of movement intention in brain-machine interface-mediated actions.},
journal = {PLoS biology},
volume = {23},
number = {4},
pages = {e3003118},
pmid = {40244939},
issn = {1545-7885},
support = {K99 NS128075/NS/NINDS NIH HHS/United States ; R00 NS128075/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; *Intention ; Movement/physiology ; Male ; Adult ; Female ; *Neurons/physiology ; Electric Stimulation ; Quadriplegia/physiopathology ; },
abstract = {Self-initiated behavior is accompanied by the experience of intending our actions. Here, we leverage the unique opportunity to examine the full intentional chain-from intention to action to environmental effects-in a tetraplegic person outfitted with a primary motor cortex (M1) brain-machine interface (BMI) generating real hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (intention, action, effect) while probing subjective experience and performing extra-cellular recordings in human M1. Behaviorally, we reveal a novel form of intentional binding: motor intentions are reflected in a perceived temporal attraction between the onset of intentions and that of actions. Neurally, we demonstrate that evoked spiking activity in M1 largely coincides in time with the onset of the experience of intention and that M1 spike counts and the onset of subjective intention may co-vary on a trial-by-trial basis. Further, population-level dynamics, as indexed by a decoder instantiating movement, reflect intention-action temporal binding. The results fill a significant knowledge gap by relating human spiking activity in M1 with the onset of subjective intention and complement prior human intracranial work examining pre-motor and parietal areas.},
}
@article {pmid40242802,
year = {2025},
author = {Iosif, R and Skrbinšek, T and Erős, N and Konec, M and Boljte, B and Jan, M and Promberger-Fürpass, B},
title = {Wolf Population Size and Composition in One of Europe's Strongholds, the Romanian Carpathians.},
journal = {Ecology and evolution},
volume = {15},
number = {4},
pages = {e71200},
pmid = {40242802},
issn = {2045-7758},
abstract = {Strategies of coexistence with large carnivores should integrate scientific evidence, population monitoring providing an opportunity for advancing outdated management paradigms. We estimated wolf population density and social dynamics across a 1400 km[2] area in a data-poor region of the Romanian Carpathians. Across three consecutive years (2017-2018 until 2019-2020), we collected and genotyped 505 noninvasive DNA wolf samples (scat, hair and urine) to identify individuals, reconstruct pedigrees, and check for the presence of hybridization with domestic dogs. We identified 27 males, 20 females, and one F1 wolf-dog hybrid male. We delineated six wolf packs, with pack size varying between two and seven individuals, and documented yearly changes in pack composition. Using a spatial capture-recapture approach, we estimated population density at 2.35 wolves/100 km[2] (95% BCI = 1.68-3.03) and population abundance at 70 individuals (95% BCI = 49-89). Noninvasive DNA data collection coupled with spatial capture-recapture has the potential to inform on wolf population size and dynamics at broader spatial scales, across different sampling areas representative of the diverse Carpathian landscapes, and across different levels of human impact, supporting wildlife decision making in one of Europe's main strongholds for large carnivores.},
}
@article {pmid40242584,
year = {2025},
author = {Hu, S and Lin, C and Wang, H and Wang, X},
title = {Psychedelics and Eating Disorders: Exploring the Therapeutic Potential for Anorexia Nervosa and Beyond.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {4},
pages = {910-916},
pmid = {40242584},
issn = {2575-9108},
abstract = {Anorexia nervosa (AN) is a severe psychiatric disorder characterized by extreme food restriction, an intense fear of weight gain, and a distorted body image, leading to significant morbidity and mortality. Conventional treatments such as cognitive-behavioral therapy (CBT) and pharmacotherapy often prove inadequate, especially in severe cases, highlighting the need for novel therapeutic approaches. Recent research into psychedelics, such as psilocybin and 3,4-methylenedioxymethamphetamine (MDMA), offers promising avenues for treating anorexia nervosa by targeting its neurobiological and psychological underpinnings. These psychedelics disrupt maladaptive neural circuits, enhance cognitive flexibility, and facilitate emotional processing, offering potential relief for patients unresponsive to traditional therapies. Early studies have shown positive outcomes with psychedelics, including reductions in anorexia nervosa symptoms and improvements in psychological well-being. However, further research is needed to establish their long-term safety, efficacy, and integration into clinical practice. Addressing the legal, ethical, and safety challenges will be crucial in determining whether psychedelics can transform the treatment landscape for anorexia nervosa and other eating disorders.},
}
@article {pmid40242456,
year = {2025},
author = {Yan, W and Luo, Q and Du, C},
title = {Channel component correlation analysis for multi-channel EEG feature component extraction.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1522964},
pmid = {40242456},
issn = {1662-4548},
abstract = {INTRODUCTION: Electroencephalogram (EEG) analysis has shown significant research value for brain disease diagnosis, neuromodulation and brain-computer interface (BCI) application. The analysis and processing of EEG signals is complex since EEG are nonstationary, nonlinear, and often contaminated by intense background noise. Principal component analysis (PCA) and independent component analysis (ICA), as the commonly used methods for multi-dimensional signal feature component extraction, still have some limitations in terms of performance and calculation.<br><br>METHODS: In this study, channel component correlation analysis (CCCA) method was proposed to extract feature components of multi-channel EEG. Firstly, empirical wavelet transform (EWT) decomposed each channel signal into different frequency bands, and reconstructed them into a multi-dimensional signal. Then the objective optimization function was constructed by maximizing the covariance between multi-dimensional signals. Finally the feature components of multi-channel EEG were extracted using the calculated weight coefficient.<br><br>RESULTS: The results showed that the CCCA method could find the most relevant frequency band between multi-channel EEG. Compared with PCA and ICA methods, CCCA could extract the common components of multi-channel EEG more effectively, which is of great significance for the accurate analysis of EEG.<br><br>DISCUSSION: The CCCA method proposed in this study has shown excellent performance in the feature component extraction of multi-channel EEG and could be considered for practical engineering applications.},
}
@article {pmid40241786,
year = {2025},
author = {Hernández-Gloria, JJ and Jaramillo-Gonzalez, A and Savić, AM and Mrachacz-Kersting, N},
title = {Toward brain-computer interface speller with movement-related cortical potentials as control signals.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1539081},
pmid = {40241786},
issn = {1662-5161},
abstract = {Brain Computer Interface spellers offer a promising alternative for individuals with Amyotrophic Lateral Sclerosis (ALS) by facilitating communication without relying on muscle activity. This study assessed the feasibility of using movement related cortical potentials (MRCPs) as a control signal for a Brain-Computer Interface speller in an offline setting. Unlike motor imagery-based BCIs, this study focused on executed movements. Fifteen healthy subjects performed three spelling tasks that involved choosing specific letters displayed on a computer screen by performing a ballistic dorsiflexion of the dominant foot. Electroencephalographic signals were recorded from 10 sites centered around Cz. Three conditions were tested to evaluate MRCP performance under varying task demands: a control condition using repeated selections of the letter "O" to isolate movement-related brain activity; a phrase spelling condition with structured text ("HELLO IM FINE") to simulate a meaningful spelling task with moderate cognitive load; and a random condition using a randomized sequence of letters to introduce higher task complexity by removing linguistic or semantic context. The success rate, defined as the presence of an MRCP, was manually determined. It was approximately 69% for both the control and phrase conditions, with a slight decrease in the random condition, likely due to increased task complexity. Significant differences in MRCP features were observed between conditions with Laplacian filtering, whereas no significant differences were found in single-site Cz recordings. These results contribute to the development of MRCP-based BCI spellers by demonstrating their feasibility in a spelling task. However, further research is required to implement and validate real-time applications.},
}
@article {pmid40241515,
year = {2025},
author = {Jin, F and Li, M and Yang, L and Yang, L and Shang, Z},
title = {Exploring the value learning in pigeons: The role of dual pathways in the basal ganglia and synaptic plasticity.},
journal = {The Journal of experimental biology},
volume = {},
number = {},
pages = {},
doi = {10.1242/jeb.249507},
pmid = {40241515},
issn = {1477-9145},
support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 252102210008//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Understanding value learning in animals is a key focus in cognitive neuroscience. Current models used in research are often simple, and while more complex models have been proposed, it remains unclear which assumptions align with actual value learning strategies of animals. This study investigated the computational mechanisms behind value learning in pigeons using a free-choice task. Three models were constructed based on different assumptions about the role of the basal ganglia's dual pathways and synaptic plasticity in value computation, followed by model comparison and neural correlation analysis. Among the three models tested, the dual-pathway reinforcement learning model with Hebbian rules most closely matched the pigeons' behavior. Furthermore, the striatal gamma band connectivity showed the highest correlation with the values estimated by this model. Additionally, enhanced beta band connectivity in the nidopallium caudolaterale supported value learning. This study provides valuable insights into reinforcement learning mechanisms in non-human animals.},
}
@article {pmid40240152,
year = {2025},
author = {Ma, YN and Karako, K and Song, P and Hu, X and Xia, Y},
title = {Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.},
journal = {Bioscience trends},
volume = {},
number = {},
pages = {},
doi = {10.5582/bst.2025.01109},
pmid = {40240152},
issn = {1881-7823},
abstract = {Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and emotional regulation. Conventional rehabilitation approaches, while partially effective, often lack individualization and yield suboptimal outcomes. In recent years, brain-computer interface (BCI) technology has emerged as a promising neurorehabilitation tool by decoding neural signals and providing real-time feedback to enhance neuroplasticity. This review systematically explores the use of BCI systems in post-stroke rehabilitation, focusing on three core domains: motor function, cognitive capacity, and emotional regulation. This review outlines the neurophysiological principles underpinning BCI-based motor rehabilitation, including neurofeedback training, Hebbian plasticity, and multimodal feedback strategies. It then examines recent advances in upper limb and gait recovery using BCI integrated with functional electrical stimulation (FES), robotics, and virtual reality (VR). Moreover, it highlights BCI's potential in cognitive and language rehabilitation through EEG-based neurofeedback and the integration of artificial intelligence (AI) and immersive VR environments. In addition, it discusses the role of BCI in monitoring and regulating post-stroke emotional disorders via closed-loop systems. While promising, BCI technologies face challenges related to signal accuracy, device portability, and clinical validation. Future research should prioritize multimodal integration, AI-driven personalization, and large-scale randomized trials to establish long-term efficacy. This review underscores BCI's transformative potential in delivering intelligent, personalized, and cross-domain rehabilitation solutions for stroke survivors.},
}
@article {pmid40239679,
year = {2025},
author = {Faes, A and Calvo Merino, E and Branco, MP and Van Hoylandt, A and Keirse, E and Theys, T and Ramsey, NF and Van Hulle, MM},
title = {Decoding sign language finger flexions from high-density electrocorticography using graph-optimized block term tensor regression.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adcd9e},
pmid = {40239679},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; *Sign Language ; *Fingers/physiology ; Male ; Gestures ; Female ; Adult ; Regression Analysis ; Middle Aged ; Movement/physiology ; },
abstract = {Objective.A novel method is introduced to regress over the sign language finger movements from human electrocorticography (ECoG) recordings.Approach.The proposed graph-optimized block-term tensor regression (Go-BTTR) method consists of two components: a deflation-based regression model that sequentially Tucker-decomposes multiway ECoG data into a series of blocks, and a causal graph process (CGP) that accounts for the complex relationship between finger movements when expressing sign language gestures. Prior to each regression block, CGP is applied to decide which fingers should be kept separate or grouped and should therefore be referred to BTTR or its extended version eBTTR, respectively.Main results.Two ECoG datasets were used, one recorded in five patients expressing four hand gestures of the American sign language alphabet, and another in two patients expressing all gestures of the Flemish sign language alphabet. As Go-BTTR combines fingers in a flexible way, it can better account for the nonlinear relationship ECoG exhibits when expressing hand gestures, including unintentional finger co-activations. This is reflected by the superior joint finger trajectory predictions compared to eBTTR, and predictions that are on par with BTTR in single-finger scenarios. For the American sign language alphabet (Utrecht dataset), the average correlation across all fingers for all subjects was 0.73 for Go-BTTR, 0.719 for eBTTR and 0.70 for BTTR. For the Flemish sign language alphabet (Leuven dataset), the average correlation across all fingers for all subjects was 0.37 for Go-BTTR, 0.34 for eBTTR and 0.33 for BTTR.Significance.Our findings show that Go-BTTR is capable of decoding complex hand gestures taken from the sign language alphabet. Go-BTTR also demonstrates computational efficiency, providing a notable benefit when intracranial electrodes are inserted during a patient's pre-surgical evaluation. This efficiency helps reduce the time required for developing and testing a brain-computer interface solution.},
}
@article {pmid40238867,
year = {2025},
author = {Li, X and Deng, Z and Zhang, W and Zhou, W and Liu, X and Quan, H and Li, J and Li, P and Li, Y and Hu, C and Li, F and Niu, L and Tian, Z and Meng, L and Zheng, H},
title = {Oscillating microbubble array-based metamaterials (OMAMs) for rapid isolation of high-purity exosomes.},
journal = {Science advances},
volume = {11},
number = {16},
pages = {eadu8915},
pmid = {40238867},
issn = {2375-2548},
mesh = {*Microbubbles ; *Exosomes/metabolism/chemistry ; Humans ; Acoustics ; },
abstract = {Exosomes secreted by cells hold substantial potential for disease diagnosis and treatment. However, the rapid isolation of high-purity exosomes and their subpopulations from biofluids (e.g., undiluted whole blood) remains challenging. This study presents oscillating microbubble array-based metamaterials (OMAMs) for enabling the rapid isolation of high-purity exosomes and their subpopulations from biofluids without labeling or preprocessing. Particularly, leveraging acoustically excited microbubble oscillation, OMAMs can generate numerous acoustofluidic traps for filtering in-fluid micro/nanoparticles, thus allowing for removing bioparticles larger than exosomes to obtain high-purity (93%) exosomes from undiluted whole blood in ~3 minutes. Moreover, exosome subpopulations in different size ranges can be isolated by tuning the microbubble oscillation amplitude. Additionally, as each oscillating microbubble functions as an ultradeep subwavelength (~λ/186) acoustic amplifier and a nonlinear source, OMAMs can generate high-resolution complex acoustic energy patterns and tune the patterns by activating different-sized microbubbles at their distinct resonance frequencies.},
}
@article {pmid40236895,
year = {2025},
author = {Wen, D and Xing, Y and Yao, Y and Liang, G and Xing, Y and Jung, TP and Yu, H and Xie, X and Wan, X and Liu, T and Duan, D and Li, D and Zhou, Y},
title = {Transforming long-term adjunctive therapy for cognitive impairment: the role of multimodal self-adaptive digital medicine.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1571817},
pmid = {40236895},
issn = {1664-2295},
}
@article {pmid40236742,
year = {2025},
author = {Wei, B and Cheng, S and Feng, Y},
title = {Neural personal information and its legal protection: evidence from China.},
journal = {Journal of law and the biosciences},
volume = {12},
number = {1},
pages = {lsaf006},
doi = {10.1093/jlb/lsaf006},
pmid = {40236742},
issn = {2053-9711},
abstract = {The rapid advancements in neuroscience highlight the pressing need to safeguard neural personal information (NPI). China has achieved significant progress in brain-computer interface technology and its clinical applications. Considering the intrinsic vulnerability of NPI and the paucity of legal scrutiny concerning NPI breaches, a thorough assessment of the adequacy of China's personal information protection legislation is essential. This analysis contends that NPI should be classified as sensitive personal information. The absence of bespoke provisions for NPI in current legislation underscores persistent challenges in its protection. To address these gaps, this work proposes the establishment of a concentric-circle hard-soft law continuum to support a hybrid governance model for NPI, rooted in fundamental human rights principles.},
}
@article {pmid40236412,
year = {2025},
author = {Jude, JJ and Levi-Aharoni, H and Acosta, AJ and Allcroft, SB and Nicolas, C and Lacayo, BE and Card, NS and Wairagkar, M and Brandman, DM and Stavisky, SD and Willett, FR and Williams, ZM and Simeral, JD and Hochberg, LR and Rubin, DB},
title = {An intuitive, bimanual, high-throughput QWERTY touch typing neuroprosthesis for people with tetraplegia.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.04.01.25324990},
pmid = {40236412},
abstract = {Recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia - one with ALS and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar, and easy-to-learn paradigm for individuals with impaired communication due to paralysis.},
}
@article {pmid40236074,
year = {2025},
author = {Busch, EL and Fincke, EC and Lajoie, G and Krishnaswamy, S and Turk-Browne, NB},
title = {Accelerated learning of a noninvasive human brain-computer interface via manifold geometry.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.29.646109},
pmid = {40236074},
issn = {2692-8205},
abstract = {Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of brain activity, or its intrinsic manifold, extracted using a data-diffusion process. We trained participants on a noninvasive BCI that allowed them to gain real-time control of an avatar in a virtual reality game by modulating functional magnetic resonance imaging (fMRI) activity in brain regions that support spatial navigation. We then perturbed the mapping between fMRI activity patterns and the movement of the avatar to test our manifold hypothesis. When the new mapping respected the intrinsic manifold, participants succeeded in regaining control of the BCI by aligning their brain activity within the manifold. When the new mapping did not respect the intrinsic manifold, participants could not learn to control the avatar again. These findings show that the manifold geometry of brain activity constrains human learning of a complex cognitive task in higher-order brain regions. Manifold geometry may be a critical ingredient for unlocking the potential of future human neurotechnologies.},
}
@article {pmid40235786,
year = {2025},
author = {Wang, LP and Yang, C and Fu, JY and Zhang, XY and Shen, XM and Shi, NL and Wu, HL and Gao, XR},
title = {A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis.},
journal = {Quantitative imaging in medicine and surgery},
volume = {15},
number = {4},
pages = {3469-3479},
pmid = {40235786},
issn = {2223-4292},
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors.<br><br>METHODS: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated.<br><br>RESULTS: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients.<br><br>CONCLUSIONS: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.},
}
@article {pmid40234729,
year = {2025},
author = {Webster, P},
title = {Can AI-powered brain-computer interfaces boost human intelligence?.},
journal = {Nature medicine},
volume = {31},
number = {4},
pages = {1045-1047},
doi = {10.1038/s41591-025-03641-7},
pmid = {40234729},
issn = {1546-170X},
}
@article {pmid40234486,
year = {2025},
author = {Zhao, W and Zhang, B and Zhou, H and Wei, D and Huang, C and Lan, Q},
title = {Multi-scale convolutional transformer network for motor imagery brain-computer interface.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12935},
pmid = {40234486},
issn = {2045-2322},
support = {3502Z202374054//Natural Science Foundation of Xiamen, China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; JAT191153 and JAT201045//Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian Province of China/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; CKZ24016//Jimei University Chengyi College Provincial and Ministerial-Level Scientific Research Cultivation Project/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; *Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencephalography (MI-EEG) signals in BCIs. However, traditional CNN-based methods face challenges such as individual variability in EEG signals and the limited receptive fields of CNNs. This study presents the Multi-Scale Convolutional Transformer (MSCFormer) model that integrates multiple CNN branches for multi-scale feature extraction and a Transformer module to capture global dependencies, followed by a fully connected layer for classification. The multi-branch multi-scale CNN structure effectively addresses individual variability in EEG signals, enhancing the model's generalization capabilities, while the Transformer encoder strengthens global feature integration and improves decoding performance. Extensive experiments on the BCI IV-2a and IV-2b datasets show that MSCFormer achieves average accuracies of 82.95% (BCI IV-2a) and 88.00% (BCI IV-2b), with kappa values of 0.7726 and 0.7599 in five-fold cross-validation, surpassing several state-of-the-art methods. These results highlight MSCFormer's robustness and accuracy, underscoring its potential in EEG-based BCI applications. The code has been released in https://github.com/snailpt/MSCFormer .},
}
@article {pmid40234393,
year = {2025},
author = {Bertoni, T and Noel, JP and Bockbrader, M and Foglia, C and Colachis, S and Orset, B and Evans, N and Herbelin, B and Rezai, A and Panzeri, S and Becchio, C and Blanke, O and Serino, A},
title = {Pre-movement sensorimotor oscillations shape the sense of agency by gating cortical connectivity.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3594},
pmid = {40234393},
issn = {2041-1723},
support = {163951//Schweizerischer Nationalfonds zur F&#xf6;rderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/ ; },
mesh = {Humans ; *Motor Cortex/physiology ; Male ; Adult ; Female ; Electroencephalography ; Young Adult ; Movement/physiology ; Brain-Computer Interfaces ; Alpha Rhythm/physiology ; Hand/physiology ; Sense of Agency ; },
abstract = {Our sense of agency, the subjective experience of controlling our actions, is a crucial component of self-awareness and motor control. It is thought to originate from the comparison between intentions and actions across broad cortical networks. However, the underlying neural mechanisms are still not fully understood. We hypothesized that oscillations in the theta-alpha range, thought to orchestrate long-range neural connectivity, may mediate sensorimotor comparisons. To test this, we manipulated the relation between intentions and actions in a tetraplegic user of a brain machine interface (BMI), decoding primary motor cortex (M1) activity to restore hand functionality. We found that the pre-movement phase of low-alpha oscillations in M1 predicted the participant's agency judgements. Further, using EEG-BMI in healthy participants, we found that pre-movement alpha oscillations in M1 and supplementary motor area (SMA) correlated with agency ratings, and with changes in their functional connectivity with parietal, temporal and prefrontal areas. These findings argue for phase-driven gating as a key mechanism for sensorimotor integration and sense of agency.},
}
@article {pmid40232894,
year = {2025},
author = {Wang, D and Wei, Q},
title = {SMANet: A Model Combining SincNet, Multi-branch Spatial-Temporal CNN and Attention Mechanism for Motor Imagery BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3560993},
pmid = {40232894},
issn = {1558-0210},
abstract = {Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an end-to-end deep learning model, Sinc-multibranch-attention network (SMANet), which combines a SincNet, a multibranch spatial-temporal convolutional neural network (MBSTCNN), and an attention mechanism for MI-BCI classification. Firstly, Sinc convolution is utilized as a band-pass filter bank for data filtering; Second, pointwise convolution facilitates the effective integration of feature information across different frequency ranges, thereby enhancing the overall feature expression capability; Next, the resulting data are fed into the MBSTCNN to learn a deep feature representation. Thereafter, the outputs of the MBSTCNN are concatenated and then passed through an efficient channel attention (ECA) module to enhance local channel feature extraction and calibrate feature mapping. Ultimately, the feature maps yielded by ECA are classified using a fully connected layer. This model SMANet enhances discriminative features through a multi-objective optimization scheme that integrates cross-entropy loss and central loss. The experimental outcomes reveal that our model attains an average accuracy of 80.21% on the four-class MI dataset (BCI Competition IV 2a), 84.02% on the two-class MI dataset (BCI Competition IV 2b), and 72.70% on the two-class MI dataset (OpenBMI). These results are superior to those of the current state-of-the-art methods. The SMANet is capable to effectively decoding the spatial-spectral-temporal information of EEG signals, thereby enhancing the performance of MI-BCIs.},
}
@article {pmid40231563,
year = {2025},
author = {Park, K and Hong, J and Shin, H and Choi, J and Xu, D and Lee, J and Ryu, J and Kim, S and Jeong, H and Choe, J and Yang, S and Yang, S and Ahn, JH},
title = {2D Material-Based Injectable Sensor for Minimally-Invasive Cerebral Blood Flow Monitoring.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e2501744},
doi = {10.1002/smll.202501744},
pmid = {40231563},
issn = {1613-6829},
support = {20012355//Ministry of Trade, Industry and Energy/ ; },
abstract = {Monitoring cerebral blood flow is an important method for diagnosing and treating brain diseases. Thermal transport caused by blood flow provides valuable information for detecting abnormalities in blood flow. Here, a minimally invasive, injectable blood flow sensor is reported, consisting of a flexible, graphene-based thin film heater and MoS2-based temperature sensor array integrated on a mesh-structured polymer substrate. Upon injection through a small skull hole in the skull, the device unfolds and achieves conformal contact on the cortical surface, aligning with the target vessel. By measuring temperature variations in response to the heater activation, the injectable sensor continuously monitors blood flow changes in the underlying vessel. This approach offers a new potential for cerebral blood flow sensing via minimally invasive implantation.},
}
@article {pmid40230710,
year = {2025},
author = {Finney, JN and Levy, IR and Chandrasekaran, S and Collinger, JL and Boninger, ML and Gaunt, RA and Helm, ER and Fisher, LE},
title = {Techniques to mitigate lead migration for percutaneous trials of cervical spinal cord stimulation.},
journal = {Frontiers in surgery},
volume = {12},
number = {},
pages = {1458572},
pmid = {40230710},
issn = {2296-875X},
abstract = {INTRODUCTION: Epidural spinal cord stimulation (SCS) is a clinical neuromodulation technique that is commonly used to treat neuropathic pain, with patients typically undergoing a one-week percutaneous trial phase before permanent implantation. Traditional SCS involves stimulation of the thoracic spinal cord, but there has been substantial recent interest in cervical SCS to treat upper extremity pain, restore sensation from the missing hand after amputation, or restore motor function to paretic limbs in people with stroke and spinal cord injury. Because of the additional mobility of the neck, as compared to the trunk, lead migration can be a major challenge for cervical SCS, especially during the percutaneous trial phase. The objective of this study was to optimize the implantation procedure of cervical SCS leads to minimize lead migration and increase lead stability during SCS trials.<br><br>METHODS: In this study, four subjects underwent percutaneous placement of three SCS leads targeting the cervical spinal segments as part of a clinical trial aiming to restore sensation for people with upper-limb amputation. The leads were maintained for up to 29 days and weekly x-ray imaging was used to measure rostrocaudal and mediolateral lead migration based on bony landmarks.<br><br>RESULTS AND DISCUSSION: Lead migration was primarily confined to the rostrocaudal axis with the most migration occurring during the first week. Iterative improvements to the implantation procedure were implemented to increase lead stability across subjects. There was a decrease in lead migration for patients who had more rostral placement of the SCS leads. The average migration from the day of lead implant to lead removal was 29.7&#x2005;mm for Subject 1 (lead placement ranging from T3-T4 to T1-T2), 41.9&#x2005;mm for Subject 2 (T2-T3 to C7-T1), 1.9&#x2005;mm for Subject 3 (T1-T2 to C7-T1), and 16.6&#x2005;mm for Subject 4 (T1-T2 to C7-T1). We found that initial placement of spinal cord stimulator leads in the cervical epidural space as rostral as possible was critical to minimizing subsequent rostrocaudal lead migration.},
}
@article {pmid40228689,
year = {2025},
author = {Hesam-Shariati, N and Alexander, L and Stapleton, F and Newton-John, T and Lin, CT and Zahara, P and Chen, K and Restrepo, S and Skinner, IW and McAuley, JH and Moseley, GL and Jensen, MP and Gustin, SM},
title = {The Effect of an EEG Neurofeedback Intervention for Corneal Neuropathic Pain: A Single-Case Experimental Design with Multiple Baselines.},
journal = {The journal of pain},
volume = {},
number = {},
pages = {105394},
doi = {10.1016/j.jpain.2025.105394},
pmid = {40228689},
issn = {1528-8447},
abstract = {Corneal neuropathic pain is a complex condition, rarely responsive to current treatments. This trial investigated the potential effect of a novel home-based self-directed EEG neurofeedback intervention on corneal neuropathic pain using a multiple-baseline single-case experimental design. Four Participants completed a predetermined baseline of 7, 10, 14, and 17 days, randomly assigned to each participant, followed by 20 intervention sessions over four weeks. Two one-week follow-ups occurred immediately and five weeks post-intervention during which participants were encouraged to practice mental strategies. Daily pain severity and pain interference observations were the outcome measures, while anxiety, depression, or sleep problems were the generalisation measures. The results showed a medium effect on pain severity and interference across participants when comparing baseline to five-week post-intervention according to Tau-U effect sizes. At the individual level, both Tau-U and NAP effect sizes indicated significant reductions in pain severity and interference for three participants when comparing baseline to five-week post-intervention. However, the reductions indicated by the visual inspection were not considered clinically meaningful. This single-case experimental design study raises the possibility that the intervention may improve pain severity and interference for some individuals, variability in outcomes highlights the need for future research to better understand individual responses and optimize the intervention effect. REGISTRATION: Australian New Zealand Clinical Trial Registry ACTRN12623000173695 PERSPECTIVE: This trial demonstrates the potential of EEG neurofeedback to reduce pain severity and interference in individuals with corneal neuropathic pain. It also highlights user preferences for technology-based interventions, emphasizing ease of use, accessibility, and self-administration to enhance adherence, especially for individuals with limited mobility or restricted healthcare access.},
}
@article {pmid40228398,
year = {2025},
author = {Grigoryan, KA and Mueller, K and Wagner, M and Masri, D and Pine, KJ and Villringer, A and Sehm, B},
title = {Short-term BCI intervention enhances functional brain connectivity associated with motor performance in chronic stroke.},
journal = {NeuroImage. Clinical},
volume = {46},
number = {},
pages = {103772},
doi = {10.1016/j.nicl.2025.103772},
pmid = {40228398},
issn = {2213-1582},
abstract = {BACKGROUND: Evidence suggests that brain-computer interface (BCI)-based rehabilitation strategies show promise in overcoming the limited recovery potential in the chronic phase of stroke. However, the specific mechanisms driving motor function improvements are not fully understood.<br><br>OBJECTIVE: We aimed at elucidating the potential functional brain connectivity changes induced by BCI training in participants with chronic stroke.<br><br>METHODS: A longitudinal crossover design was employed with two groups of participants over the span of 4&#xa0;weeks to allow for within-subject (n&#xa0;=&#xa0;21) and cross-group comparisons. Group 1 (n&#xa0;=&#xa0;11) underwent a 6-day motor imagery-based BCI training during the second week, whereas Group 2 (n&#xa0;=&#xa0;10) received the same training during the third week. Before and after each week, both groups underwent resting state functional MRI scans (4 for Group 1 and 5 for Group 2) to establish a baseline and monitor the effects of BCI training.<br><br>RESULTS: Following BCI training, an increased functional connectivity was observed between the medial prefrontal cortex of the default mode network (DMN) and motor-related areas, including the premotor cortex, superior parietal cortex, SMA, and precuneus. Moreover, these changes were correlated with the increased motor function as confirmed with upper-extremity Fugl-Meyer assessment scores, measured before and after the training.<br><br>CONCLUSIONS: Our findings suggest that BCI training can enhance brain connectivity, underlying the observed improvements in motor function. They provide a basis for developing novel rehabilitation approaches using non-invasive brain stimulation for targeting functionally relevant brain regions, thereby augmenting BCI-induced neuroplasticity and enhancing motor recovery.},
}
@article {pmid40227907,
year = {2025},
author = {Zhong, Y and Wang, Y and Farina, D and Yao, L},
title = {A Closed-Loop Tactile Stimulation Training Protocol for Motor Imagery-Based BCI: Boosting BCI Performance for BCI-Deficiency Users.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3560713},
pmid = {40227907},
issn = {1558-2531},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) enable users to control and communicate with the external environment. However, a significant challenge in BCI research is the occurrence of "BCI-illiteracy" or "BCI-deficiency", where a notable percentage of users (estimated at 15 to 30%) are unable to achieve successful BCI control. For those users, they are struggling to generate stable and distinguishable brain activity patterns, which are essential for BCI control. Existing neurofeedback training protocols, often rely on the trial-and-error process, which is time-consuming and inefficient, particularly for these low-performing users.<br><br>METHODS: To address this issue, we propose a closed-loop tactile stimulation training protocol, in which tactile stimulation training is incorporated within the closed neurofeedback loop, providing users with explicit guidance on how to correctly perform MI tasks. When a subject performs an incorrect MI trial, tactile-assisted MI training is provided to guide the user toward the correct brain state, while no training is given during correct performance.<br><br>RESULTS: The results from our study demonstrated that the proposed training protocol significantly enhances BCI decoding performance, with an improvement of 16.9%. Moreover, the BCI-deficiency rate was reduced by 61.5%. Further analysis revealed that the training process also led to enhanced motor imagery-related cortical activation.<br><br>CONCLUSION: The proposed training protocol significantly improved BCI decoding performance, enabling previously BCI-deficient users to surpass the 70% control threshold.<br><br>SIGNIFICANCE: This study demonstrates the effectiveness of closed-loop tactile-assisted training in enhancing BCI accessibility and efficiency, paving the way for more inclusive neurofeedback-based BCI training strategies.},
}
@article {pmid40227903,
year = {2025},
author = {Mai, X and Meng, J and Ding, Y and Zhu, X and Guan, C},
title = {SRRNet: Unseen SSVEP Response Regression From Stimulus for Cross-Stimulus Transfer in SSVEP-BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1460-1472},
doi = {10.1109/TNSRE.2025.3560434},
pmid = {40227903},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Electroencephalography/methods ; Female ; Algorithms ; Young Adult ; Photic Stimulation ; Regression Analysis ; Calibration ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {The prolonged calibration time required by steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) poses a significant challenge to real-life applications. Cross-stimulus transfer emerges as a promising solution, wherein a model trained on a subset of classes (seen classes) can predict both seen and unseen classes. Existing approaches extracted common components from SSVEP templates of seen classes to construct templates for unseen classes; however, they are limited by the class-specific activities and noise contained in these components, leading to imprecise templates that degrade classification performance. To address this issue, this study proposed an SSVEP Response Regression Network (SRRNet), which learned the regression mapping between sine-cosine reference signals and SSVEP templates using seen class data. This network reconstructed SSVEP templates for unseen classes utilizing their corresponding sine-cosine signals. Additionally, an SSVEP template regressing and spatial filtering (SRSF) framework was introduced, where both test data and SSVEP templates were projected by task-related component analysis (TRCA) spatial filters, and correlations were computed for target prediction. Comparative evaluations on two public datasets revealed that our method significantly outperformed state-of-the-art methods, elevating the information transfer rate (ITR) from 173.33 bits/min to 203.79 bits/min. By effectively modeling the regression from sine-cosine reference signals to SSVEP templates, SRRNet can construct SSVEP templates for unseen classes without training samples from those classes. By integrating regressed SSVEP templates with spatial filtering-based methods, our method enhances cross-stimulus transfer performance in SSVEP-BCIs, thus advancing their practical applicability. The code is available at https://github.com/MaiXiming/SRRNet.},
}
@article {pmid40227525,
year = {2025},
author = {Yan, L and Liu, Z and Wang, J and Yu, L},
title = {Integrating Hard Silicon for High-Performance Soft Electronics via Geometry Engineering.},
journal = {Nano-micro letters},
volume = {17},
number = {1},
pages = {218},
pmid = {40227525},
issn = {2150-5551},
abstract = {Soft electronics, which are designed to function under mechanical deformation (such as bending, stretching, and folding), have become essential in applications like wearable electronics, artificial skin, and brain-machine interfaces. Crystalline silicon is one of the most mature and reliable materials for high-performance electronics; however, its intrinsic brittleness and rigidity pose challenges for integrating it into soft electronics. Recent research has focused on overcoming these limitations by utilizing structural design techniques to impart flexibility and stretchability to Si-based materials, such as transforming them into thin nanomembranes or nanowires. This review summarizes key strategies in geometry engineering for integrating crystalline silicon into soft electronics, from the use of hard silicon islands to creating out-of-plane foldable silicon nanofilms on flexible substrates, and ultimately to shaping silicon nanowires using vapor-liquid-solid or in-plane solid-liquid-solid techniques. We explore the latest developments in Si-based soft electronic devices, with applications in sensors, nanoprobes, robotics, and brain-machine interfaces. Finally, the paper discusses the current challenges in the field and outlines future research directions to enable the widespread adoption of silicon-based flexible electronics.},
}
@article {pmid40226198,
year = {2025},
author = {Nirabi, A and Rahman, FA and Habaebi, MH and Sidek, KA and Yusoff, S},
title = {Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111477},
pmid = {40226198},
issn = {2352-3409},
abstract = {This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recordings designed to unravel mental stress patterns through the perspective of cognitive load. The dataset incorporates EEG signals obtained from 15 subjects, with a gender distribution of 8 females and 7 males, and a mean age of 21.5 years [1]. Recordings were collected during the subjects' engagement in diverse tasks, including the Stroop color-word test and arithmetic problem-solving tasks. The recordings are categorized into four classes representing varying levels of induced mental stress: normal, low, mid, and high. Each task was performed for a duration of 10-20 s, and three trials were conducted for comprehensive data collection. Employing an OpenBCI device with an 8-channel Cyton board, the EEG captures intricate responses of the frontal lobe to cognitive challenges posed by the Stroop and Arithmetic Tests, recorded at a sampling rate of 250 Hz. The proposed dataset serves as a valuable resource for advancing research in the realm of brain-computer interfaces and offers insights into identifying EEG patterns associated with stress. The proposed dataset serves as a valuable resource for researchers, offering insights into identifying EEG patterns that correlate with different stress states. By providing a solid foundation for the development of algorithms capable of detecting and classifying stress levels, the dataset supports innovations in non-invasive monitoring tools and contributes to personalized healthcare solutions that can adapt to the cognitive states of users. This study's foundation is crucial for advancing stress classification research, with significant implications for cognitive function and well-being.},
}
@article {pmid40225841,
year = {2025},
author = {Kashou, N},
title = {Editorial: New horizons in stroke management.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1587791},
doi = {10.3389/fnhum.2025.1587791},
pmid = {40225841},
issn = {1662-5161},
}
@article {pmid40225573,
year = {2025},
author = {Bai, R and Jia, Y and Wang, B and Wang, Z and Han, G and Liang, L and Chen, L and Ming, Y and Zhu, G and Hsu, YC and Zhao, P and Zhang, Y and Liu, Z and Liu, C and Li, Z and Liu, Y},
title = {In vivo spatiotemporal mapping of proliferation activity in gliomas via water-exchange dynamic contrast-enhanced MRI.},
journal = {Theranostics},
volume = {15},
number = {10},
pages = {4693-4707},
pmid = {40225573},
issn = {1838-7640},
mesh = {*Glioma/diagnostic imaging/pathology ; *Magnetic Resonance Imaging/methods ; Animals ; Humans ; *Cell Proliferation ; Temozolomide/pharmacology ; Cell Line, Tumor ; *Contrast Media ; Aquaporin 4/metabolism ; *Brain Neoplasms/diagnostic imaging/pathology ; *Water/metabolism ; Mice ; },
abstract = {Proliferation activity mapping is crucial for the guidance of first biopsy and treatment evaluation of gliomas due to the highly heterogenous nature of glioma tumor. Here we propose and demonstrate an ease-of-use way of in vivo spatiotemporal mapping of proliferation activity by simply tracking transmembrane water dynamics with magnetic resonance imaging (MRI). Specifically, we demonstrated that proliferation activity can accelerate the transmembrane water transport in glioma cells. Method: The transmembrane water-efflux rate (k io) measured by water-exchange dynamic contrast-enhanced (DCE) MRI. Immunofluorescence, immunohistochemistry, and immunocytochemistry staining were used to validate results obtained from the in vivo imaging studies. Results: In glioma cell cultures, k io precisely followed the dynamic changes of proliferation activity in growth cycles and response to temozolomide (TMZ) treatment. In both animal glioma model and human glioma, k io linearly and strongly correlated with the spatial heterogeneity of intra-tumoral proliferation activity. More importantly, proliferation activity predicted by the single MRI parameter k io is much more accurate than those predicted by state-of-the-art methods using multimodal standard MRIs and advanced machine learning. Upregulated aquaporin 4 (AQP4) expression were observed in most proliferating glioma cells and the knockout of AQP4 could largely slow down proliferation activity, suggesting AQP4 is the potential molecule connecting MRI-k io with proliferation activity. Conclusion: This study provides an ease-of-use, accurate, and non-invasive imaging method for the spatiotemporal monitoring of proliferation activity in glioma.},
}
@article {pmid40223771,
year = {2025},
author = {Kapur, A and Van Til, M and Daignault-Newton, S and Seibel, C and Nagpal, S and Ippolito, GM and Smith, AL and Lucioni, A and Lee, U and Suskind, A and Anger, J and Chung, D and Reynolds, WS and Cameron, A and Tenggardjaja, C and Padmanabhan, P and Brucker, BM and , },
title = {Association Between Urodynamic Findings and Urinary Retention After Onabotulinumtoxin A for Idiopathic Overactive Bladder.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.70050},
pmid = {40223771},
issn = {1520-6777},
support = {//This secondary analysis did not receive any external sources of funding. Funding for the primary analysis which utilized the same original data set as the current study was Society of Urodynamics, Female Pelvic Medicine and Urogenital Reconstruction Foundation (SUFU); National Institutes of Health, Grant/Award Number: UL1TR002240./ ; },
abstract = {INTRODUCTION: Onabotulinumtoxin A (BTX-A) is a minimally invasive therapy for idiopathic overactive bladder (iOAB). Incomplete bladder emptying is a known risk of the procedure, with an overall rate as high as 20% in male and female patients. Risk factors for incomplete bladder emptying after BTX-A have been reported in the literature, but are widely variable amongst studies and therefore patients at increased risk of this adverse effect cannot easily be identified by clinicians. The aim of this study was to evaluate whether pre-procedure urodynamics (UDS) findings are associated with incomplete bladder emptying after intradetrusor BTX-A injection for iOAB.<br><br>METHODS: Data were analyzed from the SUFU Research Network (SURN) multi-institutional retrospective database. Men and women undergoing first-time injection of 100 units BTX-A for iOAB in 2016 were included. Subjects were excluded if they did not have record of pre-procedure and post-procedure (within 1 month) post-void residual volume (PVR). The primary outcome was incidence of urinary retention within 1 month after BTX-A, defined as PVR&#x2009;>&#x2009;300&#x2009;mL and/or initiation of self-catheterization or indwelling catheter. We assessed the association of pre-procedure UDS parameters with urinary retention using Wilcoxon rank tests, Fisher's exact test, and chi-squared tests.<br><br>RESULTS: A total of 167 subjects (141 women, 26 men) were included. Ninety-nine subjects (59%) had urodynamic data. Thirty-seven subjects (22%) had urinary retention within 1 month of BTX-A. There were no significant differences in age, gender, race, or body mass index between the retention and non-retention groups. There was no statistically significant difference in median Qmax between those who did and did not have postprocedure retention (10.0 vs. 14.3&#x2009;mL/s respectively, p&#x2009;=&#x2009;0.06). Mean PVR at the start of UDS was not statistically significant when comparing the retention and non-retention groups (22.5 vs. 10.0&#x2009;mL respectively, p&#x2009;=&#x2009;0.70). Bladder outlet obstruction index (BOOI), bladder contractility index (BCI), and presence of detrusor overactivity (DO) were not found to be associated with posttreatment retention.<br><br>CONCLUSION: This retrospective multi-institutional cohort study revealed that of patients who receive UDS before BTX-A, there are no significant UDS parameters or baseline demographic factors associated with incomplete bladder emptying after intradetrusor BTX-A injections for iOAB. Future studies that focus on better defining objective evidence-based predictors of incomplete emptying after BTX are needed to optimize patient perception of efficacy and satisfaction with this therapy.},
}
@article {pmid40223534,
year = {2025},
author = {Jung, M and Abu Shihada, J and Decke, S and Koschinski, L and Graff, PS and Maruri Pazmino, S and Höllig, A and Koch, H and Musall, S and Offenhäusser, A and Rincón Montes, V},
title = {Flexible 3D Kirigami Probes for In Vitro and In Vivo Neural Applications.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2418524},
doi = {10.1002/adma.202418524},
pmid = {40223534},
issn = {1521-4095},
support = {VH-NG-1611//Helmholtz Association/ ; GRK2610//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 424556709//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; GRK2416//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; 368482240//Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)/ ; },
abstract = {3D microelectrode arrays (MEAs) are gaining popularity as brain-machine interfaces and platforms for studying electrophysiological activity. Interactions with neural tissue depend on the electrochemical, mechanical, and spatial features of the recording platform. While planar or protruding 2D MEAs are limited in their ability to capture neural activity across layers, existing 3D platforms still require advancements in manufacturing scalability, spatial resolution, and tissue integration. In this work, a customizable, scalable, and straightforward approach to fabricate flexible 3D kirigami MEAs containing both surface and penetrating electrodes, designed to interact with the 3D space of neural tissue, is presented. These novel probes feature up to 512 electrodes distributed across 128 shanks in a single flexible device, with shank heights reaching up to 1 mm. The 3D kirigami MEAs are successfully deployed in several neural applications, both in vitro and in vivo, and identified spatially dependent electrophysiological activity patterns. Flexible 3D kirigami MEAs are therefore a powerful tool for large-scale electrical sampling of complex neural tissues while improving tissue integration and offering enhanced capabilities for analyzing neural disorders and disease models where high spatial resolution is required.},
}
@article {pmid40223097,
year = {2025},
author = {Wu, Y and Liu, Y and Yang, Y and Yao, MS and Yang, W and Shi, X and Yang, L and Li, D and Liu, Y and Yin, S and Lei, C and Zhang, M and Gee, JC and Yang, X and Wei, W and Gu, S},
title = {A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3504},
pmid = {40223097},
issn = {2041-1723},
support = {62236009//National Science Foundation of China | Key Programme/ ; },
mesh = {Humans ; *Choroid Neoplasms/diagnosis/diagnostic imaging ; *Melanoma/diagnosis/diagnostic imaging ; Machine Learning ; Artificial Intelligence ; Female ; Uveal Melanoma ; Male ; *Uveal Neoplasms/diagnostic imaging/diagnosis ; Hemangioma/diagnosis/diagnostic imaging ; Middle Aged ; Diagnosis, Differential ; Multimodal Imaging/methods ; Adult ; },
abstract = {Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.},
}
@article {pmid40223012,
year = {2025},
author = {Lorente-Piera, J and Manrique-Huarte, R and Picciafuoco, S and Lima, JP and Calavia, D and Serra, V and Manrique, M},
title = {Optimization of surgical interventions in auditory rehabilitation for chronic otitis media: comparative between passive middle ear implants, bone conduction implants, and active middle ear systems.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {},
number = {},
pages = {},
pmid = {40223012},
issn = {1434-4726},
abstract = {INTRODUCTION: In otology consultations, patients with chronic otitis media (COM) often present as candidates for various hearing rehabilitation options. Selecting the most suitable approach requires careful consideration of patient preferences and expectations, the risk of disease progression, and the integrity of the bone conduction pathway. This study aims to evaluate and compare postoperative hearing outcomes in COM patients undergoing tympanoplasty (with or without passive middle ear implants), bone conduction systems (BCI), or active middle ear implants (AMEI). The objective is to assess the effectiveness of each surgical approach in hearing rehabilitation, considering the type and severity of hearing loss as well as the duration of the disease.<br><br>METHODS: Retrospective data analysis in a tertiary referral center studying average PTA across six different frequencies, speech perception at 65&#xa0;dB, influence of Eustachian tube dysfunction, reintervention rate and adverse effects, and the influence of disease duration on functional outcomes via linear regression analysis.<br><br>RESULTS: 116 patients underwent surgery due to COM between 1998 and 2024. With a slight female predominance (54.31%). AMEIs and bone conduction devices provided the highest amplification in terms of PTA and speech discrimination, with a lower reintervention rate when comparing both groups with passive middle ear implants, OR in BCI group of 0.30 (0.10; 0.89, p&#x2009;=&#x2009;0.030), OR in VSB group of 0.15 (0.04; 0.56, p&#x2009;=&#x2009;0.005). It was also observed that a longer evolution time could be associated with greater auditory gain, with a p-value&#x2009;=&#x2009;0.033.<br><br>CONCLUSIONS: The selection of each treatment option primarily depends on bone conduction thresholds, along with surgical risk, patient preferences, and MRI compatibility. In our study, AMEIs demonstrated the highest functional gain in terms of speech discrimination and frequency-specific amplification, followed by BCI. These findings support the use of implantable hearing solutions as effective alternatives for auditory rehabilitation in COM patients.},
}
@article {pmid40222332,
year = {2025},
author = {Choi, JY and Kim, YJ and Shin, JS and Choi, E and Kim, Y and Kim, MG and Kim, YT and Park, BS and Kim, JK and Kim, JG},
title = {Integrative metabolic profiling of hypothalamus and skeletal muscle in a mouse model of cancer cachexia.},
journal = {Biochemical and biophysical research communications},
volume = {763},
number = {},
pages = {151766},
doi = {10.1016/j.bbrc.2025.151766},
pmid = {40222332},
issn = {1090-2104},
abstract = {Cancer cachexia is a multifactorial metabolic syndrome characterized by progressive weight loss, muscle wasting, and systemic inflammation. Despite its clinical significance, the underlying mechanisms linking central and peripheral metabolic changes remain incompletely understood. In this study, we employed a murine model of cancer cachexia induced by intraperitoneal injection of Lewis lung carcinoma (LLC1) cells to investigate tissue-specific metabolic adaptations. Cachectic mice exhibited reduced food intake, body weight loss, impaired thermoregulation, and decreased energy expenditure. Metabolomic profiling of serum, skeletal muscle, and hypothalamus revealed distinct metabolic shifts, with increased fatty acid and ketone body utilization and altered amino acid metabolism. Notably, hypothalamic metabolite changes diverged from peripheral tissues, showing decreased neurotransmitter-related metabolites and enhanced lipid-based energy signatures. Gene expression analysis further confirmed upregulation of glycolysis- and lipid oxidation-related genes in both hypothalamus and muscle. These findings highlight coordinated yet compartmentalized metabolic remodeling in cancer cachexia and suggest that hypothalamic adaptations may play a central role in the systemic energy imbalance associated with cachexia progression.},
}
@article {pmid40221457,
year = {2025},
author = {Rybář, M and Poli, R and Daly, I},
title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {613},
pmid = {40221457},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; *Semantics ; *Imagination ; Animals ; Male ; Female ; Adult ; Brain/physiology ; },
abstract = {Semantic neural decoding aims to identify which semantic concepts an individual focuses on at a given moment based on recordings of their brain activity. We investigated the feasibility of semantic neural decoding to develop a new type of brain-computer interface (BCI) that allows direct communication of semantic concepts, bypassing the character-by-character spelling used in current BCI systems. We provide data from our study to differentiate between two semantic categories of animals and tools during a silent naming task and three intuitive sensory-based imagery tasks using visual, auditory, and tactile perception. Participants were instructed to visualize an object (animal or tool) in their minds, imagine the sounds produced by the object, and imagine the feeling of touching the object. Simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals were recorded from 12 participants. Additionally, EEG signals were recorded from 7 other participants in a follow-up experiment focusing solely on the auditory imagery task. These datasets can serve as a valuable resource for researchers investigating semantic neural decoding, brain-computer interfaces, and mental imagery.},
}
@article {pmid40219565,
year = {2025},
author = {Liu, N and Man, L and He, F and Huang, G and Zhai, J},
title = {[Correlation between urination intermittences and urodynamic parameters in benign prostatic hyperplasia patients].},
journal = {Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences},
volume = {57},
number = {2},
pages = {328-333},
pmid = {40219565},
issn = {1671-167X},
mesh = {*Prostatic Hyperplasia/complications/physiopathology ; Humans ; Male ; *Urodynamics ; *Urination ; Retrospective Studies ; Middle Aged ; Aged ; Aged, 80 and over ; *Urinary Bladder/physiopathology ; *Urination Disorders/etiology ; },
abstract = {OBJECTIVE: To explore the impact factors and the clinical significance of the urination intermittences in benign prostatic hyperplasia (BPH) patients.<br><br>METHODS: A retrospective study was performed in BPH patients who underwent urodynamic studies in Beijing Jishuitan Hospital form January 2016 to June 2021. The patients were aged 45 to 84 years with a median age of 63 years, and all the patients had no previous history of neurological disease and had no positive findings in neurological examinations. All the patients had free uroflometry followed by urethral catheterization and urodynamic tests. The voiding work of bladder was calculated using the detrusor power curve method, and the voiding power of bladder and the voiding energy consumption were also calculated. The frequency of urination intermittences generated in uroflometry was also recorded and the patients were divided into different groups according to it. The detrusor pressure at maximal flow rate (PdetQmax), the maximal flow rate (Qmax), the bladder contractile index (BCI), the bladder outlet obstruction index (BOOI), the voiding work, the voiding power, and the voiding energy consumption were compared among the different groups. Multiva-riate analyses associated with presence of urination intermittences were performed using step-wise Logistic regressions.<br><br>RESULTS: There were 272 patients included in this study, of whom, 179 had no urination intermittence (group A), 46 had urination intermittence for only one time (group B), 22 had urination intermittence for two times (group C), and 25 had urination intermittence for three times and more (group D). The BCI were 113.4&#xb1;28.2, 101.0&#xb1;30.2, 83.3&#xb1;30.2, 81.0&#xb1;30.5 in groups A, B, C, and D, respectively; The voiding power were (29.2&#xb1;14.8) mW, (16.4&#xb1;9.6) mW, (14.5&#xb1;7.1) mW, (8.5&#xb1;5.0) mW in groups A, B, C, and D, respectively, and the differences were significant (P < 0.05). The BOOI were 41.6&#xb1;29.3, 46.4&#xb1;31.0, 41.4&#xb1;29.0, 42.7&#xb1;22.8 in groups A, B, C, and D, respectively; The voiding energy consumption were (5.41&#xb1;2.21) J/L, (4.83&#xb1;2.31) J/L, (5.02&#xb1;2.54) J/L, (4.39&#xb1;2.03) J/L in groups A, B, C, and D, respectively, and the differences were insignificant (P>0.05). Among the patients, 179 cases were negative in presence of urination intermittences and 93 cases were positive. Step-wise Logistic regression analysis showed that bladder power (OR=0.814, 95%CI: 0.765-0.866, P < 0.001), BCI (OR=1.023, 95%CI: 1.008-1.038, P=0.003), and bladder work (OR=2.232, 95%CI: 1.191-4.184, P=0.012) were independent risk factors for urination intermittences in the BPH patients.<br><br>CONCLUSION: The presence of urination intermittences in the BPH patients was mainly influenced by bladder contractile functions, and was irrelevant to the degree of bladder outlet obstruction. The increase of frequency of urination intermittences seemed to be a sign of the decrease of the bladder contractile functions in the BPH patients.},
}
@article {pmid40218833,
year = {2025},
author = {Ranjbar Koleibi, E and Lemaire, W and Koua, K and Benhouria, M and Bostani, R and Serri Mazandarani, M and Gauthier, LP and Besrour, M and Ménard, J and Majdoub, M and Gosselin, B and Roy, S and Fontaine, R},
title = {Design and Implementation of a Low-Power Biopotential Amplifier in 28 nm CMOS Technology with a Compact Die-Area of 2500 μm[2] and an Ultra-High Input Impedance.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218833},
issn = {1424-8220},
abstract = {Neural signal recording demands compact, low-power, high-performance amplifiers, to enable large-scale, multi-channel electrode arrays. This work presents a bioamplifier optimized for action potential detection, designed using TSMC 28 nm HPC CMOS technology. The amplifier integrates an active low-pass filter, eliminating bulky DC-blocking capacitors and significantly reducing the size and power consumption. It achieved a high input impedance of 105.5 GΩ, ensuring minimal signal attenuation. Simulation and measurement results demonstrated a mid-band gain of 58 dB, a -3 dB bandwidth of 7 kHz, and an input-referred noise of 11.1 μVrms, corresponding to a noise efficiency factor (NEF) of 8.4. The design occupies a compact area of 2500 μm2, making it smaller than previous implementations for similar applications. Additionally, it operates with an ultra-low power consumption of 3.4 μW from a 1.2 V supply, yielding a power efficiency factor (PEF) of 85 and an area efficiency factor of 0.21. These features make the proposed amplifier well suited for multi-site in-skull neural recording systems, addressing critical constraints regarding miniaturization and power efficiency.},
}
@article {pmid40218817,
year = {2025},
author = {Andreev, A and Cattan, G and Congedo, M},
title = {The Riemannian Means Field Classifier for EEG-Based BCI Data.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218817},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {: A substantial amount of research has demonstrated the robustness and accuracy of the Riemannian minimum distance to mean (MDM) classifier for all kinds of EEG-based brain-computer interfaces (BCIs). This classifier is simple, fully deterministic, robust to noise, computationally efficient, and prone to transfer learning. Its training is very simple, requiring just the computation of a geometric mean of a symmetric positive-definite (SPD) matrix per class. We propose an improvement of the MDM involving a number of power means of SPD matrices instead of the sole geometric mean. By the analysis of 20 public databases, 10 for the motor-imagery BCI paradigm and 10 for the P300 BCI paradigm, comprising 587 individuals in total, we show that the proposed classifier clearly outperforms the MDM, approaching the state-of-the art in terms of performance while retaining the simplicity and the deterministic behavior. In order to promote reproducible research, our code will be released as open source.},
}
@article {pmid40218770,
year = {2025},
author = {Gómez-Morales, &#xd3;W and Collazos-Huertas, DF and Álvarez-Meza, AM and Castellanos-Dominguez, CG},
title = {EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218770},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; Male ; Adult ; *Brain/physiology ; Female ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only does this entail long preparation times and high costs, but it also carries the risk of losing valuable information when an electrode is damaged, further limiting its practical applicability. In this study, a signal prediction-based method is proposed to achieve high accuracy in MI classification using EEG signals recorded from only a small number of electrodes. The signal prediction model was constructed using the elastic net regression technique, allowing for the estimation of EEG signals from 22 complete channels based on just 8 centrally located channels. The predicted EEG signals from the complete channels were used for feature extraction and MI classification. The results obtained indicate a notable efficacy of the proposed prediction method, showing an average performance of 78.16% in classification accuracy. The proposed method demonstrated superior performance compared to the traditional approach that used few-channel EEG and also achieved better results than the traditional method based on full-channel EEG. Although accuracy varies among subjects, from 62.30% to an impressive 95.24%, these data indicate the capability of the method to provide accurate estimates from a reduced set of electrodes. This performance highlights its potential to be implemented in practical MI-based BCI applications, thereby mitigating the time and cost constraints associated with systems that require a high density of electrodes.},
}
@article {pmid40218647,
year = {2025},
author = {Xu, H and Hassan, SA and Haider, W and Sun, Y and Yu, X},
title = {A Frequency-Shifting Variational Mode Decomposition-Based Approach to MI-EEG Signal Classification for BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {40218647},
issn = {1424-8220},
support = {U2033202, U1333119//National Natural Science Foundation of China and Civil Aviation Administration of China/ ; 52172387//National Natural Science Foundation of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; },
abstract = {Electroencephalogram (EEG) signal analysis is crucial for understanding neural activity and advancing diagnostics in neurology. However, traditional signal decomposition (SD) techniques are hindered by two critical issues, mode mixing and mode aliasing, that compromise the quality of the decomposed signal. These challenges result in poor signal integrity, which significantly affects the accuracy of subsequent EEG interpretations and classifications. As EEG analysis is widely used in diagnosing conditions such as epilepsy, brain injuries, and sleep disorders, the impact of these shortcomings can be far-reaching, leading to misdiagnoses or delayed treatments. Despite extensive research on SD techniques, these issues remain largely unresolved, emphasizing the urgent need for a more reliable and precise approach. This study proposes a novel solution through the frequency-shifting variational mode decomposition (FS-VMD) method, which overcomes the limitations of traditional SD techniques by providing better resolution of intrinsic mode functions (IMFs). The FS-VMD method works by extracting and shifting the fundamental frequency of the EEG signal to a lower frequency range, followed by an iterative decomposition process that enhances signal clarity and reduces mode aliasing. By integrating advanced feature selection techniques and classifiers such as support vector machines (SVM), convolutional neural networks (CNN), and feature-weighted k-nearest neighbors (FWKNN), this approach offers a significant improvement in classification accuracy, with SVM achieving up to 99.99% accuracy in the 18-channel EEG setup with a standard deviation of 0.25. The results demonstrate that FS-VMD can address the critical issues of mode mixing and aliasing, providing a more accurate and efficient solution for EEG signal analysis and diagnostics.},
}
@article {pmid40215960,
year = {2025},
author = {Chen, LN and Zhou, H and Xi, K and Cheng, S and Liu, Y and Fu, Y and Ma, X and Xu, P and Ji, SY and Wang, WW and Shen, DD and Zhang, H and Shen, Q and Chai, R and Zhang, M and Yang, L and Han, F and Mao, C and Cai, X and Zhang, Y},
title = {Proton perception and activation of a proton-sensing GPCR.},
journal = {Molecular cell},
volume = {85},
number = {8},
pages = {1640-1657.e8},
doi = {10.1016/j.molcel.2025.02.030},
pmid = {40215960},
issn = {1097-4164},
mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/chemistry/genetics/ultrastructure ; *Protons ; Cryoelectron Microscopy ; HEK293 Cells ; Hydrophobic and Hydrophilic Interactions ; Histidine/metabolism/chemistry ; Hydrogen Bonding ; Protein Binding ; Models, Molecular ; Protein Conformation ; Hydrogen-Ion Concentration ; },
abstract = {Maintaining pH at cellular, tissular, and systemic levels is essential for human health. Proton-sensing GPCRs regulate physiological and pathological processes by sensing the extracellular acidity. However, the molecular mechanism of proton sensing and activation of these receptors remains elusive. Here, we present cryoelectron microscopy (cryo-EM) structures of human GPR4, a prototypical proton-sensing GPCR, in its inactive and active states. Our studies reveal that three extracellular histidine residues are crucial for proton sensing of human GPR4. The binding of protons induces substantial conformational changes in GPR4's ECLs, particularly in ECL2, which transforms from a helix-loop to a β-turn-β configuration. This transformation leads to the rearrangements of H-bond network and hydrophobic packing, relayed by non-canonical motifs to accommodate G proteins. Furthermore, the antagonist NE52-QQ57 hinders human GPR4 activation by preventing hydrophobic stacking rearrangement. Our findings provide a molecular framework for understanding the activation mechanism of a human proton-sensing GPCR, aiding future drug discovery.},
}
@article {pmid40213917,
year = {2025},
author = {Wang, N and Wang, Y and Guo, M and Wang, L and Wang, X and Zhu, N and Yang, J and Wang, L and Zheng, C and Ming, D},
title = {Dynamic gamma modulation of hippocampal place cells predominates development of theta sequences.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {40213917},
issn = {2050-084X},
support = {2022ZD0205000//National Science and Technology Innovation 2030 Major Project of China/ ; T2322021//National Natural Science Foundation of China/ ; 82271218//National Natural Science Foundation of China/ ; 12271272//National Natural Science Foundation of China/ ; 81925020//National Natural Science Foundation of China/ ; 82371886//National Natural Science Foundation of China/ ; 82202797//National Natural Science Foundation of China/ ; LG-TKN-202204-01//Space Brain Project from Lingang Laboratory/ ; 2022M712365//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Theta Rhythm/physiology ; Rats ; *Gamma Rhythm/physiology ; *Hippocampus/physiology/cytology ; *Place Cells/physiology ; Male ; Rats, Long-Evans ; },
abstract = {The experience-dependent spatial cognitive process requires sequential organization of hippocampal neural activities by theta rhythm, which develops to represent highly compressed information for rapid learning. However, how the theta sequences were developed in a finer timescale within theta cycles remains unclear. In this study, we found in rats that sweep-ahead structure of theta sequences developing with exploration was predominantly dependent on a relatively large proportion of FG-cells, that is a subset of place cells dominantly phase-locked to fast gamma rhythms. These ensembles integrated compressed spatial information by cells consistently firing at precessing slow gamma phases within the theta cycle. Accordingly, the sweep-ahead structure of FG-cell sequences was positively correlated with the intensity of slow gamma phase precession, in particular during early development of theta sequences. These findings highlight the dynamic network modulation by fast and slow gamma in the development of theta sequences which may further facilitate memory encoding and retrieval.},
}
@article {pmid40212471,
year = {2025},
author = {Feng, J and Li, Y and Huang, Z and Chen, Y and Lu, S and Hu, R and Hu, Q and Chen, Y and Wang, X and Fan, Y and He, J},
title = {Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1555690},
pmid = {40212471},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. This study proposes a Cross-Subject Heterogeneous Transfer Learning Model (CHTLM) to enhance the generalization of MI-fNIRS signal classification in stroke patients.<br><br>METHODS: CHTLM leverages labeled electroencephalogram (EEG) data from healthy individuals as the source domain. An adaptive feature matching network aligns task-relevant feature maps and convolutional layers between source (EEG) and target (fNIRS) domains. Multi-scale fNIRS features are extracted, and a sparse Bayesian extreme learning machine classifies the fused deep learning features.<br><br>RESULTS: Experiments utilized two MI-fNIRS datasets from eight stroke patients pre- and post-rehabilitation. CHTLM achieved average accuracies of 0.831 (pre-rehabilitation) and 0.913 (post-rehabilitation), with mean AUCs of 0.887 and 0.930, respectively. Compared to five baselines, CHTLM improved accuracy by 8.6-10.5% pre-rehabilitation and 11.3-15.7% post-rehabilitation.<br><br>DISCUSSION: The model demonstrates robust cross-subject generalization by transferring task-specific knowledge from heterogeneous EEG data while addressing domain discrepancies. Its performance gains post-rehabilitation suggest clinical potential for monitoring recovery progress. CHTLM advances MI-fNIRS-based brain-computer interfaces in stroke rehabilitation by mitigating data scarcity and variability challenges.},
}
@article {pmid40210930,
year = {2025},
author = {Qi, W and Zhang, Y and Su, Y and Hui, Z and Li, S and Wang, H and Zhang, J and Shi, K and Wang, M and Zhou, L and Zhu, D},
title = {Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {12285},
pmid = {40210930},
issn = {2045-2322},
mesh = {Humans ; *Cerebral Palsy/physiopathology/rehabilitation ; Child ; Male ; Female ; *Brain-Computer Interfaces ; Child, Preschool ; Electroencephalography ; *Robotics/methods ; *Lower Extremity/physiopathology ; },
abstract = {This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretical basis for applying MI-BCI in the rehabilitation of children with cerebral palsy (CP). This study included 30 subjects aged 4-6 years with GMFCS II-III grade, diagnosed with CP and classified as spastic diplegia. They sequentially completed EEG signal acquisition under REST, MI, and MI-BCI conditions. Clustering analysis was used to analyze EEG microstates and extract EEG microstate temporal parameters. Additionally, the strength of brain FC in different frequency bands was analyzed to compare the differences under various conditions. Four microstate classes (A-D) were identified to best explain the datasets of three groups. Compared to REST, the average duration and coverage rate of microstate D under MI and MI-BCI significantly increased (P < 0.05), while their frequency and the coverage rate and frequency of microstate A decreased. Compared to MI, the average duration of microstate C under MI-BCI significantly decreased (P < 0.05), while the frequency of microstate B significantly increased (P < 0.05). Additionally, the transition probability results showed that other microstates under REST had a higher transition probability to microstate A, while under MI and MI-BCI, other microstates had a higher transition probability to microstate D. The brain network results revealed significant differences in brain network connectivity among REST, MI, and MI-BCI across different frequency bands. No FC differences were found between REST, MI, and MI-BCI in the α2 frequency band. In the δ and γ frequency bands, MI and MI-BCI both had greater inter-electrode connectivity strength than REST. In the θ frequency band, REST had greater inter-electrode connectivity strength than MI-BCI, while MI-BCI had greater inter-electrode connectivity strength than both REST and MI. In the α1 frequency band, MI-BCI had greater inter-electrode connectivity strength than REST, and in the β frequency band, MI-BCI had greater inter-electrode connectivity strength than MI. MI-BCI can significantly alter the brain activity patterns of children with CP, particularly by enhancing the activity intensity of EEG microstates related to attention, motor planning, and execution, as well as the brain FC strength in different frequency bands. It holds high application value in the lower limb motor rehabilitation of children with CP.},
}
@article {pmid40210429,
year = {2025},
author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Kapageridou, E and Ahanov, O and Kontoninas, Z and Savopoulos, C and Didangelos, T},
title = {Psychometric Properties and Dimensionality of the Greek Version of the Hypoglycemic Confidence Scale.},
journal = {Journal of nursing measurement},
volume = {},
number = {},
pages = {},
doi = {10.1891/JNM-2024-0108},
pmid = {40210429},
issn = {1945-7049},
abstract = {Background and purpose: The prevalence of type 1 diabetes mellitus (T1D) is rising at an alarming rate and is projected to continue increasing in the coming years. The primary approach to preventing diabetes-related complications in individuals with T1D is the exogenous administration of insulin. However, this method can sometimes lead to hypoglycemia, a condition with a wide range of symptoms, including loss of consciousness, seizures, coma, and, in severe cases, death. This study aims to present the psychometric properties of the Greek translation of the Hypoglycemic Confidence Scale (HCS). The HCS measures an individual's sense of personal strength and comfort based on the belief that they possess the necessary resources to manage and prevent hypoglycemia-related complications. Methods: We conducted a forward and backward translation, along with a cultural adaptation, of the HCS into Greek. The psychometric properties of the scale were evaluated through confirmatory factor analysis. To assess the reliability, we calculated the intraclass correlation coefficient, while internal consistency was measured using Cronbach's coefficient α. Construct validity was evaluated through convergent and divergent validity, comparing the HCS-Gr with the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and hemoglobin A1C levels. Differential validity was assessed using the known-groups method. Results: Ninety-seven adults with T1D, aged between 18 and 57 years (mean age: 38.6 ± 11.7), completed the HCS-Gr. The two structures of the HCS-Gr demonstrated strong internal consistency, with Cronbach's coefficient α values of 0.87 for the eight-item version and 0.86 for the nine-item version. Convergent validity was supported by moderate negative correlations between both HCS-Gr versions and the DQoL-BCI subscales and total score. The HCS-Gr also showed satisfactory test-retest reliability and differential validity, confirming its robustness as a psychometric tool. Conclusion: The HCS-Gr is a valid and reliable tool for assessing confidence (or self-efficacy) in managing hypoglycemic situations among individuals with T1D in Greece.},
}
@article {pmid40209829,
year = {2025},
author = {Ruiz Ibán, MA and García Navlet, M and Marco, SM and Diaz Heredia, J and Hernando, A and Ruiz Díaz, R and Vaquero Comino, C and Alvarez Villar, S and Ávila Lafuente, JL},
title = {AUGMENTATION WITH A BOVINE BIOINDUCTIVE COLLAGEN IMPLANT OF A POSTEROSUPERIOR CUFF REPAIR SHOWS LOWER RETEAR RATES BUT SIMILAR OUTCOMES COMPARED TO NO AUGMENTATION: 2-YEAR RESULTS OF A RANDOMIZED CONTROLLED TRIAL.},
journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.arthro.2025.03.057},
pmid = {40209829},
issn = {1526-3231},
abstract = {PURPOSE: To assess the clinical and radiological outcomes of the addition of a bioinductive collagen implant (BCI) over the repair of medium-to-large posterosuperior rotator cuff tears at 24-month follow-up.<br><br>METHODS: This is an update of a randomized controlled trial that was extended from one to two-year follow-up. 124 subjects with symptomatic full-thickness posterosuperior rotator cuff tears, with fatty infiltration Goutalier grade &#x2264;2 were randomized to two groups in which a transosseous equivalent repair was performed alone (Control group) or with BCI applied over the repair (BCI group). The outcomes reassessed at 2-year follow-up were: Sugaya grade, retear rate and tendon thickness in MRI; and the clinical outcomes (pain levels, EQ-5D-5L, American Shoulder and Elbow Society[ASES] and Constant-Murley scores[CMS]).<br><br>RESULTS: There were no relevant differences in preoperative characteristics. There were no additional complications or reinterventions in the second year of follow-up. 114 (59 males-55 males, age=58.1[SD:7.35] years) of 124 randomized patients (91.9%), underwent MRI evaluation 25.4[1.95] months after surgery. There was a lower retear rate (12.3%[7/57]) in the BCI group compared to the Control group (35.1%[20/57]) (p=0.004; relative risk of retear 0.35[CI-95%:0.16 to 0.76]). Sugaya grade was also better in the BCI group (2.58[1.07] vs 3.14[1.19]; p=0.020). Two-year Clinical follow-up at 25.8[2.75] months performed in 114 of 124 patients(91.9%) showed improvements in both groups (p<0.001), with 87% improving more than the MCID for CMS and 90% for ASES, but there were no differences between groups. In subjects with both MRI and clinical assessment (n=112), those with an intact tendon presented better CMS(p=0.035), ASES(p=0.015) and pain(p=0.006) scores than those with a failed repair.<br><br>CONCLUSION: Augmentation with a BCI of a TOE repair in posterosuperior rotator cuff tears clearly reduces the retear rate at two-year follow-up without increased complication rates and similar clinical outcomes. Subjects with failed repairs had poorer clinical outcomes.<br><br>LEVEL OF EVIDENCE: Level 1, Randomized controlled trial.},
}
@article {pmid40209163,
year = {2025},
author = {Kurmanavičiūtė, D and Kataja, H and Parkkonen, L},
title = {Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0319328},
pmid = {40209163},
issn = {1932-6203},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Attention/physiology ; Young Adult ; Evoked Potentials, Auditory/physiology ; *Auditory Perception/physiology ; Support Vector Machine ; Acoustic Stimulation ; Algorithms ; },
abstract = {Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.},
}
@article {pmid40206150,
year = {2025},
author = {Zhang, H and Wang, X and Chen, G and Zhang, Y and Jian, X and He, F and Xu, M and Ming, D},
title = {Noninvasive Intracranial Source Signal Localization and Decoding with High Spatiotemporal Resolution.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0206},
pmid = {40206150},
issn = {2692-7632},
abstract = {High spatiotemporal resolution of noninvasive electroencephalography (EEG) signals is an important prerequisite for fine brain-computer manipulation. However, conventional scalp EEG has a low spatial resolution due to the volume conductor effect, making it difficult to accurately identify the intent of brain-computer manipulation. In recent years, transcranial focused ultrasound modulated EEG technology has increasingly become a research hotspot, which is expected to acquire noninvasive acoustoelectric coupling signals with a high spatial and temporal resolution. In view of this, this study established a transcranial focused ultrasound numerical simulation model and experimental platform based on a real brain model and a 128-array phased array, further constructed a 3-dimensional transcranial multisource dipole localization and decoding numerical simulation model and experimental platform based on the acoustic field platform, and developed a high-precision localization and decoding algorithm. The results show that the simulation-guided phased-array acoustic field experimental platform can achieve accurate focusing in both pure water and transcranial conditions within a safe threshold, with a modulation range of 10 mm, and the focal acoustic pressure can be enhanced by more than 200% compared with that of transducer self-focusing. In terms of dipole localization decoding results, the proposed algorithm in this study has a localization signal-to-noise ratio of 24.18 dB, which is 50.59% higher than that of the traditional algorithm, and the source signal decoding accuracy is greater than 0.85. This study provides a reliable experimental basis and technical support for high-spatiotemporal-resolution noninvasive EEG signal acquisition and precise brain-computer manipulation.},
}
@article {pmid40205860,
year = {2025},
author = {Sun, Y and Yu, N and Chen, G and Liu, T and Wen, S and Chen, W},
title = {What Else Is Happening to the Mirror Neurons?-A Bibliometric Analysis of Mirror Neuron Research Trends and Future Directions (1996-2024).},
journal = {Brain and behavior},
volume = {15},
number = {4},
pages = {e70486},
pmid = {40205860},
issn = {2162-3279},
support = {21BZX005//National Social Science Fund of China/ ; 21NDQN281YB//Philosophy and Social Sciences Project of Zhejiang Province/ ; 23QNYC19ZD//Special Project for Cultivating Leading Talents in Philosophy and Social Sciences of Zhejiang Province (Cultivation of Young Talents)/ ; },
mesh = {Animals ; Humans ; *Bibliometrics ; Brain/physiology ; *Mirror Neurons/physiology ; Neurosciences/trends ; },
abstract = {BACKGROUND: Since its discovery in the late 20th century, research on mirror neurons has become a pivotal area in neuroscience, linked to various cognitive and social functions. This bibliometric analysis explores the research trajectory, key research topics, and future trends in the field of mirror neuron research.<br><br>METHODS: We searched the Web of Science Core Collection (WoSCC) database for publications from 1996 to 2024 on mirror neuron research. Statistical and visualization analyses were performed using CiteSpace and VOSviewer.<br><br>RESULTS: Publication output on mirror neurons peaked in 2013 and remained active. High-impact journals such as Science, Brain, Neuron, PNAS, and NeuroImage frequently feature findings on the mirror neuron system, including its distribution, neural coding, and roles in intention understanding, affective empathy, motor learning, autism, and neurological disorders. Keyword clustering reveals major directions in cognitive neuroscience, motor neuroscience, and neurostimulation, whereas burst detection underscores the emerging significance of brain-computer interfaces (BCIs). Research methodologies have been evolving from traditional electrophysiological recordings to advanced techniques such as functional magnetic resonance imaging, transcranial magnetic stimulation, and BCIs, highlighting a dynamic, multidisciplinary progression.<br><br>CONCLUSIONS: This study identifies key areas associated with mirror neurons and anticipates that future work will integrate findings with artificial intelligence, clinical interventions, and novel neuroimaging techniques, providing new perspectives on complex socio-cognitive issues and their applications in both basic science and clinical practice.},
}
@article {pmid40205038,
year = {2025},
author = {Yang, L and Guo, C and Zheng, Z and Dong, Y and Xie, Q and Lv, Z and Li, M and Lu, Y and Guo, X and Deng, R and Liu, Y and Feng, Y and Mu, R and Zhang, X and Ma, H and Chen, Z and Zhang, Z and Dong, Z and Yang, W and Zhang, X and Cui, Y},
title = {Stress dynamically modulates neuronal autophagy to gate depression onset.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40205038},
issn = {1476-4687},
abstract = {Chronic stress remodels brain homeostasis, in which persistent change leads to depressive disorders[1]. As a key modulator of brain homeostasis[2], it remains elusive whether and how brain autophagy is engaged in stress dynamics. Here we discover that acute stress activates, whereas chronic stress suppresses, autophagy mainly in the lateral habenula (LHb). Systemic administration of distinct antidepressant drugs similarly restores autophagy function in the LHb, suggesting LHb autophagy as a common antidepressant target. Genetic ablation of LHb neuronal autophagy promotes stress susceptibility, whereas enhancing LHb autophagy exerts rapid antidepressant-like effects. LHb autophagy controls neuronal excitability, synaptic transmission and plasticity by means of on-demand degradation of glutamate receptors. Collectively, this study shows a causal role of LHb autophagy in maintaining emotional homeostasis against stress. Disrupted LHb autophagy is implicated in the maladaptation to chronic stress, and its reversal by autophagy enhancers provides a new antidepressant strategy.},
}
@article {pmid40204716,
year = {2025},
author = {Amann, LK and Casasnovas, V and Gail, A},
title = {Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3372},
pmid = {40204716},
issn = {2041-1723},
support = {H2020-FETPROACT-16 732266 WP1//European Commission (EC)/ ; ZN3422//Nieders&#xe4;chsische Ministerium f&#xfc;r Wissenschaft und Kultur (Lower Saxony Ministry of Science and Culture)/ ; SFB-889 C4//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; SFB 1690 B09//Deutsche Forschungsgemeinschaft (German Research Foundation)/ ; },
mesh = {Animals ; Macaca mulatta ; Male ; *Motor Cortex/physiology ; Uncertainty ; *Feedback, Sensory/physiology ; *Psychomotor Performance/physiology ; Movement/physiology ; Brain-Computer Interfaces ; Hand/physiology ; Visual Perception/physiology ; },
abstract = {Sensory uncertainty jeopardizes accurate movement. During reaching, visual uncertainty can affect the estimation of hand position (feedback) and the desired movement endpoint (target). While impairing motor learning, it is unclear how either form of uncertainty affects cortical reach goal encoding. We show that reach trajectories vary more with higher visual uncertainty of the target, but not the feedback. Accordingly, cortical motor goal activities in male rhesus monkeys are less accurate during planning and movement initiation under target but not feedback uncertainty. Yet, when monkeys critically depend on visual feedback to conduct reaches via a brain-computer interface, then visual feedback uncertainty impairs reach accuracy and neural motor goal encoding around movement initiation. Neural state space analyses reveal a dimension that separates population activity by uncertainty level in all tested conditions. Our findings demonstrate that while both target and feedback uncertainty always reflect in neural activity, uncertain feedback only deteriorates neural reach goal information and behavior when it is task-critical, i.e., when having to rely on the sensory feedback and no other more reliable sensory modalities are available. Further, uncertain target and feedback impair reach goal encoding in a time-dependent manner, suggesting that they are integrated during different stages of reach planning.},
}
@article {pmid40204228,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Effect of parasite infections on fish body condition: a systematic review and meta-analysis.},
journal = {International journal for parasitology},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.ijpara.2025.03.002},
pmid = {40204228},
issn = {1879-0135},
abstract = {Using host body condition indices (BCIs) based on the relationship between host body mass and length is a general and pervasive approach to assess the negative effects of parasites on host health. Although many researchers, especially fish biologists and fisheries managers, commonly utilize BCIs, the overall general patterns among BCI - infection relationships remain unclear. Here, we first systematically reviewed 985 fish BCI - infection relationships from 216 publications and investigated the factors affecting the strength and directionality of effects in BCI - infection relationships. We specifically predicted that the BCI measure used, parasite taxonomic group, and the infection measure used would influence the observed effect size and directionality of BCI - infection relationships. We found that most studies were heavily biased towards specific BCI measures such as Fulton's BCI and Relative BCI. Furthermore, studies using Fulton's BCI were more likely to report significant results compared with those using other BCI measures, suggesting that index choice could lead to an overestimation of the negative effects of parasites. Our meta-regressions uncovered that the use of parasite intensity as an infection measure and studies based on experimental rather than natural infections were more likely to report significant negative effects, however there were no differences among parasite taxonomic groups. Surprisingly, many studies, especially field studies, did not report significant negative correlations between BCI and infection, contrary to widespread expectations among researchers that parasites would negatively affect fish health. We discuss potential mechanisms underlying these results. Finally, we make several recommendations for the use of BCI - infection relationships in future studies.},
}
@article {pmid40204168,
year = {2025},
author = {Tan, H and Hu, YT and Goudswaard, A and Li, YJ and Balesar, R and Swaab, D and Bao, AM},
title = {Increased oxytocin/vasopressin ratio in bipolar disorder in a cohort of human postmortem adults.},
journal = {Neurobiology of disease},
volume = {209},
number = {},
pages = {106904},
doi = {10.1016/j.nbd.2025.106904},
pmid = {40204168},
issn = {1095-953X},
mesh = {Humans ; *Oxytocin/metabolism ; Female ; Male ; *Bipolar Disorder/metabolism/pathology ; Middle Aged ; Adult ; *Supraoptic Nucleus/metabolism ; *Vasopressins/metabolism ; *Paraventricular Hypothalamic Nucleus/metabolism ; Cohort Studies ; Depressive Disorder, Major/metabolism ; Aged ; *Arginine Vasopressin/metabolism ; },
abstract = {Bipolar disorder (BD) and major depressive disorder (MDD) share some common characteristics in stress-related brain circuits, but they also exhibit distinct symptoms. Our previous postmortem research on the immunoreactivity (ir) levels of neuropeptide oxytocin (OT) in the hypothalamic paraventricular nucleus (OT[PVN]) and some clinical research on plasma OT levels suggested that increased levels of OT is a potential trait marker for BD. However, dysregulation of the related neuropeptide arginine vasopressin (AVP), that often shows opposite effects for stress responses compared to OT has not been investigated in BD. Moreover, it remains so far unknown what the contribution may be of OT produced in the hypothalamic supraoptic nucleus (SON), another major source of OT (OT[SON]). Therefore, in the present postmortem study, alterations in levels of OT-ir and for the first time in AVP-ir were determined in the SON and PVN among patients with BD, MDD, and matched controls. We observed a significantly increased OT[PVN]-ir but relatively stable AVP[PVN]-ir in male BD, and a significantly decreased AVP[PVN]-ir but relatively stable OT[PVN]-ir in female BD patients. A significantly increased ratio of OT-ir/AVP-ir was observed only in BD patients in both, the PVN and SON. No significant changes in OT-ir or AVP-ir were found in MDD patients compared with controls. Our data illustrate a clear disease- and sex-specificity of the OT and AVP changes in BD. In addition, since increased AVP-ir was observed in female BD patients with lithium nephropathy, increased AVP may have a direct effect on symptoms of BD.},
}
@article {pmid40203859,
year = {2025},
author = {Pang, Y and Wang, X and Zhao, Z and Han, C and Gao, N},
title = {Multi-view collaborative ensemble classification for EEG signals based on 3D second-order difference plot and CSP.},
journal = {Physics in medicine and biology},
volume = {70},
number = {8},
pages = {},
doi = {10.1088/1361-6560/adcafa},
pmid = {40203859},
issn = {1361-6560},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Imaging, Three-Dimensional/methods ; Brain-Computer Interfaces ; },
abstract = {Objective.EEG signal analysis methods based on electrical source imaging (ESI) technique have significantly improved classification accuracy and response time. However, for the refined and informative source signals, the current studies have not fully considered their dynamic variability in feature extraction and lacked an effective integration of their dynamic variability and spatial characteristics. Additionally, the adaptability and complementarity of classifiers have not been considered comprehensively. These two aspects lead to the issue of insufficient decoding of source signals, which still limits the application of brain-computer interface (BCI). To address these challenges, this paper proposes a multi-view collaborative ensemble classification method for EEG signals based on three-dimensional second-order difference plot (3D SODP) and common spatial pattern.Approach.First, EEG signals are mapped to the source domain using the ESI technique, and then the source signals in the region of interest are obtained. Next, features from three viewpoints of the source signals are extracted, including 3D SODP features, spatial features, and the weighted fusion of both. Finally, the extracted multi-view features are integrated with subject-specific sub-classifier combination, and a voting mechanism is used to determine the final classification.Main results.The results show that the proposed method achieves classification accuracy of 81.3% and 82.6% respectively in two sessions of the OpenBMI dataset, which is nearly 5% higher than the state-of-the-art method, and maintains the analysis response time required for online BCI.Significance.This paper employs multi-view feature extraction to fully capture the characteristics of the source signals and enhances feature utilization through collaborative ensemble classification. The results demonstrate high accuracy and robust performance, providing a novel approach for online BCI.},
}
@article {pmid40203855,
year = {2025},
author = {Yin, S and Yue, Z and Qu, H and Wang, J and Shi, B and Zhang, J},
title = {Enhancing lower-limb motor imagery using a paradigm with visual and spatiotemporal tactile synchronized stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adcaec},
pmid = {40203855},
issn = {1741-2552},
mesh = {Humans ; Male ; Female ; *Imagination/physiology ; Adult ; *Brain-Computer Interfaces ; *Lower Extremity/physiology ; Young Adult ; *Photic Stimulation/methods ; *Touch/physiology ; *Motor Cortex/physiology ; Physical Stimulation/methods ; Electroencephalography/methods ; Psychomotor Performance/physiology ; },
abstract = {Objective.Vibrotactile stimulation (VS) has been widely used as an appropriate motor imagery (MI) guidance strategy to improve MI performance. However, most VS induced by a single vibrator cannot provide spatiotemporal information of tactile sensation associated with the visual guidance of the imagined motion process, not vividly providing MI guidance for subjects.Approach.This paper proposed a paradigm with visual and spatiotemporal tactile synchronized stimulation (VSTSS) to provide vivid MI guidance to help subjects perform lower-limb MI tasks and improve MI-based brain-computer interface (MI-BCI) performance, with a focus on poorly performing subjects. The proposed paradigm provided subjects with the natural spatiotemporal tactile sensation associated with the visual guidance of the foot movement process during MI. Fourteen healthy subjects were recruited to participate in the MI and Rest tasks and divided into good and poor performers. Furthermore, electrophysiological features and classification performance were analyzed to assess motor cortical activation and MI-BCI performance under no VS (NVS), VS, and VSTSS.Main results.The phenomenon of event-related desynchronization (ERD) in the sensorimotor cortex during MI under the VSTSS was more pronounced compared to the NVS and VS. Specifically, the VSTSS could improve the average ERD values in the motor cortex during the task segment by 34.70% and 14.28% than the NVS and VS in the alpha rhythm for poor performers, respectively. Additionally, the VSTSS could significantly enhance the classification accuracy between the MI and Rest tasks by 12.52% and 4.05% compared to NVS and VS for poor performers, respectively.Significance.The proposed paradigm could enhance motor cortical activation during MI and improve classification performance by providing vivid MI guidance for subjects, offering a promise for the application of lower-limb MI-BCI in stroke rehabilitation in the future.},
}
@article {pmid40203854,
year = {2025},
author = {Collinger, J and Vansteensel, MJ and Mrachacz-Kersting, N and Mattia, D and Valeriani, D and Vaughan, TM},
title = {Special Issue on Brain-Computer Interfaces: Highlighting Research from the 10th International Brain-Computer Interface Meeting.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adcaed},
pmid = {40203854},
issn = {1741-2552},
abstract = {N/A.},
}
@article {pmid40203098,
year = {2025},
author = {Sîmpetru, RC and Braun, DI and Simon, AU and März, M and Cnejevici, V and de Oliveira, DS and Weber, N and Walter, J and Franke, J and Höglinger, D and Prahm, C and Ponfick, M and Del Vecchio, A},
title = {MyoGestic: EMG interfacing framework for decoding multiple spared motor dimensions in individuals with neural lesions.},
journal = {Science advances},
volume = {11},
number = {15},
pages = {eads9150},
pmid = {40203098},
issn = {2375-2548},
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Middle Aged ; *Electromyography/instrumentation ; *Spinal Cord Injuries/rehabilitation ; *Stroke Rehabilitation/instrumentation ; *Amputation, Surgical/rehabilitation ; *Brain-Computer Interfaces ; Machine Learning ; Psychomotor Performance ; Intention ; Software Validation ; },
abstract = {Restoring motor function in individuals with spinal cord injuries (SCIs), strokes, or amputations is a crucial challenge. Recent studies show that spared motor neurons can still be voluntarily controlled using surface electromyography (EMG), even without visible movement. To harness these signals, we developed a wireless, high-density EMG bracelet and a software framework, MyoGestic. Our system enables rapid adaptation of machine learning models to users' needs, allowing real-time decoding of spared motor dimensions. In our study, we successfully decoded motor intent from two participants with traumatic SCI, two with spinal stroke, and three with amputations in real time, achieving multiple controllable motor dimensions within minutes. The decoded neural signals could control a digitally rendered hand, an orthosis, a prosthesis, or a two-dimensional cursor. MyoGestic's participant-centered approach allows a collaborative and iterative development of myocontrol algorithms, bridging the gap between researcher and participant, to advance intuitive EMG interfaces for neural lesions.},
}
@article {pmid40199879,
year = {2025},
author = {Zhao, Y and Wu, JT and Feng, JB and Cai, XY and Wang, XT and Wang, L and Xie, W and Gu, Y and Liu, J and Chen, W and Zhou, L and Shen, Y},
title = {Dual and plasticity-dependent regulation of cerebello-zona incerta circuits on anxiety-like behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3339},
pmid = {40199879},
issn = {2041-1723},
support = {2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2023YFE0206800//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 2021ZD0204000//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; 81625006//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32200620//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32225021//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170976//National Natural Science Foundation of China (National Science Foundation of China)/ ; LY21C090003//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Anxiety/physiopathology/metabolism ; Male ; *Neuronal Plasticity/physiology ; Mice ; *Zona Incerta/physiology/physiopathology ; *Cerebellum/physiology ; Mice, Inbred C57BL ; *Cerebellar Nuclei/physiology ; Neurons/physiology/metabolism ; Behavior, Animal/physiology ; Glutamic Acid/metabolism ; Neural Pathways ; Synaptic Transmission/physiology ; },
abstract = {Clinical observation has identified cerebellar cognitive affective syndrome, which is characterized by various non-motor dysfunctions such as social disorders and anxiety. Increasing evidence has revealed reciprocal mono-/poly-synaptic connections of cerebello-cerebral circuits, forming the concept of the cerebellar connectome. In this study, we demonstrate that neurons in the cerebellar nuclei (CN) of male mice project to a subset of zona incerta (ZI) neurons through long-range glutamatergic and GABAergic transmissions, both capable of encoding acute stress. Furthermore, activating or inhibiting glutamatergic and GABAergic transmissions in the CN → ZI pathway can positively or negatively regulate anxiety and place preference through presynaptic plasticity-dependent mechanisms, as well as mediate motor-induced alleviation of anxiety. Our data support the close relationship between the cerebellum and emotional processes and suggest that targeting cerebellar outputs may be an effective approach for treating anxiety.},
}
@article {pmid40199863,
year = {2025},
author = {Guttmann-Flury, E and Sheng, X and Zhu, X},
title = {Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {587},
pmid = {40199863},
issn = {2052-4463},
support = {91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91948302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Eye-Tracking Technology ; *Eye Movements ; Blinking ; Video Recording ; },
abstract = {In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potential features, providing valuable insights into users' behavior and interaction patterns. We introduce a large dataset capturing electroencephalogram (EEG) signals, eye-tracking, high-speed camera recordings, as well as subjects' mental states and characteristics, to provide a multifactor analysis of eye-related movements. Four paradigms - motor imagery, motor execution, steady-state visually evoked potentials, and P300 spellers - are selected due to their capacity to evoke various sensory-motor responses and potential influence on ocular activity. This online-available dataset contains over 46 hours of data from 31 subjects across 63 sessions, totaling 2520 trials for each of the first three paradigms, and 5670 for P300. This multimodal and multi-paradigms dataset is expected to allow the development of algorithms capable of efficiently handling eye-induced artifacts and enhancing task-specific classification. Furthermore, it offers the opportunity to evaluate the cross-paradigm robustness involving the same participants.},
}
@article {pmid40198632,
year = {2025},
author = {Ueda, M and Ueno, K and Inoue, T and Sakiyama, M and Shiroma, C and Ishii, R and Naito, Y},
title = {Detection of motor-related mu rhythm desynchronization by ear EEG.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0321107},
pmid = {40198632},
issn = {1932-6203},
mesh = {Humans ; Male ; *Electroencephalography/methods/instrumentation ; Female ; Adult ; Young Adult ; Hand/physiology ; Movement/physiology ; *Ear/physiology ; Brain-Computer Interfaces ; },
abstract = {Event-related desynchronization (ERD) of the mu rhythm (8-13 Hz) is an important indicator of motor execution, neurofeedback, and brain-computer interface in EEG. This study investigated the feasibility of an ear electroencephalography (EEG) device monitoring mu-ERD during hand grasp and release movements. The EEG data of the right hand movement and the eye opened resting condition were measured with an ear EEG device. We calculated and compared mu rhythm power and time-frequency data from 20 healthy participants during right hand movement and eye opened resting. Our results showed a significant difference of mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. These results support the utility of ear EEG in detecting motor activity-related mu-ERD. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.},
}
@article {pmid40198304,
year = {2025},
author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H},
title = {BSAN: A Self-Adapted Motor Imagery Decoding Framework Based on Contextual Information.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3557499},
pmid = {40198304},
issn = {2168-2208},
abstract = {In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.},
}
@article {pmid40197656,
year = {2025},
author = {Won, C and Cho, S and Jang, KI and Park, JU and Cho, JH and Lee, T},
title = {Emerging fiber-based neural interfaces with conductive composites.},
journal = {Materials horizons},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4mh01854k},
pmid = {40197656},
issn = {2051-6355},
abstract = {Neural interfaces that enable bidirectional communication between neural systems and external devices are crucial for treating neurological disorders and advancing brain-machine interfaces. Key requirements for these neural interfaces are the ability to modulate electrophysiological activity without causing tissue damage in the nerve system and long-term usability. Recent advances in biomedical neural electrodes aim to reduce mechanical mismatch between devices and surrounding tissues/organs while maintaining their electrical conductivity. Among these, fiber electrodes stand out as essential candidates for future neural interfaces owing to their remarkable flexibility, controllable scalability, and facile integration with systems. Herein, we introduce fiber-based devices with conductive composites, along with their fabrication technologies, and integration strategies for future neural interfaces. Compared to conventional neural electrodes, fiber electrodes readily combine with conductive materials such as metal nanoparticles, carbon-based nanomaterials, and conductive polymers. Their fabrication technologies enable high electrical performance without sacrificing mechanical properties. In addition, the neural modulation techniques of fiber electrodes; electrical, optical, and chemical, and their applications in central and peripheral nervous systems are carefully discussed. Finally, current limitations and potential advancements in fiber-based neural interfaces are highlighted for future innovations.},
}
@article {pmid40196469,
year = {2025},
author = {Tor, A and Clarke, SE and Bray, IE and Nuyujukian, P and , },
title = {Material Damage to Multielectrode Arrays after Electrolytic Lesioning is in the Noise.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.03.26.645429},
pmid = {40196469},
issn = {2692-8205},
abstract = {1The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neuroscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. Each electrode was scored in six damage categories, identified from the literature: abnormal debris, metal coating cracks, silicon tip breakage, parylene C delamination, parylene C cracks, and shank fracture. This analysis confirms previous results that observed damage on explanted arrays is more severe on the outer-edge electrodes versus inner electrodes. These findings also indicate that are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning. This work provides evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays and can be a useful tool in understanding perturbations to neural systems. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.},
}
@article {pmid40196347,
year = {2025},
author = {Yu, H and Mu, Q and Wang, Z and Guo, Y and Zhao, J and Wang, G and Wang, Q and Meng, X and Dong, X and Wang, S and Sun, J},
title = {A study on early diagnosis for fracture non-union prediction using deep learning and bone morphometric parameters.},
journal = {Frontiers in medicine},
volume = {12},
number = {},
pages = {1547588},
pmid = {40196347},
issn = {2296-858X},
abstract = {BACKGROUND: Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.<br><br>METHODS: Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.<br><br>RESULTS: A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences (p < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.<br><br>CONCLUSION: This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.},
}
@article {pmid40196232,
year = {2025},
author = {Yang, Y and Zhao, H and Hao, Z and Shi, C and Zhou, L and Yao, X},
title = {Recognition of brain activities via graph-based long short-term memory-convolutional neural network.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1546559},
pmid = {40196232},
issn = {1662-4548},
abstract = {INTRODUCTION: Human brain activities are always difficult to recognize due to its diversity and susceptibility to disturbance. With its unique capability of measuring brain activities, magnetoencephalography (MEG), as a high temporal and spatial resolution neuroimaging technique, has been used to identify multi-task brain activities. Accurately and robustly classifying motor imagery (MI) and cognitive imagery (CI) from MEG signals is a significant challenge in the field of brain-computer interface (BCI).<br><br>METHODS: In this study, a graph-based long short-term memory-convolutional neural network (GLCNet) is proposed to classify the brain activities in MI and CI tasks. It was characterized by implementing three modules of graph convolutional network (GCN), spatial convolution and long short-term memory (LSTM) to effectively extract time-frequency-spatial features simultaneously. For performance evaluation, our method was compared with six benchmark algorithms of FBCSP, FBCNet, EEGNet, DeepConvNets, Shallow ConvNet and MEGNet on two public datasets of MEG-BCI and BCI competition IV dataset 3.<br><br>RESULTS: The results demonstrated that the proposed GLCNet outperformed other models with the average accuracies of 78.65% and 65.8% for two classification and four classification on the MEG-BCI dataset, respectively.<br><br>DISCUSSION: It was concluded that the GLCNet enhanced the model's adaptability in handling individual variability with robust performance. This would contribute to the exploration of brain activates in neuroscience.},
}
@article {pmid40195935,
year = {2025},
author = {Hasegawa, R and Poulin, R},
title = {Cause or consequence? Exploring authors' interpretations of correlations between fish body condition and parasite infection.},
journal = {Journal of fish biology},
volume = {},
number = {},
pages = {},
doi = {10.1111/jfb.70048},
pmid = {40195935},
issn = {1095-8649},
support = {202460294//Japan Society for the Promotion of Science/ ; JP22KJ0086//Japan Society for the Promotion of Science/ ; },
abstract = {We reviewed 194 publications that reported relationships between fish body condition indices (BCIs) and parasite infections, and examined the authors' intention behind this cross-sectional analysis, that is, whether authors interpreted the negative correlations as the negative effects of parasites or as fish with poor BCIs being more susceptible to infections. While 89% of studies only considered parasite infections as causes of poor BCI, studies acknowledging the opposite or bidirectional causal links were rare. We recommend considering both possibilities in any given fish host and parasite association.},
}
@article {pmid40195900,
year = {2025},
author = {Shin, H and Kim, K and Lee, J and Nam, J and Baeg, E and You, C and Choi, H and Kim, M and Chung, CK and Kim, JG and Ahn, JH and Han, M and Kim, J and Yang, S and Lee, SQ and Yang, S},
title = {A Wireless Cortical Surface Implant for Diagnosing and Alleviating Parkinson's Disease Symptoms in Freely Moving Animals.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2405179},
doi = {10.1002/adhm.202405179},
pmid = {40195900},
issn = {2192-2659},
support = {//High Risk, High Return Research Program/ ; //ETRI grant (23YB1210, Collective Behavioral Modelling in Socially Interacting Group)/ ; },
abstract = {Parkinson's disease (PD), one of the most common neurodegenerative diseases, is involved in motor abnormality, primarily arising from the degeneration of dopaminergic neurons. Previous studies have examined the electrotherapeutic effects of PD using various methodological contexts, including live conditions, wireless control, diagnostic/therapeutic aspects, removable interfaces, or biocompatible materials, each of which is separately utilized for testing the diagnosis or alleviation of various brain diseases. Here, a cortical surface implant designed to improve motor function in freely moving PD animals is presented. This implant, a minimally invasive system equipped with a graphene electrode array, is the first integrated system to exhibit biocompatibility, wearability, removability, target specificity, and wireless control. The implant positioned at the motor cortical surface activates the motor cortex to maximize therapeutic effects and minimize off-target effects while monitoring motor activities. In PD animals, cortical motor surface stimulation restores motor function and brain waves, which corresponds to potentiated synaptic responses. Furthermore, these changes are associated with the upregulation of metabotropic glutamate receptor 5 (mGluR5, Grm5) and D5 dopamine receptor (D5R, Drd5) genes in the glutamatergic synapse. The newly designed wireless neural implant demonstrates capabilities in both real-time diagnostics and targeted therapeutics, suggesting its potential as a wireless system for biomedical devices for patients with PD and other neurodegenerative diseases.},
}
@article {pmid40195429,
year = {2025},
author = {Ming, Z and Yu, W and Fan, J and Ling, G and Fengming, C and Wei, T},
title = {Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11829},
pmid = {40195429},
issn = {2045-2322},
support = {XWRCHT20220045//the Xuzhou Key Medical Talents Project/ ; No.52375224//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Male ; Female ; *Upper Extremity/physiopathology ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; *Stroke/physiopathology/therapy ; Aged ; Electroencephalography ; *Kinesthesis/physiology ; Treatment Outcome ; Adult ; },
abstract = {This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. Forty-eight subacute stroke survivors were randomized to the KI-BCI, tDCS, or BCI-tDCS group. The KI-BCI group performed 30 min of KI-BCI training. Patients in tDCS group received 30 min of tDCS. Patients in BCI-tDCS group received 15 min of tDCS and 15 min of KI-BCI. The treatment cycle was five times a week, for four weeks. After all intervention, the Fugl-Meyer Assessment-Upper Extremity, Motor Status Scale, and the Modified Barthel Index scores of the KI-BCI group were superior to those of the tDCS group. The BCI-tDCS group was superior to the tDCS group in terms of the Motor Status Scale. Although quantitative EEG showed no significant group differences, the quantitative EEG indices in the tDCS group were significantly lower than before treatment. In conclusion, after treatment, although all intervention strategies improved upper limb motor function and daily living abilities in subacute stroke patients, KI-BCI demonstrated significantly better efficacy than tDCS. Under the same total treatment duration, the combined use of tDCS and KI-BCI did not achieve the hypothesized optimal outcome. Notably, tDCS reduced QEEG indices, possibly indicating favorable future outcomes in future.Trial registry number: ChiCTR2000034730.},
}
@article {pmid40194524,
year = {2025},
author = {Johnson, TR and Haddix, CA and Ajiboye, AB and Taylor, DM},
title = {Simplified control of neuromuscular stimulation systems for restoration of reach with limb stiffness as a modifiable degree of freedom.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc9e3},
pmid = {40194524},
issn = {1741-2552},
abstract = {Brain-controlled functional electrical stimulation (FES) of the upper limb has been used to restore arm function to paralyzed individuals in the lab. Able-bodied individuals naturally modulate limb stiffness throughout movements and in anticipation of perturbations. Our goal is to develop, via simulation, a framework for incorporating stiffness modulation into the currently-used 'lookup-table-based' FES control systems while addressing several practical issues: 1) optimizing stimulation across muscles with overlap in function, 2) coordinating stimulation across joints, and 3) minimizing errors due to fatigue. Our calibration process also needs to account for when current spread causes additional muscles to become activated. Approach: We developed an analytical framework for building a lookup-table-based FES controller and simulated the clinical process of calibrating and using the arm. A computational biomechanical model of a human paralyzed arm responding to stimulation was used for simulations with six muscles controlling the shoulder and elbow in the horizontal plane. Both joints had multiple muscles with overlapping functional effects, as well as biarticular muscles to reflect complex interactions between joints. Performance metrics were collected in silico, and real-time use was demonstrated with a Rhesus macaque using its cortical signals to control the computational arm model in real time. Main Results: By explicitly including stiffness as a definable degree of freedom in the lookup table, our analytical approach was able to achieve all our performance criteria. While using more empirical data during controller parameterization produced more accurate lookup tables, interpolation between sparsely sampled points (e.g., 20 degree angular intervals) still produced good results with median endpoint position errors of less than 1 cm-a range that should be easy to correct for with real-time visual feedback. Significance: Our simplified process for generating an effective FES controller now makes translating upper limb FES systems into mainstream clinical practice closer to reality. .},
}
@article {pmid40193612,
year = {2025},
author = {Kim, H and Kim, JH and Lee, YJ and Lee, J and Han, H and Yi, H and Kim, H and Kim, H and Kang, TW and Chung, S and Ban, S and Lee, B and Lee, H and Im, CH and Cho, SJ and Sohn, JW and Yu, KJ and Kang, TJ and Yeo, WH},
title = {Motion artifact-controlled micro-brain sensors between hair follicles for persistent augmented reality brain-computer interfaces.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {15},
pages = {e2419304122},
pmid = {40193612},
issn = {1091-6490},
support = {ECCS-2025462//NSF (NSF)/ ; P0017303//Korea Institute for Advancement of Technology (KIAT)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Hair Follicle/physiology ; Electroencephalography/methods/instrumentation ; *Augmented Reality ; Artifacts ; *Brain/physiology ; Motion ; Algorithms ; Electrodes ; Evoked Potentials, Visual/physiology ; },
abstract = {Modern brain-computer interfaces (BCI), utilizing electroencephalograms for bidirectional human-machine communication, face significant limitations from movement-vulnerable rigid sensors, inconsistent skin-electrode impedance, and bulky electronics, diminishing the system's continuous use and portability. Here, we introduce motion artifact-controlled micro-brain sensors between hair strands, enabling ultralow impedance density on skin contact for long-term usable, persistent BCI with augmented reality (AR). An array of low-profile microstructured electrodes with a highly conductive polymer is seamlessly inserted into the space between hair follicles, offering high-fidelity neural signal capture for up to 12 h while maintaining the lowest contact impedance density (0.03 kΩ·cm[-2]) among reported articles. Implemented wireless BCI, detecting steady-state visually evoked potentials, offers 96.4% accuracy in signal classification with a train-free algorithm even during the subject's excessive motions, including standing, walking, and running. A demonstration captures this system's capability, showing AR-based video calling with hands-free controls using brain signals, transforming digital communication. Collectively, this research highlights the pivotal role of integrated sensors and flexible electronics technology in advancing BCI's applications for interactive digital environments.},
}
@article {pmid40193313,
year = {2025},
author = {Estivalet, KM and Pettenuzzo, TSA and Mazzilli, NL and Ferreira, LF and Cechetti, F},
title = {The use of brain-machine interface, motor imagery, and action observation in the rehabilitation of individuals with Parkinson's disease: A protocol study for a randomized clinical trial.},
journal = {PloS one},
volume = {20},
number = {4},
pages = {e0315148},
pmid = {40193313},
issn = {1932-6203},
mesh = {Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Cognition ; *Imagery, Psychotherapy/methods ; *Parkinson Disease/rehabilitation/physiopathology ; Single-Blind Method ; Upper Extremity/physiopathology ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Parkinson's disease (PD) is a neurodegenerative condition that impacts motor planning and control of the upper limbs (UL) and leads to cognitive impairments. Rehabilitation approaches, including motor imagery (MI) and action observation (AO), along with the use of brain-machine interfaces (BMI), are essential in the PD population to enhance neuroplasticity and mitigate symptoms.<br><br>OBJECTIVE: To provide a description of a rehabilitation protocol for evaluating the effects of isolated and combined applications of MI and action observation (AO), along with BMI, on upper limb (UL) motor changes and cognitive function in PD.<br><br>METHODS: This study provides a detailed protocol for a single-blinded, randomized clinical trial. After selection, participants will be randomly assigned to one of five experimental groups. Each participant will be assessed at three points: pre-intervention, post-intervention, and at a follow-up four weeks after the intervention ends. The intervention consists of 10 sessions, each lasting approximately 60 minutes.<br><br>EXPECTED RESULTS: The primary outcome expected is an improvement in the Test d'&#xc9;valuation des Membres Sup&#xe9;rieurs de Personnes &#xc2;g&#xe9;es score, accompanied by a reduction in task execution time. Secondary outcomes include motor symptoms in the upper limbs, assessed via the Unified Parkinson's Disease Rating Scale - Part III and the 9-Hole Peg Test; cognitive function, assessed with the PD Cognitive Rating Scale; and occupational performance, assessed with the Canadian Occupational Performance Measure.<br><br>DISCUSSION: This study protocol is notable for its intensive daily sessions. Both MI and AO are low-cost, enabling personalized interventions that physiotherapists and occupational therapists can readily replicate in practice. While BMI use does require professionals to acquire an exoskeleton, the protocol ensures the distinctiveness of the interventions and, to our knowledge, is the first to involve individuals with PD.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05696925.},
}
@article {pmid40191683,
year = {2025},
author = {Miri, M and Abootalebi, V and Saeedi-Sourck, H and Van De Ville, D and Behjat, H},
title = {Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {7},
pmid = {40191683},
issn = {2228-7477},
abstract = {BACKGROUND: Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data.<br><br>METHODS: In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials.<br><br>RESULTS: The performance of the proposed approach was evaluated on brain-computer interface competition III (IVa) dataset.<br><br>CONCLUSIONS: Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.},
}
@article {pmid40189874,
year = {2025},
author = {Li, X and Zhang, J and Shi, B and Li, Y and Wang, Y and Shuai, K and Li, Y and Ming, G and Song, T and Pei, W and Sun, B},
title = {Freestanding Transparent Organic-Inorganic Mesh E-Tattoo for Breathable Bioelectrical Membranes with Enhanced Capillary-Driven Adhesion.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {15},
pages = {22337-22351},
doi = {10.1021/acsami.5c00565},
pmid = {40189874},
issn = {1944-8252},
mesh = {Humans ; *Wearable Electronic Devices ; *Tattooing ; Brain-Computer Interfaces ; },
abstract = {The electronic tattoo (e-tattoo), a cutting-edge wearable sensor technology adhered to human skin, has garnered significant attention for its potential in brain-computer interfaces (BCIs) and routine health monitoring. Conventionally, flexible substrates with adhesion force on dewy surfaces pursue seamless contact with skin, employing compact airtight substrates, hindering air circulation between skin and the surrounding environment, and compromising long-term wearing comfort. To address these challenges, we have developed a freestanding transparent e-tattoo featuring flexible serpentine mesh bridges with a unique full-breathable multilayer structure. The mesh e-tattoo demonstrates remarkable ductility and air permeability while maintaining robust electronic properties, even after significant mechanical deformation. Furthermore, it exhibits an impressive visible-light transmittance of up to 95%, coupled with a low sheet resistance of 0.268 Ω sq[-1], ensuring both optical clarity and electrical efficiency. By increasing the number of menisci between the mesh e-tattoo and the skin, the total adhesion force increases due to the cumulative capillary-driven effect. We also successfully demonstrated high-quality bioelectric signal collections. In particular, the controlling virtual reality (VR) objects using electrooculogram (EOG) signals collected by mesh e-tattoos were achieved to demonstrate their potential for human-computer interactions (HCIs). This freestanding transparent e-tattoo with a fully breathable mesh structure represents a significant advancement in flexible electrodes for bioelectrical signal monitoring applications.},
}
@article {pmid40189123,
year = {2025},
author = {Wang, F and Ren, J and Cai, Q and Liang, R and Wang, L and Yang, Q and Tian, Y and Zheng, C and Yang, J and Ming, D},
title = {Theta-gamma phase-amplitude coupling as a promising neurophysiological biomarker for evaluating the efficacy of low-intensity focused ultrasound stimulation on vascular dementia treatment.},
journal = {Experimental neurology},
volume = {389},
number = {},
pages = {115237},
doi = {10.1016/j.expneurol.2025.115237},
pmid = {40189123},
issn = {1090-2430},
abstract = {Low-intensity focused ultrasound stimulation (LIFUS) has garnered attention for its potential in vascular dementia (VD) treatment. However, the lack of sufficient data supporting its efficacy and elucidating its mechanisms of action limits its further clinical translation and application. Considerable researches support the idea that LIFUS can improve the disturbance of neural oscillation modes caused by a variety of neurological diseases. However, the effect of LIFUS on neural oscillation modes in VD remains unclear. Therefore, this study aims to investigate the therapeutic effects of LIFUS on neural oscillation modes in VD. To achieve this purpose, the VD model was established via the bilateral common carotid artery occlusion, followed by two weeks of LIFUS treatment targeting the bilateral hippocampus. The therapeutic effects of LIFUS were evaluated by behavioral tests and cerebral blood flow measurement. Electrophysiological signals were recorded from the hippocampal CA1 and CA3 and medial prefrontal cortex (mPFC). The results indicated LIFUS could effectively improve cognitive dysfunction in VD rats. The underlying electrophysiological mechanisms involved the restoration of phase-amplitude coupling (PAC) of theta-gamma oscillations within both the CA3-CA1 local circuit and the hippocampus-mPFC cross-brain circuit. Classification results based on PAC characteristics suggested that PAC metrics are effective for evaluating the efficacy of LIFUS in treating VD, with optimal recognition performance observed in the hippocampus-mPFC cross-brain circuit. Our findings provide neuroelectrophysiological insights into the mechanisms of LIFUS in VD treatment and propose a promising diagnostic biomarker for evaluating LIFUS efficacy in future applications.},
}
@article {pmid40187178,
year = {2025},
author = {S, P and M, S},
title = {Design of asynchronous low-complexity SSVEP-based brain control interface speller.},
journal = {Computers in biology and medicine},
volume = {190},
number = {},
pages = {110062},
doi = {10.1016/j.compbiomed.2025.110062},
pmid = {40187178},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; Wireless Technology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) provide a transformative solution, addressing communication challenges for individuals with speech impairments or neuromuscular disorders. The real-time wireless asynchronous BCI speller system utilizes electroencephalography (EEG) signals, tapping the brain's electrical activity for effective communication.<br><br>METHODS: Users interact with a screen featuring flickering stimuli, each representing cursor movement and character selection. The system includes cursor movements, displays selected characters, and produces an audio output of the complete word. Users generate real-time SSVEP responses captured wirelessly through an EEG acquisition system by directing attention to the stimulus. The single-channel EEG signal is wirelessly transmitted to a Raspberry Pi processing module through Wi-Fi. The EEG signals are decoded using modified power spectral density (PSD) analysis to identify the user's focus, maneuvering the cursor for character selection.<br><br>RESULTS: In experiments with ten subjects, the single-channel asynchronous low-complexity BCI speller system achieved 95.2% SSVEP identification accuracy with a detection time of 1.05 s for selecting each character/target and an information transfer rate (ITR) of 119.82 bits/min.<br><br>CONCLUSION: This underscores its efficacy in enabling individuals to spell words and communicate efficiently. The proposed real-time wireless BCI speller system is an effective tool for communication-challenged individuals, enhancing communication efficiency through brain signals.},
}
@article {pmid40183071,
year = {2025},
author = {Muthukrishnan, SP and Atyabi, A},
title = {Editorial: Neural mechanisms of motor planning in assisted voluntary movement.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1582214},
pmid = {40183071},
issn = {1662-5161},
}
@article {pmid40182217,
year = {2025},
author = {Wirawan, IMA and Paramarta, K},
title = {Acquisition Of Balinese Imagined Spelling using Electroencephalogram (BISE) Dataset.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111454},
pmid = {40182217},
issn = {2352-3409},
abstract = {One of the main goals of today's technology is to create a connected environment between humans and technological devices to perform daily physical activities. However, users with speech disorders cannot use this application. Loss of verbal communication can be caused by injuries and neurodegenerative diseases that affect motor production, speech articulation, and language comprehension. To overcome this problem, Brain-Computer Interfaces (BCI) use EEG signals as assistive technology to provide a new communication channel for individuals who cannot communicate due to loss of motor control. Of the several BCI studies that use EEG signals, no studies have studied Balinese characters. As a first step, this study examines the acquisition of EEG signal data for Balinese character recognition. There are several stages in obtaining EEG signal data for Balinese character spelling imagination in this study: preparation of research documents, preparation of stimulus media, submission of ethical permits, determination of participants, recording process, data presentation, and publication of datasets. The result datasets from this study are in the form of raw data, and data was analyzed for 18 Balinese and 6 vowel characters, both spelling and imagined.},
}
@article {pmid40182177,
year = {2025},
author = {Paillard, J and Hipp, JF and Engemann, DA},
title = {GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals.},
journal = {Patterns (New York, N.Y.)},
volume = {6},
number = {3},
pages = {101182},
pmid = {40182177},
issn = {2666-3899},
abstract = {Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalography (EEG) recordings while showing concord with neuroscientific domain knowledge. However, these methods rely on handcrafted rules and sequential optimization. In contrast, deep learning (DL) offers end-to-end trainable models achieving state-of-the-art performance on various prediction tasks but lacks interpretability and interoperability with established neuroscience concepts. We introduce Gabor Riemann EEGNet (GREEN), a lightweight neural network that integrates wavelet transforms and Riemannian geometry for processing raw EEG data. Benchmarking on six prediction tasks across four datasets with over 5,000 participants, GREEN outperformed non-deep state-of-the-art models and performed favorably against large DL models while using orders-of-magnitude fewer parameters. Computational experiments showed that GREEN facilitates learning sparse representations without compromising performance. By integrating domain knowledge, GREEN combines a desirable complexity-performance trade-off with interpretable representations.},
}
@article {pmid40181137,
year = {2025},
author = {Wang, G and Wang, W and Wang, Z and Huang, S and Liu, Y and Ming, D},
title = {The sixth finger illusion induced by palm outside stroking shows stable ownership and independence.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11447},
pmid = {40181137},
issn = {2045-2322},
support = {2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; 62273251//National Natural Science Foundation of China/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; MSV202418//Research Project of State Key Laboratory of Mechanical System and Vibration/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin Municipality/ ; },
mesh = {Humans ; *Illusions/physiology ; Male ; Female ; *Fingers/physiology ; Adult ; Young Adult ; *Hand/physiology ; *Touch Perception/physiology ; },
abstract = {Recently, the sixth finger illusion has been widely studied for body representation. It remains unclear how the stroking area, visual effects and the number of trials affect the illusion. We recruited 80 participants to conduct five trials by stroking the palm outside or little finger outside in conditions with and without wearing supernumerary rubber finger. The results show the stroking area has a greater impact on the intensity and independence of the illusion. And the palm outside can induce a stronger and more independent illusion. In addition, the sixth finger illusion induced by these four conditions was significantly influenced by the number of trials, and there is a significant enhancement in the intensity of the illusion induced by the palm outside as the number of trials increases. These indicate that stroking the outer lateral side of the palm can induce a relatively stronger and more independent sixth finger illusion, and the intensity of it reaches a steady state after three trials when wearing a supernumerary rubber finger and five trials when not wearing a supernumerary rubber finger. This study adds evidence to the research on multisensory integration and sensory feedback of the supernumerary robotic fingers.},
}
@article {pmid40181122,
year = {2025},
author = {Rawat, K and Sharma, T},
title = {An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {11379},
pmid = {40181122},
issn = {2045-2322},
mesh = {Humans ; Electroencephalography/methods ; Deep Learning ; *Nervous System Diseases/diagnosis/physiopathology ; Electrocardiography ; *Neural Networks, Computer ; *Mental Disorders/diagnosis/physiopathology ; Adult ; Male ; Female ; },
abstract = {Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative methodology for predicting mental illnesses such as epilepsy, sleep disorders, bipolar disorder, eating disorders, and depression using a multimodal deep learning framework that integrates neurocardiac data fusion. The proposed framework combines MEG, EEG, and ECG signals to create a more comprehensive understanding of brain and cardiac function in individuals with mental disorders. The multimodal deep learning approach uses an integrated CNN-Bi-Transformer, i.e., CardioNeuroFusionNet, which can process multiple types of inputs simultaneously, allowing for the fusion of various modalities and improving the performance of the predictive representation. The proposed framework has undergone testing on data from the Deep BCI Scalp Database and was further validated on the Kymata Atlas dataset to assess its generalizability. The model achieved promising results with high accuracy (98.54%) and sensitivity (97.77%) in predicting mental problems, including neurological and psychiatric conditions. The neurocardiac data fusion has been found to provide additional insights into the relationship between brain and cardiac function in neurological conditions, which could potentially lead to more accurate diagnosis and personalized treatment options. The suggested method overcomes the shortcomings of earlier studies, which tended to concentrate on single-modality data, lacked thorough neurocardiac data fusion, and made use of less advanced machine learning algorithms. The comprehensive experimental findings, which provide an average improvement in accuracy of 2.72%, demonstrate that the suggested work performs better than other cutting-edge AI techniques and generalizes effectively across diverse datasets.},
}
@article {pmid40180157,
year = {2025},
author = {Liang, W and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {Enhancing robustness of spatial filters in motor imagery based brain-computer interface via temporal learning.},
journal = {Journal of neuroscience methods},
volume = {418},
number = {},
pages = {110441},
doi = {10.1016/j.jneumeth.2025.110441},
pmid = {40180157},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; *Motor Activity/physiology ; },
abstract = {BACKGROUND: In motor imagery-based brain-computer interface (MI-BCI) EEG decoding, spatial filtering play a crucial role in feature extraction. Recent studies have emphasized the importance of temporal filtering for extracting discriminative features in MI tasks. While many efforts have been made to optimize feature extraction externally, stabilizing features from spatial filtering remains underexplored.<br><br>NEW METHOD: To address this problem, we propose an approach to improve the robustness of temporal features by minimizing instability in the temporal domain. Specifically, we utilize Jensen-Shannon divergence to quantify temporal instability and integrate decision variables to construct an objective function that minimizes this instability. Our method enhances the stability of variance and mean values in the extracted features, improving the identification of discriminative features and reducing the effects of instability.<br><br>RESULTS: The proposed method was applied to spatial filtering models, and tested on two publicly datasets as well as a self-collected dataset. Results demonstrate that the proposed method significantly boosts classification accuracy, confirming its effectiveness in enhancing temporal feature stability.<br><br>We compared our method with spatial filtering methods, and the-state-of-the-art models. The proposed approach achieves the highest accuracy, with 92.43&#x202f;% on BCI competition III IVa dataset, 84.45&#x202f;% on BCI competition IV 2a dataset, and 73.18&#x202f;% on self-collected dataset.<br><br>CONCLUSIONS: Enhancing the instability of temporal features contributes to improved MI-BCI performance. This not only improves classification performance but also provides a stable foundation for future advancements. The proposed method shows great potential for EEG decoding.},
}
@article {pmid40179638,
year = {2025},
author = {Thielen, J and Tangermann, M and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Towards an sEEG-based BCI using code-modulated VEP: A case study showing the influence of electrode location on decoding efficiency.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {173},
number = {},
pages = {213-215},
doi = {10.1016/j.clinph.2025.03.034},
pmid = {40179638},
issn = {1872-8952},
}
@article {pmid40177878,
year = {2025},
author = {Bhamidipaty, V and Botchu, B and Bhamidipaty, DL and Guntoory, I and Iyengar, KP},
title = {ChatGPT for speech-impaired assistance.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/17483107.2025.2483300},
pmid = {40177878},
issn = {1748-3115},
abstract = {Background: Speech and language impairments, though often used interchangeably, are two very distinct types of challenges. A speech impairment may lead to impaired ability to produce speech sounds whilst communication may be affected due to lack of fluency or articulation of words. Consequently this may affect a person's ability to articulate may affect academic achievement, social development and progress in life. ChatGPT (Generative Pretrained Transformer) is an open access AI (Artificial Intelligence) tool developed by Open AI® based on Large language models (LLMs) with the ability to respond to human prompts to generate texts using Supervised and Unsupervised Machine Learning (ML) Algorithms. This article explores the current role and future perspectives of ChatGPT AI Tool for Speech-Impaired Assistance. Methods: A cumulative search strategy using databases of PubMed, Google Scholar, Scopus and grey literature was conducted to generate this narrative review. Results: A spectrum of Enabling Technologies for Speech &amp; Language Impairment have been explored. Augmentative and Alternative Communication technology (AAC), Integration with Neuroprosthesis technology and Speech therapy applications offer considerable potential to aid speech and language impaired individuals. Conclusion: Current applications of AI, ChatGPT and other LLM's offer promising solutions in enhancing communication in people affected by Speech and Language impairment. However, further research and development is required to ensure affordability, accessibility and authenticity of these AI Tools in clinical Practice.},
}
@article {pmid40175961,
year = {2025},
author = {Guo, X and Deng, R and Lai, J and Hu, S},
title = {Is muscarinic receptor agonist effective and tolerant for schizophrenia?.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {323},
pmid = {40175961},
issn = {1471-244X},
mesh = {Humans ; *Schizophrenia/drug therapy ; *Muscarinic Agonists/adverse effects/therapeutic use ; Randomized Controlled Trials as Topic ; *Antipsychotic Agents/therapeutic use/adverse effects ; },
abstract = {BACKGROUND: Several randomized clinical trials (RCTs) have recently examined the efficacy and tolerability of muscarinic receptor agonists in schizophrenia. However, whether therapeutics targeting muscarinic receptors improve symptom management and reduce side effects remains systemically unexplored.<br><br>METHODS: Embase, PubMed, and Web of Science were searched from inception until Jan 9, 2025. Altogether, the efficacy and safety outcomes of four RCTs (397 individuals in the muscarinic receptor agonists group, and 374 in the placebo control group) were meta-analyzed. To compare scores of positive and negative syndrome scale (PANSS), response rate, discontinuation rate, and adverse events with muscarinic receptor agonists vs. placebo in patients with schizophrenia, scale changes were pooled as mean difference (MD) for continuous outcomes and risk ratio (RR) for categorical outcomes.<br><br>RESULTS: It revealed that muscarinic receptor agonists were superior to placebo in terms of decrease in the total PANSS score (MD, -&#x2009;9.92; 95% CI, -12.46 to -7.37; I[2]&#x2009;=&#x2009;0%), PANSS positive symptom subscore (MD, -&#x2009;3.21; 95% CI, -4.02 to -2.40; I[2]&#x2009;=&#x2009;0%), and PANSS negative symptom subscore (MD, -1.79; 95% CI, -2.47 to -1.11; I[2]&#x2009;=&#x2009;48%). According to the study-defined response rate, the pooled muscarinic receptor agonists vs. placebo RR was 2.08 (95% CI, 1.59 to 2.72; I[2]&#x2009;=&#x2009;0%). No significance was found in the discontinuation rate. Muscarinic receptor agonists were associated with a higher risk of nausea (RR&#x2009;=&#x2009;4.61, 95% CI, 2.65 to 8.02; I[2]&#x2009;=&#x2009;3%), and in particular, xanomeline-trospium was associated with risks of dyspepsia, vomiting, and constipation.<br><br>CONCLUSIONS: The findings highlighted an efficacy advantage with tolerated adverse event profiles for muscarinic receptor agonists in schizophrenia.},
}
@article {pmid40175631,
year = {2025},
author = {Qi, Y and Zhu, X and Xiong, X and Yang, X and Ding, N and Wu, H and Xu, K and Zhu, J and Zhang, J and Wang, Y},
title = {Human motor cortex encodes complex handwriting through a sequence of stable neural states.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40175631},
issn = {2397-3374},
support = {62276228//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62336007//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR24F020002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {How the human motor cortex (MC) orchestrates sophisticated sequences of fine movements such as handwriting remains a puzzle. Here we investigate this question through Utah array recordings from human MC during attempted handwriting of Chinese characters (n = 306, each consisting of 6.3 ± 2.0 strokes). We find that MC activity evolves through a sequence of states corresponding to the writing of stroke fragments during complicated handwriting. The directional tuning curve of MC neurons remains stable within states, but its gain or preferred direction strongly varies across states. By building models that can automatically infer the neural states and implement state-dependent directional tuning, we can significantly better explain the firing pattern of individual neurons and reconstruct recognizable handwriting trajectories with 69% improvement compared with baseline models. Our findings unveil that skilled and sophisticated movements are encoded through state-specific neural configurations.},
}
@article {pmid40175376,
year = {2025},
author = {Dong, L and Ke, Y and Zhu, X and Liu, S and Ming, D},
title = {Long-term cognitive and neurophysiological effects of mental rotation training.},
journal = {NPJ science of learning},
volume = {10},
number = {1},
pages = {16},
pmid = {40175376},
issn = {2056-7936},
support = {81741139//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Mental rotation, a crucial aspect of spatial cognition, can be improved through repeated practice. However, the long-term effects of combining training with non-invasive brain stimulation and its neurophysiological correlates are not well understood. This study examined the lasting effects of a 10-day mental rotation training with high-definition transcranial direct current stimulation (HD-tDCS) on behavioral and neural outcomes in 34 healthy participants. Participants were randomly assigned to the Active and Shan groups, with equal group sizes. Mental rotation tests and EEG recordings were conducted at baseline, 1 day, 20 days, and 90 days post-training. Although HD-tDCS showed no significant effect, training led to improved accuracy, faster response times, and enhanced task-evoked EEG responses, with benefits lasting up to 90 days. Notably, task-evoked EEG responses remained elevated 20 days post-training. Individual differences, such as gender and baseline performance, influenced the outcomes. These results emphasize the potential of mental rotation training for cognitive enhancement and suggest a need for further investigation into cognition-related neuroplasticity.},
}
@article {pmid40174604,
year = {2025},
author = {Kojima, S and Kortenbach, BE and Aalberts, C and Miloševska, S and de Wit, K and Zheng, R and Kanoh, S and Musso, M and Tangermann, M},
title = {Influence of pitch modulation on event-related potentials elicited by Dutch word stimuli in a brain-computer interface language rehabilitation task.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc83d},
pmid = {40174604},
issn = {1741-2552},
abstract = {OBJECTIVE: Recently, a novel language training using an auditory brain-computer interface (BCI) based on electroencephalogram recordings has been proposed for chronic stroke patients with aphasia. Tested with native German patients, it has shown significant and medium to large effect sizes in improving multiple aspects of language. During the training, the auditory BCI system delivers word stimuli using six spatially arranged loudspeakers. As delivering the word stimuli via headphones reduces spatial cues and makes the attention to target words more difficult, we investigate the influence of added pitch information. While pitch modulations have shown benefits for tone stimuli, they have not yet been investigated in the context of language stimuli.<br><br>APPROACH: The study translated the German experimental setup into Dutch. Seventeen native Dutch speakers participated in a single session of an exploratory study. An incomplete Dutch sentence cued them to listen to a target word embedded into a sequence of comparable non-target words while an electroencephalogram was recorded. Four conditions were compared within-subject to investigate the influence of pitch modulation: presenting the words spatially from six loudspeakers without (6D) and with pitch modulation (6D-Pitch), via stereo headphones with simulated spatial cues and pitch modulation (Stereo-Pitch), and via headphones without spatial cues or pitch modulation (Mono).<br><br>MAIN RESULTS: Comparing the 6D conditions of both language setups, the Dutch setup could be validated. For the Dutch setup, the binary AUC classification score in the 6D and the 6D-Pitch condition were 0.75 and 0.76, respectively, and adding pitch information did not significantly alter the binary classification accuracy of the event-related potential responses. The classification scores in the 6D condition and the Stereo-Pitch condition were on the same level.<br><br>SIGNIFICANCE: The competitive performance of pitch-modulated word stimuli suggests that the complex hardware setup of the 6D condition could be replaced by a headphone condition. If future studies with aphasia patients confirm the effectiveness and higher usability of a headphone-based language rehabilitation training, a simplified setup could be implemented more easily outside of clinics to deliver frequent training sessions to patients in need.},
}
@article {pmid40174326,
year = {2025},
author = {Wan, P and Xue, H and Zhang, S and Kong, W and Shao, W and Wen, B and Zhang, D},
title = {Image by co-reasoning: A collaborative reasoning-based implicit data augmentation method for dual-view CEUS classification.},
journal = {Medical image analysis},
volume = {102},
number = {},
pages = {103557},
doi = {10.1016/j.media.2025.103557},
pmid = {40174326},
issn = {1361-8423},
mesh = {Humans ; Ultrasonography/methods ; *Liver Neoplasms/diagnostic imaging ; *Breast Neoplasms/diagnostic imaging ; *Contrast Media ; Machine Learning ; Female ; Retrospective Studies ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; },
abstract = {Dual-view contrast-enhanced ultrasound (CEUS) data are often insufficient to train reliable machine learning models in typical clinical scenarios. A key issue is that limited clinical CEUS data fail to cover the underlying texture variations for specific diseases. Implicit data augmentation offers a flexible way to enrich sample diversity, however, inter-view semantic consistency has not been considered in previous studies. To address this issue, we propose a novel implicit data augmentation method for dual-view CEUS classification, which performs a sample-adaptive data augmentation with collaborative semantic reasoning across views. Specifically, the method constructs a feature augmentation distribution for each ultrasound view of an individual sample, accounting for intra-class variance. To maintain semantic consistency between the augmented views, plausible semantic changes in one view are transferred from similar instances in the other view. In this retrospective study, we validate the proposed method on the dual-view CEUS datasets of breast cancer and liver cancer, obtaining the superior mean diagnostic accuracy of 89.25% and 95.57%, respectively. Experimental results demonstrate its effectiveness in improving model performance with limited clinical CEUS data. Code: https://github.com/wanpeng16/CRIDA.},
}
@article {pmid40173067,
year = {2025},
author = {Tian, M and Li, S and Xu, R and Cichocki, A and Jin, J},
title = {An Interpretable Regression Method for Upper Limb Motion Trajectories Detection with EEG Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3557255},
pmid = {40173067},
issn = {1558-2531},
abstract = {OBJECTIVE: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.<br><br>METHODS: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.<br><br>RESULTS: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.<br><br>CONCLUSION: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.<br><br>SIGNIFICANCE: This work provides a novel perspective on the comprehensive study of movement disorders.},
}
@article {pmid40172963,
year = {2025},
author = {Yang, Z and Zheng, Y and Ma, D and Wang, L and Zhang, J and Song, T and Wang, Y and Zhang, Y and Nan, F and Su, N and Gao, Z and Guo, J},
title = {Phosphatidylinositol 4,5-bisphosphate activation mechanism of human KCNQ5.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {14},
pages = {e2416738122},
pmid = {40172963},
issn = {1091-6490},
support = {2020YFA0908501//MOST | National Key Research and Development Program of China (NKPs)/ ; 32371204//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Humans ; *Phosphatidylinositol 4,5-Diphosphate/metabolism ; *KCNQ Potassium Channels/metabolism/chemistry/genetics ; *Cryoelectron Microscopy ; Calmodulin/metabolism/chemistry ; Protein Binding ; Ion Channel Gating ; Models, Molecular ; Protein Conformation ; },
abstract = {The human voltage-gated potassium channels KCNQ2, KCNQ3, and KCNQ5 can form homo- and heterotetrameric channels that are responsible for generating the neuronal M current and maintaining the membrane potential stable. Activation of KCNQ channels requires both the depolarization of membrane potential and phosphatidylinositol 4,5-bisphosphate (PIP2). Here, we report cryoelectron microscopy structures of the human KCNQ5-calmodulin (CaM) complex in the apo, PIP2-bound, and both PIP2- and the activator HN37-bound states in either a closed or an open conformation. In the closed conformation, a PIP2 molecule binds in the middle of the groove between two adjacent voltage-sensing domains (VSDs), whereas in the open conformation, one additional PIP2 binds to the interface of VSD and the pore domain, accompanying structural rearrangement of the cytosolic domain of KCNQ and CaM. The structures, along with electrophysiology analyses, reveal the two different binding modes of PIP2 and elucidate the PIP2 activation mechanism of KCNQ5.},
}
@article {pmid40172828,
year = {2025},
author = {Li, M and Zhao, Q and Zhang, T and Ge, J and Wang, J and Xu, G},
title = {A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40172828},
issn = {1995-8218},
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) provides additional control pathways by decoding the intentions of the brain. MI ability has great intra-individual variability, and the majority of MI-BCI systems are unable to adapt to this variability, leading to poor training effects. Therefore, prediction of MI ability is needed. In this study, we propose an MI ability predictor based on multi-frequency EEG features. To validate the performance of the predictor, a video-guided paradigm and a traditional MI paradigm are designed, and the predictor is applied to both paradigms. The results demonstrate that all subjects achieved > 85% prediction precision in both applications, with a maximum of 96%. This study indicates that the predictor can accurately predict the individuals' MI ability in different states, provide the scientific basis for personalized training, and enhance the effect of MI-BCI training.},
}
@article {pmid40172544,
year = {2025},
author = {Li, H and Li, C and Zhao, H and Li, Q and Zhao, Y and Gong, J and Li, G and Yu, H and Tian, Q and Liu, Z and Han, F},
title = {Flexible fibrous electrodes for implantable biosensing.},
journal = {Nanoscale},
volume = {17},
number = {16},
pages = {9870-9894},
doi = {10.1039/d4nr04542d},
pmid = {40172544},
issn = {2040-3372},
mesh = {*Biosensing Techniques/instrumentation ; Humans ; *Electrodes, Implanted ; Animals ; Electrochemical Techniques ; },
abstract = {Flexible fibrous electrodes have emerged as a promising technology for implantable biosensing applications, offering significant advancements in the monitoring and manipulation of biological signals. This review systematically explores the key aspects of flexible fibrous electrodes, including the materials, structural designs, and fabrication methods. A detailed discussion of electrode performance metrics is provided, covering factors such as conductivity, stretchability, axial channel count, and implantation duration. The diverse applications of these electrodes in electrophysiological signal monitoring, electrochemical sensing, tissue strain monitoring, and in vivo electrical stimulation are reviewed, highlighting their potential in biomedical settings. Finally, the review discusses the eight major challenges currently faced by implantable fibrous electrodes and explores future development directions, providing critical technical analysis and potential solutions for the advancement of next-generation flexible implantable fiber-based biosensors.},
}
@article {pmid40172075,
year = {2025},
author = {Wu, X and Liang, C and Bustillo, J and Kochunov, P and Wen, X and Sui, J and Jiang, R and Yang, X and Fu, Z and Zhang, D and Calhoun, VD and Qi, S},
title = {The Impact of Atlas Parcellation on Functional Connectivity Analysis Across Six Psychiatric Disorders.},
journal = {Human brain mapping},
volume = {46},
number = {5},
pages = {e70206},
pmid = {40172075},
issn = {1097-0193},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; BE2023668//Jiangsu Provincial Key Research and Development Program/ ; RF1 NS114628/NS/NINDS NIH HHS/United States ; U01 MH108148/MH/NIMH NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; BK20220889//Natural Science Foundation of Jiangsu Province/ ; 62376124//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Atlases as Topic ; Male ; Female ; *Connectome/methods ; *Bipolar Disorder/diagnostic imaging/physiopathology/pathology ; *Schizophrenia/diagnostic imaging/physiopathology/pathology ; *Depressive Disorder, Major/diagnostic imaging/physiopathology ; *Mental Disorders/diagnostic imaging/physiopathology ; Autism Spectrum Disorder/diagnostic imaging/physiopathology ; Young Adult ; Nerve Net/diagnostic imaging/physiopathology ; Attention Deficit Disorder with Hyperactivity/diagnostic imaging/physiopathology ; Psychotic Disorders/diagnostic imaging/physiopathology/pathology ; Middle Aged ; Adolescent ; },
abstract = {Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.},
}
@article {pmid40171943,
year = {2025},
author = {Lin, C and Zhou, X and Li, M and Zhang, C and Zhai, H and Li, H and Wang, H and Wang, X},
title = {S-ketamine Alleviates Neuroinflammation and Attenuates Lipopolysaccharide-Induced Depression Via Targeting SIRT2.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2416481},
doi = {10.1002/advs.202416481},
pmid = {40171943},
issn = {2198-3844},
support = {2021ZD0203000//STI2030-Major Projects/ ; 2021ZD0203003//STI2030-Major Projects/ ; T2341003//National Natural Science Foundation of China/ ; 22207105//National Natural Science Foundation of China/ ; 2023C038-3//Jilin Provincial Development and Reform Commission/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
abstract = {Depression, a pervasive mental health condition, has increasingly been linked to neuroinflammation, as evidenced by elevated levels of pro-inflammatory markers such as TNF-α and IL-1β observed in patients, which underscores the role of inflammation in its pathophysiology. This study investigates the differential effects of S-ketamine (S-KET) and R-ketamine (R-KET) on inflammation-induced depression using a lipopolysaccharide (LPS)-induced mouse model. Results showed that S-KET, but not R-KET, significantly alleviated depressive-like behaviors and reduced levels of pro-inflammatory factors in the medial prefrontal cortex (mPFC). Activity-based protein profiling identified SIRT2 as a key intracellular target of S-KET, with direct binding observed at the Q167 residue, whereas R-KET showed no such binding. S-KET enhanced SIRT2 interaction with NF-κB subunit p65, reducing its acetylation and suppressing pro-inflammatory gene expression, effects not seen with R-KET. In vitro studies with RNA interference and the SIRT2 inhibitor AK-7, along with in vivo pharmacological blockade, confirmed that SIRT2 is crucial for the anti-inflammatory and antidepressant actions of S-KET. These findings suggest that SIRT2 mediates the therapeutic effects of S-KET, highlighting its potential as a target for treating inflammation-associated depression. This study provides novel insights into the stereospecific actions of ketamine enantiomers and the promise of targeting SIRT2 for neuroinflammatory depression.},
}
@article {pmid40170996,
year = {2025},
author = {Ping, A and Wang, J and Ángel García-Cabezas, M and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW},
title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.},
journal = {National science review},
volume = {12},
number = {4},
pages = {nwae473},
pmid = {40170996},
issn = {2053-714X},
support = {R01 MH121706/MH/NIMH NIH HHS/United States ; },
abstract = {The primate amygdala serves to evaluate the emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal, lateral and central nuclei, respectively. Recent evidence has suggested the mesoscale (millimeter-scale) nature of intra-amygdala functional organization. However, the connectivity patterns by which these mesoscale regions interact with brainwide networks remain unclear. Using infrared neural stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7-T functional magnetic resonance imaging, we have discovered that these mesoscale sites exert influence over a surprisingly extensive scope of the brain. Our findings strongly indicate that mesoscale sites within the amygdala modulate brainwide networks through a 'one-to-many' (integral) way. Meanwhile, these connections exhibit a point-to-point (focal) topography. Our work provides new insights into the functional architecture underlying emotional and social behavioral networks, thereby opening up possibilities for individualized modulation of psychological disorders.},
}
@article {pmid40169544,
year = {2025},
author = {Zhu, M and Peng, J and Wang, M and Lin, S and Zhang, H and Zhou, Y and Dai, X and Zhao, H and Yu, YQ and Shen, L and Li, XM and Chen, J},
title = {Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {3107},
pmid = {40169544},
issn = {2041-1723},
support = {81870898//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *GABAergic Neurons/metabolism ; *Zona Incerta/metabolism ; Mice ; *Transcriptome ; Male ; Behavior, Animal/physiology ; Grooming ; Optogenetics ; Mice, Inbred C57BL ; Sleep/physiology/genetics ; Wakefulness/physiology/genetics ; Periaqueductal Gray/metabolism/cytology/physiology ; Female ; Mice, Transgenic ; },
abstract = {Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIr[Ecel1] and ZIm[Pde11a]), respectively. The GABAergic projection from ZIr[Ecel1] to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZIm[Pde11a] to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.},
}
@article {pmid40168986,
year = {2025},
author = {Wang, P and Han, L and Wang, L and Tao, Q and Guo, Z and Luo, T and He, Y and Xu, Z and Yu, J and Liu, Y and Wu, Z and Xu, B and Jin, B and Wei, Y and Yang, Y and Cheng, M and Jiang, Y and Tian, C and Zheng, H and Fan, Z and Jiang, P and Gao, Y and Wu, J and Wang, S and Sun, B and Fang, Z and Lei, J and Luo, B and Wen, H and Peng, G and Tang, Y and Yang, T and Chen, J and Zhuang, Z and Su, X and Pan, C and Zhu, K and Shen, Y and Liu, S and Bao, A and Yao, J and Wang, J and Xu, X and Li, XM and Liu, L and Duan, S and Zhang, J},
title = {Molecular pathways and diagnosis in spatially resolved Alzheimer's hippocampal atlas.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.03.002},
pmid = {40168986},
issn = {1097-4199},
abstract = {We employed Stereo-seq combined with single-nucleus RNA sequencing (snRNA-seq) to investigate the gene expression and cell composition changes in human hippocampus with or without Alzheimer's disease (AD). The transcriptomic map, with single-cell precision, unveiled AD-associated alterations with spatial specificity, which include the following: (1) elevated synapse pruning gene expression in the fimbria of AD, with disrupted microglia-astrocyte communication likely leading to disorganized synaptic structure; (2) a globally increased energy generation in the cornu ammonis (CA) region, with varying degrees across its subregions; (3) a significant reduction in the number of CA1 neurons in AD, while CA4 neurons remained largely unaffected, potentially due to gene alterations in CA4 conferring resilience to AD; and (4) aggravated amyloid-beta (Aβ) plaques in CA1 and stratum lucidum, radiatum, and moleculare (SLRM), and integration of Stereo-seq map with Aβ staining revealed a sequential enrichment of microglia and astrocytes around Aβ plaques. Finally, reduced brain-derived extracellular vesicles carrying cholecystokinin (CCK) and peripheral myelin protein 2 (PMP2) in AD plasma highlighted their diagnostic potential for clinical applications.},
}
@article {pmid40168808,
year = {2025},
author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J},
title = {Validity of purulent vaginal discharge, esterase, luminometry, and three bacteriological tests for diagnosing uterine infection in dairy cows using Bayesian latent class analysis.},
journal = {Preventive veterinary medicine},
volume = {239},
number = {},
pages = {106521},
doi = {10.1016/j.prevetmed.2025.106521},
pmid = {40168808},
issn = {1873-1716},
abstract = {This prospective cross-sectional study aimed to evaluate the ability of laboratory bacterial culture, Petrifilm, Tri-Plate, luminometry, purulent vaginal discharge (PVD), and esterase to correctly identify uterine infection in dairy cows, and to assess these tests' usefulness in different situations. We sampled dairy cows between 29 and 43 days in milk in seven farms. We considered all six tests imperfect to identify uterine infection and used Bayesian latent class analyses to estimate their sensitivity and specificity. We created ten scenarios, including tests alone, in series, or in parallel, and we calculated predictive values and misclassification cost terms (MCTs). All estimates are presented with 95 % Bayesian credibility intervals (BCI). A total of 326 uterine samples were collected. The laboratory culture had the best validity (sensitivity = 0.87, 95 % BCI = 0.77-0.97; specificity = 0.71, 95 % BCI = 0.58-0.86). The other tests had similar specificity but lower sensitivity, with PVD having the lowest sensitivity (0.05, 95 % BCI = 0.01-0.10). If treating a healthy cow was considered worse than leaving a cow with a uterine infection untreated, luminometry yielded an MCT similar to the laboratory culture. These findings highlight that the on-farm tools currently used to identify cows that could benefit from intrauterine antimicrobial treatment do not identify uterine infection accurately. While the laboratory culture was the most accurate test, it cannot easily be implemented on farms. Luminometry's validity was good, but additional research is necessary to understand how it can be implemented to improve judicious intrauterine antimicrobial use.},
}
@article {pmid40166115,
year = {2025},
author = {Yu, Z and Yang, B and Wei, P and Xu, H and Shan, Y and Fan, X and Zhang, H and Wang, C and Wang, J and Yu, S and Zhao, G},
title = {Critical biomarkers for responsive deep brain stimulation and responsive focal cortex stimulation in epilepsy field.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {103-114},
pmid = {40166115},
issn = {2667-3258},
abstract = {To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems. The Detrended Fluctuation Analysis (DFA) exponent is chosen as the classification exponent, and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed. The DFA exponent exhibited a statistically significant variation among the pre-ictal, ictal period, and post-ictal stages. The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models, whereas the Naive Bayesian model necessitates the least amount of computational and storage space. The set of DFA exponents is employed as an intermediary variable in the machine learning process. The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety, specifically within the domain of neural modulation in epilepsy.},
}
@article {pmid40166113,
year = {2025},
author = {Sun, Y and Chen, X and Liu, B and Liang, L and Wang, Y and Gao, S and Gao, X},
title = {Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review.},
journal = {Fundamental research},
volume = {5},
number = {1},
pages = {3-16},
pmid = {40166113},
issn = {2667-3258},
abstract = {Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.},
}
@article {pmid40164913,
year = {2025},
author = {Cheng, YA and Sanayei, M and Chen, X and Jia, K and Li, S and Fang, F and Watanabe, T and Thiele, A and Zhang, RY},
title = {A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40164913},
issn = {2397-3374},
support = {3230085//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2421004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32441102//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100901//National Natural Science Foundation of China (National Science Foundation of China)/ ; R01EY019466//U.S. Department of Health &amp; Human Services | NIH | National Eye Institute (NEI)/ ; BCS-2241417//NSF | Directorate for Social, Behavioral &amp; Economic Sciences | Division of Behavioral and Cognitive Sciences (Behavioral &amp; Cognitive Sciences)/ ; G0700976//RCUK | Medical Research Council (MRC)/ ; },
abstract = {Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.},
}
@article {pmid40164740,
year = {2025},
author = {Littlejohn, KT and Cho, CJ and Liu, JR and Silva, AB and Yu, B and Anderson, VR and Kurtz-Miott, CM and Brosler, S and Kashyap, AP and Hallinan, IP and Shah, A and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF and Anumanchipalli, GK},
title = {A streaming brain-to-voice neuroprosthesis to restore naturalistic communication.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {902-912},
pmid = {40164740},
issn = {1546-1726},
support = {5U01DC018671//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; F30DC021872//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Voice/physiology ; Adult ; Female ; Communication Devices for People with Disabilities ; *Sensorimotor Cortex/physiology ; Communication ; Paralysis/physiopathology/rehabilitation ; },
abstract = {Natural spoken communication happens instantaneously. Speech delays longer than a few seconds can disrupt the natural flow of conversation. This makes it difficult for individuals with paralysis to participate in meaningful dialogue, potentially leading to feelings of isolation and frustration. Here we used high-density surface recordings of the speech sensorimotor cortex in a clinical trial participant with severe paralysis and anarthria to drive a continuously streaming naturalistic speech synthesizer. We designed and used deep learning recurrent neural network transducer models to achieve online large-vocabulary intelligible fluent speech synthesis personalized to the participant's preinjury voice with neural decoding in 80-ms increments. Offline, the models demonstrated implicit speech detection capabilities and could continuously decode speech indefinitely, enabling uninterrupted use of the decoder and further increasing speed. Our framework also successfully generalized to other silent-speech interfaces, including single-unit recordings and electromyography. Our findings introduce a speech-neuroprosthetic paradigm to restore naturalistic spoken communication to people with paralysis.},
}
@article {pmid40163319,
year = {2025},
author = {van Balen, B},
title = {Somatosensory Feedback in BCIs: Why Aiming for Naturalness Raises Ethical Concerns.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/21507740.2025.2478427},
pmid = {40163319},
issn = {2150-7759},
abstract = {Recent developments in the domain of bi-directional Brain-Computer Interface (BCI) technology are directed at generating naturalistic sensory perceptual experiences for disabled people. I argue that conceptualizing and operationalizing "naturalness" in this context has profound impact on disabled people and their experiences. I ask (1) what does it mean to have a "natural" perceptual experience and (2) should the bi-directional BCI-community strive for naturalness in this context? Inspired by phenomenological and 4E-cognition approaches to perception, I argue that the terms "natural" and "naturalness" should not be used in this context because of (1) polysemicity and (2) an implicit bias favoring able-bodied perception over disabled perception. I offer the phenomenological concept of transparency as a positive alternative to denote the underlying goal of embodiment and effortless use. I cash out methodological ramifications of my argument for research in bi-directional BCIs and plea for a transdisciplinary dialogue between end-users, phenomenologists and neuroscientists.},
}
@article {pmid40162212,
year = {2025},
author = {Wang, D and Ramesh, R and Azgomi, HF and Louie, K and Balakid, J and Marks, J},
title = {At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {40162212},
issn = {2693-5015},
support = {R01 NS130183/NS/NINDS NIH HHS/United States ; },
abstract = {Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.},
}
@article {pmid40162168,
year = {2025},
author = {Yang, H and Jiang, L},
title = {Regulating neural data processing in the age of BCIs: Ethical concerns and legal approaches.},
journal = {Digital health},
volume = {11},
number = {},
pages = {20552076251326123},
pmid = {40162168},
issn = {2055-2076},
abstract = {Brain-computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are uniquely sensitive due to their most intimate nature, posing unique risks and ethical concerns especially related to privacy and safe control of our neural data. In furtherance of human right protection such as mental privacy, data laws provide more detailed and enforceable rules for processing neural data which may balance the tension between privacy protection and need of the public for wellness promotion and scientific progress through data sharing. This article notes that most of the current data laws like GDPR have not covered neural data clearly, incapable of providing full protection in response to its specialty. The new legislative reforms in the U.S. states of Colorado and California made pioneering advances to incorporate neural data into data privacy laws. Yet regulatory gaps remain as such reforms have not provided special additional rules for neural data processing. Potential problems such as static consent, vague research exceptions, and loopholes in regulating non-personal neural data need to be further addressed. We recommend relevant improved measures taken through amending data laws or making special data acts.},
}
@article {pmid40161643,
year = {2025},
author = {Haro, S and Beauchene, C and Quatieri, TF and Smalt, CJ},
title = {A Brain-Computer Interface for Improving Auditory Attention in Multi-Talker Environments.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40161643},
issn = {2692-8205},
support = {T32 DC000038/DC/NIDCD NIH HHS/United States ; },
abstract = {OBJECTIVE: There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.<br><br>APPROACH: This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.<br><br>RESULTS: In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session (p = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.<br><br>SIGNIFICANCE: These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.},
}
@article {pmid40161457,
year = {2025},
author = {Ren, X and Wang, Y and Li, X and Wang, X and Liu, Z and Yang, J and Wang, L and Zheng, C},
title = {Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {56},
pmid = {40161457},
issn = {1871-4080},
abstract = {Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.},
}
@article {pmid40159374,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.},
journal = {Annals of the New York Academy of Sciences},
volume = {1546},
number = {1},
pages = {157-172},
doi = {10.1111/nyas.15322},
pmid = {40159374},
issn = {1749-6632},
support = {KAKENHI 21H04956//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Deep Learning ; *Reinforcement, Psychology ; Algorithms ; Brain/physiology ; Reinforcement Machine Learning ; },
abstract = {Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given complex environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target-approaching score, lower failure rate, and lower human workload than the EEG-NB model. We also proposed a disparity d $d$ -index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors.},
}
@article {pmid40153908,
year = {2025},
author = {Liu, J and Yang, X and Musmar, B and Hasan, DM},
title = {Trans-arterial approach for neural recording and stimulation: Present and future.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {135},
number = {},
pages = {111180},
doi = {10.1016/j.jocn.2025.111180},
pmid = {40153908},
issn = {1532-2653},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrodes, Implanted ; *Endovascular Procedures/methods ; *Brain/physiology ; Electroencephalography/methods ; },
abstract = {Neural recording and stimulation are fundamental techniques used for brain computer interfaces (BCIs). BCIs have significant potential for use in a range of brain disorders. However, for most BCIs, electrode implantation requires invasive craniotomy procedures, which have a risk of infection, hematoma, and immune responses. Such drawbacks may limit the extensive application of BCIs. There has been a rapid increase in the development of endovascular technologies and devices. Indeed, in a clinical trial, stent electrodes have been endovascularly implanted via a venous approach and provided an effective endovascular BCI to help disabled patients. Several authors have reviewed the use of endovascular recordings or endovascular BCIs. However, there is limited information on the use of trans-arterial BCIs. Herein, we reviewed the literature on the use of trans-arterial neural recording and stimulation for BCIs, and discuss their potential in terms of anatomical features, device innovations, and clinical applications. Although the use of trans-arterial recording and stimulation in the brain remains challenging, we believe it has high potential for both scientists and physicians.},
}
@article {pmid40152578,
year = {2025},
author = {Li, K and Cui, Y},
title = {The Emerging Role of Astrocytes in Learning and Memory Recall.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {3},
pages = {38721},
doi = {10.31083/JIN38721},
pmid = {40152578},
issn = {0219-6352},
support = {2022ZD0211700//STI2030-Major projects/ ; 32371057//National Natural Science Foundation of China/ ; },
}
@article {pmid40151645,
year = {2025},
author = {Iwama, S and Ueno, T and Fujimaki, T and Ushiba, J},
title = {Enhanced human sensorimotor integration via self-modulation of the somatosensory activity.},
journal = {iScience},
volume = {28},
number = {4},
pages = {112145},
pmid = {40151645},
issn = {2589-0042},
abstract = {Motor performance improvement through self-modulation of brain activity has been demonstrated through neurofeedback. However, the sensorimotor plasticity induced through the training remains unclear. Here, we combined individually tailored closed-loop neurofeedback, neurophysiology, and behavioral assessment to characterize how the training can modulate the somatosensory system and improve performance. The real-time neurofeedback of human electroencephalogram (EEG) signals enhanced participants' self-modulation ability of intrinsic neural oscillations in the primary somatosensory cortex (S1) within 30 min. Further, the short-term reorganization in S1 was corroborated by the post-training changes in somatosensory evoked potential (SEP) amplitude of the early component from S1. Meanwhile those derived from peripheral and spinal sensory fibers were maintained (N9 and N13 components), suggesting that the training manipulated S1 activities. Behavioral evaluation demonstrated improved performance during keyboard touch-typing indexed by resolved speed-accuracy trade-off. Collectively, our results provide evidence that neurofeedback training induces functional reorganization of S1 and sensorimotor function.},
}
@article {pmid40149743,
year = {2025},
author = {Zheng, J and Li, Y and Chen, L and Wang, F and Gu, B and Sun, Q and Gao, X and Zhou, F},
title = {Effects of Packet Loss on Neural Decoding Effectiveness in Wireless Transmission.},
journal = {Brain sciences},
volume = {15},
number = {3},
pages = {},
pmid = {40149743},
issn = {2076-3425},
support = {2021ZD0200405//National Key R&amp;D Program of China/ ; 2024M752811//China Postdoctoral Science Foundation under Grant Number/ ; 202204A09//Key Agricultural and Social Development Projects of Hangzhou/ ; },
abstract = {BACKGROUND: In brain-computer interfaces, neural decoding plays a central role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels; however, they are often subject to data loss, commonly referred to as "packet loss". Despite their importance, the effects of different types and degrees of packet loss on neural decoding have not yet been comprehensively studied. Understanding these effects is critical for advancing neural signal processing.<br><br>METHODS: This study addresses this gap by constructing four distinct packet loss models that simulate the congestion, distribution, and burst loss scenarios. Using macaque superior arm movement decoding experiments, we analyzed the effects of the aforementioned packet loss types on decoding performance across six parameters (position, velocity, and acceleration in the x and y dimensions). The performance was assessed using the R2 metric and statistical comparisons across different loss scenarios.<br><br>RESULTS: Our results indicate that sudden, consecutive packet loss significantly degraded decoding performance. For the same packet loss probability, burst loss led to the largest decrease in the R2 value. Notably, when the packet loss rate reached 10%, the decoding performance for acceleration dropped to 73% of the original R2 value. On the other hand, when the packet loss rate was within 2%, the neural signal decoding results across all packet loss models remained largely unaffected. However, as the packet loss rate increased, the impact became more pronounced. These findings highlight the varying degrees to which different packet loss models affect decoding outcomes.<br><br>CONCLUSIONS: This study quantitatively evaluated the relationship between packet loss and neural decoding outcomes, highlighting the differential effects of loss patterns on decoding parameters, and it proposed some methods and devices to solve the problem of packet loss. These findings offer valuable insights for the development of resilient neural signal acquisition and processing systems capable of mitigating the impact of packet loss.},
}
@article {pmid40149575,
year = {2025},
author = {Calderone, A and Manuli, A and Arcadi, FA and Militi, A and Cammaroto, S and Maggio, MG and Pizzocaro, S and Quartarone, A and De Nunzio, AM and Calabrò, RS},
title = {The Impact of Visualization on Stroke Rehabilitation in Adults: A Systematic Review of Randomized Controlled Trials on Guided and Motor Imagery.},
journal = {Biomedicines},
volume = {13},
number = {3},
pages = {},
pmid = {40149575},
issn = {2227-9059},
abstract = {Background/Objectives: Guided imagery techniques, which include mentally picturing motions or activities to help motor recovery, are an important part of neuroplasticity-based motor therapy in stroke patients. Motor imagery (MI) is a kind of guided imagery in neurorehabilitation that focuses on mentally rehearsing certain motor actions in order to improve performance. This systematic review aims to evaluate the current evidence on guided imagery techniques and identify their therapeutic potential in stroke motor rehabilitation. Methods: Randomized controlled trials (RCTs) published in the English language were identified from an online search of PubMed, Web of Science, Embase, EBSCOhost, and Scopus databases without a specific search time frame. The inclusion criteria take into account guided imagery interventions and evaluate their impact on motor recovery through validated clinical, neurophysiological, or functional assessments. This review has been registered on Open OSF with the following number: DOI 10.17605/OSF.IO/3D7MF. Results: This review synthesized 41 RCTs on MI in stroke rehabilitation, with 996 participants in the intervention group and 757 in the control group (average age 50-70, 35% female). MI showed advantages for gait, balance, and upper limb function; however, the RoB 2 evaluation revealed 'some concerns' related to allocation concealment, blinding, and selective reporting issues. Integrating MI with gait training or action observation (AO) seems to improve motor recovery, especially in balance and walking. Technological methods like brain-computer interfaces (BCIs) and hybrid models that combine MI with circuit training hold potential for enhancing functional mobility and motor results. Conclusions: Guided imagery shows promise as a beneficial adjunct in stroke rehabilitation, with the potential to improve motor recovery across several domains such as gait, upper limb function, and balance.},
}
@article {pmid40148520,
year = {2025},
author = {Li, J and Shi, J and Yu, P and Yan, X and Lin, Y},
title = {Feature-aware domain invariant representation learning for EEG motor imagery decoding.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {10664},
pmid = {40148520},
issn = {2045-2322},
support = {2023QN06002//Inner Mongolia Natural Science Foundation project of China/ ; JJ230408//Inner Mongolia University high-level talent research project/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination/physiology ; Machine Learning ; Movement/physiology ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) is extensively utilized in clinical rehabilitation and virtual reality-based movement control. Decoding EEG-based MI signals is challenging because of the inherent spatio-temporal variability of the original signal representation, coupled with a low signal-to-noise ratio (SNR), which impedes the extraction of clean and robust features. To address this issue, we propose a multi-scale spatio-temporal domain-invariant representation learning method, termed MSDI. By decomposing the original signal into spatial and temporal components, the proposed method extracts invariant features at multiple scales from both components. To further constrain the representation to invariant domains, we introduce a feature-aware shift operation that resamples the representation based on its feature statistics and feature measure, thereby projecting the features into a domain-invariant space. We evaluate our proposed method via two publicly available datasets, BNCI2014-001 and BNCI2014-004, demonstrating state-of-the-art performance on both datasets. Furthermore, our method exhibits superior time efficiency and noise resistance.},
}
@article {pmid40147158,
year = {2025},
author = {Tang, J and Xi, X and Wang, T and Li, L and Yang, J},
title = {Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network.},
journal = {Computer methods and programs in biomedicine},
volume = {265},
number = {},
pages = {108735},
doi = {10.1016/j.cmpb.2025.108735},
pmid = {40147158},
issn = {1872-7565},
mesh = {Humans ; Electromyography ; *Muscle, Skeletal/physiology/physiopathology ; Male ; *Electric Stimulation ; Female ; Middle Aged ; Stroke/physiopathology ; *Motor Cortex/physiopathology ; *Electric Stimulation Therapy ; Adult ; Stroke Rehabilitation ; *Cerebral Cortex ; Nerve Net ; },
abstract = {BACKGROUND AND OBJECTIVE: Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.<br><br>METHODS: In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.<br><br>RESULTS: The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p < 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the &#x3b3; frequency band (30-45 Hz), with marked improvements in the clustering coefficient and shortest path length (p < 0.001).<br><br>CONCLUSIONS: As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.},
}
@article {pmid40145943,
year = {2025},
author = {Jilderda, MF and Zhang, Y and Rebattu, V and Salunga, R and Mesker, W and Wong, J and de Munck, L and Fornander, T and Nordenskjöld, B and Stål, O and Anderson, AKL and Bastiaannet, E and Treuner, K and Liefers, GJ},
title = {Identification of Early-Stage Breast Cancer with Minimal Risk of Recurrence by Breast Cancer Index.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {},
number = {},
pages = {},
doi = {10.1158/1078-0432.CCR-24-3836},
pmid = {40145943},
issn = {1557-3265},
abstract = {PURPOSE: This study assessed the prognostic ability of Breast Cancer Index (BCI) to identify patients at minimal risk (<5%) of 10-year distant recurrence (DR) who are unlikely to benefit from adjuvant endocrine therapy.<br><br>EXPERIMENTAL DESIGN: This prospective translational study included postmenopausal patients with early-stage, HR+ N0 breast cancer from the Stockholm (STO-3) trial who underwent surgery alone ("untreated") or surgery plus adjuvant tamoxifen ("treated") and the Netherlands Cancer Registry (NCR; surgery alone). The primary endpoint was time to DR. An adjusted BCI model with an additional cut-point was developed that stratified patients into 4 prognostic risk groups.<br><br>RESULTS: Across cohorts, 16%-22% of patients were classified as minimal risk of 10-year DR. In the Stockholm untreated cohort (n = 283), risks in the minimal, low, intermediate, and high risk groups were 2.3%, 15.5% (hazard ratio, 4.71 [95% CI, 1.09-20.29] versus minimal risk), 19.8% (6.97 [1.61-30.18]), and 35.9% (13.21 [3.07-56.76]), respectively (P < .001). In the Stockholm treated cohort (n = 317), risks were 4.3%, 5.0% (1.16 [0.35-3.85]), 11.7% (2.45 [0.74-8.14]), and 21.1% (5.27 [1.72-16.16]; P < .001). In the NCR cohort (n = 1245), risks were 4.5%, 7.5% (sub-distribution hazard ratio, 1.67 [95% CI, 0.81-3.45]), 10.3% (2.40 [1.14-5.03]), and 13.1% (3.13 [1.50-6.55]; P = .005). BCI risk scores provided additional independent information over standard prognostic factors (likelihood ratio, c2 = 7.98; P = .004).<br><br>CONCLUSIONS: The adjusted BCI model identified women with early-stage, HR+ N0 breast cancer at minimal risk of DR who may consider de-escalating adjuvant endocrine therapy.},
}
@article {pmid40144587,
year = {2025},
author = {Marzulli, M and Bleuzé, A and Saad, J and Martel, F and Ciuciu, P and Aksenova, T and Struber, L},
title = {Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521491},
pmid = {40144587},
issn = {1662-5161},
abstract = {INTRODUCTION: Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.<br><br>METHODS: This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.<br><br>RESULTS: The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.<br><br>DISCUSSION: These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.},
}
@article {pmid40144585,
year = {2025},
author = {Saad, J and Evans, A and Jaoui, I and Roux-Sibillon, V and Hardy, E and Anghel, L},
title = {Comparison metrics and power trade-offs for BCI motor decoding circuit design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1547074},
pmid = {40144585},
issn = {1662-5161},
abstract = {Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.},
}
@article {pmid40143846,
year = {2025},
author = {Andronache, C and Curǎvale, D and Nicolae, IE and Neacşu, AA and Nicolae, G and Ivanovici, M},
title = {Tackling the possibility of extracting a brain digital fingerprint based on personal hobbies predilection.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1487175},
pmid = {40143846},
issn = {1662-4548},
abstract = {In an attempt to create a more familiar brain-machine interaction for biometric authentication applications, we investigated the efficiency of using the users' personal hobbies, interests, and memory collections. This approach creates a unique and pleasant experience that can be later utilized within an authentication protocol. This paper presents a new EEG dataset recorded while subjects watch images of popular hobbies, pictures with no point of interest and images with great personal significance. In addition, we propose several applications that can be tackled with our newly collected dataset. Namely, our study showcases 4 types of applications and we obtain state-of-the-art level results for all of them. The tackled tasks are: emotion classification, category classification, authorization process, and person identification. Our experiments show great potential for using EEG response to hobby visualization for people authentication. In our study, we show preliminary results for using predilection for personal hobbies, as measured by EEG, for identifying people. Also, we propose a novel authorization process paradigm using electroencephalograms. Code and dataset are available here.},
}
@article {pmid40141951,
year = {2025},
author = {Aydin, S and Melek, M and Gökrem, L},
title = {A Safe and Efficient Brain-Computer Interface Using Moving Object Trajectories and LED-Controlled Activation.},
journal = {Micromachines},
volume = {16},
number = {3},
pages = {},
pmid = {40141951},
issn = {2072-666X},
support = {2023/86//Tokat Gaziosmanpa&#x15f;a &#xdc;niversitesi/ ; },
abstract = {Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.},
}
@article {pmid40140571,
year = {2025},
author = {Gazit Shimoni, N and Tose, AJ and Seng, C and Jin, Y and Lukacsovich, T and Yang, H and Verharen, JPH and Liu, C and Tanios, M and Hu, E and Read, J and Tang, LW and Lim, BK and Tian, L and Földy, C and Lammel, S},
title = {Changes in neurotensin signalling drive hedonic devaluation in obesity.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40140571},
issn = {1476-4687},
abstract = {Calorie-rich foods, particularly those that are high in fat and sugar, evoke pleasure in both humans and animals[1]. However, prolonged consumption of such foods may reduce their hedonic value, potentially contributing to obesity[2-4]. Here we investigated this phenomenon in mice on a chronic high-fat diet (HFD). Although these mice preferred high-fat food over regular chow in their home cages, they showed reduced interest in calorie-rich foods in a no-effort setting. This paradoxical decrease in hedonic feeding has been reported previously[3-7], but its neurobiological basis remains unclear. We found that in mice on regular diet, neurons in the lateral nucleus accumbens (NAcLat) projecting to the ventral tegmental area (VTA) encoded hedonic feeding behaviours. In HFD mice, this behaviour was reduced and uncoupled from neural activity. Optogenetic stimulation of the NAcLat→VTA pathway increased hedonic feeding in mice on regular diet but not in HFD mice, though this behaviour was restored when HFD mice returned to a regular diet. HFD mice exhibited reduced neurotensin expression and release in the NAcLat→VTA pathway. Furthermore, neurotensin knockout in the NAcLat and neurotensin receptor blockade in the VTA each abolished optogenetically induced hedonic feeding behaviour. Enhancing neurotensin signalling via overexpression normalized aspects of diet-induced obesity, including weight gain and hedonic feeding. Together, our findings identify a neural circuit mechanism that links the devaluation of hedonic foods with obesity.},
}
@article {pmid40139011,
year = {2025},
author = {Luo, C and Zhu, X and Zhang, Y and Wen, Y and Wan, L and Qian, Z},
title = {Competitive electrochemical immunosensor for trace phosphorylated Tau181 analysis in plasma: Toward point-of-care technologies of Alzheimer's disease.},
journal = {Talanta},
volume = {292},
number = {},
pages = {128009},
doi = {10.1016/j.talanta.2025.128009},
pmid = {40139011},
issn = {1873-3573},
mesh = {*tau Proteins/blood/immunology ; *Alzheimer Disease/blood/diagnosis ; Humans ; Phosphorylation ; *Electrochemical Techniques/methods ; Immunoassay/methods ; *Biosensing Techniques/methods ; *Point-of-Care Systems ; Electrodes ; Limit of Detection ; Biomarkers/blood ; Horseradish Peroxidase/chemistry ; },
abstract = {Accurate detection of core Alzheimer's disease (AD) biomarkers in biofluids is crucial for identifying preclinical AD and predicting disease progression. Phosphorylated tau 181 (p-tau181), a key biomarker, holds promise for early diagnosis. This work presents a sensitive and rapid electrochemical immunosensor (EC-iSensor) based on screen-printed electrodes (SPEs) for p-tau181 quantification. Employing a competitive immunoassay format, the EC-iSensor utilizes biotinylated p-tau181 as a competitor against the target analyte for binding to immobilized capture antibodies. Signal transduction is achieved via horseradish peroxidase (HRP) and tetramethylbenzidine (TMB) substrate. The EC-iSensor exhibits a low detection limit of 1.91 fg/mL and a wide dynamic range spanning 6.97 fg/mL to 100 ng/mL in PBS. Furthermore, successful detection of p-tau181 in blood samples from AD patients demonstrated its practical applicability. This cost-effective SPE-based EC-iSensor offers a simple and highly sensitive platform for p-tau181 detection, presenting potential for point-of-care technologies (POCT) of AD.},
}
@article {pmid40138736,
year = {2025},
author = {Yang, W and Wang, X and Qi, W and Wang, W},
title = {LGFormer: integrating local and global representations for EEG decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc5a3},
pmid = {40138736},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain/physiology ; Brain-Computer Interfaces ; },
abstract = {Objective.Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.Approach.In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.Main results.LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.Significance.In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.},
}
@article {pmid40136841,
year = {2025},
author = {Mohamed, AK and Aharonson, V},
title = {Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136841},
issn = {2313-7673},
support = {99207 and 117965//National Research Foundation of South Africa/ ; },
abstract = {Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.},
}
@article {pmid40136836,
year = {2025},
author = {Rusev, G and Yordanov, S and Nedelcheva, S and Banderov, A and Sauter-Starace, F and Koprinkova-Hristova, P and Kasabov, N},
title = {Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136836},
issn = {2313-7673},
support = {101070891/01.10.2022//European Commission under the HORIZON-EIC action/ ; },
abstract = {Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state network (ESN)) for Motor Control Decoding (MCD). A software system, written in Python using NEST Simulator SNN library is described. It is able to adapt continuously in real time in supervised or unsupervised mode. The proposed approach was tested on several experimental data sets acquired from a tetraplegic person. First simulation results are encouraging, showing also the need for a further improvement via multiple hyper-parameters tuning. Its future implementation on a neuromorphic hardware platform that is smaller in size and significantly less power consuming is discussed too.},
}
@article {pmid40136825,
year = {2025},
author = {Li, H and Wang, Y and Fu, P},
title = {A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {40136825},
issn = {2313-7673},
support = {2024JC-YBON-0659//Natural Science Basic Research Program of Shaanxi Province/ ; TC2023JC16//Basic Research Programs of Taicang/ ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems.},
}
@article {pmid40135785,
year = {2025},
author = {Chen, J and Liu, B and Peng, G and Zhou, L and Tan, C and Qin, J and Li, J and Hong, Z and Wu, Y and Lu, M and Cai, F and Huang, Y},
title = {Achieving High-Performance Transcranial Ultrasound Transmission Through Mie and Fano Resonance in Flexible Metamaterials.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500170},
doi = {10.1002/advs.202500170},
pmid = {40135785},
issn = {2198-3844},
support = {2021YFB3801802//National Key R&amp;D Program of China/ ; 2022YFB3204300//National Key R&amp;D Program of China/ ; 226-2024-00120//Fundamental Research Funds for the Central Universities/ ; 2023R5231//Special Support Plan for High Level Talents in Zhejiang Province/ ; },
abstract = {Transcranial ultrasound holds great potential in medical applications. However, the effective transmission of ultrasound through the skull remains challenging due to the acoustic impedance mismatch, as well as the non-uniform thickness, and the curved surface. To overcome these challenges, this work introduces an innovative Mie-resonance flexible metamaterial (MRFM), which consists of periodically arranged low-speed micropillars embedded within a high-speed flexible substrate. The MRFM generates Mie-resonance, which couples with the skull to form Fano resonance, thereby enhancing ultrasound transmittance through the skull. Simulation results demonstrate that the proposed resonance solution significantly increases transcranial ultrasound transmittance from 33.7% to 75.2% at 0.309 MHz. For the fabrication of the MRFM, porous nickel foam is used as the Mie micropillars, and agarose hydrogel serves as the flexible substrate. Experimental results demonstrate enhanced ultrasound transmittance from 20.6% to 73.3% at 0.33 MHz with the MRFM, which shows good agreement with the simulation results, further validating the effectiveness of the design. The simplicity, tunability, and flexibility of the MRFM represent a significant breakthrough, addressing the limitations of conventional rigid metamaterials. This work lays a solid theoretical and experimental foundation for advancing the clinical application of transcranial ultrasound stimulation and neuromodulation.},
}
@article {pmid40134759,
year = {2025},
author = {Sakel, M and Ozolins, CA and Saunders, K and Biswas, R},
title = {A home-based EEG neurofeedback treatment for chronic neuropathic pain-a pilot study.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {6},
number = {},
pages = {1479914},
pmid = {40134759},
issn = {2673-561X},
abstract = {OBJECTIVE: This study assessed the effect of an 8-week home-based neurofeedback intervention in chronic neuropathic pain patients.<br><br>SUBJECTS/PATIENTS: A cohort of eleven individuals with chronic neuropathic pain receiving treatment within the NHS framework.<br><br>METHODS: Participants were trained to operate a home-based neurofeedback system. Each received a portable Axon system for one week of electroencephalogram (EEG) baselines, followed by an 8-week neurofeedback intervention, and subsequent 12 weeks of follow-up EEG baselines. Primary outcome measures included changes in the Brief Pain Inventory and Visual Analogue Pain Scale at post-intervention, and follow-ups compared with the baseline. Secondary outcomes included changes in depression, anxiety, stress, pain catastrophizing, central sensitization, sleep quality, and quality of life. EEG activities were monitored throughout the trial.<br><br>RESULTS: Significant improvements were noted in pain scores, with all participants experiencing overall pain reduction. Clinically significant pain improvement (&#x2265;30%) was reported by 5 participants (56%). Mood scores showed a significant decrease in depression (p&#x2009;<&#x2009;0.05), and pain catastrophizing (p&#x2009;<&#x2009;0.05) scores improved significantly at post-intervention, with continued improvement at the first-month follow-up.<br><br>CONCLUSION: The findings indicate that an 8-week home-based neurofeedback intervention improved pain and psychological well-being in this sample of chronic neuropathic pain patients. A randomized controlled trial is required to replicate these results in a larger cohort. Clinical Trial Registration: https://clinicaltrials.gov/study/NCT05464199, identifier: (NCT05464199).},
}
@article {pmid40133571,
year = {2025},
author = {Xu, X and Sha, L and Basang, S and Peng, A and Zhou, X and Liu, Y and Li, Y and Chen, L},
title = {Mortality in patients with epilepsy: a systematic review.},
journal = {Journal of neurology},
volume = {272},
number = {4},
pages = {291},
pmid = {40133571},
issn = {1432-1459},
support = {Grant Recipient//Science and Technology Major Project of Sichuan Province/ ; },
mesh = {Humans ; *Epilepsy/mortality/epidemiology ; Sudden Unexpected Death in Epilepsy/epidemiology ; },
abstract = {BACKGROUND: Epilepsy is linked to a significantly higher risk of death, yet public awareness remains low. This study aims to investigate mortality characteristics, to reduce epilepsy-related deaths and improve prevention strategies.<br><br>METHODS: This study systematically reviews mortality data in relevant literature from PubMed and Embase up until June 2024. Data quality is assessed using the Newcastle-Ottawa Scale, and analysis includes trends, regional differences, and the economic impact of premature death. Global Burden of Disease (GBD) data are used to validate trends. In addition, a review of guidelines and expert statements on sudden unexpected death in epilepsy (SUDEP) is included to explore intervention strategies and recommendations.<br><br>RESULTS: Annual mortality rates of epilepsy have gradually declined, mainly due to improvements in low-income countries, while high-income regions have experienced an upward trend. Male patients exhibit higher mortality rates than females. Age-based analysis shows that the elderly contributes most to this increase due to chronic conditions such as cardiovascular disease and pneumonia related to epilepsy. This may be a key factor contributing to the increased mortality among epilepsy patients in aging high-income regions. Accidents and suicides are more prevalent in low-income regions. The highest mortality risks occur in the early years post-diagnosis and during prolonged, uncontrolled epilepsy. SUDEP remains a leading cause of death.<br><br>CONCLUSION: This study highlights the impact of gender, region, and disease duration on epilepsy mortality. Future research should focus on elderly epilepsy patients mortality characteristics and personalized interventions for SUDEP.},
}
@article {pmid40131365,
year = {2025},
author = {Almanna, MA and Elkaim, LM and Alvi, MA and Levett, JJ and Li, B and Mamdani, M and Al-Omran, M and Alotaibi, NM},
title = {Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X.},
journal = {JMIR formative research},
volume = {},
number = {},
pages = {},
doi = {10.2196/60859},
pmid = {40131365},
issn = {2561-326X},
abstract = {BACKGROUND: Given the recent evolution and achievements in Brain-Computer interface (BCI) technologies, understanding public perception and sentiments towards such novel technologies is important for guiding their communication strategies in marketing and education.<br><br>OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (Twitter), utilizing Natural Language Processing (NLP) methods.<br><br>METHODS: A mixed-methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,926 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We utilized the Sentiment.ai tool to infer users' demographics by matching pre-defined attributes in users' profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.<br><br>RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk's announcement of Neuralink. Sentiment analysis revealed that 59.38% of posts were neutral, 32.75% were positive, and 7.85% were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic = 0.266, tau = 0.266, P<.001). Most posts were objective (77.81%), with a smaller proportion being subjective (22.02%). Biographic analysis showed that the 'Broadcasting' group contributed the most to BCI discussions (30.67%), but the 'Scientific' group, which contributed 27.58% of the discussions, had the highest overall engagement metrics. Emotional analysis identified anticipation (20.56%), trust (17.59%), and fear (13.98%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.<br><br>CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy-making, and communication strategies.},
}
@article {pmid40129720,
year = {2025},
author = {Daly, I and Matran-Fernandez, A and Lebedev, MA and Kübler, A and Valeriani, D},
title = {Editorial: Datasets for brain-computer interface applications, volume II.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1569216},
pmid = {40129720},
issn = {1662-4548},
}
@article {pmid40129499,
year = {2025},
author = {Maltezou-Papastylianou, C and Scherer, R and Paulmann, S},
title = {How do voice acoustics affect the perceived trustworthiness of a speaker? A systematic review.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1495456},
pmid = {40129499},
issn = {1664-1078},
abstract = {Trust is a multidimensional and dynamic social and cognitive construct, considered the glue of society. Gauging someone's perceived trustworthiness is essential for forming and maintaining healthy relationships across various domains. Humans have become adept at inferring such traits from speech for survival and sustainability. This skill has extended to the technological space, giving rise to humanlike voice technologies. The inclination to assign personality traits to these technologies suggests that machines may be processed along similar social and vocal dimensions as human voices. Given the increasing prevalence of voice technology in everyday tasks, this systematic review examines the factors in the psychology of voice acoustics that influence listeners' trustworthiness perception of speakers, be they human or machine. Overall, this systematic review has revealed that voice acoustics impact perceptions of trustworthiness in both humans and machines. Specifically, combining multiple acoustic features through multivariate methods enhances interpretability and yields more balanced findings compared to univariate approaches. Focusing solely on isolated features like pitch often yields inconclusive results when viewed collectively across studies without considering other factors. Crucially, situational, or contextual factors should be utilised for enhanced interpretation as they tend to offer more balanced findings across studies. Moreover, this review has highlighted the significance of cross-examining speaker-listener demographic diversity, such as ethnicity and age groups; yet, the scarcity of such efforts accentuates the need for increased attention in this area. Lastly, future work should involve listeners' own trust predispositions and personality traits with ratings of trustworthiness perceptions.},
}
@article {pmid40128831,
year = {2025},
author = {Liu, XY and Wang, WL and Liu, M and Chen, MY and Pereira, T and Doda, DY and Ke, YF and Wang, SY and Wen, D and Tong, XG and Li, WG and Yang, Y and Han, XD and Sun, YL and Song, X and Hao, CY and Zhang, ZH and Liu, XY and Li, CY and Peng, R and Song, XX and Yasi, A and Pang, MJ and Zhang, K and He, RN and Wu, L and Chen, SG and Chen, WJ and Chao, YG and Hu, CG and Zhang, H and Zhou, M and Wang, K and Liu, PF and Chen, C and Geng, XY and Qin, Y and Gao, DR and Song, EM and Cheng, LL and Chen, X and Ming, D},
title = {Recent applications of EEG-based brain-computer-interface in the medical field.},
journal = {Military Medical Research},
volume = {12},
number = {1},
pages = {14},
pmid = {40128831},
issn = {2054-9369},
support = {2021YFF1200602//The National Key R&amp;D Program of China/ ; 0401260011//The National Science Fund for Excellent Overseas Scholars/ ; c02022088//The National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences/ ; 82472098//National Natural Science Foundation of China (General Program)/ ; 82202798//The National Natural Science Foundation of China/ ; 22YF1404200//The Shanghai Sailing Program/ ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electroencephalography/methods/trends/instrumentation ; Epilepsy/physiopathology ; },
abstract = {Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.},
}
@article {pmid40127544,
year = {2025},
author = {Davis, KC and Wyse-Sookoo, KR and Raza, F and Meschede-Krasa, B and Prins, NW and Fisher, L and Brown, EN and Cajigas, I and Ivan, ME and Jagid, JR and Prasad, A},
title = {5-year follow-up of a fully implanted brain-computer interface in a spinal cord injury patient.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc48c},
pmid = {40127544},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Electrocorticography/methods/instrumentation/trends ; *Electrodes, Implanted/trends ; Follow-Up Studies ; *Spinal Cord Injuries/rehabilitation/physiopathology/diagnosis ; },
abstract = {Spinal cord injury (SCI) affects over 250 000 individuals in the US. Brain-computer interfaces (BCIs) may improve quality of life by controlling external devices. Invasive intracortical BCIs have shown promise in clinical trials but degrade in the chronic period and tether patients to acquisition hardware. Alternatively, electrocorticography (ECoG) records data from electrodes on the cortex,and studies evaluating fully implanted BCI-ECoG systems are scarce. Objective. We seek to address this need using a fully implanted ECoG-based BCI that allows for home use in SCI.Approach.The patient used a long-term BCI system, initially controlling an functional electrical stimulation orthosis in the lab and later using an external mechanical orthosis at home. To evaluate its long-term viability, electrode contact impedance, signal quality, and decoder performance were measured. Signal quality was assessed using signal-to-noise ratio and maximum bandwidth of the signal. Decoder performance was monitored using the area under the receiver operator characteristic curve (AUROC).Main results.The study analyzed data from the patient's home environment over 54 months, revealing that the device was used at home for 38 ± 24 min on average daily. After six months, we observed stable event-related desynchronization that aided in determining the onset of motor intention. The decoder's average AUROC across months was 0.959. Importantly, 40 months of the data collected was gather from the subject's home or community environment. The results indicate long-term ECoG recordings were stable for motor-imagery classification and motor control in the community environment in a case of an individual with SCI.Significance.This study presents the long-term feasibility and viability of an ECoG-based BCI system that persists in the home environment in a case of SCI. Future research should explore larger electrode counts with more participants to confirm this stability. Understanding these trends is crucial for clinical utility and chronic viability in broader patient populations.},
}
@article {pmid40127541,
year = {2025},
author = {Marissens Cueva, V and Bougrain, L and Lotte, F and Rimbert, S},
title = {Reliable predictor of BCI motor imagery performance using median nerve stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc48d},
pmid = {40127541},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Female ; Adult ; *Median Nerve/physiology ; Young Adult ; *Electroencephalography/methods ; Reproducibility of Results ; Psychomotor Performance/physiology ; Electric Stimulation/methods ; Movement/physiology ; },
abstract = {Objective.Predicting performance in brain-computer interfaces (BCIs) is crucial for enhancing user experience, optimizing training and identifying the most efficient BCI approach for each individual.Approach.This study explores the use of median nerve stimulation (MNS) as a predictor of motor imagery (MI)-BCI performance. MNS induces event related (de)synchronization (ERD/ERS) patterns in the brain that are similar to those generated during MI tasks, providing a non-invasive, user-independent, and easy-to-setup method for performance prediction.Main results.Our proposed predictor, based on the minimum value of the ERD induced by the MNS, not only exhibits a robust correlation with the MI-BCI performance accuracy (rho = -0.71,p<0.001), but also effectively predicts this performance with a significant correlation (rho = 0.61, mean absolute error = 9.0,p<0.01). These results demonstrate its validity as a reliable predictor of MI-BCI performance.Significance.By systematically analyzing patterns induced by MNS and correlating them with subsequent MI-BCI task performance, we aim to establish a robust predictive method of motor activity to each individual only based on MNS, making it possible, among other things, to passively predict BCI deficiency or proficiency, and to potentially adapt BCI parameters for an efficient BCI experience or BCI-based recovery.},
}
@article {pmid40127535,
year = {2025},
author = {Ofer, A and Ophir, A and Yoav, N and Roman, R and Oren, S},
title = {Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc48e},
pmid = {40127535},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagination/physiology ; Signal-To-Noise Ratio ; Algorithms ; Adult ; Male ; Autoencoder ; },
abstract = {Objective.Non-stationarity in electroencephalogram (EEG) signals poses significant challenges for the performance and implementation of brain-computer interfaces (BCIs).Approach.In this study, we propose a novel method for cross-session BCI tasks that employs a supervised autoencoder to reduce session-specific information while preserving task-related signals. Our approach compresses high-dimensional EEG inputs and reconstructs them, thereby mitigating non-stationary variability in the data. In addition to unsupervised minimization of the reconstruction error, the objective function of the network includes two supervised terms to ensure that the latent representations exclude session identity information and are optimized for subsequent classification.Main results.Evaluation across three different motor imagery datasets demonstrates that our approach effectively addresses domain adaptation challenges, outperforming both naïve cross-session and within-session methods.Significance.Our method eliminates the need for data from new sessions, making it fully unsupervised concerning new session data and reducing the necessity for recalibration with each session. Furthermore, the reduction of session-specific information in the reconstructed signals indicates that our approach effectively denoises non-stationary signals, thereby enhancing the accuracy of BCI models. Future applications could extend this model to a broader range of BCI tasks and explore the residual signals to investigate sources of non-stationary brain components and other cognitive processes.},
}
@article {pmid40125566,
year = {2025},
author = {Hong, W and Mao, L and Lin, K and Huang, C and Su, Y and Zhang, S and Wang, C and Wang, D and Song, J and Chen, Z},
title = {Accurate and Noninvasive Dysphagia Assessment via a Soft High-Density sEMG Electrode Array Conformal to the Submental and Infrahyoid Muscles.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500472},
doi = {10.1002/advs.202500472},
pmid = {40125566},
issn = {2198-3844},
support = {12402202//National Natural Science Foundation of China/ ; 12302223//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; 2021C03050//Zhejiang Key Research and Development Program of China/ ; },
abstract = {Accurate, noninvasive dysphagia assessment is important for rehabilitation therapy but current clinical diagnostic methods are either invasive or subjective. Surface electromyography (sEMG) that monitors muscle activity during swallowing, offers a promising alternative. However, existing sEMG electrode arrays for dysphagia assessment remain challenging in combining the advantages of a large coverage area and strong compliance to the entire swallowing muscles. Here, we report a stretchable, breathable, large-area high-density sEMG (HD-sEMG) electrode array, which enables intimate contact to complex surface of the submental and infrahyoid muscles to detect high-fidelity HD-sEMG signals during swallowing. The electrode array features a 64-channel soft on-skin sensing array for comprehensive data capture, and a stiff connector for simple and reliable connection to an external acquisition setup. Systemically experimental studies revealed the easy operability of the soft HD-sEMG electrode array for effortless integration with the skin, as well as the excellent mechanical and electrical characteristics even subject to substantial skin deformations. By comparing HD-sEMG signals collected from 38 participants, three objective indicators for quantitative dysphagia evaluation were discussed. Finally, a machine learning model was developed to accurately and automatically classify the severity of dysphagia, and the factors affecting the recognition accuracy of the model were discussed in depth.},
}
@article {pmid40124055,
year = {2025},
author = {Lee, H and Lee, S and Lee, S and Lee, J and Chou, N and Shin, H},
title = {A Highly Efficient Low-Cost Flexible Neural Probe for Scalable Mass Fabrication.},
journal = {ACS omega},
volume = {10},
number = {10},
pages = {10733-10740},
pmid = {40124055},
issn = {2470-1343},
abstract = {Neural probes capable of the precise recording and control of brain signals are essential tools for brain-computer interfaces and neuroscience research. However, conventional neural probes have not been widely adopted due to the high costs associated with semiconductor fabrication and complex packaging procedures. Herein, we present a breakthrough in this area in the form of a highly efficient flexible neural probe with a production cost of only 1.5 dollars per unit that can be mass-produced (1000 units within 3 days). The probe design is based on a standardized flexible printed circuit board (PCB) process that ensures large-scale producibility and minimizes device performance variation. The device features four independent neural probes that enable flexible targeting of multiple brain regions and a reusable interface PCB that minimizes packaging complexity. The neural signal recording performance of the fabricated probe is comparable to that of traditional silicon-based probes and is scalable with eight electrodes capable of simultaneous measurements. We anticipate that our innovative device will significantly improve the accessibility of neuroscience research.},
}
@article {pmid40122923,
year = {2025},
author = {Yang, B and Rong, F and Xie, Y and Li, D and Zhang, J and Li, F and Shi, G and Gao, X},
title = {A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {488},
pmid = {40122923},
issn = {2052-4463},
support = {62376149//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Male ; Adult ; },
abstract = {A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.},
}
@article {pmid40122080,
year = {2025},
author = {Branco, MP and Verberne, MSW and van Balen, BJ and Bekius, A and Leinders, S and Ketelaar, M and Geytenbeek, J and van Driel-Boerrigter, M and Willems-Op Het Veld, M and Rabbie-Baauw, K and Vansteensel, MJ},
title = {Stakeholder's perspective on brain-computer interfaces for children and young adults with cerebral palsy.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/17483107.2025.2481426},
pmid = {40122080},
issn = {1748-3115},
abstract = {Communication Brain-Computer Interfaces (cBCIs) are a promising tool for people with motor and speech impairment, in particular for children and young adults with communication impairments, for example due to cerebral palsy (CP). Here we aimed to create a solid basis for the user-centered design of cBCIs for children and young adults with severe CP by investigating the perspectives of their parents/caregivers and health care professionals on communication and cBCIs. We conducted an online survey on 1) current communication problems and usability of used aids, 2) interest in cBCIs, and 3) preference for specific types of cBCIs. A total of 19 parents/caregivers and 36 health care professionals who interacted directly with children and young adults (8-25 years old) with severe CP, corresponding to Gross Motor Function Classification System level IV or V, participated. Both groups of respondents indicated that motor impairment occurred the most frequently and had the greatest impact on communication. The currently used communication aids included mainly no/low-tech aids and high-tech aids. The majority of health care professionals and parents/caregivers reported an interest in cBCIs, with a slight preference for implanted electrodes over non-implanted ones, and no preference for either of the two proposed mental BCI control strategies. Results indicate that cBCIs should be considered for a subpopulation of children and young adults with severe CP, and that in the development of cBCIs for this group both visual stimuli and sensorimotor rhythms, as well as the use of implanted electrodes, should be considered.},
}
@article {pmid40121857,
year = {2025},
author = {Zhou, H and Qiao, K and Rao, L and Zhai, HJ},
title = {Nanosilica cross-linked polyurethane hybrid hydrogels to stabilize the silicone rubber based invasive electrode-neural tissue interface.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {251},
number = {},
pages = {114643},
doi = {10.1016/j.colsurfb.2025.114643},
pmid = {40121857},
issn = {1873-4367},
mesh = {*Hydrogels/chemistry/pharmacology ; Animals ; Rats ; PC12 Cells ; *Polyurethanes/chemistry ; *Silicon Dioxide/chemistry ; *Silicone Elastomers/chemistry ; Electrodes ; *Cross-Linking Reagents/chemistry ; Surface Properties ; Cell Adhesion/drug effects ; Particle Size ; },
abstract = {An unstable electrode-neural tissue interface induced by tissue inflammatory response hinders the application of invasive brain-computer interfaces (BCIs). In this work, nanosilica cross-linked polyurethane (SiO2/PU) hybrid hydrogels were prepared to serve as the coatings and to modify silicone rubber (SR), which is a commonly used encapsulation material of invasive electrodes for neural recording/stimulation. The hydrophilicity, swelling ratio, and bulk ionic conductivity of SiO2/PU hybrid hydrogels were tailored by incorporating different amount of SiO2 serving as the cross-linking agent. Correspondingly, the optimized SiO2/PU hybrid hydrogel coatings have less impact on the electrochemical properties of invasive electrodes relative to PU hydrogel. Cell affinity assays with rat pheochromocytoma cells reveal that coatings made of SiO2/PU hybrid hydrogels are more effective in enhancing their adhesion and neurite outgrowth than those made of PU hydrogels. The adsorption amount of non-specific proteins on SR is significantly reduced by 81.6 % and 92.6 % upon coating with PU hydrogels and SiO2/PU hybrid hydrogels, respectively. Histological assessment indicates that the SR implants with a SiO2/PU hybrid hydrogel coating provoke the mildest tissue response. Collectively, the SiO2/PU hybrid hydrogel is highly promising for the stabilization of electrode-neural tissue interface, which is crucial for the development of invasive BCIs.},
}
@article {pmid40119207,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Author Correction: Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {913},
doi = {10.1038/s41593-025-01941-2},
pmid = {40119207},
issn = {1546-1726},
}
@article {pmid40118966,
year = {2025},
author = {Hussain, SAH and Raza, I and Hussain, SA and Jamal, MH and Gulrez, T and Zia, A},
title = {A mental state aware brain computer interface for adaptive control of electric powered wheelchair.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9880},
pmid = {40118966},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Humans ; *Wheelchairs ; *Electroencephalography ; Male ; Machine Learning ; Persons with Disabilities/psychology ; Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively adjusts to the psychological effects of the patient could provide the foundation for refining BCI applications. This paper focuses on the collection and realization of human electroencephalogram (EEG) signals data, obtained as a response to different psychological effects of sound stimuli. Filtration and pre-processing of the data set are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26%. In addition, an automated BCI system is developed to control an electric wheelchair (EPW) while responding to the mental state of the user with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and responsive to emotions for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 s to generate the interpretable brain signal from the user.},
}
@article {pmid40118477,
year = {2025},
author = {Ren, Y and Kang, YN and Cao, SY and Meng, F and Zhang, J and Liao, R and Li, X and Chen, Y and Wen, Y and Wu, J and Xia, W and Xu, L and Wen, S and Liu, H and Li, Y and Gu, J and Lv, Q},
title = {Evaluating the performance of large language models in health education for patients with ankylosing spondylitis/spondyloarthritis: a cross-sectional, single-blind study in China.},
journal = {BMJ open},
volume = {15},
number = {3},
pages = {e097528},
pmid = {40118477},
issn = {2044-6055},
mesh = {Humans ; Cross-Sectional Studies ; *Spondylitis, Ankylosing ; China ; Male ; Single-Blind Method ; Female ; Adult ; Middle Aged ; Patient Education as Topic/methods ; Language ; Spondylarthritis ; Health Knowledge, Attitudes, Practice ; },
abstract = {OBJECTIVES: To evaluate the potential of large language models (LLMs) in health education for patients with ankylosing spondylitis (AS)/spondyloarthritis (SpA), focusing on the accuracy of information transmission, patient acceptance and performance differences between different models.<br><br>DESIGN: Cross-sectional, single-blind study.<br><br>SETTING: Multiple centres in China.<br><br>PARTICIPANTS: 182 volunteers, including 4 rheumatologists and 178 patients with AS/SpA.<br><br>Scientificity, precision and accessibility of the content of the answers provided by LLMs; patient acceptance of the answers.<br><br>RESULTS: LLMs performed well in terms of scientificity, precision and accessibility, with ChatGPT-4o and Kimi models outperforming traditional guidelines. Most patients with AS/SpA showed a higher level of understanding and acceptance of the responses from LLMs.<br><br>CONCLUSIONS: LLMs have significant potential in medical knowledge transmission and patient education, making them promising tools for future medical practice.},
}
@article {pmid40117671,
year = {2025},
author = {Revechkis, B and Aflalo, TN and Pouratian, N and Rosario, E and Ouellette, DS and Zhang, C and Pejsa, K},
title = {Effector specificity in human posterior parietal neurons and local field potentials during movement in virtual reality and online brain control.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc3ca},
pmid = {40117671},
issn = {1741-2552},
mesh = {Humans ; *Parietal Lobe/physiology ; *Virtual Reality ; *Movement/physiology ; *Neurons/physiology ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology/rehabilitation ; Adult ; Online Systems ; Psychomotor Performance/physiology ; },
abstract = {Objective. Neural prosthetics represent a significant opportunity for control of external effectors like artificial limbs and computer devices as well as a means for interacting with virtual reality. Prior studies have shown posterior parietal cortex (PPC) to be a viable source of signals for the purposes of decoding motor intentions given its representation of both visual inputs and motor outputs. Additionally, signals in parietal cortex have been shown to be associated with tool use the body schema. We investigated if more realistic movement effectors in virtual reality might elicit stronger signals at the single neuron level in parietal cortex.Approach. A quadriplegic human subject was implanted with multi-electrode recording arrays in the PPC. Neural spiking and local field potentials were recorded during attempted movement in a computer-rendered, stereoscopic, 3D virtual environment. Tuning to different movement effectors was examined using a first-person movement generation task in addition to closed loop control performance.Main results. We found single neurons and simultaneously recorded field potentials in a quadriplegic patient exhibited enhanced responses during attempted (rather than passively observed) movement of a realistic and 'attached' 3D arm relative to either a visually similar but 'detached' 2D arm or a non-anthropomorphic abstract effector. These preferences were found despite the patient having lost motor function years prior. These differences did not effect performance during closed loop brain control of the movement effectors.Significance. In human parietal cortex, single neuron activity and local field potentials responded preferentially to visually guided attempted movement of a realistic arm rather than abstract effector. However, this tuning did not affect closed loop brain control in a virtual reality environment when preceded by a text-based decoder training paradigm.},
}
@article {pmid40117159,
year = {2025},
author = {Xia, Y and Chen, J and Li, J and Gong, T and Vidal-Rosas, EE and Loureiro, R and Cooper, RJ and Zhao, H},
title = {A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1220-1230},
doi = {10.1109/TNSRE.2025.3553794},
pmid = {40117159},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Spectroscopy, Near-Infrared/methods ; *Neurofeedback/methods ; Male ; *Deep Learning ; *Algorithms ; *Tomography, Optical/methods ; Artifacts ; Female ; Adult ; Calibration ; Young Adult ; Computer Systems ; Motion ; },
abstract = {Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.},
}
@article {pmid40115973,
year = {2025},
author = {Chen, W and Chen, H and Ruan, H and Jiang, W and Chen, C and Xu, M and Xu, Y and Chen, H and Yu, Z and Chen, S},
title = {Identification of Adolescents With Major Depressive Disorder Using Random Forest Based on Nocturnal Heart Rate Variability.},
journal = {Psychophysiology},
volume = {62},
number = {3},
pages = {e70049},
doi = {10.1111/psyp.70049},
pmid = {40115973},
issn = {1469-8986},
support = {A20240472//Hangzhou Municipal General Medical and Health Plan/ ; },
mesh = {Humans ; *Heart Rate/physiology ; *Depressive Disorder, Major/physiopathology/diagnosis ; Adolescent ; Male ; Female ; *Electrocardiography ; Bayes Theorem ; Polysomnography ; Random Forest ; },
abstract = {Major depressive disorder (MDD) in adolescents is often underdiagnosed, with the current diagnosis predominantly relying on subjective assessment. Sleep disturbance and reduced heart rate variability (HRV) have been typically observed in adolescents with MDD. This study aimed to develop an automatic classification model based on nocturnal HRV features to identify adolescent MDD. Sixty-three subjects, including depressed adolescents and healthy controls, participated in the study and completed a three-night sleep electrocardiogram (ECG) monitoring, yielding 160 overnight RR interval time series and 7520 5-min short-term segments for analysis. Nineteen HRV features were extracted from the time domain, frequency domain, and nonlinear dynamics. The Bayesian-optimized random forest (BO-RF) algorithm was applied as the classifier, with performance evaluated using ten-fold cross-validation. The impact of data accumulation on the reliability of identification using short-term data and the importance of features were also examined. The BO-RF classifier based on long-term features achieved a noteworthy predictive accuracy of 80.6%, and the performance of the classifier using short-term data showed a significant improvement when more segment outcomes from the same night were included, ultimately achieving an accuracy of 75.0%. The Poincaré plot-derived features, especially heart rate asymmetry (HRA) features such as C1d, significantly contributed to distinguishing depressed adolescents from healthy subjects. Nocturnal HRV features can effectively differentiate adolescents with MDD from healthy controls. This study provides a promising diagnostic approach for adolescent MDD, with the potential to be integrated into wearable devices for broader application.},
}
@article {pmid40115889,
year = {2025},
author = {Li, J and Hu, B and Guan, ZH},
title = {AM-MTEEG: multi-task EEG classification based on impulsive associative memory.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1557287},
pmid = {40115889},
issn = {1662-4548},
abstract = {Electroencephalogram-based brain-computer interfaces (BCIs) hold promise for healthcare applications but are hindered by cross-subject variability and limited data. This article proposes a multi-task (MT) classification model, AM-MTEEG, which integrates deep learning-based convolutional and impulsive networks with bidirectional associative memory (AM) for cross-subject EEG classification. AM-MTEEG deals with the EEG classification of each subject as an independent task and utilizes common features across subjects. The model is built with a convolutional encoder-decoder and a population of impulsive neurons to extract shared features across subjects, as well as a Hebbian-learned bidirectional associative memory matrix to classify EEG within one subject. Experimental results on two BCI competition datasets demonstrate that AM-MTEEG improves average accuracy over state-of-the-art methods and reduces performance variance across subjects. Visualization of neuronal impulses in the bidirectional associative memory network reveal a precise mapping between hidden-layer neuron activities and specific movements. Given four motor imagery categories, the reconstructed waveforms resemble the real event-related potentials, highlighting the biological interpretability of the model beyond classification.},
}
@article {pmid40115888,
year = {2025},
author = {Liu, J and Xie, J and Zhang, H and Yang, H and Shao, Y and Chen, Y},
title = {Improvement of BCI performance with bimodal SSMVEPs: enhancing response intensity and reducing fatigue.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1506104},
pmid = {40115888},
issn = {1662-4548},
abstract = {Steady-state visual evoked potential (SSVEP) is a widely used brain-computer interface (BCI) paradigm, valued for its multi-target capability and limited EEG electrode requirements. Conventional SSVEP methods frequently lead to visual fatigue and decreased recognition accuracy because of the flickering light stimulation. To address these issues, we developed an innovative steady-state motion visual evoked potential (SSMVEP) paradigm that integrated motion and color stimuli, designed specifically for augmented reality (AR) glasses. Our study aimed to enhance SSMVEP response intensity and reduce visual fatigue. Experiments were conducted under controlled laboratory conditions. EEG data were analyzed using the deep learning algorithm of EEGNet and fast Fourier transform (FFT) to calculate the classification accuracy and assess the response intensity. Experimental results showed that the bimodal motion-color integrated paradigm significantly outperformed single-motion SSMVEP and single-color SSVEP paradigms, respectively, achieving the highest accuracy of 83.81% ± 6.52% under the medium brightness (M) and area ratio of C of 0.6. Enhanced signal-to-noise ratio (SNR) and reduced visual fatigue were also observed, as confirmed by objective measures and subjective reports. The findings verified the bimodal paradigm as a novel application in SSVEP-based BCIs, enhancing both brain response intensity and user comfort.},
}
@article {pmid40115887,
year = {2025},
author = {Schreiner, L and Wipprecht, A and Olyanasab, A and Sieghartsleitner, S and Pretl, H and Guger, C},
title = {Brain-computer-interface-driven artistic expression: real-time cognitive visualization in the pangolin scales animatronic dress and screen dress.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1516776},
pmid = {40115887},
issn = {1662-5161},
abstract = {This paper explores the intersection of brain-computer interfaces (BCIs) and artistic expression, showcasing two innovative projects that merge neuroscience with interactive wearable technology. BCIs, traditionally applied in clinical settings, have expanded into creative domains, enabling real-time monitoring and representation of cognitive states. The first project showcases a low-channel BCI Screen Dress, utilizing a 4-channel electroencephalography (EEG) headband to extract an engagement biomarker. The engagement is visualized through animated eyes on small screens embedded in a 3D-printed dress, which dynamically responds to the wearer's cognitive state. This system offers an accessible approach to cognitive visualization, leveraging real-time engagement estimation and demonstrating the effectiveness of low-channel BCIs in artistic applications. In contrast, the second project involves an ultra-high-density EEG (uHD EEG) system integrated into an animatronic dress inspired by pangolin scales. The uHD EEG system drives physical movements and lighting, visually and kinetically expressing different EEG frequency bands. Results show that both projects have successfully transformed brain signals into interactive, wearable art, offering a multisensory experience for both wearers and audiences. These projects highlight the vast potential of BCIs beyond traditional clinical applications, extending into fields such as entertainment, fashion, and education. These innovative wearable systems underscore the ability of BCIs to expand the boundaries of creative expression, turning the wearer's cognitive processes into art. The combination of neuroscience and fashion tech, from simplified EEG headsets to uHD EEG systems, demonstrates the scalability of BCI applications in artistic domains.},
}
@article {pmid40115885,
year = {2025},
author = {Liu, J and Li, Y and Zhao, D and Zhong, L and Wang, Y and Hao, M and Ma, J},
title = {Efficacy and safety of brain-computer interface for stroke rehabilitation: an overview of systematic review.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1525293},
pmid = {40115885},
issn = {1662-5161},
abstract = {BACKGROUND: Stroke is a major global health challenge that significantly influences public health. In stroke rehabilitation, brain-computer interfaces (BCI) offer distinct advantages over traditional training programs, including improved motor recovery and greater neuroplasticity. Here, we provide a first re-evaluation of systematic reviews and meta-analyses to further explore the safety and clinical efficacy of BCI in stroke rehabilitation.<br><br>METHODS: A standardized search was conducted in major databases up to October 2024. We assessed the quality of the literature based on the following aspects: AMSTAR-2, PRISMA, publication year, study design, homogeneity, and publication bias. The data were subsequently visualized as radar plots, enabling a comprehensive and rigorous evaluation of the literature.<br><br>RESULTS: We initially identified 908 articles and, after removing duplicates, we screened titles and abstracts of 407 articles. A total of 18 studies satisfied inclusion criteria were included. The re-evaluation showed that the quality of systematic reviews and meta-analyses concerning stroke BCI training is moderate, which can provide relatively good evidence.<br><br>CONCLUSION: It has been proven that BCI-combined treatment can improve upper limb motor function and the quality of daily life for stroke patients, especially those in the subacute phase, demonstrating good safety. However, its effects on improving speech function, lower limb motor function, and long-term outcomes require further evidence. Multicenter, long-term follow-up studies are needed to increase the reliability of the results.<br><br>CLINICAL TRIAL REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024562114, CRD42023407720.},
}
@article {pmid40112995,
year = {2025},
author = {Huang, T and Ma, Y and Chen, H and Zhang, S and Liu, L and Chen, M and Jia, R and Lin, L and Ullah, MW and Fan, Y},
title = {A silk nanofiber and hyaluronic acid composite hemostatic sponge for compressible hemostasis.},
journal = {International journal of biological macromolecules},
volume = {307},
number = {Pt 4},
pages = {142262},
doi = {10.1016/j.ijbiomac.2025.142262},
pmid = {40112995},
issn = {1879-0003},
abstract = {Uncontrolled traumatic blood loss is a leading cause of hemorrhagic shock and death, highlighting the critical need for compressible and rapid hemostatic first-aid materials. In this study, silk nanofibers (MA-SNFs) were prepared through maleic acid (MA) hydrolysis decorated with enriched carboxyl groups. The MA-SNFs were then combined with hyaluronic acid (HA) through EDC/NHS crosslinking to form a porous sponge (i.e., MA-SNF/HA) through freeze-drying. The fabricated MA-SNF/HA sponges demonstrated excellent blood compatibility (hemolysis ratio < 5 %), outstanding hemocompatibility (blood clotting index (BCI) < 35 % within 60 s), and good cytocompatibility (cell viability >85 %). Among the different sponges prepared, M4-H6 (MA-SNFs: HA = 4:6) exhibited the best liquid reabsorption capacity after 80 % compression, outperforming M6-H4 and M5-H5 sponges. Furthermore, M4-H6 sponge absorbed liquid rapidly (~30 s) while matching the liquid absorption capacity of commercial gelatin sponge (GS), which require over 5 min for similar absorption (2232.84 ± 141.69 %). These findings suggest that M4-H6 sponge is highly suitable for compressible hemostasis applications and provide further insights into its potential hemostatic mechanism.},
}
@article {pmid40112909,
year = {2025},
author = {Wang, J and Guo, M and Zhang, J and Bai, Y and Ni, G},
title = {Early audiovisual integration in target processing under continuous noise: Behavioral and EEG evidence.},
journal = {Neuropsychologia},
volume = {211},
number = {},
pages = {109128},
doi = {10.1016/j.neuropsychologia.2025.109128},
pmid = {40112909},
issn = {1873-3514},
mesh = {Humans ; Electroencephalography ; Male ; *Visual Perception/physiology ; Female ; Young Adult ; *Auditory Perception/physiology ; Reaction Time/physiology ; Photic Stimulation ; Acoustic Stimulation ; Adult ; Evoked Potentials/physiology ; *Noise ; *Brain/physiology ; Brain Mapping ; },
abstract = {Multisensory integration is interconnected across various information reception. The stage and mechanism of brain response to audiovisual integration have not been fully understood. In this study, we designed audiovisual and unisensory experiments to investigate task performance and electrophysiological characteristics associated with audiovisual integration in a continuous background interference environment using materials collected from the underwater environment. Behavioral results showed that the reaction time (RT) was shorter, and the accuracy was higher in the audiovisual experiment. The cumulative distribution function (CDF) results of RT indicated that audiovisual integration supported the co-activation model. Event-related potential (ERP) results revealed shorter latency of the P1 and N1 components in the audiovisual experiment. Microstate analysis indicated that the parietal-occipital area may play a key role in audiovisual integration. Moreover, event-related spectral perturbation (ERSP) results demonstrated the critical role of low-frequency oscillation in audiovisual integration at the early stage. Our findings support the view that the beneficial effect of audiovisual integration is predominantly upon the early stage of neural information processing, including task-independent information.},
}
@article {pmid40112768,
year = {2025},
author = {Yang, HR and Han, MR and Oh, EY and Choi, JY and Choi, JY and Kim, Y and Kim, YT and Kang, H and Kim, JG},
title = {Role of cold-inducible RNA-binding protein in hypothalamic regulation of feeding behavior during fasting and cold exposure.},
journal = {Biochemical and biophysical research communications},
volume = {757},
number = {},
pages = {151616},
doi = {10.1016/j.bbrc.2025.151616},
pmid = {40112768},
issn = {1090-2104},
mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; *Fasting/metabolism ; Male ; *Feeding Behavior/physiology ; *Hypothalamus/metabolism ; Mice ; *Cold Temperature ; *Agouti-Related Protein/metabolism/genetics ; *Mice, Inbred C57BL ; Neurons/metabolism ; Eating/physiology ; },
abstract = {Appetite regulation is a complex process that is critical for maintaining energy balance and is governed by intricate molecular and cellular mechanisms in the hypothalamus. RNA-binding proteins play vital roles in the post-transcriptional regulation of mRNA and influence feeding behavior and energy metabolism. This study explored the role of cold-inducible RNA-binding protein (Cirbp) in hypothalamic neurons under metabolic stress conditions, such as fasting and cold exposure. Next-generation sequencing (NGS) of the hypothalami from fasted mice identified 67 differentially expressed RNA-binding proteins, with Cirbp and RNA-binding motif protein 3 (Rbm3) being significantly upregulated. Immunohistochemical analysis confirmed increased Cirbp expression in the arcuate nucleus (ARC) and dorsomedial hypothalamus during fasting, indicating responsiveness to metabolic cues. Ribo-Tag analysis of agouti-related protein (AgRP) neurons demonstrated elevated Cirbp expression levels in response to fasting, linking it to hunger-regulating pathways. Intracerebroventricular injection of Cirbp antisense oligodeoxynucleotides (AS ODN) reduced Cirbp expression, leading to a decrease in food intake and a reduction in body weight, highlighting the functional role of Cirbp in appetite regulation. Cold exposure also induced Cirbp expression in the ARC, which correlated with an increase in food intake. Blockade of Cirbp by AS ODN treatment attenuated cold-induced food intake, indicating that Cirbp plays a specific role in regulating feeding behavior during cold stress. This suggests that Cirbp is a key mediator in hypothalamic responses to metabolic stress, influencing feeding behavior through its regulatory functions in AgRP neurons. Further exploration of Cirbp mechanisms may offer insights into therapeutic strategies for energy balance disorders, such as obesity and anorexia.},
}
@article {pmid40111769,
year = {2025},
author = {Wang, X and Liu, A and Cui, H and Chen, X and Wang, K and Chen, X},
title = {GZSL-Lite: A Lightweight Generalized Zero-Shot Learning Network for SSVEP-Based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3553204},
pmid = {40111769},
issn = {1558-2531},
abstract = {Generalized zero-shot learning (GZSL) networks offer promising avenues for the development of user-friendly steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs), aiming to alleviate the training burden on users. These networks only require the user to provide training data from partial classes during training, yet they demonstrate the capability to classify all classes during testing. However, these GZSL networks have a large number of trainable parameters, resulting in long training times and difficulty to practicalize. In this study, we proposed a dual-attention structure to construct a lightweight GZSL network, termed GZSL-Lite. We first embedded the input training data-constructed class templates, manually constructed sine templates, and electroencephalogram (EEG) signals using convolution-based networks. The embedding part uses the same network weights to embed the features across different stimulus frequencies while reducing the depth of the network. After embedding, two branches of the dual-attention use class and sine templates to guide the feature extraction of the EEG signal with the attention mechanism, respectively. Compared to the networks that extract all features equally, dual-attention focuses only on EEG features relative to templates, which helps classification with fewer parameters. Finally, we used depthwise convolutional blocks to output classification results. Experimental evaluations conducted on two publicly available datasets demonstrate the efficacy of the proposed network. Comparative analysis reveals a remarkable reduction in trainable parameters to less than 1% of the SOTA counterpart, concurrently showing significant performance improvement. The code is available for reproducibility at https://github.com/xtwong111/GZSL-Lite-for-SSVEP-Based-BCIs.},
}
@article {pmid40111392,
year = {2025},
author = {Tang, P and Jing, P and Luo, Z and Liu, K and Tan, W and Yao, Q and Qiu, Z and Liu, Y and Dou, Q and Yan, X},
title = {Modulating Ionic Hysteresis to Selective Interaction Mechanism toward Transition from Supercapacitor-Memristor to Supercapacitor-Diode.},
journal = {Nano letters},
volume = {25},
number = {13},
pages = {5415-5424},
doi = {10.1021/acs.nanolett.5c00596},
pmid = {40111392},
issn = {1530-6992},
abstract = {The emerging ion-confined transport supercapacitors, including supercapacitor-diodes (CAPodes) and supercapacitor-memristors (CAPistors), offer potential for neuromorphic computing, brain-computer interface, signal propagation, and logic operations. This study reports a novel transition from CAPistor to CAPode via electrochemical cycling of a ZIF-7 electrode. X-ray absorption fine structure (XAFS) and electrochemical analyses reveal a shift from "ionic hysteresis" to "ionic selective interaction" in an alkaline electrolyte, elucidating the evolution of ionic devices. The CAPodes exhibit high rectification ratios, long cycling stability, and effective current blocking in reverse bias. Additionally, they are demonstrated in ionic logic circuits ("AND" and "OR" gates), with comparisons to traditional electronic diodes. This work advances the development of functional supercapacitors and iontronic devices for future capacitive computing architectures.},
}
@article {pmid40110612,
year = {2024},
author = {Jain, S and Srivastava, R},
title = {Multi-modality NDE fusion using encoder-decoder networks for identify multiple neurological disorders from EEG signals.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {9287329241291334},
doi = {10.1177/09287329241291334},
pmid = {40110612},
issn = {1878-7401},
abstract = {BackgroundThe complexity and diversity of brain activity patterns make it difficult to accurately diagnose neurological disorders such epilepsy, Parkinson's disease, schizophrenia, stroke, and Alzheimer's disease. Integrated and effective analysis of multiple data sources is often beyond the scope of traditional diagnostic procedures. With the use of multi-modal data, recent developments in neural network approaches present encouraging opportunities for raising diagnostic accuracy.ObjectivesA novel approach has been proposed toward the integration of different Nondestructive Evaluation data with EEG signals for improving the diagnosis of neurological disorders such as stroke, epilepsy, Parkinson's disease, and schizophrenia, by leveraging advanced neural network techniques in order to improve the identification and correlation of shared latent features across heterogeneous NDE datasets.MethodsWe determined the 2D scalogram images using a specific encoder-decoder neural network after transforming the EEG signals using wavelet signal processing. Several NDE data types can be easily integrated for thorough analysis due to this network's ability to extract and correlate important aspects from each form of data. Aiming to uncover common patterns indicating of neurological disorders, the technique was evaluated on datasets containing EEG signals and corresponding NDE data.ResultsOur method demonstrated a significant improvement in diagnostic accuracy and efficiency. The encoder-decoder network effectively identified shared latent features across the heterogeneous NDE datasets, leading to more precise and reliable diagnoses. The fusion of multi-modality NDE data with EEG signals provided a robust framework for the automatic identification of multiple neurological disorders.ConclusionsThis innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.},
}
@article {pmid40110537,
year = {2025},
author = {Zhou, Y and Xu, X and Zhang, D},
title = {Cognitive load recognition in simulated flight missions: an EEG study.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1542774},
pmid = {40110537},
issn = {1662-5161},
abstract = {Cognitive load recognition (CLR) utilizing EEG signals has experienced significant advancement in recent years. However, current load-eliciting paradigms often rely on simplistic cognitive tasks such as arithmetic calculations, failing to adequately replicate real-world scenarios and lacking applicability. This study explores simulated flight missions over time to better reflect operational environments and investigate temporal dynamics of multiple load states. Thirty-six participants were recruited to perform simulated flight tasks with varying cognitive load levels of low, medium, and high. Throughout the experiments, we collected EEG load data from three sessions, pre- and post-task resting-state EEG data, subjective ratings, and objective performance metrics. Then, we employed several deep convolutional neural network (CNN) models, utilizing raw EEG data as model input, to assess cognitive load levels with six classification designs. Key findings from the study include (1) a notable distinction between resting-state and post-fatigue EEG data; (2) superior performance of shallow CNN models compared to more complex ones; and (3) temporal dynamics decline in CLR as the missions progressed. This paper establishes a potential foundation for assessing cognitive states during intricate simulated tasks across different individuals.},
}
@article {pmid40109751,
year = {2025},
author = {Wang, X and Lin, C and Wang, X},
title = {Psychedelics and Pro-Social Behaviors: A Perspective on Autism Spectrum Disorders.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {903-906},
pmid = {40109751},
issn = {2575-9108},
abstract = {Autism Spectrum Disorders (ASD) are complex neurodevelopmental conditions characterized by deficits in social interaction, communication, and repetitive behaviors. This viewpoint explores the potential mechanisms through which psychedelics such as lysergic acid diethylamide (LSD), psilocybin, and 3,4-methylenedioxymethamphetamine (MDMA) may positively influence pro-social behaviors, focusing on their implications for individuals with ASD.},
}
@article {pmid40109747,
year = {2025},
author = {Li, H and Wang, H and Wang, X},
title = {Psychedelics and the Autonomic Nervous System: A Perspective on Their Interplay and Therapeutic Potential.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {899-902},
pmid = {40109747},
issn = {2575-9108},
abstract = {Psychedelics, known for their therapeutic potential in psychiatric disorders, interact with the autonomic nervous system in ways that are not well understood. This viewpoint examines the complex relationships between psychedelics and autonomic functions, focusing on sympathetic and parasympathetic modulation. We propose a research framework to elucidate how these interactions influence cardiovascular health and contribute to therapeutic outcomes.},
}
@article {pmid40109736,
year = {2025},
author = {Li, H and Wang, X},
title = {Exploring End-of-Life Experiences and Consciousness through the Lens of Psychedelics.},
journal = {ACS pharmacology &amp; translational science},
volume = {8},
number = {3},
pages = {907-909},
pmid = {40109736},
issn = {2575-9108},
abstract = {Exploring dying through the lens of psychedelic experiences offers transformative perspectives on the end-of-life process, potentially alleviating existential distress and enriching the quality of life for those nearing death. Their potential in palliative care, therapy, and spiritual exploration is increasingly recognized, promising to revolutionize end-of-life understanding and care.},
}
@article {pmid40109381,
year = {2025},
author = {Lloyd, S and Bonventre, C},
title = {Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future.},
journal = {Science, technology &amp; human values},
volume = {50},
number = {2},
pages = {336-363},
pmid = {40109381},
issn = {0162-2439},
abstract = {In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the "deafness of history and the present," as well as experiences of brain-computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.},
}
@article {pmid40109135,
year = {2025},
author = {Sheng, T and Li, J and Zheng, L and Du, N and Xie, M and Wang, X and Gao, X and Huang, M and Wen, S and Liu, W and Guo, Y and Yao, Y and Shao, X and Liu, L and Xu, J and Wang, Y and Zhang, M},
title = {An Expandable Brain-Machine Interface Enabled by Origami Materials and Structures for Tracking Epileptic Traveling Waves.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2404947},
doi = {10.1002/adhm.202404947},
pmid = {40109135},
issn = {2192-2659},
support = {2023YFB4705500//National Key Research and Development Program of China/ ; 62350710211//National Natural Science Foundation of China/ ; 1S24080//Beijing Natural Science Foundation/ ; },
abstract = {Tracking neural activities across multiple brain regions remains a daunting challenge due to the non-negligible skull injuries during implantations of large-area electrocorticography (ECoG) grids and the limited spatial accessibility of conventional rectilinear depth probes. Here, a multiregion Brain-machine Interface (BMI) is proposed comprising an expandable bio-inspired origami ECoG electrode covering cortical areas larger than the cranial window, and an expandable origami depth probe capable of reaching multiple deep brain regions beyond a single implantation axis. Using the proposed BMI, it is observed that, in rat models of focal seizures, cortical multiband epileptiform activities mainly manifest as expanding traveling waves outward from a cortical source.},
}
@article {pmid40108144,
year = {2025},
author = {Qi, Z and Liu, H and Jin, F and Wang, Y and Lu, X and Liu, L and Yang, Z and Fan, L and Song, M and Zuo, N and Jiang, T},
title = {A wearable repetitive transcranial magnetic stimulation device.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2731},
pmid = {40108144},
issn = {2041-1723},
support = {2021ZD0200200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82202253//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31620103905//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/instrumentation/methods ; *Wearable Electronic Devices ; Adult ; Male ; Female ; Walking/physiology ; Equipment Design ; Young Adult ; Motor Cortex/physiology ; },
abstract = {Repetitive transcranial magnetic stimulation (rTMS) is widely used to treat various neuropsychiatric disorders and to explore the brain, but its considerable power consumption and large size limit its potential for broader utility, such as applications in free behaviors and in home and community settings. We addressed this challenge through lightweight magnetic core coil designs and high-power-density, high-voltage pulse driving techniques and successfully developed a battery-powered wearable rTMS device. The combined weight of the stimulator and coil is only 3 kg. The power consumption was reduced to 10% of commercial rTMS devices even though the stimulus intensity and repetition frequency are comparable. We demonstrated the effectiveness of this device during free walking, showing that neural activity associated with the legs can enhance the cortex excitability associated with the arms. This advancement allows for high-frequency rTMS modulation during free behaviors and enables convenient home and community rTMS treatments.},
}
@article {pmid40107265,
year = {2025},
author = {Jiang, R and Tian, Y and Yuan, X and Guo, F},
title = {Regulation of pre-dawn arousal in Drosophila by a pair of trissinergic descending neurons of the visual and circadian networks.},
journal = {Current biology : CB},
volume = {35},
number = {8},
pages = {1750-1764.e3},
doi = {10.1016/j.cub.2025.02.056},
pmid = {40107265},
issn = {1879-0445},
mesh = {Animals ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Drosophila melanogaster/physiology ; *Neurons/physiology ; *Arousal/physiology ; },
abstract = {Circadian neurons form a complex neural network that generates circadian oscillations. How the circadian neural network transmits circadian signals to other brain regions, thereby regulating the activity patterns in fruit flies, is not well known. Using the FlyWire database, we identified a cluster of descending neurons, DNp27, which is densely connected with key circadian neurons and the visual circuit, projecting extensively across the brain. DNp27 receives excitatory inputs from the circadian neurons DN3s at night and photo-inhibitory signals predominantly during the day, resulting in calcium oscillations that peak in the early morning and dip at dusk. Experimental manipulation of DNp27 revealed its role in activity regulation: artificial activation of DNp27 decreased flies' activity, while ablation or silencing led to an advance in the morning anticipatory peak. Similar alterations in the morning peak were observed following pan-neuronal knockdown of either Trissin or TrissinR, suggesting the involvement of this neuropeptide signaling pathway in DNp27 function. Moreover, neural circuitry and connectivity analyses indicate that DNp27 may regulate circadian neurons via extra-clock electrical oscillators (xCEOs). Lastly, we found that DNp27 modulates arousal thresholds by inhibiting light-responsive activity in the central brain, thereby promoting sleep stability, particularly in the pre-dawn period. Together, these findings suggest that DNp27 plays a crucial role in maintaining stable sleep patterns.},
}
@article {pmid40106898,
year = {2025},
author = {Hobbs, TG and Greenspon, CM and Verbaarschot, C and Valle, G and Hughes, CL and Boninger, ML and Bensmaia, SJ and Gaunt, RA},
title = {Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adc2d4},
pmid = {40106898},
issn = {1741-2552},
abstract = {OBJECTIVE: Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts.<br><br>APPROACH: In this study, we investigated whether biomimetic stimulation patterns - ICMS patterns that reproduce essential features of natural neural activity - increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation.<br><br>MAIN RESULTS: We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation.<br><br>SIGNIFICANCE: We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.},
}
@article {pmid40106847,
year = {2025},
author = {Wen, B and Su, L and Zhang, Y and Wang, A and Zhao, H and Wu, J and Gan, Z and Zhang, L and Kang, X},
title = {Fabrication of micro-wire stent electrode as a minimally invasive endovascular neural interface for vascular electrocorticography using laser ablation method.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {3},
pages = {},
doi = {10.1088/2057-1976/adc266},
pmid = {40106847},
issn = {2057-1976},
mesh = {Animals ; Rats ; *Stents ; *Laser Therapy/methods/instrumentation ; *Electrocorticography/instrumentation/methods ; Equipment Design ; Endovascular Procedures/instrumentation/methods ; Rats, Sprague-Dawley ; Electrodes ; Male ; Electrodes, Implanted ; },
abstract = {Objective. Minimally invasive endovascular stent electrode is a currently emerging technology in neural engineering with minimal damage to the neural tissue. However, the typical stent electrode still requires resistive welding and is relatively large, limiting its application mainly on the large animal or thick vessels. In this study, we investigated the feasibility of laser ablation of micro-wire stent electrode with a diameter as small as 25μmand verified it in the superior sagittal sinus (SSS) of a rat.Approach. We have developed a laser ablation technology to expose the electrode sites of the micro-wire on both sides without damaging the wire itself. During laser ablation, we applied a new method to fix and realign the micro-wires. The micro-wire stent electrode was fabricated by carefully assemble the micro-wire and stent. We tested the electrochemical performances of the electrodes as a neural interface. Finally, we deployed the stent electrode in a rat to verified the feasibility.Main result. Based on the proposed micro-wire stent electrode, we demonstrated that the stent electrode could be successfully deployed in a rat. With the benefit of the smaller design and laser fabrication technology, it can be fitted into a catheter with an inner diameter of 0.6mm. The vascular electrocorticography can be detected during the acute recording, making it promising in the application of small animals and thin vessels.Significance. The method we proposed combines the advantages of endovascular micro-wire electrode and stent, helping make the electrodes smaller. This study provided an alternative method for deploying micro-wire electrodes into thinner vessels as an endovascular neural interface.},
}
@article {pmid40106436,
year = {2025},
author = {Wang, Y and Chen, Z and Liang, K and Wang, W and Hu, Z and Mao, Y and Liang, X and Jiang, L and Liu, Z and Ma, Z},
title = {AGO2 mediates immunotherapy failure via suppressing tumor IFN-gamma response-dependent CD8[+] T cell immunity.},
journal = {Cell reports},
volume = {44},
number = {4},
pages = {115445},
doi = {10.1016/j.celrep.2025.115445},
pmid = {40106436},
issn = {2211-1247},
abstract = {Interferon-gamma (IFN-γ), a cytokine essential for activating cellular immune responses, plays a crucial role in cancer immunosurveillance and the clinical success of immune checkpoint blockade therapy. In this study, we show that Argonaute 2 (AGO2), a key mediator in small RNA-guided gene regulation, inversely correlates with tumor responsiveness to IFN-γ and the efficacy of immunotherapy. Mechanistically, IFN-γ upregulates miR-1246 expression in tumor cells, enhancing its interaction with AGO2. This miR-1246-AGO2 complex disrupts IFN-γ-mediated signal transducer and activator of transcription 1 (STAT1) phosphorylation by stabilizing protein tyrosine phosphatase non-receptor 6 (PTPN6) mRNA, thereby suppressing the expression of downstream C-X-C motif chemokine ligands (CXCLs), IFN-stimulated genes (ISGs), and human leukocyte antigen (HLA) molecules, which collectively contribute to tumor immune evasion. In preclinical cancer models, inhibiting AGO2 with BCI-137 or targeting miR-1246 with its antagomir re-sensitizes tumor cells to IFN-γ, leading to the enhanced recruitment, activation, and cytotoxicity of CD8[+] T cells and ultimately improving immunotherapy efficacy.},
}
@article {pmid40104767,
year = {2025},
author = {Memmott, T and Klee, D and Smedemark-Margulies, N and Oken, B},
title = {Artifact filtering application to increase online parity in a communication BCI: progress toward use in daily-life.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1551214},
pmid = {40104767},
issn = {1662-5161},
abstract = {A significant challenge in developing reliable Brain-Computer Interfaces (BCIs) is the presence of artifacts in the acquired brain signals. These artifacts may lead to erroneous interpretations, poor fitting of models, and subsequent reduced online performance. Furthermore, BCIs in a home or hospital setting are more susceptible to environmental noise. Artifact handling procedures aim to reduce signal interference by filtering, reconstructing, and/or eliminating unwanted signal contaminants. While straightforward conceptually and largely undisputed as essential, suitable artifact handling application in BCI systems remains unsettled and may reduce performance in some cases. A potential confound that remains unexplored in the majority of BCI studies using these procedures is the lack of parity with online usage (e.g., online parity). This manuscript compares classification performance between frequently used offline digital filtering, using the whole dataset, and an online digital filtering approach where the segmented data epochs that would be used during closed-loop control are filtered instead. In a sample of healthy adults (n = 30) enrolled in a BCI pilot study to integrate new communication interfaces, there were significant benefits to model performance when filtering with online parity. While online simulations indicated similar performance across conditions in this study, there appears to be no drawback to the approach with greater online parity.},
}
@article {pmid40103837,
year = {2024},
author = {Yektaeian Vaziri, A and Makkiabadi, B},
title = {Accelerated algorithms for source orientation detection and spatiotemporal LCMV beamforming in EEG source localization.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1505017},
pmid = {40103837},
issn = {1662-4548},
abstract = {This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional methods. Accurate EEG source localization is crucial for applications in cognitive neuroscience, neurorehabilitation, and brain-computer interfaces (BCIs). To make significant progress toward precise source orientation detection and improved signal reconstruction, we introduce the Accelerated Linear Constrained Minimum Variance (ALCMV) beamforming toolbox and the Accelerated Brain Source Orientation Detection (AORI) toolbox. The ALCMV algorithm speeds up EEG source reconstruction by utilizing recursive covariance matrix calculations, while AORI simplifies source orientation detection from three dimensions to one, reducing computational load by 66% compared to conventional methods. Using both simulated and real EEG data, we demonstrate that these algorithms maintain high accuracy, with orientation errors below 0.2% and signal reconstruction accuracy within 2%. These findings suggest that the proposed toolboxes represent a substantial advancement in the efficiency and speed of EEG source localization, making them well-suited for real-time neurotechnological applications.},
}
@article {pmid40102969,
year = {2025},
author = {Jeong, H and Song, M and Jang, SH and Kim, J},
title = {Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {61},
pmid = {40102969},
issn = {1743-0003},
support = {2022 R1 A2 C1008150//National Research Foundation of Korea/ ; NRCTR-EX23004//Translational Research Program for Rehabilitation Robots/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke Rehabilitation/methods ; Female ; Adult ; *Electroencephalography/methods ; *Learning/physiology ; *Imagination/physiology ; Middle Aged ; Neuronal Plasticity/physiology ; Young Adult ; Motor Cortex/physiology ; Spectroscopy, Near-Infrared/methods ; Neurological Rehabilitation/methods ; },
abstract = {BACKGROUND: Motor imagery-based brain-computer interface (MI-BCI) is a promising solution for neurorehabilitation. Many studies proposed that reducing false positive (FP) feedback is crucial for inducing neural plasticity by BCI technology. However, the effect of FP feedback on cortical plasticity induction during MI-BCI training is yet to be investigated.<br><br>OBJECTIVE: This study aims to validate the hypothesis that FP feedback affects the cortical plasticity of the user's MI during MI-BCI training by first comparing two different asynchronous MI-BCI paradigms (with and without FP feedback), and then comparing its effectiveness with that of conventional motor learning methods (passive and active training).<br><br>METHODS: Twelve healthy volunteers and four patients with stroke participated in the study. We implemented two electroencephalogram-driven asynchronous MI-BCI systems with different feedback conditions. The feedback was provided by a hand exoskeleton robot performing hand open/close task. We assessed the hemodynamic responses in two different feedback conditions and compared them with two conventional motor learning methods using functional near-infrared spectroscopy with an event-related design. The cortical effects of FP feedback were analyzed in different paradigms, as well as in the same paradigm via statistical analysis.<br><br>RESULTS: The MI-BCI without FP feedback paradigm induced higher cortical activation in MI, focusing on the contralateral motor area, compared to the paradigm with FP feedback. Additionally, within the same paradigm providing FP feedback, the task period immediately following FP feedback elicited a lower hemodynamic response in the channel located over the contralateral motor area compared to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.021 for healthy people; p&#x2009;=&#x2009;0.079 for people with stroke). In contrast, task trials where there was no FP feedback just before showed a higher hemodynamic response, similar to the MI-BCI paradigm without FP feedback (p&#x2009;=&#x2009;0.099 for healthy people, p&#x2009;=&#x2009;0.084 for people with stroke).<br><br>CONCLUSIONS: FP feedback reduced cortical activation for the users during MI-BCI training, suggesting a potential negative effect on cortical plasticity. Therefore, minimizing FP feedback may enhance the effectiveness of rehabilitative MI-BCI training by promoting stronger cortical activation and plasticity, particularly in the contralateral motor area.},
}
@article {pmid40102931,
year = {2025},
author = {Wu, X and Hu, Z and Yue, H and Wang, C and Li, J and Yang, Y and Luan, Z and Wang, L and Shen, Y and Gu, Y},
title = {Enhancing myelinogenesis through LIN28A rescues impaired cognition in PWMI mice.},
journal = {Stem cell research &amp; therapy},
volume = {16},
number = {1},
pages = {141},
pmid = {40102931},
issn = {1757-6512},
support = {2017YFA0104200//National Key Research and Development Program of China/ ; 32071021//National Natural Science Foundation of China/ ; 32225021//China National Funds for Distinguished Young Scientists/ ; },
mesh = {Animals ; *RNA-Binding Proteins/metabolism/genetics ; Mice ; *Myelin Sheath/metabolism ; Cell Differentiation ; White Matter/metabolism/pathology ; Oligodendrocyte Precursor Cells/metabolism ; Mice, Knockout ; Cognition ; Oligodendroglia/metabolism ; Disease Models, Animal ; },
abstract = {BACKGROUND: In premature newborn infants, preterm white matter injury (PWMI) causes motor and cognitive disabilities. Accumulating evidence suggests that PWMI may result from defected differentiation of oligodendrocyte precursor cells (OPCs) and impaired maturation of oligodendrocytes. However, the underlying mechanisms remain unclear.<br><br>METHODS: Using RNAscope, we analyzed the expression level of RNA-binding protein LIN28A in individual OPCs. Knockout of one or both alleles of Lin28a in OPCs was achieved by administrating tamoxifen to NG2[CreER]::Ai14::Lin28a[flox/+] or NG2[CreER]::Ai14::Lin28a[flox/flox] mice. Lentivirus expressing FLEX-Lin28a was used in NG2[CreER] mice to overexpress LIN28A in OPCs. A series of behavioral tests were performed to assess the cognitive functions of mice. Two-tailed unpaired t-tests was carried out for statistical analysis between groups.<br><br>RESULTS: We found that the expression of Lin28a was decreased in OPCs in a PWMI mouse model. Knockout of one or both alleles of Lin28a in OPCs postnatally resulted in reduced OPC differentiation, decreased myelinogenesis and impaired cognitive functions. Supplementing LIN28A in OPCs postnatally was able to promote OPC differentiation and enhance myelinogenesis, thus rescuing the cognitive functions in PWMI mice.<br><br>CONCLUSION: Our study reveals that LIN28A is critical in regulating postnatal myelinogenesis. Overexpression of LIN28A in OPCs rescues cognitive deficits in PWMI mice by promoting myelinogenesis, thus providing a potential strategy for the treatment of PWMI.},
}
@article {pmid40101709,
year = {2025},
author = {Chen, Q and Zhu, L and Zhang, S and Qiao, S and Ding, ZJ and Zheng, SJ and Guo, J and Su, N},
title = {Structures and mechanisms of the ABC transporter ABCB1 from Arabidopsis thaliana.},
journal = {Structure (London, England : 1993)},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.str.2025.02.008},
pmid = {40101709},
issn = {1878-4186},
abstract = {The Arabidopsis thaliana auxin transporter ABCB1 plays a fundamental role in the regulation of plant growth and development. While its homolog ABCB19 was previously shown to transport brassinosteroids (BR), another class of essential hormones, the ability of ABCB1 to mediate BR transport has remained unexplored. In this study we show that ABCB1 also transports brassinosteroids with an in vitro brassinolide (BL) transport assay. Using single-particle cryo-electron microscopy, we determined ABCB1 structures in multiple inward-facing conformations in the apo state, ANP-bound state, BL-bound state, and the both BL- and ANP-bound state. BL binds to the large cavity of two transmembrane domains, inducing a slight conformational change. Additionally, we obtained the structure of ABCB1 in an outward-facing conformation. By comparing these different conformations, we elucidated the possible mechanism of hormone transport by ABCB1. These high-resolution structures help us to understand the structural basis for hormone recognition and transport mechanisms of ABCB1.},
}
@article {pmid40101581,
year = {2025},
author = {Abdelaty, MM and Rushdi, MA and Rasmy, ME and Annaby, MH},
title = {Graph vertex and spectral features for EEG-based motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {189},
number = {},
pages = {109944},
doi = {10.1016/j.compbiomed.2025.109944},
pmid = {40101581},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Support Vector Machine ; Algorithms ; },
abstract = {Motor imagery (MI) patterns play a vital role in brain-computer interface (BCI) systems, enabling control of external devices without relying on peripheral nerves or muscles. These patterns are typically classified by analyzing the associated electroencephalogram (EEG) signals. In this work, we introduce a novel MI classification approach based on multilevel graph-theoretic modeling of multichannel EEG signals. Multivariate autoregressive modeling and coherence analysis are firstly employed to construct directed graph signals to represent the relationships among EEG channels and capture the complex correlations inherent in MI patterns. Spatial graph vertex features are thus extracted as well as graph Fourier transform coefficients. Moreover, multilevel generalizations of vertex-domain features are thus defined where edges of graph signals are pruned according to different thresholds, vertex features are extracted for each threshold level, and then all features are combined into a multilevel hierarchical graph descriptor. These graph-theoretic descriptors could be fused with different variants of common spatial patterns for improved discriminability on MI classification tasks. Different feature combinations are used to train k-nearest neighbor classifiers, support vector machines, and random forests for MI pattern classification. The proposed method demonstrates competitive performance compared to the FWCSP and SCSP methods on Dataset 2a of the BCI Competition IV, as well as robust results on Dataset 1 from the same competition. Overall, the findings highlight the potential of multilevel spatial and spectral graph features in leveraging the correlation among EEG channels towards enhanced MI classification performance.},
}
@article {pmid40101362,
year = {2025},
author = {Arpaia, P and Esposito, A and Galasso, E and Galdieri, F and Natalizio, A},
title = {A wearable brain-computer interface to play an endless runner game by self-paced motor imagery.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adc205},
pmid = {40101362},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Wearable Electronic Devices ; Female ; Adult ; Young Adult ; Electroencephalography/methods/instrumentation ; Video Games ; Running/physiology/psychology ; Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.A wearable brain-computer interface is proposed and validated experimentally in relation to the real-time control of an endless runner game by self-paced motor imagery(MI).Approach.Electroencephalographic signals were recorded via eight wet electrodes. The processing pipeline involved a filter-bank common spatial pattern approach and the combination of three binary classifiers exploiting linear discriminant analysis. This enabled the discrimination between imagining left-hand, right-hand, and no movement. Each mental task corresponded to an avatar horizontal motion within the game. Twenty-three healthy subjects participated to the experiments and their data are made publicly available. A custom metric was proposed to assess avatar control performance during the gaming phase. The game consisted of two levels, and after each, participants completed a questionnaire to self-assess their engagement and gaming experience.Main results.The mean classification accuracies resulted 73%, 73%, and 67% for left-rest, right-rest, and left-right discrimination, respectively. In the gaming phase, subjects with higher accuracies for left-rest and right-rest pair exhibited higher performance in terms of the custom metric. Correlation of the offline and real-time performance was investigated. The left-right MI did not correlate to the gaming phase performance due to the poor mean accuracy of the calibration. Finally, the engagement questionnaires revealed that level 1 and level 2 were not perceived as frustrating, despite the increasing difficulty.Significance.The work contributes to the development of wearable and self-paced interfaces for real-time control. These enhance user experience by guaranteeing a more natural interaction with respect to synchronous neural interfaces. Moving beyond benchmark datasets, the work paves the way to future applications on mobile devices for everyday use.},
}
@article {pmid40101262,
year = {2025},
author = {Choubey, C and Dhanalakshmi, M and Karunakaran, S and Londhe, GV and Vimal, V and Kirubakaran, MK},
title = {Optimizing Bioimaging: Quantum Computing-Inspired Bald Eagle Search Optimization for Motor Imaging EEG Feature Selection.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {15500594251325273},
doi = {10.1177/15500594251325273},
pmid = {40101262},
issn = {2169-5202},
abstract = {One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.},
}
@article {pmid40100843,
year = {2025},
author = {Russo, JS and Mahoney, T and Kokorin, K and Reynolds, A and Lin, CS and John, SE and Grayden, DB},
title = {Towards developing brain-computer interfaces for people with Multiple Sclerosis.},
journal = {PloS one},
volume = {20},
number = {3},
pages = {e0319811},
pmid = {40100843},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/psychology ; Female ; Male ; Adult ; Middle Aged ; Surveys and Questionnaires ; Electroencephalography ; },
abstract = {BACKGROUND: Multiple Sclerosis (MS) can be a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. Present BCI designs have also overlooked the unique pathological changes associated with MS and have not considered needs of users within their home environments. To progress this research area effectively and efficiently, we aimed to evaluate user needs and assess the feasibility and user-centric requirements of a BCI for people with MS. We hypothesised that (i) people with MS would be interested in adopting BCI technology and (ii) those with reduced independence would prefer a higher-performing invasive BCI.<br><br>METHODS: We conducted an online survey of people with MS to describe user preferences and establish the initial steps of user-centred design. The survey aimed to understand their interest in BCI applications, bionic applications, device preferences, and development considerations and related these to symptoms and assistance needs.<br><br>RESULTS: We demonstrated widespread interest for BCI applications in all stages of MS, with a preference for a non-invasive (n = 12) or minimally invasive (n = 15) BCI over carer assistance (n = 6). Descriptive analysis indicated that level of independence did not influence preference towards the higher performing but highly invasive BCI.<br><br>CONCLUSIONS: The needs of end users reported in this study are crucial for efficient development of BCI systems that can be effectively translated into the home environment. Considering the potential to enhance independence and quality of life for people living with MS, the results emphasise the importance of user-centred design for future advancement of BCIs that account for the unique pathological changes associated with MS.},
}
@article {pmid40100695,
year = {2025},
author = {Guo, Z and Xu, L and Tan, W and Chen, F},
title = {Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1180-1190},
doi = {10.1109/TNSRE.2025.3552606},
pmid = {40100695},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; Male ; *Imagination/physiology ; Female ; Adult ; *Speech/physiology ; Young Adult ; Algorithms ; Healthy Volunteers ; Stroke Rehabilitation/methods ; },
abstract = {Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the "yes" and "no" speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.},
}
@article {pmid40100694,
year = {2025},
author = {Yan, W and Lin, Y and Chen, YF and Wang, Y and Wang, J and Zhang, M},
title = {Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1156-1168},
doi = {10.1109/TNSRE.2025.3551753},
pmid = {40100694},
issn = {1558-0210},
mesh = {*Neuronal Plasticity/physiology ; Humans ; *Stroke Rehabilitation/methods ; *Recovery of Function ; *Brain-Computer Interfaces ; Stroke/physiopathology ; Robotics ; Electric Stimulation Therapy/methods ; Postural Balance/physiology ; Transcranial Magnetic Stimulation/methods ; },
abstract = {Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.},
}
@article {pmid40100553,
year = {2025},
author = {Zhou, H and Yan, ZN and Gao, WH and Lv, XX and Luo, R and Hoellwarth, JS and He, L and Yang, JM and Zhang, JY and Wang, HL and Xie, Y and Chen, XL and Xue, MD and Fang, Y and Duan, YY and Li, RY and Wang, XD and Wang, RL and Xie, M and Huang, L and Liu, PR and Ye, ZW},
title = {Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain-Computer Interface Based on Mixed Reality.},
journal = {Current medical science},
volume = {},
number = {},
pages = {},
pmid = {40100553},
issn = {2523-899X},
support = {No.82172524//the National Natural Science Foundation of China/ ; No.81974355//the National Natural Science Foundation of China/ ; No.2020021105012440//National Innovation Platform Development Program/ ; 2021BEA161//Major Program of Hubei Province/ ; JD2023BAA005//Major Key Project of Hubei Province/ ; No.2024XHYN047//Wuhan Union Hospital Free Innovation Preliminary Research Fund/ ; },
abstract = {OBJECTIVE: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.<br><br>METHODS: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0&#xa0;T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR visualization.<br><br>RESULTS: This study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical model.<br><br>CONCLUSIONS: This multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.},
}
@article {pmid40100543,
year = {2025},
author = {Gao, J and Tang, H and Wang, Z and Li, Y and Luo, N and Song, M and Xie, S and Shi, W and Yan, H and Lu, L and Yan, J and Li, P and Song, Y and Chen, J and Chen, Y and Wang, H and Liu, W and Li, Z and Guo, H and Wan, P and Lv, L and Yang, Y and Wang, H and Zhang, H and Wu, H and Ning, Y and Zhang, D and Jiang, T},
title = {Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40100543},
issn = {1995-8218},
abstract = {Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.},
}
@article {pmid40099835,
year = {2025},
author = {Jin, J and Xiao, Q and Liu, Y and Xu, T and Shen, Q},
title = {Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf058},
pmid = {40099835},
issn = {1460-2199},
support = {2022KFKT005//Open Research Fund/ ; 22dz2261100//Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; 23NDJC124YB//Zhejiang Province Philosophy and Social Science Planning Project/ ; 41005067//Fundamental Research Funds for the Central Universities/ ; 72371165//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; *Gambling/psychology/physiopathology ; *Electroencephalography ; *Risk-Taking ; *Evoked Potentials/physiology ; *Decision Making/physiology ; Reproducibility of Results ; Adult ; *Reward ; Event-Related Potentials, P300/physiology ; Brain/physiology ; },
abstract = {The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.},
}
@article {pmid40096442,
year = {2025},
author = {Shi, X and Zhai, X and Wang, R and Le, Y and Fu, S and Liu, N},
title = {Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096442},
issn = {1424-8220},
abstract = {With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs' distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations.},
}
@article {pmid40096214,
year = {2025},
author = {Bouchane, M and Guo, W and Yang, S},
title = {Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096214},
issn = {1424-8220},
support = {236Z0105G//Hebei Central Leading Local Science and Technology Development Foundation/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction from raw EEG signals is essential to improve classification accuracy while minimizing reliance on extensive preprocessing. In this study, we introduce new hybrid architectures to enhance MI classification using data augmentation and a limited number of EEG channels. The first model combines a shallow convolutional neural network and a gated recurrent unit (CNN-GRU), while the second incorporates a convolutional neural network with a bidirectional gated recurrent unit (CNN-Bi-GRU). Evaluated using the publicly available PhysioNet dataset, the CNN-GRU classifier achieved peak mean accuracy rates of 99.71%, 99.73%, 99.61%, and 99.86% for tasks involving left fist (LF), right fist (RF), both fists (LRF), and both feet (BF), respectively. The experimental results provide compelling evidence that our proposed models outperform current state-of-the-art methods, underscoring their efficiency on small-scale EEG datasets. The CNN-GRU and CNN-Bi-GRU architectures exhibit superior predictive reliability, offering a faster, cost-effective solution for user-adaptable MI-BCI applications.},
}
@article {pmid40096116,
year = {2025},
author = {González-España, JJ and Sánchez-Rodríguez, L and Pacheco-Ramírez, MA and Feng, J and Nedley, K and Chang, SH and Francisco, GE and Contreras-Vidal, JL},
title = {At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096116},
issn = {1424-8220},
support = {1827769//National Science Foundation/ ; 2137255//NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Female ; Middle Aged ; *Neurological Rehabilitation/methods/instrumentation ; *Exoskeleton Device ; Aged ; Stroke/physiopathology ; Adult ; Robotics/methods ; Electroencephalography/methods ; },
abstract = {BACKGROUND: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians.<br><br>METHODS: This paper describes the early findings of the NeuroExo brain-machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users' compliance and system performance.<br><br>RESULTS: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02).<br><br>CONCLUSIONS: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.},
}
@article {pmid40096108,
year = {2025},
author = {Alexopoulou, A and Pergantis, P and Koutsojannis, C and Triantafillou, V and Drigas, A},
title = {Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096108},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Autism Spectrum Disorder/therapy ; Adolescent ; Electroencephalography/methods ; Virtual Reality ; User-Computer Interface ; Cognition/physiology ; },
abstract = {This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD.},
}
@article {pmid40096020,
year = {2025},
author = {Vafaei, E and Hosseini, M},
title = {Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096020},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Seizures/physiopathology/diagnosis ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.},
}
@article {pmid40096019,
year = {2025},
author = {Koo, BH and Siu, HC and Newman, DJ and Roche, ET and Petersen, LG},
title = {Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
pmid = {40096019},
issn = {1424-8220},
mesh = {Humans ; *Electromyography/methods ; *Algorithms ; Male ; Adult ; Movement/physiology ; Neural Networks, Computer ; Muscle, Skeletal/physiology ; Female ; Young Adult ; Deep Learning ; Motion ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader-follower paradigms seen in today's systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application.},
}
@article {pmid40095842,
year = {2025},
author = {Tian, B and Zhang, S and Xue, D and Chen, S and Zhang, Y and Peng, K and Wang, D},
title = {Decoding Intrinsic Fluctuations of Engagement From EEG Signals During Fingertip Motor Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1271-1283},
doi = {10.1109/TNSRE.2025.3551819},
pmid = {40095842},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Fingers/physiology ; Adult ; Female ; Young Adult ; Machine Learning ; Algorithms ; Virtual Reality ; Brain-Computer Interfaces ; Psychomotor Performance/physiology ; Reproducibility of Results ; Motor Skills/physiology ; Signal Processing, Computer-Assisted ; Movement/physiology ; },
abstract = {Maintaining a high mental engagement is critical for motor rehabilitation interventions. Achieving a flow experience, often conceptualized as a highly engaged mental state, is an ideal goal for motor rehabilitation tasks. This paper proposes a virtual reality-based fine fingertip motor task in which the difficulty is maintained to match individual abilities. The aim of this study is to decode the intrinsic fluctuations of flow experience from electroencephalogram (EEG) signals during the execution of a motor task, addressing a gap in flow research that overlooks these fluctuations. To resolve the conflict between sparse self-reported flow sampling and the high dimensionality of neural signals, we use motor behavioral measures to represent flow and label the EEG data, thereby increasing the number of samples. A machine learning-based neural decoder is then established to classify each trial into high-flow or low-flow using spectral power and coherence features extracted from the EEG signals. Cross-validation reveals that the classification accuracy of the neural decoder can exceed 80%. Notably, we highlight the contributions of high-frequency bands in EEG activities to flow decoding. Additionally, EEG feature analyses reveal significant increases in the power of parietal-occipital electrodes and global coherence values, specifically in the alpha and beta bands, during high-flow durations. This study validates the feasibility of decoding the intrinsic flow fluctuations during fine motor task execution with a high accuracy. The methodology and findings in this work lay a foundation for future applications in manipulating flow experience and enhancing engagement levels in motor rehabilitation practice.},
}
@article {pmid40095346,
year = {2025},
author = {Li, J and Shao, N and Zhang, Y and Liu, X and Zhang, H and Tian, L and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2402124},
doi = {10.1002/smtd.202402124},
pmid = {40095346},
issn = {2366-9608},
support = {82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 32050410298//National Natural Science Foundation of China/ ; 32171093//National Natural Science Foundation of China/ ; LZ25H180001//Natural Science Foundation of Zhejiang Province/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024YFA1408900//National Key Research and Development Program of China/ ; 28961//Brain and Behavior Research Foundation/ ; },
abstract = {Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm[-1] red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.},
}
@article {pmid40093990,
year = {2025},
author = {Yang, KC and Xu, Y and Lin, Q and Tang, LL and Zhong, JW and An, HN and Zeng, YQ and Jia, K and Jin, Y and Yu, G and Gao, F and Zhao, L and Tong, LS},
title = {Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.},
journal = {EClinicalMedicine},
volume = {81},
number = {},
pages = {103128},
pmid = {40093990},
issn = {2589-5370},
abstract = {BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).<br><br>METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023&#xa0;at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.<br><br>FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0&#xb7;94, sensitivity 0&#xb7;96, and specificity 0&#xb7;8 for the internal testing subset; AUC 0&#xb7;85, sensitivity 0&#xb7;87, and specificity 0&#xb7;82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0&#xb7;79 vs 0&#xb7;71, sensitivity 0&#xb7;88 vs 0&#xb7;81, specificity 0&#xb7;75 vs 0&#xb7;68, p&#xa0;<&#xa0;0&#xb7;05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.<br><br>INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.<br><br>FUNDING: The work was funded by the National Key R&amp;D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&amp;D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University.},
}
@article {pmid40092993,
year = {2025},
author = {Wen, J and Li, Y and Deng, W and Li, Z},
title = {Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues.},
journal = {Frontiers in immunology},
volume = {16},
number = {},
pages = {1528363},
pmid = {40092993},
issn = {1664-3224},
mesh = {Humans ; *Neoplasms/immunology/therapy/drug therapy ; *Central Nervous System/immunology ; *Tumor Microenvironment/immunology ; Animals ; Cell Communication/immunology ; Signal Transduction ; Neuroimmunomodulation ; Immunotherapy/methods ; Immune Checkpoint Inhibitors/therapeutic use/pharmacology ; },
abstract = {Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.},
}
@article {pmid40092069,
year = {2025},
author = {Sayal, A and Direito, B and Sousa, T and Singer, N and Castelo-Branco, M},
title = {Music in the loop: a systematic review of current neurofeedback methodologies using music.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1515377},
pmid = {40092069},
issn = {1662-4548},
abstract = {Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.},
}
@article {pmid40091139,
year = {2025},
author = {Thamaraimanalan, T and Gopal, D and Vignesh, S and Kishore Kumar, K},
title = {Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9029},
pmid = {40091139},
issn = {2045-2322},
mesh = {Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Brain/physiology ; *Cognition/physiology ; *Principal Component Analysis ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method's robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.},
}
@article {pmid40089693,
year = {2025},
author = {Khamisa, N and Madala, S and Fonka, CB},
title = {Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.},
journal = {BMC nursing},
volume = {24},
number = {1},
pages = {290},
pmid = {40089693},
issn = {1472-6955},
abstract = {BACKGROUND: The wellbeing of health care workers (HCWs) has been an ongoing challenge, especially within low and middle-income countries (LMICs) such as South Africa. Evidence suggesting that HCWs are increasingly stressed and burned out is cause for concern. Nurses in particular have been impacted physically, mentally and psychosocially during the recent COVID-19 pandemic. This may leave a disproportionate consequence, affecting various aspects of their wellbeing, thereby justifying a need for a more holistic investigation of the wellbeing of South African nurses and their coping mechanisms during the peak of the pandemic.<br><br>METHODS: This was a cross-sectional study design. Online self-reported questionnaires were administered in six hospitals, sampled purposively and conveniently from three South African provinces. Using STATA 18.0, the Wilcoxon Ranksum test at 5% alpha compared the wellbeing and coping mechanisms of nursing staff and nursing management during COVID-19's peak. Univariable and multivariable linear regression analyses were performed to determine factors associated with burnout in nurses, at a 95% confidence interval (CI). Validated scales measuring burnout, coping, resilience, as well as mental and physical health were utilised.<br><br>RESULTS: Of 139 participants, 112(97.4%) were females, with 91(82%) and 20(18%) being nursing staff and management respectively. The median age of the participants was 43.3 years (n&#x2009;=&#x2009;112), with a practising duration of 12 years (n&#x2009;=&#x2009;111). There was a significant difference in the burnout score between nursing staff and nursing management (p&#x2009;=&#x2009;0.028). In the univariable linear regression model, burnout was significantly (p&#x2009;<&#x2009;0.05) associated with the Brief COPE Inventory (BCI), Conor-Davidson Resilience Scale (CDRS), Global Mental and Health Scale (GMHS), Global Physical and Health Scale (GPHS) and Hospital Anxiety and Depression Scale (HADS), as well as occupation. In the multivariable linear regression model, burnout was significantly associated with the CDRS [Coeff.=0.7, 95%CI 0.4; 0.9], GMHS [Coeff.=-2.4, 95%CI -3.2; -1.6], GPHS [Coeff.2.1, 95%CI 1.3; 2.9], and HADS [Coeff.=0.7, 95%CI 0.2; 1.2].<br><br>CONCLUSION: Investigating multiple aspects of wellbeing in this study, it's shown that coping and resilience may not be key factors in promoting the wellbeing of South African nurses. However, effective mental health interventions are crucial and should be prioritised to mitigate burnout during future health emergencies. Future studies examining the associations between general health, coping and resilience may help generate further evidence towards holistic interventions aimed at promoting nurses' wellbeing.<br><br>CLINICAL TRIAL NUMBER: Not applicable.},
}
@article {pmid40089573,
year = {2025},
author = {Sivasakthivel, R and Rajagopal, M and Anitha, G and Loganathan, K and Abbas, M and Ksibi, A and Rao, KS},
title = {Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8951},
pmid = {40089573},
issn = {2045-2322},
mesh = {Humans ; *Algorithms ; Female ; *Neurodegenerative Diseases/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Neural Networks, Computer ; Electroencephalography/methods ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.},
}
@article {pmid40087270,
year = {2025},
author = {Eby, J and Beutel, M and Koivisto, D and Achituve, I and Fetaya, E and Zariffa, J},
title = {Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {440},
pmid = {40087270},
issn = {2052-4463},
mesh = {Humans ; *Electromyography ; *Gestures ; Brain-Computer Interfaces ; },
abstract = {Neurotechnological interfaces have the potential to create new forms of human-machine interactions, by allowing devices to interact directly with neurological signals instead of via intermediates such as keystrokes. Surface electromyography (sEMG) has been used extensively in myoelectric control systems, which use bioelectric activity recorded from muscles during contractions to classify actions. This technology has been used primarily for rehabilitation applications. In order to support the development of myoelectric interfaces for a broader range of human-machine interactions, we present an sEMG dataset obtained during key presses in a typing task. This fine-grained classification dataset consists of 16-channel bilateral sEMG recordings and key logs, collected from 19 individuals in two sessions on different days. We report baseline results on intra-session, inter-session and inter-subject evaluations. Our baseline results show that within-session accuracy is relatively high, even with simple learning models. However, the results on between-session and between-participant are much lower, showing that generalizing between sessions and individuals is an open challenge.},
}
@article {pmid40086264,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Derivation of human-derived iPSC line from a male adolescent with first-episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {85},
number = {},
pages = {103694},
doi = {10.1016/j.scr.2025.103694},
pmid = {40086264},
issn = {1876-7753},
abstract = {Schizophrenia is considered to be a neurodevelopmental disorder with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were collected from a male adolescent diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by reprogramming using the factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The generated iPSC line was validated by karyotype analysis and expression of pluripotency markers. These iPSCs were capable of differentiating into derivatives of all three germ layers in vivo.},
}
@article {pmid40085468,
year = {2025},
author = {Chen, J and Yang, H and Xia, Y and Gong, T and Thomas, A and Liu, J and Chen, W and Carlson, T and Zhao, H},
title = {Simultaneous Mental Fatigue and Mental Workload Assessment With Wearable High-Density Diffuse Optical Tomography.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1242-1251},
doi = {10.1109/TNSRE.2025.3551676},
pmid = {40085468},
issn = {1558-0210},
mesh = {Humans ; *Tomography, Optical/methods/instrumentation ; *Mental Fatigue/diagnostic imaging/psychology/diagnosis ; Male ; Adult ; *Workload/psychology ; *Wearable Electronic Devices ; Machine Learning ; Female ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; Young Adult ; Reproducibility of Results ; Support Vector Machine ; Algorithms ; Brain/diagnostic imaging ; Imaging, Three-Dimensional ; },
abstract = {Accurately assessing mental states-such as mental workload and fatigue- is crucial for ensuring the reliability and effectiveness of brain-computer interface (BCI)-based applications. Relying on signals from a limited brain region with low spatial resolution may fail to capture the full scope of relevant information. To address this, high-density diffuse optical tomography (HD-DOT), an emerging form of functional near-infrared spectroscopy (fNIRS) was employed in this study, which provides higher spatial resolution for hemodynamic measurements and enables the reconstruction of 3D brain images. An experiment protocol was designed to investigate both mental workload and fatigue, two critical components of cognitive state that often fluctuate concurrently in real-world scenarios. Machine learning methods were applied for subject-specific classification, achieving 95.14% mean accuracy for fatigue/non-fatigue and 97.93% for four n-back tasks using Random Forest, outperforming Support Vector Machines. These results highlight the transformative potential of HD-DOT in advancing multifaceted cognitive state assessment, paving the way for more precise, adaptable, and powerful BCI applications.},
}
@article {pmid40084138,
year = {2025},
author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T},
title = {Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1491844},
pmid = {40084138},
issn = {1662-4548},
abstract = {OBJECTIVE: Wireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods. However, conventional wireless neural recording devices have not been optimized for ECoG recording, and few devices have been tested on freely moving primates engaged in behavioral tasks within their home cages.<br><br>METHODS: We developed a compact, low-power, 32-channel wireless ECoG headstage specifically designed for neuroscience research. To evaluate its efficacy, we established a behavioral task setup within a home cage environment.<br><br>RESULTS: The developed headstage weighed merely 1.8 g and had compact dimensions of 25 mm &#xd7; 16 mm &#xd7; 4 mm. It was efficiently powered by a 100-mAh battery (weighing 3 g), enabling continuous recording for 8.5 h. The device successfully recorded data from an unrestrained monkey performing a center-out joystick task within its home cage.<br><br>CONCLUSION: The device demonstrated excellent capability for recording ECoG data from freely moving primates in a home cage environment. This versatile device enhances task design freedom, decrease researchers' workload, and enhances data collection efficiency.},
}
@article {pmid40083893,
year = {2025},
author = {Gordienko, Y and Gordienko, N and Taran, V and Rojbi, A and Telenyk, S and Stirenko, S},
title = {Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1521805},
pmid = {40083893},
issn = {1662-5196},
abstract = {Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.},
}
@article {pmid40083152,
year = {2025},
author = {Liu, H and Bai, Y and Zheng, Q and Zhao, R and Guo, M and Zhu, J and Ni, G},
title = {Effects of spatial separation and background noise on brain functional connectivity during auditory selective spatial attention.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf054},
pmid = {40083152},
issn = {1460-2199},
support = {2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; Young Adult ; *Noise ; *Auditory Perception/physiology ; *Magnetic Resonance Imaging ; *Space Perception/physiology ; *Acoustic Stimulation/methods ; Adult ; Neural Pathways/physiology ; Brain Mapping/methods ; Signal-To-Noise Ratio ; },
abstract = {Auditory selective spatial attention (ASSA) plays an important role in "cocktail party" scenes, but the effects of spatial separation between target and distractor sources and background noise on the associated brain responses have not been thoroughly investigated. This study utilized the multilayer time-varying brain network to reveal the effect patterns of different separation degrees and signal-to-noise ratio (SNR) levels on brain functional connectivity during ASSA. Specifically, a multilayer time-varying brain network with six time-windows equally divided by each epoch was constructed to investigate the segregation and integration of brain functional connectivity. The results showed that the inter-layer connectivity strength was consistently lower than the intra-layer connectivity strength for various separation degrees and SNR levels. Moreover, the connectivity strength of the multilayer time-varying brain network increased with decreasing separation degrees and initially increased and subsequently decreased with decreasing SNR levels. The second time-window of the network showed the most significant variation under some conditions and was determined as the core layer. The topology within the core layer was mainly reflected in the connectivity between the frontal and parietal-occipital cortices. In conclusion, these results suggest that spatial separation and background noise significantly modulate brain functional connectivity during ASSA.},
}
@article {pmid40083123,
year = {2025},
author = {Meng, M and Chen, G and Chen, S and Ma, Y and Gao, Y and Luo, Z},
title = {DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/10255842.2025.2476184},
pmid = {40083123},
issn = {1476-8259},
abstract = {Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.},
}
@article {pmid40082907,
year = {2025},
author = {Bonato, P and Reinkensmeyer, D and Manto, M},
title = {Two decades of breakthroughs: charting the future of NeuroEngineering and Rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {59},
pmid = {40082907},
issn = {1743-0003},
mesh = {Humans ; *Neurological Rehabilitation/instrumentation/trends/methods ; *Biomedical Engineering/trends/methods/instrumentation ; Periodicals as Topic/trends ; Brain-Computer Interfaces/trends ; Forecasting ; Robotics/trends/instrumentation/methods ; },
abstract = {The Journal of NeuroEngineering and Rehabilitation (JNER) has become a major actor for the dissemination of knowledge in the scientific community, bridging the gaps between innovative neuroengineering and rehabilitation. Major fields of innovations have emerged these last 25 years, such as machine learning and the ongoing AI revolution, wearable technologies, human machine interfaces, robotics, advanced prosthetics, functional electrical stimulation and various neuromodulation techniques. With the major burden of disorders impacting on the central/peripheral nervous system and the musculoskeletal system both in adults and in children, successful tailored neurorehabilitation has become a major objective for the research and clinical community at a world scale. JNER contributes to this challenging goal, publishing groundbreaking cutting-edge research using the open access model. The multidisciplinary approaches at the crossroads of biomedical engineering, neuroscience, physical medicine and rehabilitation make of the journal a unique growing platform welcoming breakthrough discoveries to reshape the field and restore function.},
}
@article {pmid40082683,
year = {2025},
author = {Verwoert, M and Amigó-Vega, J and Gao, Y and Ottenhoff, MC and Kubben, PL and Herff, C},
title = {Whole-brain dynamics of articulatory, acoustic and semantic speech representations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {432},
pmid = {40082683},
issn = {2399-3642},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)/ ; },
mesh = {Humans ; *Speech/physiology ; Male ; *Brain/physiology ; *Semantics ; Female ; Adult ; Young Adult ; Speech Acoustics ; Brain Mapping ; },
abstract = {Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, ultimately, to the produced audio waveform (acoustic). In this study, we identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. Intracranial neural data is recorded from 15 participants, across 1647 electrode contacts, while overtly speaking 100 unique words. We find a bilateral spatial distribution for all three representations, with a more widespread and temporally dynamic distribution in the left compared to the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation is more widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production neural process and the potential of non-motor representations for speech brain-computer interfaces.},
}
@article {pmid40082601,
year = {2025},
author = {Wang, Y and Yang, Z and Shi, X and Han, H and Li, AN and Zhang, B and Yuan, W and Sun, YH and Li, XM and Lian, H and Li, MD},
title = {Investigating the effect of Arvcf reveals an essential role on regulating the mesolimbic dopamine signaling-mediated nicotine reward.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {429},
pmid = {40082601},
issn = {2399-3642},
mesh = {Animals ; *Dopamine/metabolism ; *Reward ; *Nicotine/pharmacology ; Mice ; Male ; *Ventral Tegmental Area/metabolism/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Mice, Knockout ; *Nucleus Accumbens/metabolism/drug effects ; Signal Transduction ; Mice, Inbred C57BL ; },
abstract = {The mesolimbic dopamine system is crucial for drug reinforcement and reward learning, leading to addiction. We previously demonstrated that Arvcf was associated significantly with nicotine and alcohol addiction through genome-wide association studies. However, the role and mechanisms of Arvcf in dopamine-mediated drug reward processes were largely unknown. In this study, we first showed that Arvcf mediates nicotine-induced reward behavior by using conditioned place preference (CPP) model on Arvcf-knockout (Arvcf-KO) animal model. Then, we revealed that Arvcf was mainly expressed in VTA dopaminergic neurons whose expression could be upregulated by nicotine treatment. Subsequently, our SnRNA-seq analysis revealed that Arvcf was directly involved in dopamine biosynthesis in VTA dopaminergic neurons. Furthermore, we found that Arvcf-KO led to a significant reduction in both the dopamine synthesis and release in the nucleus accumbens (NAc) on nicotine stimulation. Specifically, we demonstrated that inhibition of Arvcf in VTA dopaminergic neurons decreased dopamine release within VTA-NAc circuit and suppressed nicotine reward-related behavior, while overexpression of Arvcf led to the opposite results. Taken together, these findings highlight the role of Arvcf in regulating dopamine signaling and reward learning, and its enhancement of dopamine release in the VTA-NAc circuit as a novel mechanism for nicotine reward.},
}
@article {pmid40082534,
year = {2025},
author = {Muniyandi, AP and Padmanandam, K and Subbaraj, K and Khadidos, AO and Khadidos, AO and Deepa, N and Selvarajan, S},
title = {An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8782},
pmid = {40082534},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Animals ; },
abstract = {Emotion recognition and prediction plays a vital role in human-computer interaction (HCI), offering more potential for efficient intuitive and adaptive systems. This presents an innovative and efficient approach for emotion prediction from electroencephalogram (EEG) signals by using an Improved Sand Cat Optimization (ISCO) technique to enhance prediction accuracy and efficiency. EEG signals directly indicates the brain activity and these signals are rich and reliable source of data for capturing emotional states. The proposed method is improved by adapting the Cat movement which uses convex lens opposition based learning technique and this will enhance the Cat movement towards target. The proposed method converges to target identification quickly for achieving efficient emotion prediction by extending the exiting Sand Cat Optimization algorithm. The algorithm has been evaluated by using openly available EEG signals dataset, which contains 2132 labelled records of three categories of emotional classes. The performance of the proposed method is compared with other nature inspired optimization algorithms such as Practical Swam Optimization (PSO), Artificial Rabbit Optimization (ARO), Artificial Bee Colony Optimization (ABCO), and Cat Optimization (CO) algorithm. The experimental evaluation shows that the proposed technique outperforms and showcases significant improvements in emotion prediction with accuracy of 97.5% compared to the other bioinspired optimization techniques. This research article has a scope to contribute to the advancement of emotion prediction system in the field of mental health care monitoring, HCI systems, gaming systems, and affective computing.},
}
@article {pmid40081769,
year = {2025},
author = {Yin, Y and Cao, Y and Zhou, Y and Xu, Z and Luo, P and Yang, B and He, Q and Yan, H and Yang, X},
title = {Downregulation of DDIT4 levels with borneol attenuates hepatotoxicity induced by gilteritinib.},
journal = {Biochemical pharmacology},
volume = {236},
number = {},
pages = {116869},
doi = {10.1016/j.bcp.2025.116869},
pmid = {40081769},
issn = {1873-2968},
mesh = {*Chemical and Drug Induced Liver Injury/metabolism/prevention &amp; control/drug therapy ; Animals ; *Down-Regulation/drug effects/physiology ; Humans ; *Aniline Compounds/toxicity ; *Pyrazines/toxicity ; *Transcription Factors/antagonists &amp; inhibitors/metabolism/genetics ; Male ; Mice ; Dose-Response Relationship, Drug ; Hepatocytes/drug effects/metabolism ; Protein Kinase Inhibitors/toxicity ; Hep G2 Cells ; Apoptosis/drug effects ; },
abstract = {Gilteritinib, a multi-target kinase inhibitor, is currently used as standard therapy for acute myeloid leukemia. However, approximately half of the patients encounter liver-related adverse effects during the treatment with gilteritinib, which limiting its clinical applications. The underlying mechanisms of gilteritinib-induced hepatotoxicity and the development of strategies to prevent this toxicity are not well-reported. In our study, we utilized JC-1 dye, and MitoSOX to demonstrate that gilteritinib treatment leads to hepatocytes undergoing p53-mediated mitochondrial apoptosis. Furthermore, qRT-PCR analysis revealed that DNA damage-inducible transcript 4 (DDIT4), a downstream target of p53, was upregulated following gilteritinib administration and was identified as a key factor in gilteritinib-induced hepatotoxicity. After drug screening and western blot analysis, borneol, a bicyclic monoterpenoid, was found to decrease the protein level of DDIT4. This is the first compound found to downregulate DDIT4 levels and ameliorate hepatic injury caused by gilteritinib. Our findings suggest that high levels of DDIT4 are the primary driver behind gilteritinib-induced liver injury, and that borneol could potentially be a clinically safe and feasible therapeutic strategy by inhibiting DDIT4 levels.},
}
@article {pmid40081503,
year = {2025},
author = {Xu, F and Lou, Y and Deng, Y and Lun, Z and Zhao, P and Yan, D and Han, Z and Wu, Z and Feng, C and Chen, L and Leng, J},
title = {Motor imagery EEG decoding based on TS-former for spinal cord injury patients.},
journal = {Brain research bulletin},
volume = {224},
number = {},
pages = {111298},
doi = {10.1016/j.brainresbull.2025.111298},
pmid = {40081503},
issn = {1873-2747},
mesh = {Humans ; *Electroencephalography/methods ; *Spinal Cord Injuries/physiopathology/rehabilitation ; *Imagination/physiology ; Neural Networks, Computer ; Adult ; Machine Learning ; Male ; Brain-Computer Interfaces ; Female ; Signal Processing, Computer-Assisted ; Middle Aged ; Motor Activity/physiology ; },
abstract = {Traditional machine learning methods struggle with efficiency when processing large-scale data, while deep learning approaches, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), exhibit certain limitations when handling long-duration sequences. The choice of convolutional kernel size needs to be determined after several experiments, and LSTM has difficulty capturing effective information from long-time sequences. In this paper, we propose a transfer learning (TL) method based on Transformer, which constructs a new network architecture for feature extraction and classification of electroencephalogram (EEG) signals in the time-space domain, named TS-former. The frequency and spatial domain information of EEG signals is extracted using the Filter Bank Common Spatial Pattern (FBCSP), and the resulting features are subsequently processed by the Transformer to capture temporal patterns. The input features are processed by the Transformer using a multi-head attention mechanism, and the final classification outputs are generated through a fully connected layer. A classification model is pre-trained using fine-tuning techniques. When performing a new classification task, only some layers of the model are modified to adapt it to the new data and achieve good classification results. The experiments are conducted on a motor imagery (MI) EEG dataset from 16 spinal cord injury (SCI) patients. After training the model using a ten-time ten-fold cross-validation method, the average classification accuracy reached 95.09 %. Our experimental results confirm a new approach to build a brain-computer interface (BCI) system for rehabilitation training of SCI patients.},
}
@article {pmid40079091,
year = {2025},
author = {Zhang, Y and Coid, J},
title = {Testing syndemic models along pathways to psychotic spectrum disorder: implications for population-level preventive interventions.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e85},
doi = {10.1017/S0033291725000455},
pmid = {40079091},
issn = {1469-8978},
mesh = {Humans ; Male ; *Psychotic Disorders/prevention &amp; control ; Adult ; *Syndemic ; Cross-Sectional Studies ; Young Adult ; *Violence/prevention &amp; control/psychology ; Substance-Related Disorders/prevention &amp; control ; United Kingdom ; Middle Aged ; Adolescent ; Sexual Behavior ; Crime/prevention &amp; control/statistics &amp; numerical data/psychology ; Adverse Childhood Experiences/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Population-level preventive interventions are urgently needed and may be effective for psychosis due to social determinants. We tested three syndemic models along pathways from childhood adversity (CA) to psychotic spectrum disorder (PSD) and their implications for prevention.<br><br>METHODS: Cross-sectional data from 7461 British men surveyed in 5 population subgroups. We tested interactions on both additive and multiplicative scales for a syndemic of violence/criminality (VC), sexual behavior (SH), and substance misuse (SM) according to the presence of CA and adult traumatic life events; mediation analysis of path models; and partial least squares path modeling, with PSD as outcome.<br><br>RESULTS: Multiplicative synergistic interactions were found between VC, SH, and SM among men, who experienced CA and traumatic adult life events. However, when disaggregated, only SM mediated the pathway from CA to PSD. Path modeling showed traumatic life events acted on PSD through the syndemic and had no direct effect on PSD. Higher syndemic scores and living in areas of deprivation characterized men with PSD and CA.<br><br>CONCLUSIONS: Our findings support a broad division of PSD into cases due to (i) biological/inherent causes, and (ii) social determinants, the latter including a syndemic pathway determined by CA. Preventive strategies should focus primarily on preventing adverse effects of CA on developmental pathways which result in PSD. Single component prevention strategies may prevent triggering effects of SM on PSD during adolescence/early adulthood among vulnerable individuals due to CA. Future research should determine applicability and transferability of interventions based on these findings to different populations, specifically those experiencing syndemics.},
}
@article {pmid40078487,
year = {2025},
author = {Li, M and Yu, P and Shen, Y},
title = {A spatial and temporal transformer-based EEG emotion recognition in VR environment.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1517273},
pmid = {40078487},
issn = {1662-5161},
abstract = {With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios. Moreover, emotion recognition models are typically trained and tested on datasets collected in laboratory environments, with little validation of their effectiveness in real-world situations. VR, providing a highly immersive and realistic experience, is an ideal tool for emotional research. In this paper, we collect EEG data from participants while they watched VR videos. We propose a purely Transformer-based method, EmoSTT. We use two separate Transformer modules to comprehensively model the temporal and spatial information of EEG signals. We validate the effectiveness of EmoSTT on a passive paradigm collected in a laboratory environment and an active paradigm emotion dataset collected in a VR environment. Compared with state-of-the-art methods, our method achieves robust emotion classification performance and can be well transferred between different emotion elicitation paradigms.},
}
@article {pmid40074408,
year = {2025},
author = {Zhang, C and Pu, Y and Kong, XZ},
title = {Latent dimensions of brain asymmetry.},
journal = {Handbook of clinical neurology},
volume = {208},
number = {},
pages = {37-45},
doi = {10.1016/B978-0-443-15646-5.00027-0},
pmid = {40074408},
issn = {0072-9752},
mesh = {Humans ; *Functional Laterality/physiology ; *Brain/physiology/anatomy &amp; histology ; },
abstract = {Functional lateralization represents a fundamental aspect of brain organization, where certain cognitive functions are specialized in one hemisphere over the other. Deviations from typical patterns of lateralization often manifest in various brain disorders, such as autism spectrum disorder, schizophrenia, and dyslexia. However, despite its importance, uncovering the intrinsic properties of brain lateralization and its underlying structural basis remains challenging. On the one hand, functional lateralization has long been oversimplified, often reduced to a unidimensional perspective. For instance, individuals are sometimes labeled as left-brained or right-brained based on specific behavioral measures like handedness and language lateralization. Such a perspective disregards the nuanced subtypes of lateralization, each potentially attributed to distinct factors and associated with unique functional correlates. On the other hand, traditional studies of brain structural asymmetry have typically focused on localized analyses of homologous regions in the two hemispheres. This perspective fails to capture the inherent interplay between brain regions, resulting in an overly complex depiction of structural asymmetry. Such conceptual and methodological discrepancies between studies of functional lateralization and structural asymmetry pose significant obstacles to establishing meaningful links between them. To address this gap, a shift toward uncovering the dimensional structure of brain asymmetry has been proposed. This chapter introduces the concept of latent dimensions of brain asymmetry and provides an up-to-date overview of studies regarding dimensions of functional lateralization and structural asymmetry in the human brain. By transcending the traditional analysis and employing multivariate pattern techniques, these studies offer valuable insights into our understanding of the intricate organizational principles governing the human brain's lateralized functions.},
}
@article {pmid40073454,
year = {2025},
author = {Pei, Y and Zhao, S and Xie, L and Ji, B and Luo, Z and Ma, C and Gao, K and Wang, X and Sheng, T and Yan, Y and Yin, E},
title = {Toward the enhancement of affective brain-computer interfaces using dependence within EEG series.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbfc0},
pmid = {40073454},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Affect/physiology ; Reproducibility of Results ; Emotions/physiology ; Algorithms ; },
abstract = {In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances.Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs.Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test.Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier's outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy.Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.},
}
@article {pmid40073451,
year = {2025},
author = {Dong, Y and Zheng, L and Pei, W and Gao, X and Wang, Y},
title = {A 240-target VEP-based BCI system employing narrow-band random sequences.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbfc1},
pmid = {40073451},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Young Adult ; Algorithms ; Neural Networks, Computer ; },
abstract = {Objective.In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.Approach. We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm. This algorithm predicts corresponding stimulus sequences from EEG data and achieves efficient and accurate classification.Main results.Offline experiments which conducted in a sequential paradigm, resulted in an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min. In online experiments, the system demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min in a cued spelling task.Significance.This work represents an advancement in c-VEP BCI systems, offering one of the largest known instruction set in VEP-based BCIs and demonstrating robust performance metrics. The proposed system is potential for more practical and efficient BCI applications.},
}
@article {pmid40072857,
year = {2025},
author = {Ji, D and Huang, Y and Chen, Z and Zhou, X and Wang, J and Xiao, X and Xu, M and Ming, D},
title = {Enhanced Spatial Division Multiple Access BCI Performance via Incorporating MEG With EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1202-1211},
doi = {10.1109/TNSRE.2025.3550653},
pmid = {40072857},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; *Algorithms ; Male ; Adult ; Female ; Young Adult ; Reproducibility of Results ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Visual Fields/physiology ; },
abstract = {Spatial division multiple access (SDMA) is a way of encoding BCI systems based on spatial distribution of brain signal characteristics. However, SDMA-BCI based on EEG had poor system performance limited by spatial resolution. MEG-EEG fusion modality analysis can help solve this problem. According retina-cortical relationship, this study used stimulus out of the central visual field and tiny fixation points to construct a 16-command SDMA coded MEG-EEG fusion modality BCI system. We achieved this by synchronously acquiring MEG and EEG signals from 10 subjects. We compared the spatiotemporal features between MEG and EEG by analyzing signals in the occipital region. We fused MEG and EEG modalities without any signal processing and used the multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm to evaluate and compare the system performance of EEG, MEG, and MEG-EEG fusion modalities. The result showed that MEG and EEG had obvious differences in spatial distribution characteristics. MEG improves offline classification accuracy of the 16 fixation points by 27.81% over EEG at 4s data length. Specially, the MEG-EEG fusion modality achieves an impressive average offline accuracy of 91.71%, which was a significant improvement over MEG (p<0.01, ANOVA). The MEG-EEG fusion modality achieved average information transfer rate (ITR) of 60.74 bits/min with a data length of 1 s, which was a 14% improvement over MEG. The MEG-EEG fusion modality significantly enhanced the spatial features and performance of SDMA-encoded BCIs. These results highlight the potential and feasibility of MEG-EEG fusion modality BCI, and provide theoretical insights and practical value for promoting the further development and application of SDMA in BCI.},
}
@article {pmid40072172,
year = {2025},
author = {Zhang, C and Zhang, C and Liu, Y},
title = {Progress in the Development of Flexible Devices Utilizing Protein Nanomaterials.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {15},
number = {5},
pages = {},
pmid = {40072172},
issn = {2079-4991},
support = {52473014//National Natural Science Foundation of China/ ; },
abstract = {Flexible devices are soft, lightweight, and portable, making them suitable for large-area applications. These features significantly expand the scope of electronic devices and demonstrate their unique value in various fields, including smart wearable devices, medical and health monitoring, human-computer interaction, and brain-computer interfaces. Protein materials, due to their unique molecular structure, biological properties, sustainability, self-assembly ability, and good biocompatibility, can be applied in electronic devices to significantly enhance the sensitivity, stability, mechanical strength, energy density, and conductivity of the devices. Protein-based flexible devices have become an important research direction in the fields of bioelectronics and smart wearables, providing new material support for the development of more environmentally friendly and reliable flexible electronics. Currently, many proteins, such as silk fibroin, collagen, ferritin, and so on, have been used in biosensors, memristors, energy storage devices, and power generation devices. Therefore, in this paper, we provide an overview of related research in the field of protein-based flexible devices, including the concept and characteristics of protein-based flexible devices, fabrication materials, fabrication processes, characterization, and evaluation, and we point out the future development direction of protein-based flexible devices.},
}
@article {pmid40071135,
year = {2025},
author = {Kobayashi, N and Ino, M},
title = {Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1469244},
pmid = {40071135},
issn = {1662-4548},
abstract = {Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. It is also expected to reduce the workload of care workers. In this study, we focused on motor imagery (MI) classification by deep-learning to construct a system that can identify MI obtained by electroencephalography (EEG) measurements with high accuracy and a low latency response. By completing the system on the edge, the privacy of personal MI data can be ensured, and the system is ubiquitous, which improves user convenience. On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. Therefore, by optimizing the MI measurement conditions and various parameters of the deep-learning model, we attempted to reduce the power consumption and improve the response latency of the system by minimizing the computational cost while maintaining high classification accuracy. In addition, we investigated the use of a 3-dimension convolutional neural network (3D CNN), which can retain spatial locality as a feature to further improve the classification accuracy. We propose a method to maintain a high classification accuracy while enabling processing on the edge by optimizing the size and number of kernels and the layer structure. Furthermore, to develop a practical BMI system, we introduced dry electrodes, which are more comfortable for daily use, and optimized the number of parameters and memory consumption size of the proposed model to maintain classification accuracy even with fewer electrodes, less recall time, and a lower sampling rate. Compared to EEGNet, the proposed 3D CNN reduces the number of parameters, the number of multiply-accumulates, and memory footprint by approximately 75.9%, 16.3%, and 12.5%, respectively, while maintaining the same level of classification accuracy with the conditions of eight electrodes, 3.5 seconds sample window size, and 125 Hz sampling rate in 4-class dry-EEG MI.},
}
@article {pmid40070670,
year = {2025},
author = {Pan, H and Tang, C and Song, C and Li, J},
title = {Analysis of clinical efficacy of sacral magnetic stimulation for the treatment of detrusor underactivity.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1499310},
pmid = {40070670},
issn = {1664-2295},
abstract = {OBJECTIVE: The objective of this study was to investigate the effectiveness and safety of sacral magnetic stimulation (SMS) in the management of detrusor underactivity (DU).<br><br>METHODS: We retrospectively analyzed 66 patients with detrusor underactivity treated at Hangzhou Third People's Hospital from January 2020 to October 2024, divided into two groups (33 cases each). Both groups had confirmed detrusor underactivity via urodynamic studies. The control group received conventional treatment (medication, catheterization, bladder training), while the observation group received SMS therapy. Urination diaries, urodynamic parameters and self-rating anxiety scale (SAS) were collected before and after the 4-week treatment to evaluate SMS efficacy and safety.<br><br>RESULTS: All patients in the observation group completed the course of sacral magnetic stimulation without experiencing any serious complications. After treatment, the observation group showed a significant reduction in the number of daily urinations, nocturnal urinations, SAS score and residual urine volume (RUV) (p <&#x202f;0.05) compared with the control group. There was no statistically significant difference in maximum cystometric capacity (MCC) (p >&#x202f;0.05). However, improvements were observed in SAS score, Detrusor Pressure at Maximum Flow (Pdet), Bladder Contractility Index (BCI), Maximum urinary Flow Rate (Qmax) and Average Urinary Flow Rate (Qavg) (p <&#x202f;0.05). The effective rate in the observation group was 78.78%, significantly higher than that in the control group (p <&#x202f;0.05). Although there was a slight decrease in the effective rate during the 6-month follow-up, the difference was not statistically significant (p >&#x202f;0.05).<br><br>CONCLUSION: In conclusion, sacral magnetic stimulation therapy has demonstrated effectiveness in improving urinary function in patients with detrusor underactivity while maintaining a high level of safety.},
}
@article {pmid40069360,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {28},
number = {4},
pages = {748-756},
pmid = {40069360},
issn = {1546-1726},
support = {2019YFA0508701//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2022YFA1303300//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {*Amyotrophic Lateral Sclerosis/genetics/pathology/metabolism ; Animals ; Rats ; Motor Neurons/metabolism/pathology ; *Electron Transport Complex IV/genetics ; *Mitochondria/metabolism/genetics ; Humans ; Disease Models, Animal ; Mutation/genetics ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is categorized into ~10% familial and ~90% sporadic cases. While familial ALS is caused by mutations in many genes of diverse functions, the underlying pathogenic mechanisms of ALS, especially in sporadic ALS (sALS), are largely unknown. Notably, about half of the cases with sALS showed defects in mitochondrial respiratory complex IV (CIV). To determine the causal role of this defect in ALS, we used transcription activator-like effector-based mitochondrial genome editing to introduce mutations in CIV subunits in rat neurons. Our results demonstrate that neuronal CIV deficiency is sufficient to cause a number of ALS-like phenotypes, including cytosolic TAR DNA-binding protein 43 redistribution, selective motor neuron loss and paralysis. These results highlight CIV deficiency as a potential cause of sALS and shed light on the specific vulnerability of motor neurons, marking an important advance in understanding and therapeutic development of sALS.},
}
@article {pmid40068721,
year = {2025},
author = {Kopalli, SR and Shukla, M and Jayaprakash, B and Kundlas, M and Srivastava, A and Jagtap, J and Gulati, M and Chigurupati, S and Ibrahim, E and Khandige, PS and Garcia, DS and Koppula, S and Gasmi, A},
title = {Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.},
journal = {Neuroscience},
volume = {572},
number = {},
pages = {214-231},
doi = {10.1016/j.neuroscience.2025.03.017},
pmid = {40068721},
issn = {1873-7544},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Recovery of Function/physiology ; *Stroke/therapy/diagnosis ; Brain-Computer Interfaces ; },
abstract = {Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.},
}
@article {pmid40068108,
year = {2025},
author = {Li, Y and Li, H and Wang, H and Wang, X},
title = {Utilizing Caenorhabditis Elegans as a Rapid and Precise Model for Assessing Amphetamine-Type Stimulants: A Novel Approach to Evaluating New Psychoactive Substances Activity and Mechanisms.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500808},
doi = {10.1002/advs.202500808},
pmid = {40068108},
issn = {2198-3844},
support = {T2341003//National Natural Science Foundation of China/ ; T2394480(T2394483)//National Natural Science Foundation of China/ ; 2021ZD0203000(2021ZD0203003)//STI2030-Major Projects/ ; 20240304115SF//Science and Technology Development Plan Project of Jilin Province/ ; XDB0450102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
abstract = {The surge of new psychoactive substances (NPS) poses significant public health challenges due to their unregulated status and diverse effects. However, existing in vivo models for evaluating their activities are limited. To address this gap, this study utilizes the model organism Caenorhabditis elegans (C. elegans) to evaluate the activity of amphetamine-type stimulants (ATS) and their analogs. The swimming-induced paralysis (SWIP) assay is employed to measure the acute responses of C. elegans to various ATS, including amphetamine (AMPH), methamphetamine (METH), 3,4-methylenedioxymethamphetamine (MDMA) and their enantiomers. The findings reveal distinct responses in wild-type and mutant C. elegans, highlighting the roles of dopaminergic and serotonergic pathways, particularly DOP-3 and SER-4 receptors. The assay also revealed that C. elegans can distinguish between the chiral forms of ATS. Additionally, structural activity relationships (SAR) are observed, with meta-R amphetamines showing more pronounced effects than ortho-R and para-R analogs. This study demonstrates the utility of C. elegans in rapidly assessing ATS activity and toxicity, providing a cost-effective and precise method for high-throughput testing of NPS. These results contribute to a better understanding of ATS pharmacology and offer a valuable framework for future research and potential regulatory applications.},
}
@article {pmid40067734,
year = {2025},
author = {Wang, X and Qi, W and Yang, W and Wang, W},
title = {Cholesky Space for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3542801},
pmid = {40067734},
issn = {2162-2388},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms. Our proposed Cholesky space-based model, CSNet, achieves state-of-the-art (SOTA) performance in motor imagery (MI) decoding and emotion recognition and demonstrates competitive performance in error-related negativity (ERN) decoding, all without the need for data preprocessing. Furthermore, our runtime comparison shows that the Cholesky space method is more efficient than the method based on the Riemannian manifold as the matrix dimension increases. To enhance the interpretability of CSNet, we perform t-distributed stochastic neighbor embedding (t-SNE) visualization for MI, frequency band energy visualization for emotion recognition, and temporal importance visualization for ERN. The results indicate that CSNet effectively learns discriminative features, identifies important frequency bands, and focuses on important temporal features. The CSNet effectively captures the non-Euclidean characteristics of EEG signals across various BCI paradigms, while mitigating high computational costs, making it a promising candidate for future BCI algorithms. The code for this study is publicly available at: https://github.com/XingfuWang/CSNet.},
}
@article {pmid40067717,
year = {2025},
author = {Du, Y and Chen, J and Liu, Z and Wong, N and Zhang, C and Ding, Z and Liu, J and Ngai, ECH},
title = {Valence-Arousal Disentangled Representation Learning for Emotion Recognition in SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3549727},
pmid = {40067717},
issn = {2168-2208},
abstract = {Steady state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which are widely used in rehabilitation and disability assistance, can benefit from real-time emotion recognition to enhance human-machine interaction. However, the learned discriminative latent representations in SSVEP-BCIs may generalize in an unintended direction, which can lead to reduced accuracy in detecting emotional states. In this paper, we introduce a Valence-Arousal Disentangled Representation Learning (VADL) method, drawing inspiration from the classical two-dimensional emotional model, to enhance the performance and generalization of emotion recognition within SSVEP-BCIs. VADL distinctly disentangles the latent variables of valence and arousal information to improve accuracy. It utilizes the structured state space duality model to thoroughly extract global emotional features. Additionally, we propose a Multisubject Gradient Blending training strategy that individually tailors the learning pace of reconstruction and discrimination tasks within VADL on-the-fly. To verify the feasibility of our method, we have developed a comprehensive database comprising 23 subjects, in which both the emotional states and SSVEPs were effectively elicited. Experimental results indicate that VADL surpasses existing state-of-the-art benchmark algorithms.},
}
@article {pmid40064104,
year = {2025},
author = {Rodríguez-García, ME and Carino-Escobar, RI and Carrillo-Mora, P and Hernandez-Arenas, C and Ramirez-Nava, AG and Pacheco-Gallegos, MDR and Valdés-Cristerna, R and Cantillo-Negrete, J},
title = {Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbebf},
pmid = {40064104},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; *Neuronal Plasticity/physiology ; Middle Aged ; *Upper Extremity ; Aged ; *Stroke/physiopathology ; *White Matter/diagnostic imaging/physiopathology ; *Gray Matter/diagnostic imaging/physiopathology ; Electroencephalography/methods ; Adult ; Magnetic Resonance Imaging/methods ; Cerebral Cortex/physiopathology/diagnostic imaging/physiology ; Diffusion Tensor Imaging/methods ; Recovery of Function/physiology ; },
abstract = {Objective. Upper extremity (UE) motor function loss is one of the most impactful consequences of stroke. Recently, brain-computer interface (BCI) systems have been utilized in therapy programs to enhance UE motor recovery after stroke, widely attributed to neuroplasticity mechanisms. However, the effect that the BCI's closed-loop feedback can have in these programs is unclear. The aim of this study was to quantitatively assess and compare the neuroplasticity effects elicited in stroke patients by a UE motor rehabilitation BCI therapy and by its sham-BCI counterpart.Approach. Twenty patients were randomly assigned to either the experimental group (EG), who controlled the BCI system via UE motor intention, or the control group (CG), who received random feedback. The elicited neuroplasticity effects were quantified using asymmetry metrics derived from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) data acquired before, at the middle, and at the end of the intervention, alongside UE sensorimotor function evaluations. These asymmetry indexes compare the affected and unaffected hemispheres and are robust to lesion location variability.Main results. Most patients from the EG presented brain activity lateralisation to one brain hemisphere, as described by EEG (8 patients) and fMRI (6 patients) metrics. Conversely, the CG showed less pronounced lateralisations, presenting primarily bilateral activity patterns. DTI metrics showed increased white matter integrity in half of the EG patients' unaffected hemisphere, and in all but 2 CG patients' affected hemisphere. Individual patient analysis suggested that lesion location was relevant since functional and structural lateralisations occurred towards different hemispheres depending on stroke site.Significance. This study shows that a BCI intervention can elicit more pronounced neuroplasticity-related lateralisations than a sham-BCI therapy. These findings could serve as future biomarkers, helping to better select patients and increasing the impact that a BCI intervention can achieve. Clinical trial: NCT04724824.},
}
@article {pmid40064095,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Feasibility of source-level motor imagery classification for people with multiple sclerosis.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbec1},
pmid = {40064095},
issn = {1741-2552},
mesh = {Humans ; *Multiple Sclerosis/physiopathology/classification ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; *Imagination/physiology ; Middle Aged ; *Feasibility Studies ; Movement/physiology ; Electroencephalography/methods ; },
abstract = {Objective.There is limited work investigating brain-computer interface (BCI) technology in people with multiple sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by multiple sclerosis (MS) progression and BCI task-relevant signals using estimated source activity to improve classification accuracy.Approach.Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study.K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay.Main Results.Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs rest and movement vs movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis.Significance.This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology.},
}
@article {pmid40063703,
year = {2025},
author = {Ahmed, AAA and Alegret, N and Almeida, B and Alvarez-Puebla, R and Andrews, AM and Ballerini, L and Barrios-Capuchino, JJ and Becker, C and Blick, RH and Bonakdar, S and Chakraborty, I and Chen, X and Cheon, J and Chilla, G and Coelho Conceicao, AL and Delehanty, J and Dulle, M and Efros, AL and Epple, M and Fedyk, M and Feliu, N and Feng, M and Fernández-Chacón, R and Fernandez-Cuesta, I and Fertig, N and Förster, S and Garrido, JA and George, M and Guse, AH and Hampp, N and Harberts, J and Han, J and Heekeren, HR and Hofmann, UG and Holzapfel, M and Hosseinkazemi, H and Huang, Y and Huber, P and Hyeon, T and Ingebrandt, S and Ienca, M and Iske, A and Kang, Y and Kasieczka, G and Kim, DH and Kostarelos, K and Lee, JH and Lin, KW and Liu, S and Liu, X and Liu, Y and Lohr, C and Mailänder, V and Maffongelli, L and Megahed, S and Mews, A and Mutas, M and Nack, L and Nakatsuka, N and Oertner, TG and Offenhäusser, A and Oheim, M and Otange, B and Otto, F and Patrono, E and Peng, B and Picchiotti, A and Pierini, F and Pötter-Nerger, M and Pozzi, M and Pralle, A and Prato, M and Qi, B and Ramos-Cabrer, P and Genger, UR and Ritter, N and Rittner, M and Roy, S and Santoro, F and Schuck, NW and Schulz, F and Şeker, E and Skiba, M and Sosniok, M and Stephan, H and Wang, R and Wang, T and Wegner, KD and Weiss, PS and Xu, M and Yang, C and Zargarian, SS and Zeng, Y and Zhou, Y and Zhu, D and Zierold, R and Parak, WJ},
title = {Interfacing with the Brain: How Nanotechnology Can Contribute.},
journal = {ACS nano},
volume = {19},
number = {11},
pages = {10630-10717},
pmid = {40063703},
issn = {1936-086X},
support = {R01 MH111872/MH/NIMH NIH HHS/United States ; R01 MH094730/MH/NIMH NIH HHS/United States ; R01 DA045550/DA/NIDA NIH HHS/United States ; R03 NS118156/NS/NINDS NIH HHS/United States ; R21 AT010933/AT/NCCIH NIH HHS/United States ; R61 MH135106/MH/NIMH NIH HHS/United States ; },
mesh = {*Nanotechnology ; Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; Animals ; },
abstract = {Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.},
}
@article {pmid40063028,
year = {2025},
author = {Ben-Zion, Z and Simon, AJ and Rosenblatt, M and Korem, N and Duek, O and Liberzon, I and Shalev, AY and Hendler, T and Levy, I and Harpaz-Rotem, I and Scheinost, D},
title = {Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.},
journal = {JAMA network open},
volume = {8},
number = {3},
pages = {e250331},
pmid = {40063028},
issn = {2574-3805},
mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Female ; Adult ; Male ; *Connectome ; *Survivors/psychology/statistics &amp; numerical data ; *Magnetic Resonance Imaging ; Machine Learning ; Middle Aged ; Prognosis ; Israel/epidemiology ; Longitudinal Studies ; Wounds and Injuries/psychology/physiopathology/complications ; },
abstract = {IMPORTANCE: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.<br><br>OBJECTIVE: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.<br><br>This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors. The NMPTDT study was conducted from January 20, 2015, to March 11, 2020, and included adult civilians who were admitted to a general hospital emergency department in Israel and screened for early PTSD symptoms indicative of chronic PTSD risk. Enrolled participants completed comprehensive clinical assessments and functional magnetic resonance imaging (fMRI) scans at 1, 6, and 14 months post trauma. Data were analyzed from September 2023 to March 2024.<br><br>EXPOSURE: Traumatic events included motor vehicle incidents, physical assaults, robberies, hostilities, electric shocks, fires, drownings, work accidents, terror attacks, or large-scale disasters.<br><br>MAIN OUTCOMES AND MEASURES: Connectome-based predictive modeling (CPM), a whole-brain machine learning approach, was applied to resting-state and task-based fMRI data collected at 1 month post trauma. The primary outcome measure was PTSD symptom severity across the 3 time points, assessed with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Secondary outcomes included Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) PTSD symptom clusters (intrusion, avoidance, negative alterations in mood and cognition, hyperarousal).<br><br>RESULTS: A total of 162 recent trauma survivors (mean [SD] age,&#x2009;33.9&#x2009;[11.5] years; 80 women [49.4%] and 82 men [50.6%]) were included at 1 month post trauma. Follow-up assessments were completed by 136 survivors (84.0%) at 6 months and by 133 survivors (82.1%) at 14 months post trauma. Among the 162 recent trauma survivors, CPM significantly predicted PTSD severity at 1 month (&#x3c1;&#x2009;=&#x2009;0.18, P&#x2009;<&#x2009;.001) and 14 months (&#x3c1;&#x2009;=&#x2009;0.24, P&#x2009;<&#x2009;.001) post trauma, but not at 6 months post trauma (&#x3c1;&#x2009;=&#x2009;0.03, P&#x2009;=&#x2009;.39). The most predictive edges at 1 month included connections within and between the anterior default mode, motor sensory, and salience networks. These networks, with the additional contribution of the central executive and visual networks, were predictive of symptoms at 14 months. CPM predicted avoidance and negative alterations in mood and cognition at 1 month, but it predicted intrusion and hyperarousal symptoms at 14 months.<br><br>CONCLUSIONS AND RELEVANCE: In this prognostic study of recent trauma survivors, individual differences in large-scale neural networks shortly after trauma were associated with variability in PTSD symptom trajectories over the first year following trauma exposure. These findings suggest that CPM may identify potential targets for interventions.},
}
@article {pmid40062568,
year = {2025},
author = {Pilipović, K and Parpura, V},
title = {The potential of single-walled carbon nanotube-based therapeutic platforms targeting astrocytes.},
journal = {Nanomedicine (London, England)},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/17435889.2025.2476376},
pmid = {40062568},
issn = {1748-6963},
}
@article {pmid40061257,
year = {2025},
author = {Song, Y and Han, L and Zhang, T and Xu, B},
title = {Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1551656},
pmid = {40061257},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.},
}
@article {pmid40060989,
year = {2025},
author = {Huang, J and Huang, L and Li, Y and Fang, F},
title = {A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.},
journal = {Journal of multidisciplinary healthcare},
volume = {18},
number = {},
pages = {1297-1317},
pmid = {40060989},
issn = {1178-2390},
abstract = {OBJECTIVE: This study aims to conduct a bibliometric analysis of the application of brain- computer interface (BCI) in rehabilitation medicine, assessing the current state, developmental trends, and future potential of this field. By systematically analyzing relevant literature, we seek to identify key research themes and enhance understanding of BCI technology in rehabilitation.<br><br>METHODS: We utilized bibliometric analysis tools such as VOSviewer and CiteSpace to screen and analyze 426 relevant articles from the Web of Science Core Collection (WoSCC) database. We quantitatively evaluated citation patterns, publication trends, and the collaboration networks of research institutions and authors to uncover research hotspots and frontier dynamics in the field.<br><br>RESULTS: The findings indicate a continuous increase in research publications since 2003, with a notable peak occurring between 2019 and 2021. The analysis revealed that motor imagery, motor recovery, and signal processing are the predominant research themes. Furthermore, the United States and China are leading in the publication volume related to BCI and rehabilitation medicine. Key research institutions include the University of T&#xfc;bingen and the New York State Department of Health, with significant contributions from scholars like Niels Birbaumer.<br><br>CONCLUSION: Although the current research on BCI in rehabilitation medicine shows significant potential and efficacy, further exploration of certain research directions is needed, along with the promotion of interdisciplinary collaboration to comprehensively address complex real-world issues such as motor function impairment. Future research should focus on optimizing training models, enhancing technical feasibility, and exploring home rehabilitation applications to facilitate the broader adoption of BCI technology in rehabilitation medicine.},
}
@article {pmid40060849,
year = {2025},
author = {Castañeda-Valencia, G and Gama, LF and Panneerselvam, M and Vaiss, VS and Guedes, IA and Dardenne, LE and Costa, LT},
title = {Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds.},
journal = {ACS omega},
volume = {10},
number = {8},
pages = {8314-8335},
pmid = {40060849},
issn = {2470-1343},
abstract = {In this work, we performed a comprehensive benchmark study for the ground state of five small- and medium-sized platinum derivatives, PtH, PtCl, [PtCl4][2-], [Pt(NH3)4][2+], and cis-[Pt(NH3)2Cl2], in the gas phase and two cisplatin polymorphs in the solid phase. The benchmark encompassed 16 density functionals, including nonhybrids, hybrids, and double hybrids. Furthermore, Hartree-Fock (HF) and Post-HF by Møller-Plesset MP2 methods were also tested. Additionally, 11 basis sets were explored, comparing relativistic all-electron and RECP approaches. Our results indicate that the methodologies best suited for predicting structural parameters do not excel in predicting vibrational frequencies and vice versa. In the context of this theoretical framework, we also examine the derivation of partial atomic charges and bond charge increments (bci) as fundamental parameters within the MMFF94 classical force field. Our results show that the partial atomic charges of CHELPG present a slight charge fluctuation in Pt for all investigated levels of theory, and this behavior reproduces well the soft acid definition for Pt[2+], giving the best description of the chemical environment of platinum in the cisplatin complex. The average calculated bci values effectively capture the atomic charge variations in the chemical environment of Pt in the investigated species. The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. This methodology will be further implemented in the DockThor receptor-ligand docking program, allowing future molecular docking validations involving ligand compounds containing Pt atoms.},
}
@article {pmid40060267,
year = {2025},
author = {Shawki, N and Napoli, A and Vargas-Irwin, CE and Thompson, CK and Donoghue, JP and Serruya, MD},
title = {Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1544397},
pmid = {40060267},
issn = {1662-5161},
abstract = {INTRODUCTION: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia.<br><br>METHODS: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function.<br><br>RESULTS: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains.<br><br>DISCUSSION: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.},
}
@article {pmid40059723,
year = {2025},
author = {Han, JS and Jeon, MC and Lee, CM and Velasco, GC and Park, SY and Park, SN},
title = {Comparing Tinnitus Suppression in Asymmetric Hearing Loss and Single-Sided Deafness: Cochlear Versus Bone Conduction Implants.},
journal = {The Laryngoscope},
volume = {},
number = {},
pages = {},
doi = {10.1002/lary.32090},
pmid = {40059723},
issn = {1531-4995},
support = {RS-2024-00340841//National Research Foundation of Korea/ ; },
abstract = {OBJECTIVES: Implantable hearing devices, such as cochlear implants (CI) and bone conduction implants (BCI), are options for hearing rehabilitation in patients with asymmetric hearing loss (AHL) and single-sided deafness (SSD). This study aimed to compare the effects of CI and BCI on tinnitus in AHL/SSD patients with tinnitus.<br><br>METHODS: This retrospective study enrolled adult AHL/SSD patients with significant tinnitus who underwent CI or BCI placement between 2017 and 2023. Clinical characteristics, preoperative and postoperative audiologic test results, and tinnitus questionnaires (tinnitus handicap inventory, THI; visual analog scale, VAS) were collected and analyzed.<br><br>RESULTS: Of 33 AHL/SSD patients with significant tinnitus (THI &#x2265;&#x2009;18), 16 received CI and 17 BCI. In the CI group, all four VAS scores (loudness, awareness, annoyance, and effect on life) and THI scores significantly improved. In the BCI group, annoyance and effect on life categories of VAS and THI scores significantly improved, while VAS loudness and awareness remained similar. Linear mixed model analysis showed that the decrease in VAS loudness, awareness, and annoyance scores was significantly greater in the CI group compared to the BCI group. The CI group showed a significantly higher tinnitus cure rate (62.5.0%) compared with the BCI group (11.8%) at 6-months postoperative.<br><br>CONCLUSION: Both CI and BCI effectively improved tinnitus in AHL/SSD patients with tinnitus. However, CI is considered the first-line therapeutic option for tinnitus due to its stronger effect on tinnitus suppression as well as the higher cure rate in SSD/AHL patients with tinnitus.},
}
@article {pmid40059266,
year = {2025},
author = {Meng, J and Wei, Y and Mai, X and Li, S and Wang, X and Luo, R and Ji, M and Zhu, X},
title = {Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {40059266},
issn = {1741-0444},
abstract = {Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.},
}
@article {pmid40058464,
year = {2025},
author = {Zhao, Z and Li, Y and Peng, Y and Camilleri, K and Kong, W},
title = {Multi-view graph fusion of self-weighted EEG feature representations for speech imagery decoding.},
journal = {Journal of neuroscience methods},
volume = {418},
number = {},
pages = {110413},
doi = {10.1016/j.jneumeth.2025.110413},
pmid = {40058464},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; *Brain/physiology ; Adult ; Male ; Female ; },
abstract = {BACKGROUND: Electroencephalogram (EEG)-based speech imagery is an emerging brain-computer interface paradigm, which enables the speech disabled to naturally and intuitively communicate with external devices or other people. Currently, speech imagery research decoding performance is limited. One of the reasons is that there is still no consensus on which domain features are more discriminative.<br><br>NEW METHOD: To adaptively capture the complementary information from different domain features, we treat each domain as a view and propose a multi-view graph fusion of self-weighted EEG feature representations (MVGSF) model by learning a consensus graph from multi-view EEG features, based on which the imagery intentions can be effectively decoded. Considering that different EEG features in each view have different discriminative abilities, the view-dependent feature importance exploration strategy is incorporated in MVGSF.<br><br>RESULTS: (1) MVGSF exhibits outstanding performance on two public speech imagery datasets (2) The learned consensus graph from multi-view features effectively characterizes the relationships of EEG samples in a progressive manner. (3) Some task-related insights are explored including the feature importance-based identification of critical EEG channels and frequency bands in speech imagery decoding.<br><br>We compared MVGSF with single-view counterparts, other multi-view models, and state-of-the-art models. MVGSF achieved the highest accuracy, with average accuracies of 78.93% on the 2020IBCIC3 dataset and 53.85% on the KaraOne dataset.<br><br>CONCLUSIONS: MVGSF effectively integrates features from multiple domains to enhance decoding capabilities. Furthermore, through the learned feature importance, MVGSF has made certain contributions to identify the EEG spatial-frequency patterns in speech imagery decoding.},
}
@article {pmid40057290,
year = {2025},
author = {Chuang, CH and Chang, KY and Huang, CS and Bessas, AM},
title = {Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals.},
journal = {NeuroImage},
volume = {310},
number = {},
pages = {121123},
doi = {10.1016/j.neuroimage.2025.121123},
pmid = {40057290},
issn = {1095-9572},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; *Brain/physiology ; },
abstract = {Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution that simultaneously addresses multiple artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.},
}
@article {pmid40054446,
year = {2025},
author = {Natraj, N and Seko, S and Abiri, R and Miao, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K},
title = {Sampling representational plasticity of simple imagined movements across days enables long-term neuroprosthetic control.},
journal = {Cell},
volume = {188},
number = {5},
pages = {1208-1225.e32},
pmid = {40054446},
issn = {1097-4172},
support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; R01 HD111562/HD/NICHD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Neuronal Plasticity ; Adult ; *Movement ; Female ; Electrocorticography ; Middle Aged ; Quadriplegia/physiopathology ; Robotics ; Young Adult ; },
abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what the representational stability of simple well-rehearsed actions is, particularly in humans, and their adaptability to new contexts. Using an electrocorticography brain-computer interface (BCI) in tetraplegic participants, we found that the low-dimensional manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. The manifold's absolute location, however, demonstrated constrained day-to-day drift. Strikingly, neural statistics, especially variance, could be flexibly regulated to increase representational distances during BCI control without somatotopic changes. Discernability strengthened with practice and was BCI-specific, demonstrating contextual specificity. Sampling representational plasticity and drift across days subsequently uncovered a meta-representational structure with generalizable decision boundaries for the repertoire; this allowed long-term neuroprosthetic control of a robotic arm and hand for reaching and grasping. Our study offers insights into mesoscale representational statistics that also enable long-term complex neuroprosthetic control.},
}
@article {pmid40051983,
year = {2025},
author = {Lingelbach, K and Rips, J and Karstensen, L and Mathis-Ullrich, F and Vukelić, M},
title = {Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1535799},
pmid = {40051983},
issn = {2673-6195},
abstract = {INTRODUCTION: Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.<br><br>METHODS: We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.<br><br>RESULTS: Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.<br><br>DISCUSSION: The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.},
}
@article {pmid40051611,
year = {2025},
author = {Martínez-Cagigal, V and Thielen, J and Hornero, R and Desain, P},
title = {Editorial: The role of code-modulated evoked potentials in next-generation brain-computer interfacing.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1548183},
pmid = {40051611},
issn = {1662-5161},
}
@article {pmid40051554,
year = {2025},
author = {Teman, SJ and Atwood, TC and Converse, SJ and Fry, TL and Laidre, KL},
title = {Measuring polar bear health using allostatic load.},
journal = {Conservation physiology},
volume = {13},
number = {1},
pages = {coaf013},
pmid = {40051554},
issn = {2051-1434},
abstract = {The southern Beaufort Sea polar bear sub-population (Ursus maritimus) has been adversely affected by climate change and loss of sea ice habitat. Even though the sub-population is likely decreasing, it remains difficult to link individual polar bear health and physiological change to sub-population effects. We developed an index of allostatic load, which represents potential physiological dysregulation. The allostatic load index included blood- and hair-based analytes measured in physically captured southern Beaufort bears in spring. We examined allostatic load in relation to bear body condition, age, terrestrial habitat use and, over time, for bear demographic groups. Overall, allostatic load had no relationship with body condition. However, allostatic load was higher in adult females without cubs that used terrestrial habitats the prior year, indicating potential physiological dysregulation with land use. Allostatic load declined with age in adult females without cubs. Sub-adult males demonstrated decreased allostatic load over time. Our study is one of the first attempts to develop a health scoring system for free-ranging polar bears, and our findings highlight the complexity of using allostatic load as an index of health in a wild species. Establishing links between individual bear health and population dynamics is important for advancing conservation efforts.},
}
@article {pmid40050621,
year = {2025},
author = {Cui, H and Xiao, Y and Yang, Y and Pei, M and Ke, S and Fang, X and Qiao, L and Shi, K and Long, H and Xu, W and Cai, P and Lin, P and Shi, Y and Wan, Q and Wan, C},
title = {A bioinspired in-materia analog photoelectronic reservoir computing for human action processing.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2263},
pmid = {40050621},
issn = {2041-1723},
support = {92364106//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92364204//National Natural Science Foundation of China (National Science Foundation of China)/ ; BK20220121//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Algorithms ; Neural Networks, Computer ; Transistors, Electronic ; },
abstract = {Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achieved based on such system. Furthermore, falling behaviors recognition is also verified by our system with low energy consumption for processing per action (~45.78 μJ) which outperforms most previous reports on human action processing. Our results are of profound potential for advancing computer vision based on neuromorphic electronics.},
}
@article {pmid40049535,
year = {2025},
author = {Kong, L and Zhang, Q and Wang, H and Xu, Y and Xu, C and Chen, Y and Lu, J and Hu, S},
title = {Exploration of the optimized portrait of omega-3 polyunsaturated fatty acids in treating depression: A meta-analysis of randomized-controlled trials.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {489-501},
doi = {10.1016/j.jad.2025.03.006},
pmid = {40049535},
issn = {1573-2517},
mesh = {Humans ; *Fatty Acids, Omega-3/therapeutic use ; Randomized Controlled Trials as Topic ; *Depressive Disorder/drug therapy ; Treatment Outcome ; },
abstract = {BACKGROUND: According to previous studies, omega-3 polyunsaturated fatty acids (PUFAs) are controversial for the efficacy of treating depression.<br><br>AIMS: This meta-analysis aims to investigate whether omega-3 PUFAs are able to treat depression, and find out the most beneficial clinical portrait.<br><br>METHODS: More than two reviewers searched six registries, and 36 studies were eventually considered eligible. The PRISMA guidelines were used for data extraction, Cochrane Handbook for quality assessment, and random effects model for data pooling.<br><br>OUTCOMES: Significant heterogeneity and publication bias were observed. According to the results, significant efficacy was detected in the overall analysis [SMD&#xa0;=&#xa0;-0.26, 95&#xa0;% CI&#xa0;=&#xa0;(-0.41, -0.11)] and several subgroups, while total daily dosage might be a potential heterogeneity source (P&#xa0;<&#xa0;0.05). No between-group difference was observed in the rate of response [RR&#xa0;=&#xa0;0.99, 95&#xa0;% CI&#xa0;=&#xa0;(0.82, 1.20)], remission [RR&#xa0;=&#xa0;1.17, 95&#xa0;% CI&#xa0;=&#xa0;(0.92, 1.48)], and adverse events [RR&#xa0;=&#xa0;1.07, 95&#xa0;% CI&#xa0;=&#xa0;(0.90, 1.29)]. Total daily intake of eicosapentaenoic acid (EPA) and remission rate conformed to linear correlation (P&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: 1) Omega-3 PUFAs might be effective in treating depression; 2) For Asian patients with mild to moderate depression and no other baseline medication, over 8&#xa0;weeks of omega-3 PUFAs 1000-1500&#xa0;mg/day with ratio of EPA/docosahexaenoic acid (DHA) between 1:1 and 2:1 might benefit the most; 3) Omega-3 PUFAs are no superior than placebo in rates of response, remission, and adverse events. Although several limitations exist, the evidence-based information provides guidance for clinical practice and directions for further research.<br><br>PROSPERO REGISTRATION NUMBER: CRD42023464823.},
}
@article {pmid40049458,
year = {2025},
author = {Premchand, B and Toe, KK and Wang, C and Wan, KR and Selvaratnam, T and Toh, VE and Ng, WH and Libedinsky, C and Chen, W and Lim, R and Cheng, MY and Gao, Y and Ang, KK and So, RQY},
title = {Comparing a BCI communication system in a patient with Multiple System Atrophy, with an animal model.},
journal = {Brain research bulletin},
volume = {223},
number = {},
pages = {111289},
doi = {10.1016/j.brainresbull.2025.111289},
pmid = {40049458},
issn = {1873-2747},
mesh = {*Multiple System Atrophy/physiopathology ; *Brain-Computer Interfaces ; Humans ; Animals ; Male ; Disease Models, Animal ; Neural Networks, Computer ; Electroencephalography/methods ; Macaca mulatta ; Machine Learning ; Brain/physiopathology ; Middle Aged ; },
abstract = {Paralysis affects many people worldwide, and the people affected often suffer from impaired communication. We developed a microelectrode-based Brain-Computer Interface (BCI) for enabling communication in patients affected by paralysis, and implanted it in a patient with Multiple System Atrophy (MSA), a neurodegenerative disease that causes widespread neural symptoms including paralysis. To verify the effectiveness of the BCI system, it was also tested by implanting it in a non-human primate (NHP). Data from the human and NHP were used to train binary classifiers two different types of machine learning models: a Linear Discriminant Analysis (LDA) model, and a Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN). The LDA model performed at up to 72.7 % accuracy for binary decoding in the human patient, however, performance was highly variable and was much lower on most recording days. The BCI system was able to accurately decode movement vs non-movement in the NHP (accuracy using LDA: 82.7 ± 3.3 %, LSTM: 83.7 ± 2.2 %, 95 % confidence intervals), however it was not able to with recordings from the human patient (accuracy using LDA: 47.0 ± 5.1 %, LSTM: 44.6 ± 9.9 %, 95 % confidence intervals). We discuss how neurodegenerative diseases such as MSA can impede BCI-based communication, and postulate on the mechanisms by which this may occur.},
}
@article {pmid40049046,
year = {2025},
author = {Chai, C and Yang, X and Zheng, Y and Bin Heyat, MB and Li, Y and Yang, D and Chen, YH and Sawan, M},
title = {Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: Trends for wearable and noninvasive Brain-Computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {278},
number = {},
pages = {117321},
doi = {10.1016/j.bios.2025.117321},
pmid = {40049046},
issn = {1873-4235},
mesh = {Humans ; *Magnetoencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Photoacoustic Techniques/instrumentation/methods ; *Wearable Electronic Devices ; Brain/physiology/diagnostic imaging ; Electroencephalography/instrumentation ; Biosensing Techniques/instrumentation ; Spectroscopy, Near-Infrared/methods/instrumentation ; Multimodal Imaging/instrumentation ; },
abstract = {Wearable noninvasive brain-computer interface (BCI) technologies, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), have experienced significant progress since their inception. However, these technologies have not achieved revolutionary advancements, largely because of their inherently low signal-to-noise ratio and limited penetration depth. In recent years, the application of quantum-theory-based optically pumped (OP) technologies, particularly optical pumped magnetometers (OPMs) for magnetoencephalography (MEG) and photoacoustic imaging (PAI) utilizing OP pulsed laser sources, has opened new avenues for development in noninvasive BCIs. These advanced technologies have garnered considerable attention owing to their high sensitivity in tracking neural activity and detecting blood oxygen saturation. This paper represents the first attempt to discuss and compare technologies grounded in OP theory by examining the technical advantages of OPM-MEG and PAI over traditional EEG and fNIRS. Furthermore, the paper investigates the theoretical and structural feasibility of hardware reuse in OPM-MEG and PAI applications.},
}
@article {pmid40048825,
year = {2025},
author = {Kacker, K and Chetty, N and Feldman, AK and Bennett, J and Yoo, PE and Fry, A and Lacomis, D and Harel, NY and Nogueira, RG and Majidi, S and Opie, NL and Collinger, JL and Oxley, TJ and Putrino, DF and Weber, DJ},
title = {Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {40048825},
issn = {1741-2552},
support = {UH3 NS120191/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology/diagnosis ; *Motor Cortex/physiopathology/physiology ; Male ; *Brain-Computer Interfaces ; *Gamma Rhythm/physiology ; *Electrocorticography/methods/instrumentation ; Middle Aged ; Female ; Stents ; Electrodes, Implanted ; Motor Activity/physiology ; Movement/physiology ; Aged ; },
abstract = {Objective.This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (vECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to amyotrophic lateral sclerosis.Approach.vECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of vECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent.Main results.Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53% for low gamma and 54.23 ± 4.52% for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09% for low gamma and 22.53 ± 2.04% for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. vECoG amplitudes remained significantly different between rest and move periods over the 3 month testing period, with >90% accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43% for Participant 1 and 70.37% for Participant 2 in offline decoding of multiple attempted movements and rest conditions.Significance.By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 month testing period reported here.},
}
@article {pmid40048822,
year = {2025},
author = {Berger, LM and Wood, G and Kober, SE},
title = {Manipulating cybersickness in virtual reality-based neurofeedback and its effects on training performance.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbd76},
pmid = {40048822},
issn = {1741-2552},
mesh = {Humans ; Female ; Male ; *Virtual Reality ; *Neurofeedback/methods/physiology ; Adult ; Young Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Psychomotor Performance/physiology ; Heart Rate/physiology ; Nausea/etiology ; Motion Sickness/etiology/physiopathology/prevention &amp; control ; },
abstract = {Objective. Virtual reality (VR) serves as a modern and powerful tool to enrich neurofeedback (NF) and brain-computer interface (BCI) applications as well as to achieve higher user motivation and adherence to training. However, between 20%-80% of all the users develop symptoms of cybersickness (CS), namely nausea, oculomotor problems or disorientation during VR interaction, which influence user performance and behavior in VR. Hence, we investigated whether CS-inducing VR paradigms influence the success of a NF training task.Approach. We tested 39 healthy participants (20 female) in a single-session VR-based NF study. One half of the participants was presented with a high CS-inducing VR-environment where movement speed, field of view and camera angle were varied in a CS-inducing fashion throughout the session and the other half underwent NF training in a less CS-inducing VR environment, where those parameters were held constant. The NF training consisted of 6 runs of 3 min each, in which participants should increase their sensorimotor rhythm (SMR, 12-15 Hz) while keeping artifact control frequencies constant (Theta 4-7 Hz, Beta 16-30 Hz). Heart rate and subjectively experienced CS were also assessed.Main results. The high CS-inducing condition tended to lead to more subjectively experienced CS nausea symptoms than the low CS-inducing condition. Further, women experienced more CS, a higher heart rate and showed a worse NF performance compared to men. However, the SMR activity during the NF training was comparable between both the high and low CS-inducing groups. Both groups were able to increase their SMR across feedback runs, although, there was a tendency of higher SMR power for male participants in the low CS group.Significance. Hence, sickness symptoms in VR do not necessarily impair NF/BCI training success. This takes us one step further in evaluating the practicability of VR in BCI and NF applications. Nevertheless, inter-individual differences in CS susceptibility should be taken into account for VR-based NF applications.},
}
@article {pmid40048236,
year = {2025},
author = {Zhang, Y and Hedley, FE and Zhang, RY and Jin, J},
title = {Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.},
journal = {Journal of psychopathology and clinical science},
volume = {134},
number = {4},
pages = {363-388},
doi = {10.1037/abn0000972},
pmid = {40048236},
issn = {2769-755X},
support = {//National Key R&amp;D Program of China/ ; //National Natural Science Foundation of China/ ; //Zhejiang University; State Key Laboratory of Brain-Machine Intelligence/ ; //Ministry of Education; Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
mesh = {Humans ; *Phobia, Social/physiopathology/psychology ; *Models, Psychological ; *Cognitive Behavioral Therapy ; },
abstract = {Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).},
}
@article {pmid40047565,
year = {2025},
author = {Haghighi, P and Jeakle, EN and Sturgill, BS and Abbott, JR and Solis, E and Devata, VS and Vijayakumar, G and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ},
title = {Enhanced Performance of Novel Amorphous Silicon Carbide Microelectrode Arrays in Rat Motor Cortex.},
journal = {Micromachines},
volume = {16},
number = {2},
pages = {},
pmid = {40047565},
issn = {2072-666X},
support = {2R01TW104344-22S5/NH/NIH HHS/United States ; },
abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity from cortical neurons for applications that include brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic implantation conditions. This has largely been attributed to the brain's foreign body response, which is marked by neuroinflammation and gliosis in the immediate vicinity of the MEA implantation site. This has prompted the development of novel MEAs with either coatings or architectures that aim to reduce the tissue response. The present study examines the comparative performance of multi-shank planar, silicon-based devices and low-flexural-rigidity amorphous silicon carbide (a-SiC) MEAs that have a similar architecture but differ with respect to the shank cross-sectional area. Data from a-SiC arrays were previously reported in a prior study from our group. In a manner consistent with the prior work, larger cross-sectional area silicon-based arrays were implanted in the motor cortex of female Sprague-Dawley rats and weekly recordings were made for 16 weeks after implantation. Single unit metrics from the recordings were compared over the implantation period between the device types. Overall, the expression of single units measured from a-SiC devices was significantly higher than for silicon-based MEAs throughout the implantation period. Immunohistochemical analysis demonstrated reduced neuroinflammation and gliosis around the a-SiC MEAs compared to silicon-based devices. Our findings demonstrate that the a-SiC MEAs with a smaller shank cross-sectional area can record single unit activity with more stability and exhibit a reduced inflammatory response compared to the silicon-based device employed in this study.},
}
@article {pmid40046512,
year = {2025},
author = {Fang, K and Wang, Z and Tang, Y and Guo, X and Li, X and Wang, W and Liu, B and Dai, Z},
title = {Dynamically Controlled Flight Altitudes in Robo-Pigeons via Locus Coeruleus Neurostimulation.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0632},
pmid = {40046512},
issn = {2639-5274},
abstract = {Robo-pigeons, a novel class of hybrid robotic systems developed using brain-computer interface technology, hold marked promise for search and rescue missions due to their superior load-bearing capacity and sustained flight performance. However, current research remains largely confined to laboratory environments, and precise control of their flight behavior, especially flight altitude regulation, in a large-scale spatial range outdoors continues to pose a challenge. Herein, we focus on overcoming this limitation by using electrical stimulation of the locus coeruleus (LoC) nucleus to regulate outdoor flight altitude. We investigated the effects of varying stimulation parameters, including stimulation frequency (SF), interstimulus interval (ISI), and stimulation cycles (SC), on the flight altitude of robo-pigeons. The findings indicate that SF functions as a pivotal switch controlling the ascending and descending flight modes of the robo-pigeons. Specifically, 60 Hz stimulation effectively induced an average ascending flight of 12.241 m with an 87.72% success rate, while 80 Hz resulted in an average descending flight of 15.655 m with a 90.52% success rate. SF below 40 Hz did not affect flight altitude change, whereas over 100 Hz caused unstable flights. The number of SC was directly correlated with the magnitude of altitude change, enabling quantitative control of flight behavior. Importantly, electrical stimulation of the LoC nucleus had no significant effects on flight direction. This study is the first to establish that targeted variation of electrical stimulation parameters within the LoC nucleus can achieve precise altitude control in robo-pigeons, providing new insights for advancing the control of flight animal-robot systems in real-world applications.},
}
@article {pmid40044089,
year = {2025},
author = {Jialin, A and Zhang, HG and Wang, XH and Wang, JF and Zhao, XY and Wang, C and Cao, MN and Li, XJ and Li, Y and Cao, LL and Zhong, BL and Deng, W},
title = {Cortical activation patterns in generalized anxiety and major depressive disorders measured by multi-channel near-infrared spectroscopy.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {549-558},
doi = {10.1016/j.jad.2025.02.116},
pmid = {40044089},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/diagnosis/psychology ; Spectroscopy, Near-Infrared/methods ; Male ; Female ; *Anxiety Disorders/physiopathology/diagnosis/psychology ; Adult ; *Prefrontal Cortex/physiopathology ; Temporal Lobe/physiopathology ; Middle Aged ; Generalized Anxiety Disorder ; },
abstract = {BACKGROUND: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent mental disorders in psychiatry, but their overlapping symptoms often complicate precise diagnoses. This study aims to explore differential brain activation patterns in healthy controls (HC), MDD, and GAD groups through functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) to enhance the accuracy of clinical diagnoses.<br><br>METHODS: This study recruited 30 patients with MDD, 45 patients with GAD, and 34 demographically matched HCs. Hemodynamic changes in the prefrontal cortex (PFC) and temporal lobes were measured using a 48-channel fNIRS during the VFT task. Demographics information, clinical characteristics and VFT performance were also recorded.<br><br>RESULTS: Compared to HCs, both MDD and GAD share a neurobiological phenotype of hypoactivation in the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) during VFT. Moreover, MDD patients exhibited significantly greater hypoactivation in the left DLPFC and mPFC than GAD patients.<br><br>CONCLUSIONS: Although both GAD and MDD patients exhibit disrupted cortical function, the impairment is less severe in GAD. These findings provide preliminary neurophysiological evidence supporting the utility of the fNIRS-VFT paradigm in differentiating GAD from MDD. This approach may complement traditional diagnostic methods, inform targeted interventions, and ultimately enhance patient outcomes.},
}
@article {pmid40044088,
year = {2025},
author = {Liang, S and Gao, Y and Palaniyappan, L and Song, XM and Zhang, T and Han, JF and Tan, ZL and Li, T},
title = {Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {118-126},
doi = {10.1016/j.jad.2025.03.003},
pmid = {40044088},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/genetics/pathology/diagnostic imaging/psychology ; *Anhedonia/physiology ; Male ; Female ; Adult ; *Cerebral Cortex/pathology/diagnostic imaging ; Middle Aged ; Magnetic Resonance Imaging ; Neuroimaging ; *Brain Cortical Thickness ; Gene Expression ; Transcriptome ; },
abstract = {BACKGROUND: Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.<br><br>METHODS: We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.<br><br>RESULTS: Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.<br><br>LIMITATIONS: These include measuring anhedonia using interest-activity score and employing a cross-sectional design.<br><br>CONCLUSIONS: This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.},
}
@article {pmid40043367,
year = {2025},
author = {Demchenko, I and Shavit, T and Benyamini, M and Zacksenhouse, M},
title = {Self-correcting brain computer interface based on classification of multiple error-related potentials.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbcda},
pmid = {40043367},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Algorithms ; Evoked Potentials/physiology ; Reproducibility of Results ; },
abstract = {Objective.Electroencephalogram (EEG) based brain-computer interfaces (BCIs) have shown tremendous promise in facilitating direct non-invasive brain-control over external devices. However, their practical application is hampered due to errors in command interpretation. A promising strategy for improving BCI accuracy is based on detecting error-related potentials (ErrPs), which are EEG potentials evoked in response to errors. Thus, performance can be improved by undoing actions that evoke potentials that the BCI detects as ErrPs. To achieve further improvement, we aimed to classify the type of error and correct, rather than just undo, erroneous actions. The objectives of this study are to develop an error classifier (EC) and to investigate the hypothesis that correcting the actions according to the EC decisions improves performance.Approach.To evaluate our hypothesis we developed a BCI application to control the pose of virtual hands with three possible commands: change the pose of either the right or left hand and maintain pose. Thus, when an action elicits an ErrP, the identity of the correct command is still undecided. The self-correcting BCI included an EC and was developed in three phases: hand control, initial brain control and self-correcting brain control. The first two phases were conducted by 22 participants, and half of them (n= 11) also completed the last phase.Main results.Detecting the type of error and correcting actions accordingly improved the success rate of the self-correcting BCI for each participant (n= 11), with a significant average improvement of 6.6%and best improvement of 13.5%.Significance.Self-correction, based on an EC, was demonstrated to improve the accuracy of BCIs for three commands. Thus, our work presents a significant step toward the development of more reliable and user-friendly non-invasive BCIs.},
}
@article {pmid40043361,
year = {2025},
author = {Luca, IS and Vuckovic, A},
title = {How are opposite neurofeedback tasks represented at cortical and corticospinal tract levels?.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbcdb},
pmid = {40043361},
issn = {1741-2552},
mesh = {Humans ; *Neurofeedback/methods/physiology ; *Pyramidal Tracts/physiology ; Male ; Female ; Adult ; Young Adult ; *Electroencephalography/methods ; *Evoked Potentials, Motor/physiology ; Motor Cortex/physiology ; Cerebral Cortex/physiology ; },
abstract = {Objective.The study objective was to characterise indices of learning and patterns of connectivity in two neurofeedback (NF) paradigms that modulate mu oscillations in opposite directions, and the relationship with change in excitability of the corticospinal tract (CST).Approach.Forty-three healthy volunteers participated in 3 NF sessions for upregulation (N = 24) or downregulation (N = 19) of individual alpha (IA) power at central location Cz. Brain signatures from multichannel electroencephalogram (EEG) were analysed, including oscillatory (power, spindles), non-oscillatory components (Hurst exponent), and effective connectivity directed transfer function (DTF) of participants who were successful at enhancing or suppressing IA power at Cz. CST excitability was studied through leg motor-evoked potential, tested before and after the last NF session. We assessed whether participants modulated widespread alpha or central mu rhythm through the use of current source density derivation (CSD), and related the change in activity in mu and upper half of mu band, to CST excitability change.Main results.In the last session, IA/mu power suppression was achieved by 79% of participants, while 63% enhanced IA. CSD-EEG revealed that mu power was upregulated through an increase in the incidence rate of bursts of alpha band activity, while downregulation involved changes in oscillation amplitude and temporal patterns. Neuromodulation also influenced frequencies adjacent to the targeted band, indicating the use of common mental strategies within groups. DTF analysis showed, for both groups, significant connectivity between structures commonly associated with motor imagery tasks, known to modulate the excitability of the motor cortex, although most connections did not remain significant after correcting for multiple comparisons. CST excitability modulation was related to the absolute amplitude of upper mu modulation, rather than the modulation direction.Significance.The upregulation and downregulation of IA/mu power during NF, with respect to baseline were achieved via distinct mechanisms involving oscillatory and non-oscillatory EEG features. Mu enhancement and suppression post-NF and during the last NF block with respect to the baseline, respectively corresponded to opposite trends in motor-evoked potential changes post-NF. The ability of NF to modulate CST excitability could be a valuable rehabilitation tool for central nervous system disorders (stroke, spinal cord injury), where increased excitability and neural plasticity are desired. This work may inform future neuromodulation protocols, and may improve NF training effectiveness by rewarding certain EEG signatures.},
}
@article {pmid40043320,
year = {2025},
author = {Alcolea, PI and Ma, X and Bodkin, K and Miller, LE and Danziger, ZC},
title = {Less is more: selection from a small set of options improves BCI velocity control.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbcd9},
pmid = {40043320},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; Animals ; *Macaca mulatta ; Male ; Algorithms ; Female ; Adult ; Electroencephalography/methods ; Young Adult ; Psychomotor Performance/physiology ; },
abstract = {Objective.Decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities.Approach. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 naïve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF).Main Result. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per visit compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF.Significance. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control.},
}
@article {pmid40043184,
year = {2025},
author = {Chandler, JA},
title = {Inferring Mental States from Brain Data: Ethico-legal Questions about Social Uses of Brain Data.},
journal = {The Hastings Center report},
volume = {55},
number = {1},
pages = {22-32},
pmid = {40043184},
issn = {1552-146X},
support = {//ERANET-Neuron Program/ ; /CAPMC/CIHR/Canada ; },
mesh = {Humans ; *Brain ; Privacy/legislation &amp; jurisprudence ; Reproducibility of Results ; },
abstract = {Neurotechnologies that collect and interpret data about brain activity are already in use for medical and nonmedical applications. Refinements of existing noninvasive techniques and the discovery of new ones will likely encourage broader uptake. The increased collection and use of brain data and, in particular, their use to infer the existence of mental states have led to questions about whether mental privacy may be threatened. It may be threatened if the brain data actually support inferences about the mind or if decisions are made about a person in the belief that the inferences are justified. This article considers the chain of inferences lying between data about neural activity and a particular mental state as well as the ethico-legal issues raised by making these inferences, focusing here on what the threshold of reliability should be for using brain data to infer mental states.},
}
@article {pmid40043182,
year = {2025},
author = {Yang, Y and Wang, Y and Wang, X},
title = {Harnessing psychedelics for stroke recovery: therapeutic potential and mechanisms.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf093},
pmid = {40043182},
issn = {1460-2156},
}
@article {pmid40042910,
year = {2025},
author = {Xie, B and Xiong, T and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {10},
pages = {e2417040122},
pmid = {40042910},
issn = {1091-6490},
support = {22125406 22074149//MOST | National Natural Science Foundation of China (NSFC)/ ; },
abstract = {The fluidic memristor has attracted growing attention as a promising candidate for neuromorphic computing and brain-computer interfaces. However, a fluidic memristor with ion selectivity as that of natural ion channels remains a key challenge. Herein, inspired by the structure of natural biomembranes, we developed an ion-shuttling memristor (ISM) by utilizing organic solvents and artificial carriers to emulate ion channels embedded in biomembranes, which exhibited both neuromorphic functions and ion selectivity. Pinched hysteresis I-V loop curve, scan rate dependency, and distinctive impedance spectra confirmed the memristive characteristics of the as-prepared device. Moreover, the memory mechanism was discussed theoretically and validated by finite-element modeling. The ISM features multiple neuromorphic functions, such as paired-pulse facilitation, paired-pulse depression, and learning-experience behavior. More importantly, the ion selectivity of the ISM was observed, which allowed further emulation of ion-selective neural functions like resting membrane potential. Benefiting from the structural similarity to membrane-embedded ion channels, the ISM opens the door for ion-based neuromorphic computing and sophisticated chemical regulation by manipulating multifarious ions with neuromorphic functions.},
}
@article {pmid40042891,
year = {2025},
author = {Gielas, AM},
title = {Man, Hibernating Animals, and Poikilothermic Fish: The Present and Future of BCI Technology.},
journal = {Journal of special operations medicine : a peer reviewed journal for SOF medical professionals},
volume = {25},
number = {1},
pages = {50-54},
doi = {10.55460/FA29-NVKE},
pmid = {40042891},
issn = {1553-9768},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Military Personnel ; Animals ; },
abstract = {In 2024 and early 2025, several successful surgeries involving brain-computer interfaces (BCIs) gained media attention, including those conducted by Elon Musk's company Neuralink, which implanted BCIs in three paralyzed volunteers, allowing them to control computers through thought alone. While the concept of merging humans with machines dates back to the 1960s, BCI technology has now entered the clinical trial stage, with a focus on restoring communication, mobility, and sensation in individuals with severe disabilities and neurodegenerative disorders. For over two decades, BCIs have also been explored as tools to enhance the cognitive and physical abilities of military personnel. However, before Special Operations Forces (SOF) medical staff encounter BCIs in an enhancement capacity, they are likely to first come across them in medical settings. This article provides an overview of BCI technology, focusing on 1) how it works, 2) its potential significance for injured SOF servicemembers, 3) current challenges, and 4) its potential to enhance SOF in the future.},
}
@article {pmid40040918,
year = {2025},
author = {Jiang, Y and Liu, YL and Zhou, X and Shu, QQ and Dong, L and Xu, Z and Wan, JQ},
title = {A retrospective study of the Dual-channels Bolus Contrast Injection (Dc-BCI) technique during endovascular mechanical thrombectomy in the management of acute ischemic stroke due to large-vessel occlusion: a technical report.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1508976},
pmid = {40040918},
issn = {1664-2295},
abstract = {Endovascular mechanical thrombectomy (EMT) is an effective treatment for acute ischemic stroke and identifying the precise thrombus size remains key to a successful EMT. However, no imaging modality has been able to provide this information simultaneously and efficiently in an emergency setting. The present study introduces a novel technique named dual-channel bolus contrast injection (Dc-BCI) for determining thrombus size and location during EMT. In the in vitro study, the Dc-BCI demonstrated an accurate projection of the thrombus size, as the actual thrombus diameter (R[2] = 0.92, p < 0.01) and length (R[2] = 0.94, p < 0.01) exhibited a high degree of correlation with that of obtained from Dc-BCI. Consequently, between February 2023 and August 2024, 87 patients diagnosed with acute cerebral large vessel occlusions were enrolled in the study and received EMT for the treatment of acute cerebral large vessel occlusions. The Dc-BCI was successfully performed in all patients to measure the diameter and length of the thrombus. These information were used to select an appropriate stent-retriever for EMT. The restoration of blood flow was achieved in 84 patients (96.6%) to an mTICI score of 2b/3. Additionally, a low incidence of postoperative complications was observed (e.g., subarachnoid hemorrhage 8% and cerebral hemorrhage 5.7%). In conclusion, it can be posited that the Dc-BCI has the potential to enhance the outcomes of EMT, as it is capable of revealing the thrombus size information, which optimizes the interaction between the stent retriever and the thrombus, while simultaneously reducing the risk of vascular injury that is associated with the prolonged use of the stent retriever.},
}
@article {pmid40040909,
year = {2025},
author = {Tekin, U and Dener, M},
title = {A bibliometric analysis of studies on artificial intelligence in neuroscience.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1474484},
pmid = {40040909},
issn = {1664-2295},
abstract = {The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.},
}
@article {pmid40040811,
year = {2025},
author = {Feng, X and Bao, X and Huang, H and Wang, Z and Hu, W and Xue, C and Song, Z and Cai, Y and Huang, Q and Li, Y},
title = {Frontal gamma-alpha ratio reveals neural oscillatory mechanism of attention shifting in tinnitus.},
journal = {iScience},
volume = {28},
number = {3},
pages = {111929},
pmid = {40040811},
issn = {2589-0042},
abstract = {In clinical practice, the symptoms of tinnitus patients can be temporarily alleviated by diverting their attention away from disturbing sounds. However, the precise mechanisms through which this alleviation occurs are still not well understood. Here, we aimed to directly evaluate the role of attention in tinnitus alleviation by conducting distraction tasks with multilevel loads and resting-state tests among 52 adults with tinnitus and 52 healthy controls. We demonstrated that the abnormal neural oscillations in tinnitus subjects, reflected in an altered gamma/alpha ratio index in the frontal lobe, could be regulated by attention shifting in a linear manner for which the regulatory effect increased with the load of distraction. Quantitative measures of the regulation significantly correlated with symptom severity. Altogether, our work provides proof-of-concept for the role of attention in tinnitus perception and lays a solid foundation to support evidence-based applications of attention shifting in clinical interventions for tinnitus.},
}
@article {pmid40040231,
year = {2024},
author = {Chaudhry, ZA and Baxter, RH and Fu, JL and Wang, PT and Sohn, WJ and Do, AH},
title = {Feasibility of Immersive Virtual Reality Feedback for Enhancing Learning in Brain-Computer Interface Control of Ambulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782667},
pmid = {40040231},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Walking/physiology ; *Electroencephalography ; Male ; Adult ; Feasibility Studies ; Learning ; Female ; Young Adult ; Spinal Cord Injuries/rehabilitation/physiopathology ; User-Computer Interface ; },
abstract = {After prolonged paralysis, paraplegic spinal cord injury (SCI) patients typically lose the ability to generate the expected electroencephalogram (EEG) α/β modulation associated with leg movements. Brain computer interface (BCI)-controlled ambulation devices have emerged as a way to restore brain-controlled walking, but this loss of EEG signal modulation may impede the ability to operate such systems and prolonged training may be necessary to restore this physiologic phenomenon. To address this issue, this study explores the use of immersive virtual reality (VR) in providing more convincing feedback to enhance learning within a BCI training paradigm. Here, an EEG-based BCI-controlled walking simulator with an environment composed of 10 designated stop zones along a linear course was used to test this concept. Able-bodied subjects were tasked with using idling or kinesthetic motor imagery (KMI) of gait to control an avatar to either dwell at each designated stop for 5 s or advance along the course respectively. Subject performance was measured using a composite score per run and learning rate across runs. Composite scores were calculated as the geometric mean of two subscores: a stop score (reflecting the number of successful stops), and a time score (reflecting how fast the course was completed). The learning rate was calculated as the slope of the composite scores across all runs. A random walk procedure was performed to determine the statistical likelihood that each BCI run was purposeful (p≤ 0.001). Three able-bodied subjects were recruited (2 in immersive VR group and 1 in non-immersive VR group), and operated the simulator for up to 4 separate visits. The immersive VR group achieved an average composite score of 60.4% ± 12.9, while the non-VR group had an average composite score of 79.0% ± 12.2. The learning rate was 1.07%/run and 0.42%/run for the immersive and non-immersive VR groups, respectively. Purposeful control was attained in a higher proportion of runs for the immersive VR group than in the non-immersive VR group. Although limited by small sample size, this study demonstrates a conceptual framework of implementing immersive VR feedback using more convincing sensory feedback to aid training with BCI devices. Future work will test this protocol in SCI patients and with larger sample size.},
}
@article {pmid40040213,
year = {2024},
author = {Okitsu, K and Isezaki, T and Obara, K and Nishimura, Y},
title = {Enhancing Brain Machine Interface Decoding Accuracy through Domain Knowledge Integration.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782166},
pmid = {40040213},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Macaca mulatta ; Muscle, Skeletal/physiology ; Algorithms ; Humans ; },
abstract = {This paper introduces a novel decoding approach for Brain Machine Interface (BMI) that enhances the estimation accuracy and stability of muscle activity by incorporating domain knowledge of motor control. Our approach uniquely integrates domain knowledge, focusing on the relationship between torque direction and muscle activity in isometric wrist tasks. We demonstrate the effectiveness of our approach through decoding analysis with non-human primates performing a wrist torque tracking task. By implementing a Kalman filter augmented with models of muscle activity and torque for specific movement directions, we show significant improvements compared to vanilla Kalman filter in the accuracy of muscle activity estimation. The proposed approach presents a promising direction for enhancing the performance of BMI by leveraging domain-specific insights into motor control.},
}
@article {pmid40040208,
year = {2024},
author = {Li, D and Shin, HB and Yin, K and Lee, SW},
title = {Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781886},
pmid = {40040208},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification. Specifically, to learn and retain common features between subjects, we separate latent representations into subject-invariant and subject-specific features through adversarial training, while also proposing an extensible architecture to preserve features that are easily forgotten. Additionally, we incorporate a memory replay mechanism to reinforce previously acquired knowledge. Through extensive experiments, we demonstrate our framework's effectiveness in mitigating forgetting within the continual MI-EEG classification task.},
}
@article {pmid40040206,
year = {2024},
author = {Huang, CM and Lai, WL and Yang, CC and Hsieh, YJ and Wu, CM and Lee, CH},
title = {EEG Channel Localization and Selection via Training with Noise Injection for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782774},
pmid = {40040206},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) is crucial for monitoring brain activity in neuroscience and clinical applications. However, the multitude of channels recorded by scalp electrodes poses challenges, including impractical usage and high model complexity. This paper addresses the challenges of high dimensionality in EEG data and introduces an innovative EEG channel selection algorithm, LSvT-NI, based on model training and noise injection, achieving substantial reductions in channels, model size, and complexity while maintaining high classification accuracy. Validated through experiments on EEGNet and the BCI Competition IV 2a dataset, the algorithm proves beneficial for practical and cost-efficient scenarios. Specifically, experiments on the BCI Competition IV 2a dataset demonstrate that LSvT-NI with white noise and pink noise at 5dB SNR achieves a remarkable 77.3% and 72.7% reduction in channels, along with 11.7% and 11% reductions in model size, and 86.9% and 71.8% in computation complexity.},
}
@article {pmid40040181,
year = {2024},
author = {Sartipi, S and Cetin, M},
title = {Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782060},
pmid = {40040181},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance.},
}
@article {pmid40040170,
year = {2024},
author = {Meng, J and Yang, M and Zhang, S and Xu, M and Meng, L and Ming, D},
title = {An online brain-computer interface for a precise positioning of target based on rapid serial visual presentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782815},
pmid = {40040170},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Evoked Potentials/physiology ; Brain/physiology ; Photic Stimulation ; },
abstract = {The brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) provides a novel approach for efficiently optimizing traditional machine-based target detection, revealing a broad application prospect in security, entrainment, monitoring, etc. A bottleneck of current RSVP-BCI is that its detectable result is limited to a binary way, i.e., target vs. non-target, more detailed and important information about targets, such as the precise position, remains undetectable. To solve this problem, this study investigated the relationship between targets positions (up, down, left, right) and electroencephalogram (EEG) characteristics, and tested the separability of EEGs induced by the four targets positions in an online RSVP-BCI. Twelve healthy subjects participated in this study, event-related potential (ERP), topographies, laterality index (LI), discriminant canonical pattern matching (DCPM) methods were used to analyzed the EEG data. Consequently, left-right targets induced ipsilateral ERPs between bilateral hemispheres; when targets appeared at up and down positions, opposite ERPs were found between frontal and occipital areas; up-down and left-right difference reached its maximum in the 140~190ms and 190~240ms temporal window, respectively. Single-trial classification showed five-class balanced accuracy (BACC) (non-target, target at up/ down/ left/ right position) was 71.02% and 67.91% for offline and online sessions, respectively. The results provide new understanding of the RSVP features for developing BCIs.},
}
@article {pmid40040166,
year = {2024},
author = {Zhuo, F and Lv, B and Tang, F},
title = {Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782640},
pmid = {40040166},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {The existing Riemannian geometry-based approaches for brain computer interface (BCI) employ fixed time windows. However, the inherent variability and dynamic changes among subjects necessitate robust and adaptive solutions for time window optimization. Recognizing the current limitations of Riemannian classifiers, we propose a time window selection confidence metric (TWSCM) based on Riemannian geometry. This metric operates on the manifold of symmetric positive definite (SPD) matrices, providing a theoretically grounded and computationally efficient approach for time window optimization. The optimization process is unsupervised, which is able to deal with the online scenario without training labels. Experimental results on the BCI competition IV dataset IIa demonstrate that the classification performance is significantly improved for most subjects. The average performance over six subjects improved by 7.52%. The simulated online experiment shows enhanced performance in comparison to baseline experiments without time window optimization. Additionally, an in-depth analysis of TWSCM provides insights into performance variations among subjects. Overall, this paper introduces the first time window optimization method within the Riemannian geometric framework, presenting an effective and interpretable approach for optimizing time windows in motor imagery classification, providing a novel and promising perspective in EEG signal analysis.},
}
@article {pmid40040155,
year = {2024},
author = {Huang, J and Tostado-Marcos, P and Narasimha, SM and Patel, AN and Arneodo, EM and Gentner, TQ and Mishne, G and Gilja, V},
title = {Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782761},
pmid = {40040155},
issn = {2694-0604},
mesh = {Animals ; *Finches/physiology ; *Vocalization, Animal/physiology ; Humans ; Brain/physiology ; Brain-Computer Interfaces ; Speech/physiology ; },
abstract = {State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.},
}
@article {pmid40040137,
year = {2024},
author = {Idowu, OP and Kinney-Lang, E and Gulamhusein, A and Irvine, B and Kirton, A and Abou-Zeid, H},
title = {Profiling a Raspberry Pi-Based Motor Imagery Classification to Facilitate At-Home BCI for Children with Disabilities.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10781873},
pmid = {40040137},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Children with Disabilities/rehabilitation ; Neural Networks, Computer ; Imagination/physiology ; Electroencephalography/methods ; Machine Learning ; },
abstract = {There has been incremental progress in moving BCI out of the laboratory environment and into the homes of those who would benefit most, especially children living with severe physical disabilities. Practical issues, such as available computational resources and long calibration times, have slowed down the adoption of such systems. To develop an efficient and scalable machine learning framework consistent with early approaches that facilitate at-home BCI use, this study provides valuable insights into measuring the behavioral characteristics of a Raspberry Pi 4 (RPi4) during the operation and execution of standard BCI processes, including the training and evaluation of classifier models. The results, which evaluated ten standard classifiers, including the Riemannian Geometry (RG) framework and more advanced deep learning approaches like Artificial Neural Network (ANN), were profiled on RPi4. These were compared to Desktop and MacBook computations for metrics such as training time, inference time, peak memory, and incremental memory usage, with computational bottlenecks identified. Our assessment revealed comparable performance metrics (84.3% of accuracy, recall, and f1_score, and 84.7% precision) for the neural network models despite the lower computational resources. Profiling results, including 1.74 sec training time, 0.405 sec inference time, 1154.9 MiB peak memory, and 405.2 MiB incremental memory usage, also demonstrated that the RPi4 is a potentially viable device for low-cost BCI systems. However, high-resource demanding classifiers such as ANN may need to be carefully considered in their implementation, which, in turn, will scale down the potential cost and complexity of adopting practical, impactful at-home BCI systems.},
}
@article {pmid40040110,
year = {2024},
author = {Wang, Z and Liu, Y and Wu, W and Huang, S and An, X and Ming, D},
title = {EEG Pattern Comparison and Classification Performance of Motor Imagery Between Supernumerary and Inherent Limbs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782967},
pmid = {40040110},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Movement/physiology ; Robotics ; Female ; Young Adult ; },
abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm" and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm. The results showed that imagining extra limbs induced more obvious event-related desynchronization (ERD) phenomenon in sensorimotor areas compared to imagining inherent limbs. Classification results indicate well separable performance among different mental tasks (all above 86%, with a maximum of 90.5%). This work proposed a novel type of MI paradigm, and offered new way for widening the control bandwidth of the BCI system.},
}
@article {pmid40040096,
year = {2024},
author = {Lim, EY and Yin, K and Shin, HB and Lee, SW},
title = {Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781970},
pmid = {40040096},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Signal-To-Noise Ratio ; },
abstract = {Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification. Our approach employs data augmentation to strengthen model robustness, an autoencoder to extract features from baseline and MI signals, a feature alignment module to separate specific task and noise, and a classifier. We achieve state-of-the-art performance, an improvement of 5.9% and 3.7% on the BCIC IV 2a and 2b datasets, respectively. The qualitative analysis of our learned features proves superior representational power over baseline models, a critical aspect in dealing with noisy EEG signals. Our findings demonstrate the efficacy of readily available baseline signals in enhancing performance, opening possibilities for simplified BCI systems in brain-based communication applications.},
}
@article {pmid40040056,
year = {2024},
author = {Huang, S and Liu, Y and Xu, W and Wang, Z and Ming, D},
title = {Enhancement of Functional Connectivity in Frontal-Parietal Regions After BCI-Actuated Supernumerary Robotic Finger Training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781807},
pmid = {40040056},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiology ; Male ; *Robotics ; *Parietal Lobe/physiology/diagnostic imaging ; *Frontal Lobe/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Adult ; Female ; Young Adult ; },
abstract = {The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanisms of brain-actuated SRF training is not clear. In this study, a BCI-actuated SRF was used to investigate the concurrent changes in behavior and brain activity. After 4 weeks BCI-SRF training, the novel sequence operation accuracy rate enhanced by more than 350% compared with innate finger training (IFT). Task-based fMRI showed a significant increase in lateral activation of sensorimotor cortex and found a significant activation change in S1M1_L area. Moreover, BCI-SRF training significantly increase functional connectivity (FC) between S1M1_L and Frontal_Mid_L compared with IFT at post stage. And this FC increase in frontal-parietal is also significant at post vs pre in BCI-SRF group and significantly correlated with the improvement of motor sequence accuracy rate. Our findings provide useful insights into the enhanced human-machine interaction and this efficacy exhibited significant potential for clinical rehabilitation application.},
}
@article {pmid40040035,
year = {2024},
author = {Norouzi, M and Amirani, MZ and Shahriari, Y and Abiri, R},
title = {Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782784},
pmid = {40040035},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Young Adult ; },
abstract = {In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.},
}
@article {pmid40040033,
year = {2024},
author = {Ferdous, TR and Pollonini, L and Francis, JT},
title = {Enhancing Auditory BCI Performance: Incorporation of Connectivity Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782147},
pmid = {40040033},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Support Vector Machine ; Brain/physiology/diagnostic imaging ; Acoustic Stimulation ; },
abstract = {Brain connectivity analysis to classify auditory stimuli applicable to invasive auditory BCI technology, particularly intracranial electroencephalography (iEEG) remains an exciting frontier. This study revealed insights into brain network dynamics, improving analysis precision to distinguish related auditory stimuli such as speech and music. We thereby contribute to advancing auditory BCI systems to bridge the gap between noninvasive and invasive BCI by utilizing noninvasive BCI methodological frameworks to invasive BCI (iEEG) data. We focused on the viability of using connectivity matrices in BCI calculated across brain waves such as alpha, beta, theta, and gamma. The research highlights that the traditional machine learning classifier, Support Vector Machine (SVM), demonstrates exceptional capabilities in handling brain connectivity data, exhibiting an outstanding 97% accuracy in classifying brain states, surpassing previous relevant studies with an improvement of 9.64% The results are significant as we show that neural activity in the gamma band provides the best classification performance using connectivity matrices calculated with Phase Locking Values and Coherence methods.},
}
@article {pmid40040032,
year = {2024},
author = {Veeranki, YR and Posada-Quintero, HF},
title = {High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782482},
pmid = {40040032},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Algorithms ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; Adult ; Emotions/physiology ; },
abstract = {Affective computing is a critical aspect of human-computer interaction. Electroencephalographic (EEG) signals, which reflect electrical brain activity, are widely used for the understanding of human emotional states. However, these signals are nonlinear and nonstationary, making traditional analysis methods insufficient. To address these challenges, recent studies have focused on time-frequency analysis. In this paper, we propose a variable frequency complex demodulation (VFCDM) approach to obtain high-resolution time-frequency spectra (TFS) from EEG signals. First, we compute the TFS using the time-varying optimal parameter search technique to capture the spectral information. Then we generate VFCDM sub-bands and extract statistical features from each of the sub-bands. These features are then used with the Random Forest algorithm to classify arousal and valence dimensions. Our results demonstrate the robustness of this approach and its ability to accurately discriminate complex affective dimensions. The δ-VFCDM and γ-VFCDM bands produced the highest F1 scores of 71.80% for Arousal and 69.55% for Valence differentiation. This work significantly advances EEG-based affective computing and opens avenues for more emotionally attuned human-computer interaction systems.},
}
@article {pmid40039972,
year = {2024},
author = {Ye, H and Goerttler, S and He, F},
title = {EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782694},
pmid = {40039972},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Electrodes ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.},
}
@article {pmid40039945,
year = {2024},
author = {Kanda, T and Isezaki, T and Okitsu, K},
title = {A Study on Changes in Estimation Accuracy for EEG Data During Calibration and Operation in MI-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782616},
pmid = {40039945},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Calibration ; Male ; Female ; Adult ; Young Adult ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; },
abstract = {Changes in psychological factors have been suggested to cause variations in brain-computer interface (BCI) performance. More specifically, differences in psychological variables between the calibration and operation phases may cause a decrease in accuracy during operation, presenting a potential challenge for the adoption of BCI technology. The purpose of this study is to analyze the differences in accuracy between the calibration and operation phases of a BCI using a deep learning model. We structured tasks to simulate the calibration and operation phases, and participants performed motor imagery tasks under both conditions. The analysis revealed a significant decrease in accuracy for data obtained under the operation condition, highlighting the need for techniques capable of adapting to the electroencephalography signal data produced when users execute operations.},
}
@article {pmid40039943,
year = {2024},
author = {Wang, J and Li, X and Huang, Y and Xiao, D and Fan, Y and Huang, W and Hu, Y},
title = {Patient-Involved Validation of A Somatosensory ERP-BCI Facilitated by Electric Stimulation for Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10781706},
pmid = {40039943},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Electric Stimulation/methods ; Female ; Evoked Potentials, Somatosensory/physiology ; Adult ; },
abstract = {Brain-computer interface (BCI) is emerging as an effective complementary solution in the field of rehabilitation for the interaction between patients and robotic assistive devices. Specifically, the somatosensory event-related potentials (ERP) BCI has unique advantage for post-stroke motor rehabilitation scenarios and has been proven feasible on healthy subjects. We conducted the first patient-involved somatosensory ERP-BCI experiment with electric stimulation to evaluate its feasibility for real-world clinical usage. In the experiment, participant selectively attended to electric stimuli applied on either left or right wrist, which represented the operation of robot-assisted exercise of corresponding hand. An integrated platform that included exercise, stimulation, and electroencephalography (EEG) sampling modules was used. For evaluation, we used convolutional neural network (CNN) with transformer module to construct subject-specific intent decoder. The network demonstrated on average 58.95% accuracy in classifying target response from a single ERP trial. When using the classification from multiple consecutive trials, the decoder achieved a maximum of 80.12% mean accuracy in recognizing participants intent, and the highest rate from a single participant was 97.21%. The best information transfer rate (ITR) achieved was 1.956 Bit/min. These results demonstrated that the proposed BCI paradigm could be a valid choice for stroke rehabilitation. In the next stage, we anticipate the involvement of larger patient population, real-time feedback training, and the subsequent quantified motor function recovery results.},
}
@article {pmid40039935,
year = {2024},
author = {Han, HT and Kim, SJ and Lee, DH and Lee, SW},
title = {Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782698},
pmid = {40039935},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals). When conducting domain adaptation (DA) on MI signals with sparse MI signals, it could interrupt the training process. In this paper, we proposed the proxy-based masking module (PMM) for masking sparse MI signals within MI signals. The proposed module was designed to suppress the amplitude of sparse MI signals using the negative similarity-based mask generated between the proxy of rest signals and the feature vectors of MI signals. We attached our proposed module to the conventional DA methods (i.e., the DJDAN, the MAAN, and the DRDA) to verify the effectiveness in the cross-subject environment on dataset 2a of BCI competition IV. When our proposed module was attached to each conventional DA method, the average accuracy was improved by much as 4.67 %, 0.76 %, and 1.72 %, respectively. Hence, we demonstrated that our proposed module could emphasize the information related to MI characteristics. The code of our implementation is accessible on GitHub.[1].},
}
@article {pmid40039926,
year = {2024},
author = {Zhong, Y and Yao, L and Wang, Y},
title = {Enhanced BCI Performance using Diffusion Model for EEG Generation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782900},
pmid = {40039926},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {In the realm of Motor Imagery (MI)-based Brain-Computer Interface (BCI), the widespread adoption of deep learning-based algorithms has resulted in an increased demand for a larger training sample size, thereby placing a heightened burden on users. This study advocates the utilization of one of the most advanced generative models, the denoising diffusion probabilistic model (DDPM), for the artificial synthesis of Electroencephalogram (EEG) raw signals. The quality of the generated EEG signals is evaluated through both qualitative and quantitative analyses. Through dimensionality reduction projection, we observed a notable similarity in the data distributions between the generated EEG signals and real EEG signals. Additionally, spectral analysis indicates a striking similarity in energy distribution between the two, accompanied by the presence of an event-related synchronization (ERS) phenomenon in the generated EEG signals. Quantitative analysis reveals that the accuracy of generated EEG signals for left and right-hand motor imagery tasks is 89.81 ± 2.11%, with discriminative information related to classes predominantly concentrated in the motor-sensory cortex area and alpha-beta frequency band. Furthermore, the integration of generated EEG samples contributes to a 3.17% improvement in the classification performance of BCI-deficiency subjects. These artificially generated EEG signals exhibit promising potential for application in calibrating MI-BCI deep learning models, thereby alleviating the burden on participants.},
}
@article {pmid40039924,
year = {2024},
author = {Hoshino, T and Kanoga, S and Aoyama, A},
title = {Channel- and Label-Flip Data Augmentation for Motor Imagery-Based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782028},
pmid = {40039924},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Machine Learning ; Movement/physiology ; Algorithms ; Brain/physiology ; },
abstract = {Achieving high classification accuracy in motor-imagery-based brain-computer interfaces (BCIs) requires substantial amounts of training data. A challenge arises because of the impracticality of measuring large amounts of data from users. Data augmentation (DA) has emerged as a promising solution for this challenge. We propose a novel DA method called channel&amp;label-flip DA that involves not only flipping channels but also flipping class labels. This method is based on the neuroscience finding that motor imageries of left- and right-hand movements are roughly symmetrical. The efficiency of the proposed method was evaluated using the OpenBMI dataset, which comprises electroencephalograms collected from 54 participants engaged in left- and right-hand motor imagery tasks. To compare the impact on classifiers, we employed three classical machine learning models utilizing filter bank common spatial pattern features, along with a deep learning-based model that uses raw signal input. As a result, the channel&amp;label-flip DA improved the classification accuracy on average, whereas simple flipping of the channels reduced the classification accuracy compared to the case without DA.},
}
@article {pmid40039912,
year = {2024},
author = {Tan, J and Wang, Y},
title = {Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782800},
pmid = {40039912},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Reward ; *Algorithms ; Reinforcement, Psychology ; Machine Learning ; Feedback ; },
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p<0.01). These preliminary results suggests that the dynamic IRL method for feedback-driven reward estimation holds potential for improving the design of RL-based BMIs.},
}
@article {pmid40039908,
year = {2024},
author = {Patel, K and Safavi, F and Chandramouli, R and Vinjamuri, R},
title = {Transformer-Based Emotion Recognition with EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781700},
pmid = {40039908},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; },
abstract = {Emotion recognition via electroencephalography (EEG) has emerged as a pivotal domain in biomedical signal processing, offering valuable insights into affective states. This paper presents a novel approach utilizing a tailored Transformer-based model to predict valence and arousal levels from EEG signals. Diverging from traditional Transformers handling singular sequential data, our model adeptly accommodates multiple EEG channels concurrently, enhancing its ability to discern intricate temporal patterns across the brain. The modified Transformer architecture enables comprehensive exploration of spatiotemporal dynamics linked with emotional states. Demonstrating robust performance, the model achieves mean accuracies of 92.66% for valence and 91.17% for arousal prediction, validated through 10-fold cross-validation across subjects on the DEAP dataset. Trained for subject-specific analysis, our methodology offers promising avenues for enhancing understanding and applications in emotion recognition through EEG. This research contributes to a broader discourse in biomedical signal processing, paving the way for refined methodologies in decoding neural correlates of emotions with implications across various domains including brain-computer interfaces, and human-robot interaction.},
}
@article {pmid40039888,
year = {2024},
author = {Sturgill, BS and Jiang, MS and Jeakle, EN and Smith, TJ and Hoeferlin, GF and Duncan, J and Thai, TTD and Hess, JL and Alam, NN and Hernandez-Reynoso, AG and Capadona, JR and Pancrazio, JJ},
title = {Antioxidant Coated Microelectrode Arrays: Effects on Putative Inhibitory and Excitatory Neurons.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781940},
pmid = {40039888},
issn = {2694-0604},
mesh = {*Microelectrodes ; *Neurons/physiology ; Animals ; *Antioxidants/pharmacology ; Metalloporphyrins/pharmacology ; Rats ; Coated Materials, Biocompatible/chemistry ; Brain-Computer Interfaces ; },
abstract = {Intracortical microelectrode arrays (MEAs) are used to record neural activity in vivo at single-cell resolution for both neuroscience studies and for engineering restorative devices such as brain-computer interfaces (BCIs). The recording performance of these devices are known to degrade over weeks to months after implantation due, in part, to neuroinflammation and oxidative stress. Characterizing and mitigating the degradation of recording performance is of particular interest for chronic applications. Literature suggests that inhibitory neurons may be more susceptible to oxidative stress than excitatory neurons. In this study, we classify recorded neural signals as either putative inhibitory or excitatory based on their waveform characteristics and aim to identify if one preferentially benefits from the use of a Mn(III)tetrakis94-benzoic acid)porphyrin (MnTBAP) coating to reduce reactive oxygen species, which we have previously demonstrated improves chronic neural recordings. In this study, we found that the MnTBAP coating affects these two classes of neurons differently, depending on the cortical depth. The MnTBAP coating improves the number of putative inhibitory signals recorded on the middle electrode sites (L5) and putative excitatory units on the superficial (L2/3 &amp; L4) electrode sites. Our results suggest that decreases in recording performance may be influenced by both cortical depth and neuronal cell type. Furthermore, we show that the benefits of a MnTBAP coating to chronic neural recordings differ between putative inhibitory and excitatory neurons with a depth dependence.},
}
@article {pmid40039787,
year = {2024},
author = {Won, E and Lim, S and Kim, Y and Dong, SY},
title = {Toward the TCN-based Real-Time BCI System for Target Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782928},
pmid = {40039787},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Deep Learning ; Male ; Adult ; Neural Networks, Computer ; },
abstract = {This study focuses on developing a real-time Brain-Computer Interface (BCI) system, specifically designed for military applications, to enhance target detection in rapid serial visual presentation (RSVP) tasks. The proposed BCI system utilizes electroencephalogram (EEG) signals based on dry electrodes, known for their exceptional temporal resolution, to identify swiftly specific target symbols within sequences of visual stimuli. Leveraging deep learning techniques, particularly Temporal Convolutional Networks (TCN), this study demonstrates the accuracy and efficiency improvement in target detection for RSVP tasks. According to our findings, the adaptability and efficacy of TCN in handling temporal dynamics of EEG signals exhibit outstanding performance in target detection, thus offering the potential for accurate and efficient real-time BCI system.},
}
@article {pmid40039782,
year = {2024},
author = {Park, JH and Lee, SH and Lee, SW},
title = {Towards EEG-based Talking-face Generation for Brain Signal-driven Dynamic Communication.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781922},
pmid = {40039782},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology/diagnostic imaging ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Face/physiology ; Speech/physiology ; Communication ; },
abstract = {Research on decoding speech or generating images from human brain activity holds intriguing potential as neuroprosthesis for patients and innovative communication tools for general users. However, previous studies have been constrained in generating fragmented or abstract outputs, rendering them less applicable for serving as an alternative form of communication. In this paper, we propose an integrated framework that synthesizes speech from non-invasive speech-related brain signals and generates a talking-face that performs "lip-sync" using intermediate input decoded from brain signals. For realistic and dynamic brain signal-mediated communication, we generated a personalized talking-face by utilizing various forms of target data such as a real face or an avatar. Additionally, we performed a denoising process to enhance the quality of synthesized voices from brain signals, and to minimize unnecessary facial movements according to the noise. Therefore, clear and natural talking-faces, applicable to both real faces and avatars, could be generated from noisy brain signals, enabling dynamic communication. These findings serve as a pivotal contribution to the advancement of brain signal-driven face-to-face communication through the provision of integrated speech and visual interfaces. This represents a significant step towards the development of a more intuitive and dynamic brain-computer interface communication system.},
}
@article {pmid40039768,
year = {2024},
author = {He, F and Zhang, S and Yang, M and Meng, J and Xu, M and Meng, L and Ming, D},
title = {Prediction errors from distinct perspectives induce separable EEG features for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781933},
pmid = {40039768},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Algorithms ; Female ; Adult ; Young Adult ; Evoked Potentials/physiology ; },
abstract = {The ability to efficiently detect error is fundamental for human adaptive behaviors, and plays an increasingly crucial role in developing more intelligent brain-computer interface (BCI). Error-related potential (ErrP), which can reflect prediction error, has been widely used by the BCI to read whether outcomes accord with users' expectation or not. However, current ErrP-BCI cannot distinguish the prediction error is induced by user's own (first-person perspective, 1PP) or other's (third-person perspective, 3PP) wrong action, hindering it from being applied in social interactions. This study used virtual reality (VR) to make subjects aware of prediction errors from the first- or third-person perspective, and recorded electroencephalogram (EEG) data of 22 healthy subjects. Event-related potential (ERP), event-related spectral perturbation (ERSP), inter-trial coherence (ITC) and shrinkage discriminant canonical pattern matching (SKDCPM) algorithm were used to investigate EEG features and the separability of prediction errors induced by distinct perspectives. Consequently, ErrP induced by the 1PP emerged significantly earlier than that of 3PP, and caused greater ERSP and ITC in the prefrontal region in the theta and alpha bands. Decoding result achieved 76.4%± 9.13% accuracy for the two types of errors (1PP-incorrect vs 3PP-incorrect). This study fills in the fine-grained classification of different error types and provides a finer metric for the systematic error correction efficiency of two-person collaborative brain control, which is the basis for future human-machine hybrid intelligence.},
}
@article {pmid40039762,
year = {2024},
author = {Li, M and Wang, M and Wang, Y},
title = {An Adaptive Superposition Point Process Model with Neuronal Encoding Engagement Identification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781885},
pmid = {40039762},
issn = {2694-0604},
mesh = {*Neurons/physiology ; *Brain-Computer Interfaces ; *Models, Neurological ; Animals ; Humans ; Algorithms ; },
abstract = {Neuronal encoding is realized by modulating firing rates in response to various encoding factors, including external stimuli, behaviors, and complex neural interactions. The neuronal encoding engagement of various factors are dynamic, which reflects how neurons aggregate different information. The process of uncovering the extent to which these factors contribute to neuronal encoding over time is neuronal encoding engagement identification. Brain-machine interface (BMI) establishes a closed-loop framework to investigate how neurons response to different encoding factors. Since neurons don't fully participate in encoding one specific factor, accurate encoding engagement identification contributes to leveraging encoding and decoding in more naturalistic BMI application scenarios. However, previous works focus on modeling and estimating tuning properties instead of analyzing the neuronal information aggregation. We develop a dual adaptive superposition point process filter (DASPPF), which explicitly incorporates various encoding factors. DASPPF not only enables decoding kinematics but also identifies the engagement of individual kinematics encoding and functional neural connectivity encoding. DASPPF is validated on numerical simulations of monkey circle-tracking tasks. The proposed method can effectively promote decoding performance and uncover how neurons engage themselves in different effects with point process observation, which may help enhance the development of neurotechnologies.},
}
@article {pmid40039684,
year = {2024},
author = {Tang, Y and Robinson, N and Fu, X and Thomas, KP and Wai, AAP and Guan, C},
title = {Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781850},
pmid = {40039684},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Movement/physiology ; *Hand Strength/physiology ; Adult ; *Brain-Computer Interfaces ; Male ; Female ; Hand/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; },
abstract = {Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). While MTP provides continuous control signals suitable for high-precision tasks, its feasibility and applications are challenged by the low signal-to-noise ratio, especially in noninvasive settings. Previous research has predominantly focused on kinematic reconstruction of upper (e.g., arm reaching) and lower limbs (e.g., gait). However, finger movements have received much less attention, despite their crucial role in daily activities. To address this gap, our study explores the potential of noninvasive Electroencephalography (EEG) for reconstructing finger movements, specifically during hand grasping actions. A new experimental paradigm to collect multichannel EEG data from 20 healthy subjects, while performing full, natural hand opening and closing movements, was designed. Employing state-of-the-art deep learning algorithms, continuous decoding models were constructed for eight key finger joints. The Convolutional Neural Network with Attention approach achieved an average decoding performance of r=0.63. Furthermore, a post-hoc metric was proposed for hand grasp cycle detection, and 83.5% of hand grasps were successfully detected from the reconstructed motion signals, which can potentially serve as a new BCI command. Explainable AI algorithm was also applied to analyze the topographical relevance of trained features. Our findings demonstrate the feasibility of using EEG to reconstruct hand joint movements and highlight the potential of MTP-BCI in control and rehabilitation applications.},
}
@article {pmid40039675,
year = {2024},
author = {Tang, C and Jiang, D and Chen, B},
title = {MEG Channel Selection Using Copula Entropy-Based Transfer Entropy for Motor Imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782066},
pmid = {40039675},
issn = {2694-0604},
mesh = {*Magnetoencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Entropy ; Imagination/physiology ; Algorithms ; Movement/physiology ; },
abstract = {Multi-channel magnetoencephalography (MEG) data provides high spatiotemporal resolution for motor imagery (MI)-based brain-machine interfaces (BCIs). However, not all channels contribute to the performance of BCIs. Taking into account the importance of specific channels in measuring their causal relationships with other channels during MI tasks, a novel channel selection method using copula entropy-based transfer entropy (CTE) is proposed to select task-relevant channels. Experiments on a publicly available dataset validate the effectiveness of the proposed methods. Compared to using all channels, channel selection based on CTE can significantly (p < 0.05) improve single-session classification accuracy and greatly reduce the number of MEG channels. Cross-session classification also outperforms the competing method.},
}
@article {pmid40039672,
year = {2024},
author = {Irvine, B and Abou-Zeid, H and Kirton, A and Kinney-Lang, E},
title = {Benchmarking motor imagery algorithms for pediatric users of brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782164},
pmid = {40039672},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Adolescent ; *Algorithms ; Child, Preschool ; Male ; Benchmarking ; Female ; Electroencephalography/methods ; Imagination/physiology ; },
abstract = {Brain-computer interfaces (BCIs) can enable opportunities for self-expression and life participation for children with severe neurological disabilities. Unfortunately, the development and evaluation of state-of-the-art algorithms has largely neglected pediatric users. This work tests 12 state-of-the-art algorithms for motor imagery classification on three datasets of typically developing pediatric users (n=94 ages 5-17). When all datasets were combined, there were no significant differences between most non-deep learning algorithms, with all having a mean AUC score of 0.64 or 0.65. All the non-deep learning algorithms significantly outperformed the deep learning algorithms, which can be partially attributed to a lack of hyperparameter tuning. The best of the deep learning algorithms was ShallowConvNet, with a mean AUC score of 0.57. Of the algorithms tested, only the filter bank common spatial pattern (FBCSP) and ShallowConvNet exhibited significant age effects. This general lack of age effects, combined with examples of children as young as 6 having AUC scores as high as 0.8, provides evidence that young children are capable of producing measurable motor imagery activations. The age effects that were present for some algorithms suggest that the changing EEG patterns associated with development could have a measurable impact on classification algorithm outcomes, and such algorithms should be evaluated to ensure that they are not performing disproportionately poorly for younger children. This work serves as a first step towards ensuring that the state-of-the-art improvements in BCI classification can be evaluated, and where necessary, adapted to meet the needs of pediatric users.},
}
@article {pmid40039651,
year = {2024},
author = {Kolbl, N and Tziridis, K and Krauss, P and Schilling, A},
title = {Methodological Considerations in the Analysis of Acoustically Evoked Neural Signals: A Comparative Study of Active EEG, Passive EEG and MEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10782081},
pmid = {40039651},
issn = {2694-0604},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; Evoked Potentials, Auditory/physiology ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Acoustic Stimulation ; Brain/physiology ; },
abstract = {Analyzing and deciphering brain signals on a single trial base is the main goal of brain-computer interface (BCI) research as well as neurolinguistics. In the present study, we have evaluated the efficacy of three neuroimaging techniques-active electroencephalography (EEG), passive EEG, and magnetoencephalography (MEG)-in capturing and evaluating brain activity in response to auditory stimuli. The main goals of our research included two primary components: first, to identify ROIs, and second, to determine the appropriate number of stimulus samples needed to achieve a meaningful level of reliability. To estimate this number of measurement repetitions we performed step-wise sub-sampling combined with permutation testing. This involved a detailed comparison of event-related potentials resp. fields (ERPs, ERFs) elicited by auditory stimuli such as acoustic clicks and continuous speech. Our results show that active EEG outperformed passive EEG and MEG in sensor space. However, MEG demonstrated superior signal localization in source space. These results also highlight the complexity of developing real-time speech BCIs.},
}
@article {pmid40039633,
year = {2024},
author = {Lu, JB and Tsao, Y and Wang, YT},
title = {Design and Evaluate Semi-dry Watermill-like EEG Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782960},
pmid = {40039633},
issn = {2694-0604},
mesh = {*Electroencephalography/instrumentation/methods ; *Electrodes ; Humans ; *Equipment Design ; Brain-Computer Interfaces ; },
abstract = {Semi-dry electrodes act as the middle ground between wet and dry electrodes as they not only have similar contact features (equivalent circuit) with Ag/AgCl-based wet electrodes but also carry the conduct material in their cavity or sponge (e.g. absorb saline water) for long-term brain-computer interface(BCI) applications. However, the trade off between hair-layer penetration and dose control of conductive material is challenging e.g. two electrodes might be bridged when the headset continuously presses or squeezes the reservoir and electrolyte flows on the scalp. The goal of this study is to design, prototype, and evaluate watermill-like electroencephalogram (EEG) electrodes that aim to simultaneously overcome two issues: hair-layer penetration and dose control of conductive material. Two electrode profiles, straight and spiral, were 3D printed, coated and evaluated with participants' EEGs. Without any help from skilled technicians, the self-wearing mechanical design allows users to wear and acquire their EEGs in few minutes. In addition, the refillable reservoir enable the possibility for long-term BCI applications. The results show that the proposed electrodes can read neural activities on the hair-covered area. Furthermore, straight profile electrodes outperform the spiral profile in the steady-state visually evoked potential (SSVEP) response. In sum, the watermill-like EEG electrodes can shorten the preparation time as well as the dose control of conductive material for naive users. The results suggest the proposed electrodes might open opportunities for BCI users to develop real-world BCI applications in the future.},
}
@article {pmid40039626,
year = {2024},
author = {Lin, X and Eldele, E and Chen, Z and Wu, M and Ng, HW and Guan, C},
title = {Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782755},
pmid = {40039626},
issn = {2694-0604},
mesh = {Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of the EEG signal, and high inter-subject variability. In recent years, Convolutional neural networks (CNNs) have significantly enhanced decoding accuracy. Nevertheless, the majority of these CNN designs did not explicitly incorporate the inter-hemisphere functional connections, omitting crucial spatial information. Notably, in binary MI decoding of the left-hand versus right-hand, the Event-Related Desynchronization is observed in the contralateral hemisphere. Building upon this concept and various Neuroscience research, we have designed a CNN architecture that forges a functional connection between the two hemispheres. Specifically, we applied the Channel Average Referencing to one hemisphere and compared the output with all channels of the opposite hemisphere. Then, we utilized the cosine similarity to identify the most correlated channels and combined with them the original hemisphere for spatial filtering to learn the inter-hemispheric connections. This innovative technique aligns more closely with the actual brain functionality. Our method has demonstrated superior results on the Cho2017 and OpenBMI datasets, underscoring its effectiveness.},
}
@article {pmid40039616,
year = {2024},
author = {Guerrero-Mendez, CD and Rivera-Flor, H and Villa-Parra, AC and Bastos-Filho, TF},
title = {Exploring Novel Practical Approach to Post-Stroke Upper-Limb Neurorehabilitation Based on Complex Motor Imagery Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782286},
pmid = {40039616},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; Male ; Electroencephalography/methods ; Female ; Stroke/physiopathology/complications ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Middle Aged ; Neurological Rehabilitation/methods ; Adult ; },
abstract = {Motor imagery (MI) is one of the main strategies for upper-limb movement rehabilitation in post-stroke individuals. Promising results of MI applied for rehabilitation have been reported in the literature. However, there is currently a need related to the recovery of movements aimed to Activities of Daily Living (ADLs) for individuals with severe motor impairments. Therefore, this study presents the evaluation of a novel MI protocol for post-stroke upper-limb neurorehabilitation using complex tasks related to the manipulation of a drinking cup. The protocol is based on the Action Observation (AO), which was used under a first-person 2D virtual reality. Subjects had to simultaneously imagine the movements presented in AO for the manipulation of a cup varying in four positions. EEG signals were recorded from 16 channels located mainly in the motor cortex of the brain. Two computational strategies based on Riemannian Geometry (RG) with and without Feature Selection (FS) using Pair-Wise Feature Proximity (PWFP) were implemented for the binary identification of each complex MI-Task vs. MI-Rest. This approach was evaluated on 30 healthy individuals and 2 post-stroke individuals. Using Linear Discriminant Analysis (LDA) as a classifier, the results report a maximum accuracy of 0.78 for both healthy and post-stroke individuals, and a minimum FPR of 0.21 and 0.13 for healthy and post-stroke individuals, respectively. This highlights the potential use of this type of paradigms for the implementation of more robust BCI systems that allow the rehabilitation of movements close to ADLs. Therefore, complex MI tasks may be a suitable variant for rehabilitation in post-stroke individuals.},
}
@article {pmid40039598,
year = {2024},
author = {Noble, SC and Ward, T and Ringwood, JV},
title = {Assessing the Impact of Environment and Electrode Configuration on P300 Speller Performance and EEG Signal Quality.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782158},
pmid = {40039598},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/instrumentation ; *Electrodes ; Adult ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; Environment ; },
abstract = {Recent years have seen extensive use of brain-computer interfaces (BCIs) using electroencephalography (EEG). A critical element in BCI research is electrode selection, which influences performance, experiment duration, resource utilization, and consequently, cost. Electrode choice is partly dictated by the study location, as environmental electrical noise can impact EEG signal quality. This study evaluates the performance of a P300 speller and EEG signal quality using 4-, 6-, 8-, and 16-electrode configurations in two different office environments. Ten healthy adults participated in a single session, using a P300 speller to spell three words with each electrode set. Participants were split between two locations, with five individuals in each. Significant performance disparities were observed between the locations. Notably, within each location, the performance differences among 4-, 6-, and 8-electrode sets were minimal; only the 16-electrode set outperformed the others in both settings. The location associated with poorer performances also exhibited lower P300 amplitudes and higher levels of mains electricity noise.},
}
@article {pmid40039596,
year = {2024},
author = {Ben Ticha, MB and Ran, X and Roussel, P and Bocquelet, F and Le Godais, G and Aubert, M and Costecalde, T and Struber, L and Zhang, S and Charvet, G and Kahane, P and Chabardes, S and Yvert, B},
title = {A Vision Transformer Architecture For Overt Speech Decoding From ECoG Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781877},
pmid = {40039596},
issn = {2694-0604},
mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Algorithms ; Electrocorticography/methods ; },
abstract = {Speech Brain-Computer Interfaces rely on decoding algorithms that transform neural activity into speech. A current challenge is to achieve intelligible speech synthesis in real time from continuous ongoing brain activity, ideally without the need of language models that prevent free-speech production. As a first step toward this goal, we introduce here an encoder-decoder architecture, in which neural data is first encoded into a latent space using a multi-layer vision transformer (ViT), and then these latent variables are converted into acoustic coefficients using a bidirectional LSTM recurrent network. This network is compared to a more conventional architecture where the encoding is performed using a convolutional neural network. Moreover, we introduce a new data-driven data augmentation strategy based on Dynamic Time Warping (DTW) to increase a training dataset based on the intrinsic variability of its input neural features. On two ECoG datasets obtained in participants performing an overt speech task, we found that ViT-encoding outperforms CNN-encoding to predict produced speech offline and that DTW-based data augmentation also improves decoding performance.},
}
@article {pmid40039592,
year = {2024},
author = {Schrag, E and Comaduran-Marquez, D and Kirton, A and Kinney-Lang, E},
title = {Textured Stimuli Comfort and Response in SSVEP-Based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782587},
pmid = {40039592},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Adult ; *Evoked Potentials, Visual/physiology ; Male ; Female ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; Electroencephalography/methods ; },
abstract = {State of the art steady-state visual evoked potential (SSVEP) brain computer interface (BCI) stimuli are commonly high-contrast, solid color flashing objects which can contribute to visual discomfort and fatigue. The use of low-contrast, textured flashing stimuli is proposed as a more comfortable alternative stimulus presentation paradigm. Eight participants (aged 19-35) were presented with four textured stimuli at varying frequencies, alongside standard stimuli. Results indicate significant effects of stimulus type as well as an interaction between frequency and channel subset on signal-to-noise ratio (SNR) values. Comfort scores consistently favored textured stimuli over high-contrast options at all frequencies The observed lack of SNR differences between stimulus conditions supports the feasibility of using textured stimuli in BCIs. This study lays a foundation for developing comfortable and effective BCI systems. The promising results of textured stimuli suggest a potential alternative for SSEVP-based BCI systems, emphasizing the importance of balancing neural responses and user comfort in stimulus design.},
}
@article {pmid40039576,
year = {2024},
author = {Jiang, R and Qiu, S and Wang, Y and Zhang, C and He, H},
title = {Evaluation of EEG and MEG responses during Fine Motor Imagery from the same limb.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782038},
pmid = {40039576},
issn = {2694-0604},
mesh = {Humans ; *Magnetoencephalography/methods ; *Electroencephalography/methods ; Male ; *Brain-Computer Interfaces ; Adult ; *Imagination/physiology ; Female ; Movement/physiology ; },
abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. BCI systems based on fine MI can provide an intuitive control pathway of the outer device. Electroencephalography (EEG) is a widely used modality for MI due to its high temporal resolution and portability. Magnetoencephalography (MEG) has high spatial and temporal resolution, which has received more and more attention. This study designed four kinds of MI tasks of different joints from the same upper limb, including finger, wrist, elbow, and shoulder joints, and additionally added a resting task. The EEG and MEG signals of eight subjects were acquired synchronously. Analysis was conducted on the EEG and MEG data to find the time, time-frequency, and spatial difference between MI tasks of different joints from the same limb. The induced event-related desynchronization (ERD) in EEG signals at the electrode position of the left motor area are more broad and stronger in the alpha frequency band than that in MEG signals during fine MI tasks. From the topographical distribution, different MI tasks affects the area and intensity of the activated area, and topographical distribution of MEG signals in different MI tasks are more discriminative than that of EEG signals. Moreover, the analysis of movement-related cortical potentials (MRCP) showed that significant negative potentials were detected near the onset of the motor imagery events and there is a significant difference in temporal dimension between magnetoencephalogram and electroencephalogram signals. The work implies that there exist the separable differences between EEG and MEG during fine MI tasks, which can be utilized to build a multimodal classification method for fine MI-BCI systems.},
}
@article {pmid40039573,
year = {2024},
author = {Delavari, F and Santaniello, S},
title = {Role of Scalp EEG Brain Connectivity in Motor Imagery Decoding for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781532},
pmid = {40039573},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Brain/physiology/diagnostic imaging ; Scalp/physiology ; Imagination/physiology ; },
abstract = {Brain Connectivity (BC) features of multichannel EEG have been proposed for Motor Imagery (MI) decoding in Brain-Computer Interface applications, but the advantages of BC features vs. single-channel features are unclear. Here, we consider three BC features, i.e., Phase Locking Value (PLV), Granger Causality, and weighted Phase Lag Index, and investigate the relationship between the most central nodes in BC-based networks and the most influential EEG channels in single-channel classification based on common spatial pattern filtering. Then, we compare the accuracy of MI decoders that use BC features in source vs. sensor space. Applied to the BCI Competition VI Dataset 2a (left- vs. right-hand MI decoding), our study found that PLV in sensor space achieves the highest classification accuracy among BC features and has similar performance compared to single-channel features, while the transition from sensor to source space reduces the average accuracy of BC features. Across all BC measures, the network topology is similar in left- vs. right-hand MI tasks, and the most central nodes in BC-based networks partially overlap with the most influential channels in single-channel classification.},
}
@article {pmid40039515,
year = {2024},
author = {Marquez, DC and Minhas, A and Kinney-Lang, E and Kirton, A},
title = {Automated Hyper-Parameter Optimization for Eye Movement Artifact Removal.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782395},
pmid = {40039515},
issn = {2694-0604},
mesh = {Humans ; *Artifacts ; *Electroencephalography/methods ; *Eye Movements/physiology ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems allow users to control external devices with their brain waves. However, electroencephalography (EEG) signals used by most BCI systems are prone to artifacts from various sources (e.g., muscle activity, eye movements, and electrical interference). These artifacts can degrade the performance and usability of BCI systems. Many tools exist to eliminate these artifacts. However, not all methods are automated, and some might require tuning certain hyper-parameters for optimal performance. We propose a method to automatically optimize the hyper-parameters of an eye blink artifact removal tool to improve the removal of artifacts in resting state EEG. We use a subset of eye movement artifacts to optimize the hyper-parameters using the EEG Quality Index (EQI) as the objective function. The optimized hyper-parameters are then used in a test artifact to quantify the improvement of the EQI. Results show improvement in the EQI when compared to the default artifact removal hyper-parameters, and raw EEG traces. We conclude that our method can provide a personalized and robust artifact removal solution for BCI users with complex needs.},
}
@article {pmid40039504,
year = {2024},
author = {Osborn, LE and Christie, B and McMullen, DP and Thomas, TM and Thompson, MC and Nickl, RW and Pawar, AS and Wester, BA and Cantarero, GL and Celnik, PA and Crone, NE and Fifer, MS and Tenore, FV},
title = {Artificial touch feedback using microstimulation of human somatosensory cortex to convey grip force from a robotic hand.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782061},
pmid = {40039504},
issn = {2694-0604},
mesh = {Humans ; *Somatosensory Cortex/physiology/physiopathology ; *Robotics/instrumentation ; *Hand Strength/physiology ; *Hand/physiology ; *Touch/physiology ; Brain-Computer Interfaces ; Male ; Electric Stimulation ; Feedback, Sensory/physiology ; },
abstract = {Invasive brain-machine interfaces can help restore function through the control of external devices while the addition of intracortical microstimulation (ICMS) can elicit sensations of touch and help provide further benefits for individuals living with sensorimotor deficits. However, the extent of tactile information that can be conveyed through ICMS has not been fully explored. In a human participant with spinal cord injury and chronically implanted microelectrode arrays, we used ICMS to the somatosensory cortex to provide grip force feedback in the hands during grasping of objects with varying stiffness with a robotic arm. Using only ICMS-evoked touch sensations, the participant was able to identify between two and three objects with an accuracy of 92% and 67%, respectively. In a compliant grasping task with the goal of grasping a delicate object without crushing it, objects were deformed on average only 2.8 mm with ICMS-based touch feedback compared to 8.7 mm without. These results demonstrate that ICMS-evoked touch sensations to the hands can be used to provide force-based feedback for perceiving object properties and enable more precise grasping during closed-loop control of a robotic limb through a cortical interface.},
}
@article {pmid40039500,
year = {2024},
author = {Teymourlouei, A and Hu, M and Gentili, R and Reggia, J},
title = {Functional Connectivity Methods for Multi-Class Mental Workload Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782848},
pmid = {40039500},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Workload ; Brain/physiology/diagnostic imaging ; Male ; Adult ; Female ; Support Vector Machine ; },
abstract = {Recently, significant attention has been drawn to the ability of network-based features to classify EEG signals reflecting varying levels of mental workload. Such features are based on methods of functional connectivity (FC), which quantify the statistical relationship between EEG electrode potentials. Here, we compare three FC-based feature extraction methods for the classification of mental workload from the Multi-Attribute Task Battery. The approaches used are weighted phase lag index (WPLI), imaginary coherence (IC), and layer entanglement (LE). WPLI and IC are popular methods for FC analysis. LE is a new approach which was introduced in recent literature. When classifying between three levels of workload, a support vector machine classifier achieved an 88% average (person-dependent) accuracy using all FC methods together, 89% using only the LE method, 67% with the IC method, and 61% with the WPLI method. When classifying between two levels of workload, these scores improve to 97%, 97%, 86%, and 81%, respectively. These results support and extend the findings of prior work and suggest that LE-based methods may enable accurate mental workload prediction which is suitable for passive brain-computer interfaces.},
}
@article {pmid40039452,
year = {2024},
author = {N, GR and Guha, D and Mahadevappa, M},
title = {EEG Artifact Removal using Stacked Multi-Head Attention Transformer Architecture.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782044},
pmid = {40039452},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Head/physiology ; },
abstract = {This study presents a transformer attention model with stacked multi-head attention layer designed to remove noise from electroencephalogram (EEG) signals, specifically addressing the problem of signal distortion caused by artifacts such as ocular and muscular noise. This is a crucial step in improving the efficacy of EEG, for disease diagnostics and BCI applications. Deep learning (DL) models have been increasingly employed for denoising EEG data in recent years, demonstrating comparable performance to classical approaches. However, the current models have been unsuccessful in capturing temporal long-term dependencies to efficiently eliminating ocular and muscular abnormalities. In this study, we address those challenges faced in the DL models by introducing multiple multi-head attention layers in the transformer model, which surpass the performance measures of previous works in EEGdenoiseNet dataset.},
}
@article {pmid40039435,
year = {2024},
author = {Chen, J and Xia, Y and Thomas, A and Carlson, T and Zhao, H},
title = {Mental Fatigue Classification with High-Density Diffuse Optical Tomography: A Feasibility Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782566},
pmid = {40039435},
issn = {2694-0604},
mesh = {Humans ; *Tomography, Optical/methods ; *Feasibility Studies ; *Mental Fatigue/physiopathology/diagnostic imaging ; *Support Vector Machine ; Male ; Adult ; Female ; Spectroscopy, Near-Infrared/methods ; Young Adult ; },
abstract = {High-Density Diffuse Optical Tomography (HD-DOT) presents as a promising tool for not only clinical use but also daily monitoring of mental states. This study employed wearable HD-DOT to evaluate mental fatigue, specifically examining the differences in functional near-infrared spectroscopy (fNIRS) data between states of low and high fatigue among healthy participants for data collection. Data processing involved filtering, channel selection, and dimensionality reduction through Uniform Manifold Approximation (UMAP) and Projection, followed by classification using Support Vector Machines (SVM). We developed two models to assess the accuracy and generalizability of our findings: one based on individually tailored models and another employing a leave-one-participant-out cross-validation strategy. We evaluated different kernel functions, resulting in various accuracy, F1 score, and Area Under the Curve (AUC) metrics. The study achieved an average accuracy of approximately 90% for participant-specific classifiers, underscoring the effectiveness of our approach to differentiate between low and high states of mental fatigue. Our analyses led to a robust model demonstrating high classification accuracy, proving its suitability and potential for real-time Brain-Computer Interface (BCI) applications.},
}
@article {pmid40039431,
year = {2024},
author = {Ziegelman, L and Hernandez, ME},
title = {Application of a Neural ODE to Classify Motion Control Strategy using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782326},
pmid = {40039431},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; Brain-Computer Interfaces ; Wrist/physiology ; Young Adult ; },
abstract = {Speed-accuracy trade offs exist in a variety of functional tasks, which may require differences in control strategies in future neuroprosthetic devices. It is the goal of this work to evaluate the predictability of different motor control strategies during wrist rotation tasks. Participants were asked to perform a series of discrete wrist rotations. This motion data was clustered into segments of either speed or range of motion oriented control strategy, controlling for age cohort and motion type. Competing neural ordinary differential equation (NODE) and random forest (RF) models were evaluated to explore the feasibility of classifying control strategy using cortical data alone. In comparison to traditional ML techniques, such as RF models, the NODE model provided achieved comparable classification accuracy at a fraction of the time. Furthermore, the use of a single motor cluster or two frontal clusters provided similar accuracy to the full data from 4 clusters, which may due to increased information from these cortical areas. This study provided a promising initial demonstration of the benefits of NODE models for future brain-computer-interface applications that require near real-time classification.},
}
@article {pmid40039415,
year = {2024},
author = {Flores, C and Casas, P and de Carvalho, SN and Attux, R},
title = {Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782739},
pmid = {40039415},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Machine Learning ; Calibration ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male ; },
abstract = {The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject.},
}
@article {pmid40039404,
year = {2024},
author = {Rabbito, R and Cinanni, A and Bussi, L and Guiot, C and Roatta, S},
title = {A neuro-feedback prototype based on transcranial Doppler ultrasound for brain computer interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782446},
pmid = {40039404},
issn = {2694-0604},
mesh = {Humans ; *Ultrasonography, Doppler, Transcranial/methods ; *Brain-Computer Interfaces ; Adult ; Male ; Neurofeedback/methods ; Female ; Middle Cerebral Artery/diagnostic imaging/physiology ; },
abstract = {This study proposes a TCD-based neurofeedback system designed to visualize interhemispheric hemodynamic imbalance based on the bilateral monitoring of middle cerebral arteries (MCAs). The difference between cerebral blood velocities collected from the right and left side is calculated in real time and used to drive the horizontal position of the ball displayed on a screen. With this visual feedback, the user may see how different thoughts impact on the position of the ball and possibly acquire and improve control of the ball through progressive training. Four healthy volunteers participated in a preliminary assessment conducted over four training sessions, on average demonstrating increased control over the ball movement. The results provide a proof of concept of the methodology, confirm the feasibility of the approach. The system's novelty lies in its simplicity, cost-effectiveness, and focus on cerebral lateralization, which make TCD an intriguing alternative to other neurofeedback systems, typically based on EEG, fMRI or fNIRS. The results encourage larger sample size, investigations on the TCD-based neurofeedback's therapeutic and rehabilitative potential.},
}
@article {pmid40039400,
year = {2024},
author = {Kondo, S and Tanaka, H},
title = {High-Frequency SSVEP-BCI Stimulation Frequency Optimization Based on BCI accuracy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782291},
pmid = {40039400},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Electroencephalography/methods ; Male ; Photic Stimulation ; },
abstract = {This study investigates optimization of the stimulation frequency of blinking stimuli used for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) for individuals. Heare, we set the target BCI accuracy to 90%, and we propose and evaluate an efficient algorithm to search for stimulation frequencies that satisfy the accuracy target for each subject. The results of a four-input SSVEP-BCI operation experiment with various stimulation frequencies indicate that the experimental system obtained optimal stimulation frequency for the subject based on BCI accuracy. However, we found that the optimization time was greater for subjects who are not proficient at BCI operations, which caused subject fatigue.},
}
@article {pmid40039379,
year = {2024},
author = {Lim, J and Wang, PT and Joon Sohn, W and Serrano-Amenos, C and Ibrahim, M and Lin, D and Thaploo, S and Shaw, SJ and Armacost, M and Gong, H and Lee, B and Lee, D and Andersen, RA and Heydari, P and Liu, CY and Nenadic, Z and Do, AH},
title = {Early feasibility of an embedded bi-directional brain-computer interface for ambulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782271},
pmid = {40039379},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Walking ; Feasibility Studies ; Spinal Cord Injuries/rehabilitation/physiopathology ; Robotics/instrumentation/methods ; Exoskeleton Device ; Gait/physiology ; },
abstract = {Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research set-tings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80±0.08 between cues and decoded states over 5 runs.},
}
@article {pmid40039323,
year = {2024},
author = {Kasprzak, H and Niewinska, N and Komendzinski, T and Otake-Matsuura, M and Rutkowski, TM},
title = {Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782826},
pmid = {40039323},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Smell/physiology ; Female ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents can be assigned to specific commands to allow users to interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting and interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) and electrobulbography (EBG) to improve classification accuracy. Our pilot study shows promising results for a new olfactory brain-computer interface (BCI) modality that combines common spatial pattern (CSP) filtration applied to EEG and EBG to classify responses to six scent stimuli in a classical oddball paradigm.},
}
@article {pmid40039276,
year = {2024},
author = {Lim, RY and Jiang, M and Ang, KK and Lin, X and Guan, C},
title = {Brain-Computer-Brain system for individualized transcranial alternating current stimulation with concurrent EEG recording: a healthy subject pilot study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782251},
pmid = {40039276},
issn = {2694-0604},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Pilot Projects ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Motor Cortex/physiology ; Young Adult ; Healthy Volunteers ; },
abstract = {In this study, we introduce a novel brain-computer-brain (BCB) system to investigate the aftereffects of individualized, task-dependent transcranial alternating current stimulation (tACS) delivered to the motor cortex. While previous studies utilized either a generic stimulation frequency or matched it to an individual's resting frequency (e.g. individual alpha frequency, iAF), our study employed a trial-by-trial tACS stimulation design wherein the stimulation frequency delivered matches the individual's peak motor imagery (MI) performance frequency. 14 healthy subjects participated in both tACS and tACS-sham on separate days in a within-subject, randomized controlled design. We found that active tACS delivered to subjects receiving alpha (α)-tACS resulted in a decline in MI performance while that with tACS-sham did not differ significantly from baseline. However, subjects receiving beta (β)-tACS showed no significant difference in effect for both active tACS and tACS-sham conditions. These findings indirectly corroborated with that from literature advocating the notion of α tACS as functionally inhibitory; hence the consequential deterioration of MI performance observed only in α-tACS subjects. A more conclusive analysis will be conducted once more data is collected from this ongoing study.Clinical Relevance: The results gathered suggest the differential functional significance of α- and β-tACS in an individualized MI task-specific tACS delivery to the motor cortex with concurrent EEG recording. Although insignificant at the point of data analysis where sample size is small in this ongoing study, tACS-sham (30 Hz) seemed to potentially modulate neural oscillations in the direction of improving MI performance. These findings can inform future tACS study designs based on a system with personalized stimulation delivery for MI task investigations within laboratory and clinical settings - potentially beneficial towards upper limb stroke rehabilitation.},
}
@article {pmid40039273,
year = {2024},
author = {Jiang, H and Xiao, X and Mei, J and Xu, M and Wang, K and Ming, D},
title = {A Novel Real-time Algorithm Based on Phase-Locked Data Alignment for Continuously Controlled SSVEP-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782824},
pmid = {40039273},
issn = {2694-0604},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Adult ; Male ; Female ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) show good performance. However, algorithms always decode segments of electroencephalogram (EEG) and can only satisfy discrete output instructions, which limit the real-time continuous control of the BCI system. This article proposes a novel algorithm for SSVEP-BCI that can translate continuous EEG into control commands, achieving real-time monitoring of user intentions.<br><br>METHODS: A phase synchronicity maximum strategy has been employed in this algorithm, which could capture a fixed-duration SSVEP epoch near any given moment, ensuring each trial is aligned with the phase of the potential corresponding template. Then, the algorithm utilized an update strategy of a small-step sliding window to recognize and output commands in approximately real-time.<br><br>RESULTS: We constructed an SSVEP-BCI system with continuous stimulation and recruited nine subjects. The results showed that the algorithm proposed in this study efficiently decoded continuously evoked SSVEP signals. The BCI's online average accuracy and ITR were 92.03% and 143.38 bits/min, respectively.<br><br>SIGNIFICANCE: The proposed algorithm can decode SSVEP at any time theoretically, which improves command output density as well as maintains high recognition accuracy. This study provides novel methods for real-time control of external devices using SSVEP-BCIs and helps to develop BCIs that are more compatible with human control habits.},
}
@article {pmid40039271,
year = {2024},
author = {Umezawa, K and Isezaki, T and Okitsu, K and Yokoyama, O and Suzuki, M and Nishimura, Y},
title = {Refined Force Estimation in Monkey's Pinching Tasks Through Integrated EMG and ECoG Data: A Kalman Filter Method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782574},
pmid = {40039271},
issn = {2694-0604},
mesh = {*Electromyography/methods ; Animals ; *Brain-Computer Interfaces ; Electrocorticography/methods ; Algorithms ; Macaca mulatta ; },
abstract = {In the development of brain-computer interfaces (BCIs), precise decoding of motor outputs is crucial. This study presents an enhanced Kalman filter approach that integrates electromyography (EMG) with electrocorticography (ECoG) to improve force estimation in pinching tasks. By incorporating EMG data as a state variable in the filter, we aim to account for musculoskeletal dynamics, enhancing the accuracy of force predictions. This integration significantly improves the decoding performance, particularly during dynamic force phases. The results confirm the importance of embedding musculoskeletal dynamics into ECoG-based BCIs, which may help improve prosthetic control and motor rehabilitation for people with motor impairments.},
}
@article {pmid40039264,
year = {2024},
author = {Li, M and Pun, SH and Chen, F},
title = {Cross-paradigm data alignment to improve the calibration of asynchronous BCI systems in EEG-based speech imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781999},
pmid = {40039264},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Calibration ; Speech/physiology ; Algorithms ; },
abstract = {The brain-computer interfaces (BCIs) based on speech imagery with asynchronous (self-paced) paradigms enable users to directly access and manipulate BCIs with more freedom. Compared with the indirect BCIs with traditional synchronous (cue-based) paradigms, the calibration time of asynchronous paradigms was much longer and with the unbalanced number of task states and idle states. This work aimed to improve the calibration of asynchronous BCI systems by applying a data alignment (DA) approach on cue-based and self-paced paradigms. The cue-based paradigm was regarded as the calibration paradigm and the self-paced paradigm was the testing paradigm. The data alignment approach based on the parallel transport mapped their features on the same tangent space. The logistic regression was used as the classifier to classify task states and idle states. The average result with DA was 7.52% higher than that without DA (baseline), which were 78.45% and 70.92%, respectively. Specially, the best classification accuracy was for 91.82% with DA, and the largest improvement in accuracy was 22.92%. These results suggest that it is practical to use a synchronous paradigm as calibration paradigm in asynchronous BCI systems and the data alignment approach has positive impacts on the classification of task states and idle states.},
}
@article {pmid40039208,
year = {2024},
author = {Geng, Y and Yang, B and Ke, S and Chang, L and Zhang, J and Zheng, Y},
title = {Motor Imagery Decoding from EEG under Visual Distraction via Feature Map Attention EEGNet.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781898},
pmid = {40039208},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Attention/physiology ; Imagination/physiology ; Male ; Adult ; Signal Processing, Computer-Assisted ; },
abstract = {The investigation of motor imagery (MI)-based brain-computer interface (BCI) is vital to the domains of human-computer interaction and rehabilitation. Few existing studies on electroencephalogram(EEG) signals decoding based on MI consider any distractions. However, it is difficult for users to do a single MI task in real life, which is especially affected by visual distraction. In this paper, we aim to investigate the effects of visual distraction on MI decoding performance. We first design a noval MI paradigm under visual distraction and observe distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) in MI under visual distraction. Then, we propose a robust decoding method of MI under visual distraction from EEG signals by using the feature map attention EEGNet (named FMA-EEGNet) and use EEG data under conditions without and with distraction to compare the decoding performance of five methods (including the proposed method and other methods). The results demonstrate that FMA-EEGNet achieved mean accuracy of 89.1% and 82.2% without and with visual distraction, respectively, indicating superior performance compared to other methods while exhibiting minimal degradation in performance. This work contributes significantly to the advancement of practical applications in MI-BCI technology.},
}
@article {pmid40039201,
year = {2024},
author = {Li, Q and Zhang, Z and Shi, M and Tao, X},
title = {Multi-channel Neural Signal Recording System for an Implantable Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782545},
pmid = {40039201},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Electroencephalography/instrumentation ; Equipment Design ; Brain/physiology ; },
abstract = {Simultaneous recordings of neural activity at massive scope, in the long term, and under bio-safety conditions, could provide crucial information, which helps in better understanding the operation mechanism of the brain and promotes the clinical application evolution for the brain-computer interface. For this purpose, a multi-channel neural signal recording system is presented, which can record up to 2048-channel neural signals by multiple connections of a customized collection system. The system consists of a sensor array module, a central controller module, and an upper computer module. Using the modular design method, the sensor array module can be contrived by changing the number of channels. The single-channel data acquisition module has a sampling resolution of 16 bits, and a sampling rate of 30 KSamples/s. The central controller module can establish a connection between the sensor array module and the upper computer module, and control their operations. The upper computer module can display the data results. The system verifies the performance of the multi-channel data acquisition through the analog neural signal.},
}
@article {pmid40039185,
year = {2024},
author = {Raghavan, V and Patel, P and He, X and Mesgarani, N},
title = {Decoding auditory attention for real-time BCI control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782575},
pmid = {40039185},
issn = {2694-0604},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Attention/physiology ; Acoustic Stimulation ; Auditory Perception/physiology ; Male ; },
abstract = {Brain-Computer Interfaces (BCI) facilitate interaction with devices, enhancing the quality of life for individuals with disabilities and offering a more direct method for controlling smart devices. Auditory BCIs commonly utilize event-related potentials (ERPs) necessitating a sequential presentation of choices through auditory stimuli. However, such methods impose constraints on the achievable Information Transfer Rate (ITR) compared to visual BCIs due to extended stimulus presentation times. Here, we introduce an auditory BCI approach in which the selective representation of attended speech in a listener's brain enables the decoding of one target sound source from the background. The simultaneous delivery of options in our proposed method reduces presentation durations by 2.5x compared to previous auditory BCI paradigms. This approach yields an average ITR exceeding 17 bits/min, with the best subject surpassing 33 bits/min. By outdoing current state-of-the-art auditory BCI paradigms, our research represents a significant advancement in the development of practical auditory BCI technologies.},
}
@article {pmid40039137,
year = {2024},
author = {Cinquetti, E and Siviero, I and Babiloni, F and Menegaz, G and Storti, SF},
title = {Passive BCI Towards Health and Safety in Industry: Forecasting Human Vigilance 5.5 s Ahead.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782689},
pmid = {40039137},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Industry ; Forecasting ; Deep Learning ; },
abstract = {Brain-computer interfaces based on electroencephalography (EEG) recordings are gaining increasing interest in the industrial domain, aiming to enhance health, safety and performance by optimizing the cognitive load of industrial operators and facilitating human-robot interactions. This study introduces a novel experimental protocol and analysis pipeline for predicting vigilance degradation during repetitive tasks. A dataset was recorded from 10 volunteers who observed a robotic arm executing three distinct movements. The EEG power spectrum was analyzed over time using the continuous wavelet transform. Upon verifying the increased amplitude of EEG oscillations in the 8-12 Hz frequency band, we forecast its behaviour, comparing the vector autoregressive model with two deep learning recurrent architectures. The proposed encoder-decoder gated recurrent unit model obtained accurate forecasts (mean absolute error = 0.048, R[2] = 0.726) up to 5.5 s into the future. The findings suggested the feasibility of vigilance monitoring in the Industry 5.0 framework, proposing a strategy to prevent human accidents and performance decline during monotonous activities.},
}
@article {pmid40039126,
year = {2024},
author = {Amrani, H and Micucci, D and Nalin, M and Napoletano, P and Rizzi, I},
title = {EEG Acquisition and Motor Imagery Classification for Robotic Control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782723},
pmid = {40039126},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Robotics/instrumentation ; *Brain-Computer Interfaces ; Support Vector Machine ; Movement/physiology ; Imagination/physiology ; },
abstract = {The adoption of brain-computer interfaces (BCIs) has significantly increased in various application domains, particularly in the field of controlling robotic systems through motor imagery. The article contributes in two primary ways: 1) validating the effectiveness of using a minimally invasive electroencephalography (EEG) device combined with machine learning techniques to control fundamental movements in a robotic setting, and 2) demonstrating these findings practically through the construction of a robotic vehicle. In this vehicle, tasks involving motor imagery align directly with control commands for the vehicle. To validate our approach, we identified four-class and two-class classification tasks. The signals have been acquired from a portable EEG device equipped with eight dry electrodes. We employed sliding window strategies to segment the data, along with feature extraction using the Common Spatial Pattern (CSP) method. Classification modules were implemented based on Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) models. The experimentation involved five participants, each with their own personalized model. While the accuracy of results in the four-class tasks is not notably high, the outcomes in binary classification tasks are promising, boasting an average accuracy of approximately 61%. Results suggest a promising potential for this approach in the realm of robot control, particularly when employing dry-electrode EEG devices.},
}
@article {pmid40039119,
year = {2024},
author = {Rajpura, P and Meena, YK},
title = {Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781846},
pmid = {40039119},
issn = {2694-0604},
mesh = {*Electroencephalography ; Validation Studies as Topic ; *Brain-Computer Interfaces ; Models, Neurological ; Humans ; },
abstract = {Decoding Electoencephalography (EEG) during motor imagery is pivotal for the Brain-Computer Interface (BCI) system, influencing its overall performance significantly. As end-to-end data-driven learning methods advance, the challenge lies in balancing model complexity with the need for human interpretability and trust. Despite strides in EEG-based BCIs, challenges like artefacts and low signal-to-noise ratio emphasise the ongoing importance of model transparency. This work proposes using post-hoc explanations to interpret model outcomes and validate them against domain knowledge. Leveraging the GradCAM post-hoc explanation technique on the EEG motor movement/imagery dataset, this work demonstrates that relying solely on accuracy metrics may be inadequate to ensure BCI performance and acceptability. A model trained using all EEG channels of the dataset achieves 72.60% accuracy, while a model trained with motor-imagery/movement-relevant channel data has a statistically insignificant decrease of 1.75%. However, the relevant features for both are very different based on neurophysiological facts. This work demonstrates that integrating domain-specific knowledge with Explainable AI (XAI) techniques emerges as a promising paradigm for validating the neurophysiological basis of model outcomes in BCIs. Our results reveal the significance of neurophysiological validation in evaluating BCI performance, highlighting the potential risks of exclusively relying on performance metrics when selecting models for dependable and transparent BCIs.},
}
@article {pmid40039013,
year = {2024},
author = {Cetera, A and Rabiee, A and Ghafoori, S and Shahriari, Y and Abiri, R},
title = {Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782523},
pmid = {40039013},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand Strength/physiology ; Support Vector Machine ; Male ; Adult ; },
abstract = {There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to-grasp tasks in humans. However, these reports were unable to clearly show evidence of emerging neural activity from the planning (observation) phase - dominated by the vision cortices - to grasp execution - dominated by the motor cortices. In this study, we developed a novel vision-based-grasping BCI platform that distinguishes different grip types (power and precision) through the phases of plan-to-grasp tasks using EEG signals. Using our platform and extracting features from Filter Bank Common Spatial Patterns (FBCSP), we show that frequency-band specific EEG contains discriminative spatial patterns present in both the observation and movement phases. Support Vector Machine (SVM) classification (power vs precision) yielded high accuracy percentages of 74% and 68% for the observation and movement phases in the alpha band, respectively.},
}
@article {pmid40038999,
year = {2024},
author = {Farabbi, A and Mainardi, L},
title = {Advancing Brain-Computer Interface Systems: Asynchronous Classification of Error Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782785},
pmid = {40038999},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Discriminant Analysis ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {This paper explores the paradigm shift in the classification of Error-Related Potentials (ErrP) in Brain-Computer Interfaces (BCIs) by introducing an asynchronous approach. Traditional synchronous methods, relying on precise temporal alignment between stimuli presentation and neural responses, face challenges in real-world scenarios with human response variability.The proposed asynchronous classification liberates BCI systems from strict temporal constraints, allowing for a more natural interaction paradigm. The study introduces an innovative ensemble method comprising Linear Discriminant Analysis (LDA) and EEGNet for asynchronous ErrP classification.The method is evaluated on EEG data from the BNCI Horizon 2020 dataset, demonstrating high balanced accuracy. While the introduction of EEGNet refines the classification, reducing false positives, challenges persist in achieving a balanced trade-off between precision and recall.The findings suggest the ensemble method's potential for practical applications, emphasizing the need for further refinement and exploration of advanced techniques in asynchronous ErrP classification.},
}
@article {pmid40038985,
year = {2024},
author = {Lee, KY and Chang, KY and Hsu, HC and Tseng, YT and Wei, CS and Lin, SS and Chuang, CH},
title = {Utilizing Motor-Imagery Brain-Computer Interfaces for the Assessment of Developmental Coordination Disorder in Children.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781534},
pmid = {40038985},
issn = {2694-0604},
mesh = {Humans ; *Motor Skills Disorders/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Child ; *Electroencephalography/methods ; Male ; Female ; Imagination/physiology ; },
abstract = {Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder characterized by significant motor difficulties that affect daily life. Current assessment methods primarily focus on behavioral analysis, lacking in neuroscientific metrics for a comprehensive evaluation. This study introduced an electroencephalography-based motor imagery brain-computer interface classification system for evaluating children with DCD. A key of this system was the implementation of entropy-based data screening, which markedly enhanced classification performance. Notably, using mu band power in a support vector machine achieved an accuracy rate of 79.0%. These findings pave the way for developing a tool that could assist professionals in identifying children potentially affected by DCD.},
}
@article {pmid40038962,
year = {2024},
author = {Sreekantham, S and Chetty, N and Weber, DJ},
title = {Detecting and Eliminating Cardiac Artifact from Endovascular EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782938},
pmid = {40038962},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Heart/physiology/physiopathology ; },
abstract = {Paralysis is a debilitating condition that affects more than 5.4 million people in the U.S. In severe cases, the paralyzed patient is incapable of communication. Restoring this communication is a primary goal of caretakers and is critical to improving the patient's quality of life. Brain-computer interfaces (BCIs) that directly access signals from the motor cortex are a promising method of circumventing the condition causing paralysis, typically using machine learning (ML) to predict motor intent from brain signals. However, BCIs are highly invasive and subjects have primarily been limited to patients with mild to moderate paralysis. The Stentrode is a novel technology that records electroencephalographic (EEG) signals via an electrode array placed endovascularly in the superior sagittal sinus. The first clinical trials of this technology aim to enable digital communication for severely paralyzed patients, translating brain signals from attempted movements into computer control inputs like mouse clicks. However, recordings of EEG are often contaminated with artifacts, including biopotentials arising from other excitable tissues, such as the heart and skeletal muscle. This study characterizes the electrocardiographic (ECG) artifact detected in the Stentrode recordings and proposes an automated Independent Component Analysis (ICA) method for removing this artifact. We compare the effectiveness of this method to previous methods for removal. Quantifying and eliminating the cardiac artifact is critical to accurately decode signals from the motor cortex and restore patients' ability to communicate.},
}
@article {pmid40038951,
year = {2024},
author = {Song, Z and Zhang, X and Tan, J and Wang, M and Wang, Y},
title = {Facilitating Knowledge Transfer: An Approach for Matching Neural Patterns between Motor Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781969},
pmid = {40038951},
issn = {2694-0604},
mesh = {Rats ; *Brain-Computer Interfaces ; Animals ; Algorithms ; },
abstract = {Brain-machine interface (BMI) holds great promise for restoring the impaired motor functions of individuals. In real-life scenarios, BMI users often face the challenge of quickly learning new tasks to adapt to complex environments. Consequently, it becomes essential to investigate the transferability of knowledge (neural-action mapping) of the decoder gained from previously learned tasks to new tasks. This paper introduces an approach for matching neural patterns between motor tasks to facilitate knowledge transfer, which is a key step in facilitating knowledge transfer. We project neural data into a 6D jPCA feature space and observe that neural patterns associated with the same action are preserved in the last four dimensions. By utilizing the decoder trained from the previous task, we obtain a prior estimate of the matched class. This prior estimate is further refined by clustering the neural patterns in the first two dimensions, as the data demonstrates distinct cluster shapes. To validate our approach, we conducted an experiment where a rat learned two related motor tasks sequentially. The preliminary results showed that our proposed method achieved an accuracy of 87.04% in estimating the matched class compared to the ground truth. In contrast, utilizing the decoder trained from the previous task within the entire jPCA space resulted in a significantly lower accuracy of merely 39.8%. These findings highlight the efficacy of our proposed method in matching neural patterns between motor tasks, thus facilitating knowledge transfer.},
}
@article {pmid40038942,
year = {2024},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Shahriari, Y and Abiri, R},
title = {Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782674},
pmid = {40038942},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Wavelet Analysis ; *Hand Strength/physiology ; *Brain-Computer Interfaces ; Adult ; Male ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; Machine Learning ; },
abstract = {This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.},
}
@article {pmid40037510,
year = {2025},
author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, FV and Rieth, L and Andersen, RA},
title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2130 days.},
journal = {Acta biomaterialia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.actbio.2025.02.030},
pmid = {40037510},
issn = {1878-7568},
abstract = {The clinical success of brain computer interfaces (BCI) depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. This study systematically quantified damage that microelectrodes sustained during chronical implantation in three people with tetraplegia for 956-2130 days. Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from eleven Neuroport arrays tipped with platinum (Pt, n = 8) and sputtered iridium oxide film (SIROF, n = 3). Arrays were implanted/explanted from posterior parietal, motor and somatosensory cortices across three clinical sites (Caltech/UCLA, Caltech/USC, APL/Johns Hopkins). From the electron micrographs, we quantified and correlated physical damage with functional outcomes measured in vivo, prior to explant (recording quality, noise, impedance and stimulation ability). Despite greater physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt (measured by SNR). For SIROF, 1 kHz impedance significantly correlated with all physical damage metrics, recording metrics, and stimulation performance, suggesting a reliable measurement of in vivo degradation. We observed a new degradation type, primarily on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, no significant degradation due to stimulation or amount of charge delivered. We hypothesize erosion of the silicon shank accelerates damage to the electrode / tissue interface, following damage to the tip metal. These findings link quantitative measurements to the microelectrodes' physical condition and their capacity to record/stimulate. These data could lead to improved manufacturing processes or novel electrode designs to improve long-term performance of BCIs, making them vitally important as multi-year clinical trials of BCIs are becoming more common. STATEMENT OF SIGNIFICANCE: Long-term performance stability of the electrode-tissue interface is essential for clinical viability of brain computer interface (BCI) devices; currently, materials degradation is a critical component for performance loss. Across three human participants, ten micro-electrode arrays (plus one control) were implanted for 956-2130 days. Using scanning electron microscopy (SEM), we analyzed degradation of 980 electrodes, comparing two types of commonly implanted electrode tip metals: Platinum (Pt) and Sputtered Iridium Oxide Film (SIROF). We correlated observed degradation with in vivo electrode performance: recording (signal-to-noise ratio, noise, impedance) and stimulation (evoked somatosensory percepts). We hypothesize penetration of the electrode tip by biotic processes leads to erosion of the supporting silicon core, which then accelerates further tip metal damage. These data could lead to improved manufacturing processes or novel electrode designs towards the goal of a stable BCI electrical interface, spanning a multi-decade participant lifetime.},
}
@article {pmid40037493,
year = {2025},
author = {Tang, G and Chen, B and Wu, M and Sun, L and Fan, R and Hou, R and Liu, W and Kang, J and Li, Y and Wang, M and Zhang, Y and Lu, N and Guo, W and Zhang, Y and Li, X and Wei, W and Yu, H and Li, T},
title = {Effectiveness of mindfulness-based cognitive therapy for treating generalized anxiety disorder and the moderating influence of abuse during childhood: A randomized controlled trial.},
journal = {Journal of affective disorders},
volume = {379},
number = {},
pages = {510-518},
doi = {10.1016/j.jad.2025.02.103},
pmid = {40037493},
issn = {1573-2517},
mesh = {Humans ; *Anxiety Disorders/therapy/psychology ; Female ; Male ; *Mindfulness/methods ; *Cognitive Behavioral Therapy/methods ; Adult ; Treatment Outcome ; Child ; Middle Aged ; *Child Abuse/psychology ; Surveys and Questionnaires ; Generalized Anxiety Disorder ; },
abstract = {BACKGROUND: Mindfulness-based cognitive therapy (MBCT) has emerged as a promising intervention for generalized anxiety disorder (GAD). This study evaluated MBCT's effectiveness for GAD and examined whether childhood maltreatment moderates its impact.<br><br>METHODS: Individuals with GAD were randomized to receive one of two 8-week interventions, either MBCT in-person or psychoeducation on-line (n&#xa0;=&#xa0;27 per group). At baseline and after 4 and 8&#xa0;weeks of intervention, both groups were assessed using the Beck Anxiety Inventory and Penn State Worry Questionnaire as well as several secondary questionnaires. Changes in the severity of anxiety and worry over time, as determined using linear mixed modeling, were compared between the two groups as a whole and among subgroups stratified according to type of maltreatment in childhood.<br><br>RESULTS: Among all participants, severity of worry decreased significantly more in the MBCT group than in the psychoeducation group, whereas severity of anxiety decreased to a similar extent in the two groups. Among individuals who had experienced emotional abuse in childhood, MBCT reduced the severity of anxiety significantly more than psychoeducation. In fact, MBCT was significantly more effective against anxiety in individuals who had experienced emotional abuse than in those who had not.<br><br>CONCLUSIONS: MBCT might be effective in alleviating worry symptoms in GAD, while its effectiveness against anxiety symptoms appears to be influenced by the history of maltreatment, particularly emotional abuse.<br><br>TRIAL REGISTRATION: ChiCTR2400087188 (Chictr.org).},
}
@article {pmid40036596,
year = {2025},
author = {Chen, Q and Huang, X and Ju, Z and Lin, H and Tang, H and Guo, C and Fan, F and Zhao, X and Ma, Y and Luo, Y and Li, W and Zhong, W and Zhao, D},
title = {A Triband Metasurface Covering Visible, Midwave Infrared, and Long-Wave Infrared for Optical Security.},
journal = {Nano letters},
volume = {25},
number = {11},
pages = {4459-4466},
doi = {10.1021/acs.nanolett.5c00083},
pmid = {40036596},
issn = {1530-6992},
abstract = {The independent manipulation of light across multiple wavelength bands provides new opportunities for optical security. Although dual-band optical encryption methods in the visible (VIS) and infrared bands have been developed, achieving synchronized and synergistic optical security across the VIS, midwave infrared (MWIR), and long-wave infrared (LWIR) bands remains a significant challenge. Here, we experimentally demonstrate a triband metasurface that covers the VIS, MWIR, and LWIR bands. While VIS imaging is achieved by structural color, MWIR, and LWIR imaging are achieved by selective emissivity structures, with MWIR/LWIR emissivities in the MWIR imaging region of 0.81/0.17, and in the LWIR imaging region of 0.21/0.83. Importantly, the MWIR and LWIR information is completely hidden in the VIS band. We also validate the ability of metasurface to encode complex information and information-misleading encryption. This work introduces new approaches for enhancing optical security and holds significant potential for applications such as anticounterfeiting and thermal camouflage.},
}
@article {pmid40036537,
year = {2025},
author = {Ravi, A and Wolfe, P and Tung, J and Jiang, N},
title = {Signal Characteristics, Motor Cortex Engagement, and Classification Performance of Combined Action Observation, Motor Imagery and SSMVEP (CAMS) BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1004-1013},
doi = {10.1109/TNSRE.2025.3544479},
pmid = {40036537},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Motor Cortex/physiology ; *Imagination/physiology ; Female ; Adult ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Young Adult ; Healthy Volunteers ; Movement/physiology ; Algorithms ; Reproducibility of Results ; Gait/physiology ; Psychomotor Performance/physiology ; Photic Stimulation ; },
abstract = {Motor imagery (MI)-based Brain-Computer Interfaces (BCIs) have shown promise in engaging the motor cortex for recovery. However, individual responses to MI-based BCIs are highly variable and relatively weak. Conversely, combined action observation (AO) and motor imagery (MI) paradigms have demonstrated stronger responses compared to AO or MI alone, along with enhanced cortical excitability. In this study, a novel BCI called Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS) was proposed. CAMS was designed based on gait observation and imagination. Twenty-five healthy volunteers participated in the study with CAMS serving as the intervention and SSMVEP checkerboard as the control condition. We hypothesized the CAMS intervention can induce observable increases in the negativity of the movement-related cortical potential (MRCP) associated with ankle dorsiflexion. MRCP components, including Bereitschaftspotential, were measured pre- and post-intervention. Additionally, the signal characteristics of the visual and motor responses were quantified. Finally, a two-class visual BCI classification performance was assessed. A consistent increase in negativity was observed across all MRCP components in signals over the primary motor cortex, compared to the control condition. CAMS visual BCI achieved a median accuracy of 83.8%. These findings demonstrate the ability of CAMS BCI to enhance cortical excitability in relation to movement preparation and execution. The CAMS stimulus not only evokes SSMVEP-like activity and sensorimotor rhythm but also enhances the MRCP. These findings contribute to the understanding of CAMS paradigm in enhancing cortical excitability, consistent and reliable classification performance holding promise for motor rehabilitation outcomes and future BCI design considerations.},
}
@article {pmid40036449,
year = {2025},
author = {Kim, MK and Shin, HB and Cho, JH and Lee, SW},
title = {Developing Brain-Based Bare-Handed Human-Machine Interaction via On-Skin Input.},
journal = {IEEE transactions on cybernetics},
volume = {55},
number = {4},
pages = {1554-1567},
doi = {10.1109/TCYB.2025.3533088},
pmid = {40036449},
issn = {2168-2275},
mesh = {Humans ; *Touch/physiology ; Male ; Adult ; Female ; Young Adult ; Signal Processing, Computer-Assisted ; Man-Machine Systems ; Brain-Computer Interfaces ; Deep Learning ; Gestures ; Brain/physiology ; Electroencephalography/methods ; },
abstract = {Developing natural, intuitive, and human-centric input systems for mobile human-machine interaction (HMI) poses significant challenges. Existing gaze or gesture-based interaction systems are often constrained by their dependence on continuous visual engagement, limited interaction surfaces, or cumbersome hardware. To address these challenges, we propose MetaSkin, a novel neurohaptic interface that uniquely integrates neural signals with on-skin interaction for bare-handed, eyes-free interaction by exploiting human's natural proprioceptive capabilities. To support the interface, we developed a deep learning framework that employs multiscale temporal-spectral feature representation and selective feature attention to effectively decode neural signals generated by on-skin touch and motion gestures. In experiments with 12 participants, our method achieved offline accuracies of 81.95% for touch location discrimination, 71.00% for motion type identification, and 46.08% for 10-class touch-motion classification. In pseudo-online settings, accuracies reached 99.43% for touch onset detection, and 80.34% and 67.02% for classification of touch location and motion type, respectively. Neurophysiological analyses revealed distinct neural activation patterns in the sensorimotor cortex, underscoring the efficacy of our multiscale approach in capturing rich temporal and spectral dynamics. Future work will focus on optimizing the system for diverse user populations and dynamic environments, with a long-term goal of advancing human-centered, neuroadaptive interfaces for next-generation HMI systems. This work represents a significant step toward a paradigm shift in design of brain-computer interfaces, bridging sensory and motor paradigms for building more sophisticated systems.},
}
@article {pmid40035637,
year = {2025},
author = {Sun, B and Zhang, X and Zhang, X and Xu, B and Wang, Y},
title = {Data collection, enhancement, and classification of functional near-infrared spectroscopy motor execution and imagery.},
journal = {The Review of scientific instruments},
volume = {96},
number = {3},
pages = {},
doi = {10.1063/5.0236392},
pmid = {40035637},
issn = {1089-7623},
mesh = {*Spectroscopy, Near-Infrared/methods/instrumentation ; Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Electroencephalography/instrumentation/methods ; Data Collection ; Male ; Adult ; },
abstract = {Recognition and execution of motor imagery play a key role in brain-computer interface (BCI) and are prerequisites for converting thoughts into executable instructions. However, to date, data acquired through commonly used electroencephalography (EEG) methods are very sensitive to motion interference, which will affect the accuracy of the data classification. The emerging functional near-infrared spectroscopy (fNIRS) technique, while overcoming the drawbacks of EEG's susceptibility to interference and difficulty in detecting motor signals, has less publicly available data. In this paper, we designed a motor execution and imagery experiment based on a wearable fNIRS device to acquire brain signals and proposed a modified Kolmogorov-Arnold network (named SE-KAN) for recognizing fNIRS signals corresponding to the task. Due to the small number of subjects in this experiment, the Wasserstein generative adversarial network was used to enhance the data processing. For the fNIRS data recognition task, the SE-KAN method achieved 96.36 ± 2.43% single-subject accuracy and 84.72 ± 3.27% cross-subject accuracy. It is believed that the dataset and method of this paper will help the development of BCI.},
}
@article {pmid40035554,
year = {2025},
author = {Khan, WU and Shen, Z and Mugo, SM and Wang, H and Zhang, Q},
title = {Implantable hydrogels as pioneering materials for next-generation brain-computer interfaces.},
journal = {Chemical Society reviews},
volume = {54},
number = {6},
pages = {2832-2880},
doi = {10.1039/d4cs01074d},
pmid = {40035554},
issn = {1460-4744},
mesh = {*Brain-Computer Interfaces ; *Hydrogels/chemistry ; Humans ; *Electrodes, Implanted ; Biocompatible Materials/chemistry ; Brain/physiology ; Animals ; },
abstract = {Use of brain-computer interfaces (BCIs) is rapidly becoming a transformative approach for diagnosing and treating various brain disorders. By facilitating direct communication between the brain and external devices, BCIs have the potential to revolutionize neural activity monitoring, targeted neuromodulation strategies, and the restoration of brain functions. However, BCI technology faces significant challenges in achieving long-term, stable, high-quality recordings and accurately modulating neural activity. Traditional implantable electrodes, primarily made from rigid materials like metal, silicon, and carbon, provide excellent conductivity but encounter serious issues such as foreign body rejection, neural signal attenuation, and micromotion with brain tissue. To address these limitations, hydrogels are emerging as promising candidates for BCIs, given their mechanical and chemical similarities to brain tissues. These hydrogels are particularly suitable for implantable neural electrodes due to their three-dimensional water-rich structures, soft elastomeric properties, biocompatibility, and enhanced electrochemical characteristics. These exceptional features make them ideal for signal recording, neural modulation, and effective therapies for neurological conditions. This review highlights the current advancements in implantable hydrogel electrodes, focusing on their unique properties for neural signal recording and neuromodulation technologies, with the ultimate aim of treating brain disorders. A comprehensive overview is provided to encourage future progress in this field. Implantable hydrogel electrodes for BCIs have enormous potential to influence the broader scientific landscape and drive groundbreaking innovations across various sectors.},
}
@article {pmid40035293,
year = {2025},
author = {Schilling, KG and Grussu, F and Ianus, A and Hansen, B and Howard, AFD and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO},
title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 2-Ex vivo imaging: Added value and acquisition.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2535-2560},
pmid = {40035293},
issn = {1522-2594},
support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R01EB031954/NH/NIH HHS/United States ; K01EB032898/NH/NIH HHS/United States ; R56EB031765/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 CA160620/CA/NCI NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; R01EB019980/NH/NIH HHS/United States ; R01EB031765/NH/NIH HHS/United States ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; },
mesh = {*Diffusion Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging ; Animals ; *Image Processing, Computer-Assisted/methods ; Signal-To-Noise Ratio ; Humans ; Reproducibility of Results ; },
abstract = {The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher SNR and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a three-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge.},
}
@article {pmid40034942,
year = {2025},
author = {Tang, X and Fan, D and Wang, X and Xing, Z and Yu, S and Wang, A and Yu, H},
title = {Exploring how sensory dominance modulated by modality-specific expectation: an event-related potential study.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1548100},
pmid = {40034942},
issn = {1664-1078},
abstract = {The Colavita visual dominance effect refers to the phenomenon in which tend to respond only or preferentially to visual stimuli of bimodal audiovisual stimulus. Previous evidence has indicated that sensory dominance can be modulated by top-down expectation. However, it remains unclear how expectations directed toward a single sensory modality influence Colavita visual dominance at the electrophysiology level. Using event-related potential (ERP) measurements, we investigated how modality expectation modulates sensory dominance by manipulating the different unimodal target probabilities used in previous related Colavita studies. For the behavioral results, a significantly larger visual dominance effect was found when the modality expectation was directed to the visual sensory condition (40% V:10% A). Further ERPs results revealed that the mean amplitude of P2 (200-250 ms) in the central-parietal region was larger in the visual precedence auditory response (V_A) type than in the auditory precedence visual response (A_V) type when modality expectation was directed to visual sensory stimuli (40% V:10% A). In contrast, the mean amplitude of N2 (290-330 ms) in the frontal region was larger for the V_A type than in the A_V type when modality expectation was directed to the auditory sensory stimuli (10% V:40% A). Additionally, for the A_V type N1 (150-170 ms) in the frontal region was larger in visual versus auditory expectation condition. Overall, the study tentatively suggested that increasing unimodal target probability may lead to greater top-down expectation direct to target modality stimulus, and then sensory dominance emerges in the late phase when participant response to visual stimuli of bimodal audiovisual stimulus.},
}
@article {pmid40034836,
year = {2025},
author = {Alsuradi, H and Hong, J and Sarmadi, A and Volcic, R and Salam, H and Atashzar, SF and Khorrami, F and Eid, M},
title = {BandFocusNet: A Lightweight Model for Motor Imagery Classification of a Supernumerary Thumb in Virtual Reality.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {6},
number = {},
pages = {305-311},
pmid = {40034836},
issn = {2644-1276},
abstract = {Objective: Human movement augmentation through supernumerary effectors is an emerging field of research. However, controlling these effectors remains challenging due to issues with agency, control, and synchronizing movements with natural limbs. A promising control strategy for supernumerary effectors involves utilizing electroencephalography (EEG) through motor imagery (MI) functions. In this work, we investigate whether MI activity associated with a supernumerary effector could be reliably differentiated from that of a natural one, thus addressing the concern of concurrency. Twenty subjects were recruited to participate in a two-fold experiment in which they observed movements of natural and supernumerary thumbs, then engaged in MI of the observed movements, conducted in a virtual reality setting. Results: A lightweight deep-learning model that accounts for the temporal, spatial and spectral nature of the EEG data is proposed and called BandFocusNet, achieving an average classification accuracy of 70.9% using the leave-one-subject-out cross validation method. The trustworthiness of the model is examined through explainability analysis, and influential regions-of-interests are cross-validated through event-related-spectral-perturbation (ERSPs) analysis. Explainability results showed the importance of the right and left frontal cortical regions, and ERSPs analysis showed an increase in the delta and theta powers in these regions during the MI of the natural thumb but not during the MI of the supernumerary thumb. Conclusion: Evidence in the literature indicates that such activation is observed during the MI of natural effectors, and its absence could be interpreted as a lack of embodiment of the supernumerary thumb.},
}
@article {pmid40034215,
year = {2025},
author = {Li, K and Li, M and Liu, W and Wu, Y and Li, F and Xie, J and Zhou, S and Wang, S and Guo, Y and Pan, J and Wang, X},
title = {Electroencephalographic differences between waking and sleeping periods in patients with prolonged disorders of consciousness at different levels of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1521355},
pmid = {40034215},
issn = {1662-5161},
abstract = {OBJECTIVE: This study aimed to explore differences in sleep electroencephalogram (EEG) patterns in individuals with prolonged disorders of consciousness, utilizing polysomnography (PSG) to assist in distinguishing between the vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), thereby reducing misdiagnosis rates and enhancing the quality of medical treatment.<br><br>METHODS: A total of 40 patients with prolonged disorders of consciousness (pDOC; 27 patients in the VS/UWS and 13 in the MCS) underwent polysomnography. We analyzed differential EEG indices between VS/UWS and MCS groups and performed correlation analyses between these indices and the Coma Recovery Scale-Revised (CRS-R) scores. The diagnostic accuracy of the differential indices was evaluated using receiver operating characteristic (ROC) curves.<br><br>RESULTS: 1. The fractal dimension (Higuchi's fractal dimension (HFD)) of patients in the MCS tended to be higher than that of patients in the VS/UWS across all phases, with a significant difference only in the waking phase (p <&#x202f;0.05). The HFD in the waking phase was positively correlated with the CRS-R score and exhibited the highest diagnostic accuracy at 88.3%. The Teager-Kaiser energy operator (TKEO) also showed higher levels in patients in the MCS compared to those in the VS/UWS, significantly so in the NREM2 phase (p <&#x202f;0.05), with a positive correlation with the CRS-R score and diagnostic accuracy of 75.2%. The &#x3b4;-band power spectral density [PSD(&#x3b4;)] in the patients in the MCS was lower than that in those in the VS/UWS, significantly so in the waking phase (p <&#x202f;0.05), and it was negatively correlated with the CRS-R score, with diagnostic accuracy of 71.5%.<br><br>CONCLUSION: Polysomnography for the VS/UWS and MCS revealed significant differences, aiding in distinguishing between the two patient categories and reducing misdiagnosis rates. Notably, the HFD and PSD(&#x3b4;) showed significantly better performance during wakefulness compared to sleep, while the TKEO was more prominent in the NREM2 stage. Notably, the HFD exhibited a robust correlation with the CRS-R scores, the highest diagnostic accuracy, and immense promise in the clinical diagnosis of prolonged disorders of consciousness.},
}
@article {pmid40033447,
year = {2025},
author = {Li, D and Li, R and Song, Y and Qin, W and Sun, G and Liu, Y and Bao, Y and Liu, L and Jin, L},
title = {Effects of brain-computer interface based training on post-stroke upper-limb rehabilitation: a meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {44},
pmid = {40033447},
issn = {1743-0003},
mesh = {*Brain-Computer Interfaces ; Humans ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; Randomized Controlled Trials as Topic ; Stroke/complications/physiopathology ; },
abstract = {BACKGROUND: Previous research has used the brain-computer interface (BCI) to promote upper-limb motor rehabilitation. However, the results of these studies were variable, leaving efficacy unclear.<br><br>OBJECTIVES: This review aims to evaluate the effects of BCI-based training on post-stroke upper-limb rehabilitation and identify potential factors that may affect the outcome.<br><br>DESIGN: A meta-analysis including all available randomized-controlled clinical trials (RCTs) that reported the efficacy of BCI-based training on upper-limb motor rehabilitation after stroke.<br><br>DATA SOURCES AND METHODS: We searched PubMed, Cochrane Library, and Web of Science before September 15, 2024, for relevant studies. The primary efficacy outcome was the Fugl-Meyer Assessment-Upper extremity (FMA-UE). RevMan 5.4.1 with a random effect model was used for data synthesis and analysis. Mean difference (MD) and 95% confidence interval (95%CI) were calculated.<br><br>RESULTS: Twenty-one RCTs (n&#x2009;=&#x2009;886 patients) were reviewed in the meta-analysis. Compared with control, BCI-based training exerted significant effects on FMA-UE (MD&#x2009;=&#x2009;3.69, 95%CI 2.41-4.96, P&#x2009;<&#x2009;0.00001, moderate-quality evidence), Wolf Motor Function Test (WMFT) (MD&#x2009;=&#x2009;5.00, 95%CI 2.14-7.86, P&#x2009;=&#x2009;0.0006, low-quality evidence), and Action Research Arm Test (ARAT) (MD&#x2009;=&#x2009;2.04, 95%CI 0.25-3.82, P&#x2009;=&#x2009;0.03, high-quality evidence). Additionally, BCI-based training was effective on FMA-UE for both subacute (MD&#x2009;=&#x2009;4.24, 95%CI 1.81-6.67, P&#x2009;=&#x2009;0.0006) and chronic patients (MD&#x2009;=&#x2009;2.63, 95%CI 1.50-3.76, P&#x2009;<&#x2009;0.00001). BCI combined with functional electrical stimulation (FES) (MD&#x2009;=&#x2009;4.37, 95%CI 3.09-5.65, P&#x2009;<&#x2009;0.00001), robots (MD&#x2009;=&#x2009;2.87, 95%CI 0.69-5.04, P&#x2009;=&#x2009;0.010), and visual feedback (MD&#x2009;=&#x2009;4.46, 95%CI 0.24-8.68, P&#x2009;=&#x2009;0.04) exhibited significant effects on FMA-UE. BCI combined with FES significantly improved FMA-UE for both subacute (MD&#x2009;=&#x2009;5.31, 95%CI 2.58-8.03, P&#x2009;=&#x2009;0.0001) and chronic patients (MD&#x2009;=&#x2009;3.71, 95%CI 2.44-4.98, P&#x2009;<&#x2009;0.00001), and BCI combined with robots was effective for chronic patients (MD&#x2009;=&#x2009;1.60, 95%CI 0.15-3.05, P&#x2009;=&#x2009;0.03). Better results may be achieved with daily training sessions ranging from 20 to 90&#xa0;min, conducted 2-5 sessions per week for 3-4 weeks.<br><br>CONCLUSIONS: BCI-based training may be a reliable rehabilitation program to improve upper-limb motor impairment and function.<br><br>TRIAL REGISTRATION: PROSPERO registration ID: CRD42022383390.},
}
@article {pmid40033324,
year = {2025},
author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J},
title = {Correction: On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {46},
pmid = {40033324},
issn = {1743-0003},
}
@article {pmid40033273,
year = {2025},
author = {Zhang, B and Liu, S and Chen, S and Liu, X and Ke, Y and Qi, S and Wei, X and Ming, D},
title = {Disrupted small-world architecture and altered default mode network topology of brain functional network in college students with subclinical depression.},
journal = {BMC psychiatry},
volume = {25},
number = {1},
pages = {193},
pmid = {40033273},
issn = {1471-244X},
support = {2023YFF1203700//National Key Research and Development Program of China/ ; 81925020//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Universities ; *Students/psychology ; *Depression/diagnostic imaging/physiopathology/psychology ; Case-Control Studies ; *Magnetic Resonance Imaging/methods ; *Neuroimaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; Cerebral Cortex/diagnostic imaging/physiopathology ; *Default Mode Network/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND: Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD.<br><br>METHODS: Functional networks of individuals with ScD (n&#x2009;=&#x2009;26) and healthy controls (HCs) (n&#x2009;=&#x2009;33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks.<br><br>RESULTS: Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression.<br><br>CONCLUSIONS: Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.},
}
@article {pmid40033004,
year = {2025},
author = {Li, XY and Rao, Y and Li, GH and He, L and Wang, Y and He, W and Fang, P and Pei, C and Xi, L and Xie, H and Lu, YR},
title = {Single-nucleus RNA sequencing uncovers metabolic dysregulation in the prefrontal cortex of major depressive disorder patients.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {7418},
pmid = {40033004},
issn = {2045-2322},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024SSYS0016//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {Humans ; *Depressive Disorder, Major/metabolism/genetics ; Male ; Female ; Adult ; *Prefrontal Cortex/metabolism ; Middle Aged ; Sequence Analysis, RNA ; Dorsolateral Prefrontal Cortex/metabolism ; Lactic Acid/metabolism/blood ; Oxidative Phosphorylation ; Single-Cell Analysis ; Case-Control Studies ; },
abstract = {Major depressive disorder (MDD) is a widespread psychiatric condition, recognized as the third leading cause of global disease burden in 2008. In the context of MDD, alterations in synaptic transmission within the prefrontal cortex (PFC) are associated with PFC hypoactivation, a key factor in cognitive function and mood regulation. Given the high energy demands of the central nervous system, these synaptic changes suggest a metabolic imbalance within the PFC of MDD patients. However, the cellular mechanisms underlying this metabolic dysregulation remain not fully elucidated. This study employs single-nucleus RNA sequencing (snRNA-seq) data to predict metabolic alterations in the dorsolateral PFC (DLPFC) of MDD patients. Our analysis revealed cell type-specific metabolic patterns, notably the disruption of oxidative phosphorylation and carbohydrate metabolism in the DLPFC of MDD patients. Gene set enrichment analysis based on human phenotype ontology predicted alterations in serum lactate levels in MDD patients, corroborated by the observed decrease in lactate levels in MDD patients compared to 47 age-matched healthy controls (HCs). This transcriptional analysis offers novel insights into the metabolic disturbances associated with MDD and the energy dynamics underlying DLPFC hypoactivation. These findings are instrumental for comprehending the pathophysiology of MDD and may guide the development of innovative therapeutic strategies.},
}
@article {pmid40032982,
year = {2025},
author = {Wu, F and Chen, Y and Chen, X and Tong, D and Zhou, J and Du, Z and Yao, C and Yang, Y and Du, A and Ma, G},
title = {Nematode serine protease inhibitor SPI-I8 negatively regulates host NF-κB signalling by hijacking MKRN1-mediated polyubiquitination of RACK1.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {356},
pmid = {40032982},
issn = {2399-3642},
support = {32473050//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32202829//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32172877//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23C180006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Receptors for Activated C Kinase/metabolism/genetics ; *NF-kappa B/metabolism ; Mice ; *Ubiquitination ; *Signal Transduction ; Humans ; *Helminth Proteins/metabolism/genetics ; Colitis/parasitology/metabolism/genetics/chemically induced ; Mice, Inbred BALB C ; },
abstract = {Parasitic roundworms are remarkable for their ability to manipulate host immune systems and ameliorate inflammatory diseases. Although much is known about the nature of nematode effectors in immune modulation, little is known about the action mode of these molecules. Here, we report that a serine protease inhibitor SPI-I8 in the extracellular vesicles of blood-feeding nematodes like Ancylostoma ceylanicum, Haemonchus contortus and Nippostrongylus brasiliensis, effectively halts excessive inflammatory responses in vitro and in vivo. We demonstrate that H. contortus SPI-I8 promotes the role of a negative regulator of RACK1 and enhances the effects of RACK1 on tumor necrosis factor (TNF)-α-IκB kinases (IKKs)-nuclear factor kappa beta (NF-κB) axis in mammalian cells, by hijacking E3 ubiquitin protein ligase MKRN1-mediated polyubiquitination of RACK1. Administration of recombinant N. brasiliensis SPI-I8 effectively protects mice from dextran sulfate sodium (DSS)-induced colitis and lipopolysaccharide (LPS)-induced sepsis. Considering the structural and functional conservation of SPI-I8s among Strongylida nematodes and the conservation of interactive mediators (i.e., MKRN1 and RACK1) among mammals, our findings provide insights into the host-parasite interface where parasitic roundworms secret molecules to suppress host inflammatory responses. Harnessing these findings should underpin the exploitation of nematode's immunomodulators to relief excessive inflammation associated diseases in animals and humans.},
}
@article {pmid40032521,
year = {2025},
author = {Sharma, D and Lupkin, SM and McGinty, VB},
title = {Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.0789-24.2025},
pmid = {40032521},
issn = {1529-2401},
abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decisions. While much is known about how OFC neurons represent values, far less is known about information encoded in OFC local field potentials (LFPs). LFPs are important because they can reflect subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded neural activity in the OFC of male macaques performing a two-option value-based decision task. We compared the value- and decision-coding properties of high-gamma LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. HG and MUA both represented the values of decision targets, but HG signals had value-coding features that were distinct from concurrently-measured MUA. On average HG amplitude increased monotonically with value, whereas in MUA the value encoding was net neutral on average. HG encoded a signal consistent with a comparison between target values, a signal which was negligible in MUA. In individual channels, HG could predict choice outcomes more accurately than MUA; however, when channels were combined in a population-based decoder, MUA was more accurate than HG. In summary, HG signals reveal value-coding features in OFC that could not be observed from spiking activity, including representation of value comparisons and more accurate behavioral predictions. These results have implications for the role of OFC in value-based decisions, and suggest that high-frequency LFPs may be a viable - or even preferable - target for BMIs to assist cognitive function.Significance statement High-frequency LFPs are often assumed to be a mere proxy for local spiking activity. This study finds evidence to the contrary in the OFC of monkeys making value-based decisions. With respect to decision mechanisms, the results challenge previous findings by suggesting a role for OFC in computing value comparisons, evident in a comparison signal encoded in HG but not spiking. More broadly, the results add to the growing evidence for spike/LFP dissociations in prefrontal cortex, and support the idea that HG signals are an important but overlooked resource for identifying neural computations in cognitive tasks. In addition, single-channel HG signals furnished more accurate predictions about choice behavior, supporting the potential use of HG signals in cognitive neural prosthetics.},
}
@article {pmid40031838,
year = {2025},
author = {Lopez-Gordo, MA and Geirnaert, S and Bertrand, A},
title = {Unsupervised Accuracy Estimation for Brain-Computer Interfaces Based on Selective Auditory Attention Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3542253},
pmid = {40031838},
issn = {1558-2531},
abstract = {OBJECTIVE: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communication via brain-computer interfaces (BCI). Recently, it has been shown that it is possible to train such AAD decoders based on stimulus reconstruction in an unsupervised setting, where no ground truth is available regarding which sound source is attended. In many practical scenarios, such ground-truth labels are absent, making it, moreover, difficult to quantify the accuracy of the decoders. In this paper, we aim to develop a completely unsupervised algorithm to estimate the accuracy of correlation-based AAD algorithms during a competing talker listening task.<br><br>METHODS: We use principles of digital communications by modeling the AAD decision system as a binary phase-shift keying channel with additive white gaussian noise.<br><br>RESULTS: We show that the proposed unsupervised performance estimation technique can accurately determine the AAD accuracy in a transparent-for-the-user way, for different amounts of training and estimation data and decision window lengths. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the minimal amount of training required for any given target accuracy.<br><br>CONCLUSION: Our proposed estimation technique accurately predicts the performance of a correlation-based AAD algorithm without access to ground-truth labels.<br><br>SIGNIFICANCE: In neuro-steered hearing aids, the accuracy estimates provided by our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback. In BCIs, it could support a robust communication paradigm with accuracy feedback for caregivers.},
}
@article {pmid40031638,
year = {2025},
author = {Ma, Z and Yang, X and Meng, J and Wang, K and Xu, M and Ming, D},
title = {Decoding Arm Movement Direction Using Ultra-High-Density EEG.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3545856},
pmid = {40031638},
issn = {2168-2208},
abstract = {Detecting arm movement direction is significant for individuals with upper-limb motor disabilities to restore independent self-care abilities. It involves accurately decoding the fine movement patterns of the arm, which has become feasible using invasive brain-computer interfaces (BCIs). However, it is still a significant challenge for traditional electroencephalography (EEG) based BCIs to decode multi-directional arm movements effectively. This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in different directions. To extract discriminative features from UHD EEG, we proposed a spatial filtering method combining principal component analysis (PCA) and discriminative spatial pattern (DSP). We collected EEG signals from five healthy subjects (two left-handed and three right-handed) to verify the system's feasibility. The movement-related cortical potentials (MRCPs) showed a certain degree of separability both in waveforms and spatial patterns for arm movements in different directions. This study achieved an average classification accuracy of 63.15 (8.71)% for both arms (eight-class task) with a peak accuracy of 77.24%. For the dominant arm (four-class task), we obtained an average accuracy of 75.31 (9.21)% with a peak accuracy of 85.00%. For the first time, this study simultaneously decodes multi-directional movements of both arms using UHD EEG. This study provides a promising approach for detecting information about arm movement directions, which is significant for the development of BCIs.},
}
@article {pmid40031623,
year = {2025},
author = {Li, M and Chen, S and Zhang, X and Wang, Y},
title = {Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1014-1025},
doi = {10.1109/TNSRE.2025.3545206},
pmid = {40031623},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Rats ; Animals ; *Bayes Theorem ; *Algorithms ; *Neural Networks, Computer ; *Models, Neurological ; Normal Distribution ; Computer Simulation ; Action Potentials/physiology ; Neurons/physiology ; Reproducibility of Results ; Nerve Net/physiology ; },
abstract = {Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.},
}
@article {pmid40031582,
year = {2025},
author = {Berg, GLWV and Rohr, V and Platt, D and Blankertz, B},
title = {A New Canonical Log-Euclidean Kernel for Symmetric Positive Definite Matrices for EEG Analysis (Oct 2024).},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {3},
pages = {1000-1007},
doi = {10.1109/TBME.2024.3483936},
pmid = {40031582},
issn = {1558-2531},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Brain-Computer Interfaces ; Brain/physiology/diagnostic imaging ; },
abstract = {OBJECTIVE: Working with the Riemannian manifold of symmetric positive-definite (SPD) matrices has become popular in electroencephalography (EEG) analysis. Frequently selected for its speed property is the manifold geometry provided by the log-euclidean Riemannian metric. However, the kernels used in the log-euclidean framework are not canonically based on the underlying geometry. Therefore, we introduce a new canonical log-euclidean (CLE) kernel.<br><br>METHODS: We used the log-euclidean metric tensor on the SPD manifold to derive the CLE kernel. We compared it with existing kernels, namely the affine-invariant, log-euclidean, and Gaussian log-euclidean kernel. For comparison, we tested the kernels on two paradigms: classification and dimensionality reduction. Each paradigm was evaluated on five open-access brain-computer interface datasets with motor-imagery tasks across multiple sessions. Performance was measured as balanced classification accuracy using a leave-one-session-out cross-validation. Dimensionality reduction performance was measured using AUClogRNX.<br><br>RESULTS: The CLE kernel itself is simple and easily turned into code, which is provided in addition to all the analytical solutions to relevant equations in the log-euclidean framework. The CLE kernel significantly outperformed existing log-euclidean kernels in classification tasks and was several times faster than the affine-invariant kernel for most datasets.<br><br>CONCLUSION: We found that adhering to the geometrical structure significantly improves the accuracy over two commonly used log-euclidean kernels while keeping the speed advantages of the log-euclidean framework.<br><br>SIGNIFICANCE: The CLE provides a good choice as a kernel in time-critical applications and fills a gap in the kernel methods of the log-euclidean framework.},
}
@article {pmid40031574,
year = {2025},
author = {Zhang, H and Xie, J and Zhao, C and Jin, Z and Du, F and Chen, Y and Xu, G and Tao, Q and Li, M},
title = {A Novel Spatial Auditory Brain-Computer Interface based on Low-Frequency Periodic Auditory Motion Stimulation Paradigm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3544646},
pmid = {40031574},
issn = {1558-2531},
abstract = {UNLABELLED: This study aims to improve the performance of auditory brain-computer interfaces (BCIs) by developing two-target and three-target paradigms based on steady-state motion auditory evoked potential (SSMAEP) using low-frequency stimuli in a spatial audio environment. SSMAEP is elicited by auditory stimuli exhibited by periodic and discrete changes in auditory spatial position.<br><br>METHODS: We designed a periodic auditory motion stimulation paradigm to evoke SSMAEP. Two-target and three-target SSMAEP-BCIs were developed. For the two-target SSMAEP-BCI, two periodic auditory motion stimuli with different motion frequencies were located on the left (2 Hz) and right (1.6 Hz) sides of the head, respectively. For the three-target SSMAEP-BCI, three periodic auditory motion stimuli with different motion frequencies were located on the front (2 Hz), left (2.4 Hz) and right (1.6 Hz) sides of the head, respectively.<br><br>RESULTS: SSMAEP amplitudes were modulated by auditory selective attention. In the two-target BCI, the offline experiments showed a peak average information transfer rate (ITR) of 7.70 bits/min, while the online experiments had a mean accuracy of 82.83% and an ITR of 4.41 bits/min. The three-target BCI achieved a peak ITR of 12.04 bits/min offline, with an online mean accuracy of 80.45% and an ITR of 7.05 bits/min.<br><br>CONCLUSION: The study confirms the feasibility and enhanced performance of spatial low-frequency SSMAEP-BCIs.<br><br>SIGNIFICANCE: This novel approach to SSMAEP-BCI offers a promising direction for enhancing auditory BCI performance, potentially improving user experience and application in complex environments.},
}
@article {pmid40031548,
year = {2025},
author = {Liu, K and Xing, X and Yang, T and Yu, Z and Xiao, B and Wang, G and Wu, W},
title = {DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3546288},
pmid = {40031548},
issn = {2168-2208},
abstract = {OBJECTIVE: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods.<br><br>METHODS: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features.<br><br>MAIN RESULTS: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods.<br><br>CONCLUSION AND SIGNIFICANCE: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://github.com/xingxin-99/DMSANet.git.},
}
@article {pmid40031523,
year = {2024},
author = {Wang, J and Wang, Z and Xu, T and Zhou, T and Zhao, X and Hu, H},
title = {SS-MSDA: Streamlined Sample-level Multi-source Domain Adaptation for EEG Emotion Recognition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781531},
pmid = {40031523},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; },
abstract = {Affective EEG-based Brain-Computer Interface (BCI) offers extensive prospects. Yet, it grapples with notable challenges in consistently achieving accurate emotion recognition among new subjects. Mitigating this matter, Multi-Source Domain Adaptation (MSDA) has been advanced. However, they exhibit performance that falls short of expectations, necessitate complex preparations and lack solid theoretical underpinnings. Concerning these insufficiencies, we propose an innovative MSDA algorithm, effectively narrowing the Wasserstein Distance between identified subdomain and the target domain, thereby theoretically constraining the upper bound of emotion classification error. Compared with baseline model on the emotional EEG dataset SEED,SS-MSDA achieved an increase in recognition accuracy ranging from 1[~]14% (average improvement of 7.2%) across subjects, demonstrating superior performance over Domain Adaption (DA) benchmarks. Moreover, it significantly reduced the preparation time by over 99.8%, along with its minimal computational costs, thus being exceptionally apt for practical applications. Finally, the algorithm extends the theory of MSDA for affective BCI. The significance of this algorithm lies in its potential to improve the recognition accuracy of existing emotion recognition algorithms on new subjects, without the need for pre-training and data pre-collection. Moreover, it provides a novel theoretical perspective on the methods for constraining the error upper bound of the classifier.},
}
@article {pmid40031514,
year = {2024},
author = {Wang, Z and Li, A and Wang, Z and Zhou, T and Xu, T and Hu, H},
title = {Bi-Stream Adaptation Network for Motor Imagery Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782480},
pmid = {40031514},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; },
abstract = {Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information can thereby enhance network performance by having a comprehensive understanding of MI. Besides, due to the non-stationarity of EEG, the global and local distributions of cross-session EEG from an individual vary in applications. Based on these ideas, a novel Bi-Stream Adaptation Network (BSAN) is proposed to generate multi-scale context dependencies and to bridge the cross-session discrepancies in MI classification. Specifically, a Bi-attention module is proposed to cultivate multi-scale temporal dependencies and figure out the predominant brain regions. After features extraction, a Bi-discriminator is trained to implement the task of domain adaptation both globally and locally. To validate the proposed BSAN, extensive experiments were conducted based on two public MI datasets. The results revealed that the proposed BSAN improved the performance and robustness of MI classification and outperformed several state-of-the-art methods.},
}
@article {pmid40031513,
year = {2024},
author = {Li, R and Zhao, X and Wang, Z and Xu, G and Hu, H and Zhou, T and Xu, T},
title = {Narrowband-Enhanced Method for Improving Frequency Recognition in SSVEP-BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782374},
pmid = {40031513},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI) provide a non-invasive and effective means for communication and control, which fundamentally rely on the feature of frequency information. However, filter banks in conventional spatial filter classification methods do not effectively utilize narrowband information. This study proposed a narrowband-enhanced filter bank canonical correlation analysis (NE-FBCCA) to integrate narrowband signal processing with a broadband filter bank analysis. By employing adaptive signal decomposition via multivariate fast iterative filtering (MvFIF), the specific component corresponding to the stimulus frequency can be strengthened separately. To validate the efficacy of this method, we conducted a performance evaluation using public SSVEP datasets. The results demonstrate a notable enhancement of reconstructed EEG signals in the signal-to-noise ratio (SNR) of stimulus frequency responses. Furthermore, there are significant improvements observed in classification accuracy and ITRs when compared to standard canonical correlation analysis (CCA) and filter bank CCA (FBCCA) approaches. This study provides a narrowband signal processing strategy for SSVEP responses and shows its potential to improve the performance of SSVEP-based BCI systems.},
}
@article {pmid40031504,
year = {2024},
author = {Kang, H and Bao, N and Liu, H and Dong, C and Lei, D and Chen, X},
title = {A Method of Cross-Subject Transfer Learning for Ultra Short Time SSVEP Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10782593},
pmid = {40031504},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {The steady-state visual evoked potentials (SSVEP) based brain-computer interfaces (BCIs) require extensive training data for efficient classification, but existing algorithms struggle with ultra short time inputs (less than 0.2 seconds), limiting the feasibility of real-time systems. This paper proposes a novel method CSA-GSDANN consisting of CSA and GSDANN. GSDANN improves SSVEP feature extraction performance in ultra short time input scenarios by applying cross-subject transfer learning techniques, combining a Global Attention Mechanism (GAM) and an optimized SSVEPNet and pre-training method CSA selects the most suitable source subject based on accuracy and aligns it with the target subject to address the inter-subject variability. The proposed CSA-GSDANN method adopts a Domain Adversarial Neural Network (DANN) framework, which integrates an enhanced SSVEPNet algorithm with an attention mechanism to extract features from electroencephalogram (EEG) data within and across subjects. The extracted features undergo domain-adversarial transfer learning. The final stage involves frequency signal classification using a constrained convolutional network. The evaluation of the CSA-GSDANN method on the IMUT dataset containing 12 subjects shows significant improvements. A comparative analysis against eight mainstream deep learning and traditional algorithms demonstrates an average accuracy enhancement of 4.23% and an average Information Transfer Rate (ITR) improvement of 50.482 bits/min compared to state-of-the-art classification algorithms under short time (0.2s) EEG inputs, substantially improving SSVEP classification performance.},
}
@article {pmid40031501,
year = {2024},
author = {Li, Z and Shi, K and Li, W and Mu, F and Zhang, J and Huang, R and Cheng, H},
title = {A Dynamic Evaluation-Denoising Network for Motion Artifacts Removal from Single-Channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782860},
pmid = {40031501},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Artifacts ; *Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Neural Networks, Computer ; Motion ; },
abstract = {Brain-computer interfaces (BCIs) have gained significant attention in rehabilitation research as a critical step in investigating neural remodeling techniques. However, most existing methods usually overlook the randomness and diversity of motion artifacts, thereby lacking the desired generalization ability and denoising precision, which limits their practical application. To address these limitations, we propose a Dynamic Evaluation Denoising Network (DED-Net) that incorporates an evaluation model with cross-domain feature fusion for artifact detection and classification. Then dynamically selecting Bidirectional Long Short-Term Memory (Bi-LSTM) networks with varying parameters for artifact removal, which achieves superior performance compared to state-of-the-art methods. Our experiment on a semi-simulated dataset constructed by EEGdenoiseNET demonstrates that the performance of DED-Net is advanced over the state-of-the-art method, i.e., SDNet, in terms of the signal-to-noise rate (SNR) and correlation coefficient (CC). Using our method, SNR and CC are 6.0597 dB and 95.28%, respectively increasing by 20.48% and 3.15%. Experiments on real EEG data demonstrate the superior performance of the proposed method in reconstructing EEG signals, in terms of the intent recognition tasks, achieving a remarkable accuracy of 88.89%, outperforming other methods.},
}
@article {pmid40031496,
year = {2024},
author = {Tai, P and Yang, J and Qi, S and Li, G and Fu, Y and Li, Y},
title = {Exploring the Relationship Between Imitated and Associated Mechanism on Performance of Visual Imagery Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782491},
pmid = {40031496},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; *Imagination/physiology ; Adult ; Female ; Young Adult ; Brain/physiology ; Visual Perception/physiology ; },
abstract = {Visual Imagery (VI) can be defined as the manipulation of visual information derived from memory rather than perception. Currently, the brain responses underlying imitation and associative VI are not clear. In this study, we explore the differences from imitation to associative VI on the brain responses based on EEG signals. In this study, eight participants were instructed to observe visual cues from three predefined images or characters (tree, computer, or sphere), and then imagine the same cues. The results indicate that there is a significant difference in power intensity among electrode channels in the occipital lobe, posterior parietal lobe, and temporal lobe during the imagination phase between imitative tasks and associative tasks, as revealed by t-tests (p < 0.05, rejecting the null hypothesis). Overall, imitation mechanisms and associative mechanisms represent the short-term memory (STM) and long-term memory (LTM) features of objects. This study addresses a crucial research gap in VI, as there is currently a scarcity of formal simultaneous comparisons in the existing literature.},
}
@article {pmid40031495,
year = {2024},
author = {Liu, H and Yang, B and Guan, S and Rong, F and Guo, M and Fang, Y and Liu, B and Gao, Y and Gu, Y},
title = {MSDAC: A multi-source domain adversarial framework for motion prediction in intracortical brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782649},
pmid = {40031495},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Macaca mulatta ; Algorithms ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) restore motor function in patients with paralysis by converting neural activity into control signals for external devices. However, the frequent recalibration required by current decoding methods due to turnover and loss of recording neurons poses a challenge for achieving stable online decoding. To address these issues, we propose a multi-source domain adversarial classification (MSDAC) framework for cross-day decoding that utilizes an out-of-distribution (OOD) generalization approach. This framework divides the historical data into source domains by date and employs adversarial networks to minimize the distribution discrepancies among multiple source domains, thereby achieving robust domain-invariant characteristics and superior performance on unseen test data. The MSDAC framework was evaluated using five months of monkey center-out neural activity data and demonstrated exceptional performance. Without relying on test day data for model calibration or parameter updating, the MSDAC achieved an average decoding accuracy of 84.38% (day-5 to day-150, 27968 trials). These results underscore that the MSDAC-based decoding framework can be an ideal choice for establishing stable iBCI systems.},
}
@article {pmid40031494,
year = {2024},
author = {Yu, S and Liang, F and Zhang, Y and Chen, L and Dong, L and Guo, Z and Jie, J and Wang, X and Yin, M},
title = {A Three-stage Strategy Significantly Improves Hand Movement Direction Decoding of a Single Neural Unit.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781633},
pmid = {40031494},
issn = {2694-0604},
mesh = {*Hand/physiology ; *Brain-Computer Interfaces ; *Movement/physiology ; Animals ; Macaca mulatta ; Neurons/physiology ; Algorithms ; },
abstract = {Invasive brain-computer interfaces (iBCI) can record multiple neural signals with the highest temporal and spatial resolution. However, the number of available neural units decreases with the increase in implantation time, which affects the stability of the iBCI system's control. Meanwhile, most current studies utilize a population of neural units to decode a single instruction, which limits the ability to decode multi-tasks simultaneously in complex scenarios. Using a few number of neural units, possibly even a single one, to perform single-task decoding is expected to enable the simultaneous control of multitasks. Herein, a three-stage strategy is proposed to accurately decode the direction of a monkey's hand movement in a Center-out task using spiking activities from a single neural unit. First, the optimal decoding window was selected based on the time course of decoding performance. Second, a firing rate variance ratio is proposed to choose the optimal neural unit from all units. Last, hard voting is employed to classify hand movements based on a single neural unit. The results indicated that decoding with a chosen neural unit and an optimal decoding window leads to a classification accuracy of 82.0%, which is nearly equivalent to that of multi-unit decoding (84.41%). This study provides insights into controlling multiple degrees of freedom with fewer neural units in iBCI control.},
}
@article {pmid40031491,
year = {2024},
author = {Ye, Y and Mu, X and Pan, T and Li, Y and Wei, L and Fan, X and Wei, L},
title = {Cross-subject EEG-based Motor Imagery Recognition for Patient's Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782932},
pmid = {40031491},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery (MI), a kind of psychological representation without actual action, has garnered increasing attention in rehabilitation. However, the inherent differences between patients and healthy persons hinder rehabilitation by reducing the accuracy of cross-subject MI recognition. Although unsupervised domain adaptation (UDA) methods have mitigated individual differences, they still suffer from challenges in terms of selecting confusing source domains and accurately classifying MI samples at the boundary. To address these challenges, we propose a novel UDA framework with a causal graphical model and label similarity clustering. The causal graphical model is employed to estimate the similarity of EEG signals, enabling the causal selection to effectively avoid confusing healthy persons' data. In addition, label similarity clustering mechanism is utilized to establish a distinct boundary, thereby enhancing the classification accuracy. The experimental results demonstrate that our approach outperforms baseline 10.10% and 16.27% on BCI IV-2a&amp;2b, separately. MI is expected to aid rehabilitation through precise recognition and active support.},
}
@article {pmid40031490,
year = {2024},
author = {Wang, X and Lai, YH and Chen, F},
title = {Intended Speech Classification with EEG Signals Based on a Temporal Attention Mechanism: A Study of Mandarin Vowels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782383},
pmid = {40031490},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Speech/physiology ; Male ; Female ; Adult ; Young Adult ; Signal Processing, Computer-Assisted ; Language ; Attention/physiology ; },
abstract = {Speech brain-machine interfaces (BCIs) offer an effective means for patients with voice disorders to communicate, and research on decoding electroencephalography (EEG) signals related to intended speech can help to understand the mechanisms of language production in the brain. This study classified the intended speech EEG signals of four Chinese vowels, utilizing a dataset collected from 10 participants. A proposed TA-EEGNet model was employed, incorporating a temporal attention module. The model achieved an accuracy of 49.47%, surpassing other prevalent EEG classification models. The average accuracy in the binary classification of vowels was 69.83%. The vowels /u/ and /ü/ were classified with the lowest accuracy, suggesting difficulties in classifying vowels with similar articulatory movements based on intended speech EEG signals. Furthermore, the research analyzed the classification performance using data of different brain regions. The results showed that the auditory cortex, Broca's and Wernicke's areas, prefrontal cortex, and motor cortex outperformed the sensory cortex, indicating their contributions in the intended speech process of Mandarin vowels. Results also showed left hemisphere dominance. These findings contribute to the study of the neural mechanisms underlying speech production and articulatory movements, emphasizing the potential of speech BCIs to improve communication for people with speech disorders.},
}
@article {pmid40031482,
year = {2024},
author = {Zhang, Y and Yang, S and Cauwenberghs, G and Jung, TP},
title = {From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781627},
pmid = {40031482},
issn = {2694-0604},
mesh = {Humans ; *Reading ; *Electroencephalography/methods ; *Eye-Tracking Technology ; Brain-Computer Interfaces ; Language ; Comprehension/physiology ; Eye Movements/physiology ; },
abstract = {Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading. The code and data are available in github.},
}
@article {pmid40031471,
year = {2024},
author = {Gui, Z and Liu, Y and Qiu, S and Zhang, Y and Dong, K and Ming, D},
title = {Electrical stimulation-based paradigm to enhance lower limb motor imagery: initial validation in stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782372},
pmid = {40031471},
issn = {2694-0604},
mesh = {Humans ; *Lower Extremity/physiopathology ; Male ; *Stroke/physiopathology/complications ; Female ; *Brain-Computer Interfaces ; Middle Aged ; *Stroke Rehabilitation/methods ; Electric Stimulation/methods ; Aged ; Imagination/physiology ; Motor Cortex/physiopathology ; Adult ; },
abstract = {Lower limb motor dysfunction is a prevalent complication of stroke that significantly impacts patients' quality of life. Current research indicates that motor imagery-based brain-computer interface (BCI-MI) training can assist stroke patients in enhancing motor function and reconstructing neural pathways. Nevertheless, 40% of stroke patients struggle with effective motor imagery (MI), leading to challenges in applying lower limb MI in clinical settings. Electrical stimulation (ES) has demonstrated the ability to induce muscle contractions, generating a kinesthetic illusion that effectively guides subjects in performing MI. However, the existing study lacks clarity regarding the effectiveness of the ES-MI paradigm in improving lower limb MI in stroke patients. To address this gap, we recruited seven stroke patients to participate in an experiment involving the ES-MI enhancement paradigm, aiming to validate its performance in stroke patients. The results revealed that the ES-MI paradigm augmented the activation of the motor cortex in the lower limb and reactivated dormant areas, suggesting that MI training based on the ES-MI paradigm holds promise for enhancing neural remodeling effects in stroke patients. Additionally, the paradigm enhanced the classification accuracy of SVM(+1.17%), KNN(+0.93%), RF(+7.13%), LDA(+5.29%), and EEGNet(+0.96%), indicating potential improvements in the efficiency and quality of human-robot interaction in brain-controlled lower limb rehabilitation robots.},
}
@article {pmid40031469,
year = {2024},
author = {Zhou, W and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H},
title = {Enhancing Detection of SSVEP-based BCIs Using Adjacent Frequencies Fusion Method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782332},
pmid = {40031469},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) have emerged as transformative technologies, enabling direct communication between the human brain and external devices. Steady-state visual evoked potentials (SSVEP) have gained particular attention due to their potential in BCIs. Current decoding algorithms do not take advantage of the correlation coefficients of adjacent frequencies. We propose adjacent frequencies fusion filter bank canonical correlation analysis (AFF-FBCCA), which enhances accuracy and robustness by utilizing information from adjacent frequencies. This weighted fusion aims to capitalize on the inherent similarity between electroencephalogram (EEG) signals at closely spaced frequencies. The determination of weight coefficients, incorporating dynamic adjustments based on the time window, further contributes to the adaptability of AFF-FBCCA. The proposed method is validated using public benchmark datasets. The results show that AFF-FBCCA is always superior to standard FBCCA in terms of classification accuracy and information transfer rate (ITR) in all test time windows. This method maintains the advantage of training-free and provides a more accurate and user-friendly solution for SSVEP-based BCI.},
}
@article {pmid40031462,
year = {2024},
author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {A Novel SSVEP Modulation Method Utilizing VR-Based Binocular Vision.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781783},
pmid = {40031462},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Vision, Binocular/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Virtual Reality ; Algorithms ; Male ; Adult ; Young Adult ; Female ; },
abstract = {This paper presents a novel method for modulating steady-state visual evoked potentials (SSVEP) based on binocular vision in virtual reality (VR). The method involves displaying monocular frequencies in the left and right view of VR to encode nine binocular targets using only two frequencies. We constructed a VR-BCI system and validated the effectiveness of this binocular-encoded paradigm through the task-related component analysis (TRCA) algorithm, which is a supervised approach based on individual templates. The results showed a recognition accuracy of 79.05% and an information transfer rate (ITR) of 43.38 bits/min with a data length of 2 seconds. The electroencephalography (EEG) responses of the binocular combinations exhibited unique characteristics compared to traditional SSVEP, suggesting potential for further optimization in terms of performance. This proposed method reduces the frequency requirements for encoding SSVEP-speller and highlights the potential of VR-BCI in utilizing binocular characteristics, which could contribute to the practicality and high-speed implementation of SSVEP-based brain-computer interface (BCI) systems.},
}
@article {pmid40031461,
year = {2024},
author = {Li, A and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782407},
pmid = {40031461},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Speech/physiology ; *Machine Learning ; Imagination/physiology ; Algorithms ; },
abstract = {In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer Interface (BCI). In IS BCI, the classification accuracy for imagining specific words is relatively low due to the inherent complexity in high-level feature variations. However, the binary classification accuracy for IS/rest is significantly higher. FSFTL leverages the refined feature focusing capability of the binary IS/Rest classification task to effectively locate relevant features for the word-level task. The feature extractor in the IS/Rest model demonstrates robust decoding ability for low-level IS features in EEG signals. We applied this high-performance yet low-resolution feature extractor to a public dataset for five-word IS task. The classifier was retrained to handle an increased number of classification categories, and the feature extractor was further fine-tuned to accommodate higher-level classification tasks. Before the experiment, we aligned the data from the two datasets to maintain the versatility of the feature extractor. Our proposed FSFTL approach was compared with existing EEG models, showing a significant improvement. The FSFTL approach outperformed the backbone strategy with a 6% increase in mean accuracy across all fifteen subjects. This study highlights the commonality of features in EEG data of IS and their transferability across various datasets and tasks, which is beneficial for improving the decoding ability of word-level IS BCI.},
}
@article {pmid40031460,
year = {2024},
author = {Yang, J and Kulwa, F and Liu, X and Lu, Y and Fu, Y and Li, G and Huai, Y and Zhang, X and Li, Y},
title = {CEBRA Method: Decoding Brain Activity for Advanced Brain-Computer Interface Technology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10782428},
pmid = {40031460},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Brain/physiology/physiopathology ; Male ; Support Vector Machine ; Female ; Adult ; Algorithms ; Stroke/physiopathology ; Stroke Rehabilitation/methods ; },
abstract = {The emerging neurorehabilitation technology, Brain-Computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients' brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.},
}
@article {pmid40031451,
year = {2024},
author = {Luo, H and Zhao, X and Zhou, T and Wang, Z and Xu, T and Hu, H},
title = {EEG Emotion Recognition Based on 3D-CTransNet.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782401},
pmid = {40031451},
issn = {2694-0604},
mesh = {*Emotions/physiology ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Deep Learning ; },
abstract = {Emotion recognition is of great significance for brain-computer interface and emotion computing, and EEG plays a key role in this field. However, the current design of brain computer interface deep learning model is faced with algorithmic or structural constraints, and it is difficult to recognize the complex features in EEG signals with long-term dynamic changes. To solve this issue, a hybrid CNN-Transformer structure using 3D data input is proposed and named 3D-CTransNet in this paper, which solves the problem of performance degradation of the traditional CNN-LSTM hybrid structure in the recognition of long sequence signals. At the same time, the self attention mechanism and parallel mode introduced by Transformer improve the recognition accuracy and processing speed. In addition, the 3D data feature map based on electrode position mapping effectively retains the spatial characteristics of EEG signals, which makes CNN better combine the time domain and spatial domain. Finally, the Valence-Arousal classification training of emotion is carried out on the public dataset DEAP, and the classification accuracy is 97.04%, which is about 5% higher than that of the hybrid CNN-LSTM model.},
}
@article {pmid40031446,
year = {2025},
author = {Wei, Z and Lin, Y and Chen, J and Pan, S and Gao, X},
title = {Effects of 3D Stimuli With Frequency Ranges, Patterns, and Shapes on SSVEP-BCI Performance in Virtual Reality.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {890-899},
doi = {10.1109/TNSRE.2025.3544308},
pmid = {40031446},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; Male ; *Evoked Potentials, Visual/physiology ; Female ; Young Adult ; Adult ; *Electroencephalography ; *Photic Stimulation ; Healthy Volunteers ; Algorithms ; Depth Perception/physiology ; },
abstract = {Traditional steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer stability and simplicity in evoking brain responses, but their practical utility is limited by immovable screens for visual stimuli. Virtual Reality (VR) technology provides a more natural and immersive environment to evoke SSVEP signals. However, the design methods for visual stimuli in VR environments remain to be explored, especially under the stereoscopic vision conditions. This study investigated the effects of 3D stimuli with frequency ranges, patterns, and shapes on the performance and user experiences of VR-SSVEP. There were four patterns including three-dimensional (3D) flicker, two-dimensional (2D) flicker, 3D checkerboard, and 3D quick response (QR) code with four shapes comprising cube, sphere, cylinder, and cone at low (9-15Hz), medium (18-24Hz), and high frequencies (30-36Hz). Both offline and online experiments were conducted to analyze the effects of different parameter combinations on SSVEP-BCI performance, and a questionnaire was exploited to evaluate user experiences. Compared to high frequency range, the low and medium frequency ranges had better performance and lower user experiences. 3D checkerboard and 3D QR code patterns showed significantly better user experiences than 3D and 2D flickers for all frequency ranges. With a high level of classification performance, 3D checkerboard and 3D QR code patterns in medium frequency range could synthetically enhance the system performance and user experiences. These results could provide significant value for SSVEP-BCI application in VR environments.},
}
@article {pmid40031345,
year = {2025},
author = {Liu, G and Zhang, R and Tian, L and Zhou, W},
title = {Fine-Grained Spatial-Frequency-Time Framework for Motor Imagery Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3536212},
pmid = {40031345},
issn = {2168-2208},
abstract = {The Motor Imagery Brain-Computer Interfaces (MI-BCIs) have shown considerable promise for applications in neural rehabilitation. However, improving the practicality and interpretability of MI-BCIs remains a critical challenge. Unlike previous methods that focus generally on either spatial, frequency, or temporal domains with coarse-grained segmentation schemes, this study proposes a novel fine-grained spatialfrequency- time (FGSFT) framework, aiming to enhance the efficiency and reliability of MI-BCIs. Multi-channel MI EEG recordings are firstly processed through multiscale time-frequency segmentation and spatial segmentation schemes, yielding finegrained spatial-frequency-time segments (SFTSs). The key SFTSs are then selected with a tailored wrapper-based feature selection approach. Discriminative MI EEG features are extracted using a divergence-based common spatial pattern algorithm with intraclass regularization and classified using an efficient linear support vector machine (SVM). The proposed framework was evaluated on the BCI IV IIa and SDU-MI datasets, demonstrating stateof- the-art performance in terms of information transfer rate (ITR) Meanwhile, the proposed spatial segmentation strategy can significantly improve the performance of MI-BCIs when using a larger number of electrodes. Additionally, the fine-grained Motor Imagery Time-Frequency Reaction Map (MI-TFRM) and time-frequency topographical map can be obtained with the proposed framework enabling visualization of the subject-specific dynamic neural process during motor imagery tasks, facilitating the devising of personalized MI-BCIs. The FGSFT framework significantly advances the accuracy, ITR, and interoperability of MI-BCIs, paving the way for future neuroscientific research and clinical applications in neural rehabilitation and assistive technologies.},
}
@article {pmid40031268,
year = {2025},
author = {Gao, X and Gui, K and Wu, X and Metcalfe, B and Zhang, D},
title = {Effects of Different Preprocessing Pipelines on Motor Imagery-Based Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3532771},
pmid = {40031268},
issn = {2168-2208},
abstract = {In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been significantly improved by a lot of cutting-edge methods. The exploration of effective preprocessing in brain-computer interfaces, particularly in terms of identifying suitable preprocessing methods and determining the optimal sequence for their application, remains an area ripe for further investigation. To address this gap, this study explores a range of preprocessing techniques, including but not limited to independent component analysis, surface Laplacian, bandpass filtering, and baseline correction, examining their potential contributions and synergies in the context of BCI applications. In this extensive research, a variety of preprocessing pipelines were rigorously tested across four EEG data sets, all of which were pertinent to motor imagery-based BCIs. These tests incorporated five EEG machine learning models, working in tandem with the preprocessing methods discussed earlier. The study's results highlighted that baseline correction and bandpass filtering consistently provided the most beneficial preprocessing effects. From the perspective of online deployment, after testing and time complexity analysis, this study recommends baseline correction, bandpass filtering and surface Laplace as more suitable for online implementation. An interesting revelation of the study was the enhanced effectiveness of the surface Laplacian algorithm when used alongside algorithms that focus on spatial information. Using appropriate processing algorithms, we can even achieve results (92.91% and 88.11%) that exceed the SOTA feature extraction methods in some cases. Such findings are instrumental in offering critical insights for the selection of effective preprocessing pipelines in EEG signal decoding. This, in turn, contributes to the advancement and refinement of brain-computer interface technologies.},
}
@article {pmid40031262,
year = {2025},
author = {Chen, P and Liu, X and Ma, C and Wang, H and Yang, X and Grebogi, C and Gu, X and Gao, Z},
title = {Unsupervised Domain Adaptation With Synchronized Self-Training for Cross-Domain Motor Imagery Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3525577},
pmid = {40031262},
issn = {2168-2208},
abstract = {Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.},
}
@article {pmid40031240,
year = {2025},
author = {Kim, CU and Park, S and Im, CH},
title = {Performance Enhancement of an SSVEP-Based Brain-Computer Interface in Augmented Reality through Adaptive Color Adjustment of Visual Stimuli for Optimal Background Contrast.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3530421},
pmid = {40031240},
issn = {1558-0210},
abstract = {The aim of this study is to develop a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system with enhanced performance in an augmented reality (AR) environment by dynamically adjusting colors of visual stimuli to contrast with the background seen through the transparent display. Our proposed method extracts the average color value from the area surrounding the visual stimulus location. It then calculates the contrast value using the HSV color model and applies this to the stimulus color. In an offline experiment, we determined the optimal visual stimulus presentation strategy by comparing the performances of three different methods for determining the colors of visual stimuli in an AR environment. We then evaluated the feasibility of the proposed strategy through online experiments conducted in both indoor and outdoor conditions. The classification performance of the SSVEP-BCI system in an AR environment based on our proposed stimulus presentation strategy was 95.0% for a window size of 3.5 s in offline experiments performed with 17 participants. This was significantly higher than the performance of the conventional black-and-white color strategy. Additionally, it was confirmed by the online experiments that there was no large performance degradation between indoor and outdoor uses.},
}
@article {pmid40031192,
year = {2025},
author = {Li, Y and Wang, Y and Lei, B and Wang, S},
title = {SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs.},
journal = {IEEE transactions on medical imaging},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TMI.2025.3532480},
pmid = {40031192},
issn = {1558-254X},
abstract = {Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating both types of sensors on the same scalp surface. This physical constraint complicates the acquisition of high-quality hybrid signals, thereby limiting the widespread application of hybrid MI-BCIs. To address this issue, this study proposes the spatio-temporal controlled diffusion model (SCDM) as a framework for cross-modal generation from EEG to fNIRS. The model utilizes two core modules, the spatial cross-modal generation (SCG) module and the multi-scale temporal representation (MTR) module, which adaptively learn the respective latent temporal and spatial representations of both signals in a unified representation space. The SCG module further maps EEG representations to fNIRS representations by leveraging their spatial relationships. Experimental results show high similarity between synthetic and real fNIRS signals. The joint classification performance of EEG and synthetic fNIRS signals is comparable to or even better than that of EEG with real fNIRS signals. Furthermore, the synthetic signals exhibit similar spatio-temporal features to real signals while preserving spatial relationships with EEG signals. To our knowledge, it is the first work that an end-to-end framework is proposed to achieve cross-modal generation from EEG to fNIRS. Experimental results suggest that the SCDM may represent a promising paradigm for the acquisition of hybrid EEG-fNIRS signals in MI-BCI systems.},
}
@article {pmid40031046,
year = {2025},
author = {Li, R and Wang, Z and Zhao, X and Xu, G and Hu, H and Zhou, T and Xu, T},
title = {Amplitude Modulation Depth Coding Method for SSVEP-based Brain-computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3528409},
pmid = {40031046},
issn = {1558-0210},
abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the limited availability of frequency resources inherently constrains the scale of the instruction set, presenting a substantial challenge for efficient communication. As the number of stimuli increases, the comfort level of the stimulus interface also becomes increasingly demanding due to the expanded flickering area. To address these issues, we proposed a novel amplitude modulation depth coding (AMDC) method that employs Amplitude Shift Keying (ASK) technique to modulate the luminance level of stimuli dynamically. Each stimulus with a single carrier frequency was assigned a specific binary sequence to operate two modulation depths. Two experiments were conducted to comprehensively assess the effectiveness of this approach. In Experiment 1, the time-frequency responses at two modulation depths across different frequencies were examined. A 36-target paradigm based on AMDC strategy was designed and evaluated in terms of user experience and classification performance in Experiment 2. The results show that the proposed paradigm obtains an average classification accuracy of 81.7 ± 12.6% with an average information transfer rate (ITR) of 45.4 ± 11.5 bits/min. Moreover, it significantly reduces flicker perception and improves comfort level compared to traditional SSVEP stimuli with uniform modulation depth. Given its capability to improve coding efficiency for a single frequency and improve user experience, this method shows promising potential for application in large-scale command SSVEP-based BCI systems.},
}
@article {pmid40030956,
year = {2025},
author = {Wu, J and He, F and Xiao, X and Gao, R and Meng, L and Liu, X and Xu, M and Ming, D},
title = {SSVEP enhancement in mixed reality environment for brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3526950},
pmid = {40030956},
issn = {1558-0210},
abstract = {Expanding the application possibilities of brain-computer interfaces (BCIs) is possible through their implementation in mixed reality (MR) environments. However, visual stimuli are displayed against a realistic scene in the MR environment, which degrades BCI performance. The purpose of this study was to optimize stimulus colors in order to improve the MR-BCI system's performance. In the MR environment, a 10-command SSVEP-BCI was deployed. Various stimulus colors and background colors for the BCI system were tested and optimized in offline and online experiments. Color contrast ratios (CCRs) between stimulus and background colors were introduced to assess the performance difference among all conditions. Additionally, we proposed a cross-correlation task-related component analysis based on simulated annealing (SA-xTRCA), which can increase the signal-to-noise ratio (SNR) and detection accuracy by aligning SSVEP trials. The results of an offline experiment showed that the background and stimulus colors had a significant interaction effect that can impact system performance. A possible nonlinear relationship between CCR value and SSVEP detection accuracy exists. Online experiment results demonstrated that the system performed best with polychromatic stimulus on the colored background. The proposed SA-xTRCA significantly outperformed the other four traditional algorithms. The online average information transfer rate (ITR) achieved 57.58 ± 5.31 bits/min. This study proved that system performance can be effectively enhanced by optimizing stimulus color based on background color. In MR environments, CCR can be used as a quantitative criterion for choosing stimulus colors in BCI system design.},
}
@article {pmid40030957,
year = {2025},
author = {Zhang, Y and Zhang, C and Jiang, R and Qiu, S and He, H},
title = {A Distribution Adaptive Feedback Training Method to Improve Human Motor Imagery Ability.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3527629},
pmid = {40030957},
issn = {1558-0210},
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) can translate users' subjective movement-related mental state without external stimulus, which has been successfully used for replacing and repairing motor function. In contrast with studies about decoding methods, less work was reported about training users to improve the performance of MI-BCIs. This study aimed to develop a novel MI feedback training method to enhance the ability of humans to use the MI-BCI system. In this study, an adaptive MI feedback training method was proposed to improve the effectiveness of the training process. The method updated the feedback model during training process and assigned different weights to the samples to better adapt the changes in the distribution of the Electroencephalograms (EEGs). An online feedback training system was established. Each of ten subjects participated in a three-day experiment involving three different feedback methods: no feedback algorithm update, feedback algorithm update, and feedback algorithm update using the proposed adaptive method. Comparison experiments were conducted on three different feedback methods. The experimental results showed that the feedback algorithm using the proposed method can most quickly improve the MI classification accuracy and has the largest increase in accuracy. This indicates that the proposed method can enhance the effectiveness of feedback training and improve the practicality of MI-BCI systems.},
}
@article {pmid40030935,
year = {2025},
author = {Meng, J and Li, X and Li, S and Fan, X and Xu, M and Ming, D},
title = {High-Frequency Power Reflects Dual Intentions of Time and Movement for Active Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3529997},
pmid = {40030935},
issn = {1558-0210},
abstract = {Active brain-computer interface (BCI) provides a natural way for direct communications between the brain and devices. However, its detectable intention is very limited, let alone of detecting dual intentions from a single electroencephalography (EEG) feature. This study aims to develop time-based active BCI, and further investigate the feasibility of detecting time-movement dual intentions using a single EEG feature. A time-movement synchronization experiment was designed, which contained the intentions of both time (500 ms vs. 1000 ms) and movement (left vs. right). Behavioural and EEG data of 22 healthy participants were recorded and analyzed in both the before (BT) and after (AT) timing prediction training sessions. Consequently, compared to the BT sessions, AT sessions led to substantially smaller absolute deviation time behaviourally, along with larger high-frequency event-related desynchronization (ERD) in frontal-motor areas, and significantly improved decoding accuracy of time. Moreover, AT sessions achieved enhanced motor-related contralateral dominance of event-related potentials (ERP) and ERDs than the BT, which illustrated a synergistic relationship between the two intentions. The feature of 20-60 Hz power can simultaneously reflect the time and movement intentions, achieving a 73.27% averaged four-classification accuracy (500 ms-left vs. 500 ms-right vs. 1000 ms-left vs.1000 ms-right), with the highest up to 93.81%. The results initiatively verified the dual role of high-frequency (20-60 Hz) power in representing both the time and movement intentions. It not only broadens the detectable intentions of active BCI, but also enables it to read user's mind concurrently from two information dimensions.},
}
@article {pmid40030934,
year = {2025},
author = {Mahmoudi, A and Khosrotabar, M and Gramann, K and Rinderknecht, S and Sharbafi, MA},
title = {Using passive BCI for personalization of assistive wearable devices: a proof-of-concept study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3530154},
pmid = {40030934},
issn = {1558-0210},
abstract = {Assistive wearable devices can significantly enhance the quality of life for individuals with movement impairments, aid the rehabilitation process, and augment movement abilities of healthy users. However, personalizing the assistance to individual preferences and needs remains a challenge. Brain-Computer Interface (BCI) offers a promising solution for this personalization problem. The overarching goal of this study is to investigate the feasibility of utilizing passive BCI technology to personalize the assistance provided by a knee exoskeleton. Participants performed seated knee flexion-extension tasks while wearing a one-degree-of-freedom knee exoskeleton with varying levels of applied force. Their brain activities were recorded throughout the movements using electroencephalography (EEG). EEG spectral bands from several brain regions were compared between the conditions with the lowest and highest exoskeleton forces to identify statistically significant changes. A Naive Bayes classifier was trained on these spectral features to distinguish between the two conditions. Statistical analysis revealed significant increases in δ and θ activity and decreases in α and β activity in the frontal, motor, and occipital cortices. These changes suggest heightened attention, concentration, and motor engagement when the task became more difficult. The trained Naive Bayes classifier achieved an average accuracy of approximately 72% in distinguishing between the two conditions. The outcomes of our study demonstrate the potential of passive BCI in personalizing assistance provided by wearable devices. Future research should further explore integrating passive BCI into assistive wearable devices to enhance user experience.},
}
@article {pmid40030668,
year = {2025},
author = {Perna, A and Orban, G and Berdondini, L and Ribeiro, JF},
title = {Force Measurements to Advance the Design and Implantation of CMOS-Based Neural Probes.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1731-1739},
doi = {10.1109/TBME.2024.3519763},
pmid = {40030668},
issn = {1558-2531},
mesh = {*Electrodes, Implanted/adverse effects ; Equipment Design ; Microelectrodes ; Animals ; *Brain-Computer Interfaces ; Brain/physiology ; Semiconductors ; Male ; Rats ; Foreign-Body Reaction ; },
abstract = {OBJECTIVE: Tissue penetrating active neural probes provide large and densely packed microelectrode arrays for the fine-grained investigation of brain circuits and for advancing brain-machine interfaces (BMIs). To improve the electrical interfacing performances of such stiff silicon devices, which typically elicit a vigorous foreign body reaction (FBR), here we perform insertion force measurements and derive probe layout and implantation procedure optimizations.<br><br>METHODS: We performed in-vivo insertion force measurements to evaluate the impact of probe design and implantation speed on mechanically induced trauma and iatrogenic injury. Because acute damage constitutes the initial trigger of FBR, these experiments allow to characterize and minimize device invasiveness.<br><br>RESULTS: Probe sharpness outweighs cross-sectional dimensions during the dimpling stage of the implantation, when the device compresses the brain before penetration. Insertion speed does not display a major effect on dimpling magnitude. A slow speed, however, significantly increases dimpling duration.<br><br>CONCLUSION: It is crucial to use sharp devices to reduce mechanical and ischemic damage. Although slow insertion speeds typically improve the quality of acute electrophysiological recordings, we show that slow speeds should only be used upon penetration in the brain parenchyma and not during the dimpling stage. A closed-loop implantation procedure can be used to set the appropriate speed in the different insertion stages.<br><br>SIGNIFICANCE: We provide new evidence on the impact of probe layout and insertion speed on insertion force, with implications on the design and implantation procedure for minimally invasive CMOS neural probes. A novel closed-loop methodology to optimize device implantation and reduce FBR is proposed.},
}
@article {pmid40030619,
year = {2024},
author = {Tantawanich, P and Phunruangsakao, C and Izumi, SI and Hayashibe, M},
title = {A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3522168},
pmid = {40030619},
issn = {1558-0210},
abstract = {Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. Growing interest exists in decoding bimanual motor tasks, crucial for activities of daily living. This stems from the need to restore motor function, especially in individuals with deficits. This review aims to summarize neurological advancements in bimanual BCIs, encompassing neuroimaging techniques, experimental paradigms, and analysis algorithms. Thirty-six articles were reviewed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search result revealed diverse experimental paradigms, protocols, and research directions, including enhancing the decoding accuracy, advancing versatile prosthesis robots, and enabling real-time applications. Notably, within BCI studies on bimanual movement coordination, a shared objective is to achieve naturalistic movement and practical applications with neurorehabilitation potential.},
}
@article {pmid40030617,
year = {2024},
author = {Jiang, Y and Li, K and Liang, Y and Chen, D and Tan, M and Li, Y},
title = {Daily Assistance for Amyotrophic Lateral Sclerosis Patients Based on a Wearable Multimodal Brain-Computer Interface Mouse.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3520984},
pmid = {40030617},
issn = {1558-0210},
abstract = {Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease that mainly causes damage to upper and lower motor neurons. This leads to a progressive deterioration in the voluntary mobility of the upper and lower extremities in ALS patients, which underscores the pressing need for an assistance system to facilitate communication and body movement without relying on neuromuscular function. In this paper, we developed a daily assistance system for ALS patients based on a wearable multimodal brain-computer interface (BCI) mouse. The system comprises two subsystems: a mouse system assisting the upper extremity and a wheelchair system based on the mouse system assisting the lower extremity. By wearing a BCI headband, ALS patients can control a computer cursor on the screen with slight head rotation and eye blinking, and further operate a computer and drive a wheelchair with specially designed graphical user interfaces (GUIs). We designed operating tasks that simulate daily needs and invited ALS patients to perform the tasks. In total, 15 patients with upper extremity limitations performed the mouse system task and 9 patients with lower extremity mobility issues performed the wheelchair system task. To our satisfaction, all the participants fully accomplished the tasks and average accuracies of 83.9% and 87.0% for the two tasks were achieved. Furthermore, workload evaluation using NASA Task Load Index (NASA-TLX) revealed that the participants experienced a low workload when using the system. The experimental results demonstrate that the proposed system provides ALS patients with effective daily assistance and shows promising long-term application prospects.},
}
@article {pmid40030607,
year = {2025},
author = {Gao, Z and Xu, B and Wang, X and Zhang, W and Ping, J and Li, H and Song, A},
title = {Multilayer Brain Networks for Enhanced Decoding of Natural Hand Movements and Kinematic Parameters.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1708-1719},
doi = {10.1109/TBME.2024.3519348},
pmid = {40030607},
issn = {1558-2531},
mesh = {Humans ; *Hand/physiology ; Biomechanical Phenomena/physiology ; Male ; Adult ; Female ; Movement/physiology ; Young Adult ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Hand Strength/physiology ; },
abstract = {Decoding natural hand movements using Movement-Related Cortical Potentials (MRCPs) features is crucial for the natural control of neuroprosthetics. However, current research has primarily focused on the characteristics of individual channels or on brain networks within a single frequency or time segment, overlooking the potential of brain networks across multiple time-frequency domains. To address this problem, our study investigates the application of multilayer brain networks (MBNs) in decoding natural hand movements and kinematic parameters, using a combination of MRCPs features and MBNs metrics. Based on grasp taxonomy, we selected four natural movements for our study: Large Diameter (LD), Sphere 3-Finger (SF), Precision Disk (PD), and Parallel Extension (PE), each incorporating two levels of speed and force parameters. The results demonstrate that a combination of MRCPs features and MBNs metrics can successfully decode not only the types of movements and kinematic parameters but also differentiate between different grasp taxonomy characteristics, such as the number of fingers exerting force and the type of grasp. In terms of movement type, we achieved a peak four-class accuracy of 60.56%. For grasp type and number of fingers exerting force, binary classification peak accuracies reached 79.25% and 79.28%, respectively. In the case of kinematic parameters, the Precision Disk movement exhibited the highest binary classification peak accuracy at 84.65%. Moreover, our research also found the changes and patterns in brain region connectivity across both time and frequency domains. We believe that our research highlights the potential of MBNs to enhance the functionality of Brain-Computer Interface (BCI) systems for more intuitive control mechanisms and contributes valuable insights into the brain's operational mechanisms.},
}
@article {pmid40030575,
year = {2025},
author = {Xu, L and Jiang, X and Wang, R and Lin, P and Yang, Y and Leng, Y and Zheng, W and Ge, S},
title = {Decoding SSVEP Via Calibration-Free TFA-Net: A Novel Network Using Time-Frequency Features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2400-2412},
doi = {10.1109/JBHI.2024.3510740},
pmid = {40030575},
issn = {2168-2208},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Adult ; Algorithms ; Male ; Female ; Young Adult ; Deep Learning ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) signals offer high information transfer rates and non-invasive brain-to-device connectivity, making them highly practical. In recent years, deep learning techniques, particularly convolutional neural network (CNN) architectures, have gained prominence in EEG (e.g., SSVEP) decoding because of their nonlinear modeling capabilities and autonomy from manual feature extraction. However, most studies using CNNs employ temporal signals as the input and cannot directly mine the implicit frequency information, which may cause crucial frequency details to be lost and challenges in decoding. By contrast, the prevailing supervised recognition algorithms rely on a lengthy calibration phase to enhance algorithm performance, which could impede the popularization of SSVEP based BCIs. To address these problems, this study proposes the Time-Frequency Attention Network (TFA-Net), a novel CNN model tailored for SSVEP signal decoding without the calibration phase. Additionally, we introduce the Frequency Attention and Channel Recombination modules to enhance ability of TFA-Net to infer finer frequency-wise attention and extract features efficiently from SSVEP in the time-frequency domain. Classification results on a public dataset demonstrated that the proposed TFA-Net outperforms all the compared models, achieving an accuracy of 79.00% $\pm$ 0.27% and information transfer rate of 138.82 $\pm$ 0.78 bits/min with a 1-s data length. TFA-Net represents a novel approach to SSVEP identification as well as time-frequency signal analysis, offering a calibration-free solution that enhances the generalizability and practicality of SSVEP based BCIs.},
}
@article {pmid40030518,
year = {2025},
author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X},
title = {Improving Reliability of Life Applications Using Model-Based Brain Switches via SSVEP.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {5},
pages = {1636-1644},
doi = {10.1109/TBME.2024.3516733},
pmid = {40030518},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Reproducibility of Results ; Young Adult ; *Brain/physiology ; *Models, Neurological ; Signal-To-Noise Ratio ; },
abstract = {The brain switch improves the reliability of asynchronous brain-computer interface (aBCI) systems by switching the control state of the BCI system. Traditional brain switch research focuses on extracting advanced electroencephalography (EEG) features. However, a low signal-to-noise ratio (SNR) of EEG signals resulted in limited feature information and low performance of brain switches. Here, we design a virtual physical system to build the brain switch, allowing users to trigger the system through periodic brainwave modulation, fully integrating limited feature information and improving reliability. Furthermore, we designed multiple experiments to validate the effectiveness of the proposed brain switch based on steady-state visual evoked potentials (SSVEP). The results verified the performance of SSVEP brain switches based on virtual physical systems, improving the reliability of brain switches to 0.1 FP/h or even better with acceptable triggering time and calibration-free for most subjects. This represents that the proposed virtual physical model-based brain switch can utilize SSVEP features and output the reliable commands required to control external devices, promoting BCI real applications.},
}
@article {pmid40030472,
year = {2025},
author = {Ke, Y and Chen, X and Xu, W and Wang, T and Shen, S and Ming, D},
title = {High-Frequency SSVEP-BCI With Row-Column Dual-Frequency Encoding and Decoding Strategy for Reduced Training Data.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {1897-1908},
doi = {10.1109/JBHI.2024.3514794},
pmid = {40030472},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Young Adult ; *Signal Processing, Computer-Assisted ; Female ; Algorithms ; Photic Stimulation/methods ; },
abstract = {Steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCIs) have the potential to be utilized in various fields due to their high accuracies and information transfer rates (ITR). High-frequency (HF) visual stimuli have shown promise in reducing visual fatigue and enhancing user comfort. However, these HF-SSVEP-BCIs often face limitations in the number of commands and typically require extensive individual training data to achieve high performance. In this study, we proposed a row-column dual-frequency encoding and decoding method using HF stimulation to develop a comfortable BCI system that supports multiple commands and reduces training costs. We arranged 20 targets in a matrix of five rows and four columns, with each target modulated by left-and-right field stimulation using two frequency-phase combinations. Targets in each row or column share a unique frequency-phase combination, allowing EEG data from the same row or column to be used collectively to train a row/column index decoding model for target identification. To evaluate the performance of our method, we constructed a 20-target asynchronous robotic arm control system with the adaptive window method. With only four training trials per target, the online system achieved an ITR of 105.14 ± 14.15 bits/min, a true positive rate of 98.18 ± 2.87%, a false positive rate of 7.39 ± 6.73%, and a classification accuracy of 91.88 ± 5.75%, with an average data length of 925.70 ± 45.44 ms. These results indicate that the proposed protocol can deliver accurate and rapid command outputs for a comfortable SSVEP-based BCI with minimal training data and fewer frequencies.},
}
@article {pmid40030451,
year = {2024},
author = {Zhang, X and Wei, W and Qiu, S and Li, X and Wang, Y and He, H},
title = {Enhancing SSVEP-Based BCI Performance via Consensus Information Transfer Among Subjects.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2024.3506998},
pmid = {40030451},
issn = {2162-2388},
abstract = {The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has received considerable attention for its high communication speed. While large datasets provide an important opportunity to enhance decoding accuracies, the key challenge lies in the exploration of existing data to extract valuable information based on the distinctive characteristics of brain responses. In this study, we introduce ConsenNet, a framework designed to enhance SSVEP classification performance by leveraging information from the diverse perspectives of existing subjects. First, this study exploits the diversity of existing subjects to generate new samples, which retain both task-related components and variability. This effectively enhances the network generalization capability on new subjects. Second, the structured knowledge that encapsulates the interrelationships between categories has been constructed and then transferred from the teacher network to the student network, guiding the student network to extract invariant features across subjects. Finally, our model incorporates a small amount of new subject data for model calibration in the final stage. Offline experiments conducted on three public datasets demonstrate the superiority of ConsenNet over 19 methods compared in this study, while online experiments validate its feasibility for real-world applications.},
}
@article {pmid40030403,
year = {2024},
author = {Tan, J and Zhang, X and Wu, S and Song, Z and Wang, Y},
title = {Hidden Brain State-based Internal Evaluation Using Kernel Inverse Reinforcement Learning in Brain-machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3503713},
pmid = {40030403},
issn = {1558-0210},
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) assist paralyzed people in controlling neural prostheses without the need for real limb movement as supervised signals. The design of reward signal significantly impacts the learning efficiency of the RL-based decoders. Existing reward designs in the RL-based BMI framework rely on external rewards or manually labeled internal rewards, unable to accurately extract subjects' internal evaluation. In this paper, we propose a hidden brain state-based kernel inverse reinforcement learning (HBS-KIRL) method to accurately infer the subject-specific internal evaluation from neural activity during the BMI task. The state-space model is applied to project the neural state into low-dimensional hidden brain state space, which greatly reduces the exploration dimension. Then the kernel method is applied to speed up the convergence of policy, reward, and Q-value networks in reproducing kernel Hilbert space (RKHS). We tested our proposed algorithm on the data collected from the medial prefrontal cortex (mPFC) of rats when they were performing a two-lever-discrimination task. We assessed the state-value estimation performance of our proposed method and compared it with naïve IRL and PCA-based IRL. To validate that the extracted internal evaluation could contribute to the decoder training, we compared the decoding performance of decoders trained by different reward models, including manually designed reward, naïve IRL, PCA-IRL, and our proposed HBS-KIRL. The results show that the HBS-KIRL method can give a stable and accurate estimation of state-value distribution with respect to behavior. Compared with other methods, the decoder guided by HBS-KIRL achieves consistent and better decoding performance over days. This study reveals the potential of applying the IRL method to better extract subject-specific evaluation and improve the BMI decoding performance.},
}
@article {pmid40030380,
year = {2025},
author = {Chen, D and Cao, C and Gong, J and Huang, J and Xiao, J and Huang, Q and Guo, Y and Li, Y},
title = {Decoding Single-Pellet Retrieval Task From Local Field Potentials in Pre- and Post-Stroke Motor Areas: Insights Into Interhemispheric Connectivity Difference.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {4},
pages = {1316-1327},
doi = {10.1109/TBME.2024.3499319},
pmid = {40030380},
issn = {1558-2531},
mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Motor Cortex/physiopathology/physiology ; *Stroke/physiopathology ; Male ; Signal Processing, Computer-Assisted ; Forelimb/physiology/physiopathology ; Electrodes, Implanted ; },
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) hold promise for restoring communication and movement in stroke-paralyzed individuals. Recent studies have demonstrated the potential of using local field potentials (LFPs) for decoding single-pellet retrieval (SPR) tasks in iBMIs. However, most research has relied on LFPs from healthy rats rather than those affected by stroke. This study aimed to investigate the feasibility of utilizing LFPs from both the right and left (stroke) cortical forelimb areas (CFAs) for the SPR tasks decoding under both pre- and post-stroke conditions.<br><br>METHODS: LFPs were recorded via microelectrode arrays implanted into CFAs of eight rats trained to perform the SPR tasks. The relative spectral power method was used to represent frequency information, and random forest classification differentiated SPR tasks from resting states. We also assessed interhemispheric connectivity, including correlation, coherence, and phase-amplitude coupling (PAC), to compare differences between the SPR tasks and the resting states under both pre- and post-stroke conditions.<br><br>RESULTS: Our findings indicated that the relative PS method with LFPs achieves 87.10% 9.2% accuracy in post-stoke SPR decoding, where high gamma is crucial. Additionally, we observed changes in PACs from the right to the left sensorimotor cortex post-stroke during the SPR tasks compared to the resting states.<br><br>SIGNIFICANCE: Our work provides a comprehensive insight into the role of different frequency band from LFPs in motor function recovery mechanisms, highlighting the importance of the high gamma in motor function. This research lays the foundation for developing post-stoke SPR-related BMIs.},
}
@article {pmid40030325,
year = {2025},
author = {Saadatmand, H and Akbarzadeh-T, MR},
title = {Multiobjective Evolutionary Sequential Channel/ Feature Selection for EEG Motor Imagery Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2546-2556},
doi = {10.1109/JBHI.2024.3508277},
pmid = {40030325},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Fuzzy Logic ; },
abstract = {Motor imagery (MI) analysis from EEG signals constitutes a class of emerging brain-computer interface (BCI) applications that face EEG's predominant complexities arising from the multitude of channels and the vast number of possible features. This study presents a two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) for efficient EEG-based MI signal analysis. The two-step process is a non-dominant wrapper strategy that sequentially identifies the optimal channels and the minimal set of features, thereby reducing MI's combinatorial search complexity. We also employ a reptile-based search algorithm (RSA), a recent metaheuristic for efficient search in multimodal continuous domains, to optimize the classifier's hyper-parameters. The proposed MOSIFE-RSA algorithm is benchmarked against 12 representative algorithms on four standard BCI Competition databases, including IV-I, III-IVa, III-IIIa, and II. The results show that MOSIFE-RSA improves accuracy by 20%, with channel selection contributing as much as 15% and feature selection as much as 5% towards these results. Furthermore, it reduces computational complexity by 81% through channel selection and 16% through feature selection, demonstrating its effectiveness in advancing EEG-based MI signal analysis. This research has practical implications for developing more accurate and efficient brain-computer interface systems.},
}
@article {pmid40030277,
year = {2025},
author = {Ding, Y and Li, Y and Sun, H and Liu, R and Tong, C and Liu, C and Zhou, X and Guan, C},
title = {EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {1909-1918},
doi = {10.1109/JBHI.2024.3504604},
pmid = {40030277},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain/physiology ; Adult ; Male ; Young Adult ; Algorithms ; },
abstract = {Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasksâcognitive attention, driving fatigue, and mental workload detectionâconsistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks.},
}
@article {pmid40030249,
year = {2024},
author = {Kokorin, K and Zehra, SR and Mu, J and Yoo, P and Grayden, DB and John, SE},
title = {Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3500217},
pmid = {40030249},
issn = {1558-0210},
abstract = {Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully-autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected α<0.05) mean task success rate (p<0.0001, μ=36.1%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (p<0.0001, μ=-26.8%, 95% CI [-36.0%, -17.7%]), and participant workload (p=0.02, μ=-11.6, 95% CI [-21.1, -2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.},
}
@article {pmid40030248,
year = {2024},
author = {Ke, S and Yang, B and Qin, Y and Rong, F and Zhang, J and Zheng, Y},
title = {FACT-Net: a Frequency Adapter CNN with Temporal-periodicity Inception for Fast and Accurate MI-EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3499998},
pmid = {40030248},
issn = {1558-0210},
abstract = {Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy, real-time processing, and deployment. To overcome these challenges, we propose FACT-Net, an innovative deep-learning network for the fast and accurate decoding of MI-EEG signals. FACT-Net incorporates a Frequency Adapter (FA) module designed for processing the frequency features of MI-EEG data, as well as a Temporal-Periodicity Inception (TPI) module specifically for handling global periodic signals in MI. To evaluate the proposed model, we conduct the experiments on the cross-day dataset collected from 67 subjects and the BCIC-IV-2a dataset. The FACT-Net achieved an accuracy of 48.32% and 80.67% higher than the state-of-the-art (SOTA) approaches, demonstrating excellent performance in MI decoding. Additionally, it exhibits exceptional memory efficiency and inference time, indicating significant potential for practical applications. We anticipate that FACT-Net will set a new baseline for MI-EEG decoding. The code is available in https://github.com/Ktn1ga/EEG_FACT.},
}
@article {pmid40030247,
year = {2024},
author = {Rao, Z and Zhu, J and Lu, Z and Zhang, R and Li, K and Guan, Z and Li, Y},
title = {A Wearable Brain-Computer Interface with Fewer EEG Channels for Online Motor Imagery Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3502135},
pmid = {40030247},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have significant potential for neurorehabilitation and motor recovery. However, most BCI systems employ multi-channel electroencephalogram (EEG) recording devices, during which the pre-experimental preparation and post-experimental hair cleaning are time-consuming and inconvenient for stroke patients, and potentially affect their motivation for rehabilitation training. In this paper, we introduced a wearable MI-BCI system for online MI classification using a wireless headband device with four EEG channels to reduce setup time while enhancing portability. To validate the performance of the system in decoding MI-EEG signals, extensive experiments and comparisons were performed on sixty-six healthy subjects. Specifically, an offline and an online experiment with forty-six subjects were conducted, with the system achieving average offline and online accuracies of 85.21% and 76.54%, respectively. Furthermore, a comparison experiment involving another twenty subjects showed that the online performance of our headband device (77.84%) was comparable to that of a mature commercial Neuroscan device (76.50%). Compared to several existing portable systems, our wearable system achieved superior performance with fewer channels and was validated on a larger number of subjects. These results demonstrated that our wearable BCI system can reduce preparation time, enhance portability, and meet the classification performance requirements for BCI-based rehabilitation intervention, indicating its substantial potential for large-scale clinical applications in enhancing motor recovery of stroke patients.},
}
@article {pmid40030217,
year = {2025},
author = {Xu, M and Jiao, J and Chen, D and Ding, Y and Chen, Q and Wu, J and Gu, P and Pan, Y and Peng, X and Xiao, N and Yang, B and Li, Q and Guo, J},
title = {REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {3},
pages = {2136-2147},
doi = {10.1109/JBHI.2024.3498916},
pmid = {40030217},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Electrodes ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Brain/physiology/diagnostic imaging ; },
abstract = {High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.},
}
@article {pmid40030141,
year = {2025},
author = {Pankka, H and Lehtinen, J and Ilmoniemi, RJ and Roine, T},
title = {Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.},
journal = {Neural computation},
volume = {37},
number = {4},
pages = {793-814},
doi = {10.1162/neco_a_01743},
pmid = {40030141},
issn = {1530-888X},
mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; Brain-Computer Interfaces ; Brain/physiology ; Neural Networks, Computer ; Forecasting ; },
abstract = {Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task; however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies 150 ms ahead, with mean absolute errors of 1.0 ± 1.1 µV (theta) and 0.9 ± 1.1 µV (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be used to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.},
}
@article {pmid40027274,
year = {2024},
author = {Cai, Y and Li, Q and Wesselmann, U and Zhao, C},
title = {Exosomal Bupivacaine: Integrating Nerve Barrier Penetration Capability and Sustained Drug Release for Enhanced Potency in Peripheral Nerve Block and Reduced Toxicity.},
journal = {Advanced functional materials},
volume = {34},
number = {42},
pages = {},
pmid = {40027274},
issn = {1616-301X},
support = {R61 NS123196/NS/NINDS NIH HHS/United States ; },
abstract = {Peripherally injected local anesthetics exhibit limited ability to penetrate peripheral nerve barriers (PNBs), which limits their effectiveness in peripheral nerve block and increases the risk of adverse effects. In this work, we demonstrated that exosomes derived from Human Embryo Kidney (HEK) 293 cells can effectively traverse the perineurium, which is the rate-limiting barrier within PNBs that local anesthetics need to cross before acting on axons. Based on this finding, we use these exosomes as a carrier for bupivacaine (BUP), a local anesthetic commonly used in clinical settings. The in vitro assessments revealed that the prepared exosomal bupivacaine (BUP@EXO) achieves a BUP loading capacity of up to 82.33% and sustained release of BUP for over 30 days. In rats, a single peripheral injection of BUP@EXO, containing 0.75 mg of BUP, which is ineffective for BUP alone, induced a 2-hour sensory nerve blockade without significant motor impairments. Increasing the BUP dose in BUP@EXO to 2.5 mg, a highly toxic dose for BUP alone, extended the sensory nerve blockade to 12 hours without causing systemic cardiotoxicity and local neurotoxicity and myotoxicity.},
}
@article {pmid40026891,
year = {2025},
author = {Moreno-Alcayde, Y and Ruotsalo, T and Leiva, LA and Traver, VJ},
title = {Brainsourcing for temporal visual attention estimation.},
journal = {Biomedical engineering letters},
volume = {15},
number = {2},
pages = {311-326},
pmid = {40026891},
issn = {2093-985X},
abstract = {The concept of temporal visual attention in dynamic contents, such as videos, has been much less studied than its spatial counterpart, i.e., visual salience. Yet, temporal visual attention is useful for many downstream tasks, such as video compression and summarisation, or monitoring users' engagement with visual information. Previous work has considered quantifying a temporal salience score from spatio-temporal user agreements from gaze data. Instead of gaze-based or content-based approaches, we explore to what extent only brain signals can reveal temporal visual attention. We propose methods for (1) computing a temporal visual salience score from salience maps of video frames; (2) quantifying the temporal brain salience score as a cognitive consistency score from the brain signals from multiple observers; and (3) assessing the correlation between both temporal salience scores, and computing its relevance. Two public EEG datasets (DEAP and MAHNOB) are used for experimental validation. Relevant correlations between temporal visual attention and EEG-based inter-subject consistency were found, as compared with a random baseline. In particular, effect sizes, measured with Cohen's d, ranged from very small to large in one dataset, and from medium to very large in another dataset. Brain consistency among subjects watching videos unveils temporal visual attention cues. This has relevant practical implications for analysing attention for visual design in human-computer interaction, in the medical domain, and in brain-computer interfaces at large.},
}
@article {pmid40026370,
year = {2025},
author = {Pišot, R and Marušič, U and Šlosar, L},
title = {Addressing the Paradox of Rest with Innovative Technologies.},
journal = {Zdravstveno varstvo},
volume = {64},
number = {2},
pages = {68-72},
pmid = {40026370},
issn = {0351-0026},
abstract = {The paradox of rest lies in its dual nature: essential for recovery yet potentially harmful when prolonged. Prolonged physical inactivity (PI) significantly contributes to non-communicable diseases (NCDs). Studies show nearly a third of adults worldwide were insufficiently active in 2022, with the economic costs of PI projected to reach INT$520 billion by 2030. Bedrest models have illuminated the rapid onset of insulin resistance, general functional decline and muscle atrophy associated with PI, particularly in hospitalised older adults. Innovative technologies, such as extended reality (XR), offer promising solutions for mitigating the effects of PI and can enhance non-physical rehabilitation techniques such as motor imagery and action observation. These technologies provide immersive, personalised therapeutic experiences that engage multiple senses, transforming passive recovery into an active process and addressing both the physical and cognitive consequences of inactivity. Results of bedrest study showed significant preservation of muscle mass, improved strength and enhanced insulin sensitivity in the intervention group compared to controls. These findings highlight the potential of XR-based strategies in addressing structural and functional declines during inactivity. As part of the Interreg VI-A Italia-Slovenija project X-BRAIN.net, advanced XR-equipped active rooms were developed to aid post-stroke rehabilitation in acute care settings. XR technologies, particularly VR, have shown promise in providing dynamic and adaptable therapeutic environments that facilitate early and targeted interventions. Future advancements focus on integrating XR with brain-computer interfaces (BCIs) and synchronised visual-haptic neurofeedback, enhancing sensorimotor cortical activation and improving rehabilitation outcomes. Comprehensive multimodal approaches, including nutritional, physical and non-physical interventions, are emerging as effective strategies to personalise and optimise patient recovery.},
}
@article {pmid40026248,
year = {2025},
author = {Ge, Q and Yang, J and Huang, F and Dai, X and Chen, C and Guo, J and Wang, M and Zhu, M and Shao, Y and Xia, Y and Zhou, Y and Peng, J and Deng, S and Shi, J and Hu, Y and Zhang, H and Wang, Y and Wang, X and Li, XM and Chen, Z and Shu, Y and Zhu, JM and Zhang, J and Shen, Y and Duan, S and Xu, S and Shen, L and Chen, J},
title = {Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy.},
journal = {The Journal of clinical investigation},
volume = {135},
number = {5},
pages = {},
pmid = {40026248},
issn = {1558-8238},
mesh = {Humans ; *Single-Cell Analysis ; *Cellular Senescence ; Mice ; Animals ; *Drug Resistant Epilepsy/metabolism/pathology/genetics ; Male ; Pyramidal Cells/metabolism/pathology ; Female ; Biomarkers/metabolism ; Cyclin-Dependent Kinase Inhibitor p21/metabolism/genetics ; },
abstract = {The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult to determine the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA-seq approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2, and NFKBIA, which associate with mTOR pathway, inflammatory response, and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, γ-H2AX, and β-Gal, and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of patients with drug-resistant epilepsy with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent marker expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and what we believe to be new insights into the pathophysiological mechanisms of drug-resistant epilepsy.},
}
@article {pmid40025635,
year = {2025},
author = {Chen, J and Cheng, Y and Chen, L and Yang, B},
title = {Human papillomavirus (HPV) prediction for oropharyngeal cancer based on CT by using off-the-shelf features: A dual-dataset study.},
journal = {Journal of applied clinical medical physics},
volume = {},
number = {},
pages = {e70061},
doi = {10.1002/acm2.70061},
pmid = {40025635},
issn = {1526-9914},
abstract = {BACKGROUND: This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image-based HPV prediction methods are hindered by high computational demands or suboptimal performance.<br><br>METHODS: To address these issues, we propose a methodology that employs a Siamese Neural Network architecture, integrating multi-modality off-the-shelf features-handcrafted features and 3D deep features-to enhance the representation of information. We assessed the incremental benefit of combining 3D deep features from various networks and introduced manufacturer normalization. Our method was also designed for computational efficiency, utilizing transfer learning and allowing for model execution on a single-CPU platform. A substantial dataset comprising 1453 valid samples was used as internal validation, a separate independent dataset for external validation.<br><br>RESULTS: Our proposed model achieved superior performance compared to other methods, with an average area under the receiver operating characteristic curve (AUC) of 0.791 [95% (confidence interval, CI), 0.781-0.809], an average recall of 0.827 [95% CI, 0.798-0.858], and an average accuracy of 0.741 [95% CI, 0.730-0.752], indicating promise for clinical application. In the external validation, proposed method attained an AUC of 0.581 [95% CI, 0.560-0.603] and same network architecture with pure deep features achieved an AUC of 0.700 [95% CI, 0.682-0.717]. An ablation study confirmed the effectiveness of incorporating manufacturer normalization and the synergistic effect of combining different feature sets.<br><br>CONCLUSION: Overall, our proposed model not only outperforms existing counterparts for HPV status prediction but is also computationally accessible for use on a single-CPU platform, which reduces resource requirements and enhances clinical usability.},
}
@article {pmid40025160,
year = {2025},
author = {Ye, Z and Ai, Q and Liu, Y and de Rijke, M and Zhang, M and Lioma, C and Ruotsalo, T},
title = {Generative language reconstruction from brain recordings.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {346},
pmid = {40025160},
issn = {2399-3642},
support = {CHIST-ERA-20-BCI-001//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; *Brain/physiology/diagnostic imaging ; *Language ; Male ; Female ; Adult ; Brain Mapping/methods ; Young Adult ; },
abstract = {Language reconstruction from non-invasive brain recordings has been a long-standing challenge. Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. While an LLM can already generate high-quality content, our approach produces results more closely aligned with the visual or auditory language stimuli in response to which brain recordings are sampled, especially for content deemed "surprising" for the LLM. Furthermore, we show that the proposed approach can be used in an auto-regressive manner to reconstruct a 10 min-long language stimulus. Our method outperforms or is comparable to previous classification-based methods under different task settings, with the added benefit of estimating the likelihood of generating any semantic content. Our findings demonstrate the effectiveness of employing brain language interfaces in a generative setup and delineate a powerful and efficient means for mapping functional representations of language perception in the brain.},
}
@article {pmid40022809,
year = {2025},
author = {Luo, TJ and Wu, T},
title = {Sum of similarity-regularized squared correlations for enhancing SSVEP detection.},
journal = {Artificial intelligence in medicine},
volume = {162},
number = {},
pages = {103100},
doi = {10.1016/j.artmed.2025.103100},
pmid = {40022809},
issn = {1873-2860},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Calibration ; Brain/physiology ; },
abstract = {A brain-computer interface (BCI) provides a direct control pathway between human brain and external devices. Steady-state visual evoked potential based BCI (SSVEP-BCI) has been proven to be a valuable solution due to its advantages of high information transfer rate (ITR) and minimal calibration requirement. Recently, some methods have been proposed based on calibration-training techniques to compute optimal spatial filters from covariances, and have achieved good detection performance. However, these methods ignore the temporally-varying and spatially-coupled characteristics of the EEG signals, which is essentially an important clue for enhancing ITR. More importantly, existing methods cannot well deal with intrinsic noise components of electroencephalogram (EEG) signals, greatly affecting their detection performance. In this paper, we propose a novel method, termed as Sum of Similarity-Regularized Squared Correlations (SSRSC), which is extended and regularized from the sum of squared correlations. We simultaneously compute the squared correlations for both calibration data and sine-cosine harmonics templates, and mitigate variations by the similarity regularization. Moreover, we extend the SSRSC by adopting the ranking weighted ensemble strategy, termed as weSSCOR. Extensive experiments have been conducted on two benchmark SSVEP datasets, and the results demonstrated that the proposed SSRSC/weSSRSC can significantly improve accuracy and ITR of SSVEP detection with less calibration data, which has great potential in designing high ITR SSVEP-BCIs with less calibration efforts.},
}
@article {pmid40020404,
year = {2025},
author = {Wang, R and Fang, T and Zhang, Y and Cheng, Y and Wang, C and Chen, Y and Fan, Q and Zhao, X and Ming, D},
title = {The overgrowth of structure-function coupling in premature brain during infancy.},
journal = {Developmental cognitive neuroscience},
volume = {72},
number = {},
pages = {101535},
pmid = {40020404},
issn = {1878-9307},
mesh = {Humans ; *Infant, Premature/physiology ; *Magnetic Resonance Imaging/methods ; *Brain/growth &amp; development/physiology ; Male ; Female ; Infant ; Infant, Newborn ; Child Development/physiology ; Structure-Activity Relationship ; Default Mode Network/physiology/diagnostic imaging ; },
abstract = {Although the rapid growth of brain structure and function during infancy has been well documented, relatively little is known about how these two developmental processes couple-an aspect that exhibits distinct patterns in adult brain. In this study, the multimodal MRI data from the dHCP database were used to investigate the coupling between brain structure and function in infants, with a particular focus on how prematurity influences this relationship. A similar pattern of the coupling distribution between preterm and full-term infants was identified with coupling index varying across unimodal cortices such as visual and sensorimotor regions and transmodal cortices including default mode network. Notably, a widespread overgrowth of structure-function coupling and a slow developmental trajectory towards full-term infants in preterm infants at term-equivalent age were found. Collectively, the study quantified the development of structure-function relationships in preterm infants, offering new insights into the information transmission processes and developmental patterns of the early-life brain.},
}
@article {pmid40014925,
year = {2025},
author = {Dold, M and Pereira, J and Sajonz, B and Coenen, VA and Thielen, J and Janssen, MLF and Tangermann, M},
title = {Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adbb20},
pmid = {40014925},
issn = {1741-2552},
mesh = {*Deep Brain Stimulation/methods/instrumentation ; *Brain-Computer Interfaces ; Humans ; *Software ; *Parkinson Disease/therapy/physiopathology ; Electroencephalography/methods/instrumentation ; },
abstract = {Objective.This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface (BCI) research with an application focus on adaptive deep brain stimulation (aDBS). One difficulty for investigating control approaches for aDBS resides with the complex setups required for aDBS experiments, a challenge Dareplane tries to address.Approach.The key features of the platform are presented and the composition of modules into a full experimental setup is discussed in the context of a Python-based orchestration module. The performance of a typical experimental setup on Dareplane for aDBS is evaluated in three benchtop experiments, covering (a) an easy-to-replicate setup using an Arduino microcontroller, (b) a setup with hardware of an implantable pulse generator, and (c) a setup using an established and CE certified external neurostimulator. The full technical feasibility of the platform in the aDBS context is demonstrated in a first closed-loop session with externalized leads on a patient with Parkinson's disease receiving DBS treatment and further in a non-invasive BCI speller application using code-modulated visual evoked potential (c-VEP).Main results.The platform is implemented and open-source accessible onhttps://github.com/bsdlab/Dareplane. Benchtop results show that performance of the platform is sufficient for current aDBS latencies, and the platform could successfully be used in the aDBS experiment. The timing-critical c-VEP speller could be successfully implemented on the platform achieving expected information transfer rates.Significance.The Dareplane platform supports aDBS setups, and more generally the research on neurotechnological systems such as BCIs. It provides a modular, technology-agnostic, and easy-to-implement software platform to make experimental setups more resilient and replicable.},
}
@article {pmid40013095,
year = {2025},
author = {Yuan, Z and Huang, Z and Li, C and Li, S and Ren, Q and Xia, X and Jiang, Q and Zhang, D and Zhu, Q and Meng, X},
title = {Multimodal fusion model for diagnosing mild cognitive impairment in unilateral middle cerebral artery steno-occlusive disease.},
journal = {Frontiers in aging neuroscience},
volume = {17},
number = {},
pages = {1527323},
pmid = {40013095},
issn = {1663-4365},
abstract = {OBJECTIVES: To propose a multimodal functional brain network (FBN) and structural brain network (SBN) topological feature fusion technique based on resting-state functional magnetic resonance imaging (rs-fMRI), diffusion tensor imaging (DTI), 3D-T1-weighted imaging (3D-T1WI), and demographic characteristics to diagnose mild cognitive impairment (MCI) in patients with unilateral middle cerebral artery (MCA) steno-occlusive disease.<br><br>METHODS: The performances of different algorithms on the MCI dataset were evaluated using 5-fold cross-validation. The diagnostic results of the multimodal performance were evaluated using t-distributed stochastic neighbor embedding (t-SNE) analysis. The four-modal analysis method proposed in this study was applied to identify brain regions and connections associated with MCI, thus confirming its validity.<br><br>RESULTS: Based on the fusion of the topological features of the multimodal FBN and SBN, the accuracy for the diagnosis of MCI in patients with unilateral MCA steno-occlusive disease reached 90.00%. The accuracy, recall, sensitivity, and F1-score were higher than those of the other methods, as was the diagnostic efficacy (AUC = 0.9149).<br><br>CONCLUSION: The multimodal FBN and SBN topological feature fusion technique, which incorporates rs-fMRI, DTI, 3D-T1WI, and demographic characteristics, obtains the most discriminative features of MCI in patients with unilateral MCA steno-occlusive disease and can effectively identify disease-related brain areas and connections. Efficient automated diagnosis facilitates the early and accurate detection of MCI and timely intervention and treatment to delay or prevent disease progression.},
}
@article {pmid40011760,
year = {2025},
author = {Qin, R and Zhang, Y and Shi, J and Wu, P and An, C and Li, Z and Liu, N and Wan, Z and Hua, T and Li, X and Lou, J and Yin, W and Chen, W},
title = {TCR catch bonds nonlinearly control CD8 cooperation to shape T cell specificity.},
journal = {Cell research},
volume = {35},
number = {4},
pages = {265-283},
pmid = {40011760},
issn = {1748-7838},
support = {T2394511//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31971237//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12172371//National Natural Science Foundation of China (National Science Foundation of China)/ ; T2394512//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32101052//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12102389//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272216//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32090044//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12272348//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31600751//National Natural Science Foundation of China (National Science Foundation of China)/ ; KJ2070000094//Chinese Academy of Sciences (CAS)/ ; KY9100000092//University of Science and Technology of China (USTC)/ ; },
mesh = {*Receptors, Antigen, T-Cell/metabolism/immunology/chemistry ; Humans ; *CD8 Antigens/metabolism/immunology/chemistry ; *CD8-Positive T-Lymphocytes/immunology/metabolism ; Animals ; Protein Binding ; Mice ; },
abstract = {Naturally evolved T-cell receptors (TCRs) exhibit remarkably high specificity in discriminating non-self antigens from self-antigens under dynamic biomechanical modulation. In contrast, engineered high-affinity TCRs often lose this specificity, leading to cross-reactivity with self-antigens and off-target toxicity. The underlying mechanism for this difference remains unclear. Our study reveals that natural TCRs exploit mechanical force to form optimal catch bonds with their cognate antigens. This process relies on a mechanically flexible TCR-pMHC binding interface, which enables force-enhanced CD8 coreceptor binding to MHC-α1α2 domains through sequential conformational changes induced by force in both the MHC and CD8. Conversely, engineered high-affinity TCRs create rigid, tightly bound interfaces with cognate pMHCs of their parental TCRs. This rigidity prevents the force-induced conformational changes necessary for optimal catch-bond formation. Paradoxically, these high-affinity TCRs can form moderate catch bonds with non-stimulatory pMHCs of their parental TCRs, leading to off-target cross-reactivity and reduced specificity. We have also developed comprehensive force-dependent TCR-pMHC kinetics-function maps capable of distinguishing functional and non-functional TCR-pMHC pairs and identifying toxic, cross-reactive TCRs. These findings elucidate the mechano-chemical basis of the specificity of natural TCRs and highlight the critical role of CD8 in targeting cognate antigens. This work provides valuable insights for engineering TCRs with enhanced specificity and potency against non-self antigens, particularly for applications in cancer immunotherapy and infectious disease treatment, while minimizing the risk of self-antigen cross-reactivity.},
}
@article {pmid40010648,
year = {2025},
author = {Spadacenta, S and Dicke, PW and Thier, P},
title = {Minimally invasive electrocorticography (ECoG) recording in common marmosets.},
journal = {Journal of neuroscience methods},
volume = {417},
number = {},
pages = {110409},
doi = {10.1016/j.jneumeth.2025.110409},
pmid = {40010648},
issn = {1872-678X},
mesh = {Animals ; *Callithrix ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; Male ; Brain Mapping/methods ; Brain/physiology/surgery ; Female ; Cerebral Cortex/physiology/surgery ; },
abstract = {BACKGROUND: Electrocorticography (ECoG) provides a valuable compromise between spatial and temporal resolution for recording brain activity with excellent signal quality, crucial for presurgical epilepsy mapping and advancing neuroscience, including brain-machine interface development. ECoG is particularly effective in the common marmoset (Callithrix jacchus), whose lissencephalic (unfolded) brain surface provides broad cortical access. One of the key advantages of ECoG recordings is the ability to study interactions between distant brain regions. Traditional methods rely on large electrode arrays, necessitating extensive trepanations and a trade-off between size and electrode spacing.<br><br>NEW METHOD: This study introduces a refined ECoG technique for examining interactions among multiple cortical areas in marmosets, combining circumscribed trepanations with high-density electrode arrays at specific sites of interest.<br><br>Standard ECoG techniques typically require large electrode arrays and extensive trepanation, which heighten surgical risks and the likelihood of infection, while potentially compromising spatial resolution. In contrast, our method facilitates detailed and stable recordings across multiple cortical areas with minimized invasiveness and reduced complication risks, all while preserving high spatial resolution.<br><br>RESULTS: Two adult marmosets underwent ECoG implantation in frontal, temporal, and parietal regions. Postoperative monitoring confirmed rapid recovery, long-term health, and stable, high-quality neural recordings during various behavioral tasks.<br><br>CONCLUSIONS: This refined ECoG method enhances the study of cortical interactions in marmosets while minimizing surgical invasiveness and complication risks. It offers potential for broader application in other species and opens new avenues for long-term data collection, ultimately advancing both neuroscience and brain-machine interface research.},
}
@article {pmid40010602,
year = {2025},
author = {OuYang, Z and Yang, R and Wang, Y},
title = {Hotspots and Trends in Spinal Cord Stimulation Research for Spinal Cord Injury: A Bibliometric Analysis with Emphasis on Motor Recovery (2014-2024).},
journal = {World neurosurgery},
volume = {197},
number = {},
pages = {123832},
doi = {10.1016/j.wneu.2025.123832},
pmid = {40010602},
issn = {1878-8769},
abstract = {BACKGROUND: Spinal cord stimulation (SCS) has emerged as a key therapeutic strategy for enhancing motor recovery in spinal cord injury (SCI). This study employs bibliometric analysis to explore research trends and hotspots in SCS for motor recovery, highlighting advances and emerging directions over the past decade.<br><br>METHODS: This cross-sectional bibliometric study retrieved publications on SCS for motor recovery from the Web of Science Core Collection database (2014-2024). Key information, including annual publication trends, contributing countries, institutions, authors, journals, keywords, and highly cited references, was analyzed using CiteSpace and VOSviewer.<br><br>RESULTS: A total of 1033 publications were analyzed, demonstrating exponential growth in SCS research since 2014. The United States and Switzerland were identified as leading contributors, with prominent institutions such as the Swiss Federal Institute of Technology and the University of California System driving advancements. Key authors included Gr&#xe9;goire Courtine and Susan J. Harkema. Research themes have evolved through four phases: foundational studies on spinal cord mechanisms, exploration of neural circuits, application of electrical stimulation for motor recovery, and advancements in noninvasive therapies such as transcutaneous SCS. Highly cited journals, including Nature and Lancet, have published transformative studies, underscoring the field's clinical and academic significance.<br><br>CONCLUSIONS: This bibliometric analysis provides a comprehensive overview of SCS research for motor recovery post-SCI over the past decade. Interdisciplinary collaboration and technological innovation have positioned SCS as a cornerstone of SCI rehabilitation. Future efforts should focus on optimizing approaches, leveraging advanced imaging and artificial intelligence technologies, and broadening rehabilitation goals to improve outcomes for SCI patients.},
}
@article {pmid40009882,
year = {2025},
author = {Ding, L and Zou, Q and Zhu, J and Wang, Y and Yang, Y},
title = {Dynamical intracranial EEG functional network controllability localizes the seizure onset zone and predicts the epilepsy surgical outcome.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adba8d},
pmid = {40009882},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; Treatment Outcome ; Male ; *Drug Resistant Epilepsy/surgery/physiopathology ; *Seizures/physiopathology/surgery/diagnosis ; Adult ; Female ; Machine Learning ; Young Adult ; Adolescent ; Epilepsy/surgery/physiopathology/diagnosis ; Electroencephalography/methods ; Child ; },
abstract = {Objective. Seizure onset zone (SOZ) localization and SOZ resection outcome prediction are critical for the surgical treatment of drug-resistant epilepsy but have mainly relied on manual inspection of intracranial electroencephalography (iEEG) monitoring data, which can be both inaccurate and time-consuming. Therefore, automating SOZ localization and surgical outcome prediction by using appropriate iEEG neural features and machine learning models has become an emerging topic. However, current channel-wise local features, graph-theoretic network features, and system-theoretic network features cannot fully capture the spatial, temporal, and neural dynamical aspects of epilepsy, hindering accurate SOZ localization and surgical outcome prediction.Approach. Here, we develop a method for computing dynamical functional network controllability from multi-channel iEEG signals, which from a control-theoretic viewpoint, has the ability to simultaneously capture the spatial, temporal, functional, and dynamical aspects of epileptic brain networks. We then apply multiple machine learning models to use iEEG functional network controllability for localizing SOZ and predicting surgical outcomes in drug-resistant epilepsy patients and compare with existing neural features. We finally combine iEEG functional network controllability with representative local, graph-theoretic, and system-theoretic features to leverage complementary information for further improving performance.Main results. We find that iEEG functional network controllability at SOZ channels is significantly higher than that of other channels. We further show that machine learning models using iEEG functional network controllability successfully localize SOZ and predict surgical outcomes, significantly outperforming existing local, graph-theoretic, and system-theoretic features. We finally demonstrate that there exists complementary information among different types of neural features and fusing them further improves performance.Significance. Our results suggest that iEEG functional network controllability is an effective feature for automatic SOZ localization and surgical outcome prediction in epilepsy treatment.},
}
@article {pmid40009879,
year = {2025},
author = {Yan, Y and Li, J and Yin, M},
title = {EEG-based recognition of hand movement and its parameter.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adba8a},
pmid = {40009879},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Movement/physiology ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Neural Networks, Computer ; },
abstract = {Objecitve. Brain-computer interface is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage by now. There are still insufficient studies on the accuracy of ME EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-based hand movement recognition by analyzing low-frequency time-domain information.Approach. Experiments with four types of hand movements, two force parameter (picking up and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory Network (BiLSTM) model, an end-to-end serial combination of a BiLSTM and (CNN) is constructed to classify and recognize the hand movement based on the raw EEG data.Main results. According to the experimental results, the model is able to categorize four types of hand movements, picking up movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14% ± 0.49%, 99.29% ± 0.11%, 99.23% ± 0.60%, and 98.11% ± 0.23%, respectively.Significance. Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.},
}
@article {pmid40008568,
year = {2025},
author = {Jelescu, IO and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Schilling, KG},
title = {Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2507-2534},
pmid = {40008568},
issn = {1522-2594},
support = {R01 EB031954/EB/NIBIB NIH HHS/United States ; U54 AG054349/AG/NIA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01 EB019980/EB/NIBIB NIH HHS/United States ; R01 NS119605/NS/NINDS NIH HHS/United States ; P30 DA048742/DA/NIDA NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; P41 EB017183/EB/NIBIB NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; R01 AG057991/AG/NIA NIH HHS/United States ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; R01 CA160620/CA/NCI NIH HHS/United States ; P50 MH096889/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Diffusion Magnetic Resonance Imaging/methods ; *Image Processing, Computer-Assisted/methods ; Brain/diagnostic imaging ; Software ; Mice ; Reproducibility of Results ; },
abstract = {Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.},
}
@article {pmid40008460,
year = {2025},
author = {Schilling, KG and Howard, AFD and Grussu, F and Ianus, A and Hansen, B and Barrett, RLC and Aggarwal, M and Michielse, S and Nasrallah, F and Syeda, W and Wang, N and Veraart, J and Roebroeck, A and Bagdasarian, AF and Eichner, C and Sepehrband, F and Zimmermann, J and Soustelle, L and Bowman, C and Tendler, BC and Hertanu, A and Jeurissen, B and Verhoye, M and Frydman, L and van de Looij, Y and Hike, D and Dunn, JF and Miller, K and Landman, BA and Shemesh, N and Anderson, A and McKinnon, E and Farquharson, S and Dell'Acqua, F and Pierpaoli, C and Drobnjak, I and Leemans, A and Harkins, KD and Descoteaux, M and Xu, D and Huang, H and Santin, MD and Grant, SC and Obenaus, A and Kim, GS and Wu, D and Le Bihan, D and Blackband, SJ and Ciobanu, L and Fieremans, E and Bai, R and Leergaard, TB and Zhang, J and Dyrby, TB and Johnson, GA and Cohen-Adad, J and Budde, MD and Jelescu, IO},
title = {Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2561-2582},
pmid = {40008460},
issn = {1522-2594},
support = {34824//Canada Foundation for Innovation/ ; R01 EB031954/EB/NIBIB NIH HHS/United States ; PCEFP2_194260//Eccellenza Fellowship/ ; R01EB019980/NH/NIH HHS/United States ; R01EB017230/NH/NIH HHS/United States ; R01NS109090/NH/NIH HHS/United States ; K01 EB032898/EB/NIBIB NIH HHS/United States ; CIHRFDN-143263//Canadian Institute of Health Research/ ; //Research Center of Excellence of the University of Antwerp/ ; P30 DA048742/DA/NIDA NIH HHS/United States ; FDN-143263//CIHR/ ; R01 CA160620/CA/NCI NIH HHS/United States ; 12M3119N//Research Foundation Flanders/ ; 202788/Z/16/A/WT_/Wellcome Trust/United Kingdom ; P30DA048742/DA/NIDA NIH HHS/United States ; R01 EB017230/EB/NIBIB NIH HHS/United States ; R01CA160620/NH/NIH HHS/United States ; 101044180/ERC_/European Research Council/International ; 32454//Canada Foundation for Innovation/ ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; R01 EB019980/EB/NIBIB NIH HHS/United States ; //NSERC/ ; 322736//Fonds de Recherche du Qu&#xe9;bec - Sant&#xe9;/ ; FWO//Research Foundation Flanders/ ; /SNSF_/Swiss National Science Foundation/Switzerland ; R01EB031954/NH/NIH HHS/United States ; RGPIN-2019-07244//Natural Sciences and Engineering Research Council of Canada/ ; K01EB032898/NH/NIH HHS/United States ; 203139/A/16/Z/WT_/Wellcome Trust/United Kingdom ; R01EB031765/EB/NIBIB NIH HHS/United States ; R56EB031765/EB/NIBIB NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; R01 EB031765/EB/NIBIB NIH HHS/United States ; R01 NS109090/NS/NINDS NIH HHS/United States ; R01 NS125020/NS/NINDS NIH HHS/United States ; 5886,35450//Quebec BioImaging Network/ ; LCF/BQ/PR22/11920010//la Caixa/ ; R01AG057991/NH/NIH HHS/United States ; R56 EB031765/EB/NIBIB NIH HHS/United States ; R01NS125020/NH/NIH HHS/United States ; 2020 BP 00117//Government of Catalonia/ ; },
mesh = {*Diffusion Magnetic Resonance Imaging/methods ; Animals ; *Brain/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; *Diffusion Tensor Imaging/methods ; *Microscopy/methods ; Humans ; Signal-To-Noise Ratio ; },
abstract = {Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.},
}
@article {pmid40007882,
year = {2025},
author = {Crell, MR and Kostoglou, K and Sterk, K and Müller-Putz, GR},
title = {A novel paradigm for fast training data generation in asynchronous movement-based BCIs.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1540155},
pmid = {40007882},
issn = {1662-5161},
abstract = {INTRODUCTION: Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collection of 300 cued movement trials in 18 min.<br><br>METHODS: By obtaining measurements from ten participants, we demonstrate that the data produced by this paradigm exhibits characteristics similar to those observed during self-paced movement.<br><br>RESULTS AND DISCUSSION: We also show that classifiers trained on this data can be used to accurately detect executed movements with an average true positive rate of 31.8% at a maximum rate of 1.0 false positives per minute.},
}
@article {pmid40007617,
year = {2025},
author = {Wu, H and Feng, E and Yin, H and Zhang, Y and Chen, G and Zhu, B and Yue, X and Zhang, H and Liu, Q and Xiong, L},
title = {Biomaterials for neuroengineering: applications and challenges.},
journal = {Regenerative biomaterials},
volume = {12},
number = {},
pages = {rbae137},
pmid = {40007617},
issn = {2056-3418},
abstract = {Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.},
}
@article {pmid40007259,
year = {2025},
author = {Sirakov, A and Ninov, K and Sirakova, K and Sirakov, SS},
title = {Blazing the trail! Commentary on "Intra-arterial lidocaine administration in middle meningeal artery for short-term treatment of subarachoid hemorrhage-related headaches" by Qureshi et al.},
journal = {Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences},
volume = {},
number = {},
pages = {15910199251324039},
pmid = {40007259},
issn = {2385-2011},
abstract = {In their recently published INR study, Qureshi et al. present their results on intra-arterial lidocaine administration in the middle meningeal artery for the short-term treatment of subarachnoid hemorrhage (SAH)-related headaches. The authors demonstrate that their proposed intra-arterial treatment consistently alleviates headaches in patients with SAH. The purpose of this commentary is to commend the authors on their paper and the notable results they have achieved. It is always pleasant to encounter studies that not only make it to the "Latest Online" section of neurointerventional journals but also push the boundaries, advancing our understanding and care for patients in the most meaningful ways. There is no doubt that our field has witnessed remarkable progress and an expanding spectrum of interventions that endovascular neuroservices can offer. Several therapeutic approaches have emerged from similarly constructive articles, including intra-arterial chemotherapy for malignant cerebral tumors, innovative treatments for cerebrospinal fluid-venous fistulas, hydrocephalus, and chronic subdural hematomas, as well as the implantation of brain-computer interface devices.},
}
@article {pmid40006451,
year = {2025},
author = {Zaidi, SR and Khan, NA and Hasan, MA},
title = {Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
pmid = {40006451},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Emotions/physiology ; *Machine Learning ; Adult ; *Guilt ; Young Adult ; Neurosciences/methods ; Brain/physiology ; Support Vector Machine ; Sex Factors ; },
abstract = {This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the study were developed to ignite guilt and neutral emotions. Two emotions, "guilt" and "neutral", were recorded from 16 participants after these emotions were induced using storyboards as pictorial stimuli. These storyboards were developed based on various guilt-provoking events shared by another group of participants. In the pre-processing step, collected data were de-noised using bandpass filters and ICA, then segmented into smaller sections for further analysis. Two approaches were used to feed these data to the SVM classifier. First, the novel approach employed involved feeding the data to SVM classifier without computing any features. This method provided an average accuracy of 83%. In the second approach, data were divided into Alpha, Beta, Gamma, Theta and Delta frequency bands using Discrete Wavelet Decomposition. Afterward, the computed features, including entropy, Hjorth parameters and Band Power, were fed to SVM classifiers. This approach achieved an average accuracy of 63%. The findings of both classification methodologies indicate that females are more expressive in response to depicted stimuli and that their brain cells exhibit higher feature values. Moreover, females displayed higher accuracy than males in all bands except the Delta band.},
}
@article {pmid40006375,
year = {2025},
author = {Wu, Y and Cao, P and Xu, M and Zhang, Y and Lian, X and Yu, C},
title = {Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {4},
pages = {},
pmid = {40006375},
issn = {1424-8220},
support = {CEIEC-2023-ZM02-0090//Industrial Internet identification analysis System/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology ; },
abstract = {Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different periods. These challenges are exacerbated by the low spatial resolution and high signal redundancy inherent in EEG signals, which traditional linear models struggle to address. To overcome these issues, we propose a novel dual-branch framework that integrates an adaptive graph convolutional network (Adaptive GCN) and bidirectional gated recurrent units (Bi-GRUs) to enhance the decoding performance of MI-EEG signals by effectively modeling both channel correlations and temporal dependencies. The Chebyshev Type II filter decomposes the signal into multiple sub-bands giving the model frequency domain insights. The Adaptive GCN, specifically designed for the MI-EEG context, captures functional connectivity between channels more effectively than conventional GCN models, enabling accurate spatial-spectral feature extraction. Furthermore, combining Bi-GRU and Multi-Head Attention (MHA) captures the temporal dependencies across different time segments to extract deep time-spectral features. Finally, feature fusion is performed to generate the final prediction results. Experimental results demonstrate that our method achieves an average classification accuracy of 80.38% on the BCI-IV Dataset 2a and 87.49% on the BCI-I Dataset 3a, outperforming other state-of-the-art decoding approaches. This approach lays the foundation for future exploration of personalized and adaptive brain-computer interface (BCI) systems.},
}
@article {pmid40003400,
year = {2025},
author = {Teteh-Brooks, DK and Ericson, M and Bethea, TN and Dawkins-Moultin, L and Sarkaria, N and Bailey, J and Llanos, AAM and Montgomery, S},
title = {Validating the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) Among Black Breast Cancer Survivors.},
journal = {International journal of environmental research and public health},
volume = {22},
number = {2},
pages = {},
pmid = {40003400},
issn = {1660-4601},
support = {K01 MD018417/MD/NIMHD NIH HHS/United States ; L60 CA253971/CA/NCI NIH HHS/United States ; R01 CA217841/CA/NCI NIH HHS/United States ; R43 MD017966/MD/NIMHD NIH HHS/United States ; },
mesh = {Humans ; *Breast Neoplasms ; Female ; *Cancer Survivors/psychology/statistics &amp; numerical data ; Middle Aged ; *Hair Preparations ; Adult ; *Black or African American ; Surveys and Questionnaires ; Aged ; White ; },
abstract = {UNLABELLED: Personal care products containing toxic chemicals (e.g., endocrine-disrupting chemicals) may increase breast cancer risk, especially for Black women who use these products more than other racial groups. There are limited tools that examine the intersections of identity, behaviors, and attitudes surrounding product use, perceived safety, and breast cancer risk; thus, the Black Identity, Hair Product Use, and Breast Cancer Scale (BHBS) was developed to bridge this gap. While initial validations lacked diverse survivor representation, this study seeks to validate the BHBS among Black survivors.<br><br>METHODS: This study is a part of the Bench to Community Initiative (BCI), where respondents (n = 167) completed a 41-item survey including the BHBS between 2020 and 2022. The use of Principal Component Analysis (PCA) and confirmatory factor analysis (CFA) established the underlying component structures and model fit. CFA measures used to confirm component structures included the Root Mean Square Error of Approximation, the Comparative Fit Index, and the Tucker Lewis Index.<br><br>RESULTS: Black survivors on average were diagnosed with breast cancer before age 40 (37.41 &#xb1; 8.8) with Stage 1 (45%) disease. Sixty-three percent of the total variance resulted in a two-component structure. Subscale 1 (S1) measures the sociocultural perspectives about hair and identity (28% of the total variance; &#x3b1; = 0.73; 95% CI = 0.71-0.82). Subscale 2 (S2) can be used to assess perceived breast cancer risk related to hair product use (35% of the total variance; &#x3b1; = 0.86; 95% CI = 0.81-0.94). The two-component structure was confirmed with Root Mean Square Error of Approximation = 0.034, Comparative Fit Index = 0.93, and Tucker Lewis Index = 0.89.<br><br>DISCUSSION/CONCLUSIONS: The BHBS is a valid tool to measure identity, attitudes, and behaviors about product use and breast cancer risk among survivors. Hair is a significant cultural identity expression, and the health effects of styling products should be considered in future interventions.},
}
@article {pmid40002607,
year = {2025},
author = {Halkiopoulos, C and Gkintoni, E and Aroutzidis, A and Antonopoulou, H},
title = {Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {4},
pages = {},
pmid = {40002607},
issn = {2075-4418},
abstract = {Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.},
}
@article {pmid40002501,
year = {2025},
author = {Wu, C and Yao, B and Zhang, X and Li, T and Wang, J and Pu, J},
title = {The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002501},
issn = {2076-3425},
support = {RS2024X007//Key Project of Construction of Drug Regulatory Science System/ ; 2021ZD0200406//STI 2030-MajorProjects under grant/ ; 2021-I2M-1-042, 2021-I2M-1-058//the Medical and Health Innovation Project/ ; 20JCJQIC00230//the Tianjin Outstanding Youth Fund Project/ ; },
abstract = {Background: In motor imagery brain-computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8-10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.},
}
@article {pmid40002462,
year = {2025},
author = {Wu, P and Fei, K and Chen, B and Pan, L},
title = {MSEI-ENet: A Multi-Scale EEG-Inception Integrated Encoder Network for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002462},
issn = {2076-3425},
support = {61773078//the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Due to complex signal characteristics and distinct individual differences, the decoding of a motor imagery electroencephalogram (MI-EEG) is limited by the unsatisfactory performance of suboptimal traditional models.<br><br>METHODS: A subject-independent model named MSEI-ENet is proposed for multiple-task MI-EEG decoding. It employs a specially designed multi-scale structure EEG-inception module (MSEI) for comprehensive feature learning. The encoder module further helps to detect discriminative information by its multi-head self-attention layer with a larger receptive field, which enhances feature representation and improves recognition efficacy.<br><br>RESULTS: The experimental results on Competition IV dataset 2a showed that our proposed model yielded an overall accuracy of 94.30%, MF1 score of 94.31%, and Kappa of 0.92.<br><br>CONCLUSIONS: A performance comparison with state-of-the-art methods demonstrated the effectiveness and generalizability of the proposed model on challenging multi-task MI-EEG decoding.},
}
@article {pmid40002457,
year = {2025},
author = {Gu, H and Chen, T and Ma, X and Zhang, M and Sun, Y and Zhao, J},
title = {CLTNet: A Hybrid Deep Learning Model for Motor Imagery Classification.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002457},
issn = {2076-3425},
abstract = {BACKGROUND: Brain-computer interface (BCI) technology opens up new avenues for human-machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application.<br><br>METHODS: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer's self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer.<br><br>RESULTS: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods.<br><br>CONCLUSIONS: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area.},
}
@article {pmid40002431,
year = {2025},
author = {Yang, Y and Li, M and Liu, J},
title = {Generative Diffusion-Based Task Incremental Learning Method for Decoding Motor Imagery EEG.},
journal = {Brain sciences},
volume = {15},
number = {2},
pages = {},
pmid = {40002431},
issn = {2076-3425},
support = {62173010//Mingai Li/ ; },
abstract = {BACKGROUND/OBJECTIVES: Motor neurorehabilitation can be realized by gradually learning diverse motor imagery (MI) tasks. EEG-based brain-computer interfaces (BCIs) provide an effective solution. Nevertheless, existing MI decoding methods cannot balance plasticity for unseen tasks and stability for old tasks. This paper proposes a generative diffusion-based task Incremental Learning (IL) method called GD-TIL.<br><br>METHODS: First, data augmentation is employed to increase data diversity by segmenting and recombining EEG signals. Second, to capture temporal-spatial features (TSFs) from different temporal resolutions, a multi-scale temporal-spatial feature extractor (MTSFE) is developed via integrating multiscale temporal-spatial convolutions, a dual-branch pooling operation, multiple multi-head self-attention mechanisms, and a dynamic convolutional encoder. The proposed self-supervised task generalization (SSTG) mechanism introduces a regularization constraint to guide MTSFE and unified classifier updating, which combines labels and semantic similarity between the augmentation with original views to enhance model generalizability for unseen tasks. In the IL phase, a prototype-guided generative replay module (PGGR) is used to generate old tasks' TSFs by training a lightweight diffusion model based on the prototype and label of each task. Furthermore, the generated TSF is merged with a new TSF to fine-tune the convolutional encoder and update the classifier and PGGR. Finally, GD-TIL is evaluated on a self-collected ADL-MI dataset with two MI pairs and a public dataset with four MI tasks.<br><br>RESULTS: The continuous decoding accuracy reaches 80.20% and 81.32%, respectively. The experimental results exhibit the excellent plasticity and stability of GD-TIL, even beating the state-of-the-art IL methods.<br><br>CONCLUSIONS: Our work illustrates the potential of MI-based BCI and generative AI for continuous neurorehabilitation.},
}
@article {pmid40001978,
year = {2025},
author = {Al-Nafjan, A and Alshehri, H and Aldayel, M},
title = {Objective Pain Assessment Using Deep Learning Through EEG-Based Brain-Computer Interfaces.},
journal = {Biology},
volume = {14},
number = {2},
pages = {},
pmid = {40001978},
issn = {2079-7737},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority (RDIA)-Kingdom of Saudi Arabia/ ; },
abstract = {Objective pain measurements are essential in clinical settings for determining effective treatment strategies. This study aims to utilize brain-computer interface technology for reliable pain classification and detection. We developed an electroencephalography-based pain detection system comprising two main components: (1) pain/no-pain detection and (2) pain severity classification across three levels: low, moderate, and high. Deep learning models, including convolutional neural networks and recurrent neural networks, were employed to classify the wavelet features extracted through time-frequency domain analysis. Furthermore, we compared the performance of our system against conventional machine learning models, such as support vector machines and random forest classifiers. Our deep learning approach outperformed the baseline models, achieving accuracies of 91.84% for pain/no-pain detection and 87.94% for pain severity classification, respectively.},
}
@article {pmid40000219,
year = {2024},
author = {He, W and Wang, D and Meng, Q and He, F and Xu, M and Ming, D},
title = {[Applications and prospects of electroencephalography technology in neurorehabilitation assessment and treatment].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1271-1278},
pmid = {40000219},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography ; *Neurological Rehabilitation/methods/instrumentation ; *Brain-Computer Interfaces ; *Transcranial Magnetic Stimulation/methods ; Transcranial Direct Current Stimulation/methods ; Nervous System Diseases/rehabilitation ; Epilepsy/rehabilitation ; },
abstract = {With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.},
}
@article {pmid40000217,
year = {2024},
author = {Yang, H and Li, T and Zhao, L and Chen, X and Pan, J and Fu, Y},
title = {[An emerging major: brain-computer interface major].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1257-1264},
pmid = {40000217},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Brain/physiology ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major-BCI major-has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.},
}
@article {pmid40000203,
year = {2024},
author = {Wu, X and Chu, Y and Zhao, X and Zhao, Y},
title = {[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {6},
pages = {1145-1152},
pmid = {40000203},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.},
}
@article {pmid40000192,
year = {2025},
author = {Chen, Z and Huang, Y and Yu, H and Cao, C and Xu, M and Ming, D},
title = {[Research progress on the characteristics of magnetoencephalography signals in depression].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {189-196},
pmid = {40000192},
issn = {1001-5515},
mesh = {*Magnetoencephalography ; Humans ; *Brain/physiopathology/diagnostic imaging ; *Depression/diagnosis/physiopathology ; Electroencephalography ; Magnetic Resonance Imaging ; },
abstract = {Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.},
}
@article {pmid40000170,
year = {2025},
author = {Zhang, Y and Zhang, C and Sun, S and Xu, G},
title = {[Research on motor imagery recognition based on feature fusion and transfer adaptive boosting].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {1},
pages = {9-16},
pmid = {40000170},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Support Vector Machine ; Humans ; *Algorithms ; *Neural Networks, Computer ; Electroencephalography ; Imagination/physiology ; Wavelet Analysis ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; },
abstract = {This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 [th] International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.},
}
@article {pmid39999624,
year = {2025},
author = {King, SE and Waddell, JT and McDonald, AE and Corbin, WR},
title = {Are you feeling what I'm feeling? Momentary interactions between personal and perceived peer subjective response predict craving and continued drinking in young adults.},
journal = {Drug and alcohol dependence},
volume = {270},
number = {},
pages = {112601},
pmid = {39999624},
issn = {1879-0046},
support = {F31 AA030167/AA/NIAAA NIH HHS/United States ; T32 DA039772/DA/NIDA NIH HHS/United States ; },
mesh = {Humans ; Female ; Male ; *Craving ; Young Adult ; Adult ; *Alcohol Drinking/psychology ; Adolescent ; *Ecological Momentary Assessment ; *Peer Group ; Emotions ; },
abstract = {BACKGROUND: Subjective response to alcohol is a robust predictor of alcohol outcomes. It is possible that the perceived subjective response of others may influence concurrent experiences of one's own subjective response. However, no studies have examined how the perceived subjective response of others might interact with personal subjective response and how such interactions may influence levels of craving and subsequent drinking.<br><br>METHOD: Emerging adults (ages 18-25, N&#x2009;=&#x2009;131, 53.4&#x2009;% female) completed 21 days of ecological momentary assessments. During drinking events (N&#x2009;=&#x2009;1335) both personal and perceived peer subjective response (four domains encompassing high- and low-arousal positive &amp; negative effects) were assessed at drink initiation and two subsequent surveys 60 and 120min later. Current craving and drinking quantity since last report were also collected. Three-level multilevel structural equation models with Bayesian estimation tested indirect relations between subjective response and drinking continuation via craving and whether perceived subjective response moderated such relations.<br><br>RESULTS: Levels of both personal (b=0.029,95&#x2009;%BCI:[0.012,0.053]) and perceived (b=0.027,95&#x2009;%BCI:[0.012,0.051]) experiences of alcohol's rewarding, stimulating effects indirectly predicted drinking continuation via increased craving, and relations were potentiated when perceptions of peer reward were highest (b=0.015,95&#x2009;%BCI:[0.008,0.020]). Personal experiences of alcohol's relaxing, calming effects indirectly predicted a lower likelihood of drinking continuation via decreased craving (b=-0.017,95&#x2009;%BCI:[-0.036,-0.003]) whereas perceived effects directly predicted lower likelihoods of drinking (b=-0.133,95&#x2009;%CI:[-0.239, -0.031]).<br><br>CONCLUSION: Results suggest both personal and perceived peer subjective response independently influence drinking behavior even when controlling for one another. Targeted interventions focused on altering interpretations of peer subjective effects may be effective at reducing momentary risk.},
}
@article {pmid39997141,
year = {2025},
author = {Ha, J and Park, S and Han, Y and Kim, L},
title = {Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {39997141},
issn = {2313-7673},
support = {RS-2024-00340293//the National Research Foundation of Korea (NRF)/ ; },
abstract = {Brain-computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities.},
}
@article {pmid39997117,
year = {2025},
author = {Premchand, B and Zhang, Z and Ang, KK and Yu, J and Tan, IO and Lam, JPW and Choo, AXY and Sidarta, A and Kwong, PWH and Chung, LHC},
title = {A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {39997117},
issn = {2313-7673},
support = {//Institute for Infocomm Research/ ; Research Grant RRG4/2008//Rehabilitation Research Institute of Singapore/ ; },
abstract = {Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient's specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant's respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.},
}
@article {pmid39996608,
year = {2025},
author = {Hjortkjær, J and Wong, DDE and Catania, A and Märcher-Rørsted, J and Ceolini, E and Fuglsang, SA and Kiselev, I and Di Liberto, G and Liu, SC and Dau, T and Slaney, M and de Cheveigné, A},
title = {Real-time control of a hearing instrument with EEG-based attention decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad867c},
pmid = {39996608},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Attention/physiology ; *Hearing Aids ; *Brain-Computer Interfaces ; *Speech Perception/physiology ; Computer Systems ; Acoustic Stimulation/methods ; Male ; Female ; Adult ; },
abstract = {Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface system that combines a stimulus-response model based on canonical correlation analysis for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.},
}
@article {pmid39996071,
year = {2025},
author = {Samadi, E and Rahatabad, FN and Nasrabadi, AM and Dabanlou, NJ},
title = {Brain analysis to approach human muscles synergy using deep learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {44},
pmid = {39996071},
issn = {1871-4080},
abstract = {Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.},
}
@article {pmid39994209,
year = {2025},
author = {Guan, S and Dong, T and Cong, LK},
title = {Method for EEG signal recognition based on multi-domain feature fusion and optimization of multi-kernel extreme learning machine.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6601},
pmid = {39994209},
issn = {2045-2322},
support = {20220508014RC//Project supported by Jilin Provincial Science and Technology Development Plan Project/ ; },
abstract = {In response to the current issues of one-sided effective feature extraction and low classification accuracy in multi-class motor imagery recognition, this study proposes an Electroencephalogram (EEG) signal recognition method based on multi-domain feature fusion and optimized multi-kernel extreme learning machine. Firstly, the EEG signals are preprocessed using the Improved Comprehensive Ensemble Empirical Mode Decomposition (ICEEMD) algorithm combined with the Pearson correlation coefficient to eliminate noise and interference. Secondly, multivariate autoregressive (MVAR) model, wavelet packet decomposition, and Riemannian geometry methods are used to extract features from the time domain, frequency domain, and spatial domain, respectively, to construct a joint time-frequency-space feature vector. Subsequently, kernel principal component analysis (KPCA) is employed to fuse and reduce the dimensionality of the joint features, resulting in a reduced-dimensional fused feature vector. Finally, these feature vectors are input into a Radius-incorporated multi-kernel extreme learning machine (RIO-MKELM) for classification. The experimental results indicate that through multi-domain feature fusion and the incorporation of radius in a multi-kernel extreme learning machine, feature selection can be performed more effectively, eliminating redundant or irrelevant features and retaining the most useful information for classification. This approach enhances classification accuracy and other evaluation metrics, with the final classification accuracy reaching 95.49%, sensitivity at 97.88%, specificity at 98.12%, recall at 97.88%, and F1 Score at 96.67%. The findings of this study are of significant importance for the development and practical application of brain-computer interface (BCI) systems.},
}
@article {pmid39993988,
year = {2025},
author = {Guo, J and Yang, J and Li, Y},
title = {[Analysis of Brain-Computer Interface Technology in the Medical Field and the Regulation of the US FDA].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {49},
number = {1},
pages = {96-102},
doi = {10.12455/j.issn.1671-7104.240187},
pmid = {39993988},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; United States ; *United States Food and Drug Administration ; Humans ; Electroencephalography ; Device Approval ; },
abstract = {Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.},
}
@article {pmid39993333,
year = {2025},
author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, NG and Downey, JE and Chase, SM and Boninger, ML and Dekleva, BD and Collinger, JL},
title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb995},
pmid = {39993333},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Male ; *Imagination/physiology ; Adult ; Female ; *Movement/physiology ; Spinal Cord Injuries/physiopathology ; Electrodes, Implanted ; Middle Aged ; Brain Mapping/methods ; Hand/physiology ; },
abstract = {Objective:The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control.Approach:Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder.Main results:We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex.Significance:These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.},
}
@article {pmid39992333,
year = {2024},
author = {Qin, HJ and Liu, YY and Fu, EH and Liu, YW and Tian, ZL and Dong, HW and Liu, TA and Zou, DH and Cheng, YB and Liu, NG},
title = {Intelligent Recognition and Segmentation of Blunt Craniocerebral Injury CT Images Based on DeepLabV3+ Model.},
journal = {Fa yi xue za zhi},
volume = {40},
number = {5},
pages = {419-429},
doi = {10.12116/j.issn.1004-5619.2024.440801},
pmid = {39992333},
issn = {1004-5619},
mesh = {Humans ; *Tomography, X-Ray Computed/methods ; *Neural Networks, Computer ; *Head Injuries, Closed/diagnostic imaging ; Deep Learning ; Image Processing, Computer-Assisted/methods ; Craniocerebral Trauma/diagnostic imaging ; Forensic Medicine/methods ; Skull Fractures/diagnostic imaging ; },
abstract = {OBJECTIVES: To achieve intelligent recognition and segmentation of common craniocerebral injuries (hereinafter referred to as "segmentation") by training convolutional neural network DeepLabV3+ model based on CT images of blunt craniocerebral injury (BCI), and to explore the value of deep learning in automated diagnosis of BCI in forensic medicine.<br><br>METHODS: A total of 5 486 CT images of BCI from living persons were collected as the training set, validation set and test set for model training and performance evaluation. Another 255 CT images of BCI and 156 normal craniocerebral CT images from living persons were collected as the blind test set to evaluate the ability of the model to segment the five types of craniocerebral injuries including scalp hematoma, skull fracture, epidural hematoma, subdural hematoma, and brain contusion. Another 340 BCI and 120 normal craniocerebral CT images from cadavers were collected as the new blind test set to explore the application value of the model trained by living CT images in the segmentation of BCI in cadavers. The five types CT images of all BCI except the blind test set were manually labeled; then, each dataset was inputted into the model to train the model. The performance of the model was evaluated and optimized based on the loss function and accuracy curves of the training set and validation set, and the generalization ability was evaluated based on the Dice value of the test set. According to the accuracy, precision and F1 value of the blind test set, the segmentation performance of the model for five types of BCI was evaluated.<br><br>RESULTS: After training and optimizing the model, the average Dice values of the final optimal model to scalp hematoma, skull fracture, epidural hematoma, subdural hematoma and brain contusion segmentation were 0.766 4, 0.812 3, 0.938 7, 0.782 7 and 0.858 1, respectively, all greater than 0.75, meeting the expected requirements. External validation showed that the F1 values were 93.02%, 89.80%, 87.80%, 92.93% and 86.57% in living CT images, respectively; 83.92%, 44.90%, 76.47%, 64.29% and 48.89% in cadaveric CT images, respectively. The above suggested that the model was able to accurately segment various types of craniocerebral injury on living CT images, while its segmentation ability was relatively poor on cadaveric CT images, but still able to accurately segment scalp hematoma, epidural hematoma and subdural hematoma.<br><br>CONCLUSIONS: Deep learning model trained on CT images can be used for BCI segmentation. However, the direct use of living persons' BCI models for the identification of cadaveric BCI has some limitations. This study provides a new approach for intelligent segmentation of virtual anatomical data for BCI.},
}
@article {pmid39992067,
year = {2025},
author = {Wan, C and Zhang, W and Nie, Y and Qian, Y and Wang, J and Xu, H and Li, Z and Su, B and Zhang, Y and Li, Y},
title = {Impact of motor imagery-based brain-computer interface combined with virtual reality on enhancing attention, executive function, and lower-limb function in stroke: A pilot study.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1002/pmrj.13324},
pmid = {39992067},
issn = {1934-1563},
support = {No.303103136AA22//National Clinical Medical Research Centre Cultivation Program of Nanjing/ ; No.ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; No.JBGS202414//Jiangsu Province Hospital clinical diagnosis and treatment of technological innovation "Open bidding for selecting the best candidates" project/ ; },
abstract = {BACKGROUND: Brain-computer interface combined with virtual reality (BCI-VR) can reduce the difficulty of motor imagery execution and improve training performance. Few studies have focused on the effects of BCI-VR on attention, executive function, and lower-limb function in stroke.<br><br>OBJECTIVE: To evaluate feasibility and preliminary efficacy of BCI-VR pedaling training on the attention, executive function, and lower-extremity function in people after stroke. It will also provide data support for future research, especially sample size calculations.<br><br>DESIGN: A single group before-after trial design was used. All participants had a stable level of function over a 2-week period to ensure that their functional recovery was all attributable to BCI-VR training.<br><br>SETTING: The study was conducted in a specialized rehabilitation hospital.<br><br>PARTICIPANTS: Twelve participants with stroke, a certain level of motor imagery ability, capable of walking 10 meters continuously.<br><br>INTERVENTIONS: All participants received a 4-week BCI-VR pedaling training program, 5&#x2009;days per week, 30&#x2009;minutes each session.<br><br>OUTCOME MEASURES: Primary outcomes are feasibility and safety. Secondary outcomes were lower-extremity mobility, attention, and executive functions.<br><br>RESULTS: Twelve patients were recruited from inpatient rehabilitation and nine completed the study (six males/three females; 56.6&#x2009;&#xb1;&#x2009;11.6&#x2009;years). Recruitment and retention rates were 34% and 75%, respectively. Excellent adherence rate (97.7%) was obtained. No adverse events or equipment issues were reported. Following the intervention, significant improvements were found in the lower-extremity strength, balance, walking stability, attention, and general cognitive function (p&#x2009;<&#x2009;.05). A significant correlation was found between improved Berg balance scale change values and symbol digit modalities test change values (p&#x2009;<&#x2009;.05, r&#x2009;=&#x2009;0.677).<br><br>CONCLUSIONS: BCI-VR pedaling training provides a depth of feasibility and safety data, methodological detail, and preliminary results. This could provide a useful basis for future studies of BCI-VR pedaling training for stroke rehabilitation.<br><br>CLINICALTRIALS: gov registration number: ChiCTR2300071522 (http://www.chictr.org.cn).},
}
@article {pmid39990438,
year = {2025},
author = {Lin, Z and Marin-Llobet, A and Baek, J and He, Y and Lee, J and Wang, W and Zhang, X and Lee, AJ and Liang, N and Du, J and Ding, J and Li, N and Liu, J},
title = {Spike sorting AI agent.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39990438},
issn = {2692-8205},
support = {DP1 DK130673/DK/NIDDK NIH HHS/United States ; R01 LM014465/LM/NLM NIH HHS/United States ; },
abstract = {Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.},
}
@article {pmid39990331,
year = {2025},
author = {Pescatore, CRC and Zhang, H and Hadjinicolaou, AE and Paulk, AC and Rolston, JD and Richardson, RM and Williams, ZM and Cai, J and Cash, SS},
title = {Decoding semantics from natural speech using human intracranial EEG.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39990331},
issn = {2692-8205},
support = {R01 DC019653/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) hold promise for restoring natural language production capabilities in patients with speech impairments, potentially enabling smooth conversation that conveys meaningful information via synthesized words. While considerable progress has been made in decoding phonetic features of speech, our ability to extract lexical semantic information (i.e. the meaning of individual words) from neural activity remains largely unexplored. Moreover, most existing BCI research has relied on controlled experimental paradigms rather than natural conversation, limiting our understanding of semantic decoding in ecological contexts. Here, we investigated the feasibility of decoding lexical semantic information from stereo-electroencephalography (sEEG) recordings in 14 participants during spontaneous conversation. Using multivariate pattern analysis, we were able to decode word level semantic features during language production with an average accuracy of 21% across all participants compared to a chance level of 10%. This semantic decoding remained robust across different semantic representations while maintaining specificity to semantic features. Further, we identified a distributed left-lateralized network spanning precentral gyrus, pars triangularis, and middle temporal cortex, with low-frequency oscillations showing stronger contributions. Together, our results establish the feasibility of extracting word meanings from neural activity during natural speech production and demonstrate the potential for decoding semantic content from unconstrained speech.},
}
@article {pmid39989959,
year = {2025},
author = {Shoffstall, A and Li, L and Hartzler, A and Menendez-Lustri, D and Zhang, J and Chen, A and Lam, D and Traylor, B and Quill, E and Hoeferlin, G and Pawlowski, C and Bruckman, M and Gupta, SA and Capadona, J},
title = {Dexamethasone-loaded platelet-inspired nanoparticles improve intracortical microelectrode recording performance.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {39989959},
issn = {2693-5015},
support = {I01 RX003420/RX/RRD VA/United States ; R01 HL121212/HL/NHLBI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {Long-term robust intracortical microelectrode (IME) neural recording quality is negatively affected by the neuroinflammatory response following microelectrode insertion. This adversely impacts brain-machine interface (BMI) performance for patients with neurological disorders or amputations. Recent studies suggest that the leakage of blood-brain barrier (BBB) and microhemorrhage caused by the IME insertions lead to the increased neuroinflammation and reduced neural recording performance. Additionally, a sustained presence of activated platelets and coagulation factors is found near the insertion site. Thus, we hypothesized that the systemic administration of dexamethasone sodium phosphate-loaded platelet-inspired nanoparticle (SPPINDEX) can improve the neural recording performance of intracortical microelectrodes (IMEs) by promoting hemostasis, facilitating blood-brain barrier (BBB) healing, and achieving implant-targeted drug delivery. Leveraging the hemostatic and coagulation factor-binding properties of the platelet-inspired nanoparticle (PIN) drug delivery platform, SPPINDEX treatment can initially attenuate the invasion of neuroinflammatory triggers into the brain parenchyma caused by insertion-induced microhemorrhages or a compromised BBB. Furthermore, targeted delivery of the anti-inflammatory drug dexamethasone sodium phosphate (DEXSP) to the implant site via these nanoparticles can attenuate ongoing neuroinflammation, enhancing overall therapeutic efficacy. Weekly treatment with SPPINDEX for 8 weeks significantly improved the recording capabilities of IMEs compared to platelet-inspired nanoparticles alone (PIN), free dexamethasone sodium phosphate (Free DEXSP), and a diluent control trehalose buffer (TH), as assessed through extracellular single-unit recordings. Immunohistochemical analyses of neuron density, activated microglia/macrophage density, astrocyte density, and BBB permeability suggest that the improved neural recording performance may be attributed to reduced neuron degeneration, activated microglia and astrocytes at the implant interface caused by the decreased infiltration of blood-derived proteins that trigger neuroinflammation and the therapeutic effects from DEXSP. Overall, SPPINDEX treatment promotes an anti-inflammatory environment that improves neuronal density and enhances recording performance.},
}
@article {pmid39988822,
year = {2025},
author = {Park, J and Ahn, J and Choi, J and Kim, J},
title = {Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation.},
journal = {Journal of chemical information and modeling},
volume = {65},
number = {5},
pages = {2283-2296},
pmid = {39988822},
issn = {1549-960X},
mesh = {*Drug Discovery/methods ; Artificial Intelligence ; Machine Learning ; Reward ; },
abstract = {Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing approaches in generating molecules having the desired properties, including penalized LogP, QED, and celecoxib similarity, without any prior knowledge. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.},
}
@article {pmid39987217,
year = {2025},
author = {Wang, X and Chen, S and Wang, K and Cao, L},
title = {Predicted action-effects shape action representation through pre-activation of alpha oscillations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {275},
pmid = {39987217},
issn = {2399-3642},
support = {32271078//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Alpha Rhythm/physiology ; Young Adult ; Visual Cortex/physiology ; Attention/physiology ; Psychomotor Performance/physiology ; Feedback, Sensory/physiology ; },
abstract = {Actions are typically accompanied by sensory feedback (or action-effects). Action-effects, in turn, influence the action. Theoretical accounts of action control assume a pre-activation of action-effects prior to action execution. Here we show that when participants were asked to report the time of their voluntary keypress using the position of a fast-rotating clock hand, a predictable action-effect (i.e. a 250 ms delayed sound after keypress) led to a shift of visuospatial attention towards the clock hand position of action-effect onset, thus demonstrating an influence of action-effects on action representation. Importantly, the attention shift occurred about 1 second before the action execution, which was further preceded and predicted by a lateralisation of alpha oscillations in the visual cortex. Our results indicate that when the spatial location is the key feature of action-effects, the neural implementation of the action-effect pre-activation is achieved through alpha lateralisation.},
}
@article {pmid39986990,
year = {2025},
author = {Ding, N},
title = {Sequence chunking through neural encoding of ordinal positions.},
journal = {Trends in cognitive sciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tics.2025.01.014},
pmid = {39986990},
issn = {1879-307X},
abstract = {Grouping sensory events into chunks is an efficient strategy to integrate information across long sequences such as speech, music, and complex movements. Although chunks can be constructed based on diverse cues (e.g., sensory features, statistical patterns, internal knowledge) recent studies have consistently demonstrated that the chunks constructed by different cues are all tracked by low-frequency neural dynamics. Here, I review evidence that chunking cues drive low-frequency activity in modality-dependent networks, which interact to generate chunk-tracking activity in broad brain areas. Functionally, this work suggests that a core computation underlying sequence chunking may assign each event its ordinal position within a chunk and that this computation is causally implemented by chunk-tracking neural activity during predictive sequence chunking.},
}
@article {pmid39986550,
year = {2025},
author = {Zhao, W and Rao, J and Wang, R and Chai, Y and Mao, T and Quan, P and Deng, Y and Chen, W and Wang, S and Guo, B and Zhang, Q and Rao, H},
title = {Test-retest reliability of coupling between cerebrospinal fluid flow and global brain activity after normal sleep and sleep deprivation.},
journal = {NeuroImage},
volume = {309},
number = {},
pages = {121097},
doi = {10.1016/j.neuroimage.2025.121097},
pmid = {39986550},
issn = {1095-9572},
mesh = {Humans ; *Sleep Deprivation/physiopathology/cerebrospinal fluid ; Male ; Female ; Adult ; Reproducibility of Results ; *Brain ; *Glymphatic System/physiology/diagnostic imaging ; *Sleep/physiology ; Young Adult ; *Cerebrospinal Fluid/physiology ; Magnetic Resonance Imaging ; },
abstract = {The glymphatic system (GS) plays a key role in maintaining brain homeostasis by clearing metabolic waste during sleep, with the coupling between global blood-oxygen-level-dependent (gBOLD) and cerebrospinal fluid (CSF) signals serving as a potential marker for glymphatic clearance function. However, the test-retest reliability and spatial heterogeneity of gBOLD-CSF coupling after different sleep conditions remain unclear. In this study, we assessed the test-retest reliability of gBOLD-CSF coupling following either normal sleep or total sleep deprivation (TSD) in 64 healthy adults under controlled laboratory conditions. The reliability was high after normal sleep (ICC = 0.763) but decreased following TSD (ICC = 0.581). Moreover, spatial heterogeneity was evident in participants with normal sleep, with lower-order networks (visual, somatomotor, and attention) showing higher ICC values compared to higher-order networks (default-mode, limbic, and frontoparietal). This spatial variation was less distinct in the TSD group. These results demonstrate the robustness of the gBOLD-CSF coupling method and emphasize the significance of considering sleep history in glymphatic function research.},
}
@article {pmid39985774,
year = {2025},
author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Lu, J and Li, Y},
title = {Protocol to perform offline ECoG brain-to-text decoding for natural tonal sentences.},
journal = {STAR protocols},
volume = {6},
number = {1},
pages = {103650},
pmid = {39985774},
issn = {2666-1667},
mesh = {*Electrocorticography/methods ; Humans ; Brain/physiology ; Speech/physiology ; Language ; Brain-Computer Interfaces ; },
abstract = {Here, we present a protocol to decode Mandarin sentences from invasive neural recordings using a brain-to-text framework. We describe steps for preparing materials, including designing the sentence corpus and setting up electrocorticography (ECoG) recording systems. We then detail procedures for decoding, such as data preprocessing, selection of speech-responsive electrodes, speech detection, syllable and tone decoding, and language modeling. We also outline performance evaluation metrics. For complete details on the use and execution of this protocol, please refer to Zhang et al.[1].},
}
@article {pmid39984530,
year = {2025},
author = {Liu, Y and Gui, Z and Yan, D and Wang, Z and Gao, R and Han, N and Chen, J and Wu, J and Ming, D},
title = {Lower limb motor imagery EEG dataset based on the multi-paradigm and longitudinal-training of stroke patients.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {314},
pmid = {39984530},
issn = {2052-4463},
support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; *Lower Extremity/physiopathology ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Imagination ; Neuronal Plasticity ; },
abstract = {Motor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These datasets are particularly needed for accurate lower limb MI in stroke patients and for longitudinal data reflecting the rehabilitation process. This study addresses this gap by collecting EEG data from 27 stroke patients, covering two enhanced paradigms and three different time points. The dataset includes raw EEG signals, preprocessed data, and patient information. An initial analysis using CSP-SVM on the dataset yielded an average classification accuracy of 80.50%. We anticipate that this dataset will facilitate research into brain neuroplasticity in stroke patients, aid in the development of decoding algorithms for lower limb stroke, and contribute to the establishment of comprehensive stroke rehabilitation systems.},
}
@article {pmid39983772,
year = {2025},
author = {Wang, M and Zhang, Y and Wang, A and Gan, Z and Zhang, L and Kang, X},
title = {Soft neural interface with color adjusted PDMS encapsulation layer for spinal cord stimulation.},
journal = {Journal of neuroscience methods},
volume = {417},
number = {},
pages = {110402},
doi = {10.1016/j.jneumeth.2025.110402},
pmid = {39983772},
issn = {1872-678X},
mesh = {Animals ; *Dimethylpolysiloxanes/chemistry ; Mice ; *Spinal Cord Stimulation/methods/instrumentation ; Electrodes, Implanted ; Color ; Lasers ; Biocompatible Materials/chemistry ; Microtechnology/instrumentation ; Spinal Cord/physiology ; },
abstract = {BACKGROUND: Spinal cord stimulation (SCS) plays a crucial role in treating various neurological diseases. Utilizing soft spinal cord electrodes in SCS allows for a better fit with the physiological structure of the spinal cord and reduces tissue damage. Polydimethylsiloxane (PDMS) has emerged as an ideal material for soft bioelectronics. However, micromachining soft PDMS bioelectronics devices with low thermal effects and high uniformity remains challenging.<br><br>NEW METHOD: Here, we demonstrated a fully laser-micromachined soft neural interface for SCS. The native and color adjusted PDMS with variable absorbance characteristics were investigated in laser processing. In addition, we systematically evaluated the impact of electrode sizes on the electrochemical performance of neural interface. By fitting the equivalent circuit model, the electrochemical process of neural interface was revealed and the performance of the electrode was evaluated. The biocompatibility of color adjusted PDMS was confirmed by cytotoxicity assays. Finally, we validated the neural interface in mice.<br><br>RESULTS: Color adjusted PDMS has good biocompatibility and can significantly reduce the damage caused by thermal effects, enhancing the electrochemical performance of bioelectronic devices. The soft neural interface with color adjusted PDMS encapsulation layer can activate the motor function safely.<br><br>The fully laser-micromachined soft neural interface was proposed for the first time. Compared with existing methods, this method showed low thermal effects, high uniformity, and could be easily scaled up.<br><br>CONCLUSIONS: The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.},
}
@article {pmid39983236,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb90c},
pmid = {39983236},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Sleep Stages/physiology ; Male ; Female ; Adult ; Polysomnography/methods ; Young Adult ; Middle Aged ; },
abstract = {Objective.Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilitate the tedious manual labeling process. Notably, recently proposed sleep staging algorithms lack model explainability and still require performance improvement.Approach.We implemented multiscale neurophysiology-mimicking kernels to capture sleep-related electroencephalogram (EEG) activities at varying frequencies and temporal lengths; the implemented model was named 'multiscale temporal convolutional neural network (MTCNN).' Further, we evaluated its performance using an open-source dataset (Sleep-EDF Database Expanded comprising 153 d of polysomnogram data).Main results.By investigating the learned kernel weights, we observed that MTCNN detected the EEG activities specific to each sleep stage, such as the frequencies, K-complexes, and sawtooth waves. Furthermore, regarding the characterization of these neurophysiologically significant features, MTCNN demonstrated an overall accuracy (OAcc) of 91.12% and a Cohen kappa coefficient of 0.86 in the cross-subject paradigm. Notably, it demonstrated an OAcc of 88.24% and a Cohen kappa coefficient of 0.80 in the leave-few-days-out analysis. Our MTCNN model also outperformed the existing deep learning models in sleep stage classification even when it was trained with only 16% of the total EEG data, achieving an OAcc of 85.62% and a Cohen kappa coefficient of 0.75 on the remaining 84% of testing data.Significance.The proposed MTCNN enables model explainability and it can be trained with lesser amount of data, which is beneficial to its application in the real-world because large amounts of training data are not often and readily available.},
}
@article {pmid39983235,
year = {2025},
author = {T A, S and R, S and Vinod, AP and Alladi, S},
title = {On the feasibility of an online brain-computer interface-based neurofeedback game for enhancing attention and working memory in stroke and mild cognitive impairment patients.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adb8ef},
pmid = {39983235},
issn = {2057-1976},
mesh = {Humans ; *Neurofeedback/methods ; *Cognitive Dysfunction/therapy ; *Memory, Short-Term ; Male ; *Brain-Computer Interfaces ; Female ; *Electroencephalography/methods ; Middle Aged ; *Attention ; Aged ; *Stroke/complications/physiopathology ; *Feasibility Studies ; Stroke Rehabilitation/methods ; Video Games ; Brain/physiopathology ; },
abstract = {Background. Neurofeedback training (NFT) using Electroencephalogram-based Brain Computer Interface (EEG-BCI) is an emerging therapeutic tool for enhancing cognition.Methods. We developed an EEG-BCI-based NFT game for enhancing attention and working memory of stroke and Mild cognitive impairment (MCI) patients. The game involves a working memory task during which the players memorize locations of images in a matrix and refill them correctly using their attention levels. The proposed NFT was conducted across fifteen participants (6 Stroke, 7 MCI, and 2 non-patients). The effectiveness of the NFT was evaluated using the percentage of correctly filled matrix elements and EEG-based attention score. EEG varitions during working memory tasks were also investigated using EEG topographs and EEG-based indices.Results. The EEG-based attention score showed an enhancement ranging from 4.29-32.18% in the Stroke group from the first session to the third session, while in the MCI group, the improvement ranged from 4.32% to 48.25%. We observed significant differences in EEG band powers during working memory operation between the stroke and MCI groups.Significance. The proposed neurofeedback game operates based on attention and aims to improve multiple cognitive functions, including attention and working memory, in patients with stroke and MCI.Conclusions. The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.},
}
@article {pmid39981822,
year = {2025},
author = {Cai, Y and Li, Q and Banga, AK and Wesselmann, U and Zhao, C},
title = {Tetrodotoxin Delivery Pen Safely Uses Potent Natural Neurotoxin to Manage Severe Cutaneous Pain.},
journal = {Advanced healthcare materials},
volume = {14},
number = {9},
pages = {e2401549},
pmid = {39981822},
issn = {2192-2659},
support = {R61 NS123196/NS/NINDS NIH HHS/United States ; R01 GM144388/GM/NIGMS NIH HHS/United States ; R15GM139193/GM/NIGMS NIH HHS/United States ; R15 GM139193/GM/NIGMS NIH HHS/United States ; R61NS123196/NS/NINDS NIH HHS/United States ; R01GM144388/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; *Tetrodotoxin/administration &amp; dosage/pharmacology/therapeutic use ; Rats ; Rats, Sprague-Dawley ; Male ; *Skin/drug effects ; *Neurotoxins/pharmacology/administration &amp; dosage ; *Drug Delivery Systems ; *Pain/drug therapy ; Sodium Dodecyl Sulfate/chemistry ; },
abstract = {Clinically available therapies often inadequately address severe chronic cutaneous pain due to short anesthetic duration, insufficient intensity, or side effects. This study introduces a pen device delivering tetrodotoxin (TTX), a potent neurotoxin targeting nerve voltage-gated sodium channels, as a safe and effective topical anesthetic to treat severe chronic cutaneous pain. Chemical permeation enhancers, such as sodium dodecyl sulfate (SDS) and limonene (LIM), are incorporated to enhance TTX skin permeability. The device ensures precise TTX dosing down to the nanogram level, essential to avoid TTX overdose. In rats, the pen device treatment produces TTX-dose-dependent anesthetic effectiveness. An administration of 900 ng of TTX with SDS and LIM to the rat back skin produces a 393.25% increase (measurement limit) in the nociceptive skin pressure threshold, and the hypoalgesia lasts for 11.25 h, outperforming bupivacaine (28 µg), of which are 25.24% and under 1 h. Moreover, the pen device provides on-demand therapy for multiple treatments, consistently achieving prolonged anesthesia over ten sessions (1 treatment per day) without noted toxicity. Furthermore, a single topical administration of 16 µg of TTX exhibits no TTX-related toxicity in rats. The TTX delivery pen paves the way for clinical trials, offering a promising solution for severe cutaneous pain.},
}
@article {pmid39981403,
year = {2025},
author = {Liao, W and Miao, Z and Liang, S and Zhang, L and Li, C},
title = {A composite improved attention convolutional network for motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1543508},
pmid = {39981403},
issn = {1662-4548},
abstract = {INTRODUCTION: A brain-computer interface (BCI) is an emerging technology that aims to establish a direct communication pathway between the human brain and external devices. Motor imagery electroencephalography (MI-EEG) signals are analyzed to infer users' intentions during motor imagery. These signals hold potential for applications in rehabilitation training and device control. However, the classification accuracy of MI-EEG signals remains a key challenge for the development of BCI technology.<br><br>METHODS: This paper proposes a composite improved attention convolutional network (CIACNet) for MI-EEG signals classification. CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.<br><br>RESULTS: The CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. These results indicate that the CIACNet model's classification performance exceeds that of four other comparative models.<br><br>CONCLUSION: Experimental results demonstrate that the proposed CIACNet model has strong classification capabilities and low time cost. Removing one or more blocks results in a decline in the overall performance of the model, indicating that each block within the model makes a significant contribution to its overall effectiveness. These results demonstrate the ability of the CIACNet model to reduce time costs and improve performance in motor imagery brain-computer interface (MI-BCI) systems, while also highlighting its practical applicability.},
}
@article {pmid39981127,
year = {2025},
author = {Ghosh, S and Yadav, RK and Soni, S and Giri, S and Muthukrishnan, SP and Kumar, L and Bhasin, S and Roy, S},
title = {Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1532783},
pmid = {39981127},
issn = {1662-5161},
abstract = {Understanding how the brain encodes upper limb movements is crucial for developing control mechanisms in assistive technologies. Advances in assistive technologies, particularly Brain-machine Interfaces (BMIs), highlight the importance of decoding motor intentions and kinematics for effective control. EEG-based BMI systems show promise due to their non-invasive nature and potential for inducing neural plasticity, enhancing motor rehabilitation outcomes. While EEG-based BMIs show potential for decoding motor intention and kinematics, studies indicate inconsistent correlations with actual or planned movements, posing challenges for achieving precise and reliable prosthesis control. Further, the variability in predictive EEG patterns across individuals necessitates personalized tuning to improve BMI efficiency. Integrating multiple physiological signals could enhance BMI precision and reliability, paving the way for more effective motor rehabilitation strategies. Studies have shown that brain activity adapts to gravitational and inertial constraints during movement, highlighting the critical role of neural adaptation to biomechanical changes in creating control systems for assistive devices. This review aims to provide a comprehensive overview of recent progress in deciphering neural activity patterns associated with both physiological and assisted upper limb movements, highlighting avenues for future exploration in neurorehabilitation and brain-machine interface development.},
}
@article {pmid39980021,
year = {2025},
author = {Angulo-Sherman, IN and León-Domínguez, U and Martinez-Torteya, A and Fragoso-González, GA and Martínez-Pérez, MV},
title = {Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {30},
pmid = {39980021},
issn = {1743-0003},
support = {Not applicable//Universidad de Monterrey/ ; Not applicable//Universidad de Monterrey/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; *Electroencephalography ; Adult ; *Functional Laterality/physiology ; Young Adult ; Beta Rhythm/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Cortical Synchronization/physiology ; },
abstract = {BACKGROUND: Motor imagery based brain-computer interfaces (MI-BCIs) are systems that detect the mental rehearsal of movement from brain activity signals (EEG) for controlling devices that can potentiate motor neurorehabilitation. Considering the problem that MI proficiency requires training and it is not always achieved, EEG desirable features should be investigated to propose indicators of successful MI training.<br><br>METHODS: Nine healthy right-handed subjects trained with a MI-BCI for four sessions. In each session, EEG was recorded for 30 trials that consisted of a rest and a dominant-hand MI sequence, which were used for calibrating the system. Then, the subject participated in 160 trials in which a cursor was displaced on a screen by performing MI or relaxing to hit a target. The session's accuracy was calculated. For each trial from the calibration phase of the first session, the power spectral density (PSD) and the partial directed coherence (PDC) of the rest and MI EEG segments were obtained to estimate the event-related synchronization changes (ERS) and the connectivity patterns of the &#x3b8; , &#x3b1; , &#x3b2; and &#x3b3; bands that are associated with high BCI control (accuracy above 70% in at least one session). Finally, t-tests and rank-sum tests (p < 0.05 , with Benjamini-Hochberg correction) were used to compare the ERS/ERD and PDC values of subjects with high and low accuracy, respectively.<br><br>RESULTS: Proficient users showed greater &#x3b1; ERD on the right-hand motor cortex (left hemisphere). Furthermore, the &#x3b2; PDC related to the ipsilateral motor cortex is commonly weakened during motor imagery, while the contralateral motor cortex &#x3b3; PDC is enhanced.<br><br>CONCLUSIONS: Motor imagery proficiency is related to the focused and lateralized event-related &#x3b1; desynchronization patterns and the lateralization of &#x3b2; and &#x3b3; PDC. Future analysis of these features could allow complimenting the information for assessment of subject-specific BCI control and the prediction of the effectiveness of motor-imagery training.},
}
@article {pmid39979463,
year = {2025},
author = {Bhadra, K and Giraud, AL and Marchesotti, S},
title = {Learning to operate an imagined speech Brain-Computer Interface involves the spatial and frequency tuning of neural activity.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {271},
pmid = {39979463},
issn = {2399-3642},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Learning/physiology ; Imagination/physiology ; Brain/physiology ; },
abstract = {Brain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to operate a binary BCI system based on electroencephalography (EEG) signals through syllable imagery for five consecutive days. Despite considerable interindividual variability in performance and learning, a significant improvement in BCI-control was globally observed. Using a control experiment, we show that a continuous feedback about the decoded activity is necessary for learning to occur. Performance improvement was associated with a broad EEG power increase in frontal theta activity and focal enhancement in temporal low-gamma activity, showing that learning to operate an imagined-speech BCI involves dynamic changes in neural features at different spectral scales. These findings demonstrate that combining machine and human learning is a successful strategy to enhance BCI controllability.},
}
@article {pmid39979351,
year = {2025},
author = {Gupta, E and Sivakumar, R},
title = {Response coupling with an auxiliary neural signal for enhancing brain signal detection.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {6227},
pmid = {39979351},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Brain/physiology ; Adult ; Male ; Signal Processing, Computer-Assisted ; Female ; Algorithms ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling, aimed at enhancing brain signal detection and reliability by pairing a brain signal with an artificially induced auxiliary signal and leveraging their interaction. Specifically, we use error-related potentials (ErrPs) as the primary signal and steady-state visual evoked potentials (SSVEPs) as the auxiliary signal. SSVEPs, known for their phase-locked responses to rhythmic stimuli, are selected because rhythmic neural activity plays a critical role in sensory and cognitive processes, with evidence suggesting that reinforcing these oscillations can improve neural performance. By exploring the interaction between these two signals, we demonstrate that response coupling significantly improves the detection accuracy of ErrPs, especially in the parietal and occipital regions. This method introduces a new paradigm for enhancing BCI performance, where the interaction between a primary and an auxiliary signal is harnessed to enhance the detection performance. Additionally, the phase-locking properties of SSVEPs allow for unsupervised rejection of suboptimal data, further increasing BCI reliability.},
}
@article {pmid39979293,
year = {2025},
author = {Hoeferlin, GF and Grabinski, SE and Druschel, LN and Duncan, JL and Burkhart, G and Weagraff, GR and Lee, AH and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Coleman, J and Li, L and Memberg, W and Sweet, J and Hamedani, HA and Acharya, AP and Hernandez-Reynoso, AG and Donskey, C and Jaskiw, G and Ricky Chan, E and Shoffstall, AJ and Bolu Ajiboye, A and von Recum, HA and Zhang, L and Capadona, JR},
title = {Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1829},
pmid = {39979293},
issn = {2041-1723},
support = {R01 NS131502/NS/NINDS NIH HHS/United States ; R25 CA221718/CA/NCI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Microelectrodes ; Mice ; *Electrodes, Implanted/adverse effects ; *Brain-Computer Interfaces ; *Brain ; *Blood-Brain Barrier ; *Mice, Inbred C57BL ; Gastrointestinal Microbiome ; Male ; Brain-Gut Axis/physiology ; Bacteria ; Anti-Bacterial Agents/pharmacology ; },
abstract = {Brain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the brains of mice with implanted microelectrodes. These sequences changed over time. Mice treated with antibiotics showed a reduced presence of these bacteria and had a different inflammatory response, which temporarily improved microelectrode recording performance. However, long-term antibiotic use worsened performance and disrupted neurodegenerative pathways. Many bacterial sequences found were not present in the gut or in unimplanted brains. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.},
}
@article {pmid39978072,
year = {2025},
author = {Lim, MJR and Lo, JYT and Tan, YY and Lin, HY and Wang, Y and Tan, D and Wang, E and Naing Ma, YY and Wei Ng, JJ and Jefree, RA and Tseng Tsai, Y},
title = {The state-of-the-art of invasive brain-computer interfaces in humans: a systematic review and individual patient meta-analysis.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb88e},
pmid = {39978072},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electrodes, Implanted/trends ; },
abstract = {Objective.Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe challenges and opportunities in the iBCI field.Approach.Medline, EMBASE, PubMed and Cochrane databases were searched from inception until 13 April 2024. Primary studies reporting the use of iBCI in human subjects to restore function were included. Endpoints extracted include iBCI electrode type, iBCI implantation, decoder algorithm, iBCI effector, testing and training methodology and functional outcomes. Narrative synthesis of outcomes was done with a focus on hardware and software development trends over time. Individual patient data (IPD) was also collected and an IPD meta-analysis was done to identify factors significant to iBCI performance.Main results.93 studies involving 214 patients were included in this systematic review. The median task performance accuracy for cursor control tasks was 76.00% (Interquartile range [IQR] = 21.2), for motor tasks was 80.00% (IQR = 23.3), and for communication tasks was 93.27% (IQR = 15.3). Current advances in iBCI software include use of recurrent neural network architectures as decoders, while hardware advances such as intravascular stentrodes provide a less invasive alternative for neural recording. Challenges include the lack of standardized testing paradigms for specific functional outcomes and issues with portability and chronicity limiting iBCI usage to laboratory settings.Significance.Our systematic review demonstrated the exponential rate at which iBCIs have evolved over the past two decades. Yet, more work is needed for widespread clinical adoption and translation to long-term home-use.},
}
@article {pmid39976033,
year = {2025},
author = {Li, L and Li, B and Wang, G and Li, S and Li, X and Santos, J and González, AM and Guo, L and Tu, Y and Qin, Y},
title = {Research on Precision Medicine AI Algorithm for Neuro Immune Gastrointestinal Diseases based on Quantum Biochemistry and Computational Cancer Genetics.},
journal = {Current pharmaceutical biotechnology},
volume = {},
number = {},
pages = {},
doi = {10.2174/0113892010348489241210060447},
pmid = {39976033},
issn = {1873-4316},
abstract = {OBJECTIVE: The objective of this study is to conduct network toxicology analysis based on smoking habits and develop a simpler and more effective toxicology product ingestion control system.<br><br>BACKGROUND: Smoking behavior can affect the pathogenesis and prognosis of neuroimmune gastrointestinal diseases.<br><br>AIMS: The purpose of developing tools to assist clinical practice is to avoid the harm of cigarettes to the human body.<br><br>METHODS: Molecular dynamics method was used to elucidate the biophysical mechanism of TP53 gene mutation caused by harmful ingredients, and the signaling pathway of midbrain edge excitation was determined by molecular dynamics of nicotine and dopamine receptor D3. The possible involvement of nicotine in neuronal damage was determined through the molecular interaction between nicotine and ACHE. Molecular pathways were analyzed based on the aforementioned biological principles, developed artificial intelligence systems and brain computer interface systems.<br><br>RESULTS: Several signaling pathways were elucidated, and effective AI algorithms were developed.<br><br>CONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.},
}
@article {pmid39975237,
year = {2025},
author = {Cubillos, LH and Kelberman, MM and Mender, MJ and Hite, A and Temmar, H and Willsey, M and Kumar, NG and Kung, TA and Patil, PG and Chestek, C and Krishnan, C},
title = {Exploring Synergies in Brain-Machine Interfaces: Compression vs. Performance.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39975237},
issn = {2692-8205},
support = {R01 NS105132/NS/NINDS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
abstract = {Individuals with severe neurological injuries often rely on assistive technologies, but current methods have limitations in accurately decoding multi-degree-of-freedom (DoF) movements. Intracortical brain-machine interfaces (iBMIs) use neural signals to provide a more natural control method, but currently struggle with higher-DoF movements-something the brain handles effortlessly. It has been theorized that the brain simplifies high-DoF movement through muscle synergies, which link multiple muscles to function as a single unit. These synergies have been studied using dimensionality reduction techniques like principal component analysis (PCA), non-negative matrix factorization (NMF), and demixed PCA (dPCA) and successfully used to reduce noise and improve offline decoder stability in non-invasive applications. However, their effectiveness in improving decoding and generalizability for implanted recordings across varied tasks is unclear. Here, we evaluated if brain and muscle synergies can enhance iBMI performance in non-human primates performing a two-DoF finger task. Specifically, we tested if PCA, dPCA, and NMF could compress and denoise brain and muscle data and improve decoder generalization across tasks. Our results showed that while all methods effectively compressed data with minimal loss in decoding accuracy, none improved performance through denoising. Additionally, none of the methods enhanced generalization across tasks. These findings suggest that while dimensionality reduction can aid data compression, alone it may not reveal the "true" control space needed to improve decoder performance or generalizability. Further research is required to determine whether synergies are the optimal control framework or if alternative approaches are required to enhance decoder robustness in iBMI applications.},
}
@article {pmid39974888,
year = {2025},
author = {Letner, JG and Lam, JLW and Copenhaver, MG and Barrow, M and Patel, PR and Richie, JM and Lee, J and Kim, HS and Cai, D and Weiland, JD and Phillips, J and Blaauw, D and Chestek, CA},
title = {A method for efficient, rapid, and minimally invasive implantation of individual non-functional motes with penetrating subcellular-diameter carbon fiber electrodes into rat cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39974888},
issn = {2692-8205},
support = {R01 NS118606/NS/NINDS NIH HHS/United States ; RF1 MH120005/MH/NIMH NIH HHS/United States ; RF1 NS128667/NS/NINDS NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; },
abstract = {OBJECTIVE: Distributed arrays of wireless neural interfacing chips with 1-2 channels each, known as "neural dust", could enhance brain machine interfaces (BMIs) by removing the wired connection through the scalp and increasing biocompatibility with their submillimeter size. Although several approaches for neural dust have emerged, a procedure for implanting them in batches that builds upon the safety and performance of currently used electrodes remains to be demonstrated.<br><br>APPROACH: Here, we demonstrate the feasibility of implanting batches of wireless motes that rest on the cortical surface with carbon fiber electrodes of subcellular diameter (6.8-8.4 &#x3bc;m) that penetrate to a target brain depth of 1 mm without insertion aids. To simulate their implantation, we assembled more than 230 mechanically equivalent motes and affixed them to insertion tools with polyethylene glycol (PEG), a quickly dissolvable and biocompatible material. Then, we implanted mote grids of multiple configurations into rat cortex in vivo and evaluated insertion success and their arrangement on the brain surface using photos and videos captured during their implantation.<br><br>MAIN RESULTS: When placing motes onto the insertion device, we found that they aggregated in molten PEG such that the array pitch was only 5% wider than the dimensions of the mote bases themselves (240 &#xd7; 240 &#x3bc;m). Overall, we found that motes with this arrangement could be inserted into rat cortex with a high success rate, as 171/186 (92%) motes in 4&#xd7;4 (N=4) and 5&#xd7;5 (N=5) square grid configurations were successfully inserted using the insertion device alone. After implantation, measurements of how much motes tilted (22&#xb1;9&#xb0;, X&#x304;&#xb1;S) and had been displaced relative to their original positions were smaller than those measured for devices implanted inside the brain in the literature.<br><br>SIGNIFICANCE: Collectively, these data establish the viability of safely implementing motes with ultrasmall electrodes and epicortically-situated chips for use in future BMIs.},
}
@article {pmid39973870,
year = {2025},
author = {Kanagaluru, V and M, S},
title = {Artificial intelligence based BCI using SSVEP signals with single channel EEG.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {9287329241302740},
doi = {10.1177/09287329241302740},
pmid = {39973870},
issn = {1878-7401},
abstract = {BACKGROUND: Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices. Steady-state visual-evoked potentials (SSVEPs) are particularly useful in BCIs because of their rapid communication capabilities and minimal calibration requirements. Although SSVEP-based BCIs are highly effective, traditional classification methods face challenges in maintaining high accuracy with minimal EEG channels, especially in real-world applications. There is a growing need for improved classification techniques to enhance performance and efficiency.<br><br>OBJECTIVE: The aim of this research is to improve the classification of SSVEP signals using machine-learning algorithms. This involves extracting dominant frequency features from SSVEP data and applying classifiers such as Decision Tree (DT), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) to achieve high accuracy while reducing the number of EEG channels required, making the method practical for BCI applications.<br><br>METHODS: SSVEP data were collected from the Benchmark Dataset at Tsinghua BCI Lab using 64 EEG channels per subject. The Oz channel was selected as the dominant channel for analysis. Wavelet decomposition (db4) was used to extract frequency features in the range 7.8 Hz to 15.6 Hz. The frequency of the maximum amplitude within a 5-s window was extracted as the key feature, and machine learning models (DT, LDA, and SVM) were applied to classify these features.<br><br>RESULTS: The proposed method achieved a high classification accuracy, with 95.8% for DT and 96.7% for both LDA and SVM. These results show significant improvement over existing methods, indicating the potential of this approach for BCI applications.<br><br>CONCLUSION: This study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.},
}
@article {pmid39970032,
year = {2025},
author = {M, AL and R, R},
title = {Comprehensive analysis of prefrontal cortex-directional rhythms categorization for rehabilitation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2025.2467460},
pmid = {39970032},
issn = {1476-8259},
abstract = {Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.},
}
@article {pmid39969013,
year = {2025},
author = {Magalhães, SS and Lucas-Ochoa, AM and Gonzalez-Cuello, AM and Fernández-Villalba, E and Pereira Toralles, MB and Herrero, MT},
title = {The mind-machine connection: adaptive information processing and new technologies promoting mental health in older adults.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {},
number = {},
pages = {10738584251318948},
doi = {10.1177/10738584251318948},
pmid = {39969013},
issn = {1089-4098},
abstract = {The human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to learn and adapt to new technologies such as BCIs, but both (human brain and BCI) demonstrate adaptability in their responses. The human brain is skilled at quickly switching between tasks and regulating emotions, while BCIs can modify signal-processing algorithms to accommodate changes in brain activity. Furthermore, the human brain and BCI participate in knowledge acquisition; the first one strengthens cognitive abilities through exposure to new experiences, and the second one improves performance through ongoing adjustment and improvement. Current research seeks to incorporate emotional states into BCI systems to improve the user experience, despite the exceptional emotional regulation abilities of the human brain. The implementation of BCIs for older adults could be more effective, inclusive, and beneficial in improving their quality of life. This review aims to improve the understanding of brain-machine interfaces and their implications for mental health in older adults.},
}
@article {pmid39968680,
year = {2025},
author = {Hao, W and Yang, S and Sheng, Y and Ye, C and Han, L and Zhou, Z and Cui, W},
title = {Efficient expression of recombinant proteins in Bacillus subtilis using a rewired gene circuit of quorum sensing.},
journal = {Biotechnology progress},
volume = {},
number = {},
pages = {e70007},
doi = {10.1002/btpr.70007},
pmid = {39968680},
issn = {1520-6033},
support = {32171420//National Natural Science Foundation of China/ ; KLIB-KF202307//Open Project of Key Laboratory of Industrial Biotechnology, Ministry of Education/ ; 2023YFC3402402//National Key Research and Development Program of China/ ; },
abstract = {Bacillus subtilis is a favored chassis for high productivity of several high value-added product in synthetic biology. Efficient production of recombinant proteins is critical but challenging using this chassis because these expression systems in use, such as constitutive and inducible expression systems, demand for coordination of cell growth with production and addition of chemical inducers. These systems compete for intracellular resources with the host, eventually resulting in dysfunction of cell survival. To overcome the problem, in this study, LuxRI quorum sensing (QS) system from Aliivibrio fischeri was functionally reconstituted in B. subtilis for achieving coordinated protein overproduction with cell growth in a cell-density-dependent manner. Furthermore, the output-controlling promoter, PluxI, was engineered through two rounds of evolution, by which we identified four mutants, P22, P47, P56, and P58 that exhibited elevated activity compared to the original PluxI. By incorporating a strong terminator (TB5) downstream of the target gene further enhanced expression level. The expression level of this system surpasses commonly used promoter-based systems in B. subtilis like P43 and PylbP. The LuxRI QS system proves to be a potent platform for recombinant protein overproduction in B. subtilis.},
}
@article {pmid39965558,
year = {2025},
author = {Persson, AC and Eeg-Olofsson, M and Sadeghi, A and Lepp, M},
title = {Patients' Experiences of an Active Transcutaneous Implant: The Bone Conduction Implant.},
journal = {Audiology &amp; neuro-otology},
volume = {},
number = {},
pages = {1-12},
doi = {10.1159/000544774},
pmid = {39965558},
issn = {1421-9700},
abstract = {INTRODUCTION: The aim of this qualitative study was to explore and describe patients' experiences of using and living with the bone conduction implant (BCI).<br><br>METHODS: Semi-structured interviews were conducted with 10 BCI users and analyzed according to the phenomenographic approach.<br><br>RESULTS: Four conceptual themes were formed during the analysis; (1) conceptions of the process receiving the BCI, (2) conceptions of handling the BCI on a daily basis, (3) conceptions of hearing with the BCI, and (4) conceptions of health care issues related to the BCI. The participants' statements include experiences of improved hearing and self-esteem by using the BCI. Noisy situations and not being able to hear in daily life situations causes frustrations. The participants described anxiety about consequences following an MRI examination. The audio processor is easy to handle but the fact that it is not waterproof raise concerns. Despite some frustration and concerns, participants state that the audio processor has become a part of them, and they cannot imagine being without it.<br><br>CONCLUSION: The ability to hear and communicate with other people has a great impact on the participants' daily life quality, and their statements show the importance hearing has on their lives and how they perceive themselves. The BCI seems to be a good hearing rehabilitation alternative for the participants, and they state that the audio processor is easy to use and handle.},
}
@article {pmid39963381,
year = {2025},
author = {Wang, N and He, Y and Zhu, S and Liu, D and Chai, X and He, Q and Cao, T and He, J and Li, J and Si, J and Yang, Y and Zhao, J},
title = {Functional near-infrared spectroscopy for the assessment and treatment of patients with disorders of consciousness.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1524806},
pmid = {39963381},
issn = {1664-2295},
abstract = {BACKGROUND: Advances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.<br><br>AIM: This review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.<br><br>METHODS: A systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.<br><br>RESULTS: Analysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).<br><br>CONCLUSION: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.},
}
@article {pmid39957485,
year = {2025},
author = {Yi, L and Jiang, T and Ren, R and Cao, J and Edel, JB and Ivanov, AP and Tang, L},
title = {Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e202501941},
doi = {10.1002/anie.202501941},
pmid = {39957485},
issn = {1521-3773},
support = {21874119//National Natural Science Foundation of China/ ; 62127818//National Natural Science Foundation of China/ ; LR22F050003//Zhejiang Provincial Outstanding Youth Science Foundation/ ; 724300//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 875525//European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme/ ; 600322/05//Analytical Chemistry Trust Fund grant/ ; EP/V049070/1//Engineering and Physical Sciences Research Council/ ; },
abstract = {Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN)6 [3-/4-]) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN)6 [3-/4-] concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.},
}
@article {pmid39957341,
year = {2025},
author = {Zhai, H and Li, P and Wang, H and Wang, X},
title = {Temperature and steric hindrance-regulated selective synthesis of ketamine derivatives and 2-aryl-cycloketone-1-carboxamides via nucleophilic substitution and Favorskii rearrangement.},
journal = {Organic &amp; biomolecular chemistry},
volume = {23},
number = {11},
pages = {2704-2711},
doi = {10.1039/d4ob02039a},
pmid = {39957341},
issn = {1477-0539},
abstract = {A selective temperature and steric hindrance-regulated method for nucleophilic substitution or Favorskii rearrangement reactions of 2-aryl-2-bromo-cycloketones with aliphatic amines has been developed to prepare ketamine derivatives and 2-aryl-cycloketone-1-carboxamides. In the presence of secondary amines or ortho-substituted 2-aryl-2-bromocycloketones, steric hindrance directs the Favorskii rearrangement to occur. Conversely, with primary amines, the product ratio of nucleophilic substitution to Favorskii rearrangement is temperature-dependent, with higher temperatures favoring the Favorskii rearrangement. At lower temperatures (-25 °C or below), nucleophilic substitution predominates, yielding ketamine derivatives in yields of 60% to 85%. This method effectively utilizes temperature and steric hindrance to control the reaction pathway and optimize product formation.},
}
@article {pmid39957214,
year = {2025},
author = {Chinta, B and Pampana, M and M, M},
title = {An efficient deep learning approach for automatic speech recognition using EEG signals.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-21},
doi = {10.1080/10255842.2025.2456982},
pmid = {39957214},
issn = {1476-8259},
abstract = {Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.},
}
@article {pmid39956854,
year = {2025},
author = {Zou, W and Fan, Y and Liu, J and Cheng, H and Hong, H and Al-Sheikh, U and Li, S and Zhu, L and Li, R and He, L and Tang, YQ and Zhao, G and Zhang, Y and Wang, F and Zhan, R and Zheng, X and Kang, L},
title = {Anoctamin-1 is a core component of a mechanosensory anion channel complex in C. elegans.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1680},
pmid = {39956854},
issn = {2041-1723},
support = {P40 OD010440/OD/NIH HHS/United States ; 31771113//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31471023//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ22C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 2021ZD0203303//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Animals ; *Caenorhabditis elegans/metabolism/genetics ; *Mechanotransduction, Cellular ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Anoctamin-1/metabolism/genetics ; Humans ; Touch/physiology ; Neurons/metabolism ; Chloride Channels/metabolism/genetics ; },
abstract = {Mechanotransduction channels are widely expressed in both vertebrates and invertebrates, mediating various physiological processes such as touch, hearing and blood-pressure sensing. While previously known mechanotransduction channels in metazoans are primarily cation-selective, we identified Anoctamin-1 (ANOH-1), the C. elegans homolog of mammalian calcium-activated chloride channel ANO1/TMEM16A, as an essential component of a mechanosensory channel complex that contributes to the nose touch mechanosensation in C. elegans. Ectopic expression of either C. elegans or human Anoctamin-1 confers mechanosensitivity to touch-insensitive neurons, suggesting a cell-autonomous role of ANOH-1/ANO1 in mechanotransduction. Additionally, we demonstrated that the mechanosensory function of ANOH-1/ANO1 relies on CIB (calcium- and integrin- binding) proteins. Thus, our results reveal an evolutionarily conserved chloride channel involved in mechanosensory transduction in metazoans, highlighting the importance of anion channels in mechanosensory processes.},
}
@article {pmid39954182,
year = {2025},
author = {Jangir, G and Joshi, N and Purohit, G},
title = {Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {5},
pmid = {39954182},
issn = {2198-4018},
abstract = {Human brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is, statistically analyzed to understand the nature of data and outliers in it. Effectiveness of pre-processing algorithm is then visualized. The cleaned dataset has dual polarity and gaussian distribution nature which makes Tanh activation function suitable for the neural network BC4D4 model. BC4D4 uses Convolutional neural network for feature extraction, dense neural network for pattern identification and incorporating dropout &amp; regularization making the proposed model more resilient. Our model outperforms the state of the art work on the dataset 4 achieving 0.85 correlation value that is 1.85X (Winner of BCI competition 4, 2012) &amp; 1.25X (Finger Flex model, 2022).},
}
@article {pmid39953165,
year = {2025},
author = {Jordan, S and Buchmann, M and Loss, J and Okan, O},
title = {[Health literacy and health behaviour-insights into a developing field of research and action for public health].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {68},
number = {3},
pages = {255-263},
pmid = {39953165},
issn = {1437-1588},
mesh = {*Health Literacy ; Germany ; Humans ; *Health Behavior ; *Public Health ; *Health Promotion/methods ; Health Services Research ; Exercise/psychology ; },
abstract = {The research and action field of health literacy and health behaviour is increasingly differentiating. General health literacy is established and focuses on population-based studies. Specific health literacy for health behaviour offers topic-related starting points for interventions and public health strategies.There are various concepts, definitions and measurement instruments for general health literacy and specific health literacy in the areas of nutrition and physical activity. These differ in terms of the levels of action and areas of application of health literacy.Most studies show a positive association between health literacy and various health behaviours. Higher health literacy is more often associated with improved health-promoting behaviour. This applies to both general as well as specific health literacy regarding nutrition and exercise (physical activity). Some studies found no correlation for certain behaviours, while others only found correlations for certain groups, which may be due to the different measuring instruments and research contexts. This points to the importance of always considering the interaction between behaviour and circumstances in order to improve the fit between the individual and the everyday demands of dealing with health information.The behavioural and cultural insights (BCI) approach can provide insights into how to promote health literacy with regard to various health behaviours, individual barriers and facilitators that arise from life circumstances and conditions, and that take social practice into account. BCI and health literacy complement each other and have the potential to make strategies for improving health behaviour more effective and targeted.},
}
@article {pmid39950750,
year = {2024},
author = {Jiang, E and Huang, T and Yin, X},
title = {A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.},
journal = {Journal of medical engineering &amp; technology},
volume = {48},
number = {7},
pages = {262-275},
doi = {10.1080/03091902.2025.2463577},
pmid = {39950750},
issn = {1464-522X},
mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; *Fuzzy Logic ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Algorithms ; Bayes Theorem ; },
abstract = {Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose notable challenges, particularly in mental tasks like motor imagery. Introducing fuzzy components can enhance the system's ability to withstand noisy environments. The emergence of deep learning has significantly impacted artificial intelligence and data analysis, prompting extensive exploration into assessing and understanding brain signals. This work introduces a hybrid series architecture called FCLNET, which combines Compact-CNN to extract frequency and spatial features alongside the LSTM network for temporal feature extraction. The activation functions in the CNN architecture were implemented using type-2 fuzzy functions to tackle uncertainties. Hyperparameters of the FCLNET model are tuned by the Bayesian optimisation algorithm. The efficacy of this approach is assessed through the BCI Competition IV-2a database and the BCI Competition IV-1 database. By incorporating type-2 fuzzy activation functions and employing Bayesian optimisation for tuning, the proposed architecture indicates good classification accuracy compared to the literature. Outcomes showcase the exceptional achievements of the FCLNET model, suggesting that integrating fuzzy units into other classifiers could lead to advancements in motor imagery-based BCI systems.},
}
@article {pmid39949790,
year = {2025},
author = {Ding, F and Ying, Y and Jin, Y and Guo, X and Xu, Y and Yu, Z and Jiang, H},
title = {Reduced frontotemporal connectivity during a verbal fluency task in patients with anxiety, sleep, and major depressive disorders.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1542346},
pmid = {39949790},
issn = {1664-2295},
abstract = {BACKGROUND: It has been well established that psychiatric disorders are often accompanied by cognitive dysfunction. Previous studies have investigated the verbal fluency task (VFT) for detecting executive function impairment in different psychiatric disorders, but the sensitivity and specificity of this task in different psychiatric disorders have not been explored. Furthermore, clarifying the mechanisms underlying variations in executive function impairments across multiple psychiatric disorders will enhance our comprehension of brain activity alternations among these disorders. Therefore, this study combined the VFT and the functional near-infrared spectroscopy (fNIRS) to investigate the neural mechanisms underlying the impairment of executive function across psychiatric disorders including anxiety disorder (AD), sleep disorder (SD) and major depressive disorder (MDD).<br><br>METHODS: Two hundred and eight participants were enrolled including 52 AD, 52 SD, 52 MDD and 52 healthy controls (HCs). All participants completed the VFT while being monitored using fNIRS to measure changes in brain oxygenated hemoglobin (Oxy-Hb).<br><br>RESULTS: Our results demonstrated that MDD, AD and SD exhibited decreased overall connectivity strength, as well as reduced connected networks involving the frontal and temporal regions during the VFT comparing to HC. Furthermore, the MDD group showed a reduction in connected networks, specifically in the left superior temporal gyrus and precentral gyrus, compared to the AD group.<br><br>CONCLUSION: Our study offers neural evidence that the VFT combined with fNIRS could effectively detect executive function impairment in different psychiatric disorders.},
}
@article {pmid39948616,
year = {2025},
author = {Gedela, NSS and Radawiec, RD and Salim, S and Richie, J and Chestek, C and Draelos, A and Pelled, G},
title = {In vivo electrophysiology recordings and computational modeling can predict octopus arm movement.},
journal = {Bioelectronic medicine},
volume = {11},
number = {1},
pages = {4},
pmid = {39948616},
issn = {2332-8886},
abstract = {The octopus has many features that make it advantageous for revealing principles of motor circuits and control and predicting behavior. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100 ms after stimulation were predictive of the resultant movement response. Machine learning models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. For kinematic analysis, deep learning models and unsupervised dimensionality reduction identified a consistent set of features that could be used to distinguish different types of arm movements. The neural circuits and the computational models identified here generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit. This study demonstrates how real-time motor behaviors can be predicted and distinguished, contributing to the development of brain-machine interfaces. The ability to accurately model and predict complex movement patterns has broad implications for advancing technologies in robotics, neuroprosthetics, and artificial intelligence, paving the way for more sophisticated and adaptable systems.},
}
@article {pmid39947717,
year = {2025},
author = {Huang-Fu, HQ and Wang, L and Karmacharya, B and Koirala, UK and Ke, CW and Liang, D and Hao, C and Lai, YS},
title = {Spatial profiling of geographical accessibility to maternal healthcare and coverage of maternal health service utilisation in Nepal: a geospatial analysis based on demographic and health survey.},
journal = {BMJ global health},
volume = {10},
number = {2},
pages = {},
pmid = {39947717},
issn = {2059-7908},
mesh = {Humans ; Nepal ; *Health Services Accessibility/statistics &amp; numerical data ; *Maternal Health Services/statistics &amp; numerical data ; Female ; Pregnancy ; *Spatial Analysis ; Adult ; Bayes Theorem ; Health Surveys ; Patient Acceptance of Health Care/statistics &amp; numerical data ; Prenatal Care/statistics &amp; numerical data ; Young Adult ; Middle Aged ; Adolescent ; },
abstract = {BACKGROUND: Information on geographical accessibility to maternal healthcare (MHC) and coverage of maternal health service utilisation at high spatial resolution in Nepal are important for evidence-based health planning.<br><br>METHODS: Based on the Nepal Health Facility Registry dataset in 2022, we measured the geographical accessibility to MHC facilities across Nepal. Using data from 2022 Nepal Demographic and Health Survey and other sources, we assessed the relationships between geographical accessibility and the utilisation of the three major healthcare services (ie, four or more antenatal care (ANC) visits, institutional delivery and postnatal care (PNC) check-up), by applying Bayesian geostatistical models. High-resolution maps on coverage of the above services were produced.<br><br>RESULTS: The geographical accessibility showed high in the central and southern Terai belt but low in the northern mountains, with average travel-mode adjusted travel time for ANC, institutional delivery and PNC 26.74, 40.72 and 29.09&#x2009;min, respectively. Negative correlations were found between geographical accessibility with four or more ANC visits (OR 0.76, 95% Bayesian credible interval, BCI 0.65 to 0.90), institutional delivery (OR 0.76, 95% BCI 0.64 to 0.90) and PNC check-up (OR 0.87, 95% BCI 0.76 to 0.99), respectively. Population-weighted coverages for four or more ANC visits, institutional delivery and PNC check-up were estimated 83.25% (95% BCI 80.43% to 85.35%), 84.26% (95% BCI 81.30% to 86.08%) and 73.19% (95% BCI 69.43% to 76.09%), respectively, across Nepal. The northern mountains and southeastern Terai showed low coverage for the three healthcare services, while the central, eastern and western hilly regions exhibited good coverage.<br><br>CONCLUSION: Geographical accessibility is important in utilisation of maternal health services in Nepal. The high-resolution maps enable an evidence-based assessment for better health planning.},
}
@article {pmid39947551,
year = {2025},
author = {Lyu, Y and Yu, L and Qi, L and Meng, J and Wang, Y and Liu, Y and Xue, T and Zhi, C},
title = {Construction of 3D-fabric-based triple-decker agar/sodium alginate/Ca[2+] dual-network composite for wound dressing.},
journal = {International journal of biological macromolecules},
volume = {304},
number = {Pt 2},
pages = {140883},
doi = {10.1016/j.ijbiomac.2025.140883},
pmid = {39947551},
issn = {1879-0003},
mesh = {*Alginates/chemistry ; *Bandages ; *Wound Healing/drug effects ; *Agar/chemistry ; *Calcium/chemistry ; Animals ; *Anti-Bacterial Agents/pharmacology/chemistry ; *Staphylococcus aureus/drug effects ; Escherichia coli/drug effects ; Nanofibers/chemistry ; Mice ; },
abstract = {This study designed a novel multifunctional Janus structure dressing (DNCD dressing) composed of spacer fabric, agar/sodium alginate/calcium ion dual-network aerogel, methylene blue, and AgNO3-added thermoplastic polyurethane nanofiber membrane. The unidirectional liquid transport and absorbency tests prove that the DNCD dressing can unidirectionally transport liquids within just two seconds and possesses a liquid absorption ratio of 875.3 %. The unique open structure formed by the spacer fabric and liquid transport channels provides excellent air permeability as well as a suitable water vapor transmission rate, reaching 584.96 mm/s, 10.3 L/min, and 1104.82 g/m[2]/24 h, respectively. The exceptional compressive strength (216.78 kPa) and compressive modulus (515.23 kPa) of the dressing can provide protection for the wound. Antibacterial tests demonstrate that the silver ion-added DNCD dressing can eradicate >99 % of Escherichia coli and Staphylococcus aureus, while the added methylene blue can effectively monitor the survival status of bacteria. The low BCI value and the hemolysis ratio of <5 % indicate that the DNCD dressing has a certain hemostatic ability and does not cause hemolysis. The results of cytotoxicity tests and full-thickness skin defect models show that the DNCD dressing has good cytocompatibility and the potential to promote wound healing.},
}
@article {pmid39947091,
year = {2025},
author = {Zhao, H and Xu, S},
title = {Associations between panic buying and choice overload during the public health crisis in China: Testing sequential mediation models.},
journal = {Acta psychologica},
volume = {254},
number = {},
pages = {104800},
doi = {10.1016/j.actpsy.2025.104800},
pmid = {39947091},
issn = {1873-6297},
mesh = {Humans ; China/epidemiology ; *COVID-19/psychology/epidemiology ; Adult ; Male ; Female ; *Choice Behavior/physiology ; *Consumer Behavior ; Cross-Sectional Studies ; Panic ; Young Adult ; Middle Aged ; Motivation ; Surveys and Questionnaires ; Public Health ; Adolescent ; Fear ; },
abstract = {Using the COVID-19 pandemic in China as the background, the current study investigated the association between panic buying behavior and consumers' choice overload during the public health crisis, and provided the empirical evidence on the dual sequential mediating pathways from the theoretical perspective of Protection Motivation Theory and Compensatory Control Theory. Through the cross-sectional online anonymous survey method, 492 samples were collected during the COVID-19 pandemic in China when the lockdown measure and the static management were implemented. Our results identified that the fear of being infected and the perceived threat sequentially mediated the effect of the panic buying on the choice overload, and the fear of being infected and the perceived control sequentially mediated the effect of the panic buying on the choice overload during the public health crisis. Machine learning algorithms also further identified the predictive effect of all feature variables on the choice overload during the public health crisis. Our findings provided a new perspective on the understanding of consumers' behavior during the public health crisis, and further extended the application of the Protection Motivation Theory and the Compensatory Control Theory to the consumer behavior research.},
}
@article {pmid39947044,
year = {2025},
author = {Zhu, L and Xin, Y and Yang, Y and Kong, W},
title = {A multi-layer EEG fusion decoding method with channel selection for multi-brain motor imagery.},
journal = {Computer methods and programs in biomedicine},
volume = {262},
number = {},
pages = {108595},
doi = {10.1016/j.cmpb.2025.108595},
pmid = {39947044},
issn = {1872-7565},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Brain/physiology ; Algorithms ; Discriminant Analysis ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain Mapping/methods ; },
abstract = {Traditional motor imagery-based single-brain computer interfaces(BCIs) face inherent limitations, such as unstable signals and low recognition accuracy. In contrast, multi-brain BCIs offer a promising solution by leveraging group electroencephalography (EEG) data. This paper presents a novel multi-layer EEG fusion method with channel selection for motor imagery-based multi-brain BCIs. We utilize mutual information convergent cross-mapping (MCCM) to identify channels that the represent causal relationships between brains; this strategy is combined with multiple linear discriminant analysis (MLDA) for decoding intentions via both data-layer and decision-layer strategies. Our experimental results demonstrate that the proposed method improves the accuracy of multi-brain motor imagery decoding by approximately 10% over that of the traditional methods, with a further 3%-5% accuracy increase due to the effective channel selection mechanism.},
}
@article {pmid39946849,
year = {2025},
author = {Zhou, Z and Hu, Z and Lyu, H},
title = {A 0.53-μW/channel calibration-free spike detection IC with 98.8-%-accuracy based on stationary wavelet transforms and Teager energy operators.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb5c4},
pmid = {39946849},
issn = {1741-2552},
mesh = {*Wavelet Analysis ; *Action Potentials/physiology ; Humans ; *Neurons/physiology ; Brain-Computer Interfaces ; Calibration ; Algorithms ; Reproducibility of Results ; },
abstract = {Objective. The brain-computer interface is currently experiencing a surge in the number of recording channels, resulting in a vast amount of raw data. It has become crucial to reliably detect neural spikes from a large population of neurons in the presence of noise, in order to constrain the transmission bandwidth.Approach. We investigate various time-frequency analysis methods for spike detection, followed by an exploration of energy operators amplifying spikes and signal statistics for adaptive thresholding. Subsequently, we introduce a precise and computationally efficient spike detection module, leveraging stationary wavelet transform (SWT), Teager energy operator, and root-mean-square calculator. This module is capable of autonomously adapting to different levels of noise. The SWT effectively eliminates high-frequency noise, enhancing the performance of the energy operators. The hardware computational process is simplified through the use of the lifting scheme and a channel-interleaving architecture.Main results. We evaluate the proposed spike detector with adaptive threshold on the publicly available WaveClus datasets. The detector achieves an average accuracy of 98.84%. The application-specific integrated circuit (ASIC) implementation results of the spike detector demonstrate an optimized interleaving channel of 8. In a 65 nm technology, the 8-channel spike detector consumes a power of 0.532μW Ch[-1]and occupies an area of 0.00645 mm[2]Ch[-1], operating at a 1.2 V supply voltage.Significance. The proposed spike detection processor offers one of the highest accuracies among state-of-the-art spike detection methods. Importantly, the ASIC explores the considerations in the scalability and hardware costs. The proposed design provides a systematic solution on spike detection with adaptive thresholding, offering a high accuracy while maintaining low power and area consumptions.},
}
@article {pmid39946786,
year = {2025},
author = {Dutta, S and Goswami, S and Debnath, S and Adhikary, S and Majumder, A},
title = {MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment.},
journal = {Computers in biology and medicine},
volume = {188},
number = {},
pages = {109795},
doi = {10.1016/j.compbiomed.2025.109795},
pmid = {39946786},
issn = {1879-0534},
mesh = {Humans ; *Music ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiology ; Male ; Female ; Adult ; Image Processing, Computer-Assisted/methods ; Brain-Computer Interfaces ; },
abstract = {Functional magnetic resonance imaging is a popular non-invasive brain-computer interfacing technique to monitor brain activities corresponding to several physical or neurological responses by measuring blood flow changes at different brain parts. Recent studies have shown that blood flow within the brain can have signature activity patterns in response to various musical genres. However, limited studies exist in the state of the art for automatized recognition of the musical genres from functional magnetic resonance imaging. This is because the feasibility of obtaining these kinds of data is limited, and currently available open-sourced data is insufficient to build an accurate deep-learning model. To solve this, we propose a prototypical model agnostic meta-learning framework for accurately classifying musical genres by studying blood flow dynamics using functional magnetic resonance imaging. A test with open-sourced data collected from 20 human subjects with consent for 6 different mental states resulted in up to 97.25 ± 1.38% accuracy by training with only 30 samples surpassing state-of-the-art methods. Further, a detailed evaluation of the performances confirms the model's reliability.},
}
@article {pmid39946752,
year = {2025},
author = {Gherman, DE and Krol, LR and Klug, M and Zander, TO},
title = {An investigation of a passive BCI's performance for different body postures and presentation modalities.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adb58b},
pmid = {39946752},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Posture ; Male ; Female ; Adult ; *Virtual Reality ; Young Adult ; Brain/physiology ; User-Computer Interface ; Movement ; Artifacts ; },
abstract = {Passive brain-computer interfaces (passive BCIs, pBCIs) enable computers to unobtrusively decipher aspects of a user's mental state in real time from recordings of brain activity, e.g. electroencephalography (EEG). When used during human-computer interaction (HCI), this allows a computer to dynamically adapt for enhancing the subjective user experience. For transitioning from controlled laboratory environments to practical applications, understanding BCI performance in real contexts is of utmost importance. Here, Virtual Reality (VR) can play a unique role: both as a fully controllable simulation of a realistic environment and as an independent, increasingly popular real application. Given the potential of VR as a dynamic and controllable environment, and the capability of pBCIs to enable novel modes of interaction, it is tempting to envision a future where pBCI and VR are seamlessly integrated. However, the simultaneous use of these two technologies-both of which are head-mounted-presents new challenges. Due to their immediate proximity, electromagnetic artifacts can arise, contaminating the EEG. Furthermore, the active movements promoted by VR can induce mechanical and muscular artifacts in the EEG. The varying body postures and display preferences of users further complicate the practical application of pBCIs. To address these challenges, the current study investigates the influence of body posture (sitting Versus standing) and display media (computer screen Versus VR) on the performance of a pBCI in assessing cognitive load. Our results show that these conditions indeed led to some changes in the EEG data; nevertheless, the ability of pBCIs to detect cognitive load remained largely unaffected. However, when a classifier trained in one context (body posture or modality) was applied to another (e.g., cross-task application), reductions in classification accuracy were observed. As HCI moves towards increasingly adaptive and more interactive designs, these findings support the expansive potential of pBCIs in VR contexts.},
}
@article {pmid39945188,
year = {2025},
author = {Zhang, R and Sui, L and Shen, C and Xu, L and Cao, J},
title = {EEG-based real-time BCI system using drones for attention visualization.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/10255842.2025.2459272},
pmid = {39945188},
issn = {1476-8259},
abstract = {Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.},
}
@article {pmid39943246,
year = {2025},
author = {Zare, S and Beaber, SI and Sun, Y},
title = {NeuroFlex: Feasibility of EEG-Based Motor Imagery Control of a Soft Glove for Hand Rehabilitation.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {3},
pages = {},
pmid = {39943246},
issn = {1424-8220},
support = {2135620//National Science Foundation/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Hand/physiology ; *Brain-Computer Interfaces ; Robotics/instrumentation/methods ; Movement/physiology ; Hand Strength/physiology ; Feasibility Studies ; Male ; Stroke Rehabilitation/methods/instrumentation ; Imagination/physiology ; },
abstract = {Motor impairments resulting from neurological disorders, such as strokes or spinal cord injuries, often impair hand and finger mobility, restricting a person's ability to grasp and perform fine motor tasks. Brain plasticity refers to the inherent capability of the central nervous system to functionally and structurally reorganize itself in response to stimulation, which underpins rehabilitation from brain injuries or strokes. Linking voluntary cortical activity with corresponding motor execution has been identified as effective in promoting adaptive plasticity. This study introduces NeuroFlex, a motion-intent-controlled soft robotic glove for hand rehabilitation. NeuroFlex utilizes a transformer-based deep learning (DL) architecture to decode motion intent from motor imagery (MI) EEG data and translate it into control inputs for the assistive glove. The glove's soft, lightweight, and flexible design enables users to perform rehabilitation exercises involving fist formation and grasping movements, aligning with natural hand functions for fine motor practices. The results show that the accuracy of decoding the intent of fingers making a fist from MI EEG can reach up to 85.3%, with an average AUC of 0.88. NeuroFlex demonstrates the feasibility of detecting and assisting the patient's attempted movements using pure thinking through a non-intrusive brain-computer interface (BCI). This EEG-based soft glove aims to enhance the effectiveness and user experience of rehabilitation protocols, providing the possibility of extending therapeutic opportunities outside clinical settings.},
}
@article {pmid39941230,
year = {2025},
author = {Alshehri, H and Al-Nafjan, A and Aldayel, M},
title = {Decoding Pain: A Comprehensive Review of Computational Intelligence Methods in Electroencephalography-Based Brain-Computer Interfaces.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {15},
number = {3},
pages = {},
pmid = {39941230},
issn = {2075-4418},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-)//The Research, Development, and Innovation Authority 611 (RDIA) - Kingdom of Saudi Arabia/ ; },
abstract = {Objective pain evaluation is crucial for determining appropriate treatment strategies in clinical settings. Studies have demonstrated the potential of using brain-computer interface (BCI) technology for pain classification and detection. Collating knowledge and insights from prior studies, this review explores the extensive work on pain detection based on electroencephalography (EEG) signals. It presents the findings, methodologies, and advancements reported in 20 peer-reviewed articles that utilize machine learning and deep learning (DL) approaches for EEG-based pain detection. We analyze various ML and DL techniques, support vector machines, random forests, k-nearest neighbors, and convolution neural network recurrent neural networks and transformers, and their effectiveness in decoding pain neural signals. The motivation for combining AI with BCI technology lies in the potential for significant advancements in the real-time responsiveness and adaptability of these systems. We reveal that DL techniques effectively analyze EEG signals and recognize pain-related patterns. Moreover, we discuss advancements and challenges associated with EEG-based pain detection, focusing on BCI applications in clinical settings and functional requirements for effective pain classification systems. By evaluating the current research landscape, we identify gaps and opportunities for future research to provide valuable insights for researchers and practitioners.},
}
@article {pmid39937921,
year = {2025},
author = {Qiu, S and Tang, Y and Yu, H and Xie, H and Dreher, JC and Hu, Y and Zhou, X},
title = {Toward a computational understanding of bribe-taking behavior.},
journal = {Annals of the New York Academy of Sciences},
volume = {1545},
number = {1},
pages = {5-15},
doi = {10.1111/nyas.15294},
pmid = {39937921},
issn = {1749-6632},
support = {32200853//National Natural Science Foundation of China/ ; 71942001//National Natural Science Foundation of China/ ; 2021ZD0200500//STI2023-Major Projects/ ; 23ZR1418400//National Science Foundation of Shanghai/ ; 2022ECNUXWK-XK003//Fundamental Research Funds for the Central Universities/ ; ANR-16-IDEX-0005//Agence Nationale de la Recherche/ ; ANR-11-LABX-0042//Agence Nationale de la Recherche/ ; ANR-11-IDEX-007//Agence Nationale de la Recherche/ ; ANR-21-CE37-0032//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; *Decision Making/physiology ; Morals ; },
abstract = {Understanding how corrupt behavior occurs is a critical issue at the intersection of behavioral ethics, social psychology, and other related social sciences, laying the foundation for establishing effective anticorruption policies. Despite a substantial body of studies focused on bribe-taking behavior-a typical form of corruption-and its modulators, its underlying psychological processes remain poorly understood. Drawing inspiration from recent literature on neuroeconomics and moral decision-making, we argue that bribe-taking decision-making involves a value-based computational process that can be characterized by a computational framework. We show how this framework advances our understanding of bribe-taking decision-making by (1) clarifying how the cost-benefit tradeoff determines the decision to accept or reject a bribe and its neural foundations, (2) improving the prediction of bribe-taking behaviors across contexts and individuals, and (3) enhancing our comprehension of individual differences in bribe-taking behaviors. Moreover, we delineate how this framework can benefit future research on bribery by examining the mechanisms through which various modulators impact the bribe-taking behaviors or the computational processes underlying more intricate forms of corrupt behaviors. We also discussed its potential fusion with artificial intelligence techniques in offering insights for understanding cognitive processes underlying bribe-taking behaviors and designing anticorruption strategies.},
}
@article {pmid39935682,
year = {2025},
author = {Yasuhara, M and Nambu, I},
title = {Error-related potentials during multitasking involving sensorimotor control: an ERP and offline decoding study for brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1516721},
pmid = {39935682},
issn = {1662-5161},
abstract = {Humans achieve efficient behaviors by perceiving and responding to errors. Error-related potentials (ErrPs) are electrophysiological responses that occur upon perceiving errors. Leveraging ErrPs to improve the accuracy of brain-computer interfaces (BCIs), utilizing the brain's natural error-detection processes to enhance system performance, has been proposed. However, the influence of external and contextual factors on the detectability of ErrPs remains poorly understood, especially in multitasking scenarios involving both BCI operations and sensorimotor control. Herein, we hypothesized that the difficulty in sensorimotor control would lead to the dispersion of neural resources in multitasking, resulting in a reduction in ErrP features. To examine this, we conducted an experiment in which participants were instructed to keep a ball within a designated area on a board, while simultaneously attempting to control a cursor on a display through motor imagery. The BCI provided error feedback with a random probability of 30%. Three scenarios-without a ball (single-task), lightweight ball (easy-task), and heavyweight ball (hard-task)-were used for the characterization of ErrPs based on the difficulty of sensorimotor control. In addition, to examine the impact of multitasking on ErrP-BCI performance, we analyzed single-trial classification accuracy offline. Contrary to our hypothesis, varying the difficulty of sensorimotor control did not result in significant changes in ErrP features. However, multitasking significantly affected ErrP classification accuracy. Post-hoc analyses revealed that the classifier trained on single-task ErrPs exhibited reduced accuracy under hard-task scenarios. To our knowledge, this study is the first to investigate how ErrPs are modulated in a multitasking environment involving both sensorimotor control and BCI operation in an offline framework. Although the ErrP features remained unchanged, the observed variation in accuracy suggests the need to design classifiers that account for task load even before implementing a real-time ErrP-based BCI.},
}
@article {pmid39935311,
year = {2025},
author = {Wen, X and Xue, P and Zhu, M and Zhong, J and Yu, W and Ma, S and Liu, Y and Liu, P and Jing, B and Yang, M and Mo, X and Zhang, D},
title = {Alteration in Cortical Structure Mediating the Impact of Blood Oxygen-Carrying Capacity on Gross Motor Skills in Infants With Complex Congenital Heart Disease.},
journal = {Human brain mapping},
volume = {46},
number = {3},
pages = {e70155},
pmid = {39935311},
issn = {1097-0193},
support = {62476129//National Natural Science Foundation of China/ ; 81970265//National Natural Science Foundation of China/ ; 82270310//National Natural Science Foundation of China/ ; NZ2024040//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Motor Skills/physiology ; Male ; Female ; *Cerebral Cortex/diagnostic imaging/physiopathology/growth &amp; development ; *Heart Defects, Congenital/physiopathology ; Infant ; *Magnetic Resonance Imaging ; Child, Preschool ; Oxygen/blood ; },
abstract = {Congenital heart disease (CHD) is the most common congenital anomaly, leading to an increased risk of neurodevelopmental abnormalities in many children with CHD. Understanding the neurological mechanisms behind these neurodevelopmental disorders is crucial for implementing early interventions and treatments. In this study, we recruited 83 infants aged 12-26.5 months with complex CHD, along with 86 healthy controls (HCs). We collected multimodal data to explore the abnormal patterns of cerebral cortex development and explored the complex interactions among blood oxygen-carrying capacity, cortical development, and gross motor skills. We found that, compared to healthy infants, those with complex CHD exhibit significant reductions in cortical surface area development, particularly in the default mode network. Most of these developmentally abnormal brain regions are significantly correlated with the blood oxygen-carrying capacity and gross motor skills of infants with CHD. Additionally, we further discovered that the blood oxygen-carrying capacity of infants with CHD can indirectly predict their gross motor skills through cortical structures, with the left middle temporal area and left inferior temporal area showing the greatest mediation effects. This study identified biomarkers for neurodevelopmental disorders and highlighted blood oxygen-carrying capacity as an indicator of motor development risk, offering new insights for the clinical management CHD.},
}
@article {pmid39935127,
year = {2025},
author = {Xu, Q and Wang, L and Xi, Y and Ruan, T and Cao, J and Xu, M and Zheng, K and Du, Z and Wei, N and Wang, X and Yang, B and Liu, J},
title = {An Efficient MEMS Microelectrode Array with Reliable Interelectrode Insulation Processes for In Vivo Neural Recording.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {21},
number = {10},
pages = {e2407950},
doi = {10.1002/smll.202407950},
pmid = {39935127},
issn = {1613-6829},
support = {2022ZD0208601//STI 2030-Major Projects/ ; 2022ZD0208600//STI 2030-Major Projects/ ; 2022YFB3207301//National Key R&amp;D Program of China/ ; 2022YFF120301//National Key R&amp;D Program of China/ ; XDA25040100//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040200//Strategic Priority Research Program of Chinese Academy of Sciences/ ; XDA25040300//Strategic Priority Research Program of Chinese Academy of Sciences/ ; 62474109 42127807-03//National Natural Science Foundation of China/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21TQ1400203//Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University/ ; SL2023ZD205//Oceanic Interdisciplinary Program of Shanghai Jiao Tong University/ ; 21&#xd7;010301627//SJTU Trans-med Award/ ; //Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Microelectrodes ; Animals ; Mice ; Micro-Electrical-Mechanical Systems/instrumentation ; Neurons/physiology ; Electrodes, Implanted ; },
abstract = {Microelectrode arrays, particularly Utah arrays, offer irreplaceable advantages in clinical applications and play a crucial role in advancing brain-computer interactions. However, the glass-fused monolithic structure of Utah arrays limits functional expansion, and the glass insulation process is complex, costly, and time-intensive. This paper presents a microelectrode array with a simple and time-saving fabrication process, utilizing low-resistance silicon and borosilicate glass wafers as electrodes and insulation substrates, respectively. The utilization of the anodic bonding process improves production efficiency and enhances process compatibility. A one-step static wet etching process is used to form microneedle morphology to further simplify the fabrication process. Sputtered iridium oxide, as the electrode interface material, significantly reduces electrochemical impedance, and cellular experiments have confirmed its non-cytotoxicity. Moreover, the implantation into the primary visual cortex of mice has demonstrated the ability of the electrode to record in vivo electrical signals within 15 days. Movement trajectory experiments demonstrate that the mice exhibit good behavior activities following electrode implantation. The bonded microelectrode array (BMEA) presented in this work provides a universal and effective tool for neural recording, with prospective applications in multi-physiological monitoring and microelectromechanical system integration.},
}
@article {pmid39935067,
year = {2025},
author = {Yao, Y and Ahnood, A and Chambers, A and Tong, W and Prawer, S},
title = {Nitrogen-Doped Ultrananocrystalline Diamond - Optoelectronic Biointerface for Wireless Neuronal Stimulation.},
journal = {Advanced healthcare materials},
volume = {14},
number = {9},
pages = {e2403901},
pmid = {39935067},
issn = {2192-2659},
support = {2029454//National Health and Medical Research Council/ ; DE220100302//Australian Research Council/ ; DE210102750//Australian Research Council/ ; },
mesh = {Animals ; *Diamond/chemistry ; *Nitrogen/chemistry ; Rats ; *Neurons/physiology ; Retina ; *Wireless Technology ; Semiconductors ; Light ; },
abstract = {This study presents a semiconducting optoelectronic system for light-controlled non-genetic neuronal stimulation using visible light. The system architecture is entirely wireless, comprising a thin film of nitrogen-doped ultrananocrystalline diamond directly grown on a semiconducting silicon substrate. When immersed in a physiological medium and subjected to pulsed illumination in the visible (595 nm) or near-infrared wavelength (808 nm) range, charge accumulation at the device-medium interface induces a transient ionic displacement current capable of electrically stimulating neurons with high temporal resolution. With a measured photoresponsivity of 7.5 mA W[-1], the efficacy of this biointerface is demonstrated through optoelectronic stimulation of degenerate rat retinas using 595 nm irradiation, pulse durations of 50-500 ms, and irradiance levels of 1.1-4.3 mW mm[-2], all below the safe ocular threshold. This work presents the pioneering utilization of a diamond-based optoelectronic platform, capable of generating sufficiently large photocurrents for neuronal stimulation in the retina.},
}
@article {pmid39934334,
year = {2025},
author = {Chen, Y and Hu, J and Zhao, P and Fang, J and Kuang, Y and Liu, Z and Dong, S and Yao, W and Ding, Y and Wang, X and Pan, Y and Wu, J and Zhao, J and Yang, J and Xu, Z and Liu, X and Zhang, Y and Wu, C and Zhang, L and Fan, M and Feng, S and Hong, Z and Yan, Z and Xia, H and Tang, K and Yang, B and Liu, W and Sun, Q and Mei, K and Zou, W and Huang, Y and Feng, D and Yi, C},
title = {Rpl12 is a conserved ribophagy receptor.},
journal = {Nature cell biology},
volume = {27},
number = {3},
pages = {477-492},
pmid = {39934334},
issn = {1476-4679},
support = {32122028//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92254307//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32070739//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100600//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32370822//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970919//National Natural Science Foundation of China (National Science Foundation of China)/ ; LR21C070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Caenorhabditis elegans/metabolism/genetics ; *Drosophila melanogaster/metabolism/genetics ; *Ribosomal Proteins/metabolism/genetics ; *Drosophila Proteins/metabolism/genetics ; *Caenorhabditis elegans Proteins/metabolism/genetics ; *Autophagy ; Phosphorylation ; Ribosomes/metabolism/genetics ; RNA, Ribosomal/metabolism/genetics ; Autophagy-Related Protein 8 Family/metabolism/genetics ; Autophagy-Related Proteins/metabolism/genetics ; Longevity/genetics ; Starvation/metabolism/genetics ; Aging/metabolism/genetics ; Autophagy-Related Protein-1 Homolog ; },
abstract = {Ribophagy is a selective autophagic process that regulates ribosome turnover. Although NUFIP1 has been identified as a mammalian receptor for ribophagy, its homologues do not exist in yeast and nematodes. Here we demonstrate that Rpl12, a ribosomal large subunit protein, functions as a conserved ribophagy receptor in multiple organisms. Disruption of Rpl12-Atg8s binding leads to significant accumulation of ribosomal proteins and rRNA, while Atg1-mediated Rpl12 phosphorylation enhances its association with Atg11, thus triggering ribophagy during starvation. Ribophagy deficiency accelerates cell death induced by starvation and pathogen infection, leading to impaired growth and development and a shortened lifespan in both Caenorhabditis elegans and Drosophila melanogaster. Moreover, ribophagy deficiency results in motor impairments associated with ageing, while the overexpression of RPL12 significantly improves movement defects induced by starvation, ageing and Aβ accumulation in fly models. Our findings suggest that Rpl12 functions as a conserved ribophagy receptor vital for ribosome metabolism and cellular homeostasis.},
}
@article {pmid39933011,
year = {2025},
author = {Jin, X and Jiang, M and Qian, L and Tao, L and Yang, Y and Xing, L and Qian, Z and Li, W},
title = {Effect of 433 MHz double-slot microwave antennas for double-zone ablation in ex vivo swine liver experiment.},
journal = {PloS one},
volume = {20},
number = {2},
pages = {e0315678},
pmid = {39933011},
issn = {1932-6203},
mesh = {Animals ; *Microwaves ; *Liver/surgery ; Swine ; Ablation Techniques/methods/instrumentation ; Finite Element Analysis ; Equipment Design ; Computer Simulation ; },
abstract = {PURPOSE: To evaluate the effects of axial length and slot-to-slot distance of double-slot microwave antenna (DSMA) with frequency of 433 MHz on the size and shape of ablation zones created under different input microwave powers.<br><br>MATERIALS AND METHODS: The design of double slot microwave antennas (DSMAs) with axial lengths (70&#x2009;mm, 30&#x2009;mm) and slot-to-slot distance (49&#x2009;mm, 10&#x2009;mm) were optimized by numerical simulation and ex vivo liver experiments. Finite-element method simulations and forty ablations of swine liver were employed to obtain the temperature distributions within liver tissue using DSMAs at the 433 MHz operating frequency in a range of heating powers (20, 30, 40 and 50W) for 600&#x2009;s. The dependence of the effectiveness of MWA on the axial length and slot-to-slot distance of antenna as well as the input power was further evaluated by analyzing morphologic characteristics of ablated zone.<br><br>RESULTS: Two-zone ablation was achieved by two types of double-slot antennas in our study with frequency of 433 MHz, and the observed shapes of ex vivo experimental ablation zones were in good agreement with patterns predicted by simulation models. The ablation zone exhibited a 'gourd' shape after the treatment using the antenna with longer axial length and slot-to-slot distance, while the short antenna caused a guitar-shape ablation in liver tissue after MWA.<br><br>CONCLUSION: The dedicated design of our DSMAs with a frequency of 433 MHz could enable new ablation shapes with controllable dimensions, which can be applied to the clinical treatment of MWA for gourd-shaped liver tumors and other long-shaped tumors. Furthermore, research can be conducted on how to design the antenna as flexible and use it for the treatment of pulmonary nodules or varicose veins.},
}
@article {pmid39930808,
year = {2025},
author = {Cao, M and Zhu, S and Tang, E and Xue, C and Li, K and Yu, H and Zhong, T and Li, T and Chen, H and Deng, W},
title = {Neural correlates of emotional processing in trauma-related narratives.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e33},
doi = {10.1017/S0033291724003398},
pmid = {39930808},
issn = {1469-8978},
mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Male ; Adult ; Female ; Case-Control Studies ; *Emotions/physiology ; *Prefrontal Cortex/physiopathology/diagnostic imaging ; *Spectroscopy, Near-Infrared ; Narration ; Young Adult ; Middle Aged ; Natural Language Processing ; Psychological Trauma/physiopathology/diagnostic imaging ; },
abstract = {BACKGROUND: Post-traumatic stress disorder (PTSD) is a mental health condition caused by the dysregulation or overgeneralization of memories related to traumatic events. Investigating the interplay between explicit narrative and implicit emotional memory contributes to a better understanding of the mechanisms underlying PTSD.<br><br>METHODS: This case-control study focused on two groups: unmedicated patients with PTSD and a trauma-exposed control (TEC) group who did not develop PTSD. Experiments included real-time measurements of blood oxygenation changes using functional near-infrared spectroscopy during trauma narration and processing of emotional and linguistic data through natural language processing (NLP).<br><br>RESULTS: Real-time fNIRS monitoring showed that PTSD patients (mean [SD] Oxy-Hb activation, 0.153 [0.084], 95% CI 0.124 to 0.182) had significantly higher brain activity in the left anterior medial prefrontal cortex (L-amPFC) within 10&#xa0;s after expressing negative emotional words compared with the control group (0.047 [0.026], 95% CI 0.038 to 0.056; p&#xa0;<&#xa0;0.001). In the control group, there was a significant time-series correlation between the use of negative emotional memory words and activation of the L-amPFC (latency 3.82&#xa0;s, slope&#xa0;=&#xa0;0.0067, peak value&#xa0;=&#xa0;0.184, difference&#xa0;=&#xa0;0.273; Spearman's r&#xa0;=&#xa0;0.727, p&#xa0;<&#xa0;0.001). In contrast, the left anterior cingulate prefrontal cortex of PTSD patients remained in a state of high activation (peak value&#xa0;=&#xa0;0.153, difference&#xa0;=&#xa0;0.084) with no apparent latency period.<br><br>CONCLUSIONS: PTSD patients display overactivity in pathways associated with rapid emotional responses and diminished regulation in cognitive processing areas. Interventions targeting these pathways may alleviate symptoms of PTSD.},
}
@article {pmid39927921,
year = {2025},
author = {Chen, J and Ke, Y and Ni, G and Liu, S and Ming, D},
title = {Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neurom.2025.01.003},
pmid = {39927921},
issn = {1525-1403},
abstract = {BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV).<br><br>OBJECTIVE: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task.<br><br>MATERIALS AND METHODS: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks.<br><br>RESULTS: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV.<br><br>CONCLUSIONS: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions.},
}
@article {pmid39926788,
year = {2024},
author = {Sah, SK and Taksande, V and Jadhav, D and Maurya, AT},
title = {Exploring the Impact of Brain-Computer Interfaces on Health Care: Innovations, Challenges, and Future Prospects: A Review Article.},
journal = {Journal of pharmacy &amp; bioallied sciences},
volume = {16},
number = {Suppl 4},
pages = {S3037-S3040},
pmid = {39926788},
issn = {0976-4879},
abstract = {Brain-Computer Interfaces (BCIs) are an innovative technology that methods with a great possibility to revolutionize the sphere of medicine with the help of integration of human brain and external devices. In this article, we discuss how BCIs can be incorporated into hospitals and civil rehabilitation centers, possibly for rehabilitation, communication, and cognitive treatments. This review aims to discuss the advancement, usefulness, difficulties, and potential in regards to the use of BCIs in healthcare. We describe trends in the development of BCIs from simple experimental paradigms to multimedia advanced devices and their usage in clinical practice: assistive technology in patients with motor disorders, neurorehabilitation of post-stroke patients, and cognitive prosthesis for humans with neurodegenerative diseases. The article also emphasizes on present-day issues including signal quality, comfort level of the users, and the ethical parameter of the technique along with the research going on and future work streams. Thus, by evaluating the modern developments in the field and highlighting the existing problems, this article will try to give a briefing on the current stage of application of BCIs in the sphere of healthcare.},
}
@article {pmid39926014,
year = {2025},
author = {Li, C and Xu, Y and Feng, T and Wang, M and Zhang, X and Zhang, L and Cheng, R and Chen, W and Chen, W and Zhang, S},
title = {Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1532099},
pmid = {39926014},
issn = {1662-4548},
abstract = {INTRODUCTION: Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.<br><br>METHODS: Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG-EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.<br><br>RESULTS: Among the three functional connectivity methods investigated-coherence, Pearson correlation coefficient and mutual information (MI)-the MI-based EEG-EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (p&#x202f;>&#x202f;0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (p&#x202f;>&#x202f;0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.<br><br>CONCLUSION: Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.},
}
@article {pmid39925722,
year = {2025},
author = {Radwan, YA and Ahmed Mohamed, E and Metwalli, D and Barakat, M and Ahmed, A and Kiroles, AE and Selim, S},
title = {Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1484470},
pmid = {39925722},
issn = {1662-5161},
abstract = {The potential and utility of inner speech is pivotal for developing practical, everyday Brain-Computer Interface (BCI) applications, as it represents a type of brain signal that operates independently of external stimuli however it is largely underdeveloped due to the challenges faced in deciphering its signals. In this study, we evaluated the behaviors of various Machine Learning (ML) and Deep Learning (DL) models on a publicly available dataset, employing popular preprocessing methods as feature extractors to enhance model training. We face significant challenges like subject-dependent variability, high noise levels, and overfitting. To address overfitting in particular, we propose using "BruteExtraTree": a new classifier which relies on moderate stochasticity inherited from its base model, the ExtraTreeClassifier. This model not only matches the best DL model, ShallowFBCSPNet, in the subject-independent scenario in our experiments scoring 32% accuracy, but also surpasses the state-of-the-art by achieving 46.6% average per-subject accuracy in the subject-dependent case. Our results on the subject-dependent case show promise on the possibility of a new paradigm for using inner speech data inspired from LLM pretraining but we also highlight the crucial need for a drastic change in data recording or noise removal methods to open the way for more practical accuracies in the subject-independent case.},
}
@article {pmid39923980,
year = {2025},
author = {Ghasimi, A and Shamekhi, S},
title = {Enhanced EEG-based cognitive workload detection using RADWT and machine learning.},
journal = {Neuroscience},
volume = {569},
number = {},
pages = {231-244},
doi = {10.1016/j.neuroscience.2025.01.068},
pmid = {39923980},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Machine Learning ; Male ; *Wavelet Analysis ; Brain/physiology ; Adult ; Female ; Workload ; Young Adult ; Support Vector Machine ; },
abstract = {Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, brain-computer interfaces, and cognitive monitoring. In this work, different levels of cognitive workload are investigated, and a classification approach based on the Rational-Dilation Wavelet Transform (RADWT) is proposed. RADWT excels at capturing the oscillatory behavior of EEG signal sub-bands, offering high precision through its ability to adaptively analyze both temporal and spectral dynamics. Different classifications of machine learning and feature selection techniques were evaluated to get optimum classification accuracy and identify the most effective combination of features for the used dataset. The analysis shows that the most relevant brain region in differentiating cognitive workload levels is the frontal region, along with alpha and theta rhythm sub-bands. Integrating RADWT with a Linear Support Vector Machine (LSVM) and minimum Redundancy Maximum Relevance (mRMR) feature selection method yields notable classification accuracy. Concretely, the model yields accuracies of 96.6% for 0-back vs.3-back, 94.9% for 0-back vs 2-back, 92.3% for 2-back vs 3-back, and 81.7% for the three-class scenario. These results confirm the validity of the method proposed for estimating cognitive workload using the RADWT- and machine learning-based approach. The results also offer insights into neural mechanisms and a foundation for advanced applications in adaptive systems, brain-computer interfaces, and cognitive monitoring.},
}
@article {pmid39921840,
year = {2025},
author = {Huang, Y and Wang, J and Liu, N and Xu, H},
title = {Zona Incerta: A Bridge for Infant-Mother Interaction.},
journal = {Neuroscience bulletin},
volume = {41},
number = {5},
pages = {921-924},
pmid = {39921840},
issn = {1995-8218},
}
@article {pmid39921681,
year = {2025},
author = {Liyanagedera, ND and Bareham, CA and Kempton, H and Guesgen, HW},
title = {Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.},
journal = {Brain informatics},
volume = {12},
number = {1},
pages = {4},
pmid = {39921681},
issn = {2198-4018},
support = {1839RF//Neurological Foundation of New Zealand/ ; MAU2011//Royal Society of New Zealand Marsden Fast-Start/ ; },
abstract = {This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.},
}
@article {pmid39918096,
year = {2025},
author = {Chen, L and Yang, Z and Ji, S and Song, T and Li, H and Tang, Y and Chen, Y and Li, Y},
title = {Comparing the Risk of Epilepsy in Patients With Simple Congenital Heart Diseases: A Prospective Cohort Study.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {2},
pages = {e70230},
pmid = {39918096},
issn = {1755-5949},
support = {2022-I2M-C&amp;T-B-100//the Chinese Academy of Medical Sciences by Medical and Health Technology Innovation Project/ ; 2024NSFSC1652//the support from the Natural Science Foundation of Sichuan/ ; },
mesh = {Humans ; Female ; Male ; *Epilepsy/epidemiology ; *Heart Defects, Congenital/epidemiology/complications ; Prospective Studies ; Adult ; Cohort Studies ; Young Adult ; Adolescent ; Middle Aged ; Incidence ; Child ; Follow-Up Studies ; China/epidemiology ; Child, Preschool ; Risk Factors ; },
abstract = {AIMS: Simple congenital heart diseases (CHD) are associated with various central nervous system diseases, including epilepsy. This study aimed to compare the risk of epilepsy in patients with different types of simple CHD.<br><br>METHODS: In this prospective cohort study, from January 2008 to June 2022, patients with atrial septal defect (ASD), patent foramen ovale (PFO), ventricular septal defect (VSD), and patent ductus arteriosus (PDA) were recruited at the Registration Center of CHD in West China Hospital. Follow-up was conducted yearly until the diagnosis of epilepsy, loss to follow-up, or end of study. The outcomes included a comparison of epilepsy incidence according to different simple CHD types and a risk assessment of developing epilepsy. Multivariable Poisson regression was performed to adjusted factors of demographics and disease history.<br><br>RESULTS: Of 10,914 patients who met the inclusion criteria, 108 were diagnosed with epilepsy at an average follow-up of 2.19&#x2009;years. Epilepsy incidence in patients with PFO, VSD, PDA, and ASD was 8.58/1000, 4.85/1000, 3.98/1000, and 2.63/1000 person-years, respectively. Compared with ASD patients (reference group), the risk ratios (95% confidence intervals) in patients with PFO, VSD, and PDA were 3.28 (2.00-5.43), 1.47 (0.79-2.68), and 1.46 (0.70-2.82), respectively. Subgroup analyses determined that patients with simple CHD who underwent CHD surgery demonstrated a lower risk of epilepsy than those who did not.<br><br>CONCLUSION: Among the major types of simple CHD, PFO was associated with a significantly higher risk of epilepsy, while the risk was reduced in those who underwent PFO closure procedures.},
}
@article {pmid39914028,
year = {2025},
author = {Cernera, S and Gemicioglu, T and Berezutskaya, J and Csaky, R and Verwoert, M and Polyakov, D and Papadopoulos, S and Spagnolo, V and Astudillo, JG and Kumar, S and Alawieh, H and Kelly, D and Keough, JRG and Minhas, A and Dold, M and Han, Y and McClanahan, A and Mustafa, M and Gonzalez-Espana, JJ and Garro, F and Vujic, A and Kacker, K and Kapeller, C and Geukes, S and Verbaarschot, C and Wimmer, M and Sultana, M and Ahmadi, S and Herff, C and Sburlea, AI and Jeunet, C and Thompson, DE and Semprini, M and Andersen, R and Stavisky, S and Kinney-Lang, E and Lotte, F and Thielen, J and Chen, X and Peterson, V and Gunduz, A and Vaughan, T and Valeriani, D},
title = {Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb335},
pmid = {39914028},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography/methods ; },
abstract = {The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc &amp; Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.},
}
@article {pmid39914006,
year = {2025},
author = {Bakas, S and Ludwig, S and Adamos, DA and Laskaris, N and Panagakis, Y and Zafeiriou, S},
title = {Latent alignment in deep learning models for EEG decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb336},
pmid = {39914006},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/physiology ; Male ; Imagination/physiology ; Female ; Adult ; Sleep Stages/physiology ; Young Adult ; },
abstract = {Objective. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning distributions in the deep learning model's feature space is more effective for classification.Approach. We introduce the Latent Alignment method, which won the Benchmarks for EEG Transfer Learning competition. This method can be formulated as a deep set architecture applied to trials from a given subject, introducing deep sets to EEG decoding for the first time. We compare Latent Alignment to recent statistical domain adaptation techniques, carefully considering class-discriminative artifacts and the impact of class distributions on classification performance.Main results. Our experiments across motor imagery, sleep stage classification, and P300 event-related potential tasks validate Latent Alignment's effectiveness. We identify a trade-off between improved classification accuracy when alignment is performed at later modeling stages and increased susceptibility to class imbalance in the trial set used for statistical computation.Significance. Latent Alignment offers consistent improvements to subject-independent deep learning models for EEG decoding when relevant practical considerations are addressed. This work advances our understanding of statistical alignment techniques in EEG decoding and provides insights for their effective implementation in real-world BCI applications, potentially facilitating broader use of BCIs in healthcare, assistive technologies, and beyond. The model code is available athttps://github.com/StylianosBakas/LatentAlignment.},
}
@article {pmid39913287,
year = {2025},
author = {Ma, Q and Tian, JL and Lou, Y and Guo, R and Ma, XR and Wu, JB and Yang, J and Tang, BJ and Li, S and Qiu, M and Duan, S and Zhao, JW and Zhang, J and Xu, ZZ},
title = {Oligodendrocytes drive neuroinflammation and neurodegeneration in Parkinson's disease via the prosaposin-GPR37-IL-6 axis.},
journal = {Cell reports},
volume = {44},
number = {2},
pages = {115266},
doi = {10.1016/j.celrep.2025.115266},
pmid = {39913287},
issn = {2211-1247},
mesh = {Animals ; *Interleukin-6/metabolism ; *Oligodendroglia/metabolism/pathology ; *Receptors, G-Protein-Coupled/metabolism ; *Parkinson Disease/metabolism/pathology/genetics ; Mice ; Humans ; *Dopaminergic Neurons/metabolism/pathology ; *Saposins/metabolism ; Mice, Inbred C57BL ; Neuroinflammatory Diseases/metabolism/pathology ; Male ; Signal Transduction ; Substantia Nigra/metabolism/pathology ; Disease Models, Animal ; Nerve Degeneration/pathology/metabolism ; },
abstract = {Parkinson's disease (PD) is a common neurodegenerative disease and is difficult to treat due to its elusive mechanisms. Recent studies have identified a striking association between oligodendrocytes and PD progression, yet how oligodendrocytes regulate the pathogenesis of PD is still unknown. Here, we show that G-protein-coupled receptor 37 (GPR37) is upregulated in oligodendrocytes of the substantia nigra and that prosaposin (PSAP) secretion is increased in parkinsonian mice. The released PSAP can induce interleukin (IL)-6 upregulation and secretion from oligodendrocytes via a GPR37-dependent pathway, resulting in enhanced neuroinflammation, dopamine neuron degeneration, and behavioral deficits. GPR37 deficiency in oligodendrocytes prevents neurodegeneration in multiple PD models. Finally, the hallmarks of the PSAP-GPR37-IL-6 axis are observed in patients with PD. Thus, our results reveal that dopaminergic neurons interact with oligodendrocytes via secreted PSAP, and our findings identify the PSAP-GPR37-IL-6 axis as a driver of PD pathogenesis and a potential therapeutic target that might alleviate PD progression in patients.},
}
@article {pmid39912868,
year = {2025},
author = {Chen, P and Zhang, B and He, E and Xiao, Y and Liu, F and Lin, P and Wang, Z and Pan, G},
title = {Towards scalable memristive hardware for spiking neural networks.},
journal = {Materials horizons},
volume = {},
number = {},
pages = {},
doi = {10.1039/d4mh01676a},
pmid = {39912868},
issn = {2051-6355},
abstract = {Spiking neural networks (SNNs) represent a promising frontier in artificial intelligence (AI), offering event-driven, energy-efficient computation that mimics rich neural dynamics in the brain. However, running large-scale SNNs on mainstream computing hardware faces significant challenges to efficiently emulate these dynamical processes using synchronized and logical chips. Memristor based systems have recently demonstrated great potential for AI acceleration, sparking speculations and explorations of using these emerging devices for SNN tasks. This paper reviews the promises and challenges of memristive devices in SNN implementations, and our discussions are focused on the scaling and integration of neuronal and synaptic devices. We survey recent progress in device and circuit development, discuss possible pathways for chip-level integration, and finally probe into hardware-oriented algorithm designs. This review offers a system-level perspective on implementing scalable memristor based SNN platforms.},
}
@article {pmid39912736,
year = {2025},
author = {Huang, Z and Mei, T and Zhu, X and Xiao, K},
title = {Ionic Device: From Neuromorphic Computing to Interfacing with the Brain.},
journal = {Chemistry, an Asian journal},
volume = {20},
number = {7},
pages = {e202401170},
doi = {10.1002/asia.202401170},
pmid = {39912736},
issn = {1861-471X},
support = {B2401005//Shenzhen Medical Research Fund/ ; 22275079//National Natural Science Foundation of China/ ; 22474053//National Natural Science Foundation of China/ ; KQTD20221101093559017//Shenzhen Science and Technology Program/ ; JCYJ20230807093205011//Shenzhen Science and Technology Program/ ; 2024A1515012600//Guangdong Basic and Applied Basic Research Foundation/ ; 2022B1212010003//Guangdong Provincial Key Laboratory of Advanced Biomaterials/ ; //Starting Grant from Southern University of Science and Technology/ ; G03050K002//High level of special funds/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Brain/physiology ; Ions/chemistry ; Algorithms ; },
abstract = {In living organisms, the modulation of ion conductivity in ion channels of neuron cells enables intelligent behaviors, such as generating, transmitting, and storing neural signals. Drawing inspiration from these natural processes, researchers have fabricated ionic devices that replicate the functions of the nervous system. However, this field remains in its infancy, necessitating extensive foundational research in ionic device preparation, algorithm development, and biological interaction. This review summarizes recently developed neuromorphic ionic devices into three categories based on the materials states: liquid, semi-solid, and solid. The neural network algorithms embedded in these devices for neuromorphic computing are introduced, and future directions for the development of bidirectional human-computer interaction and hybrid human-computer intelligence are discussed.},
}
@article {pmid39911854,
year = {2025},
author = {Wu, X and Chu, Y and Li, Q and Luo, Y and Zhao, Y and Zhao, X},
title = {AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1540033},
pmid = {39911854},
issn = {1662-5218},
abstract = {Recently, electroencephalogram (EEG) based on motor imagery (MI) have gained significant traction in brain-computer interface (BCI) technology, particularly for the rehabilitation of paralyzed patients. But the low signal-to-noise ratio of MI EEG makes it difficult to decode effectively and hinders the development of BCI. In this paper, a method of attention-based multiscale EEGNet (AMEEGNet) was proposed to improve the decoding performance of MI-EEG. First, three parallel EEGNets with fusion transmission method were employed to extract the high-quality temporal-spatial feature of EEG data from multiple scales. Then, the efficient channel attention (ECA) module enhances the acquisition of more discriminative spatial features through a lightweight approach that weights critical channels. The experimental results demonstrated that the proposed model achieves decoding accuracies of 81.17, 89.83, and 95.49% on BCI-2a, 2b and HGD datasets. The results show that the proposed AMEEGNet effectively decodes temporal-spatial features, providing a novel perspective on MI-EEG decoding and advancing future BCI applications.},
}
@article {pmid39909352,
year = {2025},
author = {Li, X and Wei, W and Qian, L and Li, X and Li, M and Kakkos, I and Wang, Q and Yu, H and Guo, W and Ma, X and Matsopoulos, GK and Zhao, L and Deng, W and Sun, Y and Li, T},
title = {Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity.},
journal = {Brain research bulletin},
volume = {222},
number = {},
pages = {111238},
doi = {10.1016/j.brainresbull.2025.111238},
pmid = {39909352},
issn = {1873-2747},
mesh = {Humans ; *Bipolar Disorder/physiopathology/diagnostic imaging ; Male ; Female ; Adult ; *Magnetic Resonance Imaging/methods ; *Connectome/methods ; *Intelligence/physiology ; *Intelligence Tests ; Brain/physiopathology/diagnostic imaging ; Middle Aged ; Nerve Net/physiopathology/diagnostic imaging ; Young Adult ; Rest/physiology ; Neural Pathways/physiopathology/diagnostic imaging ; },
abstract = {BACKGROUND: Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.<br><br>METHODS: We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.<br><br>RESULTS: The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.<br><br>CONCLUSIONS: The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.},
}
@article {pmid39908388,
year = {2025},
author = {Xiao, Y and Liu, Y and Zhang, B and Chen, P and Zhu, H and He, E and Zhao, J and Huo, W and Jin, X and Zhang, X and Jiang, H and Ma, D and Zheng, Q and Tang, H and Lin, P and Kong, W and Pan, G},
title = {Bio-plausible reconfigurable spiking neuron for neuromorphic computing.},
journal = {Science advances},
volume = {11},
number = {6},
pages = {eadr6733},
pmid = {39908388},
issn = {2375-2548},
mesh = {*Neurons/physiology ; *Neural Networks, Computer ; *Action Potentials/physiology ; *Models, Neurological ; Humans ; },
abstract = {Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors due to high cost of emulating these biological spike patterns. Here, we propose a compact reconfigurable neuron design using the intrinsic dynamics of a NbO2-based spiking unit and excellent tunability in an electrochemical memory (ECRAM) to emulate the fast-slow dynamics in a bio-plausible neuron. The resistance of the ECRAM was effective in tuning the temporal dynamics of the membrane potential, contributing to flexible reconfiguration of various bio-plausible firing modes, such as phasic and burst spiking, and exhibiting adaptive spiking behaviors in changing environment. We used the bio-plausible neuron model to build spiking neural networks with bursting neurons and demonstrated improved classification accuracies over simplified models, showing great promises for use in more bio-plausible neuromorphic computing systems.},
}
@article {pmid39908280,
year = {2025},
author = {El-Osta, A and Al Ammouri, M and Khan, S and Altalib, S and Karki, M and Riboli-Sasco, E and Majeed, A},
title = {Community perspectives regarding brain-computer interfaces: A cross-sectional study of community-dwelling adults in the UK.},
journal = {PLOS digital health},
volume = {4},
number = {2},
pages = {e0000524},
pmid = {39908280},
issn = {2767-3170},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) represent a ground-breaking advancement in neuroscience, facilitating direct communication between the brain and external devices. This technology has the potential to significantly improve the lives of individuals with neurological disorders by providing innovative solutions for rehabilitation, communication and personal autonomy. However, despite the rapid progress in BCI technology and social media discussions around Neuralink, public perceptions and ethical considerations concerning BCIs-particularly within community settings in the UK-have not been thoroughly investigated.<br><br>OBJECTIVE: The primary aim of this study was to investigate public knowledge, attitudes and perceptions regarding BCIs including ethical considerations. The study also explored whether demographic factors were related to beliefs about BCIs increasing inequalities, support for strict regulations, and perceptions of appropriate fields for BCI design, testing and utilization in healthcare.<br><br>METHODS: This cross-sectional study was conducted between 1 December 2023 and 8 March 2024. The survey included 29 structured questions covering demographics, awareness of BCIs, ethical considerations and willingness to use BCIs for various applications. The survey was distributed via the Imperial College Qualtrics platform. Participants were recruited primarily through Prolific Academic's panel and personal networks. Data analysis involved summarizing responses using frequencies and percentages, with chi-squared tests to compare groups. All data were securely stored and pseudo-anonymized to ensure confidentiality.<br><br>RESULTS: Of the 950 invited respondents, 846 participated and 806 completed the survey. The demographic profile was diverse, with most respondents aged 36-45 years (26%) balanced in gender (52% female), and predominantly identifying as White (86%). Most respondents (98%) had never used BCIs, and 65% were unaware of them prior to the survey. Preferences for BCI types varied by condition. Ethical concerns were prevalent, particularly regarding implantation risks (98%) and costs (92%). Significant associations were observed between demographic variables and perceptions of BCIs regarding inequalities, regulation and their application in healthcare. Conclusion: Despite strong interest in BCIs, particularly for medical applications, ethical concerns, safety and privacy issues remain significant highlighting the need for clear regulatory frameworks and ethical guidelines, as well as educational initiatives to improve public understanding and trust. Promoting public discourse and involving stakeholders including potential users, ethicists and technologists in the design process through co-design principles can help align technological development with public concerns whilst also helping developers to proactively address ethical dilemmas.},
}
@article {pmid39908266,
year = {2025},
author = {Liu, X and Zhi, H and Czosnyka, M and Robba, C and Czosnyka, Z and Summers, JL and Yu, H and Tong, X and Gao, G and Xiao, G and Yu, K and Xing, Y and Mao, R and Yin, S and Chao, Y and Li, H and Pu, K and Feng, K and Pang, M and Ming, D},
title = {Advancing Hydrocephalus Management: Pathogenesis Insights, Therapeutic Innovations, and Emerging Challenges.},
journal = {Aging and disease},
volume = {},
number = {},
pages = {},
doi = {10.14336/AD.2024.1434},
pmid = {39908266},
issn = {2152-5250},
abstract = {Hydrocephalus is a prevalent neurological disorder, particularly impactful in older adults, characterized by high incidence and numerous complications that impose a significant burden on healthcare systems. This review aims to provide a comprehensive description of hydrocephalus pathogenesis, focusing on cellular and molecular insights derived from animal models. We also present the latest advances in hydrocephalus research and highlight potential therapeutic targets. Lastly, the review advocates the integration of findings from both animal and human studies to achieve better outcomes and examines the potential of emerging technologies. We wish to raise public attention about this disease in an aging society. Current animal models for hydrocephalus involve acquired hydrocephalus models and genetic/congenital hydrocephalus models. Studies from animals have shown that the main mechanisms of models can be broadly classified into nine types. A variety of drug-targeted therapy methods and non-surgical treatment methods have been used in clinical practice. But current treatment approaches primarily focus on symptomatic relief and intracranial pressure control rather than addressing the underlying pathological mechanisms. We call for the development of more accurate and representative animal models to achieve better outcomes and examine the potential of emerging technologies, such as artificial intelligence and neuroimaging. In summary, this review synthesizes recent findings in hydrocephalus research, identifies promising therapeutic targets and interventions, and critically evaluates the limitations of current research paradigms, aiming to align preclinical studies with clinical endpoints. Continued studies and multidisciplinary collaboration are essential to develop effective interventions and facilitate new treatments into bedside.},
}
@article {pmid39904453,
year = {2025},
author = {Liu, B and Wang, Y and Gao, L and Cai, Z},
title = {Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.},
journal = {Brain research},
volume = {1851},
number = {},
pages = {149484},
doi = {10.1016/j.brainres.2025.149484},
pmid = {39904453},
issn = {1872-6240},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Brain/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Imagination/physiology ; Attention/physiology ; },
abstract = {Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as nonlinearity, low signal-to-noise ratios, and large individual variations, present significant challenges for MI-EEG classification using traditional machine learning methods. To address these challenges, we propose an automatic feature extraction method rooted in deep learning for MI-EEG classification. First, original MI-EEG signals undergo noise reduction through discrete wavelet transform and common average reference. To reflect the regularity and specificity of brain neural activities, a convolutional neural network (CNN) is used to extract the time-domain features of MI-EEG. We also extracted spatial features to reflect the activity relationships and connection states of the brain in different regions. This process yields time series containing spatial information, focusing on enhancing crucial feature sequences through talking-heads attention. Finally, more abstract spatial-temporal features are extracted using a temporal convolutional network (TCN), and classification is done through a fully connected layer. Validation experiments based on the BCI Competition IV-2a dataset show that the enhanced EEG model achieves an impressive average classification accuracy of 85.53% for each subject. Compared with CNN, EEGNet, CNN-LSTM and EEG-TCNet, the classification accuracy of this model is improved by 11.24%, 6.90%, 11.18% and 6.13%, respectively. Our work underscores the potential of the proposed model to enhance intention recognition in MI-EEG significantly.},
}
@article {pmid39904383,
year = {2025},
author = {Shi, CY and Zhang, HT and Tang, Z},
title = {Large-sized trees regulating the structural diversity-productivity relationships through shaping different productive processes in a tropical forest.},
journal = {Proceedings. Biological sciences},
volume = {292},
number = {2040},
pages = {20242202},
pmid = {39904383},
issn = {1471-2954},
support = {//National Natural Science Foundation of China/ ; },
mesh = {*Trees/physiology ; *Rainforest ; *Biodiversity ; Tropical Climate ; Biomass ; Forests ; },
abstract = {Forest structural diversity, a measurement indicating the spatial and size distribution of individual trees, is critical for forest productivity, which stems from the combination of different ecological processes, such as tree mortality, recruitment and growth. Here, we evaluated the relationship between structural diversity and productivity caused by different ecological processes, and tested the roles of different-sized trees in influencing this relationship in a Forest Global Earth Observatory (ForestGEO) rainforest site on the Barro Colorado Island between 2000 and 2015. Generally, we found a negative relationship between structural diversity and forest productivity. Specifically, tree mortality-induced productivity loss increased, while tree recruitment-induced productivity gain decreased, with structural diversity. In addition, the structural diversity-productivity relationship varied with tree size, which was negative for small trees but positive for large trees. Furthermore, we revealed the important role of large-sized trees, which significantly promoted structural diversity but decreased productivity through increasing biomass loss. By disentangling the components of productivity, our results provide insights on the mechanism of the relationship between structural diversity and productivity, and highlight the role of large trees in shaping this relationship.},
}
@article {pmid39902757,
year = {2025},
author = {Wang, J and Luo, Y and Wang, H and Wang, L and Zhang, L and Gan, Z and Kang, X},
title = {FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae34},
pmid = {39902757},
issn = {1741-2552},
mesh = {*Artifacts ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; },
abstract = {Objective.Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.Approach.In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.Main results.The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.Significance.The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.},
}
@article {pmid39901197,
year = {2025},
author = {Wang, S and Ma, R and Gao, C and Tian, YN and Hu, RG and Zhang, H and Li, L and Li, Y},
title = {Unraveling the function of TSC1-TSC2 complex: implications for stem cell fate.},
journal = {Stem cell research &amp; therapy},
volume = {16},
number = {1},
pages = {38},
pmid = {39901197},
issn = {1757-6512},
support = {82073832//National Natural Science Foundation of China/ ; 82204885//National Natural Science Foundation of China/ ; 81973792//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Tuberous Sclerosis Complex 1 Protein/metabolism/genetics ; *Tuberous Sclerosis Complex 2 Protein/metabolism/genetics ; *Tuberous Sclerosis/metabolism/genetics/pathology ; *Cell Differentiation ; Animals ; Stem Cells/metabolism/cytology ; },
abstract = {BACKGROUND: Tuberous sclerosis complex is a genetic disorder caused by mutations in the TSC1 or TSC2 genes, affecting multiple systems. These genes produce proteins that regulate mTORC1 activity, essential for cell function and metabolism. While mTOR inhibitors have advanced treatment, maintaining long-term therapeutic success is still challenging. For over 20 years, significant progress has linked TSC1 or TSC2 gene mutations in stem cells to tuberous sclerosis complex symptoms.<br><br>METHODS: A comprehensive review was conducted using databases like Web of Science, Google Scholar, PubMed, and Science Direct, with search terms such as "tuberous sclerosis complex," "TSC1," "TSC2," "stem cell," "proliferation," and "differentiation." Relevant literature was thoroughly analyzed and summarized to present an updated analysis of the TSC1-TSC2 complex's role in stem cell fate determination and its implications for tuberous sclerosis complex.<br><br>RESULTS: The TSC1-TSC2 complex plays a crucial role in various stem cells, such as neural, germline, nephron progenitor, intestinal, hematopoietic, and mesenchymal stem/stromal cells, primarily through the mTOR signaling pathway.<br><br>CONCLUSIONS: This review aims shed light on the role of the TSC1-TSC2 complex in stem cell fate, its impact on health and disease, and potential new treatments for tuberous sclerosis complex.},
}
@article {pmid39900901,
year = {2025},
author = {Derosiere, G and Shokur, S and Vassiliadis, P},
title = {Reward signals in the motor cortex: from biology to neurotechnology.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {1307},
pmid = {39900901},
issn = {2041-1723},
mesh = {*Motor Cortex/physiology ; *Reward ; Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Animals ; Learning/physiology ; },
abstract = {Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learning. In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. Understanding M1 reward signals may open new avenues for enhancing motor control and rehabilitation.},
}
@article {pmid39899980,
year = {2025},
author = {Gusman, JT and Hosman, T and Crawford, R and Singer-Clark, T and Kapitonava, A and Kelemen, JN and Hahn, N and Henderson, JM and Hochberg, LR and Simeral, JD and Vargas-Irwin, CE},
title = {Multi-gesture drag-and-drop decoding in a 2D iBCI control task.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
pmid = {39899980},
issn = {1741-2552},
support = {R25 GM125500/GM/NIGMS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Gestures ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; *Motor Cortex/physiology ; *Psychomotor Performance/physiology ; Young Adult ; Quadriplegia/rehabilitation/physiopathology ; Hand/physiology ; Movement/physiology ; },
abstract = {Objective. Intracortical brain-computer interfaces (iBCIs) have demonstrated the ability to enable point and click as well as reach and grasp control for people with tetraplegia. However, few studies have investigated iBCIs during long-duration discrete movements that would enable common computer interactions such as 'click-and-hold' or 'drag-and-drop'.Approach. Here, we examined the performance of multi-class and binary (attempt/no-attempt) classification of neural activity in the left precentral gyrus of two BrainGate2 clinical trial participants performing hand gestures for 1, 2, and 4 s in duration. We then designed a novel 'latch decoder' that utilizes parallel multi-class and binary decoding processes and evaluated its performance on data from isolated sustained gesture attempts and a multi-gesture drag-and-drop task.Main results. Neural activity during sustained gestures revealed a marked decrease in the discriminability of hand gestures sustained beyond 1 s. Compared to standard direct decoding methods, the Latch decoder demonstrated substantial improvement in decoding accuracy for gestures performed independently or in conjunction with simultaneous 2D cursor control.Significance. This work highlights the unique neurophysiologic response patterns of sustained gesture attempts in human motor cortex and demonstrates a promising decoding approach that could enable individuals with tetraplegia to intuitively control a wider range of consumer electronics using an iBCI.},
}
@article {pmid39899514,
year = {2025},
author = {Chandravadia, N and Pendekanti, S and Roberts, D and Tran, R and Panchavati, S and Arnold, C and Pouratian, N and Speier, W},
title = {Comparing P300 flashing paradigms in online typing with language models.},
journal = {PloS one},
volume = {20},
number = {2},
pages = {e0303390},
pmid = {39899514},
issn = {1932-6203},
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain-Computer Interfaces ; *Language ; Male ; Female ; Adult ; Young Adult ; },
abstract = {The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.},
}
@article {pmid39898793,
year = {2025},
author = {Perkins, SM and Amematsro, EA and Cunningham, J and Wang, Q and Churchland, MM},
title = {An emerging view of neural geometry in motor cortex supports high-performance decoding.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {39898793},
issn = {2050-084X},
support = {1R01NS135240/NH/NIH HHS/United States ; T32 NS064929/NS/NINDS NIH HHS/United States ; T32 MH126036/MH/NIMH NIH HHS/United States ; R01 NS135240/NS/NINDS NIH HHS/United States ; SCGB//Simons Foundation/ ; Graduate Fellowship//National Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; *Motor Cortex/physiology ; Humans ; Machine Learning ; Neurons/physiology ; Animals ; },
abstract = {Decoders for brain-computer interfaces (BCIs) assume constraints on neural activity, chosen to reflect scientific beliefs while yielding tractable computations. Recent scientific advances suggest that the true constraints on neural activity, especially its geometry, may be quite different from those assumed by most decoders. We designed a decoder, MINT, to embrace statistical constraints that are potentially more appropriate. If those constraints are accurate, MINT should outperform standard methods that explicitly make different assumptions. Additionally, MINT should be competitive with expressive machine learning methods that can implicitly learn constraints from data. MINT performed well across tasks, suggesting its assumptions are well-matched to the data. MINT outperformed other interpretable methods in every comparison we made. MINT outperformed expressive machine learning methods in 37 of 42 comparisons. MINT's computations are simple, scale favorably with increasing neuron counts, and yield interpretable quantities such as data likelihoods. MINT's performance and simplicity suggest it may be a strong candidate for many BCI applications.},
}
@article {pmid39898755,
year = {2025},
author = {Andre, V and Abdel-Mottaleb, M and Shotbolt, M and Chen, S and Ramezini, Z and Zhang, E and Conlan, S and Telisman, O and Liang, P and Bryant, JM and Chomko, R and Khizroev, S},
title = {Foundational insights for theranostic applications of magnetoelectric nanoparticles.},
journal = {Nanoscale horizons},
volume = {10},
number = {4},
pages = {699-718},
pmid = {39898755},
issn = {2055-6764},
mesh = {Humans ; *Theranostic Nanomedicine/methods ; Drug Delivery Systems/methods ; Animals ; Neoplasms ; Nanoparticles/chemistry/therapeutic use ; Magnetite Nanoparticles/chemistry/therapeutic use ; Electroporation/methods ; },
abstract = {Reviewing emerging biomedical applications of MagnetoElectric NanoParticles (MENPs), this paper presents basic physics considerations to help understand the possibility of future theranostic applications. Currently emerging applications include wireless non-surgical neural modulation and recording, functional brain mapping, high-specificity cell electroporation for targeted cancer therapies, targeted drug delivery, early screening and diagnostics, and others. Using an ab initio analysis, each application is discussed from the perspective of its fundamental limitations. Furthermore, the review identifies the most eminent challenges and offers potential engineering solutions on the pathway to implement each application and combine the therapeutic and diagnostic capabilities of the nanoparticles.},
}
@article {pmid39896750,
year = {2025},
author = {Angulo, IN and Iáñez, E and Ubeda, A},
title = {Editorial: Recent applications of noninvasive physiological signals and artificial intelligence.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1543103},
pmid = {39896750},
issn = {1662-5196},
}
@article {pmid39896041,
year = {2025},
author = {Sarikaya, MA and Ince, G},
title = {Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning.},
journal = {PeerJ. Computer science},
volume = {11},
number = {},
pages = {e2649},
pmid = {39896041},
issn = {2376-5992},
abstract = {The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. We benchmark the efficacy of three generative adversarial network (GAN) architectures, such as conditional GAN (CGAN), conditional Wasserstein GAN (CWGAN), and CWGAN with gradient penalty (CWGAN-GP) within this framework. The proposed framework is rigorously tested on two conventional open sourced datasets, SEED-V and DEAP. Additionally, the framework was applied to an immersive three-dimensional (3D) dataset named GraffitiVR, which we collected to capture the emotional and behavioral reactions of individuals experiencing urban graffiti in a VR environment. This expanded application provides insights into emotion recognition frameworks in VR settings, providing a wider range of contexts for assessing our methodology. When the accuracy of emotion recognition classifiers trained with CWGAN-GP-generated EEG data combined with non-EEG sensory data was compared against those trained using a combination of real EEG and non-EEG sensory data, the accuracy ratios were 93% on the SEED-V dataset, 99% on the DEAP dataset, and 97% on the GraffitiVR dataset. Moreover, in the GraffitiVR dataset, using CWGAN-GP-generated EEG data with non-EEG sensory data for emotion recognition models resulted in up to a 30% reduction in calibration time compared to classifiers trained on real EEG data with non-EEG sensory data. These results underscore the robustness and versatility of the proposed approach, significantly enhancing emotion recognition processes across a variety of environmental settings.},
}
@article {pmid39890467,
year = {2025},
author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL},
title = {Mapping Eye, Arm, and Reward Information in Frontal Motor Cortices Using Electrocorticography in Nonhuman Primates.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {45},
number = {12},
pages = {},
pmid = {39890467},
issn = {1529-2401},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; Male ; *Electrocorticography/methods ; *Motor Cortex/physiology ; *Reward ; *Macaca mulatta ; *Eye Movements/physiology ; *Brain Mapping/methods ; *Psychomotor Performance/physiology ; Arm/physiology ; Frontal Lobe/physiology ; Movement/physiology ; },
abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral prefrontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the prefrontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.},
}
@article {pmid39889306,
year = {2025},
author = {Li, D and Huang, Y and Luo, R and Zhao, L and Xiao, X and Wang, K and Yi, W and Xu, M and Ming, D},
title = {Enhancing detection of SSVEPs using discriminant compacted network.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb0f2},
pmid = {39889306},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography/methods ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; Deep Learning ; Discriminant Analysis ; Photic Stimulation/methods ; },
abstract = {Objective. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a critical issue for enhancing the performance of SSVEP-BCI systems.Approach.This study proposed a novel decoding method called Discriminant Compacted Network (Dis-ComNet), which exploited the advantages of both spatial filtering and deep learning (DL). Specifically, this study enhanced SSVEP features using global template alignment and discriminant spatial pattern, and then designed a compacted temporal-spatio module (CTSM) to extract finer features. The proposed method was evaluated on a self-collected high-frequency dataset, a public Benchmark dataset and a public wearable dataset.Main Results.The results showed that Dis-ComNet significantly outperformed state-of-the-art spatial filtering methods, DL methods, and other fusion methods. Remarkably, Dis-ComNet improved the classification accuracy by 3.9%, 3.5%, 3.2%, 13.3%, 17.4%, 37.5%, and 2.5% when comparing with eTRCA, eTRCA-R, TDCA, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively in the high-frequency dataset. The achieved results were 4.7%, 4.6%, 23.6%, 52.5%, 31.7%, and 7.0% higher than those of eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net, respectively, and were comparable to those of TDCA in Benchmark dataset. The accuracy of Dis-ComNet in the wearable dataset was 9.5%, 7.1%, 36.1%, 26.3%, 15.7% and 4.7% higher than eTRCA, eTRCA-R, DNN, EEGnet, Ensemble-DNN, and TRCA-Net respectively, and comparable to TDCA. Besides, our model achieved the ITRs up to 126.0 bits/min, 236.4 bits/min and 103.6 bits/min in the high-frequency, Benchmark and the wearable datasets respectively.Significance.This study develops an effective model for the detection of SSVEPs, facilitating the development of high accuracy SSVEP-BCI systems.},
}
@article {pmid39887443,
year = {2025},
author = {Li, Z and Liang, C and He, Q and Feiweier, T and Hsu, YC and Li, J and Bai, R},
title = {Comparison of water exchange measurements between filter-exchange imaging and diffusion time-dependent kurtosis imaging in the human brain.},
journal = {Magnetic resonance in medicine},
volume = {93},
number = {6},
pages = {2357-2369},
doi = {10.1002/mrm.30454},
pmid = {39887443},
issn = {1522-2594},
support = {82111530201//National Natural Science Foundation of China (NSFC)/ ; 82222032//National Natural Science Foundation of China (NSFC)/ ; 82172050//National Natural Science Foundation of China (NSFC)/ ; 2022ZD0206000//the STI2030-Major Projects of China/ ; },
mesh = {Humans ; Male ; Adult ; *Brain/metabolism/diagnostic imaging ; Female ; *Body Water/metabolism ; *Diffusion Magnetic Resonance Imaging/methods ; Reproducibility of Results ; *Image Interpretation, Computer-Assisted/methods ; Algorithms ; *Water/metabolism ; },
abstract = {PURPOSE: Filter-exchange imaging (FEXI) and diffusion time (t)-dependent kurtosis imaging (DKI(t)) are two diffusion-based methods that have been proposed for in vivo measurements of water exchange rates. Few studies have directly compared these methods. We aimed to investigate whether FEXI and DKI(t) yield comparable water exchange measurements in the human brain in vivo.<br><br>METHODS: Eight healthy volunteers underwent multiple-direction FEXI and DKI(t) acquisitions on a 3T scanner. We performed region of interest (ROI) analysis to determine correlations between FEXI-derived apparent exchange rate (AXR) and DKI(t)-derived reciprocal of exchange time (1 / &#x3c4; ex $$ 1/{\tau}
_{ex}
$$).<br><br>RESULTS: In both white matter (WM) and gray matter (GM), DKI(t) revealed substantial diffusion-time dependence of diffusivity and kurtosis. However, at t&#x2009;&#x2265;&#x2009;100&#x2009;ms, the diffusivity showed weak time dependence. In WM, this time dependence may be due to water exchange between myelin water and "free" water with different T1 values, although other factors, such as remaining restrictive effects from microstructural barriers, cannot be excluded. We found a significant correlation between DKI(t)-derived 1 / &#x3c4; ex $$ 1/{\tau}
_{ex}
$$ and FEXI-derived AXR in the axial direction within WM. No such correlation was present in GM, although both values showed similar ranges.<br><br>CONCLUSION: These results suggest that FEXI and DKI(t) could be sensitive to the same water exchange process only when the diffusion time in DKI(t) is sufficiently long, and only in WM. In both GM and WM, the restrictive effect of microstructure is non-negligible, especially at short diffusion times (<100&#x2009;ms).},
}
@article {pmid39886803,
year = {2025},
author = {Ali, HS and Ismail, AI and El-Rabaie, EM and Abd El-Samie, FE},
title = {Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2025.2457122},
pmid = {39886803},
issn = {1476-8259},
abstract = {The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit. Moreover, high-dimensional, and irrelevant features can make it harder for a classifier to learn effectively. To address these challenges, exploring potential solutions is crucial. This paper introduces Regularized CSP with diagonal loading (DL-CSP) and Pearson correlation coefficient (PCC) based feature selection to extract the most discriminative motor imagery EEG (MI-EEG) features. Three classifiers in an ensemble are considered; bidirectional long short-term memory (Bi-LSTM), K-nearest neighbors (KNN) and naïve Bayes (NB). Decision level fusion through majority voting is exploited to leverage diverse perspectives and increase the overall system robustness. Experiments have been implemented using three publicly available datasets for MI classification; BCI competition IV-IIA (data-1), BCI Competition III-IVa (data-2), and a stroke patients' dataset (data-3). The accuracy achieved, according to the results, is 86.96% for data-1, 91.70% for data-2, and 85.75% for data-3. These percentages outperform the accuracy achieved by any state-of-the-art techniques.},
}
@article {pmid39886225,
year = {2025},
author = {Huang, J and Zhang, L and Shao, N and Zhang, Y and Xu, Y and Zhou, Y and Zhang, D and Zhang, J and Lee, HJ},
title = {Lipid Metabolic Heterogeneity during Early Embryogenesis Revealed by Hyper-3D Stimulated Raman Imaging.},
journal = {Chemical &amp; biomedical imaging},
volume = {3},
number = {1},
pages = {15-24},
pmid = {39886225},
issn = {2832-3637},
abstract = {Studying embryogenesis is fundamental to understanding developmental biology and reproductive medicine. Its process requires precise spatiotemporal regulations in which lipid metabolism plays a crucial role. However, the spatial dynamics of lipid species at the subcellular level remains obscure due to technical limitations. To address this challenge, we developed a hyperspectral 3D imaging and analysis method based on stimulated Raman scattering microscopy (hyper-3D SRS) to quantitatively assess lipid profiles in individual embryos through submicrometer resolution (x-y), 3D optical sectioning (z), and chemical bond-selective (Ω) imaging. Using hyper-3D SRS, individual lipid droplets (LDs) in single cells were identified and quantified. Our findings revealed that the LD profiles within a single embryo are not uniform, even as early as the 2-cell stage. Notably, we also discovered a dynamic relationship between the LD size and unsaturation degree as embryos develop, indicating diverse lipid metabolism during early development. Furthermore, abnormal LDs were observed in oocytes of a progeria mouse model, suggesting that LDs could serve as a potential biomarker for assessing oocyte/embryo quality. Overall, our results highlight the potential of hyper-3D SRS as a noninvasive method for studying lipid content, composition, and subcellular distribution in embryos. This technique provides valuable insights into lipid metabolism during embryonic development and has the potential for clinical applications in evaluating oocyte/embryo quality.},
}
@article {pmid39886177,
year = {2024},
author = {Murthy, V and Kashid, SR and Pal, M and Vadassery, A and Maitre, P and Arora, A and Singh, P and Joshi, A and Bakshi, G and Prakash, G},
title = {Prospective comparative study of quality of life in patients with bladder cancer undergoing cystectomy with ileal conduit or bladder preservation.},
journal = {BMJ oncology},
volume = {3},
number = {1},
pages = {e000435},
pmid = {39886177},
issn = {2752-7948},
abstract = {OBJECTIVE: To compare health-related quality of life (HRQOL) in patients undergoing radical cystectomy with ileal conduit (RC) or bladder preservation (BP) with (chemo)radiotherapy for bladder cancer.<br><br>METHODS AND ANALYSIS: Patients with bladder cancer, stage cT1-T4, cN0-N1, M0 with a minimum follow-up of 6 months from curative treatment (RC or BP) and without disease were eligible for inclusion. Two HRQOL instruments were administered: Bladder Cancer Index (BCI) for bladder cancer-specific HRQOL and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). The mean QOL scores across various domains and specific questions were compared between the two treatment groups using an independent t-test.<br><br>RESULTS: Out of the 104 enrolled patients, 56 underwent RC and 48 opted for BP, with 95 (91.3%) being male. The median time from treatment completion to QOL assessment was 22 months (IQR 10-56). The median age for the entire cohort was 62 years (IQR 55-68), 65.5 years (IQR 55-71) in BP and 59.5 years (IQR 55-66) in RC. There was no significant difference in mean BCI urinary and bowel scores in function or bother subdomains between the two groups. Overall, BCI sexual scores were low in both groups but significantly better after BP (BPmean 56.9, RCmean 41.5, p=0.01). Mean scores for sexual function subdomain were BPmean 38.4 and RCmean 25 (p=0.07) and for sexual bother were BPmean 81 RCmean 62 (p=0.02). The EORTC QLQ-C30 outcomes did not show a significant difference in either group.<br><br>CONCLUSION: The BP group showed significantly better results in the sexual domain compared with the RC group. Both groups had good QOL in terms of urinary and bowel functions.},
}
@article {pmid39883960,
year = {2025},
author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, JL and Hatsopoulos, NG and Downey, JE},
title = {How different immersive environments affect intracortical brain computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39883960},
issn = {1741-2552},
support = {R01 NS130302/NS/NINDS NIH HHS/United States ; T32 NS121763/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Virtual Reality ; *Electrodes, Implanted ; Female ; Electroencephalography/methods ; Adult ; Environment ; },
abstract = {Objective. As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device.Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor.Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered.Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.Clinical Trial: NCT01894802.},
}
@article {pmid39883957,
year = {2025},
author = {Lutes, NA and Sriram Siddhardh Nadendla, V and Krishnamurthy, K},
title = {Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb079},
pmid = {39883957},
issn = {1741-2552},
mesh = {Humans ; *Neural Networks, Computer ; *Intention ; Automobile Driving ; Male ; Electroencephalography/instrumentation/methods ; Adult ; Machine Learning ; Female ; Transfer, Psychology/physiology ; },
abstract = {Objective.This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention.Approach.Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: (1) an audio countdown in a nominal baseline, stress-free environment; (2) an audio countdown in an environment with added elements of physical fatigue and active cognitive distraction; (3) a visual cue given through stoplights in a stress-free environment. These datasets are then used to develop individual-level models from group-level models using a few-shot transfer learning method, which involves: (1) creating a group-level model by training a CNN on group-level data followed by quantization and recouping any performance loss using quantization-aware retraining; (2) converting the CNN to be compatible with Akida AKD1000 processor; and (3) training the final decision layer on individual-level data subsets to create individual-customized models using an online Akida edge-learning algorithm.Main results.Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a 1.3 × increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.Significance.Especially relevant to real-time applications, this work presents an energy-efficient, few-shot transfer learning method that is implemented on a neuromorphic processor capable of training a CSNN as new data becomes available, operating conditions change, or to customize group-level models to yield personalized models unique to each individual.},
}
@article {pmid39883956,
year = {2025},
author = {Sultana, M and Gheorghe, L and Perdikis, S},
title = {EEG correlates of acquiring race driving skills.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adb077},
pmid = {39883956},
issn = {1741-2552},
mesh = {Humans ; *Automobile Driving ; *Electroencephalography/methods ; Male ; *Transcranial Direct Current Stimulation/methods ; Adult ; Female ; Young Adult ; Motor Skills/physiology ; Psychomotor Performance/physiology ; Learning/physiology ; },
abstract = {Objective. Race driving is a complex motor task that involves multiple concurrent cognitive processes in different brain regions coordinated to maintain and optimize speed and control. Delineating the neuroplasticity accompanying the acquisition of complex and fine motor skills such as racing is crucial to elucidate how these are gradually encoded in the brain and inform new training regimes. This study aims, first, to identify the neural correlates of learning to drive a racing car using non-invasive electroencephalography (EEG) imaging and longitudinal monitoring. Second, we gather evidence on the potential role of transcranial direct current stimulation (tDCS) in enhancing the training outcome of race drivers.Approach. We collected and analyzed multimodal experimental data, including drivers' EEG and telemetry from a driving simulator to identify neuromarkers of race driving proficiency and assess the potential to improve training through anodal tDCS.Main results. Our findings indicate that theta-band EEG rhythms and alpha-band effective functional connectivity between frontocentral and occipital cortical areas are significant neuromarkers for acquiring racing skills. We also observed signs of a potential tDCS effect in accelerating the learning process.SignificanceThese results provide a foundation for future research to develop innovative race-driving training protocols using neurotechnology.},
}
@article {pmid39883372,
year = {2025},
author = {Xiong, H and Yan, Y and Chen, Y and Liu, J},
title = {Graph convolution network-based eeg signal analysis: a review.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {39883372},
issn = {1741-0444},
abstract = {With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN. An exhaustive classification of methods and a systematic analysis of key modules, such as brain map construction, node feature extraction, and GCN architecture design, are presented. In addition, we pay special attention to several key research issues related to GCN. This review enhances the understanding of the future potential of GCN in the field of EEG signal analysis. At the same time, several valuable development directions are sorted out for researchers in related fields, such as analysing the applicability of different GCN layers, building task-oriented GCN models, and improving adaptation to limited data.},
}
@article {pmid39882377,
year = {2024},
author = {Ma, Y and Huang, J and Liu, C and Shi, M},
title = {A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1502560},
pmid = {39882377},
issn = {1662-5218},
abstract = {Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.},
}
@article {pmid39880895,
year = {2025},
author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A},
title = {Author Correction: Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41551-025-01355-2},
pmid = {39880895},
issn = {2157-846X},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
}
@article {pmid39880008,
year = {2025},
author = {Loss, J and Betsch, C and Ellermann, C and Ewert, B and Grill, E and Jenny, MA and Jordan, S and Kubiak, T and Mata, J and Rebitschek, FG and Rehfuess, EA and Sniehotta, F},
title = {[Characteristics of the "Behavioural and cultural insights (BCI)" approach in Public Health - a consensus paper of the network "Behavioural Science Connect"].},
journal = {Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))},
volume = {87},
number = {4},
pages = {273-281},
doi = {10.1055/a-2528-8276},
pmid = {39880008},
issn = {1439-4421},
mesh = {Germany ; *Public Health ; Humans ; *Health Promotion/organization &amp; administration ; *Health Behavior ; *Behavioral Sciences/organization &amp; administration ; },
abstract = {Public health interventions are often intended to make it easier for people to adopt health-related behaviours, e. g., by changing the social environment and material living conditions. With the concept of "Behavioral and Cultural Insights" (BCI), the World Health Organization (WHO) has established an approach that aims at a better understanding of health-related behaviours, in order to be able to develop corresponding public health interventions in a more targeted manner. The focus is on the empirical identification of individuals as well as cultural, social and environmental barriers to and facilitators of health behaviour.The BCI approach can be used to plan preventive measures in a more evidence-based and needs-oriented manner. To this end, the current article outlines some basic features of BCI which should be taken into account for integrating the concept into a contemporary understanding of "new public health". This includes social and individual factors influencing health as well as social inequalities in health.First, the article distinguishes the BCI concept from the approach of behavioural economics (e. g., nudging). To illustrate its potential for population health and health equity, the article then explains that BCI-based measures that (a) focus on both behaviour and environment, (b) aim at health equity, (c) are developed and implemented in a participatory manner, and (d) follow the logic of the well-established Public Health Action Cycle. For BCI, it is crucial to systematically identify and analyse the factors influencing human behaviour in everyday life. BCI-based interventions must also consider the characteristics of complex interventions and be tailored to local conditions and the cultural diversity of specific population groups.The BCI approach has many similarities with other approaches of quality-assured and needs-oriented prevention measures. The focus on a systematic identification of barriers and facilitators offers an important added value in the planning of public health measures. Research into BCI and their use in prevention should be expanded in Germany.},
}
@article {pmid39878111,
year = {2025},
author = {Badr, Y and AlSawaftah, N and Husseini, G},
title = {User-Centered Design of Neuroprosthetics: Advancements and Limitations.},
journal = {CNS &amp; neurological disorders drug targets},
volume = {},
number = {},
pages = {},
doi = {10.2174/0118715273335487250102093150},
pmid = {39878111},
issn = {1996-3181},
abstract = {Neurological conditions resulting from severe spinal cord injuries, brain injuries, and other traumatic incidents often lead to the loss of essential bodily functions, including sensory and motor capabilities. Traditional prosthetic devices, though standard, have limitations in delivering the required dexterity and functionality. The advent of neuroprosthetics marks a paradigm shift, aiming to bridge the gap between prosthetic devices and the human nervous system. This review paper explores the evolution of neuroprosthetics, categorizing devices into sensory and motor neuroprosthetics and emphasizing their significance in addressing specific challenges. The discussion section delves into long-term challenges in clinical practice, encompassing device durability, ethical considerations, and issues of accessibility and affordability. Furthermore, the paper proposes potential solutions with a specific focus on enhancing sensory experiences and the importance of user-friendly interfaces. In conclusion, this paper offers a comprehensive overview of the current state of neuroprosthetics, outlining future research and development directions to guide advancements in the field.},
}
@article {pmid39875198,
year = {2025},
author = {Chen, S and Jiang, D and Li, M and Xuan, X and Li, H},
title = {Brain-Computer Interface and Electrochemical Sensor Based on Boron-Nitrogen Co-Doped Graphene-Diamond Microelectrode for EEG and Dopamine Detection.},
journal = {ACS sensors},
volume = {10},
number = {2},
pages = {868-880},
doi = {10.1021/acssensors.4c02461},
pmid = {39875198},
issn = {2379-3694},
mesh = {*Graphite/chemistry ; *Dopamine/analysis ; *Microelectrodes ; *Electroencephalography/instrumentation/methods ; *Nitrogen/chemistry ; *Boron/chemistry ; *Electrochemical Techniques/methods/instrumentation ; Humans ; *Brain-Computer Interfaces ; Biosensing Techniques/methods/instrumentation ; },
abstract = {The simultaneous detection of electroencephalography (EEG) signals and neurotransmitter levels plays an important role as biomarkers for the assessment and monitoring of emotions and cognition. This paper describes the development of boron and nitrogen codoped graphene-diamond (BNGrD) microelectrodes with a diameter of only 200 μm for sensing EEG signals and dopamine (DA) levels, which have been developed for the first time. The optimized BNGrD microelectrode responded sensitively to both EEG and DA signals, with a signal-to-noise ratio of 9 dB for spontaneous EEG signals and a limit of detection as low as 124 nM for DA. Furthermore, the BNGrD microelectrodes demonstrate excellent repeatability, reproducibility, and stability for the detection of EEG and dopamine. These results indicate that the BNGrD microelectrode creates suitable conditions for establishing a correlation between the EEG signals and neurotransmitters. A flexible printed circuit board with BNGrD microelectrodes for an eight-channel EEG headband, portable EEG collector, and light stimulation glasses are designed. The self-designed EEG collector adopts a split design strategy of digital and analog signal modules and uses miniaturized impedance-matched BNGrD microelectrodes, which effectively reduce the noise of the electrophysiological signals. The BNGrD microelectrode-based portable EEG/electrochemical analysis system detects EEG signals and DA levels in a noninvasive and minimally invasive manner and has application prospects in remote online diagnosis and treatment of patients with emotional and cognition-related diseases.},
}
@article {pmid39875073,
year = {2025},
author = {Géraudie, A and De Rossi, P and Canney, M and Carpentier, A and Delatour, B},
title = {Effects of blood-brain barrier opening using ultrasound on tauopathies: A systematic review.},
journal = {Journal of controlled release : official journal of the Controlled Release Society},
volume = {379},
number = {},
pages = {1029-1044},
doi = {10.1016/j.jconrel.2025.01.056},
pmid = {39875073},
issn = {1873-4995},
mesh = {*Blood-Brain Barrier/metabolism ; Humans ; Animals ; *Tauopathies/diagnostic imaging/metabolism ; *tau Proteins/metabolism ; Ultrasonic Therapy/methods ; },
abstract = {UNLABELLED: Blood-brain barrier opening with ultrasound can potentiate drug efficacy in the treatment of brain pathologies and also provides therapeutic effects on its own. It is an innovative tool to transiently, repeatedly and safely open the barrier, with studies showing beneficial effects in both preclinical models for Alzheimer's disease and recent clinical studies. The first preclinical and clinical work has mainly shown a decrease in amyloid burden in mice models and in patients. However, Alzheimer's disease pathology also encompasses tauopathy, which is closely related to cognitive decline, making it a crucial therapeutic target. The effects of blood-brain barrier opening with ultrasound have been rarely assessed on tau and are still unclear.<br><br>METHODS: This systematic review, conducted through searches using Pubmed, Embase, Web of Science and Cochrane Central databases, extracted results of 15 studies reporting effects of blood-brain barrier opening using ultrasound on tau proteins.<br><br>RESULTS: This review of the literature indicates that blood-brain barrier opening using ultrasound can decrease the extent of the tau pathology or potentialize the effect of a therapeutic drug. However, selected studies report paradoxically that blood-brain barrier opening can increase tau pathology burden and induce brain damage.<br><br>DISCUSSION: Apparent discrepancies between reports could originate from the variability in protocols or analytical methods that may impact the effects of blood-brain barrier opening with ultrasound on tauopathies, glial populations, tissue integrity and functional outcomes.<br><br>CONCLUSION: This calls for a better standardization effort combined with improved methodologies allowing between-studies comparisons, and for further understanding of the effects of blood-brain barrier opening on tau pathology as an essential prerequisite before translation to clinic.},
}
@article {pmid39874948,
year = {2025},
author = {Tian, F and Liu, Y and Chen, M and Schriver, KE and Roe, AW},
title = {Selective activation of mesoscale functional circuits via multichannel infrared stimulation of cortical columns in ultra-high-field 7T MRI.},
journal = {Cell reports methods},
volume = {5},
number = {1},
pages = {100961},
pmid = {39874948},
issn = {2667-2375},
mesh = {*Magnetic Resonance Imaging/methods ; *Visual Cortex/physiology/diagnostic imaging ; Humans ; Animals ; Photic Stimulation/methods ; Cats ; Infrared Rays ; Brain Mapping/methods ; },
abstract = {To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.},
}
@article {pmid39874664,
year = {2025},
author = {Qin, Y and Li, B and Wang, W and Shi, X and Peng, C and Wang, X and Wang, H},
title = {ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaf58},
pmid = {39874664},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Movement/physiology ; },
abstract = {Objective. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain. However, the performance of MI-based unimodal classification methods is low due to the limitations of EEG or fNIRS.Approach. In this paper, we propose a new multimodal fusion classification method capable of combining the potential complementary advantages of EEG and fNIRS. First, we propose a feature extraction network capable of extracting spatio-temporal features from EEG-based and fNIRS-based MI signals. Then, we successively fused the EEG and fNIRS at the feature-level and the decision-level to improve the adaptability and robustness of the model.Main results. We validate the performance of ECA-FusionNet on a publicly available EEG-fNIRS dataset. The results show that ECA-FusionNet outperforms unimodal classification methods, as well as existing fusion classification methods, in terms of classification accuracy for MI.Significance. ECA-FusionNet may provide a useful reference for the field of multimodal fusion classification.},
}
@article {pmid39874652,
year = {2025},
author = {Ziebell, P and Modde, A and Roland, E and Eidel, M and Vansteensel, MJ and Mrachacz-Kersting, N and Vaughan, TM and Kübler, A},
title = {Designing an online BCI forum: insights from researchers and end-users.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaf57},
pmid = {39874652},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Male ; Female ; Adult ; Online Systems ; Research Personnel ; Middle Aged ; Persons with Disabilities/rehabilitation ; Internet ; },
abstract = {Objective.Brain-computer interfaces (BCIs) can support non-muscular communication and device control for severely paralyzed people. However, efforts that directly involve potential or actual end-users and address their individual needs are scarce, demonstrating a translational gap. An online BCI forum supported by the BCI Society could initiate and sustainably strengthen interactions between BCI researchers and end-users to bridge this gap.Approach.We interviewed six severely paralyzed individuals and surveyed 121 BCI researchers to capture their opinions and wishes concerning an online BCI forum. Data were analyzed with a mixed-method quantitative and qualitative content analysis.Main results.All end-users and most researchers (83%) reported an interest in participating in an online BCI forum. Rating questions and open comments to identify design aspects included what should be featured most prominently, how people would get engaged in the online BCI forum, and which pitfalls should be considered.Significance.Responses support establishing an online BCI forum to serve as a meaningful resource for the entire BCI community.},
}
@article {pmid39874276,
year = {2025},
author = {Longo, L and Reilly, RB},
title = {onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0313076},
pmid = {39874276},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; Wavelet Analysis ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Signal-To-Noise Ratio ; Male ; Brain/physiology ; Adult ; Female ; },
abstract = {Electroencephalographic signals are obtained by amplifying and recording the brain's spontaneous biological potential using electrodes positioned on the scalp. While proven to help find changes in brain activity with a high temporal resolution, such signals are contaminated by non-stationary and frequent artefacts. A plethora of noise reduction techniques have been developed, achieving remarkable performance. However, they often require multi-channel information and additional reference signals, are not fully automated, require human intervention and are mostly offline. With the popularity of Brain-Computer Interfaces and the application of Electroencephalography in daily activities and other ecological settings, there is an increasing need for robust, online, near real-time denoising techniques, without additional reference signals, that is fully automated and does not require human supervision nor multi-channel information. This research contributes to the body of knowledge by introducing onEEGwaveLAD, a novel, fully automated, ONline, EEG wavelet-based Learning Adaptive Denoiser pipeline for artefact identification and reduction. It is a specific framework that can be instantiated for various types of artefacts paving the path towards real-time denoising. As the first of its kind, it is described and instantiated for the particular problem of blink detection and reduction, and evaluated across a general and a specific analysis of the signal to noise ratio across 30 participants.},
}
@article {pmid39873797,
year = {2025},
author = {Mohan, A and Anand, RS},
title = {Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.},
journal = {Brain topography},
volume = {38},
number = {2},
pages = {25},
pmid = {39873797},
issn = {1573-6792},
mesh = {Humans ; *Machine Learning ; *Speech/physiology ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.},
}
@article {pmid39872211,
year = {2025},
author = {Wang, L and Zhang, C and Hao, Z and Yao, S and Bai, L and Oliveira, JM and Wang, P and Zhang, K and Zhang, C and He, J and Reis, RL and Li, D},
title = {Bioaugmented design and functional evaluation of low damage implantable array electrodes.},
journal = {Bioactive materials},
volume = {47},
number = {},
pages = {18-31},
pmid = {39872211},
issn = {2452-199X},
abstract = {Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. In this study, we proposed a new concept for the design and bioaugmentation of implantable electrodes (bio-array electrodes) featuring a heterogeneous gradient structure. Different composite polyaniline-gelatin-alginate based conductive hydrogel formulations were developed for electrode surface coating. In addition, the design, materials, and performance of the developed electrode was optimized through a combination of numerical simulations and physio-chemical characterizations. The long-term biological performance of the bio-array electrodes were investigated in vivo using a C57 mouse model. It was found that compared to metal array electrodes, the surface charge of the bio-array electrodes increased by 1.74 times, and the impedance at 1 kHz decreased by 63.17 %, with a doubling of the average capacitance. Long-term animal experiments showed that the bio-array electrodes could consistently record 2.5 times more signals than those of the metal array electrodes, and the signal-to-noise ratio based on action potentials was 2.1 times higher. The study investigated the mechanisms of suppressing the scarring effect by the bioaugmented design, revealing reduces brain damage as a result of the interface biocompatibility between the bio-array electrodes and brain tissue, and confirmed the long-term in vivo stability of the bio-array electrodes.},
}
@article {pmid39870740,
year = {2025},
author = {Hu, D and Sato, T and Rockland, KS and Tanifuji, M and Tanigawa, H},
title = {Relationship between functional structures and horizontal connections in macaque inferior temporal cortex.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {3436},
pmid = {39870740},
issn = {2045-2322},
support = {31872776//National Natural Science Foundation of China/ ; 2018YFA0701402//National Key Research and Development Program of China/ ; },
mesh = {Animals ; *Temporal Lobe/physiology ; Visual Cortex/physiology ; Male ; Macaca mulatta ; Axons/physiology ; Photic Stimulation ; Brain Mapping/methods ; Macaca ; Visual Pathways/physiology ; },
abstract = {Horizontal connections in anterior inferior temporal cortex (ITC) are thought to play an important role in object recognition by integrating information across spatially separated functional columns, but their functional organization remains unclear. Using a combination of optical imaging, electrophysiological recording, and anatomical tracing, we investigated the relationship between stimulus-response maps and patterns of horizontal axon terminals in the macaque ITC. In contrast to the "like-to-like" connectivity observed in the early visual cortex, we found that horizontal axons in ITC do not preferentially connect sites with similar object selectivity. While some axon terminal patches shared responsiveness to specific visual features with the injection site, many connected to regions with different selectivity. Our results suggest that horizontal connections in anterior ITC exhibit diverse functional connectivity, potentially supporting flexible integration of visual information for advanced object recognition processes.},
}
@article {pmid39870272,
year = {2025},
author = {Abedi, M and Arbabi, M and Gholampour, R and Amini, J and Barandeh, Z and Hosseini, S and Abedi, A and Gholibegloo, E and Zomorrodian, H and Raoufi, M},
title = {Zinc oxide nanoparticle-embedded tannic acid/chitosan-based sponge: A highly absorbent hemostatic agent with enhanced antimicrobial activity.},
journal = {International journal of biological macromolecules},
volume = {300},
number = {},
pages = {140337},
doi = {10.1016/j.ijbiomac.2025.140337},
pmid = {39870272},
issn = {1879-0003},
mesh = {*Chitosan/chemistry/pharmacology ; *Zinc Oxide/chemistry/pharmacology ; Animals ; *Hemostatics/pharmacology/chemistry ; Rats ; *Tannins/chemistry/pharmacology ; Anti-Infective Agents/pharmacology/chemistry ; Nanoparticles/chemistry ; Blood Coagulation/drug effects ; Male ; Epichlorohydrin/chemistry ; Escherichia coli/drug effects ; Staphylococcus aureus/drug effects ; Polyphenols ; },
abstract = {This study reports the development of a highly absorbent Chitosan (CS)/Tannic Acid (TA) sponge, synthesized via chemical cross-linking with Epichlorohydrin (ECH) and integrated with zinc oxide nanoparticles (ZnO NPs) as a novel hemostatic anti-infection agent. The chemical properties of the sponges were characterized using Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and zeta potential measurements. Morphological and elemental analyses conducted through scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDAX) revealed a uniform distribution of ZnO NPs, with particle sizes below 20 nm. Compression tests indicated that the incorporation of ECH enhanced the compressive strength of the TA/CS sample, increasing from 0.614 MPa to 1.03 MPa for TA/CS-ECH and 1.16 MPa for ZnO@TA/CS-ECH, while preserving its flexibility. ZnO@TA/CS-ECH sponges exhibited high swelling ratios, consistent with their mesoporous structure revealed by porosity analysis. MTT assays confirmed that the addition of ECH did not adversely affect the biocompatibility of the final ZnO@TA/CS-ECH sample. Hemostatic performance was assessed through prothrombin time (PT), activated partial thromboplastin time (aPTT), blood clotting index (BCI), blood clotting time (BCT) assays, and platelet adhesion imaging. ZnO@TA/CS-ECH significantly reduced the BCT of untreated blood from 349 to 49 s, outperforming Celox™ (182 s). This performance was further confirmed using a rat liver hemostatic model. Moreover, ZnO@TA/CS-ECH demonstrated substantial antimicrobial activity against E. coli, S. aureus, and C. albicans, comparable to standard antibiotics and antifungals. These findings suggest that the three-dimensional ZnO@TA/CS-ECH sponge holds promise in managing infected bleeding and inspiring the next-generation of hemostatic agents.},
}
@article {pmid39870046,
year = {2025},
author = {Zhang, L and Zhang, H and Yan, S and Li, R and Yao, D and Hu, Y and Zhang, R},
title = {Improving pre-movement patterns detection with multi-dimensional EEG features for readiness potential decrease.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaef2},
pmid = {39870046},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Movement/physiology ; Male ; *Brain-Computer Interfaces ; Adult ; Female ; Young Adult ; Algorithms ; Support Vector Machine ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; },
abstract = {Objective.The readiness potential (RP) is an important neural characteristic in motor preparation-based brain-computer interface. In our previous research, we observed a significant decrease of the RP amplitude in some cases, which severely affects the pre-movement patterns detection. In this paper, we aimed to improve the accuracy (Acc) of pre-movement patterns detection in the condition of RP decrease.Approach.We analyzed multi-dimensional EEG features in terms of time-frequency, brain networks, and cross-frequency coupling (CFC). And, a multi-dimensional Electroencephalogram feature combination (MEFC) algorithm was proposed. The features used include: (1) waveforms of the RP; (2) energy in alpha and beta bands; (3) brain network in alpha and beta bands; and (4) CFC value between 2 and 10 Hz.Main results.By employing support vector machines, the MEFC method achieved an average recognition rate of 88.9% and 85.5% under normal and RP decrease conditions, respectively. Compared to classical algorithm, the average Acc for both tasks improved by 7.8% and 8.8% respectively.Significance.This method can effectively improve the Acc of pre-movement patterns decoding in the condition of RP decrease.},
}
@article {pmid39870043,
year = {2025},
author = {Candelori, B and Bardella, G and Spinelli, I and Ramawat, S and Pani, P and Ferraina, S and Scardapane, S},
title = {Spatio-temporal transformers for decoding neural movement control.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaef0},
pmid = {39870043},
issn = {1741-2552},
mesh = {Animals ; *Movement/physiology ; *Motor Cortex/physiology ; *Macaca mulatta ; Neural Networks, Computer ; Neurons/physiology ; Male ; Action Potentials/physiology ; Models, Neurological ; Deep Learning ; Psychomotor Performance/physiology ; },
abstract = {Objective. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activityin vivoremains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results.Approach. To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex of non-human primates performing a motor inhibition task.Main results. The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses.Significance. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.},
}
@article {pmid39870041,
year = {2025},
author = {Konrad, P and Gelman, KR and Lawrence, J and Bhatia, S and Jacqueline, D and Sharma, R and Ho, E and Byun, YW and Mermel, CH and Rapoport, BI},
title = {First-in-human experience performing high-resolution cortical mapping using a novel microelectrode array containing 1024 electrodes.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adaeed},
pmid = {39870041},
issn = {1741-2552},
mesh = {Humans ; *Microelectrodes ; *Brain Mapping/methods/instrumentation ; Male ; Female ; Adult ; Middle Aged ; Electrocorticography/methods/instrumentation ; Cerebral Cortex/physiology ; Aged ; Electrodes, Implanted ; Brain Neoplasms/surgery ; Brain-Computer Interfaces ; },
abstract = {Objective.Localization of function within the brain and central nervous system is an essential aspect of clinical neuroscience. Classical descriptions of functional neuroanatomy provide a foundation for understanding the functional significance of identifiable anatomic structures. However, individuals exhibit substantial variation, particularly in the presence of disorders that alter tissue structure or impact function. Furthermore, functional regions do not always correspond to identifiable structural features. Understanding function at the level of individual patients-and diagnosing and treating such patients-often requires techniques capable of correlating neural activity with cognition, behavior, and experience in anatomically precise ways.Approach. Recent advances in brain-computer interface technology have given rise to a new generation of electrophysiologic tools for scalable, nondestructive functional mapping with spatial precision in the range of tens to hundreds of micrometers, and temporal resolutions in the range of tens to hundreds of microseconds. Here we describe our initial intraoperative experience with novel, thin-film arrays containing 1024 surface microelectrodes for electrocorticographic mapping in a first-in-human study.Main results. Eight patients undergoing standard electrophysiologic cortical mapping during resection of eloquent-region brain tumors consented to brief sessions of concurrent mapping (micro-electrocorticography) using the novel arrays. Four patients underwent motor mapping using somatosensory evoked potentials (SSEPs) while under general anesthesia, and four underwent awake language mapping, using both standard paradigms and the novel microelectrode array. SSEP phase reversal was identified in the region predicted by conventional mapping, but at higher resolution (0.4 mm) and as a contour rather than as a point. In Broca's area (confirmed by direct cortical stimulation), speech planning was apparent in the micro-electrocorticogram as high-amplitude beta-band activity immediately prior to the articulatory event.Significance. These findings support the feasibility and potential clinical utility of incorporating micro-electrocorticography into the intraoperative workflow for systematic cortical mapping of functional brain regions.},
}
@article {pmid39869167,
year = {2025},
author = {Sun, L and Duan, S},
title = {The Paraventricular Hypothalamus: A Sorting Center for Visceral and Somatic Pain.},
journal = {Neuroscience bulletin},
volume = {41},
number = {4},
pages = {731-733},
pmid = {39869167},
issn = {1995-8218},
}
@article {pmid39866662,
year = {2025},
author = {Chen, L and Hu, Y and Wang, Z and Zhang, L and Jian, C and Cheng, S and Ming, D},
title = {Effects of transcutaneous auricular vagus nerve stimulation (taVNS) on motor planning: a multimodal signal study.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {35},
pmid = {39866662},
issn = {1871-4080},
abstract = {Motor planning plays a pivotal role in daily life. Transcutaneous auricular vagus nerve stimulation (taVNS) has been demonstrated to enhance decision-making efficiency, illustrating its potential use in cognitive modulation. However, current research primarily focuses on behavioral and single-modal electrophysiological signal, such as electroencephalography (EEG) and electrocardiography (ECG). To investigate the effect of taVNS on motor planning, a total of 21 subjects were recruited for this study and were divided into two groups: active group (n = 10) and sham group (n = 11). Each subject was required to be involved in a single-blind, sham-controlled, between-subject end-state comfort (ESC) experiment. The study compared behavioral indicators and electrophysiological features before and following taVNS. The results indicated a notable reduction in reaction time and an appreciable increase in the proportion of end-state comfort among the participants following taVNS, accompanied by notable alterations in motor-related cortical potential (MRCP) amplitude, low-frequency power of HRV (LF), and cortico-cardiac coherence, particularly in the parietal and occipital regions. These findings show that taVNS may impact the brain and heart, potentially enhancing their interaction, and improve participants' ability of motor planning.},
}
@article {pmid39866661,
year = {2025},
author = {Mathumitha, R and Maryposonia, A},
title = {Emotion analysis of EEG signals using proximity-conserving auto-encoder (PCAE) and ensemble techniques.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {32},
pmid = {39866661},
issn = {1871-4080},
abstract = {Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders. Recently, several deep learning (DL) based approaches have been developed for accurate emotion recognition tasks. Yet, previous works often struggle with poor recognition accuracy, high dimensionality, and high computational time. This research work designed an innovative framework named Proximity-conserving Auto-encoder (PCAE) for accurate emotion recognition based on EEG signals and resolves challenges faced by traditional emotion analysis techniques. For preserving local structures among the EEG data and reducing dimensionality, the proposed PCAE approach is introduced and it captures the essential features related to emotional states. The EEG data are collected from the EEG Brainwave dataset using a Muse EEG headband and applying preprocessing steps to enhance signal quality. The proposed PCAE model incorporates multiple convolution and deconvolution layers for encoding and decoding and deploys a Local Proximity Preservation Layer for preserving local correlations in the latent space. In addition, it develops a Proximity-conserving Squeeze-and-Excitation Auto-encoder (PC-SEAE) model to further improve the feature extraction ability of the PCAE technique. The proposed PCAE technique utilizes Maximum Mean Discrepancy (MMD) regularization to decrease the distribution discrepancy between input data and the extracted features. Moreover, the proposed model designs an ensemble model for emotion categorization that incorporates a one-versus-support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM) networks by utilizing each classifier's strength to enhance classification accuracy. Further, the performance of the proposed PCAE model is evaluated using diverse performance measures and the model attains outstanding results including accuracy, precision, and Kappa coefficient of 98.87%, 98.69%, and 0.983 respectively. This experimental validation proves that the proposed PCAE framework provides a significant contribution to accurate emotion recognition and classification systems.},
}
@article {pmid39866660,
year = {2025},
author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y},
title = {Joint disentangled representation and domain adversarial training for EEG-based cross-session biometric recognition in single-task protocols.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {31},
pmid = {39866660},
issn = {1871-4080},
abstract = {The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.},
}
@article {pmid39866658,
year = {2025},
author = {Zhou, Y and Song, Y and Song, X and He, F and Xu, M and Ming, D},
title = {Review of directional leads, stimulation patterns and programming strategies for deep brain stimulation.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {33},
pmid = {39866658},
issn = {1871-4080},
abstract = {Deep brain stimulation (DBS) is a well-established treatment for both neurological and psychiatric disorders. Directional DBS has the potential to minimize stimulation-induced side effects and maximize clinical benefits. Many new directional leads, stimulation patterns and programming strategies have been developed in recent years. Therefore, it is necessary to review new progress in directional DBS. This paper summarizes progress for directional DBS from the perspective of directional DBS leads, stimulation patterns, and programming strategies which are three key elements of DBS systems. Directional DBS leads are reviewed in electrode design and volume of tissue activated visualization strategies. Stimulation patterns are reviewed in stimulation parameters and advances in stimulation patterns. Programming strategies are reviewed in computational modeling, monopolar review, direction indicators and adaptive DBS. This review will provide a comprehensive overview of primary directional DBS leads, stimulation patterns and programming strategies, making it helpful for those who are developing DBS systems.},
}
@article {pmid39862529,
year = {2025},
author = {Lan, Z and Li, Z and Yan, C and Xiang, X and Tang, D and Wu, M and Chen, Z},
title = {RMKD: Relaxed matching knowledge distillation for short-length SSVEP-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {185},
number = {},
pages = {107133},
doi = {10.1016/j.neunet.2025.107133},
pmid = {39862529},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Evoked Potentials, Visual/physiology ; Algorithms ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Accurate decoding of electroencephalogram (EEG) signals in the shortest possible time is essential for the realization of a high-performance brain-computer interface (BCI) system based on the steady-state visual evoked potential (SSVEP). However, the degradation of decoding performance of short-length EEG signals is often unavoidable due to the reduced information, which hinders the development of BCI systems in real-world applications. In this paper, we propose a relaxed matching knowledge distillation (RMKD) method to transfer both feature-level and logit-level knowledge in a relaxed manner to improve the decoding performance of short-length EEG signals. Specifically, the long-length EEG signals and short-length EEG signals are decoded into the frequency representation by the teacher and student models, respectively. At the feature-level, the frequency-masked generation distillation is designed to improve the representation ability of student features by forcing the randomly masked student features to generate full teacher features. At the logit-level, the non-target class knowledge distillation and the inter-class relation distillation are combined to mitigate loss conflicts by imitating the distribution of non-target classes and preserve the inter-class relation in the prediction vectors of the teacher and student models. We conduct comprehensive experiments on two public SSVEP datasets in the subject-independent scenario with six different signal lengths. The extensive experimental results demonstrate that the proposed RMKD method has significantly improved the decoding performance of short-length EEG signals in SSVEP-based BCI systems.},
}
@article {pmid39860813,
year = {2025},
author = {Mallat, S and Hkiri, E and Albarrak, AM and Louhichi, B},
title = {A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {2},
pages = {},
pmid = {39860813},
issn = {1424-8220},
support = {IMSIU-RG23077//Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Algorithms ; },
abstract = {Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities. Despite this promise, extracting reliable control signals from noisy brain data remains a critical challenge. In this paper, we introduce a novel approach leveraging the collaborative synergy of five convolutional neural network (CNN) models to improve the classification accuracy of motor imagery tasks, which are essential components of BCI systems. Our method demonstrates exceptional performance, achieving an accuracy of 79.44% on the BCI Competition IV 2a dataset, surpassing existing state-of-the-art techniques in using multiple CNN models. This advancement offers significant promise for enhancing the efficacy and versatility of BCIs in a wide range of real-world applications, from assistive technologies to neurorehabilitation, thereby providing robust solutions for individuals with motor disabilities.},
}
@article {pmid39860606,
year = {2025},
author = {Baruch, Y and Barba, M and Cola, A and Frigerio, M},
title = {The Role of Anterior Vaginal Prolapse in Co-Existent Underactive Overactive Bladder Syndrome-A Retrospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
pmid = {39860606},
issn = {2077-0383},
abstract = {Background: CUOB (co-existent underactive overactive bladder) syndrome is a clinical entity that embraces storage and emptying symptoms, not strictly correlated with urodynamic findings. We assessed the differences between patients diagnosed with CUOB with/without cystocele. Methods: The study group was allocated from 2000 women who underwent urodynamic studies between 2008 and 2016. The demographic and clinical data of 369 patients with complaints consistent with CUOB were retrieved. The study group was subdivided using the Pelvic Organ Prolapse Quantification System. The International Consultation on Incontinence Questionnaire Short Form (ICIQ-UI SF) was used to quantify LUTS severity. Results: A total of 185 women had no or grade I cystocele (group 1), and 185 had grade II or III cystocele (group 2). No difference in mean age was computed. Patients from group 1 had a higher BMI (27 vs. 25, p = 0.02). Risk factors for prolapse, such as parity (1.7 vs. 2.1, p = 0.001) and maximal birthweight (3460 g vs. 3612 g, p = 0.049), were higher in group 2. Pelvic Organ Prolapse symptoms were 4.5 times more frequent in group 2 [n = 36/185 (19.5%) vs. n = 162/184 (88%) p < 0.001]. The rate of stress (70.8% vs. 55.4%, p = 0.002) and urge (64.9% vs. 50%, p = 0.04), urinary incontinence, and ICIQ-UI-SF scores (8 vs. 5, p < 0.001) were higher in group 1. Qmax measured lower in group 2 (17 vs. 15 mL/s, p = 0.008). Detrusor pressure at maximum flow was identical (24 cm H2O). The Bladder Contractility Index (BCI) was higher in group 1 (108 vs. 96.5, p = 0.017), and weak contraction (BCI < 100) was more common in group 2 (73/185; 39.5% vs. 95/184; 52.7%, p = 0.011). Conclusions: Based on our results, we assume that CUOB could be further subdivided based on its association with cystocele. The effect of prolapse repair in women with CUOB and cystocele remains to be evaluated in order to afford better counseling in the future.},
}
@article {pmid39860555,
year = {2025},
author = {Onciul, R and Tataru, CI and Dumitru, AV and Crivoi, C and Serban, M and Covache-Busuioc, RA and Radoi, MP and Toader, C},
title = {Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.},
journal = {Journal of clinical medicine},
volume = {14},
number = {2},
pages = {},
pmid = {39860555},
issn = {2077-0383},
abstract = {The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain-computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the "black-box" nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.},
}
@article {pmid39858677,
year = {2024},
author = {Wen, B and Shen, L and Kang, X},
title = {Laser Welding of Micro-Wire Stent Electrode as a Minimally Invasive Endovascular Neural Interface.},
journal = {Micromachines},
volume = {16},
number = {1},
pages = {},
pmid = {39858677},
issn = {2072-666X},
support = {61904038//National Natural Science Foundation of China/ ; 2021YFC0122700//National Key Research and Development Program of China/ ; 2021SHZDZX0103//Shanghai Municipal Science and Technology Major Project/ ; },
abstract = {Minimally invasive endovascular stent electrodes are an emerging technology in neural engineering, designed to minimize the damage to neural tissue. However, conventional stent electrodes often rely on resistive welding and are relatively bulky, restricting their use primarily to large animals or thick blood vessels. In this study, the feasibility is explored of fabricating a laser welding stent electrode as small as 300 μm. A high-precision laser welding technique was developed to join micro-wire electrodes without compromising structural integrity or performance. To ensure consistent results, a novel micro-wire welding with platinum pad method was introduced during the welding process. The fabricated electrodes were integrated with stent structures and subjected to detailed electrochemical performance testing to evaluate their potential as neural interface components. The laser-welded endovascular stent electrodes exhibited excellent electrochemical properties, including low impedance and stable charge transfer capabilities. At the same time, in this study, a simulation is conducted of the electrode distribution and arrangement on the stent structure, optimizing the utilization of the available surface area for enhanced functionality. These results demonstrate the potential of the fabricated electrodes for high-performance neural interfacing in endovascular applications. The approach provided a promising solution for advancing endovascular neural engineering technologies, particularly in applications requiring compact electrode designs.},
}
@article {pmid39856396,
year = {2025},
author = {Qin, Y and Zhang, L and Yu, B},
title = {A cross-domain-based channel selection method for motor imagery.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {39856396},
issn = {1741-0444},
support = {51977020//National Natural Science Foundation of China/ ; },
abstract = {Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.},
}
@article {pmid39856240,
year = {2025},
author = {Zhou, Y and Tang, X and Zhang, D and Lee, HJ},
title = {Machine learning empowered coherent Raman imaging and analysis for biomedical applications.},
journal = {Communications engineering},
volume = {4},
number = {1},
pages = {8},
pmid = {39856240},
issn = {2731-3395},
support = {82372011//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12074339//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.},
}
@article {pmid39856041,
year = {2025},
author = {Lai, QL and Cai, MT and Li, EC and Fang, GL and Shen, CH and Xu, YF and Qiao, S and Wang, JJ and Weng, QJ and Zhang, YX},
title = {Neurofilament light chain levels in neuronal surface antibody-associated autoimmune encephalitis: a systematic review and meta-analysis.},
journal = {Translational psychiatry},
volume = {15},
number = {1},
pages = {25},
pmid = {39856041},
issn = {2158-3188},
support = {LQ23H090004//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82222069//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073857//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Neurofilament Proteins/blood/cerebrospinal fluid/immunology ; *Autoantibodies/blood/cerebrospinal fluid ; *Hashimoto Disease/immunology/blood/cerebrospinal fluid ; *Encephalitis/immunology/blood/cerebrospinal fluid ; Biomarkers/blood/cerebrospinal fluid ; },
abstract = {BACKGROUND: Neuronal surface antibody-associated autoimmune encephalitis (NSAE) is a group of neuro-inflammatory disorders that is mediated by autoantibodies against the cell-surface and synaptic antigens. Studies have explored the role of neurofilament light chain (NfL) in NSAE and provided inconsistent data. We performed a systematic review and meta-analysis to evaluate the NfL levels in the serum and cerebrospinal fluid (CSF) of patients with NSAE.<br><br>METHODS: The National Center for Biotechnology Information (NCBI, PubMed), Web of Knowledge, and the Cochrane Library databases were searched for studies reporting NfL levels in patients with NSAE. Random-effects meta-analysis was used to pool results across studies.<br><br>RESULTS: Thirteen studies were included in the final systematic review and meta-analysis. The serum NfL levels were significantly higher in patients with NSAE compared to unaffected controls (standardized mean difference [SMD]&#x2009;=&#x2009;0.909, 95% confidence interval [CI]: 0.536-1.282). Similarly, the CSF NfL levels were elevated in patients with NSAE (SMD&#x2009;=&#x2009;0.897, 95% CI: 0.508-1.286). The serum and CSF NfL levels were not significantly correlated with disease severity, prognosis, and abnormalities in magnetic resonance imaging, electroencephalography, and CSF.<br><br>CONCLUSIONS: NfL levels in the serum and CSF were higher in patients with NSAE compared to unaffected controls. However, the NfL levels were not shown to be significantly associated with clinical or paraclinical features.},
}
@article {pmid39855567,
year = {2025},
author = {Wei, Y and Zhao, L and Wei, J and Yu, X and Wei, L and Ni, R and Li, T},
title = {Hippocampal transcriptome analysis in ClockΔ19 mice identifies pathways associated with glial cell differentiation and myelination.},
journal = {Journal of affective disorders},
volume = {376},
number = {},
pages = {280-293},
doi = {10.1016/j.jad.2025.01.039},
pmid = {39855567},
issn = {1573-2517},
mesh = {Animals ; *Hippocampus/metabolism ; Mice ; *Bipolar Disorder/genetics ; *Cell Differentiation ; *Neuroglia/metabolism ; *CLOCK Proteins/genetics ; *Gene Expression Profiling ; *Disease Models, Animal ; Myelin Sheath/genetics ; Transcriptome ; Mutation ; },
abstract = {BACKGROUND: ClockΔ19 mice demonstrate behavioral characteristics and neurobiological changes that closely resemble those observed in bipolar disorder (BD). Notably, abnormalities in the hippocampus have been observed in patients with BD, yet direct molecular investigation of human hippocampal tissue remains challenging due to its limited accessibility.<br><br>METHODS: To model BD, Clock&#x394;19 mice were employed. Weighted gene co-expression network analysis (WGCNA) was utilized to identify mutation-related modules, and changes in cell populations were determined using the computational deconvolution CIBERSORTx. Furthermore, GeneMANIA and protein-protein interactions (PPIs) were leveraged to construct a comprehensive interaction network.<br><br>RESULTS: 174 differentially expressed genes (DEGs) were identified, revealing abnormalities in rhythmic processes, mitochondrial metabolism, and various cell functions including morphology, differentiation, and receptor activity. Analysis identified 5 modules correlated with the mutation, with functional enrichment highlighting disturbances in rhythmic processes and neural cell differentiation due to the mutation. Furthermore, a decrease in neural stem cells (NSC), and an increase in astrocyte-restricted precursors (ARP), ependymocytes (EPC), and hemoglobin-expressing vascular cells (Hb-VC) in the mutant mice were observed. A network comprising 12 genes that link rhythmic processes to neural cell differentiation in the hippocampus was also identified.<br><br>LIMITATIONS: This study focused on the hippocampus of mice, hence the applicability of these findings to human patients warrants further exploration.<br><br>CONCLUSION: The Clock&#x394;19 mutation may disrupt circadian rhythm, myelination, and the differentiation of neural stem cells (NSCs) into glial cells. These abnormalities are linked to altered expression of key genes, including DPB, CIART, NR1D1, GFAP, SLC20A2, and KL. Furthermore, interactions between SLC20A2 and KL might provide a connection between circadian rhythm regulation and cell type transitions.},
}
@article {pmid39854845,
year = {2025},
author = {Bellicha, A and Struber, L and Pasteau, F and Juillard, V and Devigne, L and Karakas, S and Chabardes, S and Babel, M and Charvet, G},
title = {Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae36},
pmid = {39854845},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/instrumentation/methods ; *Wheelchairs ; *Quadriplegia/rehabilitation ; Self-Help Devices ; Male ; Adult ; Electrocorticography/methods/instrumentation ; },
abstract = {Objective.Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices. However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. This article describes assist-as-needed sensor-based shared control (SC) methods relying on the blending of BCI and depth-sensor-based control.Approach.The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram recording implant named WIMAGINE.Main results.Results showed that the proposed methods can assist BCI users in both tasks. Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided.Significance.The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed SC methods improved the performance of the two assistive devices.Clinical trial, registration number: NCT02550522.},
}
@article {pmid39854835,
year = {2025},
author = {Choi, BJ and Liu, J},
title = {A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adae35},
pmid = {39854835},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods/economics/instrumentation ; *Gestures ; *Artificial Limbs ; Male ; *Machine Learning ; Prosthesis Design ; Head/physiology ; Female ; Adult ; Neural Networks, Computer ; Algorithms ; },
abstract = {Objective.Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.Approach.To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor modeldiversitywithin these new neural network ensembles, as opposed to individual modelperformance. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.Main results.The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.Significance.These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.},
}
@article {pmid39854573,
year = {2025},
author = {Eckert, AL and Fuehrer, E and Schmitter, C and Straube, B and Fiehler, K and Endres, D},
title = {Modelling sensory attenuation as Bayesian causal inference across two datasets.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0317924},
pmid = {39854573},
issn = {1932-6203},
mesh = {Humans ; *Bayes Theorem ; Male ; Female ; Adult ; Movement/physiology ; Markov Chains ; Touch/physiology ; Young Adult ; Touch Perception/physiology ; },
abstract = {INTRODUCTION: To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.<br><br>METHODS: Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were moving vs. when their arm was moved passively. We reconsider these results from the perspective of Bayesian Causal Inference (BCI). Using a hierarchical Markov Model (HMM) and variational message passing, we first qualitatively capture patterns of task behavior and sensory attenuation in simulations. Next, we identify participant-specific model parameters for both experiments using optimization.<br><br>RESULTS: A sequential BCI model is well equipped to capture empirical patterns of SA across both datasets. Using participant-specific optimized model parameters, we find a good agreement between data and model predictions, with the model capturing both tactile detections in Experiment 1 and delay detections in Experiment 2.<br><br>DISCUSSION: BCI is an appropriate framework to model sensory attenuation in humans. Computational models of sensory attenuation may help to bridge the gap across different sensory modalities and experimental paradigms and may contribute towards an improved description and understanding of deficits in specific patient groups (e.g. schizophrenia).},
}
@article {pmid39851820,
year = {2024},
author = {Yu, W and Wu, C},
title = {Development and Validation of the Interpreting Learning Engagement Scale (ILES).},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {1},
pages = {},
pmid = {39851820},
issn = {2076-328X},
support = {20CG40//Shanghai Chenguang Talent Program/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//open project of Key Laboratory of Multilingual Education with AI/ ; C2022411//Shanghai Education and Science Research Project/ ; 2019114002//Shanghai International Studies University Planned Project/ ; },
abstract = {This study developed and validated the Interpreting Learning Engagement Scale (ILES), which was designed to measure the engagement of students in the interpreting learning context. Recognizing the crucial role of learning engagement in academic success and the acquisition of interpreting skills, which demands considerable cognitive effort and active involvement, this research addresses the gap in empirical studies on engagement within the field of interpreting. The ILES, comprising 18 items across four dimensions (behavioral, emotional, cognitive, and agentic engagement), was validated with data collected from a cohort of 306 students from five universities in China. The study employed exploratory and confirmatory factor analyses to establish the scale's theoretical underpinnings and provided further reliability and validity evidence, demonstrating its adequate psychometric properties. Additionally, the scale's scores showed a significant correlation with grit, securing the external validity of the ILES. This study not only contributes a validated instrument for assessing student engagement in interpreting learning but also provides implications for promoting engagement through potential interventions, with the ultimate aim of achieving high levels of interpreting competence.},
}
@article {pmid39851633,
year = {2024},
author = {Ding, X and Zhang, Z and Wang, K and Xiao, X and Xu, M},
title = {A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.},
journal = {Entropy (Basel, Switzerland)},
volume = {27},
number = {1},
pages = {},
pmid = {39851633},
issn = {1099-4300},
abstract = {Brain-computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. However, traditional intention-recognition methods face several challenges, such as prolonged training times and limited cross-subject adaptability, which restrict their practical application. This paper proposes an innovative method that combines a lightweight convolutional neural network (CNN) with domain adaptation. A lightweight feature extraction module is designed to extract key features from both the source and target domains, effectively reducing the model's parameters and improving the real-time performance and computational efficiency. To address differences in sample distributions, a domain adaptation strategy is introduced to optimize the feature alignment. Furthermore, domain adversarial training is employed to promote the learning of domain-invariant features, significantly enhancing the model's cross-subject generalization ability. The proposed method was evaluated on an fNIRS motor imagery dataset, achieving an average accuracy of 87.76% in a three-class classification task. Additionally, lightweight experiments were conducted from two perspectives: model structure optimization and data feature selection. The results demonstrated the potential advantages of this method for practical applications in motor imagery recognition systems.},
}
@article {pmid39851418,
year = {2025},
author = {Zhu, X and Meng, M and Yan, Z and Luo, Z},
title = {Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851418},
issn = {2076-3425},
support = {62271181 and 62171171//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain-computer interface (MI-BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial.<br><br>OBJECTIVES: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks.<br><br>METHODS: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification.<br><br>RESULTS: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively.<br><br>CONCLUSIONS: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness.},
}
@article {pmid39851397,
year = {2024},
author = {Suresh, RE and Zobaer, MS and Triano, MJ and Saway, BF and Grewal, P and Rowland, NC},
title = {Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851397},
issn = {2076-3425},
support = {P20 GM109040/GM/NIGMS NIH HHS/United States ; 5 P20 GM109040//Center of Biomedical Research Excellence (COBRE) in Stroke Recovery-Junior Investigator Research Project. Source: National Institutes of Health/ ; },
abstract = {BACKGROUND/OBJECTIVES: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation.<br><br>METHODS: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning.<br><br>RESULTS: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants.<br><br>CONCLUSIONS: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.},
}
@article {pmid39851395,
year = {2024},
author = {Pan, S and Shen, T and Lian, Y and Shi, L},
title = {A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder.},
journal = {Brain sciences},
volume = {15},
number = {1},
pages = {},
pmid = {39851395},
issn = {2076-3425},
support = {23CJL006//National Social Science Fund of China/ ; },
abstract = {BACKGROUND: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.<br><br>METHODS: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals.<br><br>RESULTS: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates.<br><br>CONCLUSIONS: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes.},
}
@article {pmid39850117,
year = {2025},
author = {Mahheidari, N and Alizadeh, M and Kamalabadi Farahani, M and Arabpour, Z and Rezaei Kolarijani, N and Djalilian, AR and Salehi, M},
title = {Regeneration of the skin wound by two different crosslinkers: In vitro and in vivo studies.},
journal = {Iranian journal of basic medical sciences},
volume = {28},
number = {2},
pages = {194-208},
pmid = {39850117},
issn = {2008-3866},
abstract = {OBJECTIVES: For designing a suitable hydrogel, two crosslinked Alginate/ Carboxymethyl cellulose (Alg/CMC) hydrogel, using calcium chloride (Ca[2+]) and glutaraldehyde (GA) as crosslinking agents were synthesized and compared.<br><br>MATERIALS AND METHODS: All samples were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Blood compatibility (BC), Blood clotting index (BCI), weight loss (WL), water absorption (WA), pH, and Electrochemical Impedance Spectroscopy (EIS). Cell viability and cell migration were investigated using the MTT assay and the wound scratch test, respectively. Besides, the wound healing potential of prepared hydrogels was evaluated on the rat models with full-thickness skin excision. To further investigation, TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were also reported by RT-PCR.<br><br>RESULTS: Water absorption and weight loss properties were compared between different crosslinker agents, and the most nontoxic crosslinker concentration was determined. We have shown that GA (20 &#xb5;l/ml) and Ca[2+] (50 or 75 mM) enhanced the physical stability of Alg-CMC hydrogel, and they are nontoxic and suitable crosslinkers for wound dressing applications. Although in vivo assessments indicated that the GA (20 &#xb5;l/ml) had a cytotoxic effect on tissue repair, Ca[2+] (75 mM) boosted the wound healing process. Further, RT-PCR results revealed that TGF &#x3b2;1, IGF-I, COL1, ACT-A (alfa-SMA), and GAPDH expression levels were increased in GA (20 &#xb5;l/ml). Moreover, this trend is the opposite in the Ca[2+] (75 mM) treatment groups.<br><br>CONCLUSION: This research shows that Ca[2+] (75 mM) boosts tissue regeneration and wound healing process.},
}
@article {pmid39850073,
year = {2024},
author = {He, J and Huang, Z and Li, Y and Shi, J and Chen, Y and Jiang, C and Feng, J},
title = {Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1481493},
pmid = {39850073},
issn = {1662-5161},
abstract = {INTRODUCTION: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.<br><br>METHODS: The proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to preserve critical EEG components. The norm-constrained ELM employs L1/L2 regularization to improve generalization and classification performance. Experimental data were collected using a Schulte Grid paradigm and TGAM sensors, along with publicly available datasets for validation.<br><br>RESULTS: The robust Kalman filter demonstrated superior denoising performance, achieving an average AUC of 0.8167 and a maximum AUC of 0.8678 on self-collected datasets, and an average AUC of 0.8344 with a maximum of 0.8950 on public datasets. The method outperformed traditional Kalman filtering, LMS adaptive filtering, and TGAM's eSense algorithm in both noise reduction and attention classification accuracy.<br><br>DISCUSSION: The study highlights the effectiveness of combining advanced signal processing and machine learning techniques to improve the robustness and generalization of EEG-based attention classification. Limitations include the small sample size and limited demographic diversity, suggesting future research should expand participant groups and explore broader applications, such as mental health monitoring and neurofeedback.},
}
@article {pmid39846559,
year = {2025},
author = {Pantaleo, A and Curatoli, L and Cavallaro, G and Auricchio, D and Murri, A and Quaranta, N},
title = {Unilateral Versus Bilateral Cochlear Implants in Adults: A Cross-Sectional Questionnaire Study Across Multiple Hearing Domains.},
journal = {Audiology research},
volume = {15},
number = {1},
pages = {},
pmid = {39846559},
issn = {2039-4330},
abstract = {AIM: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.<br><br>METHODS: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale. Statistical analyses were performed to compare the subjective experiences of the three groups.<br><br>RESULTS: Patients reported that understanding speech in noise and tinnitus perception were their main concerns. BCI users experienced fewer difficulties with understanding speech in both quiet (p < 0.001) and noisy (p = 0.008) environments and with perceiving non-vocal sounds (p = 0.038) compared to UCI and BM users; no significant differences were found for music perception (p = 0.099), tinnitus perception (p = 0.397), or voice naturalness (p = 0.157). BCI users also reported less annoyance in quiet (p = 0.004) and noisy (p = 0.047) environments, and in the perception of voices (p = 0.009) and non-vocal sounds (p = 0.019). Tinnitus-related psychological impact showed no significant differences between groups (p = 0.090).<br><br>CONCLUSIONS: Although speech perception in noise and tinnitus remain major problems for CI users, the results of our study suggest that bilateral cochlear implantation offers significant subjective advantages over unilateral implantation and bimodal stimulation in adults, particularly in difficult listening environments.},
}
@article {pmid39844431,
year = {2025},
author = {Guo, M and Han, X and Liu, H and Zhu, J and Zhang, J and Bai, Y and Ni, G},
title = {MI-Mamba: A hybrid motor imagery electroencephalograph classification model with Mamba's global scanning.},
journal = {Annals of the New York Academy of Sciences},
volume = {1544},
number = {1},
pages = {242-253},
doi = {10.1111/nyas.15288},
pmid = {39844431},
issn = {1749-6632},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long-term dependencies in sequential EEG data. Models like long short-term memory and transformers improve performance but still face challenges of computational efficiency and long sequences. Mamba, a state space model-based method, excels in modeling long sequences. To overcome the limitations of existing EEG decoding models and exploit Mamba's potential in EEG analysis, we propose MI-Mamba, a model integrating CNN with Mamba for motor imagery (MI) data decoding. MI-Mamba processes multi-channel EEG signals through a single convolutional layer to capture spatial features in the local temporal domain, followed by a Mamba module that processes global temporal features. A fully connected, layer-based classifier is used to derive classification results. Evaluated on two public MI datasets, MI-Mamba achieves 80.59% accuracy in the four-class MI task of the BCI Competition IV 2a dataset and 84.42% in the two-class task of the BCI Competition IV 2b dataset, while reducing parameter count by nearly six times compared to the most advanced previous models. These results highlight MI-Mamba's effectiveness in MI decoding and its potential as a new backbone for general EEG decoding.},
}
@article {pmid39843750,
year = {2025},
author = {O'Connell, KS and Koromina, M and van der Veen, T and Boltz, T and David, FS and Yang, JMK and Lin, KH and Wang, X and Coleman, JRI and Mitchell, BL and McGrouther, CC and Rangan, AV and Lind, PA and Koch, E and Harder, A and Parker, N and Bendl, J and Adorjan, K and Agerbo, E and Albani, D and Alemany, S and Alliey-Rodriguez, N and Als, TD and Andlauer, TFM and Antoniou, A and Ask, H and Bass, N and Bauer, M and Beins, EC and Bigdeli, TB and Pedersen, CB and Boks, MP and Børte, S and Bosch, R and Brum, M and Brumpton, BM and Brunkhorst-Kanaan, N and Budde, M and Bybjerg-Grauholm, J and Byerley, W and Cabana-Domínguez, J and Cairns, MJ and Carpiniello, B and Casas, M and Cervantes, P and Chatzinakos, C and Chen, HC and Clarence, T and Clarke, TK and Claus, I and Coombes, B and Corfield, EC and Cruceanu, C and Cuellar-Barboza, A and Czerski, PM and Dafnas, K and Dale, AM and Dalkner, N and Degenhardt, F and DePaulo, JR and Djurovic, S and Drange, OK and Escott-Price, V and Fanous, AH and Fellendorf, FT and Ferrier, IN and Forty, L and Frank, J and Frei, O and Freimer, NB and Fullard, JF and Garnham, J and Gizer, IR and Gordon, SD and Gordon-Smith, K and Greenwood, TA and Grove, J and Guzman-Parra, J and Ha, TH and Hahn, T and Haraldsson, M and Hautzinger, M and Havdahl, A and Heilbronner, U and Hellgren, D and Herms, S and Hickie, IB and Hoffmann, P and Holmans, PA and Huang, MC and Ikeda, M and Jamain, S and Johnson, JS and Jonsson, L and Kalman, JL and Kamatani, Y and Kennedy, JL and Kim, E and Kim, J and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Kranz, TM and Krebs, K and Kushner, SA and Lavebratt, C and Lawrence, J and Leber, M and Lee, HJ and Liao, C and Lucae, S and Lundberg, M and MacIntyre, DJ and Maier, W and Maihofer, AX and Malaspina, D and Manchia, M and Maratou, E and Martinsson, L and Mattheisen, M and McGregor, NW and McInnis, MG and McKay, JD and Medeiros, H and Meyer-Lindenberg, A and Millischer, V and Morris, DW and Moutsatsou, P and Mühleisen, TW and O'Donovan, C and Olsen, CM and Panagiotaropoulou, G and Papiol, S and Pardiñas, AF and Park, HY and Perry, A and Pfennig, A and Pisanu, C and Potash, JB and Quested, D and Rapaport, MH and Regeer, EJ and Rice, JP and Rivera, M and Schulte, EC and Senner, F and Shadrin, A and Shilling, PD and Sigurdsson, E and Sindermann, L and Sirignano, L and Siskind, D and Slaney, C and Sloofman, LG and Smeland, OB and Smith, DJ and Sobell, JL and Soler Artigas, M and Stein, DJ and Stein, F and Su, MH and Sung, H and Świątkowska, B and Terao, C and Tesfaye, M and Tesli, M and Thorgeirsson, TE and Thorp, JG and Toma, C and Tondo, L and Tooney, PA and Tsai, SJ and Tsermpini, EE and Vawter, MP and Vedder, H and Vreeker, A and Walters, JTR and Winsvold, BS and Witt, SH and Won, HH and Ye, R and Young, AH and Zandi, PP and Zillich, L and , and Adolfsson, R and Alda, M and Alfredsson, L and Backlund, L and Baune, BT and Bellivier, F and Bengesser, S and Berrettini, WH and Biernacka, JM and Boehnke, M and Børglum, AD and Breen, G and Carr, VJ and Catts, S and Cichon, S and Corvin, A and Craddock, N and Dannlowski, U and Dikeos, D and Etain, B and Ferentinos, P and Frye, M and Fullerton, JM and Gawlik, M and Gershon, ES and Goes, FS and Green, MJ and Grigoroiu-Serbanescu, M and Hauser, J and Henskens, FA and Hjerling-Leffler, J and Hougaard, DM and Hveem, K and Iwata, N and Jones, I and Jones, LA and Kahn, RS and Kelsoe, JR and Kircher, T and Kirov, G and Kuo, PH and Landén, M and Leboyer, M and Li, QS and Lissowska, J and Lochner, C and Loughland, C and Luykx, JJ and Martin, NG and Mathews, CA and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Medland, SE and Melle, I and Milani, L and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Müller-Myhsok, B and Myers, RM and Myung, W and Neale, BM and Nievergelt, CM and Nordentoft, M and Nöthen, MM and Nurnberger, JI and O'Donovan, MC and Oedegaard, KJ and Olsson, T and Owen, MJ and Paciga, SA and Pantelis, C and Pato, CN and Pato, MT and Patrinos, GP and Pawlak, JM and Ramos-Quiroga, JA and Reif, A and Reininghaus, EZ and Ribasés, M and Rietschel, M and Ripke, S and Rouleau, GA and Roussos, P and Saito, T and Schall, U and Schalling, M and Schofield, PR and Schulze, TG and Scott, LJ and Scott, RJ and Serretti, A and Smoller, JW and Squassina, A and Stahl, EA and Stefansson, H and Stefansson, K and Stordal, E and Streit, F and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Waldman, ID and Weickert, CS and Weickert, TW and Werge, T and Whiteman, DC and Zwart, JA and Edenberg, HJ and McQuillin, A and Forstner, AJ and Mullins, N and Di Florio, A and Ophoff, RA and Andreassen, OA and , },
title = {Genomics yields biological and phenotypic insights into bipolar disorder.},
journal = {Nature},
volume = {639},
number = {8056},
pages = {968-975},
pmid = {39843750},
issn = {1476-4687},
support = {001/WHO_/World Health Organization/International ; },
mesh = {*Bipolar Disorder/genetics ; Humans ; *Phenotype ; *Genome-Wide Association Study ; *Genomics ; Male ; Female ; White People/genetics ; Genetic Predisposition to Disease ; GABAergic Neurons/metabolism ; Asian People/genetics ; Hispanic or Latino/genetics ; Black or African American/genetics ; White ; },
abstract = {Bipolar disorder is a leading contributor to the global burden of disease[1]. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown[2]. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings[3], and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder[4], highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.},
}
@article {pmid39842626,
year = {2025},
author = {Xu, X and Gong, Q and Wang, XD},
title = {MK-801 attenuates one-trial tolerance in the elevated plus maze via the thalamic nucleus reuniens.},
journal = {Neuropharmacology},
volume = {268},
number = {},
pages = {110318},
doi = {10.1016/j.neuropharm.2025.110318},
pmid = {39842626},
issn = {1873-7064},
mesh = {Animals ; *Dizocilpine Maleate/pharmacology ; Male ; Mice ; *Anxiety/drug therapy ; *Midline Thalamic Nuclei/drug effects/physiology ; Drug Tolerance/physiology ; Diazepam/pharmacology ; Excitatory Amino Acid Antagonists/pharmacology ; Elevated Plus Maze Test ; Anti-Anxiety Agents/pharmacology ; Proto-Oncogene Proteins c-fos/metabolism ; Mice, Inbred C57BL ; Maze Learning/drug effects/physiology ; Neurons/drug effects/physiology ; },
abstract = {Anxiety, a future-oriented negative emotional state, is characterized by heightened arousal and vigilance. The elevated plus maze (EPM) test is a widely used assay of anxiety-related behaviors in rodents and shows a phenomenon where animals with prior test experience tend to avoid open arms in retest sessions. While this one-trial tolerance (OTT) phenomenon limits the reuse of the EPM test, the potential mechanisms remain unsolved. Here, we found that neither anxiogenic factors like acute restraint stress nor anxiolytic factors like diazepam (2 mg/kg) influenced the emergence of the OTT phenomenon in mice in the EPM test. In contrast, OTT was markedly attenuated by MK-801 (0.1 mg/kg), a non-competitive N-methyl-D-aspartate receptor antagonist. Through the use of c-fos mapping, MK-801 was found to increase neuronal activation in the thalamic nucleus reuniens (Re). Moreover, chemogenetic inactivation of Re neurons could prevent the effects of MK-801. Our findings suggest the Re as a crucial brain region in emotional adaptation in the EPM and shed light on the experimental design optimization and mechanistic investigation of anxiety-related behaviors.},
}
@article {pmid39841068,
year = {2025},
author = {Zhang, J and Yang, X and Liang, Z and Lou, H and Cui, T and Shen, C and Gao, Z},
title = {A brain-computer interface system for lower-limb exoskeletons based on motor imagery and stacked ensemble approach.},
journal = {The Review of scientific instruments},
volume = {96},
number = {1},
pages = {},
doi = {10.1063/5.0232481},
pmid = {39841068},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; Humans ; *Exoskeleton Device ; *Lower Extremity/physiology ; Support Vector Machine ; Algorithms ; Imagination/physiology ; Electroencephalography/instrumentation ; Male ; },
abstract = {Existing lower limb exoskeletons (LLEs) have demonstrated a lack of sufficient patient involvement during rehabilitation training. To address this issue and better incorporate the patient's motion intentions, this paper proposes an online brain-computer interface (BCI) system for LLE based motor imagery and stacked ensemble. The establishment of this online BCI system enables a comprehensive closed-loop control process, which includes the collection and decoding of brain signals, robotic control, and real-time feedback mechanisms. Additionally, an online experimental protocol that integrates visual and proprioceptive feedback is developed. To enhance decoding precision, we proposed a novel classification algorithm based on the stacking technique, termed weighted random forests-support vector machines (WRF-SVM). In this algorithm, WRFs function as the base learning models, while SVMs act as the meta-learning layer. To assess the efficacy of the BCI system and the classification algorithm, eight subjects were recruited for testing. The outcomes of both online and offline experiments exhibit high classification accuracy, confirming the viability and utility of the BCI system. We are confident that our approach holds significant promise for practical applications in the field of LLE technology.},
}
@article {pmid39840009,
year = {2024},
author = {Yang, XY and Huang, S and Fu, QJ and Galvin, J and Chen, B and Liu, JS and Tao, DD},
title = {Preliminary evaluation of the FastCAP for users of the Nurotron cochlear implant.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1523212},
pmid = {39840009},
issn = {1662-4548},
abstract = {BACKGROUND: Electrically evoked compound action potential (ECAP) can be used to measure the auditory nerve's response to electrical stimulation in cochlear implant (CI) users. In the Nurotron CI system, extracting the ECAP waveform from the stimulus artifact is time-consuming.<br><br>METHOD: We developed a new paradigm ("FastCAP") for use with Nurotron CI devices. In electrically evoked compound action potential in fast mode (FastCAP), N recordings are averaged directly on the CI hardware before data transmission, significantly reducing data transmission time. FastCAPs and ECAPs were measured across five electrodes and four stimulation levels per electrode. The FastCAP stimulation rate (33.3 Hz) is also faster than the ECAP rate (2.5 Hz).<br><br>RESULTS: Results showed strong correlations between ECAPs and FastCAPs for N1 latency (r = 0.84, p < 0.001) and N1 amplitude (r = 0.97, p < 0.001). Test-retest reliability for FastCAPs was also high, with intraclass correlation coefficients of r = 0.87 for N1 latency (p < 0.001) and r = 0.96 for N1 amplitude (p < 0.001). The mean test time was 46.9 &#xb1; 1.4 s for the FastCAP and 340.3 &#xb1; 6.3 s for the ECAP. The FastCAP measurement time was significantly shorter than the ECAP measurement time (W = -210.0, p < 0.001). FastCAP thresholds were significantly correlated with behavioral thresholds in 7/20 participants and with comfortable loudness levels in 11/20 participants. The time required to measure FastCAPs was significantly lower than that for ECAPs. The FastCAP paradigm maintained the accuracy and reliability the ECAP measurements while offering a significant reduction in time requirements.<br><br>CONCLUSION: This preliminary evaluation suggests that the FastCAP could be an effective clinical tool to optimize CI processor settings (e.g., threshold stimulation levels) in users of the Nurotron CI device.},
}
@article {pmid39839676,
year = {2024},
author = {Kusano, K and Hayashi, M and Iwama, S and Ushiba, J},
title = {Improved motor imagery skills after repetitive passive somatosensory stimulation: a parallel-group, pre-registered study.},
journal = {Frontiers in neural circuits},
volume = {18},
number = {},
pages = {1510324},
pmid = {39839676},
issn = {1662-5110},
mesh = {Humans ; Male ; Female ; Adult ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; *Electric Stimulation/methods ; Young Adult ; Motor Skills/physiology ; Somatosensory Cortex/physiology ; Sensorimotor Cortex/physiology ; },
abstract = {INTRODUCTION: Motor-imagery-based Brain-Machine Interface (MI-BMI) has been established as an effective treatment for post-stroke hemiplegia. However, the need for long-term intervention can represent a significant burden on patients. Here, we demonstrate that motor imagery (MI) instructions for BMI training, when supplemented with somatosensory stimulation in addition to conventional verbal instructions, can help enhance MI capabilities of healthy participants.<br><br>METHODS: Sixteen participants performed MI during scalp EEG signal acquisition before and after somatosensory stimulation to assess MI-induced cortical excitability, as measured using the event-related desynchronization (ERD) of the sensorimotor rhythm (SMR). The non-dominant left hand was subjected to neuromuscular electrical stimulation above the sensory threshold but below the motor threshold (St-NMES), along with passive movement stimulation using an exoskeleton. Participants were randomly divided into an intervention group, which received somatosensory stimulation, and a control group, which remained at rest without stimulation.<br><br>RESULTS: The intervention group exhibited a significant increase in SMR-ERD compared to the control group, indicating that somatosensory stimulation contributed to improving MI ability.<br><br>DISCUSSION: This study demonstrates that somatosensory stimulation, combining electrical and mechanical stimuli, can improve MI capability and enhance the excitability of the sensorimotor cortex in healthy individuals.},
}
@article {pmid39837322,
year = {2025},
author = {Coulter, ME and Gillespie, AK and Chu, J and Denovellis, EL and Nguyen, TTK and Liu, DF and Wadhwani, K and Sharma, B and Wang, K and Deng, X and Eden, UT and Kemere, C and Frank, LM},
title = {Closed-loop modulation of remote hippocampal representations with neurofeedback.},
journal = {Neuron},
volume = {113},
number = {6},
pages = {949-961.e3},
doi = {10.1016/j.neuron.2024.12.023},
pmid = {39837322},
issn = {1097-4199},
mesh = {Animals ; *Neurofeedback/methods/physiology ; Rats ; *Hippocampus/physiology ; Male ; *Rats, Long-Evans ; Place Cells/physiology ; Reward ; Cues ; Mental Recall/physiology ; },
abstract = {Humans can remember specific remote events without acting on them and influence which memories are retrieved based on internal goals. However, animal models typically present sensory cues to trigger memory retrieval and then assess retrieval based on action. Thus, it is difficult to determine whether measured neural activity patterns relate to the cue(s), the memory, or the behavior. We therefore asked whether retrieval-related neural activity could be generated in animals without cues or a behavioral report. We focused on hippocampal "place cells," which primarily represent the animal's current location (local representations) but can also represent locations away from the animal (remote representations). We developed a neurofeedback system to reward expression of remote representations and found that rats could learn to generate specific spatial representations that often jumped directly to the experimenter-defined target location. Thus, animals can deliberately engage remote representations, enabling direct study of retrieval-related activity in the brain.},
}
@article {pmid39834791,
year = {2025},
author = {Alotaibi, S and Alotaibi, MM and Alghamdi, FS and Alshehri, MA and Bamusa, KM and Almalki, ZF and Alamri, S and Alghamdi, AJ and Alhazmi, M and Osman, H and Khandaker, MU},
title = {The role of fMRI in the mind decoding process in adults: a systematic review.},
journal = {PeerJ},
volume = {13},
number = {},
pages = {e18795},
pmid = {39834791},
issn = {2167-8359},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Adult ; *Brain/physiology/diagnostic imaging ; *Brain Mapping/methods ; Mentalization/physiology ; Theory of Mind/physiology ; Temporal Lobe/physiology/diagnostic imaging ; },
abstract = {BACKGROUND: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.<br><br>METHODOLOGY: A systematic search was conducted across Web of Science, PubMed, and Google Scholar. Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms).<br><br>RESULTS: This review highlights the critical role of fMRI in uncovering the neural mechanisms of mind-reading. Key brain regions involved include the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), and temporoparietal junction (TPJ), all crucial for mentalizing (understanding others' mental states).<br><br>CONCLUSIONS: This review emphasizes the importance of fMRI in advancing our knowledge of how the brain interprets and processes mental states. It offers valuable insights into the current state of mind-reading research in adults and paves the way for future exploration in this field.},
}
@article {pmid39834638,
year = {2024},
author = {Liu, J and Yang, Z and Wang, Y and Wang, L and Li, Z},
title = {Editorial: Micro/nano devices and technologies for neural science and medical applications.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1545853},
pmid = {39834638},
issn = {2296-4185},
}
@article {pmid39833722,
year = {2025},
author = {Kocher, HM and , and , and , and Sasieni, P and Corrie, P and McNamara, MG and Sarker, D and Froeling, FEM and Christie, A and Gillmore, R and Khan, K and Propper, D},
title = {Study protocol: multi-centre, randomised controlled clinical trial exploring stromal targeting in locally advanced pancreatic cancer; STARPAC2.},
journal = {BMC cancer},
volume = {25},
number = {1},
pages = {106},
pmid = {39833722},
issn = {1471-2407},
support = {MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; MR/S036601/1/MRC_/Medical Research Council/United Kingdom ; PCRFTB//Pancreatic Cancer Research Fund/ ; PCRFTB//Pancreatic Cancer Research Fund/ ; },
mesh = {Humans ; *Pancreatic Neoplasms/drug therapy/pathology/mortality/surgery ; *Deoxycytidine/analogs &amp; derivatives/therapeutic use/administration &amp; dosage ; *Gemcitabine ; *Paclitaxel/therapeutic use/administration &amp; dosage ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Albumins/administration &amp; dosage/therapeutic use ; Carcinoma, Pancreatic Ductal/drug therapy/pathology/surgery/mortality ; Tretinoin/therapeutic use/administration &amp; dosage ; Male ; Female ; Progression-Free Survival ; Multicenter Studies as Topic ; Adult ; Clinical Trials, Phase II as Topic ; Randomized Controlled Trials as Topic ; Middle Aged ; Aged ; Stromal Cells/pathology ; },
abstract = {BACKGROUND: Pancreatic cancer (PDAC: pancreatic ductal adenocarcinoma, the commonest form), a lethal disease, is best treated with surgical excision but is feasible in less than a fifth of patients. Around a third of patients presentlocally advanced, inoperable, non-metastatic (laPDAC), whose stadrd of care is palliative chemotherapy; a small minority are down-sized sufficiently to enable surgical excision. We propose a phase II clinical trial to test whether a combination of standard chemotherapy (gemcitabine &amp; nab-Paclitaxel: GEM-NABP) and repurposing All Trans Retinoic Acid (ATRA) to target the stroma may extend progression-free survival and enable successful surgical resection for patients with laPDAC, since data from phase IB clinical trial demonstrate safety of GEM-NABP-ATRA combination to patients with advanced PDAC with potential therapeutic benefit.<br><br>METHODS: Patients with laPDAC will receive at least six cycles of GEM-NABP with 1:1 randomisation to receive this with or without ATRA to assess response, until progression or intolerance. Those with stable/responding disease may undergo surgical resection. Primary endpoint is progression free survival (PFS) defined as the time from the date of randomisation to the date of first documented tumour progression (response evaluation criteria in solid tumours [RECIST] v1.1) or death from any cause, whichever occurs first. Secondary endpoints include objective response rate (ORR), overall survival (OS), safety and tolerability, surgical resection rate, R0 surgical resection rate and patient reported outcome measures (PROMS) as measured by questionnaire EQ-5D-5L. Exploratory endpoints include a decrease or increase in CA19-9 and serum Vitamin A over time correlated with ORR, PFS, and OS.<br><br>DISCUSSION: STARPAC2 aims to assess the role of stromal targeting in laPDAC.<br><br>TRIAL REGISTRATION: EudraCT: 2019-004231-23;&#xa0;NCT04241276; ISRCTN11503604.},
}
@article {pmid39833405,
year = {2025},
author = {Willsey, MS and Shah, NP and Avansino, DT and Hahn, NV and Jamiolkowski, RM and Kamdar, FB and Hochberg, LR and Willett, FR and Henderson, JM},
title = {A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis.},
journal = {Nature medicine},
volume = {31},
number = {1},
pages = {96-104},
pmid = {39833405},
issn = {1546-170X},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01-DC017844//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; R01-DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Video Games ; *Fingers/physiopathology ; *Paralysis/rehabilitation/physiopathology ; Quadriplegia/physiopathology/rehabilitation ; Spinal Cord Injuries/physiopathology ; Male ; Adult ; },
abstract = {People with paralysis express unmet needs for peer support, leisure activities and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance, finger-based brain-computer-interface system allowing continuous control of three independent finger groups, of which the thumb can be controlled in two dimensions, yielding a total of four degrees of freedom. The system was tested in a human research participant with tetraplegia due to spinal cord injury over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets per minute and completion time of 1.58 ± 0.06 seconds-comparing favorably to prior animal studies despite a twofold increase in the decoded degrees of freedom. More importantly, finger positions were then used to control a virtual quadcopter-the number-one restorative priority for the participant-using a brain-to-finger-to-computer interface to allow dexterous navigation around fixed- and random-ringed obstacle courses. The participant expressed or demonstrated a sense of enablement, recreation and social connectedness that addresses many of the unmet needs of people with paralysis.},
}
@article {pmid39833404,
year = {2025},
author = {Ramsey, NF and Vansteensel, MJ},
title = {The expanding repertoire of brain-computer interfaces.},
journal = {Nature medicine},
volume = {31},
number = {1},
pages = {31-32},
pmid = {39833404},
issn = {1546-170X},
}
@article {pmid39833218,
year = {2025},
author = {Sun, Z and Huang, J and Ma, X and Liang, J and Sun, C and Hu, L and He, H and Yu, G},
title = {A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {109},
pmid = {39833218},
issn = {2052-4463},
support = {62076218//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Magnetic Resonance Imaging ; Humans ; *Brain/diagnostic imaging/growth &amp; development ; Infant ; Spatio-Temporal Analysis ; Infant, Newborn ; },
abstract = {Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness. The presented low-field infant structural MR data aims to manifest the feasibility of using low-field MR image to exam brain structural changes during early life in infants. The dataset comprises 100 T2 weighed MR images from infants with in-plane resolution of ~0.85 mm and ~6 mm slice thickness. To demonstrate the potential utility, we conducted atlas-based whole brain segmentations and volumetric quantifications to analyze brain development features in first 10 week in postnatal life. This dataset addresses the scarcity of a large, extended-span infant brain dataset that restricts the further tracking of infant brain development trajectories and the development of routine low-field MR imaging pipelines.},
}
@article {pmid39833018,
year = {2025},
author = {Pickles, J and Griffiths, L and McCloskey, AP and Vasey, N and Lim, E and Rathbone, AP},
title = {Developing a behaviour change intervention using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {21},
number = {4},
pages = {277-286},
doi = {10.1016/j.sapharm.2025.01.006},
pmid = {39833018},
issn = {1934-8150},
mesh = {Humans ; *Anti-Bacterial Agents/administration &amp; dosage ; *Greenhouse Gases ; Male ; Female ; Practice Patterns, Physicians' ; Primary Health Care ; England ; Climate Change ; Adult ; Middle Aged ; Motivation ; },
abstract = {INTRODUCTION: The determinants of antimicrobial prescribing often involve social influence, which can be harnessed through behaviour change techniques (BCTs). While previous studies have used BCTs to address antimicrobial resistance, there is a lack of evidence regarding their application to address climate change-related issues in antibiotic prescribing. This study aimed to develop a behaviour change intervention (BCI) using information about greenhouse gas emissions to reduce liquid antibiotic prescribing.<br><br>METHODS: A convenience sample of participants from a primary care practice in North East England participated in semi-structured interviews. The intervention design was guided by the Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation - Behaviour (COM-B) model. Data were analysed thematically, mapped to the TDF, and used to refine the BCI.<br><br>FINDINGS: Participants identified motivating factors related to high rates of liquid prescribing, climate change, and solid oral dosage form (pill) aversion. The broader context of practice, such as initiatives reduce cost and improve sustainability, provided opportunities for intervention. Participants demonstrated the capability to change prescribing behaviours and expressed willingness to share resources within their teams.<br><br>CONCLUSION: This study underscores the potential of BCIs using greenhouse gas emissions data to reduce liquid antibiotic prescribing. Further research should focus on implementing and evaluating these interventions in practice settings.},
}
@article {pmid39832646,
year = {2025},
author = {Chen, Q and Pan, C and Shen, Y and Pan, Q and Zhang, Q and Wang, J and Hu, Y and Xu, H and Gong, M and Jia, K},
title = {Atypical subcortical involvement in emotional face processing in major depressive disorder with and without comorbid social anxiety.},
journal = {Journal of affective disorders},
volume = {374},
number = {},
pages = {531-539},
doi = {10.1016/j.jad.2025.01.081},
pmid = {39832646},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/psychology ; Male ; Female ; Adult ; *Facial Expression ; *Facial Recognition/physiology ; *Emotions/physiology ; *Phobia, Social/physiopathology/epidemiology/psychology ; Comorbidity ; Young Adult ; Anger/physiology ; Middle Aged ; Sadness ; },
abstract = {Previous research on major depressive disorder (MDD) has largely focused on cognitive biases and abnormalities in cortico-limbic circuitry during emotional face processing. However, it remains unclear whether these abnormalities start at early perceptual stages via subcortical pathways and how comorbid social anxiety influences this process. Here, we investigated subcortical mechanisms in emotional face processing using a psychophysical method that measures monocular advantage (i.e., superior discrimination performance when two stimuli are presented to the same eye than to different eyes). Participants included clinical patients diagnosed with MDD (n = 32), patients with MDD comorbid with social anxiety (comorbid MDD-SAD, n = 32), and a control group of healthy participants (HC, n = 32). We assessed monocular advantage across different emotions (neutral, sad, angry) and among groups. Results indicated that individuals with MDD showed a stronger monocular advantage for sad expressions compared to neutral and angry expressions. In contrast, HC and comorbid MDD-SAD groups showed a greater monocular advantage for neural over negative expressions. Cross-group comparisons revealed that MDD group had a stronger monocular advantage for sad expressions than both HC and comorbid MDD-SAD groups. Additionally, self-reported depressive symptoms were positively correlated with monocular advantage for sad expressions, while social anxiety symptoms were negatively correlated with monocular advantage for negative expressions. These findings suggest atypical early perceptual processing of sadness in individuals with MDD via subcortical mechanisms, with comorbid social anxiety potentially counteracting this effect. This study may inform novel interventions targeting sensory processing and expand beyond cognitive bias modification.},
}
@article {pmid39832388,
year = {2025},
author = {Haddix, C and Bates, M and Garcia-Pava, S and Salmon Powell, E and Sawaki, L and Sunderam, S},
title = {Electroencephalogram features reflect effort corresponding to graded finger extension: implications for hemiparetic stroke.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/adabeb},
pmid = {39832388},
issn = {2057-1976},
mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology/complications ; Male ; *Paresis/physiopathology/etiology ; *Brain-Computer Interfaces ; *Fingers ; Female ; *Electromyography/methods ; Middle Aged ; Aged ; Movement ; Adult ; Case-Control Studies ; Hand/physiopathology ; Brain/physiopathology ; },
abstract = {Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls. Participants extended their fingers to one of four levels: low, medium, high, or 'no-go' (none), while hand, muscle (electromyography: EMG), and brain (EEG) activity were monitored. Event-related desynchronization (ERD) was measured as the change in 8-30 Hz EEG power during movement. Classifiers were trained on EEG features, EMG power, or both (EEG+EMG) to decode finger extension, and accuracy assessed via four-fold cross-validation for each hand of each participant. Mean accuracy exceeded chance (25%) for controls (n = 11) at 62% for EMG, 60% for EEG, and 71% for EEG+EMG on the left hand; and 67%, 60%, and 74%, respectively, on the right hand. Accuracies were similar on the unimpaired right hand for the stroke group (n = 3): 61%, 68%, and 78%, respectively. But on the paretic left hand, EMG only discriminated no-go from movement above chance (41%); in contrast, EEG gave 65% accuracy (68% for EEG+EMG), comparable to the non-paretic hand. The median ERD was significant (p < 0.01) over the cortical hand area in both groups and increased with each level of finger extension. But while the ERD favored the hemisphere contralateral to the active hand as expected, it was ipsilateral for the left hand of stroke due to the lesion in the right hemisphere, which may explain its discriminative ability. Hence, the ERD captures effort in finger extension regardless of success or failure at the task; and harnessing residual EMG improves the correlation. This marker could be leveraged in rehabilitative protocols that focus on fine motor control.},
}
@article {pmid39832179,
year = {2025},
author = {Jiang, M and Tan, Y and Wang, X and Hao, Y},
title = {The Phonograms' Genuine-Character Status: What the Embedded Semantic Radicals' Semantic Activation Live by.},
journal = {Brain and behavior},
volume = {15},
number = {1},
pages = {e70277},
pmid = {39832179},
issn = {2162-3279},
mesh = {Humans ; *Semantics ; Male ; Young Adult ; Female ; Adult ; Reading ; },
abstract = {BACKGROUND: In Chinese phonogram processing studies, it is widely accepted that both character and non-character semantic radicals could be semantically activated. However, little attention was paid to the underlying workings that enabled the semantic radicals' semantic activation.<br><br>PURPOSE: The present study aimed to address the above issue by conducting two experiments.<br><br>METHODS: Experiment 1 was committed to confirming whether both character and non-character semantic radicals could be semantically activated when embedded in genuine Chinese phonograms. Experiment 2 was devoted to exploring whether the same semantic radicals could also be semantically activated when incorporated in Chinese pseudo-characters.<br><br>RESULTS: Results demonstrated that both character and non-character semantic radicals embedded in the genuine phonograms were semantically activated, but those placed in the pseudo-characters underwent no semantic activation, suggesting that the semantic activation of semantic radicals was genuine-character status-dependent, irrespective of the semantic radicals' characterhood.<br><br>CONCLUSION: It seems that the genuine-character status and the meaning of the host phonogram have strong sway on the semantic activation of semantic radicals.},
}
@article {pmid39830198,
year = {2024},
author = {Liu, D and Wang, N and Song, M and Chai, X and He, Q and Cao, T and Kong, D and Song, Z and Zhang, G and Liu, L and Wang, X and Chen, G and Yin, S and Yang, Y and Zhao, J},
title = {Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1425271},
pmid = {39830198},
issn = {1664-2295},
abstract = {OBJECTIVE: This study was to employ 18F-flurodeoxyglucose (FDG-PET) to evaluate the resting-state brain glucose metabolism in a sample of 46 patients diagnosed with disorders of consciousness (DoC). The aim was to identify objective quantitative metabolic indicators and predictors that could potentially indicate the level of awareness in these patients.<br><br>METHODS: A cohort of 46 patients underwent Coma Recovery Scale-Revised (CRS-R) assessments in order to distinguish between the minimally conscious state (MCS) and the unresponsive wakefulness syndrome (UWS). Additionally, resting-state FDG-PET data were acquired from both the patient group and a control group consisting of 10 healthy individuals. The FDG-PET data underwent reorientation, spatial normalization to a stereotaxic space, and smoothing. The normalization procedure utilized a customized template following the methodology outlined by Phillips et al. Mean cortical metabolism of the overall sample was utilized for distinguishing between UWS and MCS, as well as for predicting the outcome at a 1-year follow-up through the application of receiver operating characteristic (ROC) analysis.<br><br>RESULTS: We used Global Glucose Metabolism as the Diagnostic Marker. A one-way ANOVA revealed that there was a statistically significant difference in cortical metabolic index between two groups (F(2, 53)&#x202f;=&#x202f;7.26, p&#x202f;<&#x202f;0.001). Multiple comparisons found that the mean of cortical metabolic index was significantly different between MCS (M&#x202f;=&#x202f;4.19, SD&#x202f;=&#x202f;0.64) and UWS group (M&#x202f;=&#x202f;2.74, SD&#x202f;=&#x202f;0.94,p&#x202f;<&#x202f;0.001). Also, the mean of cortical metabolic index was significantly different between MCS and healthy group (M&#x202f;=&#x202f;7.88, SD&#x202f;=&#x202f;0.80,p&#x202f;<&#x202f;0.001). Using the above diagnostic criterion, the diagnostic accuracy yielded an area under the curve (AUC) of 0.89 across the pooled cohort (95%CI 0.79-0.99). There was an 85% correct classification between MCS and UWS, with 88% sensitivity and 81% specificity for MCS. The best classification rate in the derivation cohort was achieved at a metabolic index of 3.32 (41% of the mean cortical metabolic index in healthy controls).<br><br>CONCLUSION: Our findings demonstrate that conscious awareness requires a minimum of 41% of normal cortical activity, as indicated by metabolic rates.},
}
@article {pmid39825141,
year = {2025},
author = {Oby, ER and Degenhart, AD and Grigsby, EM and Motiwala, A and McClain, NT and Marino, PJ and Yu, BM and Batista, AP},
title = {Dynamical constraints on neural population activity.},
journal = {Nature neuroscience},
volume = {28},
number = {2},
pages = {383-393},
pmid = {39825141},
issn = {1546-1726},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; NCS BCS1533672//National Science Foundation (NSF)/ ; R01 MH118929/MH/NIMH NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; NS105318//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Motor Cortex/physiology ; *Neurons/physiology ; *Macaca mulatta ; *Brain-Computer Interfaces ; Models, Neurological ; Male ; Nerve Net/physiology ; },
abstract = {The manner in which neural activity unfolds over time is thought to be central to sensory, motor and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface to challenge monkeys to violate the naturally occurring time courses of neural population activity that we observed in the motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.},
}
@article {pmid39824976,
year = {2025},
author = {Zhao, J and Shen, Q and Yong, X and Li, X and Tian, X and Sun, S and Xu, Z and Zhang, X and Zhang, L and Yang, H and Shao, Z and Xu, H and Jiang, Y and Zhang, Y and Yan, W},
title = {Cryo-EM reveals cholesterol binding in the lysosomal GPCR-like protein LYCHOS.},
journal = {Nature structural &amp; molecular biology},
volume = {},
number = {},
pages = {},
pmid = {39824976},
issn = {1545-9985},
abstract = {Cholesterol plays a pivotal role in modulating the activity of mechanistic target of rapamycin complex 1 (mTOR1), thereby regulating cell growth and metabolic homeostasis. LYCHOS, a lysosome-localized G-protein-coupled receptor-like protein, emerges as a cholesterol sensor and is capable of transducing the cholesterol signal to affect the mTORC1 function. However, the precise mechanism by which LYCHOS recognizes cholesterol remains unknown. Here, using cryo-electron microscopy, we determined the three-dimensional structural architecture of LYCHOS in complex with cholesterol molecules, revealing a unique arrangement of two sequential structural domains. Through a comprehensive analysis of this structure, we elucidated the specific structural features of these two domains and their collaborative role in the process of cholesterol recognition by LYCHOS.},
}
@article {pmid39824751,
year = {2025},
author = {Cong, L and Wang, X and Wang, J and Liu, W and Xu, W and Zhang, S and Xu, S},
title = {Three-Dimensional SERS-Active Hydrogel Microbeads Enable Highly Sensitive Homogeneous Phase Detection of Alkaline Phosphatase in Biosystems.},
journal = {ACS applied materials &amp; interfaces},
volume = {17},
number = {4},
pages = {5933-5941},
doi = {10.1021/acsami.4c18139},
pmid = {39824751},
issn = {1944-8252},
mesh = {*Alkaline Phosphatase/metabolism ; Humans ; *Spectrum Analysis, Raman/methods ; *Silver/chemistry ; *Metal Nanoparticles/chemistry ; *Hydrogels/chemistry ; *Microspheres ; Hep G2 Cells ; },
abstract = {Alkaline phosphatase (ALP) is a biomarker for many diseases, and monitoring its activity level is important for disease diagnosis and treatment. In this study, we used the microdroplet technology combined with an in situ laser-induced polymerization method to prepare the Ag nanoparticle (AgNP) doped hydrogel microbeads (HMBs) with adjustable pore sizes that allow small molecules to enter while blocking large molecules. The AgNPs embedded in the hydrogel microspheres can provide SERS activity, improving the SERS signal of small molecules that diffuse to the AgNPs. A specific hydrolysis reaction of ALP on 5-bromo-4-chloro-3-indolylphosphate (BCIP) was introduced and itsproduct 5,5'-dibromo-4,4'-dichloro-1H,1H-[2,2']bisindolyl-3,3'-dione (BCI) was employed to assess ALP activity due to its highly resonance Raman activity. The sensing platform was applied to model ALP activity in serum and evaluate ALP inhibitors. The SERS assay showed higher sensitivity than UV-vis absorption spectroscopy, with the lowest detectable ALP concentration of 1.0 × 10[-20] M. In addition, the ALP activity in HepG2 cells was evaluated using this sensing platform, showing lower ALP-expressing activity than that of controls in response to hypoxia and iron metastasis. This SERS-activated HMB shows great potential in detecting ALP and is expected to help analyze complex clinical samples.},
}
@article {pmid39823647,
year = {2025},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE},
title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39823647},
issn = {1741-2552},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Animals ; *Muscle, Skeletal/physiology/innervation ; *Motor Cortex/physiology ; *Macaca mulatta ; *Electromyography/methods ; Male ; Linear Models ; Unsupervised Machine Learning ; },
abstract = {Objective.Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.Approach.We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.Main results.We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network decoder with 10-12 clusters.Significance.This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.},
}
@article {pmid39821163,
year = {2025},
author = {Zou, Y and Xu, J and Chen, Y},
title = {Volatility, correlation and risk spillover effect between freight rates in BCI and BPI markets: Evidence from static and dynamic GARCH-Copula and dynamic CoVaR models.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0315167},
pmid = {39821163},
issn = {1932-6203},
mesh = {Humans ; *Commerce ; Models, Economic ; Ships ; Risk ; },
abstract = {The dry bulk shipping market plays a crucial role in global trade. To examine the volatility, correlation, and risk spillover between freight rates in the BCI and BPI markets, this paper employs the GARCH-Copula-CoVaR model. We analyze the dynamic behavior of the secondary market freight index for dry bulk cargo, highlighting its performance in a complex financial environment and offering empirical support for the shipping industry and financial markets. The findings reveal that: (1) There are significant differences in correlation across various routes, with the correlation between BCI and BPI routes fluctuating over time. Among all route combinations, C5 and P3A_03 exhibit the highest positive correlation. (2) A one-way risk spillover exists between P1A_03 an C5, while two-way positive risk spillover is observed between other routes. This suggests that when a risk materializes on a specific route, other routes are also exposed to potential risks, with varying intensities of spillover. (3) The distance and geographical location of routes may be key factors influencing the differing intensities of risk spillover. This highlights the need to consider the geographical characteristics of routes in understanding risk transmission. This paper aims to provide risk management strategies based on these empirical findings, assisting shipping companies and investors in developing more effective responses to market volatility.},
}
@article {pmid39820611,
year = {2025},
author = {D, J and K C, S},
title = {Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0311942},
pmid = {39820611},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Signal-To-Noise Ratio ; },
abstract = {In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.},
}
@article {pmid39819752,
year = {2025},
author = {Chen, J and Chen, X and Wang, R and Le, C and Khalilian-Gourtani, A and Jensen, E and Dugan, P and Doyle, W and Devinsky, O and Friedman, D and Flinker, A and Wang, Y},
title = {Transformer-based neural speech decoding from surface and depth electrode signals.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39819752},
issn = {1741-2552},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; R01 NS109367/NS/NINDS NIH HHS/United States ; R01 NS115929/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Electrocorticography/methods/instrumentation ; *Electrodes, Implanted ; Male ; Electroencephalography/methods ; Speech/physiology ; Female ; Brain-Computer Interfaces ; Adult ; Deep Learning ; },
abstract = {Objective.This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e. Electrocorticographic (ECoG) or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface ECoG and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements. The model should not have subject-specific layers and the trained model should perform well on participants unseen during training.Approach.We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-subject models exploiting data from multiple participants.Main results.The subject-specific models using only low-density 8 × 8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC = 0.817), overN= 43 participants, significantly outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N= 39) led to further improvement (PCC = 0.838). For participants with only sEEG electrodes (N= 9), subject-specific models still enjoy comparable performance with an average PCC = 0.798. A single multi-subject model trained on ECoG data from 15 participants yielded comparable results (PCC = 0.837) as 15 models trained individually for these participants (PCC = 0.831). Furthermore, the multi-subject models achieved high performance on unseen participants, with an average PCC = 0.765 in leave-one-out cross-validation.Significance.The proposed SwinTW decoder enables future speech decoding approaches to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. The success of the single multi-subject model when tested on participants within the training cohort demonstrates that the model architecture is capable of exploiting data from multiple participants with diverse electrode placements. The architecture's flexibility in training with both single-subject and multi-subject data, as well as grid and non-grid electrodes, ensures its broad applicability. Importantly, the generalizability of the multi-subject models in our study population suggests that a model trained using paired acoustic and neural data from multiple patients can potentially be applied to new patients with speech disability where acoustic-neural training data is not feasible.},
}
@article {pmid39819697,
year = {2025},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {Correction: CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {14},
number = {1},
pages = {3},
pmid = {39819697},
issn = {2047-9158},
}
@article {pmid39819671,
year = {2025},
author = {H Liu, D and Kumar, S and Alawieh, H and Samuel Racz, F and Del R Millán, J},
title = {Personalizedµ-transcranial alternating current stimulation improves online brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ada980},
pmid = {39819671},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Transcranial Direct Current Stimulation/methods ; Male ; Adult ; Female ; Young Adult ; *Imagination/physiology ; *Electroencephalography/methods ; Single-Blind Method ; Online Systems ; Psychomotor Performance/physiology ; Movement/physiology ; },
abstract = {Objective.A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).Approach.Previous studies have identified that the peak power spectral density value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 min of tACS at the participant-specific, peakµfrequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-stateµSMRs.Main results.After tACS, we observed significant improvements in event-related desynchronizations (ERDs) ofµSMRs, and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N= 10)-but not in an active control-stimulation control group (N= 10). Lastly, we showed a significant correlation between the resting-stateµSMRs andµERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.Significance.Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.},
}
@article {pmid39819299,
year = {2025},
author = {Tsai, PC and Akpan, A and Tang, KT and Lakany, H},
title = {Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review.},
journal = {BMC geriatrics},
volume = {25},
number = {1},
pages = {36},
pmid = {39819299},
issn = {1471-2318},
mesh = {Humans ; *Brain-Computer Interfaces ; Aged ; *Cognitive Dysfunction/psychology/therapy ; *Neurofeedback/methods/physiology ; *Electroencephalography/methods ; Cognition/physiology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) offers promising solutions to cognitive enhancement in older people. Despite the clear progress received, there is limited evidence of BCI implementation for rehabilitation. This systematic review addresses BCI applications and challenges in the standard practice of EEG-based neurofeedback (NF) training in healthy older people or older people with mild cognitive impairment (MCI).<br><br>METHODS: Articles were searched via MEDLINE, PubMed, SCOPUS, SpringerLink, and Web of Science. 16 studies between 1st January 2010 to 1st November 2024 are included after screening using PRISMA. The risk of bias, system design, and neurofeedback protocols are reviewed.<br><br>RESULTS: The successful BCI applications in NF trials in older people were biased by the randomisation process and outcome measurement. Although the studies demonstrate promising results in effectiveness of research-grade BCI for cognitive enhancement in older people, it is premature to make definitive claims about widespread BCI usability and applicability.<br><br>SIGNIFICANCE: This review highlights the common issues in the field of EEG-based BCI for older people. Future BCI research could focus on trial design and BCI performance gaps between the old and the young to develop a robust BCI system that compensates for age-related declines in cognitive and motor functions.},
}
@article {pmid39818889,
year = {2025},
author = {Marasco, PD},
title = {Navigating the complexity of touch.},
journal = {Science (New York, N.Y.)},
volume = {387},
number = {6731},
pages = {248-249},
doi = {10.1126/science.adu7929},
pmid = {39818889},
issn = {1095-9203},
mesh = {*Brain-Computer Interfaces ; *Touch/physiology ; Humans ; Somatosensory Cortex/physiology ; Animals ; Touch Perception/physiology ; Electric Stimulation ; },
abstract = {Precise cortical microstimulation improves tactile experience in brain-machine interfaces.},
}
@article {pmid39818280,
year = {2025},
author = {Li, C and Ma, K and Li, S and Meng, X and Wang, R and Zhang, D and Zhu, Q},
title = {Multi-channel spatio-temporal graph attention contrastive network for brain disease diagnosis.},
journal = {NeuroImage},
volume = {307},
number = {},
pages = {121013},
doi = {10.1016/j.neuroimage.2025.121013},
pmid = {39818280},
issn = {1095-9572},
mesh = {Humans ; *Brain Diseases/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging/methods ; *Diffusion Tensor Imaging/methods ; *Nerve Net/diagnostic imaging/physiopathology ; *Brain/diagnostic imaging/physiopathology ; Female ; Male ; Aged ; Adult ; Connectome/methods ; },
abstract = {Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases.},
}
@article {pmid39815608,
year = {2025},
author = {Dai, C and Fu, Y and Li, X and Lin, M and Li, Y and Li, X and Huang, K and Zhou, C and Xie, J and Zhao, Q and Hu, S},
title = {Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression.},
journal = {Journal of Zhejiang University. Science. B},
volume = {26},
number = {1},
pages = {26-38},
pmid = {39815608},
issn = {1862-1783},
support = {OO2020491//the Construction Fund of Key Medical Disciplines of Hangzhou/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; JNL-2023001B//the Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation&#x2012;"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; 2022KTZ004//the Chinese Medical Education Association/ ; },
mesh = {Humans ; *Vortioxetine/therapeutic use ; *Bipolar Disorder/drug therapy ; Male ; Female ; Adult ; *Valproic Acid/therapeutic use/adverse effects ; Middle Aged ; Treatment Outcome ; Prospective Studies ; Pilot Projects ; Lurasidone Hydrochloride/therapeutic use/adverse effects ; Drug Therapy, Combination ; },
abstract = {OBJECTIVES: Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains controversial. This study aimed to validate the efficacy and safety of vortioxetine in bipolar depression.<br><br>METHODS: Patients with bipolar &#x2161; depression were enrolled in this prospective, two-center, randomized, 12-week pilot trial. The main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale (MADRS) of &#x2265;50%. All eligible patients initially received four weeks of lurasidone monotherapy. Patients who responded well continued to receive this kind of monotherapy. However, no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight weeks. By comprehensively comparing the results of MADRS over a period of 4&#x200d;&#x2012;&#x200d;12 weeks, a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar depression.<br><br>RESULTS: Thirty-seven patients responded to lurasidone monotherapy, and 60 patients were randomly assigned to the valproate or vortioxetine group for eight weeks. After two weeks of combined valproate or vortioxetine treatment, the MADRS score in the vortioxetine group was significantly lower than that in the valproate group. There was no difference in the MADRS scores between the two groups at 8 and 12 weeks. The incidence of side effects did not significantly differ between the valproate and vortioxetine groups. Importantly, three patients in the vortioxetine group appeared to switch to mania or hypomania.<br><br>CONCLUSIONS: This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage, while disease progression should be monitored closely for the risk of switching to mania.},
}
@article {pmid39814210,
year = {2025},
author = {Kyoda, Y and Shibamori, K and Tachikawa, K and Nofuji, S and Saito, Y and Tabata, H and Shindo, T and Hashimoto, K and Kobayashi, K and Tanaka, T and Masumori, N},
title = {The Change of Detrusor Contractility at 5 Years After Transurethral Resection of the Prostate: A Single Center Prospective Observational Study.},
journal = {Urology},
volume = {197},
number = {},
pages = {166-173},
doi = {10.1016/j.urology.2024.12.038},
pmid = {39814210},
issn = {1527-9995},
mesh = {Humans ; Male ; Aged ; *Transurethral Resection of Prostate ; Prospective Studies ; *Urinary Bladder/physiopathology/surgery ; *Urinary Bladder Neck Obstruction/surgery/physiopathology/etiology ; *Prostatic Hyperplasia/surgery/physiopathology ; Middle Aged ; Muscle Contraction/physiology ; Time Factors ; Urodynamics ; Aged, 80 and over ; Follow-Up Studies ; },
abstract = {OBJECTIVE: To prospectively assess the impact of transurethral resection of the prostate (TURP) on detrusor function using pressure flow study (PFS) at 5 years after surgery in a single center prospective non-randomized observational study.<br><br>METHODS: Sixty consecutive male patients were prospectively enrolled and underwent TURP from November 2014 to November 2018. A questionnaire survey, free uroflowmetry, and PFS were performed at baseline, and 6, 24, and 60 months after surgery. We divided the age groups at 70 years and defined the younger group as those younger than 70 years, and the elderly group as those aged 70 years or older. The primary endpoint was the change in the bladder contractility index (BCI).<br><br>RESULTS: Of the 60 patients, 39 completed the protocol. Regardless of age, the bladder outlet obstruction indices at 6, 24, and 60 months after surgery were significantly lower than before surgery (all, P<.01). Although the BCI did not significantly change during 60 months for the entire group of 39 patients, it was significantly decreased at 60 months (85.6) after surgery compared to before surgery (102) in the elderly group (P=.02).<br><br>CONCLUSION: We prospectively evaluated detrusor contractility up to 5 years after TURP. It was significantly reduced in the elderly, in spite of which the relief of bladder outlet obstruction was maintained for 5 years after surgery.},
}
@article {pmid39814172,
year = {2025},
author = {Wei, Q and Fan, W and Li, HF and Wang, PS and Xu, M and Dong, HL and Yu, H and Lyu, J and Luo, WJ and Chen, DF and Ge, W and Wu, ZY},
title = {Biallelic variants in SREBF2 cause autosomal recessive spastic paraplegia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jgg.2025.01.004},
pmid = {39814172},
issn = {1673-8527},
abstract = {Hereditary spastic paraplegias (HSPs) refer to a genetically and clinically heterogeneous group of neurodegenerative disorders characterized by the degeneration of motor neurons. To date, a significant number of patients still have not received a definite genetic diagnosis. Therefore, identifying unreported causative genes continues to be of great importance. Here, we perform whole exome sequencing in a cohort of Chinese HSP patients. Three homozygous variants (p.L604W, p.S517F, and p.T984A) within the sterol regulatory element-binding factor 2 (SREBF2) gene are identified in one autosomal recessive family and two sporadic patients, respectively. Co-segregation is confirmed by Sanger sequencing in all available members. The three variants are rare in the public or in-house database and are predicted to be damaging. The biological impacts of variants in SREBF2 are examined by functional experiments in patient-derived fibroblasts and Drosophila. We find that the variants upregulate cellular cholesterol due to the overactivation of SREBP2, eventually impairing the autophagosomal and lysosomal functions. The overexpression of the mature form of SREBP2 leads to locomotion defects in Drosophila. Our findings identify SREBF2 as a causative gene for HSP and highlight the impairment of cholesterol as a critical pathway for HSP.},
}
@article {pmid39813939,
year = {2025},
author = {Chen, L and Yin, Z and Gu, X and Zhang, X and Cao, X and Zhang, C and Li, X},
title = {Neurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.},
journal = {Computer methods and programs in biomedicine},
volume = {261},
number = {},
pages = {108594},
doi = {10.1016/j.cmpb.2025.108594},
pmid = {39813939},
issn = {1872-7565},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; *Models, Statistical ; Signal-To-Noise Ratio ; Algorithms ; Brain/diagnostic imaging/physiology ; Normal Distribution ; Male ; Adult ; Female ; Signal Processing, Computer-Assisted ; Young Adult ; Databases, Factual ; },
abstract = {BACKGROUND AND OBJECTIVE: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.<br><br>METHODS: In this study, we proposed an EEG-fNIRS data augmentation framework based on the combination of denoising diffusion probabilistic model (DDPM) and adding Gaussian noise (EFDA-CDG), for enhancing the performance of hybrid BCI systems. Firstly, we unified the temporal and spatial dimensions of EEG and fNIRS by manually extracting features and spatial mapping interpolation to create EEG-fNIRS joint distribution samples. Then, the DDPM generative model was combined with the traditional method of adding Gaussian noise to provide richer training data for the classifier. Finally, we constructed a classification module that applies EEG feature attention and fNIRS terrain attention to improve classification accuracy.<br><br>RESULTS: In order to evaluate the effectiveness of EFDA-CDG framework, experiments were conducted and fully validated on three publicly available databases and one self-collected database. In the context of a participant-dependent training approach, our method achieves accuracy rates of 82.02% for motor imagery, 91.93% for mental arithmetic, and 90.54% for n-back tasks on public databases. Additionally, our method boasts an accuracy rate of 97.82% for drug addiction discrimination task on the self-collected database.<br><br>CONCLUSIONS: EFDA-CDG framework successfully facilitates data augmentation, thereby enhancing the performance of EEG-fNIRS hybrid BCI systems.},
}
@article {pmid39811472,
year = {2024},
author = {Maibam, PC and Pei, D and Olikkal, P and Vinjamuri, RK and Kakoty, NM},
title = {Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram.},
journal = {Wearable technologies},
volume = {5},
number = {},
pages = {e18},
pmid = {39811472},
issn = {2631-7176},
abstract = {Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses.},
}
@article {pmid39809040,
year = {2025},
author = {Xu, R and Allison, BZ and Zhao, X and Liang, W and Wang, X and Cichocki, A and Jin, J},
title = {Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {107124},
doi = {10.1016/j.neunet.2025.107124},
pmid = {39809040},
issn = {1879-2782},
mesh = {Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Evoked Potentials/physiology ; *Electroencephalography/methods ; *Attention/physiology ; Brain/physiology ; Algorithms ; Deep Learning ; },
abstract = {Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. The model integrates a multi-scale architecture, self-attention mechanism, and deep metric learning to achieve a more comprehensive, refined, and discriminative feature representation. The MS module aggregates fine-grained local features and global features with a larger receptive field within a multi-scale architecture, effectively capturing the dynamic characteristics of complex oscillatory activities in the brain at different levels of abstraction. This preserves complementary spatiotemporal representation information. The PSA module continues the multi-scale contextual modeling from the previous module and achieves adaptive recalibration of multi-scale features. By employing effective aggregation and selection mechanisms, it highlights key features while suppressing redundant information. The SOC module jointly optimizes similarity metric loss and classification loss, maintaining the feature space distribution while focusing on sample class labels. This optimization of similarity relationships between samples improves the model's generalization ability and robustness. Results from public and self-collected datasets demonstrate that the command recognition accuracy of the MS-PSA-SOC model is at least 3.1% and 2.8% higher than other advanced algorithms, achieving superior performance. Additionally, the method demonstrates a lower standard deviation across both datasets. This study also validated the network parameters based on Shannon's sampling theorem and EEG "microstates" through relevant experiments.},
}
@article {pmid39808940,
year = {2025},
author = {Gu, M and Pei, W and Gao, X and Wang, Y},
title = {Optimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaa1e},
pmid = {39808940},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography/methods ; *Photic Stimulation/methods ; Female ; Adult ; Young Adult ; },
abstract = {Objective.Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.Approach.In an offline single-target experiment, we investigated the unique characteristics of SSVEPs evoked by varying proportions in grid stimuli within low and medium frequency bands. Based on the analysis of simulation performance across a four-class brain-computer interface (BCI) task and the evaluation of user experience questionnaires, a subset of paradigms that balance performance and comfort were selected for implementation in four-target online BCI systems.Main results.Our results demonstrate that even ultra-low stimulation proportion paradigms can still evoke strong responses within specific frequency bands, effectively enhancing user experience with low and middle frequency stimuli. Notably, proportions of 0.94% and 2.10% within the 3-5 Hz range provide an optimal balance between performance and user experience. For frequencies extending up to 15 Hz, a 2.10% proportion remains ideal. At 20 Hz, slightly higher proportions of 3.75% and 8.43% maintain these benefits.Significance.These findings are crucial for advancing the development of effective and user-friendly SSVEP-based BCI systems.},
}
@article {pmid39808939,
year = {2025},
author = {Prakash, PR and Lei, T and Flint, RD and Hsieh, JK and Fitzgerald, Z and Mugler, E and Templer, J and Goldrick, MA and Tate, MC and Rosenow, J and Glaser, J and Slutzky, MW},
title = {Decoding speech intent from non-frontal cortical areas.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39808939},
issn = {1741-2552},
support = {UL1 TR000150/TR/NCATS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; R21 NS084069/NS/NINDS NIH HHS/United States ; T32 NS047987/NS/NINDS NIH HHS/United States ; F32 DC015708/DC/NIDCD NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Speech/physiology ; Male ; Female ; Adult ; *Parietal Lobe/physiology/physiopathology ; *Brain-Computer Interfaces ; Temporal Lobe/physiology/physiopathology ; Intention ; Young Adult ; Frontal Lobe/physiology/physiopathology ; Electrocorticography/methods ; Middle Aged ; },
abstract = {Objective. Brain machine interfaces (BMIs) that can restore speech have predominantly focused on decoding speech signals from the speech motor cortices. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs. The ability to use information from outside the frontal lobe could be useful not only for people with locked-in syndrome, but also to people with frontal lobe damage, which can cause nonfluent aphasia or apraxia of speech. However, temporal and parietal lobes are predominantly involved in perceptive speech processing and comprehension. Therefore, to be able to use signals from these areas in a speech BMI, it is important to ascertain that they are related to production. Here, using intracranial recordings, we sought evidence for whether, when and where neural information related to speech intent could be found in the temporal and parietal corticesApproach. Using intracranial recordings, we examined neural activity across temporal and parietal cortices to identify signals associated with speech intent. We employed causal information to distinguish speech intent from resting states and other language-related processes, such as comprehension and working memory. Neural signals were analyzed for their spatial distribution and temporal dynamics to determine their relevance to speech production.Main results. Causal information enabled us to distinguish speech intent from resting state and other processes involved in language processing or working memory. Information related to speech intent was distributed widely across the temporal and parietal lobes, including superior temporal, medial temporal, angular, and supramarginal gyri.Significance. Loss of communication due to neurological diseases can be devastating. While speech BMIs have made strides in decoding speech from frontal lobe signals, our study reveals that the temporal and parietal cortices contain information about speech production intent that can be causally decoded prior to the onset of voice. This information is distributed across a large network. This information can be used to improve current speech BMIs and potentially expand the patient population for speech BMIs to include people with frontal lobe damage from stroke or traumatic brain injury.},
}
@article {pmid39808931,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Decoding imagined movement in people with multiple sclerosis for brain-computer interface translation.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaa1d},
pmid = {39808931},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Multiple Sclerosis/physiopathology/rehabilitation ; *Imagination/physiology ; Male ; Female ; Adult ; *Movement/physiology ; Middle Aged ; *Electroencephalography/methods ; },
abstract = {Objective.Multiple sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A brain-computer interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS has been confined to exploring the P300 response and brain signals associated with attempted movement. The current study aims to expand the MS-BCI literature by highlighting the feasibility of decoding MS imagined movement.Approach.We collected electroencephalography data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement vs. rest and vs. movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared.Main Results.In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency.Significance.This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.},
}
@article {pmid39808922,
year = {2025},
author = {Dekleva, BM and Collinger, JL},
title = {Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/adaa1f},
pmid = {39808922},
issn = {1741-2552},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; U01 NS123125/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; Adult ; Female ; Hand Strength/physiology ; Electroencephalography/methods ; Young Adult ; Hand/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Algorithms ; Motor Cortex/physiology ; },
abstract = {Objective.Real-world implementation of brain-computer interfaces (BCIs) for continuous control of devices should ideally rely on fully asynchronous decoding approaches. That is, the decoding algorithm should continuously update its output by estimating the user's intended actions from real-time neural activity, without the need for any temporal alignment to an external cue. This kind of open-ended temporal flexibility is necessary to achieve naturalistic and intuitive control. However, the relation between cortical activity and behavior is not stationary: neural responses that appear related to a certain aspect of behavior (e.g. grasp force) in one context will exhibit a relationship to something else in another context (e.g. reach speed). This presents a challenge for generalizable decoding, since the applicability of a decoder for a given parameter changes over time.Approach.We developed a method to simplify the problem of continuous decoding that uses transient, end effector-specific neural responses to identify periods of relevant effector engagement. Specifically, we use transient responses in the population response observed at the onset and offset of all hand-related actions to signal the applicability of hand-related feature decoders (e.g. digit movement or force). By using this transient-based gating approach, specific feature decoding models can be simpler (owing to local linearities) and are less sensitive to interference from cross-effector interference such as combined reaching and grasping actions.Main results.The transient-based decoding approach enabled high-quality online decoding of grasp force and individual finger control in multiple behavioral paradigms. The benefits of the gated approach are most evident in tasks that require both hand and arm control, for which standard continuous decoding approaches exhibit high output variability.Significance.The approach proposed here addresses the challenge of decoder generalization across contexts. By limiting decoding to identified periods of effector engagement, this approach can support reliable BCI control in real-world applications.Clinical Trial ID: NCT01894802.},
}
@article {pmid39805390,
year = {2025},
author = {Xu, A and Huang, Y and Wu, B and Zhang, J and Deng, B and Cai, M and Cao, J and Wang, J and Yang, B and Shao, X and He, Q and Ying, M},
title = {Phase separation-based screening identifies arsenic trioxide as the N-Myc-DNA interaction inhibitor for neuroblastoma therapy.},
journal = {Cancer letters},
volume = {612},
number = {},
pages = {217449},
doi = {10.1016/j.canlet.2025.217449},
pmid = {39805390},
issn = {1872-7980},
}
@article {pmid39805258,
year = {2025},
author = {Liu, DH and Kumar, S and Alawieh, H and Racz, FS and Millan, JDR},
title = {Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada9c0},
pmid = {39805258},
issn = {1741-2552},
abstract = {OBJECTIVE: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).<br><br>APPROACH: Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak &#xb5; frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state &#xb5; SMRs.<br><br>MAIN RESULTS: After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of &#xb5; sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state &#xb5; SMRs and &#xb5; ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances.<br><br>SIGNIFICANCE: Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.},
}
@article {pmid39803338,
year = {2024},
author = {Kim, KH and Jeong, JH and Ko, MJ and Lee, S and Kwon, WK and Lee, BJ},
title = {Using Artificial Intelligence in the Comprehensive Management of Spinal Cord Injury.},
journal = {Korean journal of neurotrauma},
volume = {20},
number = {4},
pages = {215-224},
pmid = {39803338},
issn = {2234-8999},
abstract = {Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.},
}
@article {pmid39801915,
year = {2025},
author = {Gu, C and Jin, X and Zhu, L and Yi, H and Liu, H and Yang, X and Babiloni, F and Kong, W},
title = {Cross-session SSVEP brainprint recognition using attentive multi-sub-band depth identity embedding learning network.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {15},
pmid = {39801915},
issn = {1871-4080},
abstract = {Brainprint recognition technology, regarded as a promising biometric technology, encounters challenges stemming from the time-varied, low signal-to-noise ratio of brain signals, such as electroencephalogram (EEG). Steady-state visual evoked potentials (SSVEP) exhibit high signal-to-noise ratio and frequency locking, making them a promising paradigm for brainprint recognition. Consequently, the extraction of time-invariant identity information from SSVEP EEG signals is essential. In this paper, we propose an Attentive Multi-sub-band Depth Identity Embedding Learning Network for stable cross-session SSVEP brainprint recognition. To address the issue of low recognition accuracy across sessions, we introduce the Sub-band Attentive Frequency mechanism, which integrates the frequency-domain relevant characteristics of the SSVEP paradigm and focuses on exploring depth-frequency identity embedding information. Also, we employ Attentive Statistic Pooling to enhance the stability of frequency domain feature distributions across sessions. Extensive experimentation and validation were conducted on two multi-session SSVEP benchmark datasets. The experimental results show that our approach outperforms other state-of-art models on 2-second samples across sessions and has the potential to serve as a benchmark in multi-subject biometric recognition systems.},
}
@article {pmid39801913,
year = {2025},
author = {Zhou, Y and Wang, P and Gong, P and Wan, P and Wen, X and Zhang, D},
title = {Cross-subject mental workload recognition using bi-classifier domain adversarial learning.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {16},
pmid = {39801913},
issn = {1871-4080},
abstract = {To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence. This degrades the workload-discriminating ability of subject-invariant features. To deal with this problem, we propose a novel joint category-wise and domain-wise alignment Domain Adaptation (cdaDA) algorithm, using bi-classifier learning and domain discriminative adversarial learning. The bi-classifier learning approach is adopted to address the similarities and differences between categories, helping to align EEG data within the same mental workload categories. Additionally, the domain discriminative adversarial learning technique is adopted to consider global domain information by minimizing global domain discrepancy. By integrating both local category information and global domain information, the cdaDA model performs a coarse-to-fine alignment and achieves promising cross-subject MWR results.},
}
@article {pmid39801910,
year = {2025},
author = {Wang, J and Zhang, L and Chen, S and Xue, H and Du, M and Xu, Y and Liu, S and Ming, D},
title = {Individuals with high autistic traits exhibit altered interhemispheric brain functional connectivity patterns.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {9},
pmid = {39801910},
issn = {1871-4080},
abstract = {Individuals with high autistic traits (AT) encounter challenges in social interaction, similar to autistic persons. Precise screening and focused interventions positively contribute to improving this situation. Functional connectivity analyses can measure information transmission and integration between brain regions, providing neurophysiological insights into these challenges. This study aimed to investigate the patterns of brain networks in high AT individuals to offer theoretical support for screening and intervention decisions. EEG data were collected during a 4-min resting state session with eyes open and closed from 48 participants. Using the Autism Spectrum Quotient (AQ) scale, participants were categorized into the high AT group (HAT, n = 15) and low AT groups (LAT, n = 15). We computed the interhemispheric and intrahemispheric alpha coherence in two groups. The correlation between physiological indices and AQ scores was also examined. Results revealed that HAT exhibited significantly lower alpha coherence in the homologous hemispheres of the occipital cortex compared to LAT during the eyes-closed resting state. Additionally, significant negative correlations were observed between the degree of AT (AQ scores) and the alpha coherence in the occipital cortex, as well as in the right frontal and left occipital regions. The findings indicated that high AT individuals exhibit decreased connectivity in the occipital region, potentially resulting in diminished ability to process social information from visual inputs. Our discovery contributes to a deeper comprehension of the neural underpinnings of social challenges in high AT individuals, providing neurophysiological signatures for screening and intervention strategies for this population.},
}
@article {pmid39798828,
year = {2025},
author = {Wu, L and Jiang, M and Zhao, M and Hu, X and Wang, J and Zhang, K and Jia, K and Ren, F and Gao, F},
title = {Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model.},
journal = {NeuroImage},
volume = {306},
number = {},
pages = {121004},
doi = {10.1016/j.neuroimage.2025.121004},
pmid = {39798828},
issn = {1095-9572},
mesh = {Humans ; Male ; *Inhibition, Psychological ; *Magnetic Resonance Imaging ; Female ; Young Adult ; Adult ; Brain Mapping/methods ; Frontal Lobe/physiology/diagnostic imaging ; Psychomotor Performance/physiology ; Motor Cortex/physiology/diagnostic imaging ; Models, Neurological ; Executive Function/physiology ; },
abstract = {Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.},
}
@article {pmid39796947,
year = {2024},
author = {Chio, N and Quiles-Cucarella, E},
title = {A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {1},
pages = {},
pmid = {39796947},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Robotics/methods ; *Evoked Potentials, Visual/physiology ; *Bibliometrics ; Imagination/physiology ; Rehabilitation/methods ; Electroencephalography/methods ; },
abstract = {In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence. This article allows for the identification of different bibliometric indicators such as the research process, evolution, visibility, volume, influence, impact, and production in the field of brain-computer interfaces for MI and SSVEP paradigms in rehabilitation and robotics applications from 2000 to August 2024.},
}
@article {pmid39796911,
year = {2024},
author = {Özkahraman, A and Ölmez, T and Dokur, Z},
title = {Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {1},
pages = {},
pmid = {39796911},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Electrooculography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.},
}
@article {pmid39793200,
year = {2025},
author = {Peters, B and Celik, B and Gaines, D and Galvin-McLaughlin, D and Imbiriba, T and Kinsella, M and Klee, D and Lawhead, M and Memmott, T and Smedemark-Margulies, N and Wiedrick, J and Erdogmus, D and Oken, B and Vertanen, K and Fried-Oken, M},
title = {RSVP keyboard with inquiry preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
pmid = {39793200},
issn = {1741-2552},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; Middle Aged ; *Communication Devices for People with Disabilities ; Young Adult ; Computer Peripherals ; Psychomotor Performance/physiology ; },
abstract = {Objective.The RSVP Keyboard is a non-implantable, event-related potential-based brain-computer interface (BCI) system designed to support communication access for people with severe speech and physical impairments. Here we introduce inquiry preview (IP), a new RSVP Keyboard interface incorporating switch input for users with some voluntary motor function, and describe its effects on typing performance and other outcomes.Approach.Four individuals with disabilities participated in the collaborative design of possible switch input applications for the RSVP Keyboard, leading to the development of IP and a method of fusing switch input with language model and electroencephalography (EEG) evidence for typing. Twenty-four participants without disabilities and one potential end user with incomplete locked-in syndrome took part in two experiments investigating the effects of IP and two modes of switch input on typing accuracy and speed during a copy-spelling task.Main results.For participants without disabilities, IP and switch input tended to worsen typing performance compared to the standard RSVP Keyboard condition, with more consistent effects across participants for speed than for accuracy. However, there was considerable variability, with some participants demonstrating improved typing performance and better user experience (UX) with IP and switch input. Typing performance for the potential end user was comparable to that of participants without disabilities. He typed most quickly and accurately with IP and switch input and gave favorable UX ratings to those conditions, but preferred standard RSVP Keyboard.Significance.IP is a novel multimodal interface for the RSVP Keyboard BCI, incorporating switch input as an additional control signal. Typing performance and UX and preference varied widely across participants, reinforcing the need for flexible, customizable BCI systems that can adapt to individual users. ClinicalTrials.gov Identifier: NCT04468919.},
}
@article {pmid39793091,
year = {2025},
author = {Zhang, F and Pu, Y and Kong, XZ},
title = {Parallel vector memories or single memory updating?.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {1},
pages = {e2422788121},
pmid = {39793091},
issn = {1091-6490},
support = {32171031//MOST | National Natural Science Foundation of China (NSFC)/ ; 32400882//MOST | National Natural Science Foundation of China (NSFC)/ ; 2021ZD0200409//STI 2030-Major Projects/ ; },
}
@article {pmid39793054,
year = {2025},
author = {Chivukula, S and Aflalo, T and Zhang, C and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA},
title = {Population encoding of observed and actual somatosensations in the human posterior parietal cortex.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {1},
pages = {e2316012121},
pmid = {39793054},
issn = {1091-6490},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; P50MH094258//CIT | Caltech Conte Center for Social Decision Making (Caltech Conte Center for Neuroscience)/ ; R01EY015545//HHS | NIH | National Eye Institute (NEI)/ ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {*Sensation/physiology ; *Parietal Lobe/physiology ; Neurons/physiology ; Touch/physiology ; Female ; Middle Aged ; Task Performance and Analysis ; },
abstract = {Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations. Here, we hypothesize that mirror-like responses represent one facet of a broader framework in which our brains engage internal models for cognition. We recorded populations of single neurons in the human posterior parietal cortex (PPC) of a brain-machine interface clinical trial participant implanted with a microelectrode array while she either experienced actual touch, or observed diverse tactile stimuli applied to other individuals. Two body locations were tested, on each of the participant and other individuals. Some neurons exhibited mirror-like properties, consistent with earlier literature. However, they were fragile, breaking with increased task complexity. Population responses were better characterized by generalizable and compositional basic-level features encoded within neural subspaces. These features enable the population to respond to diverse actual and observed touch stimuli and are recruited similarly for similar forms of touch. Mirror-like neurons belong within these subspaces, contributing more globally to compositionality and generalizability. We speculate that at a population-level, human PPC manifests an internal model for touch, and that cognition unfolds in the high-level human cortex by versatility in its representational building blocks. In a broad sense, we speculate that the population features we demonstrate support a broad mechanism by which the high-level human cortex enables understanding.},
}
@article {pmid39789264,
year = {2025},
author = {Lin, N and Wang, S and Li, Y and Wang, B and Shi, S and He, Y and Zhang, W and Yu, Y and Zhang, Y and Zhang, X and Wong, K and Wang, S and Chen, X and Jiang, H and Zhang, X and Lin, P and Xu, X and Qi, X and Wang, Z and Shang, D and Liu, Q and Liu, M},
title = {Resistive memory-based zero-shot liquid state machine for multimodal event data learning.},
journal = {Nature computational science},
volume = {5},
number = {1},
pages = {37-47},
pmid = {39789264},
issn = {2662-8457},
support = {62422004//National Natural Science Foundation of China (National Science Foundation of China)/ ; Z210006//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
abstract = {The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware-software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.},
}
@article {pmid39788124,
year = {2025},
author = {An, D and You, Y and Ma, Q and Xu, Z and Liu, Z and Liao, R and Chen, H and Wang, Y and Wang, Y and Dai, H and Li, H and Jiang, L and Chen, Z and Hu, W},
title = {Deficiency of histamine H2 receptors in parvalbumin-positive neurons leads to hyperactivity, impulsivity, and impaired attention.},
journal = {Neuron},
volume = {113},
number = {4},
pages = {572-589.e6},
doi = {10.1016/j.neuron.2024.12.002},
pmid = {39788124},
issn = {1097-4199},
mesh = {Animals ; *Impulsive Behavior/physiology ; *Receptors, Histamine H2/metabolism/genetics ; *Attention Deficit Disorder with Hyperactivity/metabolism/genetics ; Mice ; *Parvalbumins/metabolism ; Male ; *Neurons/metabolism ; Humans ; Substantia Nigra/metabolism ; Female ; Mice, Knockout ; Dopaminergic Neurons/metabolism ; Mice, Inbred C57BL ; Attention/physiology ; Dopamine Plasma Membrane Transport Proteins/metabolism/deficiency/genetics ; },
abstract = {Attention deficit hyperactivity disorder (ADHD), affecting 4% of the population, is characterized by inattention, hyperactivity, and impulsivity; however, its neurophysiological mechanisms remain unclear. Here, we discovered that deficiency of histamine H2 receptor (H2R) in parvalbumin-positive neurons in substantia nigra pars recticulata (PV[SNr]) attenuates PV[+] neuronal activity and induces hyperactivity, impulsivity, and inattention in mice. Moreover, decreased H2R expression was observed in PV[SNr] in patients with ADHD symptoms and dopamine-transporter-deficient mice, whose behavioral phenotypes were alleviated by H2R agonist treatment. Dysfunction of PV[SNr] efferents to the substantia nigra pars compacta dopaminergic neurons and superior colliculus differently contributes to H2R-deficiency-induced behavioral disorders. Collectively, our results demonstrate that H2R deficiency in PV[+] neurons contributes to hyperactivity, impulsivity, and inattention by dampening PV[SNr] activity and involving different efferents in mice. It may enhance understanding of the molecular and circuit-level basis of ADHD and afford new potential therapeutic targets for ADHD-like psychiatric diseases.},
}
@article {pmid39787896,
year = {2025},
author = {Li, L and Menendez-Lustri, DM and Hartzler, A and Pogharian, A and Zaorski, B and Chen, A and Palen, J and Traylor, B and Quill, E and Pawlowski, CL and Bruckman, MA and Gupta, AS and Capadona, JR and Shoffstall, AJ},
title = {Systemically administered platelet-inspired nanoparticles to reduce inflammation surrounding intracortical microelectrodes.},
journal = {Biomaterials},
volume = {317},
number = {},
pages = {123082},
doi = {10.1016/j.biomaterials.2025.123082},
pmid = {39787896},
issn = {1878-5905},
support = {I01 RX003420/RX/RRD VA/United States ; },
mesh = {Animals ; Male ; *Microelectrodes ; *Rats, Sprague-Dawley ; *Nanoparticles/chemistry ; *Blood Platelets/metabolism ; *Inflammation ; Rats ; Blood-Brain Barrier/metabolism/drug effects ; Electrodes, Implanted ; },
abstract = {Intracortical microelectrodes (IMEs) are essential for neural signal acquisition in neuroscience and brain-machine interface (BMI) systems, aiding patients with neurological disorders, paralysis, and amputations. However, IMEs often fail to maintain robust signal quality over time, partly due to neuroinflammation caused by vascular damage during insertion. Platelet-inspired nanoparticles (PIN), which possess injury-targeting functions, mimic the adhesion and aggregation of active platelets through conjugated collagen-binding peptides (CBP), von Willebrand Factor-binding peptides (VBP), and fibrinogen-mimetic peptides (FMP). Systemically administered PINs can potentially enhance hemostasis and promote the resealing of IME insertion-induced leaky blood-brain barrier (BBB), thereby attenuating the influx of blood-derived proteins into the brain parenchyma that trigger neuroinflammation. This study explores the potential of PINs to mitigate neuroinflammation at implant sites. Male Sprague Dawley rats underwent craniotomies and IME implantations, followed by a single dose of Cy5 labeled PINs (2 mg/kg). Rats were sacrificed at intervals from 0 to 4 days post-implantation (DPI) for biodistribution analysis using an in vivo live imaging system (IVIS) and immunohistochemistry (IHC) to assess neuroinflammation, BBB permeability, and active platelet distribution. Another cohort of rats received weekly PINs, trehalose buffer (TH, diluent control), or control nanoparticles (CP, PEG-coated liposomes) for 4 weeks, with similar endpoint analyses. Results indicated that PIN concentrations were significantly elevated near IME interfaces acutely (0-4 DPI) and after 4 weeks of repeated dosing. At 3 DPI, peak intensities of active platelets (CD62P), activated microglia/macrophages (CD68), and PINs were observed. Immunoglobulin G (IgG) was upregulated during the first 24 h near implant sites but declined thereafter. At 4 weeks, the PINs group exhibited higher intensities of active platelets and PINs, and reduced CD68 and IgG levels compared to controls. PINs effectively targeted the IME-tissue interface, alongside endogenous activated platelets, resulting in reduced neuroinflammatory and BBB-leakage markers compared to the diluent-only-infused control group. Repeated dosing of PINs presents a promising approach for enhancing the quality of neural recordings in future studies.},
}
@article {pmid39787745,
year = {2025},
author = {Elsohaby, I and Kostoulas, P and Fayez, M and Elmoslemany, A and Alkafafy, ME and Bahhary, AM and Alzahrani, R and Morsi, AEKM and Arango-Sabogal, JC},
title = {Bayesian estimation of diagnostic accuracy of fecal smears, fecal PCR and serum ELISA for detecting Mycobacterium avium subsp. paratuberculosis infections in four domestic ruminant species in Saudi Arabia.},
journal = {Veterinary microbiology},
volume = {301},
number = {},
pages = {110377},
doi = {10.1016/j.vetmic.2025.110377},
pmid = {39787745},
issn = {1873-2542},
mesh = {Animals ; *Mycobacterium avium subsp. paratuberculosis/isolation &amp; purification/immunology/genetics ; *Paratuberculosis/diagnosis/microbiology/epidemiology ; Saudi Arabia/epidemiology ; *Bayes Theorem ; *Enzyme-Linked Immunosorbent Assay/veterinary ; *Feces/microbiology ; Cattle ; *Goats ; Sheep ; *Sensitivity and Specificity ; Cross-Sectional Studies ; *Camelus/microbiology ; *Polymerase Chain Reaction/veterinary ; *Sheep Diseases/diagnosis/microbiology/epidemiology ; Cattle Diseases/diagnosis/microbiology/epidemiology ; Goat Diseases/microbiology/diagnosis/epidemiology ; Ruminants/microbiology ; },
abstract = {Paratuberculosis, a chronic wasting disease affecting domestic and wild ruminants worldwide, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Various diagnostic tests exist for detecting MAP infection; however, none of them possess perfect accuracy to be qualified as a reference standard test, particularly due to their notably low sensitivity. Therefore, we used Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of fecal smears (FS), fecal PCR and serum ELISA for detecting MAP infections in sheep, goats, cattle, and camels older than 2 years in Saudi Arabia. Data from a cross-sectional study conducted in the Eastern Province of Saudi Arabia on 31 different farms with a history of MAP infection were analyzed. Fecal and blood samples from all animals older than 2 years in each farm were collected, resulting in a total of 220 sheep, 123 goats, 66 cattle, and 240 camels sampled. FS and IS900-PCR were performed on fecal samples to detect acid-fast bacilli and MAP DNA, respectively. The IDEXX ELISA kit was used to detect MAP antibodies in serum samples. For each ruminant species population, a BLCM was fitted to obtain posterior estimates [medians and 95 % Bayesian credible intervals (95 % BCI)] for sensitivity (Se) and specificity (Sp) of the three tests. We assumed FS and PCR to be conditionally dependent on the true animal MAP status. Prior distributions for test accuracy were used if available. FS had the highest Se among all tests and across all species with median values around 80 % in sheep, goats and camels, and near 50 % in cattle. Median Sp estimates of ELISA and PCR were higher than 90 % for all species. FS yielded the lowest Sp of the study when applied in camels, sheep, and goats. Using the prevalence observed in this study, median positive predictive value (PPV) was higher for PCR and ELISA than FS for camels, sheep, and goats. In cattle, PPV of all tests was similar with median estimates > 95 %. In camels, sheep, and goats, median negative predicative value (NPV) of all tests were > 60 %. The lowest median NPV for all tests were observed in cattle (< 30 %). Our results suggest that ELISA is a suitable option to identify MAP infected animals in farms with previous history of MAP in the Eastern region of Saudi Arabia.},
}
@article {pmid39787440,
year = {2024},
author = {Alkawadri, CI and Yan, Q and Kocuglu Kinal, AG and Spencer, DD and Alkawadri, R},
title = {Comparison of EEG Signal Characteristics of Subdural and Depth Electrodes.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {},
number = {},
pages = {},
doi = {10.1097/WNP.0000000000001139},
pmid = {39787440},
issn = {1537-1603},
abstract = {OBJECTIVES: Our study aimed to compare signal characteristics of subdural electrodes (SDE) and depth stereo EEG placed within a 5-mm vicinity in patients with drug-resistant epilepsy. We report how electrode design and placement collectively affect signal content from a shared source between these electrode types.<br><br>METHODS: In subjects undergoing invasive intracranial EEG evaluation at a surgical epilepsy center from 2012 to 2018, stereo EEG and SDE electrode contacts placed within a 5-mm vicinity were identified. Of these, 24 contacts (12 pairs) met our criteria for signal-to-noise ratio and data availability for final analysis. We used Welch method to analyze the correlation of power spectral densities of EEG segments, root mean square of 1-second windows, and fast-Fourier transform to calculate coherence across conventional frequency bands.<br><br>RESULTS: We observed a median distance of 3.7 mm between the electrode contact pairs. Time-aware analysis highlighted the coherence's strength primarily in the high-gamma band, where the median (r) was 0.889. In addition, the median power ratios between the SDE and stereo EEG signal was 1.99. This ratio decreased from high-gamma to infra-low frequencies, with medians of 2.07 and 0.97, respectively. The power spectral densities for the stereo EEG and SDE electrodes demonstrated a strong correlation, with a median correlation coefficient (r) of 0.99 and an interquartile range from 0.915 to 0.996.<br><br>CONCLUSIONS: Signals captured by standard subdural and depth (intracranial EEG) electrodes within a 5-mm radius exhibit band-specific coherence and are not identical. The association was most pronounced in the high-gamma band, with coherence decreasing with lower frequencies. Our findings underscore the combined effects of electrode size, design, placement, preferred bandwidth, and the nature of the activity source on signal recording. Particularly, SDE employed herein may offer advantages for high-frequency signals, but the impact of electrode size on recordings necessitates careful consideration in context-specific situations.<br><br>SIGNIFICANCE: The findings relate to surgical epilepsy care and may inform the design of brain-computer interface.},
}
@article {pmid39781056,
year = {2025},
author = {Cai, Z and Li, P and Cheng, L and Yuan, D and Li, M and Li, H},
title = {A high performance heterogeneous hardware architecture for brain computer interface.},
journal = {Biomedical engineering letters},
volume = {15},
number = {1},
pages = {217-227},
pmid = {39781056},
issn = {2093-985X},
abstract = {Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms. This paper proposes a heterogeneous BCI architecture based on ARM + FPGA, enabling real-time processing of electroencephalogram (EEG) signals. Adopting data quantization, layer fusion and data augmentation to optimize the compact neural network model EEGNet, and design dedicated hardware engines to accelerate the network. Experimental results show that the system achieves 93.3% classification accuracy for steady-state visual evoked potential signals, with a time delay of 0.2 ms per trail, and a power consumption of approximately (1.91 W). That is 31.5 times faster acceleration is realized at the cost of only 0.7% lower accuracy compared with the conventional processor. The results show that the BCI architecture proposed in this study has strong practicability and high research significance.},
}
@article {pmid39779796,
year = {2025},
author = {Shelishiyah, R and Thiyam, DB and Margaret, MJ and Banu, NMM},
title = {A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {1360},
pmid = {39779796},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; Deep Learning ; Adult ; Male ; Movement/physiology ; },
abstract = {The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional Near Infra-red Spectroscopy (fNIRS). Classifying contralateral and ipsilateral motor movements is found challenging among the other mental activity signals. The current work focuses on the performance of deep learning methods like - Convolutional Neural Networks (CNN) and Bidirectional Long-Short term memory (Bi-LSTM) in classifying a four-class motor execution of Right Hand, Left Hand, Right Arm and Left Arm taken from the CORE dataset. The model performance was evaluated using metrics such as Accuracy, F1 - score, Precision, Recall, AUC and ROC curve. The CNN and Hybrid CNN models have resulted in 98.3% and 99% accuracy respectively.},
}
@article {pmid39776784,
year = {2024},
author = {Ahmadi, S and Desain, P and Thielen, J},
title = {A Bayesian dynamic stopping method for evoked response brain-computer interfacing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1437965},
pmid = {39776784},
issn = {1662-5161},
abstract = {INTRODUCTION: As brain-computer interfacing (BCI) systems transition fromassistive technology to more diverse applications, their speed, reliability, and user experience become increasingly important. Dynamic stopping methods enhance BCI system speed by deciding at any moment whether to output a result or wait for more information. Such approach leverages trial variance, allowing good trials to be detected earlier, thereby speeding up the process without significantly compromising accuracy. Existing dynamic stopping algorithms typically optimize measures such as symbols per minute (SPM) and information transfer rate (ITR). However, these metrics may not accurately reflect system performance for specific applications or user types. Moreover, many methods depend on arbitrary thresholds or parameters that require extensive training data.<br><br>METHODS: We propose a model-based approach that takes advantage of the analytical knowledge that we have about the underlying classification model. By using a risk minimization approach, our model allows precise control over the types of errors and the balance between precision and speed. This adaptability makes it ideal for customizing BCI systems to meet the diverse needs of various applications.<br><br>RESULTS AND DISCUSSION: We validate our proposed method on a publicly available dataset, comparing it with established static and dynamic stopping methods. Our results demonstrate that our approach offers a broad range of accuracy-speed trade-offs and achieves higher precision than baseline stopping methods.},
}
@article {pmid39775950,
year = {2025},
author = {Zeng, X and Kang, T and Huang, W and Jin, T},
title = {How Should the BCI and BOOI Index be Correctly Applied in Patients With Low-Compliance Bladder?.},
journal = {Neurourology and urodynamics},
volume = {},
number = {},
pages = {},
doi = {10.1002/nau.25663},
pmid = {39775950},
issn = {1520-6777},
}
@article {pmid39771903,
year = {2024},
author = {Alzahrani, S and Banjar, H and Mirza, R},
title = {Systematic Review of EEG-Based Imagined Speech Classification Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771903},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Speech/physiology ; Machine Learning ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {This systematic review examines EEG-based imagined speech classification, emphasizing directional words essential for development in the brain-computer interface (BCI). This study employed a structured methodology to analyze approaches using public datasets, ensuring systematic evaluation and validation of results. This review highlights the feature extraction techniques that are pivotal to classification performance. These include deep learning, adaptive optimization, and frequency-specific decomposition, which enhance accuracy and robustness. Classification methods were explored by comparing traditional machine learning with deep learning and emphasizing the role of brain lateralization in imagined speech for effective recognition and classification. This study discusses the challenges of generalizability and scalability in imagined speech recognition, focusing on subject-independent approaches and multiclass scalability. Performance benchmarking across various datasets and methodologies revealed varied classification accuracies, reflecting the complexity and variability of EEG signals. This review concludes that challenges remain despite progress, particularly in classifying directional words. Future research directions include improved signal processing techniques, advanced neural network architectures, and more personalized, adaptive BCI systems. This review is critical for future efforts to develop practical communication tools for individuals with speech and motor impairments using EEG-based BCIs.},
}
@article {pmid39771862,
year = {2024},
author = {Khabti, J and AlAhmadi, S and Soudani, A},
title = {Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771862},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; Internet of Things ; Signal Processing, Computer-Assisted ; },
abstract = {One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs. The MI tasks are recognized through EEG signal processing and classification, which can drain sensor energy due to the complexity of the data and the presence of redundant information, often influenced by subject-dependent factors. To address these challenges, we propose in this paper a multi-subject transfer-learning approach for an efficient MI training framework in remote rehabilitation within an IoT environment. For efficient implementation, we propose an IoT architecture that includes cloud/edge computing as a solution to enhance the system's efficiency and reduce the use of network resources. Furthermore, deep-learning classification with and without channel selection is applied in the cloud, while multi-subject transfer-learning classification is utilized at the edge node. Various transfer-learning strategies, including different epochs, freezing layers, and data divisions, were employed to improve accuracy and efficiency. To validate this framework, we used the BCI IV 2a dataset, focusing on subjects 7, 8, and 9 as targets. The results demonstrated that our approach significantly enhanced the average accuracy in both multi-subject and single-subject transfer-learning classification. In three-subject transfer-learning classification, the FCNNA model achieved up to 79.77% accuracy without channel selection and 76.90% with channel selection. For two-subject and single-subject transfer learning, the application of transfer learning improved the average accuracy by up to 6.55% and 12.19%, respectively, compared to classification without transfer learning. This framework offers a promising solution for remote MI rehabilitation, providing both accurate task recognition and efficient resource usage.},
}
@article {pmid39771843,
year = {2024},
author = {Mikhaylov, D and Saeed, M and Husain Alhosani, M and F Al Wahedi, Y},
title = {Comparison of EEG Signal Spectral Characteristics Obtained with Consumer- and Research-Grade Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771843},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods/instrumentation ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Brain-Computer Interfaces ; Brain/physiology ; Wearable Electronic Devices ; Electrodes ; Young Adult ; },
abstract = {Electroencephalography (EEG) has emerged as a pivotal tool in both research and clinical practice due to its non-invasive nature, cost-effectiveness, and ability to provide real-time monitoring of brain activity. Wearable EEG technology opens new avenues for consumer applications, such as mental health monitoring, neurofeedback training, and brain-computer interfaces. However, there is still much to verify and re-examine regarding the functionality of these devices and the quality of the signal they capture, particularly as the field evolves rapidly. In this study, we recorded the resting-state brain activity of healthy volunteers via three consumer-grade EEG devices, namely PSBD Headband Pro, PSBD Headphones Lite, and Muse S Gen 2, and compared the spectral characteristics of the signal obtained with that recorded via the research-grade Brain Product amplifier (BP) with the mirroring montages. The results showed that all devices exhibited higher mean power in the low-frequency bands, which are characteristic of dry-electrode technology. PSBD Headband proved to match BP most precisely among the other examined devices. PSBD Headphones displayed a moderate correspondence with BP and signal quality issues in the central group of electrodes. Muse demonstrated the poorest signal quality, with extremely low alignment with BP. Overall, this study underscores the importance of considering device-specific design constraints and emphasizes the need for further validation to ensure the reliability and accuracy of wearable EEG devices.},
}
@article {pmid39771785,
year = {2024},
author = {Novičić, M and Djordjević, O and Miler-Jerković, V and Konstantinović, L and Savić, AM},
title = {Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771785},
issn = {1424-8220},
support = {6066223//Science Fund of the Republic of Serbia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Somatosensory/physiology ; Male ; *Machine Learning ; Adult ; Female ; Algorithms ; Touch/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory-motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.},
}
@article {pmid39771721,
year = {2024},
author = {Víg, L and Zátonyi, A and Csernyus, B and Horváth, &#xc1;C and Bojtár, M and Kele, P and Madarász, M and Rózsa, B and Fürjes, P and Hermann, P and Hakkel, O and Péter, L and Fekete, Z},
title = {Optically Controlled Drug Delivery Through Microscale Brain-Machine Interfaces Using Integrated Upconverting Nanoparticles.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771721},
issn = {1424-8220},
support = {TKP2021-EGA-42//National Research, Development and Innovation Office/ ; TKP2021-EGA-04//National Research, Development and Innovation Office/ ; VKE-2018-00032//National Research, Development and Innovation Office/ ; KFI-2018-00097//National Research, Development and Innovation Office/ ; 2020-2.1.1-ED-2022-00208//National Research, Development and Innovation Office/ ; (NAP2022I-8/2022//Hungarian Academy of Sciences/ ; Bolyai Janos Scholarship//Hungarian Academy of Sciences/ ; NKFIH FK 134403//National Research, Development and Innovation Office/ ; },
mesh = {*Nanoparticles/chemistry ; *Brain-Computer Interfaces ; *Drug Delivery Systems/methods/instrumentation ; Animals ; Brain/physiology ; Rats ; },
abstract = {The aim of this work is to incorporate lanthanide-cored upconversion nanoparticles (UCNP) into the surface of microengineered biomedical implants to create a spatially controlled and optically releasable model drug delivery device in an integrated fashion. Our approach enables silicone-based microelectrocorticography (ECoG) implants holding platinum/iridium recording sites to serve as a stable host of UCNPs. Nanoparticles excitable in the near-infrared (lower energy) regime and emitting visible (higher energy) light are utilized in a study. With the upconverted higher energy photons, we demonstrate the induction of photochemical (cleaving) reactions that enable the local release of specific dyes as a model system near the implant. The modified ECoG electrodes can be implanted in brain tissue to act as an uncaging system that releases small amounts of substance while simultaneously measuring the evoked neural response upon light activation. In this paper, several technological challenges like the surface modification of UCNPs, the immobilization of particles on the implantable platform, and measuring the stability of integrated UCNPs in in vitro and in vivo conditions are addressed in detail. Besides the chemical, mechanical, and optical characterization of the ready-to-use devices, the effect of nanoparticles on the original electrophysiological function is also evaluated. The results confirm that silicone-based brain-machine interfaces can be efficiently complemented with UCNPs to facilitate local model drug release.},
}
@article {pmid39771689,
year = {2024},
author = {Fang, F and Gao, T and Wu, J},
title = {Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771689},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Emotions/physiology ; Male ; Female ; Adult ; Young Adult ; },
abstract = {Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test". In this context, "human nature" refers to emotion and consciousness, while "test" involves a critical analysis of AI technology and an exploration of the differences between humanity and technicality. Additionally, through experimental research and literature analysis, we found that the integration of electroencephalogram (EEG) data with AI systems is becoming a significant trend. The experiment involved 20 participants, with two conditions: C1 (using EEG data) and C2 (without EEG data). The results indicated a significant increase in immersion under the C1 condition, along with a more positive emotional experience. We summarized three design directions: enhancing immersion, creating emotional experiences, and expressing philosophical concepts. Based on these findings, there is potential for further developing EEG data as a medium to enrich interactive experiences, offering new insights into the fusion of technology and human emotion.},
}
@article {pmid39771656,
year = {2024},
author = {Khalil, AEK and Perez-Diaz, JA and Cantoral-Ceballos, JA and Antelis, JM},
title = {Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {24},
pages = {},
pmid = {39771656},
issn = {1424-8220},
support = {N/A//Tecnol&#xf3;gico de Monterrey/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Computer Security ; Signal Processing, Computer-Assisted ; Algorithms ; Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user's intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.},
}
@article {pmid39768099,
year = {2024},
author = {Mahmoud, TSM and Munawar, A and Nawaz, MZ and Chen, Y},
title = {Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {12},
pages = {},
pmid = {39768099},
issn = {2306-5354},
abstract = {Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.},
}
@article {pmid39768034,
year = {2024},
author = {Geravanchizadeh, M and Shaygan Asl, A and Danishvar, S},
title = {Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {12},
pages = {},
pmid = {39768034},
issn = {2306-5354},
abstract = {Attention is one of many human cognitive functions that are essential in everyday life. Given our limited processing capacity, attention helps us focus only on what matters. Focusing attention on one speaker in an environment with many speakers is a critical ability of the human auditory system. This paper proposes a new end-to-end method based on the combined transformer and graph convolutional neural network (TraGCNN) that can effectively detect auditory attention from electroencephalograms (EEGs). This approach eliminates the need for manual feature extraction, which is often time-consuming and subjective. Here, the first EEG signals are converted to graphs. We then extract attention information from these graphs using spatial and temporal approaches. Finally, our models are trained with these data. Our model can detect auditory attention in both the spatial and temporal domains. Here, the EEG input is first processed by transformer layers to obtain a sequential representation of EEG based on attention onsets. Then, a family of graph convolutional layers is used to find the most active electrodes using the spatial position of electrodes. Finally, the corresponding EEG features of active electrodes are fed into the graph attention layers to detect auditory attention. The Fuglsang 2020 dataset is used in the experiments to train and test the proposed and baseline systems. The new TraGCNN approach, as compared with state-of-the-art attention classification methods from the literature, yields the highest performance in terms of accuracy (80.12%) as a classification metric. Additionally, the proposed model results in higher performance than our previously graph-based model for different lengths of EEG segments. The new TraGCNN approach is advantageous because attenuation detection is achieved from EEG signals of subjects without requiring speech stimuli, as is the case with conventional auditory attention detection methods. Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. The findings of this study have important implications for the understanding and assessment of auditory attention, which is crucial for many applications, such as brain-computer interface (BCI) systems, speech separation, and neuro-steered hearing aid development.},
}
@article {pmid39766488,
year = {2024},
author = {Ma, Y and Huang, Z and Yang, Y and Zhang, S and Dong, Q and Wang, R and Hu, L},
title = {Emotion Recognition Model of EEG Signals Based on Double Attention Mechanism.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766488},
issn = {2076-3425},
abstract = {BACKGROUND: Emotions play a crucial role in people's lives, profoundly affecting their cognition, decision-making, and interpersonal communication. Emotion recognition based on brain signals has become a significant challenge in the fields of affective computing and human-computer interaction.<br><br>METHODS: Addressing the issue of inaccurate feature extraction and low accuracy of existing deep learning models in emotion recognition, this paper proposes a multi-channel automatic classification model for emotion EEG signals named DACB, which is based on dual attention mechanisms, convolutional neural networks, and bidirectional long short-term memory networks. DACB extracts features in both temporal and spatial dimensions, incorporating not only convolutional neural networks but also SE attention mechanism modules for learning the importance of different channel features, thereby enhancing the network's performance. DACB also introduces dot product attention mechanisms to learn the importance of spatial and temporal features, effectively improving the model's accuracy.<br><br>RESULTS: The accuracy of this method in single-shot validation tests on the SEED-IV and DREAMER (Valence-Arousal-Dominance three-classification) datasets is 99.96% and 87.52%, 90.06%, and 89.05%, respectively. In 10-fold cross-validation tests, the accuracy is 99.73% and 84.26%, 85.40%, and 85.02%, outperforming other models.<br><br>CONCLUSIONS: This demonstrates that the DACB model achieves high accuracy in emotion classification tasks, demonstrating outstanding performance and generalization ability and providing new directions for future research in EEG signal recognition.},
}
@article {pmid39766471,
year = {2024},
author = {Adolf, A and Köllőd, CM and Márton, G and Fadel, W and Ulbert, I},
title = {The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766471},
issn = {2076-3425},
support = {KDP-2021-12, 1022428/001//National Research, Development and Innovation Office/ ; FK146115//National Research, Development and Innovation Office/ ; NAP2022-I-2/2022//Hungarian Academy of Sciences/ ; RRF-2.3.1-21-2022-00015//National Research, Development and Innovation Office/ ; },
abstract = {Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.},
}
@article {pmid39766454,
year = {2024},
author = {Beck, S and Liberman, Y and Dubljević, V},
title = {Media Representation of the Ethical Issues Pertaining to Brain-Computer Interface (BCI) Technology.},
journal = {Brain sciences},
volume = {14},
number = {12},
pages = {},
pmid = {39766454},
issn = {2076-3425},
abstract = {BACKGROUND/OBJECTIVES: Brain-computer interfaces (BCIs) are a rapidly developing technology that captures and transmits brain signals to external sources, allowing the user control of devices such as prosthetics. BCI technology offers the potential to restore physical capabilities in the body and change how we interact and communicate with computers and each other. While BCI technology has existed for decades, recent developments have caused the technology to generate a host of ethical issues and discussions in both academic and public circles. Given that media representation has the potential to shape public perception and policy, it is necessary to evaluate the space that these issues take in public discourse.<br><br>METHODS: We conducted a rapid review of media articles in English discussing ethical issues of BCI technology from 2013 to 2024 as indexed by LexisNexis. Our searches yielded 675 articles, with a final sample containing 182 articles. We assessed the themes of the articles and coded them based on the ethical issues discussed, ethical frameworks, recommendations, tone, and application of technology.<br><br>RESULTS: Our results showed a marked rise in interest in media articles over time, signaling an increased focus on this topic. The majority of articles adopted a balanced or neutral tone when discussing BCIs and focused on ethical issues regarding privacy, autonomy, and regulation.<br><br>CONCLUSIONS: Current discussion of ethical issues reflects growing news coverage of companies such as Neuralink, and reveals a mounting distrust of BCI technology. The growing recognition of ethical considerations in BCI highlights the importance of ethical discourse in shaping the future of the field.},
}
@article {pmid39760490,
year = {2025},
author = {Zhai, H and Li, P and Wang, H and Wang, X},
title = {DMSO-promoted α-bromination of α-aryl ketones for the construction of 2-aryl-2-bromo-cycloketones.},
journal = {Organic &amp; biomolecular chemistry},
volume = {23},
number = {7},
pages = {1627-1632},
doi = {10.1039/d4ob01937g},
pmid = {39760490},
issn = {1477-0539},
abstract = {A DMSO-promoted practical one-step α-bromination reaction of α-aryl ketones with NBS has been developed for the construction of 2-aryl-2-bromo-cycloketones. The desired regioselective α-bromination products were isolated in moderate to good yields, with a maximum tested scale of 15 mmol. Notably, ketamine derivatives could be smoothly synthesized in two steps.},
}
@article {pmid39760422,
year = {2025},
author = {Zhu, L and Wang, Y and Huang, A and Tan, X and Zhang, J},
title = {A multi-branch, multi-scale, and multi-view CNN with lightweight temporal attention mechanism for EEG-based motor imagery decoding.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2024.2448576},
pmid = {39760422},
issn = {1476-8259},
abstract = {Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding. Specifically, MBMSNet first extracts multi-view representations from raw EEG signals, followed by independent branches to capture spatial, spectral, temporal-spatial, and temporal-spectral features. Each branch includes a domain-specific convolutional layer, a variance layer, and a temporal attention layer. Finally, the features derived from each branch are concatenated with weights and classified through a fully connected layer. Experiments demonstrate MBMSNet outperforms state-of-the-art models, achieving accuracies of 84.60% on BCI Competition IV 2a, 87.80% on 2b, and 74.58% on OpenBMI, showcasing its potential for robust BCI applications.},
}
@article {pmid39759760,
year = {2024},
author = {Cisotto, G and Zancanaro, A and Zoppis, IF and Manzoni, SL},
title = {hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1459970},
pmid = {39759760},
issn = {1662-5196},
abstract = {INTRODUCTION: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.<br><br>METHODS: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects).<br><br>RESULTS: We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before.<br><br>DISCUSSION: Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.},
}
@article {pmid39759080,
year = {2024},
author = {Zhao, X and Xu, S and Geng, K and Zhou, T and Xu, T and Wang, Z and Feng, S and Hu, H},
title = {MP: A steady-state visual evoked potential dataset based on multiple paradigms.},
journal = {iScience},
volume = {27},
number = {11},
pages = {111030},
pmid = {39759080},
issn = {2589-0042},
abstract = {In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size" and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.},
}
@article {pmid39758181,
year = {2025},
author = {Ammar, A and Salem, A and Simak, ML and Horst, F and Schöllhorn, WI},
title = {Acute effects of motor learning models on technical efficiency in strength-coordination exercises: a comparative analysis of Olympic snatch biomechanics in beginners.},
journal = {Biology of sport},
volume = {42},
number = {1},
pages = {151-161},
pmid = {39758181},
issn = {0860-021X},
abstract = {Despite the development of various motor learning models over many decades, the question of which model is most effective under which conditions to optimize the acquisition of skills remains a heated and recurring debate. This is particularly important in connection with learning sports movements with a high strength component. This study aims to examine the acute effects of various motor learning models on technical efficiency and force production during the Olympic snatch movement. In a within-subject design, sixteen highly active male participants (mean age: 23.13 ± 2.09 years), who were absolute beginners regarding the learning task, engaged in randomized snatch learning bouts, consisting of 36 trials across different learning models: differential learning (DL), contextual interference (serial, sCI; and blocked, bCI), and repetitive learning (RL). Kinematic and kinetic data were collected from three snatch trials executed following each learning bout. Discrete data from the most commonly monitored biomechanical parameters in Olympic weightlifting were analyzed using inferential statistics to identify differences between learning models. The statistical analysis revealed no significant differences between the learning models across all tested parameters, with p-values ranging from 0.236 to 0.99. However, it was observed that only the bouts with an exercise sequence following the DL model resulted in an average antero-posterior displacement of the barbell that matched the optimal displacement. This was characterized by a mean positive displacement towards the lifter during the pulling phases, a negative displacement away from the lifter in the turnover phase, and a return to positive displacement in the catch phase. These findings indicate the limited acute impact of the exercise sequences based on the three motor learning models on Olympic snatch technical efficiency in beginners, yet they hint at a possible slight advantage for the DL model. Coaches might therefore consider incorporating the DL model to potentially enhance technical efficiency, especially during the early stages of skill acquisition. Future research, involving even bigger amounts of exercise noise, longer learning periods, or a greater number of total learning trials and sessions, is essential to verify the potential advantages of the DL model for weightlifting technical efficiency.},
}
@article {pmid39758128,
year = {2025},
author = {Wang, B and Zhang, Y and Li, H and Dou, H and Guo, Y and Deng, Y},
title = {Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.},
journal = {National science review},
volume = {12},
number = {1},
pages = {nwae301},
pmid = {39758128},
issn = {2053-714X},
abstract = {The pursuit of artificial neural networks that mirror the accuracy, efficiency and low latency of biological neural networks remains a cornerstone of artificial intelligence (AI) research. Here, we incorporated recent neuroscientific findings of self-inhibiting autapse and neuron heterogeneity for innovating a spiking neural network (SNN) with enhanced learning and memorizing capacities. A bi-level programming paradigm was formulated to respectively learn neuron-level biophysical variables and network-level synapse weights for nested heterogeneous learning. We successfully demonstrated that our biologically inspired neuron model could reproduce neural statistics at both individual and group levels, contributing to the effective decoding of brain-computer interface data. Furthermore, the heterogeneous SNN showed higher accuracy (1%-10% improvement), superior efficiency (maximal 17.83-fold reduction in energy) and lower latency (maximal 5-fold improvement) in performing several AI tasks. For the first time, we benchmarked SNN for conducting cell type identification from scRNA-seq data. The proposed model correctly identified very rare cell types associated with severe brain diseases where typical SNNs failed.},
}
@article {pmid39757218,
year = {2025},
author = {Kim, MS and Park, H and Kwon, I and An, KO and Kim, H and Park, G and Hyung, W and Im, CH and Shin, JH},
title = {Efficacy of brain-computer interface training with motor imagery-contingent feedback in improving upper limb function and neuroplasticity among persons with chronic stroke: a double-blinded, parallel-group, randomized controlled trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {1},
pmid = {39757218},
issn = {1743-0003},
support = {NRCTR-IN20001//Translational Research Program for Rehabilitation Robots, National Rehabilitation Center, Ministry of Health and Welfare, Republic of Korea/ ; },
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Chronic Disease ; Double-Blind Method ; Electroencephalography ; *Imagery, Psychotherapy/methods ; Imagination/physiology ; *Neuronal Plasticity/physiology ; *Recovery of Function/physiology ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; Treatment Outcome ; *Upper Extremity/physiopathology ; *Feedback ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) technology can enhance neural plasticity and motor recovery in persons with stroke. However, the effects of BCI training with motor imagery (MI)-contingent feedback versus MI-independent feedback remain unclear. This study aimed to investigate whether the contingent connection between MI-induced brain activity and feedback influences functional and neural plasticity outcomes. We hypothesized that BCI training, with MI-contingent feedback, would result in greater improvements in upper limb function and neural plasticity compared to BCI training, with MI-independent feedback.<br><br>METHODS: This randomized controlled trial included persons with chronic stroke who underwent BCI training involving functional electrical stimulation feedback on the affected wrist extensor. Primary outcomes included the Medical Research Council (MRC) scale score for muscle strength in the wrist extensor (MRC-WE) and active range of motion in wrist extension (AROM-WE). Resting-state electroencephalogram recordings were used to assess neural plasticity.<br><br>RESULTS: Compared to the MI-independent feedback BCI group, the MI-contingent feedback BCI group showed significantly greater improvements in MRC-WE scores (mean difference&#x2009;=&#x2009;0.52, 95% CI&#x2009;=&#x2009;0.03-1.00, p&#x2009;=&#x2009;0.036) and demonstrated increased AROM-WE at 4 weeks post-intervention (p&#x2009;=&#x2009;0.019). Enhanced functional connectivity in the affected hemisphere was observed in the MI-contingent feedback BCI group, correlating with MRC-WE and Fugl-Meyer assessment-distal scores. Improvements were also observed in the unaffected hemisphere's functional connectivity.<br><br>CONCLUSIONS: BCI training with MI-contingent feedback is more effective than MI-independent feedback in improving AROM-WE, MRC, and neural plasticity in individuals with chronic stroke. BCI technology could be a valuable addition to conventional rehabilitation for stroke survivors, enhancing recovery outcomes.<br><br>TRIAL REGISTRATION: CRIS (KCT0009013).},
}
@article {pmid39755291,
year = {2025},
author = {Vogeley, AO and Livinski, AA and Dabaghi Varnosfaderani, S and Javaheripour, N and Jamalabadi, H and Kotoula, V and Henter, ID and Hejazi, NS and Price, RB and Yavi, M and Walter, M and Zarate, CA and Kheirkhah, M},
title = {Temporal dynamics of affective scene processing in the healthy adult human brain.},
journal = {Neuroscience and biobehavioral reviews},
volume = {169},
number = {},
pages = {106003},
pmid = {39755291},
issn = {1873-7528},
support = {ZIA MH002857/ImNIH/Intramural NIH HHS/United States ; ZIA MH002927/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Humans ; *Brain/physiology ; *Emotions/physiology ; Magnetoencephalography ; Electroencephalography ; Adult ; Affect/physiology ; Time Factors ; },
abstract = {Understanding how the brain distinguishes emotional from neutral scenes is crucial for advancing brain-computer interfaces, enabling real-time emotion detection for faster, more effective responses, and improving treatments for emotional disorders like depression and anxiety. However, inconsistent research findings have arisen from differences in study settings, such as variations in the time windows, brain regions, and emotion categories examined across studies. This review sought to compile the existing literature on the timing at which the adult brain differentiates basic affective from neutral scenes in less than one second, as previous studies have consistently shown that the brain can begin recognizing emotions within just a few milliseconds. The review includes studies that used electroencephalography (EEG) or magnetoencephalography (MEG) in healthy adults to examine brain responses to emotional versus neutral images within one second. Articles of interest were limited to the English language but not to any publication year. Excluded studies involved only patients (of any diagnosis), participants under age 18 (since emotional processing can differ between adults and younger individuals), non-passive tasks, low temporal resolution techniques, time intervals over one second, and animals. Of the 3045 screened articles, 19 met these criteria. Despite the variations between studies, the earliest onset for heightened brain responses to basic affective scenes compared to neutral ones was most commonly observed within the 250-300 ms time window. To the best of our knowledge, this review is the first to synthesize data on the timing of brain differentiation between emotional and neutral scenes in healthy adults.},
}
@article {pmid39755229,
year = {2025},
author = {Ma, X and Xue, S and Ma, H and Saeed, S and Zhang, Y and Meng, Y and Chen, H and Yu, H and Wang, H and Hu, S and Cai, M},
title = {Esketamine alleviates LPS-induced depression-like behavior by activating Nrf2-mediated anti-inflammatory response in adolescent mice.},
journal = {Neuroscience},
volume = {567},
number = {},
pages = {294-307},
doi = {10.1016/j.neuroscience.2024.12.062},
pmid = {39755229},
issn = {1873-7544},
mesh = {Animals ; *Ketamine/pharmacology ; *NF-E2-Related Factor 2/metabolism ; *Lipopolysaccharides/pharmacology ; Male ; Mice ; *Depression/drug therapy/chemically induced/metabolism ; *Mice, Inbred C57BL ; Antidepressive Agents/pharmacology ; Hippocampus/drug effects/metabolism ; Anti-Inflammatory Agents/pharmacology ; Cytokines/metabolism ; Prefrontal Cortex/drug effects/metabolism ; Inflammation/metabolism/drug therapy/chemically induced ; },
abstract = {BACKGROUND: The mechanisms underlying esketamine's therapeutic effects remain elusive. The study aimed to explore the impact of single esketamine treatment on LPS-induced adolescent depressive-like behaviors and the role of Nrf2 regulated neuroinflammatory response in esketamine-produced rapid antidepressant efficacy.<br><br>METHODS: Adolescent male C57BL/6J mice were randomly assigned to three groups: control, LPS, and LPS&#xa0;+&#xa0;esketamine (15&#xa0;mg/kg, i.p.). Depressive-like behaviors were evaluated via the OFT, NFST, and TST. Protein expression of Nrf2 and inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in the hippocampus and mPFC, were measured by western blot. Moreover, the Nrf2 inhibitor, ML385, was also applied in the current study. The depressive-like behaviors and the protein expression of Nrf2, TNF-&#x3b1;, IL-1&#x3b2;, and iNOS in mPFC and hippocampus were also measured. Additionally, the plasma's pro-inflammatory cytokines and anti-inflammatory cytokines were assessed using ELISA methods with or without ML385.<br><br>RESULTS: A single administration of esketamine treatment alleviated the LPS-induced depressive-like behaviors. Esketamine increased the expression of Nrf2 and reduced the expression of the inflammatory cytokines, including TNF-&#x3b1;, IL-1&#x3b2;, and iNOS, in the mPFC and hippocampus. Notably, pharmacological inhibition of Nrf2 via ML385 administration abrogated the antidepressive-like behaviors and anti-inflammatory effects induced by esketamine. In the periphery, esketamine mitigated the LPS-induced elevation of pro-inflammatory cytokines, and the reduction of anti-inflammatory cytokines, and this effect was reversed by Nrf2 inhibition.<br><br>CONCLUSION: Esketamine treatment exerts rapid antidepressant effects and attenuates neuroinflammation in LPS-induced adolescent depressive-like behaviors, potentially through the activation of Nrf2-mediated anti-inflammatory signaling.},
}
@article {pmid39755222,
year = {2025},
author = {Huang, J and Wei, S and Gao, Z and Jiang, S and Wang, M and Sun, L and Ding, W and Zhang, D},
title = {Local structural-functional coupling with counterfactual explanations for epilepsy prediction.},
journal = {NeuroImage},
volume = {306},
number = {},
pages = {120978},
doi = {10.1016/j.neuroimage.2024.120978},
pmid = {39755222},
issn = {1095-9572},
mesh = {Humans ; *Epilepsy/diagnostic imaging/physiopathology ; *Diffusion Tensor Imaging/methods ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiopathology ; White Matter/diagnostic imaging/physiopathology ; Nerve Net/diagnostic imaging/physiopathology ; Male ; Female ; Adult ; Connectome/methods ; Brain Mapping/methods ; },
abstract = {The structural-functional brain connections coupling (SC-FC coupling) describes the relationship between white matter structural connections (SC) and the corresponding functional activation or functional connections (FC). It has been widely used to identify brain disorders. However, the existing research on SC-FC coupling focuses on global and regional scales, and few studies have investigated the impact of brain disorders on this relationship from the perspective of multi-brain region cooperation (i.e., local scale). Here, we propose the local SC-FC coupling pattern for brain disorders prediction. Compared with previous methods, the proposed patterns quantify the relationship between SC and FC in terms of subgraphs rather than whole connections or single brain regions. Specifically, we first construct structural and functional connections using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) data, subsequently organizing them into a multimodal brain network. Then, we extract subgraphs from these multimodal brain networks and select them based on their frequencies to generate local SC-FC coupling patterns. Finally, we employ these patterns to identify brain disorders while refining abnormal patterns to generate counterfactual explanations. Results on a real epilepsy dataset suggest that the proposed method not only outperforms existing methods in accuracy but also provides insights into the local SC-FC coupling pattern and their changes in brain disorders. Code available at https://github.com/UAIBC-Brain/Local-SC-FC-coupling-pattern.},
}
@article {pmid39755127,
year = {2025},
author = {Chu, T and Si, X and Song, X and Che, K and Dong, F and Guo, Y and Chen, D and Yao, W and Zhao, F and Xie, H and Shi, Y and Ma, H and Ming, D and Mao, N},
title = {Understanding structural-functional connectivity coupling in patients with major depressive disorder: A white matter perspective.},
journal = {Journal of affective disorders},
volume = {373},
number = {},
pages = {219-226},
doi = {10.1016/j.jad.2024.12.082},
pmid = {39755127},
issn = {1573-2517},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/psychology ; Male ; Female ; *White Matter/physiopathology/pathology/diagnostic imaging ; Adult ; *Suicidal Ideation ; Magnetic Resonance Imaging ; Suicide, Attempted/psychology ; Middle Aged ; Diffusion Tensor Imaging ; Case-Control Studies ; Connectome ; Young Adult ; },
abstract = {PURPOSE: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).<br><br>METHODS: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling. The primary analyses focused on investigating alterations in SC-FC coupling in WM tracts of individuals with MDD. Additionally, we explored the association between coupling and clinical symptoms. Secondary analyses examined differences among three subgroups of MDD: those with suicidal ideation (SI), those with a history of suicidal attempts (SA), and those non-suicidal (NS).<br><br>RESULTS: The study revealed increased SC-FC coupling mainly in the middle cerebellar peduncle and bilateral corticospinal tract (PFDR&#xa0;<&#xa0;0.05) in patients with MDD compared with HCs. Additionally, right cerebral peduncle coupling strength exhibited a significant positive correlation with Hamilton Anxiety Scale scores (r&#xa0;=&#xa0;0.269, PFDR&#xa0;=&#xa0;0.041), while right cingulum (hippocampus) coupling strength showed a significant negative correlation with Nurses' Global Assessment of Suicide Risk scores (r&#xa0;=&#xa0;-0.159, PFDR&#xa0;=&#xa0;0.036). An increase in left anterior limb of internal capsule (PBonferroni&#xa0;<&#xa0;0.01) and left corticospinal tract (PBonferroni&#xa0;<&#xa0;0.05) coupling has been observed in MDD with SI. Additionally, a decrease in right posterior limb of internal capsule coupling has been found in MDD with SA (PBonferroni&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: This study emphasizes the variations in SC-FC coupling in WM tracts in individuals with MDD and its subgroups, highlighting the crucial role of WM networks in the pathophysiology of MDD.},
}
@article {pmid39754303,
year = {2025},
author = {Qiu, C and Zhang, D and Wang, M and Mei, X and Chen, W and Yu, H and Yin, W and Peng, G and Hu, S},
title = {Peripheral Single-Cell Immune Characteristics Contribute to the Diagnosis of Alzheimer's Disease and Dementia With Lewy Bodies.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {31},
number = {1},
pages = {e70204},
pmid = {39754303},
issn = {1755-5949},
support = {2022YFC3602604//National Key Research and Development Program of China/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation Ten Thousand Talents Program of Zhejiang Province/ ; 2020R01001//Innovation team for precision diagnosis and treatment of major brain diseases/ ; JNL 2023001B//Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2022KTZ004//Chinese Medical Education Association/ ; 2022 F28//NINGBO Medical Health Leading Academic Discipline Project/ ; },
mesh = {Humans ; *Alzheimer Disease/immunology/diagnosis ; *Lewy Body Disease/immunology/diagnosis ; Aged ; Female ; Male ; *Leukocytes, Mononuclear/immunology/metabolism ; Aged, 80 and over ; Single-Cell Analysis/methods ; Middle Aged ; Biomarkers/blood ; T-Lymphocytes/immunology ; },
abstract = {OBJECTIVE: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are common neurodegenerative diseases with distinct but overlapping pathogenic mechanisms. The clinical similarities between these diseases often result in high misdiagnosis rates, leading to serious consequences. Peripheral blood mononuclear cells (PBMCs) are easy to collect and can accurately reflect the immune characteristics of both DLB and AD.<br><br>METHODS: We utilized time-of-flight mass cytometry (CyTOF) with single-cell resolution to quantitatively analyze peripheral PBMCs, identifying 1228 immune characteristics. Based on the top-selected immune features, we constructed immunological elastic net (iEN) models.<br><br>RESULTS: These models demonstrated high diagnostic efficacy in distinguishing diseased samples from healthy donors as well as distinguishing AD and DLB cases. The selected features reveal that the primary peripheral immune characteristic of AD is a decrease in total T cells, while DLB is characterized by low expression of I-kappa-B-alpha (IKB&#x3b1;) in the classical monocyte subset.<br><br>CONCLUSIONS: These findings suggest that peripheral immune characteristics could serve as potential biomarkers, facilitating the diagnosis of neurodegenerative diseases.},
}
@article {pmid39753090,
year = {2025},
author = {Pang, Y and Zhao, S and Zhang, Z and Xu, J and Gao, L and Zhang, R and Li, Z and Lu, F and Chen, H and Wu, H and Chen, M and Chen, K and Wang, J},
title = {Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment.},
journal = {Journal of psychiatric research},
volume = {181},
number = {},
pages = {709-715},
doi = {10.1016/j.jpsychires.2024.12.032},
pmid = {39753090},
issn = {1879-1379},
mesh = {Humans ; *Connectome ; Male ; Adult ; Female ; Young Adult ; Adolescent ; *Magnetic Resonance Imaging ; *Brain/diagnostic imaging/growth &amp; development/pathology ; *Adult Survivors of Child Abuse ; Child Abuse ; Age Factors ; Child ; },
abstract = {BACKGROUND: The long-term impact of childhood maltreatment (CM) on an individual's physical and mental health is suggested to be mediated by altered neurodevelopment. However, the exact neurobiological consequences of CM remain unclear.<br><br>METHODS: The present study aimed to investigate the relationship between CM and brain age based on structural magnetic resonance imaging data from a sample of 214 adults. The participants were divided into CM and non_CM groups according to Childhood Trauma Questionnaire. For each participant, brain connectome age was estimated from a large-scale structural covariance network through relevance vector regression. Brain predicted age difference (brain_PAD) was then calculated for each participant.<br><br>RESULTS: The brain connectome age matched well with chronological age in young adults (age range: 18-25 years) and adults (age range: 26-44 years) without CM, but not in individuals with CM. Compared with non_CM group, CM group was characterized by higher brain_PAD scores in young adults, whereas lower brain_PAD scores in adults. The finding revealed that brain development in individuals with CM seems to be accelerated in younger adults but retardation with increasing age. Moreover, individuals who suffered child abuse (but not neglect) showed higher brain_PAD scores than non_CM group, suggesting the different influence of abuse and neglect on neurodevelopment. Finally, the brain_PAD was positively correlated with attentional impulsivity in CM group.<br><br>CONCLUSIONS: CM affects different stages of adult brain development differently, and abuse and neglect have different influenced patterns, which may provide new evidence for the impact of CM on structural brain development.},
}
@article {pmid39752412,
year = {2025},
author = {Ding, C and Kim Geok, S and Sun, H and Roslan, S and Cao, S and Zhao, Y},
title = {Does music counteract mental fatigue? A systematic review.},
journal = {PloS one},
volume = {20},
number = {1},
pages = {e0316252},
pmid = {39752412},
issn = {1932-6203},
mesh = {*Mental Fatigue/prevention &amp; control ; Humans ; *Music/psychology ; *Cognition/physiology ; Music Therapy/methods ; },
abstract = {INTRODUCTION: Mental fatigue, a psychobiological state induced by prolonged and sustained cognitive tasks, impairs both cognitive and physical performance. Several studies have investigated strategies to counteract mental fatigue. However, potential health risks and contextual restrictions often limit these strategies, which hinder their practical application. Due to its noninvasive and portable nature, music has been proposed as a promising strategy to counteract mental fatigue. However, the effects of music on performance decrements vary with different music styles. Synthesizing studies that systematically report music style and its impact on counteracting performance decrements is crucial for theoretical and practical applications.<br><br>OBJECTIVES: This review aims to provide a comprehensive systematic analysis of different music styles in counteracting mental fatigue and their effects on performance decrements induced by mental fatigue. Additionally, the mechanisms by which music counteracts mental fatigue will be discussed.<br><br>METHODS: A comprehensive search was conducted across five databases-Web of Science, PubMed, SCOPUS, SPORTDiscus via EBSCOhost, and the Psychological and Behavioral Sciences Collection via EBSCOhost-up to November 18, 2023. The selected studies focused solely on music interventions, with outcomes including subjective feelings of mental fatigue, physiological markers, and both cognitive and behavioral performance.<br><br>RESULTS: Nine studies met the predetermined criteria for inclusion in this review. The types of music interventions that counteract mental fatigue include relaxing, exciting, and personal preference music, all of which were associated with decreased subjective feelings of mental fatigue and changes in objective physiological markers. Cognitive performance, particularly in inhibition and working memory tasks impaired by mental fatigue, was countered by both relaxing and exciting music. Exciting music was found to decrease reaction time more effectively than relaxing music in working memory tasks. The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. Behavioral performance in tasks such as arm-pointing, the Yo-Yo intermittent test, and the 5 km time-trial, which were impaired by mental fatigue, were counteracted by personal preference music.<br><br>CONCLUSION: Relaxing music, exciting music, and personal preference music effectively counteract mental fatigue by reducing feelings of fatigue and mitigating performance decrements. Individuals engaged in mentally demanding tasks can effectively counteract concurrent or subsequent cognitive performance decrements by simultaneously listening to relaxing or exciting music without lyrics or by using music during recovery from mental fatigue. Exciting music is more effective than relaxing music in counteracting mental fatigue. Personal preference music is effective in counteracting behavioral performance decrements in motor control and endurance tasks. Mentally fatigued individuals could apply personal preference music to counteract subsequent motor control performance decrements or simultaneously listen to it to counteract endurance performance decrements. Future studies should specify and examine the effects of different music genres, tempos, and intensities in counteracting mental fatigue. Additionally, the role of music in counteracting mental fatigue in contexts such as work productivity, traffic accident risk, and sports requires further investigation, along with the underlying mechanisms.},
}
@article {pmid39752045,
year = {2025},
author = {Xin, Q and Hu, H},
title = {To Attack or Not: A Neural Circuit Coding Sexually Dimorphic Aggression.},
journal = {Neuroscience bulletin},
volume = {41},
number = {4},
pages = {728-730},
pmid = {39752045},
issn = {1995-8218},
}
@article {pmid39748261,
year = {2025},
author = {Wu, JY and Zhang, JY and Xia, WQ and Kang, YN and Liao, RY and Chen, YL and Li, XM and Wen, Y and Meng, FX and Xu, LL and Wen, SH and Liu, HF and Li, YQ and Gu, JR and Lv, Q and Ren, Y},
title = {Predicting autoimmune thyroiditis in primary Sjogren's syndrome patients using a random forest classifier: a retrospective study.},
journal = {Arthritis research &amp; therapy},
volume = {27},
number = {1},
pages = {1},
pmid = {39748261},
issn = {1478-6362},
support = {No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; No. JCYJ20220530145001002//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; Retrospective Studies ; *Sjogren's Syndrome/immunology/diagnosis ; Female ; Male ; Middle Aged ; *Thyroiditis, Autoimmune/immunology/diagnosis ; Adult ; *Autoantibodies/immunology/blood ; *Machine Learning ; Algorithms ; Aged ; Random Forest ; },
abstract = {BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients.<br><br>METHODS: A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies.<br><br>RESULTS: The results indicated that the Random Forest Classifier algorithm (AUC&#x2009;=&#x2009;0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms.<br><br>CONCLUSIONS: Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC&#x2009;=&#x2009;0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising tool for early identification and management of AIT in pSS patients.<br><br>TRIAL REGISTRATION: This retrospective study was approved and monitored by the Ethics Committee of The Third Affiliated Hospital of Sun Yat-sen University (No.II2023-254-02).},
}
@article {pmid39748063,
year = {2025},
author = {Wei, Q and Li, C and Wang, Y and Gao, X},
title = {Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {365},
pmid = {39748063},
issn = {2045-2322},
support = {62066028//National Natural Science Foundation of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Algorithms ; *Electroencephalography/methods ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established. To address this issue, we propose a classification framework named eTRCA + sbCNN that combines an ensemble task-related component analysis (eTRCA) algorithm and a sub-band convolutional neural network (sbCNN) for recognizing the frequency of SSVEP signals. The two models are first trained separately, then their classification score vectors are added together, and finally the frequency corresponding to the maximal summed score is decided as the frequency of SSVEP signals. The proposed framework can effectively exploit the complementarity between the two kinds of feature signals and significantly improve the classification performance of SSVEP-based BCIs. The performance of the proposed method is validated on two SSVEP BCI datasets and compared with that of eTRCA, sbCNN and other state-of-the-art models. Experimental results indicate that the proposed method significantly outperform the compared algorithms, and thus helps to promote the practical application of SSVEP- BCI systems.},
}
@article {pmid39747930,
year = {2025},
author = {Anderson, L and De Ridder, D and Glue, P and Mani, R and van Sleeuwen, C and Smith, M and Adhia, DB},
title = {A safety and feasibility randomized placebo controlled trial exploring electroencephalographic effective connectivity neurofeedback treatment for fibromyalgia.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {209},
pmid = {39747930},
issn = {2045-2322},
mesh = {Humans ; *Fibromyalgia/therapy/physiopathology ; *Neurofeedback/methods ; Female ; Middle Aged ; *Electroencephalography ; Adult ; *Feasibility Studies ; Male ; Treatment Outcome ; Gyrus Cinguli/physiopathology/diagnostic imaging ; Chronic Pain/therapy/physiopathology ; },
abstract = {Fibromyalgia is a chronic pain condition contributing to significant disability worldwide. Neuroimaging studies identify abnormal effective connectivity between cortical areas responsible for descending pain modulation (pregenual anterior cingulate cortex, pgACC) and sensory components of pain experience (primary somatosensory cortex, S1). Neurofeedback, a brain-computer interface technique, can normalise dysfunctional brain activity, thereby improving pain and function. This study evaluates the safety, feasibility, and acceptability of a novel electroencephalography-based neurofeedback training, targeting effective alpha-band connectivity from the pgACC to S1 and exploring its effect on pain and function. Participants with fibromyalgia (N = 30; 15 = active, 15 = placebo) received 12 sessions of neurofeedback. Feasibility and outcome measures of pain (Brief Pain Inventory) and function (Revised Fibromyalgia Impact Questionnaire) were collected at baseline and immediately, ten-days, and one-month post-intervention. Descriptive statistics demonstrate effective connectivity neurofeedback training is feasible (recruitment rate: 6 participants per-month, mean adherence: 80.5%, dropout rate: 20%), safe (no adverse events) and highly acceptable (average 8.0/10) treatment approach for fibromyalgia. Active and placebo groups were comparable in their decrease in pain and functional impact. Future fully powered clinical trial is warranted to test the efficacy of the effective connectivity neurofeedback training in people with fibromyalgia with versus without chronic fatigue.},
}
@article {pmid39745941,
year = {2025},
author = {Stavisky, SD},
title = {Restoring Speech Using Brain-Computer Interfaces.},
journal = {Annual review of biomedical engineering},
volume = {},
number = {},
pages = {},
doi = {10.1146/annurev-bioeng-110122-012818},
pmid = {39745941},
issn = {1545-4274},
abstract = {People who have lost the ability to speak due to neurological injuries would greatly benefit from assistive technology that provides a fast, intuitive, and naturalistic means of communication. This need can be met with brain-computer interfaces (BCIs): medical devices that bypass injured parts of the nervous system and directly transform neural activity into outputs such as text or sound. BCIs for restoring movement and typing have progressed rapidly in recent clinical trials; speech BCIs are the next frontier. This review covers the clinical need for speech BCIs, surveys foundational studies that point to where and how speech can be decoded in the brain, describes recent progress in both discrete and continuous speech decoding and closed-loop speech BCIs, provides metrics for assessing these systems' performance, and highlights key remaining challenges on the road toward clinically useful speech neuroprostheses.},
}
@article {pmid39745924,
year = {2025},
author = {Trevelyan, AJ and Marks, VS and Graham, RT and Denison, T and Jackson, A and Smith, EH},
title = {On brain stimulation in epilepsy.},
journal = {Brain : a journal of neurology},
volume = {148},
number = {3},
pages = {746-752},
pmid = {39745924},
issn = {1460-2156},
mesh = {Humans ; Animals ; *Deep Brain Stimulation/methods ; *Epilepsy/therapy/physiopathology ; *Optogenetics/methods ; Vagus Nerve Stimulation/methods ; Brain/physiopathology ; Drug Resistant Epilepsy/therapy/physiopathology ; },
abstract = {Brain stimulation has, for many decades, been considered as a potential solution for the unmet needs of the many people living with drug-resistant epilepsy. Clinically, there are several different approaches in use, including vagus nerve stimulation, deep brain stimulation of the thalamus, and responsive neurostimulation. Across populations of patients, all deliver reductions in seizure load and sudden unexpected death in epilepsy risk, yet do so variably, and the improvements seem incremental rather than transformative. In contrast, within the field of experimental neuroscience, the transformational impact of optogenetic stimulation is evident; by providing a means to control subsets of neurons in isolation, it has revolutionized our ability to dissect out the functional relations within neuronal microcircuits. It is worth asking, therefore, how preclinical optogenetics research could advance clinical practice in epilepsy? Here, we review the state of the clinical field, and the recent progress in preclinical animal research. We report various breakthrough results, including the development of new models of seizure initiation, its use for seizure prediction, and for fast, closed-loop control of pathological brain rhythms, and what these experiments tell us about epileptic pathophysiology. Finally, we consider how these preclinical research advances may be translated into clinical practice.},
}
@article {pmid39745545,
year = {2025},
author = {Romano, V and Manto, M},
title = {How and where Effectively Apply Cerebellum Stimulation: The frequency-dependent Modulation of Cerebellar Output by Transcranial Alternating Current Stimulation.},
journal = {Cerebellum (London, England)},
volume = {24},
number = {1},
pages = {22},
pmid = {39745545},
issn = {1473-4230},
mesh = {Animals ; Humans ; Rats ; *Cerebellum/physiology ; *Transcranial Direct Current Stimulation/methods ; },
abstract = {As brain-machine interfaces (BMI) are growingly used in clinical settings, understanding how to apply brain stimulation is increasingly important. Despite the emergence of optogenetic techniques, ethical and medical concerns suggest that interventions that are safe and non-invasive, such as Transcranial Alternating Current Stimulation (tACS), are more likely to be employed in human in the near future. Consequently, the question of how and where to apply current stimulation is becoming increasingly important for the efficient neuromodulation of both neurological and psychiatric disorders. In this edition of The Cerebellum, Mourra et al. demonstrate how ctACS influences cerebellar output at both single-cell and population levels by stimulating Crus I in rats. As the neuron generating this output serves as a crucial convergence and divergence center in the nervous system, it can be leveraged as a strategic hub to target multiple brain structures and influence various behaviors. Accordingly, the discovery that neurons in this relatively deep brain region can be indirectly entrained through Purkinje neuron activation and optimal frequency around 80 Hz could be highly relevant for future medical interventions. In light of these findings, high-γ-tACS might be more effective in humans compared to the more commonly used low-γ (50 Hz) or θ-tACS (5 Hz). This could enhance the chance of cerebellar tACS being utilized in clinical settings and BMI.},
}
@article {pmid39744695,
year = {2025},
author = {Li, C and Miao, C and Ge, Y and Wu, J and Gao, P and Yin, S and Zhang, P and Yang, H and Tian, B and Chen, W and Chen, XQ},
title = {A molecularly distinct cell type in the midbrain regulates intermale aggression behaviors in mice.},
journal = {Theranostics},
volume = {15},
number = {2},
pages = {707-725},
pmid = {39744695},
issn = {1838-7640},
mesh = {Animals ; *Aggression/physiology ; Mice ; *Periaqueductal Gray/metabolism/physiology ; Male ; *Neurons/metabolism/physiology ; Mice, Inbred C57BL ; Tachykinins/metabolism/genetics ; Behavior, Animal/physiology ; Mesencephalon/metabolism/physiology ; Serotonin/metabolism ; },
abstract = {Rationale: The periaqueductal gray (PAG) is a central hub for the regulation of aggression, whereas the circuitry and molecular mechanisms underlying this regulation remain uncharacterized. In this study, we investigate the role of a distinct cell type, Tachykinin 2-expressing (Tac2[+]) neurons, located in the dorsomedial PAG (dmPAG) and their modulation of aggressive behavior in mice. Methods: We combined activity mapping, in vivo Ca[2+] recording, chemogenetic and pharmacological manipulation, and a viral-based translating ribosome affinity purification (TRAP) profiling using a mouse resident-intruder model. Results: We revealed that dmPAG[Tac2] neurons are selectively activated by fighting behaviors. Chemogenetic activation of these neurons evoked fighting behaviors, while inhibition or genetic ablation of dmPAG[Tac2] neurons attenuated fighting behaviors. TRAP profiling of dmPAG[Tac2] neurons revealed an enrichment of serotonin-associated transcripts in response to fighting behaviors. Finally, we validated these effects by selectively administering pharmacological agents to the dmPAG, reversing the behavioral outcomes induced by chemogenetic manipulation. Conclusions: We identify dmPAG[Tac2] neurons as critical modulators of aggressive behavior in mouse and thus suggest a distinct molecular target for the treatment of exacerbated aggressive behaviors in populations that exhibit high-level of violence.},
}
@article {pmid39743292,
year = {2024},
author = {Huang, T and Guo, X and Huang, X and Yi, C and Cui, Y and Dong, Y},
title = {Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.},
journal = {Journal of Zhejiang University. Science. B},
volume = {25},
number = {12},
pages = {1055-1065},
pmid = {39743292},
issn = {1862-1783},
support = {2022ZD0211700//the STI2030-Major Projects/ ; 32371057, 31922031, 32071017, 81971309, 32170980, 82201707 and 82200562//the National Natural Science Foundation of China/ ; LDQ24C090001//the Zhejiang Provincial Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; JCTD-2022-11//the CAS Youth Interdisciplinary Team/ ; BX20230319//the China Postdoctoral Science Foundation/ ; 2022B1515020012 and 2021A1515110121//the Guangdong Basic and Applied Basic Research Foundation/ ; 2023B1212060018//the Science and Technology Planning Project of Guangdong Province/ ; JCYJ20210324123212035, RCYX20200714114644167, ZDSYS20220606100801003, JCYJ20210324122809025 and JCYJ20230807110315031//the Shenzhen Fundamental Research Program/ ; },
mesh = {*Habenula/physiology ; Animals ; *Stress, Psychological/physiopathology ; Humans ; Neurons/physiology ; Neuronal Plasticity/physiology ; Depression/physiopathology ; Neural Pathways/physiology ; },
abstract = {Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.},
}
@article {pmid39743145,
year = {2025},
author = {Hao, X and Ma, M and Meng, F and Liang, H and Liang, C and Liu, X and Zhang, B and Ju, Y and Liu, S and Ming, D},
title = {Diminished attention network activity and heightened salience-default mode transitions in generalized anxiety disorder: Evidence from resting-state EEG microstate analysis.},
journal = {Journal of affective disorders},
volume = {373},
number = {},
pages = {227-236},
doi = {10.1016/j.jad.2024.12.095},
pmid = {39743145},
issn = {1573-2517},
mesh = {Humans ; *Anxiety Disorders/physiopathology ; Female ; Male ; *Electroencephalography ; Adult ; *Attention/physiology ; *Default Mode Network/physiopathology/diagnostic imaging ; Brain/physiopathology/diagnostic imaging ; Young Adult ; Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; Case-Control Studies ; Rest/physiology ; },
abstract = {Generalized anxiety disorder (GAD) is a common anxiety disorder characterized by excessive, uncontrollable worry and physical symptoms such as difficulty concentrating and sleep disturbances. Although functional magnetic resonance imaging (fMRI) studies have reported aberrant network-level activity related to cognition and emotion in GAD, its low temporal resolution restricts its ability to capture the rapid neural activity in mental processes. EEG microstate analysis offers millisecond-resolution for tracking the dynamic changes in brain electrical activity, thereby illuminating the neurophysiological mechanisms underlying the cognitive and emotional dysfunctions in GAD. This study collected 64-channel resting-state EEG data from 28 GAD patients and 28 healthy controls (HC), identifying five microstate classes (A-E) in both groups. Results showed that GAD patients exhibited significantly lower duration (p < 0.01), occurrence (p < 0.05), and coverage (p < 0.01) of microstate class D, potentially reflecting deficits in attention-related networks. Such alterations may contribute to the impairments in attention maintenance and cognitive control. Additionally, GAD patients displayed reduced transition probabilities in A → D, B → D, C → D, and E → D (all corrected p < 0.05), but increased in C → E (corrected p < 0.05) and E → C (corrected p < 0.01). These results highlight a significant reduction in the brain's ability to transition into microstate class D, alongside overactivity in switching between the default mode network and the salience network. Such neurophysiological changes may underlie cognitive control deficits, increased spontaneous rumination, and emotional regulation challenges observed in GAD. Together, these insights provide a new perspective for understanding the neurophysiological and pathological mechanisms underlying GAD.},
}
@article {pmid39742538,
year = {2025},
author = {Cao, L and Zhao, W and Sun, B},
title = {Emotion recognition using multi-scale EEG features through graph convolutional attention network.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {107060},
doi = {10.1016/j.neunet.2024.107060},
pmid = {39742538},
issn = {1879-2782},
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cognitive load. This process is critically important in the development and research of brain-computer interfaces, where precise and efficient recognition of emotions is paramount. In this work, we introduce a novel approach for emotion recognition employing multi-scale EEG features, denominated as the Dynamic Spatial-Spectral-Temporal Network (DSSTNet). DSSTNet includes three main parts, the first is spatial features extractor, which converts EEG signal into graph structure data, and uses graph convolutional network (GCN) to dynamically optimize the adjacency matrix during the training process to obtain the spatial features between the channels. Next, band attention module is composed of semi-global pooling, localized cross-band interaction and adaptive weighting, which further extracts frequency information. Finally, through the temporal features extractor, the deep temporal information is extracted by stacking several one-dimensional convolutional layers. In addition, in order to improve the performance of emotion recognition and filter valid channels, we add a ℓ2,1-norm regularization term to the loss function to make the adjacency matrix constraint sparse. This makes it easier to preserve emotionally relevant channels and eliminate noise in irrelevant channel. Combined with the channel selection method of graph theory, a small number of optimal channels are selected. We used a self-constructed dataset TJU-EmoEEG and a publicly available SEED dataset to evaluate DSSTNet. The experimental results demonstrate that DSSTNet outperforms current state-of-the-art (SOTA) methods in emotional recognition tasks.},
}
@article {pmid39741784,
year = {2024},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {EEG channel and feature investigation in binary and multiple motor imagery task predictions.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1525139},
pmid = {39741784},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor Imagery (MI) Electroencephalography (EEG) signals are non-stationary and dynamic physiological signals which have low signal-to-noise ratio. Hence, it is difficult to achieve high classification accuracy. Although various machine learning methods have already proven useful to that effect, the use of many features and ineffective EEG channels often leads to a complex structure of classifier algorithms. State-of-the-art studies were interested in improving classification performance with complex feature extraction and classification methods by neglecting detailed EEG channel and feature investigation in predicting MI tasks from EEGs. Here, we investigate the effects of the statistically significant feature selection method on four different feature domains (time-domain, frequency-domain, time-frequency domain, and non-linear domain) and their two different combinations to reduce the number of features and classify MI-EEG features by comparing low-dimensional matrices with well-known machine learning algorithms.<br><br>METHODS: Our main goal is not to find the best classifier performance but to perform feature and channel investigation in MI task classification. Therefore, the detailed investigation of the effect of EEG channels and features is implemented using a statistically significant feature distribution on 22 EEG channels for each feature set separately. We used the BCI Competition IV Dataset IIa and 288 samples per person. A total of 1,364 MI-EEG features were analyzed in this study. We tested nine distinct classifiers: Decision tree, Discriminant analysis, Logistic regression, Naive Bayes, Support vector machine, k-Nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.<br><br>RESULTS: Among all feature sets considered, classifications performed with non-linear and combined feature sets resulted in a maximum accuracy of 63.04% and 47.36% for binary and multiple MI task predictions, respectively. The ensemble learning classifier achieved the maximum accuracy in almost all feature sets for binary and multiple MI task classifications.<br><br>DISCUSSION: Our research thus shows that the statistically significant feature-based feature selection method significantly improves the classification performance with fewer features in almost all feature sets, enabling detailed and effective EEG channel and feature investigation.},
}
@article {pmid39741250,
year = {2024},
author = {Liang, S and Li, L and Zu, W and Feng, W and Hang, W},
title = {Adaptive deep feature representation learning for cross-subject EEG decoding.},
journal = {BMC bioinformatics},
volume = {25},
number = {1},
pages = {393},
pmid = {39741250},
issn = {1471-2105},
support = {61902197//National Natural Science Foundation of China/ ; KYCX23_1073//Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province/ ; 23KJB520012//Natural Science Research of Jiangsu Higher Education Institutions of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Deep Learning ; Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; },
abstract = {BACKGROUND: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies. Since the domain shift cannot be completely removed, target EEG samples located near the edge of clusters are also susceptible to misclassification.<br><br>METHODS: We propose a novel adaptive deep feature representation (ADFR) framework to improve the cross-subject EEG classification performance through learning transferable EEG feature representations. Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. Finally, the entropy minimization (EM) regularization is further integrated to adjust the classifier to pass through the low-density region between clusters. The synergistic learning between above regularizations during the training process enhances EEG decoding performance across subjects.<br><br>RESULTS: The effectiveness of the ADFR framework was evaluated on two public motor imagery (MI)-based EEG datasets: BCI Competition III dataset 4a and BCI Competition IV dataset 2a. In terms of average accuracy, ADFR achieved improvements of 3.0% and 2.1%, respectively, over the state-of-the-art methods on these datasets.<br><br>CONCLUSIONS: The promising results highlight the effectiveness of the ADFR algorithm for EEG decoding and show its potential for practical applications.},
}
@article {pmid39738346,
year = {2024},
author = {Le, TT and Luong, DAQ and Joo, H and Kim, D and Woo, J},
title = {Differences in spatiotemporal dynamics for processing specific semantic categories: An EEG study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31918},
pmid = {39738346},
issn = {2045-2322},
support = {NRF-2020R1A2C2003319//National Research Foundation of Korea/ ; },
mesh = {Humans ; *Electroencephalography ; *Semantics ; Male ; Female ; Adult ; *Brain Mapping ; Brain/physiology ; Young Adult ; Comprehension/physiology ; },
abstract = {Semantic processing is an essential mechanism in human language comprehension and has profound implications for speech brain-computer interface technologies. Despite recent advances in brain imaging techniques and data analysis algorithms, the mechanisms underlying human brain semantic representations remain a topic of debate, specifically whether this occurs through the activation of selectively separated cortical regions or via a network of distributed and overlapping regions. This study investigates spatiotemporal neural representation during the perception of semantic words related to faces, numbers, and animals using electroencephalography. Source-level analysis focuses on contrasting neural responses to different semantic categories. Critical intervals used in the source contrast analysis are defined using the peak duration of global field power. Effective connectivity, determined through a causality analysis of brain regions activated for semantic processing, is explored. The findings reveal the necessity of a distributed network of regions for processing specific semantic categories and provide evidence suggesting the existence of a neural substrate for semantic representations.},
}
@article {pmid39738221,
year = {2024},
author = {Wolde, HF and Clements, ACA and Gilmour, B and Alene, KA},
title = {Spatial co-distribution of tuberculosis prevalence and low BCG vaccination coverage in Ethiopia.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31561},
pmid = {39738221},
issn = {2045-2322},
support = {APP1196549//Australian National Health and Medical Research Council/ ; },
mesh = {Humans ; Ethiopia/epidemiology ; *BCG Vaccine/administration &amp; dosage ; Prevalence ; *Vaccination Coverage/statistics &amp; numerical data ; Female ; *Tuberculosis/epidemiology/prevention &amp; control ; Male ; *Bayes Theorem ; Infant ; Child, Preschool ; Adult ; Adolescent ; Vaccination/statistics &amp; numerical data ; Young Adult ; Child ; Spatial Analysis ; },
abstract = {While bacille-calmette-guerin (BCG) vaccination is one of the recommended strategies for preventing tuberculosis (TB), its coverage is low in several countries, including Ethiopia. This study investigated the spatial co-distribution and drivers of TB prevalence and low BCG coverage in Ethiopia. This ecological study was conducted using data from a national TB prevalence survey and the Ethiopian demographic and health survey (EDHS) to map the spatial co-distribution of BCG vaccination coverage and TB prevalence. A Bayesian geostatistical model was built to identify the drivers for the spatial distribution of TB prevalence and low BCG vaccination coverage. BCG vaccination coverage was defined as the number of children who received the vaccine divided by the total number of children born within five years preceding the EDHS surveys. Parameter estimation was done using binary logistic regression. Prediction maps for the co-distribution of high TB prevalence and low BCG vaccination coverage were created by overlying spatial prediction surfaces of the two outcomes. Posterior means and a 95% Bayesian credible interval (CrI) were used to summarize the parameters of the model. The national prevalence was 0.40% (95% confidence interval (CI) 0.34%, 0.47%) for TB and 47% (95% CI 46%, 48%) for vaccination coverage. Substantial spatial variation in TB prevalence and low BCG coverage was observed at a regional and local level, particularly in border areas of the country, including the Somali, Afar, and Oromia regions. Approximately 58% of the pixels (i.e., geographical area or spatial units) with high TB prevalence exhibited low BCG coverage in the same location. While travel time to cities (Mean = 0.28, 95% BCI: 0.15, 0.41) and distance to health facilities (Mean = 0.43, 95% CI 0.22, 0.63), were positively associated, population density (Mean = -0.04, 95% BCI -0.05, -0.02) was negatively associated, with the proportion of unvaccinated children for BCG indicating areas near health facilities and cities have better BCG coverage. However, there were no significant predictors for TB prevalence. Substantial spatial co-distribution between high TB prevalence and low BCG coverage was observed in some parts of the country, indicating that there are areas where the TB burden is not being adequately managed through the provision of vaccines in Ethiopia. Scaling up BCG vaccination coverage and TB diagnosis and treatment through improving access to health services in border regions such as Somalia and Afar would be important to reduce the prevalence of TB in Ethiopia.},
}
@article {pmid39738213,
year = {2024},
author = {Liu, Y and Huang, S and Xu, W and Wang, Z and Ming, D},
title = {An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.},
journal = {NPJ science of learning},
volume = {9},
number = {1},
pages = {80},
pmid = {39738213},
issn = {2056-7936},
support = {62273251//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81925020//National Natural Science Foundation of China (National Science Foundation of China)/ ; 21JCYBJC00520//Natural Science Foundation of Tianjin City (Natural Science Foundation of Tianjin)/ ; MSV202418//State Key Laboratory of Mechanical System and Vibration/ ; },
abstract = {Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.},
}
@article {pmid39735964,
year = {2024},
author = {Luo, TJ and Li, J and Li, R and Zhang, X and Wu, SR and Peng, H},
title = {Motion Cognitive Decoding of Cross-Subject Motor Imagery Guided on Different Visual Stimulus Materials.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {12},
pages = {218},
doi = {10.31083/j.jin2312218},
pmid = {39735964},
issn = {0219-6352},
support = {24JCXK01YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 24JCXK02YB//Planning Project of Philosophy and Social Science of Zhejiang Province/ ; 62106049//National Natural Science Foundation of China/ ; 61662025//National Natural Science Foundation of China/ ; 61871289//National Natural Science Foundation of China/ ; 62007016//National Natural Science Foundation of China/ ; 2022J01655//Natural Science Foundation of Fujian Province of China/ ; },
mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; Male ; *Brain-Computer Interfaces ; Female ; Motor Activity/physiology ; Psychomotor Performance/physiology ; Motion Perception/physiology ; },
abstract = {BACKGROUND: Motor imagery (MI) plays an important role in brain-computer interfaces, especially in evoking event-related desynchronization and synchronization (ERD/S) rhythms in electroencephalogram (EEG) signals. However, the procedure for performing a MI task for a single subject is subjective, making it difficult to determine the actual situation of an individual's MI task and resulting in significant individual EEG response variations during motion cognitive decoding.<br><br>METHODS: To explore this issue, we designed three visual stimuli (arrow, human, and robot), each of which was used to present three MI tasks (left arm, right arm, and feet), and evaluated differences in brain response in terms of ERD/S rhythms. To compare subject-specific variations of different visual stimuli, a novel cross-subject MI-EEG classification method was proposed for the three visual stimuli. The proposed method employed a covariance matrix centroid alignment for preprocessing of EEG samples, followed by a model agnostic meta-learning method for cross-subject MI-EEG classification.<br><br>RESULTS AND CONCLUSION: The experimental results showed that robot stimulus materials were better than arrow or human stimulus materials, with an optimal cross-subject motion cognitive decoding accuracy of 79.04%. Moreover, the proposed method produced robust classification of cross-subject MI-EEG signal decoding, showing superior results to conventional methods on collected EEG signals.},
}
@article {pmid39733553,
year = {2025},
author = {Jain, A and Kumar, L},
title = {ESI-GAL: EEG source imaging-based trajectory estimation for grasp and lift task.},
journal = {Computers in biology and medicine},
volume = {186},
number = {},
pages = {109608},
doi = {10.1016/j.compbiomed.2024.109608},
pmid = {39733553},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Biomechanical Phenomena/physiology ; Hand Strength/physiology ; Male ; Signal Processing, Computer-Assisted ; Adult ; Female ; Hand/physiology ; Deep Learning ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop Brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature.<br><br>METHOD: In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding.<br><br>RESULTS: The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with the time lag and window size of 100ms and 450ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively.<br><br>CONCLUSION: This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.},
}
@article {pmid39733023,
year = {2024},
author = {Cheng, Z and Bu, X and Wang, Q and Yang, T and Tu, J},
title = {EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31319},
pmid = {39733023},
issn = {2045-2322},
support = {2020CB-34//the Science and Technology Plan Project of Jingzhou City/ ; 2022BCE009//Key Plan of Science and Technology Department of Hubei Province/ ; },
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer. First, the Multi-Scale Dynamic CNN is used to extract complex spatial and spectral features from raw EEG signals, which not only avoids information loss but also reduces computational costs associated with the time-frequency conversion of signals. Then, the Gated Transformer Encoder is utilized to capture global dependencies of EEG signals. This encoder focuses on specific regions of the input sequence while reducing computational resources through parallel processing with the improved multi-head self-attention mechanisms. Third, the Temporal Convolution Network is used to extract temporal features from the EEG signals. Finally, the extracted abstract features are fed into a classification module for emotion recognition. The proposed method was evaluated on three publicly available datasets: DEAP, SEED, and SEED_IV. Experimental results demonstrate the high accuracy and efficiency of the proposed method for emotion recognition. This approach proves to be robust and suitable for various practical applications. By addressing challenges posed by existing methods, the proposed method provides a valuable and effective solution for the field of Brain-Computer Interface (BCI).},
}
@article {pmid39732819,
year = {2024},
author = {Jin, L and Hu, J and Li, Y and Zhu, Y and He, X and Bai, R and Wang, L},
title = {Altered neurovascular coupling and structure-function coupling in Moyamoya disease affect postoperative collateral formation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {31324},
pmid = {39732819},
issn = {2045-2322},
support = {81870910//National Natural Science Foundation of China/ ; 2022C03133//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {Humans ; *Moyamoya Disease/physiopathology/surgery/diagnostic imaging ; Female ; Male ; Adult ; *Cerebrovascular Circulation/physiology ; *Neurovascular Coupling/physiology ; *Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Middle Aged ; Collateral Circulation/physiology ; Case-Control Studies ; Brain/physiopathology/diagnostic imaging/pathology ; Young Adult ; },
abstract = {Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging. All patients had preoperative and postoperative digital subtraction angiography. Upon constructing the structural connectivity (SC) and functional connectivity (FC) networks, the SC-FC coupling was calculated. After obtaining the graph theoretical parameters, neurovascular coupling represented the spatial correlation between node degree centrality (DC) of functional networks and CBF. The CBF-DC coupling and SC-FC coupling were compared between MMD and HC groups. We further analyzed the correlation between coupling indexes and cognitive scores, as well as postoperative collateral formation. Compared with HC, CBF-DC coupling was decreased in MMD (p = 0.021), especially in the parietal lobe (p = 0.047). SC-FC coupling in MMD decreased in frontal, occipital, and subcortical regions. Cognitive scores were correlated with the CBF-DC coupling in frontal lobes (r = 0.394, p = 0.029) and SC-FC coupling (r = 0.397, p = 0.027). The CBF-DC coupling of patients with good postoperative collateral formation was higher (p = 0.041). Overall, neurovascular decoupling and structure-functional decoupling at the cortical level may be the underlying neuropathological mechanisms of MMD.},
}
@article {pmid39731856,
year = {2025},
author = {Guo, X and Feng, Y and Ji, X and Jia, N and Maimaiti, A and Lai, J and Wang, Z and Yang, S and Hu, S},
title = {Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus.},
journal = {EBioMedicine},
volume = {111},
number = {},
pages = {105530},
pmid = {39731856},
issn = {2352-3964},
mesh = {Humans ; *Genome-Wide Association Study ; *Multifactorial Inheritance ; *Genetic Predisposition to Disease ; *Polymorphism, Single Nucleotide ; *Mental Disorders/genetics ; Phenotype ; Genetic Pleiotropy ; Female ; Risk Factors ; Metabolic Diseases/genetics/epidemiology ; },
abstract = {BACKGROUND: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.<br><br>METHODS: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.<br><br>FINDINGS: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.<br><br>INTERPRETATION: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).},
}
@article {pmid39730087,
year = {2024},
author = {Kong, S and Zhang, J and Wang, L and Li, W and Guo, H and Weng, Q and He, Q and Lou, H and Ding, L and Yang, B},
title = {Mechanisms of low MHC I expression and strategies for targeting MHC I with small molecules in cancer immunotherapy.},
journal = {Cancer letters},
volume = {611},
number = {},
pages = {217432},
doi = {10.1016/j.canlet.2024.217432},
pmid = {39730087},
issn = {1872-7980},
abstract = {Major histocompatibility complex (MHC) class I load antigens and present them on the cell surface, which transduces the tumor-associated antigens to CD8[+] T cells, activating the acquired immune system. However, many tumors downregulate MHC I expression to evade immune surveillance. The low expression of MHC I not only reduce recognition by- and cytotoxicity of CD8[+] T cells, but also seriously weakens the anti-tumor effect of immunotherapy by restoring CD8[+] T cells, such as immune checkpoint inhibitors (ICIs). Accumulated evidence suggested that restoring MHC I expression is an effective strategy for enhancing tumor immunotherapy. This review focuses on mechanisms underlying MHC I downregulation include gene deletion and mutation, transcriptional inhibition, reduced mRNA stability, increased protein degradation, and disruption of endocytic trafficking. We also provide a comprehensive review of small molecules that restore or upregulate MHC I expression, as well as clinical trials involving the combination of ICIs and these small molecule drugs.},
}
@article {pmid39729483,
year = {2024},
author = {Gao, S and Sun, Y and Wu, F and Jiang, J and Peng, T and Zhang, R and Ling, C and Han, Y and Xu, Q and Zou, L and Liao, Y and Liang, C and Zhang, D and Qi, S and Tang, J and Xu, X},
title = {Effects on Multimodal Connectivity Patterns in Female Schizophrenia During 8 Weeks of Antipsychotic Treatment.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae176},
pmid = {39729483},
issn = {1745-1701},
support = {82172061//National Natural Science Foundation of China/ ; BE2022677//Key Research and Development Plan in Jiangsu/ ; WSN-166//16th Batch of Six Talent Peak Projects in Jiangsu/ ; YKK23138//Nanjing Health Technology Development Project/ ; 23-25-289//Training and Management of Young Talents in Nanjing Brain Hospital/ ; 2016YFC1306900//National Key R&amp;D Program of China/ ; },
abstract = {BACKGROUND AND HYPOTHESIS: Respective abnormal structural connectivity (SC) and functional connectivity (FC) have been reported in individuals with schizophrenia. However, transmodal associations between SC and FC following antipsychotic treatment, especially in female schizophrenia, remain unclear. We hypothesized that increased SC-FC coupling may be found in female schizophrenia, and could be normalized after antipsychotic treatment.<br><br>STUDY DESIGN: Sixty-four female drug-na&#xef;ve patients with first-diagnosed schizophrenia treated with antipsychotic drugs for 8 weeks, and 55 female healthy controls (HCs) were enrolled. Magnetic resonance imaging (MRI) data were collected from HCs at baseline and from patients at baseline and after treatment. SC and FC were analyzed by network-based statistics, calculating nonzero SC-FC coupling of the whole brain and altered connectivity following treatment. Finally, an Elastic-net logistic regression analysis was employed to establish a predictive model for evaluating the clinical efficacy treatment.<br><br>STUDY RESULTS: At baseline, female schizophrenia patients exhibited abnormal SC in cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, and limbic-cerebellar connectivity compared to HCs, while FC showed no abnormalities. Following treatment, cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar connectivity were altered in both SC and FC. Additionally, SC-FC coupling of altered connectivity was higher in patients at baseline than in HC, trending toward normalization after treatment. Furthermore, identified FC or/and SC predicted changes in psychopathological symptoms and cognitive impairment among female schizophrenia following treatment.<br><br>CONCLUSIONS: SC-FC coupling may be a potential predictive biomarker of treatment response. Cortico-cortical, frontal-limbic, frontal-striatal, limbic-striatal, temporal-cerebellar, and limbic-cerebellar could represent major targets for antipsychotic drugs in female schizophrenia.},
}
@article {pmid39727765,
year = {2024},
author = {Yousefipour, B and Rajabpour, V and Abdoljabbari, H and Sheykhivand, S and Danishvar, S},
title = {An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {12},
pages = {},
pmid = {39727765},
issn = {2313-7673},
abstract = {In recent years, significant advancements have been made in the field of brain-computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial-temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.},
}
@article {pmid39727293,
year = {2024},
author = {Gefen, N and Mazer, B and Krasovsky, T and Weiss, PL},
title = {Novel rehabilitation technologies in pediatric rehabilitation: knowledge towards translation.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/17483107.2024.2445017},
pmid = {39727293},
issn = {1748-3115},
abstract = {Purpose: Knowledge translation (KT) refers to the process of applying the most promising research outcomes into practice to ensure that new discoveries and innovations improve healthcare accessibility, effectiveness, and accountability. The objective of this perspective paper is to discuss and illustrate via examples how the KT process can be implemented in an era of rapid advancement in rehabilitation technologies that have the potential to significantly impact pediatric healthcare. Methods: Using Graham et al.'s (2006) Knowledge-to-Action cycle, which includes the knowledge creation funnel and the action cycle, we illustrate its application in implementing novel technologies into clinical practice and informing healthcare policy changes. We explore three successful applications of technology research: powered mobility, head support systems, and telerehabilitation. Additionally, we examine less clinically mature technologies such as brain-computer interfaces and robotic assistive devices, which are hindered by cost, robustness, and ease-of-use issues. Conclusions: The paper concludes by discussing how technology acceptance and usage in clinical settings are influenced by various barriers and facilitators at different stakeholder levels, including clients, families, clinicians, management, researchers, developers, and society. Recommendations include focusing on early and ongoing design partnerships, transitioning from research to real-life implementation, and identifying optimal timing for clinical adoption of new technologies.},
}
@article {pmid39726882,
year = {2024},
author = {Afrah, R and Amini, Z and Kafieh, R},
title = {An Unsupervised Feature Extraction Method based on CLSTM-AE for Accurate P300 Classification in Brain-Computer Interface Systems.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {14},
number = {6},
pages = {579-592},
pmid = {39726882},
issn = {2251-7200},
abstract = {BACKGROUND: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.<br><br>OBJECTIVE: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).<br><br>MATERIAL AND METHODS: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data. However, CNN and LSTM networks are combined to modify the classification system by extracting both spatial and temporal features. Then, the CNN-LSTM network was trained in an unsupervised learning method based on an autoencoder to improve Signal-to-noise Ratio (SNR) by extracting main components from latent space. To deal with imbalanced data, an Adaptive Synthetic Sampling Approach (ADASYN) is used and augmented without any duplication.<br><br>RESULTS: The trained model, tested on the BCI competition III dataset, including two normal subjects, with an accuracy of 95% and 94% for subjects A and B in P300 detection, respectively.<br><br>CONCLUSION: CNN-LSTM, was embedded into an autoencoder and introduced to simultaneously extract spatial and temporal features and manage the computational complexity of the method. Further, ADASYN as an augmentation method was proposed to deal with the imbalanced nature of data, which not only maintained feature space as before but also preserved anatomical features of P300. High-quality results highlight the suitable efficiency of the proposed method.},
}
@article {pmid39726001,
year = {2024},
author = {Sun, Q and Merino, EC and Yang, L and Van Hulle, MM},
title = {Unraveling EEG correlates of unimanual finger movements: insights from non-repetitive flexion and extension tasks.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {228},
pmid = {39726001},
issn = {1743-0003},
support = {202206050022//China Scholarship Council/ ; 101118964//Horizon Europe's Marie Sklodowska-Curie Action/ ; 857375//Horizon 2020 research and innovation programme/ ; C24/18/098//special research fund of the KU Leuven/ ; G0A4118N, G0A4321N, G0C1522N//Belgian Fund for Scientific Research - Flanders/ ; AKUL 043//Hercules Foundation/ ; },
mesh = {Humans ; *Fingers/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; *Movement/physiology ; Young Adult ; Brain-Computer Interfaces ; },
abstract = {BACKGROUND: The loss of finger control in individuals with neuromuscular disorders significantly impacts their quality of life. Electroencephalography (EEG)-based brain-computer interfaces that actuate neuroprostheses directly via decoded motor intentions can help restore lost finger mobility. However, the extent to which finger movements exhibit distinct and decodable EEG correlates remains unresolved. This study aims to investigate the EEG correlates of unimanual, non-repetitive finger flexion and extension.<br><br>METHODS: Sixteen healthy, right-handed participants completed multiple sessions of right-hand finger movement experiments. These included five individual (Thumb, Index, Middle, Ring, and Pinky) and four coordinated (Pinch, Point, ThumbsUp, and Fist) finger flexions and extensions, along with a rest condition (None). High-density EEG and finger trajectories were simultaneously recorded and analyzed. We examined low-frequency (0.3-3&#xa0;Hz) time series and movement-related cortical potentials (MRCPs), and event-related desynchronization/synchronization (ERD/S) in the alpha- (8-13&#xa0;Hz) and beta (13-30&#xa0;Hz) bands. A clustering approach based on Riemannian distances was used to chart similarities between the broadband EEG responses (0.3-70&#xa0;Hz) to the different finger scenarios. The contribution of different state-of-the-art features was identified across sub-bands, from low-frequency to low gamma (30-70&#xa0;Hz), and an ensemble approach was used to pairwise classify single-trial finger movements and rest.<br><br>RESULTS: A significant decrease in EEG amplitude in the low-frequency time series was observed in the contralateral frontal-central regions during finger flexion and extension. Distinct MRCP patterns were found in the pre-, ongoing-, and post-movement stages. Additionally, strong ERD was detected in the contralateral central brain regions in both alpha and beta bands during finger flexion and extension, with the beta band showing a stronger rebound (ERS) post-movement. Within the finger movement repertoire, the Thumb was most distinctive, followed by the Fist. Decoding results indicated that low-frequency time-domain amplitude better differentiates finger movements, while alpha and beta band power and Riemannian features better detect movement versus rest. Combining these features yielded over 80% finger movement detection accuracy, while pairwise classification accuracy exceeded 60% for the Thumb versus the other fingers.<br><br>CONCLUSION: Our findings confirm that non-repetitive finger movements, whether individual or coordinated, can be precisely detected from EEG. However, differentiating between specific movements is challenging due to highly overlapping neural correlates in time, spectral, and spatial domains. Nonetheless, certain finger movements, such as those involving the Thumb, exhibit distinct EEG responses, making them prime candidates for dexterous finger neuroprostheses.},
}
@article {pmid39725763,
year = {2024},
author = {Fu, R and Niu, S and Feng, X and Shi, Y and Jia, C and Zhao, J and Wen, G},
title = {Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {},
number = {},
pages = {},
pmid = {39725763},
issn = {1741-0444},
support = {62073282//the National Natural Science Foundation of China/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; F2020203070//Natural Science Foundation of Hebei Province/ ; 206Z0301G//the Central Guidance on Local Science and Technology Development Fund of Hebei Province/ ; },
abstract = {This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance the performance both in quality and reliability. The proposed MVMD-MSI algorithm combines the advantages of multivariate variational mode decomposition (MVMD) and multivariate synchronization index (MSI). Compared to widely used algorithms, the novelty of this method is its capability of decomposing nonlinear and non-stationary EEG signals into intrinsic mode functions (IMF) across different frequency bands with the best center frequency and bandwidth. Therefore, SSVEP decoding performance can be improved by this method, and the effectiveness of MVMD-MSI is evaluated by the robot with 6 degrees-of-freedom. Offline experiments were conducted to optimize the algorithm's parameters, resulting in significant improvements. Additionally, the algorithm showed good performance even with fewer channels and shorter data lengths. In online experiments, the algorithm achieved an average accuracy of 98.31% at 1.8 s, confirming its feasibility and effectiveness for real-time SSVEP BCI-based robotic arm applications. The MVMD-MSI algorithm, as proposed, represents a significant advancement in SSVEP analysis for robotic control systems. It enhances decoding performance and shows promise for practical application in this field.},
}
@article {pmid39723643,
year = {2025},
author = {Tao, T and Liu, S and He, M and Zhao, M and Chen, C and Peng, J and Wang, Y and Cai, J and Xiong, J and Lai, C and Gu, W and Ying, M and Mao, J and Li, L and Jia, X and Wu, X and Peng, W and Zhang, X and Li, Y and Li, T and Wang, J and Shu, Q},
title = {Synchronous bilateral Wilms tumors are prone to develop independently and respond differently to preoperative chemotherapy.},
journal = {International journal of cancer},
volume = {156},
number = {9},
pages = {1746-1755},
doi = {10.1002/ijc.35297},
pmid = {39723643},
issn = {1097-0215},
support = {32270853//National Natural Science Foundation of China/ ; U20A20137//National Natural Science Foundation of China/ ; LHDMZ23H160005//Joint Funds of the Zhejiang Provincial Natural Science Foundation of China/ ; 2024C03181//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang Province/ ; },
mesh = {Humans ; *Wilms Tumor/drug therapy/genetics/pathology ; *Kidney Neoplasms/drug therapy/pathology/genetics ; Male ; Female ; Infant ; *Mutation ; Child, Preschool ; DNA Copy Number Variations ; Loss of Heterozygosity ; beta Catenin/genetics/metabolism ; WT1 Proteins/genetics ; Exome Sequencing ; Neoplasms, Multiple Primary/drug therapy/genetics/pathology ; Transcriptome ; Chromosomes, Human, Pair 11/genetics ; },
abstract = {Wilms tumor (WT) is the most common kidney cancer in infants and young children. The determination of the clonality of bilateral WTs is critical to the treatment, because lineage-independent and metastatic tumors may require different treatment strategies. Here we found synchronous bilateral WT (n = 24 tumors from 12 patients) responded differently to preoperative chemotherapy. Transcriptome, whole-exome and whole-genome analysis (n = 12 tumors from 6 patients) demonstrated that each side of bilateral WT was clonally independent in terms of somatic driver mutations, copy number variations and transcriptomic profile. Molecular timing analysis revealed distinct timing and patterns of chromosomal evolution and mutational processes between the two sides of WT. Mutations in WT1, CTNNB1 and copy-neutral loss of heterozygosity of 11p15.5 provide possible genetic predisposition for the early initiation of bilateral WT. Our results provide comprehensive evidence and new insights regarding the separate initiation and early embryonic development of bilateral WT, which may benefit clinical practices in treating metastatic or refractory bilateral WT.},
}
@article {pmid39720868,
year = {2024},
author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL},
title = {A Roadmap for Implanting Electrode Arrays to Evoke Tactile Sensations Through Intracortical Stimulation.},
journal = {Human brain mapping},
volume = {45},
number = {18},
pages = {e70118},
pmid = {39720868},
issn = {1097-0193},
support = {N66001-10-C-4056//Defense Advanced Research Projects Agency/ ; UH3 NS107714/NH/NIH HHS/United States ; },
mesh = {Humans ; Male ; Adult ; *Spinal Cord Injuries/physiopathology ; *Electrodes, Implanted ; Female ; *Somatosensory Cortex/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Middle Aged ; Touch Perception/physiology ; Electric Stimulation/methods ; Brain Mapping/methods ; Hand/physiology ; Magnetic Resonance Imaging ; },
abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface (BCI) to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other BCI studies to ensure the successful placement of stimulation electrodes.},
}
@article {pmid39720668,
year = {2024},
author = {Li, X and Chu, Y and Wu, X},
title = {3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1485640},
pmid = {39720668},
issn = {1662-5218},
abstract = {Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution. Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these challenges, this paper proposes a 3D Convolutional Neural Network (P-3DCNN) decoding method that jointly learns spatial-frequency feature maps from the frequency and spatial domains of the EEG signals. First, the Welch method is used to calculate the frequency band power spectrum of the EEG, and a 2D matrix representing the spatial topology distribution of the electrodes is constructed. These spatial-frequency representations are then generated through cubic interpolation of the temporal EEG data. Next, the paper designs a 3DCNN network with 1D and 2D convolutional layers in series to optimize the convolutional kernel parameters and effectively learn the spatial-frequency features of the EEG. Batch normalization and dropout are also applied to improve the training speed and classification performance of the network. Finally, through experiments, the proposed method is compared to various classic machine learning and deep learning techniques. The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. This demonstrates the effectiveness of our approach in decoding motor imagery EEG and offers valuable insights for the development of BCI.},
}
@article {pmid39720230,
year = {2024},
author = {Hu, F and He, K and Qian, M and Liu, X and Qiao, Z and Zhang, L and Xiong, J},
title = {STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1519970},
pmid = {39720230},
issn = {1662-4548},
abstract = {INTRODUCTION: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.<br><br>METHODS: We propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification.<br><br>RESULTS: Extensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures.<br><br>DISCUSSION: In summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition.},
}
@article {pmid39719191,
year = {2025},
author = {Rudroff, T},
title = {Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.},
journal = {Brain research},
volume = {1850},
number = {},
pages = {149423},
doi = {10.1016/j.brainres.2024.149423},
pmid = {39719191},
issn = {1872-6240},
mesh = {*Brain-Computer Interfaces/ethics ; Humans ; *Artificial Intelligence ; Electroencephalography/methods ; Brain/physiology ; },
abstract = {OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.<br><br>METHODS: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019-2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.<br><br>RESULTS: Recent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5&#xa0;mm, with stable recordings over 15&#xa0;months; 2) Deep learning decoders show 40&#xa0;% improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90&#xa0;% success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55&#xa0;% while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.<br><br>CONCLUSIONS: BCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology's growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.},
}
@article {pmid39719121,
year = {2025},
author = {Li, K and Chen, P and Chen, Q and Li, X},
title = {A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ada30b},
pmid = {39719121},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Imagination/physiology ; Algorithms ; Linear Models ; Signal Processing, Computer-Assisted ; },
abstract = {Objective. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a transformer with modified locally linear embedding and sliding window convolution for EEG decoding.Approach. This network separately extracts channel and temporal features from EEG signals, subsequently fusing these features using a cross-attention mechanism. Simultaneously, manifold learning is employed to lower the computational burden of the model by mapping the high-dimensional EEG data to a low-dimensional space by its dimension reduction function.Main results. The proposed model achieves accuracy rates of 84.44%, 94.96%, and 82.79% on the BCI Competition IV dataset 2a, high gamma dataset, and a self-constructed motor imagery (MI) dataset from the left and right hand fist-clenching tests respectively. The results indicate our model outperforms the baseline models by EEG-channel transformer with dimension-reduced EEG data and window attention with sliding window convolution. Additionally, to enhance the interpretability of the model, features preceding the temporal feature extraction network were visualized. This visualization promotes the understanding of how the model prefers task-related channels.Significance. The transformer-based method makes the MI-EEG decoding more practical for further clinical applications.},
}
@article {pmid39718409,
year = {2024},
author = {Ma, X and Chen, W and Pei, Z and Zhang, J},
title = {An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.},
journal = {The Review of scientific instruments},
volume = {95},
number = {12},
pages = {},
doi = {10.1063/5.0243337},
pmid = {39718409},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; Humans ; *Lower Extremity/physiology ; *Exoskeleton Device ; *Electroencephalography/instrumentation ; Attention/physiology ; Neural Networks, Computer ; Adult ; Male ; Imagination/physiology ; },
abstract = {Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons. To address this challenge, we propose an attention-based motor imagery BCI system for lower limb exoskeletons. The decoding module of the proposed BCI system combines the convolutional neural network (CNN) with a lightweight attention module. The CNN aims to extract meaningful features from EEG signals, while the lightweight attention module aims to capture global dependencies among these features. The experiments are divided into offline and online experiments. The offline experiment is conducted to evaluate the effectiveness of different decoding methods, while the online experiment is conducted on a customized lower limb exoskeleton to evaluate the proposed BCI system. Eight subjects are recruited for the experiments. The experimental results demonstrate the great classification performance of the decoding method and validate the feasibility of the proposed BCI system. Our approach establishes a promising BCI system for the lower limb exoskeleton and is expected to achieve a more effective and user-friendly rehabilitation process.},
}
@article {pmid39715900,
year = {2024},
author = {Robinson, JT and Norman, SL and Angle, MR and Constandinou, TG and Denison, T and Donoghue, JP and Field, RM and Forsland, A and Kouider, S and Millán, JDR and Michaels, JA and Orsborn, AL and Pandarinath, C and Pruszynski, JA and Rozell, CJ and Shah, NP and Shanechi, MM and Shoaran, M and Sheth, SA and Stavisky, SD and Trautmann, E and Vachicouras, N and Xie, C},
title = {An application-based taxonomy for brain-computer interfaces.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {39715900},
issn = {2157-846X},
}
@article {pmid39712143,
year = {2024},
author = {Bai, G and Jin, J and Xu, R and Wang, X and Cichocki, A},
title = {A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3549-3563},
pmid = {39712143},
issn = {1871-4080},
abstract = {In brain-computer interfaces (BCIs) based on motor imagery (MI), reducing calibration time is gradually becoming an urgent issue in practical applications. Recently, transfer learning (TL) has demonstrated its effectiveness in reducing calibration time in MI-BCI. However, the different data distribution of subjects greatly affects the application effect of TL in MI-BCI. Therefore, this paper combines data alignment, source domain selection, and feature alignment into the MI-TL. We propose a novel dual-step transfer framework based on source domain selection and feature alignment. First, the source and target domains are aligned using a pre-calibration strategy (PS), and then a sequential reverse selection method is proposed to match the optimal source domain for each target domain with the designed dual model selection strategy. We use filter bank regularization common space pattern (FBRCSP) to obtain more features and introduce manifold embedded distribution alignment (MEDA) to correct the prediction results of the support vector machine (SVM). The experimental results on two competition public datasets (BCI competition IV Dataset 1 and Dataset 2a) and our dataset show that the average classification accuracy of the proposed framework is higher than the baseline method (no domain selection and no feature alignment), which reaches 84.12%, 79.91%, and 78.45%, respectively. And the computational cost is reduced by half compared with the baseline method.},
}
@article {pmid39712134,
year = {2024},
author = {Zhang, J and Shen, C and Chen, W and Ma, X and Liang, Z and Zhang, Y},
title = {Decoding of movement-related cortical potentials at different speeds.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3859-3872},
pmid = {39712134},
issn = {1871-4080},
abstract = {The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.},
}
@article {pmid39712133,
year = {2024},
author = {Wu, C and Wang, Y and Qiu, S and He, H},
title = {A bimodal deep learning network based on CNN for fine motor imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3791-3804},
pmid = {39712133},
issn = {1871-4080},
abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.},
}
@article {pmid39712131,
year = {2024},
author = {Meng, M and Xu, B and Ma, Y and Gao, Y and Luo, Z},
title = {STGAT-CS: spatio-temporal-graph attention network based channel selection for MI-based BCI.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3663-3678},
pmid = {39712131},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI) based on the motor imagery paradigm typically utilizes multi-channel electroencephalogram (EEG) to ensure accurate capture of physiological phenomena. However, excessive channels often contain redundant information and noise, which can significantly degrade BCI performance. Although there have been numerous studies on EEG channel selection, most of them require manual feature extraction, and the extracted features are difficult to fully represent the effective information of EEG signals. In this paper, we propose a spatio-temporal-graph attention network for channel selection (STGAT-CS) of EEG signals. We consider the EEG channels and their inter-channel connectivity as a graph and treat the channel selection problem as a node classification problem on the graph. We leverage the multi-head attention mechanism of graph attention network to dynamically capture topological relationships between nodes and update node features accordingly. Additionally, we introduce one-dimensional convolution to automatically extract temporal features from each channel in the original EEG signal, thereby obtaining more comprehensive spatiotemporal characteristics. In the classification tasks of the BCI Competition III Dataset IVa and BCI Competition IV Dataset I, STGAT-CS achieved average accuracies of 91.5% and 85.4% respectively, demonstrating the effectiveness of the proposed method.},
}
@article {pmid39712122,
year = {2024},
author = {Li, M and Li, J and Zheng, X and Ge, J and Xu, G},
title = {MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3463-3476},
pmid = {39712122},
issn = {1871-4080},
abstract = {EEG decoding plays a crucial role in the development of motor imagery brain-computer interface. Deep learning has great potential to automatically extract EEG features for end-to-end decoding. Currently, the deep learning is faced with the chanllenge of decoding from a large amount of time-variant EEG to retain a stable peroformance with different sessions. This study proposes a multi-scale residual network with hybrid attention (MSHANet) to decode four motor imagery classes. The MSHANet combines a multi-head attention and squeeze-and-excitation attention to hybridly focus on important information of the EEG features; and applies a multi-scale residual block to extracts rich EEG features, sharing part of the block parameters to extract common features. Compared with seven state-of-the-art methods, the MSHANet exhits the best accuracy on BCI Competition IV 2a with an accuracy of 83.18% for session- specific task and 80.09% for cross-session task. Thus, the proposed MSHANet decodes the time-varying EEG robustly and can save the time cost of MI-BCI, which is beneficial for long-term use.},
}
@article {pmid39712121,
year = {2024},
author = {Rahman, N and Khan, DM and Masroor, K and Arshad, M and Rafiq, A and Fahim, SM},
title = {Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3565-3583},
pmid = {39712121},
issn = {1871-4080},
abstract = {Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective. It forms the basis for Brain-Computer Interfaces (BCIs) that provide a communication channel for individuals with neurological impairments, thereby empowering them to express themselves effectively. EEG-based BCIs, especially those adapted to decode imagined speech from EEG signals, represent a significant advancement in enabling individuals with speech disabilities to communicate through text or synthesized speech. By utilizing cognitive neurodevelopmental insights, researchers have been able to develop innovative approaches for interpreting EEG signals and translating them into meaningful communication outputs. To aid researchers in effectively addressing this complex challenge, this review article synthesizes key findings from state-of-the-art significant studies. It investigates into the methodologies employed by various researchers, including preprocessing techniques, feature extraction methods, and classification algorithms utilizing Deep Learning and Machine Learning approaches and their integration. Furthermore, the review outlines the potential avenues for future research, with the goal of advancing the practical implementation of EEG-based BCI systems for decoding imagined speech from a cognitive neurodevelopmental perspective.},
}
@article {pmid39712116,
year = {2024},
author = {Tang, C and Gao, T and Wang, G and Chen, B},
title = {Coherence-based channel selection and Riemannian geometry features for magnetoencephalography decoding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3535-3548},
pmid = {39712116},
issn = {1871-4080},
abstract = {Magnetoencephalography (MEG) records the extremely weak magnetic fields on the surface of the scalp through highly sensitive sensors. Multi-channel MEG data provide higher spatial and temporal resolution when measuring brain activities, and can be applied for brain-computer interfaces as well. However, a large number of channels leads to high computational complexity and can potentially impact decoding accuracy. To improve the accuracy of MEG decoding, this paper proposes a new coherence-based channel selection method that effectively identifies task-relevant channels, reducing the presence of noisy and redundant information. Riemannian geometry is then used to extract effective features from selected channels of MEG data. Finally, MEG decoding is achieved by training a support vector machine classifier with the Radial Basis Function kernel. Experiments were conducted on two public MEG datasets to validate the effectiveness of the proposed method. The results from Dataset 1 show that Riemannian geometry achieves higher classification accuracy (compared to common spatial patterns and power spectral density) in the single-subject visual decoding task. Moreover, coherence-based channel selection significantly (P = 0.0002) outperforms the use of all channels. Moving on to Dataset 2, the results reveal that coherence-based channel selection is also significantly (P <0.0001) superior to all channels and channels around C3 and C4 in cross-session mental imagery decoding tasks. Additionally, the proposed method outperforms state-of-the-art methods in motor imagery tasks.},
}
@article {pmid39712104,
year = {2024},
author = {Yu, H and Hu, Z and Zhao, Q and Liu, J},
title = {Deep source transfer learning for the estimation of internal brain dynamics using scalp EEG.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3507-3520},
pmid = {39712104},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) provides high temporal resolution neural data for brain-computer interfacing via noninvasive electrophysiological recording. Estimating the internal brain activity by means of source imaging techniques can further improve the spatial resolution of EEG and enhance the reliability of neural decoding and brain-computer interaction. In this work, we propose a novel EEG data-driven source imaging scheme for precise and efficient estimation of macroscale spatiotemporal brain dynamics across thalamus and cortical regions with deep learning methods. A deep source imaging framework with a convolutional-recurrent neural network is designed to estimate the internal brain dynamics from high-density EEG recordings. Moreover, a brain model including 210 cortical regions and 16 thalamic nuclei is established based on human brain connectome to provide synthetic training data, which manifests intrinsic characteristics of underlying brain dynamics in spontaneous, stimulation-evoked, and pathological states. Transfer learning algorithm is further applied to the trained network to reduce the dynamical differences between synthetic and realistic EEG. Extensive experiments exhibit that the proposed deep-learning method can accurately estimate the spatial and temporal activity of brain sources and achieves superior performance compared to the state-of-the-art approaches. Moreover, the EEG data-driven source imaging framework is effective in the location of seizure onset zone in epilepsy and reconstruction of dynamical thalamocortical interactions during sensory processing of acupuncture stimulation, implying its applicability in brain-computer interfacing for neuroscience research and clinical applications.},
}
@article {pmid39712096,
year = {2024},
author = {Zhang, Z and Chen, Y and Zhao, X and Fan, W and Peng, D and Li, T and Zhao, L and Fu, Y},
title = {A review of ethical considerations for the medical applications of brain-computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3603-3614},
pmid = {39712096},
issn = {1871-4080},
abstract = {The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics. Subsequently, it introduces the ethical standards under consideration within the medical objective framework for BCI medical applications. Finally, the paper discusses and forecasts the ethical standards for BCI medical applications. The paper emphasizes the necessity to differentiate between the ethical issues of implantable and non-implantable BCIs, to approach the research on BCI-based "controlling the brain" with caution, and to establish standardized operational procedures and efficacy evaluation methods for BCI medical applications. This paper aims to provide ideas for the establishment of ethical standards in BCI medical applications.},
}
@article {pmid39712090,
year = {2024},
author = {Huang, Y and Huan, Y and Zou, Z and Wang, Y and Gao, X and Zheng, L},
title = {Data-driven natural computational psychophysiology in class.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {6},
pages = {3477-3489},
pmid = {39712090},
issn = {1871-4080},
abstract = {Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants' psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.},
}
@article {pmid39711742,
year = {2024},
author = {Li, J and Nan, Z and Qi, G and Cai, J and Zhao, X and Li, X and Liu, S and Wang, Y and Wu, Y and Miao, X and Yu, G},
title = {Assessing severity of pediatric pneumonia using multimodal transformers with multi-task learning.},
journal = {Digital health},
volume = {10},
number = {},
pages = {20552076241305168},
pmid = {39711742},
issn = {2055-2076},
abstract = {OBJECTIVE: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the robust multimodal transformer (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.<br><br>METHOD: The RMT model leverages multimodal data, integrating X-ray images and clinical text data through a sophisticated AI-driven framework. It employs a Transformer-based architecture, enhanced by multi-task learning and mask attention mechanism. This approach aims to optimize the model's performance across different modalities, particularly under conditions of modality absence.<br><br>RESULTS: The RMT model demonstrates superior performance over traditional diagnostic methods and baseline models in accuracy, precision, sensitivity, and specificity. In tests involving various scenarios, including single-modal and multimodal tasks, the model shows remarkable robustness in handling incomplete data. Its effectiveness is further validated through extensive comparative analysis and ablation studies.<br><br>CONCLUSION: The RMT model represents a substantial advancement in pediatric pneumonia severity assessment. It successfully harnesses multimodal data and advanced AI techniques to improve assessment precision. While the RMT model sets a new precedent in AI applications in medical diagnostics, the development of a comprehensive pediatric pneumonia dataset marks a pivotal contribution, providing a robust foundation for future research.},
}
@article {pmid39705724,
year = {2025},
author = {de Melo, GC and Castellano, G and Forner-Cordero, A},
title = {Identification and analysis of reference-independent movement event-related desynchronization.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {2},
pages = {},
doi = {10.1088/2057-1976/ada1dc},
pmid = {39705724},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Movement ; Male ; Female ; Adult ; Electrodes ; Signal Processing, Computer-Assisted ; Algorithms ; Cortical Synchronization/physiology ; Young Adult ; Brain/physiology ; Motor Cortex/physiology ; },
abstract = {Characterization of the electroencephalography (EEG) signals related to motor activity, such as alpha- and beta-band motor event-related desynchronizations (ERDs), is essential for Brain Computer Interface (BCI) development. Determining the best electrode combination to detect the ERD is crucial for the success of the BCI. Considering that the EEG signals are bipolar, this involves the choice of the main and reference electrodes. So far, no strategy to guarantee signals free of the activity from the reference electrode has achieved consensus among the scientific community. Therefore, mapping the ERD in terms of the spatial distribution of the main and reference electrodes can provide additional perspectives for the BCI field. The goal of this work is to identify subject-specific channels where ERD is temporally coupled to the initiation of an upper-limb motor task. We defined a criterion to determine the presence of the ERD linked to the movement onset and searched, separately for each subject, for the single channel with the most prominent ERD. The search was conducted over all available channels composed by a pair of electrodes, and the selected signals were analyzed according to their temporal and spatial characteristics. We found that alpha- and beta-band ERD temporarily linked to movement onset can be detected in atypical channels (pairs of electrodes) across the scalp. The selected channels were different across subjects. Four ERD temporal patterns were observed in terms of the initiation instant of the ERD. These patterns revealed that the M1 cortex seems to be related to later ERDs. Moreover, they were also associated to different cortical processes related to the motor task. To the best of our knowledge, this is the first time these findings are reported. Aiming at BCI development, further experiments with more subjects and with motor-imagery tasks are desirable for more robustness and applicability of these findings.},
}
@article {pmid39704693,
year = {2025},
author = {Svejgaard, B and Modrau, B and Hernández-Gloria, JJ and Wested, CL and Dosen, S and Stevenson, AJT and Mrachacz-Kersting, N},
title = {Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke.},
journal = {Journal of neurophysiology},
volume = {133},
number = {1},
pages = {333-341},
doi = {10.1152/jn.00452.2024},
pmid = {39704693},
issn = {1522-1598},
support = {2021-0008//Sundhedsinnovationspuljen, Region Nordjylland/ ; 229643//Melsen Fonden/ ; 21.584//Grosserer L. F. Foghts Fond (Grosserer LF Foghts Fund)/ ; 7683//Jascha Fonden (Jascha Foundation)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke Rehabilitation/methods ; Aged ; *Stroke/physiopathology ; *Wrist/physiology/physiopathology ; *Evoked Potentials, Motor/physiology ; Pyramidal Tracts/physiopathology/physiology ; Transcranial Magnetic Stimulation ; Motor Cortex/physiopathology/physiology ; Adult ; Electroencephalography ; Muscle, Skeletal/physiopathology/physiology ; },
abstract = {In a recently developed associative rehabilitative brain-computer interface (BCI) system, electroencephalography (EEG) is used to identify the most active phase of the motor cortex during attempted movement and deliver precisely timed peripheral stimulation during training. This approach has been demonstrated to facilitate corticospinal excitability and functional recovery in patients with lower limb weakness following stroke. The current study expands those findings by investigating changes in corticospinal excitability following the associative BCI intervention in patients with post stroke with upper limb weakness. In a randomized controlled trial, 24 patients with subacute stroke, subdivided into an intervention group and a "sham" control group, performed 30 wrist extensions. The intervention comprised 30 pairings of single peripheral nerve stimulation at the motor threshold, timed so that the generated afferent volley arrived at the motor cortex during the peak negativity of the movement-related cortical potential (MRCP), which was identified with EEG. The sham group underwent the same intervention, though the intensity of the nerve stimulation was below the perception threshold. Immediately after training, patients in the associative group exhibited significantly larger amplitudes of muscular-evoked potentials, compared with pretraining measurements in response to transcranial magnetic stimulation. These changes persisted for at least 30 min and were not observed in the sham group. We demonstrate that motor-evoked potential amplitudes increased significantly following paired associative BCI training targeting upper limb muscles in patients with subacute stroke, which is in line with results from lower limb studies.NEW &amp; NOTEWORTHY We have demonstrated that a single training session with an associative brain-computer interface increased corticospinal excitability in patients suffering from upper limb weakness following stroke. This is the first time such an effect is described in the upper limb, which paves the way for effect augmentation of existing upper limb rehabilitation protocols.},
}
@article {pmid39703669,
year = {2024},
author = {Cao, Y and Gao, S and Yu, H and Zhao, Z and Zang, D and Wang, C},
title = {A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.},
journal = {Frontiers in physiology},
volume = {15},
number = {},
pages = {1487809},
pmid = {39703669},
issn = {1664-042X},
abstract = {OBJECTIVE: Extracting deep features from participants' bioelectric signals and constructing models are key research directions in motor imagery (MI) classification tasks. In this study, we constructed a multimodal multitask hybrid brain-computer interface net (2M-hBCINet) based on deep features of electroencephalogram (EEG) and electromyography (EMG) to effectively accomplish motor imagery classification tasks.<br><br>METHODS: The model first used a variational autoencoder (VAE) network for unsupervised learning of EEG and EMG signals to extract their deep features, and subsequently applied the channel attention mechanism (CAM) to select these deep features and highlight the advantageous features and minimize the disadvantageous ones. Moreover, in this study, multitask learning (MTL) was applied to train the 2M-hBCINet model, incorporating the primary task that is the MI classification task, and auxiliary tasks including EEG reconstruction task, EMG reconstruction task, and a feature metric learning task, each with distinct loss functions to enhance the performance of each task. Finally, we designed module ablation experiments, multitask learning comparison experiments, multi-frequency band comparison experiments, and muscle fatigue experiments. Using leave-one-out cross-validation(LOOCV), the accuracy and effectiveness of each module of the 2M-hBCINet model were validated using the self-made MI-EEMG dataset and the public datasets WAY-EEG-GAL and ESEMIT.<br><br>RESULTS: The results indicated that compared to comparative models, the 2M-hBCINet model demonstrated good performance and achieved the best results across different frequency bands and under muscle fatigue conditions.<br><br>CONCLUSION: The 2M-hBCINet model constructed based on EMG and EEG data innovatively in this study demonstrated excellent performance and strong generalization in the MI classification task. As an excellent end-to-end model, 2M-hBCINet can be generalized to be used in EEG-related fields such as anomaly detection and emotion analysis.},
}
@article {pmid39703101,
year = {2024},
author = {Zhou, H and Wang, M and Xu, T and Zhang, X and Zhao, X and Tang, L and Zhao, P and Wang, D and Lai, J and Wang, F and Zhang, S and Hu, S},
title = {Cognitive Remediation in Patients With Bipolar Disorder: A Randomized Trial by Sequential tDCS and Navigated rTMS Targeting the Primary Visual Cortex.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {12},
pages = {e70179},
pmid = {39703101},
issn = {1755-5949},
support = {82201675//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021C03107//Key Research and Development Program of Zhejiang Province/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Bipolar Disorder/therapy/psychology ; *Transcranial Magnetic Stimulation/methods ; Adult ; Middle Aged ; *Cognitive Remediation/methods ; *Visual Cortex ; Young Adult ; Treatment Outcome ; },
abstract = {BACKGROUND: Non-invasive brain stimulation (NIBS), such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), has emerged as a promising alternative in the precise treatment of clinical symptoms, such as the cognitive impairment of bipolar disorder (BD). Optimizing the neurocognitive effects by combining tDCS and rTMS to strengthen the clinical outcome is a challenging research issue.<br><br>OBJECTIVE: In this randomized, controlled trial, we first combined tDCS and neuronavigated rTMS targeting the V1 region to explore the efficacy on neurocognitive function in BD patients with depressive episodes.<br><br>METHODS: Eligible individuals (n&#x2009;=&#x2009;105) were assigned into three groups, Group A (active tDCS-active rTMS), Group B (sham tDCS-active rTMS), and Group C (active tDCS-sham rTMS). All participants received 3-week treatment in which every participant received 15 sessions of stimulation through the study, 5 sessions every week, with tDCS treatment followed by neuronavigated rTMS every session. We evaluated the cognitive, emotional, and safety outcomes at week-0 (w0, baseline), week-3 (w3, immediately post-treatment), and week-8 (w8, follow-up period). The THINC-integrated tool (THINC-it), 17-item Hamilton Depression Rating Scale, and Young Mania Rating Scale were applied for evaluating the cognitive function and emotional state, respectively. Data were analyzed by repeated measure ANOVA and paired t-test.<br><br>RESULTS: Eventually, 32 patients in Group A, 27 in Group B, and 23 in Group C completed the entire treatment. Compared to Groups B and C, Group A showed greater improvement in Symbol Check items (Time and Accuracy) at W3 and Symbol Check Accuracy at W8 (p&#x2009;<&#x2009;0.01). The W0-W3 analysis indicated a significant improvement in depressive symptoms in both Group A and Group B (p&#x2009;<&#x2009;0.01). Additionally, neuroimaging data revealed increased activity in the calcarine sulcus in Group A, suggesting potential neuroplastic changes in the visual cortex following the electromagnetic stimulation.<br><br>CONCLUSIONS: These findings provide preliminary evidence that the combination of navigated rTMS with tDCS targeting V1 region may serve as a potential treatment strategy for improving cognitive impairment and depressive symptoms in BD patients.<br><br>TRIAL REGISTRATION: Clinical Trial Registry number: NCT05596461.},
}
@article {pmid39702315,
year = {2024},
author = {Capecci, M and Gandolfi, M and Straudi, S and Calabrò, RS and Baldini, N and Pepa, L and Andrenelli, E and Smania, N and Ceravolo, MG and Morone, G and Bonaiuti, D and , },
title = {Advancing public health through technological rehabilitation: insights from a national clinician survey.},
journal = {BMC health services research},
volume = {24},
number = {1},
pages = {1626},
pmid = {39702315},
issn = {1472-6963},
mesh = {Humans ; Cross-Sectional Studies ; Italy ; Surveys and Questionnaires ; Male ; Female ; *Public Health ; Physical and Rehabilitation Medicine ; Middle Aged ; Adult ; Rehabilitation ; },
abstract = {INTRODUCTION: In the evolving healthcare landscape, technology has emerged as a key component in enhancing system efficiency and offering new avenues for patient rehabilitation. Despite its growing importance, detailed information on technology's specific use, types, and applications in clinical rehabilitation settings, particularly within the Italian framework, remains unclear. This study aimed to explore the use of technology and its needs by Physical Medicine and Rehabilitation medical doctors in Italy.<br><br>METHODS: We conducted a cross-sectional online survey aimed at 186 Italian clinicians affiliated with the Italian Society of Physical and Rehabilitation Medicine (SIMFER). The online questionnaire consists of 71 structured questions designed to collect demographic and geographical data of the respondents, as well as detailed insights into the prevalence and range of technologies they use, together with their specific applications in clinical settings."<br><br>RESULTS: A broad range of technologies, predominantly commercial medical devices, has been documented. These technologies are employed for various conditions, including common neurological diseases, musculoskeletal disorders, dementia, and rheumatologic issues. The application of these technologies indicates a broadening scope beyond enhancing sensorimotor functions, addressing both physical and social aspects of patient care.<br><br>DISCUSSION: In recent years, there's been a notable surge in using technology for rehabilitation across various disorders. The upcoming challenge is to update health policies to integrate these technologies better, aiming to extend their benefits to a wider range of disabling conditions, marking a progressive shift in public health and rehabilitation practices.},
}
@article {pmid39700898,
year = {2025},
author = {Zhang, Y and Gao, S and Liang, C and Bustillo, J and Kochunov, P and Turner, JA and Calhoun, VD and Wu, L and Fu, Z and Jiang, R and Zhang, D and Jiang, J and Wu, F and Peng, T and Xu, X and Qi, S},
title = {Consistent frontal-limbic-occipital connections in distinguishing treatment-resistant and non-treatment-resistant schizophrenia.},
journal = {NeuroImage. Clinical},
volume = {45},
number = {},
pages = {103726},
pmid = {39700898},
issn = {2213-1582},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; },
mesh = {Humans ; Male ; Adult ; Female ; *Schizophrenia/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging/methods ; *Frontal Lobe/physiopathology/diagnostic imaging ; *Occipital Lobe/diagnostic imaging/physiopathology ; Schizophrenia, Treatment-Resistant/diagnostic imaging ; Young Adult ; Middle Aged ; Connectome/methods ; Limbic System/diagnostic imaging/physiopathology ; Nerve Net/diagnostic imaging/physiopathology ; Limbic Lobe/physiopathology/diagnostic imaging ; },
abstract = {BACKGROUND AND HYPOTHESIS: Treatment-resistant schizophrenia (TR-SZ) and non-treatment-resistant schizophrenia (NTR-SZ) lack specific biomarkers to distinguish from each other. This investigation aims to identify consistent dysfunctional brain connections with different atlases, multiple feature selection strategies, and several classifiers in distinguishing TR-SZ and NTR-SZ.<br><br>STUDY DESIGN: 55 TR-SZs, 239 NTR-SZs, and 87 healthy controls (HCs) were recruited from the Affiliated Brain Hospital of Nanjing Medical University. Resting-state functional connection (FC) matrices were constructed from automated anatomical labeling (AAL), Yeo-Networks (YEO) and Brainnetome (BNA) atlases. Two feature selection methods (Select From Model and Recursive Feature Elimination) and four classifiers (Adaptive Boost, Bernoulli Na&#xef;ve Bayes, Gradient Boosting and Random Forest) were combined to identify the consistent FCs in distinguishing TR-SZ and HC, NTR-SZ and HC, TR-SZ and NTR-SZ.<br><br>STUDY RESULTS: The whole brain FCs, except the temporal-occipital FC, were consistent in distinguishing SZ and HC. Abnormal frontal-limbic, frontal-parietal and occipital-temporal FCs were consistent in distinguishing TR-SZ and NTR-SZ, that were further correlated with disease progression, symptoms and medication dosage. Moreover, the frontal-limbic and frontal-parietal FCs were highly consistent for the diagnosis of SZ (TR-SZ vs. HC, NTR-SZ vs. HC and TR-SZ vs. NTR-SZ). The BNA atlas achieved the highest classification accuracy (>90&#xa0;%) comparing with AAL and YEO in the most diagnostic tasks.<br><br>CONCLUSIONS: These results indicate that the frontal-limbic and the frontal-parietal FCs are the robust neural pathways in the diagnosis of SZ, whereas the frontal-limbic, frontal-parietal and occipital-temporal FCs may be informative in recognizing those TR-SZ in the clinical practice.},
}
@article {pmid39700269,
year = {2024},
author = {Li, W and Li, J and Li, J and Wei, C and Laviv, T and Dong, M and Lin, J and Calubag, M and Colgan, LA and Jin, K and Zhou, B and Shen, Y and Li, H and Cui, Y and Gao, Z and Li, T and Hu, H and Yasuda, R and Ma, H},
title = {Boosting neuronal activity-driven mitochondrial DNA transcription improves cognition in aged mice.},
journal = {Science (New York, N.Y.)},
volume = {386},
number = {6728},
pages = {eadp6547},
doi = {10.1126/science.adp6547},
pmid = {39700269},
issn = {1095-9203},
support = {R01 MH080047/MH/NIMH NIH HHS/United States ; R35 NS116804/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Male ; Mice ; *Aging/genetics ; *Brain/metabolism/physiology ; Cell Nucleus/metabolism ; *Cognition ; Cognitive Dysfunction/genetics ; *DNA, Mitochondrial/genetics/metabolism ; Mice, Inbred C57BL ; Mitochondria/metabolism ; *Neurons/metabolism/physiology ; *Transcription, Genetic ; },
abstract = {Deciphering the complex interplay between neuronal activity and mitochondrial function is pivotal in understanding brain aging, a multifaceted process marked by declines in synaptic function and mitochondrial performance. Here, we identified an age-dependent coupling between neuronal and synaptic excitation and mitochondrial DNA transcription (E-TCmito), which operates differently compared to classic excitation-transcription coupling in the nucleus (E-TCnuc). We demonstrated that E-TCmito repurposes molecules traditionally associated with E-TCnuc to regulate mitochondrial DNA expression in areas closely linked to synaptic activation. The effectiveness of E-TCmito weakens with age, contributing to age-related neurological deficits in mice. Boosting brain E-TCmito in aged animals ameliorated these impairments, offering a potential target to counteract age-related cognitive decline.},
}
@article {pmid39698084,
year = {2024},
author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A},
title = {Tactile imagery affects cortical responses to vibrotactile stimulation of the fingertip.},
journal = {Heliyon},
volume = {10},
number = {23},
pages = {e40807},
pmid = {39698084},
issn = {2405-8440},
abstract = {Mental imagery is a crucial cognitive process, yet its underlying neural mechanisms remain less understood compared to perception. Furthermore, within the realm of mental imagery, the somatosensory domain is particularly underexplored compared to other sensory modalities. This study aims to investigate the influence of tactile imagery (TI) on cortical somatosensory processing. We explored the cortical manifestations of TI by recording EEG activity in healthy human subjects. We investigated event-related somatosensory oscillatory dynamics during TI compared to actual tactile stimulation, as well as somatosensory evoked potentials (SEPs) in response to short vibrational stimuli, examining their amplitude-temporal characteristics and spatial distribution across the scalp. EEG activity exhibited significant changes during TI compared to the no-imagery baseline. TI caused event-related desynchronization (ERD) of the contralateral μ-rhythm, with a notable correlation between ERD during imagery and real stimulation across subjects. TI also modulated several SEP components in sensorimotor and frontal areas, showing increases in the contralateral P100 and P300, contra- and ipsilateral P300, frontal P200, and parietal P600 components. The results clearly indicate that TI affects cortical processing of somatosensory stimuli, impacting EEG responses in various cortical areas. The assessment of SEPs in EEG could serve as a versatile marker of tactile imagery in practical applications. We propose incorporating TI in imagery-based brain-computer interfaces (BCIs) to enhance sensorimotor restoration and sensory substitution. This approach underscores the importance of somatosensory mental imagery in cognitive neuroscience and its potential applications in neurorehabilitation and assistive technologies.},
}
@article {pmid39697780,
year = {2024},
author = {Wan, X and Xing, S and Zhang, Y and Duan, D and Liu, T and Li, D and Yu, H and Wen, D},
title = {Combining motion performance with EEG for diagnosis of mild cognitive impairment: a new perspective.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1476730},
pmid = {39697780},
issn = {1662-4548},
}
@article {pmid39697779,
year = {2024},
author = {Cui, S and Lee, D and Wen, D},
title = {Toward brain-inspired foundation model for EEG signal processing: our opinion.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1507654},
pmid = {39697779},
issn = {1662-4548},
}
@article {pmid39696695,
year = {2024},
author = {Xue, YY and Zhang, ZS and Lin, RR and Huang, HF and Zhu, KQ and Chen, DF and Wu, ZY and Tao, QQ},
title = {CD2AP deficiency aggravates Alzheimer's disease phenotypes and pathology through p38 MAPK activation.},
journal = {Translational neurodegeneration},
volume = {13},
number = {1},
pages = {64},
pmid = {39696695},
issn = {2047-9158},
support = {81970998//National Natural Science Foundation of China/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; },
mesh = {Animals ; *Alzheimer Disease/genetics/pathology/metabolism ; *p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; Mice ; *Adaptor Proteins, Signal Transducing/genetics/deficiency ; Mice, Transgenic ; Phenotype ; Disease Models, Animal ; Humans ; Mice, Knockout ; Neurons/pathology/metabolism ; Cytoskeletal Proteins ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is the most common form of neurodegenerative disorder, which is characterized by a decline in cognitive abilities. Genome-wide association and clinicopathological studies have demonstrated that the CD2-associated protein (CD2AP) gene is one of the most important genetic risk factors for AD. However, the precise mechanisms by which CD2AP is linked to AD pathogenesis remain unclear.<br><br>METHODS: The spatiotemporal expression pattern of CD2AP was determined. Then, we generated and characterized an APP/PS1 mouse model with neuron-specific Cd2ap deletion, using immunoblotting, immunofluorescence, enzyme-linked immunosorbent assay, electrophysiology and behavioral tests. Additionally, we established a stable CD2AP-knockdown SH-SY5Y cell line to further elucidate the specific molecular mechanisms by which CD2AP contributes to AD pathogenesis. Finally, the APP/PS1 mice with neuron-specific Cd2ap deletion were treated with an inhibitor targeting the pathway identified above to further validate our findings.<br><br>RESULTS: CD2AP is widely expressed in various regions of the mouse brain, with predominant expression in neurons and vascular endothelial cells. In APP/PS1 mice, neuronal knockout of Cd2ap significantly aggravated tau pathology, synaptic impairments and cognitive deficits. Mechanistically, the knockout of Cd2ap activated p38 mitogen-activated protein kinase (MAPK) signaling, which contributed to increased tau phosphorylation, synaptic injury, neuronal apoptosis and cognitive impairment. Furthermore, the phenotypes of neuronal Cd2ap knockout were ameliorated by a p38 MAPK inhibitor.<br><br>CONCLUSION: Our study presents the first in vivo evidence that CD2AP deficiency exacerbates the phenotypes and pathology of AD through the p38 MAPK pathway, identifying CD2AP/p38 MAPK as promising therapeutic targets for AD.},
}
@article {pmid39695740,
year = {2024},
author = {Lowers, V and Kirby, R and Young, B and Harris, RV},
title = {Scoping review of fidelity strategies used in behaviour change trials delivered in primary dental care settings.},
journal = {Trials},
volume = {25},
number = {1},
pages = {824},
pmid = {39695740},
issn = {1745-6215},
mesh = {Humans ; *Randomized Controlled Trials as Topic/standards/methods ; *Primary Health Care/standards ; *Dental Care/standards ; Health Behavior ; Research Design/standards ; Behavior Therapy/methods/standards ; Oral Health/standards ; Health Knowledge, Attitudes, Practice ; },
abstract = {BACKGROUND: Primary dental care settings are strategically important locations where randomised controlled trials (RCTs) of behaviour change interventions (BCIs) can be tested to tackle oral diseases. Findings have so far produced equivocal results. Improving treatment fidelity is posed as a mechanism to improve scientific rigour, consistency and implementation of BCIs. The National Institutes of Health Behaviour Change Consortium (NIH BCC) developed a tool to assess and evaluate treatment fidelity in health behaviour change interventions, which has yet to be applied to the primary dental care BCI literature.<br><br>METHOD: We conducted a scoping review of RCTs delivered in primary dental care by dental team members (in real-world settings) between 1980 and 2023. Eligible studies were coded using the NIH BCC checklist to determine the presence of reported fidelity strategies across domains: design, training, delivery, receipt and enactment.<br><br>RESULTS: We included 34 eligible articles, reporting 21 RCTs. Fidelity reporting variations were found both between and within NIH BCC domains: strategy reporting ranged from 9.5 to 85.7% in design, 9.5 to 57.1% in training, 0 to 66.7% in delivery, 14.3 to 36.8% in receipt and 13.3 to 33.3% in enactment. The most reported domain was design (M&#x2009;=&#x2009;0.45), and the least reported domain was delivery (M&#x2009;=&#x2009;0.21). Only one study reported over 50% of the recommended strategies in every domain.<br><br>CONCLUSIONS: This review revealed inconsistencies in fidelity reporting with no evidence that fidelity guidelines or frameworks were being used within primary dental care trials. This has highlighted issues with interpretability, reliability and reproducibility of research findings. Recommendations are proposed to assist primary dental care trialists with embedding fidelity strategies into future research.},
}
@article {pmid39694240,
year = {2025},
author = {Fonseca, M and Kurban, D and Roy, JP and Santschi, DE and Molgat, E and Yang, DA and Dufour, S},
title = {Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis.},
journal = {Journal of dairy science},
volume = {108},
number = {4},
pages = {3917-3928},
doi = {10.3168/jds.2024-25403},
pmid = {39694240},
issn = {1525-3198},
mesh = {Animals ; Cattle ; Female ; *Mastitis, Bovine/diagnosis ; *Milk/cytology ; Cell Count/veterinary ; *Mammary Glands, Animal ; Dairying ; },
abstract = {Mastitis, an inflammation of the udder primarily caused by an IMI, is one of the most common diseases in dairy cattle. Somatic cell count has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential SCC (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, although it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level SCS or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (SD) of 1.4 (1.3), and, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian credible interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.},
}
@article {pmid39693762,
year = {2025},
author = {Oxley, TJ and Deo, DR and Cernera, S and Sawyer, A and Putrino, D and Ramsey, NF and Fry, A},
title = {The 'Brussels 4': essential requirements for implantable brain-computer interface user autonomy.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ada0e6},
pmid = {39693762},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; Personal Autonomy ; User-Computer Interface ; },
abstract = {Objective. Implantable brain-computer interfaces (iBCIs) hold great promise for individuals with severe paralysis and are advancing toward commercialization. The features required for successful clinical translation and patient adoption of iBCIs may be under recognized within traditional academic iBCI research and deserve further consideration.Approach. Here we consider potentially critical factors to achieve iBCI user autonomy, reflecting the authors' perspectives on discussions during various sessions and workshops across the 10th International BCI Society Meeting, Brussels, 2023.Main results. Four key considerations were identified: (1) immediate use, (2) easy to use, (3) continuous use, and (4) stable system use.Significance. Addressing these considerations may enable successful clinical translation of iBCIs.},
}
@article {pmid39693735,
year = {2024},
author = {Kim, J and Cho, YS and Kim, SP},
title = {Task-relevant stimulus design improves P300-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ada0e3},
pmid = {39693735},
issn = {1741-2552},
abstract = {OBJECTIVE: In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands.<br><br>APPROACH: In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses.<br><br>MAIN RESULTS: With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.},
}
@article {pmid39693734,
year = {2024},
author = {Srisrisawang, N and Müller-Putz, GR},
title = {Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ada0ea},
pmid = {39693734},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Hand/physiology ; *Movement/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Adult ; Biomechanical Phenomena/physiology ; },
abstract = {Objective.The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored.Approach.We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model.Main results.Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification.Significance.Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.},
}
@article {pmid39693678,
year = {2025},
author = {Shen, J and Wang, K and Gao, W and Liu, JK and Xu, Q and Pan, G and Chen, X and Tang, H},
title = {Temporal spiking generative adversarial networks for heading direction decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {184},
number = {},
pages = {106975},
doi = {10.1016/j.neunet.2024.106975},
pmid = {39693678},
issn = {1879-2782},
mesh = {Animals ; *Neural Networks, Computer ; *Neurons/physiology ; *Action Potentials/physiology ; Parietal Lobe/physiology ; Models, Neurological ; Macaca mulatta ; },
abstract = {The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological population level. The practical difficulty in collecting VIP neuronal response data hinders the application of sophisticated decoding models. To address this challenge, we propose a unified spike-based decoding framework leveraging spiking neural networks (SNNs) for both generative and decoding purposes, for their energy efficiency and suitability for neural decoding tasks. We propose the Temporal Spiking Generative Adversarial Networks (T-SGAN), a model based on a spiking transformer, to generate synthetic time-series data reflecting the neuronal response of VIP neurons. T-SGAN incorporates temporal segmentation to reduce the temporal dimension length, while spatial self-attention facilitates the extraction of associated information among VIP neurons. This is followed by recurrent SNNs decoder equipped with an attention mechanism, designed to capture the intricate spatial and temporal dynamics for heading direction decoding. Experimental evaluations conducted on biological datasets from monkeys showcase the effectiveness of the proposed framework. Results indicate that T-SGAN successfully generates realistic synthetic data, leading to a significant improvement of up to 1.75% in decoding accuracy for recurrent SNNs. Furthermore, the SNN-based decoding framework capitalizes on the low power consumption advantages, offering substantial benefits for neuronal response decoding applications.},
}
@article {pmid39692757,
year = {2025},
author = {van Oosterhout, K and Chilundo, A and Branco, MP and Aarnoutse, EJ and Timmermans, M and Fattori, M and Ramsey, NF and Cantatore, E},
title = {Brain-Computer Interfaces Using Flexible Electronics: An a-IGZO Front-End for Active ECoG Electrodes.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {6},
pages = {e2408576},
pmid = {39692757},
issn = {2198-3844},
support = {//Dutch Research Council (NWO)/ ; 17608//Stichting voor de Technische Wetenschappen/ ; 19072//Stichting voor de Technische Wetenschappen/ ; UEBIT//EWUU Alliance/ ; },
abstract = {Brain-computer interfaces (BCIs) are evolving toward higher electrode count and fully implantable solutions, which require extremely low power densities (<15mW cm[-2]). To achieve this target, and allow for a large and scalable number of channels, flexible electronics can be used as a multiplexing interface. This work introduces an active analog front-end fabricated with amorphous Indium-Gallium-Zinx-Oxide (a-IGZO) Thin-Film Transistors (TFTs) on foil capable of active matrix multiplexing. The circuit achieves only 70nV per sqrt(Hz) input referred noise, consuming 46µW, or 3.5mW cm[-2]. It demonstrates for the first time in literature a flexible front-end with a noise efficiency factor comparable with Silicon solutions (NEF = 9.8), which is more than 10X lower compared to previously reported flexible front-ends. These results have been achieved using a modified bootstrap-load amplifier. The front end is tested by playing through it recordings obtained from a conventional BCI system. A gesture classification based on the flexible front-end outputs achieves 94% accuracy. Using a flexible active front end can improve the state-of-the-art in high channel count BCI systems by lowering the multiplexer noise and enabling larger areas of the brain to be monitored while reducing power density. Therefore, this work enables a new generation of high channel-count active BCI electrode grids.},
}
@article {pmid39692430,
year = {2024},
author = {Nie, J and Huang, T and Sun, Y and Peng, Z and Dong, W and Chen, J and Zheng, D and Guo, F and Shi, W and Ling, Y and Zhao, W and Yang, H and Shui, T and Yan, X},
title = {Influence of the Enterovirus 71 Vaccine and the COVID-19 Pandemic on Hand, Foot, and Mouth Disease in China Based on Counterfactual Models: Observational Study.},
journal = {JMIR public health and surveillance},
volume = {10},
number = {},
pages = {e63146},
pmid = {39692430},
issn = {2369-2960},
mesh = {*Hand, Foot and Mouth Disease/epidemiology/prevention &amp; control ; Humans ; China/epidemiology ; *COVID-19/epidemiology/prevention &amp; control ; Infant ; Child, Preschool ; Incidence ; *Enterovirus A, Human ; *Viral Vaccines/administration &amp; dosage ; Male ; Child ; Female ; Adolescent ; Pandemics/prevention &amp; control ; },
abstract = {BACKGROUND: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control.<br><br>OBJECTIVE: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD.<br><br>METHODS: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively.<br><br>RESULTS: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from -11% to -229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions-ARIMA(1,0,1)(0,1,0)[12]-from February to September 2020, with considerable negative APEs (ranging from -31% to -2248%).<br><br>CONCLUSIONS: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.},
}
@article {pmid39691819,
year = {2024},
author = {Vieira, R and Moreno, P and Vourvopoulos, A},
title = {EEG-based action anticipation in human-robot interaction: a comparative pilot study.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1491721},
pmid = {39691819},
issn = {1662-5218},
abstract = {As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.},
}
@article {pmid39691581,
year = {2024},
author = {Eken, A and Yüce, M and Yükselen, G and Erdoğan, SB},
title = {Explainable fNIRS-based pain decoding under pharmacological conditions via deep transfer learning approach.},
journal = {Neurophotonics},
volume = {11},
number = {4},
pages = {045015},
pmid = {39691581},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.<br><br>AIM: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.<br><br>APPROACH: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90&#xa0;min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.<br><br>RESULTS: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.<br><br>CONCLUSIONS: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.},
}
@article {pmid39688131,
year = {2025},
author = {Wang, S and Song, X and Xu, J and Wang, J and Yu, L},
title = {Flexible silicon for high-performance photovoltaics, photodetectors and bio-interfaced electronics.},
journal = {Materials horizons},
volume = {12},
number = {4},
pages = {1106-1132},
doi = {10.1039/d4mh01466a},
pmid = {39688131},
issn = {2051-6355},
abstract = {Silicon (Si) is currently the most mature and reliable semiconductor material in the industry, playing a pivotal role in the development of modern microelectronics, renewable energy, and bio-electronic technologies. In recent years, widespread research attention has been devoted to the development of advanced flexible electronics, photovoltaics, and bio-interfaced sensors/detectors, boosting their emerging applications in distributed energy sources, healthcare, environmental monitoring, and brain-computer interfaces (BCIs). Despite the rigid and brittle nature of Si, a series of new fabrication technologies and integration strategies have been developed to enable a wide range of c-Si-based high-performance flexible photovoltaics and electronics, which were previously only achievable with intrinsically soft organic and polymer semiconductors. More interestingly, programmable geometric engineering of crystalline silicon (c-Si) units and logic circuits has been explored to enable the fabrication of various highly flexible nanoprobes for intracellular sensing and the deployment of soft BCI matrices to record and understand brain neural activities for the development of advanced neuroprosthetics. This review will systematically examine the latest progress in the fabrication of Si-based flexible solar cells, photodetectors, and biological probing interfaces over the past decade, identifying key design principles, mechanisms, and technological milestones achieved through novel geometry, morphology, and composition control. These advancements, when combined, will not only promote the practical applications of sustainable energy and wearable electronics but also spur new breakthroughs in emerging human-machine interfaces (HMIs) and artificial intelligence applications, which hold significant implications for understanding neural activities, implementing more efficient artificial Intelligence (AI) algorithms, and developing new therapies or treatments. Finally, we will summarize and provide an outlook on the current challenges and future opportunities of Si-based electronics, flexible optoelectronics, and bio-sensing.},
}
@article {pmid39687714,
year = {2024},
author = {Hofmann, MJ and Chang, YN and Brouwer, H and Zock, M},
title = {Editorial: Neurocomputational models of language processing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1524366},
doi = {10.3389/fnhum.2024.1524366},
pmid = {39687714},
issn = {1662-5161},
}
@article {pmid39687306,
year = {2024},
author = {Kancaoğlu, M and Kuntalp, M},
title = {Low-cost, mobile EEG hardware for SSVEP applications.},
journal = {HardwareX},
volume = {19},
number = {},
pages = {e00567},
pmid = {39687306},
issn = {2468-0672},
abstract = {The global shortage of integrated circuits due to the COVID-19 pandemic has made it challenging to build biopotential acquisition devices like electroencephalography (EEG) hardware. To address this issue, a new hardware system using common ICs has been designed, which is cost-effective, precise, and easily accessible from global distributors. The hardware system comprises 8-channel inputs EEG hardware with a mobile headset capable of acquiring 5-30Hz EEG signals. First two channels of the design is enabled for steady-state visual evoked potential (SSVEP) operations, and the remaining channels can be powered up as needed. A small 3D-printable enclosure is also designed for the hardware board, which is attached to protective glasses to be used as a head-mounted device. The board includes an additional green LED, 4 pulse width modulation (PWM) outputs for general-purpose input/output (GPIO), 2 buttons for input, and exposed programming pins and digital-to-analog converter (DAC) output from the microcontroller unit (MCU). The proposed hardware system is expected to enable students and young researchers to experiment with EEG signals, especially SSVEP, before investing in professional equipment with the availability of programming codes.},
}
@article {pmid39687114,
year = {2024},
author = {Ma, J and Rui, Z and Zou, Y and Qin, Z and Zhao, Z and Zhang, Y and Mao, Z and Bai, H and Zhang, J},
title = {Neurosurgical and BCI approaches to visual rehabilitation in occipital lobe tumor patients.},
journal = {Heliyon},
volume = {10},
number = {23},
pages = {e39072},
pmid = {39687114},
issn = {2405-8440},
abstract = {This study investigates the effects of occipital lobe tumors on visual processing and the role of brain-computer interface (BCI) technologies in post-surgical visual rehabilitation. Through a combination of pre-surgical functional magnetic resonance imaging (fMRI) and Diffusion Tensor Imaging (DTI), intra-operative direct cortical stimulation (DCS) and Electrocorticography (ECoG), and post-surgical BCI interventions, we provide insight into the complex dynamics between occipital lobe tumors and visual function. Our results highlight a discrepancy between clinical assessments of visual field damage and the patient's reported visual experiences, suggesting a residual functional capacity within the damaged occipital regions. Additionally, the absence of expected visual phenomena during surgery and the promising outcomes from BCI-driven rehabilitation underscore the complexity of visual processing and the potential of technology-enhanced rehabilitation strategies. This work emphasizes the need for an interdisciplinary approach in developing effective treatments for visual impairments related to brain tumors, illustrating the significant implications for neurosurgical practices and the advancement of rehabilitation sciences.},
}
@article {pmid39686818,
year = {2025},
author = {Lv, Q and Li, Q and Cao, P and Wei, C and Li, Y and Wang, Z and Wang, L},
title = {Designing Silk Biomaterials toward Better Future Healthcare: The Development and Application of Silk-Based Implantable Electronic Devices in Clinical Diagnosis and Therapy.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {8},
pages = {e2411946},
doi = {10.1002/adma.202411946},
pmid = {39686818},
issn = {1521-4095},
support = {2022YFC2408100//National Key Research and Development Program of China/ ; 82372511//National Natural Science Foundation of China/ ; 82302585//National Natural Science Foundation of China/ ; 82072167//National Natural Science Foundation of China/ ; 82272277//National Natural Science Foundation of China/ ; 82173315//National Natural Science Foundation of China/ ; 81974382//National Natural Science Foundation of China/ ; [2022] No.11//Hubei Province Science and Technology Innovation Team Project/ ; 2022BCA013//the Major Scientific and Technological Innovation Projects of Hubei Province/ ; 2021CFB416//Natural Science Foundation of Hubei Province/ ; 2024AFB652//Natural Science Foundation of Hubei Province/ ; 2022CFB736//Natural Science Foundation of Hubei Province/ ; },
mesh = {*Biocompatible Materials/chemistry ; *Silk/chemistry ; Humans ; Animals ; Prostheses and Implants ; Brain-Computer Interfaces ; Electronics, Medical/instrumentation ; },
abstract = {Implantable medical electronic devices (IMEDs) have attracted great attention and shown versatility for solving clinical problems ranging from real-time monitoring of physiological/ pathological states to electrical stimulation therapy and from monitoring brain cell activity to deep brain stimulation. The ongoing challenge is to select appropriate materials in target device configuration for biomedical applications. Currently, silk-based biomaterials have been developed for the design of diagnostic and therapeutic electronic devices due to their excellent properties and abundant active sites in the structure. Herein, the aim is to summarize the structural characteristics, physicochemical properties, and bioactivities of natural silk biomaterials as well as their derived materials, with a particular focus on the silk-based implantable biomedical electronic devices, such as implantable devices for invasive brain-computer interfaces, neural recording, and in vivo electrostimulation. In addition, future opportunities and challenges are also envisioned, hoping to spark the interests of researchers in interdisciplinary fields such as biomaterials, clinical medicine, and electronics.},
}
@article {pmid39686393,
year = {2024},
author = {Zhuang, W and Zhang, Y and Wang, Y and He, K},
title = {3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686393},
issn = {1424-8220},
mesh = {Humans ; *Neural Networks, Computer ; Learning/physiology ; Students ; Emotions/physiology ; Deep Learning ; Machine Learning ; },
abstract = {Evaluating students' learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students' learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.},
}
@article {pmid39686227,
year = {2024},
author = {Megalingam, RK and Sankardas, KS and Manoharan, SK},
title = {An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686227},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Artifacts ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Motion ; Signal Processing, Computer-Assisted ; Wheelchairs ; Imagination/physiology ; },
abstract = {Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain-computer interface (BCI) systems, utilizing EEG signals from motor imagery tasks. BCI is very useful for people with severe mobility issues like quadriplegics, spinal cord injury patients, stroke patients, etc., giving them the freedom to a certain extent to perform activities without the need for a caretaker, like driving a wheelchair. However, motion artifacts can significantly affect the quality of EEG recordings. The conventional EEG enhancement algorithms are effective in removing ocular and muscle artifacts for a stationary subject but not as effective when the subject is in motion, e.g., a wheelchair user. In this research study, we propose an empirical error model-based artifact removal approach for the cross-subject classification of motor imagery (MI) EEG data using a modified CNN-based deep learning algorithm, designed to assist wheelchair users with severe mobility issues. The classification method applies to real tasks with measured EEG data, focusing on accurately interpreting motor imagery signals for practical application. The empirical error model evolved from the inertial sensor-based acceleration data of the subject in motion, the weight of the wheelchair, the weight of the subject, and the surface friction of the terrain under the wheelchair. Three different wheelchairs and five different terrains, including road, brick, concrete, carpet, and marble, are used for artifact data recording. After evaluating and benchmarking the proposed CNN and empirical model, the classification accuracy achieved is 94.04% for distinguishing between four specific classes: left, right, front, and back. This accuracy demonstrates the model's effectiveness compared to other state-of-the-art techniques. The comparative results show that the proposed approach is a potentially effective way to raise the decoding efficiency of motor imagery BCI.},
}
@article {pmid39686148,
year = {2024},
author = {Li, LL and Cao, GZ and Zhang, YP and Li, WC and Cui, F},
title = {MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {23},
pages = {},
pmid = {39686148},
issn = {1424-8220},
support = {U1813212//the State Key Program of National Natural Science Foundation of China/ ; 52277061//National Natural Science Foundation of China/ ; JCYJ20220818095804009 and JSGG20200701095406010//Shenzhen Science and Technology Program/ ; 20220809200041001//Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Lower Extremity/physiology ; Imagination/physiology ; Attention/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified.},
}
@article {pmid39685825,
year = {2024},
author = {Villiger, AS and Hoehn, D and Ruggeri, G and Vaineau, C and Nirgianakis, K and Imboden, S and Kuhn, A and Mueller, MD},
title = {Lower Urinary Tract Dysfunction Among Patients Undergoing Surgery for Deep Infiltrating Endometriosis: A Prospective Cohort Study.},
journal = {Journal of clinical medicine},
volume = {13},
number = {23},
pages = {},
pmid = {39685825},
issn = {2077-0383},
abstract = {Background/Objectives: Postsurgical lower urinary tract dysfunction (LUTD) is a common problem following deep infiltrating endometriosis (DIE) resection. The condition may be caused either by surgically induced damage to the bladder innervation or by pre-existing endometriosis-associated nerve damage. The aim of this study is to evaluate the efficacy of preoperative and postoperative multichannel urodynamic testing (UD) in identifying pre-existing or surgically induced LUTD among patients with DIE. Methods: Women with suspected DIE and planned surgical resection of DIE at the Department of Obstetrics and Gynecology at the University Hospital of Bern from September 2015 to October 2022 were invited to participate in this prospective cohort study. UD was performed before and 6 weeks after surgery. The primary outcome was the maximum flow rate (uroflow), an indicator of LUTD. Secondary outcomes were further urodynamic observations of cystometry and pressure flow studies, lower urinary tract symptoms (LUTS) as assessed by the International Prostate Symptom Score (IPSS), and pain as assessed by the visual analog scale (VAS). Results: A total of 51 patients requiring surgery for DIE were enrolled in this study. All patients underwent surgical excision of the DIE. The cohort demonstrated a uroflow of 22.1 mL/s prior to surgery, which decreased postoperatively to 21.5 mL/s (p = 0.56, 95%CI -1.5-2.71). The mean bladder contractility index (BCI) exhibited a notable decline from 130.4 preoperatively to 116.6 postoperatively (p = 0.046, 95%CI 0.23-27.27). Significant improvements were observed in the prevalence of dysmenorrhea, abdominal pain, dyspareunia, and dyschezia following surgical intervention (p = <0.001). The IPSS score was within the lower moderate range both pre- and postoperatively (mean 8.37 vs. 8.51, p = 0.893, 95%CI -2.35-2.05). Subgroup analysis identified previous endometriosis surgery as a significant preoperative risk factor for elevated post-void residual (43.6 mL, p = 0.026, 95%CI 13.89-73.37). The postoperative post-void residual increased among participants with DIE on the rectum to 54.39 mL (p = 0.078, 95%CI 24.06-84.71). Participants who underwent hysterectomy exhibited a significantly decreased uroflow (16.4 mL/s, p = 0.014, 95%CI 12-20) and BCI (75.1, p = 0.036, 95%CI 34.9-115.38). Conclusions: Nerve-respecting laparoscopy for DIE may alter bladder function. UD is not advisable before surgery, but the measurement may detect patients with LUTD.},
}
@article {pmid39681925,
year = {2024},
author = {Garrott, K and Ogilvie, D and Panter, J and Petticrew, M and Sowden, A and Jones, CP and Foubister, C and Lawlor, ER and Ikeda, E and Patterson, R and van Tulleken, D and Armstrong-Moore, R and Vethanayakam, G and Bo, L and White, M and Adams, J},
title = {Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions.},
journal = {BMC global and public health},
volume = {2},
number = {1},
pages = {13},
pmid = {39681925},
issn = {2731-913X},
support = {Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; Project 05 - G109750//NIHR Public Health Policy Research Unit/ ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12015/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; MC_UU_00006/7/MRC_/Medical Research Council/United Kingdom ; },
abstract = {BACKGROUND: The 'agentic demand' of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields' ability to systematically explore these associations.<br><br>METHODS: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity-focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in harvest plots.<br><br>RESULTS: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (two levels), mechanism of action - how the intervention enables or discourages behaviour (five levels) and engagement - recipient response (two levels). When combined, these constructs form a matrix of 20 possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness.<br><br>CONCLUSIONS: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis.},
}
@article {pmid39679398,
year = {2024},
author = {Wang, S and Zhang, W and Fu, P and Zhong, Y and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Structural diversity of Alzheimer-related protein aggregations revealed using photothermal ratio-metric micro-spectroscopy.},
journal = {Biomedical optics express},
volume = {15},
number = {12},
pages = {6768-6782},
pmid = {39679398},
issn = {2156-7085},
abstract = {The crucial link between pathological protein aggregations and lipids in Alzheimer's disease pathogenesis is increasingly recognized, yet its spatial dynamics remain challenging for labeling-based microscopy. Here, we demonstrate photothermal ratio-metric infrared spectro-microscopy (PRISM) to investigate the in situ structural and molecular compositions of pathological features in brain tissues at submicron resolution. By identifying the vibrational spectroscopic signatures of protein secondary structures and lipids, PRISM tracks the structural dynamics of pathological proteins, including amyloid and hyperphosphorylated Tau (pTau). Amyloid-associated lipid features in major brain regions were observed, notably the enrichment of lipid-dissociated plaques in the hippocampus. Spectroscopic profiling of pTau revealed significant heterogeneity in phosphorylation levels and a distinct lipid-pTau relationship that contrasts with the anticipated lipid-plaque correlation. Beyond in vitro studies, our findings provide direct visualization evidence of aggregate-lipid interactions across the brain, offering new insights into mechanistic and therapeutic research of neurodegenerative diseases.},
}
@article {pmid39678727,
year = {2024},
author = {Pan, H and Song, W and Li, L and Qin, X},
title = {The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2299-2309},
pmid = {39678727},
issn = {1871-4080},
abstract = {In visual-imagery-based brain-computer interface (VI-BCI), there are problems of singleness of imagination task and insufficient description of feature information, which seriously hinder the development and application of VI-BCI technology in the field of restoring communication. In this paper, we design and optimize a multi-character classification scheme based on electroencephalogram (EEG) signals of visual imagery (VI), which is used to classify 29 characters including 26 lowercase English letters and three punctuation marks. Firstly, a new paradigm of randomly presenting characters and including preparation stage is designed to acquire EEG signals and construct a multi-character dataset, which can eliminate the influence between VI tasks. Secondly, tensor data is obtained by the Morlet wavelet transform, and a feature extraction algorithm based on tensor-uncorrelated multilinear principal component analysis is used to extract high-quality features. Finally, three classifiers, namely support vector machine, K-nearest neighbor, and extreme learning machine, are employed for classifying multi-character, and the results are compared. The experimental results demonstrate that, the proposed scheme effectively extracts character features with minimal redundancy, weak correlation, and strong representation capability, and successfully achieves an average classification accuracy 97.59% for 29 characters, surpassing existing research in terms of both accuracy and quantity of classification. The present study designs a new paradigm for acquiring EEG signals of VI, and combines the Morlet wavelet transform and UMPCA algorithm to extract the character features, enabling multi-character classification in various classifiers. This research paves a novel pathway for establishing direct brain-to-world communication.},
}
@article {pmid39678535,
year = {2024},
author = {Wang, Y and Gong, L and Zhao, Y and Yu, Y and Liu, H and Yang, X},
title = {Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1493264},
pmid = {39678535},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) establish a direct communication pathway between the brain and external devices and have been widely applied in upper limb rehabilitation for hemiplegic patients. However, significant individual variability in motor imagery electroencephalogram (MI-EEG) signals leads to poor generalization performance of MI-based BCI decoding methods to new patients. This paper proposes a Multi-scale Frequency domain Feature-based Dynamic graph Attention Network (MFF-DANet) for upper limb MI decoding in hemiplegic patients. MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. Additionally, MFF-DANet integrates a graph attention convolutional network to capture spatial topological features across different electrode channels, utilizing electrode positions as prior knowledge to construct and update the graph adjacency matrix. We validated the performance of MFF-DANet on the public PhysioNet dataset, achieving optimal decoding accuracies of 61.6% for within-subject case and 52.7% for cross-subject case. t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the features demonstrates the effectiveness of each designed module, and visualization of the adjacency matrix indicates that the extracted spatial topological features have physiological interpretability.},
}
@article {pmid39678531,
year = {2024},
author = {Wang, N and Tu, WJ},
title = {Editorial: Brain-computer interfaces in neurological disorders: expanding horizons for diagnosis, treatment, and rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1526723},
pmid = {39678531},
issn = {1662-4548},
}
@article {pmid39677402,
year = {2024},
author = {Ninenko, I and Medvedeva, A and Efimova, VL and Kleeva, DF and Morozova, M and Lebedev, MA},
title = {Olfactory neurofeedback: current state and possibilities for further development.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1419552},
pmid = {39677402},
issn = {1662-5161},
abstract = {This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing - from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.},
}
@article {pmid39674592,
year = {2025},
author = {Barker, N and Parker, H},
title = {Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures.},
journal = {Medical humanities},
volume = {50},
number = {4},
pages = {657-669},
pmid = {39674592},
issn = {1473-4265},
mesh = {Humans ; *Disabled Persons ; *Sports ; *Self-Help Devices ; Brain-Computer Interfaces ; Robotics ; Anthropology, Cultural ; Politics ; },
abstract = {Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability more present and visible across many (para) sporting cultures, and yet sport continues to operate on a tiered system that assumes a normative able body. This paper responds to this moment by offering imaginaries of future hybrid performances that critically engage with the politics and possibilities of novel technologies in sporting arenas and their wider impact on disability futures. These were generated from a collaborative ethnography that centred on becoming spectators of the Cybathlon Games. The Cybathlon Games began in 2016 as a global event where people with disabilities compete with technologies such as Brain-Computer Interfaces or robotic Prosthesis. Our imaginings are presented as three speculative fragments in the form of pages ripped from a comic book series, The In/Visibles These fragments and critical reflections are grounded on themes generated through watching the Games together. The purpose of this paper is not to offer predictions or even visions of desirable futures. Rather we present future technologised sporting bodies and spectacles with a view to extend critical posthuman discussions to these arenas. Through this we highlight: (1) The arbitrariness of where to draw the between un/natural performances; (2) The absurdities of unrestricted and open use of performance technologies when hybrid forms and functions are judged through current sporting-humanist values; and (3) The need to stay alert to socioeconomic and political drivers of sporting and disability futures. We offer these three zones of friction to guide further research when navigating the complex and shifting relations between sport, technology and the (dis)abled body now and into the future.},
}
@article {pmid39656892,
year = {2025},
author = {Guo, D and Yao, B and Shao, WW and Zuo, JC and Chang, ZH and Shi, JX and Hu, N and Bao, SQ and Chen, MM and Fan, X and Li, XH},
title = {The Critical Role of YAP/BMP/ID1 Axis on Simulated Microgravity-Induced Neural Tube Defects in Human Brain Organoids.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {12},
number = {5},
pages = {e2410188},
pmid = {39656892},
issn = {2198-3844},
support = {2021YFF1200800//National Key Research and Development Program of China/ ; 82171861//National Natural Science Foundation of China/ ; 81971782//National Natural Science Foundation of China/ ; 82101448//National Natural Science Foundation of China/ ; },
mesh = {*Organoids/metabolism ; Humans ; *Brain/metabolism/pathology ; Neural Tube/metabolism ; Weightlessness Simulation/adverse effects ; Bone Morphogenetic Proteins/metabolism/genetics ; Signal Transduction ; Weightlessness/adverse effects ; Transcription Factors/genetics/metabolism ; },
abstract = {Integrated biochemical and biophysical signals regulate embryonic development. Correct neural tube formation is critical for the development of central nervous system. However, the role of microgravity in neurodevelopment and its underlying molecular mechanisms remain unclear. In this study, the effects of stimulated microgravity (SMG) on the development of human brain organoids are investigated. SMG impairs N-cadherin-based adherens junction formation, leading to neural tube defects associated with dysregulated self-renewal capacity and neuroepithelial disorganization in human brain organoids. Bulk gene expression analyses reveal that SMG alters Hippo and BMP signaling in brain organoids. The neuropathological deficits in SMG-treated organoids can be rescued by regulating YAP/BMP/ID1 axis. Furthermore, sing-cell RNA sequencing data show that SMG results in perturbations in the number and function of neural stem and progenitor cell subpopulations. One of these subpopulations senses SMG cues and transmits BMP signals to the subpopulation responsible for tube morphogenesis, ultimately affecting the proliferating cell population. Finally, SMG intervention leads to persistent neurologic damage even after returning to normal gravity conditions. Collectively, this study reveals molecular and cellular abnormalities associated with SMG during human brain development, providing opportunities for countermeasures to maintain normal neurodevelopment in space.},
}
@article {pmid39672531,
year = {2025},
author = {Li, K and Qian, L and Zhang, C and Zhang, J and Xue, C and Zhang, Y and Deng, W},
title = {The entorhinal cortex and cognitive impairment in schizophrenia: A comprehensive review.},
journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry},
volume = {136},
number = {},
pages = {111218},
doi = {10.1016/j.pnpbp.2024.111218},
pmid = {39672531},
issn = {1878-4216},
mesh = {Humans ; *Schizophrenia/physiopathology/complications ; *Entorhinal Cortex/pathology/physiopathology ; *Cognitive Dysfunction/physiopathology/etiology ; Animals ; Schizophrenic Psychology ; },
abstract = {Schizophrenia, a severe mental illness characterized by cognitive impairment and olfactory dysfunction, remains an enigma with its pathological mechanism yet to be fully elucidated. The entorhinal cortex, a pivotal structure involved in numerous neural loop circuits related to olfaction, cognition, and emotion, has garnered significant attention due to its structural and functional abnormalities, which have been implicated in the pathogenesis of schizophrenia. This review focuses on the abnormal structural and functional changes in the entorhinal cortex in schizophrenia patients, as evidenced by neuroimaging, cellular biology, and genetic studies. These changes are posited to play a crucial role in the pathogenesis of cognitive impairment in schizophrenia. Furthermore, this review explores the various intervention strategies targeting the entorhinal cortex in current treatment modalities and proposes potential directions for future research endeavors, thereby providing a novel perspective on unraveling the complexity of neural mechanisms underlying schizophrenia and developing innovative therapeutic approaches for schizophrenia.},
}
@article {pmid39672015,
year = {2025},
author = {Hameed, I and Khan, DM and Ahmed, SM and Aftab, SS and Fazal, H},
title = {Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.},
journal = {Computers in biology and medicine},
volume = {185},
number = {},
pages = {109534},
doi = {10.1016/j.compbiomed.2024.109534},
pmid = {39672015},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Machine Learning ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Imagination/physiology ; Deep Learning ; },
abstract = {This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currently, the most used non-invasive method for measuring brain activity is the EEG, due to its high temporal resolution, user-friendliness, and safety. A Brain Computer Interface (BCI) framework can be made using these signals which can provide a new communication channel to people that are suffering from motor disabilities or other neurological disorders. However, implementing EEG-based BCI systems in real-world scenarios for motor imagery recognition presents challenges, primarily due to the inherent variability among individuals and low signal-to-noise ratio (SNR) of EEG signals. To assist researchers in navigating this complex problem, a comprehensive review article is presented, summarizing the key findings from relevant studies since 2017. This review primarily focuses on the datasets, preprocessing methods, feature extraction techniques, and deep learning models employed by various researchers. This review aims to contribute valuable insights and serve as a resource for researchers, practitioners, and enthusiasts interested in the combination of neuroscience and deep learning, ultimately hoping to contribute to advancements that bridge the gap between the human mind and machine interfaces.},
}
@article {pmid39671976,
year = {2025},
author = {Brooks, KA and Kolousek, A and Holman, EK and Evans, SS and Govil, N and Alfonso, KP},
title = {MED-EL Bonebridge implantation in pediatric patients age 11 Years and younger: Is it safe and effective?.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {188},
number = {},
pages = {112198},
doi = {10.1016/j.ijporl.2024.112198},
pmid = {39671976},
issn = {1872-8464},
mesh = {Humans ; Child ; Retrospective Studies ; Female ; Male ; *Bone Conduction ; Adolescent ; Treatment Outcome ; Child, Preschool ; Age Factors ; Hearing Loss, Conductive/surgery ; Bone-Anchored Prosthesis ; Cohort Studies ; Hearing Aids ; Speech Perception ; Prosthesis Implantation/adverse effects/methods ; },
abstract = {OBJECTIVE: To present our experience with off-label MED-EL Bonebridge implantation in pediatric patients younger than 12 years of age and compare outcomes to pediatric patients 12 years and older.<br><br>METHODS: Pediatric patients who underwent Bonebridge implantation were included in a retrospective cohort study and were categorized by off-label use (<12 years) and &#x2265;12 years at time of bone conduction implantation (BCI). Hearing outcomes were collected after implant activation, which was typically 4-8 weeks post-implantation. Mann-Whitney U tests were performed to assess for differences between audiometric outcomes. Significance was set at p&#xa0;<&#xa0;0.05.<br><br>RESULTS: Twenty patients (25 implants)&#xa0;<&#xa0;12 years of age and 17 patients (23 implants) &#x2265;12 years of age underwent BCI. Pre-BCI speech recognition threshold (SRT) was better for the older patient group (median 50&#xa0;dB) than the younger patient group (median 60&#xa0;dB). Post-BCI SRT, however, was significantly lower in the younger patient group (median 22.5&#xa0;dB) as compared to the older patient group (median 35&#xa0;dB), (p&#xa0;<&#xa0;0.001, Z&#xa0;=&#xa0;3.1). The two groups performed similarly on age-appropriate wordlists presented at 50&#xa0;dB HL in aided conditions (p&#xa0;>&#xa0;0.05, -1<Z&#xa0;<&#xa0;1). Postoperative complication rate was low for both age groups (9&#xa0;% vs 8&#xa0;%) and operative time was similar for both groups of patients (p=0.96, Z=0.52).<br><br>CONCLUSION: Pediatric patients younger than 12 years showed similar or better audiometric benefit from off-label Bonebridge implantation when compared to older patients. Pediatric patients younger than 12 years can be considered Bonebridge implant candidates if clinically indicated; Bonebridge implantation in this age group appears safe and technically feasible.},
}
@article {pmid39671798,
year = {2024},
author = {Daly, I and Williams, N and Nasuto, SJ},
title = {TMS-evoked potential propagation reflects effective brain connectivity.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9ee0},
pmid = {39671798},
issn = {1741-2552},
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Brain/physiology ; Young Adult ; Nerve Net/physiology ; Evoked Potentials/physiology ; Feedback, Sensory/physiology ; Connectome/methods ; Evoked Potentials, Motor/physiology ; Psychomotor Performance/physiology ; },
abstract = {Objective.Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity.Approach.To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling.Main results.Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence.Significance.Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback.},
}
@article {pmid39671787,
year = {2025},
author = {H Liu, D and Hsieh, JC and Alawieh, H and Kumar, S and Iwane, F and Pyatnitskiy, I and Ahmad, ZJ and Wang, H and Millán, JDR},
title = {Novel AIRTrode-based wearable electrode supports long-term, online brain-computer interface operations.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad9edf},
pmid = {39671787},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Electrodes ; *Wearable Electronic Devices ; Adult ; Female ; Young Adult ; Online Systems ; },
abstract = {Objective.Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, holding potential promise to benefit users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.Approach.In this study, four healthy participants utilized AIRTrodes in two BCI control tasks-continuous and discrete-across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).Main Results.Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.Significance.AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.},
}
@article {pmid39671281,
year = {2024},
author = {Ghodrati, MT and Aghababaei, S and Mirfathollahi, A and Shalchyan, V and Zarrindast, MR and Daliri, MR},
title = {Protocol for state-based decoding of hand movement parameters using neural signals.},
journal = {STAR protocols},
volume = {5},
number = {4},
pages = {103503},
pmid = {39671281},
issn = {2666-1667},
mesh = {Humans ; *Hand/physiology ; *Movement/physiology ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Somatosensory Cortex/physiology ; },
abstract = {We present a protocol for decoding kinematic and kinetic parameters from the primary somatosensory cortex during active and passive hand movements in a center-out reaching task using state-based and conventional decoders. We describe steps for preparing data and using the state-based model to classify movement directions into states via feature extraction and predict parameters with regression models (partial least squares and multilinear regression) trained per state. This state-based approach outperforms conventional methods, enhancing accuracy for brain-computer interface applications. For complete details on the use and execution of this protocol, please refer to Mirfathollahi et al.[1].},
}
@article {pmid39670475,
year = {2024},
author = {Xie, S and He, C},
title = {An empirical study on native Mandarin-speaking children's metonymy comprehension development.},
journal = {Journal of child language},
volume = {},
number = {},
pages = {1-28},
doi = {10.1017/S0305000924000539},
pmid = {39670475},
issn = {1469-7602},
abstract = {This study investigates Mandarin-speaking children's (age 3-7) comprehension development of novel and conventional metonymy, combining online and offline methods. Both online and offline data show significantly better performances from the oldest group (6-to-7-year-old) and a delayed acquisition of conventional metonymy compared with novel metonymy. However, part of offline data shows no significant difference between adjacent age groups, while the eye-tracking data show a chronological development from age 3-7. Furthermore, in offline tasks, the three-year-old group features a high choice randomness and the four-to-five-year-olds show the longest reaction time. Therefore, we argue that, not only age but also metonymy type can influence metonymy acquisition, and that a lack of socio-cultural experience can be a source of acquisition difficulty for children under six. Methodologically speaking, we believe that online methods should not be considered superior to offline ones as they investigate different aspects of implicit and explicit language comprehension.},
}
@article {pmid39669979,
year = {2024},
author = {Nakamura, D and Kaji, S and Kanai, R and Hayashi, R},
title = {Unsupervised method for representation transfer from one brain to another.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1470845},
pmid = {39669979},
issn = {1662-5196},
abstract = {Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. Therefore, appropriate conversion and translation of brain information is essential when decoding neural information using a model trained using another person's data or to achieving brain-to-brain communication. We propose a brain representation transfer method that involves transforming a data representation obtained from one person's brain into that obtained from another person's brain, without relying on corresponding label information between the transferred datasets. We defined the requirements to enable such brain representation transfer and developed an algorithm that distills the assumption of common similarity structure across the brain datasets into a rotational and reflectional transformation across low-dimensional hyperspheres using encoders for non-linear dimensional reduction. We first validated our proposed method using data from artificial neural networks as substitute neural activity and examining various experimental factors. We then evaluated the applicability of our method to real brain activity using functional magnetic resonance imaging response data acquired from human participants. The results of these validation experiments showed that our method successfully performed representation transfer and achieved transformations in some cases that were similar to those obtained when using corresponding label information. Additionally, we reconstructed images from individuals' data without training personalized decoders by performing brain representation transfer. The results suggest that our unsupervised transfer method is useful for the reapplication of existing models personalized to specific participants and datasets to decode brain information from other individuals. Our findings also serve as a proof of concept for the methodology, enabling the exchange of the latent properties of neural information representing individuals' sensations.},
}
@article {pmid39669288,
year = {2024},
author = {Nejatbakhsh, A and Fumarola, F and Esteki, S and Toyoizumi, T and Kiani, R and Mazzucato, L},
title = {Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.},
journal = {Physical review research},
volume = {5},
number = {4},
pages = {},
pmid = {39669288},
issn = {2643-1564},
support = {R01 DA055439/DA/NIDA NIH HHS/United States ; R01 MH109180/MH/NIMH NIH HHS/United States ; R01 MH127375/MH/NIMH NIH HHS/United States ; R01 NS118461/NS/NINDS NIH HHS/United States ; },
abstract = {A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream with high efficacy, a procedure currently achieved by labor-intensive and potentially harmful trial and error. Can one predict the effects of electrical stimulation on neural activity based on the circuit dynamics during spontaneous periods? Here we show that the effects of single-site micro-stimulation on ensemble activity in an alert monkey's prefrontal cortex can be predicted solely based on the ensemble's spontaneous activity. We first inferred the ensemble's causal flow based on the directed functional interactions inferred during spontaneous periods using convergent cross-mapping and showed that it uncovers a causal hierarchy between the recording electrodes. We find that causal flow inferred at rest successfully predicts the spatiotemporal effects of micro-stimulation. We validate the computational features underlying causal flow using ground truth data from recurrent neural network models, showing that it is robust to noise and common inputs. A detailed comparison between convergent-cross mapping and alternative methods based on information theory reveals the advantages of the former method in predicting perturbation effects. Our results elucidate the causal interactions within neural ensembles and will facilitate the design of intervention protocols and targeted circuit manipulations suitable for brain-machine interfaces.},
}
@article {pmid39667504,
year = {2025},
author = {Cao, HL and Wei, W and Meng, YJ and Tao, YJ and Yang, X and Li, T and Guo, WJ},
title = {Association of altered cortical gyrification and working memory in male early abstinent alcohol-dependent individuals.},
journal = {Brain research bulletin},
volume = {220},
number = {},
pages = {111166},
doi = {10.1016/j.brainresbull.2024.111166},
pmid = {39667504},
issn = {1873-2747},
mesh = {Humans ; Male ; *Memory, Short-Term/physiology ; *Alcoholism/pathology/diagnostic imaging/physiopathology ; Adult ; *Magnetic Resonance Imaging/methods ; *Cerebral Cortex/diagnostic imaging/pathology/physiopathology ; Middle Aged ; Neuropsychological Tests ; Alcohol Abstinence ; },
abstract = {BACKGROUND: Alcohol dependence (AD) is an addictive disorder with multifaceted neurobiological features. Recent research on the pathophysiological mechanisms of AD has emphasized the important role of dysconnectivity. Cortical gyrification is known to be a reliable marker of neural connectivity. This study aimed to explore cortical gyrification using the local gyrification index (LGI) between alcohol-dependent patients and controls.<br><br>METHODS: Magnetic resonance images were collected from 60 early abstinent patients with AD (5-12 days after stopping alcohol consumption) and 59 controls and preprocessed using FreeSurfer, followed by surface-based morphometry (SBM) analysis to compare the LGI between the two groups. Cognitive performance was assessed using the Spatial Working Memory (SWM) test in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The relationship between LGI, cognitive performance, and clinical variables was also explored in the patient group.<br><br>RESULTS: Compared with controls, patients with AD exhibited significantly decreased LGI in several regions, including the postcentral gyrus, precentral gyrus, middle frontal, superior temporal, middle temporal, insula, superior parietal, and inferior parietal cortex. AD patients did worse than controls in several SWM measures. Furthermore, decreased LGI in the left postcentral was negatively correlated with working memory performance after multiple comparison corrections in the patient group.<br><br>CONCLUSION: Alcohol-dependent individuals exhibit abnormal patterns of cortical gyrification, which may be underlying neurobiological markers of AD. Our findings further indicate that working memory deficits may be related to abnormalities in cortical gyrification in alcohol addiction.},
}
@article {pmid39667216,
year = {2025},
author = {Sun, P and De Winne, J and Zhang, M and Devos, P and Botteldooren, D},
title = {Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {107003},
doi = {10.1016/j.neunet.2024.107003},
pmid = {39667216},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; Humans ; *Attention/physiology ; *Brain-Computer Interfaces ; Acoustic Stimulation/methods ; Photic Stimulation/methods ; Brain/physiology ; Auditory Perception/physiology ; Neurons/physiology ; Visual Perception/physiology ; Neural Networks, Computer ; },
abstract = {Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset. This framework extracts patterns from audiovisual stimuli to model brain responses in EEG signals, while accounting for inherent response delays. By aligning audiovisual features with EEG signals through a shared embedding space, our approach improves the performance of brain-computer interface (BCI) systems. We also present WithMeAttention, a multimodal dataset designed to facilitate research in continuously distinguishing between target and distractor responses. Our methodology demonstrates a 3% improvement in accuracy on the WithMeAttention dataset compared to a baseline model that decodes EEG signals from scratch. This significant performance increase highlights the effectiveness of our approach Comprehensive analysis across four distinct conditions shows that rhythmic enhancement of visual information can optimize multi-sensory information processing. Notably, the two conditions featuring rhythmic target presentation - with and without accompanying beeps - achieved significantly superior performance compared to other scenarios. Furthermore, the delay distribution observed under different conditions indicates that our delay layer effectively emulates the neural processing delays in response to stimuli.},
}
@article {pmid39667215,
year = {2025},
author = {Lan, Y and Wang, Y and Zhang, Y and Zhu, H},
title = {Low-power and lightweight spiking transformer for EEG-based auditory attention detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {183},
number = {},
pages = {106977},
doi = {10.1016/j.neunet.2024.106977},
pmid = {39667215},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; *Attention/physiology ; Humans ; *Neural Networks, Computer ; Auditory Perception/physiology ; Acoustic Stimulation/methods ; Neurons/physiology ; Action Potentials/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; Models, Neurological ; },
abstract = {EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms such as cognition, emotion, and behavior. EEG-based auditory attention detection is using EEG signals to determine an individual's level of attention to specific auditory stimuli. In this technique, researchers record and analyze a subject's electrical activity to infer whether an individual is paying attention to a specific auditory stimulus. The model deployed in edge devices will be greatly convenient for subjects to use. However, most of the existing EEG-based auditory attention detection models use traditional neural network models, and their high computing load makes deployment on edge devices challenging. We present a pioneering approach in the form of a binarized spiking Transformer for EEG-based auditory attention detection, which is characterized by high accuracy, low power consumption, and lightweight design, making it highly suitable for deployment on edge devices. In terms of low power consumption, the network is constructed using spiking neurons, which emit sparse and binary spike sequences, which can effectively reduce computing power consumption. In terms of lightweight, we use a post-training quantization strategy to quantize the full-precision network weights into binary weights, which greatly reduces the model size. In addition, the structure of the Transformer ensures that the model can learn effective information and ensure its high performance. We verify the model through mainstream datasets, and experimental results show that our model performance can exceed the existing state-of-the-art models, and the model size can be reduced by more than 21 times compared with the original full-precision network counterpart.},
}
@article {pmid39665789,
year = {2024},
author = {Tang, C and Wang, P and Li, Z and Zhong, S and Yang, L and Li, G},
title = {Neural functional rehabilitation: exploring neuromuscular reconstruction technology advancements and challenges.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-24-00613},
pmid = {39665789},
issn = {1673-5374},
abstract = {Neural machine interface technology is a pioneering approach that aims to address the complex challenges of neurological dysfunctions and disabilities resulting from conditions such as congenital disorders, traumatic injuries, and neurological diseases. Neural machine interface technology establishes direct connections with the brain or peripheral nervous system to restore impaired motor, sensory, and cognitive functions, significantly improving patients' quality of life. This review analyzes the chronological development and integration of various neural machine interface technologies, including regenerative peripheral nerve interfaces, targeted muscle and sensory reinnervation, agonist-antagonist myoneural interfaces, and brain-machine interfaces. Recent advancements in flexible electronics and bioengineering have led to the development of more biocompatible and high-resolution electrodes, which enhance the performance and longevity of neural machine interface technology. However, significant challenges remain, such as signal interference, fibrous tissue encapsulation, and the need for precise anatomical localization and reconstruction. The integration of advanced signal processing algorithms, particularly those utilizing artificial intelligence and machine learning, has the potential to improve the accuracy and reliability of neural signal interpretation, which will make neural machine interface technologies more intuitive and effective. These technologies have broad, impactful clinical applications, ranging from motor restoration and sensory feedback in prosthetics to neurological disorder treatment and neurorehabilitation. This review suggests that multidisciplinary collaboration will play a critical role in advancing neural machine interface technologies by combining insights from biomedical engineering, clinical surgery, and neuroengineering to develop more sophisticated and reliable interfaces. By addressing existing limitations and exploring new technological frontiers, neural machine interface technologies have the potential to revolutionize neuroprosthetics and neurorehabilitation, promising enhanced mobility, independence, and quality of life for individuals with neurological impairments. By leveraging detailed anatomical knowledge and integrating cutting-edge neuroengineering principles, researchers and clinicians can push the boundaries of what is possible and create increasingly sophisticated and long-lasting prosthetic devices that provide sustained benefits for users.},
}
@article {pmid39664295,
year = {2024},
author = {Chen, LX and Zhang, MD and Xu, HF and Ye, HQ and Chen, DF and Wang, PS and Bao, ZW and Zou, SM and Lv, YT and Wu, ZY and Li, HF},
title = {Single-Nucleus RNA Sequencing Reveals the Spatiotemporal Dynamics of Disease-Associated Microglia in Amyotrophic Lateral Sclerosis.},
journal = {Research (Washington, D.C.)},
volume = {7},
number = {},
pages = {0548},
pmid = {39664295},
issn = {2639-5274},
abstract = {Disease-associated microglia (DAM) are observed in neurodegenerative diseases, demyelinating disorders, and aging. However, the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis (ALS) remain unclear. Using a mouse model of ALS that expresses a human SOD1 gene mutation, we found that the microglia subtype DAM begins to appear following motor neuron degeneration, primarily in the brain stem and spinal cord. Using reverse transcription quantitative polymerase chain reaction, RNAscope in situ hybridization, and flow cytometry, we found that DAM increased in number as the disease progressed, reaching their peak in the late disease stage. DAM responded to disease progression in both SOD1[G93A] mice and sporadic ALS and C9orf72-mutated patients. Motor neuron loss in SOD1[G93A] mice exhibited 2 accelerated phases: P90 to P110 (early stage) and P130 to P150 (late stage). Some markers were synchronized with the accelerated phase of motor neuron loss, suggesting that these proteins may be particularly responsive to disease progression. Through pseudotime trajectory analysis, we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia. Interestingly, we used the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX5622 to deplete microglia in SOD1[G93A] mice and observed that DAM survival is independent of CSF1R. An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes. These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS. Inducing the DAM phenotype and enhancing its function during the early phase of disease progression, or the time window between P130 and P150 where motor neuron loss slows, could serve as a neuroprotective strategy for ALS.},
}
@article {pmid39663729,
year = {2025},
author = {Qin, Y and Zhao, H and Chang, Q and Liu, Y and Jing, Z and Yu, D and Mugo, SM and Wang, H and Zhang, Q},
title = {Amylopectin-based Hydrogel Probes for Brain-machine Interfaces.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {37},
number = {6},
pages = {e2416926},
doi = {10.1002/adma.202416926},
pmid = {39663729},
issn = {1521-4095},
support = {22377122//National Natural Science Foundation of China/ ; U22A20183//National Natural Science Foundation of China/ ; SKL202402018//Jilin Province Science and Technology Development Plan/ ; 029GJHZ2024038FN//International Partnership Program of the Chinese Academy of Sciences/ ; },
mesh = {Animals ; *Hydrogels/chemistry ; *Brain-Computer Interfaces ; Rats ; *Amylopectin/chemistry ; Motor Cortex/physiology ; Stroke ; Polymers/chemistry ; Rats, Sprague-Dawley ; Biocompatible Materials/chemistry/pharmacology ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Brain ; },
abstract = {Implantable neural probes hold promise for acquiring brain data, modulating neural circuits, and treating various brain disorders. However, traditional implantable probes face significant challenges in practical applications, such as balancing sensitivity with biocompatibility and the difficulties of in situ neural information monitoring and neuromodulation. To address these challenges, this study developed an implantable hydrogel probe capable of recording neural signals, modulating neural circuits, and treating stroke. Amylopectin is integrated into the hydrogels, which can induce reorientation of the poly(3,4-ethylenedioxythiophene) (PEDOT) chain and create compliant interfaces with brain tissues, enhancing both sensitivity and biocompatibility. The hydrogel probe shows the capability of continuously recording deep brain signals for 8 weeks. The hydrogel probe is effectively utilized to study deep brain signals associated with various physiological activities. Neuromodulation and neural signal monitoring are performed directly in the primary motor cortex of rats, enabling control over their limb behaviors through evoked signals. When applied to the primary motor cortex of stroke-affected rats, neuromodulation significantly reduced the brain infarct area, promoted synaptic reorganization, and restored motor functions and balance. This research represents a significant scientific breakthrough in the design of neural probes for brain monitoring, neural circuit modulation, and the development of brain disease therapies.},
}
@article {pmid39662472,
year = {2025},
author = {Xin, Q and Zheng, D and Zhou, T and Xu, J and Ni, Z and Hu, H},
title = {Deconstructing the neural circuit underlying social hierarchy in mice.},
journal = {Neuron},
volume = {113},
number = {3},
pages = {444-459.e7},
doi = {10.1016/j.neuron.2024.11.007},
pmid = {39662472},
issn = {1097-4199},
mesh = {Animals ; Mice ; *Hierarchy, Social ; *Prefrontal Cortex/physiology ; *Neural Pathways/physiology ; Male ; *Neurons/physiology ; *Periaqueductal Gray/physiology ; Optogenetics ; Mice, Inbred C57BL ; Basolateral Nuclear Complex/physiology ; Dorsal Raphe Nucleus/physiology ; Proto-Oncogene Proteins c-fos/metabolism ; },
abstract = {Social competition determines hierarchical social status, which profoundly influences animals' behavior and health. The dorsomedial prefrontal cortex (dmPFC) plays a fundamental role in regulating social competitions, but it was unclear how the dmPFC orchestrates win- and lose-related behaviors through its downstream neural circuits. Here, through whole-brain c-Fos mapping, fiber photometry, and optogenetics- or chemogenetics-based manipulations, we identified anatomically segregated win- and lose-related neural pathways downstream of the dmPFC in mice. Specifically, layer 5 neurons projecting to the dorsal raphe nucleus (DRN) and periaqueductal gray (PAG) promote social competition, whereas layer 2/3 neurons projecting to the anterior basolateral amygdala (aBLA) suppress competition. These two neuronal populations show opposite changes in activity during effortful pushes in competition. In vivo and in vitro electrophysiology recordings revealed inhibition from the lose-related pathway to the win-related pathway. Such antagonistic interplay may represent a central principle in how the mPFC orchestrates complex behaviors through top-down control.},
}
@article {pmid39661668,
year = {2024},
author = {Fu, P and Zhang, Y and Wang, S and Ye, X and Wu, Y and Yu, M and Zhu, S and Lee, HJ and Zhang, D},
title = {INSPIRE: Single-beam probed complementary vibrational bioimaging.},
journal = {Science advances},
volume = {10},
number = {50},
pages = {eadm7687},
pmid = {39661668},
issn = {2375-2548},
mesh = {*Spectrum Analysis, Raman/methods ; *Vibration ; Humans ; *Molecular Imaging/methods ; Animals ; Mice ; Spectrophotometry, Infrared/methods ; },
abstract = {Molecular spectroscopy provides intrinsic contrast for in situ chemical imaging, linking the physiochemical properties of biomolecules to the functions of living systems. While stimulated Raman imaging has found successes in deciphering biological machinery, many vibrational modes are Raman inactive or weak, limiting the broader impact of the technique. It can potentially be mitigated by the spectral complementarity from infrared (IR) spectroscopy. However, the vastly different optical windows make it challenging to develop such a platform. Here, we introduce in situ pump-probe IR and Raman excitation (INSPIRE) microscopy, a nascent cross-modality spectroscopic imaging approach by encoding the ultrafast Raman and the IR photothermal relaxation into a single probe beam for simultaneous detection. INSPIRE inherits the merits of complementary modalities and demonstrates high-content molecular imaging of chemicals, cells, tissues, and organisms. Furthermore, INSPIRE applies to label-free and molecular tag imaging, offering possibilities for optical sensing and imaging in biomedicine and materials science.},
}
@article {pmid39661515,
year = {2024},
author = {Chen, Z and Tang, S and Xiao, X and Hong, Y and Fu, B and Li, X and Shao, Y and Chen, L and Yuan, D and Long, Y and Wang, H and Hong, H},
title = {Adiponectin receptor 1-mediated basolateral amygdala-prelimbic cortex circuit regulates methamphetamine-associated memory.},
journal = {Cell reports},
volume = {43},
number = {12},
pages = {115074},
doi = {10.1016/j.celrep.2024.115074},
pmid = {39661515},
issn = {2211-1247},
mesh = {Animals ; *Methamphetamine/pharmacology ; *Memory/drug effects/physiology ; *Receptors, Adiponectin/metabolism ; *Basolateral Nuclear Complex/metabolism/drug effects ; Male ; Mice ; *Mice, Inbred C57BL ; Neurons/metabolism/drug effects ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Signal Transduction/drug effects ; Reward ; AMP-Activated Protein Kinases/metabolism ; Piperidines ; },
abstract = {The association between drug-induced rewards and environmental cues represents a promising strategy to address addiction. However, the neural networks and molecular mechanisms orchestrating methamphetamine (MA)-associated memories remain incompletely characterized. In this study, we demonstrated that AdipoRon (AR), a specific adiponectin receptor (AdipoR) agonist, inhibits the formation of MA-induced conditioned place preference (CPP) in MA-conditioned mice, accompanied by suppression of basolateral amygdala (BLA) CaMKIIα neuron activity. Furthermore, we identified an association between the excitatory circuit from the BLA to the prelimbic cortex (PrL) and the integration of MA-induced rewards with environmental cues. We also determined that the phosphorylated AMPK (p-AMPK)/Cav1.3 signaling pathway mediates the modulatory effects of AdipoR1 in PrL-projecting BLA CaMKIIα neurons on the formation of MA reward memories, a process influenced by physical exercise. These findings highlight the critical function of AdipoR1 in the BLA[CaMKIIα]→PrL[CaMKIIα] circuit in regulating MA-related memory formation, suggesting a potential target for managing MA use disorders.},
}
@article {pmid39660042,
year = {2024},
author = {Kojima, S and Kanoh, S},
title = {Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1461960},
pmid = {39660042},
issn = {1662-5161},
abstract = {INTRODUCTION: The ASME (stands for Auditory Stream segregation Multiclass ERP) paradigm is proposed and used for an auditory brain-computer interface (BCI). In this paradigm, a sequence of sounds that are perceived as multiple auditory streams are presented simultaneously, and each stream is an oddball sequence. The users are requested to focus selectively on deviant stimuli in one of the streams, and the target of the user attention is detected by decoding event-related potentials (ERPs). To achieve multiclass ASME BCI, the number of streams must be increased. However, increasing the number of streams is not easy because of a person's limited audible frequency range. One method to achieve multiclass ASME with a limited number of streams is to increase the target stimuli in a single stream.<br><br>METHODS: Two approaches for the ASME paradigm, ASME-4stream (four streams with a single target stimulus in each stream) and ASME-2stream (two streams with two target stimuli in each stream) were investigated. Fifteen healthy subjects with no neurological disorders participated in this study. An electroencephalogram was acquired, and ERPs were analyzed. The binary classification and BCI simulation (detecting the target class of the trial out of four) were conducted with the help of linear discriminant analysis, and its performance was evaluated offline. Its usability and workload were also evaluated using a questionnaire.<br><br>RESULTS: Discriminative ERPs were elicited in both paradigms. The average accuracies of the BCI simulations were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). In the ASME-2stream paradigm, the latency and the amplitude of P300 were shorter and larger, the average binary classification accuracy was higher, and the average weighted workload was smaller.<br><br>DISCUSSION: Both four-class ASME paradigms achieved a sufficiently high accuracy (over 80%). The shorter latency and larger amplitude of P300 and the smaller workload indicated that subjects could perform the task confidently and had high usability in ASME-2stream compared to ASME-4stream paradigm. A paradigm with multiple target stimuli in a single stream could create a multiclass ASME BCI with limited streams while maintaining task difficulty. These findings expand the potential for an ASME BCI multiclass extension, offering practical auditory BCI choices for users.},
}
@article {pmid39658529,
year = {2024},
author = {Wang, WW and Ji, SY and Xu, P and Zhang, Y and Zhang, Y},
title = {The future of G protein-coupled receptor therapeutics: Apelin receptor acts as a prototype for the advancement of precision drug design.},
journal = {Clinical and translational medicine},
volume = {14},
number = {12},
pages = {e70120},
pmid = {39658529},
issn = {2001-1326},
support = {//National Natural Science Foundation of China/ ; 2024C03147//Yan Zhang [ZJU]); the ''Pioneer'' and ''Leading Goose'' R&amp;D Program of Zhejiang/ ; //Ministry of Science and Technology/ ; 2021C03039//the Key R&amp;D Projects of Zhejiang Province/ ; 2020R01006//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 82325004//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 92168114//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 32400575//the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; 2024T170783//the China Postdoctoral Science Foundation/ ; GZC20232326//Postdoctoral Fellowship Program of CPSF/ ; },
}
@article {pmid39657314,
year = {2024},
author = {Li, S and Tian, M and Xu, R and Cichocki, A and Jin, J},
title = {Decoding continuous motion trajectories of upper limb from EEG signals based on feature selection and nonlinear methods.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9cc1},
pmid = {39657314},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Nonlinear Dynamics ; *Movement/physiology ; Male ; Female ; Adult ; Algorithms ; Motion ; },
abstract = {Objective.Brain-computer interface (BCI) system has emerged as a promising technology that provides direct communication and control between the human brain and external devices. Among the various applications of BCI, limb motion decoding has gained significant attention due to its potential for patients with motor impairment to regain independence and improve their quality of life. However, the reconstruction of continuous motion trajectories in BCI systems based on electroencephalography (EEG) signals remains a challenge in practical life.Approach.This study investigates the feasibility of applying feature selection and nonlinear regression for decoding motion trajectory from EEG. We propose to fix the time window, select the optimal feature set, and reconstruct the motion trajectory of motor execution tasks using polynomial regression. The proposed approach is validated on a public dataset consisting of EEG and hand position data recorded from 15 subjects. Several methods including ridge regression and multiple linear regression are employed as comparisons.Main results.The cross-validation results show that the proposed reconstructed method has the highest correlation with actual motion trajectories, with an average value of 0.511 ± 0.019 (p< 0.05).Significance.This finding demonstrates the great potential of our approach for real-world motor kinematics BCI applications.},
}
@article {pmid39657294,
year = {2025},
author = {Du, L and Zeng, J and Yu, H and Chen, B and Deng, W and Li, T},
title = {Efficacy of bright light therapy improves outcomes of perinatal depression: A systematic review and meta-analysis of randomized controlled trials.},
journal = {Psychiatry research},
volume = {344},
number = {},
pages = {116303},
doi = {10.1016/j.psychres.2024.116303},
pmid = {39657294},
issn = {1872-7123},
mesh = {Humans ; Female ; *Phototherapy/methods ; Pregnancy ; *Randomized Controlled Trials as Topic ; Depression/therapy ; Pregnancy Complications/therapy ; Depression, Postpartum/therapy ; Outcome Assessment, Health Care ; },
abstract = {The efficacy of bright light therapy (BLT) in the context of perinatal depression remains underexplored. This meta-analysis aimed to systematically assess the effectiveness of BLT among perinatal depression. A comprehensive literature search was performed across several databases, including the Cochrane Central Register of Controlled Trials, PubMed, Embase, CNKI and the clinical trials registry platform, covering the period from the inception of each database up to January 2024. The Cochrane Collaboration's bias assessment tool was employed to evaluate the quality of the included studies. Review Manager 5.3 Software was utilized to conduct the meta-analysis. Six trials, encompassed a total of 167 participants diagnosed with perinatal depression were incorporated quantitative analysis, all of those have been published in English, with no restriction on publication year, and used BLT and dim light therapy (DLT) as intervention. The relative risk (RR) of BLT compared to DLT for perinatal depression is 1.46 (fixed effects model, p = 0.04, 95 % CI = [1.02, 2.10]), indicating a significant improvement in depression outcomes compared to DLT groups. The heterogeneity test yielded an I[2] value of 41 % (p = 0.13), indicated a low degree of heterogeneity. Considering the small sample size, we conducted a sensitivity analysis, found RR increased to 2.33 (fixed effects model, p = 0.001, CI = 1.39-3.92). Cochrane Risk of Bias Tool showed only a single study was deemed high quality. This study indicates a beneficial impact of BLT on perinatal depression, subgroup analysis finds no significant mediation effects of different parameters after sensitivity analyses. It is recommended that future studies with larger samples be conducted to explore the effects of BLT on perinatal depression.},
}
@article {pmid39652971,
year = {2024},
author = {Hinss, MF and Jahanpour, ES and Brock, AM and Roy, RN},
title = {A passive brain-computer interface for operator mental fatigue estimation in monotonous surveillance operations: time-on-task and performance labeling issues.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9bed},
pmid = {39652971},
issn = {1741-2552},
mesh = {Humans ; *Mental Fatigue/psychology/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Female ; Young Adult ; *Electroencephalography/methods ; Machine Learning ; Psychomotor Performance/physiology ; Eye Movements/physiology ; Algorithms ; },
abstract = {Objective: A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue (MF). This makes detecting MF a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT)-the popular method-and performance metrics for labels. Yet, to our knowledge, they have never been directly compared.Approach: This study was designed to address both issues: the use of a realistic task to elicit MF during a monotonous visual search task and the labeling type used for intra-participant fatigue estimation. Over four blocks of 15 min, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines.Main results: The results show that the capability of a machine learning algorithm to detect MF depends less on the input data but rather on how MF is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if MF is estimated based on behavioral performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%).Significance: TOT-based MF estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of MF are neglected.},
}
@article {pmid39652893,
year = {2024},
author = {Noorbasha, SK and Kumar, A},
title = {VME-EFD : A novel framework to eliminate the Electrooculogram artifact from single-channel EEGs.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ad9bb6},
pmid = {39652893},
issn = {2057-1976},
mesh = {Humans ; *Artifacts ; *Electrooculography/methods ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; *Fourier Analysis ; Blinking ; Eye Movements/physiology ; Computer Simulation ; Brain/physiology/diagnostic imaging ; },
abstract = {The diagnosis of neurological disorders often involves analyzing EEG data, which can be contaminated by artifacts from eye movements or blinking (EOG). To improve the accuracy of EEG-based analysis, we propose a novel framework, VME-EFD, which combines Variational Mode Extraction (VME) and Empirical Fourier Decomposition (EFD) for effective EOG artifact removal. In this approach, the EEG signal is first decomposed by VME into two segments: the desired EEG signal and the EOG artifact. The EOG component is further processed by EFD, where decomposition levels are analyzed based on energy and skewness. The level with the highest energy and skewness, corresponding to the artifact, is discarded, while the remaining levels are reintegrated with the desired EEG. Simulations on both synthetic and real EEG datasets demonstrate that VME-EFD outperforms existing methods, with lower RRMSE (0.1358 versus 0.1557, 0.1823, 0.2079, 0.2748), lower ΔPSD in theαband (0.10 ± 0.01 and 0.17 ± 0.04 versus 0.89 ± 0.91 and 0.22 ± 0.19, 1.32 ± 0.23 and 1.10 ± 0.07, 2.86 ± 1.30 and 1.19 ± 0.07, 3.96 ± 0.56 and 2.42 ± 2.48), and higher correlation coefficient (CC: 0.9732 versus 0.9695, 0.9514, 0.8994, 0.8730). The framework effectively removes EOG artifacts and preserves critical EEG features, particularly in theαband, making it highly suitable for brain-computer interface (BCI) applications.},
}
@article {pmid39651250,
year = {2024},
author = {Liang, KF and Kao, JC},
title = {A reinforcement learning based software simulator for motor brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.25.625180},
pmid = {39651250},
issn = {2692-8205},
abstract = {Intracortical motor brain-computer interfaces (BCIs) are expensive and time-consuming to design because accurate evaluation traditionally requires real-time experiments. In a BCI system, a user interacts with an imperfect decoder and continuously changes motor commands in response to unexpected decoded movements. This "closed-loop" nature of BCI leads to emergent interactions between the user and decoder that are challenging to model. The gold standard for BCI evaluation is therefore real-time experiments, which significantly limits the speed and community of BCI research. We present a new BCI simulator that enables researchers to accurately and quickly design BCIs for cursor control entirely in software. Our simulator replaces the BCI user with a deep reinforcement learning (RL) agent that interacts with a simulated BCI system and learns to optimally control it. We demonstrate that our simulator is accurate and versatile, reproducing the published results of three distinct types of BCI decoders: (1) a state-of-the-art linear decoder (FIT-KF), (2) a "two-stage" BCI decoder requiring closed-loop decoder adaptation (ReFIT-KF), and (3) a nonlinear recurrent neural network decoder (FORCE).},
}
@article {pmid39651231,
year = {2024},
author = {Sorrell, E and Wilson, DE and Rule, ME and Yang, H and Forni, F and Harvey, CD and O'Leary, T},
title = {An optical brain-machine interface reveals a causal role of posterior parietal cortex in goal-directed navigation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.11.29.626034},
pmid = {39651231},
issn = {2692-8205},
abstract = {Cortical circuits contain diverse sensory, motor, and cognitive signals, and form densely recurrent networks. This creates challenges for identifying causal relationships between neural populations and behavior. We developed a calcium imaging-based brain-machine interface (BMI) to study the role of posterior parietal cortex (PPC) in controlling navigation in virtual reality. By training a decoder to estimate navigational heading and velocity from PPC activity during virtual navigation, we discovered that mice could immediately navigate toward goal locations when control was switched to BMI. No learning or adaptation was observed during BMI, indicating that naturally occurring PPC activity patterns are sufficient to drive navigational trajectories in real time. During successful BMI trials, decoded trajectories decoupled from the mouse's physical movements, suggesting that PPC activity relates to intended trajectories. Our work demonstrates a role for PPC in navigation and offers a BMI approach for investigating causal links between neural activity and behavior.},
}
@article {pmid39648165,
year = {2024},
author = {Zhang, J and Dong, E and Zhang, Y and Zhang, Y},
title = {Harnessing the power of structure-based design: A new lease on life for cardiovascular drug development with apelin receptor modulators.},
journal = {Clinical and translational medicine},
volume = {14},
number = {12},
pages = {e70116},
pmid = {39648165},
issn = {2001-1326},
support = {2021YFF0501401//from the National Key R&amp;D Program of China/ ; 2018YFA0800501//from the National Key R&amp;D Program of China/ ; 82325004//National Natural Science Foundation of China/ ; 92168114//National Natural Science Foundation of China/ ; 92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 82300286//National Natural Science Foundation of China/ ; 92168113//National Natural Science Foundation of China/ ; 32430051//National Natural Science Foundation of China/ ; 22HHXBSS00007//Haihe Laboratory of Cell Ecosystem Innovation Fund/ ; BX20220023//China Postdoctoral Science Foundation/ ; 2022M720288//China Postdoctoral Science Foundation/ ; },
}
@article {pmid39645364,
year = {2024},
author = {Abuduaini, Y and Pu, Y and Chen, W and Kong, XZ},
title = {Imaging the Unseen: Charting Amygdalar Tau's Link to Affective Symptoms in Preclinical Alzheimer's Disease.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {9},
number = {12},
pages = {1236-1238},
doi = {10.1016/j.bpsc.2024.10.003},
pmid = {39645364},
issn = {2451-9030},
}
@article {pmid39645086,
year = {2025},
author = {Li, S and Zhou, Y and Kong, D and Miao, Y and Guan, N and Gao, G and Jin, J and Ye, H},
title = {A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice.},
journal = {Journal of controlled release : official journal of the Controlled Release Society},
volume = {378},
number = {},
pages = {27-37},
doi = {10.1016/j.jconrel.2024.12.006},
pmid = {39645086},
issn = {1873-4995},
mesh = {Animals ; *Optogenetics/methods ; *Homeostasis ; *Insulin/metabolism ; Humans ; *Blood Glucose/analysis ; *Diabetes Mellitus, Experimental/therapy ; Mice ; Male ; Mice, Inbred C57BL ; Electroencephalography ; HEK293 Cells ; Machine Learning ; },
abstract = {With the global population increasing and the demographic shifting toward an aging society, the number of patients diagnosed with conditions such as peripheral neuropathies resulting from diabetes is expected to rise significantly. This growing health burden has emphasized the need for innovative solutions, such as brain-computer interfaces. brain-computer interfaces, a multidisciplinary field that integrates neuroscience, engineering, and computer science, enable direct communication between the human brain and external devices. In this study, we developed an autonomous diabetes therapeutic system that employs visually-induced electroencephalography devices to capture and decode event-related potentials using machine learning techniques. We present the visually-induced optogenetically-engineered system for therapeutic expression regulation (VISITER), which generates diverse output commands to control illumination durations. This system regulates insulin expression through optogenetically-engineered cells, achieving blood glucose homeostasis in mice. Our results demonstrate that VISITER effectively and precisely modulates therapeutic protein expression in mammalian cells, facilitating the rapid restoration of blood glucose homeostasis in diabetic mice. These findings underscore the potential for diabetic patients to manage insulin levels autonomously by focusing on target images, paving the way for a more self-directed approach to blood glucose control.},
}
@article {pmid39644999,
year = {2025},
author = {Wang, Y and Wang, X and Wang, L and Zheng, L and An, X and Zheng, C},
title = {Attenuated task-responsive representations of hippocampal place cells induced by amyloid-beta accumulation.},
journal = {Behavioural brain research},
volume = {480},
number = {},
pages = {115384},
doi = {10.1016/j.bbr.2024.115384},
pmid = {39644999},
issn = {1872-7549},
mesh = {Animals ; *Amyloid beta-Peptides/metabolism ; Male ; *Place Cells/physiology ; *Spatial Memory/physiology ; *Hippocampus/metabolism ; Rats ; Action Potentials/physiology ; Association Learning/physiology ; },
abstract = {Alzheimer's disease (AD) is a typical neurodegenerative disease featuring deficits in spatial memory, which relies on spatial representations by hippocampal place cells. Place cells exhibit task-responsive representation to support memory encoding and retrieval processes. Yet, it remains unclear how this task-responsive spatial representation was interrupted under AD pathologies. Here, we employed a delayed match-to-place spatial memory task with associative and predictive memory processes, during which we electrophysiologically recorded hippocampal place cells with multi-tetrode hyperdrives in rats with i.c.v. amyloid/saline injection. We found that the directional selectivity of place cells coding was maintained in the Amyloid group. The firing stability was higher during predictive memory than during associative memory in both groups. However, the spatial specificity was decreased in the Amyloid group during both associative and predictive memory. Importantly, the place cells in the Amyloid group exhibited attenuated task-responsive representations, i.e. lack of spatial over-representations towards the goal zone and a higher representation of the rest zone, especially during the predictive memory stage. These results raise a hypothesis that the disrupted task-responsive representations of place cells could be an underlying mechanism of spatial memory deficits induced by amyloid proteins.},
}
@article {pmid39643773,
year = {2025},
author = {Liu, X and Zhu, J and Zheng, J and Xu, H},
title = {Role of the Thalamic Reticular Nucleus in Social Memory.},
journal = {Neuroscience bulletin},
volume = {41},
number = {2},
pages = {355-358},
pmid = {39643773},
issn = {1995-8218},
}
@article {pmid39643728,
year = {2024},
author = {Haghi, B and Aflalo, T and Kellis, S and Guan, C and Gamez de Leon, JA and Huang, AY and Pouratian, N and Andersen, RA and Emami, A},
title = {Enhanced control of a brain-computer interface by tetraplegic participants via neural-network-mediated feature extraction.},
journal = {Nature biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {39643728},
issn = {2157-846X},
abstract = {To infer intent, brain-computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.},
}
@article {pmid39642366,
year = {2024},
author = {Ikegaya, N and Mallela, AN and Warnke, PC and Kunigk, NG and Liu, F and Schone, HR and Verbaarschot, C and Hatsopoulos, NG and Downey, JE and Boninger, ML and Gaunt, R and Collinger, JL and Gonzalez-Martinez, JA},
title = {A novel robot-assisted method for implanting intracortical sensorimotor devices for brain-computer interface studies: principles, surgical techniques, and challenges.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2024.7.JNS241296},
pmid = {39642366},
issn = {1933-0693},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; U01 NS123125/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
abstract = {Precise anatomical implantation of a microelectrode array is fundamental for successful brain-computer interface (BCI) surgery, ensuring high-quality, robust signal communication between the brain and the computer interface. Robotic neurosurgery can contribute to this goal, but its application in BCI surgery has been underexplored. Here, the authors present a novel robot-assisted surgical technique to implant rigid intracortical microelectrode arrays for the BCI. Using this technique, the authors performed surgery in a 31-year-old male with tetraplegia due to a traumatic C4 spinal cord injury that occurred a decade earlier. Each of the arrays was embedded into the parenchyma with a single insertion without complication. Postoperative imaging verified that the devices were placed as intended. With the motor cortex arrays, the participant successfully accomplished 2D control of a virtual arm and hand, with a success rate of 20 of 20 attempts, and recording quality was maintained at 100 and 200 days postimplantation. Intracortical microstimulation of the somatosensory cortex arrays elicited sensations in the fingers and palm. A robotic neurosurgery technique was successfully translated into BCI device implantation as part of an early feasibility trial with the long-term goal of restoring upper-limb function. The technique was demonstrated to be accurate and subsequently contributed to high-quality signal communication.},
}
@article {pmid39642216,
year = {2024},
author = {Li, C and Zhang, S and Jiang, J and Wang, S and He, S and Song, J},
title = {Laser-induced adhesives with excellent adhesion enhancement and reduction capabilities for transfer printing of microchips.},
journal = {Science advances},
volume = {10},
number = {49},
pages = {eads9226},
pmid = {39642216},
issn = {2375-2548},
abstract = {Transfer printing based on tunable and reversible adhesive that enables the heterogeneous integration of materials is essential for developing envisioned electronic systems. An adhesive with both adhesion enhancement and reduction capabilities in a rapid and selective manner is challenging. Here, we report a laser-induced adhesive, featuring a geometrically simple shape memory polymer layer on a glass backing, with excellent adhesion modulation capability for programmable pickup and noncontact printing of microchips. Selective and rapid laser heating substantially enhances the adhesive's adhesion strength from kilopascal to megapascal within 10 ms due to the shape fixing effect, allowing for precise and programmable pickup. Conversely, the enhanced adhesion can be quickly reduced and eliminated within 3 ms through the shape recovery effect, enabling noncontact printing. Demonstrations of transfer printing microlight-emitting diodes (LEDs) and mini-LEDs onto various low-adhesive flat, rough, and curved surfaces highlight the unusual capabilities of this adhesive for deterministic assembly.},
}
@article {pmid39640342,
year = {2024},
author = {Deruelle, F},
title = {Microwave radiofrequencies, 5G, 6G, graphene nanomaterials: Technologies used in neurological warfare.},
journal = {Surgical neurology international},
volume = {15},
number = {},
pages = {439},
pmid = {39640342},
issn = {2229-5097},
abstract = {BACKGROUND: Scientific literature, with no conflicts of interest, shows that even below the limits defined by the International Commission on Non-Ionizing Radiation Protection, microwaves from telecommunication technologies cause numerous health effects: neurological, oxidative stress, carcinogenicity, deoxyribonucleic acid and immune system damage, electro-hypersensitivity. The majority of these biological effects of non-thermal microwave radiation have been known since the 1970s.<br><br>METHODS: Detailed scientific, political, and military documents were analyzed. Most of the scientific literature comes from PubMed. The other articles (except for a few) come from impacted journals . The rare scientific documents that were not peer reviewed were produced by recognized scientists in their fields. The rest of the documentation comes from official sources: political (e.g., European Union and World Health Organization), military (e.g., US Air Force and NATO), patents, and national newspapers.<br><br>RESULTS: (1) Since their emergence, the authorities have deployed and encouraged the use of wireless technologies (2G, 3G, 4G, WiFi, WiMAX, DECT, Bluetooth, cell phone towers/masts/base stations, small cells, etc.) in full awareness of their harmful effects on health. (2) Consequences of microwave radiation from communication networks are comparable to the effects of low-power directed-energy microwave weapons, whose objectives include behavioral modification through neurological (brain) targeting. Above 20 gigahertz, 5G behaves like an unconventional chemical weapon. (3) Biomedical engineering (via graphene-based nanomaterials) will enable brain-computer connections, linked wirelessly to the Internet of Everything through 5G and 6G networks (2030) and artificial intelligence, gradually leading to human-machine fusion (cyborg) before the 2050s.<br><br>CONCLUSION: Despite reports and statements from the authorities presenting the constant deployment of new wireless communication technologies, as well as medical research into nanomaterials, as society's ideal future, in-depth research into these scientific fields shows, above all, an objective linked to the current cognitive war. It could be hypothesized that, in the future, this aim will correspond to the control of humanity by machines.},
}
@article {pmid39639181,
year = {2025},
author = {Qian, M and Wang, J and Gao, Y and Chen, M and Liu, Y and Zhou, D and Lu, HD and Zhang, X and Hu, JM and Roe, AW},
title = {Multiple loci for foveolar vision in macaque monkey visual cortex.},
journal = {Nature neuroscience},
volume = {28},
number = {1},
pages = {137-149},
pmid = {39639181},
issn = {1546-1726},
support = {31627802//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52277232//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81701774//National Natural Science Foundation of China (National Science Foundation of China)/ ; 61771423//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Visual Cortex/physiology ; *Magnetic Resonance Imaging ; Visual Pathways/physiology ; Macaca mulatta ; Fovea Centralis/physiology ; Photic Stimulation/methods ; Male ; Brain Mapping ; },
abstract = {In humans and nonhuman primates, the central 1° of vision is processed by the foveola, a retinal structure that comprises a high density of photoreceptors and is crucial for primate-specific high-acuity vision, color vision and gaze-directed visual attention. Here, we developed high-spatial-resolution ultrahigh-field 7T functional magnetic resonance imaging methods for functional mapping of the foveolar visual cortex in awake monkeys. In the ventral pathway (visual areas V1-V4 and the posterior inferior temporal cortex), viewing of a small foveolar spot elicits a ring of multiple (eight) foveolar representations per hemisphere. This ring surrounds an area called the 'foveolar core', which is populated by millimeter-scale functional domains sensitive to fine stimuli and high spatial frequencies, consistent with foveolar visual acuity, color and achromatic information and motion. Thus, this elaborate rerepresentation of central vision coupled with a previously unknown foveolar core area signifies a cortical specialization for primate foveation behaviors.},
}
@article {pmid39638842,
year = {2024},
author = {Zhang, X and Wei, W and Qian, L and Yao, L and Jin, X and Xing, L and Qian, Z},
title = {Real-time monitoring of bioelectrical impedance for minimizing tissue carbonization in microwave ablation of porcine liver.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {30404},
pmid = {39638842},
issn = {2045-2322},
support = {xcxjh20230333//Nanjing University of Aeronautics and Astronautics Research and Practice Innovation Program/ ; 81727804//National Major Scientifc Instruments and Equipment Development Project Funded by National Natural Science Foundation of China/ ; 82151311//National Natural Science Foundation of China/ ; 12372306//National Natural Science Foundation of China/ ; NP2024102//Fundamental Research Funds for the Central Universities/ ; NJ2024016//Fundamental Research Funds for the Central Universities/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Microwaves ; *Electric Impedance ; *Liver/surgery/metabolism ; Swine ; Ablation Techniques/methods ; Finite Element Analysis ; },
abstract = {The charring tissue generated by the high temperature during microwave ablation can affect the therapeutic effect, such as limiting the volume of the coagulation zone and causing rejection. This paper aimed to prevent tissue carbonization while delivering an appropriate thermal dose for effective ablations by employing a treatment protocol with real-time bioelectrical impedance monitoring. Firstly, the current field response under different microwave ablation statuses is analyzed based on finite element simulation. Next, the change of impedance measured by the electrodes is correlated with the physical state of the ablated tissue, and a microwave ablation carbonization control protocol based on real-time electrical impedance monitoring was established. The finite element simulation results show that the dielectric properties of biological tissues changed dynamically during the ablation process. Finally, the relative change rule of the electrical impedance magnitude of the ex vivo porcine liver throughout the entire MWA process and the reduction of the central zone carbonization were obtained by the MWA experiment. Charring tissue was eliminated without water cooling at 40 W and significantly reduced at 50 W and 60 W. The carbonization during MWA can be reduced according to the changes in tissue electrical impedance to optimize microwave thermal ablation efficacy.},
}
@article {pmid39638804,
year = {2024},
author = {Ma, X and Chen, LN and Liao, M and Zhang, L and Xi, K and Guo, J and Shen, C and Shen, DD and Cai, P and Shen, Q and Qi, J and Zhang, H and Zang, SK and Dong, YJ and Miao, L and Qin, J and Ji, SY and Li, Y and Liu, J and Mao, C and Zhang, Y and Chai, R},
title = {Molecular insights into the activation mechanism of GPR156 in maintaining auditory function.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10601},
pmid = {39638804},
issn = {2041-1723},
mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism/genetics/chemistry ; *Cryoelectron Microscopy ; Animals ; HEK293 Cells ; Protein Binding ; Protein Multimerization ; Mice ; GTP-Binding Protein alpha Subunits, Gi-Go/metabolism/chemistry/genetics ; Models, Molecular ; },
abstract = {The class C orphan G-protein-coupled receptor (GPCR) GPR156, which lacks the large extracellular region, plays a pivotal role in auditory function through Gi2/3. Here, we firstly demonstrate that GPR156 with high constitutive activity is essential for maintaining auditory function, and further reveal the structural basis of the sustained role of GPR156. We present the cryo-EM structures of human apo GPR156 and the GPR156-Gi3 complex, unveiling a small extracellular region formed by extracellular loop 2 (ECL2) and the N-terminus. The GPR156 dimer in both apo state and Gi3 protein-coupled state adopt a transmembrane (TM)5/6-TM5/6 interface, indicating the high constitutive activity of GPR156 in the apo state. Furthermore, C-terminus in G-bound subunit of GPR156 plays a dual role in promoting G protein binding within G-bound subunit while preventing the G-free subunit from binding to additional G protein. Together, these results explain how GPR156 constitutive activity is maintained through dimerization and provide a mechanistic insight into the sustained role of GPR156 in maintaining auditory function.},
}
@article {pmid39637463,
year = {2025},
author = {Wimmer, M and Pepicelli, A and Volmer, B and ElSayed, N and Cunningham, A and Thomas, BH and Müller-Putz, GR and Veas, EE},
title = {Counting on AR: EEG responses to incongruent information with real-world context.},
journal = {Computers in biology and medicine},
volume = {185},
number = {},
pages = {109483},
doi = {10.1016/j.compbiomed.2024.109483},
pmid = {39637463},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Brain-Computer Interfaces ; Adult ; Signal Processing, Computer-Assisted ; Brain/physiology ; Young Adult ; Support Vector Machine ; Evoked Potentials/physiology ; },
abstract = {Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the visualized information or the user's mental context, can considerably impact user experience. This work characterizes the brain dynamics associated with processing incongruent information within an AR environment. To study these effects, we designed an interactive paradigm featuring the manipulation of a Rubik's cube serving as a physical referent. Congruent and incongruent information regarding the cube's current status was presented via symbolic (digits) and non-symbolic (graphs) stimuli, thus examining the impact of different means of data representation. The analysis of electroencephalographic signals from 19 participants revealed the presence of centro-parietal N400 and P600 components following the processing of incongruent information, with significantly increased latencies for non-symbolic stimuli. Additionally, we explored the feasibility of exploiting incongruency effects for brain-computer interfaces. Hence, we implemented decoders using linear discriminant analysis, support vector machines, and EEGNet, achieving comparable performances with all methods. Therefore, this work contributes to the design of adaptive AR systems by demonstrating that above-chance detection of incongruent information based on physiological signals is feasible. The successful decoding of incongruency-induced modulations can inform systems about the current mental state of users without making it explicit, aiming for more coherent and contextually appropriate AR interactions.},
}
@article {pmid39634507,
year = {2024},
author = {Wei, M and Lin, X and Xu, K and Wu, Y and Wang, C and Wang, Z and Lei, K and Bao, K and Li, J and Li, L and Li, E and Lin, H},
title = {Inverse design of compact nonvolatile reconfigurable silicon photonic devices with phase-change materials.},
journal = {Nanophotonics (Berlin, Germany)},
volume = {13},
number = {12},
pages = {2183-2192},
pmid = {39634507},
issn = {2192-8614},
abstract = {In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability. Low-loss phase change materials (PCMs) exemplified by Sb2Se3 are a promising candidate for solving this problem benefiting from their high refractive index contrast. In this work, we first developed a robust inverse design method to realize reconfigurable silicon and phase-change materials hybrid photonic devices including mode converter and optical switch. The mode converter exhibits a broadband operation of >100 nm. The optical switch shows an extinction ratio of >25 dB and a multilevel switching of 41 (>5 bits) by simply changing the crystallinity of Sb2Se3. Here, we experimentally demonstrated a Sb2Se3/Si hybrid integrated optical switch for the first time, wherein routing can be switched by the phase transition of the whole Sb2Se3. Our work provides an effective solution for the design of photonic devices that is insensitive to fabrication errors, thereby paving the way for high integration density in future photonic chips.},
}
@article {pmid39632923,
year = {2024},
author = {Karthiga, M and Suganya, E and Sountharrajan, S and Balusamy, B and Selvarajan, S},
title = {Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {30251},
pmid = {39632923},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Emotions/physiology ; *Brain-Computer Interfaces ; Heuristics ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Neural Networks, Computer ; },
abstract = {In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual's EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.},
}
@article {pmid39632897,
year = {2024},
author = {Chen, Z and Tang, Y and Liu, X and Li, W and Hu, Y and Hu, B and Xu, T and Zhang, R and Xia, L and Zhang, JX and Xiao, Z and Chen, J and Feng, Z and Zhou, Y and He, Q and Qiu, J and Lei, X and Chen, H and Qin, S and Feng, T},
title = {Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10560},
pmid = {39632897},
issn = {2041-1723},
support = {32300907//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Connectome ; Male ; Female ; *Comorbidity ; Adult ; *Depression/genetics/epidemiology ; *Anxiety/genetics/epidemiology ; Young Adult ; Genetic Markers ; Adolescent ; Child ; Anxiety Disorders/genetics/epidemiology ; Brain/diagnostic imaging/metabolism ; Magnetic Resonance Imaging ; },
abstract = {Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.},
}
@article {pmid39630811,
year = {2024},
author = {Grogan, M and Blum, KP and Wu, Y and Harston, JA and Miller, LE and Faisal, AA},
title = {Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders.},
journal = {PLoS computational biology},
volume = {20},
number = {12},
pages = {e1012614},
pmid = {39630811},
issn = {1553-7358},
support = {/WT_/Wellcome Trust/United Kingdom ; F32 MH120893/MH/NIMH NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; },
mesh = {*Proprioception/physiology ; Animals ; *Models, Neurological ; *Somatosensory Cortex/physiology/anatomy &amp; histology ; Neurons/physiology ; Movement/physiology ; Computational Biology ; Macaca mulatta ; Brain-Computer Interfaces ; Humans ; },
abstract = {Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.},
}
@article {pmid39630448,
year = {2024},
author = {Kelly, BC and Cova, TJ and Debbink, MP and Onega, T and Brewer, SC},
title = {Racial and Ethnic Disparities in Regulatory Air Quality Monitor Locations in the US.},
journal = {JAMA network open},
volume = {7},
number = {12},
pages = {e2449005},
pmid = {39630448},
issn = {2574-3805},
mesh = {Humans ; United States ; *Air Pollution/analysis/statistics &amp; numerical data ; Cross-Sectional Studies ; *Environmental Monitoring/methods ; *Air Pollutants/analysis ; Particulate Matter/analysis ; Ozone/analysis ; Ethnicity/statistics &amp; numerical data ; United States Environmental Protection Agency ; Racial Groups/statistics &amp; numerical data ; Nitrogen Dioxide/analysis ; Sulfur Dioxide/analysis ; Environmental Exposure/analysis/statistics &amp; numerical data ; },
abstract = {IMPORTANCE: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.<br><br>OBJECTIVE: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.<br><br>This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size. Bayesian mixed-effects models of the count of criteria pollutant monitors measuring an area were used, adjusting for population size and accounting for spatial autocorrelation. Data were analyzed from March to June 2024.<br><br>EXPOSURE: Census block group-level racial and ethnic composition.<br><br>MAIN OUTCOME AND MEASURES: Number of regulatory monitors measuring a census block group by criteria pollutant (particulate matter [PM], ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], lead [Pb], and carbon monoxide [CO]).<br><br>RESULTS: This analysis included 329&#x202f;725&#x202f;481 individuals living in 237&#x202f;631 block groups in the US (1&#x202f;936&#x202f;842 [0.6%] American Indian and Alaska Native, 18&#x202f;554&#x202f;697 [5.6%] Asian, 40&#x202f;196&#x202f;302 [12.2%] Black, 60&#x202f;806&#x202f;969 [18.4%] Hispanic, 555&#x202f;712 [0.2%] Native Hawaiian and Other Pacific Islander, 196&#x202f;010&#x202f;370 [59.4%] White, 1&#x202f;208&#x202f;267 [0.3%] some other race, and 10&#x202f;456&#x202f;322 [0.4%] 2 or more races). Adjusting for population size, monitoring disparities were identified for each criteria pollutant. Relative to the White non-Latino population, all groups were associated with fewer NO2, O3, Pb, and PM monitors. Disparities were consistently largest for Native Hawaiian and Other Pacific Islander populations, followed by American Indian and Alaska Native populations and those of 2 or more races. An increase in percentage of Native Hawaiian and Other Pacific Islander race was associated with fewer monitors for SO2 (adjusted odds ratio [aOR], 0.91; 95% BCI, 0.90-0.91), CO (aOR, 0.95; 95% BCI, 0.94-0.95), O3 (aOR, 0.95; 95% BCI, 0.94-0.95), NO2 (aOR, 0.97; 95% BCI, 0.91-0.94), and PM (aOR, 0.96; 95% BCI, 0.95-0.96). An increase in the percentage of those of Asian race was associated with slightly more SO2 (aOR, 1.04; 95% BCI, 1.03-1.04) monitors.<br><br>CONCLUSIONS AND RELEVANCE: This cross-sectional study of racial and ethnic disparities in the location of EPA regulatory monitors determined that data may not be equitably representative of air quality, particularly for areas with predominantly Native Hawaiian and Other Pacific Islander or American Indian or Alaska Native populations. Integration of multiple data sources may aid in filling monitoring gaps across race and ethnicity. Where possible, researchers should quantify uncertainty in exposure estimates.},
}
@article {pmid39628656,
year = {2024},
author = {Pryss, R and Vom Brocke, J and Reichert, M and Rukzio, E and Schlee, W and Weber, B},
title = {Editorial: Application of neuroscience in information systems and software engineering.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1402603},
pmid = {39628656},
issn = {1662-4548},
}
@article {pmid39628388,
year = {2024},
author = {Wang, X and Xu, M and Yang, H and Jiang, W and Jiang, J and Zou, D and Zhu, Z and Tao, C and Ni, S and Zhou, Z and Sun, L and Li, M and Nie, Y and Zhao, Y and He, F and Tao, TH and Wei, X},
title = {Ultraflexible Neural Electrodes Enabled Synchronized Long-Term Dopamine Detection and Wideband Chronic Recording Deep in Brain.},
journal = {ACS nano},
volume = {18},
number = {50},
pages = {34272-34287},
doi = {10.1021/acsnano.4c12429},
pmid = {39628388},
issn = {1936-086X},
mesh = {*Dopamine/analysis ; Animals ; *Graphite/chemistry ; *Brain/metabolism/physiology ; Rats ; Rats, Sprague-Dawley ; Electrodes ; Nanocomposites/chemistry ; Male ; Polystyrenes/chemistry ; Electrodes, Implanted ; Thiophenes ; },
abstract = {Ultraflexible neural electrodes have shown superior stability compared with rigid electrodes in long-term in vivo recordings, owing to their low mechanical mismatch with brain tissue. It is desirable to detect neurotransmitters as well as electrophysiological signals for months in brain science. This work proposes a stable electronic interface that can simultaneously detect neural electrical activity and dopamine concentration deep in the brain. This ultraflexible electrode is modified by a nanocomposite of reduced graphene oxide (rGO) and poly(3,4-ethylenedioxythiophene):poly(sodium 4-styrenesulfonate) (rGO/PEDOT:PSS), enhancing the electrical stability of the coating and increasing its specific surface area, thereby improving the sensitivity to dopamine response with 15 pA/μM. This electrode can detect dopamine fluctuations and can conduct long-term, stable recordings of local field potentials (LFPs), spiking activities, and amplitudes with high spatial and temporal resolution across multiple regions, especially in deep brain areas. The electrodes were implanted into the brains of rodent models to monitor the changes in neural and electrochemical signals across different brain regions during the administration of nomifensine. Ten minutes after drug injection, enhanced neuronal firing activity and increased LFP power were detected in the motor cortex and deeper cortical layers, accompanied by a gradual rise in dopamine levels with 192 ± 29 nM. The in vivo recording consistently demonstrates chronic high-quality neural signal monitoring with electrochemical signal stability for up to 6 weeks. These findings highlight the high quality and stability of our electrophysiological/electrochemical codetection neural electrodes, underscoring their tremendous potential for applications in neuroscience research and brain-machine interfaces.},
}
@article {pmid39627937,
year = {2024},
author = {Gielas, AM},
title = {Wounds and Vulnerabilities. The Participation of Special Operations Forces in Experimental Brain-Computer Interface Research.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {},
number = {},
pages = {1-22},
doi = {10.1017/S096318012400063X},
pmid = {39627937},
issn = {1469-2147},
abstract = {Brain-computer interfaces (BCIs) exemplify a dual-use neurotechnology with significant potential in both civilian and military contexts. While BCIs hold promise for treating neurological conditions such as spinal cord injuries and amyotrophic lateral sclerosis in the future, military decisionmakers in countries such as the United States and China also see their potential to enhance combat capabilities. Some predict that U.S. Special Operations Forces (SOF) will be early adopters of BCI enhancements. This article argues for a shift in focus: the U.S. Special Operations Command (SOCOM) should pursue translational research of medical BCIs for treating severely injured or ill SOF personnel. After two decades of continuous military engagement and on-going high-risk operations, SOF personnel face unique injury patterns, both physical and psychological, which BCI technology could help address. The article identifies six key medical applications of BCIs that could benefit wounded SOF members and discusses the ethical implications of involving SOF personnel in translational research related to these applications. Ultimately, the article challenges the traditional civilian-military divide in neurotechnology, arguing that by collaborating more closely with military stakeholders, scientists can not only help individuals with medical needs, including servicemembers, but also play a role in shaping the future military applications of BCI technology.},
}
@article {pmid39627867,
year = {2024},
author = {Patarini, F and Tamburella, F and Pichiorri, F and Mohebban, S and Bigioni, A and Ranieri, A and Di Tommaso, F and Tagliamonte, NL and Serratore, G and Lorusso, M and Ciaramidaro, A and Cincotti, F and Scivoletto, G and Mattia, D and Toppi, J},
title = {On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {211},
pmid = {39627867},
issn = {1743-0003},
support = {GR2019-12369207//Ministero della Salute/ ; RM123188F229EC72//Sapienza Universit&#xe0; di Roma/ ; },
mesh = {Humans ; *Robotics/instrumentation/methods ; Male ; Female ; *Feedback, Sensory/physiology ; Adult ; *Electroencephalography/methods ; Middle Aged ; *Eye-Tracking Technology ; Spinal Cord Injuries/rehabilitation ; Physical Therapists ; Exercise Therapy/methods/instrumentation ; Gait/physiology ; Gait Disorders, Neurologic/rehabilitation ; },
abstract = {BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.<br><br>METHODS: Ten individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.<br><br>RESULTS: We found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.<br><br>CONCLUSIONS: The type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.},
}
@article {pmid39622415,
year = {2025},
author = {Zhu, M and Yang, Y and Niu, X and Peng, Y and Liu, R and Zhang, M and Han, Y and Wang, Z},
title = {Different responses of MVL neurons when pigeons attend to local versus global information during object classification.},
journal = {Behavioural brain research},
volume = {480},
number = {},
pages = {115363},
doi = {10.1016/j.bbr.2024.115363},
pmid = {39622415},
issn = {1872-7549},
mesh = {Animals ; *Columbidae/physiology ; *Neurons/physiology ; Pattern Recognition, Visual/physiology ; Attention/physiology ; Male ; Visual Perception/physiology ; Behavior, Animal/physiology ; Photic Stimulation/methods ; },
abstract = {Most prior studies have indicated that pigeons have a tendency to rely on local information for target categorization, yet there is a lack of electrophysiological evidence to support this claim. The mesopallium ventrolaterale (MVL) is believed to play a role in processing both local and global information during visual cognition. The difference between responses of MVL neurons when pigeons are focusing on local versus global information during visual object categorization remain unknown. In this study, pigeons were trained to categorize hierarchical stimuli that maintained consistency in local and global information. Subsequently, stimuli with different local and global components were presented to examine the pigeons' behavioral preferences. Not surprisingly, the behavioral findings revealed that pigeons predominantly attended to the local elements when performing categorization tasks. Moreover, MVL neurons exhibited significantly distinct responses when pigeons prioritized local versus global information. Specifically, most recording sites showed heightened gamma band power and increased nonlinear entropy values, indicating strong neural responses and rich information when pigeons concentrated on the local components of an object. Furthermore, neural population functional connectivity was weaker when the pigeons focused on local elements, suggesting that individual neurons operated more independently and effectively when focusing on local features. These findings offer electrophysiological evidence supporting the notion of pigeons displaying a behavioral preference for local information. The study provides valuable insight into the understanding of cognitive processes of pigeons when presented with complex objects, and further sheds light on the neural mechanisms underlying pigeons' behavioral preference for attending to local information.},
}
@article {pmid39622169,
year = {2025},
author = {Wei, X and Narayan, J and Faisal, AA},
title = {The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad9957},
pmid = {39622169},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Privacy ; Brain Waves/physiology ; },
abstract = {Objective. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural activity analysis and aiding paralysed patients via brain-computer interfaces. However, training machine learning algorithms on EEG encounters two primary challenges: variability across data sets and privacy concerns using data from individuals and data centres. Our objective is to address these challenges by integrating transfer learning for data variability and federated learning for data privacy into a unified approach.Approach. We introduce the 'Sandwich' as a novel deep privacy-preserving meta-framework combining transfer learning and federated learning. The 'Sandwich' framework comprises three components: federated networks (first layers) that handle data set differences at the input level, a shared network (middle layer) learning common rules and applying transfer learning techniques, and individual classifiers (final layers) for specific brain tasks of each data set. This structure enables the central network (central server) to benefit from multiple data sets, while local branches (local servers) maintain data and label privacy.Main results. We evaluated the 'Sandwich' meta-architecture in various configurations using the BEETL motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared with baseline models likeShallow ConvNetandEEGInception, our 'Sandwich' implementations showed superior performance. The best-performing model, the Inception SanDwich with deep set alignment (Inception-SD-Deepset), exceeded baseline methods by 9%.Significance. The 'Sandwich' framework demonstrates advancements in federated deep transfer learning for diverse tasks and data sets. It outperforms conventional deep learning methods, showcasing the potential for effective use of larger, heterogeneous data sets with enhanced privacy. In addition, through its diverse implementations with various backbone architectures and transfer learning approaches, the 'Sandwich' framework shows the potential as a model-agnostic meta-framework for decoding time series data like EEG, suggesting a direction towards large-scale brainwave decoding by combining deep transfer learning with privacy-preserving federated learning.},
}
@article {pmid39622162,
year = {2025},
author = {Chen, C and Fang, H and Yang, Y and Zhou, Y},
title = {Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad9956},
pmid = {39622162},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Machine Learning ; Algorithms ; Brain-Computer Interfaces ; },
abstract = {Objective. Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy.Approach. In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable electroencephalogram-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition.Main results. We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures.Significance. Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces.},
}
@article {pmid39621862,
year = {2024},
author = {Xia, Y and He, M and Basang, S and Sha, L and Huang, Z and Jin, L and Duan, Y and Tang, Y and Li, H and Lai, W and Chen, L},
title = {Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.},
journal = {JMIR medical informatics},
volume = {12},
number = {},
pages = {e57727},
pmid = {39621862},
issn = {2291-9694},
mesh = {Humans ; *Electronic Health Records ; *Epilepsy/diagnosis/classification ; *Machine Learning ; Retrospective Studies ; *Natural Language Processing ; Female ; Male ; Adult ; Middle Aged ; China ; Adolescent ; Child ; Young Adult ; },
abstract = {BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support tools.<br><br>OBJECTIVE: We developed a symptom entity extraction tool and an epilepsy semiology ontology (ESO) and used machine learning to achieve an automated binary classification of epilepsy in this study.<br><br>METHODS: Using present history data of electronic health records from the Southwest Epilepsy Center in China, we constructed an ESO and a symptom-entity extraction tool to extract seizure duration, seizure symptoms, and seizure frequency from the unstructured text by combining manual annotation with natural language processing techniques. In addition, we achieved automatic classification of patients in the study cohort with high accuracy based on the extracted seizure feature data using multiple machine learning methods.<br><br>RESULTS: Data included present history from 10,925 cases between 2010 and 2020. Six annotators labeled a total of 2500 texts to obtain 5844 words of semiology and construct an ESO with 702 terms. Based on the ontology, the extraction tool achieved an accuracy rate of 85% in symptom extraction. Furthermore, we trained a stacking ensemble learning model combining XGBoost and random forest with an F1-score of 75.03%. The random forest model had the highest area under the curve (0.985).<br><br>CONCLUSIONS: This work demonstrated the feasibility of natural language processing-assisted structural extraction of epilepsy medical record texts and downstream tasks, providing open ontology resources for subsequent related work.},
}
@article {pmid39621615,
year = {2024},
author = {Yuan, X and Li, H and Guo, F},
title = {Temperature cues are integrated in a flexible circadian neuropeptidergic feedback circuit to remodel sleep-wake patterns in flies.},
journal = {PLoS biology},
volume = {22},
number = {12},
pages = {e3002918},
pmid = {39621615},
issn = {1545-7885},
mesh = {Animals ; *Sleep/physiology ; *Circadian Rhythm/physiology ; *Drosophila Proteins/metabolism/genetics ; *Connectome ; *Drosophila melanogaster/physiology ; *Neuropeptides/metabolism/genetics ; Neurons/physiology/metabolism ; Temperature ; Wakefulness/physiology ; Feedback, Physiological ; Brain/physiology/metabolism ; Drosophila/physiology ; Cues ; Signal Transduction ; },
abstract = {Organisms detect temperature signals through peripheral neurons, which relay them to central circadian networks to drive adaptive behaviors. Despite recent advances in Drosophila research, how circadian circuits integrate temperature cues with circadian signals to regulate sleep/wake patterns remains unclear. In this study, we used the FlyWire brain electron microscopy connectome to map neuronal connections, identifying lateral posterior neurons LPNs as key nodes for integrating temperature information into the circadian network. LPNs receive input from both circadian and temperature-sensing neurons, promoting sleep behavior. Through connectome analysis, genetic manipulation, and behavioral assays, we demonstrated that LPNs, downstream of thermo-sensitive anterior cells (ACs), suppress activity-promoting lateral dorsal neurons LNds via the AstC pathway, inducing sleep Disrupting LPN-LNd communication through either AstCR1 RNAi in LNds or in an AstCR1 mutant significantly impairs the heat-induced reduction in the evening activity peak. Conversely, optogenetic calcium imaging and behavioral assays revealed that cold-activated LNds subsequently stimulate LPNs through NPF-NPFR signaling, establishing a negative feedback loop. This feedback mechanism limits LNd activation to appropriate levels, thereby fine-tuning the evening peak increase at lower temperatures. In conclusion, our study constructed a comprehensive connectome centered on LPNs and identified a novel peptidergic circadian feedback circuit that coordinates temperature and circadian signals, offering new insights into the regulation of sleep patterns in Drosophila.},
}
@article {pmid39619679,
year = {2024},
author = {Zhang, W and Tang, X and Wang, M},
title = {Attention model of EEG signals based on reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1442398},
pmid = {39619679},
issn = {1662-5161},
abstract = {BACKGROUND: Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.<br><br>METHODS: The model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.<br><br>RESULTS: We evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.<br><br>CONCLUSION: In the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.},
}
@article {pmid39619531,
year = {2024},
author = {Patel, M and Gosai, J and Patel, P and Roy, M and Solanki, A},
title = {Insights of BDAPbI4-Based Flexible Memristor for Artificial Synapses and In-Memory Computing.},
journal = {ACS omega},
volume = {9},
number = {47},
pages = {46841-46850},
pmid = {39619531},
issn = {2470-1343},
abstract = {Inspired by brain-like spiking computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence promises to realize artificial intelligence (AI) while reducing the energy requirements of computing platforms. In this work, we show the potential of advanced learnings of butane-1,4-diammonium based low-dimensional Dion-Jacobson hybrid perovskite (BDAPbI4) memristor devices in the realm of artificial synapses and neuromorphic computing. Memristors validate Hebbian learning rules with various spike-dependent plasticity within a 10 ± 2 ms time frame, reminiscent of the human brains under flat and bending conditions (∼5 mm radium). A high recognition accuracy of ∼94% of handwritten images from the MNIST database via an artificial neural network (ANN) is achieved with only 50 epochs. An efficient demonstration of second-order memristors and the Pavlovian dog experiment exhibit significant promise in expediting learning and memory consolidation. To showcase the in-memory computing potential, a flexible 4 × 4 crossbar array is designed with measured data retention up to ∼10[3] s along with 26 multilevel resistance states. The crossbar array is successfully programmed for the facile configurability of image "Z". In conclusion, the integration of supervised, unsupervised, and associative learning holds great promise across a spectrum of future technologies, ranging from the realm of spiking neural networks to neuromorphic computing, brain-machine interfaces, and adaptive control systems.},
}
@article {pmid39615858,
year = {2025},
author = {Xue, Z and Zhong, W and Cao, Y and Liu, S and An, X},
title = {Impact of different auditory environments on task performance and EEG activity.},
journal = {Brain research bulletin},
volume = {220},
number = {},
pages = {111142},
doi = {10.1016/j.brainresbull.2024.111142},
pmid = {39615858},
issn = {1873-2747},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Young Adult ; Adult ; *Acoustic Stimulation/methods ; *Brain/physiology ; *Psychomotor Performance/physiology ; *Auditory Perception/physiology ; Noise ; Music ; Attention/physiology ; Workload/psychology ; Environment ; Task Performance and Analysis ; },
abstract = {Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation.},
}
@article {pmid39615554,
year = {2025},
author = {Cai, Z and Gao, Y and Fang, F and Zhang, Y and Du, S},
title = {Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {415},
number = {},
pages = {110332},
doi = {10.1016/j.jneumeth.2024.110332},
pmid = {39615554},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Transfer, Psychology/physiology ; *Electroencephalography/methods ; *Imagination/physiology ; *Algorithms ; Brain/physiology/diagnostic imaging ; Adult ; Machine Learning ; Male ; },
abstract = {In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.},
}
@article {pmid39612132,
year = {2025},
author = {Yang, Y and Li, M and Wang, L},
title = {An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {4},
pages = {1059-1079},
pmid = {39612132},
issn = {1741-0444},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Machine Learning ; },
abstract = {Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.},
}
@article {pmid39609786,
year = {2024},
author = {Sun, J and Chen, S and Wang, S and Guo, H and Wang, X},
title = {The relationship between work-family conflict, stress and depression among Chinese correctional officers: a mediation and network analysis study.},
journal = {BMC public health},
volume = {24},
number = {1},
pages = {3317},
pmid = {39609786},
issn = {1471-2458},
support = {2021ZD0200700//the 2030 Plan Technology and Innovation of China/ ; },
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology ; Correctional Facilities Personnel/psychology ; *Cross-Sectional Studies ; *Depression/epidemiology/psychology ; East Asian People/psychology ; Family/psychology ; Mediation Analysis ; Occupational Stress/psychology/epidemiology ; Police/psychology ; Prisons ; Social Network Analysis ; Stress, Psychological/epidemiology/psychology ; },
abstract = {BACKGROUND: Numerous studies have found that depression is prevalent among correctional officers (COs), which may be related to the work-family conflict (WFC) faced by this cohort. Role conflict theory posits that WFC emerges from the incompatibility between the demands of work and family roles, which induces stress and, in turn, results in emotional problems. Thus, this study seeks to investigate the association between WFC and depression, along with examining the mediating role of stress. Further network analysis is applied to identify the core and bridge symptoms within the network of WFC, stress, and depression, providing a basis for targeted interventions.<br><br>OBJECTIVE: This study aims to investigate the relationship between work-family conflict (WFC) and depressive symptoms among a larger sample of Chinese correctional officers (COs), exploring the potential mechanisms of stress in this population through network analysis.<br><br>METHODS: A cross-sectional study of 472 Chinese COs was conducted from October 2021 to January 2022. WFC, stress, and depressive symptoms were evaluated using the Work-Family Conflict Scale (WFCS) and the Depression Anxiety Stress Scale (DASS). Subsequently, correlation and regression analyses were conducted using SPSS 26.0, while mediation analysis was performed using Model 4 in PROCESS. By using the EBICglasso model, network analyses were utilized to estimate the network structure of WFC, stress and depression. Visualization and centrality measures were performed using the R package.<br><br>RESULTS: The results showed that (1) there was a significant positive correlation between WFC and stress and depression, as well as between stress and depression, (2) WFC and stress had a significant positive predictive effect on depression, (3) stress mediated the relationship between WFC and depression, with a total mediating effect of 0.262 (BootSE&#x2009;=&#x2009;0.031, BCI 95% = 0.278, 0.325), which accounted for 81.62% of the total effect, and (4) in the WFC, stress, and depression network model, strain-based work interference with family (SWF, (betweenness&#x2009;=&#x2009;2.24, closeness = -0.19, strength&#x2009;=&#x2009;1.40), difficult to relax (DR, betweenness&#x2009;=&#x2009;1.20, closeness&#x2009;=&#x2009;1.85, strength&#x2009;=&#x2009;1.06), and had nothing (HN, betweenness = -0.43, closeness&#x2009;=&#x2009;0.62, strength&#x2009;=&#x2009;0.73) were the core symptoms, and SWF, IT, and DH were the bridge symptoms, and (5) first-line COs had significantly higher levels of WFC, stress, and depression than non-first-line correctional officers.<br><br>CONCLUSION: Our findings elucidate the interrelationships between WFC, stress, and depression among COs. The study also enhances the understanding of the factors influencing WFC in this population and provides valuable guidance for the development of future interventions, offering practical clinical significance.},
}
@article {pmid39609406,
year = {2024},
author = {Huang, W and Jin, N and Guo, J and Shen, C and Xu, C and Xi, K and Bonhomme, L and Quast, RB and Shen, DD and Qin, J and Liu, YR and Song, Y and Gao, Y and Margeat, E and Rondard, P and Pin, JP and Zhang, Y and Liu, J},
title = {Structural basis of orientated asymmetry in a mGlu heterodimer.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10345},
pmid = {39609406},
issn = {2041-1723},
support = {ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR 18-CE11-0004-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-10-INBS-04//Agence Nationale de la Recherche (French National Research Agency)/ ; },
mesh = {*Receptors, Metabotropic Glutamate/metabolism/chemistry/ultrastructure ; *Cryoelectron Microscopy ; Humans ; *Protein Multimerization ; HEK293 Cells ; Allosteric Regulation ; Glutamic Acid/metabolism/chemistry ; Models, Molecular ; Fluorescence Resonance Energy Transfer ; Animals ; Protein Binding ; Binding Sites ; },
abstract = {The structural basis for the allosteric interactions within G protein-coupled receptors (GPCRs) heterodimers remains largely unknown. The metabotropic glutamate (mGlu) receptors are complex dimeric GPCRs important for the fine tuning of many synapses. Heterodimeric mGlu receptors with specific allosteric properties have been identified in the brain. Here we report four cryo-electron microscopy structures of mGlu2-4 heterodimer in different states: an inactive state bound to antagonists, two intermediate states bound to either mGlu2 or mGlu4 agonist only and an active state bound to both glutamate and a mGlu4 positive allosteric modulator (PAM) in complex with Gi protein. In addition to revealing a unique PAM binding pocket among mGlu receptors, our data bring important information for the asymmetric activation of mGlu heterodimers. First, we show that agonist binding to a single subunit in the extracellular domain is not sufficient to stabilize an active dimer conformation. Single-molecule FRET data show that the monoliganded mGlu2-4 can be found in both intermediate states and an active one. Second, we provide a detailed view of the asymmetric interface in seven-transmembrane (7TM) domains and identified key residues within the mGlu2 7TM that limits its activation leaving mGlu4 as the only subunit activating G proteins.},
}
@article {pmid39606007,
year = {2024},
author = {Chang, C and Piao, Y and Zhang, M and Liu, Y and Du, M and Yang, M and Mei, T and Wu, C and Wang, Y and Chen, X and Zeng, GQ and Zhang, X},
title = {Evaluation of tolerability and safety of transcranial electrical stimulation with gel particle electrodes in healthy subjects.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1441533},
pmid = {39606007},
issn = {1664-0640},
abstract = {BACKGROUND: With the advancement of transcranial electrical stimulation (tES) technology, an increasing number of stimulation devices and treatment protocols have emerged. However, safety and tolerability remain critical concerns before new strategies can be implemented. Particularly, the use of gel particle electrodes brings new challenges to the safety and tolerability of tES, which hinders its widespread adoption and further research.<br><br>OBJECTIVE: Our study utilized a specially designed and validated transcranial electrical stimulation stimulator along with preconfigured gel particle electrodes placed at F3 and F4 in the prefrontal lobes. We aimed to assess the tolerance and safety of these electrodes in healthy subjects by administering different durations and types of tES.<br><br>METHODS: Each participant underwent ten sessions of either transcranial direct current stimulation (tDCS) or transcranial alternating current stimulation (tACS), with session durations varying. In the experiment, we collected various measurement data from participants, including self-report questionnaire data and behavioral keystroke data. Tolerability was evaluated through adverse events (AEs), the relationship of adverse events with tES (AEs-rela), the Self-Rating Anxiety Scale (SAS), and the Visual Analog Mood Scale-Revised (VAMS-R). Safety was assessed using the Visual Analog Scale (VAS), the Skin Sensation Rating (SSR), Montreal Cognitive Assessment (MoCA), and Stroop task. These data were analyzed to determine the impact of different parameters on the tolerability and safety of tES.<br><br>RESULTS: There were no significant changes in the results of the MoCA and SAS scales before and after the experiment. However, significant differences were observed in VAS, SSR, AEs, and AEs-rela between tDCS and tACS. Additionally, fatigue increased, and energy levels decreased on VAMS-R with longer durations. No significant differences were found in other neuropsychological tests.<br><br>CONCLUSION: Our study revealed significant differences in tolerability and safety between tDCS and tACS, underscoring the importance of considering the stimulation type when evaluating these factors. Although tolerance and safety did not vary significantly across different stimulation durations in this study, future research may benefit from exploring shorter durations to further assess tolerability and safety efficiently.},
}
@article {pmid39605556,
year = {2024},
author = {Singer-Clark, T and Hou, X and Card, NS and Wairagkar, M and Iacobacci, C and Peracha, H and Hochberg, LR and Stavisky, SD and Brandman, DM},
title = {Speech motor cortex enables BCI cursor control and click.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39605556},
issn = {2692-8205},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; },
abstract = {Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study. We developed a cursor BCI driven by the participant's vPCG neural activity, and evaluated performance on a series of target selection tasks. The reported vPCG cursor BCI enabled rapidly-calibrating (40 seconds), accurate (2.90 bits per second) cursor control and click. The participant also used the BCI to control his own personal computer independently. These results suggest that placing electrodes in vPCG to optimize for speech decoding may also be a viable strategy for building a multi-modal BCI which enables both speech-based communication and computer control via cursor and click.},
}
@article {pmid39605372,
year = {2024},
author = {Kumaresan, V and Hung, CY and Hermann, BP and Seshu, J},
title = {Role of Dual Specificity Phosphatase 1 (DUSP1) in influencing inflammatory pathways in macrophages modulated by Borrelia burgdorferi lipoproteins.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39605372},
issn = {2692-8205},
support = {G12 MD007591/MD/NIMHD NIH HHS/United States ; R21 AI149263/AI/NIAID NIH HHS/United States ; },
abstract = {Borrelia burgdorferi (Bb), the spirochetal agent of Lyme disease, has a large array of lipoproteins that play a significant role in mediating host-pathogen interactions within ticks and vertebrates. Although there is substantial information on the effects of B. burgdorferi lipoproteins (BbLP) on immune modulatory pathways, the application of multi-omics methodologies to decode the transcriptional and proteomic patterns associated with host cell responses induced by lipoproteins in murine bone marrow-derived macrophages (BMDMs) has identified additional effectors and pathways. Single-cell RNA-Seq (scRNA-Seq) performed on BMDMs treated with various concentrations of borrelial lipoproteins revealed macrophage subsets within the BMDMs. Differential expression analysis showed that genes encoding various receptors, type I IFN-stimulated genes, signaling chemokines, and mitochondrial genes are altered in BMDMs in response to lipoproteins. Unbiased proteomics analysis of lysates of BMDMs treated with lipoproteins corroborated several of these findings. Notably, dual specificity phosphatase 1 (Dusp1) gene was upregulated during the early stages of BMDM exposure to BbLP. Pre-treatment with benzylidene-3-cyclohexylamino-1-indanone hydrochloride (BCI), an inhibitor of both DUSP1 and 6 prior to exposure to BbLP, demonstrated that DUSP1 negatively regulates NLRP3-mediated pro-inflammatory signaling and positively regulates the expression of interferon-stimulated genes and those encoding Ccl5, Il1b, and Cd274. Moreover, DUSP1, IkB kinase complex and MyD88 also modulate mitochondrial changes in BMDMs treated with borrelial lipoproteins. These findings advance the potential for exploiting DUSP1 as a therapeutic target to regulate host responses in reservoir hosts to limit survival of B. burgdorferi during its infectious cycle between ticks and mammalian hosts.},
}
@article {pmid39603445,
year = {2025},
author = {Kerezoudis, P and Jensen, MA and Huang, H and Ojemann, JG and Klassen, BT and Ince, NF and Hermes, D and Miller, KJ},
title = {Spatial and spectral changes in cortical surface potentials during pinching versusthumb and index finger flexion.},
journal = {Neuroscience letters},
volume = {845},
number = {},
pages = {138062},
doi = {10.1016/j.neulet.2024.138062},
pmid = {39603445},
issn = {1872-7972},
mesh = {Humans ; *Fingers/physiology ; Adult ; Male ; *Electrocorticography/methods ; Female ; *Motor Cortex/physiology ; *Movement/physiology ; Brain Mapping/methods ; Young Adult ; Epilepsy/physiopathology ; },
abstract = {Electrocorticographic (ECoG) signals provide high-fidelity representations of sensorimotor cortex activation during contralateral hand movements. Understanding the relationship between independent and coordinated finger movements along with their corresponding ECoG signals is crucial for precise brain mapping and neural prosthetic development. We analyzed subdural ECoG signals from three adult epilepsy patients with subdural electrode arrays implanted for seizure foci identification. Patients performed a cue-based task consisting of thumb flexion, index finger flexion or a pinching movement of both fingers together. Broadband power changes were estimated using principal component analysis of the power spectrum. All patients showed significant increases in broadband power during each movement compared to rest. We created topological maps for each movement type on brain renderings and quantified spatial overlap between movement types using a resampling metric. Pinching exhibited the highest spatial overlap with index flexion, followed by superimposed index and thumb flexion, with the least overlap observed for thumb flexion alone. This analysis provides practical insights into the complex overlap of finger representations in the motor cortex during various movement types and may help guide more nuanced approaches to brain-computer interfaces and neural prosthetics.},
}
@article {pmid39600948,
year = {2024},
author = {Adams, M and Cottrell, J},
title = {Development and characterization of an in vitro fluorescently tagged 3D bone-cartilage interface model.},
journal = {Frontiers in endocrinology},
volume = {15},
number = {},
pages = {1484912},
pmid = {39600948},
issn = {1664-2392},
mesh = {Animals ; Mice ; *Cell Differentiation ; *Osteoblasts/metabolism/cytology ; *Chondrocytes/metabolism/cytology ; *Osteoclasts/metabolism/cytology ; Bone and Bones/metabolism/cytology ; Cartilage/metabolism/cytology ; RAW 264.7 Cells ; Osteocytes/metabolism/cytology ; Cell Culture Techniques, Three Dimensional/methods ; Osteogenesis/physiology ; Cell Line ; },
abstract = {Three-dimensional cultures are widely used to study bone and cartilage. These models often focus on the interaction between osteoblasts and osteoclasts or osteoblasts and chondrocytes. A culture of osteoblasts, osteoclasts and chondrocytes would represent the cells that interact in the joint and a model with these cells could be used to study many diseases that affect the joints. The goal of this study was to develop 3D bone-cartilage interface (3D-BCI) that included osteoblasts, osteocytes, osteoclasts, and cartilage. Fluorescently tagged cell lines were developed to assess the interactions as cells differentiate to form bone and cartilage. Mouse cell line, MC3T3, was labeled with a nuclear GFP tag and differentiated into osteoblasts and osteocytes in Matrigel. Raw264.7 cells transfected with a red cytoplasmic tag were added to the system and differentiated with the MC3T3 cells to form osteoclasts. A new method was developed to differentiate chondrocyte cell line ATDC5 in a cartilage spheroid, and the ATDC5 spheroid was added to the MC3T3 and Raw264.7 cell model. We used an Incucyte and functional analysis to assess the cells throughout the differentiation process. The 3D-BCI model was found to be positive for TRAP, ALP, Alizarin red and Alcian blue staining to confirm osteoblastogenesis, osteoclastogenesis, and cartilage formation. Gene expression confirmed differentiation of cells based on increased expression of osteoblast markers: Alpl, Bglap, Col1A2, and Runx2, cartilage markers: Acan, Col2A1, Plod2, and osteoclast markers: Acp5, Rank and Ctsk. Based on staining, protein expression and gene expression results, we conclude that we successfully developed a mouse model with a 3D bone-cartilage interface.},
}
@article {pmid39600168,
year = {2024},
author = {Amandusson, &#xc5; and Nilsson, J and Pequito, S},
title = {[The role of EEG in tomorrow's medicine].},
journal = {Lakartidningen},
volume = {121},
number = {},
pages = {},
pmid = {39600168},
issn = {1652-7518},
mesh = {Humans ; Artificial Intelligence ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Wearable Electronic Devices ; },
abstract = {There is a breathtakingly rapid development in various areas that take advantage of the ever-improving possibilities to record and analyze the electrical activity generated in the brain. In this article, we attempt to briefly describe some of these areas, including AI-assisted EEG interpretation, the use of BCI (brain-computer interface) in a medical setting, and the possible new applications connected to the development of very small wearable EEG devices. Furthermore, we discuss the concerns and challenges presented by these advancements in neurotechnology.},
}
@article {pmid39598903,
year = {2024},
author = {Angulo Medina, AS and Aguilar Bonilla, MI and Rodríguez Giraldo, ID and Montenegro Palacios, JF and Cáceres Gutiérrez, DA and Liscano, Y},
title = {Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023).},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {22},
pages = {},
pmid = {39598903},
issn = {1424-8220},
support = {Convocatoria Interna No. 01-2024//Universidad Santiago de Cali/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Bibliometrics ; Artificial Intelligence ; Rehabilitation/methods ; Brain/physiology ; },
abstract = {EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.},
}
@article {pmid39597167,
year = {2024},
author = {Li, W and Zhou, J and Sheng, W and Jia, Y and Xu, W and Zhang, T},
title = {Highly Flexible and Compressible 3D Interconnected Graphene Foam for Sensitive Pressure Detection.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
pmid = {39597167},
issn = {2072-666X},
support = {12072151 and 12472153//National Natural Science Foundation of China/ ; 2019YFA0705400//National Key Research and Development Program of China/ ; MCAS-E-0124Y03//Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures/ ; },
abstract = {A flexible pressure sensor, capable of effectively detecting forces exerted on soft or deformable surfaces, has demonstrated broad application in diverse fields, including human motion tracking, health monitoring, electronic skin, and artificial intelligence systems. However, the design of convenient sensors with high sensitivity and excellent stability is still a great challenge. Herein, we present a multi-scale 3D graphene pressure sensor composed of two types of 3D graphene foam. The sensor exhibits a high sensitivity of 0.42 kPa[-1] within the low-pressure range of 0-390 Pa and 0.012 kPa[-1] within the higher-pressure range of 0.4 to 42 kPa, a rapid response time of 62 ms, and exceptional repeatability and stability exceeding 10,000 cycles. These characteristics empower the sensor to realize the sensation of a drop of water, the speed of airflow, and human movements.},
}
@article {pmid39597097,
year = {2024},
author = {Ji, W and Su, H and Jin, S and Tian, Y and Li, G and Yang, Y and Li, J and Zhou, Z and Wei, X and Tao, TH and Qin, L and Ye, Y and Sun, L},
title = {A Wireless Bi-Directional Brain-Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission.},
journal = {Micromachines},
volume = {15},
number = {11},
pages = {},
pmid = {39597097},
issn = {2072-666X},
support = {2022YFF0706500//National Key R &amp; D Program of China/ ; 2022ZD0209300//National Key R &amp; D Program of China/ ; 2021ZD0201600//National Key R &amp; D Program of China/ ; 2019YFA0905200//National Key R &amp; D Program of China/ ; 2021YFC2501500//National Key R &amp; D Program of China/ ; 2021YFF1200700//National Key R &amp; D Program of China/ ; 2022ZD0212300//National Key R &amp; D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; 21PJ1415100//Shanghai Pujiang Program/ ; 19PJ1410900//Shanghai Pujiang Program/ ; 21JM0010200//Science and Technology Commission Foundation of Shanghai/ ; 21142200300//Science and Technology Commission Foundation of Shanghai/ ; 22QA1410900//Shanghai Rising-Star Program/ ; 22YF1454700//Shanghai Sailing Program/ ; 20212ABC03W07//Jiangxi Province 03 Special Project and 5G Project/ ; 20201ZDE04013//Fund for Central Government in Guidance of Local Science and Technology Development/ ; 2021B0909060002//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; 2021B0909050004//Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province/ ; },
abstract = {Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain-computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications.},
}
@article {pmid39595855,
year = {2024},
author = {Tubbs, A and Vazquez, EA},
title = {Engineering and Technological Advancements in Repetitive Transcranial Magnetic Stimulation (rTMS): A Five-Year Review.},
journal = {Brain sciences},
volume = {14},
number = {11},
pages = {},
pmid = {39595855},
issn = {2076-3425},
abstract = {In the past five years, repetitive transcranial magnetic stimulation (rTMS) has evolved significantly, driven by advancements in device design, treatment protocols, software integration, and brain-computer interfaces (BCIs). This review evaluates how these innovations enhance the safety, efficacy, and accessibility of rTMS while identifying key challenges such as protocol standardization and ethical considerations. A structured review of peer-reviewed studies from 2019 to 2024 focused on technological and clinical advancements in rTMS, including AI-driven personalized treatments, portable devices, and integrated BCIs. AI algorithms have optimized patient-specific protocols, while portable devices have expanded access. Enhanced coil designs and BCI integration offer more precise and adaptive neuromodulation. However, challenges remain in standardizing protocols, addressing device complexity, and ensuring equitable access. While recent innovations improve rTMS's clinical utility, gaps in long-term efficacy and ethical concerns persist. Future research must prioritize standardization, accessibility, and robust ethical frameworks to ensure rTMS's sustainable impact.},
}
@article {pmid39593731,
year = {2024},
author = {Nuñez Ponasso, G and Wartman, WA and McSweeney, RC and Lai, P and Haueisen, J and Maess, B and Knösche, TR and Weise, K and Noetscher, GM and Raij, T and Makaroff, SN},
title = {Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {11},
pages = {},
pmid = {39593731},
issn = {2306-5354},
support = {R01 EB035484/EB/NIBIB NIH HHS/United States ; R01EB035484/EB/NIBIB NIH HHS/United States ; 2018 IZN 004//Free State of Thuringia/ ; R01MH130490/MH/NIMH NIH HHS/United States ; R01 MH130490/MH/NIMH NIH HHS/United States ; 01GQ2201//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 1R01NS126337/NS/NINDS NIH HHS/United States ; R01 NS126337/NS/NINDS NIH HHS/United States ; 01GQ2304A//Bundesministerium f&#xfc;r Bildung und Forschung/ ; 2018 IZN 004//European Regional Development Fund/ ; },
abstract = {Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10-20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.},
}
@article {pmid39592732,
year = {2025},
author = {Zheng, MM and Li, JY and Guo, HJ and Zhang, J and Wang, LS and Jiang, KF and Wu, HH and He, QJ and Ding, L and Yang, B},
title = {IMPDH inhibitors upregulate PD-L1 in cancer cells without impairing immune checkpoint inhibitor efficacy.},
journal = {Acta pharmacologica Sinica},
volume = {46},
number = {4},
pages = {1058-1067},
pmid = {39592732},
issn = {1745-7254},
mesh = {Humans ; *B7-H1 Antigen/metabolism/antagonists &amp; inhibitors ; *IMP Dehydrogenase/antagonists &amp; inhibitors/metabolism ; *Immune Checkpoint Inhibitors/pharmacology ; Animals ; *Up-Regulation/drug effects ; Cell Line, Tumor ; Enzyme Inhibitors/pharmacology ; Mice ; Mycophenolic Acid/pharmacology ; Mice, Inbred BALB C ; },
abstract = {Tumor cells are characterized by rapid proliferation. In order to provide purines for DNA and RNA synthesis, inosine 5'-monophosphate dehydrogenase (IMPDH), a key enzyme in the de novo guanosine biosynthesis, is highly expressed in tumor cells. In this study we investigated whether IMPDH was involved in cancer immunoregulation. We revealed that the IMPDH inhibitors AVN944, MPA or ribavirin concentration-dependently upregulated PD-L1 expression in non-small cell lung cancer cell line NCI-H292. This effect was reproduced in other non-small cell lung cancer cell lines H460, H1299 and HCC827, colon cancer cell lines HT29, RKO and HCT116, as well as kidney cancer cell line Huh7. In NCI-H292 cells, we clarified that IMPDH inhibitors increased CD274 mRNA levels by enhancing CD274 mRNA stability. IMPDH inhibitors improved the affinity of the ARE-binding protein HuR for CD274 mRNA, thereby stabilizing CD274 mRNA. Guanosine supplementation abolished the IMPDH inhibitor-induced increase in PD-L1 expression. In CT26 and EMT6 tumor models used for ICIs based studies, we showed that despite its immunosuppressive properties, the IMPDH inhibitor mycophenolate mofetil did not reduce the clinical response of checkpoint inhibitors, representing an important clinical observation given that this class of drugs is approved for use in multiple diseases. We conclude that PD-L1 induction contributes to the immunosuppressive effect of IMPDH inhibitors. Furthermore, the IMPDH inhibitor mycophenolate mofetil does not antagonize immune checkpoint blockade.},
}
@article {pmid39592434,
year = {2024},
author = {Paban, V and Feraud, L and Weills, A and Duplan, F},
title = {Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.},
journal = {The European journal of neuroscience},
volume = {60},
number = {12},
pages = {7164-7182},
pmid = {39592434},
issn = {1460-9568},
support = {214535, UMR7077//Janssen Horizon/ ; },
mesh = {Humans ; *Neurofeedback/methods ; *Cognitive Dysfunction/therapy/physiopathology/rehabilitation ; Female ; Male ; Aged ; Middle Aged ; *Electroencephalography/methods ; },
abstract = {IMPACT STATEMENT: This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals.<br><br>ABSTRACT: In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.},
}
@article {pmid39591752,
year = {2024},
author = {Sun, Y and Zhang, F and Li, Z and Liu, X and Zheng, D and Zhang, S and Fan, S and Wu, X},
title = {Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9778},
pmid = {39591752},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Scalp/physiology ; *Evoked Potentials, Visual/physiology ; Male ; Ear/physiology ; Adult ; Female ; Young Adult ; Photic Stimulation/methods ; },
abstract = {BACKGROUND: Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude.<br><br>OBJECTIVE: Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value.<br><br>APPROACH: To address this challenge, we focus on enhancing ear-EEG feature representations by training the model to learn features similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed to optimize SSVEP target classification in ear-EEG data. This framework combines signals from the scalp to obtain multi-layer distilled knowledge through the cooperation of mid-layer feature distillation and output layer response distillation.Mainresults.We improve the classification of the initial 1 s data and achieved a maximum classification accuracy of 81.1%. We evaluate the proposed MESD framework through single-session, cross-session, and cross-subject transfer decoding, comparing it with baseline methods. The results demonstrate that the proposed framework achieves the best classification results in all experiments.<br><br>SIGNIFICANCE: Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks such as auxiliary control and rehabilitation training in future endeavors.},
}
@article {pmid39591745,
year = {2024},
author = {Smedemark-Margulies, N and Wang, Y and Koike-Akino, T and Liu, J and Parsons, K and Bicer, Y and Erdoğmuş, D},
title = {Improving subject transfer in EEG classification with divergence estimation.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9777},
pmid = {39591745},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; Humans ; Algorithms ; Neural Networks, Computer ; Brain-Computer Interfaces ; },
abstract = {Objective. Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test subjects. We improve performance using new regularization techniques during model training.Approach. We propose several graphical models to describe an EEG classification task. From each model, we identify statistical relationships that should hold true in an idealized training scenario (with infinite data and a globally-optimal model) but that may not hold in practice. We design regularization penalties to enforce these relationships in two stages. First, we identify suitable proxy quantities (divergences such as Mutual Information and Wasserstein-1) that can be used to measure statistical independence and dependence relationships. Second, we provide algorithms to efficiently estimate these quantities during training using secondary neural network models.Main results. We conduct extensive computational experiments using a large benchmark EEG dataset, comparing our proposed techniques with a baseline method that uses an adversarial classifier. We first show the performance of each method across a wide range of hyperparameters, demonstrating that each method can be easily tuned to yield significant benefits over an unregularized model. We show that, using ideal hyperparameters for all methods, our first technique gives significantly better performance than the baseline regularization technique. We also show that, across hyperparameters, our second technique gives significantly more stable performance than the baseline. The proposed methods require only a small computational cost at training time that is equivalent to the cost of the baseline.Significance. The high variability in signal distribution between subjects means that typical approaches to EEG signal modeling often require time-intensive calibration for each user, and even re-calibration before every use. By improving the performance of population models in the most stringent case of zero-shot subject transfer, we may help reduce or eliminate the need for model calibration.},
}
@article {pmid39590012,
year = {2024},
author = {AlQahtani, NJ and Al-Naib, I and Ateeq, IS and Althobaiti, M},
title = {Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.},
journal = {Biosensors},
volume = {14},
number = {11},
pages = {},
pmid = {39590012},
issn = {2079-6374},
support = {KSRG-2023-195//King Salman Center for Disability Research/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared ; *Electromyography ; Male ; Adult ; Knee Prosthesis ; Knee/physiology ; Brain-Computer Interfaces ; Artificial Limbs ; },
abstract = {The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain-computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.},
}
@article {pmid39589888,
year = {2024},
author = {Liu, J and Li, Z and Sun, M and Zhou, L and Wu, X and Lu, Y and Shao, Y and Liu, C and Huang, N and Hu, B and Wu, Z and You, C and Li, L and Wang, M and Tao, L and Di, Z and Sheng, X and Mei, Y and Song, E},
title = {Flexible bioelectronic systems with large-scale temperature sensor arrays for monitoring and treatments of localized wound inflammation.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {49},
pages = {e2412423121},
pmid = {39589888},
issn = {1091-6490},
support = {2022ZD0209900//STI 2030-Major Project/ ; 62204057 62304044 12022209//the National Natural Science Foundation of China/ ; 22ZR1406400//Science and Technology Commission of Shanghai Municipality (STCSM)/ ; LG-QS-202202-02//Lingang Laboratory/ ; },
mesh = {Animals ; Humans ; *Inflammation/therapy ; *Wound Healing ; Hydrogels/chemistry ; Wearable Electronic Devices ; Temperature ; Monitoring, Physiologic/instrumentation/methods ; Rats ; Biosensing Techniques/instrumentation/methods ; },
abstract = {Continuous monitoring and closed-loop therapy of soft wound tissues is of particular interest in biomedical research and clinical practices. An important focus is on the development of implantable bioelectronics that can measure time-dependent temperature distribution related to localized inflammation over large areas of wound and offer in situ treatment. Existing approaches such as thermometers/thermocouples provide limited spatial resolution, inapplicable to a wearable/implantable format. Here, we report a conformal, scalable device package that integrates a flexible amorphous silicon-based temperature sensor array and drug-loaded hydrogel for the healing process. This system can enable the spatial temperature mapping at submillimeter resolution and high sensitivity of 0.1 °C, for dynamically localizing the inflammation regions associated with temperature change, automatically followed with heat-triggered drug delivery from hydrogel triggered by wearable infrared light-emitting-diodes. We establish the operational principles experimentally and computationally and evaluate system functionalities with a wide range of targets including live animal models and human subjects. As an example of medical utility, this system can yield closed-loop monitoring/treatments by tracking of temperature distribution over wound areas of live rats, in designs that can be integrated with automated wireless control. These findings create broad utilities of these platforms for clinical diagnosis and advanced therapy for wound healthcare.},
}
@article {pmid39589717,
year = {2024},
author = {Chan, RW and Edelman, BJ and Tsang, SY and Gao, K and Yu, AC},
title = {Opportunities for System Neuroscience.},
journal = {Advances in neurobiology},
volume = {41},
number = {},
pages = {247-253},
pmid = {39589717},
issn = {2190-5215},
mesh = {Humans ; *Neurosciences ; *Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Magnetic Resonance Imaging ; Neuroimaging ; Precision Medicine ; Nerve Net/diagnostic imaging/physiology ; },
abstract = {Systems neuroscience explores the intricate organization and dynamic function of neural circuits and networks within the brain. By elucidating how these complex networks integrate to execute mental operations, this field aims to deepen our understanding of the biological basis of cognition, behavior, and consciousness. In this chapter, we outline the promising future of systems neuroscience, highlighting the emerging opportunities afforded by powerful technological innovations and their applications. Cutting-edge tools such as awake functional MRI, ultrahigh field strength neuroimaging, functional ultrasound imaging, and optoacoustic techniques have revolutionized the field, enabling unprecedented observation and analysis of brain activity. The insights gleaned from these advanced methodologies have empowered the development of a suite of exciting applications across diverse domains. These include brain-machine interfaces (BMIs) for neural prosthetics, cognitive enhancement therapies, personalized mental health interventions, and precision medicine approaches. As our comprehension of neural systems continues to grow, it is envisioned that these and related applications will become increasingly refined and impactful in improving human health and well-being.},
}
@article {pmid39588722,
year = {2025},
author = {Xiang, Z and Yang, L and Yu, B and Zeng, Q and Huang, T and Shi, S and Yu, H and Zhang, Y and Wu, J and Zhu, M},
title = {Recent advances in polymer-based thin-film electrodes for ECoG applications.},
journal = {Journal of materials chemistry. B},
volume = {13},
number = {2},
pages = {454-471},
doi = {10.1039/d4tb02090a},
pmid = {39588722},
issn = {2050-7518},
mesh = {*Polymers/chemistry ; *Electrodes ; Humans ; *Electrocorticography/instrumentation ; Brain-Computer Interfaces ; Animals ; },
abstract = {Electrocorticography (ECoG) has garnered widespread attention owing to its superior signal resolution compared to conventional electroencephalogram (EEG). While ECoG signal acquisition entails invasiveness, the invasive rigid electrode used inevitably inflicts damage on brain tissue. Polymer electrodes that combine conductivity and transparency have garnered great interest because they not only facilitate high-quality signal acquisition but also provide additional insights while preserving the health of the brain, positioning them as the future frontier in the brain-computer interface (BCI). This review summarizes the multifaceted functions of polymers in ECoG thin-film electrodes for the BCI. We present the abilities of sensitive and structural polymers focusing on impedance reduction, signal quality improvement, good flexibility, and transparency. Typically, two sensitive polymers and four structural polymers are analyzed in detail in terms of ECoG electrode properties. Moreover, the underlying mechanism of polymer-based electrodes in signal quality enhancement is revealed. Finally, the remaining challenges and perspectives are discussed.},
}
@article {pmid39588687,
year = {2025},
author = {Wang, J and Jiang, Y and Xiong, T and Lu, J and He, X and Yu, P and Mao, L},
title = {Optically Modulated Nanofluidic Ionic Transistor for Neuromorphic Functions.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {64},
number = {7},
pages = {e202418949},
doi = {10.1002/anie.202418949},
pmid = {39588687},
issn = {1521-3773},
support = {Z230022//Natural Science Foundation of Beijing/ ; 22134002//the National Natural Science Foundation of China/ ; 22174146, 22474011//the National Natural Science Foundation of China/ ; },
mesh = {*Transistors, Electronic ; Nanotechnology/instrumentation ; Metal-Organic Frameworks/chemistry ; },
abstract = {Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.},
}
@article {pmid39586499,
year = {2025},
author = {Thenmozhi, T and Helen, R and Mythili, S},
title = {Classification of motor imagery EEG with ensemble RNCA model.},
journal = {Behavioural brain research},
volume = {479},
number = {},
pages = {115345},
doi = {10.1016/j.bbr.2024.115345},
pmid = {39586499},
issn = {1872-7549},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Bayes Theorem ; Brain/physiology ; Motor Activity/physiology ; Movement/physiology ; Adult ; },
abstract = {Motor Imagery (MI) based brain-computer interface (BCI) systems are used for regaining the motor functions of neurophysiologically affected persons. But the performance of MI tasks is degraded due to the presence of redundant EEG channels. Hence, a novel ensemble regulated neighborhood component analysis (ERNCA) method provides a perfect identification of neural region that stimulate motor movements. Domains of statistical, frequency, spatial and transform-based features narrowed down the misclassification rate. The gradient boosting method selects the relevant features thereby reduces the computational complexity. Finally, Bayesian optimized ensemble classifier finetuned the classification accuracies of 97.22 % and 91.62 % for Datasets IIIa and IVa respectively. This approach is further strengthened by analyzing real-time data with the accuracy of 93.75 %. This method qualifies out of four benchmark methods with significant percent of improvement in accuracy for these three datasets. As per the spatial distribution of refined EEG channels, majority of the brain's motor functions concentrates on frontal and central cortex regions of brain.},
}
@article {pmid39586422,
year = {2025},
author = {Mao, W and Shen, X and Bai, X and Wang, A},
title = {Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.},
journal = {Neuroscience},
volume = {564},
number = {},
pages = {214-225},
doi = {10.1016/j.neuroscience.2024.11.044},
pmid = {39586422},
issn = {1873-7544},
mesh = {Humans ; *Empathy/physiology ; Male ; *Electroencephalography/methods ; Female ; Young Adult ; *Machine Learning ; *Decision Making/physiology ; Adult ; Brain/physiology ; Evoked Potentials/physiology ; Adolescent ; Emotions/physiology ; },
abstract = {Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait empathy influence prosocial behavior. Addressing this knowledge gap is important to understand empathy-driven prosocial behavior. We employed an EEG experiment combined with interpretable machine learning methods to probe these questions. We found that empathic concern (EC) played the most pivotal role in donation decision. Behaviorally, EC negatively moderates the effect of perceived closeness and deservedness of charity projects on the willingness to donate. The machine learning results indicate that EC significantly predicts late positive potential (LPP) and beta-band activity during donation information processing. Further regression analysis results indicate that EC, rather than other dimensions of trait empathy, can positively predict LPP amplitude and negatively predict beta-band activity. These results indicated that participants with higher EC scores may experience heightened emotional arousal and the vicarious experience of others' emotions while processing donation information. Our work adds weight to understanding the relationship between trait empathy and prosocial behavior and provides electrophysiological evidence.},
}
@article {pmid39586421,
year = {2025},
author = {Hong, T and Zhou, H and Xi, W and Li, X and Du, Y and Liu, J and Geng, F and Hu, Y},
title = {Acting with awareness is positively correlated with dorsal anterior cingulate cortex glutamate concentration but both are impaired in Internet gaming disorder.},
journal = {Neuroscience},
volume = {564},
number = {},
pages = {226-235},
doi = {10.1016/j.neuroscience.2024.11.054},
pmid = {39586421},
issn = {1873-7544},
mesh = {Humans ; *Gyrus Cinguli/metabolism/diagnostic imaging ; *Glutamic Acid/metabolism ; Male ; *Internet Addiction Disorder/metabolism/physiopathology ; Young Adult ; Adult ; Female ; *Awareness/physiology ; Video Games ; Magnetic Resonance Spectroscopy ; Proton Magnetic Resonance Spectroscopy ; Adolescent ; },
abstract = {Internet gaming disorder (IGD) is increasingly recognized as a public concern for its adverse impacts on cognition and mental health. In IGD, the transition from goal-directed actions to habitual and eventually compulsive behaviors is accompanied by altered neural response within the dorsal anterior cingulate cortex (dACC), a critical region involved in conscious actions. However, the neurochemical profile of the dACC in IGD and its relationship with behavioral awareness remain poorly understood. In this study, [1]H-magnetic resonance spectroscopy was employed to quantify dACC glutamate concentration and examine its association with the capacity for 'acting with awareness' among 21 participants with IGD and 19 recreational game users. Results indicated that dACC glutamate levels and behavioral awareness were significantly lower in the IGD group compared to recreational game users. Moreover, a significant positive correlation between awareness and dACC glutamate concentration emerged in the recreational game users' group, a relationship attenuated in those with IGD. In an independent cohort of 107 participants, the positive association between awareness and dACC glutamate concentration was replicated. These findings suggest that reduced dACC glutamate in IGD may underlie diminished awareness of maladaptive habitual behaviors. Enhancing dACC neural excitability through neuromodulation or mindfulness training could represent a potential intervention to restore behavioral awareness.},
}
@article {pmid39586380,
year = {2025},
author = {Higashi, H},
title = {Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.},
journal = {Journal of neuroscience methods},
volume = {414},
number = {},
pages = {110323},
doi = {10.1016/j.jneumeth.2024.110323},
pmid = {39586380},
issn = {1872-678X},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Brain/physiology ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain-computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.},
}
@article {pmid39583262,
year = {2024},
author = {Abo Alzahab, N and Iorio, AD and Apollonio, L and Alshalak, M and Gravina, A and Antognoli, L and Baldi, M and Scalise, L and Alchalabi, B},
title = {Auditory evoked potential electroencephalography-biometric dataset.},
journal = {Data in brief},
volume = {57},
number = {},
pages = {111065},
pmid = {39583262},
issn = {2352-3409},
abstract = {This work aims to assess the use of electroencephalographic (EEG) signals as a means of biometric authentication. More than 240 recordings, each lasting 2 min, were gathered from 20 subjects involved in the data collection. Data include the results of experiments performed both in a resting state and in the presence of auditory stimuli. The resting-state EEG signals were acquired with both open and closed eyes. The auditory stimuli EEG signals consist of six experiments divided into two scenarios. The first scenario considers in-ear stimuli, while the second scenario considers bone-conducting stimuli. For each of the two scenarios, experiments include a native language song, a non-native language song and some neutral music. This data could be used to develop biometric systems for authentication or identification. Additionally, they could be used to study the effect of auditory stimuli such as music on EEG activity and to compare it with the resting state condition.},
}
@article {pmid39582417,
year = {2025},
author = {Wang, X and Zhu, K and Wu, W and Zhou, D and Lu, H and Du, J and Cai, L and Yan, X and Li, W and Qian, X and Wang, X and Ma, C and Hu, Y and Tian, C and Sun, B and Fang, Z and Wu, J and Jiang, P and Liu, J and Liu, C and Fan, J and Cui, H and Shen, Y and Duan, S and Bao, A and Yang, Y and Qiu, W and Zhang, J},
title = {Prevalence of mixed neuropathologies in age-related neurodegenerative diseases: A community-based autopsy study in China.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {1},
pages = {e14369},
pmid = {39582417},
issn = {1552-5279},
support = {2021ZD0201100//STI2030-Major Project/ ; 2024C03098//Key R&amp;D Program of Zhejiang province/ ; 2024SSYS0018//Key R&amp;D Program of Zhejiang province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; 2021-I2M-1-025//CAMS Innovation Fund for Medical Sciences/ ; 2024SSYS0018//Key Research and Development Program of Zhejiang Province/ ; 81971184//National Natural Science Foundation of China/ ; },
mesh = {Humans ; China/epidemiology ; Prevalence ; Male ; Female ; Aged ; *Neurodegenerative Diseases/pathology/epidemiology ; *Autopsy ; Aged, 80 and over ; *Brain/pathology ; Middle Aged ; Aging/pathology ; Tauopathies/pathology/epidemiology ; Lewy Body Disease/pathology/epidemiology ; Cerebrovascular Disorders/pathology/epidemiology ; Alzheimer Disease/pathology/epidemiology ; },
abstract = {INTRODUCTION: Despite extensive studies on mixed neuropathologies, data from China are limited. This study aims to fill this gap by analyzing brain samples from Chinese brain banks.<br><br>METHODS: A total of 1142 brains from six Chinese brain banks were examined using standardized methods. Independent pathologists conducted evaluations with stringent quality control. Prevalence and correlations of neurological disorders were analyzed.<br><br>RESULTS: Significant proportions of brains displayed primary age-related tauopathy (PART, 35%), limbic-predominant age-related TDP-43 encephalopathy (LATE, 46%), and aging-related tau astrogliopathy (ARTAG, 12%). Alzheimer's disease neuropathological change (ADNC, 48%), Lewy body disease (LBD, 13%), and cerebrovascular disease (CVD, 63%) were also prevalent, often co-occurring with regional variations. CVD emerged as the potential most early contributor to neuropathological changes.<br><br>DISCUSSION: This analysis highlights the prevalence of PART, LATE, ARTAG, ADNC, LBD, and CVD, with regional differences. The findings suggest CVD may be the earliest contributing factor, potentially preceding other neuropathologies. Highlights The prevalence of primary age-related tauopathy (PART), limbic-predominant age-related TDP-43 encephalopathy (LATE), aging-related tau astrogliopathy (ARTAG), Alzheimer's disease neuropathologic change, Lewy body disease, and cerebrovascular disease (CVD) in China, increasing with age, is comparable to other countries. Significant regional differences in the prevalences of diseases are noted. CVD develops prior to any other disorders, including PART, LATE, and ARTAG.},
}
@article {pmid39581346,
year = {2025},
author = {Brands, R and Bartsch, J and Thommes, M},
title = {Complemental hard modeling in Raman spectroscopy: A case study on titanium dioxide-free coating in-line monitoring.},
journal = {Journal of pharmaceutical sciences},
volume = {114},
number = {1},
pages = {577-585},
doi = {10.1016/j.xphs.2024.10.044},
pmid = {39581346},
issn = {1520-6017},
mesh = {*Titanium/chemistry ; *Spectrum Analysis, Raman/methods ; Least-Squares Analysis ; Calcium Carbonate/chemistry ; Tablets/chemistry ; Tablets, Enteric-Coated/chemistry ; },
abstract = {Tablets are coated for taste or odor modification, for modified release profiles or as a protective layer to increase the stability. Here, titanium dioxide is frequently added as a coating component due to its opaque properties. Furthermore, its Raman activity makes it an integral part of in-line monitoring models. However, due to the carcinogenic potential of titanium dioxide, calcium carbonate is utilized as a substitute, exhibiting similar opaque properties. Calcium carbonate tends to exhibit overlapped peaks with carbon hydrates in the Raman spectrum. Consequently, new models based on e.g. hard modeling are required instead of peak integration. In this study, tablets were coated with a coating including calcium carbonate. Partial Least Squares Regression (PLS) and Complemental Hard Modeling (CHM) were examined as feasible in-line monitoring approaches. Furthermore, two different measurement positions in the coater were compared, orthogonal and tangential with respect to the moving tablet bed. Cross-validation exhibited improved CHM performance with reduced RMSECV values of about 5 %. The prediction of the coating mass growth occurred comparable with RMSEP values in a similar range of 2-5 %. Despite this, the CHM´s achieved improved performance with reduced training data quantity and quality. The different measurement positions indicated no process-relevant differences.},
}
@article {pmid39581146,
year = {2024},
author = {Anwar, F and Zhang, K and Sun, C and Pang, M and Zhou, W and Li, H and He, R and Liu, X and Ming, D},
title = {Hydrocephalus: An update on latest progress in pathophysiological and therapeutic research.},
journal = {Biomedicine &amp; pharmacotherapy = Biomedecine &amp; pharmacotherapie},
volume = {181},
number = {},
pages = {117702},
doi = {10.1016/j.biopha.2024.117702},
pmid = {39581146},
issn = {1950-6007},
mesh = {Animals ; Humans ; *Genetic Therapy/methods ; *Hydrocephalus/genetics/physiopathology/therapy ; },
abstract = {Hydrocephalus is a severe and life-threatening disease associated with the imbalance of CSF dynamics and affects millions globally at any age, including infants. One cause of pathology that is wide-ranging is genetic mutations to post-traumatic injury. The most effective current pharmacological treatments provide only symptomatic relief and do not address the underlying pathology. At the same time, surgical procedures such as VP shunts performed in lower-income countries are often poorly tolerated due to insufficient diagnostic resources and suboptimal outcomes partially attributable to inferior materials. These problems are compounded by an overall lack of funding that keeps high-quality medical devices out of reach for all but the most developed countries and even among those states. There is a massive variance in treatment effectiveness. This review indicates the necessity for innovative and low-cost, accessible treatment strategies to close these gaps, focusing on current advances in novel therapies, including Pharmacological, gene therapy, and nano-based technologies, which are currently at different stages of clinical trial phases. This review provides an overview of pathophysiology, current treatments, and promising new therapeutic strategies for hydrocephalus.},
}
@article {pmid39577701,
year = {2025},
author = {Xia, G and Wang, L and Xiong, S and Deng, J},
title = {Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis.},
journal = {Journal of neuroscience methods},
volume = {414},
number = {},
pages = {110325},
doi = {10.1016/j.jneumeth.2024.110325},
pmid = {39577701},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Adult ; Male ; Algorithms ; Female ; Young Adult ; },
abstract = {BACKGROUND: In recent years, spatial filter-based frequency recognition methods have become popular in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. However, these methods are ineffective in suppressing local noise, and they rely on the length of the data. In practical applications, enhancing recognition performance with short data windows is a significant challenge for the BCI systems.<br><br>NEW METHOD: With extracting temporal information and eliminating local noise, a temporally local canonical correlation analysis based on training data-driven (TI-tdCCA) method is proposed to enhance the recognition performance of SSVEPs. Based on a novel framework, the filters are derived by incorporating the Laplacian matrix through the use of TI-CCA between the concatenated training data and individual templates. The target frequency is subsequently determined by applying the appropriate spatial filters and Laplacian matrix.<br><br>RESULTS: The experimental results on two datasets, consisting of 40 classes and recording from 35 and 70 subjects respectively, demonstrate that the proposed method consistently outperforms the eight competing methods in the majority of cases. The proposed method is simultaneously evaluated by an extended version that incorporates artificial reference signals. The extended method demonstrates a significant improvement over the proposed method. Specifically, with a time window of 0.7&#x202f;s, the average recognition accuracy of the subjects increases by 10.71&#x202f;% on the Benchmark dataset and by 6.98&#x202f;% on the BETA dataset, respectively.<br><br>Our extended method outperforms the state-of-the-art methods by at least 3&#x202f;%, and it effectively suppresses local noise and maintains excellent scalability.<br><br>The proposed method can effectively combine spatial and temporal filters to improve the recognition performance of SSVEPs.},
}
@article {pmid39577098,
year = {2025},
author = {Oxley, TJ},
title = {A 10-year journey towards clinical translation of an implantable endovascular BCI a keynote lecture given at the BCI society meeting in Brussels.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad9633},
pmid = {39577098},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; Electrocorticography/methods/instrumentation ; Electrodes, Implanted/trends ; Electroencephalography/methods/trends ; *Endovascular Procedures/methods/instrumentation/trends ; Translational Research, Biomedical/methods/trends ; },
abstract = {In the rapidly evolving field of brain-computer interfaces (BCIs), a novel modality for recording electrical brain signals has quietly emerged over the past decade. The technology is endovascular electrocorticography (ECoG), an innovation that stands alongside well-established methods such as electroencephalography, traditional ECoG, and single/multi-unit activity recording. This system was inspired by advancements in interventional cardiology, particularly the integration of electronics into various medical interventions. The breakthrough led to the development of the Stentrode system, which employs stent-mounted electrodes to record electrical brain activity for applications in a motor neuroprosthesis. This perspective explores four key areas in our quest to bring the Stentrode BCI to market: the critical patient need for autonomy driving our efforts, the hurdles and achievements in assessing BCI performance, the compelling advantages of our unique endovascular approach, and the essential steps for clinical translation and product commercialization.},
}
@article {pmid39576681,
year = {2024},
author = {Liu, D and Wei, Y},
title = {CVR-BBI: an open-source VR platform for multi-user collaborative brain to brain interfaces.},
journal = {Bioinformatics (Oxford, England)},
volume = {40},
number = {12},
pages = {},
pmid = {39576681},
issn = {1367-4811},
support = {12101570//National Natural Science Foundation of China/ ; 2024C01142//Key Research and Development Program of Zhejiang Province/ ; },
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Brain/physiology ; Virtual Reality ; Software ; Male ; Adult ; User-Computer Interface ; Female ; },
abstract = {SUMMARY: As brain imaging and neurofeedback technologies advance, the brain-to-brain interface (BBI) has emerged as an innovative field, enabling in-depth exploration of cross-brain information exchange and enhancing our understanding of collaborative intelligence. However, no open-source virtual reality (VR) platform currently supports the rapid and efficient configuration of multi-user, collaborative BBIs. To address this gap, we introduce the Collaborative Virtual Reality Brain-to-Brain Interface (CVR-BBI), an open-source platform consisting of a client and server. The CVR-BBI client enables users to participate in collaborative experiments, collect electroencephalogram (EEG) data, and manage interactive multisensory stimuli within the VR environment. Meanwhile, the CVR-BBI server manages multi-user collaboration paradigms, and performs real-time analysis of the EEG data. We evaluated the CVR-BBI platform using the SSVEP paradigm and observed that collaborative decoding outperformed individual decoding, validating the platform's effectiveness in collaborative settings. The CVR-BBI offers a pioneering platform that facilitates the development of innovative BBI applications within collaborative VR environments, thereby enhancing the understanding of brain collaboration and cognition.<br><br>CVR-BBI is released as an open-source platform, with its source code being available at https://github.com/DILIU1/CVR-BBI.},
}
@article {pmid39576281,
year = {2025},
author = {Farina, D and Merletti, R and Enoka, RM},
title = {The extraction of neural strategies from the surface EMG: 2004-2024.},
journal = {Journal of applied physiology (Bethesda, Md. : 1985)},
volume = {138},
number = {1},
pages = {121-135},
doi = {10.1152/japplphysiol.00453.2024},
pmid = {39576281},
issn = {1522-1601},
support = {810346//EC | ERC | HORIZON EUROPE European Research Council (ERC)/ ; RG-2206-39688//National Multiple Sclerosis Society/ ; EP/T020970/1//UKRI | Engineering and Physical Sciences Research Council (EPSRC)/ ; },
mesh = {*Electromyography/methods ; Humans ; *Muscle, Skeletal/physiology ; Muscle Contraction/physiology ; Motor Neurons/physiology ; Algorithms ; Animals ; Movement/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {This review follows two previous papers [Farina et al. Appl Physiol (1985) 96: 1486-1495, 2004; Farina et al. J Appl Physiol (1985) 117: 1215-1230, 2014] in which we reflected on the use of surface electromyography (EMG) in the study of the neural control of movement. This series of papers began with an analysis of the indirect approaches of EMG processing to infer the neural control strategies and then closely followed the progress in EMG technology. In this third paper, we focus on three main areas: surface EMG modeling; surface EMG processing, with an emphasis on decomposition; and interfacing applications of surface EMG recordings. We highlight the latest advances in EMG models that allow fast generation of simulated signals from realistic volume conductors, with applications ranging from validation of algorithms to identification of nonmeasurable parameters by inverse modeling. Surface EMG decomposition is currently an established state-of-the-art tool for physiological investigations of motor units. It is now possible to identify large samples of motor units, to track motor units over multiple sessions, to partially compensate for the nonstationarities in dynamic contractions, and to decompose signals in real time. The latter achievement has facilitated advances in myocontrol, by using the online decoded neural drive as a control signal, such as in the interfacing of prostheses. Looking back over the 20 yr since our first review, we conclude that the recording and analysis of surface EMG signals have seen breakthrough advances in this period. Although challenges in its application and interpretation remain, surface EMG is now a solid and unique tool for the study of the neural control of movement.},
}
@article {pmid39572612,
year = {2024},
author = {Lee, AH and Lee, J and Leung, V and Larson, L and Nurmikko, A},
title = {Patterned electrical brain stimulation by a wireless network of implantable microdevices.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {10093},
pmid = {39572612},
issn = {2041-1723},
support = {S10 OD025181/OD/NIH HHS/United States ; 232600//National Science Foundation (NSF)/ ; },
mesh = {Animals ; *Wireless Technology/instrumentation ; *Electric Stimulation/instrumentation/methods ; Rats ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Brain/physiology ; Male ; Motor Cortex/physiology ; Behavior, Animal/physiology ; },
abstract = {Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. Here, we introduce a wireless approach to multipoint patterned electrical microstimulation by a spatially distributed epicortically implanted network of silicon microchips to target specific areas of the cortex. Each sub-millimeter-sized microchip harvests energy from an external radio-frequency source and converts this into biphasic current injected focally into tissue by a pair of integrated microwires. The amplitude, period, and repetition rate of injected current from each chip are controlled across the implant network by implementing a pre-scheduled, collision-free bitmap wireless communication protocol featuring sub-millisecond latency. As a proof-of-concept technology demonstration, a network of 30 wireless stimulators was chronically implanted into motor and sensory areas of the cortex in a freely moving rat for three months. We explored the effects of patterned intracortical electrical stimulation on trained animal behavior at average RF powers well below regulatory safety limits.},
}
@article {pmid39572577,
year = {2024},
author = {Zhang, Z and Ding, X and Bao, Y and Zhao, Y and Liang, X and Qin, B and Liu, T},
title = {Chisco: An EEG-based BCI dataset for decoding of imagined speech.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1265},
pmid = {39572577},
issn = {2052-4463},
support = {U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22B2059//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176079//National Natural Science Foundation of China (National Science Foundation of China)/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; Y02022F005//Natural Science Foundation of Heilongjiang Province/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Speech ; *Imagination ; Adult ; Language ; Brain/physiology ; Young Adult ; },
abstract = {The rapid advancement of deep learning has enabled Brain-Computer Interfaces (BCIs) technology, particularly neural decoding techniques, to achieve higher accuracy and deeper levels of interpretation. Interest in decoding imagined speech has significantly increased because its concept akin to "mind reading". However, previous studies on decoding neural language have predominantly focused on brain activity patterns during human reading. The absence of imagined speech electroencephalography (EEG) datasets has constrained further research in this field. We present the Chinese Imagined Speech Corpus (Chisco), including over 20,000 sentences of high-density EEG recordings of imagined speech from healthy adults. Each subject's EEG data exceeds 900 minutes, representing the largest dataset per individual currently available for decoding neural language to date. Furthermore, the experimental stimuli include over 6,000 everyday phrases across 39 semantic categories, covering nearly all aspects of daily language. We believe that Chisco represents a valuable resource for the fields of BCIs, facilitating the development of more user-friendly BCIs.},
}
@article {pmid39571645,
year = {2024},
author = {Ma, J and Li, Z and Zheng, Q and Li, S and Zong, R and Qin, Z and Wan, L and Zhao, Z and Mao, Z and Zhang, Y and Yu, X and Bai, H and Zhang, J},
title = {Investigating unilateral and bilateral motor imagery control using electrocorticography and fMRI in awake craniotomy.},
journal = {NeuroImage},
volume = {303},
number = {},
pages = {120949},
doi = {10.1016/j.neuroimage.2024.120949},
pmid = {39571645},
issn = {1095-9572},
mesh = {Humans ; *Electrocorticography/methods ; *Magnetic Resonance Imaging/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Craniotomy/methods ; Middle Aged ; Wakefulness/physiology ; Motor Cortex/physiology/diagnostic imaging ; Young Adult ; Brain-Computer Interfaces ; Brain Mapping/methods ; Movement/physiology ; },
abstract = {BACKGROUND: The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain-computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders.<br><br>OBJECTIVE: This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs.<br><br>METHODS: We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions.<br><br>RESULTS: fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination.<br><br>CONCLUSION: Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.},
}
@article {pmid39571386,
year = {2025},
author = {Li, Y and Chen, B and Yoshimura, N and Koike, Y and Yamashita, O},
title = {Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106899},
doi = {10.1016/j.neunet.2024.106899},
pmid = {39571386},
issn = {1879-2782},
mesh = {*Bayes Theorem ; Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; *Algorithms ; Machine Learning ; Muscle, Skeletal/physiology ; },
abstract = {Sparse Bayesian learning has promoted many effective frameworks of brain activity decoding for the brain-computer interface, including the direct reconstruction of muscle activity using brain recordings. However, existing sparse Bayesian learning algorithms mainly use Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to large performance degradation for sparse Bayesian learning algorithms. The goal of this paper is to propose a novel robust implementation of sparse Bayesian learning so that robustness and sparseness can be realized simultaneously. Motivated by the exceptional robustness of maximum correntropy criterion (MCC), we proposed integrating MCC to the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC, and then leveraged it for the likelihood function. Meanwhile, we utilized the automatic relevance determination technique as the sparse prior distribution. To fully evaluate the proposed method, a synthetic example and a real-world muscle activity reconstruction task with two different brain modalities were leveraged. Experimental results showed, our proposed sparse Bayesian correntropy learning framework significantly improves the robustness for the noisy regression tasks. Our proposed algorithm could realize higher correlation coefficients and lower root mean squared errors for the real-world muscle activity reconstruction scenario. Sparse Bayesian correntropy learning provides a powerful approach for brain activity decoding which will promote the development of brain-computer interface technology.},
}
@article {pmid39570849,
year = {2024},
author = {Liang, HJ and Li, LL and Cao, GZ},
title = {FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.},
journal = {PloS one},
volume = {19},
number = {11},
pages = {e0309706},
pmid = {39570849},
issn = {1932-6203},
mesh = {*Electroencephalography/methods ; *Deep Learning ; Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagination/physiology ; },
abstract = {Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (DL)-based methods are popular for EEG decoding. Whereas the utilization efficiency of EEG features in frequency and temporal domain is not sufficient, which results in poor MI classification performance. To address this issue, an EEG-based MI classification model based on a frequency enhancement module, a deformable convolutional network, and a crop module (FDCN-C) is proposed. Firstly, the frequency enhancement module is innovatively designed to address the issue of extracting frequency information. It utilizes convolution kernels at continuous time scales to extract features across different frequency bands. These features are screened by calculating attention and integrated into the original EEG data. Secondly, for temporal feature extraction, a deformable convolution network is employed to enhance feature extraction capabilities, utilizing offset parameters to modulate the convolution kernel size. In spatial domain, a one-dimensional convolution layer is designed to integrate all channel information. Finally, a dilated convolution is used to form a crop classification module, wherein the diverse receptive fields of the EEG data are computed multiple times. Two public datasets are employed to verify the proposed FDCN-C model, the classification accuracy obtained from the proposed model is greater than that of state-of-the-art methods. The model's accuracy has improved by 14.01% compared to the baseline model, and the ablation study has confirmed the effectiveness of each module in the model.},
}
@article {pmid39570847,
year = {2024},
author = {Assiri, FY and Ragab, M},
title = {Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.},
journal = {PloS one},
volume = {19},
number = {11},
pages = {e0313261},
pmid = {39570847},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Humans ; *Electroencephalography/methods ; Imagination/physiology ; Brain/physiology ; Support Vector Machine ; Algorithms ; },
abstract = {Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people with a breakdown or motor damage, delivering a bridge between the brain's intentions and exterior actions. Employing electroencephalography (EEG) or aggressive neural recordings, machine learning (ML) methods are used to interpret patterns of brain action linked with motor image tasks. These models frequently depend upon models like support vector machine (SVM) or deep learning (DL) to distinguish among dissimilar MI classes, such as visualizing left or right limb actions. This procedure allows individuals, particularly those with motor disabilities, to utilize their opinions to command exterior devices like robotic limbs or computer borders. This article presents a Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning (BHHSHO-DL) technique based on Motor Imagery Classification for BCI. The BHHSHO-DL technique mainly exploits the hyperparameter-tuned DL approach for MI identification for BCI. Initially, the BHHSHO-DL technique performs data preprocessing utilizing the wavelet packet decomposition (WPD) model. Besides, the enhanced densely connected networks (DenseNet) model extracts the preprocessed data's complex and hierarchical feature patterns. Meanwhile, the BHHSHO technique-based hyperparameter tuning process is accomplished to elect optimal parameter values of the enhanced DenseNet model. Finally, the classification procedure is implemented by utilizing the convolutional autoencoder (CAE) model. The simulation value of the BHHSHO-DL methodology is performed on a benchmark dataset. The performance validation of the BHHSHO-DL methodology portrayed a superior accuracy value of 98.15% and 92.23% over other techniques under BCIC-III and BCIC-IV datasets.},
}
@article {pmid39569894,
year = {2024},
author = {Brannigan, JFM and Liyanage, K and Horsfall, HL and Bashford, L and Muirhead, W and Fry, A},
title = {Brain-computer interfaces patient preferences: a systematic review.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad94a6},
pmid = {39569894},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Patient Preference ; Adult ; Middle Aged ; Spinal Cord Injuries/rehabilitation/psychology/physiopathology ; Amyotrophic Lateral Sclerosis/psychology/rehabilitation/physiopathology ; },
abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to restore motor capabilities and functional independence in individuals with motor impairments. Despite accelerating advances in the performance of implanted devices, few studies have identified patient preferences underlying device design, and each study typically captures a single aetiology of motor impairment. We aimed to characterise BCI patient preferences in a large cohort across multiple aetiologies.Approach. We performed a systematic review of all published studies reporting patient preferences for BCI devices, including both qualitative and quantitative data. We searched MEDLINE, Embase, and CINAHL from inception to 18 April 2023. Two reviewers independently screened articles and extracted data on demographic information, device use, invasiveness preference, device design, and functional preferences.Main results. From 1316 articles identified, 28 studies met inclusion criteria, capturing preferences from 1701 patients (mean age 42.1-64.3 years). The most represented conditions were amyotrophic lateral sclerosis (n= 15 studies, 53.6%) and spinal cord injury (n= 13 studies 46.4%). Individuals with motor impairments prioritised device accuracy over other design characteristics. In four studies where patients ranked performance characteristics, accuracy was ranked first each time. We found that the speed and accuracy of BCI systems in recent publications exceeds reported patient preferences, however this performance has been achieved with a level of training and setup burden that would not be tolerated by most patients. Preferences varied by disease aetiology and severity; amyotrophic lateral sclerosis patients typically prioritised communication functions, whereas spinal cord injury patients emphasised limb control and sphincteric functions.Significance.Our findings highlight that despite advances in BCI performance exceeding patient expectations, there remains a need to reduce training and setup burdens to enhance usability. Moreover, patient preferences differ across conditions and impairment severities, underscoring the importance of personalised BCI configurations and tailored training regimens to meet individual needs.},
}
@article {pmid39569892,
year = {2024},
author = {Estiveira, J and Soares, E and Pires, G and Nunes, UJ and Sousa, T and Ribeiro, S and Castelo-Branco, M},
title = {SSVEP modulation via non-volitional neurofeedback: anin silicoproof of concept.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad94a5},
pmid = {39569892},
issn = {1741-2552},
mesh = {Humans ; *Neurofeedback/methods ; *Evoked Potentials, Visual/physiology ; *Visual Cortex/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Photic Stimulation/methods ; Computer Simulation ; Brain-Computer Interfaces ; Young Adult ; Volition/physiology ; },
abstract = {Objective.Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex.Approach.Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100 ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex.Main results. Response models were obtained by analyzing, EEG data (n= 8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the steady-state visual evoked potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controller's linear, time-invariant models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability.Significance. In silicoandin vivodata matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits.},
}
@article {pmid39569866,
year = {2024},
author = {Thomson, CJ and Tully, TN and Stone, ES and Morrell, CB and Scheme, EJ and Warren, DJ and Hutchinson, DT and Clark, GA and George, JA},
title = {Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
pmid = {39569866},
issn = {1741-2552},
support = {DP5 OD029571/OD/NIH HHS/United States ; },
mesh = {Humans ; Calibration ; *Electromyography/methods ; Male ; Neural Networks, Computer ; Neural Prostheses ; Amputees/rehabilitation ; Algorithms ; Artificial Limbs ; Female ; Middle Aged ; Adult ; Machine Learning ; },
abstract = {Objective.Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control.Approach.Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings.Main results.Dataset aggregation reduced the root-mean-squared error (RMSE) of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets.Significance.Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.},
}
@article {pmid39567538,
year = {2024},
author = {Forenzo, D and Zhu, H and He, B},
title = {A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1256},
pmid = {39567538},
issn = {2052-4463},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; NS127849, NS096761, NS131069, NS124564//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS124564/NS/NINDS NIH HHS/United States ; AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; EB029354//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Deep Learning ; Algorithms ; },
abstract = {This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target, instead of a single stationary target used in other traditional BCI tasks. DL methods have recently achieved promising performance in traditional BCI tasks, but most studies investigate offline data analysis using DL algorithms. This dataset consists of ~168 hours of EEG recordings from complex CP BCI experiments, collected from 28 unique human subjects over multiple sessions each, with an online DL-based decoder. The large amount of subject specific data from multiple sessions may be useful for developing new BCI decoders, especially DL methods that require large amounts of training data. By providing this dataset to the public, we hope to help facilitate the development of new or improved BCI decoding algorithms for the complex CP paradigm for continuous object control, bringing EEG-based BCIs closer to real-world applications.},
}
@article {pmid39567330,
year = {2025},
author = {Pu, Y and Francks, C and Kong, XZ},
title = {Global brain asymmetry.},
journal = {Trends in cognitive sciences},
volume = {29},
number = {2},
pages = {114-117},
doi = {10.1016/j.tics.2024.10.008},
pmid = {39567330},
issn = {1879-307X},
mesh = {Humans ; *Brain/physiology ; *Functional Laterality/physiology ; },
abstract = {Lateralization is a defining characteristic of the human brain, often studied through localized approaches that focus on interhemispheric differences between homologous pairs of regions. It is also important to emphasize an integrative perspective of global brain asymmetry, in which hemispheric differences are understood through global patterns across the entire brain.},
}
@article {pmid39567230,
year = {2024},
author = {Walters, GI and Foley, H and Huntley, CC and Naveed, A and Nettleton, K and Reilly, C and Thomas, M and Walker, C and Wheeler, K},
title = {Could a behaviour change intervention be used to address under-recognition of work-related asthma in primary care? A systematic review.},
journal = {BJGP open},
volume = {},
number = {},
pages = {},
doi = {10.3399/BJGPO.2024.0094},
pmid = {39567230},
issn = {2398-3795},
abstract = {BACKGROUND: Work-related asthma (WRA) is prevalent yet under-recognized in UK primary care.<br><br>AIM: We aimed to identify behaviour change interventions (BCI) intended for use in primary care to identify WRA, or any other chronic disease (that could be adapted for use in WRA).<br><br>DESIGN &amp; SETTING: Systematic review METHOD: We searched CCRCT, Embase, PsychINFO and Ovid-MEDLINE databases (1946-2023) for studies describing development and/or evaluation of BCIs for case finding any chronic disease in primary care settings, aimed at either healthcare professionals and/or patients. Two blinded, independent reviewers screened abstracts and assessed full text articles. We undertook narrative synthesis for outcomes of usability and effectiveness, and for BCI development processes.<br><br>RESULTS: We included 14 studies from n=768 retrieved citations, comprising 3 randomised control trials, 1 uncontrolled experimental study, and 10 studies employing recognized multi-step BC methodologies. None of the studies were concerned with identification of asthma. BCIs had been developed for facilitating screening programmes (5), implementing guidelines (3) and individual case finding (6). Five studies measured effectiveness, in terms of screening adherence rates, pre-/post-intervention competency, satisfaction and usability, for clinicians, though none measured diagnostic rates.<br><br>CONCLUSION: No single or multi-component BCIs has been developed specifically to aid identification of asthma or WRA, though other chronic diseases have been targeted. Development has used BC methodologies that involved gathering data from a range of sources, and developing content specific to defined at-risk populations, so are not immediately transferable. Such methodologies could be used similarly to develop a primary acre-based BCI for WRA.},
}
@article {pmid39565521,
year = {2025},
author = {Guan, Z and Zhang, X and Huang, W and Li, K and Chen, D and Li, W and Sun, J and Chen, L and Mao, Y and Sun, H and Tang, X and Cao, L and Li, Y},
title = {A Method for Detecting Depression in Adolescence Based on an Affective Brain-Computer Interface and Resting-State Electroencephalogram Signals.},
journal = {Neuroscience bulletin},
volume = {41},
number = {3},
pages = {434-448},
pmid = {39565521},
issn = {1995-8218},
mesh = {Humans ; Adolescent ; *Electroencephalography/methods ; Male ; Female ; *Depression/diagnosis/physiopathology ; *Brain-Computer Interfaces ; *Brain/physiopathology ; Rest/physiology ; },
abstract = {Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.},
}
@article {pmid39565505,
year = {2024},
author = {Wang, XN and Zhang, T and Han, BC and Luo, WW and Liu, WH and Yang, ZY and Disi, A and Sun, Y and Yang, JC},
title = {Wearable EEG Neurofeedback Based-on Machine Learning Algorithms for Children with Autism: A Randomized, Placebo-controlled Study.},
journal = {Current medical science},
volume = {44},
number = {6},
pages = {1141-1147},
pmid = {39565505},
issn = {2523-899X},
mesh = {Humans ; *Neurofeedback/methods/instrumentation ; Child ; Male ; Female ; *Electroencephalography/methods ; Child, Preschool ; *Machine Learning ; *Wearable Electronic Devices ; Autism Spectrum Disorder/therapy/physiopathology ; Autistic Disorder/therapy/physiopathology ; Algorithms ; },
abstract = {OBJECTIVE: Behavioral interventions have been shown to ameliorate the electroencephalogram (EEG) dynamics underlying the behavioral symptoms of autism spectrum disorder (ASD), while studies have also demonstrated that mirror neuron mu rhythm-based EEG neurofeedback training improves the behavioral functioning of individuals with ASD. This study aimed to test the effects of a wearable mu rhythm neurofeedback training system based on machine learning algorithms for children with autism.<br><br>METHODS: A randomized, placebo-controlled study was carried out on 60 participants aged 3 to 6 years who were diagnosed with autism, at two center-based intervention sites. The neurofeedback group received active mu rhythm neurofeedback training, while the control group received a sham neurofeedback training. Other behavioral intervention programs were similar between the two groups.<br><br>RESULTS: After 60 sessions of treatment, both groups showed significant improvements in several domains including language, social and problem behavior. The neurofeedback group showed significantly greater improvements in expressive language (P=0.013) and cognitive awareness (including joint attention, P=0.003) than did the placebo-controlled group.<br><br>CONCLUSION: Artificial intelligence-powered wearable EEG neurofeedback, as a type of brain-computer interface application, is a promising assistive technology that can provide targeted intervention for the core brain mechanisms underlying ASD symptoms.},
}
@article {pmid39561980,
year = {2024},
author = {Vacca, F and Galluzzi, F and Blanco-Formoso, M and Gianiorio, T and De Fazio, AF and Tantussi, F and Stürmer, S and Haq, W and Zrenner, E and Chaffiol, A and Joffrois, C and Picaud, S and Benfenati, F and De Angelis, F and Colombo, E},
title = {Solid-State Nanopores for Spatially Resolved Chemical Neuromodulation.},
journal = {Nano letters},
volume = {24},
number = {48},
pages = {15215-15225},
pmid = {39561980},
issn = {1530-6992},
mesh = {Animals ; *Nanopores ; *Neurons/drug effects/metabolism ; Mice ; *Neurotransmitter Agents/chemistry ; Synaptic Transmission/drug effects ; Glutamic Acid/chemistry ; Drug Delivery Systems ; Neural Prostheses ; Ceramics/chemistry ; },
abstract = {Most neural prosthetic devices are based on electrical stimulation, although the modulation of neuronal activity by a localized chemical delivery would better mimic physiological synaptic machinery. In the past decade, various drug delivery approaches attempted to emulate synaptic transmission, although they were hampered by poor retention of their cargo while reaching the target destination, low spatial resolution, and poor biocompatibility and stability of the materials involved. Here, we propose a planar solid-state device for multisite neurotransmitter translocation at the nanoscale consisting of a nanopatterned ceramic membrane connected to a reservoir designed to store neurotransmitters. We achieved diffusion-mediated glutamate stimulation of primary neurons, while we showed the feasibility to translocate other molecules through the pores by either pressure or diffusion, proving the versatility of the proposed technology. Finally, the system proved to be a promising neuronal stimulation interface in mice and nonhuman primates ex vivo, paving the way toward a biomimetic chemical stimulation in neural prosthetics and brain machine interfaces.},
}
@article {pmid39560446,
year = {2025},
author = {Xu, F and Shi, W and Lv, C and Sun, Y and Guo, S and Feng, C and Zhang, Y and Jung, TP and Leng, J},
title = {Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.},
journal = {International journal of neural systems},
volume = {35},
number = {1},
pages = {2450069},
doi = {10.1142/S0129065724500692},
pmid = {39560446},
issn = {1793-6462},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Male ; Stroke/physiopathology ; Middle Aged ; Stroke Rehabilitation/methods ; Female ; Adult ; Brain/physiopathology ; Aged ; Signal Processing, Computer-Assisted ; },
abstract = {Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals from stroke patients poses challenges. To address the issues of low accuracy and efficiency in EEG classification, particularly involving MI, the study proposes a residual graph convolutional network (M-ResGCN) framework based on the modified S-transform (MST), and introduces the self-attention mechanism into residual graph convolutional network (ResGCN). This study uses MST to extract EEG time-frequency domain features, derives spatial EEG features by calculating the absolute Pearson correlation coefficient (aPcc) between channels, and devises a method to construct the adjacency matrix of the brain network using aPcc to measure the strength of the connection between channels. Experimental results involving 16 stroke patients and 16 healthy subjects demonstrate significant improvements in classification quality and robustness across tests and subjects. The highest classification accuracy reached 94.91% and a Kappa coefficient of 0.8918. The average accuracy and F1 scores from 10 times 10-fold cross-validation are 94.38% and 94.36%, respectively. By validating the feasibility and applicability of brain networks constructed using the aPcc in EEG signal analysis and feature encoding, it was established that the aPcc effectively reflects overall brain activity. The proposed method presents a novel approach to exploring channel relationships in MI-EEG and improving classification performance. It holds promise for real-time applications in MI-based BCI systems.},
}
@article {pmid39560167,
year = {2025},
author = {Shah, AM},
title = {Hopeful progress in artificial vision.},
journal = {Artificial organs},
volume = {49},
number = {1},
pages = {5-6},
doi = {10.1111/aor.14912},
pmid = {39560167},
issn = {1525-1594},
mesh = {Humans ; *Brain-Computer Interfaces ; *Visual Prosthesis ; Eye, Artificial ; Prosthesis Design ; Vision, Ocular ; Vision Disorders/therapy ; },
abstract = {Visual impairment has been augmented by glasses for centuries. With the advent of newer technologies, correction of more severe visual impairment may be possible with brain-computer interface and eye implants.},
}
@article {pmid39556950,
year = {2024},
author = {Kılınç Bülbül, D and Walston, ST and Duvan, FT and Garrido, JA and Güçlü, B},
title = {Decoding sensorimotor information from somatosensory cortex by flexible epicorticalμECoG arrays in unrestrained behaving rats.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9405},
pmid = {39556950},
issn = {1741-2552},
mesh = {Animals ; Rats ; *Somatosensory Cortex/physiology ; *Brain-Computer Interfaces ; Male ; *Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Graphite ; Rats, Sprague-Dawley ; Microelectrodes ; },
abstract = {Objective.Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats, and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25μm).Approach.Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies.μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.Main results.Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.Significance.Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by usingμECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.},
}
@article {pmid39556943,
year = {2024},
author = {Leng, J and Gao, L and Jiang, X and Lou, Y and Sun, Y and Wang, C and Li, J and Zhao, H and Feng, C and Xu, F and Zhang, Y and Jung, TP},
title = {A multi-feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad9403},
pmid = {39556943},
issn = {1741-2552},
mesh = {Humans ; *Spinal Cord Injuries/physiopathology ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; *Intention ; Adult ; Female ; Attention/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Young Adult ; Middle Aged ; },
abstract = {Objective.Electroencephalogram (EEG) signals exhibit temporal-frequency-spatial multi-domain feature, and due to the nonplanar nature of the brain surface, the electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a temporal-frequency-spatial multi-domain feature fusion graph attention network (GAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients.Approach.The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between EEG channels and continuous wavelet transform to extract valid EEG information in the time-frequency domain. It then models as a graph data structure containing multi-domain information. The gated recurrent unit and GAT learn EEG's dynamic temporal-spatial information. Finally, the fully connected layer outputs the MI intention recognition results.Main results.After 10 times 10-fold cross-validation, the proposed model can achieve an average accuracy of 95.82%. Furthermore, this study analyses the event-related desynchronization/event-related synchronization and PLV brain network to explore the brain activity of SCI patients during MI.Significance.This study confirms the potential of the proposed model in terms of EEG decoding performance and provides a reference for the mechanism of neural activity in SCI patients.},
}
@article {pmid39556340,
year = {2024},
author = {Sang, Y and Li, B and Su, T and Zhan, H and Xiong, Y and Huang, Z and Wang, C and Cong, X and Du, M and Wu, Y and Yu, H and Yang, X and Ding, K and Wang, X and Miao, X and Gong, W and Wang, L and Zhao, J and Zhou, Y and Liu, W and Hu, X and Sun, Q},
title = {Visualizing ER-phagy and ER architecture in vivo.},
journal = {The Journal of cell biology},
volume = {223},
number = {12},
pages = {},
pmid = {39556340},
issn = {1540-8140},
support = {32025012//National Natural Science Foundation/ ; 2021YFC2700901//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {Animals ; *Mice, Transgenic ; *Endoplasmic Reticulum/metabolism/genetics ; Mice ; Green Fluorescent Proteins/metabolism/genetics ; Luminescent Proteins/genetics/metabolism ; Red Fluorescent Protein ; Genes, Reporter ; Mice, Inbred C57BL ; },
abstract = {ER-phagy is an evolutionarily conserved mechanism crucial for maintaining cellular homeostasis. However, significant gaps persist in our understanding of how ER-phagy and the ER network vary across cell subtypes, tissues, and organs. Furthermore, the pathophysiological relevance of ER-phagy remains poorly elucidated. Addressing these questions requires developing quantifiable methods to visualize ER-phagy and ER architecture in vivo. We generated two transgenic mouse lines expressing an ER lumen-targeting tandem RFP-GFP (ER-TRG) tag, either constitutively or conditionally. This approach enables precise spatiotemporal measurements of ER-phagy and ER structure at single-cell resolution in vivo. Systemic analysis across diverse organs, tissues, and primary cultures derived from these ER-phagy reporter mice unveiled significant variations in basal ER-phagy, both in vivo and ex vivo. Furthermore, our investigation uncovered substantial remodeling of ER-phagy and the ER network in different tissues under stressed conditions such as starvation, oncogenic transformation, and tissue injury. In summary, both reporter models represent valuable resources with broad applications in fundamental research and translational studies.},
}
@article {pmid39555298,
year = {2024},
author = {Li, J and Shi, W and Li, Y},
title = {An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2689-2707},
pmid = {39555298},
issn = {1871-4080},
abstract = {Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.},
}
@article {pmid39555297,
year = {2024},
author = {Yin, Y and Kong, W and Tang, J and Li, J and Babiloni, F},
title = {PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2883-2896},
pmid = {39555297},
issn = {1871-4080},
abstract = {Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.},
}
@article {pmid39555294,
year = {2024},
author = {Leng, J and Yu, X and Wang, C and Zhao, J and Zhu, J and Chen, X and Zhu, Z and Jiang, X and Zhao, J and Feng, C and Yang, Q and Li, J and Jiang, L and Xu, F and Zhang, Y},
title = {Functional connectivity of EEG motor rhythms after spinal cord injury.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {3015-3029},
pmid = {39555294},
issn = {1871-4080},
abstract = {Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.},
}
@article {pmid39555292,
year = {2024},
author = {Liu, H and Jin, X and Liu, D and Kong, W and Tang, J and Peng, Y},
title = {Affective EEG-based cross-session person identification using hierarchical graph embedding.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2897-2908},
pmid = {39555292},
issn = {1871-4080},
abstract = {The electroencephalogram (EEG) signal is being investigated as a more confidential biometric for person identification. Despite recent advancements, a persistent challenge lies in the influence of variations in affective states. Affective states consistently exist during data collection, regardless of the protocol used. Additionally, the inherently non-stationary nature of EEG makes it susceptible to fluctuations in affective states over time. Therefore, it would be highly crucial to perform precise EEG-based person identification under varying affective states. This paper employed an integrated Multi-scale Convolution and Graph Pooling network (MCGP) to mitigate the impact of affective state variations. MCGP utilized multiple 1D convolutions at different scales to dynamically extract and fuse features. Additionally, a graph pooling layer with an attention mechanism was incorporated to generate hierarchical graph embeddings. These embeddings were concatenated as inputs for a fully connected classification layer. Experiments were conducted on the SEED and SEED-V dataset, revealing that MCGP achieved an average accuracy of 85.51% for SEED and 88.69% for SEED-V in cross-session conditions involving mixed affective states. Under single affective state cross-session scenario, MCGP achieved an average accuracy of 85.75% for SEED and 88.06% for SEED-V for the same affective states, while obtaining 79.57% for SEED and 84.52% for SEED-V for different affective states. Results indicated that, compared to the baseline methods, MCGP effectively mitigated the impact of variations in affective states across different sessions. In single affective state cross-session scenario, identification performance for the same affective states was slightly higher than that for different affective states.},
}
@article {pmid39555282,
year = {2024},
author = {Chen, L and Gao, H and Wang, Z and Gu, B and Zhou, W and Pang, M and Zhang, K and Liu, X and Ming, D},
title = {Vagus nerve electrical stimulation in the recovery of upper limb motor functional impairment after ischemic stroke.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {3107-3124},
pmid = {39555282},
issn = {1871-4080},
abstract = {Ischemic stroke (IS) is characterized by high mortality, disability rates, and a high risk of recurrence. Motor dysfunction, such as limb hemiparesis, dysphagia, auditory disorders, and speech disorders, usually persists after stroke, which imposes a heavy burden on society and the health care system. Traditional rehabilitation therapies may be ineffective in promoting functional recovery after stroke, and alternative strategies are urgently needed. The Food and Drug Administration (FDA) has approved invasive vagus nerve stimulation (iVNS) for the improvement of refractory epilepsy, treatment-resistant depression, obesity, and moderate to severe upper limb motor impairment following chronic ischemic stroke. Additionally, the FDA has approved transcutaneous vagus nerve stimulation (tVNS) for the improvement of cluster headaches and acute migraines. Recent studies have demonstrated that vagus nerve stimulation (VNS) has neuroprotective effects in both transient and permanent cerebral ischemia animal models, significantly improving upper limb motor impairments, auditory deficits, and swallowing difficulties. Firstly, this article reviews two potential neuronal death pathways following IS, including autophagy and inflammatory responses. Then delves into the current status of preclinical and clinical research on the functional recovery following IS with VNS, as well as the potential mechanisms mediating its neuroprotective effects. Finally, the optimal parameters and timing of VNS application are summarized, and the future challenges and directions of VNS in the treatment of IS are discussed. The application of VNS in stroke rehabilitation research has reached a critical stage, and determining how to safely and effectively translate this technology into clinical practice is of utmost importance. Further preclinical and clinical studies are needed to elucidate the therapeutic mechanisms of VNS.},
}
@article {pmid39555271,
year = {2024},
author = {Liu, Y and Yu, S and Li, J and Ma, J and Wang, F and Sun, S and Yao, D and Xu, P and Zhang, T},
title = {Brain state and dynamic transition patterns of motor imagery revealed by the bayes hidden markov model.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2455-2470},
pmid = {39555271},
issn = {1871-4080},
abstract = {UNLABELLED: Motor imagery (MI) is a high-level cognitive process that has been widely applied to brain-computer inference (BCI) and motor recovery. In practical applications, however, huge individual differences and unclear neural mechanisms have seriously hindered the application of MI and BCI systems. Thus, it is urgently needed to explore MI from a new perspective. Here, we applied a hidden Markov model (HMM) to explore the dynamic organization patterns of left- and right-hand MI tasks. Eleven distinct HMM states were identified based on MI-related EEG data. We found that these states can be divided into three metastates by clustering analysis, showing a highly organized structure. We also assessed the probability activation of each HMM state across time. The results showed that the state probability activation of task-evoked have similar trends to that of event-related desynchronization/synchronization (ERD/ERS). By comparing the differences in temporal features of HMM states between left- and right-hand MI, we found notable variations in fractional occupancy, mean life time, mean interval time, and transition probability matrix across stages and states. Interestingly, we found that HMM states activated in the left occipital lobe had higher occupancy during the left-hand MI task, and conversely, during the right-hand MI task, HMM states activated in the right occipital lobe had higher occupancy. Moreover, significant correlations were observed between BCI performance and features of HMM states. Taken together, our findings explored dynamic networks underlying the MI-related process and provided a complementary understanding of different MI tasks, which may contribute to improving the MI-BCI systems.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10099-9.},
}
@article {pmid39555270,
year = {2024},
author = {Wang, Y and Zhang, M and Li, M and Cui, H and Chen, X},
title = {Development of a humanoid robot control system based on AR-BCI and SLAM navigation.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2857-2870},
pmid = {39555270},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation. An 8-target steady-state visual evoked potential (SSVEP)-based BCI was implemented to enable direct control of the humanoid robot by the user. A Microsoft HoloLens was utilized to display visual stimuli for eliciting SSVEPs. Filter bank canonical correlation analysis (FBCCA), a training-free method, was used to detect SSVEPs in this study. By leveraging SLAM technology, the proposed system alleviates the need for frequent control commands transmission from the user, thereby effectively reducing their workload. Online results from 12 healthy subjects showed this developed BCI system was able to select a command out of eight potential targets with an average accuracy of 94.79%. The autonomous navigation subsystem enabled the humanoid robot to autonomously navigate to a destination chosen utilizing the proposed BCI. Furthermore, all participants successfully completed the experimental task using the developed system without any prior training. These findings illustrate the feasibility of the developed system and its potential to contribute novel insights into humanoid robots control strategies.},
}
@article {pmid39555269,
year = {2024},
author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X},
title = {Classification algorithm for motor imagery fusing CNN and attentional mechanisms based on functional near-infrared spectroscopy brain image.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2871-2881},
pmid = {39555269},
issn = {1871-4080},
abstract = {With the continuing development of brain-computer interface technology, the analysis and interpretation of brain signals are becoming increasingly important. In the field of brain-computer interfaces, motor imagery (MI) is an important paradigm for generating specific brain signals through thought alone, rather than actual movement, for computer decoding. Functional near-infrared spectroscopy (fNIRS) imaging technology has been increasingly used in brain-computer interfaces due to its advantages of non-invasiveness, low resource requirements, low cost, and high spatial resolution. Scientists have done a lot of work in channel selection, feature selection, and then applying traditional machine learning methods for classification, but the results achieved so far are still insufficient to meet the conditions for realizing fNIRS brain-computer interfaces. To achieve a higher level of classification of fNIRS signals, we propose a method that fuses CNN and attention mechanisms to analyze the near-infrared signals of motor imagery and mental arithmetic data, which is fed into a neural network by deriving signals of changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations through the modified Beer-Lambert law, and then applied to the fNIRS dataset of 29 healthy subjects to validate the proposed method. In the fNIRS-based BCI, the average classification accuracy of the MI signal from HbR and HbO reaches 85.92% and 86.21%, respectively, and the average classification accuracy of the MA signal reaches 89.66% and 88.79%, respectively. The advantage of our approach is that it is lightweight and improves the classification accuracy of current BCI fNIRS signals.},
}
@article {pmid39555266,
year = {2024},
author = {Wang, Z and Song, X and Chen, L and Nan, J and Sun, Y and Pang, M and Zhang, K and Liu, X and Ming, D},
title = {Research progress of epileptic seizure prediction methods based on EEG.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2731-2750},
pmid = {39555266},
issn = {1871-4080},
abstract = {At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health and even the life safety of patients, and the realization of early prediction of seizures and the adoption of interventions are of great significance to the improvement of patients' quality of life. In this paper, we firstly introduce the design process of EEG-based seizure prediction methods, introduce several databases commonly used in the research, and summarize the commonly used methods in pre-processing, feature extraction, classification and identification, and post-processing. Then, based on scalp EEG and intracranial EEG respectively, we reviewed the current status of epileptic seizure prediction research from five commonly used feature analysis methods, and make a comprehensive evaluation of both. Finally, this paper describes the reasons why the current algorithms cannot be applied to the clinic, summarizes their limitations, and gives corresponding suggestions, aiming to provide improvement directions for subsequent research. In addition, deep learning algorithms have emerged in recent years, and this paper also compares the advantages and disadvantages of deep learning algorithms with traditional machine learning methods, in the hope of providing researchers with new technologies and new ideas and making significant breakthroughs in the field of epileptic seizure prediction.},
}
@article {pmid39555257,
year = {2024},
author = {Ma, J and Yang, B and Rong, F and Gao, S and Wang, W},
title = {Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2521-2534},
pmid = {39555257},
issn = {1871-4080},
abstract = {Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.},
}
@article {pmid39555252,
year = {2024},
author = {Pan, Y and Li, N and Zhang, Y and Xu, P and Yao, D},
title = {Short-length SSVEP data extension by a novel generative adversarial networks based framework.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {5},
pages = {2925-2945},
pmid = {39555252},
issn = {1871-4080},
abstract = {Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the performance of frequency identification methods heavily hinges on the amount of user calibration data and data length, which hinders the deployment in real-world applications. Recently, generative adversarial networks (GANs)-based data generation methods have been widely adopted to create synthetic electroencephalography data, holds promise to address these issues. In this paper, we proposed a GAN-based end-to-end signal transformation network for Time-window length Extension, termed as TEGAN. TEGAN transforms short-length SSVEP signals into long-length artificial SSVEP signals. Additionally, we introduced a two-stage training strategy and the LeCam-divergence regularization term to regularize the training process of GAN during the network implementation. The proposed TEGAN was evaluated on two public SSVEP datasets (a 4-class and 12-class dataset). With the assistance of TEGAN, the performance of traditional frequency recognition methods and deep learning-based methods have been significantly improved under limited calibration data. And the classification performance gap of various frequency recognition methods has been narrowed. This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system. The proposed GAN-based methods have the great potential of shortening the calibration time and cutting down the budget for various real-world BCI-based applications.},
}
@article {pmid39554850,
year = {2024},
author = {Dan, Y and Zhou, D and Wang, Z},
title = {Discriminative possibilistic clustering promoting cross-domain emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1458815},
pmid = {39554850},
issn = {1662-4548},
abstract = {The affective Brain-Computer Interface (aBCI) systems strive to enhance prediction accuracy for individual subjects by leveraging data from multiple subjects. However, significant differences in EEG (Electroencephalogram) feature patterns among subjects often hinder these systems from achieving the desired outcomes. Although studies have attempted to address this challenge using subject-specific classifier strategies, the scarcity of labeled data remains a major hurdle. In light of this, Domain Adaptation (DA) technology has gradually emerged as a prominent approach in the field of EEG-based emotion recognition, attracting widespread research interest. The crux of DA learning lies in resolving the issue of distribution mismatch between training and testing datasets, which has become a focal point of academic attention. Currently, mainstream DA methods primarily focus on mitigating domain distribution discrepancies by minimizing the Maximum Mean Discrepancy (MMD) or its variants. Nevertheless, the presence of noisy samples in datasets can lead to pronounced shifts in domain means, thereby impairing the adaptive performance of DA methods based on MMD and its variants in practical applications to some extent. Research has revealed that the traditional MMD metric can be transformed into a 1-center clustering problem, and the possibility clustering model is adept at mitigating noise interference during the data clustering process. Consequently, the conventional MMD metric can be further relaxed into a possibilistic clustering model. Therefore, we construct a distributed distance measure with Discriminative Possibilistic Clustering criterion (DPC), which aims to achieve two objectives: (1) ensuring the discriminative effectiveness of domain distribution alignment by finding a shared subspace that minimizes the overall distribution distance between domains while maximizing the semantic distribution distance according to the principle of "sames attract and opposites repel"; and (2) enhancing the robustness of distribution distance measure by introducing a fuzzy entropy regularization term. Theoretical analysis confirms that the proposed DPC is an upper bound of the existing MMD metric under certain conditions. Therefore, the MMD objective can be effectively optimized by minimizing the DPC. Finally, we propose a domain adaptation in Emotion recognition based on DPC (EDPC) that introduces a graph Laplacian matrix to preserve the geometric structural consistency between data within the source and target domains, thereby enhancing label propagation performance. Simultaneously, by maximizing the use of source domain discriminative information to minimize domain discrimination errors, the generalization performance of the DA model is further improved. Comparative experiments on several representative domain adaptation learning methods using multiple EEG datasets (i.e., SEED and SEED-IV) show that, in most cases, the proposed method exhibits better or comparable consistent generalization performance.},
}
@article {pmid39554511,
year = {2024},
author = {Herbozo Contreras, LF and Truong, ND and Eshraghian, JK and Xu, Z and Huang, Z and Bersani-Veroni, TV and Aguilar, I and Leung, WH and Nikpour, A and Kavehei, O},
title = {Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.},
journal = {PNAS nexus},
volume = {3},
number = {11},
pages = {pgae488},
pmid = {39554511},
issn = {2752-6542},
abstract = {Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment.},
}
@article {pmid39553842,
year = {2024},
author = {Zhou, L and Wu, B and Qin, B and Gao, F and Li, W and Hu, H and Zhu, Q and Qian, Z},
title = {Cortico-muscular coherence of time-frequency and spatial characteristics under movement observation, movement execution, and movement imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {3},
pages = {1079-1096},
pmid = {39553842},
issn = {1871-4080},
abstract = {Studies show that movement observation (MO), movement imagery (MI), or movement execution (ME) based brain-computer interface systems are promising in promoting the rehabilitation and reorganization of damaged motor function. This study was aimed to explore and compare the motor function rehabilitation mechanism among MO, MI, and ME. 64-channel electroencephalogram and 4-channel electromyogram data were collected from 39 healthy participants (25 males, 14 females; 18-23 years old) during MO, ME, and MI. We analyzed and compared the inter-cortical, inter-muscular, cortico-muscular, and spatial coherence under MO, ME, and MI. Under MO, ME, and MI, cortico-muscular coherence was strongest at the beta-lh band, which means the beta frequency band for cortical signals and the lh frequency band for muscular signals. 56.25-96.88% of the coherence coefficients were significantly larger than 0.5 (ps < 0.05) at the beta-lh band. MO and ME had a contralateral advantage in the spatial coherence between cortex and muscle, while MI had an ipsilateral advantage in the spatial coherence between cortex and muscle. Our results show that the cortico-muscular beta-lh band plays a critical role in the synchronous coupling between cortex and muscle. Also, our findings suggest that the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and premotor cortex (PMC) are the specific regions of MO, ME, and MI. However, their pathways of regulating muscles are different under MO, ME, and MI. This study is important for better understanding the motor function rehabilitation mechanism in MO, MI, and ME.},
}
@article {pmid39551888,
year = {2024},
author = {Lin, RR and Zhang, K},
title = {Survey of real-time brainmedia in artistic exploration.},
journal = {Visual computing for industry, biomedicine, and art},
volume = {7},
number = {1},
pages = {27},
pmid = {39551888},
issn = {2524-4442},
support = {2021JC02G114//Grant #2021JC02G114./ ; },
abstract = {This survey examines the evolution and impact of real-time brainmedia on artistic exploration, contextualizing developments within a historical framework. To enhance knowledge on the entanglement between the brain, mind, and body in an increasingly mediated world, this work defines a clear scope at the intersection of bio art and interactive art, concentrating on real-time brainmedia artworks developed in the 21st century. It proposes a set of criteria and a taxonomy based on historical notions, interaction dynamics, and media art representations. The goal is to provide a comprehensive overview of real-time brainmedia, setting the stage for future explorations of new paradigms in communication between humans, machines, and the environment.},
}
@article {pmid39551818,
year = {2024},
author = {Zhao, H and Zhang, C and Tao, R and Wang, M and Yin, Y and Xu, S},
title = {Dyadic Similarity in Social Value Orientation Modulates Hyper-Brain Network Dynamics During Interpersonal Coordination: An fNIRS-Based Hyperscanning Study.},
journal = {Brain topography},
volume = {38},
number = {1},
pages = {15},
pmid = {39551818},
issn = {1573-6792},
support = {23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 72171151//National Natural Science Foundation of China/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; },
mesh = {Humans ; Male ; Young Adult ; Female ; *Spectroscopy, Near-Infrared/methods ; *Brain/physiology ; *Interpersonal Relations ; Cooperative Behavior ; Adult ; Brain Mapping/methods ; Social Behavior ; Neural Pathways/physiology ; },
abstract = {As the fundamental dispositional determinant of social motivation, social value orientation (SVO) may modulate individuals' response patterns in interpersonal coordination contexts. Adopting fNIRS-based hyperscanning approach, the present investigation uncovered the hyper-brain network topological dynamics underlying the effect of the dyadic similarity in the social value orientation on interpersonal coordination. Our findings indicated that the dyads in proself group exhibited the higher degree of competitive intensity during the competitive coordination block, and the dyads in the prosocial group exhibited a higher degree of cooperative coordination during the cooperative coordination block. Distinct hyper-brain functional connectivity patterns and network topological characteristics were identified during the competitive and cooperative coordination blocks in the proself and prosocial groups. The nodal-network global efficiency at the right frontopolar area further mediated the effect of the dyadic deviation in social value orientation similarity on effective adjustments after the negative feedback during the cooperative coordination block in the prosocial group. Our findings manifested distinct behavioral performances and hyper-brain functional connectivity patterns underlying the effect of the dyadic similarity in social value orientation on interpersonal coordination in the real-time mode.},
}
@article {pmid39550056,
year = {2024},
author = {Chen, Y and Bai, J and Shi, N and Jiang, Y and Chen, X and Ku, Y and Gao, X},
title = {Intermodulation frequency components in steady-state visual evoked potentials: Generation, characteristics and applications.},
journal = {NeuroImage},
volume = {303},
number = {},
pages = {120937},
doi = {10.1016/j.neuroimage.2024.120937},
pmid = {39550056},
issn = {1095-9572},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Photic Stimulation/methods ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The steady-state visual evoked potentials (SSVEPs), evoked by dual-frequency or multi-frequency stimulation, likely contains intermodulation frequency components (IMs). Visual IMs are products of nonlinear integration of neural signals and can be evoked by various paradigms that induce neural interaction. IMs have demonstrated many interesting and important characteristics in cognitive psychology, clinical neuroscience, brain-computer interface and other fields, and possess substantial research potential. In this paper, we first review the definition of IMs and summarize the stimulation paradigms capable of inducing them, along with the possible neural origins of IMs. Subsequently, we describe the characteristics and derived applications of IMs in previous studies, and then introduced three signal processing methods favored by researchers to enhance the signal-to-noise ratio of IMs. Finally, we summarize the characteristics of IMs, and propose several potential future research directions related to IMs.},
}
@article {pmid39549531,
year = {2025},
author = {Imtiaz, MN and Khan, N},
title = {Enhanced cross-dataset electroencephalogram-based emotion recognition using unsupervised domain adaptation.},
journal = {Computers in biology and medicine},
volume = {184},
number = {},
pages = {109394},
doi = {10.1016/j.compbiomed.2024.109394},
pmid = {39549531},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Female ; Male ; Adult ; Algorithms ; },
abstract = {Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain-computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) signals among individuals limit the cross-domain application of EEG-based emotion recognition models. Addressing cross-dataset scenarios poses greater challenges due to changes in subject demographics, recording devices, and stimuli presented. To tackle these challenges, we propose an improved method for classifying EEG-based emotions across domains with different distributions. We propose a Gradual Proximity-guided Target Data Selection (GPTDS) technique, which gradually selects reliable target domain samples for training based on their proximity to the source clusters and the model's confidence in predicting them. This approach avoids negative transfer caused by diverse and unreliable samples. Additionally, we introduce a cost-effective test-time augmentation (TTA) technique named Prediction Confidence-aware Test-Time Augmentation (PC-TTA). Traditional TTA methods often face substantial computational burden, limiting their practical utility. By applying TTA only when necessary, based on the model's predictive confidence, our approach improves the model's performance during inference while minimizing computational costs compared to traditional TTA approaches. Experiments on the DEAP and SEED datasets demonstrate that our method outperforms state-of-the-art approaches, achieving accuracies of 67.44% when trained on DEAP and tested on SEED, and 59.68% vice versa, with improvements of 7.09% and 6.07% over the baseline. It excels in detecting both positive and negative emotions, highlighting its effectiveness for practical emotion recognition in healthcare applications. Moreover, our proposed PC-TTA technique reduces computational time by a factor of 15 compared to traditional full TTA approaches.},
}
@article {pmid39549493,
year = {2025},
author = {Hu, P and Zhang, X and Li, M and Zhu, Y and Shi, L},
title = {TSOM: Small object motion detection neural network inspired by avian visual circuit.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106881},
doi = {10.1016/j.neunet.2024.106881},
pmid = {39549493},
issn = {1879-2782},
mesh = {Animals ; *Neural Networks, Computer ; *Motion Perception/physiology ; *Visual Pathways/physiology ; *Columbidae/physiology ; Retina/physiology ; Algorithms ; Neurons/physiology ; Superior Colliculi/physiology ; Photic Stimulation/methods ; Birds/physiology ; },
abstract = {Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial-temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.},
}
@article {pmid39549492,
year = {2025},
author = {Borra, D and Magosso, E and Ravanelli, M},
title = {A protocol for trustworthy EEG decoding with neural networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {182},
number = {},
pages = {106847},
doi = {10.1016/j.neunet.2024.106847},
pmid = {39549492},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Deep Learning ; Algorithms ; Event-Related Potentials, P300/physiology ; Imagination/physiology ; Brain/physiology ; Brain-Computer Interfaces ; Adult ; Male ; },
abstract = {Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is rarely performed and limited to network-related hyperparameters. Moreover, pipelines are highly sensitive to performance fluctuations due to random initialization, hindering their reliability. Here, we design a comprehensive protocol for EEG decoding that explores the hyperparameters characterizing the entire pipeline and that includes multi-seed initialization for providing robust performance estimates. Our protocol is validated on 9 datasets about motor imagery, P300, SSVEP, including 204 participants and 26 recording sessions, and on different deep learning models. We accompany our protocol with extensive experiments on the main aspects influencing it, such as the number of participants used for hyperparameter search, the split into sequential simpler searches (multi-step search), the use of informed vs. non-informed search algorithms, and the number of random seeds for obtaining stable performance. The best protocol included 2-step hyperparameter search via an informed search algorithm, with the final training and evaluation performed using 10 random initializations. The optimal trade-off between performance and computational time was achieved by using a subset of 3-5 participants for hyperparameter search. Our protocol consistently outperformed baseline state-of-the-art pipelines, widely across datasets and models, and could represent a standard approach for neuroscientists for decoding EEG in a trustworthy and reliable way.},
}
@article {pmid39548722,
year = {2024},
author = {Farahmand, M and Mousavi, M and Azizi, F and Ramezani Tehrani, F},
title = {Exploring the Influence of Age at Menarche on Metabolic Syndrome and Its Components Across Different Women's Birth Cohorts.},
journal = {Endocrinology, diabetes &amp; metabolism},
volume = {7},
number = {6},
pages = {e70015},
pmid = {39548722},
issn = {2398-9238},
support = {43002333//Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences/ ; },
mesh = {Humans ; *Menarche/physiology ; Female ; *Metabolic Syndrome/epidemiology/etiology ; Cross-Sectional Studies ; Adult ; Adolescent ; Iran/epidemiology ; Age Factors ; Middle Aged ; Child ; Birth Cohort ; Young Adult ; Risk Factors ; Prevalence ; Cohort Studies ; },
abstract = {PURPOSE: Metabolic syndrome (MetS) is the primary cardiovascular risk factor, making it a global issue. Our objective was to assess the association between the age at menarche (AAM) and MetS and its components in different generations of women.<br><br>METHODS: In this cross-sectional study, 5500 eligible women aged &#x2265;&#x2009;20 who participated in the Tehran lipid and glucose study in 2015-2017 were selected. Participants were divided into groups by birth cohorts (BC) (born &#x2264;&#x2009;1959, 1960-1979, and &#x2265;&#x2009;1980) and AAM (&#x2264;&#x2009;11, 12-15, and &#x2265;&#x2009;16&#x2009;years, early, normal, and late, respectively). The status of MetS and its components were compared amongst participants using logistic regression.<br><br>RESULTS: Normal AAM (12-15&#x2009;years) was considered the reference group. The adjusted model revealed that AAM &#x2264;&#x2009;11 is associated with a higher risk of 34% (95% confidence interval (CI): 1.04, 1.71) in MetS, and the prevalence of MetS in the early menarche group was higher in BCI, and BCII (odds ratio (OR): 1.87; 95% CI: 1.04, 3.36 and OR: 1.33; 95% CI: 1.00, 1.89, respectively). Those with late menarche demonstrated a lower risk (OR:0.72; 95% CI: 0.57, 0.91) of abdominal obesity, and early menarche showed a higher risk (OR: 1.45; CI: 1.14, 1.86). This higher risk in early menarche was observed in BCI and BCII (OR: 1.76; 95% CI: 1.16, 2.66 and OR: 1.80; 95% CI: 1.23, 2.64, respectively). However, the protective effect of late menarche was observed in BC II and BC III (OR: 0.74; 95% CI: 0.54, 1.00 and OR: 0.64; 95% CI: 0.44, 0.96, respectively).<br><br>CONCLUSIONS: The influential effect of AAM on metabolic disturbances varies amongst different generations.},
}
@article {pmid39548177,
year = {2024},
author = {Ma, J and Ma, W and Zhang, J and Li, Y and Yang, B and Shan, C},
title = {Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {28170},
pmid = {39548177},
issn = {2045-2322},
support = {ZRJY2021-QM02//National High Level Hospital Clinical Research Funding and Elite Medical Professionals Project of China-Japan Friendship Hospital/ ; BYESS2023173//Young Elite Scientist Sponsorship Program By Bast/ ; 82272612//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke/physiopathology ; *Neural Networks, Computer ; Algorithms ; Stroke Rehabilitation/methods ; Male ; Female ; Middle Aged ; Adult ; Machine Learning ; },
abstract = {The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist stroke patients activate motor regions in the brain. In particular, the brain regions activated by unilateral upper limb multi-task are more extensive, which is more beneficial for rehabilitation, but it also increases the difficulty of decoding. In this paper, self-attention convolutional neural network based partial prior transfer learning (SACNN-PPTL) is proposed to improve the classification performance of patients' MI multi-task. The backbone network of the algorithm is SACNN, which accords with the inherent features of electroencephalogram (EEG) and contains the temporal feature module, the spatial feature module and the feature generalization module. In addition, PPTL is introduced to transfer part of the target domain while preserving the generalization of the base model while improving the specificity of the target domain. In the experiment, five backbone networks and three training modes are selected as comparison algorithms. The experimental results show that SACNN-PPTL had a classification accuracy of 55.4%±0.17 in four types of MI tasks for 22 patients, which is significantly higher than comparison algorithms (P < 0.05). SACNN-PPTL effectively improves the decoding performance of MI tasks and promotes the development of BCI-based rehabilitation for unilateral upper limb.},
}
@article {pmid39547137,
year = {2025},
author = {Wellman, SM and Forrest, AM and Douglas, MM and Subbaraman, A and Zhang, G and Kozai, TDY},
title = {Dynamic changes in the structure and function of brain mural cells around chronically implanted microelectrodes.},
journal = {Biomaterials},
volume = {315},
number = {},
pages = {122963},
doi = {10.1016/j.biomaterials.2024.122963},
pmid = {39547137},
issn = {1878-5905},
mesh = {*Microelectrodes ; *Pericytes/cytology ; Animals ; *Electrodes, Implanted/adverse effects ; *Brain/blood supply ; Male ; Mice ; Calcium/metabolism ; },
abstract = {Integration of neural interfaces with minimal tissue disruption in the brain is ideal to develop robust tools that can address essential neuroscience questions and combat neurological disorders. However, implantation of intracortical devices provokes severe tissue inflammation within the brain, which requires a high metabolic demand to support a complex series of cellular events mediating tissue degeneration and wound healing. Pericytes, peri-vascular cells involved in blood-brain barrier maintenance, vascular permeability, waste clearance, and angiogenesis, have recently been implicated as potential perpetuators of neurodegeneration in brain injury and disease. While the intimate relationship between pericytes and the cortical microvasculature have been explored in other disease states, their behavior following microelectrode implantation, which is responsible for direct blood vessel disruption and dysfunction, is currently unknown. Using two-photon microscopy we observed dynamic changes in the structure and function of pericytes during implantation of a microelectrode array over a 4-week implantation period. Pericytes respond to electrode insertion through transient increases in intracellular calcium and underlying constriction of capillary vessels. Within days following the initial insertion, we observed an influx of new, proliferating pericytes which contribute to new blood vessel formation. Additionally, we discovered a potentially novel population of reactive immune cells in close proximity to the electrode-tissue interface actively engaging in encapsulation of the microelectrode array. Finally, we determined that intracellular pericyte calcium can be modulated by intracortical microstimulation in an amplitude- and frequency-dependent manner. This study provides a new perspective on the complex biological sequelae occurring at the electrode-tissue interface and will foster new avenues of potential research consideration and lead to development of more advanced therapeutic interventions towards improving the biocompatibility of neural electrode technology.},
}
@article {pmid39545725,
year = {2025},
author = {Noneman, KK and Patrick Mayo, J},
title = {Decoding Continuous Tracking Eye Movements from Cortical Spiking Activity.},
journal = {International journal of neural systems},
volume = {35},
number = {1},
pages = {2450070},
doi = {10.1142/S0129065724500709},
pmid = {39545725},
issn = {1793-6462},
mesh = {Animals ; *Eye Movements/physiology ; *Neurons/physiology ; *Machine Learning ; *Macaca mulatta ; *Neural Networks, Computer ; Action Potentials/physiology ; Eye-Tracking Technology ; Models, Neurological ; Male ; },
abstract = {Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.},
}
@article {pmid39545148,
year = {2024},
author = {Wohns, N and Dorfman, N and Klein, E},
title = {Caregivers in implantable brain-computer interface research: a scoping review.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1490066},
pmid = {39545148},
issn = {1662-5161},
support = {R01 MH130457/MH/NIMH NIH HHS/United States ; },
abstract = {INTRODUCTION: While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention.<br><br>OBJECTIVES: This report investigates the extent to which caregivers are mentioned in publications describing implantable BCI (iBCI) research for individuals with motor dysfunction, communication impairment, and blindness.<br><br>METHODS: The scoping review was conducted in June 2024 using the PubMed and Web of Science bibliographic databases. The articles were systematically searched using query terms for caregivers, family members, and guardians, and the results were quantitatively and qualitatively analyzed.<br><br>RESULTS: Our search yielded 315 unique studies, 78 of which were included in this scoping review. Thirty-four (43.6%) of the 78 articles mentioned the study participant's caregivers. We sorted these into 5 categories: Twenty-two (64.7%) of the 34 articles thanked caregivers in the acknowledgement section, 6 (17.6%) articles described the caregiver's role with regard to the consent process, 12 (35.3%) described the caregiver's role in the technical maintenance and upkeep of the BCI system or in other procedural aspects of the study, 9 (26.5%) discussed how the BCI enhanced participant communication and goal-directed behavior with the help of a caregiver, and 3 (8.8%) articles included general comments that did not fit into the other categories but still related to the importance of caregivers in the lives of the research participants.<br><br>DISCUSSION: Caregivers were mentioned in less than half of BCI studies in this review. The studies that offered more robust discussions of caregivers provide valuable insight into the integral role that caregivers play in supporting the study participants and the research process. Attention to the role of caregivers in successful BCI research studies can help guide the responsible development of future BCI study protocols.},
}
@article {pmid39545146,
year = {2024},
author = {Wu, X and Ju, X and Dai, S and Li, X and Li, M},
title = {Multi-source domain adaptation for EEG emotion recognition based on inter-domain sample hybridization.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1464431},
pmid = {39545146},
issn = {1662-5161},
abstract = {BACKGROUND: Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance.<br><br>METHOD: This study introduces a multi-source domain adaptation common-branch network for EEG emotion recognition and proposes a novel sample hybridization method. This method enables the introduction of target domain data information by directionally hybridizing source and target domain samples without increasing the overall sample size, thereby enhancing the effectiveness of conditional distribution alignment in domain adaptation. Cross-subject and cross-session experiments were conducted on two publicly available datasets, SEED and SEED-IV, to validate the proposed model.<br><br>RESULT: In cross-subject emotion recognition, our method achieved an average accuracy of 90.27% on the SEED dataset, with eight out of 15 subjects attaining a recognition accuracy higher than 90%. For the SEED-IV dataset, the recognition accuracy also reached 73.21%. Additionally, in the cross-session experiment, we sequentially used two out of the three session data as source domains and the remaining session as the target domain for emotion recognition. The proposed model yielded average accuracies of 94.16 and 75.05% on the two datasets, respectively.<br><br>CONCLUSION: Our proposed method aims to alleviate the difficulties of emotion recognition from the limited generalization ability of EEG features across subjects and sessions. Though adapting the multi-source domain adaptation and the sample hybridization method, the proposed method can effectively transfer the emotion-related knowledge of known subjects and achieve accurate emotion recognition on unlabeled subjects.},
}
@article {pmid39544330,
year = {2024},
author = {Wang, HZ and Saeed, S and Zhang, JY and Hu, SH},
title = {Bridging Three Years of Insights: Examining the Association Between Depression and Gallstone Disease.},
journal = {Journal of clinical medicine research},
volume = {16},
number = {10},
pages = {472-482},
pmid = {39544330},
issn = {1918-3003},
abstract = {BACKGROUND: Despite sharing common pathophysiological risk factors, the relationship between gallstones and depression requires further evidence for a clearer understanding. This study combines the National Health and Nutrition Examination Survey 2017 - 2020 observational data and Mendelian randomization (MR) analysis to shed light on the potential correlation between these conditions.<br><br>METHODS: By analyzing the National Health and Nutrition Examination Survey 2017 - 2020 data through weighted multivariable-adjusted logistic regression, we examined the association between depression and gallstone risk. MR was subsequently applied, utilizing genetic instruments from a large genome-wide association study on depression (excluding 23andMe, 500,199 participants) and gallstone data (28,627 cases, 348,373 controls), employing the main inverse variance-weighted method alongside other MR methods to explore the causal relationship. Sensitivity analyses validated the study's conclusions.<br><br>RESULTS: Among the 5,303 National Health and Nutrition Examination Survey participants, a significant association was found between depressive symptoms and increased gallstone risk (initial odds ratio (OR) = 2.001; 95% confidence interval (CI) = 1.523 - 2.598; P < 0.001), with the association persisting after comprehensive adjustments (final OR = 1.687; 95% CI = 1.261 - 2.234; P < 0.001). MR findings also indicated a causal link between genetically predicted depression and higher gallstone risk (OR = 1.164; 95% CI = 1.053 - 1.286; P = 0.003).<br><br>CONCLUSIONS: Depression is significantly associated with a higher risk of gallstones, supported by genetic evidence suggesting a causal link. These findings highlight the importance of considering depression in gallstone risk assessments and management strategies.},
}
@article {pmid39543314,
year = {2024},
author = {Rybář, M and Poli, R and Daly, I},
title = {Using data from cue presentations results in grossly overestimating semantic BCI performance.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {28003},
pmid = {39543314},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Semantics ; *Cues ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; Brain/physiology ; },
abstract = {Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging. Existing EEG-based semantic decoding studies often rely on neural activity recorded when a cue is present, raising concerns about decoding reliability. To address this, we investigate the effects of cue presentation on EEG-based semantic decoding. In an experiment with a clear separation between cue presentation and mental task periods, we attempt to differentiate between semantic categories of animals and tools in four mental tasks. By using state-of-the-art decoding analyses, we demonstrate significant mean classification accuracies up to 71.3% during cue presentation but not during mental tasks, even with adapted analyses from previous studies. These findings highlight a potential issue when using neural activity recorded during cue presentation periods for semantic decoding. Additionally, our results show that semantic decoding without external cues may be more challenging than current state-of-the-art research suggests. By bringing attention to these issues, we aim to stimulate discussion and drive advancements in the field toward more effective semantic BCI applications.},
}
@article {pmid39542998,
year = {2024},
author = {Kober, SE and Wood, G and Berger, LM},
title = {Controlling Virtual Reality With Brain Signals: State of the Art of Using VR-Based Feedback in Neurofeedback Applications.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {39542998},
issn = {1573-3270},
abstract = {The rapid progress of commercial virtual reality (VR) technology, open access to VR development software as well as open-source instructions for creating brain-VR interfaces have increased the number of VR-based neurofeedback (NF) training studies. Controlling a VR environment with brain signals has potential advantages for NF applications. More entertaining, multimodal and adaptive virtual feedback modalities might positively affect subjective user experience and could consequently enhance NF training performance and outcome. Nevertheless, there are certain pitfalls and contraindications that make VR-based NF not suitable for everyone. In the present review, we summarize applications of VR-based NF and discuss positive effects of VR-based NF training as well as contraindications such as cybersickness in VR or age- and sex-related differences. The existing literature implies that VR-based feedback is a promising tool for the improvement of NF training performance. Users generally rate VR-based feedback more positively than traditional 2D feedback, albeit to draw meaningful conclusions and to rule out adverse effects of VR, more research on this topic is necessary. The pace in the development of brain-VR synchronization furthermore necessitates ethical considerations on these technologies.},
}
@article {pmid39539351,
year = {2024},
author = {Liu, M and Fang, M and Liu, M and Jin, S and Liu, B and Wu, L and Li, Z},
title = {Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1486167},
pmid = {39539351},
issn = {1662-5161},
abstract = {BACKGROUND: Although the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.<br><br>METHODS: The study retrieved relevant publications on BCI technology in rehabilitation from the Web of Science Core Collection (WoSCC) between January 1, 2004, and June 30, 2024. The search was conducted using thematic queries, and the document types included "original articles" and "review articles." Bibliometric analysis and knowledge mapping were performed using the Bibliometrix package in R software and CiteSpace software.<br><br>RESULTS: During the study period, a total of 1,431 publications on BCI technology in rehabilitation were published by 4,932 authors from 1,281 institutions across 79 countries in 386 academic journals. The volume of research literature in this field has shown a steady upward trend. The United States of America (USA) and China are the primary contributors, with Eberhard Karls University of T&#xfc;bingen being the most active research institution. The journal Frontiers in Neuroscience published the most articles, while the Journal of Neural Engineering was the most cited. Niels Birbaumer not only authored the most articles but also received the highest number of citations. The main research areas include neurology, sports medicine, and ophthalmology. The diverse applications of BCI technology in stroke and spinal cord injury rehabilitation, as well as the evaluation of BCI performance, are current research hotspots. Moreover, deep learning has demonstrated significant potential in BCI technology rehabilitation applications.<br><br>CONCLUSION: This bibliometric study provides an overview of the research landscape and developmental trends of BCI technology in rehabilitation, offering valuable reference points for researchers in formulating future research strategies.},
}
@article {pmid39539350,
year = {2024},
author = {Xia, R and Yang, S},
title = {Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1423382},
pmid = {39539350},
issn = {1662-5161},
abstract = {This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents (N = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.},
}
@article {pmid39537730,
year = {2024},
author = {Tian, P and Xu, G and Han, C and Du, C and Li, H and Chen, R and Xie, J and Wang, J and Jiang, H and Guo, X and Zhang, S and Wu, Q},
title = {A subjective and objective fusion visual fatigue assessment system for different hardware and software parameters in SSVEP-based BCI applications.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {27872},
pmid = {39537730},
issn = {2045-2322},
support = {No.20231055//The First Affiliated Hospital of Xi'an Jiaotong University/ ; No.20231056//The First Affiliated Hospital of Xi'an Jiaotong University/ ; Program No. 2023-JC-QN-0501//Natural Science Basic Research Program of Shaanxi Province/ ; No.2021ZD0204300//National Key Research and Development projects/ ; },
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; *Software ; Adult ; Algorithms ; Female ; Young Adult ; Fatigue/physiopathology ; Asthenopia/physiopathology ; Photic Stimulation ; },
abstract = {With the development of brain-computer interface industry, large amounts of related applications have entered people's vision. BCI applications based on steady-state visual evoked potentials (SSVEP) are widely used because they do not require pre-training and have high information transmission rates. However, in the actual use of SSVEP stimulus paradigm, the subjects will produce visual fatigue with the use, and fatigue will affect the transmission efficiency. In this experiment, an experimental environment consisting of two paradigm stimulus frequencies (7.5 Hz, 15 Hz), three resolutions (800 × 600, 1280 × 720, 1920 × 1080) and three refresh rates (120 Hz, 240 Hz, 360 Hz) is set up. The Likert scale is used to collect subjective fatigue and preference scores, and the EEG acquisition system and eye tracker are used to collect objective data. Using the proposed information entropy-CRITIC algorithm to combine subjective and objective indicators, a fatigue assessment system (display screen fitness-DSF) is innovated to score different experimental environments. The higher the DSF score, the better the visual experience. The results show that when using the 7.5 Hz SSVEP paradigm, the combination of 360 Hz and 1920 × 1080 can bring the best visual experience. When using the 15 Hz SSVEP paradigm, the combination of 240 Hz and 1280 × 720 is the best. DSF provides powerful help for hardware and software selection guidance and vision protection when using SSVEP-based BCI applications.},
}
@article {pmid39537459,
year = {2024},
author = {Lai, W and Sha, L and Li, R and Yu, S and Jin, L and Yang, R and Yang, C and Chen, L},
title = {Urine multi-omics markers to predict seizure one day in advance.},
journal = {Science bulletin},
volume = {69},
number = {24},
pages = {3844-3848},
doi = {10.1016/j.scib.2024.10.035},
pmid = {39537459},
issn = {2095-9281},
}
@article {pmid39536406,
year = {2025},
author = {Chen, M and Guo, K and Lu, K and Meng, K and Lu, J and Pang, Y and Zhang, L and Hu, Y and Yu, R and Zhang, R},
title = {Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network.},
journal = {Computer methods and programs in biomedicine},
volume = {258},
number = {},
pages = {108483},
doi = {10.1016/j.cmpb.2024.108483},
pmid = {39536406},
issn = {1872-7565},
mesh = {Humans ; *Drug Resistant Epilepsy/surgery/diagnostic imaging/physiopathology ; Retrospective Studies ; Female ; Male ; *Seizures/surgery/physiopathology/diagnostic imaging ; Adult ; Treatment Outcome ; Electroencephalography/methods ; Young Adult ; Adolescent ; Brain/diagnostic imaging/surgery/physiopathology ; Child ; Electrocorticography/methods ; Algorithms ; },
abstract = {BACKGROUND AND OBJECTIVE: Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.<br><br>METHODS: We conduct a retrospective study on intracranial electroencephalography (iEEG) data from 46 patients with DRE. The interactions between different brain regions are quantified by using the phase transfer entropy (PTE), based on which the causal influence index (CII) is proposed to quantify the degree of influence of nodes on the network. Subsequently, the features extracted by the CII are used to construct a random forest classification model, which the performance in identifying SOZ and the generalizability are validated in patients with successful surgeries. Then, based on the CII features of the clinically labeled SOZ, a logistic regression prediction model is constructed to predict the probability of surgical success. The statistical analysis between patients with successful and failed surgery is conducted with the Mann-Whitney U test. Finally, the consistency between the predicted SOZ and the clinically labeled SOZ is verified across different Engel classes.<br><br>RESULTS: The classification model combining the low-frequency and high-frequency features can achieve an accuracy of 82.18% (sensitivity: 85.01%, specificity: 79.69%) and an area under curve (AUC) of 0.90 in identifying SOZ. Furthermore, the model exhibits strong generalizability in identifying SOZ in patients with MRI lesional and non-lesional, as well as those implanted with electrocorticography (ECOG) and stereotactic EEG (SEEG) electrodes. Moreover, the prediction model could achieve an average accuracy of 79.8% and an AUC of 0.84. Of note, the prediction of surgical success probability is significant between patients with successful and failed surgeries (P<0.001). Correspondingly, the highest consistency between model-predicted SOZ and clinically labeled SOZ can be observed in patients with successful surgeries, but this consistency gradually decreases with increasing Engel classes.<br><br>CONCLUSIONS: These results demonstrate that the CII may be a potential biomarker for identifying the SOZ in patients with DRE, which may provide a new perspective for the treatment of epilepsy.},
}
@article {pmid39535986,
year = {2024},
author = {Officer, K and Arango-Sabogal, JC and Dufour, S and Lyashchenko, KP and Cracknell, J and Thomson, S and Cheng, S and Warren, K and Jackson, B},
title = {Bayesian accuracy estimates for diagnostic tests to detect tuberculosis in captive sun bears (Helarctos malayanus) and Asiatic black bears (Ursus thibetanus) in Cambodia and Vietnam.},
journal = {PloS one},
volume = {19},
number = {11},
pages = {e0313007},
pmid = {39535986},
issn = {1932-6203},
mesh = {Animals ; *Ursidae/microbiology ; *Bayes Theorem ; Cambodia/epidemiology ; Vietnam/epidemiology ; Diagnostic Tests, Routine/methods ; Sensitivity and Specificity ; Tuberculosis, Pulmonary/diagnosis/epidemiology/microbiology ; Tuberculosis/diagnosis/epidemiology ; Retrospective Studies ; Mycobacterium tuberculosis/isolation &amp; purification ; Bronchoalveolar Lavage Fluid/microbiology ; Male ; Female ; },
abstract = {Effective control of tuberculosis (TB) depends on early diagnosis of disease, yet available tests are unable to perfectly detect infected individuals. In novel hosts diagnostic testing methods for TB are extrapolated from other species, with unknown accuracy. The primary challenge to evaluating the accuracy of TB tests is the lack of a perfect reference test. Here we use a Bayesian latent class analysis approach to evaluate five tests available for ante-mortem detection of pulmonary TB in captive sun bears and Asiatic black bears in Southeast Asia. Using retrospective results from screening of 344 bears at three rescue centres, we estimate accuracy parameters for thoracic radiography, a serological assay (DPP VetTB), and three microbiological tests (microscopy, PCR (Xpert MTB/RIF, Xpert MTB/RIF Ultra), mycobacterial culture) performed on bronchoalveolar lavage samples. While confirming the high specificities (≥ 0.99) of the three microbiological tests, our model demonstrated their sub-optimal sensitivities (<0.7). Thoracic radiography was the only diagnostic method with sensitivity (0.95, 95% BCI: 0.76, 0.998) and specificity (0.95, 95% BCI: 0.91, 0.98) estimated above 0.9. We recommend caution when interpreting DPP VetTB results, with the increased sensitivity resulting from treatment of weakly visible reactions as positive accompanied by a drop in specificity, and we illustrate how the diagnostic value of weak DPP VetTB reactions is particularly reduced if disease prevalence and/or clinical suspicion is low. Conversely, the reduced utility of negative microbiological tests on bronchoalveolar lavage fluid samples when prevalence and/or clinical suspicion is high is demonstrated. Taken together our results suggest multiple tests should be applied and accompanied by consideration of the testing context, to minimise the consequences of misclassification of disease status of bears at risk of TB in sanctuary settings.},
}
@article {pmid39528522,
year = {2024},
author = {Hou, Z and Li, X and Yang, J and Xu, SY},
title = {Enhancing mathematical learning outcomes through a low-cost single-channel BCI system.},
journal = {NPJ science of learning},
volume = {9},
number = {1},
pages = {65},
pmid = {39528522},
issn = {2056-7936},
abstract = {This study investigates the effectiveness of a Low-Cost Single-Channel BCI system in improving mathematical learning outcomes, self-efficacy, and alpha power in university students. Eighty participants were randomly assigned to either a BCI group receiving real-time neurofeedback based on alpha rhythms or a sham feedback group. Results showed that the BCI group had significantly higher mathematical performance, self-efficacy, and alpha power compared to the sham feedback group. Mathematics performance, alpha wave intensity, and self-efficacy showed significant positive correlations after training, indicating that neurofeedback training may have promoted their interaction and integration. These findings demonstrate the potential of BCI technology in enhancing mathematical learning outcomes and highlight the importance of considering pre-test performance and self-efficacy in predicting learning outcomes, with implications for personalized learning interventions and the integration of BCI technology in educational settings.},
}
@article {pmid39534365,
year = {2024},
author = {Huang, Z and Wei, Q},
title = {Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {3},
pages = {877-892},
pmid = {39534365},
issn = {1871-4080},
abstract = {The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs. A three-way tensor is yielded by wavelet transform of a single-trial EEG signal and decomposed into three factor matrices by a regularized canonical polyadic decomposition (CPD). The channel factor matrix is used for channel selection and the important channels are selected by calculating the correlation between channels. Regularized common spatial pattern (RCSP) is employed for feature extraction and support vector machine (SVM) for classification. The proposed TCS-RCSP algorithm was evaluated on three BCI data sets and compared with the RCSP with all channels (AC-RCSP) and the RCSP with selected channels by correlation-based channel selection method (CCS-RCSP). The results indicate that TCS-RCSP achieved significantly better overall accuracy than AC-RCSP (94.4% vs. 86.3%) with ρ < 0.01 and CCS-RCSP (94.4% vs. 90.2%) with ρ < 0.05, proving the efficacy of the proposed algorithm for classifying MI tasks.},
}
@article {pmid39531567,
year = {2024},
author = {Wang, J and Cui, Y and Zhang, H and Wu, H and Yang, C},
title = {An Asynchronous Training-Free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {4120-4130},
doi = {10.1109/TNSRE.2024.3496727},
pmid = {39531567},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Algorithms ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Signal-To-Noise Ratio ; Probability ; Reproducibility of Results ; },
abstract = {SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.},
}
@article {pmid39530641,
year = {2024},
author = {Garcia Cerqueira, EM and de Medeiros, RE and da Silva Fiorin, F and de Arújo E Silva, M and Hypolito Lima, R and Azevedo Dantas, AFO and Rodrigues, AC and Delisle-Rodriguez, D},
title = {Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ad9155},
pmid = {39530641},
issn = {2057-1976},
mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Rats, Wistar ; *Seizures/therapy ; *Epilepsy/therapy ; Spinal Cord ; Motor Cortex/physiopathology ; Hippocampus ; Male ; Spinal Cord Stimulation/methods ; Algorithms ; Machine Learning ; Pentylenetetrazole ; Electric Stimulation/methods ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {Objective.This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.Approach.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, theZ-score-based PLV normalization using both modifiedk-means and Davies-Bouldin's measure for clustering is proposed here. Consequently, a generic seizure's detector is calibrated for detecting seizures on the normalized PLV, and enables the spinal cord stimulation for periods of 30 s in a closed-loop, while the BMI system detects seizure events. To calibrate the proposed BMI, a dataset with LFP signals recorded on five Wistar rats during basal state and epileptic crisis was used. The epileptic crisis was induced by injecting pentylenetetrazol (PTZ). Afterwards, two experiments without/with our BMI were carried out, inducing epileptic crisis by PTZ in Wistar rats.Main results.Stronger seizure events of high LFP amplitudes and long time periods were observed in the rat, when the BMI system was not used. In contrast, short-time seizure events of relative low intensity were observed in the rat, using the proposed BMI. The proposed system detected on unseen data the synchronized seizure activity in the hippocampus and motor cortex, provided stimulation appropriately, and consequently decreased seizure symptoms.Significance.Low-frequency LFP signals from the hippocampus and motor cortex, and cord spinal stimulation can be used to develop accurate closed-loop BMIs for early epileptic seizures inhibition, as an alternative treatment.},
}
@article {pmid39529486,
year = {2025},
author = {Blanken, TF and Kok, R and Obbels, J and Lambrichts, S and Sienaert, P and Verwijk, E},
title = {Prediction of electroconvulsive therapy outcome: A network analysis approach.},
journal = {Acta psychiatrica Scandinavica},
volume = {151},
number = {4},
pages = {521-528},
pmid = {39529486},
issn = {1600-0447},
support = {//Flemish Fund for Scientific Research/ ; //Amsterdam Brain and Cognition/ ; },
abstract = {OBJECTIVE: While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach.<br><br>METHOD: We combined individual patient data from two randomized controlled trials (total N&#x2009;=&#x2009;161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD&#x2264;7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios.<br><br>RESULTS: Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR&#x2009;=&#x2009;0.75; bootstrapped confidence interval (bCI)&#x2009;=&#x2009;0.44-1.00) whereas retardation (OR&#x2009;=&#x2009;1.21; bCI&#x2009;=&#x2009;1.00-2.02) and hypochondriasis (OR&#x2009;=&#x2009;1.31; bCI&#x2009;=&#x2009;1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found.<br><br>CONCLUSION: By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.},
}
@article {pmid39529200,
year = {2024},
author = {Liu, DY and Li, M and Yu, J and Gao, Y and Zhang, X and Hu, D and Northoff, G and Song, XM and Zhu, J},
title = {Sex differences in the human brain related to visual motion perception.},
journal = {Biology of sex differences},
volume = {15},
number = {1},
pages = {92},
pmid = {39529200},
issn = {2042-6410},
support = {2024SSYS0019//the key R&amp;D program of zhejiang/ ; 2022C03096//the key R&amp;D program of zhejiang/ ; 82272112//the national natural science foundation of china grants/ ; 62076248//the national natural science foundation of china grants/ ; 52293424//the national natural science foundation of china grants/ ; LR23E070001//zhejiang proveincial natural science foundation of china/ ; },
mesh = {Humans ; Female ; Male ; *Motion Perception/physiology ; *Magnetic Resonance Imaging ; *Sex Characteristics ; Adult ; *Brain/physiology/diagnostic imaging ; Young Adult ; Photic Stimulation ; },
abstract = {BACKGROUND: Previous studies have found that the temporal duration required for males to perceive visual motion direction is significantly shorter than that for females. However, the neural correlates of such shortened duration perception remain yet unclear. Given that motion perception is primarily associated with the neural activity of the middle temporal visual complex (MT+), we here test the novel hypothesis that the neural mechanism of these behavioral sex differences is mainly related to the MT+ region.<br><br>METHODS: We utilized ultra-high field (UHF) MRI to investigate sex differences in the MT+ brain region. A total of 95 subjects (48 females) participated in two separate studies. Cohort 1, consisting of 33 subjects (16 females), completed task-fMRI (drafting grating stimuli) experiment. Cohort 2, comprising 62 subjects (32 females), engaged in a psychophysical experiment measuring motion perception along different temporal thresholds as well as conducting structural and functional MRI scanning of MT+.<br><br>RESULTS: Our findings show pronounced sex differences in major brain parameters within the left MT+ (but not the right MT+, i.e., laterality). In particular, males demonstrate (i) larger gray matter volume (GMV) and higher brain's spontaneous activity at the fastest infra-slow frequency band in the left MT+; and (ii) stronger functional connectivity between the left MT+ and the left centromedial amygdala (CM). Meanwhile, both female and male participants exhibited comparable correlations between motion perception ability and the multimodal imaging indexes of the MT+ region, i.e., larger GMV, higher brain's spontaneous activity, and faster motion discrimination.<br><br>CONCLUSIONS: Our findings reveal sex differences of imaging indicators of structure and function in the MT+ region, which also relate to the temporal threshold of motion discrimination. Overall, these results show how behavioral sex differences in visual motion perception are generated, and advocate considering sex as a crucial biological variable in both human brain and behavioral research.},
}
@article {pmid39528479,
year = {2024},
author = {Tian, F and Zhang, Y and Schriver, KE and Hu, JM and Roe, AW},
title = {Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {9742},
doi = {10.1038/s41467-024-54090-8},
pmid = {39528479},
issn = {2041-1723},
}
@article {pmid39527418,
year = {2025},
author = {Shi, Y and Jiang, A and Zhong, J and Li, M and Zhu, Y},
title = {Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {2},
pages = {935-947},
doi = {10.1109/JBHI.2024.3496757},
pmid = {39527418},
issn = {2168-2208},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Brain/physiology ; },
abstract = {In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.},
}
@article {pmid39523453,
year = {2024},
author = {Shen, B and Yao, Q and Zhang, Y and Jiang, Y and Wang, Y and Jiang, X and Zhao, Y and Zhang, H and Dong, S and Li, D and Chen, Y and Pan, Y and Yan, J and Han, F and Li, S and Zhu, Q and Zhang, D and Zhang, L and Wu, YC},
title = {Static and Dynamic Functional Network Connectivity in Parkinson's Disease Patients With Postural Instability and Gait Disorder.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {11},
pages = {e70115},
pmid = {39523453},
issn = {1755-5949},
support = {NO.QRX17026//Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau/ ; //Nanjing Rehabilitation Medicine Center Project/ ; NO.BE2022842//Special Funds of the Jiangsu Provincial Key Research and Development Program/ ; NO.LD2021013//Jiangsu Province Elderly Health Project/ ; NO.81971185//National Natural Science Foundation of China/ ; NO.82171243//National Natural Science Foundation of China/ ; NO.82171249//National Natural Science Foundation of China/ ; NO.82204352//National Natural Science Foundation of China/ ; NO.BJ20005//Jiangsu Provincial Cadre Health Projects/ ; 23-25-2R11//Nanjing Brain Hospital Youth Talent Project/ ; 23-25-2R6//Nanjing Brain Hospital Youth Talent Project/ ; NJ2024029//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Parkinson Disease/physiopathology/diagnostic imaging ; *Postural Balance/physiology ; Female ; Male ; Middle Aged ; *Gait Disorders, Neurologic/physiopathology/etiology ; Aged ; *Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; Brain/physiopathology/diagnostic imaging ; Connectome ; Neural Pathways/physiopathology ; },
abstract = {AIMS: The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC).<br><br>METHODS: We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms.<br><br>RESULTS: Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores.<br><br>CONCLUSION: Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.},
}
@article {pmid39523287,
year = {2024},
author = {Rosenfeld, JV},
title = {Neurosurgery and the Brain-Computer Interface.},
journal = {Advances in experimental medicine and biology},
volume = {1462},
number = {},
pages = {513-527},
pmid = {39523287},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces/ethics ; Humans ; Brain/physiology ; Neurosurgical Procedures/methods ; Electrodes, Implanted ; Neurosurgery/methods/instrumentation ; },
abstract = {Brain-computer interfaces (BCIs) are devices that connect the human brain to an effector via a computer and electrode interface. BCIs may also transmit sensory data to the brain. We describe progress with the many types of surgically implanted BCIs, in which electrodes contact or penetrate the cerebral cortex. BCIs developed for restoration of movement in paralyzed limbs or control a robotic arm; restoration of somatic sensation, speech, vision, memory, hearing, and olfaction are also presented. Most devices remain experimental. Commercialization is costly, incurs financial risk, and is time consuming. There are many ethical principles that should be considered by neurosurgeons and by all those responsible for the care of people with serious neurological disability. These considerations are also paramount when the technology is used in for the purpose of enhancement of normal function and where commercial gain is a factor. A new regulatory and legislative framework is urgently required. The evolution of BCIs is occurring rapidly with advances in computer science, artificial intelligence, electronic engineering including wireless transmission, and materials science. The era of the brain-"cloud" interface is approaching.},
}
@article {pmid39521778,
year = {2024},
author = {Pacheco-Ramírez, MA and Ramírez-Moreno, MA and Kukkar, K and Rao, N and Huber, D and Brandt, AK and Noble, A and Noble, D and Ealey, B and Contreras-Vidal, JL},
title = {Neural Dynamics of Creative Movements During the Rehearsal and Performance of "LiveWire".},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1208},
pmid = {39521778},
issn = {2052-4463},
support = {IUCRC BRAIN #2137255//National Science Foundation (NSF)/ ; },
mesh = {Humans ; *Dancing/physiology ; *Electroencephalography ; *Brain/physiology ; Electrooculography ; Movement ; Music ; Creativity ; },
abstract = {This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of "LiveWire" - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.},
}
@article {pmid39520382,
year = {2025},
author = {Kim, D and Lee, JW and Kim, YT and Choe, J and Kim, G and Ha, CM and Kim, JG and Song, KH and Yang, S},
title = {Minimally Invasive Syringe-Injectable Hydrogel with Angiogenic Factors for Ischemic Stroke Treatment.},
journal = {Advanced healthcare materials},
volume = {14},
number = {6},
pages = {e2403119},
pmid = {39520382},
issn = {2192-2659},
support = {//National Research Foundation of Korea/ ; NRF-2022R1A4A5034121//Korean government/ ; NRF-2022R1A2C1007876//Korean government/ ; NRF-2021R1C1C1010633//Korean government/ ; NRF-RS-2023-00229062//Korean government/ ; 24-BR-03-02//Ministry of Science, ICT and Future Planning/ ; },
mesh = {Animals ; *Hydrogels/chemistry/pharmacology ; *Ischemic Stroke/drug therapy ; Rats ; *Rats, Sprague-Dawley ; Male ; Nanofibers/chemistry/therapeutic use ; Syringes ; Gelatin/chemistry ; Angiogenesis Inducing Agents/pharmacology/administration &amp; dosage/chemistry ; Norbornanes/chemistry ; },
abstract = {Ischemic stroke (IS) accounts for most stroke incidents and causes intractable damage to brain tissue. This condition manifests as diverse aftereffects, such as motor impairment, emotional disturbances, and dementia. However, a fundamental approach to curing IS remains unclear. This study proposes a novel approach for treating IS by employing minimally invasive and injectable jammed gelatin-norbornene nanofibrous hydrogels (GNF) infused with growth factors (GFs). The developed GNF/GF hydrogels are administered to the motor cortex of a rat IS model to evaluate their therapeutic effects on IS-induced motor dysfunction. GNFs mimic a natural fibrous extracellular matrix architecture and can be precisely injected into a targeted brain area. The syringe-injectable jammed nanofibrous hydrogel system increased angiogenesis, inflammation, and sensorimotor function in the IS-affected brain. For clinical applications, the biocompatible GNF hydrogel has the potential to efficiently load disease-specific drugs, enabling targeted therapy for treating a wide range of neurological diseases.},
}
@article {pmid39517980,
year = {2024},
author = {Chen, Y and Shi, X and De Silva, V and Dogan, S},
title = {Steady-State Visual Evoked Potential-Based Brain-Computer Interface System for Enhanced Human Activity Monitoring and Assessment.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {21},
pages = {},
pmid = {39517980},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Brain/physiology ; },
abstract = {Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.},
}
@article {pmid39517978,
year = {2024},
author = {Ma, S and Zhang, D},
title = {A Cross-Attention-Based Class Alignment Network for Cross-Subject EEG Classification in a Heterogeneous Space.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {21},
pages = {},
pmid = {39517978},
issn = {1424-8220},
support = {12271211//the National Natural Science Foundation of China/ ; 2022J011275&#xff0c;2021J01861//the Natural Science Foundation of Fujian Province/ ; ZQ2023022//the Doctoral Research Initiation Fund of Jimei University/ ; FJKX-2023XKB007//the Research Project of Fujian Association for Science and Technology Innovation Think Tank/ ; 2023SXLMMS06//Fujian Alliance Of Mathematics/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring that the source and target domains share identical feature dimensions and label sets, which is often impractical in real-world applications. Therefore, effectively addressing the challenge of EEG classification under heterogeneous spaces has emerged as a crucial research topic.<br><br>METHODS: We present a comprehensive framework that addresses the challenges of heterogeneous spaces by implementing a cross-domain class alignment strategy. We innovatively construct a cross-encoder to effectively capture the intricate dependencies between data across domains. We also introduce a tailored class discriminator accompanied by a corresponding loss function. By optimizing the loss function, we facilitate the aggregation of features with corresponding classes between the source and target domains, while ensuring that features from non-corresponding classes are dispersed.<br><br>RESULTS: Extensive experiments were conducted on two publicly available EEG datasets. Compared to advanced methods that combine label alignment with transfer learning, our method demonstrated superior performance across five heterogeneous space scenarios. Notably, in four heterogeneous label space scenarios, our method outperformed the advanced methods by an average of 7.8%. Moreover, in complex scenarios involving both heterogeneous label spaces and heterogeneous feature spaces, our method outperformed the state-of-the-art methods by an average of 4.1%.<br><br>CONCLUSIONS: This paper presents an efficient model for cross-subject EEG classification under heterogeneous spaces, which significantly addresses the challenges of EEG classification within heterogeneous spaces, thereby opening up new perspectives and avenues for research in related fields.},
}
@article {pmid39517915,
year = {2024},
author = {Wei, P and Chen, T and Zhang, J and Li, J and Hong, J and Zhang, L},
title = {Study of the Brain Functional Connectivity Processes During Multi-Movement States of the Lower Limbs.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {21},
pages = {},
pmid = {39517915},
issn = {1424-8220},
support = {52305294//National Natural Science Foundation of China/ ; xhj032021010-03//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Lower Extremity/physiology ; *Electroencephalography/methods ; *Walking/physiology ; Male ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; *Movement/physiology ; Female ; Adult ; Gait/physiology ; },
abstract = {Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain-computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time-frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal-occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke.},
}
@article {pmid39517862,
year = {2024},
author = {Rehman, M and Anwer, H and Garay, H and Alemany-Iturriaga, J and Díez, IT and Siddiqui, HUR and Ullah, S},
title = {Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {21},
pages = {},
pmid = {39517862},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Brain/physiology ; *Signal Processing, Computer-Assisted ; Visual Perception/physiology ; Algorithms ; Photic Stimulation ; },
abstract = {The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.},
}
@article {pmid39517744,
year = {2024},
author = {Rettore Andreis, F and Meijs, S and Nielsen, TGNDS and Janjua, TAM and Jensen, W},
title = {Comparison of Subdural and Intracortical Recordings of Somatosensory Evoked Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {21},
pages = {},
pmid = {39517744},
issn = {1424-8220},
support = {DNRF121//Danish National Research Foundation/ ; },
mesh = {*Evoked Potentials, Somatosensory/physiology ; Animals ; Swine ; *Somatosensory Cortex/physiology ; *Signal-To-Noise Ratio ; Brain-Computer Interfaces ; Electrocorticography/methods ; Microelectrodes ; Electric Stimulation/methods ; Electrodes, Implanted ; },
abstract = {Micro-electrocorticography (µECoG) electrodes have emerged to balance the trade-off between invasiveness and signal quality in brain recordings. However, its large-scale applicability is still hindered by a lack of comparative studies assessing the relationship between ECoG and traditional recording methods such as penetrating electrodes. This study aimed to compare somatosensory evoked potentials (SEPs) through the lenses of a µECoG and an intracortical microelectrode array (MEA). The electrodes were implanted in the pig's primary somatosensory cortex, while SEPs were generated by applying electrical stimulation to the ulnar nerve. The SEP amplitude, signal-to-noise ratio (SNR), power spectral density (PSD), and correlation structure were analysed. Overall, SEPs resulting from MEA recordings had higher amplitudes and contained significantly more spectral power, especially at higher frequencies. However, the SNRs were similar between the interfaces. These results demonstrate the feasibility of using µECoG to decode SEPs with wide-range applications in physiology monitoring and brain-computer interfaces.},
}
@article {pmid39517124,
year = {2024},
author = {Zhang, A and Jiang, J and Zhang, C and Xu, H and Yu, W and Zhang, ZN and Yuan, L and Lu, Z and Deng, Y and Fan, H and Fang, C and Wang, X and Shao, A and Chen, S and Li, H and Ni, J and Wang, W and Zhang, X and Zhang, J and Luan, B},
title = {Thermogenic Adipocytes Promote M2 Macrophage Polarization through CNNM4-Mediated Mg Secretion.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {47},
pages = {e2401140},
pmid = {39517124},
issn = {2198-3844},
support = {2023YFC2705700//National Key Research and Development Program of China/ ; 32471220//National Natural Science Foundation of China/ ; 82401598//National Natural Science Foundation of China/ ; 82350710799//National Natural Science Foundation of China/ ; 32400982//National Natural Science Foundation of China/ ; 52201300//National Natural Science Foundation of China/ ; 2023ZD0512800//Science and Technology Innovation 2030: Major national science and technology projects/ ; 2021M702090//China Postdoctoral Science Foundation/ ; 2023M742639//China Postdoctoral Science Foundation/ ; BX20240257//China Postdoctoral Science Foundation/ ; 21SG21//Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission/ ; 2023573//Shanghai Post-doctoral Excellence Program/ ; },
mesh = {Animals ; Mice ; *Macrophages/metabolism ; *Thermogenesis ; *Adipocytes/metabolism ; *Magnesium/metabolism ; Mice, Inbred C57BL ; Cation Transport Proteins/metabolism/genetics ; Signal Transduction ; Obesity/metabolism ; Disease Models, Animal ; Male ; },
abstract = {M2 macrophages promote adipose tissue thermogenesis which dissipates energy in the form of heat to combat obesity. However, the regulation of M2 macrophages by thermogenic adipocytes is unclear. Here, it is identified magnesium (Mg) as a thermogenic adipocyte-secreted factor to promote M2 macrophage polarization. Mg transporter Cyclin and CBS domain divalent metal cation transport mediator 4 (CNNM4) induced by ADRB3-PKA-CREB signaling in thermogenic adipocytes during cold exposure mediates Mg efflux and Mg in turn binds to the DFG motif in mTOR to facilitate mTORC2 activation and M2 polarization in macrophages. In obesity, downregulation of CNNM4 expression inhibits Mg secretion from thermogenic adipocytes, which leads to decreased M2 macrophage polarization and thermogenesis. As a result, CNNM4 overexpression in adipocytes or Mg supplementation in adipose tissue ameliorates obesity by promoting thermogenesis. Importantly, an Mg wire implantation (AMI) approach is introduced to achieve adipose tissue-specific long-term Mg supplement. AMI promotes M2 macrophage polarization and thermogenesis and ameliorates obesity in mice. Taken together, a reciprocal regulation of thermogenic adipocytes and M2 macrophages important for thermogenesis is identified, and AMI is offered as a promising strategy against obesity.},
}
@article {pmid39514976,
year = {2024},
author = {Ji, L and Yi, L and Huang, C and Li, H and Han, W and Zhang, N},
title = {Classification of hand movements from EEG using a FusionNet based LSTM network.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad905d},
pmid = {39514976},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Hand/physiology ; *Movement/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; },
abstract = {Objective. Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.Approach. This paper introduces a novel multi-model fusion approach, FusionNet-Long Short-Term Memory (LSTM), designed to address these issues. Specifically, it integrates Convolutional Neural Networks for spatial feature extraction, Gated Recurrent Units and LSTM networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics.Main results. Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model.Significance. This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems.},
}
@article {pmid39513467,
year = {2024},
author = {Nie, A and Li, M and Wang, Q and Zhang, C},
title = {The isolation between part-set cues and social collaboration in episodic memory is dependent: Insight from ongoing and post-collaboration.},
journal = {Scandinavian journal of psychology},
volume = {65},
number = {6},
pages = {981-999},
doi = {10.1111/sjop.13042},
pmid = {39513467},
issn = {1467-9450},
support = {202303021221150//Natural Science Foundation of Shanxi Province of China/ ; 2024-090//Research Project Supported by Shanxi Scholarship Council of China/ ; 21YJA190005//Humanities and Social Sciences, Ministry of Education of China/ ; LY21C090002//Zhejiang Provincial Natural Science Foundation of China/ ; 31300831//National Natural Science Foundation of China/ ; //Initial Scientific Research Fund of Shanxi Normal University/ ; },
mesh = {Humans ; *Cues ; Female ; Male ; *Memory, Episodic ; *Mental Recall/physiology ; Young Adult ; Adult ; *Social Interaction ; Cooperative Behavior ; Stereotyping ; Emotions/physiology ; },
abstract = {It has been demonstrated that both part-set cues and social interaction can produce detrimental effects on memory. Specifically, part-set cues lead to part-set cueing impairment, while social interaction can result in collaborative inhibition. However, there is less evidence on whether these factors have isolated or comparable impacts on memory. Additionally, it is still unknown whether the effects behave similarly on item memory and source memory, whether the effects are comparable between ongoing and post-social collaboration, and whether stimulus features influence their respective roles. To address these issues, we conducted the current experiment where participants were exposed to gender stereotype-consistent or -inconsistent words, categorized as positive, neutral, or negative. The words were read out by either a male or a female. Two recall sessions were conducted: Recall 1 was carried out either individually or collaboratively, whereas Recall 2 was always collaborative. Some participants performed Recall 1 under the part-set cued condition while others were under the no-cued condition. Both item memory and source memory were assessed in both recall sessions. The results have three implications. First, during the ongoing collaborative session, two effects were observed on item memory: part-set cueing impairment and collaborative inhibition. Further, the contributions elicited by part-set cues and social collaboration are isolated. The part-set cueing impairment was influenced by both emotional valence and stereotypical consistency. Second, post-collaboration analysis indicated that both the utilization of part-set cues and collaboration itself enhanced item memory, resulting in the part-set cueing enhancement and post-collaborative memory benefit. Additionally, there was evidence indicating that the mechanisms prompted by these two factors intertwined when emotional valence and stereotypical consistency were considered. Third, in both ongoing and post-collaboration scenarios, the detrimental and beneficial effects on item memory and source memory exhibited different patterns, thereby supporting the dual-process models. These findings enhance our comprehension of the insolation and the interplay between part-set cues and collaboration in memory.},
}
@article {pmid39511344,
year = {2025},
author = {Fan, Y and Wang, M and Fang, F and Ding, N and Luo, H},
title = {Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory.},
journal = {Nature human behaviour},
volume = {9},
number = {2},
pages = {360-375},
pmid = {39511344},
issn = {2397-3374},
support = {2023M740124//China Postdoctoral Science Foundation/ ; T2421004//Science Fund for Creative Research Groups (Fund for Creative Research Groups)/ ; 31930053//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32222035//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31930052//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Memory, Short-Term/physiology ; *Magnetoencephalography ; *Electroencephalography ; Male ; Adult ; Female ; Young Adult ; Prefrontal Cortex/physiology/diagnostic imaging ; Brain Mapping/methods ; Temporal Lobe/physiology/diagnostic imaging ; Parietal Lobe/physiology/diagnostic imaging ; },
abstract = {Working memory (WM) is constructive in nature. Instead of passively retaining information, WM reorganizes complex sequences into hierarchically embedded chunks to overcome capacity limits and facilitate flexible behaviour. Here, to investigate the neural mechanisms underlying hierarchical reorganization in WM, we performed two electroencephalography and one magnetoencephalography experiments, wherein humans retain in WM a temporal sequence of items, that is, syllables, which are organized into chunks, that is, multisyllabic words. We demonstrate that the one-dimensional sequence is represented by two-dimensional neural representational geometry in WM arising from left prefrontal and temporoparietal regions, with separate dimensions encoding item position within a chunk and chunk position in the sequence. Critically, this two-dimensional geometry is observed consistently in different experimental settings, even during tasks not encouraging hierarchical reorganization in WM and correlates with WM behaviour. Overall, these findings strongly support that complex sequences are reorganized into factorized multidimensional neural representational geometry in WM, which also speaks to general structure-based organizational principles given WM's involvement in many cognitive functions.},
}
@article {pmid39511257,
year = {2024},
author = {Xue, S and Jin, B and Jiang, J and Guo, L and Liu, J},
title = {A hybrid local-global neural network for visual classification using raw EEG signals.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {27170},
pmid = {39511257},
issn = {2045-2322},
support = {2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; 2023ZD0121201//National Science and Technology Major Project/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; U21B2043//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Photic Stimulation ; },
abstract = {EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.},
}
@article {pmid39509814,
year = {2025},
author = {Li, X and Wei, W and Qiu, S and He, H},
title = {A temporal-spectral fusion transformer with subject-specific adapter for enhancing RSVP-BCI decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {181},
number = {},
pages = {106844},
doi = {10.1016/j.neunet.2024.106844},
pmid = {39509814},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Male ; Adult ; Visual Perception/physiology ; Photic Stimulation/methods ; Brain/physiology ; },
abstract = {The Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient technology for target retrieval using electroencephalography (EEG) signals. The performance improvement of traditional decoding methods relies on a substantial amount of training data from new test subjects, which increases preparation time for BCI systems. Several studies introduce data from existing subjects to reduce the dependence of performance improvement on data from new subjects, but their optimization strategy based on adversarial learning with extensive data increases training time during the preparation procedure. Moreover, most previous methods only focus on the single-view information of EEG signals, but ignore the information from other views which may further improve performance. To enhance decoding performance while reducing preparation time, we propose a Temporal-Spectral fusion transformer with Subject-specific Adapter (TSformer-SA). Specifically, a cross-view interaction module is proposed to facilitate information transfer and extract common representations across two-view features extracted from EEG temporal signals and spectrogram images. Then, an attention-based fusion module fuses the features of two views to obtain comprehensive discriminative features for classification. Furthermore, a multi-view consistency loss is proposed to maximize the feature similarity between two views of the same EEG signal. Finally, we propose a subject-specific adapter to rapidly transfer the knowledge of the model trained on data from existing subjects to decode data from new subjects. Experimental results show that TSformer-SA significantly outperforms comparison methods and achieves outstanding performance with limited training data from new subjects. This facilitates efficient decoding and rapid deployment of BCI systems in practical use.},
}
@article {pmid39509308,
year = {2025},
author = {Liu, D and Ding, Q and Tong, C and Ai, J and Wang, F},
title = {Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {2},
pages = {984-995},
doi = {10.1109/JBHI.2024.3491096},
pmid = {39509308},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; *Algorithms ; *Principal Component Analysis ; Machine Learning ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality of SPD matrices introduces too much computational burden to hinder the real-time applications of such BCI, especially in handling dynamic tasks, like incremental learning. Directly reducing the dimensionality of SPD matrices with conventional dimensionality reduction (DR) methods will alter the fundamental properties of SPD matrices. Moreover, current DR methods for incremental learning always necessitate retaining old data to update their representations under new mapping. To this end, a bidirectional two-dimensional principal component analysis for SPD manifold (B2DPCA-SPD) is proposed to reduce the dimensionality of SPD matrices, in such way that the reduced matrices remain on SPD manifold. Afterwards, the B2DPCA-SPD is extended to adapt to incremental learning tasks without saving old data. The incremental B2DPCA-SPD can be seamlessly integrated with the matrix-formed growing neural gas network (MF-GNG) to achieve an incremental EEG classification, where the new low-dimensional representations of the prototypes in old classifiers can be easily recalculated with the updated projection matrix. Extensive experiments are conducted on two public datasets to perform the EEG classification. The results demonstrate that our method significantly reduces computation time by 38.53% and 35.96%, and outperforms conventional methods in classification accuracy by 4.21% to 19.59%.},
}
@article {pmid39508555,
year = {2024},
author = {Shah, NP and Phillips, AJ and Madugula, S and Lotlikar, A and Gogliettino, AR and Hays, MR and Grosberg, L and Brown, J and Dusi, A and Tandon, P and Hottowy, P and Dabrowski, W and Sher, A and Litke, AM and Mitra, S and Chichilnisky, EJ},
title = {Precise control of neural activity using dynamically optimized electrical stimulation.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {39508555},
issn = {2050-084X},
support = {2146755//National Science Foundation Graduate Research Fellowship/ ; 1828993//National Science Foundation/ ; F30-EY-030776-03/EY/NEI NIH HHS/United States ; T32MH-020016/MH/NIMH NIH HHS/United States ; F31-EY-033636/EY/NEI NIH HHS/United States ; DEC-2013/10/M/NZ4/00268//Polish Academy of Sciences/ ; R01-EY021271/EY/NEI NIH HHS/United States ; R01-EY029247/EY/NEI NIH HHS/United States ; P30-EY019005/EY/NEI NIH HHS/United States ; NSF/CRCNS//National Science Foundation/ ; },
mesh = {Animals ; Rats ; *Electric Stimulation/methods ; *Retinal Ganglion Cells/physiology ; Photic Stimulation ; Macaca ; Electrodes, Implanted ; Visual Prosthesis ; Macaca mulatta ; },
abstract = {Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bidirectional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration.},
}
@article {pmid39508456,
year = {2024},
author = {Park, S and Lipton, M and Dadarlat, MC},
title = {Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad83c0},
pmid = {39508456},
issn = {1741-2552},
mesh = {Animals ; *Deep Learning ; Mice ; *Movement/physiology ; *Calcium/metabolism ; Neurons/physiology ; Brain-Computer Interfaces ; Mice, Inbred C57BL ; Male ; Extremities/innervation/physiology ; },
abstract = {Objective.Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.Approach.We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.Main results.LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.Significance.Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.},
}
@article {pmid39507693,
year = {2024},
author = {Zhang, L and Xia, J and Li, B and Cao, Z and Dong, S},
title = {Multimodal integrated flexible neural probe for in situ monitoring of EEG and lactic acid.},
journal = {RSC advances},
volume = {14},
number = {48},
pages = {35520-35528},
pmid = {39507693},
issn = {2046-2069},
abstract = {In physiological activities, the brain's electroencephalogram (EEG) signal and chemical concentration change are crucial for diagnosing and treating neurological disorders. Despite the advantages of flexible neural probes, such as their flexibility and biocompatibility, it remains a challenge to achieve in situ monitoring of electrophysiological and chemical signals on a small scale simultaneously. This study developed a new method to construct an efficient dual-sided multimodal integrated flexible neural probe, which combines a density electrode array for EEG recordings and an electrochemical sensor for detecting lactic acid. The EEG electrode array includes a 6-channel recording electrode array with each electrode 30 × 50 μm in size, and the lactic acid sensor with overall contact is approximately 100 μm wide. The EEG electrodes have an average impedance of 2.57 kΩ at 1 kHz and remained stable after immersing in NS (normal saline) for 3 months. The lactic acid sensor showed a sensitivity of 52.8 nA mM[-1]. The in vivo experiments demonstrated that the probe can reliably monitor electrophysiological signals. The probe is able to be implanted into the desired site with the help of a guide port. This flexible neural probe can provide more comprehensive insights into brain activity in the field of neuroscience and clinical practices.},
}
@article {pmid39506628,
year = {2024},
author = {Theofanopoulou, C and Paez, S and Huber, D and Todd, E and Ramírez-Moreno, MA and Khaleghian, B and Sánchez, AM and Barceló, L and Gand, V and Contreras-Vidal, JL},
title = {Mobile brain imaging in butoh dancers: from rehearsals to public performance.},
journal = {BMC neuroscience},
volume = {25},
number = {1},
pages = {62},
pmid = {39506628},
issn = {1471-2202},
mesh = {Humans ; *Dancing/physiology ; *Electroencephalography/methods ; *Brain/physiology ; Female ; Male ; Adult ; Brain-Computer Interfaces ; Young Adult ; },
abstract = {BACKGROUND: Dissecting the neurobiology of dance would shed light on a complex, yet ubiquitous, form of human communication. In this experiment, we sought to study, via mobile electroencephalography (EEG), the brain activity of five experienced dancers while dancing butoh, a postmodern dance that originated in Japan.<br><br>RESULTS: We report the experimental design, methods, and practical execution of a highly interdisciplinary project that required the collaboration of dancers, engineers, neuroscientists, musicians, and multimedia artists, among others. We explain in detail how we technically validated all our EEG procedures (e.g., via impedance value monitoring) and minimized potential artifacts in our recordings (e.g., via electrooculography and inertial measurement units). We also describe the engineering details and hardware that enabled us to achieve synchronization between signals recorded at different sampling frequencies, along with a signal preprocessing and denoising pipeline that we used for data re-sampling and power line noise removal. As our experiment culminated in a live performance, where we generated a real-time visualization of the dancers' interbrain synchrony on a screen via an artistic brain-computer interface, we outline all the methodology (e.g., filtering, time-windows, equation) we used for online bispectrum estimations. Additionally, we provide access to all the raw EEG data and codes we used in our recordings. We, lastly, discuss how we envision that the data could be used to address several hypotheses, such as that of interbrain synchrony or the motor theory of vocal learning.<br><br>CONCLUSIONS: Being, to our knowledge, the first study to report synchronous and simultaneous recording from five dancers, we expect that our findings will inform future art-science collaborations, as well as dance-movement therapies.},
}
@article {pmid39505863,
year = {2024},
author = {Hu, N and Shi, JX and Chen, C and Xu, HH and Chang, ZH and Hu, PF and Guo, D and Zhang, XW and Shao, WW and Fan, X and Zuo, JC and Ming, D and Li, XH},
title = {Constructing organoid-brain-computer interfaces for neurofunctional repair after brain injury.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {9580},
pmid = {39505863},
issn = {2041-1723},
mesh = {*Brain-Computer Interfaces ; *Organoids ; Animals ; *Brain Injuries/therapy/physiopathology ; *Brain/physiology ; Humans ; Male ; Neurons/physiology ; Mice ; },
abstract = {The reconstruction of damaged neural circuits is critical for neurological repair after brain injury. Classical brain-computer interfaces (BCIs) allow direct communication between the brain and external controllers to compensate for lost functions. Importantly, there is increasing potential for generalized BCIs to input information into the brains to restore damage, but their effectiveness is limited when a large injured cavity is caused. Notably, it might be overcome by transplantation of brain organoids into the damaged region. Here, we construct innovative BCIs mediated by implantable organoids, coined as organoid-brain-computer interfaces (OBCIs). We assess the prolonged safety and feasibility of the OBCIs, and explore neuroregulatory strategies. OBCI stimulation promotes progressive differentiation of grafts and enhances structural-functional connections within organoids and the host brain, promising to repair the damaged brain via regenerating and regulating, potentially directing neurons to preselected targets and recovering functional neural networks in the future.},
}
@article {pmid39504276,
year = {2024},
author = {Yang, X and Xiong, X and Li, X and Lian, Q and Zhu, J and Zhang, J and Qi, Y and Wang, Y},
title = {Reconstructing Multi-Stroke Characters From Brain Signals Toward Generalizable Handwriting Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {4230-4239},
doi = {10.1109/TNSRE.2024.3492191},
pmid = {39504276},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Handwriting ; Male ; *Motor Cortex/physiology ; *Stroke/physiopathology ; Female ; *Algorithms ; Electroencephalography/methods ; Adult ; Young Adult ; Paralysis/rehabilitation ; },
abstract = {Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large character sets. The Chinese characters, including over 3500 commonly used characters with 10.3 strokes per character on average, represent a highly complex writing system. This paper proposes a Chinese handwriting BCI system, which reconstructs multi-stroke handwriting trajectories from brain signals. Through the recording of cortical neural signals from the motor cortex, we reveal distinct neural representations for stroke-writing and pen-lift phases. Leveraging this finding, we propose a stroke-aware approach to decode stroke-writing trajectories and pen-lift movements individually, which can reconstruct recognizable characters (accuracy of 86% with 400 characters). Our approach demonstrates high stability over 5 months, shedding light on generalized and adaptable handwriting BCIs.},
}
@article {pmid39504275,
year = {2025},
author = {Jia, T and Mo, L and McGeady, C and Sun, J and Liu, A and Ji, L and Xi, J and Li, C},
title = {Cortical Activation Patterns Determine Effectiveness of rTMS-Induced Motor Imagery Decoding Enhancement in Stroke Patients.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {3},
pages = {1200-1208},
doi = {10.1109/TBME.2024.3492977},
pmid = {39504275},
issn = {1558-2531},
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; Middle Aged ; *Stroke/physiopathology/therapy/diagnostic imaging ; *Stroke Rehabilitation/methods ; *Electroencephalography/methods ; Aged ; *Brain-Computer Interfaces ; Imagination/physiology ; Adult ; Motor Cortex/physiopathology/diagnostic imaging/physiology ; },
abstract = {Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients' individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement. We applied four kinds of rTMS treatments respectively to four groups of subacute stroke patients, twenty-six patients in total, and observed their EEG dynamics, MI decoding performance, and Fugl-Meyer assessment changes following 2-week neuromodulation. Four treatments consisted of ipsilesional 10 Hz rTMS, contralesional 1 Hz rTMS, ipsilesional 1 Hz rTMS, and sham stimulation. Results showed stroke patients with different neural reorganization patterns responded individually to rTMS therapy. Patients with cortical lesions mostly showed contralesional recruitment and patients without cortical lesions mostly presented ipsilesional focusing. Significant activation increases in the ipsilesional hemisphere (pre: -15.7% ∓ 8.2%, post: -17.3% ∓ 8.1%, p = 0.037) and MI decoding accuracy enhancement (pre: 76.3 ± 13.8%, post: 86.6 ± 8.2%, p = 0.037) were concurrently found in no-cortical-lesion patients with ipsilesional activation treatment. In the group of patients without cortical lesions, recovery rate in those receiving ipsilesional activation therapy (23.5 ± 10.4%) was higher than those receiving ipsilesional suppression therapy (9.9 ± 9.3%) (p = 0.041). This study reveals that tailoring neuromodulation therapy by recognizing cortical activation patterns is promising for improving effectiveness of the combination therapy with BCI and rTMS.},
}
@article {pmid39504274,
year = {2025},
author = {Jin, J and Zhao, X and Daly, I and Li, S and Wang, X and Cichocki, A and Jung, TP},
title = {A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-Related Potentials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {3},
pages = {1188-1199},
doi = {10.1109/TBME.2024.3492506},
pmid = {39504274},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Algorithms ; Signal Processing, Computer-Assisted ; Evoked Potentials/physiology ; Brain/physiology ; Event-Related Potentials, P300/physiology ; },
abstract = {OBJECTIVE: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.<br><br>METHODS: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF). Additionally, we proposed a "Lock a Target by Two Flashes" (LT2F) method to offer more versatile stimulus flash rules, complementing the row and column (RC) and single character (SC) modes. We applied the "Sub and Global" multi-window mode to EEGNet (mwEEGNet) to enhance classification and explored the performance of eight other representative algorithms.<br><br>RESULTS: Twenty healthy volunteers participated in the experiments. Our analysis revealed that our proposed pattern elicited more pronounced negative peaks in the parietal and occipital brain regions between 200 ms and 230 ms post-stimulus onset compared with the TF pattern. Compared to the TF pattern, the GB pattern yielded a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) when using mwEEGNet. Furthermore, results demonstrated that mwEEGNet outperformed other methods in classification performance.<br><br>CONCLUSION AND SIGNIFICANCE: These results underscore the significance of our work in advancing ERP-based BCIs.},
}
@article {pmid39502788,
year = {2024},
author = {Ahn, M and Edelman, BJ and He, B and Müller-Putz, GR and Röhrbein, F},
title = {Editorial: Advances in hybrid and application-driven BCI systems.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1498196},
doi = {10.3389/fnhum.2024.1498196},
pmid = {39502788},
issn = {1662-5161},
}
@article {pmid39501690,
year = {2024},
author = {Ji, M and Lee, D and Lee, S},
title = {Effects of wearing a KF94 face mask on performance, perceptual parameters, and physiological responses during resistance exercise.},
journal = {Physical activity and nutrition},
volume = {28},
number = {3},
pages = {17-26},
pmid = {39501690},
issn = {2733-7545},
abstract = {PURPOSE: Wearing face masks in indoor public places, including fitness centers, is an effective strategy for preventing the airborne transmission of viruses. Despite this, limited research has addressed the effects of wearing a mask during resistance exercise, which is primarily performed in indoor fitness centers. This study investigated the effects of wearing a KF94 face mask on exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses during resistance exercise.<br><br>METHODS: Twenty young men (23.8 &#xb1; 0.5 years old) participated in this randomized crossover trial. The participants performed moderate-intensity resistance exercise (60% of 1RM) sessions under two different conditions (KF94 mask vs. no mask). Cardiorespiratory parameters, exercise volume, rating of perceived exertion (RPE), and dyspnea were measured. Blood lactate concentration, blood pressure, arterial stiffness, and perceptual parameters were measured pre- and post-exercise.<br><br>RESULTS: Wearing the KF94 mask significantly reduced exercise volume, ventilation volume, and ventilation efficiency compared to exercising without a mask (p < 0.05). Although blood lactate concentration remained unchanged between the two conditions, RPE and dyspnea were significantly higher with the KF94 mask (p < 0.01). Central arterial stiffness post-exercise was significantly higher with the KF94 mask than without it (p < 0.01).<br><br>CONCLUSION: Wearing a KF94 face mask during resistance exercise affected exercise volume, perceptual parameters, and cardiorespiratory and cardiovascular responses. These findings suggest that coaches and trainers should consider the individual characteristics when designing exercise prescriptions and modifying resistance exercise variables while wearing KF94 masks.},
}
@article {pmid39500903,
year = {2024},
author = {Li, J and Zhang, F and Xia, X and Zhang, K and Wu, J and Liu, Y and Zhang, C and Cai, X and Lu, J and Xu, L and Wan, R and Hazarika, D and Xuan, W and Chen, J and Cao, Z and Li, Y and Jin, H and Dong, S and Zhang, S and Ye, Z and Yang, M and Chen, PY and Luo, J},
title = {An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {9557},
pmid = {39500903},
issn = {2041-1723},
mesh = {*Intracranial Pressure/physiology ; Monitoring, Physiologic/instrumentation/methods ; *Heart Rate/physiology ; Animals ; Humans ; Respiratory Rate ; Male ; Transducers, Pressure ; },
abstract = {The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.},
}
@article {pmid39500053,
year = {2024},
author = {Kocanaogullari, D and Gall, R and Mak, J and Huang, X and Mullen, K and Ostadabbas, S and Wittenberg, GF and Grattan, ES and Akcakaya, M},
title = {Patient-specific visual neglect severity estimation for stroke patients with neglect using EEG.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8efc},
pmid = {39500053},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Perceptual Disorders/diagnosis/physiopathology/etiology ; *Stroke/physiopathology/complications/diagnosis ; Male ; Female ; Brain-Computer Interfaces ; Middle Aged ; Aged ; Severity of Illness Index ; },
abstract = {Objective.We aim to assess the severity of spatial neglect (SN) through detailing patients' field of view (FOV) using EEG. Spatial neglect, a prevalent neurological syndrome in stroke patients, typically results from unilateral brain injuries, leading to inattention to the contralesional space. Commonly used Neglect detection methods like the Behavioral Inattention Test-conventional lack the capability to assess the full extent and severity of neglect. Although the Catherine Bergego Scale provides valuable clinical information, it does not detail the specific FOV affected in neglect patients.Approach.Building on our previously developed EEG-based brain-computer interface system, AR-guided EEG-based neglect detection, assessment, and rehabilitation system (AREEN), we aim to map neglect severity across a patient's FOV. We have demonstrated that AREEN can assess neglect severity in a patient-agnostic manner. However, its effectiveness in patient-specific scenarios, which is crucial for creating a generalizable plug-and-play system, remains unexplored. This paper introduces a novel EEG-based combined spatio-temporal network (ESTNet) that processes both time and frequency domain data to capture essential frequency band information associated with SN. We also propose a FOV correction system using Bayesian fusion, leveraging AREEN's recorded response times for enhanced accuracy by addressing noisy labels within the dataset.Main results.Extensive testing of ESTNet on our proprietary dataset has demonstrated its superiority over benchmark methods, achieving 79.62% accuracy, 76.71% sensitivity, and 86.36% specificity. Additionally, we provide saliency maps to enhance model explainability and establish clinical correlations.Significance.These findings underscore ESTNet's potential combined with Bayesian fusion-based FOV correction as an effective tool for generalized neglect assessment in clinical settings.},
}
@article {pmid39500051,
year = {2024},
author = {Dehais, F and Cabrera Castillos, K and Ladouce, S and Clisson, P},
title = {Leveraging textured flickers: a leap toward practical, visually comfortable, and high-performance dry EEG code-VEP BCI.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8ef7},
pmid = {39500051},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Female ; Male ; Adult ; Young Adult ; *Photic Stimulation/methods ; },
abstract = {Objective.Reactive brain-computer interfaces typically rely on repetitive visual stimuli, which can strain the eyes and cause attentional distraction. To address these challenges, we propose a novel approach rooted in visual neuroscience to design visual Stimuli for Augmented Response (StAR). The StAR stimuli consist of small randomly-orientedGabororRickerpatches that optimize foveal neural response while reducing peripheral distraction.Approach.In a factorial design study, 24 participants equipped with an 8-dry electrode EEG system focused on series of target flickers presented under three formats: traditionalPlainflickers,Gabor-based, orRicker-based flickers. These flickers were part of a five-class code visually evoked potentials paradigm featuring low frequency, short, and aperiodic visual flashes.Main results.Subjective ratings revealed thatGaborandRickerstimuli were visually comfortable and nearly invisible in peripheral vision compared to plain flickers. Moreover,GaborandRicker-based textures achieved higher accuracy (93.6% and 96.3%, respectively) with only 88 s of calibration data, compared to plain flickers (65.6%). A follow-up online implementation of this experiment was conducted to validate our findings within the frame of naturalistic operations. During this trial, remarkable accuracies of 97.5% in a cued task and 94.3% in an asynchronous digicode task were achieved, with a mean decoding time as low as 1.68 s.Significance.This work demonstrates the potential to expand BCI applications beyond the lab by integrating visually unobtrusive systems with gel-free, low density EEG technology, thereby making BCIs more accessible and efficient. The datasets, algorithms, and BCI implementations are shared through open-access repositories.},
}
@article {pmid39500044,
year = {2024},
author = {Tremmel, C and Krusienski, DJ and Schraefel, M},
title = {Estimating cognitive workload using a commercial in-ear EEG headset.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8ef8},
pmid = {39500044},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Workload ; Female ; *Cognition/physiology ; Adult ; Young Adult ; },
abstract = {Objective.This study investigated the potential of estimating various mental workload levels during two different tasks using a commercial in-ear electroencephalography (EEG) system, the IDUN 'Guardian'.Approach.Participants performed versions of two classical workload tasks: an n-back task and a mental arithmetic task. Both in-ear and conventional EEG data were simultaneously collected during these tasks. In an effort to facilitate a more comprehensive comparison, the complexity of the tasks was intentionally increased beyond typical levels. Special emphasis was also placed on understanding the significance ofγband activity in workload estimations. Therefore, each signal was analyzed across low frequency (1-35 Hz) and high frequency (1-100 Hz) ranges. Additionally, surrogate in-ear EEG measures, derived from the conventional EEG recordings, were extracted and examined.Main results.Workload estimation using in-ear EEG yielded statistically significant performance levels, surpassing chance levels with 44.1% for four classes and 68.4% for two classes in the n-back task and was better than a naive predictor for the mental arithmetic task. Conventional EEG exhibited significantly higher performance compared to in-ear EEG, achieving 80.3% and 92.9% accuracy for the respective tasks, along with lower error rates than the naive predictor. The developed surrogate measures achieved improved results, reaching accuracies of 57.5% and 85.5%, thus providing insights for enhancing future in-ear systems. Notably, most high frequency range signals outperformed their low frequency counterparts in terms of accuracy validating that high frequencyγband features can improve workload estimation.Significance.The application of EEG-based Brain-Computer Interfaces beyond laboratory settings is often hindered by practical limitations. In-ear EEG systems offer a promising solution to this problem, potentially enabling everyday use. This study evaluates the performance of a commercial in-ear headset and provides guidelines for increased effectiveness.},
}
@article {pmid39496663,
year = {2024},
author = {Agrawal, R and Dhule, C and Shukla, G and Singh, S and Agrawal, U and Alsubaie, N and Alqahtani, MS and Abbas, M and Soufiene, BO},
title = {Design of EEG based thought identification system using EMD &amp; deep neural network.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {26621},
pmid = {39496663},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Brain/physiology/physiopathology ; },
abstract = {Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary &amp; non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database &amp; 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.},
}
@article {pmid39496200,
year = {2024},
author = {Bunterngchit, C and Wang, J and Su, J and Wang, Y and Liu, S and Hou, ZG},
title = {Temporal attention fusion network with custom loss function for EEG-fNIRS classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8e86},
pmid = {39496200},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Spectroscopy, Near-Infrared/methods ; *Attention/physiology ; Male ; Adult ; Female ; Young Adult ; Neural Networks, Computer ; Epilepsy/physiopathology/diagnosis ; },
abstract = {Objective.Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential.Approach.To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG-fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues.Main results.Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks.Significance.This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.},
}
@article {pmid39494592,
year = {2024},
author = {Zhu, Y and Ma, J and Li, Y and Gu, M and Feng, X and Shao, Y and Tan, L and Lou, HF and Sun, L and Liu, Y and Zeng, LH and Qiu, Z and Li, XM and Duan, S and Yu, YQ},
title = {Adenosine-Dependent Arousal Induced by Astrocytes in a Brainstem Circuit.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {48},
pages = {e2407706},
pmid = {39494592},
issn = {2198-3844},
support = {2021ZD0203400//STI2030-Major Projects/ ; T2293733//National Natural Science Foundation of China Major Project/ ; T2293730//National Natural Science Foundation of China Major Project/ ; 31970939//National Natural Science Foundation of China Major Project/ ; 82288101//National Natural Science Foundation of China Major Project/ ; 82090033//National Natural Science Foundation of China Major Project/ ; U20A6005//National Natural Science Foundation of China Major Project/ ; 32171007//National Natural Science Foundation of China Major Project/ ; 32100814//National Natural Science Foundation of China Major Project/ ; LZ22H090001//Natural Science Foundation of Zhejiang Province/ ; 2024SSYS0019//Key R&amp;D Program of Zhejiang Province/ ; 2022C03034 to Y.Q.Y//Key R&amp;D Program of Zhejiang Province/ ; 2024SSYS0017//Key R&amp;D Program of Zhejiang Province/ ; 2020C03009//Key R&amp;D Program of Zhejiang Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2019B030335001//Key R&amp;D Program of Guangdong Province/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; },
mesh = {*Astrocytes/metabolism ; Animals ; *Adenosine/metabolism ; *Arousal/physiology ; Mice ; *Brain Stem/metabolism/physiology ; Male ; Sleep/physiology ; Wakefulness/physiology ; Models, Animal ; },
abstract = {Astrocytes play a crucial role in regulating sleep-wake behavior. However, how astrocytes govern a specific sleep-arousal circuit remains unknown. Here, the authors show that parafacial zone (PZ) astrocytes responded to sleep-wake cycles with state-differential Ca[2+] activity, peaking during transitions from sleep to wakefulness. Using chemogenetic and optogenetic approaches, they find that activating PZ astrocytes elicited and sustained wakefulness by prolonging arousal episodes while impeding transitions from wakefulness to non-rapid eye movement (NREM) sleep. Activation of PZ astrocytes specially induced the elevation of extracellular adenosine through the ATP hydrolysis pathway but not equilibrative nucleoside transporter (ENT) mediated transportation. Strikingly, the rise in adenosine levels induced arousal by activating A1 receptors, suggesting a distinct role for adenosine in the PZ beyond its conventional sleep homeostasis modulation observed in the basal forebrain (BF) and cortex. Moreover, at the circuit level, PZ astrocyte activation induced arousal by suppressing the GABA release from the PZ[GABA] neurons, which promote NREM sleep and project to the parabrachial nucleus (PB). Thus, their study unveils a distinctive arousal-promoting effect of astrocytes within the PZ through extracellular adenosine and elucidates the underlying mechanism at the neural circuit level.},
}
@article {pmid39493873,
year = {2024},
author = {Han, J and Wang, R and Wang, M and Yu, Z and Zhu, L and Zhang, J and Zhu, J and Zhang, S and Xi, W and Wu, H},
title = {Dynamic lateralization in contralateral-projecting corticospinal neurons during motor learning.},
journal = {iScience},
volume = {27},
number = {11},
pages = {111078},
pmid = {39493873},
issn = {2589-0042},
abstract = {Understanding the adaptability of the motor cortex in response to bilateral motor tasks is crucial for advancing our knowledge of neural plasticity and motor learning. Here we aim to investigate the dynamic lateralization of contralateral-projecting corticospinal neurons (cpCSNs) during such tasks. Utilizing in vivo two-photon calcium imaging, we observe cpCSNs in mice performing a "left-right" lever-press task. Our findings reveal heterogeneous populational dynamics in cpCSNs: a marked decrease in activity during ipsilateral motor learning, in contrast to maintained activity during contralateral motor learning. Notably, individual cpCSNs show dynamic shifts in engagement with ipsilateral and contralateral movements, displaying an evolving pattern of activation over successive days. It suggests that cpCSNs exhibit adaptive changes in activation patterns in response to ipsilateral and contralateral movements, highlighting a flexible reorganization during motor learning This reconfiguration underscores the dynamic nature of cortical lateralization in motor learning and offers insights for neuromotor rehabilitation.},
}
@article {pmid39490945,
year = {2024},
author = {Gwon, D and Ahn, M},
title = {Motor task-to-task transfer learning for motor imagery brain-computer interfaces.},
journal = {NeuroImage},
volume = {302},
number = {},
pages = {120906},
doi = {10.1016/j.neuroimage.2024.120906},
pmid = {39490945},
issn = {1095-9572},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; Female ; Adult ; Young Adult ; *Electroencephalography/methods ; Transfer, Psychology/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Movement/physiology ; },
abstract = {Motor imagery (MI) is one of the popular control paradigms in the non-invasive brain-computer interface (BCI) field. MI-BCI generally requires users to conduct the imagination of movement (e.g., left or right hand) to collect training data for generating a classification model during the calibration phase. However, this calibration phase is generally time-consuming and tedious, as users conduct the imagination of hand movement several times without being given feedback for an extended period. This obstacle makes MI-BCI non user-friendly and hinders its use. On the other hand, motor execution (ME) and motor observation (MO) are relatively easier tasks, yield lower fatigue than MI, and share similar neural mechanisms to MI. However, few studies have integrated these three tasks into BCIs. In this study, we propose a new task-to-task transfer learning approach of 3-motor tasks (ME, MO, and MI) for building a better user-friendly MI-BCI. For this study, 28 subjects participated in 3-motor tasks experiment, and electroencephalography (EEG) was acquired. User opinions regarding the 3-motor tasks were also collected through questionnaire survey. The 3-motor tasks showed a power decrease in the alpha rhythm, known as event-related desynchronization, but with slight differences in the temporal patterns. In the classification analysis, the cross-validated accuracy (within-task) was 67.05 % for ME, 65.93 % for MI, and 73.16 % for MO on average. Consistently with the results, the subjects scored MI (3.16) as the most difficult task compared with MO (1.42) and ME (1.41), with p < 0.05. In the analysis of task-to-task transfer learning, where training and testing are performed using different task datasets, the ME-trained model yielded an accuracy of 65.93 % (MI test), which is statistically similar to the within-task accuracy (p > 0.05). The MO-trained model achieved an accuracy of 60.82 % (MI test). On the other hand, combining two datasets yielded interesting results. ME and 50 % of the MI-trained model (50-shot) classified MI with a 69.21 % accuracy, which outperformed the within-task accuracy (p < 0.05), and MO and 50 % of the MI-trained model showed an accuracy of 66.75 %. Of the low performers with a within-task accuracy of 70 % or less, 90 % (n = 21) of the subjects improved in training with ME, and 76.2 % (n = 16) improved in training with MO on the MI test at 50-shot. These results demonstrate that task-to-task transfer learning is possible and could be a promising approach to building a user-friendly training protocol in MI-BCI.},
}
@article {pmid39490524,
year = {2024},
author = {Pirelli, L and Grubb, KJ and George, I and Goldsweig, AM and Nazif, TM and Dahle, G and Myers, PO and Ouzounian, M and Szeto, WY and Maisano, F and Geirsson, A and Vahl, TP and Kodali, SK and Kaneko, T and Tang, GHL},
title = {The role of cardiac surgeons in transcatheter structural heart disease interventions: The evolution of cardiac surgery.},
journal = {The Journal of thoracic and cardiovascular surgery},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jtcvs.2024.10.037},
pmid = {39490524},
issn = {1097-685X},
}
@article {pmid39490518,
year = {2024},
author = {Ahmed Taha, B and Addie, AJ and Saeed, AQ and Haider, AJ and Chaudhary, V and Arsad, N},
title = {Nanostructured Photonics Probes: A Transformative Approach in Neurotherapeutics and Brain Circuitry.},
journal = {Neuroscience},
volume = {562},
number = {},
pages = {106-124},
doi = {10.1016/j.neuroscience.2024.10.046},
pmid = {39490518},
issn = {1873-7544},
mesh = {Humans ; *Nanostructures ; *Brain/physiology ; Animals ; Optics and Photonics/methods/instrumentation ; Nanotechnology/methods ; Optogenetics/methods/instrumentation ; },
abstract = {Neuroprobes that use nanostructured photonic interfaces are capable of multimodal sensing, stimulation, and imaging with unprecedented spatio-temporal resolution. In addition to electrical recording, optogenetic modulation, high-resolution optical imaging, and molecular sensing, these advanced probes combine nanophotonic waveguides, optical transducers, nanostructured electrodes, and biochemical sensors. The potential of this technology lies in unraveling the mysteries of neural coding principles, mapping functional connectivity in complex brain circuits, and developing new therapeutic interventions for neurological disorders. Nevertheless, achieving the full potential of nanostructured photonic neural probes requires overcoming challenges such as ensuring long-term biocompatibility, integrating nanoscale components at high density, and developing robust data-analysis pipelines. In this review, we summarize and discuss the role of photonics in neural probes, trends in electrode diameter for neural interface technologies, nanophotonic technologies using nanostructured materials, advances in nanofabrication photonics interface engineering, and challenges and opportunities. Finally, interdisciplinary efforts are required to unlock the transformative potential of next-generation neuroscience therapies.},
}
@article {pmid39487147,
year = {2024},
author = {Wang, X and Wu, S and Yang, H and Bao, Y and Li, Z and Gan, C and Deng, Y and Cao, J and Li, X and Wang, Y and Ren, C and Yang, Z and Zhao, Z},
title = {Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {9442},
pmid = {39487147},
issn = {2041-1723},
mesh = {Animals ; *Electrodes, Implanted ; Sheep ; Action Potentials/physiology ; Neurons/physiology ; Brain-Computer Interfaces ; Occipital Lobe/physiology ; },
abstract = {Although intracranial neural electrodes have significantly contributed to both fundamental research and clinical treatment of neurological diseases, their implantation requires invasive surgery to open craniotomies, which can introduce brain damage and disrupt normal brain functions. Recent emergence of endovascular neural devices offers minimally invasive approaches for neural recording and stimulation. However, existing endovascular neural devices are unable to resolve single-unit activity in large animal models or human patients, impeding a broader application as neural interfaces in clinical practice. Here, we present the ultraflexible implantable neural electrode as an intravascular device (uFINE-I) for recording brain activity at single-unit resolution. We successfully implanted uFINE-Is into the sheep occipital lobe by penetrating through the confluence of sinuses and recorded both local field potentials (LFPs) and multi-channel single-unit spiking activity under spontaneous and visually evoked conditions. Imaging and histological analysis revealed minimal tissue damage and immune response. The uFINE-I provides a practical solution for achieving high-resolution neural recording with minimal invasiveness and can be readily transferred to clinical settings for future neural interface applications such as brain-machine interfaces (BMIs) and the treatment of neurological diseases.},
}
@article {pmid39484299,
year = {2024},
author = {Premchand, B and Liang, L and Phua, KS and Zhang, Z and Wang, C and Guo, L and Ang, J and Koh, J and Yong, X and Ang, KK},
title = {Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.},
journal = {NeuroSci},
volume = {5},
number = {4},
pages = {407-428},
pmid = {39484299},
issn = {2673-4087},
abstract = {Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.},
}
@article {pmid39484239,
year = {2024},
author = {Leinders, S and Aarnoutse, EJ and Branco, MP and Freudenburg, ZV and Geukes, SH and Schippers, A and Verberne, MSW and van den Boom, M and van der Vijgh, B and Crone, NE and Denison, T and Ramsey, NF and Vansteensel, MJ},
title = {DO NOT LOSE SLEEP OVER IT: IMPLANTED BRAIN-COMPUTER INTERFACE FUNCTIONALITY DURING NIGHTTIME IN LATE-STAGE AMYOTROPHIC LATERAL SCLEROSIS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39484239},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) hold promise as augmentative and alternative communication technology for people with severe motor and speech impairment (locked-in syndrome) due to neural disease or injury. Although such BCIs should be available 24/7, to enable communication at all times, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an individual with amyotrophic lateral sclerosis (ALS) who was implanted with an electrocorticography-based BCI that enabled the generation of click-commands for spelling words and call-caregiver signals.<br><br>METHODS: We investigated nocturnal dynamics of neural signal features used for BCI control, namely low (LFB: 10-30Hz) and high frequency band power (HFB: 65-95Hz). Additionally, we assessed the nocturnal performance of a BCI decoder that was trained on daytime data by quantifying the number of unintentional BCI activations at night. Finally, we developed and implemented a nightmode decoder that allowed the participant to call a caregiver at night, and assessed its performance.<br><br>RESULTS: Power and variance in HFB and LFB were significantly higher at night than during the day in the majority of the nights, with HFB variance being higher in 88% of nights. Daytime decoders caused 245 unintended selection-clicks and 13 unintended caregiver-calls per hour when applied to night data. The developed nightmode decoder functioned error-free in 79% of nights over a period of &#xb1;1.5 years, allowing the user to reliably call the caregiver, with unintended activations occurring only once every 12 nights.<br><br>DISCUSSION: Reliable nighttime use of a BCI requires decoders that are adjusted to sleep-related signal changes. This demonstration of a reliable BCI nightmode and its long-term use by an individual with advanced ALS underscores the importance of 24/7 BCI reliability.<br><br>TRIAL REGISTRATION: This trial is registered in clinicaltrials.gov under number NCT02224469 (https://clinicaltrials.gov/study/NCT02224469?term=NCT02224469&amp;rank=1). Date of submission to registry: August 21, 2014. Enrollment of first participant: September 7, 2015.},
}
@article {pmid39483493,
year = {2024},
author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Rivera-Flor, H and Fabriz-Ulhoa, PH and Fragoso-Dias, EA and de Andrade, RM and Delisle-Rodriguez, D and Bastos-Filho, TF},
title = {Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks.},
journal = {NeuroSci},
volume = {5},
number = {2},
pages = {169-183},
pmid = {39483493},
issn = {2673-4087},
abstract = {Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high-85 rpm and low-30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly (p < 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.},
}
@article {pmid39483271,
year = {2024},
author = {Yang, Y and Li, Y and Tang, L and Li, J},
title = {Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.},
journal = {Precision chemistry},
volume = {2},
number = {10},
pages = {518-538},
pmid = {39483271},
issn = {2771-9316},
abstract = {Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.},
}
@article {pmid39483192,
year = {2024},
author = {Buthut, M and Starke, G and Akmazoglu, TB and Colucci, A and Vermehren, M and van Beinum, A and Bublitz, C and Chandler, J and Ienca, M and Soekadar, SR},
title = {HYBRIDMINDS-summary and outlook of the 2023 international conference on the ethics and regulation of intelligent neuroprostheses.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1489307},
pmid = {39483192},
issn = {1662-5161},
abstract = {Neurotechnology and Artificial Intelligence (AI) have developed rapidly in recent years with an increasing number of applications and AI-enabled devices that are about to enter the market. While promising to substantially improve quality of life across various severe medical conditions, there are also concerns that the convergence of these technologies, e.g., in the form of intelligent neuroprostheses, may have undesirable consequences and compromise cognitive liberty, mental integrity, or mental privacy. Therefore, various international organizations, such as the Organization for Economic Cooperation and Development (OECD) or United Nations Educational, Scientific and Cultural Organization (UNESCO), have formed initiatives to tackle such questions and develop recommendations that mitigate risks while fostering innovation. In this context, a first international conference on the ethics and regulation of intelligent neuroprostheses was held in Berlin, Germany, in autumn 2023. The conference gathered leading experts in neuroscience, engineering, ethics, law, philosophy as well as representatives of industry, policy making and the media. Here, we summarize the highlights of the conference, underline the areas in which a broad consensus was found among participants, and provide an outlook on future challenges in development, deployment, and regulation of intelligent neuroprostheses.},
}
@article {pmid39488002,
year = {2024},
author = {Xu, Z and Khazaee, M and Duy Truong, N and Havenga, D and Nikpour, A and Ahnood, A and Kavehei, O},
title = {A leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8dfe},
pmid = {39488002},
issn = {1741-2552},
mesh = {*Electrocorticography/methods/instrumentation ; *Telemetry/instrumentation/methods ; *Wireless Technology/instrumentation ; *Brain-Computer Interfaces ; Animals ; Endovascular Procedures/methods/instrumentation ; Equipment Design/methods ; Electric Power Supplies ; Electrodes, Implanted ; Humans ; },
abstract = {Objective. Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Currently, solutions for endovascular electrocorticography (ECoG) include a stent in the brain with sensing electrodes, a chest implant to accommodate electronic components to provide power and data telemetry, and a long (tens of centimeters) cable travel through vessels with a set of wires in between. Removing this long cable is the key to the clinical viability of eBCIS as it carries risks and limitations, especially for patients with fragile vasculature.Approach. This work introduces a wireless and leadless telemetry and power transfer solution for ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent, removing the need for long, intrusive cables.Main results. The optical telemetry achieves data transmission speeds of over 2 Mbit/s, capable of supporting 41 ECoG channels at a 2 kHz sampling rate with 24-bit resolution. The FUS power transfer system delivers up to 10 mW of power to the implant through the scalp(6 mm), skull(10 mm), and subdural space(5 mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.Significance. This leadless and wireless solution eliminates the need for long cables and auxiliary implants, potentially reducing complications and enhancing the clinical applicability of eBCIs. The proposed system represents a step forward in enabling safer and more effective ECoG for a broader range of patients.},
}
@article {pmid39486261,
year = {2025},
author = {Sun, Y and Gao, Y and Shen, A and Sun, J and Chen, X and Gao, X},
title = {Creating ionic current pathways: A non-implantation approach to achieving cortical electrical signals for brain-computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {268},
number = {},
pages = {116882},
doi = {10.1016/j.bios.2024.116882},
pmid = {39486261},
issn = {1873-4235},
mesh = {*Brain-Computer Interfaces ; Animals ; Swine ; Electrodes, Implanted ; Electrocorticography/instrumentation/methods ; Biosensing Techniques/instrumentation ; Signal-To-Noise Ratio ; Cerebral Cortex/physiology ; Humans ; Brain/physiology ; Electroencephalography/instrumentation ; },
abstract = {This study introduces a novel method for acquiring brain electrical signals comparable to intracranial recordings without the health risks associated with implanted electrodes. We developed a technique using ultrasonic tools to create micro-holes in the skull and insert hollow implants, preventing natural healing. This approach establishes an artificial ionic current path (AICP) using tissue fluid, facilitating signal transmission from the cortex to the scalp surface. Experiments were conducted on pigs to validate the method's effectiveness. We synchronized our recordings with perforated electrocorticography (ECoG) for comparison. The AICP method yielded signal quality comparable to implanted ECoG in the low-frequency range, with a significant improvement in signal-to-noise ratio for evoked potentials. Our results demonstrate that this non-invasive technique can acquire high-quality brain signals, offering potential applications in neurophysiology, clinical research, and brain-computer interfaces. This innovative approach of utilizing tissue fluid as a natural conduction path opens new avenues for brain signal acquisition and analysis.},
}
@article {pmid39485790,
year = {2024},
author = {Zhang, D and Wang, Z and Qian, Y and Zhao, Z and Liu, Y and Hao, X and Li, W and Lu, S and Zhu, H and Chen, L and Xu, K and Li, Y and Lu, J},
title = {A brain-to-text framework for decoding natural tonal sentences.},
journal = {Cell reports},
volume = {43},
number = {11},
pages = {114924},
doi = {10.1016/j.celrep.2024.114924},
pmid = {39485790},
issn = {2211-1247},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Speech/physiology ; *Brain/physiology ; *Language ; Female ; Adult ; Young Adult ; Bayes Theorem ; },
abstract = {Speech brain-computer interfaces (BCIs) directly translate brain activity into speech sound and text. Despite successful applications in non-tonal languages, the distinct syllabic structures and pivotal lexical information conveyed through tonal nuances present challenges in BCI decoding for tonal languages like Mandarin Chinese. Here, we designed a brain-to-text framework to decode Mandarin sentences from invasive neural recordings. Our framework dissects speech onset, base syllables, and lexical tones, integrating them with contextual information through Bayesian likelihood and a Viterbi decoder. The results demonstrate accurate tone and syllable decoding during naturalistic speech production. The overall word error rate (WER) for 10 offline-decoded tonal sentences with a vocabulary of 40 high-frequency Chinese characters is 21% (chance: 95.3%) averaged across five participants, and tone decoding accuracy reaches 93% (chance: 25%), surpassing previous intracranial Mandarin tonal syllable decoders. This study provides a robust and generalizable approach for brain-to-text decoding of continuous tonal speech sentences.},
}
@article {pmid39481863,
year = {2025},
author = {Sellwood, D and McLeod, L and Williams, K and Brown, K and Pullin, G},
title = {Imagining alternative futures with augmentative and alternative communication: a manifesto.},
journal = {Medical humanities},
volume = {50},
number = {4},
pages = {620-623},
pmid = {39481863},
issn = {1473-4265},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Disabled Persons ; *Communication Aids for Disabled ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Communication ; Imagination ; Forecasting ; },
abstract = {This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are mainly driven by technological developments-technologies usually being developed for different markets-and are often based on ableist assumptions. In online conversations and a discussion panel at the 2023 International Society for Augmentative and Alternative Communication conference, we explored alternative futures by adopting different starting positions. Our case is presented under five headings: questioning the dominance of predictions that artificial intelligence and brain-computer interfaces will define the future of AAC; resisting disability being framed medically, as a problem to be solved, yet acknowledging both the pleasures and pains of being disabled; declaring that people who use AAC-as cyborgs of necessity rather than choice-should have choice and ownership of our technologies; challenging notions of independence as the necessary end goal for disabled bodies and considering interdependence as a human right; imagining alternative futures in which all people who use AAC are accepted and embraced for our communication and self-expression. This manifesto is an invitation for further discussion, and we welcome responses. While our focus is AAC, and three of the authors use AAC, we believe that our stance could be relevant to other disability communities in turn. This paper is about who gets to imagine disability futures and whose voices are left out. It is about how uncritical these futures can be, often presuming values that disabled people, in all their diversity, may not share.},
}
@article {pmid39480049,
year = {2024},
author = {Kaleem, MI and Javeed, S and Plog, BA and Gupta, VP and Ray, WZ},
title = {Restorative Treatments for Cervical Spinal Cord Injury, a Narrative Review.},
journal = {Clinical spine surgery},
volume = {37},
number = {9},
pages = {451-458},
doi = {10.1097/BSD.0000000000001699},
pmid = {39480049},
issn = {2380-0194},
mesh = {Humans ; *Spinal Cord Injuries/therapy ; Cervical Cord/injuries ; Recovery of Function ; Cervical Vertebrae ; Nerve Transfer/methods ; },
abstract = {STUDY DESIGN: A narrative review.<br><br>OBJECTIVE: To summarize relevant data from representative studies investigating upper limb restorative therapies for cervical spinal cord injury.<br><br>SUMMARY OF BACKGROUND DATA: Cervical spinal cord injury (SCI) is a debilitating condition resulting in tetraplegia, lifelong disability, and reduced quality of life. Given the dependence of all activities on hand function, patients with tetraplegia rank regaining hand function as one of their highest priorities. Recovery from cervical SCI is heterogeneous and often incomplete; currently, various novel therapies are under investigation to improve neurological function and eventually better quality of life in patients with tetraplegia.<br><br>METHODS: In this article, a narrative literature review was performed to identify treatment options targeting the restoration of function in patients with cervical SCI. Studies were included from available literature based on the availability of clinical data and whether they are applicable to restoration of arm and hand function in patients with cervical SCI.<br><br>RESULTS: We describe relevant studies including indications and outcomes with a focus on arm and hand function. Different treatment modalities described include nerve transfers, tendon transfers, spinal cord stimulation, functional electrical stimulation, non-invasive brain stimulation, brain-machine interfaces and neuroprosthetics, stem cell therapy, and immunotherapy. As the authors' institution leads one of the largest clinical trials on nerve transfers for cervical SCI, we also describe how patients undergoing nerve transfers are managed and followed at our center.<br><br>CONCLUSIONS: While complete recovery from cervical spinal cord injury may not be possible, novel therapies aimed at the restoration of upper limb motor function have made significant progress toward the realization of complete recovery.},
}
@article {pmid39479901,
year = {2024},
author = {Druschel, LN and Kasthuri, NM and Song, SS and Wang, JJ and Hess-Dunning, A and Chan, ER and Capadona, JR},
title = {Cell-specific spatial profiling of targeted protein expression to characterize the impact of intracortical microelectrode implantation on neuronal health.},
journal = {Journal of materials chemistry. B},
volume = {12},
number = {47},
pages = {12307-12319},
pmid = {39479901},
issn = {2050-7518},
support = {I01 RX002611/RX/RRD VA/United States ; R01 NS131502/NS/NINDS NIH HHS/United States ; R01 NS110823/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; },
mesh = {Animals ; *Microelectrodes ; *Neurons/metabolism ; Rats ; Rats, Sprague-Dawley ; Male ; Nerve Tissue Proteins/metabolism ; Microtubule-Associated Proteins/metabolism ; },
abstract = {Intracortical microelectrode arrays (MEAs) can record neuronal activity and advance brain-computer interface (BCI) devices. Implantation of the invasive MEA kills local neurons, which has been documented using immunohistochemistry (IHC). Neuronal nuclear protein (NeuN), a protein that lines the nuclei of exclusively neuronal cells, has been used as a marker for neuronal health and survival for decades in neuroscience and neural engineering. NeuN staining is often used to describe the neuronal response to intracortical microelectrode array (MEA) implantation. However, IHC is semiquantitative, relying on intensity readings rather than directly counting expressed proteins. To supplement previous IHC studies, we evaluated the expression of proteins representing different aspects of neuronal structure or function: microtubule-associated protein 2 (MAP2), neurofilament light (NfL), synaptophysin (SYP), myelin basic protein (MBP), and oligodendrocyte transcription factor 2 (OLIG2) following a neural injury caused by intracortical MEA implantation. Together, these five proteins evaluate the cytoskeletal structure, neurotransmitter release, and myelination of neurons. To fully evaluate neuronal health in NeuN-positive (NeuN+) regions, we only quantified protein expression in NeuN+ regions, making this the first-ever cell-specific spatial profiling evaluation of targeted proteins by multiplex immunochemistry following MEA implantation. We performed our protein quantification along with NeuN IHC to compare the results of the two techniques directly. We found that NeuN immunohistochemical analysis does not show the same trends as MAP2, NfL, SYP, MBP, and OLIG2 expression. Further, we found that all five quantified proteins show a decreased expression pattern that aligns more with historic intracortical MEA recording performance.},
}
@article {pmid39476487,
year = {2025},
author = {Craik, A and Dial, H and L Contreras-Vidal, J},
title = {Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad8d0a},
pmid = {39476487},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Speech/physiology ; Young Adult ; *Scalp/physiology ; Deep Learning ; Neural Networks, Computer ; },
abstract = {Objective. Neurological disorders affecting speech production adversely impact quality of life for over 7 million individuals in the US. Traditional speech interfaces like eye-tracking devices and P300 spellers are slow and unnatural for these patients. An alternative solution, speech brain-computer interfaces (BCIs), directly decodes speech characteristics, offering a more natural communication mechanism. This research explores the feasibility of decoding speech features using non-invasive EEG.Approach. Nine neurologically intact participants were equipped with a 63-channel EEG system with additional sensors to eliminate eye artifacts. Participants read aloud sentences selected for phonetic similarity to the English language. Deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks with and without attention modules, were optimized with a focus on minimizing trainable parameters and utilizing small input window sizes for real-time application. These models were employed for discrete and continuous speech decoding tasks.Main results. Statistically significant participant-independent decoding performance was achieved for discrete classes and continuous characteristics of the produced audio signal. A frequency sub-band analysis highlighted the significance of certain frequency bands (delta, theta, gamma) for decoding performance, and a perturbation analysis was used to identify crucial channels. Assessed channel selection methods did not significantly improve performance, suggesting a distributed representation of speech information encoded in the EEG signals. Leave-One-Out training demonstrated the feasibility of utilizing common speech neural correlates, reducing data collection requirements from individual participants.Significance. These findings contribute significantly to the development of EEG-enabled speech synthesis by demonstrating the feasibility of decoding both discrete and continuous speech features from EEG signals, even in the presence of EMG artifacts. By addressing the challenges of EMG interference and optimizing deep learning models for speech decoding, this study lays a strong foundation for EEG-based speech BCIs.},
}
@article {pmid39476381,
year = {2024},
author = {Sun, YH and Hu, BW and Tan, LH and Lin, L and Cao, SX and Wu, TX and Wang, H and Yu, B and Wang, Q and Lian, H and Chen, J and Li, XM},
title = {Posterior Basolateral Amygdala is a Critical Amygdaloid Area for Temporal Lobe Epilepsy.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {48},
pages = {e2407525},
pmid = {39476381},
issn = {2198-3844},
support = {82090030//National Natural Science Foundation of China/ ; 32071022//National Natural Science Foundation of China/ ; 81870898//National Natural Science Foundation of China/ ; 82090031//National Natural Science Foundation of China/ ; 82288101//National Natural Science Foundation of China/ ; 2019B030335001//Key-Area Research and Development Program of Guangdong Province/ ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Sciences/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2019YFA0110103//Ministry of Science and Technology/ ; 2021YFA1101700//National Key Research and Development Program of China/ ; LR18H090002//Zhejiang Provincial Natural Science Foundation/ ; 2021ZD0202700//STI2030-Major Projects/ ; 010904005//Grants from Nanhu Brain-computer Interface Institute/ ; },
mesh = {*Epilepsy, Temporal Lobe/physiopathology/pathology ; Animals ; Mice ; *Basolateral Nuclear Complex/metabolism ; *Disease Models, Animal ; Male ; Amygdala ; Neurons/metabolism ; },
abstract = {The amygdaloid complex consists of multiple nuclei and is a key node in controlling temporal lobe epilepsy (TLE) in both human and animal model studies. However, the specific nucleus in the amygdaloid complex and the neural circuitry governing seizures remain unknown. Here, it is discovered that activation of glutamatergic neurons in the posterior basolateral amygdala (pBLA) induces severe seizures and even mortality. The pBLA glutamatergic neurons project collateral connections to multiple brain regions, including the insular cortex (IC), bed nucleus of the stria terminalis (BNST), and central amygdala (CeA). Stimulation of pBLA-targeted IC neurons triggers seizures, whereas ablation of IC neurons suppresses seizures induced by activating pBLA glutamatergic neurons. GABAergic neurons in the BNST and CeA establish feedback inhibition on pBLA glutamatergic neurons. Deleting GABAergic neurons in the BNST or CeA leads to sporadic seizures, highlighting their role in balancing pBLA activity. Furthermore, pBLA neurons receive glutamatergic inputs from the ventral hippocampal CA1 (vCA1). Ablation of pBLA glutamatergic neurons mitigates both acute and chronic seizures in the intrahippocampal kainic acid-induced mouse model of TLE. Together, these findings identify the pBLA as a pivotal nucleus in the amygdaloid complex for regulating epileptic seizures in TLE.},
}
@article {pmid39475413,
year = {2024},
author = {Pitt, KM},
title = {Development and preliminary evaluation of a grid design application for adults and children using scanning and bci-based augmentative and alternative communication.},
journal = {Assistive technology : the official journal of RESNA},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/10400435.2024.2415368},
pmid = {39475413},
issn = {1949-3614},
abstract = {Augmentative and alternative communication (AAC) supports offer communication aids for individuals with severe speech and physical impairments. This study presents the development and proof of concept for an iPad application designed to evaluate the design preferences of both adults and children for AAC scanning and emerging P300-brain-computer interface access to AAC (BCI-AAC), both of which utilize item highlighting. Developed through a multidisciplinary and iterative process, the application incorporates customizable highlighting methods and display options for spelling-based and pictorial symbol interfaces. Initial testing involved five participants, including two adults with physical impairments and three children without physical impairments. Participants created unique interface displays using picture overlays, motion, and other highlighting methods. Feedback indicated strong usability and enjoyment during application use. Recommendations included expanded sound options and pre-made templates. This study demonstrates preliminary proof of concept for the application and supports the need for further research to explore user preferences and optimize communication outcomes across various AAC modalities. While BCI-AAC technology remains in its early stages, its integration into this application helps promote user-centered BCI-AAC development.},
}
@article {pmid39473791,
year = {2024},
author = {Zhu, Y and Bayin, C and Li, H and Shu, X and Deng, J and Yuan, H and Shen, H and Liang, Z and Li, Y},
title = {A flexible, stable, semi-dry electrode with low impedance for electroencephalography recording.},
journal = {RSC advances},
volume = {14},
number = {46},
pages = {34415-34427},
pmid = {39473791},
issn = {2046-2069},
abstract = {Brain-computer interfaces (BCIs) provide promising prospects for the field of healthcare and rehabilitation, presenting significant advantages for humanity. The development of electrodes that exhibit satisfactory performance characteristics, including high electrical conductivity, optimal comfort, and exceptional stability, is crucial for the effective implementation of electroencephalography (EEG) recording in BCI systems. The present study introduces a novel EEG electrode design that utilizes a composite material consisting of reduced graphene oxide (RGO) and polyurethane (PU) sponge. This electrode is characterized by its low impedance, stability, and flexibility. This work offers a high level of comfort while in touch with the skin and is designed to be user-friendly. Due to its notable moisturizing capacity, adaptable structure, and the presence of conductive RGO networks, the RGOPU semi-dry electrode exhibits a skin-contact impedance of less than 5.6 kΩ. This value is equivalent to that of a wet electrode and lower than that of a commercially available semi-dry electrode. The stability tests have demonstrated the outstanding electrical and mechanical performance of the material, hence confirming its suitability for long-term EEG recording. Additionally, the RGOPU semi-dry electrode demonstrates stable recording of EEG data and accurate detection of action potentials. Furthermore, the correlation coefficient between the RGOPU semi-dry electrode and wet electrodes exceeds 0.9. Additionally, it acquires electroencephalogram signals characterized by high signal-to-noise ratios (SNRs) in the context of alpha-wave and steady-state visual evoked potential (SSVEP) tests. The accuracy of the BCI is similar to that of wet electrodes, indicating a potential capability for sensing EEG in BCI applications.},
}
@article {pmid39469033,
year = {2024},
author = {Hu, F and Wang, F and Bi, J and An, Z and Chen, C and Qu, G and Han, S},
title = {HASTF: a hybrid attention spatio-temporal feature fusion network for EEG emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1479570},
pmid = {39469033},
issn = {1662-4548},
abstract = {INTRODUCTION: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging.<br><br>METHODS: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects.<br><br>RESULTS: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% &#xb1; 1.25% (SEED), 98.93% &#xb1; 1.45% (DEAP-arousal), and 98.57% &#xb1; 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology.<br><br>DISCUSSION: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition.},
}
@article {pmid39468119,
year = {2024},
author = {Keutayeva, A and Fakhrutdinov, N and Abibullaev, B},
title = {Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {25775},
pmid = {39468119},
issn = {2045-2322},
support = {OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; OPISSAI2020001//Institute of Smart Systems and Artificial Intelligence (ISSAI)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Algorithms ; },
abstract = {Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.},
}
@article {pmid39466869,
year = {2025},
author = {Ke, Y and Du, J and Liu, S and Ming, D},
title = {Corrections to "Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework".},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {33},
number = {},
pages = {1169},
doi = {10.1109/TNSRE.2024.3487206},
pmid = {39466869},
issn = {1558-0210},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Reproducibility of Results ; },
abstract = {In the above article [1], we found the formula (1) is presented incorrectly because of an error in the formula editing process. The correction is as follows.},
}
@article {pmid39466862,
year = {2025},
author = {Wei, Y and Meng, J and Luo, R and Mai, X and Li, S and Xia, Y and Zhu, X},
title = {Action Observation With Rhythm Imagery (AORI): A Novel Paradigm to Activate Motor-Related Pattern for High-Performance Motor Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {3},
pages = {1085-1096},
doi = {10.1109/TBME.2024.3487133},
pmid = {39466862},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Young Adult ; Adult ; *Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {OBJECTIVE: The Motor Imagery (MI) paradigm has been widely used in brain-computer interface (BCI) for device control and motor rehabilitation. However, the MI paradigm faces challenges such as comprehension difficulty and limited decoding accuracy. Therefore, we propose the Action Observation with Rhythm Imagery (AORI) as a natural paradigm to provide distinct features for high-performance decoding.<br><br>METHODS: Twenty subjects were recruited in the current study to perform the AORI task. Spectral-spatial, temporal and time-frequency analyses were conducted to investigate the AORI-activated brain pattern. Task-discriminant component analysis (TDCA) was utilized to perform multiclass motor decoding.<br><br>RESULTS: The results demonstrated distinct lateralized ERD in the alpha and beta bands, and clear lateralized steady-state movement-related rhythm (SSMRR) at the movement frequencies and their first harmonics. The activated brain areas included frontal, sensorimotor, posterior parietal, and occipital regions. Notably, the decoding accuracy reached 92.16% &#xb1; 7.61% in the four-class scenario.<br><br>CONCLUSION AND SIGNIFICANCE: We proposed the AORI paradigm, revealed the activated motor-related pattern and proved its efficacy for high-performance motor decoding. These findings provide new possibilities for designing a natural and robust BCI for motor control and motor rehabilitation.},
}
@article {pmid39466848,
year = {2024},
author = {Gordon, EC and Seth, AK},
title = {Ethical considerations for the use of brain-computer interfaces for cognitive enhancement.},
journal = {PLoS biology},
volume = {22},
number = {10},
pages = {e3002899},
pmid = {39466848},
issn = {1545-7885},
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Cognition/physiology ; Brain/physiology ; Privacy ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external computers, allowing processing of brain activity and the ability to control external devices. While often used for medical purposes, BCIs may also hold great promise for nonmedical purposes to unlock human neurocognitive potential. In this Essay, we discuss the prospects and challenges of using BCIs for cognitive enhancement, focusing specifically on invasive enhancement BCIs (eBCIs). We discuss the ethical, legal, and scientific implications of eBCIs, including issues related to privacy, autonomy, inequality, and the broader societal impact of cognitive enhancement technologies. We conclude that the development of eBCIs raises challenges far beyond practical pros and cons, prompting fundamental questions regarding the nature of conscious selfhood and about who-and what-we are, and ought, to be.},
}
@article {pmid39465107,
year = {2024},
author = {Lee, S and Kim, M and Ahn, M},
title = {Evaluation of consumer-grade wireless EEG systems for brain-computer interface applications.},
journal = {Biomedical engineering letters},
volume = {14},
number = {6},
pages = {1433-1443},
pmid = {39465107},
issn = {2093-985X},
abstract = {With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.},
}
@article {pmid39464623,
year = {2024},
author = {Tang, Z and Cui, Z and Wang, H and Liu, P and Xu, X and Yang, K},
title = {A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {12},
number = {},
pages = {622-634},
pmid = {39464623},
issn = {2168-2372},
mesh = {Humans ; *Electroencephalography/methods ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Adult ; Exoskeleton Device ; Evoked Potentials, Visual/physiology ; Signal Processing, Computer-Assisted ; Female ; Neural Networks, Computer ; Young Adult ; },
abstract = {Rehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more joints in the rehabilitation process. Additionally, a hybrid electroencephalogram-based (EEG-based) control approach has been developed to promote active user engagement and provide more control commands.The hybrid EEG-based control approach includes steady-state visual evoked potential (SSVEP) paradigm and motor imagery (MI) paradigm. Firstly, the rehabilitation movement was selected by SSVEP paradigm, and the multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA) method was used for SSVEP EEG recognition; then, the motion intention was obtained by MI paradigm, and the convolutional neural network (CNN) and long short-term memory network (LSTM) were used to build a CNN-LSTM model for MI EEG recognition; finally, the recognition results were translated into control commands of Bowden cables to achieve multi-degree-of-freedom rehabilitation.Experimental results show that the average classification accuracy of the CNN-LSTM model reaches to 90.07% ± 2.23%, and the overall accuracy of the hybrid EEG-based control approach reaches to 85.26% ± 1.95%. The twelve subjects involved in the usability assessment demonstrated an average system usability scale (SUS) score of 81.25 ± 5.82. Additionally, four participants who underwent a 35-day rehabilitation training demonstrated an average 10.33% increase in range of motion (ROM) across 4 joints, along with a 11.35% increase in the average electromyography (EMG) amplitude of the primary muscle involved.The exosuit demonstrates good accuracy in control, exhibits favorable usability, and shows certain efficacy in multi-joint rehabilitation. Our study has taken into account the neuroplastic principles, aiming to achieve active user engagement while introducing additional degrees of freedom, offering novel ideas and methods for potential brain-computer interface (BCI)-based rehabilitation strategies and hardware development.Clinical impact: Our study presents an exosuit with four degrees of freedom for stroke rehabilitation, enabling multi-joint movement and improved motor recovery. The hybrid EEG-based control approach enhances active user engagement, offering a promising strategy for more effective and user-driven rehabilitation, potentially improving clinical outcomes.Clinical and Translational Impact Statement: By developing an exosuit and a hybrid EEG-based control approach, this study enhances stroke rehabilitation through better user engagement and multi-joint capabilities. These innovations consider neuroplasticity principles, integrating rehabilitation theory with rehabilitation device.},
}
@article {pmid39463078,
year = {2024},
author = {Jin, K and Liu, X and Hu, S and Li, Y and Wu, Y and Li, J and Mo, C},
title = {[Discussion on Magnetic Resonance Compatibility of Implantable Brain-Computer Interface Devices].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {48},
number = {5},
pages = {486-492},
doi = {10.12455/j.issn.1671-7104.240232},
pmid = {39463078},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Humans ; Brain/diagnostic imaging ; Electrodes, Implanted ; },
abstract = {Brain-computer interface (BCI) devices are crucial tools for neural stimulation and recording, offering broad prospects in the diagnosis and treatment of neurological disorders. Furthermore, magnetic resonance imaging (MRI) is an effective and non-invasive technique for capturing whole-brain signals, providing detailed information on brain structures and activation patterns. Integrating the neural stimulation/recording capabilities of BCI devices with the non-invasive detection function of MRI is considered highly significant for brain function analysis. However, this combination imposes specific requirements on the magnetic and electronic performance of neural interface devices. The interaction between BCI devices and MRI is initially explored. Subsequently, potential safety risks arising from their combination are summarized and organized. Starting from the source of these hazards, such as the metallic electrodes and wires of BCI devices, the issues are analyzed, and current research countermeasures are summarized. In conclusion, the regulatory oversight of BCI's magnetic resonance safety is briefly discussed, and suggestions for enhancing the magnetic resonance compatibility of related BCI devices are proposed.},
}
@article {pmid39460240,
year = {2024},
author = {Belwafi, K and Ghaffari, F},
title = {Thought-Controlled Computer Applications: A Brain-Computer Interface System for Severe Disability Support.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {20},
pages = {},
pmid = {39460240},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Persons with Disabilities/rehabilitation ; Signal Processing, Computer-Assisted ; Machine Learning ; User-Computer Interface ; Male ; Adult ; Female ; Event-Related Potentials, P300/physiology ; Self-Help Devices ; },
abstract = {This study introduces an integrated computational environment that leverages Brain-Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human-Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system's Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.},
}
@article {pmid40231170,
year = {2024},
author = {Cubillos, LH and Revach, G and Mender, MJ and Costello, JT and Temmar, H and Hite, A and Zutshi, D and Wallace, DM and Ni, X and Kelberman, MM and Willsey, MS and van Sloun, RJG and Shlezinger, N and Patil, P and Draelos, A and Chestek, CA},
title = {Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces.},
journal = {Advances in neural information processing systems},
volume = {37},
number = {},
pages = {133975-133998},
pmid = {40231170},
issn = {1049-5258},
support = {R01 NS105132/NS/NINDS NIH HHS/United States ; U41 NS129436/NS/NINDS NIH HHS/United States ; },
abstract = {People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the 'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying "trust" that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data, n = 13 days) and online (real-time predictions, n = 5 days) with a simple KF and two recent deep-learning algorithms: tcFNN (non-ReFIT version) and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite this trade-off, our analysis successfully integrates traditional controls and modern deep-learning approaches to motivate high-performing yet still explainable BMI designs.},
}
@article {pmid39881836,
year = {2024},
author = {Butorova, AS and Koryukin, EA and Khomenko, NM and Sergeev, AP},
title = {Assessment of Accuracy of Spatial Object Localization by Means of Mono and Stereo Modes of Visual-to-Auditory Sensory Substitution in People with Visual Impairments (a Pilot Study).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {16},
number = {4},
pages = {29-36},
pmid = {39881836},
issn = {2309-995X},
mesh = {Humans ; Pilot Projects ; Adult ; Male ; Female ; *Vision Disorders/physiopathology ; Persons with Visual Disabilities ; },
abstract = {UNLABELLED: The aim of the study is to assess the accuracy of spatial object localization in mono and stereo modes of visual-to-auditory sensory substitution by means of the developed system tested on persons with normal or corrected-to-normal vision.<br><br>MATERIALS AND METHODS: A prototype of a visual-to-auditory sensory substitution device based on a video camera with two lenses was prepared. Software to convert the signal from a video camera into an audio signal in mono and stereo modes was developed.To assess the developed system, an experimental study with 30 blindfolded sighted participants was conducted. 15 persons were tested in mono mode, 15 - in stereo mode. All persons were trained to use the visual-to-auditory sensory substitution system. During the experiment, participants were to locate a white plastic cube with dimensions of 4&#xd7;4&#xd7;4 cm[3] on a working surface. The researcher placed the cube in one of 20 positions on the working surface in a pseudo-random order.<br><br>RESULTS: To assess the accuracy of the cube localization, deviations along the X- and Y-axes and absolute deviations were calculated. The general dynamics of localization accuracy was positive both in mono and stereo modes. Absolute deviation and X-axis deviation were significantly higher in stereo mode; there was no significant difference in Y-axis deviation between modes. On average, participants tended to underestimate the distance to the cube when it was on the left, right, or far side of the working surface, and overestimate the distance to the cube when it was on the near side of the working surface.<br><br>CONCLUSION: Tests demonstrated that the accuracy of object localization in stereo mode can be improved by increasing the time for training the participants and by showing them more presentations. The results of the study can be used to develop assistive techniques for people with visual impairments, to manufacture medical equipment, and create brain-computer interfaces.},
}
@article {pmid39483199,
year = {2023},
author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Rivera-Flor, H and De Souza, AF and Jaramillo-Isaza, S and Ruiz-Olaya, AF and Bastos-Filho, TF},
title = {Coupling Effects of Cross-Corticomuscular Association during Object Manipulation Tasks on Different Haptic Sensations.},
journal = {NeuroSci},
volume = {4},
number = {3},
pages = {195-210},
pmid = {39483199},
issn = {2673-4087},
abstract = {The effects of corticomuscular connectivity during object manipulation tasks with different haptic sensations have not been quantitatively investigated. Connectivity analyses enable the study of cortical effects and muscle responses during movements, revealing communication pathways between the brain and muscles. This study aims to examine the corticomuscular connectivity of three Electroencephalography (EEG) channels and five muscles during object manipulation tasks involving contact surfaces of Sandpaper, Suede, and Silk. The analyses included 12 healthy subjects performing tasks with their right hand. Power-Based Connectivity (PBC) and Mutual Information (MI) measures were utilized to evaluate significant differences in connectivity between contact surfaces, EEG channels, muscles, and frequency bands. The research yielded the following findings: Suede contact surface exhibited higher connectivity; Mu and Gamma frequency bands exerted greater influence; significant connectivity was observed between the three EEG channels (C 3 , C z , C 4) and the Anterior Deltoid (AD) and Brachioradialis (B) muscles; and connectivity was primarily involved during active movement in the AD muscle compared to the resting state. These findings suggest potential implementation in motor rehabilitation for more complex movements using novel alternative training systems with high effectiveness.},
}
@article {pmid39872550,
year = {2023},
author = {Huo, C and Cui, Q and Bai, G},
title = {Uncovering the mystery of genetic heterogeneity in inherited peripheral neuropathies.},
journal = {Life medicine},
volume = {2},
number = {4},
pages = {lnad026},
pmid = {39872550},
issn = {2755-1733},
}
@article {pmid39845389,
year = {2023},
author = {Tan, J and Joe, N and Kong, V and Clarke, D and Ko, J and Amey, J and Denize, B and Marsden, G and Yen, DA and Christey, G},
title = {Contemporary management of blunt colonic injuries - Experience from a level one trauma centre in New Zealand.},
journal = {Surgery in practice and science},
volume = {13},
number = {},
pages = {100179},
pmid = {39845389},
issn = {2666-2620},
abstract = {INTRODUCTION: Blunt colonic injury (BCI) is relatively rare, and literature on the topic is sparse. This study reviews our contemporary experience in its management at a level-one trauma centre in New Zealand.<br><br>MATERIALS AND METHODS: This was a retrospective study (January 2012 to December 2020) that included all patients who sustained a BCI managed at Waikato Hospital, New Zealand.<br><br>RESULTS: Of the total of 1181 patients with blunt abdominal trauma, 69 (6%) of them sustained a BCI (49% male, mean age: 36 years). 78 separate colonic injuries were identified in the 69 cases. The most commonly injured segment was the ascending colon 49% (38/78). Eighty percent (55/69) underwent a CT scan, with only 16 showing definite evidence of a colonic injury. AAST Grade 1 was the most common (81%). Fifteen patients underwent damage control surgery. All 11 grade 1 injuries were repaired primarily, whilst the other four grade 4 and 5 colonic injuries were resected, with 3 having a subsequent stoma formation and one delayed anastomosis. There were four mortalities. Patients who had negative or equivocal admission CT findings for colonic injury had delays to the operating theatre and had poorer outcomes.<br><br>CONCLUSION: BCI is rare but is associated with a prolonged hospital stay. The treatment of BCI is similar to that of penetrating colonic injury. CT appeared inaccurate in many cases.},
}
@article {pmid39944367,
year = {2023},
author = {Mokienko, OA and Lyukmanov, RK and Bobrov, PD and Suponeva, NA and Piradov, MA},
title = {Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {15},
number = {6},
pages = {63-73},
pmid = {39944367},
issn = {2309-995X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Upper Extremity ; *Stroke/therapy/physiopathology ; *Recovery of Function ; },
abstract = {Brain-computer interfaces (BCIs) are a group of technologies that allow mental training with feedback for post-stroke motor recovery. Varieties of these technologies have been studied in numerous clinical trials for more than 10 years, and their construct and software are constantly being improved. Despite the positive treatment results and the availability of registered medical devices, there are currently a number of problems for the wide clinical application of BCI technologies. This review provides information on the most studied types of BCIs and its training protocols and describes the evidence base for the effectiveness of BCIs for upper limb motor recovery after stroke. The main problems of scaling this technology and ways to solve them are also described.},
}
@article {pmid39886316,
year = {2022},
author = {Ju, J and Feleke, AG and Luo, L and Fan, X},
title = {Recognition of Drivers' Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {2022},
number = {},
pages = {9847652},
pmid = {39886316},
issn = {2692-7632},
abstract = {In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers' hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.},
}
@article {pmid39483370,
year = {2022},
author = {Simistira Liwicki, F and Gupta, V and Saini, R and De, K and Liwicki, M},
title = {Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible.},
journal = {NeuroSci},
volume = {3},
number = {2},
pages = {226-244},
pmid = {39483370},
issn = {2673-4087},
abstract = {This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain-computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.},
}
@article {pmid39460125,
year = {2024},
author = {Magruder, RD and Kukkar, KK and Contreras-Vidal, JL and Parikh, PJ},
title = {Cross-Task Differences in Frontocentral Cortical Activations for Dynamic Balance in Neurotypical Adults.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {20},
pages = {},
pmid = {39460125},
issn = {1424-8220},
support = {1P2CHD086844-01A1/NH/NIH HHS/United States ; 1R25HD106896-05A1/NH/NIH HHS/United States ; },
mesh = {Humans ; *Postural Balance/physiology ; Male ; Female ; *Electroencephalography/methods ; *Transcranial Magnetic Stimulation/methods ; Adult ; Young Adult ; Motor Cortex/physiology ; },
abstract = {Although significant progress has been made in understanding the cortical correlates underlying balance control, these studies focused on a single task, limiting the ability to generalize the findings. Different balance tasks may elicit cortical activations in the same regions but show different levels of activation because of distinct underlying mechanisms. In this study, twenty young, neurotypical adults were instructed to maintain standing balance while the standing support surface was either translated or rotated. The differences in cortical activations in the frontocentral region between these two widely used tasks were examined using electroencephalography (EEG). Additionally, the study investigated whether transcranial magnetic stimulation could modulate these cortical activations during the platform translation task. Higher delta and lower alpha relative power were found over the frontocentral region during the platform translation task when compared to the platform rotation task, suggesting greater engagement of attentional and sensory integration resources for the former. Continuous theta burst stimulation over the supplementary motor area significantly reduced delta activity in the frontocentral region but did not alter alpha activity during the platform translation task. The results provide a direct comparison of neural activations between two commonly used balance tasks and are expected to lay a strong foundation for designing neurointerventions for balance improvements with effects generalizable across multiple balance scenarios.},
}
@article {pmid39460066,
year = {2024},
author = {Senadheera, I and Hettiarachchi, P and Haslam, B and Nawaratne, R and Sheehan, J and Lockwood, KJ and Alahakoon, D and Carey, LM},
title = {AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {20},
pages = {},
pmid = {39460066},
issn = {1424-8220},
support = {2004443//National Health and Medical Research Council/ ; },
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Artificial Intelligence ; *Stroke/physiopathology ; Brain-Computer Interfaces ; Neural Networks, Computer ; Recovery of Function/physiology ; Adult ; Robotics/methods ; Machine Learning ; },
abstract = {Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.},
}
@article {pmid39457727,
year = {2024},
author = {Calderone, A and Latella, D and Bonanno, M and Quartarone, A and Mojdehdehbaher, S and Celesti, A and Calabrò, RS},
title = {Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.},
journal = {Biomedicines},
volume = {12},
number = {10},
pages = {},
pmid = {39457727},
issn = {2227-9059},
abstract = {Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.},
}
@article {pmid39455586,
year = {2024},
author = {Isaev, MR and Mokienko, OA and Lyukmanov, RK and Ikonnikova, ES and Cherkasova, AN and Suponeva, NA and Piradov, MA and Bobrov, PD},
title = {A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {1168},
pmid = {39455586},
issn = {2052-4463},
mesh = {Humans ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared ; *Stroke/physiopathology ; Male ; Female ; Middle Aged ; Aged ; Imagination ; },
abstract = {This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain-computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real-time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl-Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near-infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.},
}
@article {pmid39454612,
year = {2024},
author = {Liu, R and Song, Q and Ma, T and Pan, H and Li, H and Zhao, X},
title = {SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8b6e},
pmid = {39454612},
issn = {1741-2552},
mesh = {Humans ; *Wearable Electronic Devices ; *Shoulder/physiology ; Male ; *Movement/physiology ; Adult ; Wheelchairs ; User-Computer Interface ; Amputees/rehabilitation ; Female ; Brain-Computer Interfaces ; Spinal Cord Injuries/rehabilitation/physiopathology ; Middle Aged ; },
abstract = {Objective.Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.Approach.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.Main results.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.Significance.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.},
}
@article {pmid39453797,
year = {2024},
author = {Wang, Z and Liu, Y and Huang, S and Huang, H and Wu, W and Wang, Y and An, X and Ming, D},
title = {Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3902-3912},
doi = {10.1109/TNSRE.2024.3486551},
pmid = {39453797},
issn = {1558-0210},
mesh = {Humans ; Male ; *Imagination/physiology ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Fingers ; *Stroke/physiopathology/complications ; *Brain-Computer Interfaces ; *Sensorimotor Cortex/physiopathology ; Aged ; Electroencephalography ; Adult ; Cortical Synchronization ; Movement/physiology ; Algorithms ; },
abstract = {Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the "sixth-finger" (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A "wider range, stronger intensity, greater connection" ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation.},
}
@article {pmid39451800,
year = {2024},
author = {Drăgoi, MV and Nisipeanu, I and Frimu, A and Tălîngă, AM and Hadăr, A and Dobrescu, TG and Suciu, CP and Manea, AR},
title = {Real-Time Home Automation System Using BCI Technology.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {10},
pages = {},
pmid = {39451800},
issn = {2313-7673},
abstract = {A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.},
}
@article {pmid39451366,
year = {2024},
author = {Caffi, L and Romito, LM and Palmisano, C and Aloia, V and Arlotti, M and Rossi, L and Marceglia, S and Priori, A and Eleopra, R and Levi, V and Mazzoni, A and Isaias, IU},
title = {Adaptive vs. Conventional Deep Brain Stimulation: One-Year Subthalamic Recordings and Clinical Monitoring in a Patient with Parkinson's Disease.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {10},
pages = {},
pmid = {39451366},
issn = {2306-5354},
support = {//Grigioni Foundation for Parkinson's disease/ ; Project-ID 424778381 - TRR 295//Deutsche Forschungsgemeinschaft/ ; //New York University School of Medicine/ ; //The Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders/ ; NRRP "Fit4MedRob - Fit for Medical Robotics" Grant (# PNC0000007).//Italian Ministry of Research/ ; Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of bio-medical research in the NHS//European Union/ ; },
abstract = {Conventional DBS (cDBS) for Parkinson's disease uses constant, predefined stimulation parameters, while the currently available adaptive DBS (aDBS) provides the possibility of adjusting current amplitude with respect to subthalamic activity in the beta band (13-30 Hz). This preliminary study on one patient aims to describe how these two stimulation modes affect basal ganglia dynamics and, thus, behavior in the long term. We collected clinical data (UPDRS-III and -IV) and subthalamic recordings of one patient with Parkinson's disease treated for one year with aDBS, alternated with short intervals of cDBS. Moreover, after nine months, the patient discontinued all dopaminergic drugs while keeping aDBS. Clinical benefits of aDBS were superior to those of cDBS, both with and without medications. This improvement was paralleled by larger daily fluctuations of subthalamic beta activity. Moreover, with aDBS, subthalamic beta activity decreased during asleep with respect to awake hours, while it remained stable in cDBS. These preliminary data suggest that aDBS might be more effective than cDBS in preserving the functional role of daily beta fluctuations, thus leading to superior clinical benefit. Our results open new perspectives for a restorative brain network effect of aDBS as a more physiological, bidirectional, brain-computer interface.},
}
@article {pmid39451342,
year = {2024},
author = {Kaviri, SM and Vinjamuri, R},
title = {Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {10},
pages = {},
pmid = {39451342},
issn = {2306-5354},
support = {HCC-2053498//National Science Foundation/ ; CNS-2333292//National Science Foundation/ ; },
abstract = {Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain-computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation.},
}
@article {pmid39449239,
year = {2024},
author = {Hong, J and Cai, M and Qin, X},
title = {Multimodal human computer interaction of wheelchairs supporting lower limb active rehabilitation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2024.2417204},
pmid = {39449239},
issn = {1476-8259},
abstract = {Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.},
}
@article {pmid39448109,
year = {2024},
author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB},
title = {Relationship of SSVEP response between flash frequency conditions.},
journal = {Progress in brain research},
volume = {290},
number = {},
pages = {123-139},
doi = {10.1016/bs.pbr.2024.07.002},
pmid = {39448109},
issn = {1875-7855},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Electroencephalography ; Adult ; *Photic Stimulation/methods ; Female ; *Brain-Computer Interfaces ; Young Adult ; Visual Cortex/physiology ; },
abstract = {This study delves into the application of Brain-Computer Interfaces (BCIs), focusing on exploiting Steady-State Visual Evoked Potentials (SSVEPs) as communication tools for individuals facing mobility impairments. SSVEP-BCI systems can swiftly transmit substantial volumes of information, rendering them suitable for diverse applications. However, the efficacy of SSVEP responses can be influenced by variables such as the frequency and color of visual stimuli. Through experiments involving participants equipped with electrodes on the brain's visual cortex, visual stimuli were administered at 4, 17, 25, and 40Hz, using white, red, yellow, green, and blue light sources. The results reveal that white and green stimuli evoke higher SSVEP responses at lower frequencies, with color's impact diminishing at higher frequencies. At low light intensity (1W), white and green stimuli elicit significantly higher SSVEP responses, while at high intensity (3W), responses across colors tend to equalize. Notably, due to seizure risks, red and blue lights should be used cautiously, with white and green lights preferred for SSVEP-BCI systems. This underscores the critical consideration of color and frequency in the design of effective and safe SSVEP-BCI systems, necessitating further research to optimize designs for clinical applications.},
}
@article {pmid39448108,
year = {2024},
author = {Chang, CT and Pai, KJ and Huang, CH and Chou, CY and Liu, KW and Lin, HB},
title = {Optimizing user experience in SSVEP-BCI systems.},
journal = {Progress in brain research},
volume = {290},
number = {},
pages = {105-121},
doi = {10.1016/bs.pbr.2024.05.010},
pmid = {39448108},
issn = {1875-7855},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography ; *Photic Stimulation/methods ; Male ; *Evoked Potentials, Visual/physiology ; Adult ; Female ; Young Adult ; Visual Perception/physiology ; Signal-To-Noise Ratio ; },
abstract = {The emergence of brain-computer interface (BCI) technology provides enormous potential for human medical and daily applications. Therefore, allowing users to tolerate the visual response of SSVEP for a long time has always been an important issue in the SSVEP-BCI system. We recruited three subjects and conducted visual experiments in groups using different frequencies (17 and 25Hz) and 60Hz light. After recording the physiological signal, use FFT to perform a time-frequency analysis on the physiological signal to check whether there is any difference in the signal-to-noise ratio and amplitude of the 60Hz light source compared with a single low-frequency signal source. The results show that combining a 60Hz light source with low-frequency LEDs can reduce participants' eye discomfort while achieving effective light stimulation control. At the same time, there was no significant difference in signal-to-noise ratio and amplitude between the groups. This also means that 60Hz can make vision more continuous and improve the subject's experience and comfort. At the same time, it does not affect the performance of the original SSVEP-induced response. This study highlights the importance of considering technical aspects and user comfort when designing SSVEP-BCI systems to increase the usability of SSVEP systems for long-term flash viewing.},
}
@article {pmid39446156,
year = {2024},
author = {Muirhead, WR and Layard Horsfall, H and Aicardi, C and Carolan, J and Akram, H and Vanhoestenberghe, A and Schaefer, AT and Marcus, HJ},
title = {Implanted cortical neuroprosthetics for speech and movement restoration.},
journal = {Journal of neurology},
volume = {271},
number = {11},
pages = {7156-7168},
pmid = {39446156},
issn = {1432-1459},
support = {FC001153/WT_/Wellcome Trust/United Kingdom ; FC001153/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Cerebral Cortex/physiopathology/physiology ; Neural Prostheses ; Movement Disorders/therapy/physiopathology ; Movement/physiology ; Speech/physiology ; Speech Disorders/etiology/therapy/physiopathology ; },
abstract = {Implanted cortical neuroprosthetics (ICNs) are medical devices developed to replace dysfunctional neural pathways by creating information exchange between the brain and a digital system which can facilitate interaction with the external world. Over the last decade, researchers have explored the application of ICNs for diverse conditions including blindness, aphasia, and paralysis. Both transcranial and endovascular approaches have been used to record neural activity in humans, and in a laboratory setting, high-performance decoding of the signals associated with speech intention has been demonstrated. Particular progress towards a device which can move into clinical practice has been made with ICNs focussed on the restoration of speech and movement. This article provides an overview of contemporary ICNs for speech and movement restoration, their mechanisms of action and the unique ethical challenges raised by the field.},
}
@article {pmid39443437,
year = {2024},
author = {Heinonen, GA and Carmona, JC and Grobois, L and Kruger, LS and Velazquez, A and Vrosgou, A and Kansara, VB and Shen, Q and Egawa, S and Cespedes, L and Yazdi, M and Bass, D and Saavedra, AB and Samano, D and Ghoshal, S and Roh, D and Agarwal, S and Park, S and Alkhachroum, A and Dugdale, L and Claassen, J},
title = {A Survey of Surrogates and Health Care Professionals Indicates Support of Cognitive Motor Dissociation-Assisted Prognostication.},
journal = {Neurocritical care},
volume = {},
number = {},
pages = {},
pmid = {39443437},
issn = {1556-0961},
support = {R01 LM011826/LM/NLM NIH HHS/United States ; UL1TR001873 from NCATS/NIH//Clinical and Translational Science Awards/ ; R01 NS106014/NS/NINDS NIH HHS/United States ; LM011826//U.S. National Library of Medicine/ ; NS106014/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: Prognostication of patients with acute disorders of consciousness is imprecise but more accurate technology-supported predictions, such as cognitive motor dissociation (CMD), are emerging. CMD refers to the detection of willful brain activation following motor commands using functional magnetic resonance imaging or machine learning-supported analysis of the electroencephalogram in clinically unresponsive patients. CMD is associated with long-term recovery, but acceptance by surrogates and health care professionals is uncertain. The objective of this study was to determine receptiveness for CMD to inform goals of care (GoC) decisions and research participation among health care professionals and surrogates of behaviorally unresponsive patients.<br><br>METHODS: This was a two-center study of surrogates of and health care professionals caring for unconscious patients with severe neurological injury who were enrolled in two prospective US-based studies. Participants completed a 13-item survey to assess demographics, religiosity, minimal acceptable level of recovery, enthusiasm for research participation, and receptiveness for CMD to support GoC decisions.<br><br>RESULTS: Completed surveys were obtained from 196 participants (133 health care professionals and 63 surrogates). Across all respondents, 93% indicated that they would want their loved one or the patient they cared for to participate in a research study that supports recovery of consciousness if CMD were detected, compared to 58% if CMD were not detected. Health care professionals were more likely than surrogates to change GoC with a positive (78% vs. 59%, p&#x2009;=&#x2009;0.005) or negative (83% vs. 59%, p&#x2009;=&#x2009;0.0002) CMD result. Participants who reported religion was the most important part of their life were least likely to change GoC with or without CMD. Participants who identified as Black (odds ratio [OR] 0.12, 95% confidence interval [CI] 0.04-0.36) or Hispanic/Latino (OR 0.39, 95% CI 0.2-0.75) and those for whom religion was the most important part of their life (OR 0.18, 95% CI 0.05-0.64) were more likely to accept a lower minimum level of recovery.<br><br>CONCLUSIONS: Technology-supported prognostication and enthusiasm for clinical trial participation was supported across a diverse spectrum of health care professionals and surrogate decision-makers. Education for surrogates and health care professionals should accompany integration of technology-supported prognostication.},
}
@article {pmid39441155,
year = {2024},
author = {Demnati, B and Chabihi, Z and Boumediane, EM and Dkhissi, S and Idarrha, F and Fath Elkhir, Y and Benhima, MA and Abkari, I and Rafai, M and Ibn Moussa, S and Rahmi, M},
title = {Psychological impact of peri-implant fractures: A cross-sectional study.},
journal = {La Tunisie medicale},
volume = {102},
number = {10},
pages = {708-714},
pmid = {39441155},
issn = {2724-7031},
mesh = {Humans ; Cross-Sectional Studies ; Male ; Female ; Middle Aged ; *Quality of Life/psychology ; Adult ; *Adaptation, Psychological ; Aged ; *Depression/psychology/epidemiology/etiology ; Anxiety/psychology/etiology/epidemiology ; Periprosthetic Fractures/psychology/epidemiology/surgery ; Surveys and Questionnaires ; Stress Disorders, Post-Traumatic/psychology/epidemiology/etiology/diagnosis ; Stress, Psychological/psychology/epidemiology/etiology ; Orthopedic Procedures/psychology/adverse effects ; },
abstract = {INTRODUCTION: Peri-implant fractures (PIFs) are uncommon yet critical complications following orthopedic surgery. These complications can significantly impact a patient's psychological well-being and overall quality of life.<br><br>AIM: This study aimed to investigate the psychological effects of PIFs.<br><br>METHODS: This was a cross-sectional study that involved 136 patients who underwent surgery for PIFs between 2018 and 2022. We utilized various validated scales and questionnaires such as Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Impact of Event Scale Revised (IES-R), 36-Item Short Form Survey (SF-36), and Brief COPE Inventory (BCI) to assess their psychological state.<br><br>RESULT: The results revealed that patients with PIFs experienced higher levels of anxiety, depression, stress, and post-traumatic stress compared to the general population. Additionally, they reported lower physical and mental health. Factors such as the number of surgeries, treatment delay, post-operative pain levels, and complications significantly influenced their psychological outcomes. Notably, acceptance, positive reframing, and seeking emotional support were the most common coping mechanisms employed by these patients. Conversely, denial, substance use, and self blame were the least employed strategies.<br><br>CONCLUSION: This study suggests that psychological interventions could significantly benefit patients with PIFs, potentially reducing their distress and improving their quality of life.},
}
@article {pmid39440972,
year = {2024},
author = {Tong, C and Xiao, D and Li, Q and Gou, J and Wang, S and Zeng, Z and Xiong, W},
title = {First insights into the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus from generations worldwide.},
journal = {mSphere},
volume = {9},
number = {11},
pages = {e0070224},
pmid = {39440972},
issn = {2379-5042},
support = {2020B0301030005//Guangdong Major Project of Basic and Applied Basic Research/ ; 2023A1515030137//Guangdong Basic and Applied Basic Research Foundation/ ; },
mesh = {*Bacillus cereus/genetics/pathogenicity ; Virulence/genetics ; *Whole Genome Sequencing ; Prevalence ; *Biofilms/growth &amp; development ; Humans ; Genome, Bacterial ; Virulence Factors/genetics ; Global Health ; Gram-Positive Bacterial Infections/microbiology/epidemiology ; Foodborne Diseases/microbiology/epidemiology ; Phylogeny ; Asia/epidemiology ; Europe/epidemiology ; Oceania/epidemiology ; },
abstract = {Bacillus cereus, a global threat, is one of the major causes of toxin-induced foodborne diseases. However, a comprehensive assessment of the prevalence and characteristics of B. cereus worldwide is still lacking. Here, we applied whole-genome sequence analysis to 191 B. cereus collected in Africa, America, Asia, Europe, and Oceania from the 1900s to 2022, finding that CC142 dominated the global B. cereus clonal complex. The results provided direct evidence that B. cereus could spread through the food chain and intercontinentally. B. cereus from different generations worldwide showed coherence in the antibiotic-resistant gene and virulence and biofilm gene profiles, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.IMPORTANCEThis study first emphasized the prevalence, genetic characteristics, and pathogenicity of Bacillus cereus worldwide from the 1900s to 2022 using whole-genome sequence analysis. The CC142 dominated the global Bacillus cereus clonal complex. Moreover, we revealed a close evolutionary relationship between the isolates from different sources. B. cereus isolates from different generations worldwide showed coherence in potential pathogenicity, although with high genomic heterogeneity. The BCI-BCII-vanZF-fosB profiles and virulence and biofilm genes were detected at high rates, and we emphasized that B. cereus would pose a serious challenge to global public health and clinical treatment.},
}
@article {pmid39439491,
year = {2024},
author = {Xu, S and Liu, Y and Lee, H and Li, W},
title = {Neural interfaces: Bridging the brain to the world beyond healthcare.},
journal = {Exploration (Beijing, China)},
volume = {4},
number = {5},
pages = {20230146},
pmid = {39439491},
issn = {2766-2098},
abstract = {Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed-loop brain-computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non-invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high-resolution recording and stimulation capabilities.},
}
@article {pmid39438593,
year = {2024},
author = {Miklós, G and Halász, L and Hasslberger, M and Toth, E and Manola, L and Hagh Gooie, S and van Elswijk, G and Várkuti, B and Erőss, L},
title = {Sensory-substitution based sound perception using a spinal computer-brain interface.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {24879},
pmid = {39438593},
issn = {2045-2322},
mesh = {Humans ; Male ; Female ; Middle Aged ; *Auditory Perception/physiology ; *Brain-Computer Interfaces ; Adult ; Spinal Cord Stimulation/methods ; Aged ; Acoustic Stimulation ; Spinal Cord/physiology ; Hearing Aids ; },
abstract = {Sensory substitution offers a promising approach to restore lost sensory functions. Here we show that spinal cord stimulation (SCS), typically used for chronic pain management, can potentially serve as a novel auditory sensory substitution device. We recruited 13 patients undergoing SCS implantation and translated everyday sound samples into personalized SCS patterns during their trial phase. In a sound identification task-where chance-level performance was 33.3%-participants (n = 8) achieved a mean accuracy of 72.8% using only SCS input. We observed a weak positive correlation between stimulation bitrate and identification accuracy. A follow-up discrimination task (n = 5) confirmed that reduced bitrates significantly impaired participants' ability to distinguish between consecutive SCS patterns, indicating effective processing of additional information at higher bitrates. These findings demonstrate the feasibility of using existing SCS technology to create a novel neural interface for a sound prosthesis. Our results pave the way for future research to enhance stimulation fidelity, assess long-term training effects, and explore integration with other auditory aids for comprehensive hearing rehabilitation.},
}
@article {pmid39437806,
year = {2024},
author = {Zuo, M and Yu, B and Sui, L},
title = {Classification of EEG evoked in 2D and 3D virtual reality: traditional machine learning versus deep learning.},
journal = {Biomedical physics &amp; engineering express},
volume = {11},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ad89c5},
pmid = {39437806},
issn = {2057-1976},
mesh = {Humans ; *Electroencephalography/methods ; *Deep Learning ; *Virtual Reality ; Male ; Adult ; Female ; Support Vector Machine ; Machine Learning ; Young Adult ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Backgrounds. Virtual reality (VR) simulates real-life events and scenarios and is widely utilized in education, entertainment, and medicine. VR can be presented in two dimensions (2D) or three dimensions (3D), with 3D VR offering a more realistic and immersive experience. Previous research has shown that electroencephalogram (EEG) profiles induced by 3D VR differ from those of 2D VR in various aspects, including brain rhythm power, activation, and functional connectivity. However, studies focused on classifying EEG in 2D and 3D VR contexts remain limited.Methods. A 56-channel EEG was recorded while visual stimuli were presented in 2D and 3D VR. The recorded EEG signals were classified using two machine learning approaches: traditional machine learning and deep learning. In the traditional approach, features such as power spectral density (PSD) and common spatial patterns (CSP) were extracted, and three classifiers-support vector machines (SVM), K-nearest neighbors (KNN), and random forests (RF)-were used. For the deep learning approach, a specialized convolutional neural network, EEGNet, was employed. The classification performance of these methods was then compared.Results. In terms of accuracy, precision, recall, and F1-score, the deep learning method outperformed traditional machine learning approaches. Specifically, the classification accuracy using the EEGNet deep learning model reached up to 97.86%.Conclusions. EEGNet-based deep learning significantly outperforms conventional machine learning methods in classifying EEG signals induced by 2D and 3D VR. Given EEGNet's design for EEG-based brain-computer interfaces (BCI), this superior classification performance suggests that it can enhance the application of 3D VR in BCI systems.},
}
@article {pmid39435615,
year = {2025},
author = {Song, X and Li, R and Chu, X and Li, Q and Li, R and Li, Q and Tong, KY and Gu, X and Ming, D},
title = {Multilevel analysis of the central-peripheral-target organ pathway: contributing to recovery after peripheral nerve injury.},
journal = {Neural regeneration research},
volume = {20},
number = {10},
pages = {2807-2822},
pmid = {39435615},
issn = {1673-5374},
abstract = {Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities. Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites, neglecting multilevel pathological analysis of the overall nervous system and target organs. This has led to restrictions on current therapeutic approaches. In this paper, we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective, covering the central nervous system, peripheral nervous system, and target organs. After peripheral nerve injury, the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves; changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord. The nerve will undergo axonal regeneration, activation of Schwann cells, inflammatory response, and vascular system regeneration at the injury site. Corresponding damage to target organs can occur, including skeletal muscle atrophy and sensory receptor disruption. We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury. The main current treatments are conducted passively and include physical factor rehabilitation, pharmacological treatments, cell-based therapies, and physical exercise. However, most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway. Therefore, we should further explore multilevel treatment options that produce effective, long-lasting results, perhaps requiring a combination of passive (traditional) and active (novel) treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.},
}
@article {pmid39435350,
year = {2024},
author = {Beauchemin, N and Charland, P and Karran, A and Boasen, J and Tadson, B and Sénécal, S and Léger, PM},
title = {Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1416683},
pmid = {39435350},
issn = {1662-5161},
abstract = {Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.},
}
@article {pmid39434212,
year = {2024},
author = {Ahmadi-Dastgerdi, N and Hosseini-Nejad, H and Alinejad-Rokny, H},
title = {A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.},
journal = {International journal of neural systems},
volume = {34},
number = {12},
pages = {2450067},
doi = {10.1142/S0129065724500679},
pmid = {39434212},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; *Action Potentials/physiology ; *Brain/physiology ; Neurons/physiology ; Signal Processing, Computer-Assisted ; Humans ; Algorithms ; },
abstract = {Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm[2] per channel.},
}
@article {pmid39433844,
year = {2024},
author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR},
title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {1363},
pmid = {39433844},
issn = {2399-3642},
support = {T32MH115895//U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (NIMH)/ ; U01DC017844, R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; N2864C, A2295R, A2827R, A3803R//U.S. Department of Veterans Affairs (Department of Veterans Affairs)/ ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2NS095548, U01NS098968//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Quadriplegia/physiopathology ; Adult ; Female ; Middle Aged ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI, without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.},
}
@article {pmid39433597,
year = {2024},
author = {Candrea, DN and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, GW and Nathan, KC and Wester, BA and Anderson, WS and Rosenblatt, KR and Uchil, A and Clawson, L and Maragakis, NJ and Vansteensel, MJ and Tenore, FV and Ramsey, NF and Fifer, MS and Crone, NE},
title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling.},
journal = {Communications medicine},
volume = {4},
number = {1},
pages = {207},
pmid = {39433597},
issn = {2730-664X},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability.<br><br>METHODS: We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant's click detector using a small amount of training data (<44&#x2009;min across 4&#x2009;days) collected up to 21&#x2009;days prior to BCI use, and then tested it over a period of 90&#x2009;days without any retraining or updating.<br><br>RESULTS: Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15&#x2009;min, within 1&#x2009;day).<br><br>CONCLUSIONS: These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.},
}
@article {pmid39433073,
year = {2024},
author = {Zeng, P and Fan, L and Luo, Y and Shen, H and Hu, D},
title = {Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8963},
pmid = {39433073},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Signal-To-Noise Ratio ; Adult ; Male ; *Neural Networks, Computer ; Female ; Young Adult ; Photic Stimulation/methods ; },
abstract = {Objective.The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoising itself, disregarding the impact on subsequent tasks, which deviates from the original intention of EEG denoising. The main objective of this study is to optimize EEG denoising models with a purpose of improving the performance of BCI tasks.Approach.To this end, we proposed an innovative task-oriented EEG denoising generative adversarial network (TOED-GAN) method. This network utilizes the generator of GAN to decompose and reconstruct clean signals from the raw EEG signals, and the discriminator to learn to distinguish the generated signals from the true clean signals, resulting in a remarkable increase of the signal-to-noise ratio by simultaneously enhancing task-related components and removing task-irrelevant noise from the original contaminated signals.Main results.We evaluated the performance of the model on a public dataset and a self-collected dataset respectively, with canonical correlation analysis classification tasks of the steady-state visual evoked potential (SSVEP) based BCI. Experimental results demonstrate that TOED-GAN exhibits excellent performance in removing EEG noise and improving performance for SSVEP-BCI, with accuracy improvement rates reaching 18.47% and 21.33% in contrast to the baseline methods of convolutional neural networks, respectively.Significance.This work proves that the proposed TOED-GAN, as an EEG denoising method tailored for SSVEP tasks, contributes to enhancing the performance of BCIs in practical application scenarios.},
}
@article {pmid39433072,
year = {2024},
author = {Guetschel, P and Ahmadi, S and Tangermann, M},
title = {Review of deep representation learning techniques for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8962},
pmid = {39433072},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Deep Learning ; *Electroencephalography/methods ; },
abstract = {In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest.Objective: This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art.Approach: Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations.Main results: Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.Significance: Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.},
}
@article {pmid39433071,
year = {2024},
author = {Chen, X and Meng, L and Xu, Y and Wu, D},
title = {Adversarial artifact detection in EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad8964},
pmid = {39433071},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Artifacts ; Neural Networks, Computer ; Algorithms ; Machine Learning ; },
abstract = {Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.},
}
@article {pmid39432989,
year = {2025},
author = {Yang, H and Zhu, Z and Ni, S and Wang, X and Nie, Y and Tao, C and Zou, D and Jiang, W and Zhao, Y and Zhou, Z and Sun, L and Li, M and Tao, TH and Liu, K and Wei, X},
title = {Silk fibroin-based bioelectronic devices for high-sensitivity, stable, and prolonged in vivo recording.},
journal = {Biosensors &amp; bioelectronics},
volume = {267},
number = {},
pages = {116853},
doi = {10.1016/j.bios.2024.116853},
pmid = {39432989},
issn = {1873-4235},
mesh = {*Fibroins/chemistry ; Animals ; *Biosensing Techniques/instrumentation ; Mice ; Bombyx/chemistry ; Biocompatible Materials/chemistry ; Equipment Design ; Signal-To-Noise Ratio ; },
abstract = {Silk fibroin, recognized for its biocompatibility and modifiable properties, has significant potential in bioelectronics. Traditional silk bioelectronic devices, however, face rapid functional losses in aqueous or in vivo environments due to high water absorption of silk fibroin, which leads to expansion, structural damage, and conductive failure. In this study, we developed a novel approach by creating oriented crystallization (OC) silk fibroin through physical modification of the silk protein. This advancement enabled the fabrication of electronic interfaces for chronic biopotential recording. A pre-stretching treatment of the silk membrane allowed for tunable molecular orientation and crystallization, markedly enhancing its aqueous stability, biocompatibility, and electronic shielding capabilities. The OC devices demonstrated robust performance in sensitive detection and motion tracking of cutaneous electrical signals, long-term (over seven days) electromyographic signal acquisition in live mice with high signal-to-noise ratio (SNR >20), and accurate detection of high-frequency oscillations (HFO) in epileptic models (200-500 Hz). This work not only improves the structural and functional integrity of silk fibroin but also extends its application in durable bioelectronics and interfaces suited for long-term physiological environments.},
}
@article {pmid39432777,
year = {2024},
author = {Jiang, L and Qi, X and Lai, M and Zhou, J and Yuan, M and You, J and Liu, Q and Pan, J and Zhao, L and Ying, M and Ji, J and Li, K and Zhang, Y and Pan, W and He, Q and Yang, B and Cao, J},
title = {WDR20 prevents hepatocellular carcinoma senescence by orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {44},
pages = {e2407904121},
pmid = {39432777},
issn = {1091-6490},
support = {2021YFA1300604//National Key R&amp;D Program of China/ ; 82304514//Youth Fund of the National Natural Science Foundation of China/ ; LQ23H310004//Zhejiang Provincial Natural Science Foundation of China/ ; 226-2023-00059//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Carcinoma, Hepatocellular/metabolism/genetics/pathology ; *Liver Neoplasms/metabolism/genetics/pathology ; Animals ; *Proto-Oncogene Proteins c-myc/metabolism/genetics ; *Cellular Senescence ; Mice ; *Ubiquitination ; *Ubiquitin Thiolesterase/metabolism/genetics ; Cell Line, Tumor ; Cell Proliferation ; Gene Expression Regulation, Neoplastic ; Mice, Transgenic ; Carrier Proteins ; },
abstract = {The dysfunction of the ubiquitin-proteasome system (UPS) facilitates the malignant progression of hepatocellular carcinoma (HCC). While targeting the UPS for HCC therapy has been proposed, identifying effective targets has been challenging. In this study, we conducted a focused screen of siRNA libraries targeting UPS-related WD40 repeat (WDR) proteins and found that silencing WDR20, a deubiquitinating enzyme activating factor, selectively inhibited the proliferation of HCC cells without affecting normal hepatocytes. Moreover, the downregulation of WDR20 expression induced HCC cellular senescence and suppressed tumor progression in xenograft, sleeping beauty transposon/transposase, and hydrodynamic tail vein injection-induced HCC models, and Alb-Cre[+]/MYC[+] HCC transgenic mouse models. Mechanistically, we found that WDR20 silencing disturbed the protein stability of c-Myc, orchestrating the simultaneous USP12/46-mediated deubiquitination of c-Myc, thereby promoting the transcriptional activation of CDKN1A. Further investigation revealed a positive coexpression of WDR20 and c-Myc in a tissue microarray with 88 HCC clinical samples. By employing three patient-derived organoids from individuals with HCC, we have validated the decrease in c-Myc expression and the significant induction of senescence and growth inhibition following silencing of WDR20. This study not only uncovers the biological function of WDR20 and elucidates the molecular mechanism underlying its negative regulation of HCC cellular senescence but also highlight the potential of WDR20 as a promising target for HCC therapy.},
}
@article {pmid39430565,
year = {2024},
author = {Ratasukharom, N and Niwitpong, SA and Niwitpong, S},
title = {Estimation methods for the variance of Birnbaum-Saunders distribution containing zero values with application to wind speed data in Thailand.},
journal = {PeerJ},
volume = {12},
number = {},
pages = {e18272},
pmid = {39430565},
issn = {2167-8359},
mesh = {Thailand ; *Wind ; Models, Statistical ; Monte Carlo Method ; Air Pollution/analysis ; Humans ; Particulate Matter/analysis ; Confidence Intervals ; Computer Simulation ; },
abstract = {Thailand is currently grappling with a severe problem of air pollution, especially from small particulate matter (PM), which poses considerable threats to public health. The speed of the wind is pivotal in spreading these harmful particles across the atmosphere. Given the inherently unpredictable wind speed behavior, our focus lies in establishing the confidence interval (CI) for the variance of wind speed data. To achieve this, we will employ the delta-Birnbaum-Saunders (delta-BirSau) distribution. This statistical model allows for analyzing wind speed data and offers valuable insights into its variability and potential implications for air quality. The intervals are derived from ten different methods: generalized confidence interval (GCI), bootstrap confidence interval (BCI), generalized fiducial confidence interval (GFCI), and normal approximation (NA). Specifically, we apply GCI, BCI, and GFCI while considering the estimation of the proportion of zeros using the variance stabilized transformation (VST), Wilson, and Hannig methods. To evaluate the performance of these methods, we conduct a simulation study using Monte Carlo simulations in the R statistical software. The study assesses the coverage probabilities and average widths of the proposed confidence intervals. The simulation results reveal that GFCI based on the Wilson method is optimal for small sample sizes, GFCI based on the Hannig method excels for medium sample sizes, and GFCI based on the VST method stands out for large sample sizes. To further validate the practical application of these methods, we employ daily wind speed data from an industrial area in Prachin Buri and Rayong provinces, Thailand.},
}
@article {pmid39429223,
year = {2024},
author = {Zhu, L and Xu, M and Huang, A and Zhang, J and Tan, X},
title = {Multi-source domain transfer network based on subdomain adaptation and minimum class confusion for EEG emotion recognition.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2417212},
pmid = {39429223},
issn = {1476-8259},
abstract = {Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.},
}
@article {pmid39429090,
year = {2024},
author = {Wang, D and Zhou, H and Zhou, XL and Hu, YZ and Xu, HZ and Fu, JF},
title = {[Research advances of food addiction and obesity in children].},
journal = {Zhonghua er ke za zhi = Chinese journal of pediatrics},
volume = {62},
number = {11},
pages = {1121-1124},
doi = {10.3760/cma.j.cn112140-20240415-00268},
pmid = {39429090},
issn = {0578-1310},
mesh = {Humans ; Child ; *Food Addiction/psychology ; *Pediatric Obesity ; Weight Gain ; Feeding Behavior ; Behavior, Addictive ; },
}
@article {pmid39428886,
year = {2024},
author = {Ruiz, S and Valera, L and Ramos, P and Sitaram, R},
title = {Neurorights in the Constitution: from neurotechnology to ethics and politics.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230098},
pmid = {39428886},
issn = {1471-2970},
support = {//National Agency for Research and Development Millennium Science Initiative/ ; //Fondo Nacional de Desarrollo Cient&#xed;fico y Tecnol&#xf3;gico/ ; },
mesh = {Chile ; Humans ; *Politics ; *Neurosciences/ethics ; Neuroimaging/ethics ; Brain-Computer Interfaces/ethics ; Neurofeedback ; },
abstract = {Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}
@article {pmid39428881,
year = {2024},
author = {Sulzer, J and Papageorgiou, TD and Goebel, R and Hendler, T},
title = {Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230081},
pmid = {39428881},
issn = {1471-2970},
mesh = {*Neurofeedback/methods ; Humans ; *Brain/physiology ; Neurotransmitter Agents/metabolism ; Cognition ; },
abstract = {Neurofeedback (NF) is endogenous neuromodulation of circumscribed brain circuitry. While its use of real-time brain activity in a closed-loop system is similar to brain-computer interfaces, instead of controlling an external device like the latter, the goal of NF is to change a targeted brain function. In this special issue on NF, we present current and future methods for extracting and manipulating neural function, how these methods may reveal new insights about brain function, applications, and rarely discussed ethical considerations of guiding and interpreting the brain activity of others. Together, the articles in this issue outline the possibilities of NF use and impact in the real world, poising to influence the development of more effective and personalized NF protocols, improving the understanding of underlying psychological and neurological mechanisms and enhancing treatment precision for various neurological and psychiatric conditions.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}
@article {pmid39428875,
year = {2024},
author = {Sitaram, R and Sanchez-Corzo, A and Vargas, G and Cortese, A and El-Deredy, W and Jackson, A and Fetz, E},
title = {Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1915},
pages = {20230093},
pmid = {39428875},
issn = {1471-2970},
support = {//National Institute of Health/ ; //ATR Computational Neuroscience Laboratories/ ; },
mesh = {Humans ; *Neurofeedback ; *Brain/physiology ; *Self-Control ; Cognition/physiology ; Animals ; Brain-Computer Interfaces ; Models, Neurological ; },
abstract = {While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.},
}
@article {pmid39428037,
year = {2025},
author = {V, HM and Begum, BS},
title = {Towards imagined speech: Identification of brain states from EEG signals for BCI-based communication systems.},
journal = {Behavioural brain research},
volume = {477},
number = {},
pages = {115295},
doi = {10.1016/j.bbr.2024.115295},
pmid = {39428037},
issn = {1872-7549},
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; *Speech/physiology ; Adult ; Male ; Female ; Young Adult ; Machine Learning ; Brain/physiology ; Neural Networks, Computer ; },
abstract = {BACKGROUND: The electroencephalogram (EEG) based brain-computer interface (BCI) system employing imagined speech serves as a mechanism for decoding EEG signals to facilitate control over external devices or communication with the external world at the moment the user desires. To effectively deploy such BCIs, it is imperative to accurately discern various brain states from continuous EEG signals when users initiate word imagination.<br><br>NEW METHOD: This study involved the acquisition of EEG signals from 15 subjects engaged in four states: resting, listening, imagined speech, and actual speech, each involving a predefined set of 10 words. The EEG signals underwent preprocessing, segmentation, spatio-temporal and spectral analysis of each state, and functional connectivity analysis using the phase locking value (PLV) method. Subsequently, five features were extracted from the frequency and time-frequency domains. Classification tasks were performed using four machine learning algorithms in both pair-wise and multiclass scenarios, considering subject-dependent and subject-independent data.<br><br>RESULTS: In the subject-dependent scenario, the random forest (RF) classifier achieved a maximum accuracy of 94.60&#x202f;% for pairwise classification, while the artificial neural network (ANN) classifier achieved a maximum accuracy of 66.92&#x202f;% for multiclass classification. In the subject-independent scenario, the random forest (RF) classifier achieved maximum accuracies of 81.02&#x202f;% for pairwise classification and 55.58&#x202f;% for multiclass classification. Moreover, EEG signals were classified based on frequency bands and brain lobes, revealing that the theta (&#x3b8;) and delta (&#x3b4;) bands, as well as the frontal and temporal lobes, are sufficient for distinguishing between brain states.<br><br>CONCLUSION: The findings promise to develop a system capable of automatically segmenting imagined speech segments from continuous EEG signals.},
}
@article {pmid39426564,
year = {2025},
author = {Sleziona, D and Ely, DR and Thommes, M},
title = {Mechanisms of drug release from a melt-milled, poorly soluble drug substance.},
journal = {Journal of pharmaceutical sciences},
volume = {114},
number = {1},
pages = {394-401},
doi = {10.1016/j.xphs.2024.10.016},
pmid = {39426564},
issn = {1520-6017},
mesh = {*Solubility ; *Griseofulvin/chemistry ; *Drug Liberation ; *Particle Size ; Excipients/chemistry ; Xylitol/chemistry ; Chemistry, Pharmaceutical/methods ; Kinetics ; Crystallization/methods ; Drug Compounding/methods ; },
abstract = {Increasing the dissolution kinetics of low aqueous soluble drugs is one of the main priorities in drug formulation. New strategies must be developed, which should consider the two main dissolution mechanisms: surface reaction and diffusion. One promising tool is the so-called solid crystal suspension, a solid dispersion consisting of purely crystalline substances. In this concept, reducing the drug particle size and embedding the particles in a hydrophilic excipient increases the dissolution kinetics. Therefore, a solid crystal suspension containing submicron drug particles was produced via a modified stirred media milling process. A geometrical phase-field approach was used to model the dissolution behavior of the drug particles. A carrier material, xylitol, and the model drug substance, griseofulvin, were ground in a pearl mill. The in-vitro dissolution profile of the product was modeled to gain a deep physical understanding of the dissolution process. The used numerical tool has the potential to be a valuable approach for predicting the dissolution behavior of newly developed formulation strategies.},
}
@article {pmid39426071,
year = {2024},
author = {Chowdhury, RS and Bose, S and Ghosh, S and Konar, A},
title = {Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {183},
number = {},
pages = {109260},
doi = {10.1016/j.compbiomed.2024.109260},
pmid = {39426071},
issn = {1879-0534},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Attention/physiology ; },
abstract = {In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considerable challenges owing to their inherent complexity, non-stationary characteristics, and low signal-to-noise ratio. Notably, deep learning-based classifiers have emerged as a prominent focus for addressing the EEG signal decoding process. This study introduces a novel deep learning classifier named the Attention Induced Dual Convolutional-Capsule Network (AIDC-CN) with the specific aim of accurately categorizing various motor imagination class labels. To enhance the classifier's performance, a dual feature extraction approach leveraging spectrogram and brain connectivity networks has been employed, diversifying the feature set in the classification task. The main highlights of the proposed AIDC-CN classifier includes the introduction of a dual convolution layer to handle the brain connectivity and spectrogram features, addition of a novel self-attention module (SAM) to accentuate the relevant parts of the convolved spectrogram features, introduction of a new cross-attention module (CAM) to refine the outputs obtained from the dual convolution layers and incorporation of a Gaussian Error Linear Unit (GELU) based dynamic routing algorithm to strengthen the coupling among the primary and secondary capsule layers. Performance analysis undertaken on four public data sets depict the superior performance of the proposed model with respect to the state-of-the-art techniques. The code for this model is available at https://github.com/RiteshSurChowdhury/AIDC-CN.},
}
@article {pmid39425602,
year = {2024},
author = {Mahalungkar, SP and Shrivastava, R and Angadi, S},
title = {A brief survey on human activity recognition using motor imagery of EEG signals.},
journal = {Electromagnetic biology and medicine},
volume = {43},
number = {4},
pages = {312-327},
doi = {10.1080/15368378.2024.2415089},
pmid = {39425602},
issn = {1536-8386},
mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Motor Activity/physiology ; Human Activities ; Brain/physiology ; },
abstract = {Human being's biological processes and psychological activities are jointly connected to the brain. So, the examination of human activity is more significant for the well-being of humans. There are various models for brain activity detection considering neuroimaging for attaining decreased time requirement, increased control commands, and enhanced accuracy. Motor Imagery (MI)-based Brain-Computer Interface (BCI) systems create a way in which the brain can interact with the environment by processing Electroencephalogram (EEG) signals. Human Activity Recognition (HAR) deals with identifying the physiological activities of human beings based on sensory signals. This survey reviews the different methods available for HAR based on MI-EEG signals. A total of 50 research articles based on HAR from EEG signals are considered in this survey. This survey discusses the challenges faced by various techniques for HAR. Moreover, the papers are assessed considering various parameters, techniques, publication year, performance metrics, utilized tools, employed databases, etc. There were many techniques developed to solve the problem of HAR and they are classified as Machine Learning (ML) and Deep Learning (DL)models. At last, the research gaps and limitations of the techniques were discussed that contribute to developing an effective HAR.},
}
@article {pmid39424369,
year = {2024},
author = {Berling, D and Baroni, L and Chaffiol, A and Gauvain, G and Picaud, S and Antolík, J},
title = {Optogenetic Stimulation Recruits Cortical Neurons in a Morphology-Dependent Manner.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {49},
pages = {},
pmid = {39424369},
issn = {1529-2401},
mesh = {Animals ; *Optogenetics/methods ; Cats ; Male ; Female ; Neurons/physiology ; Photic Stimulation/methods ; Pyramidal Cells/physiology ; Cerebral Cortex/physiology/cytology ; Models, Neurological ; },
abstract = {Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bears promise for brain-machine interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cats of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than the preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.},
}
@article {pmid39423832,
year = {2024},
author = {Karpowicz, BM and Bhaduri, B and Nason-Tomaszewski, SR and Jacques, BG and Ali, YH and Flint, RD and Bechefsky, PH and Hochberg, LR and AuYong, N and Slutzky, MW and Pandarinath, C},
title = {Reducing power requirements for high-accuracy decoding in iBCIs.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
pmid = {39423832},
issn = {1741-2552},
support = {R01 NS112942/NS/NINDS NIH HHS/United States ; RF1 NS125026/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Animals ; *Action Potentials/physiology ; Macaca mulatta ; Male ; Models, Neurological ; Electric Power Supplies ; },
abstract = {Objective.Current intracortical brain-computer interfaces (iBCIs) rely predominantly on threshold crossings ('spikes') for decoding neural activity into a control signal for an external device. Spiking data can yield high accuracy online control during complex behaviors; however, its dependence on high-sampling-rate data collection can pose challenges. An alternative signal for iBCI decoding is the local field potential (LFP), a continuous-valued signal that can be acquired simultaneously with spiking activity. However, LFPs are seldom used alone for online iBCI control as their decoding performance has yet to achieve parity with spikes.Approach.Here, we present a strategy to improve the performance of LFP-based decoders by first training a neural dynamics model to use LFPs to reconstruct the firing rates underlying spiking data, and then decoding from the estimated rates. We test these models on previously-collected macaque data during center-out and random-target reaching tasks as well as data collected from a human iBCI participant during attempted speech.Main results.In all cases, training models from LFPs enables firing rate reconstruction with accuracy comparable to spiking-based dynamics models. In addition, LFP-based dynamics models enable decoding performance exceeding that of LFPs alone and approaching that of spiking-based models. In all applications except speech, LFP-based dynamics models also facilitate decoding accuracy exceeding that of direct decoding from spikes.Significance.Because LFP-based dynamics models operate on lower bandwidth and with lower sampling rate than spiking models, our findings indicate that iBCI devices can be designed to operate with lower power requirements than devices dependent on recorded spiking activity, without sacrificing high-accuracy decoding.},
}
@article {pmid39423831,
year = {2025},
author = {Carrara, I and Aristimunha, B and Corsi, MC and de Camargo, RY and Chevallier, S and Papadopoulo, T},
title = {Geometric neural network based on phase space for BCI-EEG decoding.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad88a2},
pmid = {39423831},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Algorithms ; Deep Learning ; },
abstract = {Objective.The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This is particularly true for Brain-computer interface (BCI), where the brain activity is decoded to control external devices without requiring muscle control. Electroencephalography is a widely adopted choice for designing BCI systems due to its non-invasive and cost-effective nature and excellent temporal resolution. Still, it comes at the expense of limited training data, poor signal-to-noise, and a large variability across and within-subject recordings. Finally, setting up a BCI system with many electrodes takes a long time, hindering the widespread adoption of reliable DL architectures in BCIs outside research laboratories. To improve adoption, we need to improve user comfort using, for instance, reliable algorithms that operate with few electrodes.Approach.Our research aims to develop a DL algorithm that delivers effective results with a limited number of electrodes. Taking advantage of the Augmented Covariance Method and the framework of SPDNet, we propose the Phase-SPDNet architecture and analyze its performance and the interpretability of the results. The evaluation is conducted on 5-fold cross-validation, using only three electrodes positioned above the Motor Cortex. The methodology was tested on nearly 100 subjects from several open-source datasets using the Mother Of All BCI Benchmark framework.Main results.The results of our Phase-SPDNet demonstrate that the augmented approach combined with the SPDNet significantly outperforms all the current state-of-the-art DL architecture in MI decoding.Significance.This new architecture is explainable and with a low number of trainable parameters.},
}
@article {pmid39423829,
year = {2024},
author = {Valle, C and Mendez-Orellana, C and Herff, C and Rodriguez-Fernandez, M},
title = {Identification of perceived sentences using deep neural networks in EEG.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad88a3},
pmid = {39423829},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; *Speech Perception/physiology ; *Neural Networks, Computer ; Young Adult ; Deep Learning ; Brain-Computer Interfaces ; },
abstract = {Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data.Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area.Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension.Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.},
}
@article {pmid39423826,
year = {2025},
author = {Chen, X and Li, S and Tu, Y and Wang, Z and Wu, D},
title = {User-wise perturbations for user identity protection in EEG-based BCIs.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad88a5},
pmid = {39423826},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; Machine Learning ; Neural Networks, Computer ; Computer Security ; Algorithms ; },
abstract = {Objective. An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g. user identity, emotion, disorders, etc which should be protected.Approach. We show for the first time that adding user-wise perturbations can make identity information in EEG unlearnable. We propose four types of user-wise privacy-preserving perturbations, i.e. random noise, synthetic noise, error minimization noise, and error maximization noise. After adding the proposed perturbations to EEG training data, the user identity information in the data becomes unlearnable, while the BCI task information remains unaffected.Main results. Experiments on six EEG datasets using three neural network classifiers and various traditional machine learning models demonstrated the robustness and practicability of the proposed perturbations.Significance. Our research shows the feasibility of hiding user identity information in EEG data without impacting the primary BCI task information.},
}
@article {pmid39423083,
year = {2024},
author = {Kostoglou, K and Muller-Putz, GR},
title = {Motor-Related EEG Analysis Using a Pole Tracking Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3837-3847},
doi = {10.1109/TNSRE.2024.3483294},
pmid = {39423083},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Spinal Cord Injuries/physiopathology ; Female ; *Movement/physiology ; Adult ; *Algorithms ; *Imagination/physiology ; Young Adult ; Reproducibility of Results ; Motor Cortex/physiology ; Middle Aged ; Healthy Volunteers ; Evoked Potentials, Motor/physiology ; Sensitivity and Specificity ; },
abstract = {This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.},
}
@article {pmid39421856,
year = {2024},
author = {Klee, D and Memmott, T and Oken, B},
title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain-Computer Interface Rapid Serial Visual Presentation Paradigm.},
journal = {Signals},
volume = {5},
number = {1},
pages = {18-39},
pmid = {39421856},
issn = {2624-6120},
support = {P30 AG066518/AG/NIA NIH HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain-computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or "jittered" stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.},
}
@article {pmid39421849,
year = {2024},
author = {Deng, L and Tang, H and Roy, K},
title = {Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1455530},
pmid = {39421849},
issn = {1662-5188},
}
@article {pmid39421767,
year = {2024},
author = {Wang, Z and Xiao, X and Zhou, Z and Chen, Y and Xia, T and Sheng, X and Han, Y and Gong, W and Si, K},
title = {FLUID: a fluorescence-friendly lipid-compatible ultrafast clearing method.},
journal = {Biomedical optics express},
volume = {15},
number = {10},
pages = {5609-5624},
pmid = {39421767},
issn = {2156-7085},
abstract = {Many clearing methods achieve high transparency by removing lipid components from tissues, which damages microstructure and limits their application in lipid research. As for methods which preserve lipid, it is difficult to balance transparency, fluorescence preservation and clearing speed. In this study, we propose a rapid water-based clearing method that is fluorescence-friendly and preserves lipid components. FLUID allows for preservation of endogenous fluorescence over 60 days. It shows negligible tissue distortion and is compatible with various types of fluorescent labeling and tissue staining methods. High quality imaging of human brain tissue and compatibility with pathological staining demonstrated the potential of our method for three-dimensional (3D) biopsy and clinical pathological diagnosis.},
}
@article {pmid39421626,
year = {2024},
author = {Mokienko, OA},
title = {Brain-Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments.},
journal = {Sovremennye tekhnologii v meditsine},
volume = {16},
number = {1},
pages = {78-89},
pmid = {39421626},
issn = {2309-995X},
mesh = {*Brain-Computer Interfaces ; Humans ; Electrodes, Implanted ; Prostheses and Implants ; },
abstract = {Brain-computer interfaces allow the exchange of data between the brain and an external device, bypassing the muscular system. Clinical studies of invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has been a continuous improvement of approaches to neuronal signal processing in order to improve the quality of control of external devices. Currently, brain-computer interfaces with intracortical implants allow completely paralyzed patients to control robotic limbs for self-service, use a computer or a tablet, type text, and reproduce speech at an optimal speed. Studies of invasive brain-computer interfaces regularly provide new fundamental data on functioning of the central nervous system. In recent years, breakthrough discoveries and achievements have been annually made in this sphere. This review analyzes the results of clinical experiments of brain-computer interfaces with intracortical implants, provides information on the stages of this technology development, its main discoveries and achievements.},
}
@article {pmid39421603,
year = {2024},
author = {Ye, M and Yang, C and Cheng, JX and Lee, HJ and Jiang, Y and Shi, L},
title = {Editorial: Neuromodulation technology: advances in optics and acoustics.},
journal = {Frontiers in cellular neuroscience},
volume = {18},
number = {},
pages = {1494457},
pmid = {39421603},
issn = {1662-5102},
support = {P41 EB031772/EB/NIBIB NIH HHS/United States ; },
}
@article {pmid39420525,
year = {2024},
author = {Shang, S and Shi, Y and Zhang, Y and Liu, M and Zhang, H and Wang, P and Zhuang, L},
title = {Artificial intelligence for brain disease diagnosis using electroencephalogram signals.},
journal = {Journal of Zhejiang University. Science. B},
volume = {25},
number = {10},
pages = {914-940},
pmid = {39420525},
issn = {1862-1783},
support = {2021ZD0200405//the National Key Research and Development Project of China/ ; 62271443, 32250008 and 82330064//the National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Artificial Intelligence ; *Brain Diseases/diagnosis/physiopathology ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Brain/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.},
}
@article {pmid39419976,
year = {2024},
author = {Chen, D and Zhao, Z and Shi, J and Li, S and Xu, X and Wu, Z and Tang, Y and Liu, N and Zhou, W and Ni, C and Ma, B and Wang, J and Zhang, J and Huang, L and You, Z and Zhang, P and Tang, Z},
title = {Harnessing the sensing and stimulation function of deep brain-machine interfaces: a new dawn for overcoming substance use disorders.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {440},
pmid = {39419976},
issn = {2158-3188},
support = {92148206//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82071330//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Substance-Related Disorders/therapy ; *Brain-Computer Interfaces ; *Deep Brain Stimulation/methods ; Brain/physiopathology ; Behavior, Addictive/therapy/physiopathology ; },
abstract = {Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.},
}
@article {pmid39419932,
year = {2025},
author = {Deak, F},
title = {Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.},
journal = {GeroScience},
volume = {47},
number = {2},
pages = {1557-1568},
pmid = {39419932},
issn = {2509-2723},
support = {R01 AG062655/AG/NIA NIH HHS/United States ; NIH NIA R01 AG062655/AG/NIA NIH HHS/United States ; SAGA23-1142437/ALZ/Alzheimer's Association/United States ; },
mesh = {Humans ; *Alzheimer Disease/history ; History, 20th Century ; *Artificial Intelligence/history ; History, 21st Century ; *Memory Disorders/history ; Aged ; },
abstract = {Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intelligence (AI). Both can be described as growing exponentially and presenting serious challenges. Although many questions have been addressed about the dangers of AI, we want to go beyond the fearful aspects of this topic and focus on the possible contribution of AI to solve the problem of chronic disorders of the elderly leading to cognitive impairment, like Alzheimer's disease, Parkinson's disease, and Lewy body dementia. Our second goal is to look at the ways in which modern neuroscience can influence the future design of computers and the development of AI. We wish to honor the memory of Dr. John von Neumann, who came up with many breakthrough details of the first electronic computer. Remarkably, Dr. von Neumann dedicated his last book to the comparison of the human brain and the computer as it stood in those years of the mid-1950s. We will point out how his ideas are more relevant than ever in the age of supercomputers, AI and brain implants.},
}
@article {pmid39419108,
year = {2024},
author = {de Seta, V and Colamarino, E and Pichiorri, F and Savina, G and Patarini, F and Riccio, A and Cincotti, F and Mattia, D and Toppi, J},
title = {Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad8838},
pmid = {39419108},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Stroke Rehabilitation/methods ; *Hand/physiopathology/physiology ; Middle Aged ; *Stroke/physiopathology ; Aged ; Adult ; *Muscle, Skeletal/physiopathology/physiology ; *Electroencephalography/methods ; Brain/physiopathology ; Movement/physiology ; Electromyography/methods ; },
abstract = {Objective.Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols.Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level.Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand (AH). Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their AH. Interestingly, brain features performed better in this latter condition with respect to healthy subjects.Significance.Our results provide hints to improve the design of Brain-Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients' characteristics and to the intended rehabilitative target.},
}
@article {pmid39419104,
year = {2024},
author = {Afonso, M and Sánchez-Cuesta, F and González-Zamorano, Y and Pablo Romero, J and Vourvopoulos, A},
title = {Investigating the synergistic neuromodulation effect of bilateral rTMS and VR brain-computer interfaces training in chronic stroke patients.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad8836},
pmid = {39419104},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Transcranial Magnetic Stimulation/methods ; *Stroke Rehabilitation/methods ; Aged ; *Stroke/physiopathology/therapy ; *Electroencephalography/methods ; Virtual Reality ; Adult ; Chronic Disease ; Neurofeedback/methods ; Treatment Outcome ; },
abstract = {Objective.Stroke is a major cause of adult disability worldwide, resulting in motor impairments. To regain motor function, patients undergo rehabilitation, typically involving repetitive movement training. For those who lack volitional movement, novel technology-based approaches have emerged that directly involve the central nervous system, through neuromodulation techniques such as transcranial magnetic stimulation (TMS), and closed-loop neurofeedback like brain-computer interfaces (BCIs). This, can be augmented through proprioceptive feedback delivered many times by embodied virtual reality (VR). Nonetheless, despite a growing body of research demonstrating the individual efficacy of each technique, there is limited information on their combined effects.Approach.In this study, we analyzed the Electroencephalographic (EEG) signals acquired from 10 patients with more than 4 months since stroke during a longitudinal intervention with repetitive TMS followed by VR-BCI training. From the EEG, the event related desynchronization (ERD) and individual alpha frequency (IAF) were extracted, evaluated over time and correlated with clinical outcome.Main results.Every patient's clinical outcome improved after treatment, and ERD magnitude increased during simultaneous rTMS and VR-BCI. Additionally, IAF values showed a significant correlation with clinical outcome, nonetheless, no relationship was found between differences in ERD pre- post- intervention with the clinical improvement.Significance.This study furnishes empirical evidence supporting the efficacy of the joint action of rTMS and VR-BCI in enhancing patient recovery. It also suggests a relationship between IAF and rehabilitation outcomes, that could potentially serve as a retrievable biomarker for stroke recovery.},
}
@article {pmid39419091,
year = {2024},
author = {Liu, J and Younk, R and M Drahos, L and S Nagrale, S and Yadav, S and S Widge, A and Shoaran, M},
title = {Neural decoding and feature selection methods for closed-loop control of avoidance behavior.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
pmid = {39419091},
issn = {1741-2552},
support = {R01 MH123634/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Rats ; *Avoidance Learning/physiology ; Male ; Rats, Sprague-Dawley ; Algorithms ; Amygdala/physiology ; },
abstract = {Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.},
}
@article {pmid39419024,
year = {2024},
author = {Agudelo-Toro, A and Michaels, JA and Sheng, WA and Scherberger, H},
title = {Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.},
journal = {Neuron},
volume = {112},
number = {24},
pages = {4115-4129.e8},
doi = {10.1016/j.neuron.2024.09.018},
pmid = {39419024},
issn = {1097-4199},
mesh = {Animals ; *Brain-Computer Interfaces ; *Posture/physiology ; *Hand Strength/physiology ; *Hand/physiology ; *Macaca mulatta ; Male ; Psychomotor Performance/physiology ; Motor Cortex/physiology ; Prostheses and Implants ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.},
}
@article {pmid39416663,
year = {2024},
author = {Tang, Q and Yang, X and Sun, M and He, M and Sa, R and Zhang, K and Zhu, B and Li, T},
title = {Research trends and hotspots of post-stroke upper limb dysfunction: a bibliometric and visualization analysis.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1449729},
pmid = {39416663},
issn = {1664-2295},
abstract = {BACKGROUND: The global prevalence of stroke has been increasing. Motor dysfunction is observed in approximately 55 to 75% of stroke patients, with upper limb impairment affecting around 85% of them. Following upper limb dysfunction, the body's recovery time is not only slower compared to the lower limbs, but the restoration of its fine motor skills is significantly more challenging, greatly impacting the daily lives of patients. Consequently, there is an increasing urgency for study on the upper limb function in stroke.<br><br>METHODS: A search was conducted in the Web of Science Core Collection: Science Citation Index Expanded (SCI-Expanded) database for material published from January 1, 2004 to December 31, 2023. We included all relevant literature reports and conducted an analysis of annual publications, countries/regions, institutions, journals, co-cited references, and keywords using the software packages CiteSpace, VOSviewer, and Bibliometrix R. Next, we succinctly outlined the research trends and hotspots in post-stroke upper limb dysfunction.<br><br>RESULTS: This analysis comprised 1,938 articles from 1,897 institutions, 354 journals, and 53 countries or regions. A yearly rise in the production of publications was noted. The United States is the foremost nation on the issue. Northwestern University has the most amounts of papers compared to all other institutions. The journal Neurorehabilitation and Neural Repair is a highly significant publication in this field, with Catherine E. Lang serving as the principal author. The majority of the most-cited references focus on subjects such as the reliability and validity of assessment instruments, RCT of therapies, systematic reviews, and meta-analyses. The intervention measures primarily comprise three types of high-frequency phrases that are related, as determined by keyword analysis: intelligent rehabilitation, physical factor therapy, and occupational therapy. Current areas of focus in research include randomized clinical trials, neurorehabilitation, and robot-assisted therapy.<br><br>CONCLUSION: Current research has shown a growing interest in studying upper limb function assessment, occupational therapy, physical therapy, robot-assisted therapy, virtual reality, brain-computer interface, telerehabilitation, cortical reorganisation, and neural plasticity. These topics have become popular and are expected to be the focus of future research.},
}
@article {pmid39416032,
year = {2024},
author = {Lee, JY and Lee, S and Mishra, A and Yan, X and McMahan, B and Gaisford, B and Kobashigawa, C and Qu, M and Xie, C and Kao, JC},
title = {Non-invasive brain-machine interface control with artificial intelligence copilots.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39416032},
issn = {2692-8205},
support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; },
abstract = {Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this "AI-BMI," in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3× in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.},
}
@article {pmid39415112,
year = {2024},
author = {Guo, X and Kong, L and Wen, Y and Chen, L and Hu, S},
title = {Impact of second-generation antipsychotics monotherapy or combined therapy in cytokine, lymphocyte subtype, and thyroid antibodies for schizophrenia: a retrospective study.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {695},
pmid = {39415112},
issn = {1471-244X},
support = {2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//the Innovation team for precision diagnosis and treatment of major brain diseases/ ; 226-2022-00193, 226-2022-00002//the Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Schizophrenia/drug therapy/immunology/blood ; Male ; Female ; Retrospective Studies ; Adult ; *Antipsychotic Agents/therapeutic use ; *Cytokines/blood/immunology ; Middle Aged ; *Autoantibodies/blood/immunology ; *Drug Therapy, Combination ; Lymphocyte Subsets/immunology/drug effects ; Iodide Peroxidase/immunology ; },
abstract = {BACKGROUND: Schizophrenia (SCZ) shares high clinical relevance with the immune system, and the potential interactions of psychopharmacological drugs with the immune system are still an overlooked area. Here, we aimed to identify whether the second-generation antipsychotics (SGA) monotherapy or combined therapy of SGA with other psychiatric medications influence the routine blood immunity biomarkers of patients with SCZ.<br><br>METHODS: Medical records of inpatients with SCZ from January 2019 to June 2023 were retrospectively screened from June 2023 to August 2023. The demographic data and peripheral levels of cytokines (IL-2, IL-4, IL-6, TNF-&#x3b1;, INF-&#x3b3;, and IL-17&#xa0;A), lymphocyte subtype proportions (CD3+, CD4+, CD8&#x2009;+&#x2009;T-cell, and natural killer (NK) cells), and thyroid autoimmune antibodies (thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb)) were collected and analyzed.<br><br>RESULTS: 30 drug-na&#xef;ve patients, 64 SGA monotherapy (20 for first-episode SCZ, 44 for recurrent SCZ) for at least one week, 39 combined therapies for recurrent SCZ (18 with antidepressant, 10 with benzodiazepine, and 11 with mood stabilizer) for at least two weeks, and 23 used to receive SGA monotherapy (had withdrawn for at least two weeks) were included despite specific medication. No difference in cytokines was found between the SGA monotherapy sub-groups (p&#x2009;>&#x2009;0.05). Of note, SGA monotherapy appeared to induce a down-regulation of IFN-&#x3b3; in both first (mean [95% confidence interval]: 1.08 [0.14-2.01] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.020) and recurrent (1.88 [0.71-3.05] vs. 4.60 [2.11-7.08], p&#x2009;=&#x2009;0.027) episodes compared to drug-na&#xef;ve patients. However, the lymphocyte proportions and thyroid autoimmune antibodies remained unchanged after at least two weeks of SGA monotherapy (p&#x2009;>&#x2009;0.05). In combined therapy groups, results mainly resembled the SGA monotherapy for recurrent SCZ (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: The study demonstrated that SGA monotherapy possibly achieved its comfort role via modulating IFN-&#x3b3;, and SGA combined therapy showed an overall resemblance to monotherapy.},
}
@article {pmid39413730,
year = {2024},
author = {Wang, J and Chen, ZS},
title = {Closed-loop neural interfaces for pain: Where do we stand?.},
journal = {Cell reports. Medicine},
volume = {5},
number = {10},
pages = {101662},
pmid = {39413730},
issn = {2666-3791},
support = {R01 NS121776/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; P50 MH132642/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Humans ; Brain-Computer Interfaces ; *Chronic Pain/therapy/physiopathology ; Pain Management/methods ; },
abstract = {Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.},
}
@article {pmid39413689,
year = {2024},
author = {Rebouillat, B and Barascud, N and Kouider, S},
title = {Partial awareness during voluntary endogenous decision.},
journal = {Consciousness and cognition},
volume = {125},
number = {},
pages = {103769},
doi = {10.1016/j.concog.2024.103769},
pmid = {39413689},
issn = {1090-2376},
mesh = {Humans ; *Awareness/physiology ; Male ; Adult ; Female ; *Decision Making/physiology ; Young Adult ; *Metacognition/physiology ; Electroencephalography ; },
abstract = {Despite our feeling of control over decisions, our ability to consciously access choices before execution remains debated. Recent research reveals prospective access to intention to act, allowing potential vetoes of impending decisions. However, whether the content of impending decision can be accessed remain debated. Here we track neural signals during participants' early deliberation in free decisions. Participants chose freely between two options but sometimes had to reject their current decision just before execution. The initially preferred option, tracked in real time, significantly predicts the upcoming choice, but remain mostly outside of conscious awareness. Participants often display overconfidence in their access to this content. Instead, confidence is associated with a neural marker of self-initiated decision, indicating a qualitative confusion in the confidence evaluation process. Our results challenge the notion of complete agency over choices, suggesting inflated awareness of forthcoming decisions and providing insights into metacognitive processes in free decision-making.},
}
@article {pmid39413360,
year = {2025},
author = {Trott, J and Slaymaker, C and Niznik, G and Althoff, T and Netherton, B},
title = {Brain Computer Interfaces: An Introduction for Clinical Neurodiagnostic Technologists.},
journal = {The Neurodiagnostic journal},
volume = {65},
number = {1},
pages = {32-45},
doi = {10.1080/21646821.2024.2408501},
pmid = {39413360},
issn = {2164-6821},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; },
abstract = {Brain-computer interface (BCI) is a term used to describe systems that translate biological information into commands that can control external devices such as computers, prosthetics, and other machinery. While BCI is used in military applications, home control systems, and a wide array of entertainment, much of its modern interest and funding can be attributed to its utility in the medical community, where it has rapidly propelled advancements in the restoration or replacement of critical functions robbed from victims of disease, stroke, and traumatic injury. BCI devices can allow patients to move prosthetic limbs, operate devices such as wheelchairs or computers, and communicate through writing and speech-generating devices. In this article, we aim to provide an introductory summary of the historical context and modern growing utility of BCI, with specific interest in igniting the conversation of where and how the neurodiagnostics community and its associated parties can embrace and contribute to the world of BCI.},
}
@article {pmid39412979,
year = {2024},
author = {Zhang, Y and Yu, Y and Li, H and Wu, A and Zeng, LL and Hu, D},
title = {MASER: Enhancing EEG Spatial Resolution With State Space Modeling.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3858-3868},
doi = {10.1109/TNSRE.2024.3481886},
pmid = {39412979},
issn = {1558-0210},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; Brain-Computer Interfaces ; Reproducibility of Results ; Electrodes ; },
abstract = {Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.},
}
@article {pmid39412966,
year = {2025},
author = {OLeary, G and Koerner, J and Kanchwala, M and Filho, JS and Xu, J and Valiante, TA and Genov, R},
title = {BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {19},
number = {1},
pages = {55-67},
doi = {10.1109/TBCAS.2024.3481160},
pmid = {39412966},
issn = {1940-9990},
mesh = {Humans ; *Electroencephalography/instrumentation ; *Brain/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted/instrumentation ; Machine Learning ; Brain-Computer Interfaces ; Neurons/physiology ; Algorithms ; },
abstract = {Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6µW, typical: 118µW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.},
}
@article {pmid39409506,
year = {2024},
author = {Xu, H and Haider, W and Aziz, MZ and Sun, Y and Yu, X},
title = {Transforming Motor Imagery Analysis: A Novel EEG Classification Framework Using AtSiftNet Method.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
pmid = {39409506},
issn = {1424-8220},
support = {52172387//National Natural Science Foundation of China/ ; U2033202, U1333119//Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China/ ; ILA22032-1A//Fundamental Research Funds for the Central Universities/ ; 2022Z071052001//Aeronautical Science Foundation of China/ ; 2022JGZ14//Northwestern Polytechnical University/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Principal Component Analysis ; *Machine Learning ; *Imagination/physiology ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This paper presents an innovative approach for the Feature Extraction method using Self-Attention, incorporating various Feature Selection techniques known as the AtSiftNet method to enhance the classification performance of motor imaginary activities using electrophotography (EEG) signals. Initially, the EEG signals were sorted and then denoised using multiscale principal component analysis to obtain clean EEG signals. However, we also conducted a non-denoised experiment. Subsequently, the clean EEG signals underwent the Self-Attention feature extraction method to compute the features of each trial (i.e., 350×18). The best 1 or 15 features were then extracted through eight different feature selection techniques. Finally, five different machine learning and neural network classification models were employed to calculate the accuracy, sensitivity, and specificity of this approach. The BCI competition III dataset IV-a was utilized for all experiments, encompassing the datasets of five volunteers who participated in the competition. The experiment findings reveal that the average accuracy of classification is highest among ReliefF (i.e., 99.946%), Mutual Information (i.e., 98.902%), Independent Component Analysis (i.e., 99.62%), and Principal Component Analysis (i.e., 98.884%) for both 1 and 15 best-selected features from each trial. These accuracies were obtained for motor imagery using a Support Vector Machine (SVM) as a classifier. In addition, five-fold validation was performed in this paper to assess the fair performance estimation and robustness of the model. The average accuracy obtained through five-fold validation is 99.89%. The experiments' findings indicate that the suggested framework provides a resilient biomarker with minimal computational complexity, making it a suitable choice for advancing Motor Imagery Brain-Computer Interfaces (BCI).},
}
@article {pmid39409418,
year = {2024},
author = {Borirakarawin, M and Siribunyaphat, N and Aung, ST and Punsawad, Y},
title = {The Development of a Multicommand Tactile Event-Related Potential-Based Brain-Computer Interface Utilizing a Low-Cost Wearable Vibrotactile Stimulator.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
pmid = {39409418},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods/instrumentation ; Male ; *Wearable Electronic Devices ; *Touch/physiology ; Adult ; Female ; *Vibration ; Evoked Potentials/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; },
abstract = {A tactile event-related potential (ERP)-based brain-computer interface (BCI) system is an alternative for enhancing the control and communication abilities of quadriplegic patients with visual or auditory impairments. Hence, in this study, we proposed a tactile stimulus pattern using a vibrotactile stimulator for a multicommand BCI system. Additionally, we observed a tactile ERP response to the target from random vibrotactile stimuli placed in the left and right wrist and elbow positions to create commands. An experiment was conducted to explore the location of the proposed vibrotactile stimulus and to verify the multicommand tactile ERP-based BCI system. Using the proposed features and conventional classification methods, we examined the classification efficiency of the four commands created from the selected EEG channels. The results show that the proposed vibrotactile stimulation with 15 stimulus trials produced a prominent ERP response in the Pz channels. The average classification accuracy ranged from 61.9% to 79.8% over 15 stimulus trials, requiring 36 s per command in offline processing. The P300 response in the parietal area yielded the highest average classification accuracy. The proposed method can guide the development of a brain-computer interface system for physically disabled people with visual or auditory impairments to control assistive and rehabilitative devices.},
}
@article {pmid39409405,
year = {2024},
author = {Silvoni, S and Occhigrossi, C and Di Giorgi, M and Lulé, D and Birbaumer, N},
title = {Brain Function, Learning, and Role of Feedback in Complete Paralysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
pmid = {39409405},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Learning/physiology ; *Brain/physiology/physiopathology ; Paralysis/physiopathology/psychology ; Communication ; Feedback ; },
abstract = {The determinants and driving forces of communication abilities in the locked-in state are poorly understood so far. Results from an experimental-clinical study on a completely paralyzed person involved in communication sessions after the implantation of a microelectrode array were retrospectively analyzed. The aim was to focus on the prerequisites and determinants for learning to control a brain-computer interface for communication in paralysis. A comparative examination of the communication results with the current literature was carried out in light of an ideomotor theory of thinking. We speculate that novel skill learning took place and that several aspects of the wording of sentences during the communication sessions reflect preserved cognitive and conscious processing. We also present some speculations on the operant learning procedure used for communication, which argues for the reformulation of the previously postulated hypothesis of the extinction of response planning and goal-directed ideas in the completely locked-in state. We highlight the importance of feedback and reinforcement in the thought-action-consequence associative chain necessary to maintain purposeful communication. Finally, we underline the necessity to consider the psychosocial context of patients and the duration of complete immobilization as determinants of the 'extinction of thinking' theory and to identify the actual barriers preventing communication in these patients.},
}
@article {pmid39409342,
year = {2024},
author = {Liu, M and Liu, Y and Feleke, AG and Fei, W and Bi, L},
title = {Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
pmid = {39409342},
issn = {1424-8220},
support = {JCKY2022602C024//Basic Research Plan/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Aircraft ; Young Adult ; Female ; Emergencies ; },
abstract = {Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator's electroencephalography (EEG) signals-based detection method for UAV emergencies. We found regularity characteristics similar to classic event-related potential (ERP) components like visual mismatch negativity (vMMN) and contingent negative variation (CNV). Source analysis revealed a sequential activation of the occipital, temporal, and frontal lobes following the onset of emergencies, corresponding to the processing of attention, emotion, and motor intention triggered by visual stimuli. Furthermore, an online detection system was implemented and tested. Experimental results showed that the system achieved an average accuracy of over 88% in detecting emergencies with a detection latency of 431.95 ms from the emergency onset. This work lays a foundation for understanding the brain activities of operators in emergencies and developing an EEG-based detection method for emergencies to assist UAV operations.},
}
@article {pmid39409201,
year = {2024},
author = {Shokri, R and Koolivand, Y and Shoaei, O and Caviglia, DD and Aiello, O},
title = {A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {19},
pages = {},
pmid = {39409201},
issn = {1424-8220},
mesh = {*Analog-Digital Conversion ; Humans ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain/physiology ; Neurons/physiology ; Equipment Design ; Nonlinear Dynamics ; },
abstract = {Neural recording systems play a crucial role in comprehending the intricacies of the brain and advancing treatments for neurological disorders. Within these systems, the analog-to-digital converter (ADC) serves as a fundamental component, converting the electrical signals from the brain into digital data that can be further processed and analyzed by computing units. This research introduces a novel nonlinear ADC designed specifically for spike sorting in biomedical applications. Employing MOSFET varactors and voltage-controlled oscillators (VCOs), this ADC exploits the nonlinear capacitance properties of MOSFET varactors, achieving a parabolic quantization function that digitizes the noise with low resolution and the spikes with high resolution, effectively suppressing the background noise present in biomedical signals. This research aims to develop a reconfigurable, nonlinear voltage-controlled oscillator (VCO)-based ADC, specifically designed for implantable neural recording systems used in neuroprosthetics and brain-machine interfaces. The proposed design enhances the signal-to-noise ratio and reduces power consumption, making it more efficient for real-time neural data processing. By improving the performance and energy efficiency of these devices, the research contributes to the development of more reliable medical technologies for monitoring and treating neurological disorders. The quantization step of the ADC spans from 44.8 mV in the low-amplitude range to 1.4 mV in the high-amplitude range. The circuit was designed and simulated utilizing a 180 nm CMOS process; however, no physical prototype has been fabricated at this stage. Post-layout simulations confirm the expected performance. Occupying a silicon area is 0.09 mm[2]. Operating at a sampling frequency of 16 kS/s and a supply voltage of 1 volt, this ADC consumes 62.4 µW.},
}
@article {pmid39405625,
year = {2025},
author = {Kasprzyk-Hordern, B and Jagadeesan, K and Sims, N and Farkas, K and Proctor, K and Bagnall, J and Robertson, M and Jones, DL and Wade, MJ},
title = {Multi-biomarker approach for estimating population size in a national-scale wastewater-based epidemiology study.},
journal = {Water research},
volume = {268},
number = {Pt A},
pages = {122527},
doi = {10.1016/j.watres.2024.122527},
pmid = {39405625},
issn = {1879-2448},
mesh = {*Biomarkers ; Humans ; *Wastewater ; Population Density ; England ; Wastewater-Based Epidemiological Monitoring ; },
abstract = {This study identifies biochemical markers (BCIs) that can be used as population markers in wastewater-based epidemiology (WBE) and compares their estimates with other established population size estimation (PE) methods, including census data (PECEN). Several groups of BCIs (64 targets: genetic and chemical markers) were investigated in an intercity study, including 10 cities/towns within England equating to a population of ∼7 million people. Several selection criteria were applied to identify the best BCIs to provide robust estimation of population size at a catchment level: (1) excellent performance with analytical methods; (2) excellent fit of the linear regression model which indicates PE-driven BCI daily loads; (3) low temporal variability in usage; (4) human-linked origin. Only a few tested BCIs showed a strong positive linear correlation between daily BCI loads and PE indicating their low spatiotemporal variability. These are: cimetidine, clarithromycin, metformin, cotinine, bezafibrate, metronidazole and hydroxymetronidazole, diclofenac, and benzophenone 1. However, only high/long term usage pharmaceuticals: cimetidine and metformin as well as cotinine (metabolite of nicotine) performed well when tested in two independent datasets and catchments accounting for both spatial and temporal scales. Strong seasonal usage trends were observed for antihistamines, NSAIDs (anti-inflammatories), antibiotics and UV filters, invalidating them as PE markers. Key conclusions from the study are: (1) Cimetidine is the best performing BCI; (2) Chemical markers outperform genetic markers as PE BCIs; (3) Water utility PE estimates (PEWW) align well with PECEN and PEBCI values; (4) Ammonium/orthophosphate as well as viral PE markers suffer from high temporal variability, hence, they are not recommended as PEBCI markers, and, most importantly, (5) PEBCI calibration/validation at the country/region level is advised in order to establish the best PE markers suited for local/national needs and accounting for site/region specific uncertainties.},
}
@article {pmid39402900,
year = {2025},
author = {Zhang, X and Pei, X and Shi, Y and Yang, Y and Bai, X and Chen, T and Zhao, Y and Yang, Q and Ye, J and Leng, X and Yang, Q and Bai, R and Wang, Y and Sui, B},
title = {Unveiling connections between venous disruption and cerebral small vessel disease using diffusion tensor image analysis along perivascular space (DTI-ALPS): A 7-T MRI study.},
journal = {International journal of stroke : official journal of the International Stroke Society},
volume = {20},
number = {4},
pages = {497-506},
doi = {10.1177/17474930241293966},
pmid = {39402900},
issn = {1747-4949},
mesh = {Humans ; *Cerebral Small Vessel Diseases/diagnostic imaging ; Male ; Middle Aged ; Female ; Cross-Sectional Studies ; *Diffusion Tensor Imaging/methods ; Aged ; *Glymphatic System/diagnostic imaging/pathology ; White Matter/diagnostic imaging/pathology ; Cerebral Veins/diagnostic imaging/pathology ; Prospective Studies ; Brain/diagnostic imaging/pathology/blood supply ; Magnetic Resonance Imaging/methods ; },
abstract = {BACKGROUND: Cerebral venous disruption is one of the characteristic findings in cerebral small vessel disease (CSVD), and its disruption may impede perivascular glymphatic drainage. And lower diffusivity along perivascular space (DTI-ALPS) index has been suggested to be with the presence and severity of CSVD. However, the relationships between venous disruption, DTI-ALPS index, and CSVD neuroimaging features remain unclear.<br><br>AIMS: To investigate the association between venous integrity and perivascular diffusion activity, and explore the mediating role of DTI-ALPS index between venous disruption and CSVD imaging features.<br><br>METHODS: In this cross-sectional study, 31 patients (mean age, 59.0&#x2009;&#xb1;&#x2009;9.9&#x2009;years) were prospectively enrolled and underwent 7-T magnetic resonance (MR) imaging. DTI-ALPS index was measured to quantify the perivascular diffusivity. The visibility and continuity of deep medullary veins (DMVs) were evaluated based on a brain region-based visual score on high-resolution susceptibility-weighted imaging. White matter hyperintensity (WMH) and perivascular space (PVS) were assessed using qualitative and quantitative methods. Linear regression and mediation analysis were performed to analyze the relationships among DMV scores, DTI-ALPS index, and CSVD features.<br><br>RESULTS: The DTI-ALPS index was significantly associated with the parietal DMV score (&#x3b2;&#x2009;=&#x2009;-0.573, p corrected&#x2009;=&#x2009;0.004). Parietal DMV score was associated with WMH volume (&#x3b2;&#x2009;=&#x2009;0.463, p corrected&#x2009;=&#x2009;0.013) and PVS volume in basal ganglia (&#x3b2;&#x2009;=&#x2009;0.415, p corrected&#x2009;=&#x2009;0.028). Mediation analyses showed that DTI-ALPS index manifested a full mediating effect on the association between parietal DMV score and WMH (indirect effect&#x2009;=&#x2009;0.115, Pm&#x2009;=&#x2009;43.1%), as well as between parietal DMV score and PVS volume in basal ganglia (indirect effect&#x2009;=&#x2009;0.161, Pm&#x2009;=&#x2009;42.8%).<br><br>CONCLUSION: Cerebral venous disruption is associated with glymphatic activity, and with WMH and PVS volumes. Our results suggest cerebral venous integrity may play a critical role in preserving perivascular glymphatic activity; while disruption of small veins may impair the perivascular diffusivity, thereby contributing to the development of WMH and PVS enlargement.},
}
@article {pmid39401512,
year = {2024},
author = {Eisma, YB and van Vliet, ST and Nederveen, AJ and de Winter, JCF},
title = {Assessing the influence of visual stimulus properties on steady-state visually evoked potentials and pupil diameter.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ad865d},
pmid = {39401512},
issn = {2057-1976},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Pupil/physiology ; *Photic Stimulation ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Signal-To-Noise Ratio ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEPs) are brain responses measurable via electroencephalography (EEG) in response to continuous visual stimulation at a constant frequency. SSVEPs have been instrumental in advancing our understanding of human vision and attention, as well as in the development of brain-computer interfaces (BCIs). Ongoing questions remain about which type of visual stimulus causes the most potent SSVEP response. The current study investigated the effects of color, size, and flicker frequency on the signal-to-noise ratio of SSVEPs, complemented by pupillary light reflex measurements obtained through an eye-tracker. Six participants were presented with visual stimuli that differed in terms of color (white, red, green), shape (circles, squares, triangles), size (10,000 to 30,000 pixels), flicker frequency (8 to 25 Hz), and grouping (one stimulus at a time versus four stimuli presented in a 2 × 2 matrix to simulate a BCI). The results indicated that larger stimuli elicited stronger SSVEP responses and more pronounced pupil constriction. Additionally, the results revealed an interaction between stimulus color and flicker frequency, with red being more effective at lower frequencies and white at higher frequencies. Future SSVEP research could focus on the recommended waveform, interactions between SSVEP and power grid frequency, a wider range of flicker frequencies, a larger sample of participants, and a systematic comparison of the information transfer obtained through SSVEPs, pupil diameter, and eye movements.},
}
@article {pmid39400422,
year = {2025},
author = {Vírseda-Chamorro, M and Salinas-Casado, J and Adot-Zurbano, JM and Méndez-Rubio, S and Moreno-Sierra, J},
title = {Can We Differentiate Between Organic and Functional Bladder Outlet Obstruction in Males With Parkinson's Disease?.},
journal = {Neurourology and urodynamics},
volume = {44},
number = {1},
pages = {171-177},
doi = {10.1002/nau.25599},
pmid = {39400422},
issn = {1520-6777},
support = {//The authors received no specific funding for this work./ ; },
mesh = {Humans ; *Urinary Bladder Neck Obstruction/physiopathology/diagnosis ; Male ; *Parkinson Disease/physiopathology/complications/diagnosis ; Aged ; Case-Control Studies ; *Urodynamics ; *Prostatic Hyperplasia/physiopathology/complications/diagnosis ; Middle Aged ; Urinary Bladder/physiopathology ; Pressure ; Aged, 80 and over ; Diagnosis, Differential ; },
abstract = {OBJECTIVES: To determine the type of bladder outlet obstruction (BOO) in patients with Parkinson's disease (PD).<br><br>MATERIAL AND METHOD: A case-control study was carried out in 46 patients divided into two groups. Group 1 formed by 23 PD patients with BOO (a URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Group 2 formed by 23 patients with benign prostatic hyperplasia (BPH) and compressive obstruction (an opening pressure >&#x2009;35&#x2009;cm H2O) and URA parameter &#x2265;&#x2009;29&#x2009;cm H2O). Both groups underwent a pressure-flow study to calculate Dynamic Urethral Resistance Relationship (DURR) patterns. Based on previous research, we describe two types of DURR pattern. Pattern A typical of dynamic or functional obstruction and pattern B typical of static or organic obstruction.<br><br>RESULTS: We found that PD patients had a significantly higher frequency of pattern A (70%) than BPH patients (4%). Other significant differences between groups were age (greater in PD group), bladder compliance (greater in PD group), maximum flow rate [Qmax (greater in BPH group)], maximum detrusor pressure [Pmax (greater in BPH group)], detrusor pressure at maximum flow rate [PQmax (greater in BPH group)], opening detrusor pressure (greater in BPH group), and the bladder contractility parameters BCI and Wmax (greater in BPH group). There were no significant differences in perineal voiding electromyography (EMG) activity between groups nor relationship between voiding EMG activity and the type of DURR pattern.<br><br>CONCLUSIONS: Our results are consistent with the usefulness of the DURR pattern to differentiate between functional and organic BOO in PD patients. Most PD patients have functional obstruction although a minority has organic obstruction consistent with BPH.},
}
@article {pmid39399473,
year = {2024},
author = {Zhang, K and Zhou, W and Yu, H and Pang, M and Gao, H and Anwar, F and Yu, K and Zhou, Z and Guo, F and Liu, X and Ming, D},
title = {Insights on pathophysiology of hydrocephalus rats induced by kaolin injection.},
journal = {FASEB bioAdvances},
volume = {6},
number = {9},
pages = {351-364},
pmid = {39399473},
issn = {2573-9832},
abstract = {Hydrocephalus can affect brain function and motor ability. Current treatments mostly involve invasive surgeries, with a high risk of postoperative infections and failure. A successful animal model plays a significant role in developing new treatments for hydrocephalus. Hydrocephalus was induced in Sprague-Dawley rats by injecting 25% kaolin into the subarachnoid space at the cerebral convexities with different volumes of 30, 60 and 90 μL. Magnetic resonance imaging (MRI) was performed 1 month and 4 months after kaolin injection. The behavioral performance was assessed weekly, lasting for 7 weeks. The histopathological analyses were conducted to the lateral ventricles by hematoxylin-eosin (HE) staining. Transcriptomic analysis was used between Normal Pressure Hydrocephalus (NPH) patients and hydrocephalus rats. MRI showed a progressive enlargement of ventricles in hydrocephalus group. Kaolin-60 μL and kaolin-90 μL groups showed larger ventricular size, higher anxiety level, bigger decline in body weight, motor ability and cognitive competence. These symptoms may be due to higher-grade inflammatory infiltrate and the damage of the structure of ependymal layer of the ventricles, indicated by HE staining. The overlap upregulated genes and pathways mainly involve immunity and inflammation. Transcriptomic revealed shared pathogenic genes CD40, CD44, CXCL10, and ICAM1 playing a dominance role. 60 μL injection might be recommended for the establishment of hydrocephalus animal model, with a high successful rate and high stability. The hydrocephalus model was able to resemble the inflammatory mechanism and behavioral performance observed in human NPH patients, providing insights for identifying therapeutic targets for hydrocephalus.},
}
@article {pmid39398473,
year = {2024},
author = {Hanada, GM and Kalabic, M and Ferris, DP},
title = {Mobile brain-body imaging data set of indoor treadmill walking and outdoor walking with a visual search task.},
journal = {Data in brief},
volume = {57},
number = {},
pages = {110968},
doi = {10.1016/j.dib.2024.110968},
pmid = {39398473},
issn = {2352-3409},
abstract = {To fully understand brain processes in the real world, it is necessary to record and quantitatively analyse brain processes during real world human experiences. Mobile electroencephalography (EEG) and physiological data sensors provide new opportunities for studying humans outside of the laboratory. The purpose of this study was to document data from high-density EEG and mobile physiological sensors while humans performed a visual search task both on a treadmill in a laboratory setting and overground in a natural outdoor setting. The data set includes 49 young, healthy participants on an outdoor arboretum path and on a treadmill in a laboratory with a large virtual reality screen. The data provide a valuable research tool for scientists interested in signal processing, electrocortical brain processes, mobile brain imaging, and brain-computer interfaces based on mobile EEG. Given the comparison data between laboratory and real world conditions, researchers can test the viability of new processing algorithms across conditions or investigate changes in electrocortical activity related to behavioural dynamics coded into the data.},
}
@article {pmid39397592,
year = {2025},
author = {Deepika, D and Rekha, G},
title = {A hybrid capsule attention-based convolutional bi-GRU method for multi-class mental task classification based brain-computer Interface.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {1},
pages = {90-106},
doi = {10.1080/10255842.2024.2410221},
pmid = {39397592},
issn = {1476-8259},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Attention/physiology ; },
abstract = {Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.},
}
@article {pmid39397321,
year = {2024},
author = {Gokhale, SM and Bhatia, M},
title = {Lymphoscintigraphy With SPECT-CT in Detecting the Site of Chyle Leak in Postoperative Patient.},
journal = {Clinical nuclear medicine},
volume = {49},
number = {12},
pages = {e664-e667},
doi = {10.1097/RLU.0000000000005496},
pmid = {39397321},
issn = {1536-0229},
mesh = {Humans ; Chyle/diagnostic imaging ; Esophagectomy/adverse effects ; *Lymphoscintigraphy ; Postoperative Complications/diagnostic imaging ; *Single Photon Emission Computed Tomography Computed Tomography ; },
abstract = {Here is a case of chyle leak post McKeown esophagectomy. Lymphoscintigraphy with 99m Tc-filtered sulfur colloid revealed tracer accumulation along the thoracic duct and in the left hemithorax. Precise localization of leak was done by SPECT-CT imaging. This enabled timely surgical intervention and reduced further morbidity. This procedure is not only precise but also cost-effective as compared with the other available investigations.},
}
@article {pmid39397043,
year = {2024},
author = {Shi, C and Jiang, J and Li, C and Chen, C and Jian, W and Song, J},
title = {Precision-induced localized molten liquid metal stamps for damage-free transfer printing of ultrathin membranes and 3D objects.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {8839},
pmid = {39397043},
issn = {2041-1723},
support = {U21A20502//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12225209//National Natural Science Foundation of China (National Science Foundation of China)/ ; U20A6001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12321002//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12302214//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Transfer printing, a crucial technique for heterogeneous integration, has gained attention for enabling unconventional layouts and high-performance electronic systems. Elastomer stamps are typically used for transfer printing, where localized heating for elastomer stamp can effectively control the transfer process. A key challenge is the potential damage to ultrathin membranes from the contact force of elastic stamps, especially with fragile inorganic nanomembranes. Herein, we present a precision-induced localized molten technique that employs either laser-induced transient heating or hotplate-induced directional heating to precisely melt solid gallium (Ga). By leveraging the fluidity of localized molten Ga, which provides gentle contact force and exceptional conformal adaptability, this technique avoids damage to fragile thin films and improves operational reliability compared to fully liquefied Ga stamps. Furthermore, the phase transition of Ga provides a reversible adhesion with high adhesion switchability. Once solidified, the Ga stamp hardens and securely adheres to the micro/nano-membrane during the pick-up process. The solidified stamp also exhibits the capability to maneuver arbitrarily shaped objects by generating a substantial grip force through the interlocking effects. Such a robust, damage-free, simply operable protocol illustrates its promising capabilities in transfer printing diverse ultrathin membranes and objects on complex surfaces for developing high-performance unconventional electronics.},
}
@article {pmid39396768,
year = {2024},
author = {Zhang, J and Wang, L and Guo, H and Kong, S and Li, W and He, Q and Ding, L and Yang, B},
title = {The role of Tim-3 blockade in the tumor immune microenvironment beyond T cells.},
journal = {Pharmacological research},
volume = {209},
number = {},
pages = {107458},
doi = {10.1016/j.phrs.2024.107458},
pmid = {39396768},
issn = {1096-1186},
mesh = {*Hepatitis A Virus Cellular Receptor 2/antagonists &amp; inhibitors/immunology/metabolism ; *Tumor Microenvironment/immunology ; Humans ; Animals ; *Neoplasms/immunology/drug therapy/pathology ; Killer Cells, Natural/immunology ; T-Lymphocytes/immunology/drug effects ; Macrophages/immunology ; Dendritic Cells/immunology ; },
abstract = {Numerous preclinical studies have demonstrated the inhibitory function of T cell immunoglobulin mucin domain-containing protein 3 (Tim-3) on T cells as an inhibitory receptor, leading to the clinical development of anti-Tim-3 blocking antibodies. However, recent studies have shown that Tim-3 is expressed not only on T cells but also on multiple cell types in the tumor microenvironment (TME), including dendritic cells (DCs), natural killer (NK) cells, macrophages, and tumor cells. Therefore, Tim-3 blockade in the immune microenvironment not only affect the function of T cells but also influence the functions of other cells. For example, Tim-3 blockade can enhance the ability of DCs to regulate innate and adaptive immunity. The role of Tim-3 blockade in NK cells function is controversial, as it can enhance the antitumor function of NK cells under certain conditions while having the opposite effect in other situations. Additionally, Tim-3 blockade can promote the reversal of macrophage polarization from the M2 phenotype to the M1 phenotype. Furthermore, Tim-3 blockade can inhibit tumor development by suppressing the proliferation and metastasis of tumor cells. In summary, increasing evidence has shown that Tim-3 in other cell types also plays a critical role in the efficacy of anti-Tim-3 therapy. Understanding the function of anti-Tim-3 therapy in non-T cells can help elucidate the diverse responses observed in clinical patients, leading to better development of relevant therapeutic strategies. This review aims to discuss the role of Tim-3 in the TME and emphasize the impact of Tim-3 blockade in the tumor immune microenvironment beyond T cells.},
}
@article {pmid39396567,
year = {2025},
author = {Su, H and Zhan, G and Lin, Y and Wang, L and Jia, J and Zhang, L and Gan, Z and Kang, X},
title = {Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG.},
journal = {Brain research},
volume = {1846},
number = {},
pages = {149261},
doi = {10.1016/j.brainres.2024.149261},
pmid = {39396567},
issn = {1872-6240},
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain/physiopathology/physiology ; *Stroke Rehabilitation/methods ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; Nerve Net/physiopathology/physiology ; Movement/physiology ; Adult ; Motor Cortex/physiology/physiopathology ; },
abstract = {Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.},
}
@article {pmid39396023,
year = {2024},
author = {Zhou, H and Hong, T and Chen, X and Su, C and Teng, B and Xi, W and Cadet, JL and Yang, Y and Geng, F and Hu, Y},
title = {Glutamate concentration of medial prefrontal cortex is inversely associated with addictive behaviors: a translational study.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {433},
pmid = {39396023},
issn = {2158-3188},
support = {81971245//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Glutamic Acid/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; Male ; Animals ; Rats ; Humans ; *Internet Addiction Disorder/physiopathology/metabolism ; *Behavior, Addictive/physiopathology/metabolism ; *Drug-Seeking Behavior/physiology ; Adult ; *Methamphetamine ; Young Adult ; Gyrus Cinguli/metabolism/physiopathology ; Female ; Disease Models, Animal ; Rats, Sprague-Dawley ; Translational Research, Biomedical ; Self Administration ; },
abstract = {In both preclinical and clinical settings, dysregulated frontostriatal circuits have been identified as the underlying neural substrates of compulsive seeking/taking behaviors manifested in substance use disorders and behavioral addictions including internet gaming disorder (IGD). However, the neurochemical substrates for these disorders remain elusive. The lack of comprehensive cognitive assessments in animal models has hampered our understanding of neural plasticity in addiction from these models. In this study, combining data from a rat model of compulsive taking/seeking and human participants with various levels of IGD severity, we investigated the relationship between regional glutamate (Glu) concentration and addictive behaviors. We found that Glu levels were significantly lower in the prelimbic cortex (PrL) of rats after 20-days of methamphetamine self-administration (SA), compared to controls. Glu concentration after a punishment phase negatively correlated with acute drug-seeking behavior. In addition, changes in Glu levels from a drug naïve state to compulsive drug taking patterns negatively correlated with drug-seeking during both acute and prolonged abstinence. The human data revealed a significant negative correlation between Glu concentration in the dorsal anterior cingulate cortex (dACC), the human PrL counterpart, and symptoms of IGD. Interestingly, there was a positive correlation between Glu levels in the dACC and self-control, as well as mindful awareness. Further analysis revealed that the dACC Glu concentration mediated the relationship between self-control/mindful awareness and IGD symptoms. These results provide convergent evidence for a protective role of dACC/PrL in addiction, suggesting interventions to enhance dACC glutamatergic functions as a potential strategy for addiction prevention and treatment.},
}
@article {pmid39395910,
year = {2025},
author = {Fan, J and Wang, X and Xu, H},
title = {Sex-Differential Neural Circuits and Behavioral Responses for Empathy.},
journal = {Neuroscience bulletin},
volume = {41},
number = {1},
pages = {192-194},
pmid = {39395910},
issn = {1995-8218},
}
@article {pmid39387251,
year = {2024},
author = {Zhang, W and Bai, L and Xu, W and Liu, J and Chen, Y and Lin, W and Lu, H and Wang, B and Luo, B and Peng, G and Zhang, K and Shen, C},
title = {Sirt6 Mono-ADP-Ribosylates YY1 to Promote Dystrophin Expression for Neuromuscular Transmission.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {44},
pages = {e2406390},
pmid = {39387251},
issn = {2198-3844},
support = {LZ22C110002//Natural Science Foundation of Zhejiang Province/ ; 2021YFA1101100//National Key R&amp;D Program of China/ ; 2022YFF1000500//National Key R&amp;D Program of China/ ; 32271031//National Natural Science Foundation of China/ ; 82230038//National Natural Science Foundation of China/ ; 31871203//National Natural Science Foundation of China/ ; 32071032//National Natural Science Foundation of China/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; *Sirtuins/metabolism/genetics ; *YY1 Transcription Factor/metabolism/genetics ; Mice ; *Neuromuscular Junction/metabolism/drug effects ; *Dystrophin/genetics/metabolism ; Synaptic Transmission/drug effects ; Muscle, Skeletal/metabolism/drug effects ; Disease Models, Animal ; Male ; ADP-Ribosylation ; },
abstract = {The degeneration of the neuromuscular junction (NMJ) and the decline in motor function are common features of aging, but the underlying mechanisms have remained largely unclear. This study reveals that Sirt6 is reduced in aged mouse muscles. Ablation of Sirt6 in skeletal muscle causes a reduction of Dystrophin levels, resulting in premature NMJ degeneration, compromised neuromuscular transmission, and a deterioration in motor performance. Mechanistic studies show that Sirt6 negatively regulates the stability of the Dystrophin repressor YY1 (Yin Yang 1). Specifically, Sirt6 mono-ADP-ribosylates YY1, causing its disassociation from the Dystrophin promoter and allowing YY1 to bind to the SMURF2 E3 ligase, leading to its degradation. Importantly, supplementation with nicotinamide mononucleotide (NMN) enhances the mono-ADP-ribosylation of YY1 and effectively delays NMJ degeneration and the decline in motor function in elderly mice. These findings provide valuable insights into the intricate mechanisms underlying NMJ degeneration during aging. Targeting Sirt6 could be a potential therapeutic approach to mitigate the detrimental effects on NMJ degeneration and improve motor function in the elderly population.},
}
@article {pmid39394849,
year = {2024},
author = {Li, Z and Meng, M},
title = {An SCA-based classifier for motor imagery EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2414069},
pmid = {39394849},
issn = {1476-8259},
abstract = {Efficient and accurate multi-class classification of electroencephalogram (EEG) signals poses a significant challenge in the development of motor imagery-based brain-computer interface (MI-BCI). Drawing inspiration from the sine cosine algorithm (SCA), a widely employed swarm intelligence algorithm for optimization problems, we proposed a novel population-based classification algorithm for EEG signals in this article. To fully leverage the characteristics contained in EEG signals, multi-scale sub-signals were constructed in terms of temporal windows and spectral bands simultaneously, and the common spatial pattern (CSP) features were extracted from each sub-signal. Subsequently, we integrated the multi-center optimal vectors mechanism into the classical SCA, resulting in the development of a multi-center SCA (MCSCA) classifier. During the classification stage, the label was assigned to the test trials by evaluating the Euclidean distance between their feature vectors and each optimal vector in MCSCA. Additionally, the weights of feature vectors were exploited to select the sub-signal of specific temporal windows and spectral bands for feature reduction, thereby declining computational effort and eliminating data redundancy. To validate the performance of the MCSCA classifier, we conducted four-class classification experiments using the BCI Competition IV dataset 2a, achieving an average classification accuracy of 71.89%. The experimental results show that the proposed algorithm offers a novel and effective approach for EEG classification.},
}
@article {pmid39391754,
year = {2024},
author = {Salari, V and O'Connor, R and Rodrigues, S and Oblak, D},
title = {Editorial: New approaches in Brain-Machine Interfaces with implants.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1485472},
pmid = {39391754},
issn = {1662-4548},
}
@article {pmid39391265,
year = {2024},
author = {Ren, C and Li, X and Gao, Q and Pan, M and Wang, J and Yang, F and Duan, Z and Guo, P and Zhang, Y},
title = {The effect of brain-computer interface controlled functional electrical stimulation training on rehabilitation of upper limb after stroke: a systematic review and meta-analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1438095},
pmid = {39391265},
issn = {1662-5161},
abstract = {INTRODUCTION: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients.<br><br>METHODS: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18.<br><br>RESULTS: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD&#x2009;=&#x2009;0.50, 95% CI: 0.26-0.73, I[2] =&#x2009;0%, p <&#x2009;0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD&#x2009;=&#x2009;0.37, 95% CI: 0.00-0.74, I[2] =&#x2009;21%, p =&#x2009;0.05), and the BCI-FES&#x2009;+&#x2009;CR vs. CR group (SMD&#x2009;=&#x2009;0.61, 95% CI: 0.28-0.95, I[2] =&#x2009;0%, p =&#x2009;0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD&#x2009;=&#x2009;0.56, 95% CI: 0.25-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0004) and chronic groups (SMD&#x2009;=&#x2009;0.42, 95% CI: 0.05-0.78, I[2] =&#x2009;45%, p =&#x2009;0.02). Subgroup analysis showed that both adjusting (SMD&#x2009;=&#x2009;0.55, 95% CI: 0.24-0.87, I[2] =&#x2009;0%, p =&#x2009;0.0006) and fixing (SMD&#x2009;=&#x2009;0.43, 95% CI: 0.07-0.78, I[2] =&#x2009;46%, p =&#x2009;0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD&#x2009;=&#x2009;0.41 95% CI: 0.12-0.71, I[2] =&#x2009;13%, p =&#x2009;0.006) and action observation (AO) (SMD&#x2009;=&#x2009;0.73, 95% CI: 0.26-1.20, I[2] =&#x2009;0%, p =&#x2009;0.002) as mental tasks significantly improved upper limb function in stroke patients.<br><br>DISCUSSION: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy.<br><br>Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023485744.},
}
@article {pmid39391047,
year = {2024},
author = {Jang, M and Hays, M and Yu, WH and Lee, C and Caragiulo, P and Ramkaj, A and Wang, P and Phillips, AJ and Vitale, N and Tandon, P and Yan, P and Mak, PI and Chae, Y and Chichilnisky, EJ and Murmann, B and Muratore, DG},
title = {A 1024-Channel 268 nW/pixel 36×36 μm[2]/channel Data-Compressive Neural Recording IC for High-Bandwidth Brain-Computer Interfaces.},
journal = {IEEE journal of solid-state circuits},
volume = {59},
number = {4},
pages = {1123-1136},
pmid = {39391047},
issn = {0018-9200},
support = {R01 EY021271/EY/NEI NIH HHS/United States ; R01 EY032900/EY/NEI NIH HHS/United States ; },
abstract = {This paper presents a data-compressive neural recording IC for single-cell resolution high-bandwidth brain-computer interfaces. The IC features wired-OR lossy compression during digitization, thus preventing data deluge and massive data movement. By discarding unwanted baseline samples of the neural signals, the output data rate is reduced by 146× on average while allowing the reconstruction of spike samples. The recording array consists of pulse position modulation-based active digital pixels with a global single-slope analog-to-digital conversion scheme, which enables a low-power and compact pixel design with significantly simple routing and low array readout energy. Fabricated in a 28-nm CMOS process, the neural recording IC features 1024 channels (i.e., 32 × 32 array) with a pixel pitch of 36 μm that can be directly matched to a high-density microelectrode array. The pixel achieves 7.4 μVrms input-referred noise with a -3 dB bandwidth of 300-Hz to 5-kHz while consuming only 268 nW from a single 1-V supply. The IC achieves the smallest area per channel (36 × 36 μm[2]) and the highest energy efficiency among the state-of-the-art neural recording ICs published to date.},
}
@article {pmid39389052,
year = {2024},
author = {Zheng, Z and Liu, Y and Mu, R and Guo, X and Feng, Y and Guo, C and Yang, L and Qiu, W and Zhang, Q and Yang, W and Dong, Z and Qiu, S and Dong, Y and Cui, Y},
title = {A small population of stress-responsive neurons in the hypothalamus-habenula circuit mediates development of depression-like behavior in mice.},
journal = {Neuron},
volume = {112},
number = {23},
pages = {3924-3939.e5},
doi = {10.1016/j.neuron.2024.09.012},
pmid = {39389052},
issn = {1097-4199},
mesh = {Animals ; *Habenula/physiology ; *Depression ; *Stress, Psychological ; Mice ; *Neurons/physiology ; *Hypothalamus/metabolism ; Male ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Hypothalamic Area, Lateral/physiology ; },
abstract = {Accumulating evidence has shown that various brain functions are associated with experience-activated neuronal ensembles. However, whether such neuronal ensembles are engaged in the pathogenesis of stress-induced depression remains elusive. Utilizing activity-dependent viral strategies in mice, we identified a small population of stress-responsive neurons, primarily located in the middle part of the lateral hypothalamus (mLH) and the medial part of the lateral habenula (LHbM). These neurons serve as "starter cells" to transmit stress-related information and mediate the development of depression-like behaviors during chronic stress. Starter cells in the mLH and LHbM form dominant connections, which are selectively potentiated by chronic stress. Silencing these connections during chronic stress prevents the development of depression-like behaviors, whereas activating these connections directly elicits depression-like behaviors without stress experience. Collectively, our findings dissect a core functional unit within the LH-LHb circuit that mediates the development of depression-like behaviors in mice.},
}
@article {pmid39386879,
year = {2024},
author = {Huang, Y and Yang, L and Yang, L and Xu, Z and Li, M and Shang, Z},
title = {Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy.},
journal = {Heliyon},
volume = {10},
number = {19},
pages = {e38113},
pmid = {39386879},
issn = {2405-8440},
abstract = {Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.},
}
@article {pmid39385316,
year = {2024},
author = {Wang, D and Guo, X and Huang, Q and Wang, Z and Chen, J and Hu, S},
title = {Efficacy and Safety of Transcranial Direct Current Stimulation as an Add-On Trial Treatment for Acute Bipolar Depression Patients With Suicidal Ideation.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {10},
pages = {e70077},
pmid = {39385316},
issn = {1755-5949},
support = {81971271//National Natural Science Foundation of China/ ; 82201675//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province/ ; 2020KY134//Health and Family Planning Commission of Zhejiang Province/ ; },
mesh = {Humans ; *Bipolar Disorder/therapy/psychology ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; Adult ; Double-Blind Method ; *Suicidal Ideation ; Middle Aged ; Treatment Outcome ; Psychiatric Status Rating Scales ; Quetiapine Fumarate/therapeutic use ; Young Adult ; Antipsychotic Agents/therapeutic use/adverse effects ; Combined Modality Therapy/methods ; },
abstract = {AIMS: Bipolar depression poses an overwhelming suicide risk. We aimed to examine the efficacy and safety of transcranial direct current stimulation (tDCS) combined with quetiapine in bipolar patients as a suicidal intervention.<br><br>METHODS: In a single-center, double-blind, treatment-naive bipolar depression patients with suicidal ideation were randomly assigned to quetiapine in combination with either active (n&#x2009;=&#x2009;16) or sham (n&#x2009;=&#x2009;15) tDCS over the left dorsolateral prefrontal cortex for three consecutive weeks. The 30-min, 2-mA tDCS was conducted twice a day on the weekday of the first week and then once a day on the weekdays of the two following weeks. Primary efficacy outcome measure was the change in the Beck Scale for Suicidal Ideation (BSSI). Secondary outcomes included changes on the 17-item Hamilton Depression Rating Scale (HDRS-17) and Montgomery-Asberg Depression Rating Scale (MADRS). Outcome was evaluated on Day 3 and weekend. Safety outcome was based on the reported adverse reactions.<br><br>RESULTS: Active tDCS was superior to sham tDCS on the BSSI at Day 3 and tended to sustain every weekend during the treatment process, compared to baseline. However, no difference between active and sham in HDRS-17 and MADRS was found. Response and remission rate also supported the antisuicide effect of tDCS, with higher response and remission rate in BSSI, but no antidepressant effect, compared to sham, over time. Regarding safety, active tDCS was well tolerated and all the adverse reactions reported were mild and limited to transient scalp discomfort.<br><br>CONCLUSION: The tDCS was effective as an antisuicide treatment for acute bipolar depression patients with suicidal ideation, with minimal side effects reported.},
}
@article {pmid39384601,
year = {2024},
author = {Li, D and Li, K and Xia, Y and Dong, J and Lu, R},
title = {Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23549},
pmid = {39384601},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Neural Networks, Computer ; Imagination ; Brain/physiology ; },
abstract = {In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.},
}
@article {pmid39383883,
year = {2024},
author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Wagner, L and van Dijk, JP and Kubben, PL and Herff, C},
title = {Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad851c},
pmid = {39383883},
issn = {1741-2552},
mesh = {Humans ; Male ; Female ; Adult ; *Movement/physiology ; *Brain/physiology ; *Electroencephalography/methods ; Electrodes, Implanted ; Young Adult ; Hand Strength/physiology ; Psychomotor Performance/physiology ; Motor Activity/physiology ; Electrocorticography/methods ; Epilepsy/physiopathology ; Middle Aged ; },
abstract = {Objective.Motor-related neural activity is more widespread than previously thought, as pervasive brain-wide neural correlates of motor behavior have been reported in various animal species. Brain-wide movement-related neural activity have been observed in individual brain areas in humans as well, but it is unknown to what extent global patterns exist.Approach.Here, we use a decoding approach to capture and characterize brain-wide neural correlates of movement. We recorded invasive electrophysiological data from stereotactic electroencephalographic electrodes implanted in eight epilepsy patients who performed both an executed and imagined grasping task. Combined, these electrodes cover the whole brain, including deeper structures such as the hippocampus, insula and basal ganglia. We extract a low-dimensional representation and classify movement from rest trials using a Riemannian decoder.Main results.We reveal global neural dynamics that are predictive across tasks and participants. Using an ablation analysis, we demonstrate that these dynamics remain remarkably stable under loss of information. Similarly, the dynamics remain stable across participants, as we were able to predict movement across participants using transfer learning.Significance.Our results show that decodable global motor-related neural dynamics exist within a low-dimensional space. The dynamics are predictive of movement, nearly brain-wide and present in all our participants. The results broaden the scope to brain-wide investigations, and may allow combining datasets of multiple participants with varying electrode locations or calibrationless neural decoder.},
}
@article {pmid39383715,
year = {2024},
author = {Li, K and Qian, L and Zhang, C and Li, R and Zeng, J and Xue, C and Deng, W},
title = {Deep transcranial magnetic stimulation for treatment-resistant obsessive-compulsive disorder: A meta-analysis of randomized-controlled trials.},
journal = {Journal of psychiatric research},
volume = {180},
number = {},
pages = {96-102},
doi = {10.1016/j.jpsychires.2024.09.043},
pmid = {39383715},
issn = {1879-1379},
mesh = {Humans ; *Obsessive-Compulsive Disorder/therapy ; Outcome Assessment, Health Care ; *Randomized Controlled Trials as Topic ; *Transcranial Magnetic Stimulation/methods ; },
abstract = {BACKGROUND: Deep transcranial magnetic stimulation (dTMS), an advancement of transcranial magnetic stimulation, was created to reach wider and possibly more profound regions of the brain. At present, there is insufficient high-quality evidence to support the effectiveness and safety of dTMS in treating obsessive-compulsive disorder (OCD).<br><br>OBJECTIVE: This study used a meta-analysis to evaluate the effectiveness and safety of dTMS for treating OCD.<br><br>METHODS: Four randomized controlled trials were found by searching PubMed, Embase, Web of Science, and Cochrane Library up to February 2024. The fixed effects meta-analysis model was used for the purpose of data merging in Stata17. The risk ratio (RR) value was used as the measure of effect size to compare response rates and dropout rates between active and sham dTMS.<br><br>RESULTS: The meta-analysis included four randomized-controlled trials involving 252 patients with treatment-resistant OCD. Active dTMS showed a notably greater rate of response on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) in comparison to sham dTMS after treatment (Y-BOCS: RR&#xa0;=&#xa0;3.71, 95% confidence interval [CI] 2.06 to 6.69) and at the one-month follow-up (Y-BOCS: RR&#xa0;=&#xa0;2.60, 95% CI 1.59 to 4.26). Subgroup analysis revealed that active dTMS with H-coils was more effective than sham dTMS (RR&#xa0;=&#xa0;3.57, 95%CI 1.93 to 6.60). No serious adverse events were documented in the studies that were included.<br><br>CONCLUSION: The findings suggest that dTMS demonstrates notable efficacy and safety in treating patients with treatment-resistant OCD compared to sham dTMS, with sustained effectiveness noted throughout the one-month post-treatment period.},
}
@article {pmid39381774,
year = {2024},
author = {Pilacinski, A and Christ, L and Boshoff, M and Iossifidis, I and Adler, P and Miro, M and Kuhlenkötter, B and Klaes, C},
title = {Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1383089},
pmid = {39381774},
issn = {1662-5218},
abstract = {Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.},
}
@article {pmid39379912,
year = {2024},
author = {Chen, B and Dong, J and Guo, W and Li, T},
title = {Sex-specific associations between levels of high-sensitivity C-reactive protein and severity of depression: retrospective cross-sectional analysis of inpatients in China.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {667},
pmid = {39379912},
issn = {1471-244X},
support = {82230046//Key Project of the National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *C-Reactive Protein/analysis ; Retrospective Studies ; China/epidemiology ; Middle Aged ; Cross-Sectional Studies ; Adult ; *Severity of Illness Index ; Sex Factors ; *Inpatients ; Aged ; Depression/blood ; Depressive Disorder/blood/epidemiology ; },
abstract = {BACKGROUND: We aimed to clarify the controversial relationship between levels of high-sensitivity C-reactive protein (hs-CRP) and severity of depression in men and women.<br><br>METHODS: Medical records were retrospectively analyzed for 1,236 inpatients at our medical center who were diagnosed with depression at discharge between January 2018 and August 2022. Depression severity was assessed during hospitalization using the 24-item Hamilton Depression Rating Scale. Potential associations between severity scores and hs-CRP levels were explored using multivariate linear regression as well as smooth curve fitting to detect non-linear patterns.<br><br>RESULTS: In male patients, hs-CRP levels between 2.00&#xa0;mg/L and 10.00&#xa0;mg/L showed a non-linear association with depression severity overall (fully adjusted &#x3b2;&#x2009;=&#x2009;1.69, 95% CI 0.65 to 2.72), as well as with severity of specific symptoms such as hopelessness, sluggishness, and cognitive disturbance. In female patients, hs-CRP levels showed a linear association with severity of cognitive disturbance (fully adjusted &#x3b2;&#x2009;=&#x2009;0.07, 95% CI 0.01 to 0.12). These results remained significant after adjusting for age, body mass index, diabetes, hypertension, history of drinking, history of smoking, and estradiol levels.<br><br>DISCUSSION: Levels of hs-CRP show sex-specific associations with depression severity, particularly levels between 2.00 and 10.00&#xa0;mg/L in men. These findings may help develop personalized anti-inflammatory treatments for depression, particularly for men with hs-CRP levels of 2.00-10.00&#xa0;mg/L.},
}
@article {pmid39378126,
year = {2024},
author = {Li, J and Wu, W and Chen, J and Xu, Z and Yang, B and He, Q and Yang, X and Yan, H and Luo, P},
title = {Development and safety of investigational and approved drugs targeting the RAS function regulation in RAS mutant cancers.},
journal = {Toxicological sciences : an official journal of the Society of Toxicology},
volume = {202},
number = {2},
pages = {167-178},
doi = {10.1093/toxsci/kfae129},
pmid = {39378126},
issn = {1096-0929},
support = {82373968//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Neoplasms/drug therapy/genetics ; *Antineoplastic Agents/therapeutic use ; *ras Proteins/genetics/metabolism ; *Mutation ; Animals ; Drug Approval ; Signal Transduction/drug effects ; Drugs, Investigational/therapeutic use/adverse effects ; Drug Development ; },
abstract = {The RAS gene family holds a central position in controlling key cellular activities such as migration, survival, metabolism, and other vital biological processes. The activation of RAS signaling cascades is instrumental in the development of various cancers. Although several RAS inhibitors have gained approval from the US Food and Drug Administration for their substantial antitumor effects, their widespread and severe adverse reactions significantly curtail their practical usage in the clinic. Thus, there exists a pressing need for a comprehensive understanding of these adverse events, ensuring the clinical safety of RAS inhibitors through the establishment of precise management guidelines, suitable intermittent dosing schedules, and innovative combination regimens. This review centers on the evolution of RAS inhibitors in cancer therapy, delving into the common adverse effects associated with these inhibitors, their underlying mechanisms, and the potential strategies for mitigation.},
}
@article {pmid39376540,
year = {2024},
author = {Giove, F and Zuo, XN and Calhoun, VD},
title = {Editorial: Insights in brain imaging methods: 2023.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1488845},
doi = {10.3389/fnins.2024.1488845},
pmid = {39376540},
issn = {1662-4548},
}
@article {pmid39375394,
year = {2024},
author = {Katoozian, D and Hosseini-Nejad, H and Dehaqani, MA},
title = {A new approach for neural decoding by inspiring of hyperdimensional computing for implantable intra-cortical BMIs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {23291},
pmid = {39375394},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; *Macaca mulatta ; Neurons/physiology ; Male ; Humans ; },
abstract = {In the field of Brain Machine Interface (BMI), the process of translating motor intention into a machine command is denoted as decoding. However, despite recent advancements, decoding remains a formidable challenge within BMI. The utilization of current decoding algorithms in the field of BMI often involves computational complexity and requires the use of computers. This is primarily due to the reliance on mathematical models to address the decoding issue and perform subsequent output calculations. Unfortunately, computers are not feasible for implantable BMI systems due to their size and power consumption. To address this predicament, this study proposes a pioneering approach inspired by hyperdimensional computing. This approach first involves identifying the pattern of each stimulus by considering the normal firing rate distribution of each neuron. Subsequently, the newly observed firing pattern for each input is compared with the patterns detected at each moment for each neuron. The algorithm, which shares similarities with hyperdimensional computing, identifies the most similar pattern as the final output. This approach reduces the dependence on mathematical models. The efficacy of this method is assessed through the utilization of an authentic dataset acquired from the Frontal Eye Field (FEF) of two male rhesus monkeys. The output space encompasses eight possible angles. The results demonstrate an accuracy rate of 51.5% while exhibiting significantly low computational complexity, involving a mere 2050 adder operators. Furthermore, the proposed algorithm is implemented on a field-programmable gate array (FPGA) and as an ASIC designe in a standard CMOS 180 nm technology, underscoring its suitability for real-time implantable BMI applications. The implementation required only 2.3 Kbytes of RAM, occupied an area of 2.2 mm[2], and consumed 9.32 µW at a 1.8 V power supply. Consequently, the proposed solution represents an accurate, low computational complexity, hardware-friendly, and real-time approach.},
}
@article {pmid39374625,
year = {2024},
author = {Abbasi, MAA and Abbasi, HF and Yu, X and Aziz, MZ and Yih, NTJY and Fan, Z},
title = {E-SAT: An extreme learning machine based self attention approach for decoding motor imagery EEG in subject-specific tasks.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad83f4},
pmid = {39374625},
issn = {1741-2552},
abstract = {The advancements in Brain-Computer Interface (BCI) have substantially evolved people's lives by enabling direct communication between the human brain and external peripheral devices. In recent years, the integration of machine larning (ML) and deep learning (DL) models have considerably imrpoved the performances of BCIs for decoding the motor imagery (MI) tasks. However, there still exist several limitations, e.g., extensive training time and high sensitivity to noises or outliers with those existing models, which largely hinder the rapid developments of BCIs. To address such issues, this paper proposes a novel extreme learning machine (ELM) based self-attention (E-SAT) mechanism to enhance subject-specific classification performances. Specifically, for E-SAT, ELM is employed both to imrpove self-attention module generalization ability for feature extraction and to optimize the model's parameter initialization process. Meanwhile, the extracted features are also classified using ELM, and the end-to-end ELM based setup is used to evaluate E-SAT performances on different MI EEG signals. Extensive experiments with different datasets, such as BCI Competition III Dataset IV-a, IV-b and BCI Competition IV Datasets 1,2a,2b,3, are conducted to verify the effectiveness of proposed E-SAT strategy. Results show that E-SAT outperforms several state-of-the-art (SOTA) existing methods in subject-specific classification on all the datasets, with an average classification accuracy of 99.8%,99.1%,98.9%,75.8%, 90.8%, and 95.4%, being achieved for each datasets, respectively. The experimental results not only show outstanding performance of E-SAT in feature extractions, but also demonstrate that it helps achieves the best results among nine other robust ones. In addition, results in this study also demonstrate that E-SAT achieves exceptional performance in both binary and multi-class classification tasks, as well as for noisy and non-noisy datatsets. .},
}
@article {pmid39374272,
year = {2025},
author = {Lv, R and Chang, W and Yan, G and Nie, W and Zheng, L and Guo, B and Sadiq, MT},
title = {A Novel Recognition and Classification Approach for Motor Imagery Based on Spatio-Temporal Features.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {1},
pages = {210-223},
doi = {10.1109/JBHI.2024.3464550},
pmid = {39374272},
issn = {2168-2208},
support = {//National Natural Science Foundation of China/ ; //Major Science and Technology Projects of Gansu Province/ ; //Young Doctoral Program of Gansu Department of Education/ ; //Science and Technology Program of Gansu Province/ ; },
mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; Adult ; Male ; Young Adult ; Female ; Movement/physiology ; Algorithms ; Neural Networks, Computer ; },
abstract = {Motor imagery, as a paradigm of brain-computer interface, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-computer interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88.39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99.31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.},
}
@article {pmid39373084,
year = {2024},
author = {Kong, F and He, F and Chisholm, RA},
title = {High beta diversity of gaps contributes to plot-level tree diversity in a tropical forest.},
journal = {Ecology},
volume = {105},
number = {11},
pages = {e4443},
doi = {10.1002/ecy.4443},
pmid = {39373084},
issn = {1939-9170},
support = {//China Scholarship Council/ ; },
mesh = {*Biodiversity ; *Forests ; *Trees/classification ; *Tropical Climate ; Panama ; },
abstract = {Canopy gaps are widely recognized as being crucial for maintaining the diversity of forest tree communities. But empirical studies have found mixed results because the differences in diversity between individual gaps and non-gaps are often small and statistically undetectable. One overlooked factor, however, is how small individual gap versus non-gap differences may accumulate across sites and potentially have a large effect on forest diversity at the plot scale. Our study investigated sapling richness, density, and composition in 124 treefall gaps, and 200 non-gap sites in the 50-ha tropical forest plot at Barro Colorado Island (BCI), Panama. Additionally, we analyzed species accumulation curves to understand how species richness increases with increasing stem numbers. We observed that sapling richness and density were only slightly higher in gaps 7 years after formation and statistically indistinguishable from non-gaps after 12 years. However, species accumulation curves across multiple gaps were substantially higher than those across non-gaps. Species composition showed small differences between individual gaps and non-gaps but differed significantly between collections of gaps and non-gaps. Specifically, 55 species specialized in 7-year-old gaps compared with 24 in non-gaps; of these, 23 gap-specialized species and zero non-gap species were pioneers. Our results indicate that tree species richness is higher in gaps because of both higher stem density and the presence of gap-specialized species. Our study has finally provided compelling evidence to support the idea that gaps enhance the overall diversity of tropical forest tree communities.},
}
@article {pmid39372246,
year = {2024},
author = {Sakel, M and Saunders, K and Ozolins, C and Biswas, R},
title = {Feasibility and Safety of a Home-based Electroencephalogram Neurofeedback Intervention to Reduce Chronic Neuropathic Pain: A Cohort Clinical Trial.},
journal = {Archives of rehabilitation research and clinical translation},
volume = {6},
number = {3},
pages = {100361},
pmid = {39372246},
issn = {2590-1095},
abstract = {OBJECTIVE: To evaluate the feasibility, safety, and potential health benefits of an 8-week home-based neurofeedback intervention.<br><br>DESIGN: Single-group preliminary study.<br><br>SETTING: Community-based.<br><br>PARTICIPANTS: Nine community dwelling adults with chronic neuropathic pain, 6 women and 3 men, with an average age of 51.9 years (range, 19-78 years) and with a 7-day average minimum pain score of 4 of 10 on the visual analog pain scale.<br><br>INTERVENTIONS: A minimum of 5 neurofeedback sessions per week (40min/session) for 8 consecutive weeks was undertaken with a 12-week follow-up baseline electroencephalography recording period.<br><br>MAIN OUTCOME MEASURES: Primary feasibility outcomes: accessibility, tolerability, safety (adverse events and resolution), and human and information technology (IT) resources required. Secondary outcomes: pain, sensitization, catastrophization, anxiety, depression, sleep, health-related quality of life, electroencephalographic activity, and simple participant feedback.<br><br>RESULTS: Of the 23 people screened, 11 were eligible for recruitment. One withdrew and another completed insufficient sessions for analysis, which resulted in 9 datasets analyzed. Three participants withdrew from the follow-up baselines, leaving 6 who completed the entire trial protocol. Thirteen adverse events were recorded and resolved: 1 was treatment-related, 4 were equipment-related, and 8 were administrative-related (eg, courier communication issues). The human and IT resources necessary for trial implementation were identified. There were also significant improvements in pain levels, depression, and anxiety. Six of 9 participants perceived minimal improvement or no change in symptoms after the trial, and 5 of 9 participants were satisfied with the treatment received.<br><br>CONCLUSIONS: It is feasible and safe to conduct a home-based trial of a neurofeedback intervention for people with chronic neuropathic pain, when the human and IT resources are provided and relevant governance processes are followed. Improvements in secondary outcomes merit investigation with a randomized controlled trial.},
}
@article {pmid39371523,
year = {2024},
author = {Jin, W and Zhu, X and Qian, L and Wu, C and Yang, F and Zhan, D and Kang, Z and Luo, K and Meng, D and Xu, G},
title = {Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1431815},
pmid = {39371523},
issn = {1662-5188},
abstract = {Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.},
}
@article {pmid39371161,
year = {2024},
author = {Angrick, M and Luo, S and Rabbani, Q and Joshi, S and Candrea, DN and Milsap, GW and Gordon, CR and Rosenblatt, K and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Ramsey, NF and Crone, NE},
title = {Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39371161},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice.<br><br>APPROACH: In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings.<br><br>MAIN RESULTS: Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms.<br><br>SIGNIFICANCE: To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome.<br><br>CLINICAL TRIAL INFORMATION: ClinicalTrials.gov, registration number NCT03567213.},
}
@article {pmid39369803,
year = {2024},
author = {Jin, C and Li, Y and Yin, Y and Ma, T and Hong, W and Liu, Y and Li, N and Zhang, X and Gao, JH and Zhang, X and Zha, R},
title = {The dorsomedial prefrontal cortex promotes self-control by inhibiting the egocentric perspective.},
journal = {NeuroImage},
volume = {301},
number = {},
pages = {120879},
doi = {10.1016/j.neuroimage.2024.120879},
pmid = {39369803},
issn = {1095-9572},
mesh = {Humans ; *Prefrontal Cortex/physiology/diagnostic imaging ; Male ; Female ; *Self-Control ; Adult ; *Transcranial Direct Current Stimulation ; Young Adult ; *Connectome ; Magnetic Resonance Imaging ; Reward ; Delay Discounting/physiology ; },
abstract = {The dorsomedial prefrontal cortex (dmPFC) plays a crucial role in social cognitive functions, including perspective-taking. Although perspective-taking has been linked to self-control, the mechanism by which the dmPFC might facilitate self-control remains unclear. Using the multimodal neuroimaging dataset from the Human Connectome Project (Study 1, N =978 adults), we established a reliable association between the dmPFC and self-control, as measured by discounting rate-the tendency to prefer smaller, immediate rewards over larger, delayed ones. Experiments (Study 2, N = 36 adults) involving high-definition transcranial direct current stimulation showed that anodal stimulation of the dmPFC reduces the discounting of delayed rewards and decreases the congruency effect in egocentric but not allocentric perspective in the visual perspective-taking tasks. These findings suggest that the dmPFC promotes self-control by inhibiting the egocentric perspective, offering new insights into the neural underpinnings of self-control and perspective-taking, and opening new avenues for interventions targeting disorders characterized by impaired self-regulation.},
}
@article {pmid39369514,
year = {2025},
author = {Wang, Y and Han, M and Jing, L and Jia, Q and Lv, S and Xu, Z and Liu, J and Cai, X},
title = {Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel.},
journal = {Biosensors &amp; bioelectronics},
volume = {267},
number = {},
pages = {116837},
doi = {10.1016/j.bios.2024.116837},
pmid = {39369514},
issn = {1873-4235},
mesh = {*Alginates/chemistry ; *Chitosan/chemistry ; *Dexamethasone/pharmacology/chemistry/administration &amp; dosage/analogs &amp; derivatives ; *Microelectrodes ; Animals ; *Hydrogels/chemistry ; *Biosensing Techniques/instrumentation ; *Neurons/drug effects/physiology ; Anti-Inflammatory Agents/pharmacology/chemistry ; Dopamine/chemistry/pharmacology/analysis ; Brain/drug effects/physiology ; Metal Nanoparticles/chemistry ; Platinum/chemistry ; Rats ; },
abstract = {Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.},
}
@article {pmid39369011,
year = {2024},
author = {Peng, Z and Tong, L and Shi, W and Xu, L and Huang, X and Li, Z and Yu, X and Meng, X and He, X and Lv, S and Yang, G and Hao, H and Jiang, T and Miao, X and Ye, L},
title = {Multifunctional human visual pathway-replicated hardware based on 2D materials.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {8650},
pmid = {39369011},
issn = {2041-1723},
support = {62222404, 62304084 and 92248304//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Visual Pathways/physiology ; *Retina/physiology ; *Brain-Computer Interfaces ; Visual Cortex/physiology ; Tungsten/chemistry ; Robotics/instrumentation ; Selenium/chemistry ; Artificial Intelligence ; },
abstract = {Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red-green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain-computer interfaces, and intelligent robotics.},
}
@article {pmid39368632,
year = {2024},
author = {Hu, J and Chen, C and Wu, M and Zhang, J and Meng, F and Li, T and Luo, B},
title = {Assessing consciousness in acute coma using name-evoked responses.},
journal = {Brain research bulletin},
volume = {218},
number = {},
pages = {111091},
doi = {10.1016/j.brainresbull.2024.111091},
pmid = {39368632},
issn = {1873-2747},
mesh = {Humans ; Male ; Female ; *Coma/physiopathology ; *Electroencephalography/methods ; Adult ; Middle Aged ; *Electromyography/methods ; *Acoustic Stimulation/methods ; *Consciousness/physiology ; Aged ; Glasgow Coma Scale ; Names ; Brain/physiopathology ; Young Adult ; Evoked Potentials/physiology ; Evoked Potentials, Auditory/physiology ; },
abstract = {Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.},
}
@article {pmid39368606,
year = {2024},
author = {Takemi, M and Tia, B and Kosugi, A and Castagnola, E and Ansaldo, A and Ricci, D and Fadiga, L and Ushiba, J and Iriki, A},
title = {Posture-dependent modulation of marmoset cortical motor maps detected via rapid multichannel epidural stimulation.},
journal = {Neuroscience},
volume = {560},
number = {},
pages = {263-271},
doi = {10.1016/j.neuroscience.2024.09.047},
pmid = {39368606},
issn = {1873-7544},
mesh = {Animals ; *Callithrix ; *Motor Cortex/physiology ; Male ; *Posture/physiology ; *Forelimb/physiology ; *Brain Mapping/methods ; *Electric Stimulation/methods ; Electrodes, Implanted ; Electromyography ; Muscle, Skeletal/physiology ; Epidural Space/physiology ; },
abstract = {Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state.},
}
@article {pmid39366386,
year = {2025},
author = {Zhang, L and Wang, HL and Zhang, YF and Mao, XT and Wu, TT and Huang, ZH and Jiang, WJ and Fan, KQ and Liu, DD and Yang, B and Zhuang, MH and Huang, GM and Liang, Y and Zhu, SJ and Zhong, JY and Xu, GY and Li, XM and Cao, Q and Li, YY and Jin, J},
title = {Stress triggers irritable bowel syndrome with diarrhea through a spermidine-mediated decline in type I interferon.},
journal = {Cell metabolism},
volume = {37},
number = {1},
pages = {87-103.e10},
doi = {10.1016/j.cmet.2024.09.002},
pmid = {39366386},
issn = {1932-7420},
mesh = {*Irritable Bowel Syndrome/metabolism ; *Diarrhea/metabolism ; *Spermidine/pharmacology/metabolism ; Animals ; Humans ; Male ; Stress, Psychological/complications/metabolism ; Mice, Inbred C57BL ; Interferon Type I/metabolism ; Mice ; Dendritic Cells/metabolism ; },
abstract = {Irritable bowel syndrome with diarrhea (IBS-D) is a common and chronic gastrointestinal disorder that is characterized by abdominal discomfort and occasional diarrhea. The pathogenesis of IBS-D is thought to be related to a combination of factors, including psychological stress, abnormal muscle contractions, and inflammation and disorder of the gut microbiome. However, there is still a lack of comprehensive analysis of the logical regulatory correlation among these factors. In this study, we found that stress induced hyperproduction of xanthine and altered the abundance and metabolic characteristics of Lactobacillus murinus in the gut. Lactobacillus murinus-derived spermidine suppressed the basal expression of type I interferon (IFN)-α in plasmacytoid dendritic cells by inhibiting the K63-linked polyubiquitination of TRAF3. The reduction in IFN-α unrestricted the contractile function of colonic smooth muscle cells, resulting in an increase in bowel movement. Our findings provided a theoretical basis for the pathological mechanism of, and new drug targets for, stress-exposed IBS-D.},
}
@article {pmid39366088,
year = {2024},
author = {Pan, Y and Sequestro, M and Golkar, A and Olsson, A},
title = {Handholding reduces the recovery of threat memories and magnifies prefrontal hemodynamic responses.},
journal = {Behaviour research and therapy},
volume = {183},
number = {},
pages = {104641},
doi = {10.1016/j.brat.2024.104641},
pmid = {39366088},
issn = {1873-622X},
mesh = {Humans ; *Prefrontal Cortex/physiology ; Male ; Female ; Young Adult ; *Hemodynamics/physiology ; *Extinction, Psychological/physiology ; Adult ; *Memory/physiology ; Fear/psychology/physiology ; Magnetic Resonance Imaging ; Touch/physiology ; Adolescent ; },
abstract = {Human touch is a powerful means of social and affective regulation, promoting safety behaviors. Yet, despite its importance across human contexts, it remains unknown how touch can promote the learning of new safety memories and what neural processes underlie such effects. The current study used measures of peripheral physiology and brain activity to examine the effects of interpersonal touch during safety learning (extinction) on the recovery of previously learned threat. We observed that handholding during extinction significantly reduced threat recovery, which was reflected in enhanced prefrontal hemodynamic responses. This effect was absent when learners were instructed to hold a rubber ball, independent of the presence of their partners. Our findings indicate that social touch contributes to safety learning, potentially influencing threat memories via prefrontal circuitry.},
}
@article {pmid39367153,
year = {2024},
author = {Miroshnikov, A and Yakovlev, L and Syrov, N and Vasilyev, A and Berkmush-Antipova, A and Golovanov, F and Kaplan, A},
title = {Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping.},
journal = {Brain topography},
volume = {38},
number = {1},
pages = {4},
pmid = {39367153},
issn = {1573-6792},
support = {21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; 21-75-30024//Russian Science Foundation/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Imagination/physiology ; Male ; Female ; Adult ; *Hemodynamics/physiology ; Young Adult ; *Brain Mapping/methods ; Touch Perception/physiology ; Touch/physiology ; Somatosensory Cortex/physiology/diagnostic imaging ; Brain/physiology/diagnostic imaging ; Motor Cortex/physiology/diagnostic imaging ; },
abstract = {Tactile and motor imagery are crucial components of sensorimotor functioning and cognitive neuroscience research, yet the neural mechanisms of tactile imagery remain underexplored compared to motor imagery. This study employs multichannel functional near-infrared spectroscopy (fNIRS) combined with image reconstruction techniques to investigate the neural hemodynamics associated with tactile (TI) and motor imagery (MI). In a study of 15 healthy participants, we found that MI elicited significantly greater hemodynamic responses (HRs) in the precentral area compared to TI, suggesting the involvement of different cortical areas involved in two different types of sensorimotor mental imagery. Concurrently, the HRs in S1 and parietal areas exhibited comparable patterns in both TI and MI. During MI, both motor and somatosensory areas demonstrated comparable HRs. However, in TI, somatosensory activation was observed to be more pronounced. Our results highlight the distinctive neural profiles of motor versus tactile imagery and indicate fNIRS technique to be sensitive for this. This distinction is significant for fundamental understanding of sensorimotor integration and for developing advanced neurotechnologies, including imagery-based brain-computer interfaces (BCIs) that can differentiate between different types of mental imagery.},
}
@article {pmid39366955,
year = {2024},
author = {Ullah, R and Xue, C and Wang, S and Qin, Z and Rauf, N and Zhan, S and Khan, NU and Shen, Y and Zhou, YD and Fu, J},
title = {Alternate-day fasting delays pubertal development in normal-weight mice but prevents high-fat diet-induced obesity and precocious puberty.},
journal = {Nutrition &amp; diabetes},
volume = {14},
number = {1},
pages = {82},
pmid = {39366955},
issn = {2044-4052},
support = {82350410491//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82370863//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Diet, High-Fat/adverse effects ; *Puberty, Precocious/etiology/prevention &amp; control ; Female ; Mice ; *Fasting ; *Obesity/prevention &amp; control/etiology ; Sexual Maturation/physiology ; Pregnancy ; Mice, Inbred C57BL ; Growth Hormone/blood ; },
abstract = {BACKGROUND/OBJECTIVES: Childhood obesity, particularly in girls, is linked to early puberty onset, heightening risks for adult-onset diseases. Addressing childhood obesity and precocious puberty is vital to mitigate societal burdens. Despite existing costly and invasive medical interventions, introducing lifestyle-based alternatives is essential. Our study investigates alternate-day fasting's (ADF) impact on pubertal development in normal-weight and high-fat diet (HFD)-induced obese female mice.<br><br>METHODS: Four groups of female mice were utilized, with dams initially fed control chow during and before pregnancy. Post-parturition, two groups continued on control chow, while two switched to an HFD. Offspring diets mirrored maternal exposure. One control and one HFD group were subjected to ADF. Morphometry and hormone analyses at various time points were performed.<br><br>RESULTS: Our findings demonstrate that ADF in normal-weight mice led to reduced body length, weight, uterine, and ovarian weights, accompanied by delayed puberty and lower levels of sex hormones and growth hormone (GH). Remarkably, GH treatment effectively prevented ADF-induced growth reduction but did not prevent delayed puberty. Conversely, an HFD increased body length, induced obesity and precocious puberty, and altered sex hormones and leptin levels, which were counteracted by ADF regimen. Our data indicate ADF's potential in managing childhood obesity and precocious puberty.<br><br>CONCLUSIONS: ADF reduced GH and sex hormone levels, contributing to reduced growth and delayed puberty, respectively. Therefore, parents of normal-weight children should be cautious about prolonged overnight fasting. ADF prevented HFD-induced obesity and precocious puberty, offering an alternative to medical approaches; nevertheless, further studies are needed for translation into clinical practice.},
}
@article {pmid39365711,
year = {2025},
author = {Wang, Y and Wang, J and Wang, W and Su, J and Bunterngchit, C and Hou, ZG},
title = {TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {2},
pages = {810-821},
doi = {10.1109/TBME.2024.3474049},
pmid = {39365711},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Adult ; Male ; Databases, Factual ; Young Adult ; Female ; },
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject &amp; cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.<br><br>METHODS: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.<br><br>RESULTS: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p = 2.4e-5 < 0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.<br><br>CONCLUSION/SIGNIFICANCE: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.},
}
@article {pmid39362975,
year = {2024},
author = {Yu, H and Cao, W and Fang, T and Jin, J and Pei, G},
title = {EEG β oscillations in aberrant data perception under cognitive load modulation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22995},
pmid = {39362975},
issn = {2045-2322},
support = {LGG21G010002//Zhejiang Provincial Natural Science Foundation of China/ ; LQ22C090007//Zhejiang Provincial Natural Science Foundation of China/ ; 72271166//National Natural Science Foundation of China/ ; 72401263//National Natural Science Foundation of China/ ; 2023KFKT003//Open Research Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University/ ; },
mesh = {Humans ; Male ; *Cognition/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Decision Making/physiology ; Beta Rhythm/physiology ; Brain/physiology ; Perception/physiology ; },
abstract = {Data-driven decision making (DDDM) is becoming an indispensable component of work across various fields, and the perception of aberrant data (PAD) has emerged as an essential skill. Nonetheless, the neural processing mechanisms underpinning PAD remain incompletely elucidated. Direct evidence linking neural oscillations to PAD is currently lacking, and the impact of cognitive load remains ambiguous. We address this issue using EEG time-frequency analysis. Data were collected from 21 healthy participants. The experiment employed a 2 (low vs. high cognitive load) × 2 [PAD+ (aberrant data accurately identified as aberrant) vs. PAD- (non-aberrant data correctly recognized as normal)] within-subject laboratory design. Results indicate that upper β band oscillations (26-30 Hz) were significantly enhanced in the PAD + condition compared to PAD-, with consistent activity observed in the frontal (p < 0.001, [Formula: see text] = 0.41) and parietal lobes (p = 0.028, [Formula: see text] = 0.22) within the 300-350 ms time window. Additionally, as cognitive load increased, the time window of β oscillations for distinguishing PAD+ from PAD- shifted earlier. This study enriches our understanding of the PAD neural basis by exploring the distribution of neural oscillation frequencies, decision-making neural circuits, and the windowing effect induced by cognitive load. These findings have significant implications for elucidating the pathological mechanisms of neurodegenerative disorders, as well as in the initial screening, intervention, and treatment of diseases.},
}
@article {pmid39358539,
year = {2024},
author = {Drew, L},
title = {United States sets the pace for implantable brain-computer interfaces.},
journal = {Nature},
volume = {634},
number = {8032},
pages = {S8-S10},
doi = {10.1038/d41586-024-03046-5},
pmid = {39358539},
issn = {1476-4687},
mesh = {Humans ; *Brain-Computer Interfaces/statistics &amp; numerical data/trends ; *Electrodes, Implanted/statistics &amp; numerical data/trends ; United States ; },
}
@article {pmid39358411,
year = {2024},
author = {Andreu-Sánchez, C and Martín-Pascual, M&#xc1; and Gruart, A and Delgado-García, JM},
title = {Differences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22921},
pmid = {39358411},
issn = {2045-2322},
support = {PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2021-122446NB-100//Ministerio de Ciencia e Innovaci&#xf3;n/ ; BIO-122//Junta de Andaluc&#xed;a/ ; BIO-122//Junta de Andaluc&#xed;a/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Electroencephalography/methods ; *Somatosensory Cortex/physiology ; *Hand Strength/physiology ; Young Adult ; Psychomotor Performance/physiology ; Photic Stimulation ; Brain Waves/physiology ; Visual Perception/physiology ; Brain-Computer Interfaces ; Motor Activity/physiology ; },
abstract = {Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance. We recorded subjects' brain activities with electroencephalographic (EEG) procedures, and analyzed Mu rhythm present in left (C3) and right (C4) somatosensory areas in relation to the 24 motor activities included in each visual stimulus (screen vs. reality) (24 motor and grasping actions x 40 participants x 2 conditions = 1920 trials). We found lower Mu spectral power in the somatosensory area after the onset of the motor actions in real performance than on-screened content, more pronounced in the left hemisphere. In our results, the sensorimotor Mu-ERD (event-related desynchronization) was stronger during the real-world observation compared to screen observation. This could be relevant in research areas where the somatosensory cortex is important, such as online learning, virtual reality, or brain-computer interfaces.},
}
@article {pmid39358021,
year = {2024},
author = {Graczyk, E and Hutchison, B and Valle, G and Bjanes, D and Gates, D and Raspopovic, S and Gaunt, R},
title = {Clinical Applications and Future Translation of Somatosensory Neuroprostheses.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {40},
pages = {},
pmid = {39358021},
issn = {1529-2401},
mesh = {Humans ; *Somatosensory Cortex/physiology ; Feedback, Sensory/physiology ; Translational Research, Biomedical/trends/methods ; Neural Prostheses ; Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Prostheses and Implants/trends ; },
abstract = {Somatosensory neuroprostheses restore, replace, or enhance tactile and proprioceptive feedback for people with sensory impairments due to neurological disorders or injury. Somatosensory neuroprostheses typically couple sensor inputs from a wearable device, prosthesis, robotic device, or virtual reality system with electrical stimulation applied to the somatosensory nervous system via noninvasive or implanted interfaces. While prior research has mainly focused on technology development and proof-of-concept studies, recent acceleration of clinical studies in this area demonstrates the translational potential of somatosensory neuroprosthetic systems. In this review, we provide an overview of neurostimulation approaches currently undergoing human testing and summarize recent clinical findings on the perceptual, functional, and psychological impact of somatosensory neuroprostheses. We also cover current work toward the development of advanced stimulation paradigms to produce more natural and informative sensory feedback. Finally, we provide our perspective on the remaining challenges that need to be addressed prior to translation of somatosensory neuroprostheses.},
}
@article {pmid39353205,
year = {2024},
author = {Zhang, Y and Xing, H and Li, J and Han, F and Fan, S and Zhang, Y},
title = {Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.},
journal = {ACS sensors},
volume = {9},
number = {10},
pages = {5312-5321},
doi = {10.1021/acssensors.4c01568},
pmid = {39353205},
issn = {2379-3694},
mesh = {*Fibroins/chemistry ; Humans ; Wearable Electronic Devices ; Nociceptors/physiology ; Biomimetic Materials/chemistry ; Biomimetics/instrumentation/methods ; Robotics/instrumentation ; },
abstract = {With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.},
}
@article {pmid39352826,
year = {2025},
author = {Jin, J and Chen, W and Xu, R and Liang, W and Wu, X and He, X and Wang, X and Cichocki, A},
title = {Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {1},
pages = {198-209},
doi = {10.1109/JBHI.2024.3472097},
pmid = {39352826},
issn = {2168-2208},
support = {//National Natural Science Foundation of China/ ; //Shanghai Municipal Science and Technology Major Project/ ; //Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83.62% and 89.26%, respectively. On the laboratory dataset, MSTFNet achieves 86.68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66.31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.},
}
@article {pmid39356668,
year = {2024},
author = {Simony, E and Grossman, S and Malach, R},
title = {Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {41},
pages = {e2319709121},
pmid = {39356668},
issn = {1091-6490},
mesh = {*Brain/physiology ; *Biological Evolution ; *Models, Neurological ; Humans ; Entorhinal Cortex/physiology ; Animals ; Neurons/physiology ; Neural Networks, Computer ; Nerve Net/physiology ; },
abstract = {Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.},
}
@article {pmid39355672,
year = {2024},
author = {Smith, K and Pilger, A and Amorim, MLM and Mircic, S and Reining, Z and Ristow, N and Miller, D and Leonhardt, A and Donovan, JC and Meier, M and Marzullo, TC and Serbe-Kamp, E and Steiner, AP and Gage, GJ},
title = {Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.},
journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience},
volume = {22},
number = {3},
pages = {A197-A206},
pmid = {39355672},
issn = {1544-2896},
abstract = {Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.},
}
@article {pmid39355516,
year = {2024},
author = {von Groll, VG and Leeuwis, N and Rimbert, S and Roc, A and Pillette, L and Lotte, F and Alimardani, M},
title = {Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {11},
number = {3},
pages = {87-97},
pmid = {39355516},
issn = {2326-263X},
abstract = {The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance.},
}
@article {pmid39354145,
year = {2024},
author = {Lian, YN and Cao, XW and Wu, C and Pei, CY and Liu, L and Zhang, C and Li, XY},
title = {Deconstruction the feedforward inhibition changes in the layer III of anterior cingulate cortex after peripheral nerve injury.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {1237},
pmid = {39354145},
issn = {2399-3642},
mesh = {Animals ; *Gyrus Cinguli/physiopathology ; *Peripheral Nerve Injuries/physiopathology ; Mice ; Male ; Mice, Inbred C57BL ; Neural Inhibition ; Neurons/physiology ; Peroneal Nerve/injuries/physiopathology ; Thalamus/physiopathology ; },
abstract = {The anterior cingulate cortex (ACC) is one of the critical brain areas for processing noxious information. Previous studies showed that peripheral nerve injury induced broad changes in the ACC, contributing to pain hypersensitivity. The neurons in layer 3 (L3) of the ACC receive the inputs from the mediodorsal thalamus (MD) and form the feedforward inhibition (FFI) microcircuits. The effects of peripheral nerve injury on the MD-driven FFI in L3 of ACC are unknown. In our study, we record the enhanced excitatory synaptic transmissions from the MD to L3 of the ACC in mice with common peroneal nerve ligation, affecting FFI. Chemogenetically activating the MD-to-ACC projections induces pain sensitivity and place aversion in naive mice. Furthermore, chemogenetically inactivating MD-to-ACC projections decreases pain sensitivity and promotes place preference in nerve-injured mice. Our results indicate that the peripheral nerve injury changes the MD-to-ACC projections, contributing to pain hypersensitivity and aversion.},
}
@article {pmid39352734,
year = {2024},
author = {Gao, Y and Cai, YC and Liu, DY and Yu, J and Wang, J and Li, M and Xu, B and Wang, T and Chen, G and Northoff, G and Bai, R and Song, XM},
title = {GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {39352734},
issn = {2050-084X},
support = {2021ZD0200401//STI 2030 - Major Projects/ ; 2022ZD0206000//STI 2030 - Major Projects/ ; 61876222//The National Natural Science Foundation of China/ ; 82222032//The National Natural Science Foundation of China/ ; U1909205//The National Natural Science Foundation of China/ ; 785907//Horizon 2020 Framework Programme/ ; 20YJC880095//Humanities and Social Sciences Ministry of Education/ ; 18YJA190001//Humanities and Social Sciences Ministry of Education/ ; 2022C03096//The Key R&amp;D Program of Zhejiang/ ; 2022ZJJH02-06//The Key R&amp;D Program of Zhejiang/ ; 32000761//The National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Frontal Lobe/physiology/diagnostic imaging ; Male ; *gamma-Aminobutyric Acid/metabolism ; *Intelligence/physiology ; Female ; Young Adult ; *Magnetic Resonance Imaging ; Adult ; Magnetic Resonance Spectroscopy/methods ; Space Perception/physiology ; },
abstract = {The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.},
}
@article {pmid39351695,
year = {2024},
author = {Wang, PS and Yang, XX and Wei, Q and Lv, YT and Wu, ZY and Li, HF},
title = {Clinical characterization and founder effect analysis in Chinese amyotrophic lateral sclerosis patients with SOD1 common variants.},
journal = {Annals of medicine},
volume = {56},
number = {1},
pages = {2407522},
pmid = {39351695},
issn = {1365-2060},
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Age of Onset ; *Amyotrophic Lateral Sclerosis/genetics ; China/epidemiology ; East Asian People ; Exome Sequencing ; *Founder Effect ; Genetic Association Studies ; Haplotypes ; Mutation ; Phenotype ; *Superoxide Dismutase-1/genetics ; },
abstract = {OBJECTIVE: In the Asian population, SOD1 variants are the most common cause of amyotrophic lateral sclerosis (ALS). To date, more than 200 variants have been reported in SOD1. This study aimed to summarize the genotype-phenotype correlation and determine whether the patients carrying common variants derive from a common ancestor.<br><br>METHODS: A total of 103 sporadic ALS (SALS) and 11 familial ALS (FALS) probands were included and variants were screened by whole exome sequencing. Functional analyses were performed on fibroblasts derived from patients with SOD1 p.V48A and control. Haplotype analysis was performed in the probands with p.H47R or p.V48A and their familial members.<br><br>RESULTS: A total of 25 SOD1 variants were identified in 44 probands, in which p.H47R, p.V48A and p.C112Y variants were the most common variants. 94.3% and 60% of patients with p.H47R or p.V48A had lower limb onset with predominant lower motor neurons (LMNs) involvement. Patients with p.H47R had a slow progression and prolonged survival time, while patients with p.V48A exhibited a duration of 2-5&#xa0;years. Patients with p.C112Y variant showed remarkable phenotypic variation in age at onset and disease course. SOD1[V48A] fibroblasts showed mutant SOD1 aggregate formation, enhanced intracellular reactive oxygen species level, and decreased mitochondrial membrane potential compared to the control fibroblast. Haplotype analysis showed that seven families had two different haplotypes. p.H47R and p.V48A variants did not originate from a common founder.<br><br>CONCLUSIONS: Our study expanded the understanding of the genotype-phenotype correlation of ALS with SOD1 variants and revealed that the common p.H47R or p.V48A variant did not have a founder effect.},
}
@article {pmid39350409,
year = {2024},
author = {Pang, M and Yao, H and Bao, K and Xu, R and Xi, R and Peng, R and Zhi, H and Zhang, K and He, R and Du, Y and Su, Y and Liu, X and Ming, D},
title = {Phenolic Glycoside Monomer from Reed Rhizome Inhibits Melanin Production via PI3K-Akt and Ras-Raf-MEK-ERK Pathways.},
journal = {Current medicinal chemistry},
volume = {},
number = {},
pages = {},
doi = {10.2174/0109298673341645240919072455},
pmid = {39350409},
issn = {1875-533X},
abstract = {INTRODUCTION: Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders.<br><br>METHOD: In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-&#x3b2;-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% &#xb1; 14.00% and tyrosinase in vivo by 48.26% &#xb1; 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5.<br><br>RESULTS: Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis.<br><br>CONCLUSION: These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation.},
}
@article {pmid39350194,
year = {2024},
author = {Du, YC and Ma, LH and Li, QF and Ma, Y and Dong, Y and Wu, ZY},
title = {Genotype-phenotype correlation and founder effect analysis in southeast Chinese patients with sialidosis type I.},
journal = {Orphanet journal of rare diseases},
volume = {19},
number = {1},
pages = {362},
pmid = {39350194},
issn = {1750-1172},
support = {82230062, 82071260//National Natural Science Foundation of China/ ; },
mesh = {Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; China/epidemiology ; East Asian People ; *Founder Effect ; Genetic Association Studies ; Genotype ; Haplotypes ; *Mucolipidoses/genetics ; Mutation ; Neuraminidase/genetics ; Polymorphism, Single Nucleotide ; },
abstract = {BACKGROUND: Sialidosis type 1 (ST-1) is a rare autosomal recessive disorder caused by mutation in the NEU1 gene. However, limited reports on ST-1 patients in the Chinese mainland are available.<br><br>METHODS: This study reported the genetic and clinical characteristics of 10 ST-1 patients from southeastern China. A haplotype analysis was performed using 21 single nucleotide polymorphism (SNP) markers of 500&#xa0;kb flanking the recurrent c.544&#xa0;A&#x2009;>&#x2009;G in 8 families harboring the mutation. Furthermore, this study summarized and compared previously reported ST-1 patients from Taiwan and mainland China.<br><br>RESULTS: Five mutations within NEU1 were found, including two novel ones c.557&#xa0;A&#x2009;>&#x2009;G and c.799&#xa0;C&#x2009;>&#x2009;T. The c.544&#xa0;A&#x2009;>&#x2009;G mutation was most frequent and identified in 9 patients, 6 patients were homozygous for c.544&#xa0;A&#x2009;>&#x2009;G. Haplotype analysis revealed a shared haplotype surrounding c.544&#xa0;A&#x2009;>&#x2009;G was identified, suggesting a founder effect presenting in southeast Chinese population. Through detailed assessment, 52 ST-1 patients from 45 families from Taiwan and mainland China were included. Homozygous c.544&#xa0;A&#x2009;>&#x2009;G was the most common genotype and found in 42.2% of the families, followed by the c.544&#xa0;A&#x2009;>&#x2009;G/c.239&#xa0;C&#x2009;>&#x2009;T compound genotype, which was observed in 22.2% of the families. ST-1 patients with the homozygous c.544&#xa0;A&#x2009;>&#x2009;G mutation developed the disease at a later age and had a lower incidence of cherry-red spots significantly.<br><br>CONCLUSION: The results contribute to gaps in the clinical and genetic features of ST-1 patients in southeastern mainland China and provide a deeper understanding of this disease to reduce misdiagnosis.},
}
@article {pmid39349841,
year = {2024},
author = {Fan, J and Gao, Z},
title = {Promoting glymphatic flow: A non-invasive strategy using 40 Hz light flickering.},
journal = {Purinergic signalling},
volume = {},
number = {},
pages = {},
pmid = {39349841},
issn = {1573-9546},
support = {2070974//National Natural Science Foundation of China/ ; 2070974//National Natural Science Foundation of China/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; 2021R52021//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {The glymphatic system is critical for brain homeostasis by eliminating metabolic waste, whose disturbance contributes to the accumulation of pathogenic proteins in neurodegenerative diseases. Promoting glymphatic clearance is a potential and attractive strategy for several brain disorders, including neurodegenerative diseases. Previous studies have uncovered that 40 Hz flickering augmented glymphatic flow and facilitated sleep (Zhou et al. in Cell Res 34:214-231, 2024) since sleep drives waste clearance via glymphatic flow (Xie et al. in Science 342:373-377, 2013). However, it remains unclear whether 40 Hz light flickering directly increased glymphatic flow or indirectly by promoting sleep. A recent article published in Cell Discovery by Chen et al. (Sun et al. in Cell Discov 10:81, 2024) revealed that 40 Hz light flickering facilitated glymphatic flow, by promoting the polarization of astrocytic aquaporin-4 (AQP4) and vasomotion through upregulated adenosine-A2A receptor (A2AR) signaling, independent of sleep. These findings suggest that 40 Hz light flickering may be used as a non-invasive approach to control the function of the glymphatic-lymphatic system, to help remove metabolic waste in the brain, thereby presenting a potential strategy for neurodegenerative disease treatment.},
}
@article {pmid39349588,
year = {2024},
author = {Chen, X and Cao, L and Haendel, BF},
title = {Right visual field advantage in orientation discrimination is influenced by biased suppression.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {22687},
pmid = {39349588},
issn = {2045-2322},
support = {201908060012//China Scholarship Council/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China/ ; 32271078//National Natural Science Foundation of China/ ; 677819/ERC_/European Research Council/International ; },
mesh = {Humans ; *Visual Fields/physiology ; Male ; Female ; Adult ; *Reaction Time/physiology ; *Electroencephalography/methods ; Young Adult ; Orientation/physiology ; Orientation, Spatial/physiology ; Evoked Potentials/physiology ; Photic Stimulation ; Visual Perception/physiology ; Functional Laterality/physiology ; Walking/physiology ; Attention/physiology ; },
abstract = {Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.},
}
@article {pmid39349502,
year = {2024},
author = {Fan, Y and Tao, Y and Wang, J and Gao, Y and Wei, W and Zheng, C and Zhang, X and Song, XM and Northoff, G},
title = {Irregularity of visual motion perception and negative symptoms in schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {10},
number = {1},
pages = {82},
pmid = {39349502},
issn = {2754-6993},
support = {LR23E070001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 82001410//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.},
}
@article {pmid39347924,
year = {2024},
author = {Yan, ZN and Liu, PR and Zhou, H and Zhang, JY and Liu, SX and Xie, Y and Wang, HL and Yu, JB and Zhou, Y and Ni, CM and Huang, L and Ye, ZW},
title = {Brain-computer Interaction in the Smart Era.},
journal = {Current medical science},
volume = {44},
number = {6},
pages = {1123-1131},
pmid = {39347924},
issn = {2523-899X},
mesh = {Humans ; Artificial Intelligence ; *Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Machine Learning ; },
abstract = {The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.},
}
@article {pmid39346532,
year = {2024},
author = {Zou, J and Chen, H and Chen, X and Lin, Z and Yang, Q and Tie, C and Wang, H and Niu, L and Guo, Y and Zheng, H},
title = {Noninvasive closed-loop acoustic brain-computer interface for seizure control.},
journal = {Theranostics},
volume = {14},
number = {15},
pages = {5965-5981},
pmid = {39346532},
issn = {1838-7640},
mesh = {Animals ; *Brain-Computer Interfaces ; Rats ; *Seizures/physiopathology/therapy ; *Electroencephalography/methods ; Rats, Sprague-Dawley ; Vagus Nerve Stimulation/methods ; Disease Models, Animal ; Male ; Hippocampus/physiopathology ; Vagus Nerve/physiology ; Epilepsy/therapy/physiopathology ; Brain/physiopathology/physiology ; },
abstract = {Rationale: The brain-computer interface (BCI) is core tasks in comprehensively understanding the brain, and is one of the most significant challenges in neuroscience. The development of novel non-invasive neuromodulation technique will drive major innovations and breakthroughs in the field of BCI. Methods: We develop a new noninvasive closed-loop acoustic brain-computer interface (aBCI) for decoding the seizure onset based on the electroencephalography and triggering ultrasound stimulation of the vagus nerve to terminate seizures. Firstly, we create the aBCI system and decode the onset of seizure via a multi-level threshold model based on the analysis of wireless-collected electroencephalogram (EEG) signals recorded from above the hippocampus. Then, the different acoustic parameters induced acoustic radiation force were used to stimulate the vagus nerve in a rat model of epilepsy-induced by pentylenetetrazole. Finally, the results of epileptic EEG signal triggering ultrasound stimulation of the vagus nerve to control seizures. In addition, the mechanism of aBCI control seizures were investigated by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In a rat model of epilepsy, the aBCI system selectively actives mechanosensitive neurons in the nodose ganglion while suppressing neuronal excitability in the hippocampus and amygdala, and stops seizures rapidly upon ultrasound stimulation of the vagus nerve. Physical transection or chemical blockade of the vagus nerve pathway abolish the antiepileptic effects of aBCI. In addition, aBCI shows significant antiepileptic effects compared to conventional vagus nerve electrical stimulation in an acute experiment. Conclusions: Closed-loop aBCI provides a novel, safe and effective tool for on-demand stimulation to treat abnormal neuronal discharges, opening the door to next generation non-invasive BCI.},
}
@article {pmid39346088,
year = {2024},
author = {Zhu, L and Zhang, Q and Ni, K and Yang, XJ and Jin, K and Wei, W and Preece, DA and Li, BM and Cai, XL},
title = {Assessing Emotion Regulation Difficulties Across Negative and Positive Emotions: Psychometric Properties and Clinical Applications of the Perth Emotion Regulation Competency Inventory in the Chinese Context.},
journal = {Psychology research and behavior management},
volume = {17},
number = {},
pages = {3299-3311},
pmid = {39346088},
issn = {1179-1578},
abstract = {BACKGROUND: Abnormalities of regulating positive and negative emotion have been documented in patients with mental disorders. Valid and reliable psychological instruments for measuring emotion regulation across different valences are needed. The Perth Emotion Regulation Competency Inventory (PERCI) is a 32-item self-report measure recently developed to compressively assess emotion regulation ability across both positive and negative valences.<br><br>PURPOSE: This study aimed to validate the Chinese PERCI in a large non-clinical sample and examine the clinical utility in patients with major depressive disorder (MDD).<br><br>METHODS: The Chinese PERCI was administered to 1090 Chinese participants (mean age = 20.64 years, 773 females). The factor structure, internal consistency, test-retest reliability, convergent validity, concurrent validity, and predictive validity were examined. Moreover, a MDD group (n = 50) and a matched healthy control group (n = 50) were recruited. Group comparisons and the linear discriminant analysis were conducted to assess the clinical relevance of the PERCI.<br><br>RESULTS: Confirmatory factor analysis supported the intended eight-factor structure of the PERCI in the Chinese population. The PERCI showed high internal consistency, test-retest reliability, as well as good convergent and concurrent validity. The MDD group had significantly higher PERCI scores than the healthy control group. Linear discriminant function comprised of the eight factors successfully distinguish patients with MDD from their matched controls.<br><br>CONCLUSION: The Chinese version of the PERCI is a valid and reliable instrument to compressively measure emotion regulation across positive and negative valences in the general Chinese population and patients with depression.},
}
@article {pmid39345641,
year = {2024},
author = {Karpowicz, BM and Ye, J and Fan, C and Tostado-Marcos, P and Rizzoglio, F and Washington, C and Scodeler, T and de Lucena, D and Nason-Tomaszewski, SR and Mender, MJ and Ma, X and Arneodo, EM and Hochberg, LR and Chestek, CA and Henderson, JM and Gentner, TQ and Gilja, V and Miller, LE and Rouse, AG and Gaunt, RA and Collinger, JL and Pandarinath, C},
title = {Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.15.613126},
pmid = {39345641},
issn = {2692-8205},
support = {R01 DC018446/DC/NIDCD NIH HHS/United States ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neural data are often nonstationary, which can lead to decoder failure if not accounted for. To maintain performance, users must frequently recalibrate decoders, which requires the arduous collection of new neural and behavioral data. Aiming to reduce this burden, several approaches have been developed that either limit recalibration data requirements (few-shot approaches) or eliminate explicit recalibration entirely (zero-shot approaches). However, progress is limited by a lack of standardized datasets and comparison metrics, causing methods to be compared in an ad hoc manner. Here we introduce the FALCON benchmark suite (Few-shot Algorithms for COnsistent Neural decoding) to standardize evaluation of iBCI robustness. FALCON curates five datasets of neural and behavioral data that span movement and communication tasks to focus on behaviors of interest to modern-day iBCIs. Each dataset includes calibration data, optional few-shot recalibration data, and private evaluation data. We implement a flexible evaluation platform which only requires user-submitted code to return behavioral predictions on unseen data. We also seed the benchmark by applying baseline methods spanning several classes of possible approaches. FALCON aims to provide rigorous selection criteria for robust iBCI decoders, easing their translation to real-world devices.},
}
@article {pmid39345497,
year = {2024},
author = {Gedela, NSS and Salim, S and Radawiec, RD and Richie, J and Chestek, C and Draelos, A and Pelled, G},
title = {Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39345497},
issn = {2692-8205},
support = {R01 NS098231/NS/NINDS NIH HHS/United States ; UF1 NS115817/NS/NINDS NIH HHS/United States ; },
abstract = {The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.},
}
@article {pmid39345413,
year = {2024},
author = {Wu, M and Yang, Y and Zhang, J and Efimov, AI and Vazquez-Guardado, A and Li, X and Zhang, K and Wang, Y and Gu, J and Zeng, L and Liu, J and Riahi, M and Yoon, H and Kim, M and Zhang, H and Lee, M and Kang, J and Ting, K and Cheng, S and Zhang, W and Banks, A and Good, CH and Cox, JM and Pinto, L and Huang, Y and Kozorovitskiy, Y and Rogers, JA},
title = {Patterned wireless transcranial optogenetics generates artificial perception.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.09.20.613966},
pmid = {39345413},
issn = {2692-8205},
support = {R01 MH117111/MH/NIMH NIH HHS/United States ; R01 NS107539/NS/NINDS NIH HHS/United States ; T32 MH067564/MH/NIMH NIH HHS/United States ; },
abstract = {Synthesizing perceivable artificial neural inputs independent of typical sensory channels remains a fundamental challenge in the development of next-generation brain-machine interfaces. Establishing a minimally invasive, wirelessly effective, and miniaturized platform with long-term stability is crucial for creating a clinically meaningful interface capable of mediating artificial perceptual feedback. In this study, we demonstrate a miniaturized fully implantable wireless transcranial optogenetic encoder designed to generate artificial perceptions through digitized optogenetic manipulation of large cortical ensembles. This platform enables the spatiotemporal orchestration of large-scale cortical activity for remote perception genesis via real-time wireless communication and control, with optimized device performance achieved by simulation-guided methods addressing light and heat propagation during operation. Cue discrimination during operant learning demonstrates the wireless genesis of artificial percepts sensed by mice, where spatial distance across large cortical networks and sequential order-based analyses of discrimination performance reveal principles that adhere to general perceptual rules. These conceptual and technical advancements expand our understanding of artificial neural syntax and its perception by the brain, guiding the evolution of next-generation brain-machine communication.},
}
@article {pmid39345118,
year = {2025},
author = {Rustamov, N and Souders, L and Sheehan, L and Carter, A and Leuthardt, EC},
title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.},
journal = {Neurorehabilitation and neural repair},
volume = {39},
number = {1},
pages = {74-86},
pmid = {39345118},
issn = {1552-6844},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R21 NS102696/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Male ; Middle Aged ; Female ; *Upper Extremity/physiopathology ; Aged ; *Stroke/physiopathology/complications ; Chronic Disease ; Adult ; Recovery of Function/physiology ; Electroencephalography ; Paresis/rehabilitation/physiopathology/etiology ; Motor Activity/physiology ; Gamma Rhythm/physiology ; Theta Rhythm/physiology ; Prospective Studies ; },
abstract = {BACKGROUND: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.<br><br>OBJECTIVES: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.<br><br>METHODS: Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12&#x2009;weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.<br><br>RESULTS: Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.<br><br>CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.<br><br>TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.},
}
@article {pmid39342695,
year = {2024},
author = {Wang, J and Ning, X and Xu, W and Li, Y and Jia, Z and Lin, Y},
title = {Multi-source Selective Graph Domain Adaptation Network for cross-subject EEG emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106742},
doi = {10.1016/j.neunet.2024.106742},
pmid = {39342695},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Brain/physiology ; },
abstract = {Affective brain-computer interface is an important part of realizing emotional human-computer interaction. However, existing objective individual differences among subjects significantly hinder the application of electroencephalography (EEG) emotion recognition. Existing methods still lack the complete extraction of subject-invariant representations for EEG and the ability to fuse valuable information from multiple subjects to facilitate the emotion recognition of the target subject. To address the above challenges, we propose a Multi-source Selective Graph Domain Adaptation Network (MSGDAN), which can better utilize data from different source subjects and perform more robust emotion recognition on the target subject. The proposed network extracts and selects the individual information specific to each subject, where public information refers to subject-invariant components from multi-source subjects. Moreover, the graph domain adaptation network captures both functional connectivity and regional states of the brain via a dynamic graph network and then integrates graph domain adaptation to ensure the invariance of both functional connectivity and regional states. To evaluate our method, we conduct cross-subject emotion recognition experiments on the SEED, SEED-IV, and DEAP datasets. The results demonstrate that the MSGDAN has superior classification performance.},
}
@article {pmid39342624,
year = {2024},
author = {Benioudakis, ES and Kalaitzaki, A and Karlafti, E and Ahanov, O and Kapageridou, E and Savopoulos, C and Didangelos, T},
title = {Dimensionality and psychometric properties of the Greek version of the Diabetes Impact and Device Satisfaction (DIDS) scale.},
journal = {Psychiatrike = Psychiatriki},
volume = {35},
number = {4},
pages = {314-323},
doi = {10.22365/jpsych.2024.017},
pmid = {39342624},
issn = {1105-2333},
mesh = {Humans ; *Psychometrics/instrumentation/methods ; Female ; Male ; Adult ; *Diabetes Mellitus, Type 1/psychology/drug therapy ; Greece ; Reproducibility of Results ; Middle Aged ; *Patient Satisfaction ; *Quality of Life ; Surveys and Questionnaires ; Insulin Infusion Systems ; Insulin/administration &amp; dosage ; },
abstract = {Type 1 diabetes mellitus (T1D) is a chronic condition with rising prevalence. The only treatment for individuals with T1D to prevent diabetes-related complications is exogenous insulin administration. Diabetes-related technology has significantly contributed to the management of T1D by reducing the burden of living with diabetes and providing greater flexibility in insulin management during daily activities. This study presents the psychometric properties of the Greek translation of the Diabetes Impact and Device Satisfaction (DIDS) Scale, which assesses satisfaction with the use of an insulin delivery device and the impact of diabetes management on individuals with T1D. A sample of 101 adults with T1D, mostly females (71.3%), with a mean age of 38.4 years (± 11.7), completed the translated Greek version of DIDS (DIDS-Gr). Exploratory factor analysis revealed three factors: 'Device Satisfaction', 'Diabetes Management Impact', and (new factor) 'Device Usability'. The internal consistency indices (Cronbach's alpha) for the subscales were 0.86, 0.71, and 0.60, respectively. Furthermore, convergent validity was demonstrated with moderate to high positive correlations between the DIDS-Grand the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) and its subscales, while divergent validity was also confirmed with weaker correlations with the depression subscale of the Hospital Anxiety and Depression Scale (HADS). Additionally, test-retest reliability and differential validity were present in our study. Therefore, DIDS-Gr is a valid and reliable measure for assessing the impact of diabetes on individuals with T1D and the satisfaction with the use of an insulin delivery device in Greece.},
}
@article {pmid39341475,
year = {2024},
author = {Luo, C and Ding, N},
title = {Cortical encoding of hierarchical linguistic information when syllabic rhythms are obscured by echoes.},
journal = {NeuroImage},
volume = {300},
number = {},
pages = {120875},
doi = {10.1016/j.neuroimage.2024.120875},
pmid = {39341475},
issn = {1095-9572},
mesh = {Humans ; *Speech Perception/physiology ; Male ; Female ; *Electroencephalography/methods ; Young Adult ; Adult ; Cerebral Cortex/physiology ; Linguistics ; Acoustic Stimulation ; },
abstract = {In speech perception, low-frequency cortical activity tracks hierarchical linguistic units (e.g., syllables, phrases, and sentences) on top of acoustic features (e.g., speech envelope). Since the fluctuation of speech envelope typically corresponds to the syllabic boundaries, one common interpretation is that the acoustic envelope underlies the extraction of discrete syllables from continuous speech for subsequent linguistic processing. However, it remains unclear whether and how cortical activity encodes linguistic information when the speech envelope does not provide acoustic correlates of syllables. To address the issue, we introduced a frequency-tagging speech stream where the syllabic rhythm was obscured by echoic envelopes and investigated neural encoding of hierarchical linguistic information using electroencephalography (EEG). When listeners attended to the echoic speech, cortical activity showed reliable tracking of syllable, phrase, and sentence levels, among which the higher-level linguistic units elicited more robust neural responses. When attention was diverted from the echoic speech, reliable neural tracking of the syllable level was also observed in contrast to deteriorated neural tracking of the phrase and sentence levels. Further analyses revealed that the envelope aligned with the syllabic rhythm could be recovered from the echoic speech through a neural adaptation model, and the reconstructed envelope yielded higher predictive power for the neural tracking responses than either the original echoic envelope or anechoic envelope. Taken together, these results suggest that neural adaptation and attentional modulation jointly contribute to neural encoding of linguistic information in distorted speech where the syllabic rhythm is obscured by echoes.},
}
@article {pmid39338869,
year = {2024},
author = {Miladinović, A and Accardo, A and Jarmolowska, J and Marusic, U and Ajčević, M},
title = {Optimizing Real-Time MI-BCI Performance in Post-Stroke Patients: Impact of Time Window Duration on Classification Accuracy and Responsiveness.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338869},
issn = {1424-8220},
support = {V-A Italia-Slovenia 2014-2020//Interreg/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Support Vector Machine ; *Stroke/physiopathology ; Male ; Female ; Algorithms ; Middle Aged ; Stroke Rehabilitation/methods ; Aged ; Discriminant Analysis ; Time Factors ; },
abstract = {Brain-computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1-2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios.},
}
@article {pmid39338854,
year = {2024},
author = {Frosolone, M and Prevete, R and Ognibeni, L and Giugliano, S and Apicella, A and Pezzulo, G and Donnarumma, F},
title = {Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338854},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.},
}
@article {pmid39338748,
year = {2024},
author = {Gulyás, D and Jochumsen, M},
title = {Detection of Movement-Related Brain Activity Associated with Hand and Tongue Movements from Single-Trial Around-Ear EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338748},
issn = {1424-8220},
mesh = {Humans ; *Tongue/physiology ; *Electroencephalography/methods ; *Movement/physiology ; *Brain-Computer Interfaces ; *Hand/physiology ; Male ; Adult ; Female ; Brain/physiology ; Support Vector Machine ; Young Adult ; Ear/physiology ; },
abstract = {Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.},
}
@article {pmid39338733,
year = {2024},
author = {Huang, J and Chang, Y and Li, W and Tong, J and Du, S},
title = {A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338733},
issn = {1424-8220},
support = {No.81705//Shengzhi Du/ ; Grant No.NCRCOP20230013//Yinghui Chang/ ; No. 2021ZD011//Jigang Tong/ ; No. 62303244//Wenyu Li/ ; No. 22JCQNJC01440//Wenyu Li/ ; 111-2221-E-A49 -126 -MY3//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112-2218-E-A49-027//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-002-013//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2640-E-A49-009//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 113-2218-E-A49-028//National Science and Technolo-gy Council (NSTC), Taiwan R.O.C/ ; 112UC2N006//Satellite Communications and AIoT Research Center/The Co-operation Platform of the Industry-Academia Innovation School, National Yang Ming Chiao Tung University (NYCU), Taiwan R.OC./ ; },
mesh = {*Electroencephalography/methods ; *Neural Networks, Computer ; Humans ; *Imagination/physiology ; *Semantics ; *Brain-Computer Interfaces ; *Algorithms ; Perception/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.},
}
@article {pmid39338628,
year = {2024},
author = {Mohajelin, F and Sheykhivand, S and Shabani, A and Danishvar, M and Danishvar, S and Lahijan, LZ},
title = {Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338628},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions-the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.},
}
@article {pmid39338620,
year = {2024},
author = {Razzaq, Z and Brahimi, N and Rehman, HZU and Khan, ZH},
title = {Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {18},
pages = {},
pmid = {39338620},
issn = {1424-8220},
mesh = {*Fuzzy Logic ; *Robotics/methods ; Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Algorithms ; Brain/physiology ; Neural Networks, Computer ; Machine Learning ; },
abstract = {Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.},
}
@article {pmid39335353,
year = {2024},
author = {Matulyte, G and Parciauskaite, V and Bjekic, J and Pipinis, E and Griskova-Bulanova, I},
title = {Gamma-Band Auditory Steady-State Response and Attention: A Systemic Review.},
journal = {Brain sciences},
volume = {14},
number = {9},
pages = {},
pmid = {39335353},
issn = {2076-3425},
abstract = {Auditory steady-state response (ASSR) is the result of the brain's ability to follow and entrain its oscillatory activity to the phase and frequency of periodic auditory stimulation. Gamma-band ASSR has been increasingly investigated with intentions to apply it in neuropsychiatric disorders diagnosis as well as in brain-computer interface technologies. However, it is still debatable whether attention can influence ASSR, as the results of the attention effects of ASSR are equivocal. In our study, we aimed to systemically review all known articles related to the attentional modulation of gamma-band ASSRs. The initial literature search resulted in 1283 papers. After the removal of duplicates and ineligible articles, 49 original studies were included in the final analysis. Most analyzed studies demonstrated ASSR modulation with differing attention levels; however, studies providing mixed or non-significant results were also identified. The high versatility of methodological approaches including the utilized stimulus type and ASSR recording modality, as well as tasks employed to modulate attention, were detected and emphasized as the main causality of result inconsistencies across studies. Also, the impact of training, inter-individual variability, and time of focus was addressed.},
}
@article {pmid39332212,
year = {2024},
author = {Deng, Y and Ji, Z and Wang, Y and Zhou, SK},
title = {OS-SSVEP: One-shot SSVEP classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106734},
doi = {10.1016/j.neunet.2024.106734},
pmid = {39332212},
issn = {1879-2782},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Brain-Computer Interfaces ; Neural Networks, Computer ; Discriminant Analysis ; Least-Squares Analysis ; Photic Stimulation/methods ; Adult ; Male ; Calibration ; Algorithms ; },
abstract = {It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject. Specifically, CSDuDoFN uses multi-reference least-squares transformation (MLST) to map data from both the source subjects and the target subject into the domain of sine-cosine templates, thereby reducing cross-subject domain gap and benefiting transfer learning. In addition, CSDuDoFN is fed with both transformed and original data, with an adequate fusion of their features occurring at different network layers. To capitalize on the calibration trial of the target subject, OS-SSVEP utilizes source aliasing matrix estimation (SAME)-based data augmentation to incorporate into the training process of the ensemble TRCA (eTRCA) and TDCA models. Ultimately, the outputs of CSDuDoFN, eTRCA, and TDCA are combined for the SSVEP classification. The effectiveness of our proposed approach is comprehensively evaluated on three publicly available SSVEP datasets, achieving the best performance on two datasets and competitive performance on the third. Further, it is worth noting that our method follows a different technical route from the current state-of-the-art (SOTA) method and the two are complementary. The performance is significantly improved when our method is combined with the SOTA method. This study underscores the potential to integrate the SSVEP-based brain-computer interface (BCI) into daily life. The corresponding source code is accessible at https://github.com/Sungden/One-shot-SSVEP-classification.},
}
@article {pmid39332118,
year = {2024},
author = {Crell, MR and Müller-Putz, GR},
title = {Handwritten character classification from EEG through continuous kinematic decoding.},
journal = {Computers in biology and medicine},
volume = {182},
number = {},
pages = {109132},
doi = {10.1016/j.compbiomed.2024.109132},
pmid = {39332118},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Handwriting ; Biomechanical Phenomena/physiology ; Female ; Male ; Adult ; Signal Processing, Computer-Assisted ; Hand/physiology ; Young Adult ; },
abstract = {The classification of handwritten letters from invasive neural signals has lately been subject of research to restore communication abilities in people with limited movement capacities. This study explores the classification of ten letters (a,d,e,f,j,n,o,s,t,v) from non-invasive neural signals of 20 participants, offering new insights into the neural correlates of handwriting. Letters were classified with two methods: the direct classification from low-frequency and broadband electroencephalogram (EEG) and a two-step approach comprising the continuous decoding of hand kinematics and the application of those in subsequent classification. The two-step approach poses a novel application of continuous movement decoding for the classification of letters from EEG. When using low-frequency EEG, results show moderate accuracies of 23.1% for ten letters and 39.0% for a subset of five letters with highest discriminability of the trajectories. The two-step approach yielded significantly higher performances of 26.2% for ten letters and 46.7% for the subset of five letters. Hand kinematics could be reconstructed with a correlation of 0.10 to 0.57 (average chance level: 0.04) between the decoded and original kinematic. The study shows the general feasibility of extracting handwritten letters from non-invasively recorded neural signals and indicates that the proposed two-step approach can improve performances. As an exploratory investigation of the neural mechanisms of handwriting in EEG, we found significant influence of the written letter on the low-frequency components of neural signals. Differences between letters occurred mostly in central and occipital channels. Further, our results suggest movement speed as the most informative kinematic for the decoding of short hand movements.},
}
@article {pmid39331412,
year = {2024},
author = {Wang, S and Jiang, Q and Liu, H and Yu, C and Li, P and Pan, G and Xu, K and Xiao, R and Hao, Y and Wang, C and Song, J},
title = {Mechanically adaptive and deployable intracortical probes enable long-term neural electrophysiological recordings.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {40},
pages = {e2403380121},
pmid = {39331412},
issn = {1091-6490},
support = {2021ZD0200401//STI 2030-Major Projects/ ; U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; U20A6001//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; },
mesh = {Animals ; Rats ; *Electrodes, Implanted ; Electrophysiological Phenomena ; Polymers/chemistry ; Cerebral Cortex/physiology ; Neurons/physiology ; Rats, Sprague-Dawley ; Brain/physiology ; },
abstract = {Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications.},
}
@article {pmid39331031,
year = {2024},
author = {Wang, W and Liu, Y and Wang, G and Cheng, Q and Ming, D},
title = {Oscillatory cortico-cortical connectivity during tactile discrimination between dynamic and static stimulation.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {9},
pages = {},
doi = {10.1093/cercor/bhae277},
pmid = {39331031},
issn = {1460-2199},
support = {2023YFC3603800//National Key Research and Development Program of China/ ; 62273251//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography ; *Touch Perception/physiology ; Adult ; Young Adult ; Cerebral Cortex/physiology ; Discrimination, Psychological/physiology ; Touch/physiology ; Neural Pathways/physiology ; Physical Stimulation ; Brain Mapping ; Evoked Potentials/physiology ; },
abstract = {Fine sensory modalities play an essential role in perceiving the world. However, little is known about how the cortico-cortical distinguishes between dynamic and static tactile signals. This study investigated oscillatory connectivity during a tactile discrimination task of dynamic and static stimulation via electroencephalogram (EEG) recordings and the fast oscillatory networks across widespread cortical regions. While undergoing EEG recordings, the subject felt an electro-tactile presented by a 3-dot array. Each block consisted of 3 forms of stimulation: Spatio-temporal (dynamic), Spatial (static), and Control condition (lack of electrical stimulation). The average event-related potential for the Spatial and Spatio-temporal conditions exhibited statistically significant differences between 25 and 75, 81 and 121, 174 and 204 and 459 and 489 ms after stimulus onset. Based on those times, the sLORETA approach was used to reconstruct the inverse solutions of EEG. Source localization appeared superior parietal at around 25 to 75 ms, in the primary motor cortex at 81 to 121 ms, in the central prefrontal cortex at 174 to 204 and 459 to 489 ms. To better assess spectral brain functional connectivity, we selected frequency ranges with correspondingly significant differences: for static tactile stimulation, these are concentrated in the Theta, Alpha, and Gamma bands, whereas for dynamic stimulation, the relative energy change bands are focused on the Theta and Alpha bands. These nodes' functional connectivity analysis (phase lag index) showed 3 distinct distributed networks. A tactile information discrimination network linked the Occipital lobe, Prefrontal lobe, and Postcentral gyrus. A tactile feedback network linked the Prefrontal lobe, Postcentral gyrus, and Temporal lobe. A dominant motor feedforward loop network linked the Parietal cortex, Prefrontal lobe, Frontal lobe, and Parietal cortex. Processing dynamic and static tactile signals involves discriminating tactile information, motion planning, and cognitive decision processing.},
}
@article {pmid39330094,
year = {2024},
author = {Ruan, Z and Li, H},
title = {Two Levels of Integrated Information Theory: From Autonomous Systems to Conscious Life.},
journal = {Entropy (Basel, Switzerland)},
volume = {26},
number = {9},
pages = {},
pmid = {39330094},
issn = {1099-4300},
support = {22&amp;ZD034//the Major Program of National Social Science Fund of China/ ; },
abstract = {Integrated Information Theory (IIT) is one of the most prominent candidates for a theory of consciousness, although it has received much criticism for trying to live up to expectations. Based on the relevance of three issues generalized from the developments of IITs, we have summarized the main ideas of IIT into two levels. At the second level, IIT claims to be strictly anchoring consciousness, but the first level on which it is based is more about autonomous systems or systems that have reached some other critical complexity. In this paper, we argue that the clear gap between the two levels of explanation of IIT has led to these criticisms and that its panpsychist tendency plays a crucial role in this. We suggest that the problems of IIT are far from being "pseudoscience", and by adding more necessary elements, when the first level is combined with the second level, IIT can genuinely move toward an appropriate theory of consciousness that can provide necessary and sufficient interpretations.},
}
@article {pmid39329668,
year = {2024},
author = {Tan, X and Wang, D and Xu, M and Chen, J and Wu, S},
title = {Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {9},
pages = {},
pmid = {39329668},
issn = {2306-5354},
support = {12275295//National Natural Science Foundation of China/ ; GZC20230189//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; },
abstract = {Research on electroencephalogram-based motor imagery (MI-EEG) can identify the limbs of subjects that generate motor imagination by decoding EEG signals, which is an important issue in the field of brain-computer interface (BCI). Existing deep-learning-based classification methods have not been able to entirely employ the topological information among brain regions, and thus, the classification performance needs further improving. In this paper, we propose a multi-view graph convolutional attention network (MGCANet) with residual learning structure for multi-class MI decoding. Specifically, we design a multi-view graph convolution spatial feature extraction method based on the topological relationship of brain regions to achieve more comprehensive information aggregation. During the modeling, we build an adaptive weight fusion (Awf) module to adaptively merge feature from different brain views to improve classification accuracy. In addition, the self-attention mechanism is introduced for feature selection to expand the receptive field of EEG signals to global dependence and enhance the expression of important features. The proposed model is experimentally evaluated on two public MI datasets and achieved a mean accuracy of 78.26% (BCIC IV 2a dataset) and 73.68% (OpenBMI dataset), which significantly outperforms representative comparative methods in classification accuracy. Comprehensive experiment results verify the effectiveness of our proposed method, which can provide novel perspectives for MI decoding.},
}
@article {pmid39329659,
year = {2024},
author = {Hiwaki, O},
title = {Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {9},
pages = {},
pmid = {39329659},
issn = {2306-5354},
support = {22KK-128//Mazda Motor Corporation (Japan)/ ; },
abstract = {Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.},
}
@article {pmid39329584,
year = {2024},
author = {Mounesi Rad, S and Danishvar, S},
title = {Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {9},
pages = {},
pmid = {39329584},
issn = {2313-7673},
abstract = {Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain-computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel.},
}
@article {pmid39328074,
year = {2024},
author = {Sawyer, A and Cooke, L and Breyman, E and Spohn, S and Edelman, S and Saravanan, K and Putrino, D},
title = {Meeting the Needs of People With Severe Quadriplegia in the 21st Century: The Case for Implanted Brain-Computer Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {38},
number = {11-12},
pages = {877-886},
doi = {10.1177/15459683241282783},
pmid = {39328074},
issn = {1552-6844},
mesh = {Humans ; *Quadriplegia/rehabilitation ; *Brain-Computer Interfaces ; *Self-Help Devices ; },
abstract = {BACKGROUND: In recent decades, there has been a widespread adoption of digital devices among the non-disabled population. The pervasive integration of digital devices has revolutionized how the majority of the population manages daily activities. Most of us now depend on digital platforms and services to conduct activities across the domains of communication, finance, healthcare, and work. However, a clear disparity exists for people who live with severe quadriplegia, who largely lack access to tools that would enable them to perform daily tasks digitally and communicate effectively with their environment.<br><br>OBJECTIVES: The purpose of this piece is to (i) highlight the unmet needs of people with severe quadriplegia (including cases for medical necessity and perspectives from the community), (ii) present the current landscape of assistive technology for people with severe quadriplegia, (iii) make the case for implantable BCIs (how they address needs and why they are a good solution relative to other assistive technologies), and (iv) present future directions.<br><br>RESULTS: There are technologies that are currently available to this population, but these technologies are certainly not usable with the same level of ease, efficiency, or autonomy as what has been designed for the non-disabled community. This hinders the ability of people with severe quadriplegia to achieve digital autonomy, perpetuating social isolation and limiting the expression of needs, opinions, and preferences.<br><br>CONCLUSION: Most importantly, the gap in digital equality fundamentally undermines the basic human rights of people with severe quadriplegia.},
}
@article {pmid39326769,
year = {2024},
author = {Wu, X and Xie, C and Cheng, F and Li, Z and Li, R and Xu, D and Kim, H and Zhang, J and Liu, H and Liu, M},
title = {Comparative evaluation of interpretation methods in surface-based age prediction for neonates.},
journal = {NeuroImage},
volume = {300},
number = {},
pages = {120861},
doi = {10.1016/j.neuroimage.2024.120861},
pmid = {39326769},
issn = {1095-9572},
mesh = {Humans ; Infant, Newborn ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging ; Female ; Male ; *Deep Learning ; Image Interpretation, Computer-Assisted/methods ; Neuroimaging/methods/standards ; },
abstract = {Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods-regional age prediction and the perturbation-based saliency map approach-for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.},
}
@article {pmid39326569,
year = {2025},
author = {Hurley, ET and Twomey-Kozack, J and Doyle, TR and Meyer, LE and Meyer, AM and Lorentz, SG and Bradley, KE and Dickens, JF and Klifto, CS},
title = {Bioinductive Collagen Implant Has Potential to Improve Rotator Cuff Healing: A Systematic Review.},
journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {41},
number = {2},
pages = {333-342.e2},
doi = {10.1016/j.arthro.2024.09.028},
pmid = {39326569},
issn = {1526-3231},
mesh = {Humans ; *Rotator Cuff Injuries/surgery ; *Collagen/therapeutic use ; *Rotator Cuff/surgery ; *Wound Healing/drug effects ; Prostheses and Implants ; Treatment Outcome ; },
abstract = {PURPOSE: To systematically review the literature to evaluate the clinical studies on bioinductive collagen implant (BCI) for the treatment of rotator cuff tears.<br><br>METHODS: A literature search of MEDLINE, Embase, and the Cochrane Library was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Clinical studies reporting BCI for rotator cuff tears were included. Quantitive and qualitative data were evaluated.<br><br>RESULTS: A total of 21 studies were included. In patients with full-thickness tears, 7 of the 8 studies with pre- to postoperative American Shoulder and Elbow Surgeons (ASES) scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 75% to 100% of patients meeting the minimal clinically important difference. In those with partial-thickness tears, 7 of the 8 studies with pre- to postoperative ASES scores demonstrated statistically significant improvements in mean pre- to postoperative ASES scores, with 54.4% to 100% of patients meeting the minimal clinically important difference. For studies that quantified percent increases in tendon thickness, the reported increases ranged from 13% to 44% in full-thickness tears and 14% to 60% in partial-thickness tears. Six studies evaluated rotator cuff retears after BCI treatment in the full-thickness cohort, with rates reported ranging from 0% to 9%. Five studies evaluated rotator cuff retears after BCI treatment in the partial-thickness cohort, with rates reported ranging from 0% to 18%. Two of the included studies found that BCI was cost-effective due to the increased tendon healing, with cost savings of $5,338 to $13,061 per healed rotator cuff tendon.<br><br>CONCLUSIONS: The literature on rotator cuff tear augmentation with BCI has shown consistently reported good results. Additionally, there was evidence of low retear rates and consistently improved tendon thickness with BCI, with 2 randomized controlled trials showing improved tendon healing with BCI. However, there appears to be a higher rate of adhesive capsulitis reported.<br><br>LEVEL OF EVIDENCE: Level IV, systematic review of Level I, III, and IV studies.},
}
@article {pmid39326451,
year = {2024},
author = {Erbslöh, A and Buron, L and Ur-Rehman, Z and Musall, S and Hrycak, C and Löhler, P and Klaes, C and Seidl, K and Schiele, G},
title = {Technical survey of end-to-end signal processing in BCIs using invasive MEAs.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad8031},
pmid = {39326451},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Signal Processing, Computer-Assisted ; *Microelectrodes ; Algorithms ; Electrodes, Implanted ; Electroencephalography/methods ; },
abstract = {Modern brain-computer interfaces and neural implants allow interaction between the tissue, the user and the environment, where people suffer from neurodegenerative diseases or injuries.This interaction can be achieved by using penetrating/invasive microelectrodes for extracellular recordings and stimulation, such as Utah or Michigan arrays. The application-specific signal processing of the extracellular recording enables the detection of interactions and enables user interaction. For example, it allows to read out movement intentions from recordings of brain signals for controlling a prosthesis or an exoskeleton. To enable this, computationally complex algorithms are used in research that cannot be executed on-chip or on embedded systems. Therefore, an optimization of the end-to-end processing pipeline, from the signal condition on the electrode array over the analog pre-processing to spike-sorting and finally the neural decoding process, is necessary for hardware inference in order to enable a local signal processing in real-time and to enable a compact system for achieving a high comfort level. This paper presents a survey of system architectures and algorithms for end-to-end signal processing pipelines of neural activity on the hardware of such neural devices, including (i) on-chip signal pre-processing, (ii) spike-sorting on-chip or on embedded hardware and (iii) neural decoding on workstations. A particular focus for the hardware implementation is on low-power electronic design and artifact-robust algorithms with low computational effort and very short latency. For this, current challenges and possible solutions with support of novel machine learning techniques are presented in brief. In addition, we describe our future vision for next-generation BCIs.},
}
@article {pmid39326392,
year = {2024},
author = {Magee, P and Ienca, M and Farahany, N},
title = {Beyond neural data: Cognitive biometrics and mental privacy.},
journal = {Neuron},
volume = {112},
number = {18},
pages = {3017-3028},
doi = {10.1016/j.neuron.2024.09.004},
pmid = {39326392},
issn = {1097-4199},
mesh = {Humans ; *Privacy ; *Cognition/physiology ; *Brain-Computer Interfaces/ethics ; Wearable Electronic Devices ; Biometry/methods ; Confidentiality/ethics ; },
abstract = {Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy.},
}
@article {pmid39321842,
year = {2024},
author = {Dillen, A and Omidi, M and Ghaffari, F and Vanderborght, B and Roelands, B and Romain, O and Nowé, A and De Pauw, K},
title = {A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad7f8d},
pmid = {39321842},
issn = {1741-2552},
abstract = {Objective: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals. Approach: A proof-of-concept BCI control system was developed using commercially available hardware, integrating MI with eye tracking in an augmented reality (AR) user interface to facilitate a shared control approach. This system proposes actions based on the user's gaze, enabling selection through imagined movements. A user study was conducted to evaluate the system's usability, focusing on its effectiveness and efficiency. Main results:Participants performed tasks that simulated everyday activities with the robotic arm, demonstrating the shared control system's feasibility and practicality in real-world scenarios. Despite low online decoding performance (mean accuracy: 0.52 9, F1: 0.29, Cohen's Kappa: 0.12), participants achieved a mean success rate of 0.83 in the final phase of the user study when given 15 minutes to complete the evaluation tasks. The success rate dropped below 0.5 when a 5-minute cutoff time was selected. Significance: These results indicate that integrating AR and eye tracking can significantly enhance the usability of BCI systems, despite the complexities of MI-EEG decoding. While efficiency is still low, the effectiveness of our approach was verified. This suggests that BCI systems have the potential to become a viable interaction modality for everyday applications in the future.},
}
@article {pmid39321841,
year = {2024},
author = {Wen, X and Jia, S and Han, D and Dong, Y and Gao, C and Cao, R and Hao, Y and Guo, Y and Cao, R},
title = {Filter banks guided correlational convolutional neural network for SSVEPs based BCI classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7f89},
pmid = {39321841},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/classification ; Humans ; *Evoked Potentials, Visual/physiology ; *Neural Networks, Computer ; *Electroencephalography/methods/classification ; Adult ; Male ; Photic Stimulation/methods ; Female ; },
abstract = {Objective.In the field of steady-state visual evoked potential brain computer interfaces (SSVEP-BCIs) research, convolutional neural networks (CNNs) have gradually been proved to be an effective method. Whereas, majority works apply the frequency domain characteristics in long time window to train the network, thus lead to insufficient performance of those networks in short time window. Furthermore, only the frequency domain information for classification lacks of other task-related information.Approach.To address these issues, we propose a time-frequency domain generalized filter-bank convolutional neural network (FBCNN-G) to improve the SSVEP-BCIs classification performance. The network integrates multiple frequency information of electroencephalogram (EEG) with template and predefined prior of sine-cosine signals to perform feature extraction, which contains correlation analyses in both template and signal aspects. Then the classification is performed at the end of the network. In addition, the method proposes the use of filter banks divided into specific frequency bands as pre-filters in the network to fully consider the fundamental and harmonic frequency characteristics of the signal.Main results.The proposed FBCNN-G model is compared with other methods on the public dataset Benchmark. The results manifest that this model has higher accuracy of character recognition accuracy and information transfer rates in several time windows. Particularly, in the 0.2 s time window, the mean accuracy of the proposed method reaches62.02%±5.12%, indicating its superior performance.Significance.The proposed FBCNN-G model is critical for the exploitation of SSVEP-BCIs character recognition models.},
}
@article {pmid39321840,
year = {2024},
author = {Arif, M and Ur Rehman, F and Sekanina, L and Malik, AS},
title = {A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7f8e},
pmid = {39321840},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain/physiology ; Artifacts ; Brain-Computer Interfaces ; Machine Learning ; },
abstract = {Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.},
}
@article {pmid39321611,
year = {2024},
author = {Wang, X and Qi, H},
title = {Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis.},
journal = {Computer methods and programs in biomedicine},
volume = {257},
number = {},
pages = {108425},
doi = {10.1016/j.cmpb.2024.108425},
pmid = {39321611},
issn = {1872-7565},
mesh = {*Evoked Potentials, Somatosensory/physiology ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography ; Imagination/physiology ; },
abstract = {BACKGROUND AND OBJECTIVE: Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.<br><br>METHODS: The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.<br><br>RESULTS: The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001).<br><br>CONCLUSIONS: The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.},
}
@article {pmid39321571,
year = {2024},
author = {Cioffi, E and Hutber, A and Molloy, R and Murden, S and Yurkewich, A and Kirton, A and Lin, JP and Gimeno, H and McClelland, VM},
title = {EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {167},
number = {},
pages = {143-166},
pmid = {39321571},
issn = {1872-8952},
support = {U19 AT002023/AT/NCCIH NIH HHS/United States ; },
mesh = {Humans ; *Neurofeedback/methods ; *Electroencephalography/methods ; *Neurological Rehabilitation/methods ; Child ; Adult ; Sensorimotor Cortex/physiopathology ; Cerebral Palsy/rehabilitation/physiopathology ; },
abstract = {OBJECTIVE: Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies.<br><br>METHODS: MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers.<br><br>RESULTS: Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback.<br><br>CONCLUSIONS: There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback.<br><br>SIGNIFICANCE: The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.},
}
@article {pmid39320995,
year = {2025},
author = {Huang, H and Chen, J and Xiao, J and Chen, D and Zhang, J and Pan, J and Li, Y},
title = {Real-Time Attention Regulation and Cognitive Monitoring Using a Wearable EEG-Based BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {2},
pages = {716-724},
doi = {10.1109/TBME.2024.3468351},
pmid = {39320995},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/instrumentation/methods ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; Male ; *Attention/physiology ; Adult ; Female ; *Cognition/physiology ; Young Adult ; *Signal Processing, Computer-Assisted/instrumentation ; Neurofeedback/physiology/methods ; },
abstract = {OBJECTIVE: Attention regulation is an essential ability in daily life that affects learning and work efficiency and is closely related to mental health. The effectiveness of brain-computer interface (BCI) systems in attention regulation has been proven, but most of these systems rely on bulky and expensive equipment and are still in the experimental stage. This study proposes a wearable BCI system for real-time attention regulation and cognitive monitoring.<br><br>METHODS: The BCI system integrates a wearable single-channel electroencephalogram (EEG) headband with wireless data streaming for real-time analysis. Twenty healthy subjects participated in the long-term attention regulation experiment and were evenly divided into an experimental group and a control group based on the presence of real-time neurofeedback. The neurofeedback is represented by output value of attention, which calculated from single-channel EEG data. Before and after the regulation sessions, baseline assessments were conducted for each subject, incorporating multi-channel EEG data analysis and cognitive behavioral evaluations, to verify the effectiveness of system for attention regulation.<br><br>RESULTS: The online experimental results indicate that the average attention level in the experimental group increased from 0.625 to 0.812, while no significant improvement was observed in the control group. Further comparative analysis revealed the reasons for the enhancement of attention regulation ability in terms of both brain network patterns and cognitive performance.<br><br>SIGNIFICANCE: The proposed wearable BCI system is effective at improving attention regulation ability and could be a promising tool for assisting people with attention disorders.},
}
@article {pmid39318665,
year = {2024},
author = {Chai, C and Yang, X and Gao, X and Shi, J and Wang, X and Song, H and Chen, YH and Sawan, M},
title = {Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1452865},
pmid = {39318665},
issn = {2296-4185},
abstract = {Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.},
}
@article {pmid39316474,
year = {2025},
author = {Li, X and Yang, Z and Tu, X and Wang, J and Huang, J},
title = {MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {1},
pages = {224-234},
doi = {10.1109/JBHI.2024.3467090},
pmid = {39316474},
issn = {2168-2208},
support = {//Major Program (JD) of Hubei Province/ ; //National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Convolutional Neural Networks ; },
abstract = {Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource demands of deep learning models. In this study, we propose a novel lightweight Multi-Scale Feature Residual Convolutional Neural Network (MFRC-Net). MFRC-Net primarily consists of two blocks: temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks. The former captures dynamic changes in EEG signals across various time scales through multi-scale grouped convolution and backbone temporal convolution skip connections; the latter improves local spatial feature extraction and calibrates feature mapping through the introduction of cross-domain spatial filtering layers. Furthermore, by specifically optimizing the loss function, MFRC-Net effectively reduces sensitivity to outliers. Experiment results on the BCI Competition IV 2a dataset and the SHU dataset demonstrate that, with a parameter size of only 13 K, MFRC-Net achieves accuracy of 85.1% and 69.3%, respectively, surpassing current state-of-the-art models. The integration of temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks in lightweight models significantly boosts performance, as evidenced by ablation studies and visualizations.},
}
@article {pmid39316274,
year = {2024},
author = {Dinov, ID},
title = {Neuroinformatics Applications of Data Science and Artificial Intelligence.},
journal = {Neuroinformatics},
volume = {22},
number = {4},
pages = {403-405},
pmid = {39316274},
issn = {1559-0089},
mesh = {Humans ; *Artificial Intelligence/trends ; Brain/physiology/diagnostic imaging ; Brain-Computer Interfaces/trends ; *Data Science/methods/trends ; *Neurosciences/methods/trends ; },
abstract = {Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.},
}
@article {pmid39314938,
year = {2024},
author = {Bjånes, DA and Kellis, S and Nickl, R and Baker, B and Aflalo, T and Bashford, L and Chivukula, S and Fifer, MS and Osborn, LE and Christie, B and Wester, BA and Celnik, PA and Kramer, D and Pejsa, K and Crone, NE and Anderson, WS and Pouratian, N and Lee, B and Liu, CY and Tenore, F and Rieth, L and Andersen, RA},
title = {Quantifying physical degradation alongside recording and stimulation performance of 980 intracortical microelectrodes chronically implanted in three humans for 956-2246 days.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39314938},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; UG3 NS107688/NS/NINDS NIH HHS/United States ; },
abstract = {MOTIVATION: The clinical success of brain-machine interfaces depends on overcoming both biological and material challenges to ensure a long-term stable connection for neural recording and stimulation. Therefore, there is a need to quantify any damage that microelectrodes sustain when they are chronically implanted in the human cortex.<br><br>METHODS: Using scanning electron microscopy (SEM), we imaged 980 microelectrodes from Neuroport arrays chronically implanted in the cortex of three people with tetraplegia for 956-2246 days. We analyzed eleven multi-electrode arrays in total: eight arrays with platinum (Pt) electrode tips and three with sputtered iridium oxide tips (SIROF); one Pt array was left in sterile packaging, serving as a control. The arrays were implanted/explanted across three different clinical sites surgeries (Caltech/UCLA, Caltech/USC and APL/Johns Hopkins) in the anterior intraparietal area, Brodmann's area 5, motor cortex, and somatosensory cortex.Human experts rated the electron micrographs of electrodes with respect to five damage metrics: the loss of metal at the electrode tip, the amount of separation between the silicon shank and tip metal, tissue adherence or bio-material to the electrode, damage to the shank insulation and silicone shaft. These metrics were compared to functional outcomes (recording quality, noise, impedance and stimulation ability).<br><br>RESULTS: Despite higher levels of physical degradation, SIROF electrodes were twice as likely to record neural activity than Pt electrodes (measured by SNR), at the time of explant. Additionally, 1 kHz impedance (measured in vivo prior to explant) significantly correlated with all physical damage metrics, recording, and stimulation performance for SIROF electrodes (but not Pt), suggesting a reliable measurement of in vivo degradation.We observed a new degradation type, primarily occurring on stimulated electrodes ("pockmarked" vs "cracked") electrodes; however, tip metalization damage was not significantly higher due to stimulation or amount of charge. Physical damage was centralized to specific regions of an array often with differences between outer and inner electrodes. This is consistent with degradation due to contact with the biologic milieu, influenced by variations in initial manufactured state. From our data, we hypothesize that erosion of the silicon shank often precedes damage to the tip metal, accelerating damage to the electrode / tissue interface.<br><br>CONCLUSIONS: These findings link quantitative measurements, such as impedance, to the physical condition of the microelectrodes and their capacity to record and stimulate. These data could lead to improved manufacturing or novel electrode designs to improve long-term performance of BMIs making them are vitally important as multi-year clinical trials of BMIs are becoming more common.},
}
@article {pmid39314865,
year = {2024},
author = {Luo, X},
title = {Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke: a randomized controlled study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1394424},
pmid = {39314865},
issn = {1664-2295},
abstract = {BACKGROUND: Lower limb motor dysfunction is one of the most serious consequences of stroke; however, there is insufficient evidence for optimal rehabilitation strategies. Improving lower limb motor function through effective rehabilitation strategies is a top priority for stroke patients. Neuroplasticity is a key factor in the recovery of motor function. The extent to which neuroplasticity-based rehabilitation therapy using brain-computer interface (BCI) is effective in treating lower limb motor dysfunction in acute ischemic stroke patients has not been extensively investigated.<br><br>OBJECTIVE: This study aimed to assess the impact of BCI rehabilitation on lower limb motor dysfunction in individuals with acute ischemic stroke by evaluating motor function, walking ability, and daily living activities.<br><br>METHODS: This study was conducted in a randomized controlled trial, involving 64 patients with acute ischemic stroke who experienced lower limb motor dysfunction. All patients were divided into two groups, with 32 patients assigned to the control group was given conventional rehabilitation once a day for 70&#x2009;min, 5 times a week for 2&#x2009;weeks, and the experimental group (n&#x2009;=&#x2009;32) was given BCI rehabilitation on top of the conventional rehabilitation for 1&#x2009;h a day, 30&#x2009;min of therapy in the morning and an additional 30&#x2009;min in the afternoon, for a total of 20 sessions over a two-week period. The primary outcome was lower extremity motor function, which was assessed using the lower extremity portion of the Fugl-Meyer Rating Scale (FMA-LE), and the secondary endpoints were the Functional Ambulation Scale (FAC), and the Modified Barthel index (MBI).<br><br>RESULTS: After 20 sessions of treatment, both groups improved in motor function, walking function, and activities of daily living, and the improvements in FMA-LE scores (p&#x2009;<&#x2009;0.001), FAC (p&#x2009;=&#x2009;0.031), and MBI (p&#x2009;<&#x2009;0.001) were more pronounced in the experimental group compared with the control group.<br><br>CONCLUSION: Conventional rehabilitation therapy combined with BCI rehabilitation therapy can improve the lower limb motor function of hemiplegic patients with stroke, enhance the patient's ability to perform activities of daily living, and promote the improvement of walking function, this is an effective rehabilitation policy to promote recovery from lower extremity motor function disorders.},
}
@article {pmid39314275,
year = {2024},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Miller, LE},
title = {Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39314275},
issn = {2692-8205},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; },
abstract = {OBJECTIVE: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.<br><br>APPROACH: We devised an unsupervised approach that relies on the structure of a low-dimensional neural manifold to implement a piecewise linear decoder. We created a distinctive dataset in which monkeys performed a diverse set of tasks, some trained, others innate, while we recorded neural signals from the motor cortex (M1) and electromyographs (EMGs) from upper limb muscles. We used both linear and nonlinear dimensionality reduction techniques to discover neural manifolds and applied unsupervised algorithms to identify clusters within those spaces. Finally, we fit a linear decoder of EMG for each cluster. A specific decoder was activated corresponding to the cluster each new neural data point belonged to.<br><br>MAIN RESULTS: We found clusters in the neural manifolds corresponding with the different tasks or task sub-phases. The performance of piecewise decoding improved as the number of clusters increased and plateaued gradually. With only two clusters it already outperformed a global linear decoder, and unexpectedly, it outperformed even a global recurrent neural network (RNN) decoder with 10-12 clusters.<br><br>SIGNIFICANCE: This study introduced a computationally lightweight solution for creating iBCI decoders that can function effectively across a broad range of tasks. EMG decoding is particularly challenging, as muscle activity is used, under varying contexts, to control interaction forces and limb stiffness, as well as motion. The results suggest that a piecewise linear decoder can provide a good approximation to the nonlinearity between neural activity and motor outputs, a result of our increased understanding of the structure of neural manifolds in motor cortex.},
}
@article {pmid39314155,
year = {2025},
author = {Cai, Q and Meng, L and Quan, M and Wang, L and Ren, J and Zheng, C and Yang, J and Ming, D},
title = {Progress of research in the application of ultrasound technology for the treatment of Alzheimer's disease.},
journal = {Neural regeneration research},
volume = {20},
number = {10},
pages = {2823-2837},
pmid = {39314155},
issn = {1673-5374},
abstract = {Alzheimer's disease is a common neurodegenerative disorder defined by decreased reasoning abilities, memory loss, and cognitive deterioration. The presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer's disease. The use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent years. As a safe and feasible therapeutic and drug-delivery method, ultrasound has shown promise in improving cognitive deficits. This article provides a summary of the application of ultrasound technology for treating Alzheimer's disease over the past 5 years, including standalone ultrasound treatment, ultrasound combined with microbubbles or drug therapy, and magnetic resonance imaging-guided focused ultrasound therapy. Emphasis is placed on the benefits of introducing these treatment methods and their potential mechanisms. We found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-β plaque deposition. We believe that ultrasound is an effective therapy for Alzheimer's disease, and this review provides a theoretical basis for future ultrasound treatment methods.},
}
@article {pmid39314138,
year = {2025},
author = {Lv, Y and Li, H},
title = {Blood diagnostic and prognostic biomarkers in amyotrophic lateral sclerosis.},
journal = {Neural regeneration research},
volume = {20},
number = {9},
pages = {2556-2570},
pmid = {39314138},
issn = {1673-5374},
abstract = {Amyotrophic lateral sclerosis is a devastating neurodegenerative disease for which the current treatment approaches remain severely limited. The principal pathological alterations of the disease include the selective degeneration of motor neurons in the brain, brainstem, and spinal cord, as well as abnormal protein deposition in the cytoplasm of neurons and glial cells. The biological markers under extensive scrutiny are predominantly located in the cerebrospinal fluid, blood, and even urine. Among these biomarkers, neurofilament proteins and glial fibrillary acidic protein most accurately reflect the pathologic changes in the central nervous system, while creatinine and creatine kinase mainly indicate pathological alterations in the peripheral nerves and muscles. Neurofilament light chain levels serve as an indicator of neuronal axonal injury that remain stable throughout disease progression and are a promising diagnostic and prognostic biomarker with high specificity and sensitivity. However, there are challenges in using neurofilament light chain to differentiate amyotrophic lateral sclerosis from other central nervous system diseases with axonal injury. Glial fibrillary acidic protein predominantly reflects the degree of neuronal demyelination and is linked to non-motor symptoms of amyotrophic lateral sclerosis such as cognitive impairment, oxygen saturation, and the glomerular filtration rate. TAR DNA-binding protein 43, a pathological protein associated with amyotrophic lateral sclerosis, is emerging as a promising biomarker, particularly with advancements in exosome-related research. Evidence is currently lacking for the value of creatinine and creatine kinase as diagnostic markers; however, they show potential in predicting disease prognosis. Despite the vigorous progress made in the identification of amyotrophic lateral sclerosis biomarkers in recent years, the quest for definitive diagnostic and prognostic biomarkers remains a formidable challenge. This review summarizes the latest research achievements concerning blood biomarkers in amyotrophic lateral sclerosis that can provide a more direct basis for the differential diagnosis and prognostic assessment of the disease beyond a reliance on clinical manifestations and electromyography findings.},
}
@article {pmid39313602,
year = {2025},
author = {Yin, X and Yang, C and Dong, H and Liang, J and Lin, M},
title = {Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {63},
number = {2},
pages = {355-363},
pmid = {39313602},
issn = {1741-0444},
support = {2024A1515012810//Basic and Applied Basic Research Foundation of Guangdong Province/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Female ; Young Adult ; Time Factors ; },
abstract = {The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.},
}
@article {pmid39312420,
year = {2024},
author = {Wang, J and Kim, SJ and Wu, W and Lee, J and Hinton, H and Gertner, RS and Jung, HS and Park, H and Ham, D},
title = {A Cyto-silicon Hybrid System with On-chip Closed-loop Modulation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3466549},
pmid = {39312420},
issn = {1940-9990},
abstract = {We introduce a bioelectronic interface between biological electrogenic cells and a mixed-signal CMOS integrated circuit with an array of surface electrodes, where not only is the CMOS electrode array capable of electrophysiological recording and stimulation of the cells with 1,024 recording and stimulation channels, but it can also provide low-latency artificial signal pathways from cells it records to cells it stimulates. This on-chip closed-loop modulation has an intrinsic latency less than 5 μs. To demonstrate the utility of the on-chip closed loop modulation as an artificial feedback pathway between biological cells, we develop a silicon-cardiomyocyte self-sustained oscillator with a tunable frequency to which both the relevant part of the CMOS chip and cells are locked, and also a silicon-neuron interface with a silicon inhibitory connection between neuronal cells. This line of cyto-silicon hybrid system, where the boundary between biological and semiconductor systems is blurred, may find applications in prosthesis, brain-machine interface, and fundamental biology research.},
}
@article {pmid39312418,
year = {2024},
author = {Lee, G and Jang, J and Song, K and Kim, TW},
title = {A 6-9 GHz 1.28 Gbps 76 mW Amplitude and Synchronized Time Shift Keying IR-UWB CMOS Transceiver for Brain Computer Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3465533},
pmid = {39312418},
issn = {1940-9990},
abstract = {This paper proposes a low-power, high-speed impulse radio-ultra-wideband (IR-UWB) transceiver for brain computer interfaces (BCIs) using amplitude and synchronized time shift keying technique (ASTSK). The proposed IR-UWB transmitter (Tx) generates two pulses (sync pulse and data pulse) per symbol rate. The time difference between two pulses is used for synchronized time shift keying and the amplitude of the two pulses is used for amplitude shift keying. The receiver (Rx) demodulates the time difference with a low power time-to-digital converter (TDC) and peak detector (PD) based amplitude demodulation is suggested to relax analog-to-digital converter (ADC) burden for low power receiver. Especially the Tx-based synchronized operation eliminates the need for complex clock circuitry such as phase-lock loop (PLL) and reference crystal oscillator. Therefore, it can achieve low power and high-speed operation. The prototype, fabricated in 65 nm CMOS, has a frequency range of 6-9 GHz, communication speed of 1.28 Gbps, and power consumption of 18 mW (Tx) and 58 mW (Rx). This work is a fully integrated RF transceiver adapted for high-order modulation and designed to include the receiver.},
}
@article {pmid39310800,
year = {2024},
author = {Vanderheiden, G and Marte, C and Bahram, S},
title = {Rethinking Our Approach to Accessibility in the Era of Rapidly Emerging Technologies.},
journal = {Human aspects of IT for the aged population : 10th International Conference, ITAP 2024, held as part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings. Part I. ITAP (Conference) ...},
volume = {14725},
number = {},
pages = {306-323},
pmid = {39310800},
support = {90REGE0008/ACL/ACL HHS/United States ; },
abstract = {Accessibility has always played catch-up to the detriment of people with disabilities - and this appears to be exacerbated by the rapid advancements in technology. A key question becomes, can we better predict where technology will be in 10 or 20 years and develop a plan to be better positioned to make these new technologies accessible when they make it to market? To attempt to address this question, a "Future of Interface Workshop" was convened in February 2023, chaired by Vinton Cerf and Gregg Vanderheiden that brought together leading researchers in artificial intelligence, brain-computer interfaces, computer vision, and VR/AR/XR, and disability to both a) identify barriers these new technologies might present and how to address them, and b) how these new technologies might be tapped to address current un- or under-addressed problems and populations. This paper provides an overview of the results of the workshop as well as the current version of the R&amp;D Agenda work that was initiated at the conference. It will also present an alternate approach to accessibility that is being proposed based on the new emerging technologies.},
}
@article {pmid39309467,
year = {2024},
author = {Stacy, NI and Smith, R and Sullivan, KE and Nelson, SE and Nolan, EC and De Voe, RS and Witherington, BE and Perrault, JR},
title = {Health assessment of nesting loggerhead sea turtles (Caretta caretta) in one of their largest rookeries (central eastern Florida coast, USA).},
journal = {Conservation physiology},
volume = {12},
number = {1},
pages = {coae064},
pmid = {39309467},
issn = {2051-1434},
abstract = {Reproduction is a physiologically demanding process for sea turtles. Health indicators, including morphometric indices and blood analytes, provide insight into overall health, physiology and organ function for breeding sea turtles as a way to assess population-level effects. The Archie Carr National Wildlife Refuge (ACNWR) on Florida's central eastern coast is critical nesting habitat for loggerhead sea turtles (Caretta caretta), but health variables from this location have not been documented. Objectives of the study were to (1) assess morphometrics and blood analyte data (including haematology, plasma biochemistry, protein electrophoresis, β-hydroxybutyrate, trace nutrients, vitamins and fatty acid profiles) from loggerheads nesting on or near the beaches of the ACNWR, (2) investigate correlations of body condition index (BCI) with blood analytes and (3) analyse temporal trends in morphometric and blood analyte data throughout the nesting season. Morphometric and/or blood analyte data are reported for 57 nesting loggerheads encountered between 2016 and 2019. Plasma copper and iron positively correlated with BCI. Mass tended to decline across nesting season, whereas BCI did not. Many blood analytes significantly increased or decreased across nesting season, reflecting the catabolic state and haemodynamic variations of nesting turtles. Twenty-three of 34 fatty acids declined across nesting season, which demonstrates the physiological demands of nesting turtles for vitellogenesis and reproductive activities, thus suggesting potential utility of fatty acids for the assessment of foraging status and phases of reproduction. The findings herein are relevant for future spatiotemporal and interspecies comparisons, investigating stressor effects and understanding the physiological demands in nesting sea turtles. This information provides comparative data for individual animals in rescue or managed care settings and for assessment of conservation strategies.},
}
@article {pmid39308547,
year = {2024},
author = {Villa, J and Cury, J and Kessler, L and Tan, X and Richter, CP},
title = {Enhancing biocompatibility of the brain-machine interface: A review.},
journal = {Bioactive materials},
volume = {42},
number = {},
pages = {531-549},
pmid = {39308547},
issn = {2452-199X},
support = {R01 DC018666/DC/NIDCD NIH HHS/United States ; },
abstract = {In vivo implantation of microelectrodes opens the door to studying neural circuits and restoring damaged neural pathways through direct electrical stimulation and recording. Although some neuroprostheses have achieved clinical success, electrode material properties, inflammatory response, and glial scar formation at the electrode-tissue interfaces affect performance and sustainability. Those challenges can be addressed by improving some of the materials' mechanical, physical, chemical, and electrical properties. This paper reviews materials and designs of current microelectrodes and discusses perspectives to advance neuroprosthetics performance.},
}
@article {pmid39305732,
year = {2024},
author = {Jirakittayakorn, N and Wongsawat, Y and Mitrirattanakul, S},
title = {An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.},
journal = {Computers in biology and medicine},
volume = {182},
number = {},
pages = {109138},
doi = {10.1016/j.compbiomed.2024.109138},
pmid = {39305732},
issn = {1879-0534},
mesh = {Humans ; *Deep Learning ; *Polysomnography ; *Sleep Stages/physiology ; *Electroencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Female ; Electromyography/methods ; },
abstract = {Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by 'the-last-dense' layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.},
}
@article {pmid39297701,
year = {2024},
author = {Wu, L and Liu, J and Geng, Y and Fang, J and Gao, X and Lai, J and Yao, M and Lu, S and Yin, W and Fu, P and Chen, W and Hu, S},
title = {Single-cell transcriptomic atlas reveals immune and metabolism perturbation of depression in the pathogenesis of breast cancer.},
journal = {Cancer communications (London, England)},
volume = {44},
number = {11},
pages = {1311-1315},
pmid = {39297701},
issn = {2523-3548},
support = {82172770//National Natural Science Foundation of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; },
}
@article {pmid39303815,
year = {2025},
author = {Xie, X and Li, W and Xiong, Z and Xu, J and Liao, T and Sun, L and Xu, H and Zhang, M and Zhou, J and Xiong, W and Fu, Z and Li, Z and Han, Q and Cui, D and Anthony, DC},
title = {Metformin reprograms tryptophan metabolism via gut microbiome-derived bile acid metabolites to ameliorate depression-Like behaviors in mice.},
journal = {Brain, behavior, and immunity},
volume = {123},
number = {},
pages = {442-455},
doi = {10.1016/j.bbi.2024.09.014},
pmid = {39303815},
issn = {1090-2139},
mesh = {Animals ; *Gastrointestinal Microbiome/drug effects ; *Metformin/pharmacology ; Mice ; *Bile Acids and Salts/metabolism ; *Depression/metabolism/drug therapy ; Male ; *Tryptophan/metabolism ; *Serotonin/metabolism ; *Brain/metabolism/drug effects ; Mice, Inbred C57BL ; Behavior, Animal/drug effects ; Stress, Psychological/metabolism ; Akkermansia/metabolism/drug effects ; Antidepressive Agents/pharmacology ; Disease Models, Animal ; Fecal Microbiota Transplantation/methods ; Dysbiosis/metabolism ; },
abstract = {As an adjunct therapy, metformin enhances the efficacy of conventional antidepressant medications. However, its mode of action remains unclear. Here, metformin was found to ameliorate depression-like behaviors in mice exposed to chronic restraint stress (CRS) by normalizing the dysbiotic gut microbiome. Fecal transplants from metformin-treated mice ameliorated depressive behaviors in stressed mice. Microbiome profiling revealed that Akkermansia muciniphila (A. muciniphila), in particular, was markedly increased in the gut by metformin and that oral administration of this species alone was sufficient to reverse CRS-induced depressive behaviors and normalize aberrant stress-induced 5-hydroxytryptamine (5-HT) metabolism in the brain and gut. Untargeted metabolomic profiling further identified the bile acid metabolites taurocholate and deoxycholic acid as direct A. muciniphila-derived molecules that are, individually, sufficient to rescue the CRS-induced impaired 5-HT metabolism and depression-like behaviors. Thus, we report metformin reprograms 5-HT metabolism via microbiome-brain interactions to mitigate depressive syndromes, providing novel insights into gut microbiota-derived bile acids as potential therapeutic candidates for depressive mood disorders from bench to bedside.},
}
@article {pmid39301184,
year = {2024},
author = {Fried-Oken, M and Kinsella, M and Stevens, I and Klein, E},
title = {What stakeholders with neurodegenerative conditions value about speech and accuracy in development of BCI systems for communication.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {11},
number = {1-2},
pages = {21-32},
pmid = {39301184},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {This research examined values of individuals with neurodegenerative conditions about features of speed and accuracy as they consider potential use of augmentative and alternative communication brain-computer interface systems (AAC-BCI). Sixty-six individuals with neurodegenerative disease responded to prompts about six hypothetical ethical vignettes. Data were analyzed with qualitative content analysis. The following themes emerged. (1) Disease progression may contribute to the trade-off between speed and accuracy with AAC-BCI systems. (2) Individual experiences with technology use inform their views about the speed-accuracy trade-off. (3) There is a range of views about how slow or inaccurate communication may impact relationships, the integrity of a message, and quality of life. (4) Design solutions are proposed to address trade-offs in AAC-BCI systems. With the rapid development of AAC-BCI systems, user-centered design must integrate values of potential end-users illustrating that context, partner, message, and environment impact the prioritization of speed or accuracy in any communication exchange.},
}
@article {pmid39300485,
year = {2024},
author = {Meng, L and Shi, Y and Zhao, H and Wang, D and Zhu, X and Ming, D},
title = {The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {166},
pmid = {39300485},
issn = {1743-0003},
support = {82372083//National Natural Science Foundation of China/ ; 2022YFF1202500//National Key Research and Development Program of China/ ; TJYXZDXK-060B//Tianjin Key Medical Discipline (Specialty) Construction Project/ ; 23JCYBJC00690//Tianjin Natural Science Foundation/ ; },
mesh = {Humans ; *Parkinson Disease/rehabilitation/diagnosis/physiopathology ; Male ; Female ; Middle Aged ; Aged ; *Gait/physiology ; Gait Disorders, Neurologic/rehabilitation/etiology/physiopathology/diagnosis ; },
abstract = {BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation.<br><br>METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests.<br><br>RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933).<br><br>CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.},
}
@article {pmid39296916,
year = {2024},
author = {Van Damme, S and Mumford, L and Johnson, A and Chau, T},
title = {Case report: Novel use of clinical brain-computer interfaces in recreation programming for an autistic adolescent with co-occurring attention deficit hyperactivity disorder.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1434792},
pmid = {39296916},
issn = {1662-5161},
abstract = {BACKGROUND: In recent years, several autistic children and youth have shown interest in Holland Bloorview Kids Rehabilitation Hospital's clinical brain computer interface (BCI) program. Existing literature about BCI use among autistic individuals has focused solely on cognitive skill development and remediation of challenging behaviors. To date, the benefits of recreational BCI programming with autistic children and youth have not been documented.<br><br>PURPOSE: This case report summarizes the experiences of an autistic male adolescent with co-occurring attention deficit hyperactivity disorder using a BCI for recreation and considers possible benefits with this novel user population.<br><br>METHODS: A single retrospective chart review was completed with parental guardian's consent.<br><br>FINDINGS: The participant demonstrated enjoyment in BCI sessions and requested continued opportunities to engage in BCI programming. This enjoyment correlated with improved Canadian Occupational Performance Measure (COPM) scores in BCI programming, outperforming scores from other recreational programs. Additionally, clinicians observed changes in social communication efforts and self-advocacy in this first autistic participant.<br><br>CONCLUSION: The use of brain computer interfaces in recreational programming provides a novel opportunity for engagement for autistic children and youth that may also support skill development.},
}
@article {pmid39296025,
year = {2024},
author = {Sung, DJ and Kim, KT and Jeong, JH and Kim, L and Lee, SJ and Kim, H and Kim, SJ},
title = {Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification.},
journal = {Heliyon},
volume = {10},
number = {17},
pages = {e37343},
pmid = {39296025},
issn = {2405-8440},
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs.},
}
@article {pmid39294338,
year = {2024},
author = {Fan, L and Liu, J and Hu, W and Chen, Z and Lan, J and Zhang, T and Zhang, Y and Wu, X and Zhong, Z and Zhang, D and Zhang, J and Qin, R and Chen, H and Zong, Y and Zhang, J and Chen, B and Jiang, J and Cheng, J and Zhou, J and Gao, Z and Liu, Z and Chai, Y and Fan, J and Wu, P and Chen, Y and Zhu, Y and Wang, K and Yuan, Y and Huang, P and Zhang, Y and Feng, H and Song, K and Zeng, X and Zhu, W and Hu, X and Yin, W and Chen, W and Wang, J},
title = {Author Correction: Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.},
journal = {Cell research},
volume = {34},
number = {11},
pages = {815},
doi = {10.1038/s41422-024-01003-5},
pmid = {39294338},
issn = {1748-7838},
}
@article {pmid39294121,
year = {2024},
author = {Song, H and Yang, P and Zhang, X and Tao, R and Zuo, L and Liu, W and Fu, J and Kong, Z and Tang, R and Wu, S and Pang, L and Zhang, X},
title = {Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {381},
pmid = {39294121},
issn = {2158-3188},
mesh = {Humans ; Male ; *Alcoholism/physiopathology/diagnostic imaging ; *Magnetic Resonance Imaging ; Adult ; *Frontal Lobe/physiopathology/diagnostic imaging ; Connectome ; Corpus Striatum/diagnostic imaging/physiopathology ; Case-Control Studies ; Middle Aged ; Neural Pathways/physiopathology/diagnostic imaging ; Young Adult ; },
abstract = {Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R[2] = 0.38; Michigan alcoholism screening test, MAST, R[2] = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R[2] = 0.17; MAST, R[2] = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.},
}
@article {pmid39293197,
year = {2024},
author = {Zagaria, A and Ballesio, A},
title = {Insomnia symptoms as long-term predictors of anxiety symptoms in middle-aged and older adults from the English Longitudinal Study of Ageing (ELSA), and the role of systemic inflammation.},
journal = {Sleep medicine},
volume = {124},
number = {},
pages = {120-126},
doi = {10.1016/j.sleep.2024.09.020},
pmid = {39293197},
issn = {1878-5506},
mesh = {Humans ; *Sleep Initiation and Maintenance Disorders/epidemiology/complications/blood ; Female ; Male ; Middle Aged ; Longitudinal Studies ; *Inflammation/blood ; *Anxiety ; *C-Reactive Protein/analysis ; Aged ; England/epidemiology ; Aging/psychology/physiology ; Risk Factors ; },
abstract = {Insomnia, i.e., difficulties in sleep onset and sleep maintenance, may increase the risk of anxiety symptoms, although long-term follow-up studies are rarely reported. Here, we examined whether insomnia symptoms may predict anxiety symptoms in a 9-year follow-up, and whether inflammation may play a mediating role. Data from 1355 participants (63.44 ± 7.47 years, 55.1 % females) from the English Longitudinal Study of Ageing (ELSA) were analysed. Insomnia symptoms were assessed in 2012/13. High-sensitivity C-reactive protein (hs-CRP), a marker of systemic inflammation, was measured in 2016/17. Anxiety symptoms were assessed in 2020/21. After adjusting for confounders and baseline levels, structural equation modelling (SEM) revealed that insomnia symptoms significantly predicted anxiety symptoms (β = 0.357, p < .001) but not hs-CRP (β = -0.016, p = .634). Similarly, hs-CRP was not related to anxiety symptoms (β = -0.024, p = .453). The hs-CRP mediation hypothesis was therefore rejected (β = 0.0004; 95 % BCI -0.001 to 0.005), and multi-group SEM showed that sex did not moderate these paths. However, baseline diagnoses of anxiety disorders prospectively predicted higher hs-CRP (B = 0.083, p = .030). Results of the current study suggest that individuals with baseline anxiety disorders may be at higher risk of developing low-grade chronic inflammation. Several alternative psychophysiological mechanisms linking insomnia and anxiety symptoms should be explored, including autonomic and cortical pre-sleep arousal, cortisol reactivity, and pro-inflammatory cytokines. Finally, insomnia symptoms may be a treatment target to lower the risk of anxiety symptoms in elderly.},
}
@article {pmid39292591,
year = {2025},
author = {Wang, H and Han, H and Gan, JQ and Wang, H},
title = {Lightweight Source-Free Domain Adaptation Based on Adaptive Euclidean Alignment for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {2},
pages = {909-922},
doi = {10.1109/JBHI.2024.3463737},
pmid = {39292591},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; Adult ; },
abstract = {For privacy protection of subjects in electroencephalogram (EEG)-based brain-computer interfaces (BCIs), using source-free domain adaptation (SFDA) for cross-subject recognition has proven to be highly effective. However, updating and storing a model trained on source subjects for each new subject can be inconvenient. This paper extends Euclidean alignment (EA) to propose adaptive Euclidean alignment (AEA), which learns a projection matrix to align the distribution of the target subject with the source subjects, thus eliminating domain drift issues and improving model classification performance of subject-independent BCIs. Combining the proposed AEA with various existing SFDA methods, such as SHOT, GSFDA, and NRC, this paper presents three new methods: AEA-SHOT, AEA-GSFDA, and AEA-NRC. In our experimental studies, these AEA-based SFDA methods were applied to four well-known deep learning models (i.e., EEGNet, Shallow ConvNet, Deep ConvNet, and MSFBCNN) on two motor imagery (MI) datasets, one event-related potential (ERP) dataset and one steady-state visual evoked potentials (SSVEP) dataset. The advanced cross-subject EEG classification performance demonstrates the efficacy of our proposed methods. For example, AEA-SHOT achieved the best average accuracy of 81.4% on the PhysioNet dataset.},
}
@article {pmid39292590,
year = {2025},
author = {Geirnaert, S and Yao, Y and Francart, T and Bertrand, A},
title = {Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {2},
pages = {970-983},
doi = {10.1109/JBHI.2024.3462991},
pmid = {39292590},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; Young Adult ; },
abstract = {Various new brain-computer interface technologies or neuroscience applications require decoding stimulus-following neural responses to natural stimuli such as speech and video from, e.g., electroencephalography (EEG) signals. In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus. GCCA can be used to improve the signal-to-noise ratio of the stimulus-following neural responses relative to all other irrelevant (non-)neural activity, or to quantify the correlated neural activity across multiple subjects in a group-wise coherence metric. However, the traditional GCCA technique is stimulus-unaware: no information about the stimulus is used to estimate the correlated components from the neural data of several subjects. Therefore, the GCCA technique might fail to extract relevant correlated signal components in practical situations where the amount of information is limited, for example, because of a limited amount of training data or group size. This motivates a new stimulus-informed GCCA (SI-GCCA) framework that allows taking the stimulus into account to extract the correlated components. We show that SI-GCCA outperforms GCCA in various practical settings, for both auditory and visual stimuli. Moreover, we showcase how SI-GCCA can be used to steer the estimation of the components towards the stimulus. As such, SI-GCCA substantially improves upon GCCA for various purposes, ranging from preprocessing to quantifying attention.},
}
@article {pmid39290180,
year = {2024},
author = {},
title = {Neuroprostheses move forward, in multiple languages.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {30},
number = {5},
pages = {517},
doi = {10.1177/10738584241276369},
pmid = {39290180},
issn = {1089-4098},
mesh = {Humans ; *Language ; Brain-Computer Interfaces ; Neural Prostheses ; Brain/physiology ; },
}
@article {pmid39289533,
year = {2024},
author = {Wexler, A and Feinsinger, A},
title = {Ethical challenges in translating brain-computer interfaces.},
journal = {Nature human behaviour},
volume = {8},
number = {10},
pages = {1831-1833},
pmid = {39289533},
issn = {2397-3374},
support = {RF1MH121373//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/ ; },
}
@article {pmid39288794,
year = {2025},
author = {Sawyer, A and Chetty, N and McMullen, DP and Dean, H and Eisler, J and Fried-Oken, M and Hochberg, LR and Gibbons, C and Waite, KE and Oxley, T and Fry, A and Weber, D and Putrino, D},
title = {Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop.},
journal = {Journal of neural engineering},
volume = {22},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad7bec},
pmid = {39288794},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Consensus ; Outcome Assessment, Health Care/methods ; },
abstract = {Objective: The 10th International brain computer interface (BCI) Society Meeting, 'Balancing Innovation and Translation', was held from the 6th to 9th of June 2023 in Brussels, Belgium. This report provides a summary of the workshop 'Building Consensus on Clinical Outcome Assessments (COAs) for BCI Devices'. This workshop was intended to give participants an overview of the current state of BCI, future opportunities, and how different countries and regions provide regulatory oversight to support the BCI community to develop safe and effective devices for patients.Approach: Five presentations and a panel discussion including representatives from regulators, industry, and clinical research stakeholders focused on how various stakeholders and the BCI community might best work together to ensure studies provide data that is useful for evaluating safety and effectiveness, including reaching consensus on COAs that represent clinically meaningful benefits and support regulatory and payor requirements. This report focuses on the regulatory and reimbursement requirements for medical devices and how to best measure safety and effectiveness and summarizes the presentations from five experts and the discussion between the panel and the audience.Main results: Consensus was reached on the following items specifically related to BCI: (i) the importance of and need for a new generation of COAs, (ii) the challenges facing the development of appropriate clinical outcome assessments, and (iii) that improvements in COAs should demonstrate obvious and clinically meaningful benefit(s). There was discussion on: (i) clinical trial design for BCIs and (ii) considerations for payor reimbursement and other funding.Significance: Whilst the importance of building community consensus on COAs was apparent, further collaboration will be required to reach consensus on which specific current and/or novel COAs could be used for the BCI field to evolve from research to market.},
}
@article {pmid39288164,
year = {2024},
author = {Hamidi Shishavan, H and Roy, R and Golzari, K and Singla, A and Zalozhin, D and Lohan, D and Farooq, M and Dede, EM and Kim, I},
title = {Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features.},
journal = {PloS one},
volume = {19},
number = {9},
pages = {e0308506},
pmid = {39288164},
issn = {1932-6203},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Adult ; Male ; *Photic Stimulation ; Female ; Electroencephalography/methods ; Young Adult ; Automobile Driving ; Signal-To-Noise Ratio ; },
abstract = {Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.},
}
@article {pmid39286921,
year = {2024},
author = {Cai, M and Hong, J},
title = {Joint multi-feature extraction and transfer learning in motor imagery brain computer interface.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2024.2404541},
pmid = {39286921},
issn = {1476-8259},
abstract = {Motor imagery brain computer interface (BCI) systems are considered one of the most crucial paradigms and have received extensive attention from researchers worldwide. However, the non-stationary from subject-to-subject transfer is a substantial challenge for robust BCI operations. To address this issue, this paper proposes a novel approach that integrates joint multi-feature extraction, specifically combining common spatial patterns (CSP) and wavelet packet transforms (WPT), along with transfer learning (TL) in motor imagery BCI systems. This approach leverages the time-frequency characteristics of WPT and the spatial characteristics of CSP while utilizing transfer learning to facilitate EEG identification for target subjects based on knowledge acquired from non-target subjects. Using dataset IVa from BCI Competition III, our proposed approach achieves an impressive average classification accuracy of 93.4%, outperforming five kinds of state-of-the-art approaches. Furthermore, it offers the advantage of enabling the design of various auxiliary problems to learn different aspects of the target problem from unlabeled data through transfer learning, thereby facilitating the implementation of innovative ideas within our proposed approach. Simultaneously, it demonstrates that integrating CSP and WPT while transferring knowledge from other subjects is highly effective in enhancing the average classification accuracy of EEG signals and it provides a novel solution to address subject-to-subject transfer challenges in motor imagery BCI systems.},
}
@article {pmid39286440,
year = {2024},
author = {Kew, SYN and Mok, SY and Goh, CH},
title = {Machine learning and brain-computer interface approaches in prognosis and individualized care strategies for individuals with amyotrophic lateral sclerosis: A systematic review.},
journal = {MethodsX},
volume = {13},
number = {},
pages = {102765},
pmid = {39286440},
issn = {2215-0161},
abstract = {Amyotrophic lateral sclerosis (ALS) characterized by progressive degeneration of motor neurons is a debilitating disease, posing substantial challenges in both prognosis and daily life assistance. However, with the advancement of machine learning (ML) which is renowned for tackling many real-world settings, it can offer unprecedented opportunities in prognostic studies and facilitate individuals with ALS in motor-imagery tasks. ML models, such as random forests (RF), have emerged as the most common and effective algorithms for predicting disease progression and survival time in ALS. The findings revealed that RF models had an excellent predictive performance for ALS, with a testing R2 of 0.524 and minimal treatment effects of 0.0717 for patient survival time. Despite significant limitations in sample size, with a maximum of 18 participants, which may not adequately reflect the population diversity being studied, ML approaches have been effectively applied to ALS datasets, and numerous prognostic models have been tested using neuroimaging data, longitudinal datasets, and core clinical variables. In many literatures, the constraints of ML models are seldom explicitly enunciated. Therefore, the main objective of this research is to provide a review of the most significant studies on the usage of ML models for analyzing ALS. This review covers a variation of ML algorithms involved in applications in ALS prognosis besides, leveraging ML to improve the efficacy of brain-computer interfaces (BCIs) for ALS individuals in later stages with restricted voluntary muscular control. The key future advances in individualized care and ALS prognosis may include the advancement of more personalized care aids that enable real-time input and ongoing validation of ML in diverse healthcare contexts.},
}
@article {pmid39285529,
year = {2024},
author = {Wang, N and Huang, J and Fang, Y and Du, H and Chen, Y and Zhao, S},
title = {Molecular biomarkers of blunt cardiac injury: recent advances and future perspectives.},
journal = {Expert review of molecular diagnostics},
volume = {24},
number = {11},
pages = {1023-1031},
doi = {10.1080/14737159.2024.2405919},
pmid = {39285529},
issn = {1744-8352},
mesh = {Humans ; *Biomarkers/analysis ; *Heart Injuries/diagnosis ; Natriuretic Peptide, Brain/blood ; Peptide Fragments/blood ; Troponin/blood ; Wounds, Nonpenetrating/diagnosis ; },
abstract = {INTRODUCTION: Blunt cardiac injury (BCI), associated with high morbidity and mortality, involves multiple injuries. With no widely accepted gold standard diagnostic test and molecular biomarkers still in debate and far from application in clinical practice, exploring specific molecular biomarkers of BCI is of great significance. The clarification of molecular biomarkers can improve the diagnosis of BCI, leading to more precise care for victims in various situations.<br><br>AREAS COVERED: Using the search term 'Biomarker AND Blunt cardiac injury,' we carefully reviewed related papers from June 2004 to June 2024 in PubMed and CNKI. After reviewing, we included 20 papers, summarizing the biomarkers reported in previous studies, and then reviewed molecular biomarkers such as troponins, Nterminal proBtype natriuretic peptide (NT proBNP), hearttype fatty acid binding protein (hFABP), and lactate for BCI diagnosis. Finally, valuable views on future research directions for diagnostic biomarkers of BCI were presented.<br><br>EXPERT OPINION: Several advanced technologies have been introduced into clinical medicine, which have ultimately changed the research on cardiac diseases in recent years. Some biomarkers have been identified and utilized for BCI diagnosis. Herein, we summarize the latest relevant information as a reference for clinical practice and future studies.},
}
@article {pmid39284710,
year = {2024},
author = {Duran, F and Medina, MS and Ibargüengoytía, NR and Boretto, JM},
title = {Effects of blood extraction and ecophysiological experiments on stress in adult males of Liolaemus attenboroughi.},
journal = {Biology open},
volume = {13},
number = {10},
pages = {},
pmid = {39284710},
issn = {2046-6390},
support = {PICT2020-03395//Fondo para la Investigacion Cientifica y Tecnologica/ ; UNComahue 04/B234//Universidad Nacional del Comahue/ ; PICT2020-03395//Fondo para la Investigaci&#xf3;n Cient&#xed;fica y Tecnol&#xf3;gica/ ; //INIBIOMA: Instituto de Investigaciones en Biodiversidad y Medioambiente/ ; },
mesh = {Animals ; Male ; *Stress, Physiological ; *Lizards/physiology/blood/parasitology ; Corticosterone/blood ; },
abstract = {Stress during laboratory experiments can affect the outcomes of ecophysiological studies. The serum corticosterone concentration (CORT), the leukocyte profile, heterophil/lymphocyte ratio (H/L), and the presence of blood endoparasites were analyzed as a proxy of stress and immunological state in adult males of the lizard Liolaemus attenboroughi, endemic to Patagonia, Argentina. The results of the ecophysiological variables (preferred temperature, running speed, locomotor endurance, and body condition index, BCI) were analyzed in relation to stress indicators obtained from blood samples taken at three different times: at capture, and on the third and seventh days in the laboratory. Males at capture showed a high percentage of lymphocytes and heterophils and a low of basophils, monocytes, and eosinophils. Haemogregorina-type endoparasites have been recorded in the genus Liolaemus for the first time. The proportion of infected males remained stable during captivity; however, these males showed higher CORT levels, increased percentages of basophils, and decreased percentages of lymphocytes. There was a significant increment in CORT and H/L, and a decrease in BCI during laboratory experiments, compared with baseline values at capture. The performance was not related to the CORT or the repeated blood sampling. The BCI decreased, possibly due to energy reserve mobilization caused by acute stress. This study shows that blood extraction and ecophysiological experiments over 7 days have a minor effect on the stress indicators used.},
}
@article {pmid39283802,
year = {2024},
author = {Wang, L and Li, M and Xu, D and Yang, Y},
title = {Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3636-3646},
doi = {10.1109/TNSRE.2024.3461339},
pmid = {39283802},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Algorithms ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Deep Learning ; Male ; Adult ; Female ; Cerebral Cortex/physiology ; Young Adult ; },
abstract = {Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex neural network. In consideration of the structural and functional similarity of the neurons in a neuroanatomical region, i.e., a region of interest (ROI), we propose that the comprehensive performance of each ROI may be reflected by a specific representative dipole (RD), and the time-frequency spectrums of all RDs are applied simultaneously to Random Forest algorithm to give a quantitative metric of each ROI importance (RI). Then, the more divided sub-band spectral powers are reinforced by RI, and they are interpolated to a 2-dimensional (2D) plane transformed from 3D space of all RDs, yielding an ensemble representation of RD feature image sequences (ERDFIS). Furthermore, a lightweight network, including 2D separable convolution and gated recurrent unit (2DSCG), is developed to extract and classify the frequency-spatial and temporal features from ERDFIS, forming a novel MI decoding method in cortical level (called ERDFIS-2DSCG). Based on two public datasets, the decoding accuracies of ten-fold cross-validation are 89.89% and 94.35%, respectively. The results suggest that RD can embody the overall property of ROI in time-frequency-space domains, and ROI importance is helpful to highlight the subject-based characteristics of MI-EEG. Meanwhile, 2DSCG is matched well with ERDFIS, jointly improving the decoding performance.},
}
@article {pmid39283065,
year = {2024},
author = {Jin, L and Hu, J and Han, G and Li, Y and Zhu, J and Zhu, Y and He, X and Xu, D and Zheng, L and Bai, R and Wang, L},
title = {Glymphatic System Impairment in the Advanced Stage of Moyamoya Disease.},
journal = {Journal of neuroscience research},
volume = {102},
number = {9},
pages = {e25381},
doi = {10.1002/jnr.25381},
pmid = {39283065},
issn = {1097-4547},
support = {2022C03133//Key Research and Development Program of Zhejiang Province/ ; 81870910//National Natural Science Foundation of China/ ; 82171271//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Moyamoya Disease/diagnostic imaging/surgery/pathology/physiopathology ; Female ; Male ; *Glymphatic System/diagnostic imaging/pathology ; Adult ; Middle Aged ; *Diffusion Tensor Imaging ; *Cerebrovascular Circulation/physiology ; Young Adult ; Angiography, Digital Subtraction ; Brain/diagnostic imaging/pathology ; },
abstract = {Assessing the glymphatic system activity using diffusion tensor imaging analysis along with the perivascular space (DTI-ALPS) may be helpful to understand the pathophysiology of moyamoya disease (MMD). 63 adult patients with MMD and 20 healthy controls (HCs) were included for T1-weighted images, T2-FLAIR, pseudocontinuous arterial spin labeling, and DTI. 60 patients had digital subtraction angiography more than 6 months after combined revascularization. The Suzuki stage, postoperative Matsushima grade, periventricular anastomoses (PA), enlarged perivascular spaces (EPVS), deep and subcortical white matter hyperintensities (DSWMH), DTI-ALPS, cerebral blood flow (CBF), and cognitive scales of MMD patients were assessed. MMD patients were divided into early and advanced stage based on the Suzuki stage. We detected lower DTI-ALPS in patients with advanced stage relative to HCs (p = 0.046) and patients with early stage (p = 0.004), hemorrhagic MMD compared with ischemic MMD (p = 0.048), and PA Grade 2 compared with Grade 0 (p = 0.010). DTI-ALPS was correlated with the EPVS in basal ganglia (r = -0.686, p < 0.001), Suzuki stage (r = -0.465, p < 0.001), DSWMH (r = -0.423, p = 0.001), and global CBF (r = 0.300, p = 0.017) and cognitive scores (r = 0.343, p = 0.018). The DTI-ALPS of patients with good postoperative collateral formation was higher compared to those with poor postoperative collateral formation (p = 0.038). In conclusion, the glymphatic system was impaired in advanced MMD patients and may affected cognitive function and postoperative neoangiogenesis.},
}
@article {pmid39279838,
year = {2024},
author = {Vermani, A and Dowling, M and Jeon, H and Jordan, I and Nassar, J and Bernaerts, Y and Zhao, Y and Vaerenbergh, SV and Park, IM},
title = {Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {39279838},
issn = {2331-8422},
support = {RF1 DA056404/DA/NIDA NIH HHS/United States ; },
abstract = {Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders.},
}
@article {pmid39279046,
year = {2024},
author = {Chen, Y and Tong, S and Xu, Y and Xu, Y and Wu, Z and Zhu, X and Wang, X and Li, C and Lin, C and Li, X and Zhang, C and Wang, Y and Shao, X and Fang, J and Wu, Y},
title = {Involvement of basolateral amygdala-rostral anterior cingulate cortex in mechanical allodynia and anxiety-like behaviors and potential mechanisms of electroacupuncture.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {9},
pages = {e70035},
pmid = {39279046},
issn = {1755-5949},
support = {LQ24H270003//Natural Science Foundation of Zhejiang Province/ ; LY19H270007//Natural Science Foundation of Zhejiang Province/ ; LY23H270009//Natural Science Foundation of Zhejiang Province/ ; 82074541//National Natural Science Foundation of China/ ; 202210344020//College Students' Innovative Entrepreneurial Training Plan Program/ ; 2021YKJ08//Zhejiang Chinese Medical University/ ; 2021YKJ09//Zhejiang Chinese Medical University/ ; },
mesh = {Animals ; *Electroacupuncture/methods ; *Gyrus Cinguli ; *Hyperalgesia/therapy ; *Anxiety/therapy/psychology ; Male ; Mice ; *Basolateral Nuclear Complex/metabolism ; Mice, Inbred C57BL ; Neuralgia/therapy/psychology ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Neural Pathways ; },
abstract = {AIMS: Chronic pain is highly associated with anxiety. Electroacupuncture (EA) is effective in relieving pain and anxiety. Currently, little is known about the neural mechanisms underlying the comorbidity of chronic pain and anxiety and the EA mechanism. This study investigated a potential neural circuit underlying the comorbid and EA mechanisms.<br><br>METHODS: Spared nerve injury (SNI) surgery established the chronic neuropathic pain mouse model. The neural circuit was activated or inhibited using the chemogenetic method to explore the relationship between the neural circuit and mechanical allodynia and anxiety-like behaviors. EA combined with the chemogenetic method was used to explore whether the effects of EA were related to this neural circuit.<br><br>RESULTS: EA attenuated mechanical allodynia and anxiety-like behaviors in SNI mice, which may be associated with the activity of CaMKII neurons in the basolateral amygdala (BLA). Inhibition of BLA[CaMKII]-rACC induced mechanical allodynia and anxiety-like behaviors in sham mice. Activation of the BLA[CaMKII]-rACC alleviated neuropathic pain and anxiety-like behaviors in SNI mice. The analgesic and anxiolytic effects of 2&#x2009;Hz EA were antagonized by the inhibition of the BLA[CaMKII]-rACC.<br><br>CONCLUSION: BLA[CaMKII]-rACC mediates mechanical allodynia and anxiety-like behaviors. The analgesic and anxiolytic effects of 2&#x2009;Hz EA may be associated with the BLA[CaMKII]-rACC.},
}
@article {pmid39277877,
year = {2024},
author = {Chen, H},
title = {Translational Bioethics in China: Brain-Computer Interface Research as a Case Study.},
journal = {Ethics &amp; human research},
volume = {46},
number = {5},
pages = {37-42},
doi = {10.1002/eahr.500224},
pmid = {39277877},
issn = {2578-2363},
mesh = {China ; Humans ; *Brain-Computer Interfaces/ethics ; *Translational Research, Biomedical/ethics ; *Bioethics ; Ethics, Research ; },
abstract = {The research and development of emerging technologies has potential long-term and societal impacts that pose governance challenges. This essay summarizes the development of research ethics in China over the past few decades, as well as the measures taken by the Chinese government to build its ethical governance system of science and technology after the occurrence of the CRISPR-babies incident. The essay then elaborates on the current problems of this system through the case study of ethical governance of brain-computer interface research, and explores how the transition from research ethics to translational bioethics, which encourages interdisciplinary collaboration and focuses on societal implications, may respond to the challenges of ethical governance of science and technology.},
}
@article {pmid39275712,
year = {2024},
author = {Mohamed, AK and Aswat, M and Aharonson, V},
title = {Low-Cost Dynamometer for Measuring and Regulating Wrist Extension and Flexion Motor Tasks in Electroencephalography Experiments.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {17},
pages = {},
pmid = {39275712},
issn = {1424-8220},
support = {001-404 - 8241101- 5121105//University of the Witwatersrand/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Wrist/physiology ; Male ; Adult ; Female ; Movement/physiology ; Brain-Computer Interfaces ; Young Adult ; Muscle Strength Dynamometer ; Range of Motion, Articular/physiology ; },
abstract = {A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.},
}
@article {pmid39275636,
year = {2024},
author = {Drigas, A and Sideraki, A},
title = {Brain Neuroplasticity Leveraging Virtual Reality and Brain-Computer Interface Technologies.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {17},
pages = {},
pmid = {39275636},
issn = {1424-8220},
mesh = {Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; *Neuronal Plasticity/physiology ; *Virtual Reality ; },
abstract = {This study explores neuroplasticity through the use of virtual reality (VR) and brain-computer interfaces (BCIs). Neuroplasticity is the brain's ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement.},
}
@article {pmid39274138,
year = {2024},
author = {Chang, H and Sun, Y and Lu, S and Lan, Y},
title = {Enhancing Brain-Computer Interfaces through Kriging-Based Fusion of Sparse Regression Partial Differential Equations to Counter Injection Molding View of Node Displacement Effects.},
journal = {Polymers},
volume = {16},
number = {17},
pages = {},
pmid = {39274138},
issn = {2073-4360},
abstract = {Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain-computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain-computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%.},
}
@article {pmid39273744,
year = {2024},
author = {Banyai, AD and Brișan, C},
title = {Robotics in Physical Rehabilitation: Systematic Review.},
journal = {Healthcare (Basel, Switzerland)},
volume = {12},
number = {17},
pages = {},
pmid = {39273744},
issn = {2227-9032},
abstract = {As the global prevalence of motor disabilities continues to rise, there is a pressing need for advanced solutions in physical rehabilitation. This systematic review examines the progress and challenges of implementing robotic technologies in the motor rehabilitation of patients with physical disabilities. The integration of robotic technologies such as exoskeletons, assistive training devices, and brain-computer interface systems holds significant promise for enhancing functional recovery and patient autonomy. The review synthesizes findings from the most important studies, focusing on the clinical effectiveness of robotic interventions in comparison to traditional rehabilitation methods. The analysis reveals that robotic therapies can significantly improve motor function, strength, co-ordination, and dexterity. Robotic systems also support neuroplasticity, enabling patients to relearn lost motor skills through precise, controlled, and repetitive exercises. However, the adoption of these technologies is hindered by high costs, the need for specialized training, and limited accessibility. Key insights from the review highlight the necessity of personalizing robotic therapies to meet individual patient needs, alongside addressing technical, economic, social, and cultural barriers. The review also underscores the importance of continued research to optimize these technologies and develop effective implementation strategies. By overcoming these challenges, robotic technologies can revolutionize motor rehabilitation, improving quality of life and social integration for individuals with motor disabilities.},
}
@article {pmid39273223,
year = {2024},
author = {Cho, C and Lee, S},
title = {The Effects of Blood Flow Restriction Aerobic Exercise on Body Composition, Muscle Strength, Blood Biomarkers, and Cardiovascular Function: A Narrative Review.},
journal = {International journal of molecular sciences},
volume = {25},
number = {17},
pages = {},
pmid = {39273223},
issn = {1422-0067},
support = {NRF-2022R1A2C1003657//National Research Foundation of Korea (NRF)/ ; Research Grant in 2021//Incheon National University/ ; },
mesh = {Humans ; *Body Composition ; *Exercise/physiology ; *Biomarkers/blood ; *Muscle Strength/physiology ; Regional Blood Flow ; Resistance Training/methods ; },
abstract = {Blood flow restriction exercise has emerged as a promising alternative, particularly for elderly individuals and those unable to participate in high-intensity exercise. However, existing research has predominantly focused on blood flow restriction resistance exercise. There remains a notable gap in understanding the comprehensive effects of blood flow restriction aerobic exercise (BFRAE) on body composition, lipid profiles, glycemic metabolism, and cardiovascular function. This review aims to explore the physiological effects induced by chronic BFRAE. Chronic BFRAE has been shown to decrease fat mass, increase muscle mass, and enhance muscular strength, potentially benefiting lipid profiles, glycemic metabolism, and overall function. Thus, the BFRAE offers additional benefits beyond traditional aerobic exercise effects. Notably, the BFRAE approach may be particularly suitable for individuals with low fitness levels, those prone to injury, the elderly, obese individuals, and those with metabolic disorders.},
}
@article {pmid39271023,
year = {2024},
author = {Zhang, Z and Li, M and Wei, R and Liao, W and Wang, F and Xu, G},
title = {Research on shared control of robots based on hybrid brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {412},
number = {},
pages = {110280},
doi = {10.1016/j.jneumeth.2024.110280},
pmid = {39271023},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; *Electroencephalography/methods ; Male ; *Electromyography/methods ; Young Adult ; Brain/physiology ; Adult ; Female ; },
abstract = {BACKGROUND: With the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain-computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control.<br><br>NEW METHODS: This paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion.<br><br>RESULTS: Eight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50&#x202f;%, improves the success rate of traversing obstacles by 25.00&#x202f;%, and the navigation trajectory is more consistent with expectations.<br><br>CONCLUSIONS: The HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.},
}
@article {pmid39270764,
year = {2024},
author = {Chang, W and Zhao, X and Wang, L and Zhou, X},
title = {Causal role of frontocentral beta oscillation in comprehending linguistic communicative functions.},
journal = {NeuroImage},
volume = {300},
number = {},
pages = {120853},
doi = {10.1016/j.neuroimage.2024.120853},
pmid = {39270764},
issn = {1095-9572},
mesh = {Humans ; Female ; Male ; *Beta Rhythm/physiology ; Young Adult ; Adult ; *Comprehension/physiology ; Communication ; Electroencephalography ; Frontal Lobe/physiology ; Linguistics ; },
abstract = {Linguistic communication is often considered as an action serving the function of conveying the speaker's goal to the addressee. Although neuroimaging studies have suggested a role of the motor system in comprehending communicative functions, the underlying mechanism is yet to be specified. Here, by two EEG experiments and a tACS experiment, we demonstrate that the frontocentral beta oscillation, which represents action states, plays a crucial part in linguistic communication understanding. Participants read scripts involving two interlocutors and rated the interlocutors' attitudes. Each script included a critical sentence said by the speaker expressing a context-dependent function of either promise, request, or reply to the addressee's query. These functions were behaviorally discriminated, with higher addressee's will rating for the promise than for the reply and higher speaker's will rating for the request than for the reply. EEG multivariate analyses showed that different communicative functions were represented by different patterns of the frontocentral beta activity but not by patterns of alpha activity. Further tACS results showed that, relative to alpha tACS and sham stimulation, beta tACS improved the predictability of communicative functions of request or reply, as measured by the speaker's will rating. These results convergently suggest a causal role of the frontocentral beta activities in comprehending linguistic communications.},
}
@article {pmid39269692,
year = {2024},
author = {Wang, L and Wang, J and Su, H and Zhang, X and Zhang, L and Kang, X},
title = {A zero precision loss framework for EEG channel selection: enhancing efficiency and maintaining interpretability.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2024.2401918},
pmid = {39269692},
issn = {1476-8259},
abstract = {The brain-computer interface (BCI) systems based on motor imagery typically rely on a large number of electrode channels to acquire information. The rational selection of electroencephalography (EEG) channel combinations is crucial for optimizing computational efficiency and enhancing practical applicability. However, evaluating all potential channel combinations individually is impractical. This study aims to explore a strategy for quickly achieving a balance between maximizing channel reduction and minimizing precision loss. To this end, we developed a spatio-temporal attention perception network named STAPNet. Based on the channel contributions adaptively generated by its subnetwork, we propose an extended step bi-directional search strategy that includes variable ratio channel selection (VRCS) and strided greedy channel selection (SGCS), designed to enhance global search capabilities and accelerate the optimization process. Experimental results show that on the High Gamma and BCI Competition IV 2a public datasets, the framework respectively achieved average maximum accuracies of 91.47% and 84.17%. Under conditions of zero precision loss, the average number of channels was reduced by a maximum of 87.5%. Additionally, to investigate the impact of neural information loss due to channel reduction on the interpretation of complex brain functions, we employed a heatmap visualization algorithm to verify the universal importance and complete symmetry of the selected optimal channel combination across multiple datasets. This is consistent with the brain's cooperative mechanism when processing tasks involving both the left and right hands.},
}
@article {pmid39268765,
year = {2024},
author = {Barco-Castillo, C and Sotelo Perilla, M and Rangel Amaya, J and Castaño, JC},
title = {A new nomogram for the evaluation of detrusor underactivity and bladder outlet obstruction in nonneurogenic female patients with lower urinary tract symptoms who undergo urodynamic studies.},
journal = {Neurourology and urodynamics},
volume = {43},
number = {8},
pages = {2076-2083},
doi = {10.1002/nau.25553},
pmid = {39268765},
issn = {1520-6777},
mesh = {Humans ; Female ; *Urinary Bladder Neck Obstruction/physiopathology/diagnosis ; *Nomograms ; *Urodynamics ; *Lower Urinary Tract Symptoms/physiopathology/diagnosis ; Middle Aged ; *Urinary Bladder, Underactive/physiopathology/diagnosis ; Adult ; Aged ; Urinary Bladder/physiopathology ; },
abstract = {INTRODUCTION: Micturition physiology differs in men and women. However, the results in standard urodynamic studies in women with lower urinary tract symptoms (LUTS) were extrapolated from studies in men. Nowadays, the only validated nomogram for females is Solomon-Greenwell's. However, it only evaluated bladder outlet obstruction (BOO) without considering detrusor underactivity (DU). This study aims to create a nomogram that includes an evaluation of DU and BOO in nonneurogenic women and validate it against videourodynamic studies along with other nomograms.<br><br>MATERIALS AND METHODS: For the first analysis (creation cohort), we included 183 women with LUTS who underwent videourodynamic study between 2022 and 2023. Exclusion criteria were females with neurologic diseases, renal transplantation, and trouble performing the flow-pressure study. Baseline characteristics of the patients, urodynamic parameters, and classifications on different nomograms and indexes were evaluated. A logistic regression found Qmax and PdetQmax as predictors for DU and BOO. The Barco-Castillo nomogram was created by clustering analysis and adjusted by the results of the logistic regression. A second (test) cohort was evaluated from 2023 to 2024, including 142 patients for the validation of the nomogram. A p&#x2009;<&#x2009;0.05 was considered significant.<br><br>RESULTS: All urodynamic parameters were compared between both cohorts, with no significant differences. The median age of the creation cohort was 50 years old (interquartile range [IQR] 39-63). All patients had LUTS and a previous standard urodynamic study without a clear diagnosis. The cluster analysis had a p&#x2009;<&#x2009;0.05 for two groups of BOO (yes/no) and two of DU (yes/no). We created the graph based on the logistic regression results and adjusted it according to the data. The median age of the test cohort was 44 years old (IQR 33.75-59) and had the same indication for the videourodynamic study. The receiver operating characteristic&#xa0;(ROC) curve for BOO showed an accuracy of 85.4% for Barco-Castillo nomogram, 68.5% for Blaivas-Groutz, 58.1% for Solomon-Greenwell, 57.1% for BOOI, and 50% for LinPURR. For DU, accuracy was 80.5% for PIP-1, 80.2% for Barco-Castillo, 76.6% for BCI, and 70.1% for LinPURR.<br><br>CONCLUSIONS: When evaluating women's urodynamic studies, it is important to focus on female physiology and discourage the use of parameters previously standardized in men. We encourage using the new Barco-Castillo nomogram to determine BOO and DU in women as the currently easiest and more accurate tool.},
}
@article {pmid39268310,
year = {2024},
author = {Bakrbaldawi, AAA and Al-Sheikh, U and Jiang, H and Zhu, J},
title = {Multiple Glioblastomas Ablation by Laser Interstitial Thermal Therapy (LITT): A Rare Case.},
journal = {Cureus},
volume = {16},
number = {8},
pages = {e66726},
pmid = {39268310},
issn = {2168-8184},
abstract = {Multiple glioblastomas (GBMs) are aggressive, malignant, and sporadic brain tumors. We present the case of a 58-year-old patient with two GBMs in the right frontal lobe and associated edema. The patient presented with sudden left limb weakness accompanied by abnormal gait for five consecutive days. Magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT), a minimally invasive technique that disperses thermal energy was used to cauterize the deep-seated brain lesions. Following two sessions of MRg-LITT, the patient showed full remission from symptoms. However, the disruption of the blood-brain barrier (BBB) induced vasogenic edema surrounding the necrotic GBMs. Post-operative nine-month MRI images revealed severe vasogenic edema and compression on the ventricles, shifting the midline toward the left side. Therefore the patient underwent an emergency craniectomy and continues to live with close follow-ups. Here, we established that LITT procedures were effective in cauterizing GBMs with no recurrence.},
}
@article {pmid39266806,
year = {2024},
author = {Pragya, and Bisht, S and Parashar, P},
title = {Nanotechnology-driven Microemulsion Based Intranasal Delivery to Neurotechnology-driven Neuralink: Strategies to Improve Management of Neurodegenerative Disorders.},
journal = {AAPS PharmSciTech},
volume = {25},
number = {7},
pages = {215},
pmid = {39266806},
issn = {1530-9932},
mesh = {*Administration, Intranasal/methods ; Humans ; *Neurodegenerative Diseases/drug therapy/metabolism ; *Emulsions ; *Drug Delivery Systems/methods ; *Nanotechnology/methods ; Animals ; Blood-Brain Barrier/metabolism ; Nanoparticles/chemistry/administration &amp; dosage ; },
abstract = {Neurodegenerative disorder refers to malfunctioning of neurons their degradation leading to death of neurons. Among various neurodegenerative disorders APHD (Alzheimer's, Parkinson's, and Huntington's Disease) are particularly concerning due to their progressive and debilitating nature. The therapeutic agent used for treatment and management of APHD often show unsatisfactory clinical outcome owing to poor solubility and limited permeability across blood brain barrier (BBB). The nose-to brain delivery can overcome this BBB challenge as it can transport drug directly to brain though olfactory pathways bypassing BBB. Additionally, the nanotechnology has emerged as a cutting-edge methodology to address this issue and specifically mucoadhesive micro/nanoemulsion can improve the overall performance of the drug when administered intranasally. Beyond the therapy neurotechnology has emerged as are revolutionary AI-driven BCI (Brain computer interface) aimed to restore independence in patients with function loss due to neuron degeneration/death. A promising BCI Neuralink has been recently explored for clinical trials and results revealed that a quadriplegia bearing person with implanted Neuralink chip was able to perform few normal functions of daily routine such as playing online games, text messaging, reading, and learning foreign languages online through accessing the particular websites. This review will discuss the fundamental concepts of neurodegeneration, application of micro/nanoemulsion through intranasal route and integration of neurotechnology for the management and treatment of APHD.},
}
@article {pmid39265614,
year = {2024},
author = {Kapralov, N and Jamshidi Idaji, M and Stephani, T and Studenova, A and Vidaurre, C and Ros, T and Villringer, A and Nikulin, V},
title = {Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7a24},
pmid = {39265614},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Signal-To-Noise Ratio ; Male ; Female ; Longitudinal Studies ; *Electroencephalography/methods ; Adult ; Sensorimotor Cortex/physiology ; Brain Waves/physiology ; Young Adult ; Reproducibility of Results ; },
abstract = {Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.},
}
@article {pmid39265503,
year = {2025},
author = {King, BJ and Read, GJM and Salmon, PM},
title = {Prospectively identifying risks and controls for advanced brain-computer interfaces: A Networked Hazard Analysis and Risk Management System (Net-HARMS) approach.},
journal = {Applied ergonomics},
volume = {122},
number = {},
pages = {104382},
doi = {10.1016/j.apergo.2024.104382},
pmid = {39265503},
issn = {1872-9126},
mesh = {Humans ; *Brain-Computer Interfaces ; Risk Assessment/methods ; *Risk Management/methods ; Prospective Studies ; },
abstract = {The introduction of advanced digital technologies continues to increase system complexity and introduce risks, which must be proactively identified and managed to support system resilience. Brain-computer interfaces (BCIs) are one such technology; however, the risks arising from broad societal use of the technology have yet to be identified and controlled. This study applied a structured systems thinking-based risk assessment method to prospectively identify risks and risk controls for a hypothetical future BCI system lifecycle. The application of the Networked Hazard Analysis and Risk Management System (Net-HARMS) method identified over 800 risks throughout the BCI system lifecycle, from BCI development and regulation through to BCI use, maintenance, and decommissioning. High-criticality risk themes include the implantation and degradation of unsafe BCIs, unsolicited brain stimulation, incorrect signals being sent to safety-critical technologies, and insufficiently supported BCI users. Over 600 risk controls were identified that could be implemented to support system safety and performance resilience. Overall, many highly-impactful BCI system safety and performance risks may arise throughout the BCI system lifecycle and will require collaborative efforts from a wide range of BCI stakeholders to adequately control. Whilst some of the identified controls are practical, work is required to develop a more systematic set of controls to best support the design of a resilient sociotechnical BCI system.},
}
@article {pmid39265209,
year = {2024},
author = {Walsh, BE and Schlauch, RC},
title = {Differential impact of emotional and social loneliness on daily alcohol consumption in individuals with alcohol use disorder.},
journal = {Drug and alcohol dependence},
volume = {264},
number = {},
pages = {112433},
doi = {10.1016/j.drugalcdep.2024.112433},
pmid = {39265209},
issn = {1879-0046},
mesh = {Humans ; *Loneliness/psychology ; Male ; Female ; *Alcohol Drinking/psychology/epidemiology ; *Alcoholism/psychology ; Adult ; Middle Aged ; Emotions ; Prospective Studies ; },
abstract = {BACKGROUND: Loneliness is a predisposing and maintaining factor of alcohol use behavior. Several studies have linked loneliness to daily drinking and elevated alcohol use disorder (AUD) risk; however, operationalizations of both loneliness and drinking have varied greatly.<br><br>METHODS: The current study adopted a multidimensional framework of loneliness (i.e., emotional and social subtypes) to examine daily prospective relations between loneliness and drinking among non-treatment seeking individuals with AUD. Participants (N= 60) reported on current loneliness and drinking twice daily for 14-days. Scores on emotional and social loneliness were disaggregated into within- and between-person predictors, and a multilevel hurdle model proxy was fitted with drinking likelihood (logistic) and quantity (zero truncated negative binomial) specified as separate outcomes.<br><br>RESULTS: Emotional loneliness (within-person) was associated with increased drinking likelihood (OR=1.05, 95&#x2009;% BCI [1.01, 1.10]) and quantity (IRR=1.05, 95&#x2009;% BCI [1.02, 1.09]), while social loneliness (within-person) was associated with decreases in both drinking likelihood (OR=.94, 95&#x2009;% BCI [.89,.99]) and quantity (IRR=.96, 95&#x2009;% BCI [.93,.99]). Between-person loneliness scores were unrelated to both outcomes.<br><br>CONCLUSIONS: These discrepant findings by loneliness subtype may be ascribed to differences in subjective manifestations, in that emotional loneliness is a more severe form of loneliness that overlaps significantly with other negative affective states and promotes a coping response, while social loneliness may be readily alleviated by adaptive behavioral strategies for some, and social withdrawal for others. These findings offer insight into the nuances of loneliness-drinking relations and their clinical implications.},
}
@article {pmid39264785,
year = {2024},
author = {Cho, H and Benjaber, M and Alexis Gkogkidis, C and Buchheit, M and Ruiz-Rodriguez, JF and Grannan, BL and Weaver, KE and Ko, AL and Cramer, SC and Ojemann, JG and Denison, T and Herron, JA},
title = {Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3625-3635},
pmid = {39264785},
issn = {1558-0210},
support = {K12 NS129164/NS/NINDS NIH HHS/United States ; U24 NS113637/NS/NINDS NIH HHS/United States ; UH3 NS121565/NS/NINDS NIH HHS/United States ; MC_UU_0003/3//Medical Research Council UK/ ; },
mesh = {Humans ; *Algorithms ; *Software ; Computer Systems ; Brain/physiology ; Male ; Adult ; Equipment Design ; Female ; Electrodes, Implanted ; Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.},
}
@article {pmid39264571,
year = {2025},
author = {Guo, X and Chen, L and Lai, J and Hu, S},
title = {Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder.},
journal = {Neuroscience bulletin},
volume = {41},
number = {1},
pages = {181-185},
pmid = {39264571},
issn = {1995-8218},
}
@article {pmid39264055,
year = {2024},
author = {Ma, L and Li, Y and Xue, J and Xu, L and Li, X and Chang, X},
title = {Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.},
journal = {Inquiry : a journal of medical care organization, provision and financing},
volume = {61},
number = {},
pages = {469580241275324},
pmid = {39264055},
issn = {1945-7243},
mesh = {*Medicine, Chinese Traditional/standards ; Humans ; *Tertiary Care Centers ; *Quality Indicators, Health Care ; China ; Quality of Health Care ; },
abstract = {Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals. This study collected performance assessment data from 10 tertiary TCM hospitals in Gansu Province from 2019 to 2022. Thirteen indicators with TCM characteristics were selected and categorized into 3 aspects: control of medical costs, internal operational dimensions, and comprehensive management. The level of medical quality in different hospitals and in different years were determined using the TOPSIS method for ranking and the RSR method for grading. Firstly, in terms of TCM characteristic indicators, hospital H had the highest control of medical costs and comprehensive management among different hospitals, with 45.87% and 24.20% respectively. The highest values for control of medical costs and comprehensive management were observed in 2020, with 40.65% and 18.69% respectively among different years. When evaluating the medical quality of different hospitals using the TOPSIS method, it was found that hospital H had the highest ranking from 2020 to 2022, with Ci values of 0.725, 0.778, and 0.667 respectively. Additionally, the RSR method indicated that hospital H had a high level of grading from 2020 to 2022, with Pi values of 0.687, 0.690, and 0.723 respectively. These findings suggest that the medical quality of hospital H is at a high and stable level of development. Based on the TOPSIS method to evaluate the performance appraisal results and ranking of different hospitals from 2019 to 2022. The results showed that the highest ranking was hospital B(Ci = 0.913) in 2019. The highest ranking was hospital C(Ci = 0.809)in 2020. The highest ranking was hospital D(Ci = 0.689) in 2021. The highest ranking was hospital J(Ci = 0.865) in 2022. The RSR method indicated that high grading level were hospitals B(Pi = 0.899),F(Pi = 0.795) in 2019. The highest grading level was hospital C(Pi = 0.809) in 2020. The highest grading level were hospitals A(Pi = 0.868), D(Pi = 0.813), E(Pi = 0.689), G(Pi = 0.873), J(Pi = 0.813), K(Pi = 0.842) in 2022. Based on the above results indicate that there is a large variation in the medical quality profile of different hospitals from 2019 to 2022. By comparing the results of TOPSIS and RSR method from 2019 to 2022, we found that the hospitals with identical ranking were D and J, and the hospitals with ≤2 difference in ranking was A,B,C,E in 2019, the hospitals with >2 ranking was A, F in 2020, the hospitals with >2 ranking were C, G in 2021, and the hospitals with identical ranking results were B,D,E,G,J in 2022. Comparing the ranking results of TOPSIS and RSR methods, showed that the hospitals with identical rankings were B, F from 2019 to 2022. The difference in ranking results ≤2 were A, C, D, E, G, H, J, K, indicating that high consistency between TOPSIS and RSR methods and credible results. The findings reveal significant fluctuations in medical quality across different years, while the overall level of medical quality remains relatively stable among the various hospitals. It is recommended that TCM hospitals focus on improving management efficiency, optimizing hospital operations, enhancing the utilization of medical resources, and fostering the efficient development of hospitals.},
}
@article {pmid39263239,
year = {2024},
author = {Roman-Gonzalez, A},
title = {Editorial: Multimodal perceiving technologies in neuroscience and vision applications.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1477572},
doi = {10.3389/fnins.2024.1477572},
pmid = {39263239},
issn = {1662-4548},
}
@article {pmid39262538,
year = {2024},
author = {Yukawa, Y and Higashi, T and Minakuchi, M and Naito, E and Murata, T},
title = {Vibration-Induced Illusory Movement Task Can Induce Functional Recovery in Patients With Subacute Stroke.},
journal = {Cureus},
volume = {16},
number = {8},
pages = {e66667},
pmid = {39262538},
issn = {2168-8184},
abstract = {In recent years, mental practice (MP), which involves repetitive motor imagery (MI), has been applied in rehabilitation to actively enhance exercise performance. MP is a method that involves repetitive MI, consciously evoking the intentions and content of the exercise without actual exercise. Combining actual exercise with MP promotes the development of exercise skills. However, it is possible that the MI recall ability differs greatly between individuals, affecting the therapeutic effect. In contrast, the vibration-induced illusory movement (VIM) task acts as a method to induce a motor illusion by somatosensory stimuli without actual motor. VIM, actual movement, and MI are thought to share a common neural basis in the brain. Therefore, it was hypothesized that the VIM task would complement the differences in MI recall in individual patients with hemiplegic stroke and may be a new treatment to enhance MI recall. Accordingly, in this study, we investigated the therapeutic effects of the VIM task in patients with hemiplegic stroke. In Study I, the therapeutic effect of the VIM task in 14 patients with post-stroke hemiplegia was evaluated by motor function assessment. In Study II, treatment effects were investigated by examining the ability of the same group of patients to recall MI and by neurophysiological examination of the electroencephalogram (EEG) during MI recall in four patients who consented to the study. Motor function and MI were assessed four times: before the intervention, after occupational therapy, after the VIM task (which used the motor illusion induced by tendon vibration), and one month after acceptance of therapy. Compared with occupational therapy, the VIM task showed a statistically significant improvement in upper limb function and MI ability. In addition, we found an increase in event-related desynchronization intensity during MI in the affected hemisphere only after the VIM task. It is possible that the VIM task facilitates motor function and MI. VIM task implementation of MI recall variability between individuals, which is a problem in mental practice, possible to increase the effectiveness of the brain-machine interface.},
}
@article {pmid39261006,
year = {2024},
author = {Chen, C and Song, S},
title = {Distinct Neuron Types Contribute to Hybrid Auditory Spatial Coding.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {43},
pages = {},
pmid = {39261006},
issn = {1529-2401},
mesh = {Animals ; Mice ; Female ; Male ; *Inferior Colliculi/physiology/cytology ; *Neurons/physiology ; *Sound Localization/physiology ; Acoustic Stimulation/methods ; Mice, Inbred C57BL ; Auditory Perception/physiology ; Models, Neurological ; },
abstract = {Neural decoding is a tool for understanding how activities from a population of neurons inside the brain relate to the outside world and for engineering applications such as brain-machine interfaces. However, neural decoding studies mainly focused on different decoding algorithms rather than different neuron types which could use different coding strategies. In this study, we used two-photon calcium imaging to assess three auditory spatial decoders (space map, opponent channel, and population pattern) in excitatory and inhibitory neurons in the dorsal inferior colliculus of male and female mice. Our findings revealed a clustering of excitatory neurons that prefer similar interaural level difference (ILD), the primary spatial cues in mice, while inhibitory neurons showed random local ILD organization. We found that inhibitory neurons displayed lower decoding variability under the opponent channel decoder, while excitatory neurons achieved higher decoding accuracy under the space map and population pattern decoders. Further analysis revealed that the inhibitory neurons' preference for ILD off the midline and the excitatory neurons' heterogeneous ILD tuning account for their decoding differences. Additionally, we discovered a sharper ILD tuning in the inhibitory neurons. Our computational model, linking this to increased presynaptic inhibitory inputs, was corroborated using monaural and binaural stimuli. Overall, this study provides experimental and computational insight into how excitatory and inhibitory neurons uniquely contribute to the coding of sound locations.},
}
@article {pmid39260393,
year = {2024},
author = {Johnston, R and Boulay, C and Miller, K and Sachs, A},
title = {Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ad795e},
pmid = {39260393},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; *Cognition ; *Memory, Short-Term ; *Brain Mapping/methods ; Male ; Adult ; Brain/physiology ; Female ; Electrodes, Implanted ; },
abstract = {Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates.Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions.Results.From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention.Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.},
}
@article {pmid39260008,
year = {2024},
author = {Zhou, S and Yang, B and Yuan, M and Jiang, R and Yan, R and Pan, G and Tang, H},
title = {Enhancing SNN-based spatio-temporal learning: A benchmark dataset and Cross-Modality Attention model.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106677},
doi = {10.1016/j.neunet.2024.106677},
pmid = {39260008},
issn = {1879-2782},
abstract = {Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios.},
}
@article {pmid39259621,
year = {2025},
author = {Chen, D and Song, Z and Du, Y and Chen, S and Zhang, X and Li, Y and Huang, Q},
title = {Aperiodic Component Analysis in Quantification of Steady-State Visually Evoked Potentials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {2},
pages = {468-479},
doi = {10.1109/TBME.2024.3458060},
pmid = {39259621},
issn = {1558-2531},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Male ; Adult ; Female ; Young Adult ; Algorithms ; },
abstract = {OBJECTIVE: In this study, we aimed to investigate whether and how the aperiodic component in electroencephalograms affects different quantitative processes of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces.<br><br>METHODS: We applied the Fitting Oscillations &amp; One-Over-F method to parameterize power spectra as a combination of periodic oscillations and an aperiodic component. Electroencephalographic responses and system performance were measured and compared using four prevailing methods: power spectral density analysis, canonical correlation analysis, filter bank canonical correlation analysis and the state-of-the-art method, task discriminant component analysis.<br><br>RESULTS: We found that controlling for the aperiodic component prominently downgraded the performance of brain-computer interfaces measured by canonical correlation analysis (94.9% to 82.8%), filter bank canonical correlation analysis (94.1% to 87.6%), and task discriminant component analysis (96.5% to 70.3%). However, it had almost no effect on that measured by power spectral density analysis (80.4% to 78.7%). This was accompanied by a differential aperiodic impact between power spectral density analysis and the other three methods on the differentiation of the target and non-target stimuli.<br><br>CONCLUSION: The aperiodic component distinctly impacts the quantification of steady-state visually evoked potentials and the performance of corresponding brain-computer interfaces.<br><br>SIGNIFICANCE: Our work underscores the significance of taking into account the dynamic nature of aperiodic activities in research related to the quantification of steady-state visually evoked potentials.},
}
@article {pmid39257699,
year = {2024},
author = {Rueda-Castro, V and Azofeifa, JD and Chacon, J and Caratozzolo, P},
title = {Bridging minds and machines in Industry 5.0: neurobiological approach.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1427512},
pmid = {39257699},
issn = {1662-5161},
abstract = {INTRODUCTION: In transitioning from Industry 4.0 to the forthcoming Industry 5.0, this research explores the fusion of the humanistic view and technological developments to redefine Continuing Engineering Education (CEE). Industry 5.0 introduces concepts like biomanufacturing and human-centricity, embodying the integration of sustainability and resiliency principles in CEE, thereby shaping the upskilling and reskilling initiatives for the future workforce. The interaction of sophisticated concepts such as Human-Machine Interface and Brain-Computer Interface (BCI) forms a conceptual bridge toward the approaching Fifth Industrial Revolution, allowing one to understand human beings and the impact of their biological development across diverse and changing workplace settings.<br><br>METHODS: Our research is based on recent studies into Knowledge, Skills, and Abilities taxonomies, linking these elements with dynamic labor market profiles. This work intends to integrate a biometric perspective to conceptualize and describe how cognitive abilities could be represented by linking a Neuropsychological test and a biometric assessment. We administered the brief Neuropsychological Battery in Spanish (Neuropsi Breve). At the same time, 15 engineering students used the Emotiv insight device that allowed the EEG recollection to measure performance metrics such as attention, stress, engagement, and excitement.<br><br>RESULTS: The findings of this research illustrate a methodology that allowed the first approach to the cognitive abilities of engineering students to be from neurobiological and behavioral perspectives. Additionally, two profiles were extracted from the results. The first illustrates the Neuropsi test areas, its most common mistakes, and its performance ratings regarding the students' sample. The second profile shows the interaction between the EEG and Neuropsi test, showing engineering students' cognitive and emotional states based on biometric levels.<br><br>DISCUSSIONS: The study demonstrates the potential of integrating neurobiological assessment into engineering education, highlighting a significant advancement in addressing the skills requirements of Industry 5.0. The results suggest that obtaining a comprehensive understanding of students' cognitive abilities is possible, and educational interventions can be adapted by combining neuropsychological approaches with EEG data collection. In the future, it is essential to refine these evaluation methods further and explore their applicability in different engineering disciplines. Additionally, it is necessary to investigate the long-term impact of these methods on workforce preparation and performance.},
}
@article {pmid39256727,
year = {2024},
author = {Tong, D and Wu, F and Chen, X and Du, Z and Zhou, J and Zhang, J and Yang, Y and Du, A and Ma, G},
title = {The mrp-3 gene is involved in haem efflux and detoxification in a blood-feeding nematode.},
journal = {BMC biology},
volume = {22},
number = {1},
pages = {199},
pmid = {39256727},
issn = {1741-7007},
mesh = {Animals ; *Haemonchus/genetics/metabolism ; *Heme/metabolism ; Inactivation, Metabolic/genetics ; Helminth Proteins/genetics/metabolism ; RNA Interference ; Multidrug Resistance-Associated Proteins/genetics/metabolism ; },
abstract = {BACKGROUND: Haem is essential but toxic for metazoan organisms. Auxotrophic nematodes can acquire sufficient haem from the environment or their hosts in the meanwhile eliminate or detoxify excessive haem through tightly controlled machinery. In previous work, we reported a role of the unique transporter protein HRG-1 in the haem acquisition and homeostasis of parasitic nematodes. However, little is known about the haem efflux and detoxification via ABC transporters, particularly the multiple drug resistance proteins (MRPs).<br><br>RESULTS: Here, we further elucidate that a member of the mrp family (mrp-3) is involved in haem efflux and detoxification in a blood-feeding model gastrointestinal parasite, Haemonchus contortus. This gene is haem-responsive and dominantly expressed in the intestine and inner membrane of the hypodermis of this parasite. RNA interference of mrp-3 resulted in a disturbance of genes (e.g. hrg-1, hrg-2 and gst-1) that are known to be involved in haem homeostasis and an increased formation of haemozoin in the treated larvae and lethality in vitro, particularly when exposed to exogenous haem. Notably, the nuclear hormone receptor NHR-14 appears to be associated the regulation of mrp-3 expression for haem homeostasis and detoxification. Gene knockdown of nhr-14 and/or mrp-3 increases the sensitivity of treated larvae to exogenous haem and consequently a high death rate (>&#x2009;80%).<br><br>CONCLUSIONS: These findings demonstrate that MRP-3 and the associated molecules are essential for haematophagous nematodes, suggesting novel intervention targets for these pathogens in humans and animals.},
}
@article {pmid39255823,
year = {2024},
author = {Zhao, S and Cao, Y and Yang, W and Yu, J and Xu, C and Dai, W and Li, S and Pan, G and Luo, B},
title = {DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7904},
pmid = {39255823},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Consciousness Disorders/diagnosis/physiopathology ; Female ; Male ; Adult ; Middle Aged ; Aged ; Young Adult ; Deep Learning ; Adolescent ; },
abstract = {Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.},
}
@article {pmid39255189,
year = {2024},
author = {Jia, T and Meng, L and Li, S and Liu, J and Wu, D},
title = {Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3442-3451},
doi = {10.1109/TNSRE.2024.3457504},
pmid = {39255189},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Imagination/physiology ; *Algorithms ; *Deep Learning ; *Machine Learning ; Computer Security ; Privacy ; },
abstract = {Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.},
}
@article {pmid39255188,
year = {2024},
author = {Hu, L and Gao, W and Lu, Z and Shan, C and Ma, H and Zhang, W and Li, Y},
title = {Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3543-3553},
doi = {10.1109/TNSRE.2024.3457502},
pmid = {39255188},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography ; *Wearable Electronic Devices ; *Neural Networks, Computer ; Male ; Female ; Adult ; *Algorithms ; Young Adult ; Calibration ; Machine Learning ; },
abstract = {The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross- subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. Results: The proposed method significantly boosts the average accuracy of wearable P300 BCI to 73.23±7.62 % without calibration and 78.75±6.37 % with fine-tuning. The results demonstrate the feasibility and excellent performance of our dataset and framework. A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.},
}
@article {pmid39255187,
year = {2024},
author = {Tan, X and Lian, Q and Zhu, J and Zhang, J and Wang, Y and Qi, Y},
title = {Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3432-3441},
doi = {10.1109/TNSRE.2024.3457313},
pmid = {39255187},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Algorithms ; Male ; Female ; Young Adult ; Speech/physiology ; Adult ; Phonetics ; Cluster Analysis ; Language ; },
abstract = {OBJECTIVE: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For monosyllabic languages such as Chinese Mandarin, where a word usually contains less than three phonemes, accurate decoding of phonemes plays a vital role. We found that in the neural representation space, phonemes with similar pronunciations are often inseparable, leading to confusion in phoneme classification.<br><br>METHODS: We mapped the neural signals of phoneme pronunciation into a hyperbolic space for a more distinct phoneme representation. Critically, we proposed a hyperbolic hierarchical clustering approach to specifically learn a phoneme-level structure to guide the representation.<br><br>RESULTS: We found such representation facilitated greater distance between similar phonemes, effectively reducing confusion. In the phoneme decoding task, our approach demonstrated an average accuracy of 75.21% for 21 phonemes and outperformed existing methods across different experimental days.<br><br>CONCLUSION: Our approach showed high accuracy in phoneme classification. By learning the phoneme-level neural structure, the representations of neural signals were more discriminative and interpretable.<br><br>SIGNIFICANCE: Our approach can potentially facilitate high-performance speech BCIs for Chinese and other monosyllabic languages.},
}
@article {pmid39255091,
year = {2024},
author = {Mei, L and Ingolfsson, TM and Cioflan, C and Kartsch, V and Cossettini, A and Wang, X and Benini, L},
title = {An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3457522},
pmid = {39255091},
issn = {1940-9990},
abstract = {Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes. The workflow is deployed on a wearable, parallel ultra-low power BMI platform (BioGAP). Our results based on two in-house datasets, Dataset A and Dataset B, show that the CL workflow improves average accuracy by up to 30.36% and 10.17%, respectively. Furthermore, when implementing the continual learning on a Parallel Ultra-Low Power (PULP) microcontroller (GAP9), it achieves an energy consumption as low as 0.45mJ per inference and an adaptation time of only 21.5 ms, yielding around 25 h of battery life with a small 100 mAh, 3.7 V battery on BioGAP. Our setup, coupled with the compact CNN model and on-device CL capabilities, meets users' needs for improved privacy, reduced latency, and enhanced inter-session performance, offering good promise for smart embedded real-world BMIs.},
}
@article {pmid39255081,
year = {2025},
author = {Ding, W and Liu, A and Xie, C and Wang, K and Chen, X},
title = {Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {2},
pages = {503-514},
doi = {10.1109/TBME.2024.3458389},
pmid = {39255081},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Adult ; Male ; *Signal Processing, Computer-Assisted ; Female ; Algorithms ; Young Adult ; *Deep Learning ; },
abstract = {OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification performance of deep learning methods with limited calibration data. However, effectively learning task-related knowledge from source domains during the pre-training phase remains challenging. To address this issue, this study proposes an effective data augmentation method called Reconstruction of Channel Correlation (RCC) to optimize the utilization of the source domain data.<br><br>METHODS: Concretely, RCC reconstructs training samples using probabilistically mixed eigenvector matrices derived from covariance matrices across source domains. This process manipulates the channel correlation of training samples, implicitly creating novel synthesized domains. By increasing the diversity of source domains, RCC aims to enhance the domain generalizability of the pre-trained model. The effectiveness of RCC is validated through subject-independent and subject-adaptive classification experiments.<br><br>RESULTS: The results of subject-independent classification demonstrate that RCC significantly improves the classification performance of the pre-trained model on unseen target subjects. Moreover, when compared to the fine-tuning process using the RCC-absent pre-trained model, the fine-tuning process using the RCC-enhanced pre-trained model yields significantly improved performance in the subject-adaptive classification.<br><br>CONCLUSION: RCC proves to enhance the performance of transfer learning by optimizing the utilization of the source domain data.<br><br>SIGNIFICANCE: The RCC-enhanced transfer learning has the potential to facilitate the practical implementation of SSVEP-BCIs in real-world scenarios.},
}
@article {pmid39253749,
year = {2024},
author = {Donegan, T and Sanchez-Vives, MV},
title = {Perception and control of a virtual body in immersive virtual reality for rehabilitation.},
journal = {Current opinion in neurology},
volume = {37},
number = {6},
pages = {638-644},
pmid = {39253749},
issn = {1473-6551},
mesh = {Humans ; *Virtual Reality ; Neurological Rehabilitation/methods ; Brain-Computer Interfaces ; Musculoskeletal Diseases/rehabilitation ; },
abstract = {PURPOSE OF REVIEW: This review explores recent advances in using immersive virtual reality to improve bodily perception and motor control in rehabilitation across musculoskeletal and neurological conditions, examining how virtual reality's unique capabilities can address the challenges of traditional approaches. The potential in this area of the emerging metaverse and the integration of artificial intelligence in virtual reality are discussed.<br><br>RECENT FINDINGS: In musculoskeletal rehabilitation, virtual reality shows promise in enhancing motivation, adherence, improving range of motion, and reducing kinesiophobia, particularly postsurgery. For neurological conditions like stroke and spinal cord injury, virtual reality's ability to manipulate bodily perceptions offers significant therapeutic potential, with reported improvements in upper limb function and gait performance. Balance and gait rehabilitation, especially in older adults, have also seen positive outcomes. The integration of virtual reality with brain-computer interfaces presents exciting possibilities for severe speech and motor impairments.<br><br>SUMMARY: Current research is limited by small sample sizes, short intervention durations, and variability in virtual reality systems. Future studies should focus on larger, long-term trials to confirm findings and explore underlying mechanisms. As virtual reality technology advances, its integration into rehabilitation programs could revolutionize treatment approaches, personalizing treatments, facilitating home training, and potentially improving patient outcomes across a wide variety of conditions.},
}
@article {pmid39253705,
year = {2024},
author = {AlQahtani, NJ and Al-Naib, I and Althobaiti, M},
title = {Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1454262},
pmid = {39253705},
issn = {2296-4185},
abstract = {The global rise in lower limb amputation cases necessitates advancements in prosthetic limb technology to enhance the quality of life for affected patients. This review paper explores recent advancements in the integration of EEG and fNIRS modalities for smart lower prosthetic limbs for rehabilitation applications. The paper synthesizes current research progress, focusing on the synergy between brain-computer interfaces and neuroimaging technologies to enhance the functionality and user experience of lower limb prosthetics. The review discusses the potential of EEG and fNIRS in decoding neural signals, enabling more intuitive and responsive control of prosthetic devices. Additionally, the paper highlights the challenges, innovations, and prospects associated with the incorporation of these neurotechnologies in the field of rehabilitation. The insights provided in this review contribute to a deeper understanding of the evolving landscape of smart lower prosthetic limbs and pave the way for more effective and user-friendly solutions in the realm of neurorehabilitation.},
}
@article {pmid39253519,
year = {2024},
author = {Lyu, C and Li, Z and Xu, C and Wong, KKL and Luginbuhl, DJ and McLaughlin, CN and Xie, Q and Li, T and Li, H and Luo, L},
title = {Dimensionality reduction simplifies synaptic partner matching in an olfactory circuit.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39253519},
issn = {2692-8205},
support = {R01 DC005982/DC/NIDCD NIH HHS/United States ; },
abstract = {The distribution of postsynaptic partners in three-dimensional (3D) space presents complex choices for a navigating axon. Here, we discovered a dimensionality reduction principle in establishing the 3D glomerular map in the fly antennal lobe. Olfactory receptor neuron (ORN) axons first contact partner projection neuron (PN) dendrites at the 2D spherical surface of the antennal lobe during development, regardless of whether the adult glomeruli are at the surface or interior of the antennal lobe. Along the antennal lobe surface, axons of each ORN type take a specific 1D arc-shaped trajectory that precisely intersects with their partner PN dendrites. Altering axon trajectories compromises synaptic partner matching. Thus, a 3D search problem is reduced to 1D, which simplifies synaptic partner matching and may generalize to the wiring process of more complex brains.},
}
@article {pmid39253067,
year = {2024},
author = {Xu, Y and Jie, L and Jian, W and Yi, W and Yin, H and Peng, Y},
title = {Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1447662},
pmid = {39253067},
issn = {1662-5161},
abstract = {For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison. Each paradigm corresponds to one session. Two paradigms are applied to the calibration runs of corresponding sessions. And their effectiveness is verified in the subsequent testing runs of respective sessions. Different from the non-feedback paradigm, the trial-feedback paradigm presents a topographic map and its qualitative evaluation in real time after each MI training trial, so the subjects can timely realize whether the current trial successfully induces the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon, and then they can adjust their brain rhythm in the next MI trial. Moreover, after each calibration run of the trial-feedback session, a feature distribution is visualized and quantified to show the subjects' abilities to distinguish different MI tasks and promote their self-modulation in the next calibration run. Additionally, if the subjects feel distracted during the training processes of the non-feedback and trial-feedback sessions, they can execute the blinking movement which will be captured by the electrooculogram (EOG) signals, and the corresponding MI training trial will be abandoned. Ten healthy participants sequentially performed the non-feedback and trial-feedback sessions on the different days. The experiment results showed that the trial-feedback session had better spatial filter visualization, more beneficiaries, higher average off-line and on-line classification accuracies than the non-feedback session, suggesting the trial-feedback paradigm's usefulness in subject's self-modulation and good ability to perform MI tasks.},
}
@article {pmid39252162,
year = {2024},
author = {Yeh, KT and Ho, VW and Chen, TY and Tu, JC and Lin, HY and Chan, KC},
title = {Long-term follow-up of Bonebridge BCI 601 implantation in microtia patients with aural atresia: Acoustic and subjective benefits.},
journal = {Journal of the Chinese Medical Association : JCMA},
volume = {87},
number = {12},
pages = {1090-1097},
doi = {10.1097/JCMA.0000000000001162},
pmid = {39252162},
issn = {1728-7731},
mesh = {Humans ; Female ; Male ; *Congenital Microtia/surgery ; Adult ; Adolescent ; Child ; Retrospective Studies ; Middle Aged ; Young Adult ; *Ear/abnormalities/surgery ; Follow-Up Studies ; Bone Conduction ; Hearing Aids ; Congenital Abnormalities/surgery ; },
abstract = {BACKGROUND: This study evaluated the long-term acoustic and subjective outcomes of Bonebridge bone conduction implant (BCI) 601 implantation in Taiwanese patients with microtia and aural atresia (AA).<br><br>METHODS: A total of 41 microtia patients (28 males and 13 females; 26 with bilateral AA and 15 with unilateral AA) who received Bonebridge BCI 601 implantation between December 2014 and March 2021 at Chang Gung Memorial Hospital, Linkou, Taiwan, were included in this retrospective study. Acoustic outcomes assessed included functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were assessed. Subjective outcomes were assessed using the Chinese versions of four questionnaires: the Abbreviated Profile of Hearing Aid Benefit (APHAB); the Speech, Spatial and Qualities of Hearing Scale; the International Outcome Inventory for Hearing Aids; and the Satisfaction with Amplification in Daily Living.<br><br>RESULTS: The mean age at the time of implantation was 18.9 years (range, 6.3-54.9), and the mean follow-up duration was 6.3 years (range, 2.8-9.1). The mean unaided air conduction pure tone average (PTA4) was 65.3&#x2009;&#xb1;&#x2009;8.8 decibels (dB) hearing level (HL) and the mean aided sound field PTA4 was 31.1&#x2009;&#xb1;&#x2009;9.1 dB HL, resulting in a FHG of 34.2&#x2009;&#xb1;&#x2009;11.7 dB HL (p < 0.05). After Bonebridge implantation, improvements (p < 0.05) in the mean SRT in quiet (from 58.3&#x2009;&#xb1;&#x2009;7.4 dB HL to 29.4&#x2009;&#xb1;&#x2009;7.0 dB HL), SRT in noise (from -1.4&#x2009;&#xb1;&#x2009;7.3 dB signal-to-noise ratio (SNR) to -9.6&#x2009;&#xb1;&#x2009;5.4 dB SNR), WRS in quiet (from 46.4&#x2009;&#xb1;&#x2009;26.9% to 93.8&#x2009;&#xb1;&#x2009;3.1%), and WRS in noise (from 46.7&#x2009;&#xb1;&#x2009;21.8% to 72.7&#x2009;&#xb1;&#x2009;19.3%) were found. Additionally, the bilateral AA group exhibited greater SRT and WRS improvements compared to the unilateral AA group (p < 0.05). All mean subscale scores in the four questionnaires showed improvement after Bonebridge implantation, except for the mean aversiveness to sounds subscale score in the APHAB questionnaire.<br><br>CONCLUSION: Bonebridge BCI 601 implantation provided long-term acoustic and subjective benefits for patients with microtia and AA, particularly those with bilateral AA.},
}
@article {pmid39250958,
year = {2024},
author = {Kang, T and Chen, Y and Wallraven, C},
title = {I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks.},
journal = {Journal of neural engineering},
volume = {21},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad788e},
pmid = {39250958},
issn = {1741-2552},
mesh = {*Artifacts ; Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Male ; Adult ; Algorithms ; Female ; Principal Component Analysis/methods ; Imagination/physiology ; Young Adult ; },
abstract = {Objective.In this paper, we conduct a detailed investigation on the effect of independent component (IC)-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets.Approach.We apply a pipeline matrix of two popular different independent component (IC) decomposition methods (Infomax and Adaptive Mixture Independent Component Analysis (AMICA)) with three different component rejection strategies (none, ICLabel, and multiple artifact rejection algorithm [MARA]) on three different EEG datasets (motor imagery, long-term memory formation, and visual memory). We cross-validate processed data from each pipeline with three architectures commonly used for EEG classification (two convolutional neural networks and one long short-term memory-based model. We compare decoding performances on within-participant and within-dataset levels.Main Results.Our results show that the benefit from using IC-based noise rejection for decoding analyses is at best minor, as component-rejected data did not show consistently better performance than data without rejections-especially given the significant computational resources required for independent component analysis (ICA) computations.Significance.With ever-growing emphasis on transparency and reproducibility, as well as the obvious benefits arising from streamlined processing of large-scale datasets, there has been an increased interest in automated methods for pre-processing EEG data. One prominent part of such pre-processing pipelines consists of identifying and potentially removing artifacts arising from extraneous sources. This is typically done via IC-based correction for which numerous methods have been proposed, differing not only in the decomposition of the raw data into ICs, but also in how they reject the computed ICs. While the benefits of these methods are well established in univariate statistical analyses, it is unclear whether they help in multivariate scenarios, and specifically in neural network-based decoding studies. As computational costs for pre-processing large-scale datasets are considerable, it is important to consider whether the trade-off between model performance and available resources is worth the effort.},
}
@article {pmid39250956,
year = {2024},
author = {Yin, J and Liu, A and Wang, L and Qian, R and Chen, X},
title = {Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad788d},
pmid = {39250956},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Artifacts ; Humans ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Deep Learning ; },
abstract = {Objective.Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance.Approach.We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships.Main results.The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU.Significance.The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.},
}
@article {pmid39250394,
year = {2024},
author = {Cheng, S and Liu, Y and Gao, Y and Dong, Z},
title = {"As if it were my own hand": inducing the rubber hand illusion through virtual reality for motor imagery enhancement.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {30},
number = {11},
pages = {7086-7096},
doi = {10.1109/TVCG.2024.3456147},
pmid = {39250394},
issn = {1941-0506},
mesh = {Humans ; *Virtual Reality ; *Hand/physiology ; *Brain-Computer Interfaces ; *Illusions/physiology ; *Electroencephalography/methods ; Male ; Female ; *Imagination/physiology ; Young Adult ; Adult ; Computer Graphics ; Movement/physiology ; },
abstract = {Brain-computer interfaces (BCI) are widely used in the field of disability assistance and rehabilitation, and virtual reality (VR) is increasingly used for visual guidance of BCI-MI (motor imagery). Therefore, how to improve the quality of electroencephalogram (EEG) signals for MI in VR has emerged as a critical issue. People can perform MI more easily when they visualize the hand used for visual guidance as their own, and the Rubber Hand Illusion (RHI) can increase people's ownership of the prosthetic hand. We proposed to induce RHI in VR to enhance participants' MI ability and designed five methods of inducing RHI, namely active movement, haptic stimulation, passive movement, active movement mixed with haptic stimulation, and passive movement mixed with haptic stimulation, respectively. We constructed a first-person training scenario to train participants' MI ability through the five induction methods. The experimental results showed that through the training, the participants' feeling of ownership of the virtual hand in VR was enhanced, and the MI ability was improved. Among them, the method of mixing active movement and tactile stimulation proved to have a good effect on enhancing MI. Finally, we developed a BCI system in VR utilizing the above training method, and the performance of the participants improved after the training. This also suggests that our proposed method is promising for future application in BCI rehabilitation systems.},
}
@article {pmid39250352,
year = {2024},
author = {Sultana, M and Perdikis, S},
title = {Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3422-3431},
doi = {10.1109/TNSRE.2024.3456591},
pmid = {39250352},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Algorithms ; Reproducibility of Results ; Imagination/physiology ; Machine Learning ; Adult ; },
abstract = {Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system. Our approach exploits the capability of FL to increase the reliability of decision-making by fusing heterogeneous information channels while maintaining transparency and simplicity. We show that our method leads to significant improvement in classification accuracy, feature stability and class bias when applied to large motor imagery or attempt datasets including end-users with motor disabilities. We postulate that combining data-driven methods with knowledge derived from neuroscience literature through FL can enhance the performance, explainability, and learnability of BCIs.},
}
@article {pmid39249732,
year = {2024},
author = {Luo, Y and Wu, W and Gao, Z},
title = {Unlocking the Mysteries of the Subcommissural Organ: A Patron Saint of Neuronal Development.},
journal = {Neuroscience bulletin},
volume = {40},
number = {12},
pages = {2012-2014},
pmid = {39249732},
issn = {1995-8218},
}
@article {pmid39248654,
year = {2024},
author = {Choudhari, V and Han, C and Bickel, S and Mehta, AD and Schevon, C and McKhann, GM and Mesgarani, N},
title = {Brain-Controlled Augmented Hearing for Spatially Moving Conversations in Multi-Talker Environments.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {41},
pages = {e2401379},
pmid = {39248654},
issn = {2198-3844},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; //The Marie-Josee and Henry R. Kravis Foundation/ ; //National Institute of Health's National Institute on Deafness and Other Communication Disorders/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Speech Perception/physiology ; Male ; Hearing Aids ; Female ; Hearing Loss/physiopathology ; Middle Aged ; Brain-Computer Interfaces ; Speech Intelligibility/physiology ; Adult ; Attention/physiology ; },
abstract = {Focusing on a specific conversation amidst multiple interfering talkers is challenging, especially for those with hearing loss. Brain-controlled assistive hearing devices aim to alleviate this problem by enhancing the attended speech based on the listener's neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, a realistic AAD task that involves multiple talkers taking turns as they continuously move in space in background noise is presented. Invasive electroencephalography (iEEG) data are collected from three neurosurgical patients as they focused on one of the two moving conversations. An enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model is presented. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve AAD accuracy. Subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of this approach in real-world scenarios and marks a significant step toward developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences.},
}
@article {pmid39248624,
year = {2024},
author = {Chen, C and Song, Y and Chen, D and Zhu, J and Ning, H and Xiao, R},
title = {Design and application of pneumatic rehabilitation glove system based on brain-computer interface.},
journal = {The Review of scientific instruments},
volume = {95},
number = {9},
pages = {},
doi = {10.1063/5.0225972},
pmid = {39248624},
issn = {1089-7623},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation ; *Stroke Rehabilitation/instrumentation/methods ; Equipment Design ; },
abstract = {Stroke has been the second leading cause of death and disability worldwide. With the innovation of therapeutic schedules, its death rate has decreased significantly but still guides chronic movement disorders. Due to the lack of independent activities and minimum exercise standards, the traditional rehabilitation means of occupational therapy and constraint-induced movement therapy pose challenges in stroke patients with severe impairments. Therefore, specific and effective rehabilitation methods seek innovation. To address the overlooked limitation, we design a pneumatic rehabilitation glove system. Specially, we developed a pneumatic glove, which utilizes ElectroEncephaloGram (EEG) acquisition to gain the EEG signals. A proposed EEGTran model is inserted into the system to distinguish the specific motor imagination behavior, thus, the glove can perform specific activities according to the patient's imagination, facilitating the patients with severe movement disorders and promoting the rehabilitation technology. The experimental results show that the proposed EEGTrans reached an accuracy of 87.3% and outperformed that of competitors. It demonstrates that our pneumatic rehabilitation glove system contributes to the rehabilitation training of stroke patients.},
}
@article {pmid39247844,
year = {2024},
author = {Bunterngchit, C and Wang, J and Hou, ZG},
title = {Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {12},
number = {},
pages = {600-612},
pmid = {39247844},
issn = {2168-2372},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/methods ; *Deep Learning ; Signal Processing, Computer-Assisted ; Brain/physiology/diagnostic imaging ; Adult ; Male ; Female ; },
abstract = {The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.},
}
@article {pmid39245135,
year = {2024},
author = {Feng, J and Gao, S and Hu, Y and Sun, G and Sheng, W},
title = {Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study.},
journal = {World neurosurgery},
volume = {192},
number = {},
pages = {170-187.e1},
doi = {10.1016/j.wneu.2024.08.163},
pmid = {39245135},
issn = {1878-8769},
mesh = {*Brain-Computer Interfaces/trends ; *Spinal Cord Injuries/rehabilitation ; Humans ; *Bibliometrics ; },
abstract = {BACKGROUND: Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI.<br><br>METHODS: We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field.<br><br>RESULTS: The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field.<br><br>CONCLUSIONS: In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.},
}
@article {pmid39244219,
year = {2024},
author = {Nyawanda, BO and Khagayi, S and Obor, D and Odhiambo, SB and Beloconi, A and Otieno, NA and Bigogo, G and Kariuki, S and Munga, S and Vounatsou, P},
title = {The effects of climatic and non-climatic factors on malaria mortality at different spatial scales in western Kenya, 2008-2019.},
journal = {BMJ global health},
volume = {9},
number = {9},
pages = {},
pmid = {39244219},
issn = {2059-7908},
support = {U01 GH000048/GH/CGH CDC HHS/United States ; U01 GH002133/GH/CGH CDC HHS/United States ; },
mesh = {Humans ; Kenya/epidemiology ; *Malaria/mortality ; *Bayes Theorem ; *Climate ; Infant ; Child, Preschool ; },
abstract = {BACKGROUND: Malaria mortality is influenced by several factors including climatic and environmental factors, interventions, socioeconomic status (SES) and access to health systems. Here, we investigated the joint effects of climatic and non-climatic factors on under-five malaria mortality at different spatial scales using data from a Health and Demographic Surveillance System (HDSS) in western Kenya.<br><br>METHODS: We fitted Bayesian spatiotemporal (zero-inflated) negative binomial models to monthly mortality data aggregated at the village scale and over the catchment areas of the health facilities within the HDSS, between 2008 and 2019. First order autoregressive temporal and conditional autoregressive spatial processes were included as random effects to account for temporal and spatial variation. Remotely sensed climatic and environmental variables, bed net use, SES, travel time to health facilities, proximity from water bodies/streams and altitude were included in the models to assess their association with malaria mortality.<br><br>RESULTS: Increase in rainfall (mortality rate ratio (MRR)=1.12, 95% Bayesian credible interval (BCI): 1.04-1.20), Normalized Difference Vegetation Index (MRR=1.16, 95% BCI: 1.06-1.28), crop cover (MRR=1.17, 95% BCI: 1.11-1.24) and travel time to the hospital (MRR=1.09, 95% BCI: 1.04-1.13) were associated with increased mortality, whereas increase in bed net use (MRR=0.84, 95% BCI: 0.70-1.00), distance to the nearest streams (MRR=0.89, 95% BCI: 0.83-0.96), SES (MRR=0.95, 95% BCI: 0.91-1.00) and altitude (MRR=0.86, 95% BCI: 0.81-0.90) were associated with lower mortality. The effects of travel time and SES were no longer significant when data was aggregated at the health facility catchment level.<br><br>CONCLUSION: Despite the relatively small size of the HDSS, there was spatial variation in malaria mortality that peaked every May-June. The rapid decline in malaria mortality was associated with bed nets, and finer spatial scale analysis identified additional important variables. Time and spatially targeted control interventions may be helpful, and fine spatial scales should be considered when data are available.},
}
@article {pmid39243826,
year = {2024},
author = {Shen, B and Yao, Q and Li, W and Dong, S and Zhang, H and Zhao, Y and Pan, Y and Jiang, X and Li, D and Chen, Y and Yan, J and Zhang, W and Zhu, Q and Zhang, D and Zhang, L and Wu, Y},
title = {Dynamic cerebellar and sensorimotor network compensation in tremor-dominated Parkinson's disease.},
journal = {Neurobiology of disease},
volume = {201},
number = {},
pages = {106659},
doi = {10.1016/j.nbd.2024.106659},
pmid = {39243826},
issn = {1095-953X},
mesh = {Humans ; *Parkinson Disease/physiopathology/diagnostic imaging/complications ; Male ; Female ; *Tremor/physiopathology/diagnostic imaging ; *Cerebellum/diagnostic imaging/physiopathology ; Middle Aged ; Aged ; *Magnetic Resonance Imaging ; Sensorimotor Cortex/physiopathology/diagnostic imaging ; Nerve Net/diagnostic imaging/physiopathology ; Neural Pathways/physiopathology ; },
abstract = {AIM: Parkinson's disease (PD) tremor is associated with dysfunction in the basal ganglia (BG), cerebellum (CB), and sensorimotor networks (SMN). We investigated tremor-related static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC) in PD patients.<br><br>METHODS: We analyzed the resting-state functional MRI data of 21 tremor-dominant Parkinson's disease (TDPD) patients and 29 healthy controls. We compared DFNC and SFNC between the three networks and assessed their associations with tremor severity.<br><br>RESULTS: TDPD patients exhibited increased SFNC between the SMN and BG networks. In addition, they spent more mean dwell time (MDT) in state 2, characterized by sparse connections, and less MDT in state 4, indicating stronger connections. Furthermore, enhanced DFNC between the CB and SMN was observed in state 2. Notably, the MDT of state 2 was positively associated with tremor scores.<br><br>CONCLUSION: The enhanced dynamic connectivity between the CB and SMN in TDPD patients suggests a potential compensatory mechanism. However, the tendency to remain in a state of sparse connectivity may contribute to the severity of tremor symptoms.},
}
@article {pmid39242446,
year = {2024},
author = {Imath, M and Ragavendran, C},
title = {Letter to editor: "Neuralink's brain implant: a vision for enhanced human-machine integration".},
journal = {Neurosurgical review},
volume = {47},
number = {1},
pages = {566},
doi = {10.1007/s10143-024-02806-1},
pmid = {39242446},
issn = {1437-2320},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain/surgery ; },
}
@article {pmid39241437,
year = {2024},
author = {Choo, S and Park, H and Jung, JY and Flores, K and Nam, CS},
title = {Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106665},
doi = {10.1016/j.neunet.2024.106665},
pmid = {39241437},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; Machine Learning ; Deep Learning ; Brain/physiology ; },
abstract = {In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.},
}
@article {pmid39240852,
year = {2024},
author = {Yamashiro, K and Matsumoto, N and Ikegaya, Y},
title = {Diffusion model-based image generation from rat brain activity.},
journal = {PloS one},
volume = {19},
number = {9},
pages = {e0309709},
pmid = {39240852},
issn = {1932-6203},
mesh = {Animals ; Rats ; *Brain/physiology/diagnostic imaging ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Neural Networks, Computer ; Models, Neurological ; },
abstract = {Brain-computer interface (BCI) technology has gained recognition in various fields, including clinical applications, assistive technology, and human-computer interaction research. BCI enables communication, control, and monitoring of the affective/cognitive states of users. Recently, BCI has also found applications in the artistic field, enabling real-time art composition using brain activity signals, and engaging performers, spectators, or an entire audience with brain activity-based artistic environments. Existing techniques use specific features of brain activity, such as the P300 wave and SSVEPs, to control drawing tools, rather than directly reflecting brain activity in the output image. In this study, we present a novel approach that uses a latent diffusion model, a type of deep neural network, to generate images directly from continuous brain activity. We demonstrate this technology using local field potentials from the neocortex of freely moving rats. This system continuously converted the recorded brain activity into images. Our end-to-end method for generating images from brain activity opens new possibilities for creative expression and experimentation. Notably, our results show that the generated images successfully reflect the dynamic and stochastic nature of the underlying neural activity, providing a unique procedure for visualization of brain function.},
}
@article {pmid39240342,
year = {2024},
author = {Yang, D and Sun, Q and Li, W and Wang, Y and Qian, H and Li, D},
title = {Efficiency of HoLEP in patients with detrusor underactivity and renal dysfunction secondary to BPO.},
journal = {World journal of urology},
volume = {42},
number = {1},
pages = {509},
pmid = {39240342},
issn = {1433-8726},
support = {20214Y012//General Project of Shanghai Changning District Health Commission/ ; 202040140//Surface Project of Shanghai Health Commission/ ; },
mesh = {Humans ; Male ; *Lasers, Solid-State/therapeutic use ; Aged ; *Prostatic Hyperplasia/complications/surgery ; *Urinary Bladder, Underactive/physiopathology ; Prospective Studies ; Middle Aged ; *Urinary Bladder Neck Obstruction/surgery/etiology ; Treatment Outcome ; Laser Therapy/methods ; Prostatectomy/methods/adverse effects ; Kidney Diseases/surgery/complications ; },
abstract = {PURPOSE: The purpose of this study was to assess the bladder and renal functional outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic obstruction (BPO) complicated by detrusor underactivity (DU) and secondary renal dysfunction.<br><br>METHODS: Thirty-one patients were included in this prospective study. Eligible patients had urinary retention, a bladder outlet obstruction index (BOOI) greater than 40, a bladder contractility index (BCI) less than 100, abnormal renal function at the initial diagnosis (serum creatinine&#x2009;>&#x2009;132 &#xb5;mol/L) and a renal pelvis anteroposterior diameter (PRAPD)&#x2009;>&#x2009;1.5&#xa0;cm bilaterally. All patients underwent HoLEP in a routine manner and were evaluated preoperatively and at 1, 3 and 6 months after surgery. The baseline characteristics of the patients, perioperative data, postoperative outcomes and complications were assessed.<br><br>RESULTS: Significant improvement was observed in the international prostate symptom score (IPSS), quality of life (QoL) score, maximal urinary flow rate (Qmax), post-void residual volume (PVR), Scr and RPAPD at the 6-month follow-up. Bladder wall thickness (BWT) exhibited a decreasing trend but did not significantly differ from the preoperative values. No grade 3 or higher adverse events occurred, and grade 3 and lower complications were treated conservatively. Three patients required reinsertion of indwelling catheters, and they were able to void spontaneously after two weeks of catheterisation training and medication treatment.<br><br>CONCLUSION: HoLEP is an effective treatment for men with BPO accompanied by DU and consequent renal function impairment. Patients are able to regain spontaneous voiding. Both bladder and renal functions were preserved and improved.},
}
@article {pmid39237192,
year = {2024},
author = {Pons, JL and Reys, V and Grand, F and Moreau, V and Gracy, J and Exner, TE and Labesse, G},
title = {@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking.},
journal = {Journal of molecular biology},
volume = {436},
number = {17},
pages = {168704},
doi = {10.1016/j.jmb.2024.168704},
pmid = {39237192},
issn = {1089-8638},
mesh = {Ligands ; *Molecular Docking Simulation ; *Proteins/chemistry/metabolism ; *Protein Conformation ; Binding Sites ; Protein Binding ; Software ; Drug Design ; Models, Molecular ; },
abstract = {Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.},
}
@article {pmid39237038,
year = {2024},
author = {Chen, S and Wang, Y and Lin, X and Sun, X and Li, W and Ma, W},
title = {Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.},
journal = {Journal of neuroscience methods},
volume = {411},
number = {},
pages = {110276},
doi = {10.1016/j.jneumeth.2024.110276},
pmid = {39237038},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distributions of EEG data at different subjects, has attracted great attention for cross-subject emotion recognition.<br><br>This study focuses on narrowing the domain gap between subjects through the emotional frequency bands and the relationship information between EEG channels. Emotional frequency band features represent the energy distribution of EEG data in different frequency ranges, while relationship information between EEG channels provides spatial distribution information about EEG data.<br><br>NEW METHOD: To achieve this, this paper proposes a model called the Frequency Band Attention Graph convolutional Adversarial neural Network (FBAGAN). This model includes three components: a feature extractor, a classifier, and a discriminator. The feature extractor consists of a layer with a frequency band attention mechanism and a graph convolutional neural network. The mechanism effectively extracts frequency band information by assigning weights and Graph Convolutional Networks can extract relationship information between EEG channels by modeling the graph structure. The discriminator then helps minimize the gap in the frequency information and relationship information between the source and target domains, improving the model's ability to generalize.<br><br>RESULTS: The FBAGAN model is extensively tested on the SEED, SEED-IV, and DEAP datasets. The accuracy and standard deviation scores are 88.17% and 4.88, respectively, on the SEED dataset, and 77.35% and 3.72 on the SEED-IV dataset. On the DEAP dataset, the model achieves 69.64% for Arousal and 65.18% for Valence. These results outperform most existing models.<br><br>CONCLUSIONS: The experiments indicate that FBAGAN effectively addresses the challenges of transferring EEG channel domain and frequency band domain, leading to improved performance.},
}
@article {pmid39236139,
year = {2025},
author = {Yang, X and Zhu, Z and Jiang, G and Wu, D and He, A and Wang, J},
title = {DC-ASTGCN: EEG Emotion Recognition Based on Fusion Deep Convolutional and Adaptive Spatio-Temporal Graph Convolutional Networks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2471-2483},
doi = {10.1109/JBHI.2024.3449083},
pmid = {39236139},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology/classification ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Brain-Computer Interfaces ; Deep Learning ; Adult ; },
abstract = {Thanks to advancements in artificial intelligence and brain-computer interface (BCI) research, there has been increasing attention towards emotion recognition techniques based on electroencephalogram (EEG) recently. The complexity of EEG data poses a challenge when it comes to accurately classifying emotions by integrating time, frequency, and spatial domain features. To address this challenge, this paper proposes a fusion model called DC-ASTGCN, which combines the strengths of deep convolutional neural network (DCNN) and adaptive spatio-temporal graphic convolutional neural network (ASTGCN) to comprehensively analyze and understand EEG signals. The DCNN focuses on extracting frequency-domain and local spatial features from EEG signals to identify brain region activity patterns, while the ASTGCN, with its spatio-temporal attention mechanism and adaptive brain topology layer, reveals the functional connectivity features between brain regions in different emotional states. This integration significantly enhances the model's ability to understand and recognize emotional states. Extensive experiments conducted on the DEAP and SEED datasets demonstrate that the DC-ASTGCN model outperforms existing state-of-the-art methods in terms of emotion recognition accuracy.},
}
@article {pmid39236133,
year = {2024},
author = {Rong, F and Yang, B and Guan, C},
title = {Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3399-3409},
doi = {10.1109/TNSRE.2024.3454088},
pmid = {39236133},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Machine Learning ; *Algorithms ; Healthy Volunteers ; Movement/physiology ; Upper Extremity/physiology ; Stroke Rehabilitation/methods ; },
abstract = {The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bilateral limbs paradigm and decoding, but the use scenarios for stroke rehabilitation are typically for unilateral upper limbs. There is a significant challenge to decoding unilateral MI of multitasks due to the overlapped spatial neural activities of the tasks. This study aims to formulate a novel MI-BCI experimental paradigm for unilateral limbs with multitasks. The paradigm encompasses four imagined movement directions: top-bottom, left-right, top right-bottom left, and top left-bottom right. Forty-six healthy subjects participated in this experiment. Commonly used machine learning techniques, such as FBCSP, EEGNet, deepConvNet, and FBCNet, were employed for evaluation. To improve decoding accuracy, we propose an MVCA method that introduces temporal convolution and attention mechanism to effectively capture temporal features from multiple perspectives. With the MVCA model, we have achieved 40.6% and 64.89% classification accuracies for the four-class and two-class scenarios (top right-bottom left and top left-bottom right), respectively. Conclusion: This is the first study demonstrating that motor imagery of multiple directions in unilateral limbs can be decoded. In particular, decoding two directions, right top to left bottom and left top to right bottom, provides the best accuracy, which sheds light on future studies. This study advances the development of the MI-BCI paradigm, offering preliminary evidence for the feasibility of decoding multiple directional information from EEG. This, in turn, enhances the dimensions of MI control commands.},
}
@article {pmid39234682,
year = {2025},
author = {Longo, UG and Marino, M and de Sire, A and Ruiz-Iban, MA and D'Hooghe, P},
title = {The bioinductive collagen implant yields positive histological, clinical and MRI outcomes in the management of rotator cuff tears: A systematic review.},
journal = {Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA},
volume = {33},
number = {3},
pages = {1070-1090},
pmid = {39234682},
issn = {1433-7347},
mesh = {*Rotator Cuff Injuries/surgery/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; *Collagen/therapeutic use ; Animals ; Rotator Cuff/surgery/pathology/diagnostic imaging ; *Prostheses and Implants ; Treatment Outcome ; },
abstract = {PURPOSE: The aim of this study is to report and discuss the outcomes of clinical, histological and animal studies exploring the application of bio-inductive collagen implants (BCIs) to partial and full-thickness rotator cuff tears (PT- and FT-RCTs) in addition to reporting on cost-related factors.<br><br>METHODS: Review of literature was performed using the PRISMA guidelines. A systematic electronic literature search was conducted using the CENTRAL, CINAHL, Cochrane Library, EBSCOhost, EMBASE and Google Scholar bibliographic databases. Microsoft Excel was used to create tables onto which extracted data were recorded. Tables were organized based on the research statement formulated using the PICO approach. No statistical analysis was performed.<br><br>RESULTS: Nine studies evaluated clinical and MRI outcomes of BCI augmentation for FT-RCTs, seven evaluated similar outcomes when applied to PT-RCTs, two additional studies were case reports and three studies assessed application to FT- and PT-RCTs without stratification of results, one of which also reported on histological data. Two studies reported on histological data alone, and finally, two reported on healthcare costs. BCI augmentation, alone and combined with rotator cuff repair (RCR), displays generally good histological, postoperative clinical and MRI outcomes for PT- and FT-RCT treatment. Recent economic analyses seem to be in favour of the use of this procedure, when selected and applied for appropriate patient populations.<br><br>CONCLUSION: Several studies have shown promising results of BCI application to PT- and FT-RCTs, both concomitantly and independently from RCR. Investigations report promising histological characteristics, improved clinical outcomes, increased tendon thickness, reduced defect size and lower re-tear rates.<br><br>LEVEL OF EVIDENCE: Level IV.},
}
@article {pmid39234592,
year = {2024},
author = {Tajmirriahi, M and Rabbani, H},
title = {A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.},
journal = {Journal of medical signals and sensors},
volume = {14},
number = {},
pages = {19},
pmid = {39234592},
issn = {2228-7477},
abstract = {Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure attacks are caused by hyperexcitability and anatomical lesions of special regions of the brain, and cognitive impairments and memory deficits are their most common concomitant effects. In addition to seizure reduction treatments, medical rehabilitation involving brain-computer interfaces and neurofeedback can improve cognition and quality of life in patients with focal epilepsy in most cases, in particular when resective epilepsy surgery has been considered treatment in drug-resistant epilepsy. Source estimation and precise localization of epileptic foci can improve such rehabilitation and treatment. Electroencephalography (EEG) monitoring and multimodal noninvasive neuroimaging techniques such as ictal/interictal single-photon emission computerized tomography (SPECT) imaging and structural magnetic resonance imaging are common practices for the localization of epileptic foci and have been studied in several kinds of researches. In this article, we review the most recent research on EEG-based localization of seizure foci and discuss various methods, their advantages, limitations, and challenges with a focus on model-based data processing and machine learning algorithms. In addition, we survey whether combined analysis of EEG monitoring and neuroimaging techniques, which is known as multimodal brain data fusion, can potentially increase the precision of the seizure foci localization. To this end, we further review and summarize the key parameters and challenges of processing, fusion, and analysis of multiple source data, in the framework of model-based signal processing, for the development of a multimodal brain data analyzing system. This article has the potential to be used as a valuable resource for neuroscience researchers for the development of EEG-based rehabilitation systems based on multimodal data analysis related to focal epilepsy.},
}
@article {pmid39234406,
year = {2024},
author = {Lakshminarayanan, K and Madathil, D and Murari, BM and Shah, R},
title = {Editorial: Recent advancements in brain-computer interfaces-based limb rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1466450},
doi = {10.3389/fnhum.2024.1466450},
pmid = {39234406},
issn = {1662-5161},
}
@article {pmid39234182,
year = {2024},
author = {Si, Y and Wang, Z and Xu, G and Wang, Z and Xu, T and Zhou, T and Hu, H},
title = {Group-member selection for RSVP-based collaborative brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1402154},
pmid = {39234182},
issn = {1662-4548},
abstract = {OBJECTIVE: The brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP) have been widely utilized for the detection of target and non-target images. Collaborative brain-computer interface (cBCI) effectively fuses electroencephalogram (EEG) data from multiple users to overcome the limitations of low single-user performance in single-trial event-related potential (ERP) detection in RSVP-based BCI systems. In a multi-user cBCI system, a superior group mode may lead to better collaborative performance and lower system cost. However, the key factors that enhance the collaboration capabilities of multiple users and how to further use these factors to optimize group mode remain unclear.<br><br>APPROACH: This study proposed a group-member selection strategy to optimize the group mode and improve the system performance for RSVP-based cBCI. In contrast to the conventional grouping of collaborators at random, the group-member selection strategy enabled pairing each user with a better collaborator and allowed tasks to be done with fewer collaborators. Initially, we introduced the maximum individual capability and maximum collaborative capability (MIMC) to select optimal pairs, improving the system classification performance. The sequential forward floating selection (SFFS) combined with MIMC then selected a sub-group, aiming to reduce the hardware and labor expenses in the cBCI system. Moreover, the hierarchical discriminant component analysis (HDCA) was used as a classifier for within-session conditions, and the Euclidean space data alignment (EA) was used to overcome the problem of inter-trial variability for cross-session analysis.<br><br>MAIN RESULTS: In this paper, we verified the effectiveness of the proposed group-member selection strategy on a public RSVP-based cBCI dataset. For the two-user matching task, the proposed MIMC had a significantly higher AUC and TPR and lower FPR than the common random grouping mode and the potential group-member selection method. Moreover, the SFFS with MIMC enabled a trade-off between maintaining performance and reducing the number of system users.<br><br>SIGNIFICANCE: The results showed that our proposed MIMC effectively optimized the group mode, enhanced the classification performance in the two-user matching task, and could reduce the redundant information by selecting the sub-group in the RSVP-based multi-user cBCI systems.},
}
@article {pmid39233125,
year = {2024},
author = {Mao, T and Guo, B and Quan, P and Deng, Y and Chai, Y and Xu, J and Jiang, C and Zhang, Q and Lu, Y and Goel, N and Basner, M and Dinges, DF and Rao, H},
title = {Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation.},
journal = {NeuroImage},
volume = {299},
number = {},
pages = {120833},
doi = {10.1016/j.neuroimage.2024.120833},
pmid = {39233125},
issn = {1095-9572},
mesh = {Humans ; Male ; Female ; *Magnetic Resonance Imaging ; *Hypothalamus/diagnostic imaging/physiology ; Adult ; Young Adult ; *Individuality ; Circadian Rhythm/physiology ; Sleepiness ; Neural Pathways/physiology/diagnostic imaging ; Corpus Striatum/diagnostic imaging/physiology ; Wakefulness/physiology ; Sleep/physiology ; },
abstract = {While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.},
}
@article {pmid39232389,
year = {2024},
author = {Li, N and Chen, S and Wu, Z and Dong, J and Wang, J and Lei, Y and Mo, J and Wei, W and Li, T},
title = {Secular trends in the prevalence of schizophrenia among different age, period and cohort groups between 1990 and 2019.},
journal = {Asian journal of psychiatry},
volume = {101},
number = {},
pages = {104192},
doi = {10.1016/j.ajp.2024.104192},
pmid = {39232389},
issn = {1876-2026},
mesh = {Humans ; *Schizophrenia/epidemiology ; Adult ; Middle Aged ; Prevalence ; Male ; Female ; Young Adult ; Adolescent ; Aged ; Cohort Studies ; Age Factors ; Global Health/statistics &amp; numerical data ; Global Burden of Disease/trends ; },
abstract = {BACKGROUND: Schizophrenia remains a major public health challenge, and designing efforts to manage it requires understanding its prevalence over time at different geographic scales and population groups.<br><br>METHODS: Drawing on data from the Global Burden of Disease study 2019, annual percentage change of schizophrenia was assessed across different age, period and cohort groups at different geographic scales from 1990 to 2019. We examined associations of prevalence with the sociodemographic index.<br><br>RESULTS: Global prevalence of schizophrenia in 2019 was 23.60 million (95&#x202f;% uncertainty interval: 20.23-27.15), with China, India, the USA and Indonesia accounting for 50.72&#x202f;% of it. Global prevalence increased slightly from 1990 to 2019, with an annual percentage change of 0.03&#x202f;% (95&#x202f;% confidence interval 0.01-0.05). Regions with intermediate sociodemographic index accounted for greater proportion of prevalence increasing than regions with high index. Prevalence decreased among those born after 1979 in regions with intermediate sociodemographic index, whereas it consistently improved among all birth cohorts in regions with low index. Regardless of sociodemographic index, prevalence was highest among individuals 30-59 years old than younger or older groups.<br><br>CONCLUSIONS: Prevalence of schizophrenia has shown small increases globally over the last three decades. The burden of disease is heavier in relatively less affluent regions, and it disproportionately affects individuals 30-59 years in all regions. Meanwhile, for regions with lower sociodemographic indices, the recent increasing burden among birth cohorts is more pronounced. These findings may help guide futural design of measures to manage or prevent schizophrenia in communities at higher risk.},
}
@article {pmid39232069,
year = {2024},
author = {Shang, L and Si, H and Wang, H and Pan, T and Liu, H and Li, Y and Qiu, J and Xu, M},
title = {Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20641},
pmid = {39232069},
issn = {2045-2322},
support = {NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; NJ2024029//Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. U2033202//Joint Fund of the National Natural Science Foundation of China and Civil Aviation Administration of China/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No. NS2022094//the Fundamental Research Funds for the Central Universities/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No.SYJS202207Y//the Experimental Technology Research and Development" project of Nanjing University of Aeronautics and Astronautics Project/ ; No. 202101042005//the first batch of industry-university-research cooperative collaborative education projects of the Ministry of Education in 2021/ ; No.ZDGB2021024//the Nanjing University of Aeronautics and Astronautics PhD short-term visiting scholar project/ ; },
mesh = {Humans ; *Fatigue/diagnosis ; *Algorithms ; *Neural Networks, Computer ; Aircraft ; Pilots ; Face ; Machine Learning ; Accidents, Aviation ; },
abstract = {Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.},
}
@article {pmid39231981,
year = {2024},
author = {Wang, Q and Sun, RY and Hu, JX and Sun, YH and Li, CY and Huang, H and Wang, H and Li, XM},
title = {Hypothalamic-hindbrain circuit for consumption-induced fear regulation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {7728},
pmid = {39231981},
issn = {2041-1723},
support = {82090031//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82090030//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900723//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001186//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019TQ0278//China Postdoctoral Science Foundation/ ; },
mesh = {Animals ; *Fear/physiology ; Male ; Mice ; Hypothalamus/physiology/metabolism ; Relaxin/metabolism ; Neurons/physiology/metabolism ; Mice, Inbred C57BL ; Neural Pathways/physiology ; Eating/physiology ; Conditioning, Classical/physiology ; Hypothalamic Area, Lateral/physiology/metabolism ; },
abstract = {To ensure survival, animals must sometimes suppress fear responses triggered by potential threats during feeding. However, the mechanisms underlying this process remain poorly understood. In the current study, we demonstrated that when fear-conditioned stimuli (CS) were presented during food consumption, a neural projection from lateral hypothalamic (LH) GAD2 neurons to nucleus incertus (NI) relaxin-3 (RLN3)-expressing neurons was activated, leading to a reduction in CS-induced freezing behavior in male mice. LH[GAD2] neurons established excitatory connections with the NI. The activity of this neural circuit, including NI[RLN3] neurons, attenuated CS-induced freezing responses during food consumption. Additionally, the lateral mammillary nucleus (LM), which received NI[RLN3] projections, along with RLN3 signaling in the LM, mediated the decrease in freezing behavior. Collectively, this study identified an LH[GAD2]-NI[RLN3]-LM circuit involved in modulating fear responses during feeding, thereby enhancing our understanding of how animals coordinate nutrient intake with threat avoidance.},
}
@article {pmid39231469,
year = {2024},
author = {Baberwal, SS and Magre, LA and Gunawardhana, KRSD and Parkinson, M and Ward, T and Coyle, S},
title = {Motor imagery with cues in virtual reality, audio and screen.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad775e},
pmid = {39231469},
issn = {1741-2552},
abstract = {Training plays a significant role in motor imagery (MI), particularly in applications such as Motor Imagery-based Brain-Computer Interface (MIBCI) systems and rehabilitation systems. Previous studies have investigated the intricate relationship between cues and MI signals. However, the medium of presentation still remains an emerging area to be explored, as possible factors to enhance Motor Imagery signals.. Approach: We hypothesise that the medium used for cue presentation can significantly influence both performance and training outcomes in MI tasks. To test this hypothesis, we designed and executed an experiment implementing no- feedback MI. Our investigation focused on three distinct cue presentation mediums -audio, screen, and virtual reality(VR) headsets-all of which have potential implications for BCI use in the Activities of Daily Lives. Main Results: The results of our study uncovered notable variations in MI signals depending on the medium of cue presentation, where the analysis is based on 3 EEG channels. To substantiate our findings, we employed a comprehensive approach, utilizing various evaluation metrics including Event- Related Synchronisation(ERS)/Desynchronisation(ERD), Feature Extraction (using Recursive Feature Elimination (RFE)), Machine Learning methodologies (using Ensemble Learning), and participant Questionnaires. All the approaches signify that Motor Imagery signals are enhanced when presented in VR, followed by audio, and lastly screen. Applying a Machine Learning approach across all subjects, the mean cross-validation accuracy (Mean ± Std. Error) was 69.24 ± 3.12, 68.69 ± 3.3 and 66.1±2.59 when for the VR, audio-based, and screen-based instructions respectively. Significance: This multi-faceted exploration provides evidence to inform MI- based BCI design and advocates the incorporation of different mediums into the design of MIBCI systems, experimental setups, and user studies. The influence of the medium used for cue presentation may be applied to develop more effective and inclusive MI applications in the realm of human-computer interaction and rehabilitation.},
}
@article {pmid39231466,
year = {2024},
author = {Yang, L and Sun, Q and Van Hulle, MM},
title = {Binocularly incongruent, multifrequency-coded SSVEP in VR: feasibility and characteristics.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad775f},
pmid = {39231466},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Vision, Binocular/physiology ; Male ; Female ; Adult ; *Photic Stimulation/methods ; Young Adult ; *Virtual Reality ; *Electroencephalography/methods ; *Feasibility Studies ; Brain-Computer Interfaces ; },
abstract = {Objective.Steady-state visual evoked potentials (SSVEPs) in response to flickering stimuli are popular in brain-computer interfacing but their implementation in virtual reality (VR) offers new opportunities also for clinical applications. While traditional SSVEP target selection relies on single-frequency stimulation of both eyes simultaneously, further called congruent stimulation, recent studies attempted to improve the information transfer rate by using dual-frequency-coded SSVEP where each eye is presented with a stimulus flickering at a different frequency, further called incongruent stimulation. However, few studies have investigated incongruent multifrequency-coded SSVEP (MultiIncong-SSVEP).Approach.This paper reports on a systematical investigation of incongruent dual-, triple-, and quadruple-frequency-coded SSVEP for use in VR, several of which are entirely novel, and compares their performance with that of congruent dual-frequency-coded SSVEP.Main results.We were able to confirm the presence of a summation effect when comparing monocular- and binocular single-frequency congruent stimulation, and a suppression effect when comparing monocular- and binocular dual-frequency incongruent stimulation, as both tap into the binocular vision capabilities which, when hampered, could signal amblyopia.Significance.In sum, our findings not only evidence the potential of VR-based binocularly incongruent SSVEP but also underscore the importance of paradigm choice and decoder design to optimize system performance and user comfort.},
}
@article {pmid39231465,
year = {2024},
author = {Pulferer, HS and Kostoglou, K and Müller-Putz, GR},
title = {Improving non-invasive trajectory decoding via neural correlates of continuous erroneous feedback processing.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad7762},
pmid = {39231465},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; *Electroencephalography/methods ; Young Adult ; Neural Networks, Computer ; Brain/physiology ; },
abstract = {Objective. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intocorrectorerroneousto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.Approach.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Main results.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Significance.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.},
}
@article {pmid39229920,
year = {2024},
author = {Ding, L and Guo, H and Zhang, J and Zheng, M and Zhang, W and Wang, L and Du, Q and Zhou, C and Xu, Y and Wu, H and He, Q and Yang, B},
title = {Zosuquidar Promotes Antitumor Immunity by Inducing Autophagic Degradation of PD-L1.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {41},
pages = {e2400340},
pmid = {39229920},
issn = {2198-3844},
support = {82330114//National Natural Science Foundation of China/ ; 82273949//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *B7-H1 Antigen/metabolism/genetics/immunology ; Mice ; *Autophagy ; Humans ; Disease Models, Animal ; Cell Line, Tumor ; ATP Binding Cassette Transporter, Subfamily B/metabolism/genetics ; },
abstract = {The intracellular distribution and transportation process are essential for maintaining PD-L1 (programmed death-ligand 1) expression, and intervening in this cellular process may provide promising therapeutic strategies. Here, through a cell-based high content screening, it is found that the ABCB1 (ATP binding cassette subfamily B member 1) modulator zosuquidar dramatically suppresses PD-L1 expression by triggering its autophagic degradation. Mechanistically, ABCB1 interacts with PD-L1 and impairs COP II-mediated PD-L1 transport from ER (endoplasmic reticulum) to Golgi apparatus. The treatment of zosuquidar enhances ABCB1-PD-L1 interaction and leads the ER retention of PD-L1, which is subsequently degraded in the SQSTM1-dependent selective autophagy pathway. In CT26 mouse model and a humanized xenograft mouse model, zosuquidar significantly suppresses tumor growth and accompanies by increased infiltration of cytotoxic T cells. In summary, this study indicates that ABCB1 serves as a negative regulator of PD-L1, and zosuquidar may act as a potential immunotherapy agent by triggering PD-L1 degradation in the early secretory pathway.},
}
@article {pmid39229190,
year = {2024},
author = {Vargas-Irwin, CE and Hosman, T and Gusman, JT and Pun, TK and Simeral, JD and Singer-Clark, T and Kapitonava, A and Nicolas, C and Shah, NP and Avansino, D and Kamdar, F and Williams, Z and Henderson, JM and Hochberg, LR},
title = {Gesture encoding in human left precentral gyrus neuronal ensembles.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.08.23.608325},
pmid = {39229190},
issn = {2692-8205},
abstract = {Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.},
}
@article {pmid39229047,
year = {2024},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice synthesis neuroprosthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39229047},
issn = {2692-8205},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to speak due to neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech such as prosody, intonation and immediately hearing one's own voice. Here, we demonstrate a "brain-to-voice" neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real-time to change intonation, emphasize words, and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}
@article {pmid39227389,
year = {2024},
author = {Chang, H and Sun, Y and Lu, S and Lin, D},
title = {A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain-computer interface to improve the effect of node displacement.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20420},
pmid = {39227389},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; Humans ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain-computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain-computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain-computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain-computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain-computer interface after node displacement optimization can be evaluated.},
}
@article {pmid39226926,
year = {2024},
author = {Shen, J and Garrad, M and Zhang, Q and Wong, VCH and Pirrera, A and Groh, RMJ},
title = {A rapid-response soft end effector inspired by the hummingbird beak.},
journal = {Journal of the Royal Society, Interface},
volume = {21},
number = {218},
pages = {20240148},
pmid = {39226926},
issn = {1742-5662},
support = {//Royal Academy of Engineering/ ; //Leverhulme Trust/ ; },
mesh = {Animals ; *Beak/physiology/anatomy &amp; histology ; *Birds/physiology ; Robotics ; Models, Biological ; Biomechanical Phenomena ; },
abstract = {Biology is a wellspring of inspiration in engineering design. This paper delves into the application of elastic instabilities-commonly used in biological systems to facilitate swift movement-as a power-amplification mechanism for soft robots. Specifically, inspired by the nonlinear mechanics of the hummingbird beak-and shedding further light on it-we design, build and test a novel, rapid-response, soft end effector. The hummingbird beak embodies the capacity for swift movement, achieving closure in less than [Formula: see text]. Previous work demonstrated that rapid movement is achieved through snap-through deformations, induced by muscular actuation of the beak's root. Using nonlinear finite element simulations coupled with continuation algorithms, we unveil a representative portion of the equilibrium manifold of the beak-inspired structure. The exploration involves the application of a sequence of rotations as exerted by the hummingbird muscles. Specific emphasis is placed on pinpointing and tailoring the position along the manifold of the saddle-node bifurcation at which the onset of elastic instability triggers dynamic snap-through. We show the critical importance of the intermediate rotation input in the sequence, as it results in the accumulation of elastic energy that is then explosively released as kinetic energy upon snap-through. Informed by our numerical studies, we conduct experimental testing on a prototype end effector fabricated using a compliant material (thermoplastic polyurethane). The experimental results support the trends observed in the numerical simulations and demonstrate the effectiveness of the bio-inspired design. Specifically, we measure the energy transferred by the soft end effector to a pendulum, varying the input levels in the sequence of prescribed rotations. Additionally, we demonstrate a potential robotic application in scenarios demanding explosive action. From a mechanics perspective, our work sheds light on how pre-stress fields can enable swift movement in soft robotic systems with the potential to facilitate high input-to-output energy efficiency.},
}
@article {pmid39226850,
year = {2024},
author = {Li, S and Daly, I and Guan, C and Cichocki, A and Jin, J},
title = {Inter-participant transfer learning with attention based domain adversarial training for P300 detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106655},
doi = {10.1016/j.neunet.2024.106655},
pmid = {39226850},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Attention/physiology ; Deep Learning ; },
abstract = {A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.},
}
@article {pmid39226701,
year = {2024},
author = {Ji, Y and Silva, RF and Adali, T and Wen, X and Zhu, Q and Jiang, R and Zhang, D and Qi, S and Calhoun, VD},
title = {Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders.},
journal = {NeuroImage. Clinical},
volume = {43},
number = {},
pages = {103663},
pmid = {39226701},
issn = {2213-1582},
mesh = {Humans ; Male ; Female ; Adult ; *Schizophrenia/diagnosis ; *Magnetic Resonance Imaging/methods ; Autism Spectrum Disorder/diagnosis ; Brain/physiopathology/diagnostic imaging ; Young Adult ; Mental Disorders/diagnosis ; Adolescent ; Diagnosis, Computer-Assisted/methods ; },
abstract = {Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allowed us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2 %±2.22 % and 84.8 %±2.68 % for the diagnosis of SZ and ASD, respectively. We also compared with 6 DA, 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA+LOSMFS effectively integrated multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.},
}
@article {pmid39226201,
year = {2024},
author = {Liu, J and Wang, R and Yang, Y and Zong, Y and Leng, Y and Zheng, W and Ge, S},
title = {Convolutional Transformer-Based Cross Subject Model for SSVEP-Based BCI Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {11},
pages = {6581-6593},
doi = {10.1109/JBHI.2024.3454158},
pmid = {39226201},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods/classification ; *Signal Processing, Computer-Assisted ; *Algorithms ; Adult ; Male ; },
abstract = {Steady-state visual evoked potential (SSVEP) is a commonly used brain-computer interface (BCI) paradigm. The performance of cross-subject SSVEP classification has a strong impact on SSVEP-BCI. This study designed a cross subject generalization SSVEP classification model based on an improved transformer structure that uses domain generalization (DG). The global receptive field of multi-head self-attention is used to learn the global generalized SSVEP temporal information across subjects. This is combined with a parallel local convolution module, designed to avoid oversmoothing the oscillation characteristics of temporal SSVEP data and better fit the feature. Moreover, to improve the cross-subject calibration-free SSVEP classification performance, an DG method named StableNet is combined with the proposed convolutional transformer structure to form the DG-Conformer method, which can eliminate spurious correlations between SSVEP discriminative information and background noise to improve cross-subject generalization. Experiments on two public datasets, Benchmark and BETA, demonstrated the outstanding performance of the proposed DG-Conformer compared with other calibration-free methods, FBCCA, tt-CCA, Compact-CNN, FB-tCNN, and SSVEPNet. Additionally, DG-Conformer outperforms the classic calibration-required algorithms eCCA, eTRCA and eSSCOR when calibration is used. An incomplete partial stimulus calibration scheme was also explored on the Benchmark dataset, and it was demonstrated to be a potential solution for further high-performance personalized SSVEP-BCI with quick calibration.},
}
@article {pmid39225378,
year = {2025},
author = {Zhang, H and Zheng, Z and Chen, X and Xu, L and Guo, C and Wang, J and Cui, Y and Yang, F},
title = {RADICAL: a rationally designed ion channel activated by ligand for chemogenetics.},
journal = {Protein &amp; cell},
volume = {16},
number = {2},
pages = {136-142},
pmid = {39225378},
issn = {1674-8018},
support = {32122040//National Natural Science Foundation of China/ ; LR20C050002//Zhejiang Provincial Natural Science Foundation of China/ ; },
}
@article {pmid39225144,
year = {2024},
author = {Quan, P and Mao, T and Zhang, X and Wang, R and Lei, H and Wang, J and Liu, W and Dinges, DF and Jiang, C and Rao, H},
title = {Locus coeruleus microstructural integrity is associated with vigilance vulnerability to sleep deprivation.},
journal = {Human brain mapping},
volume = {45},
number = {13},
pages = {e70013},
pmid = {39225144},
issn = {1097-0193},
support = {2023WTSCX034//Guangdong Scientific Research Platform and Projects for the Higher-educational Institution/ ; 32200889//National Natural Science Foundation of China/ ; 71942003//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; 2021KFKT012//Shanghai International Studies University Research Projects/ ; GD23XXL07//Guangdong Philosophy and Social Science Planning/ ; CTRC UL1RR024134/NH/NIH HHS/United States ; P30-NS045839/NH/NIH HHS/United States ; R01-HL102119/NH/NIH HHS/United States ; R01-MH107571/NH/NIH HHS/United States ; R21-AG051981/NH/NIH HHS/United States ; },
mesh = {Humans ; *Sleep Deprivation/diagnostic imaging/physiopathology/pathology ; *Locus Coeruleus/diagnostic imaging/pathology ; Male ; Female ; *Diffusion Tensor Imaging ; Adult ; Young Adult ; *Psychomotor Performance/physiology ; Arousal/physiology ; Anisotropy ; Neuropsychological Tests ; },
abstract = {Insufficient sleep compromises cognitive performance, diminishes vigilance, and disrupts daily functioning in hundreds of millions of people worldwide. Despite extensive research revealing significant variability in vigilance vulnerability to sleep deprivation, the underlying mechanisms of these individual differences remain elusive. Locus coeruleus (LC) plays a crucial role in the regulation of sleep-wake cycles and has emerged as a potential marker for vigilance vulnerability to sleep deprivation. In this study, we investigate whether LC microstructural integrity, assessed by fractional anisotropy (FA) through diffusion tensor imaging (DTI) at baseline before sleep deprivation, can predict impaired psychomotor vigilance test (PVT) performance during sleep deprivation in a cohort of 60 healthy individuals subjected to a rigorously controlled in-laboratory sleep study. The findings indicate that individuals with high LC FA experience less vigilance impairment from sleep deprivation compared with those with low LC FA. LC FA accounts for 10.8% of the variance in sleep-deprived PVT lapses. Importantly, the relationship between LC FA and impaired PVT performance during sleep deprivation is anatomically specific, suggesting that LC microstructural integrity may serve as a biomarker for vigilance vulnerability to sleep loss.},
}
@article {pmid39222461,
year = {2024},
author = {Wang, Z and Liu, Y and Huang, S and Qiu, S and Zhang, Y and Huang, H and An, X and Ming, D},
title = {EEG Characteristic Comparison of Motor Imagery Between Supernumerary and Inherent Limb: Sixth-Finger MI Enhances the ERD Pattern and Classification Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {12},
pages = {7078-7089},
doi = {10.1109/JBHI.2024.3452701},
pmid = {39222461},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; Adult ; Young Adult ; *Imagination/physiology ; *Fingers/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; Robotics ; Brain-Computer Interfaces ; Movement/physiology ; },
abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices. In this work, we established a MI neo-framework consisting of novel supernumerary robotic sixth-finger MI (SRF-MI) and traditional right-hand MI (RH-MI) paradigms and validated the distinctness of EEG response patterns between two MI tasks for the first time. Twenty-four subjects were recruited for this experiment involving three mental tasks. Event-related spectral perturbation was adopted to supply details about event-related desynchronization (ERD). Activation region, intensity and response time (RT) of ERD were compared between SRF-MI and RH-MI tasks. Three classical classification algorithms were utilized to verify the separability between different mental tasks. And genetic algorithm aims to select optimal combination of channels for neo-framework. A bilateral sensorimotor and prefrontal modulation was found during the SRF-MI task, whereas in RH-MI only contralateral sensorimotor modulation was exhibited. The novel SRF-MI paradigm enhanced ERD intensity by a maximum of 117% in prefrontal area and 188% in the ipsilateral somatosensory-association cortex. And, a global decrease of RT was exhibited during SRF-MI tasks compared to RH-MI. Classification results indicate well separable performance among different mental tasks (88.1% maximum for 2-class and 88.2% maximum for 3-class). This work demonstrated the difference between the SRF-MI and RH-MI paradigms, widening the control bandwidth of the BCI system.},
}
@article {pmid39220036,
year = {2024},
author = {Kim, E and Kim, Y},
title = {Exploring the potential of spiking neural networks in biomedical applications: advantages, limitations, and future perspectives.},
journal = {Biomedical engineering letters},
volume = {14},
number = {5},
pages = {967-980},
pmid = {39220036},
issn = {2093-985X},
abstract = {In this paper, a comprehensive exploration is undertaken to elucidate the utilization of Spiking Neural Networks (SNNs) within the biomedical domain. The investigation delves into the experimentally validated advantages of SNNs in comparison to alternative models like LSTM, while also critically examining the inherent limitations of SNN classifiers or algorithms. SNNs exhibit distinctive advantages that render them particularly apt for targeted applications within the biomedical field. Over time, SNNs have undergone extensive scrutiny in realms such as neuromorphic processing, Brain-Computer Interfaces (BCIs), and Disease Diagnosis. Notably, SNNs demonstrate a remarkable affinity for the processing and analysis of biomedical signals, including but not limited to electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) data. This paper initiates its exploration by introducing some of the biomedical applications of EMG, such as the classification of hand gestures and motion decoding. Subsequently, the focus extends to the applications of SNNs in the analysis of EEG and ECG signals. Moreover, the paper delves into the diverse applications of SNNs in specific anatomical regions, such as the eyes and noses. In the final sections, the paper culminates with a comprehensive analysis of the field, offering insights into the advantages, disadvantages, challenges, and opportunities introduced by various SNN models in the realm of healthcare and biomedical domains. This holistic examination provides a nuanced perspective on the potential transformative impact of SNN across a spectrum of applications within the biomedical landscape.},
}
@article {pmid39218593,
year = {2024},
author = {Guo, M and Yang, B and Geng, Y and Jie, R and Zhang, Y and Zheng, Y},
title = {[Visual object detection system based on augmented reality and steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {684-691},
pmid = {39218593},
issn = {1001-5515},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Augmented Reality ; *Electroencephalography ; Photic Stimulation ; User-Computer Interface ; Algorithms ; },
abstract = {This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.},
}
@article {pmid39218592,
year = {2024},
author = {Zhang, Y and Liu, D and Gao, F},
title = {[A deep transfer learning approach for cross-subject recognition of mental tasks based on functional near-infrared spectroscopy].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {673-683},
pmid = {39218592},
issn = {1001-5515},
mesh = {*Spectroscopy, Near-Infrared/methods ; Humans ; *Brain-Computer Interfaces ; Deep Learning ; Algorithms ; Brain/physiology/diagnostic imaging ; Neural Networks, Computer ; },
abstract = {In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.},
}
@article {pmid39218591,
year = {2024},
author = {Xie, P and Men, Y and Zhen, J and Shao, X and Zhao, J and Chen, X},
title = {[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {664-672},
pmid = {39218591},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Humans ; *Electroencephalography ; *Robotics/instrumentation ; *Algorithms ; Discriminant Analysis ; },
abstract = {Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.},
}
@article {pmid39218590,
year = {2024},
author = {Shao, X and Zhang, Y and Zhang, D and Men, Y and Wang, Z and Chen, X and Xie, P},
title = {[Virtual reality-brain computer interface hand function enhancement rehabilitation system incorporating multi-sensory stimulation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {656-663},
pmid = {39218590},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Hand/physiology ; *Virtual Reality ; *Stroke Rehabilitation/methods/instrumentation ; *Electroencephalography ; Feedback, Sensory ; User-Computer Interface ; Motor Cortex/physiology ; },
abstract = {Stroke is an acute cerebrovascular disease in which sudden interruption of blood supply to the brain or rupture of cerebral blood vessels cause damage to brain cells and consequently impair the patient's motor and cognitive abilities. A novel rehabilitation training model integrating brain-computer interface (BCI) and virtual reality (VR) not only promotes the functional activation of brain networks, but also provides immersive and interesting contextual feedback for patients. In this paper, we designed a hand rehabilitation training system integrating multi-sensory stimulation feedback, BCI and VR, which guides patients' motor imaginations through the tasks of the virtual scene, acquires patients' motor intentions, and then carries out human-computer interactions under the virtual scene. At the same time, haptic feedback is incorporated to further increase the patients' proprioceptive sensations, so as to realize the hand function rehabilitation training based on the multi-sensory stimulation feedback of vision, hearing, and haptic senses. In this study, we compared and analyzed the differences in power spectral density of different frequency bands within the EEG signal data before and after the incorporation of haptic feedback, and found that the motor brain area was significantly activated after the incorporation of haptic feedback, and the power spectral density of the motor brain area was significantly increased in the high gamma frequency band. The results of this study indicate that the rehabilitation training of patients with the VR-BCI hand function enhancement rehabilitation system incorporating multi-sensory stimulation can accelerate the two-way facilitation of sensory and motor conduction pathways, thus accelerating the rehabilitation process.},
}
@article {pmid39218589,
year = {2024},
author = {Wang, Y and Li, Y and Cui, H and Li, M and Chen, X},
title = {[A review of functional electrical stimulation based on brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {650-655},
pmid = {39218589},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Electric Stimulation/methods ; Stroke Rehabilitation/methods ; Spinal Cord Injuries/rehabilitation ; Electric Stimulation Therapy/methods ; },
abstract = {Individuals with motor dysfunction caused by damage to the central nervous system are unable to transmit voluntary movement commands to their muscles, resulting in a reduced ability to control their limbs. However, traditional rehabilitation methods have problems such as long treatment cycles and high labor costs. Functional electrical stimulation (FES) based on brain-computer interface (BCI) connects the patient's intentions with muscle contraction, and helps to promote the reconstruction of nerve function by recognizing nerve signals and stimulating the moving muscle group with electrical impulses to produce muscle convulsions or limb movements. It is an effective treatment for sequelae of neurological diseases such as stroke and spinal cord injury. This article reviewed the current research status of BCI-based FES from three aspects: BCI paradigms, FES parameters and rehabilitation efficacy, and looked forward to the future development trend of this technology, in order to improve the understanding of BCI-based FES.},
}
@article {pmid39218588,
year = {2024},
author = {Chen, Y and Zhang, Z and Wang, F and Ding, P and Zhao, L and Fu, Y},
title = {[An emerging discipline: brain-computer interfaces medicine].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {4},
pages = {641-649},
pmid = {39218588},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography ; Brain/physiology ; },
abstract = {With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.},
}
@article {pmid39218255,
year = {2024},
author = {Guellil, MS and Kies, F and Hussein, EK and Shabaz, M and Hampson, RE},
title = {Pushing the boundaries of brain-computer interfacing (BCI) and neuron-electronics.},
journal = {Journal of neuroscience methods},
volume = {411},
number = {},
pages = {110274},
doi = {10.1016/j.jneumeth.2024.110274},
pmid = {39218255},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neurons/physiology ; Brain/physiology ; },
}
@article {pmid39218135,
year = {2025},
author = {Chu, T and Si, X and Xie, H and Ma, H and Shi, Y and Yao, W and Xing, D and Zhao, F and Dong, F and Gai, Q and Che, K and Guo, Y and Chen, D and Ming, D and Mao, N},
title = {Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles.},
journal = {Biological psychiatry},
volume = {97},
number = {3},
pages = {290-301},
doi = {10.1016/j.biopsych.2024.08.022},
pmid = {39218135},
issn = {1873-2402},
mesh = {Humans ; *Depressive Disorder, Major/genetics/physiopathology ; Female ; Male ; Adult ; *Neurotransmitter Agents/metabolism ; *Brain/physiopathology/diagnostic imaging ; Magnetic Resonance Imaging ; Support Vector Machine ; Middle Aged ; Connectome ; Default Mode Network/physiopathology/diagnostic imaging ; Young Adult ; Nerve Net/physiopathology/diagnostic imaging ; Case-Control Studies ; },
abstract = {BACKGROUND: Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.<br><br>METHODS: We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.<br><br>RESULTS: We observed increased regional SC-FC coupling in the default mode network (t337&#xa0;= 3.233) and decreased coupling in the frontoparietal network (t337&#xa0;=&#xa0;-3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve&#xa0;= 0.853), support vector machine (area under the receiver operating characteristic curve&#xa0;= 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.<br><br>CONCLUSIONS: This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.},
}
@article {pmid39216077,
year = {2024},
author = {Sun, H and Cai, R and Li, R and Li, M and Gao, L and Li, X},
title = {Conjunctive processing of spatial border and locomotion in retrosplenial cortex during spatial navigation.},
journal = {The Journal of physiology},
volume = {602},
number = {19},
pages = {5017-5038},
doi = {10.1113/JP286434},
pmid = {39216077},
issn = {1469-7793},
support = {32071097//MOST | National Natural Science Foundation of China (NSFC)/ ; 31871056//MOST | National Natural Science Foundation of China (NSFC)/ ; 61703365//MOST | National Natural Science Foundation of China (NSFC)/ ; 91732302//MOST | National Natural Science Foundation of China (NSFC)/ ; 81625006//MOST | National Natural Science Foundation of China (NSFC)/ ; 2018YFC1005003//National Key R&amp;D Program of China/ ; 2019XZZX001-01-20//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2018QN81008//MOE | Fundamental Research Funds for the Central Universities (Fundamental Research Fund for the Central Universities)/ ; 2020YFB1313500//National Key Research and Development Program of the Ministry of Science and Technology of China/ ; //the MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; },
mesh = {Animals ; *Spatial Navigation/physiology ; *Locomotion/physiology ; Mice ; Male ; Neurons/physiology ; Mice, Inbred C57BL ; Gyrus Cinguli/physiology ; Cerebral Cortex/physiology ; Maze Learning/physiology ; },
abstract = {Spatial information and dynamic locomotor behaviours are equally important for achieving locomotor goals during spatial navigation. However, it remains unclear how spatial and locomotor information is integrated during the processing of self-initiated spatial navigation. Anatomically, the retrosplenial cortex (RSC) has reciprocal connections with brain regions related to spatial processing, including the hippocampus and para-hippocampus, and also receives inputs from the secondary motor cortex. In addition, RSC is functionally associated with allocentric and egocentric spatial targets and head-turning. So, RSC may be a critical region for integrating spatial and locomotor information. In this study, we first examined the role of RSC in spatial navigation using the Morris water maze and found that mice with inactivated RSC took a longer time and distance to reach their destination. Then, by imaging neuronal activity in freely behaving mice within two open fields of different sizes, we identified a large proportion of border cells, head-turning cells and locomotor speed cells in the superficial layer of RSC. Interestingly, some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals. Furthermore, these conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigator scenes using the border, turning and positive-speed conjunctive cells. Our study reveals that the RSC is an important conjunctive brain region that processes spatial and locomotor information during spatial navigation. KEY POINTS: Retrosplenial cortex (RSC) is indispensable during spatial navigation, which was displayed by the longer time and distance of mice to reach their destination after the inactivation of RSC in a water maze. The superficial layer of RSC has a larger population of spatial-related border cells, and locomotion-related head orientation and speed cells; however, it has few place cells in two-dimensional spatial arenas. Some RSC neurons exhibited conjunctive coding for both spatial and locomotor signals, and the conjunctive neurons showed higher prediction accuracy compared with simple spatial or locomotor neurons in special navigation scenes. Our study reveals that the RSC is an important conjunctive brain region that processes both spatial and locomotor information during spatial navigation.},
}
@article {pmid39215126,
year = {2024},
author = {Zhao, W and Jiang, X and Zhang, B and Xiao, S and Weng, S},
title = {CTNet: a convolutional transformer network for EEG-based motor imagery classification.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20237},
pmid = {39215126},
issn = {2045-2322},
support = {3502Z202374054//Xiamen Natural Science Foundation of China/ ; CYKYPT02//Big data technology institute of Chengyi College, Jimei University of China/ ; 2023J01785//Natural Science Foundation of Fujian Province of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Brain/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.},
}
@article {pmid39215011,
year = {2024},
author = {Egger, J and Kostoglou, K and Müller-Putz, GR},
title = {Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {20247},
pmid = {39215011},
issn = {2045-2322},
support = {101070939//HORIZON EUROPE European Innovation Council/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Female ; *Gestures ; *Hand/physiology ; Adult ; Young Adult ; Movement/physiology ; Motor Cortex/physiology ; Brain-Computer Interfaces ; },
abstract = {Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.},
}
@article {pmid39214374,
year = {2024},
author = {Zheng, Y and Yu, X and Wei, L and Chen, Q and Xu, Y and Ni, P and Deng, W and Guo, W and Hu, X and Qi, X and Li, T},
title = {LT-102, an AMPA receptor potentiator, alleviates depression-like behavior and synaptic plasticity impairments in prefrontal cortex induced by sleep deprivation.},
journal = {Journal of affective disorders},
volume = {367},
number = {},
pages = {18-30},
doi = {10.1016/j.jad.2024.08.176},
pmid = {39214374},
issn = {1573-2517},
mesh = {Animals ; Male ; Mice ; Behavior, Animal/drug effects ; Brain-Derived Neurotrophic Factor/metabolism ; *Depression/drug therapy ; Disease Models, Animal ; Disks Large Homolog 4 Protein/metabolism ; Mice, Inbred C57BL ; *Neuronal Plasticity/drug effects/physiology ; Neurons/drug effects ; *Prefrontal Cortex/metabolism/drug effects ; *Receptors, AMPA/metabolism ; Signal Transduction/drug effects ; *Sleep Deprivation/complications/physiopathology ; },
abstract = {BACKGROUND: Sleep loss is closely related to the onset and development of depression, and the mechanisms involved may include impaired synaptic plasticity. Considering the important role of glutamate α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate receptors (AMPARs) in synaptic plasticity as well as depression, we introduce LT-102, a novel AMPARs potentiator, to evaluate the potential of LT-102 in treating sleep deprivation-induced depression-like behaviors.<br><br>METHODS: We conducted a comprehensive behavioral assessment to evaluate the effects of LT-102 on depression-like symptoms in male C57BL/6J mice. This assessment included the open field test to measure general locomotor activity and anxiety-like behavior, the forced swimming test and tail suspension test to assess despair behaviors indicative of depressive states, and the sucrose preference test to quantify anhedonia, a core symptom of depression. Furthermore, to explore the impact of LT-102 on synaptic plasticity, we utilized a combination of Western blot analysis to detect protein expression levels, Golgi-Cox staining to visualize neuronal morphology, and immunofluorescence to examine the localization of synaptic proteins. Additionally, we utilized primary cortical neurons to delineate the signaling pathway modulated by LT-102.<br><br>RESULTS: Treatment with LT-102 significantly reduced depression-like behaviors associated with sleep deprivation. Quantitative Western blot (WB) analysis revealed a significant increase in GluA1 phosphorylation in the prefrontal cortex (PFC), triggering the Ca[2+]/calmodulin-dependent protein kinase II/cAMP response element-binding protein/brain-derived neurotrophic factor (CaMKII/CREB/BDNF) and forkhead box protein P2/postsynaptic density protein 95 (FoxP2/PSD95) signaling pathways. Immunofluorescence imaging confirmed that LT-102 treatment increased spine density and co-labeling of PSD95 and vesicular glutamate transporter 1 (VGLUT1) in the PFC, reversing the reductions typically observed following sleep deprivation. Golgi staining further validated these results, showing a substantial increase in neuronal dendritic spine density in sleep-deprived mice treated with LT-102. Mechanistically, application of LT-102 to primary cortical neurons, resulted in elevated levels of phosphorylated AKT (p-AKT) and phosphorylated glycogen synthase kinase-3 beta (p-GSK3&#x3b2;), key downstream molecules in the BDNF signaling pathway, which in turn upregulated FoxP2 and PSD95 expression.<br><br>LIMITATIONS: In our study, we chose to exclusively use male mice to eliminate potential influences of the estrous cycle on behavior and physiology. As there is no widely accepted positive drug control for sleep deprivation studies, we did not include one in our research.<br><br>CONCLUSION: Our results suggest that LT-102 is a promising therapeutic agent for counteracting depression-like behaviors and synaptic plasticity deficits induced by sleep deprivation, primarily through the activation of CaMKII/CREB/BDNF and AKT/GSK3&#x3b2;/FoxP2/PSD95 signaling pathways.},
}
@article {pmid39213709,
year = {2024},
author = {Si, X and Huang, D and Liang, Z and Sun, Y and Huang, H and Liu, Q and Yang, Z and Ming, D},
title = {Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition.},
journal = {Computers in biology and medicine},
volume = {181},
number = {},
pages = {108973},
doi = {10.1016/j.compbiomed.2024.108973},
pmid = {39213709},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; },
abstract = {Emotion recognition is crucial for human-computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://github.com/ThreePoundUniverse/MACTN.},
}
@article {pmid39213268,
year = {2024},
author = {Wang, T and Ke, Y and Huang, Y and He, F and Zhong, W and Liu, S and Ming, D},
title = {Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {12},
pages = {7032-7039},
doi = {10.1109/JBHI.2024.3452410},
pmid = {39213268},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Workload/classification ; *Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; Male ; Adult ; Female ; Young Adult ; Brain-Computer Interfaces ; Algorithms ; },
abstract = {Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.},
}
@article {pmid39213025,
year = {2024},
author = {Yan, X and Li, Z and Cao, C and Huang, L and Li, Y and Meng, X and Zhang, B and Yu, M and Huang, T and Chen, J and Li, W and Hao, L and Huang, D and Yi, B and Zhang, M and Zha, S and Yang, H and Yao, J and Qian, P and Leung, CK and Fan, H and Jiang, P and Shui, T},
title = {Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies.},
journal = {Journal of medical Internet research},
volume = {26},
number = {},
pages = {e54874},
pmid = {39213025},
issn = {1438-8871},
mesh = {Humans ; Internet ; Pandemics/prevention &amp; control ; *Social Media/statistics &amp; numerical data ; *Mpox, Monkeypox/epidemiology ; *Infodemic ; },
abstract = {BACKGROUND: The mpox pandemic has caused widespread public concern around the world. The spread of misinformation through the internet and social media could lead to an infodemic that poses challenges to mpox control.<br><br>OBJECTIVE: This review aims to summarize mpox-related infodemiology studies to determine the characteristics, influence, prevention, and control measures of the mpox infodemic and propose prospects for future research.<br><br>METHODS: The scoping review was conducted based on a structured 5-step methodological framework. A comprehensive search for mpox-related infodemiology studies was performed using PubMed, Web of Science, Embase, and Scopus, with searches completed by April 30, 2024. After study selection and data extraction, the main topics of the mpox infodemic were categorized and summarized in 4 aspects, including a trend analysis of online information search volume, content topics of mpox-related online posts and comments, emotional and sentiment characteristics of online content, and prevention and control measures for the mpox infodemic.<br><br>RESULTS: A total of 1607 articles were retrieved from the databases according to the keywords, and 61 studies were included in the final analysis. After the World Health Organization's declaration of an mpox public health emergency of international concern in July 2022, the number of related studies began growing rapidly. Google was the most widely used search engine platform (9/61, 15%), and Twitter was the most used social media app (32/61, 52%) for researchers. Researchers from 33 countries were concerned about mpox infodemic-related topics. Among them, the top 3 countries for article publication were the United States (27 studies), India (9 studies), and the United Kingdom (7 studies). Studies of online information search trends showed that mpox-related online search volume skyrocketed at the beginning of the mpox outbreak, especially when the World Health Organization provided important declarations. There was a large amount of misinformation with negative sentiment and discriminatory and hostile content against gay, bisexual, and other men who have sex with men. Given the characteristics of the mpox infodemic, the studies provided several positive prevention and control measures, including the timely and active publishing of professional, high-quality, and easy-to-understand information online; strengthening surveillance and early warning for the infodemic based on internet data; and taking measures to protect key populations from the harm of the mpox infodemic.<br><br>CONCLUSIONS: This comprehensive summary of evidence from previous mpox infodemiology studies is valuable for understanding the characteristics of the mpox infodemic and for formulating prevention and control measures. It is essential for researchers and policy makers to establish prediction and early warning approaches and targeted intervention methods for dealing with the mpox infodemic in the future.},
}
@article {pmid39211094,
year = {2024},
author = {Mender, MJ and Ward, AL and Cubillos, LH and Kelberman, MM and Costello, JT and Temmar, H and Wallace, DM and Lin, ET and Lam, JLW and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, PG and Chestek, CA},
title = {Functional Electrical Stimulation and Brain-Machine Interfaces for Simultaneous Control of Wrist and Finger Flexion.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.08.11.607263},
pmid = {39211094},
issn = {2692-8205},
abstract = {Brain-machine interface (BMI) controlled functional electrical stimulation (FES) is a promising treatment to restore hand movements to people with cervical spinal cord injury. Recent intracortical BMIs have shown unprecedented successes in decoding user intentions, however the hand movements restored by FES have largely been limited to predetermined grasps. Restoring dexterous hand movements will require continuous control of many biomechanically linked degrees-of-freedom in the hand, such as wrist and finger flexion, that would form the basis of those movements. Here we investigate the ability to restore simultaneous wrist and finger flexion, which would enable grasping with a controlled hand posture and assist in manipulating objects once grasped. We demonstrate that intramuscular FES can enable monkeys with temporarily paralyzed hands to move their fingers and wrist across a functional range of motion, spanning an average 88.6 degrees at the metacarpophalangeal joint flexion and 71.3 degrees of wrist flexion, and intramuscular FES can control both joints simultaneously in a real-time task. Additionally, we demonstrate a monkey using an intracortical BMI to control the wrist and finger flexion in a virtual hand, both before and after the hand is temporarily paralyzed, even achieving success rates and acquisition times equivalent to able-bodied control with BMI control after temporary paralysis in two sessions. Together, this outlines a method using an artificial brain-to-body interface that could restore continuous wrist and finger movements after spinal cord injury.},
}
@article {pmid39209118,
year = {2024},
author = {Byeon, H and Quraishi, A and Khalaf, MI and Mp, S and Khan, IR and Dutta, AK and Dasari, R and Yellu, RR and Reegu, FA and Bhatt, MW},
title = {Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.},
journal = {SLAS technology},
volume = {29},
number = {5},
pages = {100187},
doi = {10.1016/j.slast.2024.100187},
pmid = {39209118},
issn = {2472-6311},
mesh = {*Fuzzy Logic ; *Electroencephalography/methods ; Humans ; *Machine Learning ; *Brain-Computer Interfaces ; Decision Making ; Signal Processing, Computer-Assisted ; },
abstract = {One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.},
}
@article {pmid39209064,
year = {2024},
author = {Zhang, E and Shotbolt, M and Chang, CY and Scott-Vandeusen, A and Chen, S and Liang, P and Radu, D and Khizroev, S},
title = {Controlling action potentials with magnetoelectric nanoparticles.},
journal = {Brain stimulation},
volume = {17},
number = {5},
pages = {1005-1017},
doi = {10.1016/j.brs.2024.08.008},
pmid = {39209064},
issn = {1876-4754},
mesh = {Animals ; Rats ; *Action Potentials/physiology ; Rats, Sprague-Dawley ; Nanoparticles ; Magnetic Fields ; Neurons/physiology ; },
abstract = {Non-invasive or minutely invasive and wireless brain stimulation that can target any region of the brain is an open problem in engineering and neuroscience with serious implications for the treatment of numerous neurological diseases. Despite significant recent progress in advancing new methods of neuromodulation, none has successfully replicated the efficacy of traditional wired stimulation and improved on its downsides without introducing new complications. Due to the capability to convert magnetic fields into local electric fields, MagnetoElectric NanoParticle (MENP) neuromodulation is a recently proposed framework based on new materials that can locally sensitize neurons to specific, low-strength alternating current (AC) magnetic fields (50Hz 1.7 kOe field). However, the current research into this neuromodulation concept is at a very early stage, and the theoretically feasible game-changing advantages remain to be proven experimentally. To break this stalemate phase, this study leveraged understanding of the non-linear properties of MENPs and the nanoparticles' field interaction with the cellular microenvironment. Particularly, the applied magnetic field's strength and frequency were tailored to the M - H hysteresis loop of the nanoparticles. Furthermore, rectangular prisms instead of the more traditional "spherical" nanoparticle shapes were used to: (i) maximize the magnetoelectric effect and (ii) improve the nanoparticle-cell-membrane surface interface. Neuromodulation performance was evaluated in a series of exploratory in vitro experiments on 2446 rat hippocampus neurons. Linear mixed effect models were used to ensure the independence of samples by accounting for fixed adjacency effects in synchronized firing. Neural activity was measured over repeated 4-min segments, containing 90 s of baseline measurements, 90 s of stimulation measurements, and 60 s of post stimulation measurements. 87.5 % of stimulation attempts produced statistically significant (P < 0.05) changes in neural activity, with 58.3 % producing large changes (P < 0.01). In negative controls using either zero or 1.7 kOe-strength field without nanoparticles, no experiments produced significant changes in neural activity (P > 0.05 and P > 0.15 respectively). Furthermore, an exploratory analysis of a direct current (DC) magnetic field indicated that the DC field could be used with MENPs to inhibit neuron activity (P < 0.01). These experiments demonstrated the potential for magnetoelectric neuromodulation to offer a near one-to-one functionality match with conventional electrode stimulation without requiring surgical intervention or genetic modification to achieve success, instead relying on physical properties of these nanoparticles as "On/Off" control mechanisms. ONE-SENTENCE SUMMARY: This in vitro neural cell culture study explores how to exploit the non-linear and anisotropic properties of magnetoelectric nanoparticles for wireless neuromodulation, the importance of magnetic field strength and frequency matching for optimization, and demonstrates, for the first time, that magnetoelectric neuromodulation can inhibit neural responses.},
}
@article {pmid39208037,
year = {2024},
author = {Liao, C and Zhao, S and Zhang, J},
title = {Motor Imagery Recognition Based on GMM-JCSFE Model.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3348-3357},
doi = {10.1109/TNSRE.2024.3451716},
pmid = {39208037},
issn = {1558-0210},
mesh = {Humans ; *Imagination/physiology ; *Electroencephalography/methods ; *Algorithms ; Normal Distribution ; Brain-Computer Interfaces ; Reproducibility of Results ; Male ; Female ; },
abstract = {Features from EEG microstate models, such as time-domain statistical features and state transition probabilities, are typically manually selected based on experience. However, traditional microstate models assume abrupt transitions between states, and the classification features can vary among individuals due to personal differences. To date, both empirical and theoretical classification results of EEG microstate features have not been entirely satisfactory. Here, we introduce an enhanced feature extraction method that combines Joint label-Common and label-Specific Feature Exploration (JCSFE) with Gaussian Mixture Models (GMM) to explore microstate features. First, GMMs are employed to represent the smooth transitions of EEG spatiotemporal features within microstate models. Second, category-common and category-specific features are identified by applying regularization constraints to linear classifiers. Third, a graph regularizer is used to extract subject-invariant microstate features. Experimental results on publicly available datasets demonstrate that the proposed model effectively encodes microstate features and improves the accuracy of motor imagery recognition across subjects. The primary code is accessible for download from the website: https://github.com/liaoliao3450/GMM-JCSFE.},
}
@article {pmid39207622,
year = {2025},
author = {Feng, J and Wang, X and Pan, M and Li, CX and Zhang, Z and Sun, M and Liao, T and Wang, Z and Luo, J and Shi, L and Chen, YJ and Li, HF and Xu, J},
title = {The Medial Prefrontal Cortex-Basolateral Amygdala Circuit Mediates Anxiety in Shank3 InsG3680 Knock-in Mice.},
journal = {Neuroscience bulletin},
volume = {41},
number = {1},
pages = {77-92},
pmid = {39207622},
issn = {1995-8218},
mesh = {Animals ; *Prefrontal Cortex/physiopathology/metabolism ; *Basolateral Nuclear Complex/physiopathology/metabolism ; Mice ; *Anxiety/physiopathology/genetics/metabolism ; *Nerve Tissue Proteins/genetics ; Male ; Gene Knock-In Techniques ; Pyramidal Cells/physiology ; Mice, Transgenic ; Neural Pathways/physiopathology ; Mice, Inbred C57BL ; Microfilament Proteins ; },
abstract = {Anxiety disorder is a major symptom of autism spectrum disorder (ASD) with a comorbidity rate of ~40%. However, the neural mechanisms of the emergence of anxiety in ASD remain unclear. In our study, we found that hyperactivity of basolateral amygdala (BLA) pyramidal neurons (PNs) in Shank3 InsG3680 knock-in (InsG3680[+/+]) mice is involved in the development of anxiety. Electrophysiological results also showed increased excitatory input and decreased inhibitory input in BLA PNs. Chemogenetic inhibition of the excitability of PNs in the BLA rescued the anxiety phenotype of InsG3680[+/+] mice. Further study found that the diminished control of the BLA by medial prefrontal cortex (mPFC) and optogenetic activation of the mPFC-BLA pathway also had a rescue effect, which increased the feedforward inhibition of the BLA. Taken together, our results suggest that hyperactivity of the BLA and alteration of the mPFC-BLA circuitry are involved in anxiety in InsG3680[+/+] mice.},
}
@article {pmid39207066,
year = {2024},
author = {Fan, C and Yang, B and Li, X and Gao, S and Zan, P},
title = {EEG-Based Feature Classification Combining 3D-Convolutional Neural Networks with Generative Adversarial Networks for Motor Imagery.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {8},
pages = {153},
doi = {10.31083/j.jin2308153},
pmid = {39207066},
issn = {0219-6352},
support = {2022YFC3602500//National Key Research and Development Program of China/ ; 2022YFC3602504//National Key Research and Development Program of China/ ; 62376149//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Adult ; Motor Activity/physiology ; Brain/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: The adoption of convolutional neural networks (CNNs) for decoding electroencephalogram (EEG)-based motor imagery (MI) in brain-computer interfaces has significantly increased recently. The effective extraction of motor imagery features is vital due to the variability among individuals and temporal states.<br><br>METHODS: This study introduces a novel network architecture, 3D-convolutional neural network-generative adversarial network (3D-CNN-GAN), for decoding both within-session and cross-session motor imagery. Initially, EEG signals were extracted over various time intervals using a sliding window technique, capturing temporal, frequency, and phase features to construct a temporal-frequency-phase feature (TFPF) three-dimensional feature map. Generative adversarial networks (GANs) were then employed to synthesize artificial data, which, when combined with the original datasets, expanded the data capacity and enhanced functional connectivity. Moreover, GANs proved capable of learning and amplifying the brain connectivity patterns present in the existing data, generating more distinctive brain network features. A compact, two-layer 3D-CNN model was subsequently developed to efficiently decode these TFPF features.<br><br>RESULTS: Taking into account session and individual differences in EEG data, tests were conducted on both the public GigaDB dataset and the SHU laboratory dataset. On the GigaDB dataset, our 3D-CNN and 3D-CNN-GAN models achieved two-class within-session motor imagery accuracies of 76.49% and 77.03%, respectively, demonstrating the algorithm's effectiveness and the improvement provided by data augmentation. Furthermore, on the SHU dataset, the 3D-CNN and 3D-CNN-GAN models yielded two-class within-session motor imagery accuracies of 67.64% and 71.63%, and cross-session motor imagery accuracies of 58.06% and 63.04%, respectively.<br><br>CONCLUSIONS: The 3D-CNN-GAN algorithm significantly enhances the generalizability of EEG-based motor imagery brain-computer interfaces (BCIs). Additionally, this research offers valuable insights into the potential applications of motor imagery BCIs.},
}
@article {pmid39205122,
year = {2024},
author = {Moraes, CPA and Dos Santos, LH and Fantinato, DG and Neves, A and Adali, T},
title = {Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39205122},
issn = {1424-8220},
support = {2023/00640-1//S&#xe3;o Paulo Research Foundation (FAPESP)/ ; 88887.595656/2020-00//Coordena&#xe7;&#xe3;o de Aperfei&#xe7;oamento de Pessoal de N&#xed;vel Superior (CAPES)/ ; 2316420//US National Science Foundation (NSF)/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Movement/physiology ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.},
}
@article {pmid39204948,
year = {2024},
author = {Dillen, A and Omidi, M and Ghaffari, F and Romain, O and Vanderborght, B and Roelands, B and Nowé, A and De Pauw, K},
title = {User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204948},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics/methods/instrumentation ; *Electroencephalography/methods ; Male ; *Eye-Tracking Technology ; *Augmented Reality ; Female ; Adult ; *User-Computer Interface ; Middle Aged ; Young Adult ; Eye Movements/physiology ; },
abstract = {This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.},
}
@article {pmid39204910,
year = {2024},
author = {Hu, W and Ji, B and Gao, K},
title = {A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204910},
issn = {1424-8220},
support = {62106041, 62204204//the National Natural Science Foundation of China/ ; 223202100019//the Fundamental Research Funds for the Central Universities/ ; 2022ZD0208601//Science and Technology Innovation 2030-Major Project/ ; 21YF1451000//Shanghai Sailing Program/ ; },
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Neural Networks, Computer ; Electrodes ; Deep Learning ; },
abstract = {The precision of electroencephalograms (EEGs) significantly impacts the performance of brain-computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals.},
}
@article {pmid39204903,
year = {2024},
author = {Mattei, E and Lozzi, D and Di Matteo, A and Cipriani, A and Manes, C and Placidi, G},
title = {MOVING: A Multi-Modal Dataset of EEG Signals and Virtual Glove Hand Tracking.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {16},
pages = {},
pmid = {39204903},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Hand/physiology ; *Movement/physiology ; Male ; Adult ; Biomechanical Phenomena/physiology ; Female ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) are pivotal in translating neural activities into control commands for external assistive devices. Non-invasive techniques like electroencephalography (EEG) offer a balance of sensitivity and spatial-temporal resolution for capturing brain signals associated with motor activities. This work introduces MOVING, a Multi-Modal dataset of EEG signals and Virtual Glove Hand Tracking. This dataset comprises neural EEG signals and kinematic data associated with three hand movements-open/close, finger tapping, and wrist rotation-along with a rest period. The dataset, obtained from 11 subjects using a 32-channel dry wireless EEG system, also includes synchronized kinematic data captured by a Virtual Glove (VG) system equipped with two orthogonal Leap Motion Controllers. The use of these two devices allows for fast assembly (∼1 min), although introducing more noise than the gold standard devices for data acquisition. The study investigates which frequency bands in EEG signals are the most informative for motor task classification and the impact of baseline reduction on gesture recognition. Deep learning techniques, particularly EEGnetV4, are applied to analyze and classify movements based on the EEG data. This dataset aims to facilitate advances in BCI research and in the development of assistive devices for people with impaired hand mobility. This study contributes to the repository of EEG datasets, which is continuously increasing with data from other subjects, which is hoped to serve as benchmarks for new BCI approaches and applications.},
}
@article {pmid39204507,
year = {2024},
author = {Boratto, MH and Graeff, CFO and Han, S},
title = {Highly Stable Flexible Organic Electrochemical Transistors with Natural Rubber Latex Additives.},
journal = {Polymers},
volume = {16},
number = {16},
pages = {},
pmid = {39204507},
issn = {2073-4360},
support = {2024-0029//Incheon National University/ ; },
abstract = {Organic electrochemical transistors (OECTs) have attracted considerable interest in the context of wearable and implantable biosensors due to their remarkable signal amplification combined with seamless integration into biological systems. These properties underlie OECTs' potential utility across a range of bioelectronic applications. One of the main challenges to their practical applications is the mechanical limitation of PEDOT:PSS, the most typical conductive polymer used as a channel layer, when the OECTs are applied to implantable and stretchable bioelectronics. In this work, we address this critical issue by employing natural rubber latex (NRL) as an additive in PEDOT:PSS to improve flexibility and stretchability of the OECT channels. Although the inclusion of NRL leads to a decrease in transconductance, mainly due to a reduced carrier mobility from 0.3 to 0.1 cm[2]/V·s, the OECTs maintain satisfactory transconductance, exceeding 5 mS. Furthermore, it is demonstrated that the OECTs exhibit excellent mechanical stability while maintaining their performance even after 100 repetitive bending cycles. This work, therefore, suggests that the NRL/PEDOT:PSS composite film can be deployed for wearable/implantable applications, where high mechanical stability is needed. This finding opens up new avenues for practical use of OECTs in more robust and versatile wearable and implantable biosensors.},
}
@article {pmid39202770,
year = {2024},
author = {Balčiauskas, L and Balčiauskienė, L},
title = {Extreme Body Condition Index Values in Small Mammals.},
journal = {Life (Basel, Switzerland)},
volume = {14},
number = {8},
pages = {},
pmid = {39202770},
issn = {2075-1729},
abstract = {The body condition index (BCI) values in small mammals are important in understanding their survival and reproduction. The upper values could be related to the Chitty effect (presence of very heavy individuals), while the minimum ones are little known. In this study, we analyzed extremes of BCI in 12 small mammal species, snap-trapped in Lithuania between 1980 and 2023, with respect to species, animal age, sex, and participation in reproduction. The proportion of small mammals with extreme body condition indices was negligible (1.33% with a BCI < 2 and 0.52% with a BCI > 5) when considering the total number of individuals processed (n = 27,073). When compared to the expected proportions, insectivores and herbivores were overrepresented, while granivores and omnivores were underrepresented among underfit animals. The proportions of granivores and insectivores were higher, while those of omnivores and herbivores were lower than expected in overfit animals. In several species, the proportions of age groups in underfit and overfit individuals differed from that expected. The male-female ratio was not expressed, with the exception of Sorex araneus. The highest proportion of overfit and absence of underfit individuals was found in Micromys minutus. The observation that individuals with the highest body mass are not among those with the highest BCI contributes to the interpretation of the Chitty effect. For the first time in mid-latitudes, we report individuals of very high body mass in three shrew species.},
}
@article {pmid39199779,
year = {2024},
author = {Tang, F and Yan, F and Zhong, Y and Li, J and Gong, H and Li, X},
title = {Optogenetic Brain-Computer Interfaces.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199779},
issn = {2306-5354},
support = {Grant No.821RC531//Hainan Provincial Natural Science Foundation of China for High-level Talents/ ; 2022ZD0205201//STI2030-Major Projects/ ; No. T2122015//National Nature Science Foundation of China/ ; No.2019-I2M-5-014//CAMS Innovation Fund for Medical Sciences/ ; },
abstract = {The brain-computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.},
}
@article {pmid39199740,
year = {2024},
author = {Fernandes, JVMR and Alexandria, AR and Marques, JAL and Assis, DF and Motta, PC and Silva, BRDS},
title = {Emotion Detection from EEG Signals Using Machine Deep Learning Models.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199740},
issn = {2306-5354},
abstract = {Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.},
}
@article {pmid39199739,
year = {2024},
author = {Chiou, N and Günal, M and Koyejo, S and Perpetuini, D and Chiarelli, AM and Low, KA and Fabiani, M and Gratton, G},
title = {Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {8},
pages = {},
pmid = {39199739},
issn = {2306-5354},
abstract = {Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.},
}
@article {pmid39199537,
year = {2024},
author = {Fodor, MA and Herschel, H and Cantürk, A and Heisenberg, G and Volosyak, I},
title = {Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).},
journal = {Brain sciences},
volume = {14},
number = {8},
pages = {},
pmid = {39199537},
issn = {2076-3425},
support = {101118964//European Union/ ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.},
}
@article {pmid39199527,
year = {2024},
author = {Ail, BE and Ramele, R and Gambini, J and Santos, JM},
title = {An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.},
journal = {Brain sciences},
volume = {14},
number = {8},
pages = {},
pmid = {39199527},
issn = {2076-3425},
support = {ITBACyT-2020//Instituto Tecnol&#xf3;gico de Buenos Aires (ITBA)/ ; },
abstract = {This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).},
}
@article {pmid39196743,
year = {2024},
author = {El Ouahidi, Y and Gripon, V and Pasdeloup, B and Bouallegue, G and Farrugia, N and Lioi, G},
title = {A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3338-3347},
doi = {10.1109/TNSRE.2024.3451010},
pmid = {39196743},
issn = {1558-0210},
mesh = {Humans ; *Deep Learning ; *Brain-Computer Interfaces ; *Electroencephalography ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Male ; Adult ; Female ; Machine Learning ; Movement/physiology ; },
abstract = {We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, batch normalisations, ReLU activation functions and pooling functions. EEG-SimpleConv architecture is accompanied by a straightforward and tailored training routine, which is subjected to an extensive ablation study to quantify the influence of its components. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We believe that using standard components and ingredients can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.},
}
@article {pmid39196738,
year = {2024},
author = {Li, Z and Zhang, R and Li, W and Li, M and Chen, X and Cui, H},
title = {Enhancement of Hybrid BCI System Performance Based on Motor Imagery and SSVEP by Transcranial Alternating Current Stimulation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3222-3230},
doi = {10.1109/TNSRE.2024.3451015},
pmid = {39196738},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Algorithms ; Female ; *Transcranial Direct Current Stimulation/methods ; *Electroencephalography ; Adult ; Young Adult ; Healthy Volunteers ; Psychomotor Performance/physiology ; Hand/physiology ; Reproducibility of Results ; Evoked Potentials, Visual/physiology ; Evoked Potentials, Motor/physiology ; },
abstract = {The hybrid brain-computer interface (BCI) is verified to reduce disadvantages of conventional BCI systems. Transcranial electrical stimulation (tES) can also improve the performance and applicability of BCI. However, enhancement in BCI performance attained solely from the perspective of users or solely from the angle of BCI system design is limited. In this study, a hybrid BCI system combining MI and SSVEP was proposed. Furthermore, transcranial alternating current stimulation (tACS) was utilized to enhance the performance of the proposed hybrid BCI system. The stimulation interface presented a depiction of grabbing a ball with both of hands, with left-hand and right-hand flickering at frequencies of 34 Hz and 35 Hz. Subjects watched the interface and imagined grabbing a ball with either left hand or right hand to perform SSVEP and MI task. The MI and SSVEP signals were processed separately using filter bank common spatial patterns (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms, respectively. A fusion method was proposed to fuse the features extracted from MI and SSVEP. Twenty healthy subjects took part in the online experiment and underwent tACS sequentially. The fusion accuracy post-tACS reached 90.25% ± 11.40%, which was significantly different from pre-tACS. The fusion accuracy also surpassed MI accuracy and SSVEP accuracy respectively. These results indicated the superior performance of the hybrid BCI system and tACS would improve the performance of the hybrid BCI system.},
}
@article {pmid39196465,
year = {2024},
author = {Zou, Y},
title = {Genetic enhancement from the perspective of transhumanism: exploring a new paradigm of transhuman evolution.},
journal = {Medicine, health care, and philosophy},
volume = {27},
number = {4},
pages = {529-544},
pmid = {39196465},
issn = {1572-8633},
support = {2023EZX004//Shanghai Office of Philosophy and Social Science/ ; 23FZXA012//National Social Science Fund of China/ ; },
mesh = {Humans ; *Biological Evolution ; *Genetic Enhancement/ethics ; Philosophy, Medical ; Artificial Intelligence ; Humanism ; },
abstract = {Transhumanism is a movement that advocates for the enhancement of human capabilities through the use of advanced technologies such as genetic enhancement. This article explores the definition, history, and development of transhumanism. Then, it compares the stance on genetic enhancement from the perspectives of bio-conservatism, bio-liberalism, and transhumanism. This article posits that transhuman evolution has twofold implications, allowing for the integration of transhumanist research and evolutionary biology. First, it offers a compelling scientific framework for understanding genetic enhancement, avoiding technological progressivism, and incorporating concepts of evolutionary biology. Second, it represents a new evolutionary paradigm distinct from traditional Lamarckism and Darwinism. It marks the third synthesis of evolutionary biology, offering fresh perspectives on established concepts such as artificial selection and gene-culture co-evolution. In recent decades, human enhancement has captivated not only evolutionary biologists, neurobiologists, psychologists, and philosophers, but also those in fields such as cybernetics and artificial intelligence. In addition to genetic enhancement, other human enhancement technologies, including brain-computer interfaces and brain uploading, are currently under development, which the paradigm of transhuman evolution can better integrate into its framework.},
}
@article {pmid39194652,
year = {2024},
author = {Jiang, Q and Liu, M},
title = {Recent Progress in Artificial Neurons for Neuromodulation.},
journal = {Journal of functional biomaterials},
volume = {15},
number = {8},
pages = {},
pmid = {39194652},
issn = {2079-4983},
abstract = {Driven by the rapid advancement and practical implementation of biomaterials, fabrication technologies, and artificial intelligence, artificial neuron devices and systems have emerged as a promising technology for interpreting and transmitting neurological signals. These systems are equipped with multi-modal bio-integrable sensing capabilities, and can facilitate the benefits of neurological monitoring and modulation through accurate physiological recognition. In this article, we provide an overview of recent progress in artificial neuron technology, with a particular focus on the high-tech applications made possible by innovations in material engineering, new designs and technologies, and potential application areas. As a rapidly expanding field, these advancements have a promising potential to revolutionize personalized healthcare, human enhancement, and a wide range of other applications, making artificial neuron devices the future of brain-machine interfaces.},
}
@article {pmid39194625,
year = {2024},
author = {Ullah, A and Zhang, F and Song, Z and Wang, Y and Zhao, S and Riaz, W and Li, G},
title = {Surface Electromyography-Based Recognition of Electronic Taste Sensations.},
journal = {Biosensors},
volume = {14},
number = {8},
pages = {},
pmid = {39194625},
issn = {2079-6374},
mesh = {Humans ; *Electromyography ; *Taste ; Brain-Computer Interfaces ; Adult ; },
abstract = {Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.},
}
@article {pmid39194597,
year = {2024},
author = {Savić, AM and Novičić, M and Miler-Jerković, V and Djordjević, O and Konstantinović, L},
title = {Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients.},
journal = {Biosensors},
volume = {14},
number = {8},
pages = {},
pmid = {39194597},
issn = {2079-6374},
support = {6066223//Science Fund of the Republic of Serbia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke/physiopathology ; *Electroencephalography ; Female ; Middle Aged ; Aged ; Feasibility Studies ; Evoked Potentials, Somatosensory/physiology ; Stroke Rehabilitation/methods ; Adult ; Touch ; },
abstract = {This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.},
}
@article {pmid39194438,
year = {2024},
author = {Omari, S and Omari, A and Abu-Dakka, F and Abderrahim, M},
title = {EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier.},
journal = {Biomimetics (Basel, Switzerland)},
volume = {9},
number = {8},
pages = {},
pmid = {39194438},
issn = {2313-7673},
support = {2012/00605/003//Universidad Carlos III de Madrid/ ; S2018/NMT-4331//RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub/ ; KK-2022/00024 and HELDU KK-2023/00055//ELKARTEK, Basque Government (Spain)/ ; },
abstract = {Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain-computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.},
}
@article {pmid39194182,
year = {2024},
author = {Rabbani, Q and Shah, S and Milsap, G and Fifer, M and Hermansky, H and Crone, N},
title = {Iterative alignment discovery of speech-associated neural activity.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {39194182},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; *Algorithms ; *Electrocorticography/methods ; Male ; Female ; Adult ; Neural Networks, Computer ; },
abstract = {Objective. Brain-computer interfaces (BCIs) have the potential to preserve or restore speech in patients with neurological disorders that weaken the muscles involved in speech production. However, successful training of low-latency speech synthesis and recognition models requires alignment of neural activity with intended phonetic or acoustic output with high temporal precision. This is particularly challenging in patients who cannot produce audible speech, as ground truth with which to pinpoint neural activity synchronized with speech is not available.Approach. In this study, we present a new iterative algorithm for neural voice activity detection (nVAD) called iterative alignment discovery dynamic time warping (IAD-DTW) that integrates DTW into the loss function of a deep neural network (DNN). The algorithm is designed to discover the alignment between a patient's electrocorticographic (ECoG) neural responses and their attempts to speak during collection of data for training BCI decoders for speech synthesis and recognition.Main results. To demonstrate the effectiveness of the algorithm, we tested its accuracy in predicting the onset and duration of acoustic signals produced by able-bodied patients with intact speech undergoing short-term diagnostic ECoG recordings for epilepsy surgery. We simulated a lack of ground truth by randomly perturbing the temporal correspondence between neural activity and an initial single estimate for all speech onsets and durations. We examined the model's ability to overcome these perturbations to estimate ground truth. IAD-DTW showed no notable degradation (<1% absolute decrease in accuracy) in performance in these simulations, even in the case of maximal misalignments between speech and silence.Significance. IAD-DTW is computationally inexpensive and can be easily integrated into existing DNN-based nVAD approaches, as it pertains only to the final loss computation. This approach makes it possible to train speech BCI algorithms using ECoG data from patients who are unable to produce audible speech, including those with Locked-In Syndrome.},
}
@article {pmid39193830,
year = {2024},
author = {Xu, Q and Xi, Y and Wang, L and Du, Z and Xu, M and Ruan, T and Cao, J and Zheng, K and Wang, X and Yang, B and Liu, J},
title = {An Opto-electrophysiology Neural Probe with Photoelectric Artifact-Free for Advanced Single-Neuron Analysis.},
journal = {ACS nano},
volume = {18},
number = {36},
pages = {25193-25204},
doi = {10.1021/acsnano.4c07379},
pmid = {39193830},
issn = {1936-086X},
mesh = {*Neurons/physiology ; *Artifacts ; Brain-Computer Interfaces ; Animals ; Single-Cell Analysis/instrumentation ; Humans ; },
abstract = {Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.},
}
@article {pmid39191641,
year = {2025},
author = {Al-Khouja, F and Grigorian, A and Emigh, B and Schellenberg, M and Diaz, G and Duncan, TK and Tuli, R and Coimbra, R and Gilbert-Gard, K and Johnson, A and Marty, M and Jebbia, M and Obaid-Schmid, AK and Fierro, N and Ley, E and Bayat, D and Biffl, W and Ebrahimian, S and Tillou M, A and Tay-Lasso, E and Alvarez, C and Nahmias, J},
title = {24-hour Telemetry Monitoring May Not be Necessary for Patients With an Isolated Sternal Fracture and Minor ECG Abnormalities or Troponin Elevation: A Southern California Multicenter Study.},
journal = {The American surgeon},
volume = {91},
number = {1},
pages = {126-132},
doi = {10.1177/00031348241278904},
pmid = {39191641},
issn = {1555-9823},
mesh = {Humans ; Male ; Female ; Retrospective Studies ; Middle Aged ; *Electrocardiography ; *Fractures, Bone/complications ; *Telemetry ; *Sternum/injuries ; California ; *Troponin/blood ; Adult ; Aged ; Wounds, Nonpenetrating/complications/diagnosis/blood ; Heart Injuries/diagnosis/blood/complications/etiology ; Monitoring, Physiologic/methods ; },
abstract = {BACKGROUND: Current guidelines recommend 24-hour telemetry monitoring for isolated sternal fractures (ISFs) with electrocardiogram (ECG) abnormalities or troponin elevation. However, a single-center study suggested ISF patients with minor ECG abnormalities (sinus tachycardia/bradycardia, nonspecific arrhythmia/ST-changes, and bundle branch block) may not require 24-hour telemetry monitoring. This study sought to corroborate this, hypothesizing ISF patients would not develop blunt cardiac injury (BCI).<br><br>MATERIALS &amp; METHODS: A retrospective study was performed at 8 trauma centers (1/2018-8/2020). Patients with ISF (abbreviated injury scale <2 for the head/neck/face/abdomen/extremities) and minor ECG abnormalities or troponin elevations were included. Patients with multiple rib fractures or hemothorax/pneumothorax were excluded. The primary outcome was an echocardiogram confirmed BCI. The secondary outcome was significant BCI defined as cardiogenic shock, dysrhythmia requiring treatment, post-traumatic cardiac structural defects, unexplained hypotension, or cardiac-related procedures. Descriptive statistics were performed.<br><br>RESULTS: Of 124 ISF patients with minor ECG abnormalities or troponin elevation, 90% were admitted with a mean stay of 35 hours. Echocardiogram was performed for 31.5% of patients, 10 (25.6%) of which had abnormalities. However, no patient had BCI diagnosed on echocardiography. In total, 2 patients (1.6%) had a significant BCI (atrial fibrillation and supraventricular tachycardia at 10 and 82&#xa0;hours after injury). No patient died.<br><br>CONCLUSIONS: Following ISF with minor ECG changes or troponin elevation, <2% suffered significant BCI, and none had an echocardiogram diagnosed BCI, despite >30% receiving echocardiogram. These findings challenge the dogma of mandatory observation periods following ISF with associated ECG abnormalities and support the lack of utility for routine echocardiography in these patients.},
}
@article {pmid39190517,
year = {2024},
author = {Liu, K and Yang, T and Yu, Z and Yi, W and Yu, H and Wang, G and Wu, W},
title = {MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {12},
pages = {7126-7137},
doi = {10.1109/JBHI.2024.3450753},
pmid = {39190517},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Imagination/physiology ; Brain-Computer Interfaces ; Adult ; Algorithms ; Male ; Young Adult ; Female ; },
abstract = {OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms.<br><br>METHODS: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers.<br><br>RESULTS: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i.e., the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses.<br><br>CONCLUSION: MSVTNet shows superiority and robustness in enhancing MI decoding performance.},
}
@article {pmid39190511,
year = {2024},
author = {Zhou, J and Duan, Y and Chang, YC and Wang, YK and Lin, CT},
title = {BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3278-3288},
doi = {10.1109/TNSRE.2024.3450795},
pmid = {39190511},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Natural Language Processing ; Language ; Semantics ; Supervised Machine Learning ; Male ; Female ; Adult ; Reproducibility of Results ; },
abstract = {Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.},
}
@article {pmid39188923,
year = {2024},
author = {Schwarck, S and Voelkle, MC and Becke, A and Busse, N and Glanz, W and Düzel, E and Ziegler, G},
title = {Interplay of physical and recognition performance using hierarchical continuous-time dynamic modeling and a dual-task training regime in Alzheimer's patients.},
journal = {Alzheimer's &amp; dementia (Amsterdam, Netherlands)},
volume = {16},
number = {3},
pages = {e12629},
pmid = {39188923},
issn = {2352-8729},
abstract = {UNLABELLED: Training studies typically investigate the cumulative rather than the analytically challenging immediate effect of exercise on cognitive outcomes. We investigated the dynamic interplay between single-session exercise intensity and time-locked recognition speed-accuracy scores in older adults with Alzheimer's dementia (N = 17) undergoing a 24-week dual-task regime. We specified a state-of-the-art hierarchical Bayesian continuous-time dynamic model with fully connected state variables to analyze the bi-directional effects between physical and recognition scores over time. Higher physical performance was dynamically linked to improved recognition (-1.335, SD = 0.201, 95% Bayesian credible interval [BCI] [-1.725, -0.954]). The effect was short-term, lasting up to 5 days (-0.368, SD = 0.05, 95% BCI [-0.479, -0.266]). Clinical scores supported the validity of the model and observed temporal dynamics. Higher physical performance predicted improved recognition speed accuracy in a day-by-day manner, providing a proof-of-concept for the feasibility of linking exercise training and recognition in patients with Alzheimer's dementia.<br><br>HIGHLIGHTS: Hierarchical Bayesian continuous-time dynamic modeling approachA total of 72 repeated physical exercise (PP) and integrated recognition speed-accuracy (IRSA) measurementsPP is dynamically linked to session-to-session variability of IRSAHigher PP improved IRSA in subsequent sessions in subjects with Alzheimer's dementiaShort-term effect: lasting up to 4 days after training session.},
}
@article {pmid39187733,
year = {2024},
author = {Gu, J and Shao, W and Liu, L and Wang, Y and Yang, Y and Zhang, Z and Wu, Y and Xu, Q and Gu, L and Zhang, Y and Shen, Y and Zhao, H and Zeng, C and Zhang, H},
title = {Challenges and future directions of SUDEP models.},
journal = {Lab animal},
volume = {53},
number = {9},
pages = {226-243},
pmid = {39187733},
issn = {1548-4475},
support = {81771403//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81974205//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001379//National Natural Science Foundation of China (National Science Foundation of China)/ ; LZ20H090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Animals ; *Disease Models, Animal ; *Sudden Unexpected Death in Epilepsy/prevention &amp; control ; Humans ; Epilepsy/physiopathology ; Artificial Intelligence ; },
abstract = {Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among patients with epilepsy, causing a global public health burden. The underlying mechanisms of SUDEP remain elusive, and effective prevention or treatment strategies require further investigation. A major challenge in current SUDEP research is the lack of an ideal model that maximally mimics the human condition. Animal models are important for revealing the potential pathogenesis of SUDEP and preventing its occurrence; however, they have potential limitations due to species differences that prevent them from precisely replicating the intricate physiological and pathological processes of human disease. This Review provides a comprehensive overview of several available SUDEP animal models, highlighting their pros and cons. More importantly, we further propose the establishment of an ideal model based on brain-computer interfaces and artificial intelligence, hoping to offer new insights into potential advancements in SUDEP research. In doing so, we hope to provide valuable information for SUDEP researchers, offer new insights into the pathogenesis of SUDEP and open new avenues for the development of strategies to prevent SUDEP.},
}
@article {pmid39187218,
year = {2024},
author = {Ciaramidaro, A and Toppi, J and Vogel, P and Freitag, CM and Siniatchkin, M and Astolfi, L},
title = {Synergy of the mirror neuron system and the mentalizing system in a single brain and between brains during joint actions.},
journal = {NeuroImage},
volume = {299},
number = {},
pages = {120783},
doi = {10.1016/j.neuroimage.2024.120783},
pmid = {39187218},
issn = {1095-9572},
mesh = {Humans ; *Mirror Neurons/physiology ; Male ; Female ; *Electroencephalography ; Adult ; Young Adult ; *Theory of Mind/physiology ; *Brain/physiology ; Cooperative Behavior ; Mentalization/physiology ; Social Interaction ; },
abstract = {Cooperative action involves the simulation of actions and their co-representation by two or more people. This requires the involvement of two complex brain systems: the mirror neuron system (MNS) and the mentalizing system (MENT), both of critical importance for successful social interaction. However, their internal organization and the potential synergy of both systems during joint actions (JA) are yet to be determined. The aim of this study was to examine the role and interaction of these two fundamental systems-MENT and MNS-during continuous interaction. To this hand, we conducted a multiple-brain connectivity analysis in the source domain during a motor cooperation task using high-density EEG dual-recordings providing relevant insights into the roles of MNS and MENT at the intra- and interbrain levels. In particular, the intra-brain analysis demonstrated the essential function of both systems during JA, as well as the crucial role played by single brain regions of both neural mechanisms during cooperative activities. Specifically, our intra-brain analysis revealed that both neural mechanisms are essential during Joint Action (JA), showing a solid connection between MNS and MENT and a central role of the single brain regions of both mechanisms during cooperative actions. Additionally, our inter-brain study revealed increased inter-subject connections involving the motor system, MENT and MNS. Thus, our findings show a mutual influence between two interacting agents, based on synchronization of MNS and MENT systems. Our results actually encourage more research into the still-largely unknown realm of inter-brain dynamics and contribute to expand the body of knowledge in social neuroscience.},
}
@article {pmid39186838,
year = {2024},
author = {Qiu, L and Zhong, L and Li, J and Feng, W and Zhou, C and Pan, J},
title = {SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {180},
number = {},
pages = {106643},
doi = {10.1016/j.neunet.2024.106643},
pmid = {39186838},
issn = {1879-2782},
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Consciousness/physiology ; Supervised Machine Learning ; Attention/physiology ; Adult ; Male ; Female ; },
abstract = {Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emotion recognition. In this study, we propose a cross-subject EEG emotion recognition method based on a semi-supervised fine-tuning self-supervised graph attention network (SFT-SGAT). First, we model multi-channel EEG signals by constructing a graph structure that dynamically captures the spatiotemporal topological features of EEG signals. Second, we employ a self-supervised graph attention neural network to facilitate model training, mitigating the impact of signal noise on the model. Finally, a semi-supervised approach is used to fine-tune the model, enhancing its generalization ability in cross-subject classification. By combining supervised and unsupervised learning techniques, the SFT-SGAT maximizes the utility of limited labeled data in EEG emotion recognition tasks, thereby enhancing the model's performance. Experiments based on leave-one-subject-out cross-validation demonstrate that SFT-SGAT achieves state-of-the-art cross-subject emotion recognition performance on the SEED and SEED-IV datasets, with accuracies of 92.04% and 82.76%, respectively. Furthermore, experiments conducted on a self-collected dataset comprising ten healthy subjects and eight patients with disorders of consciousness (DOCs) revealed that the SFT-SGAT attains high classification performance in healthy subjects (maximum accuracy of 95.84%) and was successfully applied to DOC patients, with four patients achieving emotion recognition accuracies exceeding 60%. The experiments demonstrate the effectiveness of the proposed SFT-SGAT model in cross-subject EEG emotion recognition and its potential for assessing levels of consciousness in patients with DOC.},
}
@article {pmid39186407,
year = {2025},
author = {Edelman, BJ and Zhang, S and Schalk, G and Brunner, P and Muller-Putz, G and Guan, C and He, B},
title = {Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.},
journal = {IEEE reviews in biomedical engineering},
volume = {18},
number = {},
pages = {26-49},
pmid = {39186407},
issn = {1941-1189},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Electroencephalography/methods ; *Brain/physiology ; Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.},
}
@article {pmid39185373,
year = {2024},
author = {Yi, D and Yao, Y and Wang, Y and Chen, L},
title = {Design, Fabrication, and Implantation of Invasive Microelectrode Arrays as in vivo Brain Machine Interfaces: A Comprehensive Review.},
journal = {Journal of manufacturing processes},
volume = {126},
number = {},
pages = {185-207},
pmid = {39185373},
issn = {1526-6125},
support = {R15 NS133861/NS/NINDS NIH HHS/United States ; },
abstract = {Invasive Microelectrode Arrays (MEAs) have been a significant and useful tool for us to gain a fundamental understanding of how the brain works through high spatiotemporal resolution neuron-level recordings and/or stimulations. Through decades of research, various types of microwire, silicon, and flexible substrate-based MEAs have been developed using the evolving new materials, novel design concepts, and cutting-edge advanced manufacturing capabilities. Surgical implantation of the latest minimal damaging flexible MEAs through the hard-to-penetrate brain membranes introduces new challenges and thus the development of implantation strategies and instruments for the latest MEAs. In this paper, studies on the design considerations and enabling manufacturing processes of various invasive MEAs as in vivo brain-machine interfaces have been reviewed to facilitate the development as well as the state-of-art of such brain-machine interfaces from an engineering perspective. The challenges and solution strategies developed for surgically implanting such interfaces into the brain have also been evaluated and summarized. Finally, the research gaps have been identified in the design, manufacturing, and implantation perspectives, and future research prospects in invasive MEA development have been proposed.},
}
@article {pmid39185208,
year = {2024},
author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP},
title = {A posture subspace in primary motor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39185208},
issn = {2692-8205},
support = {T32 GM081760/GM/NIGMS NIH HHS/United States ; R01 EY035896/EY/NEI NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; RF1 NS127107/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
abstract = {To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture information in M1 than previously recognized. The compartmentalization of posture and goal information might allow the two to be flexibly combined in the service of our broad repertoire of actions.},
}
@article {pmid39185198,
year = {2024},
author = {Ouchi, T and Scholl, LR and Rajeswaran, P and Canfield, RA and Smith, LI and Orsborn, AL},
title = {Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39185198},
issn = {2692-8205},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; R01 NS134634/NS/NINDS NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; },
abstract = {Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (μECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm[2] area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate μECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.},
}
@article {pmid39184953,
year = {2024},
author = {Raghuram, V and Datye, AD and Fried, SI and Timko, BP},
title = {Transparent and Conformal Microcoil Arrays for Spatially Selective Neuronal Activation.},
journal = {Device},
volume = {2},
number = {4},
pages = {},
pmid = {39184953},
issn = {2666-9986},
support = {23TPA1057212/AHA/American Heart Association-American Stroke Association/United States ; R01 NS110575/NS/NINDS NIH HHS/United States ; R21 EB034527/EB/NIBIB NIH HHS/United States ; },
abstract = {Micromagnetic stimulation (μMS) using small, implantable microcoils is a promising method for achieving neuronal activation with high spatial resolution and low toxicity. Herein, we report a microcoil array for localized activation of cortical neurons and retinal ganglion cells. We developed a computational model to relate the electric field gradient (activating function) to the geometry and arrangement of microcoils, and selected a design that produced an anisotropic region of activation <50 μm wide. The device was comprised of an SU-8/Cu/SU-8 tri-layer structure, which was flexible, transparent and conformal and featured four individually-addressable microcoils. Interfaced with cortex or retina explants from GCaMP6-expressing mice, we observed that individual neurons localized within 40 μm of a microcoil tip could be activated repeatedly and in a dose- (power-) dependent fashion. These results demonstrate the potential of μMS devices for brain-machine interfaces and could enable routes toward bioelectronic therapies including prosthetic vision devices.},
}
@article {pmid39184542,
year = {2024},
author = {Jeon, H and Park, IM},
title = {Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {39184542},
issn = {2331-8422},
support = {RF1 DA056404/DA/NIDA NIH HHS/United States ; },
abstract = {Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for building brain machine interfaces and understanding the neural computation underlying meaningful behavior. We derive a practical upper bound to the signal-to-noise ratio (SNR) of inferred neural latent trajectories using Fisher information. We show that the SNR bound is proportional to the overdispersion factor and the Fisher information per neuron. Further numerical experiments show that inference methods that exploit the temporal regularities can achieve higher SNRs that are proportional to the bound. Our results provide insights for fitting models to data, simulating neural responses, and design of experiments.},
}
@article {pmid39181508,
year = {2024},
author = {Ho, L and Ramanujan, S and Pramod, N and Tang, S and Bena, JF and De, S},
title = {Clinical Outcomes in Patients With Hypocontractile Bladders Undergoing Holmium Laser Enucleation of the Prostate.},
journal = {Urology},
volume = {194},
number = {},
pages = {231-237},
doi = {10.1016/j.urology.2024.08.023},
pmid = {39181508},
issn = {1527-9995},
mesh = {Humans ; Male ; *Prostatic Hyperplasia/surgery/complications ; *Lasers, Solid-State/therapeutic use ; Retrospective Studies ; Aged ; *Urodynamics ; Treatment Outcome ; Prostatectomy/methods ; Urinary Bladder/surgery/physiopathology ; Middle Aged ; Laser Therapy/methods ; },
abstract = {OBJECTIVE: To compare post-operative outcomes in patients who underwent holmium laser enucleation of the prostate (HoLEP) for benign prostatic hyperplasia (BPH) and had urodynamic evidence of bladder hypocontractility versus those with normocontractile bladders.<br><br>METHODS: We retrospectively&#xa0;reviewed HoLEP patients with pre-operative urodynamic studies at a single institution, categorizing them into normocontractile and hypocontractile groups based on the bladder contractility index (BCI) (hypocontractile defined as BCI < 100). Post-void residual (PVR) volume was measured at 6 weeks and 6 months. Secondary outcomes included maximum flow rate (Qmax) and catheterization status.<br><br>RESULTS: Among 114 HoLEP patients with pre-operative urodynamic data, 49 had hypocontractile bladders. The median pre-operative PVR was 305 (202-446) mL in the hypocontractile group, higher than the median PVR of 190 (60-361) mL in the normocontractile group (P&#xa0;=&#xa0;.013). At 6 weeks post-op, the median PVR was higher in the hypocontractile compared to normocontractile group (38 [3-61]&#xa0;vs&#xa0;5 [0-44]&#xa0;mL, P&#xa0;=&#xa0;.016), but at 6 months post-op there was no significant difference (18 [0-39]&#xa0;vs&#xa0;12 (0-70) mL, P&#xa0;=&#xa0;.97). Among men who were catheter-dependent pre-operatively, 98% of hypocontractile and 100% of normocontractile patients were catheter-free post-operatively. Qmax and symptom scores were similar at both follow-up time points.<br><br>CONCLUSION: HoLEP can be an effective surgical option for BPH patients with hypocontractile bladders, including those who are catheter-dependent, with minimal differences in post-operative voiding parameters compared to those with normal bladder function.},
}
@article {pmid39180976,
year = {2024},
author = {Wang, K and Wei, W and Yi, W and Qiu, S and He, H and Xu, M and Ming, D},
title = {Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {179},
number = {},
pages = {106617},
doi = {10.1016/j.neunet.2024.106617},
pmid = {39180976},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Male ; *Arousal/physiology ; Female ; Adult ; Young Adult ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.},
}
@article {pmid39178905,
year = {2024},
author = {Kothe, C and Hanada, G and Mullen, S and Mullen, T},
title = {Decoding working-memory load duringn-back task performance from high channel fNIRS data.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad731b},
pmid = {39178905},
issn = {1741-2552},
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Memory, Short-Term/physiology ; Male ; Adult ; Female ; *Brain-Computer Interfaces ; Young Adult ; Machine Learning ; Psychomotor Performance/physiology ; },
abstract = {Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.},
}
@article {pmid39178898,
year = {2024},
author = {Kim, M and Kim, SP},
title = {Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad731e},
pmid = {39178898},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Attention/physiology ; Young Adult ; Adult ; *Electroencephalography/methods ; Evoked Potentials/physiology ; Photic Stimulation/methods ; Speech/physiology ; },
abstract = {Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.},
}
@article {pmid39178637,
year = {2024},
author = {Zheng, M and Hong, T and Zhou, H and Garland, EL and Hu, Y},
title = {The acute effect of mindfulness-based regulation on neural indices of cue-induced craving in smokers.},
journal = {Addictive behaviors},
volume = {159},
number = {},
pages = {108134},
doi = {10.1016/j.addbeh.2024.108134},
pmid = {39178637},
issn = {1873-6327},
mesh = {Humans ; *Craving/physiology ; *Cues ; Male ; *Mindfulness/methods ; Adult ; *Magnetic Resonance Imaging ; *Smokers/psychology ; Young Adult ; Amygdala/physiopathology ; Ventral Striatum/physiopathology/diagnostic imaging ; Reward ; Prefrontal Cortex/physiopathology/diagnostic imaging ; Interoception/physiology ; Emotions/physiology ; Insular Cortex/physiopathology ; Brain/physiopathology/diagnostic imaging ; Cigarette Smoking/psychology/therapy ; },
abstract = {Mindfulness has garnered attention for its potential in alleviating cigarette cravings; however, the neural mechanisms underlying its efficacy remain inadequately understood. This study (N=46, all men) aims to examine the impact of a mindfulness strategy on regulating cue-induced craving and associated brain activity. Twenty-three smokers, consuming over 10 cigarettes daily for at least 2 years， were compared to twenty-three non-smokers. During a regulation of craving task, participants were asked to practice mindfulness during smoking cue-exposure or passively view smoking cues while fMRI scans were completed. A 2 (condition: mindfulness-cigarette and look-cigarette) × 2 (phase: early, late of whole smoking cue-exposure period) repeated measures ANOVA showed a significant interaction of the craving scores between condition and phase, indicating that the mindfulness strategy dampened late-phase craving. Additionally, within the smoker group, the fMRI analyses revealed a significant main effect of mindfulness condition and its interaction with time in several brain networks involving reward, emotion, and interoception. Specifically, the bilateral insula, ventral striatum, and amygdala showed lower activation in the mindfulness condition, whereas the activation of right orbitofrontal cortex mirrored the strategy-time interaction effect of the craving change. This study illuminates the dynamic interplay between mindfulness, smoking cue-induced craving, and neural activity, offering insights into how mindfulness may effectively regulate cigarette cravings.},
}
@article {pmid39178112,
year = {2024},
author = {Tu, WY and Xu, W and Bai, L and Liu, J and Han, Y and Luo, B and Wang, B and Zhang, K and Shen, C},
title = {Local protein synthesis at neuromuscular synapses is required for motor functions.},
journal = {Cell reports},
volume = {43},
number = {9},
pages = {114661},
doi = {10.1016/j.celrep.2024.114661},
pmid = {39178112},
issn = {2211-1247},
mesh = {Animals ; *Neuromuscular Junction/metabolism ; *Protein Biosynthesis ; Mice ; *Motor Neurons/metabolism ; RNA, Messenger/metabolism/genetics ; Synaptic Transmission ; Agrin/metabolism ; Mice, Inbred C57BL ; Motor Activity ; Synapses/metabolism ; Axons/metabolism ; },
abstract = {Motor neurons are highly polarized, and their axons extend over great distances to form connections with myofibers via neuromuscular junctions (NMJs). Local translation at the NMJs in vivo has not been identified. Here, we utilized motor neuron-labeled RiboTag mice and the TRAP (translating ribosome affinity purification) technique to spatiotemporally profile the translatome at NMJs. We found that mRNAs associated with glucose catabolism, synaptic connection, and protein homeostasis are enriched at presynapses. Local translation at the synapse shifts from the assembly of cytoskeletal components during early developmental stages to energy production in adulthood. The mRNA of neuronal Agrin (Agrn), the key molecule for NMJ assembly, is present at motor axon terminals and locally translated. Disrupting the axonal location of Agrn mRNA causes impairment of synaptic transmission and motor functions in adult mice. Our findings indicate that spatiotemporal regulation of mRNA local translation at NMJs plays critical roles in synaptic transmission and motor functions in vivo.},
}
@article {pmid39175558,
year = {2024},
author = {Azadi Moghadam, M and Maleki, A},
title = {Comparative Study of Frequency Recognition Techniques for Steady-State Visual Evoked Potentials According to the Frequency Harmonics and Stimulus Number.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {14},
number = {4},
pages = {365-378},
pmid = {39175558},
issn = {2251-7200},
abstract = {BACKGROUND: A key challenge in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems is to effectively recognize frequencies within a short time window. To address this challenge, the specific characteristics of the data are needed to select the frequency recognition method. These characteristics include factors, such as the number of stimulation targets and the presence of harmonic frequencies, resulting in optimizing the performance and accuracy of SSVEP-based BCI systems.<br><br>OBJECTIVE: The current study aimed to examine the effect of data characteristics on frequency recognition accuracy.<br><br>MATERIAL AND METHODS: In this analytical study, five commonly used frequency recognition methods were examined, used to various datasets containing different numbers of frequencies, including sub-data with and without frequency harmonics.<br><br>RESULTS: The increase in the number of frequencies in the Multivariate Linear Regression (MLR) method has led to a decrease in frequency recognition accuracy by 9%. Additionally, the presence of harmonic frequencies resulted in an 8% decrease in accuracy for the MLR method.<br><br>CONCLUSION: Frequency recognition using the MLR method reduces the effect of the number of different frequencies and harmonics of the stimulation frequencies on the frequency recognition accuracy.},
}
@article {pmid39175425,
year = {2024},
author = {Yao, J and Li, Z and Zhou, Z and Bao, A and Wang, Z and Wei, H and He, H},
title = {Distinct regional vulnerability to Aβ and iron accumulation in post mortem AD brains.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {20},
number = {10},
pages = {6984-6997},
pmid = {39175425},
issn = {1552-5279},
support = {2020AAA0109502//National Key R&amp;D Program of China/ ; 82372036//National Natural Science Foundation of China/ ; 226-2023-00095//Fundamental Research Funds for the Central Universities/ ; 82151303//National Natural Science Foundation/ ; 2021ZD0204000//STI2030-Major Projects/ ; },
mesh = {Aged ; Aged, 80 and over ; Female ; Humans ; Male ; *Alzheimer Disease/pathology/metabolism/genetics ; *Amyloid beta-Peptides/metabolism ; Autopsy ; *Brain/pathology/metabolism ; *Iron/metabolism ; Magnetic Resonance Imaging ; Plaque, Amyloid/pathology/metabolism ; },
abstract = {INTRODUCTION: The paramagnetic iron, diamagnetic amyloid beta (Aβ) plaques and their interaction are crucial in Alzheimer's disease (AD) pathogenesis, complicating non-invasive magnetic resonance imaging for prodromal AD detection.<br><br>METHODS: We used a state-of-the-art sub-voxel quantitative susceptibility mapping method to simultaneously measure A&#x3b2; and iron levels in post mortem human brains, validated by histology. Further transcriptomic analysis using Allen Human Brain Atlas elucidated the underlying biological processes.<br><br>RESULTS: Regional increased paramagnetic and diamagnetic susceptibility were observed in medial prefrontal, medial parietal, and para-hippocampal cortices associated with iron deposition (R&#xa0;=&#xa0;0.836, p&#xa0;=&#xa0;0.003) and A&#x3b2; accumulation (R&#xa0;=&#xa0;0.853, p&#xa0;=&#xa0;0.002) in AD brains. Higher levels of gene expression relating to cell cycle, post-translational protein modifications, and cellular response to stress were observed.<br><br>DISCUSSION: These findings provide quantitative insights into the variable vulnerability of cortical regions to higher levels of A&#x3b2; aggregation, iron overload, and subsequent neurodegeneration, indicating changes preceding clinical symptoms.<br><br>HIGHLIGHTS: The vulnerability of distinct brain regions to amyloid beta (A&#x3b2;) and iron accumulation varies. Histological validation was performed on stained sections of ex-vivo human brains. Regional variations in susceptibility were linked to gene expression profiles. Iron and A&#x3b2; levels in ex-vivo brains were simultaneously quantified.},
}
@article {pmid39173928,
year = {2024},
author = {Cao, HL and Yu, H and Xue, R and Yang, X and Ma, X and Wang, Q and Deng, W and Guo, WJ and Li, ML and Li, T},
title = {Convergence and divergence in neurostructural signatures of unipolar and bipolar depressions: Insights from surface-based morphometry and prospective follow-up.},
journal = {Journal of affective disorders},
volume = {366},
number = {},
pages = {8-15},
doi = {10.1016/j.jad.2024.08.101},
pmid = {39173928},
issn = {1573-2517},
mesh = {Humans ; *Bipolar Disorder/diagnostic imaging/pathology ; Female ; Male ; *Magnetic Resonance Imaging ; Adult ; Follow-Up Studies ; Prospective Studies ; Depressive Disorder/diagnostic imaging/pathology ; Cerebral Cortex/diagnostic imaging/pathology ; Brain/diagnostic imaging/pathology ; Middle Aged ; Young Adult ; Case-Control Studies ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is often misidentified as unipolar depression (UD) during its early stages, typically until the onset of the first manic episode. This study aimed to explore both shared and unique neurostructural changes in patients who transitioned from UD to BD during follow-up, as compared to those with UD.<br><br>METHODS: This study utilized high-resolution structural magnetic resonance imaging (MRI) to collect brain data from individuals initially diagnosed with UD. During the average 3-year follow-up, 24 of the UD patients converted to BD (cBD). For comparison, the study included 48 demographically matched UD patients who did not convert and 48 healthy controls. The MRI data underwent preprocessing using FreeSurfer, followed by surface-based morphometry (SBM) analysis to identify cortical thickness (CT), surface area (SA), and cortical volume (CV) among groups.<br><br>RESULTS: The SBM analysis identified shared neurostructural characteristics between the cBD and UD groups, specifically thinner CT in the right precentral cortex compared to controls. Unique to the cBD group, there was a greater SA in the right inferior parietal cortex compared to the UD group. Furthermore, no significant correlations were observed between cortical morphological measures and cognitive performance and clinical features in the cBD and UD groups.<br><br>LIMITATIONS: The sample size is relatively small.<br><br>CONCLUSIONS: Our findings suggest that while cBD and UD exhibit some common alterations in cortical macrostructure, numerous distinct differences are also present. These differences offer valuable insights into the neuropathological underpinnings that distinguish these two conditions.},
}
@article {pmid39171098,
year = {2024},
author = {Freudenburg, Z and Berezutskaya, J and Herbert, C},
title = {Editorial: The ethics of speech ownership in the context of neural control of augmented assistive communication.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1468938},
pmid = {39171098},
issn = {1662-5161},
}
@article {pmid39170600,
year = {2024},
author = {Pan, H and Fu, Y and Zhang, Q and Zhang, J and Qin, X},
title = {The decoder design and performance comparative analysis for closed-loop brain-machine interface system.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {147-164},
pmid = {39170600},
issn = {1871-4080},
abstract = {Brain-machine interface (BMI) can convert electroencephalography signals (EEGs) into the control instructions of external devices, and the key of control performance is the accuracy and efficiency of decoder. However, the performance of different decoders obtaining control instructions from complex and variable EEG signals is very different and irregular in the different neural information transfer model. Aiming at this problem, the off-line and on-line performance of eight decoders based on the improved single-joint information transmission (SJIT) model is compared and analyzed in this paper, which can provide a theoretical guidance for decoder design. Firstly, in order to avoid the different types of neural activities in the decoding process on the decoder performance, eight decoders based on the improved SJIT model are designed. And then the off-line decoding performance of these decoders is tested and compared. Secondly, a closed-loop BMI system which combining by the designed decoder and the random forest encoder based on the improved SJIT model is constructed. Finally, based on the constructed closed-loop BMI system, the on-line decoding performance of decoders is compared and analyzed. The results show that the LSTM-based decoder has better on-line decoding performance than others in the improved SJIT model.},
}
@article {pmid39167669,
year = {2024},
author = {Matsiko, A},
title = {Bilingual speech neuroprosthesis.},
journal = {Science robotics},
volume = {9},
number = {93},
pages = {eads4122},
doi = {10.1126/scirobotics.ads4122},
pmid = {39167669},
issn = {2470-9476},
mesh = {Humans ; *Multilingualism ; *Speech ; *Brain-Computer Interfaces ; Neural Prostheses ; Brain/physiology ; Prosthesis Design ; Male ; },
abstract = {A neuroprosthesis could decode two languages from the brain activity of a bilingual participant who was unable to articulate speech.},
}
@article {pmid39166947,
year = {2024},
author = {Zhang, H and Jiao, L and Yang, S and Li, H and Jiang, X and Feng, J and Zou, S and Xu, Q and Gu, J and Wang, X and Wei, B},
title = {Brain-computer interfaces: the innovative key to unlocking neurological conditions.},
journal = {International journal of surgery (London, England)},
volume = {110},
number = {9},
pages = {5745-5762},
pmid = {39166947},
issn = {1743-9159},
mesh = {Humans ; *Brain-Computer Interfaces ; *Nervous System Diseases ; Artificial Intelligence ; Electroencephalography/methods ; Parkinson Disease ; },
abstract = {Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.},
}
@article {pmid39166824,
year = {2024},
author = {Hata, J and Matsuoka, K and Akaihata, H and Yaginuma, K and Meguro, S and Hoshi, S and Koguchi, T and Sato, Y and Kataoka, M and Ogawa, S and Uemura, M and Kojima, Y},
title = {Prognosis of lower urinary tract symptoms and function after robot-assisted radical prostatectomy in patients with preoperative low bladder contractility: A prospective, observational study.},
journal = {Neurourology and urodynamics},
volume = {43},
number = {8},
pages = {2240-2248},
doi = {10.1002/nau.25577},
pmid = {39166824},
issn = {1520-6777},
mesh = {Aged ; Humans ; Male ; Middle Aged ; *Lower Urinary Tract Symptoms/physiopathology/diagnosis/etiology/surgery ; *Muscle Contraction ; Prognosis ; Prospective Studies ; *Prostatectomy/adverse effects ; Prostatic Neoplasms/surgery/physiopathology ; *Robotic Surgical Procedures/adverse effects ; Time Factors ; Treatment Outcome ; *Urinary Bladder/physiopathology ; *Urodynamics ; *Preoperative Period ; },
abstract = {OBJECTIVES: To examine the prognosis of lower urinary tract symptoms and function after robot-assisted radical prostatectomy (RARP) in patients with low preoperative bladder contractility.<br><br>METHODS: A total of 115 patients who underwent RARP were enrolled and divided into two groups by preoperative urodynamic findings: normal (patients with bladder contractility index [BCI]&#x2009;&#x2265;&#x2009;100; n&#x2009;=&#x2009;70) and low contractility (patients with BCI&#x2009;<&#x2009;100; n&#x2009;=&#x2009;45) groups. Lower urinary tract symptoms and function parameters were prospectively evaluated at 1, 3, 6, 9, and 12 months after RARP in both groups.<br><br>RESULTS: International Prostatic Symptom Score voiding scores 1, 3, 6, 9, and 12 months after RARP were significantly higher (p&#x2009;<&#x2009;0.05), and the maximum flow rate (Qmax) values before and 1, 3, 9, and 12 months after RARP were significantly lower in the low contractility group (p&#x2009;<&#x2009;0.05). Comparing preoperative and postoperative parameters, IPSS voiding scores in the normal contractility group were significantly improved from 6 months after RARP, whereas those in the low contractility group were almost unchanged. Qmax and the 1-h pad test in both groups temporarily deteriorated 1 month after RARP, whereas voided volume and postvoiding residual volume significantly decreased from 1 to 12 months after RARP.<br><br>CONCLUSIONS: This observational study showed that patients with low preoperative bladder contractility might have a weak improvement in voiding symptoms and function after RARP.},
}
@article {pmid39165885,
year = {2024},
author = {Singh, AK and Bianchi, L and Valeriani, D and Nakanishi, M},
title = {Editorial: Advances and challenges to bridge computational intelligence and neuroscience for brain-computer interface.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1461494},
pmid = {39165885},
issn = {2673-6195},
}
@article {pmid39165780,
year = {2024},
author = {Tapia, JL and Lopez, A and Turner, DB and Fairley, T and Tomlin-Harris, T and Hawkins, M and Holbert, PR and Treviño, LS and Teteh-Brooks, DK},
title = {The bench to community initiative: community-based participatory research model for translating research discoveries into community solutions.},
journal = {Frontiers in public health},
volume = {12},
number = {},
pages = {1394069},
pmid = {39165780},
issn = {2296-2565},
support = {K01 MD018417/MD/NIMHD NIH HHS/United States ; L60 CA253971/CA/NCI NIH HHS/United States ; R01 CA217841/CA/NCI NIH HHS/United States ; R43 MD017966/MD/NIMHD NIH HHS/United States ; },
mesh = {*Community-Based Participatory Research ; Humans ; *Breast Neoplasms ; Female ; Translational Research, Biomedical ; Black or African American ; Quality of Life ; },
abstract = {UNLABELLED: Community-based participatory research (CBPR) is an effective methodology for translating research findings from academia to community interventions. The Bench to Community Initiative (BCI), a CBPR program, builds on prior research to engage stakeholders across multiple disciplines with the goal of disseminating interventions to reduce breast cancer disparities and improve quality of life of Black communities.<br><br>METHODS: The BCI program was established to understand sociocultural determinants of personal care product use, evaluate the biological impact of endocrine disrupting chemicals, and develop community interventions. The three pillars of the program include research, outreach and engagement as well as advocacy activities. The research pillar of the BCI includes development of multidisciplinary partnerships to understand the sociocultural and biological determinants of harmful chemical (e.g., endocrine disrupting chemicals) exposures from personal care products and to implement community interventions. The outreach and engagement pillar includes education and translation of research into behavioral practice. The research conducted through the initiative provides the foundation for advocacy engagement with applicable community-based organizations. Essential to the mission of the BCI is the participation of community members and trainees from underrepresented backgrounds who are affected by breast cancer disparities.<br><br>RESULTS: Two behavioral interventions will be developed building on prior research on environmental exposures with the focus on personal care products including findings from the BCI. In person and virtual education activities include tabling at community events with do-it-yourself product demonstrations, Salon Conversations-a virtual platform used to bring awareness, education, and pilot behavior change interventions, biennial symposiums, and social media engagement. BCI's community advisory board members support activities across the three pillars, while trainees participate in personal and professional activities that enhance their skills in research translation.<br><br>DISCUSSION: This paper highlights the three pillars of the BCI, lessons learned, testimonies from community advisory board members and trainees on the impact of the initiative, as well as BCI's mission driven approaches to achieving health equity.},
}
@article {pmid39165718,
year = {2024},
author = {Qiu, Y and Lian, YN and Wu, C and Liu, L and Zhang, C and Li, XY},
title = {Coordination between midcingulate cortex and retrosplenial cortex in pain regulation.},
journal = {Frontiers in molecular neuroscience},
volume = {17},
number = {},
pages = {1405532},
pmid = {39165718},
issn = {1662-5099},
abstract = {INTRODUCTION: The cingulate cortex, with its subregions ACC, MCC, and RSC, is key in pain processing. However, the detailed interactions among these regions in modulating pain sensation have remained unclear.<br><br>METHODS: In this study, chemogenetic tools were employed to selectively activate or inhibit neuronal activity in the MCC and RSC of rodents to elucidate their roles in pain regulation.Results: Our results showed that chemogenetic activation in both the RSC and MCC heightened pain sensitivity. Suppression of MCC activity disrupted the RSC's regulation of both mechanical and thermal pain, while RSC inhibition specifically affected the MCC's regulation of thermal pain.<br><br>DISCUSSION: The findings indicate a complex interplay between the MCC and RSC, with the MCC potentially governing the RSC's pain regulatory mechanisms. The RSC, in turn, is crucial for the MCC's control over thermal sensation, revealing a collaborative mechanism in pain processing.<br><br>CONCLUSION: This study provides evidence for the MCC and RSC's collaborative roles in pain regulation, highlighting the importance of their interactions for thermal and mechanical pain sensitivity. Understanding these mechanisms could aid in developing targeted therapies for pain disorders.},
}
@article {pmid39164787,
year = {2024},
author = {Li, JK and Tang, T and Zong, H and Wu, EM and Zhao, J and Wu, RR and Zheng, XN and Zhang, H and Li, YF and Zhou, XH and Zhang, CC and Zhang, ZL and Zhang, YH and Feng, WZ and Zhou, Y and Wang, J and Zhu, QY and Deng, Q and Zheng, JM and Yang, L and Wei, Q and Shen, BR},
title = {Intelligent medicine in focus: the 5 stages of evolution in robot-assisted surgery for prostate cancer in the past 20 years and future implications.},
journal = {Military Medical Research},
volume = {11},
number = {1},
pages = {58},
pmid = {39164787},
issn = {2054-9369},
support = {2023SCU12057//Fundamental Research Funds for the Central Universities/ ; 82373106//National Natural Science Foundation of China/ ; 82372831//National Natural Science Foundation of China/ ; 32270690//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; *Robotic Surgical Procedures/methods/history/trends ; *Prostatic Neoplasms/surgery ; Artificial Intelligence/trends ; },
abstract = {Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.},
}
@article {pmid39164105,
year = {2024},
author = {Kabir, A and Dhami, P and Dussault Gomez, MA and Blumberger, DM and Daskalakis, ZJ and Moreno, S and Farzan, F},
title = {Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {39},
pages = {},
pmid = {39164105},
issn = {1529-2401},
mesh = {Humans ; Male ; *Transcranial Magnetic Stimulation/methods ; Female ; Adult ; *Electroencephalography/methods ; Young Adult ; *Prefrontal Cortex/physiology ; *Brain/physiology ; Evoked Potentials/physiology ; },
abstract = {Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.},
}
@article {pmid39163821,
year = {2024},
author = {Cheng, C and Liu, W and Feng, L and Jia, Z},
title = {Emotion recognition using hierarchical spatial-temporal learning transformer from regional to global brain.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {179},
number = {},
pages = {106624},
doi = {10.1016/j.neunet.2024.106624},
pmid = {39163821},
issn = {1879-2782},
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain/physiology ; Brain-Computer Interfaces ; Neural Networks, Computer ; },
abstract = {Emotion recognition is an essential but challenging task in human-computer interaction systems due to the distinctive spatial structures and dynamic temporal dependencies associated with each emotion. However, current approaches fail to accurately capture the intricate effects of electroencephalogram (EEG) signals across different brain regions on emotion recognition. Therefore, this paper designs a transformer-based method, denoted by R2G-STLT, which relies on a spatial-temporal transformer encoder with regional to global hierarchical learning that learns the representative spatiotemporal features from the electrode level to the brain-region level. The regional spatial-temporal transformer (RST-Trans) encoder is designed to obtain spatial information and context dependence at the electrode level aiming to learn the regional spatiotemporal features. Then, the global spatial-temporal transformer (GST-Trans) encoder is utilized to extract reliable global spatiotemporal features, reflecting the impact of various brain regions on emotion recognition tasks. Moreover, the multi-head attention mechanism is placed into the GST-Trans encoder to empower it to capture the long-range spatial-temporal information among the brain regions. Finally, subject-independent experiments are conducted on each frequency band of the DEAP, SEED, and SEED-IV datasets to assess the performance of the proposed model. Results indicate that the R2G-STLT model surpasses several state-of-the-art approaches.},
}
@article {pmid39161850,
year = {2024},
author = {Dillen, A and Omidi, M and Díaz, MA and Ghaffari, F and Roelands, B and Vanderborght, B and Romain, O and De Pauw, K},
title = {Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1448584},
pmid = {39161850},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.},
}
@article {pmid39161655,
year = {2024},
author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y},
title = {Considerations and discussions on the clear definition and definite scope of brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1449208},
pmid = {39161655},
issn = {1662-4548},
abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.},
}
@article {pmid39160882,
year = {2024},
author = {Pitkin, M and Park, H and Frossard, L and Klishko, AN and Prilutsky, BI},
title = {Transforming the Anthropomorphic Passive Free-Flow Foot Prosthesis Into a Powered Foot Prosthesis With Intuitive Control and Sensation (Bionic FFF).},
journal = {Military medicine},
volume = {189},
number = {Suppl 3},
pages = {439-447},
doi = {10.1093/milmed/usae149},
pmid = {39160882},
issn = {1930-613X},
support = {MR150051//U.S. Department of Defense/ ; AR43290//National Center for Medical Rehabilitation Research/ ; MR150051//U.S. Department of Defense/ ; AR43290//National Center for Medical Rehabilitation Research/ ; },
mesh = {*Artificial Limbs/statistics &amp; numerical data ; Animals ; *Prosthesis Design/methods ; Cats ; Foot/physiology/physiopathology ; Amputees/rehabilitation ; Electromyography/methods/instrumentation ; Bionics/methods/instrumentation ; Walking/physiology/statistics &amp; numerical data ; Humans ; },
abstract = {INTRODUCTION: Approximately 89% of all service members with amputations do not return to duty. Restoring intuitive neural control with somatosensory sensation is a key to improving the safety and efficacy of prosthetic locomotion. However, natural somatosensory feedback from lower-limb prostheses has not yet been incorporated into any commercial prostheses.<br><br>MATERIALS AND METHODS: We developed a neuroprosthesis with intuitive bidirectional control and somatosensation and evoking phase-dependent locomotor reflexes, we aspire to significantly improve the prosthetic rehabilitation and long-term functional outcomes of U.S. amputees. We implanted the skin and bone integrated pylon with peripheral neural interface pylon into the cat distal tibia, electromyographic electrodes into the residual gastrocnemius muscle, and nerve cuff electrodes on the distal tibial and sciatic nerves. Results. The bidirectional neural interface that was developed was integrated into the existing passive Free-Flow Foot and Ankle prosthesis, WillowWood, Mount Sterling, OH. The Free-Flow Foot was chosen because it had the highest Index of Anthropomorphicity among lower-limb prostheses and was the first anthropomorphic prosthesis brought to market. Conclusion. The cats walked on a treadmill with no cutaneous feedback from the foot in the control condition and with their residual distal tibial nerve stimulated during the stance phase of walking.},
}
@article {pmid39160021,
year = {2024},
author = {Zhu, L and Wang, W and Huang, A and Ying, N and Xu, P and Zhang, J},
title = {An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.},
journal = {Medical engineering &amp; physics},
volume = {130},
number = {},
pages = {104213},
doi = {10.1016/j.medengphy.2024.104213},
pmid = {39160021},
issn = {1873-4030},
mesh = {Humans ; *Electroencephalography ; *Signal Processing, Computer-Assisted ; *Seizures/diagnosis/physiopathology ; *Neural Networks, Computer ; Epilepsy/physiopathology/diagnosis ; Algorithms ; },
abstract = {Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patient-specific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.},
}
@article {pmid39159588,
year = {2024},
author = {Liu, L and Wang, D and Luo, Y and Liu, Y and Guo, Y and Yang, GZ and Qiu, G},
title = {Intraoperative assessment of microimplantation-induced acute brain inflammation with titanium oxynitride-based plasmonic biosensor.},
journal = {Biosensors &amp; bioelectronics},
volume = {264},
number = {},
pages = {116664},
doi = {10.1016/j.bios.2024.116664},
pmid = {39159588},
issn = {1873-4235},
mesh = {Animals ; *Titanium/chemistry ; *Surface Plasmon Resonance ; Mice ; Biosensing Techniques ; Encephalitis/etiology ; Microelectrodes ; Interleukin-6/analysis/cerebrospinal fluid ; Brain ; Brain-Computer Interfaces ; Equipment Design ; Electrodes, Implanted/adverse effects ; Humans ; },
abstract = {Implantable devices for brain-machine interfaces and managing neurological disorders have experienced rapid growth in recent years. Although functional implants offer significant benefits, issues related to transient trauma and long-term biocompatibility and safety are of significant concern. Acute inflammatory reaction in the brain tissue caused by microimplants is known to be an issue but remains poorly studied. This study presents the use of titanium oxynitride (TiNO) nanofilm with defined surface plasmon resonance (SPR) properties for point-of-care characterizing of acute inflammatory responses during robot-controlled micro-neuro-implantation. By leveraging surface-enriched oxynitride, TiNO nanofilms can be biomolecular-functionalized through silanization. This label-free TiNO-SPR biosensor exhibits a high sensitivity toward the inflammatory cytokine interleukin-6 with a detection limit down to 6.3 fg ml[-1] and a short assay time of 25 min. Additionally, intraoperative monitoring of acute inflammatory responses during microelectrode implantation in the mice brain has been accomplished using the TiNO-SPR biosensors. Through intraoperative cerebrospinal fluid sampling and point-of-care plasmonic biosensing, the rhythm of acute inflammatory responses induced by the robot-controlled brain microelectrodes implantation has been successfully depicted, offering insights into intraoperative safety assessment of invasive brain-machine interfaces.},
}
@article {pmid39156926,
year = {2024},
author = {Zhu, H and Deng, X and Yakovlev, VV and Zhang, D},
title = {Dynamics of CH/n hydrogen bond networks probed by time-resolved CARS spectroscopy.},
journal = {Chemical science},
volume = {15},
number = {35},
pages = {14344-14351},
pmid = {39156926},
issn = {2041-6520},
support = {R01 GM127696/GM/NIGMS NIH HHS/United States ; R21 CA269099/CA/NCI NIH HHS/United States ; R21 GM142107/GM/NIGMS NIH HHS/United States ; },
abstract = {Hydrogen bond (HB) networks are essential for stabilizing molecular structures in solution and govern the solubility and functionality of molecules in an aqueous environment. HBs are important in biological processes such as enzyme-substrate interactions, protein folding, and DNA replication. However, the exact role of weakly polarized C-H bonds as HB proton donors in solution, such as CH/n HBs, remains mostly unknown. Here, we employ a novel approach focusing on vibrational dephasing to investigate the coherence relaxation of induced dipoles in C-H bonds within CH/n HB networks, utilizing time-resolved coherent anti-Stokes Raman scattering (T-CARS) spectroscopy. Using a representative binary system of dimethyl sulfoxide (DMSO)-water, known for its C-H backboned HB system (i.e., C-H⋯S), we observed an increase in the dephasing time of the C-H bending mode with increasing water content until a percolation threshold at a 6 : 1 water : DMSO molar ratio, where the trend is reversed. These results provide compelling evidence for the existence of C-H⋯S structures and underscore the presence of a percolation effect, suggesting a critical threshold where long-range connectivity is disputed.},
}
@article {pmid39156581,
year = {2024},
author = {Xiong, L and Cao, J and Dong, H and Song, W and Ming, D},
title = {Multidisciplinary integration of frontier technologies facilitating the development of anesthesiology and perioperative medicine in aging society.},
journal = {Fundamental research},
volume = {4},
number = {4},
pages = {795-796},
pmid = {39156581},
issn = {2667-3258},
}
@article {pmid39155721,
year = {2024},
author = {Li, Y and Su, C and Pan, Y},
title = {Spontaneous movement synchrony as an exogenous source for interbrain synchronization in cooperative learning.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1911},
pages = {20230155},
pmid = {39155721},
issn = {1471-2970},
support = {//Humanities and Social Sciences Research Project from the Ministry of Education of China/ ; //National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain/physiology ; Male ; *Movement/physiology ; Female ; *Learning/physiology ; Young Adult ; Cooperative Behavior ; Spectroscopy, Near-Infrared ; Adult ; },
abstract = {Learning through cooperation with conspecifics-'cooperative learning'-is critical to cultural evolution and survival. Recent progress has established that interbrain synchronization (IBS) between individuals predicts success in cooperative learning. However, the likely sources of IBS during learning interactions remain poorly understood. To address this dearth of knowledge, we tested whether movement synchrony serves as an exogenous factor that drives IBS, taking an embodiment perspective. We formed dyads of individuals with varying levels of prior knowledge (high-high (HH), high-low (HL), low-low (LL) dyads) and instructed them to collaboratively analyse an ancient Chinese poem. During the task, we simultaneously recorded their brain activity using functional near-infrared spectroscopy and filmed the entire experiment to parse interpersonal movement synchrony using the computer-vision motion energy analysis. Interestingly, the homogeneous groups (HH and/or LL) exhibited stronger movement synchrony and IBS compared with the heterogeneous group. Importantly, mediation analysis revealed that spontaneous and synchronized body movements between individuals contribute to IBS, hence facilitating learning. This study therefore fills a critical gap in our understanding of how interpersonal transmission of information between individual brains, associated with behavioural entrainment, shapes social learning. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.},
}
@article {pmid39154786,
year = {2024},
author = {Li, X and Wang, S and Ruan, Y and Pan, Y and Huang, Y},
title = {Taste or health: The impact of packaging cues on consumer decision-making in healthy foods.},
journal = {Appetite},
volume = {203},
number = {},
pages = {107636},
doi = {10.1016/j.appet.2024.107636},
pmid = {39154786},
issn = {1095-8304},
mesh = {Humans ; *Consumer Behavior ; *Cues ; Male ; Female ; Adult ; *Food Packaging/methods ; *Food Preferences/psychology ; *Taste ; Young Adult ; Diet, Healthy/psychology ; Decision Making ; Intention ; Choice Behavior ; Food, Organic ; Middle Aged ; Adolescent ; },
abstract = {According to the theory of dietary regulation, consumers frequently encounter conflicts between healthiness and tastiness when selecting healthy foods. This study explores how packaging cue that highlight "tasty" versus "healthy" affect consumers' intentions to purchase healthy food. After an Implicit Association Test (IAT) confirmed a perceived lack of tastiness in health foods in the Preliminary study, Study 1 analyzed pricing and packaging details of the top 200 most-popular items in each of the ten healthy food categories on a major online shopping platform. Results showed that products with taste-focused cues commanded higher prices, indicating stronger consumer acceptance of healthy foods marketed as delicious. To address the causality limitations of observational studies, Study 2 used an experimental design to directly measure the impact of these cues on purchase intentions and perceptions of energy, healthiness, and tastiness. Findings revealed that taste-focused cues significantly boosted purchase intentions compared to health-focused cues, although they also diminished the perceived healthiness of the products. Moreover, in the control group exposed to unhealthy food options, health-emphasized packaging also increased purchase intentions, indicating that consumers seek a balance between healthiness and tastiness, rather than prioritizing health alone. Study 3 further explored the impact of cognitive load over these cue influences, revealing a heightened inclination among consumers to purchase healthy products with taste-focused cue under high cognitive load state. These insights have direct implications for food packaging design, suggesting that emphasizing a balance of taste and health benefits can effectively enhance consumer engagement. The study, which conducted in China, also opens avenues for future research to explore similar effects, maybe in different cultural contexts, different consumer groups, and under varied cognitive conditions.},
}
@article {pmid39151656,
year = {2024},
author = {Kuo, CH and Liu, GT and Lee, CE and Wu, J and Casimo, K and Weaver, KE and Lo, YC and Chen, YY and Huang, WC and Ojemann, JG},
title = {Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements.},
journal = {Journal of neuroscience methods},
volume = {411},
number = {},
pages = {110251},
doi = {10.1016/j.jneumeth.2024.110251},
pmid = {39151656},
issn = {1872-678X},
mesh = {Humans ; *Fingers/physiology ; *Electrocorticography/methods ; *Movement/physiology ; *Neural Networks, Computer ; Adult ; Male ; Female ; Epilepsy/physiopathology ; Young Adult ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Electroencephalography (EEG) and electrocorticography (ECoG) recordings have been used to decode finger movements by analyzing brain activity. Traditional methods focused on single bandpass power changes for movement decoding, utilizing machine learning models requiring manual feature extraction.<br><br>NEW METHOD: This study introduces a 3D convolutional neural network (3D-CNN) model to decode finger movements using ECoG data. The model employs adaptive, explainable AI (xAI) techniques to interpret the physiological relevance of brain signals. ECoG signals from epilepsy patients during awake craniotomy were processed to extract power spectral density across multiple frequency bands. These data formed a 3D matrix used to train the 3D-CNN to predict finger trajectories.<br><br>RESULTS: The 3D-CNN model showed significant accuracy in predicting finger movements, with root-mean-square error (RMSE) values of 0.26-0.38 for single finger movements and 0.20-0.24 for combined movements. Explainable AI techniques, Grad-CAM and SHAP, identified the high gamma (HG) band as crucial for movement prediction, showing specific cortical regions involved in different finger movements. These findings highlighted the physiological significance of the HG band in motor control.<br><br>The 3D-CNN model outperformed traditional machine learning approaches by effectively capturing spatial and temporal patterns in ECoG data. The use of xAI techniques provided clearer insights into the model's decision-making process, unlike the "black box" nature of standard deep learning models.<br><br>CONCLUSIONS: The proposed 3D-CNN model, combined with xAI methods, enhances the decoding accuracy of finger movements from ECoG data. This approach offers a more efficient and interpretable solution for brain-computer interface (BCI) applications, emphasizing the HG band's role in motor control.},
}
@article {pmid39150815,
year = {2024},
author = {Wu, H and Ma, Z and Guo, Z and Wu, Y and Zhang, J and Zhou, G and Long, J},
title = {Online Privacy-Preserving EEG Classification by Source-Free Transfer Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3059-3070},
doi = {10.1109/TNSRE.2024.3445115},
pmid = {39150815},
issn = {1558-0210},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; *Machine Learning ; Privacy ; Online Systems ; Transfer, Psychology/physiology ; Adult ; Male ; },
abstract = {Electroencephalogram (EEG) signals play an important role in brain-computer interface (BCI) applications. Recent studies have utilized transfer learning to assist the learning task in the new subject, i.e., target domain, by leveraging beneficial information from previous subjects, i.e., source domains. Nevertheless, EEG signals involve sensitive personal mental and health information. Thus, privacy concern becomes a critical issue. In addition, existing methods mostly assume that a portion of the new subject's data is available and perform alignment or adaptation between the source and target domains. However, in some practical scenarios, new subjects prefer prompt BCI utilization over the time-consuming process of collecting data for calibration and adaptation, which makes the above assumption difficult to hold. To address the above challenges, we propose Online Source-Free Transfer Learning (OSFTL) for privacy-preserving EEG classification. Specifically, the learning procedure contains offline and online stages. At the offline stage, multiple model parameters are obtained based on the EEG samples from multiple source subjects. OSFTL only needs access to these source model parameters to preserve the privacy of the source subjects. At the online stage, a target classifier is trained based on the online sequence of EEG instances. Subsequently, OSFTL learns a weighted combination of the source and target classifiers to obtain the final prediction for each target instance. Moreover, to ensure good transferability, OSFTL dynamically updates the transferred weight of each source domain based on the similarity between each source classifier and the target classifier. Comprehensive experiments on both simulated and real-world applications demonstrate the effectiveness of the proposed method, indicating the potential of OSFTL to facilitate the deployment of BCI applications outside of controlled laboratory settings.},
}
@article {pmid39149481,
year = {2024},
author = {Liu, CW and Chen, SY and Wang, YM and Lu, LY and Chen, P and Liang, TY and Liu, WC and Kumar, A and Kuo, SH and Lee, JC and Lo, CC and Wu, SC and Pan, MK},
title = {The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {39149481},
issn = {2693-5015},
support = {R01 NS104423/NS/NINDS NIH HHS/United States ; R01 NS118179/NS/NINDS NIH HHS/United States ; R01 NS124854/NS/NINDS NIH HHS/United States ; },
abstract = {Cross-individual variability is considered the essence of biology, preventing precise mathematical descriptions of biological motion[1-7] like the physics law of motion. Here we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in-vivo electrophysiology and optogenetics in mice, we confirmed that deep cerebellar neurons encoded frequencies via populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism was consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform, or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validated the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating-current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for brain-computer interface for motor control.},
}
@article {pmid39145953,
year = {2024},
author = {O'Regan, RM and Zhang, Y and Fleming, GF and Francis, PA and Kammler, R and Viale, G and Dell'Orto, P and Lang, I and Bellet, M and Bonnefoi, HR and Tondini, C and Villa, F and Bernardo, A and Ciruelos, EM and Neven, P and Karlsson, P and Müller, B and Jochum, W and Zaman, K and Martino, S and Geyer, CE and Jerzak, KJ and Davidson, NE and Coleman, RE and Ingle, JN and van Mackelenbergh, MT and Loi, S and Colleoni, M and Schnabel, CA and Treuner, K and Regan, MM},
title = {Breast Cancer Index in Premenopausal Women With Early-Stage Hormone Receptor-Positive Breast Cancer.},
journal = {JAMA oncology},
volume = {10},
number = {10},
pages = {1379-1389},
pmid = {39145953},
issn = {2374-2445},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/pathology/genetics ; *Premenopause ; Adult ; Prospective Studies ; *Tamoxifen/therapeutic use ; *Biomarkers, Tumor/genetics/metabolism ; *Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; *Antineoplastic Agents, Hormonal/therapeutic use ; Middle Aged ; Retrospective Studies ; Receptors, Interleukin-17 ; Receptors, Estrogen/metabolism ; Chemotherapy, Adjuvant ; Homeodomain Proteins/genetics ; Receptors, Progesterone/metabolism ; Androstadienes/therapeutic use/administration &amp; dosage ; Neoplasm Staging ; Treatment Outcome ; Predictive Value of Tests ; Aromatase Inhibitors/therapeutic use ; },
abstract = {IMPORTANCE: Adjuvant ovarian function suppression (OFS) with oral endocrine therapy improves outcomes for premenopausal patients with hormone receptor-positive (HR+) breast cancer but adds adverse effects. A genomic biomarker for selecting patients most likely to benefit from OFS-based treatment is lacking.<br><br>OBJECTIVE: To assess the predictive and prognostic performance of the Breast Cancer Index (BCI) for OFS benefit in premenopausal women with HR+ breast cancer.<br><br>This prospective-retrospective translational study used all available tumor tissue samples from female patients from the Suppression of Ovarian Function Trial (SOFT). These individuals were randomized to receive 5 years of adjuvant tamoxifen alone, tamoxifen plus OFS, or exemestane plus OFS. BCI testing was performed blinded to clinical data and outcome. The a priori hypothesis was that BCI HOXB13/IL17BR ratio (BCI[H/I])-high tumors would benefit more from OFS and high BCI portended poorer prognosis in this population. Settings spanned multiple centers internationally. Participants included premenopausal female patients with HR+ early breast cancer with specimens in the International Breast Cancer Study Group tumor repository available for RNA extraction. Data were collected from December 2003 to April 2021 and were analyzed from May 2022 to October 2022.<br><br>MAIN OUTCOMES AND MEASURES: Primary end points were breast cancer-free interval (BCFI) for the predictive analysis and distant recurrence-free interval (DRFI) for the prognostic analyses.<br><br>RESULTS: Tumor specimens were available for 1718 of the 3047 female patients in the SOFT intention-to-treat population. The 1687 patients (98.2%) who had specimens that yielded sufficient RNA for BCI testing represented the parent trial population. The median (IQR) follow-up time was 12 (10.5-13.4) years, and 512 patients (30.3%) were younger than 40 years. Tumors were BCI(H/I)-low for 972 patients (57.6%) and BCI(H/I)-high for 715 patients (42.4%). Patients with tumors classified as BCI(H/I)-low exhibited a 12-year absolute benefit in BCFI of 11.6% from exemestane plus OFS (hazard ratio [HR], 0.48 [95% CI, 0.33-0.71]) and an absolute benefit of 7.3% from tamoxifen plus OFS (HR, 0.69 [95% CI, 0.48-0.97]) relative to tamoxifen alone. In contrast, patients with BCI(H/I)-high tumors did not benefit from either exemestane plus OFS (absolute benefit, -0.4%; HR, 1.03 [95% CI, 0.70-1.53]; P for interaction&#x2009;=&#x2009;.006) or tamoxifen plus OFS (absolute benefit, -1.2%; HR, 1.05 [95% CI, 0.72-1.54]; P for interaction&#x2009;=&#x2009;.11) compared with tamoxifen alone. BCI continuous index was significantly prognostic in the N0 subgroup for DRFI (n&#x2009;=&#x2009;1110; P&#x2009;=&#x2009;.004), with 12-year DRFI of 95.9%, 90.8%, and 86.3% in BCI low-risk, intermediate-risk, and high-risk N0 cancers, respectively.<br><br>CONCLUSIONS AND RELEVANCE: In this prospective-retrospective translational study of patients enrolled in SOFT, BCI was confirmed as prognostic in premenopausal women with HR+ breast cancer. The benefit from OFS-containing adjuvant endocrine therapy was greater for patients with BCI(H/I)-low tumors than BCI(H/I)-high tumors. BCI(H/I)-low status may identify premenopausal patients who are likely to benefit from this more intensive endocrine therapy.},
}
@article {pmid39145233,
year = {2024},
author = {Lakshminarayanan, K and Ramu, V and Shah, R and Haque Sunny, MS and Madathil, D and Brahmi, B and Wang, I and Fareh, R and Rahman, MH},
title = {Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.},
journal = {PeerJ. Computer science},
volume = {10},
number = {},
pages = {e2174},
pmid = {39145233},
issn = {2376-5992},
abstract = {BACKGROUND: The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients.<br><br>METHODS: We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot.<br><br>RESULTS: Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals.<br><br>DISCUSSION: The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.},
}
@article {pmid39144712,
year = {2024},
author = {Kim, MS and Park, H and Kwon, I and An, KO and Shin, JH},
title = {Brain-computer interface on wrist training with or without neurofeedback in subacute stroke: a study protocol for a double-blinded, randomized control pilot trial.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1376782},
pmid = {39144712},
issn = {1664-2295},
abstract = {BACKGROUND: After a stroke, damage to the part of the brain that controls movement results in the loss of motor function. Brain-computer interface (BCI)-based stroke rehabilitation involves patients imagining movement without physically moving while the system measures the perceptual-motor rhythm in the motor cortex. Visual feedback through virtual reality and functional electrical stimulation is provided simultaneously. The superiority of real BCI over sham BCI in the subacute phase of stroke remains unclear. Therefore, we aim to compare the effects of real and sham BCI on motor function and brain activity among patients with subacute stroke with weak wrist extensor strength.<br><br>METHODS: This is a double-blinded randomized controlled trial. Patients with stroke will be categorized into real BCI and sham BCI groups. The BCI task involves wrist extension for 60&#x2009;min/day, 5 times/week for 4&#x2009;weeks. Twenty sessions will be conducted. The evaluation will be conducted four times, as follows: before the intervention, 2&#x2009;weeks after the start of the intervention, immediately after the intervention, and 4&#x2009;weeks after the intervention. The assessments include a clinical evaluation, electroencephalography, and electromyography using motor-evoked potentials.<br><br>DISCUSSION: Patients will be categorized into two groups, as follows: those who will be receiving neurofeedback and those who will not receive this feedback during the BCI rehabilitation training. We will examine the importance of motor imaging feedback, and the effect of patients' continuous participation in the training rather than their being passive.Clinical Trial Registration: KCT0008589.},
}
@article {pmid39141859,
year = {2024},
author = {Chang, EF},
title = {Brain-Computer Interfaces for Restoring Communication.},
journal = {The New England journal of medicine},
volume = {391},
number = {7},
pages = {654-657},
doi = {10.1056/NEJMe2407363},
pmid = {39141859},
issn = {1533-4406},
mesh = {Humans ; *Brain-Computer Interfaces ; Communication ; *Articulation Disorders/etiology/rehabilitation ; *Amyotrophic Lateral Sclerosis/complications/rehabilitation ; },
}
@article {pmid39141854,
year = {2024},
author = {Vansteensel, MJ and Leinders, S and Branco, MP and Crone, NE and Denison, T and Freudenburg, ZV and Geukes, SH and Gosselaar, PH and Raemaekers, M and Schippers, A and Verberne, M and Aarnoutse, EJ and Ramsey, NF},
title = {Longevity of a Brain-Computer Interface for Amyotrophic Lateral Sclerosis.},
journal = {The New England journal of medicine},
volume = {391},
number = {7},
pages = {619-626},
pmid = {39141854},
issn = {1533-4406},
support = {INTENSE, 17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; UH3 NS114439/NS/NINDS NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; UGT7685, Economic Affairs SSM06011 and STW 12803//Netherlands Institute of Government/ ; },
mesh = {Female ; Humans ; Middle Aged ; *Amyotrophic Lateral Sclerosis/complications/diagnostic imaging/rehabilitation ; *Atrophy/diagnostic imaging/etiology/prevention &amp; control ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Time Factors ; Treatment Failure ; Electrodes, Implanted ; },
abstract = {The durability of communication with the use of brain-computer interfaces in persons with progressive neurodegenerative disease has not been extensively examined. We report on 7 years of independent at-home use of an implanted brain-computer interface for communication by a person with advanced amyotrophic lateral sclerosis (ALS), the inception of which was reported in 2016. The frequency of at-home use increased over time to compensate for gradual loss of control of an eye-gaze-tracking device, followed by a progressive decrease in use starting 6 years after implantation. At-home use ended when control of the brain-computer interface became unreliable. No signs of technical malfunction were found. Instead, the amplitude of neural signals declined, and computed tomographic imaging revealed progressive atrophy, which suggested that ALS-related neurodegeneration ultimately rendered the brain-computer interface ineffective after years of successful use, although alternative explanations are plausible. (Funded by the National Institute on Deafness and Other Communication Disorders and others; ClinicalTrials.gov number, NCT02224469.).},
}
@article {pmid39141853,
year = {2024},
author = {Card, NS and Wairagkar, M and Iacobacci, C and Hou, X and Singer-Clark, T and Willett, FR and Kunz, EM and Fan, C and Vahdati Nia, M and Deo, DR and Srinivasan, A and Choi, EY and Glasser, MF and Hochberg, LR and Henderson, JM and Shahlaie, K and Stavisky, SD and Brandman, DM},
title = {An Accurate and Rapidly Calibrating Speech Neuroprosthesis.},
journal = {The New England journal of medicine},
volume = {391},
number = {7},
pages = {609-618},
pmid = {39141853},
issn = {1533-4406},
support = {U01DC17844/DC/NIDCD NIH HHS/United States ; AL220043//Congressionally Directed Medical Research Programs/ ; U01 DC019430/DC/NIDCD NIH HHS/United States ; R01 MH060974/MH/NIMH NIH HHS/United States ; 872146SPI//Simons Foundation/ ; A2295-R//U.S. Department of Veterans Affairs/ ; DP2DC021055/DC/NIDCD NIH HHS/United States ; U01DC019430/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Male ; Middle Aged ; *Amyotrophic Lateral Sclerosis/complications/rehabilitation ; *Brain-Computer Interfaces ; Calibration ; Communication Devices for People with Disabilities ; *Dysarthria/rehabilitation/etiology ; Electrodes, Implanted ; Microelectrodes ; Quadriplegia/etiology/rehabilitation ; *Speech ; },
abstract = {BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.<br><br>METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.<br><br>RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.<br><br>CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).},
}
@article {pmid39140323,
year = {2024},
author = {Nakamura, T and He, X and Hattori, N and Hida, E and Hirata, M},
title = {Dilemma in patients with amyotrophic lateral sclerosis and expectations from brain-machine interfaces.},
journal = {Annals of medicine},
volume = {56},
number = {1},
pages = {2386516},
pmid = {39140323},
issn = {1365-2060},
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/psychology/therapy ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; Aged ; Surveys and Questionnaires ; *Motivation ; *Caregivers/psychology ; *Anxiety/psychology/etiology ; Adult ; Tracheostomy ; Caregiver Burden/psychology ; Locked-In Syndrome/psychology ; },
abstract = {OBJECTIVE: We hypothesized that patients with amyotrophic lateral sclerosis (ALS) face a dilemma between motivation to live and difficulty in living, and brain-machine interfaces (BMIs) can reduce this dilemma. This study aimed to investigate the present situation of patients with ALS and their expectations from BMIs.<br><br>MATERIALS AND METHODS: Our survey design consisted of an anonymous mail-in questionnaire comprising questions regarding the use of tracheostomy positive pressure ventilation (TPPV), motivation to live, anxiety about the totally locked-in state (TLS), anxiety about caregiver burden, and expectations regarding the use of BMI. Primary outcomes were scores for motivation to live and anxiety about caregiver burden and the TLS. Outcomes were evaluated using the visual analogue scale.<br><br>RESULTS: Among 460 participants, 286 (62.6%) were already supported by or had decided to use TPPV. The median scores for motivation to live, anxiety about TLS, and anxiety about caregiver burden were 8.0, 9.0, and 7.0, respectively. Overall, 49% of patients intended to use BMI. Among patients who had refused TPPV, 15.9% intended to use BMI and TPPV. Significant factors for the use of BMI were motivation to live (p = .003), anxiety about TLS (p < .001), younger age (p < .001), and advanced disease stage (p < .001).<br><br>CONCLUSIONS: These results clearly revealed a serious dilemma among patients with ALS between motivation to live and their anxiety about TLS and caregiver burden. Patients expected BMI to reduce this dilemma. Thus, the development of better BMIs may meet these expectations.},
}
@article {pmid39139629,
year = {2024},
author = {Liu, X and Mu, J and Pang, M and Fan, X and Zhou, Z and Guo, F and Yu, K and Yu, H and Ming, D},
title = {A Male Patient with Hydrocephalus via Multimodality Diagnostic Approaches: A Case Report.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {5},
number = {},
pages = {0135},
pmid = {39139629},
issn = {2692-7632},
abstract = {Introduction: Idiopathic normal pressure hydrocephalus (iNPH) is a kind of hydrocephalus that is easily to be misdiagnosed with brain atrophy due to the similarity of ventricular dilation and cognitive impairment. In this case, we present an old male patient who was diagnosed with iNPH by multimodality approaches. Outcomes: A 68-year-old male patient, with deteriorated gait, declined cognitive function for at least 3 years and urinary incontinence for 3 months. The doctors suspected him a patient with hydrocephalus or Alzheimer's disease based on his symptoms. We used multimodality diagnostic approaches including brain imaging, cerebrospinal fluid tap test, continuous intracranial pressure monitoring, and infusion study to make the final diagnosis of iNPH. He underwent ventriculoperitoneal shunt surgery and was well recovered. Conclusion: This case demonstrates the efficacy of using multimodality approaches for iNPH diagnosis, which saves patient time and clinical cost, worthy of further promotion.},
}
@article {pmid39138452,
year = {2024},
author = {Starke, G and Akmazoglu, TB and Colucci, A and Vermehren, M and van Beinum, A and Buthut, M and Soekadar, SR and Bublitz, C and Chandler, JA and Ienca, M},
title = {Qualitative studies involving users of clinical neurotechnology: a scoping review.},
journal = {BMC medical ethics},
volume = {25},
number = {1},
pages = {89},
pmid = {39138452},
issn = {1472-6939},
support = {HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; HYBRIDMIND (SNSF 32NE30_199436; BMBF, 01GP2121A and -B),//ERA-NET NEURON/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; NGBMI (759370)//European Research Council (ERC)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; SSMART (01DR21025A), NEO (13GW0483C), QHMI (03ZU1110DD), QSHIFT (01UX2211) and NeuroQ (13N16486)//Federal Ministry of Research and Education (BMBF)/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; A-2019-558//Einstein Foundation Berlin/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Qualitative Research ; Self Concept ; },
abstract = {BACKGROUND: The rise of a new generation of intelligent neuroprostheses, brain-computer interfaces (BCI) and adaptive closed-loop brain stimulation devices hastens the clinical deployment of neurotechnologies to treat neurological and neuropsychiatric disorders. However, it remains unclear how these nascent technologies may impact the subjective experience of their users. To inform this debate, it is crucial to have a solid understanding how more established current technologies already affect their users. In recent years, researchers have used qualitative research methods to explore the subjective experience of individuals who become users of clinical neurotechnology. Yet, a synthesis of these more recent findings focusing on qualitative methods is still lacking.<br><br>METHODS: To address this gap in the literature, we systematically searched five databases for original research articles that investigated subjective experiences of persons using or receiving neuroprosthetics, BCIs or neuromodulation with qualitative interviews and raised normative questions.<br><br>RESULTS: 36 research articles were included and analysed using qualitative content analysis. Our findings synthesise the current scientific literature and reveal a pronounced focus on usability and other technical aspects of user experience. In parallel, they highlight a relative neglect of considerations regarding agency, self-perception, personal identity and subjective experience.<br><br>CONCLUSIONS: Our synthesis of the existing qualitative literature on clinical neurotechnology highlights the need to expand the current methodological focus as to investigate also non-technical aspects of user experience. Given the critical role considerations of agency, self-perception and personal identity play in assessing the ethical and legal significance of these technologies, our findings reveal a critical gap in the existing literature. This review provides a comprehensive synthesis of the current qualitative research landscape on neurotechnology and the limitations thereof. These findings can inform researchers on how to study the subjective experience of neurotechnology users more holistically and build patient-centred neurotechnology.},
}
@article {pmid39138070,
year = {2024},
author = {Zhou, S and Chen, W and Yang, H},
title = {Dopamine.},
journal = {Trends in endocrinology and metabolism: TEM},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tem.2024.07.005},
pmid = {39138070},
issn = {1879-3061},
}
@article {pmid39137009,
year = {2024},
author = {Denis-Robichaud, J and Rees, EE and Daley, P and Zarowsky, C and Diouf, A and Nasri, BR and de Montigny, S and Carabin, H},
title = {Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada.},
journal = {Journal of medical Internet research},
volume = {26},
number = {},
pages = {e51325},
pmid = {39137009},
issn = {1438-8871},
mesh = {Humans ; *COVID-19/prevention &amp; control ; Cross-Sectional Studies ; Canada ; *Social Media/statistics &amp; numerical data ; Adult ; Male ; Female ; *Public Health ; Middle Aged ; Bayes Theorem ; Young Adult ; Masks/statistics &amp; numerical data ; Aged ; SARS-CoV-2 ; Surveys and Questionnaires ; Adolescent ; Patient Compliance/statistics &amp; numerical data ; Self Report ; Vaccination/statistics &amp; numerical data ; },
abstract = {BACKGROUND: The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised.<br><br>OBJECTIVE: The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter.<br><br>METHODS: Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination.<br><br>RESULTS: Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users.<br><br>CONCLUSIONS: Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.},
}
@article {pmid39136271,
year = {2024},
author = {Tong, L and Zhang, D and Huang, Z and Gao, F and Zhang, S and Chen, F and Liu, C},
title = {Calcium Ion-Coupled Polyphosphates with Different Degrees of Polymerization for Bleeding Control.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {33},
pages = {43244-43256},
doi = {10.1021/acsami.4c06698},
pmid = {39136271},
issn = {1944-8252},
mesh = {Animals ; *Polyphosphates/chemistry/pharmacology ; *Calcium/chemistry ; Rats ; *Hemorrhage/prevention &amp; control/drug therapy ; *Polymerization ; Hemostatics/chemistry/pharmacology ; Blood Coagulation/drug effects ; Rats, Sprague-Dawley ; Male ; Hemostasis/drug effects ; Ions/chemistry ; },
abstract = {The development of efficient hemostatic materials is crucial for achieving rapid hemorrhage control and effective wound healing. Inorganic polyphosphate (polyP) is recognized as an effective modulator of the blood coagulation process. However, the specific effect of polyP chain length on coagulation is not yet fully understood. Furthermore, calcium ions (Ca[2+]) are essential for the coagulation process, promoting multiple enzyme-catalyzed reactions within the coagulation cascade. Hence, calcium ion-coupled polyphosphate powders with three different degrees of polymerization (CaPP-n, n = 20, 50, and 1500) are synthesized by an ion-exchange reaction. CaPP exhibits a crystalline phase at a low polymerization degree and transitions to an amorphous phase as the polymerization degree increases. Notably, the addition of Ca[2+] enhances the wettability of polyP, and CaPP promotes hemostasis, with varying degrees of effectiveness related to chain length. CaPP-50 exhibits the most promising hemostatic performance, with the lowest blood clotting index (BCI, 12.1 ± 0.7%) and the shortest clotting time (302.0 ± 10.5 s). By combining Ca[2+] with polyP of medium-chain length, CaPP-50 demonstrates an enhanced ability to accelerate the adhesion and activation of blood cells, initiate the intrinsic coagulation cascade, and form a stable blood clot, outperforming both CaPP-20 and CaPP-1500. The hemostatic efficacy of CaPP-50 is further validated using rat liver bleeding and femoral artery puncture models. CaPP-50 is proven to possess hemostatic properties comparable to those of commercial calcium-based zeolite hemostatic powder and superior to kaolin. In addition, CaPP-50 exhibits excellent biocompatibility and long-term storage stability. These results suggest that CaPP-50 has significant clinical and commercial potential as an active inorganic hemostatic agent for rapid control of bleeding.},
}
@article {pmid39135208,
year = {2024},
author = {Liu, XY and Song, X and Czosnyka, M and Robba, C and Czosnyka, Z and Summers, JL and Yu, HJ and Gao, GY and Smielewski, P and Guo, F and Pang, MJ and Ming, D},
title = {Congenital hydrocephalus: a review of recent advances in genetic etiology and molecular mechanisms.},
journal = {Military Medical Research},
volume = {11},
number = {1},
pages = {54},
pmid = {39135208},
issn = {2054-9369},
support = {2021YFF1200602//National Key Technologies Research and Development Program/ ; },
mesh = {Humans ; *Hydrocephalus/genetics/etiology ; Animals ; Genetic Predisposition to Disease ; },
abstract = {The global prevalence rate for congenital hydrocephalus (CH) is approximately one out of every five hundred births with multifaceted predisposing factors at play. Genetic influences stand as a major contributor to CH pathogenesis, and epidemiological evidence suggests their involvement in up to 40% of all cases observed globally. Knowledge about an individual's genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making processes. However, the precise genetic etiology has only been pinpointed in fewer than 5% of human instances. More occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci. A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain disorder. This review provides a summary of pertinent genes identified through gene sequencing technologies in humans, in addition to the 4 genes currently associated with CH (two X-linked genes L1CAM and AP1S2, two autosomal recessive MPDZ and CCDC88C). Others predominantly participate in aqueduct abnormalities, ciliary movement, and nervous system development. The prospective CH-related genes revealed through animal model gene-editing techniques are further outlined, focusing mainly on 4 pathways, namely cilia synthesis and movement, ion channels and transportation, Reissner's fiber (RF) synthesis, cell apoptosis, and neurogenesis. Notably, the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid (CSF) circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this condition. So far, only a limited number of CH-associated genes have been identified in humans. The integration of genotype and phenotype for disease diagnosis represents a new trend in the medical field. Animal models provide insights into the pathogenesis of CH and contribute to our understanding of its association with related complications, such as renal cysts, scoliosis, and cardiomyopathy, as these genes may also play a role in the development of these diseases. Genes discovered in animals present potential targets for new treatments but require further validation through future human studies.},
}
@article {pmid39134592,
year = {2024},
author = {Krueger, J and Krauth, R and Reichert, C and Perdikis, S and Vogt, S and Huchtemann, T and Dürschmid, S and Sickert, A and Lamprecht, J and Huremovic, A and Görtler, M and Nasuto, SJ and Tsai, IC and Knight, RT and Hinrichs, H and Heinze, HJ and Lindquist, S and Sailer, M and Millán, JDR and Sweeney-Reed, CM},
title = {Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {18700},
pmid = {39134592},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Neuronal Plasticity ; Female ; *Stroke Rehabilitation/methods ; Middle Aged ; *Stroke/physiopathology/complications ; Aged ; *Electroencephalography ; *Recovery of Function ; *Motor Cortex/physiopathology ; *Transcranial Magnetic Stimulation/methods ; *Evoked Potentials, Motor ; },
abstract = {Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain-computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.},
}
@article {pmid39134021,
year = {2024},
author = {Bashford, L and Rosenthal, IA and Kellis, S and Bjånes, D and Pejsa, K and Brunton, BW and Andersen, RA},
title = {Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {39134021},
issn = {1741-2552},
support = {R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Movement/physiology ; *Parietal Lobe/physiology ; Middle Aged ; Single-Case Studies as Topic ; },
abstract = {Objective.A crucial goal in brain-machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data.Approach.Over a period of 1106 and 871 d respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus).Main results.We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans.Significance.These results can be practically applied to significantly expand the longevity and generalizability of brain-computer interfaces.Clinical TrialsNCT01849822, NCT01958086, NCT01964261.},
}
@article {pmid39133845,
year = {2024},
author = {Wong, MMK and Sha, Z and Lütje, L and Kong, XZ and van Heukelum, S and van de Berg, WDJ and Jonkman, LE and Fisher, SE and Francks, C},
title = {The neocortical infrastructure for language involves region-specific patterns of laminar gene expression.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {34},
pages = {e2401687121},
pmid = {39133845},
issn = {1091-6490},
support = {N/A//Max Planck Instituut voor Psycholingu&#xef;stiek (MAX-PLANCK-INSTITUT)/ ; 024.001.006//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)/ ; },
mesh = {Humans ; *Neocortex/metabolism ; *Language ; Temporal Lobe/metabolism ; Male ; Female ; Schizophrenia/genetics/metabolism ; Neurons/metabolism ; Frontal Lobe/metabolism ; Transcriptome ; Adult ; },
abstract = {The language network of the human brain has core components in the inferior frontal cortex and superior/middle temporal cortex, with left-hemisphere dominance in most people. Functional specialization and interconnectivity of these neocortical regions is likely to be reflected in their molecular and cellular profiles. Excitatory connections between cortical regions arise and innervate according to layer-specific patterns. Here, we generated a gene expression dataset from human postmortem cortical tissue samples from core language network regions, using spatial transcriptomics to discriminate gene expression across cortical layers. Integration of these data with existing single-cell expression data identified 56 genes that showed differences in laminar expression profiles between the frontal and temporal language cortex together with upregulation in layer II/III and/or layer V/VI excitatory neurons. Based on data from large-scale genome-wide screening in the population, DNA variants within these 56 genes showed set-level associations with interindividual variation in structural connectivity between the left-hemisphere frontal and temporal language cortex, and with the brain-related disorders dyslexia and schizophrenia which often involve affected language. These findings identify region-specific patterns of laminar gene expression as a feature of the brain's language network.},
}
@article {pmid39133589,
year = {2024},
author = {Zhang, Z and He, Y and Mai, W and Luo, Y and Li, X and Cheng, Y and Huang, X and Lin, R},
title = {Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2024.3437676},
pmid = {39133589},
issn = {2162-2388},
abstract = {The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manual intervention, such as channel selection and dimensionality reduction, and often struggle to achieve satisfactory classification accuracy. To achieve high classification accuracy and as little manual intervention as possible, a convolutional dynamically convergent differential neural network (ConvDCDNN) is proposed for solving the electroencephalography (EEG) signal classification problem. First, a single-layer convolutional neural network is used to replace the preprocessing steps in previous work. Then, focal loss is used to overcome the imbalance in the dataset. After that, a novel automatic dynamic convergence learning (ADCL) algorithm is proposed and proved for training neural networks. Experimental results on the BCI Competition 2003, BCI Competition III A, and BCI Competition III B datasets demonstrate that the proposed ConvDCDNN framework achieved state-of-the-art performance with accuracies of 100%, 99%, and 98%, respectively. In addition, the proposed algorithm exhibits a higher information transfer rate (ITR) compared with current algorithms.},
}
@article {pmid39133582,
year = {2024},
author = {He, C and Chen, YY and Phang, CR and Chen, IP and Tzou, SC and Jung, TP and Ko, LW},
title = {Exploring Embodied Cognition and Brain Dynamics Under Multi-Tasks Target Detection in Immerse Projector-Based Augmented Reality (IPAR) Scenarios.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3476-3485},
doi = {10.1109/TNSRE.2024.3442241},
pmid = {39133582},
issn = {1558-0210},
mesh = {Humans ; Male ; *Electroencephalography ; Female ; *Cognition/physiology ; *Augmented Reality ; Adult ; Young Adult ; *Walking/physiology ; Brain/physiology ; Multitasking Behavior/physiology ; Standing Position ; Wireless Technology ; Attention/physiology ; Healthy Volunteers ; Theta Rhythm/physiology ; Beta Rhythm/physiology ; Brain-Computer Interfaces ; },
abstract = {Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.},
}
@article {pmid39132484,
year = {2024},
author = {Kunigk, NG and Schone, HR and Gontier, C and Hockeimer, W and Tortolani, AF and Hatsopoulos, NG and Downey, JE and Chase, SM and Boninger, ML and Dekleva, BD and Collinger, JL},
title = {Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {39132484},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
abstract = {The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g., reaching vs. wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.},
}
@article {pmid39131629,
year = {2024},
author = {Huang, Z and Zhang, D and Tong, L and Gao, F and Zhang, S and Wang, X and Xie, Y and Chen, F and Liu, C},
title = {Protonated-chitosan sponge with procoagulation activity for hemostasis in coagulopathy.},
journal = {Bioactive materials},
volume = {41},
number = {},
pages = {174-192},
pmid = {39131629},
issn = {2452-199X},
abstract = {Hemostatic materials are essential for managing acute bleeding in medical settings. Chitosan (CS) shows promise in hemostasis but its underlying mechanism remains incompletely understood. We unexpectedly discovered that certain protonated-chitosan (PCS) rapidly assembled plasma proteins to form protein membrane (PM) upon contact with platelet-poor plasma (PPP). We hypothesized that the novel observation was intricately related to the procoagulant effect of chitosan. Herein, the study aimed to elucidate the conditions necessary and mechanism for PM formation, identify the proteins within the PM and PCS's procoagulant action at the molecule levels. We confirmed that the amount of -NH3 [+] groups (>4.9 mmol/g) on PCS molecules played a crucial role in promoting coagulation. The -NH3 [+] group interacted with blood's multiple active components to exert hemostatic effects: assembling plasma proteins including coagulation factors such as FII, FV, FX, activating blood cells and promoting the secretion of coagulation-related substances (FV, ADP, etc) by platelets. Notably, the hemostatic mechanism can be extended to protonated-chitosan derivatives like quaternized, alkylated, and catechol-chitosan. In the blood clotting index (BCI) experiment, compared to other groups, PCS95 achieved the lowest BCI value (∼6 %) within 30 s. Protonated-chitosan exhibited excellent biocompatibility and antibacterial properties, with PCS95 demonstrating inhibition effectiveness of over 95 % against Escherichia coli (E.coil) and Staphylococcus aureus (S. aureus). Moreover, PCS performed enhanced hemostatic effectiveness over chitosan-based commercially agents (Celox™ and ChitoGauze®XR) in diverse bleeding models. In particular, PCS95 reduced bleeding time by 70 % in rabbit models of coagulopathy. Overall, this study investigated the coagulation mechanism of materials at the molecular level, paving the way for innovative approaches in designing new hemostatic materials.},
}
@article {pmid39131333,
year = {2024},
author = {Tortolani, AF and Kunigk, NG and Sobinov, AR and Boninger, ML and Bensmaia, SJ and Collinger, JL and Hatsopoulos, NG and Downey, JE},
title = {How different immersive environments affect intracortical brain computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {39131333},
issn = {2692-8205},
support = {R01 NS130302/NS/NINDS NIH HHS/United States ; T32 NS121763/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
abstract = {As brain-computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, either viewed immersively through virtual reality goggles or at a distance on a flat television monitor. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience.},
}
@article {pmid39130131,
year = {2024},
author = {Ferro, MD and Proctor, CM and Gonzalez, A and Jayabal, S and Zhao, E and Gagnon, M and Slézia, A and Pas, J and Dijk, G and Donahue, MJ and Williamson, A and Raymond, J and Malliaras, GG and Giocomo, L and Melosh, NA},
title = {NeuroRoots, a bio-inspired, seamless brain machine interface for long-term recording in delicate brain regions.},
journal = {AIP advances},
volume = {14},
number = {8},
pages = {085109},
pmid = {39130131},
issn = {2158-3226},
support = {R56 MH106475/MH/NIMH NIH HHS/United States ; R01 MH106475/MH/NIMH NIH HHS/United States ; R01 NS072406/NS/NINDS NIH HHS/United States ; R01 DC004154/DC/NIDCD NIH HHS/United States ; R21 NS104861/NS/NINDS NIH HHS/United States ; R21 EY026365/EY/NEI NIH HHS/United States ; },
abstract = {Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain-machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain-machine interfacing.},
}
@article {pmid39127752,
year = {2024},
author = {Wei, W and Wang, K and Qiu, S and He, H},
title = {A MultiModal Vigilance (MMV) dataset during RSVP and SSVEP brain-computer interface tasks.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {867},
pmid = {39127752},
issn = {2052-4463},
mesh = {Humans ; Male ; Attention ; *Brain-Computer Interfaces ; Electrocardiography ; *Electroencephalography ; Electromyography ; Electrooculography ; *Evoked Potentials, Visual ; Eye Movements ; Female ; Young Adult ; Adult ; },
abstract = {Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.},
}
@article {pmid39124036,
year = {2024},
author = {Kabir, MH and Akhtar, NI and Tasnim, N and Miah, ASM and Lee, HS and Jang, SW and Shin, J},
title = {Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {15},
pages = {},
pmid = {39124036},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; },
abstract = {The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.},
}
@article {pmid39123966,
year = {2024},
author = {Zhou, S and Zhang, P and Chen, H},
title = {Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {15},
pages = {},
pmid = {39123966},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; Cluster Analysis ; *Brain-Computer Interfaces ; Epilepsy/diagnosis/physiopathology ; Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; },
abstract = {Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.},
}
@article {pmid39120996,
year = {2024},
author = {Li, Y and Zhu, X and Qi, Y and Wang, Y},
title = {Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {39120996},
issn = {2050-084X},
support = {no. 62336007//National Natural Science Foundation of China/ ; no. 2022C03011//Key Research and Development Program of Zhejiang/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; },
mesh = {*Motor Cortex/physiology ; Animals ; *Macaca mulatta/physiology ; *Neurons/physiology ; Behavior, Animal/physiology ; Male ; Movement/physiology ; },
abstract = {In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.},
}
@article {pmid39120991,
year = {2024},
author = {He, X and Allison, BZ and Qin, K and Liang, W and Wang, X and Cichocki, A and Jin, J},
title = {Leveraging Transfer Superposition Theory for Stable-State Visual Evoked Potential Cross-Subject Frequency Recognition.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {11},
pages = {3071-3084},
doi = {10.1109/TBME.2024.3406603},
pmid = {39120991},
issn = {1558-2531},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Young Adult ; Female ; },
abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the necessary calibration procedures take time, cause visual fatigue and reduce usability. For the calibration-free scenario, we propose a cross-subject frequency identification method based on transfer superimposed theory for SSVEP frequency decoding. First, a multi-channel signal decomposition model was constructed. Next, we used the cross least squares iterative method to create individual specific transfer spatial filters as well as source subject transfer superposition templates in the source subject. Then, we identified common knowledge among source subjects using a prototype spatial filter to make common transfer spatial filters and common impulse responses. Following, we reconstructed a global transfer superimposition template with SSVEP frequency characteristics. Finally, an ensemble cross-subject transfer learning method was proposed for SSVEP frequency recognition by combining the source-subject transfer mode, the global transfer mode, and the sine-cosine reference template. Offline tests on two public datasets show that the proposed method significantly outperforms the FBCCA, TTCCA, and CSSFT methods. More importantly, the proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm was robust, which is important for a practical BCI.},
}
@article {pmid39119268,
year = {2024},
author = {Li, P and Kim, S and Tian, B},
title = {Beyond 25 years of biomedical innovation in nano-bioelectronics.},
journal = {Device},
volume = {2},
number = {7},
pages = {},
pmid = {39119268},
issn = {2666-9986},
support = {R56 EB034289/EB/NIBIB NIH HHS/United States ; },
abstract = {Nano-bioelectronics, which blend the precision of nanotechnology with the complexity of biological systems, are evolving with innovations such as silicon nanowires, carbon nanotubes, and graphene. These elements serve applications from biochemical sensing to brain-machine interfacing. This review examines nano-bioelectronics' role in advancing biomedical interventions and discusses their potential in environmental monitoring, agricultural productivity, energy efficiency, and creative fields. The field is transitioning from molecular to ecosystem-level applications, with research exploring complex cellular mechanisms and communication. This fosters understanding of biological interactions at various levels, such as suggesting transformative approaches for ecosystem management and food security. Future research is expected to focus on refining nano-bioelectronic devices for integration with biological systems and on scalable manufacturing to broaden their reach and functionality.},
}
@article {pmid39117588,
year = {2024},
author = {Cassinadri, G and Ienca, M},
title = {Non-voluntary BCI explantation: assessing possible neurorights violations in light of contrasting mental ontologies.},
journal = {Journal of medical ethics},
volume = {},
number = {},
pages = {},
doi = {10.1136/jme-2023-109830},
pmid = {39117588},
issn = {1473-4257},
abstract = {In research involving patients with implantable brain-computer interfaces (BCIs), there is a regulatory gap concerning post-trial responsibilities and duties of sponsors and investigators towards implanted patients. In this article, we analyse the case of patient R, who underwent non-voluntary explantation of an implanted BCI, causing a discontinuation in her sense of agency and self. To clarify the post-trial duties and responsibilities involved in this case, we first define the ontological status of the BCI using both externalist (EXT) and internalist (INT) theories of cognition. We then give particular focus to the theories of extended and embedded cognition, hence considering the BCI either as a constitutive component of the patient's mind or as a causal supporter of her brain-based cognitive capacities. We argue that patient R can legitimately be considered both as an embedded and extended cognitive agent. Then, we analyse whether the non-voluntary explantation violated patient R's (neuro)rights to cognitive liberty, mental integrity, psychological continuity and mental privacy. We analyse whether and how different mental ontologies may imply morally relevant differences in interpreting these prima facie neurorights violations and the correlational duties of sponsors and investigators. We conclude that both mental ontologies support the identification of emerging neurorights of the patient and give rise to post-trial obligations of sponsors and investigators to provide for continuous technical maintenance of implanted BCIs that play a significant role in patients' agency and sense of self. However, we suggest that externalist mental ontologies better capture patient R's self-conception and support the identification of a more granular form of mental harm and associated neurorights violation, thus eliciting stricter post-trial obligations.},
}
@article {pmid39116892,
year = {2024},
author = {Xiong, H and Li, J and Liu, J and Song, J and Han, Y},
title = {Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6cf5},
pmid = {39116892},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Deep Learning ; Wavelet Analysis ; },
abstract = {Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.},
}
@article {pmid39116878,
year = {2024},
author = {Wei, B and Cheng, G and Bi, Q and Lu, C and Sun, Q and Li, L and Chen, N and Hu, M and Lu, H and Xu, X and Mao, G and Wan, S and Hu, Z and Gu, Y and Zheng, J and Zhao, L and Shen, XZ and Liu, X and Shi, P},
title = {Microglia in the hypothalamic paraventricular nucleus sense hemodynamic disturbance and promote sympathetic excitation in hypertension.},
journal = {Immunity},
volume = {57},
number = {9},
pages = {2030-2042.e8},
doi = {10.1016/j.immuni.2024.07.011},
pmid = {39116878},
issn = {1097-4180},
mesh = {*Paraventricular Hypothalamic Nucleus/metabolism ; Animals ; *Microglia/metabolism ; *Hypertension/physiopathology ; Mice ; *Hemodynamics ; *Sympathetic Nervous System/physiopathology ; Male ; Mice, Inbred C57BL ; Adenosine Triphosphate/metabolism ; Receptors, Purinergic P2Y12/metabolism ; Inflammation/immunology ; Blood Pressure ; Neurons/metabolism ; },
abstract = {Hypertension is usually accompanied by elevated sympathetic tonicity, but how sympathetic hyperactivity is triggered is not clear. Recent advances revealed that microglia-centered neuroinflammation contributes to sympathetic excitation in hypertension. In this study, we performed a temporospatial analysis of microglia at both morphological and transcriptomic levels and found that microglia in the hypothalamic paraventricular nucleus (PVN), a sympathetic center, were early responders to hypertensive challenges. Vasculature analyses revealed that the PVN was characterized by high capillary density, thin vessel diameter, and complex vascular topology relative to other brain regions. As such, the PVN was susceptible to the penetration of ATP released from the vasculature in response to hemodynamic disturbance after blood pressure increase. Mechanistically, ATP ligation to microglial P2Y12 receptor was responsible for microglial inflammatory activation and the eventual sympathetic overflow. Together, these findings identified a distinct vasculature pattern rendering vulnerability of PVN pre-sympathetic neurons to hypertension-associated microglia-mediated inflammatory insults.},
}
@article {pmid39116374,
year = {2024},
author = {Aboubakr, O and Houillier, C and Alentorn, A and Choquet, S and Dupont, S and Mokhtari, K and Leclercq, D and Nichelli, L and Kas, A and Rozenblum, L and Le Garff-Tavernier, M and Hoang-Xuan, K and Carpentier, A and Mathon, B},
title = {Epilepsy in Patients With Primary CNS Lymphoma: Prevalence, Risk Factors, and Prognostic Significance.},
journal = {Neurology},
volume = {103},
number = {5},
pages = {e209748},
doi = {10.1212/WNL.0000000000209748},
pmid = {39116374},
issn = {1526-632X},
mesh = {Humans ; Male ; Female ; Aged ; Retrospective Studies ; Middle Aged ; Risk Factors ; Prevalence ; Prognosis ; *Epilepsy/epidemiology ; *Central Nervous System Neoplasms/epidemiology/complications ; Lymphoma/epidemiology/complications ; Adult ; Aged, 80 and over ; },
abstract = {BACKGROUND AND OBJECTIVES: Epilepsy is a common comorbidity of brain tumors; however, little is known about the prevalence, onset time, semiology, and risk factors of seizures in primary CNS lymphoma (PCNSL). Our objectives were to determine the prevalence of epilepsy in PCNSL, to identify factors associated with epilepsy, and to investigate the prognostic significance of seizures in PCNSL.<br><br>METHODS: We performed an observational, retrospective single-center study at a tertiary neuro-oncology center (2011-2023) including immunocompetent patients with PCNSL and no history of seizures. We collected clinical, imaging, and treatment data; seizure status over the course of PCNSL; and oncological and seizure outcome. The primary outcome was to determine the prevalence of epilepsy. Furthermore, we aimed to identify clinical, radiologic, and treatment-related factors associated with epilepsy. Univariate analyses were conducted using the &#x3c7;[2] test for categorical variables and unpaired t test for continuous variables. Predictors identified in the unadjusted analysis were included in backward stepwise logistic regression models.<br><br>RESULTS: We included 330 patients, 157 (47.6%) were male, median age at diagnosis was 68 years, and the median Karnofsky Performance Status score was 60. Eighty-three (25.2%) patients had at least 1 seizure from initial diagnosis to the last follow-up, 40 (12.1%) as the onset symptom, 16 (4.8%) during first line of treatment, 27 (8.2%) at tumor progression and 6 (1.8%) while in remission. Focal aware seizures were the most frequent seizure type, occurring in 43 (51.8%) patients. Seizure freedom under antiseizure medication was observed in 97.6% patients. Cortical contact (odds ratio [OR] 8.6, 95% CI 4.2-15.5, p < 0.001) and a higher proliferation index (OR 5.7, 95% CI 1.3-26.2, p = 0.02) were identified as independent risk factors of epilepsy. Patients with PCNSL and epilepsy had a significantly shorter progression-free survival (median progression-free survival 9.6 vs 14.1 months, adjusted hazard ratio 1.4, 95% CI 1.0-1.9, p = 0.03), but not a significantly shorter overall survival (17 vs 44.1 months, log-rank test, p = 0.09).<br><br>DISCUSSION: Epilepsy affects a quarter of patients with PCNSL, with half experiencing it at the time of initial presentation and potentially serving as a marker of disease progression. Further research is necessary to assess the broader applicability of these findings because they are subject to the constraints of a retrospective design and tertiary center setting.},
}
@article {pmid39116252,
year = {2024},
author = {Chen, M and Ma, S and Liu, H and Dong, Y and Tang, J and Ni, Z and Tan, Y and Duan, C and Li, H and Huang, H and Li, Y and Cao, X and Lingle, CJ and Yang, Y and Hu, H},
title = {Brain region-specific action of ketamine as a rapid antidepressant.},
journal = {Science (New York, N.Y.)},
volume = {385},
number = {6709},
pages = {eado7010},
pmid = {39116252},
issn = {1095-9203},
support = {R35 GM118114/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Male ; Mice ; *Antidepressive Agents/pharmacology ; Brain-Derived Neurotrophic Factor/metabolism/genetics ; *Depression/drug therapy/metabolism ; *Habenula/drug effects/metabolism ; Hippocampus/drug effects/metabolism ; *Ketamine/pharmacology/administration &amp; dosage ; Mice, Inbred C57BL ; Mice, Knockout ; Pyramidal Cells/drug effects/metabolism ; *Receptors, N-Methyl-D-Aspartate/antagonists &amp; inhibitors/genetics ; Serotonin/metabolism ; },
abstract = {Ketamine has been found to have rapid and potent antidepressant activity. However, despite the ubiquitous brain expression of its molecular target, the N-methyl-d-aspartate receptor (NMDAR), it was not clear whether there is a selective, primary site for ketamine's antidepressant action. We found that ketamine injection in depressive-like mice specifically blocks NMDARs in lateral habenular (LHb) neurons, but not in hippocampal pyramidal neurons. This regional specificity depended on the use-dependent nature of ketamine as a channel blocker, local neural activity, and the extrasynaptic reservoir pool size of NMDARs. Activating hippocampal or inactivating LHb neurons swapped their ketamine sensitivity. Conditional knockout of NMDARs in the LHb occluded ketamine's antidepressant effects and blocked the systemic ketamine-induced elevation of serotonin and brain-derived neurotrophic factor in the hippocampus. This distinction of the primary versus secondary brain target(s) of ketamine should help with the design of more precise and efficient antidepressant treatments.},
}
@article {pmid39115894,
year = {2025},
author = {Hu, B and Lu, D and Meng, L and Zhang, Y},
title = {When time theft promotes performance: Measure development and validation of time theft motives.},
journal = {The Journal of applied psychology},
volume = {110},
number = {2},
pages = {256-281},
doi = {10.1037/apl0001229},
pmid = {39115894},
issn = {1939-1854},
support = {//National Natural Science Foundation of China/ ; //Shanghai Philosophy and Social Science Planning Project/ ; //Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
mesh = {Humans ; *Motivation ; Adult ; Male ; *Employment/psychology ; Female ; *Psychometrics/instrumentation/standards ; *Work Performance ; Young Adult ; Middle Aged ; },
abstract = {The prevailing viewpoint has long depicted employee time theft as inherently detrimental. However, this perspective may stem from a limited understanding of the underlying motives that drive such behavior. Time theft can paradoxically be motivated by neutral and even laudable intentions, such as promoting work efficiency, thus rendering it potentially beneficial and constructive. Across three mixed-methods studies, we explore the motives behind employee time theft, develop and validate an instrument to assess these motives, and examine how they differentially predict time theft behavior. Specifically, in Study 1, we use a qualitative method and identify 11 types of time theft motives. Study 2 embarks on the development of measures of these motives, subsequently validating their factor structure. Study 3 examines their incremental variance in predicting time theft behavior by controlling for personality and demographic variables. Overall, these studies reveal that employees' engagement in time theft can be driven not solely by self-oriented motives but also by others- and work-oriented motives. Further, each of these motives provides incremental value in understanding time theft behavior. Implications for both research and practice emanating from these findings are also discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).},
}
@article {pmid39115403,
year = {2024},
author = {Jeong, E and Seo, M and Kim, KS},
title = {Design of an fNIRS-EEG hybrid terminal for wearable BCI systems.},
journal = {The Review of scientific instruments},
volume = {95},
number = {8},
pages = {},
doi = {10.1063/5.0187070},
pmid = {39115403},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Spectroscopy, Near-Infrared/instrumentation/methods ; *Electroencephalography/instrumentation ; Humans ; *Wearable Electronic Devices ; Equipment Design ; },
abstract = {The importance of brain-computer interfaces (BCI) is increasing, and various methods have been developed. Among the developed BCI methods, functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are favored due to their non-invasive feature and compact device sizes. EEG monitors the electrical potentials generated by the activation of neurons, and fNIRS monitors the blood flow also generated by neurons, resulting in signals with different properties between the two methods. As the two BCI methods greatly differ in the characteristics of the acquired neural activity signals, for cases of estimating the intention or thought of a subject by BCI, it has been proven that further accurate information may be extracted by utilizing both methods simultaneously. Both systems are powered by electricity, and as EEG systems are greatly sensitive to electrical noises, application of two separate fNIRS and EEG systems together may result in electrical interference as the systems are required to be in contact with the skin and stray currents from the fNIRS system may flow along the surface of the skin into the EEG system. This research proposes a wearable fNIRS-EEG hybrid BCI system, where a single terminal is capable of operating both as a continuous wave fNIRS emitter and as a detector, and also as an EEG electrode. The system has been designed such that the fNIRS and EEG components are electrically separated to avoid electrical interference between each other. It is expected that by utilizing the developed fNIRS-EEG hybrid terminals, the development of BCI analysis may be further accelerated in various fields.},
}
@article {pmid39114591,
year = {2024},
author = {Berkmush-Antipova, A and Syrov, N and Yakovlev, L and Miroshnikov, A and Golovanov, F and Shusharina, N and Kaplan, A},
title = {Yes or no? A study of ErrPs in the "guess what I am thinking" paradigm with stimuli of different visual content.},
journal = {Frontiers in psychology},
volume = {15},
number = {},
pages = {1394496},
pmid = {39114591},
issn = {1664-1078},
abstract = {Error-related potentials (ErrPs) have attracted attention in part because of their practical potential for building brain-computer interface (BCI) paradigms. BCIs, facilitating direct communication between the brain and machines, hold great promise for brain-AI interaction. Therefore, a comprehensive understanding of ErrPs is crucial to ensure reliable BCI outcomes. In this study, we investigated ErrPs in the context of the "guess what I am thinking" paradigm. 23 healthy participants were instructed to imagine an object from a predetermined set, while an algorithm randomly selected another object that was either the same as or different from the imagined object. We recorded and analyzed the participants' EEG activity to capture their mental responses to the algorithm's "predictions". The study identified components distinguishing correct from incorrect responses. It discusses their nature and how they differ from ErrPs extensively studied in other BCI paradigms. We observed pronounced variations in the shape of ErrPs across different stimulus sets, underscoring the significant influence of visual stimulus appearance on ErrP peaks. These findings have implications for designing effective BCI systems, especially considering the less conventional BCI paradigm employed. They emphasize the necessity of accounting for stimulus factors in BCI development.},
}
@article {pmid39113778,
year = {2024},
author = {Bartkowiak, J and Agarwal, V and Lebehn, M and Nazif, TM and George, I and Kodali, SK and Vahl, TP and Hahn, RT},
title = {Strain assessment in patients with aortic regurgitation undergoing transcatheter aortic valve implantation: case series.},
journal = {European heart journal. Case reports},
volume = {8},
number = {8},
pages = {ytae261},
pmid = {39113778},
issn = {2514-2119},
abstract = {BACKGROUND: Limited data exist on strain changes after transcatheter aortic valve implantation (TAVI) in patients with aortic regurgitation (AR).<br><br>CASE SUMMARY: Three patients with AR undergoing TAVI showed an initial reduction in global longitudinal strain (GLS), followed by sustained GLS improvement within the first year.<br><br>DISCUSSION: Findings align with those of surgically treated patients with AR. There is a possible superiority of GLS to left ventricular end-diastolic diameter ratio in assessing patients with severe volume overload.},
}
@article {pmid39112576,
year = {2024},
author = {Naddaf, M and Drew, L},
title = {Second brain implant by Elon Musk's Neuralink: will it fare better than the first?.},
journal = {Nature},
volume = {632},
number = {8025},
pages = {481-482},
doi = {10.1038/d41586-024-02368-8},
pmid = {39112576},
issn = {1476-4687},
mesh = {Humans ; *Brain/surgery ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Spinal Cord Injuries/surgery/therapy ; Treatment Outcome ; },
}
@article {pmid39112461,
year = {2024},
author = {Qiu, L and Liang, C and Kochunov, P and Hutchison, KE and Sui, J and Jiang, R and Zhi, D and Vergara, VM and Yang, X and Zhang, D and Fu, Z and Bustillo, JR and Qi, S and Calhoun, VD},
title = {Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {326},
pmid = {39112461},
issn = {2158-3188},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; 62376124//National Natural Science Foundation of China (National Science Foundation of China)/ ; RF1 MH123163/MH/NIMH NIH HHS/United States ; R01 AA012207/AA/NIAAA NIH HHS/United States ; BK20220889//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; S10 OD023696/OD/NIH HHS/United States ; R01MH118695//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; R01 MH118695/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Female ; Male ; Adult ; *Psychotic Disorders/diagnostic imaging ; *Tobacco Use/adverse effects ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Young Adult ; Depressive Disorder, Major/diagnostic imaging ; Middle Aged ; Multimodal Imaging ; Alcohol Drinking/adverse effects ; Neuroimaging ; Adolescent ; Autism Spectrum Disorder/diagnostic imaging ; },
abstract = {People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol and tobacco use. However, the further associations between alcohol/tobacco use and symptoms/cognition in these disorders remain unexplored. We identified multimodal brain networks involving alcohol use (n = 707) and tobacco use (n = 281) via supervised multimodal fusion and evaluated if these networks affected symptoms and cognition in people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n = 178/134/143), depressive (major depressive disorder, n = 260) and developmental (autism spectrum disorder/attention deficit hyperactivity disorder, n = 421/346) disorders. Alcohol and tobacco use scores were used as references to guide functional and structural imaging fusion to identify alcohol/tobacco use associated multimodal patterns. Correlation analyses between the extracted brain features and symptoms or cognition were performed to evaluate the relationships between alcohol/tobacco use with symptoms/cognition in 6 psychiatric disorders. Results showed that (1) the default mode network (DMN) and salience network (SN) were associated with alcohol use, whereas the DMN and fronto-limbic network (FLN) were associated with tobacco use; (2) the DMN and fronto-basal ganglia (FBG) related to alcohol/tobacco use were correlated with symptom and cognition in psychosis; (3) the middle temporal cortex related to alcohol/tobacco use was associated with cognition in depression; (4) the DMN related to alcohol/tobacco use was related to symptom, whereas the SN and limbic system (LB) were related to cognition in developmental disorders. In summary, alcohol and tobacco use were associated with structural and functional abnormalities in DMN, SN and FLN and had significant associations with cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.},
}
@article {pmid39111412,
year = {2024},
author = {Tian, H},
title = {Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance.},
journal = {Journal of neuroscience methods},
volume = {411},
number = {},
pages = {110240},
doi = {10.1016/j.jneumeth.2024.110240},
pmid = {39111412},
issn = {1872-678X},
mesh = {Humans ; *Upper Extremity/physiology ; *Brain-Computer Interfaces ; *Robotics/instrumentation ; *Imagination/physiology ; Male ; Adult ; Female ; Deep Learning ; Young Adult ; Electroencephalography/methods ; Neural Networks, Computer ; Algorithms ; },
abstract = {BACKGROUND: Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.<br><br>NEW METHODS: In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.<br><br>Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.<br><br>RESULTS: To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7&#x202f;%.<br><br>CONCLUSION: This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.},
}
@article {pmid39111203,
year = {2024},
author = {Suwandjieff, P and Müller-Putz, GR},
title = {EEG Analyses of visual cue effects on executed movements.},
journal = {Journal of neuroscience methods},
volume = {410},
number = {},
pages = {110241},
doi = {10.1016/j.jneumeth.2024.110241},
pmid = {39111203},
issn = {1872-678X},
mesh = {Humans ; *Cues ; Adult ; Young Adult ; Male ; Female ; *Electroencephalography/methods ; Adolescent ; Psychomotor Performance/physiology ; Movement/physiology ; Brain/physiology ; Visual Perception/physiology ; Hand/physiology ; Photic Stimulation/methods ; Motor Activity/physiology ; },
abstract = {BACKGROUND: In electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks.<br><br>NEW METHOD: To address this concern, we introduced four new visual cues (Fade, Rotation, Reference, and Star) and investigated their impact on brain signals. Our objective was to identify a cue that minimizes its influence on brain activity, facilitating cue-effect free classifier training for asynchronous applications, particularly aiding individuals with severe paralysis.<br><br>RESULTS: 22 able-bodied, right-handed participants aged 18-30 performed hand movements upon presentation of the visual cues. Analysis of time-variability between movement onset and cue-aligned data, grand average MRCP, and classification outcomes revealed significant differences among cues. Rotation and Reference cue exhibited favorable results in minimizing temporal variability, maintaining MRCP patterns, and achieving comparable accuracy to self-paced signals in classification.<br><br>Our study contrasts with traditional cue-based paradigms by introducing novel visual cues designed to mitigate unintended neural activity. We demonstrate the effectiveness of Rotation and Reference cue in eliciting consistent and accurate MRCPs during motor tasks, surpassing previous methods in achieving precise timing and high discriminability for classifier training.<br><br>CONCLUSIONS: Precision in cue timing is crucial for training classifiers, where both Rotation and Reference cue demonstrate minimal variability and high discriminability, highlighting their potential for accurate classifications in online scenarios. These findings offer promising avenues for refining brain-computer interface systems, particularly for individuals with motor impairments, by enabling more reliable and intuitive control mechanisms.},
}
@article {pmid39110623,
year = {2024},
author = {Sun, Y and Liang, L and Li, Y and Chen, X and Gao, X},
title = {Dual-Alpha: a large EEG study for dual-frequency SSVEP brain-computer interface.},
journal = {GigaScience},
volume = {13},
number = {},
pages = {},
pmid = {39110623},
issn = {2047-217X},
support = {U2241208//National Natural Science Foundation of China/ ; 2023YFF1205300//National Key Research and Development Program of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual ; *Electroencephalography ; Male ; Female ; Adult ; Young Adult ; Algorithms ; },
abstract = {BACKGROUND: The domain of brain-computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field.<br><br>FINDINGS: This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks.<br><br>CONCLUSIONS: The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.},
}
@article {pmid39110554,
year = {2025},
author = {Wei, W and Li, X and Qiu, S and He, H},
title = {Preliminary Study on Rapid Serial Visualization Presentation Multi-Class Target EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {1},
pages = {90-101},
doi = {10.1109/TBME.2024.3439820},
pmid = {39110554},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Signal Processing, Computer-Assisted ; Young Adult ; Algorithms ; Brain/physiology ; },
abstract = {OBJECTIVE: Brain-Computer Interface (BCI) provides a direct communication channel between the brain and external devices. After combining with the Rapid Serial Visualization Presentation (RSVP) paradigm, the RSVP-BCI system can be utilized for human vision-based fast information retrieval. Currently only binary classification of single-trial EEG can be achieved, also the research on the multi-class target RSVP is few, which limited information transfer rate and the application scenarios of the system. In this paper, we focus on the RSVP multi-class target image retrieval task that contains two classes of targets for achieving triple classification for RSVP-EEG.<br><br>METHODS: Designed two experiments, each containing two tasks with different task difficulties. We recruited 30 subjects to participate in the experiments, collected EEG data, and made the data publicly available. Moreover, we conducted behavioral analysis, ERP analysis, and proposed a model, MDCNet, for EEG classification to study the feasibility of multi-class target RSVP and the impact of task difficulty.<br><br>RESULTS: The experimental results indicated that (1) RSVP-EEG classification that includes non-target and 2-class target is feasibility; (2) the different targets in the same task will evoke P300 with the same latency and different amplitudes, and the hit rate of the target in EEG classification is positively correlated with its amplitude; (3) the information hidden in the time dimension play an important role in EEG classification; (4) the harder the task is, the latency of P300 is longer.<br><br>CONCLUSION/SIGNIFICANCE: The experimental analysis obtained meaningful results, which provided a theoretical basis for subsequent research.},
}
@article {pmid39110553,
year = {2025},
author = {Blanco-Diaz, CF and Serafini, ERDS and Bastos-Filho, T and Dantas, AFOA and Santo, CCDE and Delisle-Rodriguez, D},
title = {A Gait Imagery-Based Brain-Computer Interface With Visual Feedback for Spinal Cord Injury Rehabilitation on Lokomat.},
journal = {IEEE transactions on bio-medical engineering},
volume = {72},
number = {1},
pages = {102-111},
doi = {10.1109/TBME.2024.3440036},
pmid = {39110553},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Adult ; Male ; Female ; *Gait/physiology ; *Feedback, Sensory/physiology ; *Imagination/physiology ; Neurofeedback/methods ; Young Adult ; Signal Processing, Computer-Assisted ; Middle Aged ; },
abstract = {OBJECTIVE: Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been proposed for the rehabilitation of people with disabilities, being a big challenge their successful application to restore motor functions in individuals with Spinal Cord Injury (SCI). This work proposes an Electroencephalography (EEG) gait imagery-based BCI to promote motor recovery on the Lokomat platform, in order to allow a clinical intervention by acting simultaneously on both central and peripheral nervous mechanisms.<br><br>METHODS: As a novelty, our BCI system accurately discriminates gait imagery tasks during walking and further provides a multi-channel EEG-based Visual Neurofeedback (VNFB) linked to (8-12 Hz) and (15-20 Hz) rhythms around Cz. VNFB is carried out through a cluster analysis strategy-based Euclidean distance, where the weighted mean MI feature vector is used as a reference to teach individuals with SCI to modulate their cortical rhythms.<br><br>RESULTS: The developed BCI reached an average classification accuracy of 74.4%. In addition, feature analysis demonstrated a reduction in cluster variance after several sessions, whereas metrics associated with self-modulation indicated a greater distance between both classes: passive walking with gait MI and passive walking without MI.<br><br>CONCLUSION: The results suggest that intervention with a gait MI-based BCI with VNFB may allow the individuals to appropriately modulate their rhythms of interest around Cz.<br><br>SIGNIFICANCE: This work contributes to the development of advanced systems for gait rehabilitation by integrating Machine Learning and neurofeedback techniques, to restore lower-limb functions of SCI individuals.},
}
@article {pmid39107957,
year = {2024},
author = {Xu, P and Liu, Y and Wang, J and Zhang, A and Wang, K and Wang, Z and Fang, Y and Wang, X and Zhang, J},
title = {Gender-specific prognosis models reveal differences in subarachnoid hemorrhage patients between sexes.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {8},
pages = {e14894},
pmid = {39107957},
issn = {1755-5949},
support = {LGD22H090001//Natural Science Foundation of Zhejiang Province/ ; 81870916//National Natural Science Foundation of China/ ; 82071287//National Natural Science Foundation of China/ ; 82201430//National Natural Science Foundation of China/ ; 82271301//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Subarachnoid Hemorrhage/diagnosis ; Male ; Female ; Middle Aged ; Prognosis ; Aged ; *Sex Characteristics ; Adult ; Retrospective Studies ; },
abstract = {BACKGROUND: Subarachnoid hemorrhage (SAH) represents a severe stroke subtype. Our study aims to develop gender-specific prognostic prediction models derived from distinct prognostic factors observed among different-gender patients.<br><br>METHODS: Inclusion comprised SAH-diagnosed patients from January 2014 to March 2016 in our institution. Collected data encompassed patients' demographics, admission severity, treatments, imaging findings, and complications. Three-month post-discharge prognoses were obtained via follow-ups. Analyses assessed gender-based differences in patient information. Key factors underwent subgroup analysis, followed by univariate and multivariate analyses to identify gender-specific prognostic factors and establish/validate gender-specific prognostic models.<br><br>RESULTS: A total of 929 patients, with a median age of 57 (16) years, were analyzed; 372 (40%) were male, and 557 (60%) were female. Differences in age, smoking history, hypertension, aneurysm presence, and treatment interventions existed between genders (p&#x2009;<&#x2009;0.01), yet no disparity in prognosis was noted. Subgroup analysis explored hypertension history, aneurysm presence, and treatment impact, revealing gender-specific variations in these factors' influence on the disease. Screening identified independent prognostic factors: age, SEBES score, admission GCS score, and complications for males; and age, admission GCS score, intraventricular hemorrhage, treatment interventions, symptomatic vasospasm, hydrocephalus, delayed cerebral ischemia, and seizures for females. Evaluation and validation of gender-specific models yielded an AUC of 0.916 (95% CI: 0.878-0.954) for males and 0.914 (95% CI: 0.885-0.944) for females in the ROC curve. Gender-specific prognostic models didn't significantly differ from the overall population-based model (model 3) but exhibited robust discriminative ability and clinical utility.<br><br>CONCLUSION: Variations in baseline and treatment-related factors among genders contribute partly to gender-based prognosis differences. Independent prognostic factors vary by gender. Gender-specific prognostic models exhibit favorable prognostic performance.},
}
@article {pmid39107319,
year = {2024},
author = {Luo, Y and Wang, L and Cao, Y and Shen, Y and Gu, Y and Wang, L},
title = {Reduced excitatory activity in the developing mPFC mediates a PVH-to-PVL transition and impaired social cognition in autism spectrum disorders.},
journal = {Translational psychiatry},
volume = {14},
number = {1},
pages = {325},
pmid = {39107319},
issn = {2158-3188},
support = {LR21C090001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; 32070975//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31820103005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071021//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Autism Spectrum Disorder/physiopathology ; Animals ; *Prefrontal Cortex/physiopathology/metabolism ; Mice ; *Interneurons/metabolism/physiology ; *Parvalbumins/metabolism ; *Disease Models, Animal ; Male ; *Social Cognition ; Behavior, Animal/physiology ; Social Behavior ; Mice, Inbred C57BL ; Female ; },
abstract = {Understanding the neuropathogenesis of impaired social cognition in autism spectrum disorders (ASD) is challenging. Altered cortical parvalbumin-positive (PV[+]) interneurons have been consistently observed in ASD, but their roles and the underlying mechanisms remain poorly understood. In our study, we observed a downward-shifted spectrum of PV expression in the developing medial prefrontal cortex (mPFC) of ASD mouse models due to decreased activity of PV[+] neurons. Surprisingly, chemogenetically suppressing PV[+] neuron activity during postnatal development failed to induce ASD-like behaviors. In contrast, lowering excitatory activity in the developing mPFC not only dampened the activity state and PV expression of individual PV[+] neurons, but also replicated ASD-like social deficits. Furthermore, enhancing excitation, but not PV[+] interneuron-mediated inhibition, rescued social deficits in ASD mouse models. Collectively, our findings propose that reduced excitatory activity in the developing mPFC may serve as a shared local circuitry mechanism triggering alterations in PV[+] interneurons and mediating impaired social functions in ASD.},
}
@article {pmid39106199,
year = {2024},
author = {Dash, D and Ferrari, P and Wang, J},
title = {Neural Decoding of Spontaneous Overt and Intended Speech.},
journal = {Journal of speech, language, and hearing research : JSLHR},
volume = {67},
number = {11},
pages = {4216-4225},
doi = {10.1044/2024_JSLHR-24-00046},
pmid = {39106199},
issn = {1558-9102},
mesh = {Humans ; *Magnetoencephalography ; Male ; Female ; *Speech/physiology ; Adult ; Young Adult ; Neural Networks, Computer ; Brain/physiology/diagnostic imaging ; Machine Learning ; Discriminant Analysis ; Brain-Computer Interfaces ; Speech Perception/physiology ; },
abstract = {PURPOSE: The aim of this study was to decode intended and overt speech from neuromagnetic signals while the participants performed spontaneous overt speech tasks without cues or prompts (stimuli).<br><br>METHOD: Magnetoencephalography (MEG), a noninvasive neuroimaging technique, was used to collect neural signals from seven healthy adult English speakers performing spontaneous, overt speech tasks. The participants randomly spoke the words yes or no at a self-paced rate without cues. Two machine learning models, namely, linear discriminant analysis (LDA) and one-dimensional convolutional neural network (1D CNN), were employed to classify the two words from the recorded MEG signals.<br><br>RESULTS: LDA and 1D CNN achieved average decoding accuracies of 79.02% and 90.40%, respectively, in decoding overt speech, significantly surpassing the chance level (50%). The accuracy for decoding intended speech was 67.19% using 1D CNN.<br><br>CONCLUSIONS: This study showcases the possibility of decoding spontaneous overt and intended speech directly from neural signals in the absence of perceptual interference. We believe that these findings make a steady step toward the future spontaneous speech-based brain-computer interface.},
}
@article {pmid39106147,
year = {2024},
author = {Lyu, J and Yang, Y and Zong, Y and Leng, Y and Zheng, W and Ge, S},
title = {Novel Sinusoidal Signal Assisted Multivariate Variational Mode Decomposition Combined With Task-Related Component Analysis for Enhancing SSVEP-Based BCI Performance.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {11},
pages = {6474-6485},
doi = {10.1109/JBHI.2024.3439391},
pmid = {39106147},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Algorithms ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Young Adult ; Female ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) have a broad application prospect owing to their multiple command output and high performance. Each harmonic component of SSVEP individually contains unique features, which can be utilized to enhance the recognition performance of SSVEP-based BCIs. However, the existing subband analysis methods for SSVEP, including those based on filter banks and existing mode decomposition methods, have limitations in extracting and utilizing independent harmonic components. This study proposes a sinusoidal signal assisted multivariate variational mode decomposition (SA-MVMD) algorithm that allows the constraint of the center frequencies and narrowband filtering structures of the intrinsic mode functions (IMFs) based on the prior frequency knowledge of the signal. It preserves the target information of the signal during decomposition while avoiding mode mixing and incorrect decomposition, thereby enabling the effective extraction of each independent harmonic component of SSVEP. Building on this, a SA-MVMD based task-related component analysis (SA-MVMD-TRCA) method is further proposed to fully utilize the features within the overall SSVEP as well as its independent harmonics, thereby enhancing the recognition performance. Testing on the public SSVEP Benchmark dataset demonstrates that the proposed method significantly outperforms the filter bank-based control methods. This study confirms the effectiveness of SA-MVMD and the potential of this approach, which analyzes and utilizes each independent harmonic of SSVEP, providing new strategies and perspectives for performance enhancement in SSVEP-based BCIs.},
}
@article {pmid39104699,
year = {2024},
author = {Rabbani, MHR and Islam, SMR},
title = {Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {4},
pages = {1489-1506},
pmid = {39104699},
issn = {1871-4080},
abstract = {The detection of the cognitive tasks performed by a subject during data acquisition of a neuroimaging method has a wide range of applications: functioning of brain-computer interface (BCI), detection of neuronal disorders, neurorehabilitation for disabled patients, and many others. Recent studies show that the combination or fusion of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) demonstrates improved classification and detection performance compared to sole-EEG and sole-fNIRS. Deep learning (DL) networks are suitable for the classification of large volume time-series data like EEG and fNIRS. This study performs the decision fusion of EEG and fNIRS. The classification of EEG, fNIRS, and decision-fused EEG-fNIRSinto cognitive task labels is performed by DL networks. Two different open-source datasets of simultaneously recorded EEG and fNIRS are examined in this study. Dataset 01 is comprised of 26 subjects performing 3 cognitive tasks: n-back, discrimination or selection response (DSR), and word generation (WG). After data acquisition, fNIRS is converted to oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HbR) in Dataset 01. Dataset 02 is comprised of 29 subjects who performed 2 tasks: motor imagery and mental arithmetic. The classification procedure of EEG and fNIRS (or HbO2, HbR) are carried out by 7 DL classifiers: convolutional neural network (CNN), long short-term memory network (LSTM), gated recurrent unit (GRU), CNN-LSTM, CNN-GRU, LSTM-GRU, and CNN-LSTM-GRU. After the classification of single modalities, their prediction scores or decisions are combined to obtain the decision-fused modality. The classification performance is measured by overall accuracy and area under the ROC curve (AUC). The highest accuracy and AUC recorded in Dataset 01 are 96% and 100% respectively; both by the decision fusion modality using CNN-LSTM-GRU. For Dataset 02, the highest accuracy and AUC are 82.76% and 90.44% respectively; both by the decision fusion modality using CNN-LSTM. The experimental result shows that decision-fused EEG-HbO2-HbR and EEG-fNIRSdeliver higher performances compared to their constituent unimodalities in most cases. For DL classifiers, CNN-LSTM-GRU in Dataset 01 and CNN-LSTM in Dataset 02 yield the highest performance.},
}
@article {pmid39104685,
year = {2024},
author = {Yin, X and Lin, M and Liang, J and Zeng, F},
title = {Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {4},
pages = {1733-1741},
pmid = {39104685},
issn = {1871-4080},
abstract = {Multivariate synchronization index (MSI), as an effective recognition algorithm for steady-state visual evoked potential (SSVEP) brain-computer interface (BCI), can accurately decode target frequencies without training. To further consider temporal features or extract harmonic components, extended MSI (EMSI), temporally local MSI (TMSI), and filter bank MSI (FBMSI) have been proposed. However, the promotion effects of the above three strategies on MSI have not been compared in detail. In this paper, the performance of EMSI, TMSI, and FBMSI under different time windows was analyzed with the same dataset. The results indicated that the improvement effect of the temporally local method on MSI was better than that of the other two methods under the short time window, and the effect of the filter bank method was better when the time window was greater than 0.8 s. Based on the idea of simultaneously extracting time-frequency features, FBEMSI and FBTMSI were proposed by integrating time delay embedding and temporally local method into FBMSI respectively. The two improved methods, which has no significant difference, can improve the recognition effect of FBMSI. But the computing time of FBEMSI was shorter, which can be a potential method for SSVEP-BCI.},
}
@article {pmid39104677,
year = {2024},
author = {Li, M and Qi, E and Xu, G and Jin, J and Zhao, Q and Guo, M and Liao, W},
title = {A delayed matching task-based study on action sequence of motor imagery.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {4},
pages = {1593-1607},
pmid = {39104677},
issn = {1871-4080},
abstract = {The way people imagine greatly affects performance of brain-computer interface (BCI) based on motion imagery (MI). Action sequence is a basic unit of imitation, learning, and memory for motor behavior. Whether it influences the MI-BCI is unknown, and how to manifest this influence is difficult since the MI is a spontaneous brain activity. To investigate the influence of the action sequence, this study proposes a novel paradigm named action sequences observing and delayed matching task to use images and videos to guide people to observe, match and reinforce the memory of sequence. Seven subjects' ERPs and MI performance are analyzed under four different levels of complexities or orders of the sequence. Results demonstrated that the action sequence in terms of complexity and sequence order significantly affects the MI. The complex action in positive order obtains stronger ERD/ERS and more pronounced MI feature distributions, and yields an MI classification accuracy that is 12.3% higher than complex action in negative order (p < 0.05). In addition, the ERP amplitudes derived from the supplementary motor area show a positive correlation to the MI. This study demonstrates a new perspective of improving imagery in the MI-BCI by considering the complexity and order of the action sequences, and provides a novel index for manifesting the MI performance by ERP.},
}
@article {pmid39104673,
year = {2024},
author = {Rezvani, S and Hosseini-Zahraei, SH and Tootchi, A and Guger, C and Chaibakhsh, Y and Saberi, A and Chaibakhsh, A},
title = {A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {4},
pages = {1419-1443},
pmid = {39104673},
issn = {1871-4080},
abstract = {Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.},
}
@article {pmid39103461,
year = {2024},
author = {Chockboondee, M and Jatupornpoonsub, T and Lertsukprasert, K and Wongsawat, Y},
title = {Effects of daily listening to 6 Hz binaural beats over one month: an event-related potentials study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {18059},
pmid = {39103461},
issn = {2045-2322},
support = {B42G670043//This project is supported by the National Higher Education Science Research and Innovation Policy Council, PMU B/ ; },
mesh = {Humans ; Male ; Female ; Adult ; Young Adult ; *Acoustic Stimulation ; Evoked Potentials, Auditory/physiology ; Electroencephalography ; Evoked Potentials/physiology ; Auditory Perception/physiology ; Event-Related Potentials, P300/physiology ; Cognition/physiology ; },
abstract = {The aim of the present study was to identify cognitive alterations, as indicated by event-related potentials (ERPs), after one month of daily exposure to theta binaural beats (BBs) for 10 minutes. The recruited healthy subjects (n = 60) were equally divided into experimental and control groups. For a month, the experimental group was required to practice BBs listening daily, while the control group did not. ERPs were assessed at three separate visits over a span of one month, with a two-week interval between each visit. At each visit, ERPs were measured before and after listening. The auditory and visual ERPs significantly increased the auditory and visual P300 amplitudes consistently at each visit. BBs enhanced the auditory N200 amplitude consistently across all visits, but the visual N200 amplitude increased only at the second and third visits. Compared to the healthy controls, daily exposure to BBs for two weeks resulted in increased auditory P300 amplitude. Additionally, four weeks of BBs exposure not only increased auditory P300 amplitude but also reduced P300 latency. These preliminary findings suggest that listening to BBs at 6 Hz for 10 minutes daily may enhance certain aspects of cognitive function. However, further research is needed to confirm these effects and to understand the underlying mechanisms. Identifying the optimal duration and practice of listening to 6 Hz BBs could potentially contribute to cognitive enhancement strategies in healthy individuals.},
}
@article {pmid39103336,
year = {2024},
author = {Sun, X and Dias, L and Peng, C and Zhang, Z and Ge, H and Wang, Z and Jin, J and Jia, M and Xu, T and Guo, W and Zheng, W and He, Y and Wu, Y and Cai, X and Agostinho, P and Qu, J and Cunha, RA and Zhou, X and Bai, R and Chen, JF},
title = {40 Hz light flickering facilitates the glymphatic flow via adenosine signaling in mice.},
journal = {Cell discovery},
volume = {10},
number = {1},
pages = {81},
pmid = {39103336},
issn = {2056-5968},
support = {31600859//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970948//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101556//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ22H090013//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {The glymphatic-lymphatic system is increasingly recognized as fundamental for the homeostasis of the brain milieu since it defines cerebral spinal fluid flow in the brain parenchyma and eliminates metabolic waste. Animal and human studies have uncovered several important physiological factors regulating the glymphatic system including sleep, aquaporin-4, and hemodynamic factors. Yet, our understanding of the modulation of the glymphatic system is limited, which has hindered the development of glymphatic-based treatment for aging and neurodegenerative disorders. Here, we present the evidence from fluorescence tracing, two-photon recording, and dynamic contrast-enhanced magnetic resonance imaging analyses that 40 Hz light flickering enhanced glymphatic influx and efflux independently of anesthesia and sleep, an effect attributed to increased astrocytic aquaporin-4 polarization and enhanced vasomotion. Adenosine-A2A receptor (A2AR) signaling emerged as the neurochemical underpinning of 40 Hz flickering-induced enhancement of glymphatic flow, based on increased cerebrofluid adenosine levels, the abolishment of enhanced glymphatic flow by pharmacological or genetic inactivation of equilibrative nucleotide transporters-2 or of A2AR, and by the physical and functional A2AR-aquaporin-4 interaction in astrocytes. These findings establish 40 Hz light flickering as a novel non-invasive strategy of enhanced glymphatic flow, with translational potential to relieve brain disorders.},
}
@article {pmid39103043,
year = {2024},
author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X},
title = {EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.},
journal = {Neuroscience},
volume = {556},
number = {},
pages = {42-51},
doi = {10.1016/j.neuroscience.2024.07.051},
pmid = {39103043},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; Brain/physiology ; },
abstract = {Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.},
}
@article {pmid39102329,
year = {2024},
author = {Shin, J and Chung, W},
title = {Multiband Convolutional Riemannian Network With Band-Wise Riemannian Triplet Loss for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {12},
pages = {7230-7238},
doi = {10.1109/JBHI.2024.3438167},
pmid = {39102329},
issn = {2168-2208},
mesh = {Humans ; Algorithms ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Imagination/physiology ; Brain-Computer Interfaces ; *Deep Learning ; },
abstract = {This paper presents a novel motor imagery classification algorithm that uses an overlapping multiscale multiband convolutional Riemannian network with band-wise Riemannian triplet loss to improve classification performance. Despite the superior performance of the Riemannian approach over the common spatial pattern filter approach, deep learning methods that generalize the Riemannian approach have received less attention. The proposed algorithm develops a state-of-the-art multiband Riemannian network that reduces the potential overfitting problem of Riemannian networks, a drawback of Riemannian networks due to their inherent large feature dimension from covariance matrix, by using fewer subbands with discriminative frequency diversity, by inserting convolutional layers before computing the subband covariance matrix, and by regularizing subband networks with Riemannian triplet loss. The proposed method is evaluated using the publicly available datasets, the BCI Competition IV dataset 2a and the OpenBMI dataset. The experimental results confirm that the proposed method improves performance, in particular achieving state-of-the-art classification accuracy among the currently studied Riemannian networks.},
}
@article {pmid39099770,
year = {2024},
author = {Chen, W and Liao, Y and Dai, R and Dong, Y and Huang, L},
title = {EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism.},
journal = {Frontiers in computational neuroscience},
volume = {18},
number = {},
pages = {1416494},
pmid = {39099770},
issn = {1662-5188},
abstract = {EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments' accuracy of 99.42% and subject-independent experiments' accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.},
}
@article {pmid39099169,
year = {2024},
author = {Wang, J and Qiu, Y and Yang, L and Wang, J and He, J and Tang, C and Yang, Z and Hong, W and Yang, B and He, Q and Weng, Q},
title = {Preserving mitochondrial homeostasis protects against drug-induced liver injury via inducing OPTN (optineurin)-dependent Mitophagy.},
journal = {Autophagy},
volume = {20},
number = {12},
pages = {2677-2696},
pmid = {39099169},
issn = {1554-8635},
mesh = {*Mitophagy/drug effects/physiology ; *Homeostasis/drug effects ; *Membrane Transport Proteins/metabolism ; Animals ; *Cell Cycle Proteins/metabolism ; Humans ; *Valosin Containing Protein/metabolism ; *Chemical and Drug Induced Liver Injury/metabolism ; *Hepatocytes/metabolism/drug effects ; Mitochondria/metabolism/drug effects ; Mice ; Transcription Factor TFIIIA/metabolism/genetics ; Mice, Inbred C57BL ; Beclin-1/metabolism ; Mitochondria, Liver/metabolism/drug effects ; },
abstract = {Disruption of mitochondrial function is observed in multiple drug-induced liver injuries (DILIs), a significant global health threat. However, how the mitochondrial dysfunction occurs and whether maintain mitochondrial homeostasis is beneficial for DILIs remains unclear. Here, we show that defective mitophagy by OPTN (optineurin) ablation causes disrupted mitochondrial homeostasis and aggravates hepatocytes necrosis in DILIs, while OPTN overexpression protects against DILI depending on its mitophagic function. Notably, mass spectrometry analysis identifies a new mitochondrial substrate, GCDH (glutaryl-CoA dehydrogenase), which can be selectively recruited by OPTN for mitophagic degradation, and a new cofactor, VCP (valosin containing protein) that interacts with OPTN to stabilize BECN1 during phagophore assembly, thus boosting OPTN-mediated mitophagy initiation to clear damaged mitochondria and preserve mitochondrial homeostasis in DILIs. Then, the accumulation of OPTN in different DILIs is further validated with a protective effect, and pyridoxine is screened and established to alleviate DILIs by inducing OPTN-mediated mitophagy. Collectively, our findings uncover a dual role of OPTN in mitophagy initiation and implicate the preservation of mitochondrial homeostasis via inducing OPTN-mediated mitophagy as a potential therapeutic approach for DILIs.Abbreviation: AILI: acetaminophen-induced liver injury; ALS: amyotrophic lateral sclerosis; APAP: acetaminophen; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CHX: cycloheximide; Co-IP: co-immunoprecipitation; DILI: drug-induced liver injury; FL: full length; GCDH: glutaryl-CoA dehydrogenase; GOT1/AST: glutamic-oxaloacetic transaminase 1; GO: gene ontology; GSEA: gene set enrichment analysis; GPT/ALT: glutamic - pyruvic transaminase; INH: isoniazid; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MMP: mitochondrial membrane potential; MST: microscale thermophoresis; MT-CO2/COX-II: mitochondrially encoded cytochrome c oxidase II; OPTN: optineurin; PINK1: PTEN induced kinase 1; PRKN: parkin RBR E3 ubiquitin protein ligase; TIMM23: translocase of inner mitochondrial membrane 23; TOMM20: translocase of outer mitochondrial membrane 20; TSN: toosendanin; VCP: valosin containing protein, WIPI2: WD repeat domain, phosphoinositide interacting 2.},
}
@article {pmid39097741,
year = {2024},
author = {El Kirat, H and van Belle, S and Khattabi, A and Belrhiti, Z},
title = {Behavioral change interventions, theories, and techniques to reduce physical inactivity and sedentary behavior in the general population: a scoping review.},
journal = {BMC public health},
volume = {24},
number = {1},
pages = {2099},
pmid = {39097741},
issn = {1471-2458},
mesh = {Humans ; *Sedentary Behavior ; Exercise/psychology ; Health Promotion/methods ; Behavior Therapy/methods ; Health Behavior ; },
abstract = {BACKGROUND: Worldwide, physical inactivity (PIA) and sedentary behavior (SB) are recognized as significant challenges hindering the achievement of the United Nations (UN) sustainable development goals (SDGs). PIA and SB are responsible for 1.6 million deaths attributed to non-communicable diseases (NCDs). The World Health Organization (WHO) has urged governments to implement interventions informed by behavioral theories aimed at reducing PIA and SB. However, limited attention has been given to the range of theories, techniques, and contextual conditions underlying the design of behavioral theories. To this end, we set out to map these interventions, their levels of action, their mode of delivery, and how extensively they apply behavioral theories, constructs, and techniques.<br><br>METHODS: Following the scoping review methodology of Arksey and O'Malley (2005), we included peer-reviewed articles on behavioral theories interventions centered on PIA and SB, published between 2010 and 2023 in Arabic, French, and English in four databases (Scopus, Web of Science [WoS], PubMed, and Google Scholar). We adopted a framework thematic analysis based on the upper-level ontology of behavior theories interventions, Behavioral theories taxonomies, and the first version (V1) taxonomy of behavior change techniques(BCTs).<br><br>RESULTS: We included 29 studies out of 1,173 that were initially screened/searched. The majority of interventions were individually focused (n&#x2009;=&#x2009;15). Few studies have addressed interpersonal levels (n&#x2009;=&#x2009;6) or organizational levels (n&#x2009;=&#x2009;6). Only two interventions can be described as systemic (i.e., addressing the individual, interpersonal, organizational, and institutional factors)(n&#x2009;=&#x2009;2). Most behavior change interventions&#xa0;use four theories: The Social cognitive theory (SCT), the socioecological model (SEM), SDT, and the transtheoretical model (TTM). Most behavior change interventions (BCIS) involve goal setting, social support, and action planning with various degrees of theoretical use&#xa0;(intensive [n&#x2009;=&#x2009;15], moderate [n&#x2009;=&#x2009;11], or low [n&#x2009;=&#x2009;3]).<br><br>DISCUSSION AND CONCLUSION: Our review suggests the need to develop systemic and complementary interventions that entail the micro-, meso- and macro-level barriers to behavioral changes. Theory informed BCI need to integrate synergistic BCTs into models that use&#xa0;micro-, meso- and macro-level theories to determine behavioral change. Future interventions need to appropriately use a mix of behavioral theories and BCTs to address the systemic nature of behavioral change as well as the heterogeneity of contexts and targeted populations.},
}
@article {pmid39095351,
year = {2024},
author = {Tian, F and Zhang, Y and Schriver, KE and Hu, JM and Roe, AW},
title = {A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {6528},
pmid = {39095351},
issn = {2041-1723},
support = {U20A20221//National Natural Science Foundation of China (National Science Foundation of China)/ ; 819611280292//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; Cats ; *Visual Cortex/physiology ; *Infrared Rays ; Photic Stimulation/methods ; Optical Fibers ; Visual Prosthesis ; },
abstract = {Cutting edge advances in electrical visual cortical prosthetics have evoked perception of shapes, motion, and letters in the blind. Here, we present an alternative optical approach using pulsed infrared neural stimulation. To interface with dense arrays of cortical columns with submillimeter spatial precision, both linear array and 100-fiber bundle array optical fiber interfaces were devised. We deliver infrared stimulation through these arrays in anesthetized cat visual cortex and monitor effects by optical imaging in contralateral visual cortex. Infrared neural stimulation modulation of response to ongoing visual oriented gratings produce enhanced responses in orientation-matched domains and suppressed responses in non-matched domains, consistent with a known higher order integration mediated by callosal inputs. Controls include dynamically applied speeds, directions and patterns of multipoint stimulation. This provides groundwork for a distinct type of prosthetic targeted to maps of visual cortical columns.},
}
@article {pmid39092732,
year = {2024},
author = {Islam, MM and Vashishat, A and Kumar, M},
title = {Advancements Beyond Limb Loss: Exploring the Intersection of AI and BCI in Prosthetic Evaluation.},
journal = {Current pharmaceutical design},
volume = {30},
number = {35},
pages = {2749-2752},
pmid = {39092732},
issn = {1873-4286},
}
@article {pmid39086375,
year = {2024},
author = {Angelopoulou, A and Chihi, I and Hemanth, J},
title = {Editorial: Methods and protocols in Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1447973},
pmid = {39086375},
issn = {1662-5161},
}
@article {pmid39086374,
year = {2024},
author = {Xavier Fidêncio, A and Klaes, C and Iossifidis, I},
title = {A generic error-related potential classifier based on simulated subjects.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1390714},
pmid = {39086374},
issn = {1662-5161},
abstract = {Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.},
}
@article {pmid39083601,
year = {2024},
author = {Jia, K and Wang, M and Steinwurzel, C and Ziminski, JJ and Xi, Y and Emir, U and Kourtzi, Z},
title = {Recurrent inhibition refines mental templates to optimize perceptual decisions.},
journal = {Science advances},
volume = {10},
number = {31},
pages = {eado7378},
pmid = {39083601},
issn = {2375-2548},
mesh = {Humans ; *Decision Making/physiology ; *Magnetic Resonance Imaging/methods ; *Visual Cortex/physiology ; Male ; Adult ; Female ; gamma-Aminobutyric Acid/metabolism ; Visual Perception/physiology ; Neuronal Plasticity/physiology ; Young Adult ; Brain Mapping ; },
abstract = {Translating sensory inputs to perceptual decisions relies on building internal representations of features critical for solving complex tasks. Yet, we still lack a mechanistic account of how the brain forms these mental templates of task-relevant features to optimize decision-making. Here, we provide evidence for recurrent inhibition: an experience-dependent plasticity mechanism that refines mental templates by enhancing γ-aminobutyric acid (GABA)-mediated (GABAergic) inhibition and recurrent processing in superficial visual cortex layers. We combine ultrahigh-field (7 T) functional magnetic resonance imaging at submillimeter resolution with magnetic resonance spectroscopy to investigate the fine-scale functional and neurochemical plasticity mechanisms for optimized perceptual decisions. We demonstrate that GABAergic inhibition increases following training on a visual (i.e., fine orientation) discrimination task, enhancing the discriminability of orientation representations in superficial visual cortex layers that are known to support recurrent processing. Modeling functional and neurochemical plasticity interactions reveals that recurrent inhibitory processing optimizes brain computations for perpetual decisions and adaptive behavior.},
}
@article {pmid39082285,
year = {2024},
author = {Khorev, V and Kurkin, S and Badarin, A and Antipov, V and Pitsik, E and Andreev, A and Grubov, V and Drapkina, O and Kiselev, A and Hramov, A},
title = {Review on the Use of Brain Computer Interface Rehabilitation Methods for Treating Mental and Neurological Conditions.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {7},
pages = {125},
doi = {10.31083/j.jin2307125},
pmid = {39082285},
issn = {0219-6352},
support = {123020600127-4//Russian Ministry of Health as part of the scientific work "Development of a multimodal biofeedback-based hardware and software system for rehabilitation of patients with cognitive and motor disorders of different nature"/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Mental Disorders/rehabilitation ; *Nervous System Diseases/rehabilitation ; Neurological Rehabilitation/methods ; },
abstract = {This review provides a comprehensive examination of recent developments in both neurofeedback and brain-computer interface (BCI) within the medical field and rehabilitation. By analyzing and comparing results obtained with various tools and techniques, we aim to offer a systematic understanding of BCI applications concerning different modalities of neurofeedback and input data utilized. Our primary objective is to address the existing gap in the area of meta-reviews, which provides a more comprehensive outlook on the field, allowing for the assessment of the current landscape and developments within the scope of BCI. Our main methodologies include meta-analysis, search queries employing relevant keywords, and a network-based approach. We are dedicated to delivering an unbiased evaluation of BCI studies, elucidating the primary vectors of research development in this field. Our review encompasses a diverse range of applications, incorporating the use of brain-computer interfaces for rehabilitation and the treatment of various diagnoses, including those related to affective spectrum disorders. By encompassing a wide variety of use cases, we aim to offer a more comprehensive perspective on the utilization of neurofeedback treatments across different contexts. The structured and organized presentation of information, complemented by accompanying visualizations and diagrams, renders this review a valuable resource for scientists and researchers engaged in the domains of biofeedback and brain-computer interfaces.},
}
@article {pmid39082284,
year = {2024},
author = {Ou, Y and Wang, F and Feng, B and Tang, L and Pan, J},
title = {Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {7},
pages = {134},
doi = {10.31083/j.jin2307134},
pmid = {39082284},
issn = {0219-6352},
support = {2021A1515011853//Guangdong Basic and Applied Basic Research Foundation/ ; 61906019//National Natural Science Foundation of China/ ; 62006082//National Natural Science Foundation of China/ ; 2022ZD0208900//STI 2030-Major Projects/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Algorithms ; Sleep Stages/physiology ; Signal Processing, Computer-Assisted ; Adult ; },
abstract = {BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations.<br><br>METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales.<br><br>RESULTS: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance.<br><br>CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.},
}
@article {pmid39081972,
year = {2023},
author = {Perez Garcia, G and Bicak, M and Buros, J and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Gage, FH and Barlow, C and Dudley, JT and Glicksberg, BS and Wang, Y and Readhead, B and Ehrlich, ME and Elder, GA and Gandy, S},
title = {Beneficial effects of physical exercise and an orally active mGluR2/3 antagonist pro-drug on neurogenesis and behavior in an Alzheimer's amyloidosis model.},
journal = {Frontiers in dementia},
volume = {2},
number = {},
pages = {1198006},
pmid = {39081972},
issn = {2813-3919},
abstract = {BACKGROUND: Modulation of physical activity represents an important intervention that may delay, slow, or prevent mild cognitive impairment (MCI) or dementia due to Alzheimer's disease (AD). One mechanism proposed to underlie the beneficial effect of physical exercise (PE) involves the apparent stimulation of adult hippocampal neurogenesis (AHN). BCI-838 is a pro-drug whose active metabolite BCI-632 is a negative allosteric modulator at group II metabotropic glutamate receptors (mGluR2/3). We previously demonstrated that administration of BCI-838 to a mouse model of brain accumulation of oligomeric Aβ[E22Q] (APP [E693Q] = "Dutch APP") reduced learning behavior impairment and anxiety, both of which are associated with the phenotype of Dutch APP mice.<br><br>METHODS: 3-month-old mice were administered BCI-838 and/or physical exercise for 1 month and then tested in novel object recognition, neurogenesis, and RNAseq.<br><br>RESULTS: Here we show that (i) administration of BCI-838 and a combination of BCI-838 and PE enhanced AHN in a 4-month old mouse model of AD amyloid pathology (APP [KM670/671NL] /PSEN1 [&#x394;exon9]= APP/PS1), (ii) administration of BCI-838 alone or with PE led to stimulation of AHN and improvement in recognition memory, (iii) the hippocampal dentate gyrus transcriptome of APP/PS1 mice following BCI-838 treatment showed up-regulation of brain-derived neurotrophic factor (BDNF), PIK3C2A of the PI3K-mTOR pathway, and metabotropic glutamate receptors, and down-regulation of EIF5A involved in modulation of mTOR activity by ketamine, and (iv) validation by qPCR of an association between increased BDNF levels and BCI-838 treatment.<br><br>CONCLUSION: Our study points to BCI-838 as a safe and orally active compound capable of mimicking the beneficial effect of PE on AHN and recognition memory in a mouse model of AD amyloid pathology.},
}
@article {pmid39080312,
year = {2024},
author = {Wang, G and Marcucci, G and Peters, B and Braidotti, MC and Muckli, L and Faccio, D},
title = {Human-centred physical neuromorphics with visual brain-computer interfaces.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {6393},
pmid = {39080312},
issn = {2041-1723},
support = {EP/T021020/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography ; Male ; Adult ; Brain/physiology ; Photic Stimulation ; Female ; Young Adult ; Neural Networks, Computer ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces (BCIs) as they provide a stable and efficient means to connect the computer to the brain with a simple flickering light. Previous studies focused on low-density frequency division multiplexing techniques, i.e. typically employing one or two light-modulation frequencies during a single flickering light stimulation. Here we show that it is possible to encode information in SSVEPs excited by high-density frequency division multiplexing, involving hundreds of frequencies. We then demonstrate the ability to transmit entire images from the computer to the brain/EEG read-out in relatively short times. High-density frequency multiplexing also allows to implement a photonic neural network utilizing SSVEPs, that is applied to simple classification tasks and exhibits promising scalability properties by connecting multiple brains in series. Our findings open up new possibilities for the field of neural interfaces, holding potential for various applications, including assistive technologies and cognitive enhancements, to further improve human-machine interactions.},
}
@article {pmid39078880,
year = {2024},
author = {Codol, O and Michaels, JA and Kashefi, M and Pruszynski, JA and Gribble, PL},
title = {MotorNet, a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {39078880},
issn = {2050-084X},
support = {RGPIN/05458-2018//Natural Sciences and Engineering Research Council of Canada/ ; PJT-175010/CAPMC/CIHR/Canada ; RGPIN-2022-04421//Natural Sciences and Engineering Research Council of Canada/ ; PJT-156241/CAPMC/CIHR/Canada ; },
mesh = {*Neural Networks, Computer ; Biomechanical Phenomena ; *Software ; Humans ; Algorithms ; },
abstract = {Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet's focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.},
}
@article {pmid39074028,
year = {2024},
author = {Lee, BH and Cho, JH and Kwon, BH and Lee, M and Lee, SW},
title = {Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2793-2804},
doi = {10.1109/TNSRE.2024.3434983},
pmid = {39074028},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Robotics ; Calibration ; *Algorithms ; Reproducibility of Results ; Male ; *Arm/physiology ; Adult ; Female ; Imagination/physiology ; Brain-Computer Interfaces ; Young Adult ; Neural Networks, Computer ; Speech/physiology ; },
abstract = {Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.},
}
@article {pmid39074021,
year = {2024},
author = {Yi, Z and Pan, J and Chen, Z and Lu, D and Cai, H and Li, J and Xie, Q},
title = {A Hybrid BCI Integrating EEG and Eye-Tracking for Assisting Clinical Communication in Patients With Disorders of Consciousness.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2759-2771},
doi = {10.1109/TNSRE.2024.3435016},
pmid = {39074021},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Female ; *Consciousness Disorders/physiopathology/diagnosis ; Adult ; *Eye-Tracking Technology ; *Event-Related Potentials, P300/physiology ; Middle Aged ; *Algorithms ; Young Adult ; Neural Networks, Computer ; Communication Devices for People with Disabilities ; Communication ; Healthy Volunteers ; Attention/physiology ; },
abstract = {Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC. Specifically, the BCI presented a question and two randomly flashing answers (yes/no). The subjects were instructed to focus on an answer. A multimodal target recognition network (MTRN) is proposed to detect P300 potentials and eye-tracking responses (i.e., pupil constriction and gaze) and identify the target in real time. In the MTRN, the dual-stream feature extraction module with two independent multiscale convolutional neural networks extracts multiscale features from multimodal data. Then, the multimodal attention strategy adaptively extracts the most relevant information about the target from multimodal data. Finally, a prototype network is designed as a classifier to facilitate small-sample data classification. Ten healthy individuals, nine DOC patients and one LIS patient were included in this study. All healthy subjects achieved 100% accuracy. Five patients could communicate with our BCI, with 76.1±7.9% accuracy. Among them, two patients who were noncommunicative on the behavioral scale exhibited communication ability via our BCI. Additionally, we assessed the performance of unimodal BCIs and compared MTRNs with other methods. All the results suggested that our BCI can yield more sensitive outcomes than the CRS-R and can serve as a valuable communication tool.},
}
@article {pmid39071181,
year = {2024},
author = {Kilani, S and Aghili, SN and Fathi, Y and Sburlea, AI},
title = {Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1360709},
pmid = {39071181},
issn = {1662-4548},
abstract = {INTRODUCTION: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.<br><br>METHODS: To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection.<br><br>RESULTS: The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <&#xbd; of the training samples needed by previous studies.<br><br>DISCUSSION: Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.},
}
@article {pmid39069564,
year = {2024},
author = {Garvayo, M and Dupont, S and Frazzini, V and Bielle, F and Adam, C and Bendary, YE and Méré, M and Samson, S and Guesdon, A and Navarro, V and Mathon, B},
title = {Resective surgery for mesial temporal lobe epilepsy associated with hippocampal sclerosis in patients over 50 years: a case-control study.},
journal = {Journal of neurology},
volume = {271},
number = {9},
pages = {6197-6208},
pmid = {39069564},
issn = {1432-1459},
mesh = {Humans ; *Epilepsy, Temporal Lobe/surgery ; Middle Aged ; Male ; Female ; *Sclerosis/surgery ; *Hippocampus/surgery/pathology ; Case-Control Studies ; Adult ; Retrospective Studies ; Aged ; Young Adult ; Drug Resistant Epilepsy/surgery/etiology ; Age Factors ; Neurosurgical Procedures/adverse effects ; Adolescent ; Follow-Up Studies ; Treatment Outcome ; Hippocampal Sclerosis ; },
abstract = {BACKGROUND: Mesial temporal lobe epilepsy associated with hippocampal sclerosis (MTLE/HS) is the most common cause of drug-resistant focal seizures and surgical resection is the primary treatment option, with seizure-free rates ranging from 60 to 80%. However, data on postsurgical seizure outcomes in patients ≥ 50 years of age are limited. This study aimed to assess the efficacy and safety of surgery in this age group compared to younger patients.<br><br>METHODS: We performed a retrospective analysis of data from resective surgeries conducted in patients with MTLE/HS between 1990 and 2022. We focused on patients aged&#x2009;&#x2265;&#x2009;50&#xa0;years and compared the surgical safety and efficacy variables between this group and a control group of patients aged&#x2009;<&#x2009;50&#xa0;years through a case-control study.<br><br>RESULTS: Among the 450 MTLE/HS patients who underwent surgery during the inclusion period, 61 (13.6%) were aged&#x2009;&#x2265;&#x2009;50&#xa0;years and matched with 183 younger patients, totaling 244 study participants. The two groups had similar characteristics. At the last follow-up (median 5.7&#xa0;years), Engel I outcomes were achieved in 80.3% of the older patients and 81.4% of the younger patients, with no significant difference (p&#x2009;=&#x2009;0.85). Postoperative cognitive and psychiatric outcomes did not differ between the groups. Major complication rates were also comparable, at 3.3% in the older group and 2.7% in the younger group (p&#x2009;=&#x2009;0.83). The extratemporal ictal abnormalities observed on video-EEG were the only variable that demonstrated a significant association with an unfavorable seizure outcome in the older group (OR 9.3, 95% CI [1.8-47.6], p&#x2009;=&#x2009;0.005).<br><br>CONCLUSIONS: This study provides grade 3 evidence that resective surgery for MTLE/HS patients aged&#x2009;&#x2265;&#x2009;50&#xa0;years is as effective and safe as it is for younger patients, and thus should be considered as the primary treatment option for drug-resistant cases.},
}
@article {pmid39069222,
year = {2024},
author = {Yeo, TS and Kim, JS and Kim, HJ and Chung, CK},
title = {Macroscopic brain dynamics beyond contralateral primary motor cortex for movement prediction.},
journal = {NeuroImage},
volume = {297},
number = {},
pages = {120727},
doi = {10.1016/j.neuroimage.2024.120727},
pmid = {39069222},
issn = {1095-9572},
mesh = {Humans ; *Motor Cortex/physiology ; Male ; *Magnetoencephalography/methods ; Adult ; Female ; *Movement/physiology ; *Brain-Computer Interfaces ; Young Adult ; Brain Mapping/methods ; },
abstract = {This study investigates the complex relationship between upper limb movement direction and macroscopic neural signals in the brain, which is critical for understanding brain-computer interfaces (BCI). Conventional BCI research has primarily focused on a local area, such as the contralateral primary motor cortex (M1), relying on the population-based decoding method with microelectrode arrays. In contrast, macroscopic approaches such as electroencephalography (EEG) and magnetoencephalography (MEG) utilize numerous electrodes to cover broader brain regions. This study probes the potential differences in the mechanisms of microscopic and macroscopic methods. It is important to determine which neural activities effectively predict movements. To investigate this, we analyzed MEG data from nine right-handed participants while performing arm-reaching tasks. We employed dynamic statistical parametric mapping (dSPM) to estimate source activity and built a decoding model composed of long short-term memory (LSTM) and a multilayer perceptron to predict movement trajectories. This model achieved a high correlation coefficient of 0.79 between actual and predicted trajectories. Subsequently, we identified brain regions sensitive to predicting movement direction using the integrated gradients (IG) method, which assesses the predictive contribution of each source activity. The resulting salience map demonstrated a distribution without significant differences across motor-related regions, including M1. Predictions based solely on M1 activity yielded a correlation coefficient of 0.42, nearly half as effective as predictions incorporating all source activities. This suggests that upper limb movements are influenced by various factors such as movement coordination, planning, body and target position recognition, and control, beyond simple muscle activity. All of the activities are needed in the decoding model using macroscopic signals. Our findings also revealed that contralateral and ipsilateral hemispheres contribute equally to movement prediction, implying that BCIs could potentially benefit patients with brain damage in the contralateral hemisphere by utilizing brain signals from the ipsilateral hemisphere. In conclusion, this study demonstrates that macroscopic activity from large brain regions significantly contributes to predicting upper limb movement. Non-invasive BCI systems would require a comprehensive collection of neural signals from multiple brain regions.},
}
@article {pmid39067986,
year = {2024},
author = {Xu, Q and Liang, R and Gao, J and Fan, Y and Dong, J and Wang, L and Zheng, C and Yang, J and Ming, D},
title = {rTMS Ameliorates time-varying depression and social behaviors in stimulated space complex environment associated with VEGF signaling.},
journal = {Life sciences in space research},
volume = {42},
number = {},
pages = {17-26},
doi = {10.1016/j.lssr.2024.04.001},
pmid = {39067986},
issn = {2214-5532},
mesh = {Animals ; *Vascular Endothelial Growth Factor A/metabolism ; Mice ; *Transcranial Magnetic Stimulation ; Male ; *Social Behavior ; *Space Flight ; *Depression ; *Mice, Inbred C57BL ; *Signal Transduction ; Prefrontal Cortex/physiology/metabolism ; Matrix Metalloproteinase 9/metabolism ; },
abstract = {Studies have indicated that medium- to long-duration spaceflight may adversely affect astronauts' emotional and social functioning. Emotion modulation can significantly impact astronauts' well-being, performance, mission safety and success. However, with the increase in flight time, the potential alterations in emotional and social performance during spaceflight and their underlying mechanisms remain to be investigated, and targeted therapeutic and preventive interventions have yet to be identified. We evaluated the changes of emotional and social functions in mice with the extension of the time in simulated space complex environment (SSCE), and simultaneously monitored changes in brain tissue of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), and inflammation-related factors. Furthermore, we assessed the regulatory role of repetitive transcranial magnetic stimulation (rTMS) in mood and socialization with the extension of the time in SSCE, as well as examining alterations of VEGF signaling in the medial prefrontal cortex (mPFC). Our findings revealed that mice exposed to SSCE for 7 days exhibited depressive-like behaviors, with these changes persisting throughout SSCE period. In addition, 14 days of rTMS treatment significantly ameliorated SSCE-induced emotional and social dysfunction, potentially through modulation of the level of VEGF signaling in mPFC. These results indicates that emotional and social disorders increase with the extension of SSCE time, and rTMS can improve the performance, which may be related to VEGF signaling. This study offers insights into potential pattern of change over time for mental health issues in astronauts. Further analysis revealed that rTMS modulates emotional and social dysfunction during SSCE exposure, with its mechanism potentially being associated with VEGF signaling.},
}
@article {pmid39066744,
year = {2024},
author = {Bartkowiak, J and Dernektsi, C and Agarwal, V and Lebehn, MA and Williams, TA and Brandwein, RA and Brugger, N and Gräni, C and Windecker, S and Vahl, TP and Nazif, TM and George, I and Kodali, SK and Praz, F and Hahn, RT},
title = {3-Dimensional Echocardiographic Prediction of Left Ventricular Outflow Tract Area Prior to Transcatheter Mitral Valve Replacement.},
journal = {JACC. Cardiovascular imaging},
volume = {17},
number = {10},
pages = {1168-1178},
doi = {10.1016/j.jcmg.2024.05.011},
pmid = {39066744},
issn = {1876-7591},
mesh = {Humans ; *Echocardiography, Three-Dimensional ; Female ; Male ; *Predictive Value of Tests ; *Mitral Valve/diagnostic imaging/physiopathology/surgery ; Retrospective Studies ; Aged ; *Heart Valve Prosthesis Implantation/instrumentation/adverse effects ; *Cardiac Catheterization/adverse effects ; Reproducibility of Results ; *Echocardiography, Transesophageal ; Treatment Outcome ; *Ventricular Function, Left ; Aged, 80 and over ; Heart Valve Prosthesis ; Middle Aged ; Hemodynamics ; Mitral Valve Insufficiency/diagnostic imaging/physiopathology/surgery ; Image Interpretation, Computer-Assisted ; },
abstract = {BACKGROUND: New postprocessing software facilitates 3-dimensional (3D) echocardiographic determination of mitral annular (MA) and neo-left ventricular outflow tract (neo-LVOT) dimensions in patients undergoing transcatheter mitral valve replacement (TMVR).<br><br>OBJECTIVES: This study aims to test the accuracy of 3D echocardiographic analysis as compared to baseline computed tomography (CT).<br><br>METHODS: A total of 105 consecutive patients who underwent TMVR at 2 tertiary care centers between October 2017 and May 2023 were retrospectively included. A virtual valve was projected in both baseline CT and 3D transesophageal echocardiography (TEE) using dedicated software. MA dimensions were measured in baseline images and neo-LVOT dimensions were measured in baseline and postprocedural images. All measurements were compared to baseline CT as a reference. The predicted neo-LVOT area was correlated with postprocedural peak LVOT gradients.<br><br>RESULTS: There was no significant bias in baseline neo-LVOT prediction between both imaging modalities. TEE significantly underestimated MA area, perimeter, and medial-lateral dimension compared to CT. Both modalities significantly underestimated the actual neo-LVOT area (mean bias pre/post TEE: 25.6&#xa0;mm[2], limit of agreement:&#xa0;-92.2&#xa0;mm[2] to 143.3&#xa0;mm[2]; P&#xa0;< 0.001; mean bias pre/post CT: 28.3&#xa0;mm[2], limit of agreement:&#xa0;-65.8&#xa0;mm[2] to 122.4&#xa0;mm[2]; P = 0.046), driven by neo-LVOT underestimation in the group treated with dedicated mitral valve bioprosthesis. Both CT- and TEE-predicted-neo-LVOT areas exhibited an inverse correlation with postprocedural LVOT gradients (r[2]&#xa0;=&#xa0;0.481; P&#xa0;< 0.001 for TEE and r[2]&#xa0;=&#xa0;0.401; P&#xa0;< 0.001 for CT).<br><br>CONCLUSIONS: TEE-derived analysis provides comparable results with CT-derived metrics in predicting the neo-LVOT area and peak gradient after TMVR.},
}
@article {pmid39064141,
year = {2024},
author = {Alvi, MA and Pedro, KM and Quddusi, AI and Fehlings, MG},
title = {Advances and Challenges in Spinal Cord Injury Treatments.},
journal = {Journal of clinical medicine},
volume = {13},
number = {14},
pages = {},
pmid = {39064141},
issn = {2077-0383},
abstract = {Spinal cord injury (SCI) is a debilitating condition that is associated with long-term physical and functional disability. Our understanding of the pathogenesis of SCI has evolved significantly over the past three decades. In parallel, significant advances have been made in optimizing the management of patients with SCI. Early surgical decompression, adequate bony decompression and expansile duraplasty are surgical strategies that may improve neurological and functional outcomes in patients with SCI. Furthermore, advances in the non-surgical management of SCI have been made, including optimization of hemodynamic management in the critical care setting. Several promising therapies have also been investigated in pre-clinical studies, with some being translated into clinical trials. Given the recent interest in advancing precision medicine, several investigations have been performed to delineate the role of imaging, cerebral spinal fluid (CSF) and serum biomarkers in predicting outcomes and curating individualized treatment plans for SCI patients. Finally, technological advancements in biomechanics and bioengineering have also found a role in SCI management in the form of neuromodulation and brain-computer interfaces.},
}
@article {pmid39061777,
year = {2024},
author = {Manero, A and Rivera, V and Fu, Q and Schwartzman, JD and Prock-Gibbs, H and Shah, N and Gandhi, D and White, E and Crawford, KE and Coathup, MJ},
title = {Emerging Medical Technologies and Their Use in Bionic Repair and Human Augmentation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {7},
pages = {},
pmid = {39061777},
issn = {2306-5354},
abstract = {As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer's disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.},
}
@article {pmid39061407,
year = {2024},
author = {Allum, JHJ and Candreia, C and Honegger, F},
title = {Trunk Instability in the Pitch, Yaw, and Roll Planes during Clinical Balance Tests: Axis Differences and Correlations to vHIT Asymmetries Following Acute Unilateral Vestibular Loss.},
journal = {Brain sciences},
volume = {14},
number = {7},
pages = {},
pmid = {39061407},
issn = {2076-3425},
abstract = {BACKGROUND: Clinical dynamic posturography concentrates on the pitch and roll but not on the yaw plane instability measures. This emphasis may not represent the axis instability observed in clinical stance and gait tasks for patients with balance deficits in comparison to healthy control (HC) subjects, nor the expected instability based on correlations with vestibulo-ocular reflex (VOR) deficits. To examine the axis stability changes with vestibular loss, we measured trunk sway in all three directions (pitch, roll, and yaw) during the stance and gait tasks of patients with acute unilateral vestibular neuritis (aUVN) and compared the results with those of HC. Concurrent changes in VORs were also examined and correlated with trunk balance deficits.<br><br>METHODS: The results of 11 patients (mean age of 61 years) recorded within 6 days of aUVN onset were compared within those of 8 age-matched healthy controls (HCs). All subjects performed a two-legged stance task-standing with eyes closed on foam (s2ecf), a semi-gait task-walking eight tandem steps (tan8), and four gait tasks-walking 3 m with head rotating laterally, pitching, or eyes closed (w3hr, w3hp, w3ec), and walking over four barriers 24 cm high, spaced 1 m apart (barr). The tasks' peak-to-peak yaw, pitch and roll angles, and angular velocities were measured with a gyroscope system (SwayStar[TM]) mounted at L1-3 and combined into three, axis-specific, balance control indexes (BCI), using angles (a) for the tandem gait and barriers task, and angular velocities (v) for all other tasks, as follows: axis BCI = (2 &#xd7; 2ecf)v + 1.5 &#xd7; (w3hr + w3hp + w3ec)v + (tan8 + 12 &#xd7; barr)a.<br><br>RESULTS: Yaw and pitch BCIs were significantly (p &#x2264; 0.004) greater (88 and 30%, respectively) than roll BCIs for aUVN patients. For HCs, only yaw but not pitch BCIs were greater (p = 0.002) than those of roll (72%). The order of BCI aUVN vs. HC differences was pitch, yaw, and roll at 55, 44, and 31%, respectively (p &#x2264; 0.002). This difference with respect to roll corresponded to the known greater yaw plane than roll plane asymmetry (40 vs. 22%) following aUVN based on VOR responses. However, the lower pitch plane asymmetry (3.5%) in VOR responses did not correspond with the pitch plane instability observed in the balance control tests. The increases in pitch plane instability in UVL subjects were, however, highly correlated with those of roll and yaw.<br><br>CONCLUSIONS: These results indicate that greater yaw than pitch and roll trunk motion during clinical balance tasks is common for aUVN patients and HCs. However, aUVN leads to a larger increase in pitch than yaw plane instability and a smaller increase in roll plane instability. This difference with respect to roll corresponds to the known greater yaw plane than roll plane asymmetry (40 vs. 22%) following aUVN observed in VOR responses. However, the lower pitch plane asymmetry (3.5%) in VOR responses does not correspond with the enhanced movements in the pitch plane, observed in balance control tasks. Whether asymmetries in vestibular-evoked myogenic potentials (Vemps) are better correlated with the deficits in pitch plane balance control remains to be investigated. The current results provide a strong rationale for the clinical testing of directional specific balance responses, especially yaw and pitch, and the linking of balance results for yaw and roll to VOR asymmetries.},
}
@article {pmid39061384,
year = {2024},
author = {Ruiz, S and Lee, S and Dalboni da Rocha, JL and Ramos-Murguialday, A and Pasqualotto, E and Soares, E and García, E and Fetz, E and Birbaumer, N and Sitaram, R},
title = {Motor Intentions Decoded from fMRI Signals.},
journal = {Brain sciences},
volume = {14},
number = {7},
pages = {},
pmid = {39061384},
issn = {2076-3425},
support = {1211510//Agencia Nacional de Investigaci&#xf3;n y Desarrollo/ ; },
abstract = {Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.},
}
@article {pmid39058783,
year = {2024},
author = {Gu, S and Mattar, MG and Tang, H and Pan, G},
title = {Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection.},
journal = {Science advances},
volume = {10},
number = {30},
pages = {eadm8430},
pmid = {39058783},
issn = {2375-2548},
mesh = {*Neural Networks, Computer ; Humans ; Learning/physiology ; Artificial Intelligence ; Recognition, Psychology/physiology ; Algorithms ; },
abstract = {Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.},
}
@article {pmid39058459,
year = {2024},
author = {Quan, W and Guo, X and Cui, H and Luo, L and Li, M},
title = {A data compression algorithm with the improved SRLE for high-throughput neural signal acquisition device.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {32},
number = {6},
pages = {3955-3966},
pmid = {39058459},
issn = {1878-7401},
mesh = {*Algorithms ; *Data Compression/methods ; *Wireless Technology/instrumentation ; *Signal Processing, Computer-Assisted/instrumentation ; Brain-Computer Interfaces ; Animals ; Brain/physiology ; },
abstract = {BACKGROUND: Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design.<br><br>OBJECTIVE: To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized.<br><br>METHODS: To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments.<br><br>RESULTS: From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding.<br><br>CONCLUSION: The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.},
}
@article {pmid39057734,
year = {2024},
author = {Ávila, FJ},
title = {An Arduino-Powered Device for the Study of White Perception beyond the Visual Chromatic Critical Flicker Fusion Frequency.},
journal = {Journal of imaging},
volume = {10},
number = {7},
pages = {},
pmid = {39057734},
issn = {2313-433X},
abstract = {Arduino microcontrollers are used for a wide range of technological and biomedical applications, such as image classification, computer vision, brain-computer interaction and vision experiments. Here, we present a new cost-effective mini-device based on RGB LED flicker stimulation for the assessment of the chromatic temporal resolution of the visual function based on the concept of critical flicker fusion frequency (CFF). The assembly of the device and its testing in thirty young subjects demonstrate the steady white visual perception of a trichromatic flicker stimulus (mixture of red, green and blue stimuli) beyond the CFF. Macular function as measured by photo-stress recovery time (PRT) was found to be independent of the CFF measurements for red, green and blue lights. However, a statistical correlation was found between the contrast modulation for CFF for red and green stimuli and PRT. Finally, wavefront measurements demonstrate that high-order aberrations improve the temporal resolution of the visual function.},
}
@article {pmid39056472,
year = {2024},
author = {Ardaillon, H and Ribault, S and Herault, C and Pisella, L and Lechopier, N and Reilly, KT and Rode, G},
title = {Striking the Balance: Embracing Technology While Upholding Humanistic Principles in Neurorehabilitation.},
journal = {Neurorehabilitation and neural repair},
volume = {38},
number = {9},
pages = {705-710},
doi = {10.1177/15459683241265887},
pmid = {39056472},
issn = {1552-6844},
mesh = {Humans ; Artificial Intelligence/ethics/trends ; Brain-Computer Interfaces/ethics/trends ; *Humanism ; *Neurological Rehabilitation/ethics/methods/trends ; Robotics/ethics/methods/trends ; Congresses as Topic ; },
abstract = {BACKGROUND: The rapid advancement of technology-focused strategies in neurorehabilitation has brought optimism to individuals with neurological disorders, caregivers, and physicians while reshaping medical practice and training.<br><br>OBJECTIVES: We critically examine the implications of technology in neurorehabilitation, drawing on discussions from the 2021 and 2024 World Congress for NeuroRehabilitation. While acknowledging the value of technology, it highlights inherent limitations and ethical concerns, particularly regarding the potential overshadowing of humanistic approaches. The integration of technologies such as robotics, artificial intelligence, neuromodulation, and brain-computer interfaces enriches neurorehabilitation by offering interdisciplinary solutions. However, ethical considerations arise regarding the balance between compensation for deficits, accessibility of technologies, and their alignment with fundamental principles of care. Additionally, the pitfalls of relying solely on neuroimaging data are discussed, stressing the necessity for a more comprehensive understanding of individual variability and clinical skills in rehabilitation.<br><br>RESULTS: From a clinical perspective, the article advocates for realistic solutions that prioritize individual needs, quality of life, and social inclusion over technological allure. It underscores the importance of modesty and honesty in responding to expectations while emphasizing the uniqueness of each individual's experience. Moreover, it argues for the preservation of human-centric approaches alongside technological advancements, recognizing the invaluable role of clinical observation and human interaction in rehabilitation.<br><br>CONCLUSION: Ultimately, the article calls for a balanced attitude that integrates both scientific and humanistic perspectives in neurorehabilitation. It highlights the symbiotic relationship between the sciences and humanities, advocating for philosophical questioning to guide the ethical implementation of new technologies and foster interdisciplinary dialogue.},
}
@article {pmid39056413,
year = {2024},
author = {Qiu, J and Zhong, S},
title = {The impact of personalized BCI-VR rehabilitation programs on the recovery of motor functions in patients with stroke-induced hemiplegia.},
journal = {Minerva surgery},
volume = {},
number = {},
pages = {},
doi = {10.23736/S2724-5691.24.10370-X},
pmid = {39056413},
issn = {2724-5438},
}
@article {pmid39056313,
year = {2025},
author = {Mathew, J and Adhia, DB and Smith, ML and De Ridder, D and Mani, R},
title = {Closed-Loop Infraslow Brain-Computer Interface can Modulate Cortical Activity and Connectivity in Individuals With Chronic Painful Knee Osteoarthritis: A Secondary Analysis of a Randomized Placebo-Controlled Clinical Trial.},
journal = {Clinical EEG and neuroscience},
volume = {56},
number = {2},
pages = {165-180},
pmid = {39056313},
issn = {2169-5202},
mesh = {Humans ; *Osteoarthritis, Knee/physiopathology/therapy ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; Double-Blind Method ; *Brain-Computer Interfaces ; *Neurofeedback/methods ; *Chronic Pain/physiopathology/therapy ; Aged ; Brain/physiopathology ; Brain Mapping/methods ; *Cerebral Cortex/physiopathology ; },
abstract = {Introduction. Chronic pain is a percept due to an imbalance in the activity between sensory-discriminative, motivational-affective, and descending pain-inhibitory brain regions. Evidence suggests that electroencephalography (EEG) infraslow fluctuation neurofeedback (ISF-NF) training can improve clinical outcomes. It is unknown whether such training can induce EEG activity and functional connectivity (FC) changes. A secondary data analysis of a feasibility clinical trial was conducted to determine whether EEG ISF-NF training can significantly alter EEG activity and FC between the targeted cortical regions in people with chronic painful knee osteoarthritis (OA). Methods. A parallel, two-arm, double-blind, randomized, sham-controlled clinical trial was conducted. People with chronic knee pain associated with OA were randomized to receive sham NF training or source-localized ratio ISF-NF training protocol to down-train ISF bands at the somatosensory (SSC), dorsal anterior cingulate (dACC), and uptrain pregenual anterior cingulate cortices (pgACC). Resting state EEG was recorded at baseline and immediate post-training. Results. The source localization mapping demonstrated a reduction (P = .04) in the ISF band activity at the left dorsolateral prefrontal cortex (LdlPFC) in the active NF group. Region of interest analysis yielded significant differences for ISF (P = .008), slow (P = .007), beta (P = .043), and gamma (P = .012) band activities at LdlPFC, dACC, and bilateral SSC. The FC between pgACC and left SSC in the delta band was negatively correlated with pain bothersomeness in the ISF-NF group. Conclusion. The EEG ISF-NF training can modulate EEG activity and connectivity in individuals with chronic painful knee osteoarthritis, and the observed EEG changes correlate with clinical pain measures.},
}
@article {pmid39053935,
year = {2024},
author = {Yan, Y and An, X and Ma, Y and Jiang, Z and Di, Y and Li, T and Wang, H and Ren, H and Ma, L and Luo, B and Huang, Y},
title = {Detection of early neurological deterioration using a quantitative electroencephalography system in patients with large vessel occlusion stroke after endovascular treatment.},
journal = {Journal of neurointerventional surgery},
volume = {},
number = {},
pages = {},
doi = {10.1136/jnis-2024-022011},
pmid = {39053935},
issn = {1759-8486},
abstract = {BACKGROUND: Early neurological deterioration (END) is a serious complication in patients with large vessel occlusion (LVO) stroke. However, modalities to monitor neurological function after endovascular treatment (EVT) are lacking. This study aimed to evaluate the diagnostic accuracy of a quantitative electroencephalography (qEEG) system for detecting END.<br><br>METHODS: In this prospective, nested case-control study, we included 47 patients with anterior circulation LVO stroke and 34 healthy adults from different clinical centers in Tianjin, China, from May 2023 to January 2024. Patients with stroke underwent EEG at admission and after EVT. The diagnostic accuracy of qEEG features for END was evaluated by receiver operating characteristic curve analysis, and the feasibility was evaluated by the percentage of artifact-free data and device-related adverse events.<br><br>RESULTS: 14 patients with stroke had END (29.8%, 95% CI 16.2% to 43.4%), with most developed within 12&#x2009;hours of recanalization (n=11). qEEG features showed significant correlations with National Institutes of Health Stroke Scale score and infarct volume. After matching, 13 patients with END and 26 controls were included in the diagnostic analysis. Relative alpha power demonstrated the highest diagnostic accuracy for the affected and unaffected hemispheres. The optimal electrode positions were FC3/4 in the unaffected hemisphere, and F7/8 and C3/4 in the affected hemisphere. No device-related adverse events were reported.<br><br>CONCLUSION: The qEEG system exhibits a high diagnostic accuracy for END and may be a promising tool for monitoring neurological function. The identification of optimal electrode positions may enhance device convenience.<br><br>CLINICAL TRIAL REGISTRATION: ChiCTR 2300070829.},
}
@article {pmid39053485,
year = {2024},
author = {Eder, M and Xu, J and Grosse-Wentrup, M},
title = {Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6793},
pmid = {39053485},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Benchmarking/methods ; *Neural Networks, Computer ; Humans ; *Electroencephalography/methods ; Algorithms ; Evoked Potentials, Visual/physiology ; Imagination/physiology ; },
abstract = {Objective.To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI Benchmarks, to compare novel convolutional neural networks to state-of-the-art Riemannian approaches across a broad range of EEG datasets, including motor imagery, P300, and steady-state visual evoked potentials paradigms.Approach.We systematically evaluated the performance of convolutional neural networks, specifically EEGNet, shallow ConvNet, and deep ConvNet, against well-established Riemannian decoding methods using MOABB processing pipelines. This evaluation included within-session, cross-session, and cross-subject methods, to provide a practical analysis of model effectiveness and to find an overall solution that performs well across different experimental settings.Main results.We find no significant differences in decoding performance between convolutional neural networks and Riemannian methods for within-session, cross-session, and cross-subject analyses.Significance.The results show that, when using traditional Brain-Computer Interface paradigms, the choice between CNNs and Riemannian methods may not heavily impact decoding performances in many experimental settings. These findings provide researchers with flexibility in choosing decoding approaches based on factors such as ease of implementation, computational efficiency or individual preferences.},
}
@article {pmid39053465,
year = {2024},
author = {Zhao, Q and Li, X and Wen, J and He, Y and Zheng, N and Li, W and Cardona, A and Gong, Z},
title = {A two-layer neural circuit controls fast forward locomotion in Drosophila.},
journal = {Current biology : CB},
volume = {34},
number = {15},
pages = {3439-3453.e5},
doi = {10.1016/j.cub.2024.06.060},
pmid = {39053465},
issn = {1879-0445},
mesh = {Animals ; *Locomotion/physiology ; *Drosophila melanogaster/physiology ; Larva/physiology ; Cholinergic Neurons/physiology ; Interneurons/physiology ; Drosophila/physiology ; },
abstract = {Fast forward locomotion is critical for animal hunting and escaping behaviors. However, how the underlying neural circuit is wired at synaptic resolution to decide locomotion direction and speed remains poorly understood. Here, we identified in the ventral nerve cord (VNC) a set of ascending cholinergic neurons (AcNs) to be command neurons capable of initiating fast forward peristaltic locomotion in Drosophila larvae. Targeted manipulations revealed that AcNs are necessary and sufficient for fast forward locomotion. AcNs can activate their postsynaptic partners, A01j and A02j; both are interneurons with locomotory rhythmicity. Activated A01j neurons form a posterior-anteriorly descendent gradient in output activity along the VNC to launch forward locomotion from the tail. Activated A02j neurons exhibit quicker intersegmental transmission in activity that enables fast propagation of motor waves. Our work revealed a global neural mechanism that coordinately controls the launch direction and propagation speed of Drosophila locomotion, furthering the understanding of the strategy for locomotion control.},
}
@article {pmid39053352,
year = {2024},
author = {Soriano-Segura, P and Ortiz, M and Iáñez, E and Azorín, JM},
title = {Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential.},
journal = {Computer methods and programs in biomedicine},
volume = {255},
number = {},
pages = {108332},
doi = {10.1016/j.cmpb.2024.108332},
pmid = {39053352},
issn = {1872-7565},
mesh = {Humans ; *Brain-Computer Interfaces ; *Exoskeleton Device ; *Lower Extremity/physiology ; Male ; *Gait/physiology ; Adult ; Electroencephalography ; Young Adult ; Female ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-Machine Interfaces (BMIs) based on a motor imagination paradigm provide an intuitive approach for the exoskeleton control during gait. However, their clinical applicability remains difficulted by accuracy limitations and sensitivity to false activations. A proposed improvement involves integrating the BMI with methods based on detecting Error Related Potentials (ErrP) to self-tune erroneous commands and enhance not only the system accuracy, but also its usability. The aim of the current research is to characterize the ErrP at the beginning of the gait with a lower limb exoskeleton to reduce the false starts in the BMI system. Furthermore, this study is valuable for determining which type of feedback, Tactile, Visual, or Visuo-Tactile, achieves the best performance in evoking and detecting the ErrP.<br><br>METHODS: The initial phase of the research concentrates on detecting ErrP at the beginning of gait to improve the efficiency of an asynchronous BMI based on motor imagery (BMI-MI) to control a lower limb exoskeleton. Initially, an experimental protocol is designed to evoke ErrP at the start of gait, employing three different stimuli: Tactile, Visual, and Visuo-Tactile. An iterative selection process is then utilized to characterize ErrP in both time and frequency domains and fine-tune various parameters, including electrode distribution, feature combinations, and classifiers. A generic classifier with 6 subjects is employed to configure an ensemble classification system for detecting ErrP and reducing the false starts.<br><br>RESULTS: The ensembled system configured with the selected parameters yields an average correction of false starts of 72.60 % &#xb1; 10.23, highlighting its corrective efficacy. Tactile feedback emerges as the most effective stimulus, outperforming Visual and Visuo-Tactile feedback in both training types.<br><br>CONCLUSIONS: The results suggest promising prospects for reducing the false starts when integrating ErrP with BMI-MI, employing Tactile feedback. Consequently, the security of the system is enhanced. Subsequent, further research efforts will focus on detecting error potential during movement for gait stop, in order to limit undesired stops.},
}
@article {pmid39052465,
year = {2025},
author = {Zhong, XC and Wang, Q and Liu, D and Chen, Z and Liao, JX and Sun, J and Zhang, Y and Fan, FL},
title = {EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2484-2495},
doi = {10.1109/JBHI.2024.3431230},
pmid = {39052465},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods/classification ; Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; *Imagination/physiology ; Adult ; },
abstract = {Motorimagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the performance of classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the test data during training. This is unrealistic and impractical in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments conducted on a simulative dataset, BCI competition IV 2a, 2b, and OpenBMI datasets, demonstrate the superiority and competitive performance of our proposed framework over other state-of-the-art methods. Specifically, EEG-DG achieves average classification accuracies of 81.79% and 87.12% on datasets IV-2a and IV-2b, respectively, and 78.37% and 76.94% for inter-session and inter-subject evaluations on dataset OpenBMI, which even outperforms some domain adaptation methods.},
}
@article {pmid39050380,
year = {2024},
author = {Porcaro, C and Diciotti, S and Madan, CR and Marzi, C},
title = {Editorial: Methods and application in fractal analysis of neuroimaging data.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1453284},
pmid = {39050380},
issn = {1662-5161},
}
@article {pmid39049815,
year = {2024},
author = {M, B and E, GMK and Raimond, K and George S, T},
title = {An automated ensemble approach using Harris Hawk optimization for visually evoked EEG signal classification.},
journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine},
volume = {238},
number = {7},
pages = {837-847},
doi = {10.1177/09544119241260553},
pmid = {39049815},
issn = {2041-3033},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Automation ; Brain-Computer Interfaces ; Machine Learning ; },
abstract = {Steady-state visually evoked potential is one of the active explorations in the brain-computer interface research. Electroencephalogram based brain computer interface studies have been widely applied to perceive solutions for real-world problems in the healthcare domain. The classification of externally bestowed visual stimuli of different frequencies on a human was experimented to identify the need of paralytic people. Although many classifiers are at the fingertip of machine learning technology, recent research has proven that ensemble learning is more efficacious than individual classifiers. Despite its efficiency, ensemble learning technology exhibits certain drawbacks like taking more time on selecting the optimal classifier subset. This research article utilizes the Harris Hawk Optimization algorithm to select the best classifier subset from the given set of classifiers. The objective of the research is to develop an efficient multi-classifier model for electroencephalogram signal classification. The proposed model utilizes the Boruta Feature Selection algorithm to select the prominent features for classification. Thus selected prominent features are fed into the multi-classifier subset which has been generated by the Harris Hawk Optimization algorithm. The results of the multi-classifier ensemble model are aggregated using Stacking, Bagging, Boosting, and Voting. The proposed model is evaluated against the acquired dataset and produces a promising accuracy of 96.1%, 98.7%, 91.91%, and 99.01% with the ensemble techniques respectively. The proposed model is also validated with other performance metrics such as sensitivity, specificity, and F1-Score. The experimental results show that the proposed model proves its supremacy in segregating the multi-class classification problem with high accuracy.},
}
@article {pmid39048655,
year = {2024},
author = {Liu, B and Gao, H and Jiang, Y and Wu, J},
title = {Research on a soft saturation nonlinear SSVEP signal feature extraction algorithm.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {17043},
pmid = {39048655},
issn = {2045-2322},
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.},
}
@article {pmid39046259,
year = {2024},
author = {Mansuri, A and Vora, P and Feuerbach, T and Winck, J and Vermeer, AWP and Hoheisel, W and Kierfeld, J and Thommes, M},
title = {A Monte Carlo simulation of tracer diffusion in amorphous polymers.},
journal = {Soft matter},
volume = {20},
number = {31},
pages = {6204-6214},
doi = {10.1039/d4sm00782d},
pmid = {39046259},
issn = {1744-6848},
abstract = {Tracer diffusion in amorphous polymers is a sought-after quantity for a range of technological applications. In this regard, a quantitative description of the so-called decoupling from the reverse proportionality between viscosity and diffusion coefficient into a fractional one remains a challenge requiring a deeper insight. This work employs a Monte Carlo simulation framework in 3 dimensions to investigate the consequences of different scenarios for estimating this fractional exponent on the diffusion coefficient of tracers in polymers near glass transition. To this end, we adopted a continuous-time random walk model for tracer diffusion in the supercooled liquid state. The waiting time distribution of the diffusants was computed based on the rotational correlation times of the polymer. This proposed procedure is of particular interest because it brings the quantity of waiting time (and its statistics) in connection with the measurable observable of rotational times. In the framework of our simulations the aforementioned fractional exponent appears in the relation between the diffusant's waiting time and the rotational time of the diffusion medium. A limited comparison with experimental diffusivities from the literature revealed a reasonable agreement with a fractional exponent on the basis of the molar volumes of the diffusant and the monomeric unit. Finally, an analysis of time-averaged mean squared displacement pointed to normal Brownian dynamics for tracer diffusion in polymers above the glass transition temperature.},
}
@article {pmid39045509,
year = {2024},
author = {Gall, R and Mcdonald, N and Huang, X and Wears, A and Price, RB and Ostadabbas, S and Akcakaya, M and Woody, ML},
title = {AttentionCARE: replicability of a BCI for the clinical application of augmented reality-guided EEG-based attention modification for adolescents at high risk for depression.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1360218},
pmid = {39045509},
issn = {1662-5161},
support = {K23 MH119225/MH/NIMH NIH HHS/United States ; },
abstract = {Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away from goal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents.},
}
@article {pmid39045104,
year = {2024},
author = {Liang, F and Song, Y and Huang, X and Ren, T and Ji, Q and Guo, Y and Li, X and Sui, Y and Xie, X and Han, L and Li, Y and Ren, Y and Xu, Z},
title = {Assessing breast disease with deep learning model using bimodal bi-view ultrasound images and clinical information.},
journal = {iScience},
volume = {27},
number = {7},
pages = {110279},
pmid = {39045104},
issn = {2589-0042},
abstract = {Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists' scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer.},
}
@article {pmid39043844,
year = {2025},
author = {Lin, S and Fan, CY and Wang, HR and Li, XF and Zeng, JL and Lan, PX and Li, HX and Zhang, B and Hu, C and Xu, J and Luo, JH},
title = {Correction: Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice.},
journal = {Molecular psychiatry},
volume = {30},
number = {1},
pages = {365-366},
doi = {10.1038/s41380-024-02658-7},
pmid = {39043844},
issn = {1476-5578},
}
@article {pmid39043690,
year = {2024},
author = {Hofmann, UG and Stieglitz, T},
title = {Why some BCI should still be called BMI.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {6207},
pmid = {39043690},
issn = {2041-1723},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Neurotechnology is becoming an integral part of clinical practice. In this Comment, the authors advocate for more scrutiny and attention when describing new neurotechnologies with particular attention to the surgical risks involved and the invasiveness.},
}
@article {pmid39038627,
year = {2024},
author = {Hou, Y and Liu, W and He, T and Chen, A},
title = {Association between the performance of executive function and the remission of depressive state after clinical treatment in patients with depression.},
journal = {Journal of affective disorders},
volume = {364},
number = {},
pages = {28-36},
doi = {10.1016/j.jad.2024.07.137},
pmid = {39038627},
issn = {1573-2517},
mesh = {Humans ; *Executive Function/physiology ; Male ; Female ; *Memory, Short-Term ; Adult ; Middle Aged ; Neuropsychological Tests ; Depression/psychology/therapy ; Reaction Time ; Remission Induction ; Follow-Up Studies ; Cognitive Dysfunction ; },
abstract = {BACKGROUND: Previous studies have reported that patients with depression have significant cognitive impairment. The aim of this study is to comprehensively evaluate the impairment of executive functions in patients with depression and whether the cognitive behavior performance of executive function is association with remission of depressive state after clinical treatment.<br><br>METHODS: We used cognitive-behavioral test to evaluate the performance of executive functions of 95 inpatients with depression before hospitalization and conducted two follow-up evaluations of their depression status on the 15th day of hospitalization and approximately 9&#xa0;months after discharge.<br><br>RESULTS: The performance of executive function except the accuracy of inhibition control in patients with depression were significantly worse than that of healthy controls. Multivariate linear regression analysis found that the reaction time of working memory not only had a significant linear relationship with the baseline depression scores of patients with depression, but also had a significant linear relationship with the reduced depression scores after two follow-up visits.<br><br>LIMITATIONS: We only used cognitive-behavioral data as indicators to evaluate the cognitive performances of participants and only measured three components of executive function.<br><br>CONCLUSIONS: The reaction time of working memory was a stable and effective predictor of symptom relief in patients with depression after clinical treatment. These results provide initial evidence for working memory to predict the clinical prognosis of inpatients with depression prospectively, which could be further leveraged to improve intervention approaches and analyze the heterogeneity of depression.},
}
@article {pmid39038520,
year = {2024},
author = {Campos-Arteaga, G and Flores-Torres, J and Rojas-Thomas, F and Morales-Torres, R and Poyser, D and Sitaram, R and Rodríguez, E and Ruiz, S},
title = {EEG subject-dependent neurofeedback training selectively impairs declarative memories consolidation process.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {203},
number = {},
pages = {112406},
doi = {10.1016/j.ijpsycho.2024.112406},
pmid = {39038520},
issn = {1872-7697},
mesh = {Humans ; *Neurofeedback/physiology/methods ; *Memory Consolidation/physiology ; Male ; Female ; Young Adult ; Adult ; *Electroencephalography ; Adolescent ; },
abstract = {The process of stabilization and storage of memories, known as consolidation, can be modulated by different interventions. Research has shown that self-regulation of brain activity through Neurofeedback (NFB) during the consolidation phase significantly impacts memory stabilization. While some studies have successfully modulated the consolidation phase using traditional EEG-based Neurofeedback (NFB) that focuses on general parameters, such as training a specific frequency band at particular electrodes, they often overlook the unique and complex neurodynamics that underlie each memory content in different individuals, potentially limiting the selective modulation of memories. The main objective of this study is to investigate the effects of a Subject-Dependent NFB (SD-NFB), based on individual models created from the brain activity of each participant, on long-term declarative memories. Participants underwent an experimental protocol involving three sessions. In the first session, they learned images of faces and houses while their brain activity was recorded. This EEG data was used to create individualized models to identify brain patterns related to learning these images. Participants were then divided into three groups, with one group receiving SD-NFB to enhance brain activity linked to faces, another to houses, and a CONTROL sham group that did not receive SD-NFB. Memory performance was evaluated 24 h and seven days later using an 'old-new' recognition task, where participants distinguished between 'old' and 'new' images. The results showed that memory contents (faces or houses) whose brain patterns were trained via SD-NFB scored lower in recognition compared to untrained contents, as evidenced 24 h and seven days post-training. In summary, this study demonstrates that SD-NFB can selectively impact the consolidation of specific declarative memories. This technique could hold significant implications for clinical applications, potentially aiding in the modulation of declarative memory strength in neuropsychiatric disorders where memories are pathologically exacerbated.},
}
@article {pmid39038519,
year = {2024},
author = {Kapgate, DD},
title = {Application of hybrid SSVEP + P300 brain computer interface to control avatar movement in mobile virtual reality gaming environment.},
journal = {Behavioural brain research},
volume = {472},
number = {},
pages = {115154},
doi = {10.1016/j.bbr.2024.115154},
pmid = {39038519},
issn = {1872-7549},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; *Avatar ; *Brain-Computer Interfaces ; *Electroencephalography ; *Event-Related Potentials, P300/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; *Video Games ; },
abstract = {INTRODUCTION: This research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex.<br><br>METHODS: A BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar's movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement.<br><br>RESULTS AND CONCLUSIONS: Conventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.},
}
@article {pmid39037771,
year = {2024},
author = {Stout, JJ and George, AE and Kim, S and Hallock, HL and Griffin, AL},
title = {Using synchronized brain rhythms to bias memory-guided decisions.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {39037771},
issn = {2050-084X},
support = {R21 MH117687/MH/NIMH NIH HHS/United States ; },
mesh = {*Prefrontal Cortex/physiology ; *Decision Making/physiology ; *Theta Rhythm/physiology ; *Hippocampus/physiology ; Animals ; Male ; Memory/physiology ; Brain-Computer Interfaces ; Humans ; Thalamus/physiology ; Optogenetics ; },
abstract = {Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain-machine interface that initiated task trials based on the magnitude of prefrontal-hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain-machine interfacing.},
}
@article {pmid39037698,
year = {2024},
author = {Ruan, K and Bai, G and Fang, Y and Li, D and Li, T and Liu, X and Lu, B and Lu, Q and Songyang, Z and Sun, S and Wang, Z and Zhang, X and Zhou, W and Zhang, H},
title = {Biomolecular condensates and disease pathogenesis.},
journal = {Science China. Life sciences},
volume = {67},
number = {9},
pages = {1792-1832},
pmid = {39037698},
issn = {1869-1889},
mesh = {Humans ; *Neurodegenerative Diseases/metabolism ; *Neoplasms/metabolism ; *Biomolecular Condensates/metabolism ; Organelles/metabolism ; Animals ; Phase Transition ; Hearing Loss/metabolism ; Immune System Diseases/metabolism ; },
abstract = {Biomolecular condensates or membraneless organelles (MLOs) formed by liquid-liquid phase separation (LLPS) divide intracellular spaces into discrete compartments for specific functions. Dysregulation of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration, tumorigenesis, and many other pathological processes. Herein, we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase separation. We then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders, hearing loss, cancers, and immunological diseases. Finally, we describe the emerging discovery of chemical modulators of phase separation.},
}
@article {pmid39037186,
year = {2024},
author = {Liu, H and Bai, Y and Zheng, Q and Liu, J and Zhu, J and Ni, G},
title = {Electrophysiological correlation of auditory selective spatial attention in the "cocktail party" situation.},
journal = {Human brain mapping},
volume = {45},
number = {11},
pages = {e26793},
pmid = {39037186},
issn = {1097-0193},
support = {2023YFF1203500//National Key Research and Development Program of China/ ; 2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 81971698//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; *Attention/physiology ; Female ; Young Adult ; *Electroencephalography ; *Space Perception/physiology ; *Evoked Potentials, Auditory/physiology ; Adult ; *Auditory Perception/physiology ; Acoustic Stimulation ; Brain Mapping ; },
abstract = {The auditory system can selectively attend to the target source in complex environments, the phenomenon known as the "cocktail party" effect. However, the spatiotemporal dynamics of electrophysiological activity associated with auditory selective spatial attention (ASSA) remain largely unexplored. In this study, single-source and multiple-source paradigms were designed to simulate different auditory environments, and microstate analysis was introduced to reveal the electrophysiological correlates of ASSA. Furthermore, cortical source analysis was employed to reveal the neural activity regions of these microstates. The results showed that five microstates could explain the spatiotemporal dynamics of ASSA, ranging from MS1 to MS5. Notably, MS2 and MS3 showed significantly lower partial properties in multiple-source situations than in single-source situations, whereas MS4 had shorter durations and MS5 longer durations in multiple-source situations than in single-source situations. MS1 had insignificant differences between the two situations. Cortical source analysis showed that the activation regions of these microstates initially transferred from the right temporal cortex to the temporal-parietal cortex, and subsequently to the dorsofrontal cortex. Moreover, the neural activity of the single-source situations was greater than that of the multiple-source situations in MS2 and MS3, correlating with the N1 and P2 components, with the greatest differences observed in the superior temporal gyrus and inferior parietal lobule. These findings suggest that these specific microstates and their associated activation regions may serve as promising substrates for decoding ASSA in complex environments.},
}
@article {pmid39036878,
year = {2024},
author = {Ye, Y and Deng, Q and Wu, J and Zhong, C and Ma, H and Shi, Y and Li, D and Tang, R and Tang, Y and Jian, J and Zhu, B and Lin, H and Li, L},
title = {Electrostatic Force-Assisted Transfer of Flexible Silicon Photodetector Focal Plane Arrays for Image Sensors.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {30},
pages = {39572-39579},
doi = {10.1021/acsami.4c05890},
pmid = {39036878},
issn = {1944-8252},
abstract = {Flexible photodetectors are pivotal in contemporary optoelectronic technology applications, such as data reception and image sensing, yet their performance and yield are often hindered by the challenge of heterogeneous integration between photoactive materials and flexible substrates. Here, we showcase the potential of an electrostatic force-assisted transfer printing technique for integrating Si PIN photodiodes onto flexible substrates. This clean and dry process eliminates the need for chemical etchants, making it a highly desirable method for manufacturing high-performance flexible photodetector arrays, expanding their widespread applications in electronic eyes, robotics, and human-machine interaction. As a demonstration, a 5 × 5 flexible Si photodetector focal plane array is constructed for imaging sensors and shaped into a convex semicylindrical form to achieve a π field of view with long-term mechanical and thermal stability. Such an approach provides a high yield rate and consistent performance, with the single photodetector demonstrating exceptional characteristics, including a responsivity of 0.61 A/W, a response speed of 39.77 MHz, a linear dynamic range of 108.53 dB, and a specific detectivity of 2.75 × 10[12] Jones at an applied voltage of -3 V at 940 nm.},
}
@article {pmid39034991,
year = {2024},
author = {Ye, Y and Xia, L and Yang, S and Luo, Y and Tang, Z and Li, Y and Han, L and Xie, H and Ren, Y and Na, N},
title = {Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.},
journal = {Frontiers in immunology},
volume = {15},
number = {},
pages = {1438247},
pmid = {39034991},
issn = {1664-3224},
mesh = {Humans ; *Deep Learning ; *Kidney Transplantation/adverse effects ; *Graft Rejection/pathology/immunology/diagnosis ; Biopsy ; Male ; Female ; Allografts/pathology ; Adult ; Middle Aged ; Kidney/pathology/immunology ; Reproducibility of Results ; },
abstract = {BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection.<br><br>METHOD: We collected H&amp;E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations.<br><br>RESULTS: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively.<br><br>CONCLUSION: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.},
}
@article {pmid39033788,
year = {2024},
author = {Chen, Y and Liu, S and Hao, Y and Zhao, Q and Ren, J and Piao, Y and Wang, L and Yang, Y and Jin, C and Wang, H and Zhou, X and Gao, JH and Zhang, X and Wei, Z},
title = {Higher emotional synchronization is modulated by relationship quality in romantic relationships and not in close friendships.},
journal = {NeuroImage},
volume = {297},
number = {},
pages = {120733},
doi = {10.1016/j.neuroimage.2024.120733},
pmid = {39033788},
issn = {1095-9572},
mesh = {Humans ; Male ; *Friends/psychology ; *Emotions/physiology ; *Interpersonal Relations ; Female ; Young Adult ; Adult ; *Electroencephalography ; Prefrontal Cortex/physiology ; Social Interaction ; },
abstract = {Emotions are fundamental to social interaction and deeply intertwined with interpersonal dynamics, especially in romantic relationships. Although the neural basis of interaction processes in romance has been widely explored, the underlying emotions and the connection between relationship quality and neural synchronization remain less understood. Our study employed EEG hyperscanning during a non-interactive video-watching paradigm to compare the emotional coordination between romantic couples and close friends. Couples showed significantly greater behavioral and prefrontal alpha synchronization than friends. Notably, couples with low relationship quality required heightened neural synchronization to maintain robust behavioral synchronization. Further support vector machine analysis underscores the crucial role of prefrontal activity in differentiating couples from friends. In summary, our research addresses gaps concerning how intrinsic emotions linked to relationship quality influence neural and behavioral synchronization by investigating a natural non-interactive context, thereby advancing our understanding of the neural mechanisms underlying emotional coordination in romantic relationships.},
}
@article {pmid39032522,
year = {2024},
author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V},
title = {Lightweight attention mechanisms for EEG emotion recognition for brain computer interface.},
journal = {Journal of neuroscience methods},
volume = {410},
number = {},
pages = {110223},
doi = {10.1016/j.jneumeth.2024.110223},
pmid = {39032522},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Attention/physiology ; Neural Networks, Computer ; },
abstract = {BACKGROUND: In the realm of brain-computer interfaces (BCI), identifying emotions from electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the intricacy of the signals, and the several channels that make up the signals.<br><br>NEW METHODS: Using dual-stream structure scaling and multiple attention mechanisms (LDMGEEG), a lightweight network is provided to maximize the accuracy and performance of EEG-based emotion identification. Reducing the number of computational parameters while maintaining the current level of classification accuracy is the aim. This network employs a symmetric dual-stream architecture to assess separately time-domain and frequency-domain spatio-temporal maps constructed using differential entropy features of EEG signals as inputs.<br><br>RESULT: The experimental results show that after significantly lowering the number of parameters, the model achieved the best possible performance in the field, with a 95.18&#x202f;% accuracy on the SEED dataset.<br><br>Moreover, it reduced the number of parameters by 98&#x202f;% when compared to existing models.<br><br>CONCLUSION: The proposed method distinct channel-time/frequency-space multiple attention and post-attention methods enhance the model's ability to aggregate features and result in lightweight performance.},
}
@article {pmid39030206,
year = {2024},
author = {Kueper, N and Kim, SK and Kirchner, EA},
title = {Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {16690},
pmid = {39030206},
issn = {2045-2322},
support = {01IW20003//Bundesministerium f&#xfc;r Bildung und Forschung/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; *Exoskeleton Device ; Calibration ; *Intention ; *Movement/physiology ; Adult ; Machine Learning ; Female ; Young Adult ; },
abstract = {Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session. This approach is validated on healthy subjects in this study. We will use the proposed approach in our future rehabilitation application, where the movement intention of the affected arm of a patient can be inferred from the EEG data recorded during bilateral arm movements enabled by the exoskeleton mirroring arm movements from the unaffected to the affected arm. For the initial evaluation, we compared two trained models for predicting unilateral and bilateral movement intentions without applying a classifier transfer. For the main evaluation, we predicted unilateral movement intentions without a calibration session by transferring the classifier trained on data from bilateral movement intentions. Our results showed that the classification performance for the transfer case was comparable to that in the non-transfer case, even with only 4 or 8 EEG channels. Our results contribute to robotic rehabilitation by eliminating the need for a calibration session, since EEG data for training is recorded during the rehabilitation session, and only a small number of EEG channels are required for model training.},
}
@article {pmid39030159,
year = {2024},
author = {Li, D and Yu, J and Zhu, C and Du, Y and Ma, Y and Dong, Y and Wu, Z},
title = {Identifying extracerebellar characteristics in a large cohort of 319 Chinese patients with spinocerebellar ataxia type 3.},
journal = {Chinese medical journal},
volume = {137},
number = {17},
pages = {2131-2133},
pmid = {39030159},
issn = {2542-5641},
mesh = {Adolescent ; Adult ; Aged ; Child ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Cohort Studies ; East Asian People ; *Machado-Joseph Disease/diagnosis/pathology ; },
}
@article {pmid39029500,
year = {2024},
author = {Rajpura, P and Cecotti, H and Kumar Meena, Y},
title = {Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6593},
pmid = {39029500},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Artificial Intelligence/trends ; Electroencephalography/methods ; },
abstract = {Objective.This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. Trust in these models can be established by incorporating reasoning or causal relationships from domain experts. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding, often used interchangeably in this context, and formulate a comprehensive framework.Approach.To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology-preferred reporting items for systematic reviews and meta-analyses to review (n = 1246) and analyse (n = 84) studies published in 2015 and onwards for key insights.Main results.The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualise and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.Significance.This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.},
}
@article {pmid39029497,
year = {2024},
author = {Habashi, AG and Azab, AM and Eldawlatly, S and Aly, GM},
title = {Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6598},
pmid = {39029497},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Neural Networks, Computer ; Calibration ; Male ; Adult ; Female ; Movement/physiology ; Young Adult ; Deep Learning ; },
abstract = {Objective.Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require recording subject-dependent data to train machine learning classifiers that are used to identify the intended motor action. This represents a challenge in developing MI BCIs as it complicates its calibration and hinders the wide adoption of such a technology.Approach.This study focuses on enhancing cross-subject (CS) MI EEG classification using EEG spectrum images. The proposed calibration-free approach employs deep learning techniques for MI classification and Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed WGAN generates synthetic spectrum images from the recorded MI-EEG to expand the training dataset; aiming to enhance the classifier's performance. The proposed approach eliminates the need for any calibration data from the target subject, making it more suitable for real-world applications.Main results.To assess the robustness and efficacy of the proposed framework, we utilized the BCI competition IV-2B, IV-2 A, and IV-1 benchmark datasets, employing leave one-subject out validation. Our results demonstrate that using the proposed modified VGG-CNN classifier in addition to WGAN-generated data for augmentation leads to an enhancement in CS accuracy outperforming state-of-the-art methods.Significance.This approach could represent one step forward towards developing calibration-free BCI systems and hence broaden their applications.},
}
@article {pmid39029496,
year = {2024},
author = {Meng, J and Li, S and Li, G and Luo, R and Sheng, X and Zhu, X},
title = {A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6595},
pmid = {39029496},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Brain/physiology ; Models, Neurological ; Movement/physiology ; },
abstract = {Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.},
}
@article {pmid39029493,
year = {2024},
author = {Zhou, Q and Zhang, Q and Wang, B and Yang, Y and Yuan, Z and Li, S and Zhao, Y and Zhu, Y and Gao, Z and Zhou, J and Wang, C},
title = {RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad658e},
pmid = {39029493},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Attention/physiology ; Female ; Young Adult ; *Electroencephalography/methods ; Adult ; *Photic Stimulation/methods ; Evoked Potentials/physiology ; Pattern Recognition, Visual/physiology ; },
abstract = {Objective.While brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is widely used in target detection, patterns of event-related potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded 'BCI-blind' users are still lacking.Approach.A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants' attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening.Main results.Statistical analysis showed that the conditions of target concealment and size significantly influenced ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, exhibited lower amplitudes and longer latencies. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets.Significance.The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.},
}
@article {pmid39028609,
year = {2024},
author = {Su, J and Wang, J and Wang, W and Wang, Y and Bunterngchit, C and Zhang, P and Hou, ZG},
title = {An Adaptive Hybrid Brain-Computer Interface for Hand Function Rehabilitation of Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2950-2960},
doi = {10.1109/TNSRE.2024.3431025},
pmid = {39028609},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Hand ; Male ; *Electroencephalography/methods ; Female ; *Evoked Potentials, Visual/physiology ; Middle Aged ; Adult ; Algorithms ; Imagination/physiology ; Stroke/physiopathology ; Gestures ; Aged ; Healthy Volunteers ; Young Adult ; Photic Stimulation ; Signal-To-Noise Ratio ; Reproducibility of Results ; },
abstract = {Motor imagery (MI) based brain computer interface (BCI) has been extensively studied to improve motor recovery for stroke patients by inducing neuroplasticity. However, due to the lower spatial resolution and signal-to-noise ratio (SNR) of electroencephalograph (EEG), MI based BCI system that involves decoding hand movements within the same limb remains lower classification accuracy and poorer practicality. To overcome the limitations, an adaptive hybrid BCI system combining MI and steady-state visually evoked potential (SSVEP) is developed to improve decoding accuracy while enhancing neural engagement. On the one hand, the SSVEP evoked by visual stimuli based on action-state flickering coding approach significantly improves the recognition accuracy compared to the pure MI based BCI. On the other hand, to reduce the impact of SSVEP on MI due to the dual-task interference effect, the event-related desynchronization (ERD) based neural engagement is monitored and employed for feedback in real-time to ensure the effective execution of MI tasks. Eight healthy subjects and six post-stroke patients were recruited to verify the effectiveness of the system. The results showed that the four-class gesture recognition accuracies of healthy individuals and patients could be improved to 94.37 ± 4.77 % and 79.38 ± 6.26 %, respectively. Moreover, the designed hybrid BCI could maintain the same degree of neural engagement as observed when subjects solely performed MI tasks. These phenomena demonstrated the interactivity and clinical utility of the developed system for the rehabilitation of hand function in stroke patients.},
}
@article {pmid39028484,
year = {2024},
author = {Blanco-Diaz, CF and Guerrero-Mendez, CD and de Andrade, RM and Badue, C and De Souza, AF and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {12},
pages = {3763-3779},
pmid = {39028484},
issn = {1741-0444},
support = {285/2021//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa e Inova&#xe7;&#xe3;o do Esp&#xed;rito Santo/ ; },
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; Biomechanical Phenomena ; Male ; *Lower Extremity/physiology ; Adult ; Female ; Brain-Computer Interfaces ; Bicycling/physiology ; Neural Networks, Computer ; Young Adult ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.},
}
@article {pmid39028220,
year = {2025},
author = {Pitt, KM and McKelvey, M and Weissling, K and Thiessen, A},
title = {Brain-computer interface for augmentative and alternative communication access: The initial training needs and learning preferences of speech-language pathologists.},
journal = {International journal of speech-language pathology},
volume = {27},
number = {1},
pages = {14-22},
doi = {10.1080/17549507.2024.2363939},
pmid = {39028220},
issn = {1754-9515},
mesh = {Humans ; *Speech-Language Pathology/education ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Learning ; Male ; Female ; Adult ; },
abstract = {PURPOSE: To enable the codesign of a training framework for brain-computer interfaces for augmentative and alternative communications access (BCI-AAC), the aim of this study is to evaluate the initial BCI-AAC training needs and preferred learning strategies of speech-language pathologists (SLPs) with AAC experience.<br><br>METHOD: Eleven SLPs employed across a broad range of settings completed a semi-structured interview. A grounded theory approach alongside peer debriefing and review, member checking, and triangulation procedures were utilised for thematic analysis to help ensure data reliability and credibility.<br><br>RESULT: Regarding critical training needs, SLPs identified the subthemes of (a) personalisation of intervention, (b) technical aspects, (c) BCI-AAC system types and access, and (d) how to support stakeholders in BCI-AAC implementation. Regarding learning strategy preferences, participants discussed (a) expert guidance and demonstrations, (b) hands-on experience, alongside (c) media and presentations.<br><br>CONCLUSION: Findings present a continuum of critical training needs ranging from more foundational information to more personalised assessment and intervention consideration. These thematic results present a first step in developing a basic framework for SLP training in BCI-AAC to utilise and build from as technology development continues, and provides an important initial starting point for the codesign of clinically focused BCI-AAC trainings.},
}
@article {pmid39026645,
year = {2024},
author = {Pang, M and Xu, R and Xi, R and Yao, H and Bao, K and Peng, R and Zhi, H and Zhang, K and He, R and Su, Y and Liu, X and Ming, D},
title = {Molecular understanding of the therapeutic potential of melanin inhibiting natural products.},
journal = {RSC medicinal chemistry},
volume = {15},
number = {7},
pages = {2226-2253},
pmid = {39026645},
issn = {2632-8682},
abstract = {With the development of society and the improvement of people's living standards, there is an increasing demand for melanin-inhibiting products that prioritize health, safety, and efficacy. Therefore, the development of natural products that can safely and efficiently inhibit melanin synthesis is of great social significance and has significant market potential. In this paper, by reviewing the literature reported in recent years, we summarized the natural products with inhibition of melanin synthesis effects that have been put into or not yet put into the market, and classified them according to the chemical groups of their compounds or the extraction methods of the natural products. Through the summary analysis, we found that these compounds mainly include terpenoids, phenylpropanoids, flavonoids and so on, while the natural product extracts mainly include methanol extracts, ethanol extracts, and aqueous extracts. Their main inhibition of melanin synthesis mechanisms include: (1) direct inhibition of tyrosinase activity; (2) down-regulation of the α-MSH-MC1R, Wnt, NO, PI3K/Akt and MAPK pathways through the expression of MITF and its downstream genes TYR, TRP-1, and TRP-2; (3) antioxidant; (4) inhibition of melanocyte growth through cytotoxicity; (5) inhibition of melanosome production and transport. This paper provides an in-depth discussion on the research progress of whitening natural products and their market value. The aim is to offer guidance for future research and development of natural skin whitening products.},
}
@article {pmid39024960,
year = {2024},
author = {Li, R and Cao, M and Fu, D and Wei, W and Wang, D and Yuan, Z and Hu, R and Deng, W},
title = {Deciphering language disturbances in schizophrenia: A study using fine-tuned language models.},
journal = {Schizophrenia research},
volume = {271},
number = {},
pages = {120-128},
doi = {10.1016/j.schres.2024.07.016},
pmid = {39024960},
issn = {1573-2509},
support = {2023C03081 AND 2024SSYS007//Key R &amp; D Program of Zhejiang/ ; },
mesh = {Humans ; *Schizophrenia/physiopathology/complications ; Male ; Female ; Adult ; *Language Disorders/etiology/diagnosis/physiopathology ; Middle Aged ; Young Adult ; Schizophrenic Psychology ; },
abstract = {This research presents two stable language metrics, namely Successful Prediction Rate (SPR) and Disfluency (DF), to objectively quantify the linguistic disturbances associated with schizophrenia. These novel language metrics can capture both off-topic responses and incoherence in patients' speech by modeling speech information and fine-tuning techniques. Additionally, these metrics exhibit cultural sensitivity while providing a more comprehensive evaluation of linguistic abnormalities in schizophrenia. This research fine-tuned the ELECTRA Pretrained Language Model on a 750 MB text corpus obtained from major Chinese mental health forums. The effectiveness of the fine-tuned language model is verified on a group comprising 38 individuals diagnosed with schizophrenia and 25 meticulously matched healthy controls. The study explores the association between the fine-tuned language model and the Positive and Negative Syndrome Scale (PANSS) items. The results demonstrate that SPR is higher in healthy controls, indicating better language understanding by the pre-trained language model. Conversely, DF is higher in individuals with schizophrenia, indicating more inconsistent language structure. The relationship between linguistic features and P2 (conceptual disorganization) reveals that patients with positive P2 exhibit lower SPR and higher DF. Binary logistic regression using the combined SPR and DF features achieves 84.5 % accuracy in classifying P2, exceeding the performance of traditional features by 20.5 %. Moreover, the proposed linguistic features outperform traditional linguistic features in discriminating FTD (formal thought disorder), as demonstrated by multivariate linear regression analysis.},
}
@article {pmid39024158,
year = {2024},
author = {Han, Y and Du, L and Huang, Q and Cui, D and Li, Y},
title = {Enhancing specialization of attention-related EEG power and phase synchronism brain patterns by meditation.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {7},
pages = {},
doi = {10.1093/cercor/bhae288},
pmid = {39024158},
issn = {1460-2199},
support = {2018B030339001//Key R&amp;D Program of Guangdong Province, China/ ; 2024A1515011690//Guangdong Natural Science Foundation General Program/ ; YG2016ZD06//Shanghai Jiao Tong University Medical Engineering Key Foundation/ ; 2022ZD0208900//STI 2030-Major Projects/ ; },
mesh = {Humans ; *Meditation ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; *Electroencephalography ; Adult ; Middle Aged ; Machine Learning ; },
abstract = {Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain.},
}
@article {pmid39022552,
year = {2024},
author = {Xi, Y and Schriver, KE and Roe, AW and Zhang, X},
title = {Quantifying tissue temperature changes induced by infrared neural stimulation: numerical simulation and MR thermometry.},
journal = {Biomedical optics express},
volume = {15},
number = {7},
pages = {4111-4131},
pmid = {39022552},
issn = {2156-7085},
abstract = {Infrared neural stimulation (INS) delivered via short pulse trains is an innovative tool that has potential for us use for studying brain function and circuitry, brain machine interface, and clinical use. The prevailing mechanism for INS involves the conversion of light energy into thermal transients, leading to neuronal membrane depolarization. Due to the potential risks of thermal damage, it is crucial to ensure that the resulting local temperature increases are within non-damaging limits for brain tissues. Previous studies have estimated damage thresholds using histological methods and have modeled thermal effects based on peripheral nerves. However, additional quantitative measurements and modeling studies are needed for the central nervous system. Here, we performed 7 T MRI thermometry on ex vivo rat brains following the delivery of infrared pulse trains at five different intensities from 0.1-1.0 J/cm[2] (each pulse train 1,875 nm, 25 us/pulse, 200 Hz, 0.5 s duration, delivered through 200 µm fiber). Additionally, we utilized the General BioHeat Transfer Model (GBHTM) to simulate local temperature changes in perfused brain tissues while delivering these laser energies to tissue (with optical parameters of human skin) via three different sizes of optical fibers at five energy intensities. The simulation results clearly demonstrate that a 0.5 second INS pulse train induces an increase followed by an immediate drop in temperature at stimulation offset. The delivery of multiple pulse trains with 2.5 s interstimulus interval (ISI) leads to rising temperatures that plateau. Both thermometry and modeling results show that, using parameters that are commonly used in biological applications (200 µm diameter fiber, 0.1-1.0 J/cm[2]), the final temperature increase at the end of the 60 sec stimuli duration does not exceed 1°C with stimulation values of 0.1-0.5 J/cm[2] and does not exceed 2°C with stimulation values of up to 1.0 J/cm[2]. Thus, the maximum temperature rise is consistent with the thermal damage threshold reported in previous studies. This study provides a quantitative evaluation of the temperature changes induced by INS, suggesting that existing practices pose minimal major safety concerns for biological tissues.},
}
@article {pmid39021269,
year = {2025},
author = {Wang, J and Huang, Y and Wu, L and Sun, Y and Zhang, X and Cao, F},
title = {Sleep-specific repetitive negative thinking processes and prenatal insomnia symptoms: A naturalistic follow-up study from mid- to late-pregnancy.},
journal = {Journal of sleep research},
volume = {34},
number = {1},
pages = {e14272},
doi = {10.1111/jsr.14272},
pmid = {39021269},
issn = {1365-2869},
support = {32071084//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Female ; *Sleep Initiation and Maintenance Disorders/psychology ; Adult ; Pregnancy ; Follow-Up Studies ; *Pregnancy Complications/psychology ; Surveys and Questionnaires ; *Anxiety/psychology ; China ; *Pessimism/psychology ; *Rumination, Cognitive/physiology ; },
abstract = {Insomnia symptoms are highly prevalent during pregnancy; therefore, identifying modifiable risk markers is important for risk prediction and early intervention. This study aimed to examine the role of sleep-specific rumination and sleep-specific worry in prenatal insomnia symptoms. A total of 859 married pregnant women without history of psychiatric illnesses (mean [standard deviation] age, 30.15 [3.86] years; 593 [69.0%] with a bachelor's degree or above) were enrolled from the obstetrical outpatient departments of two tertiary comprehensive hospitals in Shandong, China, who completed assessments of sleep-specific rumination, sleep-specific worry, and insomnia symptoms at baseline (mid-pregnancy) and follow-up (late-pregnancy). Measures included Daytime Insomnia Symptom Response Scale, Anxiety and Preoccupation about Sleep Questionnaire, and Insomnia Severity Index. Our results showed that after controlling for covariates, both sleep-specific rumination and sleep-specific worry showed significant concurrent and prospective associations with insomnia symptoms, and the increases in scores of sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of insomnia symptoms at follow-up. Moreover, the increases in sleep-specific rumination and sleep-specific worry over time were significantly associated with the increased likelihood of reporting newly developed insomnia symptoms rather than persistent normal sleep. However, the changes in sleep-specific rumination and sleep-specific worry were not significantly associated with the likelihood of reporting persistent or remitted insomnia symptoms rather than persistent normal sleep. In conclusion, sleep-specific rumination and sleep-specific worry were significantly associated with concurrent or subsequent insomnia symptoms; thus, they may be promising cognitive risk markers and intervention targets.},
}
@article {pmid39019065,
year = {2024},
author = {Mohammadi, Z and Denman, DJ and Klug, A and Lei, TC},
title = {A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {39019065},
issn = {1741-2552},
support = {R00 EY028612/EY/NEI NIH HHS/United States ; R41 NS132700/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; *Action Potentials/physiology ; Animals ; *Neurons/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Objective: The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy-but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these algorithms.Approach: Here we report our Graph nEtwork Multichannel sorting (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features.Main results: Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.Significance: GEMsort allows rapidly sort neural spikes and is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability.},
}
@article {pmid39019042,
year = {2024},
author = {Zhu, Z and Miao, L and Li, K and Ma, Q and Pan, L and Shen, C and Ge, Q and Du, Y and Yin, L and Yang, H and Xu, X and Zeng, LH and Liu, Y and Xu, H and Li, XM and Sun, L and Yu, YQ and Duan, S},
title = {A hypothalamic-amygdala circuit underlying sexually dimorphic aggression.},
journal = {Neuron},
volume = {112},
number = {18},
pages = {3176-3191.e7},
doi = {10.1016/j.neuron.2024.06.022},
pmid = {39019042},
issn = {1097-4199},
mesh = {Animals ; *Aggression/physiology ; Male ; Female ; *Sex Characteristics ; Mice ; *Hypothalamus/physiology ; *Neural Pathways/physiology ; *Amygdala/physiology ; Mice, Inbred C57BL ; Ventromedial Hypothalamic Nucleus/physiology ; },
abstract = {Male animals often display higher levels of aggression than females. However, the neural circuitry mechanisms underlying this sexually dimorphic aggression remain elusive. Here, we identify a hypothalamic-amygdala circuit that mediates male-biased aggression in mice. Specifically, the ventrolateral part of the ventromedial hypothalamus (VMHvl), a sexually dimorphic region associated with eliciting male-biased aggression, projects densely to the posterior substantia innominata (pSI), an area that promotes similar levels of attack in both sexes of mice. Although the VMHvl innervates the pSI unidirectionally through both excitatory and inhibitory connections, it is the excitatory VMHvl-pSI projections that are strengthened in males to promote aggression, whereas the inhibitory connections that reduce aggressive behavior are strengthened in females. Consequently, the convergent hypothalamic input onto the pSI leads to heightened pSI activity in males, resulting in male-biased aggression. Our findings reveal a sexually distinct excitation-inhibition balance of a hypothalamic-amygdala circuit that underlies sexually dimorphic aggression.},
}
@article {pmid39015823,
year = {2024},
author = {Herbert, C and Northoff, G},
title = {Editorial: Analyzing and computing humans - the role of language, culture, brain and health.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1439729},
pmid = {39015823},
issn = {1662-5161},
}
@article {pmid39015472,
year = {2024},
author = {He, Q and Yang, C and Xu, Y and Niu, H and Wu, H and Huang, H and Chai, X and Cao, T and Wang, N and Wong, P and He, J and Yang, Y and Zhao, J},
title = {Anatomical-related factors and outcome of percutaneous short-term spinal cord stimulation electrode shift in patients with disorders of consciousness: a retrospective study.},
journal = {Frontiers in aging neuroscience},
volume = {16},
number = {},
pages = {1403156},
pmid = {39015472},
issn = {1663-4365},
abstract = {BACKGROUND: Disorders of consciousness (DoC) represent a spectrum of neurological conditions that pose significant treatment challenges. Percutaneous short-term spinal cord stimulation (SCS) has emerged as a promising experimental diagnostic treatment to assess and potentially improve consciousness levels. However, the effectiveness of this intervention is frequently compromised by the shift of electrodes, particularly in the cervical region, which can negatively affect therapeutic outcomes.<br><br>METHODS: This retrospective study aimed to study if electrodes shift in percutaneous short-term SCS in patients with DoC would affect the outcome. We analyzed the relationship between electrode shift length and patient outcome, as well as the correlation with various anatomical parameters, including the actual length of the cervical spine, linear length, spinal canal transverse diameter, spinal canal diameter, and C2 cone height, in a cohort of patients undergoing the procedure.<br><br>RESULTS: Our findings revealed that in patients with better outcome, there are significant less patient with electrode shift (p&#x2009;=&#x2009;0.019). Further, a linear correlation was found between the length of electrode shift and patients' outcome (Rho&#x2009;=&#x2009;0.583, p&#x2009;=&#x2009;0.002), with longer shift lengths associated with poorer outcomes. Contrary to our expectations, there was no significant association between the measured anatomical parameters and the extent of electrode shift. However, a trend was found between the actual length of the cervical spine and the shift of the electrode (p&#x2009;=&#x2009;0.098). Notably, the shorter spinal canal transverse diameter was found to be significantly associated with better outcome in patients with DoC receiving percutaneous short-term SCS (p&#x2009;=&#x2009;0.033).<br><br>CONCLUSION: These results highlight the clinical importance of electrode stability in the cervical region during SCS treatment for patients with DoC. Ensuring secure placement of electrodes may play a crucial role in enhancing patients' outcome and minimize postoperative complications. Given the lack of association with expected anatomical parameters, future research should investigate other factors that could impact electrode stability to optimize this therapeutic intervention.},
}
@article {pmid39012561,
year = {2024},
author = {Lyreskog, DM and Zohny, H and Mann, SP and Singh, I and Savulescu, J},
title = {Decentralising the Self - Ethical Considerations in Utilizing Decentralised Web Technology for Direct Brain Interfaces.},
journal = {Science and engineering ethics},
volume = {30},
number = {4},
pages = {28},
pmid = {39012561},
issn = {1471-5546},
support = {/WT_/Wellcome Trust/United Kingdom ; WT203132/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; IS-BRC-1215-20005//NIHR Oxford Biomedical Research Centre/ ; },
mesh = {Humans ; *Internet ; *Privacy ; *Brain-Computer Interfaces/ethics ; *Personal Autonomy ; Social Responsibility ; Blockchain/ethics ; Computer Security/ethics ; Ownership/ethics ; Politics ; Cognition ; Safety ; Technology/ethics ; },
abstract = {The rapidly advancing field of brain-computer (BCI) and brain-to-brain interfaces (BBI) is stimulating interest across various sectors including medicine, entertainment, research, and military. The developers of large-scale brain-computer networks, sometimes dubbed 'Mindplexes' or 'Cloudminds', aim to enhance cognitive functions by distributing them across expansive networks. A key technical challenge is the efficient transmission and storage of information. One proposed solution is employing blockchain technology over Web 3.0 to create decentralised cognitive entities. This paper explores the potential of a decentralised web for coordinating large brain-computer constellations, and its associated benefits, focusing in particular on the conceptual and ethical challenges this innovation may pose pertaining to (1) Identity, (2) Sovereignty (encompassing Autonomy, Authenticity, and Ownership), (3) Responsibility and Accountability, and (4) Privacy, Safety, and Security. We suggest that while a decentralised web can address some concerns and mitigate certain risks, underlying ethical issues persist. Fundamental questions about entity definition within these networks, the distinctions between individuals and collectives, and responsibility distribution within and between networks, demand further exploration.},
}
@article {pmid39011811,
year = {2024},
author = {Blau, R and Russman, SM and Qie, Y and Shipley, W and Lim, A and Chen, AX and Nyayachavadi, A and Ah, L and Abdal, A and Esparza, GL and Edmunds, SJ and Vatsyayan, R and Dunfield, SP and Halder, M and Jokerst, JV and Fenning, DP and Tao, AR and Dayeh, SA and Lipomi, DJ},
title = {Surface-Grafted Biocompatible Polymer Conductors for Stable and Compliant Electrodes for Brain Interfaces.},
journal = {Advanced healthcare materials},
volume = {13},
number = {29},
pages = {e2402215},
pmid = {39011811},
issn = {2192-2659},
support = {DP2-EB029757/EB/NIBIB NIH HHS/United States ; //Alfred P. Sloan Foundation/ ; ECCS-1542148//National Science Foundation/ ; 898571//H2020 Marie Sk&#x142;odowska-Curie Actions/ ; //UC Irvine Materials Research Institute/ ; //UC President's Dissertation Year Fellowship/ ; CHE-1338173//National Science Foundation Major Research Instrumentation Program/ ; 1845683//National Science Foundation/ ; FA9550-19-1-0278//Air Force Office of Scientific Research/ ; UG3NS123723-01//BRAIN Initiative/ ; DMR-2011967//National Science Foundation/ ; CBET-2223566//National Science Foundation Disability and Rehabilitation Engineering/ ; //National Institutes of Health's Brain Research/ ; //Natural Sciences and Engineering Research Council of Canada/ ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; //Kuwait Foundation for the Advancement of Sciences/ ; R01NS123655-01//BRAIN Initiative/ ; DMR-2011924//National Science Foundation/ ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; },
mesh = {*Polymers/chemistry ; *Biocompatible Materials/chemistry ; Surface Properties ; Electrodes ; Electric Conductivity ; Brain/physiology ; Brain-Computer Interfaces ; Animals ; Polyethylene Glycols/chemistry ; Gold/chemistry ; },
abstract = {Durable and conductive interfaces that enable chronic and high-resolution recording of neural activity are essential for understanding and treating neurodegenerative disorders. These chronic implants require long-term stability and small contact areas. Consequently, they are often coated with a blend of conductive polymers and are crosslinked to enhance durability despite the potentially deleterious effect of crosslinking on the mechanical and electrical properties. Here the grafting of the poly(3,4 ethylenedioxythiophene) scaffold, poly(styrenesulfonate)-b-poly(poly(ethylene glycol) methyl ether methacrylate block copolymer brush to gold, in a controlled and tunable manner, by surface-initiated atom-transfer radical polymerization (SI-ATRP) is described. This "block-brush" provides high volumetric capacitance (120 F cm[─3]), strong adhesion to the metal (4 h ultrasonication), improved surface hydrophilicity, and stability against 10 000 charge-discharge voltage sweeps on a multiarray neural electrode. In addition, the block-brush film showed 33% improved stability against current pulsing. This approach can open numerous avenues for exploring specialized polymer brushes for bioelectronics research and application.},
}
@article {pmid39010893,
year = {2024},
author = {Song, J and Zhai, Q and Wang, C and Liu, J},
title = {EEGGAN-Net: enhancing EEG signal classification through data augmentation.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1430086},
pmid = {39010893},
issn = {1662-5161},
abstract = {BACKGROUND: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications.<br><br>METHODS: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks.<br><br>RESULTS: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models.<br><br>CONCLUSIONS: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.},
}
@article {pmid39009108,
year = {2024},
author = {Liu, ML and Liu, YP and Guo, XX and Wu, ZY and Zhang, XT and Roe, AW and Hu, JM},
title = {Orientation selectivity mapping in the visual cortex.},
journal = {Progress in neurobiology},
volume = {240},
number = {},
pages = {102656},
doi = {10.1016/j.pneurobio.2024.102656},
pmid = {39009108},
issn = {1873-5118},
mesh = {Male ; Female ; Animals ; Macaca ; *Visual Cortex/diagnostic imaging/physiology ; *Neuroimaging/methods ; Photic Stimulation/methods ; *Orientation/physiology ; Visual Perception/physiology ; Deep Learning ; Image Enhancement/methods ; *Optical Imaging/methods ; *Visual Pathways/diagnostic imaging/physiology ; Spatial Processing/physiology ; },
abstract = {The orientation map is one of the most well-studied functional maps of the visual cortex. However, results from the literature are of different qualities. Clear boundaries among different orientation domains and blurred uncertain distinctions were shown in different studies. These unclear imaging results will lead to an inaccuracy in depicting cortical structures, and the lack of consideration in experimental design will also lead to biased depictions of the cortical features. How we accurately define orientation domains will impact the entire field of research. In this study, we test how spatial frequency (SF), stimulus size, location, chromatic, and data processing methods affect the orientation functional maps (including a large area of dorsal V4, and parts of dorsal V1) acquired by intrinsic signal optical imaging. Our results indicate that, for large imaging fields, large grating stimuli with mixed SF components should be considered to acquire the orientation map. A diffusion model image enhancement based on the difference map could further improve the map quality. In addition, the similar outcomes of achromatic and chromatic gratings indicate two alternative types of afferents from LGN, pooling in V1 to generate cue-invariant orientation selectivity.},
}
@article {pmid39008625,
year = {2024},
author = {Liu, H and Ding, S and Lin, X and Wang, S and Wang, Y and Feng, Z and Song, J},
title = {Bone Fracture Healing under the Intervention of a Stretchable Ultrasound Array.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c02426},
pmid = {39008625},
issn = {1936-086X},
abstract = {Ultrasound treatment has been recognized as an effective and noninvasive approach to promote fracture healing. However, traditional rigid ultrasound probe is bulky, requiring cumbersome manual operations and inducing unfavorable side effects when functioning, which precludes the wide application of ultrasound in bone fracture healing. Here, we report a stretchable ultrasound array for bone fracture healing, which features high-performance 1-3 piezoelectric composites as transducers, stretchable multilayered serpentine metal films in a bridge-island pattern as electrical interconnects, soft elastomeric membranes as encapsulations, and polydimethylsiloxane (PDMS) with low curing agent ratio as adhesive layers. The resulting ultrasound array offers the benefits of large stretchability for easy skin integration and effective affecting region for simple skin alignment with good electromechanical performance. Experimental investigations of the stretchable ultrasound array on the delayed union model in femoral shafts of rats show that the callus growth is more active in the second week of treatment and the fracture site is completely osseous healed in the sixth week of treatment. Various bone quality indicators (e.g., bone modulus, bone mineral density, bone tissue/total tissue volume, and trabecular bone thickness) could be enhanced with the intervention of a stretchable ultrasound array. Histological and immunohistochemical examinations indicate that ultrasound promotes osteoblast differentiation, bone formation, and remodeling by promoting the expression of osteopontin (OPN) and runt-related transcription factor 2 (RUNX2). This work provides an effective tool for bone fracture healing in a simple and convenient manner and creates engineering opportunities for applying ultrasound in medical applications.},
}
@article {pmid39006480,
year = {2024},
author = {Wang, R and Wang, X and Platt, ML and Sheng, F},
title = {Decomposing loss aversion from a single neural signal.},
journal = {iScience},
volume = {27},
number = {7},
pages = {110153},
pmid = {39006480},
issn = {2589-0042},
abstract = {People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.},
}
@article {pmid39006157,
year = {2024},
author = {van Stuijvenberg, OC and Samlal, DPS and Vansteensel, MJ and Broekman, MLD and Jongsma, KR},
title = {The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1420334},
pmid = {39006157},
issn = {1662-5161},
abstract = {AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.},
}
@article {pmid39004179,
year = {2024},
author = {White, AJ and Kelly-Hedrick, M and Miranda, SP and Abdelbarr, MM and Lázaro-Muñoz, G and Pouratian, N and Shen, F and Nahed, BV and Williamson, T},
title = {Bioethics and Neurosurgery: An Overview of Existing and Emerging Topics for the Practicing Neurosurgeon.},
journal = {World neurosurgery},
volume = {190},
number = {},
pages = {181-186},
doi = {10.1016/j.wneu.2024.07.051},
pmid = {39004179},
issn = {1878-8769},
mesh = {Humans ; *Neurosurgery/ethics ; *Neurosurgeons/ethics ; *Bioethics ; Informed Consent/ethics ; Neurosurgical Procedures/ethics/methods ; Bioethical Issues ; },
abstract = {Neurosurgery is a field with complex ethical issues. In this article, we aim to provide an overview of key and emerging ethical issues in neurosurgery with a focus on issues relevant to practicing neurosurgeons. These issues include those of informed consent, capacity, clinical trials, emerging neurotechnology, innovation, equity and justice, and emerging bioethics areas including community engagement and organizational ethics. We argue that bioethics can help neurosurgeons think about and address these issues, and, in turn, the field of bioethics can benefit from engagement by neurosurgeons. Several ideas for increasing engagement in bioethics are proposed.},
}
@article {pmid38997424,
year = {2024},
author = {Guo, J and Zhou, YL and Yang, Y and Guo, S and You, E and Xie, X and Jiang, Y and Mao, C and Xu, HE and Zhang, Y},
title = {Structural basis of tethered agonism and G protein coupling of protease-activated receptors.},
journal = {Cell research},
volume = {34},
number = {10},
pages = {725-734},
pmid = {38997424},
issn = {1748-7838},
support = {32141004//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82121005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32130022//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82273985//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32371249//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100959//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82322070//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171187//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019YFA050880//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; 2023YFC2306800//Chinese Ministry of Science and Technology | Department of S and T for Social Development (Department of S&amp;T for Social Development)/ ; },
mesh = {Humans ; *Cryoelectron Microscopy ; *Receptor, PAR-1/metabolism/chemistry ; Protein Binding ; HEK293 Cells ; Models, Molecular ; Signal Transduction ; GTP-Binding Proteins/metabolism/chemistry ; GTP-Binding Protein alpha Subunits, Gq-G11/metabolism/chemistry ; },
abstract = {Protease-activated receptors (PARs) are a unique group within the G protein-coupled receptor superfamily, orchestrating cellular responses to extracellular proteases via enzymatic cleavage, which triggers intracellular signaling pathways. Protease-activated receptor 1 (PAR1) is a key member of this family and is recognized as a critical pharmacological target for managing thrombotic disorders. In this study, we present cryo-electron microscopy structures of PAR1 in its activated state, induced by its natural tethered agonist (TA), in complex with two distinct downstream proteins, the Gq and Gi heterotrimers, respectively. The TA peptide is positioned within a surface pocket, prompting PAR1 activation through notable conformational shifts. Contrary to the typical receptor activation that involves the outward movement of transmembrane helix 6 (TM6), PAR1 activation is characterized by the simultaneous downward shift of TM6 and TM7, coupled with the rotation of a group of aromatic residues. This results in the displacement of an intracellular anion, creating space for downstream G protein binding. Our findings delineate the TA recognition pattern and highlight a distinct role of the second extracellular loop in forming β-sheets with TA within the PAR family, a feature not observed in other TA-activated receptors. Moreover, the nuanced differences in the interactions between intracellular loops 2/3 and the Gα subunit of different G proteins are crucial for determining the specificity of G protein coupling. These insights contribute to our understanding of the ligand binding and activation mechanisms of PARs, illuminating the basis for PAR1's versatility in G protein coupling.},
}
@article {pmid38996764,
year = {2024},
author = {Montero, AS and Aliouat, I and Ribon, M and Canney, M and Goldwirt, L and Mourah, S and Berriat, F and Lobsiger, CS and Pradat, PF and Salachas, F and Bruneteau, G and Carpentier, A and Boillée, S},
title = {Effect of ultrasound-mediated blood-spinal cord barrier opening on survival and motor function in females in an amyotrophic lateral sclerosis mouse model.},
journal = {EBioMedicine},
volume = {106},
number = {},
pages = {105235},
pmid = {38996764},
issn = {2352-3964},
mesh = {Animals ; *Amyotrophic Lateral Sclerosis/metabolism/therapy ; Female ; *Disease Models, Animal ; Mice ; *Spinal Cord/metabolism ; *Blood-Brain Barrier/metabolism ; *Insulin-Like Growth Factor I/metabolism ; Mice, Transgenic ; Humans ; Motor Neurons/metabolism ; Ultrasonic Waves ; },
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive loss of motor neurons. The limited efficacy of recent therapies in clinical development may be linked to lack of drug penetration to the affected motor neurons due to the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB).<br><br>METHODS: In this work, the safety and efficacy of repeated short transient opening of the BSCB by low intensity pulsed ultrasound (US, sonication) was studied in females of an ALS mouse model (B6.Cg-Tg(SOD1&#x2217;G93A)1Gur/J). The BSCB was disrupted using a 1&#xa0;MHz ultrasound transducer coupled to the spinal cord, with and without injection of insulin-like growth factor 1 (IGF1), a neurotrophic factor that has previously shown efficacy in ALS models.<br><br>FINDINGS: Results in wild-type (WT) animals demonstrated that the BSCB can be safely disrupted and IGF1 concentrations significantly enhanced after a single session of transient BSCB disruption (176&#xa0;&#xb1;&#xa0;32&#xa0;&#x3bc;g/g vs. 0.16&#xa0;&#xb1;&#xa0;0.008&#xa0;&#x3bc;g/g, p&#xa0;<&#xa0;0.0001). Five repeated weekly US sessions performed in female ALS mice demonstrated a survival advantage in mice treated with IGF1 and US (US IGF1) compared to treatment with IGF1 alone (176 vs. 166 days, p&#xa0;=&#xa0;0.0038). Surprisingly, this survival advantage was also present in mice treated with US alone vs. untreated mice (178.5 vs. 166.5 days, p&#xa0;=&#xa0;0.0061). Muscle strength did not show difference among the groups. Analysis of glial cell immunoreactivity and microglial transcriptome showing reduced cell proliferation pathways, in addition to lymphocyte infiltration, suggested that the beneficial effect of US or US IGF1 could act through immune cell modulation.<br><br>INTERPRETATION: These results show the first step towards a possible beneficial impact of transient BSCB opening for ALS therapy and suggest implication of immune cells.<br><br>FUNDING: Fondation pour la Recherche M&#xe9;dicale (FRM). Investissements d'avenirANR-10-IAIHU-06, Soci&#xe9;t&#xe9; Fran&#xe7;aise de Neurochirurgie (SFNC), Fond d'&#xe9;tude et de Recherche du Corps Medical (FERCM), Aide &#xe0; la Recherche des Maladies du Cerveau (ARMC), SLA Fondation Recherche (SLAFR), French Ministry for High Education and Research (MENR), Carthera, Laboratoire de Recherche en Technologies Chirurgicales Avanc&#xe9;es (LRTCA).},
}
@article {pmid38996409,
year = {2024},
author = {Venot, T and Desbois, A and Corsi, MC and Hugueville, L and Saint-Bauzel, L and De Vico Fallani, F},
title = {Intentional binding for noninvasive BCI control.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad628c},
pmid = {38996409},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; *Imagination/physiology ; Female ; Robotics/methods ; Hand Strength/physiology ; Young Adult ; Intention ; Psychomotor Performance/physiology ; },
abstract = {Objective. Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation.Approach. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm.Main results. Showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions.Significance. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.},
}
@article {pmid38993375,
year = {2024},
author = {Aljuhani, W and Sayyad, Y},
title = {Orthopedic Research Funding: Assessing the Relationship between Investments and Breakthroughs.},
journal = {Orthopedic reviews},
volume = {16},
number = {},
pages = {120368},
pmid = {38993375},
issn = {2035-8164},
abstract = {Orthopedic research plays a crucial role in improving patient outcomes for musculoskeletal disorders. This narrative review explores the intricate interplay between funding patterns and the trajectory of breakthroughs achieved in this dynamic field. A meticulous search strategy identified studies illuminating the diverse sources of orthopedic research funding, including public funding (government agencies), philanthropic organizations, private sector investment, and international funding bodies. The review further delved into the spectrum of breakthroughs, encompassing fundamental scientific discoveries, technological advancements, and personalized medicine approaches. Public funding emerged as a significant pillar, supporting foundational research that lays the groundwork for future advancements. Philanthropic organizations addressed specific musculoskeletal disorders, often focusing on patient-centric applications. International funding bodies played a role in supporting research in low- and middle-income countries. Breakthroughs extended beyond cutting-edge prosthetics and minimally invasive surgeries, encompassing fundamental discoveries in areas like gene therapy and biomaterials science. Technological advancements included brain-computer interface prosthetics and 3D-printed implants. Personalized medicine offered the potential for tailored treatments based on individual needs and genetic profiles. This review underscores the complex interplay between funding patterns and breakthroughs in orthopedic research. A multifaceted approach is essential for continued progress. Fostering collaboration, optimizing funding models, and prioritizing both foundational and translational research hold the key to unlocking the true potential of orthopedic research and transforming the lives of patients suffering from musculoskeletal disorders.},
}
@article {pmid38993329,
year = {2024},
author = {Yan, T and Su, C and Xue, W and Hu, Y and Zhou, H},
title = {Mobile phone short video use negatively impacts attention functions: an EEG study.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1383913},
pmid = {38993329},
issn = {1662-5161},
abstract = {The pervasive nature of short-form video platforms has seamlessly integrated into daily routines, yet it is important to recognize their potential adverse effects on both physical and mental health. Prior research has identified a detrimental impact of excessive short-form video consumption on attentional behavior, but the underlying neural mechanisms remain unexplored. In the current study, we aimed to investigate the effect of short-form video use on attentional functions, measured through the attention network test (ANT). A total of 48 participants, consisting of 35 females and 13 males, with a mean age of 21.8 years, were recruited. The mobile phone short video addiction tendency questionnaire (MPSVATQ) and self-control scale (SCS) were conducted to assess the short video usage behavior and self-control ability. Electroencephalogram (EEG) data were recorded during the completion of the ANT task. The correlation analysis showed a significant negative relationship between MPSVATQ and theta power index reflecting the executive control in the prefrontal region (r = -0.395, p = 0.007), this result was not observed by using theta power index of the resting-state EEG data. Furthermore, a significant negative correlation was identified between MPSVATQ and SCS outcomes (r = -0.320, p = 0.026). These results suggest that an increased tendency toward mobile phone short video addiction could negatively impact self-control and diminish executive control within the realm of attentional functions. This study sheds light on the adverse consequences stemming from short video consumption and underscores the importance of developing interventions to mitigate short video addiction.},
}
@article {pmid38991197,
year = {2024},
author = {van Putten, MJAM and Ruijter, BJ and Horn, J and van Rootselaar, AF and Tromp, SC and van Kranen-Mastenbroek, V and Gaspard, N and Hofmeijer, J and , },
title = {Quantitative Characterization of Rhythmic and Periodic EEG Patterns in Patients in a Coma After Cardiac Arrest and Association With Outcome.},
journal = {Neurology},
volume = {103},
number = {3},
pages = {e209608},
doi = {10.1212/WNL.0000000000209608},
pmid = {38991197},
issn = {1526-632X},
mesh = {Humans ; *Coma/physiopathology/etiology ; *Electroencephalography/methods ; Male ; Female ; *Heart Arrest/complications/physiopathology ; Middle Aged ; Aged ; },
abstract = {OBJECTIVES: Rhythmic and periodic patterns (RPPs) on EEG in patients in a coma after cardiac arrest are associated with a poor neurologic outcome. We characterize RPPs using qEEG in relation to outcomes.<br><br>METHODS: Post hoc analysis was conducted on 172 patients in a coma after cardiac arrest from the TELSTAR trial, all with RPPs. Quantitative EEG included corrected background continuity index (BCI*), relative discharge power (RDP), discharge frequency, and shape similarity. Neurologic outcomes at 3 months after arrest were categorized as poor (CPC = 3-5) or good (CPC = 1-2).<br><br>RESULTS: A total of 16 patients (9.3%) had a good outcome. Patients with good outcomes showed later RPP onset (28.5 vs 20.1 hours after arrest, p < 0.05) and higher background continuity at RPP onset (BCI[*] = 0.83 vs BCI[*] = 0.59, p < 0.05). BCI* <0.45 at RPP onset, maximum BCI* <0.76, RDP >0.47, or shape similarity >0.75 were consistently associated with poor outcomes, identifying 36%, 22%, 40%, or 24% of patients with poor outcomes, respectively. In patients meeting both BCI* >0.44 at RPP onset and BCI* >0.75 within 72 hours, the probability of good outcomes doubled to 18%.<br><br>DISCUSSION: Sufficient EEG background continuity before and during RPPs is crucial for meaningful recovery. Background continuity, discharge power, and shape similarity can help select patients with relevant chances of recovery and may guide treatment.<br><br>February 4, 2014, ClinicalTrial.gov, NCT02056236.},
}
@article {pmid38989015,
year = {2024},
author = {Barrio-Pujante, A and Bleriot, I and Blasco, L and Fernández-Garcia, L and Pacios, O and Ortiz-Cartagena, C and Cuenca, FF and Oteo-Iglesias, J and Tomás, M},
title = {Regulation of anti-phage defense mechanisms by using cinnamaldehyde as a quorum sensing inhibitor.},
journal = {Frontiers in microbiology},
volume = {15},
number = {},
pages = {1416628},
pmid = {38989015},
issn = {1664-302X},
abstract = {BACKGROUND: Multidrug-resistant bacteria and the shortage of new antibiotics constitute a serious health problem. This problem has led to increased interest in the use of bacteriophages, which have great potential as antimicrobial agents but also carry the risk of inducing resistance. The objective of the present study was to minimize the development of phage resistance in Klebsiella pneumoniae strains by inhibiting quorum sensing (QS) and thus demonstrate the role of QS in regulating defense mechanisms.<br><br>RESULTS: Cinnamaldehyde (CAD) was added to K. pneumoniae cultures to inhibit QS and thus demonstrate the role of the signaling system in regulating the anti-phage defense mechanism. The QS inhibitory activity of CAD in K. pneumoniae was confirmed by a reduction in the quantitative expression of the lsrB gene (AI-2 pathway) and by proteomic analysis. The infection assays showed that the phage was able to infect a previously resistant K. pneumoniae strain in the cultures to which CAD was added. The results were confirmed using proteomic analysis. Thus, anti-phage defense-related proteins from different systems, such as cyclic oligonucleotide-based bacterial anti-phage signaling systems (CBASS), restriction-modification (R-M) systems, clustered regularly interspaced short palindromic repeat-Cas (CRISPR-Cas) system, and bacteriophage control infection (BCI), were present in the cultures with phage but not in the cultures with phage and CAD. When the QS and anti-phage defense systems were inhibited by the combined treatment, proteins related to phage infection and proliferation, such as the tail fiber protein, the cell division protein DamX, and the outer membrane channel protein TolC, were detected.<br><br>CONCLUSION: Inhibition of QS reduces phage resistance in K. pneumoniae, resulting in the infection of a previously resistant strain by phage, with a significant increase in phage proliferation and a significant reduction in bacterial growth. QS inhibitors could be considered for therapeutic application by including them in phage cocktails or in phage-antibiotic combinations to enhance synergistic effects and reduce the emergence of antimicrobial resistance.},
}
@article {pmid38988769,
year = {2024},
author = {Liang, J and Wang, Z and Han, J and Zhang, L},
title = {EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1421010},
pmid = {38988769},
issn = {1662-4548},
abstract = {Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.},
}
@article {pmid38988764,
year = {2024},
author = {Hong, Y and Ryun, S and Chung, CK},
title = {Evoking artificial speech perception through invasive brain stimulation for brain-computer interfaces: current challenges and future perspectives.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1428256},
pmid = {38988764},
issn = {1662-4548},
abstract = {Encoding artificial perceptions through brain stimulation, especially that of higher cognitive functions such as speech perception, is one of the most formidable challenges in brain-computer interfaces (BCI). Brain stimulation has been used for functional mapping in clinical practices for the last 70 years to treat various disorders affecting the nervous system, including epilepsy, Parkinson's disease, essential tremors, and dystonia. Recently, direct electrical stimulation has been used to evoke various forms of perception in humans, ranging from sensorimotor, auditory, and visual to speech cognition. Successfully evoking and fine-tuning artificial perceptions could revolutionize communication for individuals with speech disorders and significantly enhance the capabilities of brain-computer interface technologies. However, despite the extensive literature on encoding various perceptions and the rising popularity of speech BCIs, inducing artificial speech perception is still largely unexplored, and its potential has yet to be determined. In this paper, we examine the various stimulation techniques used to evoke complex percepts and the target brain areas for the input of speech-like information. Finally, we discuss strategies to address the challenges of speech encoding and discuss the prospects of these approaches.},
}
@article {pmid38986469,
year = {2024},
author = {Park, H and Jun, SC},
title = {Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6187},
pmid = {38986469},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; Female ; Adult ; Rest/physiology ; Young Adult ; },
abstract = {Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.},
}
@article {pmid38986468,
year = {2025},
author = {He, X and Li, H and Yu, P and Wu, H and Chen, B},
title = {DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad618a},
pmid = {38986468},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Fatigue/diagnosis/physiopathology ; *Neural Networks, Computer ; Male ; Adult ; Female ; Young Adult ; },
abstract = {Objective. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not included in the training datasets, owing to bio-individual differences and noisy labels. This study aims to develop an effective framework for cross-subject fatigue detection by addressing these challenges.Approach. In this study, we propose a novel framework, termed DP-MP, for cross-subject fatigue detection, which utilizes a domain-adversarial neural network-based prototypical representation in conjunction with Mix-up pairwise learning. Our proposed DP-MP framework aims to mitigate the impact of bio-individual differences by encoding fatigue-related semantic structures within EEG signals and exploring shared fatigue prototype features across individuals. Notably, to the best of our knowledge, this work is the first to conceptualize fatigue detection as a pairwise learning task, thereby effectively reducing the interference from noisy labels. Furthermore, we propose the Mix-up pairwise learning (MixPa) approach in the field of fatigue detection, which broadens the advantages of pairwise learning by introducing more diverse and informative relationships among samples.Main results. Cross-subject experiments were conducted on two benchmark databases, SEED-VIG and FTEF, achieving state-of-the-art performance with average accuracies of 88.14%and 97.41%, respectively. These promising results demonstrate our model's effectiveness and excellent generalization capability.Significance. This is the first time EEG-based fatigue detection has been conceptualized as a pairwise learning task, offering a novel perspective to this field. Moreover, our proposed DP-MP framework effectively tackles the challenges of bio-individual differences and noisy labels in the fatigue detection field and demonstrates superior performance. Our work provides valuable insights for future research, promoting the practical application of brain-computer interfaces for fatigue detection.},
}
@article {pmid38986465,
year = {2024},
author = {Lo, YT and Jiang, L and Woodington, B and Middya, S and Braendlein, M and Lam, JLW and Lim, MJR and Ng, VYP and Rao, JP and Chan, DWS and Ang, BT},
title = {Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad618c},
pmid = {38986465},
issn = {1741-2552},
mesh = {Animals ; Rats ; *Rats, Wistar ; *Electrocorticography/methods/instrumentation ; *Motor Cortex/physiology ; *Electrodes, Implanted ; Male ; Microelectrodes ; Action Potentials/physiology ; Equipment Design/methods ; Spatial Navigation/physiology ; Brain-Computer Interfaces ; Equipment Failure Analysis/methods ; },
abstract = {Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.},
}
@article {pmid38986460,
year = {2024},
author = {Ploesser, M and Abraham, ME and Broekman, MLD and Zincke, MT and Beach, CA and Urban, NB and Ben-Haim, S},
title = {Electrical and Magnetic Neuromodulation Technologies and Brain-Computer Interfaces: Ethical Considerations for Enhancement of Brain Function in Healthy People - A Systematic Scoping Review.},
journal = {Stereotactic and functional neurosurgery},
volume = {102},
number = {5},
pages = {308-324},
pmid = {38986460},
issn = {1423-0372},
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Brain/physiology ; Transcranial Magnetic Stimulation/methods/ethics ; Deep Brain Stimulation/ethics/methods ; },
abstract = {INTRODUCTION: This scoping review aimed to synthesize the fragmented evidence on ethical concerns related to the use of electrical and magnetic neuromodulation technologies, as well as brain-computer interfaces for enhancing brain function in healthy individuals, addressing the gaps in understanding spurred by rapid technological advancements and ongoing ethical debates.<br><br>METHODS: The following databases and interfaces were queried: MEDLINE (via PubMed), Web of Science, PhilPapers, and Google Scholar. Additional references were identified via bibliographies of included citations. References included experimental studies, reviews, opinion papers, and letters to editors published in peer-reviewed journals that explored the ethical implications of electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy adult or pediatric populations.<br><br>RESULTS: A total of 23 articles were included in the review, of which the majority explored expert opinions in the form of qualitative studies or surveys as well as reviews. Two studies explored the view of laypersons on the topic. The majority of evidence pointed to ethical concerns relating to a lack of sufficient efficacy and safety data for these new technologies, with the risks of invasive procedures potentially outweighing the benefits. Additionally, concerns about potential socioeconomic consequences were raised that could further exacerbate existing socioeconomic inequalities, as well as the risk of changes to person and environment.<br><br>CONCLUSION: This scoping review highlights a critical shortage of ethical research on electrical and magnetic neuromodulation technologies and brain-computer interfaces for enhancement of brain function in healthy individuals, with key concerns regarding the safety, efficacy, and socioeconomic impacts of neuromodulation technologies. It underscores the urgent need for integrating ethical considerations into neuroscientific research to address significant gaps and ensure equitable access and outcomes.},
}
@article {pmid38986452,
year = {2024},
author = {Jochumsen, M and Poulsen, KB and Sørensen, SL and Sulkjær, CS and Corydon, FK and Strauss, LS and Roos, JB},
title = {Single-trial movement intention detection estimation in patients with Parkinson's disease: a movement-related cortical potential study.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad6189},
pmid = {38986452},
issn = {1741-2552},
mesh = {Humans ; *Parkinson Disease/physiopathology/diagnosis ; Male ; Female ; Aged ; *Movement/physiology ; Middle Aged ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Intention ; Electromyography/methods ; },
abstract = {Objectives. Parkinson patients often suffer from motor impairments such as tremor and freezing of movement that can be difficult to treat. To unfreeze movement, it has been suggested to provide sensory stimuli. To avoid constant stimulation, episodes with freezing of movement needs to be detected which is a challenge. This can potentially be obtained using a brain-computer interface (BCI) based on movement-related cortical potentials (MRCPs) that are observed in association with the intention to move. The objective in this study was to detect MRCPs from single-trial EEG.Approach. Nine Parkinson patients executed 100 wrist movements and 100 ankle movements while continuous EEG and EMG were recorded. The experiment was repeated in two sessions on separate days. Using temporal, spectral and template matching features, a random forest (RF), linear discriminant analysis, and k-nearest neighbours (kNN) classifier were constructed in offline analysis to discriminate between epochs containing movement-related or idle brain activity to provide an estimation of the performance of a BCI. Three classification scenarios were tested: 1) within-session (using training and testing data from the same session and participant), between-session (using data from the same participant from session one for training and session two for testing), and across-participant (using data from all participants except one for training and testing on the remaining participant).Main results. The within-session classification scenario was associated with the highest classification accuracies which were in the range of 88%-89% with a similar performance across sessions. The performance dropped to 69%-75% and 70%-75% for the between-session and across-participant classification scenario, respectively. The highest classification accuracies were obtained for the RF and kNN classifiers.Significance. The results indicate that it is possible to detect movement intentions in individuals with Parkinson's disease such that they can operate a BCI which may control the delivery of sensory stimuli to unfreeze movement.},
}
@article {pmid38986186,
year = {2024},
author = {Zhang, D and Li, H and Xie, J},
title = {Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {179},
number = {},
pages = {106497},
doi = {10.1016/j.neunet.2024.106497},
pmid = {38986186},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Unsupervised Machine Learning ; Supervised Machine Learning ; Algorithms ; Brain/physiology ; },
abstract = {The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.},
}
@article {pmid38985934,
year = {2024},
author = {Cheng, Y and Yan, L and Shoukat, MU and She, J and Liu, W and Shi, C and Wu, Y and Yan, F},
title = {An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control.},
journal = {Journal of neurophysiology},
volume = {132},
number = {3},
pages = {809-821},
doi = {10.1152/jn.00029.2024},
pmid = {38985934},
issn = {1522-1598},
support = {61876137//MOST | National Natural Science Foundation of China (NSFC)/ ; S202102GG001//Science and Technology Planning Project of the third Division of Xingjiang Production and Construction Corps/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; Electroencephalography/methods ; Photic Stimulation/methods ; Aircraft ; },
abstract = {Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW &amp; NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.},
}
@article {pmid38984886,
year = {2024},
author = {Zhu, F and Cai, J and Zheng, H and Liang, Z and Zhang, Y},
title = {Suppression of negative transfer in motor imagery brain-computer interface based on mutual information and Pearson correlation coefficient.},
journal = {The Review of scientific instruments},
volume = {95},
number = {7},
pages = {},
doi = {10.1063/5.0208524},
pmid = {38984886},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Algorithms ; Humans ; Imagination/physiology ; },
abstract = {The focus of this paper is on the main challenges in brain-computer interface transfer learning: how to address data characteristic length and the source domain sample selection problems caused by individual differences. To overcome the negative migration that results from feature length, we propose a migration algorithm based on mutual information transfer (MIT), which selects effective features by calculating the entropy value of the probability distribution and conditional distribution, thereby reducing negative migration and improving learning efficiency. Source domain participants who differ too much from the target domain distribution can affect the overall classification performance. On the basis of MIT, we propose the Pearson correlation coefficient source domain automatic selection algorithm (PDAS algorithm). The PDAS algorithm can automatically select the appropriate source domain participants according to the target domain distribution, which reduces the negative migration of participant data among the source domain participants, improves experimental accuracy, and greatly reduces training time. The two proposed algorithms were tested offline and online on two public datasets, and the results were compared with those from existing advanced algorithms. The experimental results showed that the MIT algorithm and the MIT + PDAS algorithm had obvious advantages.},
}
@article {pmid38984151,
year = {2024},
author = {Darvishi, S and Datta Gupta, A and Hamilton-Bruce, A and Koblar, S and Baumert, M and Abbott, D},
title = {Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.},
journal = {PNAS nexus},
volume = {3},
number = {7},
pages = {pgae240},
pmid = {38984151},
issn = {2752-6542},
abstract = {This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients. In response, our study investigates the potential of motor imagery-based BCIs (MI-BCIs) as an alternative intervention. In this study, MI-BCIs translate imagined hand movements into actions using a combination of scalp-recorded electrical brain activity and signal processing algorithms. Our prior research on MI-BCIs, which emphasizes the benefits of proprioceptive feedback over traditional visual feedback and the importance of customizing the delay between brain activation and passive hand movement, led to the development of RehabSwift therapy. In this study, we recruited 12 chronic-stage stroke survivors to assess the effectiveness of our solution. The primary outcome measure was the Fugl-Meyer upper extremity (FMA-UE) assessment, complemented by secondary measures including the action research arm test, reaction time, unilateral neglect, spasticity, grip and pinch strength, goal attainment scale, and FMA-UE sensation. Our findings indicate a remarkable improvement in hand movement and a clinically significant reduction in poststroke arm and hand impairment following 18 sessions of neurofeedback training. The effects persisted for at least 4 weeks posttreatment. These results underscore the potential of MI-BCIs, particularly our solution, as a prospective tool in stroke rehabilitation, offering a personalized and adaptable approach to neurofeedback training.},
}
@article {pmid38982461,
year = {2024},
author = {Daigle, L and Khalid, H and Gagnon, CA and Arsenault, J and Bienzle, D and Bisson, SK and Blais, MC and Denis-Robichaud, J and Forest, C and Grenier St-Sauveur, V and Koszegi, M and MacNicol, J and Nantel-Fortier, N and Nury, C and Prystajecky, N and Fraser, E and Carabin, H and Aenishaenslin, C},
title = {High prevalence of SARS-CoV-2 antibodies and low prevalence of SARS-CoV-2 RNA in cats recently exposed to human cases.},
journal = {BMC veterinary research},
volume = {20},
number = {1},
pages = {304},
pmid = {38982461},
issn = {1746-6148},
mesh = {Cats ; Animals ; *SARS-CoV-2/immunology ; *Cat Diseases/virology/epidemiology ; *RNA, Viral ; *Antibodies, Viral/blood ; *COVID-19/veterinary/epidemiology/diagnosis/virology ; Cross-Sectional Studies ; Humans ; Female ; Male ; Prevalence ; },
abstract = {BACKGROUND: The primary objective of this cross-sectional study, conducted in Québec and Bristish Columbia (Canada) between February 2021 and January 2022, was to measure the prevalence of viral RNA in oronasal and rectal swabs and serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amongst cats living in households with at least one confirmed human case. Secondary objectives included a description of potential risk factors for the presence of SARS-CoV-2 antibodies and an estimation of the association between the presence of viral RNA in swabs as well as SARS-CoV-2 antibodies and clinical signs. Oronasal and rectal swabs and sera were collected from 55 cats from 40 households at most 15 days after a human case confirmation, and at up to two follow-up visits. A RT-qPCR assay and an ELISA were used to detect SARS-CoV-2 RNA in swabs and serum SARS-CoV-2 IgG antibodies, respectively. Prevalence and 95% Bayesian credibility intervals (BCI) were calculated, and associations were evaluated using prevalence ratio and 95% BCI obtained from Bayesian mixed log-binomial models.<br><br>RESULTS: Nine (0.16; 95% BCI&#x2009;=&#x2009;0.08-0.28) and 38 (0.69; 95% BCI&#x2009;=&#x2009;0.56-0.80) cats had at least one positive RT-qPCR and at least one positive serological test result, respectively. No risk factor was associated with the prevalence of SARS-CoV-2 serum antibodies. The prevalence of clinical signs suggestive of COVID-19 in cats, mainly sneezing, was 2.12 (95% BCI&#x2009;=&#x2009;1.03-3.98) times higher amongst cats with detectable viral RNA compared to those without.<br><br>CONCLUSIONS: We showed that cats develop antibodies to SARS-CoV-2 when exposed to recent human cases, but detection of viral RNA on swabs is rare, even when sampling occurs soon after confirmation of a human case. Moreover, cats with detectable levels of virus showed clinical signs more often than cats without signs, which can be useful for the management of such cases.},
}
@article {pmid38982198,
year = {2024},
author = {Mejia, LA},
title = {Real control in virtual rats.},
journal = {Nature neuroscience},
volume = {27},
number = {7},
pages = {1214},
doi = {10.1038/s41593-024-01708-1},
pmid = {38982198},
issn = {1546-1726},
mesh = {Animals ; Rats ; *Virtual Reality ; Brain-Computer Interfaces ; },
}
@article {pmid38980788,
year = {2024},
author = {Li, R and Bai, D and Li, Z and Yang, S and Liu, W and Zhang, Y and Zhou, J and Luo, J and Wang, W},
title = {The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2564-2578},
doi = {10.1109/TNSRE.2024.3425636},
pmid = {38980788},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Electroencephalography/methods ; Male ; *Robotics/methods ; *Hand Strength/physiology ; *Brain-Computer Interfaces ; Young Adult ; Female ; Adult ; *Neural Networks, Computer ; Healthy Volunteers ; },
abstract = {In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the existing SSVEP brain control methods cannot adapt to dynamic or unstructured environments. Moreover, the recognition accuracy from the conventional decoding algorithm still needs to improve. To address the above problems, this study proposed a steady-state hybrid visual evoked potentials (SSHVEP) paradigm using the grasping targets in their environment to improve the connection between the subjects' and their dynamic environments. Moreover, a novel EEG decoding method, using the multivariate variational mode decomposition (MVMD) algorithm for adaptive sub-band division and convolutional neural network (CNN) for target recognition, was applied to improve the decoding accuracy of the SSHVEPs. 18 subjects participated in the offline and online experiments. The offline accuracy across 18 subjects by the 9-target SSHVEP paradigm was up to 95.41 ± 2.70 %, which is a 5.80% improvement compared to the conventional algorithm. To further validate the performance of the proposed method, the brain-controlled grasping robot system using the SSHVEP paradigm was built. The average accuracy reached 93.21 ± 10.18 % for the online experiment. All the experimental results demonstrated the effectiveness of the brain-computer interaction method based on the SSHVEP paradigm and the MVMD combined CNN algorithm studied in this paper.},
}
@article {pmid38980648,
year = {2024},
author = {Pan, G and Zhao, B and Zhang, M and Guo, Y and Yan, Y and Dai, D and Zhang, X and Yang, H and Ni, J and Huang, Z and Li, X and Duan, S},
title = {Nucleus Accumbens Corticotropin-Releasing Hormone Neurons Projecting to the Bed Nucleus of the Stria Terminalis Promote Wakefulness and Positive Affective State.},
journal = {Neuroscience bulletin},
volume = {40},
number = {11},
pages = {1602-1620},
pmid = {38980648},
issn = {1995-8218},
mesh = {Animals ; *Septal Nuclei/physiology/metabolism ; *Nucleus Accumbens/metabolism/physiology ; *Corticotropin-Releasing Hormone/metabolism ; *Wakefulness/physiology ; *Neurons/physiology/metabolism ; Male ; Mice ; *Mice, Inbred C57BL ; Neural Pathways/physiology ; Anxiety/metabolism/physiopathology ; Reward ; },
abstract = {The nucleus accumbens (NAc) plays an important role in various emotional and motivational behaviors that rely on heightened wakefulness. However, the neural mechanisms underlying the relationship between arousal and emotion regulation in NAc remain unclear. Here, we investigated the roles of a specific subset of inhibitory corticotropin-releasing hormone neurons in the NAc (NAc[CRH]) in regulating arousal and emotional behaviors in mice. We found an increased activity of NAc[CRH] neurons during wakefulness and rewarding stimulation. Activation of NAc[CRH] neurons converts NREM or REM sleep to wakefulness, while inhibition of these neurons attenuates wakefulness. Remarkably, activation of NAc[CRH] neurons induces a place preference response (PPR) and decreased basal anxiety level, whereas their inactivation induces a place aversion response and anxious state. NAc[CRH] neurons are identified as the major NAc projection neurons to the bed nucleus of the stria terminalis (BNST). Furthermore, activation of the NAc[CRH]-BNST pathway similarly induced wakefulness and positive emotional behaviors. Taken together, we identified a basal forebrain CRH pathway that promotes the arousal associated with positive affective states.},
}
@article {pmid38977483,
year = {2024},
author = {Ha, J and Park, MK and Park, SN and Cho, HH and Choi, JY and Lee, CK and Lee, IW and Moon, IJ and Jung, JY and Jung, J and Lee, KY and Oh, JH and Park, HJ and Seo, JH and Song, JJ and Kim, H and Jang, JH and Choung, YH},
title = {Tinnitus reduction after active bone-conduction implantation in patients with single-sided deafness: a prospective multicenter study.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {281},
number = {11},
pages = {5677-5686},
pmid = {38977483},
issn = {1434-4726},
mesh = {Humans ; Prospective Studies ; Male ; *Tinnitus/surgery/physiopathology ; Female ; Middle Aged ; *Bone Conduction ; *Hearing Loss, Unilateral/rehabilitation/surgery/physiopathology ; Adult ; Aged ; Treatment Outcome ; Hearing Aids ; Surveys and Questionnaires ; },
abstract = {PURPOSE: Single-sided deafness (SSD) presents significant challenges for patients, including compromised sound localization, reduced speech recognition, and often, tinnitus. These issues are typically addressed using interventions such as cochlear implantation (CI) and bone conduction implant (BCI). However, evidence regarding the efficacy of BCI in reducing tinnitus in SSD patients remains limited. This study explored the ability of a novel active transcutaneous BCI (Bonebridge BCI602) to alleviate tinnitus in SSD patients.<br><br>STUDY DESIGN: Prospective cohort multicenter study.<br><br>SETTING: Tertiary referral hospitals.<br><br>METHODS: A prospective multicenter study of 30 SSD patients was conducted. The patients were divided into two groups: those with (n&#x2009;=&#x2009;19) and without (n&#x2009;=&#x2009;11) tinnitus. Audiometric assessments, subjective questionnaires including the Abbreviated Profile of Hearing Aid Benefit (APHAB) and the Bern Benefit in Single-Sided Deafness (BBSS), and tinnitus evaluations with the Tinnitus Handicap Inventory (THI) and tinnitogram were conducted before and after BCI surgery.<br><br>RESULTS: THI scores after surgery were significantly reduced in SSD patients with tinnitus. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups; however, the former group exhibited a significantly greater improvement in the APHAB questionnaire score. According to tinnitograms, the loudness of tinnitus decreased, particularly in patients with ipsilateral tinnitus. Patients with residual hearing had greater reductions in their THI scores. However, three patients without residual hearing had a relative worsening of tinnitus after surgery.<br><br>CONCLUSION: The Bonebridge BCI602 effectively reduced tinnitus in SSD patients, particularly in those with residual hearing. Subjective satisfaction improved in both the tinnitus and non-tinnitus groups. These findings demonstrate the therapeutic potential of BCI for managing SSD and associated tinnitus.},
}
@article {pmid38977410,
year = {2024},
author = {M, AL and R, R},
title = {Rehabilitation Based on BCI: An Innovative Enhancement for Sensorimotor Cortex Rhythms Systemization.},
journal = {Advanced biology},
volume = {8},
number = {9},
pages = {e2400004},
doi = {10.1002/adbi.202400004},
pmid = {38977410},
issn = {2701-0198},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Sensorimotor Cortex/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {The research proposes a novel strategy for categorizing electroencephalograms (EEG) in real-time brain-computer interfaces that have rehabilitation applications. The methodology utilizes Five Cross-Common Spatial Patterns (FCCSP) to develop a motor movement/imagery systemization model that extracts multi-domain characteristics with excellent performance. The goal is to eliminate the impact caused by EEG's nonstationarity. The article highlights the findings of a real-time technique that is incorporated into a comprehensive prediction system, and it offers an innovative method to boost accuracy in real-time Sensory-Motor cortex Rhythms (SMR). The accuracy increased from 57.14% using raw EEG to 85.71% after preprocessing, and from 58.08% to 97.94% in public domain SMR. The proposed Butterworth bandpass filter is optimized using the FCCSP to determine the ideal bandwidth that incorporates the whole EEG features in beta waves. The Hybrid Systemization of the Correlated Feature Removal classifier is then integrated with the FCCSP method to create improved predictive models. As a consequence, while applied to real-time and PhysioNet datasets, the outcome system achieved outstanding accuracy values of 85.71% and 97.94%, respectively. This demonstrates the robustness of the strategy to increase SMR prediction efficiency.},
}
@article {pmid38976678,
year = {2024},
author = {Zhu, H and Beierholm, U and Shams, L},
title = {BCI Toolbox: An open-source python package for the Bayesian causal inference model.},
journal = {PLoS computational biology},
volume = {20},
number = {7},
pages = {e1011791},
pmid = {38976678},
issn = {1553-7358},
mesh = {*Bayes Theorem ; Humans ; *Algorithms ; Computational Biology/methods ; Software ; Brain-Computer Interfaces ; },
abstract = {Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.},
}
@article {pmid38976469,
year = {2024},
author = {Wang, Z and Shen, L and Yang, Y and Ma, Y and Man Wong, C and Liu, Z and Lin, C and Tin Hon, C and Qian, T and Wan, F},
title = {A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2470-2481},
doi = {10.1109/TNSRE.2024.3424410},
pmid = {38976469},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; *Machine Learning ; Least-Squares Analysis ; Nonlinear Dynamics ; Reproducibility of Results ; Male ; },
abstract = {The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.},
}
@article {pmid38975469,
year = {2024},
author = {Kalani, M and Anjankar, A},
title = {Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment.},
journal = {Cureus},
volume = {16},
number = {6},
pages = {e61706},
pmid = {38975469},
issn = {2168-8184},
abstract = {Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.},
}
@article {pmid38975192,
year = {2024},
author = {Li, Y and Nie, Y and Quan, Z and Zhang, H and Song, R and Feng, H and Cheng, X and Liu, W and Geng, X and Sun, X and Fu, Y and Wang, S},
title = {Brain-machine interactive neuromodulation research tool with edge AI computing.},
journal = {Heliyon},
volume = {10},
number = {12},
pages = {e32609},
pmid = {38975192},
issn = {2405-8440},
abstract = {Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.},
}
@article {pmid38974661,
year = {2024},
author = {Liu, Y and Gao, X and Zhang, Y and Zeng, M and Liu, Y and Wu, Y and Hu, W and Lai, Y and Liao, J},
title = {Geographical variation in dementia prevalence across China: a geospatial analysis.},
journal = {The Lancet regional health. Western Pacific},
volume = {47},
number = {},
pages = {101117},
pmid = {38974661},
issn = {2666-6065},
abstract = {BACKGROUND: Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces.<br><br>METHODS: In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the China&#xa0;Health and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates.<br><br>FINDINGS: The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation.<br><br>INTERPRETATION: Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment.<br><br>FUNDING: National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).},
}
@article {pmid38974471,
year = {2024},
author = {Zhang, J and Zhang, Y and Zhang, X and Xu, B and Zhao, H and Sun, T and Wang, J and Lu, S and Shen, X},
title = {A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.},
journal = {iScience},
volume = {27},
number = {6},
pages = {110164},
pmid = {38974471},
issn = {2589-0042},
abstract = {This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.},
}
@article {pmid38973051,
year = {2024},
author = {Zou, X and Chen, B and Li, Y},
title = {[Research status and progress of bilateral cochlear implantation].},
journal = {Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery},
volume = {38},
number = {7},
pages = {666-670},
pmid = {38973051},
issn = {2096-7993},
mesh = {Humans ; *Cochlear Implantation/methods ; *Cochlear Implants ; Speech Perception ; Hearing Loss/surgery ; },
abstract = {With the development of social economic and technology, Cochlear Implantation has became an effective therapy for patients who suffered from severe or profound hearing impairment. In the meantime, patients' demands for sound and auditory quality are also increasing. In terms of speech recognition, localization, and auditory quality, bilateral hearing is closer to the auditory experience of normal individuals, so bilateral cochlear implantation（BCI） emerged as the times require. In this article, we will introduce the status and progress of bimodal regarding to the following aspects: the brief history, the advantages of BCI, different methods for BCI, and the problems encountered in BCI.},
}
@article {pmid38971641,
year = {2024},
author = {Casasanta, N and Patel, R and Raymond, S and Kier, MW and Blanter, J and Sohval, S and Hovstadius, M and Wu, C and Zimmerman, B and Cascetta, K and Bagiella, E and Tiersten, A},
title = {Correlating Predicted Adjuvant Therapy Benefit and Risk of Recurrence Between Breast Cancer Index (BCI) and the 21-Gene Oncotype DX Recurrence Score (RS).},
journal = {Clinical breast cancer},
volume = {24},
number = {7},
pages = {585-596},
doi = {10.1016/j.clbc.2024.06.005},
pmid = {38971641},
issn = {1938-0666},
mesh = {Humans ; Female ; *Breast Neoplasms/genetics/pathology/therapy ; Middle Aged ; *Neoplasm Recurrence, Local/genetics/pathology ; Retrospective Studies ; Adult ; Chemotherapy, Adjuvant ; Gene Expression Profiling ; Aged ; Prognosis ; Biomarkers, Tumor/genetics/metabolism ; Antineoplastic Agents, Hormonal/therapeutic use ; Receptors, Estrogen/metabolism ; Risk Assessment/methods ; },
abstract = {INTRODUCTION: Breast Cancer Index (BCI) is a genomic assay that evaluates the benefit of extending endocrine therapy (ET) from 5 to 10 years and predicts recurrence risk (RR). We evaluated the association between BCI and Oncotype DX (ODX).<br><br>PATIENTS: Women with hormone receptor (HR)-positive early-stage breast cancer (EBC) who had BCI and ODX performed were included.<br><br>METHODS: We performed a retrospective review of women with HR-positive EBC. BCI was categorized as predictive of extended ET versus not and ODX recurrence score (RS) as low (0-10), intermediate (11-25), and high (26-100). Univariate and multivariable logistic and linear regression models assessed the relationship between BCI and ODX, factors associated with each, and discordance between scores.<br><br>RESULTS: We identified 153 women, 22% were premenopausal and 18% were lymph node positive. The univariate logistic and linear models revealed an association between BCI predictive score and ODX RS (OR 7.84, CI, 2.63-23.36, P < .001) and log of BCI RR (Beta 0.04, CI, 0.02-0.06, P < .001). Seventy-four percent of BCI predictive scores were concordant with ODX RS and 83% of BCI RR was concordant with ODX RR. In a univariate logistic regression model, BCI predictive of ET benefit was associated with discordance (OR 28.00, CI, 10.58-74.02, P < .001). Higher ODX RR was associated with discordance (OR 1.92, CI, 1.42-2.59, P < .001).<br><br>CONCLUSION: We found a significant association between ODX and BCI predictive and prognostic scores. BCI predictive of extended ET benefit was associated with discordance with ODX RS. Higher predicted RR on ODX was associated with discordance with BCI predicted RR.},
}
@article {pmid38969758,
year = {2024},
author = {de Borman, A and Wittevrongel, B and Dauwe, I and Carrette, E and Meurs, A and Van Roost, D and Boon, P and Van Hulle, MM},
title = {Imagined speech event detection from electrocorticography and its transfer between speech modes and subjects.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {818},
pmid = {38969758},
issn = {2399-3642},
support = {11K2324N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4321N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0C1522N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4118N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0A4321N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; G0C1522N//Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)/ ; PDM/19/176//KU Leuven (Katholieke Universiteit Leuven)/ ; C24/18/098//KU Leuven (Katholieke Universiteit Leuven)/ ; AKUL 043//Hercules Foundation/ ; 857375//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/ ; },
mesh = {Humans ; *Electrocorticography/methods ; *Speech/physiology ; Male ; Female ; Adult ; *Brain-Computer Interfaces ; *Imagination/physiology ; Young Adult ; Motor Cortex/physiology ; },
abstract = {Speech brain-computer interfaces aim to support communication-impaired patients by translating neural signals into speech. While impressive progress was achieved in decoding performed, perceived and attempted speech, imagined speech remains elusive, mainly due to the absence of behavioral output. Nevertheless, imagined speech is advantageous since it does not depend on any articulator movements that might become impaired or even lost throughout the stages of a neurodegenerative disease. In this study, we analyzed electrocortigraphy data recorded from 16 participants in response to 3 speech modes: performed, perceived (listening), and imagined speech. We used a linear model to detect speech events and examined the contributions of each frequency band, from delta to high gamma, given the speech mode and electrode location. For imagined speech detection, we observed a strong contribution of gamma bands in the motor cortex, whereas lower frequencies were more prominent in the temporal lobe, in particular of the left hemisphere. Based on the similarities in frequency patterns, we were able to transfer models between speech modes and participants with similar electrode locations.},
}
@article {pmid38969345,
year = {2024},
author = {Kitta, T and Wada, N and Shinohara, S and Hayashi, N and Yamamura, H and Yamamoto, T and Takagi, H and Hatakeyama, T and Nagabuchi, M and Morishita, S and Tsunekawa, R and Ohtani, M and Kobayashi, S and Hori, JI and Kakizaki, H},
title = {Validation of the area under the Watts factor curve during the voiding cycle as a novel parameter for diagnosing detrusor underactivity in females.},
journal = {International journal of urology : official journal of the Japanese Urological Association},
volume = {31},
number = {10},
pages = {1121-1127},
doi = {10.1111/iju.15531},
pmid = {38969345},
issn = {1442-2042},
mesh = {Humans ; Female ; Middle Aged ; *Urodynamics ; Aged ; Adult ; *Lower Urinary Tract Symptoms/diagnosis/physiopathology/etiology ; *Urination/physiology ; *Urinary Bladder, Underactive/diagnosis/physiopathology ; *Urinary Bladder/physiopathology ; *Area Under Curve ; ROC Curve ; Muscle Contraction/physiology ; Aged, 80 and over ; Urinary Bladder Neck Obstruction/diagnosis/physiopathology/complications ; },
abstract = {OBJECTIVE: Detrusor underactivity (DU) is a common cause of lower urinary tract symptoms (LUTS). To date, no consensus has been reached on the urodynamic criteria for defining DU. We previously proposed the area under the curve of the Watts factor (WF-AUC) as a new parameter for diagnosing DU. By comparing previously reported five criteria for DU and WF-AUC, we analyzed whether the WF-AUC could assess detrusor contraction in women with LUTS.<br><br>METHODS: Using urodynamic data of consecutive 77 women with LUTS, first, we classified DU based on previously reported five criteria. Second, we assessed the potential correlation between multiple parameters and WF-AUC. Third, receiver operating characteristic curve analysis was performed to determine the cutoff value of WF-AUC for diagnosing DU based on previously reported five criteria. Fourth, a linear regression analysis was conducted and compared using multiple criteria and female bladder outlet obstruction index (BOOIf).<br><br>RESULTS: WF-AUC was positively correlated with the maximum values of WF, bladder contractility index (BCI), and projected isovolumetric pressure 1 (PIP1) with correlation coefficients of 0.63, 0.57, and 0.34, respectively. AUC for diagnosing DU based on previously reported five criteria ranging from 0.773 to 0.896 with different cutoff values of AUC-WF. The Spearman's correlation test revealed that BOOIf was significantly correlated with BCI, but not Wmax, PIP1 and WF-AUC.<br><br>CONCLUSIONS: This study demonstrated the non-inferiority of the WF-AUC compared to previously reported criteria for defining DU. Depending on the cutoff value, the WF-AUC could appropriately evaluate women with DU, regardless of the presence of BOO.},
}
@article {pmid38968976,
year = {2024},
author = {Kumari, A and Edla, DR and Reddy, RR and Jannu, S and Vidyarthi, A and Alkhayyat, A and de Marin, MSG},
title = {EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.},
journal = {Journal of neuroscience methods},
volume = {409},
number = {},
pages = {110215},
doi = {10.1016/j.jneumeth.2024.110215},
pmid = {38968976},
issn = {1872-678X},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and control. Motor imagery (MI)-based BCI systems are particularly relevant in this context. Despite their potential, achieving accurate and robust classification of MI tasks using electroencephalography (EEG) data remains a significant challenge. In this paper, we employed the Minimum Redundancy Maximum Relevance (MRMR) algorithm to optimize channel selection. Furthermore, we introduced a hybrid optimization approach that combines the War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). This hybridization significantly enhances the classification model's overall performance and adaptability. A two-tier deep learning architecture is proposed for classification, consisting of a Convolutional Neural Network (CNN) and a modified Deep Neural Network (M-DNN). The CNN focuses on capturing temporal correlations within EEG data, while the M-DNN is designed to extract high-level spatial characteristics from selected EEG channels. Integrating optimal channel selection, hybrid optimization, and the two-tier deep learning methodology in our BCI framework presents an enhanced approach for precise and effective BCI control. Our model got 95.06% accuracy with high precision. This advancement has the potential to significantly impact neurorehabilitation and assistive technology applications, facilitating improved communication and control for individuals with motor impairments.},
}
@article {pmid38968936,
year = {2024},
author = {Wu, H and Xie, Q and Yu, Z and Zhang, J and Liu, S and Long, J},
title = {Unsupervised heterogeneous domain adaptation for EEG classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5fbd},
pmid = {38968936},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; Humans ; Unsupervised Machine Learning ; Algorithms ; Neural Networks, Computer ; },
abstract = {Objective.Domain adaptation has been recognized as a potent solution to the challenge of limited training data for electroencephalography (EEG) classification tasks. Existing studies primarily focus on homogeneous environments, however, the heterogeneous properties of EEG data arising from device diversity cannot be overlooked. This motivates the development of heterogeneous domain adaptation methods that can fully exploit the knowledge from an auxiliary heterogeneous domain for EEG classification.Approach.In this article, we propose a novel model named informative representation fusion (IRF) to tackle the problem of unsupervised heterogeneous domain adaptation in the context of EEG data. In IRF, we consider different perspectives of data, i.e. independent identically distributed (iid) and non-iid, to learn different representations. Specifically, from the non-iid perspective, IRF models high-order correlations among data by hypergraphs and develops hypergraph encoders to obtain data representations of each domain. From the non-iid perspective, by applying multi-layer perceptron networks to the source and target domain data, we achieve another type of representation for both domains. Subsequently, an attention mechanism is used to fuse these two types of representations to yield informative features. To learn transferable representations, the maximum mean discrepancy is utilized to align the distributions of the source and target domains based on the fused features.Main results.Experimental results on several real-world datasets demonstrate the effectiveness of the proposed model.Significance.This article handles an EEG classification situation where the source and target EEG data lie in different spaces, and what's more, under an unsupervised learning setting. This situation is practical in the real world but barely studied in the literature. The proposed model achieves high classification accuracy, and this study is important for the commercial applications of EEG-based BCIs.},
}
@article {pmid38966123,
year = {2024},
author = {Qi, G and Liu, R and Guan, W and Huang, A},
title = {Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {5},
number = {},
pages = {0130},
pmid = {38966123},
issn = {2692-7632},
abstract = {In this study, we propose an electrophysiological analysis-based brain network method for the augmented recognition of different types of distractions during driving. Driver distractions, such as cognitive processing and visual disruptions during driving, lead to distinct alterations in the electroencephalogram (EEG) signals and the extracted brain networks. We designed and conducted a simulated experiment comprising 4 distracted driving subtasks. Three connectivity indices, including both linear and nonlinear synchronization measures, were chosen to construct the brain network. By computing connectivity strengths and topological features, we explored the potential relationship between brain network configurations and states of driver distraction. Statistical analysis of network features indicates substantial differences between normal and distracted states, suggesting a reconfiguration of the brain network under distracted conditions. Different brain network features and their combinations are fed into varied machine learning classifiers to recognize the distracted driving states. The results indicate that XGBoost demonstrates superior adaptability, outperforming other classifiers across all selected network features. For individual networks, features constructed using synchronization likelihood (SL) achieved the highest accuracy in distinguishing between cognitive and visual distraction. The optimal feature set from 3 network combinations achieves an accuracy of 95.1% for binary classification and 88.3% for ternary classification of normal, cognitively distracted, and visually distracted driving states. The proposed method could accomplish the augmented recognition of distracted driving states and may serve as a valuable tool for further optimizing driver assistance systems with distraction control strategies, as well as a reference for future research on the brain-computer interface in autonomous driving.},
}
@article {pmid38966087,
year = {2024},
author = {Sun, J and Li, C},
title = {Editorial: Advanced neurotechnology in stroke rehabilitation.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1440752},
pmid = {38966087},
issn = {1664-2295},
}
@article {pmid38965248,
year = {2024},
author = {Xu, M and Zhou, W and Shen, X and Qiu, J and Li, D},
title = {Temporal-spatial cross attention network for recognizing imagined characters.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {15432},
pmid = {38965248},
issn = {2045-2322},
support = {2023C01143//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang/ ; 19ZDA348//National Social Science Fund of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; Imagination/physiology ; Brain/physiology ; Attention/physiology ; Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features of the signals in a sequential, parallel, or single-feature manner. However, there has been limited research on the cross-relationships between temporal and spatial features, despite the inherent association between channels and sampling points in Brain-Computer Interface (BCI) signal acquisition, which holds significant information about brain activity. To address the limited research on the relationships between temporal and spatial features, we proposed a Temporal-Spatial Cross-Attention Network model, named TSCA-Net. The TSCA-Net is comprised of four modules: the Temporal Feature (TF), the Spatial Feature (SF), the Temporal-Spatial Cross (TSCross), and the Classifier. The TF combines LSTM and Transformer to extract temporal features from BCI signals, while the SF captures spatial features. The TSCross is introduced to learn the correlations between the temporal and spatial features. The Classifier predicts the label of BCI data based on its characteristics. We validated the TSCA-Net model using publicly available datasets of handwritten characters, which recorded the spiking activity from two micro-electrode arrays (MEAs). The results showed that our proposed TSCA-Net outperformed other comparison models (EEG-Net, EEG-TCNet, S3T, GRU, LSTM, R-Transformer, and ViT) in terms of accuracy, precision, recall, and F1 score, achieving 92.66 % , 92.77 % , 92.70 % , and 92.58 % , respectively. The TSCA-Net model demonstrated a 3.65 % to 7.49 % improvement in accuracy over the comparison models.},
}
@article {pmid38963812,
year = {2024},
author = {Tian, Y and Zheng, J and Zhu, X and Liu, X and Li, H and Wang, J and Yang, Q and Zeng, LH and Shi, Z and Gong, M and Hu, Y and Xu, H},
title = {A prefrontal-habenular circuitry regulates social fear behaviour.},
journal = {Brain : a journal of neurology},
volume = {147},
number = {12},
pages = {4185-4199},
doi = {10.1093/brain/awae209},
pmid = {38963812},
issn = {1460-2156},
support = {32071005//National Natural Science Foundation of China/ ; 2021YFA1101701//National Key Research and Development Program of China/ ; 010904008//Nanhu Brain-Computer Interface Institute/ ; SHSMU-ZDCX20211102//Innovative Research Team of High-level Local Universities in Shanghai/ ; //Fundamental Research Funds for the Central Universities/ ; //MOE Frontiers Science Center for Brain Science/ ; //and/ ; //Brain-Machine Integration of Zhejiang University/ ; },
mesh = {Animals ; *Fear/physiology/psychology ; *Prefrontal Cortex/physiology ; Mice ; Male ; *Habenula/physiology ; Humans ; Mice, Inbred C57BL ; Social Behavior ; Neural Pathways/physiology ; Female ; Optogenetics ; Adult ; Conditioning, Classical/physiology ; },
abstract = {The medial prefrontal cortex (mPFC) has been implicated in the pathophysiology of social impairments, including social fear. However, the precise subcortical partners that mediate mPFC dysfunction on social fear behaviour have not been identified. Using a social fear conditioning paradigm, we induced robust social fear in mice and found that the lateral habenula (LHb) neurons and LHb-projecting mPFC neurons are activated synchronously during social fear expression. Moreover, optogenetic inhibition of the mPFC-LHb projection significantly reduced social fear responses. Importantly, consistent with animal studies, we observed an elevated prefrontal-habenular functional connectivity in subclinical individuals with higher social anxiety characterized by heightened social fear. These results unravel a crucial role of the prefrontal-habenular circuitry in social fear regulation and suggest that this pathway could serve as a potential target for the treatment of social fear symptoms often observed in many psychiatric disorders.},
}
@article {pmid38963559,
year = {2024},
author = {Kukkar, KK and Rao, N and Huynh, D and Shah, S and Contreras-Vidal, JL and Parikh, PJ},
title = {Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors.},
journal = {Experimental brain research},
volume = {242},
number = {9},
pages = {2093-2112},
pmid = {38963559},
issn = {1432-1106},
support = {P2C HD086844/HD/NICHD NIH HHS/United States ; R25 HD106896/HD/NICHD NIH HHS/United States ; R25HD106896//National Institute of Child Health and Human Development/ ; P2CHDo86844//National Center for Medical Rehabilitation Research/ ; },
mesh = {Humans ; *Postural Balance/physiology ; Male ; Female ; *Stroke/physiopathology ; Middle Aged ; Aged ; *Electromyography ; *Muscle, Skeletal/physiopathology/physiology ; Chronic Disease ; Survivors ; Electroencephalography/methods ; Adult ; },
abstract = {Balance control is an important indicator of mobility and independence in activities of daily living. How the functional coupling between the cortex and the muscle for balance control is affected following stroke remains to be known. We investigated the changes in coupling between the cortex and leg muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Fourteen participants with stroke and ten healthy controls performed a challenging balance task. They stood on a computerized support surface that was either fixed (low difficulty condition) or sway-referenced with varying gain (medium and high difficulty conditions). We computed corticomuscular coherence between electrodes placed over the sensorimotor area (electroencephalography) and leg muscles (electromyography) and assessed balance performance using clinical and laboratory-based tests. We found significantly lower delta frequency band coherence in stroke participants when compared with healthy controls under medium difficulty condition, but not during low and high difficulty conditions. These differences were found for most of the distal but not for proximal leg muscle groups. No differences were found at other frequency bands. Participants with stroke showed poor balance clinical scores when compared with healthy controls, but no differences were found for laboratory-based tests. The observation of effects at distal but not at proximal muscle groups suggests differences in the (re)organization of the descending connections across two muscle groups for balance control. We argue that the observed group difference in delta band coherence indicates balance context-dependent alteration in mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support surface for balance maintenance following stroke.},
}
@article {pmid38963179,
year = {2024},
author = {Martinez-Peon, D and Garcia-Hernandez, NV and Benavides-Bravo, FG and Parra-Vega, V},
title = {Characterization and classification of kinesthetic motor imagery levels.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5f27},
pmid = {38963179},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Male ; Adult ; Female ; *Kinesthesis/physiology ; Young Adult ; *Brain-Computer Interfaces ; Muscle Contraction/physiology ; Motor Cortex/physiology ; Electromyography/methods ; Algorithms ; Movement/physiology ; Reproducibility of Results ; Support Vector Machine ; },
abstract = {Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.},
}
@article {pmid38962279,
year = {2024},
author = {Singh, AK and Bianchi, L},
title = {Encoding temporal information in deep convolution neural network.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1287794},
pmid = {38962279},
issn = {2673-6195},
abstract = {A recent development in deep learning techniques has attracted attention to the decoding and classification of electroencephalogram (EEG) signals. Despite several efforts to utilize different features in EEG signals, a significant research challenge is using time-dependent features in combination with local and global features. Several attempts have been made to remodel the deep learning convolution neural networks (CNNs) to capture time-dependency information. These features are usually either handcrafted features, such as power ratios, or splitting data into smaller-sized windows related to specific properties, such as a peak at 300 ms. However, these approaches partially solve the problem but simultaneously hinder CNNs' capability to learn from unknown information that might be present in the data. Other approaches, like recurrent neural networks, are very suitable for learning time-dependent information from EEG signals in the presence of unrelated sequential data. To solve this, we have proposed an encoding kernel (EnK), a novel time-encoding approach, which uniquely introduces time decomposition information during the vertical convolution operation in CNNs. The encoded information lets CNNs learn time-dependent features in addition to local and global features. We performed extensive experiments on several EEG data sets-physical human-robot collaborations, P300 visual-evoked potentials, motor imagery, movement-related cortical potentials, and the Dataset for Emotion Analysis Using Physiological Signals. The EnK outperforms the state of the art with an up to 6.5% reduction in mean squared error (MSE) and a 9.5% improvement in F1-scores compared to the average for all data sets together compared to base models. These results support our approach and show a high potential to improve the performance of physiological and non-physiological data. Moreover, the EnK can be applied to virtually any deep learning architecture with minimal effort.},
}
@article {pmid38962177,
year = {2024},
author = {Khazaei, S and Parshi, S and Alam, S and Amin, MR and Faghih, RT},
title = {A multimodal dataset for investigating working memory in presence of music: a pilot study.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1406814},
pmid = {38962177},
issn = {1662-4548},
abstract = {INTRODUCTION: Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.<br><br>METHODS: Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.<br><br>RESULTS: A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.<br><br>DISCUSSION: Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.},
}
@article {pmid38961531,
year = {2024},
author = {Sands, I and Demarco, R and Thurber, L and Esteban-Linares, A and Song, D and Meng, E and Chen, Y},
title = {Interface-Mediated Neurogenic Signaling: The Impact of Surface Geometry and Chemistry on Neural Cell Behavior for Regenerative and Brain-Machine Interfacing Applications.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {33},
pages = {e2401750},
pmid = {38961531},
issn = {1521-4095},
support = {1905785//National Science Foundation/ ; 2234570//National Science Foundation/ ; R01 AR072027/AR/NIAMS NIH HHS/United States ; R21 AR079153/AR/NIAMS NIH HHS/United States ; 1R21AR079153-01A1/NH/NIH HHS/United States ; //University of Connecticut/ ; 2025362//National Science Foundation/ ; U24 NS113647/NS/NINDS NIH HHS/United States ; 7R01AR072027/NH/NIH HHS/United States ; 80JSC022CA006/NASA/NASA/United States ; U01 NS126046/NS/NINDS NIH HHS/United States ; W81XWH2110274/NASA/NASA/United States ; },
mesh = {Animals ; Humans ; *Brain-Computer Interfaces ; *Signal Transduction ; *Neurons/cytology/metabolism ; *Surface Properties ; Neurogenesis ; Nanostructures/chemistry ; Tissue Scaffolds/chemistry ; Brain/metabolism/cytology/physiology ; Nerve Regeneration ; Biocompatible Materials/chemistry ; },
abstract = {Nanomaterial advancements have driven progress in central and peripheral nervous system applications such as tissue regeneration and brain-machine interfacing. Ideally, neural interfaces with native tissue shall seamlessly integrate, a process that is often mediated by the interfacial material properties. Surface topography and material chemistry are significant extracellular stimuli that can influence neural cell behavior to facilitate tissue integration and augment therapeutic outcomes. This review characterizes topographical modifications, including micropillars, microchannels, surface roughness, and porosity, implemented on regenerative scaffolding and brain-machine interfaces. Their impact on neural cell response is summarized through neurogenic outcome and mechanistic analysis. The effects of surface chemistry on neural cell signaling with common interfacing compounds like carbon-based nanomaterials, conductive polymers, and biologically inspired matrices are also reviewed. Finally, the impact of these extracellular mediated neural cues on intracellular signaling cascades is discussed to provide perspective on the manipulation of neuron and neuroglia cell microenvironments to drive therapeutic outcomes.},
}
@article {pmid38959876,
year = {2024},
author = {Van Den Kerchove, A and Si-Mohammed, H and Van Hulle, MM and Cabestaing, F},
title = {Correcting for ERP latency jitter improves gaze-independent BCI decoding.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5ec0},
pmid = {38959876},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Fixation, Ocular/physiology ; *Attention/physiology ; *Electroencephalography/methods ; Young Adult ; Photic Stimulation/methods ; Reaction Time/physiology ; Evoked Potentials, Visual/physiology ; },
abstract = {Objective.Patients suffering from heavy paralysis or Locked-in-Syndrome can regain communication using a Brain-Computer Interface (BCI). Visual event-related potential (ERP) based BCI paradigms exploit visuospatial attention (VSA) to targets laid out on a screen. However, performance drops if the user does not direct their eye gaze at the intended target, harming the utility of this class of BCIs for patients suffering from eye motor deficits. We aim to create an ERP decoder that is less dependent on eye gaze.Approach.ERP component latency jitter plays a role in covert visuospatial attention (VSA) decoding. We introduce a novel decoder which compensates for these latency effects, termed Woody Classifier-based Latency Estimation (WCBLE). We carried out a BCI experiment recording ERP data in overt and covert visuospatial attention (VSA), and introduce a novel special case of covert VSA termed split VSA, simulating the experience of patients with severely impaired eye motor control. We evaluate WCBLE on this dataset and the BNCI2014-009 dataset, within and across VSA conditions to study the dependency on eye gaze and the variation thereof during the experiment.Main results.WCBLE outperforms state-of-the-art methods in the VSA conditions of interest in gaze-independent decoding, without reducing overt VSA performance. Results from across-condition evaluation show that WCBLE is more robust to varying VSA conditions throughout a BCI operation session.Significance. Together, these results point towards a pathway to achieving gaze independence through suited ERP decoding. Our proposed gaze-independent solution enhances decoding performance in those cases where performing overt VSA is not possible.},
}
@article {pmid38957693,
year = {2024},
author = {Eidel, M and Pfeiffer, M and Ziebell, P and Kübler, A},
title = {Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1371631},
pmid = {38957693},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.},
}
@article {pmid38956454,
year = {2024},
author = {Xu, T and Ji, Z and Xu, X and Wang, L},
title = {Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.},
journal = {BMC bioinformatics},
volume = {25},
number = {1},
pages = {227},
pmid = {38956454},
issn = {1471-2105},
mesh = {*Brain-Computer Interfaces ; *Algorithms ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components.<br><br>RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively.<br><br>CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.},
}
@article {pmid38956006,
year = {2024},
author = {Huang, Q and Ding, J and Wang, X},
title = {A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.},
journal = {Neuroscience bulletin},
volume = {40},
number = {12},
pages = {1915-1930},
pmid = {38956006},
issn = {1995-8218},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Adult ; Male ; Female ; Young Adult ; Brain/physiopathology ; Signal Processing, Computer-Assisted ; },
abstract = {Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.},
}
@article {pmid38954651,
year = {2024},
author = {Köhler, RM and Binns, TS and Merk, T and Zhu, G and Yin, Z and Zhao, B and Chikermane, M and Vanhoecke, J and Busch, JL and Habets, JGV and Faust, K and Schneider, GH and Cavallo, A and Haufe, S and Zhang, J and Kühn, AA and Haynes, JD and Neumann, WJ},
title = {Dopamine and deep brain stimulation accelerate the neural dynamics of volitional action in Parkinson's disease.},
journal = {Brain : a journal of neurology},
volume = {147},
number = {10},
pages = {3358-3369},
pmid = {38954651},
issn = {1460-2156},
support = {//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Humans ; *Parkinson Disease/therapy/physiopathology ; *Deep Brain Stimulation/methods ; Female ; Male ; Middle Aged ; Aged ; *Subthalamic Nucleus/physiopathology ; *Dopamine/metabolism ; Volition ; Electrocorticography/methods ; Electromyography ; Movement/physiology ; Sensorimotor Cortex/physiopathology ; },
abstract = {The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders.},
}
@article {pmid38953082,
year = {2024},
author = {Mulpuri, RP and Konda, N and Gadde, ST and Amalakanti, S and Valiveti, SC},
title = {Artificial Intelligence and Machine Learning in Neuroregeneration: A Systematic Review.},
journal = {Cureus},
volume = {16},
number = {5},
pages = {e61400},
pmid = {38953082},
issn = {2168-8184},
abstract = {Artificial intelligence (AI) and machine learning (ML) show promise in various medical domains, including medical imaging, precise diagnoses, and pharmaceutical research. In neuroscience and neurosurgery, AI/ML advancements enhance brain-computer interfaces, neuroprosthetics, and surgical planning. They are poised to revolutionize neuroregeneration by unraveling the nervous system's complexities. However, research on AI/ML in neuroregeneration is fragmented, necessitating a comprehensive review. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, 19 English-language papers focusing on AI/ML in neuroregeneration were selected from a total of 247. Two researchers independently conducted data extraction and quality assessment using the Mixed Methods Appraisal Tool (MMAT) 2018. Eight studies were deemed high quality, 10 moderate, and four low. Primary goals included diagnosing neurological disorders (35%), robotic rehabilitation (18%), and drug discovery (12% each). Methods ranged from analyzing imaging data (24%) to animal models (24%) and electronic health records (12%). Deep learning accounted for 41% of AI/ML techniques, while standard ML algorithms constituted 29%. The review underscores the growing interest in AI/ML for neuroregenerative medicine, with increasing publications. These technologies aid in diagnosing diseases and facilitating functional recovery through robotics and targeted stimulation. AI-driven drug discovery holds promise for identifying neuroregenerative therapies. Nonetheless, addressing existing limitations remains crucial in this rapidly evolving field.},
}
@article {pmid38952818,
year = {2024},
author = {Vaccari, FE and Diomedi, S and De Vitis, M and Filippini, M and Fattori, P},
title = {Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.},
journal = {Network neuroscience (Cambridge, Mass.)},
volume = {8},
number = {2},
pages = {486-516},
pmid = {38952818},
issn = {2472-1751},
abstract = {Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.},
}
@article {pmid38952644,
year = {2024},
author = {Liu, W and Guo, Y and Xie, J and Wu, Y and Zhao, D and Xing, Z and Fu, X and Zhou, S and Zhang, H and Wang, X},
title = {Establishment and validation of a bad outcomes prediction model based on EEG and clinical parameters in prolonged disorder of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1387471},
pmid = {38952644},
issn = {1662-5161},
abstract = {OBJECTIVE: This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors.<br><br>METHODS: This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set (n&#x2009;=&#x2009;107) and verification set (n&#x2009;=&#x2009;27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model.<br><br>RESULT: The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets.<br><br>CONCLUSION: This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.},
}
@article {pmid38951635,
year = {2024},
author = {Song, H and Hsieh, TH and Yeon, SH and Shu, T and Nawrot, M and Landis, CF and Friedman, GN and Israel, EA and Gutierrez-Arango, S and Carty, MJ and Freed, LE and Herr, HM},
title = {Continuous neural control of a bionic limb restores biomimetic gait after amputation.},
journal = {Nature medicine},
volume = {30},
number = {7},
pages = {2010-2019},
pmid = {38951635},
issn = {1546-170X},
support = {R01 HD097135/HD/NICHD NIH HHS/United States ; R25 NS065743/NS/NINDS NIH HHS/United States ; R01HD097135//U.S. Department of Health &amp; Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)/ ; },
mesh = {Humans ; *Artificial Limbs ; *Gait/physiology ; *Amputation, Surgical ; *Amputees ; *Bionics ; *Biomimetics/methods ; Male ; Middle Aged ; Adult ; Female ; Muscle, Skeletal/innervation ; Walking ; Leg/surgery ; },
abstract = {For centuries scientists and technologists have sought artificial leg replacements that fully capture the versatility of their intact biological counterparts. However, biological gait requires coordinated volitional and reflexive motor control by complex afferent and efferent neural interplay, making its neuroprosthetic emulation challenging after limb amputation. Here we hypothesize that continuous neural control of a bionic limb can restore biomimetic gait after below-knee amputation when residual muscle afferents are augmented. To test this hypothesis, we present a neuroprosthetic interface consisting of surgically connected, agonist-antagonist muscles including muscle-sensing electrodes. In a cohort of seven leg amputees, the interface is shown to augment residual muscle afferents by 18% of biologically intact values. Compared with a matched amputee cohort without the afferent augmentation, the maximum neuroprosthetic walking speed is increased by 41%, enabling equivalent peak speeds to persons without leg amputation. Further, this level of afferent augmentation enables biomimetic adaptation to various walking speeds and real-world environments, including slopes, stairs and obstructed pathways. Our results suggest that even a small augmentation of residual muscle afferents restores biomimetic gait under continuous neuromodulation in individuals with leg amputation.},
}
@article {pmid38951525,
year = {2024},
author = {Li, F and Gallego, J and Tirko, NN and Greaser, J and Bashe, D and Patel, R and Shaker, E and Van Valkenburg, GE and Alsubhi, AS and Wellman, S and Singh, V and Padilla, CG and Gheres, KW and Broussard, JI and Bagwell, R and Mulvihill, M and Kozai, TDY},
title = {Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {5512},
pmid = {38951525},
issn = {2041-1723},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R01NS129632//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R21 EB028055/EB/NIBIB NIH HHS/United States ; R01NS115707//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; CAREER 1943906//NSF | ENG/OAD | Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET)/ ; R01 NS094396/NS/NINDS NIH HHS/United States ; R21EB028055//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R44 MH131514/MH/NIMH NIH HHS/United States ; R44MH131514//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01NS094396//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS105691//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 NS115707/NS/NINDS NIH HHS/United States ; },
mesh = {*Microglia/radiation effects/metabolism ; Animals ; *Microelectrodes ; *Electrodes, Implanted ; *Ultrasonic Waves ; Male ; Foreign-Body Reaction/prevention &amp; control/etiology ; Mice ; Cerebral Cortex/radiation effects/cytology ; Brain-Computer Interfaces ; Cell Movement/radiation effects ; Rats ; },
abstract = {Microglia are important players in surveillance and repair of the brain. Implanting an electrode into the cortex activates microglia, produces an inflammatory cascade, triggers the foreign body response, and opens the blood-brain barrier. These changes can impede intracortical brain-computer interfaces performance. Using two-photon imaging of implanted microelectrodes, we test the hypothesis that low-intensity pulsed ultrasound stimulation can reduce microglia-mediated neuroinflammation following the implantation of microelectrodes. In the first week of treatment, we found that low-intensity pulsed ultrasound stimulation increased microglia migration speed by 128%, enhanced microglia expansion area by 109%, and a reduction in microglial activation by 17%, indicating improved tissue healing and surveillance. Microglial coverage of the microelectrode was reduced by 50% and astrocytic scarring by 36% resulting in an increase in recording performance at chronic time. The data indicate that low-intensity pulsed ultrasound stimulation helps reduce the foreign body response around chronic intracortical microelectrodes.},
}
@article {pmid38949928,
year = {2024},
author = {Wu, X and Metcalfe, B and He, S and Tan, H and Zhang, D},
title = {A Review of Motor Brain-Computer Interfaces Using Intracranial Electroencephalography Based on Surface Electrodes and Depth Electrodes.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2408-2431},
doi = {10.1109/TNSRE.2024.3421551},
pmid = {38949928},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electrocorticography/instrumentation/methods ; *Movement/physiology ; *Electrodes, Implanted ; Deep Brain Stimulation/instrumentation ; Biomechanical Phenomena ; Electroencephalography/methods/instrumentation ; Electrodes ; Motor Cortex/physiology ; Hand/physiology ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.},
}
@article {pmid38949927,
year = {2024},
author = {Flores, C and Contreras, M and Macedo, I and Andreu-Perez, J},
title = {Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {3794-3803},
doi = {10.1109/TNSRE.2024.3420960},
pmid = {38949927},
issn = {1558-0210},
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Deep Learning ; *Electroencephalography ; *Event-Related Potentials, P300/physiology ; *Machine Learning ; *Neural Networks, Computer ; },
abstract = {Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neural data. Studies often focus on uniform single-site experiments in uniform settings, leading to high performance that may not translate well to real-world diversity. Deep learning models aim to enhance BCI classification accuracy, and transfer learning has been suggested to adapt models to individual neural patterns using a base model trained on others' data. This approach promises better generalizability and reduced overfitting, yet challenges remain in handling diverse and imbalanced datasets from different equipment, subjects, multiple centres in different countries, and both healthy and patient populations for effective model transfer and tuning. In a setting characterized by maximal heterogeneity, we proposed P300 wave detection in BCIs employing a convolutional neural network fitted with adaptive transfer learning based on Poison Sampling Disk (PDS) called Active Sampling (AS), which flexibly adjusts the transition from source data to the target domain. Our results reported for subject adaptive with 40% of adaptive fine-tuning that the averaged classification accuracy improved by 5.36% and standard deviation reduced by 12.22% using two distinct, internationally replicated datasets. These results outperformed in classification accuracy, computational time, and training efficiency, mainly due to the proposed Active Sampling (AS) method for transfer learning.},
}
@article {pmid38946233,
year = {2024},
author = {Lü, C and Wang, T and Xi, X and Wang, M and Wang, J and Zhilenko, A and Li, L},
title = {A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/10255842.2024.2371036},
pmid = {38946233},
issn = {1476-8259},
abstract = {Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.},
}
@article {pmid38945115,
year = {2024},
author = {Wang, X and Yang, W and Qi, W and Wang, Y and Ma, X and Wang, W},
title = {STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {178},
number = {},
pages = {106471},
doi = {10.1016/j.neunet.2024.106471},
pmid = {38945115},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Electroencephalography/methods ; Brain/physiology ; Movement/physiology ; },
abstract = {Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.},
}
@article {pmid38944964,
year = {2024},
author = {Wu, X and Gao, R and Tian, X and Hou, J and Wang, Y and Wang, Q and Tang, DKH and Yao, Y and Zhang, X and Wang, B and Yang, G and Li, H and Li, R},
title = {Co-composting of dewatered sludge and wheat straw with newly isolated Xenophilus azovorans: Carbon dynamics, humification, and driving pathways.},
journal = {Journal of environmental management},
volume = {365},
number = {},
pages = {121613},
doi = {10.1016/j.jenvman.2024.121613},
pmid = {38944964},
issn = {1095-8630},
mesh = {*Triticum ; *Sewage/microbiology ; *Composting ; *Carbon/metabolism ; Humic Substances ; Charcoal ; },
abstract = {Composting is a biological reaction caused by microorganisms. Composting efficiency can be adequately increased by adding biochar and/or by inoculating with exogenous microorganisms. In this study, we looked at four methods for dewatered sludge waste (DSW) and wheat straw (WS) aerobic co-composting: T1 (no additive), T2 (5% biochar), T3 (5% of a newly isolated strain, Xenophilus azovorans (XPA)), and T4 (5% of biochar-immobilized XPA (BCI-XPA)). Throughout the course of the 42-day composting period, we looked into the carbon dynamics, humification, microbial community succession, and modifications to the driving pathways. Compared to T1 and T2, the addition of XPA (T3) and BCI-XPA (T4) extended the thermophilic phase of composting without negatively affecting compost maturation. Notably, T4 exhibited a higher seed germination index (132.14%). Different from T1 and T2 treatments, T3 and T4 treatments increased CO2 and CH4 emissions in the composting process, in which the cumulative CO2 emissions increased by 18.61-47.16%, and T3 and T4 treatments also promoted the formation of humic acid. Moreover, T4 treatment with BCI-XPA addition showed relatively higher activities of urease, polyphenol oxidase, and laccase, as well as a higher diversity of microorganisms compared to other processes. The Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis showed that microorganisms involved in the carbon cycle dominated the entire composting process in all treatments, with chemoheterotrophy and aerobic chemoheterotrophy being the main pathways of organic materials degradation. Moreover, the presence of XPA accelerated the breakdown of organic materials by catabolism of aromatic compounds and intracellular parasite pathways. On the other hand, the xylanolysis pathway was aided in the conversion of organic materials to dissolved organics by the addition of BCI-XPA. These findings indicate that XPA and BCI-XPA have potential as additives to improve the efficiency of dewatered sludge and wheat straw co-composting.},
}
@article {pmid38943984,
year = {2024},
author = {Kim, JS and Kim, H and Chung, CK and Kim, JS},
title = {Dual model transfer learning to compensate for individual variability in brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {254},
number = {},
pages = {108294},
doi = {10.1016/j.cmpb.2024.108294},
pmid = {38943984},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Humans ; Neural Networks, Computer ; Electroencephalography ; Movement/physiology ; Algorithms ; Brain/physiology ; Machine Learning ; Male ; Adult ; },
abstract = {Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating complex decoding models such as deep neural networks (DNNs) to enhance performance. These models are particularly crucial for sophisticated tasks such as regression for decoding arbitrary movements. However, these BCI models trained and tested on individual data often face challenges with limited performance and generalizability across different subjects. This limitation is primarily due to a tremendous number of parameters of DNN models. Training complex models demands extensive datasets. Nevertheless, group data from many subjects may not produce sufficient decoding performance because of inherent variability in neural signals both across individuals and over time METHODS: To address these challenges, this study proposed a transfer learning approach that could effectively adapt to subject-specific variability in cortical regions. Our method involved training two separate movement decoding models: one on individual data and another on pooled group data. We then created a salience map for each cortical region from the individual model, which helped us identify the input's contribution variance across subjects. Based on the contribution variance, we combined individual and group models using a modified knowledge distillation framework. This approach allowed the group model to be universally applicable by assigning greater weights to input data, while the individual model was fine-tuned to focus on areas with significant individual variance RESULTS: Our combined model effectively encapsulated individual variability. We validated this approach with nine subjects performing arm-reaching tasks, with our method outperforming (mean correlation coefficient, r = 0.75) both individual (r = 0.70) and group models (r = 0.40) in decoding performance. In particular, there were notable improvements in cases where individual models showed low performances (e.g., r = 0.50 in the individual decoder to r = 0.61 in the proposed decoder) CONCLUSIONS: These results not only demonstrate the potential of our method for robust BCI, but also underscore its ability to generalize individual data for broader applicability.},
}
@article {pmid38943861,
year = {2024},
author = {Sun, H and Ding, Y and Bao, J and Qin, K and Tong, C and Jin, J and Guan, C},
title = {Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {178},
number = {},
pages = {106470},
doi = {10.1016/j.neunet.2024.106470},
pmid = {38943861},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Algorithms ; Imagination/physiology ; Deep Learning ; Machine Learning ; Adult ; Male ; Time Factors ; },
abstract = {Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.},
}
@article {pmid38941986,
year = {2024},
author = {Ahsan Awais, M and Ward, T and Redmond, P and Healy, G},
title = {From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5d17},
pmid = {38941986},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; Adult ; Young Adult ; *Photic Stimulation/methods ; *Artifacts ; Visual Perception/physiology ; Machine Learning ; Movement/physiology ; },
abstract = {Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.},
}
@article {pmid38941792,
year = {2024},
author = {Huang, Z and Sun, Y and Liu, S and Chen, X and Ping, J and Fei, P and Gong, Z and Zheng, N},
title = {A machine learning based method for tracking of simultaneously imaged neural activity and body posture of freely moving maggot.},
journal = {Biochemical and biophysical research communications},
volume = {727},
number = {},
pages = {150290},
doi = {10.1016/j.bbrc.2024.150290},
pmid = {38941792},
issn = {1090-2104},
mesh = {Animals ; *Larva/physiology ; *Machine Learning ; *Neurons/physiology ; *Posture/physiology ; Microscopy, Fluorescence/methods ; Drosophila melanogaster/physiology ; Drosophila/physiology ; Movement/physiology ; Behavior, Animal/physiology ; },
abstract = {To understand neural basis of animal behavior, it is necessary to monitor neural activity and behavior in freely moving animal before building relationship between them. Here we use light sheet fluorescence microscope (LSFM) combined with microfluidic chip to simultaneously capture neural activity and body movement in small freely behaving Drosophila larva. We develop a transfer learning based method to simultaneously track the continuously changing body posture and activity of neurons that move together using a sub-region tracking network with a precise landmark estimation network for the inference of target landmark trajectory. Based on the tracking of each labelled neuron, the activity of the neuron indicated by fluorescent intensity is calculated. For each video, annotation of only 20 frames in a video is sufficient to yield human-level accuracy for all other frames. The validity of this method is further confirmed by reproducing the activity pattern of PMSIs (period-positive median segmental interneurons) and larval movement as previously reported. Using this method, we disclosed the correlation between larval movement and left-right asymmetry in activity of a group of unidentified neurons labelled by R52H01-Gal4 and further confirmed the roles of these neurons in bilateral balance of body contraction during larval crawling by genetic inhibition of these neurons. Our method provides a new tool for accurate extraction of neural activities and movement of freely behaving small-size transparent animals.},
}
@article {pmid38941048,
year = {2024},
author = {Brewe, AM and Antezana, L and Carlton, CN and Gracanin, D and Richey, JA and Kim, I and White, SW},
title = {A Randomized Trial Utilizing EEG Brain Computer Interface to Improve Facial Emotion Recognition in Autistic Adults.},
journal = {Journal of autism and developmental disorders},
volume = {},
number = {},
pages = {},
pmid = {38941048},
issn = {1573-3432},
support = {R33MH100268-03/MH/NIMH NIH HHS/United States ; },
abstract = {PURPOSE: Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults.<br><br>METHODS: Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n&#x2009;=&#x2009;17) or waitlist control (n&#x2009;=&#x2009;10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint.<br><br>RESULTS: Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline.<br><br>CONCLUSION: Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.},
}
@article {pmid38938001,
year = {2024},
author = {Zhang, Z and Huang, Y and Chen, X and Li, J and Yang, Y and Lv, L and Wang, J and Wang, M and Wang, Y and Wang, Z},
title = {State-specific Regulation of Electrical Stimulation in the Intralaminar Thalamus of Macaque Monkeys: Network and Transcriptional Insights into Arousal.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {33},
pages = {e2402718},
pmid = {38938001},
issn = {2198-3844},
support = {2021ZD0204000//STI-2030-Major Projects/ ; 2022ZD0205100//STI-2030-Major Projects/ ; 2021ZD0203900//STI-2030-Major Projects/ ; 2019B030335001//Key-Area Research and Development Program of Guangdong Province/ ; 82151303//National Natural Science Foundation of China/ ; 82271292//National Natural Science Foundation of China/ ; 82301445//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *Arousal/genetics/physiology ; Macaca ; Magnetic Resonance Imaging/methods ; Electroencephalography/methods ; Electric Stimulation/methods ; Deep Brain Stimulation/methods ; Male ; Thalamus/metabolism ; },
abstract = {Long-range thalamocortical communication is central to anesthesia-induced loss of consciousness and its reversal. However, isolating the specific neural networks connecting thalamic nuclei with various cortical regions for state-specific anesthesia regulation is challenging, with the biological underpinnings still largely unknown. Here, simultaneous electroencephalogram-fuctional magnetic resonance imaging (EEG-fMRI) and deep brain stimulation are applied to the intralaminar thalamus in macaques under finely-tuned propofol anesthesia. This approach led to the identification of an intralaminar-driven network responsible for rapid arousal during slow-wave oscillations. A network-based RNA-sequencing analysis is conducted of region-, layer-, and cell-specific gene expression data from independent transcriptomic atlases and identifies 2489 genes preferentially expressed within this arousal network, notably enriched in potassium channels and excitatory, parvalbumin-expressing neurons, and oligodendrocytes. Comparison with human RNA-sequencing data highlights conserved molecular and cellular architectures that enable the matching of homologous genes, protein interactions, and cell types across primates, providing novel insight into network-focused transcriptional signatures of arousal.},
}
@article {pmid38937562,
year = {2024},
author = {Morozova, M and Nasibullina, A and Yakovlev, L and Syrov, N and Kaplan, A and Lebedev, M},
title = {Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {14862},
pmid = {38937562},
issn = {2045-2322},
support = {21-75-30024//Russian Foundation/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation/methods ; Male ; Female ; *Evoked Potentials, Motor/physiology ; Adult ; *Imagination/physiology ; Young Adult ; *Touch/physiology ; Pyramidal Tracts/physiology ; Fingers/physiology ; Motor Cortex/physiology ; Vibration ; Brain-Computer Interfaces ; },
abstract = {Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain-computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.},
}
@article {pmid38936392,
year = {2024},
author = {Savalle, E and Pillette, L and Won, K and Argelaguet, F and Lécuyer, A and J-M Macé, M},
title = {Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5cc2},
pmid = {38936392},
issn = {1741-2552},
mesh = {Humans ; *Virtual Reality ; Male ; Female ; *Electroencephalography/methods ; Adult ; Young Adult ; *Acoustic Stimulation/methods ; Event-Related Potentials, P300/physiology ; Algorithms ; Attention/physiology ; Evoked Potentials, Auditory/physiology ; },
abstract = {Objective.Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR.Approach.In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE.Main results.Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions.Significance.Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE.},
}
@article {pmid38934637,
year = {2025},
author = {Tankus, A and Stern, E and Klein, G and Kaptzon, N and Nash, L and Marziano, T and Shamia, O and Gurevitch, G and Bergman, L and Goldstein, L and Fahoum, F and Strauss, I},
title = {A Speech Neuroprosthesis in the Frontal Lobe and Hippocampus: Decoding High-Frequency Activity into Phonemes.},
journal = {Neurosurgery},
volume = {96},
number = {2},
pages = {356-364},
doi = {10.1227/neu.0000000000003068},
pmid = {38934637},
issn = {1524-4040},
support = {17630//Ministry of Science and Technology, Israel/ ; },
mesh = {Humans ; Male ; Adult ; *Hippocampus/physiology ; *Speech/physiology ; *Frontal Lobe/physiology ; Electrodes, Implanted ; *Phonetics ; *Brain-Computer Interfaces ; *Neural Prostheses ; },
abstract = {BACKGROUND AND OBJECTIVES: Loss of speech due to injury or disease is devastating. Here, we report a novel speech neuroprosthesis that artificially articulates building blocks of speech based on high-frequency activity in brain areas never harnessed for a neuroprosthesis before: anterior cingulate and orbitofrontal cortices, and hippocampus.<br><br>METHODS: A 37-year-old male neurosurgical epilepsy patient with intact speech, implanted with depth electrodes for clinical reasons only, silently controlled the neuroprosthesis almost immediately and in a natural way to voluntarily produce 2 vowel sounds.<br><br>RESULTS: During the first set of trials, the participant made the neuroprosthesis produce the different vowel sounds artificially with 85% accuracy. In the following trials, performance improved consistently, which may be attributed to neuroplasticity. We show that a neuroprosthesis trained on overt speech data may be controlled silently.<br><br>CONCLUSION: This may open the way for a novel strategy of neuroprosthesis implantation at earlier disease stages (eg, amyotrophic lateral sclerosis), while speech is intact, for improved training that still allows silent control at later stages. The results demonstrate clinical feasibility of direct decoding of high-frequency activity that includes spiking activity in the aforementioned areas for silent production of phonemes that may serve as a part of a neuroprosthesis for replacing lost speech control pathways.},
}
@article {pmid38933146,
year = {2024},
author = {Ren, G and Kumar, A and Mahmoud, SS and Fang, Q},
title = {A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1394107},
pmid = {38933146},
issn = {1662-5161},
abstract = {BACKGROUND: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets.<br><br>METHODS: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases.<br><br>RESULTS: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively.<br><br>CONCLUSIONS: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.},
}
@article {pmid38931814,
year = {2024},
author = {Jochumsen, M and Lavesen, ER and Griem, AB and Falkenberg-Andersen, C and Jensen, SKG},
title = {The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {12},
pages = {},
pmid = {38931814},
issn = {1424-8220},
mesh = {Humans ; *Caffeine/pharmacology ; Male ; *Electroencephalography/methods ; Female ; *Movement/drug effects/physiology ; Adult ; *Brain-Computer Interfaces ; Young Adult ; Coffee/chemistry ; },
abstract = {Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.},
}
@article {pmid38931769,
year = {2024},
author = {Papadopoulou, A and Hermiz, J and Grace, C and Denes, P},
title = {A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {12},
pages = {},
pmid = {38931769},
issn = {1424-8220},
support = {F1NS107667//National Institute Of Neurological Disorders And Stroke of the National Institutes of Health/ ; },
mesh = {Animals ; *Signal Processing, Computer-Assisted ; Electrophysiology/methods/instrumentation ; Neurons/physiology ; Electrophysiological Phenomena ; Electrodes ; Equipment Design ; },
abstract = {The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.},
}
@article {pmid38931751,
year = {2024},
author = {Mwata-Velu, T and Zamora, E and Vasquez-Gomez, JI and Ruiz-Pinales, J and Sossa, H},
title = {Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {12},
pages = {},
pmid = {38931751},
issn = {1424-8220},
support = {Folio SIP/1988/DI/DAI/2022, 20220002, 20230232, 20240108, 20231622, 202409//Instituto Polit&#xe9;cnico Nacional/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Algorithms ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Deep Learning ; Signal Processing, Computer-Assisted ; },
abstract = {This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).},
}
@article {pmid38931540,
year = {2024},
author = {Huang, W and Liu, X and Yang, W and Li, Y and Sun, Q and Kong, X},
title = {Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {12},
pages = {},
pmid = {38931540},
issn = {1424-8220},
support = {No.2021YFF1200700, No.2022YFF1202400, No.2023YFF1204200, No.2023YFF1203900//The National Key Research and Development Program of China/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology/diagnostic imaging ; Imagination/physiology ; },
abstract = {A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.},
}
@article {pmid38929887,
year = {2024},
author = {Krüger, L and Kamp, O and Alfen, K and Theysohn, J and Dudda, M and Becker, L},
title = {Pediatric Carotid Injury after Blunt Trauma and the Necessity of CT and CTA-A Narrative Literature Review.},
journal = {Journal of clinical medicine},
volume = {13},
number = {12},
pages = {},
pmid = {38929887},
issn = {2077-0383},
abstract = {Background: Blunt carotid injury (BCI) in pediatric trauma is quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/blunt cerebrovascular injury (BCVI) on pediatric patients after blunt trauma is, what routine diagnostics looks like, if a computed tomography (CT)/computed tomography angiography (CTA) scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects. Methods: This narrative literature review includes reviews, systematic reviews, case reports and original studies in the English language between 1999 and 2020 that deal with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure for children were included in this study. For literature research, Medline (PubMed) and the Cochrane library were used. Results: Pediatric BCI/BCVI shows an overall incidence between 0.03 and 0.5% of confirmed BCI/BCVI cases due to pediatric blunt trauma. In total, 1.1-3.5% of pediatric blunt trauma patients underwent CTA to detect BCI/BCVI. Only 0.17-1.2% of all CTA scans show a positive diagnosis for BCI/BCVI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligrays (mGy), whereas a computed tomography angiography needs at least 138 mGy. A cumulative dose of about 50 mGy almost triples the risk of leukemia, and a cumulative dose of about 60 mGy triples the risk of brain cancer. Conclusions: Given that a BCI/BCVI could have extensive neurological consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. CT and CTA are mostly used in routine BCI/BCVI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are established BCI/BCVI screening options, as well as duplex ultrasound.},
}
@article {pmid38927850,
year = {2024},
author = {Li, X and Yang, S and Fei, N and Wang, J and Huang, W and Hu, Y},
title = {A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {6},
pages = {},
pmid = {38927850},
issn = {2306-5354},
support = {GJHZ20220913143408015//Shenzhen Science and Technology Program/ ; 2022A703-3//Zhanjiang Competitive Allocation of Special Funds for Scientific and Technological Development/ ; JCYJ20230807113007015//Shenzhen Science and Technology Program/ ; SZSM202211004//Sanming Project of Medicine in Shenzhen/ ; SZXK2020084//Shenzhen Key Medical Discipline Construction Fund/ ; B2024036//Health Commission of Guangdong Province/ ; },
abstract = {The application of wearable electroencephalogram (EEG) devices is growing in brain-computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum-convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.},
}
@article {pmid38925110,
year = {2024},
author = {Wyse-Sookoo, K and Luo, S and Candrea, D and Schippers, A and Tippett, DC and Wester, B and Fifer, M and Vansteensel, MJ and Ramsey, NF and Crone, NE},
title = {Stability of ECoG high gamma signals during speech and implications for a speech BCI system in an individual with ALS: a year-long longitudinal study.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {38925110},
issn = {1741-2552},
support = {T32 EB003383/EB/NIBIB NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Longitudinal Studies ; *Electrocorticography/methods ; *Speech/physiology ; Male ; Gamma Rhythm/physiology ; Middle Aged ; Female ; Electrodes, Implanted ; },
abstract = {Objective.Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period.Approach.ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode.Main Results.We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation.Significance.Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis.Clinical Trial Information.ClinicalTrials.gov, registration number NCT03567213.},
}
@article {pmid38925086,
year = {2024},
author = {Augenstein, TE and Nagalla, D and Mohacey, A and Cubillos, LH and Lee, MH and Ranganathan, R and Krishnan, C},
title = {A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.},
journal = {Computers in biology and medicine},
volume = {178},
number = {},
pages = {108778},
doi = {10.1016/j.compbiomed.2024.108778},
pmid = {38925086},
issn = {1879-0534},
mesh = {Humans ; *Robotics ; Adult ; Male ; *Self-Help Devices ; Female ; User-Computer Interface ; Brain-Computer Interfaces ; },
abstract = {Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user's residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations.},
}
@article {pmid38923489,
year = {2024},
author = {Huang, J and Lv, Y and Zhang, ZQ and Xiong, B and Wang, Q and Wan, B and Yang, P},
title = {Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2376-2387},
doi = {10.1109/TNSRE.2024.3419013},
pmid = {38923489},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Electroencephalography/methods ; Calibration ; Male ; Adult ; Female ; Young Adult ; Reproducibility of Results ; Photic Stimulation/methods ; Healthy Volunteers ; },
abstract = {Various training-based spatial filtering methods have been proposed to decode steady-state visual evoked potentials (SSVEPs) efficiently. However, these methods require extensive calibration data to obtain valid spatial filters and temporal templates. The time-consuming data collection and calibration process would reduce the practicality of SSVEP-based brain-computer interfaces (BCIs). Therefore, we propose a temporally local weighting-based phase-locked time-shift (TLW-PLTS) data augmentation method to augment training data for calculating valid spatial filters and temporal templates. In this method, the sliding window strategy using the SSVEP response period as a time-shift step is to generate the augmented data, and the time filter which maximises the temporally local covariance between the original template signal and the sine-cosine reference signal is used to suppress the temporal noise in the augmented data. For the performance evaluation, the TLW-PLTS method was incorporated with state-of-the-art training-based spatial filtering methods to calculate classification accuracies and information transfer rates (ITRs) using three SSVEP datasets. Compared with state-of-the-art training-based spatial filtering methods and other data augmentation methods, the proposed TLW-PLTS method demonstrates superior decoding performance with fewer calibration data, which is promising for the development of fast-calibration BCIs.},
}
@article {pmid38923184,
year = {2024},
author = {Iwama, S and Tsuchimoto, S and Mizuguchi, N and Ushiba, J},
title = {EEG decoding with spatiotemporal convolutional neural network for visualization and closed-loop control of sensorimotor activities: A simultaneous EEG-fMRI study.},
journal = {Human brain mapping},
volume = {45},
number = {9},
pages = {e26767},
pmid = {38923184},
issn = {1097-0193},
support = {JPMJPR23I1//Japan Science and Technology Agency/ ; 20H05923//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Magnetic Resonance Imaging ; Adult ; *Brain-Computer Interfaces ; Male ; *Neurofeedback/methods ; Young Adult ; *Sensorimotor Cortex/physiology/diagnostic imaging ; Female ; *Neural Networks, Computer ; },
abstract = {Closed-loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high-density whole-head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain-computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI-based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.},
}
@article {pmid38922986,
year = {2024},
author = {Chen, J and Zeng, H and Cheng, Y and Yang, B},
title = {Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.},
journal = {Medical physics},
volume = {51},
number = {10},
pages = {7269-7281},
doi = {10.1002/mp.17266},
pmid = {38922986},
issn = {2473-4209},
mesh = {Humans ; *Breast Neoplasms/diagnostic imaging/genetics ; *Neural Networks, Computer ; *Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted/methods ; Contrast Media ; Female ; Receptor, ErbB-2/metabolism/genetics ; },
abstract = {BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non-invasive system for identifying HER2 and HR in breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).<br><br>METHODS: In light of the absence of high-performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I-SPY 1) and I-SPY 2, were incorporated. I-SPY 2 was utilized for model training and validation, while I-SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction.<br><br>RESULTS: The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I-SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I-SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction.<br><br>CONCLUSION: This study proposes a non-invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre-trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic.},
}
@article {pmid38920504,
year = {2024},
author = {Bardella, G and Franchini, S and Pan, L and Balzan, R and Ramawat, S and Brunamonti, E and Pani, P and Ferraina, S},
title = {Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.},
journal = {Entropy (Basel, Switzerland)},
volume = {26},
number = {6},
pages = {},
pmid = {38920504},
issn = {1099-4300},
support = {PH11715C823A9528//Sapienza University of Rome/ ; RM12117A8AD27DB1//Sapienza University of Rome/ ; EBRAINS-Italy PNRR 2023//EBRAINS-Italy PNRR 2023/ ; },
abstract = {Brain-computer interfaces have seen extraordinary surges in developments in recent years, and a significant discrepancy now exists between the abundance of available data and the limited headway made in achieving a unified theoretical framework. This discrepancy becomes particularly pronounced when examining the collective neural activity at the micro and meso scale, where a coherent formalization that adequately describes neural interactions is still lacking. Here, we introduce a mathematical framework to analyze systems of natural neurons and interpret the related empirical observations in terms of lattice field theory, an established paradigm from theoretical particle physics and statistical mechanics. Our methods are tailored to interpret data from chronic neural interfaces, especially spike rasters from measurements of single neuron activity, and generalize the maximum entropy model for neural networks so that the time evolution of the system is also taken into account. This is obtained by bridging particle physics and neuroscience, paving the way for particle physics-inspired models of the neocortex.},
}
@article {pmid38920469,
year = {2024},
author = {Neven, H and Zalcman, A and Read, P and Kosik, KS and van der Molen, T and Bouwmeester, D and Bodnia, E and Turin, L and Koch, C},
title = {Testing the Conjecture That Quantum Processes Create Conscious Experience.},
journal = {Entropy (Basel, Switzerland)},
volume = {26},
number = {6},
pages = {},
pmid = {38920469},
issn = {1099-4300},
support = {//Tiny Blue Dot Foundation/ ; },
abstract = {The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest-pain generates intense feelings and absorbs all our conscious awareness, yet nothing particularly intelligent is going on. In the hard sciences, this topic is frequently met with skepticism because, to date, no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here, we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human conscious experience through brain-quantum computer interfaces.},
}
@article {pmid38920119,
year = {2024},
author = {Samal, P and Hashmi, MF},
title = {An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-24},
doi = {10.1080/10255842.2024.2369257},
pmid = {38920119},
issn = {1476-8259},
abstract = {Emotion recognition using EEG is a difficult study because the signals' unstable behavior, which is brought on by the brain's complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model's utility in emotional computing.},
}
@article {pmid38919909,
year = {2024},
author = {Liu, Y and Liu, R and Ge, J and Wang, Y},
title = {Advancements in brain-machine interfaces for application in the metaverse.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1383319},
pmid = {38919909},
issn = {1662-4548},
abstract = {In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media interaction has become an urgent issue. The use of brain-machine interfaces for sensory simulation is one of the proposed solutions. Currently, brain-machine interfaces have demonstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scenarios: generative art in the metaverse, serious gaming for healthcare in metaverse medicine, and brain-machine interface applications for facial expression synthesis in the virtual society of the metaverse. It investigates existing commercial products and patents (such as MindWave Mobile, GVS, and Galea), draws analogies with the development processes of network security and neurosecurity, bioethics and neuroethics, and discusses the challenges and potential issues that may arise when brain-machine interfaces mature and are widely applied. Furthermore, it looks ahead to the diverse possibilities of deep and varied applications of brain-machine interfaces in the metaverse in the future.},
}
@article {pmid38919152,
year = {2024},
author = {Salmanipour, S and Rezaie, A and Alipour, N and Ghahremani-Nasab, M and Zakerhamidi, MS and Akbari-Gharalari, N and Mehdipour, A and Salehi, R and Jarolmasjed, S},
title = {Development of Polyphosphate/Nanokaolin-Modified Alginate Sponge by Gas-Foaming and Plasma Glow Discharge Methods for Ultrarapid Hemostasis in Noncompressible Bleeding.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {27},
pages = {34684-34704},
doi = {10.1021/acsami.4c05695},
pmid = {38919152},
issn = {1944-8252},
mesh = {Polyphosphates/chemistry ; Kaolin/chemistry ; *Metal Nanoparticles/chemistry ; Alginates/chemistry ; Time Factors ; Humans ; Hemostasis ; *Hemorrhage/therapy ; Porosity ; Cell Survival ; Cell Line ; Male ; Animals ; Rats ; },
abstract = {Effective bleeding management strategies in uncontrollable and noncompressible massive hemorrhage are becoming important in both clinical and combat situations. Here, a novel approach was developed to create a superporous and highly absorbable hemostatic sponge through a facile chemical gas-foaming method by cross-linking long-chain polyphosphate along with nanokaolin and Ca[2+] in an alginate structure to synergistically activate the coagulation pathway. Natural kaolin obtained from the Marand mine in East Azarbaijan was converted into pseudohexagonal-shaped kaolin nanoparticles (30 to 150 nm) using ball milling followed by a newly developed glow discharge plasma treatment method. The obtained ultralight sponges (>90% porosity) exhibit ultrarapid water/blood absorption capacity (∼4000%) and excellent shape memory, which effectively concentrates coagulation factors. The results of in vitro tests demonstrated that the proposed sponges exhibited enhanced blood clotting ability (BCI < 10%) and superior cohesion with red blood cells (∼100) and platelets (∼80%) compared to commercially available hemostatic products. The in vivo host response results exhibited biosafety with no systemic and significant local inflammatory response by hematological, pathological, and biochemical parameter assessments. In a rat femoral artery complete excision model, the application of alginate/k/polyp nanocomposite sponges resulted in a complete hemostasis time of 60 s by significant reduction of hemostasis time (∼6.7-8.3 fold) and blood loss (∼2-2.8-fold) compared to commercially available hemostatic agents (P < 0.001). In conclusion, distinct physical characteristics accompanied by unique chemical composition multifunctional sponges activate hemostasis synergistically by triggering the XII, XI, X, IX, V, and II factors and the contact pathway and have the ability of rapid hemostasis in noncompressible severe bleeding.},
}
@article {pmid38915599,
year = {2024},
author = {Jensen, MA and Schalk, G and Ince, N and Hermes, D and Brunner, P and Miller, KJ},
title = {Feasibility of Stereo EEG Based Brain Computer Interfacing in An Adult and Pediatric Cohort.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.06.12.598257},
pmid = {38915599},
issn = {2692-8205},
abstract = {INTRODUCTION: Stereoelectroencephalography (sEEG) is a mesoscale intracranial monitoring method which records from the brain volumetrically with depth electrodes. Implementation of sEEG in BCI has not been well-described across a diverse patient cohort.<br><br>METHODS: Across eighteen subjects, channels with high frequency broadband (HFB, 65-115Hz) power increases during hand, tongue, or foot movements during a motor screening task were provided real-time feedback based on these HFB power changes to control a cursor on a screen.<br><br>RESULTS: Seventeen subjects established successful control of the overt motor BCI, but only nine were able to control imagery BCI with &#x2265; 80% accuracy. In successful imagery BCI, HFB power in the two target conditions separated into distinct subpopulations, which appear to engage unique subnetworks of the motor cortex compared to cued movement or imagery alone.<br><br>CONCLUSION: sEEG-based motor BCI utilizing overt movement and kinesthetic imagery is robust across patient ages and cortical regions with substantial differences in learning proficiency between real or imagined movement.},
}
@article {pmid38915308,
year = {2024},
author = {Jin, Y and Li, J and Fan, Z and Hua, X and Wang, T and Du, S and Xi, X and Li, L},
title = {Recognition of regions of stroke injury using multi-modal frequency features of electroencephalogram.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1404816},
pmid = {38915308},
issn = {1662-4548},
abstract = {OBJECTIVE: Nowadays, increasingly studies are attempting to analyze strokes in advance. The identification of brain damage areas is essential for stroke rehabilitation.<br><br>APPROACH: We proposed Electroencephalogram (EEG) multi-modal frequency features to classify the regions of stroke injury. The EEG signals were obtained from stroke patients and healthy subjects, who were divided into right-sided brain injury group, left-sided brain injury group, bilateral brain injury group, and healthy controls. First, the wavelet packet transform was used to perform a time-frequency analysis of the EEG signal and extracted a set of features (denoted as WPT features). Then, to explore the nonlinear phase coupling information of the EEG signal, phase-locked values (PLV) and partial directed correlations (PDC) were extracted from the brain network, and the brain network produced a second set of features noted as functional connectivity (FC) features. Furthermore, we fused the extracted multiple features and used the resnet50 convolutional neural network to classify the fused multi-modal (WPT&#x2009;+&#x2009;FC) features.<br><br>RESULTS: The classification accuracy of our proposed methods was up to 99.75%.<br><br>SIGNIFICANCE: The proposed multi-modal frequency features can be used as a potential indicator to distinguish regions of brain injury in stroke patients, and are potentially useful for the optimization of decoding algorithms for brain-computer interfaces.},
}
@article {pmid38914073,
year = {2024},
author = {Martin Del Campo Vera, R and Sundaram, S and Lee, R and Lee, Y and Leonor, A and Chung, RS and Shao, A and Cavaleri, J and Gilbert, ZD and Zhang, S and Kammen, A and Mason, X and Heck, C and Liu, CY and Kellis, S and Lee, B},
title = {Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {38914073},
issn = {1741-2552},
support = {K23 NS114190/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Hippocampus/physiology ; Female ; *Beta Rhythm/physiology ; Male ; Adult ; *Arm/physiology ; Psychomotor Performance/physiology ; Movement/physiology ; Electroencephalography/methods/classification ; Principal Component Analysis ; Young Adult ; Reproducibility of Results ; Middle Aged ; },
abstract = {Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.},
}
@article {pmid38913534,
year = {2024},
author = {Li, T and Jiang, Y and Fu, X and Sun, Z and Yan, Y and Li, YF and Liu, S},
title = {Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {10},
pages = {3014-3023},
doi = {10.1109/TBME.2024.3406940},
pmid = {38913534},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; *Electrodes, Implanted ; Animals ; *Robotics/instrumentation ; Mice ; Equipment Design ; Electroencephalography/instrumentation/methods ; Nanotechnology/instrumentation ; Brain/physiology ; },
abstract = {Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Conventional rigid electrodes face the problem of foreign body reaction induced by mechanical mismatch to biological tissue, while flexible electrodes, though more preferential, lack controllability during implantation. Researchers have explored various strategies, from assistive shuttle to biodegradable coatings, to strike a balance between implantation rigidity and post-implantation flexibility. Yet, these approaches may introduce complications, including immune response, inflammations, and raising intracranial pressure. To this end, this paper proposes a novel nanorobot-based technique for direct implantation of flexible neural electrodes, leveraging the high controllability and repeatability of robotics to enhance the implantation quality. This approach features a dual-arm nanorobotic system equipped with stereo microscope, by which a flexible electrode is first visually aligned to the target neural tissue to establish contact and thereafter implanted into brain with well controlled insertion direction and depth. The key innovation is, through dual-arm coordination, the flexible electrode maintains straight along the implantation direction. With this approach, we implanted CNTf electrodes into cerebral cortex of mouse, and captured standard spiking neural signals.},
}
@article {pmid38913514,
year = {2024},
author = {Gong, L and Chen, W and Zhang, D},
title = {An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {10},
pages = {5890-5903},
doi = {10.1109/JBHI.2024.3418010},
pmid = {38913514},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Neural Networks, Computer ; Algorithms ; Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10-20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98.33/2.46 %, 92.15/5.13 %, 97.60/1.68 %(valence), and 97.48/1.42 %(arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.},
}
@article {pmid38912322,
year = {2024},
author = {Wen, H and Zhong, Y and Yao, L and Wang, Y},
title = {Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm: A High-Density EEG Source Imaging Study.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {5},
number = {},
pages = {0118},
pmid = {38912322},
issn = {2692-7632},
abstract = {Complementary to brain-computer interface (BCI) based on motor imagery (MI) task, sensory imagery (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior recording. In this study, we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram (EEG) recording, and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the tasks. In the experiment, participants were instructed to perform the left and right hand tasks in MI paradigm, sensory stimulation (SS) paradigm and SI paradigm. The statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts, frontal and right temporal areas in α bands, whereas real SS and imagined SI showed a similar activation pattern. The similarity between SS and SI may provide a way to train the BCI system, while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI performance. The combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider application. BCI studies concentrate on the hybrid decoding method combining MI or SI with SS, but the underlining neurophysiological correlates between them were unclear. MI and SI differed from each other within the ipsilateral sensorimotor cortex in alpha bands. This is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.},
}
@article {pmid38909175,
year = {2024},
author = {Dai, C and Lin, X and Xue, B and Xi, X and Gao, M and Liu, X and Han, T and Li, Q and Yuan, H and Sun, X},
title = {Correlation of bilateral M1 hand area excitability and overall functional recovery after spinal cord injury: protocol for a prospective cohort study.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {213},
pmid = {38909175},
issn = {1471-2377},
support = {82072534//National Natural Science Foundation of China/ ; 82272591//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation/physiopathology/therapy ; *Recovery of Function/physiology ; *Hand/physiopathology ; *Transcranial Magnetic Stimulation/methods ; *Motor Cortex/physiopathology ; Prospective Studies ; Evoked Potentials, Motor/physiology ; Male ; Adult ; Female ; Cohort Studies ; Middle Aged ; Spectroscopy, Near-Infrared/methods ; },
abstract = {BACKGROUND: After spinal cord injury (SCI), a large number of survivors suffer from severe motor dysfunction (MD). Although the injury site is in the spinal cord, excitability significantly decreases in the primary motor cortex (M1), especially in the lower extremity (LE) area. Unfortunately, M1 LE area-targeted repetitive transcranial magnetic stimulation (rTMS) has not achieved significant motor improvement in individuals with SCI. A recent study reported that the M1 hand area in individuals with SCl contains a compositional code (the movement-coding component of neural activity) that links matching movements from the upper extremities (UE) and the LE. However, the correlation between bilateral M1 hand area excitability and overall functional recovery is unknown.<br><br>OBJECTIVE: To clarify the changes in the excitability of the bilateral M1 hand area after SCI and its correlation with motor recovery, we aim to specify the therapeutic parameters of rTMS for SCI motor rehabilitation.<br><br>METHODS: This study is a 12-month prospective cohort study. The neurophysiological and overall functional status of the participants will be assessed. The primary outcomes included single-pulse and paired-pulse TMS. The second outcome included functional near-infrared spectroscopy (fNIRS) measurements. Overall functional status included total motor score, modified Ashworth scale score, ASIA Impairment Scale grade, spinal cord independence measure and modified Barthel index. The data will be recorded for individuals with SCI at disease durations of 1 month, 2 months, 4 months, 6 months and 12 months. The matched healthy controls will be measured during the same period of time after recruitment.<br><br>DISCUSSION: The present study is the first to analyze the role of bilateral M1 hand area excitability changes in the evaluation and prediction of overall functional recovery (including motor function and activities of daily living) after SCI, which will further expand the traditional theory of the predominant role of M1, optimize the current rTMS treatment, and explore the brain-computer interface design for individuals with SCI.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR2300068831.},
}
@article {pmid38909069,
year = {2024},
author = {Ngo, TD and Kieu, HD and Nguyen, MH and Nguyen, TH and Can, VM and Nguyen, BH and Le, TH},
title = {An EEG &amp; eye-tracking dataset of ALS patients &amp; healthy people during eye-tracking-based spelling system usage.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {664},
pmid = {38909069},
issn = {2052-4463},
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Eye-Tracking Technology ; },
abstract = {This research presents a dataset consisting of electroencephalogram and eye tracking recordings obtained from six patients with amyotrophic lateral sclerosis (ALS) in a locked-in state and one hundred seventy healthy individuals. The ALS patients exhibited varying degrees of disease progression, ranging from partial mobility and weakened speech to complete paralysis and loss of speech. Despite these physical impairments, the ALS patients retained good eye function, which allowed them to use a virtual keyboard for communication. Data from ALS patients was recorded multiple times at their homes, while data from healthy individuals was recorded once in a laboratory setting. For each data recording, the experimental design involved nine recording sessions per participant, each corresponding to a common human action or demand. This dataset can serve as a valuable benchmark for several applications, such as improving spelling systems with brain-computer interfaces, investigating motor imagination, exploring motor cortex function, monitoring motor impairment progress in patients undergoing rehabilitation, and studying the effects of ALS on cognitive and motor processes.},
}
@article {pmid38907638,
year = {2024},
author = {Selvaraj, V and Alagarsamy, M and Datchanamoorthy, K and Manickam, G},
title = {Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-14},
doi = {10.1080/10255842.2024.2356633},
pmid = {38907638},
issn = {1476-8259},
abstract = {The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy; 14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.},
}
@article {pmid38905990,
year = {2024},
author = {Zhang, Y and Zhang, H and Xu, T and Liu, J and Mu, J and Chen, R and Yang, J and Wang, P and Jian, X},
title = {A simulation study of transcranial magnetoacoustic stimulation of the basal ganglia thalamic neural network to improve pathological beta oscillations in Parkinson's disease.},
journal = {Computer methods and programs in biomedicine},
volume = {254},
number = {},
pages = {108297},
doi = {10.1016/j.cmpb.2024.108297},
pmid = {38905990},
issn = {1872-7565},
mesh = {*Parkinson Disease/physiopathology/therapy ; Humans ; *Basal Ganglia/physiopathology/diagnostic imaging ; *Thalamus/diagnostic imaging ; *Beta Rhythm ; Computer Simulation ; Transcranial Magnetic Stimulation/methods ; Nerve Net/physiopathology/diagnostic imaging ; Models, Neurological ; },
abstract = {BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively.<br><br>OBJECTIVE: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage.<br><br>METHODS: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network.<br><br>RESULTS: When the stimulating effect of ultrasound is ignored, TMAS-STN simultaneously inhibits the beta-band amplitude of the GPi nucleus, whereas TMAS-GPi fails to simultaneously have an inhibitory effect on the STN. TMAS-STN&amp;GPi can reduce the beta band amplitude. TMAS-STN&amp;GPi&amp;GPe suppressed the PD pathologic beta band amplitude of each nucleus to a greater extent. When considering the stimulatory effect of ultrasound, lower sound pressures of ultrasound do not affect the neuronal firing state, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents.<br><br>CONCLUSIONS: At 9 T static magnetic field, 0.5-1.5 MPa and 1.5-2.0 MPa ultrasound had synergistic effects on individual STN and GPi neurons. TMAS multinuclear stimulation with appropriate ultrasound intensity was the most effective in suppressing the amplitude of pathological beta oscillations in PD and may be clinically useful.},
}
@article {pmid38905761,
year = {2024},
author = {Qiao, MX and Yu, H and Li, T},
title = {Non-invasive neurostimulation to improve sleep quality and depressive symptoms in patients with major depressive disorder: A meta-analysis of randomized controlled trials.},
journal = {Journal of psychiatric research},
volume = {176},
number = {},
pages = {282-292},
doi = {10.1016/j.jpsychires.2024.06.023},
pmid = {38905761},
issn = {1879-1379},
mesh = {Humans ; *Depressive Disorder, Major/therapy ; *Randomized Controlled Trials as Topic ; *Transcranial Direct Current Stimulation ; *Transcranial Magnetic Stimulation ; *Phototherapy/methods ; Sleep Quality ; Sleep Wake Disorders/therapy/etiology ; Outcome Assessment, Health Care ; },
abstract = {BACKGROUND: Non-invasive neurostimulation, including bright light therapy (BLT), repetitive transcranial magnetic (rTMS) and transcranial direct current stimulation (tDCS), has been shown to alleviate depressive symptoms in major depressive disorder (MDD). However, the efficacy of these interventions in addressing sleep disturbances in MDD patients remains a subject of debate.<br><br>OBJECTIVE: We aimed to conduct a meta-analysis of available randomized controlled trials (RCTs) to assess the effectiveness of non-invasive neurostimulation in improving sleep disturbances and depressive symptoms in MDD patients.<br><br>METHODS: Systematic searches for relevant RCTs were conducted in the databases PubMed, Cochrane Library, Web of Science, EMBASE, Wanfang and China National Knowledge Infrastructure up to January 2024. Data on outcomes comparable across the studies were meta-analyzed using Review Manager 5.3 and Stata 14. The pooled results were reported as standardized mean differences (SMD) with their respective 95% confidence intervals (CI).<br><br>RESULTS: Our analysis encompassed 15 RCTs involving 1348 patients. Compared to sham or no stimulation, non-invasive neurostimulation significantly improved sleep quality (SMD -0.74, 95%CI -1.15 to -0.33, p&#xa0;=&#xa0;0.0004) and sleep efficiency (SMD 0.35, 95%CI 0.10 to 0.60, p&#xa0;=&#xa0;0.006). It also significantly reduced severity of depressive symptoms (SMD -0.62, 95%CI -0.90 to -0.35, p&#xa0;<&#xa0;0.00001). Subgroup analysis further demonstrated that patients experiencing sleep improvements due to neurostimulation showed a marked decrease in depressive symptoms compared to the control group (SMD&#xa0;=&#xa0;-0.90, 95% CI [-1.26, -0.54], p&#xa0;<&#xa0;0.0001).<br><br>CONCLUSION: Current evidence from RCTs suggests that neurostimulation can enhance sleep quality and efficiency in individuals with MDD, which in turn may be associated with mitigation of depressive symptoms.<br><br>PROSPERO REGISTRATION: CRD42023423844.},
}
@article {pmid38903906,
year = {2024},
author = {Klein, F},
title = {Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1286586},
pmid = {38903906},
issn = {2673-6195},
abstract = {The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.},
}
@article {pmid38903410,
year = {2024},
author = {Li, K and Fu, C and Xie, Z and Zhang, J and Zhang, C and Li, R and Gao, C and Wang, J and Xue, C and Zhang, Y and Deng, W},
title = {The impact of physical therapy on dysphagia in neurological diseases: a review.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1404398},
pmid = {38903410},
issn = {1662-5161},
abstract = {A neurogenic dysphagia is dysphagia caused by problems with the central and peripheral nervous systems, is particularly prevalent in conditions such as Parkinson's disease and stroke. It significantly impacts the quality of life for affected individuals and causes additional burdens, such as malnutrition, aspiration pneumonia, asphyxia, or even death from choking due to improper eating. Physical therapy offers a non-invasive treatment with high efficacy and low cost. Evidence supporting the use of physical therapy in dysphagia treatment is increasing, including techniques such as neuromuscular electrical stimulation, sensory stimulation, transcranial direct current stimulation, and repetitive transcranial magnetic stimulation. While initial studies have shown promising results, the effectiveness of specific treatment regimens still requires further validation. At present, there is a lack of scientific evidence to guide patient selection, develop appropriate treatment regimens, and accurately evaluate treatment outcomes. Therefore, the primary objectives of this review are to review the results of existing research, summarize the application of physical therapy in dysphagia management, we also discussed the mechanisms and treatments of physical therapy for neurogenic dysphagia.},
}
@article {pmid38903409,
year = {2024},
author = {Pan, H and Ding, P and Wang, F and Li, T and Zhao, L and Nan, W and Fu, Y and Gong, A},
title = {Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1429130},
pmid = {38903409},
issn = {1662-5161},
abstract = {Although brain-computer interface (BCI) is considered a revolutionary advancement in human-computer interaction and has achieved significant progress, a considerable gap remains between the current technological capabilities and their practical applications. To promote the translation of BCI into practical applications, the gold standard for online evaluation for classification algorithms of BCI has been proposed in some studies. However, few studies have proposed a more comprehensive evaluation method for the entire online BCI system, and it has not yet received sufficient attention from the BCI research and development community. Therefore, the qualitative leap from analyzing and modeling for offline BCI data to the construction of online BCI systems and optimizing their performance is elaborated, and then user-centred is emphasized, and then the comprehensive evaluation methods for translating BCI into practical applications are detailed and reviewed in the article, including the evaluation of the usability (including effectiveness and efficiency of systems), the evaluation of the user satisfaction (including BCI-related aspects, etc.), and the evaluation of the usage (including the match between the system and user, etc.) of online BCI systems. Finally, the challenges faced in the evaluation of the usability and user satisfaction of online BCI systems, the efficacy of online BCI systems, and the integration of BCI and artificial intelligence (AI) and/or virtual reality (VR) and other technologies to enhance the intelligence and user experience of the system are discussed. It is expected that the evaluation methods for online BCI systems elaborated in this review will promote the translation of BCI into practical applications.},
}
@article {pmid38901399,
year = {2024},
author = {Wang, L and Gu, Y},
title = {Fuse mitochondria to win peer competition.},
journal = {Neuron},
volume = {112},
number = {12},
pages = {1897-1899},
doi = {10.1016/j.neuron.2024.05.005},
pmid = {38901399},
issn = {1097-4199},
mesh = {Animals ; *Neuronal Plasticity/physiology ; *Mitochondria/metabolism ; *Neurons/physiology ; Mitochondrial Dynamics/physiology ; Humans ; },
abstract = {In this issue of Neuron, Kochan et al.[1] report that enhanced mitochondrial fusion is essential for the heightened synaptic plasticity in adult-born neurons during the critical period, thus supporting their competition with neurons of similar age for survival.},
}
@article {pmid38901186,
year = {2024},
author = {Ferrante, M and Boccato, T and Bargione, S and Toschi, N},
title = {Decoding visual brain representations from electroencephalography through knowledge distillation and latent diffusion models.},
journal = {Computers in biology and medicine},
volume = {178},
number = {},
pages = {108701},
doi = {10.1016/j.compbiomed.2024.108701},
pmid = {38901186},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain/physiology ; Neural Networks, Computer ; Brain-Computer Interfaces ; Male ; Image Processing, Computer-Assisted/methods ; Signal Processing, Computer-Assisted ; Female ; Adult ; },
abstract = {Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs knowledge distillation to train an EEG classifier and reconstruct images from the ImageNet and THINGS-EEG 2 datasets using only electroencephalography (EEG) data from participants who have viewed the images themselves (i.e. "brain decoding"). We analyzed EEG recordings from 6 participants for the ImageNet dataset and 10 for the THINGS-EEG 2 dataset, exposed to images spanning unique semantic categories. These EEG readings were converted into spectrograms, which were then used to train a convolutional neural network (CNN), integrated with a knowledge distillation procedure based on a pre-trained Contrastive Language-Image Pre-Training (CLIP)-based image classification teacher network. This strategy allowed our model to attain a top-5 accuracy of 87%, significantly outperforming a standard CNN and various RNN-based benchmarks. Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images that had elicited EEG activity. Therefore, our architecture not only decodes images from neural activity but also offers a credible image reconstruction from EEG only, paving the way for, e.g., swift, individualized feedback experiments.},
}
@article {pmid38900612,
year = {2024},
author = {Galvan, CM and Spies, RD and Milone, DH and Peterson, V},
title = {Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2346-2355},
doi = {10.1109/TNSRE.2024.3417311},
pmid = {38900612},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Computer Simulation ; *Algorithms ; Deep Learning ; },
abstract = {Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.},
}
@article {pmid38897536,
year = {2024},
author = {Kapgate, DD},
title = {Effect of inverted faces as visual stimuli on the performance of the hybrid SSVEP + P300 brain computer interface.},
journal = {Brain research},
volume = {1841},
number = {},
pages = {149092},
doi = {10.1016/j.brainres.2024.149092},
pmid = {38897536},
issn = {1872-6240},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Event-Related Potentials, P300/physiology ; Male ; Female ; *Photic Stimulation/methods ; *Electroencephalography/methods ; Young Adult ; Adult ; Face ; Pattern Recognition, Visual/physiology ; Brain/physiology ; },
abstract = {INTRODUCTION: This study proposes a hybrid brain-computer interface (BCI) system that simultaneously evokes steady-state visual evoked potentials (SSVEP) and event-related potentials (P300). The goal of this study was to improve the performance of the current hybrid SSVEP + P300 BCI systems by incorporating inverted faces into visual stimuli.<br><br>METHODS: In this study, upright and inverted faces were added to visual stimulus to elicit stronger cortical responses in a hybrid SSVEP&#xa0;+&#xa0;P300 BCI. We also considered triggering the P300 signals with facial stimuli and the SSVEP signals with non-facial stimuli. We have tested four paradigms: the upright face paradigm (UF), the inverted face paradigm (IF), the upright face and flicker paradigm (UFF), and the inverted face and flicker paradigm (IFF).<br><br>RESULTS AND CONCLUSIONS: The results showed that the IFF paradigm evoked more robust cortical responses, which led to enhanced system accuracy and ITR. The IFF paradigm had an average accuracy of 96.6% and a system communication rate of 26.45 bits per second. The UFF paradigm is the best candidate for BCI applications among other paradigms because it provides maximum comfort while maintaining a reasonable ITR.},
}
@article {pmid38897235,
year = {2024},
author = {Wang, R and Chen, ZS},
title = {Large-scale foundation models and generative AI for BigData neuroscience.},
journal = {Neuroscience research},
volume = {},
number = {},
pages = {},
pmid = {38897235},
issn = {1872-8111},
support = {UG3 NS135170/NS/NINDS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; RF1 DA056394/DA/NIDA NIH HHS/United States ; R01 MH139352/MH/NIMH NIH HHS/United States ; },
abstract = {Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-like intelligence thanks to BigData. With the help of self-supervised learning (SSL) and transfer learning, these models may potentially reshape the landscapes of neuroscience research and make a significant impact on the future. Here we present a mini-review on recent advances in foundation models and generative AI models as well as their applications in neuroscience, including natural language and speech, semantic memory, brain-machine interfaces (BMIs), and data augmentation. We argue that this paradigm-shift framework will open new avenues for many neuroscience research directions and discuss the accompanying challenges and opportunities.},
}
@article {pmid38897146,
year = {2024},
author = {Lian, S and Li, Z},
title = {An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.},
journal = {Computers in biology and medicine},
volume = {178},
number = {},
pages = {108727},
doi = {10.1016/j.compbiomed.2024.108727},
pmid = {38897146},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; },
abstract = {Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extracted from EEG signals. Currently, feature extraction heavily relies on prior knowledge to engineer features (for example from specific frequency bands); therefore, better extraction of EEG features is an important research direction. In this work, we propose an end-to-end deep neural network that automatically finds and combines features for motor imagery (MI) based EEG BCI with 4 or more imagery classes (multi-task). First, spectral domain features of EEG signals are learned by compact convolutional neural network (CCNN) layers. Then, gated recurrent unit (GRU) neural network layers automatically learn temporal patterns. Lastly, an attention mechanism dynamically combines (across EEG channels) the extracted spectral-temporal features, reducing redundancy. We test our method using BCI Competition IV-2a and a data set we collected. The average classification accuracy on 4-class BCI Competition IV-2a was 85.1 % ± 6.19 %, comparable to recent work in the field and showing low variability among participants; average classification accuracy on our 6-class data was 64.4 % ± 8.35 %. Our dynamic fusion of spectral-temporal features is end-to-end and has relatively few network parameters, and the experimental results show its effectiveness and potential.},
}
@article {pmid38896527,
year = {2025},
author = {Yang, C and Zhang, Z and Zhang, L and Zhang, Y and Li, Z and Luo, Y and Pan, G and Zhao, B},
title = {Neural Dielet 2.0: A 128-Channel 2mm2mm Battery-Free Neural Dielet Merging Simultaneous Multi-Channel Transmission Through Multi-Carrier Orthogonal Backscatter.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {19},
number = {1},
pages = {226-237},
doi = {10.1109/TBCAS.2024.3416728},
pmid = {38896527},
issn = {1940-9990},
mesh = {*Wireless Technology/instrumentation ; Brain-Computer Interfaces ; Equipment Design ; *Signal Processing, Computer-Assisted/instrumentation ; Electric Power Supplies ; Humans ; *Neurons/physiology ; },
abstract = {Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm2mm die, and then the all-digital implementation achieves 0.02mm area and 9.87W power per channel.},
}
@article {pmid38896526,
year = {2024},
author = {Xu, W and Tang, J and Qi, H},
title = {Using the Cocktail Party Effect to Add the Coding Dimension of Auditory Event Related Potential Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {10},
pages = {5953-5961},
doi = {10.1109/JBHI.2024.3416488},
pmid = {38896526},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Female ; *Signal Processing, Computer-Assisted ; Young Adult ; *Evoked Potentials, Auditory/physiology ; Adult ; Voice/physiology ; },
abstract = {OBJECTIVE: The auditory event-related potential based brain-computer interface (aERP-BCI) is a classical paradigm of brain-computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect.<br><br>METHODS: The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed.<br><br>CONCLUSION: The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC.<br><br>SIGNIFICANCE: The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.},
}
@article {pmid38895473,
year = {2024},
author = {Alcolea, P and Ma, X and Bodkin, K and Miller, LE and Danziger, ZC},
title = {Less is more: selection from a small set of options improves BCI velocity control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38895473},
issn = {2692-8205},
support = {R01 NS109257/NS/NINDS NIH HHS/United States ; },
abstract = {We designed the discrete direction selection (DDS) decoder for intracortical brain computer interface (iBCI) cursor control and showed that it outperformed currently used decoders in a human-operated real-time iBCI simulator and in monkey iBCI use. Unlike virtually all existing decoders that map between neural activity and continuous velocity commands, DDS uses neural activity to select among a small menu of preset cursor velocities. We compared closed-loop cursor control across four visits by each of 48 naïve, able-bodied human subjects using either DDS or one of three common continuous velocity decoders: direct regression with assist (an affine map from neural activity to cursor velocity), ReFIT, and the velocity Kalman Filter. DDS outperformed all three by a substantial margin. Subsequently, a monkey using an iBCI also had substantially better performance with DDS than with the Wiener filter decoder (direct regression decoder that includes time history). Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of simplifying online iBCI control.},
}
@article {pmid38895388,
year = {2024},
author = {Liu, J and Younk, R and Drahos, LM and Nagrale, SS and Yadav, S and Widge, AS and Shoaran, M},
title = {Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38895388},
issn = {2692-8205},
support = {R01 MH123634/MH/NIMH NIH HHS/United States ; },
abstract = {OBJECTIVE: Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.<br><br>APPROACH: We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.<br><br>MAIN RESULTS: Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference.<br><br>SIGNIFICANCE: Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.},
}
@article {pmid38895167,
year = {2024},
author = {Yao, Y and Hasan, WZW and Jiao, W and Dong, X and Ramli, HR and Norsahperi, NMH and Wen, D},
title = {ChatGPT and BCI-VR: a new integrated diagnostic and therapeutic perspective for the accurate diagnosis and personalized treatment of mild cognitive impairment.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1426055},
pmid = {38895167},
issn = {1662-5161},
}
@article {pmid38894334,
year = {2024},
author = {Kim, D and Kim, Y and Park, J and Choi, H and Ryu, H and Loeser, M and Seo, K},
title = {Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {11},
pages = {},
pmid = {38894334},
issn = {1424-8220},
support = {This study was financially supported by Seoul National University of Science and Technology.//This study was financially supported by Seoul National University of Science and Technology./ ; },
mesh = {Humans ; *Cognitive Dysfunction/diagnosis/physiopathology ; *Electroencephalography/methods ; Aged ; Male ; Female ; *Evoked Potentials, Visual/physiology ; *Virtual Reality ; Alzheimer Disease/physiopathology ; Neuropsychological Tests ; Activities of Daily Living ; Middle Aged ; },
abstract = {Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.},
}
@article {pmid38894311,
year = {2024},
author = {Sun, Q and Zhang, S and Dong, G and Pei, W and Gao, X and Wang, Y},
title = {High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {11},
pages = {},
pmid = {38894311},
issn = {1424-8220},
support = {62071447//National Natural Science Foundation of China/ ; 2022YFF1202303//National Key R&amp;D Program of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; Adult ; *Algorithms ; Female ; Young Adult ; Photic Stimulation ; Electrodes ; Signal Processing, Computer-Assisted ; },
abstract = {In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.},
}
@article {pmid38894107,
year = {2024},
author = {Ron-Angevin, R and Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Lespinet-Najib, V and André, JM},
title = {Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain-Computer Interface Speller under Rapid Serial Visual Presentation.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {11},
pages = {},
pmid = {38894107},
issn = {1424-8220},
support = {PID2021-127261OB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2021-127261OB-I00//Agencia Estatal de Investigaci&#xf3;n/ ; PID2021-127261OB-I00//European Regional Development Fund/ ; PID2021-127261OB-I00//Universidad de M&#xe1;laga/ ; PID2021-127261OB-I00//Instituto Universitario de Investigaci&#xf3;n en Telecomunicaci&#xf3;n de la Universidad de M&#xe1;laga/ ; PID2021-127261OB-I00//Institut Polytechnique de Bordeaux/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; *Electroencephalography/methods ; Young Adult ; *Photic Stimulation ; *Evoked Potentials/physiology ; Eye Movements/physiology ; },
abstract = {Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces-particularly those tinted red-have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems.},
}
@article {pmid38891906,
year = {2024},
author = {Venetis, K and Pescia, C and Cursano, G and Frascarelli, C and Mane, E and De Camilli, E and Munzone, E and Dellapasqua, S and Criscitiello, C and Curigliano, G and Guerini Rocco, E and Fusco, N},
title = {The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy.},
journal = {International journal of molecular sciences},
volume = {25},
number = {11},
pages = {},
pmid = {38891906},
issn = {1422-0067},
mesh = {Humans ; *Breast Neoplasms/genetics/diagnosis/therapy ; Female ; Prognosis ; *Genomics/methods ; Biomarkers, Tumor/genetics ; Genetic Testing/methods ; },
abstract = {Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score[®], MammaPrint, Prosigna[®], EndoPredict[®], and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict[®] have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.},
}
@article {pmid38891733,
year = {2024},
author = {Balčiauskas, L and Balčiauskienė, L},
title = {Insight into Body Condition Variability in Small Mammals.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {11},
pages = {},
pmid = {38891733},
issn = {2076-2615},
abstract = {The body condition index (BCI) is an indicator of both reproductive success and health in small mammals and might help to understand ecological roles of species. We analyzed BCI data from 28,567 individuals trapped in Lithuania between 1980 and 2023. We compared BCIs between species and examined differences in age groups, gender, and reproductive statuses within each species. Seven out of eighteen species had sample sizes with N < 100. In terms of species, we found that seven of the eight species with the highest average BCIs are granivores or omnivores, which can consume animal-based food at least seasonally. The two contrasting (decreasing or increasing) BCI patterns observed during ontogeny can be related to diet differences among juveniles, subadults, and adult animals. Our results demonstrate that reproductive stress has a negative impact on the BCI of adult females in all analyzed species and nearly all adult males. Although the animals with extremely low BCI consisted mostly of shrews, for the first time we found 23 common and pygmy shrews exhibiting the Chitty effect, i.e., a very high body mass resulting in a BCI > 5.0. This is the first multi-species approach of body condition at middle latitudes. The results increase our understanding of how changing environmental conditions are affecting small mammals.},
}
@article {pmid38890235,
year = {2024},
author = {Wang, S and Zhang, X and Zhao, Y and Lv, H and Li, P and Zhang, Z and Qiao, X},
title = {BCI Improves Alcohol-Induced Cognitive and Emotional Impairments by Restoring pERK-BDNF.},
journal = {Journal of molecular neuroscience : MN},
volume = {74},
number = {3},
pages = {59},
pmid = {38890235},
issn = {1559-1166},
support = {82101978//National Natural Science Foundation of China/ ; 212300410260//Natural Science Foundation of Henan Province/ ; },
mesh = {Animals ; *Brain-Derived Neurotrophic Factor/metabolism/genetics ; Mice ; Male ; *Mice, Inbred C57BL ; *Dual Specificity Phosphatase 1/metabolism/genetics ; *Prefrontal Cortex/metabolism/drug effects ; *Ethanol/toxicity/pharmacology ; Dual Specificity Phosphatase 6/metabolism/genetics ; Aminoacetonitrile/analogs &amp; derivatives/pharmacology/therapeutic use ; Anxiety/drug therapy/etiology ; MAP Kinase Signaling System ; },
abstract = {Binge drinking causes a range of problems especially damage to the nervous system, and the specific neural mechanism of brain loss and behavioral abnormalities caused by which is still unclear. Extracellular regulated protein kinases (ERK) maintain neuronal survival, growth, and regulation of synaptic plasticity by phosphorylating specific transcription factors to regulate expression of brain-derived neurotrophic factor (BDNF). Dual-specific phosphatase 1 (DUSP1) and DUSP6 dephosphorylate tyrosine and serine/threonine residues in ERK1/2 to inactivate them. To investigate the molecular mechanism by which alcohol affects memory and emotion, a chronic intermittent alcohol exposure (CIAE) model was established. The results demonstrated that mice in the CIAE group developed short-term recognition memory impairment and anxiety-like behavior; meanwhile, the expression of DUSP1 and DUSP66 in the mPFC was increased, while the levels of p-ERK and BDNF were decreased. Micro-injection of DUSP1/6 inhibitor BCI into the medial prefrontal cortex (mPFC) restored the dendritic morphology by reversing the activity of ERK-BDNF and ultimately improved cognitive and emotional impairment caused by CIAE. These findings indicate that CIAE inhibits ERK-BDNF by increasing DUSP1/6 in the mPFC that may be associated with cognitive and emotional deficits. Consequently, DUSP1 and DUSP6 appear to be potential targets for the treatment of alcoholic brain disorders.},
}
@article {pmid38889028,
year = {2024},
author = {Wu, D and Li, S and Yang, J and Sawan, M},
title = {Neuro-BERT: Rethinking Masked Autoencoding for Self-Supervised Neurological Pretraining.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3415959},
pmid = {38889028},
issn = {2168-2208},
abstract = {Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, which is often scarce and expensive to acquire, requiring specialized infrastructure and domain expertise. To address the appetite for data in deep learning, we present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. The intuition behind our approach is simple: frequency and phase distribution of neurological signals can reveal intricate neurological activities. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information using the Fourier inversion theorem. Pre-trained models can be potentially used for various downstream tasks such as sleep stage classification and gesture recognition. Unlike contrastive-based methods, which strongly rely on carefully hand-crafted augmentations and siamese structure, our approach works reasonably well with a simple transformer encoder with no augmentation requirements. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.},
}
@article {pmid38889026,
year = {2024},
author = {Amrani, H and Micucci, D and Napoletano, P},
title = {Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3416066},
pmid = {38889026},
issn = {2168-2208},
abstract = {Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, leading to uncertainty about the best approach to enhance decoding performance. Additionally, current evaluation metrics used to assess decoding effectiveness are predominantly syntactic and do not provide insights into the comprehensibility of the decoded output for human understanding. We present an end-to-end architecture for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our proposal introduces the following innovations: 1) an end-to-end deep learning architecture for open vocabulary EEG decoding, incorporating a subject-dependent representation learning module for raw EEG encoding, a BART language model, and a GPT-4 sentence refinement module; 2) a more comprehensive sentence-level evaluation metric based on the BERTScore; 3) an ablation study that analyses the contributions of each module within our proposal, providing valuable insights for future research. We evaluate our approach on two publicly available datasets, ZuCo v1.0 and v2.0, comprising EEG recordings of 30 subjects engaged in natural reading tasks. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, achieving an increment over the previous state-of-the-art by 1.40%, 2.59%, and 3.20%, respectively.},
}
@article {pmid38888742,
year = {2024},
author = {Bérubé, M and Verret, M and Bourque, L and Côté, C and Guénette, L and Richard-Denis, A and Ouellet, S and Singer, LN and Gauthier, L and Gagnon, MP and Gagnon, MA and Martorella, G},
title = {Educational needs and preferences of adult patients with acute pain: a mixed-methods systematic review.},
journal = {Pain},
volume = {165},
number = {12},
pages = {e162-e183},
pmid = {38888742},
issn = {1872-6623},
mesh = {Humans ; *Acute Pain/therapy ; *Patient Preference/psychology ; *Patient Education as Topic/methods ; *Pain Management/methods ; Adult ; },
abstract = {Many patients experience acute pain, which has been associated with numerous negative consequences. Pain education has been proposed as a strategy to improve acute pain management. However, studies report limited effects with educational interventions for acute pain in adults, which can be explained by the underuse of the person-centered approach. Thus, we aimed to systematically review and synthetize current evidence from quantitative, qualitative and mixed-methods studies describing patients' needs and preferences for acute pain education in adults. We searched original studies and gray literature in 7 databases, from January 1990 to October 2023. Methodological quality was assessed with the Mixed Methods Appraisal Tool. A total of 32 studies were included (n = 1847 patients), two-thirds of which were qualitative studies of high methodological quality. Most of the studies were conducted over the last 15 years in patients with postsurgical and posttraumatic pain, identified as White, with a low level of education. Patients expressed the greatest need for education when it came to what to expect in pain intensity and duration, as well how to take the medication and its associated adverse effects. The most frequently reported educational preferences were for in-person education while involving caregivers and to obtain information first from physicians, then by other professionals. This review has highlighted the needs and preferences to be considered in pain education interventions, which should be embedded in an approach cultivating communication and partnership with patients and their caregivers. The results still need to be confirmed with different patient populations.},
}
@article {pmid38887162,
year = {2024},
author = {Xu, L and Saeed, S and Ma, X and Cen, X and Sun, Y and Tian, Y and Zhang, X and Zhang, D and Tang, A and Zhou, H and Lai, J and Xia, H and Hu, S},
title = {Hippocampal mitophagy contributes to spatial memory via maintaining neurogenesis during the development of mice.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {6},
pages = {e14800},
pmid = {38887162},
issn = {1755-5949},
support = {LR22C070002//Natural Science Foundation of Zhejiang Province/ ; 32100598//National Natural Science Foundation of China/ ; 32222023//National Natural Science Foundation of China/ ; Y202353713//Scientific Research Fund of 577 Zhejiang Provincial Education Department/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 226-2022-00193//Fundamental Research Funds for the Central Universities/ ; 2023YFC2506200//National Key Research and Development Program of China/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2024R401199//Postgraduate Science and Technology Innovation activity and "Xinmiao" Program of Zhejiang University/ ; },
mesh = {Animals ; *Neurogenesis/physiology/drug effects ; *Mitophagy/physiology/drug effects ; *Spatial Memory/physiology ; *Hippocampus ; Male ; Mice ; Mice, Inbred C57BL ; Mitochondrial Dynamics/physiology ; Quinazolinones ; },
abstract = {BACKGROUND: Impaired mitochondrial dynamics have been identified as a significant contributing factor to reduced neurogenesis under pathological conditions. However, the relationship among mitochondrial dynamics, neurogenesis, and spatial memory during normal development remains unclear. This study aims to elucidate the role of mitophagy in spatial memory mediated by neurogenesis during development.<br><br>METHODS: Adolescent and adult male mice were used to assess spatial memory performance. Immunofluorescence staining was employed to evaluate levels of neurogenesis, and mitochondrial dynamics were assessed through western blotting and transmission electron microscopy. Pharmacological interventions further validated the causal relationship among mitophagy, neurogenesis, and behavioral performance during development.<br><br>RESULTS: The study revealed differences in spatial memory between adolescent and adult mice. Diminished neurogenesis, accompanied by reduced mitophagy, was observed in the hippocampus of adult mice compared to adolescent subjects. Pharmacological induction of mitophagy in adult mice with UMI-77 resulted in enhanced neurogenesis and prolonged spatial memory retention. Conversely, inhibition of mitophagy with Mdivi-1 in adolescent mice led to reduced hippocampal neurogenesis and impaired spatial memory.<br><br>CONCLUSION: The observed decline in spatial memory in adult mice is associated with decreased mitophagy, which affects neurogenesis in the dentate gyrus. This underscores the therapeutic potential of enhancing mitophagy to counteract age- or disease-related cognitive decline.},
}
@article {pmid38886591,
year = {2024},
author = {Momeny, M and Tienhaara, M and Sharma, M and Chakroborty, D and Varjus, R and Takala, I and Merisaari, J and Padzik, A and Vogt, A and Paatero, I and Elenius, K and Laajala, TD and Kurppa, KJ and Westermarck, J},
title = {DUSP6 inhibition overcomes neuregulin/HER3-driven therapy tolerance in HER2+ breast cancer.},
journal = {EMBO molecular medicine},
volume = {16},
number = {7},
pages = {1603-1629},
pmid = {38886591},
issn = {1757-4684},
mesh = {*Dual Specificity Phosphatase 6/metabolism/genetics ; Humans ; *Breast Neoplasms/drug therapy/pathology/metabolism/genetics ; Female ; *Receptor, ErbB-2/metabolism ; Animals ; *Receptor, ErbB-3/metabolism/genetics/antagonists &amp; inhibitors ; Cell Line, Tumor ; Mice ; Drug Resistance, Neoplasm/drug effects ; Antineoplastic Agents/pharmacology/therapeutic use ; Protein Kinase Inhibitors/pharmacology ; },
abstract = {Despite clinical benefits of tyrosine kinase inhibitors (TKIs) in cancer, most tumors can reactivate proliferation under TKI therapy. Here we present transcriptional profiling of HER2+ breast cancer cells transitioning from dormant drug tolerant cells to re-proliferating cells under continuous HER2 inhibitor (HER2i) therapy. Focusing on phosphatases, expression of dual-specificity phosphatase DUSP6 was found inhibited in dormant cells, but strongly induced upon regrowth. DUSP6 expression also selectively associated with poor patient survival in HER2+ breast cancers. DUSP6 overexpression conferred apoptosis resistance, whereas its pharmacological blockade prevented therapy tolerance development under HER2i therapy. DUSP6 targeting also synergized with clinically used HER2i combination therapies. Mechanistically DUSP6 is a positive regulator of HER3 expression, and its impact on HER2i tolerance was mediated by neuregulin-HER3 axis. In vivo, genetic targeting of DUSP6 reduced tumor growth in brain metastasis model, whereas its pharmacological targeting induced synthetic lethal therapeutic effect in combination with HER2i. Collectively this work demonstrates that DUSP6 drives escape from HER2i-induced dormancy, and that DUSP6 is a druggable target to overcome HER3-driven TKI resistance.},
}
@article {pmid38885847,
year = {2024},
author = {Gao, Y and Zhu, Z and Fang, F and Zhang, Y and Meng, M},
title = {EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.},
journal = {Journal of affective disorders},
volume = {361},
number = {},
pages = {356-366},
doi = {10.1016/j.jad.2024.06.042},
pmid = {38885847},
issn = {1573-2517},
mesh = {Humans ; *Electroencephalography ; *Emotions/physiology ; *Support Vector Machine ; Brain/physiology ; Adult ; Brain-Computer Interfaces ; Algorithms ; Female ; Male ; Young Adult ; Signal Processing, Computer-Assisted ; },
abstract = {Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lacking for selecting emotional signals in emotion recognition research, and potential associations between activation differences in brain regions and network connectivity pattern are often being overlooked. To bridge this technical gap, a data-driven signal auto-segmentation and feature fusion algorithm (DASF) is proposed in this paper. First, the Phase Locking Value (PLV) method was used to construct the brain functional adjacency matrix of each subject, and the dynamic brain functional network across subjects was then constructed. Next, tucker decomposition was performed and the Grassmann distance of the connectivity submatrix was calculated. Subsequently, different brain network states were distinguished and signal segments under emotional states were automatically extract using data-driven methods. Then, tensor sparse representation was adopted on the intercepted EEG signals to effectively extract functional connections under different emotional states. Finally, power-distribution related features (differential entropy and energy feature) and brain functional connection features were effectively combined for classification using the support vector machines (SVM) classifier. The proposed method was validated on ERN and DEAP datasets. The single-feature emotion classification accuracy of 86.57 % and 87.74 % were achieved on valence and arousal dimensions, respectively. The accuracy of the proposed feature fusion method was achieved at 89.14 % and 89.65 %, accordingly, demonstrating an improvement in emotion recognition accuracy. The results demonstrated the superior classification performance of the proposed data-driven signal auto-segmentation and feature fusion algorithm in emotion recognition compared to state-of-the-art classification methods.},
}
@article {pmid38885688,
year = {2024},
author = {Wu, X and Wellington, S and Fu, Z and Zhang, D},
title = {Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad593a},
pmid = {38885688},
issn = {1741-2552},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Speech/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Epilepsy/physiopathology/diagnosis ; Stereotaxic Techniques ; Adult ; Neural Networks, Computer ; },
abstract = {Objective.Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended speech directly. Many studies have demonstrated promising results using invasive micro-electrode arrays and electrocorticography. However, the use of stereo-electroencephalography (sEEG) for speech decoding has not been fully recognized.Approach.In this research, recently released sEEG data were used to decode Dutch words spoken by epileptic participants. We decoded speech waveforms from sEEG data using advanced deep-learning methods. Three methods were implemented: a linear regression method, an recurrent neural network (RNN)-based sequence-to-sequence model (RNN), and a transformer model.Main results.Our RNN and transformer models outperformed the linear regression significantly, while no significant difference was found between the two deep-learning methods. Further investigation on individual electrodes showed that the same decoding result can be obtained using only a few of the electrodes.Significance.This study demonstrated that decoding speech from sEEG signals is possible, and the location of the electrodes is critical to the decoding performance.},
}
@article {pmid38885683,
year = {2024},
author = {Jin, J and Bai, G and Xu, R and Qin, K and Sun, H and Wang, X and Cichocki, A},
title = {A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad593b},
pmid = {38885683},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Transfer, Psychology/physiology ; Support Vector Machine ; Electroencephalography/methods ; Movement/physiology ; Algorithms ; Machine Learning ; Databases, Factual ; Male ; },
abstract = {Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs.Approach.We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine.Main results.Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively.Significance.By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.},
}
@article {pmid38885679,
year = {2024},
author = {Whitsitt, Q and Saxena, A and Patel, B and Evans, BM and Hunt, B and Purcell, EK},
title = {Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
pmid = {38885679},
issn = {1741-2552},
support = {R01 NS107451/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Rats ; *Motor Cortex/physiology/metabolism ; *Electrodes, Implanted ; *Transcriptome ; Male ; *Microelectrodes ; Rats, Sprague-Dawley ; Brain-Computer Interfaces ; Neurons/physiology/metabolism ; },
abstract = {Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.},
}
@article {pmid38885677,
year = {2024},
author = {Chen, W and Wang, S and Bao, J and Yu, C and Jiang, Q and Song, J and Zheng, Y and Hao, Y and Xu, K},
title = {Restoration of coherent reach-grasp-pull movement via sequential intraneural peripheral nerve stimulation in rats.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5935},
pmid = {38885677},
issn = {1741-2552},
mesh = {Animals ; Rats ; *Rats, Sprague-Dawley ; *Peripheral Nerves/physiology ; *Movement/physiology ; *Hand Strength/physiology ; Muscle, Skeletal/physiology/innervation ; Male ; Electric Stimulation Therapy/methods ; Electrodes, Implanted ; Electromyography/methods ; },
abstract = {Objective.Peripheral nerve stimulation (PNS) has been demonstrated as an effective way to selectively activate muscles and to produce fine hand movements. However, sequential multi-joint upper limb movements, which are critical for paralysis rehabilitation, has not been tested with PNS. Here, we aimed to restore multiple upper limb joint movements through an intraneural interface with a single electrode, achieving coherent reach-grasp-pull movement tasks through sequential stimulation.Approach.A transverse intrafascicular multichannel electrode was implanted under the axilla of the rat's upper limb, traversing the musculocutaneous, radial, median, and ulnar nerves. Intramuscular electrodes were implanted into the biceps brachii (BB), triceps brachii (TB), flexor carpi radialis (FCR), and extensor carpi radialis (ECR) muscles to record electromyographic (EMG) activity and video recordings were used to capture the kinematics of elbow, wrist, and digit joints. Charge-balanced biphasic pulses were applied to different channels to recruit distinct upper limb muscles, with concurrent recording of EMG signals and joint kinematics to assess the efficacy of the stimulation. Finally, a sequential stimulation protocol was employed by generating coordinated pulses in different channels.Main results.BB, TB, FCR and ECR muscles were selectively activated and various upper limb movements, including elbow flexion, elbow extension, wrist flexion, wrist extension, digit flexion, and digit extension, were reliably generated. The modulation effects of stimulation parameters, including pulse width, amplitude, and frequency, on induced joint movements were investigated and reach-grasp-pull movement was elicited by sequential stimulation.Significance.Our results demonstrated the feasibility of sequential intraneural stimulation for functional multi-joint movement restoration, providing a new approach for clinical rehabilitation in paralyzed patients.},
}
@article {pmid38885099,
year = {2024},
author = {Wang, H and Wang, Z and Sun, Y and Yuan, Z and Xu, T and Li, J},
title = {A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2270-2280},
doi = {10.1109/TNSRE.2024.3415474},
pmid = {38885099},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; *Algorithms ; Neural Networks, Computer ; Photic Stimulation ; Communication Devices for People with Disabilities ; Reproducibility of Results ; Male ; },
abstract = {Visual-based brain-computer interface (BCI) enables people to communicate with others by spelling words from the brain and helps professionals recognize targets in large numbers of images. P300 signals evoked by different types of stimuli, such as words or images, may vary significantly in terms of both amplitude and latency. A unified approach is required to detect variable P300 signals, which facilitates BCI applications, as well as deepens the understanding of the P300 generation mechanism. In this study, our proposed approach involves a cascade network structure that combines xDAWN and classical EEGNet techniques. This network is designed to classify target and non-target stimuli in both P300 speller and rapid serial visual presentation (RSVP) paradigms. The proposed approach is capable of recognizing more symbols with fewer repetitions (up to 5 rounds) compared to other models while possessing a better information transfer rate (ITR) as demonstrated on Dataset II (17.22 bits/min in the second repetition round) of BCI Competition III. Additionally, our approach has the highest unweighted average recall (UAR) performance for both 5 Hz (0.8134±0.0259) and 20 Hz (0.6527±0.0321) RSVP. The results show that the cascade network structure has better performance between both the P300 Speller and RSVP paradigms, manifesting that such a cascade structure is robust enough for dealing with P300-related signals (source code is available at https://github.com/embneural/Cascade-xDAWN-EEGNet-for-ERP-Detection).},
}
@article {pmid38885097,
year = {2024},
author = {Shao, Y and Zhou, Y and Gong, P and Sun, Q and Zhang, D},
title = {A Dual-Adversarial Model for Cross-Time and Cross-Subject Cognitive Workload Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2324-2335},
doi = {10.1109/TNSRE.2024.3415364},
pmid = {38885097},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; *Cognition/physiology ; *Workload ; *Algorithms ; *Brain-Computer Interfaces ; Reproducibility of Results ; },
abstract = {Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.},
}
@article {pmid38881541,
year = {2024},
author = {Rocha, SV and Alves Dos Santos, C and Conceição, AF and Palotino-Ferreira, BM and Morais, DB and Chavane, FS and Chaves Dias, CR and Lacerda Bachi, AL and Mendes, R and Brito-Costa, S and Silva, S and Furtado, GE},
title = {Implementing regular physical activity for older individuals in the family strategy program using the RE-AIM framework to ensure feasibility and sustainability: EISI study protocol.},
journal = {Contemporary clinical trials communications},
volume = {39},
number = {},
pages = {101311},
pmid = {38881541},
issn = {2451-8654},
abstract = {The EISI study protocol aims to address the low participation rate in physical exercise programs among older individuals, emphasizing its significance as a non-pharmacological therapeutic approach for overall health and increased physical activity. The objectives include implementing physical activity (PA) and educational health programs in Jequié, Bahia, Brazil, targeting the Family Health Strategy population to enhance local physical activity levels among older individuals. The study also seeks to evaluate the program's feasibility, safety, and sustainability for large-scale implementation, along with assessing its impact on immune and inflammatory response biomarkers to the SARS-CoV virus, as well as physical-functional and brain health. Participants, aged 60 or above, will be divided into two groups: multicomponent exercise (MCE) and behavioral change interventions (BCI). The study employs a mixed-method approach, utilizing a non-randomized controlled short-term pathway model for a 4-8 weeks of pilot study and 16-week intervention impact assessment. Data collection encompasses various aspects such as sociodemographic information, mental health, physical fitness, fall risk, functional capacity, anthropometric measurements, hemodynamic assessment, habitual physical activity, and health-related quality of life. Blood and saliva samples are collected for cytokine and antibody biomarker analysis related to SARS-CoV immunity. Pre- and post-intervention evaluations for both groups will be conducted, with the hypothesis that MCE will yield more favorable responses compared to BCI. The study's holistic approach, including the assessment of feasibility, safety, and sustainability, aims to contribute to achieving Sustainable Development Goals (SDG) 3 and SDG 9 b y promoting accessible and sustainable healthcare initiatives for older individuals. This research aligns with global efforts to enhance health and well-being, emphasizing the importance of regular exercise in the aging population.},
}
@article {pmid38880343,
year = {2024},
author = {Ponrani, MA and Anand, M and Alsaadi, M and Dutta, AK and Fayaz, R and Mathew, S and Chaurasia, MA and Sunila, and Bhende, M},
title = {Brain-computer interfaces inspired spiking neural network model for depression stage identification.},
journal = {Journal of neuroscience methods},
volume = {409},
number = {},
pages = {110203},
doi = {10.1016/j.jneumeth.2024.110203},
pmid = {38880343},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Depression/physiopathology/diagnosis ; Brain/physiopathology/physiology ; Deep Learning ; Models, Neurological ; Adult ; Action Potentials/physiology ; },
abstract = {BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Interfaces inspired deep learning-assisted diagnosis based on physiological signals holds promise for improving traditional methods lacking physiological basis and leads next generation neuro-technologies. However, traditional deep learning methods rely on immense computational power and mostly involve end-to-end network learning. These learning methods also lack physiological interpretability, limiting their clinical application in assisted diagnosis.<br><br>METHODOLOGY: A brain-like learning model for diagnosing depression using electroencephalogram (EEG) is proposed. The study collects EEG data using 128-channel electrodes, producing a 128&#xd7;128 brain adjacency matrix. Given the assumption of undirected connectivity, the upper half of the 128&#xd7;128 matrix is chosen in order to minimise the input parameter size, producing 8,128-dimensional data. After eliminating 28 components derived from irrelevant or reference electrodes, a 90&#xd7;90 matrix is produced, which can be used as an input for a single-channel brain-computer interface image.<br><br>RESULT: At the functional level, a spiking neural network is constructed to classify individuals with depression and healthy individuals, achieving an accuracy exceeding 97.5&#x202f;%.<br><br>Compared to deep convolutional methods, the spiking method reduces energy consumption.<br><br>CONCLUSION: At the structural level, complex networks are utilized to establish spatial topology of brain connections and analyse their graph features, identifying potential abnormal brain functional connections in individuals with depression.},
}
@article {pmid38878764,
year = {2024},
author = {Wang, J},
title = {Can brain-computer interface promote affected upper limb movements of stroke patients?.},
journal = {Med (New York, N.Y.)},
volume = {5},
number = {6},
pages = {485-486},
doi = {10.1016/j.medj.2024.04.003},
pmid = {38878764},
issn = {2666-6340},
mesh = {Humans ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; *Stroke Rehabilitation/methods ; Movement/physiology ; Stroke/physiopathology ; Recovery of Function/physiology ; },
abstract = {Brain-computer interface (BCI) systems enable the brain to control limb movement on the side with impaired nerve conduction, showing great potential for promoting functional recovery. Wang et al.[1] demonstrated that BCI combined with functional electrical stimulation improved the motor function scores of stroke patients by conducting a multicenter RCT study.},
}
@article {pmid38876320,
year = {2024},
author = {Rao, Y and Zhang, L and Jing, R and Huo, J and Yan, K and He, J and Hou, X and Mu, J and Geng, W and Cui, H and Hao, Z and Zan, X and Ma, J and Chou, X},
title = {An optimized EEGNet decoder for decoding motor image of four class fingers flexion.},
journal = {Brain research},
volume = {1841},
number = {},
pages = {149085},
doi = {10.1016/j.brainres.2024.149085},
pmid = {38876320},
issn = {1872-6240},
mesh = {Humans ; *Fingers/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; Male ; *Neural Networks, Computer ; Female ; Young Adult ; Movement/physiology ; Brain/physiology ; },
abstract = {As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing the motor functions, it is a potential treatment way to drive mechanical equipments by the means of non-invasive BCI, which is badly depended on the accuracy of the decoded electroencephalogram (EEG) singles. In this study, an explanatory convolutional neural network namely EEGNet based on SimAM attention module was proposed to enhance the accuracy of decoding the EEG singles of index and thumb fingers for both left and right hand using sensory motor rhythm (SMR). An average classification accuracy of 72.91% the data of eight healthy subjects was obtained, which were captured from the one second before finger movement to two seconds after action. Furthermore, the character of event-related desynchronization (ERD) and event related synchronization (ERS) of index and thumb fingers was also studied in this study. These findings have significant importance for controlling external devices or other rehabilitation equipment using BCI in a fine way.},
}
@article {pmid38876042,
year = {2024},
author = {Zhang, C and Jian, L and Li, X and Guo, W and Deng, W and Hu, X and Li, T},
title = {Mendelian randomization analysis of the brain, cerebrospinal fluid, and plasma proteome identifies potential drug targets for attention deficit hyperactivity disorder.},
journal = {EBioMedicine},
volume = {105},
number = {},
pages = {105197},
pmid = {38876042},
issn = {2352-3964},
mesh = {Humans ; *Attention Deficit Disorder with Hyperactivity/genetics/cerebrospinal fluid/blood ; *Mendelian Randomization Analysis ; *Proteome/metabolism ; *Brain/metabolism/pathology ; *Biomarkers/cerebrospinal fluid ; Bayes Theorem ; Blood Proteins/metabolism ; Genetic Predisposition to Disease ; },
abstract = {BACKGROUND: The need for new therapeutics for attention deficit hyperactivity disorder (ADHD) is evident. Brain, cerebrospinal fluid (CSF), and plasma protein biomarkers with causal genetic evidence could represent potential drug targets. However, a comprehensive screen of the proteome has not yet been conducted.<br><br>METHODS: We employed a three-pronged approach using Mendelian Randomization (MR) and Bayesian colocalization analysis. Firstly, we studied 608 brains, 214 CSF, and 612 plasma proteins as potential causal mediators of ADHD using MR analysis. Secondly, we analysed the consistency of the discovered biomarkers across three distinct subtypes of ADHD: childhood, persistent, and late-diagnosed ADHD. Finally, we extended our analysis to examine the correlation between identified biomarkers and Tourette syndrome and pervasive autism spectrum disorder (ASD), conditions often linked with ADHD. To validate the MR findings, we conducted sensitivity analysis. Additionally, we performed cell type analysis on the human brain to identify risk genes that are notably enriched in various brain cell types.<br><br>FINDINGS: After applying Bonferroni correction, we found that the risk of ADHD was increased by brain proteins GMPPB, NAA80, HYI, CISD2, and HYI, TIE1 in CSF and plasma. Proteins GMPPB, NAA80, ICA1L, CISD2, TIE1, and RMDN1 showed overlapped loci with ADHD risk through Bayesian colocalization. Overexpression of GMPPB protein was linked to an increase in the risk for all three ADHD subtypes. While ICA1L provided protection against both ASD and ADHD, CISD2 increased the probability of both disorders. Cell-specific studies revealed that GMPPB, NAA80, ICA1L, and CISD2 were predominantly present on the surface of excitatory-inhibitory neurons.<br><br>INTERPRETATION: Our comprehensive MR investigation of the brain, CSF, and plasma proteomes revealed seven proteins with causal connections to ADHD. Particularly, GMPPB and TIE1 emerged as intriguing targets for potential ADHD therapy.<br><br>FUNDING: This work was partly funded by the Key R &amp; D Program of Zhejiang (T.L. 2022C03096); the National Natural Science Foundation of China Project (C.Z. 82001413); Postdoctoral Foundation of West China Hospital (C.Z. 2020HXBH163).},
}
@article {pmid38875116,
year = {2024},
author = {Zhou, S and Duan, S and Yang, H},
title = {Protocol for fiber photometry recording from deep brain regions in head-fixed mice.},
journal = {STAR protocols},
volume = {5},
number = {2},
pages = {103131},
pmid = {38875116},
issn = {2666-1667},
mesh = {Animals ; Mice ; *Photometry/methods ; *Brain/diagnostic imaging ; Optical Fibers ; Calcium/metabolism/analysis ; Stereotaxic Techniques ; Fiber Optic Technology/methods ; },
abstract = {To exclude the influence of motion on in vivo calcium imaging, animals usually need to be fixed. However, the whole-body restraint can cause stress in animals, affecting experimental results. In addition, some brain regions are prone to bleeding during surgery, which lowers the success rate of calcium imaging. Here, we present a protocol for calcium imaging using heparin-treated fiber in head-fixed mice. We describe steps for stereotaxic surgery, including virus injection and optic fiber implantation, fiber photometry, and data analysis. For complete details on the use and execution of this protocol, please refer to Du et al.[1].},
}
@article {pmid38874845,
year = {2024},
author = {Birbaumer, N},
title = {"Your Thoughts are (were) Free!": Brain-Computer-Interfaces, Neurofeedback, Detection of Deception, and the Future of Mind-Reading.},
journal = {Applied psychophysiology and biofeedback},
volume = {},
number = {},
pages = {},
pmid = {38874845},
issn = {1573-3270},
abstract = {This review describes the historical developement and rationale of clinically relevant research on neurophysiological "mind reading" paradims: Brain- Computer-Interfaces, detection of deception, brain stimulation and neurofeedback and the clinical applications in drug resistant epilepsy, chronic stroke, and communication with paralyzed locked-in persons. The emphasis lies on completely locked-in patients with amyotrophic lateral sclerosis using non-invasive and invasive brain computer interfaces and neurofeedback to restore verbal communication with the social environment. In the second part of the article we argue that success and failure of neurophysiological "mind reading" paradigms may be explained with a motor theory of thinking and emotion in combination with learning theory. The ethical implications of brain computer interface and neurofeedback approaches, particularly for severe chronic paralysis and loss of communication diseases and decisions on hastened death and euthanasia are discussed.},
}
@article {pmid38873653,
year = {2024},
author = {Alasfour, A and Gilja, V},
title = {Consistent spectro-spatial features of human ECoG successfully decode naturalistic behavioral states.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1388267},
pmid = {38873653},
issn = {1662-5161},
abstract = {OBJECTIVE: Understanding the neural correlates of naturalistic behavior is critical for extending and confirming the results obtained from trial-based experiments and designing generalizable brain-computer interfaces that can operate outside laboratory environments. In this study, we aimed to pinpoint consistent spectro-spatial features of neural activity in humans that can discriminate between naturalistic behavioral states.<br><br>APPROACH: We analyzed data from five participants using electrocorticography (ECoG) with broad spatial coverage. Spontaneous and naturalistic behaviors such as "Talking" and "Watching TV" were labeled from manually annotated videos. Linear discriminant analysis (LDA) was used to classify the two behavioral states. The parameters learned from the LDA were then used to determine whether the neural signatures driving classification performance are consistent across the participants.<br><br>MAIN RESULTS: Spectro-spatial feature values were consistently discriminative between the two labeled behavioral states across participants. Mainly, &#x3b8;, &#x3b1;, and low and high &#x3b3; in the postcentral gyrus, precentral gyrus, and temporal lobe showed significant classification performance and feature consistency across participants. Subject-specific performance exceeded 70%. Combining neural activity from multiple cortical regions generally does not improve decoding performance, suggesting that information regarding the behavioral state is non-additive as a function of the cortical region.<br><br>SIGNIFICANCE: To the best of our knowledge, this is the first attempt to identify specific spectro-spatial neural correlates that consistently decode naturalistic and active behavioral states. The aim of this work is to serve as an initial starting point for developing brain-computer interfaces that can be generalized in a realistic setting and to further our understanding of the neural correlates of naturalistic behavior in humans.},
}
@article {pmid38873652,
year = {2024},
author = {Kleih, SC and Botrel, L},
title = {Post-stroke aphasia rehabilitation using an adapted visual P300 brain-computer interface training: improvement over time, but specificity remains undetermined.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1400336},
pmid = {38873652},
issn = {1662-5161},
abstract = {INTRODUCTION: This study aimed to evaluate the efficacy of visual P300 brain-computer interface use to support rehabilitation of chronic language production deficits commonly experienced by individuals with a left-sided stroke resulting in post-stroke aphasia.<br><br>METHODS: The study involved twelve participants, but five dropped out. Additionally, data points were missing for three participants in the remaining sample of seven participants. The participants underwent four assessments-a baseline, pre-assessment, post-assessment, and follow-up assessment. Between the pre-and post-assessment, the participants underwent at least 14 sessions of visual spelling using a brain-computer interface. The study aimed to investigate the impact of this intervention on attention, language production, and language comprehension and to determine whether there were any potential effects on quality of life and well-being.<br><br>RESULTS: None of the participants showed a consistent improvement in attention. All participants showed an improvement in spontaneous speech production, and three participants experienced a reduction in aphasia severity. We found an improvement in subjective quality of life and daily functioning. However, we cannot rule out the possibility of unspecific effects causing or at least contributing to these results.<br><br>CONCLUSION: Due to challenges in assessing the patient population, resulting in a small sample size and missing data points, the results of using visual P300 brain-computer interfaces for chronic post-stroke aphasia rehabilitation are preliminary. Thus, we cannot decisively judge the potential of this approach.},
}
@article {pmid38872939,
year = {2024},
author = {Zhuo, F and Zhang, X and Tang, F and Yu, Y and Liu, L},
title = {Riemannian transfer learning based on log-Euclidean metric for EEG classification.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1381572},
pmid = {38872939},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain computer interfaces (BCI), which establish a direct interaction between the brain and the external device bypassing peripheral nerves, is one of the hot research areas. How to effectively convert brain intentions into instructions for controlling external devices in real-time remains a key issue that needs to be addressed in brain computer interfaces. The Riemannian geometry-based methods have achieved competitive results in decoding EEG signals. However, current Riemannian classifiers tend to overlook changes in data distribution, resulting in degenerated classification performance in cross-session and/or cross subject scenarios.<br><br>METHODS: This paper proposes a brain signal decoding method based on Riemannian transfer learning, fully considering the drift of the data distribution. Two Riemannian transfer learning methods based log-Euclidean metric are developed, such that historical data (source domain) can be used to aid the training of the Riemannian decoder for the current task, or data from other subjects can be used to boost the training of the decoder for the target subject.<br><br>RESULTS: The proposed methods were verified on BCI competition III, IIIa, and IV 2a datasets. Compared with the baseline that without transfer learning, the proposed algorithm demonstrates superior classification performance. In contrast to the Riemann transfer learning method based on the affine invariant Riemannian metric, the proposed method obtained comparable classification performance, but is much more computationally efficient.<br><br>DISCUSSION: With the help of proposed transfer learning method, the Riemannian classifier obtained competitive performance to existing methods in the literature. More importantly, the transfer learning process is unsupervised and time-efficient, possessing potential for online learning scenarios.},
}
@article {pmid38872529,
year = {2024},
author = {Takeuchi, N},
title = {A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {21},
number = {4},
pages = {5118-5137},
doi = {10.3934/mbe.2024226},
pmid = {38872529},
issn = {1551-0018},
mesh = {Humans ; *Brain/physiology ; *Neurosciences/methods ; Interpersonal Relations ; Social Interaction ; Transcranial Magnetic Stimulation/methods ; Neuronal Plasticity ; Psychotherapy/methods ; Neuroimaging/methods ; Social Behavior ; Brain-Computer Interfaces ; },
abstract = {Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning-a neuroimaging technique that simultaneously measures the activity of multiple brain regions-has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.},
}
@article {pmid38872209,
year = {2024},
author = {Phang, CR and Su, KH and Cheng, YY and Chen, CH and Ko, LW},
title = {Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {101},
pmid = {38872209},
issn = {1743-0003},
mesh = {Humans ; Male ; *Stroke/physiopathology/complications ; *Stroke Rehabilitation ; Female ; Middle Aged ; *Gait/physiology ; *Electroencephalography ; *Parietal Lobe/physiopathology/physiology ; Evoked Potentials, Motor/physiology ; Frontal Lobe/physiopathology/physiology ; Aged ; Adult ; Motor Cortex/physiopathology/physiology ; Support Vector Machine ; },
abstract = {BACKGROUND: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.<br><br>METHODS: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.<br><br>RESULTS: Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.<br><br>CONCLUSIONS: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.},
}
@article {pmid38872109,
year = {2024},
author = {Biswas, P and Dodakian, L and Wang, PT and Johnson, CA and See, J and Chan, V and Chou, C and Lazouras, W and McKenzie, AL and Reinkensmeyer, DJ and Nguyen, DV and Cramer, SC and Do, AH and Nenadic, Z},
title = {A single-center, assessor-blinded, randomized controlled clinical trial to test the safety and efficacy of a novel brain-computer interface controlled functional electrical stimulation (BCI-FES) intervention for gait rehabilitation in the chronic stroke population.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {200},
pmid = {38872109},
issn = {1471-2377},
support = {UL1 TR001414/TR/NCATS NIH HHS/United States ; 5R01HD095457//National Institutes of Health, United States/ ; },
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; *Brain-Computer Interfaces ; Chronic Disease ; *Electric Stimulation Therapy/methods ; Gait/physiology ; *Gait Disorders, Neurologic/rehabilitation/etiology ; Single-Blind Method ; Stroke/complications/physiopathology ; *Stroke Rehabilitation/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies.<br><br>METHODS: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (>&#x2009;6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally.<br><br>DISCUSSION: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial.<br><br>TRIAL REGISTRATION: The trial was registered as "BCI-FES Therapy for Stroke Rehabilitation" on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067.},
}
@article {pmid38871468,
year = {2024},
author = {, and , },
title = {[Chinese expert consensus on implementation and management of brain-computer interface clinical research in neurological diseases].},
journal = {Zhonghua yi xue za zhi},
volume = {104},
number = {23},
pages = {2105-2112},
doi = {10.3760/cma.j.cn112137-20240326-00690},
pmid = {38871468},
issn = {0376-2491},
support = {2021YFC2501100//National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Nervous System Diseases/therapy ; Consensus ; Delphi Technique ; China ; },
abstract = {The development of brain-computer interface (BCI) technology and its preliminary research results show great clinical application prospects. In particular, the application of BCI technologyis a hot research topic in the field of nervous system diseases at present, but the current BCI technology is still in the stage of experimental exploration, needing systematic and standardized clinical research validation. For this purpose, the consensus is led by the Society of Neurosurgery of Chinese Medical Association and Society of Cerebrovascular Surgery of Chinese Stroke Association. Based on the in-depth discussion of multidisciplinary experts and the vote of the Delphi Method, the guidelines and principles are proposed for pre-clinical qualification review, clinical research implementation and management, and long-term effect tracking and evaluation, so as to standardize research ethics and clinical research procedures and further promote the extensive application and in-depth development of BCI technology in the treatment of nervous system diseases.},
}
@article {pmid38871467,
year = {2024},
author = {Zhao, JZ},
title = {[The role and responsibility of clinical neuroscience in brain-computer interface clinical research].},
journal = {Zhonghua yi xue za zhi},
volume = {104},
number = {23},
pages = {2102-2104},
doi = {10.3760/cma.j.cn112137-20240410-00837},
pmid = {38871467},
issn = {0376-2491},
support = {2021YFC2501100//National Key Research and Development Program of China/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Neurosciences ; Biomedical Research ; Nervous System Diseases/therapy ; Electroencephalography ; Brain/physiology ; },
abstract = {Brain-computer interface (BCI) constructs the direct communication between human brain and external devices, which has been extensively applied in clinical research of the diagnosis and treatment of nervous system diseases, and plays a crucial role in functional evaluation, communication control, reconstruction, rehabilitation training, and neural regulation. This paper summarizes the different forms of BCI technology, the progress of clinical research on BCI for neurological diseases, and the importance of developing appropriate clinical trial standards and ethical norms. Further emphasis is placed on the interpretation of the scope of BCI clinical research in neurological diseases, the ethical principles of BCI clinical research in neurological diseases, and the key points of BCI clinical research implementation and management summarized in the Chinese expert Consensus on the implementation and management of BCI clinical research in Neurological diseases. Finally, the role and responsibility of clinical neuroscience in BCI clinical trials are put forward, emphasizing interdisciplinary cooperation in clinical research to better promote the clinical transformation of BCI technology.},
}
@article {pmid38869771,
year = {2024},
author = {Telli, ML and Litton, JK and Beck, JT and Jones, JM and Andersen, J and Mina, LA and Brig, R and Danso, M and Yuan, Y and Symmans, WF and Hopkins, JF and Albacker, LA and Abbattista, A and Noonan, K and Mata, M and Laird, AD and Blum, JL},
title = {Neoadjuvant talazoparib in patients with germline BRCA1/2 mutation-positive, early-stage triple-negative breast cancer: exploration of tumor BRCA mutational status.},
journal = {Breast cancer (Tokyo, Japan)},
volume = {31},
number = {5},
pages = {886-897},
pmid = {38869771},
issn = {1880-4233},
mesh = {Adult ; Aged ; Female ; Humans ; Middle Aged ; Biomarkers, Tumor/genetics ; *BRCA1 Protein/genetics ; *BRCA2 Protein/genetics ; *Germ-Line Mutation ; Loss of Heterozygosity ; *Neoadjuvant Therapy/methods ; *Phthalazines/therapeutic use/administration &amp; dosage ; Retrospective Studies ; *Triple Negative Breast Neoplasms/genetics/drug therapy/pathology ; },
abstract = {BACKGROUND: Talazoparib monotherapy in patients with germline BRCA-mutated, early-stage triple-negative breast cancer (TNBC) showed activity in the neoadjuvant setting in the phase II NEOTALA study (NCT03499353). These biomarker analyses further assessed the mutational landscape of the patients enrolled in the NEOTALA study.<br><br>METHODS: Baseline tumor tissue from the NEOTALA study was tested retrospectively using FoundationOne[&#xae;]CDx. To further hypothesis-driven correlative analyses, agnostic heat-map visualizations of the FoundationOne[&#xae;]CDx tumor dataset were used to assess overall mutational landscape and identify additional candidate predictive biomarkers of response.<br><br>RESULTS: All patients enrolled (N&#x2009;=&#x2009;61) had TNBC. In the biomarker analysis population, 75.0% (39/52) and 25.0% (13/52) of patients exhibited BRCA1 and BRCA2 mutations, respectively. Strong concordance (97.8%) was observed between tumor BRCA and germline BRCA mutations, and 90.5% (38/42) of patients with tumor BRCA mutations evaluable for somatic-germline-zygosity were predicted to exhibit BRCA loss of heterozygosity (LOH). No patients had non-BRCA germline DNA damage response (DDR) gene variants with known/likely pathogenicity, based on a panel of 14 non-BRCA DDR genes. Ninety-eight percent of patients had TP53 mutations. Genomic LOH, assessed continuously or categorically, was not associated with response.<br><br>CONCLUSION: The results from this exploratory biomarker analysis support the central role of BRCA and TP53 mutations in tumor pathobiology. Furthermore, these data support assessing germline BRCA mutational status for molecular eligibility for talazoparib in patients with TNBC.},
}
@article {pmid38869769,
year = {2024},
author = {Zhu, CH and Yu, JY and Ma, Y and Dong, Y and Wu, ZY},
title = {Progressive Ataxia due to de novo Missense Variants in the CACNA1A Gene.},
journal = {Cerebellum (London, England)},
volume = {23},
number = {5},
pages = {2197-2204},
pmid = {38869769},
issn = {1473-4230},
support = {82230062//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Mutation, Missense ; Male ; Female ; *Calcium Channels/genetics ; Middle Aged ; Ataxia/genetics ; Adult ; },
abstract = {The CACNA1A gene encodes the alpha-1A subunit of P/Q type voltage-gated calcium channel Cav2.1, which is associated with a broad clinical spectrum and variable symptomatology. While few patients with progressive ataxia caused by CACNA1A missense variants have been reported, here we report three unrelated Chinese patients with progressive ataxia due to de novo missense variants in the CACNA1A gene, including a novel pathogenic variant (c.4999C > G) and a previously reported pathogenic variant (c.4037G > A). Our findings and a systematic literature review show the unique phenotype of progressive ataxia caused by missense variants and enlarge the genetic and clinical spectrum of CACNA1A. This suggests that in addition to routine screening for dynamic mutations, screening for CACNA1A variants is important for clinicians facing patients with progressive ataxia.},
}
@article {pmid38869147,
year = {2024},
author = {Zhang, C and Yang, Z and Li, X and Zhao, L and Guo, W and Deng, W and Wang, Q and Hu, X and Li, M and Sham, PC and Xiao, X and Li, T},
title = {Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach.},
journal = {Schizophrenia bulletin},
volume = {50},
number = {5},
pages = {1185-1196},
pmid = {38869147},
issn = {1745-1701},
support = {R37 MH057881/MH/NIMH NIH HHS/United States ; R01 MH075916/MH/NIMH NIH HHS/United States ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; 2020HXBH163//Postdoctoral Foundation of West China Hospital/ ; 2022SCP001//Spring City Plan: the High-level Talent Promotion and Training Project of Kunming/ ; P50 MH084053/MH/NIMH NIH HHS/United States ; HHSN271201300031C/DA/NIDA NIH HHS/United States ; 82001413//National Natural Science Foundation of China Project/ ; P50 MH066392/MH/NIMH NIH HHS/United States ; R01 MH080405/MH/NIMH NIH HHS/United States ; 81920108018//National Natural Science Foundation of China Key Project/ ; R01 MH097276/MH/NIMH NIH HHS/United States ; P01 AG002219/AG/NIA NIH HHS/United States ; R01 MH093725/MH/NIMH NIH HHS/United States ; P50 AG005138/AG/NIA NIH HHS/United States ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence and Key Project for Hangzhou Medical Disciplines/ ; R01 MH085542/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Humans ; *Bipolar Disorder/genetics/metabolism ; Brain/metabolism ; *Genome-Wide Association Study ; Mendelian Randomization Analysis ; *NIMA-Related Kinases/genetics/metabolism ; Proteome ; *Proteomics ; Quantitative Trait Loci ; *Schizophrenia/genetics/metabolism ; },
abstract = {BACKGROUND AND HYPOTHESIS: Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets.<br><br>STUDY DESIGN: To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined.<br><br>STUDY RESULTS: Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons.<br><br>CONCLUSIONS: These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.},
}
@article {pmid38866001,
year = {2024},
author = {Jin, X and Yang, X and Kong, W and Zhu, L and Tang, J and Peng, Y and Ding, Y and Zhao, Q},
title = {TSFAN: tensorized spatial-frequency attention network with domain adaptation for cross-session EEG-based biometric recognition.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad5761},
pmid = {38866001},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Biometric Identification/methods ; Male ; Attention/physiology ; Female ; Neural Networks, Computer ; Adult ; Young Adult ; },
abstract = {Objective.Electroencephalogram (EEG) signals are promising biometrics owning to their invisibility, adapting to the application scenarios with high-security requirements. However, It is challenging to explore EEG identity features without the interference of device and state differences of the subject across sessions. Existing methods treat training sessions as a single domain, affected by the different data distribution among sessions. Although most multi-source unsupervised domain adaptation (MUDA) methods bridge the domain gap between multiple source and target domains individually, relationships among the domain-invariant features of each distribution alignment are neglected.Approach.In this paper, we propose a MUDA method, Tensorized Spatial-Frequency Attention Network (TSFAN), to assist the performance of the target domain for EEG-based biometric recognition. Specifically, significant relationships of domain-invariant features are modeled via a tensorized attention mechanism. It jointly incorporates appropriate common spatial-frequency representations of pairwise source and target but also cross-source domains, without the effect of distribution discrepancy among source domains. Additionally, considering the curse of dimensionality, our TSFAN is approximately represented in Tucker format. Benefiting the low-rank Tucker Network, the TSFAN can scale linearly in the number of domains, providing us the great flexibility to extend TSFAN to the case associated with an arbitrary number of sessions.Main results.Extensive experiments on the representative benchmarks demonstrate the effectiveness of TSFAN in EEG-based biometric recognition, outperforming state-of-the-art approaches, as verified by cross-session validation.Significance.The proposed TSFAN aims to investigate the presence of consistent EEG identity features across sessions. It is achieved by utilizing a novel tensorized attention mechanism that collaborates intra-source transferable information with inter-source interactions, while remaining unaffected by domain shifts in multiple source domains. Furthermore, the electrode selection shows that EEG-based identity features across sessions are distributed across brain regions, and 20 electrodes based on 10-20 standard system are able to extract stable identity information.},
}
@article {pmid38865781,
year = {2024},
author = {Pfeffer, MA and Ling, SSH and Wong, JKW},
title = {Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces.},
journal = {Computers in biology and medicine},
volume = {178},
number = {},
pages = {108705},
doi = {10.1016/j.compbiomed.2024.108705},
pmid = {38865781},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Brain/physiology ; },
abstract = {This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems.},
}
@article {pmid38862476,
year = {2024},
author = {Kosnoff, J and Yu, K and Liu, C and He, B},
title = {Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {4382},
pmid = {38862476},
issn = {2041-1723},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; DGE2140739//National Science Foundation (NSF)/ ; R01NS124564, RF1NS131069, R01NS096761, and NS127849//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; T32 EB029365/EB/NIBIB NIH HHS/United States ; R01AT009263//U.S. Department of Health &amp; Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH)/ ; RF1 NS131069/NS/NINDS NIH HHS/United States ; T32EB029365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; R01 NS127849/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; U18EB029354, T32EB029365//U.S. Department of Health &amp; Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Electroencephalography ; *Attention/physiology ; Adult ; Female ; Young Adult ; Visual Cortex/physiology ; Motion Perception/physiology ; Photic Stimulation/methods ; },
abstract = {A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.},
}
@article {pmid38862406,
year = {2024},
author = {Wang, XQ and Sun, HQ and Si, JY and Lin, ZY and Zhai, XM and Lu, L},
title = {Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces.},
journal = {Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih},
volume = {39},
number = {2},
pages = {131-139},
doi = {10.24920/004377},
pmid = {38862406},
issn = {1001-9294},
support = {2021ZD0201900//Ministry of Science and Technology of the People's Republic of China/ ; 2021ZD0201905//Project 5/ ; },
mesh = {Humans ; Biomedical Research/ethics ; *Brain-Computer Interfaces/ethics ; Ethical Review ; },
abstract = {Brain-computer interface (BCI) technology is rapidly advancing in medical research and application. As an emerging biomedical engineering technology, it has garnered significant attention in the clinical research of brain disease diagnosis and treatment, neurological rehabilitation, and mental health. However, BCI also raises several challenges and ethical concerns in clinical research. In this article, the authors investigate and discuss three aspects of BCI in medicine and healthcare: the state of international ethical governance, multidimensional ethical challenges pertaining to BCI in clinical research, and suggestive concerns for ethical review. Despite the great potential of frontier BCI research and development in the field of medical care, the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI. To ensure "responsible innovation" in BCI research in healthcare and medicine, the creation of an ethical global governance framework and system, along with special guidelines for cutting-edge BCI research in medicine, is suggested.},
}
@article {pmid38861961,
year = {2024},
author = {Hore, A and Bandyopadhyay, S and Chakrabarti, S},
title = {Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad56c8},
pmid = {38861961},
issn = {1741-2552},
mesh = {*Neural Networks, Computer ; *Action Potentials/physiology ; *Models, Neurological ; *Neurons/physiology ; Humans ; Synapses/physiology ; Computer Simulation ; Neural Inhibition/physiology ; Nerve Net/physiology ; },
abstract = {Objective. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory synapses are designed to establish a connection between two hardware neurons, effectively forming a network. The model demonstrates the occurrence of PIRE under strong inhibitory input. Emphasizing the significance of incorporating PIRE in neuromorphic circuits, the study showcases generation of persistent activity within cyclic and recurrent spiking neuronal networks.Approach. The neuronal and synaptic circuits are designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The operating mechanism of the PIRE phenomenon integrated into a hardware neuron is discussed. The proposed circuit encompasses several parameters for effectively controlling multiple electrophysiological features of a neuron.Main results. The neuronal circuit has been tuned to match the response of a biological neuron. The efficiency of this circuit is evaluated by computing the average power dissipation and energy consumption per spike through simulation. The sustained firing of neural spikes is observed till 1.7 s using the two neuronal networks.Significance. Persistent activity has significant implications for various cognitive functions such as working memory, decision-making, and attention. Therefore, hardware implementation of these functions will require our PIRE-integrated model. Energy-efficient neuromorphic systems are useful in many artificial intelligence applications, including human-machine interaction, IoT devices, autonomous systems, and brain-computer interfaces.},
}
@article {pmid38861953,
year = {2024},
author = {Janardhan Reddy, T and Ramasubba Reddy, M},
title = {Narrow band-pass filtered canonical correlation analysis for frequency identification in SSVEP signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {4},
pages = {},
doi = {10.1088/2057-1976/ad567f},
pmid = {38861953},
issn = {2057-1976},
mesh = {*Evoked Potentials, Visual ; Datasets as Topic ; *Image Processing, Computer-Assisted/methods ; Parietal Lobe/diagnostic imaging ; Occipital Lobe/diagnostic imaging ; Reproducibility of Results ; Humans ; Male ; Female ; Young Adult ; Adult ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {Steady-state visual evoked potentials (SSVEP) are generated in the parieto-occipital regions, accompanied by background noise and artifacts. A strong pre-processing method is required to reduce this background noise and artifacts. This study proposed a narrow band-pass filtered canonical correlation analysis (NBPFCCA) to recognize frequency components in SSVEP signals. The proposed method is tested on the publicly available 40 stimulus frequencies dataset recorded from 35 subjects and 4 class in-house dataset acquired from 10 subjects. The performance of the proposed NBPFCCA method is compared with the standard canonical correlation analysis (CCA) and the filter bank CCA (FBCCA). The mean frequency detection accuracy of the standard CCA is 86.21% for the benchmark dataset, and it is improved to 95.58% in the proposed method. Results indicate that the proposed method significantly outperforms the standard canonical correlation analysis with an increase of 9.37% and 17% in frequency recognition accuracy of the benchmark and in-house datasets, respectively.},
}
@article {pmid38857138,
year = {2024},
author = {Leng, J and Li, H and Shi, W and Gao, L and Lv, C and Wang, C and Xu, F and Zhang, Y and Jung, TP},
title = {Time-Frequency-Space EEG Decoding Model Based on Dense Graph Convolutional Network for Stroke.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {9},
pages = {5214-5226},
doi = {10.1109/JBHI.2024.3411646},
pmid = {38857138},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Stroke/physiopathology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Algorithms ; Deep Learning ; Neural Networks, Computer ; Brain/physiopathology ; Male ; Female ; Middle Aged ; Stroke Rehabilitation/methods ; Adult ; },
abstract = {Stroke, a sudden cerebrovascular ailment resulting from brain tissue damage, has prompted the use of motor imagery (MI)-based Brain-Computer Interface (BCI) systems in stroke rehabilitation. However, analyzing EEG signals from stroke patients is challenging because of their low signal-to-noise ratio and high variability. Therefore, we propose a novel approach that combines the modified S-transform (MST) and a dense graph convolutional network (DenseGCN) algorithm to enhance the MI-BCI performance across time, frequency, and space domains. MST is a time-frequency analysis method that efficiently concentrates energy in EEG signals, while DenseGCN is a deep learning model that uses EEG feature maps from each layer as inputs for subsequent layers, facilitating feature reuse and hyper-parameters optimization. Our approach outperforms conventional networks, achieving a peak classification accuracy of 90.22% and an average information transfer rate (ITR) of 68.52 bits per minute. Moreover, we conduct an in-depth analysis of the event-related desynchronization/event-related synchronization (ERD/ERS) phenomenon in the deep-level EEG features of stroke patients. Our experimental results confirm the feasibility and efficacy of the proposed approach for MI-BCI rehabilitation systems.},
}
@article {pmid38856329,
year = {2024},
author = {Ladouceur, F and Al Abed, A and Lehmann, T and Almasri, RM and Firth, J and Lovell, NH},
title = {All optical neural interfaces.},
journal = {Applied optics},
volume = {63},
number = {14},
pages = {D21-D27},
doi = {10.1364/AO.512480},
pmid = {38856329},
issn = {1539-4522},
abstract = {Brain/computer interfaces (BCIs) rely on the concurrent recording of many channels of electrical activity from excitable tissue. Traditionally such neural interfacing has been performed using cumbersome, channel-limited multielectrode arrays. We believe that BCIs can greatly benefit from using an optical approach based on simple yet powerful liquid-crystal based transducer technology. This approach potentially offers a technology platform that can sustain the necessary bandwidth, density of channels, responsivity, and conformability that are required for the long-term viability of such interfaces. In this paper we review the overall architecture of this approach, the challenges it faces, and the solutions that are being developed at UNSW Sydney.},
}
@article {pmid38855665,
year = {2024},
author = {Pang, R and Sang, H and Yi, L and Gao, C and Xu, H and Wei, Y and Zhang, L and Sun, J},
title = {Working memory load recognition with deep learning time series classification.},
journal = {Biomedical optics express},
volume = {15},
number = {5},
pages = {2780-2797},
pmid = {38855665},
issn = {2156-7085},
abstract = {Working memory load (WML) is one of the widely applied signals in the areas of human-machine interaction. The precise evaluation of the WML is crucial for this kind of application. This study aims to propose a deep learning (DL) time series classification (TSC) model for inter-subject WML decoding. We used fNIRS to record the hemodynamic signals of 27 participants during visual working memory tasks. Traditional machine learning and deep time series classification algorithms were respectively used for intra-subject and inter-subject WML decoding from the collected blood oxygen signals. The intra-subject classification accuracy of LDA and SVM were 94.6% and 79.1%. Our proposed TAResnet-BiLSTM model had the highest inter-subject WML decoding accuracy, reaching 92.4%. This study provides a new idea and method for the brain-computer interface application of fNIRS in real-time WML detection.},
}
@article {pmid38855230,
year = {2024},
author = {Okamoto, Y and Matsui, K and Ando, T and Atsuumi, K and Taniguchi, K and Hirai, H and Nishikawa, A},
title = {Pilot study of the relation between various dynamics of avatar experience and perceptual characteristics.},
journal = {PeerJ. Computer science},
volume = {10},
number = {},
pages = {e2042},
pmid = {38855230},
issn = {2376-5992},
abstract = {In recent years, due to the prevalence of virtual reality (VR) and human-computer interaction (HCI) research, along with the expectation that understanding the process of establishing sense of ownership, sense of agency, and limb heaviness (in this study, limb heaviness is replaced with comfort level) will contribute to the development of various medical rehabilitation, various studies have been actively conducted in these fields. Previous studies have indicated that each perceptual characteristics decrease in response to positive delay. However, it is still unclear how each perceptual characteristic changes in response to negative delay. Therefore, the purpose of this study was to deduce how changes occur in the perceptual characteristics when certain settings are manipulated using the avatar developed in this study. This study conducted experiments using an avatar system developed for this research that uses electromyography as the interface. Two separate experiments involved twelve participants: a preliminary experiment and a main experiment. As observed in the previous study, it was confirmed that each perceptual characteristics decreased for positive delay. In addition, the range of the preliminary experiment was insufficient for the purpose of this study, which was to confirm the perceptual characteristics for negative delay, thus confirming the validity of conducting this experiment. Meanwhile, the main experiment showed that the sense of ownership, sense of agency, and comfort level decreased gradually as delay time decreased, (i.e., this event is prior to action with intention, which could not be examined in the previous study). This suggests that control by the brain-machine interface is difficult to use when it is too fast. In addition, the distribution of the most strongly perceived settings in human perceptual characteristics was wider in regions with larger delays, suggesting this may lead to the evaluation of an internal model believed to exist in the human cerebellum. The avatar developed for this study may have the potential to create a new experimental paradigm for perceptual characteristics.},
}
@article {pmid38853549,
year = {2024},
author = {Jiang, Y and Dong, Y and Hu, H},
title = {The N-methyl-d-aspartate receptor hypothesis of ketamine's antidepressant action: evidence and controversies.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {379},
number = {1906},
pages = {20230225},
pmid = {38853549},
issn = {1471-2970},
support = {//STI2030 Major Projects/ ; //National Natural Science Foundation of China/ ; //New Cornerstone Science Foundation/ ; },
mesh = {*Ketamine/pharmacology ; *Receptors, N-Methyl-D-Aspartate/metabolism/antagonists &amp; inhibitors ; *Antidepressive Agents/pharmacology/therapeutic use ; Humans ; Animals ; Depression/drug therapy ; },
abstract = {Substantial clinical evidence has unravelled the superior antidepressant efficacy of ketamine: in comparison to traditional antidepressants targeting the monoamine systems, ketamine, as an N-methyl-d-aspartate receptor (NMDAR) antagonist, acts much faster and more potently. Surrounding the antidepressant mechanisms of ketamine, there is ample evidence supporting an NMDAR-antagonism-based hypothesis. However, alternative arguments also exist, mostly derived from the controversial clinical results of other NMDAR inhibitors. In this article, we first summarize the historical development of the NMDAR-centred hypothesis of rapid antidepressants. We then classify different NMDAR inhibitors based on their mechanisms of inhibition and evaluate preclinical as well as clinical evidence of their antidepressant effects. Finally, we critically analyse controversies and arguments surrounding ketamine's NMDAR-dependent and NMDAR-independent antidepressant action. A better understanding of ketamine's molecular targets and antidepressant mechanisms should shed light on the future development of better treatment for depression. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.},
}
@article {pmid38853074,
year = {2024},
author = {Pilz da Cunha, G and Coupé, VMH and Zonderhuis, BM and Bonjer, HJ and Erdmann, JI and Kazemier, G and Besselink, MG and Swijnenburg, RJ},
title = {Healthcare cost expenditure for robotic versus laparoscopic liver resection: a bottom-up economic evaluation.},
journal = {HPB : the official journal of the International Hepato Pancreato Biliary Association},
volume = {26},
number = {8},
pages = {971-980},
doi = {10.1016/j.hpb.2024.05.017},
pmid = {38853074},
issn = {1477-2574},
mesh = {Humans ; *Laparoscopy/economics ; *Hepatectomy/economics ; *Robotic Surgical Procedures/economics ; Male ; Retrospective Studies ; Female ; Middle Aged ; Aged ; *Health Expenditures ; Hospital Costs ; Cost-Benefit Analysis ; Operative Time ; Health Care Costs ; Treatment Outcome ; Time Factors ; },
abstract = {BACKGROUND: Minimally invasive liver surgery (MILS) is increasingly performed via the robot-assisted approach but may be associated with increased costs. This study is a post-hoc comparison of healthcare cost expenditure for robotic liver resection (RLR) and laparoscopic liver resection (LLR) in a high-volume center.<br><br>METHODS: In-hospital and 30-day postoperative healthcare costs were calculated per patient in a retrospective series (October 2015-December 2022).<br><br>RESULTS: Overall, 298 patients were included (143 RLR and 155 LLR). Benefits of RLR were lower conversion rate (2.8% vs 12.3%, p&#xa0;=&#xa0;0.002), shorter operating time (167&#xa0;min vs 198&#xa0;min, p&#xa0;=&#xa0;0.044), and less blood loss (50&#xa0;mL vs 200&#xa0;mL, p&#xa0;<&#xa0;0.001). Total per-procedure costs of RLR (&#x20ac;10260) and LLR (&#x20ac;9931) were not significantly different (mean difference &#x20ac;329 [95% bootstrapped confidence interval (BCI) &#x20ac;-1179-&#x20ac;2120]). Lower costs with RLR due to shorter surgical and operating room time were offset by higher disposable instrumentation costs resulting in comparable intraoperative costs (&#x20ac;5559 vs &#x20ac;5247, mean difference &#x20ac;312 [95% BCI &#x20ac;-25-&#x20ac;648]). Postoperative costs were similar for RLR (&#x20ac;4701) and LLR (&#x20ac;4684), mean difference &#x20ac;17 [95% BCI &#x20ac;-1357-&#x20ac;1727]. When also considering purchase and maintenance costs, RLR resulted in higher total per-procedure costs.<br><br>DISCUSSION: In a high-volume center, RLR can have similar per-procedure cost expenditure as LLR when disregarding capital investment.},
}
@article {pmid38851750,
year = {2024},
author = {Cao, B and Xu, Q and Shi, Y and Zhao, R and Li, H and Zheng, J and Liu, F and Wan, Y and Wei, B},
title = {Pathology of pain and its implications for therapeutic interventions.},
journal = {Signal transduction and targeted therapy},
volume = {9},
number = {1},
pages = {155},
pmid = {38851750},
issn = {2059-3635},
support = {82273231//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82073192//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81974166, 31872774, 32171002//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Pain/genetics/pathology ; Pain Management ; Animals ; },
abstract = {Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.},
}
@article {pmid38851575,
year = {2024},
author = {McAloon, CI and McAloon, CG and Barrett, D and Tratalos, JA and McGrath, G and Guelbenzu, M and Graham, DA and Kelly, A and O'Keeffe, K and More, SJ},
title = {Estimation of sensitivity and specificity of bulk tank milk PCR and 2 antibody ELISA tests for herd-level diagnosis of Mycoplasma bovis infection using Bayesian latent class analysis.},
journal = {Journal of dairy science},
volume = {107},
number = {10},
pages = {8464-8478},
doi = {10.3168/jds.2023-24590},
pmid = {38851575},
issn = {1525-3198},
mesh = {Animals ; Cattle ; *Mycoplasma bovis ; *Mycoplasma Infections/veterinary/diagnosis ; *Bayes Theorem ; *Enzyme-Linked Immunosorbent Assay/veterinary ; *Latent Class Analysis ; *Milk/microbiology ; *Cattle Diseases/diagnosis ; *Sensitivity and Specificity ; *Polymerase Chain Reaction/veterinary ; Female ; Ireland ; },
abstract = {Mycoplasmosis (due to infection with Mycoplasma bovis) is a serious disease of beef and dairy cattle that can adversely affect health, welfare, and productivity. Mycoplasmosis can lead to a range of often severe, clinical presentations. Mycoplasma bovis infection can present either clinically or subclinically, with the potential for recrudescence of shedding in association with stressful periods. Infection can be maintained within herds because of intermittent shedding. Mycoplasma bovis is recognized as poorly responsive to treatment, which presents a major challenge for control in infected herds. Given this, particular focus is needed on biosecurity measures to prevent introduction into uninfected herds in the first place. A robust and reliable laboratory test for surveillance is important for both herd-level prevention and control. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of 3 diagnostic tests (1 PCR and 2 ELISA tests) on bulk tank milk (BTM), for the herd-level detection of M. bovis using Bayesian latent class analysis (BLCA). In autumn 2018, BTM samples from 11,807 herds, covering the majority of the main dairy regions in Ireland had been submitted to the Department of Agriculture testing laboratory for routine surveillance and were made available for study. A stratified random sample approach was used to select a cohort of herds for testing from this larger sample set. A final study population of 728 herds had BTM samples analyzed using a Bio-X ELISA (ELISA 1), an IDvet ELISA (ELISA 2) and a PCR test. A BLCA was conducted to estimate the Se and Sp of the 3 diagnostic tests applied to BTM for the detection of herd-level infection. An overall latent class analysis was conducted on all herds within a single population (a 3-test, 1-population model). The herds were also split into 2 populations based on herd size (small herds had <82 cattle; a 3-test, 2-population model) and separately into 3 regions in Ireland (Leinster, Munster, and Connacht/Ulster; a 3-test, 3-population model). The latent variable of interest was the herd-level M. bovis infection status. In total, 363/728 (50%) were large herds, 7 (1.0%) were positive on PCR, 88 (12%) positive on ELISA 1, and 406 (56%) positive on ELISA 2. Based on the 2-population model, the Se (95% Bayesian credible interval [BCI] was 0.03 (upper and lower limits: 0.02, 0.05), 0.22 (0.18, 0.27), and 0.94 (0.88, 0.98) for PCR, ELISA 1, and ELISA 2, respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.97 (0.95, 0.99), and 0.92 (0.86, 0.97) for PCR, ELISA 1, and ELISA 2, respectively. The herd-level true prevalence was estimated at 0.43 (BCI 0.35, 0.5) for smaller herds. The true prevalence was estimated at 0.62 (BCI 0.55, 0.69) for larger herds. The true prevalence was estimated at 0.56 (BCI 0.49, 0.463) in the 1-population model. For the 3-population model, the Se (95% BCI) was 0.03 (0.02, 0.05), 0.24 (0.18, 0.29), and 0.95 (0.9, 0.98) for PCR, ELISA 1, and ELISA 2 respectively. The Sp (95% BCI) was 0.99 (0.99, 1.0), 0.98 (0.96, 0.99), and 0.88 (0.79, 0.95) for PCR, ELISA 1 and ELISA 2, respectively. The herd-level true prevalence (95% BCI) was estimated at 0.65 (0.56, 0.73), 0.38 (0.28, 0.46), and 0.53 (0.4, 0.65) for populations 1, 2, and 3 respectively. Across all 3 models, the range in true prevalence was 38% to 65% of Irish dairy herds infected with M. bovis. The operating characteristics vary substantially between tests. The IDvet ELISA had a relatively high Se (the highest Se of the 3 tests studied) but it was estimated at 0.95 at its highest in 3-test, 3-population model. This test may be an appropriate test for herd-level screening or prevalence estimation within the context of the endemically infected Irish dairy cattle population. Further work is required to optimize this test and its interpretation when applied at herd-level to offset concerns related to the lower than optimal test Sp.},
}
@article {pmid38850348,
year = {2025},
author = {Qian, SX and Bao, YF and Li, XY and Dong, Y and Zhang, XL and Wu, ZY},
title = {Multi-omics Analysis Reveals Key Gut Microbiota and Metabolites Closely Associated with Huntington's Disease.},
journal = {Molecular neurobiology},
volume = {62},
number = {1},
pages = {351-365},
pmid = {38850348},
issn = {1559-1182},
support = {2022KY1249//the Medical and Health Science and Technology Plan of Zhejiang Provincial Health Commission/ ; 2019C03039//Key Research and Development project of Zhejiang Province/ ; },
mesh = {Humans ; *Huntington Disease/microbiology/metabolism ; *Gastrointestinal Microbiome ; Male ; Female ; Middle Aged ; *Metabolomics ; *Feces/microbiology ; Adult ; Metagenomics/methods ; Metabolome ; Aged ; Multiomics ; },
abstract = {Dysbiosis of the gut microbiota is closely associated with neurodegenerative diseases, including Huntington's disease (HD). Gut microbiome-derived metabolites are key factors in host-microbiome interactions. This study aimed to investigate the crucial gut microbiome and metabolites in HD and their correlations. Fecal and serum samples from 11 to 26 patients with HD, respectively, and 16 and 23 healthy controls, respectively, were collected. The fecal samples were used for shotgun metagenomics while the serum samples for metabolomics analysis. Integrated analysis of the metagenomics and metabolomics data was also conducted. Firmicutes, Bacteroidota, Proteobacteria, Uroviricota, Actinobacteria, and Verrucomicrobia were the dominant phyla. At the genus level, the presence of Bacteroides, Faecalibacterium, Parabacteroides, Alistipes, Dialister, and Christensenella was higher in HD patients, while the abundance of Lachnospira, Roseburia, Clostridium, Ruminococcus, Blautia, Butyricicoccus, Agathobaculum, Phocaeicola, Coprococcus, and Fusicatenibacter decreased. A total of 244 differential metabolites were identified and found to be enriched in the glycerophospholipid, nucleotide, biotin, galactose, and alpha-linolenic acid metabolic pathways. The AUC value from the integrated analysis (1) was higher than that from the analysis of the gut microbiota (0.8632). No significant differences were found in the ACE, Simpson, Shannon, Sobs, and Chao indexes between HD patients and controls. Our study determined crucial functional gut microbiota and potential biomarkers associated with HD pathogenesis, providing new insights into the role of the gut microbiota-brain axis in HD occurrence and development.},
}
@article {pmid38849033,
year = {2024},
author = {El Khoury, MA and Chartier-Kastler, E and Parra, J and Vaessen, C and Roupret, M and Seisen, T and Lenfant, L},
title = {Continent cutaneous diversion: Unveiling the interplay of neuro-urology and oncological challenges.},
journal = {The French journal of urology},
volume = {34},
number = {6},
pages = {102665},
doi = {10.1016/j.fjurol.2024.102665},
pmid = {38849033},
issn = {2950-3930},
mesh = {Humans ; *Urinary Diversion/methods ; *Urinary Bladder Neoplasms/surgery ; Male ; Female ; *Cystectomy/adverse effects/methods ; Retrospective Studies ; *Quality of Life ; Aged ; Middle Aged ; Urinary Reservoirs, Continent ; Postoperative Complications/epidemiology/etiology ; Treatment Outcome ; },
abstract = {OBJECTIVES: The objective of our study is to demonstrate the practical application of continent cutaneous urinary diversion (CCUD) in oncological patients, with a focus on various aspects of the procedure: surgical challenges, functional outcomes, and quality of life.<br><br>MATERIALS AND METHODS: We studied the perioperative and follow-up data of patients who underwent cystectomy for cancer associated with CCUD (Mitrofanoff, Monti or Casale). We retrospectively analyzed complications within 30days and beyond 30days post-surgery. We evaluated oncological outcomes. Patients' quality of life was assessed using the Bladder Cancer Index (BCI) questionnaire. Results are given on an intention-to-treat basis.<br><br>RESULTS: A total of 24 patients were included in the study (July 2001 and May 2022), with a median follow-up of 62.5months. We report three deaths due to neoplasic recurrence. Forty-six percent had an early postoperative complication, two of whom required revision surgery. Overall, the medium-term complication rate was 70% and the reoperation rate was 62%. There were 8 stomal cutaneous stenoses (33%) and 3 uretero-ileal stenoses (12.5%). Overall satisfaction was rated at 9.2/10 on average, and body image was unaltered or slightly altered in 62.5% of patients. Of the patients who responded to the BCI, 75% had complete continence.<br><br>DISCUSSION: The experience gained with continent stomas in neuro-urology has allowed, in carefully selected cases, to offer patients an alternative that can improve their quality of life in a context already burdened by the shadow of cancer. CCUD can be proposed as an alternative to Bricker diversion in cases of urethral invasion or a high risk of neobladder incontinence, in selected patients.},
}
@article {pmid38848710,
year = {2024},
author = {Zhao, X and Xu, R and Xu, R and Wang, X and Cichocki, A and Jin, J},
title = {An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.},
journal = {Journal of neural engineering},
volume = {21},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ad558a},
pmid = {38848710},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Evoked Potentials/physiology ; Male ; Adult ; Female ; Young Adult ; Time Factors ; },
abstract = {Objective.Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs.Approach.This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signedR-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling.Main results.We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity.Significance.These results indicate that AWDSNet has great potential for applications in ERP decoding.},
}
@article {pmid38846893,
year = {2024},
author = {Kantawala, B and Emir Hamitoglu, A and Nohra, L and Abdullahi Yusuf, H and Jonathan Isaac, K and Shariff, S and Nazir, A and Soju, K and Yenkoyan, K and Wojtara, M and Uwishema, O},
title = {Microengineered neuronal networks: enhancing brain-machine interfaces.},
journal = {Annals of medicine and surgery (2012)},
volume = {86},
number = {6},
pages = {3535-3542},
pmid = {38846893},
issn = {2049-0801},
abstract = {The brain-machine interface (BMI), a crucial conduit between the human brain and computers, holds transformative potential for various applications in neuroscience. This manuscript explores the role of micro-engineered neuronal networks (MNNs) in advancing BMI technologies and their therapeutic applications. As the interdisciplinary collaboration intensifies, the need for innovative and user-friendly BMI technologies becomes paramount. A comprehensive literature review sourced from reputable databases (PubMed Central, Medline, EBSCOhost, and Google Scholar) aided in the foundation of the manuscript, emphasizing the pivotal role of MNNs. This study aims to synthesize and analyze the diverse facets of MNNs in the context of BMI technologies, contributing insights into neural processes, technological advancements, therapeutic potentials, and ethical considerations surrounding BMIs. MNNs, exemplified by dual-mode neural microelectrodes, offer a controlled platform for understanding complex neural processes. Through case studies, we showcase the pivotal role of MNNs in BMI innovation, addressing challenges, and paving the way for therapeutic applications. The integration of MNNs with BMI technologies marks a revolutionary stride in neuroscience, refining brain-computer interactions and offering therapeutic avenues for neurological disorders. Challenges, ethical considerations, and future trends in BMI research necessitate a balanced approach, leveraging interdisciplinary collaboration to ensure responsible and ethical advancements. Embracing the potential of MNNs is paramount for the betterment of individuals with neurological conditions and the broader community.},
}
@article {pmid38846633,
year = {2023},
author = {Saito, Y and Kamagata, K and Akashi, T and Wada, A and Shimoji, K and Hori, M and Kuwabara, M and Kanai, R and Aoki, S},
title = {Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications.},
journal = {Juntendo Iji zasshi = Juntendo medical journal},
volume = {69},
number = {4},
pages = {319-326},
pmid = {38846633},
issn = {2188-2126},
abstract = {Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or other external devices. They can extend a person's degree of freedom by either strengthening or substituting the human peripheral working capacity. Moreover, their potential clinical applications in medical fields include rehabilitation, affective computing, communication, and control. Over the last decade, noninvasive BCI systems such as electroencephalogram (EEG) have progressed from simple statistical models to deep learning models, with performance improvement over time and enhanced computational power. However, numerous challenges pertaining to the clinical use of BCI systems remain, e.g., the lack of sufficient data to learn more possible features for robust and reliable classification. However, compared with fields such as computer vision and speech recognition, the training samples in the medical BCI field are limited as they target patients who face difficulty generating EEG data compared with healthy control. Because deep learning models incorporate several parameters, they require considerably more data than other conventional methods. Thus, deep learning models have not been thoroughly leveraged in medical BCI. This study summarizes the state-of-the-art progress of the BCI system over the last decade, highlighting critical challenges and solutions.},
}
@article {pmid38844171,
year = {2024},
author = {Guo, X and Chen, Y and Huang, H and Liu, Y and Kong, L and Chen, L and Lyu, H and Gao, T and Lai, J and Zhang, D and Hu, S},
title = {Serum signature of antibodies to Toxoplasma gondii, rubella virus, and cytomegalovirus in females with bipolar disorder: A cross-sectional study.},
journal = {Journal of affective disorders},
volume = {361},
number = {},
pages = {82-90},
doi = {10.1016/j.jad.2024.06.014},
pmid = {38844171},
issn = {1573-2517},
mesh = {Humans ; *Bipolar Disorder/immunology/blood ; Female ; *Toxoplasma/immunology ; Adult ; *Rubella virus/immunology ; *Cytomegalovirus/immunology ; Cross-Sectional Studies ; *Antibodies, Viral/blood ; Young Adult ; *Immunoglobulin G/blood ; *Antibodies, Protozoan/blood ; Adolescent ; Middle Aged ; *Immunoglobulin M/blood ; Child ; Toxoplasmosis/immunology/blood ; Rubella/immunology ; Cytomegalovirus Infections/immunology ; },
abstract = {BACKGROUND AND AIM: Immunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association.<br><br>METHODS: Antibody seropositivity of IgM and IgG to T gondii, rubella virus, and CMV of females with BD and controls was extracted based on medical records from January 2018 to January 2023. Family history, type of BD, onset age, and psychotic symptom history were also collected.<br><br>RESULTS: 585 individuals with BD and 800 healthy controls were involved. Individuals with BD revealed a lower positive rate of T gondii IgG in the 10-20 aged group (OR&#xa0;=&#xa0;0.10), and a higher positive rate of rubella IgG in the 10-20 (OR&#xa0;=&#xa0;5.44) and 20-30 aged group (OR&#xa0;=&#xa0;3.15). BD with family history preferred a higher positive rate of T gondii IgG (OR&#xa0;=&#xa0;24.00). Type-I BD owned a decreased positive rate of rubella IgG (OR&#xa0;=&#xa0;0.37) and an elevated positive rate of CMV IgG (OR&#xa0;=&#xa0;2.12) compared to type-II BD, while BD with early onset showed contrast results compared to BD without early onset (Rubella IgG, OR&#xa0;=&#xa0;2.54; CMV IgG, OR&#xa0;=&#xa0;0.26). BD with psychotic symptom history displayed a lower positive rate of rubella IgG (OR&#xa0;=&#xa0;0.50).<br><br>LIMITATIONS: Absence of male evidence and control of socioeconomic status and environmental exposure.<br><br>CONCLUSIONS: Differential antibody seropositive rates of T gondii, rubella, and cytomegalovirus in BD were observed.},
}
@article {pmid38843055,
year = {2024},
author = {Kilmarx, J and Tashev, I and Millan, JDR and Sulzer, J and Lewis-Peacock, J},
title = {Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2209-2219},
pmid = {38843055},
issn = {1558-0210},
support = {R01 EY028746/EY/NEI NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Electroencephalography/methods ; Male ; Female ; *Feasibility Studies ; *Visual Perception/physiology ; Adult ; Young Adult ; Memory, Short-Term/physiology ; Photic Stimulation ; Algorithms ; Cues ; },
abstract = {Visual imagery, or the mental simulation of visual information from memory, could serve as an effective control paradigm for a brain-computer interface (BCI) due to its ability to directly convey the user's intention with many natural ways of envisioning an intended action. However, multiple initial investigations into using visual imagery as a BCI control strategies have been unable to fully evaluate the capabilities of true spontaneous visual mental imagery. One major limitation in these prior works is that the target image is typically displayed immediately preceding the imagery period. This paradigm does not capture spontaneous mental imagery as would be necessary in an actual BCI application but something more akin to short-term retention in visual working memory. Results from the present study show that short-term visual imagery following the presentation of a specific target image provides a stronger, more easily classifiable neural signature in EEG than spontaneous visual imagery from long-term memory following an auditory cue for the image. We also show that short-term visual imagery and visual perception share commonalities in the most predictive electrodes and spectral features. However, visual imagery received greater influence from frontal electrodes whereas perception was mostly confined to occipital electrodes. This suggests that visual perception is primarily driven by sensory information whereas visual imagery has greater contributions from areas associated with memory and attention. This work provides the first direct comparison of short-term and long-term visual imagery tasks and provides greater insight into the feasibility of using visual imagery as a BCI control strategy.},
}
@article {pmid38842111,
year = {2024},
author = {Luo, Y and Mu, W and Wang, L and Wang, J and Wang, P and Gan, Z and Zhang, L and Kang, X},
title = {An EEG channel selection method for motor imagery based on Fisher score and local optimization.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad504a},
pmid = {38842111},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Movement/physiology ; },
abstract = {Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.},
}
@article {pmid38841120,
year = {2024},
author = {Liu, T and Wu, Y and Ye, A and Cao, L and Cao, Y},
title = {Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1400077},
pmid = {38841120},
issn = {1662-5161},
abstract = {BACKGROUND: Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems.<br><br>METHODS: In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA.<br><br>RESULTS: The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA.<br><br>CONCLUSION: The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.},
}
@article {pmid38837930,
year = {2024},
author = {Yu, S and Mao, B and Zhou, Y and Liu, Y and Yi, C and Li, F and Yao, D and Xu, P and San Liang, X and Zhang, T},
title = {Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2187-2197},
doi = {10.1109/TNSRE.2024.3409872},
pmid = {38837930},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Bayes Theorem ; Male ; *Imagination/physiology ; *Electroencephalography/methods ; Young Adult ; Adult ; Female ; *Algorithms ; *Nerve Net/physiology ; Hand/physiology ; Cerebral Cortex/physiology ; Functional Laterality/physiology ; Movement/physiology ; },
abstract = {Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.},
}
@article {pmid38837089,
year = {2024},
author = {Khondakar, MFK and Sarowar, MH and Chowdhury, MH and Majumder, S and Hossain, MA and Dewan, MAA and Hossain, QD},
title = {A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.},
journal = {Brain informatics},
volume = {11},
number = {1},
pages = {17},
pmid = {38837089},
issn = {2198-4018},
abstract = {Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.},
}
@article {pmid38837017,
year = {2024},
author = {Bhavsar, P and Shah, P and Sinha, S and Kumar, D},
title = {Musical Neurofeedback Advancements, Feedback Modalities, and Applications: A Systematic Review.},
journal = {Applied psychophysiology and biofeedback},
volume = {49},
number = {3},
pages = {347-363},
pmid = {38837017},
issn = {1573-3270},
mesh = {Humans ; Auditory Perception/physiology ; Electroencephalography ; *Music Therapy/methods ; *Neurofeedback/methods ; },
abstract = {The field of EEG-Neurofeedback (EEG-NF) training has showcased significant promise in treating various mental disorders, while also emerging as a cognitive enhancer across diverse applications. The core principle of EEG-NF involves consciously guiding the brain in desired directions, necessitating active engagement in neurofeedback (NF) tasks over an extended period. Music listening tasks have proven to be effective stimuli for such training, influencing emotions, mood, and brainwave patterns. This has spurred the development of musical NF systems and training protocols. Despite these advancements, there exists a gap in systematic literature that comprehensively explores and discusses the various modalities of feedback mechanisms, its benefits, and the emerging applications. Addressing this gap, our review article presents a thorough literature survey encompassing studies on musical NF conducted over the past decade. This review highlights the several benefits and applications ranging from neurorehabilitation to therapeutic interventions, stress management, diagnostics of neurological disorders, and sports performance enhancement. While acknowledged for advantages and popularity of musical NF, there is an opportunity for growth in the literature in terms of the need for systematic randomized controlled trials to compare its effectiveness with other modalities across different tasks. Addressing this gap will involve developing standardized methodologies for studying protocols and optimizing parameters, presenting an exciting prospect for advancing the field.},
}
@article {pmid38836741,
year = {2024},
author = {Zhang, X and Zhang, S and Wang, R and Wang, Y and Shen, Y and Huang, H},
title = {Impact of Detrusor Muscle Activity on Short-term Prognosis Following 1470 nm Semiconductor Laser Surgery in Elderly Patients with Benign Prostatic Hyperplasia.},
journal = {Alternative therapies in health and medicine},
volume = {},
number = {},
pages = {},
pmid = {38836741},
issn = {1078-6791},
abstract = {OBJECTIVE: To investigate the influence of preoperative detrusor muscle activity on the short-term prognosis of elderly patients diagnosed with benign prostatic hyperplasia (BPH) undergoing 1470 nm semiconductor laser surgery.<br><br>METHODS: A retrospective study was conducted on clinical data from 165 elderly BPH patients who underwent 1470 nm semiconductor laser surgery between May 2019 and April 2023. Patients were stratified based on preoperative urodynamic study findings, specifically their bladder contractility index (BCI). Patients with a BCI &#x2264;100 constituted the detrusor underactivity (DU) group (n=64), while those with a BCI >100 formed the non-DU group (n=101). Surgical parameters, including duration, intraoperative blood loss, postoperative hospital stay, bladder irrigation, and catheterization duration, were compared. Additionally, changes in International Prostate Symptom Score (IPSS), Quality of Life (QOL) score, residual urine volume, and peak urinary flow rate (Qmax) were assessed before and three months after surgery in both groups.<br><br>RESULTS: There were no statistically significant differences observed between the DU and non-DU groups concerning surgical duration, intraoperative blood loss, postoperative hospitalization duration, bladder irrigation duration, and postoperative catheterization duration (P > .05). Similarly, no significant disparities were noted in the IPSS and QOL scores preoperatively and at the three-month follow-up in both groups (P > .05). Both cohorts exhibited no significant differences in residual urine volume before surgery and at the three-month mark postoperatively (P > .05). However, the postoperative Qmax was significantly reduced in the DU group compared to the non-DU group (P < .05).<br><br>CONCLUSIONS: Detrusor muscle activity does not exert a significant impact on clinical symptom improvement and quality of life in elderly BPH patients treated with 1470 nm semiconductor laser surgery. However, patients with DU exhibited inferior postoperative recovery in Qmax, underscoring the importance of preoperative urodynamic studies for early intervention and enhanced surgical outcomes in this patient population.},
}
@article {pmid38835164,
year = {2024},
author = {Gangadharan, SK and Ramakrishnan, S and Paek, A and Ravindran, A and Prasad, VA and Vidal, JLC},
title = {Characterization of Event Related Desynchronization in Chronic Stroke Using Motor Imagery Based Brain Computer Interface for Upper Limb Rehabilitation.},
journal = {Annals of Indian Academy of Neurology},
volume = {27},
number = {3},
pages = {297-306},
pmid = {38835164},
issn = {0972-2327},
abstract = {OBJECTIVE: Motor imagery-based brain-computer interface (MI-BCI) is a promising novel mode of stroke rehabilitation. The current study aims to investigate the feasibility of MI-BCI in upper limb rehabilitation of chronic stroke survivors and also to study the early event-related desynchronization after MI-BCI intervention.<br><br>METHODS: Changes in the characteristics of sensorimotor rhythm modulations in response to a short brain-computer interface (BCI) intervention for upper limb rehabilitation of stroke-disabled hand and normal hand were examined. The participants were trained to modulate their brain rhythms through motor imagery or execution during calibration, and they played a virtual marble game during the feedback session, where the movement of the marble was controlled by their sensorimotor rhythm.<br><br>RESULTS: Ipsilesional and contralesional activities were observed in the brain during the upper limb rehabilitation using BCI intervention. All the participants were able to successfully control the position of the virtual marble using their sensorimotor rhythm.<br><br>CONCLUSIONS: The preliminary results support the feasibility of BCI in upper limb rehabilitation and unveil the capability of MI-BCI as a promising medical intervention. This study provides a strong platform for clinicians to build upon new strategies for stroke rehabilitation by integrating MI-BCI with various therapeutic options to induce neural plasticity and recovery.},
}
@article {pmid38834300,
year = {2024},
author = {Harlow, TJ and Marquez, SM and Bressler, S and Read, HL},
title = {Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings.},
journal = {eNeuro},
volume = {11},
number = {6},
pages = {},
pmid = {38834300},
issn = {2373-2822},
mesh = {Humans ; *Acoustic Stimulation/methods ; *Evoked Potentials, Auditory/physiology ; Male ; Female ; *Electroencephalography/methods ; *Alpha Rhythm/physiology ; Adult ; Young Adult ; Brain/physiology ; Auditory Perception/physiology ; Algorithms ; Feasibility Studies ; },
abstract = {Following repetitive visual stimulation, post hoc phase analysis finds that visually evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. This approach has not successfully revealed an alpha phase dependence for auditory evoked or induced responses. Here, we test the feasibility of tracking alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound-evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound-evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to the estimated poststimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced post-stimulus alpha level and frequency modulations. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates the feasibility of using individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.},
}
@article {pmid38834062,
year = {2024},
author = {Roebben, A and Heintz, N and Geirnaert, S and Francart, T and Bertrand, A},
title = {'Are you even listening?' - EEG-based decoding of absolute auditory attention to natural speech.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad5403},
pmid = {38834062},
issn = {1741-2552},
mesh = {Humans ; *Attention/physiology ; *Electroencephalography/methods ; Female ; Male ; *Speech Perception/physiology ; Adult ; Young Adult ; Acoustic Stimulation/methods ; Auditory Perception/physiology ; },
abstract = {Objective.In this study, we use electroencephalography (EEG) recordings to determine whether a subject is actively listening to a presented speech stimulus. More precisely, we aim to discriminate between an active listening condition, and a distractor condition where subjects focus on an unrelated distractor task while being exposed to a speech stimulus. We refer to this task as absolute auditory attention decoding.Approach.We re-use an existing EEG dataset where the subjects watch a silent movie as a distractor condition, and introduce a new dataset with two distractor conditions (silently reading a text and performing arithmetic exercises). We focus on two EEG features, namely neural envelope tracking (NET) and spectral entropy (SE). Additionally, we investigate whether the detection of such an active listening condition can be combined with a selective auditory attention decoding (sAAD) task, where the goal is to decide to which of multiple competing speakers the subject is attending. The latter is a key task in so-called neuro-steered hearing devices that aim to suppress unattended audio, while preserving the attended speaker.Main results.Contrary to a previous hypothesis of higher SE being related with actively listening rather than passively listening (without any distractors), we find significantly lower SE in the active listening condition compared to the distractor conditions. Nevertheless, the NET is consistently significantly higher when actively listening. Similarly, we show that the accuracy of a sAAD task improves when evaluating the accuracy only on the highest NET segments. However, the reverse is observed when evaluating the accuracy only on the lowest SE segments.Significance.We conclude that the NET is more reliable for decoding absolute auditory attention as it is consistently higher when actively listening, whereas the relation of the SE between actively and passively listening seems to depend on the nature of the distractor.},
}
@article {pmid38834058,
year = {2024},
author = {Fang, H and Berman, SA and Wang, Y and Yang, Y},
title = {Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad5406},
pmid = {38834058},
issn = {1741-2552},
mesh = {*Deep Brain Stimulation/methods ; Humans ; *Parkinson Disease/therapy/physiopathology ; *Computer Simulation ; Basal Ganglia/physiopathology/physiology ; Beta Rhythm/physiology ; Models, Neurological ; Cerebral Cortex/physiopathology/physiology ; Thalamus/physiology/physiopathology ; Nonlinear Dynamics ; },
abstract = {Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.},
}
@article {pmid38834056,
year = {2024},
author = {Yang, K and Wang, J and Yang, L and Bian, L and Luo, Z and Yang, C},
title = {A diagonal masking self-attention-based multi-scale network for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad5405},
pmid = {38834056},
issn = {1741-2552},
mesh = {*Electroencephalography/methods/classification ; *Imagination/physiology ; *Brain-Computer Interfaces ; Humans ; Neural Networks, Computer ; Movement/physiology ; },
abstract = {Objective. Electroencephalography (EEG)-based motor imagery (MI) is a promising paradigm for brain-computer interface (BCI), but the non-stationarity and low signal-to-noise ratio of EEG signals make it a challenging task.Approach. To achieve high-precision MI classification, we propose a Diagonal Masking Self-Attention-based Multi-Scale Network (DMSA-MSNet) to fully develop, extract, and emphasize features from different scales. First, for local features, a multi-scale temporal-spatial block is proposed to extract features from different receptive fields. Second, an adaptive branch fusion block is specifically designed to bridge the semantic gap between these coded features from different scales. Finally, in order to analyze global information over long ranges, a diagonal masking self-attention block is introduced, which highlights the most valuable features in the data.Main results. The proposed DMSA-MSNet outperforms state-of-the-art models on the BCI Competition IV 2a and the BCI Competition IV 2b datasets.Significance. Our study achieves rich information extraction from EEG signals and provides an effective solution for MI classification.},
}
@article {pmid38830946,
year = {2024},
author = {Dubiel, M and Barghouti, Y and Kudryavtseva, K and Leiva, LA},
title = {On-device query intent prediction with lightweight LLMs to support ubiquitous conversations.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {12731},
pmid = {38830946},
issn = {2045-2322},
support = {101071147 (SYMBIOTIK)//European Innovation Council Pathfinder program/ ; CHIST-ERA-20-BCI-001//Horizon 2020 FET program/ ; },
abstract = {Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services. To address these problems, we leverage the potential of transfer learning and study how to best fine-tune lightweight pre-trained LLMs to predict the intent of user queries. Importantly, our LLMs allow for on-device deployment, making them suitable for personalised, ubiquitous, and privacy-preserving scenarios. Our experiments suggest that RoBERTa and XLNet offer the best trade-off considering these constraints. We also show that, after fine-tuning, these models perform on par with ChatGPT. We also discuss the implications of this research for relevant stakeholders, including researchers and practitioners. Taken together, this paper provides insights into LLM suitability for on-device CAs and highlights the middle ground between LLM performance and memory footprint while also considering privacy implications.},
}
@article {pmid38829759,
year = {2024},
author = {Xing, L and Casson, AJ},
title = {Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {11},
pages = {3111-3122},
doi = {10.1109/TBME.2024.3408331},
pmid = {38829759},
issn = {1558-2531},
mesh = {*Smartphone ; *Electroencephalography/methods/instrumentation ; Humans ; *Signal Processing, Computer-Assisted ; *Artifacts ; *Deep Learning ; *Brain-Computer Interfaces ; Algorithms ; Software ; Neural Networks, Computer ; },
abstract = {OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications.<br><br>METHODS: We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data.<br><br>RESULTS: DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm.<br><br>CONCLUSION: The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration.<br><br>SIGNIFICANCE: This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.},
}
@article {pmid38829754,
year = {2024},
author = {Zhou, W and Wu, L and Gao, Y and Chen, X},
title = {A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2114-2123},
doi = {10.1109/TNSRE.2024.3408273},
pmid = {38829754},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Algorithms ; *Reinforcement, Psychology ; *Bayes Theorem ; *Neural Networks, Computer ; *Electroencephalography/methods ; Discriminant Analysis ; Male ; Adult ; Young Adult ; Female ; Machine Learning ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.},
}
@article {pmid38829709,
year = {2024},
author = {Jin, J and Zheng, Q and Liu, H and Feng, K and Bai, Y and Ni, G},
title = {Musical experience enhances time discrimination: Evidence from cortical responses.},
journal = {Annals of the New York Academy of Sciences},
volume = {1536},
number = {1},
pages = {167-176},
doi = {10.1111/nyas.15153},
pmid = {38829709},
issn = {1749-6632},
support = {//the National Natural Science Foundation of China/ ; 2023YFF1203500//the Key Technologies Research and Development Program of China/ ; },
mesh = {Humans ; *Music/psychology ; Male ; *Auditory Perception/physiology ; Female ; *Electroencephalography ; Adult ; Young Adult ; Time Perception/physiology ; Reaction Time/physiology ; Acoustic Stimulation/methods ; Discrimination, Psychological/physiology ; Evoked Potentials, Auditory/physiology ; Brain/physiology ; },
abstract = {Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music-experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music-experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.},
}
@article {pmid38826644,
year = {2024},
author = {Kaya, E and Saritas, I},
title = {Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {3},
pages = {987-1003},
pmid = {38826644},
issn = {1871-4080},
abstract = {The concept of the brain-computer interface (BCI) has become one of the popular research topics of recent times because it allows people to express their thoughts and control different applications and devices without actual movement. The communication between the brain and the computer or a machine is generally provided through Electroencephalogram (EEG) signals because they are cost-effective and easy to implement in normal life, not just in healthcare facilities. On the other hand, they are hard to process efficiently due to their nonlinearity and noisy nature. Thus, the field of BCI and EEG needs constant work and improvement. This paper focuses on generalizing the most efficient EEG channels and the most significant features of motor imagery (MI) signals by analyzing the recordings of one participant obtained over 20 different days. Because the classification performance usually decreases with an increasing number of class labels, we have realized the study by analyzing the signals through a new paradigm consisting of multi-class directional labels: right, left, forward, and backward. Afterward, the results are tested on EEG data obtained from 5 participants to see if the results are consistent with each other. The average accuracy of binary and multi-class classification using the Ensemble Subspace Discriminant classifier was found as 87.39 and 61.44%, respectively, with the most efficient 3-channel combination for daily BCI evaluation of one participant. On the other hand, the average accuracy of binary and multi-class classification was found as 71.84 and 50.42%, respectively, for 5 participants, with the most efficient channel combination of 4, where the first three are the same as the daily performance of one participant. During signal processing, the outliers of the signals were discarded by considering the channels separately. An algorithm was developed to dismiss the inconsistent samples within the classes. A novel adaptive filtering approach, correlation-based adaptive variational mode decomposition (CBAVMD), was proposed. The feature selection was realized based on the standard deviation values of the features between the classes. The paradigm based on the direction movements was found to be most effective, especially for binary classification of right and left directions. The generalization of effective channels and features was found to be generally successful.},
}
@article {pmid38826642,
year = {2024},
author = {Liu, D and Cui, J and Pan, Z and Zhang, H and Cao, J and Kong, W},
title = {Machine to brain: facial expression recognition using brain machine generative adversarial networks.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {3},
pages = {863-875},
pmid = {38826642},
issn = {1871-4080},
abstract = {The human brain can effectively perform Facial Expression Recognition (FER) with a few samples by utilizing its cognitive ability. However, unlike the human brain, even the well-trained deep neural network is data-dependent and lacks cognitive ability. To tackle this challenge, this paper proposes a novel framework, Brain Machine Generative Adversarial Networks (BM-GAN), which utilizes the concept of brain's cognitive ability to guide a Convolutional Neural Network to generate LIKE-electroencephalograph (EEG) features. More specifically, we firstly obtain EEG signals triggered from facial emotion images, then we adopt BM-GAN to carry out the mutual generation of image visual features and EEG cognitive features. BM-GAN intends to use the cognitive knowledge learnt from EEG signals to instruct the model to perceive LIKE-EEG features. Thereby, BM-GAN has a superior performance for FER like the human brain. The proposed model consists of VisualNet, EEGNet, and BM-GAN. More specifically, VisualNet can obtain image visual features from facial emotion images and EEGNet can obtain EEG cognitive features from EEG signals. Subsequently, the BM-GAN completes the mutual generation of image visual features and EEG cognitive features. Finally, the predicted LIKE-EEG features of test images are used for FER. After learning, without the participation of the EEG signals, an average classification accuracy of 96.6 % is obtained on Chinese Facial Affective Picture System dataset using LIKE-EEG features for FER. Experiments demonstrate that the proposed method can produce an excellent performance for FER.},
}
@article {pmid38826067,
year = {2024},
author = {Taghi Zadeh Makouei, S and Uyulan, C},
title = {Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {69},
number = {5},
pages = {501-513},
pmid = {38826067},
issn = {1862-278X},
mesh = {*Electroencephalography/methods ; Humans ; *Deep Learning ; *Brain-Computer Interfaces ; *Hand/physiology ; *Movement/physiology ; *Neural Networks, Computer ; Arm/physiology ; Reproducibility of Results ; Spinal Cord Injuries/physiopathology ; },
abstract = {OBJECTIVES: The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI).<br><br>METHODS: The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model's robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept.<br><br>RESULTS: The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75&#x202f;% and low standard deviation (+/- 0.74&#x202f;%) in 10-fold cross-validation, confirming its reliability.<br><br>CONCLUSIONS: In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.},
}
@article {pmid38825665,
year = {2024},
author = {Meenakshinathan, J and Gupta, V and Reddy, TK and Behera, L and Sandhan, T},
title = {Session-independent subject-adaptive mental imagery BCI using selective filter-bank adaptive Riemannian features.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {11},
pages = {3293-3310},
pmid = {38825665},
issn = {1741-0444},
support = {FIG-100953//Indian Institute of Technology Roorkee/ ; SER-1968-ECD//Department of Science and Technology, Ministry of Science and Technology, India/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively.},
}
@article {pmid38825023,
year = {2024},
author = {He, Y and Ding, Y and Gong, C and Zhou, J and Gong, Z},
title = {The tail segments are required by the performance but not the accomplishment of various modes of Drosophila larval locomotion.},
journal = {Behavioural brain research},
volume = {471},
number = {},
pages = {115074},
doi = {10.1016/j.bbr.2024.115074},
pmid = {38825023},
issn = {1872-7549},
mesh = {Animals ; *Larva/physiology ; *Locomotion/physiology ; *Drosophila/physiology ; *Tail/physiology ; Neurons/physiology ; Animals, Genetically Modified ; Drosophila Proteins/metabolism ; },
abstract = {The tail plays important roles in locomotion control in many animals. But in animals with multiple body segments, the roles of the hind body segments and corresponding innervating neurons in locomotion control are not clear. Here, using the Drosophila larva as the model animal, we investigated the roles of the posterior terminal segments in various modes of locomotion and found that they participate in all of them. In forward crawling, paralysis of the larval tail by blocking the Abdb-Gal4 labeled neurons in the posterior segments of VNC led to a slower locomotion speed but did not prevent the initiation of forward peristalsis. In backward crawling, larvae with the Abdb-Gal4 neurons inhibited were unable to generate effective displacement although waves of backward peristalsis could be initiated and persist. In head swing where the movement of the tail is not obvious, disabling the larval tail by blocking Abdb-Gal4 neurons led to increased bending amplitude upon touching the head. In the case of larval lateral rolling, larval tail paralysis by inhibition of Abdb-Gal4 neurons did not prevent the accomplishment of rolling, but resulted in slower rolling speed. Our work reveals that the contribution of Drosophila larval posterior VNC segments and corresponding body segments in the tail to locomotion is comprehensive but could be compensated at least partially by other body segments. We suggest that the decentralization in locomotion control with respect to animal body parts helps to maintain the robustness of locomotion in multi-segment animals.},
}
@article {pmid38819973,
year = {2024},
author = {Guetarni, B and Windal, F and Benhabiles, H and Petit, M and Dubois, R and Leteurtre, E and Collard, D},
title = {A Vision Transformer-Based Framework for Knowledge Transfer From Multi-Modal to Mono-Modal Lymphoma Subtyping Models.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {9},
pages = {5562-5572},
doi = {10.1109/JBHI.2024.3407878},
pmid = {38819973},
issn = {2168-2208},
mesh = {Humans ; *Deep Learning ; *Image Interpretation, Computer-Assisted/methods ; *Lymphoma, Large B-Cell, Diffuse ; Lymphoma ; Algorithms ; },
abstract = {Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly expensive and time-consuming, making it less accessibility. Although alternative diagnosis methods based on IHC (immunohistochemistry) technologies exist (recommended by the WHO), they still suffer from similar limitations and are less accurate. Whole Slide Image (WSI) analysis using deep learning models has shown promising potential for cancer diagnosis, that could offer cost-effective and faster alternatives to existing methods. In this work, we propose a vision transformer-based framework for distinguishing DLBCL (Diffuse Large B-Cell Lymphoma) cancer subtypes from high-resolution WSIs. To this end, we introduce a multi-modal architecture to train a classifier model from various WSI modalities. We then leverage this model through a knowledge distillation process to efficiently guide the learning of a mono-modal classifier. Our experimental study conducted on a lymphoma dataset of 157 patients shows the promising performance of our mono-modal classification model, outperforming six recent state-of-the-art methods. In addition, the power-law curve, estimated on our experimental data, suggests that with more training data from a reasonable number of additional patients, our model could achieve competitive diagnosis accuracy with IHC technologies. Furthermore, the efficiency of our framework is confirmed through an additional experimental study on an external breast cancer dataset (BCI dataset).},
}
@article {pmid38819673,
year = {2024},
author = {Li, M and Yang, L and Liu, Y and Shang, Z and Wan, H},
title = {Dynamic temporal neural patterns based on multichannel LFPs Identify different brain states during anesthesia in pigeons: comparison of three anesthetics.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {11},
pages = {3249-3262},
pmid = {38819673},
issn = {1741-0444},
mesh = {Animals ; *Columbidae/physiology ; *Brain/physiology/drug effects ; *Anesthesia ; Anesthetics/pharmacology ; Urethane/pharmacology ; Male ; Chloral Hydrate/pharmacology ; },
abstract = {Anesthetic-induced brain activity study is crucial in avian cognitive-, consciousness-, and sleep-related research. However, the neurobiological mechanisms underlying the generation of brain rhythms and specific connectivity of birds during anesthesia are poorly understood. Although different kinds of anesthetics can be used to induce an anesthesia state, a comparison study of these drugs focusing on the neural pattern evolution during anesthesia is lacking. Here, we recorded local field potentials (LFPs) using a multi-channel micro-electrode array inserted into the nidopallium caudolateral (NCL) of adult pigeons (Columba livia) anesthetized with chloral hydrate, pelltobarbitalum natricum or urethane. Power spectral density (PSD) and functional connectivity analyses were used to measure the dynamic temporal neural patterns in NCL during anesthesia. Neural decoding analysis was adopted to calculate the probability of the pigeon's brain state and the kind of injected anesthetic. In the NCL during anesthesia, we found elevated power activity and functional connectivity at low-frequency bands and depressed power activity and connectivity at high-frequency bands. Decoding results based on the spectral and functional connectivity features indicated that the pigeon's brain states during anesthesia and the injected anesthetics can be effectively decoded. These findings provide an important foundation for future investigations on how different anesthetics induce the generation of specific neural patterns.},
}
@article {pmid38816665,
year = {2024},
author = {Liao, L and Lu, J and Wang, L and Zhang, Y and Gao, D and Wang, M},
title = {CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {10},
pages = {3233-3247},
pmid = {38816665},
issn = {1741-0444},
support = {2023YFG0018//Sichuan Science and Technology Program/ ; KYQN202241//Scientific Research Foundation for Returned Scholars of Ministry of Education/ ; },
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Neural Networks, Computer ; Algorithms ; Brain/diagnostic imaging/physiology ; Brain-Computer Interfaces ; Adult ; Deep Learning ; Male ; Brain Mapping/methods ; Young Adult ; Female ; },
abstract = {Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS classification problem. In this paper, a novel approach based on convolutional neural network and Transformer, named CT-Net, is established to guide the deep modeling for the classification of mental arithmetic (MA) tasks. We explore the effect of data representations, and design a temporal-level combination of two raw chromophore signals to improve the data utilization and enrich the feature learning of the model. We evaluate our model on two open-access datasets and achieve the classification accuracy of 98.05% and 77.61%, respectively. Moreover, we explain our model by the gradient-weighted class activation mapping, which presents a high consistent between the contributing value of features learned by the model and the mapping of brain activity in the MA task. The results suggest the feasibility and interpretability of CT-Net for decoding MA tasks.},
}
@article {pmid38815645,
year = {2024},
author = {Li, J and Wang, L and Zhang, Z and Feng, Y and Huang, M and Liang, D},
title = {Analysis and recognition of a novel experimental paradigm for musical emotion brain-computer interface.},
journal = {Brain research},
volume = {1839},
number = {},
pages = {149039},
doi = {10.1016/j.brainres.2024.149039},
pmid = {38815645},
issn = {1872-6240},
mesh = {Humans ; *Music/psychology ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; Adult ; Brain/physiology ; Auditory Perception/physiology ; Acoustic Stimulation/methods ; },
abstract = {Musical emotions have received increasing attention over the years. To better recognize the emotions by brain-computer interface (BCI), the random music-playing and sequential music-playing experimental paradigms are proposed and compared in this paper. Two experimental paradigms consist of three positive pieces, three neutral pieces and three negative pieces of music. Ten subjects participate in two experimental paradigms. The features of electroencephalography (EEG) signals are firstly analyzed in the time, frequency and spatial domains. To improve the effect of emotion recognition, a recognition model is proposed with the optimal channels selecting by Pearson's correlation coefficient, and the feature fusion combining differential entropy and wavelet packet energy. According to the analysis results, the features of sequential music-playing experimental paradigm are more different among three emotions. The classification results of sequential music-playing experimental paradigm are also better, and its average results of positive, neutral and negative emotions are 78.53%, 72.81% and 77.35%, respectively. The more obvious the changes of EEG induced by the emotions, the higher the classification accuracy will be. After analyzing two experimental paradigms, a better way for music to induce the emotions can be explored. Therefore, our research offers a novel perspective on affective BCIs.},
}
@article {pmid38815513,
year = {2024},
author = {Becker, ER and Price, AD and Whitrock, JN and Smith, M and Baucom, MR and Makley, AT and Goodman, MD},
title = {Re-evaluating the Use of High Sensitivity Troponin to Diagnose Blunt Cardiac Injury.},
journal = {The Journal of surgical research},
volume = {300},
number = {},
pages = {150-156},
doi = {10.1016/j.jss.2024.04.074},
pmid = {38815513},
issn = {1095-8673},
mesh = {Humans ; Female ; Retrospective Studies ; Male ; Middle Aged ; Adult ; *Wounds, Nonpenetrating/diagnosis/blood ; *Electrocardiography ; Aged ; Heart Injuries/diagnosis/blood ; Troponin I/blood ; Sternum/injuries ; Sensitivity and Specificity ; Biomarkers/blood ; Fractures, Bone/blood/diagnosis ; Echocardiography ; },
abstract = {INTRODUCTION: Blunt cardiac injury (BCI) can be challenging diagnostically, and if misdiagnosed, can lead to life-threatening complications. Our institution previously evaluated BCI screening with troponin and electrocardiogram (EKG) during a transition from troponin I to high sensitivity troponin (hsTnI), a more sensitive troponin I assay. The previous study found an hsTnI of 76 ng/L had the highest capability of accurately diagnosing a clinically significant BCI. The aim of this study was to determine the efficacy of the newly implemented protocol.<br><br>METHODS: Patients diagnosed with a sternal fracture from March 2022 to April 2023 at our urban level-1 trauma center were retrospectively reviewed for EKG findings, hsTnI trend, echocardiogram changes, and clinical outcomes. The BCI cohort and non-BCI cohort ordinal measures were compared using Wilcoxon's two-tailed rank sum test and categorical measures were compared with Fisher's exact test. Youden indices were used to evaluate hsTnI sensitivity and specificity.<br><br>RESULTS: Sternal fractures were identified in 206 patients, of which 183 underwent BCI screening. Of those screened, 103 underwent echocardiogram, 28 were diagnosed with clinically significant BCIs, and 15 received intervention. The peak hsTnI threshold of 76&#xa0;ng/L was found to have a Youden index of 0.31. Rather, the Youden index was highest at 0.50 at 40&#xa0;ng/L (sensitivity 0.79 and specificity 0.71) for clinically significant BCI.<br><br>CONCLUSIONS: Screening patients with sternal fractures for BCI using hsTnI and EKG remains effective. To optimize the hsTnI threshold, this study determined the hsTnI threshold should be lowered to 40&#xa0;ng/L. Further improvements to the institutional protocol may be derived from multicenter analysis.},
}
@article {pmid38813519,
year = {2024},
author = {Pan, Y and Zander, TO and Klug, M},
title = {Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1346791},
pmid = {38813519},
issn = {2673-6195},
abstract = {The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.},
}
@article {pmid38812288,
year = {2024},
author = {Ji, D and Xiao, X and Wu, J and He, X and Zhang, G and Guo, R and Liu, M and Xu, M and Lin, Q and Jung, TP and Ming, D},
title = {A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad44d8},
pmid = {38812288},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetoencephalography/methods ; *Evoked Potentials, Visual/physiology ; Adult ; Male ; Female ; *Photic Stimulation/methods ; Young Adult ; Visual Cortex/physiology ; },
abstract = {Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min[-1]with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min[-1].Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.},
}
@article {pmid38812257,
year = {2024},
author = {Kılınç Bülbül, D and Güçlü, B},
title = {Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays.},
journal = {Somatosensory &amp; motor research},
volume = {},
number = {},
pages = {1-8},
doi = {10.1080/08990220.2024.2358522},
pmid = {38812257},
issn = {1369-1651},
abstract = {AIM OF THE STUDY: Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms.<br><br>MATERIALS AND METHODS: 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and &#xb5;ECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel &#xb5;ECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such.<br><br>RESULTS: The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine.<br><br>CONCLUSION: According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.},
}
@article {pmid38812014,
year = {2024},
author = {Ma, ZZ and Wu, JJ and Cao, Z and Hua, XY and Zheng, MX and Xing, XX and Ma, J and Xu, JG},
title = {Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {91},
pmid = {38812014},
issn = {1743-0003},
support = {81902301//National Natural Science Foundation of China/ ; 81802249//National Natural Science Foundation of China/ ; 81871836//National Natural Science Foundation of China/ ; 19QA1409000//Shanghai Rising-Star Program/ ; 2018YQ02//Shanghai Municipal Commission of Health and Family Planning/ ; 22010504200//Shanghai Science and Technology Committee/ ; 2018YFC2001600//National Key R&amp;D Program of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Stroke Rehabilitation/methods ; Female ; Middle Aged ; *Upper Extremity/physiopathology ; *Magnetic Resonance Imaging ; *Imagery, Psychotherapy/methods ; *Stroke/physiopathology/complications ; Aged ; Adult ; Imagination/physiology ; Cerebral Cortex/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND: The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain-computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia.<br><br>DESIGN: A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial.<br><br>METHODS: Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures.<br><br>RESULTS: A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r&#x2009;=&#x2009;0.425, P&#x2009;<&#x2009;0.05), the mean zReHo of the right cuneus (r&#x2009;=&#x2009;0.399, P&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients.<br><br>TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry (number ChiCTR2000034848, registered July 21, 2020).},
}
@article {pmid38811613,
year = {2024},
author = {Mou, X and He, C and Tan, L and Yu, J and Liang, H and Zhang, J and Tian, Y and Yang, YF and Xu, T and Wang, Q and Cao, M and Chen, Z and Hu, CP and Wang, X and Liu, Q and Wu, H},
title = {ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {550},
pmid = {38811613},
issn = {2052-4463},
support = {2021A1515012509//Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation)/ ; },
mesh = {Humans ; Brain/physiology ; Brain-Computer Interfaces ; China ; *Electroencephalography ; Language ; Linguistics ; Natural Language Processing ; Reading ; *Semantics ; },
abstract = {An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.},
}
@article {pmid38809965,
year = {2024},
author = {Rabut, C and Norman, SL and Griggs, WS and Russin, JJ and Jann, K and Christopoulos, V and Liu, C and Andersen, RA and Shapiro, MG},
title = {Functional ultrasound imaging of human brain activity through an acoustically transparent cranial window.},
journal = {Science translational medicine},
volume = {16},
number = {749},
pages = {eadj3143},
doi = {10.1126/scitranslmed.adj3143},
pmid = {38809965},
issn = {1946-6242},
support = {R01 NS123663/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain/diagnostic imaging ; Animals ; *Skull/diagnostic imaging ; Ultrasonography/methods ; Rats ; Acoustics ; Phantoms, Imaging ; Polymethyl Methacrylate/chemistry ; Signal-To-Noise Ratio ; Male ; },
abstract = {Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game "connect the dots" task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 μm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.},
}
@article {pmid38809723,
year = {2025},
author = {Liu, S and Liu, M and Zhang, D and Ming, Z and Liu, Z and Chen, Q and Ma, L and Luo, J and Zhang, J and Suo, D and Pei, G and Yan, T},
title = {Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {4},
pages = {2932-2944},
doi = {10.1109/JBHI.2024.3406684},
pmid = {38809723},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Exoskeleton Device ; *Hand/physiology ; Male ; Adult ; *Augmented Reality ; Female ; Algorithms ; Young Adult ; Signal Processing, Computer-Assisted ; Movement/physiology ; },
abstract = {Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.},
}
@article {pmid38808372,
year = {2025},
author = {Pitt, KM and Spoor, A and Zosky, J},
title = {Considering preferences, speed and the animation of multiple symbols in developing P300 brain-computer interface for children.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {20},
number = {1},
pages = {171-183},
doi = {10.1080/17483107.2024.2359479},
pmid = {38808372},
issn = {1748-3115},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Communication Devices for People with Disabilities ; Adolescent ; Electroencephalography ; },
abstract = {PURPOSE: Prior research has begun establishing the efficacy of animation in brain-computer interfaces access to augmentative and alternative communication (BCI-AAC). However, the use of animation in P300-BCI-AAC for children is in the early stages and largely limited to single item highlighting of extended durations. In pursuit of practical application, the present study aims to evaluate children's event-related potential (ERP) characteristics and user experience during a task involving an animated P300-BCI-AAC system.<br><br>MATERIALS AND METHODS: The system utilizes multi-item zoom animations to access a 28-pictorial symbols. Participants completed a fast (100&#x2009;ms) and slow (200&#x2009;ms) zoom animation highlighting conditions wherein four pictorial symbols were highlighted concurrently.<br><br>RESULTS: The proposed display appears feasible, eliciting all targeted ERPs. However, ERP amplitudes may be reduced in comparison to single-item animation highlighting, possibly due to distraction. Ratings of mental effort were significantly higher for the 100&#x2009;ms condition, though differences in the frontal P200/P300 ERP did not achieve significance. Most participants identified a preference for the 100&#x2009;ms condition, though age may impact preference.<br><br>CONCLUSIONS: Overall, findings support the preliminary feasibility of a proposed 28-item interface that utilises group zoom animation highlighting of pictorial symbols. Further research is needed evaluating ERP characteristics and outcomes from online (real-time) use of animation-based P300-BCI-AAC for children with severe speech and physical impairments across multiple training sessions.},
}
@article {pmid38808030,
year = {2024},
author = {Xie, X and Shi, R and Yu, H and Wan, X and Liu, T and Duan, D and Li, D and Wen, D},
title = {Executive function rehabilitation and evaluation based on brain-computer interface and virtual reality: our opinion.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1377097},
pmid = {38808030},
issn = {1662-4548},
}
@article {pmid38806531,
year = {2024},
author = {Zhang, X and Wang, Q and Li, J and Gao, X and Li, B and Nie, B and Wang, J and Zhou, Z and Yang, Y and Wang, H},
title = {An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {546},
pmid = {38806531},
issn = {2052-4463},
support = {52072215//National Natural Science Foundation of China (National Science Foundation of China)/ ; U1964203//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52221005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52221005//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Adult ; Female ; Humans ; Male ; Automation ; *Automobile Driving ; Brain-Computer Interfaces ; *Cognition ; Prefrontal Cortex/physiology ; Spectroscopy, Near-Infrared ; },
abstract = {For highly autonomous vehicles, human does not need to operate continuously vehicles. The brain-computer interface system in autonomous vehicles will highly depend on the brain states of passengers rather than those of human drivers. It is a meaningful and vital choice to translate the mental activities of human beings, essentially playing the role of advanced sensors, into safe driving. Quantifying the driving risk cognition of passengers is a basic step toward this end. This study reports the creation of an fNIRS dataset focusing on the prefrontal cortex activity in fourteen types of highly automated driving scenarios. This dataset considers age, sex and driving experience factors and contains the data collected from an 8-channel fNIRS device and the data of driving scenarios. The dataset provides data support for distinguishing the driving risk in highly automated driving scenarios via brain-computer interface systems, and it also provides the possibility of preventing potential hazards in some scenarios, in which risk remains at a high value for an extended period, before hazard occurs.},
}
@article {pmid38806037,
year = {2024},
author = {Zhang, Y and Li, M and Wang, H and Zhang, M and Xu, G},
title = {Preparatory movement state enhances premovement EEG representations for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad5109},
pmid = {38806037},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Movement/physiology ; Male ; *Electroencephalography/methods ; Female ; Adult ; Young Adult ; Psychomotor Performance/physiology ; Intention ; Algorithms ; },
abstract = {Objective. Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions.Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy.Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p< 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95.Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.},
}
@article {pmid38805956,
year = {2024},
author = {Ruiz-Mateos Serrano, R and Aguzin, A and Mitoudi-Vagourdi, E and Tao, X and Naegele, TE and Jin, AT and Lopez-Larrea, N and Picchio, ML and Vinicio Alban-Paccha, M and Minari, RJ and Mecerreyes, D and Dominguez-Alfaro, A and Malliaras, GG},
title = {3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles.},
journal = {Biomaterials},
volume = {310},
number = {},
pages = {122624},
doi = {10.1016/j.biomaterials.2024.122624},
pmid = {38805956},
issn = {1878-5905},
mesh = {*Printing, Three-Dimensional ; Humans ; *Electrodes ; *Polystyrenes/chemistry ; *Textiles ; *Machine Learning ; Electric Conductivity ; Wearable Electronic Devices ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Gels/chemistry ; Polymers/chemistry ; Polyethylene Glycols/chemistry ; Electromyography/methods ; Biocompatible Materials/chemistry ; },
abstract = {The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA) to develop printable and biocompatible electrodes for long-term cutaneous electrophysiology recordings. The impact of printing parameters on the conducting properties, morphological characteristics, mechanical stability and biocompatibility of the material were investigated. The optimised eutectogel formulations were fabricated in four different patterns -flat, pyramidal, striped and wavy- to explore the influence of electrode geometry on skin conformability and mechanical contact. These electrodes were employed for impedance and forearm EMG measurements. Furthermore, arrays of twenty electrodes were embedded into a textile and used to generate body surface potential maps (BSPMs) of the forearm, where different finger movements were recorded and analysed. Finally, BSPMs for three different letters (B, I, O) in sign-language were recorded and used to train a logistic regressor classifier able to reliably identify each letter. This novel cutaneous electrode fabrication approach offers new opportunities for long-term electrophysiological recordings, online sign-language translation and brain-machine interfaces.},
}
@article {pmid38805337,
year = {2024},
author = {Wang, J and Bi, L and Fei, W and Xu, X and Liu, A and Mo, L and Genetu Feleke, A},
title = {Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2087-2095},
doi = {10.1109/TNSRE.2024.3406371},
pmid = {38805337},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Male ; *Movement/physiology ; Female ; Adult ; *Hand/physiology ; Young Adult ; *Psychomotor Performance/physiology ; Algorithms ; Motor Cortex/physiology ; Healthy Volunteers ; },
abstract = {Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.},
}
@article {pmid38804201,
year = {2024},
author = {Li, X and Zuo, Y and Lin, X and Guo, B and Jiang, H and Guan, N and Zheng, H and Huang, Y and Gu, X and Yu, B and Wang, X},
title = {Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design.},
journal = {Advanced healthcare materials},
volume = {13},
number = {28},
pages = {e2401793},
doi = {10.1002/adhm.202401793},
pmid = {38804201},
issn = {2192-2659},
support = {82371374//National Natural Science Foundation of China/ ; 2021ZD0200408//Scientific and Technological Innovation 2030 Program of China/ ; 010904006//Nanhu Brain-computer Interface Institute/ ; 2023ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2023-PT310-01//Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; 2019R01007//Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; },
mesh = {Animals ; *High-Throughput Screening Assays/methods ; *Drug Carriers/chemistry ; Polyethyleneimine/chemistry ; Mice ; Proteins/chemistry ; Polymers/chemistry ; Humans ; Drug Delivery Systems/methods ; },
abstract = {Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.},
}
@article {pmid38801716,
year = {2024},
author = {Jahangir, TN and Abdel-Azeim, S and Kandiel, TA},
title = {BiVO4 Photoanode with NiV2O6 Back Contact Interfacial Layer for Improved Hole-Diffusion Length and Photoelectrochemical Water Oxidation Activity.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {22},
pages = {28742-28755},
doi = {10.1021/acsami.4c05489},
pmid = {38801716},
issn = {1944-8252},
abstract = {The short hole diffusion length (HDL) and high interfacial recombination are among the main drawbacks of semiconductor-based solar energy systems. Surface passivation and introducing an interfacial layer are recognized for enhancing HDL and charge carrier separation. Herein, we introduced a facile recipe to design a pinholes-free BiVO4 photoanode with a NiV2O6 back contact interfacial (BCI) layer, marking a significant advancement in the HDL and photoelectrochemical activity. The fabricated BiVO4 photoanode with NiV2O6 BCI layer exhibits a 2-fold increase in the HDL compared to pristine BiVO4. Despite this improvement, we found that the front surface recombination still hinders the water oxidation process, as revealed by photoelectrochemical (PEC) studies employing Na2SO3 electron donors and by intensity-modulated photocurrent spectroscopy measurements. To address this limitation, the surface of the NiV2O6/BiVO4 photoanode was passivated with a cobalt phosphate electrocatalyst, resulting in a dramatic enhancement in the PEC performance. The optimized photoanode achieved a stable photocurrent density of 4.8 mA cm[-2] at 1.23 VRHE, which is 12-fold higher than that of the pristine BiVO4 photoanode. Density Functional Theory (DFT) simulations revealed an abrupt electrostatic potential transition at the NiV2O6/BiVO4 interface with BiVO4 being more negative than NiV2O6. A strong built-in electric field is thus generated at the interface and drifts photogenerated electrons toward the NiV2O6 BCI layer and photogenerated holes toward the BiVO4 top layer. As a result, the back-surface recombination is minimized, and ultimately, the HDL is extended in agreement with the experimental findings.},
}
@article {pmid38801273,
year = {2024},
author = {Lakshminarayanan, K and Shah, R and Ramu, V and Madathil, D and Yao, Y and Wang, I and Brahmi, B and Rahman, MH},
title = {Motor Imagery Performance through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {207},
pages = {},
doi = {10.3791/66859},
pmid = {38801273},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Imagination/physiology ; *Electroencephalography/methods ; Adult ; Neurological Rehabilitation/methods ; },
abstract = {This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) technologies with the customization of three-dimensional (3D) avatars. Traditional approaches to rehabilitation often fail to fully engage patients, primarily due to their inability to provide a deeply immersive and interactive experience. This research endeavors to fill this gap by utilizing motor imagery (MI) techniques, where participants visualize physical movements without actual execution. This method capitalizes on the brain's neural mechanisms, activating areas involved in movement execution when imagining movements, thereby facilitating the recovery process. The integration of VR's immersive capabilities with the precision of electroencephalography (EEG) to capture and interpret brain activity associated with imagined movements forms the core of this system. Digital Twins in the form of personalized 3D avatars are employed to significantly enhance the sense of immersion within the virtual environment. This heightened sense of embodiment is crucial for effective rehabilitation, aiming to bolster the connection between the patient and their virtual counterpart. By doing so, the system not only aims to improve motor imagery performance but also seeks to provide a more engaging and efficacious rehabilitation experience. Through the real-time application of BCI, the system allows for the direct translation of imagined movements into virtual actions performed by the 3D avatar, offering immediate feedback to the user. This feedback loop is essential for reinforcing the neural pathways involved in motor control and recovery. The ultimate goal of the developed system is to significantly enhance the effectiveness of motor imagery exercises by making them more interactive and responsive to the user's cognitive processes, thereby paving a new path in the field of neurological rehabilitation.},
}
@article {pmid38800720,
year = {2024},
author = {Zhu, Y and Jiang, D and Qiu, Y and Liu, X and Bian, Y and Tian, S and Wang, X and Hsia, KJ and Wan, H and Zhuang, L and Wang, P},
title = {Dynamic microphysiological system chip platform for high-throughput, customizable, and multi-dimensional drug screening.},
journal = {Bioactive materials},
volume = {39},
number = {},
pages = {59-73},
pmid = {38800720},
issn = {2452-199X},
abstract = {Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening. By employing diverse types of spheroids or organoids, it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening. High-throughput microphysiological systems that support optional, parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research. However, establishing such a system is highly challenging and requires a multidisciplinary approach. This study introduces a dynamic Microphysiological System Chip Platform (MSCP) with multiple functional microstructures that encompass the mentioned advantages. We developed a high-throughput lung cancer spheroids model and an intestine-liver-heart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs, demonstrating the feasibility of the MSCP. This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects. Moreover, the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication. The MSCP could serves as a valuable platform for microphysiological system research, making significant contributions to disease modeling, drug development, and personalized medical treatment.},
}
@article {pmid38800606,
year = {2024},
author = {Fu, P and Liu, Y and Zhu, L and Wang, M and Yu, Y and Yang, F and Zhang, W and Zhang, H and Shoham, S and Roe, AW and Xi, W},
title = {Two-photon imaging of excitatory and inhibitory neural response to infrared neural stimulation.},
journal = {Neurophotonics},
volume = {11},
number = {2},
pages = {025003},
pmid = {38800606},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Pulsed infrared neural stimulation (INS, 1875 nm) is an emerging neurostimulation technology that delivers focal pulsed heat to activate functionally specific mesoscale networks and holds promise for clinical application. However, little is known about its effect on excitatory and inhibitory cell types in cerebral cortex.<br><br>AIM: Estimates of summed population neuronal response time courses provide a potential basis for neural and hemodynamic signals described in other studies.<br><br>APPROACH: Using two-photon calcium imaging in mouse somatosensory cortex, we have examined the effect of INS pulse train application on hSyn neurons and mDlx neurons tagged with GCaMP6s.<br><br>RESULTS: We find that, in anesthetized mice, each INS pulse train reliably induces robust response in hSyn neurons exhibiting positive going responses. Surprisingly, mDlx neurons exhibit negative going responses. Quantification using the index of correlation illustrates responses are reproducible, intensity-dependent, and focal. Also, a contralateral activation is observed when INS applied.<br><br>CONCLUSIONS: In sum, the population of neurons stimulated by INS includes both hSyn and mDlx neurons; within a range of stimulation intensities, this leads to overall excitation in the stimulated population, leading to the previously observed activations at distant post-synaptic sites.},
}
@article {pmid38800199,
year = {2024},
author = {Vafopoulou, E and Christodoulou, N and Papathanasiou, IV},
title = {Treatment Adherence and Quality of Life Among Elderly Patients With Diabetes Mellitus Registered in the Community.},
journal = {Cureus},
volume = {16},
number = {4},
pages = {e58986},
pmid = {38800199},
issn = {2168-8184},
abstract = {Background This study investigates the association between medication adherence and health-related quality of life among adults with type 2 diabetes mellitus at the Health Center of Tyrnavos community level. Materials and methods This cross-sectional study involved 125 patients with type 2 diabetes mellitus, aged 60 years and older, who were visiting community healthcare facilities. The research was conducted with a structured questionnaire that included 34 questions related to socio-demographic data, self-reported compliance, and stress. The DQOL-BCI (Diabetes Quality of Life - Brief Clinical Inventory) scale was used to measure health-related quality of life. Results A total of 125 patients with a mean (SD) age of 69.2 (8.1) years were included in the study (64 women and 61 men). Based on the results of the descriptive analysis, 88.0% reported high medication adherence. However, 66% of the participants reported having high anxiety levels, with 33.6% having difficulty controlling their anxiety. Quality of life was negatively correlated with lower medication adherence (P < 0.05). Conclusions Older age and low medication adherence are associated with lower quality of life among diabetic patients. Interventions to improve the quality of life in elderly diabetic patients should consider the effect of age and medication adherence.},
}
@article {pmid38799637,
year = {2024},
author = {Wang, L and Zhang, J and Zhang, W and Zheng, M and Guo, H and Pan, X and Li, W and Yang, B and Ding, L},
title = {The inhibitory effect of adenosine on tumor adaptive immunity and intervention strategies.},
journal = {Acta pharmaceutica Sinica. B},
volume = {14},
number = {5},
pages = {1951-1964},
pmid = {38799637},
issn = {2211-3835},
abstract = {Adenosine (Ado) is significantly elevated in the tumor microenvironment (TME) compared to normal tissues. It binds to adenosine receptors (AdoRs), suppressing tumor antigen presentation and immune cell activation, thereby inhibiting tumor adaptive immunity. Ado downregulates major histocompatibility complex II (MHC II) and co-stimulatory factors on dendritic cells (DCs) and macrophages, inhibiting antigen presentation. It suppresses anti-tumor cytokine secretion and T cell activation by disrupting T cell receptor (TCR) binding and signal transduction. Ado also inhibits chemokine secretion and KCa3.1 channel activity, impeding effector T cell trafficking and infiltration into the tumor site. Furthermore, Ado diminishes T cell cytotoxicity against tumor cells by promoting immune-suppressive cytokine secretion, upregulating immune checkpoint proteins, and enhancing immune-suppressive cell activity. Reducing Ado production in the TME can significantly enhance anti-tumor immune responses and improve the efficacy of other immunotherapies. Preclinical and clinical development of inhibitors targeting Ado generation or AdoRs is underway. Therefore, this article will summarize and analyze the inhibitory effects and molecular mechanisms of Ado on tumor adaptive immunity, as well as provide an overview of the latest advancements in targeting Ado pathways in anti-tumor immune responses.},
}
@article {pmid38799297,
year = {2024},
author = {Wider, W and Mutang, JA and Chua, BS and Pang, NTP and Jiang, L and Fauzi, MA and Udang, LN},
title = {Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1339444},
pmid = {38799297},
issn = {1662-5161},
abstract = {INTRODUCTION: This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.<br><br>METHODS: It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.<br><br>RESULTS: The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.<br><br>DISCUSSION: This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.},
}
@article {pmid38798494,
year = {2025},
author = {Jung, T and Zeng, N and Fabbri, JD and Eichler, G and Li, Z and Zabeh, E and Das, A and Willeke, K and Wingel, KE and Dubey, A and Huq, R and Sharma, M and Hu, Y and Ramakrishnan, G and Tien, K and Mantovani, P and Parihar, A and Yin, H and Oswalt, D and Misdorp, A and Uguz, I and Shinn, T and Rodriguez, GJ and Nealley, C and Sanborn, S and Gonzales, I and Roukes, M and Knecht, J and Yoshor, D and Canoll, P and Spinazzi, E and Carloni, LP and Pesaran, B and Patel, S and Jacobs, J and Youngerman, B and Cotton, RJ and Tolias, A and Shepard, KL},
title = {Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38798494},
issn = {2692-8205},
support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; },
abstract = {Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating a 256×256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.},
}
@article {pmid38798438,
year = {2024},
author = {Rosenthal, IA and Bashford, L and Bjånes, D and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38798438},
issn = {2692-8205},
support = {T32 NS105595/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; },
abstract = {Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously when visual stimuli were more realistic, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts.},
}
@article {pmid38798056,
year = {2024},
author = {Mathewson, KE and Kuziek, JP and Scanlon, JEM and Robles, D},
title = {The moving wave: Applications of the mobile EEG approach to study human attention.},
journal = {Psychophysiology},
volume = {61},
number = {9},
pages = {e14603},
doi = {10.1111/psyp.14603},
pmid = {38798056},
issn = {1469-8986},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; Mobile Applications ; Event-Related Potentials, P300/physiology ; },
abstract = {Although historically confined to traditional research laboratories, electroencephalography (EEG) paradigms are now being applied to study a wide array of behaviors, from daily activities to specialized tasks in diverse fields such as sports science, neurorehabilitation, and education. This transition from traditional to real-world mobile research can provide new tools for understanding attentional processes as they occur naturally. Early mobile EEG research has made progress, despite the large size and wired connections. Recent developments in hardware and software have expanded the possibilities of mobile EEG, enabling a broader range of applications. Despite these advancements, limitations influencing mobile EEG remain that must be overcome to achieve adequate reliability and validity. In this review, we first assess the feasibility of mobile paradigms, including electrode selection, artifact correction techniques, and methodological considerations. This review underscores the importance of ecological, construct, and predictive validity in ensuring the trustworthiness and applicability of mobile EEG findings. Second, we explore studies on attention in naturalistic settings, focusing on replicating classic P3 component studies in mobile paradigms like stationary biking in our lab, and activities such as walking, cycling, and dual-tasking outside of the lab. We emphasize how the mobile approach complements traditional laboratory paradigms and the types of insights gained in naturalistic research settings. Third, we discuss promising applications of portable EEG in workplace safety and other areas including road safety, rehabilitation medicine, and brain-computer interfaces. In summary, this review explores the expanding possibilities of mobile EEG while recognizing the existing challenges in fully realizing its potential.},
}
@article {pmid38796879,
year = {2024},
author = {Chen, A and Sun, D and Gao, X and Zhang, D},
title = {A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.},
journal = {Computers in biology and medicine},
volume = {177},
number = {},
pages = {108619},
doi = {10.1016/j.compbiomed.2024.108619},
pmid = {38796879},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Brain/physiology ; Algorithms ; },
abstract = {In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.},
}
@article {pmid38796691,
year = {2024},
author = {Tao, Q and Xu, Y and He, Y and Luo, T and Li, X and Han, L},
title = {Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data.},
journal = {Briefings in bioinformatics},
volume = {25},
number = {4},
pages = {},
pmid = {38796691},
issn = {1477-4054},
support = {2021ZD0204400//National Science and Technology Innovation 2030 Major Program/ ; },
mesh = {Animals ; Mice ; *Algorithms ; *Brain/metabolism ; *Single-Cell Analysis/methods ; RNA-Seq/methods ; Transcriptome ; Sequence Analysis, RNA/methods ; Neurons/metabolism ; Gene Expression Profiling/methods ; },
abstract = {Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.},
}
@article {pmid38795979,
year = {2024},
author = {Bhatt, MW and Sharma, S},
title = {Multi-scale self-attention approach for analysing motor imagery signals in brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {408},
number = {},
pages = {110182},
doi = {10.1016/j.jneumeth.2024.110182},
pmid = {38795979},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Electroencephalography/methods ; Attention/physiology ; Motor Activity/physiology ; Brain/physiology ; Hand/physiology ; Signal Processing, Computer-Assisted ; Adult ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Motor imagery-based electroencephalogram (EEG) brain-computer interface (BCI) technology has seen tremendous advancements in the past several years. Deep learning has outperformed more traditional approaches, such next-gen neuro-technologies, in terms of productivity. It is still challenging to develop and train an end-to-end network that can sufficiently extract the possible characteristics from EEG data used in motor imaging. Brain-computer interface research is largely reliant on the fundamental problem of accurately classifying EEG data. There are still many challenges in the field of MI classification even after researchers have proposed a variety of methods, such as deep learning and machine learning techniques.<br><br>METHODOLOGY: We provide a model for four-class categorization of motor imagery EEG signals using attention mechanisms: left hand, right hand, foot, and tongue/rest. The model is built on multi-scale spatiotemporal self-attention networks. To determine the most effective channels, self-attention networks are implemented spatially to assign greater weight to channels associated with motion and lesser weight to channels unrelated to motion. To eliminate noise in the temporal domain, parallel multi-scale Temporal Convolutional Network (TCN) layers are utilized to extract temporal domain features at various scales.<br><br>RESULT: On the IV-2b dataset from the BCI Competition, the suggested model achieved an accuracy of 85.09&#x202f;%; on the IV-2a and IV-2b datasets from the HGD datasets, it was 96.26&#x202f;%.<br><br>In single-subject classification, this approach demonstrates superior accuracy when compared to existing methods.<br><br>CONCLUSION: The findings suggest that this approach exhibits commendable performance, resilience, and capacity for transfer learning.},
}
@article {pmid38795798,
year = {2024},
author = {Han, G and Jiao, B and Zhang, Y and Wang, Z and Liang, C and Li, Y and Hsu, YC and Bai, R},
title = {Arterial pulsation dependence of perivascular cerebrospinal fluid flow measured by dynamic diffusion tensor imaging in the human brain.},
journal = {NeuroImage},
volume = {297},
number = {},
pages = {120653},
doi = {10.1016/j.neuroimage.2024.120653},
pmid = {38795798},
issn = {1095-9572},
mesh = {Humans ; Adult ; Female ; Male ; Young Adult ; *Diffusion Tensor Imaging/methods ; Adolescent ; *Cerebrospinal Fluid/physiology/diagnostic imaging ; *Glymphatic System/diagnostic imaging/physiology ; Brain/physiology/diagnostic imaging/blood supply ; Pulsatile Flow/physiology ; Cerebral Arteries/diagnostic imaging/physiology ; },
abstract = {Perivascular cerebrospinal fluid (pCSF) flow is a key component of the glymphatic system. Arterial pulsation has been proposed as the main driving force of pCSF influx along the superficial and penetrating arteries; however, evidence of this mechanism in humans is limited. We proposed an experimental framework of dynamic diffusion tensor imaging with low b-values and ultra-long echo time (dynDTIlow-b) to capture pCSF flow properties during the cardiac cycle in human brains. Healthy adult volunteers (aged 17-28 years; seven men, one woman) underwent dynDTIlow-b using a 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) with simultaneously recorded cardiac output. The results showed that diffusion tensors reconstructed from pCSF were mainly oriented in the direction of the neighboring arterial flow. When switching from vasoconstriction to vasodilation, the axial and radial diffusivities of the pCSF increased by 5.7 % and 4.94 %, respectively, suggesting that arterial pulsation alters the pCSF flow both parallel and perpendicular to the arterial wall. DynDTIlow-b signal intensity at b=0 s/mm[2] (i.e., T2-weighted, [S(b=0 s/mm[2])]) decreased in systole, but this change was ∼7.5 % of a cardiac cycle slower than the changes in apparent diffusivity, suggesting that changes in S(b=0 s/mm[2]) and apparent diffusivity arise from distinct physiological processes and potential biomarkers associated with perivascular space volume and pCSF flow, respectively. Additionally, the mean diffusivities of white matter showed cardiac-cycle dependencies similar to pCSF, although a delay relative to the peak time of apparent diffusivity in pCSF was present, suggesting that dynDTIlow-b could potentially reveal the dynamics of magnetic resonance imaging-invisible pCSF surrounding small arteries and arterioles in white matter; this delay may result from pulse wave propagation along penetrating arteries. In conclusion, the vasodilation-induced increases in axial and radial diffusivities of pCSF and mean diffusivities of white matter are consistent with the notion that arterial pulsation can accelerate pCSF flow in human brain. Furthermore, the proposed dynDTIlow-b technique can capture various pCSF dynamics in artery pulsation.},
}
@article {pmid38794022,
year = {2024},
author = {Khabti, J and AlAhmadi, S and Soudani, A},
title = {Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {10},
pages = {},
pmid = {38794022},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Attention/physiology ; },
abstract = {The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.},
}
@article {pmid38793895,
year = {2024},
author = {Akhter, J and Naseer, N and Nazeer, H and Khan, H and Mirtaheri, P},
title = {Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain-Computer Interface Application.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {10},
pages = {},
pmid = {38793895},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Spectroscopy, Near-Infrared/methods ; *Algorithms ; Male ; *Neural Networks, Computer ; Adult ; Female ; Young Adult ; Brain/physiology/diagnostic imaging ; },
abstract = {Brain-computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.},
}
@article {pmid38793855,
year = {2024},
author = {Zhang, X and Li, J and Zhang, R and Liu, T},
title = {A Brain-Controlled and User-Centered Intelligent Wheelchair: A Feasibility Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {10},
pages = {},
pmid = {38793855},
issn = {1424-8220},
mesh = {*Wheelchairs ; Humans ; *Brain-Computer Interfaces ; *Feasibility Studies ; Persons with Disabilities ; Equipment Design ; Ergonomics/methods ; User-Centered Design ; Surveys and Questionnaires ; },
abstract = {Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain-machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.},
}
@article {pmid38793401,
year = {2024},
author = {Wang, S and Yan, X and Jiao, X and Yang, H},
title = {Experimental Study of the Implantation Process for Array Electrodes into Highly Transparent Agarose Gel.},
journal = {Materials (Basel, Switzerland)},
volume = {17},
number = {10},
pages = {},
pmid = {38793401},
issn = {1996-1944},
abstract = {Brain-computer interface (BCI) technology is currently a cutting-edge exploratory problem in the field of human-computer interaction. However, in experiments involving the implantation of electrodes into brain tissue, particularly high-speed or array implants, existing technologies find it challenging to observe the damage in real time. Considering the difficulties in obtaining biological brain tissue and the challenges associated with real-time observation of damage during the implantation process, we have prepared a transparent agarose gel that closely mimics the mechanical properties of biological brain tissue for use in electrode implantation experiments. Subsequently, we developed an experimental setup for synchronized observation of the electrode implantation process, utilizing the Digital Gradient Sensing (DGS) method. In the single electrode implantation experiments, with the increase in implantation speed, the implantation load increases progressively, and the tissue damage region around the electrode tip gradually diminishes. In the array electrode implantation experiments, compared to a single electrode, the degree of tissue indentation is more severe due to the coupling effect between adjacent electrodes. As the array spacing increases, the coupling effect gradually diminishes. The experimental results indicate that appropriately increasing the velocity and array spacing of the electrodes can enhance the likelihood of successful implantation. The research findings of this article provide valuable guidance for the damage assessment and selection of implantation parameters during the process of electrode implantation into real brain tissue.},
}
@article {pmid38793168,
year = {2024},
author = {Bi, M and Zhang, H and Ma, Y and Wang, H and Wang, W and Shi, Y and Sheng, W and Li, Q and Gao, G and Cai, L},
title = {Modulation Steering Motion by Quantitative Electrical Stimulation in Pigeon Robots.},
journal = {Micromachines},
volume = {15},
number = {5},
pages = {},
pmid = {38793168},
issn = {2072-666X},
support = {31500858, 61973159//National Natural Science Foundation of China/ ; 2021YFB3201200//National Key Research and Development Program of China/ ; },
abstract = {The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain-machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2-3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain-machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.},
}
@article {pmid38790494,
year = {2024},
author = {Jiang, C and Dai, Y and Ding, Y and Chen, X and Li, Y and Tang, Y},
title = {TSANN-TG: Temporal-Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition.},
journal = {Brain sciences},
volume = {14},
number = {5},
pages = {},
pmid = {38790494},
issn = {2076-3425},
support = {21ZR1481500//Science and Technology Commission of Shanghai Municipality/ ; },
abstract = {Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain-computer interfaces. In this paper, we propose TSANN-TG (temporal-spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal-spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block. Leveraging the distinct temporal scales of features from EEG signals, TSANN-TG incorporates attention mechanisms for efficient feature extraction. By constructing task-specific adjacency matrices, the graph convolutional network with an attention mechanism captures the dynamic changes in dependency information between EEG channels. Additionally, TSANN-TG emphasizes feature integration at multiple levels, leading to improved performance in emotion-recognition tasks. Our proposed TSANN-TG is applied to both our FTEHD dataset and the publicly available DEAP dataset. Comparative experiments and ablation studies highlight the excellent recognition results achieved. Compared to the baseline algorithms, TSANN-TG demonstrates significant enhancements in accuracy and F1 score on the two benchmark datasets for four types of cognitive tasks. These results underscore the significant potential of the TSANN-TG method to advance EEG-based emotion recognition.},
}
@article {pmid38790476,
year = {2024},
author = {Huang, D and Wang, Y and Fan, L and Yu, Y and Zhao, Z and Zeng, P and Wang, K and Li, N and Shen, H},
title = {Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain-Computer Interface.},
journal = {Brain sciences},
volume = {14},
number = {5},
pages = {},
pmid = {38790476},
issn = {2076-3425},
support = {JCKY2020550B003//Defense Industrial Technology Development Program/ ; },
abstract = {In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time-frequency maps using continuous wavelet transform. Based on these time-frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.},
}
@article {pmid38790456,
year = {2024},
author = {Yang, G and Liu, J},
title = {A New Framework Combining Diffusion Models and the Convolution Classifier for Generating Images from EEG Signals.},
journal = {Brain sciences},
volume = {14},
number = {5},
pages = {},
pmid = {38790456},
issn = {2076-3425},
support = {2018YFB1304600//National Key R\&amp;D Program of China/ ; JCTD-2018-11//CAS Interdisciplinary Innovation Team/ ; },
abstract = {The generation of images from electroencephalography (EEG) signals has become a popular research topic in recent research because it can bridge the gap between brain signals and visual stimuli and has wide application prospects in neuroscience and computer vision. However, due to the high complexity of EEG signals, the reconstruction of visual stimuli through EEG signals continues to pose a challenge. In this work, we propose an EEG-ConDiffusion framework that involves three stages: feature extraction, fine-tuning of the pretrained model, and image generation. In the EEG-ConDiffusion framework, classification features of EEG signals are first obtained through the feature extraction block. Then, the classification features are taken as conditions to fine-tune the stable diffusion model in the image generation block to generate images with corresponding semantics. This framework combines EEG classification and image generation means to enhance the quality of generated images. Our proposed framework was tested on an EEG-based visual classification dataset. The performance of our framework is measured by classification accuracy, 50-way top-k accuracy, and inception score. The results indicate that the proposed EEG-Condiffusion framework can extract effective classification features and generate high-quality images from EEG signals to realize EEG-to-image conversion.},
}
@article {pmid38790441,
year = {2024},
author = {Shiam, AA and Hassan, KM and Islam, MR and Almassri, AMM and Wagatsuma, H and Molla, MKI},
title = {Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.},
journal = {Brain sciences},
volume = {14},
number = {5},
pages = {},
pmid = {38790441},
issn = {2076-3425},
support = {Japan Society for the Promotion of Science//S19169/ ; },
abstract = {Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.},
}
@article {pmid38790340,
year = {2024},
author = {Polo-Hortigüela, C and Maximo, M and Jara, CA and Ramon, JL and Garcia, GJ and Ubeda, A},
title = {A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {5},
pages = {},
pmid = {38790340},
issn = {2306-5354},
abstract = {In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.},
}
@article {pmid38789029,
year = {2024},
author = {Awuah, WA and Ahluwalia, A and Darko, K and Sanker, V and Tan, JK and Tenkorang, PO and Ben-Jaafar, A and Ranganathan, S and Aderinto, N and Mehta, A and Shah, MH and Lee Boon Chun, K and Abdul-Rahman, T and Atallah, O},
title = {Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications.},
journal = {World neurosurgery},
volume = {189},
number = {},
pages = {138-153},
doi = {10.1016/j.wneu.2024.05.104},
pmid = {38789029},
issn = {1878-8769},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography/methods ; Neurosurgical Procedures/methods ; Neurosurgery ; Nervous System Diseases/surgery ; },
abstract = {Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.},
}
@article {pmid38788849,
year = {2024},
author = {Denis-Robichaud, J and Barbeau-Grégoire, N and Gauthier, ML and Dufour, S and Roy, JP and Buczinski, S and Dubuc, J},
title = {Validity of luminometry and bacteriological tests for diagnosing intramammary infection at dry-off in dairy cows.},
journal = {Journal of dairy science},
volume = {107},
number = {9},
pages = {7221-7229},
doi = {10.3168/jds.2024-24693},
pmid = {38788849},
issn = {1525-3198},
mesh = {Animals ; Cattle ; Female ; *Animal Husbandry/methods ; *Bacteriological Techniques/standards/veterinary ; Cross-Sectional Studies ; Dairying/methods ; *Mastitis, Bovine/diagnosis ; Reproducibility of Results ; },
abstract = {The objective of this cross-sectional study was to estimate the validity of laboratory culture, Petrifilm and Tri-Plate on-farm culture systems, as well as luminometry to correctly identify IMI at dry-off in dairy cows, considering all tests to be imperfect. From September 2020 until December 2021, we collected composite milk samples from cows before dry-off and divided them into 4 aliquots for luminometry, Petrifilm (aerobic count), Tri-Plate, and laboratory culture tests. We assessed multiple thresholds of relative light units (RLU) for luminometry, and we used thresholds of ≥100 cfu/mL for the laboratory culture, ≥50 cfu/mL for Petrifilm, and ≥1 cfu for Tri-Plate tests. We fitted Bayesian latent class analysis models to estimate the sensitivity (Se) and specificity (Sp) for each test to identify IMI, with 95% credibility interval (BCI). Using different prevalence measures (0.30, 0.50, and 0.70), we calculated the predictive values (PV) and misclassification cost terms (MCT) at different false negative-to-false-positive ratios (FN:FP). A total of 333 cows were enrolled in the study from one commercial Holstein herd. The validity of the luminometry was poor for all thresholds, with an Se of 0.51 (95% BCI = 0.43-0.59) and Sp of 0.38 (95% BCI = 0.26-0.50) when using a threshold of ≥150 RLU. The laboratory culture had an Se of 0.93 (95% BCI = 0.85-0.98) and Sp of 0.69 (95% BCI = 0.49-0.89); the Petrifilm had an Se of 0.91 (95% BCI = 0.80-0.98) and Sp of 0.71 (95% BCI = 0.51-0.90); and the Tri-Plate had an Se of 0.65 (95% BCI = 0.53-0.82) and Sp of 0.85 (95% BCI = 0.66-0.97). Bacteriological tests had good PV, with comparable positive PV for all 3 tests, but lower negative PV for the Tri-Plate compared with the laboratory culture and the Petrifilm. For a prevalence of IMI of 0.30, all 3 tests had similar MCT, but for prevalence of 0.50 and 0.70, the Tri-Plate had higher MCT in scenarios where leaving a cow with IMI untreated is considered to have greater detrimental effects than treating a healthy cow (i.e., FN:FP of 3:1). Our results showed that the bacteriological tests have adequate validity to diagnose IMI at dry-off, but luminometry does not. We concluded that although luminometry is not useful to identify IMI at dry-off, the Petrifilm and Tri-Plate tests performed similarly to laboratory culture, depending on the prevalence and importance of the FP and FN results.},
}
@article {pmid38788835,
year = {2024},
author = {Denis-Robichaud, J and Oliveira, AP and Sica, A and Soriano, S and Araújo, RL and Pereira, MHC and Pohler, KG and Cerri, RLA and Vasconcelos, JLM},
title = {Is prolonged luteal phase a problem in lactating Holstein cows?.},
journal = {Journal of dairy science},
volume = {107},
number = {10},
pages = {8582-8591},
doi = {10.3168/jds.2024-24792},
pmid = {38788835},
issn = {1525-3198},
mesh = {Animals ; Female ; Cattle ; *Lactation ; *Luteal Phase ; *Insemination, Artificial/veterinary ; Pregnancy ; Estrus Synchronization ; Corpus Luteum ; Estrous Cycle ; Brazil ; },
abstract = {In this study, the main objective was to assess if long luteal phases could have causes other than pregnancy loss. We enrolled Holstein dairy cows ≥50 DIM from a commercial herd in Brazil from October 2016 to August 2017. All cows received an estradiol-based synchronization protocol, and, on the day of insemination (d 0), were randomly assigned either an AI or a placebo insemination (PBO) in a 3:1 ratio. An ultrasound was used to assess the presence of a corpus luteum (CL) on d 17, 24, and 31, which, combined to the information from patches for the detection of estrus, was used to determine the length of the luteal phase following AI or PBO. Pregnancy was assessed by ultrasound on d 31 and cows that were pregnant were excluded from the analyses. The length of the estrous cycles was categorized as short (<17 d), normal (17-23 d), long (24-30 d), and very long (≥31 d). We compared the proportion of cows in each category between the AI and PBO groups using a cumulative ordinal mixed model. We define prolonged luteal phase as estrous cycles ≥24 d and tested its association with potential risk factors (parity, season, DIM, uterine size and position score, milk production, BCS, and the presence of a CL at enrollment to the synchronization protocol) using mixed logistic regression models. Results are presented as odds ratio (OR) and 95% Bayesian credible intervals (BCI). Data from 876 inseminations (AI: n = 616, PBO: n = 260) was collected. Overall, 12% of estrous cycles were short, 31% were normal, 19% were long, and 38% were very long. There was no difference in the odds of being in longer estrous cycle categories for the AI compared with the PBO group (OR = 0.92; 95% BCI = 0.76-1.10). Season and presence of a CL at enrollment were associated with prolonged luteal phase. In the AI group, there was a possible effect of early pregnancy losses on the lifespan of the CL, but not the PBO group, which led us to conclude that long and very long estrous cycles were not all caused by the embryonic loss. In fact, the high prevalence of cows with an extended CL lifespan in the present study suggests this could be an under- or miss-reported characteristic of high-producing lactating Holstein cows. This finding may have important repercussions in the understanding of the CL function physiology of lactating Holstein cows.},
}
@article {pmid38788794,
year = {2024},
author = {Semeraro, F and Schnaubelt, S and Malta Hansen, C and Bignami, EG and Piazza, O and Monsieurs, KG},
title = {Cardiac arrest and cardiopulmonary resuscitation in the next decade: Predicting and shaping the impact of technological innovations.},
journal = {Resuscitation},
volume = {200},
number = {},
pages = {110250},
doi = {10.1016/j.resuscitation.2024.110250},
pmid = {38788794},
issn = {1873-1570},
mesh = {Humans ; *Cardiopulmonary Resuscitation/methods/instrumentation ; *Heart Arrest/therapy ; Inventions ; Forecasting ; Artificial Intelligence ; Defibrillators ; },
abstract = {INTRODUCTION: Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.<br><br>METHODS: We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.<br><br>RESULTS: We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.<br><br>CONCLUSION: Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.},
}
@article {pmid38788704,
year = {2024},
author = {Chen, K and Forrest, AM and Burgos, GG and Kozai, TDY},
title = {Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
pmid = {38788704},
issn = {1741-2552},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Microelectrodes ; *Mice, Inbred C57BL ; *Electrodes, Implanted ; Mice ; Male ; Female ; Brain-Computer Interfaces ; Nerve Net/physiology ; Neurons/physiology ; Primary Visual Cortex/physiology ; Photic Stimulation/methods ; Foreign-Body Reaction/etiology ; CA1 Region, Hippocampal/physiology ; },
abstract = {Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain-computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.Approach.This study utilized single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.},
}
@article {pmid38785851,
year = {2024},
author = {Pan, Y and Wang, H and Geng, Y and Lai, J and Hu, S},
title = {Latent Profile Analysis of Suicidal Ideation in Chinese Individuals with Bipolar Disorder.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {14},
number = {5},
pages = {},
pmid = {38785851},
issn = {2076-328X},
support = {226-2022-00193, 226-2022-00002//Fundamental Research Funds for the Central Universities/ ; 2023YFC2506200//the National Key Research and Development Program of China/ ; No. 2021R52016//the Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; No. 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; },
abstract = {Individuals with bipolar disorder (BD) have a greater suicide risk than the general population. In this study, we employed latent profile analysis (LPA) to explore whether Chinese individuals with different phases of BD differed at the levels of suicidal ideation. We recruited 517 patients. Depressive symptoms were measured using the 24-item Hamilton Depression Rating Scale (HAMD-24), and manic symptoms were evaluated using the Young Mania Rating Scale (YMRS). The extent of suicidal thoughts was determined through the Beck Scale for Suicide Ideation (BSSI). The scores of HAMD and YMRS were used to perform LPA. LPA categorized participants into three classes: one exhibiting severe depressive and mild manic symptomatology, another showing severe depressive and severe manic symptomatology, and the third one displaying severe depressive and intermediate manic symptomatology. Suicidal ideation levels were found to be remarkably elevated across all three classes. Additionally, the three classes showed no significant differences in terms of suicidal ideation. Our research confirms the link between depressive symptoms and suicide, independent of the manic symptoms. These findings carry meaning as they provide insight into the suicide risk profiles within different phases of BD.},
}
@article {pmid38785685,
year = {2024},
author = {Kong, X and Wu, C and Chen, S and Wu, T and Han, J},
title = {Efficient Feature Learning Model of Motor Imagery EEG Signals with L1-Norm and Weighted Fusion.},
journal = {Biosensors},
volume = {14},
number = {5},
pages = {},
pmid = {38785685},
issn = {2079-6374},
support = {2022YFF1202400, 2023YFF1204200, 2023YFF1203900//National Key Research and Development Program of China/ ; },
mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Imagination/physiology ; },
abstract = {Brain-computer interface (BCI) for motor imagery is an advanced technology used in the field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram feature classification, BCI systems often misrecognize user commands. Although many state-of-the-art feature selection methods aim to enhance classification accuracy, they usually overlook the interrelationships between individual features, indirectly impacting the accuracy of feature classification. To overcome this issue, we propose an adaptive feature learning model that employs a Riemannian geometric approach to generate a feature matrix from electroencephalogram signals, serving as the model's input. By integrating the enhanced adaptive L1 penalty and weighted fusion penalty into the sparse learning model, we select the most informative features from the matrix. Specifically, we measure the importance of features using mutual information and introduce an adaptive weight construction strategy to penalize regression coefficients corresponding to each variable adaptively. Moreover, the weighted fusion penalty balances weight differences among correlated variables, reducing the model's overreliance on specific variables and enhancing accuracy. The performance of the proposed method was validated on BCI Competition IV datasets IIa and IIb using the support vector machine. Experimental results demonstrate the effectiveness and superiority of the proposed model compared to the existing models.},
}
@article {pmid38784100,
year = {2024},
author = {Zhang, Y and Hua, W and Zhou, Z and Zhu, H and Xiong, J and Zhang, J and Chen, D and Guo, J},
title = {A novel acupuncture technique at the Zusanli point based on virtual reality and EEG: a pilot study.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1269903},
pmid = {38784100},
issn = {1662-4548},
abstract = {INTRODUCTION: Acupuncture is a Traditional Chinese Medicine (TCM) method that achieves therapeutic effects through the interaction of neurotransmitters and neural regulation. It is generally carried out manually, making the related process expert-biased. Meanwhile, the neural stimulation effect of acupuncture is difficult to track objectively. In recent years, virtual reality (VR) in medicine has been on the fast lane to widespread use, especially in therapeutic stimulation. However, the use of related technologies in acupuncture has not been reported.<br><br>METHODS: In this work, a novel acupuncture stimulation technique using VR is proposed. To track the stimulation effect, the electroencephalogram (EEG) is used as the marker to validate brain activities under acupuncture.<br><br>RESULTS AND DISCUSSION: After statistically analyzing the data of 24 subjects during acupuncture at the "Zusanli (ST36)" acupoint, it has been determined that Virtual Acupuncture (VA) has at least a 63.54% probability of inducing similar EEG activities as in Manual Acupuncture (MA). This work may provide a new solution for researchers and clinical practitioners using Brain-Computer Interface (BCI) in acupuncture.},
}
@article {pmid38784094,
year = {2024},
author = {Gouret, A and Le Bars, S and Porssut, T and Waszak, F and Chokron, S},
title = {Advancements in brain-computer interfaces for the rehabilitation of unilateral spatial neglect: a concise review.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1373377},
pmid = {38784094},
issn = {1662-4548},
abstract = {This short review examines recent advancements in neurotechnologies within the context of managing unilateral spatial neglect (USN), a common condition following stroke. Despite the success of brain-computer interfaces (BCIs) in restoring motor function, there is a notable absence of effective BCI devices for treating cerebral visual impairments, a prevalent consequence of brain lesions that significantly hinders rehabilitation. This review analyzes current non-invasive BCIs and technological solutions dedicated to cognitive rehabilitation, with a focus on visuo-attentional disorders. We emphasize the need for further research into the use of BCIs for managing cognitive impairments and propose a new potential solution for USN rehabilitation, by combining the clinical subtleties of this syndrome with the technological advancements made in the field of neurotechnologies.},
}
@article {pmid38784093,
year = {2024},
author = {Chen, W and Liu, X and Wan, P and Chen, Z and Chen, Y},
title = {Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1393206},
pmid = {38784093},
issn = {1662-4548},
abstract = {In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends.},
}
@article {pmid38783889,
year = {2024},
author = {Arulkumaran, K and Di Vincenzo, M and Dossa, RFJ and Akiyama, S and Ogawa Lillrank, D and Sato, M and Tomeoka, K and Sasai, S},
title = {A comparison of visual and auditory EEG interfaces for robot multi-stage task control.},
journal = {Frontiers in robotics and AI},
volume = {11},
number = {},
pages = {1329270},
pmid = {38783889},
issn = {2296-9144},
abstract = {Shared autonomy holds promise for assistive robotics, whereby physically-impaired people can direct robots to perform various tasks for them. However, a robot that is capable of many tasks also introduces many choices for the user, such as which object or location should be the target of interaction. In the context of non-invasive brain-computer interfaces for shared autonomy-most commonly electroencephalography-based-the two most common choices are to provide either auditory or visual stimuli to the user-each with their respective pros and cons. Using the oddball paradigm, we designed comparable auditory and visual interfaces to speak/display the choices to the user, and had users complete a multi-stage robotic manipulation task involving location and object selection. Users displayed differing competencies-and preferences-for the different interfaces, highlighting the importance of considering modalities outside of vision when constructing human-robot interfaces.},
}
@article {pmid38782530,
year = {2024},
author = {Yaeger, K and Mocco, J},
title = {Venous Sinus Stent to Treat Paralysis.},
journal = {Neurosurgery clinics of North America},
volume = {35},
number = {3},
pages = {375-378},
doi = {10.1016/j.nec.2024.03.003},
pmid = {38782530},
issn = {1558-1349},
mesh = {Humans ; *Stents ; Animals ; *Paralysis/surgery ; Cranial Sinuses/surgery ; Electrodes, Implanted ; },
abstract = {Transvenous treatment of paralysis is a concept less than a decade old. The Stentrode (Synchron, Inc, New York, USA) is a novel electrode on stent device intended to be implanted in the superior sagittal sinus adjacent to the motor cortex. Initial animal studies in sheep demonstrated the safety of the implant as well as its accuracy in detecting neural signals at both short and long term. Early human trials have shown the safety of the device and demonstrated the use of the Stentrode system in facilitating patients with paralysis to carry out daily activities such as texting, email, and personal finance. This is an emerging technology with promise, although certainly more research is required to better understand the capabilities and limitations of the device.},
}
@article {pmid38782122,
year = {2024},
author = {Kapgate, DD},
title = {The use of happy faces as visual stimuli improves the performance of the hybrid SSVEP+P300 brain computer interface.},
journal = {Journal of neuroscience methods},
volume = {408},
number = {},
pages = {110170},
doi = {10.1016/j.jneumeth.2024.110170},
pmid = {38782122},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; *Event-Related Potentials, P300/physiology ; Young Adult ; Female ; *Electroencephalography/methods ; *Photic Stimulation/methods ; Adult ; Facial Expression ; Brain/physiology ; Facial Recognition/physiology ; },
abstract = {BACKGROUND: This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli.<br><br>NEW METHOD: In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF).<br><br>RESULTS AND CONCLUSIONS: The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.},
}
@article {pmid38781932,
year = {2024},
author = {R, V and Ramasubba Reddy, M},
title = {Optimizing motor imagery BCI models with hard trials removal and model refinement.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {4},
pages = {},
doi = {10.1088/2057-1976/ad4f8e},
pmid = {38781932},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography/methods ; *Algorithms ; Imagination ; Artificial Intelligence ; Neural Networks, Computer ; },
abstract = {Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance. The first method leverages model prediction scores to discern hard trials. The second approach employs a quantitative explainable artificial intelligence (XAI) approach, enabling a more transparent and interpretable means of hard trial identification. The identified hard trials are removed from the entire motor imagery training and validation dataset, and the deep learning model is further re-trained using the dataset without hard trials. To evaluate the efficacy of these proposed methods, experiments were conducted on the Open BMI dataset. The results for hold-out analysis show that the proposed quantitative XAI- based hard trial removal method has statistically improved the average classification accuracy of the baseline deep CNN model from 63.77% to 68.70%, withp-value =7.66e-11for the subject-specific MI classification. Additionally, analyzing the scalp map representing the average relevance scores of correctly classified trials compared to a misclassified trial provides a deeper insight into identifying hard trials. The results indicate that the proposed quantitaive-based XAI approach outperformes the prediction-score-based approach in hard trial identification.},
}
@article {pmid38781127,
year = {2024},
author = {Kojima, S and Kanoh, S},
title = {An auditory brain-computer interface based on selective attention to multiple tone streams.},
journal = {PloS one},
volume = {19},
number = {5},
pages = {e0303565},
pmid = {38781127},
issn = {1932-6203},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electroencephalography/methods ; Adult ; *Attention/physiology ; *Acoustic Stimulation/methods ; Auditory Perception/physiology ; Young Adult ; Event-Related Potentials, P300/physiology ; Electrooculography/methods ; },
abstract = {In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user's right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject's selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.},
}
@article {pmid38781061,
year = {2024},
author = {Chen, SY and Chang, CM and Chiang, KJ and Wei, CS},
title = {SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {2027-2037},
doi = {10.1109/TNSRE.2024.3404432},
pmid = {38781061},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; Male ; *Algorithms ; *Electroencephalography ; Adult ; Female ; Neural Networks, Computer ; Young Adult ; Calibration ; Reproducibility of Results ; },
abstract = {Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.},
}
@article {pmid38781054,
year = {2024},
author = {Chen, D and Huang, H and Guan, Z and Pan, J and Li, Y},
title = {An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {10},
pages = {2956-2967},
doi = {10.1109/TBME.2024.3404131},
pmid = {38781054},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Young Adult ; Brain/physiology ; Algorithms ; },
abstract = {OBJECTIVE: Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet.<br><br>METHOD: Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects.<br><br>MAIN RESULTS: DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% &#xb1; 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% &#xb1; 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%.<br><br>SIGNIFICANCE: An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.},
}
@article {pmid38780699,
year = {2024},
author = {Ge, Q and Lock, M and Yang, X and Ding, Y and Yue, J and Zhao, N and Hu, YS and Zhang, Y and Yao, M and Zang, YF},
title = {Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T.},
journal = {Neuroinformatics},
volume = {22},
number = {4},
pages = {421-435},
pmid = {38780699},
issn = {1559-0089},
mesh = {Humans ; *Magnetic Resonance Imaging/methods/standards ; *Transcranial Magnetic Stimulation/methods/standards ; Male ; Female ; Reproducibility of Results ; Adult ; *Brain/physiology/diagnostic imaging ; Young Adult ; Brain Mapping/methods ; Image Processing, Computer-Assisted/methods ; },
abstract = {US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.},
}
@article {pmid38778312,
year = {2024},
author = {Lu, J and Jin, Y and Liang, S and Wang, Q and Li, X and Li, T},
title = {Risk factors and their association network for young adults' suicidality: a cross-sectional study.},
journal = {BMC public health},
volume = {24},
number = {1},
pages = {1378},
pmid = {38778312},
issn = {1471-2458},
support = {82001440//National Natural Science Foundation of China/ ; 82230046//National Natural Science Foundation of China/ ; 2022C03096//Key Research and Development Program of Zhejiang Province/ ; 2018B030334001//Special Foundation for Brain Research from Science and Technology Program of Guangdong/ ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence &amp; Key Project for Hangzhou Medical Disciplines/ ; },
mesh = {Humans ; Male ; Risk Factors ; Female ; Cross-Sectional Studies ; Young Adult ; China/epidemiology ; *Suicidal Ideation ; *Students/psychology/statistics &amp; numerical data ; Suicide, Attempted/statistics &amp; numerical data/psychology ; Adolescent ; Universities ; Adult ; Suicide/psychology/statistics &amp; numerical data ; Machine Learning ; },
abstract = {BACKGROUND: Understanding the intricate influences of risk factors contributing to suicide among young individuals remains a challenge. The current study employed interpretable machine learning and network analysis to unravel critical suicide-associated factors in Chinese university students.<br><br>METHODS: A total of 68,071 students were recruited between Sep 2016 and Sep 2020 in China. Students reported their lifetime experiences with suicidal thoughts and behaviors, categorized as suicide ideation (SI), suicide plan (SP), and suicide attempt (SA). We assessed 36 suicide-associated factors including psychopathology, family environment, life events, and stigma. Local interpretations were provided using Shapley additive explanation (SHAP) interaction values, while a mixed graphical model facilitated a global understanding of their interplay.<br><br>RESULTS: Local explanations based on SHAP interaction values suggested that psychoticism and depression severity emerged as pivotal factors for SI, while paranoid ideation strongly correlated with SP and SA. In addition, childhood neglect significantly predicted SA. Regarding the mixed graphical model, a hierarchical structure emerged, suggesting that family factors preceded proximal psychopathological factors, with abuse and neglect retaining unique effects. Centrality indices derived from the network highlighted the importance of subjective socioeconomic status and education in connecting various risk factors.<br><br>CONCLUSIONS: The proximity of psychopathological factors to suicidality underscores their significance. The global structures of the network suggested that co-occurring factors influence suicidal behavior in a hierarchical manner. Therefore, prospective prevention strategies should take into account the hierarchical structure and unique trajectories of factors.},
}
@article {pmid38776898,
year = {2024},
author = {Junqueira, B and Aristimunha, B and Chevallier, S and de Camargo, RY},
title = {A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad4f18},
pmid = {38776898},
issn = {1741-2552},
mesh = {*Deep Learning ; *Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Algorithms ; },
abstract = {Objective:Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks. While deep learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean alignment (EA) due to its ease of use, low computational complexity, and compatibility with DL models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals.Approach:We used EA as a pre-processing step to train shared DL models with data from multiple subjects and evaluated their transferability to new subjects.Main results:Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.71%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.Significance:EA succeeds in the task of improving transfer learning performance with DL models and, could be used as a standard pre-processing technique.},
}
@article {pmid38774060,
year = {2024},
author = {Fukushi, T},
title = {East Asian perspective of responsible research and innovation in neurotechnology.},
journal = {IBRO neuroscience reports},
volume = {16},
number = {},
pages = {582-597},
pmid = {38774060},
issn = {2667-2421},
abstract = {After more than half a century of research and development (R&amp;D), Brain-computer interface (BCI)-based Neurotechnology continues to progress as one of the leading technologies of the 2020 s worldwide. Various reports and academic literature in Europe and the United States (U.S.) have outlined the trends in the R&amp;D of neurotechnology and the consideration of ethical issues, and the importance of the formulation of ethical principles, guidance and industrial standards as well as the development of relevant human resources has been discussed. However, limited number studies have focused on neurotechnology R&amp;D, the dissemination of neuroethics related to the academic foundation advancing the discussion on ethical principles, guidance and standards or human resource development in the Asian region. This study fills in this gap in understanding of Eastern Asian (China, Korea and Japan) situation based on the participation in activities to develop ethical principles, guidance, and industrial standards for appropriate use of neurotechnology, in addition to literature survey and clinical registries' search investigation reflecting the trends in neurotechnology R&amp;D as well as its social implication in Asian region. The current study compared the results with the situation in Europa and the U.S. and discussed issues that need to be addressed in the future and discussed the significance and potential of corporate consortium initiatives in Japan and examples of ethics and governance activities in Asian Countries.},
}
@article {pmid38773929,
year = {2024},
author = {Tang, H and Li, Y and Liao, S and Liu, H and Qiao, Y and Zhou, J},
title = {Multifunctional Conductive Hydrogel Interface for Bioelectronic Recording and Stimulation.},
journal = {Advanced healthcare materials},
volume = {13},
number = {22},
pages = {e2400562},
doi = {10.1002/adhm.202400562},
pmid = {38773929},
issn = {2192-2659},
support = {62201624//National Natural Science Foundation of China/ ; 32000939//National Natural Science Foundation of China/ ; 21775168//National Natural Science Foundation of China/ ; 22174167//National Natural Science Foundation of China/ ; 51861145202//National Natural Science Foundation of China/ ; U20A20168//National Natural Science Foundation of China/ ; 2024A1515012056//Guangdong Basic and Applied Basic Research Foundation/ ; 2019A1515111183//Guangdong Basic and Applied Basic Research Foundation/ ; RCBS20221008093310024//Shenzhen Science and Technology Program/ ; JCYJ20190807160401657//Shenzhen Research Funding Program/ ; JCYJ201908073000608//Shenzhen Research Funding Program/ ; JCYJ20150831192224146//Shenzhen Research Funding Program/ ; BR2023KF02010//Open Research Fund Program of Beijing National Research Center for Information Science and Technology/ ; 2020B1212060077//Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province/ ; },
mesh = {*Hydrogels/chemistry ; Humans ; *Electric Conductivity ; Wearable Electronic Devices ; Biocompatible Materials/chemistry ; Animals ; Electronics ; },
abstract = {The past few decades have witnessed the rapid advancement and broad applications of flexible bioelectronics, in wearable and implantable electronics, brain-computer interfaces, neural science and technology, clinical diagnosis, treatment, etc. It is noteworthy that soft and elastic conductive hydrogels, owing to their multiple similarities with biological tissues in terms of mechanics, electronics, water-rich, and biological functions, have successfully bridged the gap between rigid electronics and soft biology. Multifunctional hydrogel bioelectronics, emerging as a new generation of promising material candidates, have authentically established highly compatible and reliable, high-quality bioelectronic interfaces, particularly in bioelectronic recording and stimulation. This review summarizes the material basis and design principles involved in constructing hydrogel bioelectronic interfaces, and systematically discusses the fundamental mechanism and unique advantages in bioelectrical interfacing with the biological surface. Furthermore, an overview of the state-of-the-art manufacturing strategies for hydrogel bioelectronic interfaces with enhanced biocompatibility and integration with the biological system is presented. This review finally exemplifies the unprecedented advancement and impetus toward bioelectronic recording and stimulation, especially in implantable and integrated hydrogel bioelectronic systems, and concludes with a perspective expectation for hydrogel bioelectronics in clinical and biomedical applications.},
}
@article {pmid38771245,
year = {2024},
author = {Guo, B and Mao, T and Tao, R and Fu, S and Deng, Y and Liu, Z and Wang, M and Wang, R and Zhao, W and Chai, Y and Jiang, C and Rao, H},
title = {Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {5},
pages = {},
doi = {10.1093/cercor/bhae212},
pmid = {38771245},
issn = {1460-2199},
support = {32200889//National Natural Science Foundation of China/ ; 2021114002//Shanghai International Studies University Research Projects/ ; },
mesh = {Humans ; Male ; Female ; Adult ; *Cerebrovascular Circulation/physiology ; Reproducibility of Results ; *Rest/physiology ; *Brain/diagnostic imaging/physiology/blood supply ; Young Adult ; *Magnetic Resonance Imaging/methods ; Perfusion Imaging/methods ; Psychomotor Performance/physiology ; Circadian Rhythm/physiology ; Arousal/physiology ; },
abstract = {Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.},
}
@article {pmid38770525,
year = {2024},
author = {Xu, M and Zhang, Y and Zhang, Y and Liu, X and Qing, K},
title = {EEG biomarkers analysis in different cognitive impairment after stroke: an exploration study.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1358167},
pmid = {38770525},
issn = {1664-2295},
abstract = {Stroke is a cerebrovascular illness that brings about the demise of brain tissue. It is the third most prevalent cause of mortality worldwide and a significant contributor to physical impairment. Generally, stroke is triggered by blood clots obstructing the brain's blood vessels, or when these vessels rupture. And, the cognitive impairment's evaluation and detection after stroke is crucial research issue and significant project. Thus, the objective of this work is to explore an potential neuroimage tool and find their EEG biomarkers to evaluate and detect four cognitive impairment levels after stroke. In this study, power density spectrum (PSD), functional connectivity map, and one-way ANOVA methods were proposed to analyze the EEG biomarker differences, and the number of patient participants were thirty-two human including eight healthy control, mild, moderate, severe cognitive impairment levels, respectively. Finally, healthy control has significant PSD differences compared to mid, moderate and server cognitive impairment groups. And, the theta and alpha bands of severe cognitive impairment groups have presented consistent superior PSD power at the right frontal cortex, and the theta and beta bands of mild, moderated cognitive impairment (MoCI) groups have shown significant similar superior PSD power tendency at the parietal cortex. The significant gamma PSD power difference has presented at the left-frontal cortex in the mild cognitive impairment (MCI) groups, and severe cognitive impairment (SeCI) group has shown the significant PSD power at the gamma band of parietal cortex. At the point of functional connectivity map, the SeCI group appears to have stronger functional connectivity compared to the other groups. In conclusion, EEG biomarkers can be applied to classify different cognitive impairment groups after stroke. These findings provide a new approach for early detection and diagnosis of cognitive impairment after stroke and also for the development of new treatment options.},
}
@article {pmid38769157,
year = {2024},
author = {Silva, AB and Liu, JR and Metzger, SL and Bhaya-Grossman, I and Dougherty, ME and Seaton, MP and Littlejohn, KT and Tu-Chan, A and Ganguly, K and Moses, DA and Chang, EF},
title = {A bilingual speech neuroprosthesis driven by cortical articulatory representations shared between languages.},
journal = {Nature biomedical engineering},
volume = {8},
number = {8},
pages = {977-991},
pmid = {38769157},
issn = {2157-846X},
support = {T32 GM007618/GM/NIGMS NIH HHS/United States ; T32GM007618//U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (NIGMS)/ ; U01 DC018671/DC/NIDCD NIH HHS/United States ; F30 DC021872/DC/NIDCD NIH HHS/United States ; DC018671-01A1//Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)/ ; },
mesh = {Humans ; *Speech/physiology ; *Multilingualism ; *Electrocorticography/methods ; Motor Cortex/physiology ; Male ; Brain-Computer Interfaces ; Language ; Deep Learning ; Neural Prostheses ; },
abstract = {Advancements in decoding speech from brain activity have focused on decoding a single language. Hence, the extent to which bilingual speech production relies on unique or shared cortical activity across languages has remained unclear. Here, we leveraged electrocorticography, along with deep-learning and statistical natural-language models of English and Spanish, to record and decode activity from speech-motor cortex of a Spanish-English bilingual with vocal-tract and limb paralysis into sentences in either language. This was achieved without requiring the participant to manually specify the target language. Decoding models relied on shared vocal-tract articulatory representations across languages, which allowed us to build a syllable classifier that generalized across a shared set of English and Spanish syllables. Transfer learning expedited training of the bilingual decoder by enabling neural data recorded in one language to improve decoding in the other language. Overall, our findings suggest shared cortical articulatory representations that persist after paralysis and enable the decoding of multiple languages without the need to train separate language-specific decoders.},
}
@article {pmid38769115,
year = {2024},
author = {Komeiji, S and Mitsuhashi, T and Iimura, Y and Suzuki, H and Sugano, H and Shinoda, K and Tanaka, T},
title = {Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {11491},
pmid = {38769115},
issn = {2045-2322},
support = {20H00235//JSPS KAKENHI/ ; },
mesh = {Humans ; Female ; Male ; Adult ; *Brain-Computer Interfaces ; *Speech/physiology ; *Electrocorticography ; Speech Perception/physiology ; Young Adult ; Feasibility Studies ; Epilepsy/physiopathology ; Neural Networks, Computer ; Middle Aged ; Adolescent ; },
abstract = {Several attempts for speech brain-computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model's performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p > 0.05 ; d = 0.07) . Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.},
}
@article {pmid38767327,
year = {2024},
author = {Ferdi, AY and Ghazli, A},
title = {Authentication with a one-dimensional CNN model using EEG-based brain-computer interface.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2024.2355490},
pmid = {38767327},
issn = {1476-8259},
abstract = {Brain-computer interface (BCI) technology uses electroencephalogram (EEG) signals to create a direct interaction between the human body and its surroundings. Motor imagery (MI) classification using EEG signals is an important application that can help a rehabilitated or motor-impaired stroke patient perform certain tasks. Robust classification of these signals is an important step toward making the use of EEG more practical in many applications and less dependent on trained professionals. Deep learning methods have produced impressive results in BCI in recent years, especially with the availability of large electroencephalography (EEG) data sets. Dealing with EEG-MI signals is difficult because noise and other signal sources can interfere with the electrical amplitude of the brain, and its generalization ability is limited, so it is difficult to improve EEG classifiers. To address these issues, this paper presents a methodology based on one-dimensional convolutional neural networks (1-D CNN) for motor imagery (MI) recognition for the right hand, left hand, feet, and sedentary task. The proposed model is a lightweight model with fewer parameters and has an accuracy of 91.75%. Then, in an innovative exploitation of the four output classes, there is an idea that allows people with disabilities who are deprived of security measures, such as entering a secret code, to use the output classification, such as password codes. It is also an idea for a unique authentication system that is more secure and less vulnerable to theft or the like for a healthy person at the same time.},
}
@article {pmid38763997,
year = {2024},
author = {Zheng, J and Wu, X and Xu, H},
title = {Oxytocinergic Control of a Hypothalamic Social Fear Circuitry.},
journal = {Neuroscience bulletin},
volume = {40},
number = {9},
pages = {1399-1401},
pmid = {38763997},
issn = {1995-8218},
mesh = {Animals ; Humans ; *Fear/physiology/psychology ; *Hypothalamus/physiology/metabolism ; *Oxytocin/metabolism/physiology ; Social Behavior ; Mice ; },
}
@article {pmid38763932,
year = {2024},
author = {El Kaim, A and Serra, M and De Noray, H and Lallemant, A and Gobatto, C and Degos, V and Carpentier, A and Riche, M and Apra, C},
title = {Safety and practicality study of using an exoskeleton in acute neurosurgery patients.},
journal = {Acta neurochirurgica},
volume = {166},
number = {1},
pages = {221},
pmid = {38763932},
issn = {0942-0940},
mesh = {Humans ; Male ; Female ; Middle Aged ; *Exoskeleton Device ; Retrospective Studies ; Aged ; *Neurosurgical Procedures/methods ; Adult ; Early Ambulation/methods ; Patient Satisfaction ; Feasibility Studies ; },
abstract = {INTRODUCTION: Early mobilization is key in neurologically impaired persons, limiting complications and improving long-term recovery. Self-balanced exoskeletons are used in rehabilitation departments to help patients stand and walk. We report the first case series of exoskeleton use in acute neurosurgery and intensive care patients, evaluating safety, clinical feasibility and patients' satisfaction.<br><br>METHODS: We report a retrospective observational study including individuals hospitalized in the neurosurgical intensive care and neurosurgery departments. We included patients with a medical prescription for an exoskeleton session, and who met no contraindication. Patients benefited from standing sessions using a self-balanced exoskeleton (Atalante, Wandercraft, France). Patients and sessions data were collected. Safety, feasibility and adherence were evaluated.<br><br>RESULTS: Seventeen patients were scheduled for 70 standing sessions, of which 27 (39%) were completed. They were typically hospitalized for intracranial hemorrhage (74%) and presented with unilateral motor impairments, able to stand but with very insufficient weight shifting to the hemiplegic limb, requiring support (MRC 36.2&#x2009;&#xb1;&#x2009;3.70, SPB 2.0&#x2009;&#xb1;&#x2009;1.3, SPD 0.7&#x2009;&#xb1;&#x2009;0.5). The average duration of standing sessions was 16&#x2009;&#xb1;&#x2009;9&#xa0;min. The only side effect was orthostatic hypotension (18.5%), which resolved with returning to seating position. The most frequent reason for not completing a session was understaffing (75%). All patients were satisfied and expressed a desire to repeat it.<br><br>CONCLUSIONS: Physiotherapy using the exoskeleton is safe and feasible in the acute neurosurgery setting, although it requires adaptation from the staff to organize the sessions. An efficacy study is ongoing to evaluate the benefits for the patients.},
}
@article {pmid38762683,
year = {2024},
author = {Fukuma, R and Majima, K and Kawahara, Y and Yamashita, O and Shiraishi, Y and Kishima, H and Yanagisawa, T},
title = {Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {595},
pmid = {38762683},
issn = {2399-3642},
support = {JPMJCR18A5//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJER1801//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; JPMJPR2128//MEXT | Japan Science and Technology Agency (JST)/ ; JPMXS0120330644//MEXT | Japan Science and Technology Agency (JST)/ ; JP19dm0207070//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0307103//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0207070//Japan Agency for Medical Research and Development (AMED)/ ; JP19dm0307103//Japan Agency for Medical Research and Development (AMED)/ ; JP23dm0307009//Japan Agency for Medical Research and Development (AMED)/ ; 22K15623//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; 20K16465//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; },
mesh = {*Electrocorticography/methods ; Humans ; Algorithms ; Signal Processing, Computer-Assisted ; Male ; Machine Learning ; Visual Perception/physiology ; Female ; Reproducibility of Results ; Adult ; Brain-Computer Interfaces ; },
abstract = {Dynamic mode (DM) decomposition decomposes spatiotemporal signals into basic oscillatory components (DMs). DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features. However, such kernel-based machine learning algorithms have three limitations: large computational time preventing real-time application, incompatibility with non-kernel algorithms, and low interpretability. Here, we propose a mapping function corresponding to the Grassmann kernel that explicitly transforms DMs into spatial DM (sDM) features, which can be used in any machine learning algorithm. Using electrocorticographic signals recorded during various movement and visual perception tasks, the sDM features were shown to improve the decoding accuracy and computational time compared to conventional methods. Furthermore, the components of the sDM features informative for decoding showed similar characteristics to the high-γ power of the signals, but with higher trial-to-trial reproducibility. The proposed sDM features enable fast, accurate, and interpretable neural decoding.},
}
@article {pmid38760478,
year = {2024},
author = {Barret, N and Guillaumée, T and Rimmelé, T and Cortet, M and Mazza, S and Duclos, A and Rode, G and Lilot, M and Schlatter, S},
title = {Associations of coping and health-related behaviors with medical students' well-being and performance during objective structured clinical examination.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {11298},
pmid = {38760478},
issn = {2045-2322},
mesh = {Humans ; *Students, Medical/psychology ; *Adaptation, Psychological ; Female ; Male ; Cross-Sectional Studies ; Surveys and Questionnaires ; Adult ; Young Adult ; Health Behavior ; Clinical Competence ; Exercise/psychology ; Educational Measurement/methods ; },
abstract = {Objective structured clinical examination (OSCE) is a valid method to evaluate medical students' competencies. The present cross-sectional study aimed at determining how students' coping and health-related behaviors are associated with their psychological well-being and performance on the day of the OSCE. Fourth-year medical students answered a set of standardized questionnaires assessing their coping (BCI) and health-related behaviors before the examination (sleep PSQI, physical activity GPAQ). Immediately before the OSCE, they reported their level of instant psychological well-being on multi-dimensional visual analogue scales. OSCE performance was assessed by examiners blinded to the study. Associations were explored using multivariable linear regression models. A total of 482 students were included. Instant psychological well-being was positively associated with the level of positive thinking and of physical activity. It was negatively associated with the level of avoidance and of sleep disturbance. Furthermore, performance was negatively associated with the level of avoidance. Positive thinking, good sleep quality, and higher level of physical activity were all associated with improved well-being before the OSCE. Conversely, avoidance coping behaviors seem to be detrimental to both well-being and OSCE performance. The recommendation is to pay special attention to students who engage in avoidance and to consider implementing stress management programs.Clinical trial: The study protocol was registered on clinicaltrial.gov NCT05393206, date of registration: 11 June 2022.},
}
@article {pmid38760018,
year = {2024},
author = {Jiang, J and Li, C and Chen, C and Shi, C and Song, J},
title = {Tunable and Reversible Adhesive of Liquid Metal Ferrofluid Pillars for Magnetically Actuated Noncontact Transfer Printing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {29},
pages = {e2314004},
doi = {10.1002/adma.202314004},
pmid = {38760018},
issn = {1521-4095},
support = {U21A20502//National Natural Science Foundation of China/ ; 12302214//National Natural Science Foundation of China/ ; 12225209//National Natural Science Foundation of China/ ; U20A6001//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities/ ; LQ23A020006//Natural Science Foundation of Zhejiang Province/ ; },
abstract = {Transfer printing techniques based on tunable and reversible adhesives enable the heterogeneous integration of materials in desired layouts and are essential for developing both existing and envisioned electronic systems. Here, a novel tunable and reversible adhesive of liquid metal ferrofluid pillars for developing an efficient magnetically actuated noncontact transfer printing is reported. The liquid metal ferrofluid pillars offer the appealing advantages of gentle contact force by minimizing the preload effect and exceptional shape adaptability by maximizing the interfacial contact area due to their inherent fluidity, thus enabling a reliable damage-free pickup. Moreover, the liquid metal ferrofluid pillars harness the rapid stiffness increase and shape change with the magnetic field, generating an instantaneous ejection force to achieve a receiver-independent noncontact printing. Demonstrations of the adhesive of liquid metal ferrofluid pillars in transfer printing of diverse objects with different shapes, materials and dimensions onto various substrates illustrate its great potential in deterministic assembly.},
}
@article {pmid38758616,
year = {2024},
author = {Xu, G and Wang, Z and Hu, H and Zhao, X and Li, R and Zhou, T and Xu, T},
title = {Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {8},
pages = {4565-4576},
doi = {10.1109/JBHI.2024.3402324},
pmid = {38758616},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Machine Learning ; *Algorithms ; Brain/physiology ; },
abstract = {Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.},
}
@article {pmid38758613,
year = {2024},
author = {Zheng, B and Li, Y and Xu, G and Wang, G and Zheng, Y},
title = {Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1994-2004},
doi = {10.1109/TNSRE.2024.3402545},
pmid = {38758613},
issn = {1558-0210},
mesh = {Humans ; *Forearm/physiology ; *Electromyography/methods ; *Fingers/physiology ; Male ; *Motor Neurons/physiology ; Rotation ; *Algorithms ; Young Adult ; Adult ; Female ; Muscle, Skeletal/physiology ; Action Potentials/physiology ; Brain-Computer Interfaces ; Reproducibility of Results ; Muscle Contraction/physiology ; Movement/physiology ; },
abstract = {Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ([Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.},
}
@article {pmid38757738,
year = {2024},
author = {Wu, Y and Wang, L and Yan, M and Wang, X and Liao, X and Zhong, C and Ke, D and Lu, Y},
title = {Poly(3,4-Ethylenedioxythiophene)/Functional Gold Nanoparticle films for Improving the Electrode-Neural Interface.},
journal = {Advanced healthcare materials},
volume = {13},
number = {23},
pages = {e2400836},
doi = {10.1002/adhm.202400836},
pmid = {38757738},
issn = {2192-2659},
support = {T2122021//National Natural Science Foundation of China/ ; T2250710685//National Natural Science Foundation of China/ ; 32071035//National Natural Science Foundation of China/ ; 32300845//National Natural Science Foundation of China/ ; 2022ZD0209500//STI 2030-Major Projects/ ; 2019DP173024//CAS Key Laboratory of Brain Connectome and Manipulation/ ; 2017B030301017//Guangdong Key Laboratory of Brain Connectome/ ; JCYJ20220818100805013//Shenzhen Key Basic Research Project/ ; 2023M743683//China Postdoctoral Science Foundation/ ; 2024A1515010645//Guangdong Basic and Applied Basic Research Foundation/ ; },
mesh = {*Gold/chemistry ; Animals ; *Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Polymers/chemistry ; *Metal Nanoparticles/chemistry ; Mice ; Neurons/metabolism ; Electrodes, Implanted ; Male ; },
abstract = {Implantable neural electrodes are indispensable tools for recording neuron activity, playing a crucial role in neuroscience research. However, traditional neural electrodes suffer from limited electrochemical performance, compromised biocompatibility, and tentative stability, posing great challenges for reliable long-term studies in free-moving animals. In this study, a novel approach employing a hybrid film composed of poly(3,4-ethylenedioxythiophene)/functional gold nanoparticles (PEDOT/3-MPA-Au) to improve the electrode-neural interface is presented. The deposited PEDOT/3-MPA-Au demonstrates superior cathodal charge storage capacity, reduced electrochemical impedance, and remarkable electrochemical and mechanical stability. Upon implantation into the cortex of mice for a duration of 12 weeks, the modified electrodes exhibit notably decreased levels of glial fibrillary acidic protein and increased neuronal nuclei immunostaining compared to counterparts utilizing poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate). Additionally, the PEDOT/3-MPA-Au modified electrodes consistently capture high-quality, stable long-term electrophysiological signals in vivo, enabling continuous recording of target neurons for up to 16 weeks. This innovative modification strategy offers a promising solution for fabricating low-impedance, tissue-friendly, and long-term stable neural interfaces, thereby addressing the shortcomings of conventional neural electrodes. These findings mark a significant advancement toward the development of more reliable and efficacious neural interfaces, with broad implications for both research and clinical applications.},
}
@article {pmid38757187,
year = {2024},
author = {Cunlin, H and Ye, Y and Nenggang, X},
title = {Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad48bc},
pmid = {38757187},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Supervised Machine Learning ; Pattern Recognition, Automated/methods ; },
abstract = {Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.},
}
@article {pmid38754326,
year = {2024},
author = {Yang, SH and Huang, CJ and Huang, JS},
title = {Increasing Robustness of Intracortical Brain-Computer Interfaces for Recording Condition Changes via Data Augmentation.},
journal = {Computer methods and programs in biomedicine},
volume = {251},
number = {},
pages = {108208},
doi = {10.1016/j.cmpb.2024.108208},
pmid = {38754326},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Animals ; Algorithms ; Calibration ; Humans ; Signal Processing, Computer-Assisted ; Movement ; },
abstract = {BACKGROUND AND OBJECTIVE: Intracortical brain-computer interfaces (iBCIs) aim to help paralyzed individuals restore their motor functions by decoding neural activity into intended movement. However, changes in neural recording conditions hinder the decoding performance of iBCIs, mainly because the neural-to-kinematic mappings shift. Conventional approaches involve either training the neural decoders using large datasets before deploying the iBCI or conducting frequent calibrations during its operation. However, collecting data for extended periods can cause user fatigue, negatively impacting the quality and consistency of neural signals. Furthermore, frequent calibration imposes a substantial computational load.<br><br>METHODS: This study proposes a novel approach to increase iBCIs' robustness against changing recording conditions. The approach uses three neural augmentation operators to generate augmented neural activity that mimics common recording conditions. Then, contrastive learning is used to learn latent factors by maximizing the similarity between the augmented neural activities. The learned factors are expected to remain stable despite varying recording conditions and maintain a consistent correlation with the intended movement.<br><br>RESULTS: Experimental results demonstrate that the proposed iBCI outperformed the state-of-the-art iBCIs and was robust to changing recording conditions across days for long-term use on one publicly available nonhuman primate dataset. It achieved satisfactory offline decoding performance, even when a large training dataset was unavailable.<br><br>CONCLUSIONS: This study paves the way for reducing the need for frequent calibration of iBCIs and collecting a large amount of annotated training data. Potential future works aim to improve offline decoding performance with an ultra-small training dataset and improve the iBCIs' robustness to severely disabled electrodes.},
}
@article {pmid38753110,
year = {2024},
author = {Serrano-Amenos, C and Hu, F and Wang, PT and Heydari, P and Do, AH and Nenadic, Z},
title = {Simulation-Informed Power Budget Estimate of a Fully-Implantable Brain-Computer Interface.},
journal = {Annals of biomedical engineering},
volume = {52},
number = {8},
pages = {2269-2281},
pmid = {38753110},
issn = {1573-9686},
support = {1646275//National Science Foundation/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Computer Simulation ; Prostheses and Implants ; Electric Power Supplies ; },
abstract = {This study aims to estimate the maximum power consumption that guarantees a thermally safe operation for a titanium-enclosed chest wall unit (CWU) subcutaneously implanted in the pre-pectoral area. This unit is a central piece of an envisioned fully-implantable bi-directional brain-computer interface (BD-BCI). To this end, we created a thermal simulation model using the finite element method implemented in COMSOL. We also performed a sensitivity analysis to ensure that our predictions were robust against the natural variation of physiological and environmental parameters. Based on this analysis, we predict that the CWU can consume between 378 and 538 mW of power without raising the surrounding tissue's temperature above the thermal safety threshold of 2 ∘ C. This power budget should be sufficient to power all of the CWU's basic functionalities, which include training the decoder, online decoding, wireless data transmission, and cortical stimulation. This power budget assessment provides an important specification for the design of a CWU-an integral part of a fully-implantable BD-BCI system.},
}
@article {pmid38751845,
year = {2024},
author = {Lian, J and Dias Pereira, J},
title = {Editorial: IoT, UAV, BCI empowered deep learning models in precision agriculture.},
journal = {Frontiers in plant science},
volume = {15},
number = {},
pages = {1399753},
doi = {10.3389/fpls.2024.1399753},
pmid = {38751845},
issn = {1664-462X},
}
@article {pmid38750661,
year = {2024},
author = {Eck, JL and Hernández Hassan, L and Comita, LS},
title = {Intraspecific plant-soil feedback in four tropical tree species is inconsistent in a field experiment.},
journal = {American journal of botany},
volume = {111},
number = {12},
pages = {e16331},
pmid = {38750661},
issn = {1537-2197},
mesh = {*Trees/physiology ; *Seedlings/growth &amp; development/genetics/physiology ; *Tropical Climate ; Panama ; Soil Microbiology ; Soil ; Species Specificity ; Seasons ; },
abstract = {PREMISE: Soil microbes can influence patterns of diversity in plant communities via plant-soil feedbacks. Intraspecific plant-soil feedbacks occur when plant genotype leads to variations in soil microbial composition, resulting in differences in the performance of seedlings growing near their maternal plants versus seedlings growing near nonmaternal conspecific plants. How consistently such intraspecific plant-soil feedbacks occur in natural plant communities is unclear, especially in variable field conditions.<br><br>METHODS: In an in situ experiment with four native tree species on Barro Colorado Island (BCI), Panama, seedlings of each species were transplanted beneath their maternal tree or another conspecific tree in the BCI forest. Mortality and growth were assessed at the end of the wet season (~4 months post-transplant) and at the end of the experiment (~7 months post-transplant).<br><br>RESULTS: Differences in seedling performance among field treatments were inconsistent among species and eroded over time. Effects of field environment were detected at the end of the wet season in two of the four species: Virola surinamensis seedlings had higher survival beneath their maternal tree than other conspecific trees, while seedling survival of Ormosia macrocalyx was higher under other conspecific trees. However, these differences were gone by the end of the experiment.<br><br>CONCLUSIONS: Our results suggest that intraspecific plant-soil feedbacks may not be consistent in the field for tropical tree species and may have a limited role in determining seedling performance in tropical tree communities. Future studies are needed to elucidate the environmental and genetic factors that determine the incidence and direction of intraspecific plant-soil feedbacks in plant communities.},
}
@article {pmid38750479,
year = {2024},
author = {Yan, N and Hu, S},
title = {The safety and efficacy of escitalopram and sertraline in post-stroke depression: a randomized controlled trial.},
journal = {BMC psychiatry},
volume = {24},
number = {1},
pages = {365},
pmid = {38750479},
issn = {1471-244X},
mesh = {Humans ; *Sertraline/therapeutic use/adverse effects ; Male ; Aged ; Female ; Middle Aged ; *Stroke/complications/drug therapy ; Adult ; Aged, 80 and over ; *Escitalopram/therapeutic use/adverse effects ; *Activities of Daily Living ; Depression/drug therapy/etiology ; Treatment Outcome ; Selective Serotonin Reuptake Inhibitors/therapeutic use/adverse effects ; Psychiatric Status Rating Scales ; Antidepressive Agents/therapeutic use/adverse effects ; Citalopram/therapeutic use/adverse effects ; },
abstract = {OBJECTIVES: This study aims to evaluate the safety and efficacy of escitalopram and sertraline in post-stroke depression (PSD) patients, to provide more reliable therapeutics for cardiovascular and psychiatric clinical practice.<br><br>METHODS: We recruited 60 patients (aged 40-89 years old) with an ICD-10 diagnosis of PSD, who were then randomly assigned to two groups and treated with flexible doses of escitalopram (10 to 20 mg/day, n&#x2009;=&#x2009;30) or sertraline (50 to 200 mg/day, n&#x2009;=&#x2009;30) for consecutive 8 weeks, respectively. The 24-item Hamilton Depression Rating Scale (HAMD-24), the 14-item Hamilton Anxiety Rating Scale (HAMA-14), the Treatment Emergent Symptom Scale (TESS), the Montreal Cognitive Assessment Scale (MOCA), and the Activity of Daily Living scale (ADL) were used to assess patients before, during, and after treatment for depression, anxiety, adverse effects, cognitive function, and daily living activities. Repeated measures ANOVA, the Mann-Whitney U test, the chi-square test (&#x3c7;[2]), or Fisher's exact test was employed to assess baseline demographics, response rate, adverse effects rate, and changes in other clinical variables.<br><br>RESULTS: Significant reduction in HAMD-24 and HAMA-14 scores was evaluated at baseline, as well as 1, 3, 4, 6, and 8&#xa0;weeks of drug intervention (p&#x2009;<&#x2009;0.01). There was a significant group difference in post-treatment HAMD-24 scores (p&#x2009;<&#x2009;0.05), but no difference was observed in HAMA-14 scores (p&#x2009;>&#x2009;0.05). Further analysis showed a significant variance in the HAMD-24 scores between the two groups at the end of the first week (p&#x2009;<&#x2009;0.01). The incidence of adverse effects in both patient groups was mild, but there was a statistically significant difference between the two groups (p&#x2009;<&#x2009;0.05). The improvement in cognitive function and the recovery of daily living abilities were comparable between both groups (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: Escitalopram and sertraline showed comparable efficacy for anxiety symptoms, cognitive function, and daily living abilities in PSD patients. In addition, escitalopram was more appropriate for alleviating depressive symptoms. To validate the conclusion, trials with a larger sample size are in demand in the future. The registration number is ChiCTR1800017373.},
}
@article {pmid38749619,
year = {2024},
author = {Hahn, RT and Wilkoff, BL and Kodali, S and Birgersdotter-Green, UM and Ailawadi, G and Addetia, K and Andreas, M and Auricchio, A and Ehlert, F and George, I and Gupta, A and Harrison, R and Ho, EC and Kusumoto, F and Latib, A and O'Gara, P and Patton, KK and Pinney, S and Zeitler, EP and Mack, MJ and Leon, MB and Epstein, LM and , },
title = {Managing Implanted Cardiac Electronic Devices in Patients With Severe Tricuspid Regurgitation: JACC State-of-the-Art Review.},
journal = {Journal of the American College of Cardiology},
volume = {83},
number = {20},
pages = {2002-2014},
doi = {10.1016/j.jacc.2024.02.045},
pmid = {38749619},
issn = {1558-3597},
mesh = {Humans ; *Tricuspid Valve Insufficiency/surgery ; *Defibrillators, Implantable/adverse effects ; *Pacemaker, Artificial/adverse effects ; Heart Valve Prosthesis Implantation/methods/adverse effects ; Severity of Illness Index ; },
abstract = {Orthotopic transcatheter tricuspid valve replacement (TTVR) devices have been shown to be highly effective in reducing tricuspid regurgitation (TR), and interest in this therapy is growing with the recent commercial approval of the first orthotopic TTVR. Recent TTVR studies report preexisting cardiac implantable electronic device (CIED) transvalvular leads in ∼35% of patients, with entrapment during valve implantation. Concerns have been raised regarding the safety of entrapping leads and counterbalanced against the risks of transvenous lead extraction (TLE) when indicated. This Heart Valve Collaboratory consensus document attempts to define the patient population with CIED lead-associated or lead-induced TR, describe the risks of lead entrapment during TTVR, delineate the risks and benefits of TLE in this setting, and develop a management algorithm for patients considered for TTVR. An electrophysiologist experienced in CIED management should be part of the multidisciplinary heart team and involved in shared decision making.},
}
@article {pmid38746479,
year = {2024},
author = {Datta, P and Kaur, A and Sassi, N and Gulzar, Y and Jaziri, W},
title = {An evaluation of intelligent and immersive digital applications in eliciting cognitive states in humans through the utilization of Emotiv Insight.},
journal = {MethodsX},
volume = {12},
number = {},
pages = {102748},
pmid = {38746479},
issn = {2215-0161},
abstract = {The amalgamation of Virtual Reality (VR) and Artificial Intelligence (AI) results in the development of many promising applications that are helpful for society in many aspects. This research was done to study the effect of immersive and non-immersive applications on user's psychological parameters. In this paper, an intelligent, interactive, and immersive digital application was designed, and the various psychological parameters of users while using the application were analyzed through the brain computer interactive device, Emotiv. The impact of these robust and immersive applications on the emotions of human beings was analyzed. According to the observations, the stress and relaxation levels are getting minimally affected, whereas the engagement levels are high for an immersive application rather than a non-immersive application. Hence, it can be concluded that immersive applications put users "in" the application environment and provide a near-realistic experience by blurring the line between the real and virtual worlds. Deeper immersion results from the increased sensation of presence, which in turn is helpful in increasing motivation and emotional investment.•This paper demonstrates the implementation of the A* algorithm within the Unity 3D Game Engine to develop an intelligent digital application, fostering interactivity and depth.•This paper explores the integration of VR technology to transform the digital application into an immersive and interactive experience, enhancing user engagement and realism.•This paper investigates the utilization of the Emotiv Insight device to analyze cognitive parameters within both non-immersive AI-based and immersive AI &amp; VR-based applications, providing insights into user experiences.},
}
@article {pmid38745805,
year = {2024},
author = {Livanis, E and Voultsos, P and Vadikolias, K and Pantazakos, P and Tsaroucha, A},
title = {Understanding the Ethical Issues of Brain-Computer Interfaces (BCIs): A Blessing or the Beginning of a Dystopian Future?.},
journal = {Cureus},
volume = {16},
number = {4},
pages = {e58243},
pmid = {38745805},
issn = {2168-8184},
abstract = {In recent years, scientific discoveries in the field of neuroscience combined with developments in the field of artificial intelligence have led to the development of a range of neurotechnologies. Advances in neuroimaging systems, neurostimulators, and brain-computer interfaces (BCIs) are leading to new ways of enhancing, controlling, and "reading" the brain. In addition, although BCIs were developed and used primarily in the medical field, they are now increasingly applied in other fields (entertainment, marketing, education, defense industry). We conducted a literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to provide background information about ethical issues related to the use of BCIs. Among the ethical issues that emerged from the thematic data analysis of the reviewed studies included questions revolving around human dignity, personhood and autonomy, user safety, stigma and discrimination, privacy and security, responsibility, research ethics, and social justice (including access to this technology). This paper attempts to address the various aspects of these concerns. A variety of distinct ethical issues were identified, which, for the most part, were in line with the findings of prior research. However, we identified two nuances, which are related to the empirical research on ethical issues related to BCIs and the impact of BCIs on international relationships. The paper also highlights the need for the cooperation of all stakeholders to ensure the ethical development and use of this technology and concludes with several recommendations. The principles of bioethics provide an initial guiding framework, which, however, should be revised in the current artificial intelligence landscape so as to be responsive to challenges posed by the development and use of BCIs.},
}
@article {pmid38745103,
year = {2024},
author = {Silva, AB and Littlejohn, KT and Liu, JR and Moses, DA and Chang, EF},
title = {The speech neuroprosthesis.},
journal = {Nature reviews. Neuroscience},
volume = {25},
number = {7},
pages = {473-492},
pmid = {38745103},
issn = {1471-0048},
support = {F30 DC021872/DC/NIDCD NIH HHS/United States ; U01 DC018671/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Speech/physiology ; *Brain-Computer Interfaces ; Neural Prostheses ; Animals ; },
abstract = {Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore natural communication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinical epilepsy monitoring and subsequently in individuals with paralysis as part of early feasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapid and substantial progress in decoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.},
}
@article {pmid38744976,
year = {2024},
author = {Braun, JM and Fauth, M and Berger, M and Huang, NS and Simeoni, E and Gaeta, E and Rodrigues do Carmo, R and García-Betances, RI and Arredondo Waldmeyer, MT and Gail, A and Larsen, JC and Manoonpong, P and Tetzlaff, C and Wörgötter, F},
title = {A brain machine interface framework for exploring proactive control of smart environments.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {11054},
pmid = {38744976},
issn = {2045-2322},
support = {732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; 732266//Horizon 2020 Framework Programme/ ; },
mesh = {*Brain-Computer Interfaces ; Animals ; *Algorithms ; Brain/physiology ; Macaca mulatta ; },
abstract = {Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.},
}
@article {pmid38744836,
year = {2024},
author = {Fu, Y and Guo, T and Zheng, J and He, J and Shen, M and Chen, H},
title = {Children exhibit superior memory for attended but outdated information compared to adults.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {4058},
pmid = {38744836},
issn = {2041-1723},
mesh = {Humans ; Female ; Male ; Adult ; *Attention/physiology ; Child ; *Memory/physiology ; Young Adult ; Cognition/physiology ; Child, Preschool ; },
abstract = {Research on the development of cognitive selectivity predominantly focuses on attentional selection. The present study explores another facet of cognitive selectivity-memory selection-by examining the ability to filter attended yet outdated information in young children and adults. Across five experiments involving 130 children and 130 adults, participants are instructed to use specific information to complete a task, and then unexpectedly asked to report this information in a surprise test. The results consistently demonstrate a developmental reversal-like phenomenon, with children outperforming adults in reporting this kind of attended yet outdated information. Furthermore, we provide evidence against the idea that the results are due to different processing strategies or attentional deployments between adults and children. These results suggest that the ability of memory selection is not fully developed in young children, resulting in their inefficient filtering of attended yet outdated information that is not required for memory retention.},
}
@article {pmid38744273,
year = {2024},
author = {Cui, Q and Liu, Z and Bai, G},
title = {Friend or foe: The role of stress granule in neurodegenerative disease.},
journal = {Neuron},
volume = {112},
number = {15},
pages = {2464-2485},
doi = {10.1016/j.neuron.2024.04.025},
pmid = {38744273},
issn = {1097-4199},
mesh = {Humans ; *Neurodegenerative Diseases/metabolism ; Animals ; *Stress Granules/metabolism ; Cytoplasmic Granules/metabolism ; },
abstract = {Stress granules (SGs) are dynamic membraneless organelles that form in response to cellular stress. SGs are predominantly composed of RNA and RNA-binding proteins that assemble through liquid-liquid phase separation. Although the formation of SGs is considered a transient and protective response to cellular stress, their dysregulation or persistence may contribute to various neurodegenerative diseases. This review aims to provide a comprehensive overview of SG physiology and pathology. It covers the formation, composition, regulation, and functions of SGs, along with their crosstalk with other membrane-bound and membraneless organelles. Furthermore, this review discusses the dual roles of SGs as both friends and foes in neurodegenerative diseases and explores potential therapeutic approaches targeting SGs. The challenges and future perspectives in this field are also highlighted. A more profound comprehension of the intricate relationship between SGs and neurodegenerative diseases could inspire the development of innovative therapeutic interventions against these devastating diseases.},
}
@article {pmid38744056,
year = {2024},
author = {Qian, D and Zeng, H and Cheng, W and Liu, Y and Bikki, T and Pan, J},
title = {NeuroDM: Decoding and visualizing human brain activity with EEG-guided diffusion model.},
journal = {Computer methods and programs in biomedicine},
volume = {251},
number = {},
pages = {108213},
doi = {10.1016/j.cmpb.2024.108213},
pmid = {38744056},
issn = {1872-7565},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; *Brain/diagnostic imaging/physiology ; Algorithms ; Signal-To-Noise Ratio ; Signal Processing, Computer-Assisted ; Evoked Potentials, Visual/physiology ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-Computer Interface (BCI) technology has recently been advancing rapidly, bringing significant hope for improving human health and quality of life. Decoding and visualizing visually evoked electroencephalography (EEG) signals into corresponding images plays a crucial role in the practical application of BCI technology. The recent emergence of diffusion models provides a good modeling basis for this work. However, the existing diffusion models still have great challenges in generating high-quality images from EEG, due to the low signal-to-noise ratio and strong randomness of EEG signals. The purpose of this study is to address the above-mentioned challenges by proposing a framework named NeuroDM that can decode human brain responses to visual stimuli from EEG-recorded brain activity.<br><br>METHODS: In NeuroDM, an EEG-Visual-Transformer (EV-Transformer) is used to extract the visual-related features with high classification accuracy from EEG signals, then an EEG-Guided Diffusion Model (EG-DM) is employed to synthesize high-quality images from the EEG visual-related features.<br><br>RESULTS: We conducted experiments on two EEG datasets (one is a forty-class dataset, and the other is a four-class dataset). In the task of EEG decoding, we achieved average accuracies of 99.80% and 92.07% on two datasets, respectively. In the task of EEG visualization, the Inception Score of the images generated by NeuroDM reached 15.04 and 8.67, respectively. All the above results outperform existing methods.<br><br>CONCLUSIONS: The experimental results on two EEG datasets demonstrate the effectiveness of the NeuroDM framework, achieving state-of-the-art performance in terms of classification accuracy and image quality. Furthermore, our NeuroDM exhibits strong generalization capabilities and the ability to generate diverse images.},
}
@article {pmid38740991,
year = {2024},
author = {},
title = {Brain-machine-interface device translates internal speech into text.},
journal = {Nature human behaviour},
volume = {8},
number = {6},
pages = {1014-1015},
pmid = {38740991},
issn = {2397-3374},
mesh = {*Brain-Computer Interfaces ; *Speech Disorders/etiology/rehabilitation ; *Verbal Behavior/physiology ; Humans ; Action Potentials ; Neurons/physiology ; Parietal Lobe/cytology/physiology ; Quadriplegia/complications ; *Writing ; },
}
@article {pmid38740984,
year = {2024},
author = {Wandelt, SK and Bjånes, DA and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Representation of internal speech by single neurons in human supramarginal gyrus.},
journal = {Nature human behaviour},
volume = {8},
number = {6},
pages = {1136-1149},
pmid = {38740984},
issn = {2397-3374},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; U01NS098975//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Speech/physiology ; Male ; *Parietal Lobe/physiology/physiopathology ; Adult ; Neurons/physiology ; Quadriplegia/physiopathology ; Female ; Somatosensory Cortex/physiology/physiopathology ; Speech Perception/physiology ; },
abstract = {Speech brain-machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.},
}
@article {pmid38740844,
year = {2024},
author = {Mercier, M and Pepi, C and Carfi-Pavia, G and De Benedictis, A and Espagnet, MCR and Pirani, G and Vigevano, F and Marras, CE and Specchio, N and De Palma, L},
title = {The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {10887},
pmid = {38740844},
issn = {2045-2322},
support = {PE0000006//This study was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) - A Multiscale integrated approach to the study of the nervous system in health and disease/ ; 202205//Precision medicine in epilepsy: definition of a predictive model of epilepsy outcome and creation of an automated tool based on machine learning." Ricerca 5x1000 Bambino Gesu' Children' Hospital. Project code: 202205/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Child ; *Machine Learning ; Female ; Male ; Child, Preschool ; Adolescent ; Epilepsy/surgery/physiopathology/diagnosis ; Neural Networks, Computer ; Treatment Outcome ; Infant ; Sleep/physiology ; },
abstract = {Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.},
}
@article {pmid38740778,
year = {2024},
author = {Wang, J and Yang, Q and Liu, X and Li, J and Wen, YL and Hu, Y and Xu, TL and Duan, S and Xu, H},
title = {The basal forebrain to lateral habenula circuitry mediates social behavioral maladaptation.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {4013},
pmid = {38740778},
issn = {2041-1723},
support = {32125018, 32071005//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171079//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; *Habenula/physiology ; Male ; *Fear/physiology ; *Basal Forebrain/physiology/metabolism ; Mice ; *Neurons/physiology/metabolism ; Optogenetics ; Mice, Inbred C57BL ; Social Behavior ; Behavior, Animal/physiology ; Neural Pathways/physiology ; Glutamic Acid/metabolism ; Conditioning, Classical/physiology ; },
abstract = {Elucidating the neural basis of fear allows for more effective treatments for maladaptive fear often observed in psychiatric disorders. Although the basal forebrain (BF) has an essential role in fear learning, its function in fear expression and the underlying neuronal and circuit substrates are much less understood. Here we report that BF glutamatergic neurons are robustly activated by social stimulus following social fear conditioning in male mice. And cell-type-specific inhibition of those excitatory neurons largely reduces social fear expression. At the circuit level, BF glutamatergic neurons make functional contacts with the lateral habenula (LHb) neurons and these connections are potentiated in conditioned mice. Moreover, optogenetic inhibition of BF-LHb glutamatergic pathway significantly reduces social fear responses. These data unravel an important function of the BF in fear expression via its glutamatergic projection onto the LHb, and suggest that selective targeting BF-LHb excitatory circuitry could alleviate maladaptive fear in relevant disorders.},
}
@article {pmid38738941,
year = {2024},
author = {Ramezani, Z and André, V and Khizroev, S},
title = {Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.},
journal = {Biointerphases},
volume = {19},
number = {3},
pages = {},
doi = {10.1116/5.0199163},
pmid = {38738941},
issn = {1559-4106},
mesh = {*Neurons/physiology/drug effects ; Nanoparticles/chemistry ; Humans ; Models, Neurological ; Action Potentials/drug effects/physiology ; Magnetic Fields ; },
abstract = {This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.},
}
@article {pmid38738283,
year = {2024},
author = {Xue, R and Li, X and Deng, W and Liang, C and Chen, M and Chen, J and Liang, S and Wei, W and Zhang, Y and Yu, H and Xu, Y and Guo, W and Li, T},
title = {Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression.},
journal = {Psychological medicine},
volume = {54},
number = {11},
pages = {3036-3043},
doi = {10.1017/S0033291724001132},
pmid = {38738283},
issn = {1469-8978},
mesh = {Humans ; *Bipolar Disorder/physiopathology ; *Schizophrenia/physiopathology ; *Depressive Disorder, Major/physiopathology ; Male ; *Electroencephalography ; Female ; Adult ; Cross-Sectional Studies ; Middle Aged ; *Brain/physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.<br><br>METHODS: This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.<br><br>RESULTS: Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.<br><br>CONCLUSIONS: Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.},
}
@article {pmid38738213,
year = {2023},
author = {Fan, C and Hahn, N and Kamdar, F and Avansino, D and Wilson, GH and Hochberg, L and Shenoy, KV and Henderson, JM and Willett, FR},
title = {Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.},
journal = {Advances in neural information processing systems},
volume = {36},
number = {},
pages = {42258-42270},
pmid = {38738213},
issn = {1049-5258},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 EB028171/EB/NIBIB NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.},
}
@article {pmid38736974,
year = {2024},
author = {Poo, MM},
title = {China's new ethical guidelines for the use of brain-computer interfaces.},
journal = {National science review},
volume = {11},
number = {4},
pages = {nwae154},
doi = {10.1093/nsr/nwae154},
pmid = {38736974},
issn = {2053-714X},
}
@article {pmid38732846,
year = {2024},
author = {Kawaguchi, T and Ono, K and Hikawa, H},
title = {Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {9},
pages = {},
pmid = {38732846},
issn = {1424-8220},
support = {JP20K11999//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Gestures ; *Brain-Computer Interfaces ; Face/physiology ; Algorithms ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted ; Brain/physiology ; Male ; },
abstract = {Brain-computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain's status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.},
}
@article {pmid38729914,
year = {2024},
author = {Kelly, AR and Glover, DJ},
title = {Information Transmission through Biotic-Abiotic Interfaces to Restore or Enhance Human Function.},
journal = {ACS applied bio materials},
volume = {7},
number = {6},
pages = {3605-3628},
doi = {10.1021/acsabm.4c00435},
pmid = {38729914},
issn = {2576-6422},
mesh = {Humans ; *Biocompatible Materials/chemistry ; Brain-Computer Interfaces ; },
abstract = {Advancements in reliable information transfer across biotic-abiotic interfaces have enabled the restoration of lost human function. For example, communication between neuronal cells and electrical devices restores the ability to walk to a tetraplegic patient and vision to patients blinded by retinal disease. These impactful medical achievements are aided by tailored biotic-abiotic interfaces that maximize information transfer fidelity by considering the physical properties of the underlying biological and synthetic components. This Review develops a modular framework to define and describe the engineering of biotic and abiotic components as well as the design of interfaces to facilitate biotic-abiotic information transfer using light or electricity. Delineating the properties of the biotic, interface, and abiotic components that enable communication can serve as a guide for future research in this highly interdisciplinary field. Application of synthetic biology to engineer light-sensitive proteins has facilitated the control of neural signaling and the restoration of rudimentary vision after retinal blindness. Electrophysiological methodologies that use brain-computer interfaces and stimulating implants to bypass spinal column injuries have led to the rehabilitation of limb movement and walking ability. Cellular interfacing methodologies and on-chip learning capability have been made possible by organic transistors that mimic the information processing capacity of neurons. The collaboration of molecular biologists, material scientists, and electrical engineers in the emerging field of biotic-abiotic interfacing will lead to the development of prosthetics capable of responding to thought and experiencing touch sensation via direct integration into the human nervous system. Further interdisciplinary research will improve electrical and optical interfacing technologies for the restoration of vision, offering greater visual acuity and potentially color vision in the near future.},
}
@article {pmid38729262,
year = {2024},
author = {Zhang, Y and Wu, ZY},
title = {Chinese patients with adult onset leukodystrophy caused by CST3 variants.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {51},
number = {7},
pages = {778-780},
doi = {10.1016/j.jgg.2024.05.002},
pmid = {38729262},
issn = {1673-8527},
mesh = {Humans ; Age of Onset ; China ; *East Asian People ; Mutation ; *Hereditary Central Nervous System Demyelinating Diseases/genetics ; *Cystatin C/genetics ; },
}
@article {pmid38728121,
year = {2025},
author = {Hu, Z and Zhou, Z and Lyu, H},
title = {A Power-and-Area-Efficient Channel-Interleaved Neural Signal Processor for Wireless Brain-Computer Interfaces With Unsupervised Spike Sorting.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {19},
number = {1},
pages = {108-119},
doi = {10.1109/TBCAS.2024.3395353},
pmid = {38728121},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; *Wireless Technology/instrumentation ; *Signal Processing, Computer-Assisted/instrumentation ; Humans ; Algorithms ; *Action Potentials/physiology ; },
abstract = {Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neuroscience research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the 'perturbed' K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-µW power consumption and occupies 0.0057 mm[2] for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.},
}
@article {pmid38727216,
year = {2024},
author = {Alruwaili, R and Alanazi, F and Alrashidi, A and Hazazi, M and Alenezi, M},
title = {Comparative Analysis of Silicone Tube Intubation Versus Probing and Balloon Dilation for Congenital Nasolacrimal Duct Obstruction: A Systematic Review and Meta-Analysis.},
journal = {The Journal of craniofacial surgery},
volume = {35},
number = {4},
pages = {1114-1119},
doi = {10.1097/SCS.0000000000010273},
pmid = {38727216},
issn = {1536-3732},
mesh = {Humans ; Infant ; Dilatation/methods/instrumentation ; *Intubation/instrumentation ; *Lacrimal Duct Obstruction/congenital/therapy ; Nasolacrimal Duct/surgery ; *Silicones ; Treatment Outcome ; Child, Preschool ; },
abstract = {OBJECTIVE: Congenital nasolacrimal duct obstruction (CNLDO) is a pediatric disorder with a wide range of pathology. If untreated, the condition may end up with serious complications. Multiple treatment options for CNLDO exist throughout the literature, and there is an ongoing debate on the best intervention for each disease subgroup and the best timing of such interventions. This study compares the success and failure rates of silicone tube intubation (STI) against probing and balloon dilation (BD).<br><br>METHODS: The authors searched the literature for relevant articles using PubMed, Scopus, web of Science, and Cochrane Library until January 2024. Using RevMan 5.4, the authors compared STI's success and failure rates to probing and BD using risk ratios (RRs) and a random-effect model. In addition, the complication rate of monocanalicular intubation (MCI) versus bicanalicular intubation (BCI) was investigated. The authors used the leave-one-out method to check for influential studies and to resolve heterogeneity.<br><br>RESULTS: The screening process resulted in 23 eligible articles for inclusion in the authors' review. Silicone tube intubation had a higher chance of resolving the symptoms of CNLDO than probing (RR = 1.11; 95% CI: 1.04, 1.20; P = 0.004) while having less risk of surgical failure (RR = 0.48; 95% CI: 0.30, 0.76; P = 0.002]. Monocanalicular intubation showed no statistically significant difference when compared with BCI in terms of surgical success and failure; however, MCI had a lower risk of complications (RR = 0.68; 95% CI: 0.48, 0.97; P = 0.04). In addition, STI did not demonstrate any significant difference from BD.<br><br>CONCLUSION: There was no significant difference in success/failure between MCI and BCI; monocanalicular had fewer complications. Silicone tube intubation did better in terms of surgical success than probing, especially in children over 12 months, suggesting that it is the preferred intervention for older patients with CNLDO.},
}
@article {pmid38727014,
year = {2024},
author = {Zhao, C and Jiang, R and Bustillo, J and Kochunov, P and Turner, JA and Liang, C and Fu, Z and Zhang, D and Qi, S and Calhoun, VD},
title = {Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia.},
journal = {Human brain mapping},
volume = {45},
number = {7},
pages = {e26694},
pmid = {38727014},
issn = {1097-0193},
support = {R01 MH118695/MH/NIMH NIH HHS/United States ; R01MH118695/NH/NIH HHS/United States ; },
mesh = {Humans ; *Schizophrenia/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging ; Male ; Adult ; Female ; *Connectome/methods ; *Cognitive Dysfunction/diagnostic imaging/physiopathology ; Cohort Studies ; *Nerve Net/diagnostic imaging/physiopathology ; Young Adult ; Middle Aged ; },
abstract = {Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.},
}
@article {pmid38726180,
year = {2024},
author = {Al-Quraishi, MS and Tan, WH and Elamvazuthi, I and Ooi, CP and Saad, NM and Al-Hiyali, MI and Karim, HA and Azhar Ali, SS},
title = {Cortical signals analysis to recognize intralimb mobility using modified RNN and various EEG quantities.},
journal = {Heliyon},
volume = {10},
number = {9},
pages = {e30406},
pmid = {38726180},
issn = {2405-8440},
abstract = {Electroencephalogram (EEG) signals are critical in interpreting sensorimotor activities for predicting body movements. However, their efficacy in identifying intralimb movements, such as the dorsiflexion and plantar flexion of the foot, remains suboptimal. This study aims to explore whether various EEG signal quantities can effectively recognize intralimb movements to facilitate the development of Brain-Computer Interface (BCI) devices for foot rehabilitation. This research involved twenty-two healthy, right-handed participants. EEG data were collected using 21 electrodes positioned over the motor cortex, while two electromyography (EMG) electrodes recorded the onset of ankle joint movements. The study focused on analyzing slow cortical potential (SCP) and sensorimotor rhythms (SMR) in alpha and beta bands from the EEG. Five key features-fourth-order Autoregressive feature, variance, waveform length, standard deviation, and permutation entropy-were extracted. A modified Recurrent Neural Network (RNN) including Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms was developed for movement recognition. These were compared against conventional machine learning algorithms, including nonlinear Support Vector Machine (SVM) and k Nearest Neighbourhood (kNN) classifiers. The performance of the proposed models was assessed using two data schemes: within-subject and across-subjects. The findings demonstrated that the GRU and LSTM models significantly outperformed traditional machine learning algorithms in recognizing different EEG signal quantities for intralimb movement. The study indicates that deep learning models, particularly GRU and LSTM, hold superior potential over standard machine learning techniques in identifying intralimb movements using EEG signals. Where the accuracies of LSTM for within and across subjects were 98.87 ± 1.80 % and 87.38 ± 0.86 % respectively. Whereas the accuracy of GRU within and across subjects were 99.18 ± 1.28 % and 86.44 ± 0.69 % respectively. This advancement could significantly benefit the development of BCI devices aimed at foot rehabilitation, suggesting a new avenue for enhancing physical therapy outcomes.},
}
@article {pmid38724769,
year = {2024},
author = {Li, Z and Tan, X and Li, X and Yin, L},
title = {Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {10},
pages = {2961-2973},
pmid = {38724769},
issn = {1741-0444},
support = {61971374//National Natural Science Foundation of China/ ; 62073280//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/physiology/diagnostic imaging ; },
abstract = {Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannian geometry to the covariance matrix extracted by spatial filtering to obtain robust and distinct features. Then, a multiscale temporal-spectral segmentation scheme is developed to enrich the feature dimensionality. In order to determine the optimal feature configurations, we utilize a linear learning-based temporal window and spectral band (TWSB) selection method to evaluate the feature contributions, which efficiently reduces the redundant features and improves the decoding efficiency without excessive loss of accuracy. Finally, support vector machines are used to predict the classification labels based on the selected MI features. To evaluate the performance of our model, we test it on the publicly available BCI Competition IV dataset 2a and 2b. The results show that the method has an average accuracy of 79.1% and 83.1%, which outperforms other existing methods. Using TWSB feature selection instead of selecting all features improves the accuracy by up to about 6%. Moreover, the TWSB selection method can effectively reduce the computational burden. We believe that the framework reveals more interpretable feature information of motor imagery EEG signals, provides neural responses discriminative with high accuracy, and facilitates the performance of real-time MI-BCI.},
}
@article {pmid38724576,
year = {2024},
author = {Saeedinia, SA and Jahed-Motlagh, MR and Tafakhori, A and Kasabov, NK},
title = {Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {10667},
pmid = {38724576},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Epilepsy/diagnosis/physiopathology ; *Biomarkers/analysis ; Pilot Projects ; *Migraine Disorders/diagnosis/physiopathology ; *Brain/physiopathology ; *Neural Networks, Computer ; Deep Learning ; Algorithms ; Male ; Adult ; Female ; },
abstract = {The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.},
}
@article {pmid38723895,
year = {2024},
author = {Sayah Ben Aissa, NEH and Korichi, A and Lakas, A and Kerrache, CA and Calafate, CT},
title = {Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification.},
journal = {SLAS technology},
volume = {29},
number = {4},
pages = {100142},
doi = {10.1016/j.slast.2024.100142},
pmid = {38723895},
issn = {2472-6311},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; Attention/physiology ; Neural Networks, Computer ; Deep Learning ; },
abstract = {The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal role in facilitating communication for individuals with physical limitations through Brain-Computer Interface (BCI) systems. Recent strides in Attention-Based Networks (ATN) have showcased remarkable performance in EEG signal classification, presenting a promising alternative to conventional Convolutional Neural Networks (CNNs). However, while CNNs have been extensively analyzed for their resilience against adversarial attacks, the susceptibility of ATNs in comparable scenarios remains largely unexplored. This paper aims to fill this gap by investigating the robustness of ATNs in adversarial contexts. We propose a high-performing attention-based deep learning model specifically designed for classifying Motor Imagery (MI) brain signals extracted from EEG data. Subsequently, we conduct a thorough series of experiments to assess various attack strategies targeting ATNs employed in EEG-based BCI tasks. Our analysis utilizes the widely recognized BCI Competition 2a dataset to demonstrate the effectiveness of attention mechanisms in BCI endeavors. Despite achieving commendable classification results in terms of accuracy (87.15%) and kappa score (0.8287), our findings reveal the vulnerability of attention-based models to adversarial manipulations (accuracy: 9.07%, kappa score: -0.21), highlighting the imperative for bolstering the robustness of attention architectures for EEG classification tasks.},
}
@article {pmid38723868,
year = {2024},
author = {Lamba, K and Rani, S},
title = {A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare.},
journal = {Journal of neuroscience methods},
volume = {408},
number = {},
pages = {110159},
doi = {10.1016/j.jneumeth.2024.110159},
pmid = {38723868},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Magnetic Resonance Imaging/methods ; *Artificial Intelligence ; *Support Vector Machine ; *Brain/diagnostic imaging/physiology ; Neural Networks, Computer ; },
abstract = {BACKGROUND: In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradient-weighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain.<br><br>NEW METHOD: A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct classes.Afterwards, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions.<br><br>RESULTS: Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network.<br><br>The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease while highlighting the specific regions of input brain MRI images responsible for making predictions. In this study, the proposed network results in 98.92% accuracy, 98.15% precision, 99.09% sensitivity, 98.18% specificity and 98.91% dice-coefficient while identifying presence of abnormal tissues in the brain. Thus, Xception model trained on distinct dataset following transfer learning process offers remarkable diagnostic accuracy and linear support vector act as a classifier to provide efficient classification among distinct classes. In addition, the deployed explainable artificial intelligence approach helps in revealing the reasoning behind predictions made by deep neural network having black-box nature and provides a clear perspective to assist medical experts in achieving trustworthiness and transparency while diagnosing brain tumor disease in the smart healthcare.},
}
@article {pmid38723406,
year = {2024},
author = {Gong, M and Pan, C and Pan, R and Wang, X and Wang, J and Xu, H and Hu, Y and Wang, J and Jia, K and Chen, Q},
title = {Distinct patterns of monocular advantage for facial emotions in social anxiety.},
journal = {Journal of anxiety disorders},
volume = {104},
number = {},
pages = {102871},
doi = {10.1016/j.janxdis.2024.102871},
pmid = {38723406},
issn = {1873-7897},
mesh = {Humans ; Female ; Male ; *Facial Expression ; Adult ; *Emotions/physiology ; *Phobia, Social/physiopathology/psychology ; *Facial Recognition/physiology ; Young Adult ; },
abstract = {Individuals with social anxiety often exhibit atypical processing of facial expressions. Previous research in social anxiety has primarily emphasized cognitive bias associated with face processing and the corresponding abnormalities in cortico-limbic circuitry, yet whether social anxiety influences early perceptual processing of emotional faces remains largely unknown. We used a psychophysical method to investigate the monocular advantage for face perception (i.e., face stimuli are better recognized when presented to the same eye compared to different eyes), an effect that is indicative of early, subcortical processing of face stimuli. We compared the monocular advantage for different emotional expressions (neutral, angry and sad) in three groups (N = 24 per group): individuals clinically diagnosed with social anxiety disorder (SAD), individuals with high social anxiety in subclinical populations (SSA), and a healthy control (HC) group of individuals matched for age and gender. Compared to SSA and HC groups, we found that individuals with SAD exhibited a greater monocular advantage when processing neutral and sad faces. While the magnitudes of monocular advantages were similar across three groups when processing angry faces, individuals with SAD performed better in this condition when the faces were presented to different eye. The former findings suggest that social anxiety leads to an enhanced role of subcortical structures in processing nonthreatening expressions. The latter findings, on the other hand, likely reflect an enhanced cortical processing of threatening expressions in SAD group. These distinct patterns of monocular advantage indicate that social anxiety altered representation of emotional faces at various stages of information processing, starting at an early stage of the visual system.},
}
@article {pmid38722315,
year = {2024},
author = {Bi, J and Gao, Y and Peng, Z and Ma, Y},
title = {Classification of motor imagery using chaotic entropy based on sub-band EEG source localization.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad4914},
pmid = {38722315},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Imagination/physiology ; *Entropy ; *Brain-Computer Interfaces ; Nonlinear Dynamics ; Algorithms ; Support Vector Machine ; Movement/physiology ; Reproducibility of Results ; },
abstract = {Objective.Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy.Approach.To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine.Main result.The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods.Significance.The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research.},
}
@article {pmid38721102,
year = {2024},
author = {Parikh, PM and Venniyoor, A},
title = {Neuralink and Brain-Computer Interface-Exciting Times for Artificial Intelligence.},
journal = {South Asian journal of cancer},
volume = {13},
number = {1},
pages = {63-65},
pmid = {38721102},
issn = {2278-330X},
abstract = {Purvish Mahendra ParikhBrain-computer interfaces are becoming a tangible reality, capable of significantly aiding patients in real-world scenarios. The recent approval by the U.S. Food and Drug Administration for clinical human trials of Neuralink marks a monumental stride, comparable to Mr. Armstrong's moonwalk. Numerous other companies are also pioneering innovative solutions in this domain. Presently, over 150,000 patients in the United States possess brain implants. As technology advances, it holds the potential to alleviate various conditions, notably motor paralysis, cerebral palsy, and involuntary movements.},
}
@article {pmid38718901,
year = {2024},
author = {Ajrawi, SA and Rao, R and Sarkar, M},
title = {A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making.},
journal = {Journal of neuroscience methods},
volume = {408},
number = {},
pages = {110161},
doi = {10.1016/j.jneumeth.2024.110161},
pmid = {38718901},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Decision Making/physiology ; *Brain/physiology ; Algorithms ; Electrocorticography/methods ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: With the aid of a brain computer interface (BCI), users can communicate and receive signals wirelessly or over wired connections to operate smart devices. A BCI classifier's architecture is quite difficult since numerous elements should be combined. These elements are made up of brain signals, which also include high levels of weak sounds that could provide reliable participant recordings of daily activities. We must use computer vision techniques to create a model in order to control those information. The high dimension and volume of signals present the classification classifier with its primary obstacles.<br><br>NEW METHOD: Due to this, we extracted and classified the brain activity in this study, and we also presented a novel hierarchical recursive feature elimination method that we refer to as HRFE embracing noisy additions. HRFE makes a variety of categorization suggestions to eliminate bias in classifying BCI systems of different types. We put the HRFE to the test on two BCI signal data sets-specifically, dataset I and BCI contests III-using shallow and deep convolution network classification techniques. Just a grid of assets is used to create electrocorticography (ECoG) signals on the contralateral (right) motor cortex, and these signals are recorded in the BCI contests III database.<br><br>RESULTS: Using ECoG signals, we choose the top 20 features that have the biggest effects on distortion and classification selection.<br><br>The simulation findings show that HRFE has a significant computer vision enhancement when compared to comparable feature selection methods in the literature, particularly for ECoG signal, which achieves about 93% reliability.},
}
@article {pmid38718788,
year = {2024},
author = {Wimpff, M and Gizzi, L and Zerfowski, J and Yang, B},
title = {EEG motor imagery decoding: a framework for comparative analysis with channel attention mechanisms.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad48b9},
pmid = {38718788},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Attention/physiology ; Movement/physiology ; },
abstract = {Objective.The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact.Approach.We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms.Results.Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture.Significance.Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for electroencephalogram motor imagery decoding within BCIs.},
}
@article {pmid38718785,
year = {2024},
author = {Wang, X and Zhang, J and Wu, X},
title = {A feature enhanced EEG compression model using asymmetric encoding-decoding network.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad48ba},
pmid = {38718785},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Data Compression/methods ; Humans ; Wearable Electronic Devices ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {Objective.Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.Approach.Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.Main results.On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.Significance.This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.},
}
@article {pmid38716555,
year = {2024},
author = {Zhang, X and Meesen, R and Swinnen, SP and Feys, H and Woolley, DG and Cheng, HJ and Wenderoth, N},
title = {Combining muscle-computer interface guided training with bihemispheric tDCS improves upper limb function in patients with chronic stroke.},
journal = {Journal of neurophysiology},
volume = {131},
number = {6},
pages = {1286-1298},
doi = {10.1152/jn.00316.2023},
pmid = {38716555},
issn = {1522-1598},
support = {G.0758.10//Vlaamse Overheid (Government of Flanders)/ ; //National Research Foundation Singapore (NRF)/ ; },
mesh = {Humans ; Male ; Female ; *Transcranial Direct Current Stimulation/methods ; *Stroke Rehabilitation/methods ; Middle Aged ; Aged ; *Stroke/physiopathology/therapy ; Double-Blind Method ; Upper Extremity/physiopathology ; Chronic Disease ; Cross-Over Studies ; Adult ; Recovery of Function/physiology ; },
abstract = {Transcranial direct current stimulation (tDCS) may facilitate neuroplasticity but with a limited effect when administered while patients with stroke are at rest. Muscle-computer interface (MCI) training is a promising approach for training patients with stroke even if they cannot produce overt movements. However, using tDCS to enhance MCI training has not been investigated. We combined bihemispheric tDCS with MCI training of the paretic wrist and examined the effect of this intervention in patients with chronic stroke. A crossover, double-blind, randomized trial was conducted. Twenty-six patients with chronic stroke performed MCI wrist training for three consecutive days at home while receiving either real tDCS or sham tDCS in counterbalanced order and separated by at least 8 mo. The primary outcome measure was the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) that was measured 1 wk before training, on the first training day, on the last training day, and 1 wk after training. There was neither a significant difference in the baseline FMA-UE score between groups nor between intervention periods. Patients improved 3.9 ± 0.6 points in FMA-UE score when receiving real tDCS, and 1.0 ± 0.7 points when receiving sham tDCS (P = 0.003). In addition, patients also showed continuous improvement in their motor control of the MCI tasks over the training days. Our study showed that the training paradigm could lead to functional improvement in patients with chronic stroke. We argue that appropriate MCI training in combination with bihemispheric tDCS could be a useful adjuvant for neurorehabilitation in patients with stroke.NEW &amp; NOTEWORTHY Bihemispheric tDCS combined with a novel MCI training for motor control of wrist extensor can improve upper limb function especially a training-specific effect on the wrist movement in patients with chronic stroke. The training regimen can be personalized with adjustments made daily to accommodate the functional change throughout the intervention. This demonstrates that bihemispheric tDCS with MCI training could complement conventional poststroke neurorehabilitation.},
}
@article {pmid38716289,
year = {2023},
author = {Ghorbani, H and AfzalAghai, M and Soltani, S and Mottaghi, M and Tavakkoli, M and Lotfi, A},
title = {Translation, Linguistic Validation, and Cultural Adaptation of the Bladder Cancer Index (BCI) Questionnaire Into the Persian (Farsi) Language and Comparing it With WHO Quality of Life Questionnaire: An Observational Study.},
journal = {Journal of family &amp; reproductive health},
volume = {17},
number = {3},
pages = {128-135},
pmid = {38716289},
issn = {1735-8949},
abstract = {OBJECTIVE: Whether ileal conduit diversion (ICD) or orthotopic neobladder (ONB) urinary diversion provides better quality of life (QoL) is still under debate. The Bladder Cancer Index (BCI) is a specific tool for bladder cancer (BCa) patients, providing reliable results in previous studies. A validated Farsi version of the BCI concerning cultural aspects could help Farsi-speaking clinicians gain more reliable feedback on QoL following urinary diversion.<br><br>MATERIALS AND METHODS: Based on WHO suggestions, we translated the BCI questionnaire into the Persian language. Then, we performed a cross-sectional study on BCa patients who underwent ICD or ONB urinary diversion. We compared their QoL via BCI and WHO questionnaires. Chi-square and independent t-tests were used where appropriate.<br><br>RESULTS: The content validity ratio and the content validity indexes were 1 and 0.8-1.0, respectively. Of 57 participants, six patients (10.5%) were women. The ICD was performed for 38 (66.7%) and ONB diversion for 19 (33.3) participants. The mean age of ICD and ONB was 68.71 &#xb1; 7.40 and 64.28 &#xb1; 8.34 years, respectively (p-value: 0.055). In all sub-domains of BCI, except bowel habits, the mean scores were higher in the ICD group. A significant difference between ICD and ONB groups was found regarding urinary function (p-value<0.001). There was no significant difference between ICD and ONB groups in none of the domains of the WHO questionnaire.<br><br>CONCLUSION: The QoL of ICD and ONB patients did not differ significantly. Even ICD may be superior in ritual purification, while the psychological status of ONB patients was better.},
}
@article {pmid38714865,
year = {2024},
author = {Webster, P},
title = {The future of brain-computer interfaces in medicine.},
journal = {Nature medicine},
volume = {30},
number = {6},
pages = {1508-1509},
doi = {10.1038/d41591-024-00031-3},
pmid = {38714865},
issn = {1546-170X},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; Brain/physiology ; },
}
@article {pmid38713969,
year = {2024},
author = {Wang, Z and Li, S and Luo, J and Liu, J and Wu, D},
title = {Channel reflection: Knowledge-driven data augmentation for EEG-based brain-computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {176},
number = {},
pages = {106351},
doi = {10.1016/j.neunet.2024.106351},
pmid = {38713969},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Algorithms ; Brain/physiology ; Evoked Potentials, Visual/physiology ; Event-Related Potentials, P300/physiology ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: (1) CR is effective, i.e., it can noticeably improve the classification accuracy; (2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, (3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further improve the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.},
}
@article {pmid38713574,
year = {2024},
author = {Jin, J and Xu, R and Daly, I and Zhao, X and Wang, X and Cichocki, A},
title = {MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on cybernetics},
volume = {54},
number = {9},
pages = {5565-5576},
doi = {10.1109/TCYB.2024.3390805},
pmid = {38713574},
issn = {2168-2275},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *Evoked Potentials/physiology ; Deep Learning ; Brain/physiology ; Algorithms ; },
abstract = {Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.},
}
@article {pmid38713331,
year = {2024},
author = {Cao, HL and Meng, YJ and Wei, W and Li, T and Li, ML and Guo, WJ},
title = {Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.},
journal = {Brain imaging and behavior},
volume = {18},
number = {5},
pages = {951-960},
pmid = {38713331},
issn = {1931-7565},
support = {81571305//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Alcoholism/diagnostic imaging/pathology ; Male ; *Gray Matter/diagnostic imaging/pathology ; Adult ; Female ; *Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging/pathology ; Alcohol Abstinence ; Nerve Net/diagnostic imaging ; Middle Aged ; Neural Pathways/diagnostic imaging ; Cerebral Cortex/diagnostic imaging/pathology ; },
abstract = {While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.},
}
@article {pmid38712193,
year = {2025},
author = {Menéndez, JA and Hennig, JA and Golub, MD and Oby, ER and Sadtler, PT and Batista, AP and Chase, SM and Yu, BM and Latham, PE},
title = {A theory of brain-computer interface learning via low-dimensional control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38712193},
issn = {2692-8205},
support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH121533/MH/NIMH NIH HHS/United States ; R00 MH121533/MH/NIMH NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
abstract = {A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice. With a few weeks of practice, even BCIs with randomly constructed decoders can be learned. What are the biological substrates of this learning process? Here, we develop a theory based on a re-aiming strategy, whereby learning operates within a low-dimensional subspace of task-relevant inputs driving the local population of recorded neurons. Through comprehensive numerical and formal analysis, we demonstrate that this theory can provide a unifying explanation for disparate phenomena previously reported in three different BCI learning tasks, and we derive a novel experimental prediction that we verify with previously published data. By explicitly modeling the underlying neural circuitry, the theory reveals an interpretation of these phenomena in terms of biological constraints on neural activity.},
}
@article {pmid38712189,
year = {2024},
author = {Shah, NP and Willsey, MS and Hahn, N and Kamdar, F and Avansino, DT and Fan, C and Hochberg, LR and Willett, FR and Henderson, JM},
title = {A flexible intracortical brain-computer interface for typing using finger movements.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38712189},
issn = {2692-8205},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; },
abstract = {Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.},
}
@article {pmid38712177,
year = {2024},
author = {Downey, JE and Schone, HR and Foldes, ST and Greenspon, C and Liu, F and Verbaarschot, C and Biro, D and Satzer, D and Moon, CH and Coffman, BA and Youssofzadeh, V and Fields, D and Hobbs, TG and Okorokova, E and Tyler-Kabara, EC and Warnke, PC and Gonzalez-Martinez, J and Hatsopoulos, NG and Bensmaia, SJ and Boninger, ML and Gaunt, RA and Collinger, JL},
title = {A roadmap for implanting microelectrode arrays to evoke tactile sensations through intracortical microstimulation.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {38712177},
support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; },
abstract = {Intracortical microstimulation (ICMS) is a method for restoring sensation to people with paralysis as part of a bidirectional brain-computer interface to restore upper limb function. Evoking tactile sensations of the hand through ICMS requires precise targeting of implanted electrodes. Here we describe the presurgical imaging procedures used to generate functional maps of the hand area of the somatosensory cortex and subsequent planning that guided the implantation of intracortical microelectrode arrays. In five participants with cervical spinal cord injury, across two study locations, this procedure successfully enabled ICMS-evoked sensations localized to at least the first four digits of the hand. The imaging and planning procedures developed through this clinical trial provide a roadmap for other brain-computer interface studies to ensure successful placement of stimulation electrodes.},
}
@article {pmid38711277,
year = {2024},
author = {Vakilipour, P and Fekrvand, S},
title = {Brain-to-brain interface technology: A brief history, current state, and future goals.},
journal = {International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience},
volume = {84},
number = {5},
pages = {351-367},
doi = {10.1002/jdn.10334},
pmid = {38711277},
issn = {1873-474X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Brain/physiology ; History, 20th Century ; History, 21st Century ; },
abstract = {A brain-to-brain interface (BBI), defined as a combination of neuroimaging and neurostimulation methods to extract and deliver information between brains directly without the need for the peripheral nervous system, is a budding communication technique. A BBI system is made up of two parts known as the brain-computer interface part, which reads a sender's brain activity and digitalizes it, and the computer-brain interface part, which writes the delivered brain activity to a receiving brain. As with other technologies, BBI systems have gone through an evolutionary process since they first appeared. The BBI systems have been employed for numerous purposes, including rehabilitation for post-stroke patients, communicating with patients suffering from amyotrophic lateral sclerosis, locked-in syndrome and speech problems following stroke. Also, it has been proposed that a BBI system could play an important role on future battlefields. This technology was not only employed for communicating between two human brains but also for making a direct communication path among different species through which motor or sensory commands could be sent and received. However, the application of BBI systems has provoked significant challenges to human rights principles due to their ability to access and manipulate human brain information. In this study, we aimed to review the brain-computer interface and computer-brain interface technologies as components of BBI systems, the development of BBI systems, applications of this technology, arising ethical issues and expectations for future use.},
}
@article {pmid38711213,
year = {2024},
author = {Zhou, H and Gong, L and Su, C and Teng, B and Xi, W and Li, X and Geng, F and Hu, Y},
title = {White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers.},
journal = {Addiction biology},
volume = {29},
number = {5},
pages = {e13399},
pmid = {38711213},
issn = {1369-1600},
support = {81971245//National Natural Science Foundation of China/ ; 62077042//National Natural Science Foundation of China/ ; 2021ZD0200409//STI 2030-Major Projects/ ; //MOE Frontiers Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; //Zhejiang Province "Qianjiang Talent Program," Research of Basic Discipline for the 2.0 Base of Top-notch Students Training Program/ ; 20211033//Ministry of Education of China/ ; },
mesh = {Humans ; *White Matter/diagnostic imaging/pathology ; Male ; *Internet Addiction Disorder/diagnostic imaging/physiopathology ; Female ; *Diffusion Tensor Imaging/methods ; Adult ; Young Adult ; *Self-Control ; *Gray Matter/diagnostic imaging/pathology ; Ventral Striatum/diagnostic imaging/physiopathology/pathology ; Severity of Illness Index ; Neural Pathways/diagnostic imaging/physiopathology ; Corpus Striatum/diagnostic imaging/pathology/physiopathology ; Internet ; Frontal Lobe/diagnostic imaging/pathology/physiopathology ; },
abstract = {Excessive use of the internet, which is a typical scenario of self-control failure, could lead to potential consequences such as anxiety, depression, and diminished academic performance. However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self-control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self-control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (p < 0.001), but not with self-control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC-right VS pathway was negatively (p = 0.011) and positively (p = 0.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self-control (p = 0.036). These associations were not observed for the dACC-left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self-control on the relationship between FA of the dACC-right VS pathway and internet addiction severity. Our findings suggest that the dACC-right VS pathway is a critical neural substrate for both internet addiction and self-control. Deficits in this pathway may lead to impaired self-regulation over internet usage, exacerbating the severity of internet addiction.},
}
@article {pmid38709613,
year = {2024},
author = {Yang, K and Li, R and Xu, J and Zhu, L and Kong, W and Zhang, J},
title = {DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {8},
pages = {4625-4635},
doi = {10.1109/JBHI.2024.3395910},
pmid = {38709613},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; *Fingers/physiology ; *Signal Processing, Computer-Assisted ; *Machine Learning ; Imagination/physiology ; Algorithms ; Brain-Computer Interfaces ; Adult ; Male ; Female ; },
abstract = {Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of feature-dependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.},
}
@article {pmid38709472,
year = {2024},
author = {Chen, S and Liu, YJ},
title = {Microglia Suppresses Breast Cancer Brain Metastasis via a Pro-inflammatory Response.},
journal = {Neuroscience bulletin},
volume = {40},
number = {7},
pages = {1034-1036},
pmid = {38709472},
issn = {1995-8218},
mesh = {*Microglia/pathology ; *Brain Neoplasms/secondary/pathology ; *Breast Neoplasms/pathology ; Female ; Animals ; Humans ; Mice ; Inflammation/pathology ; },
}
@article {pmid38707591,
year = {2024},
author = {Bridges, NR and Stickle, M and Moxon, KA},
title = {Transitioning from global to local computational strategies during brain-machine interface learning.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1371107},
pmid = {38707591},
issn = {1662-4548},
support = {R01 NS096971/NS/NINDS NIH HHS/United States ; },
abstract = {When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control (i.e., direct neurons) can display marked changes in their firing patterns during BMI learning. However, the extent of firing pattern changes in neurons not directly involved in BMI control (i.e., indirect neurons) remains unclear. To clarify this issue, we localized direct and indirect neurons to separate hemispheres in a task designed to bilaterally engage these hemispheres while animals learned to control the position of a platform with their neural signals. Animals that learned to control the platform and improve their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. Animals that did not learn the BMI task did not shift from utilizing a global to a local strategy. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.},
}
@article {pmid38707359,
year = {2024},
author = {Li, X and Tan, Y and Song, J and Lu, H and Bian, Y and Cai, W},
title = {Combined intravenous and intra-arterial thrombolysis in hyperacute cerebral ischemia without significant corresponding vascular occlusion/stenosis: A Preliminary investigation.},
journal = {Heliyon},
volume = {10},
number = {9},
pages = {e29998},
pmid = {38707359},
issn = {2405-8440},
abstract = {OBJECTIVE: In this study, we assessed the efficacy and safety of various thrombolytic treatment protocols in patients with hyperacute cerebral infarction.<br><br>METHODS: Patients diagnosed with acute ischemic stroke within 6&#xa0;h of symptom onset and with brain computer tomography angiography confirming the absence of major vessel stenosis or occlusion were eligible for this study. The enrolled patients were subsequently randomized into two groups: all the groups received the standard intravenous thrombolysis treatment with rt-PA (0.9&#xa0;mg/kg), and the experimental group underwent sequential intra-arterial thrombolysis treatment with alteplase (0.3&#xa0;mg/kg, with a maximum dose of 22&#xa0;mg), administered directly into the target vessel via a microcatheter. Both groups were closely monitored for changes in their National Institutes of Health Stroke Scale (NIHSS) score, modified Rankin scale score, hemorrhage rate, all-cause mortality rate, and the rate of favorable outcomes at 90&#xa0;&#xb1;&#xa0;7 days.<br><br>RESULTS: Ninety-four participants were enrolled in this study, with both the control and experimental groups initiating intravenous injection of rt-PA at a median time of 29&#xa0;min. For the experimental group, the median time for arterial puncture was 123&#xa0;min. Baseline data for both groups were similar (P&#xa0;>&#xa0;0.05). Hemorrhagic transformation occurred in 24.47&#xa0;% (23 patients), with a lower intracranial hemorrhage rate observed in the experimental group compared to the control group (15.2&#xa0;% vs 33.3&#xa0;%, P&#xa0;<&#xa0;0.05). Asymptomatic hemorrhage rates were 8.7&#xa0;% for the experimental group and 12.5&#xa0;% for the control group, with no hemorrhage detected in other locations. Post-treatment median NIHSS scores were lower in the experimental group than in the control group (7 vs 9, P&#xa0;<&#xa0;0.05), but short-term NIHSS scores were similar (P&#xa0;>&#xa0;0.05). A higher proportion of patients in the experimental group achieved favorable outcomes compared to the control group (87.0&#xa0;% vs 43.8&#xa0;%, P&#xa0;<&#xa0;0.05).<br><br>CONCLUSION: In patients with acute ischemic stroke with an onset time of &#x2264;6&#xa0;h and no major intracranial vessel occlusion, combining rt-PA intravenous thrombolysis with intra-arterial thrombolysis via a microcatheter might yield superior functional outcomes.},
}
@article {pmid38703311,
year = {2024},
author = {Soler, A and Giraldo, E and Molinas, M},
title = {EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.},
journal = {Brain informatics},
volume = {11},
number = {1},
pages = {11},
pmid = {38703311},
issn = {2198-4018},
abstract = {The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.},
}
@article {pmid38701593,
year = {2024},
author = {Ma, X and Chen, W and Pei, Z and Zhang, Y and Chen, J},
title = {Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.},
journal = {Computers in biology and medicine},
volume = {175},
number = {},
pages = {108504},
doi = {10.1016/j.compbiomed.2024.108504},
pmid = {38701593},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; },
abstract = {Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain computer interface (BCI) to decode electroencephalography (EEG) signals. However, due to the limited perceptual field of convolutional kernel, CNN only extracts features from local region without considering long-term dependencies for EEG decoding. Apart from long-term dependencies, multi-modal temporal information is equally important for EEG decoding because it can offer a more comprehensive understanding of the temporal dynamics of neural processes. In this paper, we propose a novel deep learning network that combines CNN with self-attention mechanism to encapsulate multi-modal temporal information and global dependencies. The network first extracts multi-modal temporal information from two distinct perspectives: average and variance. A shared self-attention module is then designed to capture global dependencies along these two feature dimensions. We further design a convolutional encoder to explore the relationship between average-pooled and variance-pooled features and fuse them into more discriminative features. Moreover, a data augmentation method called signal segmentation and recombination is proposed to improve the generalization capability of the proposed network. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and BCI Competition IV-2b (BCIC-IV-2b) datasets show that our proposed method outperforms the state-of-the-art methods and achieves 4-class average accuracy of 85.03% on the BCIC-IV-2a dataset. The proposed method implies the effectiveness of multi-modal temporal information fusion in attention-based deep learning networks and provides a new perspective for MI-EEG decoding. The code is available at https://github.com/Ma-Xinzhi/EEG-TransNet.},
}
@article {pmid38700963,
year = {2025},
author = {Valencia, D and Mercier, PP and Alimohammad, A},
title = {An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {19},
number = {1},
pages = {130-141},
doi = {10.1109/TBCAS.2024.3396115},
pmid = {38700963},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted/instrumentation ; Neural Networks, Computer ; Humans ; *Brain/physiology ; Action Potentials/physiology ; Electroencephalography ; Animals ; },
abstract = {Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, "brain-switches" based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.},
}
@article {pmid38700806,
year = {2024},
author = {Li, Y and Fang, Y and Li, K and Yang, H and Duan, S and Sun, L},
title = {Morphological Tracing and Functional Identification of Monosynaptic Connections in the Brain: A Comprehensive Guide.},
journal = {Neuroscience bulletin},
volume = {40},
number = {9},
pages = {1364-1378},
pmid = {38700806},
issn = {1995-8218},
mesh = {Animals ; *Brain/physiology ; *Synapses/physiology ; *Optogenetics/methods ; *Neural Pathways/physiology ; Humans ; Neuroanatomical Tract-Tracing Techniques/methods ; Neurons/physiology ; },
abstract = {Behavioral studies play a crucial role in unraveling the mechanisms underlying brain function. Recent advances in optogenetics, neuronal typing and labeling, and circuit tracing have facilitated the dissection of the neural circuitry involved in various important behaviors. The identification of monosynaptic connections, both upstream and downstream of specific neurons, serves as the foundation for understanding complex neural circuits and studying behavioral mechanisms. However, the practical implementation and mechanistic understanding of monosynaptic connection tracing techniques and functional identification remain challenging, particularly for inexperienced researchers. Improper application of these methods and misinterpretation of results can impede experimental progress and lead to erroneous conclusions. In this paper, we present a comprehensive description of the principles, specific operational details, and key steps involved in tracing anterograde and retrograde monosynaptic connections. We outline the process of functionally identifying monosynaptic connections through the integration of optogenetics and electrophysiological techniques, providing practical guidance for researchers.},
}
@article {pmid38700614,
year = {2024},
author = {Pang, B and Peng, Y and Gao, J and Kong, W},
title = {Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {9},
pages = {2805-2824},
pmid = {38700614},
issn = {1741-0444},
support = {61971173//National Natural Science Foundation of China/ ; 2023YFE0114900//Key Technologies Research and Development Program/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent popularity of EEG-based emotion recognition. However, due to the non-stationary nature of EEG, inter-subject variabilities become obstacles for recognition models to well adapt to different subjects. In this paper, we propose a novel approach called semi-supervised bipartite graph construction with active EEG sample selection (SBGASS) for cross-subject emotion recognition, which offers two significant advantages. Firstly, SBGASS adaptively learns a bipartite graph to characterize the underlying relationships between labeled and unlabeled EEG samples, effectively implementing the semantic connection for samples from different subjects. Secondly, we employ active sample selection technique in this paper to reduce the impact of negative samples (outliers or noise in the data) on bipartite graph construction. Drawing from the experimental results with the SEED-IV data set, we have gained the following three insights. (1) SBGASS actively rejects negative labeled samples, which helps mitigate the impact of negative samples when constructing the optimal bipartite graph and improves the model performance. (2) Through the learned optimal bipartite graph in SBGASS, the transferability of labeled EEG samples is quantitatively analyzed, which exhibits a decreasing tendency as the distance between each labeled sample and the corresponding class centroid increases. (3) Besides the improved recognition accuracy, the spatial-frequency patterns in emotion recognition are investigated by the acquired projection matrix.},
}
@article {pmid38700460,
year = {2024},
author = {Huang, X and Xue, Z and Zhang, D and Lee, HJ},
title = {Pinpointing Fat Molecules: Advances in Coherent Raman Scattering Microscopy for Lipid Metabolism.},
journal = {Analytical chemistry},
volume = {96},
number = {20},
pages = {7945-7958},
doi = {10.1021/acs.analchem.4c01398},
pmid = {38700460},
issn = {1520-6882},
mesh = {*Spectrum Analysis, Raman/methods ; *Lipid Metabolism ; Humans ; Lipids/chemistry/analysis ; Animals ; Microscopy/methods ; },
}
@article {pmid38699918,
year = {2024},
author = {Rybář, M and Poli, R and Daly, I},
title = {Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS (2021J. Neural Eng. 18 046035).},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad417b},
pmid = {38699918},
issn = {1741-2552},
}
@article {pmid38698495,
year = {2024},
author = {Eldawlatly, S},
title = {On the role of generative artificial intelligence in the development of brain-computer interfaces.},
journal = {BMC biomedical engineering},
volume = {6},
number = {1},
pages = {4},
pmid = {38698495},
issn = {2524-4426},
abstract = {Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held promise to compensate for functions lost by people with disabilities through allowing direct communication between the brain and external devices. While research throughout the past decades has demonstrated the feasibility of BCI to act as a successful assistive technology, the widespread use of BCI outside the lab is still beyond reach. This can be attributed to a number of challenges that need to be addressed for BCI to be of practical use including limited data availability, limited temporal and spatial resolutions of brain signals recorded non-invasively and inter-subject variability. In addition, for a very long time, BCI development has been mainly confined to specific simple brain patterns, while developing other BCI applications relying on complex brain patterns has been proven infeasible. Generative Artificial Intelligence (GAI) has recently emerged as an artificial intelligence domain in which trained models can be used to generate new data with properties resembling that of available data. Given the enhancements observed in other domains that possess similar challenges to BCI development, GAI has been recently employed in a multitude of BCI development applications to generate synthetic brain activity; thereby, augmenting the recorded brain activity. Here, a brief review of the recent adoption of GAI techniques to overcome the aforementioned BCI challenges is provided demonstrating the enhancements achieved using GAI techniques in augmenting limited EEG data, enhancing the spatiotemporal resolution of recorded EEG data, enhancing cross-subject performance of BCI systems and implementing end-to-end BCI applications. GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.},
}
@article {pmid38697881,
year = {2024},
author = {Chaudhary, P and Dhankhar, N and Singhal, A and Rana, KPS},
title = {A two-stage transformer based network for motor imagery classification.},
journal = {Medical engineering &amp; physics},
volume = {128},
number = {},
pages = {104154},
doi = {10.1016/j.medengphy.2024.104154},
pmid = {38697881},
issn = {1873-4030},
mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; Motor Activity/physiology ; Movement ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.},
}
@article {pmid38697588,
year = {2024},
author = {Zhang, W and Jiang, M and Teo, KAC and Bhuvanakantham, R and Fong, L and Sim, WKJ and Guo, Z and Foo, CHV and Chua, RHJ and Padmanabhan, P and Leong, V and Lu, J and Gulyás, B and Guan, C},
title = {Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.},
journal = {NeuroImage},
volume = {293},
number = {},
pages = {120629},
doi = {10.1016/j.neuroimage.2024.120629},
pmid = {38697588},
issn = {1095-9572},
mesh = {Humans ; Male ; *Magnetic Resonance Imaging/methods ; Female ; *Speech/physiology ; Adult ; *Electroencephalography/methods ; Young Adult ; Brain/physiology/diagnostic imaging ; Brain Mapping/methods ; },
abstract = {Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.},
}
@article {pmid38697587,
year = {2024},
author = {Wen, X and Yang, M and Qi, S and Wu, X and Zhang, D},
title = {Automated individual cortical parcellation via consensus graph representation learning.},
journal = {NeuroImage},
volume = {293},
number = {},
pages = {120616},
doi = {10.1016/j.neuroimage.2024.120616},
pmid = {38697587},
issn = {1095-9572},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Connectome/methods ; *Cerebral Cortex/diagnostic imaging/physiology/anatomy &amp; histology ; Machine Learning ; Female ; Male ; Image Processing, Computer-Assisted/methods ; Adult ; Algorithms ; Reproducibility of Results ; },
abstract = {Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.},
}
@article {pmid38694882,
year = {2024},
author = {Polyakov, D and Robinson, PA and Muller, EJ and Shriki, O},
title = {Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.},
journal = {Frontiers in robotics and AI},
volume = {11},
number = {},
pages = {1362735},
pmid = {38694882},
issn = {2296-9144},
abstract = {We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.},
}
@article {pmid38694459,
year = {2024},
author = {Uszko, JM and Eichhorn, SJ and Patil, AJ and Hall, SR},
title = {Detonation of fulminating gold produces heterogeneous gold nanoparticles.},
journal = {Nanoscale advances},
volume = {6},
number = {9},
pages = {2231-2233},
pmid = {38694459},
issn = {2516-0230},
abstract = {Fulminating gold, the first high-explosive compound to be discovered, disintegrates into a mysterious cloud of purple smoke, the nature of which has been speculated upon since its discovery in the 15th century. In this work, we show that the colour of the smoke is due to the presence of gold nanoparticles.},
}
@article {pmid38694204,
year = {2024},
author = {Yao, X and Li, M and He, S and Jing, L and Li, C and Tao, J and Hui, X and Gao, F and Song, J and Chen, H and Wang, Z},
title = {Kirigami-Triggered Spoof Plasmonic Interconnects for Radiofrequency Elastronics.},
journal = {Research (Washington, D.C.)},
volume = {7},
number = {},
pages = {0367},
pmid = {38694204},
issn = {2639-5274},
abstract = {The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.},
}
@article {pmid38693175,
year = {2024},
author = {Lotun, S and Lamarche, VM and Matran-Fernandez, A and Sandstrom, GM},
title = {Author Correction: People perceive parasocial relationships to be effective at fulfilling emotional needs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {9986},
doi = {10.1038/s41598-024-60558-w},
pmid = {38693175},
issn = {2045-2322},
}
@article {pmid38692704,
year = {2024},
author = {Xu, K and Yang, Y and Ding, J and Wang, J and Fang, Y and Tian, H},
title = {Spatially Precise Genetic Engineering at the Electrode-Tissue Interface.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {29},
pages = {e2401327},
doi = {10.1002/adma.202401327},
pmid = {38692704},
issn = {1521-4095},
support = {2021ZD0202200//National Key Research and Development Program of China/ ; 2021ZD0202201//National Key Research and Development Program of China/ ; 22102040//National Natural Science Foundation of China/ ; 62371150//National Natural Science Foundation of China/ ; },
mesh = {Animals ; *PTEN Phosphohydrolase/genetics/metabolism ; Mice ; *Neurons/metabolism ; *Genetic Engineering ; RNA, Small Interfering/metabolism/genetics/chemistry ; Electrodes ; Parkinson Disease/genetics/metabolism/therapy ; Humans ; Gene Knockdown Techniques ; },
abstract = {The interface between electrodes and neural tissues plays a pivotal role in determining the efficacy and fidelity of neural activity recording and modulation. While considerable efforts have been made to improve the electrode-tissue interface, the majority of studies have primarily concentrated on the development of biocompatible neural electrodes through abiotic materials and structural engineering. In this study, an approach is presented that seamlessly integrates abiotic and biotic engineering principles into the electrode-tissue interface. Specifically, ultraflexible neural electrodes with short hairpin RNAs (shRNAs) designed to silence the expression of endogenous genes within neural tissues are combined. The system facilitates shRNA-mediated knockdown of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) and polypyrimidine tract-binding protein 1 (PTBP1), two essential genes associated in neural survival/growth and neurogenesis, within specific cell populations located at the electrode-tissue interface. Additionally, it is demonstrated that the downregulation of PTEN in neurons can result in an enlargement of neuronal cell bodies at the electrode-tissue interface. Furthermore, the system enables long-term monitoring of neuronal activities following PTEN knockdown in a mouse model of Parkinson's disease and traumatic brain injury. The system provides a versatile approach for genetically engineering the electrode-tissue interface with unparalleled precision, paving the way for the development of regenerative electronics and next-generation brain-machine interfaces.},
}
@article {pmid38692190,
year = {2024},
author = {Duan, T and Wang, Z and Li, F and Doretto, G and Adjeroh, DA and Yin, Y and Tao, C},
title = {Online continual decoding of streaming EEG signal with a balanced and informative memory buffer.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {176},
number = {},
pages = {106338},
doi = {10.1016/j.neunet.2024.106338},
pmid = {38692190},
issn = {1879-2782},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Memory/physiology ; Signal Processing, Computer-Assisted ; Brain/physiology ; Algorithms ; },
abstract = {Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.},
}
@article {pmid38689713,
year = {2024},
author = {Ma, D and Jin, X and Sun, S and Li, Y and Wu, X and Hu, Y and Yang, F and Tang, H and Zhu, X and Lin, P and Pan, G},
title = {Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.},
journal = {National science review},
volume = {11},
number = {5},
pages = {nwae102},
pmid = {38689713},
issn = {2053-714X},
abstract = {Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.},
}
@article {pmid38689706,
year = {2024},
author = {Forenzo, D and Zhu, H and Shanahan, J and Lim, J and He, B},
title = {Continuous tracking using deep learning-based decoding for noninvasive brain-computer interface.},
journal = {PNAS nexus},
volume = {3},
number = {4},
pages = {pgae145},
pmid = {38689706},
issn = {2752-6542},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; T32 EB029365/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models' midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.},
}
@article {pmid38686423,
year = {2024},
author = {Li, X and Wang, D and Zhang, B and Fan, C and Chen, J and Xu, M and Chen, Y},
title = {[A review on electroencephalogram based channel selection].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {2},
pages = {398-405},
pmid = {38686423},
issn = {1001-5515},
mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Humans ; *Algorithms ; *Signal Processing, Computer-Assisted ; Brain/physiology ; Electrodes ; Event-Related Potentials, P300/physiology ; Imagination/physiology ; },
abstract = {The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.},
}
@article {pmid38686330,
year = {2024},
author = {Liu, X and Gong, Y and Jiang, Z and Stevens, T and Li, W},
title = {Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1348434},
pmid = {38686330},
issn = {1662-4548},
abstract = {Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.},
}
@article {pmid38686326,
year = {2024},
author = {Liu, Y and Wang, H and Sha, G and Cao, Y and Chen, Y and Chen, Y and Zhang, J and Chai, C and Fan, Q and Xia, S},
title = {The covariant structural and functional neuro-correlates of cognitive impairments in patients with end-stage renal diseases.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1374948},
pmid = {38686326},
issn = {1662-4548},
abstract = {INTRODUCTION: Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD) that is associated with structural and functional changes in the brain. However, whether a joint structural and functional alteration pattern exists that is related to CI in ESRD is unclear.<br><br>METHODS: In this study, instead of looking at brain structure and function separately, we aim to investigate the covariant characteristics of both functional and structural aspects. Specifically, we took the fusion analysis approach, namely, multimodal canonical correlation analysis and joint independent component analysis (mCCA+jICA), to jointly study the discriminative features in gray matter volume (GMV) measured by T1-weighted (T1w) MRI, fractional anisotropy (FA) in white matter measured by diffusion MRI, and the amplitude of low-frequency fluctuation (ALFF) measured by blood oxygenation-level-dependent (BOLD) MRI in 78 ESRD patients versus 64 healthy controls (HCs), followed by a mediation effect analysis to explore the relationship between neuroimaging findings, cognitive impairments and uremic toxins.<br><br>RESULTS: Two joint group-discriminative independent components (ICs) were found to show covariant abnormalities across FA, GMV, and ALFF (all p&#x2009;<&#x2009;0.05). The most dominant joint IC revealed associative patterns of alterations of GMV (in the precentral gyrus, occipital lobe, temporal lobe, parahippocampal gyrus, and hippocampus), alterations of ALFF (in the precuneus, superior parietal gyrus, and superior occipital gyrus), and of white matter FA (in the corticospinal tract and inferior frontal occipital fasciculus). Another significant IC revealed associative alterations of GMV (in the dorsolateral prefrontal and orbitofrontal cortex) and FA (in the forceps minor). Moreover, the brain changes identified by FA and GMV in the above-mentioned brain regions were found to mediate the negative correlation between serum phosphate and mini-mental state examination (MMSE) scores (all p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: The mCCA+jICA method was demonstrated to be capable of revealing covariant abnormalities across neuronal features of different types in ESRD patients as contrasted to HCs, and joint brain changes may play an important role in mediating the relationship between serum toxins and CIs in ESRD. Our results show the mCCA+jICA fusion analysis approach may provide new insights into similar neurobiological studies.},
}
@article {pmid38686177,
year = {2024},
author = {Zhao, H and Liu, J and Shao, Y and Feng, X and Zhao, B and Sun, L and Liu, Y and Zeng, L and Li, XM and Yang, H and Duan, S and Yu, YQ},
title = {Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons.},
journal = {National science review},
volume = {11},
number = {4},
pages = {nwae082},
pmid = {38686177},
issn = {2053-714X},
abstract = {The nucleus of Darkschewitsch (ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons (NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock. Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior. Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray (lPAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part (GiV). Together, these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the lPAG to GiV, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.},
}
@article {pmid38683717,
year = {2024},
author = {Lee, M and Park, HY and Park, W and Kim, KT and Kim, YH and Jeong, JH},
title = {Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1767-1778},
doi = {10.1109/TNSRE.2024.3395133},
pmid = {38683717},
issn = {1558-0210},
mesh = {Humans ; Male ; Female ; Middle Aged ; *Electroencephalography/methods ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Aged ; *Algorithms ; *Machine Learning ; Imagination/physiology ; Stroke/physiopathology/complications ; Robotics ; Adult ; Psychomotor Performance ; Ischemic Stroke/physiopathology/rehabilitation ; Imagery, Psychotherapy/methods ; },
abstract = {Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.},
}
@article {pmid38682423,
year = {2024},
author = {Dawit, H and Zhao, Y and Wang, J and Pei, R},
title = {Advances in conductive hydrogels for neural recording and stimulation.},
journal = {Biomaterials science},
volume = {12},
number = {11},
pages = {2786-2800},
doi = {10.1039/d4bm00048j},
pmid = {38682423},
issn = {2047-4849},
mesh = {*Hydrogels/chemistry ; Humans ; *Electric Conductivity ; Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; Biocompatible Materials/chemistry ; Brain/physiology ; Neurons/physiology ; },
abstract = {The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.},
}
@article {pmid38682224,
year = {2024},
author = {Sadeghi, S and Maleki, A},
title = {A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and Electromyogram.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {4},
pages = {73},
doi = {10.31083/j.jin2304073},
pmid = {38682224},
issn = {0219-6352},
mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual ; *Electromyography ; Humans ; *Word Processing/methods ; },
abstract = {BACKGROUND: To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR.<br><br>METHODS: In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects.<br><br>RESULTS: The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects.<br><br>CONCLUSIONS: Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.},
}
@article {pmid38682219,
year = {2024},
author = {Yang, L and Ma, E and Yang, L and Li, M and Shang, Z and Wang, L and Ma, Z and Li, J},
title = {Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {4},
pages = {72},
doi = {10.31083/j.jin2304072},
pmid = {38682219},
issn = {0219-6352},
support = {GZC20232447//National Postdoctoral Researcher Program/ ; 62301496//National Natural Science Foundation of China/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; 222102310223//Key Scientific and Technological Projects of Henan Province/ ; },
mesh = {Animals ; *Columbidae/physiology ; *Flight, Animal/physiology ; Support Vector Machine ; Gamma Rhythm/physiology ; Midbrain Reticular Formation/physiology ; Male ; Behavior, Animal/physiology ; Mesencephalon/physiology ; },
abstract = {BACKGROUND: Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight.<br><br>METHODS: We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states.<br><br>RESULTS: Our findings indicate that the gamma band (80-150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80-150 Hz band and 0.89 using the fused features for state decoding.<br><br>CONCLUSIONS: Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.},
}
@article {pmid38681960,
year = {2024},
author = {Sarasola-Sanz, A and Ray, AM and Insausti-Delgado, A and Irastorza-Landa, N and Mahmoud, WJ and Brötz, D and Bibián-Nogueras, C and Helmhold, F and Zrenner, C and Ziemann, U and López-Larraz, E and Ramos-Murguialday, A},
title = {A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {12},
number = {},
pages = {1330330},
pmid = {38681960},
issn = {2296-4185},
abstract = {Introduction: The primary constraint of non-invasive brain-machine interfaces (BMIs) in stroke rehabilitation lies in the poor spatial resolution of motor intention related neural activity capture. To address this limitation, hybrid brain-muscle-machine interfaces (hBMIs) have been suggested as superior alternatives. These hybrid interfaces incorporate supplementary input data from muscle signals to enhance the accuracy, smoothness and dexterity of rehabilitation device control. Nevertheless, determining the distribution of control between the brain and muscles is a complex task, particularly when applied to exoskeletons with multiple degrees of freedom (DoFs). Here we present a feasibility, usability and functionality study of a bio-inspired hybrid brain-muscle machine interface to continuously control an upper limb exoskeleton with 7 DoFs. Methods: The system implements a hierarchical control strategy that follows the biologically natural motor command pathway from the brain to the muscles. Additionally, it employs an innovative mirror myoelectric decoder, offering patients a reference model to assist them in relearning healthy muscle activation patterns during training. Furthermore, the multi-DoF exoskeleton enables the practice of coordinated arm and hand movements, which may facilitate the early use of the affected arm in daily life activities. In this pilot trial six chronic and severely paralyzed patients controlled the multi-DoF exoskeleton using their brain and muscle activity. The intervention consisted of 2 weeks of hBMI training of functional tasks with the system followed by physiotherapy. Patients' feedback was collected during and after the trial by means of several feedback questionnaires. Assessment sessions comprised clinical scales and neurophysiological measurements, conducted prior to, immediately following the intervention, and at a 2-week follow-up. Results: Patients' feedback indicates a great adoption of the technology and their confidence in its rehabilitation potential. Half of the patients showed improvements in their arm function and 83% improved their hand function. Furthermore, we found improved patterns of muscle activation as well as increased motor evoked potentials after the intervention. Discussion: This underscores the significant potential of bio-inspired interfaces that engage the entire nervous system, spanning from the brain to the muscles, for the rehabilitation of stroke patients, even those who are severely paralyzed and in the chronic phase.},
}
@article {pmid38681523,
year = {2024},
author = {Wu, J and Zhao, Y},
title = {Single cocaine exposure attenuates the intrinsic excitability of CRH neurons in the ventral BNST via Sigma-1 receptors.},
journal = {Translational neuroscience},
volume = {15},
number = {1},
pages = {20220339},
pmid = {38681523},
issn = {2081-3856},
abstract = {The ventral bed nucleus of the stria terminalis (vBNST) plays a key role in cocaine addiction, especially relapse. However, the direct effects of cocaine on corticotropin-releasing hormone (CRH) neurons in the vBNST remain unclear. Here, we identify that cocaine exposure can remarkably attenuate the intrinsic excitability of CRH neurons in the vBNST in vitro. Accumulating studies reveal the crucial role of Sigma-1 receptors (Sig-1Rs) in modulating cocaine addiction. However, to the authors' best knowledge no investigations have explored the role of Sig-1Rs in the vBNST, let alone CRH neurons. Given that cocaine acts as a type of Sig-1Rs agonist, and the dramatic role of Sig-1Rs played in intrinsic excitability of neurons as well as cocaine addiction, we employ BD1063 a canonical Sig-1Rs antagonist to block the effects of cocaine, and significantly recover the excitability of CRH neurons. Together, we suggest that cocaine exposure leads to the firing rate depression of CRH neurons in the vBNST via binding to Sig-1Rs.},
}
@article {pmid38680535,
year = {2024},
author = {Jia, T and Sun, J and McGeady, C and Ji, L and Li, C},
title = {Enhancing Brain-Computer Interface Performance by Incorporating Brain-to-Brain Coupling.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {5},
number = {},
pages = {0116},
pmid = {38680535},
issn = {2692-7632},
abstract = {Human cooperation relies on key features of social interaction in order to reach desirable outcomes. Similarly, human-robot interaction may benefit from integration with human-human interaction factors. In this paper, we aim to investigate brain-to-brain coupling during motor imagery (MI)-based brain-computer interface (BCI) training using eye-contact and hand-touch interaction. Twelve pairs of friends (experimental group) and 10 pairs of strangers (control group) were recruited for MI-based BCI tests concurrent with electroencephalography (EEG) hyperscanning. Event-related desynchronization (ERD) was estimated to measure cortical activation, and interbrain functional connectivity was assessed using multilevel statistical analysis. Furthermore, we compared BCI classification performance under different social interaction conditions. In the experimental group, greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction. Notably, EEG channels with decreased power were mainly distributed around the frontal, central, and occipital regions. A significant increase in interbrain coupling was also found under social interaction conditions. BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition. However, for the strangers' group, no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions. These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling. This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.},
}
@article {pmid38679478,
year = {2024},
author = {Tao, R and Zhao, H and Zhang, C and Xu, S},
title = {Distinct neural dynamics of the observed ostracism effect in decision-making under risk and ambiguity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {4},
pages = {},
doi = {10.1093/cercor/bhae171},
pmid = {38679478},
issn = {1460-2199},
support = {72171151//National Natural Science Foundation of China/ ; 21ZR1461600//Natural Science Foundation of Shanghai/ ; 2021114003//Fundamental Research Funds for the Central Universities/ ; BHA230145//General Education Project of the National Social Science Fund/ ; 23092179-Y//Science Foundation of Zhejiang Sci-Tech University/ ; 2023DSYL028//Academic Mentoring Program of Shanghai International Studies University/ ; 2021WSYS002//Neuroeconomics Laboratory of Guangzhou Huashang College/ ; },
mesh = {Humans ; Male ; Female ; *Decision Making/physiology ; Young Adult ; *Electroencephalography ; *Risk-Taking ; Adult ; Evoked Potentials/physiology ; Brain/physiology ; Social Isolation/psychology ; Cues ; },
abstract = {Observational ostracism, as a form of social exclusion, can significantly affect human behavior. However, the effects of observed ostracism on risky and ambiguous decision-making and the underlying neural mechanisms remain unclear. This event-related potential study investigated these issues by involving participants in a wheel-of- fortune task, considering observed ostracism and inclusion contexts. The results showed that the cue-P3 component was more enhanced during the choice phase for risky decisions than for ambiguous decisions in the observed inclusion contexts but not in the observed ostracism contexts. During the outcome evaluation phase, feedback-related negativity amplitudes following both risky and ambiguous decisions were higher in the no-gain condition than in the gain condition in the observed inclusion context. In contrast, this effect was only observed following risky decisions in the observed ostracism context. The feedback-P3 component did not exhibit an observed ostracism effect in risky and ambiguous decision-making tasks. Risk levels further modulated the cue-P3 and feedback-related negativity components, while ambiguity levels further modulated the feedback-P3 components. These findings demonstrate a neural dissociation between risk and ambiguity decision-making during observed ostracism that unfolds from the choice phase to the outcome evaluation phase.},
}
@article {pmid38678941,
year = {2024},
author = {Zong, F and Wang, L and Liu, H and Xue, B and Bai, R and Liu, Y},
title = {A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI.},
journal = {Computers in biology and medicine},
volume = {175},
number = {},
pages = {108508},
doi = {10.1016/j.compbiomed.2024.108508},
pmid = {38678941},
issn = {1879-0534},
mesh = {Humans ; *Algorithms ; *Brain/diagnostic imaging ; *Image Processing, Computer-Assisted/methods ; Diffusion Magnetic Resonance Imaging/methods ; Monte Carlo Method ; },
abstract = {Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.},
}
@article {pmid38677220,
year = {2024},
author = {Shah, DD and Carter, P and Shivdasani, MN and Fong, N and Duan, W and Esrafilzadeh, D and Poole-Warren, LA and Aregueta Robles, UA},
title = {Deciphering platinum dissolution in neural stimulation electrodes: Electrochemistry or biology?.},
journal = {Biomaterials},
volume = {309},
number = {},
pages = {122575},
doi = {10.1016/j.biomaterials.2024.122575},
pmid = {38677220},
issn = {1878-5905},
mesh = {*Platinum/chemistry ; Humans ; Animals ; Electrodes, Implanted ; Electric Stimulation ; Electrochemistry/methods ; Electrodes ; },
abstract = {Platinum (Pt) is the metal of choice for electrodes in implantable neural prostheses like the cochlear implants, deep brain stimulating devices, and brain-computer interfacing technologies. However, it is well known since the 1970s that Pt dissolution occurs with electrical stimulation. More recent clinical and in vivo studies have shown signs of corrosion in explanted electrode arrays and the presence of Pt-containing particulates in tissue samples. The process of degradation and release of metallic ions and particles can significantly impact on device performance. Moreover, the effects of Pt dissolution products on tissue health and function are still largely unknown. This is due to the highly complex chemistry underlying the dissolution process and the difficulty in decoupling electrical and chemical effects on biological responses. Understanding the mechanisms and effects of Pt dissolution proves challenging as the dissolution process can be influenced by electrical, chemical, physical, and biological factors, all of them highly variable between experimental settings. By evaluating comprehensive findings on Pt dissolution mechanisms reported in the fuel cell field, this review presents a critical analysis of the possible mechanisms that drive Pt dissolution in neural stimulation in vitro and in vivo. Stimulation parameters, such as aggregate charge, charge density, and electrochemical potential can all impact the levels of dissolved Pt. However, chemical factors such as electrolyte types, dissolved gases, and pH can all influence dissolution, confounding the findings of in vitro studies with multiple variables. Biological factors, such as proteins, have been documented to exhibit a mitigating effect on the dissolution process. Other biological factors like cells and fibro-proliferative responses, such as fibrosis and gliosis, impact on electrode properties and are suspected to impact on Pt dissolution. However, the relationship between electrical properties of stimulating electrodes and Pt dissolution remains contentious. Host responses to Pt degradation products are also controversial due to the unknown chemistry of Pt compounds formed and the lack of understanding of Pt distribution in clinical scenarios. The cytotoxicity of Pt produced via electrical stimulation appears similar to Pt-based compounds, including hexachloroplatinates and chemotherapeutic agents like cisplatin. While the levels of Pt produced under clinical and acute stimulation regimes were typically an order of magnitude lower than toxic concentrations observed in vitro, further research is needed to accurately assess the mass balance and type of Pt produced during long-term stimulation and its impact on tissue response. Finally, approaches to mitigating the dissolution process are reviewed. A wide variety of approaches, including stimulation strategies, coating electrode materials, and surface modification techniques to avoid excess charge during stimulation and minimise tissue response, may ultimately support long-term and safe operation of neural stimulating devices.},
}
@article {pmid38675259,
year = {2024},
author = {Wang, X and Jiang, W and Yang, H and Ye, Y and Zhou, Z and Sun, L and Nie, Y and Tao, TH and Wei, X},
title = {Ultraflexible PEDOT:PSS/IrOx-Modified Electrodes: Applications in Behavioral Modulation and Neural Signal Recording in Mice.},
journal = {Micromachines},
volume = {15},
number = {4},
pages = {},
pmid = {38675259},
issn = {2072-666X},
support = {2019YFA0905200//National Key R &amp; D Program of China/ ; 61974154//National Natural Science Foundation of China/ ; },
abstract = {Recent advancements in neural probe technology have become pivotal in both neuroscience research and the clinical management of neurological disorders. State-of-the-art developments have led to the advent of multichannel, high-density bidirectional neural interfaces that are adept at both recording and modulating neuronal activity within the central nervous system. Despite this progress, extant bidirectional probes designed for simultaneous recording and stimulation are beset with limitations, including elicitation of inflammatory responses and insufficient charge injection capacity. In this paper, we delineate the design and application of an innovative ultraflexible bidirectional neural probe engineered from polyimide. This probe is distinguished by its ability to facilitate high-resolution recordings and precise stimulation control in deep brain regions. Electrodes enhanced with a PEDOT:PSS/IrOx composite exhibit a substantial increase in charge storage capacity, escalating from 0.14 ± 0.01 mC/cm[2] to an impressive 24.75 ± 0.18 mC/cm[2]. This augmentation significantly bolsters the electrodes' charge transfer efficacy. In tandem, we observed a notable reduction in electrode impedance, from 3.47 ± 1.77 MΩ to a mere 41.88 ± 4.04 kΩ, while the phase angle exhibited a positive shift from -72.61 ± 1.84° to -34.17 ± 0.42°. To substantiate the electrodes' functional prowess, we conducted in vivo experiments, where the probes were surgically implanted into the bilateral motor cortex of mice. These experiments involved the synchronous recording and meticulous analysis of neural signal fluctuations during stimulation and an assessment of the probes' proficiency in modulating directional turning behaviors in the subjects. The empirical evidence corroborates that targeted stimulation within the bilateral motor cortex of mice can modulate the intensity of neural signals in the stimulated locale, enabling the directional control of the mice's turning behavior to the contralateral side of the stimulation site.},
}
@article {pmid38672024,
year = {2024},
author = {Du, X and Ding, X and Xi, M and Lv, Y and Qiu, S and Liu, Q},
title = {A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.},
journal = {Brain sciences},
volume = {14},
number = {4},
pages = {},
pmid = {38672024},
issn = {2076-3425},
support = {JYTMS20230377//Educational Department of Liaoning Province/ ; },
abstract = {Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.},
}
@article {pmid38672017,
year = {2024},
author = {Du, X and Wang, X and Zhu, L and Ding, X and Lv, Y and Qiu, S and Liu, Q},
title = {Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network.},
journal = {Brain sciences},
volume = {14},
number = {4},
pages = {},
pmid = {38672017},
issn = {2076-3425},
support = {JYTMS20230377//Liaoning Provincial Education Department/ ; },
abstract = {EEG signals combined with deep learning play an important role in the study of human-computer interaction. However, the limited dataset makes it challenging to study EEG signals using deep learning methods. Inspired by the GAN network in image generation, this paper presents an improved generative adversarial network model L-C-WGAN-GP to generate artificial EEG data to augment training sets and improve the application of BCI in various fields. The generator consists of a long short-term memory (LSTM) network and the discriminator consists of a convolutional neural network (CNN) which uses the gradient penalty-based Wasserstein distance as the loss function in model training. The model can learn the statistical features of EEG signals and generate EEG data that approximate real samples. In addition, the performance of the compressed sensing reconstruction model can be improved by using augmented datasets. Experiments show that, compared with the existing advanced data amplification techniques, the proposed model produces EEG signals closer to the real EEG signals as measured by RMSE, FD and WTD indicators. In addition, in the compressed reconstruction of EEG signals, adding the new data reduces the loss by about 15% compared with the original data, which greatly improves the reconstruction accuracy of the EEG signals' compressed sensing.},
}
@article {pmid38671991,
year = {2024},
author = {Li, M and Yang, L and Wang, Z and Liu, Y and Wan, H and Shang, Z},
title = {Progress of Micro-Stimulation Techniques to Alter Pigeons' Motor Behavior: A Review from the Perspectives of the Neural Basis and Neuro-Devices.},
journal = {Brain sciences},
volume = {14},
number = {4},
pages = {},
pmid = {38671991},
issn = {2076-3425},
support = {62301496//National Natural Science Foundation of China/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 2022ZD0208500//STI 2030-Major Project/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Pigeons have natural advantages in robotics research, including a wide range of activities, low energy consumption, good concealment performance, strong long-distance weight bearing and continuous flight ability, excellent navigation, and spatial cognitive ability, etc. They are typical model animals in the field of animal robot research and have important application value. A hot interdisciplinary research topic and the core content of pigeon robot research, altering pigeon motor behavior using brain stimulation involves multiple disciplines including animal ethology, neuroscience, electronic information technology and artificial intelligence technology, etc. In this paper, we review the progress of altering pigeon motor behavior using brain stimulation from the perspectives of the neural basis and neuro-devices. The recent literature on altering pigeon motor behavior using brain stimulation was investigated first. The neural basis, structure and function of a system to alter pigeon motor behavior using brain stimulation are briefly introduced below. Furthermore, a classified review was carried out based on the representative research achievements in this field in recent years. Our summary and discussion of the related research progress cover five aspects including the control targets, control parameters, control environment, control objectives, and control system. Future directions that need to be further studied are discussed, and the development trend in altering pigeon motor behavior using brain stimulation is projected.},
}
@article {pmid38671977,
year = {2024},
author = {Zhang, C and Su, L and Li, S and Fu, Y},
title = {Differential Brain Activation for Four Emotions in VR-2D and VR-3D Modes.},
journal = {Brain sciences},
volume = {14},
number = {4},
pages = {},
pmid = {38671977},
issn = {2076-3425},
support = {62166021//National Natural Science Foundation of China/ ; 82172058//National Natural Science Foundation of China/ ; 81771926//National Natural Science Foundation of China/ ; },
abstract = {Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell's emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell's emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal-high pleasure (HAHV), high arousal-low pleasure (HALV), low arousal-low pleasure (LALV), and low arousal-high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation.},
}
@article {pmid38671798,
year = {2024},
author = {Chen, Q and Dong, Y and Gai, Y},
title = {Tactile Location Perception Encoded by Gamma-Band Power.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {4},
pages = {},
pmid = {38671798},
issn = {2306-5354},
support = {2217032//National Science Foundation/ ; },
abstract = {BACKGROUND: The perception of tactile-stimulation locations is an important function of the human somatosensory system during body movements and its interactions with the surroundings. Previous psychophysical and neurophysiological studies have focused on spatial location perception of the upper body. In this study, we recorded single-trial electroencephalography (EEG) responses evoked by four vibrotactile stimulators placed on the buttocks and thighs while the human subject was sitting in a chair with a cushion.<br><br>METHODS: Briefly, 14 human subjects were instructed to sit in a chair for a duration of 1 h or 1 h and 45 min. Two types of cushions were tested with each subject: a foam cushion and an air-cell-based cushion dedicated for wheelchair users to alleviate tissue stress. Vibrotactile stimulations were applied to the sitting interface at the beginning and end of the sitting period. Somatosensory-evoked potentials were obtained using a 32-channel EEG. An artificial neural net was used to predict the tactile locations based on the evoked EEG power.<br><br>RESULTS: We found that single-trial beta (13-30 Hz) and gamma (30-50 Hz) waves can best predict the tactor locations with an accuracy of up to 65%. Female subjects showed the highest performances, while males' sensitivity tended to degrade after the sitting period. A three-way ANOVA analysis indicated that the air-cell cushion maintained location sensitivity better than the foam cushion.<br><br>CONCLUSION: Our finding shows that tactile location information is encoded in EEG responses and provides insights on the fundamental mechanisms of the tactile system, as well as applications in brain-computer interfaces that rely on tactile stimulation.},
}
@article {pmid38671769,
year = {2024},
author = {Zhang, B and Xu, M and Zhang, Y and Ye, S and Chen, Y},
title = {Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain-Computer Interface.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {4},
pages = {},
pmid = {38671769},
issn = {2306-5354},
support = {2023M740171//China Postdoctoral Science Foundation/ ; 0420239352KF001-07//Talent Grant of Beijing Academy of Science and Technology/ ; },
abstract = {The rapid serial visual presentation-based brain-computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.},
}
@article {pmid38671062,
year = {2024},
author = {Angrick, M and Luo, S and Rabbani, Q and Candrea, DN and Shah, S and Milsap, GW and Anderson, WS and Gordon, CR and Rosenblatt, KR and Clawson, L and Tippett, DC and Maragakis, N and Tenore, FV and Fifer, MS and Hermansky, H and Ramsey, NF and Crone, NE},
title = {Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {9617},
pmid = {38671062},
issn = {2045-2322},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Amyotrophic Lateral Sclerosis/physiopathology/therapy ; Male ; *Speech/physiology ; Middle Aged ; Electrodes, Implanted ; Electrocorticography ; },
abstract = {Brain-computer interfaces (BCIs) that reconstruct and synthesize speech using brain activity recorded with intracranial electrodes may pave the way toward novel communication interfaces for people who have lost their ability to speak, or who are at high risk of losing this ability, due to neurological disorders. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a man with impaired articulation due to ALS, participating in a clinical trial (ClinicalTrials.gov, NCT03567213) exploring different strategies for BCI communication. The 3-stage approach reported here relies on recurrent neural networks to identify, decode and synthesize speech from electrocorticographic (ECoG) signals acquired across motor, premotor and somatosensory cortices. We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the participant from a vocabulary of 6 keywords previously used for decoding commands to control a communication board. Evaluation of the intelligibility of the synthesized speech indicates that 80% of the words can be correctly recognized by human listeners. Our results show that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words while preserving the participant's voice profile, and provide further evidence for the stability of ECoG for speech-based BCIs.},
}
@article {pmid38670338,
year = {2024},
author = {Donlon, JD and McAloon, CG and Mee, JF},
title = {Performance of various interpretations of clinical scoring systems for diagnosis of respiratory disease in dairy calves in a temperate climate using Bayesian latent class analysis.},
journal = {Journal of dairy science},
volume = {107},
number = {9},
pages = {7138-7152},
doi = {10.3168/jds.2023-24321},
pmid = {38670338},
issn = {1525-3198},
mesh = {Animals ; Cattle ; *Bayes Theorem ; *Latent Class Analysis ; *Cattle Diseases/diagnosis ; Prospective Studies ; Climate ; Dairying ; Sensitivity and Specificity ; Bovine Respiratory Disease Complex/diagnosis ; },
abstract = {Bovine respiratory disease (BRD) presents a challenge to farmers all over the globe, not only because it can have significant impacts on welfare and productivity, but also because diagnosis can prove challenging. Several clinical scoring systems have been developed to aid farmers in making consistent early diagnosis, 2 examples being the Wisconsin (WCS) and the California (CALIF) systems. Neither of these systems were developed in or for use in a temperate environment. As environment may lead to changes in BRD presentation, the weightings and cutoffs designed for one environmental presentation of BRD may not be appropriate when used in a temperate climate. Additionally, the interpretation of the scores recorded varies between studies; this may also influence conclusions. Hence, the objective of this work was to investigate the sensitivity (Se) and specificity (Sp) of these tests in a temperate climate and investigate the influence of varying the interpretation on the performance of the WCS. In this prospective study, 98 commercial spring-calving dairy farms were recruited (40 randomly, 58 targeted) and visited. Thoracic ultrasound and WCS were performed on 20 randomly sampled calves between 4 and 6 wk of age on each farm. On a subset of 32 farms, the CALIF score was also undertaken. The data were then used in a hierarchical Bayesian latent class model to estimate the Se and Sp of 5 different interpretations of the Wisconsin clinical score and 1 interpretation of the California clinical score. In total, 1,936 calves were examined. The Se of the Wisconsin score varied from 0.336 to 0.577 depending on the interpretation used, and the Sp varied from 0.943 to 0.977. The Se of the California score was 0.563 (95% Bayesian credible interval [BCI]: 0.452, 0.681) and the Sp was 0.919 (95% BCI: 0.899, 0.937). In conclusion, the performances of the clinical scores in a temperate environment were similar to previously published work from more extreme climates; however, the performance varied widely depending on the score interpretation. Authors should justify their use of a particular clinical score interpretation to improve clarity in publications.},
}
@article {pmid38669494,
year = {2024},
author = {Heo, SP and Choi, H and Yang, YM},
title = {Novel stability approach using Routh-Hurwitz criterion for brain computer interface applications.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {32},
number = {S1},
pages = {17-25},
pmid = {38669494},
issn = {1878-7401},
mesh = {Humans ; *Brain-Computer Interfaces ; Computer Simulation ; Algorithms ; },
abstract = {BACKGROUND: The stability criterion approach is very important for estimating precise behavior before or after fabricating brain computer interface system applications.<br><br>OBJECTIVE: A novel approach using the Routh-Hurwitz standard criterion method is proposed to easily determine and analyze the stability of brain computer interface system applications. Using this developed approach, we were able to easily test the stability of technical issue using simple programmed codes before or after brain computer interfaces fabrication applications.<br><br>METHODS: Using a MATLAB simulation program package, we are able to provide two different special case examples such as a first zero element and a row of zeros to verify the capability of our proposed Routh-Hurwitz method.<br><br>RESULTS: The MATLAB simulation program provided efficient Routh-Hurwitz standard criterion results by differentiating the highest coefficients of the s and a.<br><br>CONCLUSION: This technical paper explains how to use our proposed new Routh-Hurwitz standard condition to simply ascertain and determine the brain computer interface system stability without customized commercial simulation tools.},
}
@article {pmid38666542,
year = {2024},
author = {Li, P and Liu, J and Yuan, JH and Guo, Y and Wang, S and Zhang, P and Wang, W},
title = {Artificial Funnel Nanochannel Device Emulates Synaptic Behavior.},
journal = {Nano letters},
volume = {24},
number = {20},
pages = {6192-6200},
doi = {10.1021/acs.nanolett.3c05079},
pmid = {38666542},
issn = {1530-6992},
mesh = {*Synapses/physiology ; *Nanotechnology/instrumentation ; Electrolytes/chemistry ; Nanostructures/chemistry ; Neurons/physiology ; Electric Conductivity ; },
abstract = {Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.},
}
@article {pmid38666139,
year = {2024},
author = {Hu, Y and Pan, Y and Yue, L and Gao, X},
title = {Self-objectification and eating disorders: the psychopathological and neural processes from psychological distortion to psychosomatic illness.},
journal = {Psychoradiology},
volume = {4},
number = {},
pages = {kkae003},
pmid = {38666139},
issn = {2634-4416},
}
@article {pmid38666121,
year = {2023},
author = {Huang, Y and Weng, Y and Lan, L and Zhu, C and Shen, T and Tang, W and Lai, HY},
title = {Insight in obsessive-compulsive disorder: conception, clinical characteristics, neuroimaging, and treatment.},
journal = {Psychoradiology},
volume = {3},
number = {},
pages = {kkad025},
pmid = {38666121},
issn = {2634-4416},
abstract = {Obsessive-compulsive disorder (OCD) is a chronic disabling disease with often unsatisfactory therapeutic outcomes. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has broadened the diagnostic criteria for OCD, acknowledging that some OCD patients may lack insight into their symptoms. Previous studies have demonstrated that insight can impact therapeutic efficacy and prognosis, underscoring its importance in the treatment of mental disorders, including OCD. In recent years, there has been a growing interest in understanding the influence of insight on mental disorders, leading to advancements in related research. However, to the best of our knowledge, there is dearth of comprehensive reviews on the topic of insight in OCD. In this review article, we aim to fill this gap by providing a concise overview of the concept of insight and its multifaceted role in clinical characteristics, neuroimaging mechanisms, and treatment for OCD.},
}
@article {pmid38665897,
year = {2024},
author = {Herbert, C},
title = {Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1305445},
pmid = {38665897},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.},
}
@article {pmid38664240,
year = {2024},
author = {Armocida, D and Garbossa, D and Cofano, F},
title = {Letter: Ethical concerns and scientific communication on neuralink device.},
journal = {Neurosurgical review},
volume = {47},
number = {1},
pages = {194},
pmid = {38664240},
issn = {1437-2320},
}
@article {pmid38663401,
year = {2024},
author = {Ha, LJ and Yeo, HG and Kim, YG and Baek, I and Baeg, E and Lee, YH and Won, J and Jung, Y and Park, J and Jeon, CY and Kim, K and Min, J and Song, Y and Park, JH and Nam, KR and Son, S and Yoo, SBM and Park, SH and Choi, WS and Lim, KS and Choi, JY and Cho, JH and Lee, Y and Choi, HJ},
title = {Hypothalamic neuronal activation in non-human primates drives naturalistic goal-directed eating behavior.},
journal = {Neuron},
volume = {112},
number = {13},
pages = {2218-2230.e6},
doi = {10.1016/j.neuron.2024.03.029},
pmid = {38663401},
issn = {1097-4199},
mesh = {Animals ; *Goals ; *Feeding Behavior/physiology ; Male ; *Magnetic Resonance Imaging ; Hypothalamic Area, Lateral/physiology ; GABAergic Neurons/physiology ; Positron-Emission Tomography ; Macaca mulatta ; Hypothalamus/physiology/diagnostic imaging ; Neurons/physiology ; Female ; },
abstract = {Maladaptive feeding behavior is the primary cause of modern obesity. While the causal influence of the lateral hypothalamic area (LHA) on eating behavior has been established in rodents, there is currently no primate-based evidence available on naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHA[GABA]) neurons in eating using chemogenetics in three macaques. LHA[GABA] neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, predominantly for palatable food. Positron emission tomography and magnetic resonance spectroscopy validated chemogenetic activation. Resting-state functional magnetic resonance imaging revealed that the functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after LHA[GABA] neuron activation. Thus, our study elucidates the role of LHA[GABA] neurons in eating and obesity therapeutics for primates and humans.},
}
@article {pmid38660590,
year = {2024},
author = {Demirezen, G and Taşkaya Temizel, T and Brouwer, AM},
title = {Reproducible machine learning research in mental workload classification using EEG.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1346794},
pmid = {38660590},
issn = {2673-6195},
abstract = {This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.},
}
@article {pmid38660227,
year = {2024},
author = {Liu, H and Wang, Z and Li, R and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1367932},
pmid = {38660227},
issn = {1662-4548},
abstract = {Steady-state visual evoked potential brain-computer interfaces (SSVEP-BCI) have attracted significant attention due to their ease of deployment and high performance in terms of information transfer rate (ITR) and accuracy, making them a promising candidate for integration with consumer electronics devices. However, as SSVEP characteristics are directly associated with visual stimulus attributes, the influence of stereoscopic vision on SSVEP as a critical visual attribute has yet to be fully explored. Meanwhile, the promising combination of virtual reality (VR) devices and BCI applications is hampered by the significant disparity between VR environments and traditional 2D displays. This is not only due to the fact that screen-based SSVEP generally operates under static, stable conditions with simple and unvaried visual stimuli but also because conventional luminance-modulated stimuli can quickly induce visual fatigue. This study attempts to address these research gaps by designing SSVEP paradigms with stereo-related attributes and conducting a comparative analysis with the traditional 2D planar paradigm under the same VR environment. This study proposed two new paradigms: the 3D paradigm and the 3D-Blink paradigm. The 3D paradigm induces SSVEP by modulating the luminance of spherical targets, while the 3D-Blink paradigm employs modulation of the spheres' opacity instead. The results of offline 4-object selection experiments showed that the accuracy of 3D and 2D paradigm was 85.67 and 86.17% with canonical correlation analysis (CCA) and 86.17 and 91.73% with filter bank canonical correlation analysis (FBCCA), which is consistent with the reduction in the signal-to-noise ratio (SNR) of SSVEP harmonics for the 3D paradigm observed in the frequency-domain analysis. The 3D-Blink paradigm achieved 75.00% of detection accuracy and 27.02 bits/min of ITR with 0.8 seconds of stimulus time and task-related component analysis (TRCA) algorithm, demonstrating its effectiveness. These findings demonstrate that the 3D and 3D-Blink paradigms supported by VR can achieve improved user comfort and satisfactory performance, while further algorithmic optimization and feature analysis are required for the stereo-related paradigms. In conclusion, this study contributes to a deeper understanding of the impact of binocular stereoscopic vision mechanisms on SSVEP paradigms and promotes the application of SSVEP-BCI in diverse VR environments.},
}
@article {pmid38658998,
year = {2024},
author = {Keough, JR and Irvine, B and Kelly, D and Wrightson, J and Comaduran Marquez, D and Kinney-Lang, E and Kirton, A},
title = {Fatigue in children using motor imagery and P300 brain-computer interfaces.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {61},
pmid = {38658998},
issn = {1743-0003},
support = {1052360//Alberta Children's Hospital Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; Male ; Female ; *Event-Related Potentials, P300/physiology ; *Electroencephalography ; *Fatigue/physiopathology/psychology ; *Imagination/physiology ; Cross-Over Studies ; Adolescent ; Prospective Studies ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) technology offers children with quadriplegic cerebral palsy unique opportunities for communication, environmental exploration, learning, and game play. Research in adults demonstrates a negative impact of fatigue on BCI enjoyment, while effects on BCI performance are variable. To date, there have been no pediatric studies of BCI fatigue. The purpose of this study was to assess the effects of two different BCI paradigms, motor imagery and visual P300, on the development of self-reported fatigue and an electroencephalography (EEG) biomarker of fatigue in typically developing children.<br><br>METHODS: Thirty-seven typically-developing school-aged children were recruited to a prospective, crossover study. Participants attended three sessions: (A) motor imagery-BCI, (B) visual P300-BCI, and (C) video viewing (control). The motor imagery task involved an imagined left- or right-hand squeeze. The P300 task involved attending to one square on a 3&#x2009;&#xd7;&#x2009;3 grid during a random single flash sequence. Each paradigm had respective calibration periods and a similar visual counting game. Primary outcomes were self-reported fatigue and the power of the EEG alpha band both collected during resting-state periods pre- and post-task. Self-reported fatigue was measured using a 10-point visual analog scale. EEG alpha band power was calculated as the integrated power spectral density from 8 to 12&#xa0;Hz of the EEG spectrum.<br><br>RESULTS: Thirty-two children completed the protocol (age range 7-16, 63% female). Self-reported fatigue and EEG alpha band power increased across all sessions (F(1,155)&#x2009;=&#x2009;33.9, p&#x2009;<&#x2009;0.001; F&#x2009;=&#x2009;5.0(1,149), p&#x2009;=&#x2009;0.027 respectively). No differences in fatigue development were observed between session types. There was no correlation between self-reported fatigue and EEG alpha band power change. BCI performance varied between participants and paradigms as expected but was not associated with self-reported fatigue or EEG alpha band power.<br><br>CONCLUSION: Short periods (30-mintues) of BCI use can increase self-reported fatigue and EEG alpha band power to a similar degree in children performing motor imagery and P300 BCI paradigms. Performance was not associated with our measures of fatigue; the impact of fatigue on useability and enjoyment is unclear. Our results reflect the variability of fatigue and the BCI experience more broadly in children and warrant further investigation.},
}
@article {pmid38657615,
year = {2024},
author = {Song, M and Gwon, D and Jun, SC and Ahn, M},
title = {Signal alignment for cross-datasets in P300 brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad430d},
pmid = {38657615},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Event-Related Potentials, P300/physiology ; Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Algorithms ; },
abstract = {Objective.Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment (SA) for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning.Approach.We proposed a linear SA that uses the P300's latency, amplitude scale, and reverse factor to transform signals. For evaluation, four datasets were introduced (two from conventional P300 Speller BCIs, one from a P300 Speller with face stimuli, and the last from a standard auditory oddball paradigm).Results.Although the standard approach without SA had an average precision (AP) score of 25.5%, the approach demonstrated a 35.8% AP score, and we observed that the number of subjects showing improvement was 36.0% on average. Particularly, we confirmed that the Speller dataset with face stimuli was more comparable with other datasets.Significance.We proposed a simple and intuitive way to align ERP signals that uses the characteristics of ERP signals. The results demonstrated the feasibility of cross-dataset transfer learning even between datasets with different paradigms.},
}
@article {pmid38656833,
year = {2024},
author = {Bottosso, M and Miglietta, F and Vernaci, GM and Giarratano, T and Dieci, MV and Guarneri, V and Griguolo, G},
title = {Gene Expression Assays to Tailor Adjuvant Endocrine Therapy for HR+/HER2- Breast Cancer.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {30},
number = {14},
pages = {2884-2894},
pmid = {38656833},
issn = {1557-3265},
support = {Ricerca Corrente funding//Istituto Oncologico Veneto (IOV)/ ; DOR funding//Universit&#xe0; degli Studi di Padova (UNIPD)/ ; },
mesh = {Humans ; *Breast Neoplasms/drug therapy/genetics/pathology/metabolism ; Female ; Chemotherapy, Adjuvant/methods ; *Receptor, ErbB-2/genetics/metabolism ; *Antineoplastic Agents, Hormonal/therapeutic use/pharmacology ; *Biomarkers, Tumor/genetics ; Gene Expression Profiling ; Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Gene Expression Regulation, Neoplastic/drug effects ; Precision Medicine/methods ; },
abstract = {Adjuvant endocrine therapy (ET) represents the standard of care for almost all hormone receptor (HR)+/HER2- breast cancers, and different agents and durations are currently available. In this context, the tailoring and optimization of adjuvant endocrine treatment by reducing unnecessary toxic treatment while taking into account the biological heterogeneity of HR+/HER2- breast cancer represents a clinical priority. There is therefore a significant need for the integration of biological biomarkers in the choice of adjuvant ET beyond currently used clinicopathological characteristics. Several gene expression assays have been developed to identify patients with HR+/HER2- breast cancer who will not derive benefit from the addition of adjuvant chemotherapy. By enhancing risk stratification and predicting therapeutic response, genomic assays have also shown to be a promising tool for optimizing endocrine treatment decisions. In this study, we review evidence supporting the use of most common commercially available gene expression assays [Oncotype DX, MammaPrint, Breast Cancer Index (BCI), Prosigna, and EndoPredict] in tailoring adjuvant ET. Available data on the use of genomic tests to inform extended adjuvant treatment choice based on the risk of late relapse and on the estimated benefit of a prolonged ET are discussed. Moreover, preliminary evidence regarding the use of genomic assays to inform de-escalation of endocrine treatment, such as shorter durations or omission, for low-risk patients is reviewed. Overall, gene expression assays are emerging as potential tools to further personalize adjuvant treatment for patients with HR+/HER2- breast cancers.},
}
@article {pmid38656638,
year = {2024},
author = {Waisberg, E and Ong, J and Lee, AG},
title = {Correction: Ethical Considerations of Neuralink and Brain-Computer Interfaces.},
journal = {Annals of biomedical engineering},
volume = {52},
number = {8},
pages = {1940},
doi = {10.1007/s10439-024-03524-x},
pmid = {38656638},
issn = {1573-9686},
}
@article {pmid38654411,
year = {2024},
author = {Romanchek, BAH and Uetz, G and Scheifele, PM},
title = {Characterization of sound production by the pot-bellied seahorse (Hippocampus abdominalis) during feeding.},
journal = {Journal of fish biology},
volume = {105},
number = {1},
pages = {124-128},
doi = {10.1111/jfb.15747},
pmid = {38654411},
issn = {1095-8649},
mesh = {Animals ; *Smegmamorpha/physiology ; Female ; Male ; *Feeding Behavior ; Vocalization, Animal ; Body Size ; },
abstract = {Sound production during feeding by the pot-bellied seahorse, Hippocampus abdominalis, was quantified with an observation of clicks (acoustic signal) and snicks (visual behavior). Female, male, and juvenile seahorses had feeding sounds characterized for peak (dominant) frequency (Hz), sound pressure level (SPL), and duration (ms). Subject body size and condition was estimated by standard length (SL, cm), to determine an estimate of body condition index (BCI). An inverse correlation between mean peak frequency (Hz) of clicks and SL was found for females. A negative correlation between peak frequency (Hz) of clicks and a residual BCI was determined for both males and females, suggesting that acoustic signals may contain information regarding fitness.},
}
@article {pmid38654008,
year = {2024},
author = {Łabęcki, M and Nowicka, MM and Wróbel, A and Suffczynski, P},
title = {Frequency-dependent dynamics of steady-state visual evoked potentials under sustained flicker stimulation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {9281},
pmid = {38654008},
issn = {2045-2322},
mesh = {*Evoked Potentials, Visual/physiology ; Humans ; *Photic Stimulation ; Male ; *Electroencephalography ; Female ; Adult ; Young Adult ; Brain-Computer Interfaces ; Visual Cortex/physiology ; },
abstract = {Steady-state visual evoked potentials (SSVEP) are electroencephalographic signals elicited when the brain is exposed to a visual stimulus with a steady frequency. We analyzed the temporal dynamics of SSVEP during sustained flicker stimulation at 5, 10, 15, 20 and 40 Hz. We found that the amplitudes of the responses were not stable over time. For a 5 Hz stimulus, the responses progressively increased, while, for higher flicker frequencies, the amplitude increased during the first few seconds and often showed a continuous decline afterward. We hypothesize that these two distinct sets of frequency-dependent SSVEP signal properties reflect the contribution of parvocellular and magnocellular visual pathways generating sustained and transient responses, respectively. These results may have important applications for SSVEP signals used in research and brain-computer interface technology and may contribute to a better understanding of the frequency-dependent temporal mechanisms involved in the processing of prolonged periodic visual stimuli.},
}
@article {pmid38653131,
year = {2024},
author = {Yang, Y and Luo, S and Wang, W and Gao, X and Yao, X and Wu, T},
title = {From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications.},
journal = {NeuroImage. Clinical},
volume = {42},
number = {},
pages = {103608},
pmid = {38653131},
issn = {2213-1582},
mesh = {*Magnetoencephalography/methods ; Humans ; Brain/physiology ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Brain Mapping/methods ; Nervous System Diseases/physiopathology/diagnosis ; },
abstract = {Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.},
}
@article {pmid38651553,
year = {2024},
author = {Canale, A and Urbanelli, A and Albera, R and Gragnano, M and Bordino, V and Riva, G and Sportoletti Baduel, E and Albera, A},
title = {Binaural hearing in monaural conductive or mixed hearing loss fitted with unilateral Bonebridge.},
journal = {Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale},
volume = {44},
number = {2},
pages = {113-119},
pmid = {38651553},
issn = {1827-675X},
mesh = {Humans ; Female ; Male ; Middle Aged ; *Hearing Loss, Mixed Conductive-Sensorineural/rehabilitation/surgery ; Adult ; *Hearing Loss, Conductive/rehabilitation/surgery/physiopathology ; *Bone Conduction ; Hearing Aids ; Aged ; },
abstract = {OBJECTIVE: To determine the benefits of binaural hearing rehabilitation in patients with monaural conductive or mixed hearing loss treated with a unilateral bone conduction implant (BCI).<br><br>METHODS: This monocentric study includes 7 patients with monaural conductive or mixed hearing loss who underwent surgical implantation of a unilateral BCI (Bonebridge, Med-El). An ITA Matrix test was performed by each patient included in the study - without and with the BCI and in three different settings - to determine the summation effect, squelch effect and head shadow effect. Subjective hearing benefits were assessed using the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire.<br><br>RESULTS: The difference in signal to noise ratio of patients without and with BCI was 0.79 dB in the summation setting (p < 0.05), 4.62 dB in the head shadow setting (p < 0.05) and 1.53 dB (p = 0.063) in the squelch setting. The APHAB questionnaire revealed a subjective discomfort in the presence of unexpected sounds in patients using a unilateral BCI (aversiveness score) compared to the same environmental situations without BCI, with a mean discomfort score of 69.00% (SD &#xb1; 21.24%) with monaural BCI versus 25.67% (SD &#xb1; 16.70%) without BCI (difference: -43.33%, p < 0.05). In terms of global score, patients wearing a unilateral Bonebridge implant did not show any significant differences compared to those without hearing aid (difference: -4.00%, p = 0.310).<br><br>CONCLUSIONS: Our study shows that the use of a unilateral BCI in patients affected by monaural conductive or mixed hearing loss can improve speech perception under noise conditions due to the summation effect and to the decrease of the head shadow effect. However, since monaural BCIs might lead to discomfort under noise conditions in some subjects, a pre-operative assessment of the possible individual benefit of a monaural BCI should be carried out in patients affected by unilateral conductive or mixed hearing loss in order to investigate the possible additional effect of the fitting of hearing aids.},
}
@article {pmid38649681,
year = {2024},
author = {Wimmer, M and Weidinger, N and Veas, E and Müller-Putz, GR},
title = {Multimodal decoding of error processing in a virtual reality flight simulation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {9221},
pmid = {38649681},
issn = {2045-2322},
mesh = {Humans ; *Virtual Reality ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Adult ; Male ; *Pupil/physiology ; Female ; Young Adult ; Computer Simulation ; Brain/physiology ; Heart Rate/physiology ; },
abstract = {Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only. We examined the feasibility of decoding errors solely with pupil size data and proposed a hybrid decoding approach combining electroencephalographic (EEG) and pupillometric signals. Moreover, we analyzed if hybrid approaches can improve existing EEG-based classification approaches and focused on setups that offer increased usability for practical applications, such as the presented game-like virtual reality flight simulation. Our results indicate that classifiers trained with pupil size data can decode errors above chance. Moreover, hybrid approaches yielded improved performance compared to EEG-based decoders in setups with a reduced number of channels, which is crucial for many out-of-the-lab scenarios. These findings contribute to the development of hybrid brain-computer interfaces, particularly in combination with wearable devices, which allow for easy acquisition of additional physiological data.},
}
@article {pmid38649503,
year = {2024},
author = {Zhang, X and Lian, J and Yu, Z and Tang, H and Liang, D and Liu, J and Liu, JK},
title = {Revealing the mechanisms of semantic satiation with deep learning models.},
journal = {Communications biology},
volume = {7},
number = {1},
pages = {487},
pmid = {38649503},
issn = {2399-3642},
support = {21JR7RA510//Gansu Science and Technology Department (Science and Technology Department of Gansu Province)/ ; 62236007//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62176003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 20230484362//Beijing Nova Program/ ; },
mesh = {*Deep Learning ; *Semantics ; Humans ; Neural Networks, Computer ; Models, Neurological ; },
abstract = {The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.},
}
@article {pmid38648783,
year = {2024},
author = {Tankus, A and Rosenberg, N and Ben-Hamo, O and Stern, E and Strauss, I},
title = {Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad4179},
pmid = {38648783},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurons/physiology ; Male ; *Machine Learning ; Female ; Middle Aged ; *Speech/physiology ; Adult ; *Thalamus/physiology ; Deep Brain Stimulation/methods ; Aged ; Speech Perception/physiology ; },
abstract = {Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.},
}
@article {pmid38648782,
year = {2024},
author = {Guerreiro Fernandes, F and Raemaekers, M and Freudenburg, Z and Ramsey, N},
title = {Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad4178},
pmid = {38648782},
issn = {1741-2552},
mesh = {Adult ; Female ; Humans ; Male ; *Brain-Computer Interfaces ; Data Analysis ; Electrodes, Implanted ; *Magnetic Resonance Imaging ; Motion ; Paralysis ; Prostheses and Implants ; *Sensorimotor Cortex/anatomy &amp; histology/physiology ; *Speech/physiology ; Support Vector Machine ; Temporal Lobe/anatomy &amp; histology/physiology ; Brain Mapping ; },
abstract = {Objective.Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis.Approach.Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM).Main results.Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC.Significance.The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.},
}
@article {pmid38648781,
year = {2024},
author = {Ikegawa, Y and Fukuma, R and Sugano, H and Oshino, S and Tani, N and Tamura, K and Iimura, Y and Suzuki, H and Yamamoto, S and Fujita, Y and Nishimoto, S and Kishima, H and Yanagisawa, T},
title = {Text and image generation from intracranial electroencephalography using an embedding space for text and images.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad417a},
pmid = {38648781},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Female ; *Electrocorticography/methods ; Adult ; Electroencephalography/methods ; Middle Aged ; Electrodes, Implanted ; Young Adult ; Photic Stimulation/methods ; },
abstract = {Objective.Invasive brain-computer interfaces (BCIs) are promising communication devices for severely paralyzed patients. Recent advances in intracranial electroencephalography (iEEG) coupled with natural language processing have enhanced communication speed and accuracy. It should be noted that such a speech BCI uses signals from the motor cortex. However, BCIs based on motor cortical activities may experience signal deterioration in users with motor cortical degenerative diseases such as amyotrophic lateral sclerosis. An alternative approach to using iEEG of the motor cortex is necessary to support patients with such conditions.Approach. In this study, a multimodal embedding of text and images was used to decode visual semantic information from iEEG signals of the visual cortex to generate text and images. We used contrastive language-image pretraining (CLIP) embedding to represent images presented to 17 patients implanted with electrodes in the occipital and temporal cortices. A CLIP image vector was inferred from the high-γpower of the iEEG signals recorded while viewing the images.Main results.Text was generated by CLIPCAP from the inferred CLIP vector with better-than-chance accuracy. Then, an image was created from the generated text using StableDiffusion with significant accuracy.Significance.The text and images generated from iEEG through the CLIP embedding vector can be used for improved communication.},
}
@article {pmid38648154,
year = {2024},
author = {Chen, X and Wang, Z and Wu, D},
title = {Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1703-1714},
doi = {10.1109/TNSRE.2024.3391936},
pmid = {38648154},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Machine Learning ; *Imagination/physiology ; *Algorithms ; Evoked Potentials/physiology ; },
abstract = {Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, previous research showed that their direct extensions to BCIs degrade the classification accuracy on benign samples. This phenomenon greatly affects the applicability of adversarial defense approaches to EEG-based BCIs. To mitigate this problem, we propose alignment-based adversarial training (ABAT), which performs EEG data alignment before adversarial training. Data alignment aligns EEG trials from different domains to reduce their distribution discrepancies, and adversarial training further robustifies the classification boundary. The integration of data alignment and adversarial training can make the trained EEG classifiers simultaneously more accurate and more robust. Experiments on five EEG datasets from two different BCI paradigms (motor imagery classification, and event related potential recognition), three convolutional neural network classifiers (EEGNet, ShallowCNN and DeepCNN) and three different experimental settings (offline within-subject cross-block/-session classification, online cross-session classification, and pre-trained classifiers) demonstrated its effectiveness. It is very intriguing that adversarial attacks, which are usually used to damage BCI systems, can be used in ABAT to simultaneously improve the model accuracy and robustness.},
}
@article {pmid38648145,
year = {2024},
author = {Zhang, R and Feng, S and Hu, N and Low, S and Li, M and Chen, X and Cui, H},
title = {Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {7},
pages = {4194-4203},
doi = {10.1109/JBHI.2024.3392412},
pmid = {38648145},
issn = {2168-2208},
mesh = {Humans ; *Stroke Rehabilitation/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; *Robotics/instrumentation/methods ; Female ; Adult ; Middle Aged ; Electroencephalography/methods/instrumentation ; Evoked Potentials, Visual/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Aged ; Young Adult ; Stroke/physiopathology ; Equipment Design ; Hand/physiology/physiopathology ; },
abstract = {Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.},
}
@article {pmid38647635,
year = {2024},
author = {Juyal, R and Muthusamy, H and Kumar, N and Tiwari, A},
title = {Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.},
journal = {Physical and engineering sciences in medicine},
volume = {47},
number = {3},
pages = {939-954},
pmid = {38647635},
issn = {2662-4737},
support = {CRG/2021/007147//Core Research Grant of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (CRG/2021/007147)/ ; },
mesh = {Humans ; *Electroencephalography ; *Brain/physiology/diagnostic imaging ; Male ; Female ; Adult ; *Imagination/physiology ; Language ; Young Adult ; Brain-Computer Interfaces ; Rest ; Phonetics ; Brain Mapping ; Speech/physiology ; },
abstract = {Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers. However, more research needs to be conducted on how subject-specific characteristics such as mental state, age, handedness, nativeness and resting state activity affects the brain's output during imagined speech. In an overt speech, it is evident that native and non-native speakers' brains function differently. Therefore, this paper explores how nativeness to language affects EEG signals while imagining vowel phonemes, using brain-map analysis and scalogram and also investigates the inclusion of features extracted from resting state EEG with imagined state EEG. The Fourteen-channel EEG for Imagined Speech (FEIS) dataset was used to analyse the EEG signals recorded while imagining vowel phonemes for 16 subjects (nine native English and seven non-native Chinese). For the classification of vowel phonemes, different connectivity measures such as covariance, coherence, and Phase Synchronous Index-PSI were extracted and analysed using statistics based Multivariate Analysis of Variance (MANOVA) approach. Different fusion strategies (difference, concatenation, Common Spatial Pattern-CSP and Canonical Correlation Analysis-CCA) were carried out to incorporate resting state EEG connectivity measures with imagined state connectivity measures for enhancing the accuracy of imagined vowel phoneme recognition. Simulation results revealed that concatenating imagined state and rest state covariance and PSI features provided the maximum accuracy of 92.78% for native speakers and 94.07% for non-native speakers.},
}
@article {pmid38647423,
year = {2024},
author = {Shen, Q and Tang, X and Wen, X and Cheng, S and Xiao, P and Zang, SK and Shen, DD and Jiang, L and Zheng, Y and Zhang, H and Xu, H and Mao, C and Zhang, M and Hu, W and Sun, JP and Zhang, Y and Chen, Z},
title = {Molecular Determinant Underlying Selective Coupling of Primary G-Protein by Class A GPCRs.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {23},
pages = {e2310120},
pmid = {38647423},
issn = {2198-3844},
support = {92353303//National Natural Science Foundation of China/ ; 32141004//National Natural Science Foundation of China/ ; 32000845//National Natural Science Foundation of China/ ; U21A20418//National Natural Science Foundation of China/ ; 81973302//National Natural Science Foundation of China/ ; 62202426//National Natural Science Foundation of China/ ; 81825022//National Science Fund for Distinguished Young Scholars/ ; 2021C03039//Key R&amp;D Projects of Zhejiang Province/ ; //Fundamental Research Funds for the Central Universities/ ; 2019YFA050880//National Key Research and Development Program of China/ ; 2022ZD0205400//STI2030-Major Projects/ ; 2024C03147//"'Pioneer"' and "'Leading Goose"' R&amp;D Program of Zhejiang/ ; 2020R01006//Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang/ ; },
mesh = {*Receptors, G-Protein-Coupled/metabolism/chemistry/genetics ; Humans ; *Cryoelectron Microscopy/methods ; GTP-Binding Proteins/metabolism/chemistry/genetics ; Histamine/metabolism/chemistry ; Receptors, Histamine H2/metabolism/genetics/chemistry ; Receptors, Histamine H3/metabolism/chemistry/genetics ; Signal Transduction ; },
abstract = {G-protein-coupled receptors (GPCRs) transmit downstream signals predominantly via G-protein pathways. However, the conformational basis of selective coupling of primary G-protein remains elusive. Histamine receptors H2R and H3R couple with Gs- or Gi-proteins respectively. Here, three cryo-EM structures of H2R-Gs and H3R-Gi complexes are presented at a global resolution of 2.6-2.7 Å. These structures reveal the unique binding pose for endogenous histamine in H3R, wherein the amino group interacts with E206[5.46] of H3R instead of the conserved D114[3.32] of other aminergic receptors. Furthermore, comparative analysis of the H2R-Gs and H3R-Gi complexes reveals that the structural geometry of TM5/TM6 determines the primary G-protein selectivity in histamine receptors. Machine learning (ML)-based structuromic profiling and functional analysis of class A GPCR-G-protein complexes illustrate that TM5 length, TM5 tilt, and TM6 outward movement are key determinants of the Gs and Gi/o selectivity among the whole Class A family. Collectively, the findings uncover the common structural geometry within class A GPCRs that determines the primary Gs- and Gi/o-coupling selectivity.},
}
@article {pmid38647399,
year = {2024},
author = {Lin, RR and Jin, LL and Xue, YY and Zhang, ZS and Huang, HF and Chen, DF and Liu, Q and Mao, ZW and Wu, ZY and Tao, QQ},
title = {Hybrid Membrane-Coated Nanoparticles for Precise Targeting and Synergistic Therapy in Alzheimer's Disease.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {24},
pages = {e2306675},
pmid = {38647399},
issn = {2198-3844},
support = {2021ZD0201103//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2021ZD0201803//Science Innovation 2030-Brain Science and Brain-Inspired Intelligence Technology Major Projects/ ; 2024SSYS0018//Key R&amp;D Program of Zhejiang Province/ ; LBY21H090003//Natural Science Foundation of Zhejiang Province/ ; 2022ZDYF22//Key Science &amp; Technologies&#x2002;R&amp;D&#x2002;Program&#x2002;of Lishui City/ ; },
mesh = {*Alzheimer Disease/drug therapy/metabolism ; Animals ; Mice ; *Nanoparticles/chemistry ; *Disease Models, Animal ; *Mice, Transgenic ; *Blood-Brain Barrier/metabolism/drug effects ; *Liposomes ; Drug Delivery Systems/methods ; Cell Membrane/metabolism/drug effects ; Humans ; },
abstract = {The blood brain barrier (BBB) limits the application of most therapeutic drugs for neurological diseases (NDs). Hybrid cell membrane-coated nanoparticles derived from different cell types can mimic the surface properties and functionalities of the source cells, further enhancing their targeting precision and therapeutic efficacy. Neuroinflammation has been increasingly recognized as a critical factor in the pathogenesis of various NDs, especially Alzheimer's disease (AD). In this study, a novel cell membrane coating is designed by hybridizing the membrane from platelets and chemokine (C-C motif) receptor 2 (CCR2) cells are overexpressed to cross the BBB and target neuroinflammatory lesions. Past unsuccessful endeavors in AD drug development underscore the challenge of achieving favorable outcomes when utilizing single-mechanism drugs.Two drugs with different mechanisms of actions into liposomes are successfully loaded to realize multitargeting treatment. In a transgenic mouse model for familial AD (5xFAD), the administration of these drug-loaded hybrid cell membrane liposomes results in a significant reduction in amyloid plaque deposition, neuroinflammation, and cognitive impairments. Collectively, the hybrid cell membrane-coated nanomaterials offer new opportunities for precise drug delivery and disease-specific targeting, which represent a versatile platform for targeted therapy in AD.},
}
@article {pmid38646579,
year = {2024},
author = {Denis-Robichaud, J and Nicola, I and Chupin, H and Roy, JP and Buczinski, S and Fauteux, V and Picard-Hagen, N and Cue, R and Dubuc, J},
title = {Herd-level associations between the proportion of elevated prepartum nonesterified fatty acid concentrations and postpartum diseases, reproduction, or culling on dairy farms.},
journal = {JDS communications},
volume = {5},
number = {3},
pages = {210-214},
pmid = {38646579},
issn = {2666-9102},
abstract = {The objectives of this herd-level prospective observational cohort study were to describe the proportion of cows with elevated prepartum nonesterified fatty acid concentrations (PropElevNEFA) in dairy herds and to assess the herd-level associations between PropElevNEFA and postpartum diseases, reproductive performance, and culling. From November 2018 to December 2020, a convenience sample of 49 herds was enrolled in this study. Blood sampling (16 to 29 cows per herd) was performed during the week before and during the 2 wk following calving to quantify the concentration of nonesterified fatty acids (NEFA) and β-hydroxybutyrate acids (BHBA), respectively. Elevated NEFA was defined as ≥280 µmol/L and hyperketonemia as BHBA ≥1.4 mmol/L. Retained placenta, metritis, purulent vaginal discharge, endometritis, and mastitis were diagnosed on-farm following standardized definitions, and success at first artificial insemination (AI) and culling events were recorded. The associations between PropElevNEFA and each individual disease, success at first AI, and culling were evaluated using Bayesian aggregated binomial regression models with weakly informative priors, from the which odds ratio (OR) and the 95% credible intervals (BCI) were obtained. A total of 981 cows were included in the statistical analyses representing 16 to 29 (median = 19) cows per herd. Cows were enrolled in the prepartum period of their first to tenth (median = third) lactation, and 41% of them had an elevated prepartum NEFA concentration. At the herd level, PropElevNEFA varied between 11% and 78% (median = 39%). The odds of metritis (OR = 1.37, 95% BCI = 1.13-1.67) increased for every 10-point increase in PropElevNEFA, whereas the odds of success at first AI decreased (OR = 0.69, 95% BCI = 0.59-0.80). The PropElevNEFA was not associated with the other tested diseases or culling. Our results suggest that the herd-level proportion of cows having elevated prepartum NEFA concentrations is associated with metritis and poor success at first AI in dairy herds.},
}
@article {pmid38645586,
year = {2024},
author = {Kim, H and Won, K and Ahn, M and Jun, SC},
title = {Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset.},
journal = {Biomedical engineering letters},
volume = {14},
number = {3},
pages = {617-630},
pmid = {38645586},
issn = {2093-985X},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.},
}
@article {pmid38645254,
year = {2024},
author = {Card, NS and Wairagkar, M and Iacobacci, C and Hou, X and Singer-Clark, T and Willett, FR and Kunz, EM and Fan, C and Nia, MV and Deo, DR and Srinivasan, A and Choi, EY and Glasser, MF and Hochberg, LR and Henderson, JM and Shahlaie, K and Brandman, DM and Stavisky, SD},
title = {An accurate and rapidly calibrating speech neuroprosthesis.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {38645254},
support = {DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 DC019430/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40's with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.},
}
@article {pmid38642806,
year = {2024},
author = {Guan, S and Cong, L and Wang, F and Dong, T},
title = {A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi-Kernel Extreme Learning Machine.},
journal = {Journal of neuroscience methods},
volume = {407},
number = {},
pages = {110136},
doi = {10.1016/j.jneumeth.2024.110136},
pmid = {38642806},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Machine Learning ; *Imagination/physiology ; *Wavelet Analysis ; *Brain-Computer Interfaces ; Adult ; Young Adult ; Male ; Signal Processing, Computer-Assisted ; Brain/physiology ; Female ; Motor Activity/physiology ; Wrist/physiology ; },
abstract = {BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution.<br><br>NEW METHOD: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM).<br><br>RESULTS: After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93&#x202f;%.<br><br>We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.},
}
@article {pmid38642555,
year = {2024},
author = {Wang, A and Tian, X and Jiang, D and Yang, C and Xu, Q and Zhang, Y and Zhao, S and Zhang, X and Jing, J and Wei, N and Wu, Y and Lv, W and Yang, B and Zang, D and Wang, Y and Zhang, Y and Wang, Y and Meng, X},
title = {Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial.},
journal = {Med (New York, N.Y.)},
volume = {5},
number = {6},
pages = {559-569.e4},
doi = {10.1016/j.medj.2024.02.014},
pmid = {38642555},
issn = {2666-6340},
mesh = {Humans ; Male ; *Stroke Rehabilitation/methods ; Female ; *Brain-Computer Interfaces ; Middle Aged ; *Upper Extremity/physiopathology ; *Ischemic Stroke/rehabilitation/physiopathology ; Aged ; China ; Recovery of Function/physiology ; Treatment Outcome ; Adult ; },
abstract = {BACKGROUND: Upper limb motor dysfunction is a major problem in the rehabilitation of patients with stroke. Brain-computer interface (BCI) is a kind of communication system that converts the "ideas" in the brain into instructions and has been used in stroke rehabilitation. This study aimed to investigate the efficacy and safety of BCI in rehabilitation training on upper limb motor function among patients with ischemic stroke.<br><br>METHODS: This was an investigator-initiated, multicenter, randomized, open-label, blank-controlled clinical trial with blinded outcome assessment conducted at 17 centers in China. Patients were assigned in a 1:1 ratio to the BCI group or the control group based on traditional rehabilitation training. The primary efficacy outcome is the difference in improvement of the Fugl-Meyer Assessment upper extremity (FMA-UE) score between two groups at month 1 after randomization. The safety outcomes were any adverse events within 3&#xa0;months.<br><br>FINDINGS: A total of 296 patients with ischemic stroke were enrolled and randomly allocated to the BCI group (n&#xa0;= 150) and the control group (n&#xa0;= 146). The primary efficacy outcomes of FMA-UE score change from baseline to 1&#xa0;month were 13.17 (95% confidence interval [CI], 11.56-14.79) in the BCI group and 9.83 (95% CI, 8.19-11.47) in the control group (mean difference between groups was 3.35; 95% CI, 1.05-5.65; p&#xa0;= 0.0045). Adverse events occurred in 33 patients (22.00%) in the BCI group and in 31 patients (21.23%) in the control group.<br><br>CONCLUSIONS: BCI rehabilitation training can further improve upper limb motor function based on traditional rehabilitation training in patients with ischemic stroke. This study was registered at ClinicalTrials.gov: NCT04387474.<br><br>FUNDING: This work was supported by the National Key R&amp;D Program of China (2018YFC1312903), the National Key Research and Development Program of China (2022YFC3600600), the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), the Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), the Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).},
}
@article {pmid38642251,
year = {2024},
author = {Youn, KI and Lee, JW and Song, Y and Lee, SY and Song, KH},
title = {Development of Cell Culture Platforms for Study of Trabecular Meshwork Cells and Glaucoma Development.},
journal = {Tissue engineering and regenerative medicine},
volume = {21},
number = {5},
pages = {695-710},
pmid = {38642251},
issn = {2212-5469},
support = {Incheon National University (International Cooperative) Research Grant in 2019//Incheon National University/ ; },
mesh = {*Trabecular Meshwork/cytology ; *Glaucoma ; Humans ; *Cell Culture Techniques/methods ; Intraocular Pressure ; Aqueous Humor ; Animals ; },
abstract = {BACKGROUND: Various cell culture platforms that could display native environmental cue-mimicking stimuli were developed, and effects of environmental cues on cell behaviors were studied with the cell culture platforms. Likewise, various cell culture platforms mimicking native trabecular meshwork (TM) composed of juxtacanalicular, corneoscleral and uveal meshwork located in internal scleral sulcus were used to study effects of environmental cues and/or drug treatments on TM cells and glaucoma development. Glaucoma is a disease that could cause blindness, and cause of glaucoma is not clearly identified yet. It appears that aqueous humor (AH) outflow resistance increased by damages on pathway of AH outflow can elevate intraocular pressure (IOP). These overall possibly contribute to development of glaucoma.<br><br>METHODS: For the study of glaucoma, static and dynamic cell culture platforms were developed. Particularly, the dynamic platforms exploiting AH outflow-mimicking perfusion or increased IOP-mimicking increased pressure were used to study how perfusion or increased pressure could affect TM cells. Overall, potential mechanisms of glaucoma development, TM structures and compositions, TM cell culture platform types and researches on TM cells and glaucoma development with the platforms were described in this review.<br><br>RESULTS AND CONCLUSION: This will be useful to improve researches on TM cells and develop enhanced therapies targeting glaucoma.},
}
@article {pmid38641040,
year = {2024},
author = {Sun, R and Tang, MY and Yang, D and Zhang, YY and Xu, YH and Qiao, Y and Yu, B and Cao, SX and Wang, H and Huang, HQ and Zhang, H and Li, XM and Lian, H},
title = {C3aR in the medial prefrontal cortex modulates the susceptibility to LPS-induced depressive-like behaviors through glutamatergic neuronal excitability.},
journal = {Progress in neurobiology},
volume = {236},
number = {},
pages = {102614},
doi = {10.1016/j.pneurobio.2024.102614},
pmid = {38641040},
issn = {1873-5118},
mesh = {Animals ; *Prefrontal Cortex/metabolism/drug effects ; *Lipopolysaccharides/pharmacology ; *Neurons/metabolism/drug effects ; Mice ; *Mice, Knockout ; *Depression/metabolism/chemically induced ; Receptors, Complement/metabolism ; Mice, Inbred C57BL ; Male ; Glutamic Acid/metabolism ; },
abstract = {Complement activation and prefrontal cortical dysfunction both contribute to the pathogenesis of major depressive disorder (MDD), but their interplay in MDD is unclear. We here studied the role of complement C3a receptor (C3aR) in the medial prefrontal cortex (mPFC) and its influence on depressive-like behaviors induced by systematic lipopolysaccharides (LPS) administration. C3aR knockout (KO) or intra-mPFC C3aR antagonism confers resilience, whereas C3aR expression in mPFC neurons makes KO mice susceptible to LPS-induced depressive-like behaviors. Importantly, the excitation and inhibition of mPFC neurons have opposing effects on depressive-like behaviors, aligning with increased and decreased excitability by C3aR deletion and activation in cortical neurons. In particular, inhibiting mPFC glutamatergic (mPFC[Glu]) neurons, the main neuronal subpopulation expresses C3aR, induces depressive-like behaviors in saline-treated WT and KO mice, but not in LPS-treated KO mice. Compared to hypoexcitable mPFC[Glu] neurons in LPS-treated WT mice, C3aR-null mPFC[Glu] neurons display hyperexcitability upon LPS treatment, and enhanced excitation of mPFC[Glu] neurons is anti-depressant, suggesting a protective role of C3aR deficiency in these circumstances. In conclusion, C3aR modulates susceptibility to LPS-induced depressive-like behaviors through mPFC[Glu] neuronal excitability. This study identifies C3aR as a pivotal intersection of complement activation, mPFC dysfunction, and depression and a promising therapeutic target for MDD.},
}
@article {pmid38640695,
year = {2024},
author = {Zhou, Y and Yang, B and Wang, C},
title = {Multiband task related components enhance rapid cognition decoding for both small and similar objects.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {175},
number = {},
pages = {106313},
doi = {10.1016/j.neunet.2024.106313},
pmid = {38640695},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; *Cognition/physiology ; *Brain-Computer Interfaces ; Male ; Female ; Adult ; Young Adult ; *Evoked Potentials/physiology ; Photic Stimulation/methods ; Brain/physiology ; },
abstract = {The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.},
}
@article {pmid38639058,
year = {2024},
author = {Liu, Y and Dai, W and Liu, Y and Hu, D and Yang, B and Zhou, Z},
title = {An SSVEP-based BCI with 112 targets using frequency spatial multiplexing.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad4091},
pmid = {38639058},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; *Electroencephalography/methods ; Male ; *Photic Stimulation/methods ; Female ; Adult ; Young Adult ; Algorithms ; },
abstract = {Objective.Brain-computer interface (BCI) systems with large directly accessible instruction sets are one of the difficulties in BCI research. Research to achieve high target resolution (⩾100) has not yet entered a rapid development stage, which contradicts the application requirements. Steady-state visual evoked potential (SSVEP) based BCIs have an advantage in terms of the number of targets, but the competitive mechanism between the target stimulus and its neighboring stimuli is a key challenge that prevents the target resolution from being improved significantly.Approach.In this paper, we reverse the competitive mechanism and propose a frequency spatial multiplexing method to produce more targets with limited frequencies. In the proposed paradigm, we replicated each flicker stimulus as a 2 × 2 matrix and arrange the matrices of all frequencies in a tiled fashion to form the interaction interface. With different arrangements, we designed and tested three example paradigms with different layouts. Further we designed a graph neural network that distinguishes between targets of the same frequency by recognizing the different electroencephalography (EEG) response distribution patterns evoked by each target and its neighboring targets.Main results.Extensive experiment studies employing eleven subjects have been performed to verify the validity of the proposed method. The average classification accuracies in the offline validation experiments for the three paradigms are 89.16%, 91.38%, and 87.90%, with information transfer rates (ITR) of 51.66, 53.96, and 50.55 bits/min, respectively.Significance.This study utilized the positional relationship between stimuli and did not circumvent the competing response problem. Therefore, other state-of-the-art methods focusing on enhancing the efficiency of SSVEP detection can be used as a basis for the present method to achieve very promising improvements.},
}
@article {pmid38638416,
year = {2024},
author = {Proverbio, AM and Cesati, F},
title = {Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1357770},
pmid = {38638416},
issn = {1664-0640},
abstract = {INTRODUCTION: The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification.<br><br>METHODS: The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs).<br><br>RESULTS: Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states.<br><br>DISCUSSION: In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.},
}
@article {pmid38636376,
year = {2024},
author = {Yang, T and Zhang, P and Hu, J and Xu, W and Jiang, W and Feng, R and Lou, Y and Jin, X and Qian, Z and Gao, F and Gao, K and Liu, R and Yang, Y},
title = {Exploring the neural correlates of fat taste perception and discrimination: Insights from electroencephalogram analysis.},
journal = {Food chemistry},
volume = {450},
number = {},
pages = {139353},
doi = {10.1016/j.foodchem.2024.139353},
pmid = {38636376},
issn = {1873-7072},
mesh = {Humans ; *Taste Perception ; Female ; Young Adult ; Adult ; Male ; *Electroencephalography ; *Brain/physiology ; *Dietary Fats/metabolism/analysis ; Taste ; Fatty Acids/chemistry/metabolism ; },
abstract = {Understanding neural pathways and cognitive processes involved in the transformation of dietary fats into sensory experiences has profound implications for nutritional well-being. This study presents an efficient approach to comprehending the neural perception of fat taste using electroencephalogram (EEG). Through the examination of neural responses to different types of fatty acids (FAs) in 45 participants, we discerned distinct neural activation patterns associated with saturated versus unsaturated fatty acids. The spectrum analysis of averaged EEG signals revealed notable variations in δ and α-frequency bands across FA types. The topographical distribution and source localization results suggested that the brain encodes fat taste with specific activation timings in primary and secondary gustatory cortices. Saturated FAs elicited higher activation in cortical associated with emotion and reward processing. This electrophysiological evidence enhances our understanding of fundamental mechanisms behind fat perception, which is helpful for guiding strategies to manage hedonic eating and promote balanced fat consumption.},
}
@article {pmid38635379,
year = {2024},
author = {Danesh, AR and Pu, H and Safiallah, M and Do, AH and Nenadic, Z and Heydari, P},
title = {A CMOS BD-BCI: Neural Recorder With Two-Step Time-Domain Quantizer and Multipolar Stimulator With Dual-Mode Charge Balancing.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {6},
pages = {1354-1370},
doi = {10.1109/TBCAS.2024.3391190},
pmid = {38635379},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted/instrumentation ; Equipment Design ; Electroencephalography/instrumentation ; Electric Stimulation/instrumentation ; Semiconductors ; },
abstract = {This work presents a bi-directional brain-computer interface (BD-BCI) including a high-dynamic-range (HDR) two-step time-domain neural acquisition (TTNA) system and a high-voltage (HV) multipolar neural stimulation system incorporating dual-mode time-based charge balancing (DTCB) technique. The proposed TTNA includes four independent recording modules that can sense microvolt neural signals while tolerating large stimulation artifacts. In addition, it exhibits an integrated input-referred noise of 2.3 Vrms from 0.1- to 250-Hz and can handle a linear input-signal swing of up to 340 mVPP. The multipolar stimulator is composed of four standalone stimulators each with a maximum current of up to 14 mA (20-V of voltage compliance) and 8-bit resolution. An inter-channel interference cancellation circuitry is introduced to preserve the accuracy and effectiveness of the DTCB method in the multipolar-stimulation configuration. Fabricated in an HV 180-nm CMOS technology, the BD-BCI chipset undergoes extensive in-vitro and in-vivo evaluations. The recording system achieves a measured SNDR, SFDR, and CMRR of 84.8 dB, 89.6 dB, and 105 dB, respectively. The measurement results verify that the stimulation system is capable of performing high-precision charge balancing with 2 mV and 7.5 mV accuracy in the interpulse-bounded time-based charge balancing (TCB) and artifactless TCB modes, respectively.},
}
@article {pmid38635296,
year = {2024},
author = {Li, X and Wei, W and Wang, Q and Deng, W and Li, M and Ma, X and Zeng, J and Zhao, L and Guo, W and Hall, MH and Li, T},
title = {Identify Potential Causal Relationships Between Cortical Thickness, Mismatch Negativity, Neurocognition, and Psychosocial Functioning in Drug-Naïve First-Episode Psychosis Patients.},
journal = {Schizophrenia bulletin},
volume = {50},
number = {4},
pages = {827-838},
pmid = {38635296},
issn = {1745-1701},
support = {81920108018//National Natural Science Foundation of China/ ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence and Key Project for Hangzhou Medical Disciplines/ ; 401343//McLean Foundation Award/ ; },
mesh = {Humans ; Male ; *Psychotic Disorders/physiopathology/diagnostic imaging/pathology ; Female ; Adult ; Young Adult ; *Magnetic Resonance Imaging ; *Cognitive Dysfunction/physiopathology/diagnostic imaging/etiology ; *Psychosocial Functioning ; Temporal Lobe/physiopathology/diagnostic imaging/pathology ; Evoked Potentials, Auditory/physiology ; Electroencephalography ; Cerebral Cortex/diagnostic imaging/physiopathology/pathology ; Brain Cortical Thickness ; Adolescent ; Auditory Perception/physiology ; Mediation Analysis ; },
abstract = {BACKGROUND: Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP.<br><br>METHODS: We enrolled 28 drug-na&#xef;ve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region.<br><br>RESULTS: We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r&#x2005;=&#x2005;-.709, P&#x2005;<&#x2005;.001; right: r&#x2005;=&#x2005;-.612, P&#x2005;=&#x2005;.008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways.<br><br>CONCLUSIONS: Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP.},
}
@article {pmid38635054,
year = {2024},
author = {Yan, Y and An, X and Ren, H and Luo, B and Jin, S and Liu, L and Di, Y and Li, T and Huang, Y},
title = {Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.},
journal = {Neurosurgical review},
volume = {47},
number = {1},
pages = {169},
pmid = {38635054},
issn = {1437-2320},
support = {20JCZDJC00620//Tianjin Science and Technology Program/ ; 20JCZDJC00620//Tianjin Science and Technology Program/ ; },
mesh = {Humans ; *Intracranial Aneurysm ; Nomograms ; Retrospective Studies ; Angiography ; Hemodynamics ; },
abstract = {Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.},
}
@article {pmid38633751,
year = {2024},
author = {Behboodi, A and Kline, J and Gravunder, A and Phillips, C and Parker, SM and Damiano, DL},
title = {Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1346050},
pmid = {38633751},
issn = {1662-5161},
abstract = {In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.},
}
@article {pmid38632207,
year = {2024},
author = {Hossain, A and Khan, P and Kader, MF},
title = {Imagined speech classification exploiting EEG power spectrum features.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {8},
pages = {2529-2544},
pmid = {38632207},
issn = {1741-0444},
mesh = {Humans ; *Electroencephalography/methods ; *Speech/physiology ; Male ; Female ; *Support Vector Machine ; Adult ; Imagination/physiology ; Young Adult ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Algorithms ; Brain/physiology ; },
abstract = {Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.},
}
@article {pmid38630669,
year = {2024},
author = {Zhu, HY and Chen, HT and Lin, CT},
title = {Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure.},
journal = {PloS one},
volume = {19},
number = {4},
pages = {e0301052},
pmid = {38630669},
issn = {1932-6203},
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain/physiology ; Computer Simulation ; Alpha Rhythm ; Head ; Electroencephalography ; },
abstract = {Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup. Our study examined the effects of stress on the human brain's P300 behaviour through a height exposure experiment that incorporates complex visual, vestibular, and proprioceptive stimuli. A more complex sensory environment could produce translatable findings toward real-world behaviour and benefit emerging technologies such as brain-computer interfaces. Seventeen participants experienced our experiment that elicited the stress response through physical and virtual height exposure. We found two unique groups within our participants that exhibited contrasting behavioural performance and P300 target reaction response when exposed to stressors (from walking at heights). One group performed worse when exposed to heights and exhibited a significant decrease in parietal P300 peak amplitude and increased beta and gamma power. On the other hand, the group less affected by stress exhibited a change in their N170 peak amplitude and alpha/mu rhythm desynchronisation. The findings of our study suggest that a more individualised approach to assessing a person's behaviour performance under stress can aid in understanding P300 performance when experiencing stress.},
}
@article {pmid38628700,
year = {2024},
author = {Tao, G and Yang, S and Xu, J and Wang, L and Yang, B},
title = {Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1361235},
pmid = {38628700},
issn = {1664-2295},
abstract = {BACKGROUND: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.<br><br>METHODS: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.<br><br>RESULTS: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF&#x2009;=&#x2009;4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.<br><br>CONCLUSION: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.},
}
@article {pmid38626760,
year = {2024},
author = {Lopez-Bernal, D and Balderas, D and Ponce, P and Molina, A},
title = {Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad3f50},
pmid = {38626760},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods/classification ; Male ; *Speech/physiology ; Female ; Adult ; Support Vector Machine ; Young Adult ; Reproducibility of Results ; Algorithms ; },
abstract = {Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.},
}
@article {pmid38625770,
year = {2024},
author = {Zhang, S and Cui, H and Li, Y and Chen, X and Gao, X and Guan, C},
title = {Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1647-1656},
doi = {10.1109/TNSRE.2024.3389051},
pmid = {38625770},
issn = {1558-0210},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Electroencephalography ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Young Adult ; *Fractals ; Arousal/physiology ; Brain/physiology ; Healthy Volunteers ; Algorithms ; },
abstract = {This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI performance, and concurrently authenticates two potent electrophysiological markers for multifaceted characterization of brain states.},
}
@article {pmid38625520,
year = {2024},
author = {Della Vedova, G and Proverbio, AM},
title = {Neural signatures of imaginary motivational states: desire for music, movement and social play.},
journal = {Brain topography},
volume = {37},
number = {5},
pages = {806-825},
pmid = {38625520},
issn = {1573-6792},
mesh = {Humans ; *Motivation/physiology ; Male ; *Music ; Female ; *Electroencephalography/methods ; Adult ; Young Adult ; *Brain/physiology/diagnostic imaging ; *Brain Mapping/methods ; *Imagination/physiology ; *Movement/physiology ; Evoked Potentials/physiology ; },
abstract = {The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400-600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.},
}
@article {pmid38624364,
year = {2024},
author = {Wang, Z and Xiang, L and Zhang, R},
title = {P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network.},
journal = {The Review of scientific instruments},
volume = {95},
number = {4},
pages = {},
doi = {10.1063/5.0202770},
pmid = {38624364},
issn = {1089-7623},
mesh = {Humans ; *Intention ; *Brain ; Electrodes ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.},
}
@article {pmid38621380,
year = {2024},
author = {Barmpas, K and Panagakis, Y and Zoumpourlis, G and Adamos, DA and Laskaris, N and Zafeiriou, S},
title = {A causal perspective on brainwave modeling for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ad3eb5},
pmid = {38621380},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Machine Learning ; Electroencephalography/methods ; Brain Waves/physiology ; Brain/physiology ; Algorithms ; },
abstract = {Objective. Machine learning (ML) models have opened up enormous opportunities in the field of brain-computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting.Approach. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the ML pipeline, ranging from data collection and data pre-processing to training methods and techniques.Main results. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs.Significance. Furthermore, we present how general ML practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.},
}
@article {pmid38619940,
year = {2024},
author = {Pang, M and Wang, H and Huang, J and Vong, CM and Zeng, Z and Chen, C},
title = {Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1637-1646},
doi = {10.1109/TNSRE.2024.3389037},
pmid = {38619940},
issn = {1558-0210},
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; Female ; Male ; Machine Learning ; Artifacts ; Adult ; Neural Networks, Computer ; },
abstract = {Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.},
}
@article {pmid38618604,
year = {2024},
author = {Di Gregorio, MF and Der, C and Bravo-Torres, S and Zernotti, ME},
title = {Active Bone Conduction Implant and Adhesive Bone Conduction Device: A Comparison of Audiological Performance and Subjective Satisfaction.},
journal = {International archives of otorhinolaryngology},
volume = {28},
number = {2},
pages = {e332-e338},
pmid = {38618604},
issn = {1809-9777},
abstract = {Introduction Atresia of the external auditory canal affects 1 in every 10 thousand to 20 thousand live births, with a much higher prevalence in Latin America, at 5 to 21 out of every 10 thousand newborns. The treatment involves esthetic and functional aspects. Regarding the functional treatment, there are surgical and nonsurgical alternatives like spectacle frames and rigid and softband systems. Active transcutaneous bone conduction implants (BCIs) achieve good sound transmission and directly stimulate the bone. Objective To assess the audiological performance and subjective satisfaction of children implanted with an active transcutaneous BCI for more than one year and to compare the outcomes with a nonsurgical adhesive bone conduction device (aBCD) in the same users. Methods The present is a prospective, multicentric study. The audiological performance was evaluated at 1, 6, and 12 months postactivation, and after a 1-month trial with the nonsurgical device. Results Ten patients completed all tests. The 4-frequency pure-tone average (4PTA) in the unaided condition was of 65 dB HL, which improved significantly to 20 dB HL after using the BCI for 12 months. The speech recognition in quiet in the unaided condition was of 33% on average, which improved significantly, to 99% with the BCI, and to 91% with the aBCD. Conclusion The aBCD demonstrated sufficient hearing improvement and subjective satisfaction; thus, it is a good solution for hearing rehabilitation if surgery is not desired or not possible. If surgery is an option, the BCI is the superior device in terms of hearing outcomes, particularly background noise and subjective satisfaction.},
}
@article {pmid38618207,
year = {2024},
author = {Kong, L and Wang, H and Yan, N and Xu, C and Chen, Y and Zeng, Y and Guo, X and Lu, J and Hu, S},
title = {Effect of antipsychotics and mood stabilisers on metabolism in bipolar disorder: a network meta-analysis of randomised-controlled trials.},
journal = {EClinicalMedicine},
volume = {71},
number = {},
pages = {102581},
pmid = {38618207},
issn = {2589-5370},
abstract = {BACKGROUND: Antipsychotics and mood stabilisers are gathering attention for the disturbance of metabolism. This network meta-analysis aims to evaluate and rank the metabolic effects of the commonly used antipsychotics and mood stabilisers in treating bipolar disorder (BD).<br><br>METHODS: Registries including PubMed, Embase, Cochrane Library, Web of Science, Ovid, and Google Scholar were searched before February 15th, 2024, for randomised controlled trials (RCTs) applying antipsychotics or mood stabilisers for BD treatment. The observed outcomes were twelve metabolic indicators. The data were extracted by two reviewers independently, and confirmed by another four reviewers and a corresponding author. The above six reviewers all participated in data analyses. Data extraction was based on PRISMA guidelines, and quality assessment was conducted according to the Cochrane Handbook. Use a random effects model for data pooling. The PROSPERO registration number is CRD42023466669.<br><br>FINDINGS: Together, 5421 records were identified, and 41 publications with 11,678 complete-trial participants were confirmed eligible. After eliminating possible sensitivity, risperidone ranked 1st in elevating fasting serum glucose (SUCRA&#xa0;=&#xa0;90.7%) and serum insulin (SUCRA&#xa0;=&#xa0;96.6%). Lurasidone was most likely to elevate HbA1c (SUCRA&#xa0;=&#xa0;82.1%). Olanzapine ranked 1st in elevating serum TC (SUCRA&#xa0;=&#xa0;93.3%), TG (SUCRA&#xa0;=&#xa0;89.6%), and LDL (SUCRA&#xa0;=&#xa0;94.7%). Lamotrigine ranked 1st in reducing HDL (SUCRA&#xa0;=&#xa0;82.6%). Amisulpride ranked 1st in elevating body weight (SUCRA&#xa0;=&#xa0;100.0%). For subgroup analyses, quetiapine is more likely to affect indicators of glucose metabolism among male adult patients with bipolar mania, while long-term lurasidone tended to affect glucose metabolism among female patients with bipolar depression. Among patients under 18, divalproex tended to affect glucose metabolism, with lithium affecting lipid metabolism. In addition, most observed antipsychotics performed higher response and remission rates than placebo, and displayed a similar dropout rate with placebo, while no between-group significance of rate was observed among mood stabilisers.<br><br>INTERPRETATION: Our findings suggest that overall, antipsychotics are effective in treating BD, while they are also more likely to disturb metabolism than mood stabilisers. Attention should be paid to individual applicability in clinical practice. The results put forward evidence-based information and clinical inspiration for drug compatibility and further research of the BD mechanism.<br><br>FUNDING: The National Key Research and Development Program of China (2023YFC2506200), and the Research Project of Jinan Microecological Biomedicine Shandong Laboratory (No. JNL-2023001B).},
}
@article {pmid38617349,
year = {2024},
author = {Sharma, D and Lupkin, SM and McGinty, VB},
title = {Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38617349},
issn = {2692-8205},
support = {K01 DA036659/DA/NIDA NIH HHS/United States ; },
abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.},
}
@article {pmid38617132,
year = {2024},
author = {Qiao, Y and Mu, J and Xie, J and Hu, B and Liu, G},
title = {Music emotion recognition based on temporal convolutional attention network using EEG.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1324897},
pmid = {38617132},
issn = {1662-5161},
abstract = {Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.},
}
@article {pmid38616969,
year = {2024},
author = {Zhang, Y and Zheng, Y and Ni, P and Liang, S and Li, X and Yu, H and Wei, W and Qi, X and Yu, X and Xue, R and Zhao, L and Deng, W and Wang, Q and Guo, W and Li, T},
title = {New role of platelets in schizophrenia: predicting drug response.},
journal = {General psychiatry},
volume = {37},
number = {2},
pages = {e101347},
pmid = {38616969},
issn = {2517-729X},
abstract = {BACKGROUND: Elevated platelet count (PLTc) is associated with first-episode schizophrenia and adverse outcomes in individuals with precursory psychosis. However, the impact of antipsychotic medications on PLTc and its association with symptom improvement remain unclear.<br><br>AIMS: We aimed to investigate changes in PLTc levels following antipsychotic treatment and assess whether PLTc can predict antipsychotic responses and metabolic changes after accounting for other related variables.<br><br>METHODS: A total of 2985 patients with schizophrenia were randomised into seven groups. Each group received one of seven antipsychotic treatments and was assessed at 2, 4 and 6 weeks. Clinical symptoms were evaluated using the positive and negative syndrome scale (PANSS). Additionally, we measured blood cell counts and metabolic parameters, such as blood lipids. Repeated measures analysis of variance was used to examine the effect of antipsychotics on PLTc changes, while structural equation modelling was used to assess the predictive value of PLTc on PANSS changes.<br><br>RESULTS: PLTc significantly increased in patients treated with aripiprazole (F=6.00, p=0.003), ziprasidone (F=7.10, p<0.001) and haloperidol (F=3.59, p=0.029). It exhibited a positive association with white blood cell count and metabolic indicators. Higher baseline PLTc was observed in non-responders, particularly in those defined by the PANSS-negative subscale. In the structural equation model, PLTc, white blood cell count and a latent metabolic variable predicted the rate of change in the PANSS-negative subscale scores. Moreover, higher baseline PLTc was observed in individuals with less metabolic change, although this association was no longer significant after accounting for baseline metabolic values.<br><br>CONCLUSIONS: Platelet parameters, specifically PLTc, are influenced by antipsychotic treatment and could potentially elevate the risk of venous thromboembolism in patients with schizophrenia. Elevated PLTc levels and associated factors may impede symptom improvement by promoting inflammation. Given PLTc's easy measurement and clinical relevance, it warrants increased attention from psychiatrists.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR-TRC-10000934.},
}
@article {pmid38616204,
year = {2024},
author = {Lei, D and Dong, C and Guo, H and Ma, P and Liu, H and Bao, N and Kang, H and Chen, X and Wu, Y},
title = {A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {8616},
pmid = {38616204},
issn = {2045-2322},
support = {S20231148Z//Inner Mongolia Autonomous Region Graduate Research Innovation Project/ ; 61364018//the National Natural Science Foundation of China/ ; 61863029//the National Natural Science Foundation of China/ ; 2016JQ07//Inner Mongolia Natural Science Foundation/ ; 2020MS06020//Inner Mongolia Natural Science Foundation/ ; 2021MS06017//Inner Mongolia Natural Science Foundation/ ; CGZH2018129//Inner Mongolia Scientific and Technological Achievements Transformation Project/ ; 2023JSYD01006//Industrial Technology Innovation Program of IMAST/ ; 2021GG0264//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; 2020GG0268//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; },
abstract = {For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2 s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.},
}
@article {pmid38615362,
year = {2024},
author = {Qi, X and Yu, X and Wei, L and Jiang, H and Dong, J and Li, H and Wei, Y and Zhao, L and Deng, W and Guo, W and Hu, X and Li, T},
title = {Novel α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator LT-102: A promising therapeutic agent for treating cognitive impairment associated with schizophrenia.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {4},
pages = {e14713},
pmid = {38615362},
issn = {1755-5949},
support = {81920108018//National Natural Science Foundation of China/ ; 82371524//National Natural Science Foundation of China/ ; 82371503//National Natural Science Foundation of China/ ; 2022C03096//Key R&amp;D Program of Zhejiang Province/ ; 2018B030334001//Special Foundation for Brain Research from Science and Technology Program of Guangdong Province/ ; LY22H090009//Natural Science Foundation of Zhejiang Province/ ; 2019HXCX02//Clinical Research Innovation Project, West China Hospital, Sichuan University/ ; //Project for Hangzhou Medical Disciplines of Excellence &amp; Key Project for Hangzhou Medical Disciplines/ ; },
mesh = {Animals ; Mice ; Rats ; Phencyclidine ; *Schizophrenia/complications/drug therapy ; *Cognitive Dysfunction/drug therapy ; Isoxazoles ; *Propionates ; },
abstract = {AIMS: We aimed to evaluate the potential of a novel selective α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator, LT-102, in treating cognitive impairments associated with schizophrenia (CIAS) and elucidating its mechanism of action.<br><br>METHODS: The activity of LT-102 was examined by Ca[2+] influx assays and patch-clamp in rat primary hippocampal neurons. The structure of the complex was determined by X-ray crystallography. The selectivity of LT-102 was evaluated by hERG tail current recording and kinase-inhibition assays. The electrophysiological characterization of LT-102 was characterized by patch-clamp recording in mouse hippocampal slices. The expression and phosphorylation levels of proteins were examined by Western blotting. Cognitive function was assessed using the Morris water maze and novel object recognition tests.<br><br>RESULTS: LT-102 is a novel and selective AMPAR potentiator with little agonistic effect, which binds to the allosteric site formed by the intradimer interface of AMPAR's GluA2 subunit. Treatment with LT-102 facilitated long-term potentiation in mouse hippocampal slices and reversed cognitive deficits in a phencyclidine-induced mouse model. Additionally, LT-102 treatment increased the protein level of brain-derived neurotrophic factor and the phosphorylation of GluA1 in primary neurons and hippocampal tissues.<br><br>CONCLUSION: We conclude that LT-102 ameliorates cognitive impairments in a phencyclidine-induced model of schizophrenia by enhancing synaptic function, which could make it a potential therapeutic candidate for CIAS.},
}
@article {pmid38614892,
year = {2024},
author = {He, X and Li, W and Ma, H},
title = {Orchestrating neuronal activity-dependent translation via the integrated stress response protein GADD34.},
journal = {Trends in neurosciences},
volume = {47},
number = {5},
pages = {319-321},
doi = {10.1016/j.tins.2024.03.008},
pmid = {38614892},
issn = {1878-108X},
mesh = {Animals ; Humans ; Brain-Derived Neurotrophic Factor/metabolism ; *Neuronal Plasticity/physiology ; *Neurons/metabolism/physiology ; *Protein Biosynthesis/physiology ; *Protein Phosphatase 1/metabolism ; Stress, Physiological/physiology ; },
abstract = {In a recent study, Oliveira and colleagues revealed how growth arrest and DNA damage-inducible protein 34 (GADD34), an effector of the integrated stress response, initiates the translation of synaptic plasticity-related mRNAs following brain-derived neurotrophic factor (BDNF) stimulation. This work suggests that GADD34 may link transcriptional products with translation control upon neuronal activation, illuminating how protein synthesis is orchestrated in neuronal plasticity.},
}
@article {pmid38612334,
year = {2024},
author = {Knozowski, P and Nowakowski, JJ and Stawicka, AM and Dulisz, B and Górski, A},
title = {Effect of Management of Grassland on Prey Availability and Physiological Condition of Nestling of Red-Backed Shrike Lanius collurio.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {7},
pages = {},
pmid = {38612334},
issn = {2076-2615},
abstract = {The study aimed to determine the influence of grassland management on the potential food base of the red-backed shrike Lanius collurio and the condition of chicks in the population inhabiting semi-natural grasslands in the Narew floodplain. The grassland area was divided into three groups: extensively used meadows, intensively used meadows fertilised with mineral fertilisers, and intensively used meadows fertilised with liquid manure, and selected environmental factors that may influence food availability were determined. Using Barber traps, 1825 samples containing 53,739 arthropods were collected, and the diversity, abundance, and proportion of large arthropods in the samples were analysed depending on the grassland use type. In the bird population, the condition of the chicks was characterised by the BCI (Body Condition Index) and haematological parameters (glucose level, haemoglobin level, haematocrit, and H:L ratio). The diversity of arthropods was highest in extensively used meadows. Still, the mean abundance and proportion of arthropods over 1 cm in length differed significantly for Orthoptera, Hymenoptera, Arachne, and Carabidae between grassland use types, with the highest proportion of large arthropods and the highest abundance recorded in manure-fertilised meadows. The highest Body Condition Indexes and blood glucose levels of nestlings indicating good nestling nutrition were recorded in nests of birds associated with extensive land use. The H:L ratio as an indicator of the physiological condition of nestlings was high on manure-fertilised and extensively managed meadows, indicating stress factors associated with these environments. This suggests that consideration should be given to the effects of chemicals, such as pesticides or drug residues, that may come from slurry poured onto fields on the fitness of red-backed shrike chicks.},
}
@article {pmid38610540,
year = {2024},
author = {Clemente, L and La Rocca, M and Paparella, G and Delussi, M and Tancredi, G and Ricci, K and Procida, G and Introna, A and Brunetti, A and Taurisano, P and Bevilacqua, V and de Tommaso, M},
title = {Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610540},
issn = {1424-8220},
support = {POR Puglia FESR-FSE 2014-2020 - Asse I - Azione 1.4.b//Regione Puglia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Aging ; Brain ; Esthetics ; Perception ; },
abstract = {In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.},
}
@article {pmid38609642,
year = {2023},
author = {Zeng, J and Zhang, Y and Xiang, Y and Liang, S and Xue, C and Zhang, J and Ran, Y and Cao, M and Huang, F and Huang, S and Deng, W and Li, T},
title = {Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.},
journal = {Npj mental health research},
volume = {2},
number = {1},
pages = {4},
pmid = {38609642},
issn = {2731-4251},
abstract = {There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.},
}
@article {pmid38608677,
year = {2024},
author = {Wang, Y and Wang, X and Wang, L and Zheng, L and Meng, S and Zhu, N and An, X and Wang, L and Yang, J and Zheng, C and Ming, D},
title = {Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.},
journal = {Current biology : CB},
volume = {34},
number = {9},
pages = {1866-1879.e6},
doi = {10.1016/j.cub.2024.03.032},
pmid = {38608677},
issn = {1879-0445},
mesh = {*Prefrontal Cortex/physiology ; *Theta Rhythm/physiology ; Animals ; *Hippocampus/physiology ; Male ; *Spatial Memory/physiology ; *Goals ; Neurons/physiology ; Mice ; },
abstract = {Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aβ) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aβ accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.},
}
@article {pmid38608331,
year = {2024},
author = {Inguscio, BMS and Cartocci, G and Sciaraffa, N and Nicastri, M and Giallini, I and Aricò, P and Greco, A and Babiloni, F and Mancini, P},
title = {Two are better than one: Differences in cortical EEG patterns during auditory and visual verbal working memory processing between Unilateral and Bilateral Cochlear Implanted children.},
journal = {Hearing research},
volume = {446},
number = {},
pages = {109007},
doi = {10.1016/j.heares.2024.109007},
pmid = {38608331},
issn = {1878-5891},
mesh = {Humans ; *Cochlear Implants ; *Memory, Short-Term ; Child ; Female ; *Cochlear Implantation/instrumentation ; Male ; *Acoustic Stimulation ; *Electroencephalography ; *Persons with Hearing Disabilities/rehabilitation/psychology ; *Visual Perception ; *Auditory Perception ; Case-Control Studies ; Theta Rhythm ; Photic Stimulation ; Gamma Rhythm ; Adolescent ; Speech Perception ; Correction of Hearing Impairment/instrumentation ; Cerebral Cortex/physiopathology/physiology ; Deafness/physiopathology/rehabilitation/surgery ; Hearing ; },
abstract = {Despite the proven effectiveness of cochlear implant (CI) in the hearing restoration of deaf or hard-of-hearing (DHH) children, to date, extreme variability in verbal working memory (VWM) abilities is observed in both unilateral and bilateral CI user children (CIs). Although clinical experience has long observed deficits in this fundamental executive function in CIs, the cause to date is still unknown. Here, we have set out to investigate differences in brain functioning regarding the impact of monaural and binaural listening in CIs compared with normal hearing (NH) peers during a three-level difficulty n-back task undertaken in two sensory modalities (auditory and visual). The objective of this pioneering study was to identify electroencephalographic (EEG) marker pattern differences in visual and auditory VWM performances in CIs compared to NH peers and possible differences between unilateral cochlear implant (UCI) and bilateral cochlear implant (BCI) users. The main results revealed differences in theta and gamma EEG bands. Compared with hearing controls and BCIs, UCIs showed hypoactivation of theta in the frontal area during the most complex condition of the auditory task and a correlation of the same activation with VWM performance. Hypoactivation in theta was also observed, again for UCIs, in the left hemisphere when compared to BCIs and in the gamma band in UCIs compared to both BCIs and NHs. For the latter two, a correlation was found between left hemispheric gamma oscillation and performance in the audio task. These findings, discussed in the light of recent research, suggest that unilateral CI is deficient in supporting auditory VWM in DHH. At the same time, bilateral CI would allow the DHH child to approach the VWM benchmark for NH children. The present study suggests the possible effectiveness of EEG in supporting, through a targeted approach, the diagnosis and rehabilitation of VWM in DHH children.},
}
@article {pmid38608024,
year = {2024},
author = {Abbasi, A and Rangwani, R and Bowen, DW and Fealy, AW and Danielsen, NP and Gulati, T},
title = {Cortico-cerebellar coordination facilitates neuroprosthetic control.},
journal = {Science advances},
volume = {10},
number = {15},
pages = {eadm8246},
pmid = {38608024},
issn = {2375-2548},
support = {R00 NS097620/NS/NINDS NIH HHS/United States ; R01 NS128469/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Rats ; *Cerebellum ; *Brain-Computer Interfaces ; Cell Nucleus ; Learning ; Movement ; },
abstract = {Temporally coordinated neural activity is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence demonstrating how this coordinated activity within cortical and subcortical regions governs behavior. We investigated this between the primary motor (M1) and contralateral cerebellar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task. In neuroprosthetic task, actuator movements are causally linked to M1 "direct" neurons that drive the decoder for successful task execution. However, it is unknown how task-related M1 activity interacts with the cerebellum. We observed a notable 3 to 6 hertz coherence that emerged between these regions' local field potentials (LFPs) with learning that also modulated task-related spiking. We identified robust task-related indirect modulation in the cerebellum, which developed a preferential relationship with M1 task-related activity. Inhibiting cerebellar cortical and deep nuclei activity through optogenetics led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that cerebellar influence is necessary for M1-driven neuroprosthetic control.},
}
@article {pmid38607193,
year = {2025},
author = {Kong, D and Chen, Y and Wang, L and Lu, Y and Luo, S and Chai, H and Chen, L},
title = {Adoption of Rehabilitation Climbing Wall Combined with Brain-computer Fusion Interface in Adolescent Idiopathic Scoliosis.},
journal = {Alternative therapies in health and medicine},
volume = {31},
number = {1},
pages = {208-215},
pmid = {38607193},
issn = {1078-6791},
mesh = {Humans ; *Scoliosis/rehabilitation ; Adolescent ; Female ; Male ; Quality of Life ; *Brain-Computer Interfaces ; *Exercise Therapy/methods ; Child ; Range of Motion, Articular/physiology ; },
abstract = {BACKGROUND: As the adoption of brain-computer interface (BCI) technology in rehabilitation training is gradually maturing, the rehabilitation climbing walls combined with BCI technology are applied in adolescent idiopathic scoliosis (AIS) adoption research.<br><br>METHODS: From January 2022 to January 2023, a total of 100 AIS patients were assigned into a control group (group C, rehabilitation climbing wall training) and an observation group (group B, rehabilitation climbing wall training based on BCI technology) equally and randomly. The therapeutic effects of the patients were analyzed, including the Cobb angle, waist range of motion, and quality of life.<br><br>RESULTS: The Cobb angles of all patients after three months of treatment were obviously smaller than those preoperatively, and the Cobb angle of patients in group B was smaller than that of group C. The improvement rate of the Cobb angle of patients in group B was substantially superior to that in group C (95%CI&#xa0;17.8-42.6, P = .034). Moreover, patients in groups C and B had more extensive waist flexion, tension, and left ranges. Suitable lateral regions after three months of treatment than before and lower lumbar dysfunction scores, and group B was significantly better than group C (95%CI&#xa0;20.3-35.4, P = .042). After three months of treatment, all patients' general condition, physical pain, physiological function, and mental health scores were higher than those preoperatively, and the scores in group B were substantially superior to those in group C (95%CI&#xa0;51.3-84.2, P = .022). Furthermore, the total effective rate of patients in group B after three months was markedly superior to that in group C (96% vs. 82%) (95%CI&#xa0;79.3-97.2, P = .014).<br><br>CONCLUSION: The results of the study suggest that the rehabilitation climbing wall training method combined with brain-computer interface (BCI) technology has significant therapeutic effects on adolescent idiopathic scoliosis (AIS) patients. The intervention was found to effectively reduce the Cobb angle, increase the lumbar range of motion, improve lumbar function, and enhance the quality of life of the patients. These findings indicate that the adoption of rehabilitation climbing walls combined with BCI technology can be clinically valuable in the treatment of AIS. This approach holds promise in improving the rehabilitation outcomes for AIS patients, providing a non-invasive alternative to surgical interventions.},
}
@article {pmid38606614,
year = {2024},
author = {Xu, S and Xiao, X and Manshaii, F and Chen, J},
title = {Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.4c00815},
pmid = {38606614},
issn = {1530-6992},
abstract = {Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage in traditional optogenetics. By focusing on calcium fluorescence for imaging rather than stimulation, injectable fluorescent neural interfaces significantly minimize photothermal damage and improve the accuracy of neuronal imaging. Key advancements including the use of injectable microelectronics for targeted electrical stimulation and their integration with cell-specific genetically encoded calcium indicators have been discussed. These injectable electronics that allow for post-treatment retrieval offer a minimally invasive solution, enhancing both usability and reliability. Furthermore, the integration of genetically encoded fluorescent calcium indicators with injectable bioelectronics enables precise neuronal recording and imaging of individual neurons. This shift not only minimizes risks such as photothermal conversion but also boosts safety, specificity, and effectiveness of neural imaging. Embracing these advancements represents a significant leap forward in biomedical engineering and neuroscience, paving the way for advanced brain-machine interfaces.},
}
@article {pmid38606309,
year = {2024},
author = {Jeong, CH and Lim, H and Lee, J and Lee, HS and Ku, J and Kang, YJ},
title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1373589},
pmid = {38606309},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity.<br><br>MATERIALS AND METHODS: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.<br><br>RESULTS: The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.<br><br>CONCLUSION: The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.},
}
@article {pmid38606308,
year = {2024},
author = {Xue, Q and Song, Y and Wu, H and Cheng, Y and Pan, H},
title = {Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1309594},
pmid = {38606308},
issn = {1662-4548},
abstract = {INTRODUCTION: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.<br><br>METHODS: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.<br><br>RESULTS AND DISCUSSION: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.},
}
@article {pmid38604523,
year = {2024},
author = {Liu, L and Li, J and Ouyang, R and Zhou, D and Fan, C and Liang, W and Li, F and Lv, Z and Wu, X},
title = {Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.},
journal = {Journal of neuroscience methods},
volume = {406},
number = {},
pages = {110132},
doi = {10.1016/j.jneumeth.2024.110132},
pmid = {38604523},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Exoskeleton Device ; Male ; *Neurofeedback/methods/instrumentation ; Evoked Potentials, Visual/physiology ; Adult ; Brain/physiology/physiopathology ; Female ; Young Adult ; Imagination/physiology ; Imagery, Psychotherapy/methods ; },
abstract = {BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling.<br><br>NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients.<br><br>In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system.<br><br>RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively.<br><br>CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.},
}
@article {pmid38603901,
year = {2024},
author = {Shi, X and She, Q and Fang, F and Meng, M and Tan, T and Zhang, Y},
title = {Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.},
journal = {Computers in biology and medicine},
volume = {174},
number = {},
pages = {108445},
doi = {10.1016/j.compbiomed.2024.108445},
pmid = {38603901},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Algorithms ; *Brain-Computer Interfaces ; *Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Brain/physiology ; Female ; },
abstract = {Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL. Specifically, we first selected the source domain based on Mahalanobis distance to enhance the quality of the source domains and then used manifold feature mapping approach to map the source and target domains on the Grassmann manifold to mitigate data drift between domains. In this newly established shared space, we optimized the Mahalanobis metric by maximizing the inter-class distances while minimizing the intra-class distances in the target domain. Recognizing that significant distribution discrepancies might persist across different domains even on the manifold, to ensure similar distributions between the source and target domains, we further imposed constraints on both domains under the Mahalanobis metric. This approach aims to reduce distributional disparities and enhance the electroencephalogram (EEG) emotion recognition performance. In cross-subject experiments, the MSMMTL model exhibits average classification accuracies of 88.83 % and 65.04 % for SEED and DEAP, respectively, underscoring the superiority of our proposed MSMMTL over other state-of-the-art methods. MSMMTL can effectively solve the problem of individual differences in EEG-based affective computing.},
}
@article {pmid38603841,
year = {2024},
author = {De Rubis, G and Paudel, KR and Yeung, S and Mohamad, S and Sudhakar, S and Singh, SK and Gupta, G and Hansbro, PM and Chellappan, DK and Oliver, BGG and Dua, K},
title = {18-β-glycyrrhetinic acid-loaded polymeric nanoparticles attenuate cigarette smoke-induced markers of impaired antiviral response in vitro.},
journal = {Pathology, research and practice},
volume = {257},
number = {},
pages = {155295},
doi = {10.1016/j.prp.2024.155295},
pmid = {38603841},
issn = {1618-0631},
mesh = {Humans ; *Glycyrrhetinic Acid/pharmacology/analogs &amp; derivatives ; *Nanoparticles ; Antiviral Agents/pharmacology ; Smoke/adverse effects ; Polylactic Acid-Polyglycolic Acid Copolymer/chemistry ; Cell Line ; Pulmonary Disease, Chronic Obstructive/drug therapy/immunology ; Epithelial Cells/drug effects/virology ; Cigarette Smoking/adverse effects ; },
abstract = {Tobacco smoking is a leading cause of preventable mortality, and it is the major contributor to diseases such as COPD and lung cancer. Cigarette smoke compromises the pulmonary antiviral immune response, increasing susceptibility to viral infections. There is currently no therapy that specifically addresses the problem of impaired antiviral response in cigarette smokers and COPD patients, highlighting the necessity to develop novel treatment strategies. 18-β-glycyrrhetinic acid (18-β-gly) is a phytoceutical derived from licorice with promising anti-inflammatory, antioxidant, and antiviral activities whose clinical application is hampered by poor solubility. This study explores the therapeutic potential of an advanced drug delivery system encapsulating 18-β-gly in poly lactic-co-glycolic acid (PLGA) nanoparticles in addressing the impaired antiviral immunity observed in smokers and COPD patients. Exposure of BCi-NS1.1 human bronchial epithelial cells to cigarette smoke extract (CSE) resulted in reduced expression of critical antiviral chemokines (IP-10, I-TAC, MIP-1α/1β), mimicking what happens in smokers and COPD patients. Treatment with 18-β-gly-PLGA nanoparticles partially restored the expression of these chemokines, demonstrating promising therapeutic impact. The nanoparticles increased IP-10, I-TAC, and MIP-1α/1β levels, exhibiting potential in attenuating the negative effects of cigarette smoke on the antiviral response. This study provides a novel approach to address the impaired antiviral immune response in vulnerable populations, offering a foundation for further investigations and potential therapeutic interventions. Further studies, including a comprehensive in vitro characterization and in vivo testing, are warranted to validate the therapeutic efficacy of 18-β-gly-PLGA nanoparticles in respiratory disorders associated with compromised antiviral immunity.},
}
@article {pmid38602850,
year = {2024},
author = {Wang, Z and Hu, H and Zhou, T and Xu, T and Zhao, X},
title = {Average Time Consumption per Character: A Practical Performance Metric for Generic Synchronous BCI Spellers.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {9},
pages = {2684-2698},
doi = {10.1109/TBME.2024.3387469},
pmid = {38602850},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Communication Devices for People with Disabilities ; },
abstract = {OBJECTIVE: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed.<br><br>METHODS: In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR.<br><br>RESULTS: Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic.<br><br>CONCLUSION: The proposed ATCPC metric is valid.<br><br>SIGNIFICANCE: ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.},
}
@article {pmid38602573,
year = {2024},
author = {Waisberg, E and Ong, J and Lee, AG},
title = {Ethical Considerations of Neuralink and Brain-Computer Interfaces.},
journal = {Annals of biomedical engineering},
volume = {52},
number = {8},
pages = {1937-1939},
pmid = {38602573},
issn = {1573-9686},
mesh = {Humans ; Brain/physiology ; *Brain-Computer Interfaces/ethics ; },
abstract = {Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.},
}
@article {pmid38601924,
year = {2024},
author = {Anger, JT and Case, LK and Baranowski, AP and Berger, A and Craft, RM and Damitz, LA and Gabriel, R and Harrison, T and Kaptein, K and Lee, S and Murphy, AZ and Said, E and Smith, SA and Thomas, DA and Valdés Hernández, MDC and Trasvina, V and Wesselmann, U and Yaksh, TL},
title = {Pain mechanisms in the transgender individual: a review.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {5},
number = {},
pages = {1241015},
pmid = {38601924},
issn = {2673-561X},
abstract = {SPECIFIC AIM: Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.<br><br>METHODS: A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.<br><br>RESULTS: While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.<br><br>CONCLUSIONS: While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.},
}
@article {pmid38601801,
year = {2024},
author = {Li, H and Li, H and Ma, L and Polina, D},
title = {Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1377233},
pmid = {38601801},
issn = {1662-5161},
abstract = {INTRODUCTION: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.<br><br>METHODS: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.<br><br>RESULTS: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.<br><br>DISCUSSION: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.},
}
@article {pmid38601800,
year = {2024},
author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y},
title = {Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1391550},
pmid = {38601800},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.},
}
@article {pmid38598676,
year = {2024},
author = {Gancio, J and Masoller, C and Tirabassi, G},
title = {Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {34},
number = {4},
pages = {},
doi = {10.1063/5.0200029},
pmid = {38598676},
issn = {1089-7682},
mesh = {Humans ; Entropy ; *Brain ; *Electroencephalography/methods ; Brain Mapping/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.},
}
@article {pmid38598403,
year = {2024},
author = {Li, D and Wang, X and Dou, M and Zhao, Y and Cui, X and Xiang, J and Wang, B},
title = {Multi-Stimulus Least-Squares Transformation With Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1606-1615},
doi = {10.1109/TNSRE.2024.3387283},
pmid = {38598403},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Calibration ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Algorithms ; },
abstract = {UNLABELLED: Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA).<br><br>METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial.<br><br>RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01&#xb1;10.10 bits/min, 172.31&#xb1;7.26 bits/min, and 139.04&#xb1;14.90 bits/min for all three datasets, respectively.<br><br>CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.},
}
@article {pmid38598402,
year = {2024},
author = {Qin, K and Xu, R and Li, S and Wang, X and Cichocki, A and Jin, J},
title = {A Time-Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1596-1605},
doi = {10.1109/TNSRE.2024.3386763},
pmid = {38598402},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Pattern Recognition, Automated/methods ; Recognition, Psychology ; Electroencephalography/methods ; Algorithms ; Photic Stimulation ; },
abstract = {Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.},
}
@article {pmid38593021,
year = {2024},
author = {Niu, X and Lu, N and Yan, R and Luo, H},
title = {Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {6},
pages = {3434-3445},
doi = {10.1109/JBHI.2024.3386565},
pmid = {38593021},
issn = {2168-2208},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Algorithms ; Deep Learning ; Imagination/physiology ; Brain/physiology ; },
abstract = {Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement of the training samples makes real-time MI BCI systems impossible to implement based on deep networks, which restricts the related researches within laboratory and makes practical application hard to accomplish. To address this issue, a double-point observation deep network (DoNet) is developed to classify ultra-short samples buried in noise. First, an analytical solution is developed theoretically to perform ultra-short signal classification based on double-point couples. Then, a signal-noise model is constructed to study the interference of noise on classification based on double-point couples. Based on which, an independent identical distribution condition is utilized to improve the classification accuracy in a data-driven manner. Combining the theoretical model and data-driven mechanism, DoNet can construct a steady data-distribution for the double-point couples of the samples with the same label. Therefore, the conditional probability of each double-point couple of a test sample can be obtained. With a voting strategy, the samples can be accurately classified by fusing these conditional probabilities. Meanwhile, the noise interference can be suppressed. DoNet has been evaluated on two public EEG datasets. Compared to most state-of-the-art methods, the 1s-long EEG signal classification accuracy has been improved by more than 3%.},
}
@article {pmid38592090,
year = {2024},
author = {Ille, N},
title = {Orthogonal extended infomax algorithm.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad38db},
pmid = {38592090},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Normal Distribution ; },
abstract = {Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.},
}
@article {pmid38590363,
year = {2024},
author = {Osuna-Orozco, R and Zhao, Y and Stealey, HM and Lu, HY and Contreras-Hernandez, E and Santacruz, SR},
title = {Adaptation and learning as strategies to maximize reward in neurofeedback tasks.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1368115},
pmid = {38590363},
issn = {1662-5161},
abstract = {INTRODUCTION: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.<br><br>METHODS: Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent.<br><br>RESULTS AND DISCUSSION: Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.},
}
@article {pmid38589428,
year = {2024},
author = {Lotun, S and Lamarche, VM and Matran-Fernandez, A and Sandstrom, GM},
title = {People perceive parasocial relationships to be effective at fulfilling emotional needs.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {8185},
pmid = {38589428},
issn = {2045-2322},
mesh = {Humans ; Emotions ; Loneliness ; *Emotional Regulation ; Friends ; *Social Media ; },
abstract = {People regularly form one-sided, "parasocial" relationships (PSRs) with targets incapable of returning the sentiment. Past work has shown that people engage with PSRs to support complex psychological needs (e.g., feeling less lonely after watching a favorite movie). However, we do not know how people rate these relationships relative to traditional two-sided relationships in terms of their effectiveness in supporting psychological needs. The current research (Ntotal = 3085) examined how PSRs help people fulfil emotion regulation needs. In Studies 1 and 2, participants felt that both their YouTube creator and non-YouTube creator PSRs were more effective at fulfilling their emotional needs than in-person acquaintances, albeit less effective than close others. In Study 3, people with high self-esteem thought PSRs would be responsive to their needs when their sociometer was activated, just as they do with two-sided relationships.},
}
@article {pmid38589229,
year = {2024},
author = {Falaki, A and Quessy, S and Dancause, N},
title = {Differential Modulation of Local Field Potentials in the Primary and Premotor Cortices during Ipsilateral and Contralateral Reach to Grasp in Macaque Monkeys.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {21},
pages = {},
pmid = {38589229},
issn = {1529-2401},
mesh = {Animals ; *Motor Cortex/physiology ; *Hand Strength/physiology ; Male ; *Macaca mulatta ; *Psychomotor Performance/physiology ; *Functional Laterality/physiology ; Movement/physiology ; Hand/physiology ; },
abstract = {Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1) and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤13 Hz) and γ frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, δ oscillations were greater during contralateral reach and grasp, and β activity increased during contralateral grasp. In contrast, β oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.},
}
@article {pmid38587944,
year = {2024},
author = {Carrara, I and Papadopoulo, T},
title = {Classification of BCI-EEG Based on the Augmented Covariance Matrix.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {9},
pages = {2651-2662},
doi = {10.1109/TBME.2024.3386219},
pmid = {38587944},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; *Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {OBJECTIVE: Electroencephalography signals are recorded as multidimensional datasets. We propose a new framework based on the augmented covariance that stems from an autoregressive model to improve motor imagery classification.<br><br>METHODS: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix. The state-of the art for classifying covariance matrices is based on Riemannian Geometry. A fairly natural idea is therefore to apply this Riemannian Geometry based approach to these augmented covariance matrices. The methodology for creating the augmented covariance matrix shows a natural connection with the delay embedding theorem proposed by Takens for dynamical systems. Such an embedding method is based on the knowledge of two parameters: the delay and the embedding dimension, respectively related to the lag and the order of the autoregressive model. This approach provides new methods to compute the hyper-parameters in addition to standard grid search.<br><br>RESULTS: The augmented covariance matrix performed ACMs better than any state-of-the-art methods. We will test our approach on several datasets and several subjects using the MOABB framework, using both within-session and cross-session evaluation.<br><br>CONCLUSION: The improvement in results is due to the fact that the augmented covariance matrix incorporates not only spatial but also temporal information. As such, it contains information on the nonlinear components of the signal through the embedding procedure, which allows the leveraging of dynamical systems algorithms.<br><br>SIGNIFICANCE: These results extend the concepts and the results of the Riemannian distance based classification algorithm.},
}
@article {pmid38586334,
year = {2024},
author = {Curà, F and Sesana, R and Corsaro, L and Dugand, MM},
title = {An Active Thermography approach for materials characterisation of thermal management systems for Lithium-ion batteries.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28587},
pmid = {38586334},
issn = {2405-8440},
abstract = {The aim of this work is an alternative non destructive technique for estimating the thermal properties of four different Thermal Management System (TMS) materials. More in detail, a thermographic setup realized with the Active Thermography approach (AT) is utilized for the purpose and the data elaboration follows the ISO 18755 Standard. As well known, Phase Changes Materials (PCMs) represent an innovative solution in the Thermal Management System (TMS) of Lithium-Ion batteries and, during the years, many solutions were developed to improve its thermal properties. As a matter of fact, parameters such as the internal temperature or heat exchanges impact on both efficiency and safety of the whole battery system. Consequently, the thermal conductivity was often chosen as a performance indicator of Thermal Management System (TMS) materials. In this work, both thermal diffusivity and thermal conductivity were estimated in two different testing conditions, respectively at room temperature and higher temperature conditions. The Active Thermography (AT) technique proposed in this activity has satisfactory estimated both thermal diffusivity and thermal conductivity of Thermal Management System (TMS) materials. An analytical model was also developed to reproduce the temperature experimental profiles. Finally, results obtained with AT approach were compared to those available from commercial datasheet and literature.},
}
@article {pmid38586195,
year = {2024},
author = {Ma, P and Dong, C and Lin, R and Liu, H and Lei, D and Chen, X and Liu, H},
title = {A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1306283},
pmid = {38586195},
issn = {1662-4548},
abstract = {BACKGROUND: The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.<br><br>OBJECTIVE: This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.<br><br>METHODS: The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.<br><br>RESULTS: For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal.<br><br>CONCLUSION: The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.},
}
@article {pmid38586146,
year = {2024},
author = {Shuqfa, Z and Lakas, A and Belkacem, AN},
title = {Increasing accessibility to a large brain-computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification.},
journal = {Data in brief},
volume = {54},
number = {},
pages = {110181},
pmid = {38586146},
issn = {2352-3409},
abstract = {A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.},
}
@article {pmid38586082,
year = {2024},
author = {Mueller, NN and Kim, Y and Ocoko, MYM and Dernelle, P and Kale, I and Patwa, S and Hermoso, AC and Chirra, D and Capadona, JR and Hess-Dunning, A},
title = {Effects of Micromachining on Anti-oxidant Elution from a Mechanically-Adaptive Polymer.},
journal = {Journal of micromechanics and microengineering : structures, devices, and systems},
volume = {34},
number = {3},
pages = {},
pmid = {38586082},
issn = {0960-1317},
support = {I01 RX003083/RX/RRD VA/United States ; IK6 RX003077/RX/RRD VA/United States ; },
abstract = {Intracortical microelectrodes (IMEs) can be used to restore motor and sensory function as a part of brain-computer interfaces in individuals with neuromusculoskeletal disorders. However, the neuroinflammatory response to IMEs can result in their premature failure, leading to reduced therapeutic efficacy. Mechanically-adaptive, resveratrol-eluting (MARE) neural probes target two mechanisms believed to contribute to the neuroinflammatory response by reducing the mechanical mismatch between the brain tissue and device, as well as locally delivering an antioxidant therapeutic. To create the mechanically-adaptive substrate, a dispersion, casting, and evaporation method is used, followed by a microfabrication process to integrate functional recording electrodes on the material. Resveratrol release experiments were completed to generate a resveratrol release profile and demonstrated that the MARE probes are capable of long-term controlled release. Additionally, our results showed that resveratrol can be degraded by laser-micromachining, an important consideration for future device fabrication. Finally, the electrodes were shown to have a suitable impedance for single-unit neural recording and could record single units in vivo.},
}
@article {pmid38585364,
year = {2024},
author = {Ben Pazi, H and Jahashan, S and Har Nof, S and Zibman, S and Yanai-Kohelet, O and Prigan, L and Intrator, N and Bornstein, NM and Ribo, M},
title = {Pre-hospital stroke monitoring past, present, and future: a perspective.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1341170},
pmid = {38585364},
issn = {1664-2295},
abstract = {Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.},
}
@article {pmid38585226,
year = {2023},
author = {Yuvaraj, M and Raja, P and David, A and Burdet, E and Skm, V and Balasubramanian, S},
title = {A systematic investigation of detectors for low signal-to-noise ratio EMG signals.},
journal = {F1000Research},
volume = {12},
number = {},
pages = {429},
pmid = {38585226},
issn = {2046-1402},
mesh = {*Electromyography/methods ; Humans ; *Signal-To-Noise Ratio ; *Algorithms ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.<br><br>METHODS: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.<br><br>RESULTS: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.<br><br>CONCLUSIONS: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.},
}
@article {pmid38584867,
year = {2024},
author = {Khan, AYY and Anjum, A and Qadri, HM},
title = {Ethical tightrope: Navigating neuro-ethics in brain computer interface (BCI) technology.},
journal = {Brain &amp; spine},
volume = {4},
number = {},
pages = {102800},
pmid = {38584867},
issn = {2772-5294},
}
@article {pmid38581031,
year = {2024},
author = {Ferrero, L and Soriano-Segura, P and Navarro, J and Jones, O and Ortiz, M and Iáñez, E and Azorín, JM and Contreras-Vidal, JL},
title = {Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {48},
pmid = {38581031},
issn = {1743-0003},
support = {FPU19/03165//Ministry of Science, Innovation and Universities through the Aid for the Training of University Teachers/ ; PID2021-124111OB-C31//MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe/ ; },
mesh = {Humans ; *Deep Learning ; *Exoskeleton Device ; *Brain-Computer Interfaces ; Algorithms ; Lower Extremity ; Electroencephalography/methods ; },
abstract = {BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.<br><br>METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding.<br><br>RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.<br><br>CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.},
}
@article {pmid38580923,
year = {2024},
author = {Allonen, S and Aittoniemi, J and Vuorialho, M and Närhi, L and Panula, K and Vuento, R and Honkaniemi, J},
title = {Streptococcus intermedius causing primary bacterial ventriculitis in a patient with severe periodontitis - a case report.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {112},
pmid = {38580923},
issn = {1471-2377},
support = {EVO; 1326/2010//the State Research Funding of Vaasa Hospital District/ ; },
mesh = {Male ; Humans ; Middle Aged ; Streptococcus intermedius ; *Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy ; Anti-Bacterial Agents/therapeutic use ; *Meningitis/diagnosis ; *Central Nervous System Bacterial Infections ; *Periodontitis/complications/drug therapy ; },
abstract = {BACKGROUND: Streptococcus intermedius is a member of the S. anginosus group and is part of the normal oral microbiota. It can cause pyogenic infections in various organs, primarily in the head and neck area, including brain abscesses and meningitis. However, ventriculitis due to periodontitis has not been reported previously.<br><br>CASE PRESENTATION: A 64-year-old male was admitted to the hospital with a headache, fever and later imbalance, blurred vision, and general slowness. Neurological examination revealed nuchal rigidity and general clumsiness. Meningitis was suspected, and the patient was treated with dexamethasone, ceftriaxone and acyclovir. A brain computer tomography (CT) scan was normal, and cerebrospinal fluid (CSF) Gram staining and bacterial cultures remained negative, so the antibacterial treatment was discontinued. Nine days after admission, the patient's condition deteriorated. The antibacterial treatment was restarted, and a brain magnetic resonance imaging revealed ventriculitis. A subsequent CT scan showed hydrocephalus, so a ventriculostomy was performed. In CSF Gram staining, chains of gram-positive cocci were observed. Bacterial cultures remained negative, but a bacterial PCR detected Streptococcus intermedius. An orthopantomography revealed advanced periodontal destruction in several teeth and periapical abscesses, which were subsequently operated on. The patient was discharged in good condition after one month.<br><br>CONCLUSIONS: Poor dental health can lead to life-threatening infections in the central nervous system, even in a completely healthy individual. Primary bacterial ventriculitis is a diagnostic challenge, which may result in delayed treatment and increased mortality.},
}
@article {pmid38580626,
year = {2024},
author = {Chen, F and Zheng, J and Wang, L and Krajbich, I},
title = {Attribute latencies causally shape intertemporal decisions.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2948},
pmid = {38580626},
issn = {2041-1723},
support = {2148982//National Science Foundation (NSF)/ ; 72002195//National Natural Science Foundation of China (National Science Foundation of China)/ ; 71871199//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72371226//National Natural Science Foundation of China (National Science Foundation of China)/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People&apos;s Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Humans ; Animals ; Mice ; Time Factors ; *Delay Discounting ; Reward ; Reaction Time ; Choice Behavior/physiology ; },
abstract = {Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.},
}
@article {pmid38580047,
year = {2024},
author = {Wang, C and Sun, Y and Xing, Y and Liu, K and Xu, K},
title = {Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model.},
journal = {Brain research},
volume = {1835},
number = {},
pages = {148914},
doi = {10.1016/j.brainres.2024.148914},
pmid = {38580047},
issn = {1872-6240},
mesh = {*Habenula/physiology/physiopathology ; Animals ; *Stress, Psychological/physiopathology ; *Depression/physiopathology ; *Restraint, Physical/methods ; Male ; Disease Models, Animal ; Deep Brain Stimulation/methods ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.},
}
@article {pmid38579958,
year = {2024},
author = {Lo, YT and Lim, MJR and Kok, CY and Wang, S and Blok, SZ and Ang, TY and Ng, VYP and Rao, JP and Chua, KSG},
title = {Neural Interface-Based Motor Neuroprosthesis in Poststroke Upper Limb Neurorehabilitation: An Individual Patient Data Meta-analysis.},
journal = {Archives of physical medicine and rehabilitation},
volume = {105},
number = {12},
pages = {2336-2349},
doi = {10.1016/j.apmr.2024.04.001},
pmid = {38579958},
issn = {1532-821X},
mesh = {Humans ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/methods ; Exoskeleton Device ; Neural Prostheses ; Neurological Rehabilitation/methods ; Recovery of Function ; *Stroke Rehabilitation/methods ; *Upper Extremity/physiopathology ; },
abstract = {OBJECTIVE: To determine the efficacy of neural interface-based neurorehabilitation, including brain-computer interface, through conventional and individual patient data (IPD) meta-analysis and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.<br><br>DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.<br><br>STUDY SELECTION: Studies using neural interface-controlled physical effectors (functional electrical stimulation and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper-extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.<br><br>DATA EXTRACTION: Changes in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.<br><br>DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE scores increased by a mean of 5.23 (95% confidence interval [CI]: 3.85-6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 vs &#x2264;4 weeks. On IPD analysis, the mean time-to-improvement above minimal clinically important difference (MCID) was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41%-70%). Patients with severe impairment (P=.042) and age >50 years (P=.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (hazard ratio [HR] 0.15, P=.08 and HR 0.47, P=.06, respectively).<br><br>CONCLUSION: Neural interface-based motor rehabilitation resulted in significant, although modest, reductions in poststroke impairment and should be considered for wider applications in stroke neurorehabilitation.},
}
@article {pmid38579696,
year = {2024},
author = {Ali, YH and Bodkin, K and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, LR and Au Yong, N and Stavisky, SD and Miller, LE and Brandman, DM and Pandarinath, C},
title = {BRAND: a platform for closed-loop experiments with deep network models.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38579696},
issn = {1741-2552},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; F32 HD112173/HD/NICHD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; DP2 DC021055/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; Neural Networks, Computer ; *Brain-Computer Interfaces ; *Neurosciences ; },
abstract = {Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.},
}
@article {pmid38578854,
year = {2024},
author = {Lin, PJ and Li, W and Zhai, X and Li, Z and Sun, J and Xu, Q and Pan, Y and Ji, L and Li, C},
title = {Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1546-1555},
doi = {10.1109/TNSRE.2024.3384498},
pmid = {38578854},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; *Stroke ; Electroencephalography/methods ; *Stroke Rehabilitation/methods ; },
abstract = {Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.},
}
@article {pmid38577666,
year = {2024},
author = {Liu, F and Zheng, H and Ma, S and Zhang, W and Liu, X and Chua, Y and Shi, L and Zhao, R},
title = {Advancing brain-inspired computing with hybrid neural networks.},
journal = {National science review},
volume = {11},
number = {5},
pages = {nwae066},
pmid = {38577666},
issn = {2053-714X},
abstract = {Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.},
}
@article {pmid38576451,
year = {2024},
author = {Ning, M and Duwadi, S and Yücel, MA and von Lühmann, A and Boas, DA and Sen, K},
title = {fNIRS dataset during complex scene analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1329086},
pmid = {38576451},
issn = {1662-5161},
support = {T32 DC013017/DC/NIDCD NIH HHS/United States ; },
}
@article {pmid38572293,
year = {2024},
author = {},
title = {Correction to: Transfer learning promotes acquisition of individual BCI skills.},
journal = {PNAS nexus},
volume = {3},
number = {4},
pages = {pgae137},
doi = {10.1093/pnasnexus/pgae137},
pmid = {38572293},
issn = {2752-6542},
abstract = {[This corrects the article DOI: 10.1093/pnasnexus/pgae076.].},
}
@article {pmid38572110,
year = {2024},
author = {Wang, L and Hong, W and Zhu, H and He, Q and Yang, B and Wang, J and Weng, Q},
title = {Macrophage senescence in health and diseases.},
journal = {Acta pharmaceutica Sinica. B},
volume = {14},
number = {4},
pages = {1508-1524},
pmid = {38572110},
issn = {2211-3835},
abstract = {Macrophage senescence, manifested by the special form of durable cell cycle arrest and chronic low-grade inflammation like senescence-associated secretory phenotype, has long been considered harmful. Persistent senescence of macrophages may lead to maladaptation, immune dysfunction, and finally the development of age-related diseases, infections, autoimmune diseases, and malignancies. However, it is a ubiquitous, multi-factorial, and dynamic complex phenomenon that also plays roles in remodeled processes, including wound repair and embryogenesis. In this review, we summarize some general molecular changes and several specific biomarkers during macrophage senescence, which may bring new sight to recognize senescent macrophages in different conditions. Also, we take an in-depth look at the functional changes in senescent macrophages, including metabolism, autophagy, polarization, phagocytosis, antigen presentation, and infiltration or recruitment. Furthermore, some degenerations and diseases associated with senescent macrophages as well as the mechanisms or relevant genetic regulations of senescent macrophages are integrated, not only emphasizing the possibility of regulating macrophage senescence to benefit age-associated diseases but also has an implication on the finding of potential targets or drugs clinically.},
}
@article {pmid38570593,
year = {2024},
author = {van Stuijvenberg, OC and Broekman, MLD and Wolff, SEC and Bredenoord, AL and Jongsma, KR},
title = {Developer perspectives on the ethics of AI-driven neural implants: a qualitative study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7880},
pmid = {38570593},
issn = {2045-2322},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {*Artificial Intelligence ; Reproducibility of Results ; Qualitative Research ; Focus Groups ; *Cochlear Implants ; },
abstract = {Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users' safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.},
}
@article {pmid38570113,
year = {2024},
author = {Sun, WB and Fu, JX and Chen, YL and Li, HF and Wu, ZY and Chen, DF},
title = {Both gain- and loss-of-function variants of KCNA1 are associated with paroxysmal kinesigenic dyskinesia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {51},
number = {8},
pages = {801-810},
doi = {10.1016/j.jgg.2024.03.013},
pmid = {38570113},
issn = {1673-8527},
mesh = {Humans ; *Kv1.1 Potassium Channel/genetics ; Male ; Female ; *Pedigree ; *Dystonia/genetics/pathology ; *Mutation, Missense/genetics ; Exome Sequencing ; Loss of Function Mutation/genetics ; Adult ; Gain of Function Mutation/genetics ; Child ; Adolescent ; Genetic Predisposition to Disease ; HEK293 Cells ; Ataxia ; Myokymia ; },
abstract = {KCNA1 is the coding gene for Kv1.1 voltage-gated potassium-channel α subunit. Three variants of KCNA1 have been reported to manifest as paroxysmal kinesigenic dyskinesia (PKD), but the correlation between them remains unclear due to the phenotypic complexity of KCNA1 variants as well as the rarity of PKD cases. Using the whole exome sequencing followed by Sanger sequencing, we screen for potential pathogenic KCNA1 variants in patients clinically diagnosed with paroxysmal movement disorders and identify three previously unreported missense variants of KCNA1 in three unrelated Chinese families. The proband of one family (c.496G>A, p.A166T) manifests as episodic ataxia type 1, and the other two (c.877G>A, p.V293I and c.1112C>A, p.T371A) manifest as PKD. The pathogenicity of these variants is confirmed by functional studies, suggesting that p.A166T and p.T371A cause a loss-of-function of the channel, while p.V293I leads to a gain-of-function with the property of voltage-dependent gating and activation kinetic affected. By reviewing the locations of PKD-manifested KCNA1 variants in Kv1.1 protein, we find that these variants tend to cluster around the pore domain, which is similar to epilepsy. Thus, our study strengthens the correlation between KCNA1 variants and PKD and provides more information on genotype-phenotype correlations of KCNA1 channelopathy.},
}
@article {pmid38565846,
year = {2024},
author = {Li, H and Li, Z and Yuan, X and Tian, Y and Ye, W and Zeng, P and Li, XM and Guo, F},
title = {Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2834},
pmid = {38565846},
issn = {2041-1723},
mesh = {Animals ; Circadian Rhythm/physiology ; Temperature ; Sleep/physiology ; Drosophila ; *Circadian Clocks/physiology ; *Drosophila Proteins/genetics ; Drosophila melanogaster/physiology ; },
abstract = {The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.},
}
@article {pmid38565100,
year = {2024},
author = {Li, W and Li, H and Sun, X and Kang, H and An, S and Wang, G and Gao, Z},
title = {Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad3986},
pmid = {38565100},
issn = {1741-2552},
mesh = {*Learning ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; Algorithms ; },
abstract = {Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.},
}
@article {pmid38563704,
year = {2024},
author = {Yang, Q and Wu, B and Castagnola, E and Pwint, MY and Williams, NP and Vazquez, AL and Cui, XT},
title = {Integrated Microprism and Microelectrode Array for Simultaneous Electrophysiology and Two-Photon Imaging across All Cortical Layers.},
journal = {Advanced healthcare materials},
volume = {13},
number = {24},
pages = {e2302362},
pmid = {38563704},
issn = {2192-2659},
support = {R01 NS110564/NS/NINDS NIH HHS/United States ; R01NS089688/GF/NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; BRAINR01NS110564/GF/NIH HHS/United States ; F32 MH132145/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Microelectrodes ; Mice ; Cerebral Cortex/physiology/diagnostic imaging ; Electrophysiology/methods/instrumentation ; Microscopy, Fluorescence, Multiphoton/methods/instrumentation ; Electrophysiological Phenomena ; Neurons/physiology ; },
abstract = {Cerebral neural electronics play a crucial role in neuroscience research with increasing translational applications such as brain-computer interfaces for sensory input and motor output restoration. While widely utilized for decades, the understanding of the cellular mechanisms underlying this technology remains limited. Although two-photon microscopy (TPM) has shown great promise in imaging superficial neural electrodes, its application to deep-penetrating electrodes is technically difficult. Here, a novel device integrating transparent microelectrode arrays with glass microprisms, enabling electrophysiology recording and stimulation alongside TPM imaging across all cortical layers in a vertical plane, is introduced. Tested in Thy1-GCaMP6 mice for over 4 months, the integrated device demonstrates the capability for multisite electrophysiological recording/stimulation and simultaneous TPM calcium imaging. As a proof of concept, the impact of microstimulation amplitude, frequency, and depth on neural activation patterns is investigated using the setup. With future improvements in material stability and single unit yield, this multimodal tool greatly expands integrated electrophysiology and optical imaging from the superficial brain to the entire cortical column, opening new avenues for neuroscience research and neurotechnology development.},
}
@article {pmid38562772,
year = {2025},
author = {Rajeswaran, P and Payeur, A and Lajoie, G and Orsborn, AL},
title = {Assistive sensory-motor perturbations influence learned neural representations.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.03.20.585972},
pmid = {38562772},
issn = {2692-8205},
support = {R01 NS134634/NS/NINDS NIH HHS/United States ; },
abstract = {Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Over time, task-relevant information became concentrated in fewer neurons, unlike with fixed decoders. At the population level, task information also became largely confined to a few neural modes that accounted for an unexpectedly small fraction of the population variance. A neural network model suggests the adaptive decoders directly contribute to forming these more compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.},
}
@article {pmid38562360,
year = {2024},
author = {Ilchev, B and Chervenkov, V and Valchev, N and Nakov, V and Minchev, T and Vassilev, G and Tsvetanov, T and Laleva, L and Milev, M and Spiriev, T},
title = {Interdisciplinary Successful Revascularization of Traumatic Occlusion of the Right Common Carotid Artery.},
journal = {Cureus},
volume = {16},
number = {3},
pages = {e55395},
pmid = {38562360},
issn = {2168-8184},
abstract = {Blunt carotid artery injury (BCI) poses a rare yet severe threat following vascular trauma, often leading to significant morbidity and mortality. We present a case of a 33-year-old male who suffered complete thrombotic occlusion of the right common carotid artery (CCA) following a workplace accident. Clinical evaluation revealed profound neurological deficits, prompting multidisciplinary surgical intervention guided by the Denver criteria (Grade I - disruption inside the vessel that results in a narrowing of the lumen by less than 25%; Grade II - dissection or intramural hematoma causing greater than 25% stenosis; Grade III - comprises pseudoaneurysm formation; Grade IV - causes total vessel occlusion; Grade V - describes vessel transection with extravasation). Surgical exploration unveiled extensive arterial damage, necessitating thrombectomy, primary repair, and double-layered patch angioplasty using an autologous saphenous vein. Postoperative recovery was uneventful, with the restoration of pulsatile blood flow confirmed by Doppler ultrasound. Three-month follow-up demonstrated patent arterial reconstruction and improved cerebral perfusion, despite the persistent neurological deficits. Our case underscores the challenges in diagnosing and managing BCI, advocating for a tailored approach based on injury severity and neurological status. While conservative management remains standard, surgical intervention offers a viable option in select cases, particularly those with complete vessel occlusion and neurological compromise. Long-term surveillance is imperative to assess the durability of arterial reconstruction and monitor for recurrent thromboembolic events. Further research is warranted to refine management algorithms and elucidate optimal treatment strategies in this rare but critical vascular pathology.},
}
@article {pmid38560190,
year = {2024},
author = {Akuthota, S and K, R and Ravichander, J},
title = {Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e27198},
pmid = {38560190},
issn = {2405-8440},
abstract = {This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a &amp; 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.},
}
@article {pmid38560116,
year = {2024},
author = {Chen, D},
title = {Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28235},
pmid = {38560116},
issn = {2405-8440},
abstract = {BACKGROUND: Traditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets.<br><br>NEW METHOD: To solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification.<br><br>RESULTS: The improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets.<br><br>CONCLUSIONS: The proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.},
}
@article {pmid38558011,
year = {2024},
author = {Chen, D and Zhao, Z and Zhang, S and Chen, S and Wu, X and Shi, J and Liu, N and Pan, C and Tang, Y and Meng, C and Zhao, X and Tao, B and Liu, W and Chen, D and Ding, H and Zhang, P and Tang, Z},
title = {Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.},
journal = {Translational stroke research},
volume = {},
number = {},
pages = {},
pmid = {38558011},
issn = {1868-601X},
support = {2022B37//Research Fund of Tongji Hospital/ ; 92148206//National Natural Science Foundation of China/ ; },
abstract = {Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.},
}
@article {pmid38557253,
year = {2024},
author = {Van Horn, AL and Burgess, JR},
title = {From Blunt Cardiac Injury to Heart Transplant Following Motorcycle Collision.},
journal = {The American surgeon},
volume = {90},
number = {8},
pages = {2080-2082},
doi = {10.1177/00031348241241699},
pmid = {38557253},
issn = {1555-9823},
mesh = {Humans ; Male ; *Heart Transplantation/adverse effects ; *Accidents, Traffic ; *Motorcycles ; Young Adult ; *Wounds, Nonpenetrating/complications/surgery ; *Heart Injuries/etiology/surgery ; },
abstract = {Traumatic coronary artery occlusion and dissection is an exceedingly rare complication of blunt cardiac injury (BCI), though it has been previously noted in a number of case reports. However, it can also lead to heart transplant, which to our knowledge has not been previously described in the literature. We present a case of a healthy 24-year-old man without significant past medical history who was in a motorcycle accident, resulting in sternal fracture and BCI. He was ultimately found to have thrombotic occlusion and dissection of his left anterior descending artery (LAD), requiring mechanical thrombectomy and drug-eluting stent, as well as subsequent hospitalizations and operations due to various complications. It was suspected that he went into ventricular fibrillation and had a second motorcycle collision, resulting in cardiogenic shock. Ultimately, his progression of ischemic cardiomyopathy and mitral regurgitation led to the need for heart transplant. Blunt cardiac injury with myocardial contusion has such a broad range of pathologies. It is essential that patients with these injury patterns raise a high level of suspicion for BCI and are followed closely with appropriate diagnostic testing and rapid intervention for best possible outcomes.},
}
@article {pmid38557034,
year = {2024},
author = {Ling, W and Shang, X and Yu, C and Li, C and Xu, K and Feng, L and Wei, Y and Tang, T and Huang, X},
title = {Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.},
journal = {ACS nano},
volume = {18},
number = {15},
pages = {10596-10608},
doi = {10.1021/acsnano.4c00632},
pmid = {38557034},
issn = {1936-086X},
mesh = {Rats ; Animals ; *Dopamine/analysis ; *Metal-Organic Frameworks/metabolism ; Fluorescent Dyes/metabolism ; Fluorescence ; Brain/diagnostic imaging/metabolism ; Neurotransmitter Agents/metabolism ; },
abstract = {Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 μm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.},
}
@article {pmid38555287,
year = {2024},
author = {Wei, M and Xu, K and Tang, B and Li, J and Yun, Y and Zhang, P and Wu, Y and Bao, K and Lei, K and Chen, Z and Ma, H and Sun, C and Liu, R and Li, M and Li, L and Lin, H},
title = {Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2786},
pmid = {38555287},
issn = {2041-1723},
support = {91950204, 62105287, 61975179, and 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Monolithic integration of novel materials without modifying the existing photonic component library is crucial to advancing heterogeneous silicon photonic integrated circuits. Here we show the introduction of a silicon nitride etch stop layer at select areas, coupled with low-loss oxide trench, enabling incorporation of functional materials without compromising foundry-verified device reliability. As an illustration, two distinct chalcogenide phase change materials (PCMs) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated, offering compact phase and intensity tuning units with zero-static power consumption. By employing these building blocks, the phase error of a push-pull Mach-Zehnder interferometer optical switch could be reduced with a 48% peak power consumption reduction. Mirco-ring filters with >5-bit wavelength selective intensity modulation and waveguide-based >7-bit intensity-modulation broadband attenuators could also be achieved. This foundry-compatible platform could open up the possibility of integrating other excellent optoelectronic materials into future silicon photonic process design kits.},
}
@article {pmid38554856,
year = {2024},
author = {Bader, ER and Boro, AD and Killian, NJ and Eskandar, EN},
title = {A method for precisely timed, on-demand intracranial stimulation using the RNS device.},
journal = {Brain stimulation},
volume = {17},
number = {2},
pages = {444-447},
pmid = {38554856},
issn = {1876-4754},
mesh = {Humans ; *Deep Brain Stimulation/instrumentation/methods ; },
}
@article {pmid38554787,
year = {2024},
author = {Chunduri, V and Aoudni, Y and Khan, S and Aziz, A and Rizwan, A and Deb, N and Keshta, I and Soni, M},
title = {Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification.},
journal = {Journal of neuroscience methods},
volume = {406},
number = {},
pages = {110128},
doi = {10.1016/j.jneumeth.2024.110128},
pmid = {38554787},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; *Brain-Computer Interfaces ; Attention/physiology ; Neural Networks, Computer ; Motor Activity/physiology ; Brain/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.<br><br>NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.<br><br>RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.<br><br>In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.<br><br>CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.},
}
@article {pmid38552161,
year = {2024},
author = {Wang, W and Zhou, H and Xu, Z and Li, Z and Zhang, L and Wan, P},
title = {Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {31},
pages = {e2401035},
doi = {10.1002/adma.202401035},
pmid = {38552161},
issn = {1521-4095},
support = {52222303//National Natural Science Foundation of China/ ; XK2022-03//BRC-BC/ ; //Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Hydrogels/chemistry ; Humans ; *Wearable Electronic Devices ; *Machine Learning ; Anti-Bacterial Agents/pharmacology/chemistry ; Electric Conductivity ; Animals ; Skin ; Mice ; Adhesiveness ; Ultraviolet Rays ; Electronics ; },
abstract = {Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV) protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected electromyogram (EMG) signals is designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility, and effective hemostasis properties for promising bacterial-infected wound bleeding.},
}
@article {pmid38550646,
year = {2024},
author = {Bossi, F and Ciardo, F and Mostafaoui, G},
title = {Editorial: Neurocognitive features of human-robot and human-machine interaction.},
journal = {Frontiers in psychology},
volume = {15},
number = {},
pages = {1394970},
pmid = {38550646},
issn = {1664-1078},
}
@article {pmid38550567,
year = {2024},
author = {Racz, FS and Kumar, S and Kaposzta, Z and Alawieh, H and Liu, DH and Liu, R and Czoch, A and Mukli, P and Millán, JDR},
title = {Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1271831},
pmid = {38550567},
issn = {1662-4548},
abstract = {Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.},
}
@article {pmid38547834,
year = {2024},
author = {Abbott, JR and Jeakle, EN and Haghighi, P and Usoro, JO and Sturgill, BS and Wu, Y and Geramifard, N and Radhakrishna, R and Patnaik, S and Nakajima, S and Hess, J and Mehmood, Y and Devata, V and Vijayakumar, G and Sood, A and Doan Thai, TT and Dogra, K and Hernandez-Reynoso, AG and Pancrazio, JJ and Cogan, SF},
title = {Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex.},
journal = {Biomaterials},
volume = {308},
number = {},
pages = {122543},
pmid = {38547834},
issn = {1878-5905},
support = {R01 NS104344/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Silicon Compounds/chemistry ; *Rats, Sprague-Dawley ; *Microelectrodes ; Female ; *Motor Cortex/physiology/cytology ; *Carbon Compounds, Inorganic/chemistry ; Rats ; *Electrodes, Implanted ; Neurons/physiology ; },
abstract = {Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm[2] on week 1 and 55.9 mC/cm[2] on week 16, and at 50,000 mV/s, 4.27 mC/cm[2] on week 1 and 5.93 mC/cm[2] on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.},
}
@article {pmid38545737,
year = {2024},
author = {Ergün, E and Aydemir, &#xd6; and Korkmaz, OE},
title = {Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2024.2333924},
pmid = {38545737},
issn = {1476-8259},
abstract = {In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.},
}
@article {pmid39323876,
year = {2023},
author = {Varshney, S and Farias, D and Brandman, DM and Stavisky, SD and Miller, LM},
title = {Using Automatic Speech Recognition to Measure the Intelligibility of Speech Synthesized from Brain Signals.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2023},
number = {},
pages = {},
pmid = {39323876},
issn = {1948-3546},
support = {UL1 TR001860/TR/NCATS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) can potentially restore lost function in patients with neurological injury. A promising new application of BCI technology has focused on speech restoration. One approach is to synthesize speech from the neural correlates of a person who cannot speak, as they attempt to do so. However, there is no established gold-standard for quantifying the quality of BCI-synthesized speech. Quantitative metrics, such as applying correlation coefficients between true and decoded speech, are not applicable to anarthric users and fail to capture intelligibility by actual human listeners; by contrast, methods involving people completing forced-choice multiple-choice questionnaires are imprecise, not practical at scale, and cannot be used as cost functions for improving speech decoding algorithms. Here, we present a deep learning-based "AI Listener" that can be used to evaluate BCI speech intelligibility objectively, rapidly, and automatically. We begin by adapting several leading Automatic Speech Recognition (ASR) deep learning models - Deepspeech, Wav2vec 2.0, and Kaldi - to suit our application. We then evaluate the performance of these ASRs on multiple speech datasets with varying levels of intelligibility, including: healthy speech, speech from people with dysarthria, and synthesized BCI speech. Our results demonstrate that the multiple-language ASR model XLSR-Wav2vec 2.0, trained to output phonemes, yields superior performance in terms of speech transcription accuracy. Notably, the AI Listener reports that several previously published BCI output datasets are not intelligible, which is consistent with human listeners.},
}
@article {pmid38751471,
year = {2023},
author = {Li, S and Yu, X and Xu, Y},
title = {Breast cancer gene expression signatures: development and clinical significance-a narrative review.},
journal = {Translational breast cancer research : a journal focusing on translational research in breast cancer},
volume = {4},
number = {},
pages = {7},
pmid = {38751471},
issn = {2218-6778},
abstract = {BACKGROUND AND OBJECTIVE: Breast cancer gene expression signatures are developing rapidly and are expected to better understand the intrinsic features of the tumor, and also to optimize the treatment strategy in clinical practice. This review is to summarize the controversy and consensus in clinical practice of gene expression signatures, and to provide our perspective on these issues as well as recommendation for future direction.<br><br>METHODS: We reviewed English publications in PubMed related to breast cancer gene expression signatures from 2002 to 2022.<br><br>KEY CONTENT AND FINDINGS: Five mature commercial gene expression signatures: Oncotype, MammaPrint, Prosigna/PAM50, EndoPredict and Breast Cancer Index (BCI) are available to provide the prognostic and predictive assessment. Although they could help to evaluate the risk of recurrence and to predict the benefits of certain treatments, their applications remain challenging. Treatment decisions should be determined by a combination of related clinical pathological factors in clinical practice.<br><br>CONCLUSIONS: Gene expression signatures could assist in the determination of the adjuvant therapy of early-stage breast cancer. The prospective randomized clinical trials showed that chemotherapy may be exempted in low-risk patients. More sufficient data are expected for the application in radiotherapy, extended endocrine therapy, and neoadjuvant treatment. The treatment cannot be determined by a single factor but by comprehensive assessments of clinicopathological factors, test purpose, and cost-effectiveness. Patients will benefit from personalized treatments with the publication of further evidence.},
}
@article {pmid38699292,
year = {2024},
author = {Zhang, Y and Lv, Q and Yin, Y and Wang, H and Bueber, MA and Phillips, MR and Li, T},
title = {Research in China about the biological mechanisms that potentially link socioenvironmental changes and mental health: a scoping review.},
journal = {The Lancet regional health. Western Pacific},
volume = {45},
number = {},
pages = {100610},
pmid = {38699292},
issn = {2666-6065},
abstract = {China's rapid socioeconomic development since 1990 makes it a fitting location to summarise research about how biological changes associated with socioenvironmental changes affect population mental health and, thus, lay the groundwork for subsequent, more focused studies. An initial search identified 308 review articles in the international literature about biomarkers associated with 12 common mental health disorders. We then searched for studies conducted in China that assessed the association of the identified mental health related-biomarkers with socioenvironmental factors in English-language and Chinese-language databases. We located 1330 articles published between 1 January 1990 and 1 August 2021 that reported a total of 3567 associations between 56 specific biomarkers and 11 socioenvironmental factors: 3156 (88·5%) about six types of environmental pollution, 381 (10·7%) about four health-related behaviours (diet, physical inactivity, internet misuse, and other lifestyle factors), and 30 (0·8%) about socioeconomic inequity. Only 245 (18·4%) of the papers simultaneously considered the possible effect of the biomarkers on mental health conditions; moreover, most of these studies assessed biomarkers in animal models of mental disorders, not human subjects. Among the 245 papers, mental health conditions were linked with biomarkers of environmental pollution in 188 (76·7%), with biomarkers of health-related behaviours in 48 (19·6%), and with biomarkers of socioeconomic inequality in 9 (3·7%). The 604 biomarker-mental health condition associations reported (107 in human subjects and 497 in animal models) included 379 (62·7%) about cognitive functioning, 117 (19·4%) about anxiety, 56 (9·3%) about depression, 21 (3·5%) about neurodevelopmental conditions, and 31 (5·1%) about neurobehavioural symptoms. Improved understanding of the biological mechanisms linking socioenvironmental changes to community mental health will require expanding the range of socioenvironmental factors considered, including mental health outcomes in more of the studies about the association of biomarkers with socioenvironmental factors, and increasing the proportion of studies that assess mental health outcomes in humans.},
}
@article {pmid39169955,
year = {2022},
author = {Ukhovskyi, V and Pyskun, A and Korniienko, L and Aliekseieva, H and Moroz, O and Pyskun, O and Kyivska, G and Mezhenskyi, A},
title = {Serological prevalence of Leptospira serovars among pigs in Ukraine during the period of 2001-2019.},
journal = {Veterinarni medicina},
volume = {67},
number = {1},
pages = {13-27},
pmid = {39169955},
issn = {0375-8427},
abstract = {Leptospirosis is a widespread infection among pigs throughout the world. In most cases in Ukraine, only the microscopic agglutination test (MAT) is used for the diagnosis of leptospirosis in animals. In general, during the period of 2001-2019, 2 381 163 samples of blood sera from swine were tested in our country and 85 338 positive reactions were received, which is 3.58% [binomial confidence intervals (BCI), 3.56-3.61%]. It was established that the serovars copenhageni - 33.91% (BCI, 33.59-34.23%), bratislava - 14.14% (BCI, 13.90-14.37%), pomona - 8.58% (BCI, 8.39-8.77%), and tarassovi - 7.12% (BCI, 6.95-7.30%) play a leading role in the aetiological structure of swine leptospirosis. A large number of positive reactions to several serovars was observed - 29.78% (BCI, 29.47-30.09%) of the total number of positive cases. In addition, the article presents data according to a retrospective analysis of the eight serovars circulating among pigs in Ukraine. Thus, during the nineteen year period, there was a decrease in the number of positive reactions to bratislava, pomona, and tarassovi and an increase in the number of positive reactions to copenhageni, polonica, and kabura. Mapping Ukraine's territory for leptospirosis among pigs was carried out. This allows one to identify zones with a risk of leptospirosis infections among swine. The maps show that the highest incidence rates were identified in the eastern and central parts of Ukraine.},
}
@article {pmid38993225,
year = {2021},
author = {Osawa, T and Wei, JT and Abe, T and Honda, M and Rew, KT and Dunn, R and Yamada, S and Furumido, J and Kikuchi, H and Matsumoto, R and Sato, Y and Harabayashi, T and Takada, N and Minami, K and Morita, K and Kashiwagi, A and Fukuhara, S and Murai, S and Ito, YM and Ogasawara, K and Shinohara, N},
title = {Comparison of Health-Related Quality of Life Between Japanese and American Patients with Bladder Cancer as Measured by a Newly Developed Japanese Version of the Bladder Cancer Index.},
journal = {Bladder cancer (Amsterdam, Netherlands)},
volume = {7},
number = {1},
pages = {61-69},
pmid = {38993225},
issn = {2352-3735},
abstract = {INTRODUCTION: The aim of this study is to characterize health related quality of life (HRQOL) in Japanese patients after bladder cancer surgery and to perform cross-cultural comparison between Japanese and American patients.<br><br>METHODS: Firstly, we cross-sectionally assessed HRQOL of 371 patients in Japan using the Bladder Cancer Index (BCI-Japanese). HRQOL of the four groups of patients (native bladder without intravesical therapy, native bladder with intravesicaltherapy, cystectomy with ileal conduit, and cystectomy with neobladder) were assessed. Secondly, we compared the Japanese with the American cohort (n&#x200a;=&#x200a;315) from the original BCI paper. After adjusting for age and gender, the differences in each BCI subdomain score was analyzed.<br><br>RESULTS: Among Japanese patients, the urinary domain function score was significantly lower among the cystectomy with neobladder group, compared to the cystectomy with ileal conduit group (p&#x200a;<&#x200a;0. 01). Despite this, the urinary bother was comparable between the two groups. Although there were apparent differences between Japanese and American patients, there were few differences in Urinary and Bowel HRQOL. In three of the four treatment groups (other than native bladder with intravesical therapy), Japanese patients were more likely than Americans to report poor sexual function (p&#x200a;<&#x200a;0.05). However, Japanese patients were less likely than Americans to be bothered by their lower sexual function, regardless of treatment (p&#x200a;<&#x200a;0.05).<br><br>CONCLUSIONS: HRQOL outcomes following treatment of bladder cancer in Japan are comparable to those in the USA, except for sexual functioning and sexual bother. The BCI can be used for cross-cultural assessments of HRQOL in bladder cancer patients.},
}
@article {pmid38624425,
year = {2021},
author = {Bergsman, KC and Chudler, EH},
title = {Adapting a Neural Engineering Summer Camp for High School Students to a Fully Online Experience.},
journal = {Biomedical engineering education},
volume = {1},
number = {1},
pages = {37-42},
pmid = {38624425},
issn = {2730-5945},
abstract = {The COVID-19 pandemic and its resulting health and safety concerns caused the cancellation of many engineering education opportunities for high school students. To expose high school students to the field of neural engineering and encourage them to pursue academic pathways in biomedical engineering, the Center for Neurotechnology (CNT) at the University of Washington converted an in-person summer camp to a fully online program (Virtual REACH Program, VRP) offering both synchronous and asynchronous resources. The VRP is a five-day online program that focuses on a different daily theme (neuroscience, brain-computer interfaces, electrical stimulation, neuroethics, career/academic pathways). Each day, the VRP starts with a live videoconference meeting (lecture and interactive discussion) with a CNT faculty member. The online lectures are supported by at-home learning resources (e.g., text, videos, activities, quizzes) embedded within a digital book created using the Pressbook platform. An online bulletin board (Padlet) is also used by students to share artifacts and build community. Program evaluation will be conducted by an external evaluator. A summative survey will collect information on participants' experiences in the VRP and will help inform future iterations of the program. Although significant time was required to create a digital book, the VRP will reach a larger audience than the prior in-person program and resulted in the creation of learning tools that can be used in the future.},
}
@article {pmid38566861,
year = {2019},
author = {Fontaine, AK and Segil, JL and Caldwell, JH and Weir, RFF},
title = {Real-Time Prosthetic Digit Actuation by Optical Read-out of Activity-Dependent Calcium Signals in an Ex Vivo Peripheral Nerve.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2019},
number = {},
pages = {143-146},
pmid = {38566861},
issn = {1948-3546},
support = {OT2 OD023852/OD/NIH HHS/United States ; },
abstract = {Improved neural interfacing strategies are needed for the full articulation of advanced prostheses. To address limitations of existing control interface designs, the work of our laboratory has presented an optical approach to reading activity from individual nerve fibers using activity-dependent calcium transients. Here, we demonstrate the feasibility of such signals to control prosthesis actuation by using the axonal fluorescence signal in an ex vivo mouse nerve to drive a prosthetic digit in real-time. Additionally, signals of varying action potential frequency are streamed post hoc to the prosthesis, showing graded motor output and the potential for proportional neural control. This proof-of-concept work is a novel demonstration of the functional use of activity-dependent optical read-out in the nerve.},
}
@article {pmid39449990,
year = {2018},
author = {Mashamba-Thompson, TP and Moodley, P and Sartorius, B and Drain, PK},
title = {Evaluation of antenatal rapid human immunodeficiency virus testing in rural South Africa.},
journal = {Southern African journal of HIV medicine},
volume = {19},
number = {1},
pages = {771},
pmid = {39449990},
issn = {2078-6751},
abstract = {INTRODUCTION: South African guidelines recommend two rapid tests for diagnosing human immunodeficiency virus (HIV) using the serial HIV testing algorithm, but the accuracy and compliance to this algorithm is unknown in rural clinics. We evaluated the accuracy of HIV rapid testing and the time to receiving test results among pregnant women in rural KwaZulu-Natal (KZN).<br><br>METHOD: We observed the accuracy of rapid HIV testing algorithms for 208 consenting antenatal patients accessing voluntary HIV testing services in nine rural primary healthcare (PHC) clinics in KZN. A PHC-based HIV counsellor obtained finger-prick whole blood from each participant to perform rapid testing using the Advanced Quality&#x2122; One Step anti-HIV (1&amp;2) and/or ABON&#x2122; HIV 1/2/O Tri-Line HIV test. A research nurse obtained venous blood for an enzyme-linked immunosorbent assay (ELISA) HIV test, which is the gold standard diagnostic test. We recorded the time of receipt of HIV test results for each test.<br><br>RESULTS: Among 208 pregnant women with a mean age of 26 years, 72 women from nine rural PHC clinics were identified as HIV-positive by two rapid tests with an HIV-prevalence of 35% (95% Bayesian credibility intervals [BCI]: 28% - 41%). Of the 208 patients, 135 patients from six clinics were tested with the serial HIV testing algorithm. The estimated sensitivity and specificity for the 135 participants were 100% (95% confidence interval [CI]: 93% - 100%) and 99% (CI: 95% - 100%), respectively. The positive predictive value and negative predictive value were estimated at 98% (CI: 94% - 100%) and 95% (CI: 88% - 99%), respectively. All women received their HIV rapid test results within 20 min of testing. Test stock-out resulted in poor test availability at point-of-care, preventing performance of a second HIV test in three out of nine PHC clinics in rural KZN.<br><br>CONCLUSION: Despite the poor compliance with national guidelines for HIV rapid testing services, HIV rapid test results provided to pregnant women in rural PHC clinics in KZN were generally accurate and timely. Test stock-out was shown to be one of the barriers to test availability in rural PHC clinics, resulting in poor compliance with guidelines. We recommend a compulsory confirmation HIV rapid test for all HIV-negative test results obtained from pregnant patients in rural and resource-limited settings.},
}
@article {pmid38544185,
year = {2024},
author = {Albán-Escobar, M and Navarrete-Arroyo, P and De la Cruz-Guevara, DR and Tobar-Quevedo, J},
title = {Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {6},
pages = {},
pmid = {38544185},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Electroencephalography/methods ; *Self-Help Devices ; Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.},
}
@article {pmid38541720,
year = {2024},
author = {Pais-Vieira, C and Figueiredo, JG and Perrotta, A and Matos, D and Aguiar, M and Ramos, J and Gato, M and Poleri, T and Pais-Vieira, M},
title = {Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.},
journal = {Life (Basel, Switzerland)},
volume = {14},
number = {3},
pages = {},
pmid = {38541720},
issn = {2075-1729},
support = {UIDP/04501/2020, UIDB/04279/2020, FCT/IF/00098/2015, project CISUC -UID/CEC/00326/2020, and via the doctoral scholarship 2023.02051.BD//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 95/2016//Bial (Portugal)/ ; (grant agreement No. 779963)//European Union's Horizon 2020 Research and Innovation Programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement No. 779963) Thertact-Fb and Thertact- NEXT/ ; MC-12-2018//SANTA CASA Pr&#xe9;mios Neuroci&#xea;ncias Melo e Castro/ ; },
abstract = {Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.},
}
@article {pmid38539656,
year = {2024},
author = {Yao, X and Li, T and Ding, P and Wang, F and Zhao, L and Gong, A and Nan, W and Fu, Y},
title = {Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539656},
issn = {2076-3425},
support = {62376112, 82172058, 81771926, 61763022, and 62006246.//National Natural Science Foundation of China/ ; },
abstract = {OBJECTIVES: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification.<br><br>METHODS: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax.<br><br>RESULTS: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset.<br><br>CONCLUSIONS: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies.<br><br>SIGNIFICANCE: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.},
}
@article {pmid38539622,
year = {2024},
author = {Kuang, M and Zhan, Z and Gao, S},
title = {Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539622},
issn = {2076-3425},
support = {2019SCUH0007//Sichuan University Innovation Spark Project/ ; },
abstract = {Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain-computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node-edge interaction and a multi-scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node-edge interaction (NEI-GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi-stage reconstruction network that restricts the reconstructed images in a coarse-to-fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state-of-the-art methods in terms of accuracy in the commonly used n-way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n-way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n-way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual-decoding brain-computer interfaces.},
}
@article {pmid38539605,
year = {2024},
author = {Gu, X and Jiang, L and Chen, H and Li, M and Liu, C},
title = {Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539605},
issn = {2076-3425},
support = {2023YFC3604100//the National Key Research and Development Program of China/ ; YG2021169//the Art Planning Program of Jiangxi Province/ ; YS22247//Jiangxi University Humanities and Social Science Program/ ; },
abstract = {In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.},
}
@article {pmid38539602,
year = {2024},
author = {Niu, C and Yan, Z and Yin, K and Zhou, S},
title = {Identification and Verification of Error-Related Potentials Based on Cerebellar Targets.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539602},
issn = {2076-3425},
support = {62293552//National Natural Science Foundation of China/ ; },
abstract = {The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.},
}
@article {pmid38539585,
year = {2024},
author = {Wu, S and Bhadra, K and Giraud, AL and Marchesotti, S},
title = {Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539585},
issn = {2076-3425},
support = {#51NF40_180888/SNSF_/Swiss National Science Foundation/Switzerland ; },
abstract = {Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and remain unchanged throughout the BCI use. However, this approach might be inadequate to effectively handle the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use, as parameters are expected to change, and all the more in a real-time setting. To address this limitation, we developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI control setting. Twenty healthy participants performed two BCI control sessions based on the imagery of two syllables, using a static LDA and an adaptive LDA classifier, in randomized order. As hypothesized, the adaptive classifier led to better performances than the static one in this real-time BCI control task. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets, acquired using the same syllable imagery task. These findings highlight the effectiveness and reliability of adaptive LDA classifiers for real-time imagined speech decoding. Such an improvement can shorten the training time and favor the development of multi-class BCIs, representing a clear interest for non-invasive systems notably characterized by low decoding accuracies.},
}
@article {pmid38538229,
year = {2024},
author = {Andrade, K and Houmani, N and Guieysse, T and Razafimahatratra, S and Klarsfeld, A and Dreyfus, G and Dubois, B and Vialatte, F and Medani, T},
title = {Self-Modulation of Gamma-Band Synchronization through EEG-Neurofeedback Training in the Elderly.},
journal = {Journal of integrative neuroscience},
volume = {23},
number = {3},
pages = {67},
doi = {10.31083/j.jin2303067},
pmid = {38538229},
issn = {0219-6352},
support = {//URGOTECH/ ; },
mesh = {Humans ; Aged ; *Neurofeedback/methods ; Electroencephalography ; Brain/physiology ; Cognition/physiology ; *Alzheimer Disease/therapy ; Biomarkers ; },
abstract = {BACKGROUND: Electroencephalography (EEG) stands as a pivotal non-invasive tool, capturing brain signals with millisecond precision and enabling real-time monitoring of individuals' mental states. Using appropriate biomarkers extracted from these EEG signals and presenting them back in a neurofeedback loop offers a unique avenue for promoting neural compensation mechanisms. This approach empowers individuals to skillfully modulate their brain activity. Recent years have witnessed the identification of neural biomarkers associated with aging, underscoring the potential of neuromodulation to regulate brain activity in the elderly.<br><br>METHODS AND OBJECTIVES: Within the framework of an EEG-based brain-computer interface, this study focused on three neural biomarkers that may be disturbed in the aging brain: Peak Alpha Frequency, Gamma-band synchronization, and Theta/Beta ratio. The primary objectives were twofold: (1) to investigate whether elderly individuals with subjective memory complaints can learn to modulate their brain activity, through EEG-neurofeedback training, in a rigorously designed double-blind, placebo-controlled study; and (2) to explore potential cognitive enhancements resulting from this neuromodulation.<br><br>RESULTS: A significant self-modulation of the Gamma-band synchronization biomarker, critical for numerous higher cognitive functions and known to decline with age, and even more in Alzheimer's disease (AD), was exclusively observed in the group undergoing EEG-neurofeedback training. This effect starkly contrasted with subjects receiving sham feedback. While this neuromodulation did not directly impact cognitive abilities, as assessed by pre- versus post-training neuropsychological tests, the high baseline cognitive performance of all subjects at study entry likely contributed to this result.<br><br>CONCLUSION: The findings of this double-blind study align with a key criterion for successful neuromodulation, highlighting the significant potential of Gamma-band synchronization in such a process. This important outcome encourages further exploration of EEG-neurofeedback on this specific neural biomarker as a promising intervention to counter the cognitive decline that often accompanies brain aging and, eventually, to modify the progression of AD.},
}
@article {pmid38538143,
year = {2024},
author = {Wei 魏赣超, G and Tajik Mansouri زینب تاجیک منصوری, Z and Wang 王晓婧, X and Stevenson, IH},
title = {Calibrating Bayesian Decoders of Neural Spiking Activity.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {18},
pages = {},
pmid = {38538143},
issn = {1529-2401},
mesh = {Animals ; *Bayes Theorem ; Male ; Rats ; *Neurons/physiology ; *Action Potentials/physiology ; Calibration ; Mice ; Motor Cortex/physiology ; Macaca mulatta ; Hippocampus/physiology ; Photic Stimulation/methods ; Models, Neurological ; },
abstract = {Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.},
}
@article {pmid38538142,
year = {2024},
author = {Lee, WH and Karpowicz, BM and Pandarinath, C and Rouse, AG},
title = {Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {20},
pages = {},
pmid = {38538142},
issn = {1529-2401},
support = {DP2 NS127291/NS/NINDS NIH HHS/United States ; R00 NS101127/NS/NINDS NIH HHS/United States ; K99 NS101127/NS/NINDS NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Male ; *Macaca mulatta ; *Psychomotor Performance/physiology ; *Motor Cortex/physiology ; *Neurons/physiology ; Movement/physiology ; Deep Learning ; Action Potentials/physiology ; },
abstract = {Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements. AutoLFADS, an autoencoder-based deep-learning model, was applied to provide a clearer picture of neurons' activity on individual corrective movements across sessions. Decoding of reach velocity generalized poorly from initial to corrective submovements. Unlike initial movements, it was challenging to predict the velocity of corrective movements using traditional linear methods in a single, global neural space. We identified several locations in the neural space where corrective submovements originated after the initial reaches, signifying firing rates different than the baseline before initial movements. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating the population firing rates at the initiation of correction improved performance, highlighting the diverse neural features of corrective movements. In summary, we show neural differences between initial and corrective submovements and how the neural activity encodes specific combinations of velocity and position. These findings are inconsistent with assumptions that neural correlations with kinematic features are global and independent, emphasizing that traditional methods often fall short in describing these diverse neural processes for online corrective movements.},
}
@article {pmid38538056,
year = {2024},
author = {Brannigan, J and McClanahan, A and Hui, F and Fargen, KM and Pinter, N and Oxley, TJ},
title = {Superior cortical venous anatomy for endovascular device implantation: a systematic review.},
journal = {Journal of neurointerventional surgery},
volume = {16},
number = {12},
pages = {1353-1359},
doi = {10.1136/jnis-2023-021434},
pmid = {38538056},
issn = {1759-8486},
mesh = {Humans ; *Cerebral Veins/anatomy &amp; histology/surgery ; *Endovascular Procedures/methods ; Cerebral Cortex/anatomy &amp; histology/blood supply/surgery ; Brain-Computer Interfaces ; Electrodes, Implanted ; Superior Sagittal Sinus/anatomy &amp; histology/surgery ; },
abstract = {Endovascular electrode arrays provide a minimally invasive approach to access intracranial structures for neural recording and stimulation. These arrays are currently used as brain-computer interfaces (BCIs) and are deployed within the superior sagittal sinus (SSS), although cortical vein implantation could improve the quality and quantity of recorded signals. However, the anatomy of the superior cortical veins is heterogenous and poorly characterised. MEDLINE and Embase databases were systematically searched from inception to December 15, 2023 for studies describing the anatomy of the superior cortical veins. A total of 28 studies were included: 19 cross-sectional imaging studies, six cadaveric studies, one intraoperative anatomical study and one review. There was substantial variability in cortical vein diameter, length, confluence angle, and location relative to the underlying cortex. The mean number of SSS branches ranged from 11 to 45. The vein of Trolard was most often reported as the largest superior cortical vein, with a mean diameter ranging from 2.1 mm to 3.3 mm. The mean vein of Trolard was identified posterior to the central sulcus. One study found a significant age-related variability in cortical vein diameter and another identified myoendothelial sphincters at the base of the cortical veins. Cortical vein anatomical data are limited and inconsistent. The vein of Trolard is the largest tributary vein of the SSS; however, its relation to the underlying cortex is variable. Variability in cortical vein anatomy may necessitate individualized pre-procedural planning of training and neural decoding in endovascular BCI. Future focus on the relation to the underlying cortex, sulcal vessels, and vessel wall anatomy is required.},
}
@article {pmid38537269,
year = {2024},
author = {Soldado-Magraner, J and Antonietti, A and French, J and Higgins, N and Young, MJ and Larrivee, D and Monteleone, R},
title = {Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad3852},
pmid = {38537269},
issn = {1741-2552},
support = {F32 MH123001/MH/NIMH NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Paralysis ; *Neurosciences ; },
abstract = {Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.},
}
@article {pmid38537268,
year = {2024},
author = {Suematsu, N and Vazquez, AL and Kozai, TDY},
title = {Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38537268},
issn = {1741-2552},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R01 NS117515/NS/NINDS NIH HHS/United States ; R01 NS119410/NS/NINDS NIH HHS/United States ; },
mesh = {Mice ; Animals ; *Calcium ; Photic Stimulation ; Oxyhemoglobins ; Neurons/physiology ; *Visual Cortex ; Electric Stimulation/methods ; },
abstract = {Objective. Intracortical microstimulation (ICMS) can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site.Approach. Different microstimulation frequencies were investigatedin vivoon Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging.Main results. Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies.Significance. These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by ICMS and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.},
}
@article {pmid38536681,
year = {2024},
author = {Liang, G and Cao, D and Wang, J and Zhang, Z and Wu, Y},
title = {EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1535-1545},
doi = {10.1109/TNSRE.2024.3382226},
pmid = {38536681},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Imagination ; },
abstract = {The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine interface paradigm. However, due to the non-stationarity and individual differences among subjects in EEG signals, the decoding accuracy is limited, affecting the application of the MI-BCI. In this paper, we propose the EISATC-Fusion model for MI EEG decoding, consisting of inception block, multi-head self-attention (MSA), temporal convolutional network (TCN), and layer fusion. Specifically, we design a DS Inception block to extract multi-scale frequency band information. And design a new cnnCosMSA module based on CNN and cos attention to solve the attention collapse and improve the interpretability of the model. The TCN module is improved by the depthwise separable convolution to reduces the parameters of the model. The layer fusion consists of feature fusion and decision fusion, fully utilizing the features output by the model and enhances the robustness of the model. We improve the two-stage training strategy for model training. Early stopping is used to prevent model overfitting, and the accuracy and loss of the validation set are used as indicators for early stopping. The proposed model achieves within-subject classification accuracies of 84.57% and 87.58% on BCI Competition IV Datasets 2a and 2b, respectively. And the model achieves cross-subject classification accuracies of 67.42% and 71.23% (by transfer learning) when training the model with two sessions and one session of Dataset 2a, respectively. The interpretability of the model is demonstrated through weight visualization method.},
}
@article {pmid38533741,
year = {2024},
author = {Siu, C and Aoude, M and Andersen, J and Adams, KD},
title = {The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {19},
number = {7},
pages = {2641-2650},
doi = {10.1080/17483107.2024.2333884},
pmid = {38533741},
issn = {1748-3115},
mesh = {Humans ; *Children with Disabilities/rehabilitation ; *Brain-Computer Interfaces ; *Play and Playthings ; Male ; Child ; Female ; *Parents/psychology ; Adolescent ; Qualitative Research ; Interviews as Topic ; },
abstract = {Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children's play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children's play can be interpreted as passive or active depending on one's definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents' views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.Implications for RehabilitationAssistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factorsFuture research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselvesBrain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.},
}
@article {pmid38533483,
year = {2024},
author = {Wang, G and Tang, J and Yin, Z and Yu, S and Shi, X and Hao, X and Zhao, Z and Pan, Y and Li, S},
title = {The neurocomputational signature of decision-making for unfair offers in females under acute psychological stress.},
journal = {Neurobiology of stress},
volume = {30},
number = {},
pages = {100622},
pmid = {38533483},
issn = {2352-2895},
abstract = {Stress is a crucial factor affecting social decision-making. However, its impacts on the behavioral and neural processes of females' unfairness decision-making remain unclear. Combining computational modeling and functional near-infrared spectroscopy (fNIRS), this study attempted to illuminate the neurocomputational signature of unfairness decision-making in females. We also considered the effect of trait stress coping styles. Forty-four healthy young females (20.98 ± 2.89 years) were randomly assigned to the stress group (n = 21) and the control group (n = 23). Acute psychosocial stress was induced by the Trier Social Stress Test (TSST), and participants then completed the one-shot ultimatum game (UG) as responders. The results showed that acute psychosocial stress reduced the adaptability to fairness and lead to more random decision-making responses. Moreover, in the stress group, a high level of negative coping style predicted more deterministic decision. fNIRS results showed that stress led to an increase of oxy-hemoglobin (HbO) peak in the right temporoparietal junction (rTPJ), while decreased the activation of left middle temporal gyrus (lMTG) when presented the moderately unfair (MU) offers. This signified more involvement of the mentalization and the inhibition of moral processing. Moreover, individuals with higher negative coping scores showed more deterministic decision behaviors under stress. Taken together, our study emphasizes the role of acute psychosocial stress in affecting females' unfairness decision-making mechanisms in social interactions, and provides evidences for the "tend and befriend" pattern based on a cognitive neuroscience perspec.},
}
@article {pmid38532987,
year = {2024},
author = {Hu, S and Ng, CH and Mann, JJ},
title = {Editorial: Linking treatment target identification to biological mechanisms underlying mood disorders - Volume II.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1385955},
doi = {10.3389/fpsyt.2024.1385955},
pmid = {38532987},
issn = {1664-0640},
}
@article {pmid38532608,
year = {2024},
author = {Assi, DS and Huang, H and Karthikeyan, V and Theja, VCS and de Souza, MM and Roy, VAL},
title = {Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {27},
pages = {e2306254},
doi = {10.1002/adma.202306254},
pmid = {38532608},
issn = {1521-4095},
support = {EP/X016846/1//EPSRC/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Quantum Theory ; Synapses/physiology ; Electroencephalography ; Brain/physiology ; Evoked Potentials/physiology ; Electronics ; },
abstract = {Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.},
}
@article {pmid38531360,
year = {2024},
author = {Losey, DM and Hennig, JA and Oby, ER and Golub, MD and Sadtler, PT and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Yu, BM and Chase, SM},
title = {Learning leaves a memory trace in motor cortex.},
journal = {Current biology : CB},
volume = {34},
number = {7},
pages = {1519-1531.e4},
pmid = {38531360},
issn = {1879-0445},
support = {R01 EB026953/EB/NIBIB NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 HD090125/HD/NICHD NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; R01 NS120579/NS/NINDS NIH HHS/United States ; R00 MH121533/MH/NIMH NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; },
mesh = {*Motor Cortex ; *Brain-Computer Interfaces ; Learning ; Brain ; Brain Mapping ; Electroencephalography ; },
abstract = {How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.},
}
@article {pmid38531054,
year = {2024},
author = {Kesarwani, M and Kincaid, Z and Azhar, M and Azam, M},
title = {Enhanced MAPK signaling induced by CSF3R mutants confers dependence to DUSP1 for leukemic transformation.},
journal = {Blood advances},
volume = {8},
number = {11},
pages = {2765-2776},
pmid = {38531054},
issn = {2473-9537},
support = {R01 CA211594/CA/NCI NIH HHS/United States ; R01 CA250516/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; Humans ; Mice ; Apoptosis ; *Cell Transformation, Neoplastic/genetics/metabolism ; *Dual Specificity Phosphatase 1/metabolism/genetics ; Gene Expression Regulation, Leukemic ; Leukemia/metabolism/genetics ; *MAP Kinase Signaling System/drug effects ; Mutation ; *Receptors, Colony-Stimulating Factor/genetics/metabolism ; },
abstract = {Elevated MAPK and the JAK-STAT signaling play pivotal roles in the pathogenesis of chronic neutrophilic leukemia and atypical chronic myeloid leukemia. Although inhibitors targeting these pathways effectively suppress the diseases, they fall short in providing enduring remission, largely attributed to the cytostatic nature of these drugs. Even combinations of these drugs are ineffective in achieving sustained remission. Enhanced MAPK signaling besides promoting proliferation and survival triggers a proapoptotic response. Consequently, malignancies reliant on elevated MAPK signaling use MAPK feedback regulators to intricately modulate the signaling output, prioritizing proliferation and survival while dampening the apoptotic stimuli. Herein, we demonstrate that enhanced MAPK signaling in granulocyte colony-stimulating factor 3 receptor (CSF3R)-driven leukemia upregulates the expression of dual specificity phosphatase 1 (DUSP1) to suppress the apoptotic stimuli crucial for leukemogenesis. Consequently, genetic deletion of Dusp1 in mice conferred synthetic lethality to CSF3R-induced leukemia. Mechanistically, DUSP1 depletion in leukemic context causes activation of JNK1/2 that results in induced expression of BIM and P53 while suppressing the expression of BCL2 that selectively triggers apoptotic response in leukemic cells. Pharmacological inhibition of DUSP1 by BCI (a DUSP1 inhibitor) alone lacked antileukemic activity due to ERK1/2 rebound caused by off-target inhibition of DUSP6. Consequently, a combination of BCI with a MEK inhibitor successfully cured CSF3R-induced leukemia in a preclinical mouse model. Our findings underscore the pivotal role of DUSP1 in leukemic transformation driven by enhanced MAPK signaling and advocate for the development of a selective DUSP1 inhibitor for curative treatment outcomes.},
}
@article {pmid38530872,
year = {2024},
author = {Meng, L and He, L and Chen, M and Huang, Y},
title = {The compensation effect of competence frustration and its behavioral manifestations.},
journal = {PsyCh journal},
volume = {13},
number = {4},
pages = {654-662},
pmid = {38530872},
issn = {2046-0260},
support = {2021ZGL004//Shanghai Philosophy and Social Science Planning Project/ ; 72271165//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Frustration ; Male ; Female ; Adult ; Motivation ; Resilience, Psychological ; Personal Autonomy ; },
abstract = {The frustration of competence, one of the three basic psychological needs proposed by self-determination theory, has been widely demonstrated to negatively influence one's motivation and well-being in both work and life. However, research on the recovery mechanism of competence is still in the nascent stage. In this study, a two-stage behavioral experiment was conducted to examine the restoration of competence and the potential moderating role of resilience. Results showed that individuals who were asked to recall experience of competence frustration performed better on subsequent tasks, manifesting their behavioral efforts of competence restoration. However, resilience does not play a significant moderating role in competence restoration. Through convergent behavioral evidence, findings of this study demonstrate the compensation effect of competence frustration.},
}
@article {pmid38529662,
year = {2024},
author = {Lein, A and Baumgartner, WD and Riss, D and Gstöttner, W and Landegger, LD and Liu, DT and Thurner, T and Vyskocil, E and Brkic, FF},
title = {Early Results With the New Active Bone-Conduction Hearing Implant: A Systematic Review and Meta-Analysis.},
journal = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery},
volume = {170},
number = {6},
pages = {1630-1647},
doi = {10.1002/ohn.728},
pmid = {38529662},
issn = {1097-6817},
mesh = {Humans ; *Bone Conduction ; Hearing Loss, Conductive/surgery ; Hearing Aids ; Treatment Outcome ; Child ; Prosthesis Design ; },
abstract = {OBJECTIVE: The bone conduction implant (BCI) 602 is a new transcutaneous BCI with smaller dimensions. However, limited patient numbers restrict the statistical power and generalizability of the current studies. The present systematic review and meta-analysis summarize early audiological and medical outcomes of adult and pediatric patients implanted with the BCI 602 due to mixed or conductive hearing loss.<br><br>DATA SOURCE: Following the Preferred Reporting items for Systematic Reviews and Meta-analyses guidelines, 108 studies were reviewed, and 6 (5.6%) were included in the meta-analysis.<br><br>REVIEW METHOD: The data on study and patient characteristics, surgical outcomes, and audiological test results were extracted from each article. Meta-analysis employed the fixed-effect and random-effects models to analyze the mean differences (MDs) between pre- and postoperative performances.<br><br>RESULTS: In total, 116 patients were evaluated, including 64 (55%) adult and 52 (45%) pediatric patients. No intraoperative adverse events were reported, while postoperative complications were reported in 2&#xa0;(3.1%) adult and 2 (3.8%) pediatric patients. Studies consistently showed significant improvements in audiological outcomes, quality of life, and sound localization in the aided condition. In the meta-analysis, we observed a significant difference in the unaided compared to the aided condition in sound field thresholds (n&#x2009;=&#x2009;112; MD, -27.05&#x2009;dB; P&#x2009;<&#x2009;0.01), signal-to-noise ratio (n&#x2009;=&#x2009;96; MD, -6.35&#x2009;dB; P&#x2009;<&#x2009;0.01), and word recognition scores (n&#x2009;=&#x2009;96; MD, 68.89%; P&#x2009;<&#x2009;0.01).<br><br>CONCLUSION: The implantation of the BCI 602 was associated with minimal surgical complications and excellent audiological outcomes for both the pediatric and the adult cohort. Therefore, our analysis indicates a high level of safety and reliability. Further research should focus on direct comparisons with other BCIs and long-term functional outcomes.},
}
@article {pmid38529269,
year = {2024},
author = {Kothe, C and Hanada, G and Mullen, S and Mullen, T},
title = {On decoding of rapid motor imagery in a diverse population using a high-density NIRS device.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1355534},
pmid = {38529269},
issn = {2673-6195},
abstract = {INTRODUCTION: Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis.<br><br>METHODS: BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study.<br><br>RESULTS: We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone.<br><br>DISCUSSION: Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.},
}
@article {pmid38527459,
year = {2024},
author = {Li, G and Lan, L and He, T and Tang, Z and Liu, S and Li, Y and Huang, Z and Guan, Y and Li, X and Zhang, Y and Lai, HY},
title = {Comprehensive Assessment of Ischemic Stroke in Nonhuman Primates: Neuroimaging, Behavioral, and Serum Proteomic Analysis.},
journal = {ACS chemical neuroscience},
volume = {15},
number = {7},
pages = {1548-1559},
pmid = {38527459},
issn = {1948-7193},
mesh = {Animals ; Humans ; *Ischemic Stroke/diagnostic imaging ; Diffusion Tensor Imaging/methods ; Proteomics ; Tandem Mass Spectrometry ; *Stroke/diagnostic imaging ; Neuroimaging ; Primates ; Profilins ; },
abstract = {Ischemic strokes, prevalence and impactful, underscore the necessity of advanced research models closely resembling human physiology. Our study utilizes nonhuman primates (NHPs) to provide a detailed exploration of ischemic stroke, integrating neuroimaging data, behavioral outcomes, and serum proteomics to elucidate the complex interplay of factors involved in stroke pathophysiology. We observed a consistent pattern in infarct volume, peaking at 1-month postmiddle cerebral artery occlusion (MCAO) and then stabilized. This pattern was strongly correlated to notable changes in motor function and working memory performance. Using diffusion tensor imaging (DTI), we detected significant alterations in fractional anisotropy (FA) and mean diffusivity (MD) values, signaling microstructural changes in the brain. These alterations closely correlated with the neurological and cognitive deficits that we observed, highlighting the sensitivity of DTI metrics in stroke assessment. Behaviorally, the monkeys exhibited a reliance on their unaffected limb for compensatory movements, a common response to stroke impairment. This adaptation, along with consistent DTI findings, suggests a significant impact of stroke on motor function and spatial perception. Proteomic analysis through MS/MS functional enrichment identified two distinct groups of proteins with significant changes post-MCAO. Notably, MMP9, THBS1, MB, PFN1, and YWHAZ were identified as potential biomarkers and therapeutic targets for ischemic stroke. Our results underscore the complex nature of stroke and advocate for an integrated approach, combining neuroimaging, behavioral studies, and proteomics, for advancing our understanding and treatment of this condition.},
}
@article {pmid38526885,
year = {2024},
author = {Qin, Y and Zhang, W and Tao, X},
title = {TBEEG: A Two-Branch Manifold Domain Enhanced Transformer Algorithm for Learning EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1466-1476},
doi = {10.1109/TNSRE.2024.3380595},
pmid = {38526885},
issn = {1558-0210},
mesh = {Humans ; *Brain ; Algorithms ; Wavelet Analysis ; Electroencephalography ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {The electroencephalogram-based (EEG) brain-computer interface (BCI) has garnered significant attention in recent research. However, the practicality of EEG remains constrained by the lack of efficient EEG decoding technology. The challenge lies in effectively translating intricate EEG into meaningful, generalizable information. EEG signal decoding primarily relies on either time domain or frequency domain information. There lacks a method capable of simultaneously and effectively extracting both time and frequency domain features, as well as efficiently fuse these features. Addressing these limitations, a two-branch Manifold Domain enhanced transformer algorithm is designed to holistically capture EEG's spatio-temporal information. Our method projects the time-domain information of EEG signals into the Riemannian spaces to fully decode the time dependence of EEG signals. Using wavelet transform, the time domain information is converted into frequency domain information, and the spatial information contained in the frequency domain information of EEG signal is mined through the spectrogram. The effectiveness of the proposed TBEEG algorithm is validated on BCIC-IV-2a dataset and MAMEM-SSVEP-II datasets.},
}
@article {pmid38523306,
year = {2024},
author = {Zolfaghari, S and Yousefi Rezaii, T and Meshgini, S},
title = {Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.},
journal = {Clinical EEG and neuroscience},
volume = {55},
number = {4},
pages = {486-495},
doi = {10.1177/15500594241234836},
pmid = {38523306},
issn = {2169-5202},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; *Movement/physiology ; Adult ; Male ; Brain/physiology ; Signal Processing, Computer-Assisted ; Female ; Young Adult ; Algorithms ; Wrist/physiology ; },
abstract = {Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.},
}
@article {pmid38523252,
year = {2024},
author = {Deng, L and Wei, W and Qiao, C and Yin, Y and Li, X and Yu, H and Jian, L and Ma, X and Zhao, L and Wang, Q and Deng, W and Guo, W and Li, T},
title = {Dynamic aberrances of substantia nigra-relevant coactivation patterns in first-episode treatment-naïve patients with schizophrenia.},
journal = {Psychological medicine},
volume = {54},
number = {10},
pages = {2527-2537},
doi = {10.1017/S0033291724000655},
pmid = {38523252},
issn = {1469-8978},
mesh = {Humans ; *Schizophrenia/physiopathology/diagnostic imaging ; *Substantia Nigra/physiopathology/diagnostic imaging ; Female ; Male ; *Magnetic Resonance Imaging ; *Connectome ; Adult ; Young Adult ; Case-Control Studies ; },
abstract = {BACKGROUND: Although dopaminergic disturbances are well-known in schizophrenia, the understanding of dopamine-related brain dynamics remains limited. This study investigates the dynamic coactivation patterns (CAPs) associated with the substantia nigra (SN), a key dopaminergic nucleus, in first-episode treatment-naïve patients with schizophrenia (FES).<br><br>METHODS: Resting-state fMRI data were collected from 84 FES and 94 healthy controls (HCs). Frame-wise clustering was implemented to generate CAPs related to SN activation or deactivation. Connectome features of each CAP were derived using an edge-centric method. The occurrence for each CAP and the balance ratio for antagonistic CAPs were calculated and compared between two groups, and correlations between temporal dynamic metrics and symptom burdens were explored.<br><br>RESULTS: Functional reconfigurations in CAPs exhibited significant differences between the activation and deactivation states of SN. During SN activation, FES more frequently recruited a CAP characterized by activated default network, language network, control network, and the caudate, compared to HCs (F = 8.54, FDR-p = 0.030). Moreover, FES displayed a tilted balance towards a CAP featuring SN-coactivation with the control network, caudate, and thalamus, as opposed to its antagonistic CAP (F = 7.48, FDR-p = 0.030). During SN deactivation, FES exhibited increased recruitment of a CAP with activated visual and dorsal attention networks but decreased recruitment of its opposing CAP (F = 6.58, FDR-p = 0.034).<br><br>CONCLUSION: Our results suggest that neuroregulatory dysfunction in dopaminergic pathways involving SN potentially mediates aberrant time-varying functional reorganizations in schizophrenia. This finding enriches the dopamine hypothesis of schizophrenia from the perspective of brain dynamics.},
}
@article {pmid38520132,
year = {2024},
author = {Jeong, HJ and Lee, H and Choo, MS and Cho, SY and Jeong, SJ and Oh, SJ},
title = {Effect of detrusor underactivity on surgical outcomes of holmium laser enucleation of the prostate.},
journal = {BJU international},
volume = {133},
number = {6},
pages = {770-777},
doi = {10.1111/bju.16346},
pmid = {38520132},
issn = {1464-410X},
mesh = {Humans ; Male ; Aged ; *Prostatic Hyperplasia/surgery/complications ; *Lasers, Solid-State/therapeutic use ; Treatment Outcome ; *Urinary Bladder, Underactive/surgery/physiopathology ; Middle Aged ; Prospective Studies ; *Prostatectomy/methods/adverse effects ; Laser Therapy/methods ; Patient Satisfaction ; },
abstract = {OBJECTIVE: To evaluate the effect of detrusor underactivity (DUA) on the postoperative outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic hyperplasia (BPH).<br><br>PATIENTS AND METHODS: Patients with BPH who underwent HoLEP between January 2018 and December 2022 were enrolled in this prospective database study. Patients were divided into DUA (bladder contractility index [BCI] <100) and non-DUA (BCI &#x2265;100) groups. Objective (maximum urinary flow rate [Qmax], post-void residual urine volume [PVR]) and subjective outcomes (International Prostate Symptom Score [IPSS], Overactive Bladder Symptom Score [OABSS], satisfaction with treatment question [STQ], overall response assessment [ORA], and willingness to undergo surgery question [WUSQ]) were compared between the two groups before surgery, and at 3 and 6&#x2009;months after HoLEP.<br><br>RESULTS: A total of 689 patients, with a mean (standard deviation [SD]) age of 69.8&#x2009;(7.1)&#x2009;years, were enrolled. The mean (SD) BCI in the non-DUA (325 [47.2%]) and DUA (364 [52.8%]) groups was 123.4&#x2009;(21.4) and 78.6&#x2009;(14.2), respectively. Both objective (Qmax and PVR) and subjective (IPSS, IPSS-quality of life, and OABSS) outcomes after surgery significantly improved in both groups. The Qmax was lower in the DUA than in the non-DUA group postoperatively. At 6&#x2009;months postoperatively, the total IPSS was higher in the DUA than in the non-DUA group. There were no significant differences in surgical complications between the two groups. Responses to the STQ, ORA, and WUSQ at 6&#x2009;months postoperatively demonstrated that the patients were satisfied with the surgery (90.5% in the DUA group; 95.2% in the non-DUA group), their symptoms improved with surgery (95.9% in the DUA group; 100.0% in the non-DUA group), and they were willing to undergo surgery again (95.9% in the DUA group; 97.9% in the non-DUA group). There were no significant differences in the responses to the STQ and WUSQ between the two groups.<br><br>CONCLUSION: Our midterm results demonstrated that patients with BPH and DUA showed minimal differences in clinical outcomes after HoLEP compared to those without DUA. The overall satisfaction was high in the DUA group.},
}
@article {pmid38520009,
year = {2024},
author = {Zou, P and Liu, C and Zhang, Y and Wei, C and Liu, X and Xu, S and Ling, Q and Chen, Z and Du, G and Yuan, X},
title = {Transurethral surgical treatment for benign prostatic hyperplasia with detrusor underactivity: a systematic review and meta-analysis.},
journal = {Systematic reviews},
volume = {13},
number = {1},
pages = {93},
pmid = {38520009},
issn = {2046-4053},
mesh = {Humans ; *Prostatic Hyperplasia/surgery/complications ; Male ; *Transurethral Resection of Prostate/methods ; *Urinary Bladder, Underactive/surgery ; *Quality of Life ; Treatment Outcome ; Urodynamics ; },
abstract = {BACKGROUND: The efficacy of surgical treatment for benign prostatic hyperplasia (BPH) patients with detrusor underactivity (DU) remains controversial.<br><br>METHODS: To summarize relevant evidence, three databases (PubMed, Embase, and Web of Science) were searched from database inception to May 1, 2023. Transurethral surgical treatment modalities include transurethral prostatectomy (TURP), photoselective vaporization of the prostate (PVP), and transurethral incision of the prostate (TUIP). The efficacy of the transurethral surgical treatment was assessed according to maximal flow rate on uroflowmetry (Qmax), International Prostate Symptom Score (IPSS), postvoid residual (PVR), quality of life (QoL), voided volume, bladder contractility index (BCI) and maximal detrusor pressure at maximal flow rate (PdetQmax). Pooled mean differences (MDs) were used as summary statistics for comparison. The quality of enrolled studies was evaluated by using the Newcastle-Ottawa Scale. Sensitivity analysis and funnel plots were applied to assess possible biases.<br><br>RESULTS: In this study, 10 studies with a total of 1142 patients enrolled. In BPH patients with DU, within half a year, significant improvements in Qmax (pooled MD, 4.79; 95% CI, 2.43-7.16; P&#x2009;<&#x2009;0.05), IPSS(pooled MD,&#x2009;-&#x2009;14.29; 95%CI,&#x2009;-&#x2009;16.67-11.90; P&#x2009;<&#x2009;0.05), QoL (pooled MD,&#x2009;-&#x2009;1.57; 95% CI,&#x2009;-&#x2009;2.37-0.78; P&#x2009;<&#x2009;0.05), voided volume (pooled MD, 62.19; 95% CI, 17.91-106.48; P&#x2009;<&#x2009;0.05), BCI (pooled MD, 23.59; 95% CI, 8.15-39.04; P&#x2009;<&#x2009;0.05), and PdetQmax (pooled MD, 28.62; 95% CI, 6.72-50.52; P&#x2009;<&#x2009;0.05) were observed after surgery. In addition, after more than 1&#xa0;year, significant improvements were observed in Qmax (pooled MD, 6.75; 95%CI, 4.35-9.15; P&#x2009;<&#x2009;0.05), IPSS(pooled MD,&#x2009;-&#x2009;13.76; 95%CI,&#x2009;-&#x2009;15.17-12.35; P&#x2009;<&#x2009;0.05), PVR (pooled MD,&#x2009;-&#x2009;179.78; 95%CI,&#x2009;-&#x2009;185.12-174.44; P&#x2009;<&#x2009;0.05), QoL (pooled MD,&#x2009;-&#x2009;2.61; 95%CI,&#x2009;-&#x2009;3.12-2.09; P&#x2009;<&#x2009;0.05), and PdetQmax (pooled MD, 27.94; 95%CI, 11.70-44.19; P&#x2009;<&#x2009;0.05). Compared with DU patients who did not receive surgery, DU patients who received surgery showed better improvement in PVR (pooled MD, 137.00; 95%CI, 6.90-267.10; P&#x2009;<&#x2009;0.05) and PdetQmax (pooled MD,&#x2009;-&#x2009;8.00; 95%CI,&#x2009;-&#x2009;14.68-1.32; P&#x2009;<&#x2009;0.05).<br><br>CONCLUSIONS: Our meta-analysis results showed that transurethral surgery can improve the symptoms of BPH patients with DU. Surgery also showed advantages over pharmacological treatment for BPH patients with DU.<br><br>PROSPERO CRD42023415188.},
}
@article {pmid38518778,
year = {2024},
author = {Li, XY and Zhang, SY and Hong, YZ and Chen, ZG and Long, Y and Yuan, DH and Zhao, JJ and Tang, SS and Wang, H and Hong, H},
title = {TGR5-mediated lateral hypothalamus-dCA3-dorsolateral septum circuit regulates depressive-like behavior in male mice.},
journal = {Neuron},
volume = {112},
number = {11},
pages = {1795-1814.e10},
doi = {10.1016/j.neuron.2024.02.019},
pmid = {38518778},
issn = {1097-4199},
mesh = {Animals ; Male ; Mice ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; *Depression/metabolism ; Disease Models, Animal ; *GABAergic Neurons/metabolism/physiology ; *Hypothalamic Area, Lateral/metabolism ; Mice, Inbred C57BL ; Neural Pathways/metabolism ; *Receptors, G-Protein-Coupled/metabolism/genetics ; Septal Nuclei/metabolism ; Social Defeat ; Stress, Psychological/metabolism ; },
abstract = {Although bile acids play a notable role in depression, the pathological significance of the bile acid TGR5 membrane-type receptor in this disorder remains elusive. Using depression models of chronic social defeat stress and chronic restraint stress in male mice, we found that TGR5 in the lateral hypothalamic area (LHA) predominantly decreased in GABAergic neurons, the excitability of which increased in depressive-like mice. Upregulation of TGR5 or inhibition of GABAergic excitability in LHA markedly alleviated depressive-like behavior, whereas down-regulation of TGR5 or enhancement of GABAergic excitability facilitated stress-induced depressive-like behavior. TGR5 also bidirectionally regulated excitability of LHA GABAergic neurons via extracellular regulated protein kinases-dependent Kv4.2 channels. Notably, LHA GABAergic neurons specifically innervated dorsal CA3 (dCA3) CaMKIIα neurons for mediation of depressive-like behavior. LHA GABAergic TGR5 exerted antidepressant-like effects by disinhibiting dCA3 CaMKIIα neurons projecting to the dorsolateral septum (DLS). These findings advance our understanding of TGR5 and the LHA[GABA]→dCA3[CaMKIIα]→DLS[GABA] circuit for the development of potential therapeutic strategies in depression.},
}
@article {pmid38518426,
year = {2024},
author = {Caillet, AH and Phillips, ATM and Modenese, L and Farina, D},
title = {NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo.},
journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology},
volume = {76},
number = {},
pages = {102873},
doi = {10.1016/j.jelekin.2024.102873},
pmid = {38518426},
issn = {1873-5711},
mesh = {Humans ; *Muscle, Skeletal/physiology/innervation ; *Electromyography/methods ; *Motor Neurons/physiology ; Muscle Contraction/physiology ; Action Potentials/physiology ; Computer Simulation ; Models, Neurological ; },
abstract = {The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.},
}
@article {pmid38518365,
year = {2024},
author = {McNamara, IN and Wellman, SM and Li, L and Eles, JR and Savya, S and Sohal, HS and Angle, MR and Kozai, TDY},
title = {Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad36e1},
pmid = {38518365},
issn = {1741-2552},
mesh = {Rats ; Animals ; Electrodes, Implanted ; *Blood-Brain Barrier ; Microelectrodes ; *Inflammation ; },
abstract = {Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the 'bed-of-nails' effect and tissue dimpling.Approach. Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.Main results.Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.Significance.These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.},
}
@article {pmid38517720,
year = {2024},
author = {Han, Y and Ke, Y and Wang, R and Wang, T and Ming, D},
title = {Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1407-1415},
doi = {10.1109/TNSRE.2024.3380635},
pmid = {38517720},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Fatigue ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.},
}
@article {pmid38514730,
year = {2024},
author = {Myszor, IT and Lapka, K and Hermannsson, K and Rekha, RS and Bergman, P and Gudmundsson, GH},
title = {Bile acid metabolites enhance expression of cathelicidin antimicrobial peptide in airway epithelium through activation of the TGR5-ERK1/2 pathway.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {6750},
pmid = {38514730},
issn = {2045-2322},
mesh = {Humans ; *Bile Acids and Salts/metabolism ; *Cathelicidins/metabolism ; MAP Kinase Signaling System ; Receptors, G-Protein-Coupled/metabolism ; Epithelium/metabolism ; Lithocholic Acid/pharmacology/metabolism ; },
abstract = {Signals for the maintenance of epithelial homeostasis are provided in part by commensal bacteria metabolites, that promote tissue homeostasis in the gut and remote organs as microbiota metabolites enter the bloodstream. In our study, we investigated the effects of bile acid metabolites, 3-oxolithocholic acid (3-oxoLCA), alloisolithocholic acid (AILCA) and isolithocholic acid (ILCA) produced from lithocholic acid (LCA) by microbiota, on the regulation of innate immune responses connected to the expression of host defense peptide cathelicidin in lung epithelial cells. The bile acid metabolites enhanced expression of cathelicidin at low concentrations in human bronchial epithelial cell line BCi-NS1.1 and primary bronchial/tracheal cells (HBEpC), indicating physiological relevance for modulation of innate immunity in airway epithelium by bile acid metabolites. Our study concentrated on deciphering signaling pathways regulating expression of human cathelicidin, revealing that LCA and 3-oxoLCA activate the surface G protein-coupled bile acid receptor 1 (TGR5, Takeda-G-protein-receptor-5)-extracellular signal-regulated kinase (ERK1/2) cascade, rather than the nuclear receptors, aryl hydrocarbon receptor, farnesoid X receptor and vitamin D3 receptor in bronchial epithelium. Overall, our study provides new insights into the modulation of innate immune responses by microbiota bile acid metabolites in the gut-lung axis, highlighting the differences in epithelial responses between different tissues.},
}
@article {pmid38514500,
year = {2024},
author = {Chen, S and Xi, X and Wang, T and Li, H and Wang, M and Li, L and Lü, Z},
title = {Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {8},
pages = {2305-2318},
pmid = {38514500},
issn = {1741-0444},
support = {No. 61971169//National Natural Science Foundation of China/ ; No.2021C03031//Zhejiang Provincial Key Research and Development Program of China/ ; NO. LQ21H180005//Zhejiang Provincial Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Algorithms ; *Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Brain-Computer Interfaces ; Imagination/physiology ; Motion ; },
abstract = {The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm's ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm's effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%.},
}
@article {pmid38513290,
year = {2024},
author = {Zheng, L and Dong, Y and Tian, S and Pei, W and Gao, X and Wang, Y},
title = {A calibration-free c-VEP based BCI employing narrow-band random sequences.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad3679},
pmid = {38513290},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Calibration ; Algorithms ; Photic Stimulation ; },
abstract = {Objective.Code-modulated visual evoked potential (c-VEP) based brain-computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences.Approach.For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15-25 Hz (NBRS-15) and 8-16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8-15.8 Hz.Main results.The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS-15 frequency band achieved an average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 102.1 ± 57.59 bits/min in a simulation of a 1000-target BCI system.Significance.This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR.},
}
@article {pmid38513289,
year = {2024},
author = {Sadras, N and Pesaran, B and Shanechi, MM},
title = {Event detection and classification from multimodal time series with application to neural data.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38513289},
issn = {1741-2552},
support = {R01 MH123770/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; Neurons/physiology ; Normal Distribution ; Animals ; Models, Neurological ; Action Potentials/physiology ; Computer Simulation ; Humans ; },
abstract = {The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.},
}
@article {pmid38513274,
year = {2024},
author = {Srimadumathi, V and Ramasubba Reddy, M},
title = {Classification of Motor Imagery EEG signals using high resolution time-frequency representations and convolutional neural network.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ad3647},
pmid = {38513274},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Neural Networks, Computer ; Algorithms ; },
abstract = {A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.},
}
@article {pmid38512735,
year = {2024},
author = {Zhang, X and He, L and Gao, Q and Jiang, N},
title = {Performance of the Action Observation-Based Brain-Computer Interface in Stroke Patients and Gaze Metrics Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1370-1379},
doi = {10.1109/TNSRE.2024.3379995},
pmid = {38512735},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Stroke ; Eye Movements ; Electroencephalography/methods ; },
abstract = {Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were recruited. Four AO stimuli were designed, each presenting a decomposed action to complete the reach-and-grasp task. EEG data and eye movement data were collected. Eye movement data was utilized to analyze the reasons for individual differences in BCI performance. Task discriminative component analysis was utilized to perform online target detection. The results showed that the designed AO-based BCI could simultaneously induce steady state motion visual evoked potential (SSMVEP) from the occipital region and sensory motor rhythm from the sensorimotor region in stroke patients. The average online detection accuracy among the four AO stimuli reached 67% within 3 s in the non-hemineglect group, while the accuracy only reached 35% in the hemineglect group. Gaze metrics showed that the average total duration of fixations during the stimulus phase in the hemineglect group was only 1.31 s ± 0.532 s which was significantly lower than that in the non-hemineglect group. The results indicated that hemineglect patients have difficulty gazing at the AO stimulus, resulting in poor detection performance and weak desynchronization in the sensorimotor region. Furthermore, the degree of neglect is inversely proportional to the target detection accuracy in hemineglect stroke patients. In addition, the gaze metrics associated with cognitive load were significantly correlated with the accuracy in non-hemineglect patients. It indicated the cognitive load may affect the AO-based BCI. The current study will expedite the clinical application of AO-based BCI.},
}
@article {pmid38510513,
year = {2024},
author = {Liu, Y and Luo, Y and Zhang, N and Zhang, X and Liu, S},
title = {A scientometric review of the growing trends in transcranial alternating current stimulation (tACS).},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1362593},
pmid = {38510513},
issn = {1662-5161},
abstract = {OBJECTIVE: The aim of the current study was to provide a comprehensive picture of tACS-related research in the last decade through a bibliometric approach in order to systematically analyze the current status and cutting-edge trends in this field.<br><br>METHODS: Articles and review articles related to tACS from 2013 to 2022 were searched on the Web of Science platform. A bibliometric analysis of authors, journals, countries, institutions, references, and keywords was performed using CiteSpace (6.2.R2), VOSviewer (1.6.19), Scimago Graphica (1.0.30), and Bibliometrix (4.2.2).<br><br>RESULTS: A total of 602 papers were included. There was an overall increase in annual relevant publications in the last decade. The most contributing author was Christoph S. Herrmann. Brain Stimulation was the most prolific journal. The most prolific countries and institutions were Germany and Harvard University, respectively.<br><br>CONCLUSION: The findings reveal the development prospects and future directions of tACS and provide valuable references for researchers in the field. In recent years, the keywords "gamma," "transcranial direct current simulation," and "Alzheimer's disease" that have erupted, as well as many references cited in the outbreak, have provided certain clues for the mining of research prefaces. This will act as a guide for future researchers in determining the path of tACS research.},
}
@article {pmid38510464,
year = {2024},
author = {Tian, Z and Wu, Z and Ying, S},
title = {Editorial: Brain functional analysis and brain-like intelligence.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1383481},
pmid = {38510464},
issn = {1662-4548},
}
@article {pmid38510138,
year = {2024},
author = {Huang, C and Shi, N and Miao, Y and Chen, X and Wang, Y and Gao, X},
title = {Visual tracking brain-computer interface.},
journal = {iScience},
volume = {27},
number = {4},
pages = {109376},
pmid = {38510138},
issn = {2589-0042},
abstract = {Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.},
}
@article {pmid38510045,
year = {2024},
author = {Mainul, EA and Hossain, MF},
title = {A metamaterial unit-cell based patch radiator for brain-machine interface technology.},
journal = {Heliyon},
volume = {10},
number = {6},
pages = {e27775},
pmid = {38510045},
issn = {2405-8440},
abstract = {This paper presents a novel approach to the design of a brain implantable antenna tailored for brain-machine interface (BMI) technology. The design is based on a U-shaped unit-cell metamaterial (MTM), introducing innovative features to enhance performance and address specific challenges associated with BMI applications. The motivation behind the use of the unit-cell structure is to elongate the electric path within the antenna patch, diverging from a reliance on the electrical properties of the MTM. Consequently, the unit cell is connected to an inset-fed transmission line and shorted to the ground. This configuration serves the dual purpose of reducing the size of the antenna and enabling resonance at the 2.442 GHz band within a seven-layer brain phantom. The antenna is designed using a FR-4 substrate (εr = 4.3 and tan δ = 0.025) of 1.5 mm thickness, and it is coated with a biocompatible polyamide material (εr = 4.3 and tan δ = 0.004) of 0.05 mm thickness. The proposed antenna achieves a compact dimension of 20 × 20 × 1.6 mm3 (0.338 × 0.338 × 0.027 λg3) and demonstrates a high bandwidth of 974 MHz with its gain of -14.6 dBi in the 2.442 GHz band. It also exhibits a matched impedance of 49.41-j1.32 Ω in the implantable condition, corresponding to a 50 Ω source impedance. In comparison to a selection of relevant research works, the proposed antenna has a low specific absorption rate (SAR) of 218 W/kg and 68 W/kg at 1g and 10g brain tissue standards, respectively. An antenna prototype has been fabricated and measured for return loss in both free space and in-vivo conditions using sheep's brain. The measurement results are found to be in close agreement with the simulation results for both conditions, showing the practical applicability of the proposed antenna for BMI applications.},
}
@article {pmid38509590,
year = {2024},
author = {Liu, XY and Mu, JJ and Han, JG and Pang, MJ and Zhang, K and Zhai, WQ and Su, N and Ni, GJ and Guo, ZG and Ming, D},
title = {Heart-brain axis: low blood pressure during off-pump CABG surgery is associated with postoperative heart failure.},
journal = {Military Medical Research},
volume = {11},
number = {1},
pages = {18},
pmid = {38509590},
issn = {2054-9369},
support = {2021YFF1200602//Key Technologies Research and Development Program/ ; 0401260011//National Science Fund for Excellent Overseas Scholars/ ; c02022088//National Defense Science and Technology Innovation Fund of the Chinese Academy of Sciences/ ; 20JCZDJC00810//Tianjin science and technology program/ ; },
mesh = {Humans ; Coronary Artery Bypass/adverse effects ; Treatment Outcome ; Brain ; *Hypotension ; *Heart Failure/complications ; },
}
@article {pmid38509350,
year = {2024},
author = {Liu, X and Hu, B and Si, Y and Wang, Q},
title = {The role of eye movement signals in non-invasive brain-computer interface typing system.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {7},
pages = {1981-1990},
pmid = {38509350},
issn = {1741-0444},
mesh = {*Brain-Computer Interfaces ; Humans ; *Eye Movements/physiology ; Electroencephalography/methods ; Electrooculography ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.},
}
@article {pmid38508294,
year = {2024},
author = {Yang, H and Wu, H and Kong, L and Luo, W and Xie, Q and Pan, J and Quan, W and Hu, L and Li, D and Wu, X and Liang, H and Qin, P},
title = {Precise detection of awareness in disorders of consciousness using deep learning framework.},
journal = {NeuroImage},
volume = {290},
number = {},
pages = {120580},
doi = {10.1016/j.neuroimage.2024.120580},
pmid = {38508294},
issn = {1095-9572},
mesh = {Humans ; *Consciousness Disorders ; *Deep Learning ; Brain/diagnostic imaging ; Persistent Vegetative State ; Unconsciousness ; Consciousness ; },
abstract = {Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.},
}
@article {pmid38505100,
year = {2024},
author = {Reichert, C and Sweeney-Reed, CM and Hinrichs, H and Dürschmid, S},
title = {A toolbox for decoding BCI commands based on event-related potentials.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1358809},
pmid = {38505100},
issn = {1662-5161},
abstract = {Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.},
}
@article {pmid38505099,
year = {2024},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {EEG-based finger movement classification with intrinsic time-scale decomposition.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1362135},
pmid = {38505099},
issn = {1662-5161},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.<br><br>METHODS: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.<br><br>RESULTS: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.<br><br>DISCUSSION: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).},
}
@article {pmid38502615,
year = {2024},
author = {Peng, B and Bi, L and Wang, Z and Feleke, AG and Fei, W},
title = {Robust Decoding of Upper-Limb Movement Direction Under Cognitive Distraction With Invariant Patterns in Embedding Manifold.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1344-1354},
doi = {10.1109/TNSRE.2024.3379451},
pmid = {38502615},
issn = {1558-0210},
mesh = {Humans ; *Upper Extremity ; Movement ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Cognition ; },
abstract = {Motor brain-computer interfaces (BCIs) have gained growing research interest in motor rehabilitation, restoration, and prostheses control. Decoding upper-limb movement direction with noninvasive BCIs has been extensively investigated. However, few of them address the intervention of cognitive distraction that impairs decoding performance in practice. In this study, we propose a novel decoding model with invariant patterns in embedding manifold on a mixed dataset pooled from electroencephalograph (EEG) signals under different attentional states. We reconstruct an embedding low-dimensional manifold that intrinsically characterizes movements of the upper limb and transfer patterns of neural activities decomposed from brain functional connectivity (FC) to the manifold subspace to further preserve movement-related information. Experimental results showed that the proposed decoding model had higher robustness on the mixed dataset of attentive and distracted states compared to the baseline method. Our research provides insights into modeling a uniform underlying mechanism of movement-related EEG signals and can help enhance the practicability of BCI systems under real-world situations.},
}
@article {pmid38502151,
year = {2024},
author = {Zou, Q and Duan, H and Fang, S and Sheng, W and Li, X and Stoika, R and Finiuk, N and Panchuk, R and Liu, K and Wang, L},
title = {Fabrication of yeast β-glucan/sodium alginate/γ-polyglutamic acid composite particles for hemostasis and wound healing.},
journal = {Biomaterials science},
volume = {12},
number = {9},
pages = {2394-2407},
doi = {10.1039/d3bm02068a},
pmid = {38502151},
issn = {2047-4849},
mesh = {Animals ; *Wound Healing/drug effects ; *Alginates/chemistry/pharmacology ; *Polyglutamic Acid/chemistry/pharmacology/*analogs &amp; derivatives ; *beta-Glucans/chemistry/pharmacology ; Mice ; *Hemostasis/drug effects ; *Saccharomyces cerevisiae ; Cell Line ; Hemostatics/pharmacology/chemistry/administration &amp; dosage ; Biocompatible Materials/chemistry/pharmacology ; Male ; },
abstract = {Particles with a porous structure can lead to quick hemostasis and provide a good matrix for cell proliferation during wound healing. Recently, many particle-based wound healing materials have been clinically applied. However, these products show good hemostatic ability but with poor wound healing ability. To solve this problem, this study fabricated APGG composite particles using yeast β-glucan (obtained from Saccharomyces cerevisiae), sodium alginate, and γ-polyglutamic acid as the starting materials. The structure of yeast β-glucan was modified with many carboxymethyl groups to obtain carboxymethylated β-glucan, which could coordinate with Ca[2+] ions to form a crosslinked structure. A morphology study indicated that the APGG particles showed an irregular spheroidal structure with a low density (<0.1 g cm[-3]) and high porosity (>40%). An in vitro study revealed that the particles exhibited a low BCI value, low hemolysis ratio, and good cytocompatibility against L929 cells. The APGG particles could quickly stop bleeding in a mouse liver injury model and exhibited better hemostatic ability than the commercially available product Celox. Furthermore, the APGG particles could accelerate the healing of non-infected wounds, and the expression levels of CD31, α-SMA, and VEGF related to angiogenesis were significantly enhanced.},
}
@article {pmid38500488,
year = {2024},
author = {Sieghartsleitner, S and Sebastián-Romagosa, M and Cho, W and Grünwald, J and Ortner, R and Scharinger, J and Kamada, K and Guger, C},
title = {Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1346607},
pmid = {38500488},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.<br><br>METHODS: Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.<br><br>RESULTS: Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.<br><br>DISCUSSION: The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.},
}
@article {pmid38499913,
year = {2024},
author = {Shiina, T and Yunoki, T and Tachino, H and Hayashi, A},
title = {Comparative study of surgical outcomes regarding tear meniscus area and high-order aberrations between two different interventional methods for primary acquired nasolacrimal duct obstruction.},
journal = {Japanese journal of ophthalmology},
volume = {68},
number = {2},
pages = {139-145},
pmid = {38499913},
issn = {1613-2246},
mesh = {Male ; Humans ; Female ; Middle Aged ; Aged ; Aged, 80 and over ; *Lacrimal Duct Obstruction/diagnosis/therapy ; *Nasolacrimal Duct/surgery ; Retrospective Studies ; *Dacryocystorhinostomy/methods ; Treatment Outcome ; *Meniscus ; },
abstract = {PURPOSE: To compare endonasal dacryocystorhinostomy (EN-DCR) with sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) by evaluating tear meniscus area (TMA) and total high-order aberrations (HOAs) for primary acquired nasolacrimal duct obstruction (PANDO).<br><br>METHOD: We retrospectively reviewed 56 eyes of 42 patients (7 men, 35 women; age, 72.7&#xb1;13.1 years) who underwent EN-DCR or SG-BCI for PANDO in Toyama University Hospital from February 2020 to June 2022. In the EN-DCR and SG-BCI groups, we measured the patency of the lacrimal passage, preoperative and postoperative TMA, and HOAs of the central 4 mm of the cornea using optical coherence tomography (AS-OCT), six months postoperatively.<br><br>RESULTS: There was a positive correlation between preoperative TMA and preoperative HOAs in all cases. Postoperative patency of lacrimal passage was 100% in the EN-DCR and 80.8% in the SG-BCI group. There was a significant difference in the number of passages between the two groups (p = 0.01). Preoperative TMA and HOAs showed a significant postoperative decrease in both groups (EN-DCR group: p<0.01, p<0.01, SG-BCI group: p<0.01, p=0.03, respectively). We then calculated the rate of change of preoperative and postoperative TMA and HOAs and compared them between the two groups. The rate of change was significantly higher in the EN-DCR group than that in the SG-BCI group (TMA, p=0.03; HOAs, p=0.02).<br><br>CONCLUSION: Although both EN-DCR and SG-BCI are effective for PANDO, our results suggest that EN-DCR is more effective in improving TMA and HOAs.},
}
@article {pmid38499709,
year = {2024},
author = {Schreiner, L and Jordan, M and Sieghartsleitner, S and Kapeller, C and Pretl, H and Kamada, K and Asman, P and Ince, NF and Miller, KJ and Guger, C},
title = {Mapping of the central sulcus using non-invasive ultra-high-density brain recordings.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {6527},
pmid = {38499709},
issn = {2045-2322},
support = {RHUMBO-H2020-MSCAITN-2018-813234//European Commission project/ ; },
mesh = {*Brain/diagnostic imaging/physiology ; *Brain Mapping/methods ; Head ; Electroencephalography/methods ; Electrodes, Implanted ; Electrodes ; },
abstract = {Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.},
}
@article {pmid38498746,
year = {2024},
author = {He, Y and Ven, SV and Liaw, HP and Shi, C and Russo, P and Gourdouparis, M and Konijnenburg, M and Traferro, S and Timmermans, M and Lopez, CM and Harpe, P and Cantatore, E and Chicca, E and Liu, YH},
title = {An Event-Based Neural Compressive Telemetry With &gt;11× Loss-Less Data Reduction for High-Bandwidth Intracortical Brain Computer Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {5},
pages = {1100-1111},
pmid = {38498746},
issn = {1940-9990},
support = {101001448/ERC_/European Research Council/International ; },
mesh = {*Brain-Computer Interfaces ; *Telemetry/instrumentation/methods ; Signal Processing, Computer-Assisted/instrumentation ; Humans ; Animals ; Data Compression/methods ; Electroencephalography/methods/instrumentation ; Algorithms ; },
abstract = {Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 µW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 µV.},
}
@article {pmid38496885,
year = {2024},
author = {Klomchitcharoen, S and Wechakarn, P and Tangwattanasirikun, T and Smerwong, N and Netrapathompornkij, P and Chatmeeboon, T and Nangsue, N and Thitasirivit, V and Kaweewongsunthorn, K and Piyanopharoj, S and Phumiprathet, P and Wongsawat, Y},
title = {High-altitude balloon platform for studying the biological response of living organisms exposed to near-space environments.},
journal = {Heliyon},
volume = {10},
number = {6},
pages = {e27406},
pmid = {38496885},
issn = {2405-8440},
abstract = {The intangible desire to explore the mysteries of the universe has driven numerous advancements for humanity for centuries. Extraterrestrial journeys are becoming more realistic as a result of human curiosity and endeavors. Over the years, space biology research has played a significant role in understanding the hazardous effects of the space environment on human health during long-term space travel. The inevitable consequence of a space voyage is space ionizing radiation, which has deadly aftereffects on the human body. The paramount objective of this study is to provide a robust platform for performing biological experiments within the Earth's stratosphere by utilizing high-altitude balloons. This platform allows the use of a biological payload to simulate spaceflight missions within the unique properties of space that cannot be replicated in terrestrial facilities. This paper describes the feasibility and demonstration of a biological balloon mission suitable for students and scientists to perform space biology experiments within the boundary of the stratosphere. In this study, a high-altitude balloon was launched into the upper atmosphere (∼29 km altitude), where living microorganisms were exposed to a hazardous combination of UV irradiation, ultralow pressure and cold shock. The balloon carried the budding yeast Saccharomyces cerevisiae to investigate microbial survival potential under extreme conditions. The results indicated a notable reduction in biosample mortality two orders of magnitude (2-log) after exposure to 164.9 kJ m[-2] UV. Postflight experiments have shown strong evidence that the effect of UV irradiation on living organisms is stronger than that of other extreme conditions.},
}
@article {pmid38496552,
year = {2024},
author = {Pun, TK and Khoshnevis, M and Hosman, T and Wilson, GH and Kapitonava, A and Kamdar, F and Henderson, JM and Simeral, JD and Vargas-Irwin, CE and Harrison, MT and Hochberg, LR},
title = {Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.02.29.582733},
pmid = {38496552},
issn = {2692-8205},
support = {I01 RX003803/RX/RRD VA/United States ; I01 RX002827/RX/RRD VA/United States ; T32 MH115895/MH/NIMH NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.},
}
@article {pmid38496527,
year = {2024},
author = {Capadona, J and Hoeferlin, G and Grabinski, S and Druschel, L and Duncan, J and Burkhart, G and Weagraff, G and Lee, A and Hong, C and Bambroo, M and Olivares, H and Bajwa, T and Memberg, W and Sweet, J and Hamedani, HA and Acharya, A and Hernandez-Reynoso, A and Donskey, C and Jaskiw, G and Chan, R and Ajiboye, A and von Recum, H and Zhang, L},
title = {Bacteria Invade the Brain Following Sterile Intracortical Microelectrode Implantation.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {38496527},
issn = {2693-5015},
support = {IK6 RX003077/RX/RRD VA/United States ; R01 NS131502/NS/NINDS NIH HHS/United States ; R25 CA221718/CA/NCI NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-machine interface performance is largely affected by the neuroinflammatory responses resulting in large part from blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings strongly suggest that certain gut bacterial constituents penetrate the BBB and are resident in various brain regions of rodents and humans, both in health and disease. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could amplify dysregulation of the microbiome-gut-brain axis. Here, we report that bacteria, including those commonly found in the gut, enter the brain following intracortical microelectrode implantation in mice implanted with single-shank silicon microelectrodes. Systemic antibiotic treatment of mice implanted with microelectrodes to suppress bacteria resulted in differential expression of bacteria in the brain tissue and a reduced acute inflammatory response compared to untreated controls, correlating with temporary improvements in microelectrode recording performance. Long-term antibiotic treatment resulted in worsening microelectrode recording performance and dysregulation of neurodegenerative pathways. Fecal microbiome composition was similar between implanted mice and an implanted human, suggesting translational findings. However, a significant portion of invading bacteria was not resident in the brain or gut. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.},
}
@article {pmid38496403,
year = {2024},
author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and Lam, JL and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA},
title = {Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.03.01.583000},
pmid = {38496403},
issn = {2692-8205},
abstract = {UNLABELLED: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.<br><br>TEASER: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.},
}
@article {pmid38492454,
year = {2024},
author = {Borra, D and Filippini, M and Ursino, M and Fattori, P and Magosso, E},
title = {Convolutional neural networks reveal properties of reach-to-grasp encoding in posterior parietal cortex.},
journal = {Computers in biology and medicine},
volume = {172},
number = {},
pages = {108188},
doi = {10.1016/j.compbiomed.2024.108188},
pmid = {38492454},
issn = {1879-0534},
mesh = {*Artificial Intelligence ; Reproducibility of Results ; *Psychomotor Performance/physiology ; Parietal Lobe/physiology ; Neural Networks, Computer ; Hand Strength/physiology ; Movement/physiology ; },
abstract = {Deep neural networks (DNNs) are widely adopted to decode motor states from both non-invasively and invasively recorded neural signals, e.g., for realizing brain-computer interfaces. However, the neurophysiological interpretation of how DNNs make the decision based on the input neural activity is limitedly addressed, especially when applied to invasively recorded data. This reduces decoder reliability and transparency, and prevents the exploitation of decoders to better comprehend motor neural encoding. Here, we adopted an explainable artificial intelligence approach - based on a convolutional neural network and an explanation technique - to reveal spatial and temporal neural properties of reach-to-grasping from single-neuron recordings of the posterior parietal area V6A. The network was able to accurately decode 5 different grip types, and the explanation technique automatically identified the cells and temporal samples that most influenced the network prediction. Grip encoding in V6A neurons already started at movement preparation, peaking during movement execution. A difference was found within V6A: dorsal V6A neurons progressively encoded more for increasingly advanced grips, while ventral V6A neurons for increasingly rudimentary grips, with both subareas following a linear trend between the amount of grip encoding and the level of grip skills. By revealing the elements of the neural activity most relevant for each grip with no a priori assumptions, our approach supports and advances current knowledge about reach-to-grasp encoding in V6A, and it may represent a general tool able to investigate neural correlates of motor or cognitive tasks (e.g., attention and memory tasks) from single-neuron recordings.},
}
@article {pmid38491170,
year = {2024},
author = {Fan, L and Liu, J and Hu, W and Chen, Z and Lan, J and Zhang, T and Zhang, Y and Wu, X and Zhong, Z and Zhang, D and Zhang, J and Qin, R and Chen, H and Zong, Y and Zhang, J and Chen, B and Jiang, J and Cheng, J and Zhou, J and Gao, Z and Liu, Z and Chai, Y and Fan, J and Wu, P and Chen, Y and Zhu, Y and Wang, K and Yuan, Y and Huang, P and Zhang, Y and Feng, H and Song, K and Zeng, X and Zhu, W and Hu, X and Yin, W and Chen, W and Wang, J},
title = {Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.},
journal = {Cell research},
volume = {34},
number = {6},
pages = {407-427},
pmid = {38491170},
issn = {1748-7838},
support = {2017ZX10203205//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Humans ; *Atherosclerosis/immunology/drug therapy/pathology/therapy ; *T-Lymphocytes/immunology/metabolism ; *Programmed Cell Death 1 Receptor/metabolism/antagonists &amp; inhibitors ; Inflammation/pathology ; Antibodies, Monoclonal/therapeutic use/pharmacology ; Female ; Male ; Retrospective Studies ; Receptors, IgG/metabolism ; Plaque, Atherosclerotic/pathology/immunology/therapy/drug therapy ; Middle Aged ; },
abstract = {Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1[+] T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.},
}
@article {pmid38487836,
year = {2024},
author = {Li, S and Xu, C and Hu, S and Lai, J},
title = {Efficacy and tolerability of FDA-approved atypical antipsychotics for the treatment of bipolar depression: a systematic review and network meta-analysis.},
journal = {European psychiatry : the journal of the Association of European Psychiatrists},
volume = {67},
number = {1},
pages = {e29},
pmid = {38487836},
issn = {1778-3585},
mesh = {Humans ; *Bipolar Disorder/drug therapy ; *Antipsychotic Agents/therapeutic use/adverse effects ; *Network Meta-Analysis ; United States ; United States Food and Drug Administration ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Quetiapine Fumarate/therapeutic use/adverse effects ; Olanzapine/therapeutic use/adverse effects ; },
abstract = {We employed a Bayesian network meta-analysis for comparison of the efficacy and tolerability of US Food and Drug Administration (FDA)-approved atypical antipsychotics (AAPs) for the treatment of bipolar patients with depressive episodes. Sixteen randomized controlled trials with 7234 patients treated by one of the five AAPs (cariprazine, lumateperone, lurasidone, olanzapine, and quetiapine) were included. For the response rate (defined as an improvement of ≥50% from baseline on the Montgomery-Åsberg Depression Rating Scale [MADRS]), all AAPs were more efficacious than placebo. For the remission rate (defined as the endpoint of MADRS ≤12 or ≤ 10), cariprazine, lurasidone, olanzapine, and quetiapine had higher remission rates than placebo. In terms of tolerability, olanzapine was unexpectedly associated with lower odds of all-cause discontinuation in comparison with placebo, whereas quetiapine was associated with higher odds of discontinuation due to adverse events than placebo. Compared with placebo, lumateperone, olanzapine, and quetiapine showed higher odds of somnolence. Lumateperone had a lower rate of ≥ weight gain of 7% than placebo and other treatments. Olanzapine was associated with a significant increase from baseline in total cholesterol and triglycerides than placebo. These findings inform individualized prescriptions of AAPs for treating bipolar depression in clinical practice.},
}
@article {pmid38487198,
year = {2023},
author = {Zhang, J and Li, J and Huang, Z and Huang, D and Yu, H and Li, Z},
title = {Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.},
journal = {Health data science},
volume = {3},
number = {},
pages = {0096},
pmid = {38487198},
issn = {2765-8783},
abstract = {Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.},
}
@article {pmid38486966,
year = {2024},
author = {Wu, H and Cai, C and Ming, W and Chen, W and Zhu, Z and Feng, C and Jiang, H and Zheng, Z and Sawan, M and Wang, T and Zhu, J},
title = {Speech decoding using cortical and subcortical electrophysiological signals.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1345308},
pmid = {38486966},
issn = {1662-4548},
abstract = {INTRODUCTION: Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces.<br><br>METHODS: In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150&#x2009;Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable.<br><br>RESULTS: Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone.<br><br>DISCUSSION: This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.},
}
@article {pmid38486923,
year = {2024},
author = {Juan, JV and Martínez, R and Iáñez, E and Ortiz, M and Tornero, J and Azorín, JM},
title = {Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.},
journal = {Frontiers in neuroinformatics},
volume = {18},
number = {},
pages = {1345425},
pmid = {38486923},
issn = {1662-5196},
abstract = {INTRODUCTION: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.<br><br>METHODS: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.<br><br>RESULTS AND DISCUSSION: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.},
}
@article {pmid38485742,
year = {2024},
author = {Gao, Y and Liu, T and Hong, T and Fang, Y and Jiang, W and Zhang, X},
title = {Subwavelength dielectric waveguide for efficient travelling-wave magnetic resonance imaging.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2298},
pmid = {38485742},
issn = {2041-1723},
abstract = {Magnetic resonance imaging (MRI) has diverse applications in physics, biology, and medicine. Uniform excitation of nuclei spins through circular-polarized transverse magnetic component of electromagnetic field is vital for obtaining unbiased tissue contrasts. However, achieving this in the electrically large human body poses a significant challenge, especially at ultra-high fields (UHF) with increased working frequencies (≥297 MHz). Canonical volume resonators struggle to meet this challenge, while radiative excitation methods like travelling-wave (TW) show promise but often suffer from inadequate excitation efficiency. Here, we introduce a new technique using a subwavelength dielectric waveguide insert that enhances both efficiency and homogeneity at 7 T. Through TE11-to-TM11 mode conversion, power focusing, wave impedance matching, and phase velocity matching, we achieved a 114% improvement in TW efficiency and mitigated the center-brightening effect. This fundamental advancement in TW MRI through effective wave manipulation could promote the electromagnetic design of UHF MRI systems.},
}
@article {pmid38485630,
year = {2024},
author = {Marchi, A and Guex, R and Denis, M and El Youssef, N and Pizzo, F and Bénar, CG and Bartolomei, F},
title = {Neurofeedback and epilepsy: Renaissance of an old self-regulation method?.},
journal = {Revue neurologique},
volume = {180},
number = {4},
pages = {314-325},
doi = {10.1016/j.neurol.2024.02.386},
pmid = {38485630},
issn = {0035-3787},
mesh = {Humans ; *Neurofeedback/methods ; *Epilepsy/therapy/psychology ; *Brain-Computer Interfaces/trends ; Neuronal Plasticity/physiology ; Self-Control ; Brain/physiology/physiopathology ; },
abstract = {Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.},
}
@article {pmid38481577,
year = {2024},
author = {Cai, XL and Ye, Q and Ni, K and Zhu, L and Zhang, Q and Yin, M and Zhang, Z and Wei, W and Preece, DA and Li, BM},
title = {Chinese version of the Perth Alexithymia Questionnaire: psychometric properties and clinical applications.},
journal = {General psychiatry},
volume = {37},
number = {2},
pages = {e101281},
pmid = {38481577},
issn = {2517-729X},
abstract = {BACKGROUND: The alexithymia trait is of high clinical interest. The Perth Alexithymia Questionnaire (PAQ) was recently developed to enable detailed facet-level and valence-specific assessments of alexithymia.<br><br>AIMS: In this paper, we introduce the first Chinese version of the PAQ and examine its psychometric properties and clinical applications.<br><br>METHODS: In Study 1, the PAQ was administered to 990 Chinese participants. We examined its factor structure, internal consistency, test-retest reliability, as well as convergent, concurrent and discriminant validity. In Study 2, four groups, including a major depressive disorder (MDD) group (n=50), a matched healthy control group for MDD (n=50), a subclinical depression group (n=50) and a matched healthy control group for subclinical depression (n=50), were recruited. Group comparisons were conducted to assess the clinical relevance of the PAQ.<br><br>RESULTS: In Study 1, the intended five-factor structure of the PAQ was found to fit the data well. The PAQ showed good internal consistency and test-retest reliability, as well as good convergent, concurrent and discriminant validity. In Study 2, the PAQ was able to successfully distinguish the MDD group and the subclinical depression group from their matched healthy controls.<br><br>CONCLUSIONS: The Chinese version of the PAQ is a valid and reliable instrument for comprehensively assessing alexithymia in the general population and adults with clinical/subclinical depression.},
}
@article {pmid38480743,
year = {2024},
author = {Chen, PC and Tsai, TP and Liao, YC and Liao, YC and Cheng, HW and Weng, YH and Lin, CM and Kao, CY and Tai, CC and Ruan, JW},
title = {Intestinal dual-specificity phosphatase 6 regulates the cold-induced gut microbiota remodeling to promote white adipose browning.},
journal = {NPJ biofilms and microbiomes},
volume = {10},
number = {1},
pages = {22},
pmid = {38480743},
issn = {2055-5008},
support = {107-2320-B-006 -020 -MY2//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2320-B-006 -051 -MY3//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; 108-2320-B-006 -051 -MY3//Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan)/ ; },
mesh = {Animals ; Humans ; Mice ; Adiposity ; Cold Temperature ; Dual-Specificity Phosphatases/metabolism ; *Gastrointestinal Microbiome/physiology ; Obesity ; },
abstract = {Gut microbiota rearrangement induced by cold temperature is crucial for browning in murine white adipose tissue. This study provides evidence that DUSP6, a host factor, plays a critical role in regulating cold-induced gut microbiota rearrangement. When exposed to cold, the downregulation of intestinal DUSP6 increased the capacity of gut microbiota to produce ursodeoxycholic acid (UDCA). The DUSP6-UDCA axis is essential for driving Lachnospiraceae expansion in the cold microbiota. In mice experiencing cold-room temperature (CR) transitions, prolonged DUSP6 inhibition via the DUSP6 inhibitor (E/Z)-BCI maintained increased cecal UDCA levels and cold-like microbiota networks. By analyzing DUSP6-regulated microbiota dynamics in cold-exposed mice, we identified Marvinbryantia as a genus whose abundance increased in response to cold exposure. When inoculated with human-origin Marvinbryantia formatexigens, germ-free recipient mice exhibited significantly enhanced browning phenotypes in white adipose tissue. Moreover, M. formatexigens secreted the methylated amino acid Nε-methyl-L-lysine, an enriched cecal metabolite in Dusp6 knockout mice that reduces adiposity and ameliorates nonalcoholic steatohepatitis in mice. Our work revealed that host-microbiota coadaptation to cold environments is essential for regulating the browning-promoting gut microbiome.},
}
@article {pmid38479013,
year = {2024},
author = {Alsuradi, H and Khattak, A and Fakhry, A and Eid, M},
title = {Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad33b3},
pmid = {38479013},
issn = {1741-2552},
mesh = {Humans ; *Imagination ; Imagery, Psychotherapy ; Fingers ; *Brain-Computer Interfaces ; Brain ; Electroencephalography/methods ; Algorithms ; },
abstract = {Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.},
}
@article {pmid38478611,
year = {2024},
author = {Liu, W and Mei, T and Cao, Z and Li, C and Wu, Y and Wang, L and Xu, G and Chen, Y and Zhou, Y and Wang, S and Xue, Y and Yu, Y and Kong, XY and Chen, R and Tu, B and Xiao, K},
title = {Bioinspired carbon nanotube-based nanofluidic ionic transistor with ultrahigh switching capabilities for logic circuits.},
journal = {Science advances},
volume = {10},
number = {11},
pages = {eadj7867},
pmid = {38478611},
issn = {2375-2548},
abstract = {The voltage-gated ion channels, also known as ionic transistors, play substantial roles in biological systems and ion-ion selective separation. However, implementing the ultrafast switchable capabilities and polarity switching of ionic transistors remains a challenge. Here, we report a nanofluidic ionic transistor based on carbon nanotubes, which exhibits an on/off ratio of 10[4] at operational gate voltage as low as 1 V. By controlling the morphology of carbon nanotubes, both unipolar and ambipolar ionic transistors are realized, and their on/off ratio can be further improved by introducing an Al2O3 dielectric layer. Meanwhile, this ionic transistor enables the polarity switching between p-type and n-type by controlled surface properties of carbon nanotubes. The implementation of constructing ionic circuits based on ionic transistors is demonstrated, which enables the creation of NOT, NAND, and NOR logic gates. The ionic transistors are expected to have profound implications for low-energy consumption computing devices and brain-machine interfacing.},
}
@article {pmid38476872,
year = {2024},
author = {Lv, Z and Liu, X and Dai, M and Jin, X and Huang, X and Chen, Z},
title = {Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1361486},
pmid = {38476872},
issn = {1662-4548},
abstract = {INTRODUCTION: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.<br><br>METHODS: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.<br><br>RESULTS: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.<br><br>DISCUSSION: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).},
}
@article {pmid38475214,
year = {2024},
author = {Huang, J and Li, G and Zhang, Q and Yu, Q and Li, T},
title = {Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {5},
pages = {},
pmid = {38475214},
issn = {1424-8220},
support = {2021-I2M-1-042, 2022-I2M-C&amp;T-A-005, and 2022-I2M-C&amp;T-B-012//Chinese Academy of Medical Science health innovation project/ ; 20JCJQIC00230//Tianjin Outstanding Youth Fund Project/ ; },
mesh = {Humans ; Imagination ; *Brain-Computer Interfaces ; Imagery, Psychotherapy/methods ; *Stroke ; Electroencephalography/methods ; Algorithms ; },
abstract = {Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.},
}
@article {pmid38472417,
year = {2024},
author = {Jiao, Y and Zheng, Q and Qiao, D and Lang, X and Xie, L and Pan, Y},
title = {EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.},
journal = {Biological cybernetics},
volume = {118},
number = {1-2},
pages = {21-37},
pmid = {38472417},
issn = {1432-0770},
support = {Grant No. KQTD20200820113106007//The Shenzhen Science and Technology Program/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Multivariate Analysis ; Brain/physiology ; Computer Simulation ; },
abstract = {Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.},
}
@article {pmid38471478,
year = {2024},
author = {Sintas, JI and Bean, RH and Zhang, R and Long, TE},
title = {Nonisocyanate Polyurethane Segmented Copolymers from Bis-Carbonylimidazolides.},
journal = {Macromolecular rapid communications},
volume = {45},
number = {11},
pages = {e2400057},
doi = {10.1002/marc.202400057},
pmid = {38471478},
issn = {1521-3927},
support = {E3P-2132183//National Science Foundation/ ; //Eyring Materials Center and ASU Biodesign Institute/ ; },
mesh = {*Polyurethanes/chemistry/chemical synthesis ; Polymers/chemistry/chemical synthesis ; Imidazoles/chemistry ; Molecular Structure ; Polymerization ; Calorimetry, Differential Scanning ; },
abstract = {Bis-carbonylimidazolide (BCI) functionalization enables an efficient synthetic strategy to generate high molecular weight segmented nonisocyanate polyurethanes (NIPUs). Melt phase polymerization of ED-2003 Jeffamine, 4,4'-methylenebis(cyclohexylamine), and a BCI monomer that mimics a 1,4-butanediol chain extender enables polyether NIPUs that contain varying concentrations of hard segments ranging from 40 to 80 wt. %. Dynamic mechanical analysis and differential scanning calorimetry reveal thermal transitions for soft, hard, and mixed phases. Hard segment incorporations between 40 and 60 wt. % display up to three distinct phases pertaining to the poly(ethylene glycol) (PEG) soft segment Tg, melting transition, and hard segment Tg, while higher hard segment concentrations prohibit soft segment crystallization, presumably due to restricted molecular mobility from the hard segment. Atomic force microscopy allows for visualization and size determination of nanophase-separated regimes, revealing a nanoscale rod-like assembly of HS. Small-angle X-ray scattering confirms nanophase separation within the NIPU, characterizing both nanoscale amorphous domains and varying degrees of crystallinity. These NIPUs, which are synthesized with BCI monomers, display expected phase separation that is comparable to isocyanate-derived analogues. This work demonstrates nanophase separation in BCI-derived NIPUs and the feasibility of this nonisocyanate synthetic pathway for the preparation of segmented PU copolymers.},
}
@article {pmid38470574,
year = {2024},
author = {Pan, H and Wang, Y and Li, Z and Chu, X and Teng, B and Gao, H},
title = {A Complete Scheme for Multi-Character Classification Using EEG Signals From Speech Imagery.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {8},
pages = {2454-2462},
doi = {10.1109/TBME.2024.3376603},
pmid = {38470574},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Speech/physiology ; *Brain-Computer Interfaces ; Algorithms ; Wavelet Analysis ; Imagination/physiology ; Adult ; Male ; Female ; Neural Networks, Computer ; },
abstract = {Some classification studies of brain-computer interface (BCI) based on speech imagery show potential for improving communication skills in patients with amyotrophic lateral sclerosis (ALS). However, current research on speech imagery is limited in scope and primarily focuses on vowels or a few selected words. In this paper, we propose a complete research scheme for multi-character classification based on EEG signals derived from speech imagery. Firstly, we record 31 speech imagery contents, including 26 alphabets and five commonly used punctuation marks, from seven subjects using a 32-channel electroencephalogram (EEG) device. Secondly, we introduce the wavelet scattering transform (WST), which shares a structural resemblance to Convolutional Neural Networks (CNNs), for feature extraction. The WST is a knowledge-driven technique that preserves high-frequency information and maintains the deformation stability of EEG signals. To reduce the dimensionality of wavelet scattering coefficient features, we employ Kernel Principal Component Analysis (KPCA). Finally, the reduced features are fed into an Extreme Gradient Boosting (XGBoost) classifier within a multi-classification framework. The XGBoost classifier is optimized through hyperparameter tuning using grid search and 10-fold cross-validation, resulting in an average accuracy of 78.73% for the multi-character classification task. We utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) technology to visualize the low-dimensional representation of multi-character speech imagery. This visualization effectively enables us to observe the clustering of similar characters. The experimental results demonstrate the effectiveness of our proposed multi-character classification scheme. Furthermore, our classification categories and accuracy exceed those reported in existing research.},
}
@article {pmid38468815,
year = {2024},
author = {Larsen, OFP and Tresselt, WG and Lorenz, EA and Holt, T and Sandstrak, G and Hansen, TI and Su, X and Holt, A},
title = {A method for synchronized use of EEG and eye tracking in fully immersive VR.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1347974},
pmid = {38468815},
issn = {1662-5161},
abstract = {This study explores the synchronization of multimodal physiological data streams, in particular, the integration of electroencephalography (EEG) with a virtual reality (VR) headset featuring eye-tracking capabilities. A potential use case for the synchronized data streams is demonstrated by implementing a hybrid steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) speller within a fully immersive VR environment. The hardware latency analysis reveals an average offset of 36 ms between EEG and eye-tracking data streams and a mean jitter of 5.76 ms. The study further presents a proof of concept brain-computer interface (BCI) speller in VR, showcasing its potential for real-world applications. The findings highlight the feasibility of combining commercial EEG and VR technologies for neuroscientific research and open new avenues for studying brain activity in ecologically valid VR environments. Future research could focus on refining the synchronization methods and exploring applications in various contexts, such as learning and social interactions.},
}
@article {pmid38467434,
year = {2024},
author = {Huang, Q and Ellis, CL and Leo, SM and Velthuis, H and Pereira, AC and Dimitrov, M and Ponteduro, FM and Wong, NML and Daly, E and Murphy, DGM and Mahroo, OA and McAlonan, GM},
title = {Retinal GABAergic Alterations in Adults with Autism Spectrum Disorder.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {44},
number = {14},
pages = {},
pmid = {38467434},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Male ; Adult ; Female ; Humans ; *Autism Spectrum Disorder/drug therapy ; Retina ; Electroencephalography ; gamma-Aminobutyric Acid ; Electroretinography ; },
abstract = {Alterations in γ-aminobutyric acid (GABA) have been implicated in sensory differences in individuals with autism spectrum disorder (ASD). Visual signals are initially processed in the retina, and in this study, we explored the hypotheses that the GABA-dependent retinal response to light is altered in individuals with ASD. Light-adapted electroretinograms were recorded from 61 adults (38 males and 23 females; n = 22 ASD) in response to three stimulus protocols: (1) the standard white flash, (2) the standard 30 Hz flickering protocol, and (3) the photopic negative response protocol. Participants were administered an oral dose of placebo, 15 or 30 mg of arbaclofen (STX209, GABAB agonist) in a randomized, double-blind, crossover order before the test. At baseline (placebo), the a-wave amplitudes in response to single white flashes were more prominent in ASD, relative to typically developed (TD) participants. Arbaclofen was associated with a decrease in the a-wave amplitude in ASD, but an increase in TD, eliminating the group difference observed at baseline. The extent of this arbaclofen-elicited shift significantly correlated with the arbaclofen-elicited shift in cortical responses to auditory stimuli as measured by using an electroencephalogram in our prior study and with broader autistic traits measured with the autism quotient across the whole cohort. Hence, GABA-dependent differences in retinal light processing in ASD appear to be an accessible component of a wider autistic difference in the central processing of sensory information, which may be upstream of more complex autistic phenotypes.},
}
@article {pmid38464165,
year = {2024},
author = {Ping, A and Wang, J and García-Cabezas, M&#xc1; and Li, L and Zhang, J and Gothard, KM and Zhu, J and Roe, AW},
title = {Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38464165},
issn = {2692-8205},
support = {R01 MH121706/MH/NIMH NIH HHS/United States ; },
abstract = {The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal (BA), lateral (LA), and central (CeA) nuclei, respectively. Based on recent electrophysiological evidence suggesting mesoscale (millimeters-scale) nature of intra-amygdala functional organization, we have investigated the connectivity of these nuclei using Infrared Neural Stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7T functional Magnetic Resonance Imaging (INS-fMRI). Stimulation of multiple sites within amygdala of single individuals evoked 'mesoscale functional connectivity maps', allowing comparison of BA, LA and CeA connected brainwide networks. This revealed a mesoscale nature of connected sites, complementary spatial patterns of functional connectivity, and topographic relationships of nucleus-specific connections. Our data reveal a functional architecture of systematically organized brainwide networks mediating sensory, cognitive, and autonomic influences from the amygdala.},
}
@article {pmid38463871,
year = {2024},
author = {Zhang, X and Zhang, T and Jiang, Y and Zhang, W and Lu, Z and Wang, Y and Tao, Q},
title = {A novel brain-controlled prosthetic hand method integrating AR-SSVEP augmentation, asynchronous control, and machine vision assistance.},
journal = {Heliyon},
volume = {10},
number = {5},
pages = {e26521},
pmid = {38463871},
issn = {2405-8440},
abstract = {BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system.<br><br>METHODS: An asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios.<br><br>RESULTS: Under the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71&#xa0;&#xb1;&#xa0;3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66&#xa0;&#xb1;&#xa0;3.87% and 97.40&#xa0;&#xb1;&#xa0;2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system.<br><br>CONCLUSION: This research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.},
}
@article {pmid38459277,
year = {2024},
author = {da Cruz, SP and da Cruz, SP and Pereira, S and Saboya, C and Ramalho, A},
title = {Vitamin D and the Metabolic Phenotype in Weight Loss After Bariatric Surgery: A Longitudinal Study.},
journal = {Obesity surgery},
volume = {34},
number = {5},
pages = {1561-1568},
pmid = {38459277},
issn = {1708-0428},
mesh = {Adult ; Humans ; Vitamin D ; Longitudinal Studies ; *Obesity, Morbid/surgery ; Retrospective Studies ; Obesity/surgery ; Vitamins ; Body Mass Index ; Weight Loss ; *Gastric Bypass ; Phenotype ; Obesity, Abdominal ; },
abstract = {PURPOSE: To evaluate the influence of vitamin D (VD) concentrations coupled with metabolic phenotypes preoperatively and 6 months after Roux-en-Y gastric bypass (RYGB) on body variables and weight loss.<br><br>MATERIALS AND METHODS: A longitudinal, retrospective, analytical study comprising 30 adult individuals assessed preoperatively (T0) and 6&#xa0;months (T1) after undergoing Roux-en-Y gastric bypass. The participants were distributed preoperatively into metabolically healthy obese (MHO) and metabolically unhealthy obese individuals (MUHO) according to the HOMA-IR classification, as well as the adequacy and inadequacy of vitamin D concentrations in the form of 25(OH)D. All participants were assessed for weight, height, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), visceral adiposity index (VAI), body circularity index (BCI), body adiposity index (BAI), weight loss, and assessment of 25(OH)D and 1,25(OH)2D concentrations using high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). The statistical program used was SPSS version 21.<br><br>RESULTS: VD adequacy and a healthy phenotype in the preoperative period may play an important role concerning body fat distribution, as the body averages for WHtR (0.020*) and BCI (0.020*) were lower in MHO participants. In comparison, those with VD inadequacy and MUHOs had higher BAI averages (0.000*) in the postoperative period. Furthermore, it is possible that VD inadequacy before and after RYGB, even in the presence of an unhealthy phenotype, may contribute to the increase in VAI values (0.029*) after this surgery. Only those with inadequate VD and MUHOs had higher 25(OH)D concentrations. Besides, this unhealthy phenotype had a greater reduction in BMI in the early postoperative period (p&#x2009;<&#x2009;0.001).<br><br>CONCLUSION: This study suggests that VD adequacy and the presence of a healthy phenotype appear to have a positive impact on the reduction of visceral fat in the context of pre- and postoperative obesity. In addition, there was a greater weight reduction in those with VD inadequacy and in MUHO, which suggests that the volumetric dilution effect of VD and catabolism after bariatric surgery is more pronounced in this specific metabolic phenotype.},
}
@article {pmid38459194,
year = {2024},
author = {Lin, S and Fan, CY and Wang, HR and Li, XF and Zeng, JL and Lan, PX and Li, HX and Zhang, B and Hu, C and Xu, J and Luo, JH},
title = {Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice.},
journal = {Molecular psychiatry},
volume = {29},
number = {8},
pages = {2308-2320},
pmid = {38459194},
issn = {1476-5578},
support = {3192010300//National Natural Science Foundation of China (National Science Foundation of China)/ ; U22A20306//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31970902//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; Mice ; *Cell Adhesion Molecules, Neuronal/genetics/metabolism ; *Nucleus Accumbens/metabolism ; *Prefrontal Cortex/metabolism/physiopathology ; *Disease Models, Animal ; Male ; *Dopamine/metabolism ; *Nerve Tissue Proteins/genetics/metabolism ; *Membrane Proteins/genetics/metabolism ; *Cognition/physiology ; Autism Spectrum Disorder/genetics/metabolism/physiopathology ; Neurons/metabolism ; Reward ; Corpus Striatum/metabolism ; Gene Knock-In Techniques/methods ; Neural Pathways/metabolism/physiopathology ; Autistic Disorder/genetics/physiopathology/metabolism ; Mice, Inbred C57BL ; Choice Behavior/physiology ; },
abstract = {Cognitive and behavioral rigidity are observed in various psychiatric diseases, including in autism spectrum disorder (ASD). However, the underlying mechanism remains to be elucidated. In this study, we found that neuroligin-3 (NL3) R451C knockin mouse model of autism (KI mice) exhibited deficits in behavioral flexibility in choice selection tasks. Single-unit recording of medium spiny neuron (MSN) activity in the nucleus accumbens (NAc) revealed altered encoding of decision-related cue and impaired updating of choice anticipation in KI mice. Additionally, fiber photometry demonstrated significant disruption in dynamic mesolimbic dopamine (DA) signaling for reward prediction errors (RPEs), along with reduced activity in medial prefrontal cortex (mPFC) neurons projecting to the NAc in KI mice. Interestingly, NL3 re-expression in the mPFC, but not in the NAc, rescued the deficit of flexible behaviors and simultaneously restored NAc-MSN encoding, DA dynamics, and mPFC-NAc output in KI mice. Taken together, this study reveals the frontostriatal circuit dysfunction underlying cognitive inflexibility and establishes a critical role of the mPFC NL3 deficiency in this deficit in KI mice. Therefore, these findings provide new insights into the mechanisms of cognitive and behavioral inflexibility and potential intervention strategies.},
}
@article {pmid38458498,
year = {2024},
author = {Song, SS and Druschel, LN and Conard, JH and Wang, JJ and Kasthuri, NM and Ricky Chan, E and Capadona, JR},
title = {Depletion of complement factor 3 delays the neuroinflammatory response to intracortical microelectrodes.},
journal = {Brain, behavior, and immunity},
volume = {118},
number = {},
pages = {221-235},
doi = {10.1016/j.bbi.2024.03.004},
pmid = {38458498},
issn = {1090-2139},
mesh = {Animals ; Mice ; *Complement C3/genetics ; Electrodes, Implanted ; *Inflammation ; Microelectrodes ; },
abstract = {The neuroinflammatory response to intracortical microelectrodes (IMEs) used with brain-machine interfacing (BMI) applications is regarded as the primary contributor to poor chronic performance. Recent developments in high-plex gene expression technologies have allowed for an evolution in the investigation of individual proteins or genes to be able to identify specific pathways of upregulated genes that may contribute to the neuroinflammatory response. Several key pathways that are upregulated following IME implantation are involved with the complement system. The complement system is part of the innate immune system involved in recognizing and eliminating pathogens - a significant contributor to the foreign body response against biomaterials. Specifically, we have identified Complement 3 (C3) as a gene of interest because it is the intersection of several key complement pathways. In this study, we investigated the role of C3 in the IME inflammatory response by comparing the neuroinflammatory gene expression at the microelectrode implant site between C3 knockout (C3[-/-]) and wild-type (WT) mice. We have found that, like in WT mice, implantation of intracortical microelectrodes in C3[-/-] mice yields a dramatic increase in the neuroinflammatory gene expression at all post-surgery time points investigated. However, compared to WT mice, C3 depletion showed reduced expression of many neuroinflammatory genes pre-surgery and 4 weeks post-surgery. Conversely, depletion of C3 increased the expression of many neuroinflammatory genes at 8 weeks and 16 weeks post-surgery, compared to WT mice. Our results suggest that C3 depletion may be a promising therapeutic target for acute, but not chronic, relief of the neuroinflammatory response to IME implantation. Additional compensatory targets may also be required for comprehensive long-term reduction of the neuroinflammatory response for improved intracortical microelectrode performance.},
}
@article {pmid38458260,
year = {2024},
author = {Deng, H and Li, M and Li, J and Guo, M and Xu, G},
title = {A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding.},
journal = {Journal of neuroscience methods},
volume = {405},
number = {},
pages = {110108},
doi = {10.1016/j.jneumeth.2024.110108},
pmid = {38458260},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; Electrodiagnosis ; Intention ; Motion ; Movement ; Electroencephalography ; Algorithms ; },
abstract = {BACKGROUND: Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters.<br><br>NEW METHODS: This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects.<br><br>RESULTS: The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust.<br><br>CONCLUSIONS: The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.},
}
@article {pmid38458112,
year = {2024},
author = {Cao, HL and Meng, YJ and Zhang, YM and Deng, W and Guo, WJ and Li, ML and Li, T},
title = {The volume of gray matter mediates the relationship between glucolipid metabolism and neurocognition in first-episode, drug-naïve patients with schizophrenia.},
journal = {Journal of psychiatric research},
volume = {172},
number = {},
pages = {402-410},
doi = {10.1016/j.jpsychires.2024.02.055},
pmid = {38458112},
issn = {1879-1379},
mesh = {Humans ; Gray Matter/diagnostic imaging/pathology ; *Schizophrenia/diagnostic imaging/pathology ; *Insulin Resistance ; Magnetic Resonance Imaging/methods ; Cholesterol ; Triglycerides ; },
abstract = {We aimed to examine the hypotheses that glucolipid metabolism is linked to neurocognition and gray matter volume (GMV) and that GMV mediates the association of glucolipid metabolism with neurocognition in first-episode, drug-naïve (FEDN) patients with schizophrenia. Parameters of glucolipid metabolism, neurocognition, and magnetic resonance imaging were assessed in 63 patients and 31 controls. Compared to controls, patients exhibited higher levels of fasting glucose, triglyceride, and insulin resistance index, lower levels of cholesterol and high-density lipoprotein cholesterol, poorer neurocognitive functions, and decreased GMV in the bilateral insula, left middle occipital gyrus, and left postcentral gyrus. In the patient group, triglyceride levels and the insulin resistance index exhibited a negative correlation with Rapid Visual Information Processing (RVP) mean latency, a measure of attention within the Cambridge Neurocognitive Test Automated Battery (CANTAB), while showing a positive association with GMV in the right insula. The mediation model revealed that triglyceride and insulin resistance index had a significant positive indirect (mediated) influence on RVP mean latency through GMV in the right insula. Glucolipid metabolism was linked to both neurocognitive functions and GMV in FEDN patients with schizophrenia, with the effect pattern differing from that observed in chronic schizophrenia or schizophrenia comorbid with metabolic syndrome. Moreover, glucolipid metabolism might indirectly contribute to neurocognitive deficits through the mediating role of GMV in these patients.},
}
@article {pmid38457067,
year = {2024},
author = {Ma, C and Li, W and Ke, S and Lv, J and Zhou, T and Zou, L},
title = {Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {7},
pages = {2133-2144},
pmid = {38457067},
issn = {1741-0444},
support = {BE2021012-2//Jiangsu Provincial Key Research and Development Program/ ; BE2021012-5//Jiangsu Provincial Key Research and Development Program/ ; CE20225034//Changzhou Science and Technology Bureau Plan/ ; 2020E10010-04//Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province/ ; },
mesh = {Humans ; *Autism Spectrum Disorder/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiopathology ; Male ; *Neural Networks, Computer ; Female ; Adolescent ; Child ; Young Adult ; Adult ; Brain Mapping/methods ; Image Processing, Computer-Assisted/methods ; },
abstract = {Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.},
}
@article {pmid38457065,
year = {2024},
author = {Majdi, H and Azarnoosh, M and Ghoshuni, M and Sabzevari, VR},
title = {Direct lingam and visibility graphs for analyzing brain connectivity in BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {7},
pages = {2117-2132},
pmid = {38457065},
issn = {1741-0444},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Brain/physiology ; *Algorithms ; Bayes Theorem ; Support Vector Machine ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.},
}
@article {pmid38456888,
year = {2024},
author = {Meier, K and de Vos, CC and Bordeleau, M and van der Tuin, S and Billet, B and Ruland, T and Blichfeldt-Eckhardt, MR and Winkelmüller, M and Gulisano, HA and Gatzinsky, K and Knudsen, AL and Hedemann Sørensen, JC and Milidou, I and Cottin, SC},
title = {Examining the Duration of Carryover Effect in Patients With Chronic Pain Treated With Spinal Cord Stimulation (EChO Study): An Open, Interventional, Investigator-Initiated, International Multicenter Study.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {27},
number = {5},
pages = {887-898},
doi = {10.1016/j.neurom.2024.01.002},
pmid = {38456888},
issn = {1525-1403},
mesh = {Humans ; *Spinal Cord Stimulation/methods ; Male ; Female ; Middle Aged ; *Chronic Pain/therapy ; Aged ; Adult ; Time Factors ; Prospective Studies ; Pain Measurement/methods ; Treatment Outcome ; Internationality ; Neuralgia/therapy ; },
abstract = {OBJECTIVES: Spinal cord stimulation (SCS) is a surgical treatment for severe, chronic, neuropathic pain. It is based on one to two lead(s) implanted in the epidural space, stimulating the dorsal column. It has long been assumed that when deactivating SCS, there is a variable interval before the patient perceives the return of the pain, a phenomenon often termed echo or carryover effect. Although the carryover effect has been problematized as a source of error in crossover studies, no experimental investigation of the effect has been published. This open, prospective, international multicenter study aimed to systematically document, quantify, and investigate the carryover effect in SCS.<br><br>MATERIALS AND METHODS: Eligible patients with a beneficial effect from their SCS treatment were instructed to deactivate their SCS device in a home setting and to reactivate it when their pain returned. The primary outcome was duration of carryover time defined as the time interval from deactivation to reactivation. Central clinical parameters (age, sex, indication for SCS, SCS treatment details, pain score) were registered and correlated with carryover time using nonparametric tests (Mann-Whitney/Kruskal-Wallis) for categorical data and linear regression for continuous data.<br><br>RESULTS: In total, 158 patients were included in the analyses. A median carryover time of five hours was found (interquartile range 2.5;21 hours). Back pain as primary indication for SCS, high-frequency stimulation, and higher pain score at the time of deactivation were correlated with longer carryover time.<br><br>CONCLUSIONS: This study confirms the existence of the carryover effect and indicates a remarkably high degree of interindividual variation. The results suggest that the magnitude of carryover may be correlated to the nature of the pain condition and possibly stimulation paradigms.<br><br>CLINICAL TRIAL REGISTRATION: The Clinicaltrials.gov registration number for the study is NCT03386058.},
}
@article {pmid38454700,
year = {2024},
author = {Zhang, Y and Wu, X and Sun, J and Yue, K and Lu, S and Wang, B and Liu, W and Shi, H and Zou, L},
title = {Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {21},
number = {2},
pages = {2646-2670},
doi = {10.3934/mbe.2024117},
pmid = {38454700},
issn = {1551-0018},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Canonical Correlation Analysis ; Brain/diagnostic imaging ; Electroencephalography ; Brain Mapping/methods ; },
abstract = {Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.},
}
@article {pmid38454099,
year = {2024},
author = {Chen, Y and Stephani, T and Bagdasarian, MT and Hilsmann, A and Eisert, P and Villringer, A and Bosse, S and Gaebler, M and Nikulin, VV},
title = {Realness of face images can be decoded from non-linear modulation of EEG responses.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {5683},
pmid = {38454099},
issn = {2045-2322},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Eye ; Neurologic Examination ; Photic Stimulation ; *Brain-Computer Interfaces ; },
abstract = {Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face's eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.},
}
@article {pmid38452824,
year = {2024},
author = {Yu, Q and Liu, L and Du, M and Müller, D and Gu, Y and Gao, Z and Xin, X and Gu, Y and He, M and Marquardt, T and Wang, L},
title = {Sacral Neural Crest-Independent Origin of the Enteric Nervous System in Mouse.},
journal = {Gastroenterology},
volume = {166},
number = {6},
pages = {1085-1099},
doi = {10.1053/j.gastro.2024.02.034},
pmid = {38452824},
issn = {1528-0012},
mesh = {Animals ; *Neural Crest/cytology/embryology ; *Enteric Nervous System/embryology ; *Cell Lineage ; Mice ; *Cell Movement ; Coculture Techniques ; Mice, Transgenic ; Vagus Nerve/embryology ; Sacrum/innervation ; },
abstract = {BACKGROUND &amp; AIMS: The enteric nervous system (ENS), the gut's intrinsic nervous system critical for gastrointestinal function and gut-brain communication, is believed to mainly originate from vagal neural crest cells (vNCCs) and partially from sacral NCCs (sNCCs). Resolving the exact origins of the ENS is critical for understanding congenital ENS diseases but has been confounded by the inability to distinguish between both NCC populations in situ. Here, we aimed to resolve the exact origins of the mammalian ENS.<br><br>METHODS: We genetically engineered mouse embryos facilitating comparative lineage-tracing of either all (pan-) NCCs including vNCCs or caudal trunk and sNCCs (s/tNCCs) excluding vNCCs. This was combined with dual-lineage tracing and 3-dimensional reconstruction of pelvic plexus and hindgut to precisely pinpoint sNCC and vNCC contributions. We further used coculture assays to determine the specificity of cell migration from different neural tissues into the hindgut.<br><br>RESULTS: Both pan-NCCs and s/tNCCs contributed to established NCC derivatives but only&#xa0;pan-NCCs contributed to the ENS. Dual-lineage tracing combined with 3-dimensional reconstruction revealed that s/tNCCs settle in complex patterns in pelvic plexus and hindgut-surrounding tissues, explaining previous confusion regarding their contributions. Coculture experiments revealed unspecific cell migration from autonomic, sensory, and neural tube explants into the hindgut. Lineage tracing of ENS precursors lastly provided complimentary evidence for an exclusive vNCC origin of the murine ENS.<br><br>CONCLUSIONS: sNCCs do not contribute to the murine ENS, suggesting that the mammalian ENS exclusively originates from vNCCs. These results have immediate implications for comprehending (and devising treatments for) congenital ENS disorders, including Hirschsprung's disease.},
}
@article {pmid38450225,
year = {2024},
author = {Attallah, O and Al-Kabbany, A and Zaghlool, SB and Kholief, M},
title = {Editorial: Immersive technology and ambient intelligence for assistive living, medical, and healthcare solutions.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1376959},
pmid = {38450225},
issn = {1662-5161},
}
@article {pmid38450005,
year = {2024},
author = {Welter, M and Lotte, F},
title = {Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review.},
journal = {Frontiers in neuroergonomics},
volume = {5},
number = {},
pages = {1341790},
pmid = {38450005},
issn = {2673-6195},
abstract = {In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.},
}
@article {pmid38449664,
year = {2024},
author = {Hu, J},
title = {Augmented-reality based brain-computer interface of robot control.},
journal = {Heliyon},
volume = {10},
number = {5},
pages = {e26255},
pmid = {38449664},
issn = {2405-8440},
abstract = {Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.},
}
@article {pmid38448793,
year = {2024},
author = {Vogel, AP and Spencer, C and Burke, K and de Bruyn, D and Gibilisco, P and Blackman, S and Vojtech, JM and Kathiresan, T},
title = {Optimizing Communication in Ataxia: A Multifaceted Approach to Alternative and Augmentative Communication (AAC).},
journal = {Cerebellum (London, England)},
volume = {23},
number = {5},
pages = {2142-2151},
pmid = {38448793},
issn = {1473-4230},
support = {220100253//Australian Research Council/ ; },
mesh = {Humans ; *Ataxia/therapy/rehabilitation/physiopathology ; *Communication Devices for People with Disabilities ; Communication ; Communication Disorders/rehabilitation/therapy ; },
abstract = {The progression of multisystem neurodegenerative diseases such as ataxia significantly impacts speech and communication, necessitating adaptive clinical care strategies. With the deterioration of speech, Alternative and Augmentative Communication (AAC) can play an ever increasing role in daily life for individuals with ataxia. This review describes the spectrum of AAC resources available, ranging from unaided gestures and sign language to high-tech solutions like speech-generating devices (SGDs) and eye-tracking technology. Despite the availability of various AAC tools, their efficacy is often compromised by the physical limitations inherent in ataxia, including upper limb ataxia and visual disturbances. Traditional speech-to-text algorithms and eye gaze technology face challenges in accuracy and efficiency due to the atypical speech and movement patterns associated with the disease.In addressing these challenges, maintaining existing speech abilities through rehabilitation is prioritized, complemented by advances in digital therapeutics to provide home-based treatments. Simultaneously, projects incorporating AI driven solutions aim to enhance the intelligibility of dysarthric speech through improved speech-to-text accuracy.This review discusses the complex needs assessment for AAC in ataxia, emphasizing the dynamic nature of the disease and the importance of regular reassessment to tailor communication strategies to the changing abilities of the individual. It also highlights the necessity of multidisciplinary involvement for effective AAC assessment and intervention. The future of AAC looks promising with developments in brain-computer interfaces and the potential of voice banking, although their application in ataxia requires further exploration.},
}
@article {pmid38447577,
year = {2024},
author = {Cheng, H and Chen, D and Li, X and Al-Sheikh, U and Duan, D and Fan, Y and Zhu, L and Zeng, W and Hu, Z and Tong, X and Zhao, G and Zhang, Y and Zou, W and Duan, S and Kang, L},
title = {Phasic/tonic glial GABA differentially transduce for olfactory adaptation and neuronal aging.},
journal = {Neuron},
volume = {112},
number = {9},
pages = {1473-1486.e6},
doi = {10.1016/j.neuron.2024.02.006},
pmid = {38447577},
issn = {1097-4199},
mesh = {Animals ; *gamma-Aminobutyric Acid/metabolism ; *Caenorhabditis elegans ; *Neuroglia/metabolism/physiology ; *Adaptation, Physiological/physiology ; Smell/physiology ; Caenorhabditis elegans Proteins/metabolism/genetics ; Signal Transduction/physiology ; Cellular Senescence/physiology ; Olfactory Receptor Neurons/physiology/metabolism ; Aging/physiology/metabolism ; Receptors, GABA-A/metabolism ; },
abstract = {Phasic (fast) and tonic (sustained) inhibition of γ-aminobutyric acid (GABA) are fundamental for regulating day-to-day activities, neuronal excitability, and plasticity. However, the mechanisms and physiological functions of glial GABA transductions remain poorly understood. Here, we report that the AMsh glia in Caenorhabditis elegans exhibit both phasic and tonic GABAergic signaling, which distinctively regulate olfactory adaptation and neuronal aging. Through genetic screening, we find that GABA permeates through bestrophin-9/-13/-14 anion channels from AMsh glia, which primarily activate the metabolic GABAB receptor GBB-1 in the neighboring ASH sensory neurons. This tonic action of glial GABA regulates the age-associated changes of ASH neurons and olfactory responses via a conserved signaling pathway, inducing neuroprotection. In addition, the calcium-evoked, vesicular glial GABA release acts upon the ionotropic GABAA receptor LGC-38 in ASH neurons to regulate olfactory adaptation. These findings underscore the fundamental significance of glial GABA in maintaining healthy aging and neuronal stability.},
}
@article {pmid38446762,
year = {2024},
author = {Sabio, J and Williams, NS and McArthur, GM and Badcock, NA},
title = {A scoping review on the use of consumer-grade EEG devices for research.},
journal = {PloS one},
volume = {19},
number = {3},
pages = {e0291186},
pmid = {38446762},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Engineering ; },
abstract = {BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.<br><br>PURPOSE: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.<br><br>METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.<br><br>RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.<br><br>CONCLUSIONS: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.},
}
@article {pmid38445386,
year = {2024},
author = {Liu, Y and Ren, H and Zhang, Y and Deng, W and Ma, X and Zhao, L and Li, X and Sham, P and Wang, Q and Li, T},
title = {Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study.},
journal = {Psychological medicine},
volume = {54},
number = {9},
pages = {2054-2062},
pmid = {38445386},
issn = {1469-8978},
mesh = {Humans ; *Schizophrenia/genetics/pathology ; *Mendelian Randomization Analysis ; *Inflammation/genetics ; *Genome-Wide Association Study ; *Brain/pathology/diagnostic imaging ; *Polymorphism, Single Nucleotide ; C-Reactive Protein/genetics/metabolism ; Cytokines/genetics/metabolism ; Magnetic Resonance Imaging ; White Matter/pathology/diagnostic imaging ; },
abstract = {BACKGROUND: Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia.<br><br>METHOD: We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain.<br><br>RESULTS: We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-&#x3b2;, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum.<br><br>CONCLUSION: With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.},
}
@article {pmid38444036,
year = {2024},
author = {Guo, Z and Tian, C and Shi, Y and Song, XR and Yin, W and Tao, QQ and Liu, J and Peng, GP and Wu, ZY and Wang, YJ and Zhang, ZX and Zhang, J},
title = {Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer's disease diagnosis and differential diagnosis.},
journal = {Acta neuropathologica communications},
volume = {12},
number = {1},
pages = {38},
pmid = {38444036},
issn = {2051-5960},
support = {82020108012//Natural Science Foundation of China/ ; 82201560//Natural Science Foundation of China/ ; 2020R01001//Leading Innovation and Entrepreneurship Team in Zhejiang Province/ ; },
mesh = {Humans ; *Alzheimer Disease/diagnosis ; Diagnosis, Differential ; NAD ; Proteomics ; *Extracellular Vesicles ; },
abstract = {Accurate differential diagnosis among various dementias is crucial for effective treatment of Alzheimer's disease (AD). The study began with searching for novel blood-based neuronal extracellular vesicles (EVs) that are more enriched in the brain regions vulnerable to AD development and progression. With extensive proteomic profiling, GABRD and GPR162 were identified as novel brain regionally enriched plasma EVs markers. The performance of GABRD and GPR162, along with the AD molecule pTau217, was tested using the self-developed and optimized nanoflow cytometry-based technology, which not only detected the positive ratio of EVs but also concurrently presented the corresponding particle size of the EVs, in discovery (n = 310) and validation (n = 213) cohorts. Plasma GABRD[+]- or GPR162[+]-carrying pTau217-EVs were significantly reduced in AD compared with healthy control (HC). Additionally, the size distribution of GABRD[+]- and GPR162[+]-carrying pTau217-EVs were significantly different between AD and non-AD dementia (NAD). An integrative model, combining age, the number and corresponding size of the distribution of GABRD[+]- or GPR162[+]-carrying pTau217-EVs, accurately and sensitively discriminated AD from HC [discovery cohort, area under the curve (AUC) = 0.96; validation cohort, AUC = 0.93] and effectively differentiated AD from NAD (discovery cohort, AUC = 0.91; validation cohort, AUC = 0.90). This study showed that brain regionally enriched neuronal EVs carrying pTau217 in plasma may serve as a robust diagnostic and differential diagnostic tool in both clinical practice and trials for AD.},
}
@article {pmid38442053,
year = {2024},
author = {Lai, E and Mai, X and Ji, M and Li, S and Meng, J},
title = {High-Frequency Discrete-Interval Binary Sequence in Asynchronous C-VEP-Based BCI for Visual Fatigue Reduction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {5},
pages = {2769-2780},
doi = {10.1109/JBHI.2024.3373332},
pmid = {38442053},
issn = {2168-2208},
mesh = {Humans ; *Evoked Potentials, Visual/physiology ; *Brain-Computer Interfaces ; Male ; Adult ; *Signal Processing, Computer-Assisted ; Female ; Young Adult ; *Electroencephalography/methods ; *Algorithms ; Photic Stimulation/methods ; Asthenopia/physiopathology ; },
abstract = {In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.},
}
@article {pmid38441825,
year = {2024},
author = {Patel, P and Balasubramanian, S and Annavarapu, RN},
title = {Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.},
journal = {Brain informatics},
volume = {11},
number = {1},
pages = {7},
pmid = {38441825},
issn = {2198-4018},
abstract = {Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.},
}
@article {pmid38440775,
year = {2024},
author = {Fischer-Janzen, A and Wendt, TM and Van Laerhoven, K},
title = {A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.},
journal = {Frontiers in robotics and AI},
volume = {11},
number = {},
pages = {1326670},
pmid = {38440775},
issn = {2296-9144},
abstract = {Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.},
}
@article {pmid38439983,
year = {2024},
author = {Wang, Z and Ding, J and Tan, J and Liu, J and Zhang, T and Cai, W and Meng, S},
title = {UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.},
journal = {Frontiers in plant science},
volume = {15},
number = {},
pages = {1358965},
pmid = {38439983},
issn = {1664-462X},
abstract = {Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R[2] of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.},
}
@article {pmid38439522,
year = {2024},
author = {Zheng, M and Lou, F and Huang, Y and Pan, S and Zhang, X},
title = {MR-based electrical property tomography using a physics-informed network at 3 and 7 T.},
journal = {NMR in biomedicine},
volume = {37},
number = {8},
pages = {e5137},
doi = {10.1002/nbm.5137},
pmid = {38439522},
issn = {1099-1492},
support = {2021ZD0200401//STI 2030 - Major Projects/ ; 52277232//National Natural Science Foundation of China/ ; 52293424//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 226-2022-00136//Fundamental Research Funds for the Central Universities/ ; 226-2023-00125//Fundamental Research Funds for the Central Universities/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation/ ; BE2022049//Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//Key-Area R&amp;D Program of Guangdong Province/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging ; *Tomography ; Brain/diagnostic imaging ; Physics ; Phantoms, Imaging ; Water/chemistry ; Computer Simulation ; Male ; Female ; },
abstract = {Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of B → 1 + and B → 1 - by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.},
}
@article {pmid38438935,
year = {2024},
author = {Papaioannou, D and Sprange, K and Hamer-Kiwacz, S and Mooney, C and Moody, G and Cooper, C},
title = {Recording harms in randomised controlled trials of behaviour change interventions: a qualitative study of UK clinical trials units and NIHR trial investigators.},
journal = {Trials},
volume = {25},
number = {1},
pages = {163},
pmid = {38438935},
issn = {1745-6215},
support = {CTU Support Funding//National Institute for Health and Care Research/ ; },
mesh = {Humans ; Focus Groups ; Knowledge ; Language ; Qualitative Research ; Randomized Controlled Trials as Topic ; United Kingdom ; *Behavior Therapy ; },
abstract = {BACKGROUND: Harms, also known as adverse events (AEs), are recorded and monitored in randomised controlled trials (RCTs) to ensure participants' safety. Harms are recorded poorly or inconsistently in RCTs of Behaviour Change Interventions (BCI); however, limited guidance exists on how to record harms in BCI trials. This qualitative study explored experiences and perspectives from multi-disciplinary trial experts on recording harms in BCI trials.<br><br>METHODS: Data were collected through fifteen in-depth semi-structured qualitative interviews and three focus groups with thirty-two participants who work in the delivery and oversight of clinical trials. Participants included multi-disciplinary staff from eight CTUs, Chief investigators, and patient and public representatives. Interviews and focus group recordings were transcribed verbatim and thematic analysis was used to analyse the transcripts.<br><br>RESULTS: Five themes were identified, namely perception and understanding of harm, proportionate reporting and plausibility, the need for a multi-disciplinary approach, language of BCI harms and complex harms for complex interventions. Participants strongly believed harms should be recorded in BCI trials; however, making decisions on "how and what to record as harms" was difficult. Recording irrelevant harms placed a high burden on trial staff and participants, drained trial resources and was perceived as for little purpose. Participants believed proportionate recording was required that focused on events with a strong plausible link to the intervention. Multi-disciplinary trial team input was essential for identifying and collecting harms; however, this was difficult in practice due to lack of knowledge on harms from BCIs, lack of input or difference in opinion. The medical language of harms was recognised as a poor fit for BCI trial harms but was familiar and established within internal processes. Future guidance on this topic would be welcomed and could include summarised literature.<br><br>CONCLUSIONS: Recording harms or adverse events in behaviour change intervention trials is complex and challenging; multi-disciplinary experts in trial design and implementation welcome forthcoming guidance on this topic. Issues include the high burden of recording irrelevant harms and use of definitions originally designed for drug trials. Proportionate recording of harms focused on events with a strong plausible link to the intervention and multi-disciplinary team input into decision making are essential.},
}
@article {pmid38437792,
year = {2024},
author = {Sanft, TB and Wong, J and O'Neal, B and Siuliukina, N and Jankowitz, RC and Pegram, MD and Fox, JR and Zhang, Y and Treuner, K and O'Shaughnessy, JA},
title = {Impact of the Breast Cancer Index for Extended Endocrine Decision-Making: First Results of the Prospective BCI Registry Study.},
journal = {Journal of the National Comprehensive Cancer Network : JNCCN},
volume = {22},
number = {2},
pages = {99-107},
doi = {10.6004/jnccn.2023.7087},
pmid = {38437792},
issn = {1540-1413},
mesh = {Humans ; Female ; *Breast Neoplasms/pathology ; Prospective Studies ; *Brain-Computer Interfaces ; Chemotherapy, Adjuvant/methods ; Neoplasm Recurrence, Local/drug therapy ; },
abstract = {BACKGROUND: The Breast Cancer Index (BCI) test assay provides an individualized risk of late distant recurrence (5-10 years) and predicts the likelihood of benefitting from extended endocrine therapy (EET) in hormone receptor-positive early-stage breast cancer. This analysis aimed to assess the impact of BCI on EET decision-making in current clinical practice.<br><br>METHODS: The BCI Registry study evaluates long-term outcomes, decision impact, and medication adherence in patients receiving BCI testing as part of routine clinical care. Physicians and patients completed pre-BCI and post-BCI test questionnaires to assess a range of questions, including physician decision-making and confidence regarding EET; patient preferences and concerns about the cost, side effects, drug safety, and benefit of EET; and patient satisfaction regarding treatment recommendations. Pre-BCI and post-BCI test responses were compared using McNemar's test and Wilcoxon signed rank test.<br><br>RESULTS: Pre-BCI and post-BCI questionnaires were completed for 843 physicians and 823 patients. The mean age at enrollment was 65 years, and 88.4% of patients were postmenopausal. Of the tumors, 74.7% were T1, 53.4% were grade 2, 76.0% were N0, and 13.8% were HER2-positive. Following BCI testing, physicians changed EET recommendations in 40.1% of patients (P<.0001), and 45.1% of patients changed their preferences for EET (P<.0001). In addition, 38.8% of physicians felt more confident in their recommendation (P<.0001), and 41.4% of patients felt more comfortable with their EET decision (P<.0001). Compared with baseline, significantly more patients were less concerned about the cost (20.9%; P<.0001), drug safety (25.4%; P=.0014), and benefit of EET (29.3%; P=.0002).<br><br>CONCLUSIONS: This analysis in a large patient cohort of the BCI Registry confirms and extends previous findings on the significant decision-making impact of BCI on EET. Incorporating BCI into clinical practice resulted in changes in physician recommendations, increased physician confidence, improved patient satisfaction, and reduced patient concerns regarding the cost, drug safety, and benefit of EET.},
}
@article {pmid38437148,
year = {2024},
author = {Gu, M and Pei, W and Gao, X and Wang, Y},
title = {Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1090-1099},
doi = {10.1109/TNSRE.2024.3372594},
pmid = {38437148},
issn = {1558-0210},
mesh = {Humans ; Photic Stimulation/methods ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Computer Systems ; },
abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.},
}
@article {pmid38437070,
year = {2024},
author = {Shi, C and He, Y and Gourdouparis, M and Dolmans, G and Liu, YH},
title = {A Spatially Diverse 2TX-3RX Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {5},
pages = {1014-1023},
pmid = {38437070},
issn = {1940-9990},
support = {101001448/ERC_/European Research Council/International ; },
mesh = {*Brain-Computer Interfaces ; *Telemetry/instrumentation/methods ; Signal Processing, Computer-Assisted/instrumentation ; Humans ; Equipment Design ; Wireless Technology/instrumentation ; Animals ; Brain/physiology ; },
abstract = {A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is presented. The proposed design features a two channels transmitter and three channels receiver (2TX-3RX) topology, which introduces spatial diversity to effectively mitigate misalignments (both lateral and rotational) between the brain and the skull and recovers the path loss by 13 dB when the RX is in the worst-case blind spot. This spatial diversity also allows the presented telemetry to support the spatial division multiplexing required for a high-capacity multi-implant distributed network. It achieves a signal-to-interference ratio of 12 dB, even with the adjacent interference node placed only 8 mm away from the desired link. While consuming only 0.33 mW for each channel, the presented RX achieves a wide bandwidth of 360 MHz and a low input referred noise of 13.21 nV/√Hz. The presented telemetry achieves a 270 Mbps data rate with a BER < 10[-6] and an energy efficiency of 3.4 pJ/b and 3.7 pJ/b, respectively. The core footprint of the TX and RX modules is only 100 and 52 mm[2], respectively, minimizing the invasiveness of the surgery. The proposed transdural telemetry system has been characterized ex-vivo with a 7-mm thick porcine tissue.},
}
@article {pmid38435744,
year = {2024},
author = {Khan, H and Khadka, R and Sultan, MS and Yazidi, A and Ombao, H and Mirtaheri, P},
title = {Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1354143},
pmid = {38435744},
issn = {1662-5161},
abstract = {In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.},
}
@article {pmid38435343,
year = {2023},
author = {Dong, K and Liu, WC and Su, Y and Lyu, Y and Huang, H and Zheng, N and Rogers, JA and Nan, K},
title = {Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices.},
journal = {BME frontiers},
volume = {4},
number = {},
pages = {0034},
pmid = {38435343},
issn = {2765-8031},
abstract = {Millimeter-scale animals such as Caenorhabditis elegans, Drosophila larvae, zebrafish, and bees serve as powerful model organisms in the fields of neurobiology and neuroethology. Various methods exist for recording large-scale electrophysiological signals from these animals. Existing approaches often lack, however, real-time, uninterrupted investigations due to their rigid constructs, geometric constraints, and mechanical mismatch in integration with soft organisms. The recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability, offering unique capabilities for chronic, stable interrogation and stimulation of millimeter-scale animals and miniature tissue constructs. This review summarizes the most advanced technologies for electrophysiological studies, based on methods of 3-dimensional flexible bioelectronics. A concluding section addresses the challenges of these devices in achieving freestanding, robust, and multifunctional biointerfaces.},
}
@article {pmid38435127,
year = {2023},
author = {Herbert, C},
title = {Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1286895},
pmid = {38435127},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.},
}
@article {pmid38435056,
year = {2024},
author = {Thota, AK and Jung, R},
title = {Accelerating neurotechnology development using an Agile methodology.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1328540},
pmid = {38435056},
issn = {1662-4548},
support = {R01 EB023261/EB/NIBIB NIH HHS/United States ; R01 EB027584/EB/NIBIB NIH HHS/United States ; },
abstract = {Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.},
}
@article {pmid38433651,
year = {2024},
author = {Ma, X and Qi, Y and Xu, C and Weng, Y and Yu, J and Sun, X and Yu, Y and Wu, Y and Gao, J and Li, J and Shu, Y and Duan, S and Luo, B and Pan, G},
title = {How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study.},
journal = {Human brain mapping},
volume = {45},
number = {4},
pages = {e26586},
pmid = {38433651},
issn = {1097-0193},
support = {2021ZD0200400//STI 2030 Major Projects/ ; 61925603//Natural Science Foundation of China/ ; 62276228//Natural Science Foundation of China/ ; U1909202//Natural Science Foundation of China/ ; },
mesh = {Humans ; *Consciousness ; Reproducibility of Results ; *Wakefulness ; Benchmarking ; Electroencephalography ; Persistent Vegetative State ; },
abstract = {The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.},
}
@article {pmid38430561,
year = {2024},
author = {Bekhelifi, O and Berrached, NE and Bendahmane, A},
title = {Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ad2f58},
pmid = {38430561},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; },
abstract = {Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigue during prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% (± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 (± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.},
}
@article {pmid38429300,
year = {2024},
author = {Du, X and Liang, K and Lv, Y and Qiu, S},
title = {Fast reconstruction of EEG signal compression sensing based on deep learning.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {5087},
pmid = {38429300},
issn = {2045-2322},
support = {JYTMS20230377//Liaoning Provincial Education Department/ ; },
mesh = {Signal Processing, Computer-Assisted ; *Deep Learning ; *Data Compression/methods ; Algorithms ; Electroencephalography/methods ; },
abstract = {When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.},
}
@article {pmid38428473,
year = {2024},
author = {Wang, M and Deng, Y and Liu, Y and Suo, T and Guo, B and Eickhoff, SB and Xu, J and Rao, H},
title = {The common and distinct brain basis associated with adult and adolescent risk-taking behavior: Evidence from the neuroimaging meta-analysis.},
journal = {Neuroscience and biobehavioral reviews},
volume = {160},
number = {},
pages = {105607},
doi = {10.1016/j.neubiorev.2024.105607},
pmid = {38428473},
issn = {1873-7528},
mesh = {Adult ; Humans ; Adolescent ; *Magnetic Resonance Imaging ; *Brain/diagnostic imaging/physiology ; Frontal Lobe ; Brain Mapping ; Neuroimaging ; Risk-Taking ; },
abstract = {Risk-taking is a common, complex, and multidimensional behavior construct that has significant implications for human health and well-being. Previous research has identified the neural mechanisms underlying risk-taking behavior in both adolescents and adults, yet the differences between adolescents' and adults' risk-taking in the brain remain elusive. This study firstly employs a comprehensive meta-analysis approach that includes 73 adult and 20 adolescent whole-brain experiments, incorporating observations from 1986 adults and 789 adolescents obtained from online databases, including Web of Science, PubMed, ScienceDirect, Google Scholar and Neurosynth. It then combines functional decoding methods to identify common and distinct brain regions and corresponding psychological processes associated with risk-taking behavior in these two cohorts. The results indicated that the neural bases underlying risk-taking behavior in both age groups are situated within the cognitive control, reward, and sensory networks. Subsequent contrast analysis revealed that adolescents and adults risk-taking engaged frontal pole within the fronto-parietal control network (FPN), but the former recruited more ventrolateral area and the latter recruited more dorsolateral area. Moreover, adolescents' risk-taking evoked brain area activity within the ventral attention network (VAN) and the default mode network (DMN) compared with adults, consistent with the functional decoding analyses. These findings provide new insights into the similarities and disparities of risk-taking neural substrates underlying different age cohorts, supporting future neuroimaging research on the dynamic changes of risk-taking.},
}
@article {pmid38428423,
year = {2024},
author = {Wang, WW and Ji, SY and Zhang, W and Zhang, J and Cai, C and Hu, R and Zang, SK and Miao, L and Xu, H and Chen, LN and Yang, Z and Guo, J and Qin, J and Shen, DD and Liang, P and Zhang, Y and Zhang, Y},
title = {Structure-based design of non-hypertrophic apelin receptor modulator.},
journal = {Cell},
volume = {187},
number = {6},
pages = {1460-1475.e20},
doi = {10.1016/j.cell.2024.02.004},
pmid = {38428423},
issn = {1097-4172},
mesh = {*Apelin Receptors/agonists/chemistry/ultrastructure ; Cryoelectron Microscopy ; GTP-Binding Proteins/metabolism ; Receptors, G-Protein-Coupled/metabolism ; Signal Transduction ; Humans ; *Cardiovascular Agents/chemistry ; *Drug Design ; },
abstract = {Apelin is a key hormone in cardiovascular homeostasis that activates the apelin receptor (APLNR), which is regarded as a promising therapeutic target for cardiovascular disease. However, adverse effects through the β-arrestin pathway limit its pharmacological use. Here, we report cryoelectron microscopy (cryo-EM) structures of APLNR-Gi1 complexes bound to three agonists with divergent signaling profiles. Combined with functional assays, we have identified "twin hotspots" in APLNR as key determinants for signaling bias, guiding the rational design of two exclusive G-protein-biased agonists WN353 and WN561. Cryo-EM structures of WN353- and WN561-stimulated APLNR-G protein complexes further confirm that the designed ligands adopt the desired poses. Pathophysiological experiments have provided evidence that WN561 demonstrates superior therapeutic effects against cardiac hypertrophy and reduced adverse effects compared with the established APLNR agonists. In summary, our designed APLNR modulator may facilitate the development of next-generation cardiovascular medications.},
}
@article {pmid38426121,
year = {2024},
author = {Kumar, S and Alawieh, H and Racz, FS and Fakhreddine, R and Millán, JDR},
title = {Transfer learning promotes acquisition of individual BCI skills.},
journal = {PNAS nexus},
volume = {3},
number = {2},
pages = {pgae076},
pmid = {38426121},
issn = {2752-6542},
abstract = {Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.},
}
@article {pmid38425214,
year = {2024},
author = {Shi, C and Zhang, C and Chen, JF and Yao, Z},
title = {Enhancement of low gamma oscillations by volitional conditioning of local field potential in the primary motor and visual cortex of mice.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {2},
pages = {},
doi = {10.1093/cercor/bhae051},
pmid = {38425214},
issn = {1460-2199},
support = {2023ZY1011//Department of Science and Technology of Zhejiang Province/ ; OJQDSP2022007//Scientific Research Starting Foundation of Oujiang Laboratory/ ; LQ18C090002//Natural Science Foundation of Zhejiang Province/ ; },
mesh = {Mice ; Animals ; *Gamma Rhythm ; Brain ; *Visual Cortex ; },
abstract = {Volitional control of local field potential oscillations in low gamma band via brain machine interface can not only uncover the relationship between low gamma oscillation and neural synchrony but also suggest a therapeutic potential to reverse abnormal local field potential oscillation in neurocognitive disorders. In nonhuman primates, the volitional control of low gamma oscillations has been demonstrated by brain machine interface techniques in the primary motor and visual cortex. However, it is not clear whether this holds in other brain regions and other species, for which gamma rhythms might involve in highly different neural processes. Here, we established a closed-loop brain-machine interface and succeeded in training mice to volitionally elevate low gamma power of local field potential in the primary motor and visual cortex. We found that the mice accomplished the task in a goal-directed manner and spiking activity exhibited phase-locking to the oscillation in local field potential in both areas. Moreover, long-term training made the power enhancement specific to direct and adjacent channel, and increased the transcriptional levels of NMDA receptors as well as that of hypoxia-inducible factor relevant to metabolism. Our results suggest that volitionally generated low gamma rhythms in different brain regions share similar mechanisms and pave the way for employing brain machine interface in therapy of neurocognitive disorders.},
}
@article {pmid38423166,
year = {2024},
author = {Wang, H and Zhu, Z and Bi, H and Jiang, Z and Cao, Y and Wang, S and Zou, L},
title = {Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment.},
journal = {Neuroscience},
volume = {544},
number = {},
pages = {1-11},
doi = {10.1016/j.neuroscience.2024.02.026},
pmid = {38423166},
issn = {1873-7544},
mesh = {Humans ; *Brain Mapping/methods ; Magnetic Resonance Imaging/methods ; Neural Pathways/diagnostic imaging ; Brain/pathology ; *Cognitive Dysfunction/diagnostic imaging/pathology ; },
abstract = {Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p < 0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.},
}
@article {pmid38417170,
year = {2024},
author = {Yang, J and Zhao, S and Fu, Z and Liu, X},
title = {PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ad2e36},
pmid = {38417170},
issn = {2057-1976},
mesh = {*Evoked Potentials, Visual ; *Neural Networks, Computer ; Algorithms ; Brain/physiology ; Electroencephalography/methods ; },
abstract = {Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.},
}
@article {pmid38417162,
year = {2024},
author = {Blanco-Díaz, CF and Guerrero-Mendez, CD and Delisle-Rodriguez, D and Jaramillo-Isaza, S and Ruiz-Olaya, AF and Frizera-Neto, A and Ferreira de Souza, A and Bastos-Filho, T},
title = {Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ad2e35},
pmid = {38417162},
issn = {2057-1976},
mesh = {Humans ; Activities of Daily Living ; *Stroke ; Movement ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.},
}
@article {pmid38415197,
year = {2024},
author = {Subramaniam, S and Akay, M and Anastasio, MA and Bailey, V and Boas, D and Bonato, P and Chilkoti, A and Cochran, JR and Colvin, V and Desai, TA and Duncan, JS and Epstein, FH and Fraley, S and Giachelli, C and Grande-Allen, KJ and Green, J and Guo, XE and Hilton, IB and Humphrey, JD and Johnson, CR and Karniadakis, G and King, MR and Kirsch, RF and Kumar, S and Laurencin, CT and Li, S and Lieber, RL and Lovell, N and Mali, P and Margulies, SS and Meaney, DF and Ogle, B and Palsson, B and A Peppas, N and Perreault, EJ and Rabbitt, R and Setton, LA and Shea, LD and Shroff, SG and Shung, K and Tolias, AS and van der Meulen, MCH and Varghese, S and Vunjak-Novakovic, G and White, JA and Winslow, R and Zhang, J and Zhang, K and Zukoski, C and Miller, MI},
title = {Grand Challenges at the Interface of Engineering and Medicine.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {5},
number = {},
pages = {1-13},
doi = {10.1109/OJEMB.2024.3351717},
pmid = {38415197},
issn = {2644-1276},
support = {P41 EB027062/EB/NIBIB NIH HHS/United States ; R01 CA249799/CA/NCI NIH HHS/United States ; R01 HL076485/HL/NHLBI NIH HHS/United States ; R01 HL120046/HL/NHLBI NIH HHS/United States ; },
abstract = {Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.},
}
@article {pmid38414845,
year = {2024},
author = {Chen, S and Yao, L and Cao, L and Caimmi, M and Jia, J},
title = {Editorial: Exploration of the non-invasive brain-computer interface and neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1377665},
doi = {10.3389/fnins.2024.1377665},
pmid = {38414845},
issn = {1662-4548},
}
@article {pmid38414505,
year = {2024},
author = {Jin, Y and Wu, C and Chen, W and Li, J and Jiang, H},
title = {Gestational diabetes and risk of perinatal depression in low- and middle-income countries: a meta-analysis.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1331415},
pmid = {38414505},
issn = {1664-0640},
abstract = {BACKGROUND: The relationship between gestational diabetes (GDM) and the risk of depression has been thoroughly investigated in high-income countries on their financial basis, while it is largely unexplored in low- and middle- income countries. This meta-analysis aims to assess how GDM influences the risk of perinatal depression by searching multiple electronic databases for studies measuring the odds ratios between them in low- and middle-income countries.<br><br>METHODS: Two independent reviewers searched multiple electronic databases for studies that investigated GDM and perinatal mental disorders on August 31, 2023. Pooled odds ratios (ORs) and confidence intervals (CIs) were calculated using the random effect model. Subgroup analyses were further conducted based on the type of study design and country income level.<br><br>RESULTS: In total, 16 observational studies met the inclusion criteria. Only the number of studies on depression (n=10) satisfied the conditions to conduct a meta-analysis, showing the relationship between mental illness and GDM has been overlooked in low- and middle-income countries. Evidence shows an elevated risk of perinatal depression in women with GDM (pooled OR 1.92; 95% CI 1.24, 2.97; 10 studies). The increased risk of perinatal depression in patients with GDM was not significantly different between cross-sectional and prospective design. Country income level is a significant factor that adversely influences the risk of perinatal depression in GDM patients.<br><br>CONCLUSION: Our findings suggested that women with GDM are vulnerable to perinatal depressive symptoms, and a deeper understanding of potential risk factors and mechanisms may help inform strategies aimed at prevention of exposure to these complications during pregnancy.},
}
@article {pmid38413782,
year = {2024},
author = {Mondini, V and Sburlea, AI and Müller-Putz, GR},
title = {Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {4714},
pmid = {38413782},
issn = {2045-2322},
mesh = {Humans ; Intention ; Electroencephalography ; Evoked Potentials ; Movement ; *Brain-Computer Interfaces ; Spinal Cord ; *Neocortex ; },
abstract = {Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.},
}
@article {pmid38411720,
year = {2024},
author = {Wang, W and Wang, Y and Yin, F and Niu, H and Shin, YK and Li, Y and Kim, ES and Kim, NY},
title = {Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware.},
journal = {Nano-micro letters},
volume = {16},
number = {1},
pages = {133},
pmid = {38411720},
issn = {2150-5551},
abstract = {Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.},
}
@article {pmid38408002,
year = {2024},
author = {Wu, J and Akinin, A and Somayajulu, J and Lee, MS and Paul, A and Lu, H and Park, Y and Kim, SJ and Mercier, PP and Cauwenberghs, G},
title = {A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {2},
pages = {263-273},
pmid = {38408002},
issn = {1940-9990},
support = {UF1 NS116377/NS/NINDS NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; Equipment Design ; Electrodes ; *Electroencephalography ; *Brain Waves ; Electric Impedance ; Amplifiers, Electronic ; },
abstract = {Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 μ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 μV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.},
}
@article {pmid38406999,
year = {2024},
author = {Mao, W and Xiao, Q and Shen, X and Zhou, X and Wang, A and Jin, J},
title = {How effort-based self-interest motivation shapes altruistic donation behavior and brain responses.},
journal = {Psychophysiology},
volume = {61},
number = {7},
pages = {e14552},
doi = {10.1111/psyp.14552},
pmid = {38406999},
issn = {1469-8986},
support = {72271166//National Natural Science Foundation of China/ ; 2020011540019//Ningbo University of Technology/ ; },
mesh = {Humans ; *Motivation/physiology ; *Altruism ; Male ; Female ; Young Adult ; Adult ; *Reward ; *Evoked Potentials/physiology ; *Electroencephalography ; },
abstract = {Prosocial behaviors are central to individual and societal well-being. Although the relationship between effort and prosocial behavior is increasingly studied, the impact of effort-based self-interested motivation on prosocial behavior has received less attention. In the current study, we carried out two experiments to examine the effect of motivation to obtain a reward for oneself on donation behavior and brain response. We observed that individuals who accumulated more money in the effort-expenditure rewards task (EEfRT) donated a lower proportion of their earnings. The sigmoid model fitted participants' choices in the EEfRT task, and the effort-reward bias and sigma parameters negatively correlated with the amount of money donated in the donation task. Additionally, the effort-reward bias and sigma parameters negatively predicted N2 amplitude during processing of charitable donation-related information. We propose that individuals who exhibit a lower level of effort-based self-interest motivation may allocate more cognitive control or attentional resources when processing information related to charitable donations. Our work adds weight to understanding the relationship between effort-based self-interest motivation and prosocial behavior and provides electrophysiological evidence.},
}
@article {pmid38406207,
year = {2024},
author = {Gao, Y and Zhang, C and Huang, J and Meng, M},
title = {EEG multi-domain feature transfer based on sparse regularized Tucker decomposition.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {185-197},
pmid = {38406207},
issn = {1871-4080},
abstract = {Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.},
}
@article {pmid38406198,
year = {2024},
author = {Niu, X and Peng, Y and Jiang, Z and Huang, S and Liu, R and Zhu, M and Shi, L},
title = {Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {37-47},
pmid = {38406198},
issn = {1871-4080},
abstract = {Birds have developed visual cognitions, especially in discriminating colors due to their four types of cones in the retina. The entopallium of birds is thought to be involved in the processing of color information during visual cognition. However, there is a lack of understanding about how functional connectivity in the entopallium region of birds changes during color cognition, which is related to various input colors. We therefore trained pigeons to perform a delayed color matching task, in which two colors were randomly presented in sample stimuli phrases, and the neural activity at individual recording site and the gamma band functional connectivity among local population in entopallium during sample presentation were analyzed. Both gamma band energy and gamma band functional connectivity presented dynamics as the stimulus was presented and persisted. The response features in the early-stimulus phase were significantly different from those of baseline and the late-stimulus phase. Furthermore, gamma band energy showed significant differences between different colors during the early-stimulus phase, but the global feature of the gamma band functional network did not. Further decoding results showed that decoding accuracy was significantly enhanced by adding functional connectivity features, suggesting the global feature of the gamma band functional network did not directly contain color information, but was related to it. These results provided insight into information processing rules among local neuronal populations in the entopallium of birds during color cognition, which is important for their daily life.},
}
@article {pmid38406193,
year = {2024},
author = {Yin, X and Lin, M},
title = {Multi-information improves the performance of CCA-based SSVEP classification.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {165-172},
pmid = {38406193},
issn = {1871-4080},
abstract = {The target recognition algorithm based on canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. To reduce visual fatigue and improve the information transfer rate (ITR), how to improve the accuracy of algorithms within a short time window has become one of the main problems at present. There were filter bank CCA (FBCCA), individual template CCA (ITCCA), and temporally local CCA (TCCA), which improve the CCA algorithm from different aspects.This paper proposed to consider individual, frequency, and time information at the same time, so as to extract features more effectively. A comparison of the various methods was performed using benchmark dataset. Classification accuracy and ITR were used for performance evaluation. In the different extensions of CCA, the method incorporating the above three kinds of information simultaneously achieved the best performance within a short time window. This study explores the effect of using a variety of information to improve the CCA algorithm.},
}
@article {pmid38406192,
year = {2024},
author = {Niu, X and Peng, Y and Jiang, Z and Huang, S and Liu, R and Zhu, M and Shi, L},
title = {Correction to: Gamma-band-based dynamic functional connectivity in pigeon entopallium during sample presentation in a delayed color matching task.},
journal = {Cognitive neurodynamics},
volume = {18},
number = {1},
pages = {299},
doi = {10.1007/s11571-023-09971-x},
pmid = {38406192},
issn = {1871-4080},
abstract = {[This corrects the article DOI: 10.1007/s11571-022-09916-w.].},
}
@article {pmid38405712,
year = {2024},
author = {Kothe, C and Shirazi, SY and Stenner, T and Medine, D and Boulay, C and Grivich, MI and Mullen, T and Delorme, A and Makeig, S},
title = {The Lab Streaming Layer for Synchronized Multimodal Recording.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.02.13.580071},
pmid = {38405712},
issn = {2692-8205},
abstract = {Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.},
}
@article {pmid38404713,
year = {2024},
author = {Zhang, X and Wang, Y and Tang, Y and Wang, Z},
title = {Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.},
journal = {Health information science and systems},
volume = {12},
number = {1},
pages = {11},
pmid = {38404713},
issn = {2047-2501},
abstract = {PURPOSE: In the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined movements, and ultimately enabling individuals to control external devices. However, the low signal-to-noise ratio, multiple channels and non-linearity are the essential challenges of accurate MI classification. To tackle these issues, we investigate the role of adaptive frequency bands selection and spatial-temporal feature learning in decoding motor imagery.<br><br>METHODS: We propose an Adaptive Filter of Frequency Bands based Coordinate Attention Network (AFFB-CAN) to improve the performance of MI classification. Specifically, we design the AFFB to adaptively obtain the upper and lower limits of frequency bands in order to alleviate information loss caused by manual selection. Next, we propose the CAN-based network to emphasize the key brain regions and temporal segments. And we design a multi-scale module to enhance temporal context learning.<br><br>RESULTS: The conducted experiments on the BCI Competition IV-2a and 2b datasets reveal that our approach achieves an outstanding average accuracy, kappa values, and Macro F1-Score with 0.7825, 0.7104, and 0.7486 respectively. Similarly, for the BCI Competition IV-2b dataset, the average accuracy, kappa values, and F1-Score obtained are 0.8879, 0.7427, and 0.8734, respectively.<br><br>CONCLUSION: The proposed AFFB-CAN method improves the performance of MI classification. In addition, our study confirms previous findings that motor imagery is mainly associated with &#xb5; and &#x3b2; rhythms. Moreover, we also find that &#x3b3; rhythms also play an important role in MI classification.},
}
@article {pmid38404196,
year = {2025},
author = {Ramkumar, E and Paulraj, M},
title = {Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {1},
pages = {61-78},
doi = {10.1080/10255842.2024.2319701},
pmid = {38404196},
issn = {1476-8259},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Algorithms ; },
abstract = {The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, making a BCI is difficult in part because of the enormous dimensionality of the data. Three stages make up the suggested methodology: pre-processing, extraction of features, selection, and categorization. To remove unwanted artifacts, the EEG signals are filtered by a fifth-order Butterworth multichannel band-pass filter. This decreases execution time and memory use, both of which improve system performance. Then a novel multichannel optimized CSP-ICA feature extraction technique is used to separate and eliminate non-discriminative information from discriminative information in the EEG channels. Furthermore, CSP uses the concept of an Artificial Bee Colony (ABC) algorithm to automatically identify the simultaneous global ideal frequency band and time interval combination for the extraction and classification of common spatial pattern characteristics. Finally, a Tunable optimized feed-forward neural network (FFNN) classifier is utilized to extract and categorize the temporal and frequency domain features, which employs an FFNN classifier with Tunable-Q wavelet transform. The proposed framework, therefore optimizes signal processing, enabling enhanced EEG signal classification for BCI applications. The result shows that the models that use Tunable optimized FFNN produce higher classification accuracy of more than 20% when compared to the existing models.},
}
@article {pmid38403735,
year = {2025},
author = {Cao, HL and Wei, W and Meng, YJ and Deng, RH and Li, XJ and Deng, W and Liu, YS and Tang, Z and Du, XD and Greenshaw, AJ and Li, ML and Li, T and Guo, WJ},
title = {Interactions between overweight/obesity and alcohol dependence impact human brain white matter microstructure: evidence from DTI.},
journal = {European archives of psychiatry and clinical neuroscience},
volume = {275},
number = {2},
pages = {439-449},
pmid = {38403735},
issn = {1433-8491},
support = {81571305//National Natural Science Foundation of China/ ; 82171487//National Natural Science Foundation of China/ ; SZYJTD201715//Introduction Project of Suzhou Clinical Expert Team/ ; },
mesh = {Humans ; Male ; *Alcoholism/pathology/complications/diagnostic imaging ; Diffusion Tensor Imaging ; Female ; Adult ; *White Matter/diagnostic imaging/pathology ; *Overweight/complications/pathology/diagnostic imaging ; *Obesity/complications/pathology/diagnostic imaging ; Anisotropy ; Middle Aged ; *Brain/pathology/diagnostic imaging ; },
abstract = {There is inconsistent evidence for an association of obesity with white matter microstructural alterations. Such inconsistent findings may be related to the cumulative effects of obesity and alcohol dependence. This study aimed to investigate the possible interactions between alcohol dependence and overweight/obesity on white matter microstructure in the human brain. A total of 60 inpatients with alcohol dependence during early abstinence (44 normal weight and 16 overweight/obese) and 65 controls (42 normal weight and 23 overweight/obese) were included. The diffusion tensor imaging (DTI) measures [fractional anisotropy (FA) and radial diffusivity (RD)] of the white matter microstructure were compared between groups. We observed significant interactive effects between alcohol dependence and overweight/obesity on DTI measures in several tracts. The DTI measures were not significantly different between the overweight/obese and normal-weight groups (although widespread trends of increased FA and decreased RD were observed) among controls. However, among the alcohol-dependent patients, the overweight/obese group had widespread reductions in FA and widespread increases in RD, most of which significantly differed from the normal-weight group; among those with overweight/obesity, the alcohol-dependent group had widespread reductions in FA and widespread increases in RD, most of which were significantly different from the control group. This study found significant interactive effects between overweight/obesity and alcohol dependence on white matter microstructure, indicating that these two controllable factors may synergistically impact white matter microstructure and disrupt structural connectivity in the human brain.},
}
@article {pmid38403619,
year = {2024},
author = {Zhang, Z and Chen, Y and Zhao, X and Wang, F and Ding, P and Zhao, L and Fu, Y},
title = {[Ethical considerations for medical applications of implantable brain-computer interfaces].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {41},
number = {1},
pages = {177-183},
pmid = {38403619},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Prostheses and Implants ; Electrodes ; },
abstract = {Implantable brain-computer interfaces (BCIs) have potentially important clinical applications due to the high spatial resolution and signal-to-noise ratio of electrodes that are closer to or implanted in the cerebral cortex. However, the surgery and electrodes of implantable BCIs carry safety risks of brain tissue damage, and their medical applications face ethical challenges, with little literature to date systematically considering ethical norms for the medical applications of implantable BCIs. In order to promote the clinical translation of this type of BCI, we considered the ethics of practice for the medical application of implantable BCIs, including: reducing the risk of brain tissue damage from implantable BCI surgery and electrodes, providing patients with customized and personalized implantable BCI treatments, ensuring multidisciplinary collaboration in the clinical application of implantable BCIs, and the responsible use of implantable BCIs, among others. It is expected that this article will provide thoughts and references for the research and development of ethics of the medical application of implantable BCI.},
}
@article {pmid38400897,
year = {2024},
author = {Pirasteh, A and Shamseini Ghiyasvand, M and Pouladian, M},
title = {EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {19},
number = {8},
pages = {3183-3193},
doi = {10.1080/17483107.2024.2316312},
pmid = {38400897},
issn = {1748-3115},
mesh = {Humans ; *Amyotrophic Lateral Sclerosis/rehabilitation/physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; Support Vector Machine ; Neural Networks, Computer ; },
abstract = {Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.},
}
@article {pmid38400384,
year = {2024},
author = {Correia, G and Crosse, MJ and Lopez Valdes, A},
title = {Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400384},
issn = {1424-8220},
mesh = {Humans ; Ear ; Brain/physiology ; Electroencephalography/methods ; Electrodes ; *Brain-Computer Interfaces ; *Wearable Electronic Devices ; },
abstract = {EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.},
}
@article {pmid38400359,
year = {2024},
author = {Naseer, N and Niazi, IK and Santosa, H},
title = {Editorial: Signal Processing for Brain-Computer Interfaces-Special Issue.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400359},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Quality of Life ; Brain ; Signal Processing, Computer-Assisted ; },
abstract = {With the astounding ability to capture a wealth of brain signals, Brain-Computer Interfaces (BCIs) have the potential to revolutionize humans' quality of life [...].},
}
@article {pmid38396098,
year = {2024},
author = {Drew, L},
title = {Neuralink brain chip: advance sparks safety and secrecy concerns.},
journal = {Nature},
volume = {627},
number = {8002},
pages = {19},
doi = {10.1038/d41586-024-00550-6},
pmid = {38396098},
issn = {1476-4687},
mesh = {Humans ; *Brain/surgery ; *Brain-Computer Interfaces/adverse effects/trends ; *Confidentiality/ethics ; *Prostheses and Implants/adverse effects ; },
}
@article {pmid38396070,
year = {2024},
author = {Şekerci, Y and Kahraman, MU and Özturan, &#xd6; and Çelik, E and Ayan, S&#x15e;},
title = {Neurocognitive responses to spatial design behaviors and tools among interior architecture students: a pilot study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {4454},
pmid = {38396070},
issn = {2045-2322},
mesh = {Humans ; Pilot Projects ; *Emotions/physiology ; *Electroencephalography/methods ; Students ; Recognition, Psychology ; },
abstract = {The impact of emotions on human behavior is substantial, and the ability to recognize people's feelings has a wide range of practical applications including education. Here, the methods and tools of education are being calibrated according to the data gained over electroencephalogram (EEG) signals. The issue of which design tools would be ideal in the future of interior architecture education, is an uncertain field. It is important to measure the students' emotional states while using manual and digital design tools to determine the different impacts. Brain-computer interfaces have made it possible to monitor emotional states in a way that is both convenient and economical. In the research of emotion recognition, EEG signals have been employed, and the resulting literature explains basic emotions as well as complicated scenarios that are created from the combination of numerous basic emotions. The objective of this study is to investigate the emotional states and degrees of attachment experienced by interior architecture students while engaging in their design processes. This includes examining the use of 2D or 3D tools, whether manual or digital, and identifying any changes in design tool usage and behaviors that may be influenced by different teaching techniques. Accordingly, the hierarchical clustering which is a technique used in data analysis to group objects into a hierarchical structure of clusters based on their similarities has been conducted.},
}
@article {pmid38394680,
year = {2024},
author = {Noble, SC and Woods, E and Ward, T and Ringwood, JV},
title = {Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad2c9e},
pmid = {38394680},
issn = {1741-2552},
mesh = {Adult ; Humans ; *Neurofeedback/methods ; Electroencephalography/methods ; Single-Blind Method ; Learning ; Cognition ; },
abstract = {Objective. Neurofeedback (NFB) training through brain-computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling task. This study aims to assess the impact of task difficulty adaptation on ERP-based attention training in healthy adults. To achieve this, we introduce a novel adaptation employing iterative learning control (ILC) and compare it against an existing method and a control group with random task difficulty variation.Approach. The study involved 45 healthy participants in a single-blind, three-arm randomised controlled trial. Each group underwent one NFB training session, using different methods to adapt task difficulty in a P300 spelling task: two groups with personalised difficulty adjustments (our proposed ILC and an existing approach) and one group with random difficulty. Cognitive performance was evaluated before and after the training session using a visual spatial attention task and we gathered participant feedback through questionnaires.Main results. All groups demonstrated a significant performance improvement in the spatial attention task post-training, with an average increase of 12.63%. Notably, the group using the proposed iterative learning controller achieved a 22% increase in P300 amplitude during training and a 17% reduction in post-training alpha power, all while significantly accelerating the training process compared to other groups.Significance. Our results suggest that ERP-based NFB training using a P300 speller effectively enhances attention in healthy adults, with significant improvements observed after a single session. Personalised task difficulty adaptation using ILC not only accelerates the training but also enhances ERPs during the training. Accelerating NFB training, while maintaining its effectiveness, is vital for its acceptability by both end-users and clinicians.},
}
@article {pmid38391734,
year = {2024},
author = {Rassam, R and Chen, Q and Gai, Y},
title = {Competing Visual Cues Revealed by Electroencephalography: Sensitivity to Motion Speed and Direction.},
journal = {Brain sciences},
volume = {14},
number = {2},
pages = {},
pmid = {38391734},
issn = {2076-3425},
abstract = {Motion speed and direction are two fundamental cues for the mammalian visual system. Neurons in various places of the neocortex show tuning properties in term of firing frequency to both speed and direction. The present study applied a 32-channel electroencephalograph (EEG) system to 13 human subjects while they were observing a single object moving with different speeds in various directions from the center of view to the periphery on a computer monitor. Depending on the experimental condition, the subjects were either required to fix their gaze at the center of the monitor while the object was moving or to track the movement with their gaze; eye-tracking glasses were used to ensure that they followed instructions. In each trial, motion speed and direction varied randomly and independently, forming two competing visual features. EEG signal classification was performed for each cue separately (e.g., 11 speed values or 11 directions), regardless of variations in the other cue. Under the eye-fixed condition, multiple subjects showed distinct preferences to motion direction over speed; however, two outliers showed superb sensitivity to speed. Under the eye-tracking condition, in which the EEG signals presumably contained ocular movement signals, all subjects showed predominantly better classification for motion direction. There was a trend that speed and direction were encoded by different electrode sites. Since EEG is a noninvasive and portable approach suitable for brain-computer interfaces (BCIs), this study provides insights on fundamental knowledge of the visual system as well as BCI applications based on visual stimulation.},
}
@article {pmid38391261,
year = {2025},
author = {Kumari, R and Dybus, A and Purcell, M and Vučković, A},
title = {Motor priming to enhance the effect of physical therapy in people with spinal cord injury.},
journal = {The journal of spinal cord medicine},
volume = {48},
number = {2},
pages = {312-326},
pmid = {38391261},
issn = {2045-7723},
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation/physiopathology ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; Middle Aged ; Electroencephalography ; *Physical Therapy Modalities ; *Neurological Rehabilitation/methods ; *Electric Stimulation Therapy/methods ; },
abstract = {CONTEXT: Brain-Computer Interface (BCI) is an emerging neurorehabilitation therapy for people with spinal cord injury (SCI).<br><br>OBJECTIVE: The study aimed to test whether priming the sensorimotor system using BCI-controlled functional electrical stimulation (FES) before physical practice is more beneficial than physical practice alone.<br><br>METHODS: Ten people with subacute SCI participated in a randomized control trial where the experimental (N&#x2009;=&#x2009;5) group underwent BCI-FES priming (&#x223c;15&#x2005;min) before physical practice (30&#x2005;min), while the control (N&#x2009;=&#x2009;5) group performed physical practice (40&#x2005;min) of the dominant hand. The primary outcome measures were BCI accuracy, adherence, and perceived workload. The secondary outcome measures were manual muscle test, grip strength, the range of motion, and Electroencephalography (EEG) measured brain activity.<br><br>RESULTS: The average BCI accuracy was 85%. The experimental group found BCI-FES priming mentally demanding but not frustrating. Two participants in the experimental group did not complete all sessions due to early discharge. There were no significant differences in physical outcomes between the groups. The ratio between eyes closed to eyes opened EEG activity increased more in the experimental group (theta P&#x3b8;&#x2009;=&#x2009;0.008, low beta Pl&#x3b2;&#x2009;=&#x2009;0.009, and high beta Ph&#x3b2;&#x2009;=&#x2009;1.48e-04) indicating better neurological outcomes. There were no measurable immediate effects of BCI-FES priming.<br><br>CONCLUSION: Priming the brain before physical therapy is feasible but may require more than 15&#x2005;min. This warrants further investigation with an increased sample size.},
}
@article {pmid38390751,
year = {2024},
author = {El Khoury, J and Hermieu, N and Chesnel, C and Xylinas, E and Teng, M and Ouzaid, I and Hermieu, JF and Amarenco, G and Hentzen, C},
title = {Primary bladder neck obstruction in men: The importance of urodynamic assessment and cystourethrography in measuring its severity.},
journal = {Neurourology and urodynamics},
volume = {43},
number = {4},
pages = {874-882},
doi = {10.1002/nau.25429},
pmid = {38390751},
issn = {1520-6777},
mesh = {Male ; Adult ; Humans ; *Urinary Bladder Neck Obstruction/diagnosis ; Retrospective Studies ; Urodynamics ; Urinary Bladder ; Urination ; },
abstract = {OBJECTIVE: Primary bladder neck obstruction (PBNO) is a condition primarily affecting young men, characterized by obstruction at the bladder neck, leading to lower urinary tract symptoms. The aim of this study was to identify a correlation between the severity of bladder neck opening impairment and urinary symptoms by means of urodynamic studies.<br><br>MATERIALS AND METHODS: A retrospective analysis was conducted in adult males diagnosed with PBNO at a university neurourology department between 2015 and 2022 who underwent voiding cystourethrography (VCUG) and pressure-flow studies. The cohort was divided into two groups: absence of bladder neck opening on VCUG (Group A) and incomplete bladder neck opening (Group B).<br><br>RESULTS: Out of the 82 patients with PBNO screened, 53 were included in the analysis. Nocturia was the only symptom more prevalent in Group A (65% in Group A vs. 30% in Group B, p&#x2009;=&#x2009;0.02) but scores and subscores of the Urinary Symptom Profile questionnaire were not different between groups. In addition, the detrusor pressure at a maximum flow rate (PdetQmax), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI) were higher in Group A than in Group B [PdetQmax (A&#x2009;=&#x2009;93.7&#x2009;&#xb1;&#x2009;53.7&#x2009;cmH2O vs. B&#x2009;=&#x2009;65.7&#x2009;&#xb1;&#x2009;26.4&#x2009;cmH2O; p&#x2009;=&#x2009;0.01)-BOOI (A&#x2009;=&#x2009;77&#x2009;&#xb1;&#x2009;58.3 vs. B&#x2009;=&#x2009;48&#x2009;&#xb1;&#x2009;25.7; p&#x2009;=&#x2009;0.03)-BCI (A&#x2009;=&#x2009;136&#x2009;&#xb1;&#x2009;51.3 vs. B&#x2009;=&#x2009;110&#x2009;&#xb1;&#x2009;41.7; p&#x2009;=&#x2009;0.04)].<br><br>CONCLUSION: This study demonstrates a significant association between the extent of bladder neck opening impairment observed on VCUG and obstruction and contraction urodynamic parameters, but no association with the severity of urinary symptoms. Future studies should evaluate the predictive value of treatment response and the occurrence of complications based on clinical and urodynamic parameters.},
}
@article {pmid38390553,
year = {2024},
author = {Chapman, DP and Wu, JY},
title = {Concept for intrathecal delivery of brain recording and stimulation device.},
journal = {Frontiers in medical technology},
volume = {6},
number = {},
pages = {1211585},
pmid = {38390553},
issn = {2673-3129},
abstract = {Neurological disorders are common, yet many neurological diseases don't have efficacious treatments. The protected nature of the brain both anatomically and physiologically through the blood brain barrier (BBB) make it exceptionally hard to access. Recent advancements in interventional approaches, like the Stentrode™, have opened the possibility of using the cerebral vasculature as a highway for minimally invasive therapeutic delivery to the brain. Despite the immense success that the Stentrode™ has faced recently, it is limited to major cerebral vasculature and exists outside the BBB, making drug eluting configurations largely ineffective. The present study seeks to identify a separate anatomical pathway for therapeutic delivery to the deep brain using the ventricular system. The intrathecal route, in which drug pumps and spinal cord stimulators are delivered through a lumbar puncture, is a well-established route for delivering therapies to the spinal cord as high as C1. The present study identifies an extension of this anatomical pathway through the foramen of Magendie and into the brains ventricular system. To test this pathway, a narrow self-expanding electrical recording device was manufactured and its potential to navigate the ventricular system was assessed on human anatomical brain samples. While the results of this paper are largely preliminary and a substantial amount of safety and efficacy data is needed, this paper identifies an important anatomical pathway for delivery of therapeutic and diagnostics tools to the brain that is minimally invasive, can access limbic structures, and is within the BBB.},
}
@article {pmid38388478,
year = {2024},
author = {He, Q and Yang, Y and Ge, P and Li, S and Chai, X and Luo, Z and Zhao, J},
title = {The brain nebula: minimally invasive brain-computer interface by endovascular neural recording and stimulation.},
journal = {Journal of neurointerventional surgery},
volume = {16},
number = {12},
pages = {1237-1243},
pmid = {38388478},
issn = {1759-8486},
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Endovascular Procedures/methods ; *Brain/physiology ; *Deep Brain Stimulation/methods ; },
abstract = {A brain-computer interface (BCI) serves as a direct communication channel between brain activity and external devices, typically a computer or robotic limb. Advances in technology have led to the increasing use of intracranial electrical recording or stimulation in the treatment of conditions such as epilepsy, depression, and movement disorders. This indicates that BCIs can offer clinical neurological rehabilitation for patients with disabilities and functional impairments. They also provide a means to restore consciousness and functionality for patients with sequelae from major brain diseases. Whether invasive or non-invasive, the collected cortical or deep signals can be decoded and translated for communication. This review aims to provide an overview of the advantages of endovascular BCIs compared with conventional BCIs, along with insights into the specific anatomical regions under study. Given the rapid progress, we also provide updates on ongoing clinical trials and the prospects for current research involving endovascular electrodes.},
}
@article {pmid38386506,
year = {2024},
author = {Iwane, F and Porssut, T and Blanke, O and Chavarriaga, R and Del R Millán, J and Herbelin, B and Boulic, R},
title = {Customizing the human-avatar mapping based on EEG error related potentials.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad2c02},
pmid = {38386506},
issn = {1741-2552},
mesh = {Humans ; *Avatar ; User-Computer Interface ; *Virtual Reality ; Movement ; Electroencephalography ; },
abstract = {Objective.A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside the virtual body when they perceive erroneous interaction, i.e. a break-in-embodiment (BiE). However, the way to detect such an inadequate event is currently limited to questionnaires or spontaneous reports from users. The ability to implicitly detect BiE in real-time enables us to adjust human-avatar mapping without interruption.Approach.We propose and empirically demonstrate a novel brain computer interface (BCI) approach that monitors the occurrence of BiE based on the users' brain oscillatory activity in real-time to adjust the human-avatar mapping in VR. We collected EEG activity of 37 participants while they performed reaching movements with their avatar with different magnitude of distortion.Main results.Our BCI approach seamlessly predicts occurrence of BiE in varying magnitude of erroneous interaction. The mapping has been customized by BCI-reinforcement learning (RL) closed-loop system to prevent BiE from occurring. Furthermore, a non-personalized BCI decoder generalizes to new users, enabling 'Plug-and-Play' ErrP-based non-invasive BCI. The proposed VR system allows customization of human-avatar mapping without personalized BCI decoders or spontaneous reports.Significance.We anticipate that our newly developed VR-BCI can be useful to maintain an engaging avatar-based interaction and a compelling immersive experience while detecting when users notice a problem and seamlessly correcting it.},
}
@article {pmid38383721,
year = {2024},
author = {Wei, Z and Chen, Y and Zhao, Q and Ren, J and Piao, Y and Zhang, P and Zha, R and Qiu, B and Zhang, D and Bi, Y and Han, S and Li, C and Zhang, X},
title = {Separable amygdala activation patterns in the evaluations of robots.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {2},
pages = {},
doi = {10.1093/cercor/bhae011},
pmid = {38383721},
issn = {1460-2199},
support = {32100886//National Natural Science Foundation of China/ ; 2021ZD0202101//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; GLHZ202128//CAS-VPST Silk Road Science Fund 2021/ ; DJK-LX-2022008//Global Select Project/ ; //Institute of Health and Medicine/ ; //Hefei Comprehensive National Science Center/ ; 2023JYBKFK008//Key Laboratory of Brain-Machine Intelligence for Information Behavior-Ministry of Education/ ; },
mesh = {Humans ; *Robotics/methods ; Brain/physiology ; Neuroimaging ; Amygdala/diagnostic imaging ; Self Report ; },
abstract = {Given the increasing presence of robots in everyday environments and the significant challenge posed by social interactions with robots, it is crucial to gain a deeper understanding into the social evaluations of robots. One potentially effective approach to comprehend the fundamental processes underlying controlled and automatic evaluations of robots is to probe brain response to different perception levels of robot-related stimuli. Here, we investigate controlled and automatic evaluations of robots based on brain responses during viewing of suprathreshold (duration: 200 ms) and subthreshold (duration: 17 ms) humanoid robot stimuli. Our behavioral analysis revealed that despite participants' self-reported positive attitudes, they held negative implicit attitudes toward humanoid robots. Neuroimaging analysis indicated that subthreshold presentation of humanoid robot stimuli elicited significant activation in the left amygdala, which was associated with negative implicit attitudes. Conversely, no significant left amygdala activation was observed during suprathreshold presentation. Following successful attenuation of negative attitudes, the left amygdala response to subthreshold presentation of humanoid robot stimuli decreased, and this decrease correlated positively with the reduction in negative attitudes. These findings provide evidence for separable patterns of amygdala activation between controlled and automatic processing of robots, suggesting that controlled evaluations may influence automatic evaluations of robots.},
}
@article {pmid38382863,
year = {2024},
author = {Shi, N and Miao, Y and Huang, C and Li, X and Song, Y and Chen, X and Wang, Y and Gao, X},
title = {Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface.},
journal = {NeuroImage},
volume = {289},
number = {},
pages = {120548},
doi = {10.1016/j.neuroimage.2024.120548},
pmid = {38382863},
issn = {1095-9572},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Signal-To-Noise Ratio ; Communication ; Photic Stimulation ; Algorithms ; },
abstract = {An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.},
}
@article {pmid38382711,
year = {2024},
author = {He, L and Zhang, L and Sun, Q and Lin, X},
title = {A generative adaptive convolutional neural network with attention mechanism for driver fatigue detection with class-imbalanced and insufficient data.},
journal = {Behavioural brain research},
volume = {464},
number = {},
pages = {114898},
doi = {10.1016/j.bbr.2024.114898},
pmid = {38382711},
issn = {1872-7549},
mesh = {*Neural Networks, Computer ; Recognition, Psychology ; Upper Extremity ; },
abstract = {Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue detection systems. However, many existing methods involves tedious manual parameter tunings, a process that is both time-consuming and results in task-specific models. On the other hand, most of the researches on fatigue recognition are based on class-balanced and sufficient data, and effectively "mine" meaningful information from class-imbalanced and insufficient data for fatigue recognition is still a challenge. In this paper, we proposed two novel models, the attention-based residual adaptive multiscale fully convolutional network-long short term memory network (ARMFCN-LSTM), and the Generative ARMFCN-LSTM (GARMFCN-LSTM) aiming to address this issue. ARMFCN-LSTM excels at automatically extracting multiscale representations through adaptive multiscale temporal convolutions, while capturing temporal dependency features through LSTM. GARMFCN-LSTM integrates Wasserstein GAN with gradient penalty (WGAN-GP) into ARMFCN-LSTM to improve driver fatigue detection performance by alleviating data scarcity and addressing class imbalances. Experimental results show that ARMFCN-LSTM achieves the highest classification accuracy of 95.84% in driver fatigue detection on the class-balanced EEG dataset (binary classification), and GARMFCN-LSTM attained an improved classification accuracy of 84.70% on the class-imbalanced EOG dataset (triple classification), surpassing the competing methods. Therefore, the proposed models are promising for further implementations in online driver fatigue detection systems.},
}
@article {pmid38382104,
year = {2024},
author = {Slack, JC and Zeiser, SL and Yadav, AP},
title = {The role of stimulus periodicity on spinal cord stimulation-induced artificial sensations in rodents.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38382104},
issn = {1741-2552},
support = {DP2 NS136872/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Rats ; Animals ; Rodentia ; *Spinal Cord Stimulation/methods ; Sensation ; Learning ; *Brain-Computer Interfaces ; Spinal Cord/physiology ; },
abstract = {Objective.Sensory feedback is critical for effectively controlling brain-machine interfaces and neuroprosthetic devices. Spinal cord stimulation (SCS) is proposed as a technique to induce artificial sensory perceptions in rodents, monkeys, and humans. However, to realize the full potential of SCS as a sensory neuroprosthetic technology, a better understanding of the effect of SCS pulse train parameter changes on sensory detection and discrimination thresholds is necessary.Approach.Here we investigated whether stimulation periodicity impacts rats' ability to detect and discriminate SCS-induced perceptions at different frequencies.Main results.By varying the coefficient of variation (CV) of interstimulus pulse interval, we showed that at lower frequencies, rats could detect highly aperiodic SCS pulse trains at lower amplitudes (i.e. decreased detection thresholds). Furthermore, rats learned to discriminate stimuli with subtle differences in periodicity, and the just-noticeable differences from a highly aperiodic stimulus were smaller than those from a periodic stimulus.Significance.These results demonstrate that the temporal structure of an SCS pulse train is an integral parameter for modulating sensory feedback in neuroprosthetic applications.},
}
@article {pmid38380980,
year = {2024},
author = {Van Gerrewey, T and Navarrete, O and Vandecruys, M and Perneel, M and Boon, N and Geelen, D},
title = {Bacterially enhanced plant-growing media for controlled environment agriculture.},
journal = {Microbial biotechnology},
volume = {17},
number = {2},
pages = {e14422},
pmid = {38380980},
issn = {1751-7915},
support = {HBC.2017.0209//Agentschap Innoveren en Ondernemen/ ; },
mesh = {RNA, Ribosomal, 16S/genetics ; *Agriculture ; *Bacteria/genetics ; Plants/genetics ; Soil/chemistry ; Plant Roots/microbiology ; Soil Microbiology ; },
abstract = {Microbe-plant interactions in the root zone not only shape crop performance in soil but also in hydroponic cultivation systems. The biological and physicochemical properties of the plant-growing medium determine the root-associated microbial community and influence bacterial inoculation effectiveness, which affects plant growth. This study investigated the combined impact of plant-growing media composition and bacterial community inoculation on the root-associated bacterial community of hydroponically grown lettuce (Lactuca sativa L.). Ten plant-growing media were composed of varying raw materials, including black peat, white peat, coir pith, wood fibre, composted bark, green waste compost, perlite and sand. In addition, five different bacterial community inocula (BCI S1-5) were collected from the roots of lettuce obtained at different farms. After inoculation and cultivation inside a vertical farm, lettuce root-associated bacterial community structures, diversity and compositions were determined by evaluating 16S rRNA gene sequences. The study revealed distinct bacterial community structures among experimental replicates, highlighting the influence of raw material variations on root-associated bacterial communities, even at the batch level. However, bacterial community inoculation allowed modulation of the root-associated bacterial communities independently from the plant-growing medium composition. Bacterial diversity was identified as a key determinant of plant growth performance with green waste compost introducing Bacilli and Actinobacteria, and bacterial community inoculum S3 introducing Pseudomonas, which positively correlated with plant growth. These findings challenge the prevailing notion of hydroponic cultivation systems as sterile environments and highlight the significance of proper plant-growing media raw material selection and bacterial community inoculation in shaping root-associated microbiomes that provide stability through microbial diversity. This study supports the concept of creating bacterially enhanced plant-growing media to promote plant growth in controlled environment agriculture.},
}
@article {pmid38378830,
year = {2024},
author = {Naddaf, M},
title = {Mind-reading devices are revealing the brain's secrets.},
journal = {Nature},
volume = {626},
number = {8000},
pages = {706-708},
pmid = {38378830},
issn = {1476-4687},
mesh = {Humans ; *Brain/cytology/physiology ; *Neurosciences/instrumentation/methods/trends ; *Prostheses and Implants ; Brain-Computer Interfaces ; },
}
@article {pmid38377638,
year = {2024},
author = {Saway, BF and Palmer, C and Hughes, C and Triano, M and Suresh, RE and Gilmore, J and George, M and Kautz, SA and Rowland, NC},
title = {The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {21},
number = {3},
pages = {e00337},
pmid = {38377638},
issn = {1878-7479},
support = {IK6 RX003075/RX/RRD VA/United States ; P30 GM154630/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces/trends ; *Neuronal Plasticity/physiology ; *Stroke/therapy/physiopathology ; *Deep Brain Stimulation/methods/trends ; *Stroke Rehabilitation/methods/trends ; *Vagus Nerve Stimulation/methods/trends ; Chronic Disease ; },
abstract = {Stroke is one of the most common and debilitating neurological conditions worldwide. Those who survive experience motor, sensory, speech, vision, and/or cognitive deficits that severely limit remaining quality of life. While rehabilitation programs can help improve patients' symptoms, recovery is often limited, and patients frequently continue to experience impairments in functional status. In this review, invasive neuromodulation techniques to augment the effects of conventional rehabilitation methods are described, including vagus nerve stimulation (VNS), deep brain stimulation (DBS) and brain-computer interfaces (BCIs). In addition, the evidence base for each of these techniques, pivotal trials, and future directions are explored. Finally, emerging technologies such as functional near-infrared spectroscopy (fNIRS) and the shift to artificial intelligence-enabled implants and wearables are examined. While the field of implantable devices for chronic stroke recovery is still in a nascent stage, the data reviewed are suggestive of immense potential for reducing the impact and impairment from this globally prevalent disorder.},
}
@article {pmid38377064,
year = {2024},
author = {Oxley, TJ},
title = {The Promise of Endovascular Neurotechnology: A Brain-Computer Interface to Restore Autonomy to People With Motor Impairment.},
journal = {American journal of physical medicine &amp; rehabilitation},
volume = {103},
number = {6},
pages = {465-470},
pmid = {38377064},
issn = {1537-7385},
mesh = {Humans ; *Brain-Computer Interfaces ; Endovascular Procedures/methods ; Personal Autonomy ; Activities of Daily Living ; Paralysis/rehabilitation ; },
abstract = {This Joel A. DeLisa Lecture on endovascular brain-computer interfaces was presented by Dr Thomas Oxley on February 23, 2023, at the Association of Academic Physiatrists Annual Scientific Meeting. The lecture described how brain-computer interfaces replace lost physiological function to enable direct communication between the brain and external digital devices, such as computers, smartphones, and robotic limbs. Specifically, the potential of a novel endovascular brain-computer interface technology was discussed. The brain-computer interface uses a stent-electrode array delivered via the jugular vein and is permanently implanted in a vein adjacent to the motor cortex. In a first-in-human clinical trial, participants with upper limb paralysis who received the endovascular brain-computer interface could use the system independently and at home to operate laptop computers for various instrumental activities of daily living. A Food and Drug Administration-approved trial of the endovascular brain-computer interface in the United States is in progress. Future development of the system will provide recipients with continuous autonomy through digital access with minimal caregiver assistance. Physiatrists and occupational therapists will have a vital role in helping people with paralysis achieve the potential of implantable brain-computer interfaces.},
}
@article {pmid38376912,
year = {2024},
author = {Mamounas, EP and Bandos, H and Rastogi, P and Zhang, Y and Treuner, K and Lucas, PC and Geyer, CE and Fehrenbacher, L and Chia, SK and Brufsky, AM and Walshe, JM and Soori, GS and Dakhil, S and Paik, S and Swain, SM and Sgroi, DC and Schnabel, CA and Wolmark, N},
title = {Breast Cancer Index and Prediction of Extended Aromatase Inhibitor Therapy Benefit in Hormone Receptor-Positive Breast Cancer from the NRG Oncology/NSABP B-42 Trial.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {30},
number = {9},
pages = {1984-1991},
pmid = {38376912},
issn = {1557-3265},
support = {U10 CA180822/CA/NCI NIH HHS/United States ; U10 CA180868/CA/NCI NIH HHS/United States ; U24 CA196067/CA/NCI NIH HHS/United States ; UG1 CA189867/CA/NCI NIH HHS/United States ; UG1CA189867/BC/NCI NIH HHS/United States ; NCI U10CA 180868/BC/NCI NIH HHS/United States ; NCI U10CA 180822/BC/NCI NIH HHS/United States ; },
mesh = {Adult ; Aged ; Female ; Humans ; Middle Aged ; *Aromatase Inhibitors/therapeutic use ; *Breast Neoplasms/drug therapy/metabolism/pathology ; *Letrozole/therapeutic use/administration &amp; dosage ; Nitriles/therapeutic use ; Prognosis ; *Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Treatment Outcome ; Triazoles/therapeutic use/administration &amp; dosage ; },
abstract = {PURPOSE: BCI (H/I) has been shown to predict extended endocrine therapy (EET) benefit. We examined BCI (H/I) for EET benefit prediction in NSABP B-42, which evaluated extended letrozole therapy (ELT) in patients with hormone receptor-positive breast cancer after 5 years of ET.<br><br>EXPERIMENTAL DESIGN: A stratified Cox model was used to analyze RFI as the primary endpoint, with DR, BCFI, and DFS as secondary endpoints. Because of a nonproportional effect of ELT on DR, time-dependent analyses were performed.<br><br>RESULTS: The translational cohort included 2,178 patients (45% BCI (H/I)-High, 55% BCI (H/I)-Low). ELT showed an absolute 10-year RFI benefit of 1.6% (P = 0.10), resulting in an underpowered primary analysis (50% power). ELT benefit and BCI (H/I) did not show a significant interaction for RFI (BCI (H/I)-Low: 10 years absolute benefit 1.1% [HR, 0.70; 95% confidence interval (CI), 0.43-1.12; P = 0.13]; BCI (H/I)-High: 2.4% [HR, 0.83; 95% CI, 0.55-1.26; P = 0.38]; Pinteraction = 0.56). Time-dependent DR analysis showed that after 4 years, BCI (H/I)-High patients had significant ELT benefit (HR = 0.29; 95% CI, 0.12-0.69; P < 0.01), whereas BCI (H/I)-Low patients were less likely to benefit (HR, 0.68; 95% CI, 0.33-1.39; P = 0.29; Pinteraction = 0.14). Prediction of ELT benefit by BCI (H/I) was more apparent in the HER2- subset after 4 years (ELT-by-BCI (H/I) Pinteraction = 0.04).<br><br>CONCLUSIONS: BCI (H/I)-High versus BCI (H/I)-Low did not show a statistically significant difference in ELT benefit for the primary endpoint (RFI). However, in time-dependent DR analysis, BCI (H/I)-High patients experienced statistically significant benefit from ELT after 4 years, whereas (H/I)-Low patients did not. Because BCI (H/I) has been validated as a predictive marker of EET benefit in other trials, additional follow-up may enable further characterization of BCI's predictive ability.},
}
@article {pmid38376747,
year = {2024},
author = {Wang, X and Zheng, J and Xu, H},
title = {Neural Circuitry Involving Substance P in Male Sexual Behavior.},
journal = {Neuroscience bulletin},
volume = {40},
number = {4},
pages = {544-546},
pmid = {38376747},
issn = {1995-8218},
mesh = {Animals ; Male ; *Sexual Behavior, Animal ; *Substance P ; },
}
@article {pmid38375331,
year = {2024},
author = {Hira, R},
title = {Closed-loop experiments and brain machine interfaces with multiphoton microscopy.},
journal = {Neurophotonics},
volume = {11},
number = {3},
pages = {033405},
pmid = {38375331},
issn = {2329-423X},
abstract = {In the field of neuroscience, the importance of constructing closed-loop experimental systems has increased in conjunction with technological advances in measuring and controlling neural activity in live animals. We provide an overview of recent technological advances in the field, focusing on closed-loop experimental systems where multiphoton microscopy-the only method capable of recording and controlling targeted population activity of neurons at a single-cell resolution in vivo-works through real-time feedback. Specifically, we present some examples of brain machine interfaces (BMIs) using in vivo two-photon calcium imaging and discuss applications of two-photon optogenetic stimulation and adaptive optics to real-time BMIs. We also consider conditions for realizing future optical BMIs at the synaptic level, and their possible roles in understanding the computational principles of the brain.},
}
@article {pmid38375134,
year = {2024},
author = {Li, X and Zhang, Y and Peng, Y and Kong, W},
title = {Enhanced performance of EEG-based brain-computer interfaces by joint sample and feature importance assessment.},
journal = {Health information science and systems},
volume = {12},
number = {1},
pages = {9},
pmid = {38375134},
issn = {2047-2501},
abstract = {Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.},
}
@article {pmid38374416,
year = {2024},
author = {He, L and Zhang, L and Lin, X and Qin, Y},
title = {A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {6},
pages = {1781-1793},
pmid = {38374416},
issn = {1741-0444},
mesh = {Humans ; *Deep Learning ; *Electroencephalography/methods ; *Fatigue/diagnosis/physiopathology ; *Neural Networks, Computer ; *Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on traditional deep learning methods may led to the loss of some useful information. This paper proposed a novel τ -shaped convolutional network (τ Net) aiming to address this issue. Unlike traditional network structures, τ Net incorporates the operations of upsampling features and concatenating high- and low-level features, enabling full utilization of useful information. Moreover, considering that the fatigue state is a mental state involving temporal evolution, we proposed the novel long short-term memory (LSTM)- τ -shaped convolutional network (LSTM- τ Net), a parallel structure composed of LSTM and τ Net for fatigue detection, where τ Net extracts time-invariant features with location information, and LSTM extracts long temporal dependencies. We compared LSTM- τ Net with six competing methods based on two datasets. Results showed that the proposed algorithm achieved higher classification accuracy than the other methods, with 94.25% on EEG data (binary classification) and 82.19% on EOG data (triple classification). Additionally, the proposed algorithm exhibits low computational cost, good training stability, and robustness against insufficient training. Therefore, it is promising for further implementation of fatigue online detection systems.},
}
@article {pmid38373136,
year = {2024},
author = {Ding, W and Liu, A and Guan, L and Chen, X},
title = {A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {875-886},
doi = {10.1109/TNSRE.2024.3366930},
pmid = {38373136},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Deep Learning ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; },
abstract = {Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask encoding (EEG-ME) to mitigate overfitting. EEG-ME forces models to learn more robust features by masking partial EEG data, leading to enhanced generalization capabilities of models. Three different network architectures, including an architecture integrating convolutional neural networks (CNN) with Transformer (CNN-Former), time domain-based CNN (tCNN), and a lightweight architecture (EEGNet) are utilized to validate the effectiveness of EEG-ME on publicly available benchmark and BETA datasets. The results demonstrate that EEG-ME significantly enhances the average classification accuracy of various DL-based methods with different data lengths of time windows on two public datasets. Specifically, CNN-Former, tCNN, and EEGNet achieve respective improvements of 3.18%, 1.42%, and 3.06% on the benchmark dataset as well as 11.09%, 3.12%, and 2.81% on the BETA dataset, with the 1-second time window as an example. The enhanced performance of SSVEP classification with EEG-ME promotes the implementation of the asynchronous SSVEP-BCI system, leading to improved robustness and flexibility in human-machine interaction.},
}
@article {pmid38370697,
year = {2024},
author = {Willsey, MS and Shah, NP and Avansino, DT and Hahn, NV and Jamiolkowski, RM and Kamdar, FB and Hochberg, LR and Willett, FR and Henderson, JM},
title = {A real-time, high-performance brain-computer interface for finger decoding and quadcopter control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.02.06.578107},
pmid = {38370697},
issn = {2692-8205},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
abstract = {People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 ± 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization.},
}
@article {pmid38370431,
year = {2024},
author = {Kleeva, D and Ninenko, I and Lebedev, MA},
title = {Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1326139},
pmid = {38370431},
issn = {1662-4548},
abstract = {INTRODUCTION: Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes.<br><br>METHODS: Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications.<br><br>RESULTS: Distinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality.<br><br>DISCUSSION: We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system.},
}
@article {pmid38369007,
year = {2024},
author = {Qin, Y and Li, B and Wang, W and Shi, X and Peng, C and Lu, Y},
title = {Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism.},
journal = {Neuroscience},
volume = {542},
number = {},
pages = {59-68},
doi = {10.1016/j.neuroscience.2024.02.011},
pmid = {38369007},
issn = {1873-7544},
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; Neural Networks, Computer ; Algorithms ; Cerebral Cortex/diagnostic imaging ; Hand ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; },
abstract = {Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.},
}
@article {pmid38366607,
year = {2024},
author = {Guan, X and Shao, W and Zhang, D},
title = {T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition.},
journal = {Bioinformatics (Oxford, England)},
volume = {40},
number = {2},
pages = {},
pmid = {38366607},
issn = {1367-4811},
support = {62136004//National Natural Science Foundation of China/ ; BE2022842//Key Research and Development Plan of Jiangsu Province/ ; },
mesh = {Sequence Analysis, DNA/methods ; *Software ; *Nanopores ; Algorithms ; High-Throughput Nucleotide Sequencing/methods ; },
abstract = {MOTIVATION: Nanopore sequencing is a new macromolecular recognition and perception technology that enables high-throughput sequencing of DNA, RNA, even protein molecules. The sequences generated by nanopore sequencing span a large time frame, and the labor and time costs incurred by traditional analysis methods are substantial. Recently, research on nanopore data analysis using machine learning algorithms has gained unceasing momentum, but there is often a significant gap between traditional and deep learning methods in terms of classification results. To analyze nanopore data using deep learning technologies, measures such as sequence completion and sequence transformation can be employed. However, these technologies do not preserve the local features of the sequences. To address this issue, we propose a sequence-to-image (S2I) module that transforms sequences of unequal length into images. Additionally, we propose the Transformer-based T-S2Inet model to capture the important information and improve the classification accuracy.<br><br>RESULTS: Quantitative and qualitative analysis shows that the experimental results have an improvement of around 2% in accuracy compared to previous methods. The proposed method is adaptable to other nanopore platforms, such as the Oxford nanopore. It is worth noting that the proposed method not only aims to achieve the most advanced performance, but also provides a general idea for the analysis of nanopore sequences of unequal length.<br><br>The main program is available at https://github.com/guanxiaoyu11/S2Inet.},
}
@article {pmid38365989,
year = {2024},
author = {Chen, P and Liu, Y and Yang, J and Wang, D and Ren, R and Li, Y and Yang, L and Fu, X and Dong, R and Zhao, S},
title = {A new active bone-conduction implant: surgical experiences and audiological outcomes in patients with bilateral congenital microtia.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {281},
number = {8},
pages = {4039-4047},
pmid = {38365989},
issn = {1434-4726},
support = {81770989//National Natural Science Foundation of China/ ; 2020-2-2057//Capital Health Research and Development of Special Fund/ ; },
mesh = {Humans ; *Congenital Microtia/surgery/complications ; Male ; Female ; Prospective Studies ; Child ; Adolescent ; *Hearing Loss, Conductive/surgery/etiology ; *Bone Conduction ; Treatment Outcome ; Young Adult ; Adult ; Sound Localization/physiology ; Prosthesis Design ; },
abstract = {PURPOSE: First-generation bone bridges (BBs) have demonstrated favorable safety and audiological benefits in patients with conductive hearing loss. However, studies on the effects of second-generation BBs are limited, especially among children. In this study, we aimed to explore the surgical and audiological effects of second-generation BBs in patients with bilateral congenital microtia.<br><br>METHODS: This single-center prospective study included nine Mandarin-speaking patients with bilateral microtia. All the patients underwent BCI Generation 602 (BCI602; MED-EL, Innsbruck, Austria) implant surgery between September 2021 and June 2023. Audiological and sound localization tests were performed under unaided and BB-aided conditions.<br><br>RESULTS: The transmastoid and retrosigmoid sinus approaches were implemented in three and six patients, respectively. No patient underwent preoperative planning, lifts were unnecessary, and no sigmoid sinus or dural compression occurred. The mean function gain at 0.5-4.0&#xa0;kHz was 28.06&#x2009;&#xb1;&#x2009;4.55-dB HL. The word recognition scores improved significantly in quiet under the BB aided condition. Signal-to-noise ratio reduction by 10.56&#x2009;&#xb1;&#x2009;2.30&#xa0;dB improved the speech reception threshold in noise. Patients fitted with a unilateral BB demonstrated inferior sound source localization after the initial activation.<br><br>CONCLUSIONS: Second-generation BBs are safe and effective for patients with bilateral congenital microtia and may be suitable for children with mastoid hypoplasia without preoperative three-dimensional reconstruction.},
}
@article {pmid38361913,
year = {2024},
author = {Lee, J and Kim, M and Heo, D and Kim, J and Kim, MK and Lee, T and Park, J and Kim, H and Hwang, M and Kim, L and Kim, SP},
title = {A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1320457},
pmid = {38361913},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments.},
}
@article {pmid38359457,
year = {2024},
author = {Deng, H and Li, M and Zuo, H and Zhou, H and Qi, E and Wu, X and Xu, G},
title = {Personalized motor imagery prediction model based on individual difference of ERP.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad29d6},
pmid = {38359457},
issn = {1741-2552},
mesh = {Humans ; *Individuality ; *Brain-Computer Interfaces ; Imagination ; Evoked Potentials ; Electroencephalography/methods ; },
abstract = {Objective. Motor imagery-based brain-computer interaction (MI-BCI) is a novel method of achieving human and external environment interaction that can assist individuals with motor disorders to rehabilitate. However, individual differences limit the utility of the MI-BCI. In this study, a personalized MI prediction model based on the individual difference of event-related potential (ERP) is proposed to solve the MI individual difference.Approach.A novel paradigm named action observation-based multi-delayed matching posture task evokes ERP during a delayed matching posture task phase by retrieving picture stimuli and videos, and generates MI electroencephalogram through action observation and autonomous imagery in an action observation-based motor imagery phase. Based on the correlation between the ERP and MI, a logistic regression-based personalized MI prediction model is built to predict each individual's suitable MI action. 32 subjects conducted the MI task with or without the help of the prediction model to select the MI action. Then classification accuracy of the MI task is used to evaluate the proposed model and three traditional MI methods.Main results.The personalized MI prediction model successfully predicts suitable action among 3 sets of daily actions. Under suitable MI action, the individual's ERP amplitude and event-related desynchronization (ERD) intensity are the largest, which helps to improve the accuracy by 14.25%.Significance.The personalized MI prediction model that uses the temporal ERP features to predict the classification accuracy of MI is feasible for improving the individual's MI-BCI performance, providing a new personalized solution for the individual difference and practical BCI application.},
}
@article {pmid38358954,
year = {2024},
author = {Gao, J and Chen, H and Fang, M and Ding, N},
title = {Original speech and its echo are segregated and separately processed in the human brain.},
journal = {PLoS biology},
volume = {22},
number = {2},
pages = {e3002498},
pmid = {38358954},
issn = {1545-7885},
mesh = {Humans ; *Speech Perception/physiology ; Speech Intelligibility/physiology ; Brain ; *Auditory Cortex/physiology ; Attention ; Acoustic Stimulation ; },
abstract = {Speech recognition crucially relies on slow temporal modulations (<16 Hz) in speech. Recent studies, however, have demonstrated that the long-delay echoes, which are common during online conferencing, can eliminate crucial temporal modulations in speech but do not affect speech intelligibility. Here, we investigated the underlying neural mechanisms. MEG experiments demonstrated that cortical activity can effectively track the temporal modulations eliminated by an echo, which cannot be fully explained by basic neural adaptation mechanisms. Furthermore, cortical responses to echoic speech can be better explained by a model that segregates speech from its echo than by a model that encodes echoic speech as a whole. The speech segregation effect was observed even when attention was diverted but would disappear when segregation cues, i.e., speech fine structure, were removed. These results strongly suggested that, through mechanisms such as stream segregation, the auditory system can build an echo-insensitive representation of speech envelope, which can support reliable speech recognition.},
}
@article {pmid38358054,
year = {2024},
author = {Chai, X and Cao, T and He, Q and Wang, N and Zhang, X and Shan, X and Lv, Z and Tu, W and Yang, Y and Zhao, J},
title = {Brain-computer interface digital prescription for neurological disorders.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {2},
pages = {e14615},
pmid = {38358054},
issn = {1755-5949},
support = {2022ZD0205300//Science and Technology Innovation 2030-Young Scientists Project of Brain Science and brain-like Research/ ; Z221100002722014//International (Hong Kong, Macao, and Taiwan) Science and Technology Cooperation Project/ ; 2022GKZS0003//2022 Open Project of Key Laboratory and Engineering Technology Research Center in the Rehabilitation Field of the Ministry of Civil Affairs/ ; 2022-NKX-XM-02//Chinese Institute for Brain Research Youth Scholar Program/ ; 7232049//Beijing Natural Science Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Nervous System Diseases/therapy ; Brain ; *Brain Diseases ; Electroencephalography/methods ; },
abstract = {Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.},
}
@article {pmid38355058,
year = {2024},
author = {Zhang, R and Guo, H and Xu, Z and Hu, Y and Chen, M and Zhang, L},
title = {MGFKD: A semi-supervised multi-source domain adaptation algorithm for cross-subject EEG emotion recognition.},
journal = {Brain research bulletin},
volume = {208},
number = {},
pages = {110901},
doi = {10.1016/j.brainresbull.2024.110901},
pmid = {38355058},
issn = {1873-2747},
mesh = {Humans ; *Algorithms ; *Emotions ; Recognition, Psychology ; Electroencephalography ; },
abstract = {Currently, most models rarely consider the negative transfer problem in the research field of cross-subject EEG emotion recognition. To solve this problem, this paper proposes a semi-supervised domain adaptive algorithm based on few labeled samples of target subject, which called multi-domain geodesic flow kernel dynamic distribution alignment (MGFKD). It consists of three modules: 1) GFK common feature extractor: projects the feature distribution of source and target subjects to the Grassmann manifold space, and obtains the latent common features of the two feature distributions through GFK method. 2) Source domain selector: obtains pseudo-labels of the target subject through weak classifier, finds "golden source subjects" by using few known labels of target subjects. 3) Label corrector: uses a dynamic distribution balance strategy to correct the pseudo-labels of the target subject. We conducted comparison experiments on the SEED and SEED-IV datasets, and the results show that MGFKD outperforms unsupervised and semi-supervised domain adaptation algorithms, achieving an average accuracy of 87.51±7.68% and 68.79±8.25% on the SEED and SEED-IV datasets with only one labeled sample per video for target subject. Especially when the number of source domains is set as 6 and the number of known labels is set as 5, the accuracy increase to 90.20±7.57% and 69.99±7.38%, respectively. The above results prove that our proposed algorithm can efficiently and quickly improve the cross-subject EEG emotion classification performance. Since it only need a small number of labeled samples of new subjects, making it has strong application value in future EEG-based emotion recognition applications.},
}
@article {pmid38354793,
year = {2024},
author = {Zheng, Z and Ye, L and Xiong, W and Hu, Q and Chen, K and Sun, R and Chen, S},
title = {Prevalence and genomic characterization of the Bacillus cereus group strains contamination in food products in Southern China.},
journal = {The Science of the total environment},
volume = {921},
number = {},
pages = {170903},
doi = {10.1016/j.scitotenv.2024.170903},
pmid = {38354793},
issn = {1879-1026},
mesh = {*Bacillus ; Bacillus cereus/genetics ; Enterotoxins/analysis ; Food Microbiology ; Prevalence ; Genomics ; Food Contamination/analysis ; },
abstract = {The Bacillus cereus group, as one of the important opportunistic foodborne pathogens, is considered a risk to public health due to foodborne diseases and an important cause of economic losses to food industries. This study aimed to gain essential information on the prevalence, phenotype, and genotype of B. cereus group strains isolated from various food products in China. A total of 890 strains of B. cereus group bacteria from 1181 food samples from 2020 to 2023 were identified using the standardized detection method. These strains were found to be prevalent in various food types, with the highest contamination rates observed in cereal flour (55.8 %) and wheat/rice noodles (45.7 %). The tested strains exhibited high resistance rates against penicillin (98.5 %) and ampicillin (98.9 %). Strains isolated from cereal flour had the highest rate of meropenem resistance (7.8 %), while strains from sausages were most resistant to vancomycin (16.8 %). A total of 234 out of the 891 B. cereus group strains were randomly selected for WGS analysis, 18.4 % of which displayed multidrug resistance. The species identification by WGS analysis revealed the presence of 10 distinct species within the B. cereus group, with B. cereus species being the most prevalent. The highest level of species diversity was observed in sausages. Notably, B. anthracis strains lacking the anthrax toxin genes were detected in flour-based food products and sausages. A total of 20 antibiotic resistance genes have been identified, with β-lactam resistance genes (bla1, bla2, BcI, BcII, and blaTEM-116) being the most common. The B. tropicus strains exhibit the highest average number of virulence genes (23.4). The diarrheal virulence genes nheABC, hblACD, and cytK were found in numerous strains. Only 4 of the 234 (1.7 %) sequenced strains contain the ces gene cluster linked to emetic symptoms. These data offer valuable insights for public health policymakers on addressing foodborne B. cereus group infections and ensuring food safety.},
}
@article {pmid38352938,
year = {2023},
author = {Dong, R and Zhang, X and Li, H and Masengo, G and Zhu, A and Shi, X and He, C},
title = {EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1305850},
pmid = {38352938},
issn = {1662-4548},
abstract = {INTRODUCTION: Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.<br><br>METHODS: Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.<br><br>RESULTS: Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.<br><br>DISCUSSION: The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.},
}
@article {pmid38352723,
year = {2024},
author = {Xie, X and Chen, L and Qin, S and Zha, F and Fan, X},
title = {Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.},
journal = {Frontiers in neurorobotics},
volume = {18},
number = {},
pages = {1343249},
pmid = {38352723},
issn = {1662-5218},
abstract = {INTRODUCTION: As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.<br><br>METHODS: This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution.<br><br>RESULTS: Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively.<br><br>DISCUSSION: In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.},
}
@article {pmid38351731,
year = {2024},
author = {Gharibshahian, M and Alizadeh, M and Kamalabadi Farahani, M and Salehi, M},
title = {Fabrication of Rosuvastatin-Incorporated Polycaprolactone -Gelatin Scaffold for Bone Repair: A Preliminary In Vitro Study.},
journal = {Cell journal},
volume = {26},
number = {1},
pages = {70-80},
pmid = {38351731},
issn = {2228-5806},
abstract = {OBJECTIVE: Rosuvastatin (RSV) is a hydrophilic, effective statin with a long half-life that stimulates bone regeneration. The present study aims to develop a new scaffold and controlled release system for RSV with favourable properties for bone tissue engineering (BTE).<br><br>MATERIALS AND METHODS: In this experimental study, high porous polycaprolactone (PCL)-gelatin scaffolds that contained different concentrations of RSV (0 mg/10 ml, 0.1 mg/10 ml, 0.5 mg/10 ml, 2.5 mg/10 ml, 12.5 mg/10 ml, and 62.5 mg/10 ml) were fabricated by the thermally-induced phase separation (TIPS) method. Mechanical and biological properties of the scaffolds were evaluated by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), compressive strength, porosity, MTT, alkaline phosphatase (ALP) activity, water contact angle, degradation rate, pH alteration, blood clotting index (BCI), and hemocompatibility.<br><br>RESULTS: SEM analysis confirmed that the porous structure of the scaffolds contained interconnected pores. FTIR results showed that the RSV structure was maintained during the scaffold's fabrication. RSV (up to 62.5 mg/10 ml) increased compressive strength (16.342 &#xb1; 1.79 MPa), wettability (70.2), and degradation rate of the scaffolds. Scaffolds that contained 2.5 mg/10 ml RSV had the best effect on the human umbilical cord mesenchymal stem cell (HUC-MSCs) survival, hemocompatibility, and BCI. As a sustained release system, only 31.68 &#xb1; 0.1% of RSV was released from the PCL-Gelatin-2.5 mg/10 ml RSV scaffold over 30 days. In addition, the results of ALP activity showed that RSV increased the osteogenic differentiation potential of the scaffolds.<br><br>CONCLUSION: PCL-Gelatin-2.5 mg/10 ml RSV scaffolds have favorable mechanical, physical, and osteogenic properties for bone tissue and provide a favorable release system for RSV. They can mentioned as a a promising strategy for bone regeneration that should be further assessed in animals and clinical studies.},
}
@article {pmid38351064,
year = {2024},
author = {Gu, M and Pei, W and Gao, X and Wang, Y},
title = {An open dataset for human SSVEPs in the frequency range of 1-60 Hz.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {196},
pmid = {38351064},
issn = {2052-4463},
support = {62071447//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Healthy Volunteers ; Photic Stimulation ; },
abstract = {A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.},
}
@article {pmid38349834,
year = {2024},
author = {Wu, X and Li, G and Gao, X and Metcalfe, B and Zhang, D},
title = {Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {800-811},
doi = {10.1109/TNSRE.2024.3364752},
pmid = {38349834},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Brain ; Movement ; Algorithms ; Imagination ; },
abstract = {Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.},
}
@article {pmid38347199,
year = {2024},
author = {Wang, H and Wang, Q and Cui, L and Feng, X and Dong, P and Tan, L and Lin, L and Lian, H and Cao, S and Huang, H and Cao, P and Li, XM},
title = {A molecularly defined amygdala-independent tetra-synaptic forebrain-to-hindbrain pathway for odor-driven innate fear and anxiety.},
journal = {Nature neuroscience},
volume = {27},
number = {3},
pages = {514-526},
pmid = {38347199},
issn = {1546-1726},
support = {82090031 and 82090030//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001186//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31900723//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32071022//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Mice ; Male ; Animals ; *Odorants ; *Amygdala/physiology ; Anxiety ; Fear/physiology ; Smell/physiology ; Olfactory Bulb/physiology ; },
abstract = {Fear-related disorders (for example, phobias and anxiety) cause a substantial public health problem. To date, studies of the neural basis of fear have mostly focused on the amygdala. Here we identify a molecularly defined amygdala-independent tetra-synaptic pathway for olfaction-evoked innate fear and anxiety in male mice. This pathway starts with inputs from the olfactory bulb mitral and tufted cells to pyramidal neurons in the dorsal peduncular cortex that in turn connect to cholecystokinin-expressing (Cck[+]) neurons in the superior part of lateral parabrachial nucleus, which project to tachykinin 1-expressing (Tac1[+]) neurons in the parasubthalamic nucleus. Notably, the identified pathway is specifically involved in odor-driven innate fear. Selective activation of this pathway induces innate fear, while its inhibition suppresses odor-driven innate fear. In addition, the pathway is both necessary and sufficient for stress-induced anxiety-like behaviors. These findings reveal a forebrain-to-hindbrain neural substrate for sensory-triggered fear and anxiety that bypasses the amygdala.},
}
@article {pmid38345959,
year = {2024},
author = {Iwama, S and Ushiba, J},
title = {Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {915-922},
doi = {10.1109/TNSRE.2024.3365197},
pmid = {38345959},
issn = {1558-0210},
mesh = {Humans ; Bayes Theorem ; Reproducibility of Results ; *Brain-Computer Interfaces ; Imagination/physiology ; Electroencephalography/methods ; },
abstract = {Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent event-related desynchronization (SMR-ERD) while attempting unilateral and movement. The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closed-loop neurofeedback training.},
}
@article {pmid38345755,
year = {2024},
author = {Bartlett, JMS and Xu, K and Wong, J and Pond, G and Zhang, Y and Spears, M and Salunga, R and Mallon, E and Taylor, KJ and Hasenburg, A and Markopoulos, C and Dirix, L and van de Velde, CJH and Rea, D and Schnabel, CA and Treuner, K and Bayani, J},
title = {Validation of the Prognostic Performance of Breast Cancer Index in Hormone Receptor-Positive Postmenopausal Breast Cancer Patients in the TEAM Trial.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {30},
number = {8},
pages = {1509-1517},
pmid = {38345755},
issn = {1557-3265},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy ; Prognosis ; Tamoxifen/therapeutic use ; Postmenopause ; Risk Factors ; Neoplasm Recurrence, Local/drug therapy ; },
abstract = {PURPOSE: Patients with early-stage hormone receptor-positive (HR+) breast cancer face a prolonged risk of recurrence even after adjuvant endocrine therapy. The Breast Cancer Index (BCI) is significantly prognostic for overall (0-10 years) and late (5-10 years) distant recurrence (DR) risk in N0 and N1 patients. Here, BCI prognostic performance was evaluated in HR+ postmenopausal women from the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial.<br><br>EXPERIMENTAL DESIGN: 3,544 patients were included in the analysis (N = 1,519 N0, N = 2,025 N+). BCI risk groups were calculated using pre-specified cutoff points. Kaplan-Meier analyses and log-rank tests were used to assess the prognostic significance of BCI risk groups based on DR. Hazard ratios (HR) and confidence intervals (CI) were calculated using Cox models with and without clinical covariates.<br><br>RESULTS: For overall 10-year DR, BCI was significantly prognostic in Ni0 (N = 1,196) and N1 (N = 1,234) patients who did not receive prior chemotherapy (P < 0.001). In patients who were DR-free for 5 years, 10-year late DR rates for low- and high-risk groups were 5.4% and 9.3% (N0 cohort, N = 1,285) and 4.8% and 12.2% (N1 cohort, N = 1,625) with multivariate HRs of 2.25 (95% CI, 1.30-3.88; P = 0.004) and 2.67 (95% CI, 1.53-4.63; P < 0.001), respectively. Late DR performance was substantially improved using previously optimized cutoff points, identifying BCI low-risk groups with even lower 10-year late DR rates of 3.8% and 2.7% in N0 and N1 patients, respectively.<br><br>CONCLUSIONS: The TEAM trial represents the largest prognostic validation study for BCI to date and provides a more representative assessment of late DR risk to guide individualized treatment decision-making for HR+ patients with early-stage breast cancer.},
}
@article {pmid38343801,
year = {2024},
author = {Schippers, A and Vansteensel, MJ and Freudenburg, ZV and Ramsey, NF},
title = {Don't put words in my mouth: Speech perception can generate False Positive activation of a speech BCI.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {38343801},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {Recent studies have demonstrated that speech can be decoded from brain activity and used for brain-computer interface (BCI)-based communication. It is however also known that the area often used as a signal source for speech decoding BCIs, the sensorimotor cortex (SMC), is also engaged when people perceive speech, thus making speech perception a potential source of false positive activation of the BCI. The current study investigated if and how speech perception may interfere with reliable speech BCI control. We recorded high-density electrocorticography (HD-ECoG) data from five subjects while they performed a speech perception and speech production task and trained a support-vector machine (SVM) on the produced speech data. Our results show that decoders that are highly reliable at detecting self-produced speech from brain signals also generate false positives during the perception of speech. We conclude that speech perception interferes with reliable BCI control, and that efforts to limit the occurrence of false positives during daily-life BCI use should be implemented in BCI design to increase the likelihood of successful adaptation by end users.},
}
@article {pmid38342784,
year = {2024},
author = {Li, J and She, Q and Meng, M and Du, S and Zhang, Y},
title = {Three-stage transfer learning for motor imagery EEG recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {6},
pages = {1689-1701},
pmid = {38342784},
issn = {1741-0444},
support = {LZ22F010003//Natural Science Foundation of Zhejiang Province/ ; 62371172//National Natural Science Foundation of China/ ; 62271181//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {Motor imagery (MI) paradigms have been widely used in neural rehabilitation and drowsiness state assessment. The progress in brain-computer interface (BCI) technology has emphasized the importance of accurately and efficiently detecting motor imagery intentions from electroencephalogram (EEG). Despite the recent breakthroughs made in developing EEG-based algorithms for decoding MI, the accuracy and efficiency of these models remain limited by technical challenges posed by cross-subject heterogeneity in EEG data processing and the scarcity of EEG data for training. Inspired by the optimal transport theory, this study aims to develop a novel three-stage transfer learning (TSTL) method, which uses the existing labeled data from a source domain to improve classification performance on an unlabeled target domain. Notably, the proposed method comprises three components, namely, the Riemannian tangent space mapping (RTSM), source domain transformer (SDT), and optimal subspace mapping (OSM). The RTSM maps a symmetric positive definite matrix from the Riemannian space to the tangent space to minimize the marginal probability distribution drift. The SDT transforms the source domain to a target domain by finding the optimal transport mapping matrix to reduce the joint probability distribution differences. The OSM finally maps the transformed source domain and original target domain to the same subspace to further mitigate the distribution discrepancy. The performance of the proposed method was validated on two public BCI datasets, and the average accuracy of the algorithm on two datasets was 72.24% and 69.29%. Our results demonstrated the improved performance of EEG-based MI detection in comparison with state-of-the-art algorithms.},
}
@article {pmid38341457,
year = {2024},
author = {Demarest, P and Rustamov, N and Swift, J and Xie, T and Adamek, M and Cho, H and Wilson, E and Han, Z and Belsten, A and Luczak, N and Brunner, P and Haroutounian, S and Leuthardt, EC},
title = {A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3433},
pmid = {38341457},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Chronic Pain/therapy ; Electroencephalography ; *Neurofeedback ; Pilot Projects ; Longitudinal Studies ; Non-Randomized Controlled Trials as Topic ; },
abstract = {Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.},
}
@article {pmid38339866,
year = {2024},
author = {Wan, C and Pei, M and Shi, K and Cui, H and Long, H and Qiao, L and Xing, Q and Wan, Q},
title = {Toward a Brain-Neuromorphics Interface.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {37},
pages = {e2311288},
doi = {10.1002/adma.202311288},
pmid = {38339866},
issn = {1521-4095},
support = {2023YFE0208600//National key R&amp;D Program of China/ ; 92364106//National Natural Science Foundation of China/ ; 62174082//National Natural Science Foundation of China/ ; 62074075//National Natural Science Foundation of China/ ; 61921005//National Natural Science Foundation of China/ ; 92364204//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Brain/physiology ; Neural Networks, Computer ; Neurons/physiology ; Semiconductors ; },
abstract = {Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.},
}
@article {pmid38339635,
year = {2024},
author = {Kocejko, T and Matuszkiewicz, N and Durawa, P and Madajczak, A and Kwiatkowski, J},
title = {How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339635},
issn = {1424-8220},
support = {DS\KIB\PG//Statutory Funds of Electronics, Telecommunications and Informatics Faculty, Gdansk University of Technology/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy ; Movement ; *Wheelchairs ; Algorithms ; },
abstract = {This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.},
}
@article {pmid38339501,
year = {2024},
author = {Cao, B and Niu, H and Hao, J and Yang, X and Ye, Z},
title = {Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339501},
issn = {1424-8220},
support = {JCKY2018204B053//National Ministry Projects of China/ ; 52205513//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Imagery, Psychotherapy ; Head ; Algorithms ; },
abstract = {With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.},
}
@article {pmid38338131,
year = {2024},
author = {Jin, F and Yang, L and Yang, L and Li, J and Li, M and Shang, Z},
title = {Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {3},
pages = {},
pmid = {38338131},
issn = {2076-2615},
support = {62301496//National Natural Science Foundation of China/ ; 2022ZD0208500//STI 2030-Major Project/ ; GZC20232447//National Postdoctoral Researcher Program/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons' choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons' learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.},
}
@article {pmid38338099,
year = {2024},
author = {Zhu, JY and Zhang, ZH and Liu, G and Wan, H},
title = {Enhanced Hippocampus-Nidopallium Caudolaterale Interaction in Visual-Spatial Associative Learning of Pigeons.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {3},
pages = {},
pmid = {38338099},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; 2023KFKT005//the Open Project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
abstract = {Learning the spatial location associated with visual cues in the environment is crucial for survival. This ability is supported by a distributed interactive network. However, it is not fully understood how the most important task-related brain areas in birds, the hippocampus (Hp) and the nidopallium caudolaterale (NCL), interact in visual-spatial associative learning. To investigate the mechanisms of such coordination, synchrony and causal analysis were applied to the local field potentials of the Hp and NCL of pigeons while performing a visual-spatial associative learning task. The results showed that, over the course of learning, theta-band (4-12 Hz) oscillations in the Hp and NCL became strongly synchronized before the pigeons entered the critical choice platform for turning, with the information flowing preferentially from the Hp to the NCL. The learning process was primarily associated with the increased Hp-NCL interaction of theta rhythm. Meanwhile, the enhanced theta-band Hp-NCL interaction predicted the correct choice, supporting the pigeons' use of visual cues to guide navigation. These findings provide insight into the dynamics of Hp-NCL interaction during visual-spatial associative learning, serving to reveal the mechanisms of Hp and NCL coordination during the encoding and retrieval of visual-spatial associative memory.},
}
@article {pmid38338082,
year = {2024},
author = {Yang, L and Chen, X and Yang, L and Li, M and Shang, Z},
title = {Phase-Amplitude Coupling between Theta Rhythm and High-Frequency Oscillations in the Hippocampus of Pigeons during Navigation.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {3},
pages = {},
pmid = {38338082},
issn = {2076-2615},
support = {62301496//National Natural Science Foundation of China/ ; 2022ZD0208500//STI 2030-Major Project/ ; 232102210098//Key Scientific and Technological Projects of Henan Province/ ; 222102310223//Key Scientific and Technological Projects of Henan Province/ ; },
abstract = {Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase-amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution.},
}
@article {pmid38338074,
year = {2024},
author = {Yang, L and Jin, F and Yang, L and Li, J and Li, Z and Li, M and Shang, Z},
title = {The Hippocampus in Pigeons Contributes to the Model-Based Valuation and the Relationship between Temporal Context States.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {3},
pages = {},
pmid = {38338074},
issn = {2076-2615},
support = {62301496//National Natural Science Foundation of China/ ; },
abstract = {Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon's choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12-100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1-12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12-100 Hz) oscillations represent model-based valuations, while the low-frequency (1-12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior.},
}
@article {pmid38335958,
year = {2024},
author = {Wang, J and Luo, Y and Ye, F and Ding, ZJ and Zheng, SJ and Qiao, S and Wang, Y and Guo, J and Yang, W and Su, N},
title = {Structures and ion transport mechanisms of plant high-affinity potassium transporters.},
journal = {Molecular plant},
volume = {17},
number = {3},
pages = {409-422},
doi = {10.1016/j.molp.2024.01.007},
pmid = {38335958},
issn = {1752-9867},
mesh = {*Arabidopsis Proteins/metabolism ; Cryoelectron Microscopy ; *Symporters ; *Arabidopsis/metabolism ; Ion Transport ; Ions/metabolism ; Potassium/metabolism ; Plant Proteins/genetics/metabolism ; },
abstract = {Plant high-affinity K[+] transporters (HKTs) mediate Na[+] and K[+] uptake, maintain Na[+]/K[+] homeostasis, and therefore play crucial roles in plant salt tolerance. In this study, we present cryoelectron microscopy structures of HKTs from two classes, class I HKT1;1 from Arabidopsis thaliana (AtHKT1;1) and class II HKT2;1 from Triticum aestivum (TaHKT2;1), in both Na[+]- and K[+]-bound states at 2.6- to 3.0-Å resolutions. Both AtHKT1;1 and TaHKT2;1 function as homodimers. Each HKT subunit consists of four tandem domain units (D1-D4) with a repeated K[+]-channel-like M-P-M topology. In each subunit, D1-D4 assemble into an ion conduction pore with a pseudo-four-fold symmetry. Although both TaHKT2;1 and AtHKT1;1 have only one putative Na[+] ion bound in the selectivity filter with a similar coordination pattern, the two HKTs display different K[+] binding modes in the filter. TaHKT2;1 has three K[+] ions bound in the selectivity filter, but AtHKT1;1 has only two K[+] ions bound in the filter, which has a narrowed external entrance due to the presence of a Ser residue in the first filter motif. These structures, along with computational, mutational, and electrophysiological analyses, enable us to pinpoint key residues that are critical for the ion selectivity of HKTs. The findings provide new insights into the ion selectivity and ion transport mechanisms of plant HKTs and improve our understanding about how HKTs mediate plant salt tolerance and enhance crop growth.},
}
@article {pmid38335090,
year = {2024},
author = {Hu, S and Zhang, Z and Zhang, X and Wu, X and Valdes-Sosa, PA},
title = {[Formula: see text]-[Formula: see text]: A Nonparametric Model for Neural Power Spectra Decomposition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {5},
pages = {2624-2635},
doi = {10.1109/JBHI.2024.3364499},
pmid = {38335090},
issn = {2168-2208},
mesh = {Humans ; *Signal Processing, Computer-Assisted ; *Electroencephalography/methods ; *Brain/physiology ; Algorithms ; Statistics, Nonparametric ; Sleep/physiology ; },
abstract = {The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ- π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ- π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ- π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ- π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ- π approached the clinical findings in peak discovery. Overall, ξ- π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ- π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.},
}
@article {pmid38335021,
year = {2024},
author = {Luo, H and Li, C and Wang, S and Zhang, S and Song, J},
title = {Switchable Adhesive Based on Shape Memory Polymer with Micropillars of Different Heights for Laser-Driven Noncontact Transfer Printing.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {7},
pages = {9443-9452},
doi = {10.1021/acsami.3c16282},
pmid = {38335021},
issn = {1944-8252},
abstract = {Switchable adhesive is essential to develop transfer printing, which is an advanced heterogeneous material integration technique for developing electronic systems. Designing a switchable adhesive with strong adhesion strength that can also be easily eliminated to enable noncontact transfer printing still remains a challenge. Here, we report a simple yet robust design of switchable adhesive based on a thermally responsive shape memory polymer with micropillars of different heights. The adhesive takes advantage of the shape-fixing property of shape memory polymer to provide strong adhesion for a reliable pick-up and the various levels of shape recovery of micropillars under laser heating to eliminate the adhesion for robust printing in a noncontact way. Systematic experimental and numerical studies reveal the adhesion switch mechanism and provide insights into the design of switchable adhesives. This switchable adhesive design provides a good solution to develop laser-driven noncontact transfer printing with the capability of eliminating the influence of receivers on the performance of transfer printing. Demonstrations of transfer printing of silicon wafers, microscale Si platelets, and micro light emitting diode (μ-LED) chips onto various challenging nonadhesive receivers (e.g., sandpaper, stainless steel bead, leaf, or glass) to form desired two-dimensional or three-dimensional layouts illustrate its great potential in deterministic assembly.},
}
@article {pmid38334511,
year = {2024},
author = {Xia, J and Zhang, F and Zhang, L and Cao, Z and Dong, S and Zhang, S and Luo, J and Zhou, G},
title = {Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {14},
number = {3},
pages = {},
pmid = {38334511},
issn = {2079-4991},
support = {No. 2021ZD0200401//STI2030-Major projects/ ; No. 2022R52042//Zhejiang Province high level talent special support plan/ ; No.2021C03062//Zhejiang Province Key R &amp; D programs/ ; No.2021C03003//Zhejiang Province Key R &amp; D programs/ ; No.2023C01192//Zhejiang Province Key R &amp; D programs/ ; },
abstract = {Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.},
}
@article {pmid38334366,
year = {2024},
author = {Cao, L},
title = {A spatial-attentional mechanism underlies action-related distortions of time judgment.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {38334366},
issn = {2050-084X},
support = {32271078//National Natural Science Foundation of China/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China/ ; },
mesh = {Humans ; Psychomotor Performance ; Judgment ; *Illusions ; *Time Perception ; Reaction Time ; },
abstract = {Temporal binding has been understood as an illusion in timing judgment. When an action triggers an outcome (e.g. a sound) after a brief delay, the action is reported to occur later than if the outcome does not occur, and the outcome is reported to occur earlier than a similar outcome not caused by an action. We show here that an attention mechanism underlies the seeming illusion of timing judgment. In one method, participants watch a rotating clock hand and report event times by noting the clock hand position when the event occurs. We find that visual spatial attention is critically involved in shaping event time reports made in this way. This occurs because action and outcome events result in shifts of attention around the clock rim, thereby biasing the perceived location of the clock hand. Using a probe detection task to measure attention, we show a difference in the distribution of visual spatial attention between a single-event condition (sound only or action only) and a two-event agency condition (action plus sound). Participants accordingly report the timing of the same event (the sound or the action) differently in the two conditions: spatial attentional shifts masquerading as temporal binding. Furthermore, computational modeling based on the attention measure can reproduce the temporal binding effect. Studies that use time judgment as an implicit marker of voluntary agency should first discount the artefactual changes in event timing reports that actually reflect differences in spatial attention. The study also has important implications for related results in mental chronometry obtained with the clock-like method since Wundt, as attention may well be a critical confounding factor in the interpretation of these studies.},
}
@article {pmid38331362,
year = {2024},
author = {Saithna, A},
title = {Editorial Commentary: Bioinductive Collagen Implants Reduce Rotator Cuff Retear, yet Cost-Effectiveness and Improvement in Clinical Outcomes Are Unclear.},
journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {40},
number = {6},
pages = {1774-1776},
doi = {10.1016/j.arthro.2024.02.001},
pmid = {38331362},
issn = {1526-3231},
mesh = {Humans ; *Cost-Benefit Analysis ; *Collagen/therapeutic use ; *Rotator Cuff Injuries/surgery/economics ; *Rotator Cuff/surgery ; *Prostheses and Implants/economics ; Arthroscopy/economics ; Treatment Outcome ; },
abstract = {The estimated health care costs of failed arthroscopic rotator cuff retears (RCRs) performed in the United States represent a huge economic burden of greater than $400 million per 2-year period. Unfortunately, retear rates do not appear to have improved significantly since the 1980s, despite advances in surgical technology and the biomechanics of repair. The failure of these advances to translate into improved clinical results suggests that the limiting step in reducing retear rates is biology rather than the biomechanics of repair. Bioinductive collagen implants (BCIs) are an emerging and potentially useful option for biological augmentation. Recent meta-analysis of preclinical and clinical studies demonstrates that biological augmentation significantly lowers the risk of retear. Retrieval studies from human RCR subjects who underwent treatment with BCI demonstrate cellular incorporation, tissue formation, and maturation, providing a logical basis for a reduction in retear rates as well as small increases in tendon thickness at the footprint. Although BCIs show potential as a possible game-changing solution for reducing failure rates of RCR, concerns remain regarding cost-effectiveness analyses and demonstration of functional outcome improvement.},
}
@article {pmid38329189,
year = {2024},
author = {Ren, S and Wang, K and Jia, X and Wang, J and Xu, J and Yang, B and Tian, Z and Xia, R and Yu, D and Jia, Y and Yan, X},
title = {Fibrous MXene Synapse-Based Biomimetic Tactile Nervous System for Multimodal Perception and Memory.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {20},
number = {28},
pages = {e2400165},
doi = {10.1002/smll.202400165},
pmid = {38329189},
issn = {1613-6829},
support = {61771260//National Natural Science Foundation of China/ ; 62271269//National Natural Science Foundation of China/ ; },
mesh = {*Biomimetics/methods ; *Synapses/physiology ; Touch/physiology ; Memory/physiology ; Biomimetic Materials/chemistry ; Humans ; Polyesters/chemistry ; },
abstract = {Biomimetic tactile nervous system (BTNS) inspired by organisms has motivated extensive attention in wearable fields due to its biological similarity, low power consumption, and perception-memory integration. Though many works about planar-shape BTNS are developed, few researches could be found in the field of fibrous BTNS (FBTNS) which is superior in terms of strong flexibility, weavability, and high-density integration. Herein, a FBTNS with multimodal sensibility and memory is proposed, by fusing the fibrous poly lactic acid (PLA)/Ag/MXene/Pt artificial synapse and MXene/EMIMBF4 ionic conductive elastomer. The proposed FBTNS can successfully perceive external stimuli and generate synaptic responses. It also exhibits a short response time (23 ms) and low set power consumption (17 nW). Additionally, the proposed device demonstrates outstanding synaptic plasticity under both mechanical and electrical stimuli, which can simulate the memory function. Simultaneously, the fibrous devices are embedded into textiles to construct tactile arrays, by which biomimetic tactile perception and temporary memory functions are successfully implemented. This work demonstrates the as-prepared FBTNS can generate biomimetic synaptic signals to serve as artificial feeling signals, it is thought that it could offer a fabric electronic unit integrating with perception and memory for Human-Computer interaction, and has great potential to build lightweight and comfortable Brain-Computer interfaces.},
}
@article {pmid38328229,
year = {2024},
author = {Zhang, LA and Li, P and Callaway, EM},
title = {High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38328229},
issn = {2692-8205},
support = {R01 EY022577/EY/NEI NIH HHS/United States ; R21 NS105129/NS/NINDS NIH HHS/United States ; },
abstract = {Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing have been used to estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that laminar distributions of CSDs lack consistency and the spatial resolution required for thin layers and accurate layer boundaries. To take full advantage of high density Neuropixels arrays, we have developed approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.},
}
@article {pmid38328139,
year = {2024},
author = {Ning, M and Duwadi, S and Yücel, MA and Von Lühmann, A and Boas, DA and Sen, K},
title = {fNIRS Dataset During Complex Scene Analysis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38328139},
issn = {2692-8205},
support = {T32 DC013017/DC/NIDCD NIH HHS/United States ; },
abstract = {When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.},
}
@article {pmid38325928,
year = {2024},
author = {Zhao, J and Sun, L and Sun, Z and Zhou, X and Si, H and Zhang, D},
title = {MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image.},
journal = {Artificial intelligence in medicine},
volume = {148},
number = {},
pages = {102771},
doi = {10.1016/j.artmed.2024.102771},
pmid = {38325928},
issn = {1873-2860},
mesh = {Humans ; *Lumbosacral Plexus/diagnostic imaging ; *Magnetic Resonance Imaging ; Diagnosis, Computer-Assisted ; Image Processing, Computer-Assisted ; },
abstract = {Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnoses and surgery of spinal nerve lesions. Due to the complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue, we propose a Multi-Scale Edge Fusion Network (MSEF-Net) to fully enhance the edge feature in the encoder and adaptively fuse multi-scale features in the decoder. Specifically, to highlight the edge structure feature, we propose an edge feature fusion module (EFFM) by combining the Sobel operator edge detection and the edge-guided attention module (EAM), respectively. To adaptively fuse the multi-scale feature map in the decoder, we introduce an adaptive multi-scale fusion module (AMSF). Our proposed MSEF-Net method was evaluated on the collected spinal MRI dataset with 89 patients (a total of 2848 MR images). Experimental results demonstrate that our MSEF-Net is effective for lumbosacral plexus segmentation with MR images, when compared with several state-of-the-art segmentation methods.},
}
@article {pmid38324909,
year = {2024},
author = {Wang, X and Li, B and Lin, Y and Gao, X},
title = {Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad2710},
pmid = {38324909},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography ; Benchmarking ; Calibration ; *Brain-Computer Interfaces ; },
abstract = {Objective.Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge.Approach.This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain.Main results.The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization.Significance.The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.},
}
@article {pmid38324732,
year = {2024},
author = {Cheng, X and Wang, S and Guo, B and Wang, Q and Hu, Y and Pan, Y},
title = {How self-disclosure of negative experiences shapes prosociality?.},
journal = {Social cognitive and affective neuroscience},
volume = {19},
number = {1},
pages = {},
pmid = {38324732},
issn = {1749-5024},
support = {20220810171518001//Shenzhen Basic Research Project/ ; //Fundamental Research Funds for the Central Universities/ ; 62207025//National Natural Science Foundation of China/ ; 22YJC190017//Ministry of Education of China/ ; },
mesh = {Humans ; *Interpersonal Relations ; *Disclosure ; Brain Mapping/methods ; Emotions ; Frontal Lobe/physiology ; },
abstract = {People frequently share their negative experiences and feelings with others. Little is known, however, about the social outcomes of sharing negative experiences and the underlying neural mechanisms. We addressed this dearth of knowledge by leveraging functional near-infrared spectroscopy (fNIRS) hyperscanning: while dyad participants took turns to share their own (self-disclosure group) or a stranger's (non-disclosure group) negative and neutral experiences, their respective brain activity was recorded simultaneously by fNIRS. We observed that sharing negative (relative to neutral) experiences enhanced greater mutual prosociality, emotional empathy and interpersonal neural synchronization (INS) at the left superior frontal cortex in the self-disclosure group compared to the non-disclosure group. Importantly, mediation analyses further revealed that in the self-disclosure (but not non-disclosure) group, the increased emotional empathy and INS elicited by sharing negative experiences relative to sharing neutral experiences promoted the enhanced prosociality through increasing interpersonal liking. These results indicate that self-disclosure of negative experiences can promote prosocial behaviors via social dynamics (defined as social affective and cognitive factors, including empathy and liking) and shared neural responses. Our findings suggest that when people express negative sentiments, they incline to follow up with positive actions.},
}
@article {pmid38324109,
year = {2024},
author = {Zhang, F and Wu, H and Guo, Y},
title = {Semi-supervised multi-source transfer learning for cross-subject EEG motor imagery classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {6},
pages = {1655-1672},
pmid = {38324109},
issn = {1741-0444},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; Supervised Machine Learning ; },
abstract = {Electroencephalogram (EEG) motor imagery (MI) classification refers to the use of EEG signals to identify and classify subjects' motor imagery activities; this task has received increasing attention with the development of brain-computer interfaces (BCIs). However, the collection of EEG data is usually time-consuming and labor-intensive, which makes it difficult to obtain sufficient labeled data from the new subject to train a new model. Moreover, the EEG signals of different individuals exhibit significant differences, leading to a significant drop in the performance of a model trained on the existing subjects when directly classifying EEG signals acquired from new subjects. Therefore, it is crucial to make full use of the EEG data of the existing subjects and the unlabeled EEG data of the new target subject to improve the MI classification performance achieved for the target subject. This research study proposes a semi-supervised multi-source transfer (SSMT) learning model to address the above problems; the model learns informative and domain-invariant representations to address cross-subject MI-EEG classification tasks. In particular, a dynamic transferred weighting schema is presented to obtain the final predictions by integrating the weighted features derived from multi-source domains. The average accuracies achieved on two publicly available EEG datasets reach 83.57 % and 85.09 % , respectively, validating the effectiveness of the SSMT process. The SSMT process reveals the importance of informative and domain-invariant representations in MI classification tasks, as they make full use of the domain-invariant information acquired from each subject.},
}
@article {pmid38323603,
year = {2024},
author = {O'Keeffe, AB and Merla, A and Ashkan, K},
title = {Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?.},
journal = {British journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/02688697.2024.2311128},
pmid = {38323603},
issn = {1360-046X},
abstract = {Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.},
}
@article {pmid38323330,
year = {2024},
author = {Gómez, DMC and Braidot, AAA},
title = {Improving motor imagery through a mirror box for BCI users.},
journal = {Journal of neurophysiology},
volume = {131},
number = {5},
pages = {832-841},
doi = {10.1152/jn.00121.2023},
pmid = {38323330},
issn = {1522-1598},
support = {PID 6179//Consejo Nacional de Investigaciones Cient&#xed;ficas y T&#xe9;cnicas (CONICET)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Imagination/physiology ; Male ; Female ; Adult ; *Feedback, Sensory/physiology ; Young Adult ; Electroencephalography ; Movement/physiology ; Hand/physiology ; Motor Activity/physiology ; },
abstract = {The aim of this study was to evaluate mirror visual feedback (MVF) as a training tool for brain-computer interface (BCI) users. This is because approximately 20-30% of subjects require more training to operate a BCI system using motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of control, imagination, and the MVF with the mirror box. We constituted two groups: group 1: control, imagination, and MVF; group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (right-hand, P = 0.0403; left-hand, P = 0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r[2]s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.NEW &amp; NOTEWORTHY The increase in motor imagery recorded with EEG after MVF (mirror visual feedback) suggests that the mirror box made it easier to imagine movements. Our results demonstrate that MVF could be used as a training tool to improve motor imagery.},
}
@article {pmid38322514,
year = {2024},
author = {Zhou, Y and Lu, W and Yang, Q and Xu, Z and Li, J},
title = {[Preparation and Performance Study of a Novel Antibacterial Hemostatic Chitosan Sponge].},
journal = {Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition},
volume = {55},
number = {1},
pages = {190-197},
pmid = {38322514},
issn = {1672-173X},
mesh = {Animals ; Rabbits ; *Chitosan/chemistry ; *Hemostatics/pharmacology ; Escherichia coli ; Glycerol/pharmacology ; Staphylococcus aureus ; *Zein/pharmacology ; Hemostasis ; Anti-Bacterial Agents/pharmacology ; Hemorrhage ; Water/pharmacology ; Ethylamines/pharmacology ; Phenols/pharmacology ; },
abstract = {OBJECTIVE: To create a novel chitosan antibacterial hemostatic sponge (NCAHS) and to evaluate its material and biological properties.<br><br>METHODS: Chitosan, a polysaccharide, was used as the sponge substrate and different proportions of sodium tripolyphosphate (STPP), glycerol, and phenol sulfonyl ethylamine were added to prepare the sponges through the freeze-drying method. The whole-blood coagulation index (BCI) was used as the screening criterion to determine the optimal concentrations of chitosan and the other additives and the hemostatic sponges were prepared accordingly. Zein/calcium carbonate (Zein/CaCO3) composite microspheres loaded with ciprofloxacin hydrochloride were prepared and added to the hemostatic sponges to obtain NCAHS. Scanning electron microscope was used to observe the microscopic morphology and porosity of the NCAHS. The water absorption rate, in vitro antibacterial susceptibility rate against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), in vitro coagulation performance, and hemocompatibility of NCAHS were examined. The coagulation performance of NCAHS was evaluated by using rabbit liver injury and rabbit auricular artery hemorrhageear models and commercial hemostatic sponge (CHS) was used as a control. The in vivo biocompatibility, including such aspects as cytotoxicity, skin irritation in animals, and acute in vivo toxicity, of the NCAHS extracts was examined by using as a reference the national standards for biological evaluation of medical devices.<br><br>RESULTS: The NCAHS prepared with 1.5% chitosan (W/V), 0.01% STPP (W/V), 0% glycerol (V/V), 0.15% phenol-sulfonyl-ethylamine (V/V), Zein and CaCO3 at the mixing ratio of 5&#x2236;1 (W/W), Zein at the final mass concentration of 2.5 g/L, and ethanol at the final concentration of 17.5% (V/V) were fine and homogeneous, possessing a honeycomb-like porous structure with a pore size of about 200 &#x3bc;m. The NCAHS thus prepared had the lowest BCI value. The water absorption ([2362.16&#xb1;201.15] % vs. [1102.56&#xb1;91.79]%) and in vitro coagulation performance (31.338% vs. 1.591%) of NCAHS were significantly better than those of CHS (P<0.01). Tests with the in vivo auricular artery hemorrhage model ([36.00&#xb1;13.42] s vs. [80.00&#xb1;17.32] s) and rabbit liver bleeding model ([30.00&#xb1;0] s vs. [70.00&#xb1;17.32] s) showed that the hemostasis time of NCAHS was significantly shorter than that of CHS (P<0.01). NCAHS had significant inhibitory ability against S. aureus and E. coli. In addition, NCAHS showed good in vitro and in vivo biocompatibility.<br><br>CONCLUSION: NCAHS is a composite sponge that shows excellent antimicrobial properties, hemostatic effect, and biocompatibility. Therefore, its extensive application in clinical settings is warranted.},
}
@article {pmid38320948,
year = {2024},
author = {Talkhan, H and Stewart, D and McIntosh, T and Ziglam, H and Abdulrouf, PV and Al-Hail, M and Diab, M and Cunningham, S},
title = {Exploring determinants of antimicrobial prescribing behaviour using the Theoretical Domains Framework.},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {20},
number = {4},
pages = {401-410},
doi = {10.1016/j.sapharm.2023.12.009},
pmid = {38320948},
issn = {1934-8150},
support = {MRC-01-19-219//MRC, HMC, Qatar/ ; },
mesh = {Humans ; Pharmacists ; *Anti-Infective Agents/therapeutic use ; Qualitative Research ; *Physicians ; Inappropriate Prescribing ; },
abstract = {BACKGROUND: Few theoretically-based, qualitative studies have explored determinants of antimicrobial prescribing behaviour in hospitals. Understanding these can promote successful development and implementation of behaviour change interventions (BCIs).<br><br>OBJECTIVE: (s): To use the Theoretical Domains Framework (TDF) to explore determinants of clinicians' antimicrobial prescribing behaviour, identifying barriers (i.e., impediments) and facilitators to appropriate antimicrobial practice.<br><br>METHODS: Semi-structured interviews with purposively-sampled doctors and pharmacists with a wide range of specialties and expertise in Hamad Medical Corporation hospitals in Qatar. Interviews based on previous quantitative research and the TDF were audio-recorded, transcribed and independently analysed by two researchers using the TDF as an initial coding framework.<br><br>RESULTS: Data saturation was achieved after interviewing eight doctors and eight pharmacists. Inter-related determinants of antimicrobial prescribing behaviour linked to ten TDF domains were identified as barriers and facilitators that may contribute to inappropriate or appropriate antimicrobial prescribing. The main barriers identified were around hospital guidelines and electronic system deficiencies (environmental context and resources); knowledge gaps relating to guidelines and appropriate prescribing (knowledge); restricted roles/responsibilities of microbiologists and pharmacists (professional role and identity); challenging antimicrobial prescribing decisions (memory, attention and decision processes); and professional hierarchies and poor multidisciplinary teamworking (social influences). Key facilitators included guidelines compliance (goals and intentions), and participants' beliefs about the consequences of appropriate or inappropriate prescribing. Further education and training, and some changes to guidelines including their accessibility were also considered essential.<br><br>CONCLUSIONS: Antimicrobial prescribing behaviour in hospitals is a complex process influenced by a broad range of determinants including specific barriers and facilitators. The in-depth understanding of this complexity provided by this work may support the development of an effective BCI to promote appropriate antimicrobial stewardship.},
}
@article {pmid38319678,
year = {2024},
author = {Damasio, A and Damasio, H},
title = {Homeostatic Feelings and the Emergence of Consciousness.},
journal = {Journal of cognitive neuroscience},
volume = {36},
number = {8},
pages = {1653-1659},
doi = {10.1162/jocn_a_02119},
pmid = {38319678},
issn = {1530-8898},
support = {//BCI Research Group on Human Consciousness/ ; },
mesh = {*Consciousness/physiology ; Humans ; *Emotions/physiology ; *Homeostasis/physiology ; Animals ; Interoception/physiology ; Brain/physiology ; },
abstract = {In this article, we summarize our views on the problem of consciousness and outline the current version of a novel hypothesis for how conscious minds can be generated in mammalian organisms. We propose that a mind can be considered conscious when three processes are in place: the first is a continuous generation of interoceptive feelings, which results in experiencing of the organism's internal operations; the second is the equally continuous production of images, generated according to the organism's sensory perspective relative to its surround; the third combines feeling/experience and perspective resulting in a process of subjectivity relative to the image contents. We also propose a biological basis for these three components: the peripheral and central physiology of interoception and exteroception help explain the implementation of the first two components, whereas the third depends on central nervous system integration, at multiple levels, from spinal cord, brainstem, and diencephalic nuclei, to selected regions of the mesial cerebral cortices.},
}
@article {pmid38317650,
year = {2024},
author = {Kueper, N and Chari, K and Bütefür, J and Habenicht, J and Rossol, T and Kim, SK and Tabie, M and Kirchner, F and Kirchner, EA},
title = {EEG and EMG dataset for the detection of errors introduced by an active orthosis device.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1304311},
pmid = {38317650},
issn = {1662-5161},
}
@article {pmid38317515,
year = {2024},
author = {Lee, HG and Jung, IH and Park, BS and Yang, HR and Kim, KK and Tu, TH and Yeh, JY and Lee, S and Yang, S and Lee, BJ and Kim, JG and Nam-Goong, IS},
title = {Altered Metabolic Phenotypes and Hypothalamic Neuronal Activity Triggered by Sodium-Glucose Cotransporter 2 Inhibition (Diabetes Metab J 2023;47:784-95).},
journal = {Diabetes &amp; metabolism journal},
volume = {48},
number = {1},
pages = {159-160},
pmid = {38317515},
issn = {2233-6087},
mesh = {Humans ; *Diabetes Mellitus, Type 2/drug therapy ; *Sodium-Glucose Transporter 2 Inhibitors/pharmacology/therapeutic use ; Glucose ; Sodium ; },
}
@article {pmid38316822,
year = {2024},
author = {Tanaka, T},
title = {Evaluating the Bayesian causal inference model of intentional binding through computational modeling.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {2979},
pmid = {38316822},
issn = {2045-2322},
support = {JP20K22269//Japan Society for the Promotion of Science/ ; },
mesh = {Humans ; Bayes Theorem ; *Time Perception ; Computer Simulation ; Intention ; Algorithms ; },
abstract = {Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation than traditional models such as maximum likelihood estimation. The estimated parameter values suggested that the time compression resulted from an expectation that the actions would immediately cause sensory outcomes. Furthermore, I investigated the algorithm that realized this BCI and found probability-matching to be a plausible candidate; people might heuristically reconstruct event timing depending on causal uncertainty rather than optimally integrating causal and temporal posteriors. The evidence demonstrated the utility of computational modeling to investigate how humans infer the causal and temporal structures of events and individual differences in that process.},
}
@article {pmid38315298,
year = {2024},
author = {Zhang, X and Wang, X and Li, Y and Zhang, Y and Zhu, H and Xie, C and Zhou, Y and Shen, Y and Tong, J},
title = {Characterization of Retinal VIP-Amacrine Cell Development During the Critical Period.},
journal = {Cellular and molecular neurobiology},
volume = {44},
number = {1},
pages = {19},
pmid = {38315298},
issn = {1573-6830},
support = {822201194//The young science fund of the national nature science foundation of China/ ; XDA16040200//The Strategic Priority Research Program of Chinese Academy of Sciences/ ; 82371084//The National Nature Science Foundation of China/ ; },
mesh = {Animals ; Mice ; *Amacrine Cells ; Cell Differentiation ; Membrane Potentials/physiology ; Retina/metabolism ; *Vasoactive Intestinal Peptide/metabolism ; },
abstract = {Retinal vasoactive intestinal peptide amacrine cells (VIP-ACs) play an important role in various retinal light-mediated pathological processes related to different developmental ocular diseases and even mental disorders. It is important to characterize the developmental changes in VIP-ACs to further elucidate their mechanisms of circuit function. We bred VIP-Cre mice with Ai14 and Ai32 to specifically label retinal VIP-ACs. The VIP-AC soma and spine density generally increased, from postnatal day (P)0 to P35, reaching adult levels at P14 and P28, respectively. The VIP-AC soma density curve was different with the VIP-AC spine density curve. The total retinal VIP content reached a high level plateau at P14 but was decreased in adults. From P14 to P16, the resting membrane potential (RMP) became more negative, and the input resistance decreased. Cell membrane capacitance (MC) showed three peaks at P7, P12 and P16. The RMP and MC reached a stable level similar to the adult level at P18, whereas input resistance reached a stable level at P21. The percentage of sustained voltage-dependent potassium currents peaked at P16 and remained stable thereafter. The spontaneous excitatory postsynaptic current and spontaneous inhibitory postsynaptic current frequencies and amplitudes, as well as charge transfer, peaked at P12 to P16; however, there were also secondary peaks at different time points. In conclusion, we found that the second, third and fourth weeks after birth were important periods of VIP-AC development. Many developmental changes occurred around eye opening. The development of soma, dendrite and electrophysiological properties showed uneven dynamics of progression. Cell differentiation may contribute to soma development whereas the changes of different ion channels may play important role for spine development.},
}
@article {pmid38311896,
year = {2024},
author = {Chugh, N and Aggarwal, S},
title = {Spatial Decoding for Gaze Independent Brain-Computer Interface Based on Covert Visual Attention Shift Using Electroencephalography.},
journal = {Clinical EEG and neuroscience},
volume = {55},
number = {4},
pages = {477-485},
doi = {10.1177/15500594241229187},
pmid = {38311896},
issn = {2169-5202},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Attention/physiology ; Male ; Female ; Adult ; Young Adult ; Eye Movements/physiology ; Brain/physiology ; Fixation, Ocular/physiology ; Algorithms ; Machine Learning ; },
abstract = {The gaze-independent brain-computer interface (BCI) device is used to re-establish interaction for individuals who have abnormal eye movement. It may be possible to control the BCI by shifting your attention spatially. However, spatial attention is rarely employed to increase the effectiveness of target detection and is typically used to provide a simple "yes" or "no" response to the target recognition inquiry. To improve the effectiveness of detecting target, it is crucial to take advantage of the possible advantages of spatial attention. N2-posterior-contralateral (N2pc) component reflects correlates of visual spatial attention and is used to determine target position. In this study, a long-short-term memory (LSTM) network is used to answer "yes/no" questions by decoding covert spatial attention based on N2pc characteristics using EEG signals. The proposed LSTM-based model's average decoding accuracy is 92.79%. The target detection efficiency was successfully increased by about 4% when compared to conventional machine learning algorithms. The proposed model is tested on the independent dataset to validate its performance. The results of this work show that N2pc characteristics can be employed in gaze-independent BCIs for tracking covert attention shifts, which may help persons with poor eye mobility to connect with their environment.},
}
@article {pmid38309458,
year = {2024},
author = {Wang, J and Du, X and Yao, S and Li, L and Tanigawa, H and Zhang, X and Roe, AW},
title = {Mesoscale organization of ventral and dorsal visual pathways in macaque monkey revealed by 7T fMRI.},
journal = {Progress in neurobiology},
volume = {234},
number = {},
pages = {102584},
doi = {10.1016/j.pneurobio.2024.102584},
pmid = {38309458},
issn = {1873-5118},
mesh = {Animals ; Humans ; *Macaca ; Visual Pathways/diagnostic imaging/physiology ; Magnetic Resonance Imaging/methods ; *Visual Cortex/diagnostic imaging/physiology ; Brain Mapping ; },
abstract = {In human and nonhuman primate brains, columnar (mesoscale) organization has been demonstrated to underlie both lower and higher order aspects of visual information processing. Previous studies have focused on identifying functional preferences of mesoscale domains in specific areas; but there has been little understanding of how mesoscale domains may cooperatively respond to single visual stimuli across dorsal and ventral pathways. Here, we have developed ultrahigh-field 7 T fMRI methods to enable simultaneous mapping, in individual macaque monkeys, of response in both dorsal and ventral pathways to single simple color and motion stimuli. We provide the first evidence that anatomical V2 cytochrome oxidase-stained stripes are well aligned with fMRI maps of V2 stripes, settling a long-standing controversy. In the ventral pathway, a systematic array of paired color and luminance processing domains across V4 was revealed, suggesting a novel organization for surface information processing. In the dorsal pathway, in addition to high quality motion direction maps of MT, MST and V3A, alternating color and motion direction domains in V3 are revealed. As well, submillimeter motion domains were observed in peripheral LIPd and LIPv. In sum, our study provides a novel global snapshot of how mesoscale networks in the ventral and dorsal visual pathways form the organizational basis of visual objection recognition and vision for action.},
}
@article {pmid38308997,
year = {2024},
author = {Pérez-Velasco, S and Marcos-Martínez, D and Santamaría-Vázquez, E and Martínez-Cagigal, V and Moreno-Calderón, S and Hornero, R},
title = {Unraveling motor imagery brain patterns using explainable artificial intelligence based on Shapley values.},
journal = {Computer methods and programs in biomedicine},
volume = {246},
number = {},
pages = {108048},
doi = {10.1016/j.cmpb.2024.108048},
pmid = {38308997},
issn = {1872-7565},
mesh = {Humans ; *Artificial Intelligence ; Movement/physiology ; Brain/physiology ; Electroencephalography/methods ; Imagination/physiology ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {BACKGROUND AND OBJECTIVE: Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals.<br><br>METHODS: We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym, a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback.<br><br>RESULTS: We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% &#xb1; 10.6% on the Physionet dataset and 88.7% &#xb1; 7.0% on the Carnegie Mellon University's dataset.<br><br>CONCLUSION: Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI. Furthermore, SHAP values can optimize the requirements for out-of-laboratory BCI applications involving real users.},
}
@article {pmid38306598,
year = {2024},
author = {Gao, J and Lin, C and Zhang, C and Zhang, X and Wang, Y and Xu, H and Zhang, T and Li, H and Wang, H and Wang, X},
title = {Exploring the Function of (+)-Naltrexone Precursors: Their Activity as TLR4 Antagonists and Potential in Treating Morphine Addiction.},
journal = {Journal of medicinal chemistry},
volume = {67},
number = {4},
pages = {3127-3143},
doi = {10.1021/acs.jmedchem.3c02316},
pmid = {38306598},
issn = {1520-4804},
mesh = {Rats ; Animals ; Humans ; *Naltrexone/pharmacology ; Toll-Like Receptor 4 ; *Morphine Dependence/drug therapy ; Rats, Sprague-Dawley ; Narcotic Antagonists/pharmacology/therapeutic use ; Morphine/pharmacology ; Analgesics, Opioid/therapeutic use ; },
abstract = {Disruptions in the toll-like receptor 4 (TLR4) signaling pathway are linked to chronic inflammation, neuropathic pain, and drug addiction. (+)-Naltrexone, an opioid-derived TLR4 antagonist with a (+)-isomer configuration, does not interact with classical opioid receptors and has moderate blood-brain barrier permeability. Herein, we developed a concise 10-step synthesis for (+)-naltrexone and explored its precursors, (+)-14-hydroxycodeinone (1) and (+)-14-hydroxymorphinone (3). These precursors exhibited TLR4 antagonistic activities 100 times stronger than (+)-naltrexone, particularly inhibiting the TLR4-TRIF pathway. In vivo studies showed that these precursors effectively reduced behavioral effects of morphine, like sensitization and conditioned place preference by suppressing microglial activation and TNF-α expression in the medial prefrontal cortex and ventral tegmental area. Additionally, 3 displayed a longer half-life and higher oral bioavailability than 1. Overall, this research optimized (+)-naltrexone synthesis and identified its precursors as potent TLR4 antagonists, offering potential treatments for morphine addiction.},
}
@article {pmid38306265,
year = {2024},
author = {Chen, H and Wang, D and Xu, M and Chen, Y},
title = {CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional Autoencoder for Dual-Target RSVP-BCI Tasks.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {7},
pages = {2080-2094},
doi = {10.1109/TBME.2024.3361716},
pmid = {38306265},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Algorithms ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Evoked Potentials/physiology ; },
abstract = {OBJECTIVE: The Rapid Serial Visual Presentation (RSVP) paradigm facilitates target identification in a rapid picture stream, which is applied extensively in military target surveillance and police monitoring. Most researchers concentrate on the single target RSVP-BCI whereas the study of dual-target is scarcely conducted, limiting RSVP application considerably.<br><br>METHODS: This paper proposed a novel classification model named Common Representation Extraction-Targeted Stacked Convolutional Autoencoder (CRE-TSCAE) to detect two targets with one nontarget in RSVP tasks. CRE generated a common representation for each target class to reduce variability from different trials of the same class and distinguish the difference between two targets better. TSCAE aimed to control uncertainty in the training process while requiring less target training data. The model learned a compact and discriminative feature through the training from several learning tasks so as to distinguish each class effectively.<br><br>RESULTS: It was validated on the World Robot Contest 2021 and 2022 ERP datasets. Experimental results showed that CRE-TSCAE outperformed the state-of-the-art RSVP decoding algorithms and the Average ACC was 71.25%, improving 6.5% at least over the rest.<br><br>CONCLUSION: It demonstrated that CRE-TSCAE showed a strong ability to extract discriminative latent features in detecting the differences among two targets with nontarget, which guaranteed increased classification accuracy.<br><br>SIGNIFICANCE: CRE-TSCAE provided an innovative and effective classification model for dual-target RSVP-BCI tasks and some insights into the neurophysiological distinction between different targets.},
}
@article {pmid38305684,
year = {2024},
author = {Ma, D and Hu, M and Yang, X and Liu, Q and Ye, F and Cai, W and Wang, Y and Xu, X and Chang, S and Wang, R and Yang, W and Ye, S and Su, N and Fan, M and Xu, H and Guo, J},
title = {Structural basis for sugar perception by Drosophila gustatory receptors.},
journal = {Science (New York, N.Y.)},
volume = {383},
number = {6685},
pages = {eadj2609},
doi = {10.1126/science.adj2609},
pmid = {38305684},
issn = {1095-9203},
mesh = {Animals ; *Sugars ; *Taste/physiology ; *Taste Perception/physiology ; *Drosophila melanogaster/physiology ; *Drosophila Proteins/chemistry ; Protein Conformation ; },
abstract = {Insects rely on a family of seven transmembrane proteins called gustatory receptors (GRs) to encode different taste modalities, such as sweet and bitter. We report structures of Drosophila sweet taste receptors GR43a and GR64a in the apo and sugar-bound states. Both GRs form tetrameric sugar-gated cation channels composed of one central pore domain (PD) and four peripheral ligand-binding domains (LBDs). Whereas GR43a is specifically activated by the monosaccharide fructose that binds to a narrow pocket in LBDs, disaccharides sucrose and maltose selectively activate GR64a by binding to a larger and flatter pocket in LBDs. Sugar binding to LBDs induces local conformational changes, which are subsequently transferred to the PD to cause channel opening. Our studies reveal a structural basis for sugar recognition and activation of GRs.},
}
@article {pmid38304854,
year = {2024},
author = {Gerdle, B and Dahlqvist Leinhard, O and Lund, E and Lundberg, P and Forsgren, MF and Ghafouri, B},
title = {Pain and the biochemistry of fibromyalgia: patterns of peripheral cytokines and chemokines contribute to the differentiation between fibromyalgia and controls and are associated with pain, fat infiltration and content.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {5},
number = {},
pages = {1288024},
pmid = {38304854},
issn = {2673-561X},
abstract = {OBJECTIVES: This explorative study analyses interrelationships between peripheral compounds in saliva, plasma, and muscles together with body composition variables in healthy subjects and in fibromyalgia patients (FM). There is a need to better understand the extent cytokines and chemokines are associated with body composition and which cytokines and chemokines differentiate FM from healthy controls.<br><br>METHODS: Here, 32 female FM patients and 30 age-matched female healthy controls underwent a clinical examination that included blood sample, saliva samples, and pain threshold tests. In addition, the subjects completed a health questionnaire. From these blood and saliva samples, a panel of 68 mainly cytokines and chemokines were determined. Microdialysis of trapezius and erector spinae muscles, phosphorus-31 magnetic resonance spectroscopy of erector spinae muscle, and whole-body magnetic resonance imaging for determination of body composition (BC)-i.e., muscle volume, fat content and infiltration-were also performed.<br><br>RESULTS: After standardizing BC measurements to remove the confounding effect of Body Mass Index, fat infiltration and content are generally increased, and fat-free muscle volume is decreased in FM. Mainly saliva proteins differentiated FM from controls. When including all investigated compounds and BC variables, fat infiltration and content variables were most important, followed by muscle compounds and cytokines and chemokines from saliva and plasma. Various plasma proteins correlated positively with pain intensity in FM and negatively with pain thresholds in all subjects taken together. A mix of increased plasma cytokines and chemokines correlated with an index covering fat infiltration and content in different tissues. When muscle compounds were included in the analysis, several of these were identified as the most important regressors, although many plasma and saliva proteins remained significant.<br><br>DISCUSSION: Peripheral factors were important for group differentiation between FM and controls. In saliva (but not plasma), cytokines and chemokines were significantly associated with group membership as saliva compounds were increased in FM. The importance of peripheral factors for group differentiation increased when muscle compounds and body composition variables were also included. Plasma proteins were important for pain intensity and sensitivity. Cytokines and chemokines mainly from plasma were also significantly and positively associated with a fat infiltration and content index.<br><br>CONCLUSION: Our findings of associations between cytokines and chemokines and fat infiltration and content in different tissues confirm that inflammation and immune factors are secreted from adipose tissue. FM is clearly characterized by complex interactions between peripheral tissues and the peripheral and central nervous systems, including nociceptive, immune, and neuroendocrine processes.},
}
@article {pmid38303522,
year = {2024},
author = {Simon, A and Bech, S and Loquet, G and Østergaard, J},
title = {Cortical linear encoding and decoding of sounds: Similarities and differences between naturalistic speech and music listening.},
journal = {The European journal of neuroscience},
volume = {59},
number = {8},
pages = {2059-2074},
doi = {10.1111/ejn.16265},
pmid = {38303522},
issn = {1460-9568},
support = {//Bang &amp; Olufsen A/S/ ; 9065-00270B//Innovation Fund Denmark/ ; },
mesh = {Auditory Perception/physiology ; Speech ; *Music ; *Speech Perception/physiology ; Electroencephalography ; Acoustic Stimulation ; },
abstract = {Linear models are becoming increasingly popular to investigate brain activity in response to continuous and naturalistic stimuli. In the context of auditory perception, these predictive models can be 'encoding', when stimulus features are used to reconstruct brain activity, or 'decoding' when neural features are used to reconstruct the audio stimuli. These linear models are a central component of some brain-computer interfaces that can be integrated into hearing assistive devices (e.g., hearing aids). Such advanced neurotechnologies have been widely investigated when listening to speech stimuli but rarely when listening to music. Recent attempts at neural tracking of music show that the reconstruction performances are reduced compared with speech decoding. The present study investigates the performance of stimuli reconstruction and electroencephalogram prediction (decoding and encoding models) based on the cortical entrainment of temporal variations of the audio stimuli for both music and speech listening. Three hypotheses that may explain differences between speech and music stimuli reconstruction were tested to assess the importance of the speech-specific acoustic and linguistic factors. While the results obtained with encoding models suggest different underlying cortical processing between speech and music listening, no differences were found in terms of reconstruction of the stimuli or the cortical data. The results suggest that envelope-based linear modelling can be used to study both speech and music listening, despite the differences in the underlying cortical mechanisms.},
}
@article {pmid38303499,
year = {2024},
author = {Zhang, Y and Quan, Z and Lou, F and Fang, Y and Thompson, GJ and Chen, G and Zhang, X},
title = {A proton birdcage coil integrated with interchangeable single loops for multi-nuclear MRI/MRS.},
journal = {Journal of Zhejiang University. Science. B},
volume = {25},
number = {2},
pages = {168-180},
pmid = {38303499},
issn = {1862-1783},
support = {2021ZD0200401//the STI 2030&#x2012;Major Projects/ ; 2018YFA0701400//the National Key Research and Development Program of China/ ; 52277232, 52293424, 81701774 and 61771423//the National Natural Science Foundation of China/ ; 226-2022-00136 and 226-2023-00125//the Fundamental Research Funds for the Central Universities/ ; LR23E070001//the Zhejiang Provincial Natural Science Foundation of China/ ; BE2022049//the Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//the Key-Area R&amp;D Program of Guangdong Province/ ; },
mesh = {Animals ; *Protons ; *Magnetic Resonance Imaging/methods ; Magnetic Resonance Spectroscopy ; Phantoms, Imaging ; Signal-To-Noise Ratio ; Equipment Design ; },
abstract = {Energy metabolism is fundamental for life. It encompasses the utilization of carbohydrates, lipids, and proteins for internal processes, while aberrant energy metabolism is implicated in many diseases. In the present study, using three-dimensional (3D) printing from polycarbonate via fused deposition modeling, we propose a multi-nuclear radiofrequency (RF) coil design with integrated [1]H birdcage and interchangeable X-nuclei ([2]H, [13]C, [23]Na, and [31]P) single-loop coils for magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS). The single-loop coil for each nucleus attaches to an arc bracket that slides unrestrictedly along the birdcage coil inner surface, enabling convenient switching among various nuclei and animal handling. Compared to a commercial [1]H birdcage coil, the proposed [1]H birdcage coil exhibited superior signal-excitation homogeneity and imaging signal-to-noise ratio (SNR). For X-nuclei study, prominent peaks in spectroscopy for phantom solutions showed excellent SNR, and the static and dynamic peaks of in vivo spectroscopy validated the efficacy of the coil design in structural imaging and energy metabolism detection simultaneously.},
}
@article {pmid38303029,
year = {2024},
author = {Chen, Y and Xu, X and Ding, K and Tang, T and Cai, F and Zhang, H and Chen, Z and Qi, Y and Fu, Z and Zhu, G and Dou, Z and Xu, J and Chen, G and Wu, Q and Ji, J and Zhang, J},
title = {TRIM25 promotes glioblastoma cell growth and invasion via regulation of the PRMT1/c-MYC pathway by targeting the splicing factor NONO.},
journal = {Journal of experimental &amp; clinical cancer research : CR},
volume = {43},
number = {1},
pages = {39},
pmid = {38303029},
issn = {1756-9966},
support = {82071287//National Natural Science Foundation of China/ ; 82271301//National Natural Science Foundation of China/ ; 82203035//National Natural Science Foundation of China/ ; 82270823//National Natural Science Foundation of China/ ; LY21H160024//Natural Science Foundation of Zhejiang Province/ ; },
mesh = {Humans ; Cell Line, Tumor ; Cell Proliferation ; DNA-Binding Proteins/genetics ; Gene Expression Regulation, Neoplastic ; *Glioblastoma/metabolism/pathology ; Protein-Arginine N-Methyltransferases/genetics/metabolism ; Repressor Proteins/metabolism ; RNA Splicing Factors/metabolism ; RNA-Binding Proteins/genetics/metabolism ; *Transcription Factors/genetics/metabolism ; *Tripartite Motif Proteins ; Ubiquitin-Protein Ligases/genetics/metabolism ; Ubiquitination ; },
abstract = {BACKGROUND: Ubiquitination plays an important role in proliferating and invasive characteristic of glioblastoma (GBM), similar to many other cancers. Tripartite motif 25 (TRIM25) is a member of the TRIM family of proteins, which are involved in tumorigenesis through substrate ubiquitination.<br><br>METHODS: Difference in TRIM25 expression levels between nonneoplastic brain tissue samples and primary glioma samples was demonstrated using publicly available glioblastoma database, immunohistochemistry, and western blotting. TRIM25 knockdown GBM cell lines (LN229 and U251) and patient derived GBM stem-like cells (GSCs) GBM#021 were used to investigate the function of TRIM25 in vivo and in vitro. Co-immunoprecipitation (Co-IP) and mass spectrometry analysis were performed to identify NONO as a protein that interacts with TRIM25. The molecular mechanisms underlying the promotion of GBM development by TRIM25 through NONO were investigated by RNA-seq and validated by qRT-PCR and western blotting.<br><br>RESULTS: We observed upregulation of TRIM25 in GBM, correlating with enhanced glioblastoma cell growth and invasion, both in vitro and in vivo. Subsequently, we screened a panel of proteins interacting with TRIM25; mass spectrometry and co-immunoprecipitation revealed that NONO was a potential substrate of TRIM25. TRIM25 knockdown reduced the K63-linked ubiquitination of NONO, thereby suppressing the splicing function of NONO. Dysfunctional NONO resulted in the retention of the second intron in the pre-mRNA of PRMT1, inhibiting the activation of the PRMT1/c-MYC pathway.<br><br>CONCLUSIONS: Our study demonstrates that TRIM25 promotes glioblastoma cell growth and invasion by regulating the PRMT1/c-MYC pathway through mediation of the splicing factor NONO. Targeting the E3 ligase activity of TRIM25 or the complex interactions between TRIM25 and NONO may prove beneficial in the treatment of GBM.},
}
@article {pmid38301967,
year = {2024},
author = {Zhang, J and Guo, H and Wang, L and Zheng, M and Kong, S and Wu, H and Zhao, L and Zhao, Q and Yang, X and He, Q and Chen, X and Ding, L and Yang, B},
title = {Cediranib enhances the transcription of MHC-I by upregulating IRF-1.},
journal = {Biochemical pharmacology},
volume = {221},
number = {},
pages = {116036},
doi = {10.1016/j.bcp.2024.116036},
pmid = {38301967},
issn = {1873-2968},
mesh = {Humans ; Interferon Regulatory Factor-1/genetics ; *Carcinoma, Non-Small-Cell Lung ; *Lung Neoplasms/drug therapy ; Quinazolines/pharmacology ; *Indoles ; },
abstract = {Diminished or lost Major Histocompatibility Complex class I (MHC-I) expression is frequently observed in tumors, which obstructs the immune recognition of tumor cells by cytotoxic T cells. Restoring MHC-I expression by promoting its transcription and improving protein stability have been promising strategies for reestablishing anti-tumor immune responses. Here, through cell-based screening models, we found that cediranib significantly upregulated MHC-I expression in tumor cells. This finding was confirmed in various non-small cell lung cancer (NSCLC) cell lines and primary patient-derived lung cancer cells. Furthermore, we discovered cediranib achieved MHC-I upregulation through transcriptional regulation. interferon regulatory factor 1 (IRF-1) was required for cediranib induced MHC-I transcription and the absence of IRF-1 eliminated this effect. Continuing our research, we found cediranib triggered STAT1 phosphorylation and promoted IRF-1 transcription subsequently, thus enhancing downstream MHC-I transcription. In vivo study, we further confirmed that cediranib increased MHC-I expression, enhanced CD8[+] T cell infiltration, and improved the efficacy of anti-PD-L1 therapy. Collectively, our study demonstrated that cediranib could elevate MHC-I expression and enhance responsiveness to immune therapy, thereby providing a theoretical foundation for its potential clinical trials in combination with immunotherapy.},
}
@article {pmid38301741,
year = {2024},
author = {Pouryosef, M and Abedini-Nassab, R and Akrami, SMR},
title = {A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm.},
journal = {Neuroscience},
volume = {541},
number = {},
pages = {35-49},
doi = {10.1016/j.neuroscience.2024.01.014},
pmid = {38301741},
issn = {1873-7544},
mesh = {Humans ; Bayes Theorem ; *Signal Processing, Computer-Assisted ; *Epilepsy/diagnosis ; Seizures/diagnosis ; Algorithms ; Electroencephalography/methods ; },
abstract = {The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.},
}
@article {pmid38301424,
year = {2024},
author = {Proverbio, AM},
title = {The temporal dynamics of visual imagery and BCI: Comment on "Visual mental imagery: Evidence for a heterarchical neural architecture" by Spagna et al.},
journal = {Physics of life reviews},
volume = {48},
number = {},
pages = {174-175},
doi = {10.1016/j.plrev.2024.01.006},
pmid = {38301424},
issn = {1873-1457},
mesh = {*Brain-Computer Interfaces ; Imagery, Psychotherapy ; Visual Perception ; },
}
@article {pmid38300862,
year = {2024},
author = {Wang, Y and Seki, T and Gkoupidenis, P and Chen, Y and Nagata, Y and Bonn, M},
title = {Aqueous chemimemristor based on proton-permeable graphene membranes.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {6},
pages = {e2314347121},
pmid = {38300862},
issn = {1091-6490},
support = {None//MaxWater Initiative of the Max Planck Society/ ; },
abstract = {Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.},
}
@article {pmid38298914,
year = {2024},
author = {Liu, R and Chao, Y and Ma, X and Sha, X and Sun, L and Li, S and Chang, S},
title = {ERTNet: an interpretable transformer-based framework for EEG emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1320645},
pmid = {38298914},
issn = {1662-4548},
abstract = {BACKGROUND: Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy.<br><br>METHODS: We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state.<br><br>RESULTS: Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23%&#x2009;&#xb1;&#x2009;2.59% on the dimensional model (DEAP) and 67.17%&#x2009;&#xb1;&#x2009;1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data.<br><br>DISCUSSION: Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.},
}
@article {pmid38295915,
year = {2024},
author = {Guo, H and Zhou, C and Zheng, M and Zhang, J and Wu, H and He, Q and Ding, L and Yang, B},
title = {Insights into the role of derailed endocytic trafficking pathway in cancer: From the perspective of cancer hallmarks.},
journal = {Pharmacological research},
volume = {201},
number = {},
pages = {107084},
doi = {10.1016/j.phrs.2024.107084},
pmid = {38295915},
issn = {1096-1186},
mesh = {Humans ; *Signal Transduction/physiology ; *Neoplasms/metabolism ; Endocytosis/physiology ; Cell Membrane/metabolism ; Protein Transport ; },
abstract = {The endocytic trafficking pathway is a highly organized cellular program responsible for the regulation of membrane components and uptake of extracellular substances. Molecules internalized into the cell through endocytosis will be sorted for degradation or recycled back to membrane, which is determined by a series of sorting events. Many receptors, enzymes, and transporters on the membrane are strictly regulated by endocytic trafficking process, and thus the endocytic pathway has a profound effect on cellular homeostasis. However, the endocytic trafficking process is typically dysregulated in cancers, which leads to the aberrant retention of receptor tyrosine kinases and immunosuppressive molecules on cell membrane, the loss of adhesion protein, as well as excessive uptake of nutrients. Therefore, hijacking endocytic trafficking pathway is an important approach for tumor cells to obtain advantages of proliferation and invasion, and to evade immune attack. Here, we summarize how dysregulated endocytic trafficking process triggers tumorigenesis and progression from the perspective of several typical cancer hallmarks. The impact of endocytic trafficking pathway to cancer therapy efficacy is also discussed.},
}
@article {pmid38295471,
year = {2024},
author = {Wang, Y and Liu, W and Wang, Y and Ouyang, G and Guo, Y},
title = {Long-term HD-tDCS modulates dynamic changes of brain activity on patients with disorders of consciousness: A resting-state EEG study.},
journal = {Computers in biology and medicine},
volume = {170},
number = {},
pages = {108084},
doi = {10.1016/j.compbiomed.2024.108084},
pmid = {38295471},
issn = {1879-0534},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; Consciousness Disorders/therapy ; Electroencephalography/methods ; Brain ; },
abstract = {OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear.<br><br>METHODS: This study recruited 8 DOC patients and applied 20-min sessions of 2&#xa0;mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day's and 7,14 days' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values.<br><br>RESULT: After 1 day's parietal HD-tDCS, DOC patients' CRS-R value had not changed (8.25&#xa0;&#xb1;&#xa0;1.91). HD-tDCS improved DOC patients' CRS-R value at T2 (9.75&#xa0;&#xb1;&#xa0;1.91, p&#xa0;<&#xa0;0.05) and at T3 (11.38&#xa0;&#xb1;&#xa0;2.77, p&#xa0;<&#xa0;0.05), compared with that at T0 (8.25&#xa0;&#xb1;&#xa0;1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p&#xa0;>&#xa0;0.05), and showed significant changes at T2 and T3 (p&#xa0;<&#xa0;0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p&#xa0;<&#xa0;0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0.<br><br>CONCLUSION: Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.},
}
@article {pmid38295419,
year = {2024},
author = {Yan, S and Hu, Y and Zhang, R and Qi, D and Hu, Y and Yao, D and Shi, L and Zhang, L},
title = {Multilayer network-based channel selection for motor imagery brain-computer interface.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad2496},
pmid = {38295419},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagination ; Electroencephalography/methods ; Imagery, Psychotherapy ; Brain ; Algorithms ; },
abstract = {Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.},
}
@article {pmid38295418,
year = {2024},
author = {Mobaien, A and Boostani, R and Sanei, S},
title = {Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad2495},
pmid = {38295418},
issn = {1741-2552},
mesh = {*Evoked Potentials, Visual ; Evoked Potentials ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; *Brain-Computer Interfaces ; Algorithms ; *Cyclohexylamines ; *Indenes ; },
abstract = {Objective.the P300-based brain-computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. While various approaches have been explored to enhance the SNR of P300 signals, the impact of VEPs has been largely overlooked. The main objective of this study is to investigate how VEPs impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.Approach.our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.Main results.analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs demonstrates improved performance in P300-based BCIs. This improvement is verified through several experiments conducted with real P300 data.Significance.this study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.},
}
@article {pmid38295415,
year = {2024},
author = {John, AR and Singh, AK and Gramann, K and Liu, D and Lin, CT},
title = {Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad2494},
pmid = {38295415},
issn = {1741-2552},
mesh = {Humans ; *Robotics ; *Brain-Computer Interfaces ; Workload/psychology ; Electroencephalography/methods ; Cognition ; },
abstract = {Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.},
}
@article {pmid38292898,
year = {2023},
author = {Miziev, S and Pawlak, WA and Howard, N},
title = {Comparative analysis of energy transfer mechanisms for neural implants.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1320441},
pmid = {38292898},
issn = {1662-4548},
abstract = {As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants.},
}
@article {pmid38292443,
year = {2024},
author = {Wang, X and Ivanov, AP and Edel, JB},
title = {Biocompatible Biphasic Iontronics Enable Neuron-Like Ionic Signal Transmission.},
journal = {Research (Washington, D.C.)},
volume = {7},
number = {},
pages = {0294},
pmid = {38292443},
issn = {2639-5274},
abstract = {Biocompatible connections between external artificial devices and living organisms show promise for future neuroprosthetics and therapeutics. The study in Science by Zhao and colleagues introduces a cascade-heterogated biphasic gel (HBG) iontronic device, which facilitates electronic-to-multi-ionic signal transduction for abiotic-biotic interfaces. Inspired by neuron signaling, the HBG device demonstrated its biocompatibility by regulating neural activity in biological tissue, paving the way for wearable and implantable devices, including brain-computer interfaces.},
}
@article {pmid38289842,
year = {2024},
author = {Sartipi, S and Cetin, M},
title = {Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {718-727},
doi = {10.1109/TNSRE.2024.3360194},
pmid = {38289842},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Machine Learning ; *Brain-Computer Interfaces ; Learning ; Benchmarking ; Algorithms ; Imagination ; },
abstract = {Motor imagery (MI) classification based on electroencephalogram (EEG) is a widely-used technique in non-invasive brain-computer interface (BCI) systems. Since EEG recordings suffer from heterogeneity across subjects and labeled data insufficiency, designing a classifier that performs the MI independently from the subject with limited labeled samples would be desirable. To overcome these limitations, we propose a novel subject-independent semi-supervised deep architecture (SSDA). The proposed SSDA consists of two parts: an unsupervised and a supervised element. The training set contains both labeled and unlabeled data samples from multiple subjects. First, the unsupervised part, known as the columnar spatiotemporal auto-encoder (CST-AE), extracts latent features from all the training samples by maximizing the similarity between the original and reconstructed data. A dimensional scaling approach is employed to reduce the dimensionality of the representations while preserving their discriminability. Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part. Moreover, we employ center loss in the supervised part to minimize the embedding space distance of each point in a class to its center. The model optimizes both parts of the network in an end-to-end fashion. The performance of the proposed SSDA is evaluated on test subjects who were not seen by the model during the training phase. To assess the performance, we use two benchmark EEG-based MI task datasets. The results demonstrate that SSDA outperforms state-of-the-art methods and that a small number of labeled training samples can be sufficient for strong classification performance.},
}
@article {pmid38289841,
year = {2024},
author = {Lin, JW and Fan, ZC and Tzou, SC and Wang, LJ and Ko, LW},
title = {Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1333-1343},
doi = {10.1109/TNSRE.2024.3360137},
pmid = {38289841},
issn = {1558-0210},
mesh = {Child ; Adolescent ; Humans ; *Attention Deficit Disorder with Hyperactivity/diagnosis ; Brain ; Electroencephalography/methods ; Alpha Rhythm ; Neuropsychological Tests ; },
abstract = {Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD and provide subjective features as decision references to assist specialists and physicians. Many studies have been devoted to investigating the neural dynamics of the brain through resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance imaging (fMRI). The present study used coherence, which is one of the functional connectivity (FC) methods, to analyze the neural patterns of children and adolescents (8-16 years old) under CPT and continuous auditory test of attention (CATA) task. In the meantime, electroencephalography (EEG) oscillations were recorded by a wireless brain-computer interface (BCI). 72 children were enrolled, of which 53 participants were diagnosed with ADHD and 19 presented to be typical developing (TD). The experimental results exhibited a higher difference in alpha and theta bands between the TD group and the ADHD group. While the differences between the TD group and the ADHD group in all four frequency domains were greater than under CPT conditions. Statistically significant differences ([Formula: see text]) were observed between the ADHD and TD groups in the alpha rhythm during the CATA task in the short-range of coherence. For the temporal lobe FC during the CATA task, the TD group exhibited statistically significantly FC ([Formula: see text]) in the alpha rhythm compared to the ADHD group. These findings offering new possibilities for more techniques and diagnostic methods in finding more ADHD features. The differences in alpha and beta frequencies were more pronounced in the ADHD group during the CPT task compared to the CATA task. Additionally, the disparities in brain activity were more evident across delta, theta, alpha and beta frequency domains when the task given was a CATA as opposed to a CPT. The findings presented the underlying mechanisms of the FC differences between children and adolescents with ADHD. Moreover, these findings should extend to use machine learning approaches to assist the ADHD classification and diagnosis.},
}
@article {pmid38289338,
year = {2024},
author = {Yuan, T and Wang, Y and Jin, Y and Yang, H and Xu, S and Zhang, H and Chen, Q and Li, N and Ma, X and Song, H and Peng, C and Geng, Z and Dong, J and Duan, G and Sun, Q and Yang, Y and Yang, F and Huang, Z},
title = {Coupling of Slack and NaV1.6 sensitizes Slack to quinidine blockade and guides anti-seizure strategy development.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {38289338},
issn = {2050-084X},
support = {Nos. 31871083 and 81371432//National Natural Science Foundation of China-China Academy of General Technology Joint Fund for Basic Research/ ; Nos. 32000674//National Natural Science Foundation of China/ ; No.2021ZD0202102//Chinese National Programs for Brain Science and Brain-like Intelligence Technology/ ; },
mesh = {Animals ; Humans ; Mice ; Anticonvulsants/pharmacology/therapeutic use ; *Epilepsy ; Homozygote ; *NAV1.6 Voltage-Gated Sodium Channel/genetics ; Nerve Tissue Proteins/genetics ; *Quinidine/pharmacology ; Sodium ; },
abstract = {Quinidine has been used as an anticonvulsant to treat patients with KCNT1-related epilepsy by targeting gain-of-function KCNT1 pathogenic mutant variants. However, the detailed mechanism underlying quinidine's blockade against KCNT1 (Slack) remains elusive. Here, we report a functional and physical coupling of the voltage-gated sodium channel NaV1.6 and Slack. NaV1.6 binds to and highly sensitizes Slack to quinidine blockade. Homozygous knockout of NaV1.6 reduces the sensitivity of native sodium-activated potassium currents to quinidine blockade. NaV1.6-mediated sensitization requires the involvement of NaV1.6's N- and C-termini binding to Slack's C-terminus and is enhanced by transient sodium influx through NaV1.6. Moreover, disrupting the Slack-NaV1.6 interaction by viral expression of Slack's C-terminus can protect against Slack[G269S]-induced seizures in mice. These insights about a Slack-NaV1.6 complex challenge the traditional view of 'Slack as an isolated target' for anti-epileptic drug discovery efforts and can guide the development of innovative therapeutic strategies for KCNT1-related epilepsy.},
}
@article {pmid38287990,
year = {2023},
author = {Wu, M and Ouyang, R and Zhou, C and Sun, Z and Li, F and Li, P},
title = {A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1345770},
pmid = {38287990},
issn = {1662-4548},
abstract = {INTRODUCTION: Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application.<br><br>METHODS: In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied.<br><br>RESULTS: The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%.<br><br>DISCUSSION: The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.},
}
@article {pmid38287988,
year = {2023},
author = {Tai, P and Ding, P and Wang, F and Gong, A and Li, T and Zhao, L and Su, L and Fu, Y},
title = {Brain-computer interface paradigms and neural coding.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1345961},
pmid = {38287988},
issn = {1662-4548},
abstract = {Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.},
}
@article {pmid38286946,
year = {2024},
author = {Schiff, ND and Diringer, M and Diserens, K and Edlow, BL and Gosseries, O and Hill, NJ and Hochberg, LR and Ismail, FY and Meyer, IA and Mikell, CB and Mofakham, S and Molteni, E and Polizzotto, L and Shah, SA and Stevens, RD and Thengone, D and , },
title = {Brain-Computer Interfaces for Communication in Patients with Disorders of Consciousness: A Gap Analysis and Scientific Roadmap.},
journal = {Neurocritical care},
volume = {41},
number = {1},
pages = {129-145},
pmid = {38286946},
issn = {1556-0961},
support = {DP2 HD101400/HD/NICHD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Consciousness Disorders/physiopathology/therapy ; Communication ; },
abstract = {BACKGROUND: We developed a gap analysis that examines the role of brain-computer interfaces (BCI) in patients with disorders of consciousness (DoC), focusing on their assessment, establishment of communication, and engagement with their environment.<br><br>METHODS: The Curing Coma Campaign convened a Coma Science work group that included 16 clinicians and neuroscientists with expertise in DoC. The work group met online biweekly and performed a gap analysis of the primary question.<br><br>RESULTS: We outline a roadmap for assessing BCI readiness in patients with DoC and for advancing the use of BCI devices in patients with DoC. Additionally, we discuss preliminary studies that inform development of BCI solutions for communication and assessment of readiness for use of BCIs in DoC study participants. Special emphasis is placed on the challenges posed by the complex pathophysiologies caused by heterogeneous brain injuries and their impact on neuronal signaling. The differences between one-way and two-way communication are specifically considered. Possible implanted and noninvasive BCI solutions for acute and chronic DoC in adult and pediatric populations are also addressed.<br><br>CONCLUSIONS: We identify clinical and technical gaps hindering the use of BCI in patients with DoC in each of these contexts and provide a roadmap for research aimed at improving communication for adults and children with DoC, spanning the clinical spectrum from intensive care unit to chronic care.},
}
@article {pmid38285586,
year = {2024},
author = {Luo, J and Cui, W and Xu, S and Wang, L and Chen, H and Li, Y},
title = {A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {672-683},
doi = {10.1109/TNSRE.2024.3359191},
pmid = {38285586},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; Learning ; *Brain-Computer Interfaces ; Electric Power Supplies ; Machine Learning ; Algorithms ; },
abstract = {Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific information, leading to the poor cross-subject transfer performance. In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification. Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features. Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel time-frequency images. Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data. Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task. The source code of our model is publicly available in https://github.com/ljbuaa/CST_TVA_DRTL.},
}
@article {pmid38285578,
year = {2024},
author = {Barbruni, GL and Cordara, C and Carminati, M and Carrara, S and Ghezzi, D},
title = {A Frequency-Switching Inductive Power Transfer System for Wireless, Miniaturised and Large-Scale Neural Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {3},
pages = {679-690},
doi = {10.1109/TBCAS.2024.3359481},
pmid = {38285578},
issn = {1940-9990},
mesh = {*Wireless Technology/instrumentation ; *Equipment Design ; Humans ; Electric Power Supplies ; Miniaturization ; Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {Three-coil inductive power transfer is the state-of-the-art solution to power multiple miniaturised neural implants. However, the maximum delivered power is limited by the efficiency of the powering link and safety constrains. Here we propose a frequency-switching inductive link, where the passive resonator normally used in a three-coil link is replaced by an active resonator. It receives power from the external transmitter via a two-coil inductive link at the low frequency of 13.56 MHz. Then, it switches the operating frequency to the higher frequency of 433.92 MHz through a dedicated circuitry. Last, it transmits power to 1024 miniaturised implants via a three-coil inductive link using an array of 37 focusing resonators for a brain coverage of 163.84 mm [2]. Our simulations reported a power transfer efficiency of 0.013 % and a maximum power delivered to the load of 1970 μW under safety-constrains, which are respectively two orders of magnitude and more than six decades higher compared to an equivalent passive three-coil link. The frequency-switching inductive system is a scalable and highly versatile solution for wireless, miniaturised and large-scale neural interfaces.},
}
@article {pmid38285576,
year = {2024},
author = {Valencia, D and Mercier, PP and Alimohammad, A},
title = {Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {3},
pages = {691-701},
doi = {10.1109/TBCAS.2024.3359994},
pmid = {38285576},
issn = {1940-9990},
mesh = {*Neural Networks, Computer ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Data Compression/methods ; Animals ; *Algorithms ; Neurons/physiology ; Electroencephalography/methods ; },
abstract = {Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio.},
}
@article {pmid38282708,
year = {2023},
author = {Jiao, Y and Lei, M and Zhu, J and Chang, R and Qu, X},
title = {Advances in electrode interface materials and modification technologies for brain-computer interfaces.},
journal = {Biomaterials translational},
volume = {4},
number = {4},
pages = {213-233},
pmid = {38282708},
issn = {2096-112X},
abstract = {Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.},
}
@article {pmid38279262,
year = {2024},
author = {Miao, T and Symonds, A and Hickman, OJ and Wu, D and Wang, P and Lemoine, N and Wang, Y and Linardopoulos, S and Halldén, G},
title = {Inhibition of Bromodomain Proteins Enhances Oncolytic HAdVC5 Replication and Efficacy in Pancreatic Ductal Adenocarcinoma (PDAC) Models.},
journal = {International journal of molecular sciences},
volume = {25},
number = {2},
pages = {},
pmid = {38279262},
issn = {1422-0067},
support = {PCRF Hallden//Pancreatic Cancer Research Fund/ ; C16420/A18066//BCI CRUK Centre Grant/ ; },
mesh = {Humans ; Nuclear Proteins/genetics ; Epigenesis, Genetic ; *Oncolytic Viruses/genetics ; Transcription Factors/genetics/metabolism ; Cell Line, Tumor ; *Pancreatic Neoplasms/genetics/therapy/pathology ; *Carcinoma, Pancreatic Ductal/genetics/therapy/pathology ; *Oncolytic Virotherapy/methods ; Adenoviridae/genetics ; Tumor Microenvironment ; Bromodomain Containing Proteins ; Cell Cycle Proteins/metabolism ; },
abstract = {Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive type of pancreatic cancer, which rapidly develops resistance to the current standard of care. Several oncolytic Human AdenoViruses (HAdVs) have been reported to re-sensitize drug-resistant cancer cells and in combination with chemotherapeutics attenuate solid tumour growth. Obstacles preventing greater clinical success are rapid hepatic elimination and limited viral replication and spread within the tumour microenvironment. We hypothesised that higher intratumoural levels of the virus could be achieved by altering cellular epigenetic regulation. Here we report on the screening of an enriched epigenetics small molecule library and validation of six compounds that increased viral gene expression and replication. The greatest effects were observed with three epigenetic inhibitors targeting bromodomain (BRD)-containing proteins. Specifically, BRD4 inhibitors enhanced the efficacy of Ad5 wild type, Ad∆∆, and Ad-3∆-A20T in 3-dimensional co-culture models of PDAC and in vivo xenografts. RNAseq analysis demonstrated that the inhibitors increased viral E1A expression, altered expression of cell cycle regulators and inflammatory factors, and attenuated expression levels of tumour cell oncogenes such as c-Myc and Myb. The data suggest that the tumour-selective Ad∆∆ and Ad-3∆-A20T combined with epigenetic inhibitors is a novel strategy for the treatment of PDAC by eliminating both cancer and associated stromal cells to pave the way for immune cell access even after systemic delivery of the virus.},
}
@article {pmid38278790,
year = {2024},
author = {Wu, H and Hou, Y and Yoon, J and Knoepfel, AM and Zheng, L and Yang, D and Wang, K and Qian, J and Priya, S and Wang, K},
title = {Down-selection of biomolecules to assemble "reverse micelle" with perovskites.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {772},
pmid = {38278790},
issn = {2041-1723},
support = {DE-EE0009364//DOE | Office of Energy Efficiency &amp; Renewable Energy | Solar Energy Technologies Office (SETO)/ ; },
mesh = {*Micelles ; *Oxides/chemistry ; Semiconductors ; Biocompatible Materials ; *Titanium ; *Calcium Compounds ; },
abstract = {Biological molecule-semiconductor interfacing has triggered numerous opportunities in applied physics such as bio-assisted data storage and computation, brain-computer interface, and advanced distributed bio-sensing. The introduction of electronics into biological embodiment is being quickly developed as it has great potential in providing adaptivity and improving functionality. Reciprocally, introducing biomaterials into semiconductors to manifest bio-mimetic functionality is impactful in triggering new enhanced mechanisms. In this study, we utilize the vulnerable perovskite semiconductors as a platform to understand if certain types of biomolecules can regulate the lattice and endow a unique mechanism for stabilizing the metastable perovskite lattice. Three tiers of biomolecules have been systematically tested and the results reveal a fundamental mechanism for the formation of a "reverse-micelle" structure. Systematic exploration of a large set of biomolecules led to the discovery of guiding principle for down-selection of biomolecules which extends the classic emulsion theory to this hybrid systems. Results demonstrate that by introducing biomaterials into semiconductors, natural phenomena typically observed in biological systems can also be incorporated into semiconducting crystals, providing a new perspective to engineer existing synthetic materials.},
}
@article {pmid38278220,
year = {2024},
author = {Lamorie-Foote, K and Kramer, DR and Sundaram, S and Cavaleri, J and Gilbert, ZD and Tang, AM and Bashford, L and Liu, CY and Kellis, S and Lee, B},
title = {Primary somatosensory cortex organization for engineering artificial somatosensation.},
journal = {Neuroscience research},
volume = {204},
number = {},
pages = {1-13},
doi = {10.1016/j.neures.2024.01.005},
pmid = {38278220},
issn = {1872-8111},
mesh = {Humans ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Somatosensory Cortex/physiology ; },
abstract = {Somatosensory deficits from stroke, spinal cord injury, or other neurologic damage can lead to a significant degree of functional impairment. The primary (SI) and secondary (SII) somatosensory cortices encode information in a medial to lateral organization. SI is generally organized topographically, with more discrete cortical representations of specific body regions. SII regions corresponding to anatomical areas are less discrete and may represent a more functional rather than topographic organization. Human somatosensory research continues to map cortical areas of sensory processing with efforts primarily focused on hand and upper extremity information in SI. However, research into SII and other body regions is lacking. In this review, we synthesize the current state of knowledge regarding the cortical organization of human somatosensation and discuss potential applications for brain computer interface. In addition to accurate individualized mapping of cortical somatosensation, further research is required to uncover the neurophysiological mechanisms of how somatosensory information is encoded in the cortex.},
}
@article {pmid38278092,
year = {2024},
author = {Zhang, S and An, D and Liu, J and Chen, J and Wei, Y and Sun, F},
title = {Dynamic decomposition graph convolutional neural network for SSVEP-based brain-computer interface.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {172},
number = {},
pages = {106075},
doi = {10.1016/j.neunet.2023.12.029},
pmid = {38278092},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Photic Stimulation ; },
abstract = {The SSVEP-based paradigm serves as a prevalent approach in the realm of brain-computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.},
}
@article {pmid38277701,
year = {2024},
author = {Yao, Y and Stebner, A and Tuytelaars, T and Geirnaert, S and Bertrand, A},
title = {Identifying temporal correlations between natural single-shot videos and EEG signals.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad2333},
pmid = {38277701},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Eye Movements ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Objective.Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.Approach.We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main results.We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.Significance.This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.},
}
@article {pmid38277252,
year = {2024},
author = {Pei, Y and Xu, J and Chen, Q and Wang, C and Yu, F and Zhang, L and Luo, W},
title = {DTP-Net: Learning to Reconstruct EEG Signals in Time-Frequency Domain by Multi-Scale Feature Reuse.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {5},
pages = {2662-2673},
doi = {10.1109/JBHI.2024.3358917},
pmid = {38277252},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) signals are prone to contamination by noise, such as ocular and muscle artifacts. Minimizing these artifacts is crucial for EEG-based downstream applications like disease diagnosis and brain-computer interface (BCI). This paper presents a new EEG denoising model, DTP-Net. It is a fully convolutional neural network comprising Densely-connected Temporal Pyramids (DTPs) placed between two learnable time-frequency transformations. In the time-frequency domain, DTPs facilitate efficient propagation of multi-scale features extracted from EEG signals of any length, leading to effective noise reduction. Comprehensive experiments on two public semi-simulated datasets demonstrate that the proposed DTP-Net consistently outperforms existing state-of-the-art methods on metrics including relative root mean square error (RRMSE) and signal-to-noise ratio improvement (∆SNR). Moreover, the proposed DTP-Net is applied to a BCI classification task, yielding an improvement of up to 5.55% in accuracy. This confirms the potential of DTP-Net for applications in the fields of EEG-based neuroscience and neuro-engineering. An in-depth analysis further illustrates the representation learning behavior of each module in DTP-Net, demonstrating its robustness and reliability.},
}
@article {pmid38274525,
year = {2023},
author = {Wang, H and Xia, Q and Dong, Z and Guo, W and Deng, W and Zhang, L and Kuang, W and Li, T},
title = {Emotional distress and multimorbidity patterns in Chinese Han patients with osteoporosis: a network analysis.},
journal = {Frontiers in public health},
volume = {11},
number = {},
pages = {1242091},
pmid = {38274525},
issn = {2296-2565},
mesh = {Male ; Humans ; Female ; Multimorbidity ; Emotions ; *Osteoporosis/epidemiology ; *Psychological Distress ; China/epidemiology ; },
abstract = {With the aging of the population, the prevalence of osteoporosis and multimorbidity is increasing. Patients with osteoporosis often experience varying levels of emotional distress, including anxiety and depression. However, few studies have explored the patterns of multiple conditions and their impact on patients' emotional distress. Here, we conducted a network analysis to explore the patterns of multimorbidities and their impact on emotional distress in 13,359 Chinese Han patients with osteoporosis. The results showed that multimorbidity was prevalent in Chinese patients with osteoporosis and increased with age, and was more frequent in males than in females, with the most common pattern of multimorbidity being osteoporosis and essential (primary) hypertension. Finally, we found that patients' emotional distress increased with the number of multimorbidities, especially in female patients, and identified eight multimorbidities with high correlation to patients' emotional distress.},
}
@article {pmid38273009,
year = {2024},
author = {Henney, MA and Carstensen, M and Thorning-Schmidt, M and Kubińska, M and Grønberg, MG and Nguyen, M and Madsen, KH and Clemmensen, LKH and Petersen, PM},
title = {Brain stimulation with 40 Hz heterochromatic flicker extended beyond red, green, and blue.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {2147},
pmid = {38273009},
issn = {2045-2322},
support = {1044-00066A//Innovationsfonden (Innovation Fund Denmark)/ ; },
mesh = {Humans ; Photic Stimulation/methods ; *Amber ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Brain ; },
abstract = {Alzheimer's disease (AD) is associated with electrophysiological changes in the brain. Pre-clinical and early clinical trials have shown promising results for the possible therapy of AD with 40 Hz neurostimulation. The most notable findings used stroboscopic flicker, but this technique poses an inherent barrier for human applications due to its visible flickering and resulting high level of perceived discomfort. Therefore, alternative options should be investigated for entraining 40 Hz brain activity with light sources that appear less flickering. Previously, chromatic flicker based on red, green, and blue (RGB) have been studied in the context of brain-computer interfaces, but this is an incomplete representation of the colours in the visual spectrum. This study introduces a new kind of heterochromatic flicker based on spectral combinations of blue, cyan, green, lime, amber, and red (BCGLAR). These combinations are investigated by the steady-state visually evoked potential (SSVEP) response from the flicker with an aim of optimising the choice of 40 Hz light stimulation with spectrally similar colour combinations in BCGLAR space. Thirty healthy young volunteers were stimulated with heterochromatic flicker in an electroencephalography experiment with randomised complete block design. Responses were quantified as the 40 Hz signal-to-noise ratio and analysed using mixed linear models. The size of the SSVEP response to heterochromatic flicker is dependent on colour combinations and influenced by both visual and non-visual effects. The amber-red flicker combination evoked the highest SSVEP, and combinations that included blue and/or red consistently evoked higher SSVEP than combinations only with mid-spectrum colours. Including a colour from either extreme of the visual spectrum (blue and/or red) in at least one of the dyadic phases appears to be more important than choosing pairs of colours that are far from each other on the visual spectrum. Spectrally adjacent colour pairs appear less flickering to the perceiver, and thus the results motivate investigations into the limits for how alike the two phases can be and still evoke a 40 Hz response. Specifically, combining a colour on either extreme of the visual spectrum with another proximal colour might provide the best trade-off between flickering sensation and SSVEP magnitude.},
}
@article {pmid38272904,
year = {2024},
author = {Liu, H and Wei, P and Wang, H and Lv, X and Duan, W and Li, M and Zhao, Y and Wang, Q and Chen, X and Shi, G and Han, B and Hao, J},
title = {An EEG motor imagery dataset for brain computer interface in acute stroke patients.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {131},
pmid = {38272904},
issn = {2052-4463},
support = {82090043//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81825008//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand/physiology ; Movement/physiology ; *Stroke ; },
abstract = {The brain-computer interface (BCI) is a technology that involves direct communication with parts of the brain and has evolved rapidly in recent years; it has begun to be used in clinical practice, such as for patient rehabilitation. Patient electroencephalography (EEG) datasets are critical for algorithm optimization and clinical applications of BCIs but are rare at present. We collected data from 50 acute stroke patients with wireless portable saline EEG devices during the performance of two tasks: 1) imagining right-handed movements and 2) imagining left-handed movements. The dataset consists of four types of data: 1) the motor imagery instructions, 2) raw recording data, 3) pre-processed data after removing artefacts and other manipulations, and 4) patient characteristics. This is the first open dataset to address left- and right-handed motor imagery in acute stroke patients. We believe that the dataset will be very helpful for analysing brain activation and designing decoding methods that are more applicable for acute stroke patients, which will greatly facilitate research in the field of motor imagery-BCI.},
}
@article {pmid38271166,
year = {2024},
author = {Zhong, Y and Yao, L and Pan, G and Wang, Y},
title = {Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {662-671},
doi = {10.1109/TNSRE.2024.3358491},
pmid = {38271166},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement/physiology ; Hand/physiology ; Algorithms ; Machine Learning ; Imagination/physiology ; },
abstract = {For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.},
}
@article {pmid38270655,
year = {2024},
author = {Calzone, MR and Grossman, MD},
title = {Blunt cardiac injury in the hemodynamically stable patient.},
journal = {JAAPA : official journal of the American Academy of Physician Assistants},
volume = {37},
number = {2},
pages = {35-38},
doi = {10.1097/01.JAA.0000997692.54661.95},
pmid = {38270655},
issn = {1547-1896},
mesh = {Humans ; *Pericardial Effusion/diagnosis/etiology/therapy ; Patients ; *Wounds, Nonpenetrating/complications ; *Myocardial Contusions ; },
abstract = {Blunt cardiac injury (BCI) describes a spectrum of problems including severe, potentially life-threatening injuries from trauma. Pericardial effusion is an example of a BCI that has generally been assumed to imply serious underlying injury to the heart and should be considered hemopericardium until proven otherwise. A standard of care has been established to screen for BCI and treat hemodynamically unstable patients with an acute pericardial effusion presumably related to BCI. Less agreement exists on definitive treatment for hemodynamically stable patients with pericardial effusion after blunt cardiac trauma. This case study explores a new treatment for small to moderate hemopericardium in a stable patient after BCI.},
}
@article {pmid38268052,
year = {2024},
author = {Liang, S and Huang, Z and Wang, Y and Wu, Y and Chen, Z and Zhang, Y and Guo, W and Zhao, Z and Ford, SD and Palaniyappan, L and Li, T},
title = {Using a longitudinal network structure to subgroup depressive symptoms among adolescents.},
journal = {BMC psychology},
volume = {12},
number = {1},
pages = {46},
pmid = {38268052},
issn = {2050-7283},
support = {81920108018, 82230046//National Natural Science Foundation of China/ ; 2021ZD0200404//China Brain Project/ ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; },
mesh = {Humans ; Adolescent ; Child ; *Depression/epidemiology ; *Anxiety ; Anxiety Disorders ; Impulsive Behavior ; Learning ; },
abstract = {BACKGROUND: Network modeling has been proposed as an effective approach to examine complex associations among antecedents, mediators and symptoms. This study aimed to investigate whether the severity of depressive symptoms affects the multivariate relationships among symptoms and mediating factors over a 2-year longitudinal follow-up.<br><br>METHODS: We recruited a school-based cohort of 1480 primary and secondary school students over four semesters from January 2020 to December 2021. The participants (n&#x2009;=&#x2009;1145) were assessed at four time points (ages 10-13 years old at baseline). Based on a cut-off score of 5 on the 9-item Patient Health Questionnaire at each time point, the participants were categorized into the non-depressive symptom (NDS) and depressive symptom (DS) groups. We conducted network analysis to investigate the symptom-to-symptom influences in these two groups over time.<br><br>RESULTS: The global network metrics did not differ statistically between the NDS and DS groups at four time points. However, network connection strength varied with symptom severity. The edge weights between learning anxiety and social anxiety were prominently in the NDS group over time. The central factors for NDS and DS were oversensitivity and impulsivity (3 out of 4 time points), respectively. Moreover, both node strength and closeness were stable over time in both groups.<br><br>CONCLUSIONS: Our study suggests that interrelationships among symptoms and contributing factors are generally stable in adolescents, but a higher severity of depressive symptoms may lead to increased stability in these relationships.},
}
@article {pmid38267740,
year = {2024},
author = {Diez, P and Orosco, L and Garcés Correa, A and Carmona, L},
title = {Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {5},
pages = {1475-1490},
pmid = {38267740},
issn = {1741-0444},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Asthenopia ; Photic Stimulation ; Evoked Potentials ; Electroencephalography/methods ; Algorithms ; },
abstract = {Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.},
}
@article {pmid38267440,
year = {2024},
author = {Wei, S and Jiang, A and Sun, H and Zhu, J and Jia, S and Liu, X and Xu, Z and Zhang, J and Shang, Y and Fu, X and Li, G and Wang, P and Xia, Z and Jiang, T and Cao, A and Duan, X},
title = {Shape-changing electrode array for minimally invasive large-scale intracranial brain activity mapping.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {715},
pmid = {38267440},
issn = {2041-1723},
support = {T218810//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Male ; Animals ; Dogs ; Rats ; *Brain Mapping ; *Brain/diagnostic imaging ; Seizures ; Head ; Electrodes ; },
abstract = {Large-scale brain activity mapping is important for understanding the neural basis of behaviour. Electrocorticograms (ECoGs) have high spatiotemporal resolution, bandwidth, and signal quality. However, the invasiveness and surgical risks of electrode array implantation limit its application scope. We developed an ultrathin, flexible shape-changing electrode array (SCEA) for large-scale ECoG mapping with minimal invasiveness. SCEAs were inserted into cortical surfaces in compressed states through small openings in the skull or dura and fully expanded to cover large cortical areas. MRI and histological studies on rats proved the minimal invasiveness of the implantation process and the high chronic biocompatibility of the SCEAs. High-quality micro-ECoG activities mapped with SCEAs from male rodent brains during seizures and canine brains during the emergence period revealed the spatiotemporal organization of different brain states with resolution and bandwidth that cannot be achieved using existing noninvasive techniques. The biocompatibility and ability to map large-scale physiological and pathological cortical activities with high spatiotemporal resolution, bandwidth, and signal quality in a minimally invasive manner offer SCEAs as a superior tool for applications ranging from fundamental brain research to brain-machine interfaces.},
}
@article {pmid38267056,
year = {2024},
author = {Yang, H and Yanagisawa, T},
title = {Is Phantom Limb Awareness Necessary for the Treatment of Phantom Limb Pain?.},
journal = {Neurologia medico-chirurgica},
volume = {64},
number = {3},
pages = {101-107},
pmid = {38267056},
issn = {1349-8029},
mesh = {Humans ; *Phantom Limb/therapy ; *Motor Cortex ; *Sensorimotor Cortex ; Hand ; Neuroimaging ; },
abstract = {Phantom limb pain is attributed to abnormal sensorimotor cortical representations. Various feedback treatments have been applied to induce the reorganization of the sensorimotor cortical representations to reduce pain. We developed a training protocol using a brain-computer interface (BCI) to induce plastic changes in the sensorimotor cortical representation of phantom hand movements and demonstrated that BCI training effectively reduces phantom limb pain. By comparing the induced cortical representation and pain, the mechanisms worsening the pain have been attributed to the residual phantom hand representation. Based on our data obtained using neurofeedback training without explicit phantom hand movements and hand-like visual feedback, we suggest a direct relationship between cortical representation and pain. In this review, we summarize the results of our BCI training protocol and discuss the relationship between cortical representation and phantom limb pain. We propose a treatment for phantom limb pain based on real-time neuroimaging to induce appropriate cortical reorganization by monitoring cortical activities.},
}
@article {pmid38266054,
year = {2024},
author = {Li, C and Luo, H and Lin, X and Zhang, S and Song, J},
title = {Laser-driven noncontact bubble transfer printing via a hydrogel composite stamp.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {121},
number = {5},
pages = {e2318739121},
pmid = {38266054},
issn = {1091-6490},
support = {U21A20502//MOST | National Natural Science Foundation of China (NSFC)/ ; 12225209//MOST | National Natural Science Foundation of China (NSFC)/ ; U20A6001//MOST | National Natural Science Foundation of China (NSFC)/ ; 12321002//MOST | National Natural Science Foundation of China (NSFC)/ ; 2022YFC2401901//MOST | National Key Research and Development Program of China (NKPs)/ ; },
abstract = {Transfer printing that enables heterogeneous integration of materials into spatially organized, functional arrangements is essential for developing unconventional electronic systems. Here, we report a laser-driven noncontact bubble transfer printing via a hydrogel composite stamp, which features a circular reservoir filled with hydrogel inside a stamp body and encapsulated by a laser absorption layer and an adhesion layer. This composite structure of stamp provides a reversible thermal controlled adhesion in a rapid manner through the liquid-gas phase transition of water in the hydrogel. The ultrasoft nature of hydrogel minimizes the influence of preload on the pick-up performance, which offers a strong interfacial adhesion under a small preload for a reliable damage-free pick-up. The strong light-matter interaction at the interface induces a liquid-gas phase transition to form a bulge on the stamp surface, which eliminates the interfacial adhesion for a successful noncontact printing. Demonstrations of noncontact transfer printing of microscale Si platelets onto various challenging nonadhesive surfaces (e.g., glass, key, wrench, steel sphere, dry petal, droplet) in two-dimensional or three-dimensional layouts illustrate the unusual capabilities for deterministic assembly to develop unconventional electronic systems such as flexible inorganic electronics, curved electronics, and micro-LED display.},
}
@article {pmid38265909,
year = {2025},
author = {Yu, Z and Bu, T and Zhang, Y and Jia, S and Huang, T and Liu, JK},
title = {Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {36},
number = {2},
pages = {3396-3409},
doi = {10.1109/TNNLS.2024.3351120},
pmid = {38265909},
issn = {2162-2388},
mesh = {Humans ; *Neural Networks, Computer ; Photic Stimulation/methods ; Algorithms ; *Retina/physiology ; *Action Potentials/physiology ; Brain-Computer Interfaces ; *Visual Perception/physiology ; },
abstract = {Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.},
}
@article {pmid38264494,
year = {2023},
author = {Martínez-Saez, MC and Ros, L and López-Cano, M and Nieto, M and Navarro, B and Latorre, JM},
title = {Effect of popular songs from the reminiscence bump as autobiographical memory cues in aging: a preliminary study using EEG.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1300751},
pmid = {38264494},
issn = {1662-4548},
abstract = {INTRODUCTION: Music has the capacity to evoke emotions and memories. This capacity is influenced by whether or not the music is from the reminiscence bump (RB) period. However, research on the neural correlates of the processes of evoking autobiographical memories through songs is scant. The aim of this study was to analyze the differences at the level of frequency band activation in two situations: (1) whether or not the song is able to generate a memory; and (2) whether or not the song is from the RB period.<br><br>METHODS: A total of 35 older adults (22 women, age range: 61-73&#x2009;years) listened to 10 thirty-second musical clips that coincided with the period of their RB and 10 from the immediately subsequent 5&#x2009;years (non-RB). To record the EEG signal, a brain-computer interface (BCI) with 14 channels was used. The signal was recorded during the 30-seconds of listening to each music clip.<br><br>RESULTS: The results showed differences in the activation levels of the frequency bands in the frontal and temporal regions. It was also found that the non-retrieval of a memory in response to a song clip showed a greater activation of low frequency waves in the frontal region, compared to the trials that did generate a memory.<br><br>DISCUSSION: These results suggest the importance of analyzing not only brain activation, but also neuronal functional connectivity at older ages, in order to better understand cognitive and emotional functions in aging.},
}
@article {pmid38261272,
year = {2024},
author = {Vitória, MA and Fernandes, FG and van den Boom, M and Ramsey, N and Raemaekers, M},
title = {Decoding Single and Paired Phonemes Using 7T Functional MRI.},
journal = {Brain topography},
volume = {37},
number = {5},
pages = {731-747},
pmid = {38261272},
issn = {1573-6792},
support = {SGW-406-18-GO-086//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Male ; Female ; Adult ; *Support Vector Machine ; Young Adult ; *Speech/physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Sensorimotor Cortex/physiology/diagnostic imaging ; Phonetics ; Brain/physiology/diagnostic imaging ; },
abstract = {Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.},
}
@article {pmid38260671,
year = {2024},
author = {Suematsu, N and Vazquez, AL and Kozai, TD},
title = {Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.01.01.573814},
pmid = {38260671},
issn = {2692-8205},
abstract = {Objective . Intracortical microstimulation can be an effective method for restoring sensory perception in contemporary brain-machine interfaces. However, the mechanisms underlying better control of neuronal responses remain poorly understood, as well as the relationship between neuronal activity and other concomitant phenomena occurring around the stimulation site. Approach . Different microstimulation frequencies were investigated in vivo on Thy1-GCaMP6s mice using widefield and two-photon imaging to evaluate the evoked excitatory neural responses across multiple spatial scales as well as the induced hemodynamic responses. Specifically, we quantified stimulation-induced neuronal activation and depression in the mouse visual cortex and measured hemodynamic oxyhemoglobin and deoxyhemoglobin signals using mesoscopic-scale widefield imaging. Main results . Our calcium imaging findings revealed a preference for lower-frequency stimulation in driving stronger neuronal activation. A depressive response following the neural activation preferred a slightly higher frequency stimulation compared to the activation. Hemodynamic signals exhibited a comparable spatial spread to neural calcium signals. Oxyhemoglobin concentration around the stimulation site remained elevated during the post-activation (depression) period. Somatic and neuropil calcium responses measured by two-photon microscopy showed similar dependence on stimulation parameters, although the magnitudes measured in soma was greater than in neuropil. Furthermore, higher-frequency stimulation induced a more pronounced activation in soma compared to neuropil, while depression was predominantly induced in soma irrespective of stimulation frequencies. Significance . These results suggest that the mechanism underlying depression differs from activation, requiring ample oxygen supply, and affecting neurons. Our findings provide a novel understanding of evoked excitatory neuronal activity induced by intracortical microstimulation and offer insights into neuro-devices that utilize both activation and depression phenomena to achieve desired neural responses.},
}
@article {pmid38260549,
year = {2024},
author = {Oby, ER and Degenhart, AD and Grigsby, EM and Motiwala, A and McClain, NT and Marino, PJ and Yu, BM and Batista, AP},
title = {Dynamical constraints on neural population activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.01.03.573543},
pmid = {38260549},
issn = {2692-8205},
abstract = {The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.},
}
@article {pmid38259629,
year = {2024},
author = {Huang, S and Paul, U and Gupta, S and Desai, K and Guo, M and Jung, J and Capestany, B and Krenzer, WD and Stonecipher, D and Farahany, N},
title = {U.S. public perceptions of the sensitivity of brain data.},
journal = {Journal of law and the biosciences},
volume = {11},
number = {1},
pages = {lsad032},
doi = {10.1093/jlb/lsad032},
pmid = {38259629},
issn = {2053-9711},
abstract = {As we approach an era of potentially widespread consumer neurotechnology, scholars and organizations worldwide have started to raise concerns about the data privacy issues these devices will present. Notably absent in these discussions is empirical evidence about how the public perceives that same information. This article presents the results of a nationwide survey on public perceptions of brain data, to inform discussions of law and policy regarding brain data governance. The survey reveals that the public may perceive certain brain data as less sensitive than other 'private' information, like social security numbers, but more sensitive than some 'public' information, like media preferences. The findings also reveal that not all inferences about mental experiences may be perceived as equally sensitive, and perhaps not all data should be treated alike in ethical and policy discussions. An enhanced understanding of public perceptions of brain data could advance the development of ethical and legal norms concerning consumer neurotechnology.},
}
@article {pmid38258240,
year = {2024},
author = {Huang, P and Li, B and Wei, M and Hao, X and Chen, X and Huang, X and Huang, W and Zhou, S and Wen, X and Xie, S and Su, D},
title = {Electromagnetic Susceptibility Analysis of the Operational Amplifier to Conducted EMI Injected through the Power Supply Port.},
journal = {Micromachines},
volume = {15},
number = {1},
pages = {},
pmid = {38258240},
issn = {2072-666X},
support = {Grant 62293492, Grant 62293495//National Natural Science Foundation of China/ ; Grant No. MJZ5-2N22//Civil Aircraft Projects of China/ ; },
abstract = {Operational amplifiers (op-amps) are widely used in circuit systems. The increasing complexity of the power supply network has led to the susceptibility of the power supply port to electromagnetic interference (EMI) in circuit systems. Therefore, it is necessary to investigate the electromagnetic susceptibility (EMS) of op-amps at the power supply port. In this paper, we assessed the effect of EMI on the operational performance of op-amps through the power supply port by a bulk current injection (BCI) method. Firstly, we conducted the continuous sine wave into the power supply port by a current injection probe and measured the change in the offset voltage under EMI. Secondly, we proposed a new method of conducted susceptibility and obtained the susceptibility threshold regularities of the op-amps at the power supply port under the interference of different waveform signals. Our study provided conclusive evidence that EMI reduced the reliability of the op-amp by affecting the offset voltage of op-amps and demonstrated that the sensitivity type of op-amps was peak-sensitive at the power supply port. This study contributed to a deep understanding of the EMS mechanism and guided the design of electromagnetic compatibility (EMC) of op-amps.},
}
@article {pmid38257638,
year = {2024},
author = {Park, S and Kim, M and Nam, H and Kwon, J and Im, CH},
title = {In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {2},
pages = {},
pmid = {38257638},
issn = {1424-8220},
support = {NRF-2021M3E5D2A01019547//Korean government (MSIT)/ ; 2E32341-23-043//KIST Institutional Program/ ; },
mesh = {Humans ; Automobiles ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Eye ; Laboratories ; },
abstract = {Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver's attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the experiment, four visual stimuli were displayed on a laboratory-made head-up display (HUD). This allowed the participants to control the in-car environment by simply staring at a target visual stimulus, i.e., without pressing a button or averting their eyes from the front. The driving performances in two realistic driving tests-obstacle avoidance and car-following tests-were then compared between the manual control condition and SSVEP-BCI control condition using a driving simulator. In the obstacle avoidance driving test, where participants needed to stop the car when obstacles suddenly appeared, the participants showed significantly shorter response time (1.42 ± 0.26 s) in the SSVEP-BCI control condition than in the manual control condition (1.79 ± 0.27 s). No-response rate, defined as the ratio of obstacles that the participants did not react to, was also significantly lower in the SSVEP-BCI control condition (4.6 ± 14.7%) than in the manual control condition (20.5 ± 25.2%). In the car-following driving test, where the participants were instructed to follow a preceding car that runs at a sinusoidally changing speed, the participants showed significantly lower speed difference with the preceding car in the SSVEP-BCI control condition (15.65 ± 7.04 km/h) than in the manual control condition (19.54 ± 11.51 km/h). The in-car environment control system using SSVEP-based BCI showed a possibility that might contribute to safer driving by keeping the driver's focus on the front and thereby enhancing the overall driving performance.},
}
@article {pmid38257633,
year = {2024},
author = {Gunawardane, PDSH and MacNeil, RR and Zhao, L and Enns, JT and de Silva, CW and Chiao, M},
title = {A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {2},
pages = {},
pmid = {38257633},
issn = {1424-8220},
support = {STPGP493908//Natural Sciences and Engineering Research Council/ ; RGPIN-2018-03741//Natural Sciences and Engineering Research Council/ ; PGSD3-547166-2020//Natural Sciences and Engineering Research Council/ ; },
mesh = {Humans ; Electrooculography ; *Saccades ; *Acceleration ; Algorithms ; Brain ; },
abstract = {Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.},
}
@article {pmid38253667,
year = {2024},
author = {Wang, A and Chen, C and Mei, C and Liu, S and Xiang, C and Fang, W and Zhang, F and Xu, Y and Chen, S and Zhang, Q and Bai, X and Lin, A and Neculai, D and Xia, B and Ye, C and Zou, J and Liang, T and Feng, XH and Li, X and Shen, C and Xu, P},
title = {Innate immune sensing of lysosomal dysfunction drives multiple lysosomal storage disorders.},
journal = {Nature cell biology},
volume = {26},
number = {2},
pages = {219-234},
pmid = {38253667},
issn = {1476-4679},
support = {32321002//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31830052//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31725017//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Lysosomal Storage Diseases/genetics/metabolism/pathology ; *Niemann-Pick Disease, Type C/genetics/pathology ; Lysosomes/metabolism ; Immunity, Innate ; Nucleotidyltransferases/genetics/metabolism ; },
abstract = {Lysosomal storage disorders (LSDs), which are characterized by genetic and metabolic lysosomal dysfunctions, constitute over 60 degenerative diseases with considerable health and economic burdens. However, the mechanisms driving the progressive death of functional cells due to lysosomal defects remain incompletely understood, and broad-spectrum therapeutics against LSDs are lacking. Here, we found that various gene abnormalities that cause LSDs, including Hexb, Gla, Npc1, Ctsd and Gba, all shared mutual properties to robustly autoactivate neuron-intrinsic cGAS-STING signalling, driving neuronal death and disease progression. This signalling was triggered by excessive cytoplasmic congregation of the dsDNA and DNA sensor cGAS in neurons. Genetic ablation of cGAS or STING, digestion of neuronal cytosolic dsDNA by DNase, and repair of neuronal lysosomal dysfunction alleviated symptoms of Sandhoff disease, Fabry disease and Niemann-Pick disease, with substantially reduced neuronal loss. We therefore identify a ubiquitous mechanism mediating the pathogenesis of a variety of LSDs, unveil an inherent connection between lysosomal defects and innate immunity, and suggest a uniform strategy for curing LSDs.},
}
@article {pmid38252572,
year = {2024},
author = {Wu, H and Li, S and Wu, D},
title = {Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {527-536},
doi = {10.1109/TNSRE.2024.3356916},
pmid = {38252572},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Algorithms ; Movement ; Imagination ; },
abstract = {Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.},
}
@article {pmid38252565,
year = {2024},
author = {Falcon-Caro, A and Shirani, S and Ferreira, JF and Bird, JJ and Sanei, S},
title = {Formulation of Common Spatial Patterns for Multi-Task Hyperscanning BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {6},
pages = {1950-1957},
doi = {10.1109/TBME.2024.3356665},
pmid = {38252565},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Algorithms ; Adult ; Male ; Female ; Brain/physiology ; },
abstract = {This work proposes a new formulation for common spatial patterns (CSP), often used as a powerful feature extraction technique in brain-computer interfacing (BCI) and other neurological studies. In this approach, applied to multiple subjects' data and named as hyperCSP, the individual covariance and mutual correlation matrices between multiple simultaneously recorded subjects' electroencephalograms are exploited in the CSP formulation. This method aims at effectively isolating the common motor task between multiple heads and alleviate the effects of other spurious or undesired tasks inherently or intentionally performed by the subjects. This technique can provide a satisfactory classification performance while using small data size and low computational complexity. By using the proposed hyperCSP followed by support vector machines classifier, we obtained a classification accuracy of 81.82% over 8 trials in the presence of strong undesired tasks. We hope that this method could reduce the training error in multi-task BCI scenarios. The recorded valuable motor-related hyperscanning dataset is available for public use to promote the research in this area.},
}
@article {pmid38250330,
year = {2023},
author = {Karittevlis, C and Papadopoulos, M and Lima, V and Orphanides, GA and Tiwari, S and Antonakakis, M and Papadopoulou Lesta, V and Ioannides, AA},
title = {First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation.},
journal = {Frontiers in systems neuroscience},
volume = {17},
number = {},
pages = {1305022},
pmid = {38250330},
issn = {1662-5137},
abstract = {INTRODUCTION: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.<br><br>METHODS: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.<br><br>RESULTS: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.<br><br>DISCUSSION: It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.},
}
@article {pmid38249574,
year = {2023},
author = {Fasipe, G and Goršič, M and Rahman, MH and Rammer, J},
title = {Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1331395},
pmid = {38249574},
issn = {1662-5161},
abstract = {According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.},
}
@article {pmid38248393,
year = {2023},
author = {Qiu, Y and Ma, C and Jiang, N and Jiang, D and Yu, Z and Liu, X and Zhu, Y and Yu, W and Li, F and Wan, H and Wang, P},
title = {A Silicon-Based Field-Effect Biosensor for Drug-Induced Cardiac Extracellular Calcium Ion Change Detection.},
journal = {Biosensors},
volume = {14},
number = {1},
pages = {},
pmid = {38248393},
issn = {2079-6374},
support = {2021YFB3200801//National Key Research and Development Program of China/ ; 2021YFF1200803//National Key Research and Development Program of China/ ; 32250008//National Natural Science Foundation of China/ ; 62120106004//National Natural Science Foundation of China/ ; 61901412//National Natural Science Foundation of China/ ; LBY21H180001//Natural Science Foundation of Zhejiang Province/ ; LY21C100001//Natural Science Foundation of Zhejiang Province/ ; 2023C03104//A Key Project of Zhejiang Province/ ; },
mesh = {*Calcium ; Silicon ; *Cardiovascular Agents ; Cell Line ; Ion Channels ; },
abstract = {Calcium ions participate in the regulation of almost all biological functions of the body, especially in cardiac excitation-contraction coupling, acting as vital signaling through ion channels. Various cardiovascular drugs exert their effects via affecting the ion channels on the cell membrane. The current strategies for calcium ion monitoring are mainly based on fluorescent probes, which are commonly used for intracellular calcium ion detection (calcium imaging) and cannot achieve long-term monitoring. In this work, an all-solid-state silicone-rubber ion-sensitive membrane was fabricated on light-addressable potentiometric sensors to establish a program-controlled field-effect-based ion-sensitive light-addressable potentiometric sensor (LAPS) platform for extracellular calcium ion detection. L-type calcium channels blocker verapamil and calcium channel agonist BayK8644 were chosen to explore the effect of ion channel drugs on extracellular calcium ion concentration in HL-1 cell lines. Simultaneously, microelectrode array (MEA) chips were employed to probe the HL-1 extracellular field potential (EFP) signals. The Ca[2+] concentration and EFP parameters were studied to comprehensively evaluate the efficacy of cardiovascular drugs. This platform provides more dimensional information on cardiovascular drug efficacy that can be utilized for accurate drug screening.},
}
@article {pmid38248389,
year = {2023},
author = {Golparvar, A and Thenot, L and Boukhayma, A and Carrara, S},
title = {Soft Epidermal Paperfluidics for Sweat Analysis by Ratiometric Raman Spectroscopy.},
journal = {Biosensors},
volume = {14},
number = {1},
pages = {},
pmid = {38248389},
issn = {2079-6374},
support = {internal funding//&#xc9;cole Polytechnique F&#xe9;d&#xe9;rale de Lausanne/ ; },
mesh = {Humans ; Swine ; Animals ; *Sweat ; *Spectrum Analysis, Raman ; Epidermis ; Electronics ; Feces ; },
abstract = {The expanding interest in digital biomarker analysis focused on non-invasive human bodily fluids, such as sweat, highlights the pressing need for easily manufactured and highly efficient soft lab-on-skin solutions. Here, we report, for the first time, the integration of microfluidic paper-based devices (μPAD) and non-enhanced Raman-scattering-enabled optical biochemical sensing (Raman biosensing). Their integration merges the enormous benefits of μPAD, with high potential for commercialization and use in resource-limited settings, with biorecognition-element-free (but highly selective) optical Raman biosensing. The introduced thin (0.36 mm), ultra-lightweight (0.19 g), and compact footprint (3 cm[2]) opto-paperfluidic sweat patch is flexible, stretchable, and conforms, irritation-free, to hairless or minimally haired body regions to enable swift sweat collection. As a great advantage, this new bio-chemical sensory system excels through its absence of onboard biorecognition elements (bioreceptor-free) and omission of plasmonic nanomaterials. The proposed easy fabrication process is adaptable to mass production by following a fully sustainable and cost-effective process utilizing only basic tools by avoiding typically employed printing or laser patterning. Furthermore, efficient collection and transportation of precise sweat volumes, driven exclusively by the wicking properties of porous materials, shows high efficiency in liquid transportation and reduces biosensing latency by a factor of 5 compared to state-of-the-art epidermal microfluidics. The proposed unit enables electronic chip-free and imaging-less visual sweat loss quantification as well as optical biochemical analysis when coupled with Raman spectroscopy. We investigated the multimodal quantification of sweat urea and lactate levels ex vivo (with syntactic sweat including +30 sweat analytes on porcine skin) and achieved a linear dynamic range from 0 to 100 mmol/L during fully dynamic continuous flow characterization.},
}
@article {pmid38248307,
year = {2024},
author = {Voity, K and Lopez, T and Chan, JP and Greenwald, BD},
title = {Update on How to Approach a Patient with Locked-In Syndrome and Their Communication Ability.},
journal = {Brain sciences},
volume = {14},
number = {1},
pages = {},
pmid = {38248307},
issn = {2076-3425},
abstract = {Locked-in syndrome (LIS) is a rare and challenging condition that results in tetraplegia and cranial nerve paralysis while maintaining consciousness and variable cognitive function. Once acute management is completed, it is important to work with the patient on developing a plan to maintain and improve their quality of life (QOL). A key component towards increasing or maintaining QOL within this population involves the establishment of a functional communication system. Evaluating cognition in patients with LIS is vital for evaluating patients' communication needs along with physical rehabilitation to maximize their QOL. In the past decade or so, there has been an increase in research surrounding brain-computer interfaces to improve communication abilities for paralyzed patients. This article provides an update on the available technology and the protocol for finding the best way for patients with this condition to communicate. This article aims to increase knowledge of how to enhance and manage communication among LIS patients.},
}
@article {pmid38247907,
year = {2023},
author = {Wang, S and Luo, Z and Zhao, S and Zhang, Q and Liu, G and Wu, D and Yin, E and Chen, C},
title = {Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {11},
number = {1},
pages = {},
pmid = {38247907},
issn = {2306-5354},
support = {62332019, 62076250 , 61806146 , 82101488.//National Natural Science Foundation of China/ ; },
abstract = {In brain-computer interface (BCI) systems, challenges are presented by the recognition of motor imagery (MI) brain signals. Established recognition approaches have achieved favorable performance from patterns like SSVEP, AEP, and P300, whereas the classification methods for MI need to be improved. Hence, seeking a classification method that exhibits high accuracy and robustness for application in MI-BCI systems is essential. In this study, the Sparrow search algorithm (SSA)-optimized Deep Belief Network (DBN), called SSA-DBN, is designed to recognize the EEG features extracted by the Empirical Mode Decomposition (EMD). The performance of the DBN is enhanced by the optimized hyper-parameters obtained through the SSA. Our method's efficacy was tested on three datasets: two public and one private. Results indicate a relatively high accuracy rate, outperforming three baseline methods. Specifically, on the private dataset, our approach achieved an accuracy of 87.83%, marking a significant 10.38% improvement over the standard DBN algorithm. For the BCI IV 2a dataset, we recorded an accuracy of 86.14%, surpassing the DBN algorithm by 9.33%. In the SMR-BCI dataset, our method attained a classification accuracy of 87.21%, which is 5.57% higher than that of the conventional DBN algorithm. This study demonstrates enhanced classification capabilities in MI-BCI, potentially contributing to advancements in the field of BCI.},
}
@article {pmid38245782,
year = {2024},
author = {Brunner, I and Lundquist, CB and Pedersen, AR and Spaich, EG and Dosen, S and Savic, A},
title = {Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {10},
pmid = {38245782},
issn = {1743-0003},
support = {19-B-0147//Helsefonden/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; Pilot Projects ; *Stroke/complications ; Upper Extremity ; Paresis/rehabilitation ; },
abstract = {BACKGROUND: Restorative Brain-Computer Interfaces (BCI) that combine motor imagery with visual feedback and functional electrical stimulation (FES) may offer much-needed treatment alternatives for patients with severely impaired upper limb (UL) function after a stroke.<br><br>OBJECTIVES: This study aimed to examine if BCI-based training, combining motor imagery with FES targeting finger/wrist extensors, is more effective in improving severely impaired UL motor function than conventional therapy in the subacute phase after stroke, and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training.<br><br>METHODS: Forty patients with severe UL paresis (<&#x2009;13 on Action Research Arm Test (ARAT) were randomized to either a 12-session BCI training as part of their rehabilitation or conventional UL rehabilitation. BCI sessions were conducted 3-4 times weekly for 3-4&#xa0;weeks. At baseline, Transcranial Magnetic Stimulation (TMS) was performed to examine CST integrity. The main endpoint was the ARAT at 3&#xa0;months post-stroke. A binominal logistic regression was conducted to examine the effect of treatment group and CST integrity on achieving meaningful improvement. In the BCI group, electroencephalographic (EEG) data were analyzed to investigate changes in event-related desynchronization (ERD) during the course of therapy.<br><br>RESULTS: Data from 35 patients (15 in the BCI group and 20 in the control group) were analyzed at 3-month follow-up. Few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, 5/15 in the BCI group, 5/20 in control. An independent-samples Mann-Whitney U test revealed no differences between the two groups, p&#x2009;=&#x2009;0.382. In the logistic regression only CST integrity was a significant predictor for improving UL motor function, p&#x2009;=&#x2009;0.007. The EEG analysis showed significant changes in ERD of the affected hemisphere and its lateralization only during unaffected UL motor imagery at the end of the therapy.<br><br>CONCLUSION: This is the first RCT examining BCI training in the subacute phase where only patients with severe UL paresis were included. Though more patients in the BCI group improved relative to the group size, the difference between the groups was not significant. In the present study, preserved CTS integrity was much more vital for UL improvement than which type of intervention the patients received. Larger studies including only patients with some preserved CST integrity should be attempted.},
}
@article {pmid38244877,
year = {2024},
author = {Sánchez-Corzo, A and Baum, DM and Irani, M and Hinrichs, S and Reisenegger, R and Whitaker, GA and Born, J and Sitaram, R and Klinzing, JG},
title = {Odor cueing of declarative memories during sleep enhances coordinated spindles and slow oscillations.},
journal = {NeuroImage},
volume = {287},
number = {},
pages = {120521},
doi = {10.1016/j.neuroimage.2024.120521},
pmid = {38244877},
issn = {1095-9572},
mesh = {Humans ; *Cues ; Odorants ; Sleep/physiology ; Electroencephalography ; Memory/physiology ; *Memory Consolidation/physiology ; },
abstract = {Long-term memories are formed by repeated reactivation of newly encoded information during sleep. This process can be enhanced by using memory-associated reminder cues like sounds and odors. While auditory cueing has been researched extensively, few electrophysiological studies have exploited the various benefits of olfactory cueing. We used high-density electroencephalography in an odor-cueing paradigm that was designed to isolate the neural responses specific to the cueing of declarative memories. We show widespread cueing-induced increases in the duration and rate of sleep spindles. Higher spindle rates were most prominent over centro-parietal areas and largely overlapping with a concurrent increase in the amplitude of slow oscillations (SOs). Interestingly, greater SO amplitudes were linked to a higher likelihood of coupling a spindle and coupled spindles expressed during cueing were more numerous in particular around SO up states. We thus identify temporally and spatially coordinated enhancements of sleep spindles and slow oscillations as a candidate mechanism behind cueing-induced memory processing. Our results further demonstrate the feasibility of studying neural activity patterns linked to such processing using olfactory cueing during sleep.},
}
@article {pmid38244658,
year = {2024},
author = {Chen, L and Jiang, C and Xu, Q and Jin, J and A, S and Wang, X and Li, X and Hu, Y and Sun, H and Lu, X and Duan, S and Gao, Z and Wang, W and Wang, Y},
title = {Biphasic release of betamethasone from an injectable HA hydrogel implant for alleviating lumbar disc herniation induced sciatica.},
journal = {Acta biomaterialia},
volume = {176},
number = {},
pages = {173-189},
doi = {10.1016/j.actbio.2024.01.016},
pmid = {38244658},
issn = {1878-7568},
mesh = {Mice ; Animals ; *Sciatica/drug therapy/etiology ; *Intervertebral Disc Displacement/complications/drug therapy ; Hyaluronic Acid ; Hydrogels/pharmacology/therapeutic use ; Neuroinflammatory Diseases ; Betamethasone/pharmacology/therapeutic use ; Sulfhydryl Compounds ; },
abstract = {Epidural steroid injection (ESI) is a common therapeutic approach for managing sciatica caused by lumbar disc herniation (LDH). However, the short duration of therapeutic efficacy and the need for repeated injections pose challenges in LDH treatment. The development of a controlled delivery system capable of prolonging the effectiveness of ESI and reducing the frequency of injections, is highly significant in LDH clinical practice. In this study, we utilized a thiol-ene click chemistry to create a series of injectable hyaluronic acid (HA) based release systems loaded with diphasic betamethasone, including betamethasone dipropionate (BD) and betamethasone 21-phosphate disodium (BP) (BD/BP@HA). BD/BP@HA hydrogel implants demonstrated biocompatibility and biodegradability to matched neuronal tissues, avoiding artificial compression following injection. The sustained release of betamethasone from BD/BP@HA hydrogels effectively inhibited both acute and chronic neuroinflammation by suppressing the nuclear factor kappa-B (NF-κB) pathway. In a mouse model of LDH, the epidural administration of BD/BP@HA efficiently alleviated LDH-induced sciatica for at least 10 days by inhibiting the activation of macrophages and microglia in dorsal root ganglion and spinal dorsal horn, respectively. The newly developed HA hydrogels represent a valuable platform for achieving sustained drug release. Additionally, we provide a simple paradigm for fabricating BD/BP@HA for epidural injection, demonstrating greater and sustained efficiency in alleviating LDH-induced sciatica compared to traditional ESI and displaying potentials for clinical translation. This system has the potential to revolutionize drug delivery for co-delivery of both soluble and insoluble drugs, thereby making a significant impact in the pharmaceutical industry. STATEMENT OF SIGNIFICANCE: Lumbar disc herniation (LDH) is a common degenerative disorder leading to sciatica and spine surgery. Although epidural steroid injection (ESI) is routinely used to alleviate sciatica, the efficacy is short and repeated injections are required. There remains challenging to prolong the efficacy of ESI. Herein, an injectable hyaluronic acid (HA) hydrogel implant by crosslinking acrylated-modified HA (HA-A) with thiol-modified HA (HA-SH) was designed to achieve a biphasic release of betamethasone. The hydrogel showed biocompatibility and biodegradability to match neuronal tissues. Notably, compared to traditional ESI, the hydrogel better alleviated sciatica in vivo by synergistically inhibiting the neuroinflammation in central and peripheral nervous systems. We anticipate the injectable HA hydrogel implant has the potential for clinical translation in treating LDH-induced sciatica.},
}
@article {pmid38239461,
year = {2023},
author = {Gagliardi, M},
title = {The role of developmental caregiving programming in modulating our affiliation tendency and the vulnerability to social anxiety and eating disorders.},
journal = {Frontiers in psychology},
volume = {14},
number = {},
pages = {1259415},
pmid = {38239461},
issn = {1664-1078},
abstract = {Attachment is the evolutionarily-established process through which humans create bonds with others to receive care from them. The phenomenon is as essential to our physical survival as it is to our psychological development. An increasing number of studies demonstrates that in sensitive periods during the early years of life, our brain circuitry is programmed in the interactions with our caregivers, with the imprinting of information over multiple attachment dimensions. Adopting a basic brain-computer analogy, we can think of this knowledge as the psycho-social firmware of our mind. According to a recently proposed extension of the classical three-dimensional view, one attachment dimension - somaticity - concerns the caregiver's task of reflecting and confirming the child's (internal) states - such as sensations, emotions, and representations - to support the child's ability to identify and define those entities autonomously. Relying on multidisciplinary evidence - from neuroscientific, developmental, evolutionary, and clinical sources - we suggest that somaticity (H1) has the adaptive function to modulate our tendency to comply and affiliate with a reference group but also (H2) increases the vulnerability to developing Social Anxiety (SA) and Eating Disorders (EDs). We evaluate H1-H2, (1) indicating the evolutionary role of somaticity in modulating our affiliation tendency to optimize the ancestral threat-opportunity balance coming from infectious diseases and (2) showing the deep connection between SA-EDs and the features most closely related to somaticity - interoception and parenting style. Finally, we discuss three relevant implications of H1-H2: (A) Bringing into research focus the adaptive role of our firmware knowledge system versus the hardware (neural substrate) and software (higher cognition) ones. (B) Complementing the well-grounded Objectification and Allocentric Lock Theories, allowing us to integrate multiple levels of explanation on the etiology of psychopathology. (C) Suggesting the design of new psychological treatments. While not aiming to prove H1-H2, our analysis supports them and encourages their direct testing.},
}
@article {pmid38238759,
year = {2024},
author = {Séguin, P and Maby, E and Fouillen, M and Otman, A and Luauté, J and Giraux, P and Morlet, D and Mattout, J},
title = {The challenge of controlling an auditory BCI in the case of severe motor disability.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {9},
pmid = {38238759},
issn = {1743-0003},
support = {DEA20140629858//Fondation pour la Recherche M&#xe9;dicale/ ; ING20121226307//Fondation pour la Recherche M&#xe9;dicale/ ; ANR-17-CE40-0005//Agence Nationale de la Recherche/ ; Labex Cortex (ANR-11-LABX-0042)//Agence Nationale de la Recherche/ ; },
mesh = {Humans ; *Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography ; *Locked-In Syndrome ; *Motor Disorders ; },
abstract = {BACKGROUND: The locked-in syndrome (LIS), due to a lesion in the pons, impedes communication. This situation can also be met after some severe brain injury or in advanced Amyotrophic Lateral Sclerosis (ALS). In the most severe condition, the persons cannot communicate at all because of a complete oculomotor paralysis (Complete LIS or CLIS). This even prevents the detection of consciousness. Some studies suggest that auditory brain-computer interface (BCI) could restore a communication through a « yes-no» code.<br><br>METHODS: We developed an auditory EEG-based interface which makes use of voluntary modulations of attention, to restore a yes-no communication code in non-responding persons. This binary BCI uses repeated speech sounds (alternating "yes" on the right ear and "no" on the left ear) corresponding to either frequent (short) or rare (long) stimuli. Users are instructed to pay attention to the relevant stimuli only. We tested this BCI with 18 healthy subjects, and 7 people with severe motor disability (3 "classical" persons with locked-in syndrome and 4 persons with ALS).<br><br>RESULTS: We report online BCI performance and offline event-related potential analysis. On average in healthy subjects, online BCI accuracy reached 86% based on 50 questions. Only one out of 18 subjects could not perform above chance level. Ten subjects had an accuracy above 90%. However, most patients could not produce online performance above chance level, except for two people with ALS who obtained 100% accuracy. We report individual event-related potentials and their modulation by attention. In addition to the classical P3b, we observed a signature of sustained attention on responses to frequent sounds, but in healthy subjects and patients with good BCI control only.<br><br>CONCLUSIONS: Auditory BCI can be very well controlled by healthy subjects, but it is not a guarantee that it can be readily used by the target population of persons in LIS or CLIS. A conclusion that is supported by a few previous findings in BCI and should now trigger research to assess the reasons of such a gap in order to propose new and efficient solutions.<br><br>CLINICAL TRIAL REGISTRATIONS: No. NCT02567201 (2015) and NCT03233282 (2013).},
}
@article {pmid38238386,
year = {2024},
author = {Deo, DR and Willett, FR and Avansino, DT and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Brain control of bimanual movement enabled by recurrent neural networks.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {1598},
pmid = {38238386},
issn = {2045-2322},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; U01-NS123101/NS/NINDS NIH HHS/United States ; R01-DC014034/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Neural Networks, Computer ; Movement ; Functional Laterality ; Hand ; Paralysis ; *Brain-Computer Interfaces ; Brain ; },
abstract = {Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.},
}
@article {pmid38237182,
year = {2024},
author = {Natalizio, A and Sieghartsleitner, S and Schreiner, L and Walchshofer, M and Esposito, A and Scharinger, J and Pretl, H and Arpaia, P and Parvis, M and Solé-Casals, J and Sebastián-Romagosa, M and Ortner, R and Guger, C},
title = {Real-time estimation of EEG-based engagement in different tasks.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad200d},
pmid = {38237182},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Electrodes ; Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; },
abstract = {Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.},
}
@article {pmid38237174,
year = {2024},
author = {Wu, X and Zhang, D and Li, G and Gao, X and Metcalfe, B and Chen, L},
title = {Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad200e},
pmid = {38237174},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Benchmarking ; Brain ; Electric Power Supplies ; Electroencephalography ; },
abstract = {Objective.Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective.Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen-Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier.Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.},
}
@article {pmid38236672,
year = {2024},
author = {Kim, SJ and Lee, DH and Kwak, HG and Lee, SW},
title = {Toward Domain-Free Transformer for Generalized EEG Pre-Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {482-492},
doi = {10.1109/TNSRE.2024.3355434},
pmid = {38236672},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Brain/physiology ; *Brain-Computer Interfaces ; Electric Power Supplies ; },
abstract = {Electroencephalography (EEG) signals are the brain signals acquired using the non-invasive approach. Owing to the high portability and practicality, EEG signals have found extensive application in monitoring human physiological states across various domains. In recent years, deep learning methodologies have been explored to decode the intricate information embedded in EEG signals. However, since EEG signals are acquired from humans, it has issues with acquiring enormous amounts of data for training the deep learning models. Therefore, previous research has attempted to develop pre-trained models that could show significant performance improvement through fine-tuning when data are scarce. Nonetheless, existing pre-trained models often struggle with constraints, such as the necessity to operate within datasets of identical configurations or the need to distort the original data to apply the pre-trained model. In this paper, we proposed the domain-free transformer, called DFformer, for generalizing the EEG pre-trained model. In addition, we presented the pre-trained model based on DFformer, which is capable of seamless integration across diverse datasets without necessitating architectural modification or data distortion. The proposed model achieved competitive performance across motor imagery and sleep stage classification datasets. Notably, even when fine-tuned on datasets distinct from the pre-training phase, DFformer demonstrated marked performance enhancements. Hence, we demonstrate the potential of DFformer to overcome the conventional limitations in pre-trained model development, offering robust applicability across a spectrum of domains.},
}
@article {pmid38236517,
year = {2023},
author = {Amini Gougeh, R and Falk, TH},
title = {Enhancing motor imagery detection efficacy using multisensory virtual reality priming.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1080200},
pmid = {38236517},
issn = {2673-6195},
abstract = {Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy.},
}
@article {pmid38236207,
year = {2024},
author = {Ye, Q and Liu, Y and Zhang, S and Ni, K and Fu, S and Dou, W and Wei, W and Li, BM and Preece, DA and Cai, XL},
title = {Cross-cultural adaptation and clinical application of the Perth Empathy Scale.},
journal = {Journal of clinical psychology},
volume = {80},
number = {7},
pages = {1473-1489},
doi = {10.1002/jclp.23643},
pmid = {38236207},
issn = {1097-4679},
support = {//Starting Research Fund from Hangzhou Normal University/ ; //Fundamental Research Funds for the Central Universities/ ; 32200906//National Natural Science Foundation of China/ ; 22JZD044//Major Project of Philosophy and Social Science Research of the Ministry of Education of China/ ; 2021ZD0201705//STI 2030-Major Projects/ ; 20dz2260300//Research Project of Shanghai Science and Technology Commission/ ; },
mesh = {Humans ; *Empathy ; Male ; Female ; Adult ; *Psychometrics/instrumentation/standards ; *Depressive Disorder, Major ; Middle Aged ; Reproducibility of Results ; Young Adult ; China ; Cross-Cultural Comparison ; Adolescent ; Factor Analysis, Statistical ; },
abstract = {OBJECTIVE: Alterations of empathy have been observed in patients with various mental disorders. The Perth Empathy Scale (PES) was recently developed to measure a multidimensional construct of empathy across positive and negative emotions. However, its psychometric properties and clinical applications have not been examined in the Chinese context.<br><br>METHODS: The Chinese version of the PES was developed and administered to a large Chinese sample (n&#x2009;=&#x2009;1090). Factor structure, internal consistency, test-retest reliability, and convergent, discriminant, as well as concurrent validity were examined. Moreover, 50 patients with major depressive disorder (MDD) and 50 healthy controls were recruited to explore the clinical utility of the PES.<br><br>RESULTS: Confirmatory factor analyses supported a theoretically congruent three-factor structure of empathy, namely Cognitive Empathy, Negative Affective Empathy and Positive Affective Empathy. The PES showed good to excellent internal consistency reliability, good convergent and discriminant validity, acceptable concurrent validity, and moderate to high test-retest reliability. Patients with MDD had significantly lower PES scores compared to healthy controls. Linear discriminant function comprised of the three factors correctly differentiated 71% of participants, which further verified the clinical utility of the PES.<br><br>CONCLUSIONS: Our findings indicated that the Chinese version of the PES is a reliable and valid instrument to measure cognitive and affective empathy across negative and positive emotions, and could therefore be used in both research and clinical practice.},
}
@article {pmid38236004,
year = {2024},
author = {He, J and Hou, T and Wang, Q and Wang, Q and Jiang, Y and Chen, L and Xu, J and Qi, Y and Jia, D and Gu, Y and Gao, L and Yu, Y and Wang, L and Kang, L and Si, J and Wang, L and Chen, S},
title = {L-arginine metabolism ameliorates age-related cognitive impairment by Amuc_1100-mediated gut homeostasis maintaining.},
journal = {Aging cell},
volume = {23},
number = {4},
pages = {e14081},
pmid = {38236004},
issn = {1474-9726},
support = {2023C03163//Provincial Key R&amp;D Program of Zhejiang/ ; 82270573//National Natural Science Foundation of China/ ; 2022C03145//"Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang/ ; LQ23H250001//Natural Science Foundation of Zhejiang Province/ ; 2023ky785//Medical Science and Technology Project of Zhejiang Province/ ; 82300621//National Natural Science Foundation of China/ ; },
mesh = {Mice ; Animals ; RNA, Ribosomal, 16S ; *Verrucomicrobia ; Homeostasis ; *Cognitive Dysfunction ; Arginine ; },
abstract = {Aging-induced cognitive impairment is associated with a loss of metabolic homeostasis and plasticity. An emerging idea is that targeting key metabolites is sufficient to impact the function of other organisms. Therefore, more metabolism-targeted therapeutic intervention is needed to improve cognitive impairment. We first conducted untargeted metabolomic analyses and 16S rRNA to identify the aging-associated metabolic adaption and intestinal microbiome change. Untargeted metabolomic analyses of plasma revealed L-arginine metabolic homeostasis was altered during the aging process. Impaired L-arginine metabolic homeostasis was associated with low abundance of intestinal Akkermansia muciniphila (AKK) colonization in mice. Long-term supplementation of AKK outer membranes protein-Amuc_1100, rescued the L-arginine level and restored cognitive impairment in aging mice. Mechanically, Amuc_1100 acted directly as a source of L-arginine and enriched the L-arginine-producing bacteria. In aged brain, Amuc_1100 promoted the superoxide dismutase to alleviated oxidation stress, and increased nitric oxide, derivatives of L-arginine, to improve synaptic plasticity. Meanwhile, L-arginine repaired lipopolysaccharide-induced intestinal barrier damage and promoted growth of colon organoid. Our findings indicated that aging-related cognitive impairment was closely associated with the disorders of L-arginine metabolism. AKK-derived Amuc_1100, as a potential postbiotic, targeting the L-arginine metabolism, might provide a promising therapeutic strategy to maintain the intestinal homeostasis and cognitive function in aging.},
}
@article {pmid38235475,
year = {2022},
author = {Bianchi, L and Ferrante, R and Hu, Y and Sahonero-Alvarez, G and Zenia, NZ},
title = {Merging Brain-Computer Interface P300 speller datasets: Perspectives and pitfalls.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {1045653},
pmid = {38235475},
issn = {2673-6195},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
abstract = {BACKGROUND: In the last decades, the P300 Speller paradigm was replicated in many experiments, and collected data were released to the public domain to allow research groups, particularly those in the field of machine learning, to test and improve their algorithms for higher performances of brain-computer interface (BCI) systems. Training data is needed to learn the identification of brain activity. The more training data are available, the better the algorithms will perform. The availability of larger datasets is highly desirable, eventually obtained by merging datasets from different repositories. The main obstacle to such merging is that all public datasets are released in various file formats because no standard way is established to share these data. Additionally, all datasets necessitate reading documents or scientific papers to retrieve relevant information, which prevents automating the processing. In this study, we thus adopted a unique file format to demonstrate the importance of having a standard and to propose which information should be stored and why.<br><br>METHODS: We described our process to convert a dozen of P300 Speller datasets and reported the main encountered problems while converting them into the same file format. All the datasets are characterized by the same 6 &#xd7; 6 matrix of alphanumeric symbols (characters and numbers or symbols) and by the same subset of acquired signals (8 EEG sensors at the same recording sites).<br><br>RESULTS AND DISCUSSION: Nearly a million stimuli were converted, relative to about 7000 spelled characters and belonging to 127 subjects. The converted stimuli represent the most extensively available platform for training and testing new algorithms on the specific paradigm - the P300 Speller. The platform could potentially allow exploring transfer learning procedures to reduce or eliminate the time needed for training a classifier to improve the performance and accuracy of such BCI systems.},
}
@article {pmid38235470,
year = {2022},
author = {Grevet, E and Forge, K and Tadiello, S and Izac, M and Amadieu, F and Brunel, L and Pillette, L and Py, J and Gasq, D and Jeunet-Kelway, C},
title = {Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {1082901},
pmid = {38235470},
issn = {2673-6195},
abstract = {INTRODUCTION: Strokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants.<br><br>METHODS: The main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France (N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population.<br><br>RESULTS: Results show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention.<br><br>DISCUSSION: With this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications.},
}
@article {pmid38235338,
year = {2024},
author = {Sefati, N and Esmaeilpour, T and Salari, V and Zarifkar, A and Dehghani, F and Ghaffari, MK and Zadeh-Haghighi, H and Császár, N and Bókkon, I and Rodrigues, S and Oblak, D},
title = {Monitoring Alzheimer's disease via ultraweak photon emission.},
journal = {iScience},
volume = {27},
number = {1},
pages = {108744},
pmid = {38235338},
issn = {2589-0042},
abstract = {In an innovative experiment, we detected ultraweak photon emission (UPE) from the hippocampus of male rat brains and found significant correlations between Alzheimer's disease (AD), memory decline, oxidative stress, and UPE intensity. These findings may open up novel methods for screening, detecting, diagnosing, and classifying neurodegenerative diseases, particularly AD. The study suggests that UPE from the brain's neural tissue can serve as a valuable indicator. It also proposes the development of a minimally invasive brain-computer interface (BCI) photonic chip for monitoring and diagnosing AD, offering high spatiotemporal resolution of brain activity. The study used a rodent model of sporadic AD, demonstrating that STZ-induced sAD resulted in increased hippocampal UPE, which was associated with oxidative stress. Treatment with donepezil reduced UPE and improved oxidative stress. These findings support the potential utility of UPE as a screening and diagnostic tool for AD and other neurodegenerative diseases.},
}
@article {pmid38235188,
year = {2024},
author = {Tao, QQ and Cai, X and Xue, YY and Ge, W and Yue, L and Li, XY and Lin, RR and Peng, GP and Jiang, W and Li, S and Zheng, KM and Jiang, B and Jia, JP and Guo, T and Wu, ZY},
title = {Alzheimer's disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling.},
journal = {Innovation (Cambridge (Mass.))},
volume = {5},
number = {1},
pages = {100544},
pmid = {38235188},
issn = {2666-6758},
abstract = {Amyloid-β, tau pathology, and biomarkers of neurodegeneration make up the core diagnostic biomarkers of Alzheimer disease (AD). However, these proteins represent only a fraction of the complex biological processes underlying AD, and individuals with other brain diseases in which AD pathology is a comorbidity also test positive for these diagnostic biomarkers. More AD-specific early diagnostic and disease staging biomarkers are needed. In this study, we performed tandem mass tag proteomic analysis of paired cerebrospinal fluid (CSF) and serum samples in a discovery cohort comprising 98 participants. Candidate biomarkers were validated by parallel reaction monitoring-based targeted proteomic assays in an independent multicenter cohort comprising 288 participants. We quantified 3,238 CSF and 1,702 serum proteins in the discovery cohort, identifying 171 and 860 CSF proteins and 37 and 323 serum proteins as potential early diagnostic and staging biomarkers, respectively. In the validation cohort, 58 and 21 CSF proteins, as well as 12 and 18 serum proteins, were verified as early diagnostic and staging biomarkers, respectively. Separate 19-protein CSF and an 8-protein serum biomarker panels were built by machine learning to accurately classify mild cognitive impairment (MCI) due to AD from normal cognition with areas under the curve of 0.984 and 0.881, respectively. The 19-protein CSF biomarker panel also effectively discriminated patients with MCI due to AD from patients with other neurodegenerative diseases. Moreover, we identified 21 CSF and 18 serum stage-associated proteins reflecting AD stages. Our findings provide a foundation for developing blood-based tests for AD screening and staging in clinical practice.},
}
@article {pmid38234500,
year = {2023},
author = {Farabbi, A and Figueiredo, P and Ghiringhelli, F and Mainardi, L and Sanches, JM and Moreno, P and Santos-Victor, J and Vourvopoulos, A},
title = {Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1080794},
pmid = {38234500},
issn = {2673-6195},
abstract = {INTRODUCTION: Motor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation.<br><br>METHODS: In this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution.<br><br>RESULTS: Our results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session.<br><br>DISCUSSION: Overall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes.},
}
@article {pmid38234499,
year = {2023},
author = {Gallegos Ayala, GI and Haslacher, D and Krol, LR and Soekadar, SR and Zander, TO},
title = {Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1233722},
pmid = {38234499},
issn = {2673-6195},
abstract = {Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.},
}
@article {pmid38234491,
year = {2023},
author = {Awais, MA and Redmond, P and Ward, TE and Healy, G},
title = {AMBER: advancing multimodal brain-computer interfaces for enhanced robustness-A dataset for naturalistic settings.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1216440},
pmid = {38234491},
issn = {2673-6195},
}
@article {pmid38234482,
year = {2023},
author = {Vukelić, M and Bui, M and Vorreuther, A and Lingelbach, K},
title = {Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1274730},
pmid = {38234482},
issn = {2673-6195},
abstract = {Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.},
}
@article {pmid38234481,
year = {2023},
author = {Arvaneh, M and Tanaka, T and Raza, H and Nakanishi, M and Ward, TE},
title = {Editorial: Machine learning and signal processing for neurotechnologies and brain-computer interactions out of the lab.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1305482},
pmid = {38234481},
issn = {2673-6195},
}
@article {pmid38234475,
year = {2023},
author = {Vourvopoulos, A and Fleury, M and Tonin, L and Perdikis, S},
title = {Editorial: Neurotechnologies and brain-computer interaction for neurorehabilitation.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1203934},
pmid = {38234475},
issn = {2673-6195},
}
@article {pmid38234474,
year = {2023},
author = {Benerradi, J and Clos, J and Landowska, A and Valstar, MF and Wilson, ML},
title = {Benchmarking framework for machine learning classification from fNIRS data.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {994969},
pmid = {38234474},
issn = {2673-6195},
abstract = {BACKGROUND: While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.<br><br>METHODS: We present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-computer interface (BCI) applications. The BenchNIRS framework, using a robust methodology with nested cross-validation, enables researchers to optimise models and evaluate them without bias. The framework also enables us to produce useful metrics and figures to detail the performance of new models for comparison. To demonstrate the utility of the framework, we present a benchmarking of six baseline models [linear discriminant analysis (LDA), support-vector machine (SVM), k-nearest neighbours (kNN), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM)] on the five datasets and investigate the influence of different factors on the classification performance, including: number of training examples and size of the time window of each fNIRS sample used for classification. We also present results with a sliding window as opposed to simple classification of epochs, and with a personalised approach (within subject data classification) as opposed to a generalised approach (unseen subject data classification).<br><br>RESULTS AND DISCUSSION: Results show that the performance is typically lower than the scores often reported in literature, and without great differences between models, highlighting that predicting unseen data remains a difficult task. Our benchmarking framework provides future authors, who are achieving significant high classification scores, with a tool to demonstrate the advances in a comparable way. To complement our framework, we contribute a set of recommendations for methodology decisions and writing papers, when applying machine learning to fNIRS data.},
}
@article {pmid38234470,
year = {2023},
author = {Bosch, V and Mecacci, G},
title = {Eyes on the road: brain computer interfaces and cognitive distraction in traffic.},
journal = {Frontiers in neuroergonomics},
volume = {4},
number = {},
pages = {1171910},
pmid = {38234470},
issn = {2673-6195},
abstract = {Novel wearable neurotechnology is able to provide insight into its wearer's cognitive processes and offers ways to change or enhance their capacities. Moreover, it offers the promise of hands-free device control. These brain-computer interfaces are likely to become an everyday technology in the near future, due to their increasing accessibility and affordability. We, therefore, must anticipate their impact, not only on society and individuals broadly but also more specifically on sectors such as traffic and transport. In an economy where attention is increasingly becoming a scarce good, these innovations may present both opportunities and challenges for daily activities that require focus, such as driving and cycling. Here, we argue that their development carries a dual risk. Firstly, BCI-based devices may match or further increase the intensity of cognitive human-technology interaction over the current hands-free communication devices which, despite being widely accepted, are well-known for introducing a significant amount of cognitive load and distraction. Secondly, BCI-based devices will be typically harder than hands-free devices to both visually detect (e.g., how can law enforcement check when these extremely small and well-integrated devices are used?) and restrain in their use (e.g., how do we prevent users from using such neurotechnologies without breaching personal integrity and privacy?). Their use in traffic should be anticipated by researchers, engineers, and policymakers, in order to ensure the safety of all road users.},
}
@article {pmid38232427,
year = {2024},
author = {Liu, R and Chen, Y and Li, A and Ding, Y and Yu, H and Guan, C},
title = {Aggregating intrinsic information to enhance BCI performance through federated learning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {172},
number = {},
pages = {106100},
doi = {10.1016/j.neunet.2024.106100},
pmid = {38232427},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; Knowledge ; Electroencephalography ; Imagination ; },
abstract = {Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.},
}
@article {pmid38232381,
year = {2024},
author = {Chu, C and Zhu, L and Huang, A and Xu, P and Ying, N and Zhang, J},
title = {Transfer learning with data alignment and optimal transport for EEG based motor imagery classification.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad1f7a},
pmid = {38232381},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Imagination ; },
abstract = {Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain-Computer Interfaces (BCI) research, which requires a time-consuming specific calibration procedure to address. Transfer Learning (TL) offers a potential solution by leveraging data or models from one or more source domains to facilitate learning in the target domain, so as to address these challenges.Approach. In this paper, a novel Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the calibration problem. Firstly, the method transforms the source domain data with the resting state segment data, in order to decrease the differences between the source domain and the target domain. Subsequently, feature extraction is performed using common spatial pattern. Finally, an improved TL classifier is employed to classify the target samples. Notably, this method does not require the label information of target domain samples, while concurrently reducing the calibration workload.Main results. The proposed MTLF is assessed on Datasets 2a and 2b from the BCI Competition IV. Compared with other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets 2a and 2b respectively.Significance.Experimental results demonstrate that the MTLF framework effectively reduces the discrepancy between the source and target domains and acquires better classification performance on two motor imagery datasets.},
}
@article {pmid38232377,
year = {2024},
author = {Weiss, DA and Borsa, AM and Pala, A and Sederberg, AJ and Stanley, GB},
title = {A machine learning approach for real-time cortical state estimation.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
pmid = {38232377},
issn = {1741-2552},
support = {R21 NS112783/NS/NINDS NIH HHS/United States ; R01 NS115327/NS/NINDS NIH HHS/United States ; RF1 NS128896/NS/NINDS NIH HHS/United States ; T32 EB025816/EB/NIBIB NIH HHS/United States ; R01 EB029857/EB/NIBIB NIH HHS/United States ; R01 NS104928/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Electrophysiological Phenomena ; Machine Learning ; Software ; *Brain-Computer Interfaces ; },
abstract = {Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.},
}
@article {pmid38232123,
year = {2024},
author = {Zhang, C and Wang, H and Tang, S and Li, Z},
title = {Rhesus monkeys learn to control a directional-key inspired brain machine interface via bio-feedback.},
journal = {PloS one},
volume = {19},
number = {1},
pages = {e0286742},
pmid = {38232123},
issn = {1932-6203},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Feedback ; Biofeedback, Psychology/methods ; Algorithms ; Brain/physiology ; User-Computer Interface ; },
abstract = {Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs.},
}
@article {pmid38227524,
year = {2024},
author = {Yue, Y and Zhang, X and Lv, W and Lai, HY and Shen, T},
title = {Interplay between the glymphatic system and neurotoxic proteins in Parkinson's disease and related disorders: current knowledge and future directions.},
journal = {Neural regeneration research},
volume = {19},
number = {9},
pages = {1973-1980},
pmid = {38227524},
issn = {1673-5374},
abstract = {Parkinson's disease is a common neurodegenerative disorder that is associated with abnormal aggregation and accumulation of neurotoxic proteins, including α-synuclein, amyloid-β, and tau, in addition to the impaired elimination of these neurotoxic protein. Atypical parkinsonism, which has the same clinical presentation and neuropathology as Parkinson's disease, expands the disease landscape within the continuum of Parkinson's disease and related disorders. The glymphatic system is a waste clearance system in the brain, which is responsible for eliminating the neurotoxic proteins from the interstitial fluid. Impairment of the glymphatic system has been proposed as a significant contributor to the development and progression of neurodegenerative disease, as it exacerbates the aggregation of neurotoxic proteins and deteriorates neuronal damage. Therefore, impairment of the glymphatic system could be considered as the final common pathway to neurodegeneration. Previous evidence has provided initial insights into the potential effect of the impaired glymphatic system on Parkinson's disease and related disorders; however, many unanswered questions remain. This review aims to provide a comprehensive summary of the growing literature on the glymphatic system in Parkinson's disease and related disorders. The focus of this review is on identifying the manifestations and mechanisms of interplay between the glymphatic system and neurotoxic proteins, including loss of polarization of aquaporin-4 in astrocytic endfeet, sleep and circadian rhythms, neuroinflammation, astrogliosis, and gliosis. This review further delves into the underlying pathophysiology of the glymphatic system in Parkinson's disease and related disorders, and the potential implications of targeting the glymphatic system as a novel and promising therapeutic strategy.},
}
@article {pmid38226174,
year = {2024},
author = {Corsi, MC and Sorrentino, P and Schwartz, D and George, N and Gollo, LL and Chevallier, S and Hugueville, L and Kahn, AE and Dupont, S and Bassett, DS and Jirsa, V and De Vico Fallani, F},
title = {Measuring neuronal avalanches to inform brain-computer interfaces.},
journal = {iScience},
volume = {27},
number = {1},
pages = {108734},
pmid = {38226174},
issn = {2589-0042},
abstract = {Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). To test this hypothesis, we used source-reconstructed magneto/electroencephalography during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions, we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that neuronal avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.},
}
@article {pmid38224957,
year = {2024},
author = {Oh, E and Shin, S and Kim, SP},
title = {Brain-computer interface in critical care and rehabilitation.},
journal = {Acute and critical care},
volume = {39},
number = {1},
pages = {24-33},
pmid = {38224957},
issn = {2586-6060},
support = {1.230016.01//Ulsan National Institute of Science and Technology/ ; },
abstract = {This comprehensive review explores the broad landscape of brain-computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive "stop" mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.},
}
@article {pmid38223727,
year = {2024},
author = {Kimura, R and Nambu, I and Fujitsuka, R and Maruyama, Y and Yano, S and Wada, Y},
title = {An auditory brain-computer interface to detect changes in sound pressure level for automatic volume control.},
journal = {Heliyon},
volume = {10},
number = {1},
pages = {e23948},
pmid = {38223727},
issn = {2405-8440},
abstract = {Volume control is necessary to adjust sound levels for a comfortable audio or video listening experience. This study aims to develop an automatic volume control system based on a brain-computer interface (BCI). We thus focused on a BCI using an auditory oddball paradigm, and conducted two types of experiments. In the first experiment, the participant was asked to pay attention to a target sound where the sound level was high (70 dB) compared with the other sounds (60 dB). The brain activity measured by electroencephalogram showed large positive activity (P300) for the target sound, and classification of the target and nontarget sounds achieved an accuracy of 0.90. The second experiment adopted a two-target paradigm where a low sound level (50 dB) was introduced as the second target sound. P300 was also observed in the second experiment, and a value of 0.76 was obtained for the binary classification of the target and nontarget sounds. Further, we found that better accuracy was observed in large sound levels compared to small ones. These results suggest the possibility of using BCI for automatic volume control; however, it is necessary to improve its accuracy for application in daily life.},
}
@article {pmid38222383,
year = {2023},
author = {Ravipati, Y and Pouratian, N and Arnold, C and Speier, W},
title = {Evaluating Deep Learning Performance for P300 Neural Signal Classification.},
journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
volume = {2023},
number = {},
pages = {1218-1225},
pmid = {38222383},
issn = {1942-597X},
mesh = {Humans ; *Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Machine Learning ; Algorithms ; },
abstract = {P300 event-related potential (ERP) signals are useful neurological biomarkers, and their accurate classification is important when studying the cognitive functions in patients with neurological disorders. While many studies have proposed models for classifying these signals, results have been inconsistent. As a result, a consensus has not yet been reached on the optimal model for this classification. In this study, we evaluated the performance of classic machine learning and novel deep learning methods for P300 signal classification in both within and across subject training scenarios across a dataset of 75 subjects. Although the deep learning models attained high attended event classification F1 scores, they did not outperform Stepwise Linear Discriminant Analysis (SWLDA) in the within-subject paradigm. In the across-subject paradigm, however, EEG-Inception was able to significantly outperform SWLDA. These results suggest that deep learning models may provide a general model that do not require subject-specific training and calibration in clinical settings.},
}
@article {pmid38219680,
year = {2024},
author = {Ng, HW and Guan, C},
title = {Subject-independent meta-learning framework towards optimal training of EEG-based classifiers.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {172},
number = {},
pages = {106108},
doi = {10.1016/j.neunet.2024.106108},
pmid = {38219680},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Calibration ; Imagination ; Algorithms ; },
abstract = {Advances in deep learning have shown great promise towards the application of performing high-accuracy Electroencephalography (EEG) signal classification in a variety of tasks. However, many EEG-based datasets are often plagued by the issue of high inter-subject signal variability. Robust deep learning models are notoriously difficult to train under such scenarios, often leading to subpar or widely varying performance across subjects under the leave-one-subject-out paradigm. Recently, the model agnostic meta-learning framework was introduced as a way to increase the model's ability to generalize towards new tasks. While the original framework focused on task-based meta-learning, this research aims to show that the meta-learning methodology can be modified towards subject-based signal classification while maintaining the same task objectives and achieve state-of-the-art performance. Namely, we propose the novel implementation of a few/zero-shot subject-independent meta-learning framework towards multi-class inner speech and binary class motor imagery classification. Compared to current subject-adaptive methods which utilize large number of labels from the target, the proposed framework shows its effectiveness in training zero-calibration and few-shot models for subject-independent EEG classification. The proposed few/zero-shot subject-independent meta-learning mechanism performs well on both small and large datasets and achieves robust, generalized performance across subjects. The results obtained shows a significant improvement over the current state-of-the-art, with the binary class motor imagery achieving 88.70% and the accuracy of multi-class inner speech achieving an average of 31.15%. Codes will be made available to public upon publication.},
}
@article {pmid38219404,
year = {2024},
author = {Ille, N and Nakao, Y and Yano, S and Taura, T and Ebert, A and Bornfleth, H and Asagi, S and Kozawa, K and Itabashi, I and Sato, T and Sakuraba, R and Tsuda, R and Kakisaka, Y and Jin, K and Nakasato, N},
title = {Ongoing EEG artifact correction using blind source separation.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {158},
number = {},
pages = {149-158},
doi = {10.1016/j.clinph.2023.12.133},
pmid = {38219404},
issn = {1872-8952},
mesh = {Humans ; *Signal Processing, Computer-Assisted ; *Artifacts ; Seizures ; Electroencephalography/methods ; Algorithms ; },
abstract = {OBJECTIVE: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online.<br><br>METHODS: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts.<br><br>RESULTS: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time.<br><br>CONCLUSIONS: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals.<br><br>SIGNIFICANCE: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.},
}
@article {pmid38219006,
year = {2024},
author = {Zhang, Y and Wan, Y and Rao, H},
title = {Health involvement modulates physician preference in the brain during online health consultation.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {1269},
pmid = {38219006},
issn = {2045-2322},
support = {2023KFKT007//Open Project Funding from the Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior, Shanghai International Studies University, Shanghai, China/ ; KJQN202300634//Scientific and Technological Research Program of Chongqing Municipal Education Commission/ ; 71874018//National Natural Science Foundation of China/ ; 71942003//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Physicians/psychology ; Choice Behavior ; Prefrontal Cortex/diagnostic imaging ; Health Behavior ; Patient Preference ; },
abstract = {In traditional offline health-seeking behavior, patients consistently exhibit a preference for similar types of physicians due to limited access to physicians' information. Nevertheless, with the advent of online health consultation platforms offering comprehensive physicians' information for patients, raises the question: do patients continue to exhibit uniform preference for physicians? To address this issue, we first employed a behavioral experiment to discern patients' preferences for different types of physicians' information under different health involvement, and then conducted a functional magnetic resonance imaging (fMRI) experiment to furnish neural/physiological evidence. The results showed that health involvement modulates patients' preferences, when health involvement was low, patients had diverse preferences for physicians, that is, different types of physicians' information could individually impact patients' choice and could serve as substitutes for each other. When health involvement was high, patients' preference for physicians were uniform, highlighting that the collective influence of different types of physicians' information on patients' choice behavior. From the neural level, an explanation for the results was that the ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) brain regions, two key brain regions reflecting individual cognitive resource allocation, had different activation levels under different health involvement, indicating that patients allocated different cognitive resources.},
}
@article {pmid38218457,
year = {2024},
author = {Ye, H and Ye, L and Hu, L and Yang, Y and Ge, Y and Chen, R and Wang, S and Jin, B and Ming, W and Wang, Z and Xu, S and Xu, C and Wang, Y and Ding, Y and Zhu, J and Ding, M and Chen, Z and Wang, S and Chen, C},
title = {Widespread slow oscillations support interictal epileptiform discharge networks in focal epilepsy.},
journal = {Neurobiology of disease},
volume = {191},
number = {},
pages = {106409},
doi = {10.1016/j.nbd.2024.106409},
pmid = {38218457},
issn = {1095-953X},
mesh = {Humans ; *Electroencephalography ; *Epilepsies, Partial ; Sleep ; Electrocorticography ; Wakefulness ; },
abstract = {Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.},
}
@article {pmid38215752,
year = {2024},
author = {Mao, C and Zhao, RJ and Dong, YJ and Gao, M and Chen, LN and Zhang, C and Xiao, P and Guo, J and Qin, J and Shen, DD and Ji, SY and Zang, SK and Zhang, H and Wang, WW and Shen, Q and Sun, JP and Zhang, Y},
title = {Conformational transitions and activation of the adhesion receptor CD97.},
journal = {Molecular cell},
volume = {84},
number = {3},
pages = {570-583.e7},
doi = {10.1016/j.molcel.2023.12.020},
pmid = {38215752},
issn = {1097-4164},
mesh = {Humans ; Cell Adhesion ; Cryoelectron Microscopy ; Platelet Glycoprotein GPIb-IX Complex ; *Receptors, G-Protein-Coupled/chemistry/metabolism ; *Signal Transduction ; *Antigens, CD/chemistry/metabolism ; },
abstract = {Adhesion G protein-coupled receptors (aGPCRs) are evolutionarily ancient receptors involved in a variety of physiological and pathophysiological processes. Modulators of aGPCR, particularly antagonists, hold therapeutic promise for diseases like cancer and immune and neurological disorders. Hindered by the inactive state structural information, our understanding of antagonist development and aGPCR activation faces challenges. Here, we report the cryo-electron microscopy structures of human CD97, a prototypical aGPCR that plays crucial roles in immune system, in its inactive apo and G13-bound fully active states. Compared with other family GPCRs, CD97 adopts a compact inactive conformation with a constrained ligand pocket. Activation induces significant conformational changes for both extracellular and intracellular sides, creating larger cavities for Stachel sequence binding and G13 engagement. Integrated with functional and metadynamics analyses, our study provides significant mechanistic insights into the activation and signaling of aGPCRs, paving the way for future drug discovery efforts.},
}
@article {pmid38213340,
year = {2023},
author = {Aliyeva, A},
title = {Transhumanism: Integrating Cochlear Implants With Artificial Intelligence and the Brain-Machine Interface.},
journal = {Cureus},
volume = {15},
number = {12},
pages = {e50378},
pmid = {38213340},
issn = {2168-8184},
abstract = {The integration of cochlear implants (CI) with brain-machine interfaces (BMIs) and artificial intelligence (AI) within the framework of transhumanism is revolutionary and this editorial highlights how this synergy can transcend human sensory experiences and auditory rehabilitation. The potential of this amalgamation extends beyond restoring auditory function to enhancing human capabilities, marking a transformative step towards a future where technology harmoniously extends human faculties.},
}
@article {pmid38213061,
year = {2024},
author = {Mao, T and Rao, H},
title = {Mild sleep loss impacts food cue processing in adolescent brain.},
journal = {Sleep},
volume = {47},
number = {4},
pages = {},
doi = {10.1093/sleep/zsad074},
pmid = {38213061},
issn = {1550-9109},
support = {32200889//National Natural Science Foundation of China/ ; 2021KFKT012//Shanghai International Studies University Research/ ; },
mesh = {Adolescent ; Humans ; *Cues ; *Sleep/physiology ; Brain ; },
}
@article {pmid38213007,
year = {2024},
author = {Sadhukhan, R and Verma, SP and Mondal, S and Das, A and Banerjee, R and Mandal, A and Banerjee, M and Goswami, DK},
title = {Humidity-Induced Protein-Based Artificial Synaptic Devices for Neuroprosthetic Applications.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {20},
number = {24},
pages = {e2307439},
doi = {10.1002/smll.202307439},
pmid = {38213007},
issn = {1613-6829},
support = {DST/NM/NNetRA/ 2018(G)-IIT-KGP//Department of Science and Technology/ ; //Ministry of Electronics and Information technology/ ; MeitY//Ministry of Electronics and Information technology/ ; ) 5(1)/2017-NANO//Ministry of Electronics and Information technology/ ; 5(1)/2021-NANO//Ministry of Electronics and Information technology/ ; },
mesh = {*Humidity ; Animals ; Synapses/physiology ; Humans ; Neuronal Plasticity/physiology ; Proteins/chemistry ; },
abstract = {Neuroprosthetics and brain-machine interfaces are immensely beneficial for people with neurological disabilities, and the future generation of neural repair systems will utilize neuromorphic devices for the advantages of energy efficiency and real-time performance abilities. Conventional synaptic devices are not compatible to work in such conditions. The cerebrospinal fluid (CSF) in the central part of the nervous system is composed of 99% water. Therefore, artificial synaptic devices, which are the fundamental component of neuromorphic devices, should resemble biological nerves while being biocompatible, and functional in high-humidity environments with higher functional stability for real-time applications in the human body. In this work, artificial synaptic devices are fabricated based on gelatin-PEDOT: PSS composite as an active material to work more effectively in a highly humid environment (≈90% relative humidity). These devices successfully mimic various synaptic properties by the continuous variation of conductance, like, excitatory/inhibitory post-synaptic current(EPSC/IPSC), paired-pulse facilitation/depression(PPF/PPD), spike-voltage dependent plasticity (SVDP), spike-duration dependent plasticity (SDDP), and spike-rate dependent plasticity (SRDP) in environments at a relative humidity levels of ≈90%.},
}
@article {pmid38211460,
year = {2024},
author = {Yuan, M and Zhang, C and Wang, Z and Liu, H and Pan, G and Tang, H},
title = {Trainable Spiking-YOLO for low-latency and high-performance object detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {172},
number = {},
pages = {106092},
doi = {10.1016/j.neunet.2023.106092},
pmid = {38211460},
issn = {1879-2782},
mesh = {*Neural Networks, Computer ; },
abstract = {Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving good detection accuracy and high detection speed. To overcome the aforementioned challenges, we propose an end-to-end Trainable Spiking-YOLO (Tr-Spiking-YOLO) for low-latency and high-performance object detection. We evaluate our model on not only frame-based PASCAL VOC dataset but also event-based GEN1 Automotive Detection dataset, and investigate the impacts of different decoding methods on detection performance. The experimental results show that our model achieves competitive/better performance in terms of accuracy, latency and energy consumption compared to similar artificial neural network (ANN) and conversion-based SNN object detection model. Furthermore, when deployed on an edge device, our model achieves a processing speed of approximately from 14 to 39 FPS while maintaining a desirable mean Average Precision (mAP), which is capable of real-time detection on resource-constrained platforms.},
}
@article {pmid38211424,
year = {2024},
author = {Dong, H and Yan, J and Huang, P and Wang, X and Zhang, R and Zhang, C and Wang, W and Qian, W and Zhou, J and Zhao, Y and Gao, J and Zhang, M and Ma, X and Wang, Z and Yi, C and Zhang, J and Chen, W},
title = {miR-214-3p promotes the pathogenesis of Parkinson's disease by inhibiting autophagy.},
journal = {Biomedicine &amp; pharmacotherapy = Biomedecine &amp; pharmacotherapie},
volume = {171},
number = {},
pages = {116123},
doi = {10.1016/j.biopha.2024.116123},
pmid = {38211424},
issn = {1950-6007},
mesh = {Humans ; Animals ; Mice ; *Parkinson Disease/pathology ; *MicroRNAs ; Substantia Nigra/pathology ; Apoptosis ; Autophagy ; Dopaminergic Neurons/pathology ; Mice, Inbred C57BL ; },
abstract = {Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron death in the substantia nigra, leading to motor dysfunction. Autophagy dysregulation has been implicated in PD pathogenesis. This study explores the role of miR-214-3p in PD, focusing on its impact on autophagy and dopaminergic neuron viability. Using in vitro and in vivo models, we demonstrate that miR-214-3p inhibits autophagy and promotes dopaminergic neuron apoptosis. Behavioral assessments and molecular analyses reveal exacerbation of PD symptoms upon miR-214-3p overexpression. Furthermore, mechanistic investigations identify ATG3 as a target, shedding light on miR-214-3p's regulatory role in autophagy. These findings enhance our understanding of PD pathogenesis and propose miR-214-3p as a potential biomarker and therapeutic target for modulating autophagy and neuronal survival in PD.},
}
@article {pmid38206551,
year = {2024},
author = {Kong, L and Chen, Y and Shen, Y and Zhang, D and Wei, C and Lai, J and Hu, S},
title = {Progress and Implications from Genetic Studies of Bipolar Disorder.},
journal = {Neuroscience bulletin},
volume = {40},
number = {8},
pages = {1160-1172},
pmid = {38206551},
issn = {1995-8218},
mesh = {*Bipolar Disorder/genetics ; Humans ; *Genetic Predisposition to Disease/genetics ; *Genome-Wide Association Study ; Animals ; },
abstract = {With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.},
}
@article {pmid38203012,
year = {2023},
author = {Rodríguez-Azar, PI and Mejía-Muñoz, JM and Cruz-Mejía, O and Torres-Escobar, R and López, LVR},
title = {Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {1},
pages = {},
pmid = {38203012},
issn = {1424-8220},
abstract = {Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.},
}
@article {pmid38202942,
year = {2023},
author = {Kosmyna, N and Hauptmann, E and Hmaidan, Y},
title = {A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {1},
pages = {},
pmid = {38202942},
issn = {1424-8220},
mesh = {Humans ; Animals ; Dogs ; *Robotics ; Brainwashing ; Proof of Concept Study ; *Amyotrophic Lateral Sclerosis ; Brain ; },
abstract = {Coupling brain-computer interfaces (BCIs) and robotic systems in the future can enable seamless personal assistant systems in everyday life, with the requests that can be performed in a discrete manner, using one's brain activity only. These types of systems might be of a particular interest for people with locked-in syndrome (LIS) or amyotrophic lateral sclerosis (ALS) because they can benefit from communicating with robotic assistants using brain sensing interfaces. In this proof-of-concept work, we explored how a wireless and wearable BCI device can control a quadruped robot-Boston Dynamics' Spot. The device measures the user's electroencephalography (EEG) and electrooculography (EOG) activity of the user from the electrodes embedded in the glasses' frame. The user responds to a series of questions with YES/NO answers by performing a brain-teaser activity of mental calculus. Each question-answer pair has a pre-configured set of actions for Spot. For instance, Spot was prompted to walk across a room, pick up an object, and retrieve it for the user (i.e., bring a bottle of water) when a sequence resolved to a YES response. Our system achieved at a success rate of 83.4%. To the best of our knowledge, this is the first integration of wireless, non-visual-based BCI systems with Spot in the context of personal assistant use cases. While this BCI quadruped robot system is an early prototype, future iterations may embody friendly and intuitive cues similar to regular service dogs. As such, this project aims to pave a path towards future developments in modern day personal assistant robots powered by wireless and wearable BCI systems in everyday living conditions.},
}
@article {pmid38202932,
year = {2023},
author = {Cifuentes-Cuadros, AA and Romero, E and Caballa, S and Vega-Centeno, D and Elias, DA},
title = {The LIBRA NeuroLimb: Hybrid Real-Time Control and Mechatronic Design for Affordable Prosthetics in Developing Regions.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {1},
pages = {},
pmid = {38202932},
issn = {1424-8220},
support = {158-2020//Fondo Nacional de Desarrollo Cient&#xed;fico, Tecnol&#xf3;gico y de Innovaci&#xf3;n Tecnol&#xf3;gica (FONDECYT) del Per&#xfa;/ ; },
mesh = {Humans ; *Artificial Limbs ; Prosthesis Implantation ; Amputation, Surgical ; Electroencephalography ; Electromyography ; },
abstract = {Globally, 2.5% of upper limb amputations are transhumeral, and both mechanical and electronic prosthetics are being developed for individuals with this condition. Mechanics often require compensatory movements that can lead to awkward gestures. Electronic types are mainly controlled by superficial electromyography (sEMG). However, in proximal amputations, the residual limb is utilized less frequently in daily activities. Muscle shortening increases with time and results in weakened sEMG readings. Therefore, sEMG-controlled models exhibit a low success rate in executing gestures. The LIBRA NeuroLimb prosthesis is introduced to address this problem. It features three active and four passive degrees of freedom (DOF), offers up to 8 h of operation, and employs a hybrid control system that combines sEMG and electroencephalography (EEG) signal classification. The sEMG and EEG classification models achieve up to 99% and 76% accuracy, respectively, enabling precise real-time control. The prosthesis can perform a grip within as little as 0.3 s, exerting up to 21.26 N of pinch force. Training and validation sessions were conducted with two volunteers. Assessed with the "AM-ULA" test, scores of 222 and 144 demonstrated the prosthesis's potential to improve the user's ability to perform daily activities. Future work will prioritize enhancing the mechanical strength, increasing active DOF, and refining real-world usability.},
}
@article {pmid38202288,
year = {2024},
author = {Acuña, K and Sapahia, R and Jiménez, IN and Antonietti, M and Anzola, I and Cruz, M and García, MT and Krishnan, V and Leveille, LA and Resch, MD and Galor, A and Habash, R and DeBuc, DC},
title = {Functional Near-Infrared Spectrometry as a Useful Diagnostic Tool for Understanding the Visual System: A Review.},
journal = {Journal of clinical medicine},
volume = {13},
number = {1},
pages = {},
pmid = {38202288},
issn = {2077-0383},
support = {P30 EY014801/EY/NEI NIH HHS/United States ; },
abstract = {This comprehensive review explores the role of Functional Near-Infrared Spectroscopy (fNIRS) in advancing our understanding of the visual system. Beginning with an introduction to fNIRS, we delve into its historical development, highlighting how this technology has evolved over time. The core of the review critically examines the advantages and disadvantages of fNIRS, offering a balanced view of its capabilities and limitations in research and clinical settings. We extend our discussion to the diverse applications of fNIRS beyond its traditional use, emphasizing its versatility across various fields. In the context of the visual system, this review provides an in-depth analysis of how fNIRS contributes to our understanding of eye function, including eye diseases. We discuss the intricacies of the visual cortex, how it responds to visual stimuli and the implications of these findings in both health and disease. A unique aspect of this review is the exploration of the intersection between fNIRS, virtual reality (VR), augmented reality (AR) and artificial intelligence (AI). We discuss how these cutting-edge technologies are synergizing with fNIRS to open new frontiers in visual system research. The review concludes with a forward-looking perspective, envisioning the future of fNIRS in a rapidly evolving technological landscape and its potential to revolutionize our approach to studying and understanding the visual system.},
}
@article {pmid38200694,
year = {2024},
author = {Chae, U and Chun, H and Lim, J and Shin, H and Smith, WC and Choi, JW and Park, KD and Lee, CJ and Cho, IJ},
title = {KDS2010, a reversible MAO-B inhibitor, extends the lifetime of neural probes by preventing glial scar formation.},
journal = {Glia},
volume = {72},
number = {4},
pages = {748-758},
doi = {10.1002/glia.24500},
pmid = {38200694},
issn = {1098-1136},
support = {NRF-2019M3E5D2A01063814//Ministry of Science and ICT, South Korea/ ; NRF-2022M3E5E8081196//Ministry of Science and ICT, South Korea/ ; IBS- R001-D2//Institute for Basic Science/ ; NRF-2018R1A6A1A03023718//National Research Foundation of Korea/ ; },
mesh = {Mice ; Animals ; *Gliosis/drug therapy/prevention &amp; control ; *Astrocytes ; Monoamine Oxidase Inhibitors/pharmacology ; Monoamine Oxidase/pharmacology ; Macrophages ; },
abstract = {Implantable neural probes have been extensively utilized in the fields of neurocircuitry, systems neuroscience, and brain-computer interface. However, the long-term functionality of these devices is hampered by the formation of glial scar and astrogliosis at the surface of electrodes. In this study, we administered KDS2010, a recently developed reversible MAO-B inhibitor, to mice through ad libitum drinking in order to prevent glial scar formation and astrogliosis. The administration of KDS2010 allowed long-term recordings of neural signals with implantable devices, which remained stable over a period of 6 months and even restored diminished neural signals after probe implantation. KDS2010 effectively prevented the formation of glial scar, which consists of reactive astrocytes and activated microglia around the implant. Furthermore, it restored neural activity by disinhibiting astrocytic MAO-B dependent tonic GABA inhibition induced by astrogliosis. We suggest that the use of KDS2010 is a promising approach to prevent glial scar formation around the implant, thereby enabling long-term functionality of neural devices.},
}
@article {pmid38195150,
year = {2024},
author = {Mao, T and Guo, B and Rao, H},
title = {Unraveling the complex interplay between insomnia, anxiety, and brain networks.},
journal = {Sleep},
volume = {47},
number = {3},
pages = {},
pmid = {38195150},
issn = {1550-9109},
mesh = {Humans ; *Sleep Initiation and Maintenance Disorders/complications ; Sleep Quality ; Default Mode Network ; Anxiety/complications ; Brain ; },
}
@article {pmid38194404,
year = {2024},
author = {Wei, Q and Han, L and Zhang, T},
title = {Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications.},
journal = {IEEE transactions on cybernetics},
volume = {54},
number = {8},
pages = {4603-4616},
doi = {10.1109/TCYB.2023.3343430},
pmid = {38194404},
issn = {2168-2275},
abstract = {Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record discrete signals across multiple scales, but how to effectively process and analyze these potentially related signals remains an open challenge. This article introduces an innovative approach that merges modern control theory with spiking neural networks (SNNs) to bridge the gap among multiscale discrete information. Specifically, the macroscopic point-to-point trajectory is formulated as an optimal control problem with fixed terminal time and state, and it is iteratively solved using the direct dynamic programming (DDP) algorithm. Additionally, SNN is utilized to simulate microscale neural activities in the premotor cortex, employing the product of the weighted adjacency matrix and the mesoscale firing rate to approximate the macroscopic trajectory. The error between actual macroscale behavior and the preceding approximation is then used to update the weighted adjacency matrix through the recursive least square (RLS) method. Analysis and simulation of various tasks, including low-dimensional point-to-point tasks, high-dimensional complex Lorenz systems, and center-out-and-back tasks, verify the feasibility and interpretability of our method in processing multiscale signals ranging from spiking neurons to motion trajectory through the integration of SNN and control theory.},
}
@article {pmid38194394,
year = {2024},
author = {Qin, Y and Yang, B and Ke, S and Liu, P and Rong, F and Xia, X},
title = {M-FANet: Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {401-411},
doi = {10.1109/TNSRE.2024.3351863},
pmid = {38194394},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; Generalization, Psychological ; Neural Networks, Computer ; Signal-To-Noise Ratio ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {Motor imagery (MI) decoding methods are pivotal in advancing rehabilitation and motor control research. Effective extraction of spectral-spatial-temporal features is crucial for MI decoding from limited and low signal-to-noise ratio electroencephalogram (EEG) signal samples based on brain-computer interface (BCI). In this paper, we propose a lightweight Multi-Feature Attention Neural Network (M-FANet) for feature extraction and selection of multi-feature data. M-FANet employs several unique attention modules to eliminate redundant information in the frequency domain, enhance local spatial feature extraction and calibrate feature maps. We introduce a training method called Regularized Dropout (R-Drop) to address training-inference inconsistency caused by dropout and improve the model's generalization capability. We conduct extensive experiments on the BCI Competition IV 2a (BCIC-IV-2a) dataset and the 2019 World robot conference contest-BCI Robot Contest MI (WBCIC-MI) dataset. M-FANet achieves superior performance compared to state-of-the-art MI decoding methods, with 79.28% 4-class classification accuracy (kappa: 0.7259) on the BCIC-IV-2a dataset and 77.86% 3-class classification accuracy (kappa: 0.6650) on the WBCIC-MI dataset. The application of multi-feature attention modules and R-Drop in our lightweight model significantly enhances its performance, validated through comprehensive ablation experiments and visualizations.},
}
@article {pmid38193151,
year = {2025},
author = {Zhu, L and Xu, M and Zhu, J and Huang, A and Zhang, J},
title = {A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {5},
pages = {710-723},
doi = {10.1080/10255842.2023.2301421},
pmid = {38193151},
issn = {1476-8259},
mesh = {*Brain-Computer Interfaces ; Humans ; Electroencephalography ; Algorithms ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.},
}
@article {pmid38192730,
year = {2023},
author = {LaRocco, J and Tahmina, Q and Lecian, S and Moore, J and Helbig, C and Gupta, S},
title = {Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography.},
journal = {Frontiers in neuroinformatics},
volume = {17},
number = {},
pages = {1306277},
pmid = {38192730},
issn = {1662-5196},
abstract = {INTRODUCTION: Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain-computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.<br><br>METHODS: Although virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects.<br><br>RESULTS: Using open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 &#xb1; 0.001%, a mean F1 of 0.55 &#xb1; 0.01, and a mean AUC-ROC of 0.68 &#xb1; 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7.<br><br>DISCUSSION: However, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.},
}
@article {pmid38192510,
year = {2023},
author = {Xiong, X and Wang, Y and Song, T and Huang, J and Kang, G},
title = {Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1303648},
pmid = {38192510},
issn = {1662-4548},
abstract = {BACKGROUND: As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited.<br><br>METHODS: To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features.<br><br>RESULTS: Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.},
}
@article {pmid38191478,
year = {2024},
author = {Huang, Y and Tang, M and Hu, Z and Cai, B and Chen, G and Jiang, L and Xia, Y and Guan, P and Li, X and Mao, Z and Wan, X and Lu, W},
title = {SMYD3 promotes endometrial cancer through epigenetic regulation of LIG4/XRCC4/XLF complex in non-homologous end joining repair.},
journal = {Oncogenesis},
volume = {13},
number = {1},
pages = {3},
pmid = {38191478},
issn = {2157-9024},
support = {81971338//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32170602//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82172975, 81972438//National Natural Science Foundation of China (National Science Foundation of China)/ ; 22ZR1449400//Natural Science Foundation of Shanghai (Natural Science Foundation of Shanghai Municipality)/ ; 22QA1407400//Shanghai Science and Technology Development Foundation (Shanghai Science and Technology Development Fund)/ ; },
abstract = {Endometrial cancer (EC) stands as one of the most prevalent malignancies affecting the female genital tract, witnessing a rapid surge in incidence globally. Despite the well-established association of histone methyltransferase SMYD3 with the development and progression of various cancers, its specific oncogenic role in endometrial cancer remains unexplored. In the present study, we report that the expression level of SMYD3 is significantly upregulated in EC samples and associated with EC progression. Through meticulous in vivo and in vitro experiments, we reveal that depletion of SMYD3 curtails cell proliferation, migration, and invasion capabilities, leading to compromised non-homologous end joining repair (NHEJ) and heightened sensitivity of EC cells to radiation. Furthermore, our pathway enrichment analysis underscores the pivotal involvement of the DNA damage repair pathway in regulating EC progression. Mechanistically, in response to DNA damage, SMYD3 is recruited to these sites in a PARP1-dependent manner, specifically methylating LIG4. This methylation sets off a sequential assembly of the LIG4/XRCC4/XLF complex, actively participating in the NHEJ pathway and thereby fostering EC progression. Notably, our findings highlight the promise of SMYD3 as a crucial player in NHEJ repair and its direct correlation with EC progression. Intriguingly, pharmacological intervention targeting SMYD3 with its specific inhibitor, BCI-121, emerges as a potent strategy, markedly suppressing the tumorigenicity of EC cells and significantly enhancing the efficacy of radiotherapy. Collectively, our comprehensive data position SMYD3 as a central factor in NHEJ repair and underscore its potential as a promising pharmacological target for endometrial cancer therapy, validated through both in vitro and in vivo systems.},
}
@article {pmid38188645,
year = {2024},
author = {Li, S and Gong, L and Chen, J and Wu, X and Liu, X and Fu, H and Shou, Q},
title = {Fabricating the multibranch carboxyl-modified cellulose for hemorrhage control.},
journal = {Materials today. Bio},
volume = {24},
number = {},
pages = {100878},
pmid = {38188645},
issn = {2590-0064},
abstract = {Excessive bleeding is associated with a high mortality risk. In this study, citric acid and ascorbic acid were sequentially modified on the surface of microcrystalline cellulose (MCAA) to increase its carboxyl content, and their potential as hemostatic materials was investigated. The MCAA exhibited a carboxylic group content of 9.52 %, higher than that of citric acid grafted microcrystalline cellulose (MCA) at 4.6 %. Carboxyl functionalization of microcrystalline cellulose surfaces not only plays a fundamental role in the structure of composite materials but also aids in the absorption of plasma and stimulation of platelets. Fourier -transform infrared (FT-IR), thermogravimetric analysis (TGA) and X-ray photoelectron spectroscopy (XPS) spectra confirmed that carboxyl groups were successfully introduced onto the cellulose surface. Physical properties tests indicated that the MCAA possessed higher thermal stability (Tmax = 472.2 °C) compared to microcrystalline cellulose (MCC). Additionally, in vitro hemocompatibility, cytotoxicity and hemostatic property results demonstrated that MCAA displayed good biocompatibility (hemolysis ratio <1 %), optimal cell compatibility (cell viability exceeded 100 % after 72 h incubation), and impressive hemostatic effect (BCIMCAA = 31.3 %). Based on these findings, the hemostatic effect of covering a wound with MCAA was assessed, revealing enhanced hemostatic properties using MCAA in tail-amputation and liver-injury hemorrhage models. Furthermore, exploration into hemostatic mechanisms revealed that MCAA can significantly accelerate coagulation through rapid platelet aggregation and activation of the clotting cascade. Notably, MCAA showed remarkable biocompatibility and induced minimal skin irritation. In conclusion, the results affirmed that MCAA is a safe and potentially effective hemostatic agent for hemorrhage control.},
}
@article {pmid38188058,
year = {2023},
author = {Ni, P and Fan, L and Jiang, Y and Zhou, C and Chung, S},
title = {From cells to insights: the power of human pluripotent stem cell-derived cortical interneurons in psychiatric disorder modeling.},
journal = {Frontiers in psychiatry},
volume = {14},
number = {},
pages = {1336085},
pmid = {38188058},
issn = {1664-0640},
support = {R01 MH131610/MH/NIMH NIH HHS/United States ; R01 MH133205/MH/NIMH NIH HHS/United States ; R56 NS121541/NS/NINDS NIH HHS/United States ; },
abstract = {Psychiatric disorders, such as schizophrenia (SCZ) and autism spectrum disorders (ASD), represent a global health challenge with their poorly understood and complex etiologies. Cortical interneurons (cINs) are the primary inhibitory neurons in the cortex and their subtypes, especially those that are generated from the medial ganglionic emission (MGE) region, have been shown to play an important role in the pathogenesis of these psychiatric disorders. Recent advances in induced pluripotent stem cell (iPSC) technologies provide exciting opportunities to model and study these disorders using human iPSC-derived cINs. In this review, we present a comprehensive overview of various methods employed to generate MGE-type cINs from human iPSCs, which are mainly categorized into induction by signaling molecules vs. direct genetic manipulation. We discuss their advantages, limitations, and potential applications in psychiatric disorder modeling to aid researchers in choosing the appropriate methods based on their research goals. We also provide examples of how these methods have been applied to study the pathogenesis of psychiatric disorders. In addition, we discuss ongoing challenges and future directions in the field. Overall, iPSC-derived cINs provide a powerful tool to model the developmental pathogenesis of psychiatric disorders, thus aiding in uncovering disease mechanisms and potential therapeutic targets. This review article will provide valuable resources for researchers seeking to navigate the complexities of cIN generation methods and their applications in the study of psychiatric disorders.},
}
@article {pmid38186953,
year = {2024},
author = {Moreno-Alcayde, Y and Traver, VJ and Leiva, LA},
title = {Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing.},
journal = {Biomedical engineering letters},
volume = {14},
number = {1},
pages = {103-113},
pmid = {38186953},
issn = {2093-985X},
abstract = {Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the "data transfer rate" construct (i.e., how much data of the test samples are seen during training) and use it to examine data partitioning effects under several conditions. As a use case, we consider emotion recognition in videos using electroencephalogram (EEG) signals. Three data splits are considered, each representing a relevant BCI task: subject-independent (affective decoding), video-independent (affective annotation), and time-based (feature extraction). Model performance may change significantly (ranging e.g. from 50% to 90%) depending on how data is partitioned, in classification accuracy. This was evidenced in all experimental conditions tested. Our results show that (1) for affective decoding, it is hard to achieve performance above the baseline case (random classification) unless some data of the test subjects are considered in the training partition; (2) for affective annotation, having data from the same subject in training and test partitions, even though they correspond to different videos, also increases performance; and (3) later signal segments are generally more discriminative, but it is the number of segments (data points) what matters the most. Our findings not only have implications in how brain data are managed, but also in how experimental conditions and results are reported.},
}
@article {pmid38186945,
year = {2024},
author = {Jang, H and Park, JS and Jun, SC and Ahn, S},
title = {TSANet: multibranch attention deep neural network for classifying tactile selective attention in brain-computer interfaces.},
journal = {Biomedical engineering letters},
volume = {14},
number = {1},
pages = {45-55},
pmid = {38186945},
issn = {2093-985X},
abstract = {UNLABELLED: Brain-computer interfaces (BCIs) enable communication between the brain and a computer and electroencephalography (EEG) has been widely used to implement BCIs because of its high temporal resolution and noninvasiveness. Recently, a tactile-based EEG task was introduced to overcome the current limitations of visual-based tasks, such as visual fatigue from sustained attention. However, the classification performance of tactile-based BCIs as control signals is unsatisfactory. Therefore, a novel classification approach is required for this purpose. Here, we propose TSANet, a deep neural network, that uses multibranch convolutional neural networks and a feature-attention mechanism to classify tactile selective attention (TSA) in a tactile-based BCI system. We tested TSANet under three evaluation conditions, namely, within-subject, leave-one-out, and cross-subject. We found that TSANet achieved the highest classification performance compared with conventional deep neural network models under all evaluation conditions. Additionally, we show that TSANet extracts reasonable features for TSA by investigating the weights of spatial filters. Our results demonstrate that TSANet has the potential to be used as an efficient end-to-end learning approach in tactile-based BCIs.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13534-023-00309-4.},
}
@article {pmid38186671,
year = {2023},
author = {Kim, M and Choi, MS and Jang, GR and Bae, JH and Park, HS},
title = {EEG-controlled tele-grasping for undefined objects.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1293878},
pmid = {38186671},
issn = {1662-5218},
abstract = {This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.},
}
@article {pmid38185053,
year = {2024},
author = {Zhang, YN and Chen, XL and Guo, LY and Jiang, PR and Lu, H and Pan, K and Guo, L and Hu, YT and Bao, AM},
title = {Downregulation of peripheral luteinizing hormone rescues ovariectomy-associated cognitive deficits in APP/PS1 mice.},
journal = {Neurobiology of aging},
volume = {135},
number = {},
pages = {60-69},
doi = {10.1016/j.neurobiolaging.2023.12.007},
pmid = {38185053},
issn = {1558-1497},
mesh = {Mice ; Female ; Animals ; Humans ; Luteinizing Hormone/metabolism ; Down-Regulation ; Acetylcholinesterase ; *Cognitive Dysfunction/genetics/metabolism ; *Alzheimer Disease/metabolism ; Cognition ; Ovariectomy ; Mice, Transgenic ; Disease Models, Animal ; Hippocampus/metabolism ; },
abstract = {Alzheimer's disease (AD) is more prevalent in women than men, supposing due to the decline of estrogens in menopause, accompanied by increased gonadotropins such as luteinizing hormone (LH). We and others found that the transcription factor early growth response-1 (EGR1) regulates cholinergic function including the expression of acetylcholinesterase (AChE) and plays a significant role in cognitive decline of AD. Here we investigated in APP/PS1 mice by ovariectomy (OVX) and estradiol (E2) supplementation or inhibition of LH the effect on hippocampus-related cognition and related molecular changes. We found that OVX-associated cognitive impairment was accompanied by increased dorsal hippocampal EGR1 expression, which was rescued by downregulating peripheral LH rather than by supplementing E2. We also found in postmortem AD brains a higher expression of pituitary LH-mRNA and higher EGR1 expression in the posterior hippocampus. Both, in human and mice, there was a significant positive correlation between respectively posterior/dorsal hippocampal EGR1 and peripheral LH expression. We conclude that peripheral increased LH and increased posterior hippocampal EGR1 plays a significant role in AD pathology.},
}
@article {pmid38184362,
year = {2024},
author = {Wang, X and Liesaputra, V and Liu, Z and Wang, Y and Huang, Z},
title = {An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.},
journal = {Artificial intelligence in medicine},
volume = {147},
number = {},
pages = {102738},
doi = {10.1016/j.artmed.2023.102738},
pmid = {38184362},
issn = {1873-2860},
mesh = {Humans ; *Deep Learning ; Electroencephalography ; Brain ; *Brain-Computer Interfaces ; Communication ; },
abstract = {Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.},
}
@article {pmid38183705,
year = {2024},
author = {Çelebi, M and Öztürk, S and Kaplan, K},
title = {An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.},
journal = {Computers in biology and medicine},
volume = {169},
number = {},
pages = {107954},
doi = {10.1016/j.compbiomed.2024.107954},
pmid = {38183705},
issn = {1879-0534},
mesh = {Humans ; *Artificial Intelligence ; *Wavelet Analysis ; Neural Networks, Computer ; Emotions ; Attention ; Electroencephalography ; },
abstract = {This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion classification is one of the most basic approaches for improving the quality of emotion recognition systems. One of the two main branches of these approaches deals with the problem by extracting the features using manual engineering and the other is the famous artificial intelligence approach, which infers features of EEG data. This study proposes a novel method that considers the characteristic behavior of EEG recordings and based on the artificial intelligence method. The EEG signal is a noisy signal with a non-stationary and non-linear form. Using the Empirical Wavelet Transform (EWT) signal decomposition method, the signal's frequency components are obtained. Then, frequency-based features, linear and non-linear features are extracted. The resulting frequency-based, linear, and nonlinear features are mapped to the 2-D axis according to the positions of the EEG electrodes. By merging this 2-D images, 3-D images are constructed. In this way, the multichannel brain frequency of EEG recordings, spatial and temporal relationship are combined. Lastly, 3-D deep learning framework was constructed, which was combined with convolutional neural network (CNN), bidirectional long-short term memory (BiLSTM) and gated recurrent unit (GRU) with self-attention (AT). This model is named EWT-3D-CNN-BiLSTM-GRU-AT. As a result, we have created framework comprising handcrafted features generated and cascaded from state-of-the-art deep learning models. The framework is evaluated on the DEAP recordings based on the person-independent approach. The experimental findings demonstrate that the developed model can achieve classification accuracies of 90.57 % and 90.59 % for valence and arousal axes, respectively, for the DEAP database. Compared with existing cutting-edge emotion classification models, the proposed framework exhibits superior results for classifying human emotions.},
}
@article {pmid38183703,
year = {2024},
author = {Zhang, J and Liu, D and Chen, W and Pei, Z and Wang, J},
title = {Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation.},
journal = {Computers in biology and medicine},
volume = {169},
number = {},
pages = {107910},
doi = {10.1016/j.compbiomed.2023.107910},
pmid = {38183703},
issn = {1879-0534},
mesh = {Humans ; Electroencephalography/methods ; Brain/physiology ; *Exoskeleton Device ; Leg ; Gait ; *Brain-Computer Interfaces ; Imagination/physiology ; Algorithms ; },
abstract = {Lower-limb exoskeletons have been used extensively in many rehabilitation applications to assist disabled people with their therapies. Brain-machine interfaces (BMIs) further provide effective and natural control schemes. However, the limited performance of brain signal decoding from lower-limb kinematics restricts the broad growth of both BMI and rehabilitation industry. To address these challenges, we propose an ensemble method for lower-limb motor imagery (MI) classification. The proposed model employs multiple techniques to boost performance, including deep and shallow parts. Traditional wavelet transformation followed by filter-bank common spatial pattern (CSP) employs neurophysiologically reasonable patterns, while multi-head self-attention (MSA) followed by temporal convolutional network (TCN) extracts deeper encoded generalized patterns. Experimental results in a customized lower-limb exoskeleton on 8 subjects in 3 consecutive sessions showed that the proposed method achieved 60.27% and 64.20% for three (MI of left leg, MI of right leg, and rest) and two classes (lower-limb MI vs. rest), respectively. Besides, the proposed model achieves improvements of up to 4% and 2% accuracy for the subject-specific and subject-independent modes compared to the current state-of-the-art (SOTA) techniques, respectively. Finally, feature analysis was conducted to show discriminative brain patterns in each MI task and sessions with different feedback modalities. The proposed models integrated in the brain-actuated lower-limb exoskeleton established a potential BMI for gait training and neuroprosthesis.},
}
@article {pmid38183186,
year = {2024},
author = {Yu, S and Wang, Z and Wang, F and Chen, K and Yao, D and Xu, P and Zhang, Y and Wang, H and Zhang, T},
title = {Multiclass classification of motor imagery tasks based on multi-branch convolutional neural network and temporal convolutional network model.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {2},
pages = {},
doi = {10.1093/cercor/bhad511},
pmid = {38183186},
issn = {1460-2199},
support = {#62006197//National Natural Science Foundation of China/ ; #B2023186//Medical Science and Technology Research Fund of Guangdong Province/ ; #C008//National Center for Mental Health and Mental Hygiene Prevention and Control and the China Education Development Foundation/ ; #LSKJ202309//Lhasa Science and Technology Program/ ; #XZ202201ZY0027G//Key Research and Development Program of Tibet/ ; },
mesh = {*Imagination ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Imagery, Psychotherapy ; Electroencephalography/methods ; },
abstract = {Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.},
}
@article {pmid38180565,
year = {2024},
author = {Wishart, AE and Guerrero-Chacón, AL and Smith, R and Hawkshaw, DM and McAdam, AG and Dantzer, B and Boutin, S and Lane, JE},
title = {Inferring condition in wild mammals: body condition indices confer no benefit over measuring body mass across ecological contexts.},
journal = {Oecologia},
volume = {204},
number = {1},
pages = {161-172},
pmid = {38180565},
issn = {1432-1939},
mesh = {Humans ; Male ; Female ; Animals ; *Body Composition/physiology ; *Food ; Sciuridae/physiology ; Species Specificity ; },
abstract = {Many studies assume that it is beneficial for individuals of a species to be heavier, or have a higher body condition index (BCI), without accounting for the physiological relevance of variation in the composition of different body tissues. We hypothesized that the relationship between BCI and masses of physiologically important tissues (fat and lean) would be conditional on annual patterns of energy acquisition and expenditure. We studied three species with contrasting ecologies in their respective natural ranges: an obligate hibernator (Columbian ground squirrel, Urocitellus columbianus), a facultative hibernator (black-tailed prairie dog, Cynomys ludovicianus), and a food-caching non-hibernator (North American red squirrel, Tamiasciurus hudsonicus). We measured fat and lean mass in adults of both sexes using quantitative magnetic resonance (QMR). We measured body mass and two measures of skeletal structure (zygomatic width and right hind foot length) to develop sex- and species-specific BCIs, and tested the utility of BCI to predict body composition in each species. Body condition indices were more consistently, and more strongly correlated, with lean mass than fat mass. The indices were most positively correlated with fat when fat was expected to be very high (pre-hibernation prairie dogs). In all cases, however, BCI was never better than body mass alone in predicting fat or lean mass. While the accuracy of BCI in estimating fat varied across the natural histories and annual energetic patterns of the species considered, measuring body mass alone was as effective, or superior in capturing sufficient variation in fat and lean in most cases.},
}
@article {pmid38180037,
year = {2024},
author = {Xia, Y and Zhang, C and Xu, Z and Lu, S and Cheng, X and Wei, S and Yuan, J and Sun, Y and Li, Y},
title = {Organic iontronic memristors for artificial synapses and bionic neuromorphic computing.},
journal = {Nanoscale},
volume = {16},
number = {4},
pages = {1471-1489},
doi = {10.1039/d3nr06057h},
pmid = {38180037},
issn = {2040-3372},
mesh = {Humans ; *Bionics ; *Neural Networks, Computer ; Artificial Intelligence ; Electronics ; Synapses ; },
abstract = {To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.},
}
@article {pmid38179489,
year = {2023},
author = {González-Márquez, C},
title = {Neuromodulation and memory: exploring ethical ramifications in memory modification treatment via implantable neurotechnologies.},
journal = {Frontiers in psychology},
volume = {14},
number = {},
pages = {1282634},
pmid = {38179489},
issn = {1664-1078},
abstract = {Invasive implantable neurotechnologies capable of simultaneously altering and recording neural activity are no longer the exclusive province of science fiction but a looming reality that will revolutionize medical practice. These advancements, particularly in their memory-altering capabilities, herald a vast array of opportunities for addressing the complex landscape of neurodegenerative and psychiatric conditions linked to memory impairments. However, the panoply of ethical implications arising from such a novel neurotechnology remains relatively unexplored by the neuroethics literature. This study examines and contrasts the potential ethical implications of memory modification treatment via implantable neurotechnologies. The study contends that undesired side effects resulting from memory modulation can lead to significant identity harms, disrupting the coherence of self-narratives and impinging on our authenticity. To evince the practical impact of this moral argument, the study conducts a practical ethical assessment of how employing implantable neurotechnologies to modulate memory may jeopardize (i) our moral responsiveness to events and core system of values and (ii) the emotional component associated with the altered memory. From a first-person standpoint, changes to the way we reasonably feel and react to past events and future intentions may be deemed ethically problematic as these profound changes can yield significant moral disruptions and negatively impact our personal lives and interpersonal relationships. In addition, the study discusses further ethical conundrums from a third-person perspective as these disruptions can inhibit social activism against structural injustices, thereby hindering societal progress. Thus, taking into account this societal dimension is paramount when evaluating the ethical permissibility of memory modification procedures.},
}
@article {pmid38178839,
year = {2023},
author = {Zhang, K and Li, K and Zhang, C and Li, X and Han, S and Lv, C and Xie, J and Xia, X and Bie, L and Guo, Y},
title = {The accuracy of different mismatch negativity amplitude representations in predicting the levels of consciousness in patients with disorders of consciousness.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1293798},
pmid = {38178839},
issn = {1662-4548},
abstract = {INTRODUCTION: The mismatch negativity (MMN) index has been used to evaluate consciousness levels in patients with disorders of consciousness (DoC). Indeed, MMN has been validated for the diagnosis of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS). In this study, we evaluated the accuracy of different MMN amplitude representations in predicting levels of consciousness.<br><br>METHODS: Task-state electroencephalography (EEG) data were obtained from 67 patients with DoC (35 VS and 32 MCS). We performed a microstate analysis of the task-state EEG and used four different representations (the peak amplitude of MMN at electrode Fz (Peak), the average amplitude within a time window -25- 25 ms entered on the latency of peak MMN component (Avg for peak &#xb1; 25 ms), the average amplitude of averaged difference wave for 100-250 ms (Avg for 100-250 ms), and the average amplitude difference between the standard stimulus ("S") and the deviant stimulus ("D") at the time corresponding to Microstate 1 (MS1) (Avg for MS1) of the MMN amplitude to predict the levels of consciousness.<br><br>RESULTS: The results showed that among the four microstates clustered, MS1 showed statistical significance in terms of time proportion during the 100-250 ms period. Our results confirmed the activation patterns of MMN through functional connectivity analysis. Among the four MMN amplitude representations, the microstate-based representation showed the highest accuracy in distinguishing different levels of consciousness in patients with DoC (AUC = 0.89).<br><br>CONCLUSION: We discovered a prediction model based on microstate calculation of MMN amplitude can accurately distinguish between MCS and VS states. And the functional connection of the MS1 is consistent with the activation mode of MMN.},
}
@article {pmid38177151,
year = {2024},
author = {Mu, J and Liu, S and Burkitt, AN and Grayden, DB},
title = {Multi-frequency steady-state visual evoked potential dataset.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {26},
pmid = {38177151},
issn = {2052-4463},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; },
abstract = {The Steady-State Visual Evoked Potential (SSVEP) is a widely used modality in Brain-Computer Interfaces (BCIs). Existing research has demonstrated the capabilities of SSVEP that use single frequencies for each target in various applications with relatively small numbers of commands required in the BCI. Multi-frequency SSVEP has been developed to extend the capability of single-frequency SSVEP to tasks that involve large numbers of commands. However, the development on multi-frequency SSVEP methodologies is falling behind compared to the number of studies with single-frequency SSVEP. This dataset was constructed to promote research in multi-frequency SSVEP by making SSVEP signals collected with different frequency stimulation settings publicly available. In this dataset, SSVEPs were collected from 35 participants using single-, dual-, and tri-frequency stimulation and with three different multi-frequency stimulation variants.},
}
@article {pmid38176028,
year = {2024},
author = {Massaeli, F and Power, SD},
title = {EEG-based hierarchical classification of level of demand and modality of auditory and visual sensory processing.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad1ac1},
pmid = {38176028},
issn = {1741-2552},
mesh = {Humans ; *Visual Perception ; *Electroencephalography/methods ; Cognition ; Auditory Perception ; Attention ; },
abstract = {Objective.To date, most research on electroencephalography (EEG)-based mental workload detection for passive brain-computer interface (pBCI) applications has focused on identifying the overall level of cognitive resources required, such as whether the workload is high or low. We propose, however, that being able to determine the specific type of cognitive resources being used, such as visual or auditory, would also be useful. This would enable the pBCI to take more appropriate action to reduce the overall level of cognitive demand on the user. For example, if a high level of workload was detected and it is determined that the user is primarily engaged in visual information processing, then the pBCI could cause some information to be presented aurally instead. In our previous work we showed that EEG could be used to differentiate visual from auditory processing tasks when the level of processing is high, but the two modalities could not be distinguished when the level of cognitive processing demand was very low. The current study aims to build on this work and move toward the overall objective of developing a pBCI that is capable of predicting both the level and the type of cognitive resources being used.Approach.Fifteen individuals undertook carefully designed visual and auditory tasks while their EEG data was being recorded. In this study, we incorporated a more diverse range of sensory processing conditions including not only single-modality conditions (i.e. those requiring one of either visual or auditory processing) as in our previous study, but also dual-modality conditions (i.e. those requiring both visual and auditory processing) and no-task/baseline conditions (i.e. when the individual is not engaged in either visual or auditory processing).Main results.Using regularized linear discriminant analysis within a hierarchical classification algorithm, the overall cognitive demand was predicted with an accuracy of more than 86%, while the presence or absence of visual and auditory sensory processing were each predicted with an accuracy of approximately 70%.Significance.The findings support the feasibility of establishing a pBCI that can determine both the level and type of attentional resources required by the user at any given moment. This pBCI could assist in enhancing safety in hazardous jobs by triggering the most effective and efficient adaptation strategies when high workload conditions are detected.},
}
@article {pmid38173230,
year = {2024},
author = {Taeckens, EA and Shah, S},
title = {A spiking neural network with continuous local learning for robust online brain machine interface.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad1787},
pmid = {38173230},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Neurons ; Neural Networks, Computer ; Algorithms ; Education, Continuing ; },
abstract = {Objective.Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and they cannot be trained on a continuous input stream without pausing periodically to perform backpropagation. An ideal BMI should be capable training continuously without interruption to minimize disruption to the user and adapt to changing neural environments.Approach.We propose a continuous SNN weight update algorithm that can be trained to perform regression learning with no need for storing past spiking events in memory. As a result, the amount of memory needed for training is constant regardless of the input duration. We evaluate the accuracy of the network on recordings of neural data taken from the premotor cortex of a primate performing reaching tasks. Additionally, we evaluate the SNN in a simulated closed loop environment and observe its ability to adapt to sudden changes in the input neural structure.Main results.The continuous learning SNN achieves the same peak correlation (ρ=0.7) as existing SNN training methods when trained offline on real neural data while reducing the total memory usage by 92%. Additionally, it matches state-of-the-art accuracy in a closed loop environment, demonstrates adaptability when subjected to multiple types of neural input disruptions, and is capable of being trained online without any prior offline training.Significance.This work presents a neural decoding algorithm that can be trained rapidly in a closed loop setting. The algorithm increases the speed of acclimating a new user to the system and also can adapt to sudden changes in neural behavior with minimal disruption to the user.},
}
@article {pmid38172575,
year = {2024},
author = {Xu, L and Jia, W and Tao, X and Ye, F and Zhang, Y and Ding, ZJ and Zheng, SJ and Qiao, S and Su, N and Zhang, Y and Wu, S and Guo, J},
title = {Structures and mechanisms of the Arabidopsis cytokinin transporter AZG1.},
journal = {Nature plants},
volume = {10},
number = {1},
pages = {180-191},
pmid = {38172575},
issn = {2055-0278},
support = {LR19C050002//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Cytokinins/metabolism ; *Arabidopsis/metabolism ; Cryoelectron Microscopy ; *Arabidopsis Proteins/metabolism ; Membrane Transport Proteins/metabolism ; Plant Roots/metabolism ; Gene Expression Regulation, Plant ; },
abstract = {Cytokinins are essential for plant growth and development, and their tissue distributions are regulated by transmembrane transport. Recent studies have revealed that members of the 'Aza-Guanine Resistant' (AZG) protein family from Arabidopsis thaliana can mediate cytokinin uptake in roots. Here we present 2.7 to 3.3 Å cryo-electron microscopy structures of Arabidopsis AZG1 in the apo state and in complex with its substrates trans-zeatin (tZ), 6-benzyleaminopurine (6-BAP) or kinetin. AZG1 forms a homodimer and each subunit shares a similar topology and domain arrangement with the proteins of the nucleobase/ascorbate transporter (NAT) family. These structures, along with functional analyses, reveal the molecular basis for cytokinin recognition. Comparison of the AZG1 structures determined in inward-facing conformations and predicted by AlphaFold2 in the occluded conformation allowed us to propose that AZG1 may carry cytokinins across the membrane through an elevator mechanism.},
}
@article {pmid38171011,
year = {2024},
author = {Zheng, Y and Xue, J and Ma, B and Huan, Z and Wu, C and Zhu, Y},
title = {Mesoporous Bioactive Glass-Graphene Oxide Composite Aerogel with Effective Hemostatic and Antibacterial Activities.},
journal = {ACS applied bio materials},
volume = {7},
number = {1},
pages = {429-442},
doi = {10.1021/acsabm.3c01030},
pmid = {38171011},
issn = {2576-6422},
mesh = {Rats ; Animals ; *Hemostatics/pharmacology/therapeutic use ; Hemostasis ; Anti-Bacterial Agents/pharmacology/therapeutic use/chemistry ; Hemorrhage ; *Graphite ; },
abstract = {Hemorrhage and infection after emergency trauma are two main factors that cause deaths. It is of great importance to instantly stop bleeding and proceed with antibacterial treatment for saving lives. However, there is still a huge need and challenge to develop materials with functions of both rapid hemostasis and effective antibacterial therapy. Herein, we propose the fabrication of a composite aerogel mainly consisting of mesoporous bioactive glass (MBG) and graphene oxide (GO) through freeze-drying. This composite aerogel has a three-dimensional porous structure, high absorption, good hydrophilicity, and negative zeta potential. Moreover, it exhibits satisfactory hemostatic activities including low BCI, good hemocompatibility, and activation of intrinsic pathways. When applied to rat liver injury bleeding, it can decrease 60% hemostasis time and 75% blood loss amount compared to medical gauze. On the other hand, the composite aerogel shows excellent photothermal antibacterial capacity against Staphylococcus aureus and Escherichia coli. Animal experiments further verify that this composite aerogel can effectively kill bacteria in wound sites via photothermal treatment and promote wound healing. Hence, this MBG-GO composite aerogel makes a great choice for the therapy of emergency trauma with massive hemorrhage and bacterial infection.},
}
@article {pmid38169599,
year = {2024},
author = {Yang, L and Cao, J and Du, Y and Zhang, X and Hong, W and Peng, B and Wu, J and Weng, Q and Wang, J and Gao, J},
title = {Initial IL-10 production dominates the therapy of mesenchymal stem cell scaffold in spinal cord injury.},
journal = {Theranostics},
volume = {14},
number = {2},
pages = {879-891},
pmid = {38169599},
issn = {1838-7640},
mesh = {Animals ; Humans ; Rats ; Anti-Inflammatory Agents/metabolism ; Cytokines/metabolism ; Interleukin-10/metabolism ; *Mesenchymal Stem Cell Transplantation/methods ; *Mesenchymal Stem Cells/metabolism ; Rats, Sprague-Dawley ; *Spinal Cord Injuries ; },
abstract = {Rationale: Spinal cord injury (SCI) is an acute damage to the central nervous system that results in severe morbidity and permanent disability. Locally implanted scaffold systems with immobilized mesenchymal stem cells (MSCs) have been widely proven to promote locomotor function recovery in SCI rats; however, the underlying mechanism remains elusive. Methods and Results: In this study, we constructed a hyaluronic acid scaffold system (HA-MSC) to accelerate the adhesive growth of human MSCs and prolong their survival time in SCI rat lesions. MSCs regulate local immune responses by upregulating the expression of anti-inflammatory cytokines. Interestingly, the dramatically increased, but transient expression of interleukin 10 (IL-10) is found to be secreted by MSCs in the first week. Blocking the function of the initially produced IL-10 by the antibody completely abolished the neurological and behavioral recovery of SCI rats, indicating a core role of IL-10 in SCI therapy with HA-MSC implantation. Transcriptome analyses indicated that IL-10 selectively promotes the migration and cytokine secretion-associated programs of MSCs, which in turn helps MSCs exert their anti-inflammatory therapeutic effects. Conclusion: Our findings highlight a novel role of IL-10 in regulating MSC migration and cytokine secretion-associated programs, and determine the vital role of IL-10 in the domination of MSC treatment for spinal cord repair.},
}
@article {pmid38168744,
year = {2024},
author = {Hu, WH and Gao, XY and Li, XX and Lin, QM and He, LP and Lai, YS and Hao, YT},
title = {Spatial-temporal distribution of preterm birth in China, 1990-2020: A systematic review and modelling analysis.},
journal = {Paediatric and perinatal epidemiology},
volume = {38},
number = {2},
pages = {130-141},
doi = {10.1111/ppe.13028},
pmid = {38168744},
issn = {1365-3016},
support = {82073665//The National Natural Science Foundation of China/ ; 17-274//The China Medical Board/ ; SZSM201803061//The Sanming Project of Medicine in Shenzhen/ ; },
mesh = {Female ; Infant, Newborn ; Humans ; *Premature Birth/epidemiology ; Bayes Theorem ; China/epidemiology ; Birth Rate ; },
abstract = {BACKGROUND: Little is known about the long-term trends of preterm birth rates in China and their geographic variation by province.<br><br>OBJECTIVES: To estimate the annual spatial-temporal distribution of preterm birth rates in China by province from 1990 to 2020.<br><br>DATA SOURCES: We searched PubMed, EMBASE, Web of Science, CNKI, WANFANG and VIP from January 1990 to September 2023.<br><br>Studies that provided data on preterm births in China after 1990 were included. Data were extracted following the Guidelines for Accurate and Transparent Health Estimates Reporting.<br><br>SYNTHESIS: We assessed the quality of each survey using a 9-point checklist. We estimated the annual preterm birth risk by province using Bayesian multilevel logistic regression models considering potential socioeconomic, environmental, and sanitary predictors.<br><br>RESULTS: Based on 634 survey data from 343 included studies, we found a gradual increase in the preterm birth risk in most provinces in China since 1990, with an average annual increase of 0.7% nationally. However, the preterm birth rates in Inner Mongolia, Hubei, and Fujian Province showed a decline, while those in Sichuan were quite stable since 1990. In 2020, the estimates of preterm birth rates ranged from 2.9% (95% Bayesian credible interval [BCI] 2.1, 3.8) in Inner Mongolia to 8.5% (95% BCI 6.6, 10.9) in Jiangxi, with the national estimate of 5.9% (95% BCI 4.3, 8.1). Specifically, some provinces were identified as high-risk provinces for either consistently high preterm birth rates (e.g. Jiangxi) or relatively large increases (e.g. Shanxi) since 1990.<br><br>CONCLUSIONS: This study provides annual information on the preterm birth risk in China since 1990 and identifies high-risk provinces to assist in targeted control and intervention for this health issue.},
}
@article {pmid38167234,
year = {2024},
author = {Papadopoulos, S and Szul, MJ and Congedo, M and Bonaiuto, JJ and Mattout, J},
title = {Beta bursts question the ruling power for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad19ea},
pmid = {38167234},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Movement ; Hand ; Imagination ; Algorithms ; },
abstract = {Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.},
}
@article {pmid38165231,
year = {2024},
author = {Dong, R and Wang, L and Li, Z and Jiao, J and Wu, Y and Feng, Z and Wang, X and Chen, M and Cui, C and Lu, Y and Jiang, X},
title = {Stretchable, Self-Rolled, Microfluidic Electronics Enable Conformable Neural Interfaces of Brain and Vagus Neuromodulation.},
journal = {ACS nano},
volume = {18},
number = {2},
pages = {1702-1713},
doi = {10.1021/acsnano.3c10028},
pmid = {38165231},
issn = {1936-086X},
mesh = {*Microfluidics ; *Sciatic Nerve ; Electrodes ; Electronics ; Brain ; },
abstract = {Implantable neuroelectronic interfaces have gained significant importance in long-term brain-computer interfacing and neuroscience therapy. However, due to the mechanical and geometrical mismatches between the electrode-nerve interfaces, personalized and compatible neural interfaces remain serious issues for peripheral neuromodulation. This study introduces the stretchable and flexible electronics class as a self-rolled neural interface for neurological diagnosis and modulation. These stretchable electronics are made from liquid metal-polymer conductors with a high resolution of 30 μm using microfluidic printing technology. They exhibit high conformability and stretchability (over 600% strain) during body movements and have good biocompatibility during long-term implantation (over 8 weeks). These stretchable electronics offer real-time monitoring of epileptiform activities with excellent conformability to soft brain tissue. The study also develops self-rolled microfluidic electrodes that tightly wind the deforming nerves with minimal constraint (160 μm in diameter). The in vivo signal recording of the vagus and sciatic nerve demonstrates the potential of self-rolled cuff electrodes for sciatic and vagus neural modulation by recording action potential and reducing heart rate. The findings of this study suggest that the robust, easy-to-use self-rolled microfluidic electrodes may provide useful tools for compatible neuroelectronics and neural modulation.},
}
@article {pmid38164118,
year = {2025},
author = {Sireesha, V and Tallapragada, VVS and Naresh, M and Pradeep Kumar, GV},
title = {EEG-BCI-based motor imagery classification using double attention convolutional network.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {5},
pages = {581-600},
doi = {10.1080/10255842.2023.2298369},
pmid = {38164118},
issn = {1476-8259},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography ; *Neural Networks, Computer ; Algorithms ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data, such as intermodulation noise, crosstalk, and other unwanted noise, is removed by Modify Least Mean Square (M-LMS) in the pre-processing stage. Traditional LMSs were unable to extract all the noise from the images. After pre-processing, the required features, such as statistical features, entropy features, etc., were extracted using Common Spatial Pattern (CSP) and Pearson's Correlation Coefficient (PCC) instead of the traditional single feature extraction model. The arithmetic optimization algorithm cannot select the features accurately and fails to reduce the feature dimensionality of the data. Thus, an Extended Arithmetic operation optimization (ExAo) algorithm is used to select the most significant attributes from the extracted features. The proposed model uses Double Attention Convolutional Neural Networks (DAttnConvNet) to classify the types of EEG signals based on optimal feature selection. Here, the attention mechanism is used to select and optimize the features to improve the classification accuracy and efficiency of the model. In EEG motor imagery datasets, the proposed model has been analyzed under class, which obtained an accuracy of 99.98% in class Baseline (B), 99.82% in class Imagined movement of a right fist (R) and 99.61% in class Imagined movement of both fists (RL). In the EEG dataset, the proposed model can obtain a high accuracy of 97.94% compared to EEG datasets of other models.},
}
@article {pmid38164048,
year = {2025},
author = {Karnan, H and Uma Maheswari, D and Priyadharshini, D and Laushya, S and Thivyaprakas, TK},
title = {Cognizance detection during mental arithmetic task using statistical approach.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {4},
pages = {558-571},
doi = {10.1080/10255842.2023.2298362},
pmid = {38164048},
issn = {1476-8259},
mesh = {Humans ; Electroencephalography ; Support Vector Machine ; *Cognition/physiology ; *Problem Solving ; },
abstract = {The handheld diagnosis and analysis are highly dependent on the physiological data in the clinical sector. Detection of the defect in the neuronal-assisted activity raises the challenge to the prevailing treatment that benefits from machine learning approaches. The congregated EEG data is then utilized in design of learning applications to develop a model that classifies intricate EEG patterns into active and inactive segments. During arithmetic problem-solving EEG signal acquired from frontal lobe contributes for intelligence detection. The low intricate statistical parameters help in understanding the objective. The mean of the segmented samples and standard deviation are the features extracted for model building. The feature selection is handled using correlation and Fisher score between {Fp1 and F8}
and priority ranking of the regions with enhanced activity are selected for the classifier models to the training net. The R-studio platform is used to classify the data based on active and inactive liability. The radial basis function kernel for support vector machine (SVM) is deployed to substantiate the proposed methodology. The vulnerable regions F1 and F8 for arithmetic activity can be visualized from the correlation fit performed between regions. Using SVM classifier sensitivity of 92.5% is obtained for the selected features. A wide range of clinical problems can be diagnosed using this model and used for brain-computer interface.},
}
@article {pmid38163312,
year = {2024},
author = {Li, C and Liu, Y and Li, J and Miao, Y and Liu, J and Song, L},
title = {Decoding Bilingual EEG Signals With Complex Semantics Using Adaptive Graph Attention Convolutional Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {249-258},
doi = {10.1109/TNSRE.2023.3348981},
pmid = {38163312},
issn = {1558-0210},
mesh = {Humans ; *Semantics ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Reading ; Communication ; },
abstract = {Decoding neural signals of silent reading with Brain-Computer Interface (BCI) techniques presents a fast and intuitive communication method for severely aphasia patients. Electroencephalogram (EEG) acquisition is convenient and easily wearable with high temporal resolution. However, existing EEG-based decoding units primarily concentrate on individual words due to their low signal-to-noise ratio, rendering them insufficient for facilitating daily communication. Decoding at the word level is less efficient than decoding at the phrase or sentence level. Furthermore, with the popularity of multilingualism, decoding EEG signals with complex semantics under multiple languages is highly urgent and necessary. To the best of our knowledge, there is currently no research on decoding EEG signals during silent reading of complex semantics, let alone decoding silent reading EEG signals with complex semantics for bilingualism. Moreover, the feasibility of decoding such signals remains to be investigated. In this work, we collect silent reading EEG signals of 9 English Phrases (EP), 7 English Sentences (ES), 10 Chinese Phrases (CP), and 7 Chinese Sentences (CS) from the subject within 26 days. We propose a novel Adaptive Graph Attention Convolution Network (AGACN) for classification. Experimental results demonstrate that our proposed method outperforms state-of-the-art methods, achieving the highest classification accuracy of 54.70%, 62.26%, 44.55%, and 57.14% for silent reading EEG signals of EP, ES, CP, and CS, respectively. Moreover, our results prove the feasibility of complex semantics EEG signal decoding. This work will aid aphasic patients in achieving regular communication while providing novel ideas for neural signal decoding research.},
}
@article {pmid38161801,
year = {2023},
author = {Bi, J and Chu, M and Wang, G and Gao, X},
title = {TSPNet: a time-spatial parallel network for classification of EEG-based multiclass upper limb motor imagery BCI.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1303242},
pmid = {38161801},
issn = {1662-4548},
abstract = {The classification of electroencephalogram (EEG) motor imagery signals has emerged as a prominent research focus within the realm of brain-computer interfaces. Nevertheless, the conventional, limited categories (typically just two or four) offered by brain-computer interfaces fail to provide an extensive array of control modes. To address this challenge, we propose the Time-Spatial Parallel Network (TSPNet) for recognizing six distinct categories of upper limb motor imagery. Within TSPNet, temporal and spatial features are extracted separately, with the time dimension feature extractor and spatial dimension feature extractor performing their respective functions. Following this, the Time-Spatial Parallel Feature Extractor is employed to decouple the connection between temporal and spatial features, thus diminishing feature redundancy. The Time-Spatial Parallel Feature Extractor deploys a gating mechanism to optimize weight distribution and parallelize time-spatial features. Additionally, we introduce a feature visualization algorithm based on signal occlusion frequency to facilitate a qualitative analysis of TSPNet. In a six-category scenario, TSPNet achieved an accuracy of 49.1% ± 0.043 on our dataset and 49.7% ± 0.029 on a public dataset. Experimental results conclusively establish that TSPNet outperforms other deep learning methods in classifying data from these two datasets. Moreover, visualization results vividly illustrate that our proposed framework can generate distinctive classifier patterns for multiple categories of upper limb motor imagery, discerned through signals of varying frequencies. These findings underscore that, in comparison to other deep learning methods, TSPNet excels in intention recognition, which bears immense significance for non-invasive brain-computer interfaces.},
}
@article {pmid38158165,
year = {2024},
author = {Ruiz Ibán, M&#xc1; and García Navlet, M and Moros Marco, S and Diaz Heredia, J and Hernando Sánchez, A and Ruiz Díaz, R and Vaquero Comino, C and Rosas Ojeda, ML and Del Monte Bello, G and Ávila Lafuente, JL},
title = {Augmentation of a Transosseous-Equivalent Repair in Posterosuperior Nonacute Rotator Cuff Tears With a Bioinductive Collagen Implant Decreases the Retear Rate at 1 Year: A Randomized Controlled Trial.},
journal = {Arthroscopy : the journal of arthroscopic &amp; related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association},
volume = {40},
number = {6},
pages = {1760-1773},
doi = {10.1016/j.arthro.2023.12.014},
pmid = {38158165},
issn = {1526-3231},
mesh = {Humans ; *Rotator Cuff Injuries/surgery ; Female ; Male ; Middle Aged ; *Collagen/therapeutic use ; *Arthroscopy/methods ; Aged ; Magnetic Resonance Imaging ; Treatment Outcome ; Recurrence ; Follow-Up Studies ; Prostheses and Implants ; Wound Healing ; Rotator Cuff/surgery ; },
abstract = {PURPOSE: To determine whether the addition of a bioinductive collagen implant (BCI) over a transosseous equivalent (TOE) repair of medium-to-large posterosuperior rotator cuff tears improves the healing rate determined by magnetic resonance imaging (MRI) at 12-month follow-up.<br><br>METHODS: A Level I randomized controlled trial was performed in 124 subjects with isolated, symptomatic, reparable, full-thickness, medium-to-large posterosuperior nonacute rotator cuff tears, with fatty infiltration &#x2264;2. These were randomized to 2 groups in which an arthroscopic posterosuperior rotator cuff tear TOE repair was performed alone (Control group) or with BCI applied over the TOE repair (BCI group). The primary outcome was the retear rate (defined as Sugaya 4-5) determined by MRI at 12 months of follow-up. Secondary outcomes were characteristics of the tendon (Sugaya grade and thickness of the healed tendon) and clinical outcomes (pain levels, EQ-5D-5L, American Shoulder and Elbow Surgeons, and Constant-Murley scores) at 12 months of follow-up.<br><br>RESULTS: Of the 124 randomized patients, 122 (60 in the BCI group and 62 in the Control group) were available for MRI evaluation 12.2 &#xb1; 1.02 months after the intervention. There were no relevant differences in preoperative characteristics. Adding the BCI reduced the retear rate (8.3% [5/60] in the BCI group vs 25.8% [16/62] in the Control group, P&#xa0;= .010; relative risk of retear of 0.32 [95% confidence interval 0.13-0.83]). Sugaya grade was also better in the BCI group (P&#xa0;=&#xa0;.030). There were no differences between groups in the percentage of subjects who reached the MCID for CMS (76.7% vs 81.7%, P&#xa0;= .654) or American Shoulder and Elbow Surgeons (75% vs 80%, P&#xa0;= .829), in other clinical outcomes or in complication rates at 12.4 &#xb1; 0.73 (range 11.5-17) months of follow-up.<br><br>CONCLUSIONS: Augmentation with a BCI of a TOE repair in a medium-to-large posterosuperior rotator cuff tear reduces the retear rate at 12-month follow-up by two-thirds, yielding similar improvements in clinical outcomes and without increased complication rates.<br><br>LEVEL OF EVIDENCE: Level I, randomized controlled trial.},
}
@article {pmid38156268,
year = {2023},
author = {Guo, Y and Li, Y and Wei, HL and Zhao, Y},
title = {Editorial: New theories, models, and AI methods of brain dynamics, brain decoding and neuromodulation.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1302505},
pmid = {38156268},
issn = {1662-4548},
}
@article {pmid38153799,
year = {2024},
author = {Le, J and Lv, F and Lin, J and Wu, Y and Ren, Z and Zhang, Q and Dong, S and Luo, J and Shi, J and Chen, R and Hong, Z and Huang, Y},
title = {Novel Sandwich-Structured Flexible Composite Films with Enhanced Piezoelectric Performance.},
journal = {ACS applied materials &amp; interfaces},
volume = {16},
number = {1},
pages = {1492-1501},
doi = {10.1021/acsami.3c15046},
pmid = {38153799},
issn = {1944-8252},
abstract = {Piezoelectric poly(vinylidene fluoride) (PVDF) and its copolymers have been widely investigated for applications in wearable electric devices and sensing systems, owing to their intrinsic piezoelectricity and superior flexibility. However, their weak piezoelectricity poses major challenges for practical applications. To overcome these challenges, we propose a two-step synthesis approach to fabricate sandwich-structured piezoelectric films (BaTiO3@PDA/PVDF/BaTiO3@PDA) with significantly enhanced ferroelectric and piezoelectric properties. As compared to pristine PVDF films or conventional 0-3 composite films, a maximum polarization (Pmax) of 11.24 μC/cm[2], a remanent polarization (Pr) of 5.83 μC/cm[2], and an enhanced piezoelectric coefficient (d33 ∼ 14.6 pC/N) were achieved. Simulation and experimental results have demonstrated that the sandwich structure enhances the ability of composite films to withstand higher poling electric fields in comparison with 0-3 composites. The sandwich-structured piezoelectric films are further integrated into a wireless sensor system with a high force sensitivity of 288 mV/N, demonstrating great potential for movement monitoring applications. This facile approach shows great promise for the large-scale production of composite films with remarkable flexibility, ferroelectricity, and piezoelectricity for wearable sensing devices.},
}
@article {pmid38152952,
year = {2024},
author = {Zhu, H and Luo, H and Cai, M and Song, J},
title = {A Multifunctional Flexible Tactile Sensor Based on Resistive Effect for Simultaneous Sensing of Pressure and Temperature.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {6},
pages = {e2307693},
pmid = {38152952},
issn = {2198-3844},
support = {2022YFC2401901//National Key Research and Development Program of China/ ; 12225209//National Natural Science Foundation of China/ ; U21A20502//National Natural Science Foundation of China/ ; U20A6001//National Natural Science Foundation of China/ ; 12321002//National Natural Science Foundation of China/ ; },
abstract = {Flexible tactile sensors with multifunctional sensing functions have attracted much attention due to their wide applications in artificial limbs, intelligent robots, human-machine interfaces, and health monitoring devices. Here, a multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is reported. The sensor features a simple design with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing is achieved by the reasonable arrangement of metal layers in the patterned metal film. Systematically experimental and numerical studies are carried out to reveal the multifunctional sensing mechanism and show that the proposed sensor exhibits good linearity, fast response, high stability, good mechanical flexibility, and good microfabrication compatibility. Demonstrations of the multifunctional flexible tactile sensor to monitor touch, breathing, pulse and objects grabbing/releasing in various application scenarios involving coupled temperature/pressure stimuli illustrate its excellent capability of measuring pressure and temperature simultaneously. These results offer an effective tool for multifunctional sensing of pressure and temperature and create engineering opportunities for applications of wearable health monitoring and human-machine interfaces.},
}
@article {pmid38151948,
year = {2023},
author = {Sun, J and Meng, J and You, J and Yang, M and Jiang, J and Xu, M and Ming, D},
title = {[Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {6},
pages = {1235-1241},
pmid = {38151948},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain/physiology ; Artificial Intelligence ; Photic Stimulation/methods ; },
abstract = {Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.},
}
@article {pmid38151935,
year = {2023},
author = {Fei, K and Cai, X and Chen, S and Pan, L and Wang, W},
title = {[Recognition of motor imagery electroencephalogram based on flicker noise spectroscopy and weighted filter bank common spatial pattern].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {6},
pages = {1126-1134},
pmid = {38151935},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Algorithms ; Spectrum Analysis ; },
abstract = {Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern (wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.},
}
@article {pmid38151931,
year = {2023},
author = {Cui, X and Qin, Z and Gao, Z and Wan, W and Gu, Z},
title = {[Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {6},
pages = {1093-1101},
pmid = {38151931},
issn = {1001-5515},
mesh = {Humans ; Algorithms ; Depression/diagnosis/therapy ; *Music ; *Music Therapy ; Electroencephalography ; *Wearable Electronic Devices ; },
abstract = {Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.},
}
@article {pmid38150794,
year = {2024},
author = {Liu, H and Bai, Y and Xu, Z and Liu, J and Ni, G and Ming, D},
title = {The scalp time-varying network of auditory spatial attention in "cocktail-party" situations.},
journal = {Hearing research},
volume = {442},
number = {},
pages = {108946},
doi = {10.1016/j.heares.2023.108946},
pmid = {38150794},
issn = {1878-5891},
mesh = {Humans ; Acoustic Stimulation/methods ; *Scalp ; Temporal Lobe ; *Sound Localization ; Attention ; Auditory Perception ; },
abstract = {Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.},
}
@article {pmid38150057,
year = {2024},
author = {Zou, R and Zhao, L and He, S and Zhou, X and Yin, X},
title = {Effect of the period of EEG signals on the decoding of motor information.},
journal = {Physical and engineering sciences in medicine},
volume = {47},
number = {1},
pages = {249-260},
pmid = {38150057},
issn = {2662-4737},
support = {21S31906000//Science and Technology Commission of Shanghai Municipality/ ; 61803265//the National Natural Science Foundation of China (NSFC)/ ; 1022308524//Medical-industrial cross-project of USST Grant/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Upper Extremity ; Brain/physiology ; Movement/physiology ; Neural Networks, Computer ; },
abstract = {Decoding movement information from electroencephalogram to construct brain-computer interface has promising applications. The EEG data during the entire motor imagery (MI) period or movement execution (ME) period is generally decoded, and calculation of numerous information and massive dataset is time-consuming. In order to improve decoding efficiency, the joint topographic maps of the brain activation state of 15 subjects were studied during different periods. The results showed that the activation intensity of the preparation period in the motor imagery experiment was higher than during the exercise period, while during the exercise period, the activation intensity was higher than in the preparation period in the movement execution experiment. Hence, the wavelet neural network was used to decode the six-class movements including elbow flexion/extension, forearm pronation/supination and hand open/close in periods of MI/ME. The experimental results show that the accuracy obtained in the preparation period is the highest in the motor imagery experiment, which is 80.77%. On the other hand, the highest accuracy obtained in the exercise period of the movement execution experiment is 79.26%. It further proves that the optimized period is a key decoding factor to reduce the cost of calculation, and this new decoding method is effective to build a more intelligent brain-computer interface system.},
}
@article {pmid38148700,
year = {2023},
author = {Fateeva, VV and Kushnir, AB and Grechko, AV and Mayorova, LA},
title = {[Rehabilitation of patients with post-stroke cognitive impairment using the P300-based brain-computer interface: results of a randomized controlled trial].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {123},
number = {12. Vyp. 2},
pages = {68-74},
doi = {10.17116/jnevro202312312268},
pmid = {38148700},
issn = {1997-7298},
mesh = {Humans ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; *Cognitive Training ; *Ischemic Stroke/physiopathology/psychology/rehabilitation ; Attention ; },
abstract = {OBJECTIVE: To study the effects of a 10-day cognitive training using the brain-computer interface (BCI) technology at the P300 wavelength on the recovery of cognitive functions in poststroke patients.<br><br>MATERIAL AND METHODS: The study included 30 patients, aged 22-82 years, with ischemic stroke less than 3 months old and moderate cognitive impairment (<26 points on the Montreal Cognitive Assessment Scale (MoCA)). All patients underwent neuropsychological testing, assessment of the presence of depression, assessment of activity in daily life. Patients were randomized into two groups: patients of group 1 (main) underwent a 10-day course of cognitive rehabilitation in the form of daily exercises in the BCI environment at the P300 wave equipped with a headset for recording an electroencephalogram (EEG). Patients of group 2 (control) received a standard set of rehabilitation measures.<br><br>RESULTS: There was an increase in the mean score of the MoCA &#xab;Attention&#xbb; domain in the main group of patients (2.3&#xb1;1.24 to 5.2&#xb1;1.16 points) compared with the control group (5.9&#xb1;1.00 to 4.2&#xb1;0.94 points, p<0.05). The results of covariance analysis with repeated measures, taking into account the factors &#xab;Visit&#xbb; and &#xab;Group&#xbb;, the covariate &#xab;Depression&#xbb; and &#xab;Number of training sessions&#xbb; revealed significant effects for the MoCA domains &#xab;Naming&#xbb; (p<0.05), &#xab;Attention&#xbb; (p<0.05), &#xab;Abstraction&#xbb; (p<0.05). By the end of the 10-day cognitive training using BCI, patients of the main group showed a significant increase in the number of entered letters (20.8&#xb1;2.01 to 25.9&#xb1;1.7 characters (p=0.02) compared with the control group (21.9&#xb1;1.9 to 23.1&#xb1;1.8, p=0.06). When comparing the number of words entered by patients after 10 days, a significant difference was found between the main and control groups (p<0.05).<br><br>CONCLUSION: Rehabilitation of patients with post-stroke cognitive impairment using P300 BCI has a significant positive effect on the restoration of cognitive functions, primarily attention.},
}
@article {pmid38146541,
year = {2023},
author = {Yue, Z and Xiao, P and Wang, J and Tong, RK},
title = {Brain oscillations in reflecting motor status and recovery induced by action observation-driven robotic hand intervention in chronic stroke.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1241772},
pmid = {38146541},
issn = {1662-4548},
abstract = {Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements (p < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations (p > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, p < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, p = 0.005; high-beta, c.c = 0.71, p = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, p = 0.003; low-beta, c.c = 0.67, p = 0.009; high-beta, c.c = 0.839, p = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.},
}
@article {pmid38145752,
year = {2024},
author = {Várkuti, B and Halász, L and Hagh Gooie, S and Miklós, G and Smits Serena, R and van Elswijk, G and McIntyre, CC and Lempka, SF and Lozano, AM and Erōss, L},
title = {Conversion of a medical implant into a versatile computer-brain interface.},
journal = {Brain stimulation},
volume = {17},
number = {1},
pages = {39-48},
doi = {10.1016/j.brs.2023.12.011},
pmid = {38145752},
issn = {1876-4754},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Computers ; },
abstract = {BACKGROUND: Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications.<br><br>OBJECTIVE: Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain.<br><br>METHODS: In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task.<br><br>RESULTS: We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n&#xa0;=&#xa0;13), a Morse-decoding task (n&#xa0;=&#xa0;3), and/or two different balance/body-posture tasks (n&#xa0;=&#xa0;18; n&#xa0;=&#xa0;5). The median calibration time was 79&#xa0;min. The median training time for learning to use the interface in a subsequent task was 1:40&#xa0;min. In each task, every participant demonstrated successful performance, surpassing chance levels.<br><br>CONCLUSION: The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.},
}
@article {pmid38145348,
year = {2024},
author = {Londoño-Ramírez, H and Huang, X and Cools, J and Chrzanowska, A and Brunner, C and Ballini, M and Hoffman, L and Steudel, S and Rolin, C and Mora Lopez, C and Genoe, J and Haesler, S},
title = {Multiplexed Surface Electrode Arrays Based on Metal Oxide Thin-Film Electronics for High-Resolution Cortical Mapping.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {11},
number = {10},
pages = {e2308507},
pmid = {38145348},
issn = {2198-3844},
support = {G097022N//Fonds Wetenschappelijk Onderzoek/ ; C14/21/111//KU Leuven/ ; },
mesh = {Animals ; Mice ; *Brain Mapping ; Electrodes, Implanted ; *Cerebral Cortex/physiology ; Electrocorticography ; Electronics ; },
abstract = {Electrode grids are used in neuroscience research and clinical practice to record electrical activity from the surface of the brain. However, existing passive electrocorticography (ECoG) technologies are unable to offer both high spatial resolution and wide cortical coverage, while ensuring a compact acquisition system. The electrode count and density are restricted by the fact that each electrode must be individually wired. This work presents an active micro-electrocorticography (µECoG) implant that tackles this limitation by incorporating metal oxide thin-film transistors (TFTs) into a flexible electrode array, allowing to address multiple electrodes through a single shared readout line. By combining the array with an incremental-ΔΣ readout integrated circuit (ROIC), the system is capable of recording from up to 256 electrodes virtually simultaneously, thanks to the implemented 16:1 time-division multiplexing scheme, offering lower noise levels than existing active µECoG arrays. In vivo validation is demonstrated acutely in mice by recording spontaneous activity and somatosensory evoked potentials over a cortical surface of ≈8×8 mm[2] . The proposed neural interface overcomes the wiring bottleneck limiting ECoG arrays, holding promise as a powerful tool for improved mapping of the cerebral cortex and as an enabling technology for future brain-machine interfaces.},
}
@article {pmid38145118,
year = {2023},
author = {Khan, MA and Fares, H and Ghayvat, H and Brunner, IC and Puthusserypady, S and Razavi, B and Lansberg, M and Poon, A and Meador, KJ},
title = {A systematic review on functional electrical stimulation based rehabilitation systems for upper limb post-stroke recovery.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1272992},
pmid = {38145118},
issn = {1664-2295},
abstract = {BACKGROUND: Stroke is one of the most common neurological conditions that often leads to upper limb motor impairments, significantly affecting individuals' quality of life. Rehabilitation strategies are crucial in facilitating post-stroke recovery and improving functional independence. Functional Electrical Stimulation (FES) systems have emerged as promising upper limb rehabilitation tools, offering innovative neuromuscular reeducation approaches.<br><br>OBJECTIVE: The main objective of this paper is to provide a comprehensive systematic review of the start-of-the-art functional electrical stimulation (FES) systems for upper limb neurorehabilitation in post-stroke therapy. More specifically, this paper aims to review different types of FES systems, their feasibility testing, or randomized control trials (RCT) studies.<br><br>METHODS: The FES systems classification is based on the involvement of patient feedback within the FES control, which mainly includes "Open-Loop FES Systems" (manually controlled) and "Closed-Loop FES Systems" (brain-computer interface-BCI and electromyography-EMG controlled). Thus, valuable insights are presented into the technological advantages and effectiveness of Manual FES, EEG-FES, and EMG-FES systems.<br><br>RESULTS AND DISCUSSION: The review analyzed 25 studies and found that the use of FES-based rehabilitation systems resulted in favorable outcomes for the stroke recovery of upper limb functional movements, as measured by the FMA (Fugl-Meyer Assessment) (Manually controlled FES: mean difference = 5.6, 95% CI (3.77, 7.5), P < 0.001; BCI-controlled FES: mean difference = 5.37, 95% CI (4.2, 6.6), P < 0.001; EMG-controlled FES: mean difference = 14.14, 95% CI (11.72, 16.6), P < 0.001) and ARAT (Action Research Arm Test) (EMG-controlled FES: mean difference = 11.9, 95% CI (8.8, 14.9), P < 0.001) scores. Furthermore, the shortcomings, clinical considerations, comparison to non-FES systems, design improvements, and possible future implications are also discussed for improving stroke rehabilitation systems and advancing post-stroke recovery. Thus, summarizing the existing literature, this review paper can help researchers identify areas for further investigation. This can lead to formulating research questions and developing new studies aimed at improving FES systems and their outcomes in upper limb rehabilitation.},
}
@article {pmid38143387,
year = {2024},
author = {Zhang, M and Zhu, F and Jia, F and Wu, Y and Wang, B and Gao, L and Chu, F and Tang, W},
title = {Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: A systematic review and meta-analysis.},
journal = {NeuroRehabilitation},
volume = {54},
number = {2},
pages = {199-212},
doi = {10.3233/NRE-230215},
pmid = {38143387},
issn = {1878-6448},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Upper Extremity/physiopathology ; *Recovery of Function/physiology ; Stroke/physiopathology ; },
abstract = {BACKGROUND: The recovery of upper limb function is crucial to the daily life activities of stroke patients. Brain-computer interface technology may have potential benefits in treating upper limb dysfunction.<br><br>OBJECTIVE: To systematically evaluate the efficacy of brain-computer interfaces (BCI) in the rehabilitation of upper limb motor function in stroke patients.<br><br>METHODS: Six databases up to July 2023 were reviewed according to the PRSIMA guidelines. Randomized controlled trials of BCI-based upper limb functional rehabilitation for stroke patients were selected for meta-analysis by pooling standardized mean difference (SMD) to summarize the evidence. The Cochrane risk of bias tool was used to assess the methodological quality of the included studies.<br><br>RESULTS: Twenty-five studies were included. The studies showed that BCI had a small effect on the improvement of upper limb function after the intervention. In terms of total duration of training, <&#x200a;12 hours of training may result in better rehabilitation, but training duration greater than 12 hours suggests a non significant therapeutic effect of BCI training.<br><br>CONCLUSION: This meta-analysis suggests that BCI has a slight efficacy in improving upper limb function and has favorable long-term outcomes. In terms of total duration of training, <&#x200a;12 hours of training may lead to better rehabilitation.},
}
@article {pmid38141782,
year = {2024},
author = {Iwama, S and Takemi, M and Eguchi, R and Hirose, R and Morishige, M and Ushiba, J},
title = {Two common issues in synchronized multimodal recordings with EEG: Jitter and latency.},
journal = {Neuroscience research},
volume = {203},
number = {},
pages = {1-7},
doi = {10.1016/j.neures.2023.12.003},
pmid = {38141782},
issn = {1872-8111},
mesh = {Humans ; *Electroencephalography/methods ; Brain/physiology ; Computer Simulation ; Signal Processing, Computer-Assisted ; },
abstract = {Multimodal recording using electroencephalogram (EEG) and other biological signals (e.g., muscle activities, eye movement, pupil diameters, or body kinematics data) is ubiquitous in human neuroscience research. However, the precise time alignment of multiple data from heterogeneous sources (i.e., devices) is often arduous due to variable recording parameters of commercially available research devices and complex experimental setups. In this review, we introduced the versatility of a Lab Streaming Layer (LSL)-based application that can overcome two common issues in measuring multimodal data: jitter and latency. We discussed the issues of jitter and latency in multimodal recordings and the benefits of time-synchronization when recording with multiple devices. In addition, a computer simulation was performed to highlight how the millisecond-order jitter readily affects the signal-to-noise ratio of the electrophysiological outcome. Together, we argue that the LSL-based system can be used for research requiring precise time-alignment of datasets. Studies that detect stimulus-induced transient neural responses or test hypotheses regarding temporal relationships of different functional aspects with multimodal data would benefit most from LSL-based systems.},
}
@article {pmid38137573,
year = {2023},
author = {Virseda-Chamorro, M and Téllez, C and Salinas-Casado, J and Szczesniewski, J and Ruiz-Grana, S and Arance, I and Angulo, JC},
title = {Factors Influencing Postoperative Overactive Bladder after Adjustable Trans-Obturator Male System Implantation for Male Stress Incontinence following Prostatectomy.},
journal = {Journal of clinical medicine},
volume = {12},
number = {24},
pages = {},
pmid = {38137573},
issn = {2077-0383},
abstract = {We aimed to determine the risk factors for postoperative overactive bladder (OAB) in patients treated with an adjustable trans-obturator male system (ATOMS) for stress incontinence after radical treatment of prostate cancer. A prospective study was performed on 56 patients implanted with an ATOMS for PPI. Clinical and urodynamic information was recorded before and after ATOMS implantation. We built a multivariate model to find out the clinical and urodynamic factors that independently influenced postoperative OAB and the prognostic factors that influenced the efficacy of medical treatment of OAB. We found that the clinical risk factors were the preoperative intensity of urinary incontinence (number of daily pads used and amount of urinary leakage), International Consultation on Incontinence Questionnaire (ICIQ) score, postoperative number of ATOMS adjustments, final cushion volume, and incontinence cure. The urodynamic data associated with OAB were cystometric bladder capacity, voided volume, volume at initial involuntary contraction (IC), maximum flow rate, bladder contractility index (BCI), and urethral resistance (URA). The prognostic factors for the efficacy of oral treatment of OAB were the volume at the first IC (direct relationship) and the maximum abdominal voiding pressure (inverse relationship). The multivariate model showed that the independent clinical risk factors were the daily pad count before the implantation and the ICIQ score at baseline and after treatment. The independent urodynamic data were the volume at the first IC (inverse relationship) and the URA value (direct relationship). Both predictive factors of treatment efficacy were found to be independent. Detrusor overactivity plays an important role in postoperative OAB, although other urodynamic and clinical factors such as the degree of urethral resistance and abdominal strength may influence this condition.},
}
@article {pmid38137150,
year = {2023},
author = {Shi, Q and Gong, A and Ding, P and Wang, F and Fu, Y},
title = {Neural Mechanisms of Visual-Spatial Judgment Behavior under Visual and Auditory Constraints: Evidence from an Electroencephalograph during Handgun Shooting.},
journal = {Brain sciences},
volume = {13},
number = {12},
pages = {},
pmid = {38137150},
issn = {2076-3425},
support = {62006246//the National Natural Science Foundation(NNSF) of China/ ; 81901830//the National Natural Science Foundation(NNSF) of China/ ; 81771926//the National Natural Science Foundation(NNSF) of China/ ; 61763022//the National Natural Science Foundation(NNSF) of China/ ; 81470084//the National Natural Science Foundation(NNSF) of China/ ; 61463024//the National Natural Science Foundation(NNSF) of China/ ; 31771244//the National Natural Science Foundation(NNSF) of China/ ; },
abstract = {Light and noise are important factors affecting shooting performance, and shooters can exhibit physiological processes that differ from normal shooting when they are subjected to disturbed visual and auditory conditions. The purpose of this study was to explore the neural mechanism of shooting preparation in skilled shooters with visual and auditory limitations. We designed an experiment and recorded the electroencephalograph (EEG) and shooting performance indexes of 40 individuals skilled in marksmanship during the shooting preparation stage under three conditions: low light, noise interference, and a normal environment. EEG relative band power features and event-related desynchronization/synchronization (ERD/ERS) features were extracted and analyzed. The results showed that (1) the average score of the shooters was 8.55 under normal conditions, 7.71 under visually restricted conditions, and 8.50 under auditorily restricted conditions; (2) the relative EEG band power in the frontal lobe (Fp1, Fp2), frontal lobe (F4, F8), left temporal region (T7), central lobe (CP2), and parietal lobe (P3, PO3) in the theta band was significantly lower than in the other two environments (p < 0.05), and there was no significant difference between the power intensity of the shooter in the noisy environment and that in the normal environment; and (3) in the low-light environment, a significant negative correlation was found between the central region, the left and right temporal regions, and the parietal lobe (p < 0.05). These findings provide a basis for further understanding neural mechanisms in the brain during the shooting preparation phase under visually and auditorily restricted conditions.},
}
@article {pmid38135985,
year = {2023},
author = {Chen, J and Xia, Y and Zhou, X and Vidal Rosas, E and Thomas, A and Loureiro, R and Cooper, RJ and Carlson, T and Zhao, H},
title = {fNIRS-EEG BCIs for Motor Rehabilitation: A Review.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {12},
pages = {},
pmid = {38135985},
issn = {2306-5354},
support = {/WT_/Wellcome Trust/United Kingdom ; 101099093/ERC_/European Research Council/International ; },
abstract = {Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.},
}
@article {pmid38135189,
year = {2024},
author = {Song, Z and Zhu, Z and Zhang, H and Wang, S and Zou, L},
title = {Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children.},
journal = {Behavioural brain research},
volume = {460},
number = {},
pages = {114828},
doi = {10.1016/j.bbr.2023.114828},
pmid = {38135189},
issn = {1872-7549},
mesh = {Child ; Humans ; *Brain Mapping/methods ; *Attention Deficit Disorder with Hyperactivity/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Cluster Analysis ; },
abstract = {Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.},
}
@article {pmid38132079,
year = {2023},
author = {De Miguel-Rubio, A and Gallego-Aguayo, I and De Miguel-Rubio, MD and Arias-Avila, M and Lucena-Anton, D and Alba-Rueda, A},
title = {Effectiveness of the Combined Use of a Brain-Machine Interface System and Virtual Reality as a Therapeutic Approach in Patients with Spinal Cord Injury: A Systematic Review.},
journal = {Healthcare (Basel, Switzerland)},
volume = {11},
number = {24},
pages = {},
pmid = {38132079},
issn = {2227-9032},
abstract = {Spinal cord injury has a major impact on both the individual and society. This damage can cause permanent loss of sensorimotor functions, leading to structural and functional changes in somatotopic regions of the spinal cord. The combined use of a brain-machine interface and virtual reality offers a therapeutic alternative to be considered in the treatment of this pathology. This systematic review aimed to evaluate the effectiveness of the combined use of virtual reality and the brain-machine interface in the treatment of spinal cord injuries. A search was performed in PubMed, Web of Science, PEDro, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and Medline, including articles published from the beginning of each database until January 2023. Articles were selected based on strict inclusion and exclusion criteria. The Cochrane Collaboration's tool was used to assess the risk of bias and the PEDro scale and SCIRE systems were used to evaluate the methodological quality of the studies. Eleven articles were selected from a total of eighty-two. Statistically significant changes were found in the upper limb, involving improvements in shoulder and upper arm mobility, and weaker muscles were strengthened. In conclusion, most of the articles analyzed used the electroencephalogram as a measurement instrument for the assessment of various parameters, and most studies have shown improvements. Nonetheless, further research is needed with a larger sample size and long-term follow-up to establish conclusive results regarding the effect size of these interventions.},
}
@article {pmid38131865,
year = {2023},
author = {Mitsea, E and Drigas, A and Skianis, C},
title = {Digitally Assisted Mindfulness in Training Self-Regulation Skills for Sustainable Mental Health: A Systematic Review.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {13},
number = {12},
pages = {},
pmid = {38131865},
issn = {2076-328X},
abstract = {The onset of the COVID-19 pandemic has led to an increased demand for mental health interventions, with a special focus on digitally assisted ones. Self-regulation describes a set of meta-skills that enable one to take control over his/her mental health and it is recognized as a vital indicator of well-being. Mindfulness training is a promising training strategy for promoting self-regulation, behavioral change, and mental well-being. A growing body of research outlines that smart technologies are ready to revolutionize the way mental health training programs take place. Artificial intelligence (AI); extended reality (XR) including virtual reality (VR), augmented reality (AR), and mixed reality (MR); as well as the advancements in brain computer interfaces (BCIs) are ready to transform these mental health training programs. Mindfulness-based interventions assisted by smart technologies for mental, emotional, and behavioral regulation seem to be a crucial yet under-investigated issue. The current systematic review paper aims to explore whether and how smart technologies can assist mindfulness training for the development of self-regulation skills among people at risk of mental health issues as well as populations with various clinical characteristics. The PRISMA 2020 methodology was utilized to respond to the objectives and research questions using a total of sixty-six experimental studies that met the inclusion criteria. The results showed that digitally assisted mindfulness interventions supported by smart technologies, including AI-based applications, chatbots, virtual coaches, immersive technologies, and brain-sensing headbands, can effectively assist trainees in developing a wide range of cognitive, emotional, and behavioral self-regulation skills, leading to a greater satisfaction of their psychological needs, and thus mental wellness. These results may provide positive feedback for developing smarter and more inclusive training environments, with a special focus on people with special training needs or disabilities.},
}
@article {pmid38130433,
year = {2023},
author = {Shi, B and Yue, Z and Yin, S and Zhao, J and Wang, J},
title = {Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1292428},
pmid = {38130433},
issn = {1662-5161},
abstract = {BACKGROUND: Brain-computer interface (BCI) systems based on motor imagery (MI) have been widely used in neurorehabilitation. Feature extraction applied by the common spatial pattern (CSP) is very popular in MI classification. The effectiveness of CSP is highly affected by the frequency band and time window of electroencephalogram (EEG) segments and channels selected.<br><br>OBJECTIVE: In this study, the multi-domain feature joint optimization (MDFJO) based on the multi-view learning method is proposed, which aims to select the discriminative features enhancing the classification performance.<br><br>METHOD: The channel patterns are divided using the Fisher discriminant criterion (FDC). Furthermore, the raw EEG is intercepted for multiple sub-bands and time interval signals. The high-dimensional features are constructed by extracting features from CSP on each EEG segment. Specifically, the multi-view learning method is used to select the optimal features, and the proposed feature sparsification strategy on the time level is proposed to further refine the optimal features.<br><br>RESULTS: Two public EEG datasets are employed to validate the proposed MDFJO method. The average classification accuracy of the MDFJO in Data 1 and Data 2 is 88.29 and 87.21%, respectively. The classification result of MDFJO was significantly better than MSO (p&#x2009;<&#x2009;0.05), FBCSP32 (p&#x2009;<&#x2009;0.01), and other competing methods (p&#x2009;<&#x2009;0.001).<br><br>CONCLUSION: Compared with the CSP, sparse filter band common spatial pattern (SFBCSP), and filter bank common spatial pattern (FBCSP) methods with channel numbers 16, 32 and all channels as well as MSO, the MDFJO significantly improves the test accuracy. The feature sparsification strategy proposed in this article can effectively enhance classification accuracy. The proposed method could improve the practicability and effectiveness of the BCI system.},
}
@article {pmid38128344,
year = {2024},
author = {Ji, Y and Pearlson, G and Bustillo, J and Kochunov, P and Turner, JA and Jiang, R and Shao, W and Zhang, X and Fu, Z and Li, K and Liu, Z and Xu, X and Zhang, D and Qi, S and Calhoun, VD},
title = {Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering.},
journal = {Schizophrenia research},
volume = {264},
number = {},
pages = {130-139},
doi = {10.1016/j.schres.2023.12.013},
pmid = {38128344},
issn = {1573-2509},
support = {R01 NS114628/NS/NINDS NIH HHS/United States ; R01 EB015611/EB/NIBIB NIH HHS/United States ; RF1 NS114628/NS/NINDS NIH HHS/United States ; RF1 MH123163/MH/NIMH NIH HHS/United States ; S10 OD023696/OD/NIH HHS/United States ; },
mesh = {Humans ; Family/psychology ; *Psychotic Disorders/diagnostic imaging/genetics ; *Schizophrenia/diagnostic imaging/genetics ; *Bipolar Disorder/psychology ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND: Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences.<br><br>METHODS: 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N&#xa0;=&#xa0;453) and healthy controls (HCs, N&#xa0;=&#xa0;220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives.<br><br>RESULTS: Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms.<br><br>CONCLUSIONS: These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.},
}
@article {pmid38128128,
year = {2023},
author = {Ivanov, N and Lio, A and Chau, T},
title = {Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad17f2},
pmid = {38128128},
issn = {1741-2552},
mesh = {Adolescent ; Humans ; *Brain-Computer Interfaces ; Markov Chains ; Electroencephalography/methods ; Imagery, Psychotherapy ; Movement ; Brain ; },
abstract = {Objective.While electroencephalography (EEG)-based brain-computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user's ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill.Approach.Using unsupervisedK-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user's movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metricstaskDistinctandrelativeTaskInconsistencyto assess, respectively, a user's ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks.Main results.Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between thetaskDistinctandrelativeTaskInconsistencymetrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy.Significance.Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.},
}
@article {pmid38125371,
year = {2024},
author = {Kucukler, OF and Amira, A and Malekmohamadi, H},
title = {EEG dataset for energy data visualizations.},
journal = {Data in brief},
volume = {52},
number = {},
pages = {109933},
pmid = {38125371},
issn = {2352-3409},
abstract = {User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations. The dataset comprises EEG recordings obtained from 28 individuals who were in good health. The EEG recordings were collected using a 32-channel EMOTIV EEG device, and the international 10-20 electrode system was employed for precise electrode placement. The energy data visualizations were generated and showcased utilizing the PsychoPy software. To ascertain the participants' affective state, they were requested to rate the valence and arousal of each stimulus through the utilization of a self-assessment manikin (SAM). Additionally, three inquiries were posed for every stimulation. The dataset includes both original data visualizations and ratings. Additionally, the raw EEG data has been divided into segments consisting of data visualizations and neutral images, with the use of event markers, in order to assist analysis. The EEG recordings were recorded and stored utilizing the EMOTIVPro application, whereas the subjective reactions were captured and preserved using the PsychoPy application. Furthermore, the generation of synthetic EEG data is accomplished by employing the Generative Adversarial Network (GAN) architecture on the acquired EEG dataset. The synthetic EEG data created is integrated with empirical EEG data, and afterwards subjected to qualitative and quantitative analysis in order to improve performance. The dataset presented herein showcases a pioneering utilization of EEG investigation and offers a valuable foundation for scholars in the domains of computer science, energy conservation, artificial intelligence, brain-computer interfaces, and human-computer interaction.},
}
@article {pmid38124916,
year = {2023},
author = {Methods In Medicine, CAM},
title = {Retracted: 3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User.},
journal = {Computational and mathematical methods in medicine},
volume = {2023},
number = {},
pages = {9810435},
pmid = {38124916},
issn = {1748-6718},
abstract = {[This retracts the article DOI: 10.1155/2022/8452002.].},
}
@article {pmid38124609,
year = {2023},
author = {An, Y and Hu, S and Liu, S and Li, B},
title = {BiTCAN: A emotion recognition network based on saliency in brain cognition.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {12},
pages = {21537-21562},
doi = {10.3934/mbe.2023953},
pmid = {38124609},
issn = {1551-0018},
mesh = {*Artificial Intelligence ; *Brain ; Cognition ; Emotions ; Algorithms ; Electroencephalography ; },
abstract = {In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we construct a new spatio-temporal convolutional attention network for emotion recognition named BiTCAN. First, in the proposed method, the original EEG signals are de-baselined, and the two-dimensional mapping matrix sequence of EEG signals is constructed by combining the electrode position. Second, on the basis of the two-dimensional mapping matrix sequence, the features of saliency in brain cognition are extracted by using the Bi-hemisphere discrepancy module, and the spatio-temporal features of EEG signals are captured by using the 3-D convolution module. Finally, the saliency features and spatio-temporal features are fused into the attention module to further obtain the internal spatial relationships between brain regions, and which are input into the classifier for emotion recognition. Many experiments on DEAP and SEED (two public datasets) show that the accuracies of the proposed algorithm on both are higher than 97%, which is superior to most existing emotion recognition algorithms.},
}
@article {pmid38124568,
year = {2023},
author = {Xu, J and Li, D and Zhou, P and Li, C and Wang, Z and Tong, S},
title = {A multi-band centroid contrastive reconstruction fusion network for motor imagery electroencephalogram signal decoding.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {12},
pages = {20624-20647},
doi = {10.3934/mbe.2023912},
pmid = {38124568},
issn = {1551-0018},
mesh = {Humans ; *Electroencephalography ; *Brain ; Communication ; Learning ; Movement ; },
abstract = {Motor imagery (MI) brain-computer interface (BCI) assist users in establishing direct communication between their brain and external devices by decoding the movement intention of human electroencephalogram (EEG) signals. However, cerebral cortical potentials are highly rhythmic and sub-band features, different experimental situations and subjects have different categories of semantic information in specific sample target spaces. Feature fusion can lead to more discriminative features, but simple fusion of features from different embedding spaces leading to the model global loss is not easily convergent and ignores the complementarity of features. Considering the similarity and category contribution of different sub-band features, we propose a multi-band centroid contrastive reconstruction fusion network (MB-CCRF). We obtain multi-band spatio-temporal features by frequency division, preserving the task-related rhythmic features of different EEG signals; use a multi-stream cross-layer connected convolutional network to perform a deep feature representation for each sub-band separately; propose a centroid contrastive reconstruction fusion module, which maps different sub-band and category features into the same shared embedding space by comparing with category prototypes, reconstructing the feature semantic structure to ensure that the global loss of the fused features converges more easily. Finally, we use a learning mechanism to model the similarity between channel features and use it as the weight of fused sub-band features, thus enhancing the more discriminative features, suppressing the useless features. The experimental accuracy is 79.96% in the BCI competition Ⅳ-Ⅱa dataset. Moreover, the classification effect of sub-band features of different subjects is verified by comparison tests, the category propensity of different sub-band features is verified by confusion matrix tests and the distribution in different classes of each sub-band feature and fused feature are showed by visual analysis, revealing the importance of different sub-band features for the EEG-based MI classification task.},
}
@article {pmid38118169,
year = {2024},
author = {Steele, AG and Faraji, AH and Contreras-Vidal, JL},
title = {Electrospinography for non-invasively recording spinal sensorimotor networks in humans.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad1782},
pmid = {38118169},
issn = {1741-2552},
mesh = {Adult ; Humans ; *Electroencephalography/methods ; *Spinal Cord ; Movement/physiology ; Visual Perception ; Memory, Short-Term ; },
abstract = {Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels.Approach. We synchronously recorded electroencephalography (EEG), electromyography, and ESG in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. A multi-pronged approach was applied for removing artifacts usingH∞filtering, artifact subspace reconstruction and independent component (IC) analysis. Next, data were segmented by task and ICs of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized partial directed coherence analysis was performed within ICs and between ICs and ESG data by participant and task.Results.K-means clustering resulted in five clusters of ICs at Brodmann areas (BAs) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi-directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC's and between IC's and ESG sensors during movement when compared to the no movement condition.Significance. To our knowledge this is the first report using high density ESG for characterizing single joint lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.},
}
@article {pmid38117136,
year = {2024},
author = {Yoo, S and Kim, M and Choi, C and Kim, DH and Cha, GD},
title = {Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy.},
journal = {Advanced healthcare materials},
volume = {13},
number = {24},
pages = {e2303563},
doi = {10.1002/adhm.202303563},
pmid = {38117136},
issn = {2192-2659},
support = {IBS-R006-A1//Institute for Basic Science/ ; },
mesh = {Humans ; *Brain Neoplasms/therapy/pathology ; *Epilepsy/therapy ; },
abstract = {Soft bioelectronic technologies for neuroengineering have shown remarkable progress, which include novel soft material technologies and device design strategies. Such technological advances that are initiated from fundamental brain science are applied to clinical neuroscience and provided meaningful promises for significant improvement in the diagnosis efficiency and therapeutic efficacy of various brain diseases recently. System-level integration strategies in consideration of specific disease circumstances can enhance treatment effects further. Here, recent advances in soft implantable bioelectronics for neuroengineering, focusing on materials and device designs optimized for the treatment of intracranial disease environments, are reviewed. Various types of soft bioelectronics for neuroengineering are categorized and exemplified first, and then details for the sensing and stimulating device components are explained. Next, application examples of soft implantable bioelectronics to clinical neuroscience, particularly focusing on the treatment of brain tumor and epilepsy are reviewed. Finally, an ideal system of soft intracranial bioelectronics such as closed-loop-type fully-integrated systems is presented, and the remaining challenges for their clinical translation are discussed.},
}
@article {pmid38116236,
year = {2023},
author = {Asgher, U and Ayaz, Y and Taiar, R},
title = {Editorial: Advances in artificial intelligence (AI) in brain computer interface (BCI) and Industry 4.0 for human machine interaction (HMI).},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1320536},
doi = {10.3389/fnhum.2023.1320536},
pmid = {38116236},
issn = {1662-5161},
}
@article {pmid38116109,
year = {2023},
author = {Ding, Y and Guo, K and Li, J and Shan, Q and Guo, Y and Chen, M and Wu, Y and Wang, X},
title = {Alterations in brain network functional connectivity and topological properties in DRE patients.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1238421},
pmid = {38116109},
issn = {1664-2295},
abstract = {OBJECTIVE: The study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people.<br><br>METHODS: We retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons.<br><br>RESULTS: In the full frequency band (0.5-45&#x2009;Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p&#x2009;<&#x2009;0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p&#x2009;<&#x2009;0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p&#x2009;<&#x2009;0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p&#x2009;<&#x2009;0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p&#x2009;<&#x2009;0.05).<br><br>CONCLUSION: The topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.},
}
@article {pmid38114657,
year = {2023},
author = {Xiao, Y and Nazarian, S and Bogdan, P},
title = {GAHLS: an optimized graph analytics based high level synthesis framework.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {22655},
pmid = {38114657},
issn = {2045-2322},
abstract = {The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.},
}
@article {pmid38113536,
year = {2024},
author = {Tucci, DL},
title = {NIDCD's 5-year strategic plan seeks innovations in assistive device technologies.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
pmid = {38113536},
issn = {1741-2552},
support = {Z99 DC999999/ImNIH/Intramural NIH HHS/United States ; },
mesh = {United States ; *National Institute on Deafness and Other Communication Disorders (U.S.) ; Surveys and Questionnaires ; *Self-Help Devices ; },
}
@article {pmid38113535,
year = {2024},
author = {Carrara, I and Papadopoulo, T},
title = {Pseudo-online framework for BCI evaluation: a MOABB perspective using various MI and SSVEP datasets.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad171a},
pmid = {38113535},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Algorithms ; Imagery, Psychotherapy ; },
abstract = {Objective. BCI (Brain-Computer Interfaces) operate in three modes:online,offline, andpseudo-online. Inonlinemode, real-time EEG data is constantly analyzed. Inofflinemode, the signal is acquired and processed afterwards. Thepseudo-onlinemode processes collected data as if they were received in real-time. The main difference is that theofflinemode often analyzes the whole data, while theonlineandpseudo-onlinemodes only analyze data in short time windows.Offlineprocessing tends to be more accurate, whileonlineanalysis is better for therapeutic applications.Pseudo-onlineimplementation approximatesonlineprocessing without real-time constraints. Many BCI studies beingofflineintroduce biases compared to real-life scenarios, impacting classification algorithm performance.Approach. The objective of this research paper is therefore to extend the current MOABB framework, operating inofflinemode, so as to allow a comparison of different algorithms in apseudo-onlinesetting with the use of a technology based on overlapping sliding windows. To do this will require the introduction of a idle state event in the dataset that takes into account all different possibilities that are not task thinking. To validate the performance of the algorithms we will use the normalized Matthews correlation coefficient and the information transfer rate.Main results. We analyzed the state-of-the-art algorithms of the last 15 years over several motor imagery and steady state visually evoked potential multi-subjects datasets, showing the differences between the two approaches from a statistical point of view.Significance. The ability to analyze the performance of different algorithms inofflineandpseudo-onlinemodes will allow the BCI community to obtain more accurate and comprehensive reports regarding the performance of classification algorithms.},
}
@article {pmid38113159,
year = {2024},
author = {Huang, X and Choi, KS and Liang, S and Zhang, Y and Zhang, Y and Poon, S and Pedrycz, W},
title = {Frequency Domain Channel-Wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {5},
pages = {1587-1598},
doi = {10.1109/TBME.2023.3344295},
pmid = {38113159},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Computer Security ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Convolutional neural network (CNN), a classical structure in deep learning, has been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many methods have been proposed to evaluate the vulnerability of such CNN models, primarily by attacking them using direct temporal perturbations. In this work, we propose a novel attacking approach based on perturbations in the frequency domain instead.<br><br>METHODS: For a given natural MI trial in the frequency domain, the proposed approach, called frequency domain channel-wise attack (FDCA), generates perturbations at each channel one after another to fool the CNN classifiers. The advances of this strategy are two-fold. First, instead of focusing on the temporal domain, perturbations are generated in the frequency domain where discriminative patterns can be extracted for motor imagery (MI) classification tasks. Second, the perturbing optimization is performed based on differential evolution algorithm in a black-box scenario where detailed model knowledge is not required.<br><br>RESULTS: Experimental results demonstrate the effectiveness of the proposed FDCA which achieves a significantly higher success rate than the baselines and existing methods in attacking three major CNN classifiers on four public MI benchmarks.<br><br>CONCLUSION: Perturbations generated in the frequency domain yield highly competitive results in attacking MIBCI deployed by CNN models even in a black-box setting, where the model information is well-protected.<br><br>SIGNIFICANCE: To our best knowledge, existing MIBCI attack approaches are all gradient-based methods and require details about the victim model, e.g., the parameters and objective function. We provide a more flexible strategy that does not require model details but still produces an effective attack outcome.},
}
@article {pmid38110523,
year = {2024},
author = {Lei, A and Yu, H and Lu, S and Lu, H and Ding, X and Tan, T and Zhang, H and Zhu, M and Tian, L and Wang, X and Su, S and Xue, D and Zhang, S and Zhao, W and Chen, Y and Xie, W and Zhang, L and Zhu, Y and Zhao, J and Jiang, W and Church, G and Chan, FK and Gao, Z and Zhang, J},
title = {Author Correction: A second-generation M1-polarized CAR macrophage with antitumor efficacy.},
journal = {Nature immunology},
volume = {25},
number = {3},
pages = {576},
doi = {10.1038/s41590-023-01734-4},
pmid = {38110523},
issn = {1529-2916},
}
@article {pmid38109026,
year = {2024},
author = {Silpa, B and Hota, MK},
title = {OVME-REG: Harris hawks optimization algorithm based optimized variational mode extraction for eye blink artifact removal from EEG signal.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {3},
pages = {955-972},
pmid = {38109026},
issn = {1741-0444},
mesh = {Humans ; Animals ; *Artifacts ; Blinking ; Electroencephalography/methods ; Algorithms ; *Falconiformes ; Signal Processing, Computer-Assisted ; },
abstract = {The electroencephalogram (EEG) recordings from the human brain are useful for detecting various brain syndromes. These recordings are typically contaminated by high amplitude eye blink artifacts, which leads to deliberate misinterpretation of the EEG signal. Recently, variational mode extraction (VME) has been used to detect eye blink artifacts. But, the VME performance is impacted by the balancing parameter and center frequency selection. Therefore, this research uses two metaheuristic algorithms, particle swarm optimization and Harris hawks optimization, to determine the optimal set of the VME parameters. In the proposed method, the optimized VME (OVME) extracts the desired mode to locate the eye blink artifactual intervals. Then, the regression analysis (REG) filters the identified artifactual intervals from short EEG data segments. The significance of the proposed OVME-REG algorithm is that it is adequate for determining the optimum values of the VME algorithm. The analysis is carried out on the CHB-MIT Scalp EEG, BCI Competition, and EEG motor movement/imagery datasets. The proposed OVME-REG method provides an improved performance for suppressing single and repeated eye blink artifacts as compared to the current approaches in terms of (a) high correlation coefficient (93.08%, 87.3%, 82.17%), respectively, (b) low value of RRMSE (0.379, 0.506, 0.502), respectively, (c) high SSIM (0.892, 0.842, 0.694), and (d) low computation time and better preservation of the EEG data.},
}
@article {pmid38107525,
year = {2023},
author = {Bagheri, Z and Khosrowabadi, R and Hatami, J and Armani Kian, AR and Fatemi, MJ and Khatibi, A},
title = {Differential Cortical Oscillatory Patterns in Amputees With and Without Phantom Limb Pain.},
journal = {Basic and clinical neuroscience},
volume = {14},
number = {2},
pages = {171-184},
pmid = {38107525},
issn = {2008-126X},
abstract = {INTRODUCTION: Phantom limb pain (PLP) as neuropathic pain affects the life of amputees. It is believed an efficient PLP treatment should consider the underlying neurological mechanisms. Hereby, we investigated brain activity in PLP and its relationships to the psychological and cognitive dimensions of chronic pain. We investigate differences in resting brain activities between amputees with and without pain. We hypothesize significant differences in the motor cortex and parietal cortex activity that are related to pain perception. Also, we hypothesize two groups have significant differences in cognitive and psychological components.<br><br>METHODS: Behavioral assessment (psychological status, life satisfaction, and pain level) and EEG signals of 19 amputees (12 without pain and 7 with pain) were recorded. Data were statistically compared between the two groups. Also, the association between behavioral and neurophysiological data was computed.<br><br>RESULTS: The results showed a significant decrease in the pain group for the beta and gamma waves, as well as, for the theta and delta waves in the posterior temporal on both sides, during the eye-open condition. The eyes-closed condition showed that the delta waves were decreased on the right side of the cortex. Also, data showed a significant difference in the correlation of pain features with brain waves between the two groups.<br><br>CONCLUSION: Significant differences were mostly observed in regions related to pain perception rather than the motor cortex. This can be due to the learned strategies to deal with pain and the degree of pain. Results showed maladaptive cognitive processes had a relationship with brain wave activities. According to the result of brain wave activities, it seems that cognitive factors have a role in the experience of PLP rather than neuroplasticity through amputation.<br><br>HIGHLIGHTS: Differences found in the parietal and temporal regions of phantom limb pain's (PLP's) suggests cognition's role in the persistence of PLP.Decreased delta power at the posterior temporal cortex in PLP's could be the focus of treatments.Increased activity of the parietal cortex could be helpful in the treatment of PLP's.<br><br>PLAIN LANGUAGE SUMMARY: PLP is an annoying neurologic pain. A wide range of treatments have focused on this type of pain but couldn't be effective. Recently, researchers suggest BCI-based treatments for better treatment. For this type of treatment, we should know the neurological aspect of PLP. In most studies to investigate or treatment of neurological aspects of PLP, researchers induced pain experimentally or studied acute phantom limb pain. We believed for a better understanding of PLP, should investigate it in a natural and stabilized position. Therefore we studied brain activities in amputees with and without PLP in a resting state to find out differences. Trends in this field express the alpha band differences in the motor cortex. On the contrary, our results showed the most significant difference in high-frequency bandpasses such as beta and gamma. Also, in our study, it seems the parietal and temporal cortex that are related to pain perception is the more relevant to PLP. This study showed a psycho-cognitive aspect of pain such as pain exaggeration has a relation with PLP's brain wave activities. So, we can suggest rather than neuroplasticity through amputation, cognitive factors have a role in the experience of PLP.},
}
@article {pmid38105969,
year = {2023},
author = {Li, F and Gallego, J and Tirko, NN and Greaser, J and Bashe, D and Patel, R and Shaker, E and Van Valkenburg, GE and Alsubhi, AS and Wellman, S and Singh, V and Padill, CG and Gheres, KW and Bagwell, R and Mulvihill, M and Kozai, TDY},
title = {Low-intensity pulsed ultrasound stimulation (LIPUS) modulates microglial activation following intracortical microelectrode implantation.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.12.05.570162},
pmid = {38105969},
issn = {2692-8205},
abstract = {Microglia are important players in surveillance and repair of the brain. Their activation mediates neuroinflammation caused by intracortical microelectrode implantation, which impedes the application of intracortical brain-computer interfaces (BCIs). While low-intensity pulsed ultrasound stimulation (LIPUS) can attenuate microglial activation, its potential to modulate the microglia-mediated neuroinflammation and enhance the bio-integration of microelectrodes remains insufficiently explored. We found that LIPUS increased microglia migration speed from 0.59±0.04 to 1.35±0.07 µm/hr on day 1 and enhanced microglia expansion area from 44.50±6.86 to 93.15±8.77 µm [2] /min on day 7, indicating improved tissue healing and surveillance. Furthermore, LIPUS reduced microglial activation by 17% on day 6, vessel-associated microglia ratio from 70.67±6.15 to 40.43±3.87% on day 7, and vessel diameter by 20% on day 28. Additionally, microglial coverage of the microelectrode was reduced by 50% in week 1, indicating better tissue-microelectrode integration. These data reveal that LIPUS helps resolve neuroinflammation around chronic intracortical microelectrodes.},
}
@article {pmid38101496,
year = {2024},
author = {Chen, R and Xu, G and Zhang, H and Zhang, X and Xie, J and Tian, P and Zhang, S and Han, C},
title = {Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.},
journal = {NeuroImage},
volume = {285},
number = {},
pages = {120501},
doi = {10.1016/j.neuroimage.2023.120501},
pmid = {38101496},
issn = {1095-9572},
mesh = {Humans ; *Electroencephalography/methods ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Recognition, Psychology ; Machine Learning ; Algorithms ; Photic Stimulation ; },
abstract = {OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection.<br><br>METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition.<br><br>RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs.<br><br>CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect.<br><br>SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.},
}
@article {pmid38101408,
year = {2023},
author = {Liao, YY and Zhang, H and Shen, Q and Cai, C and Ding, Y and Shen, DD and Guo, J and Qin, J and Dong, Y and Zhang, Y and Li, XM},
title = {Snapshot of the cannabinoid receptor 1-arrestin complex unravels the biased signaling mechanism.},
journal = {Cell},
volume = {186},
number = {26},
pages = {5784-5797.e17},
doi = {10.1016/j.cell.2023.11.017},
pmid = {38101408},
issn = {1097-4172},
mesh = {*Arrestin/metabolism ; beta-Arrestin 1/metabolism ; beta-Arrestins/metabolism ; Cryoelectron Microscopy ; *Receptor, Cannabinoid, CB1/metabolism ; *Signal Transduction ; Humans ; Animals ; Cell Line ; },
abstract = {Cannabis activates the cannabinoid receptor 1 (CB1), which elicits analgesic and emotion regulation benefits, along with adverse effects, via Gi and β-arrestin signaling pathways. However, the lack of understanding of the mechanism of β-arrestin-1 (βarr1) coupling and signaling bias has hindered drug development targeting CB1. Here, we present the high-resolution cryo-electron microscopy structure of CB1-βarr1 complex bound to the synthetic cannabinoid MDMB-Fubinaca (FUB), revealing notable differences in the transducer pocket and ligand-binding site compared with the Gi protein complex. βarr1 occupies a wider transducer pocket promoting substantial outward movement of the TM6 and distinctive twin toggle switch rearrangements, whereas FUB adopts a different pose, inserting more deeply than the Gi-coupled state, suggesting the allosteric correlation between the orthosteric binding pocket and the partner protein site. Taken together, our findings unravel the molecular mechanism of signaling bias toward CB1, facilitating the development of CB1 agonists.},
}
@article {pmid38101185,
year = {2024},
author = {Yang, S and Baeg, E and Kim, K and Kim, D and Xu, D and Ahn, JH and Yang, S},
title = {Neurodiagnostic and neurotherapeutic potential of graphene nanomaterials.},
journal = {Biosensors &amp; bioelectronics},
volume = {247},
number = {},
pages = {115906},
doi = {10.1016/j.bios.2023.115906},
pmid = {38101185},
issn = {1873-4235},
mesh = {Humans ; *Graphite/chemistry ; *Biosensing Techniques ; *Nanostructures/chemistry ; Biotechnology ; *Brain Diseases ; },
abstract = {Graphene has emerged as a highly promising nanomaterial for a variety of advanced technologies, including batteries, energy, electronics, and biotechnologies. Its recent contribution to neurotechnology is particularly noteworthy because its superior conductivity, chemical resilience, biocompatibility, thermal stability, and scalable nature make it well-suited for measuring brain activity and plasticity in health and disease. Graphene-mediated compounds are microfabricated in two central methods: chemical processes with natural graphite and chemical vapor deposition of graphene in a film form. They are widely used as biosensors and bioelectronics for neurodiagnostic and neurotherapeutic purposes in several brain disorders, such as Parkinson's disease, stroke, glioma, epilepsy, tinnitus, and Alzheimer's disease. This review provides an overview of studies that have demonstrated the technical advances of graphene nanomaterials in neuroscientific and clinical applications. We also discuss current limitations and future demands in relation to the clinical application of graphene, highlighting its potential technological and clinical significance for treating brain disorders. Our review underscores the potential of graphene nanomaterials as powerful tools for advancing the understanding of the brain and developing new therapeutic strategies.},
}
@article {pmid38099883,
year = {2024},
author = {Boltcreed, E and Ersöz, A and Han, M and McConnell, GC},
title = {Short-Term Effects of Gamma Stimulation on Neuroinflammation at the Tissue-Electrode Interface in Motor Cortex.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {27},
number = {3},
pages = {500-508},
pmid = {38099883},
issn = {1525-1403},
support = {R01 DC014044/DC/NIDCD NIH HHS/United States ; },
mesh = {Rats ; Male ; Animals ; *Neuroinflammatory Diseases ; Gliosis ; *Motor Cortex ; Reproducibility of Results ; Rats, Long-Evans ; Electrodes, Implanted ; Microelectrodes ; },
abstract = {OBJECTIVES: The reliability of long-term neural recordings as therapeutic interventions for motor and sensory disorders is hampered by the brain tissue response. Previous work showed that flickering light at gamma frequencies (ie, 20-50 Hz) causes enhanced microglial recruitment in the visual cortex. The effects of gamma stimulation on glial cells surrounding implanted neural electrodes are not well understood. We hypothesized that invasive stimulation in the gamma frequency band increases microglial recruitment in the short term and reduces astrogliosis at the tissue-electrode interface.<br><br>MATERIALS AND METHODS: Male Long Evans rats were implanted with dual-shank silicon microelectrode arrays into the motor cortex. After implantation, rats received one hour of 40-Hz stimulation at a constant current of 10 &#x3bc;A using charge-balanced, biphasic pulses on one shank, and the other shank served as the nonstimulated control. Postmortem, tissue sections were stained with ectodermal&#xa0;dysplasia 1 (ED1) for activated microglia, glial fibrillary acidic protein (GFAP) for astrocytes, and 4',6-diamidino-2-phenylindole (DAPI) for nonspecific nuclei. Fluorescent intensity and cell number as a function of distance from the tissue-electrode interface were used to quantify all stained sections.<br><br>RESULTS: Fluorescent intensity for ED1 was nearly 40% lower for control than for stimulated sites (0-500 &#x3bc;m away from the implant), indicating increased microglial recruitment to the stimulated site (p&#xa0;< 0.05). Fluorescent intensity for GFAP was >67% higher for control than for stimulated sites (0-500 &#x3bc;m away from the implant), indicating reduced astrogliosis at the stimulated site (p&#xa0;< 0.05). No differences were observed in DAPI-stained sections between conditions.<br><br>CONCLUSIONS: These results suggest that short-term gamma stimulation modulates glial recruitment in the immediate vicinity of the microelectrode. Future studies will investigate the long-term effects of gamma stimulation on glial recruitment at the tissue-electrode interface as a strategy to improve long-term recording reliability.},
}
@article {pmid38097414,
year = {2024},
author = {Neumann, WJ},
title = {Cortical brain signals improve decoding of movement and tremor for clinical brain computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {157},
number = {},
pages = {143-145},
doi = {10.1016/j.clinph.2023.11.017},
pmid = {38097414},
issn = {1872-8952},
mesh = {Humans ; *Brain-Computer Interfaces ; Tremor/diagnosis ; Brain/physiology ; Movement/physiology ; Electroencephalography ; },
}
@article {pmid38095422,
year = {2023},
author = {Veeravagu, A and Kim, LH and Rao, VL and Lim, M and Shuer, LM and Harris, OA and Steinberg, GK},
title = {Stanford University School of Medicine: Our Neurosurgical Heritage.},
journal = {Neurosurgery},
volume = {},
number = {},
pages = {},
doi = {10.1227/neu.0000000000002799},
pmid = {38095422},
issn = {1524-4040},
abstract = {The legacy of Stanford University's Department of Neurosurgery began in 1858, with the establishment of a new medical school on the West Coast. Stanford Neurosurgery instilled an atmosphere of dedication to neurosurgical care, scientific research, education, and innovation. We highlight key historical events leading to the formation of the medical school and neurosurgical department, the individuals who shaped the department's vision and expansion, as well as pioneering advances in research and clinical care. The residency program was started in 1961, establishing the basis of the current education model with a strong emphasis on training future leaders, and the Moyamoya Center, founded in 1991, became the largest Moyamoya referral center in the United States. The opening of Stanford Stroke Center (1992) and seminal clinical trials resulted in a significant impact on cerebrovascular disease by expanding the treatment window of IV thrombolysis and intra-arterial thrombectomy. The invention and implementation of CyberKnife® (1994) marks another important event that revolutionized the field of radiosurgery, and the development of Stanford's innovative Brain Computer Interface program is pushing the boundaries of this specialty. The more recent launch of the Neurosurgery Virtual Reality and Simulation Center (2017) exemplifies how Stanford is continuing to evolve in this ever-changing field. The department also became a model for diversity within the school as well as nationwide. The growth of Stanford Neurosurgery from one of the youngest neurosurgery departments in the country to a prominent comprehensive neurosurgery center mirrors the history of neurosurgery itself: young, innovative, and willing to overcome challenges.},
}
@article {pmid38094245,
year = {2023},
author = {Qiu, Y and Liu, X and Zhu, Y and Jiang, D and Li, F and Yu, W and Wan, H and Zhuang, L and Pan, Y and Wang, P},
title = {Vertical impedance electrode array for spatiotemporal dynamics monitoring of 3D cells under drug diffusion effect.},
journal = {iScience},
volume = {26},
number = {12},
pages = {107962},
pmid = {38094245},
issn = {2589-0042},
abstract = {Although three-dimensional (3D) tumor models feature more accurate responses to drugs, the Matrigel scaffold affects the drug diffusion effect. Obtaining accurate drug spatiotemporal response characteristics is of great significance in the drug screening domain. However, the conventional cell-based sensors are difficult to perform spatiotemporal dynamics impedance monitoring of 3D cells and evaluate the anti-cancer pharmacological effect. Here, we proposed a biosensing platform involving a vertical impedance electrode array (VIEA) chip and a multichannel detection system. The platform can dynamically record 3D cell impedance in the vertical direction, which is consistent with time- and location-dependent drug penetration, closely related to spatiotemporal cell viability under drug effects. The subtle changes of impedance signals in different locations induced by drug diffusion can be detected, which demonstrates its high performance in drug systematic evaluation. The universal and high-content 3D cell biosensing platform is believed to have promising potential in pharmacodynamics investigation and preclinical drug screening.},
}
@article {pmid38094204,
year = {2023},
author = {George, I and Kachel, M and Nazif, T},
title = {Cerebral Filter Implantation in High-Risk Cardiac Surgery: Initial Feasibility Report and Technical Details.},
journal = {JACC. Case reports},
volume = {25},
number = {},
pages = {102031},
pmid = {38094204},
issn = {2666-0849},
abstract = {Mitral valve replacement in the setting of extensive mitral annular calcification is technically challenging and associated with high mortality and morbidity, including stroke. This is the first published report of direct surgical transcatheter valve implantation with use of a cerebral embolic protection device. (Level of Difficulty: Intermediate.).},
}
@article {pmid38094146,
year = {2023},
author = {Savić, AM and Aricò, P},
title = {Editorial: Global excellence in brain-computer interfaces: Europe.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1333272},
pmid = {38094146},
issn = {1662-5161},
}
@article {pmid38094144,
year = {2023},
author = {Kang, YH and Khorasani, A and Flint, RD and Farrokhi, B and Lee, SW},
title = {Editorial: Neural computations for brain machine interface applications.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1334636},
pmid = {38094144},
issn = {1662-5161},
}
@article {pmid38091706,
year = {2024},
author = {Kong, LZ and Lai, JB and Hu, SH},
title = {Corrigendum to "China released the latest national mental health report: A blueprint for the future" Asian J. Psychiatry 85 (2023) 103624.},
journal = {Asian journal of psychiatry},
volume = {91},
number = {},
pages = {103858},
doi = {10.1016/j.ajp.2023.103858},
pmid = {38091706},
issn = {1876-2026},
}
@article {pmid38091617,
year = {2024},
author = {Nagarajan, A and Robinson, N and Ang, KK and Chua, KSG and Chew, E and Guan, C},
title = {Transferring a deep learning model from healthy subjects to stroke patients in a motor imagery brain-computer interface.},
journal = {Journal of neural engineering},
volume = {21},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ad152f},
pmid = {38091617},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Healthy Volunteers ; *Stroke/diagnosis ; Imagery, Psychotherapy ; Electroencephalography/methods ; Algorithms ; Imagination ; },
abstract = {Objective.Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect MI in stroke patients.Approach.We introduce a new transfer learning approach where features from two-class MI data of healthy individuals are used to detect MI in stroke patients. We compare the results of the proposed method with those obtained from analyses within stroke data. Experiments were conducted using Deep ConvNet and state-of-the-art subject-specific machine learning MI classifiers, evaluated on OpenBMI two-class MI-EEG data from healthy subjects and two-class MI versus rest data from stroke patients.Main results.Results of our study indicate that through domain adaptation of a model pre-trained using healthy subjects' data, an average MI detection accuracy of 71.15% (±12.46%) can be achieved across 71 stroke patients. We demonstrate that the accuracy of the pre-trained model increased by 18.15% after transfer learning (p<0.001). Additionally, the proposed transfer learning method outperforms the subject-specific results achieved by Deep ConvNet and FBCSP, with significant enhancements of 7.64% (p<0.001) and 5.55% (p<0.001) in performance, respectively. Notably, the healthy-to-stroke transfer learning approach achieved similar performance to stroke-to-stroke transfer learning, with no significant difference (p>0.05). Explainable AI analyses using transfer models determined channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients.Significance.Transfer learning from healthy to stroke can enhance the clinical use of BCI algorithms by overcoming the challenge of insufficient clinical data for optimal training.},
}
@article {pmid38090842,
year = {2023},
author = {Deng, L and Xu, B and Gao, Z and Miao, M and Hu, C and Song, A},
title = {Decoding Natural Grasping Behaviors: Insights Into MRCP Source Features and Coupling Dynamics.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4965-4976},
doi = {10.1109/TNSRE.2023.3342426},
pmid = {38090842},
issn = {1558-0210},
mesh = {Humans ; *Cholangiopancreatography, Magnetic Resonance ; *Movement/physiology ; Motion ; Hand/physiology ; Hand Strength/physiology ; },
abstract = {The effective decoding of natural grasping behaviors is crucial for the natural control of neural prosthetics. This study aims to investigate the decoding performance of movement-related cortical potential (MRCP) source features between complex grasping actions and explore the temporal and frequency differences in inter-muscular and cortical-muscular coupling strength during movement. Based on the human grasping taxonomy and their frequency, five natural grasping motions-medium wrap, adducted thumb, adduction grip, tip pinch, and writing tripod-were chosen. We collected 64-channel electroencephalogram (EEG) and 5-channel surface electromyogram (sEMG) data from 17 healthy participants, and projected six EEG frequency bands into source space for further analysis. Results from multi-classification and binary classification demonstrated that MRCP source features could not only distinguish between power grasp and precision grasp, but also detect subtle action differences such as thumb adduction and abduction during the execution phase. Besides, we found that during natural reach-and-grasp movement, the coupling strength from cortical to muscle is lower than that from muscle to cortical, except in the hold phase of γ frequency band. Furthermore, a 12-Hz peak of inter-muscular coupling strength was found in movement execution, which might be related to movement planning and execution. We believe that this research will enhance our comprehension of the control and feedback mechanisms of human hand grasping and contributes to a natural and intuitive control for brain-computer interface.},
}
@article {pmid38090841,
year = {2024},
author = {Tao, W and Wang, Z and Wong, CM and Jia, Z and Li, C and Chen, X and Chen, CLP and Wan, F},
title = {ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {154-165},
doi = {10.1109/TNSRE.2023.3342331},
pmid = {38090841},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; Imagination ; Electroencephalography/methods ; Neural Networks, Computer ; },
abstract = {Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG μ and β rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.},
}
@article {pmid38089972,
year = {2023},
author = {Luo, TJ},
title = {Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1274320},
pmid = {38089972},
issn = {1662-4548},
abstract = {INTRODUCTION: Motor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain.<br><br>METHODS: To achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations.<br><br>RESULTS AND DISCUSSION: Empirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.},
}
@article {pmid38084608,
year = {2024},
author = {Zhang, Z and Wei, W and Wang, S and Li, M and Li, X and Li, X and Wang, Q and Yu, H and Zhang, Y and Guo, W and Ma, X and Zhao, L and Deng, W and Sham, PC and Sun, Y and Li, T},
title = {Dynamic structure-function coupling across three major psychiatric disorders.},
journal = {Psychological medicine},
volume = {54},
number = {8},
pages = {1629-1640},
doi = {10.1017/S0033291723003525},
pmid = {38084608},
issn = {1469-8978},
mesh = {Humans ; *Depressive Disorder, Major/physiopathology ; *Bipolar Disorder/physiopathology ; *Schizophrenia/physiopathology ; Adult ; Female ; Male ; Brain/physiopathology/diagnostic imaging ; Middle Aged ; Magnetic Resonance Imaging ; Nerve Net/physiopathology/diagnostic imaging ; },
abstract = {BACKGROUND: Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure-function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).<br><br>METHODS: We quantified the dynamic structure-function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure-function coupling with the topological features of functional networks to examine how the structure-function relationship facilitates brain information communication over time.<br><br>RESULTS: The dynamic structure-function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure-function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.<br><br>CONCLUSIONS: These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure-function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.},
}
@article {pmid38083794,
year = {2023},
author = {Subramanian, A and Najafizadeh, L},
title = {Hierarchical Classification Strategy for Mitigating the Impact of The Presence of Pain in fNIRS-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341152},
pmid = {38083794},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Neural Networks, Computer ; Spectrum Analysis ; Head ; },
abstract = {Brain computer interfaces (BCIs) can find applications in assistive systems for patients who experience conditions that impede their motor abilities. A BCI uses signals acquired from the brain to control external devices. As physical pain influences cortical signals, the presence of pain can negatively impact the performance of the BCI. In this work, we propose a strategy to mitigate this negative impact. Cortical signals are acquired from test subjects while they performed two mental arithmetic tasks, in the presence and the absence of painful stimuli. The task of the BCI is to reliably classify the two mental arithmetic tasks from the cortical recordings, irrespective of the presence or the absence of pain. We propose to do this classification, hierarchically, in two levels. In the first level, the data is classified into those captured in the presence and the absence of pain. Depending on the results of the classification from the first level, in the second level, the BCI performs the classification of tasks using a classifier trained either in the presence or the absence of pain. A 1-dimensional convolutional neural network (1D-CNN) is used for classification at both levels. It is observed that using this hierarchical strategy, the BCI is able to classify the tasks with an accuracy greater than 90%, irrespective of the presence or the absence of pain. Given that the presence of physical pain has shown previously to reduce the classification accuracy of a BCI to almost chance levels, this mitigation strategy will be a significant step towards enhancing the performance of BCIs when they are used in assistive systems for patients.},
}
@article {pmid38083788,
year = {2023},
author = {Liu, T and Ning, Y and Liu, P and Zhang, Y and Chua, Y and Chen, W and Zhang, S},
title = {Modularity Facilitates Classification Performance of Spiking Neural Networks for Decoding Cortical Spike Trains.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340358},
pmid = {38083788},
issn = {2694-0604},
mesh = {*Artificial Intelligence ; *Neural Networks, Computer ; Neurons/physiology ; Brain/physiology ; },
abstract = {After the introduction of recurrence, an important property of the biological brain, spiking neural networks (SNNs) have achieved unprecedented classification performance. But they still cannot outperform many artificial neural networks. Modularity is another crucial feature of the biological brain. It remains unclear if modularity can also improve the performance of SNNs. To investigate this idea, we proposed the modular SNN, and compared its performance with a uniform SNN without modularity by employing them to classify cortical spike trains. For the first time, a significant improvement was found in our modular SNN. Further, we probed into the factors influencing the performance of the modular SNN and found: (a). The modular SNN outperformed the uniform SNN more significantly when the number of neurons in the networks increased; (b). The performance of the modular SNNs increased as the number of modules dropped. These preliminary but novel findings suggest that modularity may help develop better artificial intelligence and brain-machine interfaces. Also, the modular SNN may serve as a model for the study of neuronal spike synchrony.},
}
@article {pmid38083754,
year = {2023},
author = {Dimova-Edeleva, V and Rivera, OS and Laha, R and Figueredo, LFC and Zavaglia, M and Haddadin, S},
title = {Error-related Potentials in a Virtual Pick-and-Place Experiment: Toward Real-world Shared-control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-7},
doi = {10.1109/EMBC40787.2023.10340244},
pmid = {38083754},
issn = {2694-0604},
mesh = {Humans ; *Robotics ; Electroencephalography ; *Brain-Computer Interfaces ; Brain/physiology ; Movement ; },
abstract = {In Human-Robot Collaboration setting a robot may be controlled by a user directly or through a Brain-Computer Interface that detects user intention, and it may act as an autonomous agent. As such interaction increases in complexity, conflicts become inevitable. Goal conflicts can arise from different sources, for instance, interface mistakes - related to misinterpretation of human's intention - or errors of the autonomous system to address task and human's expectations. Such conflicts evoke different spontaneous responses in the human's brain, which could be used to regulate intrinsic task parameters and to improve system response to errors - leading to improved transparency, performance, and safety. To study the possibility of detecting interface and agent errors, we designed a virtual pick and place task with sequential human and robot responsibility and recorded the electroencephalography (EEG) activity of six participants. In the virtual environment, the robot received a command from the participants through a computer keyboard or it moved as autonomous agent. In both cases, artificial errors were defined to occur in 20% - 25% of the trials. We found differences in the responses to interface and agent errors. From the EEG data, correct trials, interface errors, and agent errors were truly predicted for 51.62% ± 9.99% (chance level 38.21%) of the pick movements and 46.84%±6.62% (chance level 36.99%) for the place movements in a pseudo-asynchronous fashion. Our study suggests that in a human-robot collaboration setting one may improve the future performance of a system with intention detection and autonomous modes. Specific examples could be Neural Interfaces that replace and restore motor functions.},
}
@article {pmid38083732,
year = {2023},
author = {Yan, X and Boudrias, MH and Mitsis, GD},
title = {Investigation of Methodologies for Extracting Individual Brain Oscillations: Comparisons &amp; Insights.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340684},
pmid = {38083732},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Hand Strength ; Brain/physiology ; *Brain Waves/physiology ; Brain Mapping/methods ; },
abstract = {There is increasing evidence that the effects of non-invasive brain stimulation can be maximized when the applied intervention matches internal brain oscillations. Extracting individual brain oscillations is thus a necessary step for implementing personalized brain stimulation. In this context, different methods have been proposed for obtaining subject-specific spectral peaks from electrophysiological recordings. However, comparing the results obtained using different approaches is still lacking. Therefore, in the present work, we examined the following methodologies in terms of obtaining individual motor-related EEG spectral peaks: fast Fourier Transform analysis, power spectrum density analysis, wavelet analysis, and a principal component based time-frequency analysis. We used EEG data obtained when performing two different motor tasks - a hand grip task and a hand opening- and-closing task. Our results showed that both the motor task type and the specific method for performing the analysis had considerable impact on the extraction of subject-specific oscillation spectral peaks.Clinical Relevance-This exploratory study provides insights into the potential effects of using different methods to extract individual brain oscillations, which is important for designing personalized brain-machine-interfaces.},
}
@article {pmid38083728,
year = {2023},
author = {Borras, M and Romero, S and Rojas-Martinez, M and Serna, LY and Mananas, MA},
title = {Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340794},
pmid = {38083728},
issn = {2694-0604},
mesh = {Humans ; *Spinal Cord Injuries/rehabilitation ; Evoked Potentials/physiology ; Electroencephalography/methods ; Upper Extremity ; Movement ; },
abstract = {Spinal Cord Injury (SCI) is a common disease that usually limits the patient's independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs' amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients' mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature's amplitudes in the latter group.Clinical Relevance- This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.},
}
@article {pmid38083718,
year = {2023},
author = {Wang, J and Wang, L and Han, J and Mu, W and Wang, P and Zhang, X and Zhan, G and Zhang, L and Gan, Z and Kang, X},
title = {Using Determinant Point Process in Generative Adversarial Networks for SSVEP Signals Synthesis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340247},
pmid = {38083718},
issn = {2694-0604},
mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation/methods ; *Brain-Computer Interfaces ; Databases, Factual ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the main paradigms of brain-computer interface (BCI). However, the acquisition method of SSVEP can cause subject fatigue and discomfort, leading to the insufficiency of SSVEP databases. Inspired by generative determinantal point process (GDPP), we utilize the determinantal point process in generative adversarial network (GAN) to generate SSVEP signals. We investigate the ability of the method to synthesize signals from the Benchmark dataset. We further use some evaluation metrics to verify its validity. Results prove that the usage of this method significantly improved the authenticity of generated data and the accuracy (97.636%) of classification using deep learning in SSVEP data augmentation.},
}
@article {pmid38083705,
year = {2023},
author = {Haderlein, JF and Peterson, ADH and Burkitt, AN and Mareels, IMY and Grayden, DB},
title = {Autoregressive models for biomedical signal processing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-6},
doi = {10.1109/EMBC40787.2023.10340714},
pmid = {38083705},
issn = {2694-0604},
mesh = {*Brain ; *Signal Processing, Computer-Assisted ; Biomedical Engineering ; Algorithms ; Time Factors ; },
abstract = {Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters.Clinical relevance- This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy.},
}
@article {pmid38083697,
year = {2023},
author = {Premchand, B and Zhang, Z and Yu, J and Yang, T and Ang, KK},
title = {Synchronizing Motor Imagery Cue in fNIRS Brain-Computer Interface to reduce confounding effects of respiration.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340679},
pmid = {38083697},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Cues ; Spectroscopy, Near-Infrared/methods ; Imagination ; Respiration ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that measures oxygenated hemoglobin (HbO) levels in the brain to infer neural activity using near-infrared light. Measured HbO levels are directly affected by a person's respiration. Hence, respiration cycles tend to confound fNIRS readings in motor imagery-based fNIRS Brain-Computer Interfaces (BCI). To reduce this confounding effect, we propose a method of synchronizing the motor imagery cue timing with the subject's respiration cycle using a breathing sensor. We conducted an experiment to collect 160 single trials from 10 subjects performing motor imagery using an fNIRS-based BCI and the breathing sensor. We then compared the HbO levels in trials with and without respiration synchronization. The results showed that respiration synchronization yielded HbO levels that were less dispersed across trials, and a negative correlation between the dispersion index of HbO levels with MI decoding accuracies was found across the 10 subjects. This showed that synchronizing motor imagery cues to respiration can yield increased HbO level consistency leading to better MI performance. Hence, the proposed method holds promise to improve the decoding performance of fNIRS-BCI by reducing the confounding effects of respiration.},
}
@article {pmid38083693,
year = {2023},
author = {Russo, JS and Chodhary, M and Strik, M and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Feasibility of Using Source-Level Brain Computer Interface for People with Multiple Sclerosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340364},
pmid = {38083693},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Multiple Sclerosis ; Feasibility Studies ; Imagination ; },
abstract = {This work evaluates the feasibility of using a source level Brain-Computer Interface (BCI) for people with Multiple Sclerosis (MS). Data used was previously collected EEG of eight participants (one participant with MS and seven neurotypical participants) who performed imagined movement of the right and left hand. Equivalent current dipole cluster fitting was used to assess related brain activity at the source level and assessed using dipole location and power spectrum analysis. Dipole clusters were resolved within the motor cortices with some notable spatial difference between the MS and control participants. Neural sources that generate motor imagery originated from similar motor areas in the participant with MS compared to the neurotypical participants. Power spectral analysis indicated a reduced level of alpha power in the participant with MS during imagery tasks compared to neurotypical participants. Power in the beta band may be used to distinguish between left and right imagined movement for users with MS in BCI applications.Clinical Relevance- This paper demonstrates the cortical areas activated during imagined BCI-type tasks in a participant with Multiple Sclerosis (MS), and is a proof of concept for translating BCI research to potential users with MS.},
}
@article {pmid38083691,
year = {2023},
author = {Wimmer, M and Weidinger, N and Veas, E and Muller-Putz, GR},
title = {Neural and Pupillometric Correlates of Error Perception in an Immersive VR Flight Simulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340376},
pmid = {38083691},
issn = {2694-0604},
mesh = {Humans ; Computer Simulation ; *Virtual Reality ; Electroencephalography ; *Brain-Computer Interfaces ; Perception ; },
abstract = {Algorithms detecting erroneous events, as used in brain-computer interfaces, usually rely solely on neural correlates of error perception. The increasing availability of wearable displays with built-in pupillometric sensors enables access to additional physiological data, potentially improving error detection. Hence, we measured both electroencephalographic (EEG) and pupillometric signals of 19 participants while performing a navigation task in an immersive virtual reality (VR) setting. We found EEG and pupillometric correlates of error perception and significant differences between distinct error types. Further, we found that actively performing tasks delays error perception. We believe that the results of this work could contribute to improving error detection, which has rarely been studied in the context of immersive VR.},
}
@article {pmid38083680,
year = {2023},
author = {Chang, KY and Huang, YC and Chuang, CH},
title = {Enhancing EEG Artifact Removal Efficiency by Introducing Dense Skip Connections to IC-U-Net.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340520},
pmid = {38083680},
issn = {2694-0604},
mesh = {*Artifacts ; Algorithms ; Electroencephalography/methods ; Signal-To-Noise Ratio ; *Brain-Computer Interfaces ; },
abstract = {Electroencephalographic (EEG) data is considered contaminated with various types of artifacts. Deep learning has been successfully applied to developing EEG artifact removal techniques to increase the signal-to-noise ratio (SNR) and enhance brain-computer interface performance. Recently, our research team has proposed an end-to-end UNet-based EEG artifact removal technique, IC-U-Net, which can reconstruct signals against various artifacts. However, this model suffers from being prone to overfitting with a limited training dataset size and demanding a high computational cost. To address these issues, this study attempted to leverage the architecture of UNet++ to improve the practicability of IC-U-Net by introducing dense skip connections in the encoder-decoder architecture. Results showed that this proposed model obtained superior SNR to the original model with half the number of parameters. Also, this proposed model achieved comparable convergency using a quarter of the training data size.},
}
@article {pmid38083679,
year = {2023},
author = {Lin, C and Han, C and Mao, J and Yu, S and Zhang, Z},
title = {Multi-channel Wireless Implantable Brain-Computer Interface System.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340603},
pmid = {38083679},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Prostheses and Implants ; Electrocorticography ; Amplifiers, Electronic ; },
abstract = {The implantable brain-computer interface has been widely used in recent years due to its great application potential and research value. Few neural implants have been designed to gather neural spikes, which require a higher sampling frequency than ECoG and LFPs. These systems are still constrained by low channel counts and their bulky size. Furthermore, wire connection is still used in many neural interfaces for further data analysis, facing challenges such as tissue infection, limited movement, and increased noise interference. To address the aforementioned problems, this paper presents a compact multi-channel wireless implantable brain-computer interface system that meets the requirements of spike signals collection and miniaturization. A WiFi module is utilized to transmit information between the system and terminal equipment to eliminate the tethering effects. A 128-channel signal acquisition module, consisting of two pieces of commercial digital electrophysiology amplifier chips, is designed to realize high channel counts for capturing spike events. The proposed system has successfully recorded the analog spike signals from a digital neural signal simulator.},
}
@article {pmid38083669,
year = {2023},
author = {Leong, D and Do, TT and Lin, CT},
title = {Distinction of the object recognition and object identification in the brain-computer interfaces applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340753},
pmid = {38083669},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Brain ; Visual Perception ; },
abstract = {Object recognition is a complex cognitive process in which information is integrated and processed by various brain regions. Previous studies have shown that both the visual and temporal cortices are active during object recognition and identification. However, although object recognition and object identification are similar, these processes are considered distinct functions in the brain. Despite this, the differentiation between object recognition and identification has yet to be clearly defined for use in brain-computer interface (BCI) applications. This research aims to utilize neural features related to object recognition and identification and classify these features to differentiate between the two processes. The results demonstrate that several classifiers achieved high levels of accuracy, with the XGBoost classifier using a Linear Booster achieving the highest accuracy at 96% and a F1 score of 0.97. This ability to distinguish between object recognition and identification can be a beneficial aspect of a BCI object recognition system as it could help determine the intended target object for a user.},
}
@article {pmid38083659,
year = {2023},
author = {Meng, J and Wang, H and Sun, J and Zhao, Y and Xu, M and Ming, D},
title = {SKDCPM algorithm can improve the single-trial decoding performance of very similar error-related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340825},
pmid = {38083659},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Bayes Theorem ; Algorithms ; Discriminant Analysis ; },
abstract = {Error related potential (ErrP) is an effective control signal for the brain-computer interface (BCI). Current ErrP decoding methods can only distinguish right and wrong mental states. However, in real scenarios, error conditions often contain more detailed information, such as the degree of error, which would induce very similar ErrPs. Distinguishing such ErrPs effectively is of vital importance to provide more detailed information for optimizing BCIs. Hereto, a major challenge is the EEG differences of very similar ErrPs are very small. Thus, it is necessary to develop new efficient method for decoding very similar ErrPs. This study newly proposed an algorithm named shrinkage discriminant canonical pattern matching (SKDCPM), and compared its decoding results with the linear discriminant analysis (LDA), shrinkage LDA (SKLDA), stepwise LDA (SWLDA), Bayesian LDA (BLDA) and the DCPM, which were algorithms commonly used for ErrP decoding. A data set of 18 subjects was built, it had four conditions, i.e., right (0°), errors with varying degrees, i.e., 45°, 90°, 180° deviation from the predicted direction. As a result, the SKDCPM had high balanced accuracy (BACC) in right-wrong classification (0° vs. others). More importantly, it achieved a grand averaged BACC of 69.54% with the highest up to 74.25%, which outperformed all the other algorithms in very similar ErrPs decoding (45° vs. 90° vs. 180°) significantly. This study could provide new decoding methods for developing the ErrP-based BCI system.},
}
@article {pmid38083637,
year = {2023},
author = {Zehra, SR and Mu, J and Burkitt, AN and Grayden, DB},
title = {Effect of alpha range activity on SSVEP decoding in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340956},
pmid = {38083637},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; Photic Stimulation ; Brain/physiology ; },
abstract = {Brain-computer interfaces (BCIs) facilitate direct communication between the brain and external devices. For BCI technology to be commercialized for wide scale applications, BCIs should be accurate, efficient, and exhibit consistency in performance for a wide variety of users. A core challenge is the physiological and anatomical differences amongst people, which causes a high variability amongst participants in BCI studies. Hence, it becomes necessary to analyze the mechanisms causing this variability and address them by improving the decoding algorithms. In this paper, a publicly available steady-state visual evoked potential (SSVEP) dataset is analyzed to study the effect of SSVEP flicker on the endogenous alpha power and the subsequent overall effect on the classification accuracy of the participants. It was observed that the participants with classification accuracy below 95% showed increased alpha power in their brain activities. Incorrect prediction in the decoding algorithm was observed a maximum number of times when the predicted frequency was in the range 9-12 Hz. We conclude that frequencies between 9-12 Hz may result in below par performance in some participants when canonical correlation analysis is used for classification.Clinical relevance-If alpha-band frequencies are used for SSVEP stimulation, alpha power interference in EEG may alter BCI accuracy for some users.},
}
@article {pmid38083630,
year = {2023},
author = {Huang, Y and Zhang, X and Wang, Y},
title = {Decoding Ensemble Spike States from Extracellular Field Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341044},
pmid = {38083630},
issn = {2694-0604},
mesh = {Rats ; Animals ; Action Potentials/physiology ; *Algorithms ; Rats, Sprague-Dawley ; *Neurons/physiology ; Neural Networks, Computer ; },
abstract = {Behaviors are encoded by multi-scale brain signals, from microscopic spike signals to macroscopic extracellular Field Potentials (FPs). Extracting neuronal spike information from FPs is an important, yet challenging problem. Because FPs stem from summed contributions of a large population of neurons. Previous work inferred single-neuron spiking activity from the FPs using a generalized linear model (GLM). However, FPs reflect the states of neural ensembles more than single-neuron spike trains. In this paper, we propose a computational model to decode ensemble spike states from FPs. This framework first extracts transient features in FPs, and then detects typical ensemble spike patterns and assigns state labels accordingly. Finally, we use a neural network to decode the ensemble spike states from the FP neuromodulations. This FP-Spike decoder is tested on the FP and spike data from the M1 area of an SD rat. We show that our model can effectively decode multi-neuron spike states. Compared with the GLM method for single-neuron spike prediction, our model exhibits 37% less ensemble spike pattern decoding error. These preliminary results show that we can decode informative spike states from FPs, indicating that the decode results can further benefit long-term stable brain-machine interfaces.},
}
@article {pmid38083617,
year = {2023},
author = {Huang, R and Gao, F},
title = {Decoupling brain activations of muscle-caused activations and mental intention-cause activations using the general linear model: A functional near-infrared spectroscopy study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-5},
doi = {10.1109/EMBC40787.2023.10341029},
pmid = {38083617},
issn = {2694-0604},
mesh = {Humans ; *Intention ; Spectroscopy, Near-Infrared/methods ; Brain/physiology ; *Brain-Computer Interfaces ; Muscles ; },
abstract = {Integrating a brain-computer interface into a lower-limb medical rehabilitation assistive device can enhance rehabilitation efficiency. The latest research in the field focuses on the decoding performance of different motions. However, the difference between muscle-caused primitive activation and mental intention-caused activation has not been fully investigated. Thus, our study tried to decouple these two kinds of cerebral activation using a general linear model (GLM). Nine healthy and right-handed subjects were recruited for a two-section experiment. They were asked to extend or flex their knees while seated in the first section of the experiment or standing in the second section. Functional near-infrared spectroscopy (fNIR) was adopted to monitor their hemodynamic changes. Two groups of paradigms (one for circle-wise analysis, the other for full-section analysis) were constructed from the experimental paradigm. Each group consisted of three (the first intention, the second intention, and the muscle activation). The constructed paradigms were fed to the Balloon model for six desired hemodynamic responses (dHRFs). The regressor of GLM consisted of three dHRFs and the corresponding motion artifacts and drifts. The simulated physiological noises were included in the structured background matrix. The results showed that all subjects had similar cerebral activation patterns for the intention to extend or flex knees. The activation during musclecaused activation was less intense than that caused by both intentions. This finding can help further research on more efficient motion intention detection and the possibility of multiple motions decoding.},
}
@article {pmid38083615,
year = {2023},
author = {Ferrero, L and Quiles, V and Soriano-Segura, P and Ortiz, M and Ianez, E and Contreras-Vidal, JL and Azorin, JM},
title = {Transfer Learning with CNN Models for Brain-Machine Interfaces to command lower-limb exoskeletons: A Solution for Limited Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340008},
pmid = {38083615},
issn = {2694-0604},
mesh = {Humans ; *Exoskeleton Device ; Electroencephalography ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Machine Learning ; },
abstract = {This study evaluates the performance of two convolutional neural networks (CNNs) in a brain-machine interface (BMI) based on motor imagery (MI) by using a small dataset collected from five participants wearing a lower-limb exoskeleton. To address the issue of limited data availability, transfer learning was employed by training models on EEG signals from other subjects and subsequently fine-tuning them to specific users. A combination of common spatial patterns (CSP) and linear discriminant analysis (LDA) was used as a benchmark for comparison. The study's primary aim is to examine the potential of CNNs and transfer learning in the development of an automatic neural classification system for a BMI based on MI to command a lower-limb exoskeleton that can be used by individuals without specialized training.Clinical Relevance- BMI can be used in rehabilitation for patients with motor impairment by using mental simulation of movement to activate robotic exoskeletons. This can promote neural plasticity and aid in recovery.},
}
@article {pmid38083595,
year = {2023},
author = {Martinez-Cagigal, V and Santamaria-Vazquez, E and Perez-Velasco, S and Marcos-Martinez, D and Moreno-Calderon, S and Hornero, R},
title = {Nonparametric Early Stopping Detection for c-VEP-based Brain-Computer Interfaces: A Pilot Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341024},
pmid = {38083595},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual ; Pilot Projects ; Electroencephalography/methods ; Algorithms ; },
abstract = {Brain-computer interface (BCI) systems based on code-modulated visual evoked potentials (c-VEP) stand out for achieving excellent command selection accuracies with very short calibration times. One of the natural steps to democratize their use in plug-and-play environments is to develop early stopping algorithms. These methods allow real-time detection of the minimum number of code repetitions needed to provide reliable selections. However, such techniques are scarce in the current state-of-the-art for c-VEP-based BCI systems based on the classical circular shifting paradigm. Here, a novel nonparametric early stopping method is proposed, which approximates the distribution of unattended commands to a normal distribution and issues a selection when the correlation of the command is considered an outlier. The proposal has been evaluated offline with 15 healthy users, achieving an average accuracy of 97.08% and a speed of 1.37 s/command. Likewise, the algorithm has also been evaluated with an additional user in an online way, as a proof of concept to validate its technical feasibility, achieving an average accuracy of 96.88% with a speed of 1.67 s/command. These results suggest that the real time application of the proposed algorithm is feasible, significantly reducing the required selection time without compromising accuracy.},
}
@article {pmid38083588,
year = {2023},
author = {Zhang, H and Guo, Z and Chen, F},
title = {The Effects of Different Brain Regions on fNIRS-based Task-state Detection in Speech Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340896},
pmid = {38083588},
issn = {2694-0604},
mesh = {Humans ; *Speech/physiology ; Brain/diagnostic imaging/physiology ; Imagery, Psychotherapy ; Temporal Lobe ; *Motor Cortex/physiology ; },
abstract = {Brain-computer interface (BCI) based on speech imagery can decode users' verbal intent and help people with motor disabilities communicate naturally. Functional near-infrared spectroscopy (fNIRS) is a commonly used brain signal acquisition method. Asynchronous BCI can response to control commands at any time, which provides great convenience for users. Task state detection, defined as identifying whether user starts or continues covertly articulating, plays an important role in speech imagery BCIs. To better distinguish task state from idle state during speech imagery, this work used fNIRS signals from different brain regions to study the effects of different brain regions on task state detection accuracy. The imagined tonal syllables included four lexical tones and four vowels in Mandarin Chinese. The brain regions that were measured included Broca's area, Wernicke's area, Superior temporal cortex and Motor cortex. Task state detection accuracies of imagining tonal monosyllables with four different tones were analyzed. The average accuracy of four speech imagery tasks based on the whole brain was 0.67 and it was close to 0.69, which was the average accuracy based on Broca's area. The accuracies of Broca's area and the whole brain were significantly higher than those of other brain regions. The findings of this work demonstrated that using a few channels of Broca's area could result in a similar task state detection accuracy to that using all the channels of the brain. Moreover, it was discovered that speech imagery with tone 2/3 tasks yielded higher task state detection accuracy than speech imagery with other tones.},
}
@article {pmid38083531,
year = {2023},
author = {Mahoney, TB and Liu, PC and Grayden, DB and John, SE},
title = {Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340834},
pmid = {38083531},
issn = {2694-0604},
mesh = {Animals ; Sheep ; *Scalp/physiology ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Quality of Life ; Electroencephalography/methods ; Electrodes ; },
abstract = {Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. However, sub-scalp EEG has not yet been assessed for suitability in BCI applications. This study presents a preliminary comparison of visual evoked potentials (VEPs) recorded using sub-scalp and endovascular stent electrodes in a sheep. Sub-scalp electrodes recorded comparable VEP amplitude, signal-to-noise ratio and bandwidth to the stent electrodes.Clinical relevance-This is the first study to report a comparision between sub-scalp and stent electrode array signals. The use of sub-scalp EEG electrodes may aid in the long-term use of brain-computer interfaces.},
}
@article {pmid38083464,
year = {2023},
author = {Zhang, X and Wang, Y},
title = {A Kernel Reinforcement Learning Decoding Framework Integrating Neural and Feedback Signals for Brain Control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340203},
pmid = {38083464},
issn = {2694-0604},
mesh = {Humans ; Feedback ; *Algorithms ; *Reinforcement, Psychology ; Learning ; Brain ; },
abstract = {Brain-Machine Interfaces (BMIs) have the potential to allow subjects to brain control (BC) external devices, where their brain signals could be translated to the action of the neuro-prosthesis by reinforcement learning (RL) based decoder. During the BC task, feedback cues are provided to guide subject's learning. Subjects will adapt the neural signals according to the feedback cues. Concurrently, the RL decoding parameters are adjusted when the subject explores the BC task through trial and error, leading to a co-adaptive process between the subject and the decoder. However, when subjects receive the feedback cues and enhance their learning, the decoder does not actively utilize the feedback cues. If the RL decoder could integrate both neural signals and feedback cues, the training efficiency of the BC task would increase. A major challenge is the different temporal scales of neural signals and feedback cues, making it difficult to integrate them into a single decoder. In this paper, we propose a novel kernel RL decoding method as the first attempt to combine two signals with different temporal scales for RL decoding. The neural signals and the feedback cues comprise the decoding input, which is then projected into individual Reproducing Kernel Hilbert Spaces (RKHSs) respectively. These two RKHSs form a joint feature space, where the action of the neuro-prosthesis could be decoded linearly. We evaluate the proposed method on a simulated brain control cursor-reaching task. Our proposed method is compared with the kernel RL that only uses neural signals as the input. The proposed method has a faster learning speed and better decoding accuracy. The results demonstrate that our proposed method has successfully integrated the information of the feedback cue and facilitates the training procedure for the BC task.Clinical Relevance-This paper provides an integrated reinforcement learning decoding framework, which combines the neural signals and the feedback cues to increase the learning speed and the accuracy of the brain control task. Subjects could learn the task more easily with this decoder.},
}
@article {pmid38083424,
year = {2023},
author = {Moreno-Calderon, S and Martinez-Cagigal, V and Santamaria-Vazquez, E and Perez-Velasco, S and Marcos-Martinez, D and Hornero, R},
title = {Assessing the Potential of Brain-Computer Interface Multiplayer Video Games using c-VEPs: A Pilot Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340542},
pmid = {38083424},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Pilot Projects ; *Video Games ; Neurologic Examination ; },
abstract = {Video games have become a common and widespread form of entertainment, while non-invasive brain-computer interfaces (BCI) are emerging as potential alternative communication technologies. Combining BCIs and video games can enhance the gaming experience and make it accessible to motor-disabled individuals. Recently, code-modulated visual evoked potentials (c-VEP) have been proposed as a novel control signal able to achieve high performance with short calibration times. However, there are still no video games that use c-VEPs as a control signal. The aim of this pilot study is to develop an implementation of the 'Connect 4' multiplayer video game using a c-VEP-based BCI and test it with 10 healthy users. Participants were paired to compete in matches and carried out individual tasks. The results showed that the participants were able to control the game with an average accuracy of 94.10% and a selection time of 5.25 seconds per command, outperforming previous approaches. This suggests that the proposed video game is feasible and c-VEPs can provide smooth BCI control.},
}
@article {pmid38083416,
year = {2023},
author = {Gu, RF and Zhao, LM and Zheng, WL and Lu, BL},
title = {Tagging Continuous Labels for EEG-based Emotion Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341022},
pmid = {38083416},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Emotions ; *Brain-Computer Interfaces ; },
abstract = {EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.},
}
@article {pmid38083406,
year = {2023},
author = {Ju, C and Kobler, RJ and Guan, C},
title = {Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-7},
doi = {10.1109/EMBC40787.2023.10340899},
pmid = {38083406},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Brain/physiology ; Electroencephalography/methods ; Imagery, Psychotherapy ; Movement/physiology ; },
abstract = {The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Fréchet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.},
}
@article {pmid38083187,
year = {2023},
author = {Soriano-Segura, P and Ferrero, L and Ortiz, M and Ianez, E and Azorin, JM},
title = {Analysis of Error Potentials generated by a lower limb exoskeleton feedback in a BMI for gait control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340275},
pmid = {38083187},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; *Exoskeleton Device ; Feedback ; Body Mass Index ; Lower Extremity ; Gait ; },
abstract = {Brain-machine interfaces (BMIs) based on motor imagery (MI) for controlling lower-limb exoskeletons during the gait have been gaining importance in the rehabilitation field. However, these MI-BMI are not as precise as they should. The detection of error related potentials (ErrP) as a self-tune parameter to prevent wrong commands could be an interesting approach to improve their performance. For this reason, in this investigation ErrP elicited by the movement of a lower-limb exoskeleton against subject's will is analyzed in the time, frequency and time-frequency domain and compared with the cases where the exoskeleton is correctly commanded by motor imagery (MI). The results of the ErrP study indicate that there is statistical significative evidence of a difference between the signals in the erroneous events and the success events. Thus, ErrP could be used to increase the accuracy of BMIs which commands exoskeletons.Clinical Relevance- This investigation has the purpose of improving brain-machine interfaces (BMIs) based on motor imagery (MI) by means of the detection of error potentials. This could promote the adoption of robotic exoskeletons commanded by BMIs in rehabilitation therapies.},
}
@article {pmid38083167,
year = {2023},
author = {Eiber, CD and Aditya Tarigoppula, VS and Rind, GS},
title = {A 'Total Unique Variation Analysis' for Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340518},
pmid = {38083167},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Electrocorticography ; *Epilepsy ; Prostheses and Implants ; },
abstract = {When designing a fully implantable brain-machine interface (BMI), the primary aim is to detect as much neural information as possible with as few channels as possible. In this paper, we present a total unique variance analysis (TUVA) for evaluating the signal unique to each channel that cannot be predicted by linear combination of signals on other channels. TUVA is a statistical method for determining the total unique variance in multidimensional data, ordering channels from most to least informative, to aid in the design of maximally-efficacious BMIs. We demonstrate how this method can be applied to the design of BMIs by comparing TUVA values computed for simulated lead-field maps for high-channel-count electrocorticography (ECoG) with values computed for recordings in the interictal period in the context of surgery planning for epileptic resection.Clinical Relevance- This paper introduces a new statistical method for comparison of neural interface designs, focused on quantifying recording efficiency by minimizing channel crosstalk, which may help improve the risk-benefit profile of invasive neural recording.},
}
@article {pmid38083150,
year = {2023},
author = {Tan, J and Zhang, X and Wu, S and Wang, Y},
title = {State-space Model Based Inverse Reinforcement Learning for Reward Function Estimation in Brain-machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340953},
pmid = {38083150},
issn = {2694-0604},
mesh = {Humans ; Animals ; Rats ; *Brain-Computer Interfaces ; Reinforcement, Psychology ; Learning ; Reward ; Brain ; },
abstract = {The use of reinforcement learning (RL) in brain machine interfaces (BMIs) is considered to be a promising method for neural decoding. One key component of RL-based BMIs is the reward signal, which is used to guide decoders to update the parameters. However, designing effective and efficient rewards can be challenging, especially for complex tasks. Inverse reinforcement learning (IRL) is a method that has been proposed to estimate the internal reward function from subjects' neural activity. However, multi-channel neural activity, which may encode many sources of information, builds a large dimensions of state-action space, making it difficult to directly apply IRL methods in BMI systems. In this paper, we propose a state-space model based inverse Q-learning (SSM-IQL) method to improve the performance of the existing IRL method. The state-space model is designed to extract hidden brain state from high-dimensional neural activity. We tested the proposed method on real data collected from rats during a two-lever discrimination task. Preliminary results show that SSM-IQL provides a more accurate and stable estimation of the internal reward function than the traditional IQL algorithm. This suggests that the use of state-space model in IRL method has potential to improve the design of RL-based BMIs.},
}
@article {pmid38083118,
year = {2023},
author = {Kumar, C and Rahimi, N and Gonjari, R and McLinden, J and Hosni, SI and Shahriari, Y and Shao, M},
title = {Context-aware Multimodal Auditory BCI Classification through Graph Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10339984},
pmid = {38083118},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Neural Networks, Computer ; Brain ; Electroencephalography/methods ; },
abstract = {The prospect of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) in the presence of topological information of participants is often left unexplored in most of the brain-computer interface (BCI) systems. Additionally, the usage of these modalities together in the field of multimodality analysis to support multiple brain signals toward improving BCI performance is not fully examined. This study first presents a multimodal data fusion framework to exploit and decode the complementary synergistic properties in multimodal neural signals. Moreover, the relations among different subjects and their observations also play critical roles in classifying unknown subjects. We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the performance on an auditory task classification. We explored standard and deviant auditory EEG and fNIRS data where each subject was asked to perform an auditory oddball task and has multiple trials regarded as context-aware nodes in our graph construction. In experiments, our multimodal data fusion strategy showed an improvement up to 8.40% via SVM and 2.02% via GNN, compared to the single-modal EEG or fNIRS. In addition, our context-aware GNN achieved 5.3%, 4.07% and 4.53% higher accuracy for EEG, fNIRS and multimodal data based experiments, compared to the baseline models.},
}
@article {pmid38083086,
year = {2023},
author = {Fenton, E and Dick, JF and Hayes, A and Castles, R and Mizelle, JC and Kim, S},
title = {Exploring the Effects of Offline Paradigms and Feature Extraction Techniques on Performance of Motor Imagery Brain-Computer Interface: Longitudinal Pilot Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340368},
pmid = {38083086},
issn = {2694-0604},
mesh = {Humans ; *Imagination ; *Brain-Computer Interfaces ; Pilot Projects ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Motor Imagery (MI) Brain-Computer Interface (BCI) is a popular way of allowing disabled and healthy individuals to use brain signals to communicate with their environment, despite the technical and human factor challenges that affect MI BCI classification performance. This study explored the influence of paradigm choice and phase synchronization-based features on classification performance by comparing primary datasets to older supplemental datasets. Area Under the Curve (AUC) Receiver Operating Characteristics (ROC) curve was the metric for classification performance. Results showed that using both advanced paradigms and features significantly improved both classification and usability; TD-CSP-wPLI (16-30Hz) and S-CSP-wPLI (12-15Hz) frequency bands produced the most noticeable change in performance.},
}
@article {pmid38083059,
year = {2023},
author = {Mehdizadeh, SK and Cutrell, E and Winters, RM and Djuric, N and Cheng, Y and Tashev, IJ and Wang, YT},
title = {EEG and Eye-Tracking Error-Related Responses During Predictive Text Interactions: A BCI Case Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340598},
pmid = {38083059},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Eye-Tracking Technology ; Electroencephalography/methods ; },
abstract = {Brain-computer interfaces (BCIs) employ various paradigms which afford intuitive, augmented control for users to navigate digital technologies. In this study we explore the application of these BCI concepts to predictive text systems: commonplace interactive and assistive tools with variable usage contexts and user behaviors. We conducted an experiment to analyze user neurophysiological responses under these different usage scenarios and evaluate the feasibility of a closed-loop, adaptive BCI for use with such technologies. We recorded electroencephalogram (EEG) and eye tracking (ET) data from participants while they completed a self-paced typing task in a simulated predictive text environment. Participants completed the task with different degrees of reliance on the predictive text system (completely dependent, completely independent, or their choice) and encountered both correct and incorrect text generations. Data suggest that erroneous text generations may evoke neurophysiological responses that can be measured with both EEG and pupillometry. Moreover, these responses appear to change according to users' reliance on the predictive text system. Results show promise for use in a passive, hybrid, BCI with a closed-loop, adaptive framework, and support a neurophysiological approach to the challenge of real-time human feedback on system performance.},
}
@article {pmid38083050,
year = {2023},
author = {Chen, S and Wang, Y},
title = {A Bayesian Decoder Representing Single-Directional Connectivity between Neurons in Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340970},
pmid = {38083050},
issn = {2694-0604},
mesh = {Rats ; Animals ; *Brain-Computer Interfaces ; Bayes Theorem ; Likelihood Functions ; Neural Networks, Computer ; Neurons/physiology ; },
abstract = {Directional neural connectivity is essential to understanding how neurons encode and transmit information in the neural network. The previous studies on single neuronal encoding models illustrate how the neurons modulate the stimulus, underlying movement, and interactions with other neurons. And these encoding models have been used in the Bayesian decoders of the brain-machine interface (BMI) to explain how the neural population represents the movement intentions. However, the existing methods only consider rough correlations between neurons without directional connections, while the synapses between real neurons have explicit directions. Therefore, in these models, we cannot specify the proper functional neural connectivity and how the neurons cooperate to represent the movement intentions in truth. Therefore, we propose representing the directional neural connectivity in the Bayesian decoder in BMI. Our method derives a chain-likelihood based on Bayes' rule to form the single-directional influence between neurons. According to the derived structure, the prior causality relationship can be used to build more precise neural encoding models. Therefore, our method can represent the functional neural circuit more precisely and benefit the decoding in the BMI. We validate the proposed method in synthetic data simulating the rat's two-lever discrimination task. The results demonstrate that our method outperforms the existing methods by representing directional-neural connectivity. Besides, our method is more efficient in training because it employs fewer parameters. Consequently, our method can be used to evaluate the causality between neurons at the behavior level.Clinical Relevance-This paper proposes a decoder that can represent single-directional neural connectivity, which is potential to validate the causality relationship between neurons at behavior level.},
}
@article {pmid38083049,
year = {2023},
author = {Flotaker, S and Soler, A and Molinas, M},
title = {Primary color decoding using deep learning on source reconstructed EEG signal responses.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340033},
pmid = {38083049},
issn = {2694-0604},
mesh = {Humans ; Evoked Potentials, Visual ; *Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; },
abstract = {The brain's response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control their surroundings by looking at specific colors. Allowing the user to control certain elements in its environment, such as lighting and doors, by looking at corresponding signs of different colors could serve as an intuitive interface. This paper presents work on the development of an intra-subject classifier for red, green, and blue (RGB) visual evoked potentials (VEPs) in recordings performed with an electroencephalogram (EEG). Three deep neural networks (DNNs), proposed in earlier papers, were employed and tested for data in source- and electrode space. All the tests performed in electrode space yielded better results than those in source space. The best classifier yielded an accuracy of 77% averaged over all subjects, with the best subject having an accuracy of 96%.Clinical relevance- This paper demonstrates that deep learning can be used to classify between red, green and blue visual evoked potentials in EEG recordings with an average accuracy of 77%.},
}
@article {pmid38083036,
year = {2023},
author = {Li, M and Pun, SH and Chen, F},
title = {Impacts of Cortical Regions on EEG-based Classification of Lexical Tones and Vowels in Spoken Speech.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340428},
pmid = {38083036},
issn = {2694-0604},
mesh = {Humans ; *Speech ; Electroencephalography ; Brain ; *Auditory Cortex ; Brain Mapping ; },
abstract = {Speech impairment is one of the most serious problems for patients with communication disorders, e.g., stroke survivors. The brain-computer interface (BCI) systems have shown the potential to alternatively control or rehabilitate the neurological damages in speech production. The effects of different cortical regions in speech-based BCI systems are essential to be studied, which are favorable for improving the performance of speech-based BCI systems. This work aimed to explore the impacts of different speech-related cortical regions in the electroencephalogram (EEG) based classification of seventy spoken Mandarin monosyllables carrying four vowels and four lexical tones. Seven audible speech production-related cortical regions were studied, involving Broca's and Wernicke's areas, auditory cortex, motor cortex, prefrontal cortex, sensory cortex, left brain, right brain, and whole brain. Following the previous studies in which EEG signals were collected from ten subjects during Mandarin speech production, the features of EEG signals were extracted by the Riemannian manifold method, and a linear discriminant analysis (LDA) was regarded as a classifier to classify different vowels and lexical tones. The results showed that when using electrodes from whole brain, the classifier reached the best performances, which were 48.5% for lexical tones and 70.0% for vowels, respectively. The vowel classification results under Broca's and Wernicke's areas, auditory cortex, or prefrontal cortex were higher than those under the motor cortex or sensory cortex. No such differences were observed in the lexical tone classification task.},
}
@article {pmid38082974,
year = {2023},
author = {Ma, JL and Koorathota, S and Sajda, P},
title = {Neurophysiological Predictors of Self-Reported Difficulty in a Virtual-Reality Driving Scenario.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340597},
pmid = {38082974},
issn = {2694-0604},
mesh = {Humans ; Self Report ; *Task Performance and Analysis ; *Arousal/physiology ; Brain ; Judgment ; },
abstract = {Our perception of subjective difficulty in complex tasks, such as driving, is a judgment that is likely a result of dynamic interactions between distributed brain regions. In this paper, we investigate how neurophysiological markers associated with arousal state are informative of this perceived difficulty throughout a driving task. We do this by classifying subjective difficulty reports of subjects using set of features that include neural, autonomic, and eye behavioral markers. We subsequently assess the importance of these features in the classification. We find that though multiple EEG linked to cognitive control and, motor performance linked to classification of subjective difficulty, only pupil diameter, a measure of pupil-linked arousal, is strongly linked to both measured self-reported difficulty and actual task performance. We interpret our findings in the context of arousal pathways influencing performance and discuss their relevance to future brain-computer interface systems.},
}
@article {pmid38082970,
year = {2023},
author = {Namura, N and Kanoga, S},
title = {The Effect of Muscle Artifact Reduction Methods on Few-channel SSVEPs during Head Movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340333},
pmid = {38082970},
issn = {2694-0604},
mesh = {Humans ; Male ; Female ; *Evoked Potentials, Visual ; *Artifacts ; Electroencephalography/methods ; Head Movements ; Photic Stimulation/methods ; Muscles ; },
abstract = {Brain-computer interfaces (BCIs) with steady-state visual evoked potentials (SSVEPs) caused by flickering stimuli have caught attention as communication tools between human brains and external machines through a head-mounted display (HMD). When applying SSVEP-based BCIs to real-life environments, the head must be moved to watch the stimuli displayed in an HMD, which generates muscular artifacts and significantly reduces BCI performance. In this study, we examined four-class SSVEP identification accuracies by using four artifact reduction methods in the situation of moving the head for both simulation and real datasets. In the simulation dataset, we found that artifact subspace reconstruction (ASR) and multi-scale dictionary learning (MSDL) showed better results especially at low signal-to-noise ratio. In the real dataset, we observed that reducing muscular artifacts resulted in performance degradation for independent component analysis-based methods, while ASR and MSDL showed relatively limited degradation and in some cases improved performance. Our future work is to improve ASR and MSDL for high performance with real data and to apply them to an online SSVEP-based BCI where the user moves his/her head.},
}
@article {pmid38082886,
year = {2023},
author = {Jain, A and Kumar, L},
title = {EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341052},
pmid = {38082886},
issn = {2694-0604},
mesh = {Biomechanical Phenomena ; *Hand ; *Neural Networks, Computer ; Upper Extremity ; Electroencephalography/methods ; },
abstract = {Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data.},
}
@article {pmid38082880,
year = {2023},
author = {Milling, M and Lienhart, M and Oksymets, Y and Gebhard, A and Brugger, M and Westerhausen, C and Schuller, BW},
title = {NeuroCellCentreDB: Exploring a Novel Dataset for Neuron-like Cell Centre Detection with Deep Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340060},
pmid = {38082880},
issn = {2694-0604},
mesh = {*Neural Networks, Computer ; *Microscopy ; Automation ; Cell Proliferation ; Neurons ; },
abstract = {The manipulation and stimulation of cell growth is invaluable for neuroscience research such as brain-machine interfaces or applications of neural tissue engineering. For the implementation of such research avenues, in particular the analysis of cells' migration behaviour, and accordingly, the determination of cell positions on microscope images is essential, causing a current need for labour-intensive, manual annotation efforts of the cell positions. In an attempt towards automation of the required annotation efforts, we i) introduce NeuroCellCentreDB, a novel dataset of neuron-like cells on microscope images with annotated cell centres, ii) evaluate a common (bounding box-based) object detector, faster region-based convolutional neural network (FRCNN), for the task at hand, and iii) design and test a fully convolutional neural network, with the specific goal of cell centre detection. We achieve an F1 score of up to 0.766 on the test data with a tolerance radius of 16 pixels. Our code and dataset are publicly available.},
}
@article {pmid38082842,
year = {2023},
author = {Wang, L and Feng, L and Tang, T and Yang, D and Wei, Y},
title = {Brainprint Recognition Based on the Stable SSVEP Space-Frequency Energy Distribution.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341185},
pmid = {38082842},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Algorithms ; Photic Stimulation ; Electroencephalography/methods ; Biomarkers ; },
abstract = {Brainprint recognition has received increasing attention in information security. Electroencephalography (EEG) signals measured under task-related or task-free conditions have been exploited as brain biometrics. However, what components make the uniqueness of one's brain signals remains unclear. In this study, we proposed an interpretable biomarker based on steady-state visual evoked potentials (SSVEP) signals for EEG biometric identification. Firstly, we recovered pure SSVEP components from EEG by a point-position equivalent reconstruction (PPER) method. Then, we calculated the distribution properties of SSVEP components in space and frequency. By using the uniform manifold approximation and projection, we reduced the distribution features to 2-dimensions, which shows the separability of the subjects. Lastly, we built a long short-term memory (LSTM) network to perform brainprint recognition on the SSVEP benchmark dataset. The average recognition accuracy can reach up to 98.33%. Our results demonstrate that the space-frequency energy feature of SSVEP is an effective and interpretable biomarker for brainprint recognition. This study provides a further understanding of the uniqueness of individual EEG signal, and facilitates its potential application for personal identification.},
}
@article {pmid38082777,
year = {2023},
author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D},
title = {Experimental validation on dual-frequency outperforms single-frequency SSVEP with large numbers of targets within a given frequency range.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340718},
pmid = {38082777},
issn = {2694-0604},
mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation/methods ; *Brain-Computer Interfaces ; Neurologic Examination ; },
abstract = {Multi-frequency steady-state visual evoked potential (SSVEP) aims to increase the number of targets in SSVEP-based brain-computer interfaces. However, the effectiveness of multi-frequency SSVEP when there is a large number of targets compared to traditional single-frequency SSVEP has not been demonstrated to date. It is also unclear the degree to which multi-frequency SSVEP outperforms single-frequency SSVEP as the number of targets increases. This study directly compares single-frequency and dual-frequency SSVEPs for different numbers of targets within a fixed (5 Hz) frequency range. Our results demonstrate that dual-frequency SSVEP maintains its performance at a high level of accuracy in the range while single-frequency SSVEP performance falls as the number of targets becomes very high within the given frequency range. In this particular study, dual-frequency SSVEP has a clear advantage when there are more than 120 targets in a 5 Hz frequency range.},
}
@article {pmid38082729,
year = {2023},
author = {Tang, T and Xu, Z and Wei, Y and Feng, L and Xu, K and Yang, D and Chambers, C and Qu, S},
title = {A 4.43 TΩ ZIN 0.0128 mm[2] Cascaded Instrumentation Amplifier with Input-biased Pseudo Resistor for Implantable Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340422},
pmid = {38082729},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Equipment Design ; Prostheses and Implants ; Electric Impedance ; },
abstract = {A cascaded instrumentation amplifier (CaIA) with input-biased pseudo resistors (IBPR) is presented for implantable brain machine interfaces (BMI). The gain distribution of two-stage cascaded amplifiers, instead of a single-stage amplifier, helps to achieve an input impedance of 4.43TΩ at 100Hz, and maintain the small active area (0.0128 mm[2]). The input-biased pseudo resistors contribute to a much lower high-pass corner (fHP=0.00011Hz) compared with the conventional structure, the input-referred noise is only 3.836μVrms integrated from 0.5Hz to 10kHz with 0.98μW power consumption.Clinical Relevance- This establishes an area-efficient amplifier design with ultra-high input impedance (4.43TΩ at 100Hz) and hyper-low high-pass corner frequency (fHP=0.00011Hz), which is suitable for long-term monitoring of neural activities (including slow oscillations) in implantable brain-machine interfaces.},
}
@article {pmid38082727,
year = {2023},
author = {Khan, SU and Majid, M and Linguraru, MG and Muhammad Anwar, S},
title = {Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10341008},
pmid = {38082727},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Upper Extremity ; Electroencephalography/methods ; Movement ; Motion ; },
abstract = {An accurate classification of upper limb movements using electroencephalogram (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motions that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.Clinical relevance- This research shows that movement execution of upper limbs is classified with significant accuracy by employing a spectrogram of the EEG signals and a pre-trained deep learning model which is fine-tuned for the downstream task.},
}
@article {pmid38082707,
year = {2023},
author = {Reddy, TJ and Reddy, MR},
title = {Performance of Empirical Mode Decomposition for Frequency Identification in SSVEP Based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340245},
pmid = {38082707},
issn = {2694-0604},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Photic Stimulation ; Electroencephalography/methods ; Neurologic Examination ; },
abstract = {Empirical mode decomposition based conventional correlation (EMDCC) method is proposed to identify the frequency components in steady state visual evoked potentials (SSVEP) in electroencephalogram(EEG).The main aim of the proposed EMDCC method is to recognise narrow band frequency components that are present in SSVEP. The study is evaluated on two datasets. The first one is a 40 target benchmark dataset obtained from 35 subjects and the second is a 4 class Inhouse dataset collected from 10 healthy participants. The mean detection accuracy of the conventional correlation method is 85.64 % for the benchmark dataset and it is improved to 93.79 % in the proposed method. The mean detection accuracy of the conventional correlation method is 67.5 % for the Inhouse dataset and it is increased to 82.5 % in the proposed method. The mean detection accuracy of the proposed EMDCC method is also compared to time-weighting canonical correlation analysis (TWCCA) for the benchmark dataset. The mean detection accuracy of TWCCA is 91.04 %. Hence the results show better detection accuracies in the proposed EMDCC method than the simple conventional correlation method and also the existing TWCCA method.},
}
@article {pmid38082701,
year = {2023},
author = {Tian, Y and Zhang, L and Do, TT and Liu, J and Wang, YK and Lin, CT},
title = {Classification of inattentional blindness using brain dynamics of ERPs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-6},
doi = {10.1109/EMBC40787.2023.10340416},
pmid = {38082701},
issn = {2694-0604},
mesh = {Humans ; *Attention ; *Brain ; Cognition ; Evoked Potentials ; Blindness ; },
abstract = {Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources. To the best of our knowledge, however, past research has not fully uncovered the neurological characteristics of IB nor classified these characteristics in life-alike virtual situations. Therefore, the purpose of this study is to determine whether ERP dynamics in the brain may be utilised as a neural feature to predict the occurrence of IB using machine learning (ML) algorithms. In a virtual reality simulation of an IB experiment, 30 participants' behaviour and Electroencephalography (EEG) measurements were obtained. Participants were given a target detection task in the IB experiment without knowing the unattended shapes displayed on the background building. The targets were presented in three different sensory modalities (auditory, visual, and visual-auditory). On the post-experiment questionnaire, participants who claimed not to have noticed the unattended shapes were assigned to the IB group. Subsequently, the Aware group was formed from individuals who reported seeing the unattended shapes. Using EEGNet to classify IB and Aware groups demonstrated a high classification performance. According to the research, ERP brain dynamics are associated with the awareness of unattended shapes and have the potential to serve as a reliable indication for predicting the visual consciousness of unexpected objects.(p/)(p)Clinical relevance- This research offers a potential brain marker for the mixed-reality and BCI systems that will be used in the future to identify cognitive deterioration, maintain attentional capacity, and prevent disasters.},
}
@article {pmid38082700,
year = {2023},
author = {Rimbert, S and Trocellier, D and Lotte, F},
title = {Impact of the baseline temporal selection on the ERD/ERS analysis for Motor Imagery-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340748},
pmid = {38082700},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy/methods ; *Motor Cortex ; },
abstract = {Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) are neurotechnologies that exploit the modulation of sensorimotor rhythms over the motor cortices, respectively known as Event-Related Desynchronization (ERD) and Synchronization (ERS). The interpretation of ERD/ERS is directly related to the selection of the baseline used to estimate them, and might result in a misleading ERD/ERS visualization. In fact, in BCI paradigms, if two trials are separated by a few seconds, taking a baseline close to the end of the previous trial could result in an over-estimation of the ERD, while taking a baseline too close to the upcoming trial could result in an under-estimation of the ERD. This phenomenon may cause a functional misinterpretation of the ERD/ERS phenomena in MI-BCI studies. This may also impair BCI performances for MI vs Rest classification, since such baselines are often used as resting states. In this paper, we propose to investigate the effect of several baseline time window selections on ERD/ERS modulations and BCI performances. Our results show that considering the selected temporal baseline effect is essential to analyze the modulations of ERD/ERS during MI-BCI use.},
}
@article {pmid38082699,
year = {2023},
author = {Huang, HY and Lin, YP},
title = {Validation of Model-Basis Transfer Learning for a Personalized Electroencephalogram-Based Emotion-Classification Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340188},
pmid = {38082699},
issn = {2694-0604},
mesh = {Humans ; Reproducibility of Results ; *Emotions/physiology ; Electroencephalography ; *Brain-Computer Interfaces ; Machine Learning ; },
abstract = {The electroencephalogram (EEG)-based affective brain-computer interface (aBCI) has attracted extensive attention in multidisciplinary fields in the past decade. However, the inherent variability of emotional responses recorded in EEG signals increases the vulnerability of pre-trained machine-learning models and impedes the applicability of aBCIs with real-life settings. To overcome the shortcomings associated with the limited personal data in affective modeling, this study proposes a model-basis transfer learning (TL) approach and verifies its feasibility to construct a personalized model using less emotion-annotated data in a longitudinal eight-day dataset comprising data on 10 subjects. By performing daily reliability testing, the proposed TL approach outperformed the subject-dependent counterpart (using limited data only) by ~6% in binary valence classification after recycling a compact set of the eight most transferable models from other subjects. These empirical findings practically contribute to progress in applying TL in realistic aBCI applications.Clinical Relevance- The proposed model-basis TL approach overcomes the shortcoming of inherent variability in EEG signals, supporting realistic aBCI applications.},
}
@article {pmid38082697,
year = {2023},
author = {Li, M and Chen, S and Zhao, Z and Wang, Y},
title = {Tracking the Dynamic Neural Connectivity via Conjugate Gradient Optimization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340664},
pmid = {38082697},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; *Neurons/physiology ; *Brain-Computer Interfaces ; Probability ; Biomechanical Phenomena ; },
abstract = {Neural connectivity describes how neuron populations coordinate and create cognitive and behavioral functions. Neural connectivity performs dynamics where its population spiking responses to stimuli or intention change over time. Brain-machine interface (BMI) provides a framework for studying dynamical neural connectivity. In BMI, point process is a powerful technique in analyzing the single neuronal tuning. And generalized linear mode (GLM) as an encoding model can incorporate the tuning in kinematics and the neural connectivity. Quantification and tracking of dynamic neural connectivity can contribute to the elucidation of the generation of brain functions in a computational way. However, most of the previous work focused on single neuronal adaptation to kinematics. When a neuron is significantly modulated by some other neurons in some tasks, the shape of the log likelihood function for single neuronal observations can be narrowed in some dimensions. And the existing gradient-based methods are not able to reach the optimum in a fast and adaptive searching way. In this work, to maximize the likelihood of observations and obtain the dynamic neural connectivity tuning parameters, we proposed a conjugate gradient-based encoding model (CGE). We illustrate CGE for likelihood function using the real experimental data under manual control and brain control. The results show that the proposed CGE has better performance in tracking the dynamic neural connectivity tuning parameters and modeling neural encoding.Clinical Relevance- Not directly related.},
}
@article {pmid38082677,
year = {2023},
author = {Saha, S and Baumert, M and McEwan, A},
title = {Can Inter-Subject Associativity Predict Data-Driven BCI Performance?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340490},
pmid = {38082677},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Brain ; },
abstract = {Intra- and inter-subject variability causes covariate shifts in training and testing feature spaces, resulting in low sensorimotor (SMR) brain-computer interface (BCI) performance for practical implementation. Studies involving data-driven transfer learning strategies demonstrated improving BCI performance by covariate shift adaptation. In this study, we aim to illustrate if inter-subject associativity (e.g., subjects having similar SMR brain dynamics) can predict data-driven inter-subject BCI performance. We implemented a BCI classification pipeline with a common spatial pattern, principal component analysis and linear discriminant analysis for performance evaluation. Both intra- and inter-subject BCI were evaluated in 5-Fold Validation settings. We further proposed a Bhattacharyya distance-based covariate shift score (CSS) for assessing the difference between training and testing feature domains. We performed Pearson correlation analysis to draw the relation-ship between BCI performance and CSS. Intra-subject BCI performances were significantly and negatively correlated with CSS (r = -0.94, p < 0.05). For the inter-subject experiment, BCI performances were also highly and negatively associated with CSS (r = -0.61, p < 0.05). However, this data-driven BCI evaluation framework does not necessarily manifest inter-subject associativity in BCI performance, requiring further investigations for a conclusion.Clinical relevance- If it predicts BCI performance successfully, inter-subject associativity could reduce time-consuming and annoying subject-specific calibration for the users.},
}
@article {pmid38082644,
year = {2023},
author = {K, SG and Vinod, AP and Subasree, R},
title = {A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340319},
pmid = {38082644},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Upper Extremity ; Imagination ; Electroencephalography/methods ; *Stroke/diagnosis ; },
abstract = {Electroencephalogram (EEG) based non-invasive Brain Computer Interface (BCI) system is gaining significant attention as a promising solution for stroke rehabilitation. Accurate selection of informative EEG time segment, that accommodates the specific neural activity patterns associated with the underlying mental task can help to improve the efficacy of the BCI system. In this work, we propose a phase-based EEG epoch selection algorithm to extract the discriminative EEG time segment corresponding to bi-directional hand motor imagery. The imagined center-out hand movement in two directions is decoded using the selected epoch of the EEG, recorded from 16 stroke patients with hemiparesis and specifically hand weakness. Phase Lock Value (PLV) EEG features extracted from the selected EEG epoch is used as discriminative feature for binary classification of imagined hand movement direction using Linear Discriminant Analysis. The use of selected EEG epoch yielded an improvement of 11.5% and 11.7% in the average direction classification accuracy of calibration and feedback session data respectively, compared to the baseline method employing the whole EEG signal. In addition to improvement in decoding accuracy, the epoch selection also yielded an average Information Transfer Rate (ITR) of 39.8±24.6 bits per minute, which is 86% improvement compared to the baseline method.Clinical Relevance- The proposed Motor Imagery (MI)-BCI system may be of clinical relevance as an active rehabilitation tool for stroke-affected patients, to enhance neural plasticity and recovery of centre-out activities of affected hand and forms a strong platform for MI-BCI coupled with exoskeletons or prosthesis rehabilitation.},
}
@article {pmid38082620,
year = {2023},
author = {An, Y and Wong, JKW and Ling, SH},
title = {An EEG-based brain-computer interface for real-time multi-task robotic control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340310},
pmid = {38082620},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics ; Reproducibility of Results ; *Robotic Surgical Procedures ; Electroencephalography/methods ; },
abstract = {The Brain Computer Interface (BCI) is the communication between the human brain and the computer. Electroencephalogram (EEG) is one of the biomedical signals which can be obtained by attaching electrodes to the scalp. Some EEG related applications can be developed to help disabled people, such as EEG based wheelchair or robotic arm. A hybrid BCI real-time control system is proposed to control a multi-tasks BCI robot. In this system, a sliding window based online data segmentation strategy is proposed to segment training data, which enable the system to learn the dynamic features when the subject's brain state transfer from a rest state to a task execution state. The features help the system achieve real-time control and ensure the continuity of executing actions. In addition, Common Spatial Pattern (CSP) can better extract the spatial features of these continuous actions from the dynamic data to ensure that multiple control commands are accurately classified. In the experiment, three subjects' EEG data is collected, trained and tested the performance and reliability of the proposed control system. The system records the robot's spending time, moving distance, and the number of objects pushing down. Experimental results are given to show the feasibility of the real-time control system. Compared to real-time remote controller, the proposed system can achieve similar performance. Thus, the proposed hybrid BCI real-time control system is able to control the robot in the real-time environment and can be used to develop robot-aided arm training methods based on neurological rehabilitation principles for stroke and brain injury patients.},
}
@article {pmid38082609,
year = {2023},
author = {Wang, Z and Daly, I and Li, J},
title = {An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-4},
doi = {10.1109/EMBC40787.2023.10340894},
pmid = {38082609},
issn = {2694-0604},
mesh = {*Deep Learning ; Electroencephalography/methods ; Imagination ; Lower Extremity ; Neural Networks, Computer ; Humans ; },
abstract = {Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications. This is because CNN has the property of spatial invariance, and LSTM can capture temporal associations among features. A combination of CNN and LSTM could enhance the classification performance of EEG signals due to the complementation of their strengths. Such a combination has been applied to MI classification based on EEG. However, most studies focused on either the upper limbs or treated both lower limbs as a single class, with only limited research performed on separate lower limbs. We, therefore, explored hybrid models (different combinations of CNN and LSTM) and evaluated them in the case of individual lower limbs. In addition, we classified multiple actions: MI, real movements and movement observations using four typical hybrid models and aimed to identify which model was the most suitable. The comparison results demonstrated that no model was significantly better than the others in terms of classification accuracy, but all of them were better than the chance level. Our study informs the possibility of the use of multiple actions in BCI systems and provides useful information for further research into the classification of separate lower limb actions.},
}
@article {pmid38082602,
year = {2023},
author = {Kokorin, K and Mu, J and John, SE and Grayden, DB},
title = {Predictive Shared Control of Robotic Arms Using Simulated Brain-Computer Interface Inputs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2023},
number = {},
pages = {1-5},
doi = {10.1109/EMBC40787.2023.10340222},
pmid = {38082602},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Imagery, Psychotherapy ; Motion ; Electroencephalography/methods ; },
abstract = {Low decoding accuracy makes brain-computer interface (BCI) control of a robotic arm difficult. Shared control (SC) can overcome limitations of a BCI by leveraging external sensor data and generating commands to assist the user. Our study explored whether reaching targets with a robot end-effector was easier using SC rather than direct control (DC). We simulated a motor imagery BCI using a joystick with noise introduced to explicitly control interface accuracy to be 65% or 79%. Compared to DC, our prediction-based implementation of SC led to a significant reduction in the trajectory length of successful reaches for 4 (3) out of 5 targets using the 65% (79%) accurate interface, with failure rates being equivalent to DC for 2 (1) out of 5 targets. Therefore, this implementation of SC is likely to improve reaching efficiency but at the cost of more failures. Additionally, the NASA Task Load Index results suggest SC reduced user workload.Clinical relevance-Shared control can minimise the impact of BCI decoder errors on robot motion, making robotic arm control using noninvasive BCIs more viable.},
}
@article {pmid38082130,
year = {2023},
author = {Tozer, L},
title = {'Biocomputer' combines lab-grown brain tissue with electronic hardware.},
journal = {Nature},
volume = {624},
number = {7992},
pages = {481},
pmid = {38082130},
issn = {1476-4687},
mesh = {Humans ; *Brain ; *Brain-Computer Interfaces ; *Computers ; *Electronics/instrumentation/methods ; *Organoids ; Voice Recognition ; },
}
@article {pmid38081116,
year = {2024},
author = {Mei, J and Luo, R and Xu, L and Zhao, W and Wen, S and Wang, K and Xiao, X and Meng, J and Huang, Y and Tang, J and Cheng, L and Xu, M and Ming, D},
title = {MetaBCI: An open-source platform for brain-computer interfaces.},
journal = {Computers in biology and medicine},
volume = {168},
number = {},
pages = {107806},
doi = {10.1016/j.compbiomed.2023.107806},
pmid = {38081116},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Software ; Brain Mapping ; },
abstract = {BACKGROUND: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain.<br><br>METHOD: This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases.<br><br>RESULTS: The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities.<br><br>CONCLUSIONS: MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.},
}
@article {pmid38079202,
year = {2023},
author = {Alder, G and Taylor, D and Rashid, U and Olsen, S and Brooks, T and Terry, G and Niazi, IK and Signal, N},
title = {A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach.},
journal = {JMIR rehabilitation and assistive technologies},
volume = {10},
number = {},
pages = {e49702},
pmid = {38079202},
issn = {2369-2529},
abstract = {BACKGROUND: Rehabilitation technologies for people with stroke are rapidly evolving. These technologies have the potential to support higher volumes of rehabilitation to improve outcomes for people with stroke. Despite growing evidence of their efficacy, there is a lack of uptake and sustained use in stroke rehabilitation and a call for user-centered design approaches during technology design and development. This study focuses on a novel rehabilitation technology called exciteBCI, a complex neuromodulatory wearable technology in the prototype stage that augments locomotor rehabilitation for people with stroke. The exciteBCI consists of a brain computer interface, a muscle electrical stimulator, and a mobile app.<br><br>OBJECTIVE: This study presents the evaluation phase of an iterative user-centered design approach supported by a qualitative descriptive methodology that sought to (1) explore users' perspectives and experiences of exciteBCI and how well it fits with rehabilitation, and (2) facilitate modifications to exciteBCI design features.<br><br>METHODS: The iterative usability evaluation of exciteBCI was conducted in 2 phases. Phase 1 consisted of 3 sprint cycles consisting of single usability sessions with people with stroke (n=4) and physiotherapists (n=4). During their interactions with exciteBCI, participants used a "think-aloud" approach, followed by a semistructured interview. At the end of each sprint cycle, device requirements were gathered and the device was modified in preparation for the next cycle. Phase 2 focused on a "near-live" approach in which 2 people with stroke and 1 physiotherapist participated in a 3-week program of rehabilitation augmented by exciteBCI (n=3). Participants completed a semistructured interview at the end of the program. Data were analyzed from both phases using conventional content analysis.<br><br>RESULTS: Overall, participants perceived and experienced exciteBCI positively, while providing guidance for iterative changes. Five interrelated themes were identified from the data: (1) "This is rehab" illustrated that participants viewed exciteBCI as having a good fit with rehabilitation practice; (2) "Getting the most out of rehab" highlighted that exciteBCI was perceived as a means to enhance rehabilitation through increased engagement and challenge; (3) "It is a tool not a therapist," revealed views that the technology could either enhance or disrupt the therapeutic relationship; and (4) "Weighing up the benefits versus the burden" and (5) "Don't make me look different" emphasized important design considerations related to device set-up, use, and social acceptability.<br><br>CONCLUSIONS: This study offers several important findings that can inform the design and implementation of rehabilitation technologies. These include (1) the design of rehabilitation technology should support the therapeutic relationship between the patient and therapist, (2) social acceptability is a design priority in rehabilitation technology but its importance varies depending on the use context, and (3) there is value in using design research methods that support understanding usability in the context of sustained use.},
}
@article {pmid38075283,
year = {2023},
author = {Yun, R and Rembado, I and Perlmutter, SI and Rao, RPN and Fetz, EE},
title = {Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1273627},
pmid = {38075283},
issn = {1662-4548},
support = {F31 NS118781/NS/NINDS NIH HHS/United States ; P51 OD010425/OD/NIH HHS/United States ; P51 RR000166/RR/NCRR NIH HHS/United States ; U42 OD011123/OD/NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
abstract = {Different sleep stages have been shown to be vital for a variety of brain functions, including learning, memory, and skill consolidation. However, our understanding of neural dynamics during sleep and the role of prominent LFP frequency bands remain incomplete. To elucidate such dynamics and differences between behavioral states we collected multichannel LFP and spike data in primary motor cortex of unconstrained macaques for up to 24 h using a head-fixed brain-computer interface (Neurochip3). Each 8-s bin of time was classified into awake-moving (Move), awake-resting (Rest), REM sleep (REM), or non-REM sleep (NREM) by using dimensionality reduction and clustering on the average spectral density and the acceleration of the head. LFP power showed high delta during NREM, high theta during REM, and high beta when the animal was awake. Cross-frequency phase-amplitude coupling typically showed higher coupling during NREM between all pairs of frequency bands. Two notable exceptions were high delta-high gamma and theta-high gamma coupling during Move, and high theta-beta coupling during REM. Single units showed decreased firing rate during NREM, though with increased short ISIs compared to other states. Spike-LFP synchrony showed high delta synchrony during Move, and higher coupling with all other frequency bands during NREM. These results altogether reveal potential roles and functions of different LFP bands that have previously been unexplored.},
}
@article {pmid38075282,
year = {2023},
author = {Zhang, Y and Zhang, Y and Jiang, Z and Xu, M and Qing, K},
title = {The effect of EEG and fNIRS in the digital assessment and digital therapy of Alzheimer's disease: a systematic review.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1269359},
pmid = {38075282},
issn = {1662-4548},
abstract = {In the context of population aging, the growing problem of Alzheimer's disease (AD) poses a great challenge to mankind. Although there has been considerable progress in exploring the etiology of AD, i.e., the important role of amyloid plaques and neurofibrillary tangles in the progression of AD has been widely accepted by the scientific community, traditional treatment and monitoring modalities have significant limitations. Therefore novel evaluation and treatment modalities for Alzheimer's disease are called for emergence. In this research, we sought to review the effectiveness of digital treatment based on monitoring using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). This work searched four electronic databases using a keyword approach and focused on journals focusing on AD and geriatric cognition. Finally, 21 articles were included. The progress of digital therapy and outcome monitoring in AD was reviewed, including digital therapy approaches on different platforms and different neuromonitoring techniques. Because biomarkers such as theta coherence, alpha and beta rhythms, and oxyhemoglobin are effective in monitoring the cognitive level of AD patients, and thus the efficacy of digital therapies, this review particularly focuses on the biomarker validation results of digital therapies. The results show that digital treatment based on biomarker monitoring has good effectiveness. And the effectiveness is reflected in the numerical changes of biomarker indicators monitored by EEG and fNIRS before and after digital treatment. Increases or decreases in the values of these indicators collectively point to improvements in cognitive function (mostly moderate to large effect sizes). The study is the first to examine the state of digital therapy in AD from the perspective of multimodal monitoring, which broadens the research perspective on the effectiveness of AD and gives clinical therapists a "reference list" of treatment options. They can select a specific protocol from this "reference list" in order to tailor digital therapy to the needs of individual patients.},
}
@article {pmid38075279,
year = {2023},
author = {Ottenhoff, MC and Verwoert, M and Goulis, S and Colon, AJ and Wagner, L and Tousseyn, S and van Dijk, JP and Kubben, PL and Herff, C},
title = {Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1283491},
pmid = {38075279},
issn = {1662-4548},
abstract = {Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information.},
}
@article {pmid38075275,
year = {2023},
author = {Tian, P and Xu, G and Han, C and Zhang, X and Zheng, X and Wei, F and Zhang, S and Zhao, Z},
title = {A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1278652},
pmid = {38075275},
issn = {1662-4548},
abstract = {INTRODUCTION: In recent years, more and more attention has been paid to the visual fatigue caused by steady state visual evoked potential (SSVEP) paradigm. It is well known that the large-scale application of brain-computer interface is closely related to SSVEP, and the fatigue caused by SSVEP paradigm leads to the reduction of application effect. At present, the mainstream method of objectively quantifying visual fatigue in SSVEP paradigm is based on traditional canonical correlation analysis (CCA).<br><br>METHODS: In this paper, we propose a new SSVEP paradigm visual fatigue quantification algorithm based on underdamped second-order stochastic resonance (USSR) to accurately quantify visual fatigue caused by SSVEP paradigm in different working modes using single-channel electroencephalogram (EEG) signals. This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. We not only compared the new algorithm with CCA, but also with the traditional subjective quantitative visual fatigue gold standard Likert fatigue scale.<br><br>RESULTS: There was no significant difference (p&#x2009;=&#x2009;0.090) between the quantitative value of paradigm fatigue obtained by the single channel SSVEP processed by the new algorithm and the gold standard of subjective fatigue quantification, while there was a significant difference (p&#x2009;<&#x2009;0.001[***]) between the quantitative value of paradigm fatigue obtained by the traditional multi-channel CCA algorithm and the gold standard of subjective fatigue quantification.<br><br>DISCUSSION: The conclusion shows that the quantization value obtained by the new algorithm can better match the subjective gold standard score, which also shows that the new algorithm is more reliable, which reflects the superiority of the new algorithm.},
}
@article {pmid38075253,
year = {2023},
author = {Kim, B and Lee, B and Mandakhbayar, N and Kim, Y and Song, Y and Doh, J and Lee, JH and Jeong, B and Song, KH},
title = {Effect of lyophilized gelatin-norbornene cryogel size on calvarial bone regeneration.},
journal = {Materials today. Bio},
volume = {23},
number = {},
pages = {100868},
pmid = {38075253},
issn = {2590-0064},
abstract = {Molding processes with molds containing topographical structures have been used for fabrication of hydrogel and cryogel particles. However, they can involve difficulties in separation of fabricated particles with complex shape from the molds or repeated fabrication of the particles although the overall processes do not require much skill and equipment. In this study, molds with etched superhydrophobic patterns have been developed by etching polytetrafluoroethylene (PTFE) blocks in user-defined designs with a femtosecond (FS) laser-based etching system. Lyophilized cryogel particles with various designs and sizes were fabricated by molding precursors with these PTFE molds. Additionally, the clean and easy separation of particles from the molds allowed repeated fabrication of the particles. For an application, relatively 'big' gelatin-norbornene (GelNB) cryogel particles prepared via molding with polydimethylsiloxane (PDMS) molds, swelling in phosphate buffered saline (PBS) and slicing height in half and 'small' GelNB cryogel particles fabricated with the PTFE molds were fabricated. Then, they were used to study scaffold size effect on calvarial bone regeneration. The molds generated with the FS laser-based etching system can be useful for various applications that require the mass production of cryogel particles in various geometries.},
}
@article {pmid38073546,
year = {2024},
author = {Mammone, N and Ieracitano, C and Spataro, R and Guger, C and Cho, W and Morabito, FC},
title = {A Few-Shot Transfer Learning Approach for Motion Intention Decoding from Electroencephalographic Signals.},
journal = {International journal of neural systems},
volume = {34},
number = {2},
pages = {2350068},
doi = {10.1142/S0129065723500685},
pmid = {38073546},
issn = {1793-6462},
mesh = {*Intention ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Machine Learning ; Imagination ; Algorithms ; },
abstract = {In this study, a few-shot transfer learning approach was introduced to decode movement intention from electroencephalographic (EEG) signals, allowing to recognize new tasks with minimal adaptation. To this end, a dataset of EEG signals recorded during the preparation of complex sub-movements was created from a publicly available data collection. The dataset was divided into two parts: the source domain dataset (including 5 classes) and the support (target domain) dataset, (including 2 classes) with no overlap between the two datasets in terms of classes. The proposed methodology consists in projecting EEG signals into the space-frequency-time domain, in processing such projections (rearranged in channels × frequency frames) by means of a custom EEG-based deep neural network (denoted as EEGframeNET5), and then adapting the system to recognize new tasks through a few-shot transfer learning approach. The proposed method achieved an average accuracy of 72.45 ± 4.19% in the 5-way classification of samples from the source domain dataset, outperforming comparable studies in the literature. In the second phase of the study, a few-shot transfer learning approach was proposed to adapt the neural system and make it able to recognize new tasks in the support dataset. The results demonstrated the system's ability to adapt and recognize new tasks with an average accuracy of 80 ± 0.12% in discriminating hand opening/closing preparation and outperforming reported results in the literature. This study suggests the effectiveness of EEG in capturing information related to the motor preparation of complex movements, potentially paving the way for BCI systems based on motion planning decoding. The proposed methodology could be straightforwardly extended to advanced EEG signal processing in other scenarios, such as motor imagery or neural disorder classification.},
}
@article {pmid38073279,
year = {2024},
author = {Lai, J and Li, S and Wei, C and Chen, J and Fang, Y and Song, P and Hu, S},
title = {Mapping the global, regional and national burden of bipolar disorder from 1990 to 2019: trend analysis on the Global Burden of Disease Study 2019.},
journal = {The British journal of psychiatry : the journal of mental science},
volume = {224},
number = {2},
pages = {36-46},
doi = {10.1192/bjp.2023.127},
pmid = {38073279},
issn = {1472-1465},
support = {81971271; 82201676; 81930033//National Natural Science Foundation of China/ ; },
mesh = {Male ; Female ; Humans ; Global Burden of Disease ; *Bipolar Disorder ; Prevalence ; Incidence ; *Persons with Disabilities ; Global Health ; Quality-Adjusted Life Years ; },
abstract = {BACKGROUND: Data on trends in the epidemiological burden of bipolar disorder are scarce.<br><br>AIMS: To provide an overview of trends in bipolar disorder burden from 1990 to 2019.<br><br>METHOD: Revisiting the Global Burden of Disease Study 2019, we analysed the number of cases, calculated the age-standardised rate (per 100 000 population) and estimated annual percentage change (EAPC) of incidence, prevalence and years lived with disability (YLDs) for bipolar disorder from 1990 to 2019. The independent effects of age, period and cohort were estimated by the age-period-cohort modelling.<br><br>RESULTS: Globally, the bipolar disorder-related prevalent cases, incident cases and number of YLDs all increased from 1990 to 2019. Regionally, the World Health Organization Region of the Americas accounted for the highest estimated YLD number and rate, with the highest age-standardised prevalence rate in 1990 and 2019 and highest EAPC of prevalence. By sociodemographic index (SDI) quintiles, all five SDI regions saw an increase in estimated incident cases. Nationally, New Zealand reported the highest age-standardised rate of incidence, prevalence and YLDs in 1990 and 2019. The most prominent age effect on incidence rate was in those aged 15-19 years. Decreased effects of period on incidence, prevalence and YLD rates was observed overall and in females, not in males. The incidence, prevalence and YLD rates showed an unfavourable trend in the younger cohorts born after 1990, with males reporting a higher cohort risk than females.<br><br>CONCLUSIONS: From 1990 to 2019, the overall trend of bipolar disorder burden presents regional and national variations and differs by age, sex, period and cohort.},
}
@article {pmid38073149,
year = {2024},
author = {Ghorbanlou, M and Moradi, F and Kazemi-Galougahi, MH and Abdollahi, M},
title = {In search of subcortical and cortical morphologic alterations of a normal brain through aging: an investigation by computed tomography scan.},
journal = {Anatomy &amp; cell biology},
volume = {57},
number = {1},
pages = {45-60},
pmid = {38073149},
issn = {2093-3665},
abstract = {Morphologic changes in the brain through aging, as a physiologic process, may involve a wide range of variables including ventricular dilation, and sulcus widening. This study reports normal ranges of these changes as standard criteria. Normal brain computed tomography scans of 400 patients (200 males, 200 females) in every decade of life (20 groups each containing 20 participants) were investigated for subcortical/cortical atrophy (bicaudate width [BCW], third ventricle width [ThVW], maximum length of lateral ventricle at cella media [MLCM], bicaudate index [BCI], third ventricle index [ThVI], and cella media index 3 [CMI3], interhemispheric sulcus width [IHSW], right hemisphere sulci diameter [RHSD], and left hemisphere sulci diameter [LHSD]), ventricular symmetry. Distribution and correlation of all the variables were demonstrated with age and a multiple linear regression model was reported for age prediction. Among the various parameters of subcortical atrophy, BCW, ThVW, MLCM, and the corresponding indices of BCI, ThVI, and CMI3 demonstrated a significant correlation with age (R[2]≥0.62). All the cortical atrophy parameters including IHSW, RHSD, and LHSD demonstrated a significant correlation with age (R[2]≥0.63). This study is a thorough investigation of variables in a normal brain which can be affected by aging disclosing normal ranges of variables including major ventricular variables, derived ventricular indices, lateral ventricles asymmetry, cortical atrophy, in every decade of life introducing BW, ThVW, MLCM, BCI, ThVI, CMI3 as most significant ventricular parameters, and IHSW, RHSD, LHSD as significant cortical parameters associated with age.},
}
@article {pmid38072171,
year = {2024},
author = {Pan, H and Zhang, Y and Li, L and Qin, X},
title = {A design and implementation of multi-character classification scheme based on motor imagery EEG signals.},
journal = {Neuroscience},
volume = {538},
number = {},
pages = {22-29},
doi = {10.1016/j.neuroscience.2023.12.001},
pmid = {38072171},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; Imagery, Psychotherapy ; Movement ; *Brain-Computer Interfaces ; Brain ; Algorithms ; Imagination ; },
abstract = {In the field of brain-to-text communication, it is difficult to finish highly dexterous behaviors of writing multi-character by motor-imagery-based brain-computer interface (MI-BCI), setting a barrier to restore communication in people who have lost the ability to move and speak. In this paper, we design and implement a multi-character classification scheme based on 29 characters of motor imagery (MI) electroencephalogram (EEG) signals, which contains 26 English letters and 3 punctuation marks. Firstly, we design a novel experimental paradigm to increase the variety of BCI inputs by asking subjects to imagine the movement of writing 29 characters instead of gross motor skills such as reaching or grasping. Secondly, because of the high dimension of EEG signals, we adopt power spectral density (PSD), principal components analysis (PCA), kernel principal components analysis (KPCA) respectively to decompose EEG signals and extract feature, and then test the results with pearson product-moment correlation coefficient (PCCs). Thirdly, we respectively employ k-nearest neighbor (kNN), support vector machine (SVM), extreme learning machine (ELM) and light gradient boosting machine (LightGBM) to classify 29 characters and compare the results. We have implemented a complete scheme, including paradigm design, signal acquisition, feature extraction and classification, which can effectively classify 29 characters. The experimental results show that the KPCA has the best feature extraction effect and the kNN has the highest classification accuracy, with the final classification accuracy reaching 96.2%, which is better than other studies.},
}
@article {pmid38069840,
year = {2024},
author = {Liu, D and Wang, T and Zhao, X and Chen, J and Yang, T and Shen, Y and Zhou, YD},
title = {Saturated fatty acids stimulate cytokine production in tanycytes via the PP2Ac-dependent signaling pathway.},
journal = {Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism},
volume = {44},
number = {6},
pages = {985-999},
pmid = {38069840},
issn = {1559-7016},
mesh = {Animals ; *Signal Transduction/drug effects ; *Ependymoglial Cells/metabolism/drug effects ; *Protein Phosphatase 2/metabolism ; *Cytokines/metabolism ; Male ; Diet, High-Fat ; Rats ; Fatty Acids/metabolism ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism ; Palmitic Acid/pharmacology ; Rats, Sprague-Dawley ; Hypothalamus/metabolism ; },
abstract = {The hypothalamic tanycytes are crucial for free fatty acids (FFAs) detection, storage, and transport within the central nervous system. They have been shown to effectively respond to fluctuations in circulating FFAs, thereby regulating energy homeostasis. However, the precise molecular mechanisms by which tanycytes modulate lipid utilization remain unclear. Here, we report that the catalytic subunit of protein phosphatase 2 A (PP2Ac), a serine/threonine phosphatase, is expressed in tanycytes and its accumulation and activation occur in response to high-fat diet consumption. In vitro, tanycytic PP2Ac responds to palmitic acid (PA) exposure and accumulates and is activated at an early stage in an AMPK-dependent manner. Furthermore, activated PP2Ac boosts hypoxia-inducible factor-1α (HIF-1α) accumulation, resulting in upregulation of an array of cytokines. Pretreatment with a PP2Ac inhibitor, LB100, prevented the PA-induced elevation of vascular endothelial growth factor (VEGF), fibroblast growth factor 1 (FGF1), hepatocyte growth factor (HGF), and dipeptidyl peptidase IV (DPPIV or CD26). Our results disclose a mechanism of lipid metabolism in tanycytes that involves the activation of PP2Ac and highlight the physiological significance of PP2Ac in hypothalamic tanycytes in response to overnutrition and efficacious treatment of obesity.},
}
@article {pmid38067961,
year = {2023},
author = {Mortier, S and Turkeš, R and De Winne, J and Van Ransbeeck, W and Botteldooren, D and Devos, P and Latré, S and Leman, M and Verdonck, T},
title = {Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {23},
pages = {},
pmid = {38067961},
issn = {1424-8220},
support = {G0A0220N//Research Foundation - Flanders/ ; },
mesh = {Humans ; *Artificial Intelligence ; Acoustic Stimulation ; *Electroencephalography ; Attention ; Event-Related Potentials, P300 ; Evoked Potentials ; },
abstract = {Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject's electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal-occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain-computer interfaces. Furthermore, they validate the EEG data collected in our experiment.},
}
@article {pmid38067731,
year = {2023},
author = {Fuentes-Martinez, VJ and Romero, S and Lopez-Gordo, MA and Minguillon, J and Rodríguez-Álvarez, M},
title = {Low-Cost EEG Multi-Subject Recording Platform for the Assessment of Students' Attention and the Estimation of Academic Performance in Secondary School.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {23},
pages = {},
pmid = {38067731},
issn = {1424-8220},
support = {B-TIC-352-UGR20//Junta de Andaluc&#xed;a/ ; PID2021-128529OA-I00, MCIN / AEI / 10.13039 / 501100011033//ERDF A way of making Europe/ ; PP2021.PP-28//University of Granada/ ; PROYEXCEL_00084//Council for Economic Transformation, Industry, Knowledge and Universities, Junta de Andaluc&#xed;a 2021/ ; },
mesh = {Humans ; *Students ; Schools ; *Academic Performance ; Learning ; Electroencephalography ; },
abstract = {The level of student attention in class greatly affects their academic performance. Teachers typically rely on visual inspection to react to students' attention in time, but this subjective method leads to inconsistencies across classes. Online education exacerbates the issue as students can turn off cameras and microphones to keep their own privacy. To address this, we present a novel, low-cost EEG-based platform for assessing students' attention and estimating their academic performance. In a study involving 34 secondary school students (aged 14 to 16), participants watched an academic video and answered evaluation questions while their EEG activity was recorded using a commercial headset. The results demonstrate a significant correlation (0.53, p-value = 0.003) between the power spectral density (PSD) of the EEG beta band (12-30 Hz) and students' academic performance. Additionally, there was a notable difference in PSD-beta between high and low academic performers. These findings encourage the use of PSD-beta for the immediate and objective assessment of both the student attention and the subsequent academic performance. The platform offers valuable and objective feedback to teachers, enhancing the effectiveness of both face-to-face and online teaching and learning environments.},
}
@article {pmid38067725,
year = {2023},
author = {Rosanne, O and Alves de Oliveira, A and Falk, TH},
title = {EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {23},
pages = {},
pmid = {38067725},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Reproducibility of Results ; Electroencephalography/methods ; Algorithms ; },
abstract = {Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.},
}
@article {pmid38067674,
year = {2023},
author = {Lima, JPS and Silva, LA and Delisle-Rodriguez, D and Cardoso, VF and Nakamura-Palacios, EM and Bastos-Filho, TF},
title = {Unraveling Transformative Effects after tDCS and BCI Intervention in Chronic Post-Stroke Patient Rehabilitation-An Alternative Treatment Design Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {23},
pages = {},
pmid = {38067674},
issn = {1424-8220},
mesh = {Female ; Humans ; Male ; *Brain-Computer Interfaces ; Quality of Life ; Recovery of Function/physiology ; *Stroke ; *Stroke Rehabilitation/methods ; *Transcranial Direct Current Stimulation/methods ; Upper Extremity ; Double-Blind Method ; },
abstract = {Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies. While upper-limb recovery has been extensively studied, research on lower limbs is still limited, despite their important role in locomotion, independence, and good quality of life. As the life and social costs due to neuromuscular disability are significant, the relatively low cost, safety, and portability of tDCS devices, combined with low-cost robotic systems, can optimize therapy and reduce rehabilitation costs, increasing access to cutting-edge technologies for neuromuscular rehabilitation. This study explores a novel approach by utilizing the following processes in sequence: tDCS, a motor imagery (MI)-based brain-computer interface (BCI) with virtual reality (VR), and a motorized pedal end-effector. These are applied to enhance the brain plasticity and accelerate the motor recovery of post-stroke patients. The results are particularly relevant for post-stroke patients with severe lower-limb impairments, as the system proposed here provides motor training in a real-time closed-loop design, promoting cortical excitability around the foot area (Cz) while the patient directly commands with his/her brain signals the motorized pedal. This strategy has the potential to significantly improve rehabilitation outcomes. The study design follows an alternating treatment design (ATD), which involves a double-blind approach to measure improvements in both physical function and brain activity in post-stroke patients. The results indicate positive trends in the motor function, coordination, and speed of the affected limb, as well as sensory improvements. The analysis of event-related desynchronization (ERD) from EEG signals reveals significant modulations in Mu, low beta, and high beta rhythms. Although this study does not provide conclusive evidence for the superiority of adjuvant mental practice training over conventional therapy alone, it highlights the need for larger-scale investigations.},
}
@article {pmid38066110,
year = {2024},
author = {Zhang, X and Wang, X and Zhu, J and Chen, K and Ullah, R and Tong, J and Shen, Y},
title = {Retinal VIP-amacrine cells: their development, structure, and function.},
journal = {Eye (London, England)},
volume = {38},
number = {6},
pages = {1065-1076},
pmid = {38066110},
issn = {1476-5454},
support = {LZ19H120001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Amacrine Cells ; *Vasoactive Intestinal Peptide ; Retina/physiology ; Retinal Ganglion Cells ; gamma-Aminobutyric Acid ; },
abstract = {Amacrine cells (ACs) are the most structurally and functionally diverse neuron type in the retina. Different ACs have distinct functions, such as neuropeptide secretion and inhibitory connection. Vasoactive intestinal peptide (VIP) -ergic -ACs are retina gamma-aminobutyric acid (GABA) -ergic -ACs that were discovered long ago. They secrete VIP and form connections with bipolar cells (BCs), other ACs, and retinal ganglion cells (RGCs). They have a specific structure, density, distribution, and function. They play an important role in myopia, light stimulated responses, retinal vascular disease and other ocular diseases. Their significance in the study of refractive development and disease is increasing daily. However, a systematic review of the structure and function of retinal VIP-ACs is lacking. We discussed the detailed characteristics of VIP-ACs from every aspect across species and providing systematic knowledge base for future studies. Our review led to the main conclusion that retinal VIP-ACs develop early, and although their morphology and distribution across species are not the same, they have similar functions in a wide range of ocular diseases based on their function of secreting neuropeptides and forming inhibitory connections with other cells.},
}
@article {pmid38064955,
year = {2024},
author = {Moaveninejad, S and D'Onofrio, V and Tecchio, F and Ferracuti, F and Iarlori, S and Monteriù, A and Porcaro, C},
title = {Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {244},
number = {},
pages = {107944},
doi = {10.1016/j.cmpb.2023.107944},
pmid = {38064955},
issn = {1872-7565},
mesh = {Humans ; *Brain-Computer Interfaces ; Fractals ; Electroencephalography/methods ; Hand/physiology ; Brain/physiology ; Imagination/physiology ; Algorithms ; },
abstract = {BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features.<br><br>METHODS: In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME).<br><br>RESULTS: Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks.<br><br>CONCLUSIONS: These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.},
}
@article {pmid38064282,
year = {2023},
author = {Lin, S and Jiang, J and Huang, K and Li, L and He, X and Du, P and Wu, Y and Liu, J and Li, X and Huang, Z and Zhou, Z and Yu, Y and Gao, J and Lei, M and Wu, H},
title = {Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces.},
journal = {ACS nano},
volume = {17},
number = {24},
pages = {24487-24513},
doi = {10.1021/acsnano.3c06781},
pmid = {38064282},
issn = {1936-086X},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Electrodes ; },
abstract = {Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.},
}
@article {pmid38063378,
year = {2024},
author = {Ganjali, M and Mehridehnavi, A and Rakhshani, S and Khorasani, A},
title = {Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals.},
journal = {International journal of neural systems},
volume = {34},
number = {1},
pages = {2450006},
doi = {10.1142/S0129065724500060},
pmid = {38063378},
issn = {1793-6462},
mesh = {Animals ; Macaca mulatta ; *Motor Cortex ; Movement ; Algorithms ; Hand ; *Brain-Computer Interfaces ; },
abstract = {The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.},
}
@article {pmid38059513,
year = {2024},
author = {Valentim, WL and Tylee, DS and Polimanti, R},
title = {A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices.},
journal = {WIREs mechanisms of disease},
volume = {16},
number = {2},
pages = {e1635},
pmid = {38059513},
issn = {2692-9368},
support = {R33 DA047527/NH/NIH HHS/United States ; R21 DC018098/NH/NIH HHS/United States ; R21 DC018098/DC/NIDCD NIH HHS/United States ; RF1 MH132337/MH/NIMH NIH HHS/United States ; R33 DA047527/DA/NIDA NIH HHS/United States ; RF1MH132337/NH/NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Brain Stimulation ; Brain/physiology ; *Mental Disorders/genetics ; Genomics ; },
abstract = {Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.},
}
@article {pmid38055994,
year = {2023},
author = {Schalk, G and Shao, S and Xiao, K and Wu, Z},
title = {Detection of common EEG phenomena using individual electrodes placed outside the hair.},
journal = {Biomedical physics &amp; engineering express},
volume = {10},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ad12f9},
pmid = {38055994},
issn = {2057-1976},
mesh = {Humans ; Electrodes ; *Scalp ; Electroencephalography/methods ; Polysomnography ; *Brain-Computer Interfaces ; },
abstract = {Many studies over the past decades have provided exciting evidence that electrical signals recorded from the scalp (electroencephalogram, EEG) hold meaningful information about the brain's function or dysfunction. This information is used routinely in research laboratories to test specific hypotheses and in clinical settings to aid in diagnoses (such as during polysomnography evaluations). Unfortunately, with very few exceptions, such meaningful information about brain function has not yet led to valuable solutions that can address the needs of many people outside such research laboratories or clinics. One of the major hurdles to practical application of EEG-based neurotechnologies is the current predominant requirement to use electrodes that are placed in the hair, which greatly reduces practicality and cosmesis. While several studies reported results using one specific combination of signal/reference electrode outside the hair in one specific context (such as a brain-computer interface experiment), it has been unclear what information about brain function can be acquired using different signal/referencing locations placed outside the hair. To address this issue, in this study, we set out to determine to what extent EEG phenomena related to auditory, visual, cognitive, motor, and sleep function can be detected from different combinations of individual signal/referencing electrodes that are placed outside the hair. The results of our study from 15 subjects suggest that only a few EEG electrodes placed in locations on the forehead or around the ear can provide substantial task-related information in 6 of 7 tasks. Thus, the results of our study provide encouraging evidence and guidance that should invigorate and facilitate the translation of laboratory experiments into practical, useful, and valuable EEG-based neurotechnology solutions.},
}
@article {pmid38053651,
year = {2023},
author = {Azadi Moghadam, M and Maleki, A},
title = {Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1248474},
pmid = {38053651},
issn = {1662-5161},
abstract = {BACKGROUND: Fatigue is a serious challenge when applying a steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) in the real world. Many researchers have used quantitative indices to study the effect of visual stimuli on fatigue. According to a wide range of studies in fatigue analysis, there are contradictions and inconsistencies in the behavior of fatigue indicators.<br><br>NEW METHOD: In this study, for the first time, a systematic review and meta-analysis were performed on fatigue indices and fatigue caused by stimulation paradigm. We queried three scientific search engines for studies published between 2000 and 2022. The inclusion criteria were papers investigating mental and visual fatigue from performing a visual task using electroencephalogram (EEG) signals.<br><br>RESULTS: Attractiveness and variation are the most effective ways to reduce BCI fatigue. Therefore, zoom motion, Newton's ring motion, and cue patterns reduce fatigue. While the color of the cue could effectively reduce fatigue, its shape and background had no effect on fatigue. Additionally, the questionnaire and quantitative indicators such as frequency indices, signal-to-noise ratio (SNR), SSVEP amplitude, and multiscale entropy were utilized to assess fatigue. Meta-analysis indicated that when a person is fatigued, the spectrum amplitude of alpha, theta, and &#x3b1;+&#x3b8;/&#x3b2; increase significantly, while SNR and SSVEP amplitude decrease significantly.<br><br>CONCLUSION: The outcomes of this study can be used to design more optimal stimulation protocols that cause less fatigue. Moreover, the level of fatigue can be quantitatively assessed with indicators without the participant's self-reports.},
}
@article {pmid38053609,
year = {2023},
author = {Ribeiro, TF and Carriello, MA and de Paula, EP and Garcia, AC and da Rocha, GL and Teive, HAG},
title = {Clinical applications of neurofeedback based on sensorimotor rhythm: a systematic review and meta-analysis.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1195066},
pmid = {38053609},
issn = {1662-4548},
abstract = {BACKGROUND: Among the brain-machine interfaces, neurofeedback is a non-invasive technique that uses sensorimotor rhythm (SMR) as a clinical intervention protocol. This study aimed to investigate the clinical applications of SMR neurofeedback to understand its clinical effectiveness in different pathologies or symptoms.<br><br>METHODS: A systematic review study with meta-analysis of the clinical applications of EEG-based SMR neurofeedback performed using pre-selected publication databases. A qualitative analysis of these studies was performed using the Consensus tool on the Reporting and Experimental Design of Neurofeedback studies (CRED-nf). The Meta-analysis of clinical efficacy was carried out using Review Manager software, version 5.4.1 (RevMan 5; Cochrane Collaboration, Oxford, UK).<br><br>RESULTS: The qualitative analysis includes 44 studies, of which only 27 studies had some kind of control condition, five studies were double-blinded, and only three reported a blind follow-up throughout the intervention. The meta-analysis included a total sample of 203 individuals between stroke and fibromyalgia. Studies on multiple sclerosis, insomnia, quadriplegia, paraplegia, and mild cognitive impairment were excluded due to the absence of a control group or results based only on post-intervention scales. Statistical analysis indicated that stroke patients did not benefit from neurofeedback interventions when compared to other therapies (Std. mean. dif. 0.31, 95% CI 0.03-0.60, p = 0.03), and there was no significant heterogeneity among stroke studies, classified as moderate I[2] = 46% p-value = 0.06. Patients diagnosed with fibromyalgia showed, by means of quantitative analysis, a better benefit for the group that used neurofeedback (Std. mean. dif. -0.73, 95% CI -1.22 to -0.24, p = 0.001). Thus, on performing the pooled analysis between conditions, no significant differences were observed between the neurofeedback intervention and standard therapy (0.05, CI 95%, -0.20 to -0.30, p = 0.69), with the presence of substantial heterogeneity I[2] = 92.2%, p-value < 0.001.<br><br>CONCLUSION: We conclude that although neurofeedback based on electrophysiological patterns of SMR contemplates the interest of numerous researchers and the existence of research that presents promising results, it is currently not possible to point out the clinical benefits of the technique as a form of clinical intervention. Therefore, it is necessary to develop more robust studies with a greater sample of a more rigorous methodology to understand the benefits that the technique can provide to the population.},
}
@article {pmid38051894,
year = {2024},
author = {Huo, C and Xu, G and Xie, H and Chen, T and Shao, G and Wang, J and Li, W and Wang, D and Li, Z},
title = {Functional near-infrared spectroscopy in non-invasive neuromodulation.},
journal = {Neural regeneration research},
volume = {19},
number = {7},
pages = {1517-1522},
pmid = {38051894},
issn = {1673-5374},
abstract = {Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks, which have been widely applied in the field of central neurological diseases, such as stroke, Parkinson's disease, and mental disorders. Although significant advances have been made in neuromodulation technologies, the identification of optimal neurostimulation parameters including the cortical target, duration, and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits. Moreover, the neural mechanism underlying neuromodulation for improved behavioral performance remains poorly understood. Recently, advancements in neuroimaging have provided insight into neuromodulation techniques. Functional near-infrared spectroscopy, as a novel non-invasive optical brain imaging method, can detect brain activity by measuring cerebral hemodynamics with the advantages of portability, high motion tolerance, and anti-electromagnetic interference. Coupling functional near-infrared spectroscopy with neuromodulation technologies offers an opportunity to monitor the cortical response, provide real-time feedback, and establish a closed-loop strategy integrating evaluation, feedback, and intervention for neurostimulation, which provides a theoretical basis for development of individualized precise neurorehabilitation. We aimed to summarize the advantages of functional near-infrared spectroscopy and provide an overview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation, transcranial electrical stimulation, neurofeedback, and brain-computer interfaces. Furthermore, the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized. In conclusion, functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central neural reorganization to achieve better functional recovery from central nervous system diseases.},
}
@article {pmid38051627,
year = {2023},
author = {Jorajuria, T and Nikulin, VV and Kapralov, N and Gomez, M and Vidaurre, C},
title = {MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4931-4941},
doi = {10.1109/TNSRE.2023.3339612},
pmid = {38051627},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Prospective Studies ; Feedback ; Algorithms ; },
abstract = {Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called [Formula: see text] to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. [Formula: see text] has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.},
}
@article {pmid38051624,
year = {2023},
author = {Leong, D and Do, TT and Lin, CT},
title = {Ventral and Dorsal Stream EEG Channels: Key Features for EEG-Based Object Recognition and Identification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4862-4870},
doi = {10.1109/TNSRE.2023.3339698},
pmid = {38051624},
issn = {1558-0210},
mesh = {Humans ; *Pattern Recognition, Visual ; *Visual Perception ; Brain ; Temporal Lobe ; Magnetic Resonance Imaging ; Electroencephalography ; Brain Mapping ; },
abstract = {Object recognition and object identification are multifaceted cognitive operations that require various brain regions to synthesize and process information. Prior research has evidenced the activity of both visual and temporal cortices during these tasks. Notwithstanding their similarities, object recognition and identification are recognized as separate brain functions. Drawing from the two-stream hypothesis, our investigation aims to understand whether the channels within the ventral and dorsal streams contain pertinent information for effective model learning regarding object recognition and identification tasks. By utilizing the data we collected during the object recognition and identification experiment, we scrutinized EEGNet models, trained using channels that replicate the two-stream hypothesis pathways, against a model trained using all available channels. The outcomes reveal that the model trained solely using the temporal region delivered a high accuracy level in classifying four distinct object categories. Specifically, the object recognition and object identification models achieved an accuracy of 89% and 85%, respectively. By incorporating the channels that mimic the ventral stream, the model's accuracy was further improved, with the object recognition model and object identification model achieving an accuracy of 95% and 94%, respectively. Furthermore, the Grad-CAM result of the trained models revealed a significant contribution from the ventral and dorsal stream channels toward the training of the EEGNet model. The aim of our study is to pinpoint the optimal channel configuration that provides a swift and accurate brain-computer interface system for object recognition and identification.},
}
@article {pmid38050776,
year = {2024},
author = {Chen, J and Ke, Y and Ni, G and Liu, S and Ming, D},
title = {Evidence for modulation of EEG microstates by mental workload levels and task types.},
journal = {Human brain mapping},
volume = {45},
number = {1},
pages = {e26552},
pmid = {38050776},
issn = {1097-0193},
support = {2021YFF1200603//National Key Research and Development Program of China/ ; 61806141//National Natural Science Foundation of China/ ; 62276184//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Brain/physiology ; Brain Mapping/methods ; Cognition ; *Mental Disorders ; },
abstract = {Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.},
}
@article {pmid38049671,
year = {2025},
author = {Zhang, J and Li, L and Ji, R and Shang, D and Wen, X and Hu, J and Wang, Y and Wu, D and Zhang, L and He, F and Ye, X and Luo, B},
title = {NODDI Identifies Cognitive Associations with In Vivo Microstructural Changes in Remote Cortical Regions and Thalamocortical Pathways in Thalamic Stroke.},
journal = {Translational stroke research},
volume = {16},
number = {2},
pages = {378-391},
pmid = {38049671},
issn = {1868-601X},
mesh = {Humans ; Male ; Female ; *Thalamus/diagnostic imaging/pathology ; Middle Aged ; Aged ; *Cerebral Cortex/diagnostic imaging/pathology ; Neural Pathways/diagnostic imaging/pathology ; *Stroke/diagnostic imaging/pathology ; *Gray Matter/diagnostic imaging/pathology ; Magnetic Resonance Imaging ; *Cognition/physiology ; Diffusion Tensor Imaging ; Neuropsychological Tests ; Neurites/pathology ; },
abstract = {The roles of cerebral structures distal to isolated thalamic infarcts in cognitive deficits remain unclear. We aimed to identify the in vivo microstructural characteristics of remote gray matter (GM) and thalamic pathways and elucidate their roles across cognitive domains. Patients with isolated ischemic thalamic stroke and healthy controls underwent neuropsychological assessment and magnetic resonance imaging. Neurite orientation dispersion and density imaging (NODDI) was modeled to derive the intracellular volume fraction (VFic) and orientation dispersion index. Fiber density (FD) was determined by constrained spherical deconvolution, and tensor-derived fractional anisotropy (FA) was calculated. Voxel-wise GM analysis and thalamic pathway tractography were performed. Twenty-six patients and 26 healthy controls were included. Patients exhibited reduced VFic in remote GM regions, including ipsilesional insular and temporal subregions. The microstructural metrics VFic, FD, and FA within ipsilesional thalamic pathways decreased (false discovery rate [FDR]-p < 0.05). Noteworthy associations emerged as VFic within insular cortices (ρ = -0.791 to -0.630; FDR-p < 0.05) and FD in tracts connecting the thalamus and insula (ρ = 0.830 to 0.971; FDR-p < 0.001) were closely associated with executive function. The VFic in Brodmann area 52 (ρ = -0.839; FDR-p = 0.005) and FA within its thalamic pathway (ρ = -0.799; FDR-p = 0.003) correlated with total auditory memory scores. In conclusion, NODDI revealed neurite loss in remote normal-appearing GM regions and ipsilesional thalamic pathways in thalamic stroke. Reduced cortical dendritic density and axonal density of thalamocortical tracts in specific subregions were associated with improved cognitive functions. Subacute microstructural alterations beyond focal thalamic infarcts might reflect beneficial remodeling indicating post-stroke plasticity.},
}
@article {pmid38049438,
year = {2023},
author = {Xu, X and Lee, D and Drougard, N and Roy, RN},
title = {Signature methods for brain-computer interfaces.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {21367},
pmid = {38049438},
issn = {2045-2322},
support = {51NF40-185897//NCCR-Synapsy Phase-3 SNSF/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Movement/physiology ; Electroencephalography/methods ; Central Nervous System ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) allow direct communication between one's central nervous system and a computer without any muscle movement hence by-passing the peripheral nervous system. They can restore disabled people's ability to interact with their environment, e.g. communication and wheelchair control. However, to this day their performance is still hindered by the non-stationarity of electroencephalography (EEG) signals, as well as their susceptibility to noise from the users' environment and from their own physiological activity. Moreover, a non-negligible amount of users struggle to use BCI systems based on motor imagery. In this paper, a new method based on the path signature is introduced to tackle this problem by using features which are different from the usual power-based ones. The path signature is a series of iterated integrals computed from a multidimensional path. It is invariant under translation and time reparametrization, which makes it a robust feature for multichannel EEG time series. The performance can be further boosted by combining the path signature with the gold standard Riemannian classifier in the BCI field exploiting the geometric structure of symmetric positive definite (SPD) matrices. The results obtained on publicly available datasets show that the signature method is more robust to inter-user variability than classical ones, especially on noisy and low-quality data. Hence, this study paves the way towards the use of mathematical tools that until now have been neglected, in order to tackle the EEG-based BCI variability issue. It also sheds light on the lead-lag relationship captured by path signature which seems relevant to assess the underlying neural mechanisms.},
}
@article {pmid38049039,
year = {2024},
author = {Li, Z and Zhang, R and Zeng, Y and Tong, L and Lu, R and Yan, B},
title = {MST-net: A multi-scale swin transformer network for EEG-based cognitive load assessment.},
journal = {Brain research bulletin},
volume = {206},
number = {},
pages = {110834},
doi = {10.1016/j.brainresbull.2023.110834},
pmid = {38049039},
issn = {1873-2747},
mesh = {Humans ; *Brain ; *Electroencephalography ; Cognition ; },
abstract = {Cognitive load assessment plays a crucial role in monitoring safe production, resource allocation, and subjective initiative in human-computer interaction. Due to its high time resolution and convenient acquisition, Electroencephalography (EEG) is widely applied in brain monitoring and cognitive state assessment. In this study, a multi-scale Swin Transformer network (MST-Net) was proposed for cognitive load assessment, which extracts local features with different sensory fields using a multi-scale parallel convolution model and introduces the attention mechanism of the Swin Transformer to obtain the feature correlations among multi-scale local features. The performance of the proposed network was validated using the EEG signals collected during cognitive tasks and N-back tasks with three different load levels. Results show that the MST-Net network achieved the best classification accuracy on both local and public datasets, and was higher than the mainstream Swin Transformer and CNN. Furthermore, results of ablation experiments and feature visualization revealed that the proposed MST-Net could well characterize different cognitive loads, which not only provided novel and powerful tools for cognitive load assessment but also showed potential for broad application in brain-computer interface (BCI) systems.},
}
@article {pmid38048224,
year = {2023},
author = {Wang, JH and Wu, C and Lian, YN and Cao, XW and Wang, ZY and Dong, JJ and Wu, Q and Liu, L and Sun, L and Chen, W and Chen, WJ and Zhang, Z and Zhuo, M and Li, XY},
title = {Single-cell RNA sequencing uncovers the cell type-dependent transcriptomic changes in the retrosplenial cortex after peripheral nerve injury.},
journal = {Cell reports},
volume = {42},
number = {12},
pages = {113551},
doi = {10.1016/j.celrep.2023.113551},
pmid = {38048224},
issn = {2211-1247},
mesh = {Mice ; Animals ; Gyrus Cinguli/physiology ; *Peripheral Nerve Injuries/genetics/metabolism ; Neurons/metabolism ; Gene Expression Profiling ; *Neuralgia/genetics/metabolism ; },
abstract = {The retrosplenial cortex (RSC) is a vital area for storing remote memory and has recently been found to undergo broad changes after peripheral nerve injury. However, little is known about the role of RSC in pain regulation. Here, we examine the involvement of RSC in the pain of mice with nerve injury. Notably, reducing the activities of calcium-/calmodulin-dependent protein kinase type II-positive splenial neurons chemogenetically increases paw withdrawal threshold and extends thermal withdrawal latency in mice with nerve injury. The single-cell or single-nucleus RNA-sequencing results predict enhanced excitatory synaptic transmissions in RSC induced by nerve injury. Local infusion of 1-naphthyl acetyl spermine into RSC to decrease the excitatory synaptic transmissions relieves pain and induces conditioned place preference. Our data indicate that RSC is critical for regulating physiological and neuropathic pain. The cell type-dependent transcriptomic information would help understand the molecular basis of neuropathic pain.},
}
@article {pmid38046826,
year = {2023},
author = {Li, Q and Li, X and Bury, E and Koh, A and Lackey, K and Wesselmann, U and Yaksh, T and Zhao, C},
title = {Hydration-induced Void-containing Hydrogels for Encapsulation and Sustained Release of Small Hydrophilic Molecules.},
journal = {Advanced functional materials},
volume = {33},
number = {34},
pages = {},
pmid = {38046826},
issn = {1616-301X},
support = {R01 CA246708/CA/NCI NIH HHS/United States ; R01 GM144388/GM/NIGMS NIH HHS/United States ; },
abstract = {Efficient encapsulation and sustained release of small hydrophilic molecules from traditional hydrogel systems have been challenging due to the large mesh size of 3D networks and high water content. Furthermore, the encapsulated molecules are prone to early release from the hydrogel prior to use, resulting in a short shelf life of the formulation. Here, we present a hydration-induced void-containing hydrogel (HVH) based on hyperbranched polyglycerol-poly(propylene oxide)-hyperbranched polyglycerol (HPG-PPG-HPG) as a robust and efficient delivery system for small hydrophilic molecules. Specifically, after the HPG-PPG-HPG is incubated overnight at 4 °C in the drug solution, it is hydrated into a hydrogel containing micron-sized voids, which could encapsulate hydrophilic drugs and achieve 100% drug encapsulation efficiency. In addition, the voids are surrounded by a densely packed polymer matrix, which restricts drug transport to achieve sustained drug release. The hydrogel/drug formulation can be stored for several months without changing the drug encapsulation and release properties. HVH hydrogels are injectable due to shear thinning properties. In rats, a single injection of the HPG-PPG-HPG hydrogel containing 8 μg of tetrodotoxin (TTX) produced sciatic nerve block lasting up to 10 hours without any TTX-related systemic toxicity nor local toxicity to nerves and muscles.},
}
@article {pmid38046661,
year = {2023},
author = {Voix, J and Kidmose, P and Bleichner, MG},
title = {Editorial: Ear-centered sensing: from sensing principles to research and clinical devices, volume II.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1327801},
doi = {10.3389/fnins.2023.1327801},
pmid = {38046661},
issn = {1662-4548},
}
@article {pmid38045092,
year = {2023},
author = {Ivanov, N and Chau, T},
title = {Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1286681},
doi = {10.3389/fncom.2023.1286681},
pmid = {38045092},
issn = {1662-5188},
abstract = {[This corrects the article DOI: 10.3389/fncom.2023.1108889.].},
}
@article {pmid38042889,
year = {2023},
author = {Do, MS and Son, SJ and Jung, JH and Lee, SC and Choi, G and Nam, HK},
title = {Effects of environmental factors and intraspecific niche overlap on the body and ecological characteristics of red-tongued pit vipers (Gloydius ussuriensis).},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {21310},
pmid = {38042889},
issn = {2045-2322},
support = {NIBR No. 202206102//National Institute of Biological Resources/ ; },
mesh = {Humans ; Male ; Animals ; Female ; Infant, Newborn ; *Crotalinae ; Ecosystem ; Altitude ; Seasons ; Snakes ; *Agkistrodon ; },
abstract = {The body condition of a snake species provides important physiological, morphological, and ecological information that elucidates its habits, life cycle, and competitive relationships. We measured the body size and condition of the wild Gloydius ussuriensis population in South Korea from 2018 to 2022, analyzed the degree of intraspecific niche overlap, and identified the geographic and climatic factors affecting their body condition. We found that the females were longer than the males. The body condition index (BCI) of G. ussuriensis differed depending on sex and season; the BCI of the females and males was highest in August and October, respectively. Environmental factors related to altitude and temperature affected the body condition of G. ussuriensis; BCI increased as the mean annual temperature and winter temperature increased; however, it increased when the annual temperature range decreased. The mean Pinaka index was 0.96, indicating a high degree of niche overlap; however, the niche overlap among the neonates was less than that among the adults and juveniles. To elucidate the causes of niche overlap and mechanisms behind the intraspecific competition among G. ussuriensis individuals, the habitat and utilization of food resources at different development stages of G. ussuriensis should be further investigated.},
}
@article {pmid38042665,
year = {2025},
author = {Aboubakr, O and Houillier, C and Choquet, S and Dupont, S and Hoang-Xuan, K and Mathon, B},
title = {Epileptic seizures in patients with primary central nervous system lymphoma: A systematic review.},
journal = {Revue neurologique},
volume = {181},
number = {1-2},
pages = {3-10},
doi = {10.1016/j.neurol.2023.08.021},
pmid = {38042665},
issn = {0035-3787},
mesh = {Humans ; *Lymphoma/complications/epidemiology/therapy ; *Central Nervous System Neoplasms/complications/epidemiology/therapy ; *Epilepsy/epidemiology/etiology ; *Seizures/epidemiology/etiology ; Anticonvulsants/therapeutic use ; Risk Factors ; },
abstract = {BACKGROUND: Primary central nervous system lymphoma (PCNSL) accounts for less than 5% of primary brain tumors. Epileptic seizures are a common manifestation of brain tumors; however, literature on the prevalence, characteristics, and oncological implications of seizures in patients with PCNSL is limited, and the management of antiepileptic drugs (AEDs) is unclear. This review aimed to summarize the existing knowledge on seizures in PCNSL, their potential association with surgery, oncological treatment, survival rates, and management of AEDs.<br><br>METHODS: A systematic review was performed according to the PRISMA recommendations and included articles published between 1953 and 2023 describing seizures in patients with PCNSL.<br><br>RESULTS: The search identified 282 studies, of which 21 were included. Up to 33% of patients with PCNSL developed seizures, mostly at the initial presentation. Little information was found on changes in seizure incidence through the course of the disease, and no details were found on seizure frequency, the percentage of treatment-resistant patients, or the evolution of seizures at remission. Younger age, cortical location, and immunodeficiency have been identified as potential risk factors for seizures, but evidence is very limited. The growing use of vigorous treatments including intensive chemotherapy with autologous stem cell transplantation and immunotherapy with CAR-T cells is associated with a higher incidence of seizures. The association between seizure development and patient mortality in PCNSL remains unknown. There are no data on AED prophylaxis or the use of specific AEDs in PCNSL.<br><br>CONCLUSIONS: Further studies are needed to investigate seizures in larger cohorts of PCNSL, to clarify their prevalence, better characterize them, identify risk factors, analyze survival rates, and make recommendations on AED management. We recommend following general practice guidelines for seizures symptomatic of brain tumors and not to prescribe AED prophylaxis in PCNSL.},
}
@article {pmid38042393,
year = {2024},
author = {Tang, Y and Hu, Y and Zhuang, J and Feng, C and Zhou, X},
title = {Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns.},
journal = {NeuroImage},
volume = {285},
number = {},
pages = {120468},
doi = {10.1016/j.neuroimage.2023.120468},
pmid = {38042393},
issn = {1095-9572},
mesh = {Humans ; *Brain/diagnostic imaging ; *Prefrontal Cortex/diagnostic imaging ; Brain Mapping ; Magnetic Resonance Imaging ; },
abstract = {When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.},
}
@article {pmid38036744,
year = {2024},
author = {Griggs, WS and Norman, SL and Deffieux, T and Segura, F and Osmanski, BF and Chau, G and Christopoulos, V and Liu, C and Tanter, M and Shapiro, MG and Andersen, RA},
title = {Decoding motor plans using a closed-loop ultrasonic brain-machine interface.},
journal = {Nature neuroscience},
volume = {27},
number = {1},
pages = {196-207},
pmid = {38036744},
issn = {1546-1726},
support = {F30 EY032799/EY/NEI NIH HHS/United States ; R01 NS123663/NS/NINDS NIH HHS/United States ; T32 GM008042/GM/NIGMS NIH HHS/United States ; T32 NS105595/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Humans ; *Brain-Computer Interfaces ; Macaca mulatta ; Ultrasonics ; Hand ; Movement ; },
abstract = {Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.},
}
@article {pmid38036086,
year = {2024},
author = {Çetin, E and Bilgin, S and Bilgin, G},
title = {A novel wearable ERP-based BCI approach to explicate hunger necessity.},
journal = {Neuroscience letters},
volume = {818},
number = {},
pages = {137573},
doi = {10.1016/j.neulet.2023.137573},
pmid = {38036086},
issn = {1872-7972},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Hunger ; Algorithms ; *Wearable Electronic Devices ; },
abstract = {This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.},
}
@article {pmid38033812,
year = {2023},
author = {Zhu, Y and Yang, Y and Ni, G and Li, S and Liu, W and Gao, Z and Zhang, X and Zhang, Q and Wang, C and Zhou, J},
title = {On-demand electrically controlled melatonin release from PEDOT/SNP composite improves quality of chronic neural recording.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {11},
number = {},
pages = {1284927},
pmid = {38033812},
issn = {2296-4185},
abstract = {Long-time and high-quality signal acquisition performance from implantable electrodes is the key to establish stable and efficient brain-computer interface (BCI) connections. The chronic performance of implantable electrodes is hindered by the inflammatory response of brain tissue. In order to solve the material limitation of biological interface electrodes, we designed sulfonated silica nanoparticles (SNPs) as the dopant of Poly (3,4-ethylenedioxythiophene) (PEDOT) to modify the implantable electrodes. In this work, melatonin (MT) loaded SNPs were incorporated in PEDOT via electrochemical deposition on nickel-chromium (Ni-Cr) alloy electrode and carbon nanotube (CNT) fiber electrodes, without affecting the acute neural signal recording capacity. After coating with PEDOT/SNP-MT, the charge storage capacity of both electrodes was significantly increased, and the electrochemical impedance at 1 kHz of the Ni-Cr alloy electrodes was significantly reduced, while that of the CNT electrodes was significantly increased. In addition, this study inspected the effect of electrically triggered MT release every other day on the quality and longevity of neural recording from implanted neural electrodes in rat hippocampus for 1 month. Both MT modified Ni-Cr alloy electrodes and CNT electrodes showed significantly higher spike amplitude after 26-day recording. Significantly, the histological studies showed that the number of astrocytes around the implanted Ni-Cr alloy electrodes was significantly reduced after MT release. These results demonstrate the potent outcome of PEDOT/SNP-MT treatment in improving the chronic neural recording quality possibly through its anti-inflammatory property.},
}
@article {pmid38033535,
year = {2023},
author = {Zhao, Y and Luo, H and Chen, J and Loureiro, R and Yang, S and Zhao, H},
title = {Learning based motion artifacts processing in fNIRS: a mini review.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1280590},
pmid = {38033535},
issn = {1662-4548},
support = {/WT_/Wellcome Trust/United Kingdom ; },
abstract = {This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies.},
}
@article {pmid38032825,
year = {2023},
author = {Zou, J and Zhang, Y and Li, J and Tian, X and Ding, N},
title = {Human attention during goal-directed reading comprehension relies on task optimization.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {38032825},
issn = {2050-084X},
support = {2021ZD0204105//STI2030-Major Project/ ; 32222035//National Natural Science Foundation of China/ ; 32300856//National Natural Science Foundation of China/ ; 2019KB0AC02//Major Scientific Project of Zhejiang Laboratory/ ; 226-2023-00091//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Eye Movements ; *Comprehension ; Goals ; Attention ; Neural Networks, Computer ; },
abstract = {The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.},
}
@article {pmid38029425,
year = {2024},
author = {Shen, Z and Liang, Q and Chang, Q and Liu, Y and Zhang, Q},
title = {Topological Hydrogels for Long-Term Brain Signal Monitoring, Neuromodulation, and Stroke Treatment.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {36},
number = {7},
pages = {e2310365},
doi = {10.1002/adma.202310365},
pmid = {38029425},
issn = {1521-4095},
support = {22377122//National Natural Science Foundation of China/ ; U22A20183//National Natural Science Foundation of China/ ; 20230204107YY//Jilin Province Science and Technology Development Plan/ ; 20210509036RQ//Jilin Province Science and Technology Development Plan/ ; },
mesh = {Humans ; *Rotaxanes ; Hydrogels ; Brain/physiology ; *Stroke/therapy ; *Brain-Computer Interfaces ; },
abstract = {Stroke is the primary cause of disability without effective rehabilitation methods. Emerging brain-machine interfaces offer promise for regulating brain neural circuits and promoting the recovery of brain function disorders. Implantable probes play key roles in brain-machine interfaces, which are subject to two irreconcilable tradeoffs between conductivity and modulus match/transparency. In this work, mechanically interlocked polyrotaxane is incorporated into topological hydrogels to solve the two tradeoffs at the molecular level through the pulley effect of polyrotaxane. The unique performance of the topological hydrogels enables them to acquire brain neural information and conduct neuromodulation. The probe is capable of continuously recording local field potentials for eight weeks. Optogenetic neuromodulation in the primary motor cortex to regulate brain neural circuits and control limb behavior is realized using the probe. Most importantly, optogenetic neuromodulation is conducted using the probe, which effectively reduces the infarct regions of the brain tissue and promotes locomotor function recovery. This work exhibits a significant scientific advancement in the design concept of neural probes for developing brain-machine interfaces and seeking brain disease therapies.},
}
@article {pmid38027514,
year = {2023},
author = {Perna, A and Angotzi, GN and Berdondini, L and Ribeiro, JF},
title = {Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1275908},
pmid = {38027514},
issn = {1662-4548},
abstract = {Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.},
}
@article {pmid38027506,
year = {2023},
author = {Tao, J and Dan, Y and Zhou, D},
title = {Possibilistic distribution distance metric: a robust domain adaptation learning method.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1247082},
pmid = {38027506},
issn = {1662-4548},
abstract = {The affective Brain-Computer Interface (aBCI) systems, which achieve predictions for individual subjects through training on multiple subjects, often cannot achieve satisfactory results due to the differences in Electroencephalogram (EEG) patterns between subjects. One tried to use Subject-specific classifiers, but there was a lack of sufficient labeled data. To solve this problem, Domain Adaptation (DA) has recently received widespread attention in the field of EEG-based emotion recognition. Domain adaptation (DA) learning aims to solve the problem of inconsistent distributions between training and test datasets and has received extensive attention. Most existing methods use Maximum Mean Discrepancy (MMD) or its variants to minimize the problem of domain distribution inconsistency. However, noisy data in the domain can lead to significant drift in domain means, which can affect the adaptability performance of learning methods based on MMD and its variants to some extent. Therefore, we propose a robust domain adaptation learning method with possibilistic distribution distance measure. Firstly, the traditional MMD criterion is transformed into a novel possibilistic clustering model to weaken the influence of noisy data, thereby constructing a robust possibilistic distribution distance metric (P-DDM) criterion. Then the robust effectiveness of domain distribution alignment is further improved by a fuzzy entropy regularization term. The proposed P-DDM is in theory proved which be an upper bound of the traditional distribution distance measure method MMD criterion under certain conditions. Therefore, minimizing P-DDM can effectively optimize the MMD objective. Secondly, based on the P-DDM criterion, a robust domain adaptation classifier based on P-DDM (C-PDDM) is proposed, which adopts the Laplacian matrix to preserve the geometric consistency of instances in the source domain and target domain for improving the label propagation performance. At the same time, by maximizing the use of source domain discriminative information to minimize domain discrimination error, the generalization performance of the learning model is further improved. Finally, a large number of experiments and analyses on multiple EEG datasets (i.e., SEED and SEED-IV) show that the proposed method has superior or comparable robustness performance (i.e., has increased by around 10%) in most cases.},
}
@article {pmid38027480,
year = {2023},
author = {Xu, Y and Zhao, H and Ieracitano, C},
title = {Editorial: Advances in brain-computer interface technologies for closed-loop neuromodulation.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1327533},
doi = {10.3389/fnins.2023.1327533},
pmid = {38027480},
issn = {1662-4548},
}
@article {pmid38027473,
year = {2023},
author = {Zhang, M and Huang, J and Ni, S},
title = {Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1270785},
pmid = {38027473},
issn = {1662-4548},
abstract = {INTRODUCTION: The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.<br><br>METHODS: This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.<br><br>RESULTS: The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.<br><br>DISCUSSION: The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.},
}
@article {pmid38025739,
year = {2023},
author = {Chung, PC and Lin, IF},
title = {Sensitivity analysis of selection bias: a graphical display by bias-correction index.},
journal = {PeerJ},
volume = {11},
number = {},
pages = {e16411},
pmid = {38025739},
issn = {2167-8359},
mesh = {Humans ; Selection Bias ; Surveys and Questionnaires ; *Insurance, Health ; Odds Ratio ; *Informed Consent ; },
abstract = {BACKGROUND: In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias.<br><br>METHODS: We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked.<br><br>RESULTS: We included 15,247 people aged &#x2265;20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias.<br><br>CONCLUSIONS: When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.},
}
@article {pmid38023311,
year = {2023},
author = {Cantillo-Negrete, J and Carino-Escobar, RI and Ortega-Robles, E and Arias-Carrión, O},
title = {A comprehensive guide to BCI-based stroke neurorehabilitation interventions.},
journal = {MethodsX},
volume = {11},
number = {},
pages = {102452},
pmid = {38023311},
issn = {2215-0161},
abstract = {Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.},
}
@article {pmid38021246,
year = {2023},
author = {Hooks, K and El-Said, R and Fu, Q},
title = {Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1302647},
pmid = {38021246},
issn = {1662-5161},
abstract = {Fundamental to human movement is the ability to interact with objects in our environment. How one reaches an object depends on the object's shape and intended interaction afforded by the object, e.g., grasp and transport. Extensive research has revealed that the motor intention of reach-to-grasp can be decoded from cortical activities using EEG signals. The goal of the present study is to determine the extent to which information encoded in the EEG signals is shared between two limbs to enable cross-hand decoding. We performed an experiment in which human subjects (n = 10) were tasked to interact with a novel object with multiple affordances using either right or left hands. The object had two vertical handles attached to a horizontal base. A visual cue instructs what action (lift or touch) and whether the left or right handle should be used for each trial. EEG was recorded and processed from bilateral frontal-central-parietal regions (30 channels). We trained LDA classifiers using data from trials performed by one limb and tested the classification accuracy using data from trials performed by the contralateral limb. We found that the type of hand-object interaction can be decoded with approximately 59 and 69% peak accuracy in the planning and execution stages, respectively. Interestingly, the decoding accuracy of the reaching directions was dependent on how EEG channels in the testing dataset were spatially mirrored, and whether directions were labeled in the extrinsic (object-centered) or intrinsic (body-centered) coordinates.},
}
@article {pmid38021234,
year = {2023},
author = {Liang, W and Jin, J and Xu, R and Wang, X and Cichocki, A},
title = {Variance characteristic preserving common spatial pattern for motor imagery BCI.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1243750},
pmid = {38021234},
issn = {1662-5161},
abstract = {INTRODUCTION: The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space.<br><br>METHODS: This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly.<br><br>RESULTS: The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm.<br><br>DISCUSSION: The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.},
}
@article {pmid38021231,
year = {2023},
author = {Fernández-Rodríguez, &#xc1; and Martínez-Cagigal, V and Santamaría-Vázquez, E and Ron-Angevin, R and Hornero, R},
title = {Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1288438},
pmid = {38021231},
issn = {1662-5161},
abstract = {Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady-state visual evoked potentials (SSVEPs) have indicated that the spatial frequency of checkerboard-like stimuli influences both performance and user experience. Spatial frequency refers to the dimensions of the individual squares comprising the visual stimulus, quantified in cycles (i.e., number of black-white squares pairs) per degree of visual angle. However, the specific effects of this parameter on c-VEP-based BCIs remain unexplored. Therefore, the objective of this study is to investigate the role of spatial frequency of checkerboard-like visual stimuli in a c-VEP-based BCI. Sixteen participants evaluated selection matrices with eight spatial frequencies: C001 (0 c/°, 1×1 squares), C002 (0.15 c/°, 2×2 squares), C004 (0.3 c/°, 4×4 squares), C008 (0.6 c/°, 8×8 squares), C016 (1.2 c/°, 16×16 squares), C032 (2.4 c/°, 32×32 squares), C064 (4.79 c/°, 64×64 squares), and C128 (9.58 c/°, 128×128 squares). These conditions were tested in an online spelling task, which consisted of 18 trials each conducted on a 3×3 command interface. In addition to accuracy and information transfer rate (ITR), subjective measures regarding comfort, ocular irritation, and satisfaction were collected. Significant differences in performance and comfort were observed based on different stimulus spatial frequencies. Although all conditions achieved mean accuracy over 95% after 2.1 s of trial duration, C016 stood out in terms user experience. The proposed condition not only achieved a mean accuracy of 96.53% and 164.54 bits/min with a trial duration of 1.05s, but also was reported to be significantly more comfortable than the traditional C001 stimulus. Since both features are key for BCI development, higher spatial frequencies than the classical black-to-white stimulus might be more adequate for c-VEP systems. Hence, we assert that the spatial frequency should be carefully considered in the development of future applications for c-VEP-based BCIs.},
}
@article {pmid38019938,
year = {2024},
author = {Zhang, Y and Coid, J},
title = {Childhood Adversity Determines the Syndemic Effects of Violence, Substance Misuse, and Sexual Behavior on Psychotic Spectrum Disorder Among Men.},
journal = {Schizophrenia bulletin},
volume = {50},
number = {3},
pages = {684-694},
pmid = {38019938},
issn = {1745-1701},
support = {RP-PG-6407-10500//National Institute for Health Research/ ; 82001409//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Male ; *Psychotic Disorders/epidemiology/etiology/physiopathology ; Adult ; *Adverse Childhood Experiences/statistics &amp; numerical data ; *Syndemic ; *Substance-Related Disorders/epidemiology ; Cross-Sectional Studies ; Middle Aged ; Young Adult ; Violence/statistics &amp; numerical data ; United Kingdom/epidemiology ; Adolescent ; Adult Survivors of Child Adverse Events/statistics &amp; numerical data ; Sexual Behavior/statistics &amp; numerical data ; Latent Class Analysis ; Anxiety/epidemiology ; Aged ; },
abstract = {BACKGROUND AND HYPOTHESIS: Childhood adversity (CA) increases the risk for several adult psychiatric conditions. It is unclear why some exposed individuals experience psychotic symptoms and others do not. We investigated whether a syndemic explained a psychotic outcome determined by CA.<br><br>STUDY DESIGN: We used self-reported cross-sectional data from 7461 British men surveyed in different population subgroups. Latent class analysis (LCA) identified categorical psychopathological outcomes. LCs were tested by interaction analysis between syndemic factors derived from confirmatory factor analysis according to CA experiences. Pathway analysis using partial least squares path modeling.<br><br>RESULTS: A 4-class model with excellent fit identified an LC characterized by both psychotic and anxiety symptoms (class 4). A syndemic model of joint effects, adducing a 3-component latent variable of substance misuse (SM), high-risk sexual behavior (SH), violence and criminality (VC) showed synergy between components and explained the psychotic outcome (class 4). We found significant interactions between factor scores on the multiplicative scale, specific only to class 4 (psychosis), including SM&#x2005;&#xd7;&#x2005;SH, SH&#x2005;&#xd7;&#x2005;VC, and SM&#x2005;&#xd7;&#x2005;VC (OR&#x2005;>&#x2005;1, P&#x2005;<&#x2005;.05); and on the additive scale SM&#x2005;&#xd7;&#x2005;SH (relative excess risk due to interaction >0, P&#x2005;<&#x2005;.05), but only for men who experienced CA.<br><br>CONCLUSION: Multiplicative synergistic interactions between SM, SH, and VC constituted a mechanism determining a psychotic outcome, but not for anxiety disorder, mixed anxiety disorder/depression, or depressive disorder. This was specific to men who had experienced CA along direct and syndemic pathways. Population interventions should target SM and VC in adulthood but prioritize primary prevention strategies for CA.},
}
@article {pmid38019907,
year = {2023},
author = {Zhang, R and Pan, S and Zheng, S and Liao, Q and Jiang, Z and Wang, D and Li, X and Hu, A and Li, X and Zhu, Y and Shen, X and Lei, J and Zhong, S and Zhang, X and Huang, L and Wang, X and Huang, L and Shen, L and Song, BL and Zhao, JW and Wang, Z and Yang, B and Guo, X},
title = {Lipid-anchored proteasomes control membrane protein homeostasis.},
journal = {Science advances},
volume = {9},
number = {48},
pages = {eadj4605},
pmid = {38019907},
issn = {2375-2548},
support = {R01 GM074830/GM/NIGMS NIH HHS/United States ; R35 GM145249/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; Animals ; Mice ; *Proteasome Endopeptidase Complex/metabolism ; *Membrane Proteins/genetics/metabolism ; Proteostasis ; Endoplasmic Reticulum-Associated Degradation ; Mice, Nude ; Lipids ; },
abstract = {Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here, we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system, and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation and membrane protein trafficking. Rpt2[G2A/G2A] homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by myristoyl-anchored proteasomes in health and disease.},
}
@article {pmid38018832,
year = {2024},
author = {Brannigan, JFM and Fry, A and Opie, NL and Campbell, BCV and Mitchell, PJ and Oxley, TJ},
title = {Endovascular Brain-Computer Interfaces in Poststroke Paralysis.},
journal = {Stroke},
volume = {55},
number = {2},
pages = {474-483},
doi = {10.1161/STROKEAHA.123.037719},
pmid = {38018832},
issn = {1524-4628},
mesh = {Humans ; *Brain-Computer Interfaces ; Paralysis/etiology ; *Stroke/complications ; *Stroke Rehabilitation ; Prostheses and Implants ; },
abstract = {Stroke is a leading cause of paralysis, most frequently affecting the upper limbs and vocal folds. Despite recent advances in care, stroke recovery invariably reaches a plateau, after which there are permanent neurological impairments. Implantable brain-computer interface devices offer the potential to bypass permanent neurological lesions. They function by (1) recording neural activity, (2) decoding the neural signal occurring in response to volitional motor intentions, and (3) generating digital control signals that may be used to control external devices. While brain-computer interface technology has the potential to revolutionize neurological care, clinical translation has been limited. Endovascular arrays present a novel form of minimally invasive brain-computer interface devices that have been deployed in human subjects during early feasibility studies. This article provides an overview of endovascular brain-computer interface devices and critically evaluates the patient with stroke as an implant candidate. Future opportunities are mapped, along with the challenges arising when decoding neural activity following infarction. Limitations arise when considering intracerebral hemorrhage and motor cortex lesions; however, future directions are outlined that aim to address these challenges.},
}
@article {pmid38016453,
year = {2023},
author = {Wang, R and Zhou, T and Li, Z and Zhao, J and Li, X},
title = {Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad1054},
pmid = {38016453},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Brain ; Algorithms ; },
abstract = {Objective.In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain-computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential-based BCIs.Approach.The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis. Oscillatory features are then extracted using the spectral power of fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory, aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools by Bonferroni correctedp-values from two-way analysis of variance. Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state.Mainresults.On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features.Significance.Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.},
}
@article {pmid38016450,
year = {2024},
author = {Ahmadipour, P and Sani, OG and Pesaran, B and Shanechi, MM},
title = {Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
pmid = {38016450},
issn = {1741-2552},
support = {DP2 MH126378/MH/NIMH NIH HHS/United States ; R01 MH123770/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Neurosciences ; Normal Distribution ; },
abstract = {Objective.Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.Approach.Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior.Main results.We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.Significance.Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.},
}
@article {pmid38016198,
year = {2024},
author = {Liu, M and Jiang, N and Qin, C and Xue, Y and Wu, J and Qiu, Y and Yuan, Q and Chen, C and Huang, L and Zhuang, L and Wang, P},
title = {Multimodal spatiotemporal monitoring of basal stem cell-derived organoids reveals progression of olfactory dysfunction in Alzheimer's disease.},
journal = {Biosensors &amp; bioelectronics},
volume = {246},
number = {},
pages = {115832},
doi = {10.1016/j.bios.2023.115832},
pmid = {38016198},
issn = {1873-4235},
mesh = {Humans ; Mice ; Animals ; *Alzheimer Disease/metabolism ; *Neurodegenerative Diseases ; *Biosensing Techniques ; Mice, Transgenic ; Stem Cells/metabolism ; Organoids/metabolism ; *Olfaction Disorders/metabolism ; Amyloid beta-Peptides/metabolism ; },
abstract = {Olfactory dysfunction (OD) is a highly prevalent symptom and an early sign of neurodegenerative diseases in humans. However, the roles of peripheral olfactory system in disease progression and the mechanisms behind neurodegeneration remain to be studied. Olfactory epithelium (OE) organoid is an ideal model to study pathophysiology in vitro, yet the reliance on 3D culture condition limits continual in situ monitoring of organoid development. Here, we combined impedance biosensors and live imaging for real-time spatiotemporal analysis of OE organoids morphological and physiological features during Alzheimer's disease (AD) progression. The impedance measurements showed that organoids generated from basal stem cells of APP/PS1 transgenic mice had lower proliferation rate than that from wild-type mice. In concert with the biosensor measurements, live imaging enabled to visualize the spatial and temporal dynamics of organoid morphology. Abnormal protein aggregation and accumulation, including amyloid plaques and neurofibrillary tangles, was found in AD organoids and increased as disease progressed. This multimodal in situ bioelectrical measurement and imaging provide a new platform for investigating onset mechanisms of OD, which would shed new light on early diagnosis and treatment of neurodegenerative disease.},
}
@article {pmid38015667,
year = {2023},
author = {Li, R and Zhao, X and Wang, Z and Xu, G and Hu, H and Zhou, T and Xu, T},
title = {A Novel Hybrid Brain-Computer Interface Combining the Illusion-Induced VEP and SSVEP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4760-4772},
doi = {10.1109/TNSRE.2023.3337525},
pmid = {38015667},
issn = {1558-0210},
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Illusions ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; },
abstract = {Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated through two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.},
}
@article {pmid38015349,
year = {2024},
author = {Wu, X and Liu, Y and Wang, X and Zheng, L and Pan, L and Wang, H},
title = {Developmental Impairments of Synaptic Refinement in the Thalamus of a Mouse Model of Fragile X Syndrome.},
journal = {Neuroscience bulletin},
volume = {40},
number = {4},
pages = {439-450},
pmid = {38015349},
issn = {1995-8218},
mesh = {Mice ; Animals ; *Fragile X Syndrome/genetics ; Mice, Knockout ; *Autism Spectrum Disorder ; Disease Models, Animal ; Thalamus/metabolism ; Synapses/metabolism ; Fragile X Mental Retardation Protein/genetics/metabolism ; },
abstract = {While somatosensory over-reactivity is a common feature of autism spectrum disorders such as fragile X syndrome (FXS), the thalamic mechanisms underlying this remain unclear. Here, we found that the developmental elimination of synapses formed between the principal nucleus of V (PrV) and the ventral posterior medial nucleus (VPm) of the somatosensory system was delayed in fragile X mental retardation 1 gene knockout (Fmr1 KO) mice, while the developmental strengthening of these synapses was disrupted. Immunohistochemistry showed excessive VGluT2 puncta in mutants at P12-13, but not at P7-8 or P15-16, confirming a delay in somatic pruning of PrV-VPm synapses. Impaired synaptic function was associated with a reduction in the frequency of quantal AMPA events, as well as developmental deficits in presynaptic vesicle size and density. Our results uncovered the developmental impairment of thalamic relay synapses in Fmr1 KO mice and suggest that a thalamic contribution to the somatosensory over-reactivity in FXS should be considered.},
}
@article {pmid38013860,
year = {2023},
author = {Anton, NE and Ziliak, MC and Stefanidis, D},
title = {Augmenting mental imagery for robotic surgery using neurofeedback: results of a randomized controlled trial.},
journal = {Global surgical education : journal of the Association for Surgical Education},
volume = {2},
number = {1},
pages = {62},
pmid = {38013860},
issn = {2731-4588},
abstract = {BACKGROUND: Mental imagery (MI) can enhance surgical skills. Research has shown that through brain-computer interface (BCI), it is possible to provide feedback on MI strength. We hypothesized that adding BCI to MI training would enhance robotic skill acquisition compared with controls.<br><br>METHODS: Surgical novices were recruited. At baseline, participants completed the Mental Imagery Questionnaire (MIQ) and the Vandenburg Mental Rotation Test (MRT). Students also performed several tasks on a robotic simulator. Participants were stratified based on MIQ and robotic skill and randomized into three groups: controls, MI, and MI and BCI training. All participants completed five 2-h training sessions. One hour was devoted to practicing robotic skill on the simulator. Additionally, controls completed crosswords for one hour, the MI group completed MI training and crosswords for one hour, and the MI&#x2009;+&#x2009;BCI group completed MI training and MI-related BCI training. Following training, participants completed the same baseline assessments. A Kruskal-Wallis test was used to determine differences between groups. Mann-Whitney U tests were performed to determine specific differences between groups.<br><br>RESULTS: Twenty-seven undergraduates participated. There were post-test differences on the MRT and knot tying task. Sub-analyses revealed that the MI&#x2009;+&#x2009;BCI group significantly outperformed the other groups on knot tying. There were no appreciable differences between the control and MI groups on any measures.<br><br>CONCLUSIONS: Augmenting MI training with BCI led to significantly enhanced MI and robotic skill acquisition than traditional MI or robotic training methods. To optimize surgical skill acquisition in robotic and other surgical skills curricula, educators should consider utilizing MI with BCI training.},
}
@article {pmid38013626,
year = {2024},
author = {Wei, Q and Yu, H and Wang, PS and Xie, JJ and Dong, HL and Wu, ZY and Li, HF},
title = {Biallelic variants in the COQ4 gene caused hereditary spastic paraplegia predominant phenotype.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {4},
pages = {e14529},
pmid = {38013626},
issn = {1755-5949},
support = {82171238//National Natural Science Foundation of China/ ; 82201513//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Mitochondrial Diseases/genetics ; *Mitochondrial Proteins/genetics ; Mutation/genetics ; Pedigree ; Phenotype ; *Spastic Paraplegia, Hereditary/genetics ; },
abstract = {INTRODUCTION: Hereditary spastic paraplegias (HSPs) comprise a group of neurodegenerative disorders characterized by progressive degeneration of upper motor neurons. Homozygous or compound heterozygous variants in COQ4 have been reported to cause primary CoQ10 deficiency-7 (COQ10D7), which is a mitochondrial disease.<br><br>AIMS: We aimed to screened COQ4 variants in a cohort of HSP patients.<br><br>METHODS: A total of 87 genetically unidentified HSP index patients and their available family members were recruited. Whole exome sequencing (WES) was performed in all probands. Functional studies were performed to identify the pathogenicity of those uncertain significance variants.<br><br>RESULTS: In this study, five different COQ4 variants were identified in three Chinese HSP pedigrees and two variants were novel, c.87dupT (p.Arg30*), c.304C>T (p.Arg102Cys). More importantly, we firstly described two early-onset pure HSP caused by COQ4 variants. Functional studies in patient-derived fibroblast lines revealed a reduction cellular CoQ10 levels and the abnormal mitochondrial structure.<br><br>CONCLUSIONS: Our findings revealed that bilateral variants in the COQ4 gene caused HSP predominant phenotype, expanding the phenotypic spectrum of the COQ4-related disorders.},
}
@article {pmid38012418,
year = {2024},
author = {Lei, A and Yu, H and Lu, S and Lu, H and Ding, X and Tan, T and Zhang, H and Zhu, M and Tian, L and Wang, X and Su, S and Xue, D and Zhang, S and Zhao, W and Chen, Y and Xie, W and Zhang, L and Zhu, Y and Zhao, J and Jiang, W and Church, G and Chan, FK and Gao, Z and Zhang, J},
title = {A second-generation M1-polarized CAR macrophage with antitumor efficacy.},
journal = {Nature immunology},
volume = {25},
number = {1},
pages = {102-116},
pmid = {38012418},
issn = {1529-2916},
support = {82373238//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31871453//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91857116//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019M662035//China Postdoctoral Science Foundation/ ; },
mesh = {Humans ; Receptors, Antigen, T-Cell ; T-Lymphocytes ; Cell Line, Tumor ; *Receptors, Chimeric Antigen/genetics ; *Neoplasms ; Immunotherapy, Adoptive/methods ; Macrophages/pathology ; Tumor Microenvironment ; },
abstract = {Chimeric antigen receptor (CAR) T cell therapies have successfully treated hematological malignancies. Macrophages have also gained attention as an immunotherapy owing to their immunomodulatory capacity and ability to infiltrate solid tumors and phagocytize tumor cells. The first-generation CD3ζ-based CAR-macrophages could phagocytose tumor cells in an antigen-dependent manner. Here we engineered induced pluripotent stem cell-derived macrophages (iMACs) with toll-like receptor 4 intracellular toll/IL-1R (TIR) domain-containing CARs resulting in a markedly enhanced antitumor effect over first-generation CAR-macrophages. Moreover, the design of a tandem CD3ζ-TIR dual signaling CAR endows iMACs with both target engulfment capacity and antigen-dependent M1 polarization and M2 resistance in a nuclear factor kappa B (NF-κB)-dependent manner, as well as the capacity to modulate the tumor microenvironment. We also outline a mechanism of tumor cell elimination by CAR-induced efferocytosis against tumor cell apoptotic bodies. Taken together, we provide a second-generation CAR-iMAC with an ability for orthogonal phagocytosis and polarization and superior antitumor functions in treating solid tumors relative to first-generation CAR-macrophages.},
}
@article {pmid38010934,
year = {2024},
author = {Han, J and Gu, X and Yang, GZ and Lo, B},
title = {Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {2},
pages = {765-776},
doi = {10.1109/JBHI.2023.3337072},
pmid = {38010934},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Benchmarking ; Imagination ; },
abstract = {Motor Imagery (MI) Electroencephalography (EEG) is one of the most common Brain-Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and gaming. Although considerable research efforts have been dedicated to developing MI EEG classification algorithms, they are mostly limited in handling scenarios where the training and testing data are not from the same subject or session. Such poor generalization capability significantly limits the realization of BCI in real-world applications. In this paper, we proposed a novel framework to disentangle the representation of raw EEG data into three components, subject/session-specific, MI-task-specific, and random noises, so that the subject/session-specific feature extends the generalization capability of the system. This is realized by a joint discriminative and generative framework, supported by a series of fundamental training losses and training strategies. We evaluated our framework on three public MI EEG datasets, and detailed experimental results show that our method can achieve superior performance by a large margin compared to current state-of-the-art benchmark algorithms.},
}
@article {pmid38010159,
year = {2023},
author = {Ohkubo, M},
title = {The emergence of non-cryogenic quantum magnetic sensors: Synergistic advancement in magnetography together with SQUID.},
journal = {The Review of scientific instruments},
volume = {94},
number = {11},
pages = {},
doi = {10.1063/5.0167372},
pmid = {38010159},
issn = {1089-7623},
abstract = {Emerging non-superconductor quantum magnetic sensors, such as optically pumped magnetometer, fluxgate, magnetic tunnel junction, and diamond nitrogen-vacancy center, are approaching the performance of superconductor quantum interference devices (SQUIDs). These sensors are enabling magnetography for human bodies and brain-computer interface. Will they completely replace the SQUID magnetography in the near future?},
}
@article {pmid38007914,
year = {2023},
author = {Bordes, A and El Bendary, Y and Goudard, G and Masson, V and Gourfinkel-An, I and Mathon, B},
title = {Benefits of vagus nerve stimulation on psychomotor functions in patients with severe drug-resistant epilepsy.},
journal = {Epilepsy research},
volume = {198},
number = {},
pages = {107260},
doi = {10.1016/j.eplepsyres.2023.107260},
pmid = {38007914},
issn = {1872-6844},
mesh = {Adult ; Humans ; *Vagus Nerve Stimulation/methods ; Quality of Life ; *Drug Resistant Epilepsy/therapy ; Mental Recall ; Psychomotor Performance ; Treatment Outcome ; Vagus Nerve ; },
abstract = {PURPOSE: Patients with severe drug-resistant epilepsy (DRE) experience psychomotor disorders. Our study aimed to assess the psychomotor outcomes after vagus nerve stimulation (VNS) in this population.<br><br>METHODS: We prospectively evaluated psychomotor function in 17 adult patients with severe DRE who were referred for VNS. Psychomotor functions were examined, in the preoperative period and at 18 months post-surgery, by a psychomotor therapist using a full set of the following specific tests: the Rey-Osterrieth complex figure (ROCF) test, the Zazzo's cancelation task (ZCT), the Piaget-Head test and the paired images test.<br><br>RESULTS: At 18 months post-VNS surgery, the Piaget-head scores increased by 3 points (p&#xa0;=&#xa0;0.008) compared to baseline. Performances were also improved for ROCF test both in copy (+2.4 points, p&#xa0;=&#xa0;0.001) and recall (+2.0 points, p&#xa0;=&#xa0;0.008) tasks and for the paired images test (accuracy index: +28.6 %, p&#xa0;=&#xa0;0.03). Regarding the ZCT findings, the efficiency index increased in both single (+16 %, p&#xa0;=&#xa0;0.005) and dual (+17.1 %, p&#xa0;<&#xa0;0.001) tasks. QoL improved in 88.2 % of patients.<br><br>CONCLUSIONS: Patients with severe DRE treated with VNS experienced improved performance in terms of global psychomotor functions. Perceptual organization, visuospatial memory, laterality awareness, sustained attention, concentration, visual scanning, and inhibition were significantly improved.},
}
@article {pmid38007763,
year = {2023},
author = {Tian, C},
title = {Research on Brain Signals Classification Based on Deep Learning.},
journal = {Studies in health technology and informatics},
volume = {308},
number = {},
pages = {381-388},
doi = {10.3233/SHTI230863},
pmid = {38007763},
issn = {1879-8365},
mesh = {Humans ; *Neural Networks, Computer ; *Deep Learning ; Algorithms ; Electroencephalography/methods ; Brain/diagnostic imaging ; },
abstract = {With the continuous expansion of brain-computer communication, the precise identification of brain signals has become an essential task for brain-computer equipment. However, existing classification methods are primarily concentrated on the extraction features of brain signals and obtain unacceptable performance when directly used the model to a new brain signals data, which is caused by the different people has extraordinary brain signals. In this work, we utilize the deep learning methods not only extract the features of brain signals but also learn the order information of brain signals, which can satisfy the universal brain signals. Indeed, we utilize the classification features dimension distance loss function to optimize the proposed model and enhance the classification accuracy and we compare our model with existing classification methods to evaluate proposed model. From our extensive experimental results and analysis, we can conclude that our model can achieve the classification of brain signals with the reasonable accuracy and acceptable costs.},
}
@article {pmid38007753,
year = {2023},
author = {He, R},
title = {Perspective of Signal Processing-Based on Brain-Computer Interfaces Using Machine Learning Methods.},
journal = {Studies in health technology and informatics},
volume = {308},
number = {},
pages = {295-302},
doi = {10.3233/SHTI230853},
pmid = {38007753},
issn = {1879-8365},
mesh = {*Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The "Discussion" part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms.},
}
@article {pmid38007721,
year = {2023},
author = {Li, Y},
title = {CNN-Based Image Analysis for EEG Signal Characterization.},
journal = {Studies in health technology and informatics},
volume = {308},
number = {},
pages = {20-30},
doi = {10.3233/SHTI230820},
pmid = {38007721},
issn = {1879-8365},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; Recognition, Psychology ; *Brain-Computer Interfaces ; },
abstract = {This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.},
}
@article {pmid38007085,
year = {2024},
author = {Chen, A and Hao, S and Han, Y and Fang, Y and Miao, Y},
title = {Exploring the effects of different BCI-based attention training games on the brain: A functional near-infrared spectroscopy study.},
journal = {Neuroscience letters},
volume = {818},
number = {},
pages = {137567},
doi = {10.1016/j.neulet.2023.137567},
pmid = {38007085},
issn = {1872-7972},
mesh = {Male ; Female ; Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Brain ; Motivation ; Attention ; },
abstract = {BCI games have been widely employed as non-invasive therapeutic interventions for conditions, but their efficacy remains a subject of debate. This study explores the efficacy of two prevalent forms of Brain-Computer Interface (BCI)-based attention training games: video games (VG) and physical games (PG). The effectiveness of these games has been examined through the lens of neuroscience, using functional Near-Infrared Spectroscopy (fNIRS) to monitor cortical activation. After the fNIRS test, subjects completed an Intrinsic Motivation Inventory (IMI) questionnaire. PG tasks activated six channels (L-PFC, R-PFC and R-TL), while VG tasks activated only one (R-PFC). Furthermore, females exhibited stronger activation during PG tasks, while males had none in either. Our findings suggest that under equivalent game rules and themes, PG may prove more effective for cognitive rehabilitation than VG, with stronger intrinsic motivation. We also found this result may exhibit gender differences. Finally, this research offers valuable insights for the future design of BCI-based games from a neuroscience perspective.},
}
@article {pmid38006734,
year = {2024},
author = {Wang, X and Wang, Y and Qi, W and Kong, D and Wang, W},
title = {BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {170},
number = {},
pages = {312-324},
doi = {10.1016/j.neunet.2023.11.037},
pmid = {38006734},
issn = {1879-2782},
mesh = {*Imagination ; Movement ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to interact with the world through brain signals. To meet demands of real-time, stable, and diverse interactions, it is crucial to develop lightweight networks that can accurately and reliably decode multi-class MI tasks. In this paper, we introduce BrainGridNet, a convolutional neural network (CNN) framework that integrates two intersecting depthwise CNN branches with 3D electroencephalography (EEG) data to decode a five-class MI task. The BrainGridNet attains competitive results in both the time and frequency domains, with superior performance in the frequency domain. As a result, an accuracy of 80.26 percent and a kappa value of 0.753 are achieved by BrainGridNet, surpassing the state-of-the-art (SOTA) model. Additionally, BrainGridNet shows optimal computational efficiency, excels in decoding the most challenging subject, and maintains robust accuracy despite the random loss of 16 electrode signals. Finally, the visualizations demonstrate that BrainGridNet learns discriminative features and identifies critical brain regions and frequency bands corresponding to each MI class. The convergence of BrainGridNet's strong feature extraction capability, high decoding accuracy, steady decoding efficacy, and low computational costs renders it an appealing choice for facilitating the development of BCIs.},
}
@article {pmid38005515,
year = {2023},
author = {Wolf, P and Götzelmann, T},
title = {VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {22},
pages = {},
pmid = {38005515},
issn = {1424-8220},
abstract = {For brain-computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.},
}
@article {pmid38005437,
year = {2023},
author = {Farabbi, A and Mainardi, L},
title = {Domain-Specific Processing Stage for Estimating Single-Trail Evoked Potential Improves CNN Performance in Detecting Error Potential.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {22},
pages = {},
pmid = {38005437},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Evoked Potentials ; Neural Networks, Computer ; Wavelet Analysis ; Algorithms ; *Brain-Computer Interfaces ; },
abstract = {We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score.},
}
@article {pmid38004858,
year = {2023},
author = {Su, K and Qiu, Z and Xu, J},
title = {A 14-Bit, 12 V-to-100 V Voltage Compliance Electrical Stimulator with Redundant Digital Calibration.},
journal = {Micromachines},
volume = {14},
number = {11},
pages = {},
pmid = {38004858},
issn = {2072-666X},
support = {2021ZD0200401//STI 2030-Major Project/ ; 62176232//National Natural Science Foundation of China grant/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; },
abstract = {Electrical stimulation is an important technique for modulating the functions of the nervous system through electrical stimulus. To implement a more competitive prototype that can tackle the domain-specific difficulties of existing electrical stimulators, three key techniques are proposed in this work. Firstly, a load-adaptive power saving technique called over-voltage detection is implemented to automatically adjust the supply voltage. Secondly, redundant digital calibration (RDC) is proposed to improve current accuracy and ensure safety during long-term electrical stimulation without costing too much circuit area and power. Thirdly, a flexible waveform generator is designed to provide arbitrary stimulus waveforms for particular applications. Measurement results show the stimulator can adjust the supply voltage from 12 V to 100 V automatically, and the measured effective resolution of the stimulation current reaches 14 bits in a full range of 6.5 mA. Without applying charge balancing techniques, the average mismatch between the cathodic and anodic current pulses in biphasic stimulus is 0.0427%. The proposed electrical stimulator can generate arbitrary stimulus waveforms, including sine, triangle, rectangle, etc., and it is supposed to be competitive for implantable and wearable devices.},
}
@article {pmid38002653,
year = {2023},
author = {Zhang, Y and Zeng, H and Zhou, H and Li, J and Wang, T and Guo, Y and Cai, L and Hu, J and Zhang, X and Chen, G},
title = {Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard.},
journal = {Journal of clinical medicine},
volume = {12},
number = {22},
pages = {},
pmid = {38002653},
issn = {2077-0383},
support = {52277232, 81971099, 82171273, 82171275//National Natural Science Foundation of China/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation of China/ ; 2022C03133//Key R&amp;D Program of Zhejiang/ ; },
abstract = {Aneurysmal subarachnoid hemorrhage (aSAH) frequently causes long-term disability, but predicting outcomes remains challenging. Routine parameters such as demographics, admission status, CT findings, and blood tests can be used to predict aSAH outcomes. The aim of this study was to compare the performance of traditional logistic regression with several machine learning algorithms using readily available indicators and to generate a practical prognostic scorecard based on machine learning. Eighteen routinely available indicators were collected as outcome predictors for individuals with aSAH. Logistic regression (LR), random forest (RF), support vector machines (SVMs), and fully connected neural networks (FCNNs) were compared. A scorecard system was established based on predictor weights. The results show that machine learning models and a scorecard achieved 0.75~0.8 area under the curve (AUC) predicting aSAH outcomes (LR 0.739, RF 0.749, SVM 0.762~0.793, scorecard 0.794). FCNNs performed best (~0.95) but lacked interpretability. The scorecard model used only five factors, generating a clinically useful tool with a total cutoff score of ≥5, indicating poor prognosis. We developed and validated machine learning models proven to predict outcomes more accurately in individuals with aSAH. The parameters found to be the most strongly predictive of outcomes were NLR, lymphocyte count, monocyte count, hypertension status, and SEBES. The scorecard system provides a simplified means of applying predictive analytics at the bedside using a few key indicators.},
}
@article {pmid38002552,
year = {2023},
author = {Popa, LL and Chira, D and Strilciuc, &#x218; and Mureșanu, DF},
title = {Non-Invasive Systems Application in Traumatic Brain Injury Rehabilitation.},
journal = {Brain sciences},
volume = {13},
number = {11},
pages = {},
pmid = {38002552},
issn = {2076-3425},
abstract = {Traumatic brain injury (TBI) is a significant public health concern, often leading to long-lasting impairments in cognitive, motor and sensory functions. The rapid development of non-invasive systems has revolutionized the field of TBI rehabilitation by offering modern and effective interventions. This narrative review explores the application of non-invasive technologies, including electroencephalography (EEG), quantitative electroencephalography (qEEG), brain-computer interface (BCI), eye tracking, near-infrared spectroscopy (NIRS), functional near-infrared spectroscopy (fNIRS), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) in assessing TBI consequences, and repetitive transcranial magnetic stimulation (rTMS), low-level laser therapy (LLLT), neurofeedback, transcranial direct current stimulation (tDCS), transcranial alternative current stimulation (tACS) and virtual reality (VR) as therapeutic approaches for TBI rehabilitation. In pursuit of advancing TBI rehabilitation, this narrative review highlights the promising potential of non-invasive technologies. We emphasize the need for future research and clinical trials to elucidate their mechanisms of action, refine treatment protocols, and ensure their widespread adoption in TBI rehabilitation settings.},
}
@article {pmid38002543,
year = {2023},
author = {Lian, J and Qiao, X and Zhao, Y and Li, S and Wang, C and Zhou, J},
title = {EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions.},
journal = {Brain sciences},
volume = {13},
number = {11},
pages = {},
pmid = {38002543},
issn = {2076-3425},
support = {2021ZD0201600//the STI 2030-Major Projects/ ; Z201100006820144//the Beijing Nova Program/ ; },
abstract = {Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain-computer interfaces in EEG-based target detection areas.},
}
@article {pmid38000320,
year = {2024},
author = {Xu, F and Pan, D and Zheng, H and Ouyang, Y and Jia, Z and Zeng, H},
title = {EESCN: A novel spiking neural network method for EEG-based emotion recognition.},
journal = {Computer methods and programs in biomedicine},
volume = {243},
number = {},
pages = {107927},
doi = {10.1016/j.cmpb.2023.107927},
pmid = {38000320},
issn = {1872-7565},
mesh = {*Emotions ; *Neural Networks, Computer ; Electroencephalography ; },
abstract = {BACKGROUND AND OBJECTIVE: Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.<br><br>METHODS: We propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification.<br><br>RESULTS: EESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint.<br><br>CONCLUSIONS: EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.},
}
@article {pmid37995362,
year = {2023},
author = {Ke, Y and Wang, T and He, F and Liu, S and Ming, D},
title = {Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0f3d},
pmid = {37995362},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Workload ; Brain ; Algorithms ; *Brain-Computer Interfaces ; },
abstract = {Objective. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.Approach. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance.Main results. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%).Significance. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.},
}
@article {pmid37995162,
year = {2024},
author = {Chen, S and Zhang, X and Shen, X and Huang, Y and Wang, Y},
title = {Online Estimating Pairwise Neuronal Functional Connectivity in Brain-Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {271-281},
doi = {10.1109/TNSRE.2023.3336362},
pmid = {37995162},
issn = {1558-0210},
mesh = {Animals ; Rats ; *Brain-Computer Interfaces ; Likelihood Functions ; Algorithms ; Action Potentials/physiology ; Neurons/physiology ; Brain/physiology ; },
abstract = {Neurons respond to external stimuli and form functional networks through pairwise interactions. A neural encoding model can describe a single neuron's behavior, and brain-machine interfaces (BMIs) provide a platform to investigate how neurons adapt, functionally connect, and encode movement. Movement modulation and pairwise functional connectivity are modeled as high-dimensional tuning states, estimated from neural spike train observations. However, accurate estimation of this neural state vector can be challenging as pairwise neural interactions are highly dimensional, change in different temporal scales from movement, and could be non-stationary. We propose an Adam-based gradient descent method to online estimate high-dimensional pairwise neuronal functional connectivity and single neuronal tuning adaptation simultaneously. By minimizing negative log-likelihood based on point process observation, the proposed method adaptively adjusts the learning rate for each dimension of the neural state vectors by employing momentum and regularizer. We test the method on real recordings of two rats performing the brain control mode of a two-lever discrimination task. Our results show that our method outperforms existing methods, especially when the state is sparse. Our method is more stable and faster for an online scenario regardless of the parameter initializations. Our method provides a promising tool to track and build the time-variant functional neural connectivity, which dynamically forms the functional network and results in better brain control.},
}
@article {pmid37995161,
year = {2023},
author = {Zhu, L and Liu, Y and Liu, R and Peng, Y and Cao, J and Li, J and Kong, W},
title = {Decoding Multi-Brain Motor Imagery From EEG Using Coupling Feature Extraction and Few-Shot Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4683-4692},
doi = {10.1109/TNSRE.2023.3336356},
pmid = {37995161},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Algorithms ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.},
}
@article {pmid37993467,
year = {2023},
author = {Mao, C and Gao, M and Zang, SK and Zhu, Y and Shen, DD and Chen, LN and Yang, L and Wang, Z and Zhang, H and Wang, WW and Shen, Q and Lu, Y and Ma, X and Zhang, Y},
title = {Orthosteric and allosteric modulation of human HCAR2 signaling complex.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {7620},
pmid = {37993467},
issn = {2041-1723},
mesh = {Humans ; *Receptors, G-Protein-Coupled/metabolism ; *Niacin/pharmacology ; Ligands ; Signal Transduction ; Allosteric Regulation ; Allosteric Site ; },
abstract = {Hydroxycarboxylic acids are crucial metabolic intermediates involved in various physiological and pathological processes, some of which are recognized by specific hydroxycarboxylic acid receptors (HCARs). HCAR2 is one such receptor, activated by endogenous β-hydroxybutyrate (3-HB) and butyrate, and is the target for Niacin. Interest in HCAR2 has been driven by its potential as a therapeutic target in cardiovascular and neuroinflammatory diseases. However, the limited understanding of how ligands bind to this receptor has hindered the development of alternative drugs able to avoid the common flushing side-effects associated with Niacin therapy. Here, we present three high-resolution structures of HCAR2-Gi1 complexes bound to four different ligands, one potent synthetic agonist (MK-6892) bound alone, and the two structures bound to the allosteric agonist compound 9n in conjunction with either the endogenous ligand 3-HB or niacin. These structures coupled with our functional and computational analyses further our understanding of ligand recognition, allosteric modulation, and activation of HCAR2 and pave the way for the development of high-efficiency drugs with reduced side-effects.},
}
@article {pmid37992582,
year = {2024},
author = {Yu, C and Lu, Y and Pang, J and Li, L},
title = {A hemostatic sponge derived from chitosan and hydroxypropylmethylcellulose.},
journal = {Journal of the mechanical behavior of biomedical materials},
volume = {150},
number = {},
pages = {106240},
doi = {10.1016/j.jmbbm.2023.106240},
pmid = {37992582},
issn = {1878-0180},
mesh = {Humans ; *Hemostatics/pharmacology/chemistry ; *Chitosan/pharmacology/chemistry ; Hypromellose Derivatives/pharmacology ; Hemostasis ; Blood Coagulation ; Hemorrhage ; },
abstract = {Hemostatic materials are of great significance for rapid control of bleeding, especially in military trauma and traffic accidents. Chitosan (CS) hemostatic sponges have been widely concerned and studied due to their excellent biocompatibility. However, the hemostatic performance of pure chitosan sponges is poor due to the shortcoming of strong rigidity. In this study, CS and hydroxypropylmethylcellulose (HPMC) were combined to develop a safe and effective hemostatic composite sponges (CS/HPMC) for hemorrhage control by a simple mixed-lyophilization strategy. The CS/HPMC exhibited excellent flexibility (the flexibility was 74% higher than that of pure CS sponges). Due to the high porosity and procoagulant chemical structure of the CS/HPMC, it exhibited rapid hemostatic ability in vitro (BCI was shortened by 50% than that of pure CS sponges). The good biocompatibility of the obtained CS/HPMC was confirmed via cytotoxicity, hemocompatibility and skin irritation tests. The CS/HPMC can induced the erythrocyte and platelets adhesion, resulting in significant coagulation acceleration. The CS/HPMC had excellent performance in vivo assessments with shortest clotting time (40 s) and minimal blood loss (166 mg). All above results proved that the CS/HPMC had great potential to be a safe and rapid hemostatic material.},
}
@article {pmid37991789,
year = {2024},
author = {Khan, S and Anderson, W and Constandinou, T},
title = {Surgical Implantation of Brain Computer Interfaces.},
journal = {JAMA surgery},
volume = {159},
number = {2},
pages = {219-220},
doi = {10.1001/jamasurg.2023.2399},
pmid = {37991789},
issn = {2168-6262},
mesh = {Humans ; *Brain-Computer Interfaces ; User-Computer Interface ; Brain/surgery ; },
}
@article {pmid37991593,
year = {2024},
author = {Mizuguchi, N},
title = {Candidate brain regions for motor imagery practice: a commentary on Rieger et al., 2023.},
journal = {Psychological research},
volume = {88},
number = {6},
pages = {1868-1869},
pmid = {37991593},
issn = {1430-2772},
support = {23K10625//JSPS KAKENHI/ ; },
mesh = {Humans ; *Imagination/physiology ; *Psychomotor Performance/physiology ; *Gyrus Cinguli/physiology ; Practice, Psychological ; Motor Activity/physiology ; Brain/physiology ; },
abstract = {The mechanism through which motor imagery practice improves motor performance remains unclear. In this special issue, Rieger et al. propose a model to explain why motor imagery practice improves motor performance. According to their model, motor imagery involves a comparison between intended and predicted action effects, allowing for the modification of the internal model upon detecting errors. I believe that the anterior cingulate cortex (ACC) is a candidate as a brain region responsible for comparing intended and predicted action effects. Evidence supports this hypothesis, as a previous study has observed error-related activity in the ACC preceding incorrect responses (i.e., commission errors) in the Go/No-go task (Bediou et al., 2012, Neuroimage). Therefore, the error-related activity can be induced without any feedback. This fact also sheds light on the mechanisms of brain-computer interface. I believe that this additional literature will enhance Rieger's model.},
}
@article {pmid37990998,
year = {2023},
author = {Xu, F and Yan, Y and Zhu, J and Chen, X and Gao, L and Liu, Y and Shi, W and Lou, Y and Wang, W and Leng, J and Zhang, Y},
title = {Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.},
journal = {International journal of neural systems},
volume = {33},
number = {12},
pages = {2350066},
doi = {10.1142/S0129065723500661},
pmid = {37990998},
issn = {1793-6462},
mesh = {Humans ; *Imagination ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Cognition ; },
abstract = {Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.},
}
@article {pmid37990160,
year = {2023},
author = {Colamarino, E and Lorusso, M and Pichiorri, F and Toppi, J and Tamburella, F and Serratore, G and Riccio, A and Tomaiuolo, F and Bigioni, A and Giove, F and Scivoletto, G and Cincotti, F and Mattia, D},
title = {DiSCIoser: unlocking recovery potential of arm sensorimotor functions after spinal cord injury by promoting activity-dependent brain plasticity by means of brain-computer interface technology: a randomized controlled trial to test efficacy.},
journal = {BMC neurology},
volume = {23},
number = {1},
pages = {414},
pmid = {37990160},
issn = {1471-2377},
mesh = {Humans ; *Brain-Computer Interfaces ; Arm ; Upper Extremity ; *Spinal Cord Injuries/rehabilitation ; Neuronal Plasticity ; Recovery of Function/physiology ; },
abstract = {BACKGROUND: Traumatic cervical spinal cord injury (SCI) results in reduced sensorimotor abilities that strongly impact on the achievement of daily living activities involving hand/arm function. Among several technology-based rehabilitative approaches, Brain-Computer Interfaces (BCIs) which enable the modulation of electroencephalographic sensorimotor rhythms, are promising tools to promote the recovery of hand function after SCI. The "DiSCIoser" study proposes a BCI-supported motor imagery (MI) training to engage the sensorimotor system and thus facilitate the neuroplasticity to eventually optimize upper limb sensorimotor functional recovery in patients with SCI during the subacute phase, at the peak of brain and spinal plasticity. To this purpose, we have designed a BCI system fully compatible with a clinical setting whose efficacy in improving hand sensorimotor function outcomes in patients with traumatic cervical SCI will be assessed and compared to the hand MI training not supported by BCI.<br><br>METHODS: This randomized controlled trial will include 30 participants with traumatic cervical SCI in the subacute phase randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and the hand MI training not supported by BCI. Both interventions are delivered (3 weekly sessions; 12 weeks) as add-on to standard rehabilitation care. A multidimensional assessment will be performed at: randomization/pre-intervention and post-intervention. Primary outcome measure is the Graded Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) somatosensory sub-score. Secondary outcome measures include the motor and functional scores of the GRASSP and other clinical, neuropsychological, neurophysiological and neuroimaging measures.<br><br>DISCUSSION: We expect the BCI-based intervention to promote meaningful cortical sensorimotor plasticity and eventually maximize recovery of arm functions in traumatic cervical subacute SCI. This study will generate a body of knowledge that is fundamental to drive optimization of BCI application in SCI as a top-down therapeutic intervention, thus beyond the canonical use of BCI as assistive tool.<br><br>TRIAL REGISTRATION: Name of registry: DiSCIoser: improving arm sensorimotor functions after spinal cord injury via brain-computer interface training (DiSCIoser).<br><br>TRIAL REGISTRATION NUMBER: NCT05637775; registration date on the ClinicalTrial.gov platform: 05-12-2022.},
}
@article {pmid37986895,
year = {2023},
author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ},
title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37986895},
issn = {2692-8205},
support = {R01 EY017992/EY/NEI NIH HHS/United States ; R01 EY029247/EY/NEI NIH HHS/United States ; R01 EY034004/EY/NEI NIH HHS/United States ; },
abstract = {Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.},
}
@article {pmid37986883,
year = {2023},
author = {Chen, K and Forrest, A and Gonzalez Burgos, G and Kozai, TDY},
title = {Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.11.06.565852},
pmid = {37986883},
issn = {2692-8205},
abstract = {OBJECTIVE: This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.<br><br>APPROACH: This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.<br><br>MAIN RESULTS: The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.<br><br>SIGNIFICANCE: This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.},
}
@article {pmid37986728,
year = {2023},
author = {Fan, C and Hahn, N and Kamdar, F and Avansino, D and Wilson, GH and Hochberg, L and Shenoy, KV and Henderson, JM and Willett, FR},
title = {Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {37986728},
issn = {2331-8422},
abstract = {Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.},
}
@article {pmid37984201,
year = {2024},
author = {Borgheai, SB and Zisk, AH and McLinden, J and Mcintyre, J and Sadjadi, R and Shahriari, Y},
title = {Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.},
journal = {Computers in biology and medicine},
volume = {168},
number = {},
pages = {107658},
doi = {10.1016/j.compbiomed.2023.107658},
pmid = {37984201},
issn = {1879-0534},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.<br><br>METHOD: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.<br><br>RESULT: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value&#xa0;=&#xa0;0.011) and 0.6970 (p-value&#xa0;=&#xa0;0.017), respectively. For 81.82&#xa0;% of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18&#xa0;% when applying the correction strategies.<br><br>CONCLUSION: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.},
}
@article {pmid37982955,
year = {2024},
author = {Li, M and Li, J and Song, Z and Deng, H and Xu, J and Xu, G and Liao, W},
title = {EEGNet-based multi-source domain filter for BCI transfer learning.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {3},
pages = {675-686},
pmid = {37982955},
issn = {1741-0444},
support = {F2021202003//Natural Science Foundation of Hebei Province/ ; EERI_OY2020004//State Key Laboratory of Reliability and Intelligence of Electrical Equipment/ ; EERI_OY202000//State Key Laboratory of Reliability and Intelligence of Electrical Equipment/ ; 19277752D//the Key Research and Development Foundation of Hebei/ ; 21372002D//the Key Research and Development Foundation of Hebei/ ; JBKYXX2007//the Technology Nova of Hebei University of Technology/ ; 51977060//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Machine Learning ; Electroencephalography ; },
abstract = {Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.},
}
@article {pmid37982637,
year = {2024},
author = {Herring, EZ and Graczyk, EL and Memberg, WD and Adams, R and Fernandez Baca-Vaca, G and Hutchison, BC and Krall, JT and Alexander, BJ and Conlan, EC and Alfaro, KE and Bhat, P and Ketting-Olivier, AB and Haddix, CA and Taylor, DM and Tyler, DJ and Sweet, JA and Kirsch, RF and Ajiboye, AB and Miller, JP},
title = {Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia.},
journal = {Neurosurgery},
volume = {94},
number = {4},
pages = {864-874},
doi = {10.1227/neu.0000000000002769},
pmid = {37982637},
issn = {1524-4040},
support = {T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Humans ; Male ; *Arm/innervation ; Brain ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Hand/physiology ; Quadriplegia ; Upper Extremity ; Clinical Trials as Topic ; },
abstract = {BACKGROUND AND OBJECTIVES: Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.<br><br>METHODS: A 27-year-old right-handed man with AIS-B (motor-complete, sensory-incomplete) C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of using a brain-machine interface to read from and write to the brain for restoring motor and sensory functions of the participant's own arm and hand.<br><br>RESULTS: Multiunit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions, resulting in functional movements that the participant was able to command under brain control to perform virtual and actual arm and hand movements. The system was well tolerated with no operative complications.<br><br>CONCLUSION: The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to create bidirectional restoration of motor and sensory functions of the arm and hand after neurological injury.},
}
@article {pmid37982231,
year = {2025},
author = {Quanyu, W and Sheng, D and Weige, T and Lingjiao, P and Xiaojie, L},
title = {Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {28},
number = {1},
pages = {51-60},
doi = {10.1080/10255842.2023.2284091},
pmid = {37982231},
issn = {1476-8259},
mesh = {*Electroencephalography/methods/statistics &amp; numerical data ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Support Vector Machine ; Bayes Theorem ; },
abstract = {To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.},
}
@article {pmid37980798,
year = {2024},
author = {Ali, O and Saif-Ur-Rehman, M and Glasmachers, T and Iossifidis, I and Klaes, C},
title = {ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.},
journal = {Computers in biology and medicine},
volume = {168},
number = {},
pages = {107649},
doi = {10.1016/j.compbiomed.2023.107649},
pmid = {37980798},
issn = {1879-0534},
mesh = {Humans ; *Machine Learning ; *Brain-Computer Interfaces ; Movement ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Imagination ; },
abstract = {OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data.<br><br>APPROACH: In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals.<br><br>MAIN RESULTS: We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks).<br><br>SIGNIFICANCE: With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},
}
@article {pmid37980536,
year = {2023},
author = {Canny, E and Vansteensel, MJ and van der Salm, SMA and Müller-Putz, GR and Berezutskaya, J},
title = {Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {157},
pmid = {37980536},
issn = {1743-0003},
support = {OCENW.XS22.4.118//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; User-Computer Interface ; *Locked-In Syndrome ; Paralysis ; Electric Stimulation ; Brain/physiology ; },
abstract = {Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.},
}
@article {pmid37980496,
year = {2023},
author = {Tanamachi, K and Kuwahara, W and Okawada, M and Sasaki, S and Kaneko, F},
title = {Relationship between resting-state functional connectivity and change in motor function after motor imagery intervention in patients with stroke: a scoping review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {159},
pmid = {37980496},
issn = {1743-0003},
support = {23H00458//JSPS KAKENHI/ ; },
mesh = {Humans ; *Stroke ; Brain ; Imagery, Psychotherapy/methods ; *Stroke Rehabilitation/methods ; Recovery of Function/physiology ; },
abstract = {BACKGROUND: In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke.<br><br>REVIEW PROCESS: The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included.<br><br>CONCLUSION: All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex).},
}
@article {pmid37979923,
year = {2024},
author = {Sengupta, P and Lakshminarayanan, K},
title = {Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery.},
journal = {Behavioural brain research},
volume = {459},
number = {},
pages = {114760},
doi = {10.1016/j.bbr.2023.114760},
pmid = {37979923},
issn = {1872-7549},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Imagination/physiology ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.},
}
@article {pmid37979051,
year = {2024},
author = {Ousingsawat, J and Centeio, R and Schreiber, R and Kunzelmann, K},
title = {Niclosamide, but not ivermectin, inhibits anoctamin 1 and 6 and attenuates inflammation of the respiratory tract.},
journal = {Pflugers Archiv : European journal of physiology},
volume = {476},
number = {2},
pages = {211-227},
pmid = {37979051},
issn = {1432-2013},
mesh = {Mice ; Animals ; Anoctamin-1/metabolism ; Ivermectin/pharmacology/therapeutic use ; Niclosamide/pharmacology/therapeutic use ; Anoctamins/metabolism ; *Asthma ; Lung/metabolism ; Phospholipid Transfer Proteins/metabolism ; Calcium/metabolism ; Inflammation/drug therapy ; Anti-Inflammatory Agents ; *COVID-19 ; Chloride Channels/metabolism ; },
abstract = {Inflammatory airway diseases like cystic fibrosis, asthma and COVID-19 are characterized by high levels of pulmonary cytokines. Two well-established antiparasitic drugs, niclosamide and ivermectin, are intensively discussed for the treatment of viral inflammatory airway infections. Here, we examined these repurposed drugs with respect to their anti-inflammatory effects in airways in vivo and in vitro. Niclosamide reduced mucus content, eosinophilic infiltration and cell death in asthmatic mouse lungs in vivo and inhibited release of interleukins in the two differentiated airway epithelial cell lines CFBE and BCi-NS1.1 in vitro. Cytokine release was also inhibited by the knockdown of the Ca[2+]-activated Cl[-] channel anoctamin 1 (ANO1, TMEM16A) and the phospholipid scramblase anoctamin 6 (ANO6, TMEM16F), which have previously been shown to affect intracellular Ca[2+] levels near the plasma membrane and to facilitate exocytosis. At concentrations around 200 nM, niclosamide inhibited inflammation, lowered intracellular Ca[2+], acidified cytosolic pH and blocked activation of ANO1 and ANO6. It is suggested that niclosamide brings about its anti-inflammatory effects at least in part by inhibiting ANO1 and ANO6, and by lowering intracellular Ca[2+] levels. In contrast to niclosamide, 1 µM ivermectin did not exert any of the effects described for niclosamide. The present data suggest niclosamide as an effective anti-inflammatory treatment in CF, asthma, and COVID-19, in addition to its previously reported antiviral effects. It has an advantageous concentration-response relationship and is known to be well tolerated.},
}
@article {pmid37978295,
year = {2023},
author = {Wang, DX and Dong, ZJ and Deng, SX and Tian, YM and Xiao, YJ and Li, X and Ma, XR and Li, L and Li, P and Chang, HZ and Liu, L and Wang, F and Wu, Y and Gao, X and Zheng, SS and Gu, HM and Zhang, YN and Wu, JB and Wu, F and Peng, Y and Zhang, XW and Zhan, RY and Gao, LX and Sun, Q and Guo, X and Zhao, XD and Luo, JH and Zhou, R and Han, L and Shu, Y and Zhao, JW},
title = {GDF11 slows excitatory neuronal senescence and brain ageing by repressing p21.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {7476},
pmid = {37978295},
issn = {2041-1723},
support = {81971144//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Adult ; Mice ; Humans ; Animals ; *Caenorhabditis elegans/metabolism ; *Growth Differentiation Factors/genetics/metabolism ; Aging/genetics ; Brain/metabolism ; Neurons/metabolism ; Bone Morphogenetic Proteins ; },
abstract = {As a major neuron type in the brain, the excitatory neuron (EN) regulates the lifespan in C. elegans. How the EN acquires senescence, however, is unknown. Here, we show that growth differentiation factor 11 (GDF11) is predominantly expressed in the EN in the adult mouse, marmoset and human brain. In mice, selective knock-out of GDF11 in the post-mitotic EN shapes the brain ageing-related transcriptional profile, induces EN senescence and hyperexcitability, prunes their dendrites, impedes their synaptic input, impairs object recognition memory and shortens the lifespan, establishing a functional link between GDF11, brain ageing and cognition. In vitro GDF11 deletion causes cellular senescence in Neuro-2a cells. Mechanistically, GDF11 deletion induces neuronal senescence via Smad2-induced transcription of the pro-senescence factor p21. This work indicates that endogenous GDF11 acts as a brake on EN senescence and brain ageing.},
}
@article {pmid37978205,
year = {2023},
author = {Iwane, F and Billard, A and Millán, JDR},
title = {Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {20163},
pmid = {37978205},
issn = {2045-2322},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Brain ; Algorithms ; },
abstract = {During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varies between individuals. When passing a fragile object, risk-averse individuals may adopt a larger margin by following the longer path than risk-prone people do. However, it is not known whether this variation is associated with a personalized cost function used for the individual optimal control policies and how it is represented in our brain activity. This study investigates whether such individual variations in evaluation criteria during reaching results from differentiated weighting given to energy minimization versus comfort, and monitors brain error-related potentials (ErrPs) evoked when subjects observe a robot moving dangerously close to a fragile object. Seventeen healthy participants monitored a robot performing safe, daring and unsafe trajectories around a wine glass. Each participant displayed distinct evaluation criteria on the energy efficiency and comfort of robot trajectories. The ErrP-BCI outputs successfully inferred such individual variation. This study suggests that ErrPs could be used in conjunction with an optimal control approach to identify the personalized cost used by CNS. It further opens new avenues for the use of brain-evoked potential to train assistive robotic devices through the use of neuroprosthetic interfaces.},
}
@article {pmid37974976,
year = {2023},
author = {Karikari, E and Koshechkin, KA},
title = {Review on brain-computer interface technologies in healthcare.},
journal = {Biophysical reviews},
volume = {15},
number = {5},
pages = {1351-1358},
pmid = {37974976},
issn = {1867-2450},
abstract = {Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.},
}
@article {pmid37974580,
year = {2023},
author = {Choi, YJ and Kwon, OS and Kim, SP},
title = {Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {6},
pages = {1401-1416},
pmid = {37974580},
issn = {1871-4080},
abstract = {UNLABELLED: Non-invasive brain-computer interfaces (BCIs) based on an event-related potential (ERP) component, P300, elicited via the oddball paradigm, have been extensively developed to enable device control and communication. While most P300-based BCIs employ visual stimuli in the oddball paradigm, auditory P300-based BCIs also need to be developed for users with unreliable gaze control or limited visual processing. Specifically, auditory BCIs without additional visual support or multi-channel sound sources can broaden the application areas of BCIs. This study aimed to design optimal stimuli for auditory BCIs among artificial (e.g., beep) and natural (e.g., human voice and animal sounds) sounds in such circumstances. In addition, it aimed to investigate differences between auditory and visual stimulations for online P300-based BCIs. As a result, natural sounds led to both higher online BCI performance and larger differences in ERP amplitudes between the target and non-target compared to artificial sounds. However, no single type of sound offered the best performance for all subjects; rather, each subject indicated different preferences between the human voice and animal sound. In line with previous reports, visual stimuli yielded higher BCI performance (average 77.56%) than auditory counterparts (average 54.67%). In addition, spatiotemporal patterns of the differences in ERP amplitudes between target and non-target were more dynamic with visual stimuli than with auditory stimuli. The results suggest that selecting a natural auditory stimulus optimal for individual users as well as making differences in ERP amplitudes between target and non-target stimuli more dynamic may further improve auditory P300-based BCIs.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09901-3.},
}
@article {pmid37974284,
year = {2023},
author = {Cipriani, M and Pichiorri, F and Colamarino, E and Toppi, J and Tamburella, F and Lorusso, M and Bigioni, A and Morone, G and Tomaiuolo, F and Santoro, F and Cordella, D and Molinari, M and Cincotti, F and Mattia, D and Puopolo, M},
title = {The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a statistical analysis plan for a randomized controlled trial.},
journal = {Trials},
volume = {24},
number = {1},
pages = {736},
pmid = {37974284},
issn = {1745-6215},
support = {RF-2018-12365210//Ministero della Salute/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; Pilot Projects ; *Stroke/diagnosis/therapy/complications ; Upper Extremity ; },
abstract = {BACKGROUND: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) allow to modulate the sensorimotor rhythms and are emerging technologies for promoting post-stroke motor function recovery. The Promotoer study aims to assess the short and long-term efficacy of the Promotoer system, an EEG-based BCI assisting motor imagery (MI) practice, in enhancing post-stroke functional hand motor recovery. This paper details the statistical analysis plan of the Promotoer study.<br><br>METHODS: The Promotoer study is a randomized, controlled, assessor-blinded, single-centre, superiority trial, with two parallel groups and a 1:1 allocation ratio. Subacute stroke patients are randomized to EEG-based BCI-assisted MI training or to MI training alone (i.e. no BCI). An internal pilot study for sample size re-assessment is planned. The primary outcome is the effectiveness of the Upper Extremity Fugl-Meyer Assessment (UE-FMA) score. Secondary outcomes include clinical, functional, and user experience scores assessed at the end of intervention and at follow-up. Neurophysiological assessments are also planned. Effectiveness formulas have been specified, and intention-to-treat and per-protocol populations have been defined. Statistical methods for comparisons of groups and for development of a predictive score of significant improvement are described. Explorative subgroup analyses and methodology to handle missing data are considered.<br><br>DISCUSSION: The Promotoer study will provide robust evidence for the short/long-term efficacy of the Promotoer system in subacute stroke patients undergoing a rehabilitation program. Moreover, the development of a predictive score of response will allow transferring of the Promotoer system to optimal clinical practice. By carefully describing the statistical principles and procedures, the statistical analysis plan provides transparency in the analysis of data.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT04353297 . Registered on April 15, 2020.},
}
@article {pmid37972395,
year = {2023},
author = {Wu, Y and Li, BZ and Wang, L and Fan, S and Chen, C and Li, A and Lin, Q and Wang, P},
title = {An unsupervised real-time spike sorting system based on optimized OSort.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0d15},
pmid = {37972395},
issn = {1741-2552},
mesh = {*Neurons/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; Electrodes ; Computer Systems ; Action Potentials/physiology ; },
abstract = {Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.},
}
@article {pmid37971909,
year = {2024},
author = {Ke, Y and Liu, S and Ming, D},
title = {Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {4},
pages = {1319-1331},
doi = {10.1109/TBME.2023.3333435},
pmid = {37971909},
issn = {1558-2531},
mesh = {*Electroencephalography/methods ; Evoked Potentials, Visual ; Calibration ; Reproducibility of Results ; *Brain-Computer Interfaces ; Evoked Potentials ; Photic Stimulation ; Algorithms ; },
abstract = {OBJECTIVE: Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency.<br><br>METHODS: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment.<br><br>RESULTS: The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8&#xb1;57.3 bits/min and 191.2&#xb1;48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 &#xb1; 7.35% and ITR of 139.73&#xb1;21.04 bits/min were achieved with 0.5-s calibration data per frequency.<br><br>SIGNIFICANCE: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.},
}
@article {pmid37971908,
year = {2024},
author = {Jiang, X and Meng, L and Wang, Z and Wu, D},
title = {Deep Source Semi-Supervised Transfer Learning (DS3TL) for Cross-Subject EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {4},
pages = {1308-1318},
doi = {10.1109/TBME.2023.3333327},
pmid = {37971908},
issn = {1558-2531},
mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Cyclohexylamines ; Supervised Machine Learning ; *Indenes ; },
abstract = {OBJECTIVE: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to train a reliable EEG recognition model. However, acquiring labeled EEG data is time-consuming and user-unfriendly. Semi-supervised learning (SSL) and transfer learning can be used to exploit the unlabeled data and the auxiliary data, respectively, to reduce the amount of labeled data for a new subject.<br><br>METHODS: This paper proposes deep source semi-supervised transfer learning (DS3TL) for EEG-based BCIs, which assumes the source subject has a small number of labeled EEG trials and a large number of unlabeled ones, whereas all EEG trials from the target subject are unlabeled. DS3TL mainly includes a hybrid SSL module, a weakly-supervised contrastive module, and a domain adaptation module. The hybrid SSL module integrates pseudo-labeling and consistency regularization for SSL. The weakly-supervised contrastive module performs contrastive learning by using the true labels of the labeled data and the pseudo-labels of the unlabeled data. The domain adaptation module reduces the individual differences by uncertainty reduction.<br><br>RESULTS: Experiments on three EEG datasets from different tasks demonstrated that DS3TL outperformed a supervised learning baseline with many more labeled training data, and multiple state-of-the-art SSL approaches with the same number of labeled data.<br><br>SIGNIFICANCE: To our knowledge, this is the first approach in EEG-based BCIs that exploits the unlabeled source data for more accurate target classifier training.},
}
@article {pmid37968802,
year = {2024},
author = {Biffl, WL and Fawley, JA and Mohan, RC},
title = {Diagnosis and management of blunt cardiac injury: What you need to know.},
journal = {The journal of trauma and acute care surgery},
volume = {96},
number = {5},
pages = {685-693},
doi = {10.1097/TA.0000000000004216},
pmid = {37968802},
issn = {2163-0763},
mesh = {Humans ; *Wounds, Nonpenetrating/therapy/diagnosis/complications ; Heart Injuries/diagnosis/therapy ; Myocardial Contusions/diagnosis/therapy/complications ; Electrocardiography ; },
abstract = {Blunt cardiac injury (BCI) encompasses a wide spectrum, from occult and inconsequential contusion to rapidly fatal cardiac rupture. A small percentage of patients present with abnormal electrocardiogram or shock, but most are initially asymptomatic. The potential for sudden dysrhythmia or cardiac pump failure mandates consideration of the presence of BCI, including appropriate monitoring and management. In this review, we will present what you need to know to diagnose and manage BCI.},
}
@article {pmid37965214,
year = {2023},
author = {Xu, F and Ming, D and Jung, TP and Xu, P and Xu, M},
title = {Editorial: The application of artificial intelligence in brain-computer interface and neural system rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1290961},
doi = {10.3389/fnins.2023.1290961},
pmid = {37965214},
issn = {1662-4548},
}
@article {pmid37964432,
year = {2024},
author = {Okatan, M and Kocatürk, M},
title = {Decoding the Spike-Band Subthreshold Motor Cortical Activity.},
journal = {Journal of motor behavior},
volume = {56},
number = {2},
pages = {161-183},
doi = {10.1080/00222895.2023.2280263},
pmid = {37964432},
issn = {1940-1027},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex ; Action Potentials ; },
abstract = {Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.},
}
@article {pmid37963394,
year = {2023},
author = {Miao, M and Yang, Z and Zeng, H and Zhang, W and Xu, B and Hu, W},
title = {Explainable cross-task adaptive transfer learning for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0c61},
pmid = {37963394},
issn = {1741-2552},
mesh = {*Imagination ; Electroencephalography/methods ; Algorithms ; *Brain-Computer Interfaces ; Machine Learning ; },
abstract = {Objective. In the field of motor imagery (MI) electroencephalography (EEG)-based brain-computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability in subject-specific data for the training of robust deep learning (DL) models. Although considerable progress has been made in the cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL remains largely unexplored.Approach. We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterwards, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradient-based post-hoc explainability analysis is conducted for the visualization of important temporal-spatial features.Main results. Extensive experiments are conducted on one large ME EEG High-Gamma dataset and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for OpenBMI and GIST respectively, which outperforms several state-of-the-art algorithms. In addition, the results of the explainability analysis further validate the correlation between ME and MI EEG data and the effectiveness of ME/MI cross-task adaptation.Significance. This paper confirms that the decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and is important in a practical sense.},
}
@article {pmid37961808,
year = {2023},
author = {Liu, M and Jiang, N and Shi, Y and Wang, P and Zhuang, L},
title = {Spatiotemporal coding of natural odors in the olfactory bulb.},
journal = {Journal of Zhejiang University. Science. B},
volume = {24},
number = {11},
pages = {1057-1061},
pmid = {37961808},
issn = {1862-1783},
support = {LY21C100001 and LBY21H180001//the Zhejiang Provincial Natural Science Foundation of China/ ; 62271443 and 32250008//the National Natural Science Foundation of China/ ; },
mesh = {*Olfactory Bulb ; *Odorants ; Smell ; },
abstract = {气味是评价食品新鲜度最重要的参数之一。当气味以其自然浓度存在时，会在嗅觉系统中引发不同的神经活动模式。本研究提出了一种通过检测食物气味进行食物检测与评价的在体生物传感系统。我们通过将多通道微电极植入在清醒大鼠嗅球的僧帽/丛状细胞层上，进而对神经信号进行实时检测。结果表明，不同的气味可以引起不同的神经振荡活动，每个僧帽/丛状细胞会表现出特定气味的锋电位发放模式。单个大鼠的少量细胞携带足够的信息，可以根据锋电位发放频率变化率的极坐标图来区分不同储存天数的食物。此外，研究表明气味刺激后，β振荡比γ振荡表现出更特异的气味响应模式，这表明β振荡在气味识别中起着更重要的作用。综上，本研究提出的在体神经接口为评估食品新鲜度提供了一种可行性方法。},
}
@article {pmid37961227,
year = {2025},
author = {Francioni, V and Tang, VD and Toloza, EHS and Brown, NJ and Harnett, MT},
title = {Vectorized instructive signals in cortical dendrites during a brain-computer interface task.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37961227},
issn = {2692-8205},
support = {R01 MH135141/MH/NIMH NIH HHS/United States ; R01 NS106031/NS/NINDS NIH HHS/United States ; R01 NS113079/NS/NINDS NIH HHS/United States ; T32 GM007753/GM/NIGMS NIH HHS/United States ; },
abstract = {Vectorization of teaching signals is a key element of virtually all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments[1-5]. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We leveraged a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results provide the first biological evidence of a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.},
}
@article {pmid37960675,
year = {2023},
author = {Lyu, S and Cheung, RCC},
title = {Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {21},
pages = {},
pmid = {37960675},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Software ; Machine Learning ; Electroencephalography/methods ; },
abstract = {The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.},
}
@article {pmid37960592,
year = {2023},
author = {Lun, X and Zhang, Y and Zhu, M and Lian, Y and Hou, Y},
title = {A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {21},
pages = {},
pmid = {37960592},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy ; Electrodes ; Algorithms ; },
abstract = {A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.},
}
@article {pmid37959200,
year = {2023},
author = {Gunduz, ME and Bucak, B and Keser, Z},
title = {Advances in Stroke Neurorehabilitation.},
journal = {Journal of clinical medicine},
volume = {12},
number = {21},
pages = {},
pmid = {37959200},
issn = {2077-0383},
abstract = {Stroke is one of the leading causes of disability worldwide despite recent advances in hyperacute interventions to lessen the initial impact of stroke. Stroke recovery therapies are crucial in reducing the long-term disability burden after stroke. Stroke recovery treatment options have rapidly expanded within the last decade, and we are in the dawn of an exciting era of multimodal therapeutic approaches to improve post-stroke recovery. In this narrative review, we highlighted various promising advances in treatment and technologies targeting stroke rehabilitation, including activity-based therapies, non-invasive and minimally invasive brain stimulation techniques, robotics-assisted therapies, brain-computer interfaces, pharmacological treatments, and cognitive therapies. These new therapies are targeted to enhance neural plasticity as well as provide an adequate dose of rehabilitation and improve adherence and participation. Novel activity-based therapies and telerehabilitation are promising tools to improve accessibility and provide adequate dosing. Multidisciplinary treatment models are crucial for post-stroke neurorehabilitation, and further adjuvant treatments with brain stimulation techniques and pharmacological agents should be considered to maximize the recovery. Among many challenges in the field, the heterogeneity of patients included in the study and the mixed methodologies and results across small-scale studies are the cardinal ones. Biomarker-driven individualized approaches will move the field forward, and so will large-scale clinical trials with a well-targeted patient population.},
}
@article {pmid37956749,
year = {2024},
author = {Qin, Y and Li, B and Wang, W and Shi, X and Wang, H and Wang, X},
title = {ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.},
journal = {Brain research},
volume = {1823},
number = {},
pages = {148673},
doi = {10.1016/j.brainres.2023.148673},
pmid = {37956749},
issn = {1872-6240},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagination ; Electroencephalography/methods ; Attention ; },
abstract = {Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.},
}
@article {pmid37955998,
year = {2023},
author = {Wang, Y and Zhang, Y and Zhang, Y and Wang, Z and Guo, W and Zhang, Y and Wang, Y and Ge, Q and Wang, D},
title = {Voluntary Respiration Control: Signature Analysis by EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4624-4634},
doi = {10.1109/TNSRE.2023.3332458},
pmid = {37955998},
issn = {1558-0210},
mesh = {Humans ; *Respiration ; *Electroencephalography/methods ; Brain/physiology ; Respiratory Rate ; Consciousness ; },
abstract = {The perception of voluntary respiratory consciousness is quite important in some situations, such as respiratory assistance and respiratory rehabilitation training, and the key signatures about voluntary respiration control may lie in the neural signals from brain manifested as electroencephalography (EEG). The present work aims to explore whether there exists correlation between voluntary respiration and scalp EEG. Evoke voluntary respiration of different intensities, while collecting EEG and respiration signal synchronously. Data from 11 participants were analyzed. Spectrum characteristics at low-frequency band were studied. Computation of EEG-respiration phase lock value (PLV) and EEG sample entropy were conducted as well. When breathing voluntarily, the 0-2 Hz band EEG power is significantly enhanced in frontal and right-parietal area. The distance between main peaks belonging to the two signals in 0-2 Hz spectrum graph tends to get smaller, while EEG-respiration PLV increases in frontal area. Besides, the sample entropy of EEG shows a trend of decreasing during voluntary respiration in both areas. There's a strong correlation between voluntary respiration and scalp EEG. Significance: The discoveries will provide guidelines for developing a voluntary respiratory consciousness identifying method and make it possible to monitor people's intention of respiration by noninvasive BCI.},
}
@article {pmid37949256,
year = {2023},
author = {Cabrera Castillos, K and Ladouce, S and Darmet, L and Dehais, F},
title = {Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.},
journal = {NeuroImage},
volume = {284},
number = {},
pages = {120446},
doi = {10.1016/j.neuroimage.2023.120446},
pmid = {37949256},
issn = {1095-9572},
mesh = {Humans ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; },
abstract = {The utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain-Computer Interface (rBCI). A major advantage of the c-VEP approach is that the training of the model is independent of the number and complexity of targets, which helps reduce calibration time. Nevertheless, the existing designs of c-VEP stimuli can be further improved in terms of visual user experience but also to achieve a higher signal-to-noise ratio, while shortening the selection time and calibration process. In this study, we introduce an innovative variant of code-VEP, referred to as "Burst c-VEP". This original approach involves the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The rationale behind this design is to leverage the sensitivity of the primary visual cortex to transient changes in low-level stimuli features to reliably elicit distinctive series of visual evoked potentials. In comparison to other types of faster-paced code sequences, burst c-VEP exhibit favorable properties to achieve high bitwise decoding performance using convolutional neural networks (CNN), which yields potential to attain faster selection time with the need for less calibration data. Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP through the attenuation of visual stimuli contrast and intensity to significantly improve users' visual comfort. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were instructed to focus on c-VEP targets whose pattern (burst and maximum-length sequences) and amplitude (100% or 40% amplitude depth modulations) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6s of calibration data) to 95.6% (with 52.8s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5 s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.},
}
@article {pmid37948768,
year = {2023},
author = {Luo, R and Xiao, X and Chen, E and Meng, L and Jung, TP and Xu, M and Ming, D},
title = {Almost free of calibration for SSVEP-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0b8f},
pmid = {37948768},
issn = {1741-2552},
mesh = {Photic Stimulation/methods ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Algorithms ; },
abstract = {Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min[-1]with a peak of 242.6 bits min[-1].Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.},
}
@article {pmid37948668,
year = {2024},
author = {Das, A and Nandi, N and Ray, S},
title = {Alpha and SSVEP power outperform gamma power in capturing attentional modulation in human EEG.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {34},
number = {1},
pages = {},
doi = {10.1093/cercor/bhad412},
pmid = {37948668},
issn = {1460-2199},
support = {//Ministry of Education, Government of India/ ; IA/S/18/2/504003//Wellcome Trust/DBT India Alliance/ ; //Tata Trusts/ ; //Department of Biotechnology-Indian Institute of Science/ ; },
mesh = {Animals ; Humans ; *Electroencephalography/methods ; *Evoked Potentials ; Attention ; Brain ; Macaca ; Photic Stimulation/methods ; Evoked Potentials, Visual ; },
abstract = {Attention typically reduces power in the alpha (8-12 Hz) band and increases power in gamma (>30 Hz) band in brain signals, as reported in macaque local field potential (LFP) and human electro/magneto-encephalogram (EEG/MEG) studies. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state-visually-evoked-potential (SSVEP), whose power also increases with attention. However, effectiveness of these neural measures in capturing attentional modulation is unknown since stimuli and task paradigms vary widely across studies. In a recent macaque study, attentional modulation was more salient in the gamma band of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to 26 subjects and compared attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed negligible attentional modulation although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features are used for decoding attention, and also provide clues about spatial scales of neural mechanisms underlying attention.},
}
@article {pmid37947903,
year = {2024},
author = {Wu, L and Wang, J and Lu, Y and Huang, Y and Zhang, X and Ma, D and Xiao, Y and Cao, F},
title = {Association of intimate partner violence with offspring growth in 32 low- and middle-income countries: a population-based cross-sectional study.},
journal = {Archives of women's mental health},
volume = {27},
number = {2},
pages = {179-190},
pmid = {37947903},
issn = {1435-1102},
support = {32071084//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Female ; Adult ; *Developing Countries ; Cross-Sectional Studies ; *Intimate Partner Violence/psychology ; Mothers ; Growth Disorders/epidemiology ; Prevalence ; Sexual Partners ; Risk Factors ; },
abstract = {Intimate partner violence (IPV) against women presents a major public health challenge, especially in low-income and middle-income countries (LMICs), and its relationship with poor offspring growth is emerging but remains understudied. This study aimed to explore the impact of maternal exposure to IPV on offspring growth based on different approaches in LMICs. We conducted a population-based cross-sectional study using the most recent Demographic and Health Surveys from 32 LMICs; 81,652 mother-child dyads comprising women aged from 15 to 49 years with children aged 0 to 59 months were included. We applied logistic regression models to explore the independent and cumulative relationship between IPV, including emotional, physical, and sexual IPV, with poor child growth status, including stunting and wasting; 52.6% of mothers were under the age of 30 years with a 36% prevalence of any lifetime exposure to IPV. Maternal exposure to any IPV increased the odds of stunting, but only physical and sexual IPV were independently associated with an increased risk of stunting. Three different types of IPV exhibited a cumulative effect on stunting. Maternal exposure to physical IPV was significantly associated with an increased risk of wasting. Significant associations between maternal exposure to emotional IPV with offspring stunting and physical IPV with wasting were only observed in children aged 0 to 36 months. IPV against women remains high in LMICs and has adverse effects on offspring growth. Policy and program efforts are needed to prioritize the reduction of widespread physical and sexual IPV and to mitigate the impact of such violence.},
}
@article {pmid37943244,
year = {2024},
author = {Levett, JJ and Elkaim, LM and Niazi, F and Weber, MH and Iorio-Morin, C and Bonizzato, M and Weil, AG},
title = {Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {27},
number = {4},
pages = {597-603},
doi = {10.1016/j.neurom.2023.10.006},
pmid = {37943244},
issn = {1525-1403},
mesh = {*Spinal Cord Injuries/therapy/physiopathology/rehabilitation ; Humans ; *Brain-Computer Interfaces ; Recovery of Function/physiology ; },
abstract = {STUDY DESIGN: Systematic review of the literature.<br><br>OBJECTIVES: In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans.<br><br>MATERIALS AND METHODS: A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.<br><br>RESULTS: Of 8316 articles collected, 19 studies met&#xa0;all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n&#xa0;= 18, 85.7%) or electrocorticography (n&#xa0;= 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies.<br><br>CONCLUSIONS: Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.},
}
@article {pmid37941569,
year = {2023},
author = {Duan, D and Wu, Z and Zhou, Y and Wan, X and Wen, D},
title = {Working memory training and evaluation based on brain-computer interface and virtual reality: our opinion.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1291983},
pmid = {37941569},
issn = {1662-5161},
}
@article {pmid37939416,
year = {2024},
author = {Qin, C and Yuan, Q and Liu, M and Zhuang, L and Xu, L and Wang, P},
title = {Biohybrid tongue based on hypothalamic neuronal network-on-a-chip for real-time blood glucose sensing and assessment.},
journal = {Biosensors &amp; bioelectronics},
volume = {244},
number = {},
pages = {115784},
doi = {10.1016/j.bios.2023.115784},
pmid = {37939416},
issn = {1873-4235},
mesh = {*Blood Glucose/metabolism ; *Biosensing Techniques ; Hypothalamus/metabolism ; Glucose/metabolism ; Tongue ; Neurons/physiology ; Lab-On-A-Chip Devices ; },
abstract = {The expression of sweet receptors in the hypothalamus has been implicated in energy homeostasis control and the pathogenesis of obesity and diabetes. However, the exact mechanism by which hypothalamic glucose-sensing neurons function remains unclear. Conventional detection methods, such as fiber photometry, optogenetics, brain-machine interfaces, patch clamp and calcium imaging, pose limitations for real-time glucose perception due to their complexity, cytotoxicity and so on. Therefore, this study proposes a biohybrid tongue based on hypothalamic neuronal network (HNN)-on-a-chip coupling with microelectrode array (MEA) for real-time glucose perception. Hypothalamic neuronal cultures were cultivated on a two-dimensional "brain-on-chip" device, enabling the formation of neuronal networks and electrophysiological signal detection. Additionally, we investigated the endogenous expression of sweet taste receptors (T1R2/T1R3) in hypothalamic neuronal cells, providing the basis for the biohybrid tongue based on HNN-on-a-chip's sweetness detection capabilities. The spike signal response to sucrose and glucose stimulation was detected, and concentration-dependent responses were explored with glucose concentrations ranging from 0.01 mM to 8 mM. MEAs allow for real-time recordings, enabling the observation of dynamic changes in neuronal responses to glucose fluctuations over time. The biohybrid tongue based on HNN-on-a-chip can measure various parameters, including spike frequency and amplitude, providing insights into neuronal firing patterns and excitability. Moreover, hypothalamic glucoregulatory neurons that sense and respond to changes in blood glucose was identified, including glucose-excited neurons (GE-Neurons) and glucose-inhibited neurons (GI-Neurons). The detection range for GE-Neurons spans from 0.4 to 6 mM, while GI-Neurons demonstrate sensitivity within the range of 1-8 mM. And the glucose detection limit was firmly established at 0.01 mM. Through non-linear regression analysis, the IC50 for GI-Neurons' spike firing was determined to be 4.18 mM. In conclusion, the biohybrid tongue based on HNN-on-a-chip offers a valuable in vitro tool for studying hypothalamic neurons, elucidating glucose sensing mechanisms, and understanding hypothalamic neuronal function.},
}
@article {pmid37938944,
year = {2024},
author = {Eskandari, R and Sawan, M},
title = {Challenges and Perspectives on Impulse Radio-Ultra-Wideband Transceivers for Neural Recording Applications.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {2},
pages = {369-382},
doi = {10.1109/TBCAS.2023.3331049},
pmid = {37938944},
issn = {1940-9990},
mesh = {Artificial Intelligence ; Prostheses and Implants ; Wireless Technology ; *Brain-Computer Interfaces ; *Neural Prostheses ; },
abstract = {Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra-wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.},
}
@article {pmid37938700,
year = {2023},
author = {Drew, L},
title = {The rise of brain-reading technology: what you need to know.},
journal = {Nature},
volume = {623},
number = {7986},
pages = {241-243},
pmid = {37938700},
issn = {1476-4687},
mesh = {*Brain/physiology ; Monitoring, Physiologic/instrumentation/methods/trends ; *Thinking/physiology ; Prostheses and Implants ; Humans ; *Brain-Computer Interfaces/trends ; },
}
@article {pmid37936533,
year = {2024},
author = {Liu, M and Li, T and Zhang, X and Yang, Y and Zhou, Z and Fu, T},
title = {IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {27},
number = {15},
pages = {2175-2188},
doi = {10.1080/10255842.2023.2275244},
pmid = {37936533},
issn = {1476-8259},
mesh = {*Electroencephalography/statistics &amp; numerical data/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.},
}
@article {pmid37936521,
year = {2023},
author = {Pastötter, B and Frings, C},
title = {Prestimulus alpha power signals attention to retrieval.},
journal = {The European journal of neuroscience},
volume = {58},
number = {11},
pages = {4328-4340},
doi = {10.1111/ejn.16181},
pmid = {37936521},
issn = {1460-9568},
mesh = {Humans ; *Brain ; Learning ; Cues ; *Memory, Episodic ; Cognition ; Alpha Rhythm ; Electroencephalography ; },
abstract = {The human brain is in distinct processing modes at different times. Specifically, a distinction can be made between encoding and retrieval modes, which refer to the brain's state when it is storing new information or searching for old information, respectively. Recent research proposed the idea of a "ready-to-encode" mode, which describes a prestimulus effect in brain activity that signals (external) attention to encoding and predicts subsequent memory performance. Whether there is also a corresponding "ready-to-retrieve" mode in human brain activity is currently unclear. In this study, we examined whether prestimulus oscillations can be linked to (internal) attention to retrieval. We show that task cues to prepare for retrieval (or testing) in comparison with restudy of previously studied vocabulary word pairs led to a significant decrease of prestimulus alpha power just before the onset of word stimuli. Beamformer analysis localized this effect in the right secondary visual cortex (Brodmann area 18). Correlation analysis showed that the task cue-induced, prestimulus alpha power effect is positively related to stimulus-induced alpha/beta power, which in turn predicted participants' memory performance. The results are consistent with the idea that prestimulus alpha power signals internal attention to retrieval, which promotes the elaborative processing of episodic memories. Future research on brain-computer interfaces may find the findings interesting regarding the potential of using online measures of fluctuating alpha oscillations to trigger the presentation and sequencing of restudy and testing trials, ultimately enhancing instructional learning strategies.},
}
@article {pmid37935744,
year = {2023},
author = {Yu, H and Ni, P and Tian, Y and Zhao, L and Li, M and Li, X and Wei, W and Wei, J and Wang, Q and Guo, W and Deng, W and Ma, X and Coid, J and Li, T},
title = {Association of elevated levels of peripheral complement components with cortical thinning and impaired logical memory in drug-naïve patients with first-episode schizophrenia.},
journal = {Schizophrenia (Heidelberg, Germany)},
volume = {9},
number = {1},
pages = {79},
pmid = {37935744},
issn = {2754-6993},
support = {81630030 and 81920108018//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101598//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81871054 and 81501159//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Schizophrenia has been linked to polymorphism in genes encoding components of the complement system, and hyperactive complement activity has been linked to immune dysfunction in schizophrenia patients. Whether and how specific complement components influence brain structure and cognition in the disease is unclear. Here we compared 52 drug-naïve patients with first-episode schizophrenia and 52 healthy controls in terms of levels of peripheral complement factors, cortical thickness (CT), logical memory and psychotic symptoms. We also explored the relationship between complement factors with CT, cognition and psychotic symptoms. Patients showed significantly higher levels of C1q, C4, factor B, factor H, and properdin in plasma. Among patients, higher levels of C3 in plasma were associated with worse memory recall, while higher levels of C4, factor B and factor H were associated with thinner sensory cortex. These findings link dysregulation of specific complement components to abnormal brain structure and cognition in schizophrenia.},
}
@article {pmid37935217,
year = {2024},
author = {Venu, K and Natesan, P},
title = {Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {69},
number = {2},
pages = {125-140},
pmid = {37935217},
issn = {1862-278X},
mesh = {*Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.<br><br>METHODS: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.<br><br>RESULTS: A superior sensitivity of 0.939 is noted for HC&#xa0;+&#xa0;SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.<br><br>CONCLUSIONS: The proposed method achieved effective classification performance in terms of performance measures.},
}
@article {pmid37934650,
year = {2023},
author = {Park, D and Park, H and Kim, S and Choo, S and Lee, S and Nam, CS and Jung, JY},
title = {Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4504-4513},
doi = {10.1109/TNSRE.2023.3330922},
pmid = {37934650},
issn = {1558-0210},
mesh = {Humans ; *Artificial Intelligence ; Electroencephalography ; Learning ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; Imagination ; },
abstract = {Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification. The proposed approach exhibited better performances on two MI EEG datasets than the existing EEGNet, which uses a 2D input shape. The MI classification accuracies are improved around 1.8% and 6.1% point in average on the datasets, respectively. The permutation-based XAI method is first applied for the reliable explanation of the 3D-EEGNet. Next, to find a faster XAI method for spatio-temporal explanation, we design a novel technique based on the normalized discounted cumulative gain (NDCG) for selecting the best among a few saliency-based methods due to their higher time complexity than the permutation-based method. Among the saliency-based methods, DeepLIFT was selected because the NDCG scores indicated its results are the most similar to the permutation-based results. Finally, the fast spatio-temporal explanation using DeepLIFT provides deeper understanding for the classification results of the 3D-EEGNet and the important properties in the MI EEG experiments.},
}
@article {pmid37934649,
year = {2023},
author = {Wang, Z and Fang, J and Zhang, J},
title = {Rethinking Delayed Hemodynamic Responses for fNIRS Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4528-4538},
doi = {10.1109/TNSRE.2023.3330911},
pmid = {37934649},
issn = {1558-0210},
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Neuroimaging ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet.},
}
@article {pmid37932497,
year = {2023},
author = {Ma, S and Chen, M and Jiang, Y and Xiang, X and Wang, S and Wu, Z and Li, S and Cui, Y and Wang, J and Zhu, Y and Zhang, Y and Ma, H and Duan, S and Li, H and Yang, Y and Lingle, CJ and Hu, H},
title = {Author Correction: Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb.},
journal = {Nature},
volume = {623},
number = {7988},
pages = {E11},
doi = {10.1038/s41586-023-06814-x},
pmid = {37932497},
issn = {1476-4687},
support = {R35 GM118114/GM/NIGMS NIH HHS/United States ; },
}
@article {pmid37932250,
year = {2023},
author = {Duraivel, S and Rahimpour, S and Chiang, CH and Trumpis, M and Wang, C and Barth, K and Harward, SC and Lad, SP and Friedman, AH and Southwell, DG and Sinha, SR and Viventi, J and Cogan, GB},
title = {High-resolution neural recordings improve the accuracy of speech decoding.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {6938},
pmid = {37932250},
issn = {2041-1723},
support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; R25 NS065731/NS/NINDS NIH HHS/United States ; UG3 NS120172/NS/NINDS NIH HHS/United States ; UL1 TR002553/TR/NCATS NIH HHS/United States ; R01 NS129703/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Speech ; Quality of Life ; Electrocorticography/methods ; Communication ; Brain ; *Brain-Computer Interfaces ; },
abstract = {Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.},
}
@article {pmid37931308,
year = {2023},
author = {Han, J and Wei, X and Faisal, AA},
title = {EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad09ff},
pmid = {37931308},
issn = {1741-2552},
mesh = {*Brain ; *Brain-Computer Interfaces ; Electrodes ; Machine Learning ; Neural Networks, Computer ; Electroencephalography ; Algorithms ; },
abstract = {Objective. Brain-machine interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting in shifts in data distribution, changes in data dimensionality, and altered identity of data dimensions. Our objective is to overcome this limitation and learn from many different and diverse datasets across labs with different experimental protocols.Approach. To tackle the domain adaptation problem, we developed a novel machine learning framework combining graph neural networks (GNNs) and transfer learning methodologies for non-invasive motor imagery (MI) EEG decoding, as an example of BMI. Empirically, we focus on the challenges of learning from EEG data with different electrode layouts and varying numbers of electrodes. We utilize three MI EEG databases collected using very different numbers of EEG sensors (from 22 channels to 64) and layouts (from custom layouts to 10-20).Main results. Our model achieved the highest accuracy with lower standard deviations on the testing datasets. This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification.Significance. The findings of this study have important implications for brain-computer-interface research, as they highlight a promising method for overcoming the limitations posed by non-unified experimental setups. By enabling the integration of diverse datasets with varying electrode layouts, our proposed approach can help advance the development and application of BMI technologies.},
}
@article {pmid37931299,
year = {2023},
author = {Pan, L and Wang, K and Xu, L and Sun, X and Yi, W and Xu, M and Ming, D},
title = {Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0a01},
pmid = {37931299},
issn = {1741-2552},
mesh = {Humans ; *Learning ; Algorithms ; Brain/physiology ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Machine Learning ; Imagination/physiology ; },
abstract = {Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.},
}
@article {pmid37930905,
year = {2023},
author = {Wang, J and Bi, L and Fei, W},
title = {EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4413-4427},
doi = {10.1109/TNSRE.2023.3330500},
pmid = {37930905},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Electroencephalography/methods ; Upper Extremity ; Brain/physiology ; },
abstract = {Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.},
}
@article {pmid37928726,
year = {2023},
author = {Tong, L and Qian, Y and Peng, L and Wang, C and Hou, ZG},
title = {A learnable EEG channel selection method for MI-BCI using efficient channel attention.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1276067},
pmid = {37928726},
issn = {1662-4548},
abstract = {INTRODUCTION: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.<br><br>METHODS: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.<br><br>RESULTS AND DISCUSSION: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.},
}
@article {pmid37928600,
year = {2023},
author = {Vorreuther, A and Bastian, L and Benitez Andonegui, A and Evenblij, D and Riecke, L and Lührs, M and Sorger, B},
title = {It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.},
journal = {Neurophotonics},
volume = {10},
number = {4},
pages = {045005},
pmid = {37928600},
issn = {2329-423X},
abstract = {SIGNIFICANCE: Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity.<br><br>AIM: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2&#xa0;s), considerably increasing communication speed, and decreasing the cognitive load of BCI users.<br><br>APPROACH: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination.<br><br>RESULTS: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials.<br><br>CONCLUSIONS: The study demonstrates that an encoding time as short as 2&#xa0;s can enable immediate, efficient, and convenient fNIRS-BCI communication.},
}
@article {pmid37927423,
year = {2023},
author = {Graham, B and Ehlers, A},
title = {Development and Validation of the Bullied Cognitions Inventory (BCI).},
journal = {Cognitive therapy and research},
volume = {47},
number = {6},
pages = {1033-1045},
pmid = {37927423},
issn = {0147-5916},
support = {/WT_/Wellcome Trust/United Kingdom ; 200796/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
abstract = {BACKGROUND: Bullying increases risk of social anxiety and can produce symptoms of posttraumatic stress disorder (PTSD). According to cognitive models, these are maintained by unhelpful beliefs, which are therefore assessed and targeted in cognitive therapy. This paper describes psychometric validation of a new measure of beliefs related to bullying experiences.<br><br>METHODS: In an online survey of 1879 young people before starting university or college in the UK, 1279 reported a history of bullying (N&#x2009;=&#x2009;1279), and 854 rated their agreement with beliefs about self and others related to bullying experiences and completed symptom measures of social anxiety and PTSD related to bullying. An empirical structure for a Bullied Cognitions Inventory was established using exploratory and confirmatory factor analyses and assessed using model fit statistics and tests of reliability and validity.<br><br>RESULTS: Fifteen items clustered into four themes: "degraded in the eyes of others", "negative interpretations of reactions to bullying", "recognisable as a bullying victim" and "social defeat". The measure has acceptable reliability and validity and, accounting for existing cognitive measures, explained additional variance in symptoms of PTSD but not social anxiety.<br><br>CONCLUSIONS: The Bullied Cognitions Inventory&#xa0;(BCI) is a valid and reliable tool for measuring cognitions related to bullying. It may be useful in therapy for identifying and monitoring unhelpful cognitions in those who were bullied.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10608-023-10412-6.},
}
@article {pmid37926843,
year = {2023},
author = {Zhang, X and Wang, W and Bai, X and Zhang, X and Yuan, Z and Jiao, B and Zhang, Y and Li, Z and Zhang, P and Tang, H and Zhang, Y and Yu, X and Bai, R and Wang, Y and Sui, B},
title = {Increased glymphatic system activity in migraine chronification by diffusion tensor image analysis along the perivascular space.},
journal = {The journal of headache and pain},
volume = {24},
number = {1},
pages = {147},
pmid = {37926843},
issn = {1129-2377},
support = {Z200024//National Natural Science Foundation of Beijing/ ; 32170752, 91849104, and 31770800//National Natural Science Foundation of China/ ; 62271061//National Natural Science Foundation of China/ ; 7212028//Beijing Municipal Natural Science Foundation/ ; },
mesh = {Humans ; *Glymphatic System/diagnostic imaging ; Cross-Sectional Studies ; *Migraine Disorders/diagnostic imaging ; Headache ; *Headache Disorders ; },
abstract = {BACKGROUND: Preliminary evidence suggests that several headache disorders may be associated with glymphatic dysfunction. However, no studies have been conducted to examine the glymphatic activity in migraine chronification.<br><br>PURPOSES: To investigate the glymphatic activity of migraine chronification in patients with episodic migraine (EM) and chronic migraine (CM) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method.<br><br>METHODS: In this cross-sectional study, patients with EM, CM, and healthy controls (HCs) were included. All participants underwent a standard brain magnetic resonance imaging (MRI) examination. Bilateral DTI-ALPS indexes were calculated for all participants and compared among EM, CM, and HC groups. Correlations between the DTI-ALPS index and clinical characteristics were analyzed.<br><br>RESULTS: A total of 32 patients with EM, 24 patients with CM, and 41 age- and sex-matched HCs were included in the analysis. Significant differences were found in the right DTI-ALPS index among the three groups (p&#x2009;=&#x2009;0.011), with CM showing significantly higher values than EM (p&#x2009;=&#x2009;0.033) and HCs (p&#x2009;=&#x2009;0.015). The right DTI-ALPS index of CM group was significantly higher than the left DTI-ALPS index (p&#x2009;=&#x2009;0.005). And the headache intensity was correlated to DTI-ALPS index both in the left hemisphere (r&#x2009;=&#x2009;0.371, p&#x2009;=&#x2009;0.011) and in the right hemisphere (r&#x2009;=&#x2009;0.307, p&#x2009;=&#x2009;0.038), but there were no correlations after Bonferroni correction.<br><br>CONCLUSIONS: Glymphatic system activity is shown to be increased instead of impaired during migraine chronification. The mechanism behind this observation suggests that increased glymphatic activity is more likely to be a concomitant phenomenon of altered vascular reactivity associated with migraine pathophysiology rather than a risk factor of migraine chronification.},
}
@article {pmid37922809,
year = {2024},
author = {Gao, K and Hu, M and Li, J and Li, Z and Xu, W and Qian, Z and Gao, F and Ma, T},
title = {Drug-detecting bioelectronic nose based on odor cue memory combined with a brain computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {244},
number = {},
pages = {115797},
doi = {10.1016/j.bios.2023.115797},
pmid = {37922809},
issn = {1873-4235},
mesh = {Rats ; Animals ; Odorants/analysis ; *Brain-Computer Interfaces ; Cues ; *Biosensing Techniques ; Memory/physiology ; },
abstract = {The international drug situation is increasingly, various new drugs are hidden in public places through changing forms and packaging, which brings new challenges to drug enforcement. This study proposes a drug-detecting bioelectronic nose based on odor cue memory combined with brain-computer interface and optogenetic regulation technologies. First, the rats were trained to generate positive memories of drug odors through food reward training, and multichannel microelectrodes were implanted into the DG region of the hippocampus for responsible memory retrieval, the spike signals of individual neurons and the local field potential signals of population neurons in the brain region were collected for pattern recognition and analysis. Preliminary experimental results have shown that when low-dose drugs are buried in a hidden area, rats can find the location of the drugs in a very short time, and when close to the relevant area, there is a significant change in the energy value and time-frequency spectrum signal coupling of the returned data, which can be extracted to indicate that the rats have found the drugs. Second, we labled the neuronal activity marker c-fos and revealed more robust activation in the DG region following odor detection. We modulated these neurons through neuroregulatory technology, so that the rats could recognize drugs by retrieving memories more quickly. We conceive that the drug-detecting rat robot can detect trace amounts of various drugs in complex terrain and multiple scenes, which is of great significance for anti-drug work in the future.},
}
@article {pmid37925576,
year = {2023},
author = {Comandini, G and Ouisse, M and Ting, VP and Scarpa, F},
title = {Acoustic transmission loss in Hilbert fractal metamaterials.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {19058},
pmid = {37925576},
issn = {2045-2322},
support = {EP/L016028/1//EPSRC Centre for Doctoral Training in Advanced Composites for Innovation and Science/ ; EP/R01650X/1//EPSRC/ ; No. 101020715//ERC-2020-AdG-NEUROMETA/ ; },
abstract = {Acoustic metamaterials are increasingly being considered as a viable technology for sound insulation. Fractal patterns constitute a potentially groundbreaking architecture for acoustic metamaterials. We describe in this work the behaviour of the transmission loss of Hilbert fractal metamaterials used for sound control purposes. The transmission loss of 3D printed metamaterials with Hilbert fractal patterns related to configurations from the zeroth to the fourth order is investigated here using impedance tube tests and Finite Element models. We evaluate, in particular, the impact of the equivalent porosity and the relative size of the cavity of the fractal pattern versus the overall dimensions of the metamaterial unit. We also provide an analytical formulation that relates the acoustic cavity resonances in the fractal patterns and the frequencies associated with the maxima of the transmission losses, providing opportunities to tune the sound insulation properties through control of the fractal architecture.},
}
@article {pmid37924858,
year = {2023},
author = {Pak, S and Lee, M and Lee, S and Zhao, H and Baeg, E and Yang, S and Yang, S},
title = {Cortical surface plasticity promotes map remodeling and alleviates tinnitus in adult mice.},
journal = {Progress in neurobiology},
volume = {231},
number = {},
pages = {102543},
doi = {10.1016/j.pneurobio.2023.102543},
pmid = {37924858},
issn = {1873-5118},
mesh = {Mice ; Animals ; *Tinnitus/therapy ; *Auditory Cortex ; *Hearing Loss ; Disease Models, Animal ; Brain Mapping/methods ; Neuronal Plasticity/physiology ; },
abstract = {Tinnitus induced by hearing loss is caused primarily by irreversible damage to the peripheral auditory system, which results in abnormal neural responses and frequency map disruption in the central auditory system. It remains unclear whether and how electrical rehabilitation of the auditory cortex can alleviate tinnitus. We hypothesize that stimulation of the cortical surface can alleviate tinnitus by enhancing neural responses and promoting frequency map reorganization. To test this hypothesis, we assessed and activated cortical maps using our newly designed graphene-based electrode array with a noise-induced tinnitus animal model. We found that cortical surface stimulation increased cortical activity, reshaped sensory maps, and alleviated hearing loss-induced tinnitus behavior in adult mice. These effects were likely due to retained long-term synaptic potentiation capabilities, as shown in cortical slices from the mice model. These findings suggest that cortical surface activation can be used to facilitate practical functional recovery from phantom percepts induced by sensory deprivation. They also provide a working principle for various treatment methods that involve electrical rehabilitation of the cortex.},
}
@article {pmid37924204,
year = {2023},
author = {Ng, TTW and Davel, S and O'Connor, KD},
title = {Sulfasalazine-Induced Delayed Hypersensitivity Reaction Presenting as Fever, Aseptic Meningitis, and Mesenteric Panniculitis in a Patient with Seronegative Arthritis.},
journal = {The American journal of case reports},
volume = {24},
number = {},
pages = {e941623},
pmid = {37924204},
issn = {1941-5923},
mesh = {Female ; Humans ; Aged, 80 and over ; *Meningitis, Aseptic/chemically induced/diagnosis ; Sulfasalazine/adverse effects ; *Panniculitis, Peritoneal/complications ; *Arthritis ; Fever/chemically induced/complications ; *Sepsis/complications ; *Neoplasms/complications ; Fatigue ; *Hypersensitivity, Delayed/complications ; Steroids ; },
abstract = {BACKGROUND An 82-year-old woman presented with acute pyrexial illness and mesenteric panniculitis and developed biochemical aseptic meningitis (cerebrospinal fluid pleocytosis with no identifiable pathogen). Investigation determined her illness was likely a delayed hypersensitivity reaction caused by sulfasalazine. Sulfasalazine-induced aseptic meningitis is a rare condition often diagnosed late in a patient's admission owing to initial non-specific illness symptomatology requiring the exclusion of more common "red flag" etiologies, such as infection and malignancy. CASE REPORT An 82-year-old woman with a history of recurrent urinary tract infections and seronegative arthritis presented with a 3-day history of fatigue, headache, dyspnea, and lassitude. On admission, she was treated as presumed sepsis of uncertain source owing to pyrexia and tachycardia. Brain computer tomography (CT) revealed no acute intracranial abnormality. Furthermore, CT of the chest, abdomen, and pelvis did not reveal any source of sepsis or features of malignancy. After excluding infective etiologies with serological and cerebrospinal fluid testing, sulfasalazine-induced aseptic meningitis (SIAM) was diagnosed. The patient was then commenced on intravenous steroids, resulting in immediate defervescence and symptom resolution. CONCLUSIONS SIAM remains a diagnostic challenge since patients present with non-specific signs and symptoms, such as pyrexia, headaches, and lassitude. These patients require a thorough investigative battery starting with anamnesis, physical examination, biochemical testing, and radiologic imaging. This case illustrates the need for a high suspicion index of drug-induced hypersensitivity reaction in a rheumatological patient with pyrexial illness where infective etiologies have been confidently excluded. Prompt initiation of intravenous steroids in SIAM provides a dramatic recovery and resolution of symptoms.},
}
@article {pmid37920562,
year = {2023},
author = {Sankaran, N and Moses, D and Chiong, W and Chang, EF},
title = {Recommendations for promoting user agency in the design of speech neuroprostheses.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1298129},
pmid = {37920562},
issn = {1662-5161},
support = {U01 DC018671/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCI) that directly decode speech from brain activity aim to restore communication in people with paralysis who cannot speak. Despite recent advances, neural inference of speech remains imperfect, limiting the ability for speech BCIs to enable experiences such as fluent conversation that promote agency - that is, the ability for users to author and transmit messages enacting their intentions. Here, we make recommendations for promoting agency based on existing and emerging strategies in neural engineering. The focus is on achieving fast, accurate, and reliable performance while ensuring volitional control over when a decoder is engaged, what exactly is decoded, and how messages are expressed. Additionally, alongside neuroscientific progress within controlled experimental settings, we argue that a parallel line of research must consider how to translate experimental successes into real-world environments. While such research will ultimately require input from prospective users, here we identify and describe design choices inspired by human-factors work conducted in existing fields of assistive technology, which address practical issues likely to emerge in future real-world speech BCI applications.},
}
@article {pmid37920561,
year = {2023},
author = {Schmoigl-Tonis, M and Schranz, C and Müller-Putz, GR},
title = {Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1251690},
pmid = {37920561},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.},
}
@article {pmid37920297,
year = {2023},
author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Sieghartsleitner, S and Von Oertzen, TJ and Kamada, K and Laureys, S and Allison, BZ and Guger, C},
title = {Brain-computer interface treatment for gait rehabilitation in stroke patients.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1256077},
pmid = {37920297},
issn = {1662-4548},
abstract = {The use of Brain-Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stroke patients with gait disability were recruited for this study. Participants performed 25 sessions with the MI-BCI and assessment visits to track functional changes during the therapy. The results of this study demonstrated a clinically significant increase in walking speed of 0.19 m/s, 95%CI [0.13-0.25], p < 0.001. Patients also reduced spasticity and improved their range of motion and muscle contraction. The BCI treatment was effective in promoting long-lasting functional improvements in the gait speed of chronic stroke survivors. Patients have more movements in the lower limb; therefore, they can walk better and safer. This functional improvement can be explained by improved neuroplasticity in the central nervous system.},
}
@article {pmid37919371,
year = {2023},
author = {Ke, Y and Liu, S and Chen, L and Wang, X and Ming, D},
title = {Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.},
journal = {NPJ science of learning},
volume = {8},
number = {1},
pages = {48},
pmid = {37919371},
issn = {2056-7936},
abstract = {The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.},
}
@article {pmid37918367,
year = {2024},
author = {Myhrum, M and Heldahl, MG and Rødvik, AK and Tvete, OE and Jablonski, GE},
title = {Validation of the Norwegian Version of the Speech, Spatial and Qualities of Hearing Scale (SSQ).},
journal = {Audiology &amp; neuro-otology},
volume = {29},
number = {2},
pages = {124-135},
pmid = {37918367},
issn = {1421-9700},
mesh = {Adult ; Humans ; *Hearing Loss/diagnosis ; Speech ; Surveys and Questionnaires ; Hearing/physiology ; *Cochlear Implantation ; *Cochlear Implants ; Hearing Tests ; *Speech Perception/physiology ; },
abstract = {INTRODUCTION: The main objective of the study was to validate the Norwegian translation of the Speech, Spatial and Qualities of Hearing Scale (SSQ) and investigate the SSQ disability profiles in a cochlear implant (CI) user population.<br><br>METHODS: The study involved 152 adult CI users. The mean age at implantation was 55 (standard deviation [SD] = 16), and the mean CI experience was 5 years (SD = 4.8). The cohort was split into three groups depending on the hearing modality: bilateral CIs (BCIs), a unilateral CI (UCI), and bimodal (CI plus contralateral hearing aid; HCI). The SSQ disability profiles of each group were compared with those observed in similar studies using the English version and other translations of the SSQ. Standard values, internal consistency, sensitivity, and floor and ceiling effects were investigated, and the missing-response rates to specific questions were calculated. Relationships to speech perception were measured using monosyllabic word scores and the Norwegian Hearing in Noise Test scores.<br><br>RESULTS: In the BCI group, the average scores were around 5.0 for the speech and spatial sections and 7.0 for the qualities section (SD &#x223c;2). The average scores of the UCI and HCI groups were about one point lower than those of the BCI group. The SSQ disability profiles were comparable to the profiles in similar studies. The slopes of the linear regression lines measuring the relationships between the SSQ speech and monosyllabic word scores were 0.8 per 10% increase in the monosyllabic word score for the BCI group (explaining 35% of the variation) and 0.4 for the UCI and HCI groups (explaining 22-23% of the variation).<br><br>CONCLUSION: The Norwegian version of the SSQ measures hearing disability similar to the original English version, and the internal consistency is good. Differences in the recipients' pre-implantation variables could explain some variations we observed in the SSQ responses, and such predictors should be investigated. Data aggregation will be possible using the SSQ as a routine clinical assessment in global CI populations. Moreover, pre-implantation variables should be systematically registered so that they can be used in mixed-effects models.},
}
@article {pmid37918024,
year = {2023},
author = {Zhang, H and Zhang, Y and Wang, X and Chen, G and Jian, X and Xu, M and Ming, D},
title = {Transcranial dipole localization and decoding study based on ultrasonic phased array for acoustoelectric brain imaging.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad08f5},
pmid = {37918024},
issn = {1741-2552},
mesh = {*Ultrasonics ; *Brain/diagnostic imaging ; Computer Simulation ; Neuroimaging ; Electroencephalography ; },
abstract = {Objective. Neuroimaging is one of the effective tools to understand the functional activities of the brain, but traditional non-invasive neuroimaging techniques are difficult to combine both high temporal and spatial resolution to satisfy clinical needs. Acoustoelectric brain imaging (ABI) can combine the millimeter spatial resolution advantage of focused ultrasound with the millisecond temporal resolution advantage of electroencephalogram signals.Approach. In this study, we first explored the transcranial modulated acoustic field distribution based on ABI, and further localized and decoded single and double dipoles signals.Main results. The results show that the simulation-guided acoustic field modulation results are significantly better than those of self-focusing, which can realize precise modulation focusing of intracranial target focusing. The single dipole transcranial localization error is less than 0.4 mm and the decoding accuracy is greater than 0.93. The double dipoles transcranial localization error is less than 0.2 mm and the decoding accuracy is greater than 0.89.Significance. This study enables precise focusing of transcranial acoustic field modulation, high-precision localization of source signals and decoding of their waveforms, which provides a technical method for ABI in localizing evoked excitatory neuron areas and epileptic focus.},
}
@article {pmid37917713,
year = {2023},
author = {Lai, C and Tanaka, S and Harris, TD and Lee, AK},
title = {Volitional activation of remote place representations with a hippocampal brain-machine interface.},
journal = {Science (New York, N.Y.)},
volume = {382},
number = {6670},
pages = {566-573},
pmid = {37917713},
issn = {1095-9203},
support = {/HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; Rats ; *Brain-Computer Interfaces ; *Hippocampus/physiology ; Imagination/physiology ; Memory, Episodic ; Mental Recall/physiology ; *Volition/physiology ; Spatial Navigation ; *Brain Mapping ; },
abstract = {The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.},
}
@article {pmid37917674,
year = {2023},
author = {Coulter, ME and Kemere, C},
title = {The neural basis of mental navigation in rats.},
journal = {Science (New York, N.Y.)},
volume = {382},
number = {6670},
pages = {517-518},
doi = {10.1126/science.adl0806},
pmid = {37917674},
issn = {1095-9203},
mesh = {Animals ; Rats ; *Brain-Computer Interfaces ; *Hippocampus/physiology ; *Spatial Navigation ; *Volition/physiology ; },
abstract = {A brain-machine interface demonstrates volitional control of hippocampal activity.},
}
@article {pmid37917520,
year = {2023},
author = {Xu, G and Wang, Z and Zhao, X and Li, R and Zhou, T and Xu, T and Hu, H},
title = {Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4402-4412},
doi = {10.1109/TNSRE.2023.3329482},
pmid = {37917520},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Brain ; },
abstract = {As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.},
}
@article {pmid37915755,
year = {2023},
author = {Rainey, S},
title = {A gap between reasons for skilled use of BCI speech devices and reasons for utterances, with implications for speech ownership.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1248806},
pmid = {37915755},
issn = {1662-5161},
abstract = {The skilled use of a speech BCI device will draw upon practical experience gained through the use of that very device. The reasons a user may have for using a device in a particular way, reflecting that skill gained via familiarity with the device, may differ significantly from the reasons that a speaker might have for their utterances. The potential divergence between reasons constituting skilled use and BCI-mediated speech output may serve to make clear an instrumental relationship between speaker and BCI speech device. This will affect the way in which the device and the speech it produces for the user can be thought of as being "reasons responsive", hence the way in which the user can be said to be in control of their device. Ultimately, this divergence will come down to how ownership of produced speech can be considered. The upshot will be that skillful use of a synthetic speech device might include practices that diverge from standard speech in significant ways. This might further indicate that synthetic speech devices ought to be considered as different from, not continuous with, standard speech.},
}
@article {pmid37915592,
year = {2023},
author = {Wu, GK and Ardeshirpour, Y and Mastracchio, C and Kent, J and Caiola, M and Ye, M},
title = {Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation.},
journal = {iScience},
volume = {26},
number = {11},
pages = {108140},
pmid = {37915592},
issn = {2589-0042},
abstract = {Intracortical microstimulation (ICMS) has been used for the development of brain machine interfaces. However, further understanding about the spatiotemporal responses of neurons to different electrical stimulation parameters is necessary to inform the design of optimal therapies. In this study, we employed in vivo electrophysiological recording, two-photon calcium imaging, and electric field simulation to evaluate the acute effect of ICMS on layer II/III neurons. Our results show that stimulation frequency non-linearly modulates neuronal responses, whereas the magnitude of responses is linearly correlated to the electric field strength and stimulation amplitude before reaching a steady state. Temporal dynamics of neurons' responses depends more on stimulation frequency and their distance to the stimulation electrode. In addition, amplitude-dependent post-stimulation suppression was observed within ∼500 μm of the stimulation electrode, as evidenced by both calcium imaging and local field potentials. These findings provide insights for selecting stimulation parameters to achieve desirable spatiotemporal specificity of ICMS.},
}
@article {pmid37915185,
year = {2023},
author = {Lee, HG and Jung, IH and Park, BS and Yang, HR and Kim, KK and Tu, TH and Yeh, JY and Lee, S and Yang, S and Lee, BJ and Kim, JG and Nam-Goong, IS},
title = {Altered Metabolic Phenotypes and Hypothalamic Neuronal Activity Triggered by Sodium-Glucose Cotransporter 2 Inhibition.},
journal = {Diabetes &amp; metabolism journal},
volume = {47},
number = {6},
pages = {784-795},
pmid = {37915185},
issn = {2233-6087},
support = {2020R1I1A3072427//National Research Foundation of Korea/ ; //Ministry of Education/ ; //Incheon National University/ ; },
mesh = {Humans ; Mice ; Animals ; *Sodium-Glucose Transporter 2 Inhibitors/pharmacology ; Hypothalamus/metabolism ; Glucose/metabolism ; Phenotype ; Neurons/metabolism ; Sodium/metabolism ; },
abstract = {BACKGRUOUND: Sodium-glucose cotransporter 2 (SGLT-2) inhibitors are currently used to treat patients with diabetes. Previous studies have demonstrated that treatment with SGLT-2 inhibitors is accompanied by altered metabolic phenotypes. However, it has not been investigated whether the hypothalamic circuit participates in the development of the compensatory metabolic phenotypes triggered by the treatment with SGLT-2 inhibitors.<br><br>METHODS: Mice were fed a standard diet or high-fat diet and treated with dapagliflozin, an SGLT-2 inhibitor. Food intake and energy expenditure were observed using indirect calorimetry system. The activity of hypothalamic neurons in response to dapagliflozin treatment was evaluated by immunohistochemistry with c-Fos antibody. Quantitative real-time polymerase chain reaction was performed to determine gene expression patterns in the hypothalamus of dapagliflozin-treated mice.<br><br>RESULTS: Dapagliflozin-treated mice displayed enhanced food intake and reduced energy expenditure. Altered neuronal activities were observed in multiple hypothalamic nuclei in association with appetite regulation. Additionally, we found elevated immunosignals of agouti-related peptide neurons in the paraventricular nucleus of the hypothalamus.<br><br>CONCLUSION: This study suggests the functional involvement of the hypothalamus in the development of the compensatory metabolic phenotypes induced by SGLT-2 inhibitor treatment.},
}
@article {pmid37914959,
year = {2024},
author = {Zhu, L and Yu, F and Huang, A and Ying, N and Zhang, J},
title = {Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {2},
pages = {479-493},
pmid = {37914959},
issn = {1741-0444},
support = {2020C04009//Key Research and Development Project of Zhejiang Province/ ; 2020E10010//Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province/ ; },
mesh = {Humans ; *Electroencephalography ; *Emotions ; Learning ; },
abstract = {Electroencephalogram (EEG) emotion recognition technology is essential for improving human-computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either instance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adaptation (JDDA) and Instance-Representation Transfer (I-RT). Specifically, JDDA is different from common representation transfer methods in transfer learning. It bridges the discrepancies of marginal and conditional distributions simultaneously and combines multiple adaptive layers and kernels for deep domain adaptation. On the other hand, I-RT utilizes instance transfer to select source domain data for better representation transfer. We performed experiments and compared them with other representative methods in the SEED, SEED-IV, and SEED-V datasets. In cross-subject experiments, our approach achieved an average accuracy of 83.21% in SEED, 52.12% in SEED-IV, and 60.17% in SEED-V. Similarly, in cross-session experiments, the accuracy was 91.29% in SEED, 59.02% in SEED-IV, and 65.91% in SEED-V. These results demonstrate the improvement in the accuracy of EEG emotion recognition using the proposed approach.},
}
@article {pmid37914729,
year = {2023},
author = {Sharma, N and Upadhyay, A and Sharma, M and Singhal, A},
title = {Deep temporal networks for EEG-based motor imagery recognition.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {18813},
pmid = {37914729},
issn = {2045-2322},
mesh = {*Movement ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Electroencephalography/methods ; Imagination ; },
abstract = {The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.},
}
@article {pmid37910621,
year = {2023},
author = {Wang, X and Wu, X and Wu, H and Xiao, H and Hao, S and Wang, B and Li, C and Bleymehl, K and Kauschke, SG and Mack, V and Ferger, B and Klein, H and Zheng, R and Duan, S and Wang, H},
title = {Neural adaption in midbrain GABAergic cells contributes to high-fat diet-induced obesity.},
journal = {Science advances},
volume = {9},
number = {44},
pages = {eadh2884},
pmid = {37910621},
issn = {2375-2548},
mesh = {Mice ; Animals ; *Diet, High-Fat/adverse effects ; *Calcium/metabolism ; Obesity/etiology/metabolism ; Adipose Tissue, White/metabolism ; Mesencephalon ; Mice, Inbred C57BL ; },
abstract = {Overeating disorders largely contribute to worldwide incidences of obesity. Available treatments are limited. Here, we discovered that long-term chemogenetic activation of ventrolateral periaqueductal gray (vlPAG) GABAergic cells rescue obesity of high-fat diet-induced obesity (DIO) mice. This was associated with the recovery of enhanced mIPSCs, decreased food intake, increased energy expenditure, and inguinal white adipose tissue (iWAT) browning. In vivo calcium imaging confirmed vlPAG GABAergic suppression for DIO mice, with corresponding reduction in intrinsic excitability. Single-nucleus RNA sequencing identified transcriptional expression changes in GABAergic cell subtypes in DIO mice, highlighting Cacna2d1 as of potential importance. Overexpressing CACNA2D1 in vlPAG GABAergic cells of DIO mice rescued enhanced mIPSCs and calcium response, reversed obesity, and therefore presented here as a potential target for obesity treatment.},
}
@article {pmid37910541,
year = {2023},
author = {Ortiz, O and Kuruganti, U and Chester, V and Wilson, A and Blustein, D},
title = {Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition.},
journal = {Journal of neurophysiology},
volume = {130},
number = {6},
pages = {1588-1601},
doi = {10.1152/jn.00506.2022},
pmid = {37910541},
issn = {1522-1598},
support = {RGPIN_2021-02638//Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC)/ ; },
mesh = {Humans ; Electroencephalography/methods ; Cognition/physiology ; *Sensorimotor Cortex/physiology ; *Motor Cortex/physiology ; Biomarkers ; },
abstract = {Changes in alpha band activity (8-12 Hz) indicate the downregulation of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas) but not in error processing areas (i.e., frontoparietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect the reduction of motor drive related to motor error correction, thus, being a potential cortical electrophysiological biomarker for the process and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.NEW &amp; NOTEWORTHY We demonstrated that alpha suppression, an EEG phenomenon with high temporal resolution, occurs over the primary sensorimotor area during error correction during lift movements. Interpretations of alpha activity are often attributed to high cognitive demands, thus recognizing that it is also influenced by motor processes is important in situations where cognitive demands are paired with movement errors. This could further have application as a biomarker for error correction in human-machine interfaces, such as neuroprostheses.},
}
@article {pmid37910412,
year = {2023},
author = {Tang, Z and Wang, H and Cui, Z and Jin, X and Zhang, L and Peng, Y and Xing, B},
title = {An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4390-4401},
doi = {10.1109/TNSRE.2023.3329059},
pmid = {37910412},
issn = {1558-0210},
mesh = {Humans ; *Exoskeleton Device ; Upper Extremity ; *Stroke ; User-Computer Interface ; Electroencephalography/methods ; *Virtual Reality ; *Brain-Computer Interfaces ; },
abstract = {The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.},
}
@article {pmid37909251,
year = {2024},
author = {Wang, S and Jiang, C and Cao, K and Li, R and Gao, Z and Wang, Y},
title = {HK2 in microglia and macrophages contribute to the development of neuropathic pain.},
journal = {Glia},
volume = {72},
number = {2},
pages = {396-410},
doi = {10.1002/glia.24482},
pmid = {37909251},
issn = {1098-1136},
support = {81772382//National Natural Science Foundation of China/ ; 2020C03042//Science Technology Department of Zhejiang Province/ ; },
mesh = {Humans ; Microglia/metabolism ; Hexokinase/metabolism/pharmacology ; Neuroinflammatory Diseases ; Hyperalgesia/metabolism ; Macrophages/metabolism ; *Neuralgia/metabolism ; Ganglia, Spinal/metabolism ; Spinal Cord/metabolism ; *Peripheral Nerve Injuries/metabolism ; },
abstract = {Neuropathic pain is a complex pain condition accompanied by prominent neuroinflammation involving activation of both central and peripheral immune cells. Metabolic switch to glycolysis is an important feature of activated immune cells. Hexokinase 2 (HK2), a key glycolytic enzyme enriched in microglia, has recently been shown important in regulating microglial functions. Whether and how HK2 is involved in neuropathic pain-related neuroinflammation remains unknown. Using a HK2-tdTomato reporter line, we found that HK2 was prominently elevated in spinal microglia. Pharmacological inhibition of HK2 effectively alleviated nerve injury-induced acute mechanical pain. However, selective ablation of Hk2 in microglia reduced microgliosis in the spinal dorsal horn (SDH) with little analgesic effects. Further analyses showed that nerve injury also significantly induced HK2 expression in dorsal root ganglion (DRG) macrophages. Deletion of Hk2 in myeloid cells, including both DRG macrophages and spinal microglia, led to the alleviation of mechanical pain during the first week after injury, along with attenuated microgliosis in the ipsilateral SDH, macrophage proliferation in DRGs, and suppressed inflammatory responses in DRGs. These data suggest that HK2 plays an important role in regulating neuropathic pain-related immune cell responses at acute phase and that HK2 contributes to neuropathic pain onset primarily through peripheral monocytes and DRG macrophages rather than spinal microglia.},
}
@article {pmid37907477,
year = {2023},
author = {Zhong, C and Liao, K and Dai, T and Wei, M and Ma, H and Wu, J and Zhang, Z and Ye, Y and Luo, Y and Chen, Z and Jian, J and Sun, C and Tang, B and Zhang, P and Liu, R and Li, J and Yang, J and Li, L and Liu, K and Hu, X and Lin, H},
title = {Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {6939},
pmid = {37907477},
issn = {2041-1723},
support = {61975179//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62105287//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12104375//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.},
}
@article {pmid37907096,
year = {2023},
author = {Sun, S and Li, J and Wang, S and Li, J and Ren, J and Bao, Z and Sun, L and Ma, X and Zheng, F and Ma, S and Sun, L and Wang, M and Yu, Y and Ma, M and Wang, Q and Chen, Z and Ma, H and Wang, X and Wu, Z and Zhang, H and Yan, K and Yang, Y and Zhang, Y and Zhang, S and Lei, J and Teng, ZQ and Liu, CM and Bai, G and Wang, YJ and Li, J and Wang, X and Zhao, G and Jiang, T and Belmonte, JCI and Qu, J and Zhang, W and Liu, GH},
title = {CHIT1-positive microglia drive motor neuron ageing in the primate spinal cord.},
journal = {Nature},
volume = {624},
number = {7992},
pages = {611-620},
pmid = {37907096},
issn = {1476-4687},
mesh = {Animals ; Humans ; Biomarkers/metabolism ; *Cellular Senescence ; *Chitinases/metabolism ; *Microglia/enzymology/metabolism/pathology ; *Motor Neurons/metabolism ; Neuroinflammatory Diseases/metabolism/pathology ; *Primates/metabolism ; Reproducibility of Results ; Single-Cell Gene Expression Analysis ; *Spinal Cord/metabolism/pathology ; },
abstract = {Ageing is a critical factor in spinal-cord-associated disorders[1], yet the ageing-specific mechanisms underlying this relationship remain poorly understood. Here, to address this knowledge gap, we combined single-nucleus RNA-sequencing analysis with behavioural and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord ageing. As an underlying mechanism, we identified a neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons, and they have the ability to trigger senescence, partly by activating SMAD signalling. We further validated the driving role of secreted CHIT1 on MN senescence using multimodal experiments both in vivo, using the NHP spinal cord as a model, and in vitro, using a sophisticated system modelling the human motor-neuron-microenvironment interplay. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.},
}
@article {pmid37906969,
year = {2023},
author = {Peng, D and Zheng, WL and Liu, L and Jiang, WB and Li, Z and Lu, Y and Lu, BL},
title = {Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad085a},
pmid = {37906969},
issn = {1741-2552},
abstract = {OBJECTIVE: Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the EEG neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models.<br><br>APPROACH: We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition.<br><br>MAIN RESULTS: (1) Sex differences exist in various emotion types and both Western and Eastern cultures; (2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males; (3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) The same-sex emotion recognition models outperform the corresponding cross-sex models.<br><br>SIGNIFICANCE: These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.},
}
@article {pmid37906489,
year = {2023},
author = {Meng, J and Liu, H and Wu, Q and Zhou, H and Shi, W and Meng, L and Xu, M and Ming, D},
title = {A SSVEP-Based Brain-Computer Interface With Low-Pixel Density of Stimuli.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4439-4448},
doi = {10.1109/TNSRE.2023.3328917},
pmid = {37906489},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Fatigue ; Photic Stimulation/methods ; },
abstract = {The brain-computer interface (BCI) based on the steady-state visual evoked potential (SSVEP) has drawn widespread attention due to its high communication speed and low individual variability. However, there is still a need to enhance the comfort of SSVEP-BCI, especially considering the assurance of its effectiveness. This study aims to achieve a perfect balance between comfort and effectiveness by reducing the pixel density of SSVEP stimuli. Three experiments were conducted to determine the most suitable presentation form (flickering square vs. flickering checkerboard), pixel distribution pattern (random vs. uniform), and pixel density value (100%, 90%, 80%, 70%, 60%, 40%, 20%). Subjects' electroencephalogram (EEG) and fatigue scores were recorded, while comfort and effectiveness were measured by fatigue score and classification accuracy, respectively. The results showed that the flickering square with random pixel distribution achieved a lower fatigue score and higher accuracy. EEG responses induced by stimuli with a square-random presentation mode were then compared across various pixel densities. In both offline and online tests, the fatigue score decreased as the pixel density decreased. Strikingly, when the pixel density was above 60%, the accuracies of low-pixel-density SSVEP were all satisfactory (>90%) and showed no significant difference with that of the conventional 100%-pixel density. These results support the feasibility of using 60%-pixel density with a square-random presentation mode to improve the comfort of SSVEP-BCI, thereby promoting its practical applications in communication and control.},
}
@article {pmid37905887,
year = {2024},
author = {Lian, Y and Wu, C and Liu, L and Li, X},
title = {Prediction of cell-cell communication patterns of dorsal root ganglion cells: single-cell RNA sequencing data analysis.},
journal = {Neural regeneration research},
volume = {19},
number = {6},
pages = {1367-1374},
pmid = {37905887},
issn = {1673-5374},
abstract = {Dorsal root ganglion neurons transmit peripheral somatic information to the central nervous system, and dorsal root ganglion neuron excitability affects pain perception. Dorsal root ganglion stimulation is a new approach for managing pain sensation. Knowledge of the cell-cell communication among dorsal root ganglion cells may help in the development of new pain and itch management strategies. Here, we used the single-cell RNA-sequencing (scRNA-seq) database to investigate intercellular communication networks among dorsal root ganglion cells. We collected scRNA-seq data from six samples from three studies, yielding data on a total of 17,766 cells. Based on genetic profiles, we identified satellite glial cells, Schwann cells, neurons, vascular endothelial cells, immune cells, fibroblasts, and vascular smooth muscle cells. Further analysis revealed that eight types of dorsal root ganglion neurons mediated proprioceptive, itch, touch, mechanical, heat, and cold sensations. Moreover, we predicted several distinct forms of intercellular communication among dorsal root ganglion cells, including cell-cell contact, secreted signals, extracellular matrix, and neurotransmitter-mediated signals. The data mining predicted that Mrgpra3-positive neurons robustly express the genes encoding the adenosine Adora2b (A2B) receptor and glial cell line-derived neurotrophic factor family receptor alpha 1 (GFRα-1). Our immunohistochemistry results confirmed the coexpression of the A2B receptor and GFRα-1. Intrathecal injection of the A2B receptor antagonist PSB-603 effectively prevented histamine-induced scratching behaviour in a dose-dependent manner. Our results demonstrate the involvement of the A2B receptor in the modulation of itch sensation. Furthermore, our findings provide insight into dorsal root ganglion cell-cell communication patterns and mechanisms. Our results should contribute to the development of new strategies for the regulation of dorsal root ganglion excitability.},
}
@article {pmid37905305,
year = {2024},
author = {Mao, T and Fang, Z and Chai, Y and Deng, Y and Rao, J and Quan, P and Goel, N and Basner, M and Guo, B and Dinges, DF and Liu, J and Detre, JA and Rao, H},
title = {Sleep deprivation attenuates neural responses to outcomes from risky decision-making.},
journal = {Psychophysiology},
volume = {61},
number = {4},
pages = {e14465},
doi = {10.1111/psyp.14465},
pmid = {37905305},
issn = {1469-8986},
support = {P41 RR002305/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Humans ; *Sleep Deprivation ; *Decision Making/physiology ; Brain ; Cognition ; Gyrus Cinguli ; Magnetic Resonance Imaging ; Risk-Taking ; },
abstract = {Sleep loss impacts a broad range of brain and cognitive functions. However, how sleep deprivation affects risky decision-making remains inconclusive. This study used functional MRI to examine the impact of one night of total sleep deprivation (TSD) on risky decision-making behavior and the underlying brain responses in healthy adults. In this study, we analyzed data from N = 56 participants in a strictly controlled 5-day and 4-night in-laboratory study using a modified Balloon Analogue Risk Task. Participants completed two scan sessions in counter-balanced order, including one scan during rested wakefulness (RW) and another scan after one night of TSD. Results showed no differences in participants' risk-taking propensity and risk-induced activation between RW and TSD. However, participants showed significantly reduced neural activity in the anterior cingulate cortex and bilateral insula for loss outcomes, and in bilateral putamen for win outcomes during TSD compared with RW. Moreover, risk-induced activation in the insula negatively correlated with participants' risk-taking propensity during RW, while no such correlations were observed after TSD. These findings suggest that sleep loss may impact risky decision-making by attenuating neural responses to decision outcomes and impairing brain-behavior associations.},
}
@article {pmid37905046,
year = {2024},
author = {Forenzo, D and Zhu, H and Shanahan, J and Lim, J and He, B},
title = {Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.10.12.562084},
pmid = {37905046},
issn = {2692-8205},
abstract = {UNLABELLED: Brain-computer interfaces (BCI) using electroencephalography (EEG) provide a non-invasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online Continuous Pursuit (CP), a complex BCI task requiring the user to track an object in two-dimensional space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pre-training models on data from other subjects, and mid-session training to reduce inter-session variability. The results from these experiments showed that pre-training did not significantly improve performance, but updating the models mid-session may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help improve the quality of lives of healthy and motor-impaired individuals.<br><br>SIGNIFICANCE STATEMENT: Brain-computer Interfaces (BCI) have the potential to replace or restore motor functions for patients and can benefit the general population by providing a direct link of the brain with robotics or other devices. In this work, we developed a paradigm using deep learning (DL)-based decoders for continuous control of a BCI system and demonstrated its capabilities through extensive online experiments. We also investigate how DL performance is affected by varying amounts of training data and collected more than 150 hours of BCI data that can be used to train new models. The results of this study provide valuable information for developing future DL-based BCI decoders which can improve performance and help bring BCIs closer to practical applications and wide-spread use.},
}
@article {pmid37904095,
year = {2023},
author = {Mohammad, A and Siddiqui, F and Alam, MA and Idrees, SM},
title = {Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework.},
journal = {BMC bioinformatics},
volume = {24},
number = {1},
pages = {406},
pmid = {37904095},
issn = {1471-2105},
mesh = {Humans ; *Algorithms ; *Electroencephalography/methods ; Neural Networks, Computer ; Emotions/physiology ; },
abstract = {The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers. This work introduces EEG recognition model, where the input signal is pre-processed using band pass filter. Then, the features like discrete wavelet transform (DWT), band power, spectral flatness, and improved Entropy are extracted. Further, for recognition, tri-classifiers like long short term memory (LSTM), improved deep belief network (DBN) and recurrent neural network (RNN) are used. Also to enhance tri-model classifier performance, the weights of LSTM, improved DBN, and RNN are tuned by model named as shark smell updated BES optimization (SSU-BES). Finally, the perfection of SSU-BES is demonstrated over diverse metrics.},
}
@article {pmid37903724,
year = {2023},
author = {Yan, Y and Zhou, P and Ding, L and Hu, W and Chen, W and Su, B},
title = {T Cell Antigen Recognition and Discrimination by Electrochemiluminescence Imaging.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {62},
number = {50},
pages = {e202314588},
doi = {10.1002/anie.202314588},
pmid = {37903724},
issn = {1521-3773},
support = {22125405//National Natural Science Foundation of China/ ; 22074131//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *T-Lymphocytes ; Receptors, Antigen, T-Cell ; Antigens, Neoplasm ; *Neoplasms ; Tumor Microenvironment ; },
abstract = {Adoptive T lymphocyte (T cell) transfer and tumour-specific peptide vaccines are innovative cancer therapies. An accurate assessment of the specific reactivity of T cell receptors (TCRs) to tumour antigens is required because of the high heterogeneity of tumour cells and the immunosuppressive tumour microenvironment. In this study, we report a label-free electrochemiluminescence (ECL) imaging approach for recognising and discriminating between TCRs and tumour-specific antigens by imaging the immune synapses of T cells. Various T cell stimuli, including agonistic antibodies, auxiliary molecules, and tumour-specific antigens, were modified on the electrode's surface to allow for their interaction with T cells bearing different TCRs. The formation of immune synapses activated by specific stimuli produced a negative (shadow) ECL image, from which T cell antigen recognition and discrimination were evaluated by analysing the spreading area and the recognition intensity of T cells. This approach provides an easy way to assess TCR-antigen specificity and screen both of them for immunotherapies.},
}
@article {pmid37903108,
year = {2023},
author = {Wise, DH and Mores, RM and M Pajda-De La O, J and McCary, MA},
title = {Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation.},
journal = {PloS one},
volume = {18},
number = {10},
pages = {e0293176},
pmid = {37903108},
issn = {1932-6203},
mesh = {Humans ; Animals ; Food Chain ; Seasons ; *Spiders ; Predatory Behavior ; *Arthropods ; },
abstract = {Intraguild predation (IGP)-predation between generalist predators (IGPredator and IGPrey) that potentially compete for a shared prey resource-is a common interaction module in terrestrial food webs. Understanding temporal variation in webs with widespread IGP is relevant to testing food web theory. We investigated temporal constancy in the structure of such a system: the spider-focused food web of the forest floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders collected from the floor of a deciduous forest during spring, summer, and fall over four years. Because only spiders were defined as consumers, the web was tripartite, with 11 consumer nodes (spider families) and 22 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and the 11 spider families. Most (99%) spider-spider predation was on spider IGPrey, and ~90% of these interactions were restricted to spider families within the same broadly defined foraging mode (cursorial or web-spinning spiders). Bootstrapped-derived confidence intervals (BCI's) for two indices of web structure, restricted connectance and interaction evenness, overlapped broadly across years and seasons. A third index, % IGPrey (% IGPrey among all prey of spiders), was similar across years (~50%) but varied seasonally, with a summer rate (65%) ~1.8x higher than spring and fall. This seasonal pattern was consistent across years. Our results suggest that extensive spider predation on spider IGPrey that exhibits consistent seasonal variation in frequency, and that occurs primarily within two broadly defined spider-spider interaction pathways, must be incorporated into models of the dynamics of forest-floor food webs.},
}
@article {pmid37901886,
year = {2023},
author = {O'Shaughnessy, MR and Johnson, WG and Tournas, LN and Rozell, CJ and Rommelfanger, KS},
title = {Neuroethics guidance documents: principles, analysis, and implementation strategies.},
journal = {Journal of law and the biosciences},
volume = {10},
number = {2},
pages = {lsad025},
doi = {10.1093/jlb/lsad025},
pmid = {37901886},
issn = {2053-9711},
support = {UH3 NS103550/NS/NINDS NIH HHS/United States ; },
abstract = {Innovations in neurotechnologies have ignited conversations about ethics around the world, with implications for researchers, policymakers, and the private sector. The human rights impacts of neurotechnologies have drawn the attention of United Nations bodies; nearly 40 states are tasked with implementing the Organization for Economic Co-operation and Development's principles for responsible innovation in neurotechnology; and the United States is considering placing export controls on brain-computer interfaces. Against this backdrop, we offer the first review and analysis of neuroethics guidance documents recently issued by prominent government, private, and academic groups, focusing on commonalities and divergences in articulated goals; envisioned roles and responsibilities of different stakeholder groups; and the suggested role of the public. Drawing on lessons from the governance of other emerging technologies, we suggest implementation and evaluation strategies to guide practitioners and policymakers in operationalizing these ethical norms in research, business, and policy settings.},
}
@article {pmid37896483,
year = {2023},
author = {Di Flumeri, G and Giorgi, A and Germano, D and Ronca, V and Vozzi, A and Borghini, G and Tamborra, L and Simonetti, I and Capotorto, R and Ferrara, S and Sciaraffa, N and Babiloni, F and Aricò, P},
title = {A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {20},
pages = {},
pmid = {37896483},
issn = {1424-8220},
mesh = {Humans ; Adolescent ; Reaction Time ; Electroencephalography/methods ; *Wearable Electronic Devices ; *Automobile Driving ; Accidents, Traffic ; },
abstract = {When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.},
}
@article {pmid37894754,
year = {2023},
author = {La, VNT and Minh, DDL},
title = {Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation.},
journal = {International journal of molecular sciences},
volume = {24},
number = {20},
pages = {},
pmid = {37894754},
issn = {1422-0067},
support = {1905324//National Science Foundation/ ; },
mesh = {*Uncertainty ; Bayes Theorem ; Calorimetry/methods ; Thermodynamics ; Protein Binding ; },
abstract = {We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the simulations, where it underestimates the uncertainty of enthalpy for confidence intervals less than 70%. In both datasets, Bayesian credible intervals are much closer to observed confidence intervals.},
}
@article {pmid37893339,
year = {2023},
author = {Hoeferlin, GF and Bajwa, T and Olivares, H and Zhang, J and Druschel, LN and Sturgill, BS and Sobota, M and Boucher, P and Duncan, J and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ and Capadona, JR},
title = {Antioxidant Dimethyl Fumarate Temporarily but Not Chronically Improves Intracortical Microelectrode Performance.},
journal = {Micromachines},
volume = {14},
number = {10},
pages = {},
pmid = {37893339},
issn = {2072-666X},
support = {I01 RX002611/RX/RRD VA/United States ; R01 NS110823/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; F32 NS011082/NS/NINDS NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; },
abstract = {Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a brain-computer interface (BCI) system aimed at restoring function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (<12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals and assess its usefulness for specific applications.},
}
@article {pmid37891775,
year = {2023},
author = {Liu, T and Li, B and Zhang, C and Chen, P and Zhao, W and Yan, B},
title = {Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data.},
journal = {Brain sciences},
volume = {13},
number = {10},
pages = {},
pmid = {37891775},
issn = {2076-3425},
support = {62106285//the National Natural Science Foundation of China/ ; },
abstract = {This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.},
}
@article {pmid37890180,
year = {2024},
author = {Rocca, A and Lehner, C and Wafula-Wekesa, E and Luna, E and Fernández-Cornejo, V and Abarca-Olivas, J and Soto-Sánchez, C and Fernández-Jover, E and González-López, P},
title = {Robot-assisted implantation of a microelectrode array in the occipital lobe as a visual prosthesis: technical note.},
journal = {Journal of neurosurgery},
volume = {140},
number = {4},
pages = {1169-1176},
doi = {10.3171/2023.8.JNS23772},
pmid = {37890180},
issn = {1933-0693},
mesh = {Animals ; Humans ; *Visual Prosthesis ; Electrodes, Implanted ; Microelectrodes ; *Robotics ; *Visual Cortex/surgery ; Prosthesis Implantation ; },
abstract = {The prospect of direct interaction between the brain and computers has been investigated in recent decades, revealing several potential applications. One of these is sight restoration in profoundly blind people, which is based on the ability to elicit visual perceptions while directly stimulating the occipital cortex. Technological innovation has led to the development of microelectrodes implantable on the brain surface. The feasibility of implanting a microelectrode on the visual cortex has already been shown in animals, with promising results. Current research has focused on the implantation of microelectrodes into the occipital brain of blind volunteers. The technique raises several technical challenges. In this technical note, the authors suggest a safe and effective approach for robot-assisted implantation of microelectrodes in the occipital lobe for sight restoration.},
}
@article {pmid37887123,
year = {2023},
author = {Qin, Y and Zhang, Y and Zhang, Y and Liu, S and Guo, X},
title = {Application and Development of EEG Acquisition and Feedback Technology: A Review.},
journal = {Biosensors},
volume = {13},
number = {10},
pages = {},
pmid = {37887123},
issn = {2079-6374},
support = {12072030//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Feedback ; Electroencephalography ; *Epilepsy/diagnosis ; *Brain-Computer Interfaces ; Emotions ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.},
}
@article {pmid37885532,
year = {2023},
author = {Zotey, V and Andhale, A and Shegekar, T and Juganavar, A},
title = {Adaptive Neuroplasticity in Brain Injury Recovery: Strategies and Insights.},
journal = {Cureus},
volume = {15},
number = {9},
pages = {e45873},
pmid = {37885532},
issn = {2168-8184},
abstract = {This review addresses the relationship between neuroplasticity and recovery from brain damage. Neuroplasticity's ability to adapt becomes crucial since brain injuries frequently result in severe impairments. We begin by describing the fundamentals of neuroplasticity and how it relates to rehabilitation. Examining different forms of brain injuries and their neurological effects highlights the complex difficulties in rehabilitation. By revealing cellular processes, we shed light on synaptic adaptability following damage. Our study of synaptic plasticity digs into axonal sprouting, dendritic remodeling, and the balance of long-term potentiation. These processes depict neural resilience amid change. Then, after damage, we investigate immediate and slow neuroplastic alterations, separating reorganizations that are adaptive from those that are maladaptive. As we go on to rehabilitation, we evaluate techniques that use neuroplasticity's potential. These methods take advantage of the brain's plasticity for healing, from virtual reality and brain-computer interfaces to constraint-induced movement therapy. Ethics and individualized neurorehabilitation are explored. We scrutinize the promise of combination therapy and the difficulties in putting new knowledge into clinical practice. In conclusion, this analysis highlights neuroplasticity's critical role in brain injury recovery, providing sophisticated approaches to improve life after damage.},
}
@article {pmid37883851,
year = {2023},
author = {Jiang, X and Fan, J and Zhu, Z and Wang, Z and Guo, Y and Liu, X and Jia, F and Dai, C},
title = {Cybersecurity in neural interfaces: Survey and future trends.},
journal = {Computers in biology and medicine},
volume = {167},
number = {},
pages = {107604},
doi = {10.1016/j.compbiomed.2023.107604},
pmid = {37883851},
issn = {1879-0534},
mesh = {Humans ; *Artificial Intelligence ; Computer Security ; *Brain-Computer Interfaces ; Electromyography ; Nervous System ; },
abstract = {With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.},
}
@article {pmid37883287,
year = {2023},
author = {Zhong, Y and Yao, L and Wang, Y},
title = {Enhanced Motor Imagery Decoding by Calibration Model-Assisted With Tactile ERD.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4295-4305},
doi = {10.1109/TNSRE.2023.3327788},
pmid = {37883287},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Calibration ; Imagination/physiology ; Movement/physiology ; Touch/physiology ; *Brain-Computer Interfaces ; },
abstract = {OBJECTIVE: In this study, we propose a tactile-assisted calibration method for a motor imagery (MI) based Brain-Computer Interface (BCI) system.<br><br>METHOD: In the proposed calibration, tactile stimulation was applied to the hand wrist to assist the subjects in the MI task, which is named SA-MI task. Then, classifier training in the SA-MI Calibration was performed using the SA-MI data, while the Conventional Calibration employed the MI data. After the classifiers were trained, the performance was evaluated on a common MI dataset.<br><br>RESULTS: Our study demonstrated that the SA-MI Calibration significantly improved the performance as compared with the Conventional Calibration, with a decoding accuracy of (78.3% vs. 71.3%). Moreover, the average calibration time could be reduced by 40%. This benefit of the SA-MI Calibration effect was further validated by an independent control group, which showed no improvement when tactile stimulation was not applied during the calibration phase. Further analysis showed that when compared with MI, greater motor-related cortical activation and higher R [2] value in the alpha-beta frequency band were induced in SA-MI.<br><br>CONCLUSION: Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration.<br><br>SIGNIFICANCE: The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage.},
}
@article {pmid37883285,
year = {2023},
author = {Pousson, JE and Shen, YW and Lin, YP and Voicikas, A and Pipinis, E and Bernhofs, V and Burmistrova, L and Griskova-Bulanova, I},
title = {Exploring Spatio-Spectral Electroencephalogram Modulations of Imbuing Emotional Intent During Active Piano Playing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4347-4356},
doi = {10.1109/TNSRE.2023.3327740},
pmid = {37883285},
issn = {1558-0210},
mesh = {Humans ; *Emotions/physiology ; Electroencephalography ; Arousal/physiology ; Frontal Lobe ; *Music/psychology ; },
abstract = {Imbuing emotional intent serves as a crucial modulator of music improvisation during active musical instrument playing. However, most improvisation-related neural endeavors have been gained without considering the emotional context. This study attempts to exploit reproducible spatio-spectral electroencephalogram (EEG) oscillations of emotional intent using a data-driven independent component analysis framework in an ecological multiday piano playing experiment. Through the four-day 32-ch EEG dataset of 10 professional players, we showed that EEG patterns were substantially affected by both intra- and inter-individual variability underlying the emotional intent of the dichotomized valence (positive vs. negative) and arousal (high vs. low) categories. Less than half (3-4) of the 10 participants analogously exhibited day-reproducible (≥ three days) spectral modulations at the right frontal beta in response to the valence contrast as well as the frontal central gamma and the superior parietal alpha to the arousal counterpart. In particular, the frontal engagement facilitates a better understanding of the frontal cortex (e.g., dorsolateral prefrontal cortex and anterior cingulate cortex) and its role in intervening emotional processes and expressing spectral signatures that are relatively resistant to natural EEG variability. Such ecologically vivid EEG findings may lead to better understanding of the development of a brain-computer music interface infrastructure capable of guiding the training, performance, and appreciation for emotional improvisatory status or actuating music interaction via emotional context.},
}
@article {pmid37882881,
year = {2023},
author = {Milford, SR and Shaw, D and Starke, G},
title = {Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain.},
journal = {Science and engineering ethics},
volume = {29},
number = {6},
pages = {38},
pmid = {37882881},
issn = {1471-5546},
mesh = {Humans ; *Artificial Intelligence ; *Silicon ; Brain ; Intelligence ; Learning ; },
abstract = {The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent brain-computer interfaces as they enter clinical use. In this contribution we discuss a complementary development: the integration of a functional in vitro network of human neurons into an in silico computing environment.To do so, we draw on a recent experiment reporting the creation of silico-biological intelligence as a case study (Kagan et al., 2022b). In this experiment, multielectrode arrays were plated with stem cell-derived human neurons, creating a system which the authors call DishBrain. By embedding the system into a virtual game-world, neural clusters were able to receive electrical input signals from the game-world and to respond appropriately with output signals from pre-assigned motor regions. Using this design, the authors demonstrate how the DishBrain self-organises and successfully learns to play the computer game 'Pong', exhibiting 'sentient' and intelligent behaviour in its virtual environment.The creation of such hybrid, silico-biological intelligence raises numerous ethical challenges. Following the neuroscientific framework embraced by the authors themselves, we discuss the arising ethical challenges in the context of Karl Friston's Free Energy Principle, focusing on the risk of creating synthetic phenomenology. Following the DishBrain's creator's neuroscientific assumptions, we highlight how DishBrain's design may risk bringing about artificial suffering and argue for a congruently cautious approach to such synthetic biological intelligence.},
}
@article {pmid37881517,
year = {2023},
author = {Mang, J and Xu, Z and Qi, Y and Zhang, T},
title = {Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1271967},
pmid = {37881517},
issn = {1662-5218},
abstract = {The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.},
}
@article {pmid37881515,
year = {2023},
author = {Staffa, M and D'Errico, L and Sansalone, S and Alimardani, M},
title = {Classifying human emotions in HRI: applying global optimization model to EEG brain signals.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1191127},
pmid = {37881515},
issn = {1662-5218},
abstract = {Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future.},
}
@article {pmid37879343,
year = {2023},
author = {Fleury, M and Figueiredo, P and Vourvopoulos, A and Lécuyer, A},
title = {Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad06e1},
pmid = {37879343},
issn = {1741-2552},
mesh = {*Neurofeedback/methods ; Magnetic Resonance Imaging/methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; },
abstract = {Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.},
}
@article {pmid37878151,
year = {2024},
author = {Woolpert, KM and Ahern, TP and Lash, TL and O'Malley, DL and Stokes, AM and Cronin-Fenton, DP},
title = {Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis.},
journal = {Breast cancer research and treatment},
volume = {203},
number = {3},
pages = {407-417},
pmid = {37878151},
issn = {1573-7217},
support = {R01 CA166825/CA/NCI NIH HHS/United States ; },
mesh = {Humans ; Female ; *Breast Neoplasms ; Antineoplastic Agents, Hormonal/adverse effects ; Chemotherapy, Adjuvant ; Biomarkers ; },
abstract = {PURPOSE: Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.<br><br>METHODS: We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.<br><br>RESULTS: Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.<br><br>CONCLUSIONS: Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.},
}
@article {pmid37876899,
year = {2023},
author = {Ma, G and Yan, R and Tang, H},
title = {Exploiting noise as a resource for computation and learning in spiking neural networks.},
journal = {Patterns (New York, N.Y.)},
volume = {4},
number = {10},
pages = {100831},
pmid = {37876899},
issn = {2666-3899},
abstract = {Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.},
}
@article {pmid37876845,
year = {2023},
author = {Wang, P and Liu, J and Wang, L and Ma, H and Mei, X and Zhang, A},
title = {Effects of brain-Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial.},
journal = {Frontiers in psychology},
volume = {14},
number = {},
pages = {1241081},
pmid = {37876845},
issn = {1664-1078},
abstract = {AIM: To explore the effects of brain-computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.<br><br>BACKGROUND: The prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients' ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients' overall quality of life is the focus and difficulty of clinical rehabilitation work.<br><br>METHODS: Patients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain-computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).<br><br>RESULTS: A total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.<br><br>CONCLUSION: Brain-computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.<br><br>IMPACT: This study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.<br><br>CLINICAL REVIEW REGISTRATION: https://www.chictr.org.cn/, identifier ChiCTR2300070382.},
}
@article {pmid37875937,
year = {2023},
author = {Obukhov, NV and Naish, PLN and Solnyshkina, IE and Siourdaki, TG and Martynov, IA},
title = {Real-time assessment of hypnotic depth, using an EEG-based brain-computer interface: a preliminary study.},
journal = {BMC research notes},
volume = {16},
number = {1},
pages = {288},
pmid = {37875937},
issn = {1756-0500},
mesh = {Humans ; Hypnotics and Sedatives ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Hypnosis ; },
abstract = {OBJECTIVE: Hypnosis can be an effective treatment for many conditions, and there have been attempts to develop instrumental approaches to continuously monitor hypnotic state level ("depth"). However, there is no method that addresses the individual variability of electrophysiological hypnotic correlates. We explore the possibility of using an EEG-based passive brain-computer interface (pBCI) for real-time, individualised estimation of the hypnosis deepening process.<br><br>RESULTS: The wakefulness and deep hypnosis intervals were manually defined and labelled in 27 electroencephalographic (EEG) recordings obtained from eight outpatients after hypnosis sessions. Spectral analysis showed that EEG correlates of deep hypnosis were relatively stable in each patient throughout the treatment but varied between patients. Data from each first session was used to train classification models to continuously assess deep hypnosis probability in subsequent sessions. Models trained using four frequency bands (1.5-45, 1.5-8, 1.5-14, and 4-15&#xa0;Hz) showed accuracy mostly exceeding 85% in a 10-fold cross-validation. Real-time classification accuracy was also acceptable, so at least one of the four bands yielded results exceeding 74% in any session. The best results averaged across all sessions were obtained using 1.5-14 and 4-15&#xa0;Hz, with an accuracy of 82%. The revealed issues are also discussed.},
}
@article {pmid37875484,
year = {2023},
author = {Wang, M and Lin, T and Wang, L and Lin, A and Zou, K and Xu, X and Zhou, Y and Peng, Y and Meng, Q and Qian, Y and Deng, G and Wu, Z and Chen, J and Lin, J and Zhang, M and Zhu, W and Zhang, C and Zhang, D and Goh, RSM and Liu, Y and Pang, CP and Chen, X and Chen, H and Fu, H},
title = {Uncertainty-inspired open set learning for retinal anomaly identification.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {6757},
pmid = {37875484},
issn = {2041-1723},
mesh = {Humans ; Artificial Intelligence ; Algorithms ; Uncertainty ; Retina/diagnostic imaging ; Fundus Oculi ; *Retinal Diseases/diagnostic imaging ; *Eye Abnormalities ; },
abstract = {Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.},
}
@article {pmid37875422,
year = {2023},
author = {, and , },
title = {[Chinese expert consensus on multigene testing for adjuvant treatment of HR-positive, HER-2 negative early breast cancer(2023 edition)].},
journal = {Zhonghua zhong liu za zhi [Chinese journal of oncology]},
volume = {45},
number = {10},
pages = {863-870},
doi = {10.3760/cma.j.cn112152-20230627-00266},
pmid = {37875422},
issn = {0253-3766},
support = {2022-I2M-C&amp;T-A-014//Chinese Academy of Medical Sciences Clinical Transformation and Medical Research Fund Rolling Project/ ; 7222150//Beijing Natural Science Foundation/ ; },
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/genetics ; Consensus ; East Asian People ; Prognosis ; Chemotherapy, Adjuvant ; Receptor, ErbB-2/genetics ; },
abstract = {Breast cancer is the most common malignant tumor in women, of which the majority is early breast cancer (EBC). The strategy of postoperative adjuvant treatment relies mainly on the clinicopathologic characteristics of patients, but there are certain deficiencies if only depending on it to assess treatment benefits and disease prognosis. Multigene testing tools can evaluate the prognosis and predict therapeutic effects of breast cancer patients to guide the clinical decision-making on whether to use adjuvant chemotherapy, radiotherapy, and endocrine therapy by detecting the expression levels of specific genes. The consensus-writing expert group, based on the characteristics, validation results, and accessibility of the multigene testing tools and combined with clinical practice, described the result interpretation and clinical application of OncotypeDx(®) (21-gene), Mammaprint(®) (70-gene), RecurIndex(®) (28-gene), EndoPredict(®)(12-gene), and BreastCancerIndex(®) (BCI, 7-gene) for hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC. The development and validation process of each tool was also briefly introduced. It is expected that the consensus will help guide and standardize the clinical use of multigene testing tools and further improve the level of precise treatment for EBC.},
}
@article {pmid37875404,
year = {2023},
author = {Luo, S and Angrick, M and Coogan, C and Candrea, DN and Wyse-Sookoo, K and Shah, S and Rabbani, Q and Milsap, GW and Weiss, AR and Anderson, WS and Tippett, DC and Maragakis, NJ and Clawson, LL and Vansteensel, MJ and Wester, BA and Tenore, FV and Hermansky, H and Fifer, MS and Ramsey, NF and Crone, NE},
title = {Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {35},
pages = {e2304853},
pmid = {37875404},
issn = {2198-3844},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Speech ; *Brain-Computer Interfaces ; *Amyotrophic Lateral Sclerosis/complications ; Electrocorticography ; },
abstract = {Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.},
}
@article {pmid37875107,
year = {2023},
author = {Meng, J and Zhao, Y and Wang, K and Sun, J and Yi, W and Xu, F and Xu, M and Ming, D},
title = {Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad0650},
pmid = {37875107},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Intention ; Electroencephalography/methods ; Evoked Potentials ; Movement ; Imagination ; },
abstract = {Objective.Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention.Methods.A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection.Results.Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy.Significance.The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.},
}
@article {pmid37871461,
year = {2023},
author = {Cao, HL and Wei, W and Meng, YJ and Deng, W and Li, T and Li, ML and Guo, WJ},
title = {Disrupted white matter structural networks in individuals with alcohol dependence.},
journal = {Journal of psychiatric research},
volume = {168},
number = {},
pages = {13-21},
doi = {10.1016/j.jpsychires.2023.10.019},
pmid = {37871461},
issn = {1879-1379},
mesh = {Humans ; *White Matter/diagnostic imaging ; *Alcoholism/diagnostic imaging ; Brain/diagnostic imaging ; Diffusion Tensor Imaging/methods ; Prefrontal Cortex ; },
abstract = {Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.},
}
@article {pmid37871436,
year = {2023},
author = {Akhlaghi, P and Ghouchani, A and Rouhi, G},
title = {The effect of defect size and location on the fracture risk of proximal tibia, following tumor curettage and cementation: An in-silico investigation.},
journal = {Computers in biology and medicine},
volume = {167},
number = {},
pages = {107564},
doi = {10.1016/j.compbiomed.2023.107564},
pmid = {37871436},
issn = {1879-0534},
mesh = {Humans ; Tibia/diagnostic imaging/surgery ; Cementation ; *Fractures, Bone/pathology/surgery ; Bone Cements ; Curettage ; *Neoplasms/pathology ; Biomechanical Phenomena ; },
abstract = {Even though, proximal tibia is a common site of giant cell tumor and bone fractures, following tumor removal, nonetheless very little attention has been paid to affecting factors on the fracture risk. Here, nonlinear voxel-based finite element models based on computed tomography images were developed to predict bone fracture load with defects with different sizes, which were located in the medial, lateral, anterior, and posterior region of the proximal tibia. Critical defect size was identified using One-sample t-test to assess if the mean difference between the bone strength for a defect size was significantly different from the intact bone strength. Then, the defects larger than critical size were reconstructed with cement and the mechanics of the bone-cement interface (BCI) was investigated to find the regions prone to separation at BCI. A significant increase in fracture risk was observed for the defects larger than 20 mm, which were located in the medial, lateral and anterior regions, and defects larger than 25 mm for those located in the posterior region of the proximal tibia. Furthermore, it was found that the highest and lowest fracture risks were associated with defects located in the medial and posterior regions, respectively, highlighting the importance of selecting the initial location of a cortical window for tumor removal by the surgeon. The results of the BCI analysis showed that the location and size of the cement had a direct impact on the extent of damage and its distribution. Identification of critical regions susceptible to separation at BCI, can provide critical comments to surgeons in selecting the optimal cement augmentation technique, which may ultimately prevent unnecessary surgical intervention, such as using screws and pins.},
}
@article {pmid37870175,
year = {2023},
author = {Tian, Y and Yin, J and Wang, C and He, Z and Xie, J and Feng, X and Zhou, Y and Ma, T and Xie, Y and Li, X and Yang, T and Ren, C and Li, C and Zhao, Z},
title = {An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {33},
pages = {e2302333},
pmid = {37870175},
issn = {2198-3844},
support = {2022ZD0210300//National Science and Technology Innovation 2030 Major Program/ ; 2018SHZDZX05//Shanghai Municipal Science and Technology Major Project/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; LG202105-01-06//Lingang Laboratory/ ; },
mesh = {Animals ; Electrodes ; *Brain ; *Primates ; Single-Cell Analysis ; },
abstract = {Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.},
}
@article {pmid37869141,
year = {2023},
author = {Cao, P and Shi, D and Li, D and Zhu, Z and Zhu, J and Zhang, J and Bai, R},
title = {Modeling and in vivo experimental validation of 1,064 nm laser interstitial thermal therapy on brain tissue.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1237394},
pmid = {37869141},
issn = {1664-2295},
abstract = {INTRODUCTION: Laser interstitial thermal therapy (LITT) at 1064 nm is widely used to treat epilepsy and brain tumors; however, no numerical model exists that can predict the ablation region with careful in vivo validation.<br><br>METHODS: In this study, we proposed a model with a system of finite element methods simulating heat transfer inside the brain tissue, radiative transfer from the applicator into the brain tissue, and a model for tissue damage.<br><br>RESULTS: To speed up the computation for practical applications, we also validated P1-approximation as an efficient and fast method for calculating radiative transfer by comparing it with Monte Carlo simulation. Finally, we validated the proposed numerical model in vivo on six healthy canines and eight human patients with epilepsy and found strong agreement between the predicted temperature profile and ablation area and the magnetic resonance imaging-measured results.<br><br>DISCUSSION: Our results demonstrate the feasibility and reliability of the model in predicting the ablation area of 1,064&#x2009;nm LITT, which is important for presurgical planning when using LITT.},
}
@article {pmid37868195,
year = {2023},
author = {Liang, R and Wang, L and Yang, Q and Xu, Q and Sun, S and Zhou, H and Zhao, M and Gao, J and Zheng, C and Yang, J and Ming, D},
title = {Time-course adaptive changes in hippocampal transcriptome and synaptic function induced by simulated microgravity associated with cognition.},
journal = {Frontiers in cellular neuroscience},
volume = {17},
number = {},
pages = {1275771},
pmid = {37868195},
issn = {1662-5102},
abstract = {INTRODUCTION: The investigation of cognitive function in microgravity, both short-term and long-term, remains largely descriptive. And the underlying mechanisms of the changes over time remain unclear.<br><br>METHODS: Behavioral tests, electrophysiological recording, and RNA sequencing were used to observe differences in behavior, synaptic plasticity, and gene expression.<br><br>RESULTS: Initially, we measured the performance of spatial cognition exposed to long-term simulated microgravity (SM). Both working memory and advanced cognitive abilities were enhanced. Somewhat surprisingly, the synaptic plasticity of the hippocampal CA3-CA1 synapse was impaired. To gain insight into the mechanism of changing regularity over time, transcriptome sequencing in the hippocampus was performed. The analysis identified 20 differentially expressed genes (DEGs) in the hippocampus after short-term modeling, 19 of which were up-regulated. Gene Ontology (GO) analysis showed that these up-regulated genes were mainly enriched in synaptic-related processes, such as Stxbp5l and Epha6. This might be related to the enhancement of working memory performance under short-term SM exposure. Under exposure to long-term SM, 7 DEGs were identified in the hippocampus, all of which were up-regulated and related to oxidative stress and metabolism, such as Depp1 and Lrg1. Compensatory effects occurred with increased modeling time.<br><br>DISCUSSION: To sum up, our current research indicates that the cognitive function under SM exposure is consistently maintained or potentially even being enhanced over both short and long durations. The underlying mechanisms are intricate and potentially linked to the differential expression of hippocampal-associated genes and alterations in synaptic function, with these effects being time-dependent. The present study will lay the experimental and theoretical foundation of the multi-level mechanism of cognitive function under space flight.},
}
@article {pmid37867413,
year = {2024},
author = {Xu, J and Zu, T and Hsu, YC and Wang, X and Chan, KWY and Zhang, Y},
title = {Accelerating CEST imaging using a model-based deep neural network with synthetic training data.},
journal = {Magnetic resonance in medicine},
volume = {91},
number = {2},
pages = {583-599},
doi = {10.1002/mrm.29889},
pmid = {37867413},
issn = {1522-2594},
support = {//MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; 81971605//National Natural Science Foundation of China/ ; 2022C04031//Key R&amp;D Program of Zhejiang Province/ ; 2020R01003//Leading Innovation and Entrepreneurship Team of Zhejiang Province/ ; },
mesh = {Humans ; *Protons ; Retrospective Studies ; Algorithms ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer ; *Brain Neoplasms/diagnostic imaging ; Amides ; Image Processing, Computer-Assisted/methods ; },
abstract = {PURPOSE: To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data.<br><br>THEORY AND METHODS: Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-&#x3c9; domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods.<br><br>RESULTS: The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used.<br><br>CONCLUSIONS: The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.},
}
@article {pmid37865081,
year = {2023},
author = {Wu, F and Wu, J and Chen, X and Zhou, J and Du, Z and Tong, D and Zhang, H and Huang, Y and Yang, Y and Du, A and Ma, G},
title = {A secreted BPTI/Kunitz inhibitor domain-containing protein of barber's pole worm interacts with host NLRP3 inflammasome activation-associated G protein subunit to inhibit IL-1β and IL-18 maturation in vitro.},
journal = {Veterinary parasitology},
volume = {323},
number = {},
pages = {110052},
doi = {10.1016/j.vetpar.2023.110052},
pmid = {37865081},
issn = {1873-2550},
mesh = {Animals ; *Interleukin-1beta/metabolism/genetics ; *Helminth Proteins/genetics/metabolism/chemistry ; *Haemonchus/genetics ; *Inflammasomes/metabolism/genetics ; *Interleukin-18/metabolism/genetics ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism/genetics ; Sheep ; Humans ; Host-Parasite Interactions ; },
abstract = {Protease inhibitors are major components of excretory/secretory products released by parasitic nematodes and have been proposed to play roles in host-parasite interactions. Haemonchus contortus (the barber's pole worm) encodes for several serine protease inhibitors, and in a previous study we identified a trypsin inhibitor-like serine protease inhibitor of this blood-feeding nematode, SPI-I8, as necessary for anticoagulation. Here, we demonstrated that a bovine pancreatic trypsin inhibitor/Kunitz-type serine protease inhibitor (BPTI/Kunitz) domain-containing protein highly expressed in parasitic stages, HCON_00133150, is involved in suppressing proinflammatory cytokine production in mammalian cells. Fluorescent labelling of HCON_00133150 revealed a punctate localisation at the inner hypodermal membrane of H. contortus, an organ closely related to the excretory column. Yeast two-hybrid screening and immunoprecipitation-mass spectrometry identified that the recombinant HCON_00133150 physically interacted with a range of host proteins including the G protein subunit beta 1 of sheep (Ovis aries; OaGNB1), a negative regulator of NLRP3 inflammasome activation. Interestingly, heterologous expression of HCON_00133150 enhanced the inhibitory effect of OaGNB1 on NLRP3 inflammasome and the maturation of proinflammatory cytokines IL-1β and IL-18 in transfected cells. 1-to-1 orthologues (n = 33) of BPTI/Kunitz inhibitor domain-containing proteins were predicted in clades III, IV and V (but not clade I) parasitic nematodes. Structural (tandem BPTI/Kunitz inhibitor domains inverted into the globular reticulation) and functional (a GNB1 enhancer) characterisation of HCON_00133150 and its orthologues elucidated that these molecules might contribute to immune suppression by parasitic nematodes in animals and humans.},
}
@article {pmid37864083,
year = {2024},
author = {Zhang, CK and Wang, P and Ji, YY and Zhao, JS and Gu, JX and Yan, XX and Fan, HW and Zhang, MM and Qiao, Y and Liu, XD and Li, BJ and Wang, MH and Dong, HL and Li, HH and Huang, PC and Li, YQ and Hou, WG and Li, JL and Chen, T},
title = {Potentiation of the lateral habenula-ventral tegmental area pathway underlines the susceptibility to depression in mice with chronic pain.},
journal = {Science China. Life sciences},
volume = {67},
number = {1},
pages = {67-82},
pmid = {37864083},
issn = {1869-1889},
mesh = {Mice ; Animals ; Ventral Tegmental Area/metabolism ; *Habenula/metabolism ; Depression ; *Chronic Pain ; gamma-Aminobutyric Acid/metabolism ; },
abstract = {Chronic pain often develops severe mood changes such as depression. However, how chronic pain leads to depression remains elusive and the mechanisms determining individuals' responses to depression are largely unexplored. Here we found that depression-like behaviors could only be observed in 67.9% of mice with chronic neuropathic pain, leaving 32.1% of mice with depression resilience. We determined that the spike discharges of the ventral tegmental area (VTA)-projecting lateral habenula (LHb) glutamatergic (Glu) neurons were sequentially increased in sham, resilient and susceptible mice, which consequently inhibited VTA dopaminergic (DA) neurons through a LHb[Glu]-VTA[GABA]-VTA[DA] circuit. Furthermore, the LHb[Glu]-VTA[DA] excitatory inputs were dampened via GABAB receptors in a pre-synaptic manner. Regulation of LHb-VTA pathway largely affected the development of depressive symptoms caused by chronic pain. Our study thus identifies a pivotal role of the LHb-VTA pathway in coupling chronic pain with depression and highlights the activity-dependent contribution of LHb[Glu]-to-VTA[DA] inhibition in depressive behavioral regulation.},
}
@article {pmid37863819,
year = {2023},
author = {Lv, S and He, E and Luo, J and Liu, Y and Liang, W and Xu, S and Zhang, K and Yang, Y and Wang, M and Song, Y and Wu, Y and Cai, X},
title = {Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {33},
pages = {e2301828},
pmid = {37863819},
issn = {2198-3844},
support = {L2224042//National Natural Science Foundation of China/ ; 61960206012//National Natural Science Foundation of China/ ; 62121003//National Natural Science Foundation of China/ ; T2293731//National Natural Science Foundation of China/ ; 62171434//National Natural Science Foundation of China/ ; 61975206//National Natural Science Foundation of China/ ; 61971400//National Natural Science Foundation of China/ ; 61973292//National Natural Science Foundation of China/ ; XK2022XXC003//Frontier Interdiscipline Project of the Chinese Academy of Sciences/ ; 2022YFC2402501//National Key Research and Development Program of China/ ; 2022YFB3205602//National Key Research and Development Program of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation/ ; GJJSTD20210004//Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; },
mesh = {Humans ; *Induced Pluripotent Stem Cells/metabolism ; Microelectrodes ; Neurons/metabolism ; *Neural Stem Cells ; *Nervous System Diseases ; },
abstract = {In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.},
}
@article {pmid37861815,
year = {2023},
author = {Yang, J and Song, H and Zhan, H and Ding, M and Luan, T and Chen, J and Wei, H and Wang, J},
title = {The influence of preoperative urodynamic parameters on clinical results in patients with benign prostatic hyperplasia after transurethral resection of the prostate.},
journal = {World journal of urology},
volume = {41},
number = {12},
pages = {3679-3685},
pmid = {37861815},
issn = {1433-8726},
mesh = {Male ; Humans ; *Transurethral Resection of Prostate/methods ; *Prostatic Hyperplasia/surgery/diagnosis ; Urodynamics ; Treatment Outcome ; Prostate/surgery ; *Urinary Bladder Neck Obstruction/surgery ; *Urinary Retention/surgery ; },
abstract = {PURPOSE: To identify the urodynamic parameters affecting the clinical outcomes of transurethral resection of the prostate(TURP) surgery for patients with benign prostatic hyperplasia(BPH) by multifactor analysis and establish a regression model with diagnostic values.<br><br>METHODS: The medical records of patients who underwent TURP surgery for BPH between December 2018 and September 2021 were collected from the urology department of the Second Affiliated Hospital of Kunming Medical University, Kunming, China. The patients' clinical data and urodynamic parameters were collected before surgery. The urodynamic parameters affecting surgical efficacy were identified by multifactor analysis, and a regression model with diagnostic values was established and evaluated.<br><br>RESULTS: A total of 201 patients underwent TURP, of whom 144 had complete preoperative urodynamic data. Each urodynamic factor was subjected to multifactor analysis, and the bladder contractility index (BCI), bladder outflow obstruction index (BOOI), bladder residual urine, and bladder compliance (BC) were found to be independent influence factors on the efficacy of TURP in patients with BPH. The diagnostic value of the regression model was analyzed by receiver operating characteristics (ROC) analysis, and it was found that the AUC&#x2009;=&#x2009;0.939 (95% CI 0.886-0.972), for which the sensitivity and specificity were 95.19% and 80%, respectively.<br><br>CONCLUSIONS: The regression model had high diagnostic sensitivity and specificity in predicting the efficacy of surgery, and the diagnostic value was higher than that of individual urodynamic factors. Therefore, BCI, BOOI, bladder residual urine, and BC should be considered as independent influence factors on the efficacy of TURP surgery for BPH.},
}
@article {pmid37859766,
year = {2023},
author = {Chen, R and Xu, G and Zhang, H and Zhang, X and Li, B and Wang, J and Zhang, S},
title = {A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1246940},
pmid = {37859766},
issn = {1662-4548},
abstract = {OBJECTIVE: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).<br><br>METHODS: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.<br><br>RESULTS: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.<br><br>CONCLUSION: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.<br><br>SIGNIFICANCE: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.},
}
@article {pmid37857827,
year = {2023},
author = {Zippi, EL and Shvartsman, GF and Vendrell-Llopis, N and Wallis, JD and Carmena, JM},
title = {Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {17810},
pmid = {37857827},
issn = {2045-2322},
support = {R01 MH117763/MH/NIMH NIH HHS/United States ; R01 NS106094/NS/NINDS NIH HHS/United States ; T32 NS095939/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Cadmium ; Prefrontal Cortex/physiology ; Learning ; },
abstract = {Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.},
}
@article {pmid37857637,
year = {2023},
author = {Ma, D and Zheng, Y and Li, X and Zhou, X and Yang, Z and Zhang, Y and Wang, L and Zhang, W and Fang, J and Zhao, G and Hou, P and Nan, F and Yang, W and Su, N and Gao, Z and Guo, J},
title = {Ligand activation mechanisms of human KCNQ2 channel.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {6632},
pmid = {37857637},
issn = {2041-1723},
mesh = {Humans ; *Anticonvulsants/pharmacology ; Cryoelectron Microscopy ; Ligands ; Membrane Potentials ; *Analgesics ; KCNQ2 Potassium Channel/chemistry/metabolism ; KCNQ3 Potassium Channel/metabolism ; },
abstract = {The human voltage-gated potassium channel KCNQ2/KCNQ3 carries the neuronal M-current, which helps to stabilize the membrane potential. KCNQ2 can be activated by analgesics and antiepileptic drugs but their activation mechanisms remain unclear. Here we report cryo-electron microscopy (cryo-EM) structures of human KCNQ2-CaM in complex with three activators, namely the antiepileptic drug cannabidiol (CBD), the lipid phosphatidylinositol 4,5-bisphosphate (PIP2), and HN37 (pynegabine), an antiepileptic drug in the clinical trial, in an either closed or open conformation. The activator-bound structures, along with electrophysiology analyses, reveal the binding modes of two CBD, one PIP2, and two HN37 molecules in each KCNQ2 subunit, and elucidate their activation mechanisms on the KCNQ2 channel. These structures may guide the development of antiepileptic drugs and analgesics that target KCNQ2.},
}
@article {pmid37856256,
year = {2023},
author = {Serrano-Amenos, C and Heydari, P and Liu, CY and Do, AH and Nenadic, Z},
title = {Power Budget of a Skull Unit in a Fully-Implantable Brain-Computer Interface: Bio-Heat Model.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4029-4039},
doi = {10.1109/TNSRE.2023.3323916},
pmid = {37856256},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Hot Temperature ; Skull ; Head ; Prostheses and Implants ; },
abstract = {The aim of this study is to estimate the maximum power consumption that guarantees the thermal safety of a skull unit (SU). The SU is part of a fully-implantable bi-directional brain computer-interface (BD-BCI) system that aims to restore walking and leg sensation to those with spinal cord injury (SCI). To estimate the SU power budget, we created a bio-heat model using the finite element method (FEM) implemented in COMSOL. To ensure that our predictions were robust against the natural variation of the model's parameters, we also performed a sensitivity analysis. Based on our simulations, we estimated that the SU can nominally consume up to 70 mW of power without raising the surrounding tissues' temperature above the thermal safety threshold of 1°C. When considering the natural variation of the model's parameters, we estimated that the power budget could range between 47 and 81 mW. This power budget should be sufficient to power the basic operations of the SU, including amplification, serialization and A/D conversion of the neural signals, as well as control of cortical stimulation. Determining the power budget is an important specification for the design of the SU and, in turn, the design of a fully-implantable BD-BCI system.},
}
@article {pmid37853123,
year = {2023},
author = {Ma, S and Chen, M and Jiang, Y and Xiang, X and Wang, S and Wu, Z and Li, S and Cui, Y and Wang, J and Zhu, Y and Zhang, Y and Ma, H and Duan, S and Li, H and Yang, Y and Lingle, CJ and Hu, H},
title = {Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb.},
journal = {Nature},
volume = {622},
number = {7984},
pages = {802-809},
pmid = {37853123},
issn = {1476-4687},
support = {R35 GM118114/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Mice ; *Antidepressive Agents/administration &amp; dosage/metabolism/pharmacokinetics/pharmacology ; *Depression/drug therapy/metabolism ; *Habenula/drug effects/metabolism ; Half-Life ; *Ketamine/administration &amp; dosage/metabolism/pharmacokinetics/pharmacology ; Neurons/physiology ; *Receptors, N-Methyl-D-Aspartate/antagonists &amp; inhibitors/metabolism ; Time Factors ; Protein Binding ; },
abstract = {Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist[1], has revolutionized the treatment of depression because of its potent, rapid and sustained antidepressant effects[2-4]. Although the elimination half-life of ketamine is only 13 min in mice[5], its antidepressant activities can last for at least 24 h[6-9]. This large discrepancy poses an interesting basic biological question and has strong clinical implications. Here we demonstrate that after a single systemic injection, ketamine continues to suppress burst firing and block NMDARs in the lateral habenula (LHb) for up to 24 h. This long inhibition of NMDARs is not due to endocytosis but depends on the use-dependent trapping of ketamine in NMDARs. The rate of untrapping is regulated by neural activity. Harnessing the dynamic equilibrium of ketamine-NMDAR interactions by activating the LHb and opening local NMDARs at different plasma ketamine concentrations, we were able to either shorten or prolong the antidepressant effects of ketamine in vivo. These results provide new insights into the causal mechanisms of the sustained antidepressant effects of ketamine. The ability to modulate the duration of ketamine action based on the biophysical properties of ketamine-NMDAR interactions opens up new opportunities for the therapeutic use of ketamine.},
}
@article {pmid37853020,
year = {2023},
author = {Phunruangsakao, C and Achanccaray, D and Bhattacharyya, S and Izumi, SI and Hayashibe, M},
title = {Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {17752},
pmid = {37853020},
issn = {2045-2322},
mesh = {Humans ; Feedback ; Photic Stimulation ; *Imagination/physiology ; Brain/physiology ; Imagery, Psychotherapy ; *Neurofeedback/methods ; Electroencephalography ; },
abstract = {The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain's functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain-computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the [Formula: see text] band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.},
}
@article {pmid37853010,
year = {2023},
author = {Sujatha Ravindran, A and Contreras-Vidal, J},
title = {An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {17709},
pmid = {37853010},
issn = {2045-2322},
mesh = {*Neural Networks, Computer ; *Deep Learning ; Reproducibility of Results ; Electroencephalography/methods ; Machine Learning ; Algorithms ; },
abstract = {Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In this study, we compared multiple model explanation methods to identify the most suitable method for EEG and understand when some of these approaches might fail. A simulation framework was developed to evaluate the robustness and sensitivity of twelve back-propagation-based visualization methods by comparing to ground truth features. Multiple methods tested here showed reliability issues after randomizing either model weights or labels: e.g., the saliency approach, which is the most used visualization technique in EEG, was not class or model-specific. We found that DeepLift was consistently accurate as well as robust to detect the three key attributes tested here (temporal, spatial, and spectral precision). Overall, this study provides a review of model explanation methods for DL-based neural decoders and recommendations to understand when some of these methods fail and what they can capture in EEG.},
}
@article {pmid37852957,
year = {2023},
author = {Huang, Q and Velthuis, H and Pereira, AC and Ahmad, J and Cooke, SF and Ellis, CL and Ponteduro, FM and Puts, NAJ and Dimitrov, M and Batalle, D and Wong, NML and Kowalewski, L and Ivin, G and Daly, E and Murphy, DGM and McAlonan, GM},
title = {Exploratory evidence for differences in GABAergic regulation of auditory processing in autism spectrum disorder.},
journal = {Translational psychiatry},
volume = {13},
number = {1},
pages = {320},
pmid = {37852957},
issn = {2158-3188},
support = {MR/N026063/1/MRC_/Medical Research Council/United Kingdom ; MR/W006251/1/MRC_/Medical Research Council/United Kingdom ; /DH_/Department of Health/United Kingdom ; },
mesh = {Adult ; Humans ; *Autism Spectrum Disorder ; Auditory Perception/physiology ; GABA-B Receptor Agonists/pharmacology/therapeutic use ; *Autistic Disorder ; gamma-Aminobutyric Acid ; },
abstract = {Altered reactivity and responses to auditory input are core to the diagnosis of autism spectrum disorder (ASD). Preclinical models implicate ϒ-aminobutyric acid (GABA) in this process. However, the link between GABA and auditory processing in humans (with or without ASD) is largely correlational. As part of a study of potential biosignatures of GABA function in ASD to inform future clinical trials, we evaluated the role of GABA in auditory repetition suppression in 66 adults (n = 28 with ASD). Neurophysiological responses (temporal and frequency domains) to repetitive standard tones and novel deviants presented in an oddball paradigm were compared after double-blind, randomized administration of placebo, 15 or 30 mg of arbaclofen (STX209), a GABA type B (GABAB) receptor agonist. We first established that temporal mismatch negativity was comparable between participants with ASD and those with typical development (TD). Next, we showed that temporal and spectral responses to repetitive standards were suppressed relative to responses to deviants in the two groups, but suppression was significantly weaker in individuals with ASD at baseline. Arbaclofen reversed weaker suppression of spectral responses in ASD but disrupted suppression in TD. A post hoc analysis showed that arbaclofen-elicited shift in suppression was correlated with autistic symptomatology measured using the Autism Quotient across the entire group, though not in the smaller sample of the ASD and TD group when examined separately. Thus, our results confirm: GABAergic dysfunction contributes to the neurophysiology of auditory sensory processing alterations in ASD, and can be modulated by targeting GABAB activity. These GABA-dependent sensory differences may be upstream of more complex autistic phenotypes.},
}
@article {pmid37851060,
year = {2024},
author = {Paudel, KR and Clarence, DD and Panth, N and Manandhar, B and De Rubis, G and Devkota, HP and Gupta, G and Zacconi, FC and Williams, KA and Pont, LG and Singh, SK and Warkiani, ME and Adams, J and MacLoughlin, R and Oliver, BG and Chellappan, DK and Hansbro, PM and Dua, K},
title = {Zerumbone liquid crystalline nanoparticles protect against oxidative stress, inflammation and senescence induced by cigarette smoke extract in vitro.},
journal = {Naunyn-Schmiedeberg's archives of pharmacology},
volume = {397},
number = {4},
pages = {2465-2483},
pmid = {37851060},
issn = {1432-1912},
mesh = {Animals ; Humans ; Mice ; Anti-Inflammatory Agents/pharmacology ; Antioxidants/pharmacology ; *Cigarette Smoking ; Inflammation ; *Nanoparticles ; NF-kappa B/metabolism ; Oxidative Stress ; *Sesquiterpenes ; },
abstract = {The purpose of this study was to evaluate the potential of zerumbone-loaded liquid crystalline nanoparticles (ZER-LCNs) in the protection of broncho-epithelial cells and alveolar macrophages against oxidative stress, inflammation and senescence induced by cigarette smoke extract in vitro. The effect of the treatment of ZER-LCNs on in vitro cell models of cigarette smoke extract (CSE)-treated mouse RAW264.7 and human BCi-NS1.1 basal epithelial cell lines was evaluated for their anti-inflammatory, antioxidant and anti-senescence activities using colorimetric and fluorescence-based assays, fluorescence imaging, RT-qPCR and proteome profiler kit. The ZER-LCNs successfully reduced the expression of pro-inflammatory markers including Il-6, Il-1β and Tnf-α, as well as the production of nitric oxide in RAW 264.7 cells. Additionally, ZER-LCNs successfully inhibited oxidative stress through reduction of reactive oxygen species (ROS) levels and regulation of genes, namely GPX2 and GCLC in BCi-NS1.1 cells. Anti-senescence activity of ZER-LCNs was also observed in BCi-NS1.1 cells, with significant reductions in the expression of SIRT1, CDKN1A and CDKN2A. This study demonstrates strong in vitro anti-inflammatory, antioxidative and anti-senescence activities of ZER-LCNs paving the path for this formulation to be translated into a promising therapeutic agent for chronic respiratory inflammatory conditions including COPD and asthma.},
}
@article {pmid37850195,
year = {2023},
author = {Mao, T and Chai, Y and Guo, B and Quan, P and Rao, H},
title = {Sleep Architecture and Sleep EEG Alterations are Associated with Impaired Cognition Under Sleep Restriction.},
journal = {Nature and science of sleep},
volume = {15},
number = {},
pages = {823-838},
pmid = {37850195},
issn = {1179-1608},
abstract = {PURPOSE: Many studies have investigated the cognitive, emotional, and other impairments caused by sleep restriction. However, few studies have explored the relationship between cognitive performance and changes in sleep structure and electroencephalography (EEG) during sleep. The present study aimed to examine whether changes in sleep structure and EEG can account for cognitive impairment caused by sleep restriction.<br><br>PATIENTS AND METHODS: Sixteen young adults spent five consecutive nights (adaptation 9h, baseline 8h, 1st restriction 6h, 2nd restriction 6h, and recovery 10h) in a sleep laboratory, with polysomnography recordings taken during sleep. Throughout waking periods in each condition, participants completed the psychomotor vigilance test (PVT), which measures vigilant attention, and the Go/No-Go task, which measures inhibition control.<br><br>RESULTS: The results showed that sleep restriction significantly decreased the proportion of N1 and N2 sleep, increased the proportion of N3 sleep, and reduced the time spent awake after sleep onset (WASO) and sleep onset latency. Poorer performance on the PVT and Go/No Go task was associated with longer WASO, a larger proportion of N3 sleep, and a smaller proportion of N2 sleep. Additionally, the power spectral density of delta waves significantly increased after sleep restriction, and this increase predicted a decrease in vigilance and inhibition control the next day.<br><br>CONCLUSION: These findings suggest that sleep architecture and EEG signatures may partially explain cognitive impairment caused by sleep restriction.},
}
@article {pmid37846847,
year = {2024},
author = {Távora-Vieira, D and Marino, R and Kuthubutheen, J and Broadbent, C and Acharya, A},
title = {Decision making in bone conduction and active middle ear implants - hearing outcomes and experiences over a 10-year period.},
journal = {Cochlear implants international},
volume = {25},
number = {1},
pages = {16-22},
doi = {10.1080/14670100.2023.2267900},
pmid = {37846847},
issn = {1754-7628},
mesh = {Humans ; Retrospective Studies ; Female ; Male ; Middle Aged ; *Bone Conduction ; Adult ; Aged ; *Ossicular Prosthesis ; Quality of Life ; Treatment Outcome ; Clinical Decision-Making ; Hearing Loss, Conductive/surgery/rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Decision Making ; Speech Perception ; Cochlear Implants ; Young Adult ; Hearing Aids ; },
abstract = {OBJECTIVES: To review the decision-making paradigm in the recommendations of BCI and aMEI overlapping candidacy for patients with conductive or mixed HL, and to determine if there are differences in hearing and quality of life outcomes between these implantable hearing devices.<br><br>METHODS: Retrospective data from patients receiving BCI or aMEI in the past decade were analysed. Patients were grouped into: 1. BCI candidates, 2. BCI or aMEI candidates, and 3. aMEI candidates. We compared outcomes and examined the impact of BC threshold, age at implantation, and duration of hearing loss on candidacy.<br><br>RESULTS: 89 participants were included: 30 BCI, 37 aMEI, and 22 BCI or aMEI candidates. All groups performed similarly in aided sound field threshold testing. BCI group had lower speech scores in quiet compared to 'BCI or aMEI.' No significant differences were found in APHAB global scores. BC threshold, duration of hearing loss, and age at implantation had no significant effects.<br><br>DISCUSSION: Outcomes were generally similar across groups, except for higher effective gain in the aMEI group.<br><br>CONCLUSION: Our proposed patient pathway and decision-making approach facilitate candidate selection for aMEI and BCI, aiming to optimise outcomes.},
}
@article {pmid37845811,
year = {2024},
author = {Holt, MW and Robinson, EC and Shlobin, NA and Hanson, JT and Bozkurt, I},
title = {Intracortical brain-computer interfaces for improved motor function: a systematic review.},
journal = {Reviews in the neurosciences},
volume = {35},
number = {2},
pages = {213-223},
pmid = {37845811},
issn = {2191-0200},
mesh = {Humans ; *Brain-Computer Interfaces ; *Motor Cortex/physiology/physiopathology ; },
abstract = {In this systematic review, we address the status of intracortical brain-computer interfaces (iBCIs) applied to the motor cortex to improve function in patients with impaired motor ability. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for Systematic Reviews. Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) and the Effective Public Health Practice Project (EPHPP) were used to assess bias and quality. Advances in iBCIs in the last two decades demonstrated the use of iBCI to activate limbs for functional tasks, achieve neural typing for communication, and other applications. However, the inconsistency of performance metrics employed by these studies suggests the need for standardization. Each study was a pilot clinical trial consisting of 1-4, majority male (64.28 %) participants, with most trials featuring participants treated for more than 12 months (55.55 %). The systems treated patients with various conditions: amyotrophic lateral sclerosis, stroke, spinocerebellar degeneration without cerebellar involvement, and spinal cord injury. All participants presented with tetraplegia at implantation and were implanted with microelectrode arrays via pneumatic insertion, with nearly all electrode locations solely at the precentral gyrus of the motor cortex (88.88 %). The development of iBCI devices using neural signals from the motor cortex to improve motor-impaired patients has enhanced the ability of these systems to return ability to their users. However, many milestones remain before these devices can prove their feasibility for recovery. This review summarizes the achievements and shortfalls of these systems and their respective trials.},
}
@article {pmid37845318,
year = {2023},
author = {Karas, K and Pozzi, L and Pedrocchi, A and Braghin, F and Roveda, L},
title = {Brain-computer interface for robot control with eye artifacts for assistive applications.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {17512},
pmid = {37845318},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotics ; Artifacts ; Electroencephalography/methods ; Eye Movements ; Algorithms ; User-Computer Interface ; },
abstract = {Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.},
}
@article {pmid37844567,
year = {2023},
author = {Rizzoglio, F and Altan, E and Ma, X and Bodkin, KL and Dekleva, BM and Solla, SA and Kennedy, A and Miller, LE},
title = {From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
pmid = {37844567},
issn = {1741-2552},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; R21 NS135413/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Humans ; Haplorhini ; Arm ; *Artificial Limbs ; Paralysis ; Movement/physiology ; *Brain-Computer Interfaces ; },
abstract = {Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.},
}
@article {pmid37841873,
year = {2023},
author = {Crone, N and Candrea, D and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, G and Nathan, K and Wester, B and Anderson, W and Rosenblatt, K and Clawson, L and Maragakis, N and Vansteensel, M and Tenore, F and Ramsey, N and Fifer, M and Uchil, A},
title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {37841873},
issn = {2693-5015},
support = {T32 HD007414/HD/NICHD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability.<br><br>METHODS: We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating.<br><br>RESULTS: Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day).<br><br>CONCLUSION: These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.},
}
@article {pmid37841689,
year = {2023},
author = {Yan, T and Suzuki, K and Kameda, S and Maeda, M and Mihara, T and Hirata, M},
title = {Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1260675},
pmid = {37841689},
issn = {1662-4548},
abstract = {BACKGROUND: Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalography (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far.<br><br>METHODS: In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15&#x2009;months. We evaluated the neural activities recorded from and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo.<br><br>RESULTS: Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density showed relatively stable signal quality after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. From gain factor analysis, we found that tissue proliferation under electrodes reduced the amplitude power of signals.<br><br>CONCLUSION: This study suggests that subdural ECoG may provide chronic signal recordings for future clinical applications and neuroscience research. This study also highlights the need to reduce proliferation of reactive tissue ventral to the electrodes to enhance long-term stability.},
}
@article {pmid37840917,
year = {2023},
author = {Zeng, P and Wang, T and Zhang, L and Guo, F},
title = {Exploring the causes of augmentation in restless legs syndrome.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1160112},
pmid = {37840917},
issn = {1664-2295},
abstract = {Long-term drug treatment for Restless Legs Syndrome (RLS) patients can frequently result in augmentation, which is the deterioration of symptoms with an increased drug dose. The cause of augmentation, especially derived from dopamine therapy, remains elusive. Here, we review recent research and clinical progress on the possible mechanism underlying RLS augmentation. Dysfunction of the dopamine system highly possibly plays a role in the development of RLS augmentation, as dopamine agonists improve desensitization of dopamine receptors, disturb receptor interactions within or outside the dopamine receptor family, and interfere with the natural regulation of dopamine synthesis and release in the neural system. Iron deficiency is also indicated to contribute to RLS augmentation, as low iron levels can affect the function of the dopamine system. Furthermore, genetic risk factors, such as variations in the BTBD9 and MEIS1 genes, have been linked to an increased risk of RLS initiation and augmentation. Additionally, circadian rhythm, which controls the sleep-wake cycle, may also contribute to the worsening of RLS symptoms and the development of augmentation. Recently, Vitamin D deficiency has been suggested to be involved in RLS augmentation. Based on these findings, we propose that the progressive reduction of selective receptors, influenced by various pathological factors, reverses the overcompensation of the dopamine intensity promoted by short-term, low-dose dopaminergic therapy in the development of augmentation. More research is needed to uncover a deeper understanding of the mechanisms underlying the RLS symptom and to develop effective RLS augmentation treatments.},
}
@article {pmid37840542,
year = {2023},
author = {Yang, X and Ballini, M and Sawigun, C and Hsu, WY and Weijers, JW and Putzeys, J and Lopez, CM},
title = {An AC-Coupled 1st-order Δ-ΔΣ Readout IC for Area-Efficient Neural Signal Acquisition.},
journal = {IEEE journal of solid-state circuits},
volume = {58},
number = {4},
pages = {949-960},
pmid = {37840542},
issn = {0018-9200},
support = {U01 NS115587/NS/NINDS NIH HHS/United States ; },
abstract = {The current demand for high-channel-count neural-recording interfaces calls for more area- and power-efficient readout architectures that do not compromise other electrical performances. In this paper, we present a miniature 128-channel neural recording integrated circuit (NRIC) for the simultaneous acquisition of local field potentials (LFPs) and action potentials (APs), which can achieve a very good compromise between area, power, noise, input range and electrode DC offset cancellation. An AC-coupled 1[st]-order digitally-intensive Δ-ΔΣ architecture is proposed to achieve this compromise and to leverage the advantages of a highly-scaled technology node. A prototype NRIC, including 128 channels, a newly-proposed area-efficient bulk-regulated voltage reference, biasing circuits and a digital control, has been fabricated in 22-nm FDSOI CMOS and fully characterized. Our proposed architecture achieves a total area per channel of 0.005 mm[2], a total power per channel of 12.57 μW, and an input-referred noise of 7.7 ± 0.4 μVrms in the AP band and 11.9 ± 1.1 μVrms in the LFP band. A very good channel-to-channel uniformity is demonstrated by our measurements. The chip has been validated in vivo, demonstrating its capability to successfully record full-band neural signals.},
}
@article {pmid37840396,
year = {2023},
author = {Cho, Y and Jeong, HH and Shin, H and Pak, CJ and Cho, J and Kim, Y and Kim, D and Kim, T and Kim, H and Kim, S and Kwon, S and Hong, JP and Suh, HP and Lee, S},
title = {Hybrid Bionic Nerve Interface for Application in Bionic Limbs.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {35},
pages = {e2303728},
pmid = {37840396},
issn = {2198-3844},
support = {RS-2020-KD000196//Korea Medical Device Development Fund/ ; },
mesh = {Animals ; Rabbits ; Electromyography ; *Bionics ; *Peripheral Nerves/physiology ; Prostheses and Implants ; Nerve Regeneration/physiology ; },
abstract = {Intuitive and perceptual neuroprosthetic systems require a high degree of neural control and a variety of sensory feedback, but reliable neural interfaces for long-term use that maintain their functionality are limited. Here, a novel hybrid bionic interface is presented, fabricated by integrating a biological interface (regenerative peripheral nerve interface (RPNI)) and a peripheral neural interface to enhance the neural interface performance between a nerve and bionic limbs. This interface utilizes a shape memory polymer buckle that can be easily implanted on a severed nerve and make contact with both the nerve and the muscle graft after RPNI formation. It is demonstrated that this interface can simultaneously record different signal information via the RPNI and the nerve, as well as stimulate them separately, inducing different responses. Furthermore, it is shown that this interface can record naturally evoked signals from a walking rabbit and use them to control a robotic leg. The long-term functionality and biocompatibility of this interface in rabbits are evaluated for up to 29 weeks, confirming its promising potential for enhancing prosthetic control.},
}
@article {pmid37840179,
year = {2023},
author = {Ji, Y and Ma, BJ and Guo, XQ and Dong, HP and Ma, K},
title = {[Discussion on the composition and implementation of diagnosis and treatment strategies for whole field pain management strategy].},
journal = {Zhonghua yi xue za zhi},
volume = {103},
number = {39},
pages = {3083-3087},
doi = {10.3760/cma.j.cn112137-20230704-01135},
pmid = {37840179},
issn = {0376-2491},
mesh = {Humans ; *Pain Management/methods ; *Chronic Pain/diagnosis/therapy ; Physical Therapy Modalities ; Emotions ; Cognition ; },
abstract = {Pain is the fifth major vital sign, and chronic pain is a large category of diseases that affects health seriously. At present, the incidence of chronic pain is high, but the overall treatment satisfaction is low. It is necessary to continuously optimize pain diagnosis and treatment strategies and improve the connotation of pain management. Based on the clinical practice of our pain center, combined with relevant literature, the article proposes a diagnosis and treatment strategy of "whole field pain management" should be carried out from the four dimensions of feeling, emotion, cognition, and behavior. Innovative digital pain diagnosis and treatment technologies such as VR/MR and brain-computer interface are used to regulate emotional, cognitive, and behavioral regulation, and combined with lifestyle changes, rehabilitation physiotherapy, drugs, and minimally invasive interventional therapy to constitute a " whole field pain management strategy" to explore the new development direction of further improving the management of chronic pain.},
}
@article {pmid37839711,
year = {2024},
author = {Luo, R and Mai, X and Meng, J},
title = {Effect of motion state variability on error-related potentials during continuous feedback paradigms and their consequences for classification.},
journal = {Journal of neuroscience methods},
volume = {401},
number = {},
pages = {109982},
doi = {10.1016/j.jneumeth.2023.109982},
pmid = {37839711},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; Feedback ; *Brain-Computer Interfaces ; Computer Simulation ; },
abstract = {BACKGROUND: An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback.<br><br>NEW METHOD: In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes.<br><br>RESULTS: The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance.<br><br>Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder.<br><br>CONCLUSION: The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.},
}
@article {pmid37832939,
year = {2024},
author = {Pancholi, S and Wachs, JP and Duerstock, BS},
title = {Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities.},
journal = {Annual review of biomedical engineering},
volume = {26},
number = {1},
pages = {1-24},
doi = {10.1146/annurev-bioeng-082222-012531},
pmid = {37832939},
issn = {1545-4274},
mesh = {Humans ; *Artificial Intelligence ; *Persons with Disabilities ; *Brain-Computer Interfaces ; *Self-Help Devices ; *Robotics/methods ; Natural Language Processing ; Activities of Daily Living ; Exoskeleton Device ; User-Computer Interface ; Software ; },
abstract = {Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Integrating artificial intelligence (AI) with various agents, including electronics, robotics, and software, has revolutionized AT, resulting in groundbreaking technologies such as mind-controlled exoskeletons, bionic limbs, intelligent wheelchairs, and smart home assistants. This article provides a review of various AI techniques that have helped those with physical disabilities, including brain-computer interfaces, computer vision, natural language processing, and human-computer interaction. The current challenges and future directions for AI-powered advanced technologies are also addressed.},
}
@article {pmid37830040,
year = {2023},
author = {Chen, W and Yang, H},
title = {Editorial: New challenges and future perspectives in motivation and reward.},
journal = {Frontiers in behavioral neuroscience},
volume = {17},
number = {},
pages = {1293938},
pmid = {37830040},
issn = {1662-5153},
}
@article {pmid37829725,
year = {2023},
author = {Crétot-Richert, G and De Vos, M and Debener, S and Bleichner, MG and Voix, J},
title = {Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {895094},
pmid = {37829725},
issn = {1662-4548},
abstract = {INTRODUCTION: As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.<br><br>METHODS: In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).<br><br>RESULTS AND DISCUSSION: Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.},
}
@article {pmid37829569,
year = {2023},
author = {Liu, YF and Wang, W and Chen, XF},
title = {Progress and prospects in flexible tactile sensors.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {11},
number = {},
pages = {1264563},
pmid = {37829569},
issn = {2296-4185},
abstract = {Flexible tactile sensors have the advantages of large deformation detection, high fault tolerance, and excellent conformability, which enable conformal integration onto the complex surface of human skin for long-term bio-signal monitoring. The breakthrough of flexible tactile sensors rather than conventional tactile sensors greatly expanded application scenarios. Flexible tactile sensors are applied in fields including not only intelligent wearable devices for gaming but also electronic skins, disease diagnosis devices, health monitoring devices, intelligent neck pillows, and intelligent massage devices in the medical field; intelligent bracelets and metaverse gloves in the consumer field; as well as even brain-computer interfaces. Therefore, it is necessary to provide an overview of the current technological level and future development of flexible tactile sensors to ease and expedite their deployment and to make the critical transition from the laboratory to the market. This paper discusses the materials and preparation technologies of flexible tactile sensors, summarizing various applications in human signal monitoring, robotic tactile sensing, and human-machine interaction. Finally, the current challenges on flexible tactile sensors are also briefly discussed, providing some prospects for future directions.},
}
@article {pmid37824505,
year = {2023},
author = {Yao, J and Cai, Z and Qian, Z and Yang, B},
title = {A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.},
journal = {PloS one},
volume = {18},
number = {10},
pages = {e0286821},
pmid = {37824505},
issn = {1932-6203},
mesh = {Humans ; *Urbanization ; *Water ; Rain ; },
abstract = {As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.},
}
@article {pmid37824324,
year = {2023},
author = {Wang, X and Liu, A and Wu, L and Guan, L and Chen, X},
title = {Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4135-4145},
doi = {10.1109/TNSRE.2023.3324148},
pmid = {37824324},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Neurologic Examination ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Algorithms ; },
abstract = {Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.},
}
@article {pmid37822240,
year = {2023},
author = {Laport, F and Dapena, A and Castro, PM and Iglesias, DI and Vazquez-Araujo, FJ},
title = {Eye State Detection Using Frequency Features from 1 or 2-Channel EEG.},
journal = {International journal of neural systems},
volume = {33},
number = {12},
pages = {2350062},
doi = {10.1142/S0129065723500624},
pmid = {37822240},
issn = {1793-6462},
mesh = {Humans ; *Electroencephalography/methods ; Brain ; Algorithms ; *Brain-Computer Interfaces ; Support Vector Machine ; },
abstract = {Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.},
}
@article {pmid37820859,
year = {2023},
author = {Hao, Z and Zhai, X and Peng, B and Cheng, D and Zhang, Y and Pan, Y and Dou, W},
title = {CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG.},
journal = {NeuroImage},
volume = {282},
number = {},
pages = {120405},
doi = {10.1016/j.neuroimage.2023.120405},
pmid = {37820859},
issn = {1095-9572},
mesh = {Humans ; *Brain ; *Stroke ; Electroencephalography ; },
abstract = {Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.},
}
@article {pmid37820004,
year = {2023},
author = {Ortiz-Catalan, M and Zbinden, J and Millenaar, J and D'Accolti, D and Controzzi, M and Clemente, F and Cappello, L and Earley, EJ and Mastinu, E and Kolankowska, J and Munoz-Novoa, M and Jönsson, S and Cipriani, C and Sassu, P and Brånemark, R},
title = {A highly integrated bionic hand with neural control and feedback for use in daily life.},
journal = {Science robotics},
volume = {8},
number = {83},
pages = {eadf7360},
doi = {10.1126/scirobotics.adf7360},
pmid = {37820004},
issn = {2470-9476},
mesh = {Humans ; Feedback ; *Quality of Life ; Bionics ; Titanium ; Feedback, Sensory/physiology ; *Robotics ; Electrodes, Implanted ; },
abstract = {Restoration of sensorimotor function after amputation has remained challenging because of the lack of human-machine interfaces that provide reliable control, feedback, and attachment. Here, we present the clinical implementation of a transradial neuromusculoskeletal prosthesis-a bionic hand connected directly to the user's nervous and skeletal systems. In one person with unilateral below-elbow amputation, titanium implants were placed intramedullary in the radius and ulna bones, and electromuscular constructs were created surgically by transferring the severed nerves to free muscle grafts. The native muscles, free muscle grafts, and ulnar nerve were implanted with electrodes. Percutaneous extensions from the titanium implants provided direct skeletal attachment and bidirectional communication between the implanted electrodes and a prosthetic hand. Operation of the bionic hand in daily life resulted in improved prosthetic function, reduced postamputation, and increased quality of life. Sensations elicited via direct neural stimulation were consistently perceived on the phantom hand throughout the study. To date, the patient continues using the prosthesis in daily life. The functionality of conventional artificial limbs is hindered by discomfort and limited and unreliable control. Neuromusculoskeletal interfaces can overcome these hurdles and provide the means for the everyday use of a prosthesis with reliable neural control fixated into the skeleton.},
}
@article {pmid37819985,
year = {2023},
author = {Wang, R and Chen, X and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A},
title = {Distributed feedforward and feedback cortical processing supports human speech production.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {42},
pages = {e2300255120},
pmid = {37819985},
issn = {1091-6490},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; R01 NS109367/NS/NINDS NIH HHS/United States ; R01 NS115929/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Speech ; Feedback ; *Temporal Lobe ; Acoustic Stimulation ; },
abstract = {Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback-related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.},
}
@article {pmid37818111,
year = {2023},
author = {Jia, G and Bai, S and Lin, Y and Wang, X and Zhu, L and Lyu, C and Sun, G and An, K and Roe, AW and Li, X and Gao, L},
title = {Representation of conspecific vocalizations in amygdala of awake marmosets.},
journal = {National science review},
volume = {10},
number = {11},
pages = {nwad194},
pmid = {37818111},
issn = {2053-714X},
abstract = {Human speech and animal vocalizations are important for social communication and animal survival. Neurons in the auditory pathway are responsive to a range of sounds, from elementary sound features to complex acoustic sounds. For social communication, responses to distinct patterns of vocalization are usually highly specific to an individual conspecific call, in some species. This includes the specificity of sound patterns and embedded biological information. We conducted single-unit recordings in the amygdala of awake marmosets and presented calls used in marmoset communication, calls of other species and calls from specific marmoset individuals. We found that some neurons (47/262) in the amygdala distinguished 'Phee' calls from vocalizations of other animals and other types of marmoset vocalizations. Interestingly, a subset of Phee-responsive neurons (22/47) also exhibited selectivity to one out of the three Phees from two different 'caller' marmosets. Our findings suggest that, while it has traditionally been considered the key structure in the limbic system, the amygdala also represents a critical stage of socially relevant auditory perceptual processing.},
}
@article {pmid37817699,
year = {2023},
author = {Zhao, Y and Xiong, D and Yang, B and Xia, S and Zhang, X},
title = {Application Of Multigene Panel Detection In Breast Cancer.},
journal = {JPMA. The Journal of the Pakistan Medical Association},
volume = {73},
number = {9},
pages = {1862-1868},
doi = {10.47391/JPMA.6830},
pmid = {37817699},
issn = {0030-9982},
mesh = {Humans ; Female ; *Breast Neoplasms/diagnosis/genetics/drug therapy ; Gene Expression Profiling ; Prognosis ; Chemotherapy, Adjuvant ; Precision Medicine ; },
abstract = {Precision medicine will be the direction of future medical development, especially in cancer diagnosis and treatment. With the deepening of breast cancer-related research, new factors related to diagnosis, treatment and prognosis are constantly being discovered. Researchers combine different factorsto form a multigene panel testing, guiding clinicians' decision-making. The application scope of multigene panel detection is constantly expanding. At present, it has been tried in the prognosis evaluation of lymph node-positive and human epidermal growth factor receptor 2-positive breast cancer patients and the early screening of breast cancer. With continuous technological advancement, there will be broader application prospects in the future. The current narrative review was planned to evaluate the recent advances in applying multigene panel testing in breast cancer cases.},
}
@article {pmid37816342,
year = {2023},
author = {Wei, Y and Wang, X and Luo, R and Mai, X and Li, S and Meng, J},
title = {Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ad01de},
pmid = {37816342},
issn = {1741-2552},
mesh = {Humans ; *Upper Extremity ; Electroencephalography/methods ; Hand ; Fingers ; *Brain-Computer Interfaces ; Movement ; },
abstract = {Objective.Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface. Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.Approach.To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and filter-bank common spatial pattern features was utilized to decode movement frequencies and limbs.Main results.At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f0) and its first harmonic (f1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14 ± 15.86% and 66.30 ± 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21 ± 7.44% and 80.44 ± 7.99%.Significance.Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.},
}
@article {pmid37815970,
year = {2023},
author = {Zhi, H and Yu, Z and Yu, T and Gu, Z and Yang, J},
title = {A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3988-3998},
doi = {10.1109/TNSRE.2023.3323325},
pmid = {37815970},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; Entropy ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.},
}
@article {pmid37815969,
year = {2023},
author = {Bi, J and Chu, M},
title = {TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3958-3967},
doi = {10.1109/TNSRE.2023.3323509},
pmid = {37815969},
issn = {1558-0210},
mesh = {Humans ; *Learning ; Upper Extremity ; Electroencephalography ; Algorithms ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {The limited number of brain-computer interface based on motor imagery (MI-BCI) instruction sets for different movements of single limbs makes it difficult to meet practical application requirements. Therefore, designing a single-limb, multi-category motor imagery (MI) paradigm and effectively decoding it is one of the important research directions in the future development of MI-BCI. Furthermore, one of the major challenges in MI-BCI is the difficulty of classifying brain activity across different individuals. In this article, the transfer data learning network (TDLNet) is proposed to achieve the cross-subject intention recognition for multiclass upper limb motor imagery. In TDLNet, the Transfer Data Module (TDM) is used to process cross-subject electroencephalogram (EEG) signals in groups and then fuse cross-subject channel features through two one-dimensional convolutions. The Residual Attention Mechanism Module (RAMM) assigns weights to each EEG signal channel and dynamically focuses on the EEG signal channels most relevant to a specific task. Additionally, a feature visualization algorithm based on occlusion signal frequency is proposed to qualitatively analyze the proposed TDLNet. The experimental results show that TDLNet achieves the best classification results on two datasets compared to CNN-based reference methods and transfer learning method. In the 6-class scenario, TDLNet obtained an accuracy of 65%±0.05 on the UML6 dataset and 63%±0.06 on the GRAZ dataset. The visualization results demonstrate that the proposed framework can produce distinct classifier patterns for multiple categories of upper limb motor imagery through signals of different frequencies. The ULM6 dataset is available at https://dx.doi.org/10.21227/8qw6-f578.},
}
@article {pmid37815966,
year = {2023},
author = {Mai, X and Ai, J and Wei, Y and Zhu, X and Meng, J},
title = {Phase-Locked Time-Shift Data Augmentation Method for SSVEP Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4096-4105},
doi = {10.1109/TNSRE.2023.3323351},
pmid = {37815966},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation ; Brain/physiology ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88±4.54 bits/min to 122.61±7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.},
}
@article {pmid37812934,
year = {2023},
author = {Tan, J and Zhang, X and Wu, S and Song, Z and Chen, S and Huang, Y and Wang, Y},
title = {Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ad017d},
pmid = {37812934},
issn = {1741-2552},
mesh = {Humans ; Rats ; Animals ; *Brain-Computer Interfaces ; Rats, Sprague-Dawley ; Learning/physiology ; Algorithms ; Prefrontal Cortex ; },
abstract = {Objectives. Coadaptive brain-machine interfaces (BMIs) allow subjects and external devices to adapt to each other during the closed-loop control, which provides a promising solution for paralyzed individuals. Previous studies have focused on either improving sensory feedback to facilitate subject learning or developing adaptive algorithms to maintain stable decoder performance. In this work, we aim to design an efficient coadaptive BMI framework which not only facilitates the learning of subjects on new tasks with designed sensory feedback, but also improves decoders' learning ability by extracting sensory feedback-induced evaluation information.Approach. We designed dynamic audio feedback during the trial according to the subjects' performance when they were trained to learn a new behavioral task. We compared the learning performance of two groups of Sprague Dawley rats, one with and the other without the designed audio feedback to show whether this audio feedback could facilitate the subjects' learning. Compared with the traditional closed-loop in BMI systems, an additional closed-loop involving medial prefrontal cortex (mPFC) activity was introduced into the coadaptive framework. The neural dynamics of audio-induced mPFC activity was analyzed to investigate whether a significant neural response could be triggered. This audio-induced response was then translated into reward expectation information to guide the learning of decoders on a new task. The multiday decoding performance of the decoders with and without audio-induced reward expectation was compared to investigate whether the extracted information could accelerate decoders to learn a new task.Main results. The behavior performance comparison showed that the average days for rats to achieve 80% well-trained behavioral performance was improved by 26.4% after introducing the designed audio feedback sequence. The analysis of neural dynamics showed that a significant neural response of mPFC activity could be elicited by the audio feedback and the visualization of audio-induced neural patterns was emerged and accompanied by the behavioral improvement of subjects. The multiday decoding performance comparison showed that the decoder taking the reward expectation information could achieve faster task learning by 33.8% on average across subjects.Significance. This study demonstrates that the designed audio feedback could improve the learning of subjects and the mPFC activity induced by audio feedback can be utilized to improve the decoder's learning efficiency on new tasks. The coadaptive framework involving mPFC dynamics in the closed-loop interaction can advance the BMIs into a more adaptive and efficient system with learning ability on new tasks.},
}
@article {pmid37812554,
year = {2023},
author = {Chen, W and Liang, W and Liu, X and Lu, Z and Wan, P and Chen, Z},
title = {A Low Noise Neural Recording Frontend IC With Power Management for Closed-Loop Brain-Machine Interface Application.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {17},
number = {5},
pages = {1050-1061},
doi = {10.1109/TBCAS.2023.3321297},
pmid = {37812554},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; Prostheses and Implants ; Electric Power Supplies ; Electrodes ; },
abstract = {Brain-machine Interface (BMI) with implantable bioelectronics systems can provide an alternative way to cure neural diseases, while a power management system plays an important role in providing a stable voltage supply for the implanted chip. a prototype system of power management integrated circuit (PMIC) with heavy load capability supplying artifacts tolerable neural recording integrated circuit (ATNR-IC) is presented in this work. A reverse nested miller compensation (RNMC) low dropout regulator (LDO) with a transient enhancer is proposed for the PMIC. The power consumption is 0.55 mW and 22.5 mW at standby (SB) and full stimulation (ST) load, respectively. For a full load transition, the overshoot and downshoot of the LDO are 110 mV and 71 mV, respectively, which help improve the load transient response during neural stimulation. With the load current peak-to-peak range is about 560 μA supplied by a 4-channel stimulator, the whole PMIC can output a stable 3.3 V supply voltage, which indicates that this PMIC can be extended for more stimulating channels' scenarios. When the ATNR-IC is supplied for presented PMIC through a voltage divider network, it can amplify the signal consisting of 1 mVpp simulated neural signal and 20 mVpp simulated artifact by 28 dB with no saturation.},
}
@article {pmid37811702,
year = {2024},
author = {Kim, H and Park, MK and Park, SN and Cho, HH and Choi, JY and Lee, CK and Lee, IW and Moon, IJ and Jung, JY and Jung, J and Lee, KY and Oh, JH and Park, HJ and Seo, JH and Song, JJ and Ha, J and Jang, JH and Choung, YH},
title = {Efficacy of the Bonebridge BCI602 for Adult Patients with Single-sided Deafness: A Prospective Multicenter Study.},
journal = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery},
volume = {170},
number = {2},
pages = {490-504},
doi = {10.1002/ohn.520},
pmid = {37811702},
issn = {1097-6817},
mesh = {Adult ; Humans ; Prospective Studies ; Bone Conduction ; Hearing ; *Deafness/surgery ; *Hearing Aids ; *Tinnitus ; *Speech Perception ; Treatment Outcome ; },
abstract = {OBJECTIVE: To investigate the safety and efficacy of a novel active transcutaneous bone conduction implant (BCI) device for patients with single-sided deafness (SSD).<br><br>STUDY DESIGN: Prospective cohort study.<br><br>SETTING: Tertiary referral hospitals.<br><br>METHODS: This prospective multicenter study was conducted at 15 institutions nationwide. Thirty adult (aged &#x2265;19 years) SSD patients were recruited. They underwent implantation of an active transcutaneous BCI device (Bonebridge BCI602). Objective outcomes included aided pure-tone thresholds, aided speech discrimination scores (SDSs), and the Hearing in Noise Test (HINT) and sound localization test results. The Bern Benefit in Single-Sided Deafness (BBSS) questionnaire, the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire, and the Tinnitus Handicap Inventory (THI) were used to measure subjective benefits.<br><br>RESULTS: The mean aided pure-tone threshold was 34.2 (11.3), mean (SD), dB HL at 500 to 4000&#x2009;Hz. The mean total BBSS score was 27.5 (13.8). All APHAB questionnaire domain scores showed significant improvements: ease of communication, 33.6 (23.2) versus 22.6 (21.3), P&#x2009;=&#x2009;.025; reverberation, 44.8 (16.6) versus 32.8 (15.9), P&#x2009;=&#x2009;.002; background noise, 55.5 (23.6) versus 35.2 (18.1), P&#x2009;<&#x2009;.001; and aversiveness, 36.7 (22.8) versus 25.8 (21.4), P&#x2009;=&#x2009;.028. Moreover, the THI scores were significantly reduced [47.4 (30.1) versus 31.1 (27.0), P&#x2009;=&#x2009;.003]. Congenital SSD was a significant factor of subjective benefit (-11.643; 95% confidence interval: -21.946 to -1.340).<br><br>CONCLUSION: The BCI602 active transcutaneous BCI device can provide functional hearing gain without any adverse effects and is a feasible option for acquired SSD patients with long-term deafness.},
}
@article {pmid37810762,
year = {2023},
author = {Friedrich, EVC and Neuper, C and Scherer, R},
title = {Editorial: Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity - volume II.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1280095},
pmid = {37810762},
issn = {1662-5161},
}
@article {pmid37809054,
year = {2022},
author = {Ramírez-Moreno, MA and Cruz-Garza, JG and Acharya, A and Chatufale, G and Witt, W and Gelok, D and Reza, G and Contreras-Vidal, JL},
title = {Brain-to-brain communication during musical improvisation: a performance case study.},
journal = {F1000Research},
volume = {11},
number = {},
pages = {989},
pmid = {37809054},
issn = {2046-1402},
mesh = {*Music ; Brain ; Brain Mapping/methods ; Electroencephalography ; Communication ; },
abstract = {Understanding and predicting others' actions in ecological settings is an important research goal in social neuroscience. Here, we deployed a mobile brain-body imaging (MoBI) methodology to analyze inter-brain communication between professional musicians during a live jazz performance. Specifically, bispectral analysis was conducted to assess the synchronization of scalp electroencephalographic (EEG) signals from three expert musicians during a three-part 45 minute jazz performance, during which a new musician joined every five minutes. The bispectrum was estimated for all musician dyads, electrode combinations, and five frequency bands. The results showed higher bispectrum in the beta and gamma frequency bands (13-50 Hz) when more musicians performed together, and when they played a musical phrase synchronously. Positive bispectrum amplitude changes were found approximately three seconds prior to the identified synchronized performance events suggesting preparatory cortical activity predictive of concerted behavioral action. Moreover, a higher amount of synchronized EEG activity, across electrode regions, was observed as more musicians performed, with inter-brain synchronization between the temporal, parietal, and occipital regions the most frequent. Increased synchrony between the musicians' brain activity reflects shared multi-sensory processing and movement intention in a musical improvisation task.},
}
@article {pmid37808091,
year = {2023},
author = {Liang, R and Zhang, X and Li, Q and Wei, L and Liu, H and Kumar, A and Leadingham, KMK and Punnoose, J and Garcia, LP and Manbachi, A},
title = {Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex.},
journal = {ArXiv},
volume = {},
number = {},
pages = {},
pmid = {37808091},
issn = {2331-8422},
abstract = {While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored. We propose an artificial neural network dubbed VISION, an acronym for "Visual Interface System for Imaging Output of Neural activity," to mimic the human brain and show how it can foster neuroscientific inquiries. Using visual and contextual inputs, this multimodal model predicts the brain's functional magnetic resonance imaging (fMRI) scan response to natural images. VISION successfully predicts human hemodynamic responses as fMRI voxel values to visual inputs with an accuracy exceeding state-of-the-art performance by 45%. We further probe the trained networks to reveal representational biases in different visual areas, generate experimentally testable hypotheses, and formulate an interpretable metric to associate these hypotheses with cortical functions. With both a model and evaluation metric, the cost and time burdens associated with designing and implementing functional analysis on the visual cortex could be reduced. Our work suggests that the evolution of computational models may shed light on our fundamental understanding of the visual cortex and provide a viable approach toward reliable brain-machine interfaces.},
}
@article {pmid37806513,
year = {2023},
author = {Chen, J and Wang, T and Zhou, Y and Hong, Y and Zhang, S and Zhou, Z and Jiang, A and Liu, D},
title = {Microglia trigger the structural plasticity of GABAergic neurons in the hippocampal CA1 region of a lipopolysaccharide-induced neuroinflammation model.},
journal = {Experimental neurology},
volume = {370},
number = {},
pages = {114565},
doi = {10.1016/j.expneurol.2023.114565},
pmid = {37806513},
issn = {1090-2430},
mesh = {Mice ; Animals ; *CA1 Region, Hippocampal ; *Lipopolysaccharides/toxicity ; Microglia ; Neuroinflammatory Diseases ; GABAergic Neurons ; Synapses/physiology ; Inflammation/chemically induced ; Hippocampus ; },
abstract = {It is well-established that microglia-mediated neuroinflammatory response involves numerous neuropsychiatric and neurodegenerative diseases. While the role of microglia in excitatory synaptic transmission has been widely investigated, the impact of innate immunity on the structural plasticity of GABAergic inhibitory synapses is not well understood. To investigate this, we established an inflammation model using lipopolysaccharide (LPS) and observed a prolonged microglial response in the hippocampal CA1 region of mice, which was associated with cognitive deficits in the open field test, Y-maze test, and novel object recognition test. Furthermore, we found an increased abundance of GABAergic interneurons and GABAergic synapse formation in the hippocampal CA1 region. The cognitive impairment caused by LPS injection could be reversed by blocking GABA receptor activity with (-)-Bicuculline methiodide. These findings suggest that the upregulation of GABAergic synapses induced by LPS-mediated microglial activation leads to cognitive dysfunction. Additionally, the depletion of microglia by PLX3397 resulted in a decrease in GABAergic interneurons and GABAergic inhibitory synapses, which blocked the cognitive decline induced by LPS. In conclusion, our findings indicate that excessive reinforcement of GABAergic inhibitory synapse formation via microglial activation contributes to LPS-induced cognitive impairment.},
}
@article {pmid37805540,
year = {2023},
author = {Wirth, C and Toth, J and Arvaneh, M},
title = {Bayesian learning from multi-way EEG feedback for robot navigation and target identification.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {16925},
pmid = {37805540},
issn = {2045-2322},
mesh = {*Neurofeedback ; *Robotics/methods ; Bayes Theorem ; *Brain-Computer Interfaces ; Brain/physiology ; Electroencephalography/methods ; },
abstract = {Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed EEG information: 4-way classification of movements is performed, including specific information regarding when the target is reached. Additionally, we classify whether targets are correctly identified. Our proposed Bayesian strategy infers the most likely target location from the brain's responses. The experimental results show that our novel use of detailed information facilitates a more efficient and robust system than the state-of-the-art. Furthermore, unlike state-of-the-art approaches, we show scalability of our proposed approach: By tuning parameters appropriately, our strategy correctly identifies 98% of targets, even in large search spaces.},
}
@article {pmid37805019,
year = {2023},
author = {Csaky, R and van Es, MWJ and Jones, OP and Woolrich, M},
title = {Interpretable many-class decoding for MEG.},
journal = {NeuroImage},
volume = {282},
number = {},
pages = {120396},
pmid = {37805019},
issn = {1095-9572},
support = {MR/T033371/1/MRC_/Medical Research Council/United Kingdom ; 106183/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; RG94383/RG89702/MRC_/Medical Research Council/United Kingdom ; MR/X00757X/1/MRC_/Medical Research Council/United Kingdom ; /DH_/Department of Health/United Kingdom ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; 215573/Z/19/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Magnetoencephalography/methods ; Brain ; Electroencephalography/methods ; Brain Mapping/methods ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimised for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal.},
}
@article {pmid37804238,
year = {2023},
author = {Ni, G and Xu, Z and Bai, Y and Zheng, Q and Zhao, R and Wu, Y and Ming, D},
title = {EEG-based assessment of temporal fine structure and envelope effect in mandarin syllable and tone perception.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {23},
pages = {11287-11299},
doi = {10.1093/cercor/bhad366},
pmid = {37804238},
issn = {1460-2199},
support = {2022YFF1202400//Key Technologies Research and Development Program of China/ ; 81971698//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Speech Perception ; Noise ; Timbre Perception ; Speech Acoustics ; Electroencephalography ; Acoustic Stimulation ; },
abstract = {In recent years, speech perception research has benefited from low-frequency rhythm entrainment tracking of the speech envelope. However, speech perception is still controversial regarding the role of speech envelope and temporal fine structure, especially in Mandarin. This study aimed to discuss the dependence of Mandarin syllables and tones perception on the speech envelope and the temporal fine structure. We recorded the electroencephalogram (EEG) of the subjects under three acoustic conditions using the sound chimerism analysis, including (i) the original speech, (ii) the speech envelope and the sinusoidal modulation, and (iii) the fine structure of time and the modulation of the non-speech (white noise) sound envelope. We found that syllable perception mainly depended on the speech envelope, while tone perception depended on the temporal fine structure. The delta bands were prominent, and the parietal and prefrontal lobes were the main activated brain areas, regardless of whether syllable or tone perception was involved. Finally, we decoded the spatiotemporal features of Mandarin perception from the microstate sequence. The spatiotemporal feature sequence of the EEG caused by speech material was found to be specific, suggesting a new perspective for the subsequent auditory brain-computer interface. These results provided a new scheme for the coding strategy of new hearing aids for native Mandarin speakers.},
}
@article {pmid37803622,
year = {2023},
author = {Yang, T and Zhang, P and Xing, L and Hu, J and Feng, R and Zhong, J and Li, W and Zhang, Y and Zhu, Q and Yang, Y and Gao, F and Qian, Z},
title = {Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram.},
journal = {Food research international (Ottawa, Ont.)},
volume = {173},
number = {Pt 1},
pages = {113311},
doi = {10.1016/j.foodres.2023.113311},
pmid = {37803622},
issn = {1873-7145},
mesh = {Humans ; *Taste/physiology ; *Scalp ; Taste Perception/physiology ; Brain ; Electroencephalography ; },
abstract = {Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.},
}
@article {pmid37803140,
year = {2024},
author = {Xu, LL and Xie, JQ and Shen, JJ and Ying, MD and Chen, XZ},
title = {Neuron-derived exosomes mediate sevoflurane-induced neurotoxicity in neonatal mice via transferring lncRNA Gas5 and promoting M1 polarization of microglia.},
journal = {Acta pharmacologica Sinica},
volume = {45},
number = {2},
pages = {298-311},
pmid = {37803140},
issn = {1745-7254},
mesh = {Animals ; Mice ; Sevoflurane/toxicity ; Microglia/metabolism ; Animals, Newborn ; *RNA, Long Noncoding/genetics/metabolism ; *Exosomes/metabolism ; Neurons/metabolism ; Cytokines/metabolism ; *MicroRNAs/genetics/metabolism ; },
abstract = {Sevoflurane exposure during rapid brain development induces neuronal apoptosis and causes memory and cognitive deficits in neonatal mice. Exosomes that transfer genetic materials including long non-coding RNAs (lncRNAs) between cells play a critical role in intercellular communication. However, the lncRNAs found in exosomes derived from neurons treated with sevoflurane and their potential role in promoting neurotoxicity remain unknown. In this study, we investigated the role of cross-talk of newborn mouse neurons with microglial cells in sevoflurane-induced neurotoxicity. Mouse hippocampal neuronal HT22 cells were exposed to sevoflurane, and then co-cultured with BV2 microglial cells. We showed that sevoflurane treatment markedly increased the expression of the lncRNA growth arrest-specific 5 (Gas5) in neuron-derived extracellular vesicles, which inhibited neuronal proliferation and induced neuronal apoptosis by promoting M1 polarization of microglia and the release of inflammatory cytokines. We further revealed that the exosomal lncRNA Gas5 significantly upregulated Foxo3 as a competitive endogenous RNA of miR-212-3p in BV2 cells, and activated the NF-κB pathway to promote M1 microglial polarization and the secretion of inflammatory cytokines, thereby exacerbating neuronal damage. In neonatal mice, intracranial injection of the exosomes derived from sevoflurane-treated neurons into the bilateral hippocampi significantly increased the proportion of M1 microglia, inhibited neuronal proliferation and promoted apoptosis, ultimately leading to neurotoxicity. Similar results were observed in vitro in BV2 cells treated with the CM from HT22 cells after sevoflurane exposure. We conclude that sevoflurane induces the transfer of lncRNA Gas5-containing exosomes from neurons, which in turn regulates the M1 polarization of microglia and contributes to neurotoxicity. Thus, modulating the expression of lncRNA Gas5 or the secretion of exosomes could be a strategy for addressing sevoflurane-induced neurotoxicity.},
}
@article {pmid37798868,
year = {2024},
author = {Yang, Y and Stewart, T and Zhang, C and Wang, P and Xu, Z and Jin, J and Huang, Y and Liu, Z and Lan, G and Liang, X and Sheng, L and Shi, M and Cai, Z and Zhang, J},
title = {Erythrocytic α-Synuclein and the Gut Microbiome: Kindling of the Gut-Brain Axis in Parkinson's Disease.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {39},
number = {1},
pages = {40-52},
doi = {10.1002/mds.29620},
pmid = {37798868},
issn = {1531-8257},
support = {82020108012//National Natural Science Foundation of China/ ; 81571226//National Natural Science Foundation of China/ ; 81671187//National Natural Science Foundation of China/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; 2020R01001//Leading Innovation and Entrepreneurship Team of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; //Nanhu Brain-computer Interface Institute/ ; },
mesh = {Animals ; Mice ; *Parkinson Disease/pathology ; alpha-Synuclein/metabolism ; *Gastrointestinal Microbiome ; Brain-Gut Axis ; Erythrocytes/metabolism/pathology ; Butyrates ; },
abstract = {BACKGROUND: Progressive spreading of α-synuclein via gut-brain axis has been hypothesized in the pathogenesis of Parkinson's disease (PD). However, the source of seeding-capable α-synuclein in the gastrointestinal tract (GIT) has not been fully investigated. Additionally, the mechanism by which the GIT microbiome contributes to PD pathogenesis remains to be characterized.<br><br>OBJECTIVES: We aimed to investigate whether blood-derived &#x3b1;-synuclein might contribute to PD pathology via a gut-driven pathway and involve GIT microbiota.<br><br>METHODS: The GIT expression of &#x3b1;-synuclein and the transmission of extracellular vesicles (EVs) derived from erythrocytes/red blood cells (RBCs), with their cargo &#x3b1;-synuclein, to the GIT were explored with various methods, including radioactive labeling of RBC-EVs and direct analysis of the transfer of &#x3b1;-synuclein protein. The potential role of microbiota on the EVs transmission was further investigated by administering butyrate, the short-chain fatty acids produced by gut microbiota and studying mice with different &#x3b1;-synuclein genotypes.<br><br>RESULTS: This study demonstrated that RBC-EVs can effectively transport &#x3b1;-synuclein to the GIT in a region-dependent manner, along with variations closely associated with regional differences in the expression of gut-vascular barrier markers. The investigation further revealed that the infiltration of &#x3b1;-synuclein into the GIT was influenced significantly by butyrate and &#x3b1;-synuclein genotypes, which may also affect the GIT microbiome directly.<br><br>CONCLUSION: By demonstrating the transportation of &#x3b1;-synuclein through RBC-EVs to the GIT, and its potential association with gut-vascular barrier markers and gut microbiome, this work highlights a potential mechanism by which RBC &#x3b1;-synuclein may impact PD initiation and/or progression. &#xa9; 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.},
}
@article {pmid37794039,
year = {2023},
author = {Chen, P and Liu, F and Lin, P and Li, P and Xiao, Y and Zhang, B and Pan, G},
title = {Open-loop analog programmable electrochemical memory array.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {6184},
pmid = {37794039},
issn = {2041-1723},
support = {61925603//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.},
}
@article {pmid37792662,
year = {2023},
author = {Cui, H and Chi, X and Wang, L and Chen, X},
title = {A High-Rate Hybrid BCI System Based on High-Frequency SSVEP and sEMG.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {12},
pages = {5688-5698},
doi = {10.1109/JBHI.2023.3321722},
pmid = {37792662},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electromyography ; Photic Stimulation/methods ; Electroencephalography/methods ; },
abstract = {Recently, various biosignals have been combined with electroencephalography (EEG) to build hybrid brain-computer interface (BCI) systems to improve system performance. Since steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) are easy-to-use, non-invasive techniques, and have high signal-to-noise ratio (SNR), hybrid BCI systems combining SSVEP and sEMG have received much attention in the BCI literature. However, most existing studies regarding hybrid BCIs based on SSVEP and sEMG adopt low-frequency visual stimuli to induce SSVEPs. The comfort of these systems needs further improvement to meet the practical application requirements. The present study realized a novel hybrid BCI combining high-frequency SSVEP and sEMG signals for spelling applications. EEG and sEMG were obtained simultaneously from the scalp and skin surface of subjects, respectively. These two types of signals were analyzed independently and then combined to determine the target stimulus. Our online results demonstrated that the developed hybrid BCI yielded a mean accuracy of 88.07 ± 1.43% and ITR of 159.12 ± 4.31 bits/min. These results exhibited the feasibility and effectiveness of fusing high-frequency SSVEP and sEMG towards improving the total BCI system performance.},
}
@article {pmid37792658,
year = {2023},
author = {Chen, X and An, J and Wu, H and Li, S and Liu, B and Wu, D},
title = {Front-End Replication Dynamic Window (FRDW) for Online Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3906-3914},
doi = {10.1109/TNSRE.2023.3321640},
pmid = {37792658},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; Electroencephalography ; Algorithms ; *Brain-Computer Interfaces ; },
abstract = {Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.},
}
@article {pmid37792657,
year = {2023},
author = {Zhang, X and Chen, S and Wang, Y},
title = {Kernel Reinforcement Learning-Assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {4125-4134},
doi = {10.1109/TNSRE.2023.3321756},
pmid = {37792657},
issn = {1558-0210},
mesh = {Humans ; Animals ; Rats ; *Algorithms ; Learning ; Reinforcement, Psychology ; *Brain-Computer Interfaces ; Movement ; Brain/physiology ; },
abstract = {Brain-Machine Interfaces (BMIs) assist paralyzed people to brain control (BC) the neuro-prosthesis continuously moving in space. During the BC process, the subject imagines the movement of the real limb and adapts the brain activity according to the sensory feedback. The neural adaptation in the closed-loop control results in complex and changing brain signals. Simultaneously, the decoder interprets the time-varying functional mapping between neural activity and continuous trajectory. It is crucial and challenging to accurately and adaptively track the mapping to help the subject accomplish the BC task with a stable performance. Existing Kalman Filter (KF) based decoders achieve continuous trajectory control by linearly interpreting neural firing observations into self-evolving prosthetic states. However, the linear neural-state mapping might not accurately reflect the movement intention of the subject. In this paper, we propose a novel method that allows subjects to achieve continuous brain control efficiently and stably. The proposed method incorporates a kernel reinforcement learning method into a state-observation model to decode the nonlinearly neural observation into a continuous trajectory state. The state transition function ensures the continuity of the prosthetic state. The kernel reinforcement learning allows the quick adaptation of the nonlinear neural-movement mapping during the BC process. The proposed method is tested in an online brain control reaching task for rats. Compared with KF, our method achieved more successful trials, faster response time, shorter inter-trial time, and remained stable over days. These results demonstrate that the proposed method is an efficient tool to assist subjects in brain control tasks.},
}
@article {pmid37792654,
year = {2023},
author = {Ma, G and Kang, J and Thompson, DE and Huggins, JE},
title = {BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3968-3977},
pmid = {37792654},
issn = {1558-0210},
support = {SB1 DC015142/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Evoked Potentials ; Movement ; },
abstract = {The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.},
}
@article {pmid37791058,
year = {2023},
author = {Hao, M and Fang, Q and Wu, B and Liu, L and Tang, H and Tian, F and Chen, L and Kong, D and Li, J},
title = {Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke.},
journal = {Open life sciences},
volume = {18},
number = {1},
pages = {20220724},
pmid = {37791058},
issn = {2391-5412},
abstract = {This article aimed to explore the rehabilitation efficacy of intelligent rehabilitation training systems in hemiplegic limb spasms after stroke and provided more theoretical basis for the application of intelligent rehabilitation systems in the rehabilitation of hemiplegic limb spasms after stroke. To explore the rehabilitation efficacy of intelligent rehabilitation training system (RTS for short here) in post-stroke hemiplegic limb spasms, this study selected 99 patients with post-stroke hemiplegic limb spasms admitted to a local tertiary hospital from March 2021 to March 2023 as the research subjects. This article used blind selection to randomly divide them into three groups: control group 1, control group 2, and study group, with 33 patients in each group. Control group 1 used a conventional RTS, group 2 used the brain-computer interface RTS from reference 9, and research group used the intelligent RTS from this article. This article compared the degree of spasticity, balance ability score, motor function score, and daily living activity score of three groups of patients after 10 weeks of treatment. After 10 weeks of treatment, the number of patients in the study group with no spasms at level 0 (24) was significantly higher than the number of patients in group 1 (7) and group 2 (10), with a statistically significant difference (P < 0.05); In the comparison of Barthel index scores, after ten weeks of treatment, the total number of people in the study group with scores starting at 71-80 and 81-100 was 23. The total number of people in the score range of 71-80 and 81-100 in group 1 was 5, while in group 2, the total number of people in this score range was 8. The study group scored considerably higher than the control group and the difference was found to be statistically relevant (P < 0.05). In the Berg balance assessment scale and motor function assessment scale, after 10 weeks of treatment, the scores of the study group patients on both scales were significantly higher than those of group 1 and group 2 (P < 0.05). The intelligent RTS is beneficial for promoting the improvement of spasticity in stroke patients with hemiplegic limb spasms, as well as improving their balance ability, motor ability, and daily life activities. Its rehabilitation effect is good.},
}
@article {pmid37790428,
year = {2023},
author = {Merk, T and Köhler, R and Peterson, V and Lyra, L and Vanhoecke, J and Chikermane, M and Binns, T and Li, N and Walton, A and Bush, A and Sisterson, N and Busch, J and Lofredi, R and Habets, J and Huebl, J and Zhu, G and Yin, Z and Zhao, B and Merkl, A and Bajbouj, M and Krause, P and Faust, K and Schneider, GH and Horn, A and Zhang, J and Kühn, A and Richardson, RM and Neumann, WJ},
title = {Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {37790428},
issn = {2693-5015},
support = {R01 MH113929/MH/NIMH NIH HHS/United States ; R01 NS110424/NS/NINDS NIH HHS/United States ; R01 NS127892/NS/NINDS NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.},
}
@article {pmid37790422,
year = {2023},
author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and , and Varol, E and Hurwitz, C and Paninski, L},
title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37790422},
issn = {2692-8205},
support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH128772/MH/NIMH NIH HHS/United States ; U19 NS104649/NS/NINDS NIH HHS/United States ; U19 NS123716/NS/NINDS NIH HHS/United States ; },
abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.},
}
@article {pmid37790319,
year = {2023},
author = {Jansen, R and Milaneschi, Y and Schranner, D and Kastenmuller, G and Arnold, M and Han, X and Dunlop, BW and , and Rush, AJ and Kaddurah-Daouk, R and Penninx, BW},
title = {The Metabolome-Wide Signature of Major Depressive Disorder.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {37790319},
issn = {2693-5015},
support = {R01 AG046171/AG/NIA NIH HHS/United States ; R01 MH108348/MH/NIMH NIH HHS/United States ; R01 AG069901/AG/NIA NIH HHS/United States ; U19 AG063744/AG/NIA NIH HHS/United States ; RF1 AG059093/AG/NIA NIH HHS/United States ; U01 AG061359/AG/NIA NIH HHS/United States ; RF1 AG057452/AG/NIA NIH HHS/United States ; RF1 AG058942/AG/NIA NIH HHS/United States ; RF1 AG051550/AG/NIA NIH HHS/United States ; },
abstract = {Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.},
}
@article {pmid37788912,
year = {2024},
author = {Wang, J and Li, Y and Qi, L and Mamtilahun, M and Liu, C and Liu, Z and Shi, R and Wu, S and Yang, GY},
title = {Advanced rehabilitation in ischaemic stroke research.},
journal = {Stroke and vascular neurology},
volume = {9},
number = {4},
pages = {328-343},
pmid = {37788912},
issn = {2059-8696},
mesh = {Humans ; *Stroke Rehabilitation ; *Ischemic Stroke/rehabilitation/diagnosis/physiopathology/therapy ; Animals ; *Recovery of Function ; Treatment Outcome ; *Robotics/instrumentation ; Disease Models, Animal ; Brain-Computer Interfaces ; Physical Therapy Modalities ; },
abstract = {At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke, the mortality of patients has been greatly reduced but the number of disabled survivors is increasing, and most of them are elderly patients. Physicians and rehabilitation therapists pay attention to develop all kinds of therapist techniques including physical therapy techniques, robot-assisted technology and artificial intelligence technology, and study the molecular, cellular or synergistic mechanisms of rehabilitation therapies to promote the effect of rehabilitation therapy. Here, we discussed different animal and in vitro models of ischaemic stroke for rehabilitation studies; the compound concept and technology of neurological rehabilitation; all kinds of biological mechanisms of physical therapy; the significance, assessment and efficacy of neurological rehabilitation; the application of brain-computer interface, rehabilitation robotic and non-invasive brain stimulation technology in stroke rehabilitation.},
}
@article {pmid37787386,
year = {2024},
author = {Moratti, S and Gundlach, C and de Echegaray, J and Müller, MM},
title = {Distinct patterns of spatial attentional modulation of steady-state visual evoked magnetic fields (SSVEFs) in subdivisions of the human early visual cortex.},
journal = {Psychophysiology},
volume = {61},
number = {2},
pages = {e14452},
doi = {10.1111/psyp.14452},
pmid = {37787386},
issn = {1469-8986},
support = {MU 972/24-1//Deutsche Forschungsgemeinschaft/ ; //Universidad Complutense de Madrid/ ; },
mesh = {Humans ; *Evoked Potentials, Visual ; *Visual Cortex/physiology ; Photic Stimulation ; Visual Fields ; Magnetic Fields ; Electroencephalography ; },
abstract = {In recent years, steady-state visual evoked potentials (SSVEPs) became an increasingly valuable tool to investigate neural dynamics of competitive attentional interactions and brain-computer interfaces. This is due to their good signal-to-noise ratio, allowing for single-trial analysis, and their ongoing oscillating nature that enables to analyze temporal dynamics of facilitation and suppression. Given the popularity of SSVEPs, it is surprising that only a few studies looked at the cortical sources of these responses. This is in particular the case when searching for studies that assessed the cortical sources of attentional SSVEP amplitude modulations. To address this issue, we used a typical spatial attention task and recorded neuromagnetic fields (MEG) while presenting frequency-tagged stimuli in the left and right visual fields, respectively. Importantly, we controlled for attentional deployment in a baseline period before the shifting cue. Subjects either attended to a central fixation cross or to two peripheral stimuli simultaneously. Results clearly showed that signal sources and attention effects were restricted to the early visual cortex: V1, V2, hMT+, precuneus, occipital-parietal, and inferior-temporal cortex. When subjects attended to central fixation first, shifting attention to one of the peripheral stimuli resulted in a significant activation increase for the to-be-attended stimulus with no activation decrease for the to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but significant SSVEF decreases from V1 to occipito-parietal cortex. When attention was first deployed to both rings, shifting attention away from one ring basically resulted in a significant activation decrease in all areas for the then-to-be-ignored stimulus.},
}
@article {pmid37786664,
year = {2023},
author = {Li, M and Qiu, M and Zhu, L and Kong, W},
title = {Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {5},
pages = {1271-1281},
pmid = {37786664},
issn = {1871-4080},
abstract = {Electroencephalogram(EEG) becomes popular in emotion recognition for its capability of selectively reflecting the real emotional states. Existing graph-based methods have made primary progress in representing pairwise spatial relationships, but leaving higher-order relationships among EEG channels and higher-order relationships inside EEG series. Constructing a hypergraph is a general way of representing higher-order relations. In this paper, we propose a spatial-temporal hypergraph convolutional network(STHGCN) to capture higher-order relationships that existed in EEG recordings. STHGCN is a two-block hypergraph convolutional network, in which feature hypergraphs are constructed over the spectrum, space, and time domains, to explore spatial and temporal correlations under specific emotional states, namely the correlations of EEG channels and the dynamic relationships of temporal stamps. What's more, a self-attention mechanism is combined with the hypergraph convolutional network to initialize and update the relationships of EEG series. The experimental results demonstrate that constructed feature hypergraphs can effectively capture the correlations among valuable EEG channels and the correlations inside valuable EEG series, leading to the best emotion recognition accuracy among the graph methods. In addition, compared with other competitive methods, the proposed method achieves state-of-art results on SEED and SEED-IV datasets.},
}
@article {pmid37786654,
year = {2023},
author = {Liang, W and Jin, J and Daly, I and Sun, H and Wang, X and Cichocki, A},
title = {Novel channel selection model based on graph convolutional network for motor imagery.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {5},
pages = {1283-1296},
pmid = {37786654},
issn = {1871-4080},
abstract = {Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.},
}
@article {pmid37786651,
year = {2023},
author = {Liu, X and Wang, K and Liu, F and Zhao, W and Liu, J},
title = {3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {5},
pages = {1357-1380},
pmid = {37786651},
issn = {1871-4080},
abstract = {Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.},
}
@article {pmid37782330,
year = {2023},
author = {Ye, C and Bai, Y and Zheng, S and Yu, H and Ni, G},
title = {OCT imaging of endolymphatic hydrops in mice: association with hearing loss.},
journal = {Acta oto-laryngologica},
volume = {143},
number = {9},
pages = {759-765},
doi = {10.1080/00016489.2023.2262509},
pmid = {37782330},
issn = {1651-2251},
mesh = {Animals ; Mice ; Tomography, Optical Coherence ; Mice, Inbred C57BL ; *Endolymphatic Hydrops/complications/diagnostic imaging ; *Meniere Disease/complications/diagnostic imaging ; *Hearing Loss/etiology/complications ; *Deafness/complications ; Vasopressins ; Magnetic Resonance Imaging/methods ; },
abstract = {BACKGROUND: The etiology of Ménière's disease (MD) is still not completely clear, but it is believed to be associated with endolymphatic hydrops (EH), which is characterized by auditory functional disorders. Vasopressin injection in C57BL/6J mice can induce EH and serve as a model for MD. Optical Coherence Tomography (OCT) has shown its advantages as a non-invasive imaging method for observing EH.AimInvestigating the relationship between hearing loss and EH to assist clinical hearing assessments and indicate the severity of hydrops.<br><br>METHODS: C57BL/6J mice received 50&#x2009;&#x3bc;g/100g/day vasopressin injections to induce EH. Auditory function was assessed using auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE). OCT was used to visualize the cochlea.<br><br>RESULT: OCT observed accumulation of fluid within the scala media in the cochlear apex. ABR showed significant hearing loss after 4&#x2009;weeks. DPOAE revealed low-frequency hearing loss at 2&#x2009;weeks and widespread damage across frequencies at 4&#x2009;weeks.<br><br>CONCLUSION: The development of hearing loss in mouse models of MD is consistent with EH manifestations.SignificanceThis study demonstrates the possibility of indirectly evaluating the extent of EH through auditory assessment and emphasizes the significant value of OCT for imaging cochlear structures.},
}
@article {pmid37781630,
year = {2023},
author = {Ye, J and Collinger, JL and Wehbe, L and Gaunt, R},
title = {Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37781630},
issn = {2692-8205},
support = {R01 NS121079/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
abstract = {The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control. Code: https://github.com/joel99/context_general_bci.},
}
@article {pmid37781027,
year = {2023},
author = {Kong, L and Guo, X and Shen, Y and Xu, L and Huang, H and Lu, J and Hu, S},
title = {Pushing the Frontiers: Optogenetics for Illuminating the Neural Pathophysiology of Bipolar Disorder.},
journal = {International journal of biological sciences},
volume = {19},
number = {14},
pages = {4539-4551},
pmid = {37781027},
issn = {1449-2288},
mesh = {Animals ; Humans ; *Bipolar Disorder/genetics/complications ; Optogenetics ; Central Nervous System ; },
abstract = {Bipolar disorder (BD), a disabling mental disorder, is featured by the oscillation between episodes of depression and mania, along with disturbance in the biological rhythms. It is on an urgent demand to identify the intricate mechanisms of BD pathophysiology. Based on the continuous progression of neural science techniques, the dysfunction of circuits in the central nervous system was currently thought to be tightly associated with BD development. Yet, challenge exists since it depends on techniques that can manipulate spatiotemporal dynamics of neuron activity. Notably, the emergence of optogenetics has empowered researchers with precise timing and local manipulation, providing a possible approach for deciphering the pathological underpinnings of mental disorders. Although the application of optogenetics in BD research remains preliminary due to the scarcity of valid animal models, this technique will advance the psychiatric research at neural circuit level. In this review, we summarized the crucial aberrant brain activity and function pertaining to emotion and rhythm abnormities, thereby elucidating the underlying neural substrates of BD, and highlighted the importance of optogenetics in the pursuit of BD research.},
}
@article {pmid37778215,
year = {2023},
author = {Mijani, A and Cherloo, MN and Tang, H and Zhan, L},
title = {Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI.},
journal = {Computers in biology and medicine},
volume = {166},
number = {},
pages = {107488},
doi = {10.1016/j.compbiomed.2023.107488},
pmid = {37778215},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual/physiology ; Male ; *Signal Processing, Computer-Assisted ; *Algorithms ; Adult ; Female ; Young Adult ; },
abstract = {The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.},
}
@article {pmid37776853,
year = {2023},
author = {Dong, Y and Li, Y and Xiang, X and Xiao, ZC and Hu, J and Li, Y and Li, H and Hu, H},
title = {Stress relief as a natural resilience mechanism against depression-like behaviors.},
journal = {Neuron},
volume = {111},
number = {23},
pages = {3789-3801.e6},
doi = {10.1016/j.neuron.2023.09.004},
pmid = {37776853},
issn = {1097-4199},
mesh = {Mice ; Animals ; *Nucleus Accumbens/physiology ; Depression ; *Resilience, Psychological ; Ventral Tegmental Area/physiology ; Reward ; },
abstract = {Relief, the appetitive state after the termination of aversive stimuli, is evolutionarily conserved. Understanding the behavioral role of this well-conserved phenomenon and its underlying neurobiological mechanisms are open and important questions. Here, we discover that the magnitude of relief from physical stress strongly correlates with individual resilience to depression-like behaviors in chronic stressed mice. Notably, blocking stress relief causes vulnerability to depression-like behaviors, whereas natural rewards supplied shortly after stress promotes resilience. Stress relief is mediated by reward-related mesolimbic dopamine neurons, which show minute-long, persistent activation after stress termination. Circuitry-wise, activation or inhibition of circuits downstream of the ventral tegmental area during the transient relief period bi-directionally regulates depression resilience. These results reveal an evolutionary function of stress relief in depression resilience and identify the neural substrate mediating this effect. Importantly, our data suggest a behavioral strategy of augmenting positive valence of stress relief with natural rewards to prevent depression.},
}
@article {pmid37774694,
year = {2023},
author = {Xie, Y and Wang, K and Meng, J and Yue, J and Meng, L and Yi, W and Jung, TP and Xu, M and Ming, D},
title = {Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfe9c},
pmid = {37774694},
issn = {1741-2552},
mesh = {*Imagery, Psychotherapy ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Machine Learning ; Imagination ; Algorithms ; },
abstract = {UNLABELLED: Objective.Deep learning (DL) models have been proven to be effective in decoding motor imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success relies heavily on large amounts of training data, whereas EEG data collection is laborious and time-consuming. Recently, cross-dataset transfer learning has emerged as a promising approach to meet the data requirements of DL models. Nevertheless, transferring knowledge across datasets involving different MI tasks remains a significant challenge in cross-dataset transfer learning, limiting the full utilization of valuable data resources.<br><br>APPROACH: This study proposes a pre-training-based cross-dataset transfer learning method inspired by Hard Parameter Sharing in multi-task learning. Different datasets with distinct MI paradigms are considered as different tasks, classified with shared feature extraction layers and individual task-specific layers to allow cross-dataset classification with one unified model. Then, Pre-training and fine-tuning are employed to transfer knowledge across datasets. We also designed four fine-tuning schemes and conducted extensive experiments on them.<br><br>MAIN RESULTS: The results showed that compared to models without pre-training, models with pre-training achieved a maximum increase in accuracy of 7.76%. Moreover, when limited training data were available, the pre-training method significantly improved DL model's accuracy by 27.34% at most. The experiments also revealed that pre-trained models exhibit faster convergence and remarkable robustness. The training time per subject could be reduced by up to 102.83 s, and the variance of classification accuracy decreased by 75.22% at best.<br><br>SIGNIFICANCE: This study represents the first comprehensive investigation of the cross-dataset transfer learning method between two datasets with different MI tasks. The proposed pre-training method requires only minimal fine-tuning data when applying DL models to new MI paradigms, making MI-Brain-computer interface more practical and user-friendly.},
}
@article {pmid37773070,
year = {2023},
author = {Ma, H and Khaled, HG and Wang, X and Mandelberg, NJ and Cohen, SM and He, X and Tsien, RW},
title = {Excitation-transcription coupling, neuronal gene expression and synaptic plasticity.},
journal = {Nature reviews. Neuroscience},
volume = {24},
number = {11},
pages = {672-692},
pmid = {37773070},
issn = {1471-0048},
support = {R01 MH071739/MH/NIMH NIH HHS/United States ; R01 NS024067/NS/NINDS NIH HHS/United States ; R01 NS125271/NS/NINDS NIH HHS/United States ; RM1 HG009491/HG/NHGRI NIH HHS/United States ; T32 MH019524/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Receptors, N-Methyl-D-Aspartate/metabolism ; *Neuronal Plasticity/physiology ; Long-Term Potentiation/physiology ; Neurons/metabolism ; Synapses/metabolism ; Gene Expression ; Hippocampus/physiology ; },
abstract = {Excitation-transcription coupling (E-TC) links synaptic and cellular activity to nuclear gene transcription. It is generally accepted that E-TC makes a crucial contribution to learning and memory through its role in underpinning long-lasting synaptic enhancement in late-phase long-term potentiation and has more recently been linked to late-phase long-term depression: both processes require de novo gene transcription, mRNA translation and protein synthesis. E-TC begins with the activation of glutamate-gated N-methyl-D-aspartate-type receptors and voltage-gated L-type Ca[2+] channels at the membrane and culminates in the activation of transcription factors in the nucleus. These receptors and ion channels mediate E-TC through mechanisms that include long-range signalling from the synapse to the nucleus and local interactions within dendritic spines, among other possibilities. Growing experimental evidence links these E-TC mechanisms to late-phase long-term potentiation and learning and memory. These advances in our understanding of the molecular mechanisms of E-TC mean that future efforts can focus on understanding its mesoscale functions and how it regulates neuronal network activity and behaviour in physiological and pathological conditions.},
}
@article {pmid37772806,
year = {2023},
author = {Wang, WS and Shi, ZW and Chen, XL and Li, Y and Xiao, H and Zeng, YH and Pi, XD and Zhu, LQ},
title = {Biodegradable Oxide Neuromorphic Transistors for Neuromorphic Computing and Anxiety Disorder Emulation.},
journal = {ACS applied materials &amp; interfaces},
volume = {15},
number = {40},
pages = {47640-47648},
doi = {10.1021/acsami.3c07671},
pmid = {37772806},
issn = {1944-8252},
mesh = {Humans ; *Oxides ; *Transistors, Electronic ; Brain ; Water ; Anxiety Disorders ; },
abstract = {Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting "neurosensitization", "primary and secondary fear", and "fear-adrenaline secretion-exacerbated fear". Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in "green" electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.},
}
@article {pmid37771349,
year = {2023},
author = {Xie, X and Zhang, D and Yu, T and Duan, Y and Daly, I and He, S},
title = {Editorial: Explainable and advanced intelligent processing in the brain-machine interaction.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1280281},
pmid = {37771349},
issn = {1662-5161},
}
@article {pmid37770286,
year = {2023},
author = {Fernyhough, C and Borghi, AM},
title = {Inner speech as language process and cognitive tool.},
journal = {Trends in cognitive sciences},
volume = {27},
number = {12},
pages = {1180-1193},
doi = {10.1016/j.tics.2023.08.014},
pmid = {37770286},
issn = {1879-307X},
support = {WT108720//Wellcome Trust/ ; RG12117A5D1EB0B3//Ourselves, Sapienza Excellence Projects/ ; },
mesh = {Humans ; *Speech ; *Language ; Cognition ; },
abstract = {Many people report a form of internal language known as inner speech (IS). This review examines recent growth of research interest in the phenomenon, which has broadly supported a theoretical model in which IS is a functional language process that can confer benefits for cognition in a range of domains. A key insight to have emerged in recent years is that IS is an embodied experience characterized by varied subjective qualities, which can be usefully modeled in artificial systems and whose neural signals have the potential to be decoded through advancing brain-computer interface technologies. Challenges for future research include understanding individual differences in IS and mapping form to function across IS subtypes.},
}
@article {pmid37769865,
year = {2023},
author = {Kim, YT and Park, BS and Yang, HR and Yi, S and Nam-Goong, IS and Kim, JG},
title = {Exploring the potential hypothalamic role in mediating cisplatin-induced negative energy balance.},
journal = {Chemico-biological interactions},
volume = {385},
number = {},
pages = {110733},
doi = {10.1016/j.cbi.2023.110733},
pmid = {37769865},
issn = {1872-7786},
abstract = {Cisplatin is a chemotherapeutic drug commonly used for treating different types of cancer. However, long-term use can lead to side effects, including anorexia, nausea, vomiting, and weight loss, which negatively affect the patient's quality of life and ability to undergo chemotherapy. This study aimed to investigate the mechanisms underlying the development of a negative energy balance during cisplatin treatment. Mice treated with cisplatin exhibit reduced food intake, body weight, and energy expenditure. We observed altered neuronal activity in the hypothalamic nuclei involved in the regulation of energy metabolism in cisplatin-treated mice. In addition, we observed activation of microglia and inflammation in the hypothalamus following treatment with cisplatin. Consistent with this finding, inhibition of microglial activation effectively rescued cisplatin-induced anorexia and body weight loss. The present study identified the role of hypothalamic neurons and inflammation linked to microglial activation in the anorexia and body weight loss observed during cisplatin treatment. These findings provide insight into the mechanisms underlying the development of metabolic abnormalities during cisplatin treatment and suggest new strategies for managing these side effects.},
}
@article {pmid37769525,
year = {2023},
author = {Brands, R and Tebart, N and Thommes, M and Bartsch, J},
title = {UV/Vis spectroscopy as an in-line monitoring tool for tablet content uniformity.},
journal = {Journal of pharmaceutical and biomedical analysis},
volume = {236},
number = {},
pages = {115721},
doi = {10.1016/j.jpba.2023.115721},
pmid = {37769525},
issn = {1873-264X},
abstract = {Continuous manufacturing provides advantages compared to batch manufacturing and is increasingly gaining importance in the pharmaceutical industry. In particular, the implementation of tablet processes in continuous plants is an important part of current research. For this, in-line real-time monitoring of product quality through process analytical technology (PAT) tools is crucial. This study focuses on an in-line UV/Vis spectroscopy method for monitoring the active pharmaceutical ingredient (API) content in tablets. UV/Vis spectroscopy is particularly advantageous here, because it allows univariate data analysis without complex data processing. Experiments were conducted on a rotary tablet press. The tablets consisted of 7- 13 wt% theophylline monohydrate as API, lactose monohydrate and magnesium stearate. Two tablet production rates were investigated, 7200 and 20000 tablets per hour. The UV/Vis probe was mounted at the ejection position and measurements were taken on the tablet sidewall. Validation was according to ICH Q2 with respect to specificity, linearity, precision, accuracy and range. The specificity for this formulation was proven and linearity was sufficient with coefficients of determination of 0.9891 for the low throughput and 0.9936 for the high throughput. Repeatability and intermediate precision were investigated. Both were sufficient, indicated by coefficients of variations with a maximum of 6.46% and 6.34%, respectively. The accuracy was evaluated by mean percent recovery. This showed a higher accuracy at 20000 tablets per hour than 7200 tablets per hour. However, both throughputs demonstrate sufficient accuracy. Finally, UV/Vis spectroscopy is a promising alternative to the common NIR and Raman Spectroscopy.},
}
@article {pmid37767723,
year = {2024},
author = {Quan, Z and Yang, Z and Tang, X and Fu, C and Zhou, X and Huang, L and Xia, L and Zhang, X},
title = {A double-tuned [1] H/[31] P coil for rabbit heart metabolism detection at 3 T.},
journal = {NMR in biomedicine},
volume = {37},
number = {2},
pages = {e5049},
doi = {10.1002/nbm.5049},
pmid = {37767723},
issn = {1099-1492},
support = {226-2022-00136//Fundamental Research Funds for the Central Universities/ ; 226-2023-00125//Fundamental Research Funds for the Central Universities/ ; BE2022049//Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//Key-Area R&amp;D Program of Guangdong Province/ ; 2018YFA0701400//National Key Research and Development Program of China/ ; 81873889//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; 52293424//National Natural Science Foundation of China/ ; 52277232//National Natural Science Foundation of China/ ; 2021ZD0200401//STI 2030 - Major Projects/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation of China/ ; },
mesh = {Animals ; Rabbits ; *Magnetic Resonance Imaging/methods ; Protons ; Equipment Design ; Magnetic Resonance Spectroscopy/methods ; *Diabetes Mellitus ; Phantoms, Imaging ; },
abstract = {Magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS) employing proton nuclear resonance has emerged as a pivotal modality in clinical diagnostics and fundamental research. Nonetheless, the scope of MRI/MRS extends beyond protons, encompassing nonproton nuclei that offer enhanced metabolic insights. A notable example is phosphorus-31 ([31] P) MRS, which provides valuable information on energy metabolites within the skeletal muscle and cardiac tissues of individuals affected by diabetes. This study introduces a novel double-tuned coil tailored for [1] H and [31] P frequencies, specifically designed for investigating cardiac metabolism in rabbits. The proposed coil design incorporates a butterfly-like coil for [31] P transmission, a four-channel array for [31] P reception, and an eight-channel array for [1] H reception, all strategically arranged on a body-conformal elliptic cylinder. To assess the performance of the double-tuned coil, a comprehensive evaluation encompassing simulations and experimental investigations was conducted. The simulation results demonstrated that the proposed [31] P transmit design achieved acceptable homogeneity and exhibited comparable transmit efficiency on par with a band-pass birdcage coil. In vivo experiments further substantiated the coil's efficacy, revealing that the rabbit with experimentally induced diabetes exhibited a lower phosphocreatine/adenosine triphosphate ratio compared with its normal counterpart. These findings emphasize the potential of the proposed coil design as a promising tool for investigating the therapeutic effects of novel diabetes drugs within the context of animal experimentation. Its capability to provide detailed metabolic information establishes it as an indispensable asset within this realm of research.},
}
@article {pmid37765965,
year = {2023},
author = {Chowdhury, RR and Muhammad, Y and Adeel, U},
title = {Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {18},
pages = {},
pmid = {37765965},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Brain ; Communication ; Neural Networks, Computer ; },
abstract = {A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.},
}
@article {pmid37765916,
year = {2023},
author = {Khare, SK and Bajaj, V and Gaikwad, NB and Sinha, GR},
title = {Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {18},
pages = {},
pmid = {37765916},
issn = {1424-8220},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Wavelet Analysis ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; },
abstract = {Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.},
}
@article {pmid37765751,
year = {2023},
author = {Zhang, C and Chu, H and Ma, M},
title = {Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {18},
pages = {},
pmid = {37765751},
issn = {1424-8220},
mesh = {*Algorithms ; Neural Networks, Computer ; Electroencephalography ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; },
abstract = {EEG decoding based on motor imagery is an important part of brain-computer interface technology and is an important indicator that determines the overall performance of the brain-computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain-computer interface technology research.},
}
@article {pmid37760412,
year = {2023},
author = {Marciniak, B and Kciuk, M and Mujwar, S and Sundaraj, R and Bukowski, K and Gruszka, R},
title = {In Vitro and In Silico Investigation of BCI Anticancer Properties and Its Potential for Chemotherapy-Combined Treatments.},
journal = {Cancers},
volume = {15},
number = {18},
pages = {},
pmid = {37760412},
issn = {2072-6694},
support = {2021/05/X/NZ3/01147//National Science Center/ ; },
abstract = {BACKGROUND: DUSP6 phosphatase serves as a negative regulator of MAPK kinases involved in numerous cellular processes. BCI has been identified as a potential allosteric inhibitor with anticancer activity. Our study was designed to test the anticancer properties of BCI in colon cancer cells, to characterize the effect of this compound on chemotherapeutics such as irinotecan and oxaliplatin activity, and to identify potential molecular targets for this inhibitor.<br><br>METHODS: BCI cytotoxicity, proapoptotic activity, and cell cycle distribution were investigated in vitro on three colon cancer cell lines (DLD1, HT-29, and Caco-2). In silico investigation was prepared to assess BCI drug-likeness and identify potential molecular targets.<br><br>RESULTS: The exposure of colorectal cancer cells with BCI resulted in antitumor effects associated with cell cycle arrest and induction of apoptosis. BCI exhibited strong cytotoxicity on DLD1, HT-29, and Caco-2 cells. BCI showed no significant interaction with irinotecan, but strongly attenuated the anticancer activity of oxaliplatin when administered together. Analysis of synergy potential further confirmed the antagonistic interaction between these two compounds. In silico investigation indicated CDK5 as a potential new target of BCI.<br><br>CONCLUSIONS: Our studies point to the anticancer potential of BCI but note the need for a precise mechanism of action.},
}
@article {pmid37759889,
year = {2023},
author = {Cui, Y and Xie, S and Fu, Y and Xie, X},
title = {Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.},
journal = {Brain sciences},
volume = {13},
number = {9},
pages = {},
pmid = {37759889},
issn = {2076-3425},
support = {62220106007//National Natural Science Foundation of China/ ; 2020ZDLGY04-01//Shaanxi Provincial Key R&amp;D Program/ ; },
abstract = {Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.},
}
@article {pmid37756271,
year = {2023},
author = {Nunes, JD and Vourvopoulos, A and Blanco-Mora, DA and Jorge, C and Fernandes, JC and Bermudez I Badia, S and Figueiredo, P},
title = {Brain activation by a VR-based motor imagery and observation task: An fMRI study.},
journal = {PloS one},
volume = {18},
number = {9},
pages = {e0291528},
pmid = {37756271},
issn = {1932-6203},
mesh = {Adult ; Humans ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; *Virtual Reality ; },
abstract = {Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.},
}
@article {pmid37756179,
year = {2024},
author = {Zhao, Z and Lin, Y and Wang, Y and Gao, X},
title = {Single-Trial EEG Classification Using Spatio-Temporal Weighting and Correlation Analysis for RSVP-Based Collaborative Brain Computer Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {2},
pages = {553-562},
doi = {10.1109/TBME.2023.3309255},
pmid = {37756179},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography ; Evoked Potentials ; Algorithms ; },
abstract = {OBJECTIVE: Since single brain computer interface (BCI) is limited in performance, it is necessary to develop collaborative BCI (cBCI) systems which integrate multi-user electroencephalogram (EEG) information to improve system performance. However, there are still some challenges in cBCI systems, including effective discriminant feature extraction of multi-user EEG data, fusion algorithms, time reduction of system calibration, etc. Methods: This study proposed an event-related potential (ERP) feature extraction and classification algorithm of spatio-temporal weighting and correlation analysis (STC) to improve the performance of cBCI systems. The proposed STC algorithm consisted of three modules. First, source extraction and interval modeling were used to overcome the problem of inter-trial variability. Second, spatio-temporal weighting and temporal projection were utilized to extract effective discriminant features for multi-user information fusion and cross-session transfer. Third, correlation analysis was conducted to match target/non-target templates for classification of multi-user and cross-session datasets.<br><br>RESULTS: The collaborative cross-session datasets of rapid serial visual presentation (RSVP) from 14 subjects were used to evaluate the performance of the EEG classification algorithm. For single-user/collaborative EEG classification of within-session and cross-session datasets, STC had significantly higher performance than the existing state-of-the-art machine learning algorithms.<br><br>CONCLUSION: It was demonstrated that STC was effective to improve the classification performance of multi-user collaboration and cross-session transfer for RSVP-based BCI systems, and was helpful to reduce the system calibration time.},
}
@article {pmid37753636,
year = {2023},
author = {Csaky, R and van Es, MWJ and Jones, OP and Woolrich, M},
title = {Group-level brain decoding with deep learning.},
journal = {Human brain mapping},
volume = {44},
number = {17},
pages = {6105-6119},
pmid = {37753636},
issn = {1097-0193},
support = {MR/T033371/1/MRC_/Medical Research Council/United Kingdom ; 106183/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; MR/X00757X/1/MRC_/Medical Research Council/United Kingdom ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; 215573/Z/19/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Deep Learning ; Brain/diagnostic imaging/physiology ; Magnetoencephalography/methods ; Brain Mapping/methods ; *Brain-Computer Interfaces ; },
abstract = {Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).},
}
@article {pmid37750334,
year = {2023},
author = {Chen, B and Jiang, L and Lu, G and Li, Y and Zhang, S and Huang, X and Xu, P and Li, F and Yao, D},
title = {Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {22},
pages = {11170-11180},
doi = {10.1093/cercor/bhad355},
pmid = {37750334},
issn = {1460-2199},
support = {#2022ZD0208500//STI 2030-Major Projects/ ; #62103085//National Natural Science Foundation of China/ ; HNBBL230203//Scientific Research of Brain Science and Brain Computer Interface Technology/ ; },
mesh = {Child ; Humans ; *Autism Spectrum Disorder ; *Facial Recognition/physiology ; Brain ; Evoked Potentials/physiology ; Electroencephalography ; },
abstract = {Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.},
}
@article {pmid37748558,
year = {2023},
author = {Feng, Z and Wang, S and Qian, L and Xu, M and Wu, K and Kakkos, I and Guan, C and Sun, Y},
title = {μ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates.},
journal = {NeuroImage},
volume = {282},
number = {},
pages = {120372},
doi = {10.1016/j.neuroimage.2023.120372},
pmid = {37748558},
issn = {1095-9572},
mesh = {Humans ; Bayes Theorem ; *Brain Mapping/methods ; *Electroencephalography/methods ; Magnetoencephalography/methods ; Algorithms ; Brain/diagnostic imaging/physiology ; },
abstract = {Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method μ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the μ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the μ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, &amp; SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiologically plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the μ-STAR model for source imaging in various applications.},
}
@article {pmid37748476,
year = {2023},
author = {Iivanainen, J and Carter, TR and Trumbo, MCS and McKay, J and Taulu, S and Wang, J and Stephen, JM and Schwindt, PDD and Borna, A},
title = {Single-trial classification of evoked responses to auditory tones using OPM- and SQUID-MEG.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfcd9},
pmid = {37748476},
issn = {1741-2552},
support = {U01 EB028656/EB/NIBIB NIH HHS/United States ; R01 EB013302/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Magnetoencephalography/methods ; *Evoked Potentials, Auditory/physiology ; Adult ; Male ; Female ; *Acoustic Stimulation/methods ; Young Adult ; },
abstract = {Objective.Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject's scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses.Approach.We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding.Main results.OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement.Significance.The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing.},
}
@article {pmid37748474,
year = {2023},
author = {Kaongoen, N and Choi, J and Woo Choi, J and Kwon, H and Hwang, C and Hwang, G and Kim, BH and Jo, S},
title = {The future of wearable EEG: a review of ear-EEG technology and its applications.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfcda},
pmid = {37748474},
issn = {1741-2552},
abstract = {Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.},
}
@article {pmid37747857,
year = {2024},
author = {Torre Tresols, JJ and Chanel, CPC and Dehais, F},
title = {POMDP-BCI: A Benchmark of (Re)Active BCI Using POMDP to Issue Commands.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {3},
pages = {792-802},
doi = {10.1109/TBME.2023.3318578},
pmid = {37747857},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Algorithms ; Markov Chains ; Brain/physiology ; Electroencephalography/methods ; },
abstract = {OBJECTIVE: Past research in Brain-Computer Interfaces (BCI) have presented different decoding algorithms for different modalities. Meanwhile, highly specific decision making processes have been developed for some of these modalities, while others lack such a component in their classic pipeline. The present work proposes a model based on Partially Observable Markov Decission Process (POMDP) that works as a high-level decision making framework for three different active/reactive BCI modalities.<br><br>METHODS: We tested our approach on three different BCI modalities using publicly available datasets. We compared the general POMDP model as a decision making process with state of the art methods for each BCI modality. Accuracy, false positive (FP) trials, no-action (NA) trials and average decision time are presented as metrics.<br><br>RESULTS: Our results show how the presented POMDP models achieve comparable or better performance to the presented baseline methods, while being usable for the three proposed experiments without significant changes. Crucially, it offers the possibility of taking no-action (NA) when the decoding does not perform well.<br><br>CONCLUSION: The present work implements a flexible POMDP model that acts as a sequential decision framework for BCI systems that lack such a component, and perform comparably to those that include it.<br><br>SIGNIFICANCE: We believe the proposed POMDP framework provides several interesting properties for future BCI developments, mainly the generalizability to any BCI modality and the possible integration of other physiological or brain data pipelines under a unified decision-making framework.},
}
@article {pmid37747230,
year = {2023},
author = {Khan, NN and Sweet, T and Harvey, CA and Warschausky, S and Huggins, JE and Thompson, DE},
title = {P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {199},
pages = {},
doi = {10.3791/64959},
pmid = {37747230},
issn = {1940-087X},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Mental Processes ; },
abstract = {Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.},
}
@article {pmid37746153,
year = {2023},
author = {Liu, T and Ye, A},
title = {Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1251968},
pmid = {37746153},
issn = {1662-4548},
abstract = {BACKGROUND: For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.<br><br>METHODS: The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.<br><br>RESULTS: The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.<br><br>CONCLUSION: The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.},
}
@article {pmid37746053,
year = {2023},
author = {Huang, Z and Liao, Z and Ou, G and Chen, L and Zhang, Y},
title = {Authentication using c-VEP evoked in a mild-burdened cognitive task.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1240451},
pmid = {37746053},
issn = {1662-5161},
abstract = {In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP.},
}
@article {pmid37745380,
year = {2023},
author = {Chen, X and Wang, R and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A},
title = {A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.09.16.558028},
pmid = {37745380},
issn = {2692-8205},
support = {R01 DC018805/DC/NIDCD NIH HHS/United States ; R01 NS109367/NS/NINDS NIH HHS/United States ; },
abstract = {Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring speech function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity, and high dimensionality, and the limited publicly available source code. Here, we present a novel deep learning-based neural speech decoding framework that includes an ECoG Decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable Speech Synthesizer that maps speech parameters to spectrograms. We develop a companion audio-to-audio auto-encoder consisting of a Speech Encoder and the same Speech Synthesizer to generate reference speech parameters to facilitate the ECoG Decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Among three neural network architectures for the ECoG Decoder, the 3D ResNet model has the best decoding performance (PCC=0.804) in predicting the original speech spectrogram, closely followed by the SWIN model (PCC=0.796). Our experimental results show that our models can decode speech with high correlation even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. We successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with speech deficits resulting from left hemisphere damage. Further, we use an occlusion analysis to identify cortical regions contributing to speech decoding across our models. Finally, we provide open-source code for our two-stage training pipeline along with associated preprocessing and visualization tools to enable reproducible research and drive research across the speech science and prostheses communities.},
}
@article {pmid37744725,
year = {2023},
author = {Kuo, CH and Tu, TH and Chen, KT},
title = {Editorial: Advanced technological applications in neurosurgery.},
journal = {Frontiers in surgery},
volume = {10},
number = {},
pages = {1277997},
pmid = {37744725},
issn = {2296-875X},
}
@article {pmid37741695,
year = {2023},
author = {Gao, Z},
title = {Adenosine A2A receptor and glia.},
journal = {International review of neurobiology},
volume = {170},
number = {},
pages = {29-48},
doi = {10.1016/bs.irn.2023.08.002},
pmid = {37741695},
issn = {2162-5514},
mesh = {Humans ; Astrocytes ; Microglia ; *Neuroglia/metabolism ; Neurons ; *Receptor, Adenosine A2A/metabolism ; },
abstract = {The adenosine A2A receptor (A2AR) is abundantly expressed in the brain, including both neurons and glial cells. While the expression of A2AR is relative low in glia, its levels elevate robustly in astrocytes and microglia under pathological conditions. Elevated A2AR appears to play a detrimental role in a number of disease states, by promoting neuroinflammation and astrocytic reaction to contribute to the progression of neurodegenerative and psychiatric diseases.},
}
@article {pmid37741227,
year = {2023},
author = {Lee, S and Kim, H and Kim, JB and Kim, DJ},
title = {Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.},
journal = {Computers in biology and medicine},
volume = {166},
number = {},
pages = {107435},
doi = {10.1016/j.compbiomed.2023.107435},
pmid = {37741227},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; *Hemiplegia/physiopathology/rehabilitation ; Male ; Female ; *Electroencephalography ; Middle Aged ; Adult ; Imagination/physiology ; Aged ; Motor Cortex/physiopathology ; Functional Laterality/physiology ; Paralysis/physiopathology/rehabilitation ; },
abstract = {Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.},
}
@article {pmid37741066,
year = {2023},
author = {Li, Z and Wang, X and Xing, Y and Zhang, X and Yu, T and Li, X},
title = {Measuring multivariate phase synchronization with symbolization and permutation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {167},
number = {},
pages = {838-846},
doi = {10.1016/j.neunet.2023.07.007},
pmid = {37741066},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; Brain/physiology ; *Epilepsy ; Seizures ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.},
}
@article {pmid37740917,
year = {2023},
author = {Zhou, T and Kawasaki, K and Suzuki, T and Hasegawa, I and Roe, AW and Tanigawa, H},
title = {Mapping information flow between the inferotemporal and prefrontal cortices via neural oscillations in memory retrieval and maintenance.},
journal = {Cell reports},
volume = {42},
number = {10},
pages = {113169},
doi = {10.1016/j.celrep.2023.113169},
pmid = {37740917},
issn = {2211-1247},
mesh = {*Prefrontal Cortex ; *Cerebral Cortex ; Memory, Short-Term ; Electrocorticography ; Brain Mapping ; },
abstract = {Interaction between the inferotemporal (ITC) and prefrontal (PFC) cortices is critical for retrieving information from memory and maintaining it in working memory. Neural oscillations provide a mechanism for communication between brain regions. However, it remains unknown how information flow via neural oscillations is functionally organized in these cortices during these processes. In this study, we apply Granger causality analysis to electrocorticographic signals from both cortices of monkeys performing visual association tasks to map information flow. Our results reveal regions within the ITC where information flow to and from the PFC increases via specific frequency oscillations to form clusters during memory retrieval and maintenance. Theta-band information flow in both directions increases in similar regions in both cortices, suggesting reciprocal information exchange in those regions. These findings suggest that specific subregions function as nodes in the memory information-processing network between the ITC and the PFC.},
}
@article {pmid37739947,
year = {2023},
author = {Wang, H and Zhang, X and Li, J and Li, B and Gao, X and Hao, Z and Fu, J and Zhou, Z and Atia, M},
title = {Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {15839},
pmid = {37739947},
issn = {2045-2322},
mesh = {Humans ; *Cognition ; *Prefrontal Cortex ; Brain ; Spectrum Analysis ; Autonomous Vehicles ; },
abstract = {For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.},
}
@article {pmid37738741,
year = {2023},
author = {Chen, K and Garcia Padilla, C and Kiselyov, K and Kozai, TDY},
title = {Cell-specific alterations in autophagy-lysosomal activity near the chronically implanted microelectrodes.},
journal = {Biomaterials},
volume = {302},
number = {},
pages = {122316},
pmid = {37738741},
issn = {1878-5905},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R01 NS096755/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Microelectrodes ; Electrodes, Implanted/adverse effects ; Autophagy/physiology ; *Neurodegenerative Diseases ; *Brain Injuries ; Lysosomes ; },
abstract = {Intracortical microelectrodes that can record and stimulate brain activity have become a valuable technique for basic science research and clinical applications. However, long-term implantation of these microelectrodes can lead to progressive neurodegeneration in the surrounding microenvironment, characterized by elevation in disease-associated markers. Dysregulation of autophagy-lysosomal degradation, a major intracellular waste removal process, is considered a key factor in the onset and progression of neurodegenerative diseases. It is plausible that similar dysfunctions in autophagy-lysosomal degradation contribute to tissue degeneration following implantation-induced focal brain injury, ultimately impacting recording performance. To understand how the focal, persistent brain injury caused by long-term microelectrode implantation impairs autophagy-lysosomal pathway, we employed two-photon microscopy and immunohistology. This investigation focused on the spatiotemporal characterization of autophagy-lysosomal activity near the chronically implanted microelectrode. We observed an aberrant accumulation of immature autophagy vesicles near the microelectrode over the chronic implantation period. Additionally, we found deficits in autophagy-lysosomal clearance proximal to the chronic implant, which was associated with an accumulation of autophagy cargo and a reduction in lysosomal protease level during the chronic period. Furthermore, our evidence demonstrates reactive astrocytes have myelin-containing lysosomes near the microelectrode, suggesting its role of myelin engulfment during acute implantation period. Together, this study sheds light on the process of brain tissue degeneration caused by long-term microelectrode implantation, with a specific focus on impaired intracellular waste degradation.},
}
@article {pmid37738340,
year = {2023},
author = {Abbasi, A and Lassagne, H and Estebanez, L and Goueytes, D and Shulz, DE and Ego-Stengel, V},
title = {Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.},
journal = {Science advances},
volume = {9},
number = {38},
pages = {eadh1328},
pmid = {37738340},
issn = {2375-2548},
mesh = {Humans ; Animals ; Mice ; Feedback ; *Brain-Computer Interfaces ; Learning ; *Motor Cortex ; Motor Neurons ; },
abstract = {Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.},
}
@article {pmid37737710,
year = {2023},
author = {Xin, J and Shi, Y and Zhang, X and Yuan, X and Xin, Y and He, H and Shen, J and Blankenship, RE and Xu, X},
title = {Carotenoid assembly regulates quinone diffusion and the Roseiflexus castenholzii reaction center-light harvesting complex architecture.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {37737710},
issn = {2050-084X},
mesh = {Cytoplasm ; *Quinones ; *Carotenoids ; Chloroflexi ; },
abstract = {Carotenoid (Car) pigments perform central roles in photosynthesis-related light harvesting (LH), photoprotection, and assembly of functional pigment-protein complexes. However, the relationships between Car depletion in the LH, assembly of the prokaryotic reaction center (RC)-LH complex, and quinone exchange are not fully understood. Here, we analyzed native RC-LH (nRC-LH) and Car-depleted RC-LH (dRC-LH) complexes in Roseiflexus castenholzii, a chlorosome-less filamentous anoxygenic phototroph that forms the deepest branch of photosynthetic bacteria. Newly identified exterior Cars functioned with the bacteriochlorophyll B800 to block the proposed quinone channel between LHαβ subunits in the nRC-LH, forming a sealed LH ring that was disrupted by transmembrane helices from cytochrome c and subunit X to allow quinone shuttling. dRC-LH lacked subunit X, leading to an exposed LH ring with a larger opening, which together accelerated the quinone exchange rate. We also assigned amino acid sequences of subunit X and two hypothetical proteins Y and Z that functioned in forming the quinone channel and stabilizing the RC-LH interactions. This study reveals the structural basis by which Cars assembly regulates the architecture and quinone exchange of bacterial RC-LH complexes. These findings mark an important step forward in understanding the evolution and diversity of prokaryotic photosynthetic apparatus.},
}
@article {pmid37736411,
year = {2023},
author = {Jovanovic, LI and Jervis Rademeyer, H and Pakosh, M and Musselman, KE and Popovic, MR and Marquez-Chin, C},
title = {Scoping Review on Brain-Computer Interface-Controlled Electrical Stimulation Interventions for Upper Limb Rehabilitation in Adults: A Look at Participants, Interventions, and Technology.},
journal = {Physiotherapy Canada. Physiotherapie Canada},
volume = {75},
number = {3},
pages = {276-290},
pmid = {37736411},
issn = {0300-0508},
abstract = {PURPOSE: While current rehabilitation practice for improving arm and hand function relies on physical/occupational therapy, a growing body of research evaluates the effects of technology-enhanced rehabilitation. We review interventions that combine a brain-computer interface (BCI) with electrical stimulation (ES) for upper limb movement rehabilitation to summarize the evidence on (1) populations of study participants, (2) BCI-ES interventions, and (3) the BCI-ES systems.<br><br>METHOD: After searching seven databases, two reviewers identified 23 eligible studies. We consolidated information on the study participants, interventions, and approaches used to develop integrated BCI-ES systems. The included studies investigated the use of BCI-ES interventions with stroke and spinal cord injury (SCI) populations. All studies used electroencephalography to collect brain signals for the BCI, and functional electrical stimulation was the most common type of ES. The BCI-ES interventions were typically conducted without a therapist, with sessions varying in both frequency and duration.<br><br>RESULTS: Of the 23 eligible studies, only 3 studies involved the SCI population, compared to 20 involving individuals with stroke.<br><br>CONCLUSIONS: Future BCI-ES interventional studies could address this gap. Additionally, standardization of device and rehabilitation modalities, and study-appropriate involvement with therapists, can be considered to advance this intervention towards clinical implementation.},
}
@article {pmid37736145,
year = {2023},
author = {Boscolo Galazzo, I and Tonin, L and Miladinović, A and Storti, SF},
title = {Editorial: Brain-connectivity-based computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1281446},
pmid = {37736145},
issn = {1662-5161},
}
@article {pmid37736040,
year = {2023},
author = {Mirfathollahi, A and Ghodrati, MT and Shalchyan, V and Zarrindast, MR and Daliri, MR},
title = {Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.},
journal = {iScience},
volume = {26},
number = {10},
pages = {107808},
pmid = {37736040},
issn = {2589-0042},
abstract = {Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.},
}
@article {pmid37735237,
year = {2024},
author = {Liang, S and Zhao, L and Ni, P and Wang, Q and Guo, W and Xu, Y and Cai, J and Tao, S and Li, X and Deng, W and Palaniyappan, L and Li, T},
title = {Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders.},
journal = {Psychopharmacology},
volume = {241},
number = {1},
pages = {97-107},
pmid = {37735237},
issn = {1432-2072},
mesh = {Humans ; *Kynurenine/metabolism ; Tryptophan ; *Depressive Disorder, Major/diagnosis ; Gray Matter ; Cerebral Cortex/metabolism ; },
abstract = {RATIONALE: An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear.<br><br>OBJECTIVES: In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders.<br><br>METHODS: Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features.<br><br>RESULTS: Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures.<br><br>CONCLUSIONS: The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.},
}
@article {pmid37733286,
year = {2024},
author = {Prinsloo, S and Kaptchuk, TJ and De Ridder, D and Lyle, R and Bruera, E and Novy, D and Barcenas, CH and Cohen, LG},
title = {Brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial.},
journal = {Cancer},
volume = {130},
number = {2},
pages = {300-311},
doi = {10.1002/cncr.35027},
pmid = {37733286},
issn = {1097-0142},
support = {1K01AT008485-01//National Center for Complimentary and Integrative Health/ ; CCR-14-800//The Rising Tide Foundation/ ; },
mesh = {Humans ; Female ; *Brain-Computer Interfaces ; *Neuralgia/drug therapy ; *Breast Neoplasms/drug therapy ; Survivors ; *Antineoplastic Agents/adverse effects ; },
abstract = {BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed-loop brain-computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom report.<br><br>METHODS: Randomization to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1-month data also were examined.<br><br>RESULTS: The BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, -2.60 vs. 0.38; 95% confidence interval, -3.67, -1.46 [p&#xa0;=&#xa0;.000; effect size, 1.07]; PLC, -2.26; 95% confidence interval, -3.33, -1.19 [p&#xa0;=&#xa0;.001 vs. WLC; effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group only.<br><br>CONCLUSIONS: BCI is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long-term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the intervention.<br><br>PLAIN LANGUAGE SUMMARY: Chemotherapy-induced nerve pain (neuropathy) can be disabling for cancer survivors; however, the way symptoms are felt depends on how the brain interprets the signals from nerves in the body. We determined that the perception of neuropathy can be changed by working directly with the brain. Survivors in our trial played 20 sessions of a type of video game that was designed to change the way the brain processed sensation and movement. In this, our second trial, we again observed significant improvement in symptoms that lasted after the treatment was complete.},
}
@article {pmid37732305,
year = {2023},
author = {Li, F and Zhang, D and Chen, J and Tang, K and Li, X and Hou, Z},
title = {Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1243151},
pmid = {37732305},
issn = {1662-4548},
abstract = {BACKGROUND: The incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research.<br><br>METHODS: This study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis.<br><br>RESULTS: This study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field.<br><br>CONCLUSION: This study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work.},
}
@article {pmid37732253,
year = {2023},
author = {Kosnoff, J and Yu, K and Liu, C and He, B},
title = {Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.09.04.556252},
pmid = {37732253},
issn = {2692-8205},
abstract = {Paralysis affects roughly 1 in 50 Americans. While there is no cure for the condition, brain-computer interfaces (BCI) can allow users to control a device with their mind, bypassing the paralyzed region. Non-invasive BCIs still have high error rates, which is hypothesized to be reduced with concurrent targeted neuromodulation. This study examines whether transcranial focused ultrasound (tFUS) modulation can improve BCI outcomes, and what the underlying mechanism of action might be through high-density electroencephalography (EEG)-based source imaging (ESI) analyses. V5-targeted tFUS significantly reduced the error for the BCI speller task. ESI analyses showed significantly increased theta activity in the tFUS condition at both V5 and downstream the dorsal visual processing pathway. Correlation analysis indicates that the dorsal processing pathway connection was preserved during tFUS stimulation, whereas extraneous connections were severed. These results suggest that V5-targeted tFUS' mechanism of action is to raise the brain's feature-based attention to visual motion.},
}
@article {pmid37731047,
year = {2023},
author = {Ozcelik, F and VanRullen, R},
title = {Natural scene reconstruction from fMRI signals using generative latent diffusion.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {15666},
pmid = {37731047},
issn = {2045-2322},
mesh = {Brain/diagnostic imaging ; Brain-Computer Interfaces ; Magnetic Resonance Imaging ; *Image Processing, Computer-Assisted ; },
abstract = {In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called "Brain-Diffuser". In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling "ROI-optimal" scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.},
}
@article {pmid37728715,
year = {2024},
author = {Wang, W and Li, B and Wang, H and Wang, X and Qin, Y and Shi, X and Liu, S},
title = {EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {62},
number = {1},
pages = {107-120},
pmid = {37728715},
issn = {1741-0444},
mesh = {Humans ; *Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; Signal-To-Noise Ratio ; *Brain-Computer Interfaces ; Imagination ; Algorithms ; },
abstract = {Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.},
}
@article {pmid37727590,
year = {2023},
author = {Asanza, V and Lorente-Leyva, LL and Peluffo-Ordóñez, DH and Montoya, D and Gonzalez, K},
title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.},
journal = {Data in brief},
volume = {50},
number = {},
pages = {109540},
pmid = {37727590},
issn = {2352-3409},
abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},
}
@article {pmid37726002,
year = {2023},
author = {Bressler, S and Neely, R and Yost, RM and Wang, D and Read, HL},
title = {A wearable EEG system for closed-loop neuromodulation of sleep-related oscillations.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfb3b},
pmid = {37726002},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Sleep/physiology ; *Sleep, Slow-Wave/physiology ; Sleep Stages/physiology ; Acoustic Stimulation/methods ; },
abstract = {Objective.Healthy sleep plays a critical role in general well-being. Enhancement of slow-wave sleep by targeting acoustic stimuli to particular phases of delta (0.5-2 Hz) waves has shown promise as a non-invasive approach to improve sleep quality. Closed-loop stimulation during other sleep phases targeting oscillations at higher frequencies such as theta (4-7 Hz) or alpha (8-12 Hz) could be another approach to realize additional health benefits. However, systems to track and deliver stimulation relative to the instantaneous phase of electroencephalogram (EEG) signals at these higher frequencies have yet to be demonstrated outside of controlled laboratory settings.Approach.Here we examine the feasibility of using an endpoint-corrected version of the Hilbert transform (ecHT) algorithm implemented on a headband wearable device to measure alpha phase and deliver phase-locked auditory stimulation during the transition from wakefulness to sleep, during which alpha power is greatest. First, the ecHT algorithm is implementedin silicoto evaluate the performance characteristics of this algorithm across a range of sleep-related oscillatory frequencies. Secondly, a pilot sleep study tests feasibility to use the wearable device by users in the home setting for measurement of EEG activity during sleep and delivery of real-time phase-locked stimulation.Main results.The ecHT is capable of computing the instantaneous phase of oscillating signals with high precision, allowing auditory stimulation to be delivered at the intended phases of neural oscillations with low phase error. The wearable system was capable of measuring sleep-related neural activity with sufficient fidelity for sleep stage scoring during the at-home study, and phase-tracking performance matched simulated results. Users were able to successfully operate the system independently using the companion smartphone app to collect data and administer stimulation, and presentation of auditory stimuli during sleep initiation did not negatively impact sleep onset.Significance.This study demonstrates the feasibility of closed-loop real-time tracking and neuromodulation of a range of sleep-related oscillations using a wearable EEG device. Preliminary results suggest that this approach could be used to deliver non-invasive neuromodulation across all phases of sleep.},
}
@article {pmid37725740,
year = {2024},
author = {Ju, C and Guan, C},
title = {Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {12},
pages = {17701-17715},
doi = {10.1109/TNNLS.2023.3307470},
pmid = {37725740},
issn = {2162-2388},
mesh = {*Electroencephalography/methods/classification ; Humans ; *Neural Networks, Computer ; *Imagination/physiology ; *Brain-Computer Interfaces ; Algorithms ; Deep Learning ; Signal Processing, Computer-Assisted ; Time Factors ; Graph Neural Networks ; },
abstract = {The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep-learning-based MI-EEG classifiers from the perspective of time-frequency analysis, introducing a new architecture called Graph-CSPNet. We refer to this category of classifiers as Geometric Classifiers, highlighting their foundation in differential geometry stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel manifold-valued graph convolutional techniques to capture the EEG features in the time-frequency domain, offering heightened flexibility in signal segmentation for capturing localized fluctuations. To evaluate the effectiveness of Graph-CSPNet, we employ five commonly used publicly available MI-EEG datasets, achieving near-optimal classification accuracies in nine out of 11 scenarios. The Python repository can be found at https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet.},
}
@article {pmid37725735,
year = {2024},
author = {Yang, S and Wang, H and Pang, Y and Azghadi, MR and Linares-Barranco, B},
title = {NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {18},
number = {1},
pages = {186-199},
doi = {10.1109/TBCAS.2023.3316968},
pmid = {37725735},
issn = {1940-9990},
mesh = {*Artificial Intelligence ; *Education, Distance ; Neural Networks, Computer ; Computers ; Dendrites ; },
abstract = {Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.},
}
@article {pmid37724211,
year = {2023},
author = {Ayoub, M and Ballout, AA and Zayek, RA and Ayoub, NF},
title = {Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool.},
journal = {Cureus},
volume = {15},
number = {8},
pages = {e43690},
pmid = {37724211},
issn = {2168-8184},
abstract = {Background Generative artificial intelligence (AI) has integrated into various industries as it has demonstrated enormous potential in automating elaborate processes and enhancing complex decision-making. The ability of these chatbots to critically triage, diagnose, and manage complex medical conditions, remains unknown and requires further research. Objective This cross-sectional study sought to quantitatively analyze the appropriateness of ChatGPT (OpenAI, San Francisco, CA, US) in its ability to triage, synthesize differential diagnoses, and generate treatment plans for nine diverse but common clinical scenarios. Methods Various common clinical scenarios were developed. Each was input into ChatGPT, and the chatbot was asked to develop diagnostic and treatment plans. Five practicing physicians independently scored ChatGPT's responses to the clinical scenarios. Results The average overall score for the triage ranking was 4.2 (SD 0.7). The lowest overall score was for the completeness of the differential diagnosis at 4.1 (0.5). The highest overall scores were seen with the accuracy of the differential diagnosis, initial treatment plan, and overall usefulness of the response (all with an average score of 4.4). Variance among physician scores ranged from 0.24 for accuracy of the differential diagnosis to 0.49 for appropriateness of triage ranking. Discussion ChatGPT has the potential to augment clinical decision-making. More extensive research, however, is needed to ensure accuracy and appropriate recommendations are provided.},
}
@article {pmid37723506,
year = {2023},
author = {Khanal, S and Miani, C and Finne, E and Zielke, J and Boeckmann, M},
title = {Effectiveness of behavior change interventions for smoking cessation among expectant and new fathers: findings from a systematic review.},
journal = {BMC public health},
volume = {23},
number = {1},
pages = {1812},
pmid = {37723506},
issn = {1471-2458},
mesh = {Male ; Pregnancy ; Female ; Humans ; *Smoking Cessation ; Behavior Therapy ; Databases, Factual ; Language ; Fathers ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Smoking cessation during pregnancy and the postpartum period by both women and their partners offers multiple health benefits. However, compared to pregnant/postpartum women, their partners are less likely to actively seek smoking cessation services. There is an increased recognition about the importance of tailored approaches to smoking cessation for expectant and new fathers. While Behavior Change Interventions (BCIs) are a promising approach for smoking cessation interventions, evidence on effectiveness exclusively among expectant and new fathers are fragmented and does not allow for many firm conclusions to be drawn.<br><br>METHODS: We conducted a systematic review on effectiveness of BCIs on smoking cessation outcomes of expectant and new fathers both through individual and/or couple-based interventions. Peer reviewed articles were identified from eight databases without any date or language restriction.Two independent reviewers screened studies for relevance, assessed methodological quality of relevant studies, and extracted data from studies using a predeveloped data extraction sheet.<br><br>RESULTS: We retrieved 1222 studies, of which 39 were considered for full text screening after reviewing the titles and abstracts. An additional eight studies were identified from reviewing the reference list of review articles picked up by the databases search. A total of nine Randomised Control Trials were included in the study. Six studies targeted expectant/new fathers, two targeted couples and one primarily targeted women with an intervention component to men. While the follow-up measurements for men varied across studies, the majority reported biochemically verified quit rates at 6&#xa0;months. Most of the interventions showed positive effects on cessation outcomes. BCI were heterogenous across studies. Findings are suggestive of gender targeted interventions being more likely to have positive cessation outcomes.<br><br>CONCLUSIONS: This systematic review found limited evidence supporting the effectiveness of BCI among expectant and new fathers, although the majority of studies show positive effects of these interventions on smoking cessation outcomes. There remains a need for more research targeted at expectant and new fathers. Further, there is a need to identify how smoking cessation service delivery can better address the needs of (all) gender(s) during pregnancy.},
}
@article {pmid37721299,
year = {2024},
author = {Guo, Y and Sun, L and Zhong, W and Zhang, N and Zhao, Z and Tian, W},
title = {Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.},
journal = {Neural regeneration research},
volume = {19},
number = {3},
pages = {663-670},
pmid = {37721299},
issn = {1673-5374},
abstract = {Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury. Specifically, it can be used to analyze and process data regarding peripheral nerve injury and repair, while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms. To investigate advances in the use of artificial intelligence in the diagnosis, rehabilitation, and scientific examination of peripheral nerve injury, we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994-2023. We identified the following research hotspots in peripheral nerve injury and repair: (1) diagnosis, classification, and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques, such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy; (2) motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms, such as wearable devices and assisted wheelchair systems; (3) improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning, such as implantable peripheral nerve interfaces; (4) the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility, enabling them to control devices such as networked hand prostheses; (5) artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation, thereby reducing surgical risk and complications, and facilitating postoperative recovery. Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair, there are some limitations to this technology, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (e.g., privacy, data security, research transparency). Future research should address the issue of data collection, as large-scale, high-quality clinical datasets are required to establish effective artificial intelligence models. Multimodal data processing is also necessary, along with interdisciplinary collaboration, medical-industrial integration, and multicenter, large-sample clinical studies.},
}
@article {pmid37719745,
year = {2023},
author = {Ding, Y and Guo, K and Wang, X and Chen, M and Li, X and Wu, Y},
title = {Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy.},
journal = {Translational neuroscience},
volume = {14},
number = {1},
pages = {20220308},
pmid = {37719745},
issn = {2081-3856},
abstract = {OBJECTIVE: This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods.<br><br>METHODS: Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20&#x2009;min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons.<br><br>RESULTS: In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13&#x2009;Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization.<br><br>CONCLUSION: VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.},
}
@article {pmid37717810,
year = {2023},
author = {Mahemuti, Y and Kadeer, K and Su, R and Abula, A and Aili, Y and Maimaiti, A and Abulaiti, S and Maimaitituerxun, M and Miao, T and Jiang, S and Axier, A and Aisha, M and Wang, Y and Cheng, X},
title = {TSPO exacerbates acute cerebral ischemia/reperfusion injury by inducing autophagy dysfunction.},
journal = {Experimental neurology},
volume = {369},
number = {},
pages = {114542},
doi = {10.1016/j.expneurol.2023.114542},
pmid = {37717810},
issn = {1090-2430},
mesh = {Rats ; Animals ; *Brain Ischemia/complications ; Transcription Factors ; Infarction, Middle Cerebral Artery/complications ; *Reperfusion Injury/prevention &amp; control ; Autophagy ; },
abstract = {Autophagy is considered a double-edged sword, with a role in the regulation of the pathophysiological processes of the central nervous system (CNS) after cerebral ischemia-reperfusion injury (CIRI). The 18-kDa translocator protein (TSPO) is a highly conserved protein, with its expression level in the nervous system closely associated with the regulation of pathophysiological processes. In addition, the ligand of TSPO reduces neuroinflammation in brain diseases, but the potential role of TSPO in CIRI is largely undiscovered. On this basis, we investigated whether TSPO regulates neuroinflammatory response by affecting autophagy in microglia. In our study, increased expression of TSPO was detected in rat brain tissues with transient middle cerebral artery occlusion (tMCAO) and in BV2 microglial cells exposed to oxygen-glucose deprivation or reoxygenation (OGD/R) treatment, respectively. In addition, we confirmed that autophagy was over-activated during CIRI by increased expression of autophagy activation related proteins with Beclin-1 and LC3B, while the expression of p62 was decreased. The degradation process of autophagy was inhibited, while the expression levels of LAMP-1 and Cathepsin-D were significantly reduced. Results of confocal laser microscopy and transmission electron microscopy (TEM) indicated that autophagy flux was disordered. In contrast, inhibition of TSPO prevented autophagy over-activation both in vivo and in vitro. Interestingly, suppression of TSPO alleviated nerve cell damage by reducing reactive oxygen species (ROS) and pro-inflammatory factors, including TNF-α and IL-6 in microglia cells. In summary, these results indicated that TSPO might affect CIRI by mediating autophagy dysfunction and thus might serve as a potential target for ischemic stroke treatment.},
}
@article {pmid37717506,
year = {2023},
author = {Yang, X and Zhu, HR and Tao, YJ and Deng, RH and Tao, SW and Meng, YJ and Wang, HY and Li, XJ and Wei, W and Yu, H and Liang, R and Wang, Q and Deng, W and Zhao, LS and Ma, XH and Li, ML and Xu, JJ and Li, J and Liu, YS and Tang, Z and Du, XD and Coid, JW and Greenshaw, AJ and Li, T and Guo, WJ},
title = {Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients.},
journal = {Asian journal of psychiatry},
volume = {89},
number = {},
pages = {103767},
doi = {10.1016/j.ajp.2023.103767},
pmid = {37717506},
issn = {1876-2026},
mesh = {Humans ; Male ; *Alcoholism/therapy ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Neuroimaging ; Biomarkers ; },
abstract = {Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.},
}
@article {pmid37715921,
year = {2023},
author = {Fan, J and Xu, H},
title = {Serotonin: A Bridge for Infant-mother Bonding.},
journal = {Neuroscience bulletin},
volume = {39},
number = {11},
pages = {1741-1744},
pmid = {37715921},
issn = {1995-8218},
mesh = {Female ; Humans ; Infant ; *Mothers ; Serotonin ; Object Attachment ; Surveys and Questionnaires ; *Depression, Postpartum ; },
}
@article {pmid37714145,
year = {2023},
author = {Tian, L and Zhao, T and Dong, L and Liu, Q and Zheng, Y},
title = {Passive array micro-magnetic stimulation device based on multi-carrier wireless flexible control for magnetic neuromodulation.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfa23},
pmid = {37714145},
issn = {1741-2552},
mesh = {*Brain ; *Hippocampus ; Magnetic Fields ; Movement ; Neuronal Plasticity ; },
abstract = {Objective.The passive micro-magnetic stimulation (µMS) devices typically consist of an external transmitting coil and a single internal micro-coil, which enables a point-to-point energy supply from the external coil to the internal coil and the realization of magnetic neuromodulation via wireless energy transmission. The internal array of micro coils can achieve multi-target stimulation without movement, which improves the focus and effectiveness of magnetic stimulations. However, achieving a free selection of an appropriate external coil to deliver energy to a particular internal array of micro-coils for multiple stimulation targets has been challenging. To address this challenge, this study uses a multi-carrier modulation technique to transmit the energy of the external coil.Approach.In this study, a theoretical model of a multi-carrier resonant compensation network for the arrayµMS is established based on the principle of magnetically coupled resonance. The resonant frequency coupling parameter corresponding to each micro-coil of the arrayµMS is determined, and the magnetic field interference between the external coil and its non-resonant micro-coils is eliminated. Therefore, an effective magnetic stimulation threshold for a micro-coil corresponding to the target is determined, and wireless free control of the internal micro-coil array is achieved by using an external transmitting coil.Main results.The passiveµMS array model is designed using a multi-carrier wireless modulation method, and its synergistic modulation of the magnetic stimulation of synaptic plasticity long-term potentiation in multiple hippocampal regions is investigated using hippocampal isolated brain slices.Significance.The results presented in this study could provide theoretical and experimental bases for implantable micro-magnetic device-targeted therapy, introducing an efficient method for diagnosis and treatment of neurological diseases and providing innovative ideas for in-depth application of micro-magnetic stimulation in the neuroscience field.},
}
@article {pmid37714143,
year = {2023},
author = {Fadli, RA and Yamanouchi, Y and Jovanovic, LI and Popovic, MR and Marquez-Chin, C and Nomura, T and Milosevic, M},
title = {Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acfa22},
pmid = {37714143},
issn = {1741-2552},
mesh = {Humans ; *Brain ; *Prefrontal Cortex ; Central Nervous System ; Stereotaxic Techniques ; Electric Stimulation ; },
abstract = {Objective. Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach.Approach. The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention.Main results. M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES.Significance. Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.},
}
@article {pmid37713229,
year = {2023},
author = {Ma, R and Chen, YF and Jiang, YC and Zhang, M},
title = {A New Compound-Limbs Paradigm: Integrating Upper-Limb Swing Improves Lower-Limb Stepping Intention Decoding From EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3823-3834},
doi = {10.1109/TNSRE.2023.3315717},
pmid = {37713229},
issn = {1558-0210},
mesh = {Humans ; *Intention ; Imagination ; *Brain-Computer Interfaces ; Support Vector Machine ; Electroencephalography/methods ; Upper Extremity ; Lower Extremity ; Algorithms ; },
abstract = {Brain-computer interface (BCI) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.},
}
@article {pmid37711224,
year = {2023},
author = {Alonso-Valerdi, LM},
title = {Editorial: Improving decoding of neuroinformation: towards the diversity of neural engineering applications.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1270696},
pmid = {37711224},
issn = {1662-5161},
}
@article {pmid37707990,
year = {2023},
author = {},
title = {Erratum: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {199},
pages = {},
doi = {10.3791/6572},
pmid = {37707990},
issn = {1940-087X},
abstract = {An erratum was issued for: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke. The Authors section was updated from: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,2,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University to: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University.},
}
@article {pmid37707696,
year = {2023},
author = {Azizi, H and Fakhari, A and Farahbakhsh, M and Davtalab Esmaeili, E and Chattu, VK and Ali Asghari, N and Nazemipour, M and Mansournia, MA},
title = {Prevention of Re-attempt Suicide Through Brief Contact Interventions: A Systematic Review, Meta-analysis, and Meta-regression of Randomized Controlled Trials.},
journal = {Journal of prevention (2022)},
volume = {44},
number = {6},
pages = {777-794},
pmid = {37707696},
issn = {2731-5541},
mesh = {Female ; Humans ; *Suicide, Attempted/prevention &amp; control ; Randomized Controlled Trials as Topic ; *Suicide Prevention ; },
abstract = {Brief contact intervention (BCI) is a low-cost intervention to prevent re-attempt suicide. This meta-analysis and meta-regression study aimed to evaluate the effect of BCI on re-attempt prevention following suicide attempts (SAs). We systematically searched using defined keywords in MEDLINE, Embase, and Scopus up to April, 2023. All randomized controlled trials (RCTs) were eligible for inclusion after quality assessment. Random-effects model and subgroup analysis were used to estimate pooled risk difference (RD) and risk ratio (RR) between BCI and re-attempt prevention with 95% confidence intervals (CIs). Meta-regression analysis was carried out to explore the potential sources of heterogeneity. The pooled estimates were (RD = 4%; 95% CI 2-6%); and (RR = 0.62; 95% CI 0.48-0.77). Subgroup analysis demonstrated that more than 12 months intervention (RR = 0.46; 95% CI 0.10-0.82) versus 12 months or less (RR = 0.67; 95% CI 0.54-0.80) increased the effectiveness of BCI on re-attempt suicide reduction. Meta-regression analysis explored that BCI time (more than 12 months), BCI type, age, and female sex were the potential sources of the heterogeneity. The meta-analysis indicated that BCI could be a valuable strategy to prevent suicide re-attempts. BCI could be utilized within suicide prevention strategies as a surveillance component of mental health since BCI requires low-cost and low-educated healthcare providers.},
}
@article {pmid37706481,
year = {2023},
author = {Horner, S and Burleigh, L and Traylor, Z and Greening, SG},
title = {Looking on the bright side: the impact of ambivalent images on emotion regulation choice.},
journal = {Cognition &amp; emotion},
volume = {37},
number = {7},
pages = {1213-1229},
doi = {10.1080/02699931.2023.2256056},
pmid = {37706481},
issn = {1464-0600},
mesh = {Humans ; *Emotional Regulation ; Emotions/physiology ; Affect ; Cognition/physiology ; Cues ; },
abstract = {Previous research has found that people choose to reappraise low intensity images more often than high intensity images. However, this research does not account for image ambivalence, which is presence of both positive and negative cues in a stimulus. The purpose of this research was to determine differences in ambivalence in high intensity and low intensity images used in previous research (experiments 1-2), and if ambivalence played a role in emotion regulation choice in addition to intensity (experiments 3-4). Experiments 1 and 2 found that the low intensity images were more ambivalent than the high intensity images. Experiment 2 further found a positive relationship between ambivalence of an image and reappraisal affordances. Experiments 3 and 4 found that people chose to reappraise ambivalent images more often than non-ambivalent images, and they also chose to reappraise low intensity images more often than high intensity images. These experiments support the idea that ambivalence is a factor in emotion regulation choice. Future research should consider the impact ambivalent stimuli have on emotion regulation, including the potential for leveraging ambivalent stimuli to improve one's emotion regulation ability.},
}
@article {pmid37706155,
year = {2023},
author = {Liu, C and You, J and Wang, K and Zhang, S and Huang, Y and Xu, M and Ming, D},
title = {Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1180471},
pmid = {37706155},
issn = {1662-4548},
abstract = {OBJECTIVE: In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.<br><br>APPROACH: Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left&#x2192;Left (LL), Right&#x2192;Right (RR), Left&#x2192;Right (LR), and Right&#x2192;Left (RL)] using the left or right index finger at about 1&#x2009;s intervals under an auditory prompt of 1&#x2009;Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm.<br><br>MAIN RESULTS: As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively.<br><br>SIGNIFICANCE: This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.},
}
@article {pmid37700746,
year = {2023},
author = {Fan, C and Yang, B and Li, X and Zan, P},
title = {Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1250991},
pmid = {37700746},
issn = {1662-4548},
abstract = {Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.},
}
@article {pmid37698960,
year = {2023},
author = {Feng, X and Feng, X and Qin, B and Liu, T},
title = {Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3874-3883},
doi = {10.1109/TNSRE.2023.3314642},
pmid = {37698960},
issn = {1558-0210},
mesh = {Humans ; *Semantics ; *Language ; Brain ; Curriculum ; Electroencephalography ; },
abstract = {Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces. However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C- SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thereby reducing the discrepancy. Specifically, our C- SCL pulls semantically similar EEG representations together while pushing apart dissimilar ones. Besides, in order to introduce more meaningful contrastive pairs, we carefully employ curriculum learning to not only craft meaningful contrastive pairs but also make the learning progressively. We conduct extensive experiments on the ZuCo benchmark and our method combined with diverse models and architectures shows stable improvements across three types of metrics while achieving the new state-of-the-art. Further investigation proves not only its superiority in both the single-subject and low-resource settings but also its robust generalizability in the zero-shot setting. Our codes are available at: https://github.com/xcfcode/contrastive_eeg2text.},
}
@article {pmid37697027,
year = {2024},
author = {Marín-Medina, DS and Arenas-Vargas, PA and Arias-Botero, JC and Gómez-Vásquez, M and Jaramillo-López, MF and Gaspar-Toro, JM},
title = {New approaches to recovery after stroke.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {45},
number = {1},
pages = {55-63},
pmid = {37697027},
issn = {1590-3478},
mesh = {Humans ; *Stroke Rehabilitation ; *Stroke/therapy ; Brain ; Neuronal Plasticity/physiology ; Cerebral Cortex ; Recovery of Function ; },
abstract = {After a stroke, several mechanisms of neural plasticity can be activated, which may lead to significant recovery. Rehabilitation therapies aim to restore surviving tissue over time and reorganize neural connections. With more patients surviving stroke with varying degrees of neurological impairment, new technologies have emerged as a promising option for better functional outcomes. This review explores restorative therapies based on brain-computer interfaces, robot-assisted and virtual reality, brain stimulation, and cell therapies. Brain-computer interfaces allow for the translation of brain signals into motor patterns. Robot-assisted and virtual reality therapies provide interactive interfaces that simulate real-life situations and physical support to compensate for lost motor function. Brain stimulation can modify the electrical activity of neurons in the affected cortex. Cell therapy may promote regeneration in damaged brain tissue. Taken together, these new approaches could substantially benefit specific deficits such as arm-motor control and cognitive impairment after stroke, and even the chronic phase of recovery, where traditional rehabilitation methods may be limited, and the window for repair is narrow.},
}
@article {pmid37696689,
year = {2023},
author = {Lu, K and Pan, Y},
title = {A collective neuroscience lens on intergroup conflict.},
journal = {Trends in cognitive sciences},
volume = {27},
number = {11},
pages = {985-986},
doi = {10.1016/j.tics.2023.08.021},
pmid = {37696689},
issn = {1879-307X},
abstract = {How do team leaders and followers synchronize their behaviors and brains to effectively manage intergroup conflicts? Zhang and colleagues offered a collective neurobehavioral narrative that delves into the intricacies of intergroup conflict. Their results underscore the importance of leaders' group-oriented actions, along with leader-follower synchronization, in intergroup conflict resolution.},
}
@article {pmid37696046,
year = {2023},
author = {Pitt, KM and Cole, ZJ and Zosky, J},
title = {Promoting Simple and Engaging Brain-Computer Interface Designs for Children by Evaluating Contrasting Motion Techniques.},
journal = {Journal of speech, language, and hearing research : JSLHR},
volume = {66},
number = {10},
pages = {3974-3987},
doi = {10.1044/2023_JSLHR-23-00292},
pmid = {37696046},
issn = {1558-9102},
mesh = {Humans ; Male ; Child ; Female ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Event-Related Potentials, P300 ; Attention ; },
abstract = {PURPOSE: There is an increasing focus on using motion in augmentative and alternative communication (AAC) systems. In considering brain-computer interface access to AAC (BCI-AAC), motion may provide a simpler or more intuitive avenue for BCI-AAC control. Different motion techniques may be utilized in supporting competency with AAC devices including simple (e.g., zoom) and complex (behaviorally relevant animation) methods. However, how different pictorial symbol animation techniques impact BCI-AAC is unclear.<br><br>METHOD: Sixteen healthy children completed two experimental conditions. These conditions included highlighting of pictorial symbols via both functional (complex) and zoom (simple) animation to evaluate the effects of motion techniques on P300-based BCI-AAC signals and offline (predicted) BCI-AAC performance.<br><br>RESULTS: Functional (complex) animation significantly increased attentional-related P200/P300 event-related potential (ERP) amplitudes in the parieto-occipital area. Zoom (simple) animation significantly decreased N400 latency. N400 ERP amplitude was significantly greater, and occurred significantly earlier, on the right versus left side for the functional animation condition within the parieto-occipital bin. N200 ERP latency was significantly reduced over the left hemisphere for the zoom condition in the central bin. As hypothesized, elicitation of all targeted ERP components supported offline (predicted) BCI-AAC performance being similar between conditions.<br><br>CONCLUSION: Study findings provide continued support for the use of animation in BCI-AAC systems for children and highlight differences in neural and attentional processing between complex and simple animation techniques.<br><br>SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24085623.},
}
@article {pmid37693482,
year = {2023},
author = {Rustamov, N and Souders, L and Sheehan, L and Carter, A and Leuthardt, EC},
title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Recovery in Chronic Stroke.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.08.26.23294320},
pmid = {37693482},
abstract = {BACKGROUND AND PURPOSE: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation. This study investigated the effectiveness of the IpsiHand System, a contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery affected by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity (proximal and distal), and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.<br><br>METHODS: Thirty chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram (EEG) signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment (UEFM) served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.<br><br>RESULTS: Chronic stroke patients achieved significant motor improvement with BCI therapy. We found significant improvement in both proximal and distal upper extremity motor function. Importantly, motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3 and C4 motor electrodes following BCI therapy. We observed significant positive correlations between motor recovery and theta gamma CFC increase across BCI therapy sessions.<br><br>CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients. This therapy was significantly correlated with changes in baseline cortical dynamics, specifically theta-gamma CFC increases in both the right and left motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven motor rehabilitation in chronic stroke patients.},
}
@article {pmid37690592,
year = {2023},
author = {Huang, Y and Deng, Y and Kong, L and Zhang, X and Wei, X and Mao, T and Xu, Y and Jiang, C and Rao, H},
title = {Vigilant attention mediates the association between resting EEG alpha oscillations and word learning ability.},
journal = {NeuroImage},
volume = {281},
number = {},
pages = {120369},
doi = {10.1016/j.neuroimage.2023.120369},
pmid = {37690592},
issn = {1095-9572},
abstract = {Individuals exhibit considerable variability in their capacity to learn and retain new information, including novel vocabulary. Prior research has established the importance of vigilance and electroencephalogram (EEG) alpha rhythm in the learning process. However, the interplay between vigilant attention, EEG alpha oscillations, and an individual's word learning ability (WLA) remains elusive. To address this knowledge gap, here we conducted two experiments with a total of 140 young and middle-aged adults who underwent resting EEG recordings prior to completing a paired-associate word learning task and a psychomotor vigilance test (PVT). The results of both experiments consistently revealed significant positive correlations between WLA and resting EEG alpha oscillations in the occipital and frontal regions. Furthermore, the association between resting EEG alpha oscillations and WLA was mediated by vigilant attention, as measured by the PVT. These findings provide compelling evidence supporting the crucial role of vigilant attention in linking EEG alpha oscillations to an individual's learning ability.},
}
@article {pmid37689832,
year = {2023},
author = {Tao, R and Zhang, C and Zhao, H and Xu, S},
title = {Active vs. computer-based passive decision-making leads to discrepancies in outcome evaluation: evidence from self-reported emotional experience and brain activity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {20},
pages = {10676-10685},
doi = {10.1093/cercor/bhad317},
pmid = {37689832},
issn = {1460-2199},
mesh = {Humans ; *Electroencephalography/methods ; Self Report ; *Decision Making ; Emotions ; Evoked Potentials ; Feedback, Psychological ; Computers ; Brain ; },
abstract = {People prefer active decision-making and induce greater emotional feelings than computer-based passive mode, yet the modulation of decision-making mode on outcome evaluation remains unknown. The present study adopted event-related potentials to investigate the discrepancies in active and computer-based passive mode on outcome evaluation using a card gambling task. The subjective rating results showed that active mode elicited more cognitive effort and stronger emotional feelings than passive mode. For received outcomes, we observed no significant Feedback-Related Negativity (FRN) effect on difference waveshapes (d-FRN) between the 2 modes, but active decision-making elicited larger P300 amplitudes than the passive mode. For unchosen card outcomes, the results revealed larger d-FRN amplitudes of relative valences (Superior - Inferior) in responses to negative feedback in active mode than in passive mode. The averaged P300 results revealed an interplay among outcome feedback, decision-making mode, and relative valence, and the average P300 amplitude elicited by the received loss outcome in the active mode partially mediated the relationship between subjective cognitive effort and negative emotion ratings on loss. Our findings indicate discrepancies between active and computer-based passive modes, and cognitive effort and emotional experience involved in outcome evaluation.},
}
@article {pmid37688757,
year = {2023},
author = {Liyanagedera, ND and Hussain, AA and Singh, A and Lal, S and Kempton, H and Guesgen, HW},
title = {Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.},
journal = {Brain informatics},
volume = {10},
number = {1},
pages = {24},
pmid = {37688757},
issn = {2198-4018},
abstract = {While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.},
}
@article {pmid37687976,
year = {2023},
author = {Siviero, I and Menegaz, G and Storti, SF},
title = {Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {17},
pages = {},
pmid = {37687976},
issn = {1424-8220},
support = {"Ricerca&amp;Sviluppo"//Fondazione CariVerona/ ; "Dipartimenti di Eccellenza"//Italian Ministry of Education, University and Research/ ; DM 1061/2021//REACT-EU PON "Ricerca e Innovazione" 2014-2020/ ; },
mesh = {*Brain-Computer Interfaces ; Brain ; Electroencephalography ; Imagery, Psychotherapy ; Signal Processing, Computer-Assisted ; },
abstract = {(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.},
}
@article {pmid37685390,
year = {2023},
author = {Antony, MJ and Sankaralingam, BP and Khan, S and Almjally, A and Almujally, NA and Mahendran, RK},
title = {Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {13},
number = {17},
pages = {},
pmid = {37685390},
issn = {2075-4418},
support = {PNURSP2023R410//Princess Nourah bint Abdulrahman University/ ; },
abstract = {An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.},
}
@article {pmid37683772,
year = {2023},
author = {Sun, H and Jin, J and Daly, I and Huang, Y and Zhao, X and Wang, X and Cichocki, A},
title = {Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.},
journal = {Journal of neuroscience methods},
volume = {399},
number = {},
pages = {109969},
doi = {10.1016/j.jneumeth.2023.109969},
pmid = {37683772},
issn = {1872-678X},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Learning ; Electroencephalography/methods ; Imagination ; },
abstract = {Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.},
}
@article {pmid37683664,
year = {2023},
author = {Liu, K and Yang, M and Xing, X and Yu, Z and Wu, W},
title = {SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf7f4},
pmid = {37683664},
issn = {1741-2552},
mesh = {*Algorithms ; Imagination ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; },
abstract = {Objective.Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However, the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due to individual variability.Approach.We propose a fully end-to-end CNN called SincMSNet to address this issue. SincMSNet employs the Sinc filter to extract subject-specific frequency band information and utilizes mixed-depth convolution to extract multi-scale temporal information for each band. It then applies a spatial convolutional block to extract spatial features and uses a temporal log-variance block to obtain classification features. The model of SincMSNet is trained under the joint supervision of cross-entropy and center loss to achieve inter-class separable and intra-class compact representations of EEG signals.Main results.We evaluated the performance of SincMSNet on the BCIC-IV-2a (four-class) and OpenBMI (two-class) datasets. SincMSNet achieves impressive results, surpassing benchmark methods. In four-class and two-class inter-session analysis, it achieves average accuracies of 80.70% and 71.50% respectively. In four-class and two-class single-session analysis, it achieves average accuracies of 84.69% and 76.99% respectively. Additionally, visualizations of the learned band-pass filter bands by Sinc filters demonstrate the network's ability to extract subject-specific frequency band information from EEG.Significance.This study highlights the potential of SincMSNet in improving the performance of MI-EEG decoding and designing more robust MI-BCIs. The source code for SincMSNet can be found at:https://github.com/Want2Vanish/SincMSNet.},
}
@article {pmid37683663,
year = {2023},
author = {Xiao, X and Wang, L and Xu, M and Wang, K and Jung, TP and Ming, D},
title = {A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {20},
number = {6},
pages = {},
doi = {10.1088/1741-2552/acf7f6},
pmid = {37683663},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Signal-To-Noise Ratio ; Algorithms ; Photic Stimulation/methods ; },
abstract = {Objective.Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on the number of training samples, and the system performance would have a dramatic drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples.Approach.This study proposed a novel method for SSVEPs detection, i.e. cyclic shift trials (CSTs), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onset of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e. extended canonical correlation analysis and ensemble task-related component analysis.Main results.CST could significantly enhance the signal to noise ratios of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate could reach up to 236.19 bits min[-1]using 36 s calibration time of only one training sample for each category.Significance.The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden.},
}
@article {pmid37683653,
year = {2023},
author = {Semenkov, I and Fedosov, N and Makarov, I and Ossadtchi, A},
title = {Real-time low latency estimation of brain rhythms with deep neural networks.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf7f3},
pmid = {37683653},
issn = {1741-2552},
mesh = {Humans ; Brain ; Cognition ; *Brain-Computer Interfaces ; Neural Networks, Computer ; *Neurofeedback ; },
abstract = {Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.},
}
@article {pmid37683652,
year = {2023},
author = {Tang, J and Xi, X and Wang, T and Wang, J and Li, L and Lü, Z},
title = {Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf7f7},
pmid = {37683652},
issn = {1741-2552},
mesh = {Humans ; Brain ; Cluster Analysis ; Computer Simulation ; *Motor Cortex ; Somatosensory Cortex ; *Stroke ; },
abstract = {Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.},
}
@article {pmid37682884,
year = {2023},
author = {Khan, RA and Rashid, N and Shahzaib, M and Malik, UF and Arif, A and Iqbal, J and Saleem, M and Khan, US and Tiwana, M},
title = {A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.},
journal = {PloS one},
volume = {18},
number = {9},
pages = {e0276133},
pmid = {37682884},
issn = {1932-6203},
mesh = {Humans ; *Artificial Intelligence ; Bayes Theorem ; Logistic Models ; *Algorithms ; Electroencephalography ; },
abstract = {Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.},
}
@article {pmid37681531,
year = {2023},
author = {Zhuang, S and He, M and Feng, J and Peng, S and Jiang, H and Li, Y and Hua, N and Zheng, Y and Ye, Q and Hu, M and Nie, Y and Yu, P and Yue, X and Qian, J and Yang, W},
title = {Near-Infrared Photothermal Manipulates Cellular Excitability and Animal Behavior in Caenorhabditis elegans.},
journal = {Small methods},
volume = {7},
number = {11},
pages = {e2300848},
doi = {10.1002/smtd.202300848},
pmid = {37681531},
issn = {2366-9608},
support = {82030108//National Natural Science Foundation of China/ ; //MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; },
mesh = {Animals ; *Caenorhabditis elegans ; *Antineoplastic Agents ; Indocyanine Green ; Cell Line, Tumor ; Behavior, Animal ; Mammals ; },
abstract = {Near-infrared (NIR) photothermal manipulation has emerged as a promising and noninvasive technology for neuroscience research and disease therapy for its deep tissue penetration. NIR stimulated techniques have been used to modulate neural activity. However, due to the lack of suitable in vivo control systems, most studies are limited to the cellular level. Here, a NIR photothermal technique is developed to modulate cellular excitability and animal behaviors in Caenorhabditis elegans in vivo via the thermosensitive transient receptor potential vanilloid 1 (TRPV1) channel with an FDA-approved photothermal agent indocyanine green (ICG). Upon NIR stimuli, exogenous expression of TRPV1 in AFD sensory neurons causes Ca[2+] influx, leading to increased neural excitability and reversal behaviors, in the presence of ICG. The GABAergic D-class motor neurons can also be activated by NIR irradiation, resulting in slower thrashing behaviors. Moreover, the photothermal manipulation is successfully applied in different types of muscle cells (striated muscles and nonstriated muscles), enhancing muscular excitability, causing muscle contractions and behavior changes in vivo. Altogether, this study demonstrates a noninvasive method to precisely regulate the excitability of different types of cells and related behaviors in vivo by NIR photothermal manipulation, which may be applied in mammals and clinical therapy.},
}
@article {pmid37680264,
year = {2023},
author = {Maslova, O and Komarova, Y and Shusharina, N and Kolsanov, A and Zakharov, A and Garina, E and Pyatin, V},
title = {Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1216648},
pmid = {37680264},
issn = {1662-5161},
abstract = {The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality.},
}
@article {pmid37679900,
year = {2024},
author = {Ling, Y and Wen, X and Tang, J and Tao, Z and Sun, L and Xin, H and Luo, B},
title = {Effect of topographic comparison of electroencephalographic microstates on the diagnosis and prognosis prediction of patients with prolonged disorders of consciousness.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {3},
pages = {e14421},
pmid = {37679900},
issn = {1755-5949},
support = {2021ZD0200404//China Brain Project/ ; U22A20293//The National Natural Science Foundation of China/ ; 82071173//The National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Consciousness ; *Brain ; Consciousness Disorders/diagnosis ; Electroencephalography ; Prognosis ; },
abstract = {AIMS: The electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking.<br><br>METHODS: We conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the prognosis of patients with pDoC. An independent dataset was used to validate the conclusion.<br><br>RESULTS: Through optimized topographic analysis, the global explained variance (GEV) of microstate E increased significantly in groups with reduced levels of consciousness. However, its ability to classify the VS/UWS group was poor. In addition, the optimized GEV of microstate E exhibited a statistically significant decrease in the good prognosis group as opposed to the group with a poor prognosis. Furthermore, the optimized GEV of microstate E strongly predicted a patient's prognosis.<br><br>CONCLUSION: This technique harmonizes with the existing microstate analysis and exhibits precise and comprehensive differences in microstate topography between groups. Furthermore, this method has significant potential for evaluating the clinical prognosis of pDoC patients.},
}
@article {pmid37679881,
year = {2023},
author = {Schnitzer, SA and DeFilippis, DM and Aguilar, A and Bernal, B and Peréz, S and Valdés, A and Valdés, S and Bernal, F and Mendoza, A and Castro, B and Garcia-Leon, M},
title = {Maximum stem diameter predicts liana population demography.},
journal = {Ecology},
volume = {104},
number = {11},
pages = {e4163},
doi = {10.1002/ecy.4163},
pmid = {37679881},
issn = {1939-9170},
mesh = {*Tropical Climate ; *Forests ; Trees ; Plants ; Population Dynamics ; },
abstract = {Determining population demographic rates is fundamental to understanding differences in species' life-history strategies and their capacity to coexist. Calculating demographic rates, however, is challenging and requires long-term, large-scale censuses. Body size may serve as a simple predictor of demographic rate; can it act as a proxy for demographic rate when those data are unavailable? We tested the hypothesis that maximum body size predicts species' demographic rate using repeated censuses of the 77 most common liana species on the Barro Colorado Island, Panama (BCI) 50-ha plot. We found that maximum stem diameter does predict species' population turnover and demography. We also found that lianas on BCI can grow to the enormous diameter of 635 mm, indicating that they can store large amounts of carbon and compete intensely with tropical canopy trees. This study is the first to show that maximum stem diameter can predict plant species' demographic rates and that lianas can attain extremely large diameters. Understanding liana demography is particularly timely because lianas are increasing rapidly in many tropical forests, yet their species-level population dynamics remain chronically understudied. Determining per-species maximum liana diameters in additional forests will enable systematic comparative analyses of liana demography and potential influence across forest types.},
}
@article {pmid37678543,
year = {2024},
author = {Zhu, Y and Xie, SZ and Peng, AB and Yu, XD and Li, CY and Fu, JY and Shen, CJ and Cao, SX and Zhang, Y and Chen, J and Li, XM},
title = {Distinct Circuits From the Central Lateral Amygdala to the Ventral Part of the Bed Nucleus of Stria Terminalis Regulate Different Fear Memory.},
journal = {Biological psychiatry},
volume = {95},
number = {8},
pages = {732-744},
doi = {10.1016/j.biopsych.2023.08.022},
pmid = {37678543},
issn = {1873-2402},
mesh = {Mice ; Animals ; *Septal Nuclei ; *Central Amygdaloid Nucleus ; Neurons/physiology ; Fear/physiology ; *Basolateral Nuclear Complex ; },
abstract = {BACKGROUND: The ability to differentiate stimuli that predict fear is critical for survival; however, the underlying molecular and circuit mechanisms remain poorly understood.<br><br>METHODS: We combined transgenic mice, in&#xa0;vivo transsynaptic circuit-dissecting anatomical approaches, optogenetics, pharmacological methods, and electrophysiological recording to investigate the involvement of specific extended amygdala circuits in different fear memory.<br><br>RESULTS: We identified the projections from central lateral amygdala (CeL) protein kinase C &#x3b4; (PKC&#x3b4;)-positive neurons and somatostatin (SST)-positive neurons to GABAergic (gamma-aminobutyric acidergic) and glutamatergic neurons in the ventral part of the bed nucleus of stria terminalis (vBNST). Prolonged optogenetic activation or inhibition of the PKC&#x3b4;[CeL-vBNST] pathway specifically reduced context fear memory, whereas the SST[CeL-vBNST] pathway mainly reduced tone fear memory. Intriguingly, optogenetic manipulation of vBNST neurons that received the projection from PKC&#x3b4;[CeL] neurons exerted bidirectional regulation of context fear, whereas manipulation of vBNST neurons that received the projection from SST[CeL] neurons could bidirectionally regulate both context and tone fear memory. We subsequently demonstrated the presence of &#x3b4; and &#x3ba; opioid receptor protein expression within the CeL-vBNST circuits, potentially accounting for the discrepancy between prolonged activation of GABAergic circuits and inhibition of downstream vBNST neurons. Finally, administration of an opioid receptor antagonist cocktail on the PKC&#x3b4;[CeL-vBNST] or SST[CeL-vBNST] pathway successfully restored context or tone fear memory reduction induced by prolonged activation of the circuits.<br><br>CONCLUSIONS: Together, these findings establish a functional role for distinct CeL-vBNST circuits in the differential regulation and appropriate maintenance of fear.},
}
@article {pmid37678229,
year = {2023},
author = {Barmpas, K and Panagakis, Y and Adamos, DA and Laskaris, N and Zafeiriou, S},
title = {BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf78a},
pmid = {37678229},
issn = {1741-2552},
mesh = {Humans ; *Brain Waves ; Electroencephalography ; Recognition, Psychology ; Brain ; *Brain-Computer Interfaces ; },
abstract = {Objective.Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available.Approach.In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations.Main results.We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier.Significance.In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.},
}
@article {pmid37678222,
year = {2023},
author = {Agarwal, AK and Roy-Chaudhury, P and Mounts, P and Hurlburt, E and Pfaffle, A and Poggio, EC},
title = {Taurolidine/Heparin Lock Solution and Catheter-Related Bloodstream Infection in Hemodialysis: A Randomized, Double-Blind, Active-Control, Phase 3 Study.},
journal = {Clinical journal of the American Society of Nephrology : CJASN},
volume = {18},
number = {11},
pages = {1446-1455},
pmid = {37678222},
issn = {1555-905X},
support = {R01 DK132328/DK/NIDDK NIH HHS/United States ; },
mesh = {Adult ; Humans ; *Catheter-Related Infections/etiology ; Heparin/adverse effects ; *Central Venous Catheters/adverse effects ; Renal Dialysis/adverse effects ; *Sepsis/etiology ; *Catheterization, Central Venous/adverse effects ; },
abstract = {BACKGROUND: Catheter-related bloodstream infections (CRBSIs) are one of the most prevalent, fatal, and costly complications of hemodialysis with a central venous catheter (CVC). The LOCK IT-100 trial compared the efficacy and safety of a taurolidine/heparin catheter lock solution that combines taurolidine 13.5 mg/ml and heparin (1000 units/ml) versus heparin in preventing CRBSIs in participants receiving hemodialysis via CVC.<br><br>METHODS: LOCK IT-100 was a randomized, double-blind, active-control, multicenter, phase 3 study that enrolled adults with kidney failure undergoing maintenance hemodialysis via CVC from 70 US sites. Participants were randomized 1:1 to taurolidine/heparin catheter lock solution or heparin control catheter lock solution (1000 units/ml). The primary end point was time to CRBSI as assessed by a blinded Clinical Adjudication Committee. Secondary end points were catheter removal for any reason and loss of catheter patency. On the basis of a prespecified interim analysis, the Data and Safety Monitoring Board recommended terminating the trial early for efficacy with no safety concerns.<br><br>RESULTS: In the full analysis population (N =795), nine participants in the taurolidine/heparin arm (n =397; 2%) and 32 participants in the heparin arm (n =398; 8%) had a CRBSI. Event rates per 1000 catheter days were 0.13 and 0.46, respectively, with the difference in time to CRBSI being statistically significant, favoring taurolidine/heparin (P < 0.001). The hazard ratio was 0.29 (95% confidence interval, 0.14 to 0.62), corresponding to a 71% reduction in risk of CRBSIs with taurolidine/heparin versus heparin. There were no significant differences between study arms in time to catheter removal for any reason or loss of catheter patency. The safety of taurolidine/heparin was comparable with that of heparin, and most treatment-emergent adverse events were mild or moderate.<br><br>CONCLUSIONS: Taurolidine/heparin reduced the risk of developing a CRBSI in study participants receiving hemodialysis via CVC compared with heparin with a comparable safety profile.<br><br>Study Assessing Safety &amp; Effectiveness of a Catheter Lock Solution in Dialysis Patients to Prevent Bloodstream Infection, NCT02651428 .},
}
@article {pmid37678137,
year = {2023},
author = {Zhang, L and Li, C and Zhang, R and Sun, Q},
title = {Online semi-supervised learning for motor imagery EEG classification.},
journal = {Computers in biology and medicine},
volume = {165},
number = {},
pages = {107405},
doi = {10.1016/j.compbiomed.2023.107405},
pmid = {37678137},
issn = {1879-0534},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Supervised Machine Learning ; *Brain-Computer Interfaces ; Software ; Imagination ; },
abstract = {OBJECTIVE: Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated.<br><br>APPROACH: We proposed an online semi-supervised learning scheme to improve the classification performance of MI-based BCI. This scheme uses regularized weighted online sequential extreme learning machine (RWOS-ELM) as the base classifier and updates its model parameters with incoming balanced data chunk-by-chunk. In the initial stage, we designed a technique that combines the synthetic minority oversampling with the edited nearest neighbor rule for data augmentation to construct more discriminative initial classifiers. When used online, the incoming chunk of data is first pseudo-labeled by RWOS-ELM as well as an auxiliary classifier, and then balanced again by the above-mentioned technique. Initial classifiers are further updated based on these class-balanced data.<br><br>MAIN RESULTS: Offline experimental results on two publicly available MI datasets demonstrate the superiority of the proposed scheme over its counterparts. Further online experiments on six subjects show that their BCI performance gradually improved by learning from incoming unlabeled data.<br><br>SIGNIFICANCE: Our proposed online semi-supervised learning scheme has higher computation and memory usage efficiency, which is promising for online MI-based BCIs, especially in the case of insufficient labeled training data.},
}
@article {pmid37677045,
year = {2023},
author = {Jiang, Y and Yin, J and Zhao, B and Zhang, Y and Peng, T and Zhuang, W and Wang, S and Huang, S and Zhong, M and Zhang, Y and Tang, G and Shen, B and Ou, H and Zheng, Y and Lin, Q},
title = {Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {199},
pages = {},
doi = {10.3791/65405},
pmid = {37677045},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke ; Brain ; Cognition ; Upper Extremity ; },
abstract = {The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.},
}
@article {pmid37676244,
year = {2023},
author = {Luo, Y and Sun, C and Wei, M and Ma, H and Wu, Y and Chen, Z and Dai, H and Jian, J and Sun, B and Zhong, C and Li, J and Richardson, KA and Lin, H and Li, L},
title = {Integrated Flexible Microscale Mechanical Sensors Based on Cascaded Free Spectral Range-Free Cavities.},
journal = {Nano letters},
volume = {23},
number = {19},
pages = {8898-8906},
doi = {10.1021/acs.nanolett.3c02239},
pmid = {37676244},
issn = {1530-6992},
abstract = {Photonic mechanical sensors offer several advantages over their electronic counterparts, including immunity to electromagnetic interference, increased sensitivity, and measurement accuracy. Exploring flexible mechanical sensors on deformable substrates provides new opportunities for strain-optical coupling operations. Nevertheless, existing flexible photonics strategies often require cumbersome signal collection and analysis with bulky setups, limiting their portability and affordability. To address these challenges, we propose a waveguide-integrated flexible mechanical sensor based on cascaded photonic crystal microcavities with inherent deformation and biaxial tensile state analysis. Leveraging the advanced multiplexing capability of the sensor, for the first time, we successfully demonstrate 2D shape reconstruction and quasi-distributed strain sensing with 110 μm spatial resolution. Our microscale mechanical sensor also exhibits exceptional sensitivity with a detected force level as low as 13.6 μN in real-time measurements. This sensing platform has potential applications in various fields, including biomedical sensing, surgical catheters, aircraft and spacecraft engineering, and robotic photonic skin development.},
}
@article {pmid37674934,
year = {2023},
author = {Lugo, ZR and Cinel, C and Jeunet, C and Pichiorri, F and Riccio, A and Wriessnegger, SC},
title = {Editorial: Women in brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1260479},
pmid = {37674934},
issn = {1662-5161},
}
@article {pmid37671039,
year = {2023},
author = {Zrenner, B and Zrenner, C and Balderston, N and Blumberger, DM and Kloiber, S and Laposa, JM and Tadayonnejad, R and Trevizol, AP and Zai, G and Feusner, JD},
title = {Toward personalized circuit-based closed-loop brain-interventions in psychiatry: using symptom provocation to extract EEG-markers of brain circuit activity.},
journal = {Frontiers in neural circuits},
volume = {17},
number = {},
pages = {1208930},
pmid = {37671039},
issn = {1662-5110},
support = {R01 MH121520/MH/NIMH NIH HHS/United States ; R21 MH128815/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Psychiatry ; Transcranial Magnetic Stimulation ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Symptom provocation is a well-established component of psychiatric research and therapy. It is hypothesized that specific activation of those brain circuits involved in the symptomatic expression of a brain pathology makes the relevant neural substrate accessible as a target for therapeutic interventions. For example, in the treatment of obsessive-compulsive disorder (OCD), symptom provocation is an important part of psychotherapy and is also performed prior to therapeutic brain stimulation with transcranial magnetic stimulation (TMS). Here, we discuss the potential of symptom provocation to isolate neurophysiological biomarkers reflecting the fluctuating activity of relevant brain networks with the goal of subsequently using these markers as targets to guide therapy. We put forward a general experimental framework based on the rapid switching between psychiatric symptom states. This enable neurophysiological measures to be derived from EEG and/or TMS-evoked EEG measures of brain activity during both states. By subtracting the data recorded during the baseline state from that recorded during the provoked state, the resulting contrast would ideally isolate the specific neural circuits differentially activated during the expression of symptoms. A similar approach enables the design of effective classifiers of brain activity from EEG data in Brain-Computer Interfaces (BCI). To obtain reliable contrast data, psychiatric state switching needs to be achieved multiple times during a continuous recording so that slow changes of brain activity affect both conditions equally. This is achieved easily for conditions that can be controlled intentionally, such as motor imagery, attention, or memory retention. With regard to psychiatric symptoms, an increase can often be provoked effectively relatively easily, however, it can be difficult to reliably and rapidly return to a baseline state. Here, we review different approaches to return from a provoked state to a baseline state and how these may be applied to different symptoms occurring in different psychiatric disorders.},
}
@article {pmid37670502,
year = {2024},
author = {Kumawat, J and Yadav, A and Yadav, K and Gaur, KL},
title = {Comparison of Spectral Analysis of Gamma Band Activity During Actual and Imagined Movements as a Cognitive Tool.},
journal = {Clinical EEG and neuroscience},
volume = {55},
number = {3},
pages = {340-346},
doi = {10.1177/15500594231197100},
pmid = {37670502},
issn = {2169-5202},
mesh = {Humans ; Male ; Gamma Rays ; *Mirror Neurons ; Electroencephalography/methods ; Movement/physiology ; Cognition ; },
abstract = {Background. Imagined motor movement is a cognitive process in which a subject imagines a movement without doing it, which activates similar brain regions as during actual motor movement. Brain gamma band activity (GBA) is linked to cognitive functions such as perception, attention, memory, awareness, synaptic plasticity, motor control, and Imagination. Motor imagery can be used in sports to improve performance, raising the possibility of using it as a rehabilitation method through brain plasticity through mirror neurons. Method. A comparative observational study was conducted on 56 healthy male subjects after obtaining clearance from the Ethics Committee. EEG recordings for GBA were taken for resting, real, and imaginary motor movements and compared. The power spectrum of gamma waves was analyzed using the Kruskal-Wallis test; a p-value <.05 was considered significant. Results. The brain gamma rhythm amplitude was statistically increased during both actual and imaginary motor movement compared to baseline (resting stage) in most of the regions of the brain except the occipital region. There was no significant difference in GBA between real and imaginary movements. Conclusions. Increased gamma rhythm amplitude during both actual and imaginary motor movement than baseline (resting stage) indicating raised brain cognitive activity during both types of movements. There was no potential difference between real and imaginary movements suggesting that the real movement can be replaced by the imaginary movement to enhance work performance through mirror therapy.},
}
@article {pmid37670474,
year = {2024},
author = {Lima, EO and Silva, LM and Melo, ALV and D'arruda, JVT and Alexandre de Albuquerque, M and Ramos de Souza Neto, JM and Araújo de Oliveira, E and Andrade, SM},
title = {Transcranial Direct Current Stimulation and Brain-Computer Interfaces for Improving Post-Stroke Recovery: A Systematic Review and Meta-Analysis.},
journal = {Clinical rehabilitation},
volume = {38},
number = {1},
pages = {3-14},
doi = {10.1177/02692155231200086},
pmid = {37670474},
issn = {1477-0873},
mesh = {Humans ; *Transcranial Direct Current Stimulation ; *Brain-Computer Interfaces ; *Stroke ; *Stroke Rehabilitation ; Functional Status ; },
abstract = {OBJECTIVE: This study aimed to evaluate the effectiveness of transcranial direct current stimulation associated with brain-computer interface in stroke patients.<br><br>DATA SOURCES: The PubMed, Central, PEDro, Web of Science, SCOPUS, PsycINFO Ovid, CINAHL EBSCO, EMBASE, and ScienceDirect databases were searched from inception to April 2023 for randomized controlled studies reporting the effects of active transcranial direct current stimulation associated with brain-computer interface to a transcranial direct current stimulation sham associated with brain-computer interface condition on the outcome measure (motor performance and functional independence).<br><br>REVIEW METHODS: We searched for full-text articles which had investigated the effect of transcranial direct current stimulation associated with brain-computer interface on motor performance in the upper extremities in stroke patients. The standardized mean differences derived from the change in scores between pretreatment and post-treatment were adopted as the effect size measure, with a 95% confidence interval. Possible sources of heterogeneity were analyzed by performing subgroup analyses in order to examine the moderating effects for one variable: the level of injury severity.<br><br>RESULTS: Nine studies were included in the qualitative synthesis and the meta-analysis. The findings of the conducted analyses indicated there is not enough evidence to suggest that active transcranial direct current stimulation associated with brain-computer interface is more efficient in motor performance and functional independence when compared to sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone. In addition, the quality of evidence was rated very low. A subgroup analysis was performed for the motor performance outcome considering the injury severity level.<br><br>CONCLUSION: We found evidence that transcranial direct current stimulation associated with brain-computer interface was not more beneficial than sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone.},
}
@article {pmid37670009,
year = {2023},
author = {Dreyer, P and Roc, A and Pillette, L and Rimbert, S and Lotte, F},
title = {A large EEG database with users' profile information for motor imagery brain-computer interface research.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {580},
pmid = {37670009},
issn = {2052-4463},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; *Electroencephalography ; Hand ; },
abstract = {We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users' profiles and their BCI performances, (2) studying how EEG signals properties varies for different users' profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users' profile information into the design of EEG signal classification algorithms.},
}
@article {pmid37669261,
year = {2023},
author = {Ramirez-Nava, AG and Mercado-Gutierrez, JA and Quinzaños-Fresnedo, J and Toledo-Peral, C and Vega-Martinez, G and Gutierrez, MI and Pacheco-Gallegos, MDR and Hernández-Arenas, C and Gutiérrez-Martínez, J},
title = {Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1221160},
pmid = {37669261},
issn = {1664-2295},
abstract = {INTRODUCTION: Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients.<br><br>METHODS: A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (&#x1d6fc;&#x2009;=&#x2009;0.05).<br><br>RESULTS: After training, we found statistically significant differences between groups for FMA (p&#x2009;=&#x2009;0.012), ARAT (p&#x2009;<&#x2009;0.001), and FIM (p&#x2009;=&#x2009;0.025) scales.<br><br>DISCUSSION: It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity.<br><br>CONCLUSION: The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.},
}
@article {pmid37668293,
year = {2024},
author = {Gomez-Andres, A and Cerda-Company, X and Cucurell, D and Cunillera, T and Rodríguez-Fornells, A},
title = {Decoding agency attribution using single trial error-related brain potentials.},
journal = {Psychophysiology},
volume = {61},
number = {1},
pages = {e14434},
doi = {10.1111/psyp.14434},
pmid = {37668293},
issn = {1469-8986},
support = {BES-2016-078889//Ministerio de Econom&#xed;a y Competitividad/ ; PSI2015-69178-P//Ministerio de Econom&#xed;a y Competitividad/ ; PSI2016-79678-P//Ministerio de Econom&#xed;a y Competitividad/ ; },
mesh = {Humans ; Male ; Female ; *Brain ; Electroencephalography/methods ; Learning ; *Brain-Computer Interfaces ; },
abstract = {Being able to distinguish between self and externally generated actions is a key factor influencing learning and adaptive behavior. Previous literature has highlighted that whenever a person makes or perceives an error, a series of error-related potentials (ErrPs) can be detected in the electroencephalographic (EEG) signal, such as the error-related negativity (ERN) component. Recently, ErrPs have gained a lot of interest for the use in brain-computer interface (BCI) applications, which give the user the ability to communicate by means of decoding his/her brain activity. Here, we explored the feasibility of employing a support vector machine classifier to accurately disentangle self-agency errors from other-agency errors from the EEG signal at a single-trial level in a sample of 23 participants. Our results confirmed the viability of correctly disentangling self/internal versus other/external agency-error attributions at different stages of brain processing based on the latency and the spatial topographical distribution of key ErrP features, namely, the ERN and P600 components, respectively. These results offer a new perspective on how to distinguish self versus externally generated errors providing new potential implementations on BCI systems.},
}
@article {pmid37668071,
year = {2024},
author = {Kheirabadi, R and Omranpour, H},
title = {Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {27},
number = {12},
pages = {1649-1663},
doi = {10.1080/10255842.2023.2252953},
pmid = {37668071},
issn = {1476-8259},
mesh = {Humans ; *Electroencephalography/statistics &amp; numerical data/methods ; *Emotions/physiology ; Brain-Computer Interfaces ; Pattern Recognition, Automated/methods ; Cluster Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.},
}
@article {pmid37666761,
year = {2023},
author = {Li, Q and Zhang, T and Song, Y and Liu, Y and Sun, M},
title = {[A design and evaluation of wearable p300 brain-computer interface system based on Hololens2].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {4},
pages = {709-717},
pmid = {37666761},
issn = {1001-5515},
mesh = {Humans ; *Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Quality of Life ; Event-Related Potentials, P300 ; *Wearable Electronic Devices ; },
abstract = {Patients with amyotrophic lateral sclerosis (ALS) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.},
}
@article {pmid37666760,
year = {2023},
author = {Li, K and Lu, J and Yu, R and Zhang, R and Chen, M},
title = {[Alterations of β-γ coupling of scalp electroencephalography during epilepsy].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {4},
pages = {700-708},
pmid = {37666760},
issn = {1001-5515},
mesh = {Humans ; *Scalp ; *Epilepsy/diagnosis ; Brain ; Electroencephalography ; },
abstract = {Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.},
}
@article {pmid37666758,
year = {2023},
author = {Luo, R and Dou, X and Xiao, X and Wu, Q and Xu, M and Ming, D},
title = {[Recognition of high-frequency steady-state visual evoked potential for brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {4},
pages = {683-691},
pmid = {37666758},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms ; Discriminant Analysis ; Electroencephalography ; },
abstract = {Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.},
}
@article {pmid37666246,
year = {2023},
author = {Lim, J and Wang, PT and Bashford, L and Kellis, S and Shaw, SJ and Gong, H and Armacost, M and Heydari, P and Do, AH and Andersen, RA and Liu, CY and Nenadic, Z},
title = {Suppression of cortical electrostimulation artifacts using pre-whitening and null projection.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf68b},
pmid = {37666246},
issn = {1741-2552},
mesh = {Humans ; *Artifacts ; Electrocorticography ; Electroencephalography ; Amplifiers, Electronic ; *Electric Stimulation Therapy ; },
abstract = {Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.},
}
@article {pmid37665696,
year = {2023},
author = {Sun, Y and Shen, A and Du, C and Sun, J and Chen, X and Gao, X},
title = {A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution Without Stimulation Artifacts.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3566-3575},
doi = {10.1109/TNSRE.2023.3311750},
pmid = {37665696},
issn = {1558-0210},
mesh = {Animals ; Humans ; *Artifacts ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Quality of Life ; Head ; },
abstract = {The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.},
}
@article {pmid37663037,
year = {2023},
author = {Cui, J and Yuan, L and Wang, Z and Li, R and Jiang, T},
title = {Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1232925},
pmid = {37663037},
issn = {1662-5188},
abstract = {INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions.<br><br>METHODS: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI.<br><br>RESULTS: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results.<br><br>DISCUSSION: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.},
}
@article {pmid37662108,
year = {2023},
author = {Huang, Y and Zheng, J and Xu, B and Li, X and Liu, Y and Wang, Z and Feng, H and Cao, S},
title = {An improved model using convolutional sliding window-attention network for motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1204385},
pmid = {37662108},
issn = {1662-4548},
abstract = {INTRODUCTION: The classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.<br><br>METHODS: To solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.<br><br>RESULTS: The model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.<br><br>DISCUSSION: The experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation.},
}
@article {pmid37662099,
year = {2023},
author = {Zhang, J and Li, K and Yang, B and Han, X},
title = {Local and global convolutional transformer-based motor imagery EEG classification.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1219988},
pmid = {37662099},
issn = {1662-4548},
abstract = {Transformer, a deep learning model with the self-attention mechanism, combined with the convolution neural network (CNN) has been successfully applied for decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI). However, the extremely non-linear, nonstationary characteristics of the EEG signals limits the effectiveness and efficiency of the deep learning methods. In addition, the variety of subjects and the experimental sessions impact the model adaptability. In this study, we propose a local and global convolutional transformer-based approach for MI-EEG classification. The local transformer encoder is combined to dynamically extract temporal features and make up for the shortcomings of the CNN model. The spatial features from all channels and the difference in hemispheres are obtained to improve the robustness of the model. To acquire adequate temporal-spatial feature representations, we combine the global transformer encoder and Densely Connected Network to improve the information flow and reuse. To validate the performance of the proposed model, three scenarios including within-session, cross-session and two-session are designed. In the experiments, the proposed method achieves up to 1.46%, 7.49% and 7.46% accuracy improvement respectively in the three scenarios for the public Korean dataset compared with current state-of-the-art models. For the BCI competition IV 2a dataset, the proposed model also achieves a 2.12% and 2.21% improvement for the cross-session and two-session scenarios respectively. The results confirm that the proposed approach can effectively extract much richer set of MI features from the EEG signals and improve the performance in the BCI applications.},
}
@article {pmid37659393,
year = {2023},
author = {Yu, H and Qi, Y and Pan, G},
title = {NeuSort: an automatic adaptive spike sorting approach with neuromorphic models.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf61d},
pmid = {37659393},
issn = {1741-2552},
mesh = {*Neurons ; *Brain-Computer Interfaces ; Cell Movement ; Electrodes ; Learning ; },
abstract = {Objective.Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.Approach.NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.Results.Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.Significance.NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.},
}
@article {pmid37659115,
year = {2023},
author = {Zhang, D and Li, H and Xie, J and Li, D},
title = {MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {167},
number = {},
pages = {183-198},
doi = {10.1016/j.neunet.2023.08.008},
pmid = {37659115},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; Learning ; Electroencephalography ; Imagination ; Algorithms ; },
abstract = {Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain-computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under https://github.com/zhangdx21/MI-DAGSC.},
}
@article {pmid37657190,
year = {2023},
author = {Leinders, S and Vansteensel, MJ and Piantoni, G and Branco, MP and Freudenburg, ZV and Gebbink, TA and Pels, EGM and Raemaekers, MAH and Schippers, A and Aarnoutse, EJ and Ramsey, NF},
title = {Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {155},
number = {},
pages = {1-15},
doi = {10.1016/j.clinph.2023.08.003},
pmid = {37657190},
issn = {1872-8952},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; *Locked-In Syndrome/physiopathology/diagnostic imaging ; Female ; *Electrodes, Implanted ; *Electrocorticography/methods ; Adult ; Aged ; Brain Mapping/methods ; Sensorimotor Cortex/physiopathology/diagnostic imaging ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome.<br><br>METHODS: Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively).<br><br>RESULTS: Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95&#xa0;Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control.<br><br>CONCLUSIONS: Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates.<br><br>SIGNIFICANCE: If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.},
}
@article {pmid37652289,
year = {2023},
author = {Gu, B and Wang, K and Chen, L and He, J and Zhang, D and Xu, M and Wang, Z and Ming, D},
title = {Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.},
journal = {Neuroscience},
volume = {530},
number = {},
pages = {56-65},
doi = {10.1016/j.neuroscience.2023.08.032},
pmid = {37652289},
issn = {1873-7544},
mesh = {Humans ; *Electroencephalography/methods ; Imagery, Psychotherapy/methods ; Hand ; *Brain-Computer Interfaces ; Movement ; Imagination ; },
abstract = {Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.},
}
@article {pmid37651476,
year = {2023},
author = {Meng, L and Jiang, X and Huang, J and Li, W and Luo, H and Wu, D},
title = {User Identity Protection in EEG-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3576-3586},
doi = {10.1109/TNSRE.2023.3310883},
pmid = {37651476},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Brain ; Communication ; },
abstract = {A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.},
}
@article {pmid37650101,
year = {2023},
author = {Vargas, G and Araya, D and Sepulveda, P and Rodriguez-Fernandez, M and Friston, KJ and Sitaram, R and El-Deredy, W},
title = {Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1212549},
pmid = {37650101},
issn = {1662-4548},
support = {/WT_/Wellcome Trust/United Kingdom ; },
abstract = {INTRODUCTION: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.<br><br>METHODS: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.<br><br>RESULTS: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.<br><br>DISCUSSION: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.},
}
@article {pmid37649593,
year = {2023},
author = {Sun, Y and Zabihi, M and Li, Q and Li, X and Kim, BJ and Ubogu, EE and Raja, SN and Wesselmann, U and Zhao, C},
title = {Drug Permeability: From the Blood-Brain Barrier to the Peripheral Nerve Barriers.},
journal = {Advanced therapeutics},
volume = {6},
number = {4},
pages = {},
pmid = {37649593},
issn = {2366-3987},
support = {R01 GM144388/GM/NIGMS NIH HHS/United States ; R21 NS078226/NS/NINDS NIH HHS/United States ; R01 NS075212/NS/NINDS NIH HHS/United States ; R15 GM139193/GM/NIGMS NIH HHS/United States ; R01 NS026363/NS/NINDS NIH HHS/United States ; R21 NS073702/NS/NINDS NIH HHS/United States ; },
abstract = {Drug delivery into the peripheral nerves and nerve roots has important implications for effective local anesthesia and treatment of peripheral neuropathies and chronic neuropathic pain. Similar to drugs that need to cross the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) to gain access to the central nervous system (CNS), drugs must cross the peripheral nerve barriers (PNB), formed by the perineurium and blood-nerve barrier (BNB) to modulate peripheral axons. Despite significant progress made to develop effective strategies to enhance BBB permeability in therapeutic drug design, efforts to enhance drug permeability and retention in peripheral nerves and nerve roots are relatively understudied. Guided by knowledge describing structural, molecular and functional similarities between restrictive neural barriers in the CNS and peripheral nervous system (PNS), we hypothesize that certain CNS drug delivery strategies are adaptable for peripheral nerve drug delivery. In this review, we describe the molecular, structural and functional similarities and differences between the BBB and PNB, summarize and compare existing CNS and peripheral nerve drug delivery strategies, and discuss the potential application of selected CNS delivery strategies to improve efficacious drug entry for peripheral nerve disorders.},
}
@article {pmid37647178,
year = {2024},
author = {Wang, H and Qi, Y and Yao, L and Wang, Y and Farina, D and Pan, G},
title = {A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {12},
pages = {17534-17548},
doi = {10.1109/TNNLS.2023.3305621},
pmid = {37647178},
issn = {2162-2388},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Machine Learning ; Algorithms ; Imagination/physiology ; Brain/physiology ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.},
}
@article {pmid37645922,
year = {2023},
author = {Natraj, N and Seko, S and Abiri, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K},
title = {Flexible regulation of representations on a drifting manifold enables long-term stable complex neuroprosthetic control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37645922},
issn = {2692-8205},
support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; },
abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what is the representational stability of simple actions, particularly those that are well-rehearsed in humans, and how it changes in new contexts. Using an electrocorticography brain-computer interface (BCI), we found that the mesoscale manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. Interestingly, however, the manifold's absolute location demonstrated day-to-day drift. Strikingly, representational statistics, especially variance, could be flexibly regulated to increase discernability during BCI control without somatotopic changes. Discernability strengthened with practice and was specific to the BCI, demonstrating remarkable contextual specificity. Accounting for drift, and leveraging the flexibility of representations, allowed neuroprosthetic control of a robotic arm and hand for over 7 months without recalibration. Our study offers insight into how electrocorticography can both track representational statistics across long periods and allow long-term complex neuroprosthetic control.},
}
@article {pmid37645689,
year = {2023},
author = {Park, HJ and Lee, B},
title = {Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1186594},
pmid = {37645689},
issn = {1662-5161},
abstract = {INTRODUCTION: In this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants.<br><br>MATERIALS AND METHODS: First, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison.<br><br>RESULTS: We achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix.<br><br>DISCUSSION: Imagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.},
}
@article {pmid37643110,
year = {2023},
author = {Dong, Y and Tang, X and Li, Q and Wang, Y and Jiang, N and Tian, L and Zheng, Y and Li, X and Zhao, S and Li, G and Fang, P},
title = {An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3524-3534},
doi = {10.1109/TNSRE.2023.3309815},
pmid = {37643110},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; Artifacts ; *Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Artifact Removal Wasserstein Generative Adversarial Network (AR-WGAN), where the well-trained model can decompose input EEG, detect and delete artifacts, and then reconstruct denoised signals within a short time. The proposed approach was systematically compared with commonly used denoising methods including Denoised AutoEncoder, Wiener Filter, and Empirical Mode Decomposition, with both public and self-collected datasets. The experimental results proved the promising performance of AR-WGAN on automatic artifact removal for massive data across subjects, with correlation coefficient up to 0.726±0.033, and temporal and spatial relative root-mean-square error as low as 0.176±0.046 and 0.761±0.046, respectively. This work may demonstrate the proposed AR-WGAN as a high-performance end-to-end method for EEG denoising, with many on-line applications in clinical EEG monitoring and brain-computer interfaces.},
}
@article {pmid37640768,
year = {2023},
author = {Saal, J and Ottenhoff, MC and Kubben, PL and Colon, AJ and Goulis, S and van Dijk, JP and Krusienski, DJ and Herff, C},
title = {Towards hippocampal navigation for brain-computer interfaces.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {14021},
pmid = {37640768},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Hand ; Hippocampus ; Intention ; Movement ; },
abstract = {Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.},
}
@article {pmid37639501,
year = {2023},
author = {Cui, Z and Wu, B and Blank, I and Yu, Y and Gu, J and Zhou, T and Zhang, Y and Wang, W and Liu, Y},
title = {TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.},
journal = {Journal of agricultural and food chemistry},
volume = {71},
number = {36},
pages = {13430-13439},
doi = {10.1021/acs.jafc.3c04611},
pmid = {37639501},
issn = {1520-5118},
mesh = {Humans ; *Taste ; *Electroencephalography ; Machine Learning ; Algorithms ; Food ; },
abstract = {In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.},
}
@article {pmid37639414,
year = {2023},
author = {Lan, W and Wang, R and He, Y and Zong, Y and Leng, Y and Iramina, K and Zheng, W and Ge, S},
title = {Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3545-3555},
doi = {10.1109/TNSRE.2023.3309543},
pmid = {37639414},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Benchmarking ; Neurologic Examination ; Recognition, Psychology ; },
abstract = {The target recognition performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces can be significantly improved with a training-based approach. However, the training procedure is time consuming and often causes fatigue. Consequently, the number of training data should be limited, which may reduce the classification performance. Thus, how to improve classification accuracy without increasing the training time is crucial to SSVEP-based BCI system. This study proposes a transfer-related component analysis (TransRCA) method for addressing the above issue. In this method, the SSVEP-related components are extracted from a small number of training data of the current individual and combined with those extracted from a large number of existing training data of other individuals. The TransRCA method maximizes not only the inter-trial covariances between the source and target subjects, but also the correlation between the reference signals and SSVEP signals from the source and target subjects. The proposed method was validated on the SSVEP public Benchmark and BETA datasets, and the classification accuracy and information transmission rate of the ensemble version of the proposed TransRCA method were compared with those of the state-of-the-art eCCA, eTRCA, ttCCA, LSTeTRCA, and eIISMC methods on both datasets. The comparison results indicate that the proposed method provides a superior performance compared with these state-of-the-art methods, and thus has high potential for the development of a SSVEP-based brain-computer interface system with high classification performance that only uses a small number of training data.},
}
@article {pmid37636067,
year = {2023},
author = {Iwane, F and Sobolewski, A and Chavarriaga, R and Millán, JDR},
title = {EEG error-related potentials encode magnitude of errors and individual perceptual thresholds.},
journal = {iScience},
volume = {26},
number = {9},
pages = {107524},
pmid = {37636067},
issn = {2589-0042},
abstract = {Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks.},
}
@article {pmid37635251,
year = {2023},
author = {Prescott, RA and Pankow, AP and de Vries, M and Crosse, KM and Patel, RS and Alu, M and Loomis, C and Torres, V and Koralov, S and Ivanova, E and Dittmann, M and Rosenberg, BR},
title = {A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.},
journal = {Respiratory research},
volume = {24},
number = {1},
pages = {213},
pmid = {37635251},
issn = {1465-993X},
support = {R01 AI143639/AI/NIAID NIH HHS/United States ; R01 AI151029/AI/NIAID NIH HHS/United States ; S10 OD026880/OD/NIH HHS/United States ; U01 AI150748/AI/NIAID NIH HHS/United States ; },
mesh = {Humans ; Epithelium ; *Epithelial Cells ; Cell Differentiation ; *Interferons ; Gene Expression ; },
abstract = {BACKGROUND: The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial (HAE) cultures at air-liquid interface are a physiologically relevant in vitro model of this heterogeneous tissue and have enabled numerous studies of airway disease. HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining the capacity for differentiation to HAE. However, gene expression and innate immune function in BCi-NS1.1-derived versus primary-derived HAE cultures have not been fully characterized.<br><br>METHODS: BCi-NS1.1-derived HAE cultures (n&#x2009;=&#x2009;3 independent differentiations) and primary-derived HAE cultures (n&#x2009;=&#x2009;3 distinct donors) were characterized by immunofluorescence and single cell RNA-Seq (scRNA-Seq). Innate immune functions were evaluated in response to interferon stimulation and to infection with viral and bacterial respiratory pathogens.<br><br>RESULTS: We confirm at high resolution that BCi-NS1.1- and primary-derived HAE cultures are largely similar in morphology, cell type composition, and overall gene expression patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1-derived HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus.<br><br>CONCLUSIONS: Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.},
}
@article {pmid37629532,
year = {2023},
author = {Vatrano, M and Nemirovsky, IE and Tonin, P and Riganello, F},
title = {Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges.},
journal = {Life (Basel, Switzerland)},
volume = {13},
number = {8},
pages = {},
pmid = {37629532},
issn = {2075-1729},
abstract = {Neurofeedback is a non-invasive therapeutic approach that has gained traction in recent years, showing promising results for various neurological and psychiatric conditions. It involves real-time monitoring of brain activity, allowing individuals to gain control over their own brainwaves and improve cognitive performance or alleviate symptoms. The use of electroencephalography (EEG), such as brain-computer interface (BCI), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS), has been instrumental in developing neurofeedback techniques. However, the application of these tools in patients with disorders of consciousness (DoC) presents unique challenges. In this narrative review, we explore the use of neurofeedback in treating patients with DoC. More specifically, we discuss the advantages and challenges of using tools such as EEG neurofeedback, tDCS, TMS, and BCI for these conditions. Ultimately, we hope to provide the neuroscientific community with a comprehensive overview of neurofeedback and emphasize its potential therapeutic applications in severe cases of impaired consciousness levels.},
}
@article {pmid37628493,
year = {2023},
author = {Moreno Escobar, JJ and Morales Matamoros, O and Aguilar Del Villar, EY and Quintana Espinosa, H and Chanona Hernández, L},
title = {DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy.},
journal = {Healthcare (Basel, Switzerland)},
volume = {11},
number = {16},
pages = {},
pmid = {37628493},
issn = {2227-9032},
support = {20230629//Instituto Polit&#xe9;cnico Nacional/ ; },
abstract = {In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down's Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down's Syndrome Dataset (DSDS) has promising advantages in the field of brain-computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability.},
}
@article {pmid37627797,
year = {2023},
author = {Li, M and Qi, Y and Pan, G},
title = {Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {8},
pages = {},
pmid = {37627797},
issn = {2306-5354},
support = {2021ZD0200400//China Brain Project/ ; U1909202 and 61925603//National Natural Science Foundation of China/ ; 2020C03004//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.},
}
@article {pmid37626528,
year = {2023},
author = {Cervantes, JA and López, S and Cervantes, S and Hernández, A and Duarte, H},
title = {Social Robots and Brain-Computer Interface Video Games for Dealing with Attention Deficit Hyperactivity Disorder: A Systematic Review.},
journal = {Brain sciences},
volume = {13},
number = {8},
pages = {},
pmid = {37626528},
issn = {2076-3425},
abstract = {Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain-computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare.},
}
@article {pmid37626506,
year = {2023},
author = {Canale, A and Urbanelli, A and Gragnano, M and Bordino, V and Albera, A},
title = {Comparison of Active Bone Conduction Hearing Implant Systems in Unilateral and Bilateral Conductive or Mixed Hearing Loss.},
journal = {Brain sciences},
volume = {13},
number = {8},
pages = {},
pmid = {37626506},
issn = {2076-3425},
abstract = {BACKGROUND: To assess and compare binaural benefits and subjective satisfaction of active bone conduction implant (BCI) in patients with bilateral conductive or mixed hearing loss fitted with bilateral BCI and patients with monaural conductive hearing loss fitted with monaural BCI.<br><br>METHODS: ITA Matrix test was performed both on patients affected by bilateral conductive or mixed hearing loss fitted with monaural bone conduction hearing implant (Bonebridge, Med-El) before and after implantation of contralateral bone conduction hearing implant and on patients with monaural conductive or mixed hearing loss before and after implantation of monaural BCI. The Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire was administered to both groups of subjects and the results were compared with each other.<br><br>RESULTS: Patients of group 1 reported a difference of 4.66 dB in the summation setting compared to 0.79 dB of group 2 (p < 0.05). In the squelch setting, group 1 showed a difference of 2.42 dB compared to 1.53 dB of group 2 (p = 0.85). In the head shadow setting, patients of group 1 reported a difference of 7.5 dB, compared to 4.61 dB of group 2 (p = 0.34). As for the APHAB questionnaire, group 1 reported a mean global score difference of 11.10% while group 2 showed a difference of -4.00%.<br><br>CONCLUSIONS: Bilateral BCI in patients affected by bilateral conductive or mixed hearing loss might show more advantages in terms of sound localisation, speech perception in noise and subjective satisfaction if compared to unilateral BCI fitting in patients affected by unilateral conductive hearing impairment. This may be explained by the different individual transcranial attenuation of each subject, which might lead to different outcomes in terms of binaural hearing achievement. On the other hand, patients with unilateral conductive or mixed hearing loss fitted with monaural BCI achieved good results in terms of binaural hearing and for this reason, there is no absolute contraindication to implantation in those patients.},
}
@article {pmid37626499,
year = {2023},
author = {Cai, J and Xu, M and Cai, H and Jiang, Y and Zheng, X and Sun, H and Sun, Y and Sun, Y},
title = {Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions.},
journal = {Brain sciences},
volume = {13},
number = {8},
pages = {},
pmid = {37626499},
issn = {2076-3425},
support = {LR23F010003//Zhejiang Provincial Natural Science Foundation/ ; 82172056//National Natural Science Foundation of China/ ; },
abstract = {Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS (p = 0.011), while local efficiency was significantly increased in SS than in CS (p = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS (p = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.},
}
@article {pmid37625688,
year = {2023},
author = {Zapała, D and Augustynowicz, P and Tokovarov, M and Iwanowicz, P and Droździel, P},
title = {Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery.},
journal = {Neuroscience},
volume = {532},
number = {},
pages = {37-49},
doi = {10.1016/j.neuroscience.2023.08.022},
pmid = {37625688},
issn = {1873-7544},
mesh = {*Electroencephalography ; Brain/physiology ; Imagery, Psychotherapy ; Movement/physiology ; Kinesthesis ; *Brain-Computer Interfaces ; Imagination/physiology ; },
abstract = {It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.},
}
@article {pmid37624718,
year = {2023},
author = {Zhang, Y and Qian, K and Xie, SQ and Shi, C and Li, J and Zhang, ZQ},
title = {SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3448-3458},
doi = {10.1109/TNSRE.2023.3308778},
pmid = {37624718},
issn = {1558-0210},
mesh = {Humans ; Bayes Theorem ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Robotic Surgical Procedures ; *Robotics ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.},
}
@article {pmid37624717,
year = {2023},
author = {Zhang, R and Dong, G and Li, M and Tang, Z and Chen, X and Cui, H},
title = {A Calibration-Free Hybrid BCI Speller System Based on High-Frequency SSVEP and sEMG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3492-3500},
doi = {10.1109/TNSRE.2023.3308779},
pmid = {37624717},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electromyography ; Evoked Potentials, Visual ; Calibration ; Healthy Volunteers ; },
abstract = {Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.},
}
@article {pmid37623163,
year = {2023},
author = {Blom, C and Reis, A and Lencastre, L},
title = {Caregiver Quality of Life: Satisfaction and Burnout.},
journal = {International journal of environmental research and public health},
volume = {20},
number = {16},
pages = {},
pmid = {37623163},
issn = {1660-4601},
mesh = {Adult ; Humans ; *Caregivers ; *Quality of Life ; Cross-Sectional Studies ; Burnout, Psychological ; Health Personnel ; },
abstract = {Informal caregivers (ICs) of cancer patients play a crucial role in health care. Several of the challenges they face can affect their quality of life (QoL). This cross-sectional study explored role of burnout and caregiving satisfaction in their relationship to QoL. Portuguese ICs of adult cancer patients (N = 92) answered a sociodemographic and caregiving questionnaire, the WHOQOL-SRPB BREF, assessing physical, psychological, social, environmental, and spiritual QoL domains; the Maslach Burnout Interview, assessing the dimensions of depersonalization, emotional exhaustion, and personal accomplishment; and a Visual Analogic Scale on caregiving satisfaction. We tested correlations and a parallel mediation model for each domain of QoL, considering burnout dimensions as possible mediators between satisfaction and QoL domains. Our results show that satisfaction, burnout dimensions, and almost all QoL domains are correlated. Together, burnout dimensions seem to mediate the relationship between caregiving satisfaction and psychological, environmental, and spiritual QoL. Satisfaction had a significant indirect effect solely through emotional exhaustion on psychological QoL (β = 1.615, 95% BCI [0.590; 2.849]), environmental QoL (β = 0.904, 95% BCI [0.164; 1.876]), and spiritual QoL (β = 0.816, 95% BCI [0.019; 1.792]). It seems essential for mental health professionals to address these dimensions when providing support to an IC.},
}
@article {pmid37621168,
year = {2023},
author = {Tesch, ME},
title = {Precision medicine in extended adjuvant endocrine therapy for breast cancer.},
journal = {Current opinion in oncology},
volume = {35},
number = {6},
pages = {453-460},
doi = {10.1097/CCO.0000000000000985},
pmid = {37621168},
issn = {1531-703X},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/genetics/pathology ; Antineoplastic Agents, Hormonal/therapeutic use ; Precision Medicine ; Prognosis ; Combined Modality Therapy ; Chemotherapy, Adjuvant ; Neoplasm Recurrence, Local/drug therapy/pathology ; },
abstract = {PURPOSE OF REVIEW: In this review, the evolving role of currently available genomic assays for hormone receptor-positive, early-stage breast cancer in the selection of patients for extended adjuvant endocrine therapy will be discussed.<br><br>RECENT FINDINGS: Several studies have investigated the prognostic performance of the Oncotype DX, Breast Cancer Index (BCI), Prosigna, and EndoPredict genomic assays in the late recurrence setting (>5&#x200a;years after diagnosis), beyond standardly used clinicopathologic parameters, with mixed results. Recently, BCI has also been validated to predict the likelihood of benefit from extended endocrine therapy, though certain data limitations may need to be addressed to justify routine use in clinical practice.<br><br>SUMMARY: Even after 5&#x200a;years of adjuvant endocrine therapy, patients with hormone receptor-positive breast cancer have a significant risk for late recurrence, including distant metastases, that might be prevented with longer durations of endocrine therapy. However, the added toxicity and variable benefit derived from extended endocrine therapy make optimal patient selection crucial. Genomic assays are in development to risk-stratify patients for late recurrence and determine efficacy of extended endocrine therapy, with the aim to help guide extended endocrine therapy decisions for clinicians and individualize treatment strategies for patients.},
}
@article {pmid37619325,
year = {2023},
author = {Borra, D and Mondini, V and Magosso, E and Müller-Putz, GR},
title = {Decoding movement kinematics from EEG using an interpretable convolutional neural network.},
journal = {Computers in biology and medicine},
volume = {165},
number = {},
pages = {107323},
doi = {10.1016/j.compbiomed.2023.107323},
pmid = {37619325},
issn = {1879-0534},
mesh = {Humans ; Biomechanical Phenomena ; *Machine Learning ; Neural Networks, Computer ; Electroencephalography ; Movement ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding lack in the interpretability of the learned features and are only used to realize within-subject decoders without testing other training approaches potentially beneficial for reducing calibration time, such as transfer learning. Here, we aim to overcome these limitations by using an interpretable convolutional neural network (ICNN) to decode 2-D hand kinematics (position and velocity) from EEG in a pursuit tracking task performed by 13 participants. The ICNN is trained using both within-subject and cross-subject strategies, and also testing the feasibility of transferring the knowledge learned on other subjects on a new one. Moreover, the network eases the interpretation of learned spectral and spatial EEG features. Our ICNN outperformed most of the other state-of-the-art decoders, showing the best trade-off between performance, size, and training time. Furthermore, transfer learning improved kinematics prediction in the low data regime. The network attributed the highest relevance for decoding to the delta-band across all subjects, and to higher frequencies (alpha, beta, low-gamma) for a cluster of them; contralateral central and parieto-occipital sites were the most relevant, reflecting the involvement of sensorimotor, visual and visuo-motor processing. The approach improved the quality of kinematics prediction from the EEG, at the same time allowing interpretation of the most relevant spectral and spatial features.},
}
@article {pmid37616245,
year = {2023},
author = {Porr, B and Bohollo, LM},
title = {BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts.},
journal = {PloS one},
volume = {18},
number = {8},
pages = {e0290446},
pmid = {37616245},
issn = {1932-6203},
mesh = {*Artifacts ; *Brain-Computer Interfaces ; Consciousness ; Facial Muscles ; Electroencephalography ; },
abstract = {Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.},
}
@article {pmid37616137,
year = {2024},
author = {Hu, L and Zhu, J and Chen, S and Zhou, Y and Song, Z and Li, Y},
title = {A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {2},
pages = {504-513},
doi = {10.1109/TBME.2023.3308371},
pmid = {37616137},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electrooculography ; Electroencephalography/methods ; Communication ; *Wearable Electronic Devices ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance.<br><br>METHODS: A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection.<br><br>RESULT: The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03&#xb1;4.65%, an average information transfer rate (ITR) of 31.42&#xb1;7.39 bits/min and an average false-positive rate (FPR) of 1.78%.<br><br>CONCLUSION: The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system.<br><br>SIGNIFICANCE: Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.},
}
@article {pmid37614938,
year = {2023},
author = {Zohny, H and Lyreskog, DM and Singh, I and Savulescu, J},
title = {The Mystery of Mental Integrity: Clarifying Its Relevance to Neurotechnologies.},
journal = {Neuroethics},
volume = {16},
number = {3},
pages = {20},
pmid = {37614938},
issn = {1874-5490},
support = {/WT_/Wellcome Trust/United Kingdom ; },
abstract = {The concept of mental integrity is currently a significant topic in discussions concerning the regulation of neurotechnologies. Technologies such as deep brain stimulation and brain-computer interfaces are believed to pose a unique threat to mental integrity, and some authors have advocated for a legal right to protect it. Despite this, there remains uncertainty about what mental integrity entails and why it is important. Various interpretations of the concept have been proposed, but the literature on the subject is inconclusive. Here we consider a number of possible interpretations and argue that the most plausible one concerns neurotechnologies that bypass one's reasoning capacities, and do so specifically in ways that reliably lead to alienation from one's mental states. This narrows the scope of what constitutes a threat to mental integrity and offers a more precise role for the concept to play in the ethical evaluation of neurotechnologies.},
}
@article {pmid37612500,
year = {2023},
author = {Willett, FR and Kunz, EM and Fan, C and Avansino, DT and Wilson, GH and Choi, EY and Kamdar, F and Glasser, MF and Hochberg, LR and Druckmann, S and Shenoy, KV and Henderson, JM},
title = {A high-performance speech neuroprosthesis.},
journal = {Nature},
volume = {620},
number = {7976},
pages = {1031-1036},
pmid = {37612500},
issn = {1476-4687},
support = {R01 MH060974/MH/NIMH NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 DC019430/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Microelectrodes ; *Paralysis/physiopathology/rehabilitation ; *Speech ; Vocabulary ; *Neural Prostheses ; },
abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text[1,2] or sound[3,4]. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary[1-7]. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI[2]) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record[8] and begins to approach the speed of natural conversation (160 words per minute[9]). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.},
}
@article {pmid37611877,
year = {2023},
author = {Zhang, R and Liu, G and Wen, Y and Zhou, W},
title = {Self-attention-based convolutional neural network and time-frequency common spatial pattern for enhanced motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {398},
number = {},
pages = {109953},
doi = {10.1016/j.jneumeth.2023.109953},
pmid = {37611877},
issn = {1872-678X},
mesh = {*Imagination ; Electroencephalography/methods ; Neural Networks, Computer ; Brain ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {BACKGROUND: Motor imagery (MI) based brain-computer interfaces (BCIs) have promising potentials in the field of neuro-rehabilitation. However, due to individual variations in active brain regions during MI tasks, the challenge of decoding MI EEG signals necessitates improved classification performance for practical application.<br><br>NEW METHOD: This study proposes a self-attention-based Convolutional Neural Network (CNN) in conjunction with a time-frequency common spatial pattern (TFCSP) for enhanced MI classification. Due to the limited availability of training data, a data augmentation strategy is employed to expand the scale of MI EEG datasets. The self-attention-based CNN is trained to automatically extract the temporal and spatial information from EEG signals, allowing the self-attention module to select active channels by calculating EEG channel weights. TFCSP is further implemented to extract multiscale time-frequency-space features from EEG data. Finally, the EEG features derived from TFCSP are concatenated with those from the self-attention-based CNN for MI classification.<br><br>RESULTS: The proposed method is evaluated on two publicly accessible datasets, BCI Competition IV IIa and BCI Competition III IIIa, yielding mean accuracies of 79.28 % and 86.39 %, respectively.<br><br>CONCLUSIONS: Compared with state-of-the-art methods, our approach achieves superior classification results in accuracy. Self-attention-based CNN combining with TFCSP can make full use of the time-frequency-space information of EEG, and enhance the classification performance.},
}
@article {pmid37611567,
year = {2023},
author = {Wang, K and Qiu, S and Wei, W and Yi, W and He, H and Xu, M and Jung, TP and Ming, D},
title = {Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acf345},
pmid = {37611567},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Entropy ; Occipital Lobe ; },
abstract = {Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions.Approach.To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days.Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation.Significance.Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.},
}
@article {pmid37611109,
year = {2023},
author = {Sheng, F and Wang, R and Liang, Z and Wang, X and Platt, ML},
title = {The art of the deal: Deciphering the endowment effect from traders' eyes.},
journal = {Science advances},
volume = {9},
number = {34},
pages = {eadf2115},
pmid = {37611109},
issn = {2375-2548},
mesh = {Humans ; *Arousal ; *Emotions ; },
abstract = {People are often reluctant to trade, a reticence attributed to the endowment effect. The prevailing account attributes the endowment effect to valuation-related bias, manifesting as sellers valuing goods more than buyers, whereas an alternative account attributes it to response-related bias, manifesting as both buyers and sellers tending to stick to the status quo. Here, by tracking and modeling eye activity of buyers and sellers during trading, we accommodate both views within an evidence-accumulation framework. We find that valuation-related bias is indexed by asymmetric attentional allocation between buyers and sellers, whereas response-related bias is indexed by arousal-linked pupillary reactivity. A deal emerges when both buyers and sellers attend to their potential gains and dilate their pupils. Our study provides preliminary evidence for our computational framework of the dynamic processes mediating the endowment effect and identifies physiological biomarkers of deal-making.},
}
@article {pmid37610901,
year = {2023},
author = {Mai, X and Sheng, X and Shu, X and Ding, Y and Zhu, X and Meng, J},
title = {A Calibration-Free Hybrid Approach Combining SSVEP and EOG for Continuous Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3480-3491},
doi = {10.1109/TNSRE.2023.3307814},
pmid = {37610901},
issn = {1558-0210},
mesh = {Calibration ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Electrooculography/instrumentation/standards ; Humans ; Male ; Female ; Young Adult ; Adult ; Saccades ; Bayes Theorem ; },
abstract = {While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets. To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets. Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 ± 1.36%vs. 68.86 ± 1.08% and gaze-shifting time (0.93 ± 0.06s vs. 1.94 ± 0.08s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used. These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration. This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.},
}
@article {pmid37610705,
year = {2023},
author = {Li, X and Bi, R and Ou, X and Han, S and Sheng, Y and Chen, G and Xie, Z and Liu, C and Yue, W and Wang, Y and Hu, W and Guo, SZ},
title = {3D-Printed Intrinsically Stretchable Organic Electrochemical Synaptic Transistor Array.},
journal = {ACS applied materials &amp; interfaces},
volume = {15},
number = {35},
pages = {41656-41665},
doi = {10.1021/acsami.3c07169},
pmid = {37610705},
issn = {1944-8252},
abstract = {Organic electrochemical transistors (OECTs) for skin-like bioelectronics require mechanical stretchability, softness, and cost-effective large-scale manufacturing. However, developing intrinsically stretchable OECTs using a simple and fast-response technique is challenging due to limitations in functional materials, substrate wettability, and integrated processing of multiple materials. In this regard, we propose a fabrication method devised by combining the preparation of a microstructured hydrophilic substrate, multi-material printing of functional inks with varying viscosities, and optimization of the device channel geometries. The resulting intrinsically stretchable OECT array with synaptic properties was successfully manufactured. These devices demonstrated high transconductance (22.5 mS), excellent mechanical softness (Young's modulus ∼ 2.2 MPa), and stretchability (∼30%). Notably, the device also exhibited artificial synapse functionality, mimicking the biological synapse with features such as paired-pulse depression, short-term plasticity, and long-term plasticity. This study showcases a promising strategy for fabricating intrinsically stretchable OECTs and provides valuable insights for the development of brain-computer interfaces.},
}
@article {pmid37610645,
year = {2023},
author = {Xue, R and Li, X and Chen, J and Liang, S and Yu, H and Zhang, Y and Wei, W and Xu, Y and Deng, W and Guo, W and Li, T},
title = {Shared and Distinct Topographic Alterations of Alpha-Range Resting EEG Activity in Schizophrenia, Bipolar Disorder, and Depression.},
journal = {Neuroscience bulletin},
volume = {39},
number = {12},
pages = {1887-1890},
pmid = {37610645},
issn = {1995-8218},
mesh = {Humans ; *Bipolar Disorder ; *Schizophrenia ; Depression ; Electroencephalography ; },
}
@article {pmid37610441,
year = {2024},
author = {Zhang, X and Wang, Y and Jiao, B and Wang, Z and Shi, J and Zhang, Y and Bai, X and Li, Z and Li, S and Bai, R and Sui, B},
title = {Glymphatic system impairment in Alzheimer's disease: associations with perivascular space volume and cognitive function.},
journal = {European radiology},
volume = {34},
number = {2},
pages = {1314-1323},
pmid = {37610441},
issn = {1432-1084},
support = {XDB39000000//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; Z181100001518005//Beijing Municipal Science and Technology Commission/ ; },
mesh = {Humans ; *Glymphatic System/diagnostic imaging ; *Alzheimer Disease/complications/diagnostic imaging ; Positron Emission Tomography Computed Tomography ; Cognition ; *Cognitive Dysfunction/complications ; Hypertrophy ; },
abstract = {OBJECTIVES: To investigate glymphatic function in Alzheimer's disease (AD) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and to explore the associations between DTI-ALPS index and perivascular space (PVS) volume, as well as between DTI-ALPS index and cognitive function.<br><br>METHODS: Thirty patients with PET-CT-confirmed AD (15 AD dementia; 15 mild cognitive impairment due to AD) and 26 age- and sex-matched cognitively normal controls (NCs) were included in this study. All participants underwent neurological MRI and cognitive assessments. Bilateral DTI-ALPS indices were calculated. PVS volume fractions were quantitatively measured at three locations: basal ganglia (BG), centrum semiovale, and lateral ventricle body level. DTI-ALPS index and PVS volume fractions were compared among three groups; correlations among the DTI-ALPS index, PVS volume fraction, and cognitive scales were analyzed.<br><br>RESULTS: Patients with AD dementia showed a significantly lower DTI-ALPS index in the whole brain (p&#x2009;=&#x2009;0.009) and in the left hemisphere (p&#x2009;=&#x2009;0.012) compared with NCs. The BG-PVS volume fraction in patients with AD was significantly larger than the fraction in NCs (p&#x2009;=&#x2009;0.045); it was also negatively correlated with the DTI-ALPS index (r&#x2009;=&#x2009;&#x2009;-&#x2009;0.433, p&#x2009;=&#x2009;0.021). Lower DTI-ALPS index was correlated with worse performance in the Boston Naming Test (&#x3b2;&#x2009;=&#x2009;0.515, p&#x2009;=&#x2009;0.008), Trail Making Test A (&#x3b2;&#x2009;=&#x2009;&#x2009;-&#x2009;0.391, p&#x2009;=&#x2009;0.048), and Digit Span Test (&#x3b2;&#x2009;=&#x2009;0.408, p&#x2009;=&#x2009;0.038).<br><br>CONCLUSIONS: The lower DTI-ALPS index was found in patients with AD dementia, which may suggest impaired glymphatic system function. DTI-ALPS index was correlated with BG-PVS enlargement and worse cognitive performance in certain cognitive domains.<br><br>CLINICAL RELEVANCE STATEMENT: Diffusion tensor image analysis along the perivascular space index may be applied as a useful indicator to evaluate the glymphatic system function. The impaired glymphatic system in patients with Alzheimer's disease (AD) dementia may provide a new perspective for understanding the pathophysiology of AD.<br><br>KEY POINTS: &#x2022; Patients with Alzheimer's disease dementia displayed a lower diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, possibly indicating glymphatic impairment. &#x2022; A lower DTI-ALPS index was associated with the enlargement of perivascular space and cognitive impairment. &#x2022; DTI-ALPS index could be a promising biomarker of the glymphatic system in Alzheimer's disease dementia.},
}
@article {pmid37610305,
year = {2023},
author = {Ma, X and Rizzoglio, F and Bodkin, KL and Perreault, E and Miller, LE and Kennedy, A},
title = {Using adversarial networks to extend brain computer interface decoding accuracy over time.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {37610305},
issn = {2050-084X},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Acclimatization ; Brain ; *Coleoptera ; Heart ; },
abstract = {Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called 'Adversarial Domain Adaptation Network' (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by Factor Analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.},
}
@article {pmid37609450,
year = {2023},
author = {Gruenwald, J and Sieghartsleitner, S and Kapeller, C and Scharinger, J and Kamada, K and Brunner, P and Guger, C},
title = {Characterization of High-Gamma Activity in Electrocorticographic Signals.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1206120},
pmid = {37609450},
issn = {1662-4548},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; },
abstract = {INTRODUCTION: Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information.<br><br>METHODS: To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA.<br><br>RESULTS: The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks.<br><br>DISCUSSION: This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.},
}
@article {pmid37609258,
year = {2023},
author = {Ruszala, B and Mazurek, KA and Schieber, MH},
title = {Somatosensory cortex microstimulation modulates primary motor and ventral premotor cortex neurons with extensive spatial convergence and divergence.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.08.05.552025},
pmid = {37609258},
issn = {2692-8205},
abstract = {UNLABELLED: Intracortical microstimulation (ICMS) is known to affect distant neurons transynaptically, yet the extent to which ICMS pulses delivered in one cortical area modulate neurons in other cortical areas remains largely unknown. Here we assessed how the individual pulses of multi-channel ICMS trains delivered in the upper extremity representation of the macaque primary somatosensory area (S1) modulate neuron firing in the primary motor cortex (M1) and in the ventral premotor cortex (PMv). S1-ICMS pulses modulated the majority of units recorded both in the M1 upper extremity representation and in PMv, producing more inhibition than excitation. Effects converged on individual neurons in both M1 and PMv from extensive S1 territories. Conversely, effects of ICMS delivered in a small region of S1 diverged to wide territories in both M1 and PMv. The effects of this direct modulation of M1 and PMv neurons produced by multi-electrode S1-ICMS like that used here may need to be taken into account by bidirectional brain-computer interfaces that decode intended movements from neural activity in these cortical motor areas.<br><br>SIGNIFICANCE STATEMENT: Although ICMS is known to produce effects transynaptically, relatively little is known about how ICMS in one cortical area affects neurons in other cortical areas. We show that the effects of multi-channel ICMS in a small patch of S1 diverge to affect neurons distributed widely in both M1 and PMv, and conversely, individual neurons in each of these areas can be affected by ICMS converging from much of the S1 upper extremity representation. Such direct effects of ICMS may complicate the decoding of motor intent from M1 or PMv when artificial sensation is delivered via S1-ICMS in bidirectional brain-computer interfaces.},
}
@article {pmid37609167,
year = {2023},
author = {Ali, YH and Bodkin, K and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, LR and Yong, NA and Stavisky, SD and Miller, LE and Brandman, DM and Pandarinath, C},
title = {BRAND: A platform for closed-loop experiments with deep network models.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.08.08.552473},
pmid = {37609167},
issn = {2692-8205},
support = {F32 HD112173/HD/NICHD NIH HHS/United States ; },
abstract = {Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes , which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.},
}
@article {pmid37607973,
year = {2023},
author = {Yeom, HG and Kim, JS and Chung, CK},
title = {A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {552},
pmid = {37607973},
issn = {2052-4463},
support = {2019R1A2C1009674//National Research Foundation of Korea (NRF)/ ; 2019R1A2C1009674//National Research Foundation of Korea (NRF)/ ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Knowledge ; *Magnetoencephalography ; },
abstract = {Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.},
}
@article {pmid37606742,
year = {2023},
author = {Kancheva, I and van der Salm, SMA and Ramsey, NF and Vansteensel, MJ},
title = {Association between lesion location and sensorimotor rhythms in stroke - a systematic review with narrative synthesis.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {44},
number = {12},
pages = {4263-4289},
pmid = {37606742},
issn = {1590-3478},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; U01DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Stroke/pathology ; Brain/pathology ; Movement/physiology ; Electroencephalography ; },
abstract = {BACKGROUND: Stroke causes alterations in the sensorimotor rhythms (SMRs) of the brain. However, little is known about the influence of lesion location on the SMRs. Understanding this relationship is relevant for the use of SMRs in assistive and rehabilitative therapies, such as Brain-Computer Interfaces (BCIs)..<br><br>METHODS: We reviewed current evidence on the association between stroke lesion location and SMRs through systematically searching PubMed and Embase and generated a narrative synthesis of findings.<br><br>RESULTS: We included 12 articles reporting on 161 patients. In resting-state studies, cortical and pontine damage were related to an overall decrease in alpha (&#x223c;8-12&#xa0;Hz) and increase in delta (&#x223c;1-4&#xa0;Hz) power. In movement paradigm studies, attenuated alpha and beta (&#x223c;15-25&#xa0;Hz) event-related desynchronization (ERD) was shown in stroke patients during (attempted) paretic hand movement, compared to controls. Stronger reductions in alpha and beta ERD in the ipsilesional, compared to contralesional hemisphere, were observed for cortical lesions. Subcortical stroke was found to affect bilateral ERD and ERS, but results were highly variable.<br><br>CONCLUSIONS: Findings suggest a link between stroke lesion location and SMR alterations, but heterogeneity across studies and limited lesion location descriptions precluded a meta-analysis.<br><br>SIGNIFICANCE: Future research would benefit from more uniformly defined outcome measures, homogeneous methodologies, and improved lesion location reporting.},
}
@article {pmid37604750,
year = {2023},
author = {Biffl, WL and Castelo, M and Dandan, IS and Lu, N and Rivera, P and Bayat, D},
title = {Exploring the role of endovascular interventions in blunt carotid and vertebral artery trauma.},
journal = {American journal of surgery},
volume = {226},
number = {5},
pages = {688-691},
doi = {10.1016/j.amjsurg.2023.07.030},
pmid = {37604750},
issn = {1879-1883},
mesh = {Humans ; *Carotid Artery Injuries/surgery ; *Craniocerebral Trauma ; *Neck Injuries ; Prospective Studies ; Retrospective Studies ; *Stroke ; Treatment Outcome ; Vertebral Artery/surgery/injuries ; *Wounds, Nonpenetrating/complications/therapy ; },
abstract = {BACKGROUND: The role of endovascular interventions (EI) for blunt carotid and vertebral artery injuries (BCI and BVI) is poorly defined. The purpose of this study was to assess the efficacy of EI compared with antithrombotic therapy (AT) to inform future prospective study.<br><br>METHODS: Retrospective review (2017-2022) of records at a Level I trauma center to determine injury, treatment, and outcome information. Primary outcome was stroke.<br><br>RESULTS: 96 patients suffered 106 injuries (74 BVI, 32 BCI). 12 patients underwent 13 EI- 4 therapeutic, 9 prophylactic. Stroke occurred in 12 patients- 6 who had EI. In grade IV BVI, stroke rates are low with both EI and AT. Thrombectomy after stroke improved neurologic function in 4 (100%) of 4 patients.<br><br>CONCLUSIONS: Most strokes occur prior to preventive therapy. Neither AT nor EI is 100% effective in preventing stroke. Thrombectomy may improve neurologic outcomes after stroke. Prospective multicenter study is imperative.},
}
@article {pmid37604228,
year = {2023},
author = {Selvam, A and Aggarwal, T and Mukherjee, M and Verma, YK},
title = {Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0.},
journal = {Biotechnology advances},
volume = {68},
number = {},
pages = {108237},
doi = {10.1016/j.biotechadv.2023.108237},
pmid = {37604228},
issn = {1873-1899},
mesh = {Humans ; *Friends ; *Robotics ; Regenerative Medicine ; Tissue Engineering ; },
abstract = {The evolution of industries have introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 through the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine, which can be extended to target modern warfare and smart healthcare.},
}
@article {pmid37604119,
year = {2023},
author = {Liu, K and Yao, Z and Zheng, L and Wei, Q and Pei, W and Gao, X and Wang, Y},
title = {A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/acf242},
pmid = {37604119},
issn = {1741-2552},
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Retinal Diseases ; Povidone ; Algorithms ; },
abstract = {Objective. Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55-62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness.Approach. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method, offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification.Main results. The online BCI system achieved an average accuracy of 88.87% ± 3.05% and an information transfer rate of 51.83 ± 2.77 bits min[-1]under the low flickering perception condition.Significance. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology.},
}
@article {pmid37603133,
year = {2023},
author = {Al-Qaysi, ZT and Albahri, AS and Ahmed, MA and Mohammed, SM},
title = {Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.},
journal = {Physical and engineering sciences in medicine},
volume = {46},
number = {4},
pages = {1519-1534},
pmid = {37603133},
issn = {2662-4737},
mesh = {Humans ; *Electroencephalography/methods ; Imagination ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; Learning ; },
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.},
}
@article {pmid37602829,
year = {2023},
author = {Li, T},
title = {Methods And Protocols For Live Imaging In Development.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {191},
pages = {},
doi = {10.3791/64642},
pmid = {37602829},
issn = {1940-087X},
mesh = {Animals ; *Zebrafish ; *Brain ; Caenorhabditis elegans ; Drosophila ; Time-Lapse Imaging ; },
abstract = {Januschke, J., Loyer, N. Applications of immobilization of Drosophila tissues with fibrin clots for live imaging. Journal of Visualized Experiments. (166), 10.3791/61954 (2020). Li, T., Luo, L. An explant system for time-lapse imaging studies of olfactory circuit assembly in Drosophila. Journal of Visualized Experiments. (176), 10.3791/62983 (2021). Schramm, P., Hetsch, F., Meier, J. C., Koster, R. W. In vivo imaging of fully active brain tissue in awake zebrafish larvae and juveniles by skull and skin removal. Journal of Visualized Experiments. (168), 10.3791/62166 (2021). Ratke, J., Kramer, F., Strobl, F. Simultaneous live imaging of multiple insect embryos in sample chamber-based light sheet fluorescence microscopes. Journal of Visualized Experiments. (163), 10.3791/61713 (2020). Terzi, A., Alam, S. M. S., Suter, D. M. ROS live cell imaging during neuronal development. Journal of Visualized Experiments. (168), 10.3791/62165 (2021). Mutlu, A. S., Chen, T., Deng, D., Wang, M. C. Label-Free imaging of lipid storage dynamics in Caenorhabditis elegans using stimulated Raman scattering microscopy. Journal of Visualized Experiments. (171), 10.3791/61870 (2021). Boutillon, A., Escot, S., David, N. B. Deep and spatially controlled volume ablations using a two-photon microscope in the zebrafish gastrula. Journal of Visualized Experiments. (173), 10.3791/62815 (2021).},
}
@article {pmid37602262,
year = {2023},
author = {Cui, Y and Cong, F and Huang, F and Zeng, M and Yan, R},
title = {Cortical activation of neuromuscular electrical stimulation synchronized mirror neuron rehabilitation strategies: an fNIRS study.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1232436},
pmid = {37602262},
issn = {1664-2295},
abstract = {BACKGROUND: The mirror neuron system (MNS) plays a key role in the neural mechanism underlying motor learning and neural plasticity. Action observation (AO), action execution (AE), and a combination of both, known as action imitation (AI), are the most commonly used rehabilitation strategies based on MNS. It is possible to enhance the cortical activation area and amplitude by combining traditional neuromuscular electrical stimulation (NMES) with other top-down and active rehabilitation strategies based on the MNS theory.<br><br>OBJECTIVE: This study aimed to explore the cortical activation patterns induced by NMES synchronized with rehabilitation strategies based on MNS, namely NMES+AO, NMES+AE, and NMES+AI. In addition, the study aimed to assess the feasibility of these three novel rehabilitative treatments in order to provide insights and evidence for the design, implementation, and application of brain-computer interfaces.<br><br>METHODS: A total of 70 healthy adults were recruited from July 2022 to February 2023, and 66 of them were finally included in the analysis. The cortical activation patterns during NMES+AO, NMES+AE, and NMES+AI were detected using the functional Near-Infrared Spectroscopy (fNIRS) technique. The action to be observed, executed, or imitated was right wrist and hand extension, and two square-shaped NMES electrodes were placed on the right extensor digitorum communis. A block design was adopted to evaluate the activation intensity of the left MNS brain regions.<br><br>RESULTS: General linear model results showed that compared with the control condition, the number of channels significantly activated (PFDR&#x2009;<&#x2009;0.05) in the NMES+AO, NMES+AE, and NMES+AI conditions were 3, 9, and 9, respectively. Region of interest (ROI) analysis showed that 2 ROIs were significantly activated (PFDR&#x2009;<&#x2009;0.05) in the NMES+AO condition, including BA6 and BA44; 5 ROIs were significantly activated in the NMES+AE condition, including BA6, BA40, BA44, BA45, and BA46; and 6 ROIs were significantly activated in the NMES+AI condition, including BA6, BA7, BA40, BA44, BA45, and BA46.<br><br>CONCLUSION: The MNS was activated during neuromuscular electrical stimulation combined with an AO, AE, and AI intervention. The synchronous application of NMES and mirror neuron rehabilitation strategies is feasible in clinical rehabilitation. The fNIRS signal patterns observed in this study could be used to develop brain-computer interface and neurofeedback therapy rehabilitation devices.},
}
@article {pmid37600556,
year = {2023},
author = {Moreno-Calderón, S and Martínez-Cagigal, V and Santamaría-Vázquez, E and Pérez-Velasco, S and Marcos-Martínez, D and Hornero, R},
title = {Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1227727},
pmid = {37600556},
issn = {1662-5161},
abstract = {INTRODUCTION AND OBJECTIVE: Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known 'Connect 4' video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally.<br><br>METHODS: The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires.<br><br>RESULTS: The average accuracy achieved was 93.74% &#xb1; 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly.<br><br>CONCLUSIONS: This c-VEP based multiplayer video game has reached suitable performance on 22 users, supported by high motivation and minimal workload. Consequently, compared to other versions of "Connect 4" that utilized different control signals, this version has exhibited superior performance.},
}
@article {pmid37600142,
year = {2023},
author = {Xu, B and Liu, D and Xue, M and Miao, M and Hu, C and Song, A},
title = {Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance.},
journal = {Computational and structural biotechnology journal},
volume = {22},
number = {},
pages = {3-16},
pmid = {37600142},
issn = {2001-0370},
abstract = {Although the electroencephalography (EEG) based brain-computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose a continuous shared control strategy combining continuous BCI and autonomous navigation for a mobile robot system. The weight of shared control is designed to dynamically adjust the fusion of continuous BCI control and autonomous navigation. During this process, the system uses the visual-based simultaneous localization and mapping (SLAM) method to construct environmental maps. After obtaining the global optimal path, the system utilizes the brain-based shared control dynamic window approach (BSC-DWA) to evaluate safe and reachable trajectories while considering shared control velocity. Eight subjects participated in two-stage training, and six of these eight subjects participated in online shared control experiments. The training results demonstrated that naïve subjects could achieve continuous control performance with an average percent valid correct rate of approximately 97 % and an average total correct rate of over 80 %. The results of online shared control experiments showed that all of the subjects could complete navigation tasks in an unknown corridor with continuous shared control. Therefore, our experiments verified the feasibility and effectiveness of the proposed system combining continuous BCI, shared control, autonomous navigation, and visual SLAM. The proposed continuous shared control framework shows great promise in BCI-driven tasks, especially navigation tasks for brain-driven assistive mobile robots and wheelchairs in daily applications.},
}
@article {pmid37600011,
year = {2023},
author = {Yan, W and He, B and Zhao, J},
title = {SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1161511},
pmid = {37600011},
issn = {1662-4548},
abstract = {INTRODUCTION: As an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals. Currently, the majority SSVEP feature recognition methods use a static classifier. However, electroencephalogram (EEG) signals are non-stationary and time-varying. Hence, an adaptive classification method would be an alternative option to a static classifier for tracking the changes in EEG feature distribution, as its parameters can be re-estimated and updated with the input of new EEG data.<br><br>METHODS: In this study, an unsupervised adaptive classification algorithm is designed based on the self-similarity of same-frequency signals. The proposed classification algorithm saves the EEG data that has undergone feature recognition as a template signal in accordance with its estimated label, and the new testing signal is superimposed with the template signals at each stimulus frequency as the new test signals to be analyzed. With the continuous input of EEG data, the template signals are continuously updated.<br><br>RESULTS: By comparing the classification accuracy of the original testing signal and the testing signal superimposed with the template signals, this study demonstrates the effectiveness of using the self-similarity of same-frequency signals in the adaptive classification algorithm. The experimental results also show that the longer the SSVEP-BCI system is used, the better the responses of users on SSVEP are, and the more significantly the adaptive classification algorithm performs in terms of feature recognition. The testing results of two public datasets show that the adaptive classification algorithm outperforms the static classification method in terms of feature recognition.<br><br>DISCUSSION: The proposed adaptive classification algorithm can update the parameters with the input of new EEG data, which is of favorable impact for the accurate analysis of EEG data with time-varying characteristics.},
}
@article {pmid37599998,
year = {2023},
author = {Tsoneva, T and Garcia-Molina, G and Desain, P},
title = {Electrophysiological model of human temporal contrast sensitivity based on SSVEP.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1180829},
pmid = {37599998},
issn = {1662-4548},
abstract = {The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1-50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40-60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions.},
}
@article {pmid37593487,
year = {2023},
author = {Healthcare Engineering, JO},
title = {Retracted: Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.},
journal = {Journal of healthcare engineering},
volume = {2023},
number = {},
pages = {9851304},
pmid = {37593487},
issn = {2040-2309},
abstract = {[This retracts the article DOI: 10.1155/2021/4710044.].},
}
@article {pmid37588651,
year = {2023},
author = {Hatton, SL and Rathore, S and Vilinsky, I and Stowasser, A},
title = {Quantitative and Qualitative Representation of Introductory and Advanced EEG Concepts: An Exploration of Different EEG Setups.},
journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience},
volume = {21},
number = {2},
pages = {A142-A150},
pmid = {37588651},
issn = {1544-2896},
abstract = {Electroencephalograms (EEGs) are the gold standard test used in the medical field to diagnose epilepsy and aid in the diagnosis of many other neurological and mental disorders. Growing in popularity in terms of nonmedical applications, the EEG is also used in research, neurofeedback, and brain-computer interface, making it increasingly relevant to student learning. Recent innovations have made EEG setups more accessible and affordable, thus allowing their integration into neuroscience educational settings. Introducing students to EEGs, however, can be daunting due to intricate setup protocols, individual variation, and potentially expensive equipment. This paper aims to provide guidance for introducing students and educators to fundamental beginning and advanced level EEG concepts. Specifically, this paper tested the potential of three different setups, with varying channel number and wired or wireless connectivity, for introducing students to qualitative and quantitative exploration of alpha enhancement when eyes are closed, and observation of the alpha/beta anterior to posterior gradient. The setups were compared to determine their relative advantages and their robustness in detecting these well-established parameters. The basic 1- or 2-channel setups are sufficient for observing alpha and beta waves, while more advanced systems containing 8 or 16 channels are required for consistent observation of an anterior-posterior gradient. In terms of localization, the 16-channel setup, in principle, was more adept. The 8-channel setup, however, was more effective than the 16-channel setup with regards to displaying the anterior to posterior gradient. Thus, an 8-channel setup is sufficient in an education setting to display these known trends. Modification of the 16-channel setup may provide a better observation of the anterior to posterior gradient.},
}
@article {pmid37588619,
year = {2023},
author = {Zheng, H and Niu, L and Qiu, W and Liang, D and Long, X and Li, G and Liu, Z and Meng, L},
title = {The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface.},
journal = {Research (Washington, D.C.)},
volume = {6},
number = {},
pages = {0200},
pmid = {37588619},
issn = {2639-5274},
abstract = {A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.},
}
@article {pmid37588309,
year = {2023},
author = {Kumar, J and Patel, T and Sugandh, F and Dev, J and Kumar, U and Adeeb, M and Kachhadia, MP and Puri, P and Prachi, F and Zaman, MU and Kumar, S and Varrassi, G and Syed, ARS},
title = {Innovative Approaches and Therapies to Enhance Neuroplasticity and Promote Recovery in Patients With Neurological Disorders: A Narrative Review.},
journal = {Cureus},
volume = {15},
number = {7},
pages = {e41914},
pmid = {37588309},
issn = {2168-8184},
abstract = {Brain rehabilitation and recovery for people with neurological disorders, such as stroke, traumatic brain injury (TBI), and neurodegenerative diseases, depend mainly on neuroplasticity, the brain's capacity to restructure and adapt. This literature review aims to look into cutting-edge methods and treatments that support neuroplasticity and recovery in these groups. A thorough search of electronic databases revealed a wide range of research and papers investigating several neuroplasticity-targeting methods, such as cognitive training, physical activity, non-invasive brain stimulation, and pharmaceutical interventions. The results indicate that these therapies can control neuroplasticity and improve motor, mental, and sensory function. In addition, cutting-edge approaches, such as virtual reality (VR) and brain-computer interfaces (BCIs), promise to increase neuroplasticity and foster rehabilitation. However, many issues and restrictions still need to be resolved, including the demand for individualized treatments and the absence of defined standards. In conclusion, this review emphasizes the significance of neuroplasticity in brain rehabilitation. It identifies novel strategies and treatments that promise to enhance recovery in patients with neurological illnesses. Future studies should concentrate on improving these therapies and developing evidence-based standards to direct clinical practice and enhance outcomes for this vulnerable population.},
}
@article {pmid37585625,
year = {2023},
author = {Li, L and Wang, S and Duan, X and Wang, Z and Chang, KC},
title = {Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface.},
journal = {ACS applied materials &amp; interfaces},
volume = {15},
number = {34},
pages = {40753-40761},
doi = {10.1021/acsami.3c07684},
pmid = {37585625},
issn = {1944-8252},
mesh = {*Brain-Computer Interfaces ; Synapses/chemistry ; Action Potentials ; Cold Temperature ; },
abstract = {A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.},
}
@article {pmid37583792,
year = {2023},
author = {Zhang, P and Pan, Y and Zha, R and Song, H and Yuan, C and Zhao, Q and Piao, Y and Ren, J and Chen, Y and Liang, P and Tao, R and Wei, Z and Zhang, X},
title = {Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder.},
journal = {General psychiatry},
volume = {36},
number = {4},
pages = {e100985},
pmid = {37583792},
issn = {2517-729X},
abstract = {BACKGROUND: Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder.<br><br>AIMS: We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD.<br><br>METHODS: Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected.<br><br>RESULTS: Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed.<br><br>CONCLUSIONS: Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.},
}
@article {pmid37582062,
year = {2023},
author = {Bellier, L and Llorens, A and Marciano, D and Gunduz, A and Schalk, G and Brunner, P and Knight, RT},
title = {Music can be reconstructed from human auditory cortex activity using nonlinear decoding models.},
journal = {PLoS biology},
volume = {21},
number = {8},
pages = {e3002176},
pmid = {37582062},
issn = {1545-7885},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R13 NS118932/NS/NINDS NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Auditory Cortex/physiology ; *Music ; Brain Mapping ; Auditory Perception/physiology ; Temporal Lobe/physiology ; Acoustic Stimulation ; },
abstract = {Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior-posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain-computer interface (BCI) applications.},
}
@article {pmid37581962,
year = {2023},
author = {Shao, Z and Dou, W and Ma, D and Zhai, X and Xu, Q and Pan, Y},
title = {A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3375-3383},
doi = {10.1109/TNSRE.2023.3305474},
pmid = {37581962},
issn = {1558-0210},
mesh = {Humans ; Hemiplegia ; Diffusion Tensor Imaging ; *Brain-Computer Interfaces ; Reproducibility of Results ; *Stroke ; Magnetic Resonance Imaging ; *Neurological Rehabilitation ; },
abstract = {It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.},
}
@article {pmid37581121,
year = {2023},
author = {Lee, JW and Song, KH},
title = {Fibrous hydrogels by electrospinning: Novel platforms for biomedical applications.},
journal = {Journal of tissue engineering},
volume = {14},
number = {},
pages = {20417314231191881},
pmid = {37581121},
issn = {2041-7314},
abstract = {Hydrogels, hydrophilic and biocompatible polymeric networks, have been used for numerous biomedical applications because they have exhibited abilities to mimic features of extracellular matrix (ECM). In particular, the hydrogels engineered with electrospinning techniques have shown great performances in biomedical applications. Electrospinning techniques are to generate polymeric micro/nanofibers that can mimic geometries of natural ECM by drawing micro/nanofibers from polymer precursors with electrical forces, followed by structural stabilization of them. By exploiting the electrospinning techniques, the fibrous hydrogels have been fabricated and utilized as 2D/3D cell culture platforms, implantable scaffolds, and wound dressings. In addition, some hydrogels that respond to external stimuli have been used to develop biosensors. For comprehensive understanding, this review covers electrospinning processes, hydrogel precursors used for electrospinning, characteristics of fibrous hydrogels and specific biomedical applications of electrospun fibrous hydrogels and highlight their potential to promote use in biomedical applications.},
}
@article {pmid37578926,
year = {2023},
author = {Huang, J and Zhang, ZQ and Xiong, B and Wang, Q and Wan, B and Li, F and Yang, P},
title = {Cross-Subject Transfer Method Based on Domain Generalization for Facilitating Calibration of SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3307-3319},
doi = {10.1109/TNSRE.2023.3305202},
pmid = {37578926},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Calibration ; Electroencephalography/methods ; Neurologic Examination ; Photic Stimulation ; Algorithms ; },
abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.},
}
@article {pmid37578918,
year = {2023},
author = {Deny, P and Cheon, S and Son, H and Choi, KW},
title = {Hierarchical Transformer for Motor Imagery-Based Brain Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {11},
pages = {5459-5470},
doi = {10.1109/JBHI.2023.3304646},
pmid = {37578918},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagination ; Algorithms ; Electroencephalography ; },
abstract = {In this paper, we propose a novel transformer-based classification algorithm for the brain computer interface (BCI) using a motor imagery (MI) electroencephalogram (EEG) signal. To design the MI classification algorithm, we apply an up-to-date deep learning model, the transformer, that has revolutionized the natural language processing (NLP) and successfully widened its application to many other domains such as the computer vision. Within a long MI trial spanning a few seconds, the classification algorithm should give more attention to the time periods during which the intended motor task is imagined by the subject without any artifact. To achieve this goal, we propose a hierarchical transformer architecture that consists of a high-level transformer (HLT) and a low-level transformer (LLT). We break down a long MI trial into a number of short-term intervals. The LLT extracts a feature from each short-term interval, and the HLT pays more attention to the features from more relevant short-term intervals by using the self-attention mechanism of the transformer. We have done extensive tests of the proposed scheme on four open MI datasets, and shown that the proposed hierarchical transformer excels in both the subject-dependent and subject-independent tests.},
}
@article {pmid37577389,
year = {2023},
author = {Huang, M and Hua, N and Zhuang, S and Fang, Q and Shang, J and Wang, Z and Tao, X and Niu, J and Li, X and Yu, P and Yang, W},
title = {Cux1[+] proliferative basal cells promote epidermal hyperplasia in chronic dry skin disease identified by single-cell RNA transcriptomics.},
journal = {Journal of pharmaceutical analysis},
volume = {13},
number = {7},
pages = {745-759},
pmid = {37577389},
issn = {2214-0883},
abstract = {Pathological dry skin is a disturbing and intractable healthcare burden, characterized by epithelial hyperplasia and severe itch. Atopic dermatitis (AD) and psoriasis models with complications of dry skin have been studied using single-cell RNA sequencing (scRNA-seq). However, scRNA-seq analysis of the dry skin mouse model (acetone/ether/water (AEW)-treated model) is still lacking. Here, we used scRNA-seq and in situ hybridization to identify a novel proliferative basal cell (PBC) state that exclusively expresses transcription factor CUT-like homeobox 1 (Cux1). Further in vitro study demonstrated that Cux1 is vital for keratinocyte proliferation by regulating a series of cyclin-dependent kinases (CDKs) and cyclins. Clinically, Cux1[+] PBCs were increased in patients with psoriasis, suggesting that Cux1[+] PBCs play an important part in epidermal hyperplasia. This study presents a systematic knowledge of the transcriptomic changes in a chronic dry skin mouse model, as well as a potential therapeutic target against dry skin-related dermatoses.},
}
@article {pmid37576014,
year = {2023},
author = {Raizen, D and Bhavsar, R and Keenan, BT and Liu, PZ and Kegelman, TP and Chao, HH and Vapiwala, N and Rao, H},
title = {Increased posterior cingulate cortex blood flow in cancer-related fatigue.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1135462},
pmid = {37576014},
issn = {1664-2295},
abstract = {Fatigue is a common symptom associated with cancer treatments. Brain mechanisms underlying cancer-related fatigue (CRF) and its progression following therapy are poorly understood. Previous studies have suggested a role of the default mode network (DMN) in fatigue. In this study we used arterial spin labeling (ASL) perfusion functional magnetic resonance imaging (fMRI) and compared resting cerebral blood flow (CBF) differences in the posterior cingulate cortex (PCC), a core hub of the DMN, between 16 patients treated with radiation therapy (RAT) for prostate (9 males) or breast (7 females) cancer and 18 healthy controls (HC). Resting CBF in patients was also measured immediately after the performance of a fatiguing 20-min psychomotor vigilance task (PVT). Twelve of 16 cancer patients were further followed between 3 and 7 months after completion of the RAT (post-RAT). Patients reported elevated fatigue on RAT in comparison to post-RAT, but no change in sleepiness, suggesting that the underlying neural mechanisms of CRF progression are distinct from those regulating sleep drive progression. Compared to HC, patients showed significantly increased resting CBF in the PCC and the elevated PCC CBF persisted during the follow up visit. Post-PVT, but not pre-PVT, resting CBF changes in the PCC correlated with fatigue changes after therapy in patients with CRF, suggesting that PCC CBF following a fatiguing cognitive task may be a biomarker for CRF recovery.},
}
@article {pmid37569786,
year = {2023},
author = {Géraudie, A and Riche, M and Lestra, T and Trotier, A and Dupuis, L and Mathon, B and Carpentier, A and Delatour, B},
title = {Effects of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in P301S Mice Modeling Alzheimer's Disease Tauopathies.},
journal = {International journal of molecular sciences},
volume = {24},
number = {15},
pages = {},
pmid = {37569786},
issn = {1422-0067},
support = {ANR-10-IAIHU-06//Agence Nationale de la Recherche/ ; ANR-11-INBS-0011-NeurATRIS//Agence Nationale de la Recherche/ ; LRTCA grant to B.D. (no grant number)//Laboratoire de Recherche en Technologies Chirurgicales Avanc&#xe9;es/ ; NeurATRIS grant to B.D. and A.C. (no grant number)//NeurATRIS/ ; },
mesh = {Mice ; Animals ; *Alzheimer Disease/genetics/therapy/pathology ; Blood-Brain Barrier/pathology ; *Tauopathies/therapy/pathology ; Mice, Transgenic ; Ultrasonic Waves ; },
abstract = {Alzheimer's disease (AD) is the leading cause of dementia. No treatments have led to clinically meaningful impacts. A major obstacle for peripherally administered therapeutics targeting the central nervous system is related to the blood-brain barrier (BBB). Ultrasounds associated with microbubbles have been shown to transiently and safely open the BBB. In AD mouse models, the sole BBB opening with no adjunct drugs may be sufficient to reduce lesions and mitigate cognitive decline. However, these therapeutic effects are for now mainly assessed in preclinical mouse models of amyloidosis and remain less documented in tau lesions. The aim of the present study was therefore to evaluate the effects of repeated BBB opening using low-intensity pulsed ultrasounds (LIPU) in tau transgenic P301S mice with two main readouts: tau-positive lesions and microglial cells. Our results show that LIPU-induced BBB opening does not decrease tau pathology and may even potentiate the accumulation of pathological tau in selected brain regions. In addition, LIPU-BBB opening in P301S mice strongly reduced microglia densities in brain parenchyma, suggesting an anti-inflammatory action. These results provide a baseline for future studies using LIPU-BBB opening, such as adjunct drug therapies, in animal models and in AD patients.},
}
@article {pmid37567915,
year = {2023},
author = {Pan, Y and Hao, N and Liu, N and Zhao, Y and Cheng, X and Ku, Y and Hu, Y},
title = {Mnemonic-trained brain tuning to a regular odd-even pattern subserves digit memory in children.},
journal = {NPJ science of learning},
volume = {8},
number = {1},
pages = {27},
pmid = {37567915},
issn = {2056-7936},
support = {71942001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62207025//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171082//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {It is said that our species use mnemonics - that "magic of memorization" - to engrave an enormous amount of information in the brain. Yet, it is unclear how mnemonics affect memory and what the neural underpinnings are. In this electroencephalography study, we examined the hypotheses whether mnemonic training improved processing-efficiency and/or altered encoding-pattern to support memory enhancement. By 22-day training of a digit-image mnemonic (a custom memory technique used by world-class mnemonists), a group of children showed increased short-term memory after training, but with limited gain generalization. This training resulted in regular odd-even neural patterns (i.e., enhanced P200 and theta power during the encoding of digits at even- versus odd- positions in a sequence). Critically, the P200 and theta power effects predicted the training-induced memory improvement. These findings provide evidence of how mnemonics alter encoding pattern, as reflected in functional brain organization, to support memory enhancement.},
}
@article {pmid37567222,
year = {2023},
author = {Wallace, DM and Benyamini, M and Nason-Tomaszewski, SR and Costello, JT and Cubillos, LH and Mender, MJ and Temmar, H and Willsey, MS and Patil, PG and Chestek, CA and Zacksenhouse, M},
title = {Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
pmid = {37567222},
issn = {1741-2552},
support = {T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Male ; *Brain-Computer Interfaces ; Macaca mulatta ; Electrodes, Implanted ; Fingers ; Movement ; },
abstract = {Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below5%, it was possible to achieve mean true positive rate of28.1%online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.},
}
@article {pmid37564400,
year = {2023},
author = {Lee, I and Kim, D and Kim, S and Kim, HJ and Chung, US and Lee, JJ},
title = {Cognitive training based on functional near-infrared spectroscopy neurofeedback for the elderly with mild cognitive impairment: a preliminary study.},
journal = {Frontiers in aging neuroscience},
volume = {15},
number = {},
pages = {1168815},
pmid = {37564400},
issn = {1663-4365},
abstract = {INTRODUCTION: Mild cognitive impairment (MCI) is often described as an intermediate stage of the normal cognitive decline associated with aging and dementia. There is a growing interest in various non-pharmacological interventions for MCI to delay the onset and inhibit the progressive deterioration of daily life functions. Previous studies suggest that cognitive training (CT) contributes to the restoration of working memory and that the brain-computer-interface technique can be applied to elicit a more effective treatment response. However, these techniques have certain limitations. Thus, in this preliminary study, we applied the neurofeedback paradigm during CT to increase the working memory function of patients with MCI.<br><br>METHODS: Near-infrared spectroscopy (NIRS) was used to provide neurofeedback by measuring the changes in oxygenated hemoglobin in the prefrontal cortex. Thirteen elderly MCI patients who received CT-neurofeedback sessions four times on the left dorsolateral prefrontal cortex (dlPFC) once a week were recruited as participants.<br><br>RESULTS: Compared with pre-intervention, the activity of the targeted brain region increased when the participants first engaged in the training; after 4 weeks of training, oxygen saturation was significantly decreased in the left dlPFC. The participants demonstrated significantly improved working memory compared with pre-intervention and decreased activity significantly correlated with improved cognitive performance.<br><br>CONCLUSION: Our results suggest that the applications for evaluating brain-computer interfaces can aid in elucidation of the subjective mental workload that may create additional or decreased task workloads due to CT.},
}
@article {pmid37563528,
year = {2023},
author = {Lin, CT and Wang, Y and Chen, SF and Huang, KC and Liao, LD},
title = {Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {11},
pages = {3003-3019},
pmid = {37563528},
issn = {1741-0444},
mesh = {Electroencephalography/methods ; Evoked Potentials ; *Brain-Computer Interfaces ; Cerebral Cortex ; *Wearable Electronic Devices ; Evoked Potentials, Visual ; },
abstract = {Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.},
}
@article {pmid37558464,
year = {2023},
author = {Kim, B and Erickson, BA and Fernandez-Nunez, G and Rich, R and Mentzelopoulos, G and Vitale, F and Medaglia, JD},
title = {EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio.},
journal = {eNeuro},
volume = {10},
number = {9},
pages = {},
pmid = {37558464},
issn = {2373-2822},
support = {R01 NS121219/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Rest/physiology ; Cognition ; Brain/physiology ; },
abstract = {EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.},
}
@article {pmid37556336,
year = {2023},
author = {Jung, J and Moon, H and Yu, G and Hwang, H},
title = {Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {11},
pages = {5622-5633},
doi = {10.1109/JBHI.2023.3303494},
pmid = {37556336},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; },
abstract = {Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality.},
}
@article {pmid37554408,
year = {2023},
author = {Ma, H and Li, C and Zhu, Y and Peng, Y and Sun, L},
title = {Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1205858},
pmid = {37554408},
issn = {1662-5161},
abstract = {Accurate recognition of patients' movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation.},
}
@article {pmid37552980,
year = {2023},
author = {Gordon, SM and McDaniel, JR and King, KW and Lawhern, VJ and Touryan, J},
title = {Decoding neural activity to assess individual latent state in ecologically valid contexts.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/acee20},
pmid = {37552980},
issn = {1741-2552},
mesh = {Humans ; *Visual Perception ; Task Performance and Analysis ; Research Design ; *Brain-Computer Interfaces ; Discrimination, Psychological ; },
abstract = {Objective.Currently, there exists very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make accurate inferences about latent states, associated cognitive processes, or proximal behavior. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks.Approach.Domain generalization methods, borrowed from the work of the brain-computer interface community, have the potential to capture high-dimensional patterns of neural activity in a way that can be reliably applied across experimental datasets in order to address this specific challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks while perched atop a six-degrees-of-freedom ride-motion simulator.Main Results.Using the pretrained models, we estimate latent state and the associated patterns of neural activity. As the patterns of neural activity become more similar to those patterns observed in the training data, we find changes in behavior and task performance that are consistent with the observations from the original, laboratory-based paradigms.Significance.These results lend ecological validity to the original, highly controlled, experimental designs and provide a methodology for understanding the relationship between neural activity and behavior during complex tasks.},
}
@article {pmid37552978,
year = {2023},
author = {Liang, T and Yu, X and Liu, X and Wang, H and Liu, X and Dong, B},
title = {EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/acee1f},
pmid = {37552978},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Movement ; Neural Networks, Computer ; Electroencephalography/methods ; Imagination ; },
abstract = {Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.},
}
@article {pmid37552589,
year = {2024},
author = {Li, S and Wang, Z and Luo, H and Ding, L and Wu, D},
title = {T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {2},
pages = {423-432},
doi = {10.1109/TBME.2023.3303289},
pmid = {37552589},
issn = {1558-2531},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Brain ; Learning ; },
abstract = {OBJECTIVE: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available.<br><br>METHODS: This article proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized.<br><br>RESULTS: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches.<br><br>SIGNIFICANCE: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.},
}
@article {pmid37550947,
year = {2023},
author = {Zhang, J and Xu, B and Lou, X and Wu, Y and Shen, X},
title = {MI-based BCI with accurate real-time three-class classification processing and light control application.},
journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine},
volume = {237},
number = {8},
pages = {1017-1028},
doi = {10.1177/09544119231187287},
pmid = {37550947},
issn = {2041-3033},
mesh = {*Imagination ; *Brain-Computer Interfaces ; Reproducibility of Results ; Algorithms ; Electroencephalography ; Support Vector Machine ; },
abstract = {The use of brain-computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.},
}
@article {pmid37550747,
year = {2023},
author = {Fu, Z and Tian, Z and Chen, Y and Jia, Z and Wang, C and Zhang, X and Zhang, W and Li, G and Wei, X and Huang, Y},
title = {Analysis of the efficacy of a single subumbilical stoma for bilateral cutaneous ureterostomy after radical cystectomy.},
journal = {European journal of medical research},
volume = {28},
number = {1},
pages = {273},
pmid = {37550747},
issn = {2047-783X},
support = {BE2020654//Key Research and Development Program of Jiangsu Province under Grant Agreement/ ; },
mesh = {Humans ; Ureterostomy/methods ; Cystectomy/methods ; Quality of Life ; Retrospective Studies ; *Urinary Diversion/methods ; *Urinary Bladder Neoplasms/surgery ; },
abstract = {BACKGROUND: Radical cystectomy and urinary diversion are the standard surgical treatments for patients with muscle-invasive or high-risk, or recurrent non-muscle-invasive bladder cancer. Although this approach significantly prolongs patient survival, it can lead to postoperative complications. This study aims to compare the efficacy and complications of bilateral cutaneous ureterostomy with a single subumbilical stoma to those of cutaneous ureterostomy with two stomas and an ileal conduit as a means of urinary diversion after radical cystectomy. The findings of this study will provide valuable information for healthcare providers in selecting the appropriate urinary diversion method for their patients.<br><br>METHODS: The clinical data for 108 patients who received bilateral cutaneous ureterostomy with a single subumbilical stoma (ureterostomy with a single stoma group), cutaneous ureterostomy with two stomas (ureterostomy with two stomas group), or an ileal conduit (ileal conduit group) after radical cystectomy were retrospectively analysed. The operative time, pathological stage, survival status, perioperative complication rate, rate of successful first extubation, rehospitalization rate at 6&#xa0;months after surgery,ostomy-related medical costs,and postoperative quality of life were compared between the three groups of patients.<br><br>RESULTS: A significant difference in the operative time was found between the three groups (P&#x2009;=&#x2009;0.001). No significant differences in pathological stage, survival status, perioperative complication rate, rehospitalization rate at 6&#xa0;months after surgery, or bladder cancer index (BCI) score were identified among the three groups. The difference in the successful first extubation rate between the three groups of patients was significant (P&#x2009;=&#x2009;0.001). Significant differences in ostomy-related medical costs were observed among the three groups of patients (P&#x2009;=&#x2009;0.006).<br><br>CONCLUSION: A single subumbilical stoma for bilateral cutaneous ureterostomy after radical cystectomy may result in shorter surgery time, increased success rates for initial catheter removal, and lower medical expenses. However, to confirm these findings, further prospective randomized clinical trials are necessary.},
}
@article {pmid37548304,
year = {2023},
author = {Cheng, Z and Hu, S and Han, G and Fang, K and Jin, X and Ordinola, A and Özarslan, E and Bai, R},
title = {Using deep learning to accelerate magnetic resonance measurements of molecular exchange.},
journal = {The Journal of chemical physics},
volume = {159},
number = {5},
pages = {},
doi = {10.1063/5.0159343},
pmid = {37548304},
issn = {1089-7690},
abstract = {Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.},
}
@article {pmid37547152,
year = {2023},
author = {Wang, F and Wan, Y and Li, Z and Qi, F and Li, J},
title = {A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1167125},
pmid = {37547152},
issn = {1662-4548},
abstract = {BACKGROUND: Brain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients' EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient's own data and performs poorly.<br><br>METHODS: In this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients' P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data.<br><br>RESULTS: The results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment.<br><br>CONCLUSION: These results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients.},
}
@article {pmid37547144,
year = {2023},
author = {Li, JW and Lin, D and Che, Y and Lv, JJ and Chen, RJ and Wang, LJ and Zeng, XX and Ren, JC and Zhao, HM and Lu, X},
title = {An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1221512},
pmid = {37547144},
issn = {1662-4548},
abstract = {INTRODUCTION: Efficiently recognizing emotions is a critical pursuit in brain-computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.<br><br>METHODS: These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.<br><br>RESULTS: The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83-92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.<br><br>DISCUSSION: Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.},
}
@article {pmid37547006,
year = {2023},
author = {Noel, JP and Bockbrader, M and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A},
title = {Human primary motor cortex indexes the onset of subjective intention in brain-machine-interface mediated actions.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37547006},
issn = {2692-8205},
support = {K99 NS128075/NS/NINDS NIH HHS/United States ; },
abstract = {Self-initiated behavior is accompanied by the experience of willing our actions. Here, we leverage the unique opportunity to examine the full intentional chain - from will (W) to action (A) to environmental effects (E) - in a tetraplegic person fitted with a primary motor cortex (M1) brain machine interface (BMI) generating hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (W, A, and E) while performing extra-cellular recordings and probing subjective experience. Our results reveal single-cell, multi-unit, and population-level dynamics in human M1 that encode W and may predict its subjective onset. Further, we show that the proficiency of a neural decoder in M1 reflects the degree of W-A binding, tracking the participant's subjective experience of intention in (near) real time. These results point to M1 as a critical node in forming the subjective experience of intention and demonstrate the relevance of intention-related signals for translational neuroprosthetics.},
}
@article {pmid37546532,
year = {2023},
author = {Huang, Z and Ma, Y and Su, J and Shi, H and Jia, S and Yuan, B and Li, W and Geng, J and Yang, T},
title = {CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition.},
journal = {Frontiers in physiology},
volume = {14},
number = {},
pages = {1200656},
pmid = {37546532},
issn = {1664-042X},
abstract = {EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1-Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.},
}
@article {pmid37543245,
year = {2023},
author = {Ullah, R and Shen, Y and Zhou, YD and Fu, J},
title = {Perinatal metabolic inflammation in the hypothalamus impairs the development of homeostatic feeding circuitry.},
journal = {Metabolism: clinical and experimental},
volume = {147},
number = {},
pages = {155677},
doi = {10.1016/j.metabol.2023.155677},
pmid = {37543245},
issn = {1532-8600},
mesh = {Child ; Animals ; Female ; Humans ; Pregnancy ; *Leptin/metabolism ; Ghrelin ; Agouti-Related Protein ; *Pediatric Obesity/metabolism ; Pro-Opiomelanocortin/metabolism ; Hypothalamus/metabolism ; Arcuate Nucleus of Hypothalamus/metabolism ; Insulin/metabolism ; },
abstract = {Over the past few decades, there has been a global increase in childhood obesity. This rise in childhood obesity contributes to the susceptibility of impaired metabolism during both childhood and adulthood. The hypothalamus, specifically the arcuate nucleus (ARC), houses crucial neurons involved in regulating homeostatic feeding. These neurons include proopiomelanocortin (POMC) and agouti-related peptide (AGRP) secreting neurons. They play a vital role in sensing nutrients and metabolic hormones like insulin, leptin, and ghrelin. The neurogenesis of AGRP and POMC neurons completes at birth; however, axon development and synapse formation occur during the postnatal stages in rodents. Insulin, leptin, and ghrelin are the essential regulators of POMC and AGRP neurons. Maternal obesity and postnatal overfeeding or a high-fat diet (HFD) feeding cause metabolic inflammation, disrupted signaling of metabolic hormones, netrin-1, and neurogenic factors, neonatal obesity, and defective neuronal development in animal models; however, the mechanism is unclear. Within the hypothalamus and other brain areas, there exists a wide range of interconnected neuronal populations that regulate various aspects of feeding. However, this review aims to discuss how perinatal metabolic inflammation influences the development of POMC and AGRP neurons within the hypothalamus.},
}
@article {pmid37540396,
year = {2024},
author = {Mahrooz, MH and Fattahzadeh, F and Gharibzadeh, S},
title = {Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback.},
journal = {Applied psychophysiology and biofeedback},
volume = {49},
number = {1},
pages = {47-53},
pmid = {37540396},
issn = {1573-3270},
mesh = {Humans ; *Neurofeedback/methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Confusion ; },
abstract = {Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.},
}
@article {pmid37540385,
year = {2023},
author = {Maiseli, B and Abdalla, AT and Massawe, LV and Mbise, M and Mkocha, K and Nassor, NA and Ismail, M and Michael, J and Kimambo, S},
title = {Brain-computer interface: trend, challenges, and threats.},
journal = {Brain informatics},
volume = {10},
number = {1},
pages = {20},
pmid = {37540385},
issn = {2198-4018},
abstract = {Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.},
}
@article {pmid37539380,
year = {2023},
author = {Huang, Y and Huan, Y and Zou, Z and Pei, W and Gao, X and Wang, Y and Zheng, L},
title = {A wearable group-synchronized EEG system for multi-subject brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1176344},
pmid = {37539380},
issn = {1662-4548},
abstract = {OBJECTIVE: The multi-subject brain-computer interface (mBCI) is becoming a key tool for the analysis of group behaviors. It is necessary to adopt a neural recording system for collaborative brain signal acquisition, which is usually in the form of a fixed wire.<br><br>APPROACH: In this study, we designed a wireless group-synchronized neural recording system that supports real-time mBCI and event-related potential (ERP) analysis. This system uses a wireless synchronizer to broadcast events to multiple wearable EEG amplifiers. The simultaneously received broadcast signals are marked in data packets to achieve real-time event correlation analysis of multiple targets in a group.<br><br>MAIN RESULTS: To evaluate the performance of the proposed real-time group-synchronized neural recording system, we conducted collaborative signal sampling on 10 wireless mBCI devices. The average signal correlation reached 99.8%, the amplitude of average noise was 0.87 &#x3bc;V, and the average common mode rejection ratio (CMRR) reached 109.02 dB. The minimum synchronization error is 237 &#x3bc;s. We also tested the system in real-time processing of the steady-state visual-evoked potential (SSVEP) ranging from 8 to 15.8 Hz. Under 40 target stimulators, with 2 s data length, the average information transfer rate (ITR) reached 150 &#xb1; 20 bits/min, and the highest reached 260 bits/min, which was comparable to the marketing leading EEG system (the average: 150 &#xb1; 15 bits/min; the highest: 280 bits/min). The accuracy of target recognition in 2 s was 98%, similar to that of the Synamps2 (99%), but a higher signal-to-noise ratio (SNR) of 5.08 dB was achieved. We designed a group EEG cognitive experiment; to verify, this system can be used in noisy settings.<br><br>SIGNIFICANCE: The evaluation results revealed that the proposed real-time group-synchronized neural recording system is a high-performance tool for real-time mBCI research. It is an enabler for a wide range of future applications in collaborative intelligence, cognitive neurology, and rehabilitation.},
}
@article {pmid37537987,
year = {2024},
author = {Potter, SJ and Erdody, ML and Bamford, NJ and Knowles, EJ and Menzies-Gow, N and Morrison, PK and Argo, CM and McIntosh, BJ and Kaufman, K and Harris, PA and Bailey, SR},
title = {Development of a body condition index to estimate adiposity in ponies and horses from morphometric measurements.},
journal = {Equine veterinary journal},
volume = {56},
number = {2},
pages = {299-308},
doi = {10.1111/evj.13975},
pmid = {37537987},
issn = {2042-3306},
support = {LP100200224//Australian Research Council/ ; //MARS Petcare UK/ ; },
mesh = {Humans ; Horses ; Animals ; *Adiposity ; Retrospective Studies ; Body Composition ; Obesity/veterinary ; Body Weight ; *Horse Diseases/diagnosis/epidemiology ; },
abstract = {BACKGROUND: There is a high prevalence of obesity in ponies and pleasure horses. This may be associated with equine metabolic syndrome and an increased risk of laminitis. Body condition scoring (BCS) systems are widely used but are subjective and not very sensitive.<br><br>OBJECTIVES: To derive a body condition index (BCI), based on objective morphometric measurements, that correlates with % body fat.<br><br>STUDY DESIGN: Retrospective cohort study.<br><br>METHODS: Morphometric measurements were obtained from 21 ponies and horses in obese and moderate body condition. Percentage body fat was determined using the deuterium dilution method and the BCI was derived to give the optimal correlation with body fat, applying appropriate weightings. The index was then validated by assessing inter-observer variation and correlation with % body fat in a separate population of Welsh ponies; and finally, the correlation between BCI and BCS was evaluated in larger populations from studies undertaken in Australia, the United Kingdom and the United States.<br><br>RESULTS: The BCI correlated well with adiposity in the ponies and horses, giving a Pearson r value of 0.74 (P&#x2009;<&#x2009;0.001); however, it was found to slightly overestimate the % body fat in leaner animals and underestimate in more obese animals. In field studies, the correlation between BCI and BCS varied particularly in Shetlands and miniature ponies, presumably due to differences in body shape.<br><br>MAIN LIMITATIONS: Further work may be required to adapt the BCI to a method that is more applicable for Shetlands and miniature ponies.<br><br>CONCLUSIONS: This BCI was able to provide an index of adiposity which compared favourably with condition scoring in terms of accuracy of estimating adiposity; and was more consistent and repeatable when used by inexperienced assessors. Therefore, this may be a useful tool for assessing adiposity; and may be more sensitive than condition scoring for tracking weight gain or weight loss in individual animals.},
}
@article {pmid37536094,
year = {2023},
author = {Li, S and Tang, Z and Yang, L and Li, M and Shang, Z},
title = {Application of deep reinforcement learning for spike sorting under multi-class imbalance.},
journal = {Computers in biology and medicine},
volume = {164},
number = {},
pages = {107253},
doi = {10.1016/j.compbiomed.2023.107253},
pmid = {37536094},
issn = {1879-0534},
mesh = {Action Potentials/physiology ; *Signal Processing, Computer-Assisted ; *Neurons/physiology ; Microelectrodes ; Algorithms ; },
abstract = {Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.},
}
@article {pmid37535483,
year = {2023},
author = {Leoni, J and Strada, SC and Tanelli, M and Proverbio, AM},
title = {MIRACLE: MInd ReAding CLassification Engine.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3212-3222},
doi = {10.1109/TNSRE.2023.3301507},
pmid = {37535483},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Evoked Potentials ; Electrodes ; *Brain-Computer Interfaces ; },
abstract = {Brain-computer interfaces (BCIs) have revolutionized the way humans interact with machines, particularly for patients with severe motor impairments. EEG-based BCIs have limited functionality due to the restricted pool of stimuli that they can distinguish, while those elaborating event-related potentials up to now employ paradigms that require the patient's perception of the eliciting stimulus. In this work, we propose MIRACLE: a novel BCI system that combines functional data analysis and machine-learning techniques to decode patients' minds from the elicited potentials. MIRACLE relies on a hierarchical ensemble classifier recognizing 10 different semantic categories of imagined stimuli. We validated MIRACLE on an extensive dataset collected from 20 volunteers, with both imagined and perceived stimuli, to compare the system performance on the two. Furthermore, we quantify the importance of each EEG channel in the decision-making process of the classifier, which can help reduce the number of electrodes required for data acquisition, enhancing patients' comfort.},
}
@article {pmid37533980,
year = {2023},
author = {Sarhan, SM and Al-Faiz, MZ and Takhakh, AM},
title = {A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients.},
journal = {Heliyon},
volume = {9},
number = {8},
pages = {e18308},
pmid = {37533980},
issn = {2405-8440},
abstract = {Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.},
}
@article {pmid37533587,
year = {2023},
author = {Pais-Vieira, M and Aksenova, T and Tsytsarev, V and Faber, J},
title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1243226},
pmid = {37533587},
issn = {1662-5161},
}
@article {pmid37531857,
year = {2023},
author = {Mirzabagherian, H and Menhaj, MB and Suratgar, AA and Talebi, N and Abbasi Sardari, MR and Sajedin, A},
title = {Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury.},
journal = {Computers in biology and medicine},
volume = {164},
number = {},
pages = {107159},
doi = {10.1016/j.compbiomed.2023.107159},
pmid = {37531857},
issn = {1879-0534},
mesh = {Humans ; Bayes Theorem ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Movement ; Electroencephalography/methods ; Imagination ; },
abstract = {Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals. Using EEG signals in five different hand movement classes of SCI people, we compare the effectiveness of TSCIR-Net and TSCR-Net models with some competitive methods. We use the bayesian hyperparameter optimization algorithm to tune the hyperparameters of compact convolutional neural networks. In order to show the high generalizability of the proposed models, we compare the results of the models in different frequency ranges. Our proposed models decoded distinctive characteristics of different movement efforts and obtained higher classification accuracy than previous deep neural networks. Our findings indicate that TSCIR-Net and TSCR-Net models fulfills a better classification accuracy of 71.11%, and 64.55% for EEG_All and 57.74%, and 67.87% for EEG_Low frequency data sets than the compared methods in the literature.},
}
@article {pmid37529233,
year = {2023},
author = {Chandrasekaran, S and Bhagat, NA and Ramdeo, R and Ebrahimi, S and Sharma, PD and Griffin, DG and Stein, A and Harkema, SJ and Bouton, CE},
title = {Case study: persistent recovery of hand movement and tactile sensation in peripheral nerve injury using targeted transcutaneous spinal cord stimulation.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1210544},
pmid = {37529233},
issn = {1662-4548},
abstract = {Peripheral nerve injury can lead to chronic pain, paralysis, and loss of sensation, severely affecting quality of life. Spinal cord stimulation has been used in the clinic to provide pain relief arising from peripheral nerve injuries, however, its ability to restore function after peripheral nerve injury have not been explored. Neuromodulation of the spinal cord through transcutaneous spinal cord stimulation (tSCS), when paired with activity-based training, has shown promising results towards restoring volitional limb control in people with spinal cord injury. We show, for the first time, the effectiveness of targeted tSCS in restoring strength (407% increase from 1.79 ± 1.24 N to up to 7.3 ± 0.93 N) and significantly increasing hand dexterity in an individual with paralysis due to a peripheral nerve injury (PNI). Furthermore, this is the first study to document a persisting 3-point improvement during clinical assessment of tactile sensation in peripheral injury after receiving 6 weeks of tSCS. Lastly, the motor and sensory gains persisted for several months after stimulation was received, suggesting tSCS may lead to long-lasting benefits, even in PNI. Non-invasive spinal cord stimulation shows tremendous promise as a safe and effective therapeutic approach with broad applications in functional recovery after debilitating injuries.},
}
@article {pmid37528087,
year = {2023},
author = {Fang, A and Wang, Y and Guan, N and Zuo, Y and Lin, L and Guo, B and Mo, A and Wu, Y and Lin, X and Cai, W and Chen, X and Ye, J and Abdelrahman, Z and Li, X and Zheng, H and Wu, Z and Jin, S and Xu, K and Huang, Y and Gu, X and Yu, B and Wang, X},
title = {Author Correction: Porous microneedle patch with sustained delivery of extracellular vesicles mitigates severe spinal cord injury.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {4603},
doi = {10.1038/s41467-023-40368-w},
pmid = {37528087},
issn = {2041-1723},
}
@article {pmid37527325,
year = {2024},
author = {Razzak, I and Bouadjenek, MR and Saris, RA and Ding, W},
title = {Support Matrix Machine via Joint ℓ2,1 and Nuclear Norm Minimization Under Matrix Completion Framework for Classification of Corrupted Data.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {11},
pages = {16341-16352},
doi = {10.1109/TNNLS.2023.3293888},
pmid = {37527325},
issn = {2162-2388},
abstract = {Traditional support vector machines (SVMs) are fragile in the presence of outliers; even a single corrupt data point can arbitrarily alter the quality of the approximation. If even a small fraction of columns is corrupted, then classification performance will inevitably deteriorate. This article considers the problem of high-dimensional data classification, where a number of the columns are arbitrarily corrupted. An efficient Support Matrix Machine that simultaneously performs matrix Recovery (SSMRe) is proposed, i.e. feature selection and classification through joint minimization of l2,1 (the nuclear norm of L). The data are assumed to consist of a low-rank clean matrix plus a sparse noisy matrix. SSMRe works under incoherence and ambiguity conditions and is able to recover an intrinsic matrix of higher rank in the presence of data densely corrupted. The objective function is a spectral extension of the conventional elastic net; it combines the property of matrix recovery along with low rank and joint sparsity to deal with complex high-dimensional noisy data. Furthermore, SSMRe leverages structural information, as well as the intrinsic structure of data, avoiding the inevitable upper bound. Experimental results on different real-time applications, supported by the theoretical analysis and statistical testing, show significant gain for BCI, face recognition, and person identification datasets, especially in the presence of outliers, while preserving a reasonable number of support vectors.},
}
@article {pmid37527288,
year = {2023},
author = {Liang, S and Xuan, C and Hang, W and Lei, B and Wang, J and Qin, J and Choi, KS and Zhang, Y},
title = {Domain-Generalized EEG Classification With Category-Oriented Feature Decorrelation and Cross-View Consistency Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3285-3296},
doi = {10.1109/TNSRE.2023.3300961},
pmid = {37527288},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; Neural Networks, Computer ; Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {Generalizing the electroencephalogram (EEG) decoding methods to unseen subjects is an important research direction for realizing practical application of brain-computer interfaces (BCIs). Since distribution shifts across subjects, the performance of most current deep neural networks for decoding EEG signals degrades when dealing with unseen subjects. Domain generalization (DG) aims to tackle this issue by learning invariant representations across subjects. To this end, we propose a novel domain-generalized EEG classification framework, named FDCL, to generalize EEG decoding through category-relevant and -irrelevant Feature Decorrelation and Cross-view invariant feature Learning. Specifically, we first devise data augmented regularization through mixing the segments of same-category features from multiple subjects, which increases the diversity of EEG data by spanning the space of subjects. Furthermore, we introduce feature decorrelation regularization to learn the weights of the augmented EEG trials to remove the dependencies between their features, so that the true mapping relationship between relevant features and corresponding labels can be better established. To further distill subject-invariant EEG feature representations, cross-view consistency learning regularization is introduced to encourage consistent predictions of category-relevant features induced from different augmented EEG views. We seamlessly integrate three complementary regularizations into a unified DG framework to jointly improve the generalizability and robustness of the model on unseen subjects. Experimental results on motor imagery (MI) based EEG datasets validate that the proposed FDCL outperforms the available state-of-the-art methods.},
}
@article {pmid37524520,
year = {2024},
author = {Sawyer, A and Cooke, L and Ramsey, NF and Putrino, D},
title = {The digital motor output: a conceptual framework for a meaningful clinical performance metric for a motor neuroprosthesis.},
journal = {Journal of neurointerventional surgery},
volume = {16},
number = {5},
pages = {443-446},
doi = {10.1136/jnis-2023-020316},
pmid = {37524520},
issn = {1759-8486},
mesh = {Humans ; *Brain-Computer Interfaces ; Neural Prostheses ; Movement/physiology ; },
abstract = {In recent years, the majority of the population has become increasingly reliant on continuous and independent control of smart devices to conduct activities of daily living. Upper extremity movement is typically required to generate the motor outputs that control these interfaces, such as rapidly and accurately navigating and clicking a mouse, or activating a touch screen. For people living with tetraplegia, these abilities are lost, significantly compromising their ability to interact with their environment. Implantable brain computer interfaces (BCIs) hold promise for restoring lost neurologic function, including motor neuroprostheses (MNPs). An implantable MNP can directly infer motor intent by detecting brain signals and transmitting the motor signal out of the brain to generate a motor output and subsequently control computer actions. This physiological function is typically performed by the motor neurons in the human body. To evaluate the use of these implanted technologies, there is a need for an objective measurement of the effectiveness of MNPs in restoring motor outputs. Here, we propose the concept of digital motor outputs (DMOs) to address this: a motor output decoded directly from a neural recording during an attempted limb or orofacial movement is transformed into a command that controls an electronic device. Digital motor outputs are diverse and can be categorized as discrete or continuous representations of motor control, and the clinical utility of the control of a single, discrete DMO has been reported in multiple studies. This sets the stage for the DMO to emerge as a quantitative measure of MNP performance.},
}
@article {pmid37524073,
year = {2023},
author = {Sadras, N and Sani, OG and Ahmadipour, P and Shanechi, MM},
title = {Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
doi = {10.1088/1741-2552/acec14},
pmid = {37524073},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Brain/physiology ; Decision Making/physiology ; },
abstract = {Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.},
}
@article {pmid37522623,
year = {2023},
author = {Moon, J and Chau, T},
title = {Online Ternary Classification of Covert Speech by Leveraging the Passive Perception of Speech.},
journal = {International journal of neural systems},
volume = {33},
number = {9},
pages = {2350048},
doi = {10.1142/S012906572350048X},
pmid = {37522623},
issn = {1793-6462},
mesh = {Humans ; Speech ; Electroencephalography/methods ; Vocabulary ; Auditory Perception ; *Speech Perception ; *Brain-Computer Interfaces ; },
abstract = {Brain-computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally fatiguing. Research on CS and speech perception (SP) identifies common spatiotemporal patterns in their respective electroencephalographic (EEG) signals, pointing towards shared encoding mechanisms. The goal of this study was to investigate whether a model that leverages the signal similarities between SP and CS can differentiate speech-related EEG signals online. Ten participants completed a dyadic protocol where in each trial, they listened to a randomly selected word and then subsequently mentally rehearsed the word. In the offline sessions, eight words were presented to participants. For the subsequent online sessions, the two most distinct words (most separable in terms of their EEG signals) were chosen to form a ternary classification problem (two words and rest). The model comprised a functional mapping derived from SP and CS signals of the same speech token (features are extracted via a Riemannian approach). An average ternary online accuracy of 75.3% (60% chance level) was achieved across participants, with individual accuracies as high as 93%. Moreover, we observed that the signal-to-noise ratio (SNR) of CS signals was enhanced by perception-covert modeling according to the level of high-frequency ([Formula: see text]-band) correspondence between CS and SP. These findings may lead to less burdensome data collection for training speech BCIs, which could eventually enhance the rate at which the vocabulary can grow.},
}
@article {pmid37522052,
year = {2023},
author = {Li, M and Wu, L and Lin, F and Guo, M and Xu, G},
title = {Dual stimuli interface with logical division using local move stimuli.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {4},
pages = {965-973},
pmid = {37522052},
issn = {1871-4080},
abstract = {Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain-computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules.},
}
@article {pmid37522042,
year = {2023},
author = {Hualiang, L and Xupeng, Y and Yuzhong, L and Tingjun, X and Wei, T and Yali, S and Qiru, W and Chaolin, X and Yu, W and Weilin, L and Long, J},
title = {A novel noninvasive brain-computer interface by imagining isometric force levels.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {4},
pages = {975-983},
pmid = {37522042},
issn = {1871-4080},
abstract = {Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain-computer interfaces, e.g., different limb imaginations.},
}
@article {pmid37521708,
year = {2023},
author = {Sun, C and Mou, C},
title = {Survey on the research direction of EEG-based signal processing.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1203059},
pmid = {37521708},
issn = {1662-4548},
abstract = {Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.},
}
@article {pmid37520987,
year = {2023},
author = {Lei, Y and Wang, D and Wang, W and Qu, H and Wang, J and Shi, B},
title = {Improving single-hand open/close motor imagery classification by error-related potentials correction.},
journal = {Heliyon},
volume = {9},
number = {8},
pages = {e18452},
pmid = {37520987},
issn = {2405-8440},
abstract = {OBJECTIVE: The ability of a brain-computer interface (BCI) to classify brain activity in electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance indicator. Because the cortical regions that drive the single-handed open and closed tasks overlap, it is difficult to classify the EEG signals during executing both tasks.<br><br>APPROACH: The addition of special EEG features can improve the accuracy of classifying single-hand open and closed tasks. In this work, we designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI) and proposed a strategy to correct the classification results of MI by using ErrP information. The ErrP and MI features of EEG data from 11 subjects were superimposed.<br><br>MAIN RESULTS: The corrected strategy improved the classification accuracy of single-hand open/close MI tasks from 52.3% to 73.7%, an increase of approximately 21%.<br><br>SIGNIFICANCE: Our hybrid BCI paradigm improves the classification accuracy of single-hand MI by adding ErrP information, which provides a new approach for improving the classification performance of BCI.},
}
@article {pmid37519930,
year = {2023},
author = {Dong, Y and Wang, S and Huang, Q and Berg, RW and Li, G and He, J},
title = {Neural Decoding for Intracortical Brain-Computer Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {4},
number = {},
pages = {0044},
pmid = {37519930},
issn = {2692-7632},
abstract = {Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.},
}
@article {pmid37519929,
year = {2023},
author = {Si, X and He, H and Yu, J and Ming, D},
title = {Cross-Subject Emotion Recognition Brain-Computer Interface Based on fNIRS and DBJNet.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {4},
number = {},
pages = {0045},
pmid = {37519929},
issn = {2692-7632},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.},
}
@article {pmid37519868,
year = {2023},
author = {Rouzitalab, A and Boulay, CB and Park, J and Sachs, AJ},
title = {Intracortical brain-computer interfaces in primates: a review and outlook.},
journal = {Biomedical engineering letters},
volume = {13},
number = {3},
pages = {375-390},
pmid = {37519868},
issn = {2093-985X},
abstract = {Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.},
}
@article {pmid37518828,
year = {2023},
author = {Trotier, A and Bagnoli, E and Walski, T and Evers, J and Pugliese, E and Lowery, M and Kilcoyne, M and Fitzgerald, U and Biggs, M},
title = {Micromotion Derived Fluid Shear Stress Mediates Peri-Electrode Gliosis through Mechanosensitive Ion Channels.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {27},
pages = {e2301352},
pmid = {37518828},
issn = {2198-3844},
support = {/SFI_/Science Foundation Ireland/Ireland ; },
mesh = {Rats ; Animals ; *Gliosis ; *Ion Channels/metabolism/pharmacology ; Neuroglia/metabolism ; Astrocytes/metabolism ; Electrodes ; },
abstract = {The development of bioelectronic neural implant technologies has advanced significantly over the past 5 years, particularly in brain-machine interfaces and electronic medicine. However, neuroelectrode-based therapies require invasive neurosurgery and can subject neural tissues to micromotion-induced mechanical shear, leading to chronic inflammation, the formation of a peri-electrode void and the deposition of reactive glial scar tissue. These structures act as physical barriers, hindering electrical signal propagation and reducing neural implant functionality. Although well documented, the mechanisms behind the initiation and progression of these processes are poorly understood. Herein, in silico analysis of micromotion-induced peri-electrode void progression and gliosis is described. Subsequently, ventral mesencephalic cells exposed to milliscale fluid shear stress in vitro exhibited increased expression of gliosis-associated proteins and overexpression of mechanosensitive ion channels PIEZO1 (piezo-type mechanosensitive ion channel component 1) and TRPA1 (transient receptor potential ankyrin 1), effects further confirmed in vivo in a rat model of peri-electrode gliosis. Furthermore, in vitro analysis indicates that chemical inhibition/activation of PIEZO1 affects fluid shear stress mediated astrocyte reactivity in a mitochondrial-dependent manner. Together, the results suggest that mechanosensitive ion channels play a major role in the development of a peri-electrode void and micromotion-induced glial scarring at the peri-electrode region.},
}
@article {pmid37517788,
year = {2023},
author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X},
title = {Classification Algorithm for Electroencephalogram-based Motor Imagery Using Hybrid Neural Network with Spatio-temporal Convolution and Multi-head Attention Mechanism.},
journal = {Neuroscience},
volume = {527},
number = {},
pages = {64-73},
doi = {10.1016/j.neuroscience.2023.07.020},
pmid = {37517788},
issn = {1873-7544},
mesh = {Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; },
abstract = {Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.},
}
@article {pmid37515875,
year = {2023},
author = {Chen, F and Wang, J and Liu, H and Kong, W and Zhao, Z and Ma, L and Liao, H and Zhang, D},
title = {Frequency constraint-based adversarial attack on deep neural networks for medical image classification.},
journal = {Computers in biology and medicine},
volume = {164},
number = {},
pages = {107248},
doi = {10.1016/j.compbiomed.2023.107248},
pmid = {37515875},
issn = {1879-0534},
mesh = {*Neural Networks, Computer ; *Semantics ; Thorax ; },
abstract = {The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversarial attacks that can embed hidden, malicious behaviors into the system. However, designing a unified attack method that can generate imperceptible attack samples with high content similarity and be applied to diverse medical image classification systems is challenging due to the diversity of medical imaging modalities and dimensionalities. Most existing attack methods are designed to attack natural image classification models, which inevitably corrupt the semantics of pixels by applying spatial perturbations. To address this issue, we propose a novel frequency constraint-based adversarial attack method capable of delivering attacks in various medical image classification tasks. Specially, our method introduces a frequency constraint to inject perturbation into high-frequency information while preserving low-frequency information to ensure content similarity. Our experiments include four public medical image datasets, including a 3D CT dataset, a 2D chest X-Ray image dataset, a 2D breast ultrasound dataset, and a 2D thyroid ultrasound dataset, which contain different imaging modalities and dimensionalities. The results demonstrate the superior performance of our model over other state-of-the-art adversarial attack methods for attacking medical image classification tasks on different imaging modalities and dimensionalities.},
}
@article {pmid37515595,
year = {2024},
author = {Cai, R and Liu, Y and Wang, X and Wei, H and Wang, J and Cao, Y and Lei, J and Li, D},
title = {Influences of standardized clinical probing on peri-implant soft tissue seal in a situation of peri-implant mucositis: A histomorphometric study in dogs.},
journal = {Journal of periodontology},
volume = {95},
number = {3},
pages = {233-243},
doi = {10.1002/JPER.23-0167},
pmid = {37515595},
issn = {1943-3670},
support = {2017YFB1104100//Ministry of Science and Technology of the People's Republic of China: The National Key Research and Development Program of China/ ; LCC202202//The Fourth Military Medical University: The National Clinical Research Center for Oral Diseases/ ; },
mesh = {Animals ; Dogs ; *Peri-Implantitis ; *Dental Implants/adverse effects ; *Mucositis ; Cross-Sectional Studies ; Wound Healing ; },
abstract = {BACKGROUND: Clinical probing is commonly recommended to evaluate peri-implant conditions. In a situation of peri-implant mucositis or peri-implantitis, the peri-implant seal healing from the disruption of soft tissue caused by probing has not yet been studied. This study aimed to investigate soft tissue healing after standardized clinical probing around osseointegrated implants with peri-implant mucositis in a dog model.<br><br>METHODS: Three transmucosal implants in each hemi-mandible of six dogs randomly assigned to the peri-implant healthy group or peri-implant mucositis group were probed randomly in the mesial or distal site as probing groups (PH or PM), the cross-sectional opposite sites as unprobed control groups. Histomorphometric measurements of implant shoulder (IS)-most coronal level of alveolar bone contact to the implant surface (BCI), apical termination of the junctional epithelium (aJE)-BCI, mucosal margin (MM)-BCI, and MM-aJE were performed at 1&#xa0;day, 1&#xa0;week, and 2&#xa0;weeks after probing. Apoptosis, proliferation, proinflammatory cytokines, and matrix metalloproteinases (MMPs) of peri-implant soft tissue were estimated by immunofluorescent analysis.<br><br>RESULTS: In the PM group, apical migration of junctional epithelium was revealed by significantly decreased aJE-BCI from 1&#xa0;day to 2&#xa0;weeks in comparison to unprobed sites (p&#xa0;<&#xa0;0.05), while no significant differences were found in the PH group. Immunofluorescent analysis showed higher levels of interleukin-1&#x3b2; (IL-1&#x3b2;), IL-6, tumor necrosis factor-&#x3b1; (TNF-&#x3b1;), MMP-1, and MMP-8, together with exaggerated apoptosis and proliferation of peri-implant soft tissue in the PM group.<br><br>CONCLUSION: Within the limitations, standardized clinical probing might lead to apical migration of the junctional epithelium in a situation of peri-implant mucositis.},
}
@article {pmid37514728,
year = {2023},
author = {Chaddad, A and Wu, Y and Kateb, R and Bouridane, A},
title = {Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {14},
pages = {},
pmid = {37514728},
issn = {1424-8220},
mesh = {*Signal Processing, Computer-Assisted ; Sleep ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Databases, Factual ; Algorithms ; },
abstract = {The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.},
}
@article {pmid37514603,
year = {2023},
author = {Liang, L and Zhang, Q and Zhou, J and Li, W and Gao, X},
title = {Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {14},
pages = {},
pmid = {37514603},
issn = {1424-8220},
support = {No. 2021YFF0601801//China Academy of Information and Communications Technology/ ; },
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Photic Stimulation ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.},
}
@article {pmid37514082,
year = {2023},
author = {Bourdin, A and Ortoli, M and Karadayi, R and Przegralek, L and Sennlaub, F and Bodaghi, B and Guillonneau, X and Carpentier, A and Touhami, S},
title = {Efficacy and Safety of Low-Intensity Pulsed Ultrasound-Induced Blood-Retinal Barrier Opening in Mice.},
journal = {Pharmaceutics},
volume = {15},
number = {7},
pages = {},
pmid = {37514082},
issn = {1999-4923},
support = {ANR-18-IAHU-01//IHU FOReSIGHT/ ; none//ASTRL/ ; },
abstract = {Systemic drugs can treat various retinal pathologies such as retinal cancers; however, their ocular diffusion may be limited by the blood-retina barrier (BRB). Sonication corresponds to the use of ultrasound (US) to increase the permeability of cell barriers including in the BRB. The objective was to study the efficacy and safety of sonication using microbubble-assisted low-intensity pulsed US in inducing a transient opening of the BRB. The eyes of C57/BL6J mice were sonicated at different acoustic pressures (0.10 to 0.50 MPa). Efficacy analyses consisted of fluorescein angiography (FA) performed at different timepoints and the size of the leaked molecules was assessed using FITC-marked dextrans. Tolerance was assessed by fundus photographs, optical coherence tomography, immunohistochemistry, RT-qPCR, and electroretinograms. Sonication at 0.15 MPa was the most suitable pressure for transient BRB permeabilization without altering the morphology or function of the retina. It did not increase the expression of inflammation or apoptosis markers in the retina, retinal pigment epithelium, or choroid. The dextran assay suggested that drugs up to 150 kDa in size can cross the BRB. Microbubble-assisted sonication at an optimized acoustic pressure of 0.15 MPa provides a non-invasive method to transiently open the BRB, increasing the retinal diffusion of systemic drugs without inducing any noticeable side-effect.},
}
@article {pmid37509941,
year = {2023},
author = {Zhu, JY and Li, MM and Zhang, ZH and Liu, G and Wan, H},
title = {Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions.},
journal = {Entropy (Basel, Switzerland)},
volume = {25},
number = {7},
pages = {},
pmid = {37509941},
issn = {1099-4300},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory-spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual-spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4-12 Hz) in visual-spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.},
}
@article {pmid37509039,
year = {2023},
author = {Dong, Y and Wen, X and Gao, F and Gao, C and Cao, R and Xiang, J and Cao, R},
title = {Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.},
journal = {Brain sciences},
volume = {13},
number = {7},
pages = {},
pmid = {37509039},
issn = {2076-3425},
support = {62206196//the National Natural Science Foundation of China/ ; 202103021223035//the Natural Science Foundation of Shanxi/ ; },
abstract = {A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient's energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.},
}
@article {pmid37508946,
year = {2023},
author = {Seifpour, S and Šatka, A},
title = {Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report.},
journal = {Brain sciences},
volume = {13},
number = {7},
pages = {},
pmid = {37508946},
issn = {2076-3425},
support = {2/0023/22//Ministry of Education, Science, Research, and Sport of the Slovak Republic (VEGA)/ ; },
abstract = {Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.},
}
@article {pmid37508828,
year = {2023},
author = {Li, M and Qi, Y and Pan, G},
title = {Optimal Feature Analysis for Identification Based on Intracranial Brain Signals with Machine Learning Algorithms.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {7},
pages = {},
pmid = {37508828},
issn = {2306-5354},
support = {2021ZD0200400//China Brain Project/ ; U1909202 and 61925603//National Natural Science Foundation of China/ ; 2020C03004//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.},
}
@article {pmid37508789,
year = {2023},
author = {Mathon, B and Duarte Rocha, V and Py, JB and Falcan, A and Bergeret, T},
title = {An Air-Filled Bicycle Helmet for Mitigating Traumatic Brain Injury.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {7},
pages = {},
pmid = {37508789},
issn = {2306-5354},
support = {X//ALLIANZ FRANCE/ ; },
abstract = {We created a novel air-filled bicycle helmet. The aims of this study were (i) to assess the head injury mitigation performance of the proposed helmet and (ii) to compare those performance results against the performance results of an expanded polystyrene (EPS) traditional bicycle helmet. Two bicycle helmet types were subjected to impacts in guided vertical drop tests onto a flat anvil: EPS helmets and air-filled helmets (Bumpair). The maximum acceleration value recorded during the test on the Bumpair helmet was 86.76 ± 3.06 g, while the acceleration during the first shock on the traditional helmets reached 207.85 ± 5.55 g (p < 0.001). For the traditional helmets, the acceleration increased steadily over the number of shocks. There was a strong correlation between the number of impacts and the response of the traditional helmet (cor = 0.94; p < 0.001), while the Bumpair helmets showed a less significant dependence over time (cor = 0.36; p = 0.048), meaning previous impacts had a lower consequence. The air-filled helmet significantly reduced the maximal linear acceleration when compared to an EPS traditional helmet, showing improvements in impact energy mitigation, as well as in resistance to repeated impacts. This novel helmet concept could improve head injury mitigation in cyclists.},
}
@article {pmid37507031,
year = {2023},
author = {Song, S and Druschel, LN and Chan, ER and Capadona, JR},
title = {Differential expression of genes involved in the chronic response to intracortical microelectrodes.},
journal = {Acta biomaterialia},
volume = {169},
number = {},
pages = {348-362},
pmid = {37507031},
issn = {1878-7568},
support = {I01 RX002611/RX/RRD VA/United States ; IK6 RX003077/RX/RRD VA/United States ; R01 NS110823/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Mice ; Animals ; Microelectrodes ; Electrodes, Implanted ; *Neuroinflammatory Diseases ; *Inflammation/genetics/pathology ; Immunity, Innate ; },
abstract = {Brain-Machine Interface systems (BMIs) are clinically valuable devices that can provide functional restoration for patients with spinal cord injury or improved integration for patients requiring prostheses. Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for precisely controlling BMIs. However, intracortical microelectrodes have a demonstrated history of progressive decline in the recording performance with time, inhibiting their usefulness. One major contributor to decreased performance is the neuroinflammatory response to the implanted microelectrodes. The neuroinflammatory response can lead to neurodegeneration and the formation of a glial scar at the implant site. Historically, histological imaging of relatively few known cellular and protein markers has characterized the neuroinflammatory response to implanted microelectrode arrays. However, neuroinflammation requires many molecular players to coordinate the response - meaning traditional methods could result in an incomplete understanding. Taking advantage of recent advancements in tools to characterize the relative or absolute DNA/RNA expression levels, a few groups have begun to explore gene expression at the microelectrode-tissue interface. We have utilized a custom panel of ∼813 neuroinflammatory-specific genes developed with NanoString for bulk tissue analysis at the microelectrode-tissue interface. Our previous studies characterized the acute innate immune response to intracortical microelectrodes. Here we investigated the gene expression at the microelectrode-tissue interface in wild-type (WT) mice chronically implanted with nonfunctioning probes. We found 28 differentially expressed genes at chronic time points (4WK, 8WK, and 16WK), many in the complement and extracellular matrix system. Further, the expression levels were relatively stable over time. Genes identified here represent chronic molecular players at the microelectrode implant sites and potential therapeutic targets for the long-term integration of microelectrodes. STATEMENT OF SIGNIFICANCE: Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for the precise control of Brain-Machine Interface systems (BMIs). However, intracortical microelectrodes have a demonstrated history of progressive declines in the recording performance with time, inhibiting their usefulness. One major contributor to the decline in these devices is the neuroinflammatory response against the implanted microelectrodes. Historically, neuroinflammation to implanted microelectrode arrays has been characterized by histological imaging of relatively few known cellular and protein markers. Few studies have begun to develop a more in-depth understanding of the molecular pathways facilitating device-mediated neuroinflammation. Here, we are among the first to identify genetic pathways that could represent targets to improve the host response to intracortical microelectrodes, and ultimately device performance.},
}
@article {pmid37506011,
year = {2023},
author = {Altindis, F and Banerjee, A and Phlypo, R and Yilmaz, B and Congedo, M},
title = {Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {10},
pages = {4696-4706},
doi = {10.1109/JBHI.2023.3299837},
pmid = {37506011},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Databases, Factual ; },
abstract = {This article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12±1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.},
}
@article {pmid37506000,
year = {2023},
author = {Wang, W and Qi, F and Wipf, DP and Cai, C and Yu, T and Li, Y and Zhang, Y and Yu, Z and Wu, W},
title = {Sparse Bayesian Learning for End-to-End EEG Decoding.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {45},
number = {12},
pages = {15632-15649},
doi = {10.1109/TPAMI.2023.3299568},
pmid = {37506000},
issn = {1939-3539},
mesh = {*Algorithms ; Brain/diagnostic imaging ; Bayes Theorem ; Machine Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination/physiology ; },
abstract = {Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets (N=192) and an emotion recognition EEG dataset (N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.},
}
@article {pmid37503096,
year = {2023},
author = {Kukkar, KK and Rao, N and Huynh, D and Shah, S and Contreras-Vidal, JL and Parikh, PJ},
title = {Task-dependent Alteration in Delta Band Corticomuscular Coherence during Standing in Chronic Stroke Survivors.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {37503096},
support = {P2C HD086844/HD/NICHD NIH HHS/United States ; R25 HD106896/HD/NICHD NIH HHS/United States ; },
abstract = {Balance control is an important indicator of mobility and independence in activities of daily living. How the changes in functional integrity of corticospinal tract due to stroke affects the maintenance of upright stance remains to be known. We investigated the changes in functional coupling between the cortex and lower limb muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Eleven stroke patients and nine healthy controls performed a challenging balance task. They stood on a computerized platform with/without somatosensory input distortion created by sway-referencing the support surface, thereby varying the difficulty levels of the task. We computed corticomuscular coherence between Cz (electroencephalography) and leg muscles and assessed balance performance using Berg Balance scale (BBS), Timed-up and go (TUG) and center of pressure (COP) measures. We found lower delta frequency band coherence in stroke patients when compared with healthy controls under medium difficulty condition for distal but not proximal leg muscles. For both groups, we found similar coherence at other frequency bands. On BBS and TUG, stroke patients showed poor balance. However, similar group differences were not consistently observed across COP measures. The presence of distal versus proximal effect suggests differences in the (re)organization of the corticospinal connections across the two muscles groups for balance control. We argue that the observed group difference in the delta coherence might be due to altered mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support platform for balance control.},
}
@article {pmid37502681,
year = {2023},
author = {Chen, D and Huang, H and Bao, X and Pan, J and Li, Y},
title = {An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1194554},
pmid = {37502681},
issn = {1662-4548},
abstract = {INTRODUCTION: Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects.<br><br>METHODS: In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects.<br><br>RESULTS AND DISCUSSION: We achieved an intra-subject average classification accuracy of 85.05% &#xb1; 6.87% and an inter-subject average classification accuracy of 81.60% &#xb1; 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in &#x3b1;, &#x3b2; and &#x3b8; bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.},
}
@article {pmid37501450,
year = {2023},
author = {Ding, X and Yang, L and Li, C},
title = {Study of MI-BCI classification method based on the Riemannian transform of personalized EEG spatiotemporal features.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {7},
pages = {12454-12471},
doi = {10.3934/mbe.2023554},
pmid = {37501450},
issn = {1551-0018},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; Algorithms ; Support Vector Machine ; },
abstract = {Motor imagery (MI) is a traditional paradigm of brain-computer interface (BCI) and can assist users in creating direct connections between their brains and external equipment. The common spatial patterns algorithm is the most popular spatial filtering technique for collecting EEG signal features in MI-based BCI systems. Due to the defect that it only considers the spatial information of EEG signals and is susceptible to noise interference and other issues, its performance is diminished. In this study, we developed a Riemannian transform feature extraction method based on filter bank fusion with a combination of multiple time windows. First, we proposed the multi-time window data segmentation and recombination method by combining it with a filter group to create new data samples. This approach could capture individual differences due to the variation in time-frequency patterns across different participants, thereby improving the model's generalization performance. Second, Riemannian geometry was used for feature extraction from non-Euclidean structured EEG data. Then, considering the non-Gaussian distribution of EEG signals, the neighborhood component analysis (NCA) algorithm was chosen for feature selection. Finally, to meet real-time requirements and a low complexity, we employed a Support Vector Machine (SVM) as the classification algorithm. The proposed model achieved improved accuracy and robustness. In this study, we proposed an algorithm with superior performance on the BCI Competition IV dataset 2a, achieving an accuracy of 89%, a kappa value of 0.73 and an AUC of 0.9, demonstrating advanced capabilities. Furthermore, we analyzed data collected in our laboratory, and the proposed method achieved an accuracy of 77.4%, surpassing other comparative models. This method not only significantly improved the classification accuracy of motor imagery EEG signals but also bore significant implications for applications in the fields of brain-computer interfaces and neural engineering.},
}
@article {pmid38235464,
year = {2022},
author = {Fox, EL and Ugolini, M and Houpt, JW},
title = {Predictions of task using neural modeling.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {1007673},
pmid = {38235464},
issn = {2673-6195},
abstract = {INTRODUCTION: A well-designed brain-computer interface (BCI) can make accurate and reliable predictions of a user's state through the passive assessment of their brain activity; in turn, BCI can inform an adaptive system (such as artificial intelligence, or AI) to intelligently and optimally aid the user to maximize the human-machine team (HMT) performance. Various groupings of spectro-temporal neural features have shown to predict the same underlying cognitive state (e.g., workload) but vary in their accuracy to generalize across contexts, experimental manipulations, and beyond a single session. In our work we address an outstanding challenge in neuroergonomic research: we quantify if (how) identified neural features and a chosen modeling approach will generalize to various manipulations defined by the same underlying psychological construct, (multi)task cognitive workload.<br><br>METHODS: To do this, we train and test 20 different support vector machine (SVM) models, each given a subset of neural features as recommended from previous research or matching the capabilities of commercial devices. We compute each model's accuracy to predict which (monitoring, communications, tracking) and how many (one, two, or three) task(s) were completed simultaneously. Additionally, we investigate machine learning model accuracy to predict task(s) within- vs. between-sessions, all at the individual-level.<br><br>RESULTS: Our results indicate gamma activity across all recording locations consistently outperformed all other subsets from the full model. Our work demonstrates that modelers must consider multiple types of manipulations which may each influence a common underlying psychological construct.<br><br>DISCUSSION: We offer a novel and practical modeling solution for system designers to predict task through brain activity and suggest next steps in expanding our framework to further contribute to research and development in the neuroergonomics community. Further, we quantified the cost in model accuracy should one choose to deploy our BCI approach using a mobile EEG-systems with fewer electrodes-a practical recommendation from our work.},
}
@article {pmid38235457,
year = {2022},
author = {Novak, VD and Kostoulas, T and Muszynski, M and Cinel, C and Nijholt, A},
title = {Editorial: Harnessing physiological synchronization and hyperscanning to enhance collaboration and communication.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {956087},
pmid = {38235457},
issn = {2673-6195},
}
@article {pmid38235467,
year = {2022},
author = {Giles, J and Ang, KK and Phua, KS and Arvaneh, M},
title = {A Transfer Learning Algorithm to Reduce Brain-Computer Interface Calibration Time for Long-Term Users.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {837307},
pmid = {38235467},
issn = {2673-6195},
support = {MC_PC_19051/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation.},
}
@article {pmid38235478,
year = {2022},
author = {Dehais, F and Ladouce, S and Darmet, L and Nong, TV and Ferraro, G and Torre Tresols, J and Velut, S and Labedan, P},
title = {Dual Passive Reactive Brain-Computer Interface: A Novel Approach to Human-Machine Symbiosis.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {824780},
pmid = {38235478},
issn = {2673-6195},
abstract = {The present study proposes a novel concept of neuroadaptive technology, namely a dual passive-reactive Brain-Computer Interface (BCI), that enables bi-directional interaction between humans and machines. We have implemented such a system in a realistic flight simulator using the NextMind classification algorithms and framework to decode pilots' intention (reactive BCI) and to infer their level of attention (passive BCI). Twelve pilots used the reactive BCI to perform checklists along with an anti-collision radar monitoring task that was supervised by the passive BCI. The latter simulated an automatic avoidance maneuver when it detected that pilots missed an incoming collision. The reactive BCI reached 100% classification accuracy with a mean reaction time of 1.6 s when exclusively performing the checklist task. Accuracy was up to 98.5% with a mean reaction time of 2.5 s when pilots also had to fly the aircraft and monitor the anti-collision radar. The passive BCI achieved a F1-score of 0.94. This first demonstration shows the potential of a dual BCI to improve human-machine teaming which could be applied to a variety of applications.},
}
@article {pmid38235234,
year = {2021},
author = {Pham Xuan, R and Andreessen, LM and Zander, TO},
title = {Investigating the Single Trial Detectability of Cognitive Face Processing by a Passive Brain-Computer Interface.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {754472},
pmid = {38235234},
issn = {2673-6195},
abstract = {An automated recognition of faces enables machines to visually identify a person and to gain access to non-verbal communication, including mimicry. Different approaches in lab settings or controlled realistic environments provided evidence that automated face detection and recognition can work in principle, although applications in complex real-world scenarios pose a different kind of problem that could not be solved yet. Specifically, in autonomous driving-it would be beneficial if the car could identify non-verbal communication of pedestrians or other drivers, as it is a common way of communication in daily traffic. Automated identification from observation whether pedestrians or other drivers communicate through subtle cues in mimicry is an unsolved problem so far, as intent and other cognitive factors are hard to derive from observation. In contrast, communicating persons usually have clear understanding whether they communicate or not, and such information is represented in their mindsets. This work investigates whether the mental processing of faces can be identified through means of a Passive Brain-Computer Interface (pBCI). This then could be used to support the cars' autonomous interpretation of facial mimicry of pedestrians to identify non-verbal communication. Furthermore, the attentive driver can be utilized as a sensor to improve the context awareness of the car in partly automated driving. This work presents a laboratory study in which a pBCI is calibrated to detect responses of the fusiform gyrus in the electroencephalogram (EEG), reflecting face recognition. Participants were shown pictures from three different categories: faces, abstracts, and houses evoking different responses used to calibrate the pBCI. The resulting classifier could distinguish responses to faces from that evoked by other stimuli with accuracy above 70%, in a single trial. Further analysis of the classification approach and the underlying data identified activation patterns in the EEG that corresponds to face recognition in the fusiform gyrus. The resulting pBCI approach is promising as it shows better-than-random accuracy and is based on relevant and intended brain responses. Future research has to investigate whether it can be transferred from the laboratory to the real world and how it can be implemented into artificial intelligences, as used in autonomous driving.},
}
@article {pmid38235453,
year = {2022},
author = {Roy, RN and Hinss, MF and Darmet, L and Ladouce, S and Jahanpour, ES and Somon, B and Xu, X and Drougard, N and Dehais, F and Lotte, F},
title = {Retrospective on the First Passive Brain-Computer Interface Competition on Cross-Session Workload Estimation.},
journal = {Frontiers in neuroergonomics},
volume = {3},
number = {},
pages = {838342},
pmid = {38235453},
issn = {2673-6195},
abstract = {As is the case in several research domains, data sharing is still scarce in the field of Brain-Computer Interfaces (BCI), and particularly in that of passive BCIs-i.e., systems that enable implicit interaction or task adaptation based on a user's mental state(s) estimated from brain measures. Moreover, research in this field is currently hindered by a major challenge, which is tackling brain signal variability such as cross-session variability. Hence, with a view to develop good research practices in this field and to enable the whole community to join forces in working on cross-session estimation, we created the first passive brain-computer interface competition on cross-session workload estimation. This competition was part of the 3rd International Neuroergonomics conference. The data were electroencephalographic recordings acquired from 15 volunteers (6 females; average 25 y.o.) who performed 3 sessions-separated by 7 days-of the Multi-Attribute Task Battery-II (MATB-II) with 3 levels of difficulty per session (pseudo-randomized order). The data -training and testing sets-were made publicly available on Zenodo along with Matlab and Python toy code (https://doi.org/10.5281/zenodo.5055046). To this day, the database was downloaded more than 900 times (unique downloads of all version on the 10th of December 2021: 911). Eleven teams from 3 continents (31 participants) submitted their work. The best achieving processing pipelines included a Riemannian geometry-based method. Although better than the adjusted chance level (38% with an α at 0.05 for a 3-class classification problem), the results still remained under 60% of accuracy. These results clearly underline the real challenge that is cross-session estimation. Moreover, they confirmed once more the robustness and effectiveness of Riemannian methods for BCI. On the contrary, chance level results were obtained by one third of the methods-4 teams- based on Deep Learning. These methods have not demonstrated superior results in this contest compared to traditional methods, which may be due to severe overfitting. Yet this competition is the first step toward a joint effort to tackle BCI variability and to promote good research practices including reproducibility.},
}
@article {pmid38235245,
year = {2021},
author = {Brophy, E and Redmond, P and Fleury, A and De Vos, M and Boylan, G and Ward, T},
title = {Denoising EEG Signals for Real-World BCI Applications Using GANs.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {805573},
pmid = {38235245},
issn = {2673-6195},
abstract = {As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.},
}
@article {pmid37521509,
year = {2022},
author = {Martini, M and Kemper, C},
title = {[Cybersecurity of brain-computer interfaces].},
journal = {International cybersecurity law review},
volume = {3},
number = {1},
pages = {191-243},
pmid = {37521509},
issn = {2662-9739},
abstract = {Brain-computer interfaces inspire visions of superhuman powers, enabling users to control protheses and other devices solely with their thoughts. But the rapid development and commercialization of this technology also brings security risks. Attacks on brain-computer interfaces may cause harrowing consequences for users, from eavesdropping on neurological data to manipulating brain activity. At present, data protection law, the regulation of medical devices, and the new rules on the sale of goods with digital elements all govern aspects of cybersecurity. There are, nevertheless, significant gaps. The article analyzes how the legal system currently addresses the risks of cyberattacks on brain-computer interfaces-and how policymakers could address such risks in the future.},
}
@article {pmid38235241,
year = {2021},
author = {Susnoschi Luca, I and Putri, FD and Ding, H and Vuckovič, A},
title = {Brain Synchrony in Competition and Collaboration During Multiuser Neurofeedback-Based Gaming.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {749009},
pmid = {38235241},
issn = {2673-6195},
abstract = {EEG hyperscanning during multiuser gaming offers opportunities to study brain characteristics of social interaction under various paradigms. In this study, we aimed to characterize neural signatures and phase-based functional connectivity patterns of gaming strategies during collaborative and competitive alpha neurofeedback games. Twenty pairs of participants with no close relationship took part in three sessions of collaborative or competitive multiuser neurofeedback (NF), with identical graphical user interface, using Relative Alpha (RA) power as a control signal. Collaborating dyads had to keep their RA within 5% of each other for the team to be awarded a point, while members of competitive dyads scored points if their RA was 10% above their opponent's. Interbrain synchrony existed only during gaming but not during baseline in either collaborative or competitive gaming. Spectral analysis and interbrain connectivity showed that in collaborative gaming, players with higher resting state alpha content were more active in regulating their RA to match those of their partner. Moreover, interconnectivity was the strongest between homologous brain structures of the dyad in theta and alpha bands, indicating a similar degree of planning and social exchange. Competitive gaming emphasized the difference between participants who were able to relax and, in this way, maintain RA, and those who had an unsuccessful approach. Analysis of interbrain connections shows engagement of frontal areas in losers, but not in winners, indicating the formers' attempt to mentalise and apply strategies that might be suitable for conventional gaming, but inappropriate for the alpha neurofeedback-based game. We show that in gaming based on multiplayer non-verbalized NF, the winning strategy is dependent on the rules of the game and on the behavior of the opponent. Mental strategies that characterize successful gaming in the physical world might not be adequate for NF-based gaming.},
}
@article {pmid38235225,
year = {2021},
author = {Chen, P and Hendrikse, S and Sargent, K and Romani, M and Oostrik, M and Wilderjans, TF and Koole, S and Dumas, G and Medine, D and Dikker, S},
title = {Hybrid Harmony: A Multi-Person Neurofeedback Application for Interpersonal Synchrony.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {687108},
pmid = {38235225},
issn = {2673-6195},
abstract = {Recent years have seen a dramatic increase in studies measuring brain activity, physiological responses, and/or movement data from multiple individuals during social interaction. For example, so-called "hyperscanning" research has demonstrated that brain activity may become synchronized across people as a function of a range of factors. Such findings not only underscore the potential of hyperscanning techniques to capture meaningful aspects of naturalistic interactions, but also raise the possibility that hyperscanning can be leveraged as a tool to help improve such naturalistic interactions. Building on our previous work showing that exposing dyads to real-time inter-brain synchrony neurofeedback may help boost their interpersonal connectedness, we describe the biofeedback application Hybrid Harmony, a Brain-Computer Interface (BCI) that supports the simultaneous recording of multiple neurophysiological datastreams and the real-time visualization and sonification of inter-subject synchrony. We report results from 236 dyads experiencing synchrony neurofeedback during naturalistic face-to-face interactions, and show that pairs' social closeness and affective personality traits can be reliably captured with the inter-brain synchrony neurofeedback protocol, which incorporates several different online inter-subject connectivity analyses that can be applied interchangeably. Hybrid Harmony can be used by researchers who wish to study the effects of synchrony biofeedback, and by biofeedback artists and serious game developers who wish to incorporate multiplayer situations into their practice.},
}
@article {pmid38505122,
year = {2021},
author = {Raj, V and Jagadish, C and Gautam, V},
title = {Understanding, engineering, and modulating the growth of neural networks: An interdisciplinary approach.},
journal = {Biophysics reviews},
volume = {2},
number = {2},
pages = {021303},
pmid = {38505122},
issn = {2688-4089},
abstract = {A deeper understanding of the brain and its function remains one of the most significant scientific challenges. It not only is required to find cures for a plethora of brain-related diseases and injuries but also opens up possibilities for achieving technological wonders, such as brain-machine interface and highly energy-efficient computing devices. Central to the brain's function is its basic functioning unit (i.e., the neuron). There has been a tremendous effort to understand the underlying mechanisms of neuronal growth on both biochemical and biophysical levels. In the past decade, this increased understanding has led to the possibility of controlling and modulating neuronal growth in vitro through external chemical and physical methods. We provide a detailed overview of the most fundamental aspects of neuronal growth and discuss how researchers are using interdisciplinary ideas to engineer neuronal networks in vitro. We first discuss the biochemical and biophysical mechanisms of neuronal growth as we stress the fact that the biochemical or biophysical processes during neuronal growth are not independent of each other but, rather, are complementary. Next, we discuss how utilizing these fundamental mechanisms can enable control over neuronal growth for advanced neuroengineering and biomedical applications. At the end of this review, we discuss some of the open questions and our perspectives on the challenges and possibilities related to controlling and engineering the growth of neuronal networks, specifically in relation to the materials, substrates, model systems, modulation techniques, data science, and artificial intelligence.},
}
@article {pmid38235228,
year = {2021},
author = {Trambaiolli, LR and Tiwari, A and Falk, TH},
title = {Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {678981},
pmid = {38235228},
issn = {2673-6195},
abstract = {Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of "in-the-wild" protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.},
}
@article {pmid38235231,
year = {2021},
author = {Lingelbach, K and Dreyer, AM and Schöllhorn, I and Bui, M and Weng, M and Diederichs, F and Rieger, JW and Petermann-Stock, I and Vukelić, M},
title = {Brain Oscillation Entrainment by Perceptible and Non-perceptible Rhythmic Light Stimulation.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {646225},
pmid = {38235231},
issn = {2673-6195},
abstract = {Objective and Background: Decades of research in the field of steady-state visual evoked potentials (SSVEPs) have revealed great potential of rhythmic light stimulation for brain-computer interfaces. Additionally, rhythmic light stimulation provides a non-invasive method for entrainment of oscillatory activity in the brain. Especially effective protocols enabling non-perceptible rhythmic stimulation and, thereby, reducing eye fatigue and user discomfort are favorable. Here, we investigate effects of (1) perceptible and (2) non-perceptible rhythmic light stimulation as well as attention-based effects of the stimulation by asking participants to focus (a) on the stimulation source directly in an overt attention condition or (b) on a cross-hair below the stimulation source in a covert attention condition. Method: SSVEPs at 10 Hz were evoked with a light-emitting diode (LED) driven by frequency-modulated signals and amplitudes of the current intensity either below or above a previously estimated individual threshold. Furthermore, we explored the effect of attention by asking participants to fixate on the LED directly in the overt attention condition and indirectly attend it in the covert attention condition. By measuring electroencephalography, we analyzed differences between conditions regarding the detection of reliable SSVEPs via the signal-to-noise ratio (SNR) and functional connectivity in occipito-frontal(-central) regions. Results: We could observe SSVEPs at 10 Hz for the perceptible and non-perceptible rhythmic light stimulation not only in the overt but also in the covert attention condition. The SNR and SSVEP amplitudes did not differ between the conditions and SNR values were in all except one participant above significance thresholds suggested by previous literature indicating reliable SSVEP responses. No difference between the conditions could be observed in the functional connectivity in occipito-frontal(-central) regions. Conclusion: The finding of robust SSVEPs even for non-intrusive rhythmic stimulation protocols below an individual perceptibility threshold and without direct fixation on the stimulation source reveals strong potential as a safe stimulation method for oscillatory entrainment in naturalistic applications.},
}
@article {pmid38235238,
year = {2021},
author = {Brouwer, AM},
title = {Challenges and Opportunities in Consumer Neuroergonomics.},
journal = {Frontiers in neuroergonomics},
volume = {2},
number = {},
pages = {606646},
pmid = {38235238},
issn = {2673-6195},
}
@article {pmid38234308,
year = {2020},
author = {Kenny, B and Power, SD},
title = {Toward a Subject-Independent EEG-Based Neural Indicator of Task Proficiency During Training.},
journal = {Frontiers in neuroergonomics},
volume = {1},
number = {},
pages = {618632},
pmid = {38234308},
issn = {2673-6195},
abstract = {This study explores the feasibility of developing an EEG-based neural indicator of task proficiency based on subject-independent mental state classification. Such a neural indicator could be used in the development of a passive brain-computer interface to potentially enhance training effectiveness and efficiency. A spatial knowledge acquisition training protocol was used in this study. Fifteen participants acquired spatial knowledge in a novel virtual environment via 60 navigation trials (divided into ten blocks). Task performance (time required to complete trials), perceived task certainty, and EEG signal data were collected. For each participant, 1 s epochs of EEG data were classified as either from the "low proficiency, 0" or "high proficiency, 1" state using a support vector machine classifier trained on data from the remaining 14 participants. The average epoch classification per trial was used to calculate a neural indicator (NI) ranging from 0 ("low proficiency") to 1 ("high proficiency"). Trends in the NI throughout the session-from the first to the last trial-were analyzed using a repeated measure mixed model linear regression. There were nine participants for whom the neural indicator was quite effective in tracking the progression from low to high proficiency. These participants demonstrated a significant (p < 0.001) increase in the neural indicator throughout the training from NI = 0.15 in block 1 to NI = 0.81 (on average) in block 10, with the average NI reaching a plateau after block 7. For the remaining participants, the NI did not effectively track the progression of task proficiency. The results support the potential of a subject-independent EEG-based neural indicator of task proficiency and encourage further research toward this objective.},
}
@article {pmid38234309,
year = {2020},
author = {Belkhiria, C and Peysakhovich, V},
title = {Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020).},
journal = {Frontiers in neuroergonomics},
volume = {1},
number = {},
pages = {606719},
pmid = {38234309},
issn = {2673-6195},
abstract = {Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.},
}
@article {pmid38234311,
year = {2020},
author = {Fairclough, SH and Lotte, F},
title = {Grand Challenges in Neurotechnology and System Neuroergonomics.},
journal = {Frontiers in neuroergonomics},
volume = {1},
number = {},
pages = {602504},
pmid = {38234311},
issn = {2673-6195},
}
@article {pmid37499661,
year = {2023},
author = {Du, Y and Zhou, S and Ma, C and Chen, H and Du, A and Deng, G and Liu, Y and Tose, AJ and Sun, L and Liu, Y and Wu, H and Lou, H and Yu, YQ and Zhao, T and Lammel, S and Duan, S and Yang, H},
title = {Dopamine release and negative valence gated by inhibitory neurons in the laterodorsal tegmental nucleus.},
journal = {Neuron},
volume = {111},
number = {19},
pages = {3102-3118.e7},
doi = {10.1016/j.neuron.2023.06.021},
pmid = {37499661},
issn = {1097-4199},
mesh = {Mice ; Animals ; *Ventral Tegmental Area/physiology ; *Dopamine/physiology ; Nucleus Accumbens ; Dopaminergic Neurons/physiology ; gamma-Aminobutyric Acid ; Mammals ; },
abstract = {GABAergic neurons in the laterodorsal tegmental nucleus (LDT[GABA]) encode aversion by directly inhibiting mesolimbic dopamine (DA). Yet, the detailed cellular and circuit mechanisms by which these cells relay unpleasant stimuli to DA neurons and regulate behavioral output remain largely unclear. Here, we show that LDT[GABA] neurons bidirectionally respond to rewarding and aversive stimuli in mice. Activation of LDT[GABA] neurons promotes aversion and reduces DA release in the lateral nucleus accumbens. Furthermore, we identified two molecularly distinct LDT[GABA] cell populations. Somatostatin-expressing (Sst[+]) LDT[GABA] neurons indirectly regulate the mesolimbic DA system by disinhibiting excitatory hypothalamic neurons. In contrast, Reelin-expressing LDT[GABA] neurons directly inhibit downstream DA neurons. The identification of separate GABAergic subpopulations in a single brainstem nucleus that relay unpleasant stimuli to the mesolimbic DA system through direct and indirect projections is critical for establishing a circuit-level understanding of how negative valence is encoded in the mammalian brain.},
}
@article {pmid37499295,
year = {2023},
author = {Luo, J and Wang, Y and Xia, S and Lu, N and Ren, X and Shi, Z and Hei, X},
title = {A shallow mirror transformer for subject-independent motor imagery BCI.},
journal = {Computers in biology and medicine},
volume = {164},
number = {},
pages = {107254},
doi = {10.1016/j.compbiomed.2023.107254},
pmid = {37499295},
issn = {1879-0534},
mesh = {Humans ; *Electroencephalography/methods ; Imagination ; *Brain-Computer Interfaces ; Learning ; Algorithms ; },
abstract = {OBJECTIVE: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI.<br><br>APPROACH: In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects.<br><br>MAIN RESULTS: The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification.<br><br>SIGNIFICANCE: This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.},
}
@article {pmid37499267,
year = {2024},
author = {Abdou, H and Treffalls, RN and Stonko, DP and Kundi, R and Morrison, JJ},
title = {Endovascular stenting techniques for blunt carotid injury.},
journal = {Vascular},
volume = {32},
number = {5},
pages = {1055-1062},
doi = {10.1177/17085381231193062},
pmid = {37499267},
issn = {1708-539X},
mesh = {Humans ; *Carotid Artery Injuries/diagnostic imaging/therapy/surgery/etiology ; Clinical Decision-Making ; *Endovascular Procedures/instrumentation/adverse effects ; Patient Selection ; Risk Factors ; *Stents ; Treatment Outcome ; *Wounds, Nonpenetrating/diagnostic imaging/therapy/surgery ; },
abstract = {OBJECTIVES: While methods of endovascular carotid artery stenting have improved over time, concerns surrounding the safety and efficacy of stenting for blunt carotid injury (BCI) remain. This study aims to present our approach to carotid artery stenting (CAS) by incorporating new technologies such as flow-diverting stents and circuits.<br><br>METHODS: There is no robust evidence to support routine carotid artery stenting; however, there are several therapeutic options and approaches for treating BCI that currently require an individualized approach. Endovascular stenting and specific stent selection are largely dictated by the disease process the surgeon intends to treat. We will discuss patient selection, medical management, and the most common revascularization techniques, including transfemoral stenting, trans-carotid arterial revascularization using flow reversal, and stent-assisting coiling.<br><br>RESULTS: It must be stressed that endovascular intervention is not an alternative to or preclusive of antithrombotic or anticoagulant therapy. In the setting of BCI, transfemoral CAS is most appropriate in patients who are symptomatic, have a rapidly progressing or large lesion, and do not have a soft thrombus present due to risk of embolism. Unlike transfemoral CAS, TCAR offers an elegant solution for embolic protection when patients have a soft thrombus present. In the case of a large pseudoaneurysm, we perform stent-assisted coiling.<br><br>CONCLUSIONS: We practice selective endovascular intervention, stenting lesions that are flow-limiting or have large or rapidly expanding pseudoaneurysms, and only in patients for whom anticoagulation and antiplatelet agents are not contraindicated. As technology and investigation progress, the concerns regarding the safety and the role of endovascular intervention in the treatment of BCI will be more clearly defined.},
}
@article {pmid37498754,
year = {2023},
author = {Ma, X and Chen, W and Pei, Z and Liu, J and Huang, B and Chen, J},
title = {A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3188-3200},
doi = {10.1109/TNSRE.2023.3299355},
pmid = {37498754},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Imagination ; },
abstract = {Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.},
}
@article {pmid37498753,
year = {2023},
author = {Zhou, Y and Yu, T and Gao, W and Huang, W and Lu, Z and Huang, Q and Li, Y},
title = {Shared Three-Dimensional Robotic Arm Control Based on Asynchronous BCI and Computer Vision.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3163-3175},
doi = {10.1109/TNSRE.2023.3299350},
pmid = {37498753},
issn = {1558-0210},
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Movement/physiology ; Computers ; Electroencephalography/methods ; },
abstract = {OBJECTIVE: A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge.<br><br>APPROACH: In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically.<br><br>RESULTS: Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control.<br><br>SIGNIFICANCE: Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.},
}
@article {pmid37498009,
year = {2023},
author = {van Merode, NAM and Nijholt, IM and Heesakkers, JP and van Koeveringe, GA and Steffens, MG and Witte, LPW},
title = {Effect of bladder outlet procedures on urodynamic assessments in men with an acontractile or underactive detrusor: A systematic review and meta-analysis.},
journal = {Neurourology and urodynamics},
volume = {42},
number = {8},
pages = {1822-1838},
doi = {10.1002/nau.25253},
pmid = {37498009},
issn = {1520-6777},
support = {//Isala Science and Innovation Fund Grant/ ; },
mesh = {Male ; Adult ; Humans ; Urinary Bladder/surgery ; *Urinary Bladder, Underactive/surgery ; Quality of Life ; Urodynamics ; *Urinary Bladder Neck Obstruction/surgery ; },
abstract = {OBJECTIVE: To review the effect of bladder outlet procedures on urodynamic outcomes and symptom scores in males with detrusor underactivity (DU) or acontractile detrusors (AD).<br><br>MATERIALS AND METHODS: We performed a systematic review and meta-analysis of research publications derived from PubMed, Embase, Web of Science, and Ovid Medline to identify clinical studies of adult men with non-neurogenic DU or AD who underwent any bladder outlet procedure. Outcomes comprised the detrusor pressure at maximum flow (Pdet Qmax), maximum flow rate (Qmax), international prostate symptom score (IPSS), and quality of life (QoL). This study is registered under PROSPERO CRD42020215832.<br><br>RESULTS: We included 13 studies of bladder outlet procedures, of which 6 reported decreased and 7 reported improved Pdet Qmax after the procedure. Meta-analysis revealed an increase in the pooled mean Pdet Qmax of 5.99&#x2009;cmH2 0 after surgery (95%&#xa0;CI:&#xa0;0.59-11.40; p&#x2009;=&#x2009;0.03; I[2] 95%). Notably, the Pdet Qmax improved in all subgroups with a preoperative bladder contractility index (BCI)&#xa0;<50 and decreased in all subgroups with a BCI&#xa0;&#x2265;50. All studies reported an improved Qmax after surgery, with a pooled mean difference of 5.87&#x2009;mL/s (95%&#xa0;CI:&#xa0;4.25-7.49; I[2] 93%). Only three studies reported QoL, but pooling suggested significant improvements after surgery (mean, -2.41 points; 95%&#xa0;CI:&#xa0;-2.81 to -2.01; p&#x2009;=&#x2009;0.007). All seven studies reporting IPSS demonstrated improvement (mean, -12.82; 95%&#xa0;CI:&#xa0;-14.76 to -10.88; p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: This review shows that Pdet Qmax and Qmax increases after surgical bladder outlet procedures in men with DU&#xa0;and AD. Bladder outlet procedures should be discussed as part of the shared decision-making process for this group. The evidence was of low to very low certainty.},
}
@article {pmid37497042,
year = {2023},
author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y},
title = {Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1223307},
pmid = {37497042},
issn = {1662-5161},
abstract = {In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.},
}
@article {pmid37497040,
year = {2023},
author = {Kleih-Dahms, SC and Botrel, L},
title = {Neurofeedback therapy to improve cognitive function in patients with chronic post-stroke attention deficits: a within-subjects comparison.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1155584},
pmid = {37497040},
issn = {1662-5161},
abstract = {INTRODUCTION: We investigated a slow-cortical potential (SCP) neurofeedback therapy approach for rehabilitating chronic attention deficits after stroke. This study is the first attempt to train patients who survived stroke with SCP neurofeedback therapy.<br><br>METHODS: We included N = 5 participants in a within-subjects follow-up design. We assessed neuropsychological and psychological performance at baseline (4 weeks before study onset), before study onset, after neurofeedback training, and at 3 months follow-up. Participants underwent 20 sessions of SCP neurofeedback training.<br><br>RESULTS: Participants learned to regulate SCPs toward negativity, and we found indications for improved attention after the SCP neurofeedback therapy in some participants. Quality of life improved throughout the study according to engagement in activities of daily living. The self-reported motivation was related to mean SCP activation in two participants.<br><br>DISCUSSION: We would like to bring attention to the potential of SCP neurofeedback therapy as a new rehabilitation method for treating post-stroke cognitive deficits. Studies with larger samples are warranted to corroborate the results.},
}
@article {pmid37496741,
year = {2023},
author = {Xu, H and Cao, K and Chen, H and Abudusalamu, A and Wu, W and Xue, Y},
title = {Emotional brain network decoded by biological spiking neural network.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1200701},
pmid = {37496741},
issn = {1662-4548},
abstract = {INTRODUCTION: Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown.<br><br>METHODS: To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions.<br><br>RESULTS: The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the &#x3b1; frequency band could effectively represent negative emotions, while the &#x3b1; frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation.<br><br>DISCUSSION: The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.},
}
@article {pmid37496515,
year = {2023},
author = {Lu, Z and Wang, T and Zhang, R},
title = {Editorial: Affective brain-computer interface in emotion artificial intelligence and medical engineering.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1237252},
pmid = {37496515},
issn = {1662-5188},
}
@article {pmid37496395,
year = {2023},
author = {Estaño, LA and Jumawan, JC},
title = {The prevailing infection of Schistosoma japonicum and other zoonotic parasites in bubaline reservoir hosts in the ricefield of lake ecosystem: the case of Lake Mainit, Philippines.},
journal = {Parasitology},
volume = {150},
number = {9},
pages = {786-791},
pmid = {37496395},
issn = {1469-8161},
mesh = {Humans ; Animals ; Cattle ; *Schistosoma japonicum ; *Schistosomiasis japonica/epidemiology/veterinary/parasitology ; *Parasites ; *Fascioliasis/epidemiology/veterinary ; Ecosystem ; Lakes ; Philippines/epidemiology ; *Schistosomiasis ; China/epidemiology ; },
abstract = {Bovines are important reservoir hosts of schistosomiasis, placing humans and animals in rice fields areas at risk of infection. This study reported the prevailing infection of zoonotic parasites from bovine feces in the rice fields adjacent to Lake Mainit, Philippines. Formalin Ethyl Acetate Sedimentation was performed on 124 bovine fecal samples from rice fields and documented eggs and cysts from seven parasites: Schistosoma japonicum, Fasciola gigantica, Ascaris sp., Strongyloides sp., Balantidium coli, coccidian oocyst and a hookworm species. Among these parasites, F. gigantica harboured the highest infection with a 100% prevalence rate, followed by hookworms (51.61%), B. coli (30.64%) and S. japonicum (12.09%), respectively. The intensity of infection of S. japonicum eggs per gram (MPEG = 4.19) among bovines is categorized as ‘light.’ Bovine contamination index (BCI) calculations revealed that, on average, infected bovines in rice fields excrete 104 750 S. japonicum eggs daily. However, across all ricefield stations, bovines were heavily infected with fascioliasis with BCI at 162 700 F. gigantica eggs per day. The study reports that apart from the persistent cases of schistosomiasis in the area, bovines in these rice fields are also heavily infected with fascioliasis. The study confirms the critical role of bovines as a reservoir host for continued infection of schistosomiasis, fascioliasis and other diseases in the rice fields of Lake Mainit. Immediate intervention to manage the spread of these diseases in bovines is recommended.},
}
@article {pmid37494734,
year = {2023},
author = {Zhang, X and Wang, X and Zhu, H and Zhang, D and Chen, J and Wen, Y and Li, Y and Jin, L and Xie, C and Guo, D and Luo, T and Tong, J and Zhou, Y and Shen, Y},
title = {Short-wavelength artificial light affects visual neural pathway development in mice.},
journal = {Ecotoxicology and environmental safety},
volume = {263},
number = {},
pages = {115282},
doi = {10.1016/j.ecoenv.2023.115282},
pmid = {37494734},
issn = {1090-2414},
mesh = {Animals ; Mice ; Mice, Inbred C57BL ; *Retina/metabolism ; *Retinal Cone Photoreceptor Cells/physiology ; Retinal Ganglion Cells/physiology ; Neural Pathways ; Mammals ; },
abstract = {Nearly all modern life depends on artificial light; however, it does cause health problems. With certain restrictions of artificial light emitting technology, the influence of the light spectrum is inevitable. The most remarkable problem is its overload in the short wavelength component. Short wavelength artificial light has a wide range of influences from ocular development to mental problems. The visual neuronal pathway, as the primary light-sensing structure, may contain the fundamental mechanism of all light-induced abnormalities. However, how the artificial light spectrum shapes the visual neuronal pathway during development in mammals is poorly understood. We placed C57BL/6 mice in three different spectrum environments (full-spectrum white light: 400-750 nm; violet light: 400 ± 20 nm; green light: 510 ± 20 nm) beginning at eye opening, with a fixed light time of 7:00-19:00. During development, we assessed the ocular axial dimension, visual function and retinal neurons. After two weeks under short wavelength conditions, the ocular axial length (AL), anterior chamber depth (ACD) and length of lens thickness, real vitreous chamber depth and retinal thickness (LLVR) were shorter, visual acuity (VA) decreased, and retinal electrical activity was impaired. The density of S-cones in the dorsal and ventral retinas both decreased after one week under short wavelength conditions. In the ventral retina, it increased after three weeks. Retinal ganglion cell (RGC) density and axon thickness were not influenced; however, the axonal terminals in the lateral geniculate nucleus (LGN) were less clustered and sparse. Amacrine cells (ACs) were significantly more activated. Green light has few effects. The KEGG and GO enrichment analyses showed that many genes related to neural circuitry, synaptic formation and neurotransmitter function were differentially expressed in the short wavelength light group. In conclusion, exposure to short wavelength artificial light in the early stage of vision-dependent development in mice delayed the development of the visual pathway. The axon terminus structure and neurotransmitter function may be the major suffering.},
}
@article {pmid37494151,
year = {2024},
author = {Forenzo, D and Liu, Y and Kim, J and Ding, Y and Yoon, T and He, B},
title = {Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {1},
pages = {282-294},
pmid = {37494151},
issn = {1558-2531},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 NS131069/NS/NINDS NIH HHS/United States ; R01 NS127849/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Electroencephalography/methods ; Imagination ; *Brain-Computer Interfaces ; Brain ; Attention ; },
abstract = {OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.<br><br>METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI, and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).<br><br>RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), and statistically outperforms both MI alone (42%) and OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.<br><br>CONCLUSION: Integrating MI and OSA leads to improved performance over both individual methods at the group level and is the best BCI paradigm option for some subjects.<br><br>SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.},
}
@article {pmid37492903,
year = {2023},
author = {Zhang, Y and Valsecchi, M and Gegenfurtner, KR and Chen, J},
title = {Laplacian reference is optimal for steady-state visual-evoked potentials.},
journal = {Journal of neurophysiology},
volume = {130},
number = {3},
pages = {557-568},
doi = {10.1152/jn.00469.2022},
pmid = {37492903},
issn = {1522-1598},
mesh = {Humans ; *Electroencephalography/methods ; Reproducibility of Results ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Signal-To-Noise Ratio ; Photic Stimulation/methods ; Algorithms ; },
abstract = {Steady-state visual-evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces (BCIs). Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (Chen J, Valsecchi M, Gegenfurtner KR. J Neurophysiol 118: 749-754, 2017; Chen J, Valsecchi M, Gegenfurtner KR. Neuropsychologia 102: 206-216, 2017; Chen J, McManus M, Valsecchi M, Harris LR, Gegenfurtner KR. J Vis 19: 8, 2019) and three public datasets from other studies (Baker DH, Vilidaite G, Wade AR. PLoS Comput Biol 17: e1009507, 2021; Lygo FA, Richard B, Wade AR, Morland AB, Baker DH. NeuroImage 230: 117780, 2021; Vilidaite G, Norcia AM, West RJH, Elliott CJH, Pei F, Wade AR, Baker DH. Proc R Soc B 285: 20182255, 2018), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred as it requires only data from the maximally activated electrode and a few surrounding electrodes.NEW &amp; NOTEWORTHY The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis.},
}
@article {pmid37491998,
year = {2023},
author = {Wang, J and Wang, X and Zou, J and Duan, J and Shen, Z and Xu, N and Chen, Y and Zhang, J and He, H and Bi, Y and Ding, N},
title = {Neural substrate underlying the learning of a passage with unfamiliar vocabulary and syntax.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {18},
pages = {10036-10046},
doi = {10.1093/cercor/bhad263},
pmid = {37491998},
issn = {1460-2199},
mesh = {Humans ; *Vocabulary ; Learning ; Language ; Speech ; Phonetics ; *Speech Perception/physiology ; Magnetic Resonance Imaging/methods ; Comprehension/physiology ; Brain Mapping ; },
abstract = {Speech comprehension is a complex process involving multiple stages, such as decoding of phonetic units, recognizing words, and understanding sentences and passages. In this study, we identify cortical networks beyond basic phonetic processing using a novel passage learning paradigm. Participants learn to comprehend a story composed of syllables of their native language, but containing unfamiliar vocabulary and syntax. Three learning methods are employed, each resulting in some degree of learning within a 12-min learning session. Functional magnetic resonance imaging results reveal that, when listening to the same story, the classic temporal-frontal language network is significantly enhanced by learning. Critically, activation of the left anterior and posterior temporal lobe correlates with the learning outcome that is assessed behaviorally through, e.g. word recognition and passage comprehension tests. This study demonstrates that a brief learning session is sufficient to induce neural plasticity in the left temporal lobe, which underlies the transformation from phonetic units to the units of meaning, such as words and sentences.},
}
@article {pmid37491837,
year = {2024},
author = {Li, S and Lv, D and Qian, C and Jiang, J and Zhang, P and Xi, C and Wu, L and Gao, X and Fu, Y and Zhang, D and Chen, Y and Huang, H and Zhu, Y and Wang, X and Lai, J and Hu, S},
title = {Circulating T-cell subsets discrepancy between bipolar disorder and major depressive disorder during mood episodes: A naturalistic, retrospective study of 1015 cases.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {2},
pages = {e14361},
pmid = {37491837},
issn = {1755-5949},
support = {2020R01001//Innovation Group Program of Zhejiang Province/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation/ ; 81971271//National Natural Science Foundation of China/ ; 82201676//National Natural Science Foundation of China/ ; LQ20H090013//Natural Science Foundation of Zhejiang Province/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; },
mesh = {Humans ; *Bipolar Disorder/diagnosis ; *Depressive Disorder, Major/diagnosis ; Retrospective Studies ; T-Lymphocyte Subsets ; Biomarkers ; },
abstract = {AIMS: We aimed to investigate whether peripheral T-cell subsets could be a biomarker to distinguish major depressive disorder (MDD) and bipolar disorder (BD).<br><br>METHODS: Medical records of hospitalized patients in the Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, from January 2015 to September 2020 with a discharge diagnosis of MDD or BD were reviewed. Patients who underwent peripheral blood examination of T-cell subtype proportions, including CD3+, CD4+, CD8+ T-cell, and natural killer (NK) cells, were enrolled. The Chi-square test, t-test, or one-way analysis of variance were used to analyze group differences. Demographic profiles and T-cell data were used to construct a random forest classifier-based diagnostic model.<br><br>RESULTS: Totally, 98 cases of BD mania, 459 cases of BD depression (BD-D), and 458 cases of MDD were included. There were significant differences in the proportions of CD3+, CD4+, CD8+ T-cell, and NK cells among the three groups. Compared with MDD, the BD-D group showed higher CD8+ but lower CD4+ T-cell and a significantly lower ratio of CD4+ and CD8+ proportions. The random forest model achieved an area under the curve of 0.77 (95% confidence interval: 0.71-0.83) to distinguish BD-D from MDD patients.<br><br>CONCLUSION: These findings imply that BD and MDD patients may harbor different T-cell inflammatory patterns, which could be a potential diagnostic biomarker for mood disorders.},
}
@article {pmid37491671,
year = {2023},
author = {Jiang, H and Chen, P and Sun, Z and Liang, C and Xue, R and Zhao, L and Wang, Q and Li, X and Deng, W and Gao, Z and Huang, F and Huang, S and Zhang, Y and Li, T},
title = {Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
volume = {48},
number = {13},
pages = {1920-1930},
pmid = {37491671},
issn = {1740-634X},
mesh = {Adult ; Male ; Adolescent ; Humans ; *Schizophrenia/diagnosis ; Neural Networks, Computer ; Electroencephalography/methods ; Biomarkers ; },
abstract = {Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.},
}
@article {pmid37491649,
year = {2024},
author = {Shao, YR and Yu, JY and Ma, Y and Dong, Y and Wu, ZY},
title = {CAT Interruption as a Protective Factor in Chinese Patients with Spinocerebellar Ataxia Type 1.},
journal = {Cerebellum (London, England)},
volume = {23},
number = {3},
pages = {1211-1214},
pmid = {37491649},
issn = {1473-4230},
mesh = {Humans ; Male ; *Spinocerebellar Ataxias/genetics/epidemiology ; Female ; Middle Aged ; Adult ; *Asian People/genetics ; Ataxin-1/genetics ; China/epidemiology ; Young Adult ; Age of Onset ; Cohort Studies ; Protective Factors ; Aged ; Trinucleotide Repeat Expansion/genetics ; Alleles ; East Asian People ; },
abstract = {Spinocerebellar ataxia type 1 (SCA1) is the third most common type of spinocerebellar ataxias in China. CAT interruptions in the pathogenic alleles of SCA1 patients had only been reported by limited documents and there was a lack of data based on the Chinese population. In this study, we detected CAT interrupted pathogenic alleles in SCA1 patients from 4 out of 79 (5.1%) Chinese families. Their total CAG repeats were larger (median 58 vs. 47, p < 0.001) but ages at onset were later (median 46 vs. 38, p = 0.020). The longest uninterrupted CAG repeats could explain 65.4% of the AAO variance, making an increase of 28.0% compared to the total CAG repeats. The interruption pattern was greatly different between Chinese cohort and Caucasian cohort, indicating the effect of race.},
}
@article {pmid37488871,
year = {2024},
author = {Tao, Q and Chao, H and Fang, D and Dou, D},
title = {Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022.},
journal = {Neural regeneration research},
volume = {19},
number = {1},
pages = {226-232},
pmid = {37488871},
issn = {1673-5374},
abstract = {The National Natural Science Foundation of China is one of the major funding agencies for neurorehabilitation research in China. This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and worldwide. We used data from the Web of Science Core Collection (WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information. In addition, the prospects for neurorehabilitation research in China are discussed. From 2010 to 2022, a total of 74,220 publications in neurorehabilitation were identified, with there being an overall upward tendency. During this period, the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neurorehabilitation research in China. With the support of the National Natural Science Foundation of China, China has made some achievements in neurorehabilitation research. Research related to neurorehabilitation is believed to be making steady and significant progress in China.},
}
@article {pmid37487829,
year = {2023},
author = {Zhao, Y and Chen, Y and Cheng, K and Huang, W},
title = {Artificial intelligence based multimodal language decoding from brain activity: A review.},
journal = {Brain research bulletin},
volume = {201},
number = {},
pages = {110713},
doi = {10.1016/j.brainresbull.2023.110713},
pmid = {37487829},
issn = {1873-2747},
mesh = {Humans ; *Artificial Intelligence ; *Brain ; Language ; Speech ; },
abstract = {Decoding brain activity is conducive to the breakthrough of brain-computer interface (BCI) technology. The development of artificial intelligence (AI) continually promotes the progress of brain language decoding technology. Existent research has mainly focused on a single modality and paid insufficient attention to AI methods. Therefore, our objective is to provide an overview of relevant decoding research from the perspective of different modalities and methodologies. The modalities involve text, speech, image, and video, whereas the core method is using AI-built decoders to translate brain signals induced by multimodal stimuli into text or vocal language. The semantic information of brain activity can be successfully decoded into a language at various levels, ranging from words through sentences to discourses. However, the decoding effect is affected by various factors, such as the decoding model, vector representation model, and brain regions. Challenges and future directions are also discussed. The advances in brain language decoding and BCI technology will potentially assist patients with clinical aphasia in regaining the ability to communicate.},
}
@article {pmid37487487,
year = {2023},
author = {Thomas, TM and Singh, A and Bullock, LP and Liang, D and Morse, CW and Scherschligt, X and Seymour, JP and Tandon, N},
title = {Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ace9fb},
pmid = {37487487},
issn = {1741-2552},
mesh = {Humans ; Speech ; Phonetics ; Language ; Electroencephalography/methods ; *Sensorimotor Cortex ; *Brain-Computer Interfaces ; },
abstract = {Objective.The speech production network relies on a widely distributed brain network. However, research and development of speech brain-computer interfaces (speech-BCIs) has typically focused on decoding speech only from superficial subregions readily accessible by subdural grid arrays-typically placed over the sensorimotor cortex. Alternatively, the technique of stereo-electroencephalography (sEEG) enables access to distributed brain regions using multiple depth electrodes with lower surgical risks, especially in patients with brain injuries resulting in aphasia and other speech disorders.Approach.To investigate the decoding potential of widespread electrode coverage in multiple cortical sites, we used a naturalistic continuous speech production task. We obtained neural recordings using sEEG from eight participants while they read aloud sentences. We trained linear classifiers to decode distinct speech components (articulatory components and phonemes) solely based on broadband gamma activity and evaluated the decoding performance using nested five-fold cross-validation.Main Results.We achieved an average classification accuracy of 18.7% across 9 places of articulation (e.g. bilabials, palatals), 26.5% across 5 manner of articulation (MOA) labels (e.g. affricates, fricatives), and 4.81% across 38 phonemes. The highest classification accuracies achieved with a single large dataset were 26.3% for place of articulation, 35.7% for MOA, and 9.88% for phonemes. Electrodes that contributed high decoding power were distributed across multiple sulcal and gyral sites in both dominant and non-dominant hemispheres, including ventral sensorimotor, inferior frontal, superior temporal, and fusiform cortices. Rather than finding a distinct cortical locus for each speech component, we observed neural correlates of both articulatory and phonetic components in multiple hubs of a widespread language production network.Significance.These results reveal the distributed cortical representations whose activity can enable decoding speech components during continuous speech through the use of this minimally invasive recording method, elucidating language neurobiology and neural targets for future speech-BCIs.},
}
@article {pmid37486136,
year = {2023},
author = {Arpaia, P and Esposito, A and Moccaldi, N and Parvis, M},
title = {A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {197},
pages = {},
doi = {10.3791/65007},
pmid = {37486136},
issn = {1940-087X},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; User-Computer Interface ; Electroencephalography ; Delivery of Health Care ; *Wearable Electronic Devices ; Photic Stimulation ; },
abstract = {The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.},
}
@article {pmid37484920,
year = {2023},
author = {Bates, M and Sunderam, S},
title = {Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1121481},
pmid = {37484920},
issn = {1662-5161},
abstract = {INTRODUCTION: Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.<br><br>METHODS: Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.<br><br>RESULTS: A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.<br><br>DISCUSSION: We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.},
}
@article {pmid37484690,
year = {2023},
author = {Xu, M and Qian, L and Wang, S and Cai, H and Sun, Y and Thakor, N and Qi, X and Sun, Y},
title = {Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing.},
journal = {Frontiers in aging neuroscience},
volume = {15},
number = {},
pages = {1193292},
pmid = {37484690},
issn = {1663-4365},
abstract = {Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.},
}
@article {pmid37483349,
year = {2023},
author = {Chandrasekaran, S and Bhagat, NA and Ramdeo, R and Ebrahimi, S and Sharma, PD and Griffin, DG and Stein, A and Harkema, SJ and Bouton, CE},
title = {Targeted transcutaneous spinal cord stimulation promotes persistent recovery of upper limb strength and tactile sensation in spinal cord injury: a pilot study.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1210328},
pmid = {37483349},
issn = {1662-4548},
abstract = {Long-term recovery of limb function is a significant unmet need in people with paralysis. Neuromodulation of the spinal cord through epidural stimulation, when paired with intense activity-based training, has shown promising results toward restoring volitional limb control in people with spinal cord injury. Non-invasive neuromodulation of the cervical spinal cord using transcutaneous spinal cord stimulation (tSCS) has shown similar improvements in upper-limb motor control rehabilitation. However, the motor and sensory rehabilitative effects of activating specific cervical spinal segments using tSCS have largely remained unexplored. We show in two individuals with motor-complete SCI that targeted stimulation of the cervical spinal cord resulted in up to a 1,136% increase in exerted force, with weekly activity-based training. Furthermore, this is the first study to document up to a 2-point improvement in clinical assessment of tactile sensation in SCI after receiving tSCS. Lastly, participant gains persisted after a one-month period void of stimulation, suggesting that targeted tSCS may lead to persistent recovery of motor and sensory function.},
}
@article {pmid37480644,
year = {2023},
author = {Chen, L and Liao, H and Kong, W and Zhang, D and Chen, F},
title = {Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images.},
journal = {Computer methods and programs in biomedicine},
volume = {240},
number = {},
pages = {107642},
doi = {10.1016/j.cmpb.2023.107642},
pmid = {37480644},
issn = {1872-7565},
mesh = {Humans ; *Liver/diagnostic imaging/surgery ; Ultrasonography ; Computer Simulation ; Learning ; *Physicians ; },
abstract = {In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.},
}
@article {pmid37480186,
year = {2023},
author = {Werner, CM and Willing, LB and Goudsward, HJ and McBride, AR and Stella, SL and Holmes, GM},
title = {Plasticity of colonic enteric nervous system following spinal cord injury in male and female rats.},
journal = {Neurogastroenterology and motility},
volume = {35},
number = {11},
pages = {e14646},
pmid = {37480186},
issn = {1365-2982},
support = {R01 NS105987/NS/NINDS NIH HHS/United States ; R01-NS-105987/NS/NINDS NIH HHS/United States ; },
mesh = {Rats ; Male ; Female ; Animals ; *Neurogenic Bowel ; Quality of Life ; *Enteric Nervous System ; Myenteric Plexus ; Colon ; Motor Neurons ; *Spinal Cord Injuries/complications ; },
abstract = {BACKGROUND: Neurogenic bowel is a dysmotility disorder following spinal cord injury (SCI) that negatively impacts quality of life, social integration, and physical health. Colonic transit is directly modulated by the enteric nervous system. Interstitial Cells of Cajal (ICC) distributed throughout the small intestine and colon serve as specialized pacemaker cells, generating rhythmic electrical slow waves within intestinal smooth muscle, or serve as an interface between smooth muscle cells and enteric motor neurons of the myenteric plexus. Interstitial Cells of Cajal loss has been reported for other preclinical models of dysmotility, and our previous experimental SCI study provided evidence of reduced excitatory and inhibitory enteric neuronal count and smooth muscle neural control.<br><br>METHODS: Immunohistochemistry for the ICC-specific marker c-Kit was utilized to examine neuromuscular remodeling of the distal colon in male and female rats with experimental SCI.<br><br>KEY RESULTS: Myenteric plexus ICC (ICC-MP) exhibited increased cell counts 3&#x2009;days following SCI in male rats, but did not significantly increase in females until 3&#x2009;weeks after SCI. On average, ICC-MP total primary arborization length increased significantly in male rats at 3-day, 3-week, and 6-week time points, whereas in females, this increase occurred most frequently at 6&#x2009;weeks post-SCI. Conversely, circular muscle ICC (ICC-CM) did not demonstrate post-SCI changes.<br><br>CONCLUSIONS AND INFERENCES: These data demonstrate resiliency of the ICC-MP in neurogenic bowel following SCI, unlike seen in other related disease states. This plasticity underscores the need to further understand neuromuscular changes driving colonic dysmotility after SCI in order to advance therapeutic targets for neurogenic bowel treatment.},
}
@article {pmid37478039,
year = {2023},
author = {Wang, H and Cao, L and Huang, C and Jia, J and Dong, Y and Fan, C and de Albuquerque, VHC},
title = {A Novel Algorithmic Structure of EEG Channel Attention Combined With Swin Transformer for Motor Patterns Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3132-3141},
doi = {10.1109/TNSRE.2023.3297654},
pmid = {37478039},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; Imagination ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Attention ; },
abstract = {With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.},
}
@article {pmid37473638,
year = {2023},
author = {Zhang, S and Shi, E and Wu, L and Wang, R and Yu, S and Liu, Z and Xu, S and Liu, T and Zhao, S},
title = {Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {165},
number = {},
pages = {1035-1049},
doi = {10.1016/j.neunet.2023.06.040},
pmid = {37473638},
issn = {1879-2782},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; Brain ; *Brain-Computer Interfaces ; Surgical Instruments ; Imagination ; },
abstract = {EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level. However, present classification performance is still far from satisfactory. We have identified two major focal points for improvements: one is about organizing the input EEG signals, and the other is related to the design of the RNN architecture. To optimize the above-mentioned issues and achieve better brain state classification performance, we propose a novel multi-clip random fragment strategy-based interactive bidirectional recurrent neural network (McRFS-IBiRNN) model in this work. This model has two advantages over previous methods. First, the McRFS component is designed to re-organize the input EEG signals to make them more suitable for the RNN architecture. Second, the IBiRNN component is an innovative design to model the RNN layers with interaction connections to enhance the fusion of bidirectional features. By adopting the proposed model, promising brain states classification performances are obtained. For example, 96.97% and 99.34% of individual and group level four-category classification accuracies are successfully obtained on the EEG motor/imagery dataset, respectively. A 99.01% accuracy can be observed for four-category classification tasks with new subjects not seen before, which demonstrates the generalization of our proposed method. Compared with existing methods, our model outperforms them with superior results. Overall, the proposed McRFS-IBiRNN model demonstrates great superiority in differentiating brain states on EEG signals.},
}
@article {pmid37471544,
year = {2023},
author = {Timko, BP},
title = {Neural implants without brain surgery.},
journal = {Science (New York, N.Y.)},
volume = {381},
number = {6655},
pages = {268-269},
pmid = {37471544},
issn = {1095-9203},
support = {23TPA1057212/AHA/American Heart Association-American Stroke Association/United States ; },
mesh = {Animals ; Humans ; *Brain/blood supply/physiology ; *Neurons/physiology ; *Brain-Computer Interfaces ; *Biosensing Techniques ; Single-Cell Analysis ; *Blood Vessels/physiology ; },
abstract = {Injectable bioprobes record single-neuron activity from within blood vessels.},
}
@article {pmid37467742,
year = {2023},
author = {Hu, R and Ming, G and Wang, Y and Gao, X},
title = {A sub-region combination scheme for spatial coding in a high-frequency SSVEP-based BCI.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ace8bd},
pmid = {37467742},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Visual Fields ; Photic Stimulation/methods ; },
abstract = {Objective.In studying the spatial coding mechanism of visual evoked potentials, it is significant to construct a model that shows the relationship between steady-state visual evoked potential (SSVEP) responses to the local and global visual field stimulation. In order to investigate whether SSVEPs produced by sub-region stimulation can predict that produced by joint region stimulation, a sub-region combination scheme for spatial coding in a high-frequency SSVEP-based brain-computer interface (BCI) is developed innovatively.Approach.An annular visual field is divided equally into eight sub-regions. The 60 Hz visual stimuli in different sub-regions and joint regions are presented separately to participants. The SSVEP produced by the sub-region stimulation is superimposed to simulate the SSVEP produced by the joint region stimulation with different spatial combinations. A four-class spatially-coded BCI paradigm is used to evaluate the simulated classification performance, and the performance ranking of all simulated SSVEPs is obtained. Six representative stimulus patterns from two performance levels and three stimulus areas are applied to the online BCI system for each participant.Main results.The experimental result shows that the proposed scheme can implement a spatially-coded visual BCI system and realize satisfactory performance with imperceptible flicker. Offline analysis indicates that the classification accuracy and information transfer rate (ITR) are 89.69 ± 8.75% and 24.35 ± 7.09 bits min[-1]with 3 s data length under the 3/8 stimulus area. The online BCI system reaches an average classification accuracy of 87.50 ± 9.13% with 3 s data length, resulting in an ITR of 22.48 ± 6.71 bits min[-1]under the 3/8 stimulus area.Significance.This study proves the feasibility of using the sub-region's response to predict the joint region's response. It has the potential to extend to other frequency bands and lays a foundation for future research on more complex spatial coding methods.},
}
@article {pmid37467739,
year = {2023},
author = {Berezutskaya, J and Freudenburg, ZV and Vansteensel, MJ and Aarnoutse, EJ and Ramsey, NF and van Gerven, MAJ},
title = {Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.},
journal = {Journal of neural engineering},
volume = {20},
number = {5},
pages = {},
pmid = {37467739},
issn = {1741-2552},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Speech ; *Deep Learning ; *Brain-Computer Interfaces ; *Sensorimotor Cortex ; Communication ; Electrocorticography/methods ; },
abstract = {Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.},
}
@article {pmid37467714,
year = {2023},
author = {Giraud, AL and Su, Y},
title = {Reconstructing language from brain signals and deconstructing adversarial thought-reading.},
journal = {Cell reports. Medicine},
volume = {4},
number = {7},
pages = {101115},
pmid = {37467714},
issn = {2666-3791},
mesh = {*Reading ; Brain ; Language ; *Brain-Computer Interfaces ; },
abstract = {Tang et al.[1] report a noninvasive brain-computer interface (BCI) that reconstructs perceived and intended continuous language from semantic brain responses. The study offers new possibilities to radically facilitate neural speech decoder applications and addresses concerns about misuse in non-medical scenarios.},
}
@article {pmid37467641,
year = {2023},
author = {Wang, M and Shao, W and Huang, S and Zhang, D},
title = {Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based Alzheimer's Disease diagnosis.},
journal = {Medical image analysis},
volume = {89},
number = {},
pages = {102883},
doi = {10.1016/j.media.2023.102883},
pmid = {37467641},
issn = {1361-8423},
mesh = {Humans ; *Alzheimer Disease/diagnostic imaging/genetics ; Multimodal Imaging/methods ; Neuroimaging/methods ; Magnetic Resonance Imaging/methods ; Positron-Emission Tomography/methods ; Brain/diagnostic imaging ; Algorithms ; },
abstract = {Recent studies show that multi-modal data fusion techniques combining information from diverse sources are helpful to diagnose and predict complex brain disorders. However, most existing diagnosis methods have only simply employed a feature combination strategy for multiple imaging and genetic data, ignoring the imaging phenotypes associated with the risk gene information. To this end, we present a hypergraph-regularized multimodal learning by graph diffusion (HMGD) for joint association learning and outcome prediction. Specifically, we first present a graph diffusion method for enhancing similarity measures among subjects given from multi-modality phenotypes, which fully uses multiple input similarity graphs and integrates them into a unified graph with valuable geometric structures among different imaging phenotypes. Then, we employ the unified graph to represent the high-order similarity relationships among subjects, and enforce a hypergraph-regularized term to incorporate both inter- and cross-modality information for selecting the imaging phenotypes associated with the risk single nucleotide polymorphism (SNP). Finally, a multi-kernel support vector machine (MK-SVM) is adopted to fuse such phenotypic features selected from different modalities for the final diagnosis and prediction. The proposed approach is experimentally explored on brain imaging genetic data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Relevant results present that the proposed approach is superior to several competing algorithms, and realizes strong associations and discovers significant consistent and robust ROIs across different imaging phenotypes associated with the genetic risk biomarkers to guide disease interpretation and prediction.},
}
@article {pmid37466825,
year = {2024},
author = {Poppe, C and Elger, BS},
title = {Brain-Computer Interfaces, Completely Locked-In State in Neurodegenerative Diseases, and End-of-Life Decisions.},
journal = {Journal of bioethical inquiry},
volume = {21},
number = {1},
pages = {19-27},
pmid = {37466825},
issn = {1872-4353},
mesh = {Humans ; *Brain-Computer Interfaces/ethics ; *Terminal Care/ethics ; *Neurodegenerative Diseases ; *Quality of Life ; *Decision Making/ethics ; Suicide, Assisted/ethics ; Locked-In Syndrome ; Mental Competency ; },
abstract = {In the future, policies surrounding end-of-life decisions will be faced with the question of whether competent people in a completely locked-in state should be enabled to make end-of-life decisions via brain-computer interfaces (BCI). This article raises ethical issues with acting through BCIs in the context of these decisions, specifically self-administration requirements within assisted suicide policies. We argue that enabling patients to end their life even once they have entered completely locked-in state might, paradoxically, prolong and uphold their quality of life.},
}
@article {pmid37465143,
year = {2023},
author = {Shah, NP and Willsey, MS and Hahn, N and Kamdar, F and Avansino, DT and Hochberg, LR and Shenoy, KV and Henderson, JM},
title = {A brain-computer typing interface using finger movements.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2023},
number = {},
pages = {},
pmid = {37465143},
issn = {1948-3546},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; },
abstract = {Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.},
}
@article {pmid37464883,
year = {2023},
author = {Li, G and Jiang, S and Meng, J and Wu, Z and Jiang, H and Fan, Z and Hu, J and Sheng, X and Zhang, D and Schalk, G and Chen, L and Zhu, X},
title = {Spatio-temporal evolution of human neural activity during visually cued hand movements.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {17},
pages = {9764-9777},
doi = {10.1093/cercor/bhad242},
pmid = {37464883},
issn = {1460-2199},
mesh = {Humans ; *Cues ; *Hand ; Brain/physiology ; Movement/physiology ; Brain Mapping/methods ; Electroencephalography/methods ; },
abstract = {Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.},
}
@article {pmid37463076,
year = {2024},
author = {Li, P and Gao, X and Li, C and Yi, C and Huang, W and Si, Y and Li, F and Cao, Z and Tian, Y and Xu, P},
title = {Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {11},
pages = {16181-16195},
doi = {10.1109/TNNLS.2023.3292179},
pmid = {37463076},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Brain/physiology/diagnostic imaging ; Neural Networks, Computer ; Algorithms ; Imagination/physiology ; Computer Simulation ; Signal Processing, Computer-Assisted ; Nerve Net/physiology ; },
abstract = {Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.},
}
@article {pmid37461446,
year = {2023},
author = {Bashford, L and Rosenthal, I and Kellis, S and Bjånes, D and Pejsa, K and Brunton, BW and Andersen, RA},
title = {Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37461446},
issn = {2692-8205},
support = {R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
abstract = {A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.},
}
@article {pmid37461360,
year = {2023},
author = {Comita, LS and Aguilar, S and Hubbell, SP and Pérez, R},
title = {Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama.},
journal = {Ecology},
volume = {104},
number = {9},
pages = {e4140},
doi = {10.1002/ecy.4140},
pmid = {37461360},
issn = {1939-9170},
mesh = {Humans ; *Seedlings ; *Censuses ; Colorado ; Phylogeny ; Tropical Climate ; Forests ; Plants ; Panama ; },
abstract = {Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50 ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-year intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m[2] seedling plot in the center of every 5 × 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m[2] seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1-2 years. Here, we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1 M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1 cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography. This data set can be freely used for non-commercial purposes; we request that users of these data cite this data paper in all publications resulting from the use of this data set.},
}
@article {pmid37460730,
year = {2023},
author = {Si, X and Zhou, Y and Li, S and Zhang, X and Han, S and Xiang, S and Ming, D},
title = {Brain-Computer Interfaces in Visualized Medicine.},
journal = {Advances in experimental medicine and biology},
volume = {1199},
number = {},
pages = {127-153},
pmid = {37460730},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; },
abstract = {The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.},
}
@article {pmid37459853,
year = {2023},
author = {Meng, K and Goodarzy, F and Kim, E and Park, YJ and Kim, JS and Cook, MJ and Chung, CK and Grayden, DB},
title = {Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ace7f6},
pmid = {37459853},
issn = {1741-2552},
mesh = {Humans ; *Speech/physiology ; *Phonetics ; Brain ; Frontal Lobe ; Prefrontal Cortex ; Brain Mapping/methods ; },
abstract = {Objective. Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.Approach. Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.Main results. Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.Significance.As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.},
}
@article {pmid37457015,
year = {2023},
author = {Li, Y and Chen, B and Wang, G and Yoshimura, N and Koike, Y},
title = {Partial maximum correntropy regression for robust electrocorticography decoding.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1213035},
pmid = {37457015},
issn = {1662-4548},
abstract = {The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.},
}
@article {pmid37457014,
year = {2023},
author = {Li, R and Zhang, Y and Fan, G and Li, Z and Li, J and Fan, S and Lou, C and Liu, X},
title = {Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1193950},
pmid = {37457014},
issn = {1662-4548},
abstract = {INTRODUCTION: The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI.<br><br>METHODS: For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive.<br><br>RESULTS: The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully.<br><br>CONCLUSION: The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.},
}
@article {pmid37456795,
year = {2023},
author = {Wolpaw, JR and Thompson, AK},
title = {Enhancing neurorehabilitation by targeting beneficial plasticity.},
journal = {Frontiers in rehabilitation sciences},
volume = {4},
number = {},
pages = {1198679},
pmid = {37456795},
issn = {2673-6861},
support = {R01 NS114279/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R01 NS061823/NS/NINDS NIH HHS/United States ; U54 GM104941/GM/NIGMS NIH HHS/United States ; P2C HD086844/HD/NICHD NIH HHS/United States ; R01 NS069551/NS/NINDS NIH HHS/United States ; },
abstract = {Neurorehabilitation is now one of the most exciting areas in neuroscience. Recognition that the central nervous system (CNS) remains plastic through life, new understanding of skilled behaviors (skills), and novel methods for engaging and guiding beneficial plasticity combine to provide unprecedented opportunities for restoring skills impaired by CNS injury or disease. The substrate of a skill is a distributed network of neurons and synapses that changes continually through life to ensure that skill performance remains satisfactory as new skills are acquired, and as growth, aging, and other life events occur. This substrate can extend from cortex to spinal cord. It has recently been given the name "heksor." In this new context, the primary goal of rehabilitation is to enable damaged heksors to repair themselves so that their skills are once again performed well. Skill-specific practice, the mainstay of standard therapy, often fails to optimally engage the many sites and kinds of plasticity available in the damaged CNS. New noninvasive technology-based interventions can target beneficial plasticity to critical sites in damaged heksors; these interventions may thereby enable much wider beneficial plasticity that enhances skill recovery. Targeted-plasticity interventions include operant conditioning of a spinal reflex or corticospinal motor evoked potential (MEP), paired-pulse facilitation of corticospinal connections, and brain-computer interface (BCI)-based training of electroencephalographic (EEG) sensorimotor rhythms. Initial studies in people with spinal cord injury, stroke, or multiple sclerosis show that these interventions can enhance skill recovery beyond that achieved by skill-specific practice alone. After treatment ends, the repaired heksors maintain the benefits.},
}
@article {pmid37452047,
year = {2023},
author = {Wang, Z and Shi, N and Zhang, Y and Zheng, N and Li, H and Jiao, Y and Cheng, J and Wang, Y and Zhang, X and Chen, Y and Chen, Y and Wang, H and Xie, T and Wang, Y and Ma, Y and Gao, X and Feng, X},
title = {Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {4213},
pmid = {37452047},
issn = {2041-1723},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Calibration ; Language ; Photic Stimulation ; Algorithms ; },
abstract = {Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2[nd] harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.},
}
@article {pmid37450357,
year = {2024},
author = {Yamamoto, MS and Sadatnejad, K and Tanaka, T and Islam, MR and Dehais, F and Tanaka, Y and Lotte, F},
title = {Modeling Complex EEG Data Distribution on the Riemannian Manifold Toward Outlier Detection and Multimodal Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {2},
pages = {377-387},
doi = {10.1109/TBME.2023.3295769},
pmid = {37450357},
issn = {1558-2531},
mesh = {*Algorithms ; Reproducibility of Results ; *Brain-Computer Interfaces ; Machine Learning ; Electroencephalography/methods ; },
abstract = {OBJECTIVE: The usage of Riemannian geometry for Brain-computer interfaces (BCIs) has gained momentum in recent years. Most of the machine learning techniques proposed for Riemannian BCIs consider the data distribution on a manifold to be unimodal. However, the distribution is likely to be multimodal rather than unimodal since high-data variability is a crucial limitation of electroencephalography (EEG). In this paper, we propose a novel data modeling method for considering complex data distributions on a Riemannian manifold of EEG covariance matrices, aiming to improve BCI reliability.<br><br>METHODS: Our method, Riemannian spectral clustering (RiSC), represents EEG covariance matrix distribution on a manifold using a graph with proposed similarity measurement based on geodesic distances, then clusters the graph nodes through spectral clustering. This allows flexibility to model both a unimodal and a multimodal distribution on a manifold. RiSC can be used as a basis to design an outlier detector named outlier detection Riemannian spectral clustering (odenRiSC) and a multimodal classifier named multimodal classifier Riemannian spectral clustering (mcRiSC). All required parameters of odenRiSC/mcRiSC are selected in data-driven manner. Moreover, there is no need to pre-set a threshold for outlier detection and the number of modes for multimodal classification.<br><br>RESULTS: The experimental evaluation revealed odenRiSC can detect EEG outliers more accurately than existing methods and mcRiSC outperformed the standard unimodal classifier, especially on high-variability datasets.<br><br>CONCLUSION: odenRiSC/mcRiSC are anticipated to contribute to making real-life BCIs outside labs and neuroergonomics applications more robust.<br><br>SIGNIFICANCE: RiSC can work as a robust EEG outlier detector and multimodal classifier.},
}
@article {pmid37450213,
year = {2023},
author = {Adama, S and Bogdan, M},
title = {Assessing consciousness in patients with disorders of consciousness using soft-clustering.},
journal = {Brain informatics},
volume = {10},
number = {1},
pages = {16},
pmid = {37450213},
issn = {2198-4018},
abstract = {Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person's consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients' states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients' consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain-computer interfaces (BCI)-based communication systems.},
}
@article {pmid37448967,
year = {2023},
author = {Zhou, Y and Yang, H and Wang, X and Yang, H and Sun, K and Zhou, Z and Sun, L and Zhao, J and Tao, TH and Wei, X},
title = {A mosquito mouthpart-like bionic neural probe.},
journal = {Microsystems &amp; nanoengineering},
volume = {9},
number = {},
pages = {88},
pmid = {37448967},
issn = {2055-7434},
abstract = {Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system's nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces. A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.},
}
@article {pmid37447926,
year = {2023},
author = {White, J and Power, SD},
title = {k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447926},
issn = {1424-8220},
support = {RGPIN-2016-04210//Natural Sciences and Engineering Research Council/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Research Design ; Algorithms ; },
abstract = {In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter "epochs" to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can result in unreliable estimates of mental state separability (due to autocorrelation in the samples derived from the same trial), k-fold CV is still commonly used and reported in passive BCI studies. What is not known is the extent to which k-fold CV misrepresents true mental state separability. This makes it difficult to interpret the results of studies that use it. Furthermore, if the seriousness of the problem were clearly known, perhaps more researchers would be aware that they should avoid it. In this work, a novel experiment explored how the degree of correlation among samples within a class affects EEG-based mental state classification accuracy estimated by k-fold CV. Results were compared to a ground-truth (GT) accuracy and to "block-wise" CV, an alternative to k-fold which is purported to alleviate the autocorrelation issues. Factors such as the degree of true class separability and the feature set and classifier used were also explored. The results show that, under some conditions, k-fold CV inflated the GT classification accuracy by up to 25%, but block-wise CV underestimated the GT accuracy by as much as 11%. It is our recommendation that the number of samples derived from the same trial should be reduced whenever possible in single-subject analysis, and that both the k-fold and block-wise CV results are reported.},
}
@article {pmid37447852,
year = {2023},
author = {Soangra, R and Smith, JA and Rajagopal, S and Yedavalli, SVR and Anirudh, ER},
title = {Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447852},
issn = {1424-8220},
support = {R15 HD110941/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; Walking ; *Brain-Computer Interfaces ; Machine Learning ; },
abstract = {Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries.},
}
@article {pmid37447849,
year = {2023},
author = {Peksa, J and Mamchur, D},
title = {State-of-the-Art on Brain-Computer Interface Technology.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447849},
issn = {1424-8220},
mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Brain ; Algorithms ; Technology ; },
abstract = {This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.},
}
@article {pmid37447780,
year = {2023},
author = {Craik, A and González-España, JJ and Alamir, A and Edquilang, D and Wong, S and Sánchez Rodríguez, L and Feng, J and Francisco, GE and Contreras-Vidal, JL},
title = {Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447780},
issn = {1424-8220},
support = {1827769//National Science Foundation/ ; 1650536//National Science Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Reproducibility of Results ; Electroencephalography ; Brain ; Eye Movements ; },
abstract = {Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.},
}
@article {pmid37447728,
year = {2023},
author = {Gracia, DI and Ortiz, M and Candela, T and Iáñez, E and Sánchez, RM and Díaz, C and Azorín, JM},
title = {Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447728},
issn = {1424-8220},
support = {GVA COVID19/2021/062//Generalitat Valenciana/ ; },
mesh = {Humans ; *COVID-19/complications/therapy ; Anosmia/therapy/etiology ; SARS-CoV-2 ; *Olfaction Disorders/therapy/epidemiology/etiology ; *Transcranial Direct Current Stimulation ; Smell/physiology ; },
abstract = {A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world's population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.},
}
@article {pmid37447686,
year = {2023},
author = {Arpaia, P and Coyle, D and Esposito, A and Natalizio, A and Parvis, M and Pesola, M and Vallefuoco, E},
title = {Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {13},
pages = {},
pmid = {37447686},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Telerehabilitation ; Electroencephalography/methods ; Imagery, Psychotherapy/methods ; *Wearable Electronic Devices ; },
abstract = {The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor imagery. The sample was divided into two equal-sized groups: a "neurofeedback" group, which performed motor imagery while receiving feedback, and a "control" group, which performed motor imagery with no feedback. Questionnaires were administered to participants aiming to investigate the usability of the proposed system and an individual's ability to imagine movements. The highest mean classification accuracy across the subjects of the control group was about 62% with 3% associated type A uncertainty, and it was 69% with 3% uncertainty for the neurofeedback group. Moreover, the results in some cases were significantly higher for the neurofeedback group. The perceived usability by all participants was high. Overall, the study aimed at highlighting the advantages and the pitfalls of using a wearable brain-computer interface with dry sensors. Notably, this technology can be adopted for safe and economically viable tele-rehabilitation.},
}
@article {pmid37446512,
year = {2023},
author = {Luo, M and Chen, R and Zhu, Z and Cheng, C and Ning, X and Huang, B},
title = {A Broadband Photodetector Based on PbS Quantum Dots and Graphene with High Responsivity and Detectivity.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {37446512},
issn = {2079-4991},
abstract = {A high-efficiency photodetector consisting of colloidal PbS quantum dots (QDs) and single-layer graphene was prepared in this research. In the early stage, PbS QDs were synthesized and characterized, and the results showed that the product conformed with the characteristics of high-quality PbS QDs. Afterwards, the photodetector was derived through steps, including the photolithography and etching of indium tin oxide (ITO) electrodes and the graphene active region, as well as the spin coating and ligand substitution of the PbS QDs. After application testing, the photodetector, which was prepared in this research, exhibited outstanding properties. Under visible and near-infrared light, the highest responsivities were up to 202 A/W and 183 mA/W, respectively, and the highest detectivities were up to 2.24 × 10[11] Jones and 2.47 × 10[8] Jones, respectively, with light densities of 0.56 mW/cm[2] and 1.22 W/cm[2], respectively. In addition to these results, the response of the device and the rise and fall times for the on/off illumination cycles showed its superior performance, and the fastest response times were approximately 0.03 s and 1.0 s for the rise and fall times, respectively. All the results illustrated that the photodetector based on PbS and graphene, which was prepared in this research, possesses the potential to be applied in reality.},
}
@article {pmid37443930,
year = {2023},
author = {Medill, SA and Janz, DM and McLoughlin, PD},
title = {Hair Cortisol Concentrations in Feral Horses and the Influence of Physiological and Social Factors.},
journal = {Animals : an open access journal from MDPI},
volume = {13},
number = {13},
pages = {},
pmid = {37443930},
issn = {2076-2615},
support = {371535-2009//Natural Sciences and Engineering Research Council/ ; 25046//Canada Foundation for Innovation/ ; },
abstract = {Cortisol is a glucocorticoid hormone produced during activation of the hypothalamic-pituitary-adrenal axis (HPA) in response to psychological or physiological demands. High amounts of circulating cortisol can be found in individuals experiencing energetically demanding physiological events, such as pregnancy, lactation, injury, or starvation, but, also, in individuals who may have less obvious HPA activation from social situations. The feral horse population on Sable Island (Nova Scotia, Canada) provides an opportunity to look at hair cortisol concentration (HCC) as a proxy for circulating cortisol concentration to better understand physiological correlates. The horse's complex social structure also allows us to look at how the population and group structure may influence HPA activation. Hair samples (n = 282) were analyzed from 113 females and 135 males. Females with dependent offspring (foals) had higher HCC than those females without dependent offspring (p = 0.005). Horses in poor body condition were also more likely to have higher HCC (females: p < 0.001, males: p = 0.028); females had greater variation in the body condition index (BCI), which also correlated with foal production. In general, the top-ranked models describing female cortisol levels included age, BCI, presence of a foal, as well as social measures such as harem size and the number of bachelors in the vicinity. The top model describing male cortisol levels included age, BCI, and year of collection only, and the number of bachelors in the home range appeared in subsequent, though still high-ranked, models. Among the variables not of direct interest, we found some significant results relating to hair color and hair texture. Differences in HCC patterns between feral and domestically kept horses (e.g., age and sex) are likely linked to periods of resource limitations, particularly for individuals experiencing energetically demanding processes such as reproduction, illness/parasitism, or related to experiencing the full range of social and reproductive behaviors.},
}
@article {pmid37443535,
year = {2023},
author = {Işık, &#xdc; and Güven, A and Batbat, T},
title = {Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {13},
number = {13},
pages = {},
pmid = {37443535},
issn = {2075-4418},
support = {122E592//Scientific and Technological Research Council of Turkey/ ; },
abstract = {Recent achievements have made emotion studies a rising field contributing to many areas, such as health technologies, brain-computer interfaces, psychology, etc. Emotional states can be evaluated in valence, arousal, and dominance (VAD) domains. Most of the work uses only VA due to the easiness of differentiation; however, very few studies use VAD like this study. Similarly, segment comparisons of emotion analysis with handcrafted features also use VA space. At this point, we primarily focused on VAD space to evaluate emotions and segmentations. The DEAP dataset is used in this study. A comprehensive analytical approach is implemented with two sub-studies: first, segmentation (Segments I-VIII), and second, binary cross-comparisons and evaluations of eight emotional states, in addition to comparisons of selected segments (III, IV, and V), class separation levels (5, 4-6, and 3-7), and unbalanced and balanced data with SMOTE. In both sub-studies, Wavelet Transform is applied to electroencephalography signals to separate the brain waves into their bands (α, β, γ, and θ bands), twenty-four attributes are extracted, and Sequential Minimum Optimization, K-Nearest Neighbors, Fuzzy Unordered Rule Induction Algorithm, Random Forest, Optimized Forest, Bagging, Random Committee, and Random Subspace are used for classification. In our study, we have obtained high accuracy results, which can be seen in the figures in the second part. The best accuracy result in this study for unbalanced data is obtained for Low Arousal-Low Valence-High Dominance and High Arousal-High Valence-Low Dominance emotion comparisons (Segment III and 4.5-5.5 class separation), and an accuracy rate of 98.94% is obtained with the IBk classifier. Data-balanced results mostly seem to outperform unbalanced results.},
}
@article {pmid37443193,
year = {2023},
author = {Wei, W and Deng, L and Qiao, C and Yin, Y and Zhang, Y and Li, X and Yu, H and Jian, L and Li, M and Guo, W and Wang, Q and Deng, W and Ma, X and Zhao, L and Sham, PC and Palaniyappan, L and Li, T},
title = {Neural variability in three major psychiatric disorders.},
journal = {Molecular psychiatry},
volume = {28},
number = {12},
pages = {5217-5227},
pmid = {37443193},
issn = {1476-5578},
support = {82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81920108018//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82001410//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Depressive Disorder, Major/physiopathology/metabolism/genetics ; Female ; *Bipolar Disorder/physiopathology/genetics/metabolism ; Male ; *Schizophrenia/physiopathology/metabolism/genetics ; Adult ; *Magnetic Resonance Imaging/methods ; *Brain/metabolism/physiopathology ; Middle Aged ; Cognition/physiology ; Mental Disorders/physiopathology/metabolism/genetics ; Oxygen/blood/metabolism ; },
abstract = {Across the major psychiatric disorders (MPDs), a shared disruption in brain physiology is suspected. Here we investigate the neural variability at rest, a well-established behavior-relevant marker of brain function, and probe its basis in gene expression and neurotransmitter receptor profiles across the MPDs. We recruited 219 healthy controls and 279 patients with schizophrenia, major depressive disorder, or bipolar disorders (manic or depressive state). The standard deviation of blood oxygenation level-dependent signal (SDBOLD) obtained from resting-state fMRI was used to characterize neural variability. Transdiagnostic disruptions in SDBOLD patterns and their relationships with clinical symptoms and cognitive functions were tested by partial least-squares correlation. Moving beyond the clinical sample, spatial correlations between the observed patterns of SDBOLD disruption and postmortem gene expressions, Neurosynth meta-analytic cognitive functions, and neurotransmitter receptor profiles were estimated. Two transdiagnostic patterns of disrupted SDBOLD were discovered. Pattern 1 is exhibited in all diagnostic groups and is most pronounced in schizophrenia, characterized by higher SDBOLD in the language/auditory networks but lower SDBOLD in the default mode/sensorimotor networks. In comparison, pattern 2 is only exhibited in unipolar and bipolar depression, characterized by higher SDBOLD in the default mode/salience networks but lower SDBOLD in the sensorimotor network. The expression of pattern 1 related to the severity of clinical symptoms and cognitive deficits across MPDs. The two disrupted patterns had distinct spatial correlations with gene expressions (e.g., neuronal projections/cellular processes), meta-analytic cognitive functions (e.g., language/memory), and neurotransmitter receptor expression profiles (e.g., D2/serotonin/opioid receptors). In conclusion, neural variability is a potential transdiagnostic biomarker of MPDs with a substantial amount of its spatial distribution explained by gene expressions and neurotransmitter receptor profiles. The pathophysiology of MPDs can be traced through the measures of neural variability at rest, with varying clinical-cognitive profiles arising from differential spatial patterns of aberrant variability.},
}
@article {pmid37442973,
year = {2023},
author = {Wang, DQ and Zhang, JJ and Chen, JN and Li, RY and Luo, YX and Deng, W},
title = {Exergames improves cognitive functions in adolescents with depression: study protocol of a prospective, assessor-blind, randomized controlled trial.},
journal = {BMC psychiatry},
volume = {23},
number = {1},
pages = {507},
pmid = {37442973},
issn = {1471-244X},
mesh = {Adolescent ; Humans ; Cognition/physiology ; *Depression/therapy ; Exercise/psychology ; *Exergaming ; Prospective Studies ; Randomized Controlled Trials as Topic ; Treatment Outcome ; },
abstract = {BACKGROUND: Depression is a condition that imposes a significant disease burden, with cognitive impairment being one of its costly symptoms. While cognitive rehabilitation is crucial, it is also challenging. Although some studies have investigated the impact of exergames on cognitive function improvement, these have primarily focused on the elderly population, with limited attention given to individuals with depression. Consequently, this study aims to investigate the effects of exergames on cognitive functions in adolescents with depression and compare the effectiveness of exergames with traditional exercise.<br><br>METHOD: The present investigation is a single-center randomized controlled trial that employs the ANOVA method to calculate the sample size using G*Power software, assuming a 25% dropout rate. The study enrolls fifty-four eligible patients with depression who are randomly allocated to one of three treatment groups: the exergames group, which receives standard treatment and exergames intervention; the exercise group, which receives standard treatment and traditional exercise intervention; and the control group, which receives standard treatment exclusively. The study provides a comprehensive regimen of 22 supervised exercise and exergame sessions over an 8-week period, with a frequency of twice per week for the initial two weeks and three times per week for the subsequent six weeks. The researchers gather cognitive, mood, and sleep metrics at the onset of the first week, as well as at the conclusion of the fourth and eighth weeks. The researchers employ a wearable device to track participants' heart rate during each intervention session and evaluate the Borg Rating of Perceived Exertion scale at the conclusion of each session.<br><br>DISCUSSION: The findings from this study make several contributions to the current literature. First, this study comprehensively reports the efficacy of an exergames intervention for multidimensional symptoms in adolescents with depression. Second, this study also compares the efficacy of exergames with that of traditional exercise. These findings provide a theoretical basis for the use of exergames as an adjunctive intervention for depression and lay the groundwork for future research.<br><br>TRIAL REGISTRATION: This trial is registered with the Chinese Clinical Trials Registry (Registration number: ChiCTR2100052709; Registration Status: Prospective registration;) 3/11/2021, URL:&#xa0; &#xa0; http://www.chictr.org.cn/edit.aspx?pid=135663&amp;htm=4 .},
}
@article {pmid37442139,
year = {2023},
author = {Iwane, F and Dash, D and Salamanca-Giron, RF and Hayward, W and Bönstrup, M and Buch, ER and Cohen, LG},
title = {Combined low-frequency brain oscillatory activity and behavior predict future errors in human motor skill.},
journal = {Current biology : CB},
volume = {33},
number = {15},
pages = {3145-3154.e5},
doi = {10.1016/j.cub.2023.06.040},
pmid = {37442139},
issn = {1879-0445},
mesh = {Humans ; *Motor Skills ; *Brain ; Gyrus Cinguli ; },
abstract = {Human skills are composed of sequences of individual actions performed with utmost precision. When occasional errors occur, they may have serious consequences, for example, when pilots are manually landing a plane. In such cases, the ability to predict an error before it occurs would clearly be advantageous. Here, we asked whether it is possible to predict future errors in a keyboard procedural human motor skill. We report that prolonged keypress transition times (KTTs), reflecting slower speed, and anomalous delta-band oscillatory activity in cingulate-entorhinal-precuneus brain regions precede upcoming errors in skill. Combined anomalous low-frequency activity and prolonged KTTs predicted up to 70% of future errors. Decoding strength (posterior probability of error) increased progressively approaching the errors. We conclude that it is possible to predict future individual errors in skill sequential performance.},
}
@article {pmid37442010,
year = {2023},
author = {Zhong, XC and Wang, Q and Liu, D and Liao, JX and Yang, R and Duan, S and Ding, G and Sun, J},
title = {A deep domain adaptation framework with correlation alignment for EEG-based motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {163},
number = {},
pages = {107235},
doi = {10.1016/j.compbiomed.2023.107235},
pmid = {37442010},
issn = {1879-0534},
mesh = {*Electroencephalography/methods ; Algorithms ; Adaptation, Physiological ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {It is impractical to collect sufficient and well-labeled EEG data in Brain-computer interface because of the time-consuming data acquisition and costly annotation. Conventional classification methods reusing EEG data from different subjects and time periods (across domains) significantly decrease the classification accuracy of motor imagery. In this paper, we propose a deep domain adaptation framework with correlation alignment (DDAF-CORAL) to solve the problem of distribution divergence for motor imagery classification across domains. Specifically, a two-stage framework is adopted to extract deep features for raw EEG data. The distribution divergence caused by subjected-related and time-related variations is further minimized by aligning the covariance of the source and target EEG feature distributions. Finally, the classification loss and adaptation loss are optimized simultaneously to achieve sufficient discriminative classification performance and low feature distribution divergence. Extensive experiments on three EEG datasets demonstrate that our proposed method can effectively reduce the distribution divergence between the source and target EEG data. The results show that our proposed method delivers outperformance (an average classification accuracy of 92.9% for within-session, an average kappa value of 0.761 for cross-session, and an average classification accuracy of 83.3% for cross-subject) in two-class classification tasks compared to other state-of-the-art methods.},
}
@article {pmid37441434,
year = {2023},
author = {Gemborn Nilsson, M and Tufvesson, P and Heskebeck, F and Johansson, M},
title = {An open-source human-in-the-loop BCI research framework: method and design.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1129362},
pmid = {37441434},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil).},
}
@article {pmid37437575,
year = {2023},
author = {Bigoni, C and Beanato, E and Harquel, S and Hervé, J and Oflar, M and Crema, A and Espinosa, A and Evangelista, GG and Koch, P and Bonvin, C and Turlan, JL and Guggisberg, A and Morishita, T and Wessel, MJ and Zandvliet, SB and Hummel, FC},
title = {Novel personalized treatment strategy for patients with chronic stroke with severe upper-extremity impairment: The first patient of the AVANCER trial.},
journal = {Med (New York, N.Y.)},
volume = {4},
number = {9},
pages = {591-599.e3},
doi = {10.1016/j.medj.2023.06.006},
pmid = {37437575},
issn = {2666-6340},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Stroke Rehabilitation/methods ; Precision Medicine ; Treatment Outcome ; *Stroke/therapy ; Upper Extremity ; },
abstract = {BACKGROUND: Around 25% of patients who have had a stroke suffer from severe upper-limb impairment and lack effective rehabilitation strategies. The AVANCER proof-of-concept clinical trial (NCT04448483) tackles this issue through an intensive and personalized-dosage cumulative intervention that combines multiple non-invasive neurotechnologies.<br><br>METHODS: The therapy consists of two sequential interventions, lasting until the patient shows no further motor improvement, for a minimum of 11 sessions each. The first phase involves a brain-computer interface governing an exoskeleton and multi-channel functional electrical stimulation enabling full upper-limb movements. The second phase adds anodal transcranial direct current stimulation of the motor cortex of the lesioned hemisphere. Clinical, electrophysiological, and neuroimaging examinations are performed before, between, and after the two interventions (T0, T1, and T2). This case report presents the results from the first patient of the study.<br><br>FINDINGS: The primary outcome (i.e., 4-point improvement in the Fugl-Meyer assessment of the upper extremity) was met in the first patient, with an increase from 6 to 11 points between T0 and T2. This improvement was paralleled by changes in motor-network structure and function. Resting-state and transcranial magnetic stimulation-evoked electroencephalography revealed brain functional changes, and magnetic resonance imaging (MRI) measures detected structural and task-related functional changes.<br><br>CONCLUSIONS: These first results are promising, pointing to feasibility, safety, and potential efficacy of this personalized approach acting synergistically on the nervous and musculoskeletal systems. Integrating multi-modal data may provide valuable insights into underlying mechanisms driving the improvements and providing predictive information regarding treatment response and outcomes.<br><br>FUNDING: This work was funded by the Wyss-Center for Bio and Neuro Engineering (WCP-030), the Defitech Foundation, PHRT-#2017-205, ERA-NET-NEURON (Discover), and SNSF (320030L_197899, NiBS-iCog).},
}
@article {pmid37436869,
year = {2023},
author = {Wu, R and Jin, J and Daly, I and Wang, X and Cichocki, A},
title = {Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {3075-3085},
doi = {10.1109/TNSRE.2023.3294815},
pmid = {37436869},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electroencephalography/methods ; Recognition, Psychology ; Algorithms ; },
abstract = {Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to accurately classify MI-related EEG activity. Recently, the development of deep learning has begun to draw increasing attention in the BCI research community because it does not need to use sophisticated signal preprocessing and can automatically extract features. In this paper, we propose a deep learning model for use in MI-based BCI systems. Our model makes use of a convolutional neural network based on a multi-scale and channel-temporal attention module (CTAM), which called MSCTANN. The multi-scale module is able to extract a large number of features, while the attention module includes both a channel attention module and a temporal attention module, which together allow the model to focus attention on the most important features extracted from the data. The multi-scale module and the attention module are connected by a residual module, which avoids the degradation of the network. Our network model is built from these three core modules, which combine to improve the recognition ability of the network for EEG signals. Our experimental results on three datasets (BCI competition IV 2a, III IIIa and IV 1) show that our proposed method has better performance than other state-of-the-art methods, with accuracy rates of 80.6%, 83.56% and 79.84%. Our model has stable performance in decoding EEG signals and achieves efficient classification performance while using fewer network parameters than other comparable state-of-the-art methods.},
}
@article {pmid37436864,
year = {2024},
author = {Ali, MU and Zafar, A and Kallu, KD and Masood, H and Mannan, MMN and Ibrahim, MM and Kim, S and Khan, MA},
title = {Correlation-Filter-Based Channel and Feature Selection Framework for Hybrid EEG-fNIRS BCI Applications.},
journal = {IEEE journal of biomedical and health informatics},
volume = {28},
number = {6},
pages = {3361-3370},
doi = {10.1109/JBHI.2023.3294586},
pmid = {37436864},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; *Spectroscopy, Near-Infrared/methods ; *Signal Processing, Computer-Assisted ; *Brain/physiology/diagnostic imaging ; Support Vector Machine ; Algorithms ; Adult ; },
abstract = {The proposed study is based on a feature and channel selection strategy that uses correlation filters for brain-computer interface (BCI) applications using electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) brain imaging modalities. The proposed approach fuses the complementary information of the two modalities to train the classifier. The channels most closely correlated with brain activity are extracted using a correlation-based connectivity matrix for fNIRS and EEG separately. Furthermore, the training vector is formed through the identification and fusion of the statistical features of both modalities (i.e., slope, skewness, maximum, skewness, mean, and kurtosis). The constructed fused feature vector is passed through various filters (including ReliefF, minimum redundancy maximum relevance, chi-square test, analysis of variance, and Kruskal-Wallis filters) to remove redundant information before training. Traditional classifiers such as neural networks, support-vector machines, linear discriminant analysis, and ensembles were used for the purpose of training and testing. A publicly available dataset with motor imagery information was used for validation of the proposed approach. Our findings indicate that the proposed correlation-filter-based channel and feature selection framework significantly enhances the classification accuracy of hybrid EEG-fNIRS. The ReliefF-based filter outperformed other filters with the ensemble classifier with a high accuracy of 94.77 ± 4.26%. The statistical analysis also validated the significance (p < 0.01) of the results. A comparison of the proposed framework with the prior findings was also presented. Our results show that the proposed approach can be used in future EEG-fNIRS-based hybrid BCI applications.},
}
@article {pmid37434221,
year = {2023},
author = {Luo, J and Li, J and Mao, Q and Shi, Z and Liu, H and Ren, X and Hei, X},
title = {Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.},
journal = {BioData mining},
volume = {16},
number = {1},
pages = {19},
pmid = {37434221},
issn = {1756-0381},
abstract = {BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition.<br><br>METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label.<br><br>RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods.<br><br>CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.},
}
@article {pmid37433293,
year = {2023},
author = {Zaidi, M and Aggarwal, G and Shah, NP and Karniol-Tambour, O and Goetz, G and Madugula, SS and Gogliettino, AR and Wu, EG and Kling, A and Brackbill, N and Sher, A and Litke, AM and Chichilnisky, EJ},
title = {Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
pmid = {37433293},
issn = {1741-2552},
support = {R01 EY021271/EY/NEI NIH HHS/United States ; F31 EY027166/EY/NEI NIH HHS/United States ; R01 EY032900/EY/NEI NIH HHS/United States ; F30 EY030776/EY/NEI NIH HHS/United States ; P30 EY019005/EY/NEI NIH HHS/United States ; R01 EY029247/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Retinal Ganglion Cells/physiology ; Action Potentials/physiology ; Electric Stimulation/methods ; Retina/physiology ; *Retinal Degeneration ; Macaca ; },
abstract = {Objective. Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs.Approach.First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated.Main results.A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses (an upper bound). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data.Significance.These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.},
}
@article {pmid37432835,
year = {2024},
author = {Cai, S and Schultz, T and Li, H},
title = {Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {71},
number = {1},
pages = {171-182},
doi = {10.1109/TBME.2023.3294242},
pmid = {37432835},
issn = {1558-2531},
mesh = {Humans ; *Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Brain ; *Brain-Computer Interfaces ; },
abstract = {OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG). However, to our knowledge, topological relationships between individual channels have not yet been considered in any study. In this work, we introduced a novel architecture that exploits the topology of the human brain to perform auditory spatial attention detection (ASAD) from EEG signals.<br><br>METHODS: We propose EEG-Graph Net, an EEG-graph convolutional network, which employs a neural attention mechanism. This mechanism models the topology of the human brain in terms of the spatial pattern of EEG signals as a graph. In the EEG-Graph, each EEG channel is represented by a node, while the relationship between two EEG channels is represented by an edge between the respective nodes. The convolutional network takes the multi-channel EEG signals as a time series of EEG-graphs and learns the node and edge weights from the contribution of the EEG signals to the ASAD task. The proposed architecture supports the interpretation of the experimental results by data visualization.<br><br>RESULTS: We conducted experiments on two publicly available databases. The experimental results showed that EEG-Graph Net significantly outperforms the state-of-the-art methods in terms of decoding performance. In addition, the analysis of the learned weight patterns provides insights into the processing of continuous speech in the brain and confirms findings from neuroscientific studies.<br><br>CONCLUSION: We showed that modeling brain topology with EEG-graphs yields highly competitive results for auditory spatial attention detection.<br><br>SIGNIFICANCE: The proposed EEG-Graph Net is more lightweight and accurate than competing baselines and provides explanations for the results. Also, the architecture can be easily transferred to other brain-computer interface (BCI) tasks.},
}
@article {pmid37432820,
year = {2023},
author = {Tortora, S and Tonin, L and Sieghartsleitner, S and Ortner, R and Guger, C and Lennon, O and Coyle, D and Menegatti, E and Del Felice, A},
title = {Effect of Lower Limb Exoskeleton on the Modulation of Neural Activity and Gait Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2988-3003},
doi = {10.1109/TNSRE.2023.3294435},
pmid = {37432820},
issn = {1558-0210},
mesh = {Humans ; *Exoskeleton Device ; Gait ; Walking ; *Robotics/methods ; Lower Extremity ; },
abstract = {Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten healthy volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13±3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3±4.8% , while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4±11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy.},
}
@article {pmid37431690,
year = {2023},
author = {Zheng, R and Xu, FX and Zhou, L and Xu, J and Shen, Y and Hao, K and Wang, XT and Deng, J},
title = {Ablation of KIF2C in Purkinje cells impairs metabotropic glutamate receptor trafficking and motor coordination in male mice.},
journal = {The Journal of physiology},
volume = {601},
number = {17},
pages = {3905-3920},
doi = {10.1113/JP284214},
pmid = {37431690},
issn = {1469-7793},
mesh = {Animals ; Male ; Mice ; Carrier Proteins/metabolism ; Cell Cycle Proteins/metabolism ; Cerebellum/metabolism ; Kinesins/genetics/metabolism ; *Purkinje Cells/physiology ; Receptors, AMPA/metabolism ; *Receptors, Metabotropic Glutamate/metabolism ; Synapses/metabolism ; },
abstract = {Kinesin family member 2C (KIF2C)/mitotic centromere-associated kinesin (MCAK), is thought to be oncogenic as it is involved in tumour progression and metastasis. Moreover, it also plays a part in neurodegenerative conditions like Alzheimer's disease and psychiatric disorders such as suicidal schizophrenia. Our previous study conducted on mice demonstrated that KIF2C is widely distributed in various regions of the brain, and is localized in synaptic spines. Additionally, it regulates microtubule dynamic properties through its own microtubule depolymerization activity, thereby affecting AMPA receptor transport and cognitive behaviour in mice. In this study, we show that KIF2C regulates the transport of mGlu1 receptors in Purkinje cells by binding to Rab8. KIF2C deficiency in Purkinje cells results in abnormal gait, reduced balance ability and motor incoordination in male mice. These data suggest that KIF2C is essential for maintaining normal transport and synaptic function of mGlu1 and motor coordination in mice. KEY POINTS: KIF2C is localized in synaptic spines of hippocampus neurons, and regulates excitatory transmission, synaptic plasticity and cognitive behaviour. KIF2C is extensively expressed in the cerebellum, and we investigated its functions in development and synaptic transmission of cerebellar Purkinje cells. KIF2C deficiency in Purkinje cells alters the expression of metabotropic glutamate receptor 1 (mGlu1) and the AMPA receptor GluA2 subunit at Purkinje cell synapses, and changes excitatory synaptic transmission, but not inhibitory transmission. KIF2C regulates the transport of mGlu1 receptors in Purkinje cells by binding to Rab8. KIF2C deficiency in Purkinje cells affects motor coordination, but not social behaviour in male mice.},
}
@article {pmid37431592,
year = {2023},
author = {Fan, S and Wu, E and Cao, M and Xu, T and Liu, T and Yang, L and Su, J and Liu, J},
title = {Flexible In-Ga-Zn-N-O synaptic transistors for ultralow-power neuromorphic computing and EEG-based brain-computer interfaces.},
journal = {Materials horizons},
volume = {10},
number = {10},
pages = {4317-4328},
doi = {10.1039/d3mh00759f},
pmid = {37431592},
issn = {2051-6355},
mesh = {Humans ; *Brain-Computer Interfaces ; *COVID-19 ; Neural Networks, Computer ; Electroencephalography/methods ; Zinc ; },
abstract = {Designing low-power and flexible artificial neural devices with artificial neural networks is a promising avenue for creating brain-computer interfaces (BCIs). Herein, we report the development of flexible In-Ga-Zn-N-O synaptic transistors (FISTs) that can simulate essential and advanced biological neural functions. These FISTs are optimized to achieve ultra-low power consumption under a super-low or even zero channel bias, making them suitable for wearable BCI applications. The effective tunability of synaptic behaviors promotes the realization of associative and non-associative learning, facilitating Covid-19 chest CT edge detection. Importantly, FISTs exhibit high tolerance to long-term exposure under an ambient environment and bending deformation, indicating their suitability for wearable BCI systems. We demonstrate that an array of FISTs can classify vision-evoked EEG signals with up to ∼87.9% and 94.8% recognition accuracy for EMNIST-Digits and MindBigdata, respectively. Thus, FISTs have enormous potential to significantly impact the development of various BCI techniques.},
}
@article {pmid37431519,
year = {2022},
author = {Shull, G and Shin, Y and Viventi, J and Jochum, T and Morizio, J and Seo, KJ and Fang, H},
title = {Design and Simulation of a Low Power 384-channel Actively Multiplexed Neural Interface.},
journal = {IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference},
volume = {2022},
number = {},
pages = {477-481},
pmid = {37431519},
support = {U01 NS123668/NS/NINDS NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCIs) provide clinical benefits including partial restoration of lost motor control, vision, speech, and hearing. A fundamental limitation of existing BCIs is their inability to span several areas (> cm[2]) of the cortex with fine (<100 μm) resolution. One challenge of scaling neural interfaces is output wiring and connector sizes as each channel must be independently routed out of the brain. Time division multiplexing (TDM) overcomes this by enabling several channels to share the same output wire at the cost of added noise. This work leverages a 130-nm CMOS process and transfer printing to design and simulate a 384-channel actively multiplexed array, which minimizes noise by adding front end filtering and amplification to every electrode site (pixel). The pixels are 50 μm × 50 μm and enable recording of all 384 channels at 30 kHz with a gain of 22.3 dB, noise of 9.57 μV rms, bandwidth of 0.1 Hz - 10 kHz, while only consuming 0.63 μW/channel. This work can be applied broadly across neural interfaces to create high channel-count arrays and ultimately improve BCIs.},
}
@article {pmid37430568,
year = {2023},
author = {Chen, X and Gupta, RS and Gupta, L},
title = {Multidomain Convolution Neural Network Models for Improved Event-Related Potential Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {10},
pages = {},
pmid = {37430568},
issn = {1424-8220},
mesh = {Humans ; *Artifacts ; Brain ; *Brain Diseases ; Evoked Potentials ; Neural Networks, Computer ; },
abstract = {Two convolution neural network (CNN) models are introduced to accurately classify event-related potentials (ERPs) by fusing frequency, time, and spatial domain information acquired from the continuous wavelet transform (CWT) of the ERPs recorded from multiple spatially distributed channels. The multidomain models fuse the multichannel Z-scalograms and the V-scalograms, which are generated from the standard CWT scalogram by zeroing-out and by discarding the inaccurate artifact coefficients that are outside the cone of influence (COI), respectively. In the first multidomain model, the input to the CNN is generated by fusing the Z-scalograms of the multichannel ERPs into a frequency-time-spatial cuboid. The input to the CNN in the second multidomain model is formed by fusing the frequency-time vectors of the V-scalograms of the multichannel ERPs into a frequency-time-spatial matrix. Experiments are designed to demonstrate (a) customized classification of ERPs, where the multidomain models are trained and tested with the ERPs of individual subjects for brain-computer interface (BCI)-type applications, and (b) group-based ERP classification, where the models are trained on the ERPs from a group of subjects and tested on single subjects not included in the training set for applications such as brain disorder classification. Results show that both multidomain models yield high classification accuracies for single trials and small-average ERPs with a small subset of top-ranked channels, and the multidomain fusion models consistently outperform the best unichannel classifiers.},
}
@article {pmid37430058,
year = {2023},
author = {Kesarwani, M and Kincaid, Z and Azhar, M and Menke, J and Schwieterman, J and Ansari, S and Reaves, A and Deininger, ME and Levine, R and Grimes, HL and Azam, M},
title = {MAPK-negative feedback regulation confers dependence to JAK2[V617F] signaling.},
journal = {Leukemia},
volume = {37},
number = {8},
pages = {1686-1697},
pmid = {37430058},
issn = {1476-5551},
support = {R01 CA211594/CA/NCI NIH HHS/United States ; P30 CA008748/CA/NCI NIH HHS/United States ; R35 CA197594/CA/NCI NIH HHS/United States ; R01 CA250516/CA/NCI NIH HHS/United States ; R21 CA280723/CA/NCI NIH HHS/United States ; },
mesh = {Humans ; Feedback ; Tumor Suppressor Protein p53/metabolism ; Signal Transduction ; *Antineoplastic Agents/therapeutic use ; Cytokines/metabolism ; Janus Kinase 2/metabolism ; *Myeloproliferative Disorders/drug therapy ; Mutation ; },
abstract = {Despite significant advances in developing selective JAK2 inhibitors, JAK2 kinase inhibitor (TKI) therapy is ineffective in suppressing the disease. Reactivation of compensatory MEK-ERK and PI3K survival pathways sustained by inflammatory cytokine signaling causes treatment failure. Concomitant inhibition of MAPK pathway and JAK2 signaling showed improved in vivo efficacy compared to JAK2 inhibition alone but lacked clonal selectivity. We hypothesized that cytokine signaling in JAK2[V617F] induced MPNs increases the apoptotic threshold that causes TKI persistence or refractoriness. Here, we show that JAK2[V617F] and cytokine signaling converge to induce MAPK negative regulator, DUSP1. Enhanced DUSP1 expression blocks p38 mediated p53 stabilization. Deletion of Dusp1 increases p53 levels in the context of JAK2[V617F] signaling that causes synthetic lethality to Jak2[V617F] expressing cells. However, inhibition of Dusp1 by a small molecule inhibitor (BCI) failed to impart Jak2[V617F] clonal selectivity due to pErk1/2 rebound caused by off-target inhibition of Dusp6. Ectopic expression of Dusp6 and BCI treatment restored clonal selectively and eradicated the Jak2[V617F] cells. Our study shows that inflammatory cytokines and JAK2[V617F] signaling converge to induce DUSP1, which downregulates p53 and establishes a higher apoptotic threshold. These data suggest that selectively targeting DUSP1 may provide a curative response in JAK2[V617F]-driven MPN.},
}
@article {pmid37429288,
year = {2023},
author = {Wu, X and Lin, DT and Chen, R and Bhattacharyya, SS},
title = {Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
pmid = {37429288},
issn = {1741-2552},
support = {R01 NS110421/NS/NINDS NIH HHS/United States ; },
mesh = {*Neural Networks, Computer ; Neurons ; Algorithms ; *Brain-Computer Interfaces ; Calcium ; },
abstract = {Objective.Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models.Approach.On top of GRS, JGRS implements a 'jump mechanism', which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding.Main results.Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS.Significance.In our experiments, we demonstrate that JGRS achieves on average 9%-20% more compressed models compared to GRS with 2-8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.},
}
@article {pmid37426775,
year = {2023},
author = {Reddy, A and Hosseini, MR and Patel, A and Sharaf, R and Reddy, V and Tabarestani, A and Lucke-Wold, B},
title = {Deep brain stimulation, lesioning, focused ultrasound: update on utility.},
journal = {AIMS neuroscience},
volume = {10},
number = {2},
pages = {87-108},
pmid = {37426775},
issn = {2373-7972},
abstract = {Procedures for neurological disorders such as Parkinsons Disease (PD), Essential Tremor (ET), Obsessive Compulsive Disorder (OCD), Tourette's Syndrome (TS), and Major Depressive Disorder (MDD) tend to overlap. Common therapeutic procedures include deep brain stimulation (DBS), lesioning, and focused ultrasound (FUS). There has been significant change and innovation regarding targeting mechanisms and new advancements in this field allowing for better clinical outcomes in patients with severe cases of these conditions. In this review, we discuss advancements and recent discoveries regarding these three procedures and how they have led to changes in utilization in certain conditions. We further discuss the advantages and drawbacks of these treatments in certain conditions and the emerging advancements in brain-computer interface (BCI) and its utility as a therapeutic for neurological disorders.},
}
@article {pmid37425721,
year = {2023},
author = {Angrick, M and Luo, S and Rabbani, Q and Candrea, DN and Shah, S and Milsap, GW and Anderson, WS and Gordon, CR and Rosenblatt, KR and Clawson, L and Maragakis, N and Tenore, FV and Fifer, MS and Hermansky, H and Ramsey, NF and Crone, NE},
title = {Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {37425721},
support = {UH3 NS114439/NS/NINDS NIH HHS/United States ; },
abstract = {Recent studies have shown that speech can be reconstructed and synthesized using only brain activity recorded with intracranial electrodes, but until now this has only been done using retrospective analyses of recordings from able-bodied patients temporarily implanted with electrodes for epilepsy surgery. Here, we report online synthesis of intelligible words using a chronically implanted brain-computer interface (BCI) in a clinical trial participant (ClinicalTrials.gov, NCT03567213) with dysarthria due to amyotrophic lateral sclerosis (ALS). We demonstrate a reliable BCI that synthesizes commands freely chosen and spoken by the user from a vocabulary of 6 keywords originally designed to allow intuitive selection of items on a communication board. Our results show for the first time that a speech-impaired individual with ALS can use a chronically implanted BCI to reliably produce synthesized words that are intelligible to human listeners while preserving the participants voice profile.},
}
@article {pmid37425292,
year = {2023},
author = {Le Godais, G and Roussel, P and Bocquelet, F and Aubert, M and Kahane, P and Chabardès, S and Yvert, B},
title = {Overt speech decoding from cortical activity: a comparison of different linear methods.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1124065},
pmid = {37425292},
issn = {1662-5161},
abstract = {INTRODUCTION: Speech BCIs aim at reconstructing speech in real time from ongoing cortical activity. Ideal BCIs would need to reconstruct speech audio signal frame by frame on a millisecond-timescale. Such approaches require fast computation. In this respect, linear decoder are good candidates and have been widely used in motor BCIs. Yet, they have been very seldomly studied for speech reconstruction, and never for reconstruction of articulatory movements from intracranial activity. Here, we compared vanilla linear regression, ridge-regularized linear regressions, and partial least squares regressions for offline decoding of overt speech from cortical activity.<br><br>METHODS: Two decoding paradigms were investigated: (1) direct decoding of acoustic vocoder features of speech, and (2) indirect decoding of vocoder features through an intermediate articulatory representation chained with a real-time-compatible DNN-based articulatory-to-acoustic synthesizer. Participant's articulatory trajectories were estimated from an electromagnetic-articulography dataset using dynamic time warping. The accuracy of the decoders was evaluated by computing correlations between original and reconstructed features.<br><br>RESULTS: We found that similar performance was achieved by all linear methods well above chance levels, albeit without reaching intelligibility. Direct and indirect methods achieved comparable performance, with an advantage for direct decoding.<br><br>DISCUSSION: Future work will address the development of an improved neural speech decoder compatible with fast frame-by-frame speech reconstruction from ongoing activity at a millisecond timescale.},
}
@article {pmid37424160,
year = {2024},
author = {Kong, L and Shen, Y and Hu, S and Lai, J},
title = {The impact of quetiapine monotherapy or in combination with lithium on the thyroid function in patients with bipolar depression: A retrospective study.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {30},
number = {2},
pages = {e14342},
pmid = {37424160},
issn = {1755-5949},
support = {//Innovation team for precision diagnosis and treatment of major brain diseases/ ; //Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; //Zhejiang Provincial Key Research and Development Program/ ; },
mesh = {Humans ; *Triiodothyronine ; Thyroid Gland/physiology ; Thyroxine/therapeutic use ; Retrospective Studies ; Lithium ; *Bipolar Disorder/drug therapy ; Quetiapine Fumarate/therapeutic use ; Thyroid Function Tests ; Thyrotropin ; },
abstract = {OBJECTIVE: This study aims to investigate whether quetiapine monotherapy or in combination with lithium significantly disturbs thyroid function in depressed patients with bipolar disorder (BD), and whether difference exists in the post-treatment thyroid function between the two therapies.<br><br>METHODS: Based on the electric medical records, outpatients and inpatients with a current depressive episode of BD from January 2016 to December 2022 were screened. All patients were treated with quetiapine monotherapy or in combination with lithium. In addition to the demographic data and depression scale, thyroid profiles including total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and antithyroglobulin antibody (TGAb) were recorded, analyzed, and compared before and after the treatment.<br><br>RESULTS: Totally, 73 eligible patients were enrolled, including 53 in the monotherapy group (MG) and 20 in the combined therapy group (CG). No significant differences in thyroid profiles were detected between the two groups at the baseline (p&#x2009;>&#x2009;0.05). After one-month treatment, in the MG, serum levels of TT4, TT3, FT4, and FT3 reduced significantly (p&#x2009;<&#x2009;0.05), while TSH, TPOAb, and TGAb increased significantly (p&#x2009;<&#x2009;0.05). In the CG, serum levels of TT4, TT3, and FT4 reduced and TSH increased following one-month treatment (p&#x2009;<&#x2009;0.05), with no significant change in FT3, TPOAb, or TGAb (p&#x2009;>&#x2009;0.05). After one-month treatment, no difference of TT4, TT3, FT4, FT3, and TSH was found between the two groups (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: Both quetiapine monotherapy and a combined therapy with lithium significantly disturbed thyroid function in patients with bipolar depression, while quetiapine monotherapy seems to be associated with immune dysregulation in the thyroid.},
}
@article {pmid37423417,
year = {2023},
author = {Mansuri, A and Völkel, M and Mihiranga, D and Feuerbach, T and Winck, J and Vermeer, AWP and Hoheisel, W and Thommes, M},
title = {Predicting self-diffusion coefficients in semi-crystalline and amorphous solid dispersions using free volume theory.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {190},
number = {},
pages = {107-120},
doi = {10.1016/j.ejpb.2023.07.001},
pmid = {37423417},
issn = {1873-3441},
mesh = {*Povidone/chemistry ; *Chemistry, Pharmaceutical/methods ; Solubility ; Polymers/chemistry ; },
abstract = {The self-diffusion coefficient of active ingredients (AI) in polymeric solid dispersions is one of the essential parameters for the rational formulation design in life sciences. Measuring this parameter for products in their application temperature range can, however, be difficult to realise and time-consuming (due to the slow kinetics of diffusion). The aim of this study is to present a simple and time-saving platform for predicting the AI self-diffusivity in amorphous and semi-crystalline polymers on the basis of a modified version of Vrentas' and Duda's free volume theory (FVT) [A. Mansuri, M. Völkel, T. Feuerbach, J. Winck, A.W.P. Vermeer, W. Hoheisel, M. Thommes, Modified free volume theory for self-diffusion of small molecules in amorphous polymers, Macromolecules. (2023)]. The predictive model discussed in this work requires pure-component properties as its input and covers the approximate temperature range of T < 1.2 Tg, the whole compositional range of the binary mixtures (as long as a molecular mixture is present), and the whole crystallinity range of the polymer. In this context, the self-diffusion coefficients of the AIs imidacloprid, indomethacin, and deltamethrin were predicted in polyvinylpyrrolidone, polyvinylpyrrolidone/vinyl acetate, polystyrene, polyethylene, and polypropylene. The results highlight the profound importance of the kinetic fragility of the solid dispersion on the molecular migration; a property which in some cases might entail higher self-diffusion coefficients despite an increase in the molecular weight of the polymer. We interpret this observation within the context of the theory of heterogeneous dynamics in glass-formers [M.D. Ediger, Spatially heterogeneous dynamics in supercooled liquids, Annu. Rev. Phys. Chem. 51 (2000) 99-128] by attributing it to the stronger presence of "fluid-like" mobile regions in fragile polymers offering facilitated routes for the AI diffusion within the dispersion. The modified FVT further allows for identifying the influence of some structural and thermophysical material properties on the translational mobility of AIs in binary dispersions with polymers. In addition, estimates of self-diffusivity in semi-crystalline polymers are provided by further accounting for the tortuosity of the diffusion paths and the chain immobilisation at the interface of the amorphous and crystalline phases.},
}
@article {pmid37422072,
year = {2023},
author = {Nie, L and Ku, Y},
title = {Decoding emotion from high-frequency steady state visual evoked potential (SSVEP).},
journal = {Journal of neuroscience methods},
volume = {395},
number = {},
pages = {109919},
doi = {10.1016/j.jneumeth.2023.109919},
pmid = {37422072},
issn = {1872-678X},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography ; Photic Stimulation ; *Brain-Computer Interfaces ; Emotions ; },
abstract = {BACKGROUND: Steady-state visual evoked potential (SSVEP) by flickering sensory stimuli has been widely applied in the brain-machine interface (BMI). Yet, it remains largely unexplored whether affective information could be decoded from the signal of SSVEP, especially from the frequencies higher than the critical flicker frequency (an upper-frequency limit one can see the flicker).<br><br>NEW METHOD: Participants fixated on visual stimuli presented at 60&#xa0;Hz above the critical flicker frequency. The stimuli were pictures with different affective valance (positive, neutral, negative) in distinctive semantic categories (human, animal, scene). SSVEP entrainment in the brain evoked by the flickering stimuli at 60&#xa0;Hz was used to decode the affective and semantic information.<br><br>RESULTS: During the presentation of stimuli (1&#xa0;s), the affective valance could be decoded from the SSVEP signals at 60&#xa0;Hz, while the semantic categories could not. In contrast, neither affective nor semantic information could be decoded from the brain signal one second before the onset of stimuli.<br><br>Previous studies focused mainly on EEG activity tagged at frequencies lower than the critical flickering frequency and investigated whether the affective valence of stimuli drew participants' attention. The current study was the first to use SSVEP signals from high-frequency (60&#xa0;Hz) above the critical flickering frequency to decode affective information from stimuli. The high-frequency flickering was invisible and thus substantially reduced the fatigue of participants.<br><br>CONCLUSIONS: We found that affective information could be decoded from high-frequency SSVEP and the current finding could be added to designing affective BMI in the future.},
}
@article {pmid37421553,
year = {2023},
author = {Du, Y and Sui, J and Wang, S and Fu, R and Jia, C},
title = {Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {10},
pages = {2665-2676},
pmid = {37421553},
issn = {1741-0444},
support = {62073282//National Natural Science Foundation of China/ ; 11832009//National Natural Science Foundation of China/ ; 206Z0301G//Central Guidance on Local Science and Technology Development Fund of Hebei Province/ ; F2022203092//Natural Science Foundation of Hebei Province/ ; F2020203061//Natural Science Foundation of Hebei Province/ ; 2021HBQZYCSB003//Full-time Introduction of National High level Innovation Talents Research Project of Hebei Province/ ; },
abstract = {The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left-right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery.},
}
@article {pmid37420942,
year = {2023},
author = {Xu, S and Liu, Y and Yang, Y and Zhang, K and Liang, W and Xu, Z and Wu, Y and Luo, J and Zhuang, C and Cai, X},
title = {Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays.},
journal = {Micromachines},
volume = {14},
number = {4},
pages = {},
pmid = {37420942},
issn = {2072-666X},
support = {No. L2224042, T2293731,62121003, 61960206012, 62171434, 61971400, 61975206, and 61973292//The National Natural Science Foundation of China/ ; No. XK2022XXC003//the Frontier Interdisciplinary Project of the Chinese Academy of Sciences/ ; No. GJJSTD20210004//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; (No.2022YFC2402501//the National Key R&amp;D Program/ ; No. 2021ZD0201603//Major program of scientific and technical innovation 2030/ ; },
abstract = {Recent years have witnessed a spurt of progress in the application of the encoding and decoding of neural activities to drug screening, diseases diagnosis, and brain-computer interactions. To overcome the constraints of the complexity of the brain and the ethical considerations of in vivo research, neural chip platforms integrating microfluidic devices and microelectrode arrays have been raised, which can not only customize growth paths for neurons in vitro but also monitor and modulate the specialized neural networks grown on chips. Therefore, this article reviews the developmental history of chip platforms integrating microfluidic devices and microelectrode arrays. First, we review the design and application of advanced microelectrode arrays and microfluidic devices. After, we introduce the fabrication process of neural chip platforms. Finally, we highlight the recent progress on this type of chip platform as a research tool in the field of brain science and neuroscience, focusing on neuropharmacology, neurological diseases, and simplified brain models. This is a detailed and comprehensive review of neural chip platforms. This work aims to fulfill the following three goals: (1) summarize the latest design patterns and fabrication schemes of such platforms, providing a reference for the development of other new platforms; (2) generalize several important applications of chip platforms in the field of neurology, which will attract the attention of scientists in the field; and (3) propose the developmental direction of neural chip platforms integrating microfluidic devices and microelectrode arrays.},
}
@article {pmid37420741,
year = {2023},
author = {Sen, O and Sheehan, AM and Raman, PR and Khara, KS and Khalifa, A and Chatterjee, B},
title = {Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {12},
pages = {},
pmid = {37420741},
issn = {1424-8220},
mesh = {Humans ; *Speech ; *Brain-Computer Interfaces ; Brain ; Machine Learning ; Handwriting ; },
abstract = {Brain-Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.},
}
@article {pmid37419902,
year = {2023},
author = {Fang, A and Wang, Y and Guan, N and Zuo, Y and Lin, L and Guo, B and Mo, A and Wu, Y and Lin, X and Cai, W and Chen, X and Ye, J and Abdelrahman, Z and Li, X and Zheng, H and Wu, Z and Jin, S and Xu, K and Huang, Y and Gu, X and Yu, B and Wang, X},
title = {Porous microneedle patch with sustained delivery of extracellular vesicles mitigates severe spinal cord injury.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {4011},
pmid = {37419902},
issn = {2041-1723},
mesh = {Humans ; Porosity ; *Spinal Cord Injuries ; Spinal Cord/pathology ; Axons/pathology ; *Extracellular Vesicles/pathology ; },
abstract = {The transplantation of mesenchymal stem cells-derived secretome, particularly extracellular vesicles is a promising therapy to suppress spinal cord injury-triggered neuroinflammation. However, efficient delivery of extracellular vesicles to the injured spinal cord, with minimal damage, remains a challenge. Here we present a device for the delivery of extracellular vesicles to treat spinal cord injury. We show that the device incorporating mesenchymal stem cells and porous microneedles enables the delivery of extracellular vesicles. We demonstrate that topical application to the spinal cord lesion beneath the spinal dura, does not damage the lesion. We evaluate the efficacy of our device in a contusive spinal cord injury model and find that it reduces the cavity and scar tissue formation, promotes angiogenesis, and improves survival of nearby tissues and axons. Importantly, the sustained delivery of extracellular vesicles for at least 7 days results in significant functional recovery. Thus, our device provides an efficient and sustained extracellular vesicles delivery platform for spinal cord injury treatment.},
}
@article {pmid37418412,
year = {2023},
author = {Kang, C and Novak, D and Yao, X and Xie, J and Hu, Y},
title = {Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2964-2973},
doi = {10.1109/TNSRE.2023.3293051},
pmid = {37418412},
issn = {1558-0210},
mesh = {Humans ; *Depressive Disorder, Major/diagnosis ; Neural Networks, Computer ; Brain/physiology ; Algorithms ; Electroencephalography/methods ; },
abstract = {Major Depressive Disorder (MDD) - can be evaluated by advanced neurocomputing and traditional machine learning techniques. This study aims to develop an automatic system based on a Brain-Computer Interface (BCI) to classify and score depressive patients by specific frequency bands and electrodes. In this study, two Residual Neural Networks (ResNets) based on electroencephalogram (EEG) monitoring are presented for classifying depression (classifier) and for scoring depressive severity (regression). Significant frequency bands and specific brain regions are selected to improve the performance of the ResNets. The algorithm, which is estimated by 10-fold cross-validation, attained an average accuracy rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error (RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific EEG channels, we obtained the best-classifying accuracy at 0.871 and the smallest RMSE at 2.80. It was discovered that signals extracted from the beta band are more distinctive in depression classification, and these selected channels tend to perform better on scoring depressive severity. Our study also uncovered the different brain architectural connections by relying on phase coherence analysis. Increased delta deactivation accompanied by strong beta activation is the main feature of depression when the depression symptom is becoming more severe. We can therefore conclude that the model developed here is acceptable for classifying depression and for scoring depressive severity. Our model can offer physicians a model that consists of topological dependency, quantified semantic depressive symptoms and clinical features by using EEG signals. These selected brain regions and significant beta frequency bands can improve the performance of the BCI system for detecting depression and scoring depressive severity.},
}
@article {pmid37414984,
year = {2023},
author = {Gu, Y and Ge, S},
title = {Hypothalamic-Modified New Hippocampal Neurons for Alzheimer's Disease.},
journal = {Neuroscience bulletin},
volume = {39},
number = {11},
pages = {1735-1737},
pmid = {37414984},
issn = {1995-8218},
mesh = {Humans ; *Alzheimer Disease ; Hippocampus ; Neurons ; Hypothalamus ; },
}
@article {pmid37414861,
year = {2023},
author = {Kurmanavičiūtė, D and Kataja, H and Jas, M and Välilä, A and Parkkonen, L},
title = {Target of selective auditory attention can be robustly followed with MEG.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {10959},
pmid = {37414861},
issn = {2045-2322},
mesh = {Humans ; Acoustic Stimulation ; *Auditory Perception/physiology ; Electroencephalography/methods ; Evoked Potentials, Auditory/physiology ; *Auditory Cortex/physiology ; },
abstract = {Selective auditory attention enables filtering of relevant acoustic information from irrelevant. Specific auditory responses, measurable by magneto- and electroencephalography (MEG/EEG), are known to be modulated by attention to the evoking stimuli. However, such attention effects have typically been studied in unnatural conditions (e.g. during dichotic listening of pure tones) and have been demonstrated mostly in averaged auditory evoked responses. To test how reliably we can detect the attention target from unaveraged brain responses, we recorded MEG data from 15 healthy subjects that were presented with two human speakers uttering continuously the words "Yes" and "No" in an interleaved manner. The subjects were asked to attend to one speaker. To investigate which temporal and spatial aspects of the responses carry the most information about the target of auditory attention, we performed spatially and temporally resolved classification of the unaveraged MEG responses using a support vector machine. Sensor-level decoding of the responses to attended vs. unattended words resulted in a mean accuracy of [Formula: see text] (N = 14) for both stimulus words. The discriminating information was mostly available 200-400 ms after the stimulus onset. Spatially-resolved source-level decoding indicated that the most informative sources were in the auditory cortices, in both the left and right hemisphere. Our result corroborates attention modulation of auditory evoked responses and shows that such modulations are detectable in unaveraged MEG responses at high accuracy, which could be exploited e.g. in an intuitive brain-computer interface.},
}
@article {pmid37414004,
year = {2023},
author = {TaghiBeyglou, B and Shamsollahi, MB},
title = {ETucker: a constrained tensor decomposition for single trial ERP extraction.},
journal = {Physiological measurement},
volume = {44},
number = {7},
pages = {},
doi = {10.1088/1361-6579/ace510},
pmid = {37414004},
issn = {1361-6579},
mesh = {*Algorithms ; Evoked Potentials/physiology ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; *Brain-Computer Interfaces ; Brain/physiology ; },
abstract = {Objective.In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition.Approach.We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings. The dataset is manipulated to contain the P300 ERP component and to cover different SNR conditions, ranging from 0 to -30 dB, to simulate the presence of the P300 component in extremely noisy recordings. Furthermore, in order to assess the practicality of the proposed methodology in real-world scenarios, we utilized the brain-computer interface (BCI) competition III-dataset II.Main results.Our primary results demonstrate the superior performance of our approach compared to conventional methods commonly employed for single-trial estimation. Additionally, our method outperformed both Tucker decomposition and non-negative Tucker decomposition in the synthesized dataset. Furthermore, the results obtained from real-world data exhibited meaningful performance and provided insightful interpretations for the extracted P300 component.Significance.The findings suggest that the proposed decomposition is eminently capable of extracting the target P300 component's waveform, including latency and amplitude as well as its spatial location, using single-trial EEG recordings.},
}
@article {pmid37413842,
year = {2023},
author = {Chen, K and Cambi, F and Kozai, TDY},
title = {Pro-myelinating clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health.},
journal = {Biomaterials},
volume = {301},
number = {},
pages = {122210},
pmid = {37413842},
issn = {1878-5905},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R03 AG072218/AG/NIA NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; },
mesh = {Microelectrodes ; *Clemastine/metabolism ; Electrodes, Implanted ; *Neurons/metabolism ; Brain ; },
abstract = {Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over the chronic implantation period. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.},
}
@article {pmid37410639,
year = {2023},
author = {Shin, J and Chung, W},
title = {Multi-Band CNN With Band-Dependent Kernels and Amalgamated Cross Entropy Loss for Motor Imagery Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {9},
pages = {4466-4477},
doi = {10.1109/JBHI.2023.3292909},
pmid = {37410639},
issn = {2168-2208},
mesh = {Humans ; *Algorithms ; Imagination ; Entropy ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; },
abstract = {In this paper, we present a novel MI classification method based on multi-band convolutional neural network (CNN) with band-dependent kernel sizes, named MBK-CNN, to improve classification performance, by resolving the subject dependency issue of the widely used CNN-based approaches due to the kernel size optimization problem. The proposed structure exploits the frequency diversity of the EEG signals and simultaneously resolves the subject dependent kernel size issue. EEG signal is decomposed into overlapping multi-band and passed through multiple CNNs (termed 'branch-CNNs') with different kernel sizes to generate frequency dependent features, which are combined by a simple weighted sum. In contrast to the existing works where single-band multi-branch CNNs with different kernel sizes are used to resolve the subject dependency issue, a unique kernel size per frequency band is used. To prevent possible overfitting induced by a weighted sum, each branch-CNN is additionally trained by tentative cross entropy loss while overall network is optimized by the end-to-end cross entropy loss, which is named amalgamated cross entropy loss. In addition, we further propose multi-band CNN with enhanced spatial diversity, named MBK-LR-CNN, by replacing each branch-CNN with several sub branch-CNNs applied for channel subsets (termed 'local region') to improve the classification performance. We evaluated the performance of the proposed methods, MBK-CNN and MBK-LR-CNN, on publicly available datasets, BCI Competition IV dataset 2a and High Gamma Dataset. The experimental results confirm the performance improvement of the proposed methods compared to the currently existing MI classification methods.},
}
@article {pmid37410276,
year = {2023},
author = {Mahey, P and Toussi, N and Purnomu, G and Herdman, AT},
title = {Generative Adversarial Network (GAN) for Simulating Electroencephalography.},
journal = {Brain topography},
volume = {36},
number = {5},
pages = {661-670},
pmid = {37410276},
issn = {1573-6792},
mesh = {Humans ; *Brain/diagnostic imaging ; *Electroencephalography ; Neuroimaging ; },
abstract = {Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often difficult to obtain the required datasets. Generative adversarial networks are robust deep learning framework which have proven themselves to be capable of synthesizing data. The robust nature of a generative adversarial network was used to generate multi-channel electroencephalography data in order to see if generative adversarial networks could reconstruct the spatio-temporal aspects of multi-channel electroencephalography signals. We were able to find that the synthetic electroencephalography data was able to replicate fine details of electroencephalography data and could potentially help us to generate large sample synthetic resting-state electroencephalography data for use in simulation testing of neuroimaging analyses. Generative adversarial networks (GANs) are robust deep-learning frameworks that can be trained to be convincing replicants of real data GANs were capable of generating "fake" EEG data that replicated fine details and topographies of "real" resting-state EEG data.},
}
@article {pmid37409628,
year = {2023},
author = {Yao, S and Zhu, Q and Zhang, Q and Cai, Y and Liu, S and Pang, L and Jing, Y and Yin, X and Cheng, H},
title = {Managing Cancer and Living Meaningfully (CALM) alleviates chemotherapy related cognitive impairment (CRCI) in breast cancer survivors: A pilot study based on resting-state fMRI.},
journal = {Cancer medicine},
volume = {12},
number = {15},
pages = {16231-16242},
pmid = {37409628},
issn = {2045-7634},
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy ; Magnetic Resonance Imaging/methods ; Pilot Projects ; *Chemotherapy-Related Cognitive Impairment ; *Cancer Survivors ; *Percutaneous Coronary Intervention ; Quality of Life ; Brain/diagnostic imaging ; },
abstract = {BACKGROUND: Chemotherapy related cognitive impairment (CRCI) is a type of memory and cognitive impairment induced by chemotherapy and has become a growing clinical problem. Breast cancer survivors (BCs) refer to patients from the moment of breast cancer diagnosis to the end of their lives. Managing Cancer and Living Meaningfully (CALM) is a convenient and easy-to-apply psychological intervention that has been proven to improve quality of life and alleviate CRCI in BCs. However, the underlying neurobiological mechanisms remain unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) has become an effective method for understanding the neurobiological mechanisms of brain networks in CRCI. The fractional amplitude of low-frequency fluctuations (fALFF) and ALFF have often been used in analyzing the power and intensity of spontaneous regional resting state neural activity.<br><br>METHODS: The recruited BCs were randomly divided into the CALM group and the care as usual (CAU) group. All BCs were evaluated by the Functional Assessment of Cancer Therapy Cognitive Function (FACT-Cog) before and after CALM or CAU. The rs-fMRI imaging was acquired before and after CALM intervention in CALM group BCs. The BCs were defined as before CALM intervention (BCI) group and after CALM intervention (ACI) group.<br><br>RESULTS: There were 32 BCs in CALM group and 35 BCs in CAU group completed the overall study. There were significant differences between the BCI group and the ACI group in the FACT-Cog-PCI scores. Compared with the BCI group, the ACI group showed lower fALFF signal in the left medial frontal gyrus and right sub-gyral and higher fALFF in the left occipital_sup and middle occipital gyrus. There was a significant positive correlation between hippocampal ALFF value and FACT-Cog-PCI scores.<br><br>CONCLUSIONS: CALM intervention may have an effective function in alleviating CRCI of BCs. The altered local synchronization and regional brain activity may be correlated with the improved cognitive function of BCs who received the CALM intervention. The ALFF value of hippocampus seems to be an important factor in reflect cognitive function in BCs with CRCI and the neural network mechanism of CALM intervention deserves further exploration to promote its application.},
}
@article {pmid37409102,
year = {2023},
author = {Wang, W and Shi, B and Wang, D and Wang, J and Liu, G},
title = {Enhanced lower-limb motor imagery by kinesthetic illusion.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1077479},
pmid = {37409102},
issn = {1662-4548},
abstract = {Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.},
}
@article {pmid37407726,
year = {2023},
author = {Aurucci, GV and Preatoni, G and Damiani, A and Raspopovic, S},
title = {Brain-Computer Interface to Deliver Individualized Multisensory Intervention for Neuropathic Pain.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {20},
number = {5},
pages = {1316-1329},
pmid = {37407726},
issn = {1878-7479},
mesh = {Humans ; *Brain-Computer Interfaces ; *Transcutaneous Electric Nerve Stimulation/methods ; *Neuralgia/therapy ; Pain Management ; Electroencephalography ; },
abstract = {To unravel the complexity of the neuropathic pain experience, researchers have tried to identify reliable pain signatures (biomarkers) using electroencephalography (EEG) and skin conductance (SC). Nevertheless, their use as a clinical aid to design personalized therapies remains scarce and patients are prescribed with common and inefficient painkillers. To address this need, novel non-pharmacological interventions, such as transcutaneous electrical nerve stimulation (TENS) to activate peripheral pain relief via neuromodulation and virtual reality (VR) to modulate patients' attention, have emerged. However, all present treatments suffer from the inherent bias of the patient's self-reported pain intensity, depending on their predisposition and tolerance, together with unspecific, pre-defined scheduling of sessions which does not consider the timing of pain episodes onset. Here, we show a Brain-Computer Interface (BCI) detecting in real-time neurophysiological signatures of neuropathic pain from EEG combined with SC and accordingly triggering a multisensory intervention combining TENS and VR. After validating that the multisensory intervention effectively decreased experimentally induced pain, the BCI was tested with thirteen healthy subjects by electrically inducing pain and showed 82% recall in decoding pain in real time. Such constructed BCI was then validated with eight neuropathic patients reaching 75% online pain precision, and consequently releasing the intervention inducing a significant decrease (50% NPSI score) in neuropathic patients' pain perception. Our results demonstrate the feasibility of real-time pain detection from objective neurophysiological signals, and the effectiveness of a triggered combination of VR and TENS to decrease neuropathic pain. This paves the way towards personalized, data-driven pain therapies using fully portable technologies.},
}
@article {pmid37407676,
year = {2023},
author = {Barreiro, R and Sanabria-Macías, F and Posada, J and Martín-Mateos, P and de Dios, C},
title = {Experimental demonstration of a new near-infrared spectroscopy technique based on optical dual-comb: DC-NIRS.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {10924},
pmid = {37407676},
issn = {2045-2322},
abstract = {We present a novel near-infrared spectroscopy technique based on Dual-Comb optical interrogation (DC-NIRS) applied to dispersive media. The technique recovers the frequency response of the medium under investigation by sampling its spectral response in amplitude and phase. The DC-NIRS reference and sample signals are generated using electro-optic modulation which offers a cost-effective, integrable solution while providing high adaptability to the interrogated medium. A careful choice of both line spacing and optical span of the frequency comb ensures that the retrieved information enables the reconstruction of the temporal impulse response of the medium, known as the diffuse-time-of-flight (DTOF), to obtain its optical properties with a 70 µs temporal resolution and 32 ps photon propagation delay resolution. Furthermore, the DC-NIRS technique also offers enhanced penetration due to noiseless optical amplification (interferometric detection). The presented technique was demonstrated on a static bio-mimetic phantom of known optical properties reproducing a typical brain's optical response. The DTOF and optical properties of the phantom were measured, showing the capabilities of this new technique on the estimation of absolute optical properties with a deviation under 3%. Compared to current technologies, our DC-NIRS technique provides enhanced temporal resolution, spatial location capabilities, and penetration depth, with an integrable and configurable cost-effective architecture, paving the way to next-generation, non-invasive and portable systems for functional brain imaging, and brain-computer interfaces, among other. The system is patent pending PCT/ES2022/070176.},
}
@article {pmid37405822,
year = {2023},
author = {Noble, SC and Woods, E and Ward, T and Ringwood, JV},
title = {Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.},
journal = {JMIR research protocols},
volume = {12},
number = {},
pages = {e46135},
pmid = {37405822},
issn = {1929-0748},
abstract = {BACKGROUND: The number of people with cognitive deficits and diseases, such as stroke, dementia, or attention-deficit/hyperactivity disorder, is rising due to an aging, or in the case of attention-deficit/hyperactivity disorder, a growing population. Neurofeedback training using brain-computer interfaces is emerging as a means of easy-to-use and noninvasive cognitive training and rehabilitation. A novel application of neurofeedback training using a P300-based brain-computer interface has previously shown potential to improve attention in healthy adults.<br><br>OBJECTIVE: This study aims to accelerate attention training using iterative learning control to optimize the task difficulty in an adaptive P300 speller task. Furthermore, we hope to replicate the results of a previous study using a P300 speller for attention training, as a benchmark comparison. In addition, the effectiveness of personalizing the task difficulty during training will be compared to a nonpersonalized task difficulty adaptation.<br><br>METHODS: In this single-blind, parallel, 3-arm randomized controlled trial, 45 healthy adults will be recruited and randomly assigned to the experimental group or 1 of 2 control groups. This study involves a single training session, where participants receive neurofeedback training through a P300 speller task. During this training, the task's difficulty is progressively increased, which makes it more difficult for the participants to maintain their performance. This encourages the participants to improve their focus. Task difficulty is either adapted based on the participants' performance (in the experimental group and control group 1) or chosen randomly (in control group 2). Changes in brain patterns before and after training will be analyzed to study the effectiveness of the different approaches. Participants will complete a random dot motion task before and after the training so that any transfer effects of the training to other cognitive tasks can be evaluated. Questionnaires will be used to estimate the participants' fatigue and compare the perceived workload of the training between groups.<br><br>RESULTS: This study has been approved by the Maynooth University Ethics Committee (BSRESC-2022-2474456) and is registered on ClinicalTrials.gov (NCT05576649). Participant recruitment and data collection began in October 2022, and we expect to publish the results in 2023.<br><br>CONCLUSIONS: This study aims to accelerate attention training using iterative learning control in an adaptive P300 speller task, making it a more attractive training option for individuals with cognitive deficits due to its ease of use and speed. The successful replication of the results from the previous study, which used a P300 speller for attention training, would provide further evidence to support the effectiveness of this training tool.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05576649; https://clinicaltrials.gov/ct2/show/NCT05576649.<br><br>DERR1-10.2196/46135.},
}
@article {pmid37404786,
year = {2023},
author = {Garrison, WJ and Qing, K and He, M and Zhao, L and Tustison, NJ and Patrie, JT and Mata, JF and Shim, YM and Ropp, AM and Altes, TA and Mugler, JP and Miller, GW},
title = {Lung Volume Dependence and Repeatability of Hyperpolarized [129]Xe MRI Gas Uptake Metrics in Healthy Volunteers and Participants with COPD.},
journal = {Radiology. Cardiothoracic imaging},
volume = {5},
number = {3},
pages = {e220096},
pmid = {37404786},
issn = {2638-6135},
support = {R01 HL132177/HL/NHLBI NIH HHS/United States ; R01 HL133889/HL/NHLBI NIH HHS/United States ; R01 HL167202/HL/NHLBI NIH HHS/United States ; UL1 TR003015/TR/NCATS NIH HHS/United States ; },
abstract = {PURPOSE: To assess the effect of lung volume on measured values and repeatability of xenon 129 ([129]Xe) gas uptake metrics in healthy volunteers and participants with chronic obstructive pulmonary disease (COPD).<br><br>MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant prospective study included data (March 2014-December 2015) from 49 participants (19 with COPD [mean age, 67 years &#xb1; 9 (SD)]; nine women]; 25 older healthy volunteers [mean age, 59 years &#xb1; 10; 20 women]; and five young healthy women [mean age, 23 years &#xb1; 3]). Thirty-two participants underwent repeated [129]Xe and same-breath-hold proton MRI at residual volume plus one-third forced vital capacity (RV+FVC/3), with 29 also undergoing one examination at total lung capacity (TLC). The remaining 17 participants underwent imaging at TLC, RV+FVC/3, and residual volume (RV). Signal ratios between membrane, red blood cell (RBC), and gas-phase compartments were calculated using hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (ie, IDEAL). Repeatability was assessed using coefficient of variation and intraclass correlation coefficient, and volume relationships were assessed using Spearman correlation and Wilcoxon rank sum tests.<br><br>RESULTS: Gas uptake metrics were repeatable at RV+FVC/3 (intraclass correlation coefficient = 0.88 for membrane/gas; 0.71 for RBC/gas, and 0.88 for RBC/membrane). Relative ratio changes were highly correlated with relative volume changes for membrane/gas (r = -0.97) and RBC/gas (r = -0.93). Membrane/gas and RBC/gas measured at RV+FVC/3 were significantly lower in the COPD group than the corresponding healthy group (P &#x2264; .001). However, these differences lessened upon correction for individual volume differences (P = .23 for membrane/gas; P = .09 for RBC/gas).<br><br>CONCLUSION: Dissolved-phase [129]Xe MRI-derived gas uptake metrics were repeatable but highly dependent on lung volume during measurement.Keywords: Blood-Air Barrier, MRI, Chronic Obstructive Pulmonary Disease, Pulmonary Gas Exchange, Xenon Supplemental material is available for this article &#xa9; RSNA, 2023.},
}
@article {pmid37402376,
year = {2023},
author = {Athalye, VR and Khanna, P and Gowda, S and Orsborn, AL and Costa, RM and Carmena, JM},
title = {Invariant neural dynamics drive commands to control different movements.},
journal = {Current biology : CB},
volume = {33},
number = {14},
pages = {2962-2976.e15},
pmid = {37402376},
issn = {1879-0445},
support = {K99 NS128250/NS/NINDS NIH HHS/United States ; U19 NS104649/NS/NINDS NIH HHS/United States ; R01 NS106094/NS/NINDS NIH HHS/United States ; K99 NS124748/NS/NINDS NIH HHS/United States ; F32 MH120891/MH/NIMH NIH HHS/United States ; F32 MH118714/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Macaca mulatta ; Movement/physiology ; Feedback ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; },
abstract = {It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.},
}
@article {pmid37402372,
year = {2023},
author = {Wu, Y and Dong, JH and Dai, YF and Zhu, MZ and Wang, MY and Zhang, Y and Pan, YD and Yuan, XR and Guo, ZX and Wang, CX and Li, YQ and Zhu, XH},
title = {Hepatic soluble epoxide hydrolase activity regulates cerebral Aβ metabolism and the pathogenesis of Alzheimer's disease in mice.},
journal = {Neuron},
volume = {111},
number = {18},
pages = {2847-2862.e10},
doi = {10.1016/j.neuron.2023.06.002},
pmid = {37402372},
issn = {1097-4199},
mesh = {Animals ; Mice ; *Alzheimer Disease/metabolism ; Amyloid beta-Peptides/metabolism ; Brain/metabolism ; Disease Models, Animal ; Epoxide Hydrolases/genetics/metabolism ; Liver/metabolism/pathology ; },
abstract = {Alzheimer's disease (AD) is caused by a complex interaction between genetic and environmental factors. However, how the role of peripheral organ changes in response to environmental stimuli during aging in AD pathogenesis remains unknown. Hepatic soluble epoxide hydrolase (sEH) activity increases with age. Hepatic sEH manipulation bidirectionally attenuates brain amyloid-β (Aβ) burden, tauopathy, and cognitive deficits in AD mouse models. Moreover, hepatic sEH manipulation bidirectionally regulates the plasma level of 14,15-epoxyeicosatrienoic acid (-EET), which rapidly crosses the blood-brain barrier and modulates brain Aβ metabolism through multiple pathways. A balance between the brain levels of 14,15-EET and Aβ is essential for preventing Aβ deposition. In AD models, 14,15-EET infusion mimicked the neuroprotective effects of hepatic sEH ablation at biological and behavioral levels. These results highlight the liver's key role in AD pathology, and targeting the liver-brain axis in response to environmental stimuli may constitute a promising therapeutic approach for AD prevention.},
}
@article {pmid37401033,
year = {2023},
author = {Xu, Y and Zhu, X and Chen, Y and Chen, Y and Zhu, Y and Xiao, S and Wu, M and Wang, Y and Zhang, C and Wu, Z and He, X and Liu, B and Shen, Z and Shao, X and Fang, J},
title = {Electroacupuncture alleviates mechanical allodynia and anxiety-like behaviors induced by chronic neuropathic pain via regulating rostral anterior cingulate cortex-dorsal raphe nucleus neural circuit.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {29},
number = {12},
pages = {4043-4058},
pmid = {37401033},
issn = {1755-5949},
support = {//National Natural Science Foundation of China (Grant numbers: 82274635, 81873360, 82074518)/ ; },
mesh = {Rats ; Humans ; Mice ; Animals ; Hyperalgesia/therapy ; *Electroacupuncture ; Gyrus Cinguli ; Dorsal Raphe Nucleus/metabolism ; *Anti-Anxiety Agents ; Rats, Sprague-Dawley ; *Neuralgia/therapy/metabolism ; Analgesics ; Anxiety/therapy ; Disease Models, Animal ; },
abstract = {AIMS: Epidemiological studies in patients with neuropathic pain have demonstrated a strong association between neuropathic pain and psychiatric conditions such as anxiety. Preclinical and clinical work has demonstrated that electroacupuncture (EA) effectively alleviates anxiety-like behaviors induced by chronic neuropathic pain. In this study, a potential neural circuitry underlying the therapeutic action of EA was investigated.<br><br>METHODS: The effects of EA stimulation on mechanical allodynia and anxiety-like behaviors in animal models of spared nerve injury (SNI) were examined. EA plus chemogenetic manipulation of glutamatergic (Glu) neurons projecting from the rostral anterior cingulate cortex (rACC[Glu]) to the dorsal raphe nucleus (DRN) was used to explore the changes of mechanical allodynia and anxiety-like behaviors in SNI mice.<br><br>RESULTS: Electroacupuncture significantly alleviated both mechanical allodynia and anxiety-like behaviors with increased activities of glutamatergic neurons in the rACC and serotoninergic neurons in the DRN. Chemogenetic activation of the rACC[Glu] -DRN projections attenuated both mechanical allodynia and anxiety-like behaviors in mice at day 14 after SNI. Chemogenetic inhibition of the rACC[Glu] -DRN pathway did not induce mechanical allodynia and anxiety-like behaviors under physiological conditions, but inhibiting this pathway produced anxiety-like behaviors in mice at day 7 after SNI; this effect was reversed by EA. EA plus activation of the rACC[Glu] -DRN circuit did not produce a synergistic effect on mechanical allodynia and anxiety-like behaviors. The analgesic and anxiolytic effects of EA could be blocked by inhibiting the rACC[Glu] -DRN pathway.<br><br>CONCLUSIONS: The role of rACC[Glu] -DRN circuit may be different during the progression of chronic neuropathic pain and these changes may be related to the serotoninergic neurons in the DRN. These findings describe a novel rACC[Glu] -DRN pathway through which EA exerts analgesic and anxiolytic effects in SNI mice exhibiting anxiety-like behaviors.},
}
@article {pmid37399992,
year = {2023},
author = {Zhang, Y and Peng, Y and Li, J and Kong, W},
title = {SIFIAE: An adaptive emotion recognition model with EEG feature-label inconsistency consideration.},
journal = {Journal of neuroscience methods},
volume = {395},
number = {},
pages = {109909},
doi = {10.1016/j.jneumeth.2023.109909},
pmid = {37399992},
issn = {1872-678X},
mesh = {Humans ; Male ; Female ; *Emotions/physiology ; *Recognition, Psychology ; Affect ; Electroencephalography/methods ; },
abstract = {BACKGROUND: A common but easily overlooked affective overlap problem has not been received enough attention in electroencephalogram (EEG)-based emotion recognition research. In real life, affective overlap refers to the current emotional state of human being is sometimes influenced easily by his/her historical mood. In stimulus-evoked EEG collection experiment, due to the short rest interval in consecutive trials, the inner mechanisms of neural responses make subjects cannot switch their emotion state easily and quickly, which might lead to the affective overlap. For example, we might be still in sad state to some extent even if we are watching a comedy because we just saw a tragedy before. In pattern recognition, affective overlap usually means that there exists the feature-label inconsistency in EEG data.<br><br>NEW METHODS: To alleviate the impact of inconsistent EEG data, we introduce a variable to adaptively explore the sample inconsistency in emotion recognition model development. Then, we propose a semi-supervised emotion recognition model for joint sample inconsistency and feature importance exploration (SIFIAE). Accordingly, an efficient optimization method to SIFIAE model is proposed.<br><br>RESULTS: Extensive experiments on the SEED-V dataset demonstrate the effectiveness of SIFIAE. Specifically, SIFIAE achieves 69.10%, 67.01%, 71.50%, 73.26%, 72.07% and 71.35% average accuracies in six cross-session emotion recognition tasks.<br><br>CONCLUSION: The results illustrated that the sample weights have a rising trend in the beginning of most trials, which coincides with the affective overlap hypothesis. The feature importance factor indicated the critical bands and channels are more obvious compared with some models without considering EEG feature-label inconsistency.},
}
@article {pmid37399806,
year = {2023},
author = {Deng, Y and Sun, Q and Wang, C and Wang, Y and Zhou, SK},
title = {TRCA-Net: using TRCA filters to boost the SSVEP classification with convolutional neural network.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ace380},
pmid = {37399806},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Neural Networks, Computer ; Algorithms ; *Brain-Computer Interfaces ; Photic Stimulation ; },
abstract = {Objective.The steady-state visual evoked potential (SSVEP)-based brain-computer interface has received extensive attention in research due to its simple system, less training data, and high information transfer rate. There are currently two prominent methods dominating the classification of SSVEP signals. One is the knowledge-based task-related component analysis (TRCA) method, whose core idea is to find the spatial filters by maximizing the inter-trial covariance. The other is the deep learning-based approach, which directly learns a classification model from data. However, how to integrate the two methods to achieve better performance has not been studied before.Approach.In this study, we develop a novel algorithm named TRCA-Net (TRCA-Net) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model. Specifically, the proposed TRCA-Net first performs TRCA to obtain spatial filters, which extract task-related components of data. Then the TRCA-filtered features from different filters are rearranged as new multi-channel signals for a deep convolutional neural network (CNN) for classification. Introducing the TRCA filters to a deep learning-based approach improves the signal-to-noise ratio of input data, hence benefiting the deep learning model.Main results.We evaluate the performance of TRCA-Net using two publicly available large-scale benchmark datasets, and the results demonstrate the effectiveness of TRCA-Net. Additionally, offline and online experiments separately testing ten and five subjects further validate the robustness of TRCA-Net. Further, we conduct ablation studies on different CNN backbones and demonstrate that our approach can be transplanted into other CNN models to boost their performance.Significance.The proposed approach is believed to have a promising potential for SSVEP classification and promote its practical applications in communication and control. The code is available athttps://github.com/Sungden/TRCA-Net.},
}
@article {pmid37399423,
year = {2023},
author = {Chen, H and Xu, X and Hu, W and Wu, S and Xiao, J and Wu, P and Wang, X and Han, X and Zhang, Y and Zhang, Y and Jiang, N and Liu, W and Lou, C and Chen, W and Xu, C and Lou, J},
title = {Self-programmed dynamics of T cell receptor condensation.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {28},
pages = {e2217301120},
pmid = {37399423},
issn = {1091-6490},
mesh = {*Receptors, Antigen, T-Cell ; *Lymphocyte Specific Protein Tyrosine Kinase p56(lck) ; Signal Transduction/physiology ; Phosphorylation ; Antigens/metabolism ; },
abstract = {A common event upon receptor-ligand engagement is the formation of receptor clusters on the cell surface, in which signaling molecules are specifically recruited or excluded to form signaling hubs to regulate cellular events. These clusters are often transient and can be disassembled to terminate signaling. Despite the general relevance of dynamic receptor clustering in cell signaling, the regulatory mechanism underlying the dynamics is still poorly understood. As a major antigen receptor in the immune system, T cell receptors (TCR) form spatiotemporally dynamic clusters to mediate robust yet temporal signaling to induce adaptive immune responses. Here we identify a phase separation mechanism controlling dynamic TCR clustering and signaling. The TCR signaling component CD3ε chain can condensate with Lck kinase through phase separation to form TCR signalosomes for active antigen signaling. Lck-mediated CD3ε phosphorylation, however, switched its binding preference to Csk, a functional suppressor of Lck, to cause the dissolvement of TCR signalosomes. Modulating TCR/Lck condensation by targeting CD3ε interactions with Lck or Csk directly affects T cell activation and function, highlighting the importance of the phase separation mechanism. The self-programmed condensation and dissolvement is thus a built-in mechanism of TCR signaling and might be relevant to other receptors.},
}
@article {pmid37398400,
year = {2023},
author = {Ahmadipour, P and Sani, OG and Pesaran, B and Shanechi, MM},
title = {Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.05.26.542509},
pmid = {37398400},
issn = {2692-8205},
abstract = {Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.},
}
@article {pmid37398375,
year = {2023},
author = {Celotto, M and Bím, J and Tlaie, A and De Feo, V and Lemke, S and Chicharro, D and Nili, H and Bieler, M and Hanganu-Opatz, IL and Donner, TH and Brovelli, A and Panzeri, S},
title = {An information-theoretic quantification of the content of communication between brain regions.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37398375},
issn = {2692-8205},
support = {R01 NS108410/NS/NINDS NIH HHS/United States ; R01 NS109961/NS/NINDS NIH HHS/United States ; U19 NS107464/NS/NINDS NIH HHS/United States ; },
abstract = {Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.},
}
@article {pmid37398143,
year = {2023},
author = {Zippi, EL and Shvartsman, GF and Vendrell-Llopis, N and Wallis, JD and Carmena, JM},
title = {Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37398143},
issn = {2692-8205},
support = {R01 MH117763/MH/NIMH NIH HHS/United States ; R01 NS106094/NS/NINDS NIH HHS/United States ; },
abstract = {Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC→M1 throughout trials across both control types and Cd→M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.},
}
@article {pmid37397858,
year = {2023},
author = {Rutkowski, TM and Abe, MS and Komendzinski, T and Sugimoto, H and Narebski, S and Otake-Matsuura, M},
title = {Machine learning approach for early onset dementia neurobiomarker using EEG network topology features.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1155194},
pmid = {37397858},
issn = {1662-5161},
abstract = {INTRODUCTION: Modern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called "AI for social good" domain contributes to improving the well-being of individuals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches with neuro-biomarker feedback may be helpful for older adults to remain independent and improve their wellbeing. We report research results on early-onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies.<br><br>METHODS: We present an empirical task in the EEG-based passive brain-computer interface application framework to assess working memory decline for forecasting a mild cognitive impairment. The EEG responses are analyzed in a framework of a network neuroscience technique applied to EEG time series for evaluation and to confirm the initial hypothesis of possible ML application modeling mild cognitive impairment prediction.<br><br>RESULTS: We report findings from a pilot study group in Poland for a cognitive decline prediction. We utilize two emotional working memory tasks by analyzing EEG responses to facial emotions reproduced in short videos. A reminiscent interior image oddball task is also employed to validate the proposed methodology further.<br><br>DISCUSSION: The proposed three experimental tasks in the current pilot study showcase the critical utilization of artificial intelligence for early-onset dementia prognosis in older adults.},
}
@article {pmid37397128,
year = {2023},
author = {Morozova, M and Bikbavova, A and Bulanov, V and Lebedev, MA},
title = {An olfactory-based Brain-Computer Interface: electroencephalography changes during odor perception and discrimination.},
journal = {Frontiers in behavioral neuroscience},
volume = {17},
number = {},
pages = {1122849},
pmid = {37397128},
issn = {1662-5153},
abstract = {Brain-Computer Interfaces (BCIs) are devices designed for establishing communication between the central nervous system and a computer. The communication can occur through different sensory modalities, and most commonly visual and auditory modalities are used. Here we propose that BCIs can be expanded by the incorporation of olfaction and discuss the potential applications of such olfactory BCIs. To substantiate this idea, we present results from two olfactory tasks: one that required attentive perception of odors without any overt report, and the second one where participants discriminated consecutively presented odors. In these experiments, EEG recordings were conducted in healthy participants while they performed the tasks guided by computer-generated verbal instructions. We emphasize the importance of relating EEG modulations to the breath cycle to improve the performance of an olfactory-based BCI. Furthermore, theta-activity could be used for olfactory-BCI decoding. In our experiments, we observed modulations of theta activity over the frontal EEG leads approximately 2 s after the inhalation of an odor. Overall, frontal theta rhythms and other types of EEG activity could be incorporated in the olfactory-based BCIs which utilize odors either as inputs or outputs. These BCIs could improve olfactory training required for conditions like anosmia and hyposmia, and mild cognitive impairment.},
}
@article {pmid37395863,
year = {2023},
author = {Yap, AU and Dewi, NL and Pragustine, Y and Marpaung, C},
title = {Oral behaviors in young adults: a multidimensional evaluation of the influence of personality, coping, and distress.},
journal = {Clinical oral investigations},
volume = {27},
number = {9},
pages = {5083-5093},
pmid = {37395863},
issn = {1436-3771},
mesh = {Humans ; Young Adult ; Adult ; *Stress, Psychological/psychology ; Adaptation, Psychological ; Personality ; Anxiety/psychology ; *Psychological Distress ; },
abstract = {OBJECTIVES: This study explored the relationship of oral parafunction to the psychological variables of personality, coping, and distress. Correlates of sleeping/waking-state oral activities with the different psychological factors were also examined, along with psychological predictors for high parafunction.<br><br>MATERIALS AND METHODS: Young adults from a large private university were enrolled. The frequency of oral behaviors was appraised with the oral behavior checklist (OBC), and participants were stratified into low and high parafunction (LP/HP) groups following the DC/TMD. Personality traits, coping styles, and psychological distress were assessed with the Big Five Personality Inventory-10 (BFI-10), brief-COPE Inventory (BCI), and Depression, Anxiety, Stress Scales-21 (DASS-21) correspondingly. Statistical evaluations were performed using the chi-square/Mann-Whitney U tests, Spearman's correlation, and logistic regression analyses (&#x3b1;&#x2009;=&#x2009;0.05).<br><br>RESULTS: Among the 507 participants (mean age 22.2&#x2009;&#xb1;&#x2009;1.5&#xa0;years), 84.6% and 15.4% had low and high parafunction respectively. While personality profiles did not vary substantially, the HP group exhibited significantly greater emotion-focused/dysfunctional coping, general distress, depression, anxiety, and stress scores than the LP group. Associations between OBC and the various psychological variables were weak when significant or insignificant. Neuroticism and dysfunctional coping were moderately correlated to general distress, depression, anxiety, and stress (rs&#x2009;=&#x2009;0.44-0.60/0.45-0.51). Multivariate analyses indicated that high parafunction was predicted by dysfunctional coping style (OR&#x2009;=&#x2009;2.55) and anxiety (OR&#x2009;=&#x2009;1.33).<br><br>CONCLUSIONS: Dysfunctional coping was the main risk factor for high parafunction, increasing its odds by about 2.5 times.<br><br>CLINICAL RELEVANCE: Oral parafunction appears to be a dysfunctional coping response to psychological distress.},
}
@article {pmid37394031,
year = {2023},
author = {Nie, A and Zhou, W and Xiao, Y},
title = {Sensitivity of late ERP old/new effects in source memory to self-referential encoding focus and stimulus emotionality.},
journal = {Neurobiology of learning and memory},
volume = {203},
number = {},
pages = {107795},
doi = {10.1016/j.nlm.2023.107795},
pmid = {37394031},
issn = {1095-9564},
mesh = {*Evoked Potentials ; Recognition, Psychology ; *Memory, Episodic ; Emotions ; Electroencephalography ; Mental Recall ; },
abstract = {In episodic memory, the old/new effect, the contrast of the waveforms elicited by the correctly recognized studied items and the correctly rejected novel items, has been broadly concerned. However, the contribution of self-referential encoding to the old/new effect in source memory (i.e., source-SRE), is far from clarification; further, it remains unclear whether the contribution is susceptible to the factor of stimulus emotionality. To address these issues, adopting the event-related potential (ERP) technique, this study applied words of three types of emotional valences (positive, neutral, vs. negative) in the self-focus vs. external-focus encoding tasks. In the course of the test, four ERP old/new effects were identified: (a) the familiarity- and recollection-reflected mid-frontal effect (FN400) and late positive component (LPC) were both independent of source-SRE and stimulus emotionality; (b) the reconstruction-driven late posterior negativity (LPN) exhibited an adverse pattern of source-SRE and was susceptible to the emotional valence by encoding focus; and (c) the right frontal old/new effect (RFE), reflecting post-retrieval process, exhibited a source-SRE in emotional words. These effects provide compelling evidence for the influences of both stimulus valence and encoding focus on SRE in source memory, especially during the late processes. Further directions considering more perspectives are put forward.},
}
@article {pmid37393355,
year = {2023},
author = {Liang, X and Yu, Y and Liu, Y and Liu, K and Liu, Y and Zhou, Z},
title = {EEG-based emergency braking intention detection during simulated driving.},
journal = {Biomedical engineering online},
volume = {22},
number = {1},
pages = {65},
pmid = {37393355},
issn = {1475-925X},
support = {U19A2083//joint Funds of National Natural Science Foundation of China/ ; U19A2083//joint Funds of National Natural Science Foundation of China/ ; U19A2083//joint Funds of National Natural Science Foundation of China/ ; U19A2083//joint Funds of National Natural Science Foundation of China/ ; U19A2083//joint Funds of National Natural Science Foundation of China/ ; U19A2083//joint Funds of National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Intention ; *Electroencephalography ; Algorithms ; Machine Learning ; ROC Curve ; },
abstract = {BACKGROUND: Current research related to electroencephalogram (EEG)-based driver's emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features.<br><br>METHODS: To this end, a novel EEG-based driver's emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input.<br><br>RESULTS: We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200&#xa0;ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking.<br><br>CONCLUSIONS: The study provides a user-centered framework for human-vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.},
}
@article {pmid37393010,
year = {2023},
author = {Zhou, L and Hu, H and Qin, B and Zhu, Q and Qian, Z},
title = {Brain activity differences between susceptible and non-susceptible populations under visually induced motion sickness based on sensor-space and source-space analyses.},
journal = {Brain research},
volume = {1815},
number = {},
pages = {148474},
doi = {10.1016/j.brainres.2023.148474},
pmid = {37393010},
issn = {1872-6240},
mesh = {Humans ; *Motion Sickness ; Electroencephalography ; Occipital Lobe ; },
abstract = {The neural mechanisms underlying visually induced motion sickness (VIMS) in different susceptible populations are unclear, as it is not clear how brain activity changes in different susceptible populations during the vection section (VS). This study aimed to analyze the brain activity changes in different susceptible populations during VS. Twenty subjects were included in this study and divided into the VIMS-susceptible group (VIMSSG) and VIMS-resistant group (VIMSRG) based on a motion sickness questionnaire. 64-channel electroencephalogram (EEG) data from these subjects during VS were collected. The brain activities during VS for VIMSSG and VIMSRG were analyzed with time-frequency based sensor-space analysis and EEG source imaging based source-space analysis. Under VS, delta and theta energies were significantly increased in VIMSSG and VIMSRG, while alpha and beta energies were only significantly increased in VIMSRG. Also, the superior and middle temporal were activated in VIMSSG and VIMSRG, while lateral occipital, supramarginal gyrus, and precentral gyrus were activated only in VIMSSG. The spatiotemporal differences in brain activity observed between VIMSSG and VIMSRG may be attributed to the different susceptibility of participants in each group and the different severity of MS symptoms experienced. Long-term vestibular training can effectively improve the ability of anti-VIMS. The knowledge gained from this study helps advance understanding of the neural mechanism of VIMS in different susceptible populations.},
}
@article {pmid37392815,
year = {2023},
author = {Sabé, M and Sulstarova, A and Chen, C and Hyde, J and Poulet, E and Aleman, A and Downar, J and Brandt, V and Mallet, L and Sentissi, O and Nitsche, MA and Bikson, M and Brunoni, AR and Cortese, S and Solmi, M},
title = {A century of research on neuromodulation interventions: A scientometric analysis of trends and knowledge maps.},
journal = {Neuroscience and biobehavioral reviews},
volume = {152},
number = {},
pages = {105300},
doi = {10.1016/j.neubiorev.2023.105300},
pmid = {37392815},
issn = {1873-7528},
mesh = {Child ; Humans ; *Deep Brain Stimulation/methods ; *Vagus Nerve Stimulation ; *Epilepsy/therapy ; },
abstract = {Interest in neurostimulation interventions has significantly grown in recent decades, yet a scientometric analysis objectively mapping scientific knowledge and recent trends remains unpublished. Using relevant keywords, we conducted a search in the Web of Science Core Collection on September 23, 2022, retrieving a total of 47,681 documents with 987,979 references. We identified two prominent research trends: 'noninvasive brain stimulation' and 'invasive brain stimulation.' These methods have interconnected over time, forming a cluster focused on evidence synthesis. Noteworthy emerging research trends encompassed 'transcutaneous auricular vagus nerve stimulation,' 'DBS/epilepsy in the pediatric population,' 'spinal cord stimulation,' and 'brain-machine interface.' While progress has been made for various neurostimulation interventions, their approval as adjuvant treatments remains limited, and optimal stimulation parameters lack consensus. Enhancing communication between experts of both neurostimulation types and encouraging novel translational research could foster further development. These findings offer valuable insights for funding agencies and research groups, guiding future directions in the field.},
}
@article {pmid37392806,
year = {2023},
author = {Takeda, Y and Gomi, T and Umebayashi, R and Tomita, S and Suzuki, K and Hiroe, N and Saikawa, J and Munaka, T and Yamashita, O},
title = {Sensor array design of optically pumped magnetometers for accurately estimating source currents.},
journal = {NeuroImage},
volume = {277},
number = {},
pages = {120257},
doi = {10.1016/j.neuroimage.2023.120257},
pmid = {37392806},
issn = {1095-9572},
mesh = {Humans ; *Magnetoencephalography/methods ; Computer Simulation ; *Brain ; },
abstract = {An optically pumped magnetometer (OPM) is a new generation of magnetoencephalography (MEG) devices that is small, light, and works at room temperature. Due to these characteristics, OPMs enable flexible and wearable MEG systems. On the other hand, if we have a limited number of OPM sensors, we need to carefully design their sensor arrays depending on our purposes and regions of interests (ROIs). In this study, we propose a method that designs OPM sensor arrays for accurately estimating the cortical currents at the ROIs. Based on the resolution matrix of minimum norm estimate (MNE), our method sequentially determines the position of each sensor to optimize its inverse filter pointing to the ROIs and suppressing the signal leakage from the other areas. We call this method the Sensor array Optimization based on Resolution Matrix (SORM). We conducted simple and realistic simulation tests to evaluate its characteristics and efficacy for real OPM-MEG data. SORM designed the sensor arrays so that their leadfield matrices had high effective ranks as well as high sensitivities to ROIs. Although SORM is based on MNE, the sensor arrays designed by SORM were effective not only when we estimated the cortical currents by MNE but also when we did so by other methods. With real OPM-MEG data we confirmed its validity for real data. These analyses suggest that SORM is especially useful when we want to accurately estimate ROIs' activities with a limited number of OPM sensors, such as brain-machine interfaces and diagnosing brain diseases.},
}
@article {pmid37391697,
year = {2023},
author = {Foldi, J and Tsagianni, A and Salganik, M and Schnabel, CA and Brufsky, A and van Londen, GJ and Pusztai, L and Sanft, T},
title = {Persistence to extended adjuvant endocrine therapy following Breast Cancer Index (BCI) testing in women with early-stage hormone receptor-positive (HR +) breast cancer.},
journal = {BMC cancer},
volume = {23},
number = {1},
pages = {606},
pmid = {37391697},
issn = {1471-2407},
support = {UL1 TR001863/TR/NCATS NIH HHS/United States ; },
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy ; *Brain-Computer Interfaces ; Adjuvants, Immunologic ; Combined Modality Therapy ; Recurrence ; },
abstract = {PURPOSE: Extending adjuvant endocrine therapy (ET) beyond the standard 5 years offers added protection against late breast cancer recurrences in women with early-stage hormone receptor-positive (HR +) breast cancer. Little is known about treatment persistence to extended ET (EET) and the role that genomic assays may play. In this study, we evaluated persistence to EET in women who had Breast Cancer Index (BCI) testing.<br><br>METHODS: Women with stage I-III HR&#x2009;+&#x2009;breast cancer who had BCI testing after at least 3.5&#xa0;years of adjuvant ET and&#x2009;&#x2265;&#x2009;7&#xa0;years of follow-up after diagnosis were included (n&#x2009;=&#x2009;240). Data on medication persistence was based on prescriptions in the electronic health record.<br><br>RESULTS: BCI predicted 146 (61%) patients to have low - BCI (H/I)-low - and 94 (39%) patients to have high likelihood of benefit from EET (BCI (H/I)-high). Continuation of ET after BCI occurred in 76 (81%) (H/I)-high and 39 (27%) (H/I)-low patients. Non-persistence rates were 19% in the (H/I)-high and 38% in the (H/I)-low group. The most common reason for non-persistence was intolerable side effects. Patients on EET underwent more DXA bone density scans than those who stopped ET at 5&#xa0;years (mean 2.09 versus 1.27; p&#x2009;<&#x2009;0.001). At a median follow-up of 10&#xa0;years from diagnosis, there were 6 metastatic recurrences.<br><br>CONCLUSIONS: In patients who continued ET after BCI testing, the rates of persistence to EET were high, particularly in patients with predicted high likelihood of benefit from EET. Use of EET is associated with increased use of DXA scans.},
}
@article {pmid37390795,
year = {2023},
author = {Chen, F and Chen, L and Xu, T and Ye, H and Liao, H and Zhang, D},
title = {Precise angle estimation of capsule robot in ultrasound using heatmap guided two-stage network.},
journal = {Computer methods and programs in biomedicine},
volume = {240},
number = {},
pages = {107605},
doi = {10.1016/j.cmpb.2023.107605},
pmid = {37390795},
issn = {1872-7565},
mesh = {Animals ; Swine ; Humans ; *Robotics/methods ; Ultrasonography ; },
abstract = {PURPOSE: A capsule robot can be controlled inside gastrointestinal (GI) tract by an external permanent magnet outside of human body for finishing non-invasive diagnosis and treatment. Locomotion control of capsule robot relies on the precise angle feedback that can be achieved by ultrasound imaging. However, ultrasound-based angle estimation of capsule robot is interfered by gastric wall tissue and the mixture of air, water, and digestive matter existing in the stomach.<br><br>METHODS: To tackle these issues, we introduce a heatmap guided two-stage network to detect the position and estimate the angle of the capsule robot in ultrasound images. Specifically, this network proposes the probability distribution module and skeleton extraction-based angle calculation to obtain accurate capsule robot position and angle estimation.<br><br>RESULTS: Extensive experiments were finished on the ultrasound image dataset of capsule robot within porcine stomach. Empirical results showed that our method obtained small position center error of 0.48&#x202f;mm and high angle estimation accuracy of 96.32%.<br><br>CONCLUSION: Our method can provide precise angle feedback for locomotion control of capsule robot.},
}
@article {pmid37390271,
year = {2023},
author = {Shou, YZ and Wang, XH and Yang, GF},
title = {Verum versus Sham brain-computer interface on upper limb function recovery after stroke: A systematic review and meta-analysis of randomized controlled trials.},
journal = {Medicine},
volume = {102},
number = {26},
pages = {e34148},
pmid = {37390271},
issn = {1536-5964},
mesh = {Humans ; *Brain-Computer Interfaces ; Recovery of Function ; Randomized Controlled Trials as Topic ; *Stroke ; Upper Extremity ; },
abstract = {BACKGROUND: Previous clinical trials have reported that the brain-computer interface (BCI) is a useful management tool for upper limb function recovery (ULFR) in stroke. However, there is insufficient evidence regarding this topic. Thus, this study aimed to investigate the effectiveness of verum versus sham BCI on the ULFR in stroke patients.<br><br>METHODS: We comprehensively searched the Cochrane Library, PUBMED, EMBASE, Web of Science, and China National Knowledge Infrastructure databases from their inception to January 1, 2023. Randomized clinical trials (RCTs) assessing the effectiveness and safety of BCI for ULFR after stroke were included. The outcomes were the Fugl-Meyer Assessment for Upper Extremity, Wolf Motor Function Test, Modified Barthel Index, motor activity log, and Action Research Arm Test. The methodological quality of all the included randomized controlled trials was evaluated using the Cochrane risk-of-bias tool. Statistical analysis was performed using RevMan 5.4 software.<br><br>RESULTS: Eleven eligible studies involving 334 patients were included. The results of the meta-analysis showed significant differences in the Fugl-Meyer Assessment for Upper Extremity (mean difference [MD] = 4.78, 95% confidence interval [CI] [1.90, 7.65], I2 = 0%, P = .001) and Modified Barthel Index (MD = 7.37, 95% CI [1.89, 12.84], I2 = 19%, P = .008). However, no significant differences were found on motor activity log (MD = -0.70, 95% CI [-3.17, 1.77]), Action Research Arm Test (MD = 3.05, 95% CI [-8.33, 14.44], I2 = 0%, P = .60), and Wolf Motor Function Test (MD = 4.23, 95% CI [-0.55, 9.01], P = .08).<br><br>CONCLUSION: BCI may be an effective management strategy for ULFR in stroke patients. Future studies with larger sample size and strict design are still needed to warrant the current findings.},
}
@article {pmid37390128,
year = {2023},
author = {Boccardo, A and Ferraro, S and Sala, G and Ferrulli, V and Pravettoni, D and Buczinski, S},
title = {Bayesian evaluation of the accuracy of a thoracic auscultation scoring system in dairy calves with bronchopneumonia using a standard lung sound nomenclature.},
journal = {Journal of veterinary internal medicine},
volume = {37},
number = {4},
pages = {1603-1613},
pmid = {37390128},
issn = {1939-1676},
mesh = {Animals ; Cattle ; *Bronchopneumonia/diagnosis/veterinary/pathology ; Respiratory Sounds/veterinary ; Bayes Theorem ; Lung/pathology ; Auscultation/veterinary ; *Cattle Diseases/diagnosis/pathology ; },
abstract = {BACKGROUND: Although thoracic auscultation (AUSC) in calves is quick and easy to perform, the definition of lung sounds is highly variable and leads to poor to moderate accuracy in diagnosing bronchopneumonia (BP).<br><br>HYPOTHESIS/OBJECTIVES: Evaluate the diagnostic accuracy of an AUSC scoring system based on a standard lung sound nomenclature at different cut-off values, accounting for the absence of a gold standard test for BP diagnosis.<br><br>ANIMALS: Three hundred thirty-one calves.<br><br>METHODS: We considered the following pathological lung sounds: increased breath sounds (score 1), wheezes and crackles (score 2), increased bronchial sounds (score 3), and pleural friction rubs (score 4). Thoracic auscultation was categorized as AUSC1 (positive calves for scores &#x2265;1), AUSC2 (positive calves for scores &#x2265;2), and AUSC3 (positive calves for scores &#x2265;3). The accuracy of AUSC categorizations was determined using 3 imperfect diagnostic tests with a Bayesian latent class model and sensitivity analysis (informative vs weakly informative vs noninformative priors and with vs without covariance between ultrasound and clinical scoring).<br><br>RESULTS: Based on the priors used, the sensitivity (95% Bayesian confidence interval [BCI]) of AUSC1 ranged from 0.89 (0.80-0.97) to 0.95 (0.86-0.99), with a specificity (95% BCI) of 0.54 (0.45-0.71) to 0.60 (0.47-0.94). Removing increased breath sounds from the categorizations resulted in increased specificity (ranging between 0.97 [0.93-0.99] and 0.98 [0.94-0.99] for AUSC3) at the cost of decreased sensitivity (0.66 [0.54-0.78] to 0.81 [0.65-0.97]).<br><br>A standardized definition of lung sounds improved AUSC accuracy for BP diagnosis in calves.},
}
@article {pmid37389361,
year = {2023},
author = {Hu, L and Hong, W and Liu, L},
title = {MSATNet: multi-scale adaptive transformer network for motor imagery classification.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1173778},
pmid = {37389361},
issn = {1662-4548},
abstract = {Motor imagery brain-computer interface (MI-BCI) can parse user motor imagery to achieve wheelchair control or motion control for smart prostheses. However, problems of poor feature extraction and low cross-subject performance exist in the model for motor imagery classification tasks. To address these problems, we propose a multi-scale adaptive transformer network (MSATNet) for motor imagery classification. Therein, we design a multi-scale feature extraction (MSFE) module to extract multi-band highly-discriminative features. Through the adaptive temporal transformer (ATT) module, the temporal decoder and multi-head attention unit are used to adaptively extract temporal dependencies. Efficient transfer learning is achieved by fine-tuning target subject data through the subject adapter (SA) module. Within-subject and cross-subject experiments are performed to evaluate the classification performance of the model on the BCI Competition IV 2a and 2b datasets. The MSATNet outperforms benchmark models in classification performance, reaching 81.75 and 89.34% accuracies for the within-subject experiments and 81.33 and 86.23% accuracies for the cross-subject experiments. The experimental results demonstrate that the proposed method can help build a more accurate MI-BCI system.},
}
@article {pmid37388414,
year = {2023},
author = {Lin, J and Lai, D and Wan, Z and Feng, L and Zhu, J and Zhang, J and Wang, Y and Xu, K},
title = {Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1168017},
pmid = {37388414},
issn = {1662-5161},
abstract = {INTRODUCTION: In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs).<br><br>METHODS: The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands.<br><br>RESULTS: The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals.<br><br>DISCUSSION: The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology.<br><br>CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.},
}
@article {pmid37386026,
year = {2023},
author = {Zeng, Y and Ma, G and Cai, F and Wang, P and Liang, H and Zhang, R and Deng, J and Liu, Y},
title = {SMYD3 drives the proliferation in gastric cancer cells via reducing EMP1 expression in an H4K20me3-dependent manner.},
journal = {Cell death &amp; disease},
volume = {14},
number = {6},
pages = {386},
pmid = {37386026},
issn = {2041-4889},
mesh = {Humans ; *Stomach Neoplasms/genetics ; Proto-Oncogene Proteins c-akt ; Cell Proliferation/genetics ; Histone-Lysine N-Methyltransferase/genetics ; },
abstract = {Protein lysine methyltransferase SET and MYND domain-containing 3 (SMYD3) is aberrantly expressed in various cancer settings. The mechanisms that SMYD3 activates the expression of critical pro-tumoral genes in an H3K4me3-dependent manner have been well described in previous reports. Besides H3K4me3, H4K20me3 is another catalytic product of SMYD3, however it is a transcriptionally repressive hallmark. Since it is not clear that how SMYD3-elicited transcriptionally repressive program functions in cancer, we used gastric cancer (GC) as a model to investigate the roles of SMYD3-H4K20me3. Herein, online bioinformatics tools, quantitative PCR, western blotting and immunohistochemistry assays demonstrated that SMYD3 expression was markedly increased in GC tissues from our institutional and The Cancer Genome Atlas (TCGA) cohort. Additionally, aberrantly increased SMYD3 expression was closely associated with aggressive clinical characteristics and poor prognosis. Depletion of endogenous SMYD3 expression using shRNAs significantly attenuates the proliferation in GC cells and Akt signaling pathway in vitro and in vivo. Mechanistically, chromatin immunoprecipitation (ChIP) assay showed that SMYD3 epigenetically repressed the expression of epithelial membrane protein 1 (EMP1) in an H4K20me3-dependent manner. Gain-of-function and rescue experiments validated that EMP1 inhibited the propagation of GC cells and reduced p-Akt (S473) level. Based on these data, pharmaceutical inhibition of SMYD3 activity using the small inhibitor BCI-121 deactivated Akt signaling pathway in GC cells and further impaired the cellular viability in vitro and in vivo. Together, these results demonstrate that SMYD3 promotes the proliferation in GC cells and may be a valid target for therapeutic intervention of patients with GC.},
}
@article {pmid37384193,
year = {2023},
author = {Lajtos, M and Barradas-Chacón, LA and Wriessnegger, SC},
title = {Effects of handedness on brain oscillatory activity during imagery and execution of upper limb movements.},
journal = {Frontiers in psychology},
volume = {14},
number = {},
pages = {1161613},
pmid = {37384193},
issn = {1664-1078},
abstract = {Brain activation during left- and right-hand motor imagery is a popular feature used for brain-computer interfaces. However, most studies so far have only considered right-handed participants in their experiments. This study aimed to investigate how handedness influences brain activation during the processes of imagining and executing simple hand movements. EEG signals were recorded using 32 channels while participants repeatedly squeezed or imagined squeezing a ball using their left, right, or both hands. The data of 14 left-handed and 14 right-handed persons were analyzed with a focus on event-related desynchronization/synchronization patterns (ERD/S). Both handedness groups showed activation over sensorimotor areas; however, the right-handed group tended to display more bilateral patterns than the left-handed group, which is in contrast to earlier research results. Furthermore, a stronger activation during motor imagery than during motor execution could be found in both groups.},
}
@article {pmid37383105,
year = {2023},
author = {Smith, TJ and Wu, Y and Cheon, C and Khan, AA and Srinivasan, H and Capadona, JR and Cogan, SF and Pancrazio, JJ and Engineer, CT and Hernandez-Reynoso, AG},
title = {Behavioral paradigm for the evaluation of stimulation-evoked somatosensory perception thresholds in rats.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1202258},
pmid = {37383105},
issn = {1662-4548},
abstract = {Intracortical microstimulation (ICMS) of the somatosensory cortex via penetrating microelectrode arrays (MEAs) can evoke cutaneous and proprioceptive sensations for restoration of perception in individuals with spinal cord injuries. However, ICMS current amplitudes needed to evoke these sensory percepts tend to change over time following implantation. Animal models have been used to investigate the mechanisms by which these changes occur and aid in the development of new engineering strategies to mitigate such changes. Non-human primates are commonly the animal of choice for investigating ICMS, but ethical concerns exist regarding their use. Rodents are a preferred animal model due to their availability, affordability, and ease of handling, but there are limited choices of behavioral tasks for investigating ICMS. In this study, we investigated the application of an innovative behavioral go/no-go paradigm capable of estimating ICMS-evoked sensory perception thresholds in freely moving rats. We divided animals into two groups, one receiving ICMS and a control group receiving auditory tones. Then, we trained the animals to nose-poke - a well-established behavioral task for rats - following either a suprathreshold ICMS current-controlled pulse train or frequency-controlled auditory tone. Animals received a sugar pellet reward when nose-poking correctly. When nose-poking incorrectly, animals received a mild air puff. After animals became proficient in this task, as defined by accuracy, precision, and other performance metrics, they continued to the next phase for perception threshold detection, where we varied the ICMS amplitude using a modified staircase method. Finally, we used non-linear regression to estimate perception thresholds. Results indicated that our behavioral protocol could estimate ICMS perception thresholds based on ~95% accuracy of rat nose-poke responses to the conditioned stimulus. This behavioral paradigm provides a robust methodology for evaluating stimulation-evoked somatosensory percepts in rats comparable to the evaluation of auditory percepts. In future studies, this validated methodology can be used to study the performance of novel MEA device technologies on ICMS-evoked perception threshold stability using freely moving rats or to investigate information processing principles in neural circuits related to sensory perception discrimination.},
}
@article {pmid37383098,
year = {2023},
author = {Lakshminarayanan, K and Shah, R and Daulat, SR and Moodley, V and Yao, Y and Madathil, D},
title = {The effect of combining action observation in virtual reality with kinesthetic motor imagery on cortical activity.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1201865},
pmid = {37383098},
issn = {1662-4548},
abstract = {INTRODUCTION: In the past, various techniques have been used to improve motor imagery (MI), such as immersive virtual-reality (VR) and kinesthetic rehearsal. While electroencephalography (EEG) has been used to study the differences in brain activity between VR-based action observation and kinesthetic motor imagery (KMI), there has been no investigation into their combined effect. Prior research has demonstrated that VR-based action observation can enhance MI by providing both visual information and embodiment, which is the perception of oneself as part of the observed entity. Additionally, KMI has been found to produce similar brain activity to physically performing a task. Therefore, we hypothesized that utilizing VR to offer an immersive visual scenario for action observation while participants performed kinesthetic motor imagery would significantly improve cortical activity related to MI.<br><br>METHODS: In this study, 15 participants (9 male, 6 female) performed kinesthetic motor imagery of three hand tasks (drinking, wrist flexion-extension, and grabbing) both with and without VR-based action observation.<br><br>RESULTS: Our results indicate that combining VR-based action observation with KMI enhances brain rhythmic patterns and provides better task differentiation compared to KMI without action observation.<br><br>DISCUSSION: These findings suggest that using VR-based action observation alongside kinesthetic motor imagery can improve motor imagery performance.},
}
@article {pmid37380398,
year = {2023},
author = {Chen, Q and Yuan, T and Zhang, L and Gong, J and Fu, L and Han, X and Ruan, M and Yu, Z},
title = {[The research status and development trends of brain-computer interfaces in medicine].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {3},
pages = {566-572},
pmid = {37380398},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; *Medicine ; Algorithms ; Artificial Intelligence ; Brain ; },
abstract = {Brain-computer interfaces (BCIs) have become one of the cutting-edge technologies in the world, and have been mainly applicated in medicine. In this article, we sorted out the development history and important scenarios of BCIs in medical application, analyzed the research progress, technology development, clinical transformation and product market through qualitative and quantitative analysis, and looked forward to the future trends. The results showed that the research hotspots included the processing and interpretation of electroencephalogram (EEG) signals, the development and application of machine learning algorithms, and the detection and treatment of neurological diseases. The technological key points included hardware development such as new electrodes, software development such as algorithms for EEG signal processing, and various medical applications such as rehabilitation and training in stroke patients. Currently, several invasive and non-invasive BCIs are in research. The R&amp;D level of BCIs in China and the United State is leading the world, and have approved a number of non-invasive BCIs. In the future, BCIs will be applied to a wider range of medical fields. Related products will develop shift from a single mode to a combined mode. EEG signal acquisition devices will be miniaturized and wireless. The information flow and interaction between brain and machine will give birth to brain-machine fusion intelligence. Last but not least, the safety and ethical issues of BCIs will be taken seriously, and the relevant regulations and standards will be further improved.},
}
@article {pmid37380379,
year = {2023},
author = {Li, H and Liu, H and Chen, H and Zhang, R},
title = {[Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {3},
pages = {418-425},
pmid = {37380379},
issn = {1001-5515},
mesh = {Humans ; Time Factors ; *Brain ; *Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.},
}
@article {pmid37380378,
year = {2023},
author = {Zhao, W and Xu, L and Xiao, X and Yi, W and Chen, Y and Wang, K and Xu, M and Ming, D},
title = {[Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {3},
pages = {409-417},
pmid = {37380378},
issn = {1001-5515},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Healthy Volunteers ; Signal-To-Noise Ratio ; },
abstract = {High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.},
}
@article {pmid37380213,
year = {2023},
author = {Estupiñán-Romero, F and Pinilla Dominguez, J and Bernal-Delgado, E and , },
title = {Differences in acute ischaemic stroke in-hospital mortality across referral stroke hospitals in Spain: a retrospective, longitudinal observational study.},
journal = {BMJ open},
volume = {13},
number = {6},
pages = {e068183},
pmid = {37380213},
issn = {2044-6055},
mesh = {Humans ; *Stroke/therapy ; Retrospective Studies ; Hospital Mortality ; Spain/epidemiology ; Bayes Theorem ; *Brain Ischemia/therapy ; *Ischemic Stroke ; Hospitals ; Referral and Consultation ; },
abstract = {OBJECTIVE: To assess differences in acute ischaemic stroke (AIS) in-hospital mortality between referral stroke hospitals and provide evidence on the association of those differences with the overtime adoption of effective reperfusion therapies.<br><br>DESIGN: Retrospective, longitudinal observational study using administrative data for virtually all hospital admissions from 2003 to 2015.<br><br>SETTING: Thirty-seven referral stroke hospitals in the Spanish National Health System.<br><br>PARTICIPANTS: Patients aged 18 years and older with a hospital episode with an admission diagnosis of AIS in any referral stroke hospital (196 099 admissions). MAIN ENDPOINTS: (1) Hospital variation in 30-day in-hospital mortality measured in terms of the intraclass correlation coefficient (ICC); and (2) the difference in mortality between the hospital of treatment and the trend of utilisation of reperfusion therapies (including intravenous fibrinolysis and endovascular mechanical thrombectomy) in terms of median OR (MOR).<br><br>RESULTS: Adjusted 30-day AIS in-hospital mortality decreased over the study period. Adjusted in-hospital mortality after AIS rates varied from 6.66% to 16.01% between hospitals. Beyond differences in patient characteristics, the relative contribution of the hospital of treatment was higher in the case of patients undergoing reperfusion therapies (ICC=0.031 (95% Bayesian credible interval (BCI)=0.017 to 0.057)) than in the case of those who did not (ICC=0.016 (95% BCI=0.010 to 0.026)). Using the MOR, the difference in risk of death was as high as 46% between the hospital with the highest risk and the hospital with the lowest risk of patients undergoing reperfusion therapy (MOR 1.46 (95% BCI 1.32 to 1.68)); in patients not undergoing any reperfusion therapy, the risk was 31% higher (MOR 1.31 (95% BCI 1.24 to 1.41)).<br><br>CONCLUSIONS: In the referral stroke hospitals of the Spanish National Health System, the overall adjusted in-hospital mortality decreased between 2003 and 2015. However, between-hospital variations in mortality persisted.},
}
@article {pmid37379192,
year = {2024},
author = {An, S and Kim, S and Chikontwe, P and Park, SH},
title = {Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {11},
pages = {15479-15493},
doi = {10.1109/TNNLS.2023.3287181},
pmid = {37379192},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods/classification ; *Imagination/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Deep Learning ; Attention/physiology ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Algorithms ; },
abstract = {Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challenging due to intersubject variability, scarcity of labeled unseen subject data, and low signal-to-noise ratio (SNR). In this context, we propose a novel two-way few-shot network able to efficiently learn how to learn representative features of unseen subject categories and classify them with limited MI EEG data. The pipeline includes an embedding module that learns feature representations from a set of signals, a temporal-attention module to emphasize important temporal features, an aggregation-attention module for key support signal discovery, and a relation module for final classification based on relation scores between a support set and a query signal. In addition to the unified learning of feature similarity and a few-shot classifier, our method can emphasize informative features in support data relevant to the query, which generalizes better on unseen subjects. Furthermore, we propose to fine-tune the model before testing by arbitrarily sampling a query signal from the provided support set to adapt to the distribution of the unseen subject. We evaluate our proposed method with three different embedding modules on cross-subject and cross-dataset classification tasks using brain-computer interface (BCI) competition IV 2a, 2b, and GIST datasets. Extensive experiments show that our model significantly improves over the baselines and outperforms existing few-shot approaches.},
}
@article {pmid37371365,
year = {2023},
author = {Fernández-Rodríguez, &#xc1; and Ron-Angevin, R and Velasco-Álvarez, F and Diaz-Pineda, J and Letouzé, T and André, JM},
title = {Evaluation of Single-Trial Classification to Control a Visual ERP-BCI under a Situation Awareness Scenario.},
journal = {Brain sciences},
volume = {13},
number = {6},
pages = {},
pmid = {37371365},
issn = {2076-3425},
support = {PID2021-127261OB-I00 (SICODIS)//MCIN (Ministerio de Ciencia e Innovaci&#xf3;n) /AEI (Agencia Estatal de Investigaci&#xf3;n) /10.13039/501100011033/ FEDER, UE (Fondo Europeo de Desarrollo Regional)/ ; },
abstract = {An event-related potential (ERP)-based brain-computer interface (BCI) can be used to monitor a user's cognitive state during a surveillance task in a situational awareness context. The present study explores the use of an ERP-BCI for detecting new planes in an air traffic controller (ATC). Two experiments were conducted to evaluate the impact of different visual factors on target detection. Experiment 1 validated the type of stimulus used and the effect of not knowing its appearance location in an ERP-BCI scenario. Experiment 2 evaluated the effect of the size of the target stimulus appearance area and the stimulus salience in an ATC scenario. The main results demonstrate that the size of the plane appearance area had a negative impact on the detection performance and on the amplitude of the P300 component. Future studies should address this issue to improve the performance of an ATC in stimulus detection using an ERP-BCI.},
}
@article {pmid37370616,
year = {2023},
author = {Yoo, SH and Huang, G and Hong, KS},
title = {Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {6},
pages = {},
pmid = {37370616},
issn = {2306-5354},
support = {RS-2023-00207954//National Research Foundation of Korea/ ; },
abstract = {Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary.},
}
@article {pmid37370595,
year = {2023},
author = {Shi, Y and Li, Y and Koike, Y},
title = {Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {6},
pages = {},
pmid = {37370595},
issn = {2306-5354},
abstract = {Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75-96.9% of channels) with a 1.65-5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2-15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain-computer interface (BCI).},
}
@article {pmid37370580,
year = {2023},
author = {Abdulghani, MM and Walters, WL and Abed, KH},
title = {Imagined Speech Classification Using EEG and Deep Learning.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {6},
pages = {},
pmid = {37370580},
issn = {2306-5354},
abstract = {In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The long-short term memory recurrent neural network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: up, down, left, and right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration was implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based brain-computer interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50%, and 92.62% for precision, recall, and F1-score, respectively.},
}
@article {pmid37368194,
year = {2023},
author = {Zhu, L and Zheng, D and Li, R and Shen, CJ and Cai, R and Lyu, C and Tang, B and Sun, H and Wang, X and Ding, Y and Xu, B and Jia, G and Li, X and Gao, L and Li, XM},
title = {Induction of Anxiety-Like Phenotypes by Knockdown of Cannabinoid Type-1 Receptors in the Amygdala of Marmosets.},
journal = {Neuroscience bulletin},
volume = {39},
number = {11},
pages = {1669-1682},
pmid = {37368194},
issn = {1995-8218},
mesh = {Animals ; *Callithrix ; Receptors, Cannabinoid ; Anxiety ; Amygdala ; *Cannabinoids ; Phenotype ; },
abstract = {The amygdala is an important hub for regulating emotions and is involved in the pathophysiology of many mental diseases, such as depression and anxiety. Meanwhile, the endocannabinoid system plays a crucial role in regulating emotions and mainly functions through the cannabinoid type-1 receptor (CB1R), which is strongly expressed in the amygdala of non-human primates (NHPs). However, it remains largely unknown how the CB1Rs in the amygdala of NHPs regulate mental diseases. Here, we investigated the role of CB1R by knocking down the cannabinoid receptor 1 (CNR1) gene encoding CB1R in the amygdala of adult marmosets through regional delivery of AAV-SaCas9-gRNA. We found that CB1R knockdown in the amygdala induced anxiety-like behaviors, including disrupted night sleep, agitated psychomotor activity in new environments, and reduced social desire. Moreover, marmosets with CB1R-knockdown had up-regulated plasma cortisol levels. These results indicate that the knockdown of CB1Rs in the amygdala induces anxiety-like behaviors in marmosets, and this may be the mechanism underlying the regulation of anxiety by CB1Rs in the amygdala of NHPs.},
}
@article {pmid37368040,
year = {2023},
author = {Ledesma-Ramírez, CI and Hernández-Gloria, JJ and Bojorges-Valdez, E and Yanez-Suarez, O and Piña-Ramírez, O},
title = {Recurrence quantification analysis during a mental calculation task.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {33},
number = {6},
pages = {},
doi = {10.1063/5.0147321},
pmid = {37368040},
issn = {1089-7682},
mesh = {*Nonlinear Dynamics ; *Electroencephalography/methods ; Brain ; Rest ; },
abstract = {The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.},
}
@article {pmid37365814,
year = {2023},
author = {Vigué-Guix, I and Soto-Faraco, S},
title = {Using occipital ⍺-bursts to modulate behavior in real-time.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {16},
pages = {9465-9477},
doi = {10.1093/cercor/bhad217},
pmid = {37365814},
issn = {1460-2199},
mesh = {*Brain/physiology ; Electroencephalography ; Photic Stimulation ; *Visual Perception/physiology ; Humans ; },
abstract = {Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.},
}
@article {pmid37365367,
year = {2023},
author = {Qin, C and Su, J and Xiao, Y and Qiang, Y and Xiong, S},
title = {Assessing the Beautiful China Initiative from an environmental perspective: indicators, goals, and provincial performance.},
journal = {Environmental science and pollution research international},
volume = {30},
number = {35},
pages = {84412-84424},
pmid = {37365367},
issn = {1614-7499},
support = {2019YFC0507803//National Key Research and Development Project of China/ ; },
mesh = {*Goals ; *Sustainable Development ; Cities ; Government ; China ; Economic Development ; },
abstract = {The Beautiful China Initiative (BCI) is part of China's national strategy for implementing the long-term goals of building an ecological civilization and promoting sustainable development. However, currently, there is no goal-oriented, comparable, and standardized indicator framework for monitoring the performance of the BCI. Here, we established the BCI from an environmental perspective (BCIE) index comprising 40 indicators and targets in eight fields and used a systematic approach to measure the distance and progress towards the goal of building a "Beautiful China" by 2035 at the national and subnational levels. Our analyses indicate that the BCIE index score (range: [0, 1]) was 0.757 at the national level and 0.628-0.869 at the provincial level in 2020. Between 2015 and 2020, the BCIE index scores of all provinces improved; however, large spatio-temporal variations were evident. Provinces with better BCIE performances exhibited relatively balanced scores across different sectors and cities. Our study revealed that the BCIE index scores at the city level surpassed provincial administrative boundaries, resulting in a wider range of aggregation. By focusing on the strategic arrangement of BCI, this study provides an effective index system and evaluation method for dynamic monitoring and phased evaluations at all levels of government in China.},
}
@article {pmid37362726,
year = {2023},
author = {Yadav, H and Maini, S},
title = {Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities.},
journal = {Multimedia tools and applications},
volume = {},
number = {},
pages = {1-45},
pmid = {37362726},
issn = {1380-7501},
abstract = {Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.},
}
@article {pmid37360173,
year = {2023},
author = {Ramu, V and Lakshminarayanan, K},
title = {Enhanced motor imagery of digits within the same hand via vibrotactile stimulation.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1152563},
pmid = {37360173},
issn = {1662-4548},
abstract = {PURPOSE: The aim of the present study is to evaluate the effect of vibrotactile stimulation prior to repeated complex motor imagery of finger movements using the non-dominant hand on motor imagery (MI) performance.<br><br>METHODS: Ten healthy right-handed adults (4 females and 6 males) participated in the study. The subjects performed motor imagery tasks with and without a brief vibrotactile sensory stimulation prior to performing motor imagery using either their left-hand index, middle, or thumb digits. Mu- and beta-band event-related desynchronization (ERD) at the sensorimotor cortex and an artificial neural network-based digit classification was evaluated.<br><br>RESULTS: The ERD and digit discrimination results from our study showed that ERD was significantly different between the vibration conditions for the index, middle, and thumb. It was also found that digit classification accuracy with-vibration (mean&#x2009;&#xb1;&#x2009;SD&#x2009;=&#x2009;66.31&#x2009;&#xb1;&#x2009;3.79%) was significantly higher than without-vibration (mean&#x2009;&#xb1;&#x2009;SD&#x2009;=&#x2009;62.68&#x2009;&#xb1;&#x2009;6.58%).<br><br>CONCLUSION: The results showed that a brief vibration was more effective at improving MI-based brain-computer interface classification of digits within a single limb through increased ERD compared to performing MI without vibrotactile stimulation.},
}
@article {pmid37359245,
year = {2023},
author = {Meng, L and Lin, X and Du, J and Zhang, X and Lu, X},
title = {Autonomy support and prosocial impact facilitate meaningful work: A daily diary study.},
journal = {Motivation and emotion},
volume = {},
number = {},
pages = {1-16},
pmid = {37359245},
issn = {0146-7239},
abstract = {This study pays attention to within-person fluctuations in meaningful work and its antecedents and consequences. Considering self- and other-oriented dimensions as crucial pathways to meaningful work, effects of daily perceived autonomy support and prosocial impact on one's meaningful work were examined. A daily diary study was conducted in which 86 nurses from varied hospitals reported their work experiences for 10 consecutive workdays (860 occasions). Results of multilevel modeling showed that both day-level perceived autonomy support and prosocial impact were positively related to day-level meaningful work, which served as the mediator between them and work engagement. Prosocial orientation strengthened the positive relationship between day-level perceived prosocial impact and day-level meaningful work. However, autonomy orientation negatively moderated the effect of day-level perceived autonomy support on day-level meaningful work, suggesting the necessity to distinguish between assisted and asserted autonomy orientation. Our findings illustrate the transient and dynamic nature of meaningful work and provide empirical evidences linking suggested managerial practices to employees' meaningful work.},
}
@article {pmid37358955,
year = {2023},
author = {León, R and Gutiérrez, DA and Pinto, C and Morales, C and de la Fuente, C and Riquelme, C and Cortés, BI and González-Martin, A and Chamorro, D and Espinosa, N and Fuentealba, P and Cancino, GI and Zanlungo, S and Dulcey, AE and Marugan, JJ and Álvarez Rojas, A},
title = {c-Abl tyrosine kinase down-regulation as target for memory improvement in Alzheimer's disease.},
journal = {Frontiers in aging neuroscience},
volume = {15},
number = {},
pages = {1180987},
pmid = {37358955},
issn = {1663-4365},
abstract = {BACKGROUND: Growing evidence suggests that the non-receptor tyrosine kinase, c-Abl, plays a significant role in the pathogenesis of Alzheimer's disease (AD). Here, we analyzed the effect of c-Abl on the cognitive performance decline of APPSwe/PSEN1ΔE9 (APP/PS1) mouse model for AD.<br><br>METHODS: We used the conditional genetic ablation of c-Abl in the brain (c-Abl-KO) and pharmacological treatment with neurotinib, a novel allosteric c-Abl inhibitor with high brain penetrance, imbued in rodent's chow.<br><br>RESULTS: We found that APP/PS1/c-Abl-KO mice and APP/PS1 neurotinib-fed mice had improved performance in hippocampus-dependent tasks. In the object location and Barnes-maze tests, they recognized the displaced object and learned the location of the escape hole faster than APP/PS1 mice. Also, APP/PS1 neurotinib-fed mice required fewer trials to reach the learning criterion in the memory flexibility test. Accordingly, c-Abl absence and inhibition caused fewer amyloid plaques, reduced astrogliosis, and preserved neurons in the hippocampus.<br><br>DISCUSSION: Our results further validate c-Abl as a target for AD, and the neurotinib, a novel c-Abl inhibitor, as a suitable preclinical candidate for AD therapies.},
}
@article {pmid37358299,
year = {2023},
author = {Sun, H and Li, R and Lin, Y and Cao, X and Fan, L and Sun, G and Xie, M and Zhu, L and Yu, C and Cai, R and Lyu, C and Wang, X and Zhang, Y and Bai, S and Qi, R and Tang, B and Jia, G and Li, X and Gao, L},
title = {Hand-Rearing Method for Infant Marmosets.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {196},
pages = {},
doi = {10.3791/65296},
pmid = {37358299},
issn = {1940-087X},
mesh = {Animals ; Female ; *Callithrix ; *Food ; },
abstract = {The common marmoset (Callithrix jacchus) is a small and highly social New World monkey with high reproduction rates, which has been proven to be a compelling non-human primate model for biomedical and neuroscience research. Some females give birth to triplets; however, the parents cannot raise all of them. To save these infants, we have developed a hand-rearing method for raising newborn marmosets. In this protocol, we describe the formula of the food, the time for feeding, the configuration of the temperature and humidity, as well as the adaptation of the hand-reared infants to the colony environment. This hand-rearing method significantly increases the survival rate of marmoset infants (without hand-rearing: 45%; with hand-rearing: 86%) and provides the opportunity to study the development of marmoset infants with similar genetic backgrounds raised in different postnatal environments. As the method is practical and easy to use, we anticipate that it could also be applied to other labs working with common marmosets.},
}
@article {pmid37356599,
year = {2023},
author = {Lou, F and Tang, X and Quan, Z and Qian, M and Wang, J and Qu, S and Gao, Y and Wang, Y and Pan, G and Lai, HY and Roe, AW and Zhang, X},
title = {A 16-channel loop array for in vivo macaque whole-brain imaging at 7 T.},
journal = {Magnetic resonance imaging},
volume = {102},
number = {},
pages = {179-183},
doi = {10.1016/j.mri.2023.06.014},
pmid = {37356599},
issn = {1873-5894},
mesh = {Animals ; *Macaca ; *Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Head ; Signal-To-Noise Ratio ; Neuroimaging/methods ; Phantoms, Imaging ; Equipment Design ; },
abstract = {Combining multimodal approaches with functional magnetic resonance imaging (fMRI) has catapulted the research on brain circuitries of non-human primates (NHPs) into a new era. However, many studies are constrained by a lack of appropriate RF coils. In this study, a single loop transmit and 16-channel receive array coil was constructed for brain imaging of macaques at 7 Tesla (7 T). The 16 receive channels were mounted on a 3D-printed helmet-shaped form closely fitting the macaque head, with fourteen openings arranged for multimodal devices around the cortical regions. Coil performance was evaluated by quantifying and comparing signal-to-noise ratio (SNR) maps, noise correlations, g-factor maps and flip-angle maps with a 28-channel commercial knee coil. The in vivo results suggested that the macaque coil has higher SNR in cortical regions and better acceleration ability in parallel imaging, which may benefit revealing mesoscale organizations in the brain.},
}
@article {pmid37351363,
year = {2023},
author = {Almajidy, RK and Mottaghi, S and Ajwad, AA and Boudria, Y and Mankodiya, K and Besio, W and Hofmann, UG},
title = {A case for hybrid BCIs: combining optical and electrical modalities improves accuracy.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1162712},
pmid = {37351363},
issn = {1662-5161},
abstract = {Near-infrared spectroscopy (NIRS) is a promising research tool that found its way into the field of brain-computer interfacing (BCI). BCI is crucially dependent on maximized usability thus demanding lightweight, compact, and low-cost hardware. We designed, built, and validated a hybrid BCI system incorporating one optical and two electrical modalities ameliorating usability issues. The novel hardware consisted of a NIRS device integrated with an electroencephalography (EEG) system that used two different types of electrodes: Regular gelled gold disk electrodes and tri-polar concentric ring electrodes (TCRE). BCI experiments with 16 volunteers implemented a two-dimensional motor imagery paradigm in off- and online sessions. Various non-canonical signal processing methods were used to extract and classify useful features from EEG, tEEG (EEG through TCRE electrodes), and NIRS. Our analysis demonstrated evidence of improvement in classification accuracy when using the TCRE electrodes compared to disk electrodes and the NIRS system. Based on our synchronous hybrid recording system, we could show that the combination of NIRS-EEG-tEEG performed significantly better than either single modality only.},
}
@article {pmid37349602,
year = {2024},
author = {Claassen, J and Kondziella, D and Alkhachroum, A and Diringer, M and Edlow, BL and Fins, JJ and Gosseries, O and Hannawi, Y and Rohaut, B and Schnakers, C and Stevens, RD and Thibaut, A and Monti, M and , },
title = {Cognitive Motor Dissociation: Gap Analysis and Future Directions.},
journal = {Neurocritical care},
volume = {40},
number = {1},
pages = {81-98},
pmid = {37349602},
issn = {1556-0961},
support = {NS128326/NS/NINDS NIH HHS/United States ; RF1 MH121378/MH/NIMH NIH HHS/United States ; NS112760/NS/NINDS NIH HHS/United States ; R01 NS106014/NS/NINDS NIH HHS/United States ; R21 NS128326/NS/NINDS NIH HHS/United States ; NS106014/NS/NINDS NIH HHS/United States ; NS126577/NS/NINDS NIH HHS/United States ; R03 NS112760/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Consciousness Disorders ; Brain ; Consciousness/physiology ; *Brain Injuries ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND: Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance.<br><br>METHODS: The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs.<br><br>RESULTS: The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces.<br><br>CONCLUSIONS: To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.},
}
@article {pmid37348695,
year = {2023},
author = {Kieffer, L and Sørås, R and Ciesielski, TM and Stawski, C},
title = {Species and reproductive status influence element concentrations in bat fur.},
journal = {Environmental pollution (Barking, Essex : 1987)},
volume = {333},
number = {},
pages = {122092},
doi = {10.1016/j.envpol.2023.122092},
pmid = {37348695},
issn = {1873-6424},
mesh = {Humans ; Male ; Female ; Animals ; Lactation ; Lead ; *Mercury ; Reproduction ; *Environmental Pollutants ; *Arsenic ; *Chiroptera ; },
abstract = {To assess the impact of increasing pollutant levels on wildlife, we measured chemical contaminant loads in bats with different habitat and dietary preferences. Samples were taken from the fur of bats (Eptesicus nilssonii, Myotis brandtii, Myotis mystacinus and Plecotus auritus) to measure concentrations of 55 elements by inductively coupled plasma mass spectrometry (ICP-MS). Variations in element concentrations between different bat groups (species, sex, reproductive status) were analysed with a focus on arsenic (As), mercury (Hg) and lead (Pb) as these are known to cause specific health concerns in wildlife. For M. brandtii we found the highest As concentrations, especially in lactating bats, with a maximum exceeding those from other studies where bats had compromised health. Whereas for M. mystacinus there was a negative correlation between body condition index (BCI) and As concentration, indicating a potential danger for bats in the study area. In M. mystacinus and M. brandtii Hg concentrations were higher for sixteen individuals than in other studies where bats suffered genotoxic effects, although median levels were still below this threshold. Lactating bats from P. auritus and M. brandtii had higher Hg concentrations than bats of other reproductive status, which could endanger offspring as Hg can be transferred through lactation. In females from M. mystacinus Pb concentrations were more than three times higher compared to males. There was also a negative correlation between Pb concentration and BCI, which could mean that Pb has an adverse effect on health. Although many other biotic and abiotic factors should be considered, some of the variations in element concentrations could be due to different behaviours (foraging, roosting, etc.) in the studied species. The high levels of chemical contamination in some of the bats in our study, particularly reproductive individuals, is of conservation concern as bats are important agents for insect control.},
}
@article {pmid37348393,
year = {2023},
author = {Yang, T and Yang, Y and Zhang, P and Li, W and Ge, Q and Yu, H and Wu, M and Xing, L and Qian, Z and Gao, F and Liu, R},
title = {Quantitative proteomics analysis on the meat quality of processed pale, soft, and exudative (PSE)-like broiler pectoralis major by different heating methods.},
journal = {Food chemistry},
volume = {426},
number = {},
pages = {136602},
doi = {10.1016/j.foodchem.2023.136602},
pmid = {37348393},
issn = {1873-7072},
mesh = {Animals ; *Pectoralis Muscles ; *Proteomics ; Heating ; Chickens ; Meat/analysis ; Myoglobin ; },
abstract = {This study aims to assess and compare the influences of different heating methods on the quality characteristics of pale, soft, and exudative (PSE)-like and normal (NOR) pectoralis major through quantitative proteomic analysis. A total of 632 proteins were identified, and there were 84, 89, 50, and 43 differentially abundant proteins (DAPs) between processed PSE and NOR samples after four thermal treatments, including boiling (BO), steaming (ST), roasting (RO), and microwaving (MV), respectively, where moist heating conditions led to more different protein abundance. Processed PSE muscles resulted in significant changes in structural proteins related to myofibrillar and connective tissue, which could be associated with their structural instability and degraded quality. Collagen, tropomyosin, myoglobin, and hemoglobin could be potential indicators of PSE muscles color stability and variation during thermal processing. The quantitative proteomic analysis will help correlate molecular changes with processed meat quality towards future optimization of PSE poultry meat processing.},
}
@article {pmid37346414,
year = {2023},
author = {Agres, KR and Dash, A and Chua, P},
title = {AffectMachine-Classical: a novel system for generating affective classical music.},
journal = {Frontiers in psychology},
volume = {14},
number = {},
pages = {1158172},
pmid = {37346414},
issn = {1664-1078},
abstract = {This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal (R[2] = 0.96) to listeners, and is also quite convincing in terms of Valence (R[2] = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.},
}
@article {pmid37346164,
year = {2023},
author = {Ma, ZZ and Wu, JJ and Hua, XY and Zheng, MX and Xing, XX and Ma, J and Shan, CL and Xu, JG},
title = {Evidence of neuroplasticity with brain-computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1135466},
pmid = {37346164},
issn = {1664-2295},
abstract = {BACKGROUND: Brain-computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients.<br><br>METHODS: Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks.<br><br>RESULTS: The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p&#x2009;=&#x2009;0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including &#x3b3; (p&#x2009;=&#x2009;0.035) and &#x3c3; (p&#x2009;=&#x2009;0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p&#x2009;=&#x2009;0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R&#x2009;=&#x2009;0.498, p&#x2009;=&#x2009;0.011).<br><br>CONCLUSION: Brain-computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain's visuospatial processing ability, which can be used to optimize BCI strategies.<br><br>CLINICAL TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.},
}
@article {pmid37344392,
year = {2023},
author = {Chen, F and Chen, L and Han, H and Zhang, S and Zhang, D and Liao, H},
title = {The ability of Segmenting Anything Model (SAM) to segment ultrasound images.},
journal = {Bioscience trends},
volume = {17},
number = {3},
pages = {211-218},
doi = {10.5582/bst.2023.01128},
pmid = {37344392},
issn = {1881-7823},
mesh = {*Algorithms ; Ultrasonography/methods ; },
abstract = {Accurate ultrasound (US) image segmentation is important for disease screening, diagnosis, and prognosis assessment. However, US images typically have shadow artifacts and ambiguous boundaries that affect US segmentation. Recently, Segmenting Anything Model (SAM) from Meta AI has demonstrated remarkable potential in a wide range of applications. The purpose of this paper was to conduct an initial evaluation of the ability for SAM to segment US images, particularly in the event of shadow artifacts and ambiguous boundaries. We evaluated SAM's performance on three US datasets of different tissues, including multi-structure cardiac tissue, thyroid nodules, and the fetal head. Results indicated that SAM generally performs well with US images with clear tissue structures, but it has limited performance in the event of shadow artifacts and ambiguous boundaries. Thus, creating an improved SAM that considers the characteristics of US images is significant for automatic and accurate US segmentation.},
}
@article {pmid37343466,
year = {2023},
author = {Farina, D and Enoka, RM},
title = {Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing.},
journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology},
volume = {71},
number = {},
pages = {102796},
doi = {10.1016/j.jelekin.2023.102796},
pmid = {37343466},
issn = {1873-5711},
mesh = {Humans ; Electromyography/methods ; *Muscle, Skeletal/physiology ; *Muscle Contraction/physiology ; Muscle Fibers, Skeletal ; Electrophysiology ; },
abstract = {Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.},
}
@article {pmid37343138,
year = {2023},
author = {Zhang, XY and Dong, HL and Wu, ZY},
title = {Axonal Charcot-Marie-Tooth disease due to COQ7 mutation: expanding the genetic and clinical spectrum.},
journal = {Brain : a journal of neurology},
volume = {146},
number = {12},
pages = {e117-e119},
doi = {10.1093/brain/awad212},
pmid = {37343138},
issn = {1460-2156},
support = {//research foundation/ ; 188020-193810101/089//Zhejiang University/ ; //Fundamental Research Funds/ ; //Central Universities/ ; },
mesh = {Humans ; *Charcot-Marie-Tooth Disease/genetics ; Mutation/genetics ; Axons ; Phenotype ; Pedigree ; },
}
@article {pmid37342949,
year = {2023},
author = {Wei, Q and Zhang, Y and Wang, Y and Gao, X},
title = {A Canonical Correlation Analysis-Based Transfer Learning Framework for Enhancing the Performance of SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2809-2821},
doi = {10.1109/TNSRE.2023.3288397},
pmid = {37342949},
issn = {1558-0210},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; *Electroencephalography/methods ; Evoked Potentials, Visual ; Machine Learning ; Photic Stimulation ; },
abstract = {A steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) can either achieve high classification accuracy in the case of sufficient training data or suppress the training stage at the cost of low accuracy. Although some researches attempted to conquer the dilemma between performance and practicality, a highly effective approach has not yet been established. In this paper, we propose a canonical correlation analysis (CCA)-based transfer learning framework for improving the performance of an SSVEP BCI and reducing its calibration effort. Three spatial filters are optimized by a CCA algorithm with intra- and inter-subject EEG data (IISCCA), two template signals are estimated separately with the EEG data from the target subject and a set of source subjects and six coefficients are yielded by correlation analysis between a testing signal and each of the two templates after they are filtered by each of the three spatial filters. The feature signal used for classification is extracted by the sum of squared coefficients multiplied by their signs and the frequency of the testing signal is recognized by template matching. To reduce the individual discrepancy between subjects, an accuracy-based subject selection (ASS) algorithm is developed for screening those source subjects whose EEG data are more similar to those of the target subject. The proposed ASS-IISCCA integrates both subject-specific models and subject-independent information for the frequency recognition of SSVEP signals. The performance of ASS-IISCCA was evaluated on a benchmark data set with 35 subjects and compared with the state-of-the-art algorithm task-related component analysis (TRCA). The results show that ASS-IISCCA can significantly improve the performance of SSVEP BCIs with a small number of training trials from a new user, thus helping to facilitate their applications in real world.},
}
@article {pmid37342822,
year = {2023},
author = {Nam, H and Kim, JM and Choi, W and Bak, S and Kam, TE},
title = {The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1205881},
pmid = {37342822},
issn = {1662-5161},
abstract = {INTRODUCTION: The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.<br><br>METHODS: In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.<br><br>RESULTS AND DISCUSSION: The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.},
}
@article {pmid37342712,
year = {2023},
author = {Zhang, Y and Liu, D and Li, T and Zhang, P and Li, Z and Gao, F},
title = {CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.},
journal = {Biomedical optics express},
volume = {14},
number = {6},
pages = {2934-2954},
pmid = {37342712},
issn = {2156-7085},
abstract = {Functional near-infrared spectroscopy (fNIRS) is increasingly used to investigate different mental tasks for brain-computer interface (BCI) control due to its excellent environmental and motion robustness. Feature extraction and classification strategy for fNIRS signal are essential to enhance the classification accuracy of voluntarily controlled BCI systems. The limitation of traditional machine learning classifiers (MLCs) lies in manual feature engineering, which is considered as one of the drawbacks that reduce accuracy. Since the fNIRS signal is a typical multivariate time series with multi-dimensionality and complexity, it makes the deep learning classifier (DLC) ideal for classifying neural activation patterns. However, the inherent bottleneck of DLCs is the requirement of substantial-scale, high-quality labeled training data and expensive computational resources to train deep networks. The existing DLCs for classifying mental tasks do not fully consider the temporal and spatial properties of fNIRS signals. Therefore, a specifically-designed DLC is desired to classify multi-tasks with high accuracy in fNIRS-BCI. To this end, we herein propose a novel data-augmented DLC to accurately classify mental tasks, which employs a convolution-based conditional generative adversarial network (CGAN) for data augmentation and a revised Inception-ResNet (rIRN) based DLC. The CGAN is utilized to generate class-specific synthetic fNIRS signals to augment the training dataset. The network architecture of rIRN is elaborately designed in accordance with the characteristics of the fNIRS signal, with serial multiple spatial and temporal feature extraction modules (FEMs), where each FEM performs deep and multi-scale feature extraction and fusion. The results of the paradigm experiments show that the proposed CGAN-rIRN approach improves the single-trial accuracy for mental arithmetic and mental singing tasks in both the data augmentation and classifier, as compared to the traditional MLCs and the commonly used DLCs. The proposed fully data-driven hybrid deep learning approach paves a promising way to improve the classification performance of volitional control fNIRS-BCI.},
}
@article {pmid37342465,
year = {2023},
author = {Xiao, X and Gao, R and Zhou, X and Yi, W and Xu, F and Wang, K and Xu, M and Ming, D},
title = {A novel visual brain-computer interfaces paradigm based on evoked related potentials evoked by weak and small number of stimuli.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1178283},
pmid = {37342465},
issn = {1662-4548},
abstract = {INTRODUCTION: Traditional visual Brain-Computer Interfaces (v-BCIs) usually use large-size stimuli to attract more attention from users and then elicit more distinct and robust EEG responses, which would cause visual fatigue and limit the length of use of the system. On the contrary, small-size stimuli always need multiple and repeated stimulus to code more instructions and increase separability among each code. These common v-BCIs paradigms can cause problems such as redundant coding, long calibration time, and visual fatigue.<br><br>METHODS: To address these problems, this study presented a novel v-BCI paradigm using weak and small number of stimuli, and realized a nine-instruction v-BCI system that controlled by only three tiny stimuli. Each of these stimuli were located between instructions, occupied area with eccentricities subtended 0.4&#xb0;, and flashed in the row-column paradigm. The weak stimuli around each instruction would evoke specific evoked related potentials (ERPs), and a template-matching method based on discriminative spatial pattern (DSP) was employed to recognize these ERPs containing the intention of users. Nine subjects participated in the offline and online experiments using this novel paradigm.<br><br>RESULTS: The average accuracy of the offline experiment was 93.46% and the online average information transfer rate (ITR) was 120.95 bits/min. Notably, the highest online ITR achieved 177.5 bits/min.<br><br>DISCUSSION: These results demonstrate the feasibility of using a weak and small number of stimuli to implement a friendly v-BCI. Furthermore, the proposed novel paradigm achieved higher ITR than traditional ones using ERPs as the controlled signal, which showed its superior performance and may have great potential of being widely used in various fields.},
}
@article {pmid37339875,
year = {2023},
author = {Pierrieau, E and Berret, B and Lepage, JF and Bernier, PM},
title = {From Motivation to Action: Action Cost Better Predicts Changes in Premovement Beta-Band Activity than Speed.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {43},
number = {28},
pages = {5264-5275},
pmid = {37339875},
issn = {1529-2401},
mesh = {Humans ; *Motivation ; *Motor Cortex ; Movement ; Hand ; Beta Rhythm ; Electroencephalography ; Cortical Synchronization ; },
abstract = {Although premovement beta-band event-related desynchronization (β-ERD; 13-30 Hz) from sensorimotor regions is modulated by movement speed, current evidence does not support a strict monotonic association between the two. Given that β-ERD is thought to increase information encoding capacity, we tested the hypothesis that it might be related to the expected neurocomputational cost of movement, here referred to as action cost. Critically, action cost is greater both for slow and fast movements compared with a medium or "preferred" speed. Thirty-one right-handed participants performed a speed-controlled reaching task while recording their EEG. Results revealed potent modulations of beta power as a function of speed, with β-ERD being significantly greater both for movements performed at high and low speeds compared with medium speed. Interestingly, medium-speed movements were more often chosen by participants than low-speed and high-speed movements, suggesting that they were evaluated as less costly. In line with this, modeling of action cost revealed a pattern of modulation across speed conditions that strikingly resembled the one found for β-ERD. Indeed, linear mixed models showed that estimated action cost predicted variations of β-ERD significantly better than speed. This relationship with action cost was specific to beta power, as it was not found when averaging activity in the mu band (8-12 Hz) and gamma band (31-49 Hz) bands. These results demonstrate that increasing β-ERD may not merely speed up movements, but instead facilitate the preparation of high-speed and low-speed movements through the allocation of additional neural resources, thereby enabling flexible motor control.SIGNIFICANCE STATEMENT Heightened beta activity has been associated with movement slowing in Parkinson's disease, and modulations of beta activity are commonly used to decode movement parameters in brain-computer interfaces. Here we show that premovement beta activity is better explained by the neurocomputational cost of the action rather than its speed. Instead of being interpreted as a mere reflection of changes in movement speed, premovement changes in beta activity might therefore be used to infer the amount of neural resources that are allocated for motor planning.},
}
@article {pmid37339227,
year = {2023},
author = {Chai, Y and Gehrman, P and Yu, M and Mao, T and Deng, Y and Rao, J and Shi, H and Quan, P and Xu, J and Zhang, X and Lei, H and Fang, Z and Xu, S and Boland, E and Goldschmied, JR and Barilla, H and Goel, N and Basner, M and Thase, ME and Sheline, YI and Dinges, DF and Detre, JA and Zhang, X and Rao, H},
title = {Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {26},
pages = {e2214505120},
pmid = {37339227},
issn = {1091-6490},
support = {R01 NS113889/NS/NINDS NIH HHS/United States ; R01 MH107571/MH/NIMH NIH HHS/United States ; UL1 RR024134/RR/NCRR NIH HHS/United States ; R01 HL102119/HL/NHLBI NIH HHS/United States ; R21 AG051981/AG/NIA NIH HHS/United States ; },
mesh = {Adult ; Humans ; *Depressive Disorder, Major/diagnostic imaging/drug therapy ; Sleep Deprivation/diagnostic imaging ; Amygdala/diagnostic imaging ; Gyrus Cinguli/diagnostic imaging ; Antidepressive Agents/pharmacology/therapeutic use ; Magnetic Resonance Imaging/methods ; },
abstract = {Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.},
}
@article {pmid37338891,
year = {2023},
author = {Kirton, A},
title = {A Moral Imperative to Advance Brain-Computer Interfaces for Children With Neurological Disability.},
journal = {JAMA pediatrics},
volume = {177},
number = {8},
pages = {751-752},
doi = {10.1001/jamapediatrics.2023.1744},
pmid = {37338891},
issn = {2168-6211},
mesh = {Humans ; Child ; *Brain-Computer Interfaces ; User-Computer Interface ; Morals ; },
}
@article {pmid37336205,
year = {2023},
author = {Zhao, H and Zheng, L and Yuan, M and Wang, Y and Gao, X and Liu, R and Pei, W},
title = {Optimization of ear electrodes for SSVEP-based BCI.},
journal = {Journal of neural engineering},
volume = {20},
number = {4},
pages = {},
doi = {10.1088/1741-2552/acdf85},
pmid = {37336205},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Electrodes ; Hydrogels ; Photic Stimulation/methods ; },
abstract = {Objective.Current ear electrodes often require complex placing or long stimulation durations to achieve good detection of steady-state visual evoked potential (SSVEP). To improve the practicability of ear electrode-based SSVEP-BCI (brain-computer interface) system, we developed a high-performance ear electrode that can be easily placed.Approach.Hydrogel based disposable and replaceable semi-dry electrodes are developed to improve the contact impedance and wear feeling. The best combination of electrodes for SSVEP-BCI application around the ear is optimized by assessing the electrode on volunteers, and the performance of the electrode was compared with that of the occipital electrode.Main results.The developed ear hydrogel electrode can achieve an impedance close to that of the wet electrode. Three combinations of ear electrode groups demonstrate high information transfer rate (ITR) and accuracy in SSVEP-BCI applications. According to the rating of the comprehensive assessment and BCI performance in the online session, the behind-aural electrode is the best electrode combination for recording SSVEP in the ear region. The average preparation time is the shortest, and the average impedance is the lowest. The ITR of the behind-aural electrode based SSVEP-BCI system can reach 37.5 ± 18 bits min[-1]. The stimulus duration was as low as 3 s compared to 5 s or 10 s in other studies.Significance.The accuracy, ITR, and wear feeling can be improved by introducing a semi-dry ear electrode and optimizing the position and the combination of ear electrode. By providing a better trade-off between performance and convenience, the ear electrode-based SSVEP-BCI promises to be used in daily life.},
}
@article {pmid37336030,
year = {2023},
author = {Zhang, D and Li, H and Xie, J},
title = {MI-CAT: A transformer-based domain adaptation network for motor imagery classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {165},
number = {},
pages = {451-462},
doi = {10.1016/j.neunet.2023.06.005},
pmid = {37336030},
issn = {1879-2782},
mesh = {*Imagination ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain-computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer's self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain-computer interfaces (BCIs).},
}
@article {pmid37333834,
year = {2023},
author = {Rodriguez, F and He, S and Tan, H},
title = {The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1134599},
pmid = {37333834},
issn = {1662-5161},
abstract = {Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems.},
}
@article {pmid37332859,
year = {2023},
author = {Wang, P and Cao, X and Zhou, Y and Gong, P and Yousefnezhad, M and Shao, W and Zhang, D},
title = {A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1086472},
pmid = {37332859},
issn = {1662-4548},
abstract = {The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.},
}
@article {pmid37332856,
year = {2023},
author = {Zhang, H and Ji, H and Yu, J and Li, J and Jin, L and Liu, L and Bai, Z and Ye, C},
title = {Subject-independent EEG classification based on a hybrid neural network.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1124089},
pmid = {37332856},
issn = {1662-4548},
abstract = {A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI.},
}
@article {pmid37331820,
year = {2023},
author = {Abdalkader, M and Hui, F and Amans, MR and Raz, E and Hanning, U and Ma, A and Brinjikji, W and Malek, AM and Oxley, TJ and Nguyen, TN},
title = {Cerebral venous disorders: Diagnosis and endovascular management.},
journal = {Journal of neuroradiology = Journal de neuroradiologie},
volume = {50},
number = {6},
pages = {581-592},
doi = {10.1016/j.neurad.2023.06.002},
pmid = {37331820},
issn = {0150-9861},
mesh = {Humans ; *Intracranial Hypertension ; Cerebral Angiography ; *Endovascular Procedures ; },
abstract = {The role of the venous circulation in neurological diseases has been underestimated. In this review, we present an overview of the intracranial venous anatomy, venous disorders of the central nervous system, and options for endovascular management. We discuss the role the venous circulation plays in various neurological diseases including cerebrospinal fluid (CSF) disorders (intracranial hypertension and intracranial hypotension), arteriovenous diseases, and pulsatile tinnitus. We also shed light on emergent cerebral venous interventions including transvenous brain-computer interface implantation, transvenous treatment of communicating hydrocephalus, and the endovascular treatment of CSF-venous disorders.},
}
@article {pmid37330614,
year = {2023},
author = {Inker, LA and Collier, W and Greene, T and Miao, S and Chaudhari, J and Appel, GB and Badve, SV and Caravaca-Fontán, F and Del Vecchio, L and Floege, J and Goicoechea, M and Haaland, B and Herrington, WG and Imai, E and Jafar, TH and Lewis, JB and Li, PKT and Maes, BD and Neuen, BL and Perrone, RD and Remuzzi, G and Schena, FP and Wanner, C and Wetzels, JFM and Woodward, M and Heerspink, HJL and , },
title = {A meta-analysis of GFR slope as a surrogate endpoint for kidney failure.},
journal = {Nature medicine},
volume = {29},
number = {7},
pages = {1867-1876},
pmid = {37330614},
issn = {1546-170X},
support = {MR/N006178/1/MRC_/Medical Research Council/United Kingdom ; UL1 TR002538/TR/NCATS NIH HHS/United States ; },
mesh = {Humans ; Glomerular Filtration Rate ; *Kidney Failure, Chronic ; Bayes Theorem ; Disease Progression ; *Renal Insufficiency, Chronic ; Biomarkers ; },
abstract = {Glomerular filtration rate (GFR) decline is causally associated with kidney failure and is a candidate surrogate endpoint for clinical trials of chronic kidney disease (CKD) progression. Analyses across a diverse spectrum of interventions and populations is required for acceptance of GFR decline as an endpoint. In an analysis of individual participant data, for each of 66 studies (total of 186,312 participants), we estimated treatment effects on the total GFR slope, computed from baseline to 3 years, and chronic slope, starting at 3 months after randomization, and on the clinical endpoint (doubling of serum creatinine, GFR < 15 ml min[-1] per 1.73 m[2] or kidney failure with replacement therapy). We used a Bayesian mixed-effects meta-regression model to relate treatment effects on GFR slope with those on the clinical endpoint across all studies and by disease groups (diabetes, glomerular diseases, CKD or cardiovascular diseases). Treatment effects on the clinical endpoint were strongly associated with treatment effects on total slope (median coefficient of determination (R[2]) = 0.97 (95% Bayesian credible interval (BCI) 0.82-1.00)) and moderately associated with those on chronic slope (R[2] = 0.55 (95% BCI 0.25-0.77)). There was no evidence of heterogeneity across disease. Our results support the use of total slope as a primary endpoint for clinical trials of CKD progression.},
}
@article {pmid37329623,
year = {2023},
author = {Barnova, K and Mikolasova, M and Kahankova, RV and Jaros, R and Kawala-Sterniuk, A and Snasel, V and Mirjalili, S and Pelc, M and Martinek, R},
title = {Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction.},
journal = {Computers in biology and medicine},
volume = {163},
number = {},
pages = {107135},
doi = {10.1016/j.compbiomed.2023.107135},
pmid = {37329623},
issn = {1879-0534},
mesh = {Humans ; *Artificial Intelligence ; Quality of Life ; Algorithms ; Machine Learning ; Computers ; *Brain-Computer Interfaces ; Brain ; },
abstract = {Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.},
}
@article {pmid37327754,
year = {2023},
author = {Ma, W and Wang, C and Sun, X and Lin, X and Niu, L and Wang, Y},
title = {MBGA-Net: A multi-branch graph adaptive network for individualized motor imagery EEG classification.},
journal = {Computer methods and programs in biomedicine},
volume = {240},
number = {},
pages = {107641},
doi = {10.1016/j.cmpb.2023.107641},
pmid = {37327754},
issn = {1872-7565},
mesh = {*Algorithms ; Imagination ; Electroencephalography/methods ; Movement ; *Brain-Computer Interfaces ; },
abstract = {BACKGROUND AND OBJECTIVE: The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual's EEG signal is recognized with precision.<br><br>METHODS: We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data.<br><br>RESULTS: We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49% and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71%, 85.83%, and 86.99%, respectively.<br><br>CONCLUSIONS: The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification.},
}
@article {pmid37326721,
year = {2023},
author = {Wang, W and Wang, Y and Qi, X and He, L},
title = {Spatial pattern and environmental drivers of breast cancer incidence in Chinese women.},
journal = {Environmental science and pollution research international},
volume = {30},
number = {34},
pages = {82506-82516},
doi = {10.1007/s11356-023-28206-4},
pmid = {37326721},
issn = {1614-7499},
support = {Grant No. 42001164//National Natural Science Foundation of China/ ; Grant No. 2018ZDCXL-SF-02-03-01//Key R &amp; D Program of Shaanxi Province/ ; Grant No. 20YJC840014//Ministry of Education, Humanities and Social Sciences Research Youth Foundation/ ; },
mesh = {Humans ; Female ; Incidence ; *Breast Neoplasms/epidemiology ; East Asian People ; *Air Pollution/analysis ; Spatial Analysis ; China/epidemiology ; *Air Pollutants/analysis ; },
abstract = {Breast cancer (BC) had the highest incidence of all cancers in Chinese women. However, studies on spatial pattern and environmental drivers of BC were still lacked as they were either limited in a small area or few considered the comprehensive impact of multiple risk factors. In this study, we firstly performed spatial visualization and the spatial autocorrelation analysis based on Chinese women breast cancer incidence (BCI) data of 2012-2016. Then, we explored the environmental drivers related to BC by applying univariate correlation analysis and geographical detector model. We found that the BC high-high clusters were mainly distributed in the eastern and central regions, such as Liaoning, Hebei, Shandong, Henan, and Anhui Provinces. The BCI in Shenzhen was significantly higher than other prefectures. Urbanization rate (UR), per capita GDP (PGDP), average years of school attainment (AYSA), and average annual wind speed (WIND) had higher explanatory power on spatial variability of the BCI. PM10, NO2, and PGDP had significant nonlinear enhanced effect on other factors. Besides, normalized difference vegetation index (NDVI) was negatively associated with BCI. Therefore, high socioeconomic status, serious air pollution, high wind speed, and low vegetation cover were the risk factors for BC. Our study may able to provide evidence for BC etiology research and precise identification of areas requiring focused screening.},
}
@article {pmid37326668,
year = {2024},
author = {Ross, CF and Laurence-Chasen, JD and Li, P and Orsbon, C and Hatsopoulos, NG},
title = {Biomechanical and Cortical Control of Tongue Movements During Chewing and Swallowing.},
journal = {Dysphagia},
volume = {39},
number = {1},
pages = {1-32},
pmid = {37326668},
issn = {1432-0460},
support = {R01 DE023816/DE/NIDCR NIH HHS/United States ; T32 HD007009/HD/NICHD NIH HHS/United States ; R01-DE023816/DE/NIDCR NIH HHS/United States ; },
mesh = {Animals ; Humans ; *Deglutition/physiology ; *Deglutition Disorders ; Mastication/physiology ; Tongue/physiology ; Hyoid Bone ; Biomechanical Phenomena ; },
abstract = {Tongue function is vital for chewing and swallowing and lingual dysfunction is often associated with dysphagia. Better treatment of dysphagia depends on a better understanding of hyolingual morphology, biomechanics, and neural control in humans and animal models. Recent research has revealed significant variation among animal models in morphology of the hyoid chain and suprahyoid muscles which may be associated with variation in swallowing mechanisms. The recent deployment of XROMM (X-ray Reconstruction of Moving Morphology) to quantify 3D hyolingual kinematics has revealed new details on flexion and roll of the tongue during chewing in animal models, movements similar to those used by humans. XROMM-based studies of swallowing in macaques have falsified traditional hypotheses of mechanisms of tongue base retraction during swallowing, and literature review suggests that other animal models may employ a diversity of mechanisms of tongue base retraction. There is variation among animal models in distribution of hyolingual proprioceptors but how that might be related to lingual mechanics is unknown. In macaque monkeys, tongue kinematics-shape and movement-are strongly encoded in neural activity in orofacial primary motor cortex, giving optimism for development of brain-machine interfaces for assisting recovery of lingual function after stroke. However, more research on hyolingual biomechanics and control is needed for technologies interfacing the nervous system with the hyolingual apparatus to become a reality.},
}
@article {pmid37325190,
year = {2023},
author = {Anaya, D and Batra, G and Bracewell, P and Catoen, R and Chakraborty, D and Chevillet, M and Damodara, P and Dominguez, A and Emms, L and Jiang, Z and Kim, E and Klumb, K and Lau, F and Le, R and Li, J and Mateo, B and Matloff, L and Mehta, A and Mugler, EM and Murthy, A and Nakagome, S and Orendorff, R and Saung, EF and Schwarz, R and Sethi, R and Sevile, R and Srivastava, A and Sundberg, J and Yang, Y and Yin, A},
title = {Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).},
journal = {Journal of biomedical optics},
volume = {28},
number = {6},
pages = {065003},
pmid = {37325190},
issn = {1560-2281},
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Hemodynamics/physiology ; Hand ; },
abstract = {SIGNIFICANCE: We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature.<br><br>AIM: Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research.<br><br>APPROACH: We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules.<br><br>RESULTS: The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy.<br><br>CONCLUSIONS: The advances presented here should serve to make fNIRS more accessible for BCI applications.},
}
@article {pmid37324415,
year = {2023},
author = {Tang, X and Zhang, W and Wang, H and Wang, T and Tan, C and Zou, M and Xu, Z},
title = {Dynamic pruning group equivariant network for motor imagery EEG recognition.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {11},
number = {},
pages = {917328},
pmid = {37324415},
issn = {2296-4185},
abstract = {Introduction: The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them. Methods: In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed. Group convolutional networks can learn powerful representations based on symmetric patterns, but they lack clear methods to learn meaningful relationships between them. The dynamic pruning equivariant group convolution proposed in this paper is used to enhance meaningful symmetric combinations and suppress unreasonable and misleading symmetric combinations. At the same time, a new dynamic pruning method is proposed to dynamically evaluate the importance of parameters, which can restore the pruned connections. Results and Discussion: The experimental results show that the pruning group equivariant convolution network is superior to the traditional benchmark method in the benchmark motor imagery EEG data set. This research can also be transferred to other research areas.},
}
@article {pmid37323929,
year = {2023},
author = {Ran, S and Zhong, W and Duan, D and Ye, L and Zhang, Q},
title = {SSTM-IS: simplified STM method based on instance selection for real-time EEG emotion recognition.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1132254},
pmid = {37323929},
issn = {1662-5161},
abstract = {INTRODUCTION: EEG signals can non-invasively monitor the brain activities and have been widely used in brain-computer interfaces (BCI). One of the research areas is to recognize emotions objectively through EEG. In fact, the emotion of people changes over time, however, most of the existing affective BCIs process data and recognize emotions offline, and thus cannot be applied to real-time emotion recognition.<br><br>METHODS: In order to solve this problem, we introduce the instance selection strategy into transfer learning and propose a simplified style transfer mapping algorithm. In the proposed method, the informative instances are firstly selected from the source domain data, and then the update strategy of hyperparameters is also simplified for style transfer mapping, making the model training more quickly and accurately for a new subject.<br><br>RESULTS: To verify the effectiveness of our algorithm, we carry out the experiments on SEED, SEED-IV and the offline dataset collected by ourselves, and achieve the recognition accuracies up to 86.78%, 82.55% and 77.68% in computing time of 7s, 4s and 10s, respectively. Furthermore, we also develop a real-time emotion recognition system which integrates the modules of EEG signal acquisition, data processing, emotion recognition and result visualization.<br><br>DISCUSSION: Both the results of offline and online experiments show that the proposed algorithm can accurately recognize emotions in a short time, meeting the needs of real-time emotion recognition applications.},
}
@article {pmid37323927,
year = {2023},
author = {Tang, S and Liang, Y and Li, Z},
title = {Mind wandering state detection during video-based learning via EEG.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1182319},
pmid = {37323927},
issn = {1662-5161},
abstract = {The aim of this study is to explore the potential of technology for detecting mind wandering, particularly during video-based distance learning, with the ultimate benefit of improving learning outcomes. To overcome the challenges of previous mind wandering research in ecological validity, sample balance, and dataset size, this study utilized practical electroencephalography (EEG) recording hardware and designed a paradigm consisting of viewing short-duration video lectures under a focused learning condition and a future planning condition. Participants estimated statistics of their attentional state at the end of each video, and we combined this rating scale feedback with self-caught key press responses during video watching to obtain binary labels for classifier training. EEG was recorded using an 8-channel system, and spatial covariance features processed by Riemannian geometry were employed. The results demonstrate that a radial basis function kernel support vector machine classifier, using Riemannian-processed covariance features from delta, theta, alpha, and beta bands, can detect mind wandering with a mean area under the receiver operating characteristic curve (AUC) of 0.876 for within-participant classification and AUC of 0.703 for cross-lecture classification. Furthermore, our results suggest that a short duration of training data is sufficient to train a classifier for online decoding, as cross-lecture classification remained at an average AUC of 0.689 when using 70% of the training set (about 9 min). The findings highlight the potential for practical EEG hardware in detecting mind wandering with high accuracy, which has potential application to improving learning outcomes during video-based distance learning.},
}
@article {pmid37322987,
year = {2023},
author = {Sha, T and Zhang, Y and Peng, Y and Kong, W},
title = {Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {6},
pages = {11379-11402},
doi = {10.3934/mbe.2023505},
pmid = {37322987},
issn = {1551-0018},
mesh = {*Emotions/physiology ; *Brain/physiology ; Algorithms ; Learning ; Electroencephalography ; },
abstract = {Electroencephalogram (EEG) signals are widely used in the field of emotion recognition since it is resistant to camouflage and contains abundant physiological information. However, EEG signals are non-stationary and have low signal-noise-ratio, making it more difficult to decode in comparison with data modalities such as facial expression and text. In this paper, we propose a model termed semi-supervised regression with adaptive graph learning (SRAGL) for cross-session EEG emotion recognition, which has two merits. On one hand, the emotional label information of unlabeled samples is jointly estimated with the other model variables by a semi-supervised regression in SRAGL. On the other hand, SRAGL adaptively learns a graph to depict the connections among EEG data samples which further facilitates the emotional label estimation process. From the experimental results on the SEED-IV data set, we have the following insights. 1) SRAGL achieves superior performance compared to some state-of-the-art algorithms. To be specific, the average accuracies are 78.18%, 80.55%, and 81.90% in the three cross-session emotion recognition tasks. 2) As the iteration number increases, SRAGL converges quickly and optimizes the emotion metric of EEG samples gradually, leading to a reliable similarity matrix finally. 3) Based on the learned regression projection matrix, we obtain the contribution of each EEG feature, which enables us to automatically identify critical frequency bands and brain regions in emotion recognition.},
}
@article {pmid37322947,
year = {2023},
author = {Hua, X and Li, J and Wang, T and Wang, J and Pi, S and Li, H and Xi, X},
title = {Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {6},
pages = {10530-10551},
doi = {10.3934/mbe.2023465},
pmid = {37322947},
issn = {1551-0018},
mesh = {Humans ; *Muscle, Skeletal ; *Stroke ; Electromyography/methods ; Movement ; Electroencephalography ; Biomarkers ; },
abstract = {Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.},
}
@article {pmid37322937,
year = {2023},
author = {Chang, Y and Wang, L and Zhao, Y and Liu, M and Zhang, J},
title = {Research on two-class and four-class action recognition based on EEG signals.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {6},
pages = {10376-10391},
doi = {10.3934/mbe.2023455},
pmid = {37322937},
issn = {1551-0018},
mesh = {Humans ; *Electroencephalography ; Neural Networks, Computer ; Algorithms ; Motion ; *Brain-Computer Interfaces ; },
abstract = {BMI has attracted widespread attention in the past decade, which has greatly improved the living conditions of patients with motor disorders. The application of EEG signals in lower limb rehabilitation robots and human exoskeleton has also been gradually applied by researchers. Therefore, the recognition of EEG signals is of great significance. In this paper, a CNN-LSTM neural network model is designed to study the two-class and four-class motion recognition of EEG signals. In this paper, a brain-computer interface experimental scheme is designed. Combining the characteristics of EEG signals, the time-frequency characteristics of EEG signals and event-related potential phenomena are analyzed, and the ERD/ERS characteristics are obtained. Pre-process EEG signals, and propose a CNN-LSTM neural network model to classify the collected binary and four-class EEG signals. The experimental results show that the CNN-LSTM neural network model has a good effect, and its average accuracy and kappa coefficient are higher than the other two classification algorithms, which also shows that the classification algorithm selected in this paper has a good classification effect.},
}
@article {pmid37322168,
year = {2023},
author = {Koitschev, A and Neudert, M and Lenarz, T},
title = {A bone conduction implant using self-drilling screws : Self-drilling screws as a new fixation method of an active transcutaneous bone conduction hearing implant.},
journal = {HNO},
volume = {71},
number = {Suppl 1},
pages = {61-66},
pmid = {37322168},
issn = {1433-0458},
mesh = {Humans ; Bone Conduction ; Bone Screws ; *Hearing Aids ; Hearing Loss, Conductive ; *Hearing Loss, Mixed Conductive-Sensorineural ; Prospective Studies ; Quality of Life ; *Speech Perception ; Treatment Outcome ; Adolescent ; Young Adult ; Adult ; Middle Aged ; },
abstract = {BACKGROUND: The active transcutaneous bone conduction implant (tBCI; BONEBRIDGE™ BCI 601; MED-EL, Innsbruck, Austria) is fixed to the skull with two self-tapping screws in predrilled screw channels. The aim of this prospective study was to evaluate the safety and effectiveness of fixation with self-drilling screws instead of the self-tapping screws, in order to simplify the surgical procedure.<br><br>MATERIALS AND METHODS: Nine patients (mean age 37&#x202f;&#xb1;&#x2009;16&#xa0;years, range 14-57&#xa0;years) were examined pre- and 12&#xa0;months postoperatively for word recognition scores (WRS) at 65&#x202f;dB SPL, sound-field (SF) thresholds, bone conduction thresholds (BC), health-related quality of life (Assessment of Quality of Life, AQOL-8D questionnaire), and adverse events (AE).<br><br>RESULTS: Due to avoidance of one surgical step, the surgical technique was simplified. Mean WRS in SF was 11.1&#x202f;&#xb1;&#x2009;22.2% (range 0-55%) pre- and 77.2&#x202f;&#xb1;&#x2009;19.9% (range 30-95%) postoperatively; mean SF threshold (pure tone audiometry, PTA4) improved from 61.2&#x202f;&#xb1;&#x2009;14.3&#x202f;dB HL (range 37.0-75.3&#x202f;dB HL) to 31.9&#x202f;&#xb1;&#x2009;7.2&#x202f;dB HL (range 22.8-45.0&#x202f;dB HL); mean BC thresholds were constant at 16.7&#x202f;&#xb1;&#x2009;6.8&#x202f;dB HL (range 6.3-27.5&#x202f;dB HL) pre- and 14.2&#x202f;&#xb1;&#x2009;6.2&#x202f;dB HL (range 5.8-23.8&#x202f;dB HL) postoperatively. AQOL-8D mean utility score increased from 0.65&#x202f;&#xb1;&#x2009;0.18 preoperatively to 0.82&#x202f;&#xb1;&#x2009;0.17 postoperatively. No device-related adverse events occurred.<br><br>CONCLUSION: Implant fixation by means of self-drilling screws was safe and effective in all nine patients. There was significant audiological benefit 12&#xa0;months after implantation.},
}
@article {pmid37322080,
year = {2023},
author = {Iwama, S and Morishige, M and Kodama, M and Takahashi, Y and Hirose, R and Ushiba, J},
title = {High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {385},
pmid = {37322080},
issn = {2052-4463},
support = {JPMJMS2012//MEXT | Japan Science and Technology Agency (JST)/ ; },
mesh = {Humans ; Brain/physiology ; *Brain-Computer Interfaces ; Computers ; Electroencephalography/methods ; *Scalp ; },
abstract = {Real-time functional imaging of human neural activity and its closed-loop feedback enable voluntary control of targeted brain regions. In particular, a brain-computer interface (BCI), a direct bridge of neural activities and machine actuation is one promising clinical application of neurofeedback. Although a variety of studies reported successful self-regulation of motor cortical activities probed by scalp electroencephalogram (EEG), it remains unclear how neurophysiological, experimental conditions or BCI designs influence variability in BCI learning. Here, we provide the EEG data during using BCIs based on sensorimotor rhythm (SMR), consisting of 4 separate datasets. All EEG data were acquired with a high-density scalp EEG setup containing 128 channels covering the whole head. All participants were instructed to perform motor imagery of right-hand movement as the strategy to control BCIs based on the task-related power attenuation of SMR magnitude, that is event-related desynchronization. This dataset would allow researchers to explore the potential source of variability in BCI learning efficiency and facilitate follow-up studies to test the explicit hypotheses explored by the dataset.},
}
@article {pmid37322034,
year = {2023},
author = {Wilson, H and Golbabaee, M and Proulx, MJ and Charles, S and O'Neill, E},
title = {EEG-based BCI Dataset of Semantic Concepts for Imagination and Perception Tasks.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {386},
pmid = {37322034},
issn = {2052-4463},
support = {EP/S515279/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; EP/X001091/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; EP/T022523/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/ ; AH/T004673/1//RCUK | Arts and Humanities Research Council (AHRC)/ ; AH/T004673/1//RCUK | Arts and Humanities Research Council (AHRC)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Perception ; Semantics ; },
abstract = {Electroencephalography (EEG) is a widely-used neuroimaging technique in Brain Computer Interfaces (BCIs) due to its non-invasive nature, accessibility and high temporal resolution. A range of input representations has been explored for BCIs. The same semantic meaning can be conveyed in different representations, such as visual (orthographic and pictorial) and auditory (spoken words). These stimuli representations can be either imagined or perceived by the BCI user. In particular, there is a scarcity of existing open source EEG datasets for imagined visual content, and to our knowledge there are no open source EEG datasets for semantics captured through multiple sensory modalities for both perceived and imagined content. Here we present an open source multisensory imagination and perception dataset, with twelve participants, acquired with a 124 EEG channel system. The aim is for the dataset to be open for purposes such as BCI related decoding and for better understanding the neural mechanisms behind perception, imagination and across the sensory modalities when the semantic category is held constant.},
}
@article {pmid37321520,
year = {2023},
author = {Shimamura, M and Katayama, N and Ohura, H},
title = {Mean 14-year Outcomes of Hybrid Total Hip Arthroplasty Using Bulk Femoral Head Autografts for Acetabular Reconstruction.},
journal = {The Journal of arthroplasty},
volume = {38},
number = {12},
pages = {2667-2672},
doi = {10.1016/j.arth.2023.06.013},
pmid = {37321520},
issn = {1532-8406},
mesh = {Humans ; *Arthroplasty, Replacement, Hip/methods ; Autografts/surgery ; Retrospective Studies ; Femur Head/surgery ; Acetabulum/surgery ; *Hip Prosthesis ; Follow-Up Studies ; },
abstract = {BACKGROUND: We aimed to evaluate the mean 14-year outcomes of hybrid total hip arthroplasty (THA) with cementless acetabular cups using bulk femoral head autografts in acetabular reconstruction and specify the radiological characteristics of cementless acetabular cups using this technique.<br><br>METHODS: This retrospective study included 98 patients (123 hips) who underwent hybrid THA with a cementless acetabular cup using bulk femoral head autografts for bone deficiency in acetabular dysplasia and who were followed-up for a mean of 14 years (range, 10 to 19.6). The percentage of bone coverage index (BCI) and cup center-edge (CE) angles were evaluation radiologically of acetabular host bone coverage. The survival rate of the cementless acetabular cup and autograft bone ingrowth were assessed.<br><br>RESULTS: The survival rate with all revisions of cementless acetabular cups was 97.1% (95% confidence interval: 91.2 to 99.1). The autograft bone was remodeled or reoriented in all cases except in 2 hips where the bulk femoral head autograft collapsed. Radiological evaluation revealed a mean cup CE angle of&#xa0;-17.8&#xb0; (range,&#xa0;-52 to&#xa0;-7&#xb0;) and a BCI of 44.4% (range, 10 to 75.4%).<br><br>CONCLUSION: Cementless acetabular cups using bulk femoral head autografts for bone deficiency of the acetabular roof remained stable even if the average BCI was 44.4% and the average cup CE angle was&#xa0;-17.8&#xb0;. Cementless acetabular cups using these techniques showed good 10-year to 19.6-year outcomes and viabilities of graft bones.},
}
@article {pmid37321179,
year = {2023},
author = {Guerrero-Mendez, CD and Blanco-Diaz, CF and Ruiz-Olaya, AF and López-Delis, A and Jaramillo-Isaza, S and Milanezi Andrade, R and Ferreira De Souza, A and Delisle-Rodriguez, D and Frizera-Neto, A and Bastos-Filho, TF},
title = {EEG motor imagery classification using deep learning approaches in naïve BCI users.},
journal = {Biomedical physics &amp; engineering express},
volume = {9},
number = {4},
pages = {},
doi = {10.1088/2057-1976/acde82},
pmid = {37321179},
issn = {2057-1976},
mesh = {Humans ; *Deep Learning ; *Brain-Computer Interfaces ; Imagination ; Reproducibility of Results ; Electroencephalography/methods ; },
abstract = {Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p< 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.},
}
@article {pmid37318970,
year = {2023},
author = {Ma, R and Zhang, H and Zhang, J and Zhong, X and Yu, Z and Li, Y and Yu, T and Gu, Z},
title = {Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2789-2799},
doi = {10.1109/TNSRE.2023.3286688},
pmid = {37318970},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Bayes Theorem ; Uncertainty ; Reproducibility of Results ; Algorithms ; },
abstract = {P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.},
}
@article {pmid37318349,
year = {2023},
author = {Litton, JK and Beck, JT and Jones, JM and Andersen, J and Blum, JL and Mina, LA and Brig, R and Danso, M and Yuan, Y and Abbattista, A and Noonan, K and Niyazov, A and Chakrabarti, J and Czibere, A and Symmans, WF and Telli, ML},
title = {Neoadjuvant Talazoparib in Patients With Germline BRCA1/2 Mutation-Positive, Early-Stage Triple-Negative Breast Cancer: Results of a Phase II Study.},
journal = {The oncologist},
volume = {28},
number = {10},
pages = {845-855},
pmid = {37318349},
issn = {1549-490X},
mesh = {Humans ; *BRCA1 Protein/genetics ; Neoadjuvant Therapy ; *Triple Negative Breast Neoplasms/drug therapy/genetics ; BRCA2 Protein/genetics ; Quality of Life ; Antineoplastic Combined Chemotherapy Protocols/adverse effects ; Poly(ADP-ribose) Polymerase Inhibitors/adverse effects ; Germ-Line Mutation ; Anthracyclines/therapeutic use ; },
abstract = {BACKGROUND: The undetermined efficacy of the current standard-of-care neoadjuvant treatment, anthracycline/platinum-based chemotherapy, in patients with early-stage triple-negative breast cancer (TNBC) and germline BRCA mutations emphasizes the need for biomarker-targeted treatment, such as poly(ADP-ribose) polymerase inhibitors, in this setting. This phase II, single-arm, open-label study evaluated the efficacy and safety of neoadjuvant talazoparib in patients with germline BRCA1/2-mutated early-stage TNBC.<br><br>PATIENTS AND METHODS: Patients with germline BRCA1/2-mutated early-stage TNBC received talazoparib 1 mg once daily for 24 weeks (0.75 mg for moderate renal impairment) followed by surgery. The primary endpoint was pathologic complete response (pCR) by independent central review (ICR). Secondary endpoints included residual cancer burden (RCB) by ICR. Safety and tolerability of talazoparib and patient-reported outcomes were assessed.<br><br>RESULTS: Of 61 patients, 48 received &#x2265;80% talazoparib doses, underwent surgery, and were assessed for pCR or progressed before pCR assessment and considered nonresponders. pCR rate was 45.8% (95% confidence interval [CI], 32.0%-60.6%) and 49.2% (95% CI, 36.7%-61.6%) in the evaluable and intent-to-treat (ITT) population, respectively. RCB 0/I rate was 45.8% (95% CI, 29.4%-63.2%) and 50.8% (95% CI, 35.5%-66.0%) in the evaluable and ITT population, respectively. Treatment-related adverse events (TRAE) were reported in 58 (95.1%) patients. Most common grade 3 and 4 TRAEs were anemia (39.3%) and neutropenia (9.8%). There was no clinically meaningful detriment in quality of life. No deaths occurred during the reporting period; 2 deaths due to progressive disease occurred during long-term follow-up (>400 days after first dose).<br><br>CONCLUSIONS: Neoadjuvant talazoparib monotherapy was active despite pCR rates not meeting the prespecified threshold; these rates were comparable to those observed with combination anthracycline- and taxane-based chemotherapy regimens. Talazoparib was generally well tolerated.<br><br>CLINICALTRIALS.GOV IDENTIFIER: NCT03499353.},
}
@article {pmid37315657,
year = {2023},
author = {Pei, G and Xiao, Q and Pan, Y and Li, T and Jin, J},
title = {Neural evidence of face processing in social anxiety disorder: A systematic review with meta-analysis.},
journal = {Neuroscience and biobehavioral reviews},
volume = {152},
number = {},
pages = {105283},
doi = {10.1016/j.neubiorev.2023.105283},
pmid = {37315657},
issn = {1873-7528},
mesh = {Humans ; *Phobia, Social/psychology ; *Facial Recognition ; Emotions ; Evoked Potentials ; Fear ; Facial Expression ; },
abstract = {Numerous previous studies have used event-related potentials (ERPs) to examine facial processing deficits in individuals with social anxiety disorder (SAD). However, researchers still need to determine whether the deficits are general or specific and what the dominant factors are behind different cognitive stages. Meta-analysis was performed to quantitatively identify face processing deficits in individuals with SAD. Ninety-seven results in 27 publications involving 1032 subjects were calculated using Hedges' g. The results suggest that the face itself elicits enlarged P1 amplitudes, threat-related facial expressions induce larger P2 amplitudes, and negative facial expressions lead to enhanced P3/LPP amplitudes in SAD individuals compared with controls. That is, there is face perception attentional bias in the early phase (P1), threat attentional bias in the mid-term phase (P2), and negative emotion attentional bias in the late phase (P3/LPP), which can be summarized into a three-phase SAD face processing deficit model. These findings provide an essential theoretical basis for cognitive behavioral therapy and have significant application value for the initial screening, intervention, and treatment of social anxiety.},
}
@article {pmid37313471,
year = {2023},
author = {Xue, H and Wang, D and Jin, M and Gao, H and Wang, X and Xia, L and Li, D and Sun, K and Wang, H and Dong, X and Zhang, C and Cong, F and Lin, J},
title = {Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition.},
journal = {Microsystems &amp; nanoengineering},
volume = {9},
number = {},
pages = {79},
pmid = {37313471},
issn = {2055-7434},
abstract = {Noninvasive brain-computer interfaces (BCIs) show great potential in applications including sleep monitoring, fatigue alerts, neurofeedback training, etc. While noninvasive BCIs do not impose any procedural risk to users (as opposed to invasive BCIs), the acquisition of high-quality electroencephalograms (EEGs) in the long term has been challenging due to the limitations of current electrodes. Herein, we developed a semidry double-layer hydrogel electrode that not only records EEG signals at a resolution comparable to that of wet electrodes but is also able to withstand up to 12 h of continuous EEG acquisition. The electrode comprises dual hydrogel layers: a conductive layer that features high conductivity, low skin-contact impedance, and high robustness; and an adhesive layer that can bond to glass or plastic substrates to reduce motion artifacts in wearing conditions. Water retention in the hydrogel is stable, and the measured skin-contact impedance of the hydrogel electrode is comparable to that of wet electrodes (conductive paste) and drastically lower than that of dry electrodes (metal pin). Cytotoxicity and skin irritation tests show that the hydrogel electrode has excellent biocompatibility. Finally, the developed hydrogel electrode was evaluated in both N170 and P300 event-related potential (ERP) tests on human volunteers. The hydrogel electrode captured the expected ERP waveforms in both the N170 and P300 tests, showing similarities in the waveforms generated by wet electrodes. In contrast, dry electrodes fail to detect the triggered potential due to low signal quality. In addition, our hydrogel electrode can acquire EEG for up to 12 h and is ready for recycled use (7-day tests). Altogether, the results suggest that our semidry double-layer hydrogel electrodes are able to detect ERPs in the long term in an easy-to-use fashion, potentially opening up numerous applications in real-life scenarios for noninvasive BCI.},
}
@article {pmid37311807,
year = {2023},
author = {Simistira Liwicki, F and Gupta, V and Saini, R and De, K and Abid, N and Rakesh, S and Wellington, S and Wilson, H and Liwicki, M and Eriksson, J},
title = {Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {378},
pmid = {37311807},
issn = {2052-4463},
mesh = {Humans ; Brain ; Electroencephalography ; Magnetic Resonance Imaging ; *Speech ; *Speech Perception ; },
abstract = {The recognition of inner speech, which could give a 'voice' to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.},
}
@article {pmid37309580,
year = {2023},
author = {Akiyama, N and Patel, KD and Jang, EJ and Shannon, MR and Patel, R and Patel, M and Perriman, AW},
title = {Tubular nanomaterials for bone tissue engineering.},
journal = {Journal of materials chemistry. B},
volume = {11},
number = {27},
pages = {6225-6248},
doi = {10.1039/d3tb00905j},
pmid = {37309580},
issn = {2050-7518},
support = {MR/S016430/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Tissue Engineering/methods ; *Nanotubes, Carbon/chemistry ; Bone and Bones ; Biocompatible Materials/chemistry ; Durapatite/chemistry ; },
abstract = {Nanomaterial composition, morphology, and mechanical performance are critical parameters for tissue engineering. Within this rapidly expanding space, tubular nanomaterials (TNs), including carbon nanotubes (CNTs), titanium oxide nanotubes (TNTs), halloysite nanotubes (HNTs), silica nanotubes (SiNTs), and hydroxyapatite nanotubes (HANTs) have shown significant potential across a broad range of applications due to their high surface area, versatile surface chemistry, well-defined mechanical properties, excellent biocompatibility, and monodispersity. These include drug delivery vectors, imaging contrast agents, and scaffolds for bone tissue engineering. This review is centered on the recent developments in TN-based biomaterials for structural tissue engineering, with a strong focus on bone tissue regeneration. It includes a detailed literature review on TN-based orthopedic coatings for metallic implants and composite scaffolds to enhance in vivo bone regeneration.},
}
@article {pmid37308588,
year = {2023},
author = {Zuo, Y and Ye, J and Cai, W and Guo, B and Chen, X and Lin, L and Jin, S and Zheng, H and Fang, A and Qian, X and Abdelrahman, Z and Wang, Z and Zhang, Z and Chen, Z and Yu, B and Gu, X and Wang, X},
title = {Controlled delivery of a neurotransmitter-agonist conjugate for functional recovery after severe spinal cord injury.},
journal = {Nature nanotechnology},
volume = {18},
number = {10},
pages = {1230-1240},
pmid = {37308588},
issn = {1748-3395},
mesh = {Rats ; Animals ; Reactive Oxygen Species ; *Spinal Cord Injuries/drug therapy/pathology ; Neurons/pathology ; Neurotransmitter Agents/pharmacology ; },
abstract = {Despite considerable unmet medical needs, effective pharmacological treatments that promote functional recovery after spinal cord injury remain limited. Although multiple pathological events are implicated in spinal cord injuries, the development of a microinvasive pharmacological approach that simultaneously targets the different mechanisms involved in spinal cord injury remains a formidable challenge. Here we report the development of a microinvasive nanodrug delivery system that consists of amphiphilic copolymers responsive to reactive oxygen species and an encapsulated neurotransmitter-conjugated KCC2 agonist. Upon intravenous administration, the nanodrugs enter the injured spinal cord due to a disruption in the blood-spinal cord barrier and disassembly due to damage-triggered reactive oxygen species. The nanodrugs exhibit dual functions in the injured spinal cord: scavenging accumulated reactive oxygen species in the lesion, thereby protecting spared tissues, and facilitating the integration of spared circuits into the host spinal cord through targeted modulation of inhibitory neurons. This microinvasive treatment leads to notable functional recovery in rats with contusive spinal cord injury.},
}
@article {pmid37307771,
year = {2023},
author = {Zhao, P and Guo, Z and Wang, H and Zhou, B and Huang, F and Dong, S and Yang, J and Li, B and Wang, X},
title = {A multi-crosslinking strategy of organic and inorganic compound bio-adhesive polysaccharide-based hydrogel for wound hemostasis.},
journal = {Biomaterials advances},
volume = {152},
number = {},
pages = {213481},
doi = {10.1016/j.bioadv.2023.213481},
pmid = {37307771},
issn = {2772-9508},
mesh = {Humans ; Adhesives/pharmacology ; Tissue Adhesions ; Hydrogels/pharmacology ; Hemostasis ; *Hemostatics/pharmacology/chemistry ; Polysaccharides/pharmacology ; *Inorganic Chemicals/pharmacology ; },
abstract = {Polysaccharides are naturally occurring polymers with exceptional biodegradable and biocompatible qualities that are used as hemostatic agents. In this study, photoinduced CC bond network and dynamic bond network binding was used to give polysaccharide-based hydrogels the requisite mechanical strength and tissue adhesion. The designed hydrogel was composed of modified carboxymethyl chitosan (CMCS-MA) and oxidized dextran (OD), and introduced hydrogen bond network through tannic acid (TA) doping. Halloysite nanotubes (HNTs) were also added, and the effects of various doping amount on the performance of the hydrogel were examined, in order to enhance the hemostatic property of hydrogel. Experiments on vitro degradation and swelling demonstrated the strong structural stability of hydrogels. The hydrogel has improved tissue adhesion strength, with a maximum adhesion strength of 157.9 kPa, and demonstrated improved compressive strength, with a maximum compressive strength of 80.9 kPa. Meanwhile, the hydrogel had a low hemolysis rate and had no inhibition on cell proliferation. The created hydrogel exhibited a significant aggregation effect on platelets and a reduced blood clotting index (BCI). Importantly, the hydrogel can quickly adhere to seal the wound and has good hemostatic effect in vivo. Our work successfully prepared a polysaccharide-based bio-adhesive hydrogel dressing with stable structure, appropriate mechanical strength, and good hemostatic properties.},
}
@article {pmid37307178,
year = {2023},
author = {Song, Y and Zheng, Q and Wang, Q and Gao, X and Heng, PA},
title = {Global Adaptive Transformer for Cross-Subject Enhanced EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2767-2777},
doi = {10.1109/TNSRE.2023.3285309},
pmid = {37307178},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; *Learning ; Machine Learning ; Software ; Electric Power Supplies ; },
abstract = {Due to the individual difference, EEG signals from other subjects (source) can hardly be used to decode the mental intentions of the target subject. Although transfer learning methods have shown promising results, they still suffer from poor feature representation or neglect long-range dependencies. In light of these limitations, we propose Global Adaptive Transformer (GAT), an domain adaptation method to utilize source data for cross-subject enhancement. Our method uses parallel convolution to capture temporal and spatial features first. Then, we employ a novel attention-based adaptor that implicitly transfers source features to the target domain, emphasizing the global correlation of EEG features. We also use a discriminator to explicitly drive the reduction of marginal distribution discrepancy by learning against the feature extractor and the adaptor. Besides, an adaptive center loss is designed to align the conditional distribution. With the aligned source and target features, a classifier can be optimized to decode EEG signals. Experiments on two widely used EEG datasets demonstrate that our method outperforms state-of-the-art methods, primarily due to the effectiveness of the adaptor. These results indicate that GAT has good potential to enhance the practicality of BCI.},
}
@article {pmid37305363,
year = {2023},
author = {Ninenko, I and Kleeva, DF and Bukreev, N and Lebedev, MA},
title = {An experimental paradigm for studying EEG correlates of olfactory discrimination.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1117801},
pmid = {37305363},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) correlates of olfaction are of fundamental and practical interest for many reasons. In the field of neural technologies, olfactory-based brain-computer interfaces (BCIs) represent an approach that could be useful for neurorehabilitation of anosmia, dysosmia and hyposmia. While the idea of a BCI that decodes neural responses to different odors and/or enables odor-based neurofeedback is appealing, the results of previous EEG investigations into the olfactory domain are rather inconsistent, particularly when non-primary processing of olfactory signals is concerned. Here we developed an experimental paradigm where EEG recordings are conducted while a participant executes an olfaction-based instructed-delay task. We utilized an olfactory display and a sensor of respiration to deliver odors in a strictly controlled fashion. We showed that with this approach spatial and spectral EEG properties could be analyzed to assess neural processing of olfactory stimuli and their conversion into a motor response. We conclude that EEG recordings are suitable for detecting active processing of odors. As such they could be integrated in a BCI that strives to rehabilitate olfactory disabilities or uses odors for hedonistic purposes.},
}
@article {pmid37305362,
year = {2023},
author = {López-Larraz, E and Escolano, C and Robledo-Menéndez, A and Morlas, L and Alda, A and Minguez, J},
title = {A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1135153},
pmid = {37305362},
issn = {1662-5161},
abstract = {This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.},
}
@article {pmid37303934,
year = {2023},
author = {Zhou, M and Zhang, YM and Li, T},
title = {Knowledge, attitudes and experiences of genetic testing for autism spectrum disorders among caregivers, patients, and health providers: A systematic review.},
journal = {World journal of psychiatry},
volume = {13},
number = {5},
pages = {247-261},
pmid = {37303934},
issn = {2220-3206},
abstract = {BACKGROUND: Several genetic testing techniques have been recommended as a first-tier diagnostic tool in clinical practice for diagnosing autism spectrum disorder (ASD). However, the actual usage rate varies dramatically. This is due to various reasons, including knowledge and attitudes of caregivers, patients, and health providers toward genetic testing. Several studies have therefore been conducted worldwide to investigate the knowledge, experiences, and attitudes toward genetic testing among caregivers of children with ASD, adolescent and adult ASD patients, and health providers who provide medical services for them. However, no systematic review has been done.<br><br>AIM: To systematically review research on knowledge, experiences, and attitudes towards genetic testing among caregivers of children with ASD, adolescent and adult ASD patients, and health providers.<br><br>METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and searched the literature in three English language databases (PubMed, Web of Science, and PsychInfo) and two Chinese databases (CNKI and Wanfang). Searched literature was screened independently by two reviewers and discussed when inconsistency existed. Information on characteristics of the study, characteristics of participants, and main findings regarding knowledge, experience, and attitudes of caregivers of children with ASD, adolescent and adult ASD patients, and health providers concerning ASD genetic testing were extracted from included papers into a charting form for analysis.<br><br>RESULTS: We included 30 studies published between 2012 and 2022 and conducted in 9 countries. Most of the studies (n = 29) investigated caregivers of children with ASD, one study also included adolescent and adult patients, and two covered health providers. Most (51.0%-100%) of the caregivers/patients knew there was a genetic cause for ASD and 17.0% to 78.1% were aware of ASD genetic testing. However, they lacked full understanding of genetic testing. They acquired relevant and necessary information from physicians, the internet, ASD organizations, and other caregivers. Between 9.1% to 72.7% of caregivers in different studies were referred for genetic testing, and between 17.4% to 61.7% actually obtained genetic testing. Most caregivers agreed there are potential benefits following genetic testing, including benefits for children, families, and others. However, two studies compared perceived pre-test and post-test benefits with conflicting findings. Caregivers concerns included high costs, unhelpful results, negative influences (e.g., causing family conflicts, causing stress/risk/pain to children etc.) prevented some caregivers from using genetic testing. Nevertheless, 46.7% to 95.0% caregivers without previous genetic testing experience intended to obtain it in the future, and 50.5% to 59.6% of parents previously obtaining genetic testing would recommend it to other parents. In a single study of child and adolescent psychiatrists, 54.9% of respondents had ordered ASD genetic testing for their patients in the prior 12 mo, which was associated with greater knowledge of genetic testing.<br><br>CONCLUSION: Most caregivers are willing to learn about and use genetic testing. However, the review showed their current knowledge is limited and usage rates varied widely in different studies.},
}
@article {pmid37302715,
year = {2023},
author = {Okahara, Y and Takano, K and Odaka, K and Uchino, Y and Kansaku, K},
title = {Detecting passive and active response in patients with behaviourally diagnosed unresponsive wakefulness syndrome.},
journal = {Neuroscience research},
volume = {196},
number = {},
pages = {23-31},
doi = {10.1016/j.neures.2023.06.002},
pmid = {37302715},
issn = {1872-8111},
mesh = {Humans ; *Wakefulness ; *Persistent Vegetative State/diagnostic imaging ; Magnetic Resonance Imaging ; Electroencephalography/methods ; },
abstract = {The diagnosis of unresponsive wakefulness syndrome depends mostly on the motor response following verbal commands. However, there is a potential for misdiagnosis in patients who understand verbal commands (passive response) but cannot perform voluntary movements (active response). To evaluate passive and active responses in such patients, this study used an approach combining functional magnetic resonance imaging and passive listening tasks to evaluate the level of speech comprehension, with portable brain-computer interface modalities that were applied to elicit an active response to attentional modulation tasks at the bedside. We included ten patients who were clinically diagnosed as unresponsive wakefulness syndrome. Two of ten patients showed no significant activation, while limited activation in the auditory cortex was found in six patients. The remaining two patients showed significant activation in language areas, and were able to control the brain-computer interface with reliable accuracy. Using a combined passive/active approach, we identified unresponsive wakefulness syndrome patients who showed both active and passive neural responses. This suggests that some patients with unresponsive wakefulness syndrome diagnosed behaviourally are both wakeful and responsive, and the combined approach is useful for distinguishing a minimally conscious state from unresponsive wakefulness syndrome physiologically.},
}
@article {pmid37300785,
year = {2023},
author = {Fan, J and Zhou, F and Zheng, J and Xu, H},
title = {Rapid Eye Movement Sleep Consolidates Social Memory.},
journal = {Neuroscience bulletin},
volume = {39},
number = {10},
pages = {1598-1600},
pmid = {37300785},
issn = {1995-8218},
mesh = {Humans ; *Sleep, REM ; Memory ; Sleep ; *REM Sleep Behavior Disorder ; },
}
@article {pmid37300066,
year = {2023},
author = {López-Ahumada, R and Jiménez-Naharro, R and Gómez-Bravo, F},
title = {A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {11},
pages = {},
pmid = {37300066},
issn = {1424-8220},
mesh = {Humans ; *Blinking ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Algorithms ; Computers ; Artifacts ; },
abstract = {Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human-computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain-computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software.},
}
@article {pmid37299873,
year = {2023},
author = {Gannouni, S and Belwafi, K and Aledaily, A and Aboalsamh, H and Belghith, A},
title = {Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {11},
pages = {},
pmid = {37299873},
issn = {1424-8220},
support = {14-INF3139-02//National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia/ ; },
mesh = {Humans ; *User-Centered Design ; *Deep Learning ; User-Computer Interface ; Emotions ; Electroencephalography/methods ; Software ; },
abstract = {It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usability testing based on emotion detection using EEG signals, which can significantly affect software production and user satisfaction. This approach can provide an in-depth understanding of user satisfaction accurately and precisely, making it a valuable tool in software development. The proposed framework includes a recurrent neural network algorithm as a classifier, a feature extraction algorithm based on event-related desynchronization and event-related synchronization analysis, and a new method for selecting EEG sources adaptively for emotion recognition. The framework results are promising, achieving 92.13%, 92.67%, and 92.24% for the valence-arousal-dominance dimensions, respectively.},
}
@article {pmid37299791,
year = {2023},
author = {Batistić, L and Sušanj, D and Pinčić, D and Ljubic, S},
title = {Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {11},
pages = {},
pmid = {37299791},
issn = {1424-8220},
support = {uniri-mladi-tehnic-22-2//University of Rijeka/ ; },
mesh = {Humans ; *Imagery, Psychotherapy ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Support Vector Machine ; Algorithms ; },
abstract = {Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain-computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human-computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.},
}
@article {pmid37299779,
year = {2023},
author = {Shuqfa, Z and Belkacem, AN and Lakas, A},
title = {Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {11},
pages = {},
pmid = {37299779},
issn = {1424-8220},
support = {12R011//Strategic Research Program - Emirates Center for Mobility Research/ ; },
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; },
abstract = {The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.},
}
@article {pmid37296480,
year = {2023},
author = {Kopecek, M and Kremlacek, J},
title = {Eye-tracking control of an adjustable electric bed: construction and validation by immobile patients with multiple sclerosis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {75},
pmid = {37296480},
issn = {1743-0003},
support = {TG01010108//Technology Agency of the Czech Republic/ ; },
mesh = {Male ; Humans ; Female ; *Multiple Sclerosis ; Eye-Tracking Technology ; Eye Movements ; *Persons with Disabilities ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: In severe conditions of limited motor abilities, frequent position changes for work or passive and active rest are essential bedside activities to prevent further health complications. We aimed to develop a system using eye movements for bed positioning and to verify its functionality in a control group and a group of patients with significant motor limitation caused by multiple sclerosis.<br><br>METHODS: The eye-tracking system utilized an innovative digital-to-analog converter module to control the positioning bed via a novel graphical user interface. We verified the ergonomics and usability of the system by performing a fixed sequence of positioning tasks, in which the leg and head support was repeatedly raised and then lowered. Fifteen women and eleven men aged 42.7&#x2009;&#xb1;&#x2009;15.9&#xa0;years in the control group and nine women and eight men aged 60.3&#x2009;&#xb1;&#x2009;9.14&#xa0;years in the patient group participated in the experiment. The degree of disability, according to the Expanded Disability Status Scale (EDSS), ranged from 7 to 9.5 points in the patients. We assessed the speed and efficiency of the bed control and the improvement during testing. In a questionnaire, we evaluated satisfaction with the system.<br><br>RESULTS: The control group mastered the task in 40.2&#xa0;s (median) with an interquartile interval from 34.5 to 45.5&#xa0;s, and patients mastered the task in in 56.5 (median) with an interquartile interval from 46.5 to 64.9&#xa0;s. The efficiency of solving the task (100% corresponds to an optimal performance) was 86.3 (81.6; 91.0) % for the control group and 72.1 (63.0; 75.2) % for the patient group. Throughout testing, the patients learned to communicate with the system, and their efficiency and task time improved. A&#xa0;correlation analysis showed a negative relationship (rho&#x2009;=&#x2009;-&#x2009;0.587) between efficiency improvement and the degree of impairment (EDSS). In the control group, the learning was not significant. On the questionnaire survey, sixteen patients reported gaining confidence in bed control. Seven patients preferred the offered form of bed control, and in six cases, they would choose another form of interface.<br><br>CONCLUSIONS: The proposed system and communication through eye movements are reliable for positioning the bed in people affected by advanced multiple sclerosis. Seven of 17 patients indicated that they would choose this system for bed control and wished to extend it for another application.},
}
@article {pmid37294753,
year = {2023},
author = {Liu, Y and Zhao, Z and Xu, M and Yu, H and Zhu, Y and Zhang, J and Bu, L and Zhang, X and Lu, J and Li, Y and Ming, D and Wu, J},
title = {Decoding and synthesizing tonal language speech from brain activity.},
journal = {Science advances},
volume = {9},
number = {23},
pages = {eadh0478},
pmid = {37294753},
issn = {2375-2548},
mesh = {Humans ; *Speech ; *Language ; Neural Networks, Computer ; Brain ; },
abstract = {Recent studies have shown that the feasibility of speech brain-computer interfaces (BCIs) as a clinically valid treatment in helping nontonal language patients with communication disorders restore their speech ability. However, tonal language speech BCI is challenging because additional precise control of laryngeal movements to produce lexical tones is required. Thus, the model should emphasize the features from the tonal-related cortex. Here, we designed a modularized multistream neural network that directly synthesizes tonal language speech from intracranial recordings. The network decoded lexical tones and base syllables independently via parallel streams of neural network modules inspired by neuroscience findings. The speech was synthesized by combining tonal syllable labels with nondiscriminant speech neural activity. Compared to commonly used baseline models, our proposed models achieved higher performance with modest training data and computational costs. These findings raise a potential strategy for approaching tonal language speech restoration.},
}
@article {pmid37294411,
year = {2023},
author = {Wang, L and Li, M and Zhang, L},
title = {Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {8},
pages = {2013-2032},
pmid = {37294411},
issn = {1741-0444},
support = {2021YFA1000200//National Key Research and Development Program of China/ ; 62173010//National Natural Science Foundation of China/ ; 11832003//National Natural Science Foundation of China/ ; },
mesh = {*Algorithms ; Reproducibility of Results ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {Deep learning has been applied to the recognition of motor imagery electroencephalograms (MI-EEG) in brain-computer interface, and the performance results depend on data representation as well as neural network structure. Especially, MI-EEG is so complex with the characteristics of non-stationarity, specific rhythms, and uneven distribution; however, its multidimensional feature information is difficult to be fused and enhanced simultaneously in the existing recognition methods. In this paper, a novel channel importance (NCI) based on time-frequency analysis is proposed to develop an image sequence generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Each electrode of MI-EEG is converted to a time-frequency spectrum by utilizing short-time Fourier transform; the corresponding part to 8-30 Hz is combined with random forest algorithm for computing NCI; and it is further divided into three sub-images covered by α (8-13 Hz), β1 (13-21 Hz), and β2 (21-30 Hz) bands; their spectral powers are further weighted by NCI and interpolated to 2-dimensional electrode coordinates, producing three main sub-band image sequences. Then, a parallel multi-branch convolutional neural network and gate recurrent unit (PMBCG) is designed to successively extract and identify the spatial-spectral and temporal features from the image sequences. Two public four-class MI-EEG datasets are adopted; the proposed classification method respectively achieves the average accuracies of 98.26% and 80.62% by 10-fold cross-validation experiment; and its statistical performance is also evaluated by multi-indexes, such as Kappa value, confusion matrix, and ROC curve. Extensive experiment results show that NCI-ISG + PMBCG can yield great performance on MI-EEG classification compared to state-of-the-art methods. The proposed NCI-ISG can enhance the feature representation of time-frequency-space domains and match well with PMBCG, which improves the recognition accuracies of MI tasks and demonstrates the preferable reliability and distinguishable ability. This paper proposes a novel channel importance (NCI) based on time-frequency analysis to develop an image sequences generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Then, a parallel multi-branch convolutional neural network and gate recurrent unit (PMBCG) is designed to successively extract and identify the spatial-spectral and temporal features from the image sequences.},
}
@article {pmid37292755,
year = {2023},
author = {Costello, JT and Temmar, H and Cubillos, LH and Mender, MJ and Wallace, DM and Willsey, MS and Patil, PG and Chestek, CA},
title = {Balancing Memorization and Generalization in RNNs for High Performance Brain-Machine Interfaces.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.05.28.542435},
pmid = {37292755},
issn = {2692-8205},
abstract = {Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.},
}
@article {pmid37292583,
year = {2023},
author = {Martineau, T and He, S and Vaidyanathan, R and Tan, H},
title = {Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1111590},
pmid = {37292583},
issn = {1662-5161},
support = {MC_UU_00003/2/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; MR/V00655X/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {INTRODUCTION: Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience.<br><br>METHODS: This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson's disease patients.<br><br>RESULTS: Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 &#xb1; 0.06 (mean &#xb1; SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models.<br><br>DISCUSSION: Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study's findings can inform future design iterations of neural decoders for adaptive DBS and BCI.},
}
@article {pmid37292117,
year = {2023},
author = {Yang, S and Li, M and Wang, J and Shi, Z and He, B and Xie, J and Xu, G},
title = {A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1161187},
pmid = {37292117},
issn = {1662-5218},
abstract = {INTRODUCTION: Hemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use.<br><br>METHODS: To address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs.<br><br>RESULTS: This method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention.<br><br>DISCUSSION: For the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems.},
}
@article {pmid37291106,
year = {2023},
author = {Wang, D and Tang, L and Xi, C and Luo, D and Liang, Y and Huang, Q and Wang, Z and Chen, J and Zhao, X and Zhou, H and Wang, F and Hu, S},
title = {Targeted visual cortex stimulation (TVCS): a novel neuro-navigated repetitive transcranial magnetic stimulation mode for improving cognitive function in bipolar disorder.},
journal = {Translational psychiatry},
volume = {13},
number = {1},
pages = {193},
pmid = {37291106},
issn = {2158-3188},
support = {81971271//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82201675//National Natural Science Foundation of China (National Science Foundation of China)/ ; LQ20H090012//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Transcranial Magnetic Stimulation ; *Bipolar Disorder/therapy ; Prefrontal Cortex ; Cerebral Cortex ; Cognition ; Treatment Outcome ; },
abstract = {A more effective and better-tolerated site for repetitive transcranial magnetic stimulation (rTMS) for treating cognitive dysfunction in patients with bipolar disorder (BD) is needed. The primary visual cortex (V1) may represent a suitable site. To investigate the use of the V1, which is functionally linked to the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), as a potential site for improving cognitive function in BD. Seed-based functional connectivity (FC) analysis was used to locate targets in the V1 that had significant FC with the DLPFC and ACC. Subjects were randomly assigned to 4 groups, namely, the DLPFC active-sham rTMS (A1), DLPFC sham-active rTMS (A2), ACC active-sham rTMS (B1), and ACC sham-active rTMS groups (B2). The intervention included the rTMS treatment once daily, with five treatments a week for four weeks. The A1 and B1 groups received 10 days of active rTMS treatment followed by 10 days of sham rTMS treatment. The A2 and B2 groups received the opposite. The primary outcomes were changes in the scores of five tests in the THINC-integrated tool (THINC-it) at week 2 (W2) and week 4 (W4). The secondary outcomes were changes in the FC between the DLPFC/ACC and the whole brain at W2 and W4. Of the original 93 patients with BD recruited, 86 were finally included, and 73 finished the trial. Significant interactions between time and intervention type (Active/Sham) were observed in the scores of the accuracy of the Symbol Check in the THINC-it tests at baseline (W0) and W2 in groups B1 and B2 (F = 4.736, p = 0.037) using a repeated-measures analysis of covariance approach. Group B1 scored higher in the accuracy of Symbol Check at W2 compared with W0 (p < 0.001), while the scores of group B2 did not differ significantly between W0 and W2. No significant interactions between time and intervention mode were seen between groups A1 and A2, nor was any within-group significance of FC between DLPFC/ACC and the whole brain observed between baseline (W0) and W2/W4 in any group. One participant in group B1 experienced disease progression after 10 active and 2 sham rTMS sessions. The present study demonstrated that V1, functionally correlated with ACC, is a potentially effective rTMS stimulation target for improving neurocognitive function in BD patients. Further investigation using larger samples is required to confirm the clinical efficacy of TVCS.},
}
@article {pmid37290398,
year = {2023},
author = {Roe, AW},
title = {BMI 2.0: Toward a technological interface with brainwide networks.},
journal = {Neuron},
volume = {111},
number = {11},
pages = {1687-1688},
doi = {10.1016/j.neuron.2023.05.012},
pmid = {37290398},
issn = {1097-4199},
mesh = {Animals ; Mice ; Body Mass Index ; *Neurons/physiology ; *Brain-Computer Interfaces ; },
abstract = {The field of brain machine interface has long sought a technology for brainwide interaction. In this issue of Neuron, Kim et al.[1] present a novel method for dynamic, patterned, and precise optogenetic stimulation of mouse cortex in ultra-high-field MRI that portends such an interface.},
}
@article {pmid37288633,
year = {2023},
author = {Su, T and Deng, C and Li, X},
title = {[Roadmap of Medical Device for Implanted Brain-computer Interface].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {47},
number = {3},
pages = {304-308},
doi = {10.3969/j.issn.1671-7104.2023.03.014},
pmid = {37288633},
issn = {1671-7104},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain/physiology ; Electrodes, Implanted ; },
abstract = {Implanted brain-computer interface (iBCI) is a system that establishes a direct communication channel between human brain and computer or an external devices by implanted neural electrode. Because of the good functional extensibility, iBCI devices as a platform technology have the potential to bring benefit to people with nervous system disease and progress rapidly from fundamental neuroscience discoveries to translational applications and market access. In this report, the industrialization process of implanted neural regulation medical devices is reviewed, and the translational pathway of iBCI in clinical application is proposed. However, the Food and Drug Administration (FDA) regulations and guidances for iBCI were expounded as a breakthrough medical device. Furthermore, several iBCI products in the process of applying for medical device registration certificate were briefly introduced and compared recently. Due to the complexity of iBCI in clinical application, the translational applications and industrialization of iBCI as a medical device need the closely cooperation between regulatory departments, companies, universities, institutes and hospitals in the future.},
}
@article {pmid37287074,
year = {2023},
author = {Li, XY and Xie, JJ and Wang, JH and Bao, YF and Dong, Y and Gao, B and Shen, T and Huang, PY and Ying, HC and Xu, H and Roe, AW and Lai, HY and Wu, ZY},
title = {Perivascular spaces relate to the course and cognition of Huntington's disease.},
journal = {Translational neurodegeneration},
volume = {12},
number = {1},
pages = {30},
pmid = {37287074},
issn = {2047-9158},
mesh = {Humans ; *Huntington Disease/diagnostic imaging/genetics ; Cognition ; Neuropsychological Tests ; },
}
@article {pmid37286432,
year = {2023},
author = {Chai, Y and Sheline, YI and Oathes, DJ and Balderston, NL and Rao, H and Yu, M},
title = {Functional connectomics in depression: insights into therapies.},
journal = {Trends in cognitive sciences},
volume = {27},
number = {9},
pages = {814-832},
pmid = {37286432},
issn = {1879-307X},
support = {K01 MH121777/MH/NIMH NIH HHS/United States ; R01 MH111886/MH/NIMH NIH HHS/United States ; RF1 MH116920/MH/NIMH NIH HHS/United States ; U01 MH109991/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Connectome/methods ; Magnetic Resonance Imaging/methods ; Depression/therapy ; Brain/diagnostic imaging ; Neuroimaging ; },
abstract = {Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.},
}
@article {pmid37284744,
year = {2023},
author = {Mender, MJ and Nason-Tomaszewski, SR and Temmar, H and Costello, JT and Wallace, DM and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, P and Chestek, CA},
title = {The impact of task context on predicting finger movements in a brain-machine interface.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {37284744},
issn = {2050-084X},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; R01 NS105132/NS/NINDS NIH HHS/United States ; Grant Number R01NS105132/NS/NINDS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; R01 GM111293/GM/NIGMS NIH HHS/United States ; Grant Number T32NS007222/NS/NINDS NIH HHS/United States ; Grant Number R01GM111293/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Fingers/physiology ; Movement/physiology ; Hand/physiology ; Electromyography/methods ; },
abstract = {A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.},
}
@article {pmid37284019,
year = {2023},
author = {Han, G and Zhou, Y and Zhang, K and Jiao, B and Hu, J and Zhang, Y and Wang, Z and Lou, M and Bai, R},
title = {Age- and time-of-day dependence of glymphatic function in the human brain measured via two diffusion MRI methods.},
journal = {Frontiers in aging neuroscience},
volume = {15},
number = {},
pages = {1173221},
pmid = {37284019},
issn = {1663-4365},
abstract = {Advanced age, accompanied by impaired glymphatic function, is a key risk factor for many neurodegenerative diseases. To study age-related differences in the human glymphatic system, we measured the influx and efflux activities of the glymphatic system via two non-invasive diffusion magnetic resonance imaging (MRI) methods, ultra-long echo time and low-b diffusion tensor imaging (DTIlow-b) measuring the subarachnoid space (SAS) flow along the middle cerebral artery and DTI analysis along the perivascular space (DTI-ALPS) along medullary veins in 22 healthy volunteers (aged 21-75 years). We first evaluated the circadian rhythm dependence of the glymphatic activity by repeating the MRI measurements at five time points from 8:00 to 23:00 and found no time-of-day dependence in the awake state under the current sensitivity of MRI measurements. Further test-retest analysis demonstrated high repeatability of both diffusion MRI measurements, suggesting their reliability. Additionally, the influx rate of the glymphatic system was significantly higher in participants aged >45 years than in participants aged 21-38, while the efflux rate was significantly lower in those aged >45 years. The mismatched influx and efflux activities in the glymphatic system might be due to age-related changes in arterial pulsation and aquaporin-4 polarization.},
}
@article {pmid37282888,
year = {2023},
author = {Quaranta, N and Baguley, D and Fanizzi, P and Murri, A and Pontillo, V and Cutler, JM and Cavallaro, G},
title = {The effect of cochlear implant and bimodal stimulation on tinnitus: a multinational survey.},
journal = {Acta oto-laryngologica},
volume = {143},
number = {6},
pages = {476-480},
doi = {10.1080/00016489.2023.2210618},
pmid = {37282888},
issn = {1651-2251},
mesh = {Humans ; *Tinnitus/diagnosis ; *Cochlear Implants ; Surveys and Questionnaires ; Emotions ; Electric Stimulation ; *Cochlear Implantation ; },
abstract = {BACKGROUND: Tinnitus is a frequent symptom in cochlear implant (CI) patients. Many studies have shown that a CI leads to a significant change in the perception of tinnitus.<br><br>AIMS: The aim of the present study was to evaluate the effect of CI on tinnitus in patients with Unilateral Cochlear Implant (UCI), Bilateral Cochlear Implant (BCI), and Bimodal Stimulation (BMS).<br><br>MATERIAL AND METHODS: A survey was administered online to CI patients. The Tinnitus Handicap Inventory (THI) score was calculated. Emotional, functional, and catastrophic subscales scores were calculated. The intensity and annoyance of tinnitus were graded using a scale from 1 to 10.<br><br>RESULTS: 130 participants represented the study group; the Average THI score was 38.3 (SD: 26.3) in UCI, 32.4 (SD 25.8) in BCI users, and 42.5 (SD 28.2) in BMS: no significant difference was found among the three groups. CI users for less than 1&#x2009;year showed significantly higher THI scores compared to CI users for more than 5&#x2009;years (p&#x2009;=&#x2009;.0275). The intensity and annoyance of tinnitus significantly decreased with the CI on compared to the CI off condition.<br><br>CONCLUSIONS AND SIGNIFICANCE: Taken together, our findings support CI's efficacy in reducing the perception of tinnitus. No significant differences were evident between unilateral and bilateral electrical stimulation in terms of tinnitus improvement.},
}
@article {pmid37276408,
year = {2023},
author = {Fu, XX and Zhuo, DH and Zhang, YJ and Li, YF and Liu, X and Xing, YY and Huang, Y and Wang, YF and Cheng, T and Wang, D and Chen, SH and Chen, YJ and Jiang, GN and Lu, FI and Feng, Y and Huang, X and Ma, J and Liu, W and Bai, G and Xu, PF},
title = {A spatiotemporal barrier formed by Follistatin is required for left-right patterning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {24},
pages = {e2219649120},
pmid = {37276408},
issn = {1091-6490},
mesh = {Animals ; *Zebrafish/genetics/metabolism ; *Zebrafish Proteins/genetics/metabolism ; Follistatin/genetics/metabolism ; Body Patterning/genetics ; Transforming Growth Factor beta/metabolism ; Gene Expression Regulation, Developmental ; },
abstract = {How left-right (LR) asymmetry emerges in a patterning field along the anterior-posterior axis remains an unresolved problem in developmental biology. Left-biased Nodal emanating from the LR organizer propagates from posterior to anterior (PA) and establishes the LR pattern of the whole embryo. However, little is known about the regulatory mechanism of the PA spread of Nodal and its asymmetric activation in the forebrain. Here, we identify bilaterally expressed Follistatin (Fst) as a regulator blocking the propagation of the zebrafish Nodal ortholog Southpaw (Spaw) in the right lateral plate mesoderm (LPM), and restricting Spaw transmission in the left LPM to facilitate the establishment of a robust LR asymmetric Nodal patterning. In addition, Fst inhibits the Activin-Nodal signaling pathway in the forebrain thus preventing Nodal activation prior to the arrival, at a later time, of Spaw emanating from the left LPM. This contributes to the orderly propagation of asymmetric Nodal activation along the PA axis. The LR regulation function of Fst is further confirmed in chick and frog embryos. Overall, our results suggest that a robust LR patterning emerges by counteracting a Fst barrier formed along the PA axis.},
}
@article {pmid37275985,
year = {2023},
author = {Yu, H and Ni, P and Zhao, L and Tian, Y and Li, M and Li, X and Wei, W and Wei, J and Deng, W and Du, X and Wang, Q and Guo, W and Ma, X and Coid, J and Li, T},
title = {Decreased plasma neuropeptides in first-episode schizophrenia, bipolar disorder, major depressive disorder: associations with clinical symptoms and cognitive function.},
journal = {Frontiers in psychiatry},
volume = {14},
number = {},
pages = {1180720},
pmid = {37275985},
issn = {1664-0640},
abstract = {BACKGROUND: There is an urgent need to identify differentiating and disease-monitoring biomarkers of schizophrenia, bipolar disorders (BD), and major depressive disorders (MDD) to improve treatment and management.<br><br>METHODS: We recruited 54 first-episode schizophrenia (FES) patients, 52 BD patients, 35 MDD patients, and 54 healthy controls from inpatient and outpatient clinics. &#x3b1;-Melanocyte Stimulating Hormone (&#x3b1;-MSH), &#x3b2;-endorphin, neurotensin, orexin-A, oxytocin, and substance P were investigated using quantitative multiplex assay method. Psychotic symptoms were measured using the Brief Psychiatric Rating Scale (BPRS) and Positive and Negative Syndrome Scale (PANSS), manic symptoms using the Young Mania Rating Scale (YMRS), and depressive symptoms using 17 item-Hamilton Depression Rating Scale (HAMD). We additionally measured cognitive function by using a battery of tests given to all participants.<br><br>RESULTS: &#x3b1;-MSH, neurotensin, orexin-A, oxytocin, and substance P were decreased in the three patient groups compared with controls. Neurotensin outperformed all biomarkers in differentiating patient groups from controls. There were no significant differences for 6 neuropeptides in their ability to differentiate between the three patient groups. Higher neurotensin was associated with better executive function across the entire sample. Lower oxytocin and higher substance p were associated with more psychotic symptoms in FES and BD groups. &#x3b2;-endorphin was associated with early morning wakening symptom in all three patient groups.<br><br>CONCLUSION: Our research shows decreased circulating neuropeptides have the potential to differentiate severe mental illnesses from controls. These neuropeptides are promising treatment targets for improving clinical symptoms and cognitive function in FES, BD, and MDD.},
}
@article {pmid37275343,
year = {2023},
author = {Nitta, T and Horikawa, J and Iribe, Y and Taguchi, R and Katsurada, K and Shinohara, S and Kawai, G},
title = {Linguistic representation of vowels in speech imagery EEG.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1163578},
pmid = {37275343},
issn = {1662-5161},
abstract = {Speech imagery recognition from electroencephalograms (EEGs) could potentially become a strong contender among non-invasive brain-computer interfaces (BCIs). In this report, first we extract language representations as the difference of line-spectra of phones by statistically analyzing many EEG signals from the Broca area. Then we extract vowels by using iterative search from hand-labeled short-syllable data. The iterative search process consists of principal component analysis (PCA) that visualizes linguistic representation of vowels through eigen-vectors φ(m), and subspace method (SM) that searches an optimum line-spectrum for redesigning φ(m). The extracted linguistic representation of Japanese vowels /i/ /e/ /a/ /o/ /u/ shows 2 distinguished spectral peaks (P1, P2) in the upper frequency range. The 5 vowels are aligned on the P1-P2 chart. A 5-vowel recognition experiment using a data set of 5 subjects and a convolutional neural network (CNN) classifier gave a mean accuracy rate of 72.6%.},
}
@article {pmid37274188,
year = {2023},
author = {Chailloux Peguero, JD and Hernández-Rojas, LG and Mendoza-Montoya, O and Caraza, R and Antelis, JM},
title = {SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1142892},
pmid = {37274188},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC).<br><br>METHODS: We set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data.<br><br>RESULTS: The results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation.<br><br>DISCUSSION: Finally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.},
}
@article {pmid37273160,
year = {2024},
author = {Zhao, R and Yue, T and Xu, Z and Zhang, Y and Wu, Y and Bai, Y and Ni, G and Ming, D},
title = {Electroencephalogram-based objective assessment of cognitive function level associated with age-related hearing loss.},
journal = {GeroScience},
volume = {46},
number = {1},
pages = {431-446},
pmid = {37273160},
issn = {2509-2723},
support = {2022YFF1202400//National Key Research and Development Program of China/ ; 81971698//National Natural Science Foundation of China/ ; 82202290//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Longitudinal Studies ; Cognition ; *Presbycusis/psychology ; *Cognitive Dysfunction/diagnosis ; Electroencephalography ; },
abstract = {Age-Related Hearing Loss (ARHL) is a common problem in aging. Numerous longitudinal cohort studies have revealed that ARHL is closely related to cognitive function, leading to a significant risk of cognitive decline and dementia. This risk gradually increases with the severity of hearing loss. We designed dual auditory Oddball and cognitive task paradigms for the ARHL subjects, then obtained the Montreal Cognitive Assessment (MoCA) scale evaluation results for all the subjects. Multi-dimensional EEG characteristics helped explore potential biomarkers to evaluate the cognitive level of the ARHL group, having a significantly lower P300 peak amplitude coupled with a prolonged latency. Moreover, visual memory, auditory memory, and logical calculation were investigated during the cognitive task paradigm. In the ARHL groups, the alpha-to-beta rhythm energy ratio in the visual and auditory memory retention period and the wavelet packet entropy value within the logical calculation period were significantly reduced. Correlation analysis between the above specificity indicators and the subjective scale results of the ARHL group revealed that the auditory P300 component characteristics could assess attention resources and information processing speed. The alpha and beta rhythm energy ratio and wavelet packet entropy can become potential indicators to determine working memory and logical cognitive computation-related cognitive ability.},
}
@article {pmid37270412,
year = {2023},
author = {Adams, J and Hasan, M and Thorp, J},
title = {Using neurotechnology in the emergency and safety management for creating a safer work environment.},
journal = {Journal of emergency management (Weston, Mass.)},
volume = {21},
number = {2},
pages = {133-139},
doi = {10.5055/jem.0768},
pmid = {37270412},
issn = {1543-5865},
mesh = {Humans ; *Working Conditions ; *Brain-Computer Interfaces ; },
abstract = {Brain-computer interfaces are emerging neurotechnology conducting specific commands or outputs based on acquiring brain signals or inputs. This study examines the common hazards present in industries, which can be managed by neurotechnology, as well as compares two types of brain-computer interfaces in the neurotechnology area. The findings from this work suggest acknowledging current safety management practices and technology that can promote a safer work environment, in addition to increasing probable applications of use of the current research findings related to neurotechnology. This study advises understanding the risks associated between noninvasive and invasive neurotechnologies, whereas noninvasive technologies are safer that exhibit lesser degrees of accuracy or applications of use compared to its counterpart, which is invasive technology. This study proposes future development of this technology, which can integrate components based on common practices by industry.},
}
@article {pmid37269957,
year = {2023},
author = {Miao, Z and Zhao, M and Zhang, X and Ming, D},
title = {LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability.},
journal = {NeuroImage},
volume = {276},
number = {},
pages = {120209},
doi = {10.1016/j.neuroimage.2023.120209},
pmid = {37269957},
issn = {1095-9572},
mesh = {Humans ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Generalization, Psychological ; Imagination/physiology ; },
abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically, EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. To address these limitations, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net is able to effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for evoked responses and endogenous activities. By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general decoding model for various EEG tasks.},
}
@article {pmid37269019,
year = {2023},
author = {Amin, HU and Ullah, R and Reza, MF and Malik, AS},
title = {Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {70},
pmid = {37269019},
issn = {1743-0003},
mesh = {Humans ; *Wavelet Analysis ; *Electroencephalography/methods ; Evoked Potentials/physiology ; Machine Learning ; Area Under Curve ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.<br><br>METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.<br><br>RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.<br><br>CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.},
}
@article {pmid37268619,
year = {2023},
author = {Ortiz, M and de la Ossa, L and Juan, J and Iáñez, E and Torricelli, D and Tornero, J and Azorín, JM},
title = {An EEG database for the cognitive assessment of motor imagery during walking with a lower-limb exoskeleton.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {343},
pmid = {37268619},
issn = {2052-4463},
support = {779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; 779963//EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priorioty Industrial Leadership | H2020 Industrial Leadership - Leadership in Enabling and Industrial Technologies | H2020 LEIT Information and Communication Technologies (H2020 Leadership in Enabling and Industrial Technologies - Information and Communication Technologies)/ ; },
mesh = {Cognition ; *Electroencephalography/methods ; *Exoskeleton Device ; Lower Extremity ; Walking ; Humans ; },
abstract = {One important point in the development of a brain-machine Interface (BMI) commanding an exoskeleton is the assessment of the cognitive engagement of the subject during the motor imagery tasks conducted. However, there are not many databases that provide electroencephalography (EEG) data during the use of a lower-limb exoskeleton. The current paper presents a database designed with an experimental protocol aiming to assess not only motor imagery during the control of the device, but also the attention to gait on flat and inclined surfaces. The research was conducted as an EUROBENCH subproject in the facilities sited in Hospital Los Madroños, Brunete (Madrid). The data validation reaches accuracies over 70% in the assessment of motor imagery and attention to gait, which marks the present database as a valuable resource for researches interested on developing and testing new EEG-based BMIs.},
}
@article {pmid37266326,
year = {2023},
author = {Gwon, D and Won, K and Song, M and Nam, CS and Jun, SC and Ahn, M},
title = {Corrigendum: Review of public motor imagery and execution datasets in brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1205419},
doi = {10.3389/fnhum.2023.1205419},
pmid = {37266326},
issn = {1662-5161},
abstract = {[This corrects the article DOI: 10.3389/fnhum.2023.1134869.].},
}
@article {pmid37265653,
year = {2023},
author = {Hu, Y and Wang, Y and Zhang, R and Hu, Y and Fang, M and Li, Z and Shi, L and Zhang, Y and Zhang, Z and Gao, J and Zhang, L},
title = {Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {3},
pages = {661-669},
pmid = {37265653},
issn = {1871-4080},
abstract = {The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.},
}
@article {pmid37263791,
year = {2023},
author = {Yakovlev, L and Syrov, N and Miroshnikov, A and Lebedev, M and Kaplan, A},
title = {Event-Related Desynchronization Induced by Tactile Imagery: an EEG Study.},
journal = {eNeuro},
volume = {10},
number = {6},
pages = {},
pmid = {37263791},
issn = {2373-2822},
mesh = {Humans ; *Imagination/physiology ; Electroencephalography/methods ; Hand/physiology ; Movement/physiology ; *Motor Cortex/physiology ; },
abstract = {It is well known that both hand movements and mental representations of movement lead to event-related desynchronization (ERD) of the electroencephalogram (EEG) recorded over the corresponding cortical motor areas. However, the relationship between ERD in somatosensory cortical areas and mental representations of tactile sensations is not well understood. In this study, we employed EEG recordings in healthy humans to compare the effects of real and imagined vibrotactile stimulation of the right hand. Both real and imagined sensations produced contralateral ERD patterns, particularly in the μ-band and most significantly in the C3 region. Building on these results and the previous literature, we discuss the role of tactile imagery as part of the complex body image and the potential for using EEG patterns induced by tactile imagery as control signals in brain-computer interfaces (BCIs). Combining this approach with motor imagery (MI) could improve the performance of BCIs intended for rehabilitation of sensorimotor function after stroke and neural trauma.},
}
@article {pmid37263368,
year = {2023},
author = {Li, C and Guo, J and Zhao, Y and Sun, K and Abdelrahman, Z and Cao, X and Zhang, J and Zheng, Z and Yuan, C and Huang, H and Chen, Y and Liu, Z and Chen, Z},
title = {Visit-to-visit HbA1c variability, dementia, and hippocampal atrophy among adults without diabetes.},
journal = {Experimental gerontology},
volume = {178},
number = {},
pages = {112225},
doi = {10.1016/j.exger.2023.112225},
pmid = {37263368},
issn = {1873-6815},
mesh = {Male ; Humans ; Middle Aged ; Aged ; Female ; Glycated Hemoglobin ; *Diabetes Mellitus, Type 2/complications ; *Neurodegenerative Diseases/pathology ; Hippocampus/diagnostic imaging/pathology ; *Dementia/complications ; Atrophy/pathology ; Risk Factors ; Blood Glucose ; },
abstract = {OBJECTIVES: Adults without diabetes are not completely healthy; they are probably heterogeneous with several potential health problems. The management of hemoglobin A1c (HbA1c) is crucial among patients with diabetes; but whether similar management strategy is needed for adults without diabetes is unclear. Thus, this study aimed to investigate the associations of visit-to-visit HbA1c variability with incident dementia and hippocampal volume among middle-aged and older adults without diabetes, providing potential insights into this question.<br><br>METHODS: We conducted a prospective analysis for incident dementia in 10,792 participants (mean age 58.9&#xa0;years, 47.8&#xa0;% men) from the UK Biobank. A subgroup of 3793 participants (mean age 57.8&#xa0;years, 48.6&#xa0;% men) was included in the analysis for hippocampal volume. We defined HbA1c variability as the difference in HbA1c divided by the mean HbA1c over the 2 sequential visits ([latter - former]/mean). Dementia was identified using hospital inpatient records with ICD-9 codes. T1-structural brain magnetic resonance imaging was conducted to derive hippocampal volume (normalized for head size). The nonlinear and linear associations were examined using restricted cubic spline (RCS) models, Cox regression models, and multiple linear regression models.<br><br>RESULTS: During a mean follow-up (since the second round) of 8.4&#xa0;years, 90 (0.8&#xa0;%) participants developed dementia. The RCS models suggested no significant nonlinear associations of HbA1c variability with incident dementia and hippocampal volume, respectively (All P&#xa0;>&#xa0;0.05). Above an optimal cutoff of HbA1c variability at 0.08, high HbA1c variability (increment in HbA1c) was associated with an increased risk of dementia (Hazard Ratio, 1.88; 95&#xa0;% Confidence Interval, 1.13 to 3.14, P&#xa0;=&#xa0;0.015), and lower hippocampal volume (coefficient, -96.84&#xa0;mm[3], P&#xa0;=&#xa0;0.037), respectively, in models with adjustment of covariates including age, sex, etc. Similar results were found for a different cut-off of 0. A series of sensitivity analyses verified the robustness of the findings.<br><br>CONCLUSIONS: Among middle-aged and older adults without diabetes, increasing visit-to-visit HbA1c variability was associated with an increased dementia risk and lower hippocampal volume. The findings highlight the importance of monitoring and controlling HbA1c fluctuation in apparently healthy adults without diabetes.},
}
@article {pmid37263044,
year = {2023},
author = {Kong, LZ and Lai, JB and Hu, SH},
title = {China released the latest national mental health report: A blueprint for the future.},
journal = {Asian journal of psychiatry},
volume = {85},
number = {},
pages = {103624},
doi = {10.1016/j.ajp.2023.103624},
pmid = {37263044},
issn = {1876-2026},
mesh = {Humans ; *Mental Health ; *Mental Disorders/epidemiology/therapy/psychology ; China ; Depression/psychology ; },
}
@article {pmid37262122,
year = {2023},
author = {Xu, H and Hsu, SH and Nakanishi, M and Lin, Y and Jung, TP and Cauwenberghs, G},
title = {Stimulus Design for Visual Evoked Potential Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2545-2551},
doi = {10.1109/TNSRE.2023.3280081},
pmid = {37262122},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Neurologic Examination ; Algorithms ; },
abstract = {Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying the need for a trade-off in design. In this study, we comprehensively and systematically compared various combinations of amplitude contrast and spectral content parameters in the stimulus design to quantify their impact on decoding performance and subject comfort. Specifically, three parameters were investigated: 1) contrast level, 2) temporal pattern (periodic steady-state or pseudo-random code-modulated), and 3) frequency range. We collected electroencephalogram (EEG) data and subjective perception ratings from ten subjects and evaluated the decoding accuracy and subject comfort rating for different combinations of the stimulus parameters. Our results indicate that while high-frequency steady-state VEP (SSVEP) stimuli were rated the most comfortable, they also had the lowest decoding accuracy. Conversely, low-frequency SSVEP stimuli were rated the least comfortable but had the highest decoding accuracy. Standard and high-frequency M-sequence code-modulated VEPs (c-VEPs) produced intermediates between the two. We observed a consistent trade-off relationship between decoding accuracy and subjective comfort level across all parameters. Based on our findings, we offer c-VEP as a preferable stimulus for achieving reliable decoding accuracy while maintaining a reasonable level of comfortability.},
}
@article {pmid37262121,
year = {2023},
author = {Wang, Z and Yang, L and Zhou, Y and Chen, L and Gu, B and Liu, S and Xu, M and He, F and Ming, D},
title = {Incorporating EEG and fNIRS Patterns to Evaluate Cortical Excitability and MI-BCI Performance During Motor Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2872-2882},
doi = {10.1109/TNSRE.2023.3281855},
pmid = {37262121},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Electroencephalography/methods ; *Neurofeedback ; *Cortical Excitability ; },
abstract = {As electroencephalography (EEG) is nonlinear and nonstationary in nature, an imperative challenge for brain-computer interfaces (BCIs) is to construct a robust classifier that can survive for a long time and monitor the brain state stably. To this end, this research aims to improve BCI performance by incorporation of electroencephalographic and cerebral hemodynamic patterns. A motor imagery (MI)-BCI based visual-haptic neurofeedback training (NFT) experiment was designed with sixteen participants. EEG and functional near infrared spectroscopy (fNIRS) signals were simultaneously recorded before and after this transient NFT. Cortical activation was significantly improved after repeated and continuous NFT through time-frequency and topological analysis. A classifier calibration strategy, weighted EEG-fNIRS patterns (WENP), was proposed, in which elementary classifiers were constructed by using both the EEG and fNIRS information and then integrated into a strong classifier with their independent accuracy-based weight assessment. The results revealed that the classifier constructed on integrating EEG and fNIRS patterns was significantly superior to that only with independent information (∼ 10% and ∼ 18% improvement respectively), reaching ∼ 89% in mean classification accuracy. The WENP is a classifier calibration strategy that can effectively improve the performance of the MI-BCI and could also be used to other BCI paradigms. These findings validate that our proposed methods are feasible and promising for optimizing conventional motor training methods and clinical rehabilitation.},
}
@article {pmid37261079,
year = {2023},
author = {Kirton, A and Kinney-Lang, E and Norton, J and Chau, T},
title = {Editorial: BCIs: research and development in children.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1201623},
doi = {10.3389/fnhum.2023.1201623},
pmid = {37261079},
issn = {1662-5161},
}
@article {pmid37260144,
year = {2023},
author = {Shang, X and Ling, W and Chen, Y and Li, C and Huang, X},
title = {Construction of a Flexible Optogenetic Device for Multisite and Multiregional Optical Stimulation Through Flexible µ-LED Displays on the Cerebral Cortex.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {19},
number = {39},
pages = {e2302241},
doi = {10.1002/smll.202302241},
pmid = {37260144},
issn = {1613-6829},
mesh = {Rats ; Animals ; *Optogenetics/methods ; *Prostheses and Implants ; Optical Fibers ; Wireless Technology ; Cerebral Cortex ; },
abstract = {Precisely delivering light to multiple locations in biological tissue is crucial for advancing multiregional optogenetics in neuroscience research. However, conventional implantable devices typically have rigid geometries and limited light sources, allowing only single or dual probe placement with fixed spacing. Here, a fully flexible optogenetic device with multiple thin-film microscale light-emitting diode (µ-LED) displays scattering from a central controller is presented. Each display is heterogeneously integrated with thin-film 5 × 10 µ-LEDs and five optical fibers 125 µm in diameter to achieve cellular-scale spatial resolution. Meanwhile, the device boasts a compact, flexible circuit capable of multichannel configuration and wireless transmission, with an overall weight of 1.31 g, enabling wireless, real-time neuromodulation of freely moving rats. Characterization results and finite element analysis have demonstrated excellent optical properties and mechanical stability, while cytotoxicity tests further ensure the biocompatibility of the device for implantable applications. Behavior studies under optogenetic modulation indicate great promise for wirelessly modulating neural functions in freely moving animals. The device with multisite and multiregional optogenetic modulation capability offers a comprehensive platform to advance both fundamental neuroscience studies and potential applications in brain-computer interfaces.},
}
@article {pmid37259938,
year = {2024},
author = {Benioudakis, ES and Karlafti, E and Kalaitzaki, A and Kalpou, MA and Georgiou, ED and Savopoulos, C and Didangelos, T},
title = {Comparison of the Sensor-Augmented Pump System with the Advanced Hybrid Closed-Loop Delivery System: Quality of Life, Diabetes Distress, and Glycaemic Outcomes in a Real-Life Context.},
journal = {Current diabetes reviews},
volume = {20},
number = {3},
pages = {e310523217505},
doi = {10.2174/1573399820666230531161858},
pmid = {37259938},
issn = {1875-6417},
mesh = {Adult ; Female ; Humans ; Male ; *Diabetes Mellitus, Type 1/drug therapy ; Hypoglycemic Agents/therapeutic use ; Quality of Life ; Insulin Infusion Systems ; Insulin/therapeutic use ; Blood Glucose Self-Monitoring ; *Pancreatic Diseases ; Glucose ; Blood Glucose ; },
abstract = {BACKGROUND: Type 1 diabetes mellitus (T1D) is a chronic disease that requires exogenous insulin administration and intensive management to prevent any complications. Recent innovations in T1D management technologies include the Advanced Hybrid Closed-Loop delivery system (AHCL). The pioneer AHCL system provides automated basal and automated bolus corrections when needed.<br><br>OBJECTIVE: This study aimed to compare the Advanced Hybrid Closed-Loop (AHCL) system and the Sensor-Augmented Pump (SAP) with Predictive Low Glucose Management (PLGM) system, in relation to glycaemic outcomes, general and diabetes-related Quality of Life (QoL), and diabetes distress.<br><br>METHODS: General and diabetes-related QoL were assessed with the Diabetes Quality of Life Brief Clinical Inventory (DQOL-BCI) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Diabetes distress was assessed with the Diabetes Distress Scale for Type 1 diabetes (T1-DDS).<br><br>RESULTS: Eighty-nine T1D adults participated in the study, mostly females (65.2%), with a mean age of 39.8 (&#xb1; 11.5 years). They had on average 23 years of diabetes (&#xb1; 10.7) and they were on continuous subcutaneous insulin infusion therapy. Significant differences favoring the AHCL over the SAP + PLGM system were demonstrated by lower mean glucose levels, less time above range, lower scores on DQOL-BCI, T1-DDS, and higher scores on WHOQOL-BREF. Finally, the linear regression models revealed the association of time in range in most of the above aspects.<br><br>CONCLUSION: This study highlighted the advantages of the AHCL system over the SAP + PLGM system in the real-world setting in relation to general and diabetes-related QoL, diabetes distress, and glycaemic outcomes.},
}
@article {pmid37259431,
year = {2023},
author = {Wang, Z and Zhang, D and Du, Y and Wang, Y and Huang, T and Ng, CH and Huang, H and Pan, Y and Lai, J and Hu, S},
title = {Efficacy of Quetiapine Monotherapy and Combination Therapy for Patients with Bipolar Depression with Mixed Features: A Randomized Controlled Pilot Study.},
journal = {Pharmaceuticals (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {},
pmid = {37259431},
issn = {1424-8247},
support = {2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation - 'Ten Thousand Talents Program' of Zhejiang Province/ ; LQ20H090010//he Natural Science Foundation of Zhejiang province/ ; 81971271//the National Natural Science Foundation of China/ ; },
abstract = {Effective pharmacotherapy of bipolar depression with mixed features defined by DSM-5 remains unclear in clinical treatment guidelines. Quetiapine (QTP) and valproate have potential treatment utility but are often inadequate as monotherapy. Meanwhile, the efficacy of combination therapies of QTP plus valproate or lithium have yet to be verified. Hence, we conducted a randomized controlled pilot study to evaluate the efficacy of QTP monotherapy in patients with bipolar depression with mixed features defined by DSM-5 and compared the combination therapy of QTP plus valproate (QTP + V) versus QTP plus lithium (QTP + L) for those patients who responded insufficiently to QTP monotherapy. Data was analyzed according to the intent-to-treat population. Generalized linear mixed model was performed by using "nlme" package in R software. A total 56 patients were enrolled, among which, 35 patients responded to QTP alone, and 11 and 10 patients were randomly assigned to QTP + V and QTP + L group, respectively. Nearly 60% enrolled patients responded to QTP monotherapy at the first two weeks treatment. No statistically significant difference in efficacy between QTP + V and QTP + L was observed. In conclusion, QTP monotherapy appeared to be efficacious in patients with bipolar depression with mixed features, and for those who responded insufficiently to QTP, combining with either valproate or lithium appeared to have positive effects.},
}
@article {pmid37256332,
year = {2023},
author = {Tayebi, H and Azadnajafabad, S and Maroufi, SF and Pour-Rashidi, A and Khorasanizadeh, M and Faramarzi, S and Slavin, KV},
title = {Applications of brain-computer interfaces in neurodegenerative diseases.},
journal = {Neurosurgical review},
volume = {46},
number = {1},
pages = {131},
pmid = {37256332},
issn = {1437-2320},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Neurodegenerative Diseases/therapy ; Brain ; Central Nervous System ; },
abstract = {Brain-computer interfaces (BCIs) provide the central nervous system with channels of direct communication to the outside world, without having to go through the peripheral nervous system. Neurodegenerative diseases (NDs) are notoriously incurable and burdensome medical conditions that will result in progressive deterioration of the nervous system. The applications of BCIs in NDs have been studied for decades now through different approaches, resulting in a considerable amount of literature in all related areas. In this study, we begin by introducing BCIs and proceed by explaining the principles of BCI-based neurorehabilitation. Then, we go through four specific types of NDs, including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and spinal muscular atrophy, and review some of the applications of BCIs in the neural rehabilitation of these diseases. We conclude with a discussion of the characteristics, challenges, and future possibilities of research in the field. Going through the uses of BCIs in NDs, we can see that approaches and strategies employed to tackle the wide range of limitations caused by NDs are numerous and diverse. Furthermore, NDs can fall under different categories based on the target area of neurodegeneration and thus require different methods of BCI-based rehabilitation. In recent years, neurotechnology companies have substantially invested in research on BCIs, focusing on commercializing BCIs and bringing BCI-based technologies from bench to bedside. This can mean the beginning of a new era for BCI-based neurorehabilitation, with an anticipated spike in interest among researchers, practitioners, engineers, and entrepreneurs alike.},
}
@article {pmid37256201,
year = {2023},
author = {Kim, DH and Shin, DH and Kam, TE},
title = {Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1194751},
pmid = {37256201},
issn = {1662-5161},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) facilitate direct interaction between the human brain and computers, enabling individuals to control external devices through cognitive processes. Despite its potential, the problem of BCI illiteracy remains one of the major challenges due to inter-subject EEG variability, which hinders many users from effectively utilizing BCI systems. In this study, we propose a subject-to-subject semantic style transfer network (SSSTN) at the feature-level to address the BCI illiteracy problem in electroencephalogram (EEG)-based motor imagery (MI) classification tasks.<br><br>METHODS: Our approach uses the continuous wavelet transform method to convert high-dimensional EEG data into images as input data. The SSSTN 1) trains a classifier for each subject, 2) transfers the distribution of class discrimination styles from the source subject (the best-performing subject for the classifier, i.e., BCI expert) to each subject of the target domain (the remaining subjects except the source subject, specifically BCI illiterates) through the proposed style loss, and applies a modified content loss to preserve the class-relevant semantic information of the target domain, and 3) finally merges the classifier predictions of both source and target subject using an ensemble technique.<br><br>RESULTS AND DISCUSSION: We evaluate the proposed method on the BCI Competition IV-2a and IV-2b datasets and demonstrate improved classification performance over existing methods, especially for BCI illiterate users. The ablation experiments and t-SNE visualizations further highlight the effectiveness of the proposed method in achieving meaningful feature-level semantic style transfer.},
}
@article {pmid37252870,
year = {2023},
author = {Li, M and Chen, X and Cui, H},
title = {A High-Frequency SSVEP-BCI System Based on Simultaneous Modulation of Luminance and Motion Using Intermodulation Frequencies.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2603-2611},
doi = {10.1109/TNSRE.2023.3281416},
pmid = {37252870},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Algorithms ; },
abstract = {The low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) tend to induce visual fatigue in the subjects. In order to enhance the comfort of SSVEP-BCIs, a novel SSVEP-BCI encoding method based on simultaneous modulation of luminance and motion is proposed. In this work, sixteen stimulus targets are simultaneously flickered and radially zoomed using a sampled sinusoidal stimulation method. The flicker frequency is set to a 30 Hz for all the targets, while assigning different radial zoom frequencies (ranging from 0.4 Hz to 3.4 Hz, with an interval of 0.2 Hz) are assigned to each target separately. Accordingly, an extended vision of the filter bank canonical correlation analysis (eFBCCA) is proposed to detect the intermodulation (IM) frequencies and classify the targets. In addition, we adopt the comfort level scale to evaluate the subjective comfort experience. By optimizing the combination of IM frequencies for the classification algorithm, the average recognition accuracy of the offline and online experiments reaches 92.74 ± 1.53% and 93.33 ± 0.01%, respectively. Most importantly, the average comfort scores are above 5. These results demonstrate the feasibility and comfort of the proposed system using IM frequencies, which provides new ideas for the further development of highly comfortable SSVEP-BCIs.},
}
@article {pmid37251598,
year = {2023},
author = {Wan, Z and Liu, T and Ran, X and Liu, P and Chen, W and Zhang, S},
title = {The influence of non-stationarity of spike signals on decoding performance in intracortical brain-computer interface: a simulation study.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1135783},
pmid = {37251598},
issn = {1662-5188},
abstract = {INTRODUCTION: Intracortical Brain-Computer Interfaces (iBCI) establish a new pathway to restore motor functions in individuals with paralysis by interfacing directly with the brain to translate movement intention into action. However, the development of iBCI applications is hindered by the non-stationarity of neural signals induced by the recording degradation and neuronal property variance. Many iBCI decoders were developed to overcome this non-stationarity, but its effect on decoding performance remains largely unknown, posing a critical challenge for the practical application of iBCI.<br><br>METHODS: To improve our understanding on the effect of non-stationarity, we conducted a 2D-cursor simulation study to examine the influence of various types of non-stationarities. Concentrating on spike signal changes in chronic intracortical recording, we used the following three metrics to simulate the non-stationarity: mean firing rate (MFR), number of isolated units (NIU), and neural preferred directions (PDs). MFR and NIU were decreased to simulate the recording degradation while PDs were changed to simulate the neuronal property variance. Performance evaluation based on simulation data was then conducted on three decoders and two different training schemes. Optimal Linear Estimation (OLE), Kalman Filter (KF), and Recurrent Neural Network (RNN) were implemented as decoders and trained using static and retrained schemes.<br><br>RESULTS: In our evaluation, RNN decoder and retrained scheme showed consistent better performance under small recording degradation. However, the serious signal degradation would cause significant performance to drop eventually. On the other hand, RNN performs significantly better than the other two decoders in decoding simulated non-stationary spike signals, and the retrained scheme maintains the decoders' high performance when changes are limited to PDs.<br><br>DISCUSSION: Our simulation work demonstrates the effects of neural signal non-stationarity on decoding performance and serves as a reference for selecting decoders and training schemes in chronic iBCI. Our result suggests that comparing to KF and OLE, RNN has better or equivalent performance using both training schemes. Performance of decoders under static scheme is influenced by recording degradation and neuronal property variation while decoders under retrained scheme are only influenced by the former one.},
}
@article {pmid37250399,
year = {2023},
author = {Fu, J and Chen, S and Shu, X and Lin, Y and Jiang, Z and Wei, D and Gao, J and Jia, J},
title = {Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1146146},
pmid = {37250399},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) have been proven to be effective for hand motor recovery after stroke. Facing kinds of dysfunction of the paretic hand, the motor task of BCIs for hand rehabilitation is relatively single, and the operation of many BCI devices is complex for clinical use. Therefore, we proposed a functional-oriented, portable BCI equipment and explored the efficiency of hand motor recovery after a stroke.<br><br>MATERIALS AND METHODS: Stroke patients were randomly assigned to the BCI group and the control group. The BCI group received BCI-based grasp/open motor training, while the control group received task-oriented guidance training. Both groups received 20 sessions of motor training in 4 weeks, and each session lasted for 30 min. The Fugl-Meyer assessment of the upper limb (FMA-UE) was applied for the assessment of rehabilitation outcomes, and the EEG signals were obtained for processing.<br><br>RESULTS: The progress of FMA-UE between the BCI group [10.50 (5.75, 16.50)] and the control group [5.00 (4.00, 8.00)] was significantly different (Z = -2.834, P = 0.005). Meanwhile, the FMA-UE of both groups improved significantly (P < 0.001). A total of 24 patients in the BCI group achieved the minimal clinically important difference (MCID) of FMA-UE with an effective rate of 80%, and 16 in the control group achieved the MCID, with an effective rate of 51.6%. The lateral index of the open task in the BCI group was significantly decreased (Z = -2.704, P = 0.007). The average BCI accuracy for 24 stroke patients in 20 sessions was 70.7%, which was improved by 5.0% in the final session compared with the first session.<br><br>CONCLUSION: Targeted hand movement and two motor task modes, namely grasp and open, to be applied in a BCI design may be suitable in stroke patients with hand dysfunction. The functional-oriented, portable BCI training can promote hand recovery after a stroke, and it is expected to be widely used in clinical practice. The lateral index change of inter-hemispheric balance may be the mechanism of motor recovery.<br><br>TRIAL REGISTRATION NUMBER: ChiCTR2100044492.},
}
@article {pmid37250394,
year = {2023},
author = {Qi, Y and Chen, J and Wang, Y},
title = {Neuromorphic computing facilitates deep brain-machine fusion for high-performance neuroprosthesis.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1153985},
pmid = {37250394},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMI) have developed rapidly in recent years, but still face critical issues such as accuracy and stability. Ideally, a BMI system would be an implantable neuroprosthesis that would be tightly connected and integrated into the brain. However, the heterogeneity of brains and machines hinders deep fusion between the two. Neuromorphic computing models, which mimic the structure and mechanism of biological nervous systems, present a promising approach to developing high-performance neuroprosthesis. The biologically plausible property of neuromorphic models enables homogeneous information representation and computation in the form of discrete spikes between the brain and the machine, promoting deep brain-machine fusion and bringing new breakthroughs for high-performance and long-term usable BMI systems. Furthermore, neuromorphic models can be computed at ultra-low energy costs and thus are suitable for brain-implantable neuroprosthesis devices. The intersection of neuromorphic computing and BMI has great potential to lead the development of reliable, low-power implantable BMI devices and advance the development and application of BMI.},
}
@article {pmid37250338,
year = {2023},
author = {Huo, H and Liu, X and Tang, Z and Dong, Y and Zhao, D and Chen, D and Tang, M and Qiao, X and Du, X and Guo, J and Wang, J and Fan, Y},
title = {Interhemispheric multisensory perception and Bayesian causal inference.},
journal = {iScience},
volume = {26},
number = {5},
pages = {106706},
pmid = {37250338},
issn = {2589-0042},
abstract = {In daily life, our brain needs to eliminate irrelevant signals and integrate relevant signals to facilitate natural interactions with the surrounding. Previous study focused on paradigms without effect of dominant laterality and found that human observers process multisensory signals consistent with Bayesian causal inference (BCI). However, most human activities are of bilateral interaction involved in processing of interhemispheric sensory signals. It remains unclear whether the BCI framework also fits to such activities. Here, we presented a bilateral hand-matching task to understand the causal structure of interhemispheric sensory signals. In this task, participants were asked to match ipsilateral visual or proprioceptive cues with the contralateral hand. Our results suggest that interhemispheric causal inference is most derived from the BCI framework. The interhemispheric perceptual bias may vary strategy models to estimate the contralateral multisensory signals. The findings help to understand how the brain processes the uncertainty information coming from interhemispheric sensory signals.},
}
@article {pmid37250318,
year = {2023},
author = {Ferrero, L and Quiles, V and Ortiz, M and Iáñez, E and Gil-Agudo, &#xc1; and Azorín, JM},
title = {Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton.},
journal = {iScience},
volume = {26},
number = {5},
pages = {106675},
pmid = {37250318},
issn = {2589-0042},
abstract = {This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training session to accelerate training with the BCI. Results from this group were compared with a control group of five able-bodied subjects, and it was found that the employment of shorter training by VR did not reduce the effectiveness of the BCI and even improved it in some cases. Patients gave positive feedback about the system and were able to handle experimental sessions without reaching high levels of physical and mental exertion. These results are promising for the inclusion of BCI in rehabilitation programs, and future research should investigate the potential of the MI-based BCI system.},
}
@article {pmid37248479,
year = {2023},
author = {Shen, Y and Kong, L and Lai, J and Hu, S},
title = {Shifting levels of peripheral inflammatory profiles as an indicator for comorbid multiple autoimmune diseases and bipolar disorder: a case report.},
journal = {BMC psychiatry},
volume = {23},
number = {1},
pages = {375},
pmid = {37248479},
issn = {1471-244X},
mesh = {Female ; Humans ; Adult ; *Bipolar Disorder/complications/diagnosis ; *Antipsychotic Agents/therapeutic use ; Cytokines ; Biomarkers ; *Autoimmune Diseases/complications/diagnosis ; },
abstract = {Autoimmune diseases (AID) cause inflammatory changes in the peripheral blood, which might be a predisposing factor for the development of comorbid bipolar disorder (BD). The levels of peripheral inflammatory indicators and cytokines may also serve as potential biomarkers for predicting BD susceptibility and the efficacy of antipsychotics in patients with AID. Herein, we present the case of a 43-year-old female who has suffered from AID for over 16 years and was recently diagnosed with "bipolar and related disorder due to another medical condition".},
}
@article {pmid37248393,
year = {2023},
author = {Guan, S and Tian, H and Yang, Y and Liu, M and Ding, J and Wang, J and Fang, Y},
title = {Self-assembled ultraflexible probes for long-term neural recordings and neuromodulation.},
journal = {Nature protocols},
volume = {18},
number = {6},
pages = {1712-1744},
pmid = {37248393},
issn = {1750-2799},
support = {61971150//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32061143013//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Microelectrodes ; Neurons ; *Optogenetics ; },
abstract = {Ultraflexible microelectrode arrays (MEAs) that can stably record from a large number of neurons after their chronic implantation offer opportunities for understanding neural circuit mechanisms and developing next-generation brain-computer interfaces. The implementation of ultraflexible MEAs requires their reliable implantation into deep brain tissues in a minimally invasive manner, as well as their precise integration with optogenetic tools to enable the simultaneous recording of neural activity and neuromodulation. Here, we describe the process for the preparation of elastocapillary self-assembled ultraflexible MEAs, their use in combination with adeno-associated virus vectors carrying opsin genes and promoters to form an optrode probe and their in vivo experimental use in the brains of rodents, enabling electrophysiological recordings and optical modulation of neuronal activity over long periods of time (on the order of weeks to months). The procedures, including device fabrication, probe assembly and implantation, can be completed within 3 weeks. The protocol is intended to facilitate the applications of ultraflexible MEAs for long-term neuronal activity recording and combined electrophysiology and optogenetics. The protocol requires users with expertise in clean room facilities for the fabrication of ultraflexible MEAs.},
}
@article {pmid37247319,
year = {2023},
author = {Wang, Z and Wong, CM and Wang, B and Feng, Z and Cong, F and Wan, F},
title = {Compact Artificial Neural Network Based on Task Attention for Individual SSVEP Recognition With Less Calibration.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2525-2534},
doi = {10.1109/TNSRE.2023.3276745},
pmid = {37247319},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Calibration ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Photic Stimulation ; Neural Networks, Computer ; Algorithms ; },
abstract = {OBJECTIVE: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP recognition.<br><br>METHOD: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable parameters.<br><br>RESULTS: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than 90% and 80% respectively, and boosts the individual recognition performance by at least 57% and 7% respectively.<br><br>CONCLUSION: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.},
}
@article {pmid37245559,
year = {2023},
author = {Schroeder, ML and Sherafati, A and Ulbrich, RL and Wheelock, MD and Svoboda, AM and Klein, ED and George, TG and Tripathy, K and Culver, JP and Eggebrecht, AT},
title = {Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography.},
journal = {NeuroImage},
volume = {276},
number = {},
pages = {120190},
pmid = {37245559},
issn = {1095-9572},
support = {R01 NS090874/NS/NINDS NIH HHS/United States ; R21 MH109775/MH/NIMH NIH HHS/United States ; R01 MH122751/MH/NIMH NIH HHS/United States ; R00 EB029343/EB/NIBIB NIH HHS/United States ; R01 NS109486/NS/NINDS NIH HHS/United States ; K01 MH103594/MH/NIMH NIH HHS/United States ; P50 HD103525/HD/NICHD NIH HHS/United States ; },
mesh = {Humans ; *Brain/diagnostic imaging/physiology ; Brain Mapping/methods ; Comprehension ; *Tomography, Optical/methods ; Language ; },
abstract = {Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., "silent") language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., "out loud") language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces.},
}
@article {pmid37245048,
year = {2023},
author = {Kyriazidis, IP and Jakob, DA and Vargas, JAH and Franco, OH and Degiannis, E and Dorn, P and Pouwels, S and Patel, B and Johnson, I and Houdlen, CJ and Whiteley, GS and Head, M and Lala, A and Mumtaz, H and Soler, JA and Mellor, K and Rawaf, D and Ahmed, AR and Ahmad, SJS and Exadaktylos, A},
title = {Accuracy of diagnostic tests in cardiac injury after blunt chest trauma: a systematic review and meta-analysis.},
journal = {World journal of emergency surgery : WJES},
volume = {18},
number = {1},
pages = {36},
pmid = {37245048},
issn = {1749-7922},
mesh = {Humans ; *Thoracic Injuries/complications/diagnosis ; *Wounds, Nonpenetrating/complications/diagnosis ; *Heart Injuries/diagnosis/complications ; *Myocardial Contusions/diagnosis/complications ; Troponin I ; Troponin T ; Diagnostic Tests, Routine ; },
abstract = {INTRODUCTION: The diagnosis of cardiac contusion, caused by blunt chest trauma, remains a challenge due to the non-specific symptoms it causes and the lack of ideal tests to diagnose myocardial damage. A cardiac contusion can be life-threatening if not diagnosed and treated promptly. Several diagnostic tests have been used to evaluate the risk of cardiac complications, but the challenge of identifying patients with contusions nevertheless remains.<br><br>AIM OF THE STUDY: To evaluate the accuracy of diagnostic tests for detecting blunt cardiac injury (BCI) and its complications, in patients with severe chest injuries, who are assessed in an emergency department or by any front-line emergency physician.<br><br>METHODS: A targeted search strategy was performed using Ovid MEDLINE and Embase databases from 1993 up to October 2022. Data on at least one of the following diagnostic tests: electrocardiogram (ECG), serum creatinine phosphokinase-MB level (CPK-MB), echocardiography (Echo), Cardiac troponin I (cTnI) or Cardiac troponin T (cTnT). Diagnostic tests for cardiac contusion were evaluated for their accuracy in meta-analysis. Heterogeneity was assessed using the I[2] and the QUADAS-2 tool was used to assess bias of the studies.<br><br>RESULTS: This systematic review yielded 51 studies (n&#x2009;=&#x2009;5,359). The weighted mean incidence of myocardial injuries after sustaining a blunt force trauma stood at 18.3% of cases. Overall weighted mean mortality among patients with blunt cardiac injury was 7.6% (1.4-36.4%). Initial ECG, cTnI, cTnT and transthoracic echocardiography TTE all showed high specificity (>&#x2009;80%), but lower sensitivity (<&#x2009;70%). TEE had a specificity of 72.1% (range 35.8-98.2%) and sensitivity of 86.7% (range 40-99.2%) in diagnosing cardiac contusion. CK-MB had the lowest diagnostic odds ratio of 3.598 (95% CI: 1.832-7.068). Normal ECG accompanied by normal cTnI showed a high sensitivity of 85% in ruling out cardiac injuries.<br><br>CONCLUSION: Emergency physicians face great challenges in diagnosing cardiac injuries in patients following blunt trauma. In the majority of cases, joint use of ECG and cTnI was a pragmatic and cost-effective approach to rule out cardiac injuries. In addition, TEE may be highly accurate in identifying cardiac injuries in suspected cases.},
}
@article {pmid37244370,
year = {2023},
author = {Hughes, C and Kozai, T},
title = {Dynamic amplitude modulation of microstimulation evokes biomimetic onset and offset transients and reduces depression of evoked calcium responses in sensory cortices.},
journal = {Brain stimulation},
volume = {16},
number = {3},
pages = {939-965},
pmid = {37244370},
issn = {1876-4754},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R01 NS129632/NS/NINDS NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Mice ; *Calcium ; Electric Stimulation/methods ; *Neurons/physiology ; Somatosensory Cortex/physiology ; Brain ; Microelectrodes ; },
abstract = {BACKGROUND: Intracortical microstimulation (ICMS) is an emerging approach to restore sensation to people with neurological injury or disease. Biomimetic microstimulation, or stimulus trains that mimic neural activity in the brain through encoding of onset and offset transients, could improve the utility of ICMS for brain-computer interface (BCI) applications, but how biomimetic microstimulation affects neural activation is not understood. Current "biomimetic" ICMS trains aim to reproduce the strong onset and offset transients evoked in the brain by sensory input through dynamic modulation of stimulus parameters. Stimulus induced depression of neural activity (decreases in evoked intensity over time) is also a potential barrier to clinical implementation of sensory feedback, and dynamic microstimulation may reduce this effect.<br><br>OBJECTIVE: We evaluated how bio-inspired ICMS trains with dynamic modulation of amplitude and/or frequency change the calcium response, spatial distribution, and depression of neurons in the somatosensory and visual cortices.<br><br>METHODS: Calcium responses of neurons were measured in Layer 2/3 of visual and somatosensory cortices of anesthetized GCaMP6s mice in response to ICMS trains with fixed amplitude and frequency (Fixed) and three dynamic ICMS trains that increased the stimulation intensity during the onset and offset of stimulation by modulating the amplitude (DynAmp), frequency (DynFreq), or amplitude and frequency (DynBoth). ICMS was provided for either 1-s with 4-s breaks (Short) or for 30-s with 15-s breaks (Long).<br><br>RESULTS: DynAmp and DynBoth trains evoked distinct onset and offset transients in recruited neural populations, while DynFreq trains evoked population activity similar to Fixed trains. Individual neurons had heterogeneous responses primarily based on how quickly they depressed to ICMS, where neurons farther from the electrode depressed faster and a small subpopulation (1-5%) were modulated by DynFreq trains. Neurons that depressed to Short trains were also more likely to depress to Long trains, but Long trains induced more depression overall due to the increased stimulation length. Increasing the amplitude during the hold phase resulted in an increase in recruitment and intensity which resulted in more depression and reduced offset responses. Dynamic amplitude modulation reduced stimulation induced depression by 14.6&#xa0;&#xb1;&#xa0;0.3% for Short and 36.1&#xa0;&#xb1;&#xa0;0.6% for Long trains. Ideal observers were 0.031&#xa0;&#xb1;&#xa0;0.009&#xa0;s faster for onset detection and 1.33&#xa0;&#xb1;&#xa0;0.21&#xa0;s faster for offset detection with dynamic amplitude encoding.<br><br>CONCLUSIONS: Dynamic amplitude modulation evokes distinct onset and offset transients, reduces depression of neural calcium activity, and decreases total charge injection for sensory feedback in BCIs by lowering recruitment of neurons during long maintained periods of ICMS. In contrast, dynamic frequency modulation evokes distinct onset and offset transients in a small subpopulation of neurons but also reduces depression in recruited neurons by reducing the rate of activation.},
}
@article {pmid37244320,
year = {2023},
author = {Qu, S and Shi, S and Quan, Z and Gao, Y and Wang, M and Wang, Y and Pan, G and Lai, HY and Roe, AW and Zhang, X},
title = {Design and application of a multimodality-compatible 1Tx/6Rx RF coil for monkey brain MRI at 7T.},
journal = {NeuroImage},
volume = {276},
number = {},
pages = {120185},
doi = {10.1016/j.neuroimage.2023.120185},
pmid = {37244320},
issn = {1095-9572},
mesh = {Animals ; Haplorhini ; Pilot Projects ; *Transcranial Direct Current Stimulation ; Magnetic Resonance Imaging ; Neuroimaging ; Brain/diagnostic imaging ; Macaca ; Equipment Design ; Phantoms, Imaging ; Radio Waves ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales.<br><br>APPROACH: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted.<br><br>MAIN RESULTS: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images.<br><br>SIGNIFICANCE: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks.},
}
@article {pmid37241639,
year = {2023},
author = {Kim, Y and Mueller, NN and Schwartzman, WE and Sarno, D and Wynder, R and Hoeferlin, GF and Gisser, K and Capadona, JR and Hess-Dunning, A},
title = {Fabrication Methods and Chronic In Vivo Validation of Mechanically Adaptive Microfluidic Intracortical Devices.},
journal = {Micromachines},
volume = {14},
number = {5},
pages = {},
pmid = {37241639},
issn = {2072-666X},
support = {I01 RX003083/RX/RRD VA/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {Intracortical neural probes are both a powerful tool in basic neuroscience studies of brain function and a critical component of brain computer interfaces (BCIs) designed to restore function to paralyzed patients. Intracortical neural probes can be used both to detect neural activity at single unit resolution and to stimulate small populations of neurons with high resolution. Unfortunately, intracortical neural probes tend to fail at chronic timepoints in large part due to the neuroinflammatory response that follows implantation and persistent dwelling in the cortex. Many promising approaches are under development to circumvent the inflammatory response, including the development of less inflammatory materials/device designs and the delivery of antioxidant or anti-inflammatory therapies. Here, we report on our recent efforts to integrate the neuroprotective effects of both a dynamically softening polymer substrate designed to minimize tissue strain and localized drug delivery at the intracortical neural probe/tissue interface through the incorporation of microfluidic channels within the probe. The fabrication process and device design were both optimized with respect to the resulting device mechanical properties, stability, and microfluidic functionality. The optimized devices were successfully able to deliver an antioxidant solution throughout a six-week in vivo rat study. Histological data indicated that a multi-outlet design was most effective at reducing markers of inflammation. The ability to reduce inflammation through a combined approach of drug delivery and soft materials as a platform technology allows future studies to explore additional therapeutics to further enhance intracortical neural probes performance and longevity for clinical applications.},
}
@article {pmid37241600,
year = {2023},
author = {Wang, S and Ji, B and Shao, D and Chen, W and Gao, K},
title = {A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals.},
journal = {Micromachines},
volume = {14},
number = {5},
pages = {},
pmid = {37241600},
issn = {2072-666X},
support = {62106041//National Natural Science Foundation of China/ ; 62204204//National Natural Science Foundation of China/ ; 223202100019//Fundamental Research Funds for the Central Universities/ ; 21YF1451000//Shanghai Sailing Program/ ; 2022GY-001//Key Research and Development Program of Shaanxi/ ; cstc2021jcyj-msxmX0825//Natural Science Foundation of Chongqing/ ; },
abstract = {In this paper, we propose a classification algorithm of EEG signal based on canonical correlation analysis (CCA) and integrated with adaptive filtering. It can enhance the detection of steady-state visual evoked potentials (SSVEPs) in a brain-computer interface (BCI) speller. An adaptive filter is employed in front of the CCA algorithm to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. The ensemble method is developed to integrate recursive least squares (RLS) adaptive filter corresponding to multiple stimulation frequencies. The method is tested by the SSVEP signal recorded from six targets by actual experiment and the EEG in a public SSVEP dataset of 40 targets from Tsinghua University. The accuracy rates of the CCA method and the CCA-based integrated RLS filter algorithm (RLS-CCA method) are compared. Experiment results show that the proposed RLS-CCA-based method significantly improves the classification accuracy compared with the pure CCA method. Especially when the number of EEG leads is low (three occipital electrodes and five non occipital electrodes), its advantage is more significant, and accuracy reaches 91.23%, which is more suitable for wearable environments where high-density EEG is not easy to collect.},
}
@article {pmid37239297,
year = {2023},
author = {Wu, Q and Lei, H and Mao, T and Deng, Y and Zhang, X and Jiang, Y and Zhong, X and Detre, JA and Liu, J and Rao, H},
title = {Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies.},
journal = {Brain sciences},
volume = {13},
number = {5},
pages = {},
pmid = {37239297},
issn = {2076-3425},
support = {R01-HL102119; R21-AG051981/NH/NIH HHS/United States ; },
abstract = {Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.},
}
@article {pmid37239254,
year = {2023},
author = {Candreia, C and Rust, HM and Honegger, F and Allum, JHJ},
title = {The Effects of Vibro-Tactile Biofeedback Balance Training on Balance Control and Dizziness in Patients with Persistent Postural-Perceptual Dizziness (PPPD).},
journal = {Brain sciences},
volume = {13},
number = {5},
pages = {},
pmid = {37239254},
issn = {2076-3425},
abstract = {BACKGROUND: Patients with persistent postural-perceptual dizziness (PPPD) frequently report having problems with balance control. Artificial systems providing vibro-tactile feedback (VTfb) of trunk sway to the patient could aid recalibration of "falsely" programmed natural sensory signal gains underlying unstable balance control and dizziness. Thus, the question we examine, retrospectively, is whether such artificial systems improve balance control in PPPD patients and simultaneously reduce the effects of dizziness on their living circumstances. Therefore, we assessed in PPPD patients the effects of VTfb of trunk sway on balance control during stance and gait tests, and on their perceived dizziness.<br><br>METHODS: Balance control was assessed in 23 PPPD patients (11 of primary PPPD origin) using peak-to-peak amplitudes of trunk sway measured in the pitch and roll planes with a gyroscope system (SwayStar&#x2122;) during 14 stance and gait tests. The tests included standing eyes closed on foam, walking tandem steps, and walking over low barriers. The measures of trunk sway were combined into a Balance Control Index (BCI) and used to determine whether the patient had a quantified balance deficit (QBD) or dizziness only (DO). The Dizziness Handicap Inventory (DHI) was used to assess perceived dizziness. The subjects first underwent a standard balance assessment from which the VTfb thresholds in eight directions, separated by 45 deg, were calculated for each assessment test based on the 90% range of the trunk sway angles in the pitch and roll directions for the test. A headband-mounted VTfb system, connected to the SwayStar&#x2122;, was active in one of the eight directions when the threshold for that direction was exceeded. The subjects trained for 11 of the 14 balance tests with VTfb twice per week for 30 min over a total of 2 consecutive weeks. The BCI and DHI were reassessed each week and the thresholds were reset after the first week of training.<br><br>RESULTS: On average, the patients showed an improved balance control in the BCI values after 2 weeks of VTfb training (24% p = 0.0001). The improvement was greater for the QBD patients than for the DO patients (26 vs. 21%), and greater for the gait tests than the stance tests. After 2 weeks, the mean BCI values of the DO patients, but not the QBD patients, were significantly less (p = 0.0008) than the upper 95% limit of normal age-matched reference values. A subjective benefit in balance control was spontaneously reported by 11 patients. Lower (36%), but less significant DHI values were also achieved after VTfb training (p = 0.006). The DHI changes were identical for the QBD and DO patients and approximately equal to the minimum clinical important difference.<br><br>CONCLUSIONS: These initial results show, as far as we are aware for the first time, that providing VTfb of trunk sway to PPPD subjects yields a significant improvement in balance control, but a far less significant change in DHI-assessed dizziness. The intervention benefitted the gait trials more than the stance trials and benefited the QBD group of PPPD patients more than the DO group. This study increases our understanding of the pathophysiologic processes underlying PPPD and provides a basis for future interventions.},
}
@article {pmid37239253,
year = {2023},
author = {Xu, D and Tang, F and Li, Y and Zhang, Q and Feng, X},
title = {FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization.},
journal = {Brain sciences},
volume = {13},
number = {5},
pages = {},
pmid = {37239253},
issn = {2076-3425},
support = {QYZDY-SSW-JSC005//Chinese Academy of Sciences/ ; },
abstract = {The brain-computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 ± 6.18% and 80.58 ± 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided.},
}
@article {pmid37239238,
year = {2023},
author = {Novičić, M and Savić, AM},
title = {Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training.},
journal = {Brain sciences},
volume = {13},
number = {5},
pages = {},
pmid = {37239238},
issn = {2076-3425},
support = {6066223//Science Fund, Republic of Serbia/ ; },
abstract = {Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user's forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.},
}
@article {pmid37239182,
year = {2023},
author = {Zhu, S and Yang, J and Ding, P and Wang, F and Gong, A and Fu, Y},
title = {Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph.},
journal = {Brain sciences},
volume = {13},
number = {5},
pages = {},
pmid = {37239182},
issn = {2076-3425},
support = {82172058, 81771926, 61763022, 62006246.//National Natural Science Foundation of China/ ; },
abstract = {The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.},
}
@article {pmid37237679,
year = {2023},
author = {Tan, X and Wang, D and Chen, J and Xu, M},
title = {Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {5},
pages = {},
pmid = {37237679},
issn = {2306-5354},
support = {Project 12275295//National Natural Science Foundation of China/ ; },
abstract = {Exploring the effective signal features of electroencephalogram (EEG) signals is an important issue in the research of brain-computer interface (BCI), and the results can reveal the motor intentions that trigger electrical changes in the brain, which has broad research prospects for feature extraction from EEG data. In contrast to previous EEG decoding methods that are based solely on a convolutional neural network, the traditional convolutional classification algorithm is optimized by combining a transformer mechanism with a constructed end-to-end EEG signal decoding algorithm based on swarm intelligence theory and virtual adversarial training. The use of a self-attention mechanism is studied to expand the receptive field of EEG signals to global dependence and train the neural network by optimizing the global parameters in the model. The proposed model is evaluated on a real-world public dataset and achieves the highest average accuracy of 63.56% in cross-subject experiments, which is significantly higher than that found for recently published algorithms. Additionally, good performance is achieved in decoding motor intentions. The experimental results show that the proposed classification framework promotes the global connection and optimization of EEG signals, which can be further applied to other BCI tasks.},
}
@article {pmid37237678,
year = {2023},
author = {Ali, MU and Kim, KS and Kallu, KD and Zafar, A and Lee, SW},
title = {OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS-Brain Computer Interface.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {5},
pages = {},
pmid = {37237678},
issn = {2306-5354},
support = {2022R1C1C2003637//National Research Foundation of Korea/ ; NRF2021R1I1A2059735//National Research Foundation of Korea/ ; },
abstract = {Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.},
}
@article {pmid37237623,
year = {2023},
author = {Perpetuini, D and Günal, M and Chiou, N and Koyejo, S and Mathewson, K and Low, KA and Fabiani, M and Gratton, G and Chiarelli, AM},
title = {Fast Optical Signals for Real-Time Retinotopy and Brain Computer Interface.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {5},
pages = {},
pmid = {37237623},
issn = {2306-5354},
support = {P41 EB031772/EB/NIBIB NIH HHS/United States ; },
abstract = {A brain-computer interface (BCI) allows users to control external devices through brain activity. Portable neuroimaging techniques, such as near-infrared (NIR) imaging, are suitable for this goal. NIR imaging has been used to measure rapid changes in brain optical properties associated with neuronal activation, namely fast optical signals (FOS) with good spatiotemporal resolution. However, FOS have a low signal-to-noise ratio, limiting their BCI application. Here FOS were acquired with a frequency-domain optical system from the visual cortex during visual stimulation consisting of a rotating checkerboard wedge, flickering at 5 Hz. We used measures of photon count (Direct Current, DC light intensity) and time of flight (phase) at two NIR wavelengths (690 nm and 830 nm) combined with a machine learning approach for fast estimation of visual-field quadrant stimulation. The input features of a cross-validated support vector machine classifier were computed as the average modulus of the wavelet coherence between each channel and the average response among all channels in 512 ms time windows. An above chance performance was obtained when differentiating visual stimulation quadrants (left vs. right or top vs. bottom) with the best classification accuracy of ~63% (information transfer rate of ~6 bits/min) when classifying the superior and inferior stimulation quadrants using DC at 830 nm. The method is the first attempt to provide generalizable retinotopy classification relying on FOS, paving the way for the use of FOS in real-time BCI.},
}
@article {pmid37237385,
year = {2023},
author = {Yang, Z and Liu, F and Li, Z and Liu, N and Yao, X and Zhou, Y and Zhang, L and Jiang, P and Liu, H and Kong, L and Lang, C and Xu, X and Jia, J and Nakajima, T and Gu, W and Zheng, L and Zhang, Z},
title = {Histone lysine methyltransferase SMYD3 promotes oral squamous cell carcinoma tumorigenesis via H3K4me3-mediated HMGA2 transcription.},
journal = {Clinical epigenetics},
volume = {15},
number = {1},
pages = {92},
pmid = {37237385},
issn = {1868-7083},
mesh = {Humans ; Carcinogenesis/genetics ; Cell Line, Tumor ; Cell Transformation, Neoplastic/genetics ; DNA Methylation ; Gene Expression Regulation, Neoplastic ; *Histone-Lysine N-Methyltransferase/metabolism ; Histones/metabolism ; *Mouth Neoplasms/genetics ; *Squamous Cell Carcinoma of Head and Neck/genetics ; },
abstract = {BACKGROUND: Epigenetic dysregulation is essential to the tumorigenesis of oral squamous cell carcinoma (OSCC). SET and MYND domain-containing protein 3 (SMYD3), a histone lysine methyltransferase, is implicated in gene transcription regulation and tumor development. However, the roles of SMYD3 in OSCC initiation are not fully understood. The present study investigated the biological functions and mechanisms involved in the SMYD3-mediated tumorigenesis of OSCC utilizing bioinformatic approaches and validation assays with the aim of informing the development of targeted therapies for OSCC.<br><br>RESULTS: 429 chromatin regulators were screened by a machine learning approach and aberrant expression of SMYD3 was found to be closely associated with OSCC formation and poor prognosis. Data profiling of single-cell and tissue demonstrated that upregulated SMYD3 significantly correlated with aggressive clinicopathological features of OSCC. Alterations in copy number and DNA methylation patterns may contribute to SMYD3 overexpression. Functional experimental results suggested that SMYD3 enhanced cancer cell stemness and proliferation in vitro and tumor growth in vivo. SMYD3 was observed to bind to the High Mobility Group AT-Hook 2 (HMGA2) promoter and elevated tri-methylation of histone H3 lysine 4 at the corresponding site was responsible for transactivating HMGA2. SMYD3 also was positively linked to HMGA2 expression in OSCC samples. Furthermore, treatment with the SMYD3 chemical inhibitor BCI-121 exerted anti-tumor effects.<br><br>CONCLUSIONS: Histone methyltransferase activity and transcription-potentiating function of SMYD3 were found to be essential for tumorigenesis and the SMYD3-HMGA2 is a potential therapeutic target in OSCC.},
}
@article {pmid37236786,
year = {2023},
author = {Nadra, JG and Bengson, JJ and Morales, AB and Mangun, GR},
title = {Attention without Constraint: Alpha Lateralization in Uncued Willed Attention.},
journal = {eNeuro},
volume = {10},
number = {6},
pages = {},
pmid = {37236786},
issn = {2373-2822},
mesh = {Humans ; *Attention/physiology ; *Volition/physiology ; Vision, Ocular ; Motivation ; Cues ; Space Perception/physiology ; Reaction Time/physiology ; },
abstract = {Studies of voluntary visual spatial attention have used attention-directing cues, such as arrows, to induce or instruct observers to focus selective attention on relevant locations in visual space to detect or discriminate subsequent target stimuli. In everyday vision, however, voluntary attention is influenced by a host of factors, most of which are quite different from the laboratory paradigms that use attention-directing cues. These factors include priming, experience, reward, meaning, motivations, and high-level behavioral goals. Attention that is endogenously directed in the absence of external attention-directing cues has been referred to as "self-initiated attention" or, as in our prior work, as "willed attention" where volunteers decide where to attend in response to a prompt to do so. Here, we used a novel paradigm that eliminated external influences (i.e., attention-directing cues and prompts) about where and/or when spatial attention should be directed. Using machine learning decoding methods, we showed that the well known lateralization of EEG alpha power during spatial attention was also present during purely self-generated attention. By eliminating explicit cues or prompts that affect the allocation of voluntary attention, this work advances our understanding of the neural correlates of attentional control and provides steps toward the development of EEG-based brain-computer interfaces that tap into human intentions.},
}
@article {pmid37236176,
year = {2023},
author = {Cui, Y and Xie, S and Xie, X and Zheng, D and Tang, H and Duan, K and Chen, X and Jiang, Y},
title = {LDER: a classification framework based on ERP enhancement in RSVP task.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/acd95d},
pmid = {37236176},
issn = {1741-2552},
mesh = {Humans ; *Evoked Potentials ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Discriminant Analysis ; },
abstract = {Objective.Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications.Approach.To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework.Main results.Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain-computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes.Significance.As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.},
}
@article {pmid37235473,
year = {2023},
author = {Lycke, R and Kim, R and Zolotavin, P and Montes, J and Sun, Y and Koszeghy, A and Altun, E and Noble, B and Yin, R and He, F and Totah, N and Xie, C and Luan, L},
title = {Low-threshold, high-resolution, chronically stable intracortical microstimulation by ultraflexible electrodes.},
journal = {Cell reports},
volume = {42},
number = {6},
pages = {112554},
pmid = {37235473},
issn = {2211-1247},
support = {R01 NS102917/NS/NINDS NIH HHS/United States ; R01 NS109361/NS/NINDS NIH HHS/United States ; U01 NS115588/NS/NINDS NIH HHS/United States ; },
mesh = {Mice ; Animals ; *Somatosensory Cortex/physiology ; Electrodes ; Electric Stimulation/methods ; Electrodes, Implanted ; },
abstract = {Intracortical microstimulation (ICMS) enables applications ranging from neuroprosthetics to causal circuit manipulations. However, the resolution, efficacy, and chronic stability of neuromodulation are often compromised by adverse tissue responses to the indwelling electrodes. Here we engineer ultraflexible stim-nanoelectronic threads (StimNETs) and demonstrate low activation threshold, high resolution, and chronically stable ICMS in awake, behaving mouse models. In vivo two-photon imaging reveals that StimNETs remain seamlessly integrated with the nervous tissue throughout chronic stimulation periods and elicit stable, focal neuronal activation at low currents of 2 μA. Importantly, StimNETs evoke longitudinally stable behavioral responses for over 8 months at a markedly low charge injection of 0.25 nC/phase. Quantified histological analyses show that chronic ICMS by StimNETs induces no neuronal degeneration or glial scarring. These results suggest that tissue-integrated electrodes provide a path for robust, long-lasting, spatially selective neuromodulation at low currents, which lessens risk of tissue damage or exacerbation of off-target side effects.},
}
@article {pmid37234419,
year = {2023},
author = {Haggie, L and Schmid, L and Röhrle, O and Besier, T and McMorland, A and Saini, H},
title = {Linking cortex and contraction-Integrating models along the corticomuscular pathway.},
journal = {Frontiers in physiology},
volume = {14},
number = {},
pages = {1095260},
pmid = {37234419},
issn = {1664-042X},
abstract = {Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.},
}
@article {pmid37234411,
year = {2023},
author = {Centeio, R and Cabrita, I and Schreiber, R and Kunzelmann, K},
title = {TMEM16A/F support exocytosis but do not inhibit Notch-mediated goblet cell metaplasia of BCi-NS1.1 human airway epithelium.},
journal = {Frontiers in physiology},
volume = {14},
number = {},
pages = {1157704},
pmid = {37234411},
issn = {1664-042X},
abstract = {Cl[-] channels such as the Ca[2+] activated Cl[-] channel TMEM16A and the Cl[-] permeable phospholipid scramblase TMEM16F may affect the intracellular Cl[-] concentration ([Cl[-]]i), which could act as an intracellular signal. Loss of airway expression of TMEM16A induced a massive expansion of the secretory cell population like goblet and club cells, causing differentiation into a secretory airway epithelium. Knockout of the Ca[2+]-activated Cl[-] channel TMEM16A or the phospholipid scramblase TMEM16F leads to mucus accumulation in intestinal goblet cells and airway secretory cells. We show that both TMEM16A and TMEM16F support exocytosis and release of exocytic vesicles, respectively. Lack of TMEM16A/F expression therefore causes inhibition of mucus secretion and leads to goblet cell metaplasia. The human basal epithelial cell line BCi-NS1.1 forms a highly differentiated mucociliated airway epithelium when grown in PneumaCult™ media under an air liquid interface. The present data suggest that mucociliary differentiation requires activation of Notch signaling, but not the function of TMEM16A. Taken together, TMEM16A/F are important for exocytosis, mucus secretion and formation of extracellular vesicles (exosomes or ectosomes) but the present data do no not support a functional role of TMEM16A/F in Notch-mediated differentiation of BCi-NS1.1 cells towards a secretory epithelium.},
}
@article {pmid37232857,
year = {2023},
author = {Yang, G and Wang, Y and Xu, Z and Zhang, X and Ruan, W and Mo, F and Lu, B and Fan, P and Dai, Y and He, E and Song, Y and Wang, C and Liu, J and Cai, X},
title = {PtNPs/PEDOT:PSS-Modified Microelectrode Arrays for Detection of the Discharge of Head Direction Cells in the Retrosplenial Cortex of Rats under Dissociation between Visual and Vestibular Inputs.},
journal = {Biosensors},
volume = {13},
number = {5},
pages = {},
pmid = {37232857},
issn = {2079-6374},
support = {T2293731//National Natural Science Foundation of China/ ; 62121003//National Natural Science Foundation of China/ ; 61960206012//National Natural Science Foundation of China/ ; 62171434//National Natural Science Foundation of China/ ; 61971400//National Natural Science Foundation of China/ ; 61975206//National Natural Science Foundation of China/ ; 61973292//National Natural Science Foundation of China/ ; 2021ZD0201600//STI 2030 - Major Projects/ ; 2022YFC2402501//the National Key R&amp;D Program of China/ ; GJJSTD20210004//the Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; with title of "Novel Micro Devices and Technologies for Brain Computer Interface"//the Frontier Interdisciplinary Project of the Chinese Academy of Sciences/ ; },
mesh = {Animals ; Rats ; *Gyrus Cinguli ; Microelectrodes ; Neurons/physiology ; },
abstract = {The electrophysiological activities of head direction (HD) cells under visual and vestibular input dissociation are important to understanding the formation of the sense of direction in animals. In this paper, we fabricated a PtNPs/PEDOT:PSS-modified MEA to detect changes in the discharge of HD cells under dissociated sensory conditions. The electrode shape was customized for the retrosplenial cortex (RSC) and was conducive to the sequential detection of neurons at different depths in vivo when combined with a microdriver. The recording sites of the electrode were modified with PtNPs/PEDOT:PSS to form a three-dimensional convex structure, leading to closer contact with neurons and improving the detection performance and signal-to-noise ratio of the MEA. We designed a rotating cylindrical arena to separate the visual and vestibular information of the rats and detected the changes in the directional tuning of the HD cells in the RSC. The results showed that after visual and vestibular sensory dissociation, HD cells used visual information to establish newly discharged directions which differed from the original direction. However, with the longer time required to process inconsistent sensory information, the function of the HD system gradually degraded. After recovery, the HD cells reverted to their newly established direction rather than the original direction. The research based on our MEAs revealed how HD cells process dissociated sensory information and contributes to the study of the spatial cognitive navigation mechanism.},
}
@article {pmid37230207,
year = {2023},
author = {Van de Wauw, C and Riecke, L and Goebel, R and Kaas, A and Sorger, B},
title = {Talking with hands and feet: Selective somatosensory attention and fMRI enable robust and convenient brain-based communication.},
journal = {NeuroImage},
volume = {276},
number = {},
pages = {120172},
doi = {10.1016/j.neuroimage.2023.120172},
pmid = {37230207},
issn = {1095-9572},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Reproducibility of Results ; Electroencephalography/methods ; Brain/diagnostic imaging ; Hand ; *Brain-Computer Interfaces ; Somatosensory Cortex/diagnostic imaging/physiology ; },
abstract = {In brain-based communication, voluntarily modulated brain signals (instead of motor output) are utilized to interact with the outside world. The possibility to circumvent the motor system constitutes an important alternative option for severely paralyzed. Most communication brain-computer interface (BCI) paradigms require intact visual capabilities and impose a high cognitive load, but for some patients, these requirements are not given. In these situations, a better-suited, less cognitively demanding information-encoding approach may exploit auditorily-cued selective somatosensory attention to vibrotactile stimulation. Here, we propose, validate and optimize a novel communication-BCI paradigm using differential fMRI activation patterns evoked by selective somatosensory attention to tactile stimulation of the right hand or left foot. Using cytoarchitectonic probability maps and multi-voxel pattern analysis (MVPA), we show that the locus of selective somatosensory attention can be decoded from fMRI-signal patterns in (especially primary) somatosensory cortex with high accuracy and reliability, with the highest classification accuracy (85.93%) achieved when using Brodmann area 2 (SI-BA2) at a probability level of 0.2. Based on this outcome, we developed and validated a novel somatosensory attention-based yes/no communication procedure and demonstrated its high effectiveness even when using only a limited amount of (MVPA) training data. For the BCI user, the paradigm is straightforward, eye-independent, and requires only limited cognitive functioning. In addition, it is BCI-operator friendly given its objective and expertise-independent procedure. For these reasons, our novel communication paradigm has high potential for clinical applications.},
}
@article {pmid37228852,
year = {2023},
author = {Deverett, B},
title = {Anesthesia for non-traditional consciousness.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1146242},
pmid = {37228852},
issn = {1662-5161},
}
@article {pmid37228786,
year = {2023},
author = {Baker, JL and Toth, R and Deli, A and Zamora, M and Fleming, JE and Benjaber, M and Goerzen, D and Ryou, JW and Purpura, KP and Schiff, ND and Denison, T},
title = {Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2023},
number = {},
pages = {10123754},
pmid = {37228786},
issn = {1948-3546},
support = {MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; R01 NS111019/NS/NINDS NIH HHS/United States ; },
abstract = {Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.},
}
@article {pmid37228451,
year = {2023},
author = {Dale, R and O'sullivan, TD and Howard, S and Orihuela-Espina, F and Dehghani, H},
title = {System Derived Spatial-Temporal CNN for High-Density fNIRS BCI.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {4},
number = {},
pages = {85-95},
pmid = {37228451},
issn = {2644-1276},
support = {R01 EB029595/EB/NIBIB NIH HHS/United States ; },
abstract = {An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.},
}
@article {pmid37227915,
year = {2023},
author = {Jia, H and Feng, F and Caiafa, CF and Duan, F and Zhang, Y and Sun, Z and Sole-Casals, J},
title = {Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {8},
pages = {3867-3877},
doi = {10.1109/JBHI.2023.3278747},
pmid = {37227915},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Upper Extremity ; Movement ; *Brain-Computer Interfaces ; },
abstract = {The classification of limb movements can provide with control commands in non-invasive brain-computer interface. Previous studies on the classification of limb movements have focused on the classification of left/right limbs; however, the classification of different types of upper limb movements has often been ignored despite that it provides more active-evoked control commands in the brain-computer interface. Nevertheless, few machine learning method can be used as the state-of-the-art method in the multi-class classification of limb movements. This work focuses on the multi-class classification of upper limb movements and proposes the multi-class filter bank task-related component analysis (mFBTRCA) method, which consists of three steps: spatial filtering, similarity measuring and filter bank selection. The spatial filter, namely the task-related component analysis, is first used to remove noise from EEG signals. The canonical correlation measures the similarity of the spatial-filtered signals and is used for feature extraction. The correlation features are extracted from multiple low-frequency filter banks. The minimum-redundancy maximum-relevance selects the essential features from all the correlation features, and finally, the support vector machine is used to classify the selected features. The proposed method compared against previously used models is evaluated using two datasets. mFBTRCA achieved a classification accuracy of 0.4193 ± 0.0780 (7 classes) and 0.4032 ± 0.0714 (5 classes), respectively, which improves on the best accuracies achieved using the compared methods (0.3590 ± 0.0645 and 0.3159 ± 0.0736, respectively). The proposed method is expected to provide more control commands in the applications of non-invasive brain-computer interfaces.},
}
@article {pmid37227910,
year = {2023},
author = {Rathee, G and Kerrache, CA and Bilal, M},
title = {An Accurate and Inter-Operatable Fuzzy-Based System Using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2726-2733},
doi = {10.1109/TNSRE.2023.3275009},
pmid = {37227910},
issn = {1558-0210},
mesh = {Humans ; *Canonical Correlation Analysis ; Electroencephalography/methods ; Brain ; *Brain-Computer Interfaces ; Internet ; Algorithms ; },
abstract = {The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics.},
}
@article {pmid37225984,
year = {2023},
author = {Lorach, H and Galvez, A and Spagnolo, V and Martel, F and Karakas, S and Intering, N and Vat, M and Faivre, O and Harte, C and Komi, S and Ravier, J and Collin, T and Coquoz, L and Sakr, I and Baaklini, E and Hernandez-Charpak, SD and Dumont, G and Buschman, R and Buse, N and Denison, T and van Nes, I and Asboth, L and Watrin, A and Struber, L and Sauter-Starace, F and Langar, L and Auboiroux, V and Carda, S and Chabardes, S and Aksenova, T and Demesmaeker, R and Charvet, G and Bloch, J and Courtine, G},
title = {Walking naturally after spinal cord injury using a brain-spine interface.},
journal = {Nature},
volume = {618},
number = {7963},
pages = {126-133},
pmid = {37225984},
issn = {1476-4687},
mesh = {Humans ; *Brain/physiology ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/instrumentation/methods ; Quadriplegia/etiology/rehabilitation/therapy ; Reproducibility of Results ; *Spinal Cord/physiology ; *Spinal Cord Injuries/complications/rehabilitation/therapy ; *Walking/physiology ; Leg/physiology ; *Neurological Rehabilitation/instrumentation/methods ; Male ; },
abstract = {A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis[1,2]. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain-spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals[3] and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking[4-6]. A highly reliable BSI is calibrated within a few minutes. This reliability has remained stable over one year, including during independent use at home. The participant reports that the BSI enables natural control over the movements of his legs to stand, walk, climb stairs and even traverse complex terrains. Moreover, neurorehabilitation supported by the BSI improved neurological recovery. The participant regained the ability to walk with crutches overground even when the BSI was switched off. This digital bridge establishes a framework to restore natural control of movement after paralysis.},
}
@article {pmid37225819,
year = {2023},
author = {Lewis, D},
title = {Brain-spine interface allows paralysed man to walk using his thoughts.},
journal = {Nature},
volume = {618},
number = {7963},
pages = {18},
pmid = {37225819},
issn = {1476-4687},
mesh = {Humans ; Male ; *Brain/physiology ; *Brain-Computer Interfaces ; *Paralysis/etiology/rehabilitation/therapy ; *Spinal Cord/physiology ; *Spinal Cord Injuries/complications/rehabilitation/therapy ; *Thinking/physiology ; *Walking ; },
}
@article {pmid37223547,
year = {2023},
author = {Li, M and Wei, R and Zhang, Z and Zhang, P and Xu, G and Liao, W},
title = {CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {4},
number = {},
pages = {0024},
pmid = {37223547},
issn = {2692-7632},
abstract = {Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.},
}
@article {pmid37221270,
year = {2023},
author = {Xu, P and Huang, S and Krumm, BE and Zhuang, Y and Mao, C and Zhang, Y and Wang, Y and Huang, XP and Liu, YF and He, X and Li, H and Yin, W and Jiang, Y and Zhang, Y and Roth, BL and Xu, HE},
title = {Structural genomics of the human dopamine receptor system.},
journal = {Cell research},
volume = {33},
number = {8},
pages = {604-616},
pmid = {37221270},
issn = {1748-7838},
support = {R01 MH112205/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Receptors, Dopamine/metabolism ; Ligands ; Dopamine/metabolism/therapeutic use ; *Parkinson Disease/genetics/drug therapy ; Genomics ; },
abstract = {The dopaminergic system, including five dopamine receptors (D1R to D5R), plays essential roles in the central nervous system (CNS); and ligands that activate dopamine receptors have been used to treat many neuropsychiatric disorders, including Parkinson's Disease (PD) and schizophrenia. Here, we report cryo-EM structures of all five subtypes of human dopamine receptors in complex with G protein and bound to the pan-agonist, rotigotine, which is used to treat PD and restless legs syndrome. The structures reveal the basis of rotigotine recognition in different dopamine receptors. Structural analysis together with functional assays illuminate determinants of ligand polypharmacology and selectivity. The structures also uncover the mechanisms of dopamine receptor activation, unique structural features among the five receptor subtypes, and the basis of G protein coupling specificity. Our work provides a comprehensive set of structural templates for the rational design of specific ligands to treat CNS diseases targeting the dopaminergic system.},
}
@article {pmid37220058,
year = {2024},
author = {Li, D and Wang, J and Xu, J and Fang, X and Ji, Y},
title = {Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {10},
pages = {13472-13482},
doi = {10.1109/TNNLS.2023.3269512},
pmid = {37220058},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Neural Networks, Computer ; *Algorithms ; Brain/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Machine Learning ; },
abstract = {In recent years, with the rapid development of deep learning, various deep learning frameworks have been widely used in brain-computer interface (BCI) research for decoding motor imagery (MI) electroencephalogram (EEG) signals to understand brain activity accurately. The electrodes, however, record the mixed activities of neurons. If different features are directly embedded in the same feature space, the specific and mutual features of different neuron regions are not considered, which will reduce the expression ability of the feature itself. We propose a cross-channel specific-mutual feature transfer learning (CCSM-FT) network model to solve this problem. The multibranch network extracts the specific and mutual features of brain's multiregion signals. Effective training tricks are used to maximize the distinction between the two kinds of features. Suitable training tricks can also improve the effectiveness of the algorithm compared with novel models. Finally, we transfer two kinds of features to explore the potential of mutual and specific features to enhance the expressive power of the feature and use the auxiliary set to improve identification performance. The experimental results show that the network has a better classification effect in the BCI Competition IV-2a and the HGD datasets.},
}
@article {pmid37216253,
year = {2023},
author = {Zhang, Z and Feng, P and Oprea, A and Constandinou, TG},
title = {Calibration-Free and Hardware-Efficient Neural Spike Detection for Brain Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {17},
number = {4},
pages = {725-740},
doi = {10.1109/TBCAS.2023.3278531},
pmid = {37216253},
issn = {1940-9990},
mesh = {Humans ; Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Action Potentials ; *Data Compression ; Algorithms ; },
abstract = {Recent translational efforts in brain-machine interfaces (BMI) are demonstrating the potential to help people with neurological disorders. The current trend in BMI technology is to increase the number of recording channels to the thousands, resulting in the generation of vast amounts of raw data. This in turn places high bandwidth requirements for data transmission, which increases power consumption and thermal dissipation of implanted systems. On-implant compression and/or feature extraction are therefore becoming essential to limiting this increase in bandwidth, but add further power constraints - the power required for data reduction must remain less than the power saved through bandwidth reduction. Spike detection is a common feature extraction technique used for intracortical BMIs. In this article, we develop a novel firing-rate-based spike detection algorithm that requires no external training and is hardware efficient and therefore ideally suited for real-time applications. Key performance and implementation metrics such as detection accuracy, adaptability in chronic deployment, power consumption, area utilization, and channel scalability are benchmarked against existing methods using various datasets. The algorithm is first validated using a reconfigurable hardware (FPGA) platform and then ported to a digital ASIC implementation in both 65 nm and 0.18 μm CMOS technologies. The 128-channel ASIC design implemented in a 65 nm CMOS technology occupies 0.096 mm[2] silicon area and consumes 4.86 μW from a 1.2 V power supply. The adaptive algorithm achieves a 96% spike detection accuracy on a commonly used synthetic dataset, without the need for any prior training.},
}
@article {pmid37214970,
year = {2023},
author = {Huang, S and Liu, X and Lin, S and Glynn, C and Felix, K and Sahasrabudhe, A and Maley, C and Xu, J and Chen, W and Hong, E and Crosby, AJ and Wang, Q and Rao, S},
title = {Control of Polymers' Amorphous-crystalline Transition for Hydrogel Bioelectronics Miniaturization and Multifunctional Integration.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {37214970},
issn = {2693-5015},
support = {R00 MH120279/MH/NIMH NIH HHS/United States ; },
abstract = {Bioelectronic devices made of soft elastic materials exhibit motion-adaptive properties suitable for brain-machine interfaces and for investigating complex neural circuits. While two-dimensional microfabrication strategies enable miniaturizing devices to access delicate nerve structures, creating 3D architecture for expansive implementation requires more accessible and scalable manufacturing approaches. Here we present a fabrication strategy through the control of metamorphic polymers' amorphous-crystalline transition (COMPACT), for hydrogel bioelectronics with miniaturized fiber shape and multifunctional interrogation of neural circuits. By introducing multiple cross-linkers, acidification treatment, and oriented polymeric crystalline growth under deformation, we observed about an 80% diameter decrease in chemically cross-linked polyvinyl alcohol (PVA) hydrogel fibers, stably maintained in a fully hydrated state. We revealed that the addition of cross-linkers and acidification facilitated the oriented polymetric crystalline growth under mechanical stretching, which contributed to the desired hydrogel fiber diameter decrease. Our approach enabled the control of hydrogels' properties, including refractive index (RI 1.37-1.40 at 480 nm), light transmission (> 96%), stretchability (95% - 111%), and elastic modulus (10-63 MPa). To exploit these properties, we fabricated step-index hydrogel optical probes with contrasting RIs and applied them in optogenetics and photometric recordings in the mouse brain region of the ventral tegmental area (VTA) with concurrent social behavioral assessment. To extend COMPACT hydrogel multifunctional scaffolds to assimilate conductive nanomaterials and integrate multiple components of optical waveguide and electrodes, we developed carbon nanotubes (CNTs)-PVA hydrogel microelectrodes for hindlimb muscle electromyographic and brain electrophysiological recordings of light-triggered neural activities in transgenic mice expressing Channelrhodopsin-2 (ChR2).},
}
@article {pmid37213933,
year = {2023},
author = {Syrov, N and Yakovlev, L and Miroshnikov, A and Kaplan, A},
title = {Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1180056},
pmid = {37213933},
issn = {1662-5161},
abstract = {Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.},
}
@article {pmid37213929,
year = {2023},
author = {Wang, X and Dai, X and Liu, Y and Chen, X and Hu, Q and Hu, R and Li, M},
title = {Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1175399},
pmid = {37213929},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual differences among different subjects, existing classification models have low accuracy and poor generalization ability in MI classification tasks.<br><br>METHODS: To solve this problem, this paper proposes a electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning. Firstly, the source domain and target domain data are preprocessed, and then common space mode (CSP) and power spectral density (PSD) are used to extract spatial and frequency domain features respectively, which are combined into EEG joint features. Finally, an ensemble learning algorithm based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) is used to classify MI-EEG.<br><br>RESULTS: To validate the effectiveness of the algorithm, this paper compared and analyzed different algorithms on the BCI Competition IV Dataset 2a, and further verified the stability and effectiveness of the algorithm on the BCI Competition IV Dataset 2b. The experimental results show that the algorithm has an average accuracy of 91.5% and 83.7% on Dataset 2a and Dataset 2b, respectively, which is significantly better than other algorithms.<br><br>DISCUSSION: The statement explains that the algorithm fully exploits EEG signals and enriches EEG features, improves the recognition of the MI signals, and provides a new approach to solving the above problem.},
}
@article {pmid37213927,
year = {2023},
author = {Feng, B and Yu, T and Wang, H and Liu, K and Wu, W and Long, W},
title = {Editorial: Machine learning and deep learning in biomedical signal analysis.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1183840},
doi = {10.3389/fnhum.2023.1183840},
pmid = {37213927},
issn = {1662-5161},
}
@article {pmid37211699,
year = {2023},
author = {Ni, P and Zhou, C and Liang, S and Jiang, Y and Liu, D and Shao, Z and Noh, H and Zhao, L and Tian, Y and Zhang, C and Wei, J and Li, X and Yu, H and Ni, R and Yu, X and Qi, X and Zhang, Y and Ma, X and Deng, W and Guo, W and Wang, Q and Sham, PC and Chung, S and Li, T},
title = {YBX1-Mediated DNA Methylation-Dependent SHANK3 Expression in PBMCs and Developing Cortical Interneurons in Schizophrenia.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {20},
pages = {e2300455},
pmid = {37211699},
issn = {2198-3844},
support = {R01 MH107884/MH/NIMH NIH HHS/United States ; MH107884/MH/NIMH NIH HHS/United States ; R56 NS121541/NS/NINDS NIH HHS/United States ; NS121541/NS/NINDS NIH HHS/United States ; R01 MH131610/MH/NIMH NIH HHS/United States ; R01 MH133205/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *DNA Methylation/genetics ; Leukocytes, Mononuclear/metabolism ; *Schizophrenia/genetics ; Interneurons/metabolism/pathology ; DNA/metabolism ; Y-Box-Binding Protein 1/genetics/metabolism ; Nerve Tissue Proteins/genetics ; },
abstract = {Schizophrenia (SCZ) is a severe psychiatric and neurodevelopmental disorder. The pathological process of SCZ starts early during development, way before the first onset of psychotic symptoms. DNA methylation plays an important role in regulating gene expression and dysregulated DNA methylation is involved in the pathogenesis of various diseases. The methylated DNA immunoprecipitation-chip (MeDIP-chip) is performed to investigate genome-wide DNA methylation dysregulation in peripheral blood mononuclear cells (PBMCs) of patients with first-episode SCZ (FES). Results show that the SHANK3 promoter is hypermethylated, and this hypermethylation (HyperM) is negatively correlated with the cortical surface area in the left inferior temporal cortex and positively correlated with the negative symptom subscores in FES. The transcription factor YBX1 is further found to bind to the HyperM region of SHANK3 promoter in induced pluripotent stem cells (iPSCs)-derived cortical interneurons (cINs) but not glutamatergic neurons. Furthermore, a direct and positive regulatory effect of YBX1 on the expression of SHANK3 is confirmed in cINs using shRNAs. In summary, the dysregulated SHANK3 expression in cINs suggests the potential role of DNA methylation in the neuropathological mechanism underlying SCZ. The results also suggest that HyperM of SHANK3 in PBMCs can serve as a potential peripheral biomarker of SCZ.},
}
@article {pmid37211686,
year = {2023},
author = {Wang, X and Zhang, A and Yu, Q and Wang, Z and Wang, J and Xu, P and Liu, Y and Lu, J and Zheng, J and Li, H and Qi, Y and Zhang, J and Fang, Y and Xu, S and Zhou, J and Wang, K and Chen, S and Zhang, J},
title = {Single-Cell RNA Sequencing and Spatial Transcriptomics Reveal Pathogenesis of Meningeal Lymphatic Dysfunction after Experimental Subarachnoid Hemorrhage.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {21},
pages = {e2301428},
pmid = {37211686},
issn = {2198-3844},
support = {81870916//National Natural Science Foundation of China/ ; 82071287//National Natural Science Foundation of China/ ; 82271301//National Natural Science Foundation of China/ ; 81971107//National Natural Science Foundation of China/ ; 82002634//National Natural Science Foundation of China/ ; 82201430//National Natural Science Foundation of China/ ; 82201512//National Natural Science Foundation of China/ ; },
mesh = {Humans ; *Subarachnoid Hemorrhage/genetics/cerebrospinal fluid/pathology ; CD47 Antigen ; Transcriptome/genetics ; *Lymphatic Vessels/pathology ; Sequence Analysis, RNA ; },
abstract = {Subarachnoid hemorrhage (SAH) is a devastating subtype of stroke with high mortality and disability rate. Meningeal lymphatic vessels (mLVs) are a newly discovered intracranial fluid transport system and are proven to drain extravasated erythrocytes from cerebrospinal fluid into deep cervical lymph nodes after SAH. However, many studies have reported that the structure and function of mLVs are injured in several central nervous system diseases. Whether SAH can cause mLVs injury and the underlying mechanism remain unclear. Herein, single-cell RNA sequencing and spatial transcriptomics are applied, along with in vivo/vitro experiments, to investigate the alteration of the cellular, molecular, and spatial pattern of mLVs after SAH. First, it is demonstrated that SAH induces mLVs impairment. Then, through bioinformatic analysis of sequencing data, it is discovered that thrombospondin 1 (THBS1) and S100A6 are strongly associated with SAH outcome. Furthermore, the THBS1-CD47 ligand-receptor pair is found to function as a key role in meningeal lymphatic endothelial cell apoptosis via regulating STAT3/Bcl-2 signaling. The results illustrate a landscape of injured mLVs after SAH for the first time and provide a potential therapeutic strategy for SAH based on mLVs protection by disrupting THBS1 and CD47 interaction.},
}
@article {pmid37209578,
year = {2023},
author = {Zhao, Y and Zeng, H and Zheng, H and Wu, J and Kong, W and Dai, G},
title = {A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition.},
journal = {Computer methods and programs in biomedicine},
volume = {238},
number = {},
pages = {107593},
doi = {10.1016/j.cmpb.2023.107593},
pmid = {37209578},
issn = {1872-7565},
mesh = {*Brain ; *Algorithms ; Electroencephalography ; Cognition ; },
abstract = {BACKGROUND AND OBJECTIVE: Extracting cognitive representation and computational representation information simultaneously from electroencephalography (EEG) data and constructing corresponding information interaction models can effectively improve the recognition capability of brain cognitive status. However, due to the huge gap in the interaction between the two types of information, existing studies have yet to consider the advantages of the interaction of both.<br><br>METHODS: This paper introduces a novel architecture named the bidirectional interaction-based hybrid network (BIHN) for EEG cognitive recognition. BIHN consists of two networks: a cognitive-based network named CogN (e.g., graph convolution network, GCN; capsule network, CapsNet) and a computing-based network named ComN (e.g., EEGNet). CogN is responsible for extracting cognitive representation features from EEG data, while ComN is responsible for extracting computational representation features. Additionally, a bidirectional distillation-based coadaptation (BDC) algorithm is proposed to facilitate information interaction between CogN and ComN to realize the coadaptation of the two networks through bidirectional closed-loop feedback.<br><br>RESULTS: Cross-subject cognitive recognition experiments were performed on the Fatigue-Awake EEG dataset (FAAD, 2-class classification) and SEED dataset (3-class classification), and hybrid network pairs of GCN&#xa0;+&#xa0;EEGNet and CapsNet&#xa0;+&#xa0;EEGNet were verified. The proposed method achieved average accuracies of 78.76% (GCN&#xa0;+&#xa0;EEGNet) and 77.58% (CapsNet&#xa0;+&#xa0;EEGNet) on FAAD and 55.38% (GCN&#xa0;+&#xa0;EEGNet) and 55.10% (CapsNet&#xa0;+&#xa0;EEGNet) on SEED, outperforming the hybrid networks without the bidirectional interaction strategy.<br><br>CONCLUSIONS: Experimental results show that BIHN can achieve superior performance on two EEG datasets and enhance the ability of both CogN and ComN in EEG processing as well as cognitive recognition. We also validated its effectiveness with different hybrid network pairs. The proposed method could greatly promote the development of brain-computer collaborative intelligence.},
}
@article {pmid37209444,
year = {2023},
author = {Chen, J and Zhang, Y and Pan, Y and Xu, P and Guan, C},
title = {A transformer-based deep neural network model for SSVEP classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {164},
number = {},
pages = {521-534},
doi = {10.1016/j.neunet.2023.04.045},
pmid = {37209444},
issn = {1879-2782},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Photic Stimulation ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.},
}
@article {pmid37209127,
year = {2023},
author = {Hong, W and Liang, P and Pan, Y and Jin, J and Luo, L and Li, Y and Jin, C and Lü, W and Wang, M and Liu, Y and Chen, H and Gou, H and Wei, W and Ma, Z and Tao, R and Zha, R and Zhang, X},
title = {Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.},
journal = {Journal of behavioral addictions},
volume = {12},
number = {2},
pages = {458-470},
pmid = {37209127},
issn = {2063-5303},
mesh = {Humans ; Brain Mapping/methods ; Internet Addiction Disorder/diagnostic imaging ; *Video Games ; Brain/diagnostic imaging ; *Behavior, Addictive/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Internet ; },
abstract = {BACKGROUND AND AIMS: Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD).<br><br>METHODS: In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT.<br><br>RESULTS: PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community pro&#xfb01;le similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD.<br><br>DISCUSSION AND CONCLUSIONS: The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.},
}
@article {pmid37205052,
year = {2023},
author = {Li, J and Qi, Y and Pan, G},
title = {Phase-amplitude coupling-based adaptive filters for neural signal decoding.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1153568},
pmid = {37205052},
issn = {1662-4548},
abstract = {Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30-200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.},
}
@article {pmid37201274,
year = {2023},
author = {Marcos-Martínez, D and Santamaría-Vázquez, E and Martínez-Cagigal, V and Pérez-Velasco, S and Rodríguez-González, V and Martín-Fernández, A and Moreno-Calderón, S and Hornero, R},
title = {ITACA: An open-source framework for Neurofeedback based on Brain-Computer Interfaces.},
journal = {Computers in biology and medicine},
volume = {160},
number = {},
pages = {107011},
doi = {10.1016/j.compbiomed.2023.107011},
pmid = {37201274},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Neurofeedback/methods ; Humans ; },
abstract = {BACKGROUND AND OBJECTIVE: Neurofeedback (NF) is a paradigm that allows users to self-modulate patterns of brain activity. It is implemented with a closed-loop brain-computer interface (BCI) system that analyzes the user's brain activity in real-time and provides continuous feedback. This paradigm is of great interest due to its potential as a non-pharmacological and non-invasive alternative to treat non-degenerative brain disorders. Nevertheless, currently available NF frameworks have several limitations, such as the lack of a wide variety of real-time analysis metrics or overly simple training scenarios that may negatively affect user performance. To overcome these limitations, this work proposes ITACA: a novel open-source framework for the design, implementation and evaluation of NF training paradigms.<br><br>METHODS: ITACA is designed to be easy-to-use, flexible and attractive. Specifically, ITACA includes three different gamified training scenarios with a choice of five brain activity metrics as real-time feedback. Among them, novel metrics based on functional connectivity and network theory stand out. It is complemented with five different computerized versions of widespread cognitive assessment tests. To validate the proposed framework, a computational efficiency analysis and an NF training protocol focused on frontal-medial theta modulation were conducted.<br><br>RESULTS: Efficiency analysis proved that all implemented metrics allow an optimal feedback update rate for conducting NF sessions. Furthermore, conducted NF protocol yielded results that support the use of ITACA in NF research studies.<br><br>CONCLUSIONS: ITACA implements a wide variety of features for designing, conducting and evaluating NF studies with the goal of helping researchers expand the current state-of-the-art in NF training.},
}
@article {pmid37200132,
year = {2023},
author = {Ai, J and Meng, J and Mai, X and Zhu, X},
title = {BCI Control of a Robotic Arm Based on SSVEP With Moving Stimuli for Reach and Grasp Tasks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {8},
pages = {3818-3829},
doi = {10.1109/JBHI.2023.3277612},
pmid = {37200132},
issn = {2168-2208},
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Robotic Surgical Procedures ; Photic Stimulation ; },
abstract = {Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.},
}
@article {pmid37196071,
year = {2023},
author = {Murphy, RR},
title = {Sci-fi imagines how good brain-machine interfaces will amplify bad choices.},
journal = {Science robotics},
volume = {8},
number = {78},
pages = {eadi2192},
doi = {10.1126/scirobotics.adi2192},
pmid = {37196071},
issn = {2470-9476},
mesh = {Male ; Humans ; *Brain-Computer Interfaces ; *Robotics ; },
abstract = {Machine Man and The Andromeda Evolution explore personal and societal ramifications of brain-machine interfaces.},
}
@article {pmid37195562,
year = {2023},
author = {Phillips, MM and Pavlyk, I and Allen, M and Ghazaly, E and Cutts, R and Carpentier, J and Berry, JS and Nattress, C and Feng, S and Hallden, G and Chelala, C and Bomalaski, J and Steele, J and Sheaff, M and Balkwill, F and Szlosarek, PW},
title = {Correction: A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma.},
journal = {Pharmacological reports : PR},
volume = {75},
number = {3},
pages = {753},
doi = {10.1007/s43440-023-00487-z},
pmid = {37195562},
issn = {2299-5684},
}
@article {pmid37193899,
year = {2023},
author = {O'Leary, K},
title = {MRI decoders translate thoughts into words.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/d41591-023-00044-4},
pmid = {37193899},
issn = {1546-170X},
}
@article {pmid37191865,
year = {2023},
author = {Niu, L and Bin, J and Wang, JKS and Zhan, G and Jia, J and Zhang, L and Gan, Z and Kang, X},
title = {Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {9},
pages = {2481-2495},
pmid = {37191865},
issn = {1741-0444},
support = {Grant Nos. 61904038//National Natural Science Foundation of China/ ; U1913216//National Natural Science Foundation of China/ ; U19YF1403600//Shanghai Sailing Program/ ; X190021TB190//Ji Hua Laboratory/ ; X190021TB193//Ji Hua Laboratory/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; *Virtual Reality ; },
abstract = {A brain-computer interface (BCI) system and virtual reality (VR) are integrated as a more interactive hybrid system (BCI-VR) that allows the user to manipulate the car. A virtual scene in the VR system that is the same as the physical environment is built, and the object's movement can be observed in the VR scene. The four-class three-dimensional (3D) paradigm is designed and moves synchronously in virtual reality. The dynamic paradigm may affect their attention according to the experimenters' feedback. Fifteen subjects in our experiment steered the car according to a specified motion trajectory. According to our online experimental result, different motion trajectories of the paradigm have various effects on the system's performance, and training can mitigate this adverse effect. Moreover, the hybrid system using frequencies between 5 and 10 Hz indicates better performance than those using lower or higher stimulation frequencies. The experiment results show a maximum average accuracy of 0.956 and a maximum information transfer rate (ITR) of 41.033 bits/min. It suggests that a hybrid system provides a high-performance way of brain-computer interaction. This research could encourage more interesting applications involving BCI and VR technologies.},
}
@article {pmid37190647,
year = {2023},
author = {Al-Nafjan, A and Aldayel, M and Kharrat, A},
title = {Systematic Review and Future Direction of Neuro-Tourism Research.},
journal = {Brain sciences},
volume = {13},
number = {4},
pages = {},
pmid = {37190647},
issn = {2076-3425},
abstract = {Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain-computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field.},
}
@article {pmid37190630,
year = {2023},
author = {Vanutelli, ME and Salvadore, M and Lucchiari, C},
title = {BCI Applications to Creativity: Review and Future Directions, from little-c to C[2].},
journal = {Brain sciences},
volume = {13},
number = {4},
pages = {},
pmid = {37190630},
issn = {2076-3425},
support = {Departments of Excellence 2018-2022//Ministry of Education, Universities and Research/ ; },
abstract = {BCI devices are increasingly being used to create interactive interfaces between users and their own psychophysiological signals. Over the years, these systems have seen strong development as they can enable people with limited mobility to make certain decisions to alter their environment. Additionally, their portability and ease of use have allowed a field of research to flourish for the study of cognitive and emotional processes in natural settings. The study of creativity, especially little creativity (little-c), is one example, although the results of this cutting-edge research are often poorly systematized. The purpose of the present paper, therefore, was to conduct a scoping review to describe and systematize the various studies that have been conducted on the application potential of BCI to the field of creativity. Twenty-two papers were selected that collect information on different aspects of creativity, including clinical applications; art experience in settings with high ecological validity; BCI for creative content creation, and participants' engagement. Critical issues and potentialities of this promising area of study are also presented. Implications for future developments towards multi-brain creativity settings and C[2] are discussed.},
}
@article {pmid37190621,
year = {2023},
author = {Lakshminarayanan, K and Shah, R and Daulat, SR and Moodley, V and Yao, Y and Sengupta, P and Ramu, V and Madathil, D},
title = {Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery.},
journal = {Brain sciences},
volume = {13},
number = {4},
pages = {},
pmid = {37190621},
issn = {2076-3425},
support = {SRG/2021/000283//Department of Science &amp; Technology/ ; },
abstract = {Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain-computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices.},
}
@article {pmid37190591,
year = {2023},
author = {Yang, J and Wang, M and Lv, Y and Chen, J},
title = {Cortical Layer Markers Expression and Increased Synaptic Density in Interstitial Neurons of the White Matter from Drug-Resistant Epilepsy Patients.},
journal = {Brain sciences},
volume = {13},
number = {4},
pages = {},
pmid = {37190591},
issn = {2076-3425},
support = {2019YFA0110103//Ministry of Science and Technology/ ; 81870898//National Natural Science Foundation of China/ ; 2021FZZX001-37//Fundamental Research Funds for the Central Universities/ ; LR18H090002//Zhejiang Provincial Natural Science Foundation/ ; },
abstract = {The interstitial neurons in the white matter of the human and non-human primate cortex share a similar developmental origin with subplate neurons and deep-layer cortical neurons. A subset of interstitial neurons expresses the molecular markers of subplate neurons, but whether interstitial neurons express cortical layer markers in the adult human brain remains unexplored. Here we report the expression of cortical layer markers in interstitial neurons in the white matter of the adult human brain, supporting the hypothesis that interstitial neurons could be derived from cortical progenitor cells. Furthermore, we found increased non-phosphorylated neurofilament protein (NPNFP) expression in interstitial neurons in the white matter of drug-resistant epilepsy patients. We also identified the expression of glutamatergic and g-aminobutyric acid (GABAergic) synaptic puncta that were distributed in the perikarya and dendrites of interstitial neurons. The density of glutamatergic and GABAergic synaptic puncta was increased in interstitial neurons in the white matter of drug-resistant epilepsy patients compared with control brain tissues with no history of epilepsy. Together, our results provide important insights of the molecular identity of interstitial neurons in the adult human white matter. Increased synaptic density of interstitial neurons could result in an imbalanced synaptic network in the white matter and participate as part of the epileptic network in drug-resistant epilepsy.},
}
@article {pmid37190517,
year = {2023},
author = {Nie, A and Wu, Y},
title = {Differentiation of the Contribution of Familiarity and Recollection to the Old/New Effects in Associative Recognition: Insight from Semantic Relation.},
journal = {Brain sciences},
volume = {13},
number = {4},
pages = {},
pmid = {37190517},
issn = {2076-3425},
support = {LY21C090002//Zhejiang Provincial Natural Science Foundation of China/ ; },
abstract = {Previous research has revealed two different old/new effects, the early mid-frontal old/new effect (a.k.a., FN400) and the late parietal old/new effect (a.k.a., LPC), which relate to familiarity and recollection processes, respectively. Although associative recognition is thought to be more based on recollection, recent studies have confirmed that familiarity can make a great contribution when the items of a pair are unitized. However, it remains unclear whether the old/new effects are sensitive to the nature of different semantic relations. The current ERP (event-related potentials) study aimed to address this, where picture pairs of thematic, taxonomic, and unrelated relations served as stimuli and participants were required to discriminate the pair type: intact, rearranged, "old + new", or new. We confirmed both FN400 and LPC. Our findings, by comparing the occurrence and the amplitudes of these two components, implicate that the neural activity of associative recognition is sensitive to the semantic relation of stimuli and depends more on stimulus properties, that the familiarity of a single item can impact the neural activities in discriminating associative pairs, and that the interval length between encoding and test modulates the familiarity of unrelated pairs. In addition, the dissociation between FN400 and LPC reinforces the dual-process models.},
}
@article {pmid37189929,
year = {2023},
author = {Sahli, F and Sahli, H and Trabelsi, O and Jebabli, N and Zghibi, M and Haddad, M},
title = {Peer Verbal Encouragement Enhances Offensive Performance Indicators in Handball Small-Sided Games.},
journal = {Children (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
pmid = {37189929},
issn = {2227-9067},
abstract = {OBJECTIVE: This study aimed at assessing the effects of two verbal encouragement modalities on the different offensive and defensive performance indicators in handball small-sided games practiced in physical education settings.<br><br>METHODS: A total of 14 untrained secondary school male students, aged 17 to 18, took part in a three-session practical intervention. Students were divided into two teams of seven players (four field players, a goalkeeper, and two substitutes). During each experimental session, each team played one 8 min period under teacher verbal encouragement (TeacherEN) and another under peer verbal encouragement (PeerEN). All sessions were videotaped for later analysis using a specific grid focusing on the balls played, balls won, balls lost, shots on goal, goals scored, as well as the ball conservation index (BCI), and the defensive efficiency index (DEI).<br><br>RESULTS: The findings showed no significant differences in favor of TeacherEN in all the performance indicators that were measured, whereas significant differences in favor of PeerEN were observed in balls played and shots on goal.<br><br>CONCLUSIONS: When implemented in handball small-sided games, peer verbal encouragement can produce greater positive effects than teacher verbal encouragement in terms of offensive performance.},
}
@article {pmid37188006,
year = {2023},
author = {Liang, F and Yu, S and Pang, S and Wang, X and Jie, J and Gao, F and Song, Z and Li, B and Liao, WH and Yin, M},
title = {Non-human primate models and systems for gait and neurophysiological analysis.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1141567},
pmid = {37188006},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) have garnered extensive interest and become a groundbreaking technology to restore movement, tactile sense, and communication in patients. Prior to their use in human subjects, clinical BCIs require rigorous validation and verification (V&amp;V). Non-human primates (NHPs) are often considered the ultimate and widely used animal model for neuroscience studies, including BCIs V&amp;V, due to their proximity to humans. This literature review summarizes 94 NHP gait analysis studies until 1 June, 2022, including seven BCI-oriented studies. Due to technological limitations, most of these studies used wired neural recordings to access electrophysiological data. However, wireless neural recording systems for NHPs enabled neuroscience research in humans, and many on NHP locomotion, while posing numerous technical challenges, such as signal quality, data throughout, working distance, size, and power constraint, that have yet to be overcome. Besides neurological data, motion capture (MoCap) systems are usually required in BCI and gait studies to capture locomotion kinematics. However, current studies have exclusively relied on image processing-based MoCap systems, which have insufficient accuracy (error: ≥4° and 9 mm). While the role of the motor cortex during locomotion is still unclear and worth further exploration, future BCI and gait studies require simultaneous, high-speed, accurate neurophysiological, and movement measures. Therefore, the infrared MoCap system which has high accuracy and speed, together with a high spatiotemporal resolution neural recording system, may expand the scope and improve the quality of the motor and neurophysiological analysis in NHPs.},
}
@article {pmid37184907,
year = {2023},
author = {Xu, F and Wang, C and Yu, X and Zhao, J and Liu, M and Zhao, J and Gao, L and Jiang, X and Zhu, Z and Wu, Y and Wang, D and Feng, S and Yin, S and Zhang, Y and Leng, J},
title = {One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.},
journal = {International journal of neural systems},
volume = {33},
number = {6},
pages = {2350030},
doi = {10.1142/S0129065723500302},
pmid = {37184907},
issn = {1793-6462},
mesh = {Humans ; *Neuralgia ; *Spinal Cord Injuries/rehabilitation ; Electroencephalography ; Brain/diagnostic imaging ; Brain Mapping ; },
abstract = {Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.},
}
@article {pmid37183188,
year = {2023},
author = {Bu, Y and Harrington, DL and Lee, RR and Shen, Q and Angeles-Quinto, A and Ji, Z and Hansen, H and Hernandez-Lucas, J and Baumgartner, J and Song, T and Nichols, S and Baker, D and Rao, R and Lerman, I and Lin, T and Tu, XM and Huang, M},
title = {Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {14},
pages = {8942-8955},
doi = {10.1093/cercor/bhad173},
pmid = {37183188},
issn = {1460-2199},
mesh = {Humans ; Magnetoencephalography ; *Brain-Computer Interfaces ; *Deep Learning ; Gestures ; Electroencephalography/methods ; Algorithms ; },
abstract = {Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.},
}
@article {pmid37180552,
year = {2023},
author = {Barradas-Chacón, LA and Brunner, C and Wriessnegger, SC},
title = {Stylized faces enhance ERP features used for the detection of emotional responses.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1160800},
pmid = {37180552},
issn = {1662-5161},
abstract = {For their ease of accessibility and low cost, current Brain-Computer Interfaces (BCI) used to detect subjective emotional and affective states rely largely on electroencephalographic (EEG) signals. Public datasets are available for researchers to design models for affect detection from EEG. However, few designs focus on optimally exploiting the nature of the stimulus elicitation to improve accuracy. The RSVP protocol is used in this experiment to present human faces of emotion to 28 participants while EEG was measured. We found that artificially enhanced human faces with exaggerated, cartoonish visual features significantly improve some commonly used neural correlates of emotion as measured by event-related potentials (ERPs). These images elicit an enhanced N170 component, well known to relate to the facial visual encoding process. Our findings suggest that the study of emotion elicitation could exploit consistent, high detail, AI generated stimuli transformations to study the characteristics of electrical brain activity related to visual affective stimuli. Furthermore, this specific result might be useful in the context of affective BCI design, where a higher accuracy in affect decoding from EEG can improve the experience of a user.},
}
@article {pmid37179906,
year = {2023},
author = {Jia, X and Wang, W and Liang, J and Ma, X and Chen, W and Wu, D and Zhang, H and Ni, S and Wu, J and Lai, C and Zhang, Y},
title = {Application of amide proton transfer imaging to pretreatment risk stratification of childhood neuroblastoma: comparison with neuron-specific enolase.},
journal = {Quantitative imaging in medicine and surgery},
volume = {13},
number = {5},
pages = {3001-3012},
pmid = {37179906},
issn = {2223-4292},
abstract = {BACKGROUND: The diagnosis and treatment of childhood neuroblastoma (NB) varies with different risk groups, thus requiring accurate preoperative risk assessment. This study aimed to verify the feasibility of amide proton transfer (APT) imaging in risk stratification of abdominal NB in children, and compare it with the serum neuron-specific enolase (NSE).<br><br>METHODS: This prospective study enrolled 86 consecutive pediatric volunteers with suspected NB, and all subjects underwent abdominal APT imaging on a 3T magnetic resonance imaging scanner. A 4-pool Lorentzian fitting model was used to mitigate motion artifacts and separate the APT signal from the contaminating ones. The APT values were measured from tumor regions delineated by two experienced radiologists. The one-way analysis of variance, independent-sample t-test, Mann-Whitney U-test, and receiver operating characteristic analysis were performed to evaluate and compare the risk stratification performance of the APT value and serum NSE index-a routine biomarker of NB in clinics.<br><br>RESULTS: Thirty-four cases (mean age, 38.6&#xb1;32.4 months; 5 very-low-risk, 5 low-risk, 8 intermediate-risk and 16 high-risk ones) were included in the final analysis. The APT values were significantly higher in high-risk NB (5.80%&#xb1;1.27%) than in the non-high-risk group (3.88%&#xb1;1.01%) composed of the other three risk groups (P<0.001). However, there was no significant difference (P=0.18) in NSE levels between the high-risk (93.05&#xb1;97.14 ng/mL) and non-high-risk groups (41.45&#xb1;30.99 ng/mL). The associated area under the curve (AUC) of the APT parameter (AUC =0.89) in differentiating high-risk NB from non-high-risk NB was significantly higher (P=0.03) than that of NSE (AUC =0.64).<br><br>CONCLUSIONS: As an emerging non-invasive magnetic resonance imaging technique, APT imaging has a promising prospect for distinguishing high-risk NB from non-high-risk NB in routine clinical applications.},
}
@article {pmid37177761,
year = {2023},
author = {Knierim, MT and Bleichner, MG and Reali, P},
title = {A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {9},
pages = {},
pmid = {37177761},
issn = {1424-8220},
support = {Project ID 411333557 and Project ID 490839860//DFG, German Research Foundation/ ; },
mesh = {Electroencephalography/methods ; *Hearing Aids ; Electrodes ; *Brain-Computer Interfaces ; Noise ; },
abstract = {Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research.},
}
@article {pmid37177482,
year = {2023},
author = {de Brito Guerra, TC and Nóbrega, T and Morya, E and de M Martins, A and de Sousa, VA},
title = {Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {9},
pages = {},
pmid = {37177482},
issn = {1424-8220},
mesh = {Humans ; *Imagery, Psychotherapy ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Machine Learning ; },
abstract = {Electroencephalography (EEG) is a fundamental tool for understanding the brain's electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.},
}
@article {pmid37177443,
year = {2023},
author = {Ortega-Rodríguez, J and Gómez-González, JF and Pereda, E},
title = {Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {9},
pages = {},
pmid = {37177443},
issn = {1424-8220},
support = {ProID2017010100//Consejer&#xed;a de Econom&#xed;a, Industria, Comercio y Conocimiento of the Canary Islands Government/ ; TEC2016-80063-C3-2-R//Ministry of Economy, Industry and Competitiveness/ ; MACBIOIDI2 MAC2/1.1b/352//European Regional Development Fund (ERDF)/ ; },
mesh = {Humans ; Electroencephalography/methods ; Brain ; *Biometric Identification ; Electrodes ; Biometry ; *Brain-Computer Interfaces ; },
abstract = {Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.},
}
@article {pmid37172612,
year = {2023},
author = {Saini, M and Satija, U},
title = {State-of-the-art mental tasks classification based on electroencephalograms: a review.},
journal = {Physiological measurement},
volume = {44},
number = {6},
pages = {},
doi = {10.1088/1361-6579/acd51b},
pmid = {37172612},
issn = {1361-6579},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Artifacts ; Databases, Factual ; Brain/physiology ; Algorithms ; },
abstract = {Electroencephalograms (EEGs) play an important role in analyzing different mental tasks and neurological disorders. Hence, they are a critical component for designing various applications, such as brain-computer interfaces, neurofeedback, etc. Mental task classification (MTC) is one of the research focuses in these applications. Therefore, numerous MTC techniques have been proposed in literary works. Although various literature reviews exist based on EEG signals for different neurological disorders and behavior analysis, there is a lack of reviews of state-of-the-art MTC techniques. Therefore, this paper presents a detailed review of MTC techniques, including the classification of mental tasks and mental workload. A brief description of EEGs along with their physiological and nonphysiological artifacts is also presented. Furthermore, we include information on several publicly available databases, features, classifiers, and performance metrics used in MTC studies. We implement and evaluate some of the commonly used existing MTC techniques in the presence of different artifacts and subjects, based on which the challenges and directions are highlighted for future research in MTC.},
}
@article {pmid37172446,
year = {2023},
author = {Zhang, F and Yang, Y and Xin, Y and Sun, Y and Wang, C and Zhu, J and Tang, T and Zhang, J and Xu, K},
title = {Efficacy of different strategies of responsive neurostimulation on seizure control and their association with acute neurophysiological effects in rats.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {143},
number = {},
pages = {109212},
doi = {10.1016/j.yebeh.2023.109212},
pmid = {37172446},
issn = {1525-5069},
mesh = {Animals ; Rats ; *Deep Brain Stimulation ; Seizures/therapy ; *Drug Resistant Epilepsy/therapy ; *Epilepsy, Temporal Lobe/therapy ; Electrocorticography ; },
abstract = {Responsive neurostimulation (RNS) has shown promising but limited efficacy in the treatment of drug-resistant epilepsy. The clinical utility of RNS is hindered by the incomplete understanding of the mechanism behind its therapeutic effects. Thus, assessing the acute effects of responsive stimulation (AERS) based on intracranial EEG recordings in the temporal lobe epilepsy rat model may provide a better understanding of the potential therapeutic mechanisms underlying the antiepileptic effect of RNS. Furthermore, clarifying the correlation between AERS and seizure severity may help guide the optimization of RNS parameter settings. In this study, RNS with high (130 Hz) and low frequencies (5 Hz) was applied to the subiculum (SUB) and CA1. To quantify the changes induced by RNS, we calculated the AERS during synchronization by Granger causality and analyzed the band power ratio in the classic power band after different stimulations were delivered in the interictal and seizure onset periods, respectively. This demonstrates that only targets combined with an appropriate stimulation frequency could be efficient for seizure control. High-frequency stimulation of CA1 significantly shortened the ongoing seizure duration, which may be causally related to increased synchronization after stimulation. Both high-frequency stimulation of the CA1 and low-frequency stimulation delivered to the SUB reduced seizure frequency, and the reduced seizure risk may correlate with the change in power ratio near the theta band. It indicated that different stimulations may control seizures in diverse manners, perhaps with disparate mechanisms. More focus should be placed on understanding the correlation between seizure severity and synchronization and rhythm around theta bands to simplify the process of parameter optimization.},
}
@article {pmid37171929,
year = {2023},
author = {Wang, Z and Yang, L and Wang, M and Zhou, Y and Chen, L and Gu, B and Liu, S and Xu, M and He, F and Ming, D},
title = {Motor Imagery and Action Observation Induced Electroencephalographic Activations to Guide Subject-Specific Training Paradigm: A Pilot Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2457-2467},
doi = {10.1109/TNSRE.2023.3275572},
pmid = {37171929},
issn = {1558-0210},
mesh = {Humans ; Pilot Projects ; Electroencephalography/methods ; Imagery, Psychotherapy/methods ; *Brain-Computer Interfaces ; *Stroke ; Imagination/physiology ; },
abstract = {Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha-band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between groups and different motor paradigms within group, respectively. The results showed that there were significant differences in sensorimotor activations between two groups of subjects. Specifically, the group with weaker MI induced EEG features, could achieve stronger sensorimotor activations in AO than that of other conditions. The group with stronger MI induced EEG features, could achieve stronger sensorimotor activations in the MI+AO than that of other conditions. We also explored their classification and brain network differences, which might try to explain the EEG mechanism in different individuals and help stroke patients to choose appropriate subject-specific motor training paradigm for their rehabilitation and better treatment outcomes.},
}
@article {pmid37171927,
year = {2023},
author = {Zhao, J and Shi, Y and Liu, W and Zhou, T and Li, Z and Li, X},
title = {A Hybrid Method Fusing Frequency Recognition With Attention Detection to Enhance an Asynchronous Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2391-2398},
doi = {10.1109/TNSRE.2023.3275547},
pmid = {37171927},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Brain/physiology ; Electroencephalography/methods ; Algorithms ; },
abstract = {OBJECTIVE: One critical problem in controlling an asynchronous brain-computer interface (BCI) system is to discriminate between control and idle states. This paper proposes a hybrid attention detection and frequency recognition method based on weighted Dempster-Shafer theory (ADFR-DS), which integrates information of different aspects of the task from two brain regions, to enhance asynchronous control performance of a steady-state visual evoked potential (SSVEP)-based BCI system.<br><br>METHODS: The ADFR-DS method utilizes a hybrid architecture to process electroencephalogram (EEG) data from different channels simultaneously: an individualized frequency band based optimized complex network (IFBOCN) algorithm processes neural activity from the prefrontal area for attention detection, and an ensemble task-related component analysis (eTRCA) algorithm processes data from the occipital area for frequency recognition. The ADFR-DS method then fuses their classification results at decision level to generate the final output of the BCI system. A novel weighted Dempster-Shafer fusion method was proposed to enhance the fusion performance. This study evaluated the proposed method using a 40-target dataset recorded from 35 participants.<br><br>MAIN RESULTS: The proposed method outperformed the eTRCA algorithm in the true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and information transfer rate (ITR). Specifically, ADFR-DS improved the average ACC of eTRCA from 62.71% to 69.30%, and improved the average ITR from 184.28 bits/min to 216.89 bits/min (data length 0.3 s).<br><br>CONCLUSION: The results suggest that the proposed ADFR-DS method can enhance asynchronous SSVEP-based BCI systems.},
}
@article {pmid37170160,
year = {2022},
author = {Li, M and Cheng, S and Fan, J and Shang, Z and Wan, H and Yang, L and Yang, L},
title = {Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons.},
journal = {BMC zoology},
volume = {7},
number = {1},
pages = {54},
pmid = {37170160},
issn = {2056-3132},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: The hippocampus plays an important role to support path planning and adjustment in goal-directed spatial navigation. While we still only have limited knowledge about how do the hippocampal neural activities, especially the functional connectivity patterns, change during the spatial path adjustment. In this study, we measured the behavioural indicators and local field potentials of the pigeon (Columba livia, male and female) during a goal-directed navigational task with the detour paradigm, exploring the changing patterns of the hippocampal functional network connectivity of the bird during the spatial path learning and adjustment.<br><br>RESULTS: Our study demonstrates that the pigeons progressively learned to solve the path adjustment task after the preferred path is blocked suddenly. Behavioural results show that both the total duration and the path lengths pigeons completed the task during the phase of adjustment are significantly longer than those during the acquisition and recovery phases. Furthermore, neural results show that hippocampal functional connectivity selectively changed during path adjustment. Specifically, we identified depressed connectivity in lower bands (delta and theta) and elevated connectivity in higher bands (slow-gamma and fast-gamma).<br><br>CONCLUSIONS: These results feature both the behavioural response and neural representation of the avian spatial cognitive learning process, suggesting that the functional disarrangement and reorganization of the connectivity in the avian hippocampus during different phases may contribute to our further understanding of the potential mechanism of path learning and adjustment.},
}
@article {pmid37169016,
year = {2023},
author = {Sun, H and Li, C and Zhang, H},
title = {Design of virtual BCI channels based on informer.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1150316},
pmid = {37169016},
issn = {1662-5161},
abstract = {The precision and reliability of electroencephalogram (EEG) data are essential for the effective functioning of a brain-computer interface (BCI). As the number of BCI acquisition channels increases, more EEG information can be gathered. However, having too many channels will reduce the practicability of the BCI system, raise the likelihood of poor-quality channels, and lead to information misinterpretation. These issues pose challenges to the advancement of BCI systems. Determining the optimal configuration of BCI acquisition channels can minimize the number of channels utilized, but it is challenging to maintain the original operating system and accommodate individual variations in channel layout. To address these concerns, this study introduces the EEG-completion-informer (EC-informer), which is based on the Informer architecture known for its effectiveness in time-series problems. By providing input from four BCI acquisition channels, the EC-informer can generate several virtual acquisition channels to extract additional EEG information for analysis. This approach allows for the direct inheritance of the original model, significantly reducing researchers' workload. Moreover, EC-informers demonstrate strong performance in damaged channel repair and poor channel identification. Using the Informer as a foundation, the study proposes the EC-informer, tailored to BCI requirements and demanding only a small number of training samples. This approach eliminates the need for extensive computing units to train an efficient, lightweight model while preserving comprehensive information about target channels. The study also confirms that the proposed model can be transferred to other operators with minimal loss, exhibiting robust applicability. The EC-informer's features enable original BCI devices to adapt to a broader range of classification algorithms and relax the operational requirements of BCI devices, which could facilitate the promotion of the use of BCI devices in daily life.},
}
@article {pmid37168809,
year = {2023},
author = {Albahri, AS and Al-Qaysi, ZT and Alzubaidi, L and Alnoor, A and Albahri, OS and Alamoodi, AH and Bakar, AA},
title = {A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.},
journal = {International journal of telemedicine and applications},
volume = {2023},
number = {},
pages = {7741735},
pmid = {37168809},
issn = {1687-6415},
abstract = {The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.},
}
@article {pmid37167975,
year = {2023},
author = {Cao, K and Qiu, L and Lu, X and Wu, W and Hu, Y and Cui, Z and Jiang, C and Luo, Y and Shao, Y and Xi, W and Zeng, LH and Xu, H and Ma, H and Zhang, Z and Peng, J and Duan, S and Gao, Z},
title = {Microglia modulate general anesthesia through P2Y12 receptor.},
journal = {Current biology : CB},
volume = {33},
number = {11},
pages = {2187-2200.e6},
doi = {10.1016/j.cub.2023.04.047},
pmid = {37167975},
issn = {1879-0445},
mesh = {Mice ; Animals ; *Microglia/metabolism ; *Anesthetics/metabolism ; Brain ; Anesthesia, General ; Signal Transduction/physiology ; },
abstract = {General anesthesia (GA) is an unconscious state produced by anesthetic drugs, which act on neurons to cause overall suppression of neuronal activity in the brain. Recent studies have revealed that GA also substantially enhances the dynamics of microglia, the primary brain immune cells, with increased process motility and territory surveillance. However, whether microglia are actively involved in GA modulation remains unknown. Here, we report a previously unrecognized role for microglia engaging in multiple GA processes. We found that microglial ablation reduced the sensitivity of mice to anesthetics and substantially shortened duration of loss of righting reflex (LORR) or unconsciousness induced by multiple anesthetics, thereby promoting earlier emergence from GA. Microglial repopulation restored the regular anesthetic recovery, and chemogenetic activation of microglia prolonged the duration of LORR. In addition, anesthesia-accompanying analgesia and hypothermia were also attenuated after microglial depletion. Single-cell RNA sequencing analyses showed that anesthesia prominently affected the transcriptional levels of chemotaxis and migration-related genes in microglia. By pharmacologically targeting different microglial motility pathways, we found that blocking P2Y12 receptor (P2Y12R) reduced the duration of LORR of mice. Moreover, genetic ablation of P2Y12R in microglia also promoted quicker recovery in mice from anesthesia, verifying the importance of microglial P2Y12R in anesthetic regulation. Our work presents the first evidence that microglia actively participate in multiple processes of GA through P2Y12R-mediated signaling and expands the non-immune roles of microglia in the brain.},
}
@article {pmid37167876,
year = {2023},
author = {Zhou, L and Xu, Y and Song, F and Li, W and Gao, F and Zhu, Q and Qian, Z},
title = {The effect of TENS on sleep: A pilot study.},
journal = {Sleep medicine},
volume = {107},
number = {},
pages = {126-136},
doi = {10.1016/j.sleep.2023.04.029},
pmid = {37167876},
issn = {1878-5506},
mesh = {Humans ; *Transcutaneous Electric Nerve Stimulation/methods ; Pilot Projects ; *Sleep Initiation and Maintenance Disorders/therapy ; Sleep ; },
abstract = {BACKGROUND: Insomnia is the second most common neuropsychiatric disorder, but the current treatments are not very effective. There is therefore an urgent need to develop better treatments. Transcutaneous electrical nerve stimulation (TENS) may be a promising means of treating insomnia.<br><br>OBJECTIVE: This work aims to explore whether and how TENS modulate sleep and the effect of stimulation waveforms on sleep.<br><br>METHODS: Forty-five healthy subjects participated in this study. Electroencephalography (EEG) data were recorded before and after four mode low-frequency (1&#xa0;Hz) TENS with different waveforms, which were formed by superimposing sine waves of different high frequencies (60-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). The four waveform modes are formed by combining sine waves of varying frequencies. Mode 1 (M1) consists of a combination of high frequencies (60-110&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). Mode 2 (M2) is made up of high frequencies (60-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). Mode 3 (M3) consists of high frequencies (110-160&#xa0;Hz) and low frequencies (1-6&#xa0;Hz), while mode 4 (M4) is composed of high frequencies (160-210&#xa0;Hz) and low frequencies (1-6&#xa0;Hz). For M1, M3 and M4, the high frequency portions of the stimulus waveforms account for 50%, while for M2, the high frequency portion of the waveform accounts for 65%. For each mode, the current intensities ranged from 4&#xa0;mA to 7&#xa0;mA, with values for each participant adjusted according to individual tolerance. During stimulation, the subjects were stimulated at the greater occipital nerve by the four mode TENS.<br><br>RESULTS: M1, M3, and M4 slowed down the frequency of neural activity, broadened the distribution of theta waves, and caused a decrease in activity in wakefulness-related regions and an increase in activity in sleep-related regions. However, M2 has the opposite modulation effect.<br><br>CONCLUSION: These results indicated that low-frequency TENS (1&#xa0;Hz) may facilitate sleep in a waveform-specific manner. Our findings provide new insights into the mechanisms of sleep modulation by TENS and the design of effective insomnia treatments.},
}
@article {pmid37167054,
year = {2023},
author = {Wang, P and Li, Z and Gong, P and Zhou, Y and Chen, F and Zhang, D},
title = {MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2349-2358},
doi = {10.1109/TNSRE.2023.3275172},
pmid = {37167054},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Upper Extremity ; Motion ; Electroencephalography/methods ; Movement ; },
abstract = {Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints.},
}
@article {pmid37166297,
year = {2024},
author = {Padfield, N and Agius Anastasi, A and Camilleri, T and Fabri, S and Bugeja, M and Camilleri, K},
title = {BCI-controlled wheelchairs: end-users' perceptions, needs, and expectations, an interview-based study.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {19},
number = {4},
pages = {1539-1551},
doi = {10.1080/17483107.2023.2211602},
pmid = {37166297},
issn = {1748-3115},
mesh = {Humans ; *Wheelchairs ; Male ; *Brain-Computer Interfaces ; Female ; Adult ; *Spinal Cord Injuries/rehabilitation ; Middle Aged ; *Interviews as Topic ; Persons with Disabilities/rehabilitation ; Equipment Design ; Perception ; Aged ; },
abstract = {PURPOSE: Brain-computer interface (BCI)-controlled wheelchairs have the potential to improve the independence of people with mobility impairments. The low uptake of BCI devices has been linked to a lack of knowledge among researchers of the needs of end-users that should influence BCI development.<br><br>MATERIALS AND METHODS: This study used semi-structured interviews to learn about the perceptions, needs, and expectations of spinal cord injury (SCI) patients with regards to a BCI-controlled wheelchair. Topics discussed in the interview include: paradigms, shared control, safety, robustness, channel selection, hardware, and experimental design. The interviews were recorded and then transcribed. Analysis was carried out using coding based on grounded theory principles.<br><br>RESULTS: The majority of participants had a positive view of BCI-controlled wheelchair technology and were willing to use the technology. Core issues were raised regarding safety, cost and aesthetics. Interview discussions were linked to state-of-the-art BCI technology. The results challenge the current reliance of researchers on the motor-imagery paradigm by suggesting end-users expect highly intuitive paradigms. There also needs to be a stronger focus on obstacle avoidance and safety features in BCI wheelchairs. Finally, the development of control approaches that can be personalized for individual users may be instrumental for widespread adoption of these devices.<br><br>CONCLUSIONS: This study, based on interviews with SCI patients, indicates that BCI-controlled wheelchairs are a promising assistive technology that would be well received by end-users. Recommendations for a more person-centered design of BCI controlled wheelchairs are made and clear avenues for future research are identified.},
}
@article {pmid37163609,
year = {2023},
author = {Song, S and Fallegger, F and Trouillet, A and Kim, K and Lacour, SP},
title = {Deployment of an electrocorticography system with a soft robotic actuator.},
journal = {Science robotics},
volume = {8},
number = {78},
pages = {eadd1002},
doi = {10.1126/scirobotics.add1002},
pmid = {37163609},
issn = {2470-9476},
mesh = {Animals ; Swine ; Electrocorticography/methods ; *Robotics ; Swine, Miniature ; Brain ; *Brain-Computer Interfaces ; },
abstract = {Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs among cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences and often limit the use of the mapping technology to the operating room. In this work, we report on a scalable technique for the fabrication of large-area soft robotic electrode arrays and their deployment on the cortex through a square-centimeter burr hole using a pressure-driven actuation mechanism called eversion. The deployable system consists of up to six prefolded soft legs, and it is placed subdurally on the cortex using an aqueous pressurized solution and secured to the pedestal on the rim of the small craniotomy. Each leg contains soft, microfabricated electrodes and strain sensors for real-time deployment monitoring. In a proof-of-concept acute surgery, a soft robotic electrode array was successfully deployed on the cortex of a minipig to record sensory cortical activity. This soft robotic neurotechnology opens promising avenues for minimally invasive cortical surgery and applications related to neurological disorders such as motor and sensory deficits.},
}
@article {pmid37162924,
year = {2023},
author = {Qi, C and Verheijen, BM and Kokubo, Y and Shi, Y and Tetter, S and Murzin, AG and Nakahara, A and Morimoto, S and Vermulst, M and Sasaki, R and Aronica, E and Hirokawa, Y and Oyanagi, K and Kakita, A and Ryskeldi-Falcon, B and Yoshida, M and Hasegawa, M and Scheres, SHW and Goedert, M},
title = {Tau Filaments from Amyotrophic Lateral Sclerosis/Parkinsonism-Dementia Complex (ALS/PDC) adopt the CTE Fold.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {37162924},
issn = {2692-8205},
support = {MC_UP_1201/25/MRC_/Medical Research Council/United Kingdom ; R01 AG054641/AG/NIA NIH HHS/United States ; },
abstract = {The amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS/PDC) of the island of Guam and the Kii peninsula of Japan is a fatal neurodegenerative disease of unknown cause that is characterised by the presence of abundant filamentous tau inclusions in brains and spinal cords. Here we used electron cryo-microscopy (cryo-EM) to determine the structures of tau filaments from the cerebral cortex of three cases of ALS/PDC from Guam and eight cases from Kii, as well as from the spinal cord of two of the Guam cases. Tau filaments had the chronic traumatic encephalopathy (CTE) fold, with variable amounts of Type I and Type II filaments. Paired helical tau filaments were also found in two Kii cases. We also identified a novel Type III CTE tau filament, where protofilaments pack against each other in an anti-parallel fashion. ALS/PDC is the third known tauopathy with CTE-type filaments and abundant tau inclusions in cortical layers II/III, the others being CTE and subacute sclerosing panencephalitis. Because these tauopathies are believed to have environmental causes, our findings support the hypothesis that ALS/PDC is caused by exogenous factors.},
}
@article {pmid37162904,
year = {2023},
author = {Herring, EZ and Graczyk, EL and Memberg, WD and Adams, RD and Baca-Vaca, GF and Hutchison, BC and Krall, JT and Alexander, BJ and Conlan, EC and Alfaro, KE and Bhat, PR and Ketting-Olivier, AB and Haddix, CA and Taylor, DM and Tyler, DJ and Kirsch, RF and Ajiboye, AB and Miller, JP},
title = {Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration after Tetraplegia.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {37162904},
support = {I01 RX002654/RX/RRD VA/United States ; R01 NS119160/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {BACKGROUND: Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia.<br><br>OBJECTIVE: We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.<br><br>METHODS: A right-handed man with motor-complete C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of a bidirectional brain-machine interface to restore function of the participant's own arm and hand.<br><br>RESULTS: Multi-unit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions. The system was well tolerated with no operative complications.<br><br>CONCLUSION: The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to allow restoration of motor and sensory functions of the arm and hand after neurological injury.},
}
@article {pmid37161677,
year = {2024},
author = {Shen, Q and Fu, S and Jiang, X and Huang, X and Lin, D and Xiao, Q and Khadijah, S and Yan, Y and Xiong, X and Jin, J and Ebstein, RP and Xu, T and Wang, Y and Feng, J},
title = {Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study.},
journal = {Psychological medicine},
volume = {54},
number = {2},
pages = {256-266},
doi = {10.1017/S0033291723001307},
pmid = {37161677},
issn = {1469-8978},
mesh = {Humans ; Adolescent ; *Learning ; Reinforcement, Psychology ; Reward ; *Depressive Disorder, Major ; Conditioning, Operant ; },
abstract = {BACKGROUND: The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.<br><br>METHODS: We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).<br><br>RESULTS: The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.<br><br>CONCLUSIONS: By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.},
}
@article {pmid37160488,
year = {2023},
author = {Emigh, B and Grigorian, A and Dilday, J and Condon, F and Nahmias, J and Schellenberg, M and Martin, M and Matsushima, K and Inaba, K},
title = {Risk factors and outcomes in pediatric blunt cardiac injuries.},
journal = {Pediatric surgery international},
volume = {39},
number = {1},
pages = {195},
pmid = {37160488},
issn = {1437-9813},
mesh = {Adolescent ; Adult ; Humans ; Child ; Hemothorax ; Risk Factors ; *Contusions ; *Wounds, Nonpenetrating/epidemiology ; *Myocardial Contusions ; },
abstract = {PURPOSE: Unlike adults, less is known of the etiology and risk factors for blunt cardiac injury (BCI) in children. Identifying risk factors for BCI in pediatric patients will allow for more specific screening practices following blunt trauma.<br><br>METHODS: A retrospective review was performed using the Trauma Quality Improvement Program (TQIP) database from 2017 to 2019. All patients &#x2264;&#x2009;16&#xa0;years injured following blunt trauma were included. Demographics, mechanism, associated injuries, injury severity, and outcomes were collected. Univariate and multivariate regression was used to determine specific risk factors for BCI.<br><br>RESULTS: Of 266,045 pediatric patients included in the analysis, the incidence of BCI was less than 0.2%. The all-cause mortality seen in patients with BCI was 26%. Motor-vehicle collisions (MVCs) were the most common mechanism, although no association with seatbelt use was seen in adolescents (p&#x2009;=&#x2009;0.158). The strongest independent risk factors for BCI were pulmonary contusions (OR 15.4, p&#x2009;<&#x2009;0.001) and hemothorax (OR 8.9, p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: Following trauma, the presence of pulmonary contusions or hemothorax should trigger additional screening investigations specific for BCI in pediatric patients.},
}
@article {pmid37160127,
year = {2023},
author = {Guan, C and Aflalo, T and Kadlec, K and Gámez de Leon, J and Rosario, ER and Bari, A and Pouratian, N and Andersen, RA},
title = {Decoding and geometry of ten finger movements in human posterior parietal cortex and motor cortex.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
pmid = {37160127},
issn = {1741-2552},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {Humans ; *Motor Cortex ; Fingers ; Movement ; Hand ; Parietal Lobe ; },
abstract = {Objective. Enable neural control of individual prosthetic fingers for participants with upper-limb paralysis.Approach. Two tetraplegic participants were each implanted with a 96-channel array in the left posterior parietal cortex (PPC). One of the participants was additionally implanted with a 96-channel array near the hand knob of the left motor cortex (MC). Across tens of sessions, we recorded neural activity while the participants attempted to move individual fingers of the right hand. Offline, we classified attempted finger movements from neural firing rates using linear discriminant analysis with cross-validation. The participants then used the neural classifier online to control individual fingers of a brain-machine interface (BMI). Finally, we characterized the neural representational geometry during individual finger movements of both hands.Main Results. The two participants achieved 86% and 92% online accuracy during BMI control of the contralateral fingers (chance = 17%). Offline, a linear decoder achieved ten-finger decoding accuracies of 70% and 66% using respective PPC recordings and 75% using MC recordings (chance = 10%). In MC and in one PPC array, a factorized code linked corresponding finger movements of the contralateral and ipsilateral hands.Significance. This is the first study to decode both contralateral and ipsilateral finger movements from PPC. Online BMI control of contralateral fingers exceeded that of previous finger BMIs. PPC and MC signals can be used to control individual prosthetic fingers, which may contribute to a hand restoration strategy for people with tetraplegia.},
}
@article {pmid37158939,
year = {2023},
author = {Wang, J and Yin, C and Pan, Y and Yang, Y and Li, W and Ni, H and Liu, B and Nie, H and Xu, R and Wei, H and Zhang, Y and Li, Y and Hu, Q and Tai, Y and Shao, X and Fang, J and Liu, B},
title = {CXCL13 contributes to chronic pain of a mouse model of CRPS-I via CXCR5-mediated NF-κB activation and pro-inflammatory cytokine production in spinal cord dorsal horn.},
journal = {Journal of neuroinflammation},
volume = {20},
number = {1},
pages = {109},
pmid = {37158939},
issn = {1742-2094},
support = {82105014//National Natural Science Foundation of China/ ; 81873365//National Natural Science Foundation of China/ ; LZ23H270001//Natural Science Funds of Zhejiang Province/ ; },
mesh = {Animals ; Mice ; *Chemokine CXCL13/metabolism ; *Chronic Pain ; Disease Models, Animal ; Hyperalgesia ; Neuroinflammatory Diseases ; NF-kappa B ; *Reflex Sympathetic Dystrophy ; Spinal Cord Dorsal Horn ; *Receptors, CXCR5/metabolism ; },
abstract = {BACKGROUND: Complex regional pain syndrome type-I (CRPS-I) causes excruciating pain that affect patients' life quality. However, the mechanisms underlying CRPS-I are incompletely understood, which hampers the development of target specific therapeutics.<br><br>METHODS: The mouse chronic post-ischemic pain (CPIP) model&#xa0;was established to mimic CRPS-I. qPCR, Western blot, immunostaining, behavioral assay and pharmacological methods were used to study mechanisms underlying neuroinflammation and chronic pain in spinal cord dorsal horn (SCDH) of CPIP mice.<br><br>RESULTS: CPIP mice developed robust and long-lasting mechanical allodynia in bilateral hindpaws. The expression of inflammatory chemokine CXCL13 and its receptor CXCR5 was significantly upregulated in ipsilateral SCDH of CPIP mice. Immunostaining revealed CXCL13 and CXCR5 was predominantly expressed in spinal neurons. Neutralization of spinal CXCL13 or genetic deletion of Cxcr5 (Cxcr5[-/-]) significantly reduced mechanical allodynia, as well as spinal glial cell overactivation and c-Fos activation in SCDH of CPIP mice. Mechanical pain causes affective disorder in CPIP mice, which was attenuated in Cxcr5[-/-] mice. Phosphorylated STAT3 co-expressed with CXCL13 in SCDH neurons and contributed to CXCL13 upregulation and mechanical allodynia in CPIP mice. CXCR5 coupled with NF-&#x3ba;B signaling in SCDH neurons to trigger pro-inflammatory cytokine gene Il6 upregulation, contributing to mechanical allodynia. Intrathecal CXCL13 injection produced mechanical allodynia via CXCR5-dependent NF-&#x3ba;B activation. Specific overexpression of CXCL13 in SCDH neurons is sufficient to induce persistent mechanical allodynia in na&#xef;ve mice.<br><br>CONCLUSIONS: These results demonstrated a previously unidentified role of CXCL13/CXCR5 signaling in mediating spinal neuroinflammation and mechanical pain in an animal model of CRPS-I. Our work suggests that targeting CXCL13/CXCR5 pathway may lead to novel therapeutic approaches for CRPS-I.},
}
@article {pmid37158916,
year = {2023},
author = {Su, W and Ju, J and Gu, M and Wang, X and Liu, S and Yu, J and Mu, D},
title = {SARS-CoV-2 envelope protein triggers depression-like behaviors and dysosmia via TLR2-mediated neuroinflammation in mice.},
journal = {Journal of neuroinflammation},
volume = {20},
number = {1},
pages = {110},
pmid = {37158916},
issn = {1742-2094},
support = {2022SF20//National High Level Hospital Clinical Research Funding (Scientific Research Seed Fund of Peking University First Hospital)/ ; },
mesh = {Female ; Male ; Animals ; Mice ; *COVID-19 ; Depression/etiology ; Interleukin-6 ; Neuroinflammatory Diseases ; SARS-CoV-2 ; Toll-Like Receptor 2 ; *Olfaction Disorders/etiology ; },
abstract = {BACKGROUND: Depression and dysosmia have been regarded as primary neurological symptoms in COVID-19 patients, the mechanism of which remains unclear. Current studies have demonstrated that the SARS-CoV-2 envelope (E) protein is a pro-inflammatory factor sensed by Toll-like receptor 2 (TLR2), suggesting the pathological feature of E protein is independent of viral infection. In this study, we aim to ascertain the role of E protein in depression, dysosmia and associated neuroinflammation in the central nervous system (CNS).<br><br>METHODS: Depression-like behaviors and olfactory function were observed in both female and male mice receiving intracisternal injection of E protein. Immunohistochemistry was applied in conjunction with RT-PCR to evaluate glial activation, blood-brain barrier status and mediators synthesis in the cortex, hippocampus and olfactory bulb. TLR2 was pharmacologically blocked to determine its role in E protein-related depression-like behaviors and dysosmia in mice.<br><br>RESULTS: Intracisternal injection of E protein evoked depression-like behaviors and dysosmia in both female and male mice. Immunohistochemistry suggested that the E protein upregulated IBA1 and GFAP in the cortex, hippocampus and olfactory bulb, while ZO-1 was downregulated. Moreover, IL-1&#x3b2;, TNF-&#x3b1;, IL-6, CCL2, MMP2 and CSF1 were upregulated in both cortex and hippocampus, whereas IL-1&#x3b2;, IL-6 and CCL2 were upregulated in the olfactory bulb. Furtherly, inhibiting microglia, rather than astrocytes, alleviated depression-like behaviors and dysosmia induced by E protein. Finally, RT-PCR and immunohistochemistry suggested that TLR2 was upregulated in the cortex, hippocampus and olfactory bulb, the blocking of which mitigated depression-like behaviors and dysosmia induced by E protein.<br><br>CONCLUSIONS: Our study demonstrates that envelope protein could directly induce depression-like behaviors, dysosmia, and obvious neuroinflammation in CNS. TLR2 mediated depression-like behaviors and dysosmia induced by envelope protein, which could serve as a promising therapeutic target for neurological manifestation in COVID-19 patients.},
}
@article {pmid37155399,
year = {2023},
author = {Li, H and Xu, G and Li, Z and Zhang, K and Zheng, X and Du, C and Han, C and Kuang, J and Du, Y and Zhang, S},
title = {A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2486-2496},
doi = {10.1109/TNSRE.2023.3274121},
pmid = {37155399},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation ; Recognition, Psychology ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods.<br><br>METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with different Phase (DP-MAFD) is proposed to achieve signal extension. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after extension.<br><br>RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the proposed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accuracy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s.<br><br>CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.},
}
@article {pmid37152432,
year = {2023},
author = {Wu, C and Shang, HF and Wang, YJ and Wang, JH and Zuo, ZX and Lian, YN and Liu, L and Zhang, C and Li, XY},
title = {Cingulate protein arginine methyltransferases 1 regulates peripheral hypersensitivity via fragile X messenger ribonucleoprotein.},
journal = {Frontiers in molecular neuroscience},
volume = {16},
number = {},
pages = {1153870},
pmid = {37152432},
issn = {1662-5099},
abstract = {The deficit of fragile X messenger ribonucleoprotein (FMRP) leads to intellectual disability in human and animal models, which also leads to desensitization of pain after nerve injury. Recently, it was shown that the protein arginine methyltransferases 1 (PRMT1) regulates the phase separation of FMRP. However, the role of PRMT1 in pain regulation has been less investigated. Here we showed that the downregulation of PRMT1 in the anterior cingulate cortex (ACC) contributes to the development of peripheral pain hypersensitivity. We observed that the peripheral nerve injury decreased the expression of PRMT1 in the ACC; knockdown of the PRMT1 via shRNA in the ACC decreased the paw withdrawal thresholds (PWTs) of naïve mice. Moreover, the deficits of FMRP abolished the effects of PRMT1 on pain sensation. Furthermore, overexpression of PRMT1 in the ACC increased the PWTs of mice with nerve injury. These observations indicate that the downregulation of cingulate PRMT1 was necessary and sufficient to develop peripheral hypersensitivity after nerve injury. Thus, we provided evidence that PRMT1 is vital in regulating peripheral pain hypersensitivity after nerve injury via the FMRP.},
}
@article {pmid37149621,
year = {2023},
author = {Catalán, JM and Trigili, E and Nann, M and Blanco-Ivorra, A and Lauretti, C and Cordella, F and Ivorra, E and Armstrong, E and Crea, S and Alcañiz, M and Zollo, L and Soekadar, SR and Vitiello, N and García-Aracil, N},
title = {Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {61},
pmid = {37149621},
issn = {1743-0003},
mesh = {Male ; Female ; Humans ; Adult ; Middle Aged ; Aged ; *Activities of Daily Living ; *Exoskeleton Device ; Quality of Life ; Reproducibility of Results ; Brain ; },
abstract = {BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator.<br><br>METHODS: Ten impaired participants (5 males and 5 females, mean age 52 &#xb1; 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3&#x2009;s and a reliable control in case it remained below 5&#x2009;s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability.<br><br>RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3&#x2009;s in case of the exoskeleton and bellow 5s in case of the external manipulator).<br><br>CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.},
}
@article {pmid37148587,
year = {2023},
author = {King, BJ and Read, GJM and Salmon, PM},
title = {Identifying risk controls for future advanced brain-computer interfaces: A prospective risk assessment approach using work domain analysis.},
journal = {Applied ergonomics},
volume = {111},
number = {},
pages = {104028},
doi = {10.1016/j.apergo.2023.104028},
pmid = {37148587},
issn = {1872-9126},
mesh = {Humans ; *Brain-Computer Interfaces ; Prospective Studies ; Risk Assessment ; Electroencephalography/methods ; },
abstract = {Brain-computer interface (BCI) technologies are progressing rapidly and may eventually be implemented widely within society, yet their risks have arguably not yet been comprehensively identified, nor understood. This study analysed an anticipated invasive BCI system lifecycle to identify the individual, organisational, and societal risks associated with BCIs, and controls that could be used to mitigate or eliminate these risks. A BCI system lifecycle work domain analysis model was developed and validated with 10 subject matter experts. The model was subsequently used to undertake a systems thinking-based risk assessment approach to identify risks that could emerge when functions are either undertaken sub-optimally or not undertaken at all. Eighteen broad risk themes were identified that could negatively impact the BCI system lifecycle in a variety of unique ways, while a larger number of controls for these risks were also identified. The most concerning risks included inadequate regulation of BCI technologies and inadequate training of BCI stakeholders, such as users and clinicians. In addition to specifying a practical set of risk controls to inform BCI device design, manufacture, adoption, and utilisation, the results demonstrate the complexity involved in managing BCI risks and suggests that a system-wide coordinated response is required. Future research is required to evaluate the comprehensiveness of the identified risks and the practicality of implementing the risk controls.},
}
@article {pmid37148558,
year = {2023},
author = {Li, XY and Bao, YF and Xie, JJ and Gao, B and Qian, SX and Dong, Y and Wu, ZY},
title = {Application Value of Serum Neurofilament Light Protein for Disease Staging in Huntington's Disease.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {38},
number = {7},
pages = {1307-1315},
doi = {10.1002/mds.29430},
pmid = {37148558},
issn = {1531-8257},
mesh = {Humans ; *Huntington Disease/pathology ; Tumor Necrosis Factor Ligand Superfamily Member 14 ; Brain/pathology ; Intermediate Filaments ; Disease Progression ; Biomarkers ; },
abstract = {BACKGROUND: Neurofilament light protein (NfL) has been proven to be a sensitive biomarker for Huntington's disease (HD). However, these studies did not include HD patients at advanced stages or with larger CAG repeats (>50), leading to a knowledge gap of the characteristics of NfL.<br><br>METHODS: Serum NfL (sNfL) levels were quantified using an ultrasensitive immunoassay. Participants were assessed by clinical scales and 7.0&#x2009;T magnetic resonance imaging. Longitudinal samples and clinical data were obtained.<br><br>RESULTS: Baseline samples were available from 110 controls, 90 premanifest HD (pre-HD) and 137 HD individuals. We found levels of sNfL significantly increased in HD compared to pre-HD and controls (both P&#x2009;<&#x2009;0.0001). The increase rates of sNfL were differed by CAG repeat lengths. However, there was no difference in sNfL levels in manifest HD from early to late stages. In addition, sNfL levels were associated with cognitive measures in pre-HD and manifest HD group, respectively. The increased levels of sNfL were also closely related to microstructural changes in white matter. In the longitudinal analysis, baseline sNfL did not correlate with subsequent clinical function decline. Random forest analysis revealed that sNfL had good power for predicting disease onset.<br><br>CONCLUSIONS: Although sNfL levels are independent of disease stages in manifest HD, it is still an optimal indicator for predicting disease onset and has potential use as a surrogate biomarker of treatment effect in clinical trials. &#xa9; 2023 International Parkinson and Movement Disorder Society.},
}
@article {pmid37148553,
year = {2023},
author = {Neumann, WJ and Gilron, R and Little, S and Tinkhauser, G},
title = {Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {38},
number = {6},
pages = {937-948},
doi = {10.1002/mds.29415},
pmid = {37148553},
issn = {1531-8257},
support = {K23 NS120037/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Deep Brain Stimulation/methods ; *Parkinson Disease/therapy ; Neurophysiology ; },
abstract = {Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.},
}
@article {pmid37147908,
year = {2023},
author = {Ni, RJ and Wang, YY and Gao, TH and Wang, QR and Wei, JX and Zhao, LS and Ma, YR and Ma, XH and Li, T},
title = {Depletion of microglia with PLX3397 attenuates MK-801-induced hyperactivity associated with regulating inflammation-related genes in the brain.},
journal = {Zoological research},
volume = {44},
number = {3},
pages = {543-555},
pmid = {37147908},
issn = {2095-8137},
mesh = {Mice ; Animals ; *Dizocilpine Maleate/pharmacology/metabolism ; Microglia/metabolism ; Brain/metabolism ; *Inflammation/chemically induced/drug therapy/genetics/veterinary ; gamma-Aminobutyric Acid/metabolism ; Membrane Glycoproteins/metabolism ; Receptors, Immunologic/metabolism ; },
abstract = {Acute administration of MK-801 (dizocilpine), an N-methyl-D-aspartate receptor (NMDAR) antagonist, can establish animal models of psychiatric disorders. However, the roles of microglia and inflammation-related genes in these animal models of psychiatric disorders remain unknown. Here, we found rapid elimination of microglia in the prefrontal cortex (PFC) and hippocampus (HPC) of mice following administration of the dual colony-stimulating factor 1 receptor (CSF1R)/c-Kit kinase inhibitor PLX3397 (pexidartinib) in drinking water. Single administration of MK-801 induced hyperactivity in the open-field test (OFT). Importantly, PLX3397-induced depletion of microglia prevented the hyperactivity and schizophrenia-like behaviors induced by MK-801. However, neither repopulation of microglia nor inhibition of microglial activation by minocycline affected MK-801-induced hyperactivity. Importantly, microglial density in the PFC and HPC was significantly correlated with behavioral changes. In addition, common and distinct glutamate-, GABA-, and inflammation-related gene (116 genes) expression patterns were observed in the brains of PLX3397- and/or MK-801-treated mice. Moreover, 10 common inflammation-related genes (CD68, CD163, CD206, TMEM119, CSF3R, CX3CR1, TREM2, CD11b, CSF1R, and F4/80) with very strong correlations were identified in the brain using hierarchical clustering analysis. Further correlation analysis demonstrated that the behavioral changes in the OFT were most significantly associated with the expression of inflammation-related genes (NLRP3, CD163, CD206, F4/80, TMEM119, and TMEM176a), but not glutamate- or GABA-related genes in PLX3397- and MK-801-treated mice. Thus, our results suggest that microglial depletion via a CSF1R/c-Kit kinase inhibitor can ameliorate the hyperactivity induced by an NMDAR antagonist, which is associated with modulation of immune-related genes in the brain.},
}
@article {pmid37145943,
year = {2023},
author = {Meng, L and Jiang, X and Huang, J and Zeng, Z and Yu, S and Jung, TP and Lin, CT and Chavarriaga, R and Wu, D},
title = {EEG-Based Brain-Computer Interfaces are Vulnerable to Backdoor Attacks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2224-2234},
doi = {10.1109/TNSRE.2023.3273214},
pmid = {37145943},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Algorithms ; Machine Learning ; Brain ; },
abstract = {Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.},
}
@article {pmid37143288,
year = {2023},
author = {Wang, J and Wang, T and Liu, H and Wang, K and Moses, K and Feng, Z and Li, P and Huang, W},
title = {Flexible Electrodes for Brain-Computer Interface System.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {35},
number = {47},
pages = {e2211012},
doi = {10.1002/adma.202211012},
pmid = {37143288},
issn = {1521-4095},
support = {52003224//National Natural Science Foundation of China/ ; 52073230//National Natural Science Foundation of China/ ; 62288102//National Natural Science Foundation of China/ ; 2023-JC-JQ-32//Natural Science Basic Research Program of Shaanxi Province/ ; 2019JQ-157//Natural Science Basic Research Program of Shaanxi Province/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Electrodes ; Brain ; },
abstract = {Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body, and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided.},
}
@article {pmid37143057,
year = {2023},
author = {Won, K and Kim, H and Gwon, D and Ahn, M and Nam, CS and Jun, SC},
title = {Can vibrotactile stimulation and tDCS help inefficient BCI users?.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {60},
pmid = {37143057},
issn = {1743-0003},
mesh = {Humans ; *Transcranial Direct Current Stimulation/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Movement/physiology ; Electroencephalography/methods ; },
abstract = {Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation's (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter's BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p < 0.01), and while the tDCS group subjects' performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.},
}
@article {pmid37143020,
year = {2023},
author = {Batistić, L and Lerga, J and Stanković, I},
title = {Detection of motor imagery based on short-term entropy of time-frequency representations.},
journal = {Biomedical engineering online},
volume = {22},
number = {1},
pages = {41},
pmid = {37143020},
issn = {1475-925X},
support = {IP-2018-01-3739//Hrvatska Zaklada za Znanost/ ; uniri-tehnic-18-17//University of Rijeka/ ; uniri-tehnic-18-15//University of RIjeka/ ; 101087348//EU Horizon project INNO2MARE/ ; 101083838//EU Digital project EDIH ADRIA/ ; },
mesh = {*Imagination/physiology ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement ; Entropy ; },
abstract = {BACKGROUND: Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain-computer interface (BCI). This paper provides a comparison of different time-frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only.<br><br>RESULTS: When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner-Ville time-frequency representation.<br><br>CONCLUSIONS: Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.},
}
@article {pmid37141863,
year = {2023},
author = {Ziai, Y and Zargarian, SS and Rinoldi, C and Nakielski, P and Sola, A and Lanzi, M and Truong, YB and Pierini, F},
title = {Conducting polymer-based nanostructured materials for brain-machine interfaces.},
journal = {Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology},
volume = {15},
number = {5},
pages = {e1895},
doi = {10.1002/wnan.1895},
pmid = {37141863},
issn = {1939-0041},
mesh = {Polymers/chemistry ; *Brain-Computer Interfaces ; Artificial Intelligence ; Hydrogels/chemistry ; *Nanostructures ; },
abstract = {As scientists discovered that raw neurological signals could translate into bioelectric information, brain-machine interfaces (BMI) for experimental and clinical studies have experienced massive growth. Developing suitable materials for bioelectronic devices to be used for real-time recording and data digitalizing has three important necessitates which should be covered. Biocompatibility, electrical conductivity, and having mechanical properties similar to soft brain tissue to decrease mechanical mismatch should be adopted for all materials. In this review, inorganic nanoparticles and intrinsically conducting polymers are discussed to impart electrical conductivity to systems, where soft materials such as hydrogels can offer reliable mechanical properties and a biocompatible substrate. Interpenetrating hydrogel networks offer more mechanical stability and provide a path for incorporating polymers with desired properties into one strong network. Promising fabrication methods, like electrospinning and additive manufacturing, allow scientists to customize designs for each application and reach the maximum potential for the system. In the near future, it is desired to fabricate biohybrid conducting polymer-based interfaces loaded with cells, giving the opportunity for simultaneous stimulation and regeneration. Developing multi-modal BMIs, Using artificial intelligence and machine learning to design advanced materials are among the future goals for this field. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.},
}
@article {pmid37141094,
year = {2023},
author = {Zhu, L and Wang, M and Liu, Y and Fu, P and Zhang, W and Zhang, H and Roe, AW and Xi, W},
title = {Single-microvessel occlusion produces lamina-specific microvascular flow vasodynamics and signs of neurodegenerative change.},
journal = {Cell reports},
volume = {42},
number = {5},
pages = {112469},
doi = {10.1016/j.celrep.2023.112469},
pmid = {37141094},
issn = {2211-1247},
mesh = {Humans ; *Neurodegenerative Diseases ; Microvessels ; Blood-Brain Barrier ; Hemodynamics ; Capillaries ; },
abstract = {Recent studies have highlighted the importance of understanding the architecture and function of microvasculature, and dysfunction of these microvessels may underlie neurodegenerative disease. Here, we utilize a high-precision ultrafast laser-induced photothrombosis (PLP) method to occlude single capillaries and then quantitatively study the effects on vasodynamics and surrounding neurons. Analysis of the microvascular architecture and hemodynamics after single-capillary occlusion reveals distinct changes upstream vs. downstream branches, which shows rapid regional flow redistribution and local downstream blood-brain barrier (BBB) leakage. Focal ischemia via capillary occlusions surrounding labeled target neurons induces dramatic and rapid lamina-specific changes in neuronal dendritic architecture. Further, we find that micro-occlusion at two different depths within the same vascular arbor results in distinct effects on flow profiles in layers 2/3 vs layer 4. The current results reveal laminar-scale regulation distinctions in microinfarct response and raise the possibility that relatively greater impacts on microvascular function contribute to cognitive decline in neurodegenerative disease.},
}
@article {pmid37141070,
year = {2023},
author = {Wen, D and Liang, B and Li, J and Wu, L and Wan, X and Dong, X and Lan, X and Song, H and Zhou, Y},
title = {Feature Extraction Method of EEG Signals Evaluating Spatial Cognition of Community Elderly With Permutation Conditional Mutual Information Common Space Model.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2370-2380},
doi = {10.1109/TNSRE.2023.3273119},
pmid = {37141070},
issn = {1558-0210},
mesh = {Humans ; Aged ; *Signal Processing, Computer-Assisted ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Cognition ; Imagination ; },
abstract = {In order to improve the traditional common space pattern (CSP) algorithm pattern in EEG feature extraction, this study proposes a feature extraction method of EEG signals based on permutation conditional mutual information common space pattern (PCMICSP), which used the sum of the permutation condition mutual information matrices of each lead to replacing the mixed spatial covariance matrix in the traditional CSP algorithm, and its eigenvectors and eigenvalues are used to construct a new spatial filter. Then the spatial features in the different time domains and frequency domains are combined to construct the two-dimensional pixel map, Finally, a convolutional neural network (CNN) is used for binary classification. The EEG signals of 7 community elderly before and after spatial cognitive training in virtual reality (VR) scenes were used as the test data set. The average classification accuracy of the PCMICSP algorithm for pre-test and post-test EEG signals is 98%, which was higher than that of CSP based on CMI (conditional mutual information), CSP based on MI (mutual information), and traditional CSP in the combination of four frequency bands. Compared with the traditional CSP method, PCMICSP can be used as a more effective method to extract the spatial features of EEG signals. Therefore, this paper provides a new approach to solving the strict linear hypothesis of CSP and can be used as a valuable biomarker for the spatial cognitive evaluation of the elderly in the community.},
}
@article {pmid37141006,
year = {2023},
author = {Wang, Y and Zhao, T and Jiao, Y and Huang, H and Zhang, Y and Fang, A and Wang, X and Zhou, Y and Gu, H and Wu, Q and Chang, J and Li, F and Xu, K},
title = {Silicate Nanoplatelets Promotes Neuronal Differentiation of Neural Stem Cells and Restoration of Spinal Cord Injury.},
journal = {Advanced healthcare materials},
volume = {12},
number = {19},
pages = {e2203051},
doi = {10.1002/adhm.202203051},
pmid = {37141006},
issn = {2192-2659},
mesh = {Humans ; Cell Differentiation ; *Neural Stem Cells ; *Spinal Cord Injuries/therapy/pathology ; Spinal Cord/pathology ; Stem Cell Transplantation/methods ; Silicates/pharmacology ; },
abstract = {Neural stem cell (NSC) transplantation has been suggested as a promising therapeutic strategy to replace lost neurons after spinal cord injury (SCI). However, the low survival rate and neuronal differentiation efficiency of implanted NSCs within the lesion cavity limit the application. Furthermore, it is difficult for transplanted cells to form connections with host cells. Thus, effective and feasible methods to enhance the efficacy of cell transplantation are needed. In this study, the effect of Laponite nanoplatelets, a type of silicate nanoplatelets, on stem cell therapy is explored. Laponite nanoplatelets can induce the neuronal differentiation of NSCs in vitro within five days, and RNA sequencing and protein expression analysis demonstrated that the NF-κB pathway is involved in this process. Moreover, histological results revealed that Laponite nanoplatelets can increase the survival rate of transplanted NSCs and promote NSCs to differentiate into mature neurons. Finally, the formation of connections between transplanted cells and host cells is confirmed by axon tracing. Hence, Laponite nanoplatelets, which drove neuronal differentiation and the maturation of NSCs both in vitro and in vivo, can be considered a convenient and practical biomaterial to promote repair of the injured spinal cord by enhancing the efficacy of NSC transplantation.},
}
@article {pmid37140225,
year = {2023},
author = {Kosal, M and Putney, J},
title = {Neurotechnology and international security: Predicting commercial and military adoption of brain-computer interfaces (BCIs) in the United States and China.},
journal = {Politics and the life sciences : the journal of the Association for Politics and the Life Sciences},
volume = {42},
number = {1},
pages = {81-103},
doi = {10.1017/pls.2022.2},
pmid = {37140225},
issn = {1471-5457},
mesh = {Humans ; United States ; *Brain-Computer Interfaces ; Arm ; *Military Personnel ; *Artificial Limbs ; Brain ; },
abstract = {In the past decade, international actors have launched "brain projects" or "brain initiatives." One of the emerging technologies enabled by these publicly funded programs is brain-computer interfaces (BCIs), which are devices that allow communication between the brain and external devices like a prosthetic arm or a keyboard. BCIs are poised to have significant impacts on public health, society, and national security. This research presents the first analytical framework that attempts to predict the dissemination of neurotechnologies to both the commercial and military sectors in the United States and China. While China started its project later with less funding, we find that it has other advantages that make earlier adoption more likely. We also articulate national security risks implicit in later adoption, including the inability to set international ethical and legal norms for BCI use, especially in wartime operating environments, and data privacy risks for citizens who use technology developed by foreign actors.},
}
@article {pmid37140029,
year = {2023},
author = {Zhang, X and Wang, W and Zhang, X and Bai, X and Yuan, Z and Zhang, P and Bai, R and Jiao, B and Zhang, Y and Li, Z and Tang, H and Zhang, Y and Yu, X and Wang, Y and Sui, B},
title = {Normal glymphatic system function in patients with new daily persistent headache using diffusion tensor image analysis along the perivascular space.},
journal = {Headache},
volume = {63},
number = {5},
pages = {663-671},
doi = {10.1111/head.14514},
pmid = {37140029},
issn = {1526-4610},
support = {7212028//Beijing Municipal Natural Science Foundation/ ; Z200024//National Natural Science Foundation of Beijing/ ; 31770800//National Natural Science Foundation of China/ ; 32170752//National Natural Science Foundation of China/ ; 91849104//National Natural Science Foundation of China/ ; },
mesh = {Male ; Female ; Humans ; *Glymphatic System/diagnostic imaging ; Cross-Sectional Studies ; *Headache Disorders ; Headache ; Neurologic Examination ; },
abstract = {OBJECTIVES: To investigate the glymphatic function in patients with new daily persistent headache (NDPH) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method.<br><br>BACKGROUND: NDPH, a rare and treatment-refractory primary headache disorder, is poorly understood. There is limited evidence to suggest that headaches are associated with glymphatic dysfunction. Thus far, no studies have evaluated glymphatic function in patients with NDPH.<br><br>METHODS: In this cross-sectional study conducted in the Headache Center of Beijing Tiantan Hospital, patients with NDPH and healthy controls were enrolled. All participants underwent brain magnetic resonance imaging examinations. Clinical characteristics and neuropsychological evaluation were examined in patients with NDPH. ALPS indexes for both hemispheres were measured to determine the glymphatic system function in patients with NDPH and healthy controls.<br><br>RESULTS: In total, 27 patients with NDPH (14 males, 13 females; age [mean&#x2009;&#xb1;&#x2009;standard deviation (SD)]: 36.6&#x2009;&#xb1;&#x2009;20.6) and 33 healthy controls (15 males, 18 females; age [mean&#x2009;&#xb1;&#x2009;SD]: 36.0&#x2009;&#xb1;&#x2009;10.8) were included in the analysis. No significant differences between groups were observed in the left ALPS index (1.583&#x2009;&#xb1;&#x2009;0.182 vs. 1.586&#x2009;&#xb1;&#x2009;0.175, mean difference&#x2009;=&#x2009;0.003, 95% confidence interval [CI] of difference&#x2009;=&#x2009;-0.089 to 0.096, p&#x2009;=&#x2009;0.942), or right ALPS index (1.578&#x2009;&#xb1;&#x2009;0.230 vs. 1.559&#x2009;&#xb1;&#x2009;0.206, mean difference&#x2009;=&#x2009;-0.027, 95% CI of difference&#x2009;=&#x2009;-0.132 to 0.094, p&#x2009;=&#x2009;0.738). Additionally, ALPS indexes were not correlated with clinical characteristics or neuropsychiatric scores.<br><br>CONCLUSION: No glymphatic dysfunction was detected in patients with NDPH by means of the ALPS method. Additional studies with larger samples are needed to confirm these preliminary findings and improve the understanding of glymphatic function in NDPH.},
}
@article {pmid37139769,
year = {2023},
author = {Zhang, Z and Zhao, X and Ma, Y and Ding, P and Nan, W and Gong, A and Fu, Y},
title = {[Ethics considerations on brain-computer interface technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {2},
pages = {358-364},
pmid = {37139769},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Technology ; Brain ; User-Computer Interface ; Electroencephalography ; },
abstract = {The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.},
}
@article {pmid37139522,
year = {2023},
author = {Li, H and Ji, H and Yu, J and Li, J and Jin, L and Liu, L and Bai, Z and Ye, C},
title = {A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1125230},
pmid = {37139522},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.<br><br>METHODS: This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.<br><br>RESULTS: A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.<br><br>DISCUSSION: This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.},
}
@article {pmid37139055,
year = {2023},
author = {Zhu, Y and Wang, C and Li, J and Zeng, L and Zhang, P},
title = {Effect of different modalities of artificial intelligence rehabilitation techniques on patients with upper limb dysfunction after stroke-A network meta-analysis of randomized controlled trials.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1125172},
pmid = {37139055},
issn = {1664-2295},
abstract = {BACKGROUND: This study aimed to observe the effects of six different types of AI rehabilitation techniques (RR, IR, RT, RT + VR, VR and BCI) on upper limb shoulder-elbow and wrist motor function, overall upper limb function (grip, grasp, pinch and gross motor) and daily living ability in subjects with stroke. Direct and indirect comparisons were drawn to conclude which AI rehabilitation techniques were most effective in improving the above functions.<br><br>METHODS: From establishment to 5 September 2022, we systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CNKI, VIP and Wanfang. Only randomized controlled trials (RCTs) that met the inclusion criteria were included. The risk of bias in studies was evaluated using the Cochrane Collaborative Risk of Bias Assessment Tool. A cumulative ranking analysis by SUCRA was performed to compare the effectiveness of different AI rehabilitation techniques for patients with stroke and upper limb dysfunction.<br><br>RESULTS: We included 101 publications involving 4,702 subjects. According to the results of the SUCRA curves, RT + VR (SUCRA = 84.8%, 74.1%, 99.6%) was most effective in improving FMA-UE-Distal, FMA-UE-Proximal and ARAT function for subjects with upper limb dysfunction and stroke, respectively. IR (SUCRA = 70.5%) ranked highest in improving FMA-UE-Total with upper limb motor function amongst subjects with stroke. The BCI (SUCRA = 73.6%) also had the most significant advantage in improving their MBI daily living ability.<br><br>CONCLUSIONS: The network meta-analysis (NMA) results and SUCRA rankings suggest RT + VR appears to have a greater advantage compared with other interventions in improving upper limb motor function amongst subjects with stroke in FMA-UE-Proximal and FMA-UE-Distal and ARAT. Similarly, IR had shown the most significant advantage over other interventions in improving the FMA-UE-Total upper limb motor function score of subjects with stroke. The BCI also had the most significant advantage in improving their MBI daily living ability. Future studies should consider and report on key patient characteristics, such as stroke severity, degree of upper limb impairment, and treatment intensity/frequency and duration.<br><br>www.crd.york.ac.uk/prospero/#recordDetail, identifier: CRD42022337776.},
}
@article {pmid37138756,
year = {2023},
author = {Abdelrahman, Z and Wang, X and Wang, D and Zhang, T and Zhang, Y and Wang, X and Chen, Z},
title = {Identification of novel pathways and immune profiles related to sarcopenia.},
journal = {Frontiers in medicine},
volume = {10},
number = {},
pages = {928285},
pmid = {37138756},
issn = {2296-858X},
abstract = {INTRODUCTION: Sarcopenia is a progressive deterioration of skeletal muscle mass strength and function.<br><br>METHODS: To uncover the underlying cellular and biological mechanisms, we studied the association between sarcopenia's three stages and the patient's ethnicity, identified a gene regulatory network based on motif enrichment in the upregulated gene set of sarcopenia, and compared the immunological landscape among sarcopenia stages.<br><br>RESULTS: We found that sarcopenia (S) was associated with GnRH, neurotrophin, Rap1, Ras, and p53 signaling pathways. Low muscle mass (LMM) patients showed activated pathways of VEGF signaling, B-cell receptor signaling, ErbB signaling, and T-cell receptor signaling. Low muscle mass and physical performance (LMM_LP) patients showed lower enrichment scores in B-cell receptor signaling, apoptosis, HIF-1 signaling, and the adaptive immune response pathways. Five common genes among DEGs and the elastic net regression model, TTC39DP, SLURP1, LCE1C, PTCD2P1, and OR7E109P, were expressed between S patients and healthy controls. SLURP1 and LCE1C showed the highest expression levels among sarcopenic Chinese descent than Caucasians and Afro-Caribbeans. Gene regulatory analysis of top upregulated genes in S patients yielded a top-scoring regulon containing GATA1, GATA2, and GATA3 as master regulators and nine predicted direct target genes. Two genes were associated with locomotion: POSTN and SLURP1. TTC39DP upregulation was associated with a better prognosis and stronger immune profile in S patients. The upregulation of SLURP1 and LCE1C was associated with a worse prognosis and weaker immune profile.<br><br>CONCLUSION: This study provides new insight into sarcopenia's cellular and immunological prospects and evaluates the age and sarcopenia-related modifications of skeletal muscle.},
}
@article {pmid37137713,
year = {2023},
author = {Ullah, A and Liu, Y and Wang, Y and Gao, H and Luo, Z and Li, G},
title = {Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography.},
journal = {Perceptual and motor skills},
volume = {130},
number = {3},
pages = {938-957},
doi = {10.1177/00315125231169882},
pmid = {37137713},
issn = {1558-688X},
mesh = {Humans ; Male ; Female ; *Taste Perception/physiology ; *Taste/physiology ; Electromyography ; Sex Factors ; Touch ; },
abstract = {Males and females respond differently at the muscular level to various tastes and show varied responses when eating different foods. In this study, we used surface electromyography (sEMG) as a novel approach to examine gender differences in taste sensations. We collected sEMG data from 30 participants (15 males, 15 females) over various sessions for six taste states: a no-stimulation physiological state, sweet, sour, salty, bitter, and umami. We applied a Fast Fourier Transformation to the sEMG-filtered data and used a two-sample t-test algorithm to analyze and evaluate the resulting frequency spectrum. Our results showed that the female participants had more sEMG channels with low frequencies and fewer channels with high frequencies than the male participants during all taste states except the bitter taste sensation, meaning that for most sensations, the female participants had better tactile and fewer gustatory responses than the male participants. The female participants responded better to gustatory and tactile perceptions during bitter tasting because they had more channels throughout the frequency distribution. Moreover, the facial muscles of the female participants twitched with low frequencies, while the facial muscles of the male participants twitched with high frequencies for all taste states except the bitter sensation, for which the female facial muscles twitched throughout the range of the frequency distribution. This gender-dependent variation in sEMG frequency distribution provides new evidence of differentiated taste sensations between males and females.},
}
@article {pmid37137387,
year = {2023},
author = {Gilbert, F and Ienca, M and Cook, M},
title = {How I became myself after merging with a computer: Does human-machine symbiosis raise human rights issues?.},
journal = {Brain stimulation},
volume = {16},
number = {3},
pages = {783-789},
doi = {10.1016/j.brs.2023.04.016},
pmid = {37137387},
issn = {1876-4754},
mesh = {Humans ; Female ; *Symbiosis ; Algorithms ; Computers ; Cognition ; Electroencephalography ; Human Rights ; *Brain-Computer Interfaces ; },
abstract = {Novel usages of brain stimulation combined with artificially intelligent (AI) systems promise to address a large range of diseases. These new conjoined technologies, such as brain-computer interfaces (BCI), are increasingly used in experimental and clinical settings to predict and alleviate symptoms of various neurological and psychiatric disorders. Due to their reliance on AI algorithms for feature extraction and classification, these BCI systems enable a novel, unprecedented, and direct connection between human cognition and artificial information processing. In this paper, we present the results of a study that investigates the phenomenology of human-machine symbiosis during a first-in-human experimental BCI trial designed to predict epileptic seizures. We employed qualitative semi-structured interviews to collect user experience data from a participant over a six-years period. We report on a clinical case where a specific embodied phenomenology emerged: namely, after BCI implantation, the patient reported experiences of increased agential capacity and continuity; and after device explantation, the patient reported persistent traumatic harms linked to agential discontinuity. To our knowledge, this is the first reported clinical case of a patient experiencing persistent agential discontinuity due to BCI explantation and potential evidence of an infringement on patient right, where the implanted person was robbed of her de novo agential capacities when the device was removed.},
}
@article {pmid37137282,
year = {2023},
author = {Jia, K and Goebel, R and Kourtzi, Z},
title = {Ultra-High Field Imaging of Human Visual Cognition.},
journal = {Annual review of vision science},
volume = {9},
number = {},
pages = {479-500},
doi = {10.1146/annurev-vision-111022-123830},
pmid = {37137282},
issn = {2374-4650},
support = {205067/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; 223131/Z/21/Z/WT_/Wellcome Trust/United Kingdom ; BB/P021255/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; H012508/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; },
mesh = {Humans ; *Brain Mapping/methods ; *Brain/diagnostic imaging ; Cognition ; Magnetic Resonance Imaging/methods ; },
abstract = {Functional magnetic resonance imaging (fMRI), the key methodology for mapping the functions of the human brain in a noninvasive manner, is limited by low temporal and spatial resolution. Recent advances in ultra-high field (UHF) fMRI provide a mesoscopic (i.e., submillimeter resolution) tool that allows us to probe laminar and columnar circuits, distinguish bottom-up versus top-down pathways, and map small subcortical areas. We review recent work demonstrating that UHF fMRI provides a robust methodology for imaging the brain across cortical depths and columns that provides insights into the brain's organization and functions at unprecedented spatial resolution, advancing our understanding of the fine-scale computations and interareal communication that support visual cognition.},
}
@article {pmid37133926,
year = {2023},
author = {He, Y and Tang, Z and Sun, G and Cai, C and Wang, Y and Yang, G and Bao, Z},
title = {Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial.},
journal = {JMIR mHealth and uHealth},
volume = {11},
number = {},
pages = {e44855},
pmid = {37133926},
issn = {2291-5222},
mesh = {Humans ; *Atrial Fibrillation/therapy ; *Brain-Computer Interfaces ; *Meditation/methods ; *Mindfulness/methods ; *Mobile Applications ; Pilot Projects ; *Catheter Ablation/adverse effects/methods ; Fatigue ; },
abstract = {BACKGROUND: Radiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice.<br><br>OBJECTIVE: This study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA.<br><br>METHODS: This single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app-delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation.<br><br>RESULTS: BCI-based app-delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001), and Brief Fatigue Inventory (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01) scores. No significant differences were observed in hemodynamic parameters or the amounts of parecoxib and dexmedetomidine used in RFCA between the 2 groups. The intervention group exhibited a significant decrease in fentanyl use compared to the control group, with a mean dose of 3.96 (SD 1.37) mcg/kg versus 4.85 (SD 1.25) mcg/kg in the control group (P=.003).The incidence of adverse events was lower in the intervention group (5/40) than in the control group (10/40), though this difference was not significant (P=.15).<br><br>CONCLUSIONS: BCI-based app-delivered mindfulness meditation effectively relieved physical and psychological discomfort and may reduce the doses of sedative medication used in RFCA for patients with AF.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05306015; https://clinicaltrials.gov/ct2/show/NCT05306015.},
}
@article {pmid37132740,
year = {2023},
author = {Coelho, HRS and Neves, SC and Menezes, JNS and Antoniolli-Silva, ACMB and Oliveira, RJ},
title = {Cell therapy with adipose tissue-derived human stem cells in the urinary bladder improves detrusor contractility and reduces voiding residue.},
journal = {Brazilian journal of biology = Revista brasleira de biologia},
volume = {83},
number = {},
pages = {e268540},
doi = {10.1590/1519-6984.268540},
pmid = {37132740},
issn = {1678-4375},
mesh = {Humans ; *Urinary Bladder ; *Quality of Life ; Stem Cells ; Cell- and Tissue-Based Therapy ; },
abstract = {Detrusor hypocontractility (DH) is a disease without a gold standard treatment in traditional medicine. Therefore, there is a need to develop innovative therapies. The present report presents the case of a patient with DH who was transplanted with 2 x 106 adipose tissue-derived mesenchymal stem cells twice and achieved significant improvements in their quality of life. The results showed that cell therapy reduced the voiding residue from 1,800 mL to 800 mL, the maximum cystometric capacity from 800 to 550 mL, and bladder compliance from 77 to 36.6 mL/cmH2O. Cell therapy also increased the maximum flow from 3 to 11 mL/s, the detrusor pressure from 08 to 35 cmH2O, the urine volume from 267 to 524 mL and the bladder contractility index (BCI) value from 23 to 90. The International Continence on Incontinence Questionnaire - Short Form score decreased from 17 to 8. Given the above, it is inferred that the transplantation of adipose tissue-derived mesenchymal stem cells is an innovative and efficient therapeutic strategy for DH treatment and improves the quality of life of patients affected by this disease.},
}
@article {pmid37131830,
year = {2023},
author = {Deo, DR and Willett, FR and Avansino, DT and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Translating deep learning to neuroprosthetic control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.04.21.537581},
pmid = {37131830},
issn = {2692-8205},
abstract = {Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.},
}
@article {pmid37130514,
year = {2023},
author = {Borra, D and Filippini, M and Ursino, M and Fattori, P and Magosso, E},
title = {Motor decoding from the posterior parietal cortex using deep neural networks.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/acd1b6},
pmid = {37130514},
issn = {1741-2552},
mesh = {Humans ; Animals ; Bayes Theorem ; *Neural Networks, Computer ; Parietal Lobe ; Neurons/physiology ; Macaca fascicularis ; Movement/physiology ; *Brain-Computer Interfaces ; },
abstract = {Objective.Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs.Approach.Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded nine reaching endpoints in 3D space or five grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding.Main results.DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness.Significance.Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.},
}
@article {pmid37130248,
year = {2023},
author = {Zhang, R and Wang, C and He, S and Zhao, C and Zhang, K and Wang, X and Li, Y},
title = {An Adaptive Brain-Computer Interface to Enhance Motor Recovery After Stroke.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2268-2278},
doi = {10.1109/TNSRE.2023.3272372},
pmid = {37130248},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; Recovery of Function/physiology ; *Stroke ; Upper Extremity ; },
abstract = {Brain computer interfaces (BCIs) have been demonstrated to have the potential to enhance motor recovery after stroke. However, some stroke patients with severe paralysis have difficulty achieving the BCI performance required for participating in BCI-based rehabilitative interventions, limiting their clinical benefits. To address this issue, we presented a BCI intervention approach that can adapt to patients' BCI performance and reported that adaptive BCI-based functional electrical stimulation (FES) treatment induced clinically significant, long-term improvements in upper extremity motor function after stroke more effectively than FES treatment without BCI intervention. These improvements were accompanied by a more optimized brain functional reorganization. Further comparative analysis revealed that stroke patients with low BCI performance (LBP) had no significant difference from patients with high BCI performance in rehabilitation efficacy improvement. Our findings suggested that the current intervention may be an effective way for LBP patients to engage in BCI-based rehabilitation treatment and may promote lasting motor recovery, thus contributing to expanding the applicability of BCI-based rehabilitation treatments to pave the way for more effective rehabilitation treatments.},
}
@article {pmid37129900,
year = {2024},
author = {Blanco-Díaz, CF and Guerrero-Mendez, CD and Delisle-Rodriguez, D and de Souza, AF and Badue, C and Bastos-Filho, TF},
title = {Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {27},
number = {7},
pages = {867-877},
doi = {10.1080/10255842.2023.2207705},
pmid = {37129900},
issn = {1476-8259},
mesh = {Humans ; *Electroencephalography ; Biomechanical Phenomena ; Lower Extremity/physiology ; Bicycling/physiology ; Brain-Computer Interfaces ; Male ; Signal Processing, Computer-Assisted ; Adult ; Algorithms ; Young Adult ; },
abstract = {Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling. The results demonstrated maximum decoding accuracy using slow cortical potentials in the delta band (0.1-4 Hz) of 0.33 for Pearson's r-value and 8 for the signal-to-noise ratio (SNR). This leaves an open door to the development of closed-loop EEG-based BCI systems for kinematic monitoring during pedaling rehabilitation tasks.},
}
@article {pmid37127759,
year = {2023},
author = {Tang, J and LeBel, A and Jain, S and Huth, AG},
title = {Semantic reconstruction of continuous language from non-invasive brain recordings.},
journal = {Nature neuroscience},
volume = {26},
number = {5},
pages = {858-866},
pmid = {37127759},
issn = {1546-1726},
support = {R01 DC020088/DC/NIDCD NIH HHS/United States ; },
mesh = {Semantics ; Brain ; Language ; Brain Mapping/methods ; *Speech Perception ; Magnetic Resonance Imaging/methods ; *Brain-Computer Interfaces ; },
abstract = {A brain-computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain-computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain-computer interfaces.},
}
@article {pmid37125350,
year = {2023},
author = {Yu, K and Sun, J and He, B},
title = {Editorial: Brain stimulation and interfacing.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1179166},
doi = {10.3389/fnhum.2023.1179166},
pmid = {37125350},
issn = {1662-5161},
}
@article {pmid37124570,
year = {2023},
author = {Donlon, JD and Mee, JF and McAloon, CG},
title = {Prevalence of respiratory disease in Irish preweaned dairy calves using hierarchical Bayesian latent class analysis.},
journal = {Frontiers in veterinary science},
volume = {10},
number = {},
pages = {1149929},
pmid = {37124570},
issn = {2297-1769},
abstract = {INTRODUCTION: Bovine respiratory disease (BRD) has a significant impact on the health and welfare of dairy calves. It can result in increased antimicrobial usage, decreased growth rate and reduced future productivity. There is no gold standard antemortem diagnostic test for BRD in calves and no estimates of the prevalence of respiratory disease in seasonal calving dairy herds.<br><br>METHODS: To estimate BRD prevalence in seasonal calving dairy herds in Ireland, 40 dairy farms were recruited and each farm was visited once during one of two calving seasons (spring 2020 &amp; spring 2021). At that visit the prevalence of BRD in 20 calves between 4 and 6 weeks of age was determined using thoracic ultrasound score (&#x2265;3) and the Wisconsin respiratory scoring system (&#x2265;5). Hierarchical Bayesian latent class analysis was used to estimate the calf-level true prevalence of BRD, and the within-herd prevalence distribution, accounting for the imperfect nature of both diagnostic tests.<br><br>RESULTS: In total, 787 calves were examined, of which 58 (7.4%) had BRD as defined by a Wisconsin respiratory score &#x2265;5 only, 37 (4.7%) had BRD as defined by a thoracic ultrasound score of &#x2265;3 only and 14 (1.8%) calves had BRD based on both thoracic ultrasound and clinical scoring. The primary model assumed both tests were independent and used informed priors for test characteristics. Using this model the true prevalence of BRD was estimated as 4%, 95% Bayesian credible interval (BCI) (1%, 8%). This prevalence estimate is lower or similar to those found in other dairy production systems. Median within herd prevalence varied from 0 to 22%. The prevalence estimate was not sensitive to whether the model was constructed with the tests considered conditionally dependent or independent. When the case definition for thoracic ultrasound was changed to a score &#x2265;2, the prevalence estimate increased to 15% (95% BCI: 6%, 27%).<br><br>DISCUSSION: The prevalence of calf respiratory disease, however defined, was low, but highly variable, in these seasonal calving dairy herds.},
}
@article {pmid37121218,
year = {2023},
author = {Li, S and Huang, S and Hu, S and Lai, J},
title = {Psychological consequences among veterans during the COVID-19 pandemic: A scoping review.},
journal = {Psychiatry research},
volume = {324},
number = {},
pages = {115229},
pmid = {37121218},
issn = {1872-7123},
mesh = {Humans ; *COVID-19/epidemiology ; Pandemics ; *Veterans/psychology ; Anxiety/epidemiology/psychology ; *Stress Disorders, Post-Traumatic/epidemiology/psychology ; },
abstract = {Although there is an increasing number of studies reporting the psychological impact of COVID-19 on the general population and healthcare workers, relatively less attention has been paid to the veterans. This study aimed to review the existing literature regarding the psychological consequences of COVID-19 on veterans. A systematic search was conducted on PubMed, Embase, and the Cochrane Library from inception to December 3, 2022. A total of twenty-three studies were included with moderate-quality of evidence. Veterans experienced more mental health problems than civilians. The prevalence rates of alcohol use, anxiety, depression, post-traumatic stress disorder, stress, loneliness, and suicide ideation significantly increased during the pandemic, ranging from 9.6% to 47.4%, 9.4% to 53.5%, 8.6% to 55.1%, 4.1% to 58.0%, 4.3% to 39.4%, 15.9% to 28.4%, and 7.8% to 22.0%, respectively. The main risk factors of negative consequences included pandemic-related stress, poor family relationships, lack of social support, financial problems, and preexisting mental disorders. In contrast, higher household income and greater community interaction and support appeared to be resilience factors. In conclusion, the COVID-19 pandemic has increased adverse mental health consequences among veterans. Tackling mental health issues due to the COVID-19 pandemic among veterans should be a priority.},
}
@article {pmid37118880,
year = {2023},
author = {Liu, H and Tang, M and Yu, H and Liu, T and Cui, Q and Gu, L and Wu, ZY and Sheng, N and Yang, XL and Zeng, L and Bai, G},
title = {CMT2D neuropathy is influenced by vitamin D-mediated environmental pathway.},
journal = {Journal of molecular cell biology},
volume = {15},
number = {4},
pages = {},
pmid = {37118880},
issn = {1759-4685},
support = {R35 GM139627/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; Vitamin D ; *Charcot-Marie-Tooth Disease/metabolism ; *Diabetes Mellitus, Type 2 ; },
}
@article {pmid37116888,
year = {2023},
author = {Bergeron, D and Iorio-Morin, C and Bonizzato, M and Lajoie, G and Orr Gaucher, N and Racine, &#xc9; and Weil, AG},
title = {Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations.},
journal = {Journal of child neurology},
volume = {38},
number = {3-4},
pages = {223-238},
pmid = {37116888},
issn = {1708-8283},
mesh = {Child ; Humans ; Artificial Intelligence ; *Brain-Computer Interfaces ; *Persons with Disabilities ; *Neurosurgery ; Neurosurgical Procedures ; },
abstract = {Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.},
}
@article {pmid37113774,
year = {2023},
author = {Aghili, SN and Kilani, S and Khushaba, RN and Rouhani, E},
title = {A spatial-temporal linear feature learning algorithm for P300-based brain-computer interfaces.},
journal = {Heliyon},
volume = {9},
number = {4},
pages = {e15380},
pmid = {37113774},
issn = {2405-8440},
abstract = {Speller brain-computer interface (BCI) systems can help neuromuscular disorders patients write their thoughts by using the electroencephalogram (EEG) signals by just focusing on the speller tasks. For practical speller-based BCI systems, the P300 event-related brain potential is measured by using the EEG signal. In this paper, we design a robust machine-learning algorithm for P300 target detection. The novel spatial-temporal linear feature learning (STLFL) algorithm is proposed to extract high-level P300 features. The STLFL method is a modified linear discriminant analysis technique focusing on the spatial-temporal aspects of information extraction. A new P300 detection structure is then proposed based on the combination of the novel STLFL feature extraction and discriminative restricted Boltzmann machine (DRBM) for the classification approach (STLFL + DRBM). The effectiveness of the proposed technique is evaluated using two state-of-the-art P300 BCI datasets. Across the two available databases, we show that in terms of average target recognition accuracy and standard deviation values, the proposed STLFL + DRBM method outperforms traditional methods by 33.5, 78.5, 93.5, and 98.5% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition III datasets II and by 71.3, 100, 100, and 100% for 1, 5, 10, and 15 repetitions, respectively, in BCI competition II datasets II and by 67.5 ± 4, 84.2 ± 2.5, 93.5 ± 1, 96.3 ± 1, and 98.4 ± 0.5% for rapid serial visual presentation (RSVP) based dataset in repetitions 1-5. The method has some advantages over the existing variants including its efficiency, robustness with a small number of training samples, and a high ability to create discriminative features between classes.},
}
@article {pmid37112504,
year = {2023},
author = {Mwata-Velu, T and Niyonsaba-Sebigunda, E and Avina-Cervantes, JG and Ruiz-Pinales, J and Velu-A-Gulenga, N and Alonso-Ramírez, AA},
title = {Motor Imagery Multi-Tasks Classification for BCIs Using the NVIDIA Jetson TX2 Board and the EEGNet Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {8},
pages = {},
pmid = {37112504},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Imagery, Psychotherapy ; Software ; },
abstract = {Nowadays, Brain-Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT's public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems' requirements, dealing with short processing times and reliable classification accuracy.},
}
@article {pmid37112230,
year = {2023},
author = {Al-Qazzaz, NK and Aldoori, AA and Ali, SHBM and Ahmad, SA and Mohammed, AK and Mohyee, MI},
title = {EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {8},
pages = {},
pmid = {37112230},
issn = {1424-8220},
support = {[FRGS/1/2021/TK0/UKM/01/4]//University Kebangsaan Malaysia and Ministry of Education, Malaysia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; *Stroke ; Imagery, Psychotherapy ; Electroencephalography/methods ; Algorithms ; },
abstract = {The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain-computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals' performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.},
}
@article {pmid37106582,
year = {2023},
author = {Mesin, L and Cipriani, GE and Amanzio, M},
title = {Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {4},
pages = {},
pmid = {37106582},
issn = {2306-5354},
abstract = {The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment. Although subjective cognitive decline may be part of normal aging, negative changes objectified as cognitive impairment are present in neurocognitive disorders and functional abilities are most impaired in patients with dementia. Electroencephalography-based brain-machine interfaces (BMI) are being used to assist older people in their daily activities and to improve their quality of life with neuro-rehabilitative applications. This paper provides an overview of BMI used to assist older adults. Both technical issues (detection of signals, extraction of features, classification) and application-related aspects with respect to the users' needs are considered.},
}
@article {pmid37106143,
year = {2024},
author = {Koitschev, A and Neudert, M and Lenarz, T},
title = {[Transcutaneous bone conduction implant with self-drilling screws : A new method for fixation of an active transcutaneous bone conduction implant. German version].},
journal = {HNO},
volume = {72},
number = {8},
pages = {554-560},
pmid = {37106143},
issn = {1433-0458},
mesh = {Humans ; Adult ; Male ; Female ; Middle Aged ; Adolescent ; *Bone Screws ; Treatment Outcome ; Young Adult ; *Bone Conduction ; Germany ; Prosthesis Design ; Hearing Aids ; Equipment Failure Analysis ; Translating ; Bone-Anchored Prosthesis ; },
abstract = {BACKGROUND: The active transcutaneous bone conduction implant (tBCI; BONEBRIDGE™ BCI 601; MED-EL, Innsbruck, Austria) is fixed to the skull with two self-tapping screws in predrilled screw channels. The aim of this prospective study was to evaluate the safety and effectiveness of fixation with self-drilling screws instead of the self-tapping screws, in order to simplify the surgical procedure.<br><br>MATERIALS AND METHODS: Nine patients (mean age 37&#x202f;&#xb1;&#x2009;16&#xa0;years, range 14-57&#xa0;years) were examined pre- and 12&#xa0;months postoperatively for word recognition scores (WRS) at 65&#x202f;dB SPL, sound-field (SF) thresholds, bone conduction thresholds (BC), health-related quality of life (Assessment of Quality of Life, AQOL-8D questionnaire), and adverse events (AE).<br><br>RESULTS: Due to avoidance of one surgical step, the surgical technique was simplified. Mean WRS in SF was 11.1&#x202f;&#xb1;&#x2009;22.2% (range 0-55%) pre- and 77.2&#x202f;&#xb1;&#x2009;19.9% (range 30-95%) postoperatively; mean SF threshold (pure tone audiometry, PTA4) improved from 61.2&#x202f;&#xb1;&#x2009;14.3&#x202f;dB HL (range 37.0-75.3&#x202f;dB HL) to 31.9&#x202f;&#xb1;&#x2009;7.2&#x202f;dB HL (range 22.8-45.0&#x202f;dB HL); mean BC thresholds were constant at 16.7&#x202f;&#xb1;&#x2009;6.8&#x202f;dB HL (range 6.3-27.5&#x202f;dB HL) pre- and 14.2&#x202f;&#xb1;&#x2009;6.2&#x202f;dB HL (range 5.8-23.8&#x202f;dB HL) postoperatively. AQOL-8D mean utility score increased from 0.65&#x202f;&#xb1;&#x2009;0.18 preoperatively to 0.82&#x202f;&#xb1;&#x2009;0.17 postoperatively. No device-related adverse events occurred.<br><br>CONCLUSION: Implant fixation by means of self-drilling screws was safe and effective in all nine patients. There was significant audiological benefit 12&#xa0;months after implantation.},
}
@article {pmid37105806,
year = {2023},
author = {Neumann, WJ and Horn, A and Kühn, AA},
title = {Insights and opportunities for deep brain stimulation as a brain circuit intervention.},
journal = {Trends in neurosciences},
volume = {46},
number = {6},
pages = {472-487},
doi = {10.1016/j.tins.2023.03.009},
pmid = {37105806},
issn = {1878-108X},
support = {R01 MH113929/MH/NIMH NIH HHS/United States ; R01 NS127892/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Deep Brain Stimulation ; *Parkinson Disease/therapy ; Brain ; *Connectome ; },
abstract = {Deep brain stimulation (DBS) is an effective treatment and has provided unique insights into the dynamic circuit architecture of brain disorders. This Review illustrates our current understanding of the pathophysiology of movement disorders and their underlying brain circuits that are modulated with DBS. It proposes principles of pathological network synchronization patterns like beta activity (13-35 Hz) in Parkinson's disease. We describe alterations from microscale including local synaptic activity via modulation of mesoscale hypersynchronization to changes in whole-brain macroscale connectivity. Finally, an outlook on advances for clinical innovations in next-generation neurotechnology is provided: from preoperative connectomic targeting to feedback controlled closed-loop adaptive DBS as individualized network-specific brain circuit interventions.},
}
@article {pmid37101843,
year = {2022},
author = {Portes, JP and Schmid, C and Murray, JM},
title = {Distinguishing Learning Rules with Brain Machine Interfaces.},
journal = {Advances in neural information processing systems},
volume = {35},
number = {},
pages = {25937-25950},
pmid = {37101843},
issn = {1049-5258},
support = {R00 NS114194/NS/NINDS NIH HHS/United States ; },
abstract = {Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.},
}
@article {pmid37097795,
year = {2023},
author = {Chung, M and Kim, T and Jeong, E and Chung, CK and Kim, JS and Kwon, OS and Kim, SP},
title = {Decoding Imagined Musical Pitch From Human Scalp Electroencephalograms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2154-2163},
doi = {10.1109/TNSRE.2023.3270175},
pmid = {37097795},
issn = {1558-0210},
mesh = {Humans ; *Scalp ; Electroencephalography ; *Brain-Computer Interfaces ; Cognition ; Imagination ; },
abstract = {Brain-computer interfaces (BCIs) can restore impaired cognitive functions in people with neurological disorders such as stroke. Musical ability is a cognitive function that is correlated with non-musical cognitive functions, and restoring it can enhance other cognitive functions. Pitch sense is the most relevant function to musical ability according to previous studies of amusia, and thus decoding pitch information is crucial for BCIs to be able to restore musical ability. This study evaluated the feasibility of decoding pitch imagery information directly from human electroencephalography (EEG). Twenty participants performed a random imagery task with seven musical pitches (C4-B4). We used two approaches to explore EEG features of pitch imagery: multiband spectral power at individual channels (IC) and differences between bilaterally symmetric channels (DC). The selected spectral power features revealed remarkable contrasts between left and right hemispheres, low- (< 13 Hz) and high-frequency (13 Hz) bands, and frontal and parietal areas. We classified two EEG feature sets, IC and DC, into seven pitch classes using five types of classifiers. The best classification performance for seven pitches was obtained using IC and multiclass Support Vector Machine with an average accuracy of 35.68±7.47% (max. 50%) and an information transfer rate (ITR) of 0.37±0.22 bits/sec. When grouping the pitches to vary the number of classes (K = 2-6), the ITR was similar across K and feature sets, suggesting the efficiency of DC. This study demonstrates for the first time the feasibility of decoding imagined musical pitch directly from human EEG.},
}
@article {pmid37095297,
year = {2023},
author = {Wu, Z and Tang, X and Wu, J and Huang, J and Shen, J and Hong, H},
title = {Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {9},
pages = {2391-2404},
pmid = {37095297},
issn = {1741-0444},
support = {LY20E070005//Natural Science Foundation of Zhejiang Province/ ; },
mesh = {Humans ; *Deep Learning ; Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electroencephalography/methods ; Imagination ; },
abstract = {Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the application of deep-learning techniques in independent portable brain-computer-interface (BCI) devices has not been extensively explored. In this study, we proposed a high-accuracy MI EEG decoder by incorporating spatial-attention mechanism into convolution neural network (CNN), and deployed it on fully integrated single-chip microcontroller unit (MCU). After the CNN model was trained on workstation computer using GigaDB MI datasets (52 subjects), its parameters were then extracted and converted to build deep-learning architecture interpreter on MCU. For comparison, EEG-Inception model was also trained using the same dataset, and was deployed on MCU. The results indicate that our deep-learning model can independently decode imaginary left-/right-hand motions. The mean accuracy of the proposed compact CNN reaches 96.75 ± 2.41% (8 channels: Frontocentral3 (FC3), FC4, Central1 (C1), C2, Central-Parietal1 (CP1), CP2, C3, and C4), versus 76.96 ± 19.08% of EEG-Inception (6 channels: FC3, FC4, C1, C2, CP1, and CP2). To the best of our knowledge, this is the first portable deep-learning decoder for MI EEG signals. The findings demonstrate high-accuracy deep-learning decoding of MI EEG in a portable mode, which has great implications for hand-disabled patients. Our portable system can be used for developing artificial-intelligent wearable BCI devices, as it is less computationally expensive and convenient for real-life application.},
}
@article {pmid37094717,
year = {2023},
author = {Jiang, Z and Liu, Y and Li, W and Dai, Y and Zou, L},
title = {Integration of simultaneous fMRI and EEG source localization in emotional decision problems.},
journal = {Behavioural brain research},
volume = {448},
number = {},
pages = {114445},
doi = {10.1016/j.bbr.2023.114445},
pmid = {37094717},
issn = {1872-7549},
mesh = {Male ; Humans ; Female ; *Magnetic Resonance Imaging/methods ; *Brain Mapping/methods ; Bayes Theorem ; Brain/diagnostic imaging/physiology ; Electroencephalography/methods ; },
abstract = {Simultaneous EEG-fMRI has been a powerful technique to understand the mechanism of the brain in recent years. In this paper, we develop an integrating method by integrating the EEG data into the fMRI data based on the parametric empirical Bayesian (PEB) model to improve the accuracy of the brain source location. The gambling task, a classic paradigm, is used for the emotional decision-making study in this paper. The proposed method was conducted on 21 participants, including 16 men and 5 women. Contrary to the previous method that only localizes the area widely distributed across the ventral striatum and orbitofrontal cortex, the proposed method localizes accurately at the orbital frontal cortex during the process of the brain's emotional decision-making. The activated brain regions extracted by source localization were mainly located in the prefrontal and orbitofrontal lobes; the activation of the temporal pole regions unrelated to reward processing disappeared, and the activation of the somatosensory cortex and motor cortex was significantly reduced. The log evidence shows that the integration of simultaneous fMRI and EEG method based on synchronized data evidence is 22,420, the largest value among the three methods. The integration method always takes on a larger value of log evidence and describes a better performance in analysis associated with source localization. DATA AVAILABILITY: The data used in the current study are available from the corresponding author upon on reasonable request.},
}
@article {pmid37093731,
year = {2023},
author = {Li, Y and Chen, B and Shi, Y and Yoshimura, N and Koike, Y},
title = {Correntropy-Based Logistic Regression With Automatic Relevance Determination for Robust Sparse Brain Activity Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {8},
pages = {2416-2429},
doi = {10.1109/TBME.2023.3246599},
pmid = {37093731},
issn = {1558-2531},
mesh = {Humans ; Logistic Models ; *Brain/diagnostic imaging ; Brain Mapping/methods ; Learning ; Magnetic Resonance Imaging/methods ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {OBJECTIVE: Recent studies have used sparse classifications to predict categorical variables from high-dimensional brain activity signals to expose human's mental states and intentions, selecting the relevant features automatically in the model training process. However, existing sparse classification models will likely be prone to the performance degradation which is caused by the noise inherent in the brain recordings. To address this issue, we aim to propose a new robust and sparse classification algorithm in this study.<br><br>METHODS: To this end, we introduce the correntropy learning framework into the automatic relevance determination based sparse classification model, proposing a new correntropy-based robust sparse logistic regression algorithm. To demonstrate the superior brain activity decoding performance of the proposed algorithm, we evaluate it on a synthetic dataset, an electroencephalogram (EEG) dataset, and a functional magnetic resonance imaging (fMRI) dataset.<br><br>RESULTS: The extensive experimental results confirm that not only the proposed method can achieve higher classification accuracy in a noisy and high-dimensional classification task, but also it would select those more informative features for the decoding tasks.<br><br>CONCLUSION: Integrating the correntropy learning approach with the automatic relevance determination technique will significantly improve the robustness with respect to the noise, leading to more adequate robust sparse brain decoding algorithm.<br><br>SIGNIFICANCE: It provides a more powerful approach in the real-world brain activity decoding and the brain-computer interfaces.},
}
@article {pmid37092505,
year = {2023},
author = {Mayorova, L and Kushnir, A and Sorokina, V and Pradhan, P and Radutnaya, M and Zhdanov, V and Petrova, M and Grechko, A},
title = {Rapid Effects of BCI-Based Attention Training on Functional Brain Connectivity in Poststroke Patients: A Pilot Resting-State fMRI Study.},
journal = {Neurology international},
volume = {15},
number = {2},
pages = {549-559},
pmid = {37092505},
issn = {2035-8385},
abstract = {The prevalence of stroke-induced cognitive impairment is high. Effective approaches to the treatment of these cognitive impairments after stroke remain a serious and perhaps underestimated challenge. A BCI-based task-focused training that results in repetitive recruitment of the normal motor or cognitive circuits may strengthen stroke-affected neuronal connectivity, leading to functional improvements. In the present controlled study, we attempted to evaluate the modulation of neuronal circuits under the influence of 10 days of training in a P3-based BCI speller in subacute ischemic stroke patients.},
}
@article {pmid37090803,
year = {2023},
author = {Urdaneta, ME and Kunigk, NG and Peñaloza-Aponte, JD and Currlin, S and Malone, IG and Fried, SI and Otto, KJ},
title = {Layer-dependent stability of intracortical recordings and neuronal cell loss.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1096097},
pmid = {37090803},
issn = {1662-4548},
support = {T32 HL134621/HL/NHLBI NIH HHS/United States ; },
abstract = {Intracortical recordings can be used to voluntarily control external devices via brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.},
}
@article {pmid37089477,
year = {2023},
author = {Zhang, T and Chen, WT and He, Q and Li, Y and Peng, H and Xie, J and Hu, H and Qin, C},
title = {Coping strategies following the diagnosis of a fetal anomaly: A scoping review.},
journal = {Frontiers in public health},
volume = {11},
number = {},
pages = {1055562},
pmid = {37089477},
issn = {2296-2565},
support = {P30 MH058107/MH/NIMH NIH HHS/United States ; },
mesh = {Female ; Pregnancy ; Humans ; *Adaptation, Psychological ; *Pregnant People ; Anxiety ; Qualitative Research ; },
abstract = {INTRODUCTION: Many women experience severe emotional distress (such as grief, depression, and anxiety) following a diagnosis of fetal anomaly. The ability to cope with stressful events and regulate emotions across diverse situations may play a primary role in psychological wellbeing. This study aims to present coping strategies after disclosing a fetal anomaly to pregnant women.<br><br>METHODS: This is a scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews (PRISMA-ScR). Electronic databases, including Web of Science (WOS, BCI, KJD, MEDLINE, RSCI, SCIELO), CINAHL, and EBSCO PsycARTICLES, were used to search for primary studies from the inception of each database to 2021. The keywords were determined by existing literature and included: "fetal anomaly," "fetal abnormality," "fetal anomaly," "fetal abnormality" AND "cope," "coping," "deal," "manage," "adapt[*]," "emotion[*] regulate[*]," with the use of Boolean operators AND/OR. A total of 16 articles were reviewed, followed by advancing scoping review methodology of Arksey and O'Malley's framework.<br><br>RESULTS: In this review, we identified 52 coping strategies using five questionnaires in seven quantitative studies and one mixed-method study. The relationship between coping strategies and mental distress was explored. However, the results were inconsistent and incomparable. We synthesized four coping categories from qualitative studies and presented them in an intersection.<br><br>CONCLUSION: This scoping review identified the coping strategies of women with a diagnosis of a fetal anomaly during pregnancy. The relationship between coping strategies and mental distress was uncertain and needs more exploration. We considered an appropriate measurement should be necessary for the research of coping in women diagnosed with fetal anomaly pregnancy.},
}
@article {pmid37084719,
year = {2023},
author = {Nason-Tomaszewski, SR and Mender, MJ and Kennedy, E and Lambrecht, JM and Kilgore, KL and Chiravuri, S and Ganesh Kumar, N and Kung, TA and Willsey, MS and Chestek, CA and Patil, PG},
title = {Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/accf36},
pmid = {37084719},
issn = {1741-2552},
support = {R01 GM111293/GM/NIGMS NIH HHS/United States ; R01 EB024522/EB/NIBIB NIH HHS/United States ; F31 HD098804/HD/NICHD NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Upper Extremity ; Quadriplegia ; Movement/physiology ; Haplorhini ; Primates ; },
abstract = {Objective.Brain-machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user's own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand.Approach.We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI's predictions to control FES of the monkey's finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI's predictions to control movements of virtual fingers, with no FES.Main results.In the BCFES task, the monkey improved his success rate to 83% (1.5 s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5 s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time.Significance.These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.},
}
@article {pmid37083516,
year = {2023},
author = {Fan, Z and Xi, X and Gao, Y and Wang, T and Fang, F and Houston, M and Zhang, Y and Li, L and Lu, Z},
title = {Joint Filter-Band-Combination and Multi-View CNN for Electroencephalogram Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2101-2110},
doi = {10.1109/TNSRE.2023.3269055},
pmid = {37083516},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography ; },
abstract = {Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.},
}
@article {pmid37083413,
year = {2023},
author = {Lim, H and Jeong, CH and Kang, YJ and Ku, J},
title = {Attentional State-Dependent Peripheral Electrical Stimulation During Action Observation Enhances Cortical Activations in Stroke Patients.},
journal = {Cyberpsychology, behavior and social networking},
volume = {26},
number = {6},
pages = {408-416},
doi = {10.1089/cyber.2022.0176},
pmid = {37083413},
issn = {2152-2723},
mesh = {Humans ; Electroencephalography/methods ; *Brain-Computer Interfaces ; *Stroke ; Brain ; *Stroke Rehabilitation/methods ; },
abstract = {Brain-computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8-13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.},
}
@article {pmid37082149,
year = {2023},
author = {Müller-Putz, GR and Collinger, JL and Kobler, RJ},
title = {Editorial: Towards dependable brain computer/machine interfaces for movement control.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1186423},
doi = {10.3389/fnhum.2023.1186423},
pmid = {37082149},
issn = {1662-5161},
}
@article {pmid37081753,
year = {2023},
author = {Nath, RK and Somasundaram, C},
title = {Double Fascicular Nerve Transfer Restored Nearly Normal Functional Movements in a Completely Paralyzed Upper Extremity Resulting from an ACDF Surgery: A Case Report and Review of Recent Literature.},
journal = {The American journal of case reports},
volume = {24},
number = {},
pages = {e938650},
pmid = {37081753},
issn = {1941-5923},
mesh = {Female ; Humans ; Middle Aged ; *Nerve Transfer/methods ; *Brachial Plexus/injuries ; Muscle, Skeletal ; Paralysis/surgery ; Upper Extremity/surgery ; },
abstract = {BACKGROUND Cervical spine deformities can occur because of genetic, congenital, inflammatory, degenerative, or iatrogenic causes. CASE REPORT We report a 45-year-old woman who presented to our clinic with complete paralysis of the left upper extremity 5 months after C4-C6 discectomy and fusion surgery. The electrodiagnostic and EMG reports 3 months after her previous surgery revealed left C5-C7 polyradiculopathy involving the upper trunk, lateral and posterior cords, and atrophy of the left deltoids, triceps, and biceps muscles. She underwent the following nerve transfer procedures with the senior author (RKN): The median nerve fascicles were transferred to the biceps and brachialis branches of the musculocutaneous nerve. Radial nerve triceps branches were transferred to the deltoid and teres minor branches of the axillary nerve. The patient could fully abduct her left shoulder to 170°, and the LUE functions were restored to nearly normal 17 months after the surgery. CONCLUSIONS Neurolysis combined with nerve transfer might be the most effective treatment for cervical spinal root injuries. Advances in peripheral nerve rewiring, transcranial magnetic stimulation, brain-computer interface robotic technologies, and emerging rehabilitation will undoubtedly increase the possibility of reviving the extremities in patients with central pathology by restoring the descending motor signals through the residual neural network connections.},
}
@article {pmid37081142,
year = {2023},
author = {Shen, K and Chen, O and Edmunds, JL and Piech, DK and Maharbiz, MM},
title = {Translational opportunities and challenges of invasive electrodes for neural interfaces.},
journal = {Nature biomedical engineering},
volume = {7},
number = {4},
pages = {424-442},
pmid = {37081142},
issn = {2157-846X},
mesh = {Reproducibility of Results ; Electrodes ; *Brain-Computer Interfaces ; },
abstract = {Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.},
}
@article {pmid37080420,
year = {2023},
author = {Shang, Q and Chen, J and Fu, H and Wang, C and Pei, G and Jin, J},
title = {"Guess You Like It" - How personalized recommendation timing and product type influence consumers' acceptance: An ERP study.},
journal = {Neuroscience letters},
volume = {807},
number = {},
pages = {137261},
doi = {10.1016/j.neulet.2023.137261},
pmid = {37080420},
issn = {1872-7972},
mesh = {*Evoked Potentials ; *Mental Processes ; Consumer Behavior ; },
abstract = {Personalized recommendation has been increasingly used in online shopping environment, and improving the effectiveness of personalized recommendation is an important issue. On the basis of two-stage decision theory and preference inconsistency theory, our study adopted the neuroscientific methodology of event-related potential to investigate the decision-making process and psychological mechanism of consumers for personalized recommendation under different recommendation timings (browsing and decision stages) and recommended product types (similar and related). Behavioral results showed that consumers' acceptance of similar product recommendations was higher than that of related product recommendations during the browsing stage, whereas no difference was observed in consumers' acceptance of the two product types during the decision stage. More importantly, neurophysiology results provided underlying psychological mechanism for exploring consumers' decision-making process for personalized recommendations. Consumers' psychological mechanism of the personalized recommendations might be divided into two processes, the early automatic cognitive process indicated by the N2 component, and the late advanced cognitive process indicated by the P3 component. We suggested that N2 reflects the perceptual mismatch between the recommended products and the target products, and P3 reflects the attention capture during categorization evaluation of the recommended product and the target product. These findings have important theoretical and practical significance for the deeper understanding of consumers' decision-making process and psychological mechanism in personalized recommendation, as well as improving the effectiveness of personalized recommendation.},
}
@article {pmid37080210,
year = {2023},
author = {Zhang, Z and Constandinou, TG},
title = {Firing-rate-modulated spike detection and neural decoding co-design.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/accece},
pmid = {37080210},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Neurons/physiology ; Computers ; Algorithms ; Signal Processing, Computer-Assisted ; Action Potentials/physiology ; },
abstract = {Objective. Translational efforts on spike-signal-based implantable brain-machine interfaces (BMIs) are increasingly aiming to minimise bandwidth while maintaining decoding performance. Developing these BMIs requires advances in neuroscience and electronic technology, as well as using low-complexity spike detection algorithms and high-performance machine learning models. While some state-of-the-art BMI systems jointly design spike detection algorithms and machine learning models, it remains unclear how the detection performance affects decoding.Approach. We propose the co-design of the neural decoder with an ultra-low complexity spike detection algorithm. The detection algorithm is designed to attain a target firing rate, which the decoder uses to modulate the input features preserving statistical invariance in long term (over several months).Main results. We demonstrate a multiplication-free fixed-point spike detection algorithm with an average detection accuracy of 97% across different noise levels on a synthetic dataset and the lowest hardware complexity among studies we have seen. By co-designing the system to incorporate statistically invariant features, we observe significantly improved long-term stability, with decoding accuracy degrading by less than 10% after 80 days of operation. Our analysis also reveals a nonlinear relationship between spike detection and decoding performance. Increasing the detection sensitivity improves decoding accuracy and long-term stability, which means the activity of more neurons is beneficial despite the detection of more noise. Reducing the spike detection sensitivity still provides acceptable decoding accuracy whilst reducing the bandwidth by at least 30%.Significance. Our findings regarding the relationship between spike detection and decoding performance can provide guidance on setting the threshold for spike detection rather than relying on training or trial-and-error. The trade-off between data bandwidth and decoding performance can be effectively managed using appropriate spike detection settings. We demonstrate improved decoding performance by maintaining statistical invariance of input features. We believe this approach can motivate further research focused on improving decoding performance through the manipulation of data itself (based on a hypothesis) rather than using more complex decoding models.},
}
@article {pmid37080005,
year = {2023},
author = {She, Q and Shi, X and Fang, F and Ma, Y and Zhang, Y},
title = {Cross-subject EEG emotion recognition using multi-source domain manifold feature selection.},
journal = {Computers in biology and medicine},
volume = {159},
number = {},
pages = {106860},
doi = {10.1016/j.compbiomed.2023.106860},
pmid = {37080005},
issn = {1879-0534},
mesh = {*Emotions ; Algorithms ; Electroencephalography/methods ; Learning ; *Brain-Computer Interfaces ; },
abstract = {Recent researches on emotion recognition suggests that domain adaptation, a form of transfer learning, has the capability to solve the cross-subject problem in Affective brain-computer interface (aBCI) field. However, traditional domain adaptation methods perform single to single domain transfer or simply merge different source domains into a larger domain to realize the transfer of knowledge, resulting in negative transfer. In this study, a multi-source transfer learning framework was proposed to promote the performance of multi-source electroencephalogram (EEG) emotion recognition. The method first used the data distribution similarity ranking (DDSA) method to select the appropriate source domain for each target domain off-line, and reduced data drift between domains through manifold feature mapping on Grassmann manifold. Meanwhile, the minimum redundancy maximum correlation algorithm (mRMR) was employed to select more representative manifold features and minimized the conditional distribution and marginal distribution of the manifold features, and then learned the domain-invariant classifier by summarizing structural risk minimization (SRM). Finally, the weighted fusion criterion was applied to further improve recognition performance. We compared our method with several state-of-the-art domain adaptation techniques using the SEED and DEAP dataset. Results showed that, compared with the conventional MEDA algorithm, the recognition accuracy of our proposed algorithm on SEED and DEAP dataset were improved by 6.74% and 5.34%, respectively. Besides, compared with TCA, JDA, and other state-of-the-art algorithms, the performance of our proposed method was also improved with the best average accuracy of 86.59% on SEED and 64.40% on DEAP. Our results demonstrated that the proposed multi-source transfer learning framework is more effective and feasible than other state-of-the-art methods in recognizing different emotions by solving the cross-subject problem.},
}
@article {pmid37079422,
year = {2023},
author = {Zhou, J and Duan, Y and Zou, Y and Chang, YC and Wang, YK and Lin, CT},
title = {Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2140-2153},
doi = {10.1109/TNSRE.2023.3268751},
pmid = {37079422},
issn = {1558-0210},
mesh = {Humans ; *Speech ; Imagination ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electroencephalography/methods ; Algorithms ; },
abstract = {Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data. Inspired by the waveform characteristics and processing methods shared between EEG and speech signals, we propose Speech2EEG, a novel EEG recognition method that leverages pretrained speech features to improve the accuracy of EEG recognition. Specifically, a pretrained speech processing model is adapted to the EEG domain to extract multichannel temporal embeddings. Then, several aggregation methods, including the weighted average, channelwise aggregation, and channel-and-depthwise aggregation, are implemented to exploit and integrate the multichannel temporal embeddings. Finally, a classification network is used to predict EEG categories based on the integrated features. Our work is the first to explore the use of pretrained speech models for EEG signal analysis as well as the effective ways to integrate the multichannel temporal embeddings from the EEG signal. Extensive experimental results suggest that the proposed Speech2EEG method achieves state-of-the-art performance on two challenging motor imagery (MI) datasets, the BCI IV-2a and BCI IV-2b datasets, with accuracies of 89.5% and 84.07% , respectively. Visualization analysis of the multichannel temporal embeddings show that the Speech2EEG architecture can capture useful patterns related to MI categories, which can provide a novel solution for subsequent research under the constraints of a limited dataset scale.},
}
@article {pmid37079420,
year = {2023},
author = {Liang, S and Kuang, S and Wang, D and Yuan, Z and Zhang, H and Sun, L},
title = {An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2120-2131},
doi = {10.1109/TNSRE.2023.3268979},
pmid = {37079420},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods ; Imagination ; },
abstract = {While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model. The framework learns the latent feature distributions of real data and uses Gaussian noise to synthesize artificial data. The experimental evaluation reveals that the proposed method effectively preserves the time-frequency-spatial features of the real data and enhances the classification performance of the model using limited training data and is easy to implement, which outperforms the common data augmentation methods. The average accuracy of the decoding model designed in this work is improved by (4.72±0.98)% on the BCI competition IV 2a dataset. Furthermore, the framework is applicable to other deep learning-based decoders. The finding provides a novel way to generate artificial signals for enhancing classification performance when there are insufficient data, thus reducing data acquisition consuming in the BCI field.},
}
@article {pmid37077575,
year = {2023},
author = {Chen, L and Lu, X and Jin, Q and Gao, Z and Wang, Y},
title = {Sensory innervation of the lumbar 5/6 intervertebral disk in mice.},
journal = {Frontiers in neurology},
volume = {14},
number = {},
pages = {1084209},
pmid = {37077575},
issn = {1664-2295},
abstract = {INTRODUCTION: Over the years, most back pain-related biological studies focused on the pathogenesis of disk degeneration. It is known that nerve distributions at the outer layer of the annulus fibrosus (AF) may be an important contributor to back pain symptoms. However, the types and origins of sensory nerve terminals in the mouse lumbar disks have not been widely studied. Using disk microinjection and nerve retrograde tracing methods, the current study aimed to characterize the nerve types and neuropathway of the lumbar 5/6 (L5/6) disk in mice.<br><br>METHODS: Using an anterior peritoneal approach, the L5/6 disk of adult C57BL/6 mice (males, 8-12 weeks) disk microinjection was performed. Fluorogold (FG) was injected into the L5/6 disk using the Hamilton syringe with a homemade glass needle driven by a pressure microinjector. The lumbar spine and bilateral thoracic 13 (Th13) to L6 DRGs were harvested at 10 days after injection. The number of FG[+] neurons among different levels was counted and analyzed. Different nerve markers, including anti-neurofilament 160/200 (NF160/200), anti-calcitonin gene-related peptide (CGRP), anti-parvalbumin (PV), and anti-tyrosine hydroxylase (TH), were used to identify different types of nerve terminals in AF and their origins in DRG neurons.<br><br>RESULTS: There were at least three types of nerve terminals at the outer layer of L5/6 AF in mice, including NF160/200[+] (indicating A&#x3b2; fibers), CGRP[+] (A&#x3b4; and C fibers), and PV[+] (proprioceptive fibers). No TH[+] fibers (sympathetic nerve fibers and some C-low threshold mechanoreceptors) were noticed in either. Using retrograde tracing methods, we found that nerve terminals in the L5/6 disk were multi-segmentally from Th13-L6 DRGs, with L1 and L5 predominately. An immunofluorescence analysis revealed that FG[+] neurons in DRGs were co-localized with NF160/200, CGRP, and PV, but not TH.<br><br>CONCLUSION: Intervertebral disks were innervated by multiple types of nerve fibers in mice, including A&#x3b2;, A&#x3b4;, C, and proprioceptive fibers. No sympathetic nerve fibers were found in AF. The nerve network of the L5/6 disk in mice was multi-segmentally innervated by the Th13-L6 DRGs (mainly L1 and L5 DRGs). Our results may serve as a reference for preclinical studies of discogenic pain in mice.},
}
@article {pmid37075914,
year = {2023},
author = {Ma, J and Yang, B and Qiu, W and Zhang, J and Yan, L and Wang, W},
title = {Recognizable rehabilitation movements of multiple unilateral upper limb: An fMRI study of motor execution and motor imagery.},
journal = {Journal of neuroscience methods},
volume = {392},
number = {},
pages = {109861},
doi = {10.1016/j.jneumeth.2023.109861},
pmid = {37075914},
issn = {1872-678X},
mesh = {Humans ; *Magnetic Resonance Imaging ; *Imagination/physiology ; Movement/physiology ; Upper Extremity ; Brain/physiology ; },
abstract = {BACKGROUND: This paper presents a study investigating the recognizability of multiple unilateral upper limb movements in stroke rehabilitation.<br><br>METHODS: A functional magnetic experiment is employed to study motor execution (ME) and motor imagery (MI) of four movements for the unilateral upper limb: hand-grasping, hand-handling, arm-reaching, and wrist-twisting. The functional magnetic resonance imaging (fMRI) images of ME and MI tasks are statistically analyzed to delineate the region of interest (ROI). Then parameter estimation associated with ROIs for each ME and MI task are evaluated, where differences in ROIs for different movements are compared using analysis of covariance (ANCOVA).<br><br>RESULTS: All movements of ME and MI tasks activate motor areas of the brain, and there are significant differences (p&#xa0;<&#xa0;0.05) in ROIs evoked by different movements. The activation area is larger when executing the hand-grasping task instead of the others.<br><br>CONCLUSION: The four movements we propose can be adopted as MI tasks, especially for stroke rehabilitation, since they are highly recognizable and capable of activating more brain areas during MI and ME.},
}
@article {pmid37074913,
year = {2023},
author = {Yang, GM and Xu, L and Wang, RM and Tao, X and Zheng, ZW and Chang, S and Ma, D and Zhao, C and Dong, Y and Wu, S and Guo, J and Wu, ZY},
title = {Structures of the human Wilson disease copper transporter ATP7B.},
journal = {Cell reports},
volume = {42},
number = {5},
pages = {112417},
doi = {10.1016/j.celrep.2023.112417},
pmid = {37074913},
issn = {2211-1247},
mesh = {Humans ; *Cation Transport Proteins/genetics/metabolism ; Copper/metabolism ; Copper Transport Proteins ; Copper-Transporting ATPases/genetics/metabolism ; Cryoelectron Microscopy ; *Hepatolenticular Degeneration/metabolism ; },
abstract = {The P-type ATPase ATP7B exports cytosolic copper and plays an essential role in the regulation of cellular copper homeostasis. Mutants of ATP7B cause Wilson disease (WD), an autosomal recessive disorder of copper metabolism. Here, we present cryoelectron microscopy (cryo-EM) structures of human ATP7B in the E1 state in the apo, the putative copper-bound, and the putative cisplatin-bound forms. In ATP7B, the N-terminal sixth metal-binding domain (MBD6) binds at the cytosolic copper entry site of the transmembrane domain (TMD), facilitating the delivery of copper from the MBD6 to the TMD. The sulfur-containing residues in the TMD of ATP7B mark the copper transport pathway. By comparing structures of the E1 state human ATP7B and E2-Pi state frog ATP7B, we propose the ATP-driving copper transport model of ATP7B. These structures not only advance our understanding of the mechanisms of ATP7B-mediated copper export but can also guide the development of therapeutics for the treatment of WD.},
}
@article {pmid37074883,
year = {2023},
author = {Zhao, S and Yang, J and Wang, J and Fang, C and Liu, T and Zhang, S and Sawan, M},
title = {A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {17},
number = {3},
pages = {598-609},
doi = {10.1109/TBCAS.2023.3268502},
pmid = {37074883},
issn = {1940-9990},
mesh = {*Neural Networks, Computer ; *Brain-Computer Interfaces ; Humans ; },
abstract = {Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency. 1) Hybrid neural network design: The processor supports artificial neural network (ANN)- and spiking neural network (SNN)-based neuromorphic processing where ANN is used to support the processing of ExG signals and SNN is used for handling neural spike signals. 2) Event-driven processing: The processor can perform always-on binary neural network (BNN)-based event detection with low-energy consumption, and it only switches to the high-accuracy convolutional neural network (CNN)-based recognition mode when events are detected. 3) Reconfigurable architecture: By exploiting the computational similarity of different neural networks, the processor supports critical BNN, CNN, and SNN operations with the same processing elements, achieving significant area reduction and energy efficiency improvement over those of a naive implementation. It achieves 90.05% accuracy and 4.38 uJ/class in a center-out reaching task with an SNN and 99.4% sensitivity, 98.6% specificity, and 1.93 uJ/class in an EEG-based seizure prediction task with dual neural network-based event-driven processing. Moreover, it achieves a classification accuracy of 99.92%, 99.38%, and 86.39% and energy consumption of 1.73, 0.99, and 1.31 uJ/class for EEG-based epileptic seizure detection, ECG-based arrhythmia detection, and EMG-based gesture recognition, respectively.},
}
@article {pmid37071937,
year = {2023},
author = {Macías-Macías, JM and Ramírez-Quintana, JA and Chacón-Murguía, MI and Torres-García, AA and Corral-Martínez, LF},
title = {Interpretation of a deep analysis of speech imagery features extracted by a capsule neural network.},
journal = {Computers in biology and medicine},
volume = {159},
number = {},
pages = {106909},
doi = {10.1016/j.compbiomed.2023.106909},
pmid = {37071937},
issn = {1879-0534},
mesh = {*Speech/physiology ; Capsules ; Electroencephalography/methods ; Neural Networks, Computer ; Brain/physiology ; *Brain-Computer Interfaces ; Imagination/physiology ; Algorithms ; },
abstract = {Speech imagery has been successfully employed in developing Brain-Computer Interfaces because it is a novel mental strategy that generates brain activity more intuitively than evoked potentials or motor imagery. There are many methods to analyze speech imagery signals, but those based on deep neural networks achieve the best results. However, more research is necessary to understand the properties and features that describe imagined phonemes and words. In this paper, we analyze the statistical properties of speech imagery EEG signals from the KaraOne dataset to design a method that classifies imagined phonemes and words. With this analysis, we propose a Capsule Neural Network that categorizes speech imagery patterns into bilabial, nasal, consonant-vocal, and vowels/iy/ and/uw/. The method is called Capsules for Speech Imagery Analysis (CapsK-SI). The input of CapsK-SI is a set of statistical features of EEG speech imagery signals. The architecture of the Capsule Neural Network is composed of a convolution layer, a primary capsule layer, and a class capsule layer. The average accuracy reached is 90.88%±7 for bilabial, 90.15%±8 for nasal, 94.02%±6 for consonant-vowel, 89.70%±8 for word-phoneme, 94.33%± for/iy/ vowel and, 94.21%±3 for/uw/ vowel detection. Finally, with the activity vectors of the CapsK-SI capsules, we generated brain maps to represent brain activity in the production of bilabial, nasal, and consonant-vocal signals.},
}
@article {pmid37067974,
year = {2023},
author = {Yan, W and He, B and Zhao, J and Wu, Y and Du, C and Xu, G},
title = {Frequency Domain Filtering Method for SSVEP-EEG Preprocessing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {2079-2089},
doi = {10.1109/TNSRE.2023.3266488},
pmid = {37067974},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Recognition, Psychology ; Photic Stimulation/methods ; },
abstract = {Steady-state visual evoked potential (SSVEP) signal collected from the scalp typically contains other types of electric signals, and it is important to remove these noise components from the actual signal by application of a pre-processing step for accurate analysis. High-pass or bandpass filtering of the SSVEP signal in the time domain is the most common pre-processing method. Because frequency is the most important feature information contained in the SSVEP signal, a technique for frequency-domain filtering of SSVEP was proposed here. In this method, the time-domain signal is extended to multi-dimensional signal by empirical mode decomposition (EMD), where each dimension represents a intrinsic mode function (IMF). The multi-dimensional signal is transformed to a frequency-domain signal by 2-D Fourier transform, the Gaussian high-pass filter function is constructed to perform high-pass filtering, and then the filtered signal is transformed to time domain by 2-D inverse Fourier transform. Finally, the filtered multi-dimensional intrinsic mode function is superimposed and averaged as the frequency-domain filtered signal. Compared with the time-domain filtering method, the experimental results revealed that frequency-domain filtering method effectively removed the baseline drift in signal and effectively suppressed the low-frequency interference component. This method was tested using data from public datasets and the results show that the proposed frequency-domain filtering method can significantly improve the feature recognition performance of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA) methods. Thus, the results suggest that the application of frequency-domain filtering in the pre-processing stage allows improved noise removal. The proposed method extends SSVEP signal filtering from time-domain to frequency-domain, and the results suggest that this analysis tool significantly promotes the practical application of SSVEP systems.},
}
@article {pmid37067278,
year = {2023},
author = {Fallegger, F and Trouillet, A and Lacour, SP},
title = {Subdural Soft Electrocorticography (ECoG) Array Implantation and Long-Term Cortical Recording in Minipigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {193},
pages = {},
doi = {10.3791/64997},
pmid = {37067278},
issn = {1940-087X},
mesh = {Animals ; Humans ; Swine ; *Electrocorticography/methods ; Swine, Miniature ; *Brain/physiology ; Electrodes ; Evoked Potentials ; Electrodes, Implanted ; },
abstract = {Neurological impairments and diseases can be diagnosed or treated using electrocorticography (ECoG) arrays. In drug-resistant epilepsy, these help delineate the epileptic region to resect. In long-term applications such as brain-computer interfaces, these epicortical electrodes are used to record the movement intention of the brain, to control the robotic limbs of paralyzed patients. However, current stiff electrode grids do not answer the need for high-resolution brain recordings and long-term biointegration. Recently, conformable electrode arrays have been proposed to achieve long-term implant stability with high performance. However, preclinical studies for these new implant technologies are needed to validate their long-term functionality and safety profile for their translation to human patients. In this context, porcine models are routinely employed in developing medical devices due to their large organ sizes and easy animal handling. However, only a few brain applications are described in the literature, mostly due to surgery limitations and integration of the implant system on a living animal. Here, we report the method for long-term implantation (6 months) and evaluation of soft ECoG arrays in the minipig model. The study first presents the implant system, consisting of a soft microfabricated electrode array integrated with a magnetic resonance imaging (MRI)-compatible polymeric transdermal port that houses instrumentation connectors for electrophysiology recordings. Then, the study describes the surgical procedure, from subdural implantation to animal recovery. We focus on the auditory cortex as an example target area where evoked potentials are induced by acoustic stimulation. We finally describe a data acquisition sequence that includes MRI of the whole brain, implant electrochemical characterization, intraoperative and freely moving electrophysiology, and immunohistochemistry staining of the extracted brains. This model can be used to investigate the safety and function of novel design of cortical prostheses; mandatory preclinical study to envision translation to human patients.},
}
@article {pmid37067101,
year = {2023},
author = {Zhang, J and Huang, Y and Jiang, C and Xu, Y and Rao, H and Xu, H},
title = {Dynamic brain responses to Russian word acquisition among Chinese adult learners: An event-related potential study.},
journal = {Human brain mapping},
volume = {44},
number = {9},
pages = {3717-3729},
pmid = {37067101},
issn = {1097-0193},
mesh = {Adult ; Female ; Humans ; Male ; Brain/diagnostic imaging/physiology ; East Asian People ; *Electroencephalography ; *Evoked Potentials/physiology ; *Language ; },
abstract = {Human learners are capable to acquire foreign language vocabulary at an impressive speed even in adulthood. Previous studies have examined the neural mechanisms underlying rapid acquisition of Latin-alphabet vocabulary and revealed dynamic changes in several event-related potential (ERP) components during novel word learning. However, scant attention has been paid to the acquisition of Russian words. The present study used ERP and examined dynamic brain responses to rapid Russian word acquisition in 53 native Chinese speakers with no prior knowledge of Russian language. Behavioral data showed robust individual differences in Russian word acquisition, with most participants being able to rapidly learn a subset of novel Russian words in a few exposures. ERP results revealed significant learning effects in the P200, N400, and P600 amplitudes. Moreover, P600 amplitude changes predicted participants' word acquisition after learning. These findings demonstrated dynamic brain responses to rapid Russian word learning and suggested that the P600 component may serve as a bio-marker for individual learning ability in Russian word acquisition.},
}
@article {pmid37065928,
year = {2023},
author = {Qi, Y and Sun, Y and Liu, Q and Zhang, Q and Cai, H and Zheng, Q},
title = {Editorial: The intersection of artificial intelligence and brain for high-performance neuroprosthesis and cyborg systems.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1133002},
pmid = {37065928},
issn = {1662-4548},
}
@article {pmid37064914,
year = {2023},
author = {Arif, S and Munawar, S and Ali, H},
title = {Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.},
journal = {Frontiers in physiology},
volume = {14},
number = {},
pages = {1153268},
pmid = {37064914},
issn = {1664-042X},
abstract = {Introduction: Drowsy driving is a significant factor causing dire road crashes and casualties around the world. Detecting it earlier and more effectively can significantly reduce the lethal aftereffects and increase road safety. As physiological conditions originate from the human brain, so neurophysiological signatures in drowsy and alert states may be investigated for this purpose. In this preface, A passive brain-computer interface (pBCI) scheme using multichannel electroencephalography (EEG) brain signals is developed for spatially localized and accurate detection of human drowsiness during driving tasks. Methods: This pBCI modality acquired electrophysiological patterns of 12 healthy subjects from the prefrontal (PFC), frontal (FC), and occipital cortices (OC) of the brain. Neurological states are recorded using six EEG channels spread over the right and left hemispheres in the PFC, FC, and OC of the sleep-deprived subjects during simulated driving tasks. In post-hoc analysis, spectral signatures of the δ, θ, α, and β rhythms are extracted in terms of spectral band powers and their ratios with a temporal correlation over the complete span of the experiment. Minimum redundancy maximum relevance, Chi-square, and ReliefF feature selection methods are used and aggregated with a Z-score based approach for global feature ranking. The extracted drowsiness attributes are classified using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machines, k-nearest neighbors, and ensemble classifiers. The binary classification results are reported with confusion matrix-based performance assessment metrics. Results: In inter-classifier comparison, the optimized ensemble model achieved the best results of drowsiness classification with 85.6% accuracy and precision, 89.7% recall, 87.6% F1-score, 80% specificity, 70.3% Matthews correlation coefficient, 70.2% Cohen's kappa score, and 91% area under the receiver operating characteristic curve with 76-ms execution time. In inter-channel comparison, the best results were obtained at the F8 electrode position in the right FC of the brain. The significance of all the results was validated with a p-value of less than 0.05 using statistical hypothesis testing methods. Conclusions: The proposed scheme has achieved better results for driving drowsiness detection with the accomplishment of multiple objectives. The predictor importance approach has reduced the feature extraction cost and computational complexity is minimized with the use of conventional machine learning classifiers resulting in low-cost hardware and software requirements. The channel selection approach has spatially localized the most promising brain region for drowsiness detection with only a single EEG channel (F8) which reduces the physical intrusiveness in normal driving operation. This pBCI scheme has a good potential for practical applications requiring earlier, more accurate, and less disruptive drowsiness detection using the spectral information of EEG biosignals.},
}
@article {pmid37063105,
year = {2023},
author = {Gwon, D and Won, K and Song, M and Nam, CS and Jun, SC and Ahn, M},
title = {Review of public motor imagery and execution datasets in brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1134869},
pmid = {37063105},
issn = {1662-5161},
abstract = {The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.},
}
@article {pmid37062374,
year = {2023},
author = {Kieffaber, PD and Osborne, J and Norton, E and Hilimire, M},
title = {Deconstructing the functional significance of the error-related negativity (ERN) and midline frontal theta oscillations using stepwise time-locking and single-trial response dynamics.},
journal = {NeuroImage},
volume = {274},
number = {},
pages = {120113},
doi = {10.1016/j.neuroimage.2023.120113},
pmid = {37062374},
issn = {1095-9572},
mesh = {Humans ; Reaction Time/physiology ; *Electroencephalography ; *Brain ; Evoked Potentials/physiology ; Psychomotor Performance/physiology ; },
abstract = {Error-related electroencephalographic potentials have been used for decades to develop theoretical models of response monitoring processes, study altered cognitive functioning in clinical populations, and more recently, to improve the performance of brain-computer interfaces. However, the vast majority of this research relies on discrete behavioral responses that confound error detection, response cancelation, error correction, and post-error cognitive and affective processes. By contrast, the present study demonstrates a novel, complementary method for isolating the functional correlates of error-related electroencephalographic responses using single-trial kinematic analyses of cursor trajectories and a stepwise time-locking analysis. The results reveal that the latency of the ERN, Pe, and medial-frontal theta oscillations are all strongly positively correlated with the latency at which an initiated error response is canceled, as indicated by the peak deceleration of the initiated movement prior to a corrective response. Results are discussed with respect to current theoretical models of error-related brain potentials and potential relevance to clinical applications.},
}
@article {pmid37062178,
year = {2023},
author = {Li, J and Wang, F and Huang, H and Qi, F and Pan, J},
title = {A novel semi-supervised meta learning method for subject-transfer brain-computer interface.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {163},
number = {},
pages = {195-204},
doi = {10.1016/j.neunet.2023.03.039},
pmid = {37062178},
issn = {1879-2782},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Supervised Machine Learning ; Brain ; Algorithms ; },
abstract = {The brain-computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.},
}
@article {pmid37061673,
year = {2023},
author = {Fu, J and Jiang, Z and Shu, X and Chen, S and Jia, J},
title = {Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study.},
journal = {Biomedical engineering online},
volume = {22},
number = {1},
pages = {36},
pmid = {37061673},
issn = {1475-925X},
support = {91948302//National Natural Integration Project/ ; 91948302//National Natural Integration Project/ ; 91948302//National Natural Integration Project/ ; 91948302//National Natural Integration Project/ ; 91948302//National Natural Integration Project/ ; 82021002//National Natural Innovation Research Group Project/ ; 82021002//National Natural Innovation Research Group Project/ ; 82021002//National Natural Innovation Research Group Project/ ; 82021002//National Natural Innovation Research Group Project/ ; 22YF1404200//Shanghai Sailing Program/ ; 22YF1404200//Shanghai Sailing Program/ ; 22YF1404200//Shanghai Sailing Program/ ; 22YF1404200//Shanghai Sailing Program/ ; },
mesh = {Humans ; Cross-Sectional Studies ; *Brain-Computer Interfaces ; Recovery of Function/physiology ; *Stroke ; Upper Extremity ; *Stroke Rehabilitation ; Hand Strength ; },
abstract = {BACKGROUND AND AIMS: Brain-computer interfaces (BCIs) are emerging as a promising tool for upper limb recovery after stroke, and motor tasks are an essential part of BCIs for patient training and control of rehabilitative/assistive BCIs. However, the correlation between brain activation with different levels of motor impairment and motor tasks in BCIs is still not so clear. Thus, we aim to compare the brain activation of different levels of motor impairment in performing the hand grasping and opening tasks in BCIs.<br><br>METHODS: We instructed stroke patients to perform motor attempts (MA) to grasp and open the affected hand for 30 trials, respectively. During this period, they underwent EEG acquisition and BCIs accuracy recordings. They also received detailed history records and behavioral scale assessments (the Fugl-Meyer assessment of upper limb, FMA-UE).<br><br>RESULTS: The FMA-UE was negatively correlated with the event-related desynchronization (ERD) of the affected hemisphere during open MA (R&#x2009;=&#x2009;-&#xa0;0.423, P&#x2009;=&#x2009;0.009) but not with grasp MA (R&#x2009;=&#x2009;-&#xa0;0.058, P&#x2009;=&#x2009;0.733). Then we divided the stroke patients into group 1 (Brunnstrom recovery stages between I to II, n&#x2009;=&#x2009;19) and group 2 (Brunnstrom recovery stages between III to VI, n&#x2009;=&#x2009;23). No difference during the grasping task (t&#x2009;=&#x2009;0.091, P&#x2009;=&#x2009;0.928), but a significant difference during the open task (t&#x2009;=&#x2009;2.156, P&#x2009;=&#x2009;0.037) was found between the two groups on the affected hemisphere. No significant difference was found in the unaffected hemisphere.<br><br>CONCLUSIONS: The study indicated that brain activation is positively correlated with the hand function of stroke in open-hand tasks. In the grasping task, the patients in the different groups have a similar brain response, while in the open task, mildly injured patients have more brain activation in open the hand than the poor hand function patients.},
}
@article {pmid37061103,
year = {2023},
author = {Ma, Z and Li, W and Zhuang, L and Wen, T and Wang, P and Yu, H and Liu, Y and Yu, Y},
title = {TMEM59 ablation leads to loss of olfactory sensory neurons and impairs olfactory functions via interaction with inflammation.},
journal = {Brain, behavior, and immunity},
volume = {111},
number = {},
pages = {151-168},
doi = {10.1016/j.bbi.2023.04.005},
pmid = {37061103},
issn = {1090-2139},
mesh = {Animals ; *Olfactory Receptor Neurons ; Olfactory Mucosa/metabolism ; Inflammation/metabolism ; Neurogenesis ; NF-kappa B/metabolism ; Mammals ; },
abstract = {The olfactory epithelium undergoes constant neurogenesis throughout life in mammals. Several factors including key signaling pathways and inflammatory microenvironment regulate the maintenance and regeneration of the olfactory epithelium. In this study, we identify TMEM59 (also known as DCF1) as a critical regulator to the epithelial maintenance and regeneration. Single-cell RNA-Seq data show downregulation of TMEM59 in multiple epithelial cell lineages with aging. Ablation of TMEM59 leads to apparent alteration at the transcriptional level, including genes associated with olfactory transduction and inflammatory/immune response. These differentially expressed genes are key components belonging to several signaling pathways, such as NF-κB, chemokine, etc. TMEM59 deletion impairs olfactory functions, attenuates proliferation, causes loss of both mature and immature olfactory sensory neurons, and promotes infiltration of inflammatory cells, macrophages, microglia cells and neutrophils into the olfactory epithelium and lamina propria. TMEM59 deletion deteriorates regeneration of the olfactory epithelium after injury, with significant reduction in the number of proliferative cells, immature and mature sensory neurons, accompanied by the increasing number of inflammatory cells and macrophages. Anti-inflammation by dexamethasone recovers neuronal generation and olfactory functions in the TMEM59-KO animals, suggesting the correlation between TMEM59 and inflammation in regulating the epithelial maintenance. Collectively, TMEM59 regulates olfactory functions, as well as neuronal generation in the olfactory epithelium via interaction with inflammation, suggesting a potential role in therapy against olfactory dysfunction associated with inflamm-aging.},
}
@article {pmid37059125,
year = {2023},
author = {Hosseini, SM and Aminitabar, AH and Shalchyan, V},
title = {Investigating the application of graph theory features in hand movement directions decoding using EEG signals.},
journal = {Neuroscience research},
volume = {194},
number = {},
pages = {24-35},
doi = {10.1016/j.neures.2023.04.002},
pmid = {37059125},
issn = {1872-8111},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Hand ; Brain ; Movement ; Imagination ; },
abstract = {In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude-squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.},
}
@article {pmid37056962,
year = {2023},
author = {Long, T and Wan, M and Jian, W and Dai, H and Nie, W and Xu, J},
title = {Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1143027},
pmid = {37056962},
issn = {1662-5161},
abstract = {INTRODUCTION: The volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system.<br><br>METHODS: Dataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction.<br><br>RESULTS: Experiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols.<br><br>DISCUSSION: Our work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.},
}
@article {pmid37056480,
year = {2023},
author = {Jain, P and Conte, MM and Voss, HU and Victor, JD and Schiff, ND},
title = {Low-level language processing in brain-injured patients.},
journal = {Brain communications},
volume = {5},
number = {2},
pages = {fcad094},
pmid = {37056480},
issn = {2632-1297},
abstract = {Assessing cognitive function-especially language processing-in severely brain-injured patients is critical for prognostication, care, and development of communication devices (e.g. brain-computer interfaces). In patients with diminished motor function, language processing has been probed using EEG measures of command-following in motor imagery tasks. While such tests eliminate the need for motor response, they require sustained attention. However, passive listening tasks, with an EEG response measure can reduce both motor and attentional demands. These considerations motivated the development of two assays of low-level language processing-identification of differential phoneme-class responses and tracking of the natural speech envelope. This cross-sectional study looks at a cohort of 26 severely brain-injured patient subjects and 10 healthy controls. Patients' level of function was assessed via the coma recovery scale-revised at the bedside. Patients were also tested for command-following via EEG and/or MRI assays of motor imagery. For the present investigation, EEG was recorded while presenting a 148 s audio clip of Alice in Wonderland. Time-locked EEG responses to phoneme classes were extracted and compared to determine a differential phoneme-class response. Tracking of the natural speech envelope was assessed from the same recordings by cross-correlating the EEG response with the speech envelope. In healthy controls, the dynamics of the two measures were temporally similar but spatially different: a central parieto-occipital component of differential phoneme-class response was absent in the natural speech envelope response. The differential phoneme-class response was present in all patient subjects, including the six classified as vegetative state/unresponsive wakefulness syndrome by behavioural assessment. However, patient subjects with evidence of language processing either by behavioural assessment or motor imagery tests had an early bilateral response in the first 50 ms that was lacking in patient subjects without any evidence of language processing. The natural speech envelope tracking response was also present in all patient subjects and responses in the first 100 ms distinguished patient subjects with evidence of language processing. Specifically, patient subjects with evidence of language processing had a more global response in the first 100 ms whereas those without evidence of language processing had a frontopolar response in that period. In summary, we developed two passive EEG-based methods to probe low-level language processing in severely brain-injured patients. In our cohort, both assays showed a difference between patient subjects with evidence of command-following and those with no evidence of command-following: a more prominent early bilateral response component.},
}
@article {pmid37051328,
year = {2023},
author = {Guo, R and Lin, Y and Luo, X and Gao, X and Zhang, S},
title = {A robotic arm control system with simultaneous and sequential modes combining eye-tracking with steady-state visual evoked potential in virtual reality environment.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1146415},
pmid = {37051328},
issn = {1662-5218},
abstract = {At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects' gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system.},
}
@article {pmid37050823,
year = {2023},
author = {Cardona-Álvarez, YN and Álvarez-Meza, AM and Cárdenas-Peña, DA and Castaño-Duque, GA and Castellanos-Dominguez, G},
title = {A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {7},
pages = {},
pmid = {37050823},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Software ; Imagery, Psychotherapy ; Electrodes ; },
abstract = {An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.},
}
@article {pmid37050774,
year = {2023},
author = {Zafar, A and Hussain, SJ and Ali, MU and Lee, SW},
title = {Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {7},
pages = {},
pmid = {37050774},
issn = {1424-8220},
support = {NRF2021R1I1A2059735//National Research Foundation of Korea/ ; },
mesh = {*Imagination ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; Imagery, Psychotherapy ; Algorithms ; Electroencephalography/methods ; },
abstract = {In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.},
}
@article {pmid37050653,
year = {2023},
author = {Srisrisawang, N and Müller-Putz, GR},
title = {Transfer Learning in Trajectory Decoding: Sensor or Source Space?.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {7},
pages = {},
pmid = {37050653},
issn = {1424-8220},
support = {ERC-CoG-2015 681231/ERC_/European Research Council/International ; n.d.//Government of Thailand/ ; },
abstract = {In this study, across-participant and across-session transfer learning was investigated to minimize the calibration time of the brain-computer interface (BCI) system in the context of continuous hand trajectory decoding. We reanalyzed data from a study with 10 able-bodied participants across three sessions. A leave-one-participant-out (LOPO) model was utilized as a starting model. Recursive exponentially weighted partial least squares regression (REW-PLS) was employed to overcome the memory limitation due to the large pool of training data. We considered four scenarios: generalized with no update (Gen), generalized with cumulative update (GenC), and individual models with cumulative (IndC) and non-cumulative (Ind) updates, with each one trained with sensor-space features or source-space features. The decoding performance in generalized models (Gen and GenC) was lower than the chance level. In individual models, the cumulative update (IndC) showed no significant improvement over the non-cumulative model (Ind). The performance showed the decoder's incapability to generalize across participants and sessions in this task. The results suggested that the best correlation could be achieved with the sensor-space individual model, despite additional anatomical information in the source-space features. The decoding pattern showed a more localized pattern around the precuneus over three sessions in Ind models.},
}
@article {pmid37050605,
year = {2023},
author = {Borkin, D and Nemethova, A and Nemeth, M and Tanuska, P},
title = {Control of a Production Manipulator with the Use of BCI in Conjunction with an Industrial PLC.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {7},
pages = {},
pmid = {37050605},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; *Persons with Disabilities ; Automation ; Logic ; },
abstract = {Research in the field of gathering and analyzing biological signals is growing. The sensors are becoming more available and more non-invasive for examining such signals, which in the past required the inconvenient acquisition of data. This was achieved mainly by the fact that biological sensors were able to be built into wearable and portable devices. The representation and analysis of EEGs (electroencephalograms) is nowadays commonly used in various application areas. The application of the use of the EEG signals to the field of automation is still an unexplored area and therefore provides opportunities for interesting research. In our research, we focused on the area of processing automation; especially the use of the EEG signals to bridge the communication between control of individual processes and a human. In this study, the real-time communication between a PLC (programmable logic controller) and BCI (brain computer interface) was investigated and described. In the future, this approach can help people with physical disabilities to control certain machines or devices and therefore it could find applicability in overcoming physical disabilities. The main contribution of the article is, that we have demonstrated the possibility of interaction between a person and a manipulator controlled by a PLC with the help of a BCI. Potentially, with the expansion of functionality, such solutions will allow a person with physical disabilities to participate in the production process.},
}
@article {pmid37046941,
year = {2023},
author = {Gao, S and Zhou, K and Zhang, J and Cheng, Y and Mao, S},
title = {Effects of Background Music on Mental Fatigue in Steady-State Visually Evoked Potential-Based BCIs.},
journal = {Healthcare (Basel, Switzerland)},
volume = {11},
number = {7},
pages = {},
pmid = {37046941},
issn = {2227-9032},
support = {2022YFF1202500//National Key Research and Development Program of China/ ; 2022YFF1202504//National Key Research and Development Program of China/ ; },
abstract = {As a widely used brain-computer interface (BCI) paradigm, steady-state visually evoked potential (SSVEP)-based BCIs have the advantages of high information transfer rates, high tolerance for artifacts, and robust performance across diverse users. However, the incidence of mental fatigue from prolonged, repetitive stimulation is a critical issue for SSVEP-based BCIs. Music is often used as a convenient, non-invasive means of relieving mental fatigue. This study investigates the compensatory effect of music on mental fatigue through the introduction of different modes of background music in long-duration, SSVEP-BCI tasks. Changes in electroencephalography power index, SSVEP amplitude, and signal-to-noise ratio were used to assess participants' mental fatigue. The study's results show that the introduction of exciting background music to the SSVEP-BCI task was effective in relieving participants' mental fatigue. In addition, for continuous SSVEP-BCI tasks, a combination of musical modes that used soothing background music during the rest interval phase proved more effective in reducing users' mental fatigue. This suggests that background music can provide a practical solution for long-duration SSVEP-based BCI implementation.},
}
@article {pmid37046310,
year = {2023},
author = {Perez-Valero, E and Gutierrez, CAM and Lopez-Gordo, MA and Alcalde, SL},
title = {Evaluating the feasibility of cognitive impairment detection in Alzheimer's disease screening using a computerized visual dynamic test.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {43},
pmid = {37046310},
issn = {1743-0003},
mesh = {Humans ; *Alzheimer Disease/diagnosis ; *Neurodegenerative Diseases ; Feasibility Studies ; *Cognitive Dysfunction/diagnosis ; *Cognition Disorders ; Neuropsychological Tests ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease without known cure. However, early medical treatment can help control its progression and postpone intellectual decay. Since AD is preceded by a period of cognitive deterioration, the effective assessment of cognitive capabilities is crucial to develop reliable screening procedures. For this purpose, cognitive tests are extensively used to evaluate cognitive areas such as language, attention, or memory.<br><br>METHODS: In this work, we analyzed the potential of a visual dynamics evaluation, the rapid serial visual presentation task (RSVP), for the detection of cognitive impairment in AD. We compared this evaluation with two of the most extended brief cognitive tests applied in Spain: the Clock-drawing test (CDT) and the Phototest. For this purpose, we assessed a group of patients (mild AD and mild cognitive impairment) and controls, and we evaluated the ability of the three tests for the discrimination of the two groups.<br><br>RESULTS: The preliminary results obtained suggest the RSVP performance is statistically higher for the controls than for the patients (p-value = 0.013). Furthermore, we obtained promising classification results for this test (mean accuracy of 0.91 with 95% confidence interval 0.72, 0.97).<br><br>CONCLUSIONS: Since the RSVP is a computerized, auto-scored, and potentially self-administered brief test, it could contribute to speeding-up cognitive impairment screening and to reducing the associated costs. Furthermore, this evaluation could be combined with other tests to augment the efficiency of cognitive impairment screening protocols and to potentially monitor patients under medical treatment.},
}
@article {pmid37044093,
year = {2023},
author = {Bonizzato, M and Guay Hottin, R and Côté, SL and Massai, E and Choinière, L and Macar, U and Laferrière, S and Sirpal, P and Quessy, S and Lajoie, G and Martinez, M and Dancause, N},
title = {Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.},
journal = {Cell reports. Medicine},
volume = {4},
number = {4},
pages = {101008},
pmid = {37044093},
issn = {2666-3791},
mesh = {Rats ; Animals ; *Spinal Cord Injuries/therapy ; *Motor Cortex ; Haplorhini ; Bayes Theorem ; },
abstract = {Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve "prior" expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.},
}
@article {pmid37043349,
year = {2023},
author = {Yao, T and Vanduffel, W},
title = {Spike rates of frontal eye field neurons predict reaction times in a spatial attention task.},
journal = {Cell reports},
volume = {42},
number = {4},
pages = {112384},
pmid = {37043349},
issn = {2211-1247},
mesh = {Animals ; Humans ; Reaction Time/physiology ; *Visual Fields ; Photic Stimulation ; Macaca mulatta ; *Attention/physiology ; Frontal Lobe/physiology ; Neurons/physiology ; Saccades ; },
abstract = {Which neuronal signal(s) predict reaction times when subjects respond to a target at covertly attended locations? Although recent studies showed that spike rates are not predictive, it remains a highly contested question. Therefore, we record single-unit activity from frontal eye field (FEF) neurons while macaques are performing a covert spatial attention task. We find that the attentional modulation of spike rates of FEF neurons is strongly correlated with behavioral reaction times. Moreover, this correlation already emerges 1 s before target dimming, which triggers the behavioral responses. This prediction of reaction times by spike rates is found in neurons showing attention-dependent enhanced and suppressed activity for targets and distractors, respectively, yet in varying degrees across subjects. Thus, spike rates of FEF neurons can predict reaction times persistently and well before the operant behavior during selective attention tasks. Such long prediction windows will be useful for developing spike-based brain-machine interfaces.},
}
@article {pmid37043313,
year = {2023},
author = {Xia, M and Chen, C and Xu, Y and Li, Y and Sheng, X and Ding, H},
title = {Extracting Individual Muscle Drive and Activity From High-Density Surface Electromyography Signals Based on the Center of Gravity of Motor Unit.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {10},
pages = {2852-2862},
doi = {10.1109/TBME.2023.3266575},
pmid = {37043313},
issn = {1558-2531},
mesh = {Humans ; Electromyography/methods ; *Muscle, Skeletal/physiology ; *Hand ; Movement ; Isometric Contraction ; Muscle Contraction/physiology ; },
abstract = {Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.},
}
@article {pmid37040738,
year = {2023},
author = {Chiang, KJ and Dong, S and Cheng, CK and Jung, TP},
title = {Using EEG signals to assess workload during memory retrieval in a real-world scenario.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/accbed},
pmid = {37040738},
issn = {1741-2552},
mesh = {Humans ; *Workload/psychology ; *Electroencephalography/methods ; Memory ; Machine Learning ; },
abstract = {Objective. The electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants' typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement.Approach. We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: (1) a single-monitor setup and (2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states.Main results. The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study.Significance. The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies.},
}
@article {pmid37038142,
year = {2023},
author = {Habashi, AG and Azab, AM and Eldawlatly, S and Aly, GM},
title = {Generative adversarial networks in EEG analysis: an overview.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {40},
pmid = {37038142},
issn = {1743-0003},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Image Processing, Computer-Assisted/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; },
abstract = {Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.},
}
@article {pmid37035943,
year = {2023},
author = {Duan, Y and Wang, S and Yuan, Q and Shi, Y and Jiang, N and Jiang, D and Song, J and Wang, P and Zhuang, L},
title = {Long-Term Flexible Neural Interface for Synchronous Recording of Cross-Regional Sensory Processing along the Olfactory Pathway.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {19},
number = {29},
pages = {e2205768},
doi = {10.1002/smll.202205768},
pmid = {37035943},
issn = {1613-6829},
mesh = {Animals ; Humans ; *Olfactory Pathways/physiology ; *Smell/physiology ; Olfactory Bulb/physiology ; Odorants ; Perception ; Mammals ; },
abstract = {Humans perceive the world through five senses, of which olfaction is the oldest evolutionary sense that enables the detection of chemicals in the external environment. Recent progress in bioinspired electronics has boosted the development of artificial sensory systems. Here, a biohybrid olfactory system is proposed by integrating living mammals with implantable flexible neural electrodes, to employ the outstanding properties of mammalian olfactory system. In olfactory perception, the peripheral organ-olfactory epithelium (OE) projects axons into the olfactory relay station-olfactory bulb (OB). The olfactory information encoded in the neural activity is recorded from both OE and OB simultaneously using flexible neural electrodes. Results reveal that spontaneous slow oscillations (<12 Hz) in both OE and OB closely follow respiration. This respiration-locked rhythm modulates the amplitude of fast oscillations (>20 Hz), which are associated with odor perception. Further, by extracting the characteristics of odor-evoked oscillatory signals, responses of different odors are identified and classified with 80% accuracy. This study demonstrates for the first time that the flexible electrode enables chronic stable electrophysiological recordings of the peripheral and central olfactory system in vivo. Overall, the method provides a novel neural interface for olfactory biosensing and cognitive processing.},
}
@article {pmid37034665,
year = {2024},
author = {Stout, JJ and George, AE and Kim, S and Hallock, HL and Griffin, AL},
title = {Using synchronized brain rhythms to bias memory-guided decisions.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.04.02.535279},
pmid = {37034665},
issn = {2692-8205},
support = {R21 MH117687/MH/NIMH NIH HHS/United States ; },
abstract = {Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain machine interface that initiated task trials based on the magnitude of prefrontal hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain machine interfacing.},
}
@article {pmid37034169,
year = {2023},
author = {Wan, Z and Li, M and Liu, S and Huang, J and Tan, H and Duan, W},
title = {EEGformer: A transformer-based brain activity classification method using EEG signal.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1148855},
pmid = {37034169},
issn = {1662-4548},
abstract = {BACKGROUND: The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain.<br><br>METHOD: Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG).<br><br>RESULTS: The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance.<br><br>CONCLUSION: EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.},
}
@article {pmid37034156,
year = {2023},
author = {Li, Y and Yang, B and Wang, Z and Huang, R and Lu, X and Bi, X and Zhou, S},
title = {EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1133834},
pmid = {37034156},
issn = {1662-4548},
abstract = {In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.},
}
@article {pmid37033911,
year = {2023},
author = {Belkacem, AN and Jamil, N and Khalid, S and Alnajjar, F},
title = {On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1085173},
pmid = {37033911},
issn = {1662-5161},
abstract = {Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.},
}
@article {pmid37033908,
year = {2023},
author = {Savić, AM and Novičić, M and Ðorđević, O and Konstantinović, L and Miler-Jerković, V},
title = {Novel electrotactile brain-computer interface with somatosensory event-related potential based control.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1096814},
pmid = {37033908},
issn = {1662-5161},
abstract = {OBJECTIVE: A brain computer interface (BCI) allows users to control external devices using non-invasive brain recordings, such as electroencephalography (EEG). We developed and tested a novel electrotactile BCI prototype based on somatosensory event-related potentials (sERP) as control signals, paired with a tactile attention task as a control paradigm.<br><br>APPROACH: A novel electrotactile BCI comprises commercial EEG device, an electrical stimulator and custom software for EEG recordings, electrical stimulation control, synchronization between devices, signal processing, feature extraction, selection, and classification. We tested a novel BCI control paradigm based on tactile attention on a sensation at a target stimulation location on the forearm. Tactile stimuli were electrical pulses delivered at two proximal locations on the user's forearm for stimulating branches of radial and median nerves, with equal probability of the target and distractor stimuli occurrence, unlike in any other ERP-based BCI design. We proposed a compact electrical stimulation electrodes configuration for delivering electrotactile stimuli (target and distractor) using 2 stimulation channels and 3 stimulation electrodes. We tested the feasibility of a single EEG channel BCI control, to determine pseudo-online BCI performance, in ten healthy subjects. For optimizing the BCI performance we compared the results for two classifiers, sERP averaging approaches, and novel dedicated feature extraction/selection methods via cross-validation procedures.<br><br>MAIN RESULTS: We achieved a single EEG channel BCI classification accuracy in the range of 75.1 to 88.1% for all subjects. We have established an optimal combination of: single trial averaging to obtain sERP, feature extraction/selection methods and classification approach.<br><br>SIGNIFICANCE: The obtained results demonstrate that a novel electrotactile BCI paradigm with equal probability of attended (target) and unattended (distractor) stimuli and proximal stimulation sites is feasible. This method may be used to drive restorative BCIs for sensory retraining in stroke or brain injury, or assistive BCIs for communication in severely disabled users.},
}
@article {pmid37030758,
year = {2023},
author = {Li, A and Wang, Z and Zhao, X and Xu, T and Zhou, T and Hu, H},
title = {MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1743-1753},
doi = {10.1109/TNSRE.2023.3259730},
pmid = {37030758},
issn = {1558-0210},
abstract = {In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify the proposed strategy. For the four-classes dataset BCIC IV dataset 2a, the proposed MDTL achieves 80.86%, 81.95%, and 75.00% mean prediction accuracy using the three models, which outperforms those without MDTL by 5.79%, 6.64%, and 11.42%. For the binary-classes dataset Lee2019, MDTL achieves 88.2% mean accuracy using the model DeepConvNet. It outperforms the accuracy without MDTL by 23.48%. The achieved 81.95% and 88.2% are also better than the existing deep transfer learning strategy. Besides, the training time of MDTL is reduced by 93.94%. MDTL is an easy-to-deploy, scalable and reliable transfer learning strategy for existing deep learning models, which significantly improves model performance and reduces preparation time without changing model architecture.},
}
@article {pmid37030737,
year = {2023},
author = {Zhang, R and Cao, L and Xu, Z and Zhang, Y and Zhang, L and Hu, Y and Chen, M and Yao, D},
title = {Improving AR-SSVEP Recognition Accuracy Under High Ambient Brightness Through Iterative Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1796-1806},
doi = {10.1109/TNSRE.2023.3260842},
pmid = {37030737},
issn = {1558-0210},
mesh = {Humans ; Evoked Potentials, Visual ; *Augmented Reality ; Photic Stimulation ; Electroencephalography/methods ; Recognition, Psychology ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.},
}
@article {pmid37030734,
year = {2023},
author = {Gao, W and Huang, W and Li, M and Gu, Z and Pan, J and Yu, T and Yu, ZL and Li, Y},
title = {Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1754-1763},
doi = {10.1109/TNSRE.2023.3259991},
pmid = {37030734},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Calibration ; Event-Related Potentials, P300 ; Neural Networks, Computer ; Electroencephalography/methods ; },
abstract = {A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.},
}
@article {pmid37030724,
year = {2023},
author = {Liu, D and Dai, W and Zhang, H and Jin, X and Cao, J and Kong, W},
title = {Brain-Machine Coupled Learning Method for Facial Emotion Recognition.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {45},
number = {9},
pages = {10703-10717},
doi = {10.1109/TPAMI.2023.3257846},
pmid = {37030724},
issn = {1939-3539},
mesh = {Humans ; *Algorithms ; *Facial Recognition ; Brain/diagnostic imaging ; Emotions ; Neural Networks, Computer ; Electroencephalography/methods ; },
abstract = {Neural network models of machine learning have shown promising prospects for visual tasks, such as facial emotion recognition (FER). However, the generalization of the model trained from a dataset with a few samples is limited. Unlike the machine, the human brain can effectively realize the required information from a few samples to complete the visual tasks. To learn the generalization ability of the brain, in this article, we propose a novel brain-machine coupled learning method for facial emotion recognition to let the neural network learn the visual knowledge of the machine and cognitive knowledge of the brain simultaneously. The proposed method utilizes visual images and electroencephalogram (EEG) signals to couple training the models in the visual and cognitive domains. Each domain model consists of two types of interactive channels, common and private. Since the EEG signals can reflect brain activity, the cognitive process of the brain is decoded by a model following reverse engineering. Decoding the EEG signals induced by the facial emotion images, the common channel in the visual domain can approach the cognitive process in the cognitive domain. Moreover, the knowledge specific to each domain is found in each private channel using an adversarial strategy. After learning, without the participation of the EEG signals, only the concatenation of both channels in the visual domain is used to classify facial emotion images based on the visual knowledge of the machine and the cognitive knowledge learned from the brain. Experiments demonstrate that the proposed method can produce excellent performance on several public datasets. Further experiments show that the proposed method trained from the EEG signals has good generalization ability on new datasets and can be applied to other network models, illustrating the potential for practical applications.},
}
@article {pmid37028310,
year = {2023},
author = {Hsu, WY and Cheng, YW},
title = {EEG-Channel-Temporal-Spectral-Attention Correlation for Motor Imagery EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1659-1669},
doi = {10.1109/TNSRE.2023.3255233},
pmid = {37028310},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; Brain ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {In brain-computer interface (BCI) work, how correctly identifying various features and their corresponding actions from complex Electroencephalography (EEG) signals is a challenging technology. However, most current methods do not consider EEG feature information in spatial, temporal and spectral domains, and the structure of these models cannot effectively extract discriminative features, resulting in limited classification performance. To address this issue, we propose a novel text motor-imagery EEG discrimination method, namely wavelet-based temporal-spectral-attention correlation coefficient (WTS-CC), to simultaneously consider the features and their weighting in spatial, EEG-channel, temporal and spectral domains in this study. The initial Temporal Feature Extraction (iTFE) module extracts the initial important temporal features of MI EEG signals. The Deep EEG-Channel-attention (DEC) module is then proposed to automatically adjust the weight of each EEG channel according to its importance, thereby effectively enhancing more important EEG channels and suppressing less important EEG channels. Next, the Wavelet-based Temporal-Spectral-attention (WTS) module is proposed to obtain more significant discriminative features between different MI tasks by weighting features on two-dimensional time-frequency maps. Finally, a simple discrimination module is used for MI EEG discrimination. The experimental results indicate that the proposed text WTS-CC method can achieve promising discrimination performance that outperforms the state-of-the-art methods in terms of classification accuracy, Kappa coefficient, F1 score, and AUC on three public datasets.},
}
@article {pmid37028281,
year = {2023},
author = {Hu, Y and Liu, Y and Zhang, S and Zhang, T and Dai, B and Peng, B and Yang, H and Dai, Y},
title = {A Cross-Space CNN With Customized Characteristics for Motor Imagery EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1554-1565},
doi = {10.1109/TNSRE.2023.3249831},
pmid = {37028281},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; Algorithms ; Neural Networks, Computer ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {The classification of motor imagery-electroencephalogram(MI-EEG)based brain-computer interface(BCI)can be used to decode neurological activities, which has been widely applied in the control of external devices. However, two factors still hinder the improvement of classification accuracy and robustness, especially in multi-class tasks. First, existing algorithms are based on a single space (measuring or source space). They suffer from the holistic low spatial resolution of the measuring space or the locally high spatial resolution information accessed from the source space, failing to provide holistic and high-resolution representations. Second, the subject specificity is not sufficiently characterized, resulting in the loss of personalized intrinsic information. Therefore, we propose a cross-space convolutional neural network (CS-CNN) with customized characteristics for four-class MI-EEG classification. This algorithm uses the modified customized band common spatial patterns (CBCSP) and duplex mean-shift clustering (DMSClustering) to express the specific rhythms and source distribution information in cross-space. At the same time, multi-view features from the time, frequency and space domains are extracted, connecting with CNN to fuse the characteristics from two spaces and classify them. MI-EEG was collected from 20 subjects. Lastly, the classification accuracy of the proposed is 96.05% with real MRI information and 94.79% without MRI in the private dataset. And the results in the BCI competition IV-2a show that CS-CNN outperforms the state-of-the-art algorithms, achieving an accuracy improvement of 1.98%, and a standard deviation reduction of 5.15%.},
}
@article {pmid37028070,
year = {2023},
author = {Zhang, Y and Xie, SQ and Shi, C and Li, J and Zhang, ZQ},
title = {Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1574-1583},
doi = {10.1109/TNSRE.2023.3250953},
pmid = {37028070},
issn = {1558-0210},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.},
}
@article {pmid37028028,
year = {2023},
author = {Lou, H and Ye, Z and Yao, L and Zhang, Y},
title = {Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1888-1899},
doi = {10.1109/TNSRE.2023.3252610},
pmid = {37028028},
issn = {1558-0210},
abstract = {Electroencephalography (EEG) signals are gaining popularity in Brain-Computer Interface (BCI)-based rehabilitation and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account. Specifically, we construct a task-adaptive graph representation of the brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. We empirically show that the proposed approach outperforms the state-of-the-art, with around 1% and 11% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data, suggesting a possible shift in direction for future works other than simply scaling up the model.},
}
@article {pmid37027669,
year = {2023},
author = {Gong, P and Wang, P and Zhou, Y and Zhang, D},
title = {A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1440-1450},
doi = {10.1109/TNSRE.2023.3246989},
pmid = {37027669},
issn = {1558-0210},
abstract = {Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes. Moreover, most are designed for specific BCI tasks and lack some generality. Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEG-based BCIs. Specifically, we first adopt a learnable spike encoder to convert the raw EEG signals into spike trains. Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. Finally, we design the spike-based LSTM units to further capture the temporal dependencies of the spikes. We evaluate our proposed model on two publicly available datasets from two representative fields of BCI, notably emotion recognition, and motor imagery decoding. The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.},
}
@article {pmid37027653,
year = {2024},
author = {Wang, H and Chen, P and Zhang, M and Zhang, J and Sun, X and Li, M and Yang, X and Gao, Z},
title = {EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {8},
pages = {10698-10712},
doi = {10.1109/TNNLS.2023.3243339},
pmid = {37027653},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Algorithms ; Neural Networks, Computer ; Movement/physiology ; Brain/physiology ; },
abstract = {A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.},
}
@article {pmid37027633,
year = {2023},
author = {Fan, Y and Mao, H and Li, Q},
title = {A Model-Agnostic Feature Attribution Approach to Magnetoencephalography Predictions Based on Shapley Value.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {5},
pages = {2524-2535},
doi = {10.1109/JBHI.2023.3248139},
pmid = {37027633},
issn = {2168-2208},
mesh = {Humans ; *Magnetoencephalography/methods ; Brain/physiology ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; *Brain-Computer Interfaces ; },
abstract = {Deep learning has greatly enhanced the predictive performance of magnetoencephalography (MEG) decoding. However, the lack of interpretability has become a major obstacle to the practical application of deep learning-based MEG decoding algorithms, which may lead to non-compliance with legal requirements and distrust among end-users. To address this issue, this article proposes a feature attribution approach, which can provide interpretative support for each individual MEG prediction for the first time. The approach first transforms a MEG sample into a feature set, then assigns contribution weights to each feature using modified Shapley values, which are optimized by filtering reference samples and generating antithetic sample pairs. Experimental results show that the Area Under the Deletion test Curve (AUDC) of the approach is as low as 0.005, which means a better attribution accuracy compared to typical computer vision algorithms. Visualization analysis reveals that the key features of the model decisions are consistent with neurophysiological theories. Based on these key features, the input signal can be compressed to one-sixteenth of its original size with only a 0.19% loss in classification performance. Another benefit of our approach is that it is model-agnostic, enabling its utilization for various decoding models and brain-computer interface (BCI) applications.},
}
@article {pmid37027569,
year = {2023},
author = {Kalra, J and Mittal, P and Mittal, N and Arora, A and Tewari, U and Chharia, A and Upadhyay, R and Kumar, V and Longo, L},
title = {How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape: A PRISMA Compliant Systematic Review.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1429-1439},
doi = {10.1109/TNSRE.2023.3246588},
pmid = {37027569},
issn = {1558-0210},
mesh = {Humans ; Attention ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Neural Networks, Computer ; },
abstract = {Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants' age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.},
}
@article {pmid37027558,
year = {2023},
author = {Ke, Y and Du, J and Liu, S and Ming, D},
title = {Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1405-1417},
doi = {10.1109/TNSRE.2023.3246359},
pmid = {37027558},
issn = {1558-0210},
abstract = {This study proposed a novel frequency-specific (FS) algorithm framework for enhancing control state detection using short data length toward high-performance asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCI). The FS framework sequentially incorporated task-related component analysis (TRCA)-based SSVEP identification and a classifier bank containing multiple FS control state detection classifiers. For an input EEG epoch, the FS framework first identified its potential SSVEP frequency using the TRCA-based method and then recognized its control state using one of the classifiers trained on the features specifically related to the identified frequency. A frequency-unified (FU) framework that conducted control state detection using a unified classifier trained on features related to all candidate frequencies was proposed to compare with the FS framework. Offline evaluation using data lengths within 1 s found that the FS framework achieved excellent performance and significantly outperformed the FU framework. 14-target FS and FU asynchronous systems were separately constructed by incorporating a simple dynamic stopping strategy and validated using a cue-guided selection task in an online experiment. Using averaged data length of 591.63±5.65 ms, the online FS system significantly outperformed the FU system and achieved an information transfer rate, true positive rate, false positive rate, and balanced accuracy of 124.95±12.35 bits/min, 93.16±4.4%, 5.21±5.85%, and 92.89±4.02%, respectively. The FS system was also of higher reliability by accepting more correctly identified SSVEP trials and rejecting more wrongly identified ones. These results suggest that the FS framework has great potential to enhance the control state detection for high-speed asynchronous SSVEP-BCIs.},
}
@article {pmid37027528,
year = {2023},
author = {Shi, N and Li, X and Liu, B and Yang, C and Wang, Y and Gao, X},
title = {Representative-Based Cold Start for Adaptive SSVEP-BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1521-1531},
doi = {10.1109/TNSRE.2023.3245654},
pmid = {37027528},
issn = {1558-0210},
abstract = {OBJECTIVE: The tradeoff between calibration effort and model performance still hinders the user experience for steady-state visual evoked brain-computer interfaces (SSVEP-BCI). To address this issue and improve model generalizability, this work investigated the adaptation from the cross-dataset model to avoid the training process, while maintaining high prediction ability.<br><br>METHODS: When a new subject enrolls, a group of user-independent (UI) models is recommended as the representative from a multi-source data pool. The representative model is then augmented with online adaptation and transfer learning techniques based on user-dependent (UD) data. The proposed method is validated on both offline (N=55) and online (N=12) experiments.<br><br>RESULTS: Compared with the UD adaptation, the recommended representative model relieved approximately 160 trials of calibration efforts for a new user. In the online experiment, the time window decreased from 2 s to 0.56&#xb1;0.2 s, while maintaining high prediction accuracy of 0.89-0.96. Finally, the proposed method achieved the average information transfer rate (ITR) of 243.49 bits/min, which is the highest ITR ever reported in a complete calibration-free setting. The results of the offline result were consistent with the online experiment.<br><br>CONCLUSION: Representatives can be recommended even in a cross-subject/device/session situation. With the help of represented UI data, the proposed method can achieve sustained high performance without a training process.<br><br>SIGNIFICANCE: This work provides an adaptive approach to the transferable model for SSVEP-BCIs, enabling a more generalized, plug-and-play and high-performance BCI free of calibrations.},
}
@article {pmid37027527,
year = {2023},
author = {Wang, J and Bi, L and Feleke, AG and Fei, W},
title = {MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1384-1393},
doi = {10.1109/TNSRE.2023.3245617},
pmid = {37027527},
issn = {1558-0210},
abstract = {Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients' residual or intact movement functions, is a more intuitive and natural paradigm. Based on the ME paradigm, we can decode voluntary hand movement intentions from electroencephalography (EEG) signals. Numerous studies have investigated EEG-based unimanual movement decoding. Moreover, some studies have explored bimanual movement decoding since bimanual coordination is important in daily-life assistance and bilateral neurorehabilitation therapy. However, the multi-class classification of the unimanual and bimanual movements shows weak performance. To address this problem, in this work, we propose a neurophysiological signatures-driven deep learning model utilizing the movement-related cortical potentials (MRCPs) and event-related synchronization/ desynchronization (ERS/D) oscillations for the first time, inspired by the finding that brain signals encode motor-related information with both evoked potentials and oscillation components in ME. The proposed model consists of a feature representation module, an attention-based channel-weighting module, and a shallow convolutional neural network module. Results show that our proposed model has superior performance to the baseline methods. Six-class classification accuracies of unimanual and bimanual movements achieved 80.3%. Besides, each feature module of our model contributes to the performance. This work is the first to fuse the MRCPs and ERS/D oscillations of ME in deep learning to enhance the multi-class unimanual and bimanual movements' decoding performance. This work can facilitate the neural decoding of unimanual and bimanual movements for neurorehabilitation and assistance.},
}
@article {pmid37027253,
year = {2023},
author = {Wang, J and Yao, L and Wang, Y},
title = {IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1900-1911},
doi = {10.1109/TNSRE.2023.3257319},
pmid = {37027253},
issn = {1558-0210},
mesh = {Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography ; },
abstract = {OBJECTIVE: The key principle of motor imagery (MI) decoding for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from spectral, spatial, and temporal domains jointly and efficiently, whereas limited, noisy, and non-stationary EEG samples challenge the advanced design of decoding algorithms.<br><br>METHODS: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. IFNet first extracts spectro-spatial features in low and high-frequency bands, respectively. Then the interplay between the two bands is learned using an element-wise addition operation followed by temporal average pooling. Combined with repeated trial augmentation as a regularizer, IFNet yields spectro-spatio-temporally robust features for the final MI classification. We conduct extensive experiments on two benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset.<br><br>RESULTS: Compared with state-of-the-art MI decoding algorithms, IFNet achieves significantly superior classification performance on both datasets while improving the winner's result in BCIC-IV-2a by 11%. Moreover, by conducting sensitivity analysis on decision windows, we show IFNet attains the best trade-off between decoding speed and accuracy. Detailed analysis and visualization verify IFNet can capture the coupling across frequency bands along with the known MI signatures.<br><br>CONCLUSION: We demonstrate the effectiveness and superiority of the proposed IFNet for MI decoding.<br><br>SIGNIFICANCE: This study suggests IFNet holds promise for rapid response and accurate control in MI-BCI applications.},
}
@article {pmid37025702,
year = {2023},
author = {Xiang, L and Harel, A and Todorova, R and Gao, H and Sara, SJ and Wiener, SI},
title = {Locus coeruleus noradrenergic neurons phase-lock to prefrontal and hippocampal infra-slow rhythms that synchronize to behavioral events.},
journal = {Frontiers in cellular neuroscience},
volume = {17},
number = {},
pages = {1131151},
pmid = {37025702},
issn = {1662-5102},
abstract = {The locus coeruleus (LC) is the primary source of noradrenergic projections to the forebrain, and, in prefrontal cortex, is implicated in decision-making and executive function. LC neurons phase-lock to cortical infra-slow wave oscillations during sleep. Such infra-slow rhythms are rarely reported in awake states, despite their interest, since they correspond to the time scale of behavior. Thus, we investigated LC neuronal synchrony with infra-slow rhythms in awake rats performing an attentional set-shifting task. Local field potential (LFP) oscillation cycles in prefrontal cortex and hippocampus on the order of 0.4 Hz phase-locked to task events at crucial maze locations. Indeed, successive cycles of the infra-slow rhythms showed different wavelengths, as if they are periodic oscillations that can reset phase relative to salient events. Simultaneously recorded infra-slow rhythms in prefrontal cortex and hippocampus could show different cycle durations as well, suggesting independent control. Most LC neurons (including optogenetically identified noradrenergic neurons) recorded here were phase-locked to these infra-slow rhythms, as were hippocampal and prefrontal units recorded on the LFP probes. The infra-slow oscillations also phase-modulated gamma amplitude, linking these rhythms at the time scale of behavior to those coordinating neuronal synchrony. This would provide a potential mechanism where noradrenaline, released by LC neurons in concert with the infra-slow rhythm, would facilitate synchronization or reset of these brain networks, underlying behavioral adaptation.},
}
@article {pmid37023989,
year = {2023},
author = {Michalke, L and Dreyer, AM and Borst, JP and Rieger, JW},
title = {Inter-individual single-trial classification of MEG data using M-CCA.},
journal = {NeuroImage},
volume = {273},
number = {},
pages = {120079},
doi = {10.1016/j.neuroimage.2023.120079},
pmid = {37023989},
issn = {1095-9572},
mesh = {Humans ; *Magnetoencephalography/methods ; Canonical Correlation Analysis ; Brain/physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; },
abstract = {Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants' recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-subject alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-subject variability of sensor locations over the brain due to the use of a fixed helmet. Hence, an approach to combine MEG data over individual brains should relax the assumptions that a) brain anatomy and function are tightly linked and b) that the same sensors capture functionally comparable brain activation across individuals. Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activations recorded from 15 participants performing a grasping task. The M-CCA algorithm was applied to transform the data of a set of multiple participants into a common space with maximum correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this common representation. This makes it useful for applications that require transfer of models derived from a group of individuals to new individuals. We demonstrate the usefulness and superiority of the approach with respect to previously used approaches. Finally, we show that our approach requires only a small number of labeled data from the new participant. The proposed method demonstrates that functionally motivated common spaces have potential applications in reducing training time of online brain-computer interfaces, where models can be pre-trained on previous participants/sessions. Moreover, inter-subject alignment via M-CCA has the potential for combining data of different participants and could become helpful in future endeavors on large open datasets.},
}
@article {pmid37023639,
year = {2023},
author = {Peng, B and Zhang, Y and Wang, M and Chen, J and Gao, D},
title = {T-A-MFFNet: Multi-feature fusion network for EEG analysis and driving fatigue detection based on time domain network and attention network.},
journal = {Computational biology and chemistry},
volume = {104},
number = {},
pages = {107863},
doi = {10.1016/j.compbiolchem.2023.107863},
pmid = {37023639},
issn = {1476-928X},
mesh = {*Electroencephalography/methods ; Time Factors ; Entropy ; },
abstract = {Driving fatigue detection based on EEG signals is a research hotspot in applying brain-computer interfaces. EEG signal is complex, unstable, and nonlinear. Most existing methods rarely analyze the data characteristics from multiple dimensions, so it takes work to analyze the data comprehensively. To analyze EEG signals more comprehensively, this paper evaluates a feature extraction strategy of EEG data based on differential entropy (DE). This method combines the characteristics of different frequency bands, extracts the frequency domain characteristics of EEG, and retains the spatial information between channels. This paper proposes a multi-feature fusion network (T-A-MFFNet) based on the time domain and attention network. The model is composed of a time domain network (TNet), channel attention network (CANet), spatial attention network (SANet), and multi-feature fusion network(MFFNet) based on a squeeze network. T-A-MFFNet aims to learn more valuable features from the input data to achieve good classification results. Specifically, the TNet network extracts high-level time series information from EEG data. CANet and SANet are used to fuse channel and spatial features. They use MFFNet to merge multi-dimensional features and realize classification. The validity of the model is verified on the SEED-VIG dataset. The experimental results show that the accuracy of the proposed method reaches 85.65 %, which is superior to the current popular model. The proposed method can learn more valuable information from EEG signals to improve the ability to identify fatigue status and promote the development of the research field of driving fatigue detection based on EEG signals.},
}
@article {pmid37023540,
year = {2023},
author = {Gao, Y and Zhang, C and Fang, F and Cammon, J and Zhang, Y},
title = {Multi-domain feature analysis method of MI-EEG signal based on Sparse Regularity Tensor-Train decomposition.},
journal = {Computers in biology and medicine},
volume = {158},
number = {},
pages = {106887},
doi = {10.1016/j.compbiomed.2023.106887},
pmid = {37023540},
issn = {1879-0534},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Brain/diagnostic imaging ; Imagination ; },
abstract = {Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.},
}
@article {pmid37023162,
year = {2023},
author = {Barmpas, K and Panagakis, Y and Bakas, S and Adamos, DA and Laskaris, N and Zafeiriou, S},
title = {Improving Generalization of CNN-Based Motor-Imagery EEG Decoders via Dynamic Convolutions.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1997-2005},
doi = {10.1109/TNSRE.2023.3265304},
pmid = {37023162},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; Neural Networks, Computer ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Generalization, Psychological ; Imagination/physiology ; },
abstract = {Deep Convolutional Neural Networks (CNNs) have recently demonstrated impressive results in electroencephalogram (EEG) decoding for several Brain-Computer Interface (BCI) paradigms, including Motor-Imagery (MI). However, neurophysiological processes underpinning EEG signals vary across subjects causing covariate shifts in data distributions and hence hindering the generalization of deep models across subjects. In this paper, we aim to address the challenge of inter-subject variability in MI. To this end, we employ causal reasoning to characterize all possible distribution shifts in the MI task and propose a dynamic convolution framework to account for shifts caused by the inter-subject variability. Using publicly available MI datasets, we demonstrate improved generalization performance (up to 5%) across subjects in various MI tasks for four well-established deep architectures.},
}
@article {pmid37022899,
year = {2023},
author = {Chen, X and Liu, B and Wang, Y and Cui, H and Dong, J and Ma, R and Li, N and Gao, X},
title = {Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1277-1286},
doi = {10.1109/TNSRE.2023.3243786},
pmid = {37022899},
issn = {1558-0210},
abstract = {The brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have been extensively explored due to their advantages in terms of high communication speed and smaller calibration time. The visual stimuli in the low- and medium-frequency ranges are adopted in most of the existing studies for eliciting SSVEPs. However, there is a need to further improve the comfort of these systems. The high-frequency visual stimuli have been used to build BCI systems and are generally considered to significantly improve the visual comfort, but their performance is relatively low. The distinguishability of 16-class SSVEPs encoded by the three frequency ranges, i.e., 31-34.75 Hz with an interval of 0.25 Hz, 31-38.5 Hz with an interval of 0.5 Hz, 31-46 Hz with an interval of 1 Hz, is explored in this study. We compare classification accuracy and information transfer rate (ITR) of the corresponding BCI system. According to the optimized frequency range, this study builds an online 16-target high frequency SSVEP-BCI and verifies the feasibility of the proposed system based on 21 healthy subjects. The BCI based on visual stimuli with the narrowest frequency range, i.e., 31-34.5 Hz, have the highest ITR. Therefore, the narrowest frequency range is adopted to build an online BCI system. An averaged ITR obtained from the online experiment is 153.79 ±6.39 bits/min. These findings contribute to the development of more efficient and comfortable SSVEP-based BCIs.},
}
@article {pmid37022898,
year = {2023},
author = {Jia, H and Yu, S and Yin, S and Liu, L and Yi, C and Xue, K and Li, F and Yao, D and Xu, P and Zhang, T},
title = {A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1311-1320},
doi = {10.1109/TNSRE.2023.3243992},
pmid = {37022898},
issn = {1558-0210},
abstract = {Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information and low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the movement intentions of users. In this study, we proposed an end-to-end deep learning model, a multi-branch spectral-temporal convolutional neural network with channel attention and LightGBM model (MBSTCNN-ECA-LightGBM), to decode MI-EEG tasks. We first constructed a multi branch CNN module to learn spectral-temporal domain features. Subsequently, we added an efficient channel attention mechanism module to obtain more discriminative features. Finally, LightGBM was applied to decode the MI multi-classification tasks. The within-subject cross-session training strategy was used to validate classification results. The experimental results showed that the model achieved an average accuracy of 86% on the two-class MI-BCI data and an average accuracy of 74% on the four-class MI-BCI data, which outperformed current state-of-the-art methods. The proposed MBSTCNN-ECA-LightGBM can efficiently decode the spectral and temporal domain information of EEG, improving the performance of MI-based BCIs.},
}
@article {pmid37022880,
year = {2023},
author = {Ge, S and Yang, H and Wang, R and Leng, Y and Iramina, K and Lin, P and Wang, H},
title = {Block Distributed Joint Temporal-Frequency- Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {4},
pages = {1835-1844},
doi = {10.1109/JBHI.2023.3244277},
pmid = {37022880},
issn = {2168-2208},
abstract = {How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81 $\pm$ 9.41% and 91.36 $\pm$ 6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based BCI.},
}
@article {pmid37022873,
year = {2023},
author = {Zhang, S and Gao, X and Cui, H and Chen, X},
title = {Transcranial Direct Current Stimulation-Based Neuromodulation Improves the Performance of Brain-Computer Interfaces Based on Steady-State Visual Evoked Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1364-1373},
doi = {10.1109/TNSRE.2023.3245079},
pmid = {37022873},
issn = {1558-0210},
abstract = {The study of brain state estimation and intervention methods is of great significance for the utility of brain-computer interfaces (BCIs). In this paper, a neuromodulation technology using transcranial direct current stimulation (tDCS) is explored to improve the performance of steady-state visual evoked potential (SSVEP)-based BCIs. The effects of pre-stimulation, sham-tDCS and anodal-tDCS are analyzed through a comparison of the EEG oscillations and fractal component characteristics. In addition, in this study, a novel brain state estimation method is introduced to assess neuromodulation-induced changes in brain arousal for SSVEP-BCIs. The results suggest that tDCS, and anodal-tDCS in particular, can be used to increase SSVEP amplitude and further improve the performance of SSVEP-BCIs. Furthermore, evidence from fractal features further validates that tDCS-based neuromodulation induces an increased level of brain state arousal. The findings of this study provide insights into the improvement of BCI performance based on personal state interventions and provide an objective method for quantitative brain state monitoring that may be used for EEG modeling of SSVEP-BCIs.},
}
@article {pmid37022842,
year = {2023},
author = {Wei, F and Xu, X and Jia, T and Zhang, D and Wu, X},
title = {A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1258-1267},
doi = {10.1109/TNSRE.2023.3243257},
pmid = {37022842},
issn = {1558-0210},
abstract = {Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects. To address these issues, we introduce transfer joint matching and improve it to multi-source transfer joint matching (MSTJM) and weighted MSTJM (wMSTJM). Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. It mainly consists of three modules: covariance matrix centroid alignment in the Riemannian space, source selection in the Euclidean space after tangent space mapping to reduce negative transfer and computation overhead, and further distribution alignment by MSTJM or wMSTJM. The superiority of this framework is verified on two common public MI datasets from BCI competition IV. The average classification accuracy of the MSTJM and wMSTJ methods outperformed other state-of-the-art methods by at least 4.24% and 2.62% respectively. It's promising to advance the practical applications of MI-BCI.},
}
@article {pmid37022841,
year = {2023},
author = {Nakanishi, M and Miner, A and Jung, TP and Graves, J},
title = {Novel Moving Steady-State Visual Evoked Potential Stimulus to Assess Afferent and Efferent Dysfunction in Multiple Sclerosis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1297-1301},
doi = {10.1109/TNSRE.2023.3243554},
pmid = {37022841},
issn = {1558-0210},
abstract = {Afferent and efferent visual dysfunction are prominent features of multiple sclerosis (MS). Visual outcomes have been shown to be robust biomarkers of the overall disease state. Unfortunately, precise measurement of afferent and efferent function is typically limited to tertiary care facilities, which have the equipment and analytical capacity to make these measurements, and even then, only a few centers can accurately quantify both afferent and efferent dysfunction. These measurements are currently unavailable in acute care facilities (ER, hospital floors). We aimed to develop a moving multifocal steady-state visual evoked potential (mfSSVEP) stimulus to simultaneously assess afferent and efferent dysfunction in MS for application on a mobile platform. The brain-computer interface (BCI) platform consists of a head-mounted virtual-reality headset with electroencephalogram (EEG) and electrooculogram (EOG) sensors. To evaluate the platform, we recruited consecutive patients who met the 2017 MS McDonald diagnostic criteria and healthy controls for a pilot cross-sectional study. Nine MS patients (mean age 32.7 years, SD 4.33) and ten healthy controls (24.9 years, SD 7.2) completed the research protocol. The afferent measures based on mfSSVEPs showed a significant difference between the groups (signal-to-noise ratio of mfSSVEPs for controls: 2.50 ± 0.72 vs. MS: 2.04 ± 0.47) after controlling for age (p = 0.049). In addition, the moving stimulus successfully induced smooth pursuit movement that can be measured by the EOG signals. There was a trend for worse smooth pursuit tracking in cases vs. controls, but this did not reach nominal statistical significance in this small pilot sample. This study introduces a novel moving mfSSVEP stimulus for a BCI platform to evaluate neurologic visual function. The moving stimulus showed a reliable capability to assess both afferent and efferent visual functions simultaneously.},
}
@article {pmid37022824,
year = {2023},
author = {Wong, CM and Wang, Z and Wang, B and Rosa, A and Jung, TP and Wan, F},
title = {Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1343-1352},
doi = {10.1109/TNSRE.2023.3243290},
pmid = {37022824},
issn = {1558-0210},
abstract = {OBJECTIVE: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance.<br><br>APPROACH: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases.<br><br>MAIN RESULTS: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III.<br><br>SIGNIFICANCE: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.},
}
@article {pmid37022818,
year = {2023},
author = {Mammone, N and Ieracitano, C and Adeli, H and Morabito, FC},
title = {AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {5},
pages = {2365-2376},
doi = {10.1109/JBHI.2023.3243698},
pmid = {37022818},
issn = {2168-2208},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Electroencephalography/methods ; Imagination ; },
abstract = {The present paper introduces a novel method, named AutoEncoder-Filter Bank Common Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography (EEG). AE-FBCSP is an extension of the well-established FBCSP and is based on a global (cross-subject) and subsequent transfer learning subject-specific (intra-subject) approach. A multi-way extension of AE-FBCSP is also introduced in this paper. Features are extracted from high-density EEG (64 electrodes), by means of FBCSP, and used to train a custom AE, in an unsupervised way, to project the features into a compressed latent space. Latent features are used to train a supervised classifier (feed forward neural network) to decode the imagined movement. The proposed method was tested using a public dataset of EEGs collected from 109 subjects. The dataset consists of right-hand, left-hand, both hands, both feet motor imagery and resting EEGs. AE-FBCSP was extensively tested in the 3-way classification (right hand vs left hand vs resting) and also in the 2-way, 4-way and 5-way ones, both in cross- and intra-subject analysis. AE-FBCSP outperformed standard FBCSP in a statistically significant way (p > 0.05) and achieved a subject-specific average accuracy of 89.09% in the 3-way classification. The proposed methodology performed subject-specific classification better than other comparable methods in the literature, applied to the same dataset, also in the 2-way, 4-way and 5-way tasks. One of the most interesting outcomes is that AE-FBCSP remarkably increased the number of subjects that responded with a very high accuracy, which is a fundamental requirement for BCI systems to be applied in practice.},
}
@article {pmid37022448,
year = {2023},
author = {Gu, Y and Zhong, X and Qu, C and Liu, C and Chen, B},
title = {A Domain Generative Graph Network for EEG-Based Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {5},
pages = {2377-2386},
doi = {10.1109/JBHI.2023.3242090},
pmid = {37022448},
issn = {2168-2208},
mesh = {Humans ; *Emotions/physiology ; Neural Networks, Computer ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition is important for the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used in emotion recognition in recent years, emotion recognition based on electroencephalography (EEG) is still a challenging task in practical applications. Herein, we proposed a novel hybrid model that employs generative adversarial networks to generate potential representations of EEG signals while combining graph convolutional neural networks and long short-term memory networks to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets show that the proposed model achieved the promising emotion classification performance compared with the state-of-the-art methods.},
}
@article {pmid37022416,
year = {2023},
author = {Wu, X and Jiang, S and Li, G and Liu, S and Metcalfe, B and Chen, L and Zhang, D},
title = {Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {5},
pages = {2387-2398},
doi = {10.1109/JBHI.2023.3242262},
pmid = {37022416},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Neural Networks, Computer ; *Epilepsy/diagnosis ; Electroencephalography/methods ; Algorithms ; },
abstract = {OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals.<br><br>METHODS: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN.<br><br>RESULTS: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 &#xb1; 6.1%, 38 &#xb1; 4.9%, 60 &#xb1; 3.9%, 60 &#xb1; 3.3%, 61 &#xb1; 3.2%, and 63 &#xb1; 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain.<br><br>CONCLUSION: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives.<br><br>SIGNIFICANCE: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.},
}
@article {pmid37022411,
year = {2023},
author = {Tang, X and Yang, C and Sun, X and Zou, M and Wang, H},
title = {Motor Imagery EEG Decoding Based on Multi-Scale Hybrid Networks and Feature Enhancement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1208-1218},
doi = {10.1109/TNSRE.2023.3242280},
pmid = {37022411},
issn = {1558-0210},
abstract = {Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain's intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What's more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals' advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.},
}
@article {pmid37022389,
year = {2024},
author = {Li, C and Li, P and Zhang, Y and Li, N and Si, Y and Li, F and Cao, Z and Chen, H and Chen, B and Yao, D and Xu, P},
title = {Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {8},
pages = {10258-10272},
doi = {10.1109/TNNLS.2023.3238519},
pmid = {37022389},
issn = {2162-2388},
mesh = {Humans ; *Emotions/physiology/classification ; *Electroencephalography/methods ; *Brain/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Male ; Adult ; Female ; Young Adult ; Algorithms ; Pattern Recognition, Automated/methods ; Machine Learning ; },
abstract = {Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and increase the stability of emotion recognition, we propose an effective emotion recognition model that performs multicategory emotion recognition with multiple emotion-related spatial network topology patterns (MESNPs) by learning discriminative graph topologies in EEG brain networks. To evaluate the performance of our proposed MESNP model, we conducted single-subject and multisubject four-class classification experiments on two public datasets, MAHNOB-HCI and DEAP. Compared with existing feature extraction methods, the MESNP model significantly enhances the multiclass emotional classification performance in the single-subject and multisubject conditions. To evaluate the online version of the proposed MESNP model, we designed an online emotion monitoring system. We recruited 14 participants to conduct the online emotion decoding experiments. The average online experimental accuracy of the 14 participants was 84.56%, indicating that our model can be applied in affective brain-computer interface (aBCI) systems. The offline and online experimental results demonstrate that the proposed MESNP model effectively captures discriminative graph topology patterns and significantly improves emotion classification performance. Moreover, the proposed MESNP model provides a new scheme for extracting features from strongly coupled array signals.},
}
@article {pmid37022370,
year = {2023},
author = {Li, M and Li, N and Gao, X and Ma, R and Dong, J and Chen, X and Cui, H},
title = {A Novel SSVEP Brain-Computer Interface System Based on Simultaneous Modulation of Luminance and Motion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1149-1157},
doi = {10.1109/TNSRE.2023.3241629},
pmid = {37022370},
issn = {1558-0210},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have received significant attention owing to their high information transfer rate (ITR) and low training requirements. Previous SSVEP-based BCIs mostly adopt the stationary visual flickers where only a few studies have explored the effect of moving visual flickers on the SSVEP-BCI. In this study, a novel stimulus encoding method based on the simultaneous modulation of luminance and motion was proposed. We adopted the sampled sinusoidal stimulation method to encode the frequencies and phases of stimulus targets. In addition to luminance modulation, at the same time, visual flickers also moved horizontally towards right and left at different frequencies (i.e., 0, 0.2, 0.4, and 0.6 Hz) following a sinusoidal function. Accordingly, a nine-target SSVEP-BCI was built to evaluate the influence of motion modulation on the BCI performance. Filter bank canonical correlation analysis (FBCCA) approach was adopted to identify the stimulus targets. Offline experimental results of 17 subjects revealed that the system performance decreased with the increase of superimposed horizontal periodic motion frequency. Our online experimental results showed that the subjects achieved 85.00 ± 6.77 % and 83.15 ± 9.88 % accuracy for the superimposed horizontal periodic motion frequencies of 0 and 0.2 Hz, respectively. These results verified the feasibility of the proposed systems. In addition, the system with 0.2 Hz horizontal motion frequency provided the best visual experience for subjects. These results indicated that moving visual stimulus can provide an alternative option for SSVEP-BCIs. Furthermore, the proposed paradigm is expected to develop a more comfortable BCI system.},
}
@article {pmid37022367,
year = {2023},
author = {Gao, Y and Li, M and Peng, Y and Fang, F and Zhang, Y},
title = {Double Stage Transfer Learning for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1128-1136},
doi = {10.1109/TNSRE.2023.3241301},
pmid = {37022367},
issn = {1558-0210},
abstract = {In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals are difficult to collect in large quantities due to the non-stationary nature and long calibration time required. Transfer learning (TL), which transfers knowledge learned from existing subjects to new subjects, can be applied to solve this problem. Some existing EEG-based TL algorithms cannot achieve good results because they only extract partial features. To achieve effective transfer, a double-stage transfer learning (DSTL) algorithm which applied transfer learning to both preprocessing stage and feature extraction stage of typical BCIs was proposed. First, Euclidean alignment (EA) was used to align EEG trials from different subjects. Second, aligned EEG trials in the source domain were reweighted by the distance between the covariance matrix of each trial in the source domain and the mean covariance matrix of the target domain. Lastly, after extracting spatial features with common spatial patterns (CSP), transfer component analysis (TCA) was adopted to reduce the differences between different domains further. Experiments on two public datasets in two transfer paradigms (multi-source to single-target (MTS) and single-source to single-target (STS)) verified the effectiveness of the proposed method. The proposed DSTL achieved better classification accuracy on two datasets: 84.64% and 77.16% in MTS, 73.38% and 68.58% in STS, which shows that DSTL performs better than other state-of-the-art methods. The proposed DSTL can reduce the difference between the source domain and the target domain, providing a new method for EEG data classification without training dataset.},
}
@article {pmid37022366,
year = {2023},
author = {She, Q and Chen, T and Fang, F and Zhang, J and Gao, Y and Zhang, Y},
title = {Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1137-1148},
doi = {10.1109/TNSRE.2023.3241846},
pmid = {37022366},
issn = {1558-0210},
abstract = {Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.},
}
@article {pmid37022234,
year = {2023},
author = {Pham, TD},
title = {Classification of Motor-Imagery Tasks Using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1097-1107},
doi = {10.1109/TNSRE.2023.3241241},
pmid = {37022234},
issn = {1558-0210},
abstract = {Brain-computer or brain-machine interface technology allows humans to control machines using their thoughts via brain signals. In particular, these interfaces can assist people with neurological diseases for speech understanding or physical disabilities for operating devices such as wheelchairs. Motor-imagery tasks play a basic role in brain-computer interfaces. This study introduces an approach for classifying motor-imagery tasks in a brain-computer interface environment, which remains a challenge for rehabilitation technology using electroencephalogram sensors. Methods used and developed for addressing the classification include wavelet time and image scattering networks, fuzzy recurrence plots, support vector machines, and classifier fusion. The rationale for combining outputs from two classifiers learning on wavelet-time and wavelet-image scattering features of brain signals, respectively, is that they are complementary and can be effectively fused using a novel fuzzy rule-based system. A large-scale challenging electroencephalogram dataset of motor imagery-based brain-computer interface was used to test the efficacy of the proposed approach. Experimental results obtained from within-session classification show the potential application of the new model that achieves an improvement of 7% in classification accuracy over the best existing classifier using state-of-the-art artificial intelligence (76% versus 69%, respectively). For the cross-session experiment, which imposes a more challenging and practical classification task, the proposed fusion model improves the accuracy by 11% (54% versus 65%). The technical novelty presented herein and its further exploration are promising for developing a reliable sensor-based intervention for assisting people with neurodisability to improve their quality of life.},
}
@article {pmid37022076,
year = {2023},
author = {Kwak, Y and Kong, K and Song, WJ and Kim, SE},
title = {Subject-Invariant Deep Neural Networks Based on Baseline Correction for EEG Motor Imagery BCI.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {4},
pages = {1801-1812},
doi = {10.1109/JBHI.2023.3238421},
pmid = {37022076},
issn = {2168-2208},
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems have been extensively used in various applications, such as communication, control, and rehabilitation. However, individual anatomical and physiological differences cause subject-specific variability of EEG signals for the same task, and BCI systems thus require a calibration procedure that adjusts system parameters to each subject. To overcome this problem, we propose a subject-invariant deep neural network (DNN) using baseline-EEG signals that can be recorded from subjects resting in comfortable states. We first modeled the deep features of EEG signals as a decomposition of subject-invariant and subject-variant features corrupted by anatomical/physiological characteristics. Subject-variant features were then removed from the deep features by learning the network with a baseline correction module (BCM) using the underlying individual information in baseline-EEG signals. The subject-invariant loss forces the BCM to assemble subject-invariant features that have the same class, irrespective of the subject. Using 1-min baseline-EEG signals of the new subject, our algorithm can eliminate subject-variant components from test data without the calibration process. The experimental results show that our subject-invariant DNN framework significantly increases decoding accuracies of the conventional DNN methods for BCI systems. Furthermore, feature visualizations illustrate that the proposed BCM extracts subject-invariant features that are close to each other in the same class.},
}
@article {pmid37022027,
year = {2023},
author = {Grover, N and Chharia, A and Upadhyay, R and Longo, L},
title = {Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {464-473},
doi = {10.1109/TNSRE.2023.3237375},
pmid = {37022027},
issn = {1558-0210},
abstract = {Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents $\textit {Schizo-Net}
$ , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the $\textit {first}
$ which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. $\textit {Schizo-Net}
$ surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.},
}
@article {pmid37021989,
year = {2024},
author = {Ding, Y and Robinson, N and Tong, C and Zeng, Q and Guan, C},
title = {LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {7},
pages = {9773-9786},
doi = {10.1109/TNNLS.2023.3236635},
pmid = {37021989},
issn = {2162-2388},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Brain/physiology ; Attention/physiology ; Machine Learning ; Algorithms ; Emotions/physiology ; Cognition/physiology ; },
abstract = {Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-global-graph network (LGGNet), a novel neurologically inspired graph neural network (GNN), to learn local-global-graph (LGG) representations of electroencephalography (EEG) for brain-computer interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multiscale 1-D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local- and global-graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art (SOTA) methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, regularized graph neural network (RGNN), attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN), hierarchical recurrent neural network (HRNN), and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant () in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG.},
}
@article {pmid37021904,
year = {2023},
author = {Wang, Z and Chen, C and Li, J and Wan, F and Sun, Y and Wang, H},
title = {ST-CapsNet: Linking Spatial and Temporal Attention With Capsule Network for P300 Detection Improvement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {991-1000},
doi = {10.1109/TNSRE.2023.3237319},
pmid = {37021904},
issn = {1558-0210},
abstract = {A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 det- ection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.},
}
@article {pmid37021903,
year = {2023},
author = {Park, S and Ha, J and Kim, L},
title = {Improving Performance of Motor Imagery-Based Brain-Computer Interface in Poorly Performing Subjects Using a Hybrid-Imagery Method Utilizing Combined Motor and Somatosensory Activity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1064-1074},
doi = {10.1109/TNSRE.2023.3237583},
pmid = {37021903},
issn = {1558-0210},
abstract = {The phenomena of brain-computer interface-inefficiency in transfer rates and reliability can hinder development and use of brain-computer interface technology. This study aimed to enhance the classification performance of motor imagery-based brain-computer interface (three-class: left hand, right hand, and right foot) of poor performers using a hybrid-imagery approach that combined motor and somatosensory activity. Twenty healthy subjects participated in these experiments involving the following three paradigms: (1) Control-condition: motor imagery only, (2) Hybrid-condition I: combined motor and somatosensory stimuli (same stimulus: rough ball), and (3) Hybrid-condition II: combined motor and somatosensory stimuli (different stimulus: hard and rough, soft and smooth, and hard and rough ball). The three paradigms for all participants, achieved an average accuracy of 63.60± 21.62%, 71.25± 19.53%, and 84.09± 12.79% using the filter bank common spatial pattern algorithm (5-fold cross-validation), respectively. In the poor performance group, the Hybrid-condition II paradigm achieved an accuracy of 81.82%, showing a significant increase of 38.86% and 21.04% in accuracy compared to the control-condition (42.96%) and Hybrid-condition I (60.78%), respectively. Conversely, the good performance group showed a pattern of increasing accuracy, with no significant difference between the three paradigms. The Hybrid-condition II paradigm provided high concentration and discrimination to poor performers in the motor imagery-based brain-computer interface and generated the enhanced event-related desynchronization pattern in three modalities corresponding to different types of somatosensory stimuli in motor and somatosensory regions compared to the Control-condition and Hybrid-condition I. The hybrid-imagery approach can help improve motor imagery-based brain-computer interface performance, especially for poorly performing users, thus contributing to the practical use and uptake of brain-computer interface.},
}
@article {pmid37021872,
year = {2022},
author = {Abrams, Z},
title = {The Future of Brain-Computer Interfaces.},
journal = {IEEE pulse},
volume = {13},
number = {6},
pages = {21-24},
doi = {10.1109/MPULS.2022.3227806},
pmid = {37021872},
issn = {2154-2317},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Touch ; *Video Games ; Electroencephalography ; },
abstract = {Operating a drone, playing video games, or controlling a robot simply by thinking are exciting applications of brain-computer interfaces (BCIs) that pave the way for more mind-bending breakthroughs. Crucially, BCIs, which enable the brain to exchange signals with an outside device, also represent a powerful tool to restore movement, speech, touch, and other functions to patients with brain damage. Despite recent progress in the field, technological innovation is needed and plenty of scientific and ethical questions remain unanswered. Still, researchers say BCIs hold great promise for patients with the most severe impairments-and that major breakthroughs are within reach.},
}
@article {pmid37021322,
year = {2023},
author = {Nyawanda, BO and Beloconi, A and Khagayi, S and Bigogo, G and Obor, D and Otieno, NA and Lange, S and Franke, J and Sauerborn, R and Utzinger, J and Kariuki, S and Munga, S and Vounatsou, P},
title = {The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019.},
journal = {Parasite epidemiology and control},
volume = {21},
number = {},
pages = {e00297},
pmid = {37021322},
issn = {2405-6731},
abstract = {BACKGROUND: Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya.<br><br>METHODS: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence.<br><br>RESULTS: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR]&#xa0;=&#xa0;0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR&#xa0;=&#xa0;1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59&#xa0;months (IRR&#xa0;=&#xa0;0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population.<br><br>CONCLUSIONS: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59&#xa0;months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.},
}
@article {pmid37019099,
year = {2023},
author = {Bryan, MJ and Preston Jiang, L and P N Rao, R},
title = {Neural co-processors for restoring brain function: results from a cortical model of grasping.},
journal = {Journal of neural engineering},
volume = {20},
number = {3},
pages = {},
doi = {10.1088/1741-2552/accaa9},
pmid = {37019099},
issn = {1741-2552},
mesh = {Humans ; *Artificial Intelligence ; Algorithms ; Brain/physiology ; Neural Networks, Computer ; *Deep Brain Stimulation/methods ; },
abstract = {Objective.A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and different objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation.Approach.To achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a 'neural co-processor' which uses artificial neural networks and deep learning to learn optimal closed-loop stimulation policies. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for futurein vivotests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity in preparation for futurein vivotests.Main results.Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function in the range 75%-90%.Significance.Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations andin vivoapplications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.},
}
@article {pmid37018725,
year = {2023},
author = {Wei, Q and Ding, X},
title = {Intra- and Inter-Subject Common Spatial Pattern for Reducing Calibration Effort in MI-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {904-916},
doi = {10.1109/TNSRE.2023.3236372},
pmid = {37018725},
issn = {1558-0210},
abstract = {One major problem limiting the practicality of a brain-computer interface (BCI) is the need for large amount of labeled data to calibrate its classification model. Although the effectiveness of transfer learning (TL) for conquering this problem has been evidenced by many studies, a highly recognized approach has not yet been established. In this paper, we propose a Euclidean alignment (EA)-based intra- and inter-subject common spatial pattern (EA-IISCSP) algorithm for estimating four spatial filters, which aim at exploiting intra- and inter-subject similarities and variability to enhance the robustness of feature signals. Based on the algorithm, a TL-based classification framework was developed for enhancing the performance of motor imagery (MI) BCIs, in which the feature vector extracted by each filter is dimensionally reduced by linear discriminant analysis (LDA) and a support vector machine (SVM) is used for classification. The performance of the proposed algorithm was evaluated on two MI data sets and compared with that of three state-of-the-art TL algorithms. Experimental results showed that the proposed algorithm significantly outperforms these competing algorithms for training trials per class from 15 to 50 and can reduce the amount of training data while maintaining an acceptable accuracy, thus facilitating the practical application of MI-based BCIs.},
}
@article {pmid37018712,
year = {2023},
author = {Naser, MYM and Bhattacharya, S},
title = {Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1030-1044},
doi = {10.1109/TNSRE.2023.3236251},
pmid = {37018712},
issn = {1558-0210},
abstract = {The use of brain signals in controlling wheelchairs is a promising solution for many disabled individuals, specifically those who are suffering from motor neuron disease affecting the proper functioning of their motor units. Almost two decades since the first work, the applicability of EEG-driven wheelchairs is still limited to laboratory environments. In this work, a systematic review study has been conducted to identify the state-of-the-art and the different models adopted in the literature. Furthermore, a strong emphasis is devoted to introducing the challenges impeding a broad use of the technology as well as the latest research trends in each of those areas.},
}
@article {pmid37018635,
year = {2023},
author = {Wang, X and Liu, A and Wu, L and Li, C and Liu, Y and Chen, X},
title = {A Generalized Zero-Shot Learning Scheme for SSVEP-Based BCI System.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {863-874},
doi = {10.1109/TNSRE.2023.3235804},
pmid = {37018635},
issn = {1558-0210},
abstract = {The steady-state visual evoked potential (SSVEP) has been widely used in building multi-target brain-computer interfaces (BCIs) based on electroencephalogram (EEG). However, methods for high-accuracy SSVEP systems require training data for each target, which needs significant calibration time. This study aimed to use the data of only part of the targets for training while achieving high classification accuracy on all targets. In this work, we proposed a generalized zero-shot learning (GZSL) scheme for SSVEP classification. We divided the target classes into seen and unseen classes and trained the classifier only using the seen classes. During the test time, the search space contained both seen classes and unseen classes. In the proposed scheme, the EEG data and the sine waves are embedded into the same latent space using convolutional neural networks (CNN). We use the correlation coefficient of the two outputs in the latent space for classification. Our method was tested on two public datasets and reached 89.9% of the classification accuracy of the state-of-the-art (SOTA) data-driven method, which needs the training data of all targets. Compared to the SOTA training-free method, our method achieved a multifold improvement. This work shows that it is promising to build an SSVEP classification system that does not need the training data of all targets.},
}
@article {pmid37018580,
year = {2023},
author = {Zeng, H and Shen, Y and Sun, D and Hu, X and Wen, P and Song, A},
title = {Extended Control With Hybrid Gaze-BCI for Multi-Robot System Under Hands-Occupied Dual-Tasking.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {829-840},
doi = {10.1109/TNSRE.2023.3234971},
pmid = {37018580},
issn = {1558-0210},
abstract = {Currently there still remains a critical need of human involvements for multi-robot system (MRS) to successfully perform their missions in real-world applications, and the hand-controller has been commonly used for the operator to input MRS control commands. However, in more challenging scenarios involving concurrent MRS control and system monitoring tasks, where the operator's both hands are busy, the hand-controller alone is inadequate for effective human-MRS interaction. To this end, our study takes a first step toward a multimodal interface by extending the hand-controller with a hands-free input based on gaze and brain-computer interface (BCI), i.e., a hybrid gaze-BCI. Specifically, the velocity control function is still designated to the hand-controller that excels at inputting continuous velocity commands for MRS, while the formation control function is realized with a more intuitive hybrid gaze-BCI, rather than with the hand-controller via a less natural mapping. In a dual-task experimental paradigm that simulated the hands-occupied manipulation condition in real-world applications, operators achieved improved performance for controlling simulated MRS (average formation inputting accuracy increases 3%, average finishing time decreases 5 s), reduced cognitive load (average reaction time for secondary task decreases 0.32 s) and perceived workload (average rating score decreases 15.84) with the hand-controller extended by the hybrid gaze-BCI, over those with the hand-controller alone. These findings reveal the potential of the hands-free hybrid gaze-BCI to extend the traditional manual MRS input devices for creating a more operator-friendly interface, in challenging hands-occupied dual-tasking scenarios.},
}
@article {pmid37018579,
year = {2023},
author = {Wu, D and Shi, Y and Wang, Z and Yang, J and Sawan, M},
title = {C[2]SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {841-850},
doi = {10.1109/TNSRE.2023.3235390},
pmid = {37018579},
issn = {1558-0210},
abstract = {Recent developments in brain-machine interface technology have rendered seizure prediction possible. However, the transmission of a large volume of electrophysiological signals between sensors and processing apparatuses and the related computation become two major bottlenecks for seizure prediction systems due to the constrained bandwidth and limited computational resources, especially for power-critical wearable and implantable medical devices. Although many data compression methods can be adopted to compress the signals to reduce communication bandwidth requirement, they require complex compression and reconstruction procedures before the signal can be used for seizure prediction. In this paper, we propose $\text{C}
^{{2}
}
$ SP-Net, a framework to jointly solve compression, prediction, and reconstruction without extra computation overhead. The framework consists of a plug-and-play in-sensor compression matrix to reduce transmission bandwidth requirements. The compressed signal can be utilized for seizure prediction without additional reconstruction steps. Reconstruction of the original signal can also be carried out in high fidelity. Compression and classification overhead from the energy consumption perspective, prediction accuracy, sensitivity, false prediction rate, and reconstruction quality of the proposed framework are evaluated using various compression ratios. The experimental results illustrate that our proposed framework is energy efficient and outperforms the competitive state-of-the-art baselines by a large margin in prediction accuracy. In particular, our proposed method produces an average loss of 0.6% in prediction accuracy with a compression ratio ranging from 1/2 to 1/16.},
}
@article {pmid37015706,
year = {2023},
author = {Wang, H and Zheng, H and Wu, H and Long, J},
title = {Behavior-Dependent Corticocortical Contributions to Imagined Grasping: A BCI-Triggered TMS Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {519-529},
doi = {10.1109/TNSRE.2022.3227511},
pmid = {37015706},
issn = {1558-0210},
abstract = {Previous studies have indicated that corticocortical neural mechanisms differ during various grasping behaviors. However, the literature rarely considers corticocortical contributions to various imagined grasping behaviors. To address this question, we examine their mechanisms by transcranial magnetic stimulation (TMS) triggered when detecting event-related desynchronization during right-hand grasping behavior imagination through a brain-computer interface (BCI) system. Based on the BCI system, we designed two experiments. In Experiment 1, we explored differences in motor evoked potentials (MEPs) between power grip and resting conditions. In Experiment 2, we used the three TMS coil orientations (lateral-medial (LM), posterior-anterior (PA), and anterior-posterior (AP) directions) over the primary motor cortex to elicit MEPs during imagined index finger abduction, precision grip, and power grip. We found that larger MEP amplitudes and shorter latencies were obtained in imagined power grip than in resting. We also detected lower MEP amplitudes during imagined power grip, while MEP amplitudes remained similar across imagined precision grip and index finger abduction in each TMS coil orientation. Differences in AP-LM latency were longer when subjects imagined a power grip compared with precision grip and index finger abduction. Based on our results, higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. Our results suggests that higher cortical excitability may be achieved when humans imagine precision grip and index finger abduction. We also propose that preferential recruitment of late synaptic inputs to corticospinal neurons may occur when humans imagine a power grip.},
}
@article {pmid37015688,
year = {2023},
author = {Ahn, HJ and Lee, DH and Jeong, JH and Lee, SW},
title = {Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {646-656},
doi = {10.1109/TNSRE.2022.3229330},
pmid = {37015688},
issn = {1558-0210},
abstract = {A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.},
}
@article {pmid37015587,
year = {2023},
author = {Luo, R and Xu, M and Zhou, X and Xiao, X and Jung, TP and Ming, D},
title = {Data Augmentation of SSVEPs Using Source Aliasing Matrix Estimation for Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {6},
pages = {1775-1785},
doi = {10.1109/TBME.2022.3227036},
pmid = {37015587},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Algorithms ; Calibration ; Photic Stimulation ; },
abstract = {OBJECTIVE: Currently, ensemble task-related component analysis (eTRCA) and task discriminative component analysis (TDCA) are the state-of-the-art algorithms for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). However, training the BCIs requires multiple calibration trials. With insufficient calibration data, the accuracy of the BCI will degrade, or even become invalid with only one calibration trial. However, collecting a large amount of electroencephalography (EEG) data for calibration is a time-consuming and laborious process, which hinders the practical use of eTRCA and TDCA.<br><br>METHODS: This study proposed a novel method, namely Source Aliasing Matrix Estimation (SAME), to augment the calibration data for SSVEP-BCIs. SAME could generate artificial EEG trials with the featured SSVEPs. Its effectiveness was evaluated using two public datasets (i.e., Benchmark, BETA).<br><br>RESULTS: When combined with SAME, both eTRCA and TDCA had significantly improved performance with a limited number of calibration data. Specifically, SAME increased the average accuracy of eTRCA and TDCA by about 12% and 3%, respectively, with as few as two calibration trials. Notably, SAME enabled eTRCA and TDCA to work well with a single calibration trial, achieving an average accuracy >90% for the Benchmark dataset and >70% for the BETA dataset with 1-second EEG.<br><br>CONCLUSION: SAME is an effective method for SSVEP-BCIs to augment the calibration data, thereby significantly enhancing the performance of eTRCA and TDCA.<br><br>SIGNIFICANCE: We propose a new data-augmentation method that is compatible with the state-of-the-art algorithms of SSVEP-based BCIs. It can significantly reduce the efforts required to calibrate SSVEP-BCIs, which is promising for the development of practical BCIs.},
}
@article {pmid37015471,
year = {2023},
author = {Park, S and Ha, J and Park, J and Lee, K and Im, CH},
title = {Brain-Controlled, AR-Based Home Automation System Using SSVEP-Based Brain-Computer Interface and EOG-Based Eye Tracker: A Feasibility Study for the Elderly End User.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {544-553},
doi = {10.1109/TNSRE.2022.3228124},
pmid = {37015471},
issn = {1558-0210},
abstract = {Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user's preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.},
}
@article {pmid37015470,
year = {2023},
author = {Guo, Z and Chen, F},
title = {Decoding Articulation Motor Imagery Using Early Connectivity Information in the Motor Cortex: A Functional Near-Infrared Spectroscopy Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {506-518},
doi = {10.1109/TNSRE.2022.3227595},
pmid = {37015470},
issn = {1558-0210},
abstract = {Brain computer interface (BCI) based on speech imagery can help people with motor disorders communicate their thoughts to the outside world in a natural way. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) is preferred for developing BCIs. Previous BCIs based on fNIRS mainly relied on activation information, which ignored the functional connectivity between neural areas. In this study, a 4-class speech imagery BCI based on fNIRS is presented to decode simplified articulation motor imagery (only the movements of jaw and lip were retained) of different vowels. Synchronization information in the motor cortex was extracted as features. In multiclass (four classes) settings, the mean subject-dependent classification accuracies approximated or exceeded 40% in the 0-2.5 s and 0-10 s time windows, respectively. In binary class settings (the average classification accuracies of all pairwise comparisons between two vowels), the mean subject-dependent classification accuracies exceeded 70% in the 0-2.5 s and 0-10 s time windows. These results demonstrate that connectivity features can effectively differentiate different vowels even if the time window size was reduced from 10 s to 2.5 s and the decoding performance in both the time windows was almost the same. This finding suggests that speech imagery BCI based on fNIRS can be further optimized in terms of feature extraction and command generation time reduction. In addition, simplified articulation motor imagery of vowels can be distinguished, and therefore, the potential contribution of articulation motor imagery information extracted from the motor cortex should be emphasized in speech imagery BCI based on fNIRS to improve decoding performance.},
}
@article {pmid37015468,
year = {2023},
author = {Huang, X and Liang, S and Zhang, Y and Zhou, N and Pedrycz, W and Choi, KS},
title = {Relation Learning Using Temporal Episodes for Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {530-543},
doi = {10.1109/TNSRE.2022.3228216},
pmid = {37015468},
issn = {1558-0210},
abstract = {For practical motor imagery (MI) brain-computer interface (BCI) applications, generating a reliable model for a target subject with few MI trials is important since the data collection process is labour-intensive and expensive. In this paper, we address this issue by proposing a few-shot learning method called temporal episode relation learning (TERL). TERL models MI with only limited trials from the target subject by the ability to compare MI trials through episode-based training. It can be directly applied to a new user without being re-trained, which is vital to improve user experience and realize real-world MIBCI applications. We develop a new and effective approach where, unlike the original episode learning, the temporal pattern between trials in each episode is encoded during the learning to boost the classification performance. We also perform an online evaluation simulation, in addition to the offline analysis that the previous studies only conduct, to better understand the performance of different approaches in real-world scenario. Extensive experiments are completed on four publicly available MIBCI datasets to evaluate the proposed TERL. Results show that TERL outperforms baseline and recent state-of-the-art methods, demonstrating competitive performance for subject-specific MIBCI where few trials are available from a target subject and a considerable number of trials from other source subjects.},
}
@article {pmid37015437,
year = {2022},
author = {Zou, B and Zheng, Y and Shen, M and Luo, Y and Li, L and Zhang, L},
title = {BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {16},
number = {6},
pages = {1287-1298},
doi = {10.1109/TBCAS.2022.3230500},
pmid = {37015437},
issn = {1940-9990},
abstract = {Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition systems' hardware and software are usually closed-source. Its inability to flexible expansion and secondary development is a major obstacle to real-time BCI research. This paper presents the Beijing University of Posts and Telecommunications EEG Acquisition Tool System named BEATS. It implements a comprehensive system from hardware to software, composed of the analog front end, microprocessor, and software platform. BEATS is capable of collecting 32-channel EEG signals at a guaranteed sampling rate of 4 kHz with wireless transmission. Compared to state-of-the-art systems used in many EEG fields, it displays a better sampling rate. Using techniques including direct memory access, first in first out, and timer, the precision and stability of the acquisition are ensured at the microsecond level. An evaluation is conducted during 24 hours of continuous acquisitions. There are no packet losses and the average maximum delay is only 0.07 s/h. Moreover, as an open-source system, BEATS provides detailed design files, and adopts a plug-in structure and easy-to-access materials, which makes it can be quickly reproduced.},
}
@article {pmid37015133,
year = {2023},
author = {Canal, G and Diaz-Mercado, Y and Egerstedt, M and Rozell, C},
title = {A Low-Complexity Brain-Computer Interface for High-Complexity Robot Swarm Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1816-1825},
doi = {10.1109/TNSRE.2023.3257261},
pmid = {37015133},
issn = {1558-0210},
mesh = {Humans ; *Robotics/methods ; *Brain-Computer Interfaces ; Algorithms ; Feedback ; Electroencephalography/methods ; User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device to record the human user's brain activity, which is then processed into commands that drive a system end effector. BCIs involve either invasive measurements which allow for high-complexity control but are generally infeasible, or noninvasive measurements which offer lower quality signals but are more practical to use. In general, BCI systems have not been developed that efficiently, robustly, and scalably perform high-complexity control while retaining the practicality of noninvasive measurements. Here we leverage recent results from feedback information theory to fill this gap by modeling BCIs as a communications system and deploying a human-implementable interaction algorithm for noninvasive control of a high-complexity robot swarm. We construct a scalable dictionary of robotic behaviors that can be searched simply and efficiently by a BCI user, as we demonstrate through a large-scale user study testing the feasibility of our interaction algorithm, a user test of the full BCI system on (virtual and real) robot swarms, and simulations that verify our results against theoretical models. Our results provide a proof of concept for how a large class of high-complexity effectors (even beyond robotics) can be effectively controlled by a BCI system with low-complexity and noisy inputs.},
}
@article {pmid37015116,
year = {2023},
author = {Luo, X and Lin, Y and Guo, R and Gao, X and Zhang, S},
title = {ERP and Pupillometry Synchronization Analysis on Rapid Serial Visual Presentation of Words, Numbers, Pictures.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1933-1942},
doi = {10.1109/TNSRE.2023.3263502},
pmid = {37015116},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials ; *Electroencephalography/methods ; Event-Related Potentials, P300 ; Attention ; Recognition, Psychology ; },
abstract = {Hybrid brain-computer interfaces (HBCI) combining eye-tracker has attracted the attentions of researchers in target recognition. However, there are still many issues to be addressed in rapid sequence visual presentation (RSVP) tasks, such as the effect of presentation rates and target types on event-related potentials (ERP) and pupillometry, synchronization analysis of electroencephalography (EEG) and eye-tracking, and so on. In this study, the RSVP experiments with three different target types of pictures, words and numbers at the presentation rates of 100 and 200 ms were conducted. EEG data and pupillometry data were synchronously collected from 20 university students. The results of ERP analysis showed that, among three different target types at the presentation rate of 100 ms, the picture P300 component had the largest amplitude and the longest latency. From the 100 ms presentation rates to 200 ms one for the three target types, the P300 amplitudes became smaller, and the P300 latencies became shorter. The results of pupillometry analysis showed that, at the presentation rates of 100 and 200 ms, the pupil dilation of pictures had the smallest amplitude and the shortest latency. At the two presentation rates, no significant differences of pupil size and latency were found for the three target types. For the early pupil dilation within 1000 ms, the picture pupil size was significantly smaller than the other ones, and the picture pupil acceleration had the largest average amplitude and the shortest latency. These pupillometry features within 1000 ms combining with the P300 features could be taken as the effective ones for target classification. Through synchronization analysis of the EEG data and pupillometry data, the effects of target type and presentation rate on ERP and pupil dilation were different. These results could contribute to developing the fusion methods between EEG and eye-tracking, and provide valuable references for the multi-target recognition of hybrid BCI based on eye-tracking.},
}
@article {pmid37015115,
year = {2023},
author = {Wang, X and Ma, Y and Cammon, J and Fang, F and Gao, Y and Zhang, Y},
title = {Self-Supervised EEG Emotion Recognition Models Based on CNN.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {1952-1962},
doi = {10.1109/TNSRE.2023.3263570},
pmid = {37015115},
issn = {1558-0210},
mesh = {Humans ; *Neural Networks, Computer ; *Emotions ; Algorithms ; Machine Learning ; Electroencephalography/methods ; },
abstract = {Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.},
}
@article {pmid37012508,
year = {2023},
author = {Cadoni, S and Demené, C and Alcala, I and Provansal, M and Nguyen, D and Nelidova, D and Labernède, G and Lubetzki, J and Goulet, R and Burban, E and Dégardin, J and Simonutti, M and Gauvain, G and Arcizet, F and Marre, O and Dalkara, D and Roska, B and Sahel, JA and Tanter, M and Picaud, S},
title = {Ectopic expression of a mechanosensitive channel confers spatiotemporal resolution to ultrasound stimulations of neurons for visual restoration.},
journal = {Nature nanotechnology},
volume = {18},
number = {6},
pages = {667-676},
pmid = {37012508},
issn = {1748-3395},
support = {P30 EY008098/EY/NEI NIH HHS/United States ; },
mesh = {*Ectopic Gene Expression ; Neurons/metabolism ; Retina ; Vision, Ocular ; *Visual Cortex ; },
abstract = {Remote and precisely controlled activation of the brain is a fundamental challenge in the development of brain-machine interfaces for neurological treatments. Low-frequency ultrasound stimulation can be used to modulate neuronal activity deep in the brain, especially after expressing ultrasound-sensitive proteins. But so far, no study has described an ultrasound-mediated activation strategy whose spatiotemporal resolution and acoustic intensity are compatible with the mandatory needs of brain-machine interfaces, particularly for visual restoration. Here we combined the expression of large-conductance mechanosensitive ion channels with uncustomary high-frequency ultrasonic stimulation to activate retinal or cortical neurons over millisecond durations at a spatiotemporal resolution and acoustic energy deposit compatible with vision restoration. The in vivo sonogenetic activation of the visual cortex generated a behaviour associated with light perception. Our findings demonstrate that sonogenetics can deliver millisecond pattern presentations via an approach less invasive than current brain-machine interfaces for visual restoration.},
}
@article {pmid37010783,
year = {2023},
author = {Phillips, MM and Pavlyk, I and Allen, M and Ghazaly, E and Cutts, R and Carpentier, J and Berry, JS and Nattress, C and Feng, S and Hallden, G and Chelala, C and Bomalaski, J and Steele, J and Sheaff, M and Balkwill, F and Szlosarek, PW},
title = {A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma.},
journal = {Pharmacological reports : PR},
volume = {75},
number = {3},
pages = {570-584},
pmid = {37010783},
issn = {2299-5684},
support = {MRCCRTF11-8/MRC_/Medical Research Council/United Kingdom ; C12522/A8632/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Animals ; Humans ; Mice ; Arginine/metabolism ; Argininosuccinate Synthase/genetics/metabolism ; Cell Line, Tumor ; *Mesothelioma/drug therapy/genetics ; *Mesothelioma, Malignant ; Neoplasm Recurrence, Local ; Polyethylene Glycols/pharmacology ; Tumor Microenvironment ; Vascular Endothelial Growth Factor A ; *Macrophages ; *Drug Resistance, Neoplasm ; },
abstract = {BACKGROUND: Pegylated arginine deiminase (ADI-PEG20; pegargiminase) depletes arginine and improves survival outcomes for patients with argininosuccinate synthetase 1 (ASS1)-deficient malignant pleural mesothelioma (MPM). Optimisation of ADI-PEG20-based therapy will require a deeper understanding of resistance mechanisms, including those mediated by the tumor microenvironment. Here, we sought to reverse translate increased tumoral macrophage infiltration in patients with ASS1-deficient MPM relapsing on pegargiminase therapy.<br><br>METHODS: Macrophage-MPM tumor cell line (2591, MSTO, JU77) co-cultures treated with ADI-PEG20 were analyzed by flow cytometry. Microarray experiments of gene expression profiling were performed in ADI-PEG20-treated MPM tumor cells, and macrophage-relevant genetic "hits" were validated by qPCR, ELISA, and LC/MS. Cytokine and argininosuccinate analyses were performed using plasma from pegargiminase-treated patients with MPM.<br><br>RESULTS: We identified that ASS1-expressing macrophages promoted viability of ADI-PEG20-treated ASS1-negative MPM cell lines. Microarray gene expression data revealed a dominant CXCR2-dependent chemotactic signature and co-expression of VEGF-A and IL-1&#x3b1; in ADI-PEG20-treated MPM cell lines. We confirmed that ASS1 in macrophages was IL-1&#x3b1;-inducible and that the argininosuccinate concentration doubled in the cell supernatant sufficient to restore MPM cell viability under co-culture conditions with ADI-PEG20. For further validation, we detected elevated plasma VEGF-A and CXCR2-dependent cytokines, and increased argininosuccinate in patients with MPM progressing on ADI-PEG20. Finally, liposomal clodronate depleted ADI-PEG20-driven macrophage infiltration and suppressed growth significantly in the MSTO xenograft murine model.<br><br>CONCLUSIONS: Collectively, our data indicate that ADI-PEG20-inducible cytokines orchestrate argininosuccinate fuelling of ASS1-deficient mesothelioma by macrophages. This novel stromal-mediated resistance pathway may be leveraged to optimize arginine deprivation therapy for mesothelioma and related arginine-dependent cancers.},
}
@article {pmid37009261,
year = {2023},
author = {Lyreskog, DM and Zohny, H and Savulescu, J and Singh, I},
title = {Merging Minds: The Conceptual and Ethical Impacts of Emerging Technologies for Collective Minds.},
journal = {Neuroethics},
volume = {16},
number = {1},
pages = {12},
pmid = {37009261},
issn = {1874-5490},
abstract = {A growing number of technologies are currently being developed to improve and distribute thinking and decision-making. Rapid progress in brain-to-brain interfacing and swarming technologies promises to transform how we think about collective and collaborative cognitive tasks across domains, ranging from research to entertainment, and from therapeutics to military applications. As these tools continue to improve, we are prompted to monitor how they may affect our society on a broader level, but also how they may reshape our fundamental understanding of agency, responsibility, and other key concepts of our moral landscape. In this paper we take a closer look at this class of technologies - Technologies for Collective Minds - to see not only how their implementation may react with commonly held moral values, but also how they challenge our underlying concepts of what constitutes collective or individual agency. We argue that prominent contemporary frameworks for understanding collective agency and responsibility are insufficient in terms of accurately describing the relationships enabled by Technologies for Collective Minds, and that they therefore risk obstructing ethical analysis of the implementation of these technologies in society. We propose a more multidimensional approach to better understand this set of technologies, and to facilitate future research on the ethics of Technologies for Collective Minds.},
}
@article {pmid37008216,
year = {2023},
author = {Minciacchi, D and Bravi, R and Rosenboom, D},
title = {Editorial: Sonification, aesthetic representation of physical quantities.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1162383},
pmid = {37008216},
issn = {1662-4548},
}
@article {pmid37008204,
year = {2023},
author = {Bai, X and Li, M and Qi, S and Ng, ACM and Ng, T and Qian, W},
title = {A hybrid P300-SSVEP brain-computer interface speller with a frequency enhanced row and column paradigm.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1133933},
pmid = {37008204},
issn = {1662-4548},
abstract = {OBJECTIVE: This study proposes a new hybrid brain-computer interface (BCI) system to improve spelling accuracy and speed by stimulating P300 and steady-state visually evoked potential (SSVEP) in electroencephalography (EEG) signals.<br><br>METHODS: A frequency enhanced row and column (FERC) paradigm is proposed to incorporate the frequency coding into the row and column (RC) paradigm so that the P300 and SSVEP signals can be evoked simultaneously. A flicker (white-black) with a specific frequency from 6.0 to 11.5 Hz with an interval of 0.5 Hz is assigned to one row or column of a 6 &#xd7; 6 layout, and the row/column flashes are carried out in a pseudorandom sequence. A wavelet and support vector machine (SVM) combination is adopted for P300 detection, an ensemble task-related component analysis (TRCA) method is used for SSVEP detection, and the two detection possibilities are fused using a weight control approach.<br><br>RESULTS: The implemented BCI speller achieved an accuracy of 94.29% and an information transfer rate (ITR) of 28.64 bit/min averaged across 10 subjects during the online tests. An accuracy of 96.86% is obtained during the offline calibration tests, higher than that of only using P300 (75.29%) or SSVEP (89.13%). The SVM in P300 outperformed the previous linear discrimination classifier and its variants (61.90-72.22%), and the ensemble TRCA in SSVEP outperformed the canonical correlation analysis method (73.33%).<br><br>CONCLUSION: The proposed hybrid FERC stimulus paradigm can improve the performance of the speller compared with the classical single stimulus paradigm. The implemented speller can achieve comparable accuracy and ITR to its state-of-the-art counterparts with advanced detection algorithms.},
}
@article {pmid37007683,
year = {2023},
author = {Proverbio, AM and Pischedda, F},
title = {Measuring brain potentials of imagination linked to physiological needs and motivational states.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1146789},
pmid = {37007683},
issn = {1662-5161},
abstract = {INTRODUCTION: While EEG signals reflecting motor and perceptual imagery are effectively used in brain computer interface (BCI) contexts, little is known about possible indices of motivational states. In the present study, electrophysiological markers of imagined motivational states, such as craves and desires were investigated.<br><br>METHODS: Event-related potentials (ERPs) were recorded in 31 participants during perception and imagery elicited by the presentation of 360 pictograms. Twelve micro-categories of needs, subdivided into four macro-categories, were considered as most relevant for a possible BCI usage, namely: primary visceral needs (e.g., hunger, linked to desire of food); somatosensory thermal and pain sensations (e.g., cold, linked to desire of warm), affective states (e.g., fear: linked to desire of reassurance) and secondary needs (e.g., desire to exercise or listen to music). Anterior N400 and centroparietal late positive potential (LPP) were measured and statistically analyzed.<br><br>RESULTS: N400 and LPP were differentially sensitive to the various volition stats, depending on their sensory, emotional and motivational poignancy. N400 was larger to imagined positive appetitive states (e.g., play, cheerfulness) than negative ones (sadness or fear). In addition, N400 was of greater amplitude during imagery of thermal and nociceptive sensations than other motivational or visceral states. Source reconstruction of electromagnetic dipoles showed the activation of sensorimotor areas and cerebellum for movement imagery, and of auditory and superior frontal areas for music imagery.<br><br>DISCUSSION: Overall, ERPs were smaller and more anteriorly distributed during imagery than perception, but showed some similarity in terms of lateralization, distribution, and category response, thus indicating some overlap in neural processing, as also demonstrated by correlation analyses. In general, anterior frontal N400 provided clear markers of subjects' physiological needs and motivational states, especially cold, pain, and fear (but also sadness, the urgency to move, etc.), than can signal life-threatening conditions. It is concluded that ERP markers might potentially allow the reconstruction of mental representations related to various motivational states through BCI systems.},
}
@article {pmid37007682,
year = {2023},
author = {Jadavji, Z and Kirton, A and Metzler, MJ and Zewdie, E},
title = {BCI-activated electrical stimulation in children with perinatal stroke and hemiparesis: A pilot study.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1006242},
pmid = {37007682},
issn = {1662-5161},
abstract = {BACKGROUND: Perinatal stroke (PS) causes most hemiparetic cerebral palsy (CP) and results in lifelong disability. Children with severe hemiparesis have limited rehabilitation options. Brain computer interface- activated functional electrical stimulation (BCI-FES) of target muscles may enhance upper extremity function in hemiparetic adults. We conducted a pilot clinical trial to assess the safety and feasibility of BCI-FES in children with hemiparetic CP.<br><br>METHODS: Thirteen participants (mean age = 12.2 years, 31% female) were recruited from a population-based cohort. Inclusion criteria were: (1) MRI-confirmed PS, (2) disabling hemiparetic CP, (3) age 6-18 years, (4) informed consent/assent. Those with neurological comorbidities or unstable epilepsy were excluded. Participants attended two BCI sessions: training and rehabilitation. They wore an EEG-BCI headset and two forearm extensor stimulation electrodes. Participants' imagination of wrist extension was classified on EEG, after which muscle stimulation and visual feedback were provided when the correct visualization was detected.<br><br>RESULTS: No serious adverse events or dropouts occurred. The most common complaints were mild headache, headset discomfort and muscle fatigue. Children ranked the experience as comparable to a long car ride and none reported as unpleasant. Sessions lasted a mean of 87 min with 33 min of stimulation delivered. Mean classification accuracies were (M = 78.78%, SD = 9.97) for training and (M = 73.48, SD = 12.41) for rehabilitation. Mean Cohen's Kappa across rehabilitation trials was M = 0.43, SD = 0.29, range = 0.019-1.00, suggesting BCI competency.<br><br>CONCLUSION: Brain computer interface-FES was well -tolerated and feasible in children with hemiparesis. This paves the way for clinical trials to optimize approaches and test efficacy.},
}
@article {pmid37007675,
year = {2023},
author = {Śliwowski, M and Martin, M and Souloumiac, A and Blanchart, P and Aksenova, T},
title = {Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1111645},
pmid = {37007675},
issn = {1662-5161},
abstract = {INTRODUCTION: In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal characteristics (e.g., non-stationarity), can we achieve higher decoding performance with more data to train decoders? What is the perspective for further improvement with time in the case of long-term BCI studies? In this study, we investigated the impact of long-term recordings on motor imagery decoding from two main perspectives: model requirements regarding dataset size and potential for patient adaptation.<br><br>METHODS: We evaluated the multilinear model and two deep learning (DL) models on a long-term BCI &amp; Tetraplegia (ClinicalTrials.gov identifier: NCT02550522) clinical trial dataset containing 43 sessions of ECoG recordings performed with a tetraplegic patient. In the experiment, a participant executed 3D virtual hand translation using motor imagery patterns. We designed multiple computational experiments in which training datasets were increased or translated to investigate the relationship between models' performance and different factors influencing recordings.<br><br>RESULTS: Our results showed that DL decoders showed similar requirements regarding the dataset size compared to the multilinear model while demonstrating higher decoding performance. Moreover, high decoding performance was obtained with relatively small datasets recorded later in the experiment, suggesting motor imagery patterns improvement and patient adaptation during the long-term experiment. Finally, we proposed UMAP embeddings and local intrinsic dimensionality as a way to visualize the data and potentially evaluate data quality.<br><br>DISCUSSION: DL-based decoding is a prospective approach in BCI which may be efficiently applied with real-life dataset size. Patient-decoder co-adaptation is an important factor to consider in long-term clinical BCI.},
}
@article {pmid37007206,
year = {2023},
author = {Li, MA and Ruan, ZW},
title = {Decoding motor imagery with a simplified distributed dipoles model at source level.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {2},
pages = {445-457},
pmid = {37007206},
issn = {1871-4080},
abstract = {Motor imagery (MI) based brain computer interface significantly oriented the development of neuro-rehabilitation, and the crucial issue is how to accurately detect the changes of cerebral cortex for MI decoding. The brain activity can be calculated based on the head model and observed scalp EEG, providing insights regarding cortical dynamics by using equivalent current dipoles with high spatial and temporal resolution. Now, all the dipoles within entire cortex or partial regions of interest are directly applied to data representation, this may make the key information weakened or lost, and it is worth studying how to choose the most important from numerous dipoles. In this paper, we devote to building a simplified distributed dipoles model (SDDM), which is combined with convolutional neural network (CNN), generating a MI decoding method at source level (called SDDM-CNN). First, all channels of raw MI-EEG signals are subdivided by a series of bandpass filters with width of 1 Hz, the average energies associated with any sub-band signals are calculated and ranked in a descending order to screen the top n sub-bands; then, the MI-EEG signals over each selected sub-band are mapped into source space by using EEG source imaging technology, and for each scout of neuroanatomical Desikan-Killiany partition, a centered dipole is selected as the most relevant dipole and put together to build a SDDM to reflect the neuroelectric activity of entire cerebral cortex; finally, the 4 dimensional (4D) magnitude matrix is constructed for each SDDM and fused into a novel data representation, which is further input to a well-designed 3DCNN with n parallel branches (nB3DCNN) to extract and classify the comprehensive features from time-frequency-space dimensions. Experiments are carried out on three public datasets, and the average ten-fold CV decoding accuracies achieve 95.09%, 97.98% and 94.53% respectively, and the statistical analysis is fulfilled by standard deviation, kappa value and confusion matrix. Experiment results suggest that it is beneficial to pick out the most sensitive sub-bands in sensor domain, and SDDM can sufficiently describe the dynamic changing of entire cortex, improving decoding performance while greatly reducing number of source signals. Also, nB3DCNN is capable of exploring spatial-temporal features from multi sub-bands.},
}
@article {pmid37007202,
year = {2023},
author = {Pan, H and Li, Z and Tian, C and Wang, L and Fu, Y and Qin, X and Liu, F},
title = {The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {2},
pages = {373-384},
pmid = {37007202},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI) can obtain text information by decoding language induced electroencephalogram (EEG) signals, so as to restore communication ability for patients with language impairment. At present, the BCI system based on speech imagery of Chinese characters has the problem of low accuracy of features classification. In this paper, the light gradient boosting machine (LightGBM) is adopted to recognize Chinese characters and solve the above problems. Firstly, the Db4 wavelet basis function is selected to decompose the EEG signals in six-layer of full frequency band, and the correlation features of Chinese characters speech imagery with high time resolution and high frequency resolution are extracted. Secondly, the two core algorithms of LightGBM, gradient-based one-side sampling and exclusive feature bundling, are used to classify the extracted features. Finally, we verify that classification performance of LightGBM is more accurate and applicable than the traditional classifiers according to the statistical analysis methods. We evaluate the proposed method through contrast experiment. The experimental results show that the average classification accuracy of the subjects' silent reading of Chinese characters "(left)", "(one)" and simultaneous silent reading is improved by 5.24%, 4.90% and 12.44% respectively.},
}
@article {pmid37007196,
year = {2023},
author = {Martín-Chinea, K and Ortega, J and Gómez-González, JF and Pereda, E and Toledo, J and Acosta, L},
title = {Effect of time windows in LSTM networks for EEG-based BCIs.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {2},
pages = {385-398},
pmid = {37007196},
issn = {1871-4080},
abstract = {People with impaired motor function could be helped by an effective brain-computer interface (BCI) based on a real-time electroencephalogram (EEG) and artificial intelligence algorithms. However, current methodologies for interpreting patient instructions from an EEG are not accurate enough to be completely safe in a real-world situation , where a poor decision would place their physical integrity at risk, such as when traveling in an electric wheelchair in a city. For various reasons, such as the low signal-to-noise ratio of portable EEGs or the effects of signal contamination (disturbances due to user movement, temporal variation of the features of EEG signals, etc.), a long short-term memory network (LSTM) (a type of recurrent neural network) that is able to learn data flow patterns from EEG signals could improve the classification of the actions taken by the user. In this paper, the effectiveness of using an LSTM with a low-cost wireless EEG device in real time is tested, and the time window that maximizes its classification accuracy is studied. The goal is to be able to implement it in the BCI of a smart wheelchair with a simple coded command protocol, such as opening or closing the eyes, which could be executed by patients with reduced mobility. Results show a higher resolution of the LSTM with an accuracy range between 77.61 and 92.14% compared to traditional classifiers (59.71%), and an optimal time window of around 7 s for the task done by users in this work. In addition, tests in real-life contexts show that a trade-off between accuracy and response times is necessary to ensure detection.},
}
@article {pmid37007193,
year = {2023},
author = {Chen, M and Zhu, Y and Zhang, R and Yu, R and Hu, Y and Wan, H and Yao, D and Guo, D},
title = {A model description of beta oscillations in the external globus pallidus.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {2},
pages = {477-487},
pmid = {37007193},
issn = {1871-4080},
abstract = {The external globus pallidus (GPe), a subcortical nucleus located in the indirect pathway of the basal ganglia, is widely considered to have tight associations with abnormal beta oscillations (13-30 Hz) observed in Parkinson's disease (PD). Despite that many mechanisms have been put forward to explain the emergence of these beta oscillations, however, it is still unclear the functional contributions of the GPe, especially, whether the GPe itself can generate beta oscillations. To investigate the role played by the GPe in producing beta oscillations, we employ a well described firing rate model of the GPe neural population. Through extensive simulations, we find that the transmission delay within the GPe-GPe pathway contributes significantly to inducing beta oscillations, and the impacts of the time constant and connection strength of the GPe-GPe pathway on generating beta oscillations are non-negligible. Moreover, the GPe firing patterns can be significantly modulated by the time constant and connection strength of the GPe-GPe pathway, as well as the transmission delay within the GPe-GPe pathway. Interestingly, both increasing and decreasing the transmission delay can push the GPe firing pattern from beta oscillations to other firing patterns, including oscillation and non-oscillation firing patterns. These findings suggest that if the transmission delays within the GPe are at least 9.8 ms, beta oscillations can be produced originally in the GPe neural population, which also may be the origin of PD-related beta oscillations and should be regarded as a promising target for treatments for PD.},
}
@article {pmid37003976,
year = {2023},
author = {Liu, X and Zhang, W and Li, W and Zhang, S and Lv, P and Yin, Y},
title = {Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial.},
journal = {BMC neurology},
volume = {23},
number = {1},
pages = {136},
pmid = {37003976},
issn = {1471-2377},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Hemiplegia/etiology ; Recovery of Function/physiology ; Electroencephalography/methods ; *Stroke/complications ; Upper Extremity ; },
abstract = {BACKGROUND: Seeking positive and comprehensive rehabilitation methods after stroke is an urgent problem to be solved, which is very important to improve the dysfunction of stroke. The aim of this study was to investigate the effects of motor imagery-based brain-computer interface training (MI-BCI) on upper limb function and attention in stroke patients with hemiplegia.<br><br>METHODS: Sixty stroke patients with impairment of upper extremity function and decreased attention were randomly assigned to the control group (CR group) or the experimental group (BCI group) in a 1:1 ratio. Patients in the CR group received conventional rehabilitation. Patients in the BCI group received 20&#xa0;min of MI-BCI training five times a week for 3 weeks (15 sessions) in addition to conventional rehabilitation. The primary outcome measures were the changes in Fugl-Meyer Motor Function Assessment of Upper Extremities (FMA-UE) and Attention Network Test (ANT) from baseline to 3 weeks.<br><br>RESULTS: About 93% of the patients completed the allocated training. Compared with the CR group, among those in the BCI group, FMA-UE was increased by 8.0 points (95%CI, 5.0 to 10.0; P&#x2009;<&#x2009;0.001). Alert network response time (32.4ms; 95%CI, 58.4 to 85.6; P&#x2009;<&#x2009;0.001), orienting network response (5.6ms; 95%CI, 29.8 to 55.8; P&#x2009;=&#x2009;0.010), and corrects number (8.0; 95%CI, 17.0 to 28.0; P&#x2009;<&#x2009;0.001) also increased in the BCI group compared with the CR group. Additionally, the executive control network response time (-&#x2009;105.9ms; 95%CI, -&#x2009;68.3 to -&#x2009;23.6; P&#x2009;=&#x2009;0.002), the total average response time (-&#x2009;244.8ms; 95%CI, -&#x2009;155.8 to -&#x2009;66.2; P&#x2009;=&#x2009;0.002), and total time (-&#x2009;122.0ms; 95%CI, -&#x2009;80.0 to -&#x2009;35.0; P&#x2009;=&#x2009;0.001) were reduced in the BCI group compared with the CR group.<br><br>CONCLUSION: MI-BCI combined with conventional rehabilitation training could better enhance upper limb motor function and attention in stroke patients. This training method may be feasible and suitable for individuals with stroke.<br><br>TRIAL REGISTRATION: This study was registered in the Chinese Clinical Trial Registry with Portal Number ChiCTR2100050430(27/08/2021).},
}
@article {pmid37002922,
year = {2023},
author = {Rosenthal, IA and Bashford, L and Kellis, S and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus.},
journal = {Cell reports},
volume = {42},
number = {4},
pages = {112312},
pmid = {37002922},
issn = {2211-1247},
support = {T32 NS105595/NS/NINDS NIH HHS/United States ; U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Physical Stimulation ; *Somatosensory Cortex/physiology ; Fingers ; *Touch Perception/physiology ; Brain Mapping ; },
abstract = {Recent literature suggests that tactile events are represented in the primary somatosensory cortex (S1) beyond its long-established topography; in addition, the extent to which S1 is modulated by vision remains unclear. To better characterize S1, human electrophysiological data were recorded during touches to the forearm or finger. Conditions included visually observed physical touches, physical touches without vision, and visual touches without physical contact. Two major findings emerge from this dataset. First, vision strongly modulates S1 area 1, but only if there is a physical element to the touch, suggesting that passive touch observation is insufficient to elicit neural responses. Second, despite recording in a putative arm area of S1, neural activity represents both arm and finger stimuli during physical touches. Arm touches are encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization but also more generally, encompassing other areas of the body.},
}
@article {pmid37001511,
year = {2023},
author = {Mahapatra, NC and Bhuyan, P},
title = {EEG-based classification of imagined digits using a recurrent neural network.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acc976},
pmid = {37001511},
issn = {1741-2552},
mesh = {Humans ; *Algorithms ; Alprostadil ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electroencephalography/methods ; },
abstract = {Objective.In recent years, imagined speech brain-computer (machine) interface applications have been an important field of study that can improve the lives of patients with speech problems through alternative verbal communication. This study aims to classify the imagined speech of numerical digits from electroencephalography (EEG) signals by exploiting the past and future temporal characteristics of the signal using several deep learning models.Approach.This study proposes a methodological combination of EEG signal processing techniques and deep learning models for the recognition of imagined speech signals. EEG signals were filtered and preprocessed using the discrete wavelet transform to remove artifacts and retrieve feature information. To classify the preprocessed imagined speech neural signals, multiple versions of multilayer bidirectional recurrent neural networks were used.Main results.The method is examined by leveraging MUSE and EPOC signals from MNIST imagined digits in the MindBigData open-access database. The presented methodology's classification performance accuracy was noteworthy, with the model's multiclass overall classification accuracy reaching a maximum of 96.18% on MUSE signals and 71.60% on EPOC signals.Significance.This study shows that the proposed signal preprocessing approach and the stacked bidirectional recurrent network model are suitable for extracting the high temporal resolution of EEG signals in order to classify imagined digits, indicating the unique neural identity of each imagined digit class that distinguishes it from the others.},
}
@article {pmid37001500,
year = {2023},
author = {Li, HY and Zhu, MZ and Yuan, XR and Guo, ZX and Pan, YD and Li, YQ and Zhu, XH},
title = {A thalamic-primary auditory cortex circuit mediates resilience to stress.},
journal = {Cell},
volume = {186},
number = {7},
pages = {1352-1368.e18},
doi = {10.1016/j.cell.2023.02.036},
pmid = {37001500},
issn = {1097-4172},
mesh = {Mice ; Animals ; *Auditory Cortex/metabolism ; Thalamus/physiology ; Neurons/metabolism ; Geniculate Bodies ; Interneurons/physiology ; Parvalbumins/metabolism ; },
abstract = {Resilience enables mental elasticity in individuals when rebounding from adversity. In this study, we identified a microcircuit and relevant molecular adaptations that play a role in natural resilience. We found that activation of parvalbumin (PV) interneurons in the primary auditory cortex (A1) by thalamic inputs from the ipsilateral medial geniculate body (MG) is essential for resilience in mice exposed to chronic social defeat stress. Early attacks during chronic social defeat stress induced short-term hyperpolarizations of MG neurons projecting to the A1 (MG[A1] neurons) in resilient mice. In addition, this temporal neural plasticity of MG[A1] neurons initiated synaptogenesis onto thalamic PV neurons via presynaptic BDNF-TrkB signaling in subsequent stress responses. Moreover, optogenetic mimicking of the short-term hyperpolarization of MG[A1] neurons, rather than merely activating MG[A1] neurons, elicited innate resilience mechanisms in response to stress and achieved sustained antidepressant-like effects in multiple animal models, representing a new strategy for targeted neuromodulation.},
}
@article {pmid37000276,
year = {2023},
author = {Willenborg, K and Lenarz, T and Busch, S},
title = {Surgical and audiological outcomes with a new transcutaneous bone conduction device with reduced transducer thickness in children.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {280},
number = {10},
pages = {4381-4389},
pmid = {37000276},
issn = {1434-4726},
support = {Germany's Excellence Strategy EXC 2177/1 "Hearing4all 2.0" Project ID 390895286//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Humans ; Child ; Child, Preschool ; *Hearing Aids ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery/rehabilitation ; Bone Conduction ; Audiometry, Pure-Tone ; *Ossicular Prosthesis ; },
abstract = {PURPOSE: Due to smaller bone thickness, young children with conductive or mixed hearing loss or single-sided deafness were previously most commonly treated with a percutaneous osseointegrated bone-anchored hearing aid (BAHA) or an active middle-ear implant. While the BAHA increases the risk of implant infections, skin infection, overgrowth of the screw or involvement of the implant in head trauma, middle-ear implant surgery involves manipulation of the ossicles with possible risk of surgical trauma. These complications can be omitted with transcutaneous bone conduction implant systems like the MED-EL Bonebridge system. The purpose of this study was to analyze whether the second generation of the Bonebridge (BCI 602) that features a decreased implant thickness with a reduced surgical drilling depth can be implanted safely in young children with good postoperative hearing performance.<br><br>METHODS: In this study, 14 patients under 12&#xa0;years were implanted with the second generation of the Bonebridge. Preoperative workup comprised a CT scan, an MRI scan, pure tone audiometry, or alternatively a BERA (bone conduction, air conduction). Since children under 12&#xa0;years often have a lower bone thickness, the CT was performed to determine the suitability of the temporal bone for optimal implant placement using the Otoplan software.<br><br>RESULTS: All patients (including three under the age of five) were successfully implanted and showed a good postoperative hearing performance.<br><br>CONCLUSION: With adequate preoperative workup, this device can be safely implanted in children and even children under 5&#xa0;years of age and allows for an extension of indication criteria toward younger children.},
}
@article {pmid36999132,
year = {2023},
author = {Liao, W and Li, J and Zhang, X and Li, C},
title = {Motor imagery brain-computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1117670},
pmid = {36999132},
issn = {1662-5161},
abstract = {This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI) combined with physiotherapy and physiotherapy alone in ischemic stroke before and after rehabilitation training. We wanted to explore whether the rehabilitation effect of MI-BCI is affected by the severity of the patient's condition and whether MI-BCI was effective for all patients. Forty hospitalized patients with ischemic stroke with motor deficits participated in this study. The patients were divided into MI and control groups. Functional assessments were performed before and after rehabilitation training. The Fugl-Meyer Assessment (FMA) was used as the primary outcome measure, and its shoulder and elbow scores and wrist scores served as secondary outcome measures. The motor assessment scale (MAS) was used to assess motor function recovery. We used non-contrast CT (NCCT) to investigate the influence of different types of middle cerebral artery high-density signs on the prognosis of ischemic stroke. Brain topographic maps can directly reflect the neural activity of the brain, so we used them to detect changes in brain function and brain topological power response after stroke. Compared the MI group and control group after rehabilitation training, better functional outcome was observed after MI-BCI rehabilitation, including a significantly higher probability of achieving a relevant increase in the Total FMA scores (MI = 16.70 ± 12.79, control = 5.34 ± 10.48), FMA shoulder and elbow scores (MI = 12.56 ± 6.37, control = 2.45 ± 7.91), FMA wrist scores (MI = 11.01 ± 3.48, control = 3.36 ± 5.79), the MAS scores (MI = 3.62 ± 2.48, control = 1.85 ± 2.89), the NCCT (MI = 21.94 ± 2.37, control = 17.86 ± 3.55). The findings demonstrate that MI-BCI rehabilitation training could more effectively improve motor function after upper limb motor dysfunction after stroke compared with routine rehabilitation training, which verifies the feasibility of active induction of neural rehabilitation. The severity of the patient's condition may affect the rehabilitation effect of the MI-BCI system.},
}
@article {pmid36998726,
year = {2023},
author = {Quiles, V and Ferrero, L and Iáñez, E and Ortiz, M and Gil-Agudo, &#xc1; and Azorín, JM},
title = {Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1154480},
pmid = {36998726},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.<br><br>MATERIAL AND METHODS: First, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.<br><br>RESULTS AND DISCUSSION: The results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.},
}
@article {pmid36997155,
year = {2023},
author = {Wang, DX and Ng, N and Seger, SE and Ekstrom, AD and Kriegel, JL and Lega, BC},
title = {Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {13},
pages = {8150-8163},
pmid = {36997155},
issn = {1460-2199},
support = {R01 NS107357/NS/NINDS NIH HHS/United States ; R01 NS125250/NS/NINDS NIH HHS/United States ; U01 NS113198/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain/physiology ; Brain-Computer Interfaces ; Cluster Analysis ; *Electrodes/standards ; *Electroencephalography/methods/standards ; *Memory, Episodic ; Mental Recall ; *Random Forest ; *Support Vector Machine ; Unsupervised Machine Learning ; },
abstract = {Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.},
}
@article {pmid36993576,
year = {2023},
author = {Tou, SLJ and Warschausky, SA and Karlsson, P and Huggins, JE},
title = {Individualized Electrode Subset Improves the Calibration Accuracy of an EEG P300-design Brain-Computer Interface for People with Severe Cerebral Palsy.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.03.22.533775},
pmid = {36993576},
issn = {2692-8205},
support = {UL1 TR000433/TR/NCATS NIH HHS/United States ; },
abstract = {OBJECTIVE: This study examined the effect of individualized electroencephalogram (EEG) electrode location selection for non-invasive P300-design brain-computer interfaces (BCIs) in people with varying severity of cerebral palsy (CP).<br><br>APPROACH: A forward selection algorithm was used to select the best performing 8 electrodes (of an available 32) to construct an individualized electrode subset for each participant. BCI accuracy of the individualized subset was compared to accuracy of a widely used default subset.<br><br>MAIN RESULTS: Electrode selection significantly improved BCI calibration accuracy for the group with severe CP. Significant group effect was not found for the group of typically developing controls and the group with mild CP. However, several individuals with mild CP showed improved performance. Using the individualized electrode subsets, there was no significant difference in accuracy between calibration and evaluation data in the mild CP group, but there was a reduction in accuracy from calibration to evaluation in controls.<br><br>SIGNIFICANCE: The findings suggested that electrode selection can accommodate developmental neurological impairments in people with severe CP, while the default electrode locations are sufficient for many people with milder impairments from CP and typically developing individuals.},
}
@article {pmid36991884,
year = {2023},
author = {Hashem, HA and Abdulazeem, Y and Labib, LM and Elhosseini, MA and Shehata, M},
title = {An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {6},
pages = {},
pmid = {36991884},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Quality of Life ; Electroencephalography/methods ; Algorithms ; Machine Learning ; },
abstract = {Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.},
}
@article {pmid36988341,
year = {2023},
author = {Nie, A and Guo, B},
title = {Benefits and Detriments of Social Collaborative Memory in Turn-Taking and Directed Forgetting.},
journal = {Perceptual and motor skills},
volume = {130},
number = {3},
pages = {1040-1076},
doi = {10.1177/00315125231163626},
pmid = {36988341},
issn = {1558-688X},
mesh = {Humans ; *Mental Recall/physiology ; *Recognition, Psychology/physiology ; Emotions/physiology ; Inhibition, Psychological ; Cues ; },
abstract = {Collaborative recall by groups of people can evoke both memory detriments (e.g., collaborative inhibition) and benefits (e.g., error pruning and post-collaborative memory benefit). Yet, it remains indeterminate whether these effects are due to the emotional valence of stimuli and/or the specific subtypes of episodic memory tested (i.e., item memory and source memory), and whether they are related to the research procedure of directed forgetting (DF). We introduced item-method DF into collaborative memory research using a turn-taking procedure. The to-be-recalled words were studied in different emotional valences and were followed by either an R or F cue, which, respectively, instructed participants to remember or forget the words presented. We conducted two recall sessions (Recall 1 and Recall 2) that included the two subtypes of episodic memory. Recall 1 was performed either individually or collaboratively, while Recall 2 was always performed individually. We observed three major findings: (a) a collaborative memory decrement - collaborative inhibition - was minimally affected in both item memory and source memory tasks by either the emotional valence of the stimuli or the DF cue; (b) a collaborative memory benefit - error pruning of item memory - persisted within both ongoing and post-collaboration, while error pruning of source memory only presented in ongoing collaboration, thus demonstrating the relevance of dual-process models that differentiate automatic familiarity and effortful recollection processes; and (c) there was no post-collaborative memory benefit, indicating the importance of the type of collaborative procedure. We discuss these results in terms of various theories, including the retrieval strategy disruption hypothesis (RSDH) which asserts that memory strategies tend to be disrupted in collaboration but are facilitated within post-collaboration. Also, we describe the implications of these results and directions for exploring other influential factors in future research.},
}
@article {pmid36987698,
year = {2023},
author = {Youssofzadeh, V and Roy, S and Chowdhury, A and Izadysadr, A and Parkkonen, L and Raghavan, M and Prasad, G},
title = {Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG.},
journal = {Human brain mapping},
volume = {44},
number = {8},
pages = {3324-3342},
pmid = {36987698},
issn = {1097-0193},
mesh = {Humans ; Magnetoencephalography ; Imagination ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; *Motor Cortex ; },
abstract = {Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.},
}
@article {pmid36985341,
year = {2023},
author = {Li, A and He, Y and Yang, C and Lu, N and Bao, J and Gao, S and Hosyanto, FF and He, X and Fu, H and Yan, H and Ding, N and Xu, L},
title = {Methylprednisolone Promotes Mycobacterium smegmatis Survival in Macrophages through NF-κB/DUSP1 Pathway.},
journal = {Microorganisms},
volume = {11},
number = {3},
pages = {},
pmid = {36985341},
issn = {2076-2607},
support = {no. cstc2017jcyjAX0409//Chongqing Municipal Science and Technology Commission/ ; W 0091//CQMU Program for Youth Innovation in Future Medicine/ ; },
abstract = {BACKGROUND: Mycobacterium tuberculosis (M. tuberculosis) is the causative agent of tuberculosis. As an important component of host immunity, macrophages are not only the first line of defense against M. tuberculosis but also the parasitic site of M. tuberculosis in the host. Glucocorticoids can cause immunosuppression, which is considered to be one of the major risk factors for active tuberculosis, but the mechanism is unclear.<br><br>OBJECTIVE: To study the effect of methylprednisolone on the proliferation of mycobacteria in macrophages and try to find key molecules of this phenomenon.<br><br>METHODS: The macrophage line RAW264.7 infected by M. smegmatis was treated with methylprednisolone, and the intracellular bacterial CFU, Reactive Oxygen Species (ROS), cytokine secretion, autophagy, and apoptosis were measured. After the cells were treated with NF-&#x3ba;B inhibitor BAY 11-7082 and DUSP1 inhibitor BCI, respectively, the intracellular bacterial CFU, ROS, IL-6, and TNF-&#x3b1; secretion were detected.<br><br>RESULTS: After treatment with methylprednisolone, the CFU of intracellular bacteria increased, the level of ROS decreased, and the secretion of IL-6 and TNF-&#x3b1; decreased in infected macrophages. After BAY 11-7082 treatment, the CFU of M. smegmatis in macrophages increased, and the level of ROS production and the secretion of IL-6 by macrophages decreased. Transcriptome high-throughput sequencing and bioinformatics analysis suggested that DUSP1 was the key molecule in the above phenomenon. Western blot analysis confirmed that the expression level of DUSP1 was increased in the infected macrophages treated with methylprednisolone and BAY 11-7082, respectively. After BCI treatment, the level of ROS produced by infected macrophages increased, and the secretion of IL-6 increased. After the treatment of BCI combined with methylprednisolone or BAY 11-7082, the level of ROS produced and the secretion of IL-6 by macrophages were increased.<br><br>CONCLUSION: methylprednisolone promotes the proliferation of mycobacteria in macrophages by suppressing cellular ROS production and IL-6 secretion through down-regulating NF-&#x3ba;B and up-regulating DUSP1 expression. BCI, an inhibitor of DUSP1, can reduce the level of DUSP1 in the infected macrophages and inhibit the proliferation of intracellular mycobacteria by promoting cellular ROS production and IL-6 secretion. Therefore, BCI may become a new molecule for host-directed therapy of tuberculosis, as well as a new strategy for the prevention of tuberculosis when treated with glucocorticoids.},
}
@article {pmid36985087,
year = {2023},
author = {Jeakle, EN and Abbott, JR and Usoro, JO and Wu, Y and Haghighi, P and Radhakrishna, R and Sturgill, BS and Nakajima, S and Thai, TTD and Pancrazio, JJ and Cogan, SF and Hernandez-Reynoso, AG},
title = {Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex.},
journal = {Micromachines},
volume = {14},
number = {3},
pages = {},
pmid = {36985087},
issn = {2072-666X},
support = {R01NS104344/NH/NIH HHS/United States ; },
abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague-Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between 1 and 500 Hz of recordings were compared over the implantation period for both device types. Initially, the bandpower of the a-SiC devices and standard MEAs was comparable. However, the standard MEAs showed a consistent decline in both bandpower and power spectral density throughout the 16 weeks post-implantation, whereas the a-SiC MEAs showed substantially more stable performance. These differences in bandpower and spectral density between standard and a-SiC MEAs were statistically significant from week 6 post-implantation until the end of the study at 16 weeks. These results support the use of ultra-thin a-SiC MEAs to develop chronic, reliable brain-machine interfaces.},
}
@article {pmid36982255,
year = {2023},
author = {Li, J and Cheng, Y and Gu, M and Yang, Z and Zhan, L and Du, Z},
title = {Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface.},
journal = {International journal of molecular sciences},
volume = {24},
number = {6},
pages = {},
pmid = {36982255},
issn = {1422-0067},
support = {2018B030331001, 2018B030338001//Key-Area Research and Development Program of Guangdong Province/ ; 2022ZD0209800//Scientific and Technological Innovation 2030 Key Project of Ministry of Science and Technology of China/ ; 31930047,31700936//NSFC project/ ; 2020YFC2008503, 2018YFA0701400, 2017YFC1310503//National Key R&amp;D Program of China/ ; 2017A030310496//Doctoral Initiation Project of the Guangdong Province/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Prostheses and Implants ; Electrodes ; Technology ; User-Computer Interface ; },
abstract = {Implantable brain-computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials' contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.},
}
@article {pmid36981352,
year = {2023},
author = {Sheng, J and Xu, J and Li, H and Liu, Z and Zhou, H and You, Y and Song, T and Zuo, G},
title = {A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements.},
journal = {Entropy (Basel, Switzerland)},
volume = {25},
number = {3},
pages = {},
pmid = {36981352},
issn = {1099-4300},
support = {2022C03029//The Key Research and Development Program of Zhejiang Province/ ; },
abstract = {In motor imagery (MI) brain-computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time-frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time-frequency-space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.},
}
@article {pmid36980430,
year = {2023},
author = {García-Murillo, DG and Álvarez-Meza, AM and Castellanos-Dominguez, CG},
title = {KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {13},
number = {6},
pages = {},
pmid = {36980430},
issn = {2075-4418},
support = {82729//Colciencias/ ; 55063//Universidad Nacional de Colombia/ ; },
abstract = {This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.},
}
@article {pmid36979295,
year = {2023},
author = {Liu, Y and Zhang, Y and Jiang, Z and Kong, W and Zou, L},
title = {Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.},
journal = {Brain sciences},
volume = {13},
number = {3},
pages = {},
pmid = {36979295},
issn = {2076-3425},
support = {BE2021012-2//Ling Zou/ ; BE2021012-5//Ling Zou/ ; 2020E10010-04//Ling Zou/ ; CE20225034//Ling Zou/ ; KYCX22_3075//Yuchao Liu/ ; },
abstract = {BACKGROUND: It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition.<br><br>METHODS: First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices.<br><br>RESULTS: The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli.<br><br>CONCLUSION: This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.},
}
@article {pmid36979293,
year = {2023},
author = {Xu, D and Tang, F and Li, Y and Zhang, Q and Feng, X},
title = {An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.},
journal = {Brain sciences},
volume = {13},
number = {3},
pages = {},
pmid = {36979293},
issn = {2076-3425},
support = {QYZDY-SSW-JSC005//Chinese Academy of Sciences/ ; },
abstract = {The brain-computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011-2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.},
}
@article {pmid36978672,
year = {2023},
author = {Wang, T and Chen, YH and Sawan, M},
title = {Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {3},
pages = {},
pmid = {36978672},
issn = {2306-5354},
support = {041030080118//Westlake University/ ; 2021C03002//Zhejiang Key R&amp;D Program from Science and Technology Department Zhejiang Province/ ; },
abstract = {Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.},
}
@article {pmid36972585,
year = {2023},
author = {Zhang, T and Rahimi Azghadi, M and Lammie, C and Amirsoleimani, A and Genov, R},
title = {Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acc7cc},
pmid = {36972585},
issn = {1741-2552},
mesh = {*Signal Processing, Computer-Assisted ; Action Potentials/physiology ; *Algorithms ; Computers ; Microelectrodes ; Models, Neurological ; },
abstract = {Objective. Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including brain machine interfaces (BMIs), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases.Approach. We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities.Main results. In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional '3-step' algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including application-specific integrated circuits, field-programmable gate arrays, and in-memory computing devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed.Significance. This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.},
}
@article {pmid36968496,
year = {2023},
author = {Rueckauer, B and van Gerven, M},
title = {An in-silico framework for modeling optimal control of neural systems.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1141884},
pmid = {36968496},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning.<br><br>METHODS: To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability.<br><br>RESULTS: We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation.<br><br>DISCUSSION: We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.},
}
@article {pmid36968493,
year = {2023},
author = {Hu, YT and Chen, XL and Zhang, YN and McGurran, H and Stormmesand, J and Breeuwsma, N and Sluiter, A and Zhao, J and Swaab, D and Bao, AM},
title = {Sex differences in hippocampal β-amyloid accumulation in the triple-transgenic mouse model of Alzheimer's disease and the potential role of local estrogens.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1117584},
pmid = {36968493},
issn = {1662-4548},
abstract = {INTRODUCTION: Epidemiological studies show that women have a higher prevalence of Alzheimer's disease (AD) than men. Peripheral estrogen reduction during aging in women is proposed to play a key role in this sex-associated prevalence, however, the underlying mechanism remains elusive. We previously found that transcription factor early growth response-1 (EGR1) significantly regulates cholinergic function. EGR1 stimulates acetylcholinesterase (AChE) gene expression and is involved in AD pathogenesis. We aimed to investigate whether the triple-transgenic AD (3xTg-AD) mice harboring PS1 [M146V] , APP [Swe] , and Tau [P301L] show sex differences in β-amyloid (Aβ) and hyperphosphorylated tau (p-Tau), the two primary AD hallmarks, and how local 17β-estradiol (E2) may regulate the expression of EGR1 and AChE.<br><br>METHODS: We first sacrificed male and female 3xTg-AD mice at 3-4, 7-8, and 11-12 months and measured the levels of A&#x3b2;, p-Tau, EGR1, and AChE in the hippocampal complex. Second, we infected SH-SY5Y cells with lentivirus containing the amyloid precursor protein construct C99, cultured with or without E2 administration we measured the levels of extracellular A&#x3b2; and intracellular EGR1 and AChE.<br><br>RESULTS: Female 3xTg-AD mice had higher levels of A&#x3b2; compared to males, while no p-Tau was found in either group. In SH-SY5Y cells infected with lentivirus containing the amyloid precursor protein construct C99, we observed significantly increased extracellular A&#x3b2; and decreased expression of intracellular EGR1 and AChE. By adding E2 to the culture medium, extracellular A&#x3b2;(l-42) was significantly decreased while intracellular EGR1 and AChE expression were elevated.<br><br>DISCUSSION: This data shows that the 3xTg-AD mouse model can be useful for studying the human sex differences of AD, but only in regards to Ap. Furthermore, in vitro data shows local E2 may be protective for EGR1 and cholinergic functions in AD while suppressing soluble A&#x3b2;(1-42) levels. Altogether, this study provides further in vivo and in vitro data supporting the human epidemiological data indicating a higher prevalence of AD in women is related to changes in brain estrogen levels.},
}
@article {pmid36967197,
year = {2023},
author = {Carpenter, KA and Thurlow, KE and Craig, SEL and Grainger, S},
title = {Wnt regulation of hematopoietic stem cell development and disease.},
journal = {Current topics in developmental biology},
volume = {153},
number = {},
pages = {255-279},
pmid = {36967197},
issn = {1557-8933},
support = {R00 HL133458/HL/NHLBI NIH HHS/United States ; R35 GM142779/GM/NIGMS NIH HHS/United States ; },
mesh = {*beta Catenin/metabolism ; *Wnt Proteins/metabolism ; Hematopoietic Stem Cells/metabolism ; Hematopoiesis ; Cell Differentiation/physiology ; Wnt Signaling Pathway ; },
abstract = {Hematopoietic stem cells (HSCs) are multipotent stem cells that give rise to all cells of the blood and most immune cells. Due to their capacity for unlimited self-renewal, long-term HSCs replenish the blood and immune cells of an organism throughout its life. HSC development, maintenance, and differentiation are all tightly regulated by cell signaling pathways, including the Wnt pathway. Wnt signaling is initiated extracellularly by secreted ligands which bind to cell surface receptors and give rise to several different downstream signaling cascades. These are classically categorized either β-catenin dependent (BCD) or β-catenin independent (BCI) signaling, depending on their reliance on the β-catenin transcriptional activator. HSC development, homeostasis, and differentiation is influenced by both BCD and BCI, with a high degree of sensitivity to the timing and dosage of Wnt signaling. Importantly, dysregulated Wnt signals can result in hematological malignancies such as leukemia, lymphoma, and myeloma. Here, we review how Wnt signaling impacts HSCs during development and in disease.},
}
@article {pmid36963740,
year = {2023},
author = {Wu, H and Xie, Q and Pan, J and Liang, Q and Lan, Y and Guo, Y and Han, J and Xie, M and Liu, Y and Jiang, L and Wu, X and Li, Y and Qin, P},
title = {Identifying patients with cognitive motor dissociation using resting-state temporal stability.},
journal = {NeuroImage},
volume = {272},
number = {},
pages = {120050},
doi = {10.1016/j.neuroimage.2023.120050},
pmid = {36963740},
issn = {1095-9572},
mesh = {Humans ; *Brain/diagnostic imaging ; *Unconsciousness ; Cognition ; Consciousness ; Consciousness Disorders ; Magnetic Resonance Imaging/methods ; },
abstract = {Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.},
}
@article {pmid36960685,
year = {2023},
author = {Chen, XL and Fortes, JM and Hu, YT and van Iersel, J and He, KN and van Heerikhuize, J and Balesar, R and Swaab, D and Bao, AM},
title = {Sexually dimorphic age-related molecular differences in the entorhinal cortex of cognitively intact elderly: Relation to early Alzheimer's changes.},
journal = {Alzheimer's &amp; dementia : the journal of the Alzheimer's Association},
volume = {19},
number = {9},
pages = {3848-3857},
doi = {10.1002/alz.13037},
pmid = {36960685},
issn = {1552-5279},
mesh = {Male ; Humans ; Female ; Aged ; *Entorhinal Cortex ; *Alzheimer Disease/genetics ; Aging ; },
abstract = {INTRODUCTION: Women are more vulnerable to Alzheimer's disease (AD) than men. The entorhinal cortex (EC) is one of the earliest structures affected in AD. We identified in cognitively intact elderly different molecular changes in the EC in relation to age.<br><br>METHODS: Changes in 12 characteristic molecules in relation to age were determined by quantitative immunohistochemistry or in situ hybridization in the EC. They were arbitrarily grouped into sex steroid-related molecules, markers of neuronal activity, neurotransmitter-related molecules, and cholinergic activity-related molecules.<br><br>RESULTS: The changes in molecules indicated increasing local estrogenic and neuronal activity accompanied by a higher and faster hyperphosphorylated tau accumulation in women's EC in relation to age, versus a mainly stable local estrogenic/androgenic and neuronal activity in men's EC.<br><br>DISCUSSION: EC employs a different neurobiological strategy in women and men to maintain cognitive function, which seems to be accompanied by an earlier start of AD in women.<br><br>HIGHLIGHTS: Local estrogen system is activated with age only in women's entorhinal cortex (EC). EC neuronal activity increased with age only in elderly women with intact cognition. Men and women have different molecular strategies to retain cognition with aging. P-tau accumulation in the EC was higher and faster in cognitively intact elderly women.},
}
@article {pmid36960172,
year = {2023},
author = {Ma, Z and Wang, K and Xu, M and Yi, W and Xu, F and Ming, D},
title = {Transformed common spatial pattern for motor imagery-based brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1116721},
pmid = {36960172},
issn = {1662-4548},
abstract = {OBJECTIVE: The motor imagery (MI)-based brain-computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.<br><br>APPROACH: This study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.<br><br>MAIN RESULTS: As a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.<br><br>SIGNIFICANCE: The results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.},
}
@article {pmid36959601,
year = {2023},
author = {Velasco, I and Sipols, A and De Blas, CS and Pastor, L and Bayona, S},
title = {Motor imagery EEG signal classification with a multivariate time series approach.},
journal = {Biomedical engineering online},
volume = {22},
number = {1},
pages = {29},
pmid = {36959601},
issn = {1475-925X},
support = {C080020-09//Spanish Ministry of Economy and Competitiveness/ ; 785907//European Union's Horizon 2020 Framework Programme for Research and Innovation/ ; PID2020-113013RB-C21//Spanish Ministry of Science and Innovation/ ; PID2019-108311GB-I00//Agencia Estatal de Investigaci&#xf3;n/ ; PID2019-106254RB-I00//Agencia Estatal de Investigaci&#xf3;n/ ; },
mesh = {Time Factors ; *Algorithms ; Electroencephalography/methods ; Wavelet Analysis ; Hand ; *Brain-Computer Interfaces ; Imagination ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.<br><br>CONCLUSIONS: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.},
}
@article {pmid36951376,
year = {2024},
author = {Saraswat, M and Dubey, AK},
title = {EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {27},
number = {3},
pages = {378-399},
doi = {10.1080/10255842.2023.2187662},
pmid = {36951376},
issn = {1476-8259},
mesh = {*Neural Networks, Computer ; *Brain-Computer Interfaces ; Heuristics ; Time Factors ; Algorithms ; Electroencephalography/methods ; },
abstract = {Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.},
}
@article {pmid36950505,
year = {2023},
author = {Fang, H and Yang, Y},
title = {Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1119685},
pmid = {36950505},
issn = {1662-5188},
abstract = {INTRODUCTION: Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.<br><br>METHODS: Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.<br><br>RESULTS: We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.<br><br>DISCUSSION: Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.},
}
@article {pmid36950147,
year = {2023},
author = {Moly, A and Aksenov, A and Martel, F and Aksenova, T},
title = {Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1075666},
pmid = {36950147},
issn = {1662-5161},
abstract = {INTRODUCTION: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.<br><br>METHODS: The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1.<br><br>RESULTS: The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA.<br><br>DISCUSSION: The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.},
}
@article {pmid36948359,
year = {2023},
author = {Gao, X and Zhang, S and Liu, K and Tan, Z and Zhao, G and Han, Y and Cheng, Y and Li, C and Li, P and Tian, Y and Li, F},
title = {An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification.},
journal = {Journal of neuroscience methods},
volume = {390},
number = {},
pages = {109841},
doi = {10.1016/j.jneumeth.2023.109841},
pmid = {36948359},
issn = {1872-678X},
mesh = {*Artifacts ; Reproducibility of Results ; *Canonical Correlation Analysis ; Algorithms ; Emotions ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.<br><br>NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.<br><br>RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.<br><br>Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.<br><br>CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.},
}
@article {pmid36945749,
year = {2023},
author = {Lebani, BR and Barcelos, ADS and Gouveia, DSES and Girotti, ME and Remaille, EP and Skaff, M and Almeida, FG},
title = {The role of transurethral resection of prostate (TURP) in patients with underactive bladder: 12 months follow-up in different grades of detrusor contractility.},
journal = {The Prostate},
volume = {83},
number = {9},
pages = {857-862},
doi = {10.1002/pros.24526},
pmid = {36945749},
issn = {1097-0045},
mesh = {Humans ; Male ; Prostate/surgery ; *Transurethral Resection of Prostate/methods ; Follow-Up Studies ; *Urinary Bladder, Underactive/surgery ; *Prostatic Hyperplasia/surgery ; *Urinary Bladder Neck Obstruction/etiology/surgery ; Urodynamics ; },
abstract = {INTRODUCTION AND OBJECTIVE: Male detrusor underactivity (DUA) definition remains controversial and no effective treatment is consolidated. Transurethral resection of the prostate (TURP) is one of the cornerstones surgical treatments recommended in bladder outlet obstruction (BOO). However, the role of prostatic surgery in male DUA is not clear. The primary endpoint was the clinical and voiding improvement based on IPSS and the maximum flow rate in uroflowmetry (Qmax) within 12 months.<br><br>MATERIALS AND METHODS: We analyzed an ongoing prospective database that embraces benign prostata hyperplasia (BPH) male patients with lower urinary tract symptoms who have undergone to TURP. All patients were evaluated pre and postoperatively based on IPSS questionnaires, prostate volume measured by ultrasound, postvoid residual urine volume (PVR), Prostate Specific Antigen measurement and urodynamic study (UDS) before the procedure. After surgery, all patients were evaluated at 1-, 3-, 6- and 12-months. Patients were categorized in 3 groups: Group 1-Detrusor Underactive (Bladder Contractility Index (BCI) [BCI]&#x2009;<&#x2009;100 and BOO index [BOOI]&#x2009;<&#x2009;40); Group 2-Detrusor Underactive and BOO (BCI&#x2009;<&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;40); Group 3-BOO (BCI&#x2009;&#x2265;&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;0).<br><br>RESULTS: It was included 158 patients underwent monopolar or bipolar TURP since November 2015 to March 2021. According to UDS, patients were categorized in: group 1 (n&#x2009;=&#x2009;39 patients); group 2 (n&#x2009;=&#x2009;41 patients); group 3 (n&#x2009;=&#x2009;77 patients). Preoperative IPSS was similar between groups (group 1-24.9&#x2009;&#xb1;&#x2009;6.33; group 2-24.8&#x2009;&#xb1;&#x2009;7.33; group 3-24.5&#x2009;&#xb1;&#x2009;6.23). Qmax was statistically lower in the group 2 (group 1-5.43&#x2009;&#xb1;&#x2009;3.69; group 2-3.91&#x2009;&#xb1;&#x2009;2.08; group 3-6.3&#x2009;&#xb1;&#x2009;3.18) as well as greater PVR. The 3 groups presented similar outcomes regard to IPSS score during the follow-up. There was a significant increase in Qmax in the 3 groups. However, group 1 presented the lowest Qmax improvement.<br><br>CONCLUSION: There were different objective outcomes depending on the degree of DUA at 12 months follow-up. Patients with DUA had similar IPSS improvement. However, DUA patients had worst Qmax improvement than men with normal bladder contraction.},
}
@article {pmid36939855,
year = {2023},
author = {Karoly, HC and Drennan, ML and Prince, MA and Zulic, L and Dooley, G},
title = {Consuming oral cannabidiol prior to a standard alcohol dose has minimal effect on breath alcohol level and subjective effects of alcohol.},
journal = {Psychopharmacology},
volume = {240},
number = {5},
pages = {1119-1129},
pmid = {36939855},
issn = {1432-2072},
support = {K23 AA028238/AA/NIAAA NIH HHS/United States ; UL1 TR002535/TR/NCATS NIH HHS/United States ; K23AA028238/AA/NIAAA NIH HHS/United States ; },
mesh = {Humans ; *Cannabidiol/pharmacology ; Cross-Over Studies ; Ethanol/pharmacology ; *Alcoholism ; Alcohol Drinking/drug therapy ; Double-Blind Method ; },
abstract = {RATIONALE: Cannabidiol (CBD) is found in the cannabis plant and has garnered attention as a potential treatment for alcohol use disorder (AUD). CBD reduces alcohol consumption and other markers of alcohol dependence in rodents, but human research on CBD and alcohol is limited. It is unknown whether CBD reduces drinking in humans, and mechanisms through which CBD could impact behavioral AUD phenotypes are unknown.<br><br>OBJECTIVES: This study explores effects of oral CBD on breath alcohol level (BrAC), and subjective effects of alcohol in human participants who report heavy drinking.<br><br>METHODS: In this placebo-controlled, crossover study, participants consumed 30&#xa0;mg CBD, 200&#xa0;mg CBD, or placebo CBD before receiving a standardized alcohol dose. Participants were blind to which CBD dose they received at each session and completed sessions in random order. Thirty-six individuals completed at least one session and were included in analyses.<br><br>RESULTS: Differences in outcomes across the three conditions and by sex were explored using multilevel structural equation models. BrAC fell fastest in the placebo condition, followed by 30&#xa0;mg and 200&#xa0;mg CBD. Stimulation decreased more slowly in the 200&#xa0;mg CBD condition than in placebo (b&#x2009;=&#x2009;&#x2009;-&#x2009;2.38, BCI [-&#x2009;4.46,&#x2009;-&#x2009;.03]). Sedation decreased more slowly in the 30&#xa0;mg CBD condition than in placebo (b&#x2009;=&#x2009;&#x2009;-&#x2009;2.41, BCI [-&#x2009;4.61,&#x2009;-&#x2009;.09]). However, the magnitude of condition differences in BrAC and subjective effects was trivial.<br><br>CONCLUSIONS: CBD has minimal influence on BrAC and subjective effects of alcohol. Further research is needed to test whether CBD impacts alcohol consumption in humans, and if so, what mechanism(s) may explain this effect.},
}
@article {pmid36938361,
year = {2023},
author = {Hanna, J and Flöel, A},
title = {An accessible and versatile deep learning-based sleep stage classifier.},
journal = {Frontiers in neuroinformatics},
volume = {17},
number = {},
pages = {1086634},
pmid = {36938361},
issn = {1662-5196},
abstract = {Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.},
}
@article {pmid36937688,
year = {2023},
author = {Sawai, S and Murata, S and Fujikawa, S and Yamamoto, R and Shima, K and Nakano, H},
title = {Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1148336},
pmid = {36937688},
issn = {1662-4548},
abstract = {INTRODUCTION: Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB.<br><br>METHODS: Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and &#x3bc;-event-related desynchronization (ERD) and vividness MI were evaluated.<br><br>RESULTS: &#x3bc;-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training.<br><br>DISCUSSION: Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.},
}
@article {pmid36937679,
year = {2023},
author = {Lai, D and Wan, Z and Lin, J and Pan, L and Ren, F and Zhu, J and Zhang, J and Wang, Y and Hao, Y and Xu, K},
title = {Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1133928},
pmid = {36937679},
issn = {1662-4548},
abstract = {INTRODUCTION: How the human brain coordinates bimanual movements is not well-established.<br><br>METHODS: Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron.<br><br>RESULTS: We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side.<br><br>DISCUSSION: These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.},
}
@article {pmid36936191,
year = {2023},
author = {Islam, MK and Rastegarnia, A},
title = {Editorial: Recent advances in EEG (non-invasive) based BCI applications.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1151852},
pmid = {36936191},
issn = {1662-5188},
}
@article {pmid36935358,
year = {2023},
author = {Gerasimov, JY and Tu, D and Hitaishi, V and Harikesh, PC and Yang, CY and Abrahamsson, T and Rad, M and Donahue, MJ and Ejneby, MS and Berggren, M and Forchheimer, R and Fabiano, S},
title = {A Biologically Interfaced Evolvable Organic Pattern Classifier.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {14},
pages = {e2207023},
pmid = {36935358},
issn = {2198-3844},
support = {ERC-2018-ADG/ERC_/European Research Council/International ; },
mesh = {Reproducibility of Results ; *Electronics ; *Neurons ; Signal Processing, Computer-Assisted ; Transistors, Electronic ; },
abstract = {Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.},
}
@article {pmid36933706,
year = {2023},
author = {Wang, A and Fan, Z and Zhang, Y and Wang, J and Zhang, X and Wang, P and Mu, W and Zhan, G and Wang, M and Zhang, L and Gan, Z and Kang, X},
title = {Resting-state SEEG-based brain network analysis for the detection of epileptic area.},
journal = {Journal of neuroscience methods},
volume = {390},
number = {},
pages = {109839},
doi = {10.1016/j.jneumeth.2023.109839},
pmid = {36933706},
issn = {1872-678X},
mesh = {Humans ; *Brain Mapping/methods ; Electroencephalography/methods ; Brain ; *Epilepsy ; Seizures/diagnostic imaging ; },
abstract = {BACKGROUND: Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.<br><br>NEW METHOD: The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.<br><br>RESULTS: By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p&#xa0;<&#xa0;0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94&#xa0;&#xb1;&#xa0;0.08 for the epilepsy zone.<br><br>CONCLUSIONS AND SIGNIFICANCE: The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.},
}
@article {pmid36931795,
year = {2023},
author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS},
title = {Neurosurgical lesioning for Tourette syndrome - Authors' reply.},
journal = {The Lancet. Neurology},
volume = {22},
number = {4},
pages = {292-293},
doi = {10.1016/S1474-4422(23)00079-0},
pmid = {36931795},
issn = {1474-4465},
mesh = {Humans ; *Tourette Syndrome/surgery ; *Deep Brain Stimulation ; },
}
@article {pmid36930206,
year = {2023},
author = {Wang, F and Chen, Y and Lin, Y and Wang, X and Li, K and Han, Y and Wu, J and Shi, X and Zhu, Z and Long, C and Hu, X and Duan, S and Gao, Z},
title = {A parabrachial to hypothalamic pathway mediates defensive behavior.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {36930206},
issn = {2050-084X},
mesh = {Rats ; Animals ; *Hypothalamus/metabolism ; Paraventricular Hypothalamic Nucleus/metabolism ; Cholecystokinin/metabolism ; Neurons/physiology ; *Parabrachial Nucleus/physiology ; },
abstract = {Defensive behaviors are critical for animal's survival. Both the paraventricular nucleus of the hypothalamus (PVN) and the parabrachial nucleus (PBN) have been shown to be involved in defensive behaviors. However, whether there are direct connections between them to mediate defensive behaviors remains unclear. Here, by retrograde and anterograde tracing, we uncover that cholecystokinin (CCK)-expressing neurons in the lateral PBN (LPB[CCK]) directly project to the PVN. By in vivo fiber photometry recording, we find that LPB[CCK] neurons actively respond to various threat stimuli. Selective photoactivation of LPB[CCK] neurons promotes aversion and defensive behaviors. Conversely, photoinhibition of LPB[CCK] neurons attenuates rat or looming stimuli-induced flight responses. Optogenetic activation of LPB[CCK] axon terminals within the PVN or PVN glutamatergic neurons promotes defensive behaviors. Whereas chemogenetic and pharmacological inhibition of local PVN neurons prevent LPB[CCK]-PVN pathway activation-driven flight responses. These data suggest that LPB[CCK] neurons recruit downstream PVN neurons to actively engage in flight responses. Our study identifies a previously unrecognized role for the LPB[CCK]-PVN pathway in controlling defensive behaviors.},
}
@article {pmid36928694,
year = {2023},
author = {Berger, CC and Coppi, S and Ehrsson, HH},
title = {Synchronous motor imagery and visual feedback of finger movement elicit the moving rubber hand illusion, at least in illusion-susceptible individuals.},
journal = {Experimental brain research},
volume = {241},
number = {4},
pages = {1021-1039},
pmid = {36928694},
issn = {1432-1106},
support = {2017-00276//Vetenskapsr&#xe5;det/ ; },
mesh = {Humans ; *Illusions/physiology ; *Touch Perception/physiology ; Feedback, Sensory ; Hand/physiology ; Fingers ; Proprioception/physiology ; Visual Perception/physiology ; Body Image ; },
abstract = {Recent evidence suggests that imagined auditory and visual sensory stimuli can be integrated with real sensory information from a different sensory modality to change the perception of external events via cross-modal multisensory integration mechanisms. Here, we explored whether imagined voluntary movements can integrate visual and proprioceptive cues to change how we perceive our own limbs in space. Participants viewed a robotic hand wearing a glove repetitively moving its right index finger up and down at a frequency of 1 Hz, while they imagined executing the corresponding movements synchronously or asynchronously (kinesthetic-motor imagery); electromyography (EMG) from the participants' right index flexor muscle confirmed that the participants kept their hand relaxed while imagining the movements. The questionnaire results revealed that the synchronously imagined movements elicited illusory ownership and a sense of agency over the moving robotic hand-the moving rubber hand illusion-compared with asynchronously imagined movements; individuals who affirmed experiencing the illusion with real synchronous movement also did so with synchronous imagined movements. The results from a proprioceptive drift task further demonstrated a shift in the perceived location of the participants' real hand toward the robotic hand in the synchronous versus the asynchronous motor imagery condition. These results suggest that kinesthetic motor imagery can be used to replace veridical congruent somatosensory feedback from a moving finger in the moving rubber hand illusion to trigger illusory body ownership and agency, but only if the temporal congruence rule of the illusion is obeyed. This observation extends previous studies on the integration of mental imagery and sensory perception to the case of multisensory bodily awareness, which has potentially important implications for research into embodiment of brain-computer interface controlled robotic prostheses and computer-generated limbs in virtual reality.},
}
@article {pmid36927003,
year = {2023},
author = {Hou, Y and Ling, Y and Wang, Y and Wang, M and Chen, Y and Li, X and Hou, X},
title = {Learning from the Brain: Bioinspired Nanofluidics.},
journal = {The journal of physical chemistry letters},
volume = {14},
number = {11},
pages = {2891-2900},
doi = {10.1021/acs.jpclett.2c03930},
pmid = {36927003},
issn = {1948-7185},
mesh = {Humans ; *Artificial Intelligence ; *Brain ; },
abstract = {The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.},
}
@article {pmid36925628,
year = {2023},
author = {Carino-Escobar, RI and Rodríguez-García, ME and Carrillo-Mora, P and Valdés-Cristerna, R and Cantillo-Negrete, J},
title = {Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1015464},
pmid = {36925628},
issn = {1662-5218},
abstract = {INTRODUCTION: Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.<br><br>METHODS: An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.<br><br>RESULTS: Significantly higher BCI performance (65.4 &#xb1; 17.9% with the continuous and 62.1 &#xb1; 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.<br><br>DISCUSSION: It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.},
}
@article {pmid36924669,
year = {2023},
author = {Wei, L and Qi, X and Yu, X and Zheng, Y and Luo, X and Wei, Y and Ni, P and Zhao, L and Wang, Q and Ma, X and Deng, W and Guo, W and Hu, X and Li, T},
title = {3,4-Dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs act as potential AMPA receptor potentiators with antidepressant activity.},
journal = {European journal of medicinal chemistry},
volume = {251},
number = {},
pages = {115252},
doi = {10.1016/j.ejmech.2023.115252},
pmid = {36924669},
issn = {1768-3254},
mesh = {Rats ; Mice ; Animals ; Receptors, AMPA ; *Depressive Disorder, Major/drug therapy ; Antidepressive Agents/pharmacology ; *Ketamine/pharmacology ; Neurons ; },
abstract = {Major depressive disorder is a common psychiatric disorder, with ∼30% of patients suffering from treatment-resistant depression. Based on preclinical studies on ketamine, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) activation may be a promising therapeutic approach. In this study, we synthesized a series of novel 3,4-dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs and analyzed their potential as AMPAR potentiators. Compounds 5aa and 7k exhibited high potentiation with little agonist activity in a high-throughput screen using a calcium influx assay in cultured hippocampal primary neurons. In rats, compound 7k had better pharmacokinetic properties and oral bioavailability (F = 67.19%); it also exhibited an acceptable safety profile in vital internal organs based on hematoxylin and eosin staining. We found that 7k produced a rapid antidepressant-like effect in chronic restraint stress-induced mice 1 h after intraperitoneal administration. Our study presented a series of novel AMPAR potentiators and identified 7k as a promising drug-like candidate against major depressive disorders.},
}
@article {pmid36923936,
year = {2023},
author = {Kong, L and Zhang, D and Huang, S and Lai, J and Lu, L and Zhang, J and Hu, S},
title = {Extracellular Vesicles in Mental Disorders: A State-of-art Review.},
journal = {International journal of biological sciences},
volume = {19},
number = {4},
pages = {1094-1109},
pmid = {36923936},
issn = {1449-2288},
mesh = {Humans ; *Depressive Disorder, Major ; *Extracellular Vesicles/physiology ; *MicroRNAs/genetics ; Central Nervous System ; *Mental Disorders ; },
abstract = {Extracellular vesicles (EVs) are nanoscale particles with various physiological functions including mediating cellular communication in the central nervous system (CNS), which indicates a linkage between these particles and mental disorders such as schizophrenia, bipolar disorder, major depressive disorder, etc. To date, known characteristics of mental disorders are mainly neuroinflammation and dysfunctions of homeostasis in the CNS, and EVs are proven to be able to regulate these pathological processes. In addition, studies have found that some cargo of EVs, especially miRNAs, were significantly up- or down-regulated in patients with mental disorders. For many years, interest has been generated in exploring new diagnostic and therapeutic methods for mental disorders, but scale assessment and routine drug intervention are still the first-line applications so far. Therefore, underlying the downstream functions of EVs and their cargo may help uncover the pathogenetic mechanisms of mental disorders as well as provide novel biomarkers and therapeutic candidates. This review aims to address the connection between EVs and mental disorders, and discuss the current strategies that focus on EVs-related psychiatric detection and therapy.},
}
@article {pmid36922925,
year = {2023},
author = {Khodaei, F and Sadati, SH and Doost, M and Lashgari, R},
title = {LFP polarity changes across cortical and eccentricity in primary visual cortex.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1138602},
pmid = {36922925},
issn = {1662-4548},
abstract = {Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.},
}
@article {pmid36921432,
year = {2023},
author = {Rokai, J and Ulbert, I and Márton, G},
title = {Edge computing on TPU for brain implant signal analysis.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {162},
number = {},
pages = {212-224},
doi = {10.1016/j.neunet.2023.02.036},
pmid = {36921432},
issn = {1879-2782},
mesh = {*Signal Processing, Computer-Assisted ; *Artificial Intelligence ; Neurons ; Algorithms ; Brain ; Action Potentials ; },
abstract = {The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.},
}
@article {pmid36915907,
year = {2023},
author = {Zhu, Y and Wu, L and Ye, S and Fu, Y and Huang, H and Lai, J and Shi, C and Hu, S},
title = {The Chinese Version of Oxford Depression Questionnaire: A Validation Study in Patients with Mood Disorders.},
journal = {Neuropsychiatric disease and treatment},
volume = {19},
number = {},
pages = {547-556},
pmid = {36915907},
issn = {1176-6328},
abstract = {BACKGROUND: Emotional blunting is prevalent in patients with mood disorders and adversely affects the overall treatment outcome. The Oxford Depression Questionnaire is a validated psychometric instrument for assessing emotional blunting. We aimed to evaluate the reliability and validity of the Chinese version of the ODQ (ODQ) in Chinese patients with mood disorders.<br><br>METHODS: 136 mood disorders patients and 95 healthy control participants were recruited at the First Affiliated Hospital of Zhejiang University, School of Medicine. Patients were assessed using the ODQ, Beck Depression Inventory-II (BDI-II), and Montgomery-Asberg Depression Rating Scale (MADRS). Internal consistency reliability and test-retest reliability were analyzed. Confirmatory factor analysis and correlation analysis were used to evaluate construct and convergent validity.<br><br>RESULTS: A total of 136 patients with mood disorders and 95 healthy controls participated in this study. Cronbach &#x3b1; values were 0.928 (ODQ-20) and 0.945 (ODQ-26). Test-retest reliability coefficients were 0.798 (ODQ-20) and 0.836 (ODQ-26) (p<0.05); intraclass correlation coefficient values were 0.777 (ODQ-20) and 0.781 (ODQ-26) (p<0.01). The score of ODQ was positively correlated with BDI-II and MADRS (r=0.326~0.719, 0.235~0.537, p<0.01). The differences in the ODQ scores between the patient and control groups were statistically significant.<br><br>CONCLUSION: The reliability, structural validity, and criterion validity of the ODQ applied to patients with mood disorders meet the psychometric requirements, and the scale can be used to assess emotional blunting in Chinese patients with mood disorders.},
}
@article {pmid36915631,
year = {2023},
author = {McDermott, EJ and Metsomaa, J and Belardinelli, P and Grosse-Wentrup, M and Ziemann, U and Zrenner, C},
title = {Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.},
journal = {Virtual reality},
volume = {27},
number = {1},
pages = {347-369},
pmid = {36915631},
issn = {1359-4338},
abstract = {Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.},
}
@article {pmid36914265,
year = {2023},
author = {Dougherty, LL and Dutta, S and Avasthi, P},
title = {The ERK activator, BCI, inhibits ciliogenesis and causes defects in motor behavior, ciliary gating, and cytoskeletal rearrangement.},
journal = {Life science alliance},
volume = {6},
number = {6},
pages = {},
pmid = {36914265},
issn = {2575-1077},
support = {P20 GM113132/GM/NIGMS NIH HHS/United States ; P30 CA023108/CA/NCI NIH HHS/United States ; R35 GM128702/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Humans ; *Brain-Computer Interfaces ; Mitogen-Activated Protein Kinases ; Mammals ; },
abstract = {MAPK pathways are well-known regulators of the cell cycle, but they have also been found to control ciliary length in a wide variety of organisms and cell types from Caenorhabditis elegans neurons to mammalian photoreceptors through unknown mechanisms. ERK1/2 is a MAP kinase in human cells that is predominantly phosphorylated by MEK1/2 and dephosphorylated by the phosphatase DUSP6. We have found that the ERK1/2 activator/DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), inhibits ciliary maintenance in Chlamydomonas and hTERT-RPE1 cells and assembly in Chlamydomonas These effects involve inhibition of total protein synthesis, microtubule organization, membrane trafficking, and KAP-GFP motor dynamics. Our data provide evidence for various avenues for BCI-induced ciliary shortening and impaired ciliogenesis that gives mechanistic insight into how MAP kinases can regulate ciliary length.},
}
@article {pmid36913120,
year = {2024},
author = {Zhang, Y and Zeng, H and Lou, F and Tan, X and Zhang, X and Chen, G},
title = {SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage.},
journal = {Translational stroke research},
volume = {15},
number = {3},
pages = {556-571},
pmid = {36913120},
issn = {1868-601X},
mesh = {Animals ; Mice ; Biomarkers/metabolism ; *Cerebral Hemorrhage/metabolism/genetics/diagnostic imaging ; Disease Models, Animal ; Mice, Inbred C57BL ; Oligodendroglia/metabolism ; *White Matter/metabolism/pathology/diagnostic imaging ; *Monosaccharide Transport Proteins/genetics/metabolism ; },
abstract = {Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.},
}
@article {pmid36911809,
year = {2023},
author = {Tao, QQ and Lin, RR and Wu, ZY},
title = {Early Diagnosis of Alzheimer's Disease: Moving Toward a Blood-Based Biomarkers Era.},
journal = {Clinical interventions in aging},
volume = {18},
number = {},
pages = {353-358},
pmid = {36911809},
issn = {1178-1998},
mesh = {Humans ; *Alzheimer Disease/diagnosis ; Biomarkers ; Early Diagnosis ; Amyloid beta-Peptides ; tau Proteins ; },
}
@article {pmid36909601,
year = {2023},
author = {Prescott, RA and Pankow, AP and de Vries, M and Crosse, K and Patel, RS and Alu, M and Loomis, C and Torres, V and Koralov, S and Ivanova, E and Dittmann, M and Rosenberg, BR},
title = {A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {36909601},
issn = {2692-8205},
support = {P30 CA016087/CA/NCI NIH HHS/United States ; U01 AI150748/AI/NIAID NIH HHS/United States ; S10 OD026880/OD/NIH HHS/United States ; R01 AI137336/AI/NIAID NIH HHS/United States ; R01 AI151029/AI/NIAID NIH HHS/United States ; R01 AI143639/AI/NIAID NIH HHS/United States ; },
abstract = {The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial cultures at air-liquid interface (HAE) are a physiologically relevant in vitro model of this heterogeneous tissue, enabling numerous studies of airway disease [1â€"7] . HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining capacity for differentiation to HAE [5] . However, gene expression and innate immune function in HAE derived from BCi-NS1.1 versus primary cells have not been fully characterized. Here, combining single cell RNA-Seq (scRNA-Seq), immunohistochemistry, and functional experimentation, we confirm at high resolution that BCi-NS1.1 and primary HAE cultures are largely similar in morphology, cell type composition, and overall transcriptional patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1 HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus . Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.},
}
@article {pmid36908799,
year = {2023},
author = {Du, P and Li, P and Cheng, L and Li, X and Su, J},
title = {Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1132290},
pmid = {36908799},
issn = {1662-4548},
abstract = {INTRODUCTION: Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.<br><br>METHODS: In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.<br><br>RESULTS: In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.<br><br>DISCUSSION: The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.},
}
@article {pmid36908782,
year = {2023},
author = {Zhang, R and Chen, Y and Xu, Z and Zhang, L and Hu, Y and Chen, M},
title = {Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1129049},
pmid = {36908782},
issn = {1662-4548},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.},
}
@article {pmid36907708,
year = {2023},
author = {Gavaret, M and Iftimovici, A and Pruvost-Robieux, E},
title = {EEG: Current relevance and promising quantitative analyses.},
journal = {Revue neurologique},
volume = {179},
number = {4},
pages = {352-360},
doi = {10.1016/j.neurol.2022.12.008},
pmid = {36907708},
issn = {0035-3787},
mesh = {Humans ; *Brain Mapping ; Electroencephalography ; Brain ; Evoked Potentials ; *Epilepsy ; },
abstract = {Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.},
}
@article {pmid36905065,
year = {2023},
author = {Tao, T and Gao, Y and Jia, Y and Chen, R and Li, P and Xu, G},
title = {A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {5},
pages = {},
pmid = {36905065},
issn = {1424-8220},
support = {2020KWZ-003//Key Research and Development Program of Shaanxi/ ; },
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Sensitivity and Specificity ; },
abstract = {An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.},
}
@article {pmid36905004,
year = {2023},
author = {Saibene, A and Caglioni, M and Corchs, S and Gasparini, F},
title = {EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {5},
pages = {},
pmid = {36905004},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Wearable Electronic Devices ; *Brain Waves ; },
abstract = {In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.},
}
@article {pmid36904950,
year = {2023},
author = {Collazos-Huertas, DF and Álvarez-Meza, AM and Cárdenas-Peña, DA and Castaño-Duque, GA and Castellanos-Domínguez, CG},
title = {Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {5},
pages = {},
pmid = {36904950},
issn = {1424-8220},
support = {57579//Colciencias/ ; },
mesh = {Humans ; *Motor Skills ; Electroencephalography/methods ; Imagery, Psychotherapy ; Neural Networks, Computer ; Brain/physiology ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.},
}
@article {pmid36904683,
year = {2023},
author = {Oikonomou, VP and Georgiadis, K and Kalaganis, F and Nikolopoulos, S and Kompatsiaris, I},
title = {A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {5},
pages = {},
pmid = {36904683},
issn = {1424-8220},
support = {RESEARCH CREATE INNOVATE (T2EDK-03661)//General Secretariat for Research and Technology/ ; },
mesh = {Bayes Theorem ; *Electroencephalography/methods ; Brain ; Algorithms ; *Brain-Computer Interfaces ; Cognition ; },
abstract = {In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).},
}
@article {pmid36904629,
year = {2023},
author = {Oikonomou, VP},
title = {Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {5},
pages = {},
pmid = {36904629},
issn = {1424-8220},
mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Photic Stimulation/methods ; },
abstract = {Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.},
}
@article {pmid36899699,
year = {2023},
author = {Niu, K and An, Z and Yao, Z and Chen, C and Yang, L and Xiong, J},
title = {Effects of Different Bedding Materials on Production Performance, Lying Behavior and Welfare of Dairy Buffaloes.},
journal = {Animals : an open access journal from MDPI},
volume = {13},
number = {5},
pages = {},
pmid = {36899699},
issn = {2076-2615},
support = {CARS-36//Modern Agro-industry Technology Research System/ ; },
abstract = {Different bedding materials have important effects on the behavioristics, production performance and welfare of buffalo. This study aimed to compare the effects of two bedding materials on lying behavior, production performance and animal welfare of dairy buffaloes. More than 40 multiparous lactating buffaloes were randomly divided into two groups, which were raised on fermented manure bedding (FMB) and chaff bedding (CB). The results showed that the application of FMB improved the lying behavior of buffaloes, the average daily lying time (ADLT) of buffaloes in FMB increased by 58 min compared to those in CB, with a significant difference (p < 0.05); the average daily standing time (ADST) decreased by 30 min, with a significant difference (p < 0.05); and the buffalo comfort index (BCI) increased, but the difference was not significant (p > 0.05). The average daily milk yield of buffaloes in FMB increased by 5.78% compared to buffaloes in CB. The application of FMB improved the hygiene of buffaloes. The locomotion score and hock lesion score were not significantly different between the two groups and all buffaloes did not show moderate and severe lameness. The price of FMB was calculated to be 46% of CB, which greatly reduced the cost of bedding material. In summary, FMB has significantly improved the lying behavior, production performance and welfare of buffaloes and significantly reduce the cost of bedding material.},
}
@article {pmid36899597,
year = {2023},
author = {Zhao, SN and Cui, Y and He, Y and He, Z and Diao, Z and Peng, F and Cheng, C},
title = {Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {2},
pages = {3638-3660},
doi = {10.3934/mbe.2023170},
pmid = {36899597},
issn = {1551-0018},
mesh = {*Brain-Computer Interfaces ; *Robotics ; Evoked Potentials, Visual ; Brain/physiology ; Electroencephalography ; },
abstract = {This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.},
}
@article {pmid36899543,
year = {2023},
author = {Yang, L and Shi, T and Lv, J and Liu, Y and Dai, Y and Zou, L},
title = {A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {2},
pages = {2482-2500},
doi = {10.3934/mbe.2023116},
pmid = {36899543},
issn = {1551-0018},
mesh = {Humans ; Electroencephalography ; Bayes Theorem ; *Brain-Computer Interfaces ; Upper Extremity ; *Stroke ; Algorithms ; },
abstract = {To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.},
}
@article {pmid36899504,
year = {2023},
author = {Gan, L and Yin, X and Huang, J and Jia, B},
title = {Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {2},
pages = {1695-1715},
doi = {10.3934/mbe.2023077},
pmid = {36899504},
issn = {1551-0018},
mesh = {Humans ; *Artificial Intelligence ; Cerebrovascular Circulation/physiology ; *Cerebrovascular Disorders ; Brain ; Ultrasonography, Doppler, Transcranial/methods ; Computers ; },
abstract = {Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.},
}
@article {pmid36896512,
year = {2023},
author = {Wu, Z and She, Q and Hou, Z and Li, Z and Tian, K and Ma, Y},
title = {Multi-source online transfer algorithm based on source domain selection for EEG classification.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {20},
number = {3},
pages = {4560-4573},
doi = {10.3934/mbe.2023211},
pmid = {36896512},
issn = {1551-0018},
mesh = {*Algorithms ; Electroencephalography ; *Brain-Computer Interfaces ; Learning ; },
abstract = {The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.},
}
@article {pmid36883755,
year = {2023},
author = {Greenwell, D and Vanderkolff, S and Feigh, J},
title = {Understanding de novo learning for brain-machine interfaces.},
journal = {Journal of neurophysiology},
volume = {129},
number = {4},
pages = {749-750},
doi = {10.1152/jn.00496.2022},
pmid = {36883755},
issn = {1522-1598},
mesh = {*Brain-Computer Interfaces ; Learning ; Adaptation, Physiological ; },
abstract = {De novo motor learning is a form of motor learning characterized by the development of an entirely new and distinct motor controller to accommodate a novel motor demand. Inversely, adaptation is a form of motor learning characterized by rapid, unconscious modifications in a previously established motor controller to accommodate small deviations in task demands. As most of the motor learning involves the adaptation of previously established motor controllers, de novo learning can be challenging to isolate and observe. The recent publication from Haith et al. (Haith AM, Yang CS, Pakpoor J, Kita K. J Neurophysiol 128: 982-993, 2022.) details a novel method to investigate de novo learning using a complex bimanual cursor control task. This research is especially important in the context of future brain-machine interface devices that will present users with an entirely novel motor learning demand, requiring de novo learning.},
}
@article {pmid36878831,
year = {2023},
author = {Mathon, B and Navarro, V and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Safety Profile of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in Non-epileptic Mice and in a Mouse Model of Mesial Temporal Lobe Epilepsy.},
journal = {Ultrasound in medicine &amp; biology},
volume = {49},
number = {5},
pages = {1327-1336},
doi = {10.1016/j.ultrasmedbio.2023.02.002},
pmid = {36878831},
issn = {1879-291X},
mesh = {Mice ; Animals ; *Blood-Brain Barrier/metabolism ; *Epilepsy, Temporal Lobe/therapy/chemically induced ; Mice, Inbred C57BL ; Disease Models, Animal ; Albumins ; Hippocampus ; },
abstract = {OBJECTIVE: It is unknown whether ultrasound-induced blood-brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication.<br><br>METHODS: To gain more insight into the safety profile of ultrasound (US)-induced BBB opening, we determined BBB permeability as well as histological modifications in C57BL/6 adult control mice and in the kainate (KA) model for mesial temporal lobe epilepsy in mice after sonication with low-intensity pulsed ultrasound (LIPU). Microglial and astroglial changes in ipsilateral hippocampus were examined at different time points following BBB disruption by respectively analyzing Iba1 and glial fibrillary acidic protein immunoreactivity. Using intracerebral EEG recordings, we further studied the possible electrophysiological repercussions of a repeated disrupted BBB for seizure generation in nine non-epileptic mice.<br><br>RESULTS: LIPU-induced BBB opening led to transient albumin extravasation and reversible mild astrogliosis, but not to microglial activation in the hippocampus of non-epileptic mice. In KA mice, the transient albumin extravasation into the hippocampus mediated by LIPU-induced BBB opening did not aggravate inflammatory processes and histologic changes that characterize the hippocampal sclerosis. Three LIPU-induced BBB opening did not induce epileptogenicity in non-epileptic mice implanted with depth EEG electrodes.<br><br>CONCLUSION: Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases.},
}
@article {pmid36878727,
year = {2023},
author = {Ma, J and Hu, Z and Yue, H and Luo, Y and Wang, C and Wu, X and Gu, Y and Wang, L},
title = {GRM2 Regulates Functional Integration of Adult-Born DGCs by Paradoxically Modulating MEK/ERK1/2 Pathway.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {43},
number = {16},
pages = {2822-2836},
pmid = {36878727},
issn = {1529-2401},
mesh = {Male ; Female ; Mice ; Animals ; *Dentate Gyrus/physiology ; *MAP Kinase Signaling System ; Neurons/physiology ; Hippocampus/physiology ; Mitogen-Activated Protein Kinase Kinases ; Neurogenesis/physiology ; },
abstract = {Metabotropic glutamate receptor 2 (GRM2) is highly expressed in hippocampal dentate granule cells (DGCs), regulating synaptic transmission and hippocampal functions. Newborn DGCs are continuously generated throughout life and express GRM2 when they are mature. However, it remained unclear whether and how GRM2 regulates the development and integration of these newborn neurons. We discovered that the expression of GRM2 in adult-born DGCs increased with neuronal development in mice of both sexes. Lack of GRM2 caused developmental defects of DGCs and impaired hippocampus-dependent cognitive functions. Intriguingly, our data showed that knockdown of Grm2 resulted in decreased b/c-Raf kinases and paradoxically led to an excessive activation of MEK/ERK1/2 pathway. Inhibition of MEK ameliorated the developmental defects caused by Grm2 knockdown. Together, our results indicate that GRM2 is necessary for the development and functional integration of newborn DGCs in the adult hippocampus through regulating the phosphorylation and activation state of MEK/ERK1/2 pathway.SIGNIFICANCE STATEMENT Metabotropic glutamate receptor 2 (GRM2) is highly expressed in mature dentate granule cells (DGCs) in the hippocampus. It remains unclear whether GRM2 is required for the development and integration of adult-born DGCs. We provided in vivo and in vitro evidence to show that GRM2 regulates the development of adult-born DGCs and their integration into existing hippocampal circuits. Lack of GRM2 in a cohort of newborn DGCs impaired object-to-location memory in mice. Moreover, we revealed that GRM2 knockdown paradoxically upregulated MEK/ERK1/2 pathway by suppressing b/c-Raf in developing neurons, which is likely a common mechanism underlying the regulation of the development of neurons expressing GRM2. Thus, Raf/MEK/ERK1/2 pathway could be a potential target for brain diseases related to GRM2 abnormality.},
}
@article {pmid36877853,
year = {2023},
author = {Qi, HX and Reed, JL and Liao, CC and Kaas, JH},
title = {Regressive changes in sizes of somatosensory cuneate nucleus after sensory loss in primates.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {11},
pages = {e2222076120},
pmid = {36877853},
issn = {1091-6490},
support = {R01 NS016446/NS/NINDS NIH HHS/United States ; R01 NS067017/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain Stem ; *Primates ; Hand ; Upper Extremity ; Atrophy ; },
abstract = {Neurons in the early stages of processing sensory information suffer transneuronal atrophy when deprived of their activating inputs. For over 40 y, members of our laboratory have studied the reorganization of the somatosensory cortex during and after recovering from different types of sensory loss. Here, we took advantage of the preserved histological material from these studies of the cortical effects of sensory loss to evaluate the histological consequences in the cuneate nucleus of the lower brainstem and the adjoining spinal cord. The neurons in the cuneate nucleus are activated by touch on the hand and arm, and relay this activation to the contralateral thalamus, and from the thalamus to the primary somatosensory cortex. Neurons deprived of activating inputs tend to shrink and sometimes die. We considered the effects of differences in species, type and extent of sensory loss, recovery time after injury, and age at the time of injury on the histology of the cuneate nucleus. The results indicate that all injuries that deprived part or all of the cuneate nucleus of sensory activation result in some atrophy of neurons as reflected by a decrease in nucleus size. The extent of the atrophy is greater with greater sensory loss and with longer recovery times. Based on supporting research, atrophy appears to involve a reduction in neuron size and neuropil, with little or no neuron loss. Thus, the potential exists for restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or biologically with hand replacement surgery.},
}
@article {pmid36877440,
year = {2023},
author = {He, C and Duan, S},
title = {Novel Insight into Glial Biology and Diseases.},
journal = {Neuroscience bulletin},
volume = {39},
number = {3},
pages = {365-367},
pmid = {36877440},
issn = {1995-8218},
mesh = {*Neuroglia ; *Brain ; Biology ; },
}
@article {pmid36876801,
year = {2023},
author = {Kumar, A and Sah, DK and Khanna, K and Rai, Y and Yadav, AK and Ansari, MS and Bhatt, AN},
title = {A calcium and zinc composite alginate hydrogel for pre-hospital hemostasis and wound care.},
journal = {Carbohydrate polymers},
volume = {299},
number = {},
pages = {120186},
doi = {10.1016/j.carbpol.2022.120186},
pmid = {36876801},
issn = {1879-1344},
mesh = {Humans ; Animals ; Mice ; *Calcium ; *Hydrogels ; Zinc ; Alginates ; Hemostasis ; },
abstract = {We developed, characterized, and examined the hemostatic potential of sodium alginate-based Ca[2+] and Zn[2+] composite hydrogel (SA-CZ). SA-CZ hydrogel showed substantial in-vitro efficacy, as observed by the significant reduction in coagulation time with better blood coagulation index (BCI) and no evident hemolysis in human blood. SA-CZ significantly reduced bleeding time (≈60 %) and mean blood loss (≈65 %) in the tail bleeding and liver incision in the mice hemorrhage model (p ≤ 0.001). SA-CZ also showed enhanced cellular migration (1.58-fold) in-vitro and improved wound closure (≈70 %) as compared with betadine (≈38 %) and saline (≈34 %) at the 7th-day post-wound creation in-vivo (p < 0.005). Subcutaneous implantation and intra-venous gamma-scintigraphy of hydrogel revealed ample body clearance and non-considerable accumulation in any vital organ, proving its non-thromboembolic nature. Overall, SA-CZ showed good biocompatibility along with efficient hemostasis and wound healing qualities, making it suitable as a safe and effective aid for bleeding wounds.},
}
@article {pmid36875646,
year = {2023},
author = {Porcaro, C and Avanaki, K and Arias-Carrion, O and Mørup, M},
title = {Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1152394},
pmid = {36875646},
issn = {1662-4548},
}
@article {pmid36875236,
year = {2023},
author = {Zhang, J and Gao, S and Zhou, K and Cheng, Y and Mao, S},
title = {An online hybrid BCI combining SSVEP and EOG-based eye movements.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1103935},
pmid = {36875236},
issn = {1662-5161},
abstract = {Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.},
}
@article {pmid36868861,
year = {2023},
author = {Wang, Y and Lin, J and Li, J and Yan, L and Li, W and He, X and Ma, H},
title = {Chronic Neuronal Inactivity Utilizes the mTOR-TFEB Pathway to Drive Transcription-Dependent Autophagy for Homeostatic Up-Scaling.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {43},
number = {15},
pages = {2631-2652},
pmid = {36868861},
issn = {1529-2401},
mesh = {Rats ; Animals ; Male ; Female ; Rats, Sprague-Dawley ; *Neurons/physiology ; Homeostasis/physiology ; *Neuronal Plasticity/physiology ; TOR Serine-Threonine Kinases/metabolism ; Autophagy ; Transcription Factors/metabolism ; Mammals ; Basic Helix-Loop-Helix Leucine Zipper Transcription Factors/metabolism ; },
abstract = {Activity-dependent changes in protein expression are critical for neuronal plasticity, a fundamental process for the processing and storage of information in the brain. Among the various forms of plasticity, homeostatic synaptic up-scaling is unique in that it is induced primarily by neuronal inactivity. However, precisely how the turnover of synaptic proteins occurs in this homeostatic process remains unclear. Here, we report that chronically inhibiting neuronal activity in primary cortical neurons prepared from embryonic day (E)18 Sprague Dawley rats (both sexes) induces autophagy, thereby regulating key synaptic proteins for up-scaling. Mechanistically, chronic neuronal inactivity causes dephosphorylation of ERK and mTOR, which induces transcription factor EB (TFEB)-mediated cytonuclear signaling and drives transcription-dependent autophagy to regulate αCaMKII and PSD95 during synaptic up-scaling. Together, these findings suggest that mTOR-dependent autophagy, which is often triggered by metabolic stressors such as starvation, is recruited and sustained during neuronal inactivity to maintain synaptic homeostasis, a process that ensures proper brain function and if impaired can cause neuropsychiatric disorders such as autism.SIGNIFICANCE STATEMENT In the mammalian brain, protein turnover is tightly controlled by neuronal activation to ensure key neuronal functions during long-lasting synaptic plasticity. However, a long-standing question is how this process occurs during synaptic up-scaling, a process that requires protein turnover but is induced by neuronal inactivation. Here, we report that mTOR-dependent signaling, which is often triggered by metabolic stressors such as starvation, is "hijacked" by chronic neuronal inactivation, which then serves as a nucleation point for transcription factor EB (TFEB) cytonuclear signaling that drives transcription-dependent autophagy for up-scaling. These results provide the first evidence of a physiological role of mTOR-dependent autophagy in enduing neuronal plasticity, thereby connecting major themes in cell biology and neuroscience via a servo loop that mediates autoregulation in the brain.},
}
@article {pmid36868167,
year = {2023},
author = {Yang, Y and Zhang, F and Gao, X and Feng, L and Xu, K},
title = {Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {141},
number = {},
pages = {109120},
doi = {10.1016/j.yebeh.2023.109120},
pmid = {36868167},
issn = {1525-5069},
mesh = {Rats ; Animals ; *Epilepsy, Temporal Lobe/chemically induced/complications/therapy ; *Epilepsy ; Seizures ; Brain ; Hippocampus ; },
abstract = {OBJECTIVE: Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically.<br><br>METHODS: Long-term local field potentials (LFPs) were recorded over a period of 1 to 4&#xa0;months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage.<br><br>RESULTS: Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data.<br><br>SIGNIFICANCE: Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.},
}
@article {pmid36866539,
year = {2023},
author = {Chen, S and Guan, X and Xie, L and Liu, C and Li, C and He, M and Hu, J and Fan, H and Li, Q and Xie, L and Yang, M and Zhang, X and Xiao, S and Tang, J},
title = {Aloe-emodin targets multiple signaling pathways by blocking ubiquitin-mediated degradation of DUSP1 in nasopharyngeal carcinoma cells.},
journal = {Phytotherapy research : PTR},
volume = {37},
number = {7},
pages = {2979-2994},
doi = {10.1002/ptr.7793},
pmid = {36866539},
issn = {1099-1573},
support = {20-065-76//Fund of Guangxi Key laboratory of Metabolic Diseases Research/ ; S2020106//Guangxi Medical and Health Appropriate Technology Development and Promotion Project/ ; GXZYC20220317//Guangxi Zhuang Autonomous Region Traditional Chinese Medicine Administration self-funded scientific research project/ ; 20210227-8-7//Guilin Science and Technology Plan Project/ ; 81760491//National Natural Science Foundation of China/ ; 82060500//National Natural Science Foundation of China/ ; 82260476//National Natural Science Foundation of China/ ; 2020GXNSFAA159040//Natural Science Foundation of Guangxi Province of China/ ; 2022GXNSFAA103002//Natural Science Foundation of Guangxi Province of China/ ; 2022KY0502//The project of improving the basic scientific research ability of young and middle-aged teachers in Guangxi universities/ ; },
mesh = {Humans ; *Emodin/pharmacology ; *Aloe ; Nasopharyngeal Carcinoma ; Ubiquitin ; Molecular Docking Simulation ; Signal Transduction ; Apoptosis ; p38 Mitogen-Activated Protein Kinases/metabolism ; *Nasopharyngeal Neoplasms/drug therapy ; Cell Line, Tumor ; Cell Proliferation ; Dual Specificity Phosphatase 1/metabolism ; },
abstract = {Aloe-emodin (AE) has been shown to inhibit the proliferation of several cancer cell lines, including human nasopharyngeal carcinoma (NPC) cell lines. In this study, we confirmed that AE inhibited malignant biological behaviors, including cell viability, abnormal proliferation, apoptosis, and migration of NPC cells. Western blotting analysis revealed that AE upregulated the expression of DUSP1, an endogenous inhibitor of multiple cancer-associated signaling pathways, resulting in blockage of the extracellular signal-regulated kinase (ERK)-1/2, protein kinase B (AKT), and p38-mitogen activated protein kinase（p38-MAPK） signaling pathways in NPC cell lines. Moreover, the selective inhibitor of DUSP1, BCI-hydrochloride, partially reversed the AE-induced cytotoxicity and blocked the aforementioned signaling pathways in NPC cells. In addition, the binding between AE and DUSP1 was predicted via molecular docking analysis using AutoDock-Vina software and further verified via a microscale thermophoresis assay. The binding amino acid residues were adjacent to the predicted ubiquitination site (Lys192) of DUSP1. Immunoprecipitation with the ubiquitin antibody, ubiquitinated DUSP1 was shown to be upregulated by AE. Our findings revealed that AE can stabilize DUSP1 by blocking its ubiquitin-proteasome-mediated degradation and proposed an underlying mechanism by which AE-upregulated DUSP1 may potentially target multiple pathways in NPC cells.},
}
@article {pmid36866306,
year = {2023},
author = {Patel, HH and Berlinberg, EJ and Nwachukwu, B and Williams, RJ and Mandelbaum, B and Sonkin, K and Forsythe, B},
title = {Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation.},
journal = {Arthroscopy, sports medicine, and rehabilitation},
volume = {5},
number = {1},
pages = {e207-e216},
pmid = {36866306},
issn = {2666-061X},
abstract = {UNLABELLED: Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: "quadriceps muscle," "neurofeedback," "biofeedback," "muscle activation," "motor learning," "anterior cruciate ligament," and "cortical plasticity." We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps "exercise" the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care.<br><br>LEVEL OF EVIDENCE: V, expert opinion.},
}
@article {pmid36865207,
year = {2023},
author = {Forenzo, D and Liu, Y and Kim, J and Ding, Y and Yoon, T and He, B},
title = {Integrating simultaneous motor imagery and spatial attention for EEG-BCI control.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.02.20.529307},
pmid = {36865207},
issn = {2692-8205},
abstract = {OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.<br><br>METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).<br><br>RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.<br><br>CONCLUSION: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects.<br><br>SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.},
}
@article {pmid36863014,
year = {2023},
author = {Li, G and Liu, Y and Chen, Y and Li, M and Song, J and Li, K and Zhang, Y and Hu, L and Qi, X and Wan, X and Liu, J and He, Q and Zhou, H},
title = {Polyvinyl alcohol/polyacrylamide double-network hydrogel-based semi-dry electrodes for robust electroencephalography recording at hairy scalp for noninvasive brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acc098},
pmid = {36863014},
issn = {1741-2552},
mesh = {Humans ; *Scalp ; *Brain-Computer Interfaces ; Polyvinyl Alcohol ; Electroencephalography/methods ; Hydrogels ; Electrodes ; },
abstract = {Objective.Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography (EEG) signals, which is essential for real-life brain-computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust EEG recording at hairy scalp.Approach.The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants.Main results.The results show that the PVA/PAM DNHs with 7.5 wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 ± 8.9 kΩ at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 ± 0.4μV min[-1]). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes.Significance.Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs.},
}
@article {pmid36862765,
year = {2023},
author = {Mao, C and Xiao, P and Tao, XN and Qin, J and He, QT and Zhang, C and Guo, SC and Du, YQ and Chen, LN and Shen, DD and Yang, ZS and Zhang, HQ and Huang, SM and He, YH and Cheng, J and Zhong, YN and Shang, P and Chen, J and Zhang, DL and Wang, QL and Liu, MX and Li, GY and Guo, Y and Xu, HE and Wang, C and Zhang, C and Feng, S and Yu, X and Zhang, Y and Sun, JP},
title = {Unsaturated bond recognition leads to biased signal in a fatty acid receptor.},
journal = {Science (New York, N.Y.)},
volume = {380},
number = {6640},
pages = {eadd6220},
doi = {10.1126/science.add6220},
pmid = {36862765},
issn = {1095-9203},
mesh = {Cryoelectron Microscopy ; Ligands ; *Receptors, G-Protein-Coupled/agonists/chemistry/genetics ; *Fatty Acids, Omega-3/chemistry/metabolism ; Humans ; Biphenyl Compounds/chemistry/pharmacology ; Phenylpropionates/chemistry/pharmacology ; *Drug Design ; Protein Conformation ; Eicosapentaenoic Acid/chemistry/metabolism ; Mutation, Missense ; Polymorphism, Single Nucleotide ; },
abstract = {Individual free fatty acids (FAs) play important roles in metabolic homeostasis, many through engagement with more than 40G protein-coupled receptors. Searching for receptors to sense beneficial omega-3 FAs of fish oil enabled the identification of GPR120, which is involved in a spectrum of metabolic diseases. Here, we report six cryo-electron microscopy structures of GPR120 in complex with FA hormones or TUG891 and Gi or Giq trimers. Aromatic residues inside the GPR120 ligand pocket were responsible for recognizing different double-bond positions of these FAs and connect ligand recognition to distinct effector coupling. We also investigated synthetic ligand selectivity and the structural basis of missense single-nucleotide polymorphisms. We reveal how GPR120 differentiates rigid double bonds and flexible single bonds. The knowledge gleaned here may facilitate rational drug design targeting to GPR120.},
}
@article {pmid36861900,
year = {2023},
author = {Shah, AM},
title = {New development in brain-computer interface platforms: 1-year results from the SWITCH trial.},
journal = {Artificial organs},
volume = {47},
number = {4},
pages = {615-616},
doi = {10.1111/aor.14511},
pmid = {36861900},
issn = {1525-1594},
mesh = {Humans ; *Brain-Computer Interfaces ; Speech ; *Motor Cortex ; Prostheses and Implants ; Electroencephalography ; },
abstract = {Synchron publishes SWITCH trial results demonstrating the safety and efficacy of stentrode™ device. The stentrode™ is an endovascularly implanted brain-computer interface communication device capable of relaying neural activity from the motor cortex of paralyzed patients. The platform has been used to recover speech.},
}
@article {pmid36861042,
year = {2023},
author = {Valeriani, D and Cecotti, H and Thelen, A and Herff, C},
title = {Editorial: Translational brain-computer interfaces: From research labs to the market and back.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {1152466},
pmid = {36861042},
issn = {1662-5161},
}
@article {pmid36860620,
year = {2023},
author = {Huang, G and Zhao, Z and Zhang, S and Hu, Z and Fan, J and Fu, M and Chen, J and Xiao, Y and Wang, J and Dan, G},
title = {Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1122661},
pmid = {36860620},
issn = {1662-4548},
abstract = {INTRODUCTION: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.<br><br>METHODS: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.<br><br>RESULTS: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.<br><br>DISCUSSION: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.},
}
@article {pmid36860616,
year = {2023},
author = {Ivanov, N and Chau, T},
title = {Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1108889},
pmid = {36860616},
issn = {1662-5188},
abstract = {Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.},
}
@article {pmid36856917,
year = {2023},
author = {Song, Y and Sun, Z and Sun, W and Luo, M and Du, Y and Jing, J and Wang, Y},
title = {Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study.},
journal = {Brain topography},
volume = {36},
number = {3},
pages = {283-293},
pmid = {36856917},
issn = {1573-6792},
mesh = {Humans ; Functional Laterality/physiology ; Hemiplegia/diagnostic imaging ; Magnetic Resonance Imaging/methods ; *Stroke/diagnostic imaging ; *Sensorimotor Cortex/diagnostic imaging ; Neuronal Plasticity/physiology ; },
abstract = {To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = - 0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = - 0.60 for the RH group and - 0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.},
}
@article {pmid36855290,
year = {2023},
author = {Çevik Saldıran, T and Kara, &#x130; and Dinçer, E and Öztürk, &#xd6; and Çakıcı, R and Burroughs, T},
title = {Cross-cultural adaptation and validation of Diabetes Quality of Life Brief Clinical Inventory in Turkish patients with type 2 diabetes mellitus.},
journal = {Disability and rehabilitation},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/09638288.2023.2182917},
pmid = {36855290},
issn = {1464-5165},
abstract = {PURPOSE: To translate and culturally adapt the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) into Turkish and assess the psychometric properties of the translated version.<br><br>METHODS: A forward-backward translation process was conducted in conformity with international guidelines. A total of 150 patients with type 2 diabetes mellitus (T2DM) completed the Turkish version of DQoL-BCI (DQoL-BCI-Tr). The factor structure, test-retest reliability, and construct validity were evaluated.<br><br>RESULTS: In the DQoL-BCI-Tr, the three-factor structure was found optimal and explained 68.7% of the variance. The DQoL-BCI-Tr showed excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.98). Cronbach's alpha values ranged from 0.85 to 0.91 for subscales (satisfaction, worry, impact). A negative correlation was found between the total scores of the DQoL-BCI-Tr and the EuroQoL-5 dimensions (EQ-5D) indexes (r=&#x2009;-0.22, p&#x2009;<&#x2009;0.01). The DQoL-BCI-Tr total score and satisfaction and worry subscale scores differentiated between groups defined by glycated hemoglobin (HbA1c>9%) and the use of insulin.<br><br>CONCLUSIONS: The study results showed that the DQoL-BCI-Tr can be served as a reliable and valid instrument to obtain information from Turkish patients with T2DM diagnosis, including satisfaction with treatment, the impact of the disease, and worry about the social/vocational issues.Implications for rehabilitationThe Turkish version of the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) is a valid and reliable instrument.The DQoL-BCI Questionnaire in Turkish (DQoL-BCI-Tr) is an easy and quick way to determine satisfaction with treatment, impact of disease, and worry about the social/vocational issues.The DQoL-BCI-Tr is a reliable instrument for assessing disease-specific effects, emotional loads, and satisfaction of Turkish patients with type 2 diabetes in clinical and research settings.},
}
@article {pmid36854561,
year = {2023},
author = {Zheng, C and Liu, Y and Xiao, X and Zhou, X and Xu, F and Xu, M and Ming, D},
title = {[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {40},
number = {1},
pages = {155-162},
pmid = {36854561},
issn = {1001-5515},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.},
}
@article {pmid36854262,
year = {2023},
author = {Dadarlat, MC and Canfield, RA and Orsborn, AL},
title = {Neural Plasticity in Sensorimotor Brain-Machine Interfaces.},
journal = {Annual review of biomedical engineering},
volume = {25},
number = {},
pages = {51-76},
pmid = {36854262},
issn = {1545-4274},
support = {TL1 RR025016/RR/NCRR NIH HHS/United States ; TL1 TR002318/TR/NCATS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Learning ; Movement ; Neuronal Plasticity ; },
abstract = {Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.},
}
@article {pmid36854181,
year = {2023},
author = {Zhang, Y and Qiu, S and He, H},
title = {Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acbfdf},
pmid = {36854181},
issn = {1741-2552},
mesh = {*Imagination ; Electroencephalography/methods ; Brain ; Movement ; Neural Networks, Computer ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {Objective. A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. Multimodal MI-BCI that uses multiple neural signals contains rich common and complementary information and is promising for enhancing the decoding accuracy of MI-BCI. However, the heterogeneity of different modalities makes the multimodal decoding task difficult. How to effectively utilize multimodal information remains to be further studied.Approach. In this study, a multimodal MI decoding neural network was proposed. Spatial feature alignment losses were designed to enhance the feature representations extracted from the heterogeneous data and guide the fusion of features from different modalities. An attention-based modality fusion module was built to align and fuse the features in the temporal dimension. To evaluate the proposed decoding method, a five-class MI electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) dataset were constructed.Main results and significance. The comparison experimental results showed that the proposed decoding method achieved higher decoding accuracy than the compared methods on both the self-collected dataset and a public dataset. The ablation results verified the effectiveness of each part of the proposed method. Feature distribution visualization results showed that the proposed losses enhance the feature representation of EEG and fNIRS modalities. The proposed method based on EEG and fNIRS modalities has significant potential for improving decoding performance of MI tasks.},
}
@article {pmid36851970,
year = {2023},
author = {Amiri, M and Nazari, S and Jafari, AH and Makkiabadi, B},
title = {A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies.},
journal = {Heliyon},
volume = {9},
number = {3},
pages = {e13766},
pmid = {36851970},
issn = {2405-8440},
abstract = {BACKGROUND: The bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain.<br><br>NEW METHOD: In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device.<br><br>RESULTS: The sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid "spike train" algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus "spike train" algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm "spike train" on simulations and experimental data.},
}
@article {pmid36851960,
year = {2023},
author = {Lin, CL and Chen, LT},
title = {Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.},
journal = {Heliyon},
volume = {9},
number = {3},
pages = {e13745},
pmid = {36851960},
issn = {2405-8440},
abstract = {Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.},
}
@article {pmid36850850,
year = {2023},
author = {Ghodousi, M and Pousson, JE and Bernhofs, V and Griškova-Bulanova, I},
title = {Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {4},
pages = {},
pmid = {36850850},
issn = {1424-8220},
support = {S-LLT-19-3//Lietuvos mokslo taryba/ ; LV-LT-TW/2021/1//State Education Development Agency/ ; },
mesh = {Humans ; Expressed Emotion ; *Music ; Emotions ; Arousal ; *Brain-Computer Interfaces ; },
abstract = {A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.},
}
@article {pmid36850667,
year = {2023},
author = {Siribunyaphat, N and Punsawad, Y},
title = {Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {4},
pages = {},
pmid = {36850667},
issn = {1424-8220},
support = {CGS-RF-2022/01//Walailak University/ ; },
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Asthenopia ; Algorithms ; Brain ; },
abstract = {Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.},
}
@article {pmid36850530,
year = {2023},
author = {Xie, Y and Oniga, S},
title = {Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {4},
pages = {},
pmid = {36850530},
issn = {1424-8220},
mesh = {Humans ; *Neural Networks, Computer ; *Algorithms ; Electroencephalography ; Imagery, Psychotherapy ; Intention ; },
abstract = {In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.},
}
@article {pmid36848679,
year = {2023},
author = {Letner, JG and Patel, PR and Hsieh, JC and Smith Flores, IM and Della Valle, E and Walker, LA and Weiland, JD and Chestek, CA and Cai, D},
title = {Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
pmid = {36848679},
issn = {1741-2552},
support = {RF1 MH120005/MH/NIMH NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; },
mesh = {Male ; Rats ; Animals ; Carbon Fiber ; Electrodes, Implanted ; Electrodes ; *Neurons/physiology ; *Cerebral Cortex/physiology ; Electrophysiology ; Microelectrodes ; },
abstract = {Objective.Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.Approach.Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.Main results.Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6µm,X-± S) in layer V motor cortex.Significance.Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.},
}
@article {pmid36848586,
year = {2023},
author = {Gupta, A and Daniel, R and Rao, A and Roy, PP and Chandra, S and Kim, BG},
title = {Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.},
journal = {Big data},
volume = {11},
number = {4},
pages = {307-319},
doi = {10.1089/big.2021.0204},
pmid = {36848586},
issn = {2167-647X},
mesh = {Humans ; *Deep Learning ; Electroencephalography/methods ; Algorithms ; Workload ; Cognition ; },
abstract = {With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.},
}
@article {pmid36847833,
year = {2023},
author = {Yang, Y and Garringer, HJ and Shi, Y and Lövestam, S and Peak-Chew, S and Zhang, X and Kotecha, A and Bacioglu, M and Koto, A and Takao, M and Spillantini, MG and Ghetti, B and Vidal, R and Murzin, AG and Scheres, SHW and Goedert, M},
title = {New SNCA mutation and structures of α-synuclein filaments from juvenile-onset synucleinopathy.},
journal = {Acta neuropathologica},
volume = {145},
number = {5},
pages = {561-572},
pmid = {36847833},
issn = {1432-0533},
support = {P30 AG010133/AG/NIA NIH HHS/United States ; MC_UP_A025-1013/MRC_/Medical Research Council/United Kingdom ; G-1703/PUK_/Parkinson's UK/United Kingdom ; MC_UP_1201/14/MRC_/Medical Research Council/United Kingdom ; U01 NS110437/NS/NINDS NIH HHS/United States ; MC_U105184291/MRC_/Medical Research Council/United Kingdom ; RF1 AG071177/AG/NIA NIH HHS/United States ; MC_UP_A025_1013/MRC_/Medical Research Council/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; P30 AG072976/AG/NIA NIH HHS/United States ; },
mesh = {Humans ; alpha-Synuclein/genetics/metabolism ; *Synucleinopathies/genetics ; Nigeria ; *Multiple System Atrophy/genetics/metabolism ; Mutation/genetics ; },
abstract = {A 21-nucleotide duplication in one allele of SNCA was identified in a previously described disease with abundant α-synuclein inclusions that we now call juvenile-onset synucleinopathy (JOS). This mutation translates into the insertion of MAAAEKT after residue 22 of α-synuclein, resulting in a protein of 147 amino acids. Both wild-type and mutant proteins were present in sarkosyl-insoluble material that was extracted from frontal cortex of the individual with JOS and examined by electron cryo-microscopy. The structures of JOS filaments, comprising either a single protofilament, or a pair of protofilaments, revealed a new α-synuclein fold that differs from the folds of Lewy body diseases and multiple system atrophy (MSA). The JOS fold consists of a compact core, the sequence of which (residues 36-100 of wild-type α-synuclein) is unaffected by the mutation, and two disconnected density islands (A and B) of mixed sequences. There is a non-proteinaceous cofactor bound between the core and island A. The JOS fold resembles the common substructure of MSA Type I and Type II dimeric filaments, with its core segment approximating the C-terminal body of MSA protofilaments B and its islands mimicking the N-terminal arm of MSA protofilaments A. The partial similarity of JOS and MSA folds extends to the locations of their cofactor-binding sites. In vitro assembly of recombinant wild-type α-synuclein, its insertion mutant and their mixture yielded structures that were distinct from those of JOS filaments. Our findings provide insight into a possible mechanism of JOS fibrillation in which mutant α-synuclein of 147 amino acids forms a nucleus with the JOS fold, around which wild-type and mutant proteins assemble during elongation.},
}
@article {pmid36845071,
year = {2023},
author = {Chen, D and Liu, K and Guo, J and Bi, L and Xiang, J},
title = {Editorial: Brain-computer interface and its applications.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1140508},
doi = {10.3389/fnbot.2023.1140508},
pmid = {36845071},
issn = {1662-5218},
}
@article {pmid36844572,
year = {2023},
author = {Chen, PW and Ji, DH and Zhang, YS and Lee, C and Yeh, MY},
title = {Electroactive and Stretchable Hydrogels of 3,4-Ethylenedioxythiophene/thiophene Copolymers.},
journal = {ACS omega},
volume = {8},
number = {7},
pages = {6753-6761},
pmid = {36844572},
issn = {2470-1343},
abstract = {Hydrogels are conductive and stretchable, allowing for their use in flexible electronic devices, such as electronic skins, sensors, human motion monitoring, brain-computer interface, and so on. Herein, we synthesized the copolymers having various molar ratios of 3,4-ethylenedioxythiophene (EDOT) to thiophene (Th), which served as conductive additives. With doping engineering and incorporation with P(EDOT-co-Th) copolymers, hydrogels have presented excellent physical/chemical/electrical properties. It was found that the mechanical strength, adhesion ability, and conductivity of hydrogels were highly dependent on the molar ratio of EDOT to Th of the copolymers. The more the EDOT, the stronger the tensile strength and the greater the conductivity, but the lower the elongation break tends to be. By comprehensively evaluating the physical/chemical/electrical properties and cost of material use, the hydrogel incorporated with a 7:3 molar ratio P(EDOT-co-Th) copolymer was an optimal formulation for soft electronic devices.},
}
@article {pmid36844419,
year = {2022},
author = {Song, M and Huang, Y and Shen, Y and Shi, C and Breeschoten, A and Konijnenburg, M and Visser, H and Romme, J and Dutta, B and Alavi, MS and Bachmann, C and Liu, YH},
title = {A 1.66Gb/s and 5.8pJ/b Transcutaneous IR-UWB Telemetry System with Hybrid Impulse Modulation for Intracortical Brain-Computer Interfaces.},
journal = {Digest of technical papers. IEEE International Solid-State Circuits Conference},
volume = {2022},
number = {},
pages = {394-396},
pmid = {36844419},
issn = {0193-6530},
support = {101001448/ERC_/European Research Council/International ; },
}
@article {pmid36843389,
year = {2023},
author = {Branco, MP and Geukes, SH and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Nine decades of electrocorticography: A comparison between epidural and subdural recordings.},
journal = {The European journal of neuroscience},
volume = {57},
number = {8},
pages = {1260-1288},
doi = {10.1111/ejn.15941},
pmid = {36843389},
issn = {1460-9568},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; *Electrocorticography/methods ; *Subdural Space ; Dura Mater ; Electrodes, Implanted ; },
abstract = {In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.},
}
@article {pmid36842495,
year = {2023},
author = {Hua, SS and Ding, JJ and Sun, TC and Guo, C and Zhang, Y and Yu, ZH and Cao, YQ and Zhong, LH and Wu, Y and Guo, LY and Luo, JH and Cui, YH and Qiu, S},
title = {NMDA Receptor-Dependent Synaptic Potentiation via APPL1 Signaling Is Required for the Accessibility of a Prefrontal Neuronal Assembly in Retrieving Fear Extinction.},
journal = {Biological psychiatry},
volume = {94},
number = {3},
pages = {262-277},
doi = {10.1016/j.biopsych.2023.02.013},
pmid = {36842495},
issn = {1873-2402},
mesh = {Mice ; Animals ; *Extinction, Psychological/physiology ; *Fear/physiology ; Receptors, N-Methyl-D-Aspartate/metabolism ; Neurons/physiology ; Signal Transduction ; Prefrontal Cortex/metabolism ; Mice, Transgenic ; },
abstract = {BACKGROUND: The ventromedial prefrontal cortex has been viewed as a locus for storage and recall of extinction memory. However, the synaptic and cellular mechanisms underlying these processes remain elusive.<br><br>METHODS: We combined transgenic mice, electrophysiological recording, activity-dependent cell labeling, and chemogenetic manipulation to analyze the role of adaptor protein APPL1 in the ventromedial prefrontal cortex in fear extinction retrieval.<br><br>RESULTS: We found that both constitutive and conditional APPL1 knockout decreased NMDA receptor (NMDAR) function in the ventromedial prefrontal cortex and impaired fear extinction retrieval. Moreover, APPL1 undergoes nuclear translocation during extinction retrieval. Blocking APPL1 nucleocytoplasmic translocation reduced NMDAR currents and disrupted extinction retrieval. We also identified a prefrontal neuronal ensemble that is both necessary and sufficient for the storage of extinction memory. Inducible APPL1 knockout in this ensemble abolished NMDAR-dependent synaptic potentiation and disrupted extinction retrieval, while chemogenetic activation of this ensemble simultaneously rescued the impaired behaviors.<br><br>CONCLUSIONS: Our results indicate that a prefrontal neuronal ensemble stores extinction memory, and APPL1 signaling supports these neurons in retrieving extinction memory by controlling NMDAR-dependent potentiation.},
}
@article {pmid36842221,
year = {2023},
author = {Liu, Z and Wang, L and Xu, S and Lu, K},
title = {A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification.},
journal = {Computers in biology and medicine},
volume = {155},
number = {},
pages = {106196},
doi = {10.1016/j.compbiomed.2022.106196},
pmid = {36842221},
issn = {1879-0534},
mesh = {Bayes Theorem ; *Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Imagination ; },
abstract = {Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.},
}
@article {pmid36839377,
year = {2023},
author = {De Rubis, G and Paudel, KR and Manandhar, B and Singh, SK and Gupta, G and Malik, R and Shen, J and Chami, A and MacLoughlin, R and Chellappan, DK and Oliver, BGG and Hansbro, PM and Dua, K},
title = {Agarwood Oil Nanoemulsion Attenuates Cigarette Smoke-Induced Inflammation and Oxidative Stress Markers in BCi-NS1.1 Airway Epithelial Cells.},
journal = {Nutrients},
volume = {15},
number = {4},
pages = {},
pmid = {36839377},
issn = {2072-6643},
mesh = {Antioxidants/pharmacology ; *Cigarette Smoking ; *Pulmonary Disease, Chronic Obstructive/metabolism ; Oxidative Stress ; Inflammation/metabolism ; Anti-Inflammatory Agents/pharmacology ; Epithelial Cells ; Nicotiana ; },
abstract = {Chronic obstructive pulmonary disease (COPD) is an irreversible inflammatory respiratory disease characterized by frequent exacerbations and symptoms such as cough and wheezing that lead to irreversible airway damage and hyperresponsiveness. The primary risk factor for COPD is chronic cigarette smoke exposure, which promotes oxidative stress and a general pro-inflammatory condition by stimulating pro-oxidant and pro-inflammatory pathways and, simultaneously, inactivating anti-inflammatory and antioxidant detoxification pathways. These events cause progressive damage resulting in impaired cell function and disease progression. Treatments available for COPD are generally aimed at reducing the symptoms of exacerbation. Failure to regulate oxidative stress and inflammation results in lung damage. In the quest for innovative treatment strategies, phytochemicals, and complex plant extracts such as agarwood essential oil are promising sources of molecules with antioxidant and anti-inflammatory activity. However, their clinical use is limited by issues such as low solubility and poor pharmacokinetic properties. These can be overcome by encapsulating the therapeutic molecules using advanced drug delivery systems such as polymeric nanosystems and nanoemulsions. In this study, agarwood oil nanoemulsion (agarwood-NE) was formulated and tested for its antioxidant and anti-inflammatory potential in cigarette smoke extract (CSE)-treated BCi-NS1.1 airway basal epithelial cells. The findings suggest successful counteractivity of agarwood-NE against CSE-mediated pro-inflammatory effects by reducing the expression of the pro-inflammatory cytokines IL-1α, IL-1β, IL-8, and GDF-15. In addition, agarwood-NE induced the expression of the anti-inflammatory mediators IL-10, IL-18BP, TFF3, GH, VDBP, relaxin-2, IFN-γ, and PDGF. Furthermore, agarwood-NE also induced the expression of antioxidant genes such as GCLC and GSTP1, simultaneously activating the PI3K pro-survival signalling pathway. This study provides proof of the dual anti-inflammatory and antioxidant activity of agarwood-NE, highlighting its enormous potential for COPD treatment.},
}
@article {pmid36837864,
year = {2023},
author = {Lin, A and Wang, T and Li, C and Pu, F and Abdelrahman, Z and Jin, M and Yang, Z and Zhang, L and Cao, X and Sun, K and Hou, T and Liu, Z and Chen, L and Chen, Z},
title = {Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study.},
journal = {Metabolites},
volume = {13},
number = {2},
pages = {},
pmid = {36837864},
issn = {2218-1989},
support = {2022010002//Research Center of Prevention and Treatment of Senescence Syndrome, School of Medicine Zhejiang University/ ; 2020E10004//Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province/ ; 188170-11103//Zhejiang University Global Partnership Fund/ ; },
abstract = {The relationship between skeletal muscle and cognitive disorders has drawn increasing attention. This study aims to examine the associations of sarcopenia with cognitive function and dementia risk score. Data on 1978 participants (aged 65 years and older) from the 2011 wave of the China Health and Retirement Longitudinal Study, with four follow-up waves to 2018, were used. Cognitive function was assessed by four dimensions, with a lower score indicating lower cognitive function. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), with a higher score indicating a greater risk. Sarcopenia was defined when low muscle mass plus low muscle strength or low physical performance were met. We used generalized estimating equations to examine the associations of sarcopenia. In the fully adjusted models, sarcopenia was significantly associated with lower cognitive function (standardized, β = -0.15; 95% CIs: -0.26, -0.04) and a higher BDRM score (standardized, β = 0.42; 95% CIs: 0.29, 0.55). Our findings may provide a new avenue for alleviating the burden of cognitive disorders by preventing sarcopenia.},
}
@article {pmid36837157,
year = {2023},
author = {Liu, Q and Deng, WY and Zhang, LY and Liu, CX and Jie, WW and Su, RX and Zhou, B and Lu, LM and Liu, SW and Huang, XG},
title = {Modified Bamboo Charcoal as a Bifunctional Material for Methylene Blue Removal.},
journal = {Materials (Basel, Switzerland)},
volume = {16},
number = {4},
pages = {},
pmid = {36837157},
issn = {1996-1944},
support = {22064010, 51862014//National Natural Science Foundation of China/ ; GJJ200430//Foundation of Jiangxi Educational Committee/ ; 20192BBEL50029//Jiangxi Province Key Research and Development Project/ ; },
abstract = {Biomass-derived raw bamboo charcoal (BC), NaOH-impregnated bamboo charcoal (BC-I), and magnetic bamboo charcoal (BC-IM) were fabricated and used as bio-adsorbents and Fenton-like catalysts for methylene blue removal. Compared to the raw biochar, a simple NaOH impregnation process significantly optimized the crystal structure, pore size distribution, and surface functional groups and increase the specific surface area from 1.4 to 63.0 m[2]/g. Further magnetization of the BC-I sample not only enhanced the surface area to 84.7 m[2]/g, but also improved the recycling convenience due to the superparamagnetism. The maximum adsorption capacity of BC, BC-I, and BC-IM for methylene blue at 328 K was 135.13, 220.26 and 497.51 mg/g, respectively. The pseudo-first-order rate constants k at 308 K for BC, BC-I, and BC-IM catalytic degradation in the presence of H2O2 were 0.198, 0.351, and 1.542 h[-1], respectively. A synergistic mechanism between adsorption and radical processes was proposed.},
}
@article {pmid36836747,
year = {2023},
author = {Omejc, N and Peskar, M and Miladinović, A and Kavcic, V and Džeroski, S and Marusic, U},
title = {On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.},
journal = {Life (Basel, Switzerland)},
volume = {13},
number = {2},
pages = {},
pmid = {36836747},
issn = {2075-1729},
support = {No. 431 P5-0381 and No. P2-0103//Slovenian Research Agency/ ; No. 952401 (TwinBrain)//European Union's Horizon 2020/ ; },
abstract = {The utilization of a non-invasive electroencephalogram (EEG) as an input sensor is a common approach in the field of the brain-computer interfaces (BCI). However, the collected EEG data pose many challenges, one of which may be the age-related variability of event-related potentials (ERPs), which are often used as primary EEG BCI signal features. To assess the potential effects of aging, a sample of 27 young and 43 older healthy individuals participated in a visual oddball study, in which they passively viewed frequent stimuli among randomly occurring rare stimuli while being recorded with a 32-channel EEG set. Two types of EEG datasets were created to train the classifiers, one consisting of amplitude and spectral features in time and another with extracted time-independent statistical ERP features. Among the nine classifiers tested, linear classifiers performed best. Furthermore, we show that classification performance differs between dataset types. When temporal features were used, maximum individuals' performance scores were higher, had lower variance, and were less affected overall by within-class differences such as age. Finally, we found that the effect of aging on classification performance depends on the classifier and its internal feature ranking. Accordingly, performance will differ if the model favors features with large within-class differences. With this in mind, care must be taken in feature extraction and selection to find the correct features and consequently avoid potential age-related performance degradation in practice.},
}
@article {pmid36836659,
year = {2023},
author = {Vasilyev, AN and Yashin, AS and Shishkin, SL},
title = {Quasi-Movements and "Quasi-Quasi-Movements": Does Residual Muscle Activation Matter?.},
journal = {Life (Basel, Switzerland)},
volume = {13},
number = {2},
pages = {},
pmid = {36836659},
issn = {2075-1729},
support = {22-29-01361//Russian Science Foundation/ ; },
abstract = {Quasi-movements (QM) are observed when an individual minimizes a movement to an extent that no related muscle activation is detected. Likewise to imaginary movements (IM) and overt movements, QMs are accompanied by the event-related desynchronization (ERD) of EEG sensorimotor rhythms. Stronger ERD was observed under QMs compared to IMs in some studies. However, the difference could be caused by the remaining muscle activation in QMs that could escape detection. Here, we re-examined the relation between the electromyography (EMG) signal and ERD in QM using sensitive data analysis procedures. More trials with signs of muscle activation were observed in QMs compared with a visual task and IMs. However, the rate of such trials was not correlated with subjective estimates of actual movement. Contralateral ERD did not depend on the EMG but still was stronger in QMs compared with IMs. These results suggest that brain mechanisms are common for QMs in the strict sense and "quasi-quasi-movements" (attempts to perform the same task accompanied by detectable EMG elevation) but differ between them and IMs. QMs could be helpful in research aimed at better understanding motor action and at modeling the use of attempted movements in the brain-computer interfaces with healthy participants.},
}
@article {pmid36831864,
year = {2023},
author = {Lakshminarayanan, K and Ramu, V and Rajendran, J and Chandrasekaran, KP and Shah, R and Daulat, SR and Moodley, V and Madathil, D},
title = {The Effect of Tactile Imagery Training on Reaction Time in Healthy Participants.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831864},
issn = {2076-3425},
support = {SRG/2021/000283//Department of Science &amp; Technology/ ; },
abstract = {BACKGROUND: Reaction time is an important measure of sensorimotor performance and coordination and has been shown to improve with training. Various training methods have been employed in the past to improve reaction time. Tactile imagery (TI) is a method of mentally simulating a tactile sensation and has been used in brain-computer interface applications. However, it is yet unknown whether TI can have a learning effect and improve reaction time.<br><br>OBJECTIVE: The purpose of this study was to investigate the effect of TI on reaction time in healthy participants.<br><br>METHODS: We examined the reaction time to vibratory stimuli before and after a TI training session in an experimental group and compared the change in reaction time post-training with pre-training in the experimental group as well as the reaction time in a control group. A follow-up evaluation of reaction time was also conducted.<br><br>RESULTS: The results showed that TI training significantly improved reaction time after TI compared with before TI by approximately 25% (pre-TI right-hand mean &#xb1; SD: 456.62 &#xb1; 124.26 ms, pre-TI left-hand mean &#xb1; SD: 448.82 &#xb1; 124.50 ms, post-TI right-hand mean &#xb1; SD: 340.32 &#xb1; 65.59 ms, post-TI left-hand mean &#xb1; SD: 335.52 &#xb1; 59.01 ms). Furthermore, post-training reaction time showed significant reduction compared with the control group and the improved reaction time had a lasting effect even after four weeks post-training.<br><br>CONCLUSION: These findings indicate that TI training may serve as an alternate imagery strategy for improving reaction time without the need for physical practice.},
}
@article {pmid36831857,
year = {2023},
author = {Peketi, S and Dhok, SB},
title = {Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831857},
issn = {2076-3425},
abstract = {Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.},
}
@article {pmid36831846,
year = {2023},
author = {Cattan, GH and Quemy, A},
title = {Case-Based and Quantum Classification for ERP-Based Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831846},
issn = {2076-3425},
abstract = {Low transfer rates are a major bottleneck for brain-computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers. In this study, we investigated the performance of variational quantum, quantum-enhanced support vector, and hypergraph case-based reasoning classifiers in the binary classification of EEG data from a P300 experiment. On the one hand, quantum classification is a promising technology to reduce computational time and improve learning outcomes. On the other hand, case-based reasoning has an excellent potential to simplify the preprocessing steps of EEG analysis. We found that the balanced training (prediction) accuracy of each of these three classifiers was 56.95 (51.83), 83.17 (50.25), and 71.10% (52.04%), respectively. In addition, case-based reasoning performed significantly lower with a simplified (49.78%) preprocessing pipeline. These results demonstrated that all classifiers were able to learn from the data and that quantum classification of EEG data was implementable; however, more research is required to enable a greater prediction accuracy because none of the classifiers were able to generalize from the data. This could be achieved by improving the configuration of the quantum classifiers (e.g., increasing the number of shots) and increasing the number of trials for hypergraph case-based reasoning classifiers through transfer learning.},
}
@article {pmid36831811,
year = {2023},
author = {Liang, X and Liu, Y and Yu, Y and Liu, K and Liu, Y and Zhou, Z},
title = {Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831811},
issn = {2076-3425},
support = {62006239//National Natural Science Foundation of China/ ; 62036013//National Natural Science Foundation of China/ ; 61722313//National Natural Science Foundation of China/ ; U19A2083//joint funds of the National Natural Science Foundation of China/ ; JCKY2020550B003//Defense Industrial Technology Development Program/ ; },
abstract = {Convolutional neural networks (CNNs) have shown great potential in the field of brain-computer interfaces (BCIs) due to their ability to directly process raw electroencephalogram (EEG) signals without artificial feature extraction. Some CNNs have achieved better classification accuracy than that of traditional methods. Raw EEG signals are usually represented as a two-dimensional (2-D) matrix composed of channels and time points, ignoring the spatial topological information of electrodes. Our goal is to make a CNN that takes raw EEG signals as inputs have the ability to learn spatial topological features and improve its classification performance while basically maintaining its original structure. We propose an EEG topographic representation module (TRM). This module consists of (1) a mapping block from raw EEG signals to a 3-D topographic map and (2) a convolution block from the topographic map to an output with the same size as the input. According to the size of the convolutional kernel used in the convolution block, we design two types of TRMs, namely TRM-(5,5) and TRM-(3,3). We embed the two TRM types into three widely used CNNs (ShallowConvNet, DeepConvNet and EEGNet) and test them on two publicly available datasets (the Emergency Braking During Simulated Driving Dataset (EBDSDD) and the High Gamma Dataset (HGD)). Results show that the classification accuracies of all three CNNs are improved on both datasets after using the TRMs. With TRM-(5,5), the average classification accuracies of DeepConvNet, EEGNet and ShallowConvNet are improved by 6.54%, 1.72% and 2.07% on the EBDSDD and by 6.05%, 3.02% and 5.14% on the HGD, respectively; with TRM-(3,3), they are improved by 7.76%, 1.71% and 2.17% on the EBDSDD and by 7.61%, 5.06% and 6.28% on the HGD, respectively. We improve the classification performance of three CNNs on both datasets through the use of TRMs, indicating that they have the capability to mine spatial topological EEG information. More importantly, since the output of a TRM has the same size as the input, CNNs with raw EEG signals as inputs can use this module without changing their original structures.},
}
@article {pmid36831784,
year = {2023},
author = {Arı, E and Taçgın, E},
title = {Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831784},
issn = {2076-3425},
abstract = {EEG signals are interpreted, analyzed and classified by many researchers for use in brain-computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03-89.29% classification accuracy and 2-23 s epoch time were obtained for 2A dataset, 64.84-84.94% classification accuracy and 4-10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.},
}
@article {pmid36831764,
year = {2023},
author = {Hu, H and Yue, K and Guo, M and Lu, K and Liu, Y},
title = {Subject Separation Network for Reducing Calibration Time of MI-Based BCI.},
journal = {Brain sciences},
volume = {13},
number = {2},
pages = {},
pmid = {36831764},
issn = {2076-3425},
abstract = {Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.},
}
@article {pmid36829694,
year = {2023},
author = {Nawaz, R and Wood, G and Nisar, H and Yap, VV},
title = {Exploring the Effects of EEG-Based Alpha Neurofeedback on Working Memory Capacity in Healthy Participants.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {36829694},
issn = {2306-5354},
support = {IPSR/RMC/UTARRF/2021-C2/H03//Universiti Tunku Abdul Rahman/ ; ICM-2020-00116//Austrian Agency for International Cooperation in Education and Research/ ; },
abstract = {Neurofeedback, an operant conditioning neuromodulation technique, uses information from brain activities in real-time via brain-computer interface (BCI) technology. This technique has been utilized to enhance the cognitive abilities, including working memory performance, of human beings. The aims of this study are to investigate how alpha neurofeedback can improve working memory performance in healthy participants and to explore the underlying neural mechanisms in a working memory task before and after neurofeedback. Thirty-six participants divided into the NFT group and the control group participated in this study. This study was not blinded, and both the participants and the researcher were aware of their group assignments. Increasing power in the alpha EEG band was used as a neurofeedback in the eyes-open condition only in the NFT group. The data were collected before and after neurofeedback while they were performing the N-back memory task (N = 1 and N = 2). Both groups showed improvement in their working memory performance. There was an enhancement in the power of their frontal alpha and beta activities with increased working memory load (i.e., 2-back). The experimental group showed improvements in their functional connections between different brain regions at the theta level. This effect was absent in the control group. Furthermore, brain hemispheric lateralization was found during the N-back task, and there were more intra-hemisphere connections than inter-hemisphere connections of the brain. These results suggest that healthy participants can benefit from neurofeedback and from having their brain networks changed after the training.},
}
@article {pmid36829681,
year = {2023},
author = {Lee, PL and Chen, SH and Chang, TC and Lee, WK and Hsu, HT and Chang, HH},
title = {Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {},
pmid = {36829681},
issn = {2306-5354},
support = {110-2221-E-008 -095 -MY3//National Science and Technology Council, Taiwan./ ; 111-2811-E-008 -002 -MY3//National Science and Technology Council, Taiwan./ ; },
abstract = {The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.},
}
@article {pmid36827704,
year = {2023},
author = {Ming, G and Zhong, H and Pei, W and Gao, X and Wang, Y},
title = {A new grid stimulus with subtle flicker perception for user-friendly SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acbee0},
pmid = {36827704},
issn = {1741-2552},
mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Perception ; Algorithms ; },
abstract = {Objective.The traditional uniform flickering stimulation pattern shows strong steady-state visual evoked potential (SSVEP) responses and poor user experience with intense flicker perception. To achieve a balance between performance and comfort in SSVEP-based brain-computer interface (BCI) systems, this study proposed a new grid stimulation pattern with reduced stimulation area and low spatial contrast.Approach.A spatial contrast scanning experiment was conducted first to clarify the relationship between the SSVEP characteristics and the signs and values of spatial contrast. Four stimulation patterns were involved in the experiment: the ON and OFF grid stimulation patterns that separately activated the positive or negative contrast information processing pathways, the ON-OFF grid stimulation pattern that simultaneously activated both pathways, and the uniform flickering stimulation pattern that served as a control group. The contrast-intensity and contrast-user experience curves were obtained for each stimulation pattern. Accordingly, the optimized stimulation schemes with low spatial contrast (the ON-50% grid stimulus, the OFF-50% grid stimulus, and the Flicker-30% stimulus) were applied in a 12-target and a 40-target BCI speller and compared with the traditional uniform flickering stimulus (the Flicker-500% stimulus) in the evaluation of BCI performance and subjective experience.Main results.The OFF-50% grid stimulus showed comparable online performance (12-target, 2 s: 69.87 ± 0.74 vs. 69.76 ± 0.58 bits min[-1], 40-target, 4 s: 57.02 ± 2.53 vs. 60.79 ± 1.08 bits min[-1]) and improved user experience (better comfortable level, weaker flicker perception and higher preference level) compared to the traditional Flicker-500% stimulus in both multi-targets BCI spellers.Significance.Selective activation of the negative contrast information processing pathway using the new OFF-50% grid stimulus evoked robust SSVEP responses. On this basis, high-performance and user-friendly SSVEP-based BCIs have been developed and implemented, which has important theoretical significance and application value in promoting the development of the visual BCI technology.},
}
@article {pmid36827271,
year = {2023},
author = {McCaffrey, KR and Balaguera-Reina, SA and Falk, BG and Gati, EV and Cole, JM and Mazzotti, FJ},
title = {How to estimate body condition in large lizards? Argentine black and white tegu (Salvator merianae, Duméril and Bibron, 1839) as a case study.},
journal = {PloS one},
volume = {18},
number = {2},
pages = {e0282093},
pmid = {36827271},
issn = {1932-6203},
mesh = {Animals ; *Lizards ; Florida ; },
abstract = {Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat, because it was the least-associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton's K as the best performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complimentary measures of health or fitness should be considered.},
}
@article {pmid36825130,
year = {2023},
author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y},
title = {QEEG characteristics associated with malnutrition-inflammation complex syndrome.},
journal = {Frontiers in human neuroscience},
volume = {17},
number = {},
pages = {944988},
pmid = {36825130},
issn = {1662-5161},
abstract = {End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.},
}
@article {pmid36825118,
year = {2023},
author = {Wang, X and Xing, K and He, M and He, T and Xiang, X and Chen, T and Zhang, L and Li, H},
title = {Time-restricted feeding is an intervention against excessive dark-phase sleepiness induced by obesogenic diet.},
journal = {National science review},
volume = {10},
number = {1},
pages = {nwac222},
pmid = {36825118},
issn = {2053-714X},
abstract = {High-fat diet (HFD)-induced obesity is a growing epidemic and major health concern. While excessive daytime sleepiness (EDS) is a common symptom of HFD-induced obesity, preliminary findings suggest that reduced wakefulness could be improved with time-restricted feeding (TRF). At present, however, the underlying neural mechanisms remain largely unknown. The paraventricular thalamic nucleus (PVT) plays a role in maintaining wakefulness. We found that chronic HFD impaired the activity of PVT neurons. Notably, inactivation of the PVT was sufficient to reduce and fragment wakefulness during the active phase in lean mice, similar to the sleep-wake alterations observed in obese mice with HFD-induced obesity. On the other hand, enhancing PVT neuronal activity consolidated wakefulness in mice with HFD-induced obesity. We observed that the fragmented wakefulness could be eliminated and reversed by TRF. Furthermore, TRF prevented the HFD-induced disruptions on synaptic transmission in the PVT, in a feeding duration-dependent manner. Collectively, our findings demonstrate that ad libitum access to a HFD results in inactivation of the PVT, which is critical to impaired nocturnal wakefulness and increased sleep, while TRF can prevent and reverse diet-induced PVT dysfunction and excessive sleepiness. We establish a link between TRF and neural activity, through which TRF can potentially serve as a lifestyle intervention against diet/obesity-related EDS.},
}
@article {pmid36824667,
year = {2023},
author = {Chen, M and Chen, Z and Xiao, X and Zhou, L and Fu, R and Jiang, X and Pang, M and Xia, J},
title = {Corticospinal circuit neuroplasticity may involve silent synapses: Implications for functional recovery facilitated by neuromodulation after spinal cord injury.},
journal = {IBRO neuroscience reports},
volume = {14},
number = {},
pages = {185-194},
pmid = {36824667},
issn = {2667-2421},
abstract = {Spinal cord injury (SCI) leads to devastating physical consequences, such as severe sensorimotor dysfunction even lifetime disability, by damaging the corticospinal system. The conventional opinion that SCI is intractable due to the poor regeneration of neurons in the adult central nervous system (CNS) needs to be revisited as the CNS is capable of considerable plasticity, which underlie recovery from neural injury. Substantial spontaneous neuroplasticity has been demonstrated in the corticospinal motor circuitry following SCI. Some of these plastic changes appear to be beneficial while others are detrimental toward locomotor function recovery after SCI. The beneficial corticospinal plasticity in the spared corticospinal circuits can be harnessed therapeutically by multiple contemporary neuromodulatory approaches, especially the electrical stimulation-based modalities, in an activity-dependent manner to improve functional outcomes in post-SCI rehabilitation. Silent synapse generation and unsilencing contribute to profound neuroplasticity that is implicated in a variety of neurological disorders, thus they may be involved in the corticospinal motor circuit neuroplasticity following SCI. Exploring the underlying mechanisms of silent synapse-mediated neuroplasticity in the corticospinal motor circuitry that may be exploited by neuromodulation will inform a novel direction for optimizing therapeutic repair strategies and rehabilitative interventions in SCI patients.},
}
@article {pmid36822385,
year = {2023},
author = {Zhang, Y and Zou, J and Ding, N},
title = {Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope.},
journal = {Neuroscience and biobehavioral reviews},
volume = {147},
number = {},
pages = {105111},
doi = {10.1016/j.neubiorev.2023.105111},
pmid = {36822385},
issn = {1873-7528},
mesh = {Humans ; *Speech ; *Speech Perception ; Acoustic Stimulation ; Acoustics ; Periodicity ; },
abstract = {The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.},
}
@article {pmid36822277,
year = {2023},
author = {Gan, A and Gong, A and Ding, P and Yuan, X and Chen, M and Fu, Y and Cheng, Y},
title = {Computer-aided diagnosis of schizophrenia based on node2vec and Transformer.},
journal = {Journal of neuroscience methods},
volume = {389},
number = {},
pages = {109824},
doi = {10.1016/j.jneumeth.2023.109824},
pmid = {36822277},
issn = {1872-678X},
mesh = {Humans ; Brain/diagnostic imaging ; Computers ; Diagnosis, Computer-Assisted ; Magnetic Resonance Imaging/methods ; *Schizophrenia/diagnostic imaging ; Case-Control Studies ; },
abstract = {OBJECTIVE: Compared with the healthy control (HC) group, the brain structure and function of schizophrenia (SZ) patients are significantly abnormal, so brain imaging methods can be used to achieve the aided diagnosis of SZ. However, a brain network based on brain imaging data is non-Euclidean, and its intrinsic features cannot be learned effectively by general deep learning models. Furthermore, in the majority of existing studies, brain network features were manually specified as the input of machine learning models.<br><br>METHODS: In this study, brain functional network constructed from the subject's fMRI data is analyzed, and its small-world value is calculated and t-tested; the node2vec algorithm in graph embedding is introduced to transform the constructed brain network into low-dimensional dense vectors, and the brain network's non-Euclidean spatial structure characteristics are retained to the greatest extent, so that its intrinsic features can be extracted by deep learning models; GridMask is used to randomly mask part of the information in the vectors to enhance the data; and then features can be extracted using the Transformer model to identify SZ.<br><br>RESULTS: It is again shown that the small-world value of the brain network in SZ is significantly lower than that in HC by t-test (p=0.014&#xa1;0.05). 97.78% classification accuracy is achieved by the proposed methods (node2vec + GridMask + Transformer) in 30 SZ patients and 30 healthy people.<br><br>CONCLUSION: The experiment shows that the node2vec used in this paper can effectively solve the problem of brain network features being difficult to learn by general deep learning models. The high-precision computer-aided diagnosis of SZ can be obtained by combining node2vec with Transformer and GridMask.<br><br>SIGNIFICANCE: The proposed methods in the paper are expected to be used for aided diagnosis of SZ.},
}
@article {pmid36821640,
year = {2023},
author = {Jeunet, C and N'Kaoua, B and Subramanian, S and Hachet, M and Lotte, F},
title = {Correction: Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.},
journal = {PloS one},
volume = {18},
number = {2},
pages = {e0282281},
pmid = {36821640},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0143962.].},
}
@article {pmid36821578,
year = {2023},
author = {Goodrich, JA and Walker, DI and He, J and Lin, X and Baumert, BO and Hu, X and Alderete, TL and Chen, Z and Valvi, D and Fuentes, ZC and Rock, S and Wang, H and Berhane, K and Gilliland, FD and Goran, MI and Jones, DP and Conti, DV and Chatzi, L},
title = {Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study.},
journal = {Environmental health perspectives},
volume = {131},
number = {2},
pages = {27005},
pmid = {36821578},
issn = {1552-9924},
support = {U2C ES030163/ES/NIEHS NIH HHS/United States ; P50 MD017344/MD/NIMHD NIH HHS/United States ; R21 ES029681/ES/NIEHS NIH HHS/United States ; P01 ES022845/ES/NIEHS NIH HHS/United States ; R01 ES030691/ES/NIEHS NIH HHS/United States ; R01 DK059211/DK/NIDDK NIH HHS/United States ; R21 ES028903/ES/NIEHS NIH HHS/United States ; R01 ES032831/ES/NIEHS NIH HHS/United States ; P01 ES011627/ES/NIEHS NIH HHS/United States ; R00 ES027870/ES/NIEHS NIH HHS/United States ; T32 ES013678/ES/NIEHS NIH HHS/United States ; P30 ES019776/ES/NIEHS NIH HHS/United States ; K12 ES033594/ES/NIEHS NIH HHS/United States ; R21 ES031824/ES/NIEHS NIH HHS/United States ; R01 ES029944/ES/NIEHS NIH HHS/United States ; P30 ES007048/ES/NIEHS NIH HHS/United States ; R21 ES029328/ES/NIEHS NIH HHS/United States ; R25 GM143298/GM/NIGMS NIH HHS/United States ; P30 ES023515/ES/NIEHS NIH HHS/United States ; R01 ES032189/ES/NIEHS NIH HHS/United States ; U2C ES030859/ES/NIEHS NIH HHS/United States ; R01 ES030364/ES/NIEHS NIH HHS/United States ; R24 ES029490/ES/NIEHS NIH HHS/United States ; P01 CA196569/CA/NCI NIH HHS/United States ; R01 ES033688/ES/NIEHS NIH HHS/United States ; R00 ES027853/ES/NIEHS NIH HHS/United States ; },
mesh = {Adolescent ; Humans ; Young Adult ; *Alkanesulfonic Acids ; Bayes Theorem ; Cohort Studies ; *Environmental Pollutants/toxicity ; *Fluorocarbons ; Tyrosine ; },
abstract = {BACKGROUND: Exposure to per- and polyfluoroalkyl substances (PFAS) is ubiquitous and has been associated with an increased risk of several cardiometabolic diseases. However, the metabolic pathways linking PFAS exposure and human disease are unclear.<br><br>OBJECTIVE: We examined associations of PFAS mixtures with alterations in metabolic pathways in independent cohorts of adolescents and young adults.<br><br>METHODS: Three hundred twelve overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) and 137 young adults from the Southern California Children's Health Study (CHS) were included in the analysis. Plasma PFAS and the metabolome were determined using liquid-chromatography/high-resolution mass spectrometry. A metabolome-wide association study was performed on log-transformed metabolites using Bayesian regression with a g-prior specification and g-computation for modeling exposure mixtures to estimate the impact of exposure to a mixture of six ubiquitous PFAS (PFOS, PFHxS, PFHpS, PFOA, PFNA, and PFDA). Pathway enrichment analysis was performed using Mummichog and Gene Set Enrichment Analysis. Significance across cohorts was determined using weighted Z-tests.<br><br>RESULTS: In the SOLAR and CHS cohorts, PFAS exposure was associated with alterations in tyrosine metabolism (meta-analysis p=0.00002) and de novo fatty acid biosynthesis (p=0.03), among others. For example, when increasing all PFAS in the mixture from low (&#x223c;30th percentile) to high (&#x223c;70th percentile), thyroxine (T4), a thyroid hormone related to tyrosine metabolism, increased by 0.72 standard deviations (SDs; equivalent to a standardized mean difference) in the SOLAR cohort (95% Bayesian credible interval (BCI): 0.00, 1.20) and 1.60 SD in the CHS cohort (95% BCI: 0.39, 2.80). Similarly, when going from low to high PFAS exposure, arachidonic acid increased by 0.81 SD in the SOLAR cohort (95% BCI: 0.37, 1.30) and 0.67 SD in the CHS cohort (95% BCI: 0.00, 1.50). In general, no individual PFAS appeared to drive the observed associations.<br><br>DISCUSSION: Exposure to PFAS is associated with alterations in amino acid metabolism and lipid metabolism in adolescents and young adults. https://doi.org/10.1289/EHP11372.},
}
@article {pmid36821341,
year = {2023},
author = {Chen, Y and Chen, S and Zhang, X and Zhang, S and Jia, K and Anderson, BA and Gong, M},
title = {Reward history modulates attention based on feature relationship.},
journal = {Journal of experimental psychology. General},
volume = {152},
number = {7},
pages = {1937-1950},
doi = {10.1037/xge0001384},
pmid = {36821341},
issn = {1939-2222},
support = {//National Natural Science Foundation of China/ ; //Fundamental Research Funds for the Central Universities of China/ ; //National Science and Technology Innovation 2030-Major Project/ ; //Zhejiang University; MOE Frontiers Science Center for Brain Science &amp; Brain-Machine Integration/ ; },
mesh = {Humans ; *Learning ; *Reward ; Reaction Time ; },
abstract = {Prioritizing attention to reward-predictive items is critical for survival, but challenging because these items rarely appear in the same feature or within the same environment. However, whether attention selection can be adaptively tuned to items that matched the context-dependent, relative feature of previously rewarded items remains largely unknown. In four experiments (N = 40 per experiment), we trained participants to learn the color-reward association and then adopted visual search tasks in which the color of a singleton distractor matched either the feature value (e.g., red or yellow) or feature relationship (i.e., redder or yellower) of previously rewarded colors. We consistently found enhanced attentional capture by a singleton distractor when it was relationally matched to the high reward compared with the low reward relationship, in addition to observing the typical effect of learned value on singletons matching the previously rewarded colors. Our findings provide novel evidence for the flexibility of value-driven attention via feature relationship, which is particularly useful given the changeable sensory inputs in real-world searches. (PsycInfo Database Record (c) 2023 APA, all rights reserved).},
}
@article {pmid36820790,
year = {2022},
author = {Bretton-Granatoor, Z and Stealey, H and Santacruz, SR and Lewis-Peacock, JA},
title = {Estimating Intrinsic Manifold Dimensionality to Classify Task-Related Information in Human and Non-Human Primate Data.},
journal = {IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference},
volume = {2022},
number = {},
pages = {650-654},
pmid = {36820790},
support = {R01 EY028746/EY/NEI NIH HHS/United States ; T32 MH106454/MH/NIMH NIH HHS/United States ; },
abstract = {Feature selection, or dimensionality reduction, has become a standard step in reducing large-scale neural datasets into usable signals for brain-machine interface and neurofeedback decoders. Current techniques in fMRI data reduce the number of voxels (features) by performing statistics on individual voxels or using traditional techniques that utilize linear combinations of features (e.g., principal component analysis (PCA)). However, these methods often do not account for the cross-correlations found across voxels and do not sufficiently reduce the feature space to support efficient real-time feedback. To overcome these limitations, we propose using factor analysis on fMRI data. This technique has become increasingly popular for extracting a minimal number of latent features to explain high-dimensional data in non-human primates (NHPs). Here, we demonstrate these methods in both NHP and human data. In NHP subjects (n=2), we reduced the number of features to an average of 26.86% and 14.86% of the total feature space to build our multinomial classifier. In one NHP subject, the average accuracy of classifying eight target locations over 64 sessions was 62.43% (+/-6.19%) compared to a PCA-based classifier with 60.26% (+/-6.02%). In healthy fMRI subjects, we reduced the feature space to an average of 0.33% of the initial space. Group average (n=5) accuracy of FA-based category classification was 74.33% (+/- 4.91%) compared to a PCA-based classifier with 68.42% (+/-4.79%). FA-based classifiers can maintain the performance fidelity observed with PCA-based decoders. Importantly, FA-based methods allow researchers to address specific hypotheses about how underlying neural activity relates to behavior.},
}
@article {pmid36817868,
year = {2023},
author = {Xiao, HY and Chai, JY and Fang, YY and Lai, YS},
title = {The spatial-temporal risk profiling of Clonorchis sinensis infection over 50 years implies the effectiveness of control programs in South Korea: a geostatistical modeling study.},
journal = {The Lancet regional health. Western Pacific},
volume = {33},
number = {},
pages = {100697},
pmid = {36817868},
issn = {2666-6065},
abstract = {BACKGROUND: Over the past 50 years, two national control programs on Clonorchis sinensis infection have been conducted in South Korea. Spatial-temporal profiles of infection risk provide useful information on assessing the effectiveness of the programs and planning spatial-targeted control strategies.<br><br>METHODS: Advanced Bayesian geostatistical joint models with spatial-temporal random effects were developed to analyze disease data collecting by a systematic review with potential influencing factors, and to handle issues of preferential sampling and data heterogeneities. Changes of the infection risk were analyzed.<br><br>FINDINGS: We presented the first spatial-temporal risk maps of C.&#xa0;sinensis infection at 5&#xa0;&#xd7;&#xa0;5&#xa0;km[2] resolution from 1970 to 2020 in South Korea. Moderate-to-high risk areas were shrunk, but temporal variances were shown in different areas. The population-adjusted estimated prevalence across the country was 5.99% (95% BCI: 5.09-7.01%) in 1970, when the first national deworming campaign began. It declined to 3.95% (95% BCI: 2.88-3.95%) in 1995, when the campaign suspended, and increased to 4.73% (95% BCI: 4.00-5.42%) in 2004, just before the Clonorchiasis Eradication Program (CEP). The population-adjusted prevalence was estimated at 2.77% (95% BCI: 1.67-4.34%) in 2020, 15 years after CEP started, corresponding to 1.42 (95% BCI: 0.85-2.23) million infected people.<br><br>INTERPRETATION: The first nationwide campaign and the CEP showed effectiveness on control of C.&#xa0;sinensis infection. Moderate-to-high risk areas identified by risk maps should be prioritized for control and intervention.<br><br>FUNDING: The National Natural Science Foundation of China (project no. 82073665) and the Natural Science Foundation of Guangdong Province (project no. 2022A1515010042).},
}
@article {pmid36817607,
year = {2023},
author = {Shang, YF and Shen, YY and Zhang, MC and Lv, MC and Wang, TY and Chen, XQ and Lin, J},
title = {Progress in salivary glands: Endocrine glands with immune functions.},
journal = {Frontiers in endocrinology},
volume = {14},
number = {},
pages = {1061235},
pmid = {36817607},
issn = {1664-2392},
mesh = {*Salivary Glands/physiology ; Saliva/chemistry ; *Endocrine Glands ; Immunity ; },
abstract = {The production and secretion of saliva is an essential function of the salivary glands. Saliva is a complicated liquid with different functions, including moistening, digestion, mineralization, lubrication, and mucosal protection. This review focuses on the mechanism and neural regulation of salivary secretion, and saliva is secreted in response to various stimuli, including odor, taste, vision, and mastication. The chemical and physical properties of saliva change dynamically during physiological and pathophysiological processes. Moreover, the central nervous system modulates salivary secretion and function via various neurotransmitters and neuroreceptors. Smell, vision, and taste have been investigated for the connection between salivation and brain function. The immune and endocrine functions of the salivary glands have been explored recently. Salivary glands play an essential role in innate and adaptive immunity and protection. Various immune cells such as B cells, T cells, macrophages, and dendritic cells, as well as immunoglobins like IgA and IgG have been found in salivary glands. Evidence supports the synthesis of corticosterone, testosterone, and melatonin in salivary glands. Saliva contains many potential biomarkers derived from epithelial cells, gingival crevicular fluid, and serum. High level of matrix metalloproteinases and cytokines are potential markers for oral carcinoma, infectious disease in the oral cavity, and systemic disease. Further research is required to monitor and predict potential salivary biomarkers for health and disease in clinical practice and precision medicine.},
}
@article {pmid36817318,
year = {2023},
author = {Zhang, Z and Li, D and Zhao, Y and Fan, Z and Xiang, J and Wang, X and Cui, X},
title = {A flexible speller based on time-space frequency conversion SSVEP stimulation paradigm under dry electrode.},
journal = {Frontiers in computational neuroscience},
volume = {17},
number = {},
pages = {1101726},
pmid = {36817318},
issn = {1662-5188},
abstract = {INTRODUCTION: Speller is the best way to express the performance of the brain-computer interface (BCI) paradigm. Due to its advantages of short analysis time and high accuracy, the SSVEP paradigm has been widely used in the BCI speller system based on the wet electrode. It is widely known that the wet electrode operation is cumbersome and that the subjects have a poor experience. In addition, in the asynchronous SSVEP system based on threshold analysis, the system flickers continuously from the beginning to the end of the experiment, which leads to visual fatigue. The dry electrode has a simple operation and provides a comfortable experience for subjects. The EOG signal can avoid the stimulation of SSVEP for a long time, thus reducing fatigue.<br><br>METHODS: This study first designed the brain-controlled switch based on continuous blinking EOG signal and SSVEP signal to improve the flexibility of the BCI speller. Second, in order to increase the number of speller instructions, we designed the time-space frequency conversion (TSFC) SSVEP stimulus paradigm by constantly changing the time and space frequency of SSVEP sub-stimulus blocks, and designed a speller in a dry electrode environment.<br><br>RESULTS: Seven subjects participated and completed the experiments. The results showed that the accuracy of the brain-controlled switch designed in this study was up to 94.64%, and all the subjects could use the speller flexibly. The designed 60-character speller based on the TSFC-SSVEP stimulus paradigm has an accuracy rate of 90.18% and an information transmission rate (ITR) of 117.05 bits/min. All subjects can output the specified characters in a short time.<br><br>DISCUSSION: This study designed and implemented a multi-instruction SSVEP speller based on dry electrode. Through the combination of EOG and SSVEP signals, the speller can be flexibly controlled. The frequency of SSVEP stimulation sub-block is recoded in time and space by TSFC-SSVEP stimulation paradigm, which greatly improves the number of output instructions of BCI system in dry electrode environment. This work only uses FBCCA algorithm to test the stimulus paradigm, which requires a long stimulus time. In the future, we will use trained algorithms to study stimulus paradigm to improve its overall performance.},
}
@article {pmid36816135,
year = {2023},
author = {Wang, M and Zhou, H and Li, X and Chen, S and Gao, D and Zhang, Y},
title = {Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.},
journal = {Frontiers in neuroscience},
volume = {17},
number = {},
pages = {1113593},
pmid = {36816135},
issn = {1662-4548},
abstract = {Motor imagery (MI) electroencephalogram (EEG) signals have a low signal-to-noise ratio, which brings challenges in feature extraction and feature selection with high classification accuracy. In this study, we proposed an approach that combined an improved lasso with relief-f to extract the wavelet packet entropy features and the topological features of the brain function network. For signal denoising and channel filtering, raw MI EEG was filtered based on an R[2] map, and then the wavelet soft threshold and one-to-one multi-class score common spatial pattern algorithms were used. Subsequently, the relative wavelet packet entropy and corresponding topological features of the brain network were extracted. After feature fusion, mutcorLasso and the relief-f method were applied for feature selection, followed by three classifiers and an ensemble classifier, respectively. The experiments were conducted on two public EEG datasets (BCI Competition III dataset IIIa and BCI Competition IV dataset IIa) to verify this proposed method. The results showed that the brain network topology features and feature selection methods can retain the information of EEG more effectively and reduce the computational complexity, and the average classification accuracy for both public datasets was above 90%; hence, this algorithms is suitable in MI-BCI and has potential applications in rehabilitation and other fields.},
}
@article {pmid36813927,
year = {2023},
author = {Shang, Y and Gao, X and An, A},
title = {Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {6},
pages = {1581-1602},
pmid = {36813927},
issn = {1741-0444},
support = {61563032//National Natural Science Foundation of China/ ; 61963025//National Natural Science Foundation of China/ ; 2019KFJJ02//Open Fund Project of Industrial Process Advanced Control of Gansu Province/ ; },
mesh = {*Support Vector Machine ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Imagination ; Algorithms ; },
abstract = {Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.},
}
@article {pmid36812763,
year = {2023},
author = {Barrier, ML and Myszor, IT and Sahariah, P and Sigurdsson, S and Carmena-Bargueño, M and Pérez-Sánchez, H and Gudmundsson, GH},
title = {Aroylated phenylenediamine HO53 modulates innate immunity, histone acetylation and metabolism.},
journal = {Molecular immunology},
volume = {155},
number = {},
pages = {153-164},
doi = {10.1016/j.molimm.2023.02.003},
pmid = {36812763},
issn = {1872-9142},
mesh = {*Histones/metabolism ; Acetylation ; *Histone Deacetylase Inhibitors/pharmacology ; Immunity, Innate ; Phenylenediamines/pharmacology ; Cathelicidins ; },
abstract = {In the current context of antibiotic resistance, the need to find alternative treatment strategies is urgent. Our research aimed to use synthetized aroylated phenylenediamines (APDs) to induce the expression of cathelicidin antimicrobial peptide gene (CAMP) to minimize the necessity of antibiotic use during infection. One of these compounds, HO53, showed promising results in inducing CAMP expression in bronchial epithelium cells (BCi-NS1.1 hereafter BCi). Thus, to decipher the cellular effects of HO53 on BCi cells, we performed RNA sequencing (RNAseq) analysis after 4, 8 and 24 h treatment of HO53. The number of differentially expressed transcripts pointed out an epigenetic modulation. Yet, the chemical structure and in silico modeling indicated HO53 as a histone deacetylase (HDAC) inhibitor. When exposed to a histone acetyl transferase (HAT) inhibitor, BCi cells showed a decreased expression of CAMP. Inversely, when treated with a specific HDAC3 inhibitor (RGFP996), BCi cells showed an increased expression of CAMP, indicating acetylation status in cells as determinant for the induction of the expression of the gene CAMP expression. Interestingly, a combination treatment with both HO53 and HDAC3 inhibitor RGFP966 leads to a further increase of CAMP expression. Moreover, HDAC3 inhibition by RGFP966 leads to increased expression of STAT3 and HIF1A, both previously demonstrated to be involved in pathways regulating CAMP expression. Importantly, HIF1α is considered as a master regulator in metabolism. A significant number of genes of metabolic enzymes were detected in our RNAseq data with enhanced expression conveying a shift toward enhanced glycolysis. Overall, we are demonstrating that HO53 might have a translational value against infections in the future through a mechanism leading to innate immunity strengthening involving HDAC inhibition and shifting the cells towards an immunometabolism, which further favors innate immunity activation.},
}
@article {pmid36808912,
year = {2023},
author = {Li, R and Hu, H and Zhao, X and Wang, Z and Xu, G},
title = {A static paradigm based on illusion-induced VEP for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/acbdc0},
pmid = {36808912},
issn = {1741-2552},
abstract = {OBJECTIVE: Visual evoked potentials (VEPs) have been commonly applied in brain-computer interfaces (BCIs) due to their satisfactory classification performance recently. However, most existing methods with flickering or oscillating stimuli will induce visual fatigue under long-term training, thus restricting the implementation of VEP-based BCIs. To address this issue, a novel paradigm adopting static motion illusion based on illusion-induced visual evoked potential (IVEP) is proposed for BCIs to enhance visual experience and practicality.<br><br>APPROACH: This study explored the responses to baseline and illusion tasks including the Rotating-Tilted-Lines (RTL) illusion and Rotating-Snakes (RS) illusion. The distinguishable features were examined between different illusions by analyzing the event-related potentials (ERPs) and amplitude modulation of evoked oscillatory responses.<br><br>MAIN RESULTS: The illusion stimuli elicited VEPs in an early time window encompassing a negative component (N1) from 110 to 200 ms and a positive component (P2) between 210 and 300 ms. Based on the feature analysis, a filter bank was designed to extract discriminative signals. The task-related component analysis (TRCA) was used to evaluate the binary classification task performance of the proposed method. Then the highest accuracy of 86.67% was achieved with a data length of 0.6 s.<br><br>SIGNIFICANCE: The results of this study demonstrate that the static motion illusion paradigm has the feasibility of implementation and is promising for VEP-based BCI applications.},
}
@article {pmid36805270,
year = {2023},
author = {Liu, X and Whalen, AJ and Ryu, SB and Lee, SW and Fried, SI and Kim, K and Cai, C and Lauritzen, M and Bertram, N and Chang, B and Yu, T and Han, A},
title = {MEMS micro-coils for magnetic neurostimulation.},
journal = {Biosensors &amp; bioelectronics},
volume = {227},
number = {},
pages = {115143},
doi = {10.1016/j.bios.2023.115143},
pmid = {36805270},
issn = {1873-4235},
support = {R01 EY029022/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Mice ; *Micro-Electrical-Mechanical Systems ; *Biosensing Techniques ; Metals ; Brain ; Electric Conductivity ; },
abstract = {Micro-coil magnetic stimulation of brain tissue presents new challenges for MEMS micro-coil probe fabrication. The main challenges are threefold; (i) low coil resistance for high power efficiency, (ii) low leak current from the probe into the in vitro experimental set-up, (iii) adaptive MEMS process technology because of the dynamic research area, which requires agile design changes. Taking on these challenges, we present a MEMS fabrication process that has three main features; (i) multilayer resist lift-off process to pattern up to 1800-nm-thick metal films, and special care is taken to obtain high conductivity thin-films by physical vapor deposition, and (ii) all micro-coil Al wires are encapsulated in at least 200 nm of ALD alumina and 6-μm-thick parylene C such the leak resistance is high (>210 GΩ), (iii) combining a multi-step DRIE process and maskless photolithography for adaptive design and device fabrication. The entire process requires four lithography steps. Because we avoided SOI wafers and lithography mask fabrication, the design-to-device time is shortened significantly. The resulting probes are 4-mm-long, 60-μm-thick, and down to 150 μm-wide. Selected MEMS coil devices were validated in vivo using mice and compared to previous work.},
}
@article {pmid36805091,
year = {2023},
author = {Zhang, Y and Wang, Y and Li, Z and Wang, Z and Cheng, J and Bai, X and Hsu, YC and Sun, Y and Li, S and Shi, J and Sui, B and Bai, R},
title = {Vascular-water-exchange MRI (VEXI) enables the detection of subtle AXR alterations in Alzheimer's disease without MRI contrast agent, which may relate to BBB integrity.},
journal = {NeuroImage},
volume = {270},
number = {},
pages = {119951},
doi = {10.1016/j.neuroimage.2023.119951},
pmid = {36805091},
issn = {1095-9572},
mesh = {Humans ; Blood-Brain Barrier/diagnostic imaging ; *Alzheimer Disease ; Contrast Media ; Water ; Magnetic Resonance Imaging/methods ; *Cognitive Dysfunction/diagnostic imaging ; },
abstract = {Blood-brain barrier (BBB) impairment is an important pathophysiological process in Alzheimer's disease (AD) and a potential biomarker for early diagnosis of AD. However, most current neuroimaging methods assessing BBB function need the injection of exogenous contrast agents (or tracers), which limits the application of these methods in a large population. In this study, we aim to explore the feasibility of vascular water exchange MRI (VEXI), a diffusion-MRI-based method proposed to assess the BBB permeability to water molecules without using a contrast agent, in the detection of the BBB breakdown in AD. We tested VEXI on a 3T MRI scanner on three groups: AD patients (AD group), mild cognitive impairment (MCI) patients due to AD (MCI group), and the age-matched normal cognition subjects (NC group). Interestingly, we find that the apparent water exchange across the BBB (AXRBBB) measured by VEXI shows higher values in MCI compared with NC, and this higher AXRBBB happens specifically in the hippocampus. This increase in AXRBBB value gets larger and extends to more brain regions (medial orbital frontal cortex and thalamus) from MCI group to the AD group. Furthermore, we find that the AXRBBB values of these three regions is correlated significantly with the impairment of respective cognitive domains independent of age, sex and education. These results suggest VEXI is a promising method to assess the BBB breakdown in AD.},
}
@article {pmid36801814,
year = {2023},
author = {Lakshminarayanan, K and Shah, R and Yao, Y and Madathil, D},
title = {The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery.},
journal = {Motor control},
volume = {27},
number = {3},
pages = {559-572},
doi = {10.1123/mc.2022-0061},
pmid = {36801814},
issn = {1087-1640},
mesh = {Adult ; Male ; Female ; Humans ; *Electroencephalography/methods ; Imagination/physiology ; Movement/physiology ; Imagery, Psychotherapy ; *Sensorimotor Cortex ; },
abstract = {Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.},
}
@article {pmid36801453,
year = {2023},
author = {Zhao, K and Zhu, J and Yang, L and Shang, Z and Wan, H},
title = {Goal given moment modulates the time period of gamma oscillations in nidopallium caudolaterale during the goal-directed behavior of pigeon.},
journal = {Brain research},
volume = {1806},
number = {},
pages = {148288},
doi = {10.1016/j.brainres.2023.148288},
pmid = {36801453},
issn = {1872-6240},
mesh = {Animals ; *Columbidae ; *Goals ; },
abstract = {The cognitive processes of goal-directed navigation are believed to be organized around and serve the identification and selection of goals. Differences in LFP signals in avian nidopallium caudolaterale (NCL) under different goal location/distance information in the goal-directed behavior have been studied. However, for goals that are multifarious constructs that include various information, the modulation of goal time information on the LFP of NCL during goal-directed behavior remains unclear. In this study, we recorded the LFP activity from the NCL of eight pigeons as they performed two goal-directed decision-making tasks in a plus-maze. During the two tasks with different goal time information, spectral analysis revealed significant LFP power selectively enhanced in the slow gamma band (40-60 Hz), while the slow gamma band of LFP which could effectively decode the behavioral goal of the pigeons existed in different time periods. These findings suggest that the LFP activity in the gamma band correlates with the goal-time information, and help to shed light on the contribution of the gamma rhythm recorded from the NCL in goal-directed behavior.},
}
@article {pmid36801435,
year = {2023},
author = {Chikhi, S and Matton, N and Sanna, M and Blanchet, S},
title = {Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults.},
journal = {Biological psychology},
volume = {178},
number = {},
pages = {108521},
doi = {10.1016/j.biopsycho.2023.108521},
pmid = {36801435},
issn = {1873-6246},
mesh = {Humans ; Young Adult ; *Neurofeedback ; Electroencephalography/methods ; Rest ; },
abstract = {Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.},
}
@article {pmid36801241,
year = {2023},
author = {Gu, L and Jiang, J and Han, H and Gan, JQ and Wang, H},
title = {Recognition of unilateral lower limb movement based on EEG signals with ERP-PCA analysis.},
journal = {Neuroscience letters},
volume = {800},
number = {},
pages = {137133},
doi = {10.1016/j.neulet.2023.137133},
pmid = {36801241},
issn = {1872-7972},
mesh = {Humans ; *Imagination/physiology ; Electroencephalography/methods ; Evoked Potentials/physiology ; Upper Extremity ; Lower Extremity ; Movement/physiology ; *Brain-Computer Interfaces ; },
abstract = {It has been confirmed that motor imagery (MI) and motor execution (ME) share a subset of mechanisms underlying motor cognition. In contrast to the well-studied laterality of upper limb movement, the laterality hypothesis of lower limb movement also exists, but it needs to be characterized by further investigation. This study used electroencephalographic (EEG) recordings of 27 subjects to compare the effects of bilateral lower limb movement in the MI and ME paradigms. Event-related potential (ERP) recorded was decomposed into meaningful and useful representatives of the electrophysiological components, such as N100 and P300. Principal components analysis (PCA) was used to trace the characteristics of ERP components temporally and spatially, respectively. The hypothesis of this study is that the functional opposition of unilateral lower limbs of MI and ME should be reflected in the different alterations of the spatial distribution of lateralized activity. Meanwhile, the significant ERP-PCA components of the EEG signals as identifiable feature sets were applied with support vector machine to identify left and right lower limb movement tasks. The average classification accuracy over all subjects is up to 61.85% for MI and 62.94% for ME. The proportion of subjects with significant results are 51.85% for MI and 59.26% for ME, respectively. Therefore, a potential new classification model for lower limb movement can be applied on brain computer interface (BCI) systems in the future.},
}
@article {pmid36800979,
year = {2023},
author = {Hambridge, T and Coffeng, LE and de Vlas, SJ and Richardus, JH},
title = {Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach.},
journal = {Infectious diseases of poverty},
volume = {12},
number = {1},
pages = {12},
pmid = {36800979},
issn = {2049-9957},
support = {RIA2017NIM-1839-PEP4LEP//European and Developing Countries Clinical Trials Partnership/ ; 707.19.58//Leprosy Research Initiative/ ; },
mesh = {Humans ; Bayes Theorem ; *Leprosy/diagnosis/epidemiology/drug therapy ; Mycobacterium leprae ; *Leprosy, Multibacillary ; *Leprosy, Paucibacillary ; },
abstract = {BACKGROUND: Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.<br><br>METHODS: Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors.<br><br>RESULTS: For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: -1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14-2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08-2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review.<br><br>CONCLUSIONS: The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.},
}
@article {pmid36800288,
year = {2023},
author = {Shu, X and Wei, C and Tu, WY and Zhong, K and Qi, S and Wang, A and Bai, L and Zhang, SX and Luo, B and Xu, ZZ and Zhang, K and Shen, C},
title = {Negative regulation of TREM2-mediated C9orf72 poly-GA clearance by the NLRP3 inflammasome.},
journal = {Cell reports},
volume = {42},
number = {2},
pages = {112133},
doi = {10.1016/j.celrep.2023.112133},
pmid = {36800288},
issn = {2211-1247},
mesh = {Animals ; Mice ; *Amyotrophic Lateral Sclerosis/genetics/metabolism ; C9orf72 Protein/genetics/metabolism ; Dipeptides/metabolism ; DNA Repeat Expansion ; *Frontotemporal Dementia/genetics ; Inflammasomes ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics ; Proteins/genetics ; Disease Models, Animal ; },
abstract = {Expansion of the hexanucleotide repeat GGGGCC in the C9orf72 gene is the most common genetic factor in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Poly-Gly-Ala (poly-GA), one form of dipeptide repeat proteins (DPRs) produced from GGGGCC repeats, tends to form neurotoxic protein aggregates. The C9orf72 GGGGCC repeats and microglial receptor TREM2 are both associated with risk for ALS/FTD. The role and regulation of TREM2 in C9orf72-ALS/FTD remain unclear. Here, we found that poly-GA proteins activate the microglial NLRP3 inflammasome to produce interleukin-1β (IL-1β), which promotes ADAM10-mediated TREM2 cleavage and inhibits phagocytosis of poly-GA. The inhibitor of the NLRP3 inflammasome, MCC950, reduces the TREM2 cleavage and poly-GA aggregates, resulting in the alleviation of motor deficits in poly-GA mice. Our study identifies a crosstalk between NLRP3 and TREM2 signaling, suggesting that targeting the NLRP3 inflammasome to sustain TREM2 is an approach to treat C9orf72-ALS/FTD.},
}
@article {pmid36799296,
year = {2023},
author = {Liu, Y and Shen, X and Zhang, Y and Zheng, X and Cepeda, C and Wang, Y and Duan, S and Tong, X},
title = {Interactions of glial cells with neuronal synapses, from astrocytes to microglia and oligodendrocyte lineage cells.},
journal = {Glia},
volume = {71},
number = {6},
pages = {1383-1401},
doi = {10.1002/glia.24343},
pmid = {36799296},
issn = {1098-1136},
mesh = {Animals ; *Astrocytes/physiology ; *Microglia/physiology ; Cell Lineage ; Neuroglia/physiology ; Neurons/physiology ; Oligodendroglia/physiology ; Synapses/physiology ; Mammals ; },
abstract = {The mammalian brain is a complex organ comprising neurons, glia, and more than 1 × 10[14] synapses. Neurons are a heterogeneous group of electrically active cells, which form the framework of the complex circuitry of the brain. However, glial cells, which are primarily divided into astrocytes, microglia, oligodendrocytes (OLs), and oligodendrocyte precursor cells (OPCs), constitute approximately half of all neural cells in the mammalian central nervous system (CNS) and mainly provide nutrition and tropic support to neurons in the brain. In the last two decades, the concept of "tripartite synapses" has drawn great attention, which emphasizes that astrocytes are an integral part of the synapse and regulate neuronal activity in a feedback manner after receiving neuronal signals. Since then, synaptic modulation by glial cells has been extensively studied and substantially revised. In this review, we summarize the latest significant findings on how glial cells, in particular, microglia and OL lineage cells, impact and remodel the structure and function of synapses in the brain. Our review highlights the cellular and molecular aspects of neuron-glia crosstalk and provides additional information on how aberrant synaptic communication between neurons and glia may contribute to neural pathologies.},
}
@article {pmid36799225,
year = {2023},
author = {Tu, WY and Xu, W and Zhang, J and Qi, S and Bai, L and Shen, C and Zhang, K},
title = {C9orf72 poly-GA proteins impair neuromuscular transmission.},
journal = {Zoological research},
volume = {44},
number = {2},
pages = {331-340},
pmid = {36799225},
issn = {2095-8137},
mesh = {Animals ; Agrin ; *Amyotrophic Lateral Sclerosis/genetics/metabolism/pathology ; C9orf72 Protein/genetics/metabolism ; Dipeptides/metabolism ; Disease Models, Animal ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is a devastating motoneuron disease, in which lower motoneurons lose control of skeletal muscles. Degeneration of neuromuscular junctions (NMJs) occurs at the initial stage of ALS. Dipeptide repeat proteins (DPRs) from G4C2 repeat-associated non-ATG (RAN) translation are known to cause C9orf72-associated ALS (C9-ALS). However, DPR inclusion burdens are weakly correlated with neurodegenerative areas in C9-ALS patients, indicating that DPRs may exert cell non-autonomous effects, in addition to the known intracellular pathological mechanisms. Here, we report that poly-GA, the most abundant form of DPR in C9-ALS, is released from cells. Local administration of poly-GA proteins in peripheral synaptic regions causes muscle weakness and impaired neuromuscular transmission in vivo. The NMJ structure cannot be maintained, as evidenced by the fragmentation of postsynaptic acetylcholine receptor (AChR) clusters and distortion of presynaptic nerve terminals. Mechanistic study demonstrated that extracellular poly-GA sequesters soluble Agrin ligands and inhibits Agrin-MuSK signaling. Our findings provide a novel cell non-autonomous mechanism by which poly-GA impairs NMJs in C9-ALS. Thus, targeting NMJs could be an early therapeutic intervention for C9-ALS.},
}
@article {pmid36798295,
year = {2023},
author = {Pollmann, EH and Yin, H and Uguz, I and Dubey, A and Wingel, KE and Choi, JS and Moazeni, S and Gilhotra, Y and Pavlovsky, VA and Banees, A and Boominathan, V and Robinson, J and Veeraraghavan, A and Pieribone, VA and Pesaran, B and Shepard, KL},
title = {Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.02.07.527500},
pmid = {36798295},
issn = {2692-8205},
abstract = {Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.},
}
@article {pmid36792239,
year = {2023},
author = {Ziafati, A and Maleki, A},
title = {Genetic algorithm based ensemble system using MLR and MsetCCA methods for SSVEP frequency recognition.},
journal = {Medical engineering &amp; physics},
volume = {111},
number = {},
pages = {103945},
doi = {10.1016/j.medengphy.2022.103945},
pmid = {36792239},
issn = {1873-4030},
mesh = {Humans ; *Evoked Potentials, Visual ; Linear Models ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; },
abstract = {BCI systems provide a direct communication channel between the human and the machine using brain signals. Among the various methods of steady-state visual evoked potential (SSVEP) stimulation frequency detection, multiple linear regression (MLR), and multiset canonical correlation analysis (MsetCCA) methods have achieved high accurate results in recent studies. The purpose of this study is to utilize both approaches and benefit from them using a genetic algorithm (GA). This algorithm leads to high-performance optimization due to its large number of regulatory parameters. Signal analysis was performed for the windows with 0.5 to 4 s duration length and with 0.5-second incremental steps. In this paper, we were able to achieve 100% accuracy of recognition for 2-second time-windows using the genetic algorithm to optimally ensemble SSVEP stimulation frequency detection methods. The accuracy of the proposed system indicates a significant improvement in detection compared to either MLR or MsetCCA alone and indicates that the ensemble system is correctly optimized using the genetic algorithm. Genetic algorithm is one of the most widely used algorithms because of its high regulatory parameters leading to its high flexibility. The improvement in detection of the proposed system is due to the use of the strengths of both two methods, and the optimal choice of the system response to visual stimuli.},
}
@article {pmid36789010,
year = {2023},
author = {Matsuki, E and Kawamoto, S and Morikawa, Y and Yahagi, N},
title = {The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection.},
journal = {Open forum infectious diseases},
volume = {10},
number = {2},
pages = {ofad039},
pmid = {36789010},
issn = {2328-8957},
abstract = {BACKGROUND: Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.<br><br>METHODS: A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.<br><br>RESULTS: The mean temperature of the prior week was positively associated with the number of newly diagnosed cases (0.107 [95% Bayesian credible interval {BCI},
.106-.109]), whereas the mean difference in the temperature of the prior week was negatively associated (-0.835 [95% BCI, -.840 to -.830]). The product of the mean temperature and mean difference in the temperature of the previous week were also negatively associated with the number of newly diagnosed cases (-0.192 [95% BCI, -.197 to -.187]).<br><br>CONCLUSIONS: The mean temperature and the mean difference in temperature affected the number of newly diagnosed influenza cases differently. Our data suggest that high ambient temperature and a drop in the temperature and their interaction increase the risk of infection. Therefore, the highest risk of infection is attributable to a steep fall in temperature in a relatively warm environment.},
}
@article {pmid36788214,
year = {2023},
author = {Yu, B and Zhang, Q and Lin, L and Zhou, X and Ma, W and Wen, S and Li, C and Wang, W and Wu, Q and Wang, X and Li, XM},
title = {Molecular and cellular evolution of the amygdala across species analyzed by single-nucleus transcriptome profiling.},
journal = {Cell discovery},
volume = {9},
number = {1},
pages = {19},
pmid = {36788214},
issn = {2056-5968},
support = {32000714//National Science Foundation of China | Young Scientists Fund/ ; },
abstract = {The amygdala, or an amygdala-like structure, is found in the brains of all vertebrates and plays a critical role in survival and reproduction. However, the cellular architecture of the amygdala and how it has evolved remain elusive. Here, we generated single-nucleus RNA-sequencing data for more than 200,000 cells in the amygdala of humans, macaques, mice, and chickens. Abundant neuronal cell types from different amygdala subnuclei were identified in all datasets. Cross-species analysis revealed that inhibitory neurons and inhibitory neuron-enriched subnuclei of the amygdala were well-conserved in cellular composition and marker gene expression, whereas excitatory neuron-enriched subnuclei were relatively divergent. Furthermore, LAMP5[+] interneurons were much more abundant in primates, while DRD2[+] inhibitory neurons and LAMP5[+]SATB2[+] excitatory neurons were dominant in the human central amygdalar nucleus (CEA) and basolateral amygdalar complex (BLA), respectively. We also identified CEA-like neurons and their species-specific distribution patterns in chickens. This study highlights the extreme cell-type diversity in the amygdala and reveals the conservation and divergence of cell types and gene expression patterns across species that may contribute to species-specific adaptations.},
}
@article {pmid36786985,
year = {2022},
author = {Bobrova, EV and Reshetnikova, VV and Vershinina, EA and Grishin, AA and Isaev, MR and Bobrov, PD and Gerasimenko, YP},
title = {Dependence of Brain-Computer Interface Control Training on Personality Traits.},
journal = {Doklady. Biochemistry and biophysics},
volume = {507},
number = {1},
pages = {273-277},
pmid = {36786985},
issn = {1608-3091},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Movement ; Personality ; },
abstract = {Personality traits (PTs) are predictors of the success of control of brain-computer interfaces (BCIs); however, it is unknown how the PTs that are optimal for BCI control changes during training. The paper for the first time analyzes the correlations between PTs and the accuracy of the classification (AC) of brain states in imagining the movements of the hands, feet, and locomotion during 10-day training of ten volunteers in BCI control. In the first 3 days of training, the AC is higher for more stressed and anxious volunteers; in the last days, for calmer ones. In the middle of the training period, AC is higher in low-demonstrativeness persons, it is more pronounced when imagining foot movements. Correlations of low demonstrativeness, as well as of foresight and self-control with AC when imagining foot movements are revealed significantly more often than when imagining hand movements and locomotions. During almost the entire period of training, AC with locomotion imagination is higher in individualists. The results make it possible to propose individually-oriented recommendations for the use of BCI based on the imagination of movements for the rehabilitation of patients with motor disorders.},
}
@article {pmid36780814,
year = {2023},
author = {Mattheiss, JP and Breyta, R and Kurath, G and LaDeau, SL and Páez, DJ and Ferguson, PFB},
title = {Coproduction and modeling spatial contact networks prevent bias about infectious hematopoietic necrosis virus transmission for Snake River Basin salmonids.},
journal = {Journal of environmental management},
volume = {334},
number = {},
pages = {117415},
doi = {10.1016/j.jenvman.2023.117415},
pmid = {36780814},
issn = {1095-8630},
mesh = {Animals ; *Salmonidae ; *Infectious hematopoietic necrosis virus ; Rivers ; Ecosystem ; Bayes Theorem ; *Fish Diseases ; Salmon ; },
abstract = {Much remains unknown about variation in pathogen transmission across the geographic range of a free-ranging fish or animal species and about the influence of movement (associated with husbandry practices or animal behavior) on pathogen transmission. Salmonid hatcheries are an ideal system in which to study these processes. Salmonid hatcheries are managed for endangered species recovery, supplementation of threatened or at-risk fish stocks, support of fisheries, and ecosystem stability. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus of significant concern to salmon aquaculture. Landscape IHNV transmission dynamics previously had been estimated only for salmonid hatcheries in the Lower Columbia River Basin (LCRB). The objectives of this study were to estimate IHNV transmission dynamics in a unique geographic region, the Snake River Basin (SRB), and to quantitatively estimate the effect of model coproduction on inference because previous assessments of coproduction have been qualitative. In contrast to the LCRB, the SRB has hatchery complexes consisting of a main hatchery and ≥1 satellite facility. Knowledge about hatchery complexes was held by a subset of project researchers but would not have been available to project modelers without coproduction. Project modelers generated and tested multiple versions of Bayesian susceptible-exposedinfected models to realistically represent the SRB and estimate the effect of coproduction. Models estimated the frequency of transmission routes, route-specific infection probabilities, and infection probabilities for combinations of salmonid hosts and IHNV lineages. Model results indicated that in the SRB, avoiding exposure to IHNV-positive adult salmonids is the most important action to prevent juvenile infections. Migrating adult salmonids exposed juvenile cohort-sites most frequently, and the infection probability was greatest following exposure to migrating adults. Without coproduction, the frequency of exposure by migrating adults would have been overestimated by 70 cohort-sites, and the infection probability following exposure to migrating adults would have been underestimated by∼0.09. The coproduced model had less uncertainty in the infection probability if no transmission route could be identified (Bayesian credible interval (BCI) width = 0.12) compared to the model without coproduction (BCI width = 0.34). Evidence for virus lineage MD specialization on steelhead and rainbow trout (both Oncorhynchus mykiss) was apparent without model coproduction. In the SRB, we found a greater probability of virus lineage UC infection in Chinook salmon (Oncorhynchus tshawytscha) compared to in O. mykiss, whereas in the LCRB, UC more clearly exhibited a generalist approach. Coproduction influenced estimates that depended on transmission routes, which operated differently at main hatcheries and satellite sites within hatchery complexes. Hatchery complexes are found outside of the SRB and are not specific to salmonid hatcheries alone. There is great potential for coproduction and modeling spatial contact networks to advance understanding about infectious disease transmission in complex production systems and surrounding free-ranging animal populations.},
}
@article {pmid36780560,
year = {2023},
author = {Chen, X and Ma, R and Zhang, W and Zeng, GQ and Wu, Q and Yimiti, A and Xia, X and Cui, J and Liu, Q and Meng, X and Bu, J and Chen, Q and Pan, Y and Yu, NX and Wang, S and Deng, ZD and Sack, AT and Laughlin, MM and Zhang, X},
title = {Alpha oscillatory activity is causally linked to working memory retention.},
journal = {PLoS biology},
volume = {21},
number = {2},
pages = {e3001999},
pmid = {36780560},
issn = {1545-7885},
mesh = {Humans ; *Memory, Short-Term/physiology ; Brain/physiology ; Cognition ; *Transcranial Direct Current Stimulation ; },
abstract = {Although previous studies have reported correlations between alpha oscillations and the "retention" subprocess of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain oscillations. Conventional transcranial alternating current stimulation (tACS) is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention subprocess). Here, we developed an online phase-corrected tACS system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits the modulation of brain oscillations at the target stimulation frequency within a short stimulation period and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to participants during the retention subprocess of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. These findings strongly support a causal link between alpha oscillations and WM retention and illustrate the broad application prospects of phase-corrected tACS.},
}
@article {pmid36778458,
year = {2023},
author = {Wilson, GH and Willett, FR and Stein, EA and Kamdar, F and Avansino, DT and Hochberg, LR and Shenoy, KV and Druckmann, S and Henderson, JM},
title = {Long-term unsupervised recalibration of cursor BCIs.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.02.03.527022},
pmid = {36778458},
issn = {2692-8205},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.},
}
@article {pmid36778360,
year = {2023},
author = {Chen, K and Cambi, F and Kozai, TDY},
title = {Pro-myelinating Clemastine administration improves recording performance of chronically implanted microelectrodes and nearby neuronal health.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.01.31.526463},
pmid = {36778360},
issn = {2692-8205},
abstract = {Intracortical microelectrodes have become a useful tool in neuroprosthetic applications in the clinic and to understand neurological disorders in basic neurosciences. Many of these brain-machine interface technology applications require successful long-term implantation with high stability and sensitivity. However, the intrinsic tissue reaction caused by implantation remains a major failure mechanism causing loss of recorded signal quality over time. Oligodendrocytes remain an underappreciated intervention target to improve chronic recording performance. These cells can accelerate action potential propagation and provides direct metabolic support for neuronal health and functionality. However, implantation injury causes oligodendrocyte degeneration and leads to progressive demyelination in surrounding brain tissue. Previous work highlighted that healthy oligodendrocytes are necessary for greater electrophysiological recording performance and the prevention of neuronal silencing around implanted microelectrodes over chronic implantation. Thus, we hypothesize that enhancing oligodendrocyte activity with a pharmaceutical drug, Clemastine, will prevent the chronic decline of microelectrode recording performance. Electrophysiological evaluation showed that the promyelination Clemastine treatment significantly elevated the signal detectability and quality, rescued the loss of multi-unit activity, and increased functional interlaminar connectivity over 16-weeks of implantation. Additionally, post-mortem immunohistochemistry showed that increased oligodendrocyte density and myelination coincided with increased survival of both excitatory and inhibitory neurons near the implant. Overall, we showed a positive relationship between enhanced oligodendrocyte activity and neuronal health and functionality near the chronically implanted microelectrode. This study shows that therapeutic strategy that enhance oligodendrocyte activity is effective for integrating the functional device interface with brain tissue over chronic implantation period.},
}
@article {pmid36776946,
year = {2022},
author = {Chen, XJ and Collins, LM and Mainsah, BO},
title = {Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2022},
number = {},
pages = {1642-1647},
pmid = {36776946},
issn = {1062-922X},
support = {R21 DC018347/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs), such as the P300 speller, can provide a means of communication for individuals with severe neuromuscular limitations. BCIs interpret electroencephalography (EEG) signals in order to translate embedded information about a user's intent into executable commands to control external devices. However, EEG signals are inherently noisy and nonstationary, posing a challenge to extended BCI use. Conventionally, a BCI classifier is trained via supervised learning in an offline calibration session; once trained, the classifier is deployed for online use and is not updated. As the statistics of a user's EEG data change over time, the performance of a static classifier may decline with extended use. It is therefore desirable to automatically adapt the classifier to current data statistics without requiring offline recalibration. In an existing semi-supervised learning approach, the classifier is trained on labeled EEG data and is then updated using incoming unlabeled EEG data and classifier-predicted labels. To reduce the risk of learning from incorrect predictions, a threshold is imposed to exclude unlabeled data with low-confidence label predictions from the expanded training set when retraining the adaptive classifier. In this work, we propose the use of a language model for spelling error correction and disambiguation to provide information about label correctness during semi-supervised learning. Results from simulations with multi-session P300 speller user EEG data demonstrate that our language-guided semi-supervised approach significantly improves spelling accuracy relative to conventional BCI calibration and threshold-based semi-supervised learning.},
}
@article {pmid36776560,
year = {2023},
author = {Li, S and Al-Sheikh, U and Chen, Y and Kang, L},
title = {Nematode homologs of the sour taste receptor Otopetrin1 are evolutionarily conserved acid-sensitive proton channels.},
journal = {Frontiers in cell and developmental biology},
volume = {11},
number = {},
pages = {1133890},
pmid = {36776560},
issn = {2296-634X},
abstract = {Numerous taste receptors and related molecules have been identified in vertebrates and invertebrates. Otopetrin1 has recently been identified as mammalian sour taste receptor which is essential for acid sensation. However, whether other Otopetrin proteins are involved in PH-sensing remains unknown. In C. elegans, there are eight otopetrin homologous genes but their expression patterns and functions have not been reported so far. Through heterologous expression in HEK293T cells, we found that ceOTOP1a can be activated by acid in NMDG[+] solution without conventional cations, which generated inward currents and can be blocked by zinc ions. Moreover, we found that Otopetrin channels are widely expressed in numerous tissues, especially in sensory neurons in the nematode. These results suggest that the biophysical characteristics of the Otopetrin channels in nematodes are generally conserved. However, a series of single gene mutations of otopetrins, which were constructed by CRISPR-Cas9 method, did not affect either calcium responses in ASH polymodal sensory neurons to acid stimulation or acid avoidance behaviors, suggesting that Otopetrin channels might have diverse functions among species. This study reveals that nematode Otopetrins are evolutionarily conserved acid-sensitive proton channels, and provides a framework for further revealing the function and mechanisms of Otopetrin channels in both invertebrates and vertebrates.},
}
@article {pmid36776220,
year = {2022},
author = {Cajigas, I and Davis, KC and Prins, NW and Gallo, S and Naeem, JA and Fisher, L and Ivan, ME and Prasad, A and Jagid, JR},
title = {Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1077416},
pmid = {36776220},
issn = {1662-5161},
support = {K12 NS129164/NS/NINDS NIH HHS/United States ; R25 NS108937/NS/NINDS NIH HHS/United States ; },
abstract = {Introduction: Most spinal cord injuries (SCI) result in lower extremities paralysis, thus diminishing ambulation. Using brain-computer interfaces (BCI), patients may regain leg control using neural signals that actuate assistive devices. Here, we present a case of a subject with cervical SCI with an implanted electrocorticography (ECoG) device and determined whether the system is capable of motor-imagery-initiated walking in an assistive ambulator. Methods: A 24-year-old male subject with cervical SCI (C5 ASIA A) was implanted before the study with an ECoG sensing device over the sensorimotor hand region of the brain. The subject used motor-imagery (MI) to train decoders to classify sensorimotor rhythms. Fifteen sessions of closed-loop trials followed in which the subject ambulated for one hour on a robotic-assisted weight-supported treadmill one to three times per week. We evaluated the stability of the best-performing decoder over time to initiate walking on the treadmill by decoding upper-limb (UL) MI. Results: An online bagged trees classifier performed best with an accuracy of 84.15% averaged across 9 weeks. Decoder accuracy remained stable following throughout closed-loop data collection. Discussion: These results demonstrate that decoding UL MI is a feasible control signal for use in lower-limb motor control. Invasive BCI systems designed for upper-extremity motor control can be extended for controlling systems beyond upper extremity control alone. Importantly, the decoders used were able to use the invasive signal over several weeks to accurately classify MI from the invasive signal. More work is needed to determine the long-term consequence between UL MI and the resulting lower-limb control.},
}
@article {pmid36772822,
year = {2023},
author = {Arioka, M and Koyano, K and Nakao, Y and Ozaki, M and Nakamura, S and Kiuchi, H and Okada, H and Itoh, S and Murao, K and Kusaka, T},
title = {Quantitative effects of bilirubin structural photoisomers on the measurement of direct bilirubin via the vanadate oxidation method.},
journal = {Annals of clinical biochemistry},
volume = {60},
number = {3},
pages = {177-183},
doi = {10.1177/00045632231154748},
pmid = {36772822},
issn = {1758-1001},
mesh = {Infant, Newborn ; Humans ; *Phototherapy/methods ; *Vanadates ; Light ; Bilirubin ; Isomerism ; },
abstract = {BACKGROUND: Exposing blood serum samples to ambient white light-emitting diode (WLED) light may accelerate bilirubin photoisomer production. We previously demonstrated the quantitative effect of bilirubin configurational isomers (BCI) on direct bilirubin (DB) value using the vanadate oxidation method. However, the effects of bilirubin structural photoisomers (BSI) remain unclear.<br><br>METHODS: In Study 1, the relationship between WLED irradiation time and BSI production was examined. Serum samples from five neonates were irradiated with WLED light for 0, 10, 30, 60 and 180 min. Bilirubin isomer concentration and BSI production rates were calculated. In Study 2, we performed quantitative investigation of BSI effect on DB values: Differences in DB, BCI and BSI values before and after irradiation were calculated as &#x22bf;DB, &#x22bf;BCI and &#x22bf;BSI, respectively. Assuming the coefficient of BCI affecting DB values was 'a', relational expression was &#x22bf;DB = a*&#x22bf;BSI + 0.19*&#x22bf;BCI. Serum samples from 15 neonates were irradiated with green LED light for 10 and 30&#xa0;s. The respective bilirubin isomer levels were measured, and the coefficient was derived.<br><br>RESULTS: In Study 1, the median BSI production rate was 0.022 mg/dL per min in specimens with an unconjugated bilirubin concentration of 10.88 mg/dL. In Study 2, assuming that &#x22bf;DB-0.19*&#x22bf;BCI was Y and &#x22bf;BSI was X, the relational expression was Y = 0.34X-0.03 (R[2] = 0.87; p < .01) and a = 0.34.<br><br>CONCLUSIONS: Under ambient WLED light, serum sample generated 1.3 mg/dL BSIs in 1 h. Approximately 34% (0.44&#xa0;mg/dL) of BSI concentrations was measured as DB when using the vanadate oxidation method according to the above equation.},
}
@article {pmid36772744,
year = {2023},
author = {Yang, L and Van Hulle, MM},
title = {Real-Time Navigation in Google Street View[[®]] Using a Motor Imagery-Based BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772744},
issn = {1424-8220},
support = {1S65622N//Research Foundation - Flanders/ ; 857375//European Union's Horizon 2020/ ; C24/18/098//Special research fund of the KU Leuven/ ; G0A4118N//Research Foundation - Flanders/ ; G0A4321N//Research Foundation - Flanders/ ; G0C1522N//Research Foundation - Flanders/ ; AKUL043//Hercules Foundation/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Search Engine ; Electroencephalography/methods ; Imagery, Psychotherapy ; Brain/physiology ; },
abstract = {Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View[[®]]. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.},
}
@article {pmid36772731,
year = {2023},
author = {Ranieri, A and Pichiorri, F and Colamarino, E and de Seta, V and Mattia, D and Toppi, J},
title = {Parallel Factorization to Implement Group Analysis in Brain Networks Estimation.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772731},
issn = {1424-8220},
support = {GR-2018-12365874//Italian Ministry of Health/ ; RF-2018-12365210//Italian Ministry of Health/ ; RF-2019-12369396//Italian Ministry of Health/ ; GR2019-12369207//Italian Ministry of Health/ ; RM120172B8899B8C//Sapienza University of Rome - Progetti di Ateneo 2020/ ; },
mesh = {Humans ; Algorithms ; *Brain ; *Brain Mapping/methods ; },
abstract = {When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation.},
}
@article {pmid36772444,
year = {2023},
author = {Li, M and Qiu, M and Kong, W and Zhu, L and Ding, Y},
title = {Fusion Graph Representation of EEG for Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772444},
issn = {1424-8220},
support = {2017YFE0116800//National Key R\&amp;D Program of China for Intergovernmental International Science and Technology Innovation Cooperation Project/ ; U20B2074, U1909202//National Natural Science Foundation of China/ ; 2020E10010//Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province/ ; 2021C03003//Key R$\&amp;$D Project of Zhejiang Province/ ; },
mesh = {*Emotions ; *Recognition, Psychology ; Brain ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Various relations existing in Electroencephalogram (EEG) data are significant for EEG feature representation. Thus, studies on the graph-based method focus on extracting relevancy between EEG channels. The shortcoming of existing graph studies is that they only consider a single relationship of EEG electrodes, which results an incomprehensive representation of EEG data and relatively low accuracy of emotion recognition. In this paper, we propose a fusion graph convolutional network (FGCN) to extract various relations existing in EEG data and fuse these extracted relations to represent EEG data more comprehensively for emotion recognition. First, the FGCN mines brain connection features on topology, causality, and function. Then, we propose a local fusion strategy to fuse these three graphs to fully utilize the valuable channels with strong topological, causal, and functional relations. Finally, the graph convolutional neural network is adopted to represent EEG data for emotion recognition better. Experiments on SEED and SEED-IV demonstrate that fusing different relation graphs are effective for improving the ability in emotion recognition. Furthermore, the emotion recognition accuracy of 3-class and 4-class is higher than that of other state-of-the-art methods.},
}
@article {pmid36772343,
year = {2023},
author = {Ron-Angevin, R and Fernández-Rodríguez, &#xc1; and Dupont, C and Maigrot, J and Meunier, J and Tavard, H and Lespinet-Najib, V and André, JM},
title = {Comparison of Two Paradigms Based on Stimulation with Images in a Spelling Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772343},
issn = {1424-8220},
support = {PID2021-127261OB-I00//European Regional Development Fund/ ; PID2021-127261OB-I00//University of Malaga/ ; PID2021-127261OB-I00//Spanish Ministry of Science, Innovation, and Universities/ ; PID2021-127261OB-I00//State Research Agency/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Emotions/physiology ; Surveys and Questionnaires ; Photic Stimulation/methods ; },
abstract = {A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain-computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller.},
}
@article {pmid36772275,
year = {2023},
author = {Yedukondalu, J and Sharma, LD},
title = {Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772275},
issn = {1424-8220},
mesh = {Humans ; *Wavelet Analysis ; Electrooculography/methods ; *Artifacts ; Eye Movements ; Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.},
}
@article {pmid36772115,
year = {2023},
author = {Lin, CF and Lin, HC},
title = {IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {3},
pages = {},
pmid = {36772115},
issn = {1424-8220},
mesh = {*Algorithms ; Signal Processing, Computer-Assisted ; Delta Rhythm ; Movement ; *Brain-Computer Interfaces ; Electroencephalography/methods ; },
abstract = {This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert-Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5-4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500-600, 800-900, 800-900, and 500-600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.},
}
@article {pmid36767088,
year = {2023},
author = {Alvarado, C and Castillo-Aguilar, M and Villegas, V and Estrada Goic, C and Harris, K and Barria, P and Moraes, MM and Mendes, TT and Arantes, RME and Valdés-Badilla, P and Núñez-Espinosa, C},
title = {Physical Activity, Seasonal Sensitivity and Psychological Well-Being of People of Different Age Groups Living in Extreme Environments.},
journal = {International journal of environmental research and public health},
volume = {20},
number = {3},
pages = {},
pmid = {36767088},
issn = {1660-4601},
mesh = {Humans ; Male ; Female ; *Seasonal Affective Disorder/epidemiology/prevention &amp; control/psychology ; Seasons ; Cross-Sectional Studies ; Psychological Well-Being ; Exercise ; },
abstract = {Physical activity can prevent many organic and mental pathologies. For people living in extreme southern high-latitude environments, weather conditions can affect these activities, altering their psychological well-being and favoring the prevalence of seasonal sensitivity (SS). This study aims to determine the relationships between the practice of physical activity, seasonal sensitivity and well-being in people living in high southern latitudes. A cross-sectional study was conducted, using the Seasonal Pattern Assessment Questionnaire (SPAQ), applying a psychological well-being scale, and determining sports practice according to the recommendations of the World Health Organization (WHO) for the 370 male (n = 209; 55%) and female (n = 173; 45%) participants. The main results indicated that 194 people (52 ± 7.7 years) reported physical activity. High-intensity physical activity practitioners recorded a significantly lower proportion of SS. In terms of psychological well-being, an adverse effect was found between the Seasonal Score Index (SSI) and five subcategories of the Ryff well-being scale. In conclusion, those who perform high-intensity physical activity have a lower SS, and those who have a higher SS have a lower psychological well-being.},
}
@article {pmid36766680,
year = {2023},
author = {Küçükakarsu, M and Kavsaoğlu, AR and Alenezi, F and Alhudhaif, A and Alwadie, R and Polat, K},
title = {A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {13},
number = {3},
pages = {},
pmid = {36766680},
issn = {2075-4418},
abstract = {This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.},
}
@article {pmid36765392,
year = {2023},
author = {Nair, L and Winkle, B and Senanayake, E},
title = {Managing blunt cardiac injury.},
journal = {Journal of cardiothoracic surgery},
volume = {18},
number = {1},
pages = {71},
pmid = {36765392},
issn = {1749-8090},
mesh = {Humans ; *Heart Injuries/diagnosis/etiology/surgery ; Heart ; *Myocardial Contusions/diagnosis/therapy/complications ; *Heart Rupture/complications ; *Wounds, Nonpenetrating/diagnosis/surgery/complications ; Rupture ; *Thoracic Injuries/complications/diagnosis/surgery ; },
abstract = {Blunt cardiac injury (BCI) encompasses a spectrum of pathologies ranging from clinically silent, transient arrhythmias to deadly cardiac wall rupture. Of diagnosed BCIs, cardiac contusion is most common. Suggestive symptoms may be unrelated to BCI, while some injuries may be clinically asymptomatic. Cardiac rupture is the most devastating complication of BCI. Most patients who sustain rupture of a heart chamber do not reach the emergency department alive. The incidence of BCI following blunt thoracic trauma remains variable and no gold standard exists to either diagnose cardiac injury or provide management. Diagnostic tests should be limited to identifying those patients who are at risk of developing cardiac complications as a result of cardiac in jury. Therapeutic interventions should be directed to treat the complications of cardiac injury. Prompt, appropriate and well-orchestrated surgical treatment is invaluable in the management of the unstable patients.},
}
@article {pmid36765285,
year = {2023},
author = {Bae, CM and Cho, JY and Jung, H and Son, SA},
title = {Serum pro-B-type natriuretic peptide levels and cardiac index as adjunctive tools of blunt cardiac injury.},
journal = {BMC cardiovascular disorders},
volume = {23},
number = {1},
pages = {81},
pmid = {36765285},
issn = {1471-2261},
mesh = {Adolescent ; Adult ; Aged ; Humans ; Middle Aged ; Young Adult ; Biomarkers/blood/metabolism ; Critical Care ; Intensive Care Units ; Lactates ; *Myocardial Contusions/blood/metabolism ; *Natriuretic Peptide, Brain/blood/metabolism ; Peptide Fragments ; },
abstract = {BACKGROUND: Blunt cardiac injury (BCI) has a variety of symptoms that may be a potentially life-threatening injury that can lead to death. Depending on the diagnosis of BCI, treatment direction and length of stay may vary. In addition, the utility of other diagnostic tests for cardiac disease as diagnostic tools for BCI remain unclear. The purpose of this study was to investigate the competence of N-terminal pro-B-type natriuretic peptide (NT pro-BNP) and cardiac index (C.I) as adjunctive diagnostic tools for BCI.<br><br>METHODS: From January 2018 to March 2020, severe trauma patients with sternum fracture who were admitted to the traumatic intensive care unit (TICU) were included this study. Patients with sternum fracture, 18&#xa0;years of age or older, and with an injury severity score&#x2009;>&#x2009;16 who required intensive care were included. Invasive measurement for the analysis of the pulse contour for C.I monitoring and intravenous blood sampling for NT pro-BNP measurement were performed. Sampling and 12-lead electrocardiogram were performed at different time points as follows: immediately after TICU admission and at 24&#xa0;h and 48&#xa0;h after trauma.<br><br>RESULTS: Among 103; 33 patients with factors that could affect NT pro-BNP were excluded; therefore, 63 patients were included in this study. According to the American Association for the Surgery of Trauma Cardiac Injury Scale, 33 patients were diagnosed with non-BCI, and 30 patients constituted with BCI. The median ages of the patients were 58 (52-69), and 60 (45-69) years in the non-BCI and BCI groups, respectively (p&#x2009;=&#x2009;0.77). The median NT pro-BNP values were higher in the BCI group on admission, hospital day (HD) 2, and HD 3, however, no statistical difference was observed (125 (49-245) vs. 130 (47-428) pg/mL, p&#x2009;=&#x2009;0.08, 124 (68-224) vs. 187 (55-519) pg/mL, p&#x2009;=&#x2009;0.09, and 121(59-225) vs. 133 (56-600) pg/mL, p&#x2009;=&#x2009;0.17, respectively). On the contrary, significantly lower values were observed in the median C.I measurement on admission and HD 3 in the BCI group (3.2 (2.8-3.5) vs. 2.6 (2.3-3.5) L/min/m[2], p&#x2009;<&#x2009;0.01 and 3.2 (3.1-3.9) vs. 2.9 (2.4-3.2) L/min/m[2], p&#x2009;<&#x2009;0.01, respectively); however, no significant difference was observed on HD 2 (3.4 (3.0-3.7) vs. 2.6 (2.4-3.4) L/min/m[2], p&#x2009;=&#x2009;0.17), Furthermore, The median lactate levels in the BCI group upon admission, HD 2, and HD 3 were significantly higher than those in the non-BCI group (1.8 (1.1-2.6) vs. 3.1 (2.1-4.4) mmol/L, p&#x2009;<&#x2009;0.01; 1.3 (0.8-2.3) vs. 3.0 (2.2-4.7) mmol/L, p&#x2009;<&#x2009;0.01; and 1.5 (0.9-1.5) vs. 2.2 (1.3-3.7) mmol/L, p&#x2009;<&#x2009;0.01, respectively).<br><br>CONCLUSION: Consecutive values of NT pro-BNP and C.I show no correlation with ECG-based BCI diagnosis. However, lactate level measurement may help in the early recognition of BCI as an adjunctive tool. It should be noted that this is a hypothesis-generating study for BCI diagnosis. Further studies should be conducted in larger populations with a prospective approach.},
}
@article {pmid36765121,
year = {2023},
author = {Hinss, MF and Jahanpour, ES and Somon, B and Pluchon, L and Dehais, F and Roy, RN},
title = {Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.},
journal = {Scientific data},
volume = {10},
number = {1},
pages = {85},
pmid = {36765121},
issn = {2052-4463},
mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Cognition ; *Electroencephalography ; },
abstract = {Brain-Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. Testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. The COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community. Furthermore, a proof of concept is given with an example of mental workload estimation pipeline and results, to ensure that the data can be used for the design and evaluation of pBCI pipelines. This body of work presents a large effort to promote the use of pBCIs in an open science framework.},
}
@article {pmid36764125,
year = {2023},
author = {Kong, LZ and Shen, YT and Zhang, DH and Lai, JB and Hu, SH},
title = {Free long-acting injectables for patients with psychosis: A step forward.},
journal = {Asian journal of psychiatry},
volume = {83},
number = {},
pages = {103476},
doi = {10.1016/j.ajp.2023.103476},
pmid = {36764125},
issn = {1876-2026},
mesh = {Humans ; *Psychotic Disorders/drug therapy ; Injections ; Delayed-Action Preparations ; *Antipsychotic Agents/therapeutic use ; },
}
@article {pmid36763992,
year = {2023},
author = {Chen, J and Wang, D and Yi, W and Xu, M and Tan, X},
title = {Filter bank sinc-convolutional network with channel self-attention for high performance motor imagery decoding.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
doi = {10.1088/1741-2552/acbb2c},
pmid = {36763992},
issn = {1741-2552},
mesh = {*Imagination ; Imagery, Psychotherapy ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Intention ; Algorithms ; },
abstract = {Objective.Motor Imagery Brain-Computer Interface (MI-BCI) is an active Brain-Computer Interface (BCI) paradigm focusing on the identification of motor intention, which is one of the most important non-invasive BCI paradigms. In MI-BCI studies, deep learning-based methods (especially lightweight networks) have attracted more attention in recent years, but the decoding performance still needs further improving.Approach.To solve this problem, we designed a filter bank structure with sinc-convolutional layers for spatio-temporal feature extraction of MI-electroencephalography in four motor rhythms. The Channel Self-Attention method was introduced for feature selection based on both global and local information, so as to build a model called Filter Bank Sinc-convolutional Network with Channel Self-Attention for high performance MI-decoding. Also, we proposed a data augmentation method based on multivariate empirical mode decomposition to improve the generalization capability of the model.Main results.We performed an intra-subject evaluation experiment on unseen data of three open MI datasets. The proposed method achieved mean accuracy of 78.20% (4-class scenario) on BCI Competition IV IIa, 87.34% (2-class scenario) on BCI Competition IV IIb, and 72.03% (2-class scenario) on Open Brain Machine Interface (OpenBMI) dataset, which are significantly higher than those of compared deep learning-based methods by at least 3.05% (p= 0.0469), 3.18% (p= 0.0371), and 2.27% (p= 0.0024) respectively.Significance.This work provides a new option for deep learning-based MI decoding, which can be employed for building BCI systems for motor rehabilitation.},
}
@article {pmid36763058,
year = {2022},
author = {Ma, D and Zhong, L and Yan, Z and Yao, J and Zhang, Y and Ye, F and Huang, Y and Lai, D and Yang, W and Hou, P and Guo, J},
title = {Structural mechanisms for the activation of human cardiac KCNQ1 channel by electro-mechanical coupling enhancers.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {119},
number = {45},
pages = {e2207067119},
pmid = {36763058},
issn = {1091-6490},
mesh = {Humans ; *KCNQ1 Potassium Channel/metabolism ; Cryoelectron Microscopy ; *Heart ; Piperidines ; },
abstract = {The cardiac KCNQ1 potassium channel carries the important IKs current and controls the heart rhythm. Hundreds of mutations in KCNQ1 can cause life-threatening cardiac arrhythmia. Although KCNQ1 structures have been recently resolved, the structural basis for the dynamic electro-mechanical coupling, also known as the voltage sensor domain-pore domain (VSD-PD) coupling, remains largely unknown. In this study, utilizing two VSD-PD coupling enhancers, namely, the membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2) and a small-molecule ML277, we determined 2.5-3.5 Å resolution cryo-electron microscopy structures of full-length human KCNQ1-calmodulin (CaM) complex in the apo closed, ML277-bound open, and ML277-PIP2-bound open states. ML277 binds at the "elbow" pocket above the S4-S5 linker and directly induces an upward movement of the S4-S5 linker and the opening of the activation gate without affecting the C-terminal domain (CTD) of KCNQ1. PIP2 binds at the cleft between the VSD and the PD and brings a large structural rearrangement of the CTD together with the CaM to activate the PD. These findings not only elucidate the structural basis for the dynamic VSD-PD coupling process during KCNQ1 gating but also pave the way to develop new therapeutics for anti-arrhythmia.},
}
@article {pmid36760796,
year = {2022},
author = {Xu, F and Zhao, J and Liu, M and Yu, X and Wang, C and Lou, Y and Shi, W and Liu, Y and Gao, L and Yang, Q and Zhang, B and Lu, S and Tang, J and Leng, J},
title = {Exploration of sleep function connection and classification strategies based on sub-period sleep stages.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1088116},
pmid = {36760796},
issn = {1662-4548},
abstract = {BACKGROUND: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.<br><br>METHODS: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.<br><br>RESULTS: The experimental results have shown that when the number of sub-periods is 30, the &#x3b1; (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.<br><br>CONCLUSION: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.},
}
@article {pmid36760718,
year = {2022},
author = {Singh, AK and Krishnan, S},
title = {Trends in EEG signal feature extraction applications.},
journal = {Frontiers in artificial intelligence},
volume = {5},
number = {},
pages = {1072801},
pmid = {36760718},
issn = {2624-8212},
abstract = {This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.},
}
@article {pmid36750362,
year = {2023},
author = {Esparza-Iaizzo, M and Vigué-Guix, I and Ruzzoli, M and Torralba-Cuello, M and Soto-Faraco, S},
title = {Long-Range α-Synchronization as Control Signal for BCI: A Feasibility Study.},
journal = {eNeuro},
volume = {10},
number = {3},
pages = {},
pmid = {36750362},
issn = {2373-2822},
mesh = {Adult ; Humans ; *Brain-Computer Interfaces ; Feasibility Studies ; Electroencephalography ; Attention/physiology ; },
abstract = {Shifts in spatial attention are associated with variations in α band (α, 8-14 Hz) activity, specifically in interhemispheric imbalance. The underlying mechanism is attributed to local α-synchronization, which regulates local inhibition of neural excitability, and frontoparietal synchronization reflecting long-range communication. The direction-specific nature of this neural correlate brings forward its potential as a control signal in brain-computer interfaces (BCIs). In the present study, we explored whether long-range α-synchronization presents lateralized patterns dependent on voluntary attention orienting and whether these neural patterns can be picked up at a single-trial level to provide a control signal for active BCI. We collected electroencephalography (EEG) data from a cohort of healthy adults (n = 10) while performing a covert visuospatial attention (CVSA) task. The data show a lateralized pattern of α-band phase coupling between frontal and parieto-occipital regions after target presentation, replicating previous findings. This pattern, however, was not evident during the cue-to-target orienting interval, the ideal time window for BCI. Furthermore, decoding the direction of attention trial-by-trial from cue-locked synchronization with support vector machines (SVMs) was at chance level. The present findings suggest EEG may not be capable of detecting long-range α-synchronization in attentional orienting on a single-trial basis and, thus, highlight the limitations of this metric as a reliable signal for BCI control.},
}
@article {pmid36750151,
year = {2023},
author = {Kikkert, S and Sonar, HA and Freund, P and Paik, J and Wenderoth, N},
title = {Hand and face somatotopy shown using MRI-safe vibrotactile stimulation with a novel soft pneumatic actuator (SPA)-skin interface.},
journal = {NeuroImage},
volume = {269},
number = {},
pages = {119932},
doi = {10.1016/j.neuroimage.2023.119932},
pmid = {36750151},
issn = {1095-9572},
mesh = {Humans ; *Hand ; *Magnetic Resonance Imaging/methods ; Fingers ; Touch ; Skin ; Somatosensory Cortex/physiology ; Brain Mapping/methods ; Physical Stimulation/methods ; },
abstract = {The exact somatotopy of the human facial representation in the primary somatosensory cortex (S1) remains debated. One reason that progress has been hampered is due to the methodological challenge of how to apply automated vibrotactile stimuli to face areas in a manner that is: (1) reliable despite differences in the curvatures of face locations; and (2) MR-compatible and free of MR-interference artefacts when applied in the MR head-coil. Here we overcome this challenge by using soft pneumatic actuator (SPA) technology. SPAs are made of a soft silicon material and can be in- or deflated by means of airflow, have a small diameter, and are flexible in structure, enabling good skin contact even on curved body surfaces (as on the face). To validate our approach, we first mapped the well-characterised S1 finger layout using this novel device and confirmed that tactile stimulation of the fingers elicited characteristic somatotopic finger activations in S1. We then used the device to automatically and systematically deliver somatosensory stimulation to different face locations. We found that the forehead representation was least distant from the representation of the hand. Within the face representation, we found that the lip representation is most distant from the forehead representation, with the chin represented in between. Together, our results demonstrate that this novel MR compatible device produces robust and clear somatotopic representational patterns using vibrotactile stimulation through SPA-technology.},
}
@article {pmid36749989,
year = {2023},
author = {Massaeli, F and Bagheri, M and Power, SD},
title = {EEG-based detection of modality-specific visual and auditory sensory processing.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb9be},
pmid = {36749989},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Auditory Perception ; Visual Perception ; Attention ; Workload ; },
abstract = {Objective.A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the 'level' of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific 'type' of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.Approach.15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.Main results.The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.Significance.These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.},
}
@article {pmid36749645,
year = {2023},
author = {Ziemba, AM and Woodson, MCC and Funnell, JL and Wich, D and Balouch, B and Rende, D and Amato, DN and Bao, J and Oprea, I and Cao, D and Bajalo, N and Ereifej, ES and Capadona, JR and Palermo, EF and Gilbert, RJ},
title = {Development of a Slow-Degrading Polymerized Curcumin Coating for Intracortical Microelectrodes.},
journal = {ACS applied bio materials},
volume = {6},
number = {2},
pages = {806-818},
pmid = {36749645},
issn = {2576-6422},
support = {IK2 RX002628/RX/RRD VA/United States ; R01 NS092754/NS/NINDS NIH HHS/United States ; },
mesh = {Rats ; Animals ; Microelectrodes ; *Curcumin/pharmacology ; Electrodes, Implanted ; Neurons ; Polymers ; },
abstract = {Intracortical microelectrodes are used with brain-computer interfaces to restore lost limb function following nervous system injury. While promising, recording ability of intracortical microelectrodes diminishes over time due, in part, to neuroinflammation. As curcumin has demonstrated neuroprotection through anti-inflammatory activity, we fabricated a 300 nm-thick intracortical microelectrode coating consisting of a polyurethane copolymer of curcumin and polyethylene glycol (PEG), denoted as poly(curcumin-PEG1000 carbamate) (PCPC). The uniform PCPC coating reduced silicon wafer hardness by two orders of magnitude and readily absorbed water within minutes, demonstrating that the coating is soft and hydrophilic in nature. Using an in vitro release model, curcumin eluted from the PCPC coating into the supernatant over 1 week; the majority of the coating was intact after an 8-week incubation in buffer, demonstrating potential for longer term curcumin release and softness. Assessing the efficacy of PCPC within a rat intracortical microelectrode model in vivo, there were no significant differences in tissue inflammation, scarring, neuron viability, and myelin damage between the uncoated and PCPC-coated probes. As the first study to implant nonfunctional probes with a polymerized curcumin coating, we have demonstrated the biocompatibility of a PCPC coating and presented a starting point in the design of poly(pro-curcumin) polymers as coating materials for intracortical electrodes.},
}
@article {pmid36745927,
year = {2023},
author = {Li, B and Zhang, S and Hu, Y and Lin, Y and Gao, X},
title = {Assembling global and local spatial-temporal filters to extract discriminant information of EEG in RSVP task.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb96f},
pmid = {36745927},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Learning ; Benchmarking ; Algorithms ; },
abstract = {Objective.Brain-computer interface (BCI) system has developed rapidly in the past decade. And rapid serial visual presentation (RSVP) is an important BCI paradigm to detect the targets in high-speed image streams. For decoding electroencephalography (EEG) in RSVP task, the ensemble-model methods have better performance than the single-model ones.Approach.This study proposed a method based on ensemble learning to extract discriminant information of EEG. An extreme gradient boosting framework was utilized to sequentially generate the sub models, including one global spatial-temporal filter and a group of local ones. EEG was reshaped into a three-dimensional form by remapping the electrode dimension into a 2D array to learn the spatial-temporal features from real local space.Main results.A benchmark RSVP EEG dataset was utilized to evaluate the performance of the proposed method, where EEG data of 63 subjects were analyzed. Compared with several state-of-the-art methods, the spatial-temporal patterns of proposed method were more consistent with P300, and the proposed method can provide significantly better classification performance.Significance.The ensemble model in this study was end-to-end optimized, which can avoid error accumulation. The sub models optimized by gradient boosting theory can extract discriminant information complementarily and non-redundantly.},
}
@article {pmid36745911,
year = {2023},
author = {Adhikary, S and Jain, K and Saha, B and Chowdhury, D},
title = {Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface.},
journal = {Biomedical physics &amp; engineering express},
volume = {9},
number = {3},
pages = {},
doi = {10.1088/2057-1976/acb942},
pmid = {36745911},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Brain/physiology ; Algorithms ; },
abstract = {Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG's relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration.Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.},
}
@article {pmid36743394,
year = {2022},
author = {Song, M and Huang, Y and Visser, HJ and Romme, J and Liu, YH},
title = {An Energy-Efficient and High-Data-Rate IR-UWB Transmitter for Intracortical Neural Sensing Interfaces.},
journal = {IEEE journal of solid-state circuits},
volume = {57},
number = {12},
pages = {3656-3668},
pmid = {36743394},
issn = {0018-9200},
support = {101001448/ERC_/European Research Council/International ; },
abstract = {This paper presents an implantable impulse-radio ultra-wideband (IR-UWB) wireless telemetry system for intracortical neural sensing interfaces. A 3-dimensional (3-D) hybrid impulse modulation that comprises phase shift keying (PSK), pulse position modulation (PPM) and pulse amplitude modulation (PAM) is proposed to increase modulation order without significantly increasing the demodulation requirement, thus leading to a high data rate of 1.66 Gbps and an increased air-transmission range. Operating in 6 - 9 GHz UWB band, the presented transmitter (TX) supports the proposed hybrid modulation with a high energy efficiency of 5.8 pJ/bit and modulation quality (EVM< -21 dB). A low-noise injection-locked ring oscillator supports 8-PSK with a phase error of 2.6°. A calibration free delay generator realizes a 4-PPM with only 115 μW and avoids potential cross-modulation between PPM and PSK. A switch-cap power amplifier with an asynchronous pulse-shaping performs 4-PAM with high energy efficiency and linearity. The TX is implemented in 28 nm CMOS technology, occupying 0.155mm[2] core area. The wireless module including a printed monopole antenna has a module area of only 1.05 cm[2]. The transmitter consumes in total 9.7 mW when transmitting -41.3 dBm/MHz output power. The wireless telemetry module has been validated ex-vivo with a 15-mm multi-layer porcine tissue, and achieves a communication (air) distance up to 15 cm, leading to at least 16× improvement in distance-moralized energy efficiency of 45 pJ/bit/meter compared to state-of-the-art.},
}
@article {pmid36741783,
year = {2022},
author = {Shibu, CJ and Sreedharan, S and Arun, KM and Kesavadas, C and Sitaram, R},
title = {Explainable artificial intelligence model to predict brain states from fNIRS signals.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1029784},
pmid = {36741783},
issn = {1662-5161},
abstract = {Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.},
}
@article {pmid36741671,
year = {2023},
author = {Almosallam, W and Aljoujou, AA and Ayoubi, HR and Alzoubi, H},
title = {Evaluation of the Effect of Antihypertensive Drugs on the Values of Dental Pulp Oxygen Saturation in Hypertension Patients: A Case-Control Study.},
journal = {Cureus},
volume = {15},
number = {1},
pages = {e33245},
pmid = {36741671},
issn = {2168-8184},
abstract = {Purpose This study aimed to know about the positive or negative effect of antihypertensive drugs of different groups on the values of dental pulp oxygen saturation in hypertension patients. Materials and Methods A case-control study to evaluate the impact of the antihypertensive drugs on the values of dental pulp oxygen saturation in hypertension patients. The studied sample consisted of 40 participants, and they were distributed into two groups: Group I (n=20): Hypertension patients treated with antihypertensive drugs, and Group II (n=20): Healthy participants. A finger pulse oximeter was recorded after a rest period of 15 minutes by BCI® Advisor® vital signs monitor. The patient was then asked to use a chlorhexidine digluconate mouth rinse for five minutes, and the two dental pulp pulse oximeters for the central upper incisors were also recorded for all participants. Data were analyzed using the Mann-Whitney U test. Results The results showed that there was no significant difference between the finger pulse oximeters of the two studied groups (P-value = 0.421). The two dental pulp oxygen saturation was higher than the control group with statistically significant (P-value = 0.043, P-value = 0.002). Conclusions Within the limitation of this study, it can be concluded that antihypertensive drugs increase the dental pulp oxygen saturation in patients with hypertension who are treated with antihypertensive drugs, and thus there is a positive effect of these drugs in stimulating the dental pulp.},
}
@article {pmid36738734,
year = {2023},
author = {Cui, Q and Bi, H and Lv, Z and Wu, Q and Hua, J and Gu, B and Huo, C and Tang, M and Chen, Y and Chen, C and Chen, S and Zhang, X and Wu, Z and Lao, Z and Sheng, N and Shen, C and Zhang, Y and Wu, ZY and Jin, Z and Yang, P and Liu, H and Li, J and Bai, G},
title = {Diverse CMT2 neuropathies are linked to aberrant G3BP interactions in stress granules.},
journal = {Cell},
volume = {186},
number = {4},
pages = {803-820.e25},
doi = {10.1016/j.cell.2022.12.046},
pmid = {36738734},
issn = {1097-4172},
mesh = {Animals ; Mice ; *Charcot-Marie-Tooth Disease/genetics/metabolism/pathology ; Cytoplasm ; Motor Neurons ; *Stress Granules ; *RNA Recognition Motif Proteins/metabolism ; },
abstract = {Complex diseases often involve the interplay between genetic and environmental factors. Charcot-Marie-Tooth type 2 neuropathies (CMT2) are a group of genetically heterogeneous disorders, in which similar peripheral neuropathology is inexplicably caused by various mutated genes. Their possible molecular links remain elusive. Here, we found that upon environmental stress, many CMT2-causing mutant proteins adopt similar properties by entering stress granules (SGs), where they aberrantly interact with G3BP and integrate into SG pathways. For example, glycyl-tRNA synthetase (GlyRS) is translocated from the cytoplasm into SGs upon stress, where the mutant GlyRS perturbs the G3BP-centric SG network by aberrantly binding to G3BP. This disrupts SG-mediated stress responses, leading to increased stress vulnerability in motoneurons. Disrupting this aberrant interaction rescues SG abnormalities and alleviates motor deficits in CMT2D mice. These findings reveal a stress-dependent molecular link across diverse CMT2 mutants and provide a conceptual framework for understanding genetic heterogeneity in light of environmental stress.},
}
@article {pmid36736668,
year = {2023},
author = {Deng, J and Sun, J and Lu, S and Yue, K and Liu, W and Shi, H and Zou, L},
title = {Exploring neural activity in inflammatory bowel diseases using functional connectivity and DKI-fMRI fusion.},
journal = {Behavioural brain research},
volume = {443},
number = {},
pages = {114325},
doi = {10.1016/j.bbr.2023.114325},
pmid = {36736668},
issn = {1872-7549},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Brain Mapping/methods ; Brain ; Diffusion Tensor Imaging ; Signal Processing, Computer-Assisted ; Neural Pathways ; },
abstract = {Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.},
}
@article {pmid36736571,
year = {2023},
author = {Yan, K and Tao, R and Huang, X and Zhang, E},
title = {Influence of advisees' facial feedback on subsequent advice-giving by advisors: Evidence from the behavioral and neurophysiological approach.},
journal = {Biological psychology},
volume = {177},
number = {},
pages = {108506},
doi = {10.1016/j.biopsycho.2023.108506},
pmid = {36736571},
issn = {1873-6246},
mesh = {Humans ; Feedback ; *Decision Making ; *Smiling ; Facial Expression ; },
abstract = {Previous work has demonstrated the interpersonal implications of advisees' decisions (acceptance or rejection) on advisors' advice-giving behavior in subsequent exchanges. Here, using an ERP technique, we investigated how advisees' facial feedback (smiling, neutral, or frowning) accompanying their decisions (acceptance or rejection) influenced advisors' feedback evaluation from advisees and their advice-giving in subsequent exchanges. Behaviorally, regardless of whether the advice was accepted or rejected, advisors who received smiling-expression feedback would show higher willingness rates in subsequent advice-giving decisions, while advisors who received frowning-expression feedback would show lower willingness rates. On the neural level, in the feedback evaluation stage, the FRN and P3 responses were not sensitive to facial feedback. In contrast, frowning-expression feedback elicited a larger LPC amplitude than neutral- and smiling-expression feedback, regardless of whether the advice was accepted or rejected. In the advice decision stage, advisors who received neutral-expression feedback showed a larger N2 in making decisions than advisors who received frowning-expression feedback only after the advice was rejected. Additionally, Advisors who received smiling- and neutral-expression feedback showed a larger P3 in making decisions than advisors who received frowning-expression feedback only after the advice was accepted. In sum, the current findings extended previous research findings by showing that the effect of advisees' facial expressions on the advisors' advice-giving existed in multiple stages, including both the feedback evaluation stage and the advice decision stage.},
}
@article {pmid36736001,
year = {2023},
author = {Mao, J and Qiu, S and Wei, W and He, H},
title = {Cross-modal guiding and reweighting network for multi-modal RSVP-based target detection.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {161},
number = {},
pages = {65-82},
doi = {10.1016/j.neunet.2023.01.009},
pmid = {36736001},
issn = {1879-2782},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials ; },
abstract = {Rapid Serial Visual Presentation (RSVP) based Brain-Computer Interface (BCI) facilities the high-throughput detection of rare target images by detecting evoked event-related potentials (ERPs). At present, the decoding accuracy of the RSVP-based BCI system limits its practical applications. This study introduces eye movements (gaze and pupil information), referred to as EYE modality, as another useful source of information to combine with EEG-based BCI and forms a novel target detection system to detect target images in RSVP tasks. We performed an RSVP experiment, recorded the EEG signals and eye movements simultaneously during a target detection task, and constructed a multi-modal dataset including 20 subjects. Also, we proposed a cross-modal guiding and fusion network to fully utilize EEG and EYE modalities and fuse them for better RSVP decoding performance. In this network, a two-branch backbone was built to extract features from these two modalities. A Cross-Modal Feature Guiding (CMFG) module was proposed to guide EYE modality features to complement the EEG modality for better feature extraction. A Multi-scale Multi-modal Reweighting (MMR) module was proposed to enhance the multi-modal features by exploring intra- and inter-modal interactions. And, a Dual Activation Fusion (DAF) was proposed to modulate the enhanced multi-modal features for effective fusion. Our proposed network achieved a balanced accuracy of 88.00% (±2.29) on the collected dataset. The ablation studies and visualizations revealed the effectiveness of the proposed modules. This work implies the effectiveness of introducing the EYE modality in RSVP tasks. And, our proposed network is a promising method for RSVP decoding and further improves the performance of RSVP-based target detection systems.},
}
@article {pmid36733372,
year = {2023},
author = {Gams, A and Naik, GR},
title = {Editorial: Neurorobotics explores gait movement in the sporting community.},
journal = {Frontiers in neurorobotics},
volume = {17},
number = {},
pages = {1127994},
pmid = {36733372},
issn = {1662-5218},
}
@article {pmid36731812,
year = {2023},
author = {Soroush, PZ and Herff, C and Ries, SK and Shih, JJ and Schultz, T and Krusienski, DJ},
title = {The nested hierarchy of overt, mouthed, and imagined speech activity evident in intracranial recordings.},
journal = {NeuroImage},
volume = {269},
number = {},
pages = {119913},
doi = {10.1016/j.neuroimage.2023.119913},
pmid = {36731812},
issn = {1095-9572},
mesh = {Humans ; *Speech/physiology ; Brain/physiology ; Mouth ; Face ; Electroencephalography/methods ; *Brain-Computer Interfaces ; },
abstract = {Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.},
}
@article {pmid36731770,
year = {2023},
author = {Pan, L and Ping, A and Schriver, KE and Roe, AW and Zhu, J and Xu, K},
title = {Infrared neural stimulation in human cerebral cortex.},
journal = {Brain stimulation},
volume = {16},
number = {2},
pages = {418-430},
doi = {10.1016/j.brs.2023.01.1678},
pmid = {36731770},
issn = {1876-4754},
mesh = {Animals ; Humans ; *Brain ; *Neurons/physiology ; Brain Mapping/methods ; Cerebral Cortex ; Electric Stimulation/methods ; },
abstract = {BACKGROUND: Modulation of brain circuits by electrical stimulation has led to exciting and powerful therapies for diseases such as Parkinson's. Because human brain organization is based in mesoscale (millimeter-scale) functional nodes, having a method that can selectively target such nodes could enable more precise, functionally specific stimulation therapies. Infrared Neural Stimulation (INS) is an emerging stimulation technology that stimulates neural tissue via delivery of tiny heat pulses. In nonhuman primates, this optical method provides focal intensity-dependent stimulation of the brain without tissue damage. However, whether INS application to the human central nervous system (CNS) is similarly effective is unknown.<br><br>OBJECTIVE: To examine the effectiveness of INS on human cerebral cortex in intraoperative setting and to evaluate INS damage threshholds.<br><br>METHODS: Five epileptic subjects undergoing standard lobectomy for epilepsy consented to this study. Cortical response to INS was assessed by intrinsic signal optical imaging (OI, a method that detects changes in tissue reflectance due to neuronal activity). A custom integrated INS and OI system was developed specifically for short-duration INS and OI acquisition during surgical procedures. Single pulse trains of INS with intensities from 0.2 to 0.8&#xa0;J/cm[2] were delivered to the somatosensory cortex and responses were recorded via optical imaging. Following tissue resection, histological analysis was conducted to evaluate damage threshholds.<br><br>RESULTS: As assessed by OI, and similar to results in monkeys, INS induced responses in human cortex were highly focal (millimeter sized) and led to relative suppression of nearby cortical sites. Intensity dependence was observed at both stimulated and functionally connected sites. Histological analysis of INS-stimulated human cortical tissue provided damage threshold estimates.<br><br>CONCLUSION: This is the first study demonstrating application of INS to human CNS and shows feasibility for stimulating single cortical nodes and associated sites and provided INS damage threshold estimates for cortical tissue. Our results suggest that INS is a promising tool for stimulation of functionally selective mesoscale circuits in the human brain, and may lead to advances in the future of precision medicine.},
}
@article {pmid36731636,
year = {2023},
author = {Jin, J and Chen, X and Zhang, D and Liang, Z},
title = {Editorial for the special issue "Visual evoked brain computer interface studies".},
journal = {Journal of neuroscience methods},
volume = {388},
number = {},
pages = {109806},
doi = {10.1016/j.jneumeth.2023.109806},
pmid = {36731636},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; User-Computer Interface ; Brain/physiology ; Evoked Potentials, Visual ; Electroencephalography ; Photic Stimulation ; },
}
@article {pmid36729587,
year = {2023},
author = {Rimbert, S and Lelarge, J and Guerci, P and Bidgoli, SJ and Meistelman, C and Cheron, G and Cebolla Alvarez, AM and Schmartz, D},
title = {Detection of Motor Cerebral Activity After Median Nerve Stimulation During General Anesthesia (STIM-MOTANA): Protocol for a Prospective Interventional Study.},
journal = {JMIR research protocols},
volume = {12},
number = {},
pages = {e43870},
pmid = {36729587},
issn = {1929-0748},
abstract = {BACKGROUND: Accidental awareness during general anesthesia (AAGA) is defined as an unexpected awareness of the patient during general anesthesia. This phenomenon occurs in 1%-2% of high-risk practice patients and can cause physical suffering and psychological after-effects, called posttraumatic stress disorder. In fact, no monitoring techniques are satisfactory enough to effectively prevent AAGA; therefore, new alternatives are needed. Because the first reflex for a patient during an AAGA is to move, but cannot do so because of the neuromuscular blockers, we believe that it is possible to design a brain-computer interface (BCI) based on the detection of movement intention to warn the anesthetist. To do this, we propose to describe and detect the changes in terms of motor cortex oscillations during general anesthesia with propofol, while a median nerve stimulation is performed. We believe that our results could enable the design of a BCI based on median nerve stimulation, which could prevent AAGA.<br><br>OBJECTIVE: To our knowledge, no published studies have investigated the detection of electroencephalographic (EEG) patterns in relation to peripheral nerve stimulation over the sensorimotor cortex during general anesthesia. The main objective of this study is to describe the changes in terms of event-related desynchronization and event-related synchronization modulations, in the EEG signal over the motor cortex during general anesthesia with propofol while a median nerve stimulation is performed.<br><br>METHODS: STIM-MOTANA is an interventional and prospective study conducted with patients scheduled for surgery under general anesthesia, involving EEG measurements and median nerve stimulation at two different times: (1) when the patient is awake before surgery (2) and under general anesthesia. A total of 30 patients will receive surgery under complete intravenous anesthesia with a target-controlled infusion pump of propofol.<br><br>RESULTS: The changes in event-related desynchronization and event-related synchronization during median nerve stimulation according to the various propofol concentrations for 30 patients will be analyzed. In addition, we will apply 4 different offline machine learning algorithms to detect the median nerve stimulation at the cerebral level. Recruitment began in December 2022. Data collection is expected to conclude in June 2024.<br><br>CONCLUSIONS: STIM-MOTANA will be the first protocol to investigate median nerve stimulation cerebral motor effect during general anesthesia for the detection of intraoperative awareness. Based on strong practical and theoretical scientific reasoning from our previous studies, our innovative median nerve stimulation-based BCI would provide a way to detect intraoperative awareness during general anesthesia.<br><br>TRIAL REGISTRATION: Clinicaltrials.gov NCT05272202; https://clinicaltrials.gov/ct2/show/NCT05272202.<br><br>PRR1-10.2196/43870.},
}
@article {pmid36729246,
year = {2023},
author = {Knopf, S and Frahm, N and M Pfotenhauer, S},
title = {How Neurotech Start-Ups Envision Ethical Futures: Demarcation, Deferral, Delegation.},
journal = {Science and engineering ethics},
volume = {29},
number = {1},
pages = {4},
pmid = {36729246},
issn = {1471-5546},
mesh = {*Technology ; *Biotechnology/ethics ; *Neurobiology/ethics ; *Industry/ethics ; },
abstract = {Like many ethics debates surrounding emerging technologies, neuroethics is increasingly concerned with the private sector. Here, entrepreneurial visions and claims of how neurotechnology innovation will revolutionize society-from brain-computer-interfaces to neural enhancement and cognitive phenotyping-are confronted with public and policy concerns about the risks and ethical challenges related to such innovations. But while neuroethics frameworks have a longer track record in public sector research such as the U.S. BRAIN Initiative, much less is known about how businesses-and especially start-ups-address ethics in tech development. In this paper, we investigate how actors in the field frame and enact ethics as part of their innovative R&amp;D processes and business models. Drawing on an empirical case study on direct-to-consumer (DTC) neurotechnology start-ups, we find that actors engage in careful boundary-work to anticipate and address public critique of their technologies, which allows them to delineate a manageable scope of their ethics integration. In particular, boundaries are drawn around four areas: the technology's actual capability, purpose, safety and evidence-base. By drawing such lines of demarcation, we suggest that start-ups make their visions of ethical neurotechnology in society more acceptable, plausible and desirable, favoring their innovations while at the same time assigning discrete responsibilities for ethics. These visions establish a link from the present into the future, mobilizing the latter as promissory place where a technology's benefits will materialize and to which certain ethical issues can be deferred. In turn, the present is constructed as a moment in which ethical engagement could be delegated to permissive regulatory standards and scientific authority. Our empirical tracing of the construction of 'ethical realities' in and by start-ups offers new inroads for ethics research and governance in tech industries beyond neurotechnology.},
}
@article {pmid36726940,
year = {2023},
author = {Liu, Y and Xu, S and Yang, Y and Zhang, K and He, E and Liang, W and Luo, J and Wu, Y and Cai, X},
title = {Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review.},
journal = {Microsystems &amp; nanoengineering},
volume = {9},
number = {},
pages = {13},
pmid = {36726940},
issn = {2055-7434},
abstract = {A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.},
}
@article {pmid36726556,
year = {2022},
author = {Hossain, KM and Islam, MA and Hossain, S and Nijholt, A and Ahad, MAR},
title = {Status of deep learning for EEG-based brain-computer interface applications.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {1006763},
pmid = {36726556},
issn = {1662-5188},
abstract = {In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.},
}
@article {pmid36723288,
year = {2023},
author = {Yang, T and Wang, SC and Ye, L and Maimaitiyiming, Y and Naranmandura, H},
title = {Targeting viral proteins for restraining SARS-CoV-2: focusing lens on viral proteins beyond spike for discovering new drug targets.},
journal = {Expert opinion on drug discovery},
volume = {18},
number = {3},
pages = {247-268},
doi = {10.1080/17460441.2023.2175812},
pmid = {36723288},
issn = {1746-045X},
mesh = {Humans ; Antiviral Agents/pharmacology/chemistry ; *COVID-19 ; *SARS-CoV-2/genetics ; Viral Nonstructural Proteins/metabolism ; Viral Proteins/metabolism ; },
abstract = {INTRODUCTION: Emergence of highly infectious SARS-CoV-2 variants are reducing protection provided by current vaccines, requiring constant updates in antiviral approaches. The virus encodes four structural and sixteen nonstructural proteins which play important roles in viral genome replication and transcription, virion assembly, release , entry into cells, and compromising host cellular defenses. As alien proteins to host cells, many viral proteins represent potential targets for combating the SARS-CoV-2.<br><br>AREAS COVERED: Based on literature from PubMed and Web of Science databases, the authors summarize the typical characteristics of SARS-CoV-2 from the whole viral particle to the individual viral proteins and their corresponding functions in virus life cycle. The authors also discuss the potential and emerging targeted interventions to curb virus replication and spread in detail to provide unique insights into SARS-CoV-2 infection and countermeasures against it.<br><br>EXPERT OPINION: Our comprehensive analysis highlights the rationale to focus on non-spike viral proteins that are less mutated but have important functions. Examples of this include: structural proteins (e.g. nucleocapsid protein, envelope protein) and extensively-concerned nonstructural proteins (e.g. NSP3, NSP5, NSP12) along with the ones with relatively less attention (e.g. NSP1, NSP10, NSP14 and NSP16), for developing novel drugs to overcome resistance of SARS-CoV-2 variants to preexisting vaccines and antibody-based treatments.},
}
@article {pmid36721006,
year = {2023},
author = {Li, Z and Zheng, Y and Diao, X and Li, R and Sun, N and Xu, Y and Li, X and Duan, S and Gong, W and Si, K},
title = {Robust and adjustable dynamic scattering compensation for high-precision deep tissue optogenetics.},
journal = {Communications biology},
volume = {6},
number = {1},
pages = {128},
pmid = {36721006},
issn = {2399-3642},
mesh = {Mice ; Animals ; Rats ; *Optogenetics ; Brain ; Light ; },
abstract = {The development of high-precision optogenetics in deep tissue is limited due to the strong optical scattering induced by biological tissue. Although various wavefront shaping techniques have been developed to compensate the scattering, it is still a challenge to non-invasively characterize the dynamic scattered optical wavefront inside the living tissue. Here, we present a non-invasive scattering compensation system with fast multidither coherent optical adaptive technique (fCOAT), which allows the rapid wavefront correction and stable focusing in dynamic scattering medium. We achieve subcellular-resolution focusing through 500-μm-thickness brain slices, or even three pieces overlapped mouse skulls after just one iteration with a 589 nm CW laser. Further, focusing through dynamic scattering medium such as live rat ear is also successfully achieved. The formed focus can maintain longer than 60 s, which satisfies the requirements of stable optogenetics manipulation. Moreover, the focus size is adjustable from subcellular level to tens of microns to freely match the various manipulation targets. With the specially designed fCOAT system, we successfully achieve single-cellular optogenetic manipulation through the brain tissue, with a stimulation efficiency enhancement up to 300% compared with that of the speckle.},
}
@article {pmid36720854,
year = {2023},
author = {Duan, J and Xu, P and Zhang, H and Luan, X and Yang, J and He, X and Mao, C and Shen, DD and Ji, Y and Cheng, X and Jiang, H and Jiang, Y and Zhang, S and Zhang, Y and Xu, HE},
title = {Mechanism of hormone and allosteric agonist mediated activation of follicle stimulating hormone receptor.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {519},
pmid = {36720854},
issn = {2041-1723},
mesh = {Female ; Humans ; Follicle Stimulating Hormone ; Hydrocortisone ; *Infertility ; *Receptors, FSH/agonists ; },
abstract = {Follicle stimulating hormone (FSH) is an essential glycoprotein hormone for human reproduction, which functions are mediated by a G protein-coupled receptor, FSHR. Aberrant FSH-FSHR signaling causes infertility and ovarian hyperstimulation syndrome. Here we report cryo-EM structures of FSHR in both inactive and active states, with the active structure bound to FSH and an allosteric agonist compound 21 f. The structures of FSHR are similar to other glycoprotein hormone receptors, highlighting a conserved activation mechanism of hormone-induced receptor activation. Compound 21 f formed extensive interactions with the TMD to directly activate FSHR. Importantly, the unique residue H615[7.42] in FSHR plays an essential role in determining FSHR selectivity for various allosteric agonists. Together, our structures provide a molecular basis of FSH and small allosteric agonist-mediated FSHR activation, which could inspire the design of FSHR-targeted drugs for the treatment of infertility and controlled ovarian stimulation for in vitro fertilization.},
}
@article {pmid36720164,
year = {2023},
author = {Li, Z and Zhang, G and Wang, L and Wei, J and Dang, J},
title = {Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb79e},
pmid = {36720164},
issn = {1741-2552},
mesh = {Humans ; *Emotions ; *Recognition, Psychology ; Brain ; Electroencephalography ; Artificial Intelligence ; },
abstract = {Objective.Constructing an efficient human emotion recognition model based on electroencephalogram (EEG) signals is significant for realizing emotional brain-computer interaction and improving machine intelligence.Approach.In this paper, we present a spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition. First, we combined the single-channel differential entropy (DE) feature with the cross-channel functional connectivity (FC) feature to extract both the temporal variation and spatial topological information of EEG. After that, a novel convolutional graph attention network was used to fuse the DE and FC features and further extract higher-level graph structural information with sufficient expressive power for emotion recognition. Furthermore, we introduced a multi-headed attention mechanism in graph neural networks to improve the generalization ability of the model.Main results.We evaluated the emotion recognition performance of our proposed model on the public SEED and DEAP datasets, which achieved a classification accuracy of 99.11% ± 0.83% and 94.83% ± 3.41% in the subject-dependent and subject-independent experiments on the SEED dataset, and achieved an accuracy of 91.19% ± 1.24% and 92.03% ± 4.57% for discrimination of arousal and valence in subject-independent experiments on DEAP dataset. Notably, our model achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets. In addition, we gained insight into the proposed frame through both the ablation experiments and the analysis of spatial patterns of FC and DE features.Significance.All these results prove the effectiveness of the STFCGAT architecture for emotion recognition and also indicate that there are significant differences in the spatial-temporal characteristics of the brain under different emotional states.},
}
@article {pmid36720162,
year = {2023},
author = {Sindhu, KR and Ngo, D and Ombao, H and Olaya, JE and Shrey, DW and Lopour, BA},
title = {A novel method for dynamically altering the surface area of intracranial EEG electrodes.},
journal = {Journal of neural engineering},
volume = {20},
number = {2},
pages = {},
pmid = {36720162},
issn = {1741-2552},
support = {R01 NS116273/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Electrocorticography/methods ; Electroencephalography/methods ; Brain ; *Epilepsy ; Electrodes ; },
abstract = {Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.},
}
@article {pmid36719563,
year = {2023},
author = {Dong, Y and Wang, L and Li, M},
title = {Applying correlation analysis to electrode optimization in source domain.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {5},
pages = {1225-1238},
pmid = {36719563},
issn = {1741-0444},
mesh = {*Electroencephalography/methods ; Electrodes ; Algorithms ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; },
abstract = {In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.},
}
@article {pmid36716553,
year = {2023},
author = {Fan, C and Zha, R and Liu, Y and Wei, Z and Wang, Y and Song, H and Lv, W and Ren, J and Hong, W and Gou, H and Zhang, P and Chen, Y and Zhou, Y and Pan, Y and Zhang, X},
title = {Altered white matter functional network in nicotine addiction.},
journal = {Psychiatry research},
volume = {321},
number = {},
pages = {115073},
doi = {10.1016/j.psychres.2023.115073},
pmid = {36716553},
issn = {1872-7123},
mesh = {Humans ; *White Matter ; *Tobacco Use Disorder ; Nicotine ; Diffusion Tensor Imaging/methods ; Neural Pathways ; Brain ; Magnetic Resonance Imaging ; },
abstract = {Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.},
}
@article {pmid36716494,
year = {2023},
author = {Delisle-Rodriguez, D and Silva, L and Bastos-Filho, T},
title = {EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb73b},
pmid = {36716494},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Calibration ; Feedback, Sensory ; Imagery, Psychotherapy ; Electroencephalography/methods ; Imagination/physiology ; Algorithms ; },
abstract = {Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.},
}
@article {pmid36711591,
year = {2023},
author = {Willett, FR and Kunz, E and Fan, C and Avansino, D and Wilson, G and Choi, EY and Kamdar, F and Hochberg, LRH and Druckmann, S and Shenoy, K and Henderson, J},
title = {A high-performance speech neuroprosthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2023.01.21.524489},
pmid = {36711591},
issn = {2692-8205},
abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.},
}
@article {pmid36711163,
year = {2023},
author = {Cho, YK and Koh, CS and Lee, Y and Park, M and Kim, TJ and Jung, HH and Chang, JW and Jun, SB},
title = {Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle.},
journal = {Biomedical engineering letters},
volume = {13},
number = {1},
pages = {85-95},
pmid = {36711163},
issn = {2093-985X},
abstract = {Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.},
}
@article {pmid36711161,
year = {2023},
author = {Valencia, D and Alimohammad, A},
title = {Partially binarized neural networks for efficient spike sorting.},
journal = {Biomedical engineering letters},
volume = {13},
number = {1},
pages = {73-83},
pmid = {36711161},
issn = {2093-985X},
abstract = {While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 μ W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm 2 of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.},
}
@article {pmid36711153,
year = {2022},
author = {Sohn, WJ and Lim, J and Wang, PT and Pu, H and Malekzadeh-Arasteh, O and Shaw, SJ and Armacost, M and Gong, H and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {Benchtop and bedside validation of a low-cost programmable cortical stimulator in a testbed for bi-directional brain-computer-interface research.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1075971},
pmid = {36711153},
issn = {1662-4548},
abstract = {INTRODUCTION: Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback.<br><br>METHODS: A custom programmable direct cortical stimulator (DCS) capable of eliciting artificial sensorimotor response was integrated into an embedded BCI system to form a safe, independent, wireless, and battery powered testbed to explore BD-BCI concepts at a low cost. The BD-BCI stimulator output was tested in phantom brain tissue by assessing its ability to deliver electrical stimulation equivalent to an FDA-approved commercial electrical cortical stimulator. Subsequently, the stimulator was tested in an epilepsy patient with subcortical electrocorticographic (ECoG) implants covering the sensorimotor cortex to assess its ability to elicit equivalent responses as the FDA-approved counterpart. Additional safety features (impedance monitoring, artifact mitigation, and passive and active charge balancing mechanisms) were also implemeneted and tested in phantom brain tissue. Finally, concurrent operation with interleaved stimulation and BCI decoding was tested in a phantom brain as a proof-of-concept operation of BD-BCI system.<br><br>RESULTS: The benchtop prototype BD-BCI stimulator's basic output features (current amplitude, pulse frequency, pulse width, train duration) were validated by demonstrating the output-equivalency to an FDA-approved commercial cortical electrical stimulator (R [2] > 0.99). Charge-neutral stimulation was demonstrated with pulse-width modulation-based correction algorithm preventing steady state voltage deviation. Artifact mitigation achieved a 64.5% peak voltage reduction. Highly accurate impedance monitoring was achieved with R [2] > 0.99 between measured and actual impedance, which in-turn enabled accurate charge density monitoring. An online BCI decoding accuracy of 93.2% between instructional cues and decoded states was achieved while delivering interleaved stimulation. The brain stimulation mapping via ECoG grids in an epilepsy patient showed that the two stimulators elicit equivalent responses.<br><br>SIGNIFICANCE: This study demonstrates clinical validation of a fully-programmable electrical stimulator, integrated into an embedded BCI system. This low-cost BD-BCI system is safe and readily applicable as a testbed for BD-BCI research. In particular, it provides an all-inclusive hardware platform that approximates the limitations in a near-future implantable BD-BCI. This successful benchtop/human validation of the programmable electrical stimulator in a BD-BCI system is a critical milestone toward fully-implantable BD-BCI systems.},
}
@article {pmid36711141,
year = {2022},
author = {Li, H and Liu, M and Yu, X and Zhu, J and Wang, C and Chen, X and Feng, C and Leng, J and Zhang, Y and Xu, F},
title = {Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1097660},
pmid = {36711141},
issn = {1662-4548},
abstract = {BACKGROUND: Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.<br><br>METHODS: According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.<br><br>RESULTS: The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.<br><br>CONCLUSION: The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.},
}
@article {pmid36710855,
year = {2022},
author = {Sajno, E and Bartolotta, S and Tuena, C and Cipresso, P and Pedroli, E and Riva, G},
title = {Machine learning in biosignals processing for mental health: A narrative review.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {1066317},
pmid = {36710855},
issn = {1664-1078},
abstract = {Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.},
}
@article {pmid36709613,
year = {2023},
author = {Cai, J and Xie, M and Zhao, L and Li, X and Liang, S and Deng, W and Guo, W and Ma, X and Sham, PC and Wang, Q and Li, T},
title = {White matter changes and its relationship with clinical symptom in medication-naive first-episode early onset schizophrenia.},
journal = {Asian journal of psychiatry},
volume = {82},
number = {},
pages = {103482},
doi = {10.1016/j.ajp.2023.103482},
pmid = {36709613},
issn = {1876-2026},
mesh = {Adolescent ; Humans ; *Schizophrenia/diagnostic imaging ; *White Matter/diagnostic imaging ; Diffusion Tensor Imaging/methods ; Corpus Callosum ; Brain/diagnostic imaging ; },
abstract = {Previous studies have highlighted the role of white matter (WM) alterations as biomarkers of the disease state and prognosis of schizophrenia. However, less is known about WM abnormalities in the rarely occurring adolescent early onset schizophrenia (EOS). In this study, T1-weighted and diffusion-weighted images were collected in 56 medication-naive first-episode participants with EOS and 43 healthy controls (HCs). Using Tract-based Spatial Statistics, we calculate case-control differences in scalar diffusion measures, i.e. fractional anisotropy (FA) and mean diffusivity (MD), and investigated their association with clinical feature in participants with EOS. Compared with HCs, decreased MD was found in EOS group most notably in the inferior longitudinal fasciculus, anterior thalamic radiation, inferior fronto-occipital fasciculus and corticospinal tract in the right hemisphere. No significant difference was found in FA between these two groups. The FA values of the forceps minor and the right superior longitudinal fasciculus were suggested to be related to the severity of clinical symptom in participants with EOS. These results provide clues about the neural basis of schizophrenia and a potential biomarker for clinical studies.},
}
@article {pmid36707885,
year = {2023},
author = {Angerhöfer, C and Vermehren, M and Colucci, A and Nann, M and Koßmehl, P and Niedeggen, A and Kim, WS and Chang, WK and Paik, NJ and Hömberg, V and Soekadar, SR},
title = {The Berlin Bimanual Test for Tetraplegia (BeBiTT): development, psychometric properties, and sensitivity to change in assistive hand exoskeleton application.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {17},
pmid = {36707885},
issn = {1743-0003},
support = {759370/ERC_/European Research Council/International ; },
mesh = {Humans ; *Exoskeleton Device ; Psychometrics ; Reproducibility of Results ; Berlin ; Hand ; Quadriplegia/diagnosis/rehabilitation ; *Spinal Cord Injuries/rehabilitation ; },
abstract = {BACKGROUND: Assistive hand exoskeletons are promising tools to restore hand function after cervical spinal cord injury (SCI) but assessing their specific impact on bimanual hand and arm function is limited due to lack of reliable and valid clinical tests. Here, we introduce the Berlin Bimanual Test for Tetraplegia (BeBiTT) and demonstrate its psychometric properties and sensitivity to assistive hand exoskeleton-related improvements in bimanual task performance.<br><br>METHODS: Fourteen study participants with subacute cervical SCI performed the BeBiTT unassisted (baseline). Thereafter, participants repeated the BeBiTT while wearing a brain/neural hand exoskeleton (B/NHE) (intervention). Online control of the B/NHE was established via a hybrid sensorimotor rhythm-based brain-computer interface (BCI) translating electroencephalographic (EEG) and electrooculographic (EOG) signals into open/close commands. For reliability assessment, BeBiTT scores were obtained by four independent observers. Besides internal consistency analysis, construct validity was assessed by correlating baseline BeBiTT scores with the Spinal Cord Independence Measure III (SCIM III) and Quadriplegia Index of Function (QIF). Sensitivity to differences in bimanual task performance was assessed with a bootstrapped paired t-test.<br><br>RESULTS: The BeBiTT showed excellent interrater reliability (intraclass correlation coefficients&#x2009;>&#x2009;0.9) and internal consistency (&#x3b1;&#x2009;=&#x2009;0.91). Validity of the BeBiTT was evidenced by strong correlations between BeBiTT scores and SCIM III as well as QIF. Wearing a B/NHE (intervention) improved the BeBiTT score significantly (p&#x2009;<&#x2009;0.05) with high effect size (d&#x2009;=&#x2009;1.063), documenting high sensitivity to intervention-related differences in bimanual task performance.<br><br>CONCLUSION: The BeBiTT is a reliable and valid test for evaluating bimanual task performance in persons with tetraplegia, suitable to assess the impact of assistive hand exoskeletons on bimanual function.},
}
@article {pmid36706879,
year = {2023},
author = {Li, H and Shen, S and Yu, K and Wang, H and Fu, J},
title = {Construction of porous structure-based carboxymethyl chitosan/ sodium alginate/ tea polyphenols for wound dressing.},
journal = {International journal of biological macromolecules},
volume = {233},
number = {},
pages = {123404},
doi = {10.1016/j.ijbiomac.2023.123404},
pmid = {36706879},
issn = {1879-0003},
mesh = {*Polyphenols/pharmacology/chemistry ; Alginates/chemistry ; *Chitosan/chemistry ; Tea/chemistry ; Porosity ; Anti-Bacterial Agents/pharmacology/chemistry ; Bandages ; },
abstract = {Polysaccharide-based materials with porous structure were selected as the basic skeleton to prepare a flexible and biodegradable wound dressing. The carboxymethyl chitosan/sodium alginate/tea polyphenols (CC/SA/TP) with a two-layer porous structure exhibits a variety of performances. The specific combined structure with ordered and lamellar porous structure was constructed by high-speed homogenized foaming, Ca[2+] crosslinking and two-step freeze-drying methods. Moreover, the CC/SA/TP porous structure owns better shape retention and recovery because of the 3D network with an "egg-box" structure formed by impregnation. Tea polyphenols are efficiently encapsulated into a porous structure and released in a sustained pattern. After storing for 60 days, the CC/SA/TP porous structure still exhibits great suitable water vapor transmittance, efficient antibacterial activity and ultrarapid antioxidant activity. Meanwhile, the relatively low differential blood clotting index (BCI) and cytotoxicity of the CC/SA/TP porous structure indicate that it possesses the possibility of adjusting and controlling wound bleeding. The test results reveal that the CC/SA/TP porous structure might be expected to play a great potential role in biomedical applications of wound dressing.},
}
@article {pmid36705845,
year = {2023},
author = {Zhao, ZD and Zhang, L and Xiang, X and Kim, D and Li, H and Cao, P and Shen, WL},
title = {Neurocircuitry of Predatory Hunting.},
journal = {Neuroscience bulletin},
volume = {39},
number = {5},
pages = {817-831},
pmid = {36705845},
issn = {1995-8218},
mesh = {Animals ; Motivation ; Neurons/physiology ; *Predatory Behavior/physiology ; *Zebrafish ; },
abstract = {Predatory hunting is an important type of innate behavior evolutionarily conserved across the animal kingdom. It is typically composed of a set of sequential actions, including prey search, pursuit, attack, and consumption. This behavior is subject to control by the nervous system. Early studies used toads as a model to probe the neuroethology of hunting, which led to the proposal of a sensory-triggered release mechanism for hunting actions. More recent studies have used genetically-trackable zebrafish and rodents and have made breakthrough discoveries in the neuroethology and neurocircuits underlying this behavior. Here, we review the sophisticated neurocircuitry involved in hunting and summarize the detailed mechanism for the circuitry to encode various aspects of hunting neuroethology, including sensory processing, sensorimotor transformation, motivation, and sequential encoding of hunting actions. We also discuss the overlapping brain circuits for hunting and feeding and point out the limitations of current studies. We propose that hunting is an ideal behavioral paradigm in which to study the neuroethology of motivated behaviors, which may shed new light on epidemic disorders, including binge-eating, obesity, and obsessive-compulsive disorders.},
}
@article {pmid36704636,
year = {2023},
author = {Lyu, X and Ding, P and Li, S and Dong, Y and Su, L and Zhao, L and Gong, A and Fu, Y},
title = {Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {1},
pages = {105-118},
pmid = {36704636},
issn = {1871-4080},
abstract = {Existing brain-computer interface (BCI) research has made great progress in improving the accuracy and information transfer rate (ITR) of BCI systems. However, the practicability of BCI is still difficult to achieve. One of the important reasons for this difficulty is that human factors are not fully considered in the research and development of BCI. As a result, BCI systems have not yet reached users' expectations. In this study, we investigate a BCI system of motor imagery for lower limb synchronous rehabilitation as an example. From the perspective of human factors engineering of BCI, a comprehensive evaluation method of BCI system development is proposed based on the concept of human-centered design and evaluation. Subjects' satisfaction ratings for BCI sensors, visual analog scale (VAS), subjects' satisfaction rating of the BCI system, and the mental workload rating for subjects manipulating the BCI system, as well as interview/follow-up comprehensive evaluation of motor imagery of BCI (MI-BCI) system satisfaction were used. The methods and concepts proposed in this study provide useful insights for the design of personalized MI-BCI. We expect that the human factors engineering of BCI could be applied to the design and satisfaction evaluation of MI-BCI, so as to promote the practical application of this kind of BCI.},
}
@article {pmid36704625,
year = {2023},
author = {Cui, Z and Lin, J and Fu, X and Zhang, S and Li, P and Wu, X and Wang, X and Chen, W and Zhu, S and Li, Y},
title = {Construction of the dynamic model of SCI rehabilitation using bidirectional stimulation and its application in rehabilitating with BCI.},
journal = {Cognitive neurodynamics},
volume = {17},
number = {1},
pages = {169-181},
pmid = {36704625},
issn = {1871-4080},
abstract = {UNLABELLED: Patients with complete spinal cord injury have a complete loss of motor and sensory functions below the injury plane, leading to a complete loss of function of the nerve pathway in the injured area. Improving the microenvironment in the injured area of patients with spinal cord injury, promoting axon regeneration of the nerve cells is challenging research fields. The brain-computer interface rehabilitation system is different from the other rehabilitation techniques. It can exert bidirectional stimulation on the spinal cord injury area, and can make positively rehabilitation effects of the patient with complete spinal cord injury. A dynamic model was constructed for the patient with spinal cord injury under-stimulation therapy, and the mechanism of the brain-computer interface in rehabilitation training was explored. The effects of the three current rehabilitation treatment methods on the microenvironment in a microscopic nonlinear model were innovatively unified and a complex system mapping relationship from the microscopic axon growth to macroscopic motor functions was constructed. The basic structure of the model was determined by simulating and fitting the data of the open rat experiments. A clinical rehabilitation experiment of spinal cord injury based on brain-computer interface was built, recruiting a patient with complete spinal cord injury, and the rehabilitation training and follow-up were conducted. The changes in the motor function of the patient was simulated and predicted through the constructed model, and the trend in the motor function improvement was successfully predicted over time. This proposed model explores the mechanism of brain-computer interface in rehabilitating patients with complete spinal cord injury, and it is also an application of complex system theory in rehabilitation medicine.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09804-3.},
}
@article {pmid36704007,
year = {2022},
author = {de Oliveira, IH and Rodrigues, AC},
title = {Empirical comparison of deep learning methods for EEG decoding.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1003984},
pmid = {36704007},
issn = {1662-4548},
abstract = {Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.},
}
@article {pmid36699986,
year = {2023},
author = {Shang, Q and Ma, H and Wang, C and Gao, L},
title = {Effects of Background Fitting of e-Commerce Live Streaming on Consumers' Purchase Intentions: A Cognitive-Affective Perspective.},
journal = {Psychology research and behavior management},
volume = {16},
number = {},
pages = {149-168},
pmid = {36699986},
issn = {1179-1578},
abstract = {PURPOSE: The purpose of this paper is to explore the effects of the background fitting of e-commerce live streaming on consumers' purchase intentions and the relevant internal psychological mechanism from the cognitive-affective perspective.<br><br>METHODS: In this study, a theoretical framework model of SOR comprising six variables is established. SPSS and SmartPLS are used to test the model and analyze data collected from a comprehensive questionnaire survey of 424 Chinese online consumers.<br><br>RESULTS: Results demonstrate that the impact of background fitting in e-commerce live streaming on consumers' purchase intentions can be divided into three stages. In the first stage, background fitting (comprised of both product-background fit and anchor-background fit) positively affect consumer cognitive process (perceived trust and perceived value). Perceived trust is mainly affected by anchor-background fit, while perceived value is mainly affected by product-background fit. In the second stage, consumers' cognitive process subsequently affects their affective process (perceived pleasure). Perceived value also has a greater positive effect on consumers' perceived pleasure than perceived trust, although perceived trust is a prerequisite for improving perceived value. In the third stage, the affective process further promotes consumers' purchase intentions.<br><br>CONCLUSION: Combining both SOR theory and cognitive-affective perspective, this study reveals that the internal influence mechanism of background fitting in e-commerce live streaming on consumers' purchase intentions is divided into three stages. Theoretically, this study not only expands the application of SOR theory in the research field of e-commerce live streaming from the perspective of external background stimulation, but also importantly contributes to the application of cognitive-emotional perspective in e-commerce live streaming. Practically, the study suggests optimizing background fitting as an effective way to improve consumer purchase intention in e-commerce live streaming, and it is better to optimize background fitting from the perspective of improving perceived trust, perceived value, and perceived pleasure.},
}
@article {pmid36699541,
year = {2022},
author = {Hu, J and Wang, Y and Tong, Y and Lin, G and Li, Y and Chen, J and Xu, D and Wang, L and Bai, R},
title = {Thalamic structure and anastomosis in different hemispheres of moyamoya disease.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1058137},
pmid = {36699541},
issn = {1662-4548},
abstract = {OBJECTIVE: The progression of the asymptomatic hemisphere of moyamoya disease (MMD) is largely unknown. In this study, we investigated the differences in subcortical gray matter structure and angiographic features between asymptomatic and symptomatic hemispheres in patients with MMD.<br><br>METHODS: We retrospectively reviewed patients with MMD in consecutive cases in our center. We compared subcortical gray matter volume and three types of collaterals (lenticulostriate anastomosis, thalamic anastomosis, and choroidal anastomosis) between symptomatic and asymptomatic hemispheres. Symptomatic hemispheres were classified as ischemic hemisphere (i-hemisphere) and hemorrhagic hemisphere (h-hemisphere). Asymptomatic hemispheres were classified as contralateral asymptomatic hemisphere of i-hemisphere (ai-hemisphere), contralateral asymptomatic hemisphere of h-hemisphere (ah-hemisphere), bilateral asymptomatic hemispheres in asymptomatic group (aa-hemisphere).<br><br>RESULTS: A total of 117 MMD patients were reviewed, and 49 of them met the inclusion criteria, with 98 hemispheres being analyzed. The thalamic volume was found to differ significantly between the i- and ai-hemispheres (P = 0.010), between the i- and ah-hemispheres (P = 0.004), as well as between the h- and ai-hemispheres (P = 0.002), between the h- and ah-hemispheres (P < 0.001). There was a higher incidence of thalamic anastomosis in the ai-hemispheres than i-hemispheres (31.3% vs. 6.3%, P = 0.070), and in the ah-hemispheres than h-hemispheres (29.6% vs. 11.1%, P = 0.088). Additionally, the hemispheres with thalamic anastomosis had a significantly greater volume than those without thalamic anastomosis (P = 0.024). Univariate and multivariate logistic regression analysis showed that thalamic volume was closely associated with thalamic anastomosis.<br><br>CONCLUSION: The thalamic volume and the incidence of thalamic anastomosis increase in asymptomatic hemispheres and decrease in symptomatic hemispheres. Combining these two characteristics may be helpful in assessing the risk of stroke in the asymptomatic hemispheres of MMD as well as understanding the pathological evolution of the disease.},
}
@article {pmid36699533,
year = {2022},
author = {Li, Y and Zhang, X and Ming, D},
title = {Early-stage fusion of EEG and fNIRS improves classification of motor imagery.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1062889},
pmid = {36699533},
issn = {1662-4548},
abstract = {INTRODUCTION: Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear.<br><br>METHODS: In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages.<br><br>RESULTS: The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.},
}
@article {pmid36698872,
year = {2022},
author = {Zanona, AF and Piscitelli, D and Seixas, VM and Scipioni, KRDDS and Bastos, MSC and de Sá, LCK and Monte-Silva, K and Bolivar, M and Solnik, S and De Souza, RF},
title = {Brain-computer interface combined with mental practice and occupational therapy enhances upper limb motor recovery, activities of daily living, and participation in subacute stroke.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1041978},
pmid = {36698872},
issn = {1664-2295},
abstract = {BACKGROUND: We investigated the effects of brain-computer interface (BCI) combined with mental practice (MP) and occupational therapy (OT) on performance in activities of daily living (ADL) in stroke survivors.<br><br>METHODS: Participants were randomized into two groups: experimental (n = 23, BCI controlling a hand exoskeleton combined with MP and OT) and control (n = 21, OT). Subjects were assessed with the functional independence measure (FIM), motor activity log (MAL), amount of use (MAL-AOM), and quality of movement (MAL-QOM). The box and blocks test (BBT) and the Jebsen hand functional test (JHFT) were used for the primary outcome of performance in ADL, while the Fugl-Meyer Assessment was used for the secondary outcome. Exoskeleton activation and the degree of motor imagery (measured as event-related desynchronization) were assessed in the experimental group. For the BCI, the EEG electrodes were placed on the regions of FC3, C3, CP3, FC4, C4, and CP4, according to the international 10-20 EEG system. The exoskeleton was placed on the affected hand. MP was based on functional tasks. OT consisted of ADL training, muscle mobilization, reaching tasks, manipulation and prehension, mirror therapy, and high-frequency therapeutic vibration. The protocol lasted 1 h, five times a week, for 2 weeks.<br><br>RESULTS: There was a difference between baseline and post-intervention analysis for the experimental group in all evaluations: FIM (p = 0.001, d = 0.56), MAL-AOM (p = 0.001, d = 0.83), MAL-QOM (p = 0.006, d = 0.84), BBT (p = 0.004, d = 0.40), and JHFT (p = 0.001, d = 0.45). Within the experimental group, post-intervention improvements were detected in the degree of motor imagery (p < 0.001) and the amount of exoskeleton activations (p < 0.001). For the control group, differences were detected for MAL-AOM (p = 0.001, d = 0.72), MAL-QOM (p = 0.013, d = 0.50), and BBT (p = 0.005, d = 0.23). Notably, the effect sizes were larger for the experimental group. No differences were detected between groups at post-intervention.<br><br>CONCLUSION: BCI combined with MP and OT is a promising tool for promoting sensorimotor recovery of the upper limb and functional independence in subacute post-stroke survivors.},
}
@article {pmid36698168,
year = {2023},
author = {Lim, CG and Soh, CP and Lim, SSY and Fung, DSS and Guan, C and Lee, TS},
title = {Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial.},
journal = {Child and adolescent psychiatry and mental health},
volume = {17},
number = {1},
pages = {15},
pmid = {36698168},
issn = {1753-2000},
abstract = {BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a prevalent child neurodevelopmental disorder that is treated in clinics and in schools. Previous trials suggested that our brain-computer interface (BCI)-based attention training program could improve ADHD symptoms. We have since developed a tablet version of the training program which can be paired with wireless EEG headsets. In this trial, we investigated the feasibility of delivering this tablet-based BCI intervention at home.<br><br>METHODS: Twenty children diagnosed with ADHD, who did not receive any medication for the preceding month, were randomised to receive the 8-week tablet-based BCI intervention either in the clinic or at home. Those in the home intervention group received instructions before commencing the program and got reminders if they were lagging on the training sessions. The ADHD Rating Scale was completed by a blinded clinician at baseline and at week 8. Adverse events were monitored during any contact with the child throughout the trial and at week 8.<br><br>RESULTS: Children in both groups could complete the tablet-based intervention easily on their own with minimal support from the clinic therapist or their parents (at home). The intervention was safe with few reported adverse effects. Clinician-rated inattentive symptoms on the ADHD-Rating Scale reduced by 3.2 (SD 6.20) and 3.9 (SD 5.08) for the home-based and clinic-based groups respectively, suggesting that home-based intervention was comparable to clinic-based intervention.<br><br>CONCLUSIONS: This trial demonstrated that the tablet version of our BCI-based attention training program can be safely delivered to children in the comfort of their own home. Trial registration This trial is registered at clinicaltrials.gov as NCT01344044.},
}
@article {pmid36696073,
year = {2023},
author = {Öztürk, S and Devecioğlu, &#x130; and Güçlü, B},
title = {Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex.},
journal = {Journal of computational neuroscience},
volume = {51},
number = {2},
pages = {207-222},
pmid = {36696073},
issn = {1573-6873},
mesh = {Rats ; Male ; Female ; Animals ; Bayes Theorem ; Models, Neurological ; *Sensorimotor Cortex ; *Brain-Computer Interfaces ; Somatosensory Cortex ; },
abstract = {Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (Rd) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean Rd was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.},
}
@article {pmid36693374,
year = {2023},
author = {Fan, Z and Chang, J and Liang, Y and Zhu, H and Zhang, C and Zheng, D and Wang, J and Xu, Y and Li, QJ and Hu, H},
title = {Neural mechanism underlying depressive-like state associated with social status loss.},
journal = {Cell},
volume = {186},
number = {3},
pages = {560-576.e17},
doi = {10.1016/j.cell.2022.12.033},
pmid = {36693374},
issn = {1097-4172},
mesh = {Mice ; Animals ; *Social Status ; Reward ; Social Behavior ; *Habenula/physiology ; Depression ; },
abstract = {Downward social mobility is a well-known mental risk factor for depression, but its neural mechanism remains elusive. Here, by forcing mice to lose against their subordinates in a non-violent social contest, we lower their social ranks stably and induce depressive-like behaviors. These rank-decline-associated depressive-like behaviors can be reversed by regaining social status. In vivo fiber photometry and single-unit electrophysiological recording show that forced loss, but not natural loss, generates negative reward prediction error (RPE). Through the lateral hypothalamus, the RPE strongly activates the brain's anti-reward center, the lateral habenula (LHb). LHb activation inhibits the medial prefrontal cortex (mPFC) that controls social competitiveness and reinforces retreats in contests. These results reveal the core neural mechanisms mutually promoting social status loss and depressive behaviors. The intertwined neuronal signaling controlling mPFC and LHb activities provides a mechanistic foundation for the crosstalk between social mobility and psychological disorder, unveiling a promising target for intervention.},
}
@article {pmid36693292,
year = {2023},
author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Marcos-Martínez, D and Rodríguez-González, V and Pérez-Velasco, S and Moreno-Calderón, S and Hornero, R},
title = {MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research.},
journal = {Computer methods and programs in biomedicine},
volume = {230},
number = {},
pages = {107357},
doi = {10.1016/j.cmpb.2023.107357},
pmid = {36693292},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; *Cognitive Neuroscience ; Reproducibility of Results ; Ecosystem ; Electroencephalography ; Software ; },
abstract = {BACKGROUND AND OBJECTIVE: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations.<br><br>METHODS: We followed strict development practices to optimize MEDUSA&#xa9; for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages.<br><br>RESULTS: MEDUSA&#xa9; provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility.<br><br>CONCLUSIONS: MEDUSA&#xa9; is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.},
}
@article {pmid36693278,
year = {2023},
author = {Johnston, R and Abbass, M and Corrigan, B and Gulli, R and Martinez-Trujillo, J and Sachs, A},
title = {Decoding spatial locations from primate lateral prefrontal cortex neural activity during virtual navigation.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb5c2},
pmid = {36693278},
issn = {1741-2552},
support = {//CIHR/Canada ; },
mesh = {Animals ; Humans ; *Prefrontal Cortex/physiology ; Primates ; Brain/physiology ; Neurons/physiology ; *Spatial Navigation/physiology ; Macaca ; },
abstract = {Objective. Decoding the intended trajectories from brain signals using a brain-computer interface system could be used to improve the mobility of patients with disabilities.Approach. Neuronal activity associated with spatial locations was examined while macaques performed a navigation task within a virtual environment.Main results.Here, we provide proof of principle that multi-unit spiking activity recorded from the lateral prefrontal cortex (LPFC) of non-human primates can be used to predict the location of a subject in a virtual maze during a navigation task. The spatial positions within the maze that require a choice or are associated with relevant task events can be better predicted than the locations where no relevant events occur. Importantly, within a task epoch of a single trial, multiple locations along the maze can be independently identified using a support vector machine model.Significance. Considering that the LPFC of macaques and humans share similar properties, our results suggest that this area could be a valuable implant location for an intracortical brain-computer interface system used for spatial navigation in patients with disabilities.},
}
@article {pmid36689427,
year = {2023},
author = {Pattisapu, S and Ray, S},
title = {Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier.},
journal = {PloS one},
volume = {18},
number = {1},
pages = {e0279881},
pmid = {36689427},
issn = {1932-6203},
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; *Evoked Potentials ; Brain/physiology ; Brain Mapping/methods ; Noise ; },
abstract = {Stimulus-induced narrow-band gamma oscillations (30-70 Hz) in human electro-encephalograph (EEG) have been linked to attentional and memory mechanisms and are abnormal in mental health conditions such as autism, schizophrenia and Alzheimer's Disease. However, since the absolute power in EEG decreases rapidly with increasing frequency following a "1/f" power law, and the gamma band includes line noise frequency, these oscillations are highly susceptible to instrument noise. Previous studies that recorded stimulus-induced gamma oscillations used expensive research-grade EEG amplifiers to address this issue. While low-cost EEG amplifiers have become popular in Brain Computer Interface applications that mainly rely on low-frequency oscillations (< 30 Hz) or steady-state-visually-evoked-potentials, whether they can also be used to measure stimulus-induced gamma oscillations is unknown. We recorded EEG signals using a low-cost, open-source amplifier (OpenBCI) and a traditional, research-grade amplifier (Brain Products GmbH), both connected to the OpenBCI cap, in male (N = 6) and female (N = 5) subjects (22-29 years) while they viewed full-screen static gratings that are known to induce two distinct gamma oscillations: slow and fast gamma, in a subset of subjects. While the EEG signals from OpenBCI were considerably noisier, we found that out of the seven subjects who showed a gamma response in Brain Products recordings, six showed a gamma response in OpenBCI as well. In spite of the noise in the OpenBCI setup, the spectral and temporal profiles of these responses in alpha (8-13 Hz) and gamma bands were highly correlated between OpenBCI and Brain Products recordings. These results suggest that low-cost amplifiers can potentially be used in stimulus-induced gamma response detection.},
}
@article {pmid36683147,
year = {2023},
author = {Jin, J and Xu, Z and Zhang, L and Zhang, C and Zhao, X and Mao, Y and Zhang, H and Liang, X and Wu, J and Yang, Y and Zhang, J},
title = {Gut-derived β-amyloid: Likely a centerpiece of the gut-brain axis contributing to Alzheimer's pathogenesis.},
journal = {Gut microbes},
volume = {15},
number = {1},
pages = {2167172},
pmid = {36683147},
issn = {1949-0984},
mesh = {Mice ; Humans ; Animals ; Aged ; Amyloid beta-Peptides/metabolism ; *Alzheimer Disease ; Amyloid Precursor Protein Secretases ; Brain-Gut Axis ; RNA, Ribosomal, 16S ; Mice, Transgenic ; *Gastrointestinal Microbiome/physiology ; Aspartic Acid Endopeptidases ; Amyloid beta-Protein Precursor/genetics/metabolism ; Disease Models, Animal ; },
abstract = {Peripheral β-amyloid (Aβ), including those contained in the gut, may contribute to the formation of Aβ plaques in the brain, and gut microbiota appears to exert an impact on Alzheimer's disease (AD) via the gut-brain axis, although detailed mechanisms are not clearly defined. The current study focused on uncovering the potential interactions among gut-derived Aβ in aging, gut microbiota, and AD pathogenesis. To achieve this goal, the expression levels of Aβ and several key proteins involved in Aβ metabolism were initially assessed in mouse gut, with key results confirmed in human tissue. The results demonstrated that a high level of Aβ was detected throughout the gut in both mice and human, and gut Aβ42 increased with age in wild type and mutant amyloid precursor protein/presenilin 1 (APP/PS1) mice. Next, the gut microbiome of mice was characterized by 16S rRNA sequencing, and we found the gut microbiome altered significantly in aged APP/PS1 mice and fecal microbiota transplantation (FMT) of aged APP/PS1 mice increased gut BACE1 and Aβ42 levels. Intra-intestinal injection of isotope or fluorescence labeled Aβ combined with vagotomy was also performed to investigate the transmission of Aβ from gut to brain. The data showed that, in aged mice, the gut Aβ42 was transported to the brain mainly via blood rather than the vagal nerve. Furthermore, FMT of APP/PS1 mice induced neuroinflammation, a phenotype that mimics early AD pathology. Taken together, this study suggests that the gut is likely a critical source of Aβ in the brain, and gut microbiota can further upregulate gut Aβ production, thereby potentially contributing to AD pathogenesis.},
}
@article {pmid36682180,
year = {2023},
author = {Peng, G and Zhao, K and Zhang, H and Xu, D and Kong, X},
title = {Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis.},
journal = {Computers in biology and medicine},
volume = {154},
number = {},
pages = {106537},
doi = {10.1016/j.compbiomed.2023.106537},
pmid = {36682180},
issn = {1879-0534},
mesh = {*Emotions ; *Brain ; Electroencephalography/methods ; Cerebral Cortex ; Electrodes ; },
abstract = {Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.},
}
@article {pmid36682005,
year = {2023},
author = {Guo, B and Zheng, H and Jiang, H and Li, X and Guan, N and Zuo, Y and Zhang, Y and Yang, H and Wang, X},
title = {Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.},
journal = {Briefings in bioinformatics},
volume = {24},
number = {2},
pages = {},
doi = {10.1093/bib/bbac628},
pmid = {36682005},
issn = {1477-4054},
mesh = {Protein Binding ; *Proteins/chemistry ; *Machine Learning ; Models, Theoretical ; },
abstract = {Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.},
}
@article {pmid36680589,
year = {2023},
author = {Afreen, A and Ahmed, Z and Khalid, N and Ferheen, I and Ahmed, I},
title = {Optimization and cholesterol-lowering activity of exopolysaccharide from Lactiplantibacillus paraplantarum NCCP 962.},
journal = {Applied microbiology and biotechnology},
volume = {107},
number = {4},
pages = {1189-1204},
pmid = {36680589},
issn = {1432-0614},
support = {NRPU-8709//Higher Education Commision, Pakistan/ ; },
mesh = {*Lactose ; *Lactobacillus ; Viscosity ; Monosaccharides ; Polysaccharides, Bacterial/chemistry ; },
abstract = {Exopolysaccharides (EPSs) are biological polymers with unique structural features have gained particular interest in the fields of food, chemistry and medicine, and food industry. EPS from the food-grade lactic acid bacteria (LAB) can be used as a natural food additives to commercial ones in the processing and development of functional foods and nutraceuticals. The current study was aimed to explore the EPS-producing LAB from the dahi; to optimize the fermentation conditions through Plackett-Burman (PB) and response surface methodology (RSM); and to study its physicochemical, rheological, functional attributes, and cholesterol-lowering activity. Lactiplantibacillus paraplantarum NCCP 962 was isolated among the 08 strains screened at the initial stage. The PB design screened out four independent factors that had a significant positive effect, i.e., lactose, yeast extract, CaCl2, and tryptone, while the remaining seven had a non-significant effect. The RSM exhibited lactose, yeast extract, and CaCl2, significantly contributing to EPS yield. The maximum EPS yield (0.910 g/L) was obtained at 6.57% lactose, 0.047% yeast extract, 0.59% CaCl2, and 1.37% tryptone. The R[2] value above 97% explains the higher variability and depicts the model's validity. The resulted EPS was a heteropolysaccharide in nature with mannose, glucose, and galactose monosaccharides. FTIR spectrum reflected the presence of functional groups, i.e., O-H, C-H, C = O, C-O-H, and CH2. SEM revealed a porous and rough morphology of EPS, also found to be thermally stable and negligible weight loss, i.e., 14.0% at 257 °C and 35.4% at 292.9 °C was observed in the 1st and 2nd phases, respectively. Rheological attributes revealed that strain NCCP 962 had high viscosity by increasing the EPS concentration, low pH, and temperature with respectable water holding, oil capacities, foaming abilities, and stability. NCCP 962 EPS possessed up to 46.4% reduction in cholesterol concentration in the supernatant. Conclusively, these results suggested that strain NCCP 962 can be used in food processing applications and other medical fields. KEY POINTS: • The fermentation conditions affect EPS yield from L. paraplantarum and significantly increased yield to 0.910 g/L. • The EPS was heteropolysaccharide in nature and thermally stable with amorphous morphology. • Good cholesterol-lowering potential with the best rheological, emulsifying, and foaming capacities.},
}
@article {pmid36679557,
year = {2023},
author = {Lupenko, S and Butsiy, R and Shakhovska, N},
title = {Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {2},
pages = {},
pmid = {36679557},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Fourier Analysis ; Models, Theoretical ; Electroencephalography/methods ; },
abstract = {In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.},
}
@article {pmid36679501,
year = {2023},
author = {Milanés-Hermosilla, D and Trujillo-Codorniú, R and Lamar-Carbonell, S and Sagaró-Zamora, R and Tamayo-Pacheco, JJ and Villarejo-Mayor, JJ and Delisle-Rodriguez, D},
title = {Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {2},
pages = {},
pmid = {36679501},
issn = {1424-8220},
support = {PN223LH004-026//National Science Project "Development of upper limb exo-503 skeletons for rehabilitation task"/ ; },
mesh = {*Electroencephalography/methods ; Bayes Theorem ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.},
}
@article {pmid36676348,
year = {2023},
author = {Wen, J and Tang, L and Zhang, S and Zhan, Q and Wang, Y},
title = {Qualitative and Quantitative Investigations on the Failure Effect of Critical Fissures in Rock Specimens under Plane Strain Compression.},
journal = {Materials (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {},
pmid = {36676348},
issn = {1996-1944},
support = {2021YFC3090103//National Key R&amp;D Program of China/ ; Y421006;Y421008;Y422001//Fundamental Research Funds for Central Public Welfare Research Institutes of China/ ; },
abstract = {To investigate the failure effects of critical fissures in rock specimens subjected to plane strain compression (PSC), five types of internal fissures in rock specimens were designed and twelve PSC tests were conducted for two lithologies based on the discrete element method (DEM). The results were analyzed in terms of the fracture mode, data characteristics, and crack evolution. The results indicated the following. (1) The rock samples with a critical fissure under PSC showed a weak face shear fracture mode, which was influenced by lithology, fissure angle, and fissure surface direction. (2) There were four critical expansion points (CEPs) of axial stress of the rocks under PSC, which were the stage signs of rock materials from local damage to complete fracture. The rock-bearing capacity index (RockBCI) was further proposed. (3) The bearing capacity of rock samples with horizontal fissures, fissures whose angles coincided with the fracture surface, and fissures whose surface was perpendicular to the lateral confine direction was the worst; their BCI[2] values were found to be 80.6%, 70.8%, and 56.9% of the rock samples without any fissures, respectively. The delayed fracture situation under PSC was identified and analyzed. (4) The crack evolution followed the unified law of localization, and the fissures in the rocks changed the mode of crack development and the path of the deepening and connecting of crack clusters, as well as affecting the time process from damage to collapse. This research innovatively investigated the behavior characteristics of rock samples with a fissure under PSC, and it qualitatively and quantitatively analyzed the bearing capacity of rock mass from local damage to fracture.},
}
@article {pmid36675707,
year = {2022},
author = {Ma, Y and Gong, A and Nan, W and Ding, P and Wang, F and Fu, Y},
title = {Personalized Brain-Computer Interface and Its Applications.},
journal = {Journal of personalized medicine},
volume = {13},
number = {1},
pages = {},
pmid = {36675707},
issn = {2075-4426},
support = {82172058, 81771926, 61763022, 81470084, 61463024, 62006246//National Natural Science Foundation of China/ ; },
abstract = {Brain-computer interfaces (BCIs) are a new technology that subverts traditional human-computer interaction, where the control signal source comes directly from the user's brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.},
}
@article {pmid36675486,
year = {2023},
author = {Morone, G and Pichiorri, F},
title = {Post-Stroke Rehabilitation: Challenges and New Perspectives.},
journal = {Journal of clinical medicine},
volume = {12},
number = {2},
pages = {},
pmid = {36675486},
issn = {2077-0383},
abstract = {A stroke is determined by insufficient blood supply to the brain due to vessel occlusion (ischemic stroke) or rupture (hemorrhagic stroke), resulting in immediate neurological impairment to differing degrees [...].},
}
@article {pmid36672726,
year = {2023},
author = {Coelho, HRS and Neves, SCD and Menezes, JNDS and Antoniolli-Silva, ACMB and Oliveira, RJ},
title = {Mesenchymal Stromal Cell Therapy Reverses Detrusor Hypoactivity in a Chronic Kidney Patient.},
journal = {Biomedicines},
volume = {11},
number = {1},
pages = {},
pmid = {36672726},
issn = {2227-9059},
abstract = {Detrusor hypoactivity (DH) is characterized by low detrusor pressure or a short contraction associated with low urinary flow. This condition can progress to chronic renal failure (CRF) and result in the need for dialysis. The present case report demonstrates that a patient diagnosed with DH and CRF who received two transplants with 2 × 10[6] autologous mesenchymal stromal cells at an interval of 30 days recovered the contractile strength of the bladder and normalized his renal function. The patient had a score of 19 on the ICIQ-SF before cell therapy, and that score was reduced to 1 after transplantation. These results demonstrate that there was an improvement in his voiding function, urinary stream and urine volume as evaluated by urofluxometry. In addition, a urodynamic study carried out after treatment showed an increase in the maximum flow from 2 mL/s to 23 mL/s, the detrusor pressure in the maximum flow from 21 cm H2O to 46 cm H2O and a BCI that went from 31 to 161, characterizing good detrusor contraction. Thus, in the present case, the transplantation of autologous mesenchymal stromal cells proved to be a viable therapeutic option to allow the patient to recover the contractile strength of the bladder, and reversed the CRF.},
}
@article {pmid36672115,
year = {2023},
author = {Zhao, ZP and Nie, C and Jiang, CT and Cao, SH and Tian, KX and Yu, S and Gu, JW},
title = {Modulating Brain Activity with Invasive Brain-Computer Interface: A Narrative Review.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672115},
issn = {2076-3425},
abstract = {Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.},
}
@article {pmid36672052,
year = {2022},
author = {Pepi, C and Mercier, M and Carfì Pavia, G and de Benedictis, A and Vigevano, F and Rossi-Espagnet, MC and Falcicchio, G and Marras, CE and Specchio, N and de Palma, L},
title = {Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672052},
issn = {2076-3425},
abstract = {OBJECTIVES: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome.<br><br>METHODS: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy.<br><br>RESULTS: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified.<br><br>CONCLUSIONS: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT.<br><br>SIGNIFICANCE: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.},
}
@article {pmid36672050,
year = {2022},
author = {Gao, T and Hu, Y and Zhuang, J and Bai, Y and Lu, R},
title = {Repetitive Transcranial Magnetic Stimulation of the Brain Region Activated by Motor Imagery Involving a Paretic Wrist and Hand for Upper-Extremity Motor Improvement in Severe Stroke: A Preliminary Study.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672050},
issn = {2076-3425},
support = {20194Y0103//Shanghai Municipal Health Commission/ ; 81902280//National Natural Science Foundation of China/ ; },
abstract = {Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain-computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention.},
}
@article {pmid36672046,
year = {2022},
author = {Adama, S and Bogdan, M},
title = {Application of Soft-Clustering to Assess Consciousness in a CLIS Patient.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672046},
issn = {2076-3425},
abstract = {Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.},
}
@article {pmid36672038,
year = {2022},
author = {Fu, J and Chen, S and Jia, J},
title = {Sensorimotor Rhythm-Based Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672038},
issn = {2076-3425},
support = {2018YFC2002300//National Key Research and Development Program Project of China/ ; 91948302//National Natural Integration Project/ ; 82021002//National Natural Innovation Research Group Project/ ; 22YF1404200//Shanghai Sailing Program/ ; },
abstract = {Brain-computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients' difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.},
}
@article {pmid36672034,
year = {2022},
author = {Gao, X and Yang, Y and Zhang, F and Zhou, F and Zhu, J and Sun, J and Xu, K and Chen, Y},
title = {A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.},
journal = {Brain sciences},
volume = {13},
number = {1},
pages = {},
pmid = {36672034},
issn = {2076-3425},
support = {2021ZD0200405//National Key R&amp;D program of China/ ; 31627802//National Natural Science Foundation of China/ ; 81873911//National Natural Science Foundation of China/ ; 2019C03033//Public Projects of Zhejiang province/ ; 2021FZZX002-05//Fundamental Research Funds for the Central Universities/ ; 2021KYY600403-0001//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.},
}
@article {pmid36671894,
year = {2022},
author = {Xu, M and Zhao, Y and Xu, G and Zhang, Y and Sun, S and Sun, Y and Wang, J and Pei, R},
title = {Recent Development of Neural Microelectrodes with Dual-Mode Detection.},
journal = {Biosensors},
volume = {13},
number = {1},
pages = {},
pmid = {36671894},
issn = {2079-6374},
support = {31900999, 32071392//National Natural Science Foundation of China/ ; BE2020766, BK20221264//Natural Science Foundation of Jiangsu Province/ ; 2021T140500//China Postdoctoral Science Foundation/ ; 2021K070A//Jiangsu Planned Projects for Postdoctoral Research Funds/ ; SJC2022006//Basic Research Pilot Project in Suzhou/ ; },
mesh = {Microelectrodes ; *Brain/physiology ; *Neurons/physiology ; },
abstract = {Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.},
}
@article {pmid36669202,
year = {2023},
author = {Ming, G and Pei, W and Gao, X and Wang, Y},
title = {A high-performance SSVEP-based BCI using imperceptible flickers.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb50e},
pmid = {36669202},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation/methods ; *Visual Cortex ; },
abstract = {Objective.Existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) struggle to balance user experience and system performance. This study proposed an individualized space and phase modulation method to code imperceptible flickers at 60 Hz towards a user-friendly SSVEP-based BCI with high performance.Approach.The individualized customization of visual stimulation took the subject-to-subject variability in cortex geometry into account. An annulus global-stimulation was divided into local-stimulations of eight annular sectors and presented to subjects separately. The local-stimulation SSVEPs were superimposed to simulate global-stimulation SSVEPs with 4[7]space and phase coding combinations. A four-class phase-coded BCI diagram was used to evaluate the simulated classification performance. The performance ranking of all simulated global-stimulation SSVEPs were obtained and three performance levels (optimal, medium, worst) of individualized modulation groups were searched for each subject. The standard-modulation group conforming to the V1 'cruciform' geometry and the non-modulation group were involved as controls. A four-target phase-coded BCI system with SSVEPs at 60 Hz was implemented with the five modulation groups and questionnaires were used to evaluate user experience.Main results.The proposed individualized space and phase modulation method effectively modulated the SSVEP intensity without affecting the user experience. The online BCI system using the 60 Hz stimuli achieved mean information transfer rates of 52.8 ± 1.9 bits min[-1], 16.8 ± 2.4 bits min[-1], and 42.4 ± 3.0 bits min[-1]with individualized optimal-modulation, individualized worst-modulation, and non-modulation groups, respectively.Significance.Structural and functional characteristics of the human visual cortex were exploited to enhance the response intensity of SSVEPs at 60 Hz, resulting in a high-performance BCI system with good user experience. This study has important theoretical significance and application value for promoting the development of the visual BCI technology.},
}
@article {pmid36662378,
year = {2023},
author = {Li, J and Wang, J and Wang, T and Kong, W and Xi, X},
title = {Quantification of body ownership awareness induced by the visual movement illusion of the lower limbs: a study of electroencephalogram and surface electromyography.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {5},
pages = {951-965},
pmid = {36662378},
issn = {1741-0444},
support = {61971169//National Natural Science Foundation of China/ ; 62061044//National Natural Science Foundation of China/ ; U20B2074//National Natural Science Foundation of China/ ; 2021C03031//Zhejiang Provincial Key Research and Development Program of China/ ; LQ21H180005//Zhejiang Provincial Natural Science Foundation of China/ ; GK199900299012-016//Fundamental Research Funds for the Provincial Universities of Zhejiang/ ; },
mesh = {Humans ; Electromyography ; *Illusions/physiology ; Ownership ; Electroencephalography ; Movement/physiology ; Lower Extremity ; Hand/physiology ; },
abstract = {The visual movement illusion (VMI) is a subjective experience. This illusion is produced by watching the subject's motion video. At the same time, VMI evokes awareness of body ownership. We applied the power spectral density (PSD) matrix and the partial directed correlation (PDC) matrix to build the PPDC matrix for the γ2 band (34-98.5 Hz), combining cerebral cortical and musculomotor cortical complexity and PPDC to quantify the degree of body ownership. Thirty-five healthy subjects were recruited to participate in this experiment. The subjects' electroencephalography (EEG) and surface electromyography (sEMG) data were recorded under resting conditions, observation conditions, illusion conditions, and actual seated front-kick movements. The results show the following: (1) VMI activates the cerebral cortex to some extent; (2) VMI enhances cortical muscle excitability in the rectus femoris and medial vastus muscles; (3) VMI induces a sense of body ownership; (4) the use of PPDC values, fuzzy entropy values of muscles, and fuzzy entropy values of the cerebral cortex can quantify whether VMI induces awareness of body ownership. These results illustrate that PPDC can be used as a biomarker to show that VMI affects changes in the cerebral cortex and as a quantitative tool to show whether body ownership awareness arises.},
}
@article {pmid36662082,
year = {2023},
author = {Karbalaei Akbari, M and Siraj Lopa, N and Shahriari, M and Najafzadehkhoee, A and Galusek, D and Zhuiykov, S},
title = {Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing.},
journal = {Journal of functional biomaterials},
volume = {14},
number = {1},
pages = {},
pmid = {36662082},
issn = {2079-4983},
abstract = {Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.},
}
@article {pmid36658415,
year = {2023},
author = {Gu, J and Jiang, J and Ge, S and Wang, H},
title = {Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {5},
pages = {1083-1092},
pmid = {36658415},
issn = {1741-0444},
support = {62176054//National Natural Science Foundation of China/ ; BE2022157//Key Research and Development Plan (Industry Foresight and Common Key Technology) of Jiangsu Province, China/ ; },
mesh = {*Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods ; Imagination ; },
abstract = {The common spatial patterns (CSP) technique is an effective strategy for the classification of multichannel electroencephalogram (EEG) signals. However, the objective function expression of the conventional CSP algorithm is based on the L2-norm, which makes the performance of the method easily affected by outliers and noise. In this paper, we consider a new extension to CSP, which is termed capped L21-norm-based common spatial patterns (CCSP-L21), by using the capped L21-norm rather than the L2-norm for robust modeling. L21-norm considers the L1-norm sum which largely alleviates the influence of outliers and noise for the sake of robustness. The capped norm is further used to mitigate the effects of extreme outliers whose signal amplitude is much higher than that of the normal signal. Moreover, a non-greedy iterative procedure is derived to solve the proposed objective function. The experimental results show that the proposed method achieves the highest average recognition rates on the three real data sets of BCI competitions, which are 91.67%, 85.07%, and 82.04%, respectively. Capped L21-norm-based common spatial patterns-a robust model for EEG signals classification.},
}
@article {pmid36657633,
year = {2023},
author = {Perez-Garcia, G and Bicak, M and Haure-Mirande, JV and Perez, GM and Otero-Pagan, A and Gama Sosa, MA and De Gasperi, R and Sano, M and Barlow, C and Gage, FH and Readhead, B and Ehrlich, ME and Gandy, S and Elder, GA},
title = {BCI-838, an orally active mGluR2/3 receptor antagonist pro-drug, rescues learning behavior deficits in the PS19 MAPT[P301S] mouse model of tauopathy.},
journal = {Neuroscience letters},
volume = {797},
number = {},
pages = {137080},
pmid = {36657633},
issn = {1872-7972},
support = {I01 RX002333/RX/RRD VA/United States ; P30 AG072980/AG/NIA NIH HHS/United States ; I01 RX000684/RX/RRD VA/United States ; P30 AG019610/AG/NIA NIH HHS/United States ; I21 RX003459/RX/RRD VA/United States ; I01 BX004067/BX/BLRD VA/United States ; I01 RX002660/RX/RRD VA/United States ; P30 AG066514/AG/NIA NIH HHS/United States ; I01 RX003846/RX/RRD VA/United States ; },
mesh = {Male ; Mice ; Humans ; Animals ; *Prodrugs/therapeutic use ; *Tauopathies/pathology ; tau Proteins/genetics ; *Receptors, Metabotropic Glutamate ; *Alzheimer Disease/pathology ; Mice, Transgenic ; Disease Models, Animal ; },
abstract = {Tauopathies are a heterogeneous group of neurodegenerative disorders that are clinically and pathologically distinct from Alzheimer's disease (AD) having tau inclusions in neurons and/or glia as their most prominent neuropathological feature. BCI-838 (MGS00210) is a group II metabotropic glutamate receptor (mGluR2/3) antagonist pro-drug. Previously, we reported that orally administered BCI-838 improved learning behavior and reduced anxiety in Dutch (APP[E693Q]) transgenic mice, a model of the pathological accumulation of Aβ oligomers found in AD. Herein, we investigated effects of BCI-838 on PS19 male mice that express the tauopathy mutation MAPT[P301S] associated with human frontotemporal lobar degeneration (FTLD). These mice develop an aging-related tauopathy without amyloid accumulation. Mice were divided into three experimental groups: (1) non-transgenic wild type mice treated with vehicle, (2) PS19 mice treated with vehicle and (3) PS19 mice treated with 5 mg/kg BCI-838. Groups of 10-13 mice were utilized. Vehicle or BCI-838 was administered by oral gavage for 4 weeks. Behavioral testing consisting of a novel object recognition task was conducted after drug administration. Two studies were performed beginning treatment of mice at 3 or 7 months of age. One month of BCI-838 treatment rescued deficits in recognition memory in PS19 mice whether treatment was begun at 3 or 7 months of age. These studies extend the potential utility of BCI-838 to neurodegenerative conditions that have tauopathy as their underlying basis. They also suggest an mGluR2/3 dependent mechanism as a basis for the behavioral deficits in PS19 mice.},
}
@article {pmid36657242,
year = {2023},
author = {Proverbio, AM and Tacchini, M and Jiang, K},
title = {What do you have in mind? ERP markers of visual and auditory imagery.},
journal = {Brain and cognition},
volume = {166},
number = {},
pages = {105954},
doi = {10.1016/j.bandc.2023.105954},
pmid = {36657242},
issn = {1090-2147},
mesh = {Animals ; Humans ; Male ; Female ; *Evoked Potentials ; *Electroencephalography ; Imagination/physiology ; Auditory Perception ; },
abstract = {This study aimed to investigate the psychophysiological markers of imagery processes through EEG/ERP recordings. Visual and auditory stimuli representing 10 different semantic categories were shown to 30 healthy participants. After a given interval and prompted by a light signal, participants were asked to activate a mental image corresponding to the semantic category for recording synchronized electrical potentials. Unprecedented electrophysiological markers of imagination were recorded in the absence of sensory stimulation. The following peaks were identified at specific scalp sites and latencies, during imagination of infants (centroparietal positivity, CPP, and late CPP), human faces (anterior negativity, AN), animals (anterior positivity, AP), music (P300-like), speech (N400-like), affective vocalizations (P2-like) and sensory (visual vs auditory) modality (PN300). Overall, perception and imagery conditions shared some common electro/cortical markers, but during imagery the category-dependent modulation of ERPs was long latency and more anterior, with respect to the perceptual condition. These ERP markers might be precious tools for BCI systems (pattern recognition, classification, or A.I. algorithms) applied to patients affected by consciousness disorders (e.g., in a vegetative or comatose state) or locked-in-patients (e.g., spinal or SLA patients).},
}
@article {pmid36656873,
year = {2023},
author = {Pang, J and Peng, S and Hou, C and Zhao, H and Fan, Y and Ye, C and Zhang, N and Wang, T and Cao, Y and Zhou, W and Sun, D and Wang, K and Rümmeli, MH and Liu, H and Cuniberti, G},
title = {Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles.},
journal = {ACS sensors},
volume = {8},
number = {2},
pages = {482-514},
doi = {10.1021/acssensors.2c02790},
pmid = {36656873},
issn = {2379-3694},
mesh = {Humans ; *Graphite ; Touch ; Muscles ; Nervous System ; },
abstract = {Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.},
}
@article {pmid36655886,
year = {2022},
author = {Breen, JR and Pensini, P},
title = {Grounded by Mother Nature's Revenge.},
journal = {Experimental psychology},
volume = {69},
number = {5},
pages = {284-294},
doi = {10.1027/1618-3169/a000566},
pmid = {36655886},
issn = {2190-5142},
mesh = {Humans ; Female ; Middle Aged ; Adolescent ; Male ; *COVID-19/epidemiology ; Australia/epidemiology ; Attitude ; Travel ; Emotions ; },
abstract = {Leisure air travel is a popular form of tourism, but its emissions are a major contributor to anthropogenic climate change. Restrictions to leisure air travel have previously received little support; however, the same restrictions to mitigate the spread of COVID-19 have been popular. This support is unlikely to persist in a postpandemic world, highlighting the need for alternative ways to improve support for reducing leisure air travel. Anthropomorphism of nature has consistently predicted proenvironmental behavior, which has been mediated by guilt felt for harm to the environment. This research is the first empirical study to explore this relationship in the context of COVID-19, where it examined support for restricting leisure air travel to help mitigate (1) COVID-19 and (2) climate change. In an experimental online study, Australian residents (N = 325, Mage = 54.48, SDage = 14.63, 62% women) were recruited through social media. Anthropomorphism of nature in the context of COVID-19 (AMP-19) was manipulated through exposure to a news article. Participants then completed measures of environmental guilt and support for restricting leisure air travel to mitigate COVID-19 (LAT-19) and to mitigate climate change (LAT-CC). A significant indirect effect was observed in both models, such that AMP-19 predicted environmental guilt which in turn predicted LAT-19 (f[2] = .26; BCI [0.66, 3.87]) and LAT-CC (f[2] = .45; BCI [0.84, 5.06]). The results imply that anthropomorphism of nature in the context of COVID-19 can improve attitudes toward this proenvironmental behavior, with greater support when this was to mitigate climate change. Implications are discussed.},
}
@article {pmid36654858,
year = {2023},
author = {Jin, S and Chen, X and Zheng, H and Cai, W and Lin, X and Kong, X and Ni, Y and Ye, J and Li, X and Shen, L and Guo, B and Abdelrahman, Z and Zhou, S and Mao, S and Wang, Y and Yao, C and Gu, X and Yu, B and Wang, Z and Wang, X},
title = {Downregulation of UBE4B promotes CNS axon regrowth and functional recovery after stroke.},
journal = {iScience},
volume = {26},
number = {1},
pages = {105885},
pmid = {36654858},
issn = {2589-0042},
abstract = {The limited intrinsic regrowth capacity of corticospinal axons impedes functional recovery after cortical stroke. Although the mammalian target of rapamycin (mTOR) and p53 pathways have been identified as the key intrinsic pathways regulating CNS axon regrowth, little is known about the key upstream regulatory mechanism by which these two major pathways control CNS axon regrowth. By screening genes that regulate ubiquitin-mediated degradation of the p53 proteins in mice, we found that ubiquitination factor E4B (UBE4B) represses axonal regrowth in retinal ganglion cells and corticospinal neurons. We found that axonal regrowth induced by UBE4B depletion depended on the cooperative activation of p53 and mTOR. Importantly, overexpression of UbV.E4B, a competitive inhibitor of UBE4B, in corticospinal neurons promoted corticospinal axon sprouting and facilitated the recovery of corticospinal axon-dependent function in a cortical stroke model. Thus, our findings provide a translatable strategy for restoring corticospinal tract-dependent functions after cortical stroke.},
}
@article {pmid36652620,
year = {2023},
author = {Abbasi, J and Suran, M},
title = {From Thought to Text: How an Endovascular Brain-Computer Interface Could Help Patients With Severe Paralysis Communicate.},
journal = {JAMA},
volume = {329},
number = {5},
pages = {360-362},
doi = {10.1001/jama.2022.24343},
pmid = {36652620},
issn = {1538-3598},
mesh = {Humans ; *Brain-Computer Interfaces ; Paralysis ; User-Computer Interface ; },
}
@article {pmid36652475,
year = {2023},
author = {Liang, W and Balasubramanian, K and Papadourakis, V and Hatsopoulos, NG},
title = {Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {120},
number = {4},
pages = {e2212227120},
pmid = {36652475},
issn = {1091-6490},
support = {R01 NS111982/NS/NINDS NIH HHS/United States ; R35 NS122333/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Motor Cortex ; Macaca mulatta ; Biomechanical Phenomena ; Movement ; *Brain-Computer Interfaces ; Action Potentials ; },
abstract = {Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400 Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.},
}
@article {pmid36650644,
year = {2023},
author = {Jiang, J and Fu, Y and Tang, A and Gao, X and Zhang, D and Shen, Y and Mou, T and Hu, S and Gao, J and Lai, J},
title = {Sex difference in prebiotics on gut and blood-brain barrier dysfunction underlying stress-induced anxiety and depression.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {29 Suppl 1},
number = {Suppl 1},
pages = {115-128},
pmid = {36650644},
issn = {1755-5949},
support = {2020R01001//Innovation Group Program of Zhejiang Province/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation - "Ten Thousand Talents Program" of Zhejiang Province/ ; 81971271//National Natural Science Foundation of China/ ; LQ20H090013//Natural Science Foundation of Zhejiang Province/ ; 2023KY701//the Health and Family Planning Commission of Zhejiang Province/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; },
mesh = {Female ; Male ; Mice ; Animals ; *Prebiotics ; Blood-Brain Barrier/metabolism ; Depression/etiology/metabolism ; Sex Characteristics ; Anxiety/etiology ; *Anti-Anxiety Agents ; Cytokines/metabolism ; Oligosaccharides/pharmacology ; },
abstract = {BACKGROUND: Most of the previous studies have demonstrated the potential antidepressive and anxiolytic role of prebiotic supplement in male subjects, yet few have females enrolled. Herein, we explored whether prebiotics administration during chronic stress prevented depression-like and anxiety-like behavior in a sex-specific manner and the mechanism of behavioral differences caused by sex.<br><br>METHODS: Female and male C57 BL/J mice on normal diet were supplemented with or without a combination of fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS) during 3- and 4-week chronic restraint stress (CRS) treatment, respectively. C57 BL/J mice on normal diet without CRS were used as controls. Behavior consequences, gut microbiota, dysfunction of gut and brain-blood barriers, and inflammatory profiles were measured.<br><br>RESULTS: In the 3rd week, FOS&#x2009;+&#x2009;GOS administration attenuated stress-induced anxiety-like behavior in female, but not in male mice, and the anxiolytic effects in males were observed until the 4th week. However, protective effects of prebiotics on CRS-induced depression were not observed. Changes in the gene expression of tight junction proteins in the distal colon and hippocampus, and decreased number of colon goblet cells following CRS were restored by prebiotics only in females. In both female and male mice, prebiotics alleviated stress-induced BBB dysfunction and elevation in pro-inflammatory cytokines levels, and modulated gut microbiota caused by stress. Furthermore, correlation analysis revealed that anxiety-like behaviors were significantly correlated with levels of pro-inflammatory cytokines and gene expression of tight junction proteins in the hippocampus of female mice, and the abundance of specific gut microbes was also correlated with anxiety-like behaviors, pro-inflammatory cytokines, and gene expression of tight junction proteins in the hippocampus of female mice.<br><br>CONCLUSION: Female mice were more vulnerable to stress and prebiotics than males. The gut microbiota, gut and blood-brain barrier, and inflammatory response may mediate the protective effects of prebiotics on anxiety-like behaviors in female mice.},
}
@article {pmid36650410,
year = {2023},
author = {Zhang, J and Wang, X and Xu, B and Wu, Y and Lou, X and Shen, X},
title = {An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {5},
pages = {1047-1056},
pmid = {36650410},
issn = {1741-0444},
support = {81371663//National Natural Science Foundation of China/ ; 61534003//National Natural Science Foundation of China/ ; SWYY-116//Six Talent Peaks Project in Jiangsu Province/ ; KYCX21_3085//Graduate Research and Innovation Projects of Jiangsu Province/ ; },
mesh = {Humans ; *Imagery, Psychotherapy ; Foot ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; Imagination ; },
abstract = {The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.},
}
@article {pmid36645915,
year = {2023},
author = {Li, M and Zuo, H and Zhou, H and Xu, G and Qi, E},
title = {A study of action difference on motor imagery based on delayed matching posture task.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb386},
pmid = {36645915},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Evoked Potentials ; Imagery, Psychotherapy ; *Brain-Computer Interfaces ; Imagination ; Posture ; },
abstract = {Objective. Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.Approach.To explore action differences, this study proposes a novel paradigm named as action observation based delayed matching posture task. Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.Main results.Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p< 0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28μV of grasping, 5.64μV and 5.25μV higher than the cutting and the writing, respectively (p< 0.05).Significance.The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.},
}
@article {pmid36645913,
year = {2023},
author = {Valencia, D and Leone, G and Keller, N and Mercier, PP and Alimohammad, A},
title = {Power-efficientin vivobrain-machine interfaces via brain-state estimation.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb385},
pmid = {36645913},
issn = {1741-2552},
mesh = {*Quality of Life ; Brain ; Prostheses and Implants ; Computers ; *Brain-Computer Interfaces ; },
abstract = {Objective.Advances in brain-machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.Approach.To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of anin vivointention-aware interface via brain-state estimation.Main Results.It is shown that incorporating brain-state estimation reduces thein vivopower consumption and reduces total energy dissipation by over 1.8× compared to those of the current systems, enabling longer better life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180 nm CMOS process occupies 0.03 mm[2]of silicon area and consumes 0.63 µW of power per channel, which is the least power consumption among the currentin vivoASIC realizations.Significance.The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs.},
}
@article {pmid36644311,
year = {2022},
author = {Sui, Y and Yu, H and Zhang, C and Chen, Y and Jiang, C and Li, L},
title = {Deep brain-machine interfaces: sensing and modulating the human deep brain.},
journal = {National science review},
volume = {9},
number = {10},
pages = {nwac212},
pmid = {36644311},
issn = {2053-714X},
abstract = {Different from conventional brain-machine interfaces that focus more on decoding the cerebral cortex, deep brain-machine interfaces enable interactions between external machines and deep brain structures. They sense and modulate deep brain neural activities, aiming at function restoration, device control and therapeutic improvements. In this article, we provide an overview of multiple deep brain recording and stimulation techniques that can serve as deep brain-machine interfaces. We highlight two widely used interface technologies, namely deep brain stimulation and stereotactic electroencephalography, for technical trends, clinical applications and brain connectivity research. We discuss the potential to develop closed-loop deep brain-machine interfaces and achieve more effective and applicable systems for the treatment of neurological and psychiatric disorders.},
}
@article {pmid36643889,
year = {2023},
author = {Alharbi, H},
title = {Identifying Thematics in a Brain-Computer Interface Research.},
journal = {Computational intelligence and neuroscience},
volume = {2023},
number = {},
pages = {2793211},
pmid = {36643889},
issn = {1687-5273},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Artificial Intelligence ; Software ; *Neurosciences ; User-Computer Interface ; },
abstract = {This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020-2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.},
}
@article {pmid36639665,
year = {2023},
author = {Pichiorri, F and Toppi, J and de Seta, V and Colamarino, E and Masciullo, M and Tamburella, F and Lorusso, M and Cincotti, F and Mattia, D},
title = {Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {20},
number = {1},
pages = {5},
pmid = {36639665},
issn = {1743-0003},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation ; Electroencephalography ; *Stroke ; Upper Extremity ; Electromyography ; },
abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) promote upper limb recovery in stroke patients reinforcing motor related brain activity (from electroencephalogaphy, EEG). Hybrid BCIs which include peripheral signals (electromyography, EMG) as control features could be employed to monitor post-stroke motor abnormalities. To ground the use of corticomuscular coherence (CMC) as a hybrid feature for a rehabilitative BCI, we analyzed high-density CMC networks (derived from multiple EEG and EMG channels) and their relation with upper limb motor deficit by comparing data from stroke patients with healthy participants during simple hand tasks.<br><br>METHODS: EEG (61 sensors) and EMG (8 muscles per arm) were simultaneously recorded from 12 stroke (EXP) and 12 healthy participants (CTRL) during simple hand movements performed with right/left (CTRL) and unaffected/affected hand (EXP, UH/AH). CMC networks were estimated for each movement and their properties were analyzed by means of indices derived ad-hoc from graph theory and compared among groups.<br><br>RESULTS: Between-group analysis showed that CMC weight of the whole brain network was significantly reduced in patients during AH movements. The network density was increased especially for those connections entailing bilateral non-target muscles. Such reduced muscle-specificity observed in patients was confirmed by muscle degree index (connections per muscle) which indicated a connections' distribution among non-target and contralateral muscles and revealed a higher involvement of proximal muscles in patients. CMC network properties correlated with upper-limb motor impairment as assessed by Fugl-Meyer Assessment and Manual Muscle Test in patients.<br><br>CONCLUSIONS: High-density CMC networks can capture motor abnormalities in stroke patients during simple hand movements. Correlations with upper limb motor impairment support their use in a BCI-based rehabilitative approach.},
}
@article {pmid36639237,
year = {2023},
author = {Rubin, DB and Ajiboye, AB and Barefoot, L and Bowker, M and Cash, SS and Chen, D and Donoghue, JP and Eskandar, EN and Friehs, G and Grant, C and Henderson, JM and Kirsch, RF and Marujo, R and Masood, M and Mernoff, ST and Miller, JP and Mukand, JA and Penn, RD and Shefner, J and Shenoy, KV and Simeral, JD and Sweet, JA and Walter, BL and Williams, ZM and Hochberg, LR},
title = {Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System.},
journal = {Neurology},
volume = {100},
number = {11},
pages = {e1177-e1192},
pmid = {36639237},
issn = {1526-632X},
support = {R01 NS062092/NS/NINDS NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; IK6 RX004284/RX/RRD VA/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; R25 NS065743/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Humans ; Feasibility Studies ; Prospective Studies ; *Brain-Computer Interfaces ; Quadriplegia ; *Spinal Cord Injuries/surgery ; },
abstract = {BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, nonrandomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI.<br><br>METHODS: Adults aged 18-75 years with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through 7 clinical sites in the United States. Participants underwent surgical implantation of 1 or 2 microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events (SAEs) requiring device explantation or resulting in death or permanently increased disability during the 1-year postimplant evaluation period. The secondary outcomes included the type and frequency of other adverse events and the feasibility of the BrainGate system for controlling a computer or other assistive technologies.<br><br>RESULTS: From 2004 to 2021, 14 adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related SAEs. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device.<br><br>DISCUSSION: The BrainGate Neural Interface system has a safety record comparable with other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development.<br><br>ClinicalTrials.gov Identifier: NCT00912041.<br><br>CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explantation, resulting in death, or resulting in permanently increased disability during the 1-year postimplant period.},
}
@article {pmid36638268,
year = {2023},
author = {Wu, J and Chen, C and Qin, C and Li, Y and Jiang, N and Yuan, Q and Duan, Y and Liu, M and Wei, X and Yu, Y and Zhuang, L and Wang, P},
title = {Mimicking the Biological Sense of Taste In Vitro Using a Taste Organoids-on-a-Chip System.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {7},
pages = {e2206101},
pmid = {36638268},
issn = {2198-3844},
support = {2020AAA0105900//National Key Research and Development Program of China/ ; 62120106004//National Natural Science Foundation of China/ ; 62271443//National Natural Science Foundation of China/ ; LBY21H180001//Natural Science Foundation of Zhejiang Province/ ; LY21C100001//Natural Science Foundation of Zhejiang Province/ ; 2021M702859//Postdoctoral Research Foundation of China/ ; 2021T140605//Postdoctoral Research Foundation of China/ ; },
mesh = {Humans ; *Taste ; *Microphysiological Systems ; Tongue ; Cell Survival ; Drug Evaluation, Preclinical ; },
abstract = {Thanks to the gustatory system, humans can experience the flavors in foods and drinks while avoiding the intake of some harmful substances. Although great advances in the fields of biotechnology, microfluidics, and nanotechnologies have been made in recent years, this astonishing recognition system can hardly be replaced by any artificial sensors designed so far. Here, taste organoids are coupled with an extracellular potential sensor array to form a novel bioelectronic organoid and developed a taste organoids-on-a-chip system (TOS) for highly mimicking the biological sense of taste ex vivo with high stability and repeatability. The taste organoids maintain key taste receptors expression after the third passage and high cell viability during 7 days of on-chip culture. Most importantly, the TOS not only distinguishs sour, sweet, bitter, and salt stimuli with great specificity, but also recognizes varying concentrations of the stimuli through an analytical method based on the extraction of signal features and principal component analysis. It is hoped that this bioelectronic tongue can facilitate studies in food quality controls, disease modelling, and drug screening.},
}
@article {pmid36637269,
year = {2023},
author = {Hu, J and Wang, Y and Zhu, Y and Li, Y and Chen, J and Zhang, Y and Xu, D and Bai, R and Wang, L},
title = {Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease.},
journal = {Neurosurgery},
volume = {92},
number = {2},
pages = {344-352},
pmid = {36637269},
issn = {1524-4040},
mesh = {Humans ; *Moyamoya Disease/diagnostic imaging/surgery ; Brain/diagnostic imaging/surgery ; Magnetic Resonance Imaging ; Cerebral Infarction ; *Cerebral Revascularization ; },
abstract = {BACKGROUND: In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.<br><br>OBJECTIVE: To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.<br><br>METHODS: We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.<br><br>RESULTS: Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.<br><br>CONCLUSION: Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.},
}
@article {pmid36636754,
year = {2023},
author = {Hudson, HM and Guggenmos, DJ and Azin, M and Vitale, N and McKenzie, KA and Mahnken, JD and Mohseni, P and Nudo, RJ},
title = {Broad Therapeutic Time Window for Driving Motor Recovery After TBI Using Activity-Dependent Stimulation.},
journal = {Neurorehabilitation and neural repair},
volume = {37},
number = {6},
pages = {384-393},
doi = {10.1177/15459683221145144},
pmid = {36636754},
issn = {1552-6844},
mesh = {Male ; Animals ; Rats ; Time Factors ; *Motor Disorders/etiology/therapy ; Motor Cortex ; Brain Injuries, Traumatic/complications ; Recovery of Function ; Behavior, Animal ; Electric Stimulation Therapy ; },
abstract = {BACKGROUND: After an acquired injury to the motor cortex, the ability to generate skilled movements is impaired, leading to long-term motor impairment and disability. While rehabilitative therapy can improve outcomes in some individuals, there are no treatments currently available that are able to fully restore lost function.<br><br>OBJECTIVE: We previously used activity-dependent stimulation (ADS), initiated immediately after an injury, to drive motor recovery. The objective of this study was to determine if delayed application of ADS would still lead to recovery and if the recovery would persist after treatment was stopped.<br><br>METHODS: Rats received a controlled cortical impact over primary motor cortex, microelectrode arrays were implanted in ipsilesional premotor and somatosensory areas, and a custom brain-machine interface was attached to perform the ADS. Stimulation was initiated either 1, 2, or 3&#x2009;weeks after injury and delivered constantly over a 4-week period. An additional group was monitored for 8&#x2009;weeks after terminating ADS to assess persistence of effect. Results were compared to rats receiving no stimulation.<br><br>RESULTS: ADS was delayed up to 3&#x2009;weeks from injury onset and still resulted in significant motor recovery, with maximal recovery occurring in the 1-week delay group. The improvements in motor performance persisted for at least 8&#x2009;weeks following the end of treatment.<br><br>CONCLUSIONS: ADS is an effective method to treat motor impairments following acquired brain injury in rats. This study demonstrates the clinical relevance of this technique as it could be initiated in the post-acute period and could be explanted/ceased once recovery has occurred.},
}
@article {pmid36636584,
year = {2022},
author = {Truong, MT and Liu, YC and Kohn, J and Chinnadurai, S and Zopf, DA and Tribble, M and Tanner, PB and Sie, K and Chang, KW},
title = {Integrated microtia and aural atresia management.},
journal = {Frontiers in surgery},
volume = {9},
number = {},
pages = {944223},
pmid = {36636584},
issn = {2296-875X},
abstract = {OBJECTIVES: To present recommendations for the coordinated evaluation and management of the hearing and reconstructive needs of patients with microtia and aural atresia.<br><br>METHODS: A national working group of 9 experts on microtia and atresia evaluated a working document on the evaluation and treatment of patients. Treatment options for auricular reconstruction and hearing habilitation were reviewed and integrated into a coordinated care timeline.<br><br>RESULTS: Recommendations were created for children with microtia and atresia, including diagnostic considerations, surgical and non-surgical options for hearing management and auricular reconstruction, and the treatment timeline for each option. These recommendations are based on the collective opinion of the group and are intended for otolaryngologists, audiologists, plastic surgeons, anaplastologists, and any provider caring for a patient with microtia and ear canal atresia. Close communication between atresia/hearing reconstruction surgeon and microtia repair surgeon is strongly recommended.},
}
@article {pmid36635340,
year = {2023},
author = {Daly, I},
title = {Neural decoding of music from the EEG.},
journal = {Scientific reports},
volume = {13},
number = {1},
pages = {624},
pmid = {36635340},
issn = {2045-2322},
mesh = {Humans ; *Brain Mapping/methods ; *Music ; Electroencephalography ; Auditory Perception ; Auscultation ; },
abstract = {Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% ([Formula: see text], [Formula: see text]). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% ([Formula: see text], [Formula: see text]). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.},
}
@article {pmid36634598,
year = {2023},
author = {Zhu, S and Hosni, SI and Huang, X and Wan, M and Borgheai, SB and McLinden, J and Shahriari, Y and Ostadabbas, S},
title = {A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.},
journal = {Computers in biology and medicine},
volume = {153},
number = {},
pages = {106498},
doi = {10.1016/j.compbiomed.2022.106498},
pmid = {36634598},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Brain ; Electroencephalography/methods ; Algorithms ; Imagination ; },
abstract = {Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics. Moreover, existing graph theoretic approaches are mostly used to reveal the topological patterns of brain functional networks based on synchronization between signals from distinctive spatial regions, instead of interdependence between states at different timestamps. In this study, we present a robust fold-wise hyperparameter optimization framework utilizing a series of conventional graph-based measurements combined with spectral graph features and investigate its discriminative performance on classification of a designed mental task in 6 participants with amyotrophic lateral sclerosis (ALS). Across all of our participants, we reached an average accuracy of 71.1%±4.5% for mental task classification by combining the global graph-based measurements and the spectral graph features, higher than the conventional non-graph based feature performance (67.1%±7.5%). Compared to using either one of the graphic features (66.3%±6.5% for the eigenvalues and 65.9%±5.2% for the global graph features), our feature combination strategy shows considerable improvement in both accuracy and robustness performance. Our results indicate the feasibility and advantage of the presented fold-wise optimization framework utilizing graph-based features in BCI systems targeted at end-users.},
}
@article {pmid36633302,
year = {2023},
author = {Sample, M and Sattler, S and Boehlen, W and Racine, E},
title = {Brain-computer interfaces, disability, and the stigma of refusal: A factorial vignette study.},
journal = {Public understanding of science (Bristol, England)},
volume = {32},
number = {4},
pages = {522-542},
pmid = {36633302},
issn = {1361-6609},
mesh = {Humans ; *Brain-Computer Interfaces ; Social Stigma ; Attitude ; Technology ; Emotions ; },
abstract = {As brain-computer interfaces are promoted as assistive devices, some researchers worry that this promise to "restore" individuals worsens stigma toward disabled people and fosters unrealistic expectations. In three web-based survey experiments with vignettes, we tested how refusing a brain-computer interface in the context of disability affects cognitive (blame), emotional (anger), and behavioral (coercion) stigmatizing attitudes (Experiment 1, N = 222) and whether the effect of a refusal is affected by the level of brain-computer interface functioning (Experiment 2, N = 620) or the risk of malfunctioning (Experiment 3, N = 620). We found that refusing a brain-computer interface increased blame and anger, while brain-computer interface functioning did change the effect of a refusal. Higher risks of device malfunctioning partially reduced stigmatizing attitudes and moderated the effect of refusal. This suggests that information about disabled people who refuse a technology can increase stigma toward them. This finding has serious implications for brain-computer interface regulation, media coverage, and the prevention of ableism.},
}
@article {pmid36630716,
year = {2023},
author = {Abrego, AM and Khan, W and Wright, CE and Islam, MR and Ghajar, MH and Bai, X and Tandon, N and Seymour, JP},
title = {Sensing local field potentials with a directional and scalable depth electrode array.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb230},
pmid = {36630716},
issn = {1741-2552},
support = {UG3 NS125487/NS/NINDS NIH HHS/United States ; },
mesh = {Rats ; Animals ; *Electroencephalography/methods ; Brain/physiology ; *Brain-Computer Interfaces ; Microelectrodes ; },
abstract = {Objective. A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (a) access to all cortical regions in a minimally invasive manner; (b) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (c) access to spatially distant multiple brain regions that constitute distributed cognitive networks.Approach.We modeled, designed, and demonstrated a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode and combined with radially distributed microelectrodes.Main results. Electro-quasistatic models demonstrate that the lead body amplifies and shields LFP sources based on direction, enablingdirectional sensitivity andscalability, referred to as thedirectional andscalable (DISC) array.In vivo,DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critical for future translation, DISC demonstrated a higher signal to noise ratio (SNR) than virtual ring electrodes and a noise floor approaching that of large ring electrodes in an unshielded environment after common average referencing. DISC also revealed independent, stereoscopic current source density measures whose direction was verified after histology.Significance. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting.},
}
@article {pmid36630714,
year = {2023},
author = {Guo, Z and Chen, F},
title = {Impacts of simplifying articulation movements imagery to speech imagery BCI performance.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb232},
pmid = {36630714},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Speech/physiology ; Imagery, Psychotherapy ; Brain/physiology ; Movement ; Electroencephalography/methods ; Imagination/physiology ; },
abstract = {Objective.Speech imagery (SI) can be used as a reliable, natural, and user-friendly activation task for the development of brain-computer interface (BCI), which empowers individuals with severe disabilities to interact with their environment. The functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, insensitive to motion artifacts, and having relatively high spatial resolution.Approach.To improve the classification performance of SI BCI based on fNIRS, a novel paradigm was developed in this work by simplifying the articulation movements in SI to make the articulation movement differences clearer between different words imagery tasks. A SI BCI was proposed to directly answer questions by covertly rehearsing the word '' or '' ('yes' or 'no' in English), and an unconstrained rest task also was contained in this BCI. The articulation movements of SI were simplified by retaining only the movements of the jaw and lips of vowels in Chinese Pinyin for words '' and ''.Main results.Compared with conventional speech imagery, simplifying the articulation movements in SI could generate more different brain activities among different tasks, which led to more differentiable temporal features and significantly higher classification performance. The average 3-class classification accuracies of the proposed paradigm across all 20 participants reached 69.6% and 60.2% which were about 10.8% and 5.6% significantly higher than those of the conventional SI paradigm operated in the 0-10 s and 0-2.5 s time windows, respectively.Significance.These results suggested that simplifying the articulation movements in SI is promising for improving the classification performance of intuitive BCIs based on speech imagery.},
}
@article {pmid36628907,
year = {2023},
author = {Lai, JB and Kong, LZ and Chen, J and Hu, SH},
title = {From strict quarantine to an optimized policy: Are we psychologically prepared?.},
journal = {Asian journal of psychiatry},
volume = {81},
number = {},
pages = {103435},
doi = {10.1016/j.ajp.2022.103435},
pmid = {36628907},
issn = {1876-2026},
mesh = {Humans ; *Quarantine ; *COVID-19 ; SARS-CoV-2 ; Policy ; },
}
@article {pmid36626112,
year = {2023},
author = {Dos Santos, EM and San-Martin, R and Fraga, FJ},
title = {Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {61},
number = {3},
pages = {835-845},
pmid = {36626112},
issn = {1741-0444},
support = {#2015/09510-7//FAPESP/ ; #2017/15243-7//FAPESP/ ; },
mesh = {Humans ; *Electroencephalography ; Support Vector Machine ; *Brain-Computer Interfaces ; Discriminant Analysis ; Imagery, Psychotherapy ; Imagination ; Algorithms ; },
abstract = {Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.},
}
@article {pmid36625869,
year = {2023},
author = {Sprinzl, G and Toner, J and Koitschev, A and Berger, N and Keintzel, T and Rasse, T and Baumgartner, WD and Honeder, C and Magele, A and Plontke, S and Götze, G and Schmutzhard, J and Zelger, P and Corkill, S and Lenarz, T and Salcher, R},
title = {Multicentric study on surgical information and early safety and performance results with the Bonebridge BCI 602: an active transcutaneous bone conduction hearing implant.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {280},
number = {4},
pages = {1565-1579},
pmid = {36625869},
issn = {1434-4726},
mesh = {Adult ; Humans ; Child ; Bone Conduction ; *Brain-Computer Interfaces ; *Hearing Aids ; Hearing ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Hearing Loss, Conductive/surgery ; *Deafness/surgery ; *Hearing Loss/surgery ; *Hearing Loss, Sensorineural/surgery ; *Speech Perception ; Treatment Outcome ; Multicenter Studies as Topic ; },
abstract = {AIM: This European multicentric study aimed to prove safety and performance of the Bonebridge BCI 602 in children and adults suffering from either conductive hearing loss (CHL), mixed hearing loss (MHL), or single-sided sensorineural deafness (SSD).<br><br>METHODS: 33 patients (13 adults and 10 children with either CHL or MHL and 10 patients with SSD) in three study groups were included. Patients were their own controls (single-subject repeated measures), comparing the unaided or pre-operative to the 3-month post-operative outcomes. Performance was evaluated by sound field thresholds (SF), word recognition scores (WRS) and/or speech reception thresholds in quiet (SRT) and in noise (SNR). Safety was demonstrated with a device-specific surgical questionnaire, adverse event reporting and stable pure-tone measurements.<br><br>RESULTS: The Bonebridge BCI 602 significantly improved SF thresholds (+&#x2009;25.5&#xa0;dB CHL/MHL/SSD), speech intelligibility in WRS (+&#x2009;68.0% CHL/MHL) and SRT in quiet (-&#x2009;16.5&#xa0;dB C/MHL) and in noise (-&#xa0;3.51&#xa0;dB SNR SSD). Air conduction (AC) and bone conduction (BC) thresholds remained stable over time. All adverse events were resolved, with none unanticipated. Mean audio processor wearing times in hours [h] per day for the CHL/MHL group were&#x2009;~&#x2009;13&#xa0;h for adults,&#x2009;~&#x2009;11&#xa0;h for paediatrics and&#x2009;~&#x2009;6&#xa0;h for the SSD group. The average surgical length was 57&#xa0;min for the CHL/MHL group and 42&#xa0;min for the SSD group. The versatility of the BCI 602 (reduced drilling depth and ability to bend the transition for optimal placement) allows for treatment of normal, pre-operated and malformed anatomies. All audiological endpoints were reached.<br><br>CONCLUSIONS: The Bonebridge BCI 602 significantly improved hearing thresholds and speech understanding. Since implant placement follows the patient's anatomy instead of the shape of the device and the duration of surgery is shorter than with its predecessor, implantation is easier with the BCI 602. Performance and safety were proven for adults and children as well as for the CHL/MHL and SSD indications 3 months post-operatively.},
}
@article {pmid36624409,
year = {2023},
author = {Rusé, J and Clenet, A and Vaiva, G and Debien, C and Arbus, C and Salles, J},
title = {The association between reattempted suicide and incoming calls to the brief contact intervention service, VigilanS: a study of the clinical profile of callers.},
journal = {BMC psychiatry},
volume = {23},
number = {1},
pages = {21},
pmid = {36624409},
issn = {1471-244X},
mesh = {Humans ; Retrospective Studies ; *Suicide, Attempted/prevention &amp; control ; Risk Factors ; France/epidemiology ; },
abstract = {BACKGROUND: Suicide is a major health problem globally. As attempted suicide is a major risk factor for suicide, specific prevention strategies have been designed for use thereafter. An example is the brief contact intervention (BCI). In this regard, France employs a composite BCI, VigilanS, which utilizes three types of contact: phone calls, postcards and a 'who to contact in a crisis' card. Previous studies have found that this system is effective at preventing suicide. Nevertheless, VigilanS was not effective in the same way for all the patients included. This observation raises the question of specific adaptation during follow-up for populations that were less receptive to the service. In consideration of this issue, we identified one study which found that incoming calls to the service were linked with a higher risk of suicide reattempts. However, this study did not document the profiles of the patients who made these calls. Better understanding of why this population is more at risk is important in terms of identifying factors that could be targeted to improve follow-up. This research therefore aims to bring together such data.<br><br>METHODS: We performed a retrospective analysis of 579 patients referred to VigilanS by Toulouse University Hospital (France). We examined the sociodemographics, clinical characteristics, and follow-ups in place and compared the patients who made incoming calls to the service versus those who did not. Subsequently, we conducted a regression analysis using the significantly associated element of patients calling VigilanS. Then, in order to better understand this association, we analyzed the factors, including such calls, that were linked to the risk of suicide reattempts.<br><br>RESULTS: We found that 22% of the patients in our sample called the VigilanS service. These individuals: were older, at 41.4&#x2009;years versus 37.9&#x2009;years for the non-callers; were more likely to have a borderline personality disorder (BPD) diagnosis (28.9% versus 19.3%); and had a history of suicide attempts (71.9% versus 54.6%). Our analysis confirmed that incoming calls to VigilanS (OR&#x2009;=&#x2009;2.9) were associated with reattempted suicide, as were BPD (OR&#x2009;=&#x2009;1.8) and a history of suicide attempts (OR&#x2009;=&#x2009;1.7).<br><br>CONCLUSION: There was a high risk that the patients calling VigilanS would make another suicide attempt. However, this association was present regardless of the clinical profile. We postulate that this link between incoming calls and reattempted suicide may arise because this form of contact is, in fact, a way in which patients signal that a further attempt will be made.},
}
@article {pmid36622685,
year = {2023},
author = {Mitchell, P and Lee, SCM and Yoo, PE and Morokoff, A and Sharma, RP and Williams, DL and MacIsaac, C and Howard, ME and Irving, L and Vrljic, I and Williams, C and Bush, S and Balabanski, AH and Drummond, KJ and Desmond, P and Weber, D and Denison, T and Mathers, S and O'Brien, TJ and Mocco, J and Grayden, DB and Liebeskind, DS and Opie, NL and Oxley, TJ and Campbell, BCV},
title = {Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study.},
journal = {JAMA neurology},
volume = {80},
number = {3},
pages = {270-278},
pmid = {36622685},
issn = {2168-6157},
support = {UH3 NS120191/NS/NINDS NIH HHS/United States ; },
mesh = {Aged ; Humans ; Male ; Middle Aged ; Brain ; *Brain-Computer Interfaces ; Cerebral Cortex ; Paralysis/etiology ; Prospective Studies ; },
abstract = {IMPORTANCE: Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown.<br><br>OBJECTIVE: To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought.<br><br>The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022.<br><br>INTERVENTIONS: Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control.<br><br>MAIN OUTCOMES AND MEASURES: The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control.<br><br>RESULTS: Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI.<br><br>CONCLUSIONS AND RELEVANCE: Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03834857.},
}
@article {pmid36620442,
year = {2022},
author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D},
title = {Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1057010},
pmid = {36620442},
issn = {1662-4548},
abstract = {OBJECTIVE: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods.<br><br>METHODS: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses.<br><br>RESULTS: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments.<br><br>CONCLUSION: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies.<br><br>SIGNIFICANCE: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.},
}
@article {pmid36619242,
year = {2022},
author = {Wang, L and Lan, Z and Wang, Q and Bai, X and Ma, F},
title = {An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples.},
journal = {Journal of healthcare engineering},
volume = {2022},
number = {},
pages = {4509612},
pmid = {36619242},
issn = {2040-2309},
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Learning ; Electroencephalography/methods ; Movement ; },
abstract = {Rehabilitation technologies based on brain-computer interface (BCI) have become a promising approach for patients with dyskinesia to regain movement. In BCI experiment, there is often a necessary stage of calibration measurement before the feedback applications. To reduce the time required for initial training, it is of great importance to have a method which can learn to classify electroencephalogram (EEG) signals with a little amount of training data. In this paper, the novel combination of feature extraction and classification algorithm is proposed for classification of EEG signals with a small number of training samples. For feature extraction, the motor imagery EEG signals are pre-processed, and a relative distance criterion is defined to select the optimal combination of channels. Subsequently, common spatial subspace decomposition (CSSD) algorithm and extreme learning machine with kernel (ELM_Kernel) algorithm are used to perform the types of tasks classification of motor imagery EEG signals. Simulation results demonstrate that the proposed method produces a high average classification accuracy of 99.1% on BCI Competition III dataset IVa and 76.92% on BCI Competition IV dataset IIa outperforming state-of-the-art algorithms and obtains a good classification accuracy.},
}
@article {pmid36619090,
year = {2022},
author = {Chen, J and Zhao, Z and Shu, Q and Cai, G},
title = {Feature extraction based on microstate sequences for EEG-based emotion recognition.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {1065196},
pmid = {36619090},
issn = {1664-1078},
abstract = {Recognizing emotion from Electroencephalography (EEG) is a promising and valuable research issue in the field of affective brain-computer interfaces (aBCI). To improve the accuracy of emotion recognition, an emotional feature extraction method is proposed based on the temporal information in the EEG signal. This study adopts microstate analysis as a spatio-temporal analysis for EEG signals. Microstates are defined as a series of momentary quasi-stable scalp electric potential topographies. Brain electrical activity could be modeled as being composed of a time sequence of microstates. Microstate sequences provide an ideal macroscopic window for observing the temporal dynamics of spontaneous brain activity. To further analyze the fine structure of the microstate sequence, we propose a feature extraction method based on k-mer. K-mer is a k-length substring of a given sequence. It has been widely used in computational genomics and sequence analysis. We extract features that are based on the D 2 ∗ statistic of k-mer. In addition, we also extract four parameters (duration, occurrence, time coverage, GEV) of each microstate class as features at the coarse level. We conducted experiments on the DEAP dataset to evaluate the performance of the proposed features. The experimental results demonstrate that the fusion of features in fine and coarse levels can effectively improve classification accuracy.},
}
@article {pmid36618996,
year = {2022},
author = {Pan, J and Chen, X and Ban, N and He, J and Chen, J and Huang, H},
title = {Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1077717},
pmid = {36618996},
issn = {1662-5161},
abstract = {A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.},
}
@article {pmid36618992,
year = {2022},
author = {Feng, J and Li, Y and Jiang, C and Liu, Y and Li, M and Hu, Q},
title = {Classification of motor imagery electroencephalogram signals by using adaptive cross-subject transfer learning.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1068165},
pmid = {36618992},
issn = {1662-5161},
abstract = {INTRODUCTION: Electroencephalogram (EEG)-based motor imagery (MI) classification is an important aspect in brain-computer interfaces (BCIs), which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, due to the small number of training samples of MI electroencephalogram (MI-EEG) for a single subject and the great individual differences of MI-EEG among different subjects, the generalization and accuracy of the model on the specific MI task may be poor.<br><br>METHODS: To solve these problems, an adaptive cross-subject transfer learning algorithm is proposed, which is based on kernel mean matching (KMM) and transfer learning adaptive boosting (TrAdaBoost) method. First, the common spatial pattern (CSP) is used to extract the spatial features. Then, in order to make the feature distribution more similar among different subjects, the KMM algorithm is used to compute a sample weight matrix for aligning the mean between source and target domains and reducing distribution differences among different subjects. Finally, the sample weight matrix from KMM is used as the initialization weight of TrAdaBoost, and then TrAdaBoost is used to adaptively select source domain samples that are closer to the target task distribution to assist in building a classification model.<br><br>RESULTS: In order to verify the effectiveness and feasibility of the proposed method, the algorithm is applied to BCI Competition IV datasets and in-house datasets. The results show that the average classification accuracy of the proposed method on the public datasets is 89.1%, and the average classification accuracy on the in-house datasets is 80.4%.<br><br>DISCUSSION: Compared with the existing methods, the proposed method effectively improves the classification accuracy of MI-EEG signals. At the same time, this paper also applies the proposed algorithm to the in-house dataset, the results verify the effectiveness of the algorithm again, and the results of this study have certain clinical guiding significance for brain rehabilitation.},
}
@article {pmid36617977,
year = {2023},
author = {Khan, NN and Ganai, NA and Ahmad, T and Shanaz, S and Majid, R and Mir, MA and Ahmad, SF},
title = {Morphometric indices of native sheep breeds of the Himalayan region of India using multivariate principal component analysis.},
journal = {Zygote (Cambridge, England)},
volume = {31},
number = {2},
pages = {157-162},
doi = {10.1017/S0967199422000636},
pmid = {36617977},
issn = {1469-8730},
mesh = {Animals ; Sheep ; *Principal Component Analysis ; India ; },
abstract = {This study was performed to analyze the morphometric traits and indices in 3000 animals of five registered sheep breeds in the Himalayan region under a multivariate approach. Data were recorded under field conditions with equal coverage of the five breeds, viz., Karnah, Gurez, Poonchi, Bakerwal and Changthangi on body length (BL), height at withers (HW), chest girth (CG), ear length (EL), and tail length (TL). Furthermore, four derived traits (indices) were studied, which included an index of body frame (IBF), an index of thorax development (ITD), a Baron-Crevat index (BCI), and an index of body weight (IBW). Multivariate principal component analysis (PCA) was undertaken on nine morphometric traits. Kaiser's criterion was used to reduce the number of principal components for further analysis and interpretation. The adequacy of sampling was evaluated using Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity. The mean BL ranged from 52.15 (Changthangi) to 71.13 (Gurez). The estimates of HW, CG, EL and TL were highest in Gurez (63.49), Bakerwal (84.82), Bakerwal (7.26), and Karnah (8.18) breeds, respectively. Among the derived traits, the highest IBF was observed in the Gurez breed with an estimate of 112.22. Upon multivariate PCA on the dataset, the first four principal components were able to explain 92.117% of the total variance. The KMO test, Bartlett's test of sphericity and estimated communalities showed the appropriateness of PCA on the evaluated traits. Four eigenvalues were greater than one and were extracted for further analysis. Morphometric traits were highly correlated, except for EL and TL that showed lower correlation estimates with other traits. The Changthangi population showed the lowest estimates of BL, HW, CG and rectangular body frame. The present study ascertained important morphometric traits/indices that can help in developing selection criteria and formulating sustainable breeding and conservation plans vis-à-vis the unique sheep breeds of the temperate Himalayas.},
}
@article {pmid36617798,
year = {2023},
author = {Lee, Y and Lee, HJ and Tae, KS},
title = {Classification of EEG signals related to real and imagery knee movements using deep learning for brain computer interfaces.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {31},
number = {3},
pages = {933-942},
doi = {10.3233/THC-220363},
pmid = {36617798},
issn = {1878-7401},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Movement ; Neural Networks, Computer ; Imagination ; Algorithms ; },
abstract = {BACKGROUND: Non-invasive Brain-Computer Interface (BCI) uses an electroencephalogram (EEG) to obtain information on brain neural activity. Because EEG can be contaminated by various artifacts during the collection process, it has primarily evolved into motor imagery (MI) with a low risk of contamination. However, MI has a disadvantage in that accurate data is difficult to obtain.<br><br>OBJECTIVE: The goal of this study was to determine which motor imagery and movement execution (ME) of the knee has the best classification performance.<br><br>METHODS: Ten subjects were selected to provide MI and ME data for four different types of knee exercise. The experiment was conducted to keep the left, right, and both knees extend or bend for five seconds, and there was a five seconds break between each movement. Each motion was performed 20 times and the MI was carried out in the same protocol. Motions were classified through a modified model of the Lenet-5 of CNN (Convolution Neural Network).<br><br>RESULTS: The deep learning data was classified, and a study discovered that ME (98.91%) could be classified significantly more accurately than MI (98.37%) (p< 0.001).<br><br>CONCLUSION: If future studies on other body movements are conducted, we anticipate that BCI can be further developed to be more accurate. And such advancements in BCI can be used to facilitate the patient's communication by analyzing the user's movement intention. These results can also be used for various controls such as robots using a combination of MI and ME.},
}
@article {pmid36617098,
year = {2023},
author = {Alotaibi, FM and Fawad, },
title = {An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.},
journal = {Sensors (Basel, Switzerland)},
volume = {23},
number = {1},
pages = {},
pmid = {36617098},
issn = {1424-8220},
support = {IFPIP: 109-830-1443//Ministry of Education and King Abdul Aziz University/ ; },
mesh = {Humans ; *Neural Networks, Computer ; *Emotions ; Algorithms ; Electroencephalography/methods ; Recognition, Psychology ; },
abstract = {The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based on diverse biosignals. In particular, previous research has shown that an Electroencephalogram (EEG) can provide deep insight into the state of emotion. Recently, various handcrafted and deep neural network (DNN) models were proposed by researchers for extracting emotion-relevant features, which offer limited robustness to noise that leads to reduced precision and increased computational complexity. The DNN models developed to date were shown to be efficient in extracting robust features relevant to emotion classification; however, their massive feature dimensionality problem leads to a high computational load. In this paper, we propose a bag-of-hybrid-deep-features (BoHDF) extraction model for classifying EEG signals into their respective emotion class. The invariance and robustness of the BoHDF is further enhanced by transforming EEG signals into 2D spectrograms before the feature extraction stage. Such a time-frequency representation fits well with the time-varying behavior of EEG patterns. Here, we propose to combine the deep features from the GoogLeNet fully connected layer (one of the simplest DNN models) together with the OMTLBP_SMC texture-based features, which we recently developed, followed by a K-nearest neighbor (KNN) clustering algorithm. The proposed model, when evaluated on the DEAP and SEED databases, achieves a 93.83 and 96.95% recognition accuracy, respectively. The experimental results using the proposed BoHDF-based algorithm show an improved performance in comparison to previously reported works with similar setups.},
}
@article {pmid36610247,
year = {2023},
author = {Robinette, K and Sims, J and Pang, B and Babu, S},
title = {Transcutaneous versus percutaneous bone-anchored hearing aids: A quality of life comparison.},
journal = {American journal of otolaryngology},
volume = {44},
number = {2},
pages = {103758},
doi = {10.1016/j.amjoto.2022.103758},
pmid = {36610247},
issn = {1532-818X},
mesh = {Humans ; *Hearing Aids ; Retrospective Studies ; Quality of Life ; Bone Conduction ; Suture Anchors ; Hearing Loss, Conductive ; Treatment Outcome ; },
abstract = {PURPOSE: To determine whether patients have improved quality of life outcomes with percutaneous bone conduction implant (p-BCI) versus transcutaneous bone conduction implant (t-BCI).<br><br>MATERIALS &amp; METHODS: Retrospective chart review of patients who have undergone placement of a BCI in the Ascension St John Providence Health System from 2013 to 2018. Patient satisfaction of t-BCI and p-BCI was measured using a questionnaire that incorporated the Glasgow Benefit Inventory (GBI) and BAHA, aesthetic, hygiene &amp; use (BAHU) survey. Key outcome variables were separated into 2 categories: (1) evaluation of wound healing and implant-associated complications, and (2) quality of life improvements.<br><br>RESULTS: Comparative analysis of the 27 p-BCI patients and 10 t-BCI patients showed overall positive benefit with no statistically significant difference on quality of life improvement between the two groups. Total complication rates for p-BCI (48.1&#xa0;%) vs t-BCI (10&#xa0;%) was marginally significant (p&#xa0;=&#xa0;0.056). Rate of revision for p-BCI versus t-BCI was 14.8&#xa0;% vs 0&#xa0;%, respectively.<br><br>CONCLUSION: This study provides a much-needed comparative insight in patient's experience with these two devices. Understanding which device is preferable in the patient's view will offer helpful information for guiding proper implant selection.},
}
@article {pmid36610205,
year = {2023},
author = {Tang, W and Shen, T and Huang, Y and Zhu, W and You, S and Zhu, C and Zhang, L and Ma, J and Wang, Y and Zhao, J and Li, T and Lai, HY},
title = {Exploring structural and functional alterations in drug-naïve obsessive-compulsive disorder patients: An ultrahigh field multimodal MRI study.},
journal = {Asian journal of psychiatry},
volume = {81},
number = {},
pages = {103431},
doi = {10.1016/j.ajp.2022.103431},
pmid = {36610205},
issn = {1876-2026},
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Brain ; Gray Matter/pathology ; Frontal Lobe ; *Obsessive-Compulsive Disorder ; },
abstract = {BACKGROUND: Brain structural and functional alterations have been reported in obsessive-compulsive disorder (OCD) patients; however, these findings were inconsistent across studies due to several limitations, including small sample sizes, different inclusion/exclusion criteria, varied demographic characteristics and symptom dimensions, comorbidity, and medication status. Prominent and replicable neuroimaging biomarkers remain to be discovered.<br><br>METHODS: This study explored the gray matter structure, neural activity, and white matter microstructure differences in 40 drug-na&#xef;ve OCD patients and 57 matched healthy controls using ultrahigh field 7.0&#xa0;T multimodal magnetic resonance imaging, which increased the spatial resolution and detection power. We also evaluated correlations among different modalities, imaging features and clinical symptoms.<br><br>RESULTS: Drug-na&#xef;ve OCD patients exhibited significantly increased gray matter volume in the frontal cortex, especially in the orbitofrontal cortex, as well as volumetric reduction in the temporal lobe, occipital lobe and cerebellum. Increased neural activities were observed in the cingulate gyri and precuneus. Increased temporal-middle cingulate and posterior cingulate-precuneus functional connectivities and decreased frontal-middle cingulate connectivity were further detected. Decreased fractional anisotropy values were found in the cingulum-hippocampus gyrus and inferior fronto-occipital fascicle in OCD patients. Moreover, significantly altered imaging features were related to OCD symptom severity. Altered functional and structural neural connectivity might influence compulsive and obsessive features, respectively.<br><br>CONCLUSIONS: Altered structure and function of the classical cortico-striato-thalamo-cortical circuit, limbic system, default mode network, visual, language and sensorimotor networks play important roles in the neurophysiology of OCD.},
}
@article {pmid36609445,
year = {2023},
author = {Williams, JB and Cao, Q and Wang, W and Lee, YH and Qin, L and Zhong, P and Ren, Y and Ma, K and Yan, Z},
title = {Inhibition of histone methyltransferase Smyd3 rescues NMDAR and cognitive deficits in a tauopathy mouse model.},
journal = {Nature communications},
volume = {14},
number = {1},
pages = {91},
pmid = {36609445},
issn = {2041-1723},
support = {F99 NS118745/NS/NINDS NIH HHS/United States ; R01 AG064656/AG/NIA NIH HHS/United States ; R01 AG079797/AG/NIA NIH HHS/United States ; R21 AG067597/AG/NIA NIH HHS/United States ; },
mesh = {Animals ; Mice ; *Alzheimer Disease/complications/genetics/metabolism ; Cognition ; Disease Models, Animal ; Histone Methyltransferases ; Histone-Lysine N-Methyltransferase/genetics ; Mice, Transgenic ; tau Proteins/metabolism ; *Tauopathies/genetics ; Receptors, N-Methyl-D-Aspartate/metabolism ; },
abstract = {Pleiotropic mechanisms have been implicated in Alzheimer's disease (AD), including transcriptional dysregulation, protein misprocessing and synaptic dysfunction, but how they are mechanistically linked to induce cognitive deficits in AD is unclear. Here we find that the histone methyltransferase Smyd3, which catalyzes histone H3 lysine 4 trimethylation (H3K4me3) to activate gene transcription, is significantly elevated in prefrontal cortex (PFC) of AD patients and P301S Tau mice, a model of tauopathies. A short treatment with the Smyd3 inhibitor, BCI-121, rescues cognitive behavioral deficits, and restores synaptic NMDAR function and expression in PFC pyramidal neurons of P301S Tau mice. Fbxo2, which encodes an E3 ubiquitin ligase controlling the degradation of NMDAR subunits, is identified as a downstream target of Smyd3. Smyd3-induced upregulation of Fbxo2 in P301S Tau mice is linked to the increased NR1 ubiquitination. Fbxo2 knockdown in PFC leads to the recovery of NMDAR function and cognitive behaviors in P301S Tau mice. These data suggest an integrated mechanism and potential therapeutic strategy for AD.},
}
@article {pmid36608342,
year = {2023},
author = {Han, J and Xu, M and Xiao, X and Yi, W and Jung, TP and Ming, D},
title = {A high-speed hybrid brain-computer interface with more than 200 targets.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb105},
pmid = {36608342},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Occipital Lobe ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Photic Stimulation/methods ; Algorithms ; },
abstract = {Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.Main results.The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% ± 7.49% and 86.00% ± 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 ± 39.20 bits min[-1]and 204.47 ± 37.56 bits min[-1], respectively. Notably, the peak ITR could reach up to 367.83 bits min[-1].Significance.This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.},
}
@article {pmid36608339,
year = {2023},
author = {Tao, T and Jia, Y and Xu, G and Liang, R and Zhang, Q and Chen, L and Gao, Y and Chen, R and Zheng, X and Yu, Y},
title = {Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acb102},
pmid = {36608339},
issn = {1741-2552},
mesh = {Humans ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Brain ; Imagination ; *Motor Cortex ; },
abstract = {Objective. Motor imagery (MI) is a process of autonomously modulating the motor area to rehearse action mentally without actual execution. Based on the neuroplasticity of the cerebral cortex, MI can promote the functional rehabilitation of the injured cerebral cortex motor area. However, it usually takes several days to a few months to train individuals to acquire the necessary MI ability to control rehabilitation equipment in current studies, which greatly limits the clinical application of rehabilitation training systems based on the MI brain-computer interface (BCI).Approach. A novel MI training paradigm combined with the error related potential (ErrP) is proposed, and online adaptive training of the MI classifier was performed using ErrP. ErrP is used to correct the output of the MI classification to obtain a higher accuracy of kinesthetic feedback based on the imagination intention of subjects while generating simulated labels for MI online adaptive training. In this way, we improved the MI training efficiency. Thirteen subjects were randomly divided into an experimental group using the proposed paradigm and a control group using the traditional MI training paradigm to participate in six MI training experiments.Main results. The proposed paradigm enabled the experimental group to obtain a higher event-related desynchronization modulation level in the contralateral brain region compared with the control group and 69.76% online classification accuracy of MI after three MI training experiments. The online classification accuracy reached 72.76% and the whole system recognized the MI intention of the subjects with an online accuracy of 82.61% after six experiments.Significance. Compared with the conventional unimodal MI training strategy, the proposed approach enables subjects to use the MI-BCI based system directly and achieve a better performance after only three training experiments with training left and right hands simultaneously. This greatly improves the usability of the MI-BCI-based rehabilitation system and makes it more convenient for clinical use.},
}
@article {pmid36607454,
year = {2023},
author = {LaMarca, K and Gevirtz, R and Lincoln, AJ and Pineda, JA},
title = {Brain-Computer Interface Training of mu EEG Rhythms in Intellectually Impaired Children with Autism: A Feasibility Case Series.},
journal = {Applied psychophysiology and biofeedback},
volume = {48},
number = {2},
pages = {229-245},
pmid = {36607454},
issn = {1573-3270},
mesh = {Humans ; Child ; *Autistic Disorder ; Electroencephalography/methods ; *Brain-Computer Interfaces ; *Autism Spectrum Disorder/therapy ; Feasibility Studies ; *Neurofeedback/methods ; },
abstract = {Prior studies show that neurofeedback training (NFT) of mu rhythms improves behavior and EEG mu rhythm suppression during action observation in children with autism spectrum disorder (ASD). However, intellectually impaired persons were excluded because of their behavioral challenges. We aimed to determine if intellectually impaired children with ASD, who were behaviorally prepared to take part in a mu-NFT study using conditioned auditory reinforcers, would show improvements in symptoms and mu suppression following mu-NFT. Seven children with ASD (ages 6-8; mean IQ 70.6 ± 7.5) successfully took part in mu-NFT. Four cases demonstrated positive learning trends (hit rates) during mu-NFT (learners), and three cases did not (non-learners). Artifact-creating behaviors were present during tests of mu suppression for all cases, but were more frequent in non-learners. Following NFT, learners showed behavioral improvements and were more likely to show evidence of a short-term increase in mu suppression relative to non-learners who showed little to no EEG or behavior improvements. Results support mu-NFT's application in some children who otherwise may not have been able to take part without enhanced behavioral preparations. Children who have more limitations in demonstrating learning during NFT, or in providing data with relatively low artifact during task-dependent EEG tests, may have less chance of benefiting from mu-NFT. Improving the identification of ideal mu-NFT candidates, mu-NFT learning rates, source analyses, EEG outcome task performance, population-specific artifact-rejection methods, and the theoretical bases of NFT protocols, could aid future BCI-based, neurorehabilitation efforts.},
}
@article {pmid36607323,
year = {2023},
author = {Zhang, Y and Schriver, KE and Hu, JM and Roe, AW},
title = {Spatial frequency representation in V2 and V4 of macaque monkey.},
journal = {eLife},
volume = {12},
number = {},
pages = {},
pmid = {36607323},
issn = {2050-084X},
mesh = {Animals ; *Macaca ; Haplorhini ; Visual Pathways ; Visual Perception ; *Visual Cortex ; Brain Mapping ; Photic Stimulation/methods ; },
abstract = {Spatial frequency (SF) is an important attribute in the visual scene and is a defining feature of visual processing channels. However, there remain many unsolved questions about how extrastriate areas in primate visual cortex code this fundamental information. Here, using intrinsic signal optical imaging in visual areas of V2 and V4 of macaque monkeys, we quantify the relationship between SF maps and (1) visual topography and (2) color and orientation maps. We find that in orientation regions, low to high SF is mapped orthogonally to orientation; in color regions, which are reported to contain orthogonal axes of color and lightness, low SFs tend to be represented more frequently than high SFs. This supports a population-based SF fluctuation related to the 'color/orientation' organizations. We propose a generalized hypercolumn model across cortical areas, comprised of two orthogonal parameters with additional parameters.},
}
@article {pmid36606248,
year = {2022},
author = {Kim, J and Jiang, X and Forenzo, D and Liu, Y and Anderson, N and Greco, CM and He, B},
title = {Immediate effects of short-term meditation on sensorimotor rhythm-based brain-computer interface performance.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1019279},
pmid = {36606248},
issn = {1662-5161},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
abstract = {INTRODUCTION: Meditation has been shown to enhance a user's ability to control a sensorimotor rhythm (SMR)-based brain-computer interface (BCI). For example, prior work have demonstrated that long-term meditation practices and an 8-week mindfulness-based stress reduction (MBSR) training have positive behavioral and neurophysiological effects on SMR-based BCI. However, the effects of short-term meditation practice on SMR-based BCI control are still unknown.<br><br>METHODS: In this study, we investigated the immediate effects of a short, 20-minute meditation on SMR-based BCI control. Thirty-seven subjects performed several runs of one-dimensional cursor control tasks before and after two types of 20-minute interventions: a guided mindfulness meditation exercise and a recording of a narrator reading a journal article.<br><br>RESULTS: We found that there is no significant change in BCI performance and Electroencephalography (EEG) BCI control signal following either 20-minute intervention. Moreover, the change in BCI performance between the meditation group and the control group was found to be not significant.<br><br>DISCUSSION: The present results suggest that a longer period of meditation is needed to improve SMR-based BCI control.},
}
@article {pmid36604821,
year = {2024},
author = {Echtioui, A and Zouch, W and Ghorbel, M and Mhiri, C and Hamam, H},
title = {Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.},
journal = {Clinical EEG and neuroscience},
volume = {55},
number = {4},
pages = {455-464},
doi = {10.1177/15500594221148285},
pmid = {36604821},
issn = {2169-5202},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; *Electroencephalography/methods ; *Imagination/physiology ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology/physiopathology ; Discriminant Analysis ; },
abstract = {Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.},
}
@article {pmid36604739,
year = {2023},
author = {Sun, G and McCartin, M and Liu, W and Zhang, Q and Kenefati, G and Chen, ZS and Wang, J},
title = {Temporal pain processing in the primary somatosensory cortex and anterior cingulate cortex.},
journal = {Molecular brain},
volume = {16},
number = {1},
pages = {3},
pmid = {36604739},
issn = {1756-6606},
support = {GM115384/GM/NIGMS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; NS121776/NS/NINDS NIH HHS/United States ; R01 GM115384/GM/NIGMS NIH HHS/United States ; },
mesh = {Humans ; *Gyrus Cinguli/physiology ; *Time Perception ; Somatosensory Cortex ; Pain ; Cerebral Cortex/physiology ; },
abstract = {Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.},
}
@article {pmid36604186,
year = {2023},
author = {Zhang, P and Zhang, D and Lai, J and Fu, Y and Wu, L and Huang, H and Pan, Y and Jiang, J and Xi, C and Che, Z and Song, X and Hu, S},
title = {Characteristics of the gut microbiota in bipolar depressive disorder patients with distinct weight.},
journal = {CNS neuroscience &amp; therapeutics},
volume = {29 Suppl 1},
number = {Suppl 1},
pages = {74-83},
pmid = {36604186},
issn = {1755-5949},
support = {81971271//National Natural Science Foundation of China/ ; 2021C03107//the Zhejiang Provincial Key Research and Development Program/ ; LQ20H090013//the Zhejiang Provincial Natural Science Foundation/ ; 2020KY548//the Program from the Health and Family Planning Commission of Zhejiang Province/ ; 2021R52016//the Leading Talent of Scientific and Technological Innovation-'Ten Thousand Talents Programme' of Zhejiang Province/ ; },
mesh = {Humans ; Overweight ; *Gastrointestinal Microbiome/genetics ; *Bipolar Disorder ; Obesity ; Amino Acids ; *Depressive Disorder ; Lipids ; },
abstract = {BACKGROUND: Preliminary studies have indicated metabolic dysfunction and gut dysbiosis in patients with bipolar disorder (BD). In this study, we aimed to clarify the impact of the gut microbial composition and function on metabolic dysfunction in BD patients with an acute depressive episode.<br><br>METHODS: Fresh fecal samples were provided from 58 patients with BD depression, including 29 with normal weight (NW) and 29 with overweight/obesity (OW), and 31 healthy controls (HCs). The hypervariable region of 16&#x2009;S rRNA gene (V3-V4) sequencing was performed using IonS5TMXL platform to evaluate the bacterial communities. Differences of microbial community and correlation to clinical parameters across different groups were analyzed.<br><br>RESULTS: Compared to NW and HCs, the OW group showed a decreased tendency in alpha diversity index. Beta diversity was markedly different among these groups (PERMANOVA: R[2] &#xa0;=&#xa0;0.034, p&#xa0;=&#xa0;0.01) and was higher in patients versus HCs. A total number of 24 taxa displayed significantly different abundance among OW, NW, and HCs. At the family level, the abundance of three taxa was remarkably increased in NW, one in OW, and one in HCs. At the genus level, five taxa were enriched in OW, eight in NW, and two in HCs. The relative abundance of the genera Megamonas was positively associated with BMI, while Eggerthella was negatively correlated with BMI. Functional prediction analysis revealed the metabolism of cofactors and vitamins and amino acid were highly enriched in OW compared to HCs. In addition, microbial functions involved in "lipid metabolism" were depleted while the "fructose and mannose metabolism" was enriched in OW compared to NW group.<br><br>CONCLUSIONS: Specific bacterial taxa involved in pathways regulating the lipid, energy, and amino acid metabolisms may underlie the weight concerns in depressed BD patients. Potential targeting gut microbial therapy is provided for overweight/obesity patients with BD, which still need further studies in the future.},
}
@article {pmid36603232,
year = {2023},
author = {Yu, W and Zhao, F and Ren, Z and Jin, D and Yang, X and Zhang, X},
title = {Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image.},
journal = {Computer methods and programs in biomedicine},
volume = {230},
number = {},
pages = {107330},
doi = {10.1016/j.cmpb.2022.107330},
pmid = {36603232},
issn = {1872-7565},
mesh = {Humans ; *Autism Spectrum Disorder/diagnosis ; Fixation, Ocular ; Eye Movements ; Social Behavior ; },
abstract = {BACKGROUND AND OBJECTIVE: Attention allocation reflects the way of humans filtering and organizing the information. On one hand, different task scenarios seriously affect human's rule of attention distribution, on the other hand, visual attention reflecting the cognitive and psychological process. Most of the previous studies on visual attention allocation are based on cognitive models, predicted models, or statistical analysis of eye movement data or visual images, however, these methods are inadequate to provide an inside view of gaze behavior to reveal the attention distribution pattern within scenario context. Moreover, they seldom study the association rules of these patterns. Therefore, we adopted the big data mining approach to discover the paradigm of visual attention distribution.<br><br>METHODS: We applied the data mining method to extract the gaze patterns to discover the regularities of attention distribution behavior within the scenario context. The proposed method consists of three components, tasks scenario segmented and clustered, gaze pattern mining, and association rule of frequent pattern mining.<br><br>RESULTS: The proposed approach is tested on the operation platform. The complex operation task is simultaneously segmented and clustered with the TICC-based method and evaluated by the BCI index. The operator's eye movement frequent patterns and their association rule are discovered. The results demonstrate that our method can associate the eye-tracking data with the task-oriented scene data.<br><br>DISCUSSION: The proposed method provides the benefits of being able to explicitly express and quantitatively&#xa0;analyze people's visual attention patterns. The proposed method can not only be applied in the field of aerospace medicine and aviation psychology, but also can likely be applied to computer-aided diagnosis and follow-up tool for neurological disease and cognitive impairment related disease, such as ADHD (Attention Deficit Hyperactivity Disorder), neglect syndrome, social attention differences in ASD (Autism spectrum disorder).},
}
@article {pmid36601593,
year = {2022},
author = {Schalk, G and Worrell, S and Mivalt, F and Belsten, A and Kim, I and Morris, JM and Hermes, D and Klassen, BT and Staff, NP and Messina, S and Kaufmann, T and Rickert, J and Brunner, P and Worrell, GA and Miller, KJ},
title = {Toward a fully implantable ecosystem for adaptive neuromodulation in humans: Preliminary experience with the CorTec BrainInterchange device in a canine model.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {932782},
pmid = {36601593},
issn = {1662-4548},
support = {KL2 TR002379/TR/NCATS NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R01 MH122258/MH/NIMH NIH HHS/United States ; U01 NS128612/NS/NINDS NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; UH3 NS095495/NS/NINDS NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
abstract = {This article describes initial work toward an ecosystem for adaptive neuromodulation in humans by documenting the experience of implanting CorTec's BrainInterchange (BIC) device in a beagle canine and using the BCI2000 environment to interact with the BIC device. It begins with laying out the substantial opportunity presented by a useful, easy-to-use, and widely available hardware/software ecosystem in the current landscape of the field of adaptive neuromodulation, and then describes experience with implantation, software integration, and post-surgical validation of recording of brain signals and implant parameters. Initial experience suggests that the hardware capabilities of the BIC device are fully supported by BCI2000, and that the BIC/BCI2000 device can record and process brain signals during free behavior. With further development and validation, the BIC/BCI2000 ecosystem could become an important tool for research into new adaptive neuromodulation protocols in humans.},
}
@article {pmid36601085,
year = {2022},
author = {Yan, L and Hou, Z and Fu, W and Yu, Y and Cui, R and Miao, Z and Lou, X and Ma, N},
title = {Association of periprocedural perfusion non-improvement with recurrent stroke after endovascular treatment for Intracranial Atherosclerotic Stenosis.},
journal = {Therapeutic advances in neurological disorders},
volume = {15},
number = {},
pages = {17562864221143178},
pmid = {36601085},
issn = {1756-2856},
abstract = {BACKGROUND: Predictors of recurrent stroke after endovascular treatment for symptomatic intracranial atherosclerotic stenosis (ICAS) remain uncertain.<br><br>OBJECTIVES: Among baseline characteristics, lesion features, and cerebral perfusion changes, we try to explore which factors are associated with the risk of recurrent stroke in symptomatic ICAS after endovascular treatment.<br><br>DESIGN: Consecutive patients with symptomatic ICAS of 70-99% receiving endovascular treatment were enrolled. All patients underwent whole-brain computer tomography perfusion (CTP) within 3&#x2009;days before and 3&#x2009;days after the endovascular treatment. Baseline characteristics, lesion features, and cerebral perfusion changes were collected.<br><br>METHODS: Cerebral perfusion changes were evaluated with RAPID software and calculated as preprocedural cerebral blood flow (CBF)&#x2009;<&#x2009;30%, time to maximum of the residue function (Tmax)&#x2009;>&#x2009;6&#x2009;s, and Tmax&#x2009;>&#x2009;4&#x2009;s volumes minus postprocedural. Cerebral perfusion changes were divided into periprocedural perfusion improvement (>0&#x2009;ml) and non-improvement (&#x2a7d;&#x2009;0&#x2009;ml). Recurrent stroke within 180&#x2009;days was collected. The Cox proportional hazards analysis analyses were performed to evaluate factors associated with recurrent stroke.<br><br>RESULTS: From March 2021 to December 2021, 107 patients with symptomatic ICAS were enrolled. Of the 107 enrolled patients, 30 (28.0%) patients underwent balloon angioplasty alone and 77 patients (72.0%) underwent stenting. The perioperative complications occurred in three patients. Among CBF&#x2009;<&#x2009;30%, Tmax&#x2009;>&#x2009;6&#x2009;s, and Tmax&#x2009;>&#x2009;4&#x2009;s volumes, Tmax&#x2009;>&#x2009;4&#x2009;s volume was available to evaluate cerebral perfusion changes. Periprocedural perfusion improvement was found in 77 patients (72.0%) and non-improvement in 30 patients (28.0%). Nine patients (8.4%) suffered from recurrent stroke in 180-day follow-up. In Cox proportional hazards analysis adjusted for age and sex, perfusion non-improvement was associated with recurrent stroke [hazards ratio (HR): 4.472; 95% CI: 1.069-18.718; p&#x2009;=&#x2009;0.040].<br><br>CONCLUSION: In patients with symptomatic ICAS treated with endovascular treatment, recurrent stroke may be related to periprocedural cerebral perfusion non-improvement.<br><br>REGISTRATION: http://www.chictr.org.cn. Unique identifier: ChiCTR2100052925.},
}
@article {pmid36600620,
year = {2024},
author = {Zhang, Y and Tao, S and Coid, J and Wei, W and Wang, Q and Yue, W and Yan, H and Tan, L and Chen, Q and Yang, G and Lu, T and Wang, L and Zhang, F and Yang, J and Li, K and Lv, L and Tan, Q and Zhang, H and Ma, X and Yang, F and Li, L and Wang, C and Zhao, L and Deng, W and Guo, W and Ma, X and Zhang, D and Li, T},
title = {The Role of Total White Blood Cell Count in Antipsychotic Treatment for Patients with Schizophrenia.},
journal = {Current neuropharmacology},
volume = {22},
number = {1},
pages = {159-167},
pmid = {36600620},
issn = {1875-6190},
support = {81920108018, 82001409//National Natural Science Foundation of China/ ; 2022C03096//Key R &amp; D Program of Zhejiang/ ; 2021HXBH037//Post-Doctor Research Project, West China Hospital, Sichuan University/ ; },
mesh = {Humans ; *Antipsychotic Agents/therapeutic use ; *Schizophrenia/drug therapy/metabolism ; Olanzapine/therapeutic use ; Risperidone/therapeutic use ; Quetiapine Fumarate/therapeutic use ; Haloperidol/therapeutic use ; Perphenazine/therapeutic use ; Benzodiazepines/adverse effects ; Glucose/therapeutic use ; Inflammation/drug therapy ; },
abstract = {BACKGROUND: Total white blood cell count (TWBCc), an index of chronic and low-grade inflammation, is associated with clinical symptoms and metabolic alterations in patients with schizophrenia. The effect of antipsychotics on TWBCc, predictive values of TWBCc for drug response, and role of metabolic alterations require further study.<br><br>METHODS: Patients with schizophrenia were randomized to monotherapy with risperidone, olanzapine, quetiapine, aripiprazole, ziprasidone, perphenazine or haloperidol in a 6-week pharmacological trial. We repeatedly measured clinical symptoms, TWBCc, and metabolic measures (body mass index, blood pressure, waist circumference, fasting blood lipids and glucose). We used mixed-effect linear regression models to test whether TWBCc can predict drug response. Mediation analysis to investigate metabolic alteration effects on drug response.<br><br>RESULTS: At baseline, TWBCc was higher among patients previously medicated. After treatment with risperidone, olanzapine, quetiapine, perphenazine, and haloperidol, TWBCc decreased significantly (p < 0.05). Lower baseline TWBCc predicted greater reductions in Positive and Negative Syndrome Scale (PANSS) total and negative scores over time (p < 0.05). We found significant mediation of TWBCc for effects of waist circumference, fasting low-density lipoprotein cholesterol, and glucose on reductions in PANSS total scores and PANSS negative subscale scores (p < 0.05).<br><br>CONCLUSION: TWBCc is affected by certain antipsychotics among patients with schizophrenia, with decreases observed following short-term, but increases following long-term treatment. TWBCc is predictive of drug response, with lower TWBCc predicting better responses to antipsychotics. It also mediates the effects of certain metabolic measures on improvement of negative symptoms. This indicates that the metabolic state may affect clinical manifestations through inflammation.},
}
@article {pmid36600612,
year = {2023},
author = {Kodama, M and Iwama, S and Morishige, M and Ushiba, J},
title = {Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {11},
pages = {6573-6584},
doi = {10.1093/cercor/bhac525},
pmid = {36600612},
issn = {1460-2199},
mesh = {Humans ; *Neurofeedback/physiology ; Imagination/physiology ; Electroencephalography ; *Sensorimotor Cortex/physiology ; Imagery, Psychotherapy ; },
abstract = {Neurofeedback training using electroencephalogram (EEG)-based brain-computer interfaces (BCIs) combined with mental rehearsals of motor behavior has demonstrated successful self-regulation of motor cortical excitability. However, it remains unclear whether the acquisition of skills to voluntarily control neural excitability is accompanied by structural plasticity boosted by neurofeedback. Here, we sought short-term changes in cortical structures induced by 30 min of BCI-based neurofeedback training, which aimed at the regulation of sensorimotor rhythm (SMR) in scalp EEG. When participants performed kinesthetic motor imagery of right finger movement with online feedback of either event-related desynchronisation (ERD) of SMR magnitude from the contralateral sensorimotor cortex (SM1) or those from other participants (i.e. placebo), the learning rate of SMR-ERD control was significantly different. Although overlapped structural changes in gray matter volumes were found in both groups, significant differences revealed by group-by-group comparison were spatially different; whereas the veritable neurofeedback group exhibited sensorimotor area-specific changes, the placebo exhibited spatially distributed changes. The white matter change indicated a significant decrease in the corpus callosum in the verum group. Furthermore, the learning rate of SMR regulation was correlated with the volume changes in the ipsilateral SM1, suggesting the involvement of interhemispheric motor control circuitries in BCI control tasks.},
}
@article {pmid36598169,
year = {2023},
author = {Sinha, S and Hashim, H and Finazzi-Agrò, E and Dmochowski, RR and Iacovelli, V},
title = {The bladder contractility and bladder outlet obstruction indices in children. Results of a global Delphi consensus study.},
journal = {Neurourology and urodynamics},
volume = {42},
number = {2},
pages = {472-477},
doi = {10.1002/nau.25129},
pmid = {36598169},
issn = {1520-6777},
mesh = {Male ; Adult ; Humans ; Child ; Female ; Urinary Bladder ; Delphi Technique ; *Urinary Bladder Neck Obstruction ; *Urethral Obstruction ; Urodynamics ; },
abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI), Bladder Outlet Obstruction Index (BOOI), and the related evidence. This manuscript deals with children and follows previous manuscripts reporting on adult men and women.<br><br>METHODS: Eighteen experts were invited to answer the two-round survey including three&#xa0;foundation questions and four&#xa0;survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in Round 1 was classified into "strongly agree,"&#xa0;"agree,"&#xa0;"neutral,"&#xa0;"disagree," and "strongly disagree"&#xa0;for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in children.<br><br>RESULTS: Eleven experts participated in the survey with 100% completion. Consensus was not noted with regard to any of the questions. There was a general trend toward disagreement with the utility of the BCI and BOOI in children. Systematic search yielded one&#xa0;publication pertaining the value of the indices in predicting long-term outcome in boys treated for posterior urethral valves.<br><br>CONCLUSIONS: This global Delphi survey of experts showed a general disinclination to use numerical indices for bladder contractility and bladder outflow obstruction in children. There is very little data on the use of the BCI and BOOI indices in children. The establishment of urodynamic indices in children might help refine the treatment of functional urological disorders in children.},
}
@article {pmid36595316,
year = {2023},
author = {Yasemin, M and Cruz, A and Nunes, UJ and Pires, G},
title = {Single trial detection of error-related potentials in brain-machine interfaces: a survey and comparison of methods.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acabe9},
pmid = {36595316},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Reproducibility of Results ; Brain ; Man-Machine Systems ; Algorithms ; },
abstract = {Objective.Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).Approach.With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.Main results.From our analysis, we have found that shrinkage-regularized linear discriminant analysis is the most robust method for classification, and for feature extraction, using Fisher criterion beamformer spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).Significance.This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.},
}
@article {pmid36594734,
year = {2023},
author = {Huang, Z and Wu, J and Zhao, Y and Zhang, D and Tong, L and Gao, F and Liu, C and Chen, F},
title = {Starch-based shape memory sponge for rapid hemostasis in penetrating wounds.},
journal = {Journal of materials chemistry. B},
volume = {11},
number = {4},
pages = {852-864},
doi = {10.1039/d2tb02364d},
pmid = {36594734},
issn = {2050-7518},
mesh = {Animals ; Rabbits ; *Gelatin/pharmacology ; Staphylococcus aureus ; Starch ; Escherichia coli ; Hemostasis ; Hemorrhage/drug therapy ; *Wounds, Penetrating ; },
abstract = {Death caused by excessive blood loss has always been a global concern. Timely control of bleeding in incompressible penetrated wounds remains a great challenge. Here, we developed a shape memory sponge (SQG) based on modified starch and gelatin (Gel) to control the hemorrhage of penetrating wounds. The porous structure of SQG greatly enhanced the absorption of blood, and the adhesion of erythrocytes and platelets. The water absorption rate of SQG reached 1178.72 ± 12.18% in 10 s. SQG quickly recovered its shape in water (∼3 s) and exhibited high mechanical strength (∼38 kPa), acting as a physically packed barrier to facilitate hemostasis. Furthermore, the positively charged sponges were conducive to activating platelets and promoting the release of coagulation factors. SQG sponges possessed the lowest blood coagulation index (BCI) of 21.32 ± 0.19%, and presented good biocompatibility and obvious inhibitory effect on Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Moreover, SQG sponges controlled complete bleeding in 69 ± 20 s and a bleeding loss of 334 ± 138 mg was observed, nearly 50% lower than that of gelatin sponge in rabbit liver penetrating wounds. Overall, SQG possesses a combination of potent shape recovery, rapid hemostasis, and excellent antibacterial and degradation ability, enabling promising applications for hemostasis in non-compressible penetrating wounds.},
}
@article {pmid36591913,
year = {2023},
author = {Wang, W and Li, B},
title = {A novel model based on a 1D-ResCNN and transfer learning for processing EEG attenuation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {26},
number = {16},
pages = {1980-1993},
doi = {10.1080/10255842.2022.2162339},
pmid = {36591913},
issn = {1476-8259},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Machine Learning ; },
abstract = {EEG signals are valuable signals in clinical medicine, brain research, and the study of neurological illnesses. However, EEG signal attenuation may occur at any time from signal generation through BCI device acquisition due to defects in the brain-computer interface (BCI) devices, restrictions in the dynamic network, and individual variations across the subjects. The attenuation of EEG data will alter the data distribution and lead to information fuzziness, substantially influencing subsequent EEG research. A model based on one-dimensional residual convolutional neural networks (1D-ResCNN) and transfer learning is proposed in this article to reduce the negative impacts of EEG attenuation. An end-to-end manner maps an attenuated EEG signal to a normal EEG signal. The structure employs a multi-level residual connection structure with varying weight coefficients, transferring characteristics from the bottom to the top of the convolutional neural network, enhancing feature learning. In addition, we initialize the subsequent denoising model using the transfer learning method. The combination of these two networks can well solve the attenuation problem of EEG signals. Experiments are carried out using the EEG-denoisenet data set. According to the findings, the model can yield a clear waveform with a decent SNR and RRMSE value.},
}
@article {pmid36590466,
year = {2022},
author = {Tonin, L and Perdikis, S and Kuzu, TD and Pardo, J and Orset, B and Lee, K and Aach, M and Schildhauer, TA and Martínez-Olivera, R and Millán, JDR},
title = {Learning to control a BMI-driven wheelchair for people with severe tetraplegia.},
journal = {iScience},
volume = {25},
number = {12},
pages = {105418},
pmid = {36590466},
issn = {2589-0042},
abstract = {Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the hypothesis that BMI skill acquisition by end-users is fundamental to control a non-invasive brain-actuated intelligent wheelchair in real-world settings. We demonstrate that three tetraplegic spinal-cord injury users could be trained to operate a non-invasive, self-paced thought-controlled wheelchair and execute complex navigation tasks. However, only the two users exhibiting increasing decoding performance and feature discriminancy, significant neuroplasticity changes and improved BMI command latency, achieved high navigation performance. In addition, we show that dexterous, continuous control of robots is possible through low-degree of freedom, discrete and uncertain control channels like a motor imagery BMI, by blending human and artificial intelligence through shared-control methodologies. We posit that subject learning and shared-control are the key components paving the way for translational non-invasive BMI.},
}
@article {pmid36589747,
year = {2022},
author = {Benito-León, M and Gil-Redondo, JC and Perez-Sen, R and Delicado, EG and Ortega, F and Gomez-Villafuertes, R},
title = {BCI, an inhibitor of the DUSP1 and DUSP6 dual specificity phosphatases, enhances P2X7 receptor expression in neuroblastoma cells.},
journal = {Frontiers in cell and developmental biology},
volume = {10},
number = {},
pages = {1049566},
pmid = {36589747},
issn = {2296-634X},
abstract = {P2X7 receptor (P2RX7) is expressed strongly by most human cancers, including neuroblastoma, where high levels of P2RX7 are correlated with a poor prognosis for patients. Tonic activation of P2X7 receptor favors cell metabolism and angiogenesis, thereby promoting cancer cell proliferation, immunosuppression, and metastasis. Although understanding the mechanisms that control P2X7 receptor levels in neuroblastoma cells could be biologically and clinically relevant, the intracellular signaling pathways involved in this regulation remain poorly understood. Here we show that (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), an allosteric inhibitor of dual specificity phosphatases (DUSP) 1 and 6, enhances the expression of P2X7 receptor in N2a neuroblastoma cells. We found that exposure to BCI induces the phosphorylation of mitogen-activated protein kinases p38 and JNK, while it prevents the phosphorylation of ERK1/2. BCI enhanced dual specificity phosphatase 1 expression, whereas it induced a decrease in the dual specificity phosphatase 6 transcripts, suggesting that BCI-dependent inhibition of dual specificity phosphatase 1 may be responsible for the increase in p38 and JNK phosphorylation. The weaker ERK phosphorylation induced by BCI was reversed by p38 inhibition, indicating that this MAPK is involved in the regulatory loop that dampens ERK activity. The PP2A phosphatase appears to be implicated in the p38-dependent dephosphorylation of ERK1/2. In addition, the PTEN phosphatase inhibition also prevented ERK1/2 dephosphorylation, probably through p38 downregulation. By contrast, inhibition of the p53 nuclear factor decreased ERK phosphorylation, probably enhancing the activity of p38. Finally, the inhibition of either p38 or Sp1-dependent transcription halved the increase in P2X7 receptor expression induced by BCI. Moreover, the combined inhibition of both p38 and Sp1 completely prevented the effect exerted by BCI. Together, our results indicate that dual specificity phosphatase 1 acts as a novel negative regulator of P2X7 receptor expression in neuroblastoma cells due to the downregulation of the p38 pathway.},
}
@article {pmid36589278,
year = {2022},
author = {Shi, B and Chen, X and Yue, Z and Zeng, F and Yin, S and Wang, B and Wang, J},
title = {Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {1004301},
pmid = {36589278},
issn = {1662-5188},
abstract = {BACKGROUND: Effectively decoding electroencephalogram (EEG) pattern for specific mental tasks is a crucial topic in the development of brain-computer interface (BCI). Extracting common spatial pattern (CSP) features from motor imagery EEG signals is often highly dependent on the selection of frequency band and time interval. Therefore, optimizing frequency band and time interval would contribute to effective feature extraction and accurate EEG decoding.<br><br>OBJECTIVE: This study proposes an approach based on an improved novel global harmony search (INGHS) to optimize frequency-time parameters for effective CSP feature extraction.<br><br>METHODS: The INGHS algorithm is applied to find the optimal frequency band and temporal interval. The linear discriminant analysis and support vector machine are used for EEG pattern decoding. Extensive experimental studies are conducted on three EEG datasets to assess the effectiveness of our proposed method.<br><br>RESULTS: The average test accuracy obtained by the time-frequency parameters selected by the proposed INGHS method is slightly better than artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms. Furthermore, the INGHS algorithm is superior to PSO and ABC in running time.<br><br>CONCLUSION: These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.},
}
@article {pmid36588886,
year = {2022},
author = {Xu, G and Hao, F and Zhao, W and Qiu, J and Zhao, P and Zhang, Q},
title = {The influential factors and non-pharmacological interventions of cognitive impairment in children with ischemic stroke.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1072388},
pmid = {36588886},
issn = {1664-2295},
abstract = {BACKGROUND: The prevalence of pediatric ischemic stroke rose by 35% between 1990 and 2013. Affected patients can experience the gradual onset of cognitive impairment in the form of impaired language, memory, intelligence, attention, and processing speed, which affect 20-50% of these patients. Only few evidence-based treatments are available due to significant heterogeneity in age, pathological characteristics, and the combined epilepsy status of the affected children.<br><br>METHODS: We searched the literature published by Web of Science, Scopus, and PubMed, which researched non-pharmacological rehabilitation interventions for cognitive impairment following pediatric ischemic stroke. The search period is from the establishment of the database to January 2022.<br><br>RESULTS: The incidence of such impairment is influenced by patient age, pathological characteristics, combined epilepsy status, and environmental factors. Non-pharmacological treatments for cognitive impairment that have been explored to date mainly include exercise training, psychological intervention, neuromodulation strategies, computer-assisted cognitive training, brain-computer interfaces (BCI), virtual reality, music therapy, and acupuncture. In childhood stroke, the only interventions that can be retrieved are psychological intervention and neuromodulation strategies.<br><br>CONCLUSION: However, evidence regarding the efficacy of these interventions is relatively weak. In future studies, the active application of a variety of interventions to improve pediatric cognitive function will be necessary, and neuroimaging and electrophysiological measurement techniques will be of great value in this context. Larger multi-center prospective longitudinal studies are also required to offer more accurate evidence-based guidance for the treatment of patients with pediatric stroke.},
}
@article {pmid36587114,
year = {2023},
author = {Pan, W and Huang, X and Yu, Z and Ding, Q and Xia, L and Hua, J and Gu, B and Xiong, Q and Yu, H and Wang, J and Xu, Z and Zeng, L and Bai, G and Liu, H},
title = {Netrin-3 Suppresses Diabetic Neuropathic Pain by Gating the Intra-epidermal Sprouting of Sensory Axons.},
journal = {Neuroscience bulletin},
volume = {39},
number = {5},
pages = {745-758},
pmid = {36587114},
issn = {1995-8218},
mesh = {Mice ; Animals ; *Diabetes Mellitus, Experimental/complications/metabolism ; Axons/physiology ; *Diabetic Neuropathies ; Sensory Receptor Cells/metabolism ; *Neuralgia/metabolism ; },
abstract = {Diabetic neuropathic pain (DNP) is the most common disabling complication of diabetes. Emerging evidence has linked the pathogenesis of DNP to the aberrant sprouting of sensory axons into the epidermal area; however, the underlying molecular events remain poorly understood. Here we found that an axon guidance molecule, Netrin-3 (Ntn-3), was expressed in the sensory neurons of mouse dorsal root ganglia (DRGs), and downregulation of Ntn-3 expression was highly correlated with the severity of DNP in a diabetic mouse model. Genetic ablation of Ntn-3 increased the intra-epidermal sprouting of sensory axons and worsened the DNP in diabetic mice. In contrast, the elevation of Ntn-3 levels in DRGs significantly inhibited the intra-epidermal axon sprouting and alleviated DNP in diabetic mice. In conclusion, our studies identified Ntn-3 as an important regulator of DNP pathogenesis by gating the aberrant sprouting of sensory axons, indicating that Ntn-3 is a potential druggable target for DNP treatment.},
}
@article {pmid36586179,
year = {2023},
author = {Havaei, P and Zekri, M and Mahmoudzadeh, E and Rabbani, H},
title = {An efficient deep learning framework for P300 evoked related potential detection in EEG signal.},
journal = {Computer methods and programs in biomedicine},
volume = {229},
number = {},
pages = {107324},
doi = {10.1016/j.cmpb.2022.107324},
pmid = {36586179},
issn = {1872-7565},
mesh = {Electroencephalography/methods ; *Deep Learning ; Algorithms ; Evoked Potentials/physiology ; Neural Networks, Computer ; *Brain-Computer Interfaces ; },
abstract = {BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can exhibit their characteristics to reveal an effective solution in the detection of P300 evoked related potential (ERP). By applying a drastic number of convolutional layers, the majority of deep networks elicit sufficient properties for the output determination, leading to gigantic and time-consuming structures. In this paper, we propose a novel deep learning framework by the combination of tuned GT, and modified HOG with the CNN as "TGT-MHOG-CNN" for detection of P300 ERP in EEG signal.<br><br>METHOD: In the proposed method, GT is tuned based on triangular function for EEG signals, and then spectrograms including time-frequency information are captured. The function's parameters are justified to differentiate the signals with the P300 component. Furthermore, HOG is modified (MHOG) for the 2-D EEG signal, and consequently, gradients patterns are extracted for the target potentials. MHOG is potent in distinguishing the positive peak in the general waveform; however, GT unravels time-frequency information, which is ignored in the gradient histogram. These outputs of GT and MHOG do not share the same nature in the images nor overlap. Therefore, more extensive information is reached without redundancy or excessive information by fusing them. Combining GT and MHOG provides different patterns which benefit CNN for more precise detection. Consequently, TGT-MHOG-CNN ends in a more straightforward structure than other networks, and therefore, the whole performance is acceptable with faster rates and very high accuracy.<br><br>RESULTS: BCI Competition II and III datasets are used to evaluate the performance of the proposed method. These datasets include a complete record for P300 ERP with BCI2000 using a paradigm, and it has numerous noises, including power and muscle-based noises. The objective is to predict the correct character in each provided character selection epochs. Compared to state-of-the-art methods, simulation results indicate striking abilities of the proposed framework for P300 ERP detection. Our best record reached the P300 ERP classification rates of over 98.7% accuracy and 98.7% precision for BCI Competition II and 99% accuracy and 100% precision for BCI Competition III datasets, with superiority in execution time for the mentioned datasets.},
}
@article {pmid36586146,
year = {2023},
author = {Borda, E and Medagoda, DI and Airaghi Leccardi, MJI and Zollinger, EG and Ghezzi, D},
title = {Conformable neural interface based on off-stoichiometry thiol-ene-epoxy thermosets.},
journal = {Biomaterials},
volume = {293},
number = {},
pages = {121979},
doi = {10.1016/j.biomaterials.2022.121979},
pmid = {36586146},
issn = {1878-5905},
mesh = {Mice ; Animals ; *Sulfhydryl Compounds/chemistry ; *Nervous System ; Electrodes ; Prostheses and Implants ; Brain ; },
abstract = {Off-stoichiometry thiol-ene-epoxy (OSTE+) thermosets show low permeability to gases and little absorption of dissolved molecules, allow direct low-temperature dry bonding without surface treatments, have a low Young's modulus, and can be manufactured via UV polymerisation. For these reasons, OSTE+ thermosets have recently gained attention for the rapid prototyping of microfluidic chips. Moreover, their compatibility with standard clean-room processes and outstanding mechanical properties make OSTE+ an excellent candidate as a novel material for neural implants. Here we exploit OSTE+ to manufacture a conformable multilayer micro-electrocorticography array with 16 platinum electrodes coated with platinum black. The mechanical properties allow conformability to curved surfaces such as the brain. The low permeability and strong adhesion between layers improve the stability of the device. Acute experiments in mice show the multimodal capacity of the array to record and stimulate the neural tissue by smoothly conforming to the mouse cortex. Devices are not cytotoxic, and immunohistochemistry stainings reveal only modest foreign body reaction after two and six weeks of chronic implantation. This work introduces OSTE+ as a promising material for implantable neural interfaces.},
}
@article {pmid36583387,
year = {2022},
author = {Sato, A and Nakatani, S},
title = {Independent bilateral-eye stimulation for gaze pattern recognition based on steady-state pupil light reflex.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/acab31},
pmid = {36583387},
issn = {1741-2552},
mesh = {Humans ; *Pupil/physiology ; *Vision, Binocular/physiology ; Evoked Potentials, Visual ; Vision, Monocular/physiology ; Reflex ; Photic Stimulation ; },
abstract = {Objective:recently, pupil oscillations synchronized with steady visual stimuli were used as input for an interface. The proposed system, inspired by a brain-computer interface based on steady-state visual evoked potentials, does not require contact with the participant. However, the pupil oscillation mechanism limits the stimulus frequency to 2.5 Hz or less, making it hard to enhance the information transfer rate (ITR).Approach:here, we compared multiple conditions for stimulation to increase the ITR of the pupil vibration-based interface, which were called monocular-single, monocular-superposed, and binocular-independent conditions. The binocular-independent condition stimulates each eye at different frequencies respectively and mixes them by using the visual stereoscopic perception of users. The monocular-superposed condition stimulates both eyes by a mixed signal of two different frequencies. We selected the shape of the stimulation signal, evaluated the amount of spectral leakage in the monocular-superposed and binocular-independent conditions, and compared the power spectrum density at the stimulation frequency. Moreover, 5, 10, and 15 patterns of stimuli were classified in each condition.Main results:a square wave, which causes an efficient pupil response, was used as the stimulus. Spectral leakage at the beat frequency was higher in the monocular-superposed condition than in the binocular-independent one. The power spectral density of stimulus frequencies was greatest in the monocular-single condition. Finally, we could classify the 15-stimulus pattern, with ITRs of 14.4 (binocular-independent, using five frequencies), 14.5 (monocular-superimposed, using five frequencies), and 23.7 bits min[-1](monocular-single, using 15 frequencies). There were no significant differences for the binocular-independent and monocular-superposed conditions.Significance:this paper shows a way to increase the number of stimuli that can be simultaneously displayed without decreasing ITR, even when only a small number of frequencies are available. This could lead to the provision of an interface based on pupil oscillation to a wider range of users.},
}
@article {pmid36583011,
year = {2022},
author = {Phunruangsakao, C and Achanccaray, D and Izumi, SI and Hayashibe, M},
title = {Multibranch convolutional neural network with contrastive representation learning for decoding same limb motor imagery tasks.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1032724},
pmid = {36583011},
issn = {1662-5161},
abstract = {INTRODUCTION: Emerging deep learning approaches to decode motor imagery (MI) tasks have significantly boosted the performance of brain-computer interfaces. Although recent studies have produced satisfactory results in decoding MI tasks of different body parts, the classification of such tasks within the same limb remains challenging due to the activation of overlapping brain regions. A single deep learning model may be insufficient to effectively learn discriminative features among tasks.<br><br>METHODS: The present study proposes a framework to enhance the decoding of multiple hand-MI tasks from the same limb using a multi-branch convolutional neural network. The CNN framework utilizes feature extractors from established deep learning models, as well as contrastive representation learning, to derive meaningful feature representations for classification.<br><br>RESULTS: The experimental results suggest that the proposed method outperforms several state-of-the-art methods by obtaining a classification accuracy of 62.98% with six MI classes and 76.15 % with four MI classes on the Tohoku University MI-BCI and BCI Competition IV datasets IIa, respectively.<br><br>DISCUSSION: Despite requiring heavy data augmentation and multiple optimization steps, resulting in a relatively long training time, this scheme is still suitable for online use. However, the trade-of between the number of base learners, training time, prediction time, and system performance should be carefully considered.},
}
@article {pmid36582164,
year = {2022},
author = {Kotov, SV and Slyunkova, EV and Borisova, VA and Isakova, EV},
title = {[Effectiveness of brain-computer interfaces and cognitive training using computer technologies in restoring cognitive functions in patients after stroke].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {122},
number = {12. Vyp. 2},
pages = {67-75},
doi = {10.17116/jnevro202212212267},
pmid = {36582164},
issn = {1997-7298},
mesh = {Aged ; Humans ; Middle Aged ; *Brain-Computer Interfaces ; Cognition ; *Cognitive Training ; Computers ; *Stroke/complications/therapy/psychology ; *Stroke Rehabilitation/methods ; },
abstract = {OBJECTIVE: To study the effectiveness of brain-computer interfaces (BCI) and cognitive training using computer technologies in restoring cognitive functions in poststroke patients.<br><br>MATERIAL AND METHODS: Thirty-four stroke patients (mean age 59.3&#xb1;10.8 years) with stroke duration of 5.1&#xb1;4.7 months, were included. To assess the effectiveness of treatment, patients before and after treatment were tested using memorization of words according to the method of Luria A.R. &#xab;10 words&#xbb;, the Montreal Cognitive Assessment Scale (MoCA), the Clock Drawing Test (CDT). All patients received standard rehabilitation therapy (exercise therapy, physiotherapy, sessions with a speech therapist-neuropsychologist). Patients of the first group additionally received training on the &#xab;Neurochat&#xbb; complex, patients of the second group - on the &#xab;Exokist-2&#xbb; complex, patients of the third group - cognitive training according to standard programs using computer technology and visual material.<br><br>RESULTS: Patients of the three groups showed a significant improvement in the total MoCA score: in the 1[st] and 2[nd] groups - p<0.01, in the 3[rd] group - p<0.05. According to CDT, there was a significant change in the 2[nd] group (p=0.018). The Luria method &#xab;10 words&#xbb; revealed an improvement in memory in all groups (p<0.01, p<0.05), being more pronounced in the 1[st] and 2[nd] groups.<br><br>CONCLUSION: The effectiveness of BCI in restoring cognitive functions in patients after a stroke in comparison with cognitive training without BCI has been demonstrated. However, there are reasons to believe that various BCIs have a specific effect on cognitive functions and have their own target group.},
}
@article {pmid36582163,
year = {2022},
author = {Borisova, VA and Isakova, EV and Kotov, SV},
title = {[Possibilities of the brain-computer interface in the correction of post-stroke cognitive impairments].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {122},
number = {12. Vyp. 2},
pages = {60-66},
doi = {10.17116/jnevro202212212260},
pmid = {36582163},
issn = {1997-7298},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Stroke ; *Neurological Rehabilitation ; *Cognitive Dysfunction/etiology ; },
abstract = {In recent years, brain-computer interfaces have been widely used in neurorehabilitation, and an extensive database of results from clinical studies conducted around the world has been accumulated, demonstrating their effectiveness in restoring motor function after a stroke. Currently, their use in post-stroke cognitive impairment is expanding. This article discusses the potential and prospects for using brain-computer interfaces for the treatment of cognitive disorders, reviews the experience of using it, presents the results of clinical studies in stroke patients, evaluates the possibilities of using this technology, describes the prospects, new directions of work on studying its effects.},
}
@article {pmid36579369,
year = {2022},
author = {Goueytes, D and Lassagne, H and Shulz, DE and Ego-Stengel, V and Estebanez, L},
title = {Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/acab87},
pmid = {36579369},
issn = {1741-2552},
mesh = {Mice ; Animals ; Feedback ; *Brain-Computer Interfaces ; Optogenetics/methods ; Learning ; Movement ; },
abstract = {Objective.Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface (BMI). Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop BMI.Approach.We tested the ability of mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint.Main results.We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control.Significance.These findings suggest that in the context of a closed-loop BMI, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.},
}
@article {pmid36578777,
year = {2022},
author = {Alwasiti, H and Yusoff, MZ},
title = {Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {3},
number = {},
pages = {171-177},
pmid = {36578777},
issn = {2644-1276},
abstract = {Goal: Building a DL model that can be trained on small EEG training set of a single subject presents an interesting challenge that this work is trying to address. In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. Methods: A customized Convolutional Neural Network with mixup augmentation was trained with [Formula: see text]120 EEG trials for only one subject per model. Results: Modified ResNet18 and DenseNet121 models with mixup augmentation achieved 0.920 (95% Confidence Interval: 0.908, 0.933) and 0.933 (95% Confidence Interval: 0.922, 0.945) classification accuracy, respectively. Conclusions: We show that the designed classifiers resulted in a higher classification performance in comparison to other DL classifiers of previous studies on the same dataset, despite the limited training dataset used in this work.},
}
@article {pmid36577882,
year = {2023},
author = {Cao, K and Hu, Y and Gao, Z},
title = {Sense to Tune: Engaging Microglia with Dynamic Neuronal Activity.},
journal = {Neuroscience bulletin},
volume = {39},
number = {3},
pages = {553-556},
pmid = {36577882},
issn = {1995-8218},
mesh = {*Microglia/physiology ; *Neurons/physiology ; Cells, Cultured ; },
}
@article {pmid36577144,
year = {2023},
author = {Duan, X and Xie, S and Lv, Y and Xie, X and Obermayer, K and Yan, H},
title = {A transfer learning-based feedback training motivates the performance of SMR-BCI.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acaee7},
pmid = {36577144},
issn = {1741-2552},
mesh = {Humans ; Feedback ; *Brain-Computer Interfaces ; Learning/physiology ; Electroencephalography/methods ; Machine Learning ; },
abstract = {Objective. Feedback training is a practical approach to brain-computer interface (BCI) end-users learning to modulate their sensorimotor rhythms (SMRs). BCI self-regulation learning has been shown to be influenced by subjective psychological factors, such as motivation. However, few studies have taken into account the users' self-motivation as additional guidance for the cognitive process involved in BCI learning. In this study we tested a transfer learning (TL) feedback method designed to increase self-motivation by providing information about past performance.Approach. Electroencephalography (EEG) signals from the previous runs were affine transformed and displayed as points on the screen, along with the newly recorded EEG signals in the current run, giving the subjects a context for self-motivation. Subjects were asked to separate the feedback points for the current run under the display of the separability of prior training. We conducted a between-subject feedback training experiment, in which 24 healthy SMR-BCI naive subjects were trained to imagine left- and right-hand movements. The participants were provided with either TL feedback or typical cursor-bar (CB) feedback (control condition), for three sessions on separate days.Main results. The behavioral results showed an increased challenge and stable mastery confidence, suggesting that subjects' motivation grew as the feedback training went on. The EEG results showed favorable overall training effects with TL feedback in terms of the class distinctiveness and EEG discriminancy. Performance was 28.5% higher in the third session than in the first. About 41.7% of the subjects were 'learners' including not only low-performance subjects, but also good-performance subjects who might be affected by the ceiling effect. Subjects were able to control BCI with TL feedback with a higher performance of 60.5% during the last session compared to CB feedback.Significance. The present study demonstrated that the proposed TL feedback method boosted psychological engagement through the self-motivated context, and further allowed subjects to modulate SMR effectively. The proposed TL feedback method also provided an alternative to typical CB feedback.},
}
@article {pmid36576451,
year = {2023},
author = {Rinoldi, C and Ziai, Y and Zargarian, SS and Nakielski, P and Zembrzycki, K and Haghighat Bayan, MA and Zakrzewska, AB and Fiorelli, R and Lanzi, M and Kostrzewska-Księżyk, A and Czajkowski, R and Kublik, E and Kaczmarek, L and Pierini, F},
title = {In Vivo Chronic Brain Cortex Signal Recording Based on a Soft Conductive Hydrogel Biointerface.},
journal = {ACS applied materials &amp; interfaces},
volume = {15},
number = {5},
pages = {6283-6296},
doi = {10.1021/acsami.2c17025},
pmid = {36576451},
issn = {1944-8252},
mesh = {Mice ; Animals ; *Hydrogels/pharmacology ; *Brain ; Electric Conductivity ; Cerebral Cortex ; },
abstract = {In neuroscience, the acquisition of neural signals from the brain cortex is crucial to analyze brain processes, detect neurological disorders, and offer therapeutic brain-computer interfaces. The design of neural interfaces conformable to the brain tissue is one of today's major challenges since the insufficient biocompatibility of those systems provokes a fibrotic encapsulation response, leading to an inaccurate signal recording and tissue damage precluding long-term/permanent implants. The design and production of a novel soft neural biointerface made of polyacrylamide hydrogels loaded with plasmonic silver nanocubes are reported herein. Hydrogels are surrounded by a silicon-based template as a supporting element for guaranteeing an intimate neural-hydrogel contact while making possible stable recordings from specific sites in the brain cortex. The nanostructured hydrogels show superior electroconductivity while mimicking the mechanical characteristics of the brain tissue. Furthermore, in vitro biological tests performed by culturing neural progenitor cells demonstrate the biocompatibility of hydrogels along with neuronal differentiation. In vivo chronic neuroinflammation tests on a mouse model show no adverse immune response toward the nanostructured hydrogel-based neural interface. Additionally, electrocorticography acquisitions indicate that the proposed platform permits long-term efficient recordings of neural signals, revealing the suitability of the system as a chronic neural biointerface.},
}
@article {pmid36575664,
year = {2022},
author = {Guo, Z and Wang, F and Wang, L and Tu, K and Jiang, C and Xi, Y and Hong, W and Xu, Q and Wang, X and Yang, B and Sun, B and Lin, Z and Liu, J},
title = {A flexible neural implant with ultrathin substrate for low-invasive brain-computer interface applications.},
journal = {Microsystems &amp; nanoengineering},
volume = {8},
number = {},
pages = {133},
pmid = {36575664},
issn = {2055-7434},
abstract = {Implantable brain-computer interface (BCI) devices are an effective tool to decipher fundamental brain mechanisms and treat neural diseases. However, traditional neural implants with rigid or bulky cross-sections cause trauma and decrease the quality of the neuronal signal. Here, we propose a MEMS-fabricated flexible interface device for BCI applications. The microdevice with a thin film substrate can be readily reduced to submicron scale for low-invasive implantation. An elaborate silicon shuttle with an improved structure is designed to reliably implant the flexible device into brain tissue. The flexible substrate is temporarily bonded to the silicon shuttle by polyethylene glycol. On the flexible substrate, eight electrodes with different diameters are distributed evenly for local field potential and neural spike recording, both of which are modified by Pt-black to enhance the charge storage capacity and reduce the impedance. The mechanical and electrochemical characteristics of this interface were investigated in vitro. In vivo, the small cross-section of the device promises reduced trauma, and the neuronal signals can still be recorded one month after implantation, demonstrating the promise of this kind of flexible BCI device as a low-invasive tool for brain-computer communication.},
}
@article {pmid36575091,
year = {2022},
author = {Wang, H and Wang, S and Qiu, Z and Zhang, Q and Xu, S},
title = {[Design and preliminary application of outdoor flying pigeon-robot].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {6},
pages = {1209-1217},
pmid = {36575091},
issn = {1001-5515},
mesh = {Animals ; *Columbidae/physiology ; *Robotics/methods ; Cerebral Cortex ; },
abstract = {Control at beyond-visual ranges is of great significance to animal-robots with wide range motion capability. For pigeon-robots, such control can be done by the way of onboard preprogram, but not constitute a closed-loop yet. This study designed a new control system for pigeon-robots, which integrated the function of trajectory monitoring to that of brain stimulation. It achieved the closed-loop control in turning or circling by estimating pigeons' flight state instantaneously and the corresponding logical regulation. The stimulation targets located at the formation reticularis medialis mesencephali (FRM) in the left and right brain, for the purposes of left- and right-turn control, respectively. The stimulus was characterized by the waveform mimicking the nerve cell membrane potential, and was activated intermittently. The wearable control unit weighted 11.8 g totally. The results showed a 90% success rate by the closed-loop control in pigeon-robots. It was convenient to obtain the wing shape during flight maneuver, by equipping a pigeon-robot with a vivo camera. It was also feasible to regulate the evolution of pigeon flocks by the pigeon-robots at different hierarchical level. All of these lay the groundwork for the application of pigeon-robots in scientific researches.},
}
@article {pmid36575087,
year = {2022},
author = {Pan, L and Ding, Y and Wang, S and Song, A},
title = {[Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {6},
pages = {1173-1180},
pmid = {36575087},
issn = {1001-5515},
mesh = {Humans ; *Imagination ; Signal Processing, Computer-Assisted ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms ; },
abstract = {Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.},
}
@article {pmid36575075,
year = {2022},
author = {Song, H and Xu, S and Liu, G and Liu, J and Xiong, P},
title = {[Automatic removal algorithm of electrooculographic artifacts in non-invasive brain-computer interface based on independent component analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {6},
pages = {1074-1081},
pmid = {36575075},
issn = {1001-5515},
mesh = {Humans ; Electrooculography/methods ; *Artifacts ; *Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.},
}
@article {pmid36575074,
year = {2022},
author = {Hu, Y and Liu, Y and Cheng, C and Geng, C and Dai, B and Peng, B and Zhu, J and Dai, Y},
title = {[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {6},
pages = {1065-1073},
pmid = {36575074},
issn = {1001-5515},
mesh = {Humans ; Adult ; *Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Electroencephalography/methods ; Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; },
abstract = {The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.},
}
@article {pmid36572173,
year = {2023},
author = {Zhu, X and Zhou, H and Geng, F and Wang, J and Xu, H and Hu, Y},
title = {Functional Connectivity Between Basal Forebrain and Superficial Amygdala Negatively Correlates with Social Fearfulness.},
journal = {Neuroscience},
volume = {510},
number = {},
pages = {72-81},
doi = {10.1016/j.neuroscience.2022.12.020},
pmid = {36572173},
issn = {1873-7544},
mesh = {Male ; Female ; Young Adult ; Humans ; *Basal Forebrain ; Fear/physiology ; Amygdala/diagnostic imaging ; Emotions/physiology ; Anger ; Brain Mapping ; Magnetic Resonance Imaging/methods ; Facial Expression ; },
abstract = {Social anxiety is characterized by an intense fear of evaluation from others and/or withdrawal from social situations. Extreme social anxiety can lead to social anxiety disorder. There remains an urgent need to investigate the neural substrates of subclinical social anxiety for early diagnosis and intervention to reduce the risk to develop social anxiety disorder. Twenty-nine young adults were recruited (10 males/19 females; mean age (SD) = 20.34 (2.29)). Trait-like social anxiety was assessed by Liebowitz Social Anxiety Scale. Functional magnetic resonance imaging was used with an emotional face-matching paradigm to probe brain activation in response to emotional stimuli including angry, fearful, and happy faces, with shape-matching as a control condition. Behavioral results showed positive correlations between Liebowitz Social Anxiety Scale scores and the reaction time in both angry and fearful conditions. The activation of superficial amygdala and the deactivation of basal forebrain in response to angry condition showed positive correlations with the level of social anxiety. In addition, the resting-state functional connectivity between these two regions was negatively correlated with the level of social anxiety. These results may help to understand the individual difference and corresponding neural underpinnings of social anxiety in the subclinical population, and might provide some insight to develop strategies for early diagnosis and interventions of social anxiety to reduce the risk of deterioration from subclinical to clinical level of social anxiety.},
}
@article {pmid36569472,
year = {2022},
author = {Lee Friesen, C and Lawrence, M and Ingram, TGJ and Boe, SG},
title = {Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1023246},
pmid = {36569472},
issn = {1662-5161},
abstract = {INTRODUCTION: Improved understanding of the relationship between post-stroke rehabilitation interventions and functional motor outcomes could result in improvements in the efficacy of post-stroke physical rehabilitation. The laterality of motor cortex activity (M1-LAT) during paretic upper-extremity movement has been documented as a useful biomarker of post-stroke motor recovery. However, the expensive, labor intensive, and laboratory-based equipment required to take measurements of M1-LAT limit its potential clinical utility in improving post-stroke physical rehabilitation. The present study tested the ability of a mobile functional near-infrared spectroscopy (fNIRS) system (designed to enable independent measurement by stroke survivors) to measure cerebral hemodynamics at the motor cortex in the homes of chronic stroke survivors.<br><br>METHODS: Eleven chronic stroke survivors, ranging widely in their level of upper-extremity motor deficit, used their stroke-affected upper-extremity to perform a simple unilateral movement protocol in their homes while a wireless prototype fNIRS headband took measurements at the motor cortex. Measures of participants' upper-extremity impairment and function were taken.<br><br>RESULTS: Participants demonstrated either a typically lateralized response, with an increase in contralateral relative oxyhemoglobin (&#x394;HbO), or response showing a bilateral pattern of increase in &#x394;HbO during the motor task. During the simple unilateral task, M1-LAT correlated significantly with measures of both upper-extremity impairment and function, indicating that participants with more severe motor deficits had more a more atypical (i.e., bilateral) pattern of lateralization.<br><br>DISCUSSION: These results indicate it is feasible to gain M1-LAT measures from stroke survivors in their homes using fNIRS. These findings represent a preliminary step toward the goals of using ergonomic functional neuroimaging to improve post-stroke rehabilitative care, via the capture of neural biomarkers of post-stroke motor recovery, and/or via use as part of an accessible rehabilitation brain-computer-interface.},
}
@article {pmid36567362,
year = {2022},
author = {Sorkhi, M and Jahed-Motlagh, MR and Minaei-Bidgoli, B and Daliri, MR},
title = {Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {22334},
pmid = {36567362},
issn = {2045-2322},
mesh = {*Artificial Intelligence ; Bayes Theorem ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.},
}
@article {pmid36565984,
year = {2023},
author = {West, TO and Duchet, B and Farmer, SF and Friston, KJ and Cagnan, H},
title = {When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states.},
journal = {Progress in neurobiology},
volume = {221},
number = {},
pages = {102397},
pmid = {36565984},
issn = {1873-5118},
support = {205103/WT_/Wellcome Trust/United Kingdom ; 205103/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; MR/R020418/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/1/MRC_/Medical Research Council/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Beta Rhythm/physiology ; Psychomotor Performance/physiology ; *Motor Cortex/physiology ; Movement/physiology ; Electrocorticography ; },
abstract = {Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.},
}
@article {pmid36563409,
year = {2023},
author = {Liang, Q and Shen, Z and Sun, X and Yu, D and Liu, K and Mugo, SM and Chen, W and Wang, D and Zhang, Q},
title = {Electron Conductive and Transparent Hydrogels for Recording Brain Neural Signals and Neuromodulation.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {35},
number = {9},
pages = {e2211159},
doi = {10.1002/adma.202211159},
pmid = {36563409},
issn = {1521-4095},
support = {20210509036RQ//Jilin Province Science and Technology Development Plan/ ; 2021SYHZ0038//Jilin Province Science and Technology Development Plan/ ; 2018YFD1100503//National Key Research and Development Program of China/ ; 2020YFA0713601//National Key Research and Development Program of China/ ; U22A20183and32260244//National Natural Science Foundation of China/ ; 21ZGY04//Changchun Province Science and Technology Development Plan/ ; U22A20183//National Natural Science Foundation of China/ ; },
mesh = {Rats ; Animals ; *Polymers/chemistry ; Hydrogels/chemistry ; Electrons ; *Neurodegenerative Diseases ; Pyrroles ; Brain ; },
abstract = {Recording brain neural signals and optogenetic neuromodulations open frontiers in decoding brain neural information and neurodegenerative disease therapeutics. Conventional implantable probes suffer from modulus mismatch with biological tissues and an irreconcilable tradeoff between transparency and electron conductivity. Herein, a strategy is proposed to address these tradeoffs, which generates conductive and transparent hydrogels with polypyrrole-decorated microgels as cross-linkers. The optical transparency of the electrodes can be attributed to the special structures that allow light waves to bypass the microgel particles and minimize their interaction. Demonstrated by probing the hippocampus of rat brains, the biomimetic electrode shows a prolonged capacity for simultaneous optogenetic neuromodulation and recording of brain neural signals. More importantly, an intriguing brain-machine interaction is realized, which involves signal input to the brain, brain neural signal generation, and controlling limb behaviors. This breakthrough work represents a significant scientific advancement toward decoding brain neural information and developing neurodegenerative disease therapies.},
}
@article {pmid36560369,
year = {2022},
author = {Akram, F and Alwakeel, A and Alwakeel, M and Hijji, M and Masud, U},
title = {A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {24},
pages = {},
pmid = {36560369},
issn = {1424-8220},
support = {001/1443//Sensor Networks and Cellular Systems Research Center (SNCS)/ ; },
mesh = {Humans ; Event-Related Potentials, P300 ; *Brain-Computer Interfaces ; Brain ; *Telecommunications ; Writing ; Electroencephalography ; },
abstract = {Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results.},
}
@article {pmid36560172,
year = {2022},
author = {Kartsch, VJ and Kumaravel, VP and Benatti, S and Vallortigara, G and Benini, L and Farella, E and Buiatti, M},
title = {Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {24},
pages = {},
pmid = {36560172},
issn = {1424-8220},
support = {842243/ERC_/European Research Council/International ; },
mesh = {Electroencephalography/methods ; Evoked Potentials, Visual ; Canonical Correlation Analysis ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; *Wearable Electronic Devices ; Algorithms ; },
abstract = {Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.},
}
@article {pmid36560158,
year = {2022},
author = {Saichoo, T and Boonbrahm, P and Punsawad, Y},
title = {Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {24},
pages = {},
pmid = {36560158},
issn = {1424-8220},
support = {CGS-RF-2020/11//Walailak University Graduate Research Fund/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Movement/physiology ; *Wheelchairs ; },
abstract = {The research on the electroencephalography (EEG)-based brain-computer interface (BCI) is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency. Therefore, we focused on BCI tasks and protocols to yield high efficiency from the robust EEG features of individual users. This study proposes a task-based brain activity to gain the power of the alpha band, which included eyes closed for alpha response at the occipital area, attention to an upward arrow for alpha response at the frontal area, and an imagined left/right motor for alpha event-related desynchronization at the left/right motor cortex. An EPOC X neuroheadset was used to acquire the EEG signals. We also proposed user proficiency in motor imagery sessions with limb movement paradigms by recommending motor imagination tasks. Using the proposed system, we verified the feature extraction algorithms and command translation. Twelve volunteers participated in the experiment, and the conventional paradigm of motor imagery was used to compare the efficiencies. With utilized user proficiency in motor imagery, an average accuracy of 83.7% across the left and right commands was achieved. The recommended MI paradigm via user proficiency achieved an approximately 4% higher accuracy than the conventional MI paradigm. Moreover, the real-time control results of a simulated wheelchair revealed a high efficiency based on the time condition. The time results for the same task as the joystick-based control were still approximately three times longer. We suggest that user proficiency be used to recommend an individual MI paradigm for beginners. Furthermore, the proposed BCI system can be used for electric wheelchair control by people with severe disabilities.},
}
@article {pmid36557761,
year = {2022},
author = {Kiel, A and Creutz, I and Rückert, C and Kaltschmidt, BP and Hütten, A and Niehaus, K and Busche, T and Kaltschmidt, B and Kaltschmidt, C},
title = {Genome-Based Analysis of Virulence Factors and Biofilm Formation in Novel P. aeruginosa Strains Isolated from Household Appliances.},
journal = {Microorganisms},
volume = {10},
number = {12},
pages = {},
pmid = {36557761},
issn = {2076-2607},
abstract = {In household washing machines, opportunistic pathogens such as Pseudomonas aeruginosa are present, which represent the household as a possible reservoir for clinical pathogens. Here, four novel P. aeruginosa strains, isolated from different sites of household appliances, were investigated regarding their biofilm formation. Only two isolates showed strong surface-adhered biofilm formation. In consequence of these phenotypic differences, we performed whole genome sequencing using Oxford Nanopore Technology together with Illumina MiSeq. Whole genome data were screened for the prevalence of 285 virulence- and biofilm-associated genes as well as for prophages. Linking biofilm phenotypes and parallelly appearing gene compositions, we assume a relevancy of the las quorum sensing system and the phage-encoded bacteriophage control infection gene bci, which was found on integrated phi297 DNA in all biofilm-forming isolates. Additionally, only the isolates revealing strong biofilm formation harbored the ϕCTX-like prophage Dobby, implicating a role of this prophage on biofilm formation. Investigations on clinically relevant pathogens within household appliances emphasize their adaptability to harsh environments, with high concentrations of detergents, providing greater insights into pathogenicity and underlying mechanisms. This in turn opens the possibility to map and characterize potentially relevant strains even before they appear as pathogens in society.},
}
@article {pmid36553544,
year = {2022},
author = {Jardillier, R and Koca, D and Chatelain, F and Guyon, L},
title = {Optimal microRNA Sequencing Depth to Predict Cancer Patient Survival with Random Forest and Cox Models.},
journal = {Genes},
volume = {13},
number = {12},
pages = {},
pmid = {36553544},
issn = {2073-4425},
mesh = {Humans ; Proportional Hazards Models ; Random Forest ; Gene Expression Profiling ; *MicroRNAs/genetics ; *Lung Neoplasms/genetics ; RNA, Messenger/genetics ; },
abstract = {(1) Background: tumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Results: we first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at a lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups.},
}
@article {pmid36552139,
year = {2022},
author = {Qiu, P and Dai, J and Wang, T and Li, H and Ma, C and Xi, X},
title = {Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation.},
journal = {Brain sciences},
volume = {12},
number = {12},
pages = {},
pmid = {36552139},
issn = {2076-3425},
support = {2021C03031//Zhejiang Provincial Key Research and Development Program of China/ ; 61971169//National Natural Science Foundation of China/ ; LQ21H180005//Zhejiang Provincial Natural Science Foundation of China/ ; },
abstract = {Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.},
}
@article {pmid36551135,
year = {2022},
author = {Luo, J and Xue, N and Chen, J},
title = {A Review: Research Progress of Neural Probes for Brain Research and Brain-Computer Interface.},
journal = {Biosensors},
volume = {12},
number = {12},
pages = {},
pmid = {36551135},
issn = {2079-6374},
support = {2021YFB2011600//National Key R&amp;D Program of China/ ; No. 61901440//National Natural Science Foundation of China/ ; No. 4202080//Beijing Municipal Natural Science Foundation/ ; No.YESS20210341//Young Elite Scientists Sponsorship Program by CAST/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Brain/physiology ; Microelectrodes ; Neurons/physiology ; Microfluidics ; Electrodes, Implanted ; },
abstract = {Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, two new types of neural probes have been developed in recent years: optoprobes based on optogenetics and magnetrodes that record neural magnetic signals. In this review, we give a comprehensive overview of these three kinds of neural probes. We firstly discuss the development of microelectrodes and strategies for their flexibility, which is mainly represented by the selection of flexible substrates and new electrode materials. Subsequently, the concept of optogenetics is introduced, followed by the review of several novel structures of optoprobes, which are divided into multifunctional optoprobes integrated with microfluidic channels, artifact-free optoprobes, three-dimensional drivable optoprobes, and flexible optoprobes. At last, we introduce the fundamental perspectives of magnetoresistive (MR) sensors and then review the research progress of magnetrodes based on it.},
}
@article {pmid36551100,
year = {2022},
author = {Said, RR and Heyat, MBB and Song, K and Tian, C and Wu, Z},
title = {A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials.},
journal = {Biosensors},
volume = {12},
number = {12},
pages = {},
pmid = {36551100},
issn = {2079-6374},
support = {2019YFE0196700//National Science and Technology Ministry of China/ ; 2022YFH0046//Science &amp; Technology Department of Sichuan Province/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Robotics ; *Virtual Reality ; Brain ; },
abstract = {To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.},
}
@article {pmid36550974,
year = {2022},
author = {Orban, M and Elsamanty, M and Guo, K and Zhang, S and Yang, H},
title = {A Review of Brain Activity and EEG-Based Brain-Computer Interfaces for Rehabilitation Application.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {9},
number = {12},
pages = {},
pmid = {36550974},
issn = {2306-5354},
support = {BE2021012;//the Key Research and Development Program of Jiangsu Province,:/ ; 154232KYSB20200016;//the International Partnership Program of the Chinese Academy of Science,:/ ; },
abstract = {Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.},
}
@article {pmid36550932,
year = {2022},
author = {Abdullah, and Faye, I and Islam, MR},
title = {EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {9},
number = {12},
pages = {},
pmid = {36550932},
issn = {2306-5354},
abstract = {Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.},
}
@article {pmid36550229,
year = {2023},
author = {Latheef, S},
title = {Brain to Brain Interfaces (BBIs) in future military operations; blurring the boundaries of individual responsibility.},
journal = {Monash bioethics review},
volume = {41},
number = {1},
pages = {49-66},
pmid = {36550229},
issn = {1836-6716},
mesh = {Humans ; Female ; *Military Personnel ; Brain/physiology ; *Brain-Computer Interfaces ; Social Behavior ; },
abstract = {Developments in neurotechnology took a leap forward with the demonstration of the first Brain to Brain Interface (BBI). BBIs enable direct communication between two brains via a Brain Computer Interface (BCI) and bypasses the peripheral nervous system. This discovery promises new possibilities for future battlefield technology. As battlefield technology evolves, it is more likely to place greater demands on future soldiers. Future soldiers are more likely to process large amounts of data derived from an extensive networks of humans and machines. This raises several ethical and philosophical concerns. This paper will look at BBI technology in current stages of research, future BBI applications in the military and how the potential use of BBIs in military operations challenges the way we understand the concept of responsibility. In this paper, I propose that an individual connected to a BBI ought not to be held fully responsible for her actions. The justification for this proposition is based on three key points such as an individual connected to a BBI does not have the ability to act freely, has a diminished sense of self-agency and may not be able to demonstrate authenticity of the thoughts and memories generated when connected to the interface.},
}
@article {pmid36548997,
year = {2023},
author = {Nagarajan, A and Robinson, N and Guan, C},
title = {Relevance-based channel selection in motor imagery brain-computer interface.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acae07},
pmid = {36548997},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy ; Imagination/physiology ; Algorithms ; },
abstract = {Objective.Channel selection in the electroencephalogram (EEG)-based brain-computer interface (BCI) has been extensively studied for over two decades, with the goal being to select optimal subject-specific channels that can enhance the overall decoding efficacy of the BCI. With the emergence of deep learning (DL)-based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood.Approach.Here, we propose a novel methodology for implementing subject-independent channel selection in DL-based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from the Korea University EEG dataset.Main Results.Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p = 0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance-based channel selections provide significantly better accuracies compared to conventional weight-based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p = 0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p = 0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification.Significance.The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.},
}
@article {pmid36548189,
year = {2022},
author = {Tsiamalou, A and Dardiotis, E and Paterakis, K and Fotakopoulos, G and Liampas, I and Sgantzos, M and Siokas, V and Brotis, AG},
title = {EEG in Neurorehabilitation: A Bibliometric Analysis and Content Review.},
journal = {Neurology international},
volume = {14},
number = {4},
pages = {1046-1061},
pmid = {36548189},
issn = {2035-8385},
abstract = {BACKGROUND: There is increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community.<br><br>METHODS: We performed an electronic search in Scopus, looking for studies reporting on rehabilitation in patients with neurological disabilities. We used the most influential papers to outline the knowledge base and carried out a word co-occurrence analysis to identify the research hotspots. We also used depicted collaboration networks between universities, authors, and countries after analyzing the cocitations. The results were presented in summary tables, plots, and maps. Finally, a content review based on the top-20 most cited articles completed our study.<br><br>RESULTS: Our current bibliometric study was based on 874 records from 420 sources. There was vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by L.F. Nicolas-Alfonso and J. Gomez-Gill, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by J. Daly and J.R. Wolpaw (708 citations). The US, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of functional magnetic imaging to EEG-based brain-machine interface, motor imagery, and deep learning.<br><br>CONCLUSIONS: EEG constitutes the most significant input in brain-computer interfaces (BCIs) and can be successfully used in the neurorehabilitation of patients with stroke symptoms, amyotrophic lateral sclerosis, and traumatic brain and spinal injuries. EEG-based BCI facilitates the training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG-filtering algorithms.},
}
@article {pmid36547803,
year = {2023},
author = {Zhou, F and Zheng, J and Xu, H},
title = {Lighting up Oxytocin Neurons to Nurture the Brain.},
journal = {Neuroscience bulletin},
volume = {39},
number = {5},
pages = {866-868},
pmid = {36547803},
issn = {1995-8218},
mesh = {*Brain/metabolism ; *Oxytocin ; Light ; },
}
@article {pmid36545350,
year = {2022},
author = {Galiotta, V and Quattrociocchi, I and D'Ippolito, M and Schettini, F and Aricò, P and Sdoia, S and Formisano, R and Cincotti, F and Mattia, D and Riccio, A},
title = {EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1040816},
pmid = {36545350},
issn = {1662-5161},
abstract = {BACKGROUND: Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).<br><br>OBJECTIVES: The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.<br><br>METHODS: The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.<br><br>RESULTS: Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.<br><br>CONCLUSION: Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.},
}
@article {pmid36543809,
year = {2022},
author = {Shen, T and Yue, Y and Ba, F and He, T and Tang, X and Hu, X and Pu, J and Huang, C and Lv, W and Zhang, B and Lai, HY},
title = {Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson's disease.},
journal = {NPJ Parkinson's disease},
volume = {8},
number = {1},
pages = {174},
pmid = {36543809},
issn = {2373-8057},
support = {61673346//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101323//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81771216//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {The brain glymphatic system is involved in the clearance of misfolding α-synuclein, the impaired glymphatic system may contribute to the progression of Parkinson's disease (PD). We aimed to analyze the diffusion tensor image along the perivascular space (DTI-ALPS) and perivascular space (PVS) burden to reveal the relationship between the glymphatic system and PD. A cross-sectional study using a 7 T MRI of 76 PD patients and 48 controls was performed to evaluate the brain's glymphatic system. The DTI-ALPS and PVS burden in basal ganglia were calculated. Correlation analyses were conducted between DTI-ALPS, PVS burden and clinical features. We detected lower DTI-ALPS in the PD subgroup relative to controls, and the differences were more pronounced in patients with Hoehn &amp; Yahr stage greater than two. The decreased DTI-ALPS was only evident in the left hemisphere in patients in the early stage but involved both hemispheres in more advanced PD patients. Decreased DTI-ALPS were also correlated with longer disease duration, higher Unified Parkinson's Disease Rating Scale motor score (UPDRS III) and UPDRS total scores, as well as higher levodopa equivalent daily dose. Moreover, the decreased DTI-ALPS correlated with increased PVS burden, and both indexes correlated with PD disease severity. This study demonstrated decreased DTI-ALPS in PD, which might initiate from the left hemisphere and progressively involve right hemisphere with the disease progression. Decreased DTI-ALPS index correlated with increased PVS burden, indicating that both metrics could provide supporting evidence of an impaired glymphatic system. MRI evaluation of the PVS burden and diffusion along PVS are potential imaging biomarkers for PD for disease progression.},
}
@article {pmid36542992,
year = {2023},
author = {Wang, X and Sun, X and Ma, C and Zhang, Y and Kong, L and Huang, Z and Hu, Y and Wan, H and Wang, P},
title = {Multifunctional AuNPs@HRP@FeMOF immune scaffold with a fully automated saliva analyzer for oral cancer screening.},
journal = {Biosensors &amp; bioelectronics},
volume = {222},
number = {},
pages = {114910},
doi = {10.1016/j.bios.2022.114910},
pmid = {36542992},
issn = {1873-4235},
mesh = {Humans ; *Biosensing Techniques/methods ; Early Detection of Cancer ; Gold ; *Metal Nanoparticles ; *Mouth Neoplasms/diagnosis ; Saliva ; Iron/chemistry ; },
abstract = {Delayed diagnosis of cancer-causing death is a worldwide concern. General diagnosis methods are invasive, time-consuming, and operation complicated, which are not suitable for preliminary screening. To address these challenges, the sensing platform based on immune scaffold and fully automated saliva analyzer (FASA) was proposed for oral cancer screening for the first time by non-invasive detection of Cyfra21-1 in saliva. Through one-step synthesis method with unique covalent and electrostatic adsorption strategy, AuNPs@HRP@FeMOF immune scaffold features multiple functions including antibody carrier, catalytic activity, and signal amplification. Highly integrated FASA with the immune scaffold provides automatic testing to avoid false-positive results and reduce pretreatment time without any user intervention. Compared with the commercial analyzer, FASA has comparable performance for Cyfra21-1 detection with a detection range of 3.1-50.0 ng/mL and R[2] of 0.971, and superior features in full automation, high integration, time saving and low cost. Oral cancer patients could be distinguished accurately by the platform with an excellent correlation (R[2] of 0.904) and average RSD (5.578%) without sample dilution. The proposed platform provides an effective and promising tool for cancer screening in point-of-care applications, which can be further extended for biomarker detection in universal body fluids, disease screening, prognosis review and homecare monitoring.},
}
@article {pmid36541542,
year = {2022},
author = {Fang, T and Wang, J and Mu, W and Song, Z and Zhang, X and Zhan, G and Wang, P and Bin, J and Niu, L and Zhang, L and Kang, X},
title = {Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/aca82d},
pmid = {36541542},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms ; },
abstract = {Objective.The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system.Approach.In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms.Main results.The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability.Significance.The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.},
}
@article {pmid36541535,
year = {2022},
author = {Pires, G and Cruz, A and Jesus, D and Yasemin, M and Nunes, UJ and Sousa, T and Castelo-Branco, M},
title = {A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/aca798},
pmid = {36541535},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Autism Spectrum Disorder ; Learning ; Reinforcement, Psychology ; },
abstract = {Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.},
}
@article {pmid36541532,
year = {2022},
author = {Wang, X and Chen, HT and Lin, CT},
title = {Error-related potential-based shared autonomy via deep recurrent reinforcement learning.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/aca4fb},
pmid = {36541532},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Reinforcement, Psychology ; Computer Simulation ; },
abstract = {Objective.Error-related potential (ErrP)-based brain-computer interfaces (BCIs) have received a considerable amount of attention in the human-robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human-robot interaction.Approach.We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users.Main results.The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster.Significance.The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human-robot interaction task.},
}
@article {pmid36538406,
year = {2022},
author = {Kostenko, EV and Petrova, LV and Pogonchenkova, IV and Neprintseva, NV and Shurupova, ST and Kopasheva, VD and Rylsky, AV},
title = {[Innovative technologies and multimodal correction in medical rehabilitation of motor and neuropsychological disturbances due to stroke].},
journal = {Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury},
volume = {99},
number = {6},
pages = {67-78},
doi = {10.17116/kurort20229906167},
pmid = {36538406},
issn = {0042-8787},
mesh = {Humans ; *Stroke/complications/psychology ; *Stroke Rehabilitation/methods ; Upper Extremity ; },
abstract = {The article presents an overview of innovative technologies based on the methods of sensorimotor retraining of the patient using various types of biofeedback (BFB) as the most promising in the medical rehabilitation (MR) of patients with cerebral stroke (CS). The works of a high level of evidence (RCTs, national and international clinical guidelines, meta-analyses, systematic reviews) of the Medline, Pubmed, PubMed Cochrane Library databases are analyzed, ClinicalTrials.gov. It is emphasized that training with multisensory effects on visual, auditory, vestibular and kinesthetic analyzers have a beneficial effect on cognitive-motor training and retraining, neuropsychological status of the patient and increase the level of motivation to achieve success in the rehabilitation process. The synergy of multimodal effects of digital technologies, BFB, virtual reality, and the brain-computer interface will expand the capabilities and improve the efficiency of MR of after stroke-patients.},
}
@article {pmid36536134,
year = {2022},
author = {Hu, Y and Cao, K and Wang, F and Wu, W and Mai, W and Qiu, L and Luo, Y and Ge, WP and Sun, B and Shi, L and Zhu, J and Zhang, J and Wu, Z and Xie, Y and Duan, S and Gao, Z},
title = {Dual roles of hexokinase 2 in shaping microglial function by gating glycolytic flux and mitochondrial activity.},
journal = {Nature metabolism},
volume = {4},
number = {12},
pages = {1756-1774},
pmid = {36536134},
issn = {2522-5812},
mesh = {Mice ; Male ; Animals ; Microglia/metabolism ; *Brain Ischemia/genetics/metabolism ; *Stroke/genetics/metabolism ; Hexokinase/genetics/metabolism ; Mitochondria/metabolism ; },
abstract = {Microglia continuously survey the brain parenchyma and actively shift status following stimulation. These processes demand a unique bioenergetic programme; however, little is known about the metabolic determinants in microglia. By mining large datasets and generating transgenic tools, here we show that hexokinase 2 (HK2), the most active isozyme associated with mitochondrial membrane, is selectively expressed in microglia in the brain. Genetic ablation of HK2 reduced microglial glycolytic flux and energy production, suppressed microglial repopulation, and attenuated microglial surveillance and damage-triggered migration in male mice. HK2 elevation is prominent in immune-challenged or disease-associated microglia. In ischaemic stroke models, however, HK2 deletion promoted neuroinflammation and potentiated cerebral damages. The enhanced inflammatory responses after HK2 ablation in microglia are associated with aberrant mitochondrial function and reactive oxygen species accumulation. Our study demonstrates that HK2 gates both glycolytic flux and mitochondrial activity to shape microglial functions, changes of which contribute to metabolic abnormalities and maladaptive inflammation in brain diseases.},
}
@article {pmid36535036,
year = {2023},
author = {Berke Guney, O and Ozkan, H},
title = {Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/acacca},
pmid = {36535036},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Reproducibility of Results ; Evoked Potentials, Visual ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Machine Learning ; Photic Stimulation/methods ; },
abstract = {Objective.Steady-state visually evoked potentials (SSVEPs), measured with electroencephalogram (EEG), yield decent information transfer rates (ITRs) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a novel target identification method based on an ensemble of deep neural networks (DNNs), which does not require any sort of user-specific training.Approach.We exploit already-existing literature datasets from participants of previously conducted EEG experiments to train a global target identifier DNN first, which is then fine-tuned to each participant. We transfer this ensemble of fine-tuned DNNs to the new user instance, determine thekmost representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.Main results.The proposed method significantly outperforms all the state-of-the-art alternatives for all stimulation durations in [0.2-1.0] s on two large-scale benchmark and BETA datasets, and achieves impressive 155.51 bits/min and 114.64 bits/min ITRs. Code is available for reproducibility:https://github.com/osmanberke/Ensemble-of-DNNs.Significance.Our Ensemble-DNN method has the potential to promote the practical widespread deployment of BCI spellers in daily lives as we provide the highest performance while enabling the immediate system use without any user-specific training.},
}
@article {pmid36535004,
year = {2022},
author = {Yacine, F and Salah, H and Amar, K and Ahmad, K},
title = {A novel ANN adaptive Riemannian-based kernel classification for motor imagery.},
journal = {Biomedical physics &amp; engineering express},
volume = {9},
number = {1},
pages = {},
doi = {10.1088/2057-1976/acaca2},
pmid = {36535004},
issn = {2057-1976},
mesh = {*Neural Networks, Computer ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Electroencephalography/methods ; },
abstract = {More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify these matrices. This paper presents a novel Artificial Neural Network approach based on an Adaptive Riemannian Kernel, named ARK-ANN, to classify Electroencephalographic (EEG) motor imaging signals in the context of Brain Computer Interface (BCI). A multilayer perceptron is used to classify the covariance matrices of Motor Imagery (MI) signals employing an adaptive optimization of the testing set. The contribution of a geodesic filter is also assessed for the ANN and the original method which uses an SVM classifier. The results demonstrate that the ARK-ANN performs better than the other methods and the geodesic filter gives slightly better results in the ARK-SVM, considered here as the reference method, in the case of inter-subject classification (accuracy of 87.4% and 86% for ARK-ANN and ARK-SVM, respectively). Regarding the cross-subject classification, the proposed method gives an accuracy of 77.3% and increases the precision by 8.2% in comparison to the SVM based method.},
}
@article {pmid36534700,
year = {2023},
author = {Zhang, L and Liu, C and Zhou, X and Zhou, H and Luo, S and Wang, Q and Yao, Z and Chen, JF},
title = {Neural representation and modulation of volitional motivation in response to escalating efforts.},
journal = {The Journal of physiology},
volume = {601},
number = {3},
pages = {631-645},
pmid = {36534700},
issn = {1469-7793},
mesh = {Mice ; Animals ; *Motivation ; *Dopamine/pharmacology ; Calcium/metabolism ; Learning ; Reward ; Nucleus Accumbens ; },
abstract = {Task-dependent volitional control of the selected neural activity in the cortex is critical to neuroprosthetic learning to achieve reliable and robust control of the external device. The volitional control of neural activity is driven by a motivational factor (volitional motivation), which directly reinforces the target neurons via real-time biofeedback. However, in the absence of motor behaviour, how do we evaluate volitional motivation? Here, we defined the criterion (ΔF/F) of the calcium fluorescence signal in a volitionally controlled neural task, then escalated the efforts by progressively increasing the number of reaching the criterion or holding time after reaching the criterion. We devised calcium-based progressive threshold-crossing events (termed 'Calcium PTE') and calcium-based progressive threshold-crossing holding-time (termed 'Calcium PTH') for quantitative assessment of volitional motivation in response to progressively escalating efforts. Furthermore, we used this novel neural representation of volitional motivation to explore the neural circuit and neuromodulator bases for volitional motivation. As with behavioural motivation, chemogenetic activation and pharmacological blockade of the striatopallidal pathway decreased and increased, respectively, the breakpoints of the 'Calcium PTE' and 'Calcium PTH' in response to escalating efforts. Furthermore, volitional and behavioural motivation shared similar dopamine dynamics in the nucleus accumbens in response to trial-by-trial escalating efforts. In general, the development of a neural representation of volitional motivation may open a new avenue for smooth and effective control of brain-machine interface tasks. KEY POINTS: Volitional motivation is quantitatively evaluated by M1 neural activity in response to progressively escalating volitional efforts. The striatopallidal pathway and adenosine A2A receptor modulate volitional motivation in response to escalating efforts. Dopamine dynamics encode prediction signal for reward in response to repeated escalating efforts during motor and volitional conditioning. Mice learn to modulate neural activity to compensate for repeated escalating efforts in volitional control.},
}
@article {pmid36532389,
year = {2022},
author = {Andrews, A},
title = {Mind Power: Thought-controlled Augmented Reality for Basic Science Education.},
journal = {Medical science educator},
volume = {32},
number = {6},
pages = {1571-1573},
pmid = {36532389},
issn = {2156-8650},
abstract = {The integration of augmented reality (AR) and brain-computer interface (BCI) technologies holds a tremendous potential to improve learning, communication, and teamwork in basic science education. The current study presents a novel interface technology solution to enable AR-BCI interoperability and allow learners to control digital objects in AR using neural commands.},
}
@article {pmid36531919,
year = {2022},
author = {Lyu, J and Maýe, A and Görner, M and Ruppel, P and Engel, AK and Zhang, J},
title = {Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {1068274},
pmid = {36531919},
issn = {1662-5218},
abstract = {In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.},
}
@article {pmid36530202,
year = {2022},
author = {Bleuzé, A and Mattout, J and Congedo, M},
title = {Tangent space alignment: Transfer learning for Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1049985},
pmid = {36530202},
issn = {1662-5161},
abstract = {Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability. This method aims to reduce calibration time and even improve accuracy of BCI systems by aligning EEG data from one subject to the other in the tangent space of the positive definite matrices Riemannian manifold. We tested the method on 18 BCI databases comprising a total of 349 subjects pertaining to three BCI paradigms, namely, event related potentials (ERP), motor imagery (MI), and steady state visually evoked potentials (SSVEP). We employ a support vector classifier for feature classification. The results demonstrate a significant improvement of classification accuracy, as compared to a classical training-test pipeline, in the case of the ERP paradigm, whereas for both the MI and SSVEP paradigm no deterioration of performance is observed. A global 2.7% accuracy improvement is obtained compared to a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Interestingly, tangent space alignment has an intrinsic ability to deal with transfer learning for sets of data that have different number of channels, naturally applying to inter-dataset transfer learning.},
}
@article {pmid36529022,
year = {2023},
author = {Li, H and Zhang, D and Xie, J},
title = {MI-DABAN: A dual-attention-based adversarial network for motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {152},
number = {},
pages = {106420},
doi = {10.1016/j.compbiomed.2022.106420},
pmid = {36529022},
issn = {1879-0534},
mesh = {Humans ; *Imagination ; *Brain-Computer Interfaces ; Learning ; Electroencephalography/methods ; Algorithms ; },
abstract = {The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) is widely used because of its convenience and safety. However, due to the distributional disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier. For efficient use of the labeled data, domain transfer learning and adversarial learning are gradually applied to BCI classification tasks. While these methods improve classification performance, they only align globally and ignore task-specific class boundaries, which may lead to the blurring of features near the classification boundaries. Simultaneously, they employ fully shared generators to extract features, resulting in the loss of domain-specific information and the destruction of performance. To address these issues, we propose a novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN). Our framework leverages multiple subjects' knowledge to improve a single subject's motor imagery classification performance by cleverly using a novel adversarial learning method and two unshared attention blocks. Specifically, without introducing additional domain discriminators, we iteratively maximize and minimize the output difference between the two classifiers to implement adversarial learning to ensure accurate domain alignment. Among them, maximization is used to identify easily confused samples near the decision boundary, and minimization is used to align the source and target domain distributions. Moreover, for the shallow features from source and target domains, we use two non-shared attention blocks to preserve domain-specific information, which can prevent the negative transfer of domain information and further improve the classification performance on test data. We conduct extensive experiments on two publicly available EEG datasets, namely BCI Competition IV Datasets 2a and 2b. The experiment results demonstrate our method's effectiveness and superiority.},
}
@article {pmid36528312,
year = {2023},
author = {Kim, H and Kim, JS and Chung, CK},
title = {Identification of cerebral cortices processing acceleration, velocity, and position during directional reaching movement with deep neural network and explainable AI.},
journal = {NeuroImage},
volume = {266},
number = {},
pages = {119783},
doi = {10.1016/j.neuroimage.2022.119783},
pmid = {36528312},
issn = {1095-9572},
mesh = {Humans ; *Psychomotor Performance/physiology ; Artificial Intelligence ; Movement/physiology ; Neural Networks, Computer ; *Motor Cortex/physiology ; Acceleration ; },
abstract = {Cerebral cortical representation of motor kinematics is crucial for understanding human motor behavior, potentially extending to efficient control of the brain-computer interface. Numerous single-neuron studies have found the existence of a relationship between neuronal activity and motor kinematics such as acceleration, velocity, and position. Despite differences between kinematic characteristics, it is hard to distinguish neural representations of these kinematic characteristics with macroscopic functional images such as electroencephalography (EEG) and magnetoencephalography (MEG). The reason might be because cortical signals are not sensitive enough to segregate kinematic characteristics due to their limited spatial and temporal resolution. Considering different roles of each cortical area in producing movement, there might be a specific cortical representation depending on characteristics of acceleration, velocity, and position. Recently, neural network modeling has been actively pursued in the field of decoding. We hypothesized that neural features of each kinematic parameter could be identified with a high-performing model for decoding with an explainable AI method. Time-series deep neural network (DNN) models were used to measure the relationship between cortical activity and motor kinematics during reaching movement. With DNN models, kinematic parameters of reaching movement in a 3D space were decoded based on cortical source activity obtained from MEG data. An explainable artificial intelligence (AI) method was then adopted to extract the map of cortical areas, which strongly contributed to decoding each kinematics from DNN models. We found that there existed differed as well as shared cortical areas for decoding each kinematic attribute. Shared areas included bilateral supramarginal gyri and superior parietal lobules known to be related to the goal of movement and sensory integration. On the other hand, dominant areas for each kinematic parameter (the contralateral motor cortex for acceleration, the contralateral parieto-frontal network for velocity, and bilateral visuomotor areas for position) were mutually exclusive. Regarding the visuomotor reaching movement, the motor cortex was found to control the muscle force, the parieto-frontal network encoded reaching movement from sensory information, and visuomotor areas computed limb and gaze coordination in the action space. To the best of our knowledge, this is the first study to discriminate kinematic cortical areas using DNN models and explainable AI.},
}
@article {pmid36527133,
year = {2022},
author = {Sgroi, DC and Treuner, K and Zhang, Y and Piper, T and Salunga, R and Ahmed, I and Doos, L and Thornber, S and Taylor, KJ and Brachtel, E and Pirrie, S and Schnabel, CA and Rea, D and Bartlett, JMS},
title = {Correlative studies of the Breast Cancer Index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine therapy benefit: a Trans-aTTom study.},
journal = {Breast cancer research : BCR},
volume = {24},
number = {1},
pages = {90},
pmid = {36527133},
issn = {1465-542X},
support = {25354/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/genetics/pathology ; Receptors, Androgen/genetics ; Progesterone ; Receptors, Estrogen/metabolism ; Ki-67 Antigen/genetics ; Prognosis ; Estrogens ; Receptors, Progesterone/genetics/metabolism ; Biomarkers, Tumor/metabolism ; Receptor, ErbB-2 ; Homeodomain Proteins ; },
abstract = {BACKGROUND: Multiple clinical trials demonstrate consistent but modest benefit of adjuvant extended endocrine therapy (EET) in HR + breast cancer patients. Predictive biomarkers to identify patients that benefit from EET are critical to balance modest reductions in risk against potential side effects of EET. This study compares the performance of the Breast Cancer Index, BCI (HOXB13/IL17BR, H/I), with expression of estrogen (ER), progesterone (PR), and androgen receptors (AR), and Ki67, for prediction of EET benefit.<br><br>METHODS: Node-positive (N+) patients from the Trans-aTTom study with available tissue specimen and BCI results (N&#x2009;=&#x2009;789) were included. Expression of ER, PR, AR, and Ki67 was assessed by quantitative immunohistochemistry. BCI&#xa0;(H/I) gene expression analysis was conducted by quantitative RT-PCR. Statistical significance of the treatment by biomarker interaction was evaluated by likelihood ratio tests based on multivariate Cox proportional models, adjusting for age, tumor size, grade, and HER2 status. Pearson's correlation coefficients were calculated to evaluate correlations between BCI (H/I) versus ER, PR, AR, Ki67 and AR/ER ratio.<br><br>RESULTS: EET benefit, measured by the difference in risk of recurrence between patients treated with tamoxifen for 10 versus 5&#xa0;years, is significantly associated with increasing values of BCI&#xa0;(H/I) (interaction P&#x2009;=&#x2009;0.01). In contrast, expression of ER (P&#x2009;=&#x2009;0.83), PR (P&#x2009;=&#x2009;0.66), AR (P&#x2009;=&#x2009;0.78), Ki67 (P&#x2009;=&#x2009;0.87) and AR/ER ratio (P&#x2009;=&#x2009;0.84) exhibited no significant relationship with EET benefit. BCI&#xa0;(H/I) showed a very weak negative correlation with ER (r&#x2009;=&#x2009;-&#x2009;0.18), PR (r&#x2009;=&#x2009;-&#x2009;0.25), and AR (r&#x2009;=&#x2009;-&#x2009;0.14) expression, but no correlation with either Ki67 (r&#x2009;=&#x2009;0.04) or AR/ER ratio (r&#x2009;=&#x2009;0.02).<br><br>CONCLUSION: These findings are consistent with the growing body of evidence that BCI&#xa0;(H/I) is significantly predictive of response to EET and outcome. Results from this direct comparison demonstrate that expression of ER, PR, AR, Ki67 or AR/ER ratio are not predictive of benefit from EET. BCI&#xa0;(H/I) is the only clinically validated biomarker that predicts EET benefit.},
}
@article {pmid36525745,
year = {2023},
author = {Kern, K and Vukelić, M and Guggenberger, R and Gharabaghi, A},
title = {Oscillatory neurofeedback networks and poststroke rehabilitative potential in severely impaired stroke patients.},
journal = {NeuroImage. Clinical},
volume = {37},
number = {},
pages = {103289},
pmid = {36525745},
issn = {2213-1582},
mesh = {Humans ; *Neurofeedback ; *Stroke/complications ; *Stroke Rehabilitation ; *Sensorimotor Cortex ; Imagery, Psychotherapy ; },
abstract = {Motor restoration after severe stroke is often limited. However, some of the severely impaired stroke patients may still have a rehabilitative potential. Biomarkers that identify these patients are sparse. Eighteen severely impaired chronic stroke patients with a lack of volitional finger extension participated in an EEG study. During sixty-six trials of kinesthetic motor imagery, a brain-machine interface turned event-related beta-band desynchronization of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. A subgroup of eight patients participated in a subsequent four-week rehabilitation training. Changes of the movement extent were captured with sensors which objectively quantified even discrete improvements of wrist movement. Albeit with the same motor impairment level, patients could be differentiated into two groups, i.e., with and without task-related increase of bilateral cortico-cortical phase synchronization between frontal/premotor and parietal areas. This fronto-parietal integration (FPI) was associated with a significantly higher volitional beta modulation range in the ipsilesional sensorimotor cortex. Following the four-week training, patients with FPI showed significantly higher improvement in wrist movement than those without FPI. Moreover, only the former group improved significantly in the upper extremity Fugl-Meyer-Assessment score. Neurofeedback-related long-range oscillatory coherence may differentiate severely impaired stroke patients with regard to their rehabilitative potential, a finding that needs to be confirmed in larger patient cohorts.},
}
@article {pmid36524791,
year = {2023},
author = {Sinha, S and Dmochowski, RR and Hashim, H and Finazzi-Agrò, E and Iacovelli, V},
title = {The bladder contractility and bladder outlet obstruction indices in adult women. Results of a global Delphi consensus study.},
journal = {Neurourology and urodynamics},
volume = {42},
number = {2},
pages = {453-462},
doi = {10.1002/nau.25114},
pmid = {36524791},
issn = {1520-6777},
mesh = {Humans ; Male ; Adult ; Female ; Urinary Bladder ; Delphi Technique ; *Urinary Bladder Neck Obstruction/drug therapy ; *Urinary Incontinence, Stress ; Muscle Contraction ; Urodynamics ; },
abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of the bladder contractility index (BCI), bladder outlet obstruction index (BOOI), and the related evidence. This manuscript deals with adult women and follows a previous manuscript reporting on adult men.<br><br>METHODS: Twenty-nine experts were invited to answer the two-round survey including three foundation questions and 12 survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree," "agree," "neutral," "disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in women.<br><br>RESULTS: Eighteen experts participated in the survey with 100% completion. Consensus was noted with regard to 2 of 12 questions, both in the negative. The experts had a consensus that BOOI was neither accurate nor useful and a similar negative trend was noted with regard to BCI. However, there was support, short of consensus, for the utility on an index of bladder contractility and bladder outflow obstruction. Systematic search yielded eight publications pertaining to stress urinary incontinence (n&#x2009;=&#x2009;6), pelvic organ prolapse (n&#x2009;=&#x2009;1), and intra-sphincteric botulinum toxin (n&#x2009;=&#x2009;1).<br><br>CONCLUSIONS: Experts had significant concerns with regard to the use of the male BCI and BOOI in adult women despite a general recognition of the need for numerical indices of contractility and obstruction. Systematic search showed a striking lack of evidence in this regard.},
}
@article {pmid36523756,
year = {2022},
author = {Proverbio, AM and Tacchini, M and Jiang, K},
title = {Event-related brain potential markers of visual and auditory perception: A useful tool for brain computer interface systems.},
journal = {Frontiers in behavioral neuroscience},
volume = {16},
number = {},
pages = {1025870},
pmid = {36523756},
issn = {1662-5153},
abstract = {OBJECTIVE: A majority of BCI systems, enabling communication with patients with locked-in syndrome, are based on electroencephalogram (EEG) frequency analysis (e.g., linked to motor imagery) or P300 detection. Only recently, the use of event-related brain potentials (ERPs) has received much attention, especially for face or music recognition, but neuro-engineering research into this new approach has not been carried out yet. The aim of this study was to provide a variety of reliable ERP markers of visual and auditory perception for the development of new and more complex mind-reading systems for reconstructing the mental content from brain activity.<br><br>METHODS: A total of 30 participants were shown 280 color pictures (adult, infant, and animal faces; human bodies; written words; checkerboards; and objects) and 120 auditory files (speech, music, and affective vocalizations). This paradigm did not involve target selection to avoid artifactual waves linked to decision-making and response preparation (e.g., P300 and motor potentials), masking the neural signature of semantic representation. Overall, 12,000 ERP waveforms &#xd7; 126 electrode channels (1 million 512,000 ERP waveforms) were processed and artifact-rejected.<br><br>RESULTS: Clear and distinct category-dependent markers of perceptual and cognitive processing were identified through statistical analyses, some of which were novel to the literature. Results are discussed from the view of current knowledge of ERP functional properties and with respect to machine learning classification methods previously applied to similar data.<br><br>CONCLUSION: The data showed a high level of accuracy (p &#x2264; 0.01) in the discriminating the perceptual categories eliciting the various electrical potentials by statistical analyses. Therefore, the ERP markers identified in this study could be significant tools for optimizing BCI systems [pattern recognition or artificial intelligence (AI) algorithms] applied to EEG/ERP signals.},
}
@article {pmid36523527,
year = {2022},
author = {Kophamel, S and Ward, LC and Konovalov, DA and Mendez, D and Ariel, E and Cassidy, N and Bell, I and Balastegui Martínez, MT and Munns, SL},
title = {Field-based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning.},
journal = {Ecology and evolution},
volume = {12},
number = {12},
pages = {e9610},
pmid = {36523527},
issn = {2045-7758},
abstract = {Loss of adipose tissue in vertebrate wildlife species is indicative of decreased nutritional and health status and is linked to environmental stress and diseases. Body condition indices (BCI) are commonly used in ecological studies to estimate adipose tissue mass across wildlife populations. However, these indices have poor predictive power, which poses the need for quantitative methods for improved population assessments. Here, we calibrate bioelectrical impedance spectroscopy (BIS) as an alternative approach for assessing the nutritional status of vertebrate wildlife in ecological studies. BIS is a portable technology that can estimate body composition from measurements of body impedance and is widely used in humans. BIS is a predictive technique that requires calibration using a reference body composition method. Using sea turtles as model organisms, we propose a calibration protocol using computed tomography (CT) scans, with the prediction equation being: adipose tissue mass (kg) = body mass - (-0.03 [intercept] - 0.29 * length[2]/resistance at 50 kHz + 1.07 * body mass - 0.11 * time after capture). CT imaging allows for the quantification of body fat. However, processing the images manually is prohibitive due to the extensive time requirement. Using a form of artificial intelligence (AI), we trained a computer model to identify and quantify nonadipose tissue from the CT images, and adipose tissue was determined by the difference in body mass. This process enabled estimating adipose tissue mass from bioelectrical impedance measurements. The predictive performance of the model was built on 2/3 samples and tested against 1/3 samples. Prediction of adipose tissue percentage had greater accuracy when including impedance parameters (mean bias = 0.11%-0.61%) as predictor variables, compared with using body mass alone (mean bias = 6.35%). Our standardized BIS protocol improves on conventional body composition assessment methods (e.g., BCI) by quantifying adipose tissue mass. The protocol can be applied to other species for the validation of BIS and to provide robust information on the nutritional and health status of wildlife, which, in turn, can be used to inform conservation decisions at the management level.},
}
@article {pmid36523445,
year = {2022},
author = {Ferracuti, F and Freddi, A and Iarlori, S and Monteriù, A and Omer, KIM and Porcaro, C},
title = {A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.},
journal = {Frontiers in robotics and AI},
volume = {9},
number = {},
pages = {909971},
pmid = {36523445},
issn = {2296-9144},
abstract = {Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.},
}
@article {pmid36522455,
year = {2022},
author = {Iwama, S and Yanagisawa, T and Hirose, R and Ushiba, J},
title = {Beta rhythmicity in human motor cortex reflects neural population coupling that modulates subsequent finger coordination stability.},
journal = {Communications biology},
volume = {5},
number = {1},
pages = {1375},
pmid = {36522455},
issn = {2399-3642},
mesh = {Humans ; *Motor Cortex/physiology ; Movement/physiology ; Transcranial Magnetic Stimulation/methods ; Electroencephalography ; Periodicity ; },
abstract = {Human behavior is not performed completely as desired, but is influenced by the inherent rhythmicity of the brain. Here we show that anti-phase bimanual coordination stability is regulated by the dynamics of pre-movement neural oscillations in bi-hemispheric primary motor cortices (M1) and supplementary motor area (SMA). In experiment 1, pre-movement bi-hemispheric M1 phase synchrony in beta-band (M1-M1 phase synchrony) was online estimated from 129-channel scalp electroencephalograms. Anti-phase bimanual tapping preceded by lower M1-M1 phase synchrony exhibited significantly longer duration than tapping preceded by higher M1-M1 phase synchrony. Further, the inter-individual variability of duration was explained by the interaction of pre-movement activities within the motor network; lower M1-M1 phase synchrony and spectral power at SMA were associated with longer duration. The necessity of cortical interaction for anti-phase maintenance was revealed by sham-controlled repetitive transcranial magnetic stimulation over SMA in another experiment. Our results demonstrate that pre-movement cortical oscillatory coupling within the motor network unknowingly influences bimanual coordination performance in humans after consolidation, suggesting the feasibility of augmenting human motor ability by covertly monitoring preparatory neural dynamics.},
}
@article {pmid36515725,
year = {2023},
author = {Kruppa, C and Benner, S and Brinkemper, A and Aach, M and Reimertz, C and Schildhauer, TA},
title = {[New technologies and robotics].},
journal = {Unfallchirurgie (Heidelberg, Germany)},
volume = {126},
number = {1},
pages = {9-18},
pmid = {36515725},
issn = {2731-703X},
mesh = {Humans ; *Robotics ; Artificial Intelligence ; *Exoskeleton Device ; Gait/physiology ; Paraplegia ; },
abstract = {The development of increasingly more complex computer and electromotor technologies enables the increasing use and expansion of robot-assisted systems in trauma surgery rehabilitation; however, the currently available devices are rarely comprehensively applied but are often used within pilot projects and studies. Different technological approaches, such as exoskeletal systems, functional electrical stimulation, soft robotics, neurorobotics and brain-machine interfaces are used and combined to read and process the communication between, e.g., residual musculature or brain waves, to transfer them to the executing device and to enable the desired execution.Currently, the greatest amount of evidence exists for the use of exoskeletal systems with different modes of action in the context of gait and stance rehabilitation in paraplegic patients; however, their use also plays a role in the rehabilitation of fractures close to the hip joint and endoprosthetic care. So-called single joint systems are also being tested in the rehabilitation of functionally impaired extremities, e.g., after knee prosthesis implantation. At this point, however, the current data situation is still too limited to be able to make a clear statement about the use of these technologies in the trauma surgery "core business" of rehabilitation after fractures and other joint injuries.For rehabilitation after limb amputation, in addition to the further development of myoelectric prostheses, the current development of "sentient" prostheses is of great interest. The use of 3D printing also plays a role in the production of individualized devices.Due to the current progress of artificial intelligence in all fields, ground-breaking further developments and widespread application possibilities in the rehabilitation of trauma patients are to be expected.},
}
@article {pmid36509440,
year = {2023},
author = {Shapiro, SB and Llerena, PA and Mowery, TM and Miele, EA and Wackym, PA},
title = {Subtemporalis Muscle Middle Cranial Fossa Bone-Island Craniotomy Technique for Placement of an Active Transcutaneous Bone-Conduction Implant.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {44},
number = {1},
pages = {54-60},
pmid = {36509440},
issn = {1537-4505},
mesh = {Humans ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Bone Conduction/physiology ; Cranial Fossa, Middle/surgery ; *Hearing Aids ; Muscles ; Hearing Loss, Conductive/surgery ; Treatment Outcome ; *Speech Perception ; },
abstract = {OBJECTIVE: Placement of an active transcutaneous bone-conduction implant (BCI) requires drilling of a precise bone bed to accommodate the device and allow for fixation points to make appropriate contact with bone, which can be difficult even when lifts are used. We describe a subtemporalis muscle middle cranial fossa bone-island craniotomy technique that simplifies the procedure and obviates the need for lifts in securing the device.<br><br>STUDY DESIGN: Prospective case series.<br><br>SETTING: Tertiary academic medical center.<br><br>PATIENTS: Seventeen patients underwent surgery for placement of 18 transcutaneous BCIs, 14 for conductive or mixed hearing loss, and 4 for single-sided deafness.<br><br>INTERVENTIONS: Surgical placement of a transcutaneous BCI with a bone-island craniotomy technique.<br><br>MAIN OUTCOME MEASURES: Functional gain in air-conduction thresholds, aided air-bone gap, frequency of need for lifts, and minor and major complications.<br><br>RESULTS: For the conductive or mixed hearing loss cohort, with the transcutaneous BCI in place, there was a highly statistically significant mean functional gain of 35.4 dB hearing level (HL) (range, 16.7-50.25 dB HL; standard deviation, 12.4 dB HL) compared with the unaided condition (p < 0.0001; 95% confidence interval, 36.6-51.6 dB HL). Lifts were not needed in any case. There was one minor complication requiring a second procedure in a patient who had previously received radiation and no major complications. There was no device loss or failure.<br><br>CONCLUSIONS: A subtemporalis muscle middle cranial fossa bone-island craniotomy technique eliminates the need for lifts and is a safe and effective method for placement of a transcutaneous BCI.},
}
@article {pmid36507325,
year = {2022},
author = {Du, Y and Huang, J and Huang, X and Shi, K and Zhou, N},
title = {Dual attentive fusion for EEG-based brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1044631},
pmid = {36507325},
issn = {1662-4548},
abstract = {The classification based on Electroencephalogram (EEG) is a challenging task in the brain-computer interface (BCI) field due to data with a low signal-to-noise ratio. Most current deep learning based studies in this challenge focus on designing a desired convolutional neural network (CNN) to learn and classify the raw EEG signals. However, only CNN itself may not capture the highly discriminative patterns of EEG due to a lack of exploration of attentive spatial and temporal dynamics. To improve information utilization, this study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information by modeling the interdependencies between the features from the EEG signals. To our best knowledge, our method is the first to fuse spatial and temporal dimensions in an interactive attention module. This module improves the expression ability of the extracted features. Extensive experiments implemented on four publicly available datasets demonstrate that our method outperforms state-of-the-art methods. Meanwhile, this work also indicates the effectiveness of Dual Attentive Fusion Module.},
}
@article {pmid36507305,
year = {2022},
author = {La Fisca, L and Vandenbulcke, V and Wauthia, E and Miceli, A and Simoes Loureiro, I and Ris, L and Lefebvre, L and Gosselin, B and Pernet, CR},
title = {Biases in BCI experiments: Do we really need to balance stimulus properties across categories?.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {900571},
pmid = {36507305},
issn = {1662-5188},
abstract = {Brain Computer Interfaces (BCIs) consist of an interaction between humans and computers with a specific mean of communication, such as voice, gestures, or even brain signals that are usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI algorithm typically involves the classification of the input signals into predefined task-specific categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled experimental conditions, namely covariates, that are unbalanced across the categories. This issue led to the current solution of forcing the balance of these covariates across the different categories which is time consuming and drastically decreases the dataset diversity. The purpose of this research is to evaluate the need for this forced balance in BCI experiments involving EEG data. A typical design of neural BCIs involves repeated experimental trials using visual stimuli to trigger the so-called Event-Related Potential (ERP). The classifier is expected to learn spatio-temporal patterns specific to categories rather than patterns related to uncontrolled stimulus properties, such as psycho-linguistic variables (e.g., phoneme number, familiarity, and age of acquisition) and image properties (e.g., contrast, compactness, and homogeneity). The challenges are then to know how biased the decision is, which features affect the classification the most, which part of the signal is impacted, and what is the probability to perform neural categorization per se. To address these problems, this research has two main objectives: (1) modeling and quantifying the covariate effects to identify spatio-temporal regions of the EEG allowing maximal classification performance while minimizing the biasing effect, and (2) evaluating the need to balance the covariates across categories when studying brain mechanisms. To solve the modeling problem, we propose using a linear parametric analysis applied to some observable and commonly studied covariates to them. The biasing effect is quantified by comparing the regions highly influenced by the covariates with the regions of high categorical contrast, i.e., parts of the ERP allowing a reliable classification. The need to balance the stimulus's inner properties across categories is evaluated by assessing the separability between category-related and covariate-related evoked responses. The procedure is applied to a visual priming experiment where the images represent items belonging to living or non-living entities. The observed covariates are the commonly controlled psycho-linguistic variables and some visual features of the images. As a result, we identified that the category of the stimulus mostly affects the late evoked response. The covariates, when not modeled, have a biasing effect on the classification, essentially in the early evoked response. This effect increases with the diversity of the dataset and the complexity of the algorithm used. As the effects of both psycho-linguistic variables and image features appear outside of the spatio-temporal regions of significant categorical contrast, the proper selection of the region of interest makes the classification reliable. Having proved that the covariate effects can be separated from the categorical effect, our framework can be further used to isolate the category-dependent evoked response from the rest of the EEG to study neural processes involved when seeing living vs. non-living entities.},
}
@article {pmid36507057,
year = {2022},
author = {Wang, Y and Liu, S and Wang, H and Zhao, Y and Zhang, XD},
title = {Neuron devices: emerging prospects in neural interfaces and recognition.},
journal = {Microsystems &amp; nanoengineering},
volume = {8},
number = {},
pages = {128},
pmid = {36507057},
issn = {2055-7434},
abstract = {Neuron interface devices can be used to explore the relationships between neuron firing and synaptic transmission, as well as to diagnose and treat neurological disorders, such as epilepsy and Alzheimer's disease. It is crucial to exploit neuron devices with high sensitivity, high biocompatibility, multifunctional integration and high-speed data processing. During the past decades, researchers have made significant progress in neural electrodes, artificial sensory neuron devices, and neuromorphic optic neuron devices. The main part of the review is divided into two sections, providing an overview of recently developed neuron interface devices for recording electrophysiological signals, as well as applications in neuromodulation, simulating the human sensory system, and achieving memory and recognition. We mainly discussed the development, characteristics, functional mechanisms, and applications of neuron devices and elucidated several key points for clinical translation. The present review highlights the advances in neuron devices on brain-computer interfaces and neuroscience research.},
}
@article {pmid36504642,
year = {2022},
author = {Ren, Z and Han, X and Wang, B},
title = {The performance evaluation of the state-of-the-art EEG-based seizure prediction models.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1016224},
pmid = {36504642},
issn = {1664-2295},
abstract = {The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.},
}
@article {pmid36502931,
year = {2023},
author = {Ojeda, A and Wagner, M and Maric, V and Ramanathan, D and Mishra, J},
title = {EEG source derived salience network coupling supports real-world attention switching.},
journal = {Neuropsychologia},
volume = {178},
number = {},
pages = {108445},
doi = {10.1016/j.neuropsychologia.2022.108445},
pmid = {36502931},
issn = {1873-3514},
mesh = {Humans ; *Cerebral Cortex ; *Magnetic Resonance Imaging ; Brain ; Brain Mapping ; Electroencephalography ; },
abstract = {While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.},
}
@article {pmid36502205,
year = {2022},
author = {Fernández-Rodríguez, &#xc1; and Darves-Bornoz, A and Velasco-Álvarez, F and Ron-Angevin, R},
title = {Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {23},
pages = {},
pmid = {36502205},
issn = {1424-8220},
support = {PID2021-127261OB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2021-127261OB-I00//Agencia Estatal de Investigaci&#xf3;n/ ; PID2021-127261OB-I00//European Regional Development Fund/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials ; Eye Movements ; Electroencephalography/methods ; },
abstract = {Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain-computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.},
}
@article {pmid36501860,
year = {2022},
author = {Gannouni, S and Belwafi, K and Alangari, N and AboAlsamh, H and Belghith, A},
title = {Classification Strategies for P300-Based BCI-Spellers Adopting the Row Column Paradigm.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {23},
pages = {},
pmid = {36501860},
issn = {1424-8220},
support = {14-INF3139-02//National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Electroencephalography ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Acknowledging the importance of the ability to communicate with other people, the researcher community has developed a series of BCI-spellers, with the goal of regaining communication and interaction capabilities with the environment for people with disabilities. In order to bridge the gap in the digital divide between the disabled and the non-disabled people, we believe that the development of efficient signal processing algorithms and strategies will go a long way towards achieving novel assistive technologies using new human-computer interfaces. In this paper, we present various classification strategies that would be adopted by P300 spellers adopting the row/column paradigm. The presented strategies have obtained high accuracy rates compared with existent similar research works.},
}
@article {pmid36501753,
year = {2022},
author = {Jochumsen, M and Hougaard, BI and Kristensen, MS and Knoche, H},
title = {Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {23},
pages = {},
pmid = {36501753},
issn = {1424-8220},
support = {22357//The Velux Foundations/ ; },
mesh = {Humans ; *Stroke Rehabilitation ; Electroencephalography ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Feedback ; *Stroke ; },
abstract = {Brain-computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games.},
}
@article {pmid36495049,
year = {2023},
author = {Sorinas, J and Troyano, JCF and Ferrández, JM and Fernandez, E},
title = {Unraveling the Development of an Algorithm for Recognizing Primary Emotions Through Electroencephalography.},
journal = {International journal of neural systems},
volume = {33},
number = {1},
pages = {2250057},
doi = {10.1142/S0129065722500575},
pmid = {36495049},
issn = {1793-6462},
mesh = {Humans ; *Emotions ; Electroencephalography/methods ; Algorithms ; *Brain-Computer Interfaces ; },
abstract = {The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective brain-computer interface (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. Twelve seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-based emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent (SD) approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Moreover, new insights regarding subject-independent (SI) approximation have been discussed, although the results were not conclusive.},
}
@article {pmid36494390,
year = {2022},
author = {Rouanne, V and Costecalde, T and Benabid, AL and Aksenova, T},
title = {Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {21316},
pmid = {36494390},
issn = {2045-2322},
support = {PHRC-15-15-0124//Minist&#xe8;re des Solidarit&#xe9;s et de la Sant&#xe9;/ ; },
mesh = {*Brain-Computer Interfaces ; Task Performance and Analysis ; Electrocorticography ; Brain ; Computer Simulation ; Electroencephalography ; },
abstract = {Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.},
}
@article {pmid36483633,
year = {2022},
author = {de Seta, V and Toppi, J and Colamarino, E and Molle, R and Castellani, F and Cincotti, F and Mattia, D and Pichiorri, F},
title = {Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1016862},
pmid = {36483633},
issn = {1662-5161},
abstract = {Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.},
}
@article {pmid36481698,
year = {2022},
author = {Chinchani, AM and Paliwal, S and Ganesh, S and Chandrasekhar, V and Yu, BM and Sridharan, D},
title = {Tracking momentary fluctuations in human attention with a cognitive brain-machine interface.},
journal = {Communications biology},
volume = {5},
number = {1},
pages = {1346},
pmid = {36481698},
issn = {2399-3642},
support = {IA/I/15/2/502089/WTDBT_/DBT-Wellcome Trust India Alliance/India ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; R01 EB026953/EB/NIBIB NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Cognition ; },
abstract = {Selective attention produces systematic effects on neural states. It is unclear whether, conversely, momentary fluctuations in neural states have behavioral significance for attention. We investigated this question in the human brain with a cognitive brain-machine interface (cBMI) for tracking electrophysiological steady-state visually evoked potentials (SSVEPs) in real-time. Discrimination accuracy (d') was significantly higher when target stimuli were triggered at high, versus low, SSVEP power states. Target and distractor SSVEP power was uncorrelated across the hemifields, and target d' was unaffected by distractor SSVEP power states. Next, we trained participants on an auditory neurofeedback paradigm to generate biased, cross-hemispheric competitive interactions between target and distractor SSVEPs. The strongest behavioral effects emerged when competitive SSVEP dynamics unfolded at a timescale corresponding to the deployment of endogenous attention. In sum, SSVEP power dynamics provide a reliable readout of attentional state, a result with critical implications for tracking and training human attention.},
}
@article {pmid36481619,
year = {2023},
author = {Yuan, TF and Ng, CH and Hu, S},
title = {Addressing the mental health of children in quarantine with COVID-19 during the Omicron variant era.},
journal = {Asian journal of psychiatry},
volume = {80},
number = {},
pages = {103371},
pmid = {36481619},
issn = {1876-2026},
mesh = {Child ; Humans ; *COVID-19/prevention &amp; control ; *Mental Health ; SARS-CoV-2 ; *Communicable Disease Control ; },
}
@article {pmid36478044,
year = {2023},
author = {Zhang, Z and Chen, Y and Zheng, L and Du, J and Wei, S and Zhu, X and Xiong, JW},
title = {A DUSP6 inhibitor suppresses inflammatory cardiac remodeling and improves heart function after myocardial infarction.},
journal = {Disease models &amp; mechanisms},
volume = {16},
number = {5},
pages = {},
pmid = {36478044},
issn = {1754-8411},
support = {2018YFA0800501//National Key Research and Development Program of China/ ; 31730061//National Natural Science Foundation of China/ ; //AstraZeneca/ ; //Synogen Biopharma/ ; //Peking University/ ; },
mesh = {Animals ; Rats ; Dual Specificity Phosphatase 6 ; Fibrosis ; *Myocardial Infarction/complications/drug therapy ; Ventricular Remodeling ; },
abstract = {Acute myocardial infarction (MI) results in loss of cardiomyocytes and abnormal cardiac remodeling with severe inflammation and fibrosis. However, how cardiac repair can be achieved by timely resolution of inflammation and cardiac fibrosis remains incompletely understood. Our previous findings have shown that dual-specificity phosphatase 6 (DUSP6) is a regeneration repressor from zebrafish to rats. In this study, we found that intravenous administration of the DUSP6 inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) improved heart function and reduced cardiac fibrosis in MI rats. Mechanistic analysis revealed that BCI attenuated macrophage inflammation through NF-κB and p38 signaling, independent of DUSP6 inhibition, leading to the downregulation of various cytokines and chemokines. In addition, BCI suppressed differentiation-related signaling pathways and decreased bone-marrow cell differentiation into macrophages through inhibiting DUSP6. Furthermore, intramyocardial injection of poly (D, L-lactic-co-glycolic acid)-loaded BCI after MI had a notable effect on cardiac repair. In summary, BCI improves heart function and reduces abnormal cardiac remodeling by inhibiting macrophage formation and inflammation post-MI, thus providing a promising pro-drug candidate for the treatment of MI and related heart diseases. This article has an associated First Person interview with the first author of the paper.},
}
@article {pmid36476879,
year = {2022},
author = {Cao, W and Li, JH and Lin, S and Xia, QQ and Du, YL and Yang, Q and Ye, YZ and Zeng, LH and Li, XY and Xu, J and Luo, JH},
title = {NMDA receptor hypofunction underlies deficits in parvalbumin interneurons and social behavior in neuroligin 3 R451C knockin mice.},
journal = {Cell reports},
volume = {41},
number = {10},
pages = {111771},
doi = {10.1016/j.celrep.2022.111771},
pmid = {36476879},
issn = {2211-1247},
mesh = {Animals ; Mice ; *Autism Spectrum Disorder ; *Parvalbumins ; Receptors, N-Methyl-D-Aspartate ; Social Behavior ; },
abstract = {Neuroligins (NLs), a family of postsynaptic cell-adhesion molecules, have been associated with autism spectrum disorder. We have reported that dysfunction of the medial prefrontal cortex (mPFC) leads to social deficits in an NL3 R451C knockin (KI) mouse model of autism. However, the underlying molecular mechanism remains unclear. Here, we find that N-methyl-D-aspartate receptor (NMDAR) function and parvalbumin-positive (PV+) interneuron number and expression are reduced in the mPFC of the KI mice. Selective knockdown of NMDAR subunit GluN1 in the mPFC PV+ interneuron decreases its intrinsic excitability. Restoring NMDAR function by its partial agonist D-cycloserine rescues the PV+ interneuron dysfunction and social deficits in the KI mice. Interestingly, early D-cycloserine administration at adolescence prevents adult KI mice from social deficits. Together, our results suggest that NMDAR hypofunction and the resultant PV+ interneuron dysfunction in the mPFC may constitute a central node in the pathogenesis of social deficits in the KI mice.},
}
@article {pmid36476748,
year = {2023},
author = {Asahina, T and Shimba, K and Kotani, K and Jimbo, Y},
title = {Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies.},
journal = {Journal of neuroscience methods},
volume = {385},
number = {},
pages = {109764},
doi = {10.1016/j.jneumeth.2022.109764},
pmid = {36476748},
issn = {1872-678X},
mesh = {Animals ; *Brain-Computer Interfaces ; Haplorhini ; Quality of Life ; Movement ; Action Potentials ; },
abstract = {BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body.<br><br>Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method.<br><br>CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data.<br><br>NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study.<br><br>RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.},
}
@article {pmid36471144,
year = {2022},
author = {Bex, A and Mathon, B},
title = {Advances, technological innovations, and future prospects in stereotactic brain biopsies.},
journal = {Neurosurgical review},
volume = {46},
number = {1},
pages = {5},
pmid = {36471144},
issn = {1437-2320},
mesh = {Humans ; *Brain Neoplasms/diagnosis/surgery/pathology ; Inventions ; Stereotaxic Techniques ; Biopsy/methods ; Brain/surgery/pathology ; },
abstract = {Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.},
}
@article {pmid36471022,
year = {2022},
author = {Chen, W and Wu, J and Wei, R and Wu, S and Xia, C and Wang, D and Liu, D and Zheng, L and Zou, T and Li, R and Qi, X and Zhang, X},
title = {Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.},
journal = {Insights into imaging},
volume = {13},
number = {1},
pages = {184},
pmid = {36471022},
issn = {1869-4101},
support = {2018YFA0701400//Key Technologies Research and Development Program/ ; 52277232//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 2021ZD0200401//China Brain Project/ ; 226-2022-00136//Fundamental Research Funds for the Central Universities/ ; 2021C03001//Key R&amp;D Program of Zhejiang Province/ ; BE2022049-4//Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//Key-Area R&amp;D Program of Guangdong Province/ ; },
abstract = {OBJECTIVE: This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).<br><br>METHODS: Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.<br><br>RESULTS: In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4&#x2009;&#xb1;&#x2009;93.6&#xa0;min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7&#xa0;s (p&#x2009;=&#x2009;0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.<br><br>CONCLUSIONS: With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.},
}
@article {pmid36470437,
year = {2023},
author = {Wang, H and Xia, H and Xu, Z and Natsuki, T and Ni, QQ},
title = {Effect of surface structure on the antithrombogenicity performance of poly(-caprolactone)-cellulose acetate small-diameter tubular scaffolds.},
journal = {International journal of biological macromolecules},
volume = {226},
number = {},
pages = {132-142},
doi = {10.1016/j.ijbiomac.2022.11.315},
pmid = {36470437},
issn = {1879-0003},
mesh = {*Tissue Scaffolds/chemistry ; Tissue Engineering ; *Blood Substitutes ; Polyesters/pharmacology/chemistry ; },
abstract = {Small-diameter artificial blood vessels have always faced the problem of thrombosis. In this research, three types of poly(-caprolactone)-cellulose acetate (PCL-CA) composite nanofiber membranes were prepared by various collectors to make into a tubular scaffold with a 4.5-mm diameter. The collector consisted of two sizes of stainless steel wire mesh large-mesh (LM) and small-mesh (SM), respectively. There is also a random flat (RF) that acts as the third type collector. The nanofiber membrane's surface structure mimicked the collectors' surface morphology, they named LM, SM and RF scaffolds. The water contact angles of RF and LM scaffolds are 126.5° and 105.5°, and the distinct square-groove construction greatly improves the contact angle of LM. The tubular scaffolds' radial mechanical property test demonstrated that the large-mesh (LM) tubular scaffold enhanced the strain and tensile strength; the tensile strength and strain are 30 % and 148 % higher than that of the random-flat (RF) tubular scaffold, respectively. The suture retention strength value of the LM tubular scaffold was 103 % higher than that of the RF tubular scaffold. The cytotoxicity and antithrombogenicity performance were also evaluated, the LM tubular scaffold has 88 % cell viability, and the 5-min blood coagulation index (BCI) value was 89 %, which is much higher than other tubular scaffolds. The findings indicate that changing the tubular scaffold's surface morphology cannot only enhance the mechanical and hydrophilic properties but also increase cell survival and antithrombogenicity performance. Thus, the development of a small-diameter artificial blood vessel will be a big step toward solving the problem on thrombosis. Furthermore, artificial blood vessel is expected to be a candidate material for biomedical applications.},
}
@article {pmid36468060,
year = {2022},
author = {Carino-Escobar, RI and Rodríguez-García, ME and Ramirez-Nava, AG and Quinzaños-Fresnedo, J and Ortega-Robles, E and Arias-Carrion, O and Valdés-Cristerna, R and Cantillo-Negrete, J},
title = {A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1010328},
pmid = {36468060},
issn = {1664-2295},
abstract = {COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.},
}
@article {pmid36466619,
year = {2022},
author = {Mussi, MG and Adams, KD},
title = {EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1007136},
pmid = {36466619},
issn = {1662-5161},
abstract = {Most hybrid brain-computer interfaces (hBCI) aim at improving the performance of single-input BCI. Many combinations are possible to configure an hBCI, such as using multiple brain input signals, different stimuli or more than one input system. Multiple studies have been done since 2010 where such interfaces have been tested and analyzed. Results and conclusions are promising but little has been discussed as to what is the best approach for the pediatric population, should they use hBCI as an assistive technology. Children might face greater challenges when using BCI and might benefit from less complex interfaces. Hence, in this scoping review we included 42 papers that developed hBCI systems for the purpose of control of assistive devices or communication software, and we analyzed them through the lenses of potential use in clinical settings and for children. We extracted taxonomic categories proposed in previous studies to describe the types of interfaces that have been developed. We also proposed interface characteristics that could be observed in different hBCI, such as type of target, number of targets and number of steps before selection. Then, we discussed how each of the extracted characteristics could influence the overall complexity of the system and what might be the best options for applications for children. Effectiveness and efficiency were also collected and included in the analysis. We concluded that the least complex hBCI interfaces might involve having a brain inputs and an external input, with a sequential role of operation, and visual stimuli. Those interfaces might also use a minimal number of targets of the strobic type, with one or two steps before the final selection. We hope this review can be used as a guideline for future hBCI developments and as an incentive to the design of interfaces that can also serve children who have motor impairments.},
}
@article {pmid36463881,
year = {2022},
author = {Senathirajah, Y and Solomonides, AE},
title = {Best Papers in Human Factors and Sociotechnical Development.},
journal = {Yearbook of medical informatics},
volume = {31},
number = {1},
pages = {221-225},
pmid = {36463881},
issn = {2364-0502},
mesh = {Humans ; *COVID-19 ; *Medical Informatics ; Electronic Health Records ; MEDLINE ; *Social Media ; },
abstract = {OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021.<br><br>METHODS: A rigorous extraction process based on queries from Web of Science&#xae; and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.<br><br>RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section.<br><br>CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.},
}
@article {pmid36463200,
year = {2022},
author = {Yan, JJ and Ding, XJ and He, T and Chen, AX and Zhang, W and Yu, ZX and Cheng, XY and Wei, CY and Hu, QD and Liu, XY and Zhang, YL and He, M and Xie, ZY and Zha, X and Xu, C and Cao, P and Li, H and Xu, XH},
title = {A circuit from the ventral subiculum to anterior hypothalamic nucleus GABAergic neurons essential for anxiety-like behavioral avoidance.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {7464},
pmid = {36463200},
issn = {2041-1723},
mesh = {Male ; Animals ; Mice ; *GABAergic Neurons ; *Anterior Hypothalamic Nucleus ; Anxiety Disorders ; Anxiety ; Hippocampus ; },
abstract = {Behavioral observations suggest a connection between anxiety and predator defense, but the underlying neural mechanisms remain unclear. Here we examine the role of the anterior hypothalamic nucleus (AHN), a node in the predator defense network, in anxiety-like behaviors. By in vivo recordings in male mice, we find that activity of AHN GABAergic (AHN[Vgat+]) neurons shows individually stable increases when animals approach unfamiliar objects in an open field (OF) or when they explore the open-arm of an elevated plus-maze (EPM). Moreover, object-evoked AHN activity overlap with predator cue responses and correlate with the object and open-arm avoidance. Crucially, exploration-triggered optogenetic inhibition of AHN[Vgat+] neurons reduces object and open-arm avoidance. Furthermore, retrograde viral tracing identifies the ventral subiculum (vSub) of the hippocampal formation as a significant input to AHN[Vgat+] neurons in driving avoidance behaviors in anxiogenic situations. Thus, convergent activation of AHN[Vgat+] neurons serves as a shared mechanism between anxiety and predator defense to promote behavioral avoidance.},
}
@article {pmid36460220,
year = {2023},
author = {Pan, H and Fu, Y and Li, Z and Wen, F and Hu, J and Wu, B},
title = {Images Reconstruction from Functional Magnetic Resonance Imaging Patterns Based on the Improved Deep Generative Multiview Model.},
journal = {Neuroscience},
volume = {509},
number = {},
pages = {103-112},
doi = {10.1016/j.neuroscience.2022.11.021},
pmid = {36460220},
issn = {1873-7544},
mesh = {Humans ; *Image Processing, Computer-Assisted/methods ; *Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Head ; },
abstract = {Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. This paper proposes an improved deep generative multiview model to further promote the accuracy of reconstructing visual stimulus images. Firstly, an encoder based on residual-in-residual dense blocks is designed to fit the deep and multiview visual features of human natural state, and extract the features of visual stimulus images. Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.},
}
@article {pmid36456595,
year = {2022},
author = {Dimova-Edeleva, V and Ehrlich, SK and Cheng, G},
title = {Brain computer interface to distinguish between self and other related errors in human agent collaboration.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {20764},
pmid = {36456595},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Support Vector Machine ; Movement ; Acclimatization ; },
abstract = {When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.},
}
@article {pmid36456558,
year = {2022},
author = {Tian, X and Chen, Y and Majka, P and Szczupak, D and Perl, YS and Yen, CC and Tong, C and Feng, F and Jiang, H and Glen, D and Deco, G and Rosa, MGP and Silva, AC and Liang, Z and Liu, C},
title = {An integrated resource for functional and structural connectivity of the marmoset brain.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {7416},
pmid = {36456558},
issn = {2041-1723},
support = {R24 AG073190/AG/NIA NIH HHS/United States ; },
mesh = {Animals ; *Callithrix ; *Brain/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; Computer Simulation ; },
abstract = {Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.},
}
@article {pmid36455079,
year = {2023},
author = {Bian, R and Wu, H and Liu, B and Wu, D},
title = {Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {446-455},
doi = {10.1109/TNSRE.2022.3225878},
pmid = {36455079},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Electroencephalography ; Photic Stimulation ; Algorithms ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.},
}
@article {pmid36452175,
year = {2022},
author = {Weisinger, B and Pandey, DP and Saver, JL and Hochberg, A and Bitton, A and Doniger, GM and Lifshitz, A and Vardi, O and Shohami, E and Segal, Y and Reznik Balter, S and Djemal Kay, Y and Alter, A and Prasad, A and Bornstein, NM},
title = {Frequency-tuned electromagnetic field therapy improves post-stroke motor function: A pilot randomized controlled trial.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {1004677},
pmid = {36452175},
issn = {1664-2295},
abstract = {BACKGROUND AND PURPOSE: Impaired upper extremity (UE) motor function is a common disability after ischemic stroke. Exposure to extremely low frequency and low intensity electromagnetic fields (ELF-EMF) in a frequency-specific manner (Electromagnetic Network Targeting Field therapy; ENTF therapy) is a non-invasive method available to a wide range of patients that may enhance neuroplasticity, potentially facilitating motor recovery. This study seeks to quantify the benefit of the ENTF therapy on UE motor function in a subacute ischemic stroke population.<br><br>METHODS: In a randomized, sham-controlled, double-blind trial, ischemic stroke patients in the subacute phase with moderately to severely impaired UE function were randomly allocated to active or sham treatment with a novel, non-invasive, brain computer interface-based, extremely low frequency and low intensity ENTF therapy (1-100 Hz, < 1 G). Participants received 40 min of active ENTF or sham treatment 5 days/week for 8 weeks; ~three out of the five treatments were accompanied by 10 min of concurrent physical/occupational therapy. Primary efficacy outcome was improvement on the Fugl-Meyer Assessment - Upper Extremity (FMA-UE) from baseline to end of treatment (8 weeks).<br><br>RESULTS: In the per protocol set (13 ENTF and 8 sham participants), mean age was 54.7 years (&#xb1;15.0), 19% were female, baseline FMA-UE score was 23.7 (&#xb1;11.0), and median time from stroke onset to first stimulation was 11 days (interquartile range (IQR) 8-15). Greater improvement on the FMA-UE from baseline to week 4 was seen with ENTF compared to sham stimulation, 23.2 &#xb1; 14.1 vs. 9.6 &#xb1; 9.0, p = 0.007; baseline to week 8 improvement was 31.5 &#xb1; 10.7 vs. 23.1 &#xb1; 14.1. Similar favorable effects at week 8 were observed for other UE and global disability assessments, including the Action Research Arm Test (Pinch, 13.4 &#xb1; 5.6 vs. 5.3 &#xb1; 6.5, p = 0.008), Box and Blocks Test (affected hand, 22.5 &#xb1; 12.4 vs. 8.5 &#xb1; 8.6, p < 0.0001), and modified Rankin Scale (-2.5 &#xb1; 0.7 vs. -1.3 &#xb1; 0.7, p = 0.0005). No treatment-related adverse events were reported.<br><br>CONCLUSIONS: ENTF stimulation in subacute ischemic stroke patients was associated with improved UE motor function and reduced overall disability, and results support its safe use in the indicated population. These results should be confirmed in larger multicenter studies.<br><br>CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04039178, identifier: NCT04039178.},
}
@article {pmid36450968,
year = {2023},
author = {Vansteensel, MJ and Klein, E and van Thiel, G and Gaytant, M and Simmons, Z and Wolpaw, JR and Vaughan, TM},
title = {Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.},
journal = {Journal of neurology},
volume = {270},
number = {3},
pages = {1323-1336},
pmid = {36450968},
issn = {1432-1459},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Electroencephalography/methods ; *Brain-Computer Interfaces ; *Amyotrophic Lateral Sclerosis/therapy ; Speech ; *Self-Help Devices ; },
abstract = {Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.},
}
@article {pmid36450871,
year = {2022},
author = {Kaushik, P and Moye, A and Vugt, MV and Roy, PP},
title = {Decoding the cognitive states of attention and distraction in a real-life setting using EEG.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {20649},
pmid = {36450871},
issn = {2045-2322},
mesh = {Humans ; Animals ; Electroencephalography ; *Brain-Computer Interfaces ; *Automobile Driving ; *Gastropoda ; Attention ; Cognition ; },
abstract = {Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.},
}
@article {pmid36447267,
year = {2022},
author = {Li, Y and Qu, T and Li, D and Jing, J and Deng, Q and Wan, X},
title = {Human herpesvirus 7 encephalitis in an immunocompetent adult and a literature review.},
journal = {Virology journal},
volume = {19},
number = {1},
pages = {200},
pmid = {36447267},
issn = {1743-422X},
mesh = {Adult ; Child ; Humans ; *Herpesvirus 7, Human/genetics ; *Encephalitis, Herpes Simplex ; *Roseolovirus Infections/complications/diagnosis ; Electroencephalography ; High-Throughput Nucleotide Sequencing ; },
abstract = {BACKGROUND: Human herpesvirus 7 (HHV-7) is a common virus that infects children early and is accompanied by lifelong latency in cells, which is easy to reactivate in immunodeficient adults, but the underlying pathological mechanism is uncertain in immunocompetent adults without peculiar past medical history. Even though the clinical manifestation of the encephalitis caused by HHV-7 is uncommon in immunocompetent adults, the HHV-7 infection should not be neglected for encephalitis for unknown reasons.<br><br>CASE PRESENTATION: We reported here a case of HHV-7 encephalitis with epileptic seizures. While the brain computer tomography was standard, electroencephalography displayed slow waves in the temporal and bilateral frontal areas, then HHV-7 DNA was detected in the metagenomic next-generation sequencing of cerebrospinal fluid. Fortunately, the patient recovered after treatment and was discharged 2&#xa0;months later. We also collected the related cases and explored a better way to illuminate the underlying mechanism.<br><br>CONCLUSION: The case indicates clinicians should memorize HHV-7 as an unusual etiology of encephalitis to make an early diagnosis and therapy.},
}
@article {pmid36446933,
year = {2022},
author = {Yu, XD and Zhu, Y and Sun, QX and Deng, F and Wan, J and Zheng, D and Gong, W and Xie, SZ and Shen, CJ and Fu, JY and Huang, H and Lai, HY and Jin, J and Li, Y and Li, XM},
title = {Distinct serotonergic pathways to the amygdala underlie separate behavioral features of anxiety.},
journal = {Nature neuroscience},
volume = {25},
number = {12},
pages = {1651-1663},
pmid = {36446933},
issn = {1546-1726},
mesh = {Animals ; Mice ; Amygdala ; Anxiety ; *Anxiety Disorders ; *Basolateral Nuclear Complex ; Receptors, GABA-B ; *Serotonin ; },
abstract = {Anxiety-like behaviors in mice include social avoidance and avoidance of bright spaces. Whether these features are distinctly regulated is unclear. We demonstrate that in mice, social and anxiogenic stimuli, respectively, increase and decrease serotonin (5-HT) levels in basal amygdala (BA). In dorsal raphe nucleus (DRN), 5-HT∩vGluT3 neurons projecting to BA parvalbumin (DRN[5-HT∩vGluT3]-BA[PV]) and pyramidal (DRN[5-HT∩vGluT3]-BA[Pyr]) neurons have distinct intrinsic properties and gene expression and respond to anxiogenic and social stimuli, respectively. Activation of DRN[5-HT∩vGluT3]→BA[PV] inhibits 5-HT release via GABAB receptors on serotonergic terminals in BA, inducing social avoidance and avoidance of bright spaces. Activation of DRN[5-HT∩vGluT3]→BA neurons inhibits two subsets of BA[Pyr] neurons via 5-HT1A receptors (HTR1A) and 5-HT1B receptors (HTR1B). Pharmacological inhibition of HTR1A and HTR1B in BA induces avoidance of bright spaces and social avoidance, respectively. These findings highlight the functional significance of heterogenic inputs from DRN to BA subpopulations in the regulation of separate anxiety-related behaviors.},
}
@article {pmid36446797,
year = {2022},
author = {Nicolelis, MAL and Alho, EJL and Donati, ARC and Yonamine, S and Aratanha, MA and Bao, G and Campos, DSF and Almeida, S and Fischer, D and Shokur, S},
title = {Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {20545},
pmid = {36446797},
issn = {2045-2322},
mesh = {Adult ; Male ; Humans ; Feedback ; *Brain-Computer Interfaces ; Pilot Projects ; Brazil ; Paraplegia ; Locomotion ; *Spinal Cord Injuries/therapy ; },
abstract = {In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.},
}
@article {pmid36444397,
year = {2023},
author = {Heubel-Moenen, FCJI and Ansems, LEM and Verhezen, PWM and Wetzels, RJH and van Oerle, RGM and Straat, RJMHE and Megy, K and Downes, K and Henskens, YMC and Beckers, EAM and Joore, MA},
title = {Effectiveness and costs of a stepwise versus an all-in-one approach to diagnose mild bleeding disorders.},
journal = {British journal of haematology},
volume = {200},
number = {6},
pages = {792-801},
doi = {10.1111/bjh.18570},
pmid = {36444397},
issn = {1365-2141},
mesh = {Adult ; Humans ; *Blood Coagulation Disorders/diagnosis ; *Hemorrhagic Disorders/diagnosis ; Hemorrhage ; Fibrinolysis ; Cost-Benefit Analysis ; },
abstract = {The diagnostic work-up of patients referred to the haematologist for bleeding evaluation is performed in a stepwise way: bleeding history and results of screening laboratory tests guide further diagnostic evaluation. This can be ineffective, time-consuming and burdensome for patients. To improve this strategy, the initial laboratory investigation can be extended. In a model-based approach, effectiveness and costs of a conventional stepwise versus a newly proposed all-in-one diagnostic approach for bleeding evaluation were evaluated and compared, using data from an observational patient cohort study, including adult patients referred for bleeding evaluation. In the all-in-one approach, specialized platelet function tests, coagulation factors, and fibrinolysis tests were included in the initial investigation. Final diagnosis, hospital resource use and costs and patient burden were compared. A total of 150 patients were included. Compared to the stepwise approach, in the all-in-one approach, 19 additional patients reached a diagnosis and patient burden was lower, but total costs per patient were higher [€359, 95% bootstrapped confidence interval (BCI) 283-518, p = 0.001]. For bleeding evaluation of patients referred to the haematologist, an all-in-one diagnostic approach has a higher diagnostic yield and reduces patient burden, at a higher cost. This raises the question what costs justify the diagnosis of a bleeding disorder and a less burdensome diagnostic strategy.},
}
@article {pmid36441876,
year = {2023},
author = {Strypsteen, T and Bertrand, A},
title = {Bandwidth-Efficient Distributed Neural Network Architectures With Application to Neuro-Sensor Networks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {2},
pages = {933-943},
doi = {10.1109/JBHI.2022.3225019},
pmid = {36441876},
issn = {2168-2208},
mesh = {Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; *Data Compression ; },
abstract = {In this paper, we describe a design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data over bandwidth-limited communication channels to solve a classification task. Our design methodology starts from a centralized neural network and transforms it into a distributed architecture in which the channels are distributed over different nodes. The distributed network consists of two parallel branches, whose outputs are fused at the fusion center. The first branch collects classification results from local, node-specific classifiers while the second branch compresses each node's signal and then reconstructs the multi-channel time series for classification at the fusion center. We further improve bandwidth gains by dynamically activating the compression path when the local classifications do not suffice. We validate this method on a motor execution task in an emulated EEG sensor network and analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that the proposed framework enables up to a factor 20 in bandwidth reduction and factor 9 in power reduction with minimal loss (up to 2%) in classification accuracy compared to the centralized baseline on the demonstrated task. The proposed method offers a way to transform a centralized architecture to a distributed, bandwidth-efficient network amenable for low-power sensor networks. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.},
}
@article {pmid36441469,
year = {2023},
author = {Wood, CR and Xi, Y and Yang, WJ and Wang, H},
title = {Insight into Neuroethical Considerations of the Newly Emerging Technologies and Techniques of the Global Brain Initiatives.},
journal = {Neuroscience bulletin},
volume = {39},
number = {4},
pages = {685-689},
pmid = {36441469},
issn = {1995-8218},
mesh = {*Brain ; *Head ; },
}
@article {pmid36440598,
year = {2022},
author = {Kong, X and Shu, X and Wang, J and Liu, D and Ni, Y and Zhao, W and Wang, L and Gao, Z and Chen, J and Yang, B and Guo, X and Wang, Z},
title = {Fine-tuning of mTOR signaling by the UBE4B-KLHL22 E3 ubiquitin ligase cascade in brain development.},
journal = {Development (Cambridge, England)},
volume = {149},
number = {24},
pages = {},
pmid = {36440598},
issn = {1477-9129},
mesh = {Animals ; Mice ; *Brain/growth &amp; development ; *Neural Stem Cells/metabolism ; Sirolimus ; *TOR Serine-Threonine Kinases/metabolism ; *Ubiquitin-Protein Ligases/metabolism ; *Adaptor Proteins, Signal Transducing/metabolism ; },
abstract = {Spatiotemporal regulation of the mechanistic target of rapamycin (mTOR) pathway is pivotal for establishment of brain architecture. Dysregulation of mTOR signaling is associated with a variety of neurodevelopmental disorders. Here, we demonstrate that the UBE4B-KLHL22 E3 ubiquitin ligase cascade regulates mTOR activity in neurodevelopment. In a mouse model with UBE4B conditionally deleted in the nervous system, animals display severe growth defects, spontaneous seizures and premature death. Loss of UBE4B in the brains of mutant mice results in depletion of neural precursor cells and impairment of neurogenesis. Mechanistically, UBE4B polyubiquitylates and degrades KLHL22, an E3 ligase previously shown to degrade the GATOR1 component DEPDC5. Deletion of UBE4B causes upregulation of KLHL22 and hyperactivation of mTOR, leading to defective proliferation and differentiation of neural precursor cells. Suppression of KLHL22 expression reverses the elevated activity of mTOR caused by acute local deletion of UBE4B. Prenatal treatment with the mTOR inhibitor rapamycin rescues neurogenesis defects in Ube4b mutant mice. Taken together, these findings demonstrate that UBE4B and KLHL22 are essential for maintenance and differentiation of the precursor pool through fine-tuning of mTOR activity.},
}
@article {pmid36438642,
year = {2022},
author = {Sisti, HM and Beebe, A and Bishop, M and Gabrielsson, E},
title = {A brief review of motor imagery and bimanual coordination.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1037410},
pmid = {36438642},
issn = {1662-5161},
support = {P20 GM103449/GM/NIGMS NIH HHS/United States ; },
abstract = {Motor imagery is increasingly being used in clinical settings, such as in neurorehabilitation and brain computer interface (BCI). In stroke, patients lose upper limb function and must re-learn bimanual coordination skills necessary for the activities of daily living. Physiotherapists integrate motor imagery with physical rehabilitation to accelerate recovery. In BCIs, users are often asked to imagine a movement, often with sparse instructions. The EEG pattern that coincides with this cognitive task is captured, then used to execute an external command, such as operating a neuroprosthetic device. As such, BCIs are dependent on the efficient and reliable interpretation of motor imagery. While motor imagery improves patient outcome and informs BCI research, the cognitive and neurophysiological mechanisms which underlie it are not clear. Certain types of motor imagery techniques are more effective than others. For instance, focusing on kinesthetic cues and adopting a first-person perspective are more effective than focusing on visual cues and adopting a third-person perspective. As motor imagery becomes more dominant in neurorehabilitation and BCIs, it is important to elucidate what makes these techniques effective. The purpose of this review is to examine the research to date that focuses on both motor imagery and bimanual coordination. An assessment of current research on these two themes may serve as a useful platform for scientists and clinicians seeking to use motor imagery to help improve bimanual coordination, either through augmenting physical therapy or developing more effective BCIs.},
}
@article {pmid36437049,
year = {2023},
author = {Robinson, DA and Foster, ME and Bennett, CH and Bhandarkar, A and Webster, ER and Celebi, A and Celebi, N and Fuller, EJ and Stavila, V and Spataru, CD and Ashby, DS and Marinella, MJ and Krishnakumar, R and Allendorf, MD and Talin, AA},
title = {Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analog Enables Stable and Efficient Biocompatible Artificial Synapses.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {35},
number = {37},
pages = {e2207595},
doi = {10.1002/adma.202207595},
pmid = {36437049},
issn = {1521-4095},
support = {//Laboratory-Directed Research and Development/ ; //DOE Office of Science Research Program for Microelectronics Codesign/ ; DE-NA-0003525//U.S. Department of Energy's National Nuclear Security Administration/ ; },
abstract = {Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analog (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory computations and application of electron transfer theory to in situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity, and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing.},
}
@article {pmid36433362,
year = {2022},
author = {Wang, H and Zhu, C and Jin, W and Tang, J and Wu, Z and Chen, K and Hong, H},
title = {A Linear-Power-Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {22},
pages = {},
pmid = {36433362},
issn = {1424-8220},
support = {2022C01119//the "Pioneer" and "Leading Goose" R&amp;D Program of Zhejiang/ ; 62171169//National Natural Science Foundation of China/ ; },
mesh = {*Wireless Technology ; *Hot Temperature ; Prostheses and Implants ; Electric Impedance ; Body Temperature Regulation ; },
abstract = {Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain-computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications.},
}
@article {pmid36429765,
year = {2022},
author = {Chang, D and Xiang, Y and Zhao, J and Qian, Y and Li, F},
title = {Exploration of Brain-Computer Interaction for Supporting Children's Attention Training: A Multimodal Design Based on Attention Network and Gamification Design.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {22},
pages = {},
pmid = {36429765},
issn = {1660-4601},
mesh = {Child ; Humans ; *Brain-Computer Interfaces ; Gamification ; Brain ; Cognition ; Computers ; },
abstract = {Recent developments in brain-computer interface (BCI) technology have shown great potential in terms of estimating users' mental state and supporting children's attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children's attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants' attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.},
}
@article {pmid36428885,
year = {2022},
author = {Shovon, MSH and Islam, MJ and Nabil, MNAK and Molla, MM and Jony, AI and Mridha, MF},
title = {Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {11},
pages = {},
pmid = {36428885},
issn = {2075-4418},
abstract = {Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&amp;E (hematoxylin &amp; eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.},
}
@article {pmid36428289,
year = {2023},
author = {Si, C and Qin, H and Chuanzhuang, Y and Wei, T and Lin, X},
title = {Study of event-related potentials by withdrawal friction on the fingertip.},
journal = {Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)},
volume = {29},
number = {1},
pages = {e13232},
pmid = {36428289},
issn = {1600-0846},
support = {51805218//National Natural Science Foundation of China/ ; 51905222//National Natural Science Foundation of China/ ; 51875566//National Natural Science Foundation of China/ ; BK20170552//Youth project of Jiangsu Natural Science Foundation of China/ ; 2018M632239//China Postdoctoral Science Foundation Funded Project/ ; 1701063C//Jiangsu Planned Projects for Postdoctoral Research Funds/ ; },
mesh = {Humans ; Male ; Female ; Young Adult ; Adult ; Friction ; *Touch/physiology ; *Touch Perception/physiology ; Skin ; Fingers/physiology ; Evoked Potentials ; },
abstract = {OBJECTIVES: The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain-computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface.<br><br>METHODS: When friction is suddenly removed from the surface of an item, a technique called event-related potential, (ERP) is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A 64-channels EEG collecting system was utilized to acquire EEG data from the individuals. [Corrections added on 5 December 2022, after first online publication: The preceding sentence has been updated.] The event-related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years.<br><br>RESULTS: It was discovered that the P3 component of event-related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed.<br><br>CONCLUSIONS: It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain-computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation.},
}
@article {pmid36427669,
year = {2023},
author = {Deng, X and Wang, Z and Liu, K and Xiang, X},
title = {A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction.},
journal = {Journal of neuroscience methods},
volume = {384},
number = {},
pages = {109747},
doi = {10.1016/j.jneumeth.2022.109747},
pmid = {36427669},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; *Brain-Computer Interfaces ; Brain/diagnostic imaging/physiology ; Reproduction ; },
abstract = {In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.},
}
@article {pmid36426541,
year = {2022},
author = {Colucci, A and Vermehren, M and Cavallo, A and Angerhöfer, C and Peekhaus, N and Zollo, L and Kim, WS and Paik, NJ and Soekadar, SR},
title = {Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?.},
journal = {Neurorehabilitation and neural repair},
volume = {36},
number = {12},
pages = {747-756},
pmid = {36426541},
issn = {1552-6844},
mesh = {Humans ; *Exoskeleton Device ; *Brain-Computer Interfaces ; *Neurological Rehabilitation ; Brain ; *Robotics ; },
abstract = {The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that-upon repeated use over several weeks-B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.},
}
@article {pmid36423320,
year = {2022},
author = {Chen, YF and Fu, R and Wu, J and Song, J and Ma, R and Jiang, YC and Zhang, M},
title = {Continuous Bimanual Trajectory Decoding of Coordinated Movement From EEG Signals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {12},
pages = {6012-6023},
doi = {10.1109/JBHI.2022.3224506},
pmid = {36423320},
issn = {2168-2208},
mesh = {Humans ; *Electroencephalography/methods ; Hand ; Upper Extremity ; *Brain-Computer Interfaces ; Movement ; },
abstract = {While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.},
}
@article {pmid36422533,
year = {2022},
author = {Diao, X and Luo, D and Wang, D and Lai, J and Li, Q and Zhang, P and Huang, H and Wu, L and Lu, S and Hu, S},
title = {Lurasidone versus Quetiapine for Cognitive Impairments in Young Patients with Bipolar Depression: A Randomized, Controlled Study.},
journal = {Pharmaceuticals (Basel, Switzerland)},
volume = {15},
number = {11},
pages = {},
pmid = {36422533},
issn = {1424-8247},
support = {2021C03107//Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation - &quot;Ten Thousand Talents Program&quot; of Zhejiang Province/ ; 2020R01001//Innovation Team for Precision Diagnosis and Treatment of Major Brain Diseases/ ; },
abstract = {The clinical efficacy of lurasidone and quetiapine, two commonly prescribed atypical antipsychotics for bipolar depression, has been inadequately studied in young patients. In this randomized and controlled study, we aimed to compare the effects of these two drugs on cognitive function, emotional status, and metabolic profiles in children and adolescents with bipolar depression. We recruited young participants (aged 10-17 years old) with a DSM-5 diagnosis of bipolar disorder during a depressive episode, who were then randomly assigned to two groups and treated with flexible doses of lurasidone (60 to 120 mg/day) or quetiapine (300 to 600 mg/day) for consecutive 8 weeks, respectively. All the participants were clinically evaluated on cognitive function using the THINC-it instrument at baseline and week 8, and emotional status was assessed at baseline and the end of week 2, 4, and 8. Additionally, the changes in weight and serum metabolic profiles (triglyceride, cholesterol, and fasting blood glucose) during the trial were also analyzed. In results, a total of 71 patients were randomly assigned to the lurasidone group (n = 35) or the quetiapine group (n = 36), of which 31 patients completed the whole treatment course. After an 8-week follow-up, participants in the lurasidone group showed better performance in the Symbol Check Reaction and Accuracy Tests, when compared to those in the quetiapine group. No inter-group difference was observed in the depression scores, response rate, or remission rate throughout the trial. In addition, there was no significant difference in serum metabolic profiles between the lurasidone group and the quetiapine group, including triglyceride level, cholesterol level, and fasting blood glucose level. However, the quetiapine group presented a more apparent change in body weight than the lurasidone group. In conclusion, the present study provided preliminary evidence that quetiapine and lurasidone had an equivalent anti-depressive effect, and lurasidone appeared to be superior to quetiapine in improving the cognitive function of young patients with bipolar depression.},
}
@article {pmid36421880,
year = {2022},
author = {Li, J and Huang, B and Wang, F and Xie, Q and Xu, C and Huang, H and Pan, J},
title = {A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.},
journal = {Brain sciences},
volume = {12},
number = {11},
pages = {},
pmid = {36421880},
issn = {2076-3425},
support = {2022ZD0208900//the Science and Technology Innovation 2030 - &quot;Brain Science and Brain-Like Intelligence Technology&quot; Key Project/ ; 62006082, 61906019, 82171174, 81974154//National Natural Science Foundation of China/ ; 2021A1515011600, 2020A1515110294, 2021A1515011853//Guangdong Basic and Applied Basic Research Foundation/ ; 202102020877//Guangzhou Science and Technology Plan Project/ ; },
abstract = {For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.},
}
@article {pmid36421877,
year = {2022},
author = {Zavala Hernández, JG and Barbosa-Santillán, LI},
title = {Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.},
journal = {Brain sciences},
volume = {12},
number = {11},
pages = {},
pmid = {36421877},
issn = {2076-3425},
support = {544587//Consejo Nacional de Ciencia y Tecnolog&#xed;a/ ; },
abstract = {The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain-computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.},
}
@article {pmid36419166,
year = {2022},
author = {Jervis-Rademeyer, H and Ong, K and Djuric, A and Munce, S and Musselman, KE and Marquez-Chin, C},
title = {Therapists' perspectives on using brain-computer interface-triggered functional electrical stimulation therapy for individuals living with upper extremity paralysis: a qualitative case series study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {19},
number = {1},
pages = {127},
pmid = {36419166},
issn = {1743-0003},
support = {//CIHR/Canada ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Paralysis ; Qualitative Research ; Upper Extremity ; },
abstract = {BACKGROUND: Brain computer interface-triggered functional electrical stimulation therapy (BCI-FEST) has shown promise as a therapy to improve upper extremity function for individuals who have had a stroke or spinal cord injury. The next step is to determine whether BCI-FEST could be used clinically as part of broader therapy practice. To do this, we need to understand therapists' opinions on using the BCI-FEST and what limitations potentially exist. Therefore, we conducted a qualitative exploratory study to understand the perspectives of therapists on their experiences delivering BCI-FEST and the feasibility of large-scale clinical implementation.<br><br>METHODS: Semi-structured interviews were conducted with physical therapists (PTs) and occupational therapists (OTs) who have delivered BCI-FEST. Interview questions were developed using the COM-B (Capability, Opportunity, Motivation-Behaviour) model of behaviour change. COM-B components were used to inform deductive content analysis while other subthemes were detected using an inductive approach.<br><br>RESULTS: We interviewed PTs (n&#x2009;=&#x2009;3) and OTs (n&#x2009;=&#x2009;3), with 360 combined hours of experience delivering BCI-FEST. Components and subcomponents of the COM-B determined deductively included: (1) Capability (physical, psychological), (2) Opportunity (physical, social), and (3) Motivation (automatic, reflective). Under each deductive subcomponent, one to two inductive subthemes were identified (n&#x2009;=&#x2009;8). Capability and Motivation were perceived as strengths, and therefore supported therapists' decisions to use BCI-FEST. Under Opportunity, for both subcomponents (physical, social), therapists recognized the need for more support to clinically implement BCI-FEST.<br><br>CONCLUSIONS: We identified facilitating and limiting factors to BCI-FEST delivery in a clinical setting according to clinicians. These factors implied that education, training, a support network or mentors, and restructuring the physical environment (e.g., scheduling) should be targeted as interventions. The results of this study may help to inform future development of new technologies and interventions.},
}
@article {pmid36418525,
year = {2023},
author = {Baudry, AS and Vanlemmens, L and Congard, A and Untas, A and Segura-Djezzar, C and Lefeuvre-Plesse, C and Coussy, F and Guiu, S and Frenel, JS and Sauterey, B and Yakimova, S and Christophe, V},
title = {Emotional processes in partners' quality of life at various stages of breast cancer pathway: a longitudinal study.},
journal = {Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation},
volume = {32},
number = {4},
pages = {1085-1094},
pmid = {36418525},
issn = {1573-2649},
mesh = {Humans ; Female ; *Breast Neoplasms ; Quality of Life/psychology ; Longitudinal Studies ; Depression/psychology ; Emotions ; Anxiety/psychology ; },
abstract = {INTRODUCTION: Several studies have shown that emotional competence (EC) impacts cancer adjustment via anxiety and depression symptoms. The objective was to test this model for the quality of life (QoL) of partners: first, the direct effect of partners' EC on their QoL, anxiety and depression symptoms after cancer diagnosis (T1), after chemotherapy (T2) and after radiotherapy (T3); Second, the indirect effects of partners' EC at T1 on their QoL at T2 and T3 through anxiety and depression symptoms.<br><br>METHODS: 192 partners of women with breast cancer completed a questionnaire at T1, T2 and T3 to assess their EC (PEC), anxiety and depression symptoms (HADS) and QoL (Partner-YW-BCI). Partial correlations and regression analyses were performed to test direct and indirect effects of EC on issues.<br><br>RESULTS: EC at T1 predicted fewer anxiety and depression symptoms at each time and all dimensions of QoL, except for career management and financial difficulties. EC showed different significant indirect effects (i.e. via anxiety or depression symptoms) on all sub-dimensions of QoL, except for financial difficulties, according to the step of care pathway (T2 and T3). Anxiety and depression played a different role in the psychological processes that influence QoL.<br><br>CONCLUSION: Findings confirm the importance of taking emotional processes into account in the adjustment of partners, especially regarding their QoL and the support they may provide to patients. It, thus, seems important to integrate EC in future health models and psychosocial interventions focused on partners or caregivers.},
}
@article {pmid36408731,
year = {2023},
author = {Kucewicz, MT and Worrell, GA and Axmacher, N},
title = {Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.},
journal = {Brain : a journal of neurology},
volume = {146},
number = {6},
pages = {2214-2226},
doi = {10.1093/brain/awac435},
pmid = {36408731},
issn = {1460-2156},
mesh = {Humans ; *Brain/physiology ; *Memory/physiology ; Mental Recall/physiology ; Electric Stimulation ; Cognition ; },
abstract = {Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive as to how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation or recall in response to direct electrical stimulation are only now being unravelled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learnt. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioural outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with an open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials will shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioural states. Next-generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.},
}
@article {pmid36408095,
year = {2022},
author = {Hu, J and Zou, J and Wan, Y and Yao, Q and Dong, P and Li, G and Wu, X and Zhang, L and Liang, D and Zeng, Q and Huang, G},
title = {Rehabilitation of motor function after stroke: A bibliometric analysis of global research from 2004 to 2022.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {1024163},
pmid = {36408095},
issn = {1663-4365},
abstract = {BACKGROUND AND AIMS: The mortality rate of stroke has been increasing worldwide. Poststroke somatic dysfunctions are common. Motor function rehabilitation of patients with such somatic dysfunctions enhances the quality of life and has long been the primary practice to achieve functional recovery. In this regard, we aimed to delineate the new trends and frontiers in stroke motor function rehabilitation literature published from 2004 to 2022 using a bibliometric software.<br><br>METHODS: All documents related to stroke rehabilitation and published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Publication output, research categories, countries/institutions, authors/cocited authors, journals/cocited journals, cocited references, and keywords were assessed using VOSviewer v.1.6.15.0 and CiteSpace version 5.8. The cocitation map was plotted according to the analysis results to intuitively observe the research hotspots.<br><br>RESULTS: Overall, 3,302 articles were retrieved from 78 countries or regions and 564 institutions. Over time, the publication outputs increased annually. In terms of national contribution, the United States published the most papers, followed by China, Japan, South Korea, and Canada. Yeungnam University had the most articles among all institutions, followed by Emory University, Fudan University, and National Taiwan University. Jang Sung Ho and Wolf S.L. were the most productive (56 published articles) and influential (cited 1,121 times) authors, respectively. "Effect of constraint-induced movement therapy on upper extremity function 3-9&#x2009;months after stroke: the Extremity Constraint Induced Therapy Evaluation randomized clinical trial" was the most frequently cited reference. Analysis of keywords showed that upper limbs, Fugl-Meyer assessment, electromyography, virtual reality, telerehabilitation, exoskeleton, and brain-computer interface were the research development trends and focus areas for this topic.<br><br>CONCLUSION: Publications regarding motor function rehabilitation following stroke are likely to continuously increase. Research on virtual reality, telemedicine, electroacupuncture, the brain-computer interface, and rehabilitation robots has attracted increasing attention, with these topics becoming the hotspots of present research and the trends of future research.},
}
@article {pmid36408074,
year = {2022},
author = {Cui, Z and Li, Y and Huang, S and Wu, X and Fu, X and Liu, F and Wan, X and Wang, X and Zhang, Y and Qiu, H and Chen, F and Yang, P and Zhu, S and Li, J and Chen, W},
title = {BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {6},
pages = {1283-1301},
pmid = {36408074},
issn = {1871-4080},
abstract = {UNLABELLED: In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09801-6.},
}
@article {pmid36405787,
year = {2022},
author = {Jaipriya, D and Sriharipriya, KC},
title = {A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {1010770},
pmid = {36405787},
issn = {1662-5188},
abstract = {In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.},
}
@article {pmid36403238,
year = {2023},
author = {Zhang, F and Zhang, L and Xia, J and Zhao, W and Dong, S and Ye, Z and Pan, G and Luo, J and Zhang, S},
title = {Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {2},
pages = {e2204467},
pmid = {36403238},
issn = {2198-3844},
support = {2021ZD0200401//China Brain Project/ ; 2021C03003//Zhejiang Province Key Research &amp; Development programs/ ; 2021C03062//Zhejiang Province Key Research &amp; Development programs/ ; 2021C03108//Zhejiang Province Key Research &amp; Development programs/ ; },
mesh = {Mice ; Animals ; *Zinc Oxide ; Electrocorticography ; Electrodes ; Brain/physiology ; Brain Mapping/methods ; },
abstract = {Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid (13.2 dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2 dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.},
}
@article {pmid36403143,
year = {2024},
author = {Klein, E and Kinsella, M and Stevens, I and Fried-Oken, M},
title = {Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {19},
number = {3},
pages = {1041-1051},
pmid = {36403143},
issn = {1748-3115},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Neurodegenerative Diseases ; *Brain Diseases ; Technology ; Language ; },
abstract = {PURPOSE: To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies.<br><br>METHODS: Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory.<br><br>RESULTS: Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies.<br><br>CONCLUSIONS: With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.},
}
@article {pmid36400152,
year = {2023},
author = {Xi, C and Li, A and Lai, J and Huang, X and Zhang, P and Yan, S and Jiao, M and Huang, H and Hu, S},
title = {Brain-gut microbiota multimodal predictive model in patients with bipolar depression.},
journal = {Journal of affective disorders},
volume = {323},
number = {},
pages = {140-152},
doi = {10.1016/j.jad.2022.11.026},
pmid = {36400152},
issn = {1573-2517},
mesh = {Humans ; *Bipolar Disorder/diagnostic imaging/drug therapy ; Quetiapine Fumarate/therapeutic use ; *Gastrointestinal Microbiome ; Brain/diagnostic imaging ; Gray Matter ; Magnetic Resonance Imaging/methods ; },
abstract = {BACKGROUND: The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction.<br><br>METHODS: At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders.<br><br>RESULTS: The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified&#xff0c;Porphyromonas_asaccharolytica&#xff0c;Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality.<br><br>LIMITATIONS: Lack post-treatment and external validation datasets; size of HCs is modest.<br><br>CONCLUSIONS: Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.},
}
@article {pmid36398685,
year = {2023},
author = {Bhuvaneshwari, M and Grace Mary Kanaga, E and George, ST},
title = {Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.},
journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine},
volume = {237},
number = {1},
pages = {134-143},
doi = {10.1177/09544119221135714},
pmid = {36398685},
issn = {2041-3033},
mesh = {Animals ; *Foxes ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Evoked Potentials ; },
abstract = {Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.},
}
@article {pmid36398508,
year = {2022},
author = {Rainey, S and Dague, KO and Crisp, R},
title = {Brain-State Transitions, Responsibility, and Personal Identity.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {31},
number = {4},
pages = {453-463},
doi = {10.1017/S096318012100092X},
pmid = {36398508},
issn = {1469-2147},
mesh = {Humans ; *Self Concept ; *Brain-Computer Interfaces ; Morals ; Brain ; },
abstract = {This article examines the emerging possibility of "brain-state transitioning," in which one brain state is prompted through manipulating the dynamics of the active brain. The technique, still in its infancy, is intended to provide the basis for novel treatments for brain-based disorders. Although a detailed literature exists covering topics around brain-machine interfaces, where targets of brain-based activity include artificial limbs, hardware, and software, there is less concentration on the brain itself as a target for instrumental intervention. This article examines some of the science behind brain-state transitioning, before extending beyond current possibilities in order to explore philosophical and ethical questions about how transitions could be seen to impact on assessment of responsibility and personal identity. It concludes with some thoughts on how best to pursue this nascent approach while accounting for the philosophical and ethical issues.},
}
@article {pmid36398434,
year = {2023},
author = {Zhang, J and Wang, L and Xue, Y and Lei, IM and Chen, X and Zhang, P and Cai, C and Liang, X and Lu, Y and Liu, J},
title = {Engineering Electrodes with Robust Conducting Hydrogel Coating for Neural Recording and Modulation.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {35},
number = {3},
pages = {e2209324},
doi = {10.1002/adma.202209324},
pmid = {36398434},
issn = {1521-4095},
support = {2022ZD0209500//National Science and Technology Innovation 2030-"Brain Science and Brain-Like Intelligence Technology" Major Project/ ; 2020A1515110288//Natural Science Foundation of Guangdong Province/ ; 2022A1515010152//Natural Science Foundation of Guangdong Province/ ; 2020B1515020042//Natural Science Foundation of Guangdong Province/ ; JCYJ20210324105211032//Basic Research Program of Shenzhen/ ; GJHZ20210705141809030//Basic Research Program of Shenzhen/ ; Y01346002//MechERE Centers at MIT and SUSTech/ ; ZDSYS20200811143601004//Science, Technology and Innovation Commission of Shenzhen Municipality/ ; T2122021//National Natural Science Foundation of China/ ; 32071035//National Natural Science Foundation of China/ ; },
mesh = {Animals ; Mice ; *Hydrogels/chemistry ; Reproducibility of Results ; Electrodes ; *Polymers/chemistry ; Electric Conductivity ; },
abstract = {Coating conventional metallic electrodes with conducting polymers has enabled the essential characteristics required for bioelectronics, such as biocompatibility, electrical conductivity, mechanical compliance, and the capacity for structural and chemical functionalization of the bioelectrodes. However, the fragile interface between the conducting polymer and the electrode in wet physiological environment greatly limits their utility and reliability. Here, a general yet reliable strategy to seamlessly interface conventional electrodes with conducting hydrogel coatings is established, featuring tissue-like modulus, highly-desirable electrochemical properties, robust interface, and long-term reliability. Numerical modeling reveals the role of toughening mechanism, synergy of covalent anchorage of long-chain polymers, and chemical cross-linking, in improving the long-term robustness of the interface. Through in vivo implantation in freely-moving mouse models, it is shown that stable electrophysiological recording can be achieved, while the conducting hydrogel-electrode interface remains robust during the long-term low-voltage electrical stimulation. This simple yet versatile design strategy addresses the long-standing technical challenges in functional bioelectrode engineering, and opens up new avenues for the next-generation diagnostic brain-machine interfaces.},
}
@article {pmid36395140,
year = {2024},
author = {Faes, A and Camarrone, F and Van Hulle, MM},
title = {Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {7},
pages = {8897-8908},
doi = {10.1109/TNNLS.2022.3216589},
pmid = {36395140},
issn = {2162-2388},
mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiology ; *Algorithms ; Movement/physiology ; Brain/physiology/diagnostic imaging ; Electrocorticography/methods ; Male ; Signal Processing, Computer-Assisted ; Adult ; Electroencephalography/methods ; },
abstract = {Multiway- or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector- or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector- or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.},
}
@article {pmid36394044,
year = {2022},
author = {Insausti-Delgado, A and López-Larraz, E and Nishimura, Y and Ziemann, U and Ramos-Murguialday, A},
title = {Non-invasive brain-spine interface: Continuous control of trans-spinal magnetic stimulation using EEG.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {975037},
pmid = {36394044},
issn = {2296-4185},
abstract = {Brain-controlled neuromodulation has emerged as a promising tool to promote functional recovery in patients with motor disorders. Brain-machine interfaces exploit this neuromodulatory strategy and could be used for restoring voluntary control of lower limbs. In this work, we propose a non-invasive brain-spine interface (BSI) that processes electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS), as an approach for lower-limb neurorehabilitation. This novel platform allows to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. We tested this closed-loop system in 10 healthy participants using different stimulation conditions. This BSI efficiently removed stimulation artifacts from EEG regardless of ts-MS intensity used, allowing continuous monitoring of cortical activity and real-time closed-loop control of ts-MS. Our BSI induced afferent and efferent evoked responses, being this activation ts-MS intensity-dependent. We demonstrated the feasibility, safety and usability of this non-invasive BSI. The presented system represents a novel non-invasive means of brain-controlled neuromodulation and opens the door towards its integration as a therapeutic tool for lower-limb rehabilitation.},
}
@article {pmid36389253,
year = {2022},
author = {Li, P and Su, J and Belkacem, AN and Cheng, L and Chen, C},
title = {Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1024150},
doi = {10.3389/fnins.2022.1024150},
pmid = {36389253},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2022.971039.].},
}
@article {pmid36389231,
year = {2022},
author = {Hou, X and Zhao, J and Zhang, H},
title = {Reconstruction of perceived face images from brain activities based on multi-attribute constraints.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1015752},
pmid = {36389231},
issn = {1662-4548},
abstract = {Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.},
}
@article {pmid36388974,
year = {2022},
author = {Keogh, C and FitzGerald, JJ},
title = {Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.},
journal = {iScience},
volume = {25},
number = {11},
pages = {105428},
pmid = {36388974},
issn = {2589-0042},
abstract = {The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.},
}
@article {pmid36387766,
year = {2022},
author = {Bak, S and Yeu, M and Jeong, J},
title = {Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1034983},
pmid = {36387766},
issn = {1687-5273},
mesh = {Humans ; *Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Prefrontal Cortex ; Brain ; },
abstract = {It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.},
}
@article {pmid36387584,
year = {2022},
author = {Cui, X and Wu, Y and Wu, J and You, Z and Xiahou, J and Ouyang, M},
title = {A review: Music-emotion recognition and analysis based on EEG signals.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {997282},
pmid = {36387584},
issn = {1662-5196},
abstract = {Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.},
}
@article {pmid36381629,
year = {2022},
author = {Lee, SH and Thunemann, M and Lee, K and Cleary, DR and Tonsfeldt, KJ and Oh, H and Azzazy, F and Tchoe, Y and Bourhis, AM and Hossain, L and Ro, YG and Tanaka, A and Kılıç, K and Devor, A and Dayeh, SA},
title = {Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces.},
journal = {Advanced functional materials},
volume = {32},
number = {25},
pages = {},
pmid = {36381629},
issn = {1616-301X},
support = {F32 MH120886/MH/NIMH NIH HHS/United States ; P50 NS022343/NS/NINDS NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; P30 CA023100/CA/NCI NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; },
abstract = {The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.},
}
@article {pmid36379711,
year = {2022},
author = {Heusser, MR and Bourrelly, C and Gandhi, NJ},
title = {Decoding the Time Course of Spatial Information from Spiking and Local Field Potential Activities in the Superior Colliculus.},
journal = {eNeuro},
volume = {9},
number = {6},
pages = {},
pmid = {36379711},
issn = {2373-2822},
support = {R01 EY024831/EY/NEI NIH HHS/United States ; T32 GM081760/GM/NIGMS NIH HHS/United States ; P30 EY008098/EY/NEI NIH HHS/United States ; R01 EY022854/EY/NEI NIH HHS/United States ; R21 EY030667/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Superior Colliculi/physiology ; Macaca mulatta ; *Saccades ; Eye Movements ; Neurons/physiology ; },
abstract = {Place code representation is ubiquitous in circuits that encode spatial parameters. For visually guided eye movements, neurons in many brain regions emit spikes when a stimulus is presented in their receptive fields and/or when a movement is directed into their movement fields. Crucially, individual neurons respond for a broad range of directions or eccentricities away from the optimal vector, making it difficult to decode the stimulus location or the saccade vector from each cell's activity. We investigated whether it is possible to decode the spatial parameter with a population-level analysis, even when the optimal vectors are similar across neurons. Spiking activity and local field potentials (LFPs) in the superior colliculus (SC) were recorded with a laminar probe as monkeys performed a delayed saccade task to one of eight targets radially equidistant in direction. A classifier was applied offline to decode the spatial configuration as the trial progresses from sensation to action. For spiking activity, decoding performance across all eight directions was highest during the visual and motor epochs and lower but well above chance during the delay period. Classification performance followed a similar pattern for LFP activity too, except the performance during the delay period was limited mostly to the preferred direction. Increasing the number of neurons in the population consistently increased classifier performance for both modalities. Overall, this study demonstrates the power of population activity for decoding spatial information not possible from individual neurons.},
}
@article {pmid36376487,
year = {2023},
author = {Jia, Y and Xu, S and Han, G and Wang, B and Wang, Z and Lan, C and Zhao, P and Gao, M and Zhang, Y and Jiang, W and Qiu, B and Liu, R and Hsu, YC and Sun, Y and Liu, C and Liu, Y and Bai, R},
title = {Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.},
journal = {Nature biomedical engineering},
volume = {7},
number = {3},
pages = {236-252},
pmid = {36376487},
issn = {2157-846X},
mesh = {Humans ; Rats ; Animals ; *Water/metabolism ; *Glioma/diagnostic imaging/metabolism ; Aquaporin 4/metabolism ; Biomarkers ; Magnetic Resonance Imaging ; },
abstract = {The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.},
}
@article {pmid36376067,
year = {2022},
author = {Iwama, S and Zhang, Y and Ushiba, J},
title = {De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.},
journal = {eNeuro},
volume = {9},
number = {6},
pages = {},
pmid = {36376067},
issn = {2373-2822},
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; *Cerebral Cortex ; Brain/physiology ; Algorithms ; Computers ; },
abstract = {Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.},
}
@article {pmid36373709,
year = {2022},
author = {Lyu, C and Yu, C and Sun, G and Zhao, Y and Cai, R and Sun, H and Wang, X and Jia, G and Fan, L and Chen, X and Zhou, L and Shen, Y and Gao, L and Li, X},
title = {Deconstruction of Vermal Cerebellum in Ramp Locomotion in Mice.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {10},
number = {1},
pages = {e2203665},
pmid = {36373709},
issn = {2198-3844},
support = {2022ZD0205000//National Science and Technology Innovation 2030 Major Program/ ; 32071097//National Natural Science Foundation of China/ ; 32170991//National Natural Science Foundation of China/ ; 31871056//National Natural Science Foundation of China/ ; 61703365//National Natural Science Foundation of China/ ; 91732302//National Natural Science Foundation of China/ ; 81625006//National Natural Science Foundation of China/ ; 81901316//National Natural Science Foundation of China/ ; 2018YFC1005003//National Key R&amp;D Program of China/ ; 2019XZZX001-01-20//Fundamental Research Funds for the Central Universities/ ; 2018QN81008//Fundamental Research Funds for the Central Universities/ ; //MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University/ ; 2020YFB1313500//National Key Research and Development Program of the Ministry of Science and Technology of China/ ; LD22H090003//Zhejiang Province Natural Science Foundation of China/ ; },
abstract = {The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.},
}
@article {pmid36371498,
year = {2022},
author = {Willsey, MS and Nason-Tomaszewski, SR and Ensel, SR and Temmar, H and Mender, MJ and Costello, JT and Patil, PG and Chestek, CA},
title = {Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {6899},
pmid = {36371498},
issn = {2041-1723},
mesh = {Animals ; Male ; *Brain-Computer Interfaces ; Macaca mulatta ; Neural Networks, Computer ; Movement ; Algorithms ; },
abstract = {Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.},
}
@article {pmid36368035,
year = {2022},
author = {Rommel, C and Paillard, J and Moreau, T and Gramfort, A},
title = {Data augmentation for learning predictive models on EEG: a systematic comparison.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/aca220},
pmid = {36368035},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Sleep Stages ; Sleep ; },
abstract = {Objective.The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis.Approach.We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments.Main results.We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.Significance.Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.},
}
@article {pmid36366726,
year = {2023},
author = {Wu, L and Ren, K and Chen, G and Wang, H and Li, H and Xu, L},
title = {Hemostatic effect and safety evaluation of the absorbable macroporous polysaccharides composite hemostatic material prepared by a green fabrication approach.},
journal = {Journal of biomaterials applications},
volume = {37},
number = {8},
pages = {1486-1496},
doi = {10.1177/08853282221139026},
pmid = {36366726},
issn = {1530-8022},
mesh = {Rats ; Animals ; *Hemostatics/chemistry ; *Chitosan/chemistry ; Rats, Sprague-Dawley ; Hemostasis ; Polysaccharides ; },
abstract = {Carboxymethyl chitosan is widely used in the medical field such as wound healing and other medical fields. We previously fabricated the absorbable macroporous polysaccharides composite hemostatics (AMPCs) mainly composed of carboxymethyl chitosan which possess excellent hemostatic effect. To further elucidate the impact of CMCTs on the hemostatic effect and biosafety of AMPCs, carboxymethyl chitosan with different properties were used to prepare AMPCs. By comparing the physical and chemical properties, AMPCs performed high water absorption ability, especially Group 1 (swelling ratio reached 5792%), which facilitated the rapid formation of blood clots. It was confirmed by blood clotting index (BCI) and blood coagulation tests in vitro that Group 1 showed a slightly higher coagulation capacity than groups 2 and 3, which may be due to the positive charge on the surface of the cations in the salts attaches to the negative charge on the surface of the red blood cells, an electrostatic neutralization reaction occurs. The biosafety was a preliminary evaluation by implanted AMPCs into the back of Sprague-Dawley rats and the tissue was harvested after feeding for 28 days. The AMPCs exhibited good biosafety for whole blood and major organs during the degradation in vivo: during the degradation of AMPCs, excluding changes in some serum indicators, no tissue necrosis or inflammatory cell infiltration was observed in these organs, either by gross observation or histological analysis. These findings demonstrate that expecting to develop a highly functional and safe hemostatic agent based on Group 1 for rapid hemostasis applications in emergencies.},
}
@article {pmid36366225,
year = {2022},
author = {Hu, H and Pu, Z and Li, H and Liu, Z and Wang, P},
title = {Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {21},
pages = {},
pmid = {36366225},
issn = {1424-8220},
support = {2018YFB2003201//National Key Research and Development Program of China/ ; },
mesh = {*Brain-Computer Interfaces ; Imagination ; Signal Processing, Computer-Assisted ; Brain ; Electroencephalography/methods ; Algorithms ; },
abstract = {The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.},
}
@article {pmid36366001,
year = {2022},
author = {Chuang, CC and Lee, CC and So, EC and Yeng, CH and Chen, YJ},
title = {Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {21},
pages = {},
pmid = {36366001},
issn = {1424-8220},
support = {ANHRF111-26 and ANHRF110-22//An Nan Hospital, China Medical University/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; *Amyotrophic Lateral Sclerosis ; Neural Networks, Computer ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; },
abstract = {Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain-computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.},
}
@article {pmid36365948,
year = {2022},
author = {Emsawas, T and Morita, T and Kimura, T and Fukui, KI and Numao, M},
title = {Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {21},
pages = {},
pmid = {36365948},
issn = {1424-8220},
support = {JPMJCE1310//Japan Science and Technology Agency/ ; },
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Emotions ; Palliative Care ; },
abstract = {Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain-computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model's learning capacity.},
}
@article {pmid36365824,
year = {2022},
author = {Khare, SK and Gaikwad, N and Bokde, ND},
title = {An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {21},
pages = {},
pmid = {36365824},
issn = {1424-8220},
mesh = {Humans ; *Electroencephalography/methods ; Wavelet Analysis ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; Algorithms ; Support Vector Machine ; Signal Processing, Computer-Assisted ; },
abstract = {Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.},
}
@article {pmid36364633,
year = {2022},
author = {Frenzel, J and Kupferer, A and Zink, M and Mayr, SG},
title = {Laminin Adsorption and Adhesion of Neurons and Glial Cells on Carbon Implanted Titania Nanotube Scaffolds for Neural Implant Applications.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {12},
number = {21},
pages = {},
pmid = {36364633},
issn = {2079-4991},
support = {100331694//Saxon State Ministry for Science and the Arts/ ; },
abstract = {Interfacing neurons persistently to conductive matter constitutes one of the key challenges when designing brain-machine interfaces such as neuroelectrodes or retinal implants. Novel materials approaches that prevent occurrence of loss of long-term adhesion, rejection reactions, and glial scarring are highly desirable. Ion doped titania nanotube scaffolds are a promising material to fulfill all these requirements while revealing sufficient electrical conductivity, and are scrutinized in the present study regarding their neuron-material interface. Adsorption of laminin, an essential extracellular matrix protein of the brain, is comprehensively analyzed. The implantation-dependent decline in laminin adsorption is revealed by employing surface characteristics such as nanotube diameter, ζ-potential, and surface free energy. Moreover, the viability of U87-MG glial cells and SH-SY5Y neurons after one and four days are investigated, as well as the material's cytotoxicity. The higher conductivity related to carbon implantation does not affect the viability of neurons, although it impedes glial cell proliferation. This gives rise to novel titania nanotube based implant materials with long-term stability, and could reduce undesirable glial scarring.},
}
@article {pmid36359646,
year = {2022},
author = {Wang, K and Tian, F and Xu, M and Zhang, S and Xu, L and Ming, D},
title = {Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {11},
pages = {},
pmid = {36359646},
issn = {1099-4300},
support = {2022ZD0208900//National Key Research and Development Program of China/ ; 2021GXRC071//Introduce Innovative Teams of 2021 "New High School 20 Items" Project/ ; 62206198, 62122059, 61976152, 81925020//National Natural Science Foundation of China/ ; },
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.},
}
@article {pmid36359454,
year = {2022},
author = {Syrov, N and Yakovlev, L and Nikolaeva, V and Kaplan, A and Lebedev, M},
title = {Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {11},
pages = {},
pmid = {36359454},
issn = {2075-4418},
support = {21-75-30024//Russian Science Foundation/ ; FZWM-2020-0013//State assignment/ ; },
abstract = {Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.},
}
@article {pmid36358440,
year = {2022},
author = {Adhia, DB and Mani, R and Turner, PR and Vanneste, S and De Ridder, D},
title = {Infraslow Neurofeedback Training Alters Effective Connectivity in Individuals with Chronic Low Back Pain: A Secondary Analysis of a Pilot Randomized Placebo-Controlled Study.},
journal = {Brain sciences},
volume = {12},
number = {11},
pages = {},
pmid = {36358440},
issn = {2076-3425},
support = {NA//Otago Medical School Trust Fund/ ; NA//Brain Health Research Centre, University of Otago/ ; },
abstract = {This study explored the effect of electroencephalographic infraslow neurofeedback (EEG ISF-NF) training on effective connectivity and tested whether such effective connectivity changes are correlated with changes in pain and disability in people with chronic low back pain. This involved secondary analysis of a pilot double-blinded randomised placebo-controlled study. Participants (n = 60) were randomised to receive ISF-NF targeting either the pregenual anterior cingulate cortex (pgACC), dorsal anterior cingulate and somatosensory cortex (dACC + S1), ratio of pgACC*2/dACC + S1, or Sham-NF. Resting-state EEG and clinical outcomes were assessed at baseline, immediately after intervention, and at one-week and one-month follow-up. Kruskal-Wallis tests demonstrated significant between-group differences in effective connectivity from pgACC to S1L at one-month follow up and marginal significant changes from S1L to pgACC at one-week and one-month follow up. Mann-Whitney U tests demonstrated significant increases in effective connectivity in the ISF-NF up-training pgACC group when compared to the Sham-NF group (pgACC to S1L at one-month (p = 0.013), and S1L to pgACC at one-week (p = 0.008) and one-month follow up (p = 0.016)). Correlational analyses demonstrated a significant negative correlation (ρ = -0.630, p = 0.038) between effective connectivity changes from pgACC to S1L and changes in pain severity at one-month follow-up. The ISF-NF training pgACC can reduce pain via influencing effective connectivity between pgACC and S1L.},
}
@article {pmid36358428,
year = {2022},
author = {Cao, L and Wu, H and Chen, S and Dong, Y and Zhu, C and Jia, J and Fan, C},
title = {A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.},
journal = {Brain sciences},
volume = {12},
number = {11},
pages = {},
pmid = {36358428},
issn = {2076-3425},
support = {2018YFC2002300 and 2018YFC2002301//the National Key R&amp;D Program of China/ ; 62102242 and 62103258//the National Natural Science Young Foundation of China/ ; C2022152//Shanghai Education Research Program/ ; MS201944 and T201906//the Project of Wuxi Health Commission/ ; },
abstract = {Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.},
}
@article {pmid36356391,
year = {2022},
author = {Wan, Z and Yang, R and Huang, M and Alsaadi, FE and Sheikh, MM and Wang, Z},
title = {Segment alignment based cross-subject motor imagery classification under fading data.},
journal = {Computers in biology and medicine},
volume = {151},
number = {Pt A},
pages = {106267},
doi = {10.1016/j.compbiomed.2022.106267},
pmid = {36356391},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Imagination ; Algorithms ; },
abstract = {Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.},
}
@article {pmid36356309,
year = {2022},
author = {Petrosyan, A and Voskoboinikov, A and Sukhinin, D and Makarova, A and Skalnaya, A and Arkhipova, N and Sinkin, M and Ossadtchi, A},
title = {Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/aca1e1},
pmid = {36356309},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; Speech/physiology ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Electrodes ; },
abstract = {Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes.Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation.Mainresults. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature.Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.},
}
@article {pmid36355738,
year = {2023},
author = {Chen, R and Xu, G and Jia, Y and Zhou, C and Wang, Z and Pei, J and Han, C and Wang, Y and Zhang, S},
title = {Enhancement of Time-Frequency Energy for the Classification of Motor Imagery Electroencephalogram Based on an Improved FitzHugh-Nagumo Neuron System.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {282-293},
doi = {10.1109/TNSRE.2022.3219450},
pmid = {36355738},
issn = {1558-0210},
mesh = {Humans ; *Imagination/physiology ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Algorithms ; Motor Neurons ; },
abstract = {Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness.},
}
@article {pmid36354027,
year = {2023},
author = {Johnson, KA and Worbe, Y and Foote, KD and Butson, CR and Gunduz, A and Okun, MS},
title = {Tourette syndrome: clinical features, pathophysiology, and treatment.},
journal = {The Lancet. Neurology},
volume = {22},
number = {2},
pages = {147-158},
pmid = {36354027},
issn = {1474-4465},
support = {U01 NS119562/NS/NINDS NIH HHS/United States ; R01 NS096008/NS/NINDS NIH HHS/United States ; UH3 NS119844/NS/NINDS NIH HHS/United States ; R25 NS108939/NS/NINDS NIH HHS/United States ; R01 NR014852/NR/NINR NIH HHS/United States ; },
mesh = {Humans ; *Tourette Syndrome/diagnosis/genetics/therapy ; Quality of Life ; *Tics/diagnosis ; Treatment Outcome ; Brain/pathology ; },
abstract = {Tourette syndrome is a chronic neurodevelopmental disorder characterised by motor and phonic tics that can substantially diminish the quality of life of affected individuals. Evaluating and treating Tourette syndrome is complex, in part due to the heterogeneity of symptoms and comorbidities between individuals. The underlying pathophysiology of Tourette syndrome is not fully understood, but recent research in the past 5 years has brought new insights into the genetic variations and the alterations in neurophysiology and brain networks contributing to its pathogenesis. Treatment options for Tourette syndrome are expanding with novel pharmacological therapies and increased use of deep brain stimulation for patients with symptoms that are refractory to pharmacological or behavioural treatments. Potential predictors of patient responses to therapies for Tourette syndrome, such as specific networks modulated during deep brain stimulation, can guide clinical decisions. Multicentre data sharing initiatives have enabled several advances in our understanding of the genetics and pathophysiology of Tourette syndrome and will be crucial for future large-scale research and in refining effective treatments.},
}
@article {pmid36351832,
year = {2022},
author = {Fischer, L and Mojica Soto-Albors, R and Tang, VD and Bicknell, B and Grienberger, C and Francioni, V and Naud, R and Palmer, LM and Takahashi, N},
title = {Dendritic Mechanisms for In Vivo Neural Computations and Behavior.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {45},
pages = {8460-8467},
pmid = {36351832},
issn = {1529-2401},
mesh = {*Dendrites/physiology ; *Pyramidal Cells/physiology ; Neurons/physiology ; Hippocampus ; Learning ; Models, Neurological ; Action Potentials/physiology ; },
abstract = {Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output. Ex vivo and theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain-computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical for in vivo learning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.},
}
@article {pmid36351413,
year = {2022},
author = {McNamara, CG and Rothwell, M and Sharott, A},
title = {Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium.},
journal = {Cell reports},
volume = {41},
number = {6},
pages = {111616},
pmid = {36351413},
issn = {2211-1247},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; 209120/WT_/Wellcome Trust/United Kingdom ; 209120/Z/17/Z/WT_/Wellcome Trust/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; MC_UU_00003/6/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Animals ; Rats ; *Deep Brain Stimulation ; *Parkinson Disease/therapy ; Basal Ganglia/physiology ; Neurons/physiology ; Brain ; },
abstract = {Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.},
}
@article {pmid36350872,
year = {2023},
author = {Zhang, M and Wu, J and Song, J and Fu, R and Ma, R and Jiang, YC and Chen, YF},
title = {Decoding Coordinated Directions of Bimanual Movements From EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {248-259},
doi = {10.1109/TNSRE.2022.3220884},
pmid = {36350872},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography/methods ; Hand ; Neural Networks, Computer ; Movement ; *Brain-Computer Interfaces ; Functional Laterality ; },
abstract = {Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35 %. The binary classification accuracies achieved 80.24 ± 6.25 , 82.62 ± 7.82 , and 86.28 ± 5.50 % for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50 %, 75.67 ± 7.18 %, and 77.79 ± 5.65 %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.},
}
@article {pmid36350815,
year = {2022},
author = {Sattler, S and Pietralla, D},
title = {Public attitudes towards neurotechnology: Findings from two experiments concerning Brain Stimulation Devices (BSDs) and Brain-Computer Interfaces (BCIs).},
journal = {PloS one},
volume = {17},
number = {11},
pages = {e0275454},
pmid = {36350815},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Public Opinion ; Stereotaxic Techniques ; Morals ; Brain/physiology ; },
abstract = {This study contributes to the emerging literature on public perceptions of neurotechnological devices (NTDs) in their medical and non-medical applications, depending on their invasiveness, framing effects, and interindividual differences related to personal needs and values. We conducted two web-based between-subject experiments (2×2×2) using a representative, nation-wide sample of the adult population in Germany. Using vignettes describing how two NTDs, brain stimulation devices (BSDs; NExperiment 1 = 1,090) and brain-computer interfaces (BCIs; NExperiment 2 = 1,089), function, we randomly varied the purpose (treatment vs. enhancement) and invasiveness (noninvasive vs. invasive) of the NTD, and assessed framing effects (variable order of assessing moral acceptability first vs. willingness to use first). We found a moderate moral acceptance and willingness to use BSDs and BCIs. Respondents preferred treatment over enhancement purposes and noninvasive over invasive devices. We also found a framing effect and explored the role of personal characteristics as indicators of personal needs and values (e.g., stress, religiosity, and gender). Our results suggest that the future demand for BSDs or BCIs may depend on the purpose, invasiveness, and personal needs and values. These insights can inform technology developers about the public's needs and concerns, and enrich legal and ethical debates.},
}
@article {pmid36349931,
year = {2022},
author = {Zwerus, EL and van Deurzen, DFP and van den Bekerom, MPJ and The, B and Eygendaal, D},
title = {Distal Biceps Tendon Ruptures: Diagnostic Strategy Through Physical Examination.},
journal = {The American journal of sports medicine},
volume = {50},
number = {14},
pages = {3956-3962},
pmid = {36349931},
issn = {1552-3365},
mesh = {Humans ; Cohort Studies ; Reproducibility of Results ; *Physical Examination ; },
abstract = {BACKGROUND: Distinguishing a complete from a partial distal biceps tendon rupture is essential, as a complete rupture may require repair on short notice to restore function, whereas partial ruptures can be treated nonsurgically in most cases. Reliability of physical examination is crucial to determine the right workup and treatment in patients with a distal biceps tendon rupture.<br><br>PURPOSES: The primary aim of this study was to find a (combination of) test(s) that serves best to diagnose a complete rupture with certainty in the acute phase (&#x2264;1 month) without missing any complete ruptures. The secondary aims were to determine the best (combination of) test(s) to identify a chronic (>1 month) rupture of the distal biceps tendon and indicate additional imaging in case partial ruptures or tendinitis are suspected.<br><br>STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2.<br><br>METHODS: A total of 86 patients with anterior elbow complaints or suspected distal biceps injury underwent standardized physical examination, including the Hook test, passive forearm pronation test, biceps crease interval (BCI), and biceps crease ratio. Diagnosis was confirmed intraoperatively (68 cases), by magnetic resonance imaging (13 cases), or by ultrasound (5 cases).<br><br>RESULTS: A combination of the Hook test and BCI (ie, both tests are positive) was most accurate for both acute and chronic ruptures but with a different purpose. For acute complete ruptures, sensitivity was 94% and specificity was 100%. In chronic cases, specificity was also 100%. Weakness on active supination and palpation of the tendon footprint provided excellent sensitivity of 100% for chronic complete ruptures and partial ruptures, respectively.<br><br>CONCLUSION: The combination of a positive Hook test and BCI serves best to accurately diagnose acute complete ruptures of the distal biceps tendon. Weakness on active supination and pain on palpation of the tendon footprint provide excellent sensitivity for chronic complete ruptures and partial ruptures. Using these tests in all suspected distal biceps ruptures allows a physician to refrain from imaging for a diagnostic purpose in certain cases, to limit treatment delay and thereby provide better treatment outcome, and to avoid hospital and social costs.},
}
@article {pmid36349662,
year = {2023},
author = {Peterson, V and Merk, T and Bush, A and Nikulin, V and Kühn, AA and Neumann, WJ and Richardson, RM},
title = {Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.},
journal = {Experimental neurology},
volume = {359},
number = {},
pages = {114261},
doi = {10.1016/j.expneurol.2022.114261},
pmid = {36349662},
issn = {1090-2430},
mesh = {Humans ; *Brain-Computer Interfaces ; Movement/physiology ; Electrocorticography ; Brain/physiology ; *Parkinson Disease/therapy ; },
abstract = {The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.},
}
@article {pmid36349568,
year = {2023},
author = {Khademi, Z and Ebrahimi, F and Kordy, HM},
title = {A review of critical challenges in MI-BCI: From conventional to deep learning methods.},
journal = {Journal of neuroscience methods},
volume = {383},
number = {},
pages = {109736},
doi = {10.1016/j.jneumeth.2022.109736},
pmid = {36349568},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Algorithms ; Imagination ; },
abstract = {Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools to control, for example, wheelchair for people with disabilities, robotic control, and exoskeleton control. This success largely depends on the machine learning (ML) approaches like deep learning (DL) models. DL algorithms provide effective and powerful models to analyze compact and complex EEG data optimally for MI-BCI applications. DL models with CNN network have revolutionized computer vision through end-to-end learning from raw data. Meanwhile, RNN networks have been able to decode EEG signals by processing sequences of time series data. However, many challenges in the MI-BCI field have affected the performance of DL models. A major challenge is the individual differences in the EEG signal of different subjects. Therefore, the model must be retrained from the scratch for each new subject, which leads to computational costs. Analyzing the EEG signals is challenging due to its low signal to noise ratio and non-stationary nature. Additionally, limited size of existence datasets can lead to overfitting which can be prevented by using transfer learning (TF) approaches. The main contributions of this study are discovering major challenges in the MI-BCI field by reviewing the state of art machine learning models and then suggesting solutions to address these challenges by focusing on feature selection, feature extraction and classification methods.},
}
@article {pmid36346867,
year = {2024},
author = {Fu, K and Du, C and Wang, S and He, H},
title = {Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {7},
pages = {9026-9040},
doi = {10.1109/TNNLS.2022.3217767},
pmid = {36346867},
issn = {2162-2388},
mesh = {Humans ; *Emotions/physiology ; *Brain/physiology ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; Electroencephalography ; Algorithms ; },
abstract = {Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.},
}
@article {pmid36346232,
year = {2022},
author = {Sake, SM and Kosch, C and Blockus, S and Haid, S and Gunesch, AP and Zhang, X and Friesland, M and Trummer, SB and Grethe, C and Kühnel, A and Rückert, J and Duprex, WP and Huang, J and Rameix-Welti, MA and Empting, M and Fischer, N and Hirsch, AKH and Schulz, TF and Pietschmann, T},
title = {Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages.},
journal = {Antimicrobial agents and chemotherapy},
volume = {66},
number = {12},
pages = {e0103222},
pmid = {36346232},
issn = {1098-6596},
mesh = {Humans ; *Respiratory Syncytial Virus Infections/drug therapy ; *Respiratory Syncytial Virus, Human/genetics ; Antiviral Agents/therapeutic use ; Lung ; *Respiratory Tract Infections ; },
abstract = {Human respiratory syncytial virus (hRSV) infection is a leading cause of severe respiratory tract infections. Effective, directly acting antivirals against hRSV are not available. We aimed to discover new and chemically diverse candidates to enrich the hRSV drug development pipeline. We used a two-step screen that interrogates compound efficacy after primary infection and a consecutive virus passaging. We resynthesized selected hit molecules and profiled their activities with hRSV lentiviral pseudotype cell entry, replicon, and time-of-addition assays. The breadth of antiviral activity was tested against recent RSV clinical strains and human coronavirus (hCoV-229E), and in pseudotype-based entry assays with non-RSV viruses. Screening 6,048 molecules, we identified 23 primary candidates, of which 13 preferentially scored in the first and 10 in the second rounds of infection, respectively. Two of these molecules inhibited hRSV cell entry and selected for F protein resistance within the fusion peptide. One molecule inhibited transcription/replication in hRSV replicon assays, did not select for phenotypic hRSV resistance and was active against non-hRSV viruses, including hCoV-229E. One compound, identified in the second round of infection, did not measurably inhibit hRSV cell entry or replication/transcription. It selected for two coding mutations in the G protein and was highly active in differentiated BCi-NS1.1 lung cells. In conclusion, we identified four new hRSV inhibitor candidates with different modes of action. Our findings build an interesting platform for medicinal chemistry-guided derivatization approaches followed by deeper phenotypical characterization in vitro and in vivo with the aim of developing highly potent hRSV drugs.},
}
@article {pmid36343405,
year = {2022},
author = {Pu, X and Yi, P and Chen, K and Ma, Z and Zhao, D and Ren, Y},
title = {EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.},
journal = {Computers in biology and medicine},
volume = {151},
number = {Pt A},
pages = {106248},
doi = {10.1016/j.compbiomed.2022.106248},
pmid = {36343405},
issn = {1879-0534},
mesh = {*Electroencephalography/methods ; Artifacts ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Muscles ; Algorithms ; },
abstract = {Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.},
}
@article {pmid36343004,
year = {2022},
author = {Zhang, D and Liu, S and Zhang, J and Li, G and Suo, D and Liu, T and Luo, J and Ming, Z and Wu, J and Yan, T},
title = {Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {12},
pages = {6138-6149},
doi = {10.1109/JBHI.2022.3219812},
pmid = {36343004},
issn = {2168-2208},
mesh = {*Robotics/instrumentation/methods ; *Environment ; *Brain-Computer Interfaces ; *Brain/physiology ; Electroencephalography ; Spatial Navigation ; Humans ; Male ; Female ; Adolescent ; Young Adult ; Adult ; Photic Stimulation ; Biomechanical Phenomena ; Avoidance Learning ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.<br><br>METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.<br><br>RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).<br><br>SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.},
}
@article {pmid36340769,
year = {2022},
author = {Lee, HS and Schreiner, L and Jo, SH and Sieghartsleitner, S and Jordan, M and Pretl, H and Guger, C and Park, HS},
title = {Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1009878},
pmid = {36340769},
issn = {1662-4548},
abstract = {Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.},
}
@article {pmid36340754,
year = {2022},
author = {Tang, C and Gao, T and Li, Y and Chen, B},
title = {EEG channel selection based on sequential backward floating search for motor imagery classification.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1045851},
pmid = {36340754},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.},
}
@article {pmid36337859,
year = {2022},
author = {He, C and Du, Y and Zhao, X},
title = {A separable convolutional neural network-based fast recognition method for AR-P300.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {986928},
pmid = {36337859},
issn = {1662-5161},
abstract = {Augmented reality-based brain-computer interface (AR-BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR-SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR-P300). SepCNN achieved single extraction of AR-P300 features and improved the recognition speed. A nine-target AR-P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR-P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.},
}
@article {pmid36337857,
year = {2022},
author = {Floreani, ED and Rowley, D and Kelly, D and Kinney-Lang, E and Kirton, A},
title = {On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {1007199},
pmid = {36337857},
issn = {1662-5161},
abstract = {INTRODUCTION: Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.<br><br>MATERIALS AND METHODS: Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.<br><br>RESULTS: All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 &#xb1; 12.1%, participants were more reliably able to activate (95.7 &#xb1; 11.3%) the device than hold still (62.1 &#xb1; 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 &#xb1; 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.<br><br>DISCUSSION: BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.},
}
@article {pmid36337363,
year = {2022},
author = {Li, L},
title = {Preface to special topic on brain-machine interface.},
journal = {National science review},
volume = {9},
number = {10},
pages = {nwac211},
doi = {10.1093/nsr/nwac211},
pmid = {36337363},
issn = {2053-714X},
}
@article {pmid36337269,
year = {2022},
author = {Song, X and Zeng, Y and Tong, L and Shu, J and Yang, Q and Kou, J and Sun, M and Yan, B},
title = {Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {9819804},
pmid = {36337269},
issn = {1687-5273},
abstract = {[This corrects the article DOI: 10.1155/2022/4752450.].},
}
@article {pmid36335198,
year = {2022},
author = {Smrdel, A},
title = {Use of common spatial patterns for early detection of Parkinson's disease.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {18793},
pmid = {36335198},
issn = {2045-2322},
mesh = {Humans ; *Parkinson Disease/diagnosis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Brain ; Early Diagnosis ; },
abstract = {One of the most common diseases that affects human brain is Parkinson's disease. Detection of Parkinson's disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.},
}
@article {pmid36335102,
year = {2022},
author = {Zhao, LH and Lin, J and Ji, SY and Zhou, XE and Mao, C and Shen, DD and He, X and Xiao, P and Sun, J and Melcher, K and Zhang, Y and Yu, X and Xu, HE},
title = {Structure insights into selective coupling of G protein subtypes by a class B G protein-coupled receptor.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {6670},
pmid = {36335102},
issn = {2041-1723},
mesh = {*Heterotrimeric GTP-Binding Proteins/metabolism ; Urocortins/metabolism ; },
abstract = {The ability to couple with multiple G protein subtypes, such as Gs, Gi/o, or Gq/11, by a given G protein-coupled receptor (GPCR) is critical for many physiological processes. Over the past few years, the cryo-EM structures for all 15 members of the medically important class B GPCRs, all in complex with Gs protein, have been determined. However, no structure of class B GPCRs with Gq/11 has been solved to date, limiting our understanding of the precise mechanisms of G protein coupling selectivity. Here we report the structures of corticotropin releasing factor receptor 2 (CRF2R) bound to Urocortin 1 (UCN1), coupled with different classes of heterotrimeric G proteins, G11 and Go. We compare these structures with the structure of CRF2R in complex with Gs to uncover the structural differences that determine the selective coupling of G protein subtypes by CRF2R. These results provide important insights into the structural basis for the ability of CRF2R to couple with multiple G protein subtypes.},
}
@article {pmid36333482,
year = {2022},
author = {Gao, Z and Pang, Z and Chen, Y and Lei, G and Zhu, S and Li, G and Shen, Y and Xu, W},
title = {Restoring After Central Nervous System Injuries: Neural Mechanisms and Translational Applications of Motor Recovery.},
journal = {Neuroscience bulletin},
volume = {38},
number = {12},
pages = {1569-1587},
pmid = {36333482},
issn = {1995-8218},
mesh = {Animals ; *Spinal Cord Injuries/therapy ; Motor Neurons/physiology ; Brain ; *Stroke ; Recovery of Function/physiology ; },
abstract = {Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain-computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.},
}
@article {pmid36332422,
year = {2022},
author = {Zhan, Q and Wang, L and Ren, L and Huang, X},
title = {A novel heterogeneous transfer learning method based on data stitching for the sequential coding brain computer interface.},
journal = {Computers in biology and medicine},
volume = {151},
number = {Pt A},
pages = {106220},
doi = {10.1016/j.compbiomed.2022.106220},
pmid = {36332422},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Imagination ; },
abstract = {OBJECTIVE: For the brain computer interface (BCI), it is necessary to collect enough electroencephalography (EEG) signals to train the classification model. When the operation dimension of BCI is large, it will bring great burden to data acquisition. Fortunately, this problem can be solved by our proposed transfer learning method.<br><br>METHOD: For the sequential coding experimental paradigm, the multi-band data stitching with label alignment and tangent space mapping (MDSLATSM) algorithm is proposed as a novel heterogeneous transfer learning method. After filtering by multi-band filtering, the artificial signals can be obtained by data stitching from the source domain, which build a bridge between the source domain and target domain. To make the distribution of two domains closer, their covariance matrices are aligned by label alignment. After mapping to the tangent space, the features are extracted from the Riemannian manifold. Finally, the classification results are obtained with feature selection and classification.<br><br>RESULTS: Our data set includes the EEG signals from 16 subjects. For the heterogeneous transfer learning of cross-label, the average classification accuracy is 78.28%. MDSLATSM is also tested for cross-subject, and the average classification accuracy is 64.01%, which is better than existing methods.<br><br>SIGNIFICANCE: Combining multi-band filtering, data stitching, label alignment and tangent space mapping, a novel heterogeneous transfer learning method can be achieved with superior performance, which promotes the practical application of the BCI systems.},
}
@article {pmid36331650,
year = {2024},
author = {Zhang, S and Wu, L and Yu, S and Shi, E and Qiang, N and Gao, H and Zhao, J and Zhao, S},
title = {An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {6},
pages = {7339-7350},
doi = {10.1109/TNNLS.2022.3214225},
pmid = {36331650},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain/physiology ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Deep Learning ; },
abstract = {Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.},
}
@article {pmid36331633,
year = {2023},
author = {Wang, R and Liu, Y and Shi, J and Peng, B and Fei, W and Bi, L},
title = {Sound Target Detection Under Noisy Environment Using Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {229-237},
doi = {10.1109/TNSRE.2022.3219595},
pmid = {36331633},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Sound ; },
abstract = {As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.},
}
@article {pmid36329083,
year = {2022},
author = {Mencel, J and Marusiak, J and Jaskólska, A and Kamiński, &#x141; and Kurzyński, M and Wołczowski, A and Jaskólski, A and Kisiel-Sajewicz, K},
title = {Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {18610},
pmid = {36329083},
issn = {2045-2322},
support = {DEC-2011/03/B/NZ7/00588//National Science Centre of the Republic of Poland/ ; },
mesh = {Humans ; Brain-Computer Interfaces ; Electroencephalography ; Goals ; *Hand Strength/physiology ; Imagination/physiology ; *Motor Skills/physiology ; Evoked Potentials/physiology ; *Imagery, Psychotherapy/education ; },
abstract = {The study aimed to determine whether four weeks of motor imagery training (MIT) of goal-directed reaching (reaching to grasp task) would affect the cortical activity during motor imagery of reaching (MIR) and grasping (MIG) in the same way. We examined cortical activity regarding event-related potentials (ERPs) in healthy young participants. Our study also evaluated the subjective vividness of the imagery. Furthermore, we aimed to determine the relationship between the subjective assessment of motor imagery (MI) ability to reach and grasp and the cortical activity during those tasks before and after training to understand the underlying neuroplasticity mechanisms. Twenty-seven volunteers participated in MIT of goal-directed reaching and two measurement sessions before and after MIT. During the sessions 128-channel electroencephalography (EEG) was recorded during MIR and MIG. Also, participants assessed the vividness of the MI tasks using a visual analog scale (VAS). The vividness of imagination improved significantly (P < .05) after MIT. A repeated measures ANOVA showed that the task (MIR/MIG) and the location of electrodes had a significant effect on the ERP's amplitude (P < .05). The interaction between the task, location, and session (before/after MIT) also had a significant effect on the ERP's amplitude (P < .05). Finally, the location of electrodes and the interaction between location and session had a significant effect on the ERP's latency (P < .05). We found that MIT influenced the EEG signal associated with reaching differently than grasping. The effect was more pronounced for MIR than for MIG. Correlation analysis showed that changes in the assessed parameters due to MIT reduced the relationship between the subjective evaluation of imagining and the EEG signal. This finding means that the subjective evaluation of imagining cannot be a simple, functional insight into the bioelectrical activity of the cerebral cortex expressed by the ERPs in mental training. The changes we noted in ERPs after MIT may benefit the use of non-invasive EEG in the brain-computer interface (BCI) context.Trial registration: NCT04048083.},
}
@article {pmid36327603,
year = {2022},
author = {Fu, Y and Zhu, Y and Zhang, Y and Hu, S},
title = {Is AlphaFold a perfect experimental assistant of psychiatric drug discovery in precision psychiatry era?.},
journal = {Asian journal of psychiatry},
volume = {78},
number = {},
pages = {103305},
doi = {10.1016/j.ajp.2022.103305},
pmid = {36327603},
issn = {1876-2026},
mesh = {Humans ; *Psychiatry ; *Mental Disorders/drug therapy ; Drug Discovery ; },
}
@article {pmid36323230,
year = {2022},
author = {Al-Sheikh, U and Kang, L},
title = {Kir2.1 channel: Macrophage plasticity in tumor microenvironment.},
journal = {Cell metabolism},
volume = {34},
number = {11},
pages = {1613-1615},
doi = {10.1016/j.cmet.2022.10.009},
pmid = {36323230},
issn = {1932-7420},
mesh = {Humans ; *Tumor Microenvironment ; Macrophages/metabolism ; *Neoplasms/metabolism ; },
abstract = {Diverse ion channels have dysregulated functional expression in the tumor microenvironment (TME). In this issue of Cell Metabolism, Chen et al. reveal that high intratumoral K[+] ions restrict the plasticity of tumor-associated macrophages (TAMs). Inhibition of the Kir2.1 potassium channel induced metabolic reprogramming and repolarization of pro-tumor M2-TAMs to tumoricidal M1-like states.},
}
@article {pmid36318565,
year = {2023},
author = {Bian, Y and Zhao, L and Li, J and Guo, T and Fu, X and Qi, H},
title = {Improvements in Classification of Left and Right Foot Motor Intention Using Modulated Steady-State Somatosensory Evoked Potential Induced by Electrical Stimulation and Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {150-159},
doi = {10.1109/TNSRE.2022.3218682},
pmid = {36318565},
issn = {1558-0210},
mesh = {Humans ; *Intention ; Electroencephalography/methods ; Imagination/physiology ; Evoked Potentials, Somatosensory/physiology ; Electric Stimulation ; *Brain-Computer Interfaces ; },
abstract = {In recent years, motor imagery-based brain-computer interface (MI-BCI) has been applied to motor rehabilitation in patients with motor dysfunction. However, traditional MI-BCI is rarely used for foot motor intention recognition because the motor cortex regions of both feet are anatomically close to each other, and traditional event-related desynchronization (ERD) patterns for MI-BCI have insufficient spatial discrimination. This study introduced steady-state somatosensory evoked potentials (SSSEPs) by synchronous bilateral feet electrical stimulation at different frequencies, which were used as carrier signals modulated by unilateral foot motor intention. Fifteen subjects participated in MI and MI-SSSEP tasks. A Riemannian geometry classifier with a task-related component analysis (TRCA) spatial filter was proposed to demodulate the variation in SSSEP features and discriminate the left and right foot motor intentions. The feature outcomes indicated that the amplitude and phase synchronization of the SSSEPs could be well modulated by unilateral foot MI tasks under the MI-SSSEP paradigm. The classification results revealed that the modulated SSSEP features played an important role in the recognition of left-right foot discrimination. Moreover, the proposed TRCA-based method outperformed the other three methods and improved the foot average classification accuracy to 81.07± 13.29%, with the highest accuracy attained at 97.00%. Compared with the traditional MI paradigm, the foot motor intention recognition accuracy of the MI-SSSEP paradigm was significantly improved, from nearly 60% to more than 80%. This work provides a practical method for left-right foot motor intention recognition and expands the application of MI-BCI in the field of lower-extremity motor function rehabilitation.},
}
@article {pmid36318386,
year = {2022},
author = {Gorur, K and Eraslan, B},
title = {The single-channel dry electrode SSVEP-based biometric approach: data augmentation techniques against overfitting for RNN-based deep models.},
journal = {Physical and engineering sciences in medicine},
volume = {45},
number = {4},
pages = {1219-1240},
pmid = {36318386},
issn = {2662-4737},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Neural Networks, Computer ; Electrodes ; Biometry ; },
abstract = {Biometric studies based on electroencephalography (EEG) have received increasing attention because each individual has a dynamic and unique pattern. However, classic EEG-based biometrics have significant deficiencies, including noise-prone signals, gel-based electrodes, and the need for multi-training/multi-channel acquisition and high mental effort. In contrast, steady-state visually evoked potential (SSVEP)-based biometrics have the important advantages of high signal-to-noise ratio and untrained usage. Dynamic brain potential responses are a natural subconscious activity and can be elicited by flickering lights having distinct frequencies, such as cell phone flashes, without extra physical or mental effort. Few studies involving multi-channel/multi-trial SSVEP-based biometric research are available in the current literature. Moreover, there is a lack of research comparing them to the single-channel single-trial dry electrode-implemented SSVEP-based biometric approach using Recurrent Neural Networks (RNN). Furthermore, to the best of our knowledge, no prior work has proposed an SSVEP-based biometric comparison of the RNNs using data augmentation strategies against overfitting. It was observed that the biometric recognition results were promising, achieving up to 100% accuracy and > 97% sensitivity and specificity scores for 11 subjects. F-scores were also yielded as > 97% values. This single-channel SSVEP-based biometric approach using RNN deep models may offer low-cost, user-friendly, and reliable individual identification authentication, leading to significant application domains.},
}
@article {pmid36317357,
year = {2022},
author = {Ogino, M and Hamada, N and Mitsukura, Y},
title = {Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9edd},
pmid = {36317357},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Acoustic Stimulation/methods ; Evoked Potentials ; Supervised Machine Learning ; Probability ; Electroencephalography/methods ; Event-Related Potentials, P300 ; },
abstract = {Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min[-1], in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min[-1], respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.},
}
@article {pmid36317288,
year = {2022},
author = {Jia, H and Sun, Z and Duan, F and Zhang, Y and Caiafa, CF and Solé-Casals, J},
title = {Improving pre-movement pattern detection with filter bank selection.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9e75},
pmid = {36317288},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Support Vector Machine ; Movement ; Upper Extremity ; Algorithms ; Imagination ; },
abstract = {Objective. Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states.Approach. The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract CCPs. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) is an alternative approach to select canonical correlation patterns.Main Results. Three methods were evaluated using EEG signals in the time window from 2 s before the movement onset to 1 s after the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8968 ± 0.0847 while the accuracies of STRCA and CNN were 0.8228 ± 0.1149 and 0.8828 ± 0.0917, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.6611 ± 0.1432, 0.6993 ± 0.1271, 0.7178 ± 0.1274, respectively. Feature selection using filter banks, as in FBTRCA, produces comparable results to STRCA.Significance. The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices.},
}
@article {pmid36317255,
year = {2022},
author = {Guo, Z and Chen, F},
title = {Decoding lexical tones and vowels in imagined tonal monosyllables using fNIRS signals.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9e1d},
pmid = {36317255},
issn = {1741-2552},
mesh = {Humans ; *Speech ; Language ; *Speech Perception ; Imagery, Psychotherapy ; },
abstract = {Objective.Speech is a common way of communication. Decoding verbal intent could provide a naturalistic communication way for people with severe motor disabilities. Active brain computer interaction (BCI) speller is one of the most commonly used speech BCIs. To reduce the spelling time of Chinese words, identifying vowels and tones that are embedded in imagined Chinese words is essential. Functional near-infrared spectroscopy (fNIRS) has been widely used in BCI because it is portable, non-invasive, safe, low cost, and has a relatively high spatial resolution.Approach.In this study, an active BCI speller based on fNIRS is presented by covertly rehearsing tonal monosyllables with vowels (i.e. /a/, /i/, /o/, and /u/) and four lexical tones in Mandarin Chinese (i.e. tones 1, 2, 3, and 4) for 10 s.Main results.fNIRS results showed significant differences in the right superior temporal gyrus between imagined vowels with tone 2/3/4 and those with tone 1 (i.e. more activations and stronger connections to other brain regions for imagined vowels with tones 2/3/4 than for those with tone 1). Speech-related areas for tone imagery (i.e. the right hemisphere) provided majority of information for identifying tones, while the left hemisphere had advantages in vowel identification. Having decoded both vowels and tones during the post-stimulus 15 s period, the average classification accuracies exceeded 40% and 70% in multiclass (i.e. four classes) and binary settings, respectively. To spell words more quickly, the time window size for decoding was reduced from 15 s to 2.5 s while the classification accuracies were not significantly reduced.Significance.For the first time, this work demonstrated the possibility of discriminating lexical tones and vowels in imagined tonal syllables simultaneously. In addition, the reduced time window for decoding indicated that the spelling time of Chinese words could be significantly reduced in the fNIRS-based BCIs.},
}
@article {pmid37168652,
year = {2022},
author = {Shi, Y and Ananthakrishnan, A and Oh, S and Liu, X and Hota, G and Cauwenberghs, G and Kuzum, D},
title = {A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting.},
journal = {IEEE transactions on electron devices},
volume = {69},
number = {4},
pages = {2137-2144},
pmid = {37168652},
issn = {0018-9383},
support = {DP2 EB030992/EB/NIBIB NIH HHS/United States ; },
abstract = {Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuOx resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuOx memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.},
}
@article {pmid37181286,
year = {2022},
author = {Khrulev, AE and Kuryatnikova, KM and Belova, &#x410;N and Popova, PS and Khrulev, S&#x415;},
title = {Modern Rehabilitation Technologies of Patients with Motor Disorders at an Early Rehabilitation of Stroke (Review).},
journal = {Sovremennye tekhnologii v meditsine},
volume = {14},
number = {6},
pages = {64-78},
pmid = {37181286},
issn = {2309-995X},
mesh = {Adult ; Humans ; *Stroke Rehabilitation/methods ; *Transcranial Direct Current Stimulation/methods ; *Motor Disorders ; Recovery of Function ; *Stroke ; },
abstract = {Cerebral stroke is one of the leading disability causes among adult population worldwide. The number of post-stroke patients, who need rehabilitation including motor recovery, keeps growing annually. Standard motor rehabilitation techniques have a limited effect on recovering extremity motor defunctionalization. In this regard, in recent years, new technologies of post-stroke rehabilitation are being suggested. The present review summarizes the existing literature data on current techniques applied in patients with motor disorders at an early rehabilitation period of cerebral stroke. The current modern technologies are divided into the methods based on "interhemispheric inhibition" theory (repetitive transcranial magnetic stimulation, transcranial direct current stimulation), and on "mirror neurons" theory (virtual reality systems and brain-computer interfaces). The authors present the neurophysiological causes and feasible protocols of using the techniques in clinical practice, the clinical research findings due to the initial severity level of motor disorders and stroke age, as well as the factors contributing to the motor rehabilitation efficiency when using these methods.},
}
@article {pmid36908334,
year = {2022},
author = {Huggins, JE and Krusienski, D and Vansteensel, MJ and Valeriani, D and Thelen, A and Stavisky, S and Norton, JJS and Nijholt, A and Müller-Putz, G and Kosmyna, N and Korczowski, L and Kapeller, C and Herff, C and Halder, S and Guger, C and Grosse-Wentrup, M and Gaunt, R and Dusang, AN and Clisson, P and Chavarriaga, R and Anderson, CW and Allison, BZ and Aksenova, T and Aarnoutse, E},
title = {Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {9},
number = {2},
pages = {69-101},
pmid = {36908334},
issn = {2326-263X},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; R13 DC018466/DC/NIDCD NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; },
abstract = {The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.},
}
@article {pmid37118342,
year = {2021},
author = {Chen, S and Chen, L and Qi, Y and Xu, J and Ge, Q and Fan, Y and Chen, D and Zhang, Y and Wang, L and Hou, T and Yang, X and Xi, Y and Si, J and Kang, L and Wang, L},
title = {Bifidobacterium adolescentis regulates catalase activity and host metabolism and improves healthspan and lifespan in multiple species.},
journal = {Nature aging},
volume = {1},
number = {11},
pages = {991-1001},
pmid = {37118342},
issn = {2662-8465},
mesh = {Animals ; Mice ; *Bifidobacterium adolescentis ; Longevity ; Caenorhabditis elegans/genetics ; Catalase ; Drosophila melanogaster ; Fibroblasts ; },
abstract = {To identify candidate bacteria associated with aging, we performed fecal microbiota sequencing in young, middle-aged and older adults, and found lower Bifidobacterium adolescentis abundance in older individuals aged ≥60 years. Dietary supplementation of B. adolescentis improved osteoporosis and neurodegeneration in a mouse model of premature aging (Terc[-/-]) and increased healthspan and lifespan in Drosophila melanogaster and Caenorhabditis elegans. B. adolescentis supplementation increased the activity of the catalase (CAT) enzyme in skeletal muscle and brain tissue from Terc[-/-] mice, and suppressed cellular senescence in mouse embryonic fibroblasts. Transgenic deletion of catalase (ctl-2) in C. elegans abolished the effects of B. adolescentis on the lifespan and healthspan. B. adolescentis feeding also led to changes in oxidative stress-associated metabolites in Terc[-/-] mouse feces. These results suggest a role for B. adolescentis in improving the healthspan and lifespan through the regulation of CAT activity and host metabolism.},
}
@article {pmid36925573,
year = {2021},
author = {Lehnertz, K and Rings, T and Bröhl, T},
title = {Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks.},
journal = {Frontiers in network physiology},
volume = {1},
number = {},
pages = {755016},
pmid = {36925573},
issn = {2674-0109},
abstract = {Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.},
}
@article {pmid36654371,
year = {2021},
author = {Zhu, J and Chen, F and Luo, L and Wu, W and Dai, J and Zhong, J and Lin, X and Chai, C and Ding, P and Liang, L and Wang, S and Ding, X and Chen, Y and Wang, H and Qiu, J and Wang, F and Sun, C and Zeng, Y and Fang, J and Jiang, X and Liu, P and Tang, G and Qiu, X and Zhang, X and Ruan, Y and Jiang, S and Li, J and Zhu, S and Xu, X and Li, F and Liu, Z and Cao, G and Chen, D},
title = {Single-cell atlas of domestic pig cerebral cortex and hypothalamus.},
journal = {Science bulletin},
volume = {66},
number = {14},
pages = {1448-1461},
doi = {10.1016/j.scib.2021.04.002},
pmid = {36654371},
issn = {2095-9281},
abstract = {The brain of the domestic pig (Sus scrofa domesticus) has drawn considerable attention due to its high similarities to that of humans. However, the cellular compositions of the pig brain (PB) remain elusive. Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB (frontal lobe, parietal lobe, temporal lobe, occipital lobe, and hypothalamus) and identified 21 cell subpopulations. The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution. Furthermore, we identified cell types and molecular pathways closely associated with neurological disorders, bridging the gap between gene mutations and pathogenesis. We reported, to our knowledge, the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species, providing extensive resources for future research regarding neural science, evolutionary developmental biology, and regenerative medicine.},
}
@article {pmid37427003,
year = {2021},
author = {Horowitz, AJ and Guger, C and Korostenskaja, M},
title = {What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?.},
journal = {HCA healthcare journal of medicine},
volume = {2},
number = {3},
pages = {163-179},
pmid = {37427003},
issn = {2689-0216},
abstract = {Description In this review article, we aimed to create a summary of the effects of internal variables on the performance of sensorimotor rhythm-based brain computer interfaces (SMR-BCIs). SMR-BCIs can be potentially used for interfacing between the brain and devices, bypassing usual central nervous system output, such as muscle activity. The careful consideration of internal factors, affecting SMR-BCI performance, can maximize BCI application in both healthy and disabled people. Internal variables may be generalized as descriptors of the processes mainly dependent on the BCI user and/or originating within the user. The current review aimed to critically evaluate and summarize the currently accumulated body of knowledge regarding the effect of internal variables on SMR-BCI performance. The examples of such internal variables include motor imagery, hand coordination, attention, motivation, quality of life, mood and neurophysiological signals other than SMR. We will conclude our review with the discussion about the future developments regarding the research on the effects of internal variables on SMR-BCI performance. The end-goal of this review paper is to provide current BCI users and researchers with the reference guide that can help them optimize the SMR-BCI performance by accounting for possible influences of various internal factors.},
}
@article {pmid37427002,
year = {2021},
author = {Horowitz, AJ and Guger, C and Korostenskaja, M},
title = {What External Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?.},
journal = {HCA healthcare journal of medicine},
volume = {2},
number = {3},
pages = {143-162},
pmid = {37427002},
issn = {2689-0216},
abstract = {Description Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) are used for the acquisition and translation of motor imagery-related brain signals into machine control commands, bypassing the usual central nervous system output. The selection of optimal external variable configuration can maximize SMR-BCI performance in both healthy and disabled people. This performance is especially important now when the BCI is targeted for everyday use in the environment beyond strictly regulated laboratory settings. In this review article, we summarize and critically evaluate the current body of knowledge pertaining to the effect of the external variables on SMR-BCI performance. When assessing the relationship between SMR-BCI performance and external variables, we broadly characterize them as elements that are less dependent on the BCI user and originate from beyond the user. These elements include such factors as BCI type, distractors, training, visual and auditory feedback, virtual reality and magneto electric feedback, proprioceptive and haptic feedback, carefulness of electroencephalography (EEG) system assembling and positioning of EEG electrodes as well as recording-related artifacts. At the end of this review paper, future developments are proposed regarding the research into the effects of external variables on SMR-BCI performance. We believe that our critical review will be of value for academic BCI scientists and developers and clinical professionals working in the field of BCIs as well as for SMR-BCI users.},
}
@article {pmid36742108,
year = {2021},
author = {Jee, S},
title = {Brain Oscillations and Their Implications for Neurorehabilitation.},
journal = {Brain &amp; NeuroRehabilitation},
volume = {14},
number = {1},
pages = {e7},
pmid = {36742108},
issn = {2383-9910},
abstract = {Neural oscillation is rhythmic or repetitive neural activities, which can be observed at all levels of the central nervous system (CNS). The large-scale oscillations measured by electroencephalography have long been used in clinical practice and may have a potential for the usage in neurorehabilitation for people with various CNS disorders. The recent advancement of computational neuroscience has opened up new opportunities to explore clinical application of the results of neural oscillatory activity analysis to evaluation and diagnosis; monitoring the rehab progress; prognostication; and personalized rehabilitation planning in neurorehabilitation. In addition, neural oscillation is catching more attention to its role as a target of noninvasive neuromodulation in neurological disorders.},
}
@article {pmid37387779,
year = {2021},
author = {Huang, H and Chen, L and Chopp, M and Young, W and Robert Bach, J and He, X and Sarnowaska, A and Xue, M and Chunhua Zhao, R and Shetty, A and Siniscalco, D and Guo, X and Khoshnevisan, A and Hawamdeh, Z},
title = {The 2020 Yearbook of Neurorestoratology.},
journal = {Journal of neurorestoratology},
volume = {9},
number = {1},
pages = {1-12},
pmid = {37387779},
issn = {2324-2426},
abstract = {COVID-19 has been an emerging and rapidly evolving risk to people of the world in 2020. Facing this dangerous situation, many colleagues in Neurorestoratology did their best to avoid infection if themselves and their patients, and continued their work in the research areas described in the 2020 Yearbook of Neurorestoratology. Neurorestorative achievements and progress during 2020 includes recent findings on the pathogenesis of neurological diseases, neurorestorative mechanisms and clinical therapeutic achievements. Therapeutic progress during this year included advances in cell therapies, neurostimulation/neuromodulation, brain-computer interface (BCI), and pharmaceutical neurorestorative therapies, which improved neurological functions and quality of life for patients. Four clinical guidelines or standards of Neurorestoratology were published in 2020. Milestone examples include: 1) a multicenter randomized, double-blind, placebo-controlled study of olfactory ensheathing cell treatment of chronic stroke showed functional improvements; 2) patients after transhumeral amputation experienced increased sensory acuity and had improved effectiveness in work and other activities of daily life using a prosthesis; 3) a patient with amyotrophic lateral sclerosis used a steady-state visual evoked potential (SSVEP)-based BCI to achieve accurate and speedy computer input; 4) a patient with complete chronic spinal cord injury recovered both motor function and touch sensation with a BCI and restored ability to detect objects by touch and several sensorimotor functions. We hope these achievements motivate and encourage other scientists and physicians to increase neurorestorative research and its therapeutic applications.},
}
@article {pmid36590862,
year = {2019},
author = {Sharpee, TO and Berkowitz, JA},
title = {Linking neural responses to behavior with information-preserving population vectors.},
journal = {Current opinion in behavioral sciences},
volume = {29},
number = {},
pages = {37-44},
pmid = {36590862},
issn = {2352-1546},
support = {R01 EY019493/EY/NEI NIH HHS/United States ; },
abstract = {All systems for processing signals, both artificial and within animals, must obey fundamental statistical laws for how information can be processed. We discuss here recent results using information theory that provide a blueprint for building circuits where signals can be read-out without information loss. Many properties that are necessary to build information-preserving circuits are actually observed in real neurons, at least approximately. One such property is the use of logistic nonlinearity for relating inputs to neural response probability. Such nonlinearities are common in neural and intracellular networks. With this nonlinearity type, there is a linear combination of neural responses that is guaranteed to preserve Shannon information contained in the response of a neural population, no matter how many neurons it contains. This read-out measure is related to a classic quantity known as the population vector that has been quite successful in relating neural responses to animal behavior in a wide variety of cases. Nevertheless, the population vector did not withstand the scrutiny of detailed information-theoretical analyses that showed that it discards substantial amounts of information contained in the responses of a neural population. We discuss recent theoretical results showing how to modify the population vector expression to make it 'information-preserving', and what is necessary in terms of neural circuit organization to allow for lossless information transfer. Implementing these strategies within artificial systems is likely to increase their efficiency, especially for brain-machine interfaces.},
}
@article {pmid36317254,
year = {2022},
author = {Lee, C and Vaskov, AK and Gonzalez, MA and Vu, PP and Davis, AJ and Cederna, PS and Chestek, CA and Gates, DH},
title = {Use of regenerative peripheral nerve interfaces and intramuscular electrodes to improve prosthetic grasp selection: a case study.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
pmid = {36317254},
issn = {1741-2552},
support = {R01 NS105132/NS/NINDS NIH HHS/United States ; },
mesh = {Female ; Humans ; *Artificial Limbs ; Electrodes ; Electromyography/methods ; Hand/physiology ; Hand Strength ; *Muscle, Skeletal/physiology ; Peripheral Nerves/physiology ; },
abstract = {Objective.Advanced myoelectric hands enable users to select from multiple functional grasps. Current methods for controlling these hands are unintuitive and require frequent recalibration. This case study assessed the performance of tasks involving grasp selection, object interaction, and dynamic postural changes using intramuscular electrodes with regenerative peripheral nerve interfaces (RPNIs) and residual muscles.Approach.One female with unilateral transradial amputation participated in a series of experiments to compare the performance of grasp selection controllers with RPNIs and intramuscular control signals with controllers using surface electrodes. These experiments included a virtual grasp-matching task with and without a concurrent cognitive task and physical tasks with a prosthesis including standardized functional assessments and a functional assessment where the individual made a cup of coffee ('Coffee Task') that required grasp transitions.Main results.In the virtual environment, the participant was able to select between four functional grasps with higher accuracy using the RPNI controller (92.5%) compared to surface controllers (81.9%). With the concurrent cognitive task, performance of the virtual task was more consistent with RPNI controllers (reduced accuracy by 1.1%) compared to with surface controllers (4.8%). When RPNI signals were excluded from the controller with intramuscular electromyography (i.e. residual muscles only), grasp selection accuracy decreased by up to 24%. The participant completed the Coffee Task with 11.7% longer completion time with the surface controller than with the RPNI controller. She also completed the Coffee Task with 11 fewer transition errors out of a maximum of 25 total errors when using the RPNI controller compared to surface controller.Significance.The use of RPNI signals in concert with residual muscles and intramuscular electrodes can improve grasp selection accuracy in both virtual and physical environments. This approach yielded consistent performance without recalibration needs while reducing cognitive load associated with pattern recognition for myoelectric control (clinical trial registration number NCT03260400).},
}
@article {pmid36317171,
year = {2022},
author = {Imambocus, BN and Formozov, A and Zhou, F and Soba, P},
title = {A two-choice assay for noxious light avoidance with temporal distribution analysis in Drosophila melanogaster larvae.},
journal = {STAR protocols},
volume = {3},
number = {4},
pages = {101787},
pmid = {36317171},
issn = {2666-1667},
support = {P40 OD018537/OD/NIH HHS/United States ; },
mesh = {Animals ; *Drosophila melanogaster ; Larva ; *Drosophila ; Biological Assay ; },
abstract = {Two-choice assays allow assessing of different behaviors including light avoidance in Drosophila larvae. Typically, the readout is limited to a preference index at a specific end point. We provide a detailed protocol to set up light avoidance assays and map the temporal distribution of larvae based on analysis of larval intensities. We describe the assay setup and implementation of scripts for analysis, which can be easily adapted to other two-choice assays and different model organisms. For complete details on the use and execution of this protocol, please refer to Imambocus et al. (2022).},
}
@article {pmid36315547,
year = {2023},
author = {Phang, CR and Chen, CH and Cheng, YY and Chen, YJ and Ko, LW},
title = {Frontoparietal Dysconnection in Covert Bipedal Activity for Enhancing the Performance of the Motor Preparation-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {139-149},
doi = {10.1109/TNSRE.2022.3217298},
pmid = {36315547},
issn = {1558-0210},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; *Neurofeedback ; *Stroke ; Imagination ; },
abstract = {Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.},
}
@article {pmid36315544,
year = {2023},
author = {Gao, Y and Liu, Y and She, Q and Zhang, J},
title = {Domain Adaptive Algorithm Based on Multi-Manifold Embedded Distributed Alignment for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {27},
number = {1},
pages = {296-307},
doi = {10.1109/JBHI.2022.3218453},
pmid = {36315544},
issn = {2168-2208},
mesh = {Humans ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Electroencephalography/methods ; },
abstract = {The use of transfer learning in brain-computer interfaces (BCIs) has potential applications. As electroencephalogram (EEG) signals vary among different paradigms and subjects, existing EEG transfer learning algorithms mainly focus on the alignment of the original space. They may not discover hidden details owing to the low-dimensional structure of EEG. To effectively transfer data from a source to target domain, a multi-manifold embedding domain adaptive algorithm is proposed for BCI. First, we aligned the EEG covariance matrix in the Riemannian manifold and extracted the characteristics of each source domain in the tangent space to reflect the differences between different source domains. Subsequently, we mapped the extracted characteristics to the Grassmann manifold to obtain a common feature representation. In domain adaptation, the geometric and statistical attributes of EEG data were considered simultaneously, and the target domain divergence matrix was updated with pseudo-labels to maximize the inter-class distance and minimize the intra-class distance. Datasets generated via BCIs were used to verify the effectiveness of the algorithm. Under two experimental paradigms, namely single-source to single-target and multi-source to single-target, the average accuracy of the algorithm on three datasets was 73.31% and 81.02%, respectively, which is more than that of several state-of-the-art EEG cross-domain classification approaches. Our multi-manifold embedded domain adaptive method achieved satisfactory results on EEG transfer learning. The method can achieve effective EEG classification without a same subject's training set.},
}
@article {pmid36315056,
year = {2022},
author = {Pan, Y and Zhu, Y and Xu, C and Pan, C and Shi, Y and Zou, J and Li, Y and Hu, X and Zhou, B and Zhao, C and Gao, Q and Zhang, J and Wu, A and Chen, X and Li, J},
title = {Biomimetic Yolk-Shell Nanocatalysts for Activatable Dual-Modal-Image-Guided Triple-Augmented Chemodynamic Therapy of Cancer.},
journal = {ACS nano},
volume = {16},
number = {11},
pages = {19038-19052},
doi = {10.1021/acsnano.2c08077},
pmid = {36315056},
issn = {1936-086X},
mesh = {Humans ; Biomimetics ; Hydrogen Peroxide/metabolism ; Manganese Compounds/pharmacology ; Cell Line, Tumor ; Oxides ; *Neoplasms/diagnostic imaging/drug therapy ; Glutathione/metabolism ; Glucose Oxidase/metabolism ; *Nanoparticles ; Tumor Microenvironment ; },
abstract = {Fenton reaction-based chemodynamic therapy (CDT), which applies metal ions to convert less active hydrogen peroxide (H2O2) into more harmful hydroxyl peroxide (·OH) for tumor treatment, has attracted increasing interest recently. However, the CDT is substantially hindered by glutathione (GSH) scavenging effect on ·OH, low intracellular H2O2 level, and low reaction rate, resulting in unsatisfactory efficacy. Here, a cancer cell membrane (CM)-camouflaged Au nanorod core/mesoporous MnO2 shell yolk-shell nanocatalyst embedded with glucose oxidase (GOD) and Dox (denoted as AMGDC) is constructed for synergistic triple-augmented CDT and chemotherapy of tumor under MRI/PAI guidance. Benefiting from the homologous adhesion and immune escaping property of the cancer CM, the nanocatalysts can target tumor and gradually accumulate in tumor site. For triple-augmented CDT, first, the MnO2 shell reacts with intratumoral GSH to generate Mn[2+] and glutathione disulfide, which achieves Fenton-like ion delivery and weakening of GSH-mediated scavenging effect, leading to GSH depletion-enhanced CDT. Second, the intratumoral glucose can be oxidized to H2O2 and gluconic acid by GOD, achieving supplementary H2O2-enhanced CDT. Next, the AuNRs absorbing in NIR-II elevate the local tumor temperature upon NIR-II laser irradiation, achieving photothermal-enhanced CDT. Dox is rapidly released for adjuvant chemotherapy due to responsive degradation of MnO2 shell. Moreover, GSH-activated PAI/MRI can be used to monitor CDT process. This study provides a great paradigm for enhancing CDT-mediated antitumor efficacy.},
}
@article {pmid36313812,
year = {2022},
author = {Cui, Y and Xie, S and Xie, X and Zhang, X and Liu, X},
title = {Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {1006361},
pmid = {36313812},
issn = {1662-5188},
abstract = {BACKGROUND: Rapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses.<br><br>METHODS: Dynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features.<br><br>RESULTS: A nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 &#xb1; 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 &#xb1; 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 &#xb1; 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%.<br><br>CONCLUSION: Experimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.},
}
@article {pmid36313593,
year = {2022},
author = {Ma, T and Li, Y and Huggins, JE and Zhu, J and Kang, J},
title = {Bayesian Inferences on Neural Activity in EEG-Based Brain-Computer Interface.},
journal = {Journal of the American Statistical Association},
volume = {117},
number = {539},
pages = {1122-1133},
pmid = {36313593},
issn = {0162-1459},
support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; },
abstract = {A brain-computer interface (BCI) is a system that translates brain activity into commands to operate technology. A common design for an electroencephalogram (EEG) BCI relies on the classification of the P300 event-related potential (ERP), which is a response elicited by the rare occurrence of target stimuli among common non-target stimuli. Few existing ERP classifiers directly explore the underlying mechanism of the neural activity. To this end, we perform a novel Bayesian analysis of the probability distribution of multi-channel real EEG signals under the P300 ERP-BCI design. We aim to identify relevant spatial temporal differences of the neural activity, which provides statistical evidence of P300 ERP responses and helps design individually efficient and accurate BCIs. As one key finding of our single participant analysis, there is a 90% posterior probability that the target ERPs of the channels around visual cortex reach their negative peaks around 200 milliseconds post-stimulus. Our analysis identifies five important channels (PO7, PO8, Oz, P4, Cz) for the BCI speller leading to a 100% prediction accuracy. From the analyses of nine other participants, we consistently select the identified five channels, and the selection frequencies are robust to small variations of bandpass filters and kernel hyper-parameters.},
}
@article {pmid36312030,
year = {2022},
author = {Lim, J and Wang, PT and Shaw, SJ and Gong, H and Armacost, M and Liu, CY and Do, AH and Heydari, P and Nenadic, Z},
title = {Artifact propagation in subdural cortical electrostimulation: Characterization and modeling.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1021097},
pmid = {36312030},
issn = {1662-4548},
abstract = {Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R [2] = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.},
}
@article {pmid36310494,
year = {2022},
author = {Li, M and Gong, A and Nan, W and Xu, B and Ding, P and Fu, Y},
title = {[Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {5},
pages = {1041-1049},
pmid = {36310494},
issn = {1001-5515},
mesh = {*Neurofeedback/methods ; Spectroscopy, Near-Infrared/methods ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Technology ; },
abstract = {Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.},
}
@article {pmid36310493,
year = {2022},
author = {Cao, H and Jung, TP and Chen, Y and Mei, J and Li, A and Xu, M and Ming, D},
title = {[Research advances in non-invasive brain-computer interface control strategies].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {5},
pages = {1033-1040},
pmid = {36310493},
issn = {1001-5515},
mesh = {Humans ; Electroencephalography ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; User-Computer Interface ; Brain/physiology ; },
abstract = {Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.},
}
@article {pmid36306641,
year = {2022},
author = {Scott, CJ and de Mestre, AM and Verheyen, KL and Arango-Sabogal, JC},
title = {Bayesian accuracy estimates and fit for purpose thresholds of cytology and culture of endometrial swab samples for detecting endometritis in mares.},
journal = {Preventive veterinary medicine},
volume = {209},
number = {},
pages = {105783},
doi = {10.1016/j.prevetmed.2022.105783},
pmid = {36306641},
issn = {1873-1716},
mesh = {Horses ; Animals ; Female ; *Endometritis/diagnosis/veterinary/microbiology ; Retrospective Studies ; Bayes Theorem ; *Horse Diseases/diagnosis/epidemiology/microbiology ; Endometrium ; },
abstract = {The overall aim of this work was to identify the potential impact of misclassification errors associated with routine screening and diagnostic testing for endometritis in mares. Using Bayesian latent class models (BLCM), specific objectives were to: 1) estimate the diagnostic accuracy of cytology and culture of endometrial swab samples to detect endometritis in mares; 2) assess the impact of different cytology thresholds on test accuracy and misclassification costs; and 3) assess the sensitivity (Se) and specificity (Sp) of a diagnostic strategy including both tests interpreted in series and parallel. Diagnostic and pre-breeding endometrial swab samples collected from 3448 mares based at breeding premises located in the South East of England between 2014 and 2020 were retrospectively analysed. Culture results were classified as positive according to three different case definitions: (A) > 90% of the growth colonies were a monoculture; (B) pathogenic or pathogenic and non-pathogenic bacteria were identified; and (C) any growth was observed. Endometrial smears were graded based on the percent of polymorphonuclear cells (PMN) per high power field (HPF). A hierarchical BLCM was fitted using the cross-tabulated results of the three culture case definitions with a cytology threshold fixed at > 0.5% PMN. Fit for purpose cytology thresholds were proposed using a misclassification cost analysis in the context of good antimicrobial stewardship and for varying endometritis prevalence estimates. Median [95% Bayesian credible intervals (BCI)] cytology Se estimates were 6.5% (2.2-11.6), 6.4% (2.2-10.8) and 6.3% (2.2-10.8) for scenario A, B and C, respectively. Median (95% BCI) cytology Sp estimates were 88.8% (83.1-94.8), 88.9% (83.9-93.8) and 88.8% (84.0-93.8) for scenarios A, B and C, respectively. Median (95% BCI) culture Se estimates were 37.5% (29.9-46.0), 42.3% (33.8-51.1) and 46.4% (35.7-55.9) for scenarios A, B and C, respectively. Median (95% BCI) culture Sp estimates were 92.8% (84.3-99.0), 91.5% (82.5-98.0) and 90.8% (80.1-97.4) for scenarios A, B and C, respectively. Regardless of the culture case definition, Se and Sp of cytology (> 0.5% PMN) was lower than previously reported for swab samples in studies using histology as the reference standard test. The misclassification cost term decreased as the cytology threshold increased for all scenarios and all prevalence contexts, suggesting that, regardless of the endometritis prevalence in the population, increasing the cytology threshold would reduce the misclassification costs associated with false positive mares contributing to good antimicrobial stewardship.},
}
@article {pmid36306303,
year = {2023},
author = {Lee, T and Nam, S and Hyun, DJ},
title = {Adaptive Window Method Based on FBCCA for Optimal SSVEP Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {78-86},
doi = {10.1109/TNSRE.2022.3217789},
pmid = {36306303},
issn = {1558-0210},
mesh = {Humans ; *Evoked Potentials, Visual ; Photic Stimulation ; Recognition, Psychology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; },
abstract = {In the conventional studies related to steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the window length (detection time) was typically predetermined through the offline analysis, which had limitations of practical applicability of a BCI system due to the inter-subject/trial variability of electroencephalography (EEG) signals. To address these limitations, this study aims to automatically optimize the window length for each trial based on training-free approaches and proposes a novel adaptive window method (ANCOVA-based filter-bank canonical correlation analysis, ABFCCA) for SSVEP-based BCIs. The proposed method is based on analysis of covariance (ANCOVA) which is applied after feature extraction by the conventional training-free SSVEP recognition approaches. To evaluate the performance of the proposed method, conventional fixed window and recent adaptive window methods were compared using two open-access datasets. In the Benchmark dataset, the average information transfer rate (ITR) was 146.81 bits/min, the average accuracy 93.55%, and the average window length 1.53 s. In the OpenBMI dataset, the average ITR was 119.01 bits/min, the average accuracy 83.50%, and the average window length 0.65 s. The proposed method significantly outperformed the conventional approaches with fixed window in terms of the accuracy and ITR, and is applicable to various SSVEP-based BCI paradigms based on the criterion of significance level without offline analysis to find optimal hyper-parameters. ABFCCA is enabled the practical use of various BCI systems by automatically optimizing the window length independently.},
}
@article {pmid36304780,
year = {2022},
author = {Valeriani, D and Santoro, F and Ienca, M},
title = {The present and future of neural interfaces.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {953968},
pmid = {36304780},
issn = {1662-5218},
abstract = {The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.},
}
@article {pmid36303917,
year = {2022},
author = {Liu, S},
title = {Applying antagonistic activation pattern to the single-trial classification of mental arithmetic.},
journal = {Heliyon},
volume = {8},
number = {10},
pages = {e11102},
pmid = {36303917},
issn = {2405-8440},
abstract = {BACKGROUND: At present, the application of fNIRS in the field of brain-computer interface (BCI) is being a hot topic. By fNIRS-BCI, the brain realizes the control of external devices. A state-of-the-art BCI system has five steps which are cerebral cortex signal acquisition, data pre-processing, feature selection and extraction, feature classification and application interface. Proper feature selection and extraction are crucial to the final fNIRS-BCI effect. This paper proposes a feature selection and extraction method for the mental arithmetic task. Specifically, we modified the antagonistic activation pattern approach and used the combination of antagonistic activation patterns to extract features for enhancement of the classification accuracy with low calculation costs.<br><br>METHODS: Experiments are conducted on an open-acquisition dataset including fNIRS signals of eight healthy subjects of mental arithmetic (MA) tasks and rest tasks. First, the signals are filtered using band-pass filters to remove noise. Second, channels are selected by prior knowledge about antagonistic activation patterns. We used cerebral blood volume (CBV) and cerebral oxygen exchange (COE) of selected each channel to build novel attributes. Finally, we proposed three groups of attributes which are CBV, COE and CBV + COE. Based on attributes generated by the proposed method, we calculated temporal statistical measures (average, variance, maximum, minimum and slope). Any two of five statistical measures were combined as feature sets.<br><br>MAIN RESULTS: With the LDA, QDA, and SVM classifiers, the proposed method obtained higher classification accuracies the basic control method. The maximum classification accuracies achieved by the proposed method are 67.45 &#xb1; 14.56% with LDA classifier, 89.73 &#xb1; 5.71% with QDA classifier, and 87.04 &#xb1; 6.88% with SVM classifier. The novel method reduced the running time by 3.75 times compared with the method incorporating all channels into the feature set. Therefore, the novel method reduces the computational costs while maintaining high classification accuracy. The results are validated by another open-access dataset including MA and rest tasks of 29 healthy subjects.},
}
@article {pmid36300170,
year = {2022},
author = {Zhang, Y and Liu, D and Zhang, P and Li, T and Li, Z and Gao, F},
title = {Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {938518},
pmid = {36300170},
issn = {1662-4548},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.},
}
@article {pmid36299440,
year = {2022},
author = {Xu, Y and Yin, H and Yi, W and Huang, X and Jian, W and Wang, C and Hu, R},
title = {Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1603104},
pmid = {36299440},
issn = {1687-5273},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms ; Calibration ; Imagination ; },
abstract = {A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.},
}
@article {pmid36298430,
year = {2022},
author = {Ng, CR and Fiedler, P and Kuhlmann, L and Liley, D and Vasconcelos, B and Fonseca, C and Tamburro, G and Comani, S and Lui, TK and Tse, CY and Warsito, IF and Supriyanto, E and Haueisen, J},
title = {Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {20},
pages = {},
pmid = {36298430},
issn = {1424-8220},
support = {57452734//German Academic Exchange Service/ ; 813483//European Union/ ; 101007521//European Union/ ; 2018 IZN 004//Free State of Thuringia/ ; },
mesh = {Humans ; Reproducibility of Results ; Electrodes ; *Electroencephalography ; *Brain-Computer Interfaces ; Electric Impedance ; },
abstract = {Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.},
}
@article {pmid36298196,
year = {2022},
author = {Zhang, S and Li, H and Li, L and Lu, J and Zuo, Z},
title = {A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {20},
pages = {},
pmid = {36298196},
issn = {1424-8220},
support = {No.62172132//National Natural Science Foundation of China/ ; No.LGF21F020014//Public Welfare Technology Research Project of Zhejiang Province/ ; },
mesh = {*Image Processing, Computer-Assisted/methods ; *Algorithms ; Neural Networks, Computer ; },
abstract = {Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.},
}
@article {pmid36298064,
year = {2022},
author = {Oikonomou, VP},
title = {An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {20},
pages = {},
pmid = {36298064},
issn = {1424-8220},
support = {T2EDK-03661//Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH CREATE INNOVATE/ ; },
mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Bayes Theorem ; Algorithms ; *Brain-Computer Interfaces ; Photic Stimulation ; },
abstract = {Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.},
}
@article {pmid36297922,
year = {2022},
author = {Liu, N and Wang, H and Wang, S and Xu, B and Qu, L},
title = {Liquid Oxygen Compatibility and Ultra-Low-Temperature Mechanical Properties of Modified epoxy Resin Containing Phosphorus and Nitrogen.},
journal = {Polymers},
volume = {14},
number = {20},
pages = {},
pmid = {36297922},
issn = {2073-4360},
support = {No.12090031//National Natural Science Foundation of China/ ; No. 2018YFA0702804//National Key Research and Development of China/ ; },
abstract = {Endowing epoxy resin (EP) with prospective liquid oxygen compatibility (LOC) as well as enhanced ultra-low-temperature mechanical properties is urgently required in order to broaden its applications in aerospace engineering. In this study, a reactive phosphorus/nitrogen-containing aromatic ethylenediamine (BSEA) was introduced as a reactive component to enhance the LOC and ultra-low-temperature mechanical properties of an EP/biscitraconimide resin (BCI) system. The resultant EP thermosets showed no sensitivity reactions in the 98J liquid oxygen impact test (LOT) when the BSEA content reached 4 wt% or 5 wt%, indicating that they were compatible with liquid oxygen. Moreover, the bending properties, fracture toughness and impact strength of BSEA-modified EP were greatly enhanced at RT and cryogenic temperatures (77 K) at an appropriate level of BSEA content. The bending strength (251.64 MPa) increased by 113.67%, the fracture toughness (2.97 MPa·m[1/2]) increased by 81.10%, and the impact strength (31.85 kJ·m[-2]) increased by 128.81% compared with that of pure EP at 77 K. All the above results demonstrate that the BSEA exhibits broad application potential in liquid oxygen tanks and in the cryogenic field.},
}
@article {pmid36291203,
year = {2022},
author = {Ning, Y and Wan, G and Liu, T and Zhang, S},
title = {Volitional Generation of Reproducible, Efficient Temporal Patterns.},
journal = {Brain sciences},
volume = {12},
number = {10},
pages = {},
pmid = {36291203},
issn = {2076-3425},
support = {2022ZD0208600,2021ZD0200401//China Brain Project/ ; },
abstract = {One of the extraordinary characteristics of the biological brain is the low energy expense it requires to implement a variety of biological functions and intelligence as compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as a contributor for the brain to run on low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how it evolves throughout learning have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm. Two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1) by learning the BMI paradigm. Moreover, most neurons that were not directly assigned to control the BMI did not boost their excitability, and they demonstrated an overall energy-efficient manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting that a cohesive rather than dissociable processing underlies the refinement of energy-efficient temporal patterns.},
}
@article {pmid36290990,
year = {2022},
author = {Filho, G and Júnior, C and Spinelli, B and Damasceno, I and Fiuza, F and Morya, E},
title = {All-Polymeric Electrode Based on PEDOT:PSS for In Vivo Neural Recording.},
journal = {Biosensors},
volume = {12},
number = {10},
pages = {},
pmid = {36290990},
issn = {2079-6374},
mesh = {Animals ; Rats ; *Neurons/physiology ; Rats, Wistar ; *Polymers ; Microelectrodes ; },
abstract = {One of the significant challenges today in the brain-machine interfaces that use invasive methods is the stability of the chronic record. In recent years, polymer-based electrodes have gained notoriety for achieving mechanical strength values close to that of brain tissue, promoting a lower immune response to the implant. In this work, we fabricated fully polymeric electrodes based on PEDOT:PSS for neural recording in Wistar rats. We characterized the electrical properties and both in vitro and in vivo functionality of the electrodes. Additionally, we employed histological processing and microscopical visualization to evaluate the tecidual immune response at 7, 14, and 21 days post-implant. Electrodes with 400-micrometer channels showed a 12 dB signal-to-noise ratio. Local field potentials were characterized under two conditions: anesthetized and free-moving. There was a proliferation of microglia at the tissue-electrode interface in the early days, though there was a decrease after 14 days. Astrocytes also migrated to the interface, but there was not continuous recruitment of these cells in the tissue; there was inflammatory stability by day 21. The signal was not affected by this inflammatory action, demonstrating that fully polymeric electrodes can be an alternative means to prolong the valuable time of neural recordings.},
}
@article {pmid36290910,
year = {2022},
author = {Chen, W and Chen, SK and Liu, YH and Chen, YJ and Chen, CS},
title = {An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System.},
journal = {Biosensors},
volume = {12},
number = {10},
pages = {},
pmid = {36290910},
issn = {2079-6374},
support = {MOST 109-2221-E-027 -044 -MY3, NTUT-MMH-101-09//Ministry of Science and Technology (Taiwan), National Taipei University of Technology and Mackay Memorial Hospital Joint Research Program (Taiwan)/ ; },
mesh = {Humans ; Evoked Potentials, Visual ; *Brain-Computer Interfaces ; *Wheelchairs ; Reactive Oxygen Species ; Photic Stimulation ; Electroencephalography/methods ; Algorithms ; },
abstract = {Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain-computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human-machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.},
}
@article {pmid36289356,
year = {2022},
author = {Bak, S and Jeong, Y and Yeu, M and Jeong, J},
title = {Brain-computer interface to predict impulse buying behavior using functional near-infrared spectroscopy.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {18024},
pmid = {36289356},
issn = {2045-2322},
mesh = {Humans ; *Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods ; *COVID-19 ; Prefrontal Cortex/diagnostic imaging/physiology ; Biomarkers ; },
abstract = {As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people's preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain-Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain's prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.},
}
@article {pmid36289267,
year = {2022},
author = {An, KM and Shim, JH and Kwon, H and Lee, YH and Yu, KK and Kwon, M and Chun, WY and Hirosawa, T and Hasegawa, C and Iwasaki, S and Kikuchi, M and Kim, K},
title = {Detection of the 40 Hz auditory steady-state response with optically pumped magnetometers.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {17993},
pmid = {36289267},
issn = {2045-2322},
mesh = {*Helium ; *Magnetoencephalography/methods ; Brain/diagnostic imaging/physiology ; Neuroimaging ; Functional Neuroimaging ; },
abstract = {Magnetoencephalography (MEG) is a functional neuroimaging technique that noninvasively detects the brain magnetic field from neuronal activations. Conventional MEG measures brain signals using superconducting quantum interference devices (SQUIDs). SQUID-MEG requires a cryogenic environment involving a bulky non-magnetic Dewar flask and the consumption of liquid helium, which restricts the variability of the sensor array and the gap between the cortical sources and sensors. Recently, miniature optically pumped magnetometers (OPMs) have been developed and commercialized. OPMs do not require cryogenic cooling and can be placed within millimeters from the scalp. In the present study, we arranged six OPM sensors on the temporal area to detect auditory-related brain responses in a two-layer magnetically shielded room. We presented the auditory stimuli of 1 kHz pure-tone bursts with 200 ms duration and obtained the M50 and M100 components of auditory-evoked fields. We delivered the periodic stimuli with a 40 Hz repetition rate and observed the gamma-band power changes and inter-trial phase coherence of auditory steady-state responses at 40 Hz. We found that the OPM sensors have a performance comparable to that of conventional SQUID-MEG sensors, and our results suggest the feasibility of using OPM sensors for functional neuroimaging and brain-computer interface applications.},
}
@article {pmid36288717,
year = {2022},
author = {Wang, L and Zhan, G and Maimaitiyiming, Y and Su, Y and Lin, S and Liu, J and Su, K and Lin, J and Shen, S and He, W and Wang, F and Chen, J and Sun, S and Xue, Y and Gu, J and Chen, X and Zhang, J and Zhang, L and Wang, Q and Chang, KJ and Chiou, SH and Björklund, M and Naranmandura, H and Cheng, X and Hsu, CH},
title = {m[6]A modification confers thermal vulnerability to HPV E7 oncotranscripts via reverse regulation of its reader protein IGF2BP1 upon heat stress.},
journal = {Cell reports},
volume = {41},
number = {4},
pages = {111546},
doi = {10.1016/j.celrep.2022.111546},
pmid = {36288717},
issn = {2211-1247},
mesh = {Humans ; *Alphapapillomavirus/metabolism ; Carcinogenesis ; Heat-Shock Proteins ; Heat-Shock Response ; Papillomaviridae ; Papillomavirus E7 Proteins/genetics/metabolism ; *Papillomavirus Infections ; Proteasome Endopeptidase Complex ; RNA, Messenger/genetics/metabolism ; RNA, Viral/genetics ; Ubiquitin ; RNA-Binding Proteins ; },
abstract = {Human papillomavirus (HPV)-induced carcinogenesis critically depends on the viral early protein 7 (E7), making E7 an attractive therapeutic target. Here, we report that the E7 messenger RNA (mRNA)-containing oncotranscript complex can be selectively targeted by heat treatment. In HPV-infected cells, viral E7 mRNA is modified by N[6]-methyladenosine (m[6]A) and stabilized by IGF2BP1, a cellular m[6]A reader. Heat treatment downregulates E7 mRNA and protein by destabilizing IGF2BP1 without the involvement of canonical heat-shock proteins and reverses HPV-associated carcinogenesis in vitro and in vivo. Mechanistically, heat treatment promotes IGF2BP1 aggregation only in the presence of m[6]A-modified E7 mRNA to form distinct heat-induced m[6]A E7 mRNA-IGF2BP1 granules, which are resolved by the ubiquitin-proteasome system. Collectively, our results not only show a mutual regulation between m[6]A RNA and its reader but also provide a heat-treatment-based therapeutic strategy for HPV-associated malignancies by specifically downregulating E7 mRNA-IGF2BP1 oncogenic complex.},
}
@article {pmid36288219,
year = {2023},
author = {Kalafatovich, J and Lee, M and Lee, SW},
title = {Learning Spatiotemporal Graph Representations for Visual Perception Using EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {97-108},
doi = {10.1109/TNSRE.2022.3217344},
pmid = {36288219},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Visual Perception ; },
abstract = {Perceiving and recognizing objects enable interaction with the external environment. Recently, decoding brain signals based on brain-computer interface (BCI) that recognize the user's intentions by just looking at objects has attracted attention as a next-generation intuitive interface. However, classifying signals from different objects is very challenging, and in practice, decoding performance for visual perception is not yet high enough to be used in real environments. In this study, we aimed to classify single-trial electroencephalography signals evoked by visual stimuli into their corresponding semantic category. We proposed a two-stream convolutional neural network to increase classification performance. The model consists of a spatial stream and a temporal stream that use graph convolutional neural network and channel-wise convolutional neural network respectively. Two public datasets were used to evaluate the proposed model; (i) SU DB (a set of 72 photographs of objects belonging to 6 semantic categories) and MPI DB (8 exemplars belonging to two categories). Our results outperform state-of-the-art methods, with accuracies of 54.28 ± 7.89% for SU DB (6-class) and 84.40 ± 8.03% for MPI DB (2-class). These results could facilitate the application of intuitive BCI systems based on visual perception.},
}
@article {pmid36288214,
year = {2023},
author = {Jin, J and Qu, T and Xu, R and Wang, X and Cichocki, A},
title = {Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {31},
number = {},
pages = {58-67},
doi = {10.1109/TNSRE.2022.3217573},
pmid = {36288214},
issn = {1558-0210},
mesh = {Humans ; *Imagination ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; },
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The open-access BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p < 0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.},
}
@article {pmid36287496,
year = {2022},
author = {Henriques, DHN and Alves, AMH and Kuntze, MM and Garcia, LDFR and Bortoluzzi, EA and Teixeira, CDS},
title = {Effect of dental tissue thickness on the measurement of oxygen saturation by two different pulse oximeters.},
journal = {Brazilian dental journal},
volume = {33},
number = {5},
pages = {26-34},
pmid = {36287496},
issn = {1806-4760},
mesh = {Humans ; *Oxygen Saturation ; *Oximetry ; Oxygen ; Dental Enamel ; Diamond ; },
abstract = {This study aimed to evaluate the influence of different dental tissue thickness on the measurement of oxygen saturation (SpO2) levels in high (HP) and low (LP) blood perfusion by comparing the values obtained from two different pulse oximeters (POs) - BCI and Sense 10. Thirty freshly extracted human teeth had their crowns interposed between the POs and an optical simulator, which emulated the SpO2 and heart beats per minute (bpm) at HP (100% SpO2/75 bpm) and LP (86% SpO2/75 bpm) modes. Afterwards, the palatine/lingual surfaces of the dental crowns were worn with diamond drills. The reading of SpO2 was performed again using the POs alternately through the buccal surface of each dental crown. Data were analyzed by the Wilcoxon, Mann-Whitney and Kendall Tau-b tests (α=5%). The results showed significant difference at the HP and LP modes in the SpO2 readouts through the different dental thicknesses with the use of BCI, and at the LP mode with the use of Sense 10, which had a significant linear correlation (p<0.0001) and lower SpO2 readout values in relation to the increase of the dental thickness. Irrespective of tooth thickness, Sense 10 had significantly higher readout values (p<0.0001) than BCI at both perfusion modes. The interposition of different thicknesses of enamel and dentin influenced the POs measurement of SpO2, specially at the low perfusion mode. The POs were more accurate in SpO2 measurement when simulated perfusion levels were higher.},
}
@article {pmid36286988,
year = {2023},
author = {Zhu, L and Wang, M and Fu, P and Liu, Y and Zhang, H and Roe, AW and Xi, W},
title = {Precision 1070 nm Ultrafast Laser-Induced Photothrombosis of Depth-Targeted Vessels In Vivo.},
journal = {Small methods},
volume = {7},
number = {1},
pages = {e2200917},
doi = {10.1002/smtd.202200917},
pmid = {36286988},
issn = {2366-9608},
support = {2018YFA0701400//National Key R&amp;D program of China/ ; 91632105//National Natural Science Foundation of China/ ; U20A20221//National Natural Science Foundation of China/ ; 81961128029//National Natural Science Foundation of China/ ; 2022C03096//Key R&amp;D Program of Zhejiang/ ; 2020C03004//Key Research and Development Program of Zhejiang Province/ ; 226-2022-00083//Fundamental Research Funds for the Central Universities/ ; 2019XZZX003-20//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; *Lasers ; *Thrombosis ; Rose Bengal/pharmacology ; Brain ; },
abstract = {The cerebrovasculature plays an essential role in neurovascular and homeostatic functions in health and disease conditions. Many efforts have been made for developing vascular thrombosis methods to study vascular dysfunction in vivo, while technical challenges remain, such as accuracy and depth-selectivity to target a single vessel in the cerebral cortex. Herein, this paper first demonstrates the evaluation and quantification of the feasibility and effects of Rose Bengal (RB)-induced photothrombosis with 720-1070 nm ultrafast lasers in a raster scan. A flexible and reproducible approach is then proposed to employ a 1070 nm ultrafast laser with a spiral scan for producing RB-induced occlusion, which is described as precision ultrafast laser-induced photothrombosis (PLP). Combine with two-photon microscopy imaging, this PLP displays highly precise and fast occlusion induction of various vessel types, sizes, and depths, which enhances the precision and power of the photothrombosis protocol. Overall, the PLP method provides a real-time, practical, precise, and depth-selected single-vessel photothrombosis technology in the cerebral cortex with commercially available optical equipment, which is crucial for exploring brain vascular function with high spatial-temporal resolution in the brain.},
}
@article {pmid36285909,
year = {2022},
author = {Cavallaro, G and Murri, A and Nelson, E and Gorrasi, R and Quaranta, N},
title = {The Impact of the COVID-19 Lockdown on Quality of Life in Adult Cochlear Implant Users: A Survey Study.},
journal = {Audiology research},
volume = {12},
number = {5},
pages = {518-526},
pmid = {36285909},
issn = {2039-4330},
abstract = {BACKGROUND: The COVID-19 pandemic rapidly spread through Europe in the first months of 2020. On the 9th of March 2020, the Italian government ordered a national lock-down. The study's objectives were: to investigate the effect of lockdown on CI users; and to detect the difference in the perception of discomfort existing between unilateral cochlear implant (UCI) users and bilateral cochlear implant (BCI) users, due to the lockdown experience.<br><br>METHODS: A 17-item, web-based, anonymous online survey was administered to 57 CI users, exploring hearing performance, emotions, practical issues, behavior, and tinnitus. Participation in the study was voluntary.<br><br>RESULTS: all CI users obtained an abnormal score in all questionnaire themes. For the emotion theme and the practical issue theme, the age range 61-90 showed a significant difference between UCI and BCI users in favor of BCI users (emotion theme: UCI mean = 3.9, BCI mean = 2.3, p = 0.0138; practical issues: UCI mean = 4, BCI mean = 3, p = 0.0031).<br><br>CONCLUSIONS: CI users experienced the lockdown negatively as regards behavior, emotions, hearing performance, and in practical issues. CI subjects with UCI in old age suffered more from the experience of lockdown than subjects with BCI in the same age, with regards to emotions and practical issues.},
}
@article {pmid36285542,
year = {2023},
author = {Yu, H and Ni, P and Tian, Y and Zhao, L and Li, M and Li, X and Wei, W and Wei, J and Du, X and Wang, Q and Guo, W and Deng, W and Ma, X and Coid, J and Li, T},
title = {Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders.},
journal = {Psychological medicine},
volume = {53},
number = {13},
pages = {6102-6112},
doi = {10.1017/S0033291722003282},
pmid = {36285542},
issn = {1469-8978},
mesh = {Humans ; *Depressive Disorder, Major/diagnostic imaging/pathology ; *Bipolar Disorder/diagnostic imaging/pathology ; Complement Factor H ; Properdin ; Complement C1q ; Magnetic Resonance Imaging ; Brain/diagnostic imaging/pathology ; Gray Matter/diagnostic imaging/pathology ; *Motor Cortex ; },
abstract = {BACKGROUND: Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.<br><br>METHODS: A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.<br><br>RESULTS: GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.<br><br>CONCLUSIONS: BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.},
}
@article {pmid36284139,
year = {2022},
author = {Li, P and Garg, AK and Zhang, LA and Rashid, MS and Callaway, EM},
title = {Cone opponent functional domains in primary visual cortex combine signals for color appearance mechanisms.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {6344},
pmid = {36284139},
issn = {2041-1723},
support = {R01 EY022577/EY/NEI NIH HHS/United States ; T32 GM007198/GM/NIGMS NIH HHS/United States ; },
mesh = {*Calcium ; *Primary Visual Cortex ; Retinal Cone Photoreceptor Cells/physiology ; Color Perception/physiology ; Retina/physiology ; Photic Stimulation/methods ; Color ; },
abstract = {Studies of color perception have led to mechanistic models of how cone-opponent signals from retinal ganglion cells are integrated to generate color appearance. But it is unknown how this hypothesized integration occurs in the brain. Here we show that cone-opponent signals transmitted from retina to primary visual cortex (V1) are integrated through highly organized circuits within V1 to implement the color opponent interactions required for color appearance. Combining intrinsic signal optical imaging (ISI) and 2-photon calcium imaging (2PCI) at single cell resolution, we demonstrate cone-opponent functional domains (COFDs) that combine L/M cone-opponent and S/L + M cone-opponent signals following the rules predicted from psychophysical studies of color perception. These give rise to an orderly organization of hue preferences of the neurons within the COFDs and the generation of hue "pinwheels". Thus, spatially organized neural circuits mediate an orderly transition from cone-opponency to color appearance that begins in V1.},
}
@article {pmid36283830,
year = {2022},
author = {Xing, D and Truccolo, W and Borton, DA},
title = {Emergence of Distinct Neural Subspaces in Motor Cortical Dynamics during Volitional Adjustments of Ongoing Locomotion.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {49},
pages = {9142-9157},
pmid = {36283830},
issn = {1529-2401},
mesh = {Male ; Animals ; Cats ; *Motor Cortex/physiology ; Locomotion/physiology ; Gait/physiology ; Walking/physiology ; *Brain-Computer Interfaces ; },
abstract = {The ability to modulate ongoing walking gait with precise, voluntary adjustments is what allows animals to navigate complex terrains. However, how the nervous system generates the signals to precisely control the limbs while simultaneously maintaining locomotion is poorly understood. One potential strategy is to distribute the neural activity related to these two functions into distinct cortical activity coactivation subspaces so that both may be conducted simultaneously without disruptive interference. To investigate this hypothesis, we recorded the activity of primary motor cortex in male nonhuman primates during obstacle avoidance on a treadmill. We found that the same neural population was active during both basic unobstructed locomotion and volitional obstacle avoidance movements. We identified the neural modes spanning the subspace of the low-dimensional dynamics in primary motor cortex and found a subspace that consistently maintains the same cyclic activity throughout obstacle stepping, despite large changes in the movement itself. All of the variance corresponding to this large change in movement during the obstacle avoidance was confined to its own distinct subspace. Furthermore, neural decoders built for ongoing locomotion did not generalize to decoding obstacle avoidance during locomotion. Our findings suggest that separate underlying subspaces emerge during complex locomotion that coordinates ongoing locomotor-related neural dynamics with volitional gait adjustments. These findings may have important implications for the development of brain-machine interfaces.SIGNIFICANCE STATEMENT Locomotion and precise, goal-directed movements are two distinct movement modalities with known differing requirements of motor cortical input. Previous studies have characterized the cortical activity during obstacle avoidance while walking in rodents and felines, but, to date, no such studies have been completed in primates. Additionally, in any animal model, it is unknown how these two movements are represented in primary motor cortex (M1) low-dimensional dynamics when both activities are performed at the same time, such as during obstacle avoidance. We developed a novel obstacle avoidance paradigm in freely moving nonhuman primates and discovered that the rhythmic locomotion-related dynamics and the voluntary, gait-adjustment movement separate into distinct subspaces in M1 cortical activity. Our analysis of decoding generalization may also have important implications for the development of brain-machine interfaces.},
}
@article {pmid36280665,
year = {2022},
author = {Ji, SY and Dong, YJ and Chen, LN and Zang, SK and Wang, J and Shen, DD and Guo, J and Qin, J and Zhang, H and Wang, WW and Shen, Q and Zhang, Y and Song, Z and Mao, C},
title = {Molecular basis for the activation of thyrotropin-releasing hormone receptor.},
journal = {Cell discovery},
volume = {8},
number = {1},
pages = {116},
pmid = {36280665},
issn = {2056-5968},
support = {81922071//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32100959//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
}
@article {pmid36280089,
year = {2022},
author = {Xue, Y and Zhu, J and Huang, X and Xu, X and Li, X and Zheng, Y and Zhu, Z and Jin, K and Ye, J and Gong, W and Si, K},
title = {A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed.},
journal = {Journal of biomedical informatics},
volume = {136},
number = {},
pages = {104233},
doi = {10.1016/j.jbi.2022.104233},
pmid = {36280089},
issn = {1532-0480},
mesh = {Humans ; *Deep Learning ; *Glaucoma/diagnosis ; Diagnostic Techniques, Ophthalmological ; Photography/methods ; Diagnosis, Computer-Assisted/methods ; },
abstract = {Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.},
}
@article {pmid36278046,
year = {2022},
author = {Cao, X and Zhu, L and Qi, R and Wang, X and Sun, G and Ying, Y and Chen, R and Li, X and Gao, L},
title = {Effect of a High Estrogen Level in Early Pregnancy on the Development and Behavior of Marmoset Offspring.},
journal = {ACS omega},
volume = {7},
number = {41},
pages = {36175-36183},
pmid = {36278046},
issn = {2470-1343},
abstract = {The use of assisted reproductive technology (ART) has risen steadily worldwide over the past 3 decades and helps many infertile families. However, ART treatments lead to an abnormal internal environment in the uterus, which may increase the risks of health problems for the offspring. Higher maternal estradiol (E2) is a notable feature in women who use ART treatments, and this has been suggested as a key factor for the risk of diseases in the offspring. In the current study, we have established a marmoset model with a high E2 level in early pregnancy to examine its potential risk to the development and behavior of the offspring. In comparison with the normal group, babies of the high E2 group exhibited lower average survival rates and birth weights. However, those who survived in the high E2 group demonstrated normal vocal production with rich call repertoires, normal speed during locomotion, and normal behaviors in the home cage. In contrast to the normal group, surviving babies of the high E2 group spent more time sleeping during development without signs of sleep disorders. In summary, our study revealed that high estrogen in early pregnancy may cause low survival rates and birth weights of the offspring, though the surviving infants did not show obvious behavioral deficiencies during development. The current study is a valuable and highly important non-human primate study for evaluating the safety of ART treatments. However, it is worth noting that some results did not reach the significant level, which may be due to the small sample size caused by animal shortage stemming from the COVID-19 epidemic.},
}
@article {pmid36277512,
year = {2023},
author = {Jamil, N and Belkacem, AN and Lakas, A},
title = {On enhancing students' cognitive abilities in online learning using brain activity and eye movements.},
journal = {Education and information technologies},
volume = {28},
number = {4},
pages = {4363-4397},
pmid = {36277512},
issn = {1360-2357},
abstract = {The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure students' cognitive skills during online learning. We found that, because many experimental tasks depend on recorded rather than real-time video, students don't have direct and real-time interaction with their teacher. Further, we found no evidence in any of the reviewed papers for brain-to-brain synchronization during remote learning. This points to a potentially fruitful future application of brain computer interfaces in education, investigating whether the brains of student-teacher pairs who interact with the same course content have increasingly similar brain patterns.},
}
@article {pmid36277476,
year = {2022},
author = {Wang, G and Cerf, M},
title = {Brain-Computer Interface using neural network and temporal-spectral features.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {952474},
pmid = {36277476},
issn = {1662-5196},
abstract = {Brain-Computer Interfaces (BCIs) are increasingly useful for control. Such BCIs can be used to assist individuals who lost mobility or control over their limbs, for recreational purposes such as gaming or semi-autonomous driving, or as an interface toward man-machine integration. Thus far, the performance of algorithms used for thought decoding has been limited. We show that by extracting temporal and spectral features from electroencephalography (EEG) signals and, following, using deep learning neural network to classify those features, one can significantly improve the performance of BCIs in predicting which motor action was imagined by a subject. Our movement prediction algorithm uses Sequential Backward Selection technique to jointly choose temporal and spectral features and a radial basis function neural network for the classification. The method shows an average performance increase of 3.50% compared to state-of-the-art benchmark algorithms. Using two popular public datasets our algorithm reaches 90.08% accuracy (compared to an average benchmark of 79.99%) on the first dataset and 88.74% (average benchmark: 82.01%) on the second dataset. Given the high variability within- and across-subjects in EEG-based action decoding, we suggest that using features from multiple modalities along with neural network classification protocol is likely to increase the performance of BCIs across various tasks.},
}
@article {pmid36273413,
year = {2023},
author = {Sinha, S and Finazzi-Agrò, E and Dmochowski, RR and Hashim, H and Iacovelli, V},
title = {The bladder contractility and bladder outlet obstruction indices in adult men: Results of a global Delphi consensus study.},
journal = {Neurourology and urodynamics},
volume = {42},
number = {1},
pages = {229-238},
doi = {10.1002/nau.25073},
pmid = {36273413},
issn = {1520-6777},
mesh = {Adult ; Humans ; Male ; Delphi Technique ; Muscle Contraction ; *Urinary Bladder ; *Urinary Bladder Neck Obstruction/drug therapy ; Urodynamics ; },
abstract = {AIMS: This Delphi study was planned to examine global expert consensus with regard to utility, accuracy, and categorization of Bladder Contractility Index (BCI) and Bladder Outlet Obstruction Index (BOOI) and the related evidence.<br><br>METHODS: Twenty-eight experts were invited to answer the two-round survey including three foundation questions and 15 survey questions. Consensus was defined as &#x2265;75% agreement. The ordinal scale (0-10) in round 1 was classified into "strongly agree,"&#xa0;"agree,"&#xa0;"neutral,"&#xa0;"disagree," and "strongly disagree" for the final round. A systematic search for evidence was conducted for therapeutic studies that have examined outcome stratified by the indices in men.<br><br>RESULTS: Nineteen experts participated in the survey with 100% completion. Consensus was noted with regard to 6 of 19 questions. Experts strongly agreed with utility of quantifying bladder contractility and bladder outflow obstruction with near unanimity regarding the latter. There was consensus that BCI and BOOI were accurate, that BCI was clinically useful, and for defining severe bladder outflow obstruction as BOOI&#x2009;>&#x2009;80. Systematic search yielded 69 publications (BCI 45; BOOI 50). Most studies examined the indices as a continuous variable or by standard cutoffs (BCI 100, 150; BOOI 20, 40).<br><br>CONCLUSION: There is general agreement among experts on need for indices to quantify bladder contractility and bladder outflow obstruction as well as with regard to accuracy and utility of BCI and BOOI indices. Few studies have examined the discriminant power of existing cutoffs or explored new ones. This is an extraordinary knowledge gap in the field of urology.},
}
@article {pmid36272285,
year = {2022},
author = {A, W and Du, F and He, Y and Wu, B and Liu, F and Liu, Y and Zheng, W and Li, G and Wang, X},
title = {Graphene oxide reinforced hemostasis of gelatin sponge in noncompressible hemorrhage via synergistic effects.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {220},
number = {},
pages = {112891},
doi = {10.1016/j.colsurfb.2022.112891},
pmid = {36272285},
issn = {1873-4367},
mesh = {Humans ; *Gelatin/pharmacology ; Hemostasis ; *Hemostatics/pharmacology ; Hemorrhage/drug therapy ; },
abstract = {Effective hemostasis for noncompressible bleeding has been a key challenge because of the deep, narrow, and irregular wounds. Swellable gelatin is an available hemostatic material but is limited by weak mechanical strength and slow liquid absorption. Herein, the design of a gelatin and graphene oxide (GO) composite sponge (GP-GO) that possesses stable cross-linked networks and excellent absorbability, is reported. The GP-GOs are constructed via the thermal radical polymerization technique, using methacrylate gelatin (Gel-MA) and poly(ethylene glycol) diacrylate (PEGDA) as the crosslinker, while GO is uniformly fixed in the network via the curing reaction to further strengthen the stability. The optimized GP-GO5 with GO addition (5 wt%) exhibits high porosity (> 90%), distinguished liquid absorption rate (106 ms), rapidly responsive swelling (422% expansion within 10 s), and stable mechanical properties. The addition of GO effectively reinforces coagulation stimulation of GP-GOs though the stimulation of platelets and the enrichment effect at the interface, significantly reducing the blood coagulation index (BCI) (< 17.5%). Hemostatic mechanism study indicated the liquid absorbability of GP-GOs is the critical foundation to trigger the subsequent physical expansion, blood cells enrichment, and coagulation stimulations. Besides, GP-GO5 exhibits excellent biosafety assessed by hemolysis and cytotoxicity. Under the synergistic effects, the biocompatible GP-GO5 showed excellent hemostatic properties in the hemostasis of severe bleeding and noncompressible wounds compared with a pure gelatin sponge (GP) and the commercial hemostatic agent Celox™. This study demonstrated a promising candidate for practical application of noncompressible wound hemostasis.},
}
@article {pmid36271004,
year = {2022},
author = {Zhai, X and Mao, C and Shen, Q and Zang, S and Shen, DD and Zhang, H and Chen, Z and Wang, G and Zhang, C and Zhang, Y and Liu, Z},
title = {Molecular insights into the distinct signaling duration for the peptide-induced PTH1R activation.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {6276},
pmid = {36271004},
issn = {2041-1723},
mesh = {*Receptor, Parathyroid Hormone, Type 1/genetics ; *Teriparatide/pharmacology ; Ligands ; Cryoelectron Microscopy ; Amino Acid Sequence ; Parathyroid Hormone/pharmacology ; Peptides/chemistry ; Receptors, G-Protein-Coupled ; },
abstract = {The parathyroid hormone type 1 receptor (PTH1R), a class B1 G protein-coupled receptor, plays critical roles in bone turnover and Ca[2+] homeostasis. Teriparatide (PTH) and Abaloparatide (ABL) are terms as long-acting and short-acting peptide, respectively, regarding their marked duration distinctions of the downstream signaling. However, the mechanistic details remain obscure. Here, we report the cryo-electron microscopy structures of PTH- and ABL-bound PTH1R-Gs complexes, adapting similar overall conformations yet with notable differences in the receptor ECD regions and the peptide C-terminal portions. 3D variability analysis and site-directed mutagenesis studies uncovered that PTH-bound PTH1R-Gs complexes display less motions and are more tolerant of mutations in affecting the receptor signaling than ABL-bound complexes. Furthermore, we combined the structural analysis and signaling assays to delineate the molecular basis of the differential signaling durations induced by these peptides. Our study deepens the mechanistic understanding of ligand-mediated prolonged or transient signaling.},
}
@article {pmid36270622,
year = {2022},
author = {Liu, Y and Luo, C and Zheng, J and Liang, J and Ding, N},
title = {Working memory asymmetrically modulates auditory and linguistic processing of speech.},
journal = {NeuroImage},
volume = {264},
number = {},
pages = {119698},
doi = {10.1016/j.neuroimage.2022.119698},
pmid = {36270622},
issn = {1095-9572},
mesh = {Humans ; *Memory, Short-Term/physiology ; Speech/physiology ; Linguistics ; *Speech Perception/physiology ; Language ; },
abstract = {Working memory load can modulate speech perception. However, since speech perception and working memory are both complex functions, it remains elusive how each component of the working memory system interacts with each speech processing stage. To investigate this issue, we concurrently measure how the working memory load modulates neural activity tracking three levels of linguistic units, i.e., syllables, phrases, and sentences, using a multiscale frequency-tagging approach. Participants engage in a sentence comprehension task and the working memory load is manipulated by asking them to memorize either auditory verbal sequences or visual patterns. It is found that verbal and visual working memory load modulate speech processing in similar manners: Higher working memory load attenuates neural activity tracking of phrases and sentences but enhances neural activity tracking of syllables. Since verbal and visual WM load similarly influence the neural responses to speech, such influences may derive from the domain-general component of WM system. More importantly, working memory load asymmetrically modulates lower-level auditory encoding and higher-level linguistic processing of speech, possibly reflecting reallocation of attention induced by mnemonic load.},
}
@article {pmid36270502,
year = {2022},
author = {Sosulski, J and Tangermann, M},
title = {Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9c98},
pmid = {36270502},
issn = {1741-2552},
mesh = {Humans ; Discriminant Analysis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Algorithms ; },
abstract = {Objective.Covariance matrices of noisy multichannel electroencephalogram (EEG) time series data provide essential information for the decoding of brain signals using machine learning methods. However, small datasets and high dimensionality make it hard to estimate these matrices. In brain-computer interfaces (BCI) based on event-related potentials (ERP) and a linear discriminant analysis (LDA) classifier, the state of the art covariance estimation uses shrinkage regularization. As this is a general covariance regularization approach, we aim at improving LDA further by better exploiting the domain-specific characteristics of the EEG to regularize the covariance estimates.Approach.We propose to enforce a block-Toeplitz structure for the covariance matrix of the LDA, which implements an assumption of signal stationarity in short time windows for each channel.Main results.An offline re-analysis of data collected from 213 subjects under 13 different event-related potential BCI protocols showed a significantly increased binary classification performance of this 'ToeplitzLDA' compared to shrinkage regularized LDA (up to 6 AUC points,p < 0.001) and Riemannian classification approaches (up to 2 AUC points,p < 0.001). In an unsupervised visual speller application, this improvement would translate to a relative reduction of spelling errors by 81% on average for 25 subjects. Additionally, aside from lower memory and reduced time complexity for LDA training, ToeplitzLDA proves to be robust against drastic increases of the number of temporal features.Significance.The proposed covariance estimation allows BCI researchers to improve classification rates and reduce calibration times of BCI protocols using event-related potentials and thus support the usability of corresponding applications. Its lower computational and memory needs could make it a valuable algorithm especially for mobile BCIs.},
}
@article {pmid36270467,
year = {2022},
author = {Liu, S and Zhang, J and Wang, A and Wu, H and Zhao, Q and Long, J},
title = {Subject adaptation convolutional neural network for EEG-based motor imagery classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9c94},
pmid = {36270467},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Imagination ; },
abstract = {Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.},
}
@article {pmid36270430,
year = {2023},
author = {Spencer, M and Kameneva, T and Grayden, DB and Burkitt, AN and Meffin, H},
title = {Quantifying visual acuity for pre-clinical testing of visual prostheses.},
journal = {Journal of neural engineering},
volume = {20},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac9c95},
pmid = {36270430},
issn = {1741-2552},
mesh = {*Visual Prosthesis ; Visual Acuity ; Vision, Ocular ; Visual Perception/physiology ; Retina/physiology ; },
abstract = {Objective.Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.Approach.This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized 'C' and 'E' optotypes.Main results.The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.Significance.Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.},
}
@article {pmid36269910,
year = {2022},
author = {Sakkalis, V and Krana, M and Farmaki, C and Bourazanis, C and Gaitatzis, D and Pediaditis, M},
title = {Augmented Reality Driven Steady-State Visual Evoked Potentials for Wheelchair Navigation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2960-2969},
doi = {10.1109/TNSRE.2022.3215695},
pmid = {36269910},
issn = {1558-0210},
mesh = {Humans ; Evoked Potentials, Visual ; *Augmented Reality ; *Wheelchairs ; Electroencephalography ; *Brain-Computer Interfaces ; Photic Stimulation ; },
abstract = {Medically oriented Brain Computer Interfaces (BCIs) have been proposed as a promising approach addressed to individuals suffering from severe paralysis. Steady-State Visual Evoked Potentials (SSVEPs) in particular have been proven successful in many different applications, achieving high information throughput with short or even no training. However, efficient electric wheelchair navigation combining high accuracy and comfort is still not demonstrated. In this paper, we propose the use of an SSVEP-based universal control system featuring augmented reality (AR) glasses in an attempt to increase ease of use and patient acceptability without making compromises on BCI performance. The system received positive user-feedback, reaching a mean accuracy of 90%. Merits and pitfalls of the system proposed are also addressed.},
}
@article {pmid36269374,
year = {2023},
author = {Margenau, EL and Wood, PB and Brown, DJ and Ryan, CW},
title = {Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.},
journal = {Environmental management},
volume = {71},
number = {2},
pages = {321-333},
pmid = {36269374},
issn = {1432-1009},
mesh = {Animals ; *Ecosystem ; *Forests ; Trees ; Soil ; Caudata ; Forestry/methods ; },
abstract = {Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.},
}
@article {pmid36264857,
year = {2022},
author = {Yan, Z and Yang, X and Jin, Y},
title = {Considerate motion imagination classification method using deep learning.},
journal = {PloS one},
volume = {17},
number = {10},
pages = {e0276526},
pmid = {36264857},
issn = {1932-6203},
mesh = {*Deep Learning ; Algorithms ; Imagination ; *Brain-Computer Interfaces ; Electroencephalography/methods ; },
abstract = {In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.},
}
@article {pmid36264734,
year = {2022},
author = {Feng, L and Shan, H and Zhang, Y and Zhu, Z},
title = {An Efficient Model-Compressed EEGNet Accelerator for Generalized Brain-Computer Interfaces With Near Sensor Intelligence.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {16},
number = {6},
pages = {1239-1249},
doi = {10.1109/TBCAS.2022.3215962},
pmid = {36264734},
issn = {1940-9990},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Electroencephalography/methods ; Intelligence ; },
abstract = {Brain-computer interfaces (BCIs) is promising in interacting with machines through electroencephalogram (EEG) signal. The compact end-to-end neural network model for generalized BCIs, EEGNet, has been implemented in hardware to get near sensor intelligence, but without enough efficiency. To utilize EEGNet in low-power wearable device for long-term use, this paper proposes an efficient EEGNet inference accelerator. Firstly, the EEGNet model is compressed by embedded channel selection, normalization merging, and product quantization. The customized accelerator based on the compressed model is then designed. The multilayer convolutions are achieved by reusing multiplying-accumulators and processing elements (PEs) to minimize area of logic circuits, and the weights and intermediate results are quantized to minimize memory sizes. The PEs are clock-gated to save power. Experimental results in FPGA on three datasets show the good generalizing ability of the proposed design across three BCI diagrams, which only consumes 3.31% area and 1.35% power compared to the one-to-one parallel design. The speedup factors of 1.4, 3.5, and 3.7 are achieved by embedded channel selection with negligible loss of accuracy (-0.80%). The presented accelerator is also synthesized in 65 nm CMOS low power (LP) process and consumes 0.23M gates, 24.4 ms/inference, 0.267 mJ/inference, which is 87.22% more efficient than the implementation of EEGNet in a RISC-V MCU realized in 40 nm CMOS LP process in terms of area, and 20.77% more efficient in terms of energy efficiency on BCIC-IV-2a dataset.},
}
@article {pmid36264427,
year = {2022},
author = {Ishida, S and Matsukawa, Y and Yuba, T and Naito, Y and Matsuo, K and Majima, T and Gotoh, M},
title = {Urodynamic risk factors of asymptomatic bacteriuria in men with non-neurogenic lower urinary tract symptoms.},
journal = {World journal of urology},
volume = {40},
number = {12},
pages = {3035-3041},
pmid = {36264427},
issn = {1433-8726},
mesh = {Middle Aged ; Male ; Humans ; Aged ; Urodynamics ; Retrospective Studies ; *Bacteriuria/epidemiology/complications ; Urinary Bladder ; *Lower Urinary Tract Symptoms/epidemiology/complications ; *Urinary Bladder Neck Obstruction/complications ; Risk Factors ; },
abstract = {PURPOSE: To investigate the prevalence of asymptomatic bacteriuria (ASB) in middle-aged and older men with non-neurogenic lower urinary tract symptoms (LUTS) and clarify urodynamic factors related to the presence of ASB.<br><br>METHODS: We retrospectively reviewed the clinical data of men with LUTS who underwent urine culture examination, LUTS severity assessment, and urodynamic studies. The patients were allocated into two groups (the ASB&#x2009;+&#x2009;LUTS and LUTS-only) according to presence or absence of ASB. The patients' characteristics and urodynamic factors related to the development of ASB were assessed using univariate, binomial logistic regression, and receiver-operating characteristic (ROC) curve analyses.<br><br>RESULTS: Of 440 men, 93 (21.1%) had ASB. Parameters related to voiding functions, such as maximum flow rate, post-void residual urine volume, bladder voiding efficiency (BVE), and bladder contractility index (BCI), were significantly reduced in the ASB&#x2009;+&#x2009;LUTS group, while bladder outlet obstruction index was not different between the groups. Binomial logistic regression analysis showed that the presence of diabetes, lower BCI, and lower BVE were significantly associated with the presence of ASB. In addition, ROC analysis identified 55% as the optimal cutoff value of BVE for the presence of ASB, with a sensitivity of 84% and specificity of 83%.<br><br>CONCLUSIONS: ASB was found in&#x2009;>&#x2009;20% of men with non-neurogenic LUTS and was associated with decreased bladder contractility and decreased BVE. BVE could predict presence of ASB with high sensitivity and specificity.},
}
@article {pmid36261030,
year = {2022},
author = {Song, CY and Hsieh, HL and Pesaran, B and Shanechi, MM},
title = {Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9b94},
pmid = {36261030},
issn = {1741-2552},
support = {R01 MH123770/MH/NIMH NIH HHS/United States ; },
mesh = {*Models, Neurological ; Algorithms ; *Brain-Computer Interfaces ; Normal Distribution ; Brain ; },
abstract = {Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. Also, for single-scale linear Gaussian observations, the new switching smoother can better generalize to diverse system settings compared to prior switching smoothers.Significance.These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.},
}
@article {pmid36260252,
year = {2023},
author = {Lin, S and Zhu, MY and Tang, MY and Wang, M and Yu, XD and Zhu, Y and Xie, SZ and Yang, D and Chen, J and Li, XM},
title = {Somatostatin-Positive Neurons in the Rostral Zona Incerta Modulate Innate Fear-Induced Defensive Response in Mice.},
journal = {Neuroscience bulletin},
volume = {39},
number = {2},
pages = {245-260},
pmid = {36260252},
issn = {1995-8218},
mesh = {Mice ; Animals ; *Zona Incerta/metabolism ; Neurons/metabolism ; Fear/physiology ; Somatostatin/metabolism ; },
abstract = {Defensive behaviors induced by innate fear or Pavlovian fear conditioning are crucial for animals to avoid threats and ensure survival. The zona incerta (ZI) has been demonstrated to play important roles in fear learning and fear memory, as well as modulating auditory-induced innate defensive behavior. However, whether the neuronal subtypes in the ZI and specific circuits can mediate the innate fear response is largely unknown. Here, we found that somatostatin (SST)-positive neurons in the rostral ZI of mice were activated by a visual innate fear stimulus. Optogenetic inhibition of SST-positive neurons in the rostral ZI resulted in reduced flight responses to an overhead looming stimulus. Optogenetic activation of SST-positive neurons in the rostral ZI induced fear-like defensive behavior including increased immobility and bradycardia. In addition, we demonstrated that manipulation of the GABAergic projections from SST-positive neurons in the rostral ZI to the downstream nucleus reuniens (Re) mediated fear-like defensive behavior. Retrograde trans-synaptic tracing also revealed looming stimulus-activated neurons in the superior colliculus (SC) that projected to the Re-projecting SST-positive neurons in the rostral ZI (SC-ZIr[SST]-Re pathway). Together, our study elucidates the function of SST-positive neurons in the rostral ZI and the SC-ZIr[SST]-Re tri-synaptic circuit in mediating the innate fear response.},
}
@article {pmid36257830,
year = {2022},
author = {Ni, RJ and Gao, TH and Wang, YY and Tian, Y and Wei, JX and Zhao, LS and Ni, PY and Ma, XH and Li, T},
title = {Chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway.},
journal = {Zoological research},
volume = {43},
number = {6},
pages = {989-1004},
pmid = {36257830},
issn = {2095-8137},
mesh = {Male ; Mice ; Animals ; *Ketamine/toxicity ; Phosphatidylinositol 3-Kinases/genetics/metabolism/pharmacology ; Proto-Oncogene Proteins c-akt/genetics/metabolism/pharmacology ; Lithium/pharmacology ; Mania ; Phosphatidylinositol 3-Kinase/genetics/metabolism/pharmacology ; *Depressive Disorder, Major ; RNA, Small Interfering ; TOR Serine-Threonine Kinases/genetics ; Signal Transduction ; Antidepressive Agents/therapeutic use/pharmacology ; Sirolimus/pharmacology ; Lithium Compounds/pharmacology ; Mammals ; },
abstract = {Ketamine, a rapid-acting antidepressant drug, has been used to treat major depressive disorder and bipolar disorder (BD). Recent studies have shown that ketamine may increase the potential risk of treatment-induced mania in patients. Ketamine has also been applied to establish animal models of mania. At present, however, the underlying mechanism is still unclear. In the current study, we found that chronic lithium exposure attenuated ketamine-induced mania-like behavior and c-Fos expression in the medial prefrontal cortex (mPFC) of adult male mice. Transcriptome sequencing was performed to determine the effect of lithium administration on the transcriptome of the PFC in ketamine-treated mice, showing inactivation of the phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway. Pharmacological inhibition of AKT signaling by MK2206 (40 mg/kg), a selective AKT inhibitor, reversed ketamine-induced mania. Furthermore, selective knockdown of AKT via AAV-AKT-shRNA-EGFP in the mPFC also reversed ketamine-induced mania-like behavior. Importantly, pharmacological activation of AKT signaling by SC79 (40 mg/kg), an AKT activator, contributed to mania in low-dose ketamine-treated mice. Inhibition of PI3K signaling by LY294002 (25 mg/kg), a specific PI3K inhibitor, reversed the mania-like behavior in ketamine-treated mice. However, pharmacological inhibition of mammalian target of rapamycin (mTOR) signaling with rapamycin (10 mg/kg), a specific mTOR inhibitor, had no effect on ketamine-induced mania-like behavior. These results suggest that chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway, which may be a novel target for the development of BD treatment.},
}
@article {pmid36257070,
year = {2022},
author = {Xu, F and Li, J and Dong, G and Li, J and Chen, X and Zhu, J and Hu, J and Zhang, Y and Yue, S and Wen, D and Leng, J},
title = {EEG decoding method based on multi-feature information fusion for spinal cord injury.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {156},
number = {},
pages = {135-151},
doi = {10.1016/j.neunet.2022.09.016},
pmid = {36257070},
issn = {1879-2782},
mesh = {Humans ; Reproducibility of Results ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement/physiology ; Algorithms ; *Spinal Cord Injuries/diagnosis ; Imagination/physiology ; },
abstract = {To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.},
}
@article {pmid36253948,
year = {2022},
author = {Le Moigne, V and Blouquit-Laye, S and Desquesnes, A and Girard-Misguich, F and Herrmann, JL},
title = {Liposomal amikacin and Mycobacterium abscessus: intimate interactions inside eukaryotic cells.},
journal = {The Journal of antimicrobial chemotherapy},
volume = {77},
number = {12},
pages = {3496-3503},
doi = {10.1093/jac/dkac348},
pmid = {36253948},
issn = {1460-2091},
mesh = {Humans ; Amikacin/pharmacology ; *Mycobacterium abscessus ; Eukaryotic Cells ; *Mycobacterium Infections, Nontuberculous/drug therapy/microbiology ; Anti-Bacterial Agents/pharmacology/therapeutic use ; Liposomes ; *Mycobacterium ; Microbial Sensitivity Tests ; },
abstract = {BACKGROUND: Mycobacterium abscessus (Mabs), a rapidly growing Mycobacterium species, is considered an MDR organism. Among the standard antimicrobial multi-drug regimens against Mabs, amikacin is considered as one of the most effective. Parenteral amikacin, as a consequence of its inability to penetrate inside the cells, is only active against extracellular mycobacteria. The use of inhaled liposomal amikacin may yield improved intracellular efficacy by targeting Mabs inside the cells, while reducing its systemic toxicity.<br><br>OBJECTIVES: To evaluate the colocalization of an amikacin liposomal inhalation suspension (ALIS) with intracellular Mabs, and then to measure its intracellular anti-Mabs activity.<br><br>METHODS: We evaluated the colocalization of ALIS with Mabs in eukaryotic cells such as macrophages (THP-1 and J774.2) or pulmonary epithelial cells (BCi-NS1.1 and MucilAir), using a fluorescent ALIS and GFP-expressing Mabs, to test whether ALIS reaches intracellular Mabs. We then evaluated the intracellular anti-Mabs activity of ALIS inside macrophages using cfu and/or luminescence.<br><br>RESULTS: Using confocal microscopy, we demonstrated fluorescent ALIS and GFP-Mabs colocalization in macrophages and epithelial cells. We also showed that ALIS was active against intracellular Mabs at a concentration of 32 to 64&#x2005;mg/L, at 3 and 5&#x2005;days post-infection. Finally, ALIS intracellular activity was confirmed when tested against 53 clinical Mabs isolates, showing intracellular growth reduction for nearly 80% of the isolates.<br><br>CONCLUSIONS: Our experiments demonstrate the intracellular localization and intracellular contact between Mabs and ALIS, and antibacterial activity against intracellular Mabs, showing promise for its future use for Mabs pulmonary infections.},
}
@article {pmid36252368,
year = {2022},
author = {Gao, Y and Liu, A and Cui, X and Qian, R and Chen, X},
title = {A general sample-weighted framework for epileptic seizure prediction.},
journal = {Computers in biology and medicine},
volume = {150},
number = {},
pages = {106169},
doi = {10.1016/j.compbiomed.2022.106169},
pmid = {36252368},
issn = {1879-0534},
mesh = {Humans ; Child ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Seizures/diagnosis ; *Epilepsy/diagnosis ; Machine Learning ; Algorithms ; },
abstract = {OBJECTIVE: Effective epileptic seizure prediction can make the patients know the onset of the seizure in advance to take timely preventive measures. Many studies based on machine learning methods have been proposed to tackle this problem and achieve significant progress in recent years. However, most studies treat each EEG training sample's contribution to the model as equal, while different samples have different predictive effects on epileptic seizures (e.g., preictal samples from different times). To this end, in this paper, we propose a general sample-weighted framework for patient-specific epileptic seizure prediction.<br><br>METHODS: Specifically, we define the mapping from the sample weights of training sets to the performance of the validation sets as the fitness function to be optimized. Then, the genetic algorithm is employed to optimize this fitness function and obtain the optimal sample weights. Finally, we obtain the final model by using the training sets with optimized sample weights.<br><br>RESULTS: To evaluate the effectiveness of our framework, we conduct extensive experiments on both traditional machine learning methods and prevalent deep learning methods. Our framework can significantly improve performance based on these methods. Among them, our framework based on Transformer achieves an average sensitivity of 94.6%, an average false prediction rate of 0.06/h, and an average AUC of 0.939 in 12 pediatric patients from the CHB-MIT database with the leave-one-out method, which outperforms the state-of-the-art methods.<br><br>CONCLUSION: This study provides new insights into the field of epileptic seizure prediction by considering the discrepancies between EEG samples. Moreover, we develop a general sample-weighted framework, which applies to almost all classical classification methods and can significantly improve performance based on these methods.},
}
@article {pmid36251899,
year = {2023},
author = {Jeong, JH and Cho, JH and Lee, BH and Lee, SW},
title = {Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.},
journal = {IEEE transactions on cybernetics},
volume = {53},
number = {12},
pages = {7469-7482},
doi = {10.1109/TCYB.2022.3211694},
pmid = {36251899},
issn = {2168-2275},
mesh = {Humans ; *Electroencephalography ; Brain ; *Brain-Computer Interfaces ; Communication ; Speech ; },
abstract = {Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.},
}
@article {pmid36251867,
year = {2022},
author = {Elliott, C and Sutherland, D and Gerhard, D and Theys, C},
title = {An Evaluation of the P300 Brain-Computer Interface, EyeLink Board, and Eye-Tracking Camera as Augmentative and Alternative Communication Devices.},
journal = {Journal of speech, language, and hearing research : JSLHR},
volume = {65},
number = {11},
pages = {4280-4290},
doi = {10.1044/2022_JSLHR-21-00572},
pmid = {36251867},
issn = {1558-9102},
mesh = {Humans ; *Brain-Computer Interfaces ; Eye-Tracking Technology ; *Communication Devices for People with Disabilities ; *Communication Disorders ; Communication ; Electroencephalography ; },
abstract = {PURPOSE: Augmentative and alternative communication (AAC) systems are important to support communication for individuals with complex communication needs. A recent addition to AAC system options is the brain-computer interface (BCI). This study aimed to compare the clinical application of the P300 speller BCI with two more common AAC systems, the EyeLink board, and an eye-tracking camera.<br><br>METHOD: Ten participants without communication impairment (18-35 years of age) used each of the three AAC systems to spell three-letter words in one session. Accuracy and speed of letter selection were measured, and questionnaires were administered to evaluate usability, cognitive workload, and user preferences.<br><br>RESULTS: The results showed that the BCI was significantly less accurate, slower, and with lower usability and higher cognitive workload compared to the eye-tracking camera and EyeLink board. Participants rated the eye-tracking camera as the most favorable AAC system on all measures.<br><br>CONCLUSIONS: The results demonstrated that while the P300 speller BCI was usable by most participants, it did not function as well as the eye-tracking camera and EyeLink board. The clinical use of the BCI is, therefore, currently difficult to justify for most individuals, particularly when considering the substantial cost and setup resourcing needed.<br><br>SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.21291384.},
}
@article {pmid36248616,
year = {2023},
author = {Klein, F and Lührs, M and Benitez-Andonegui, A and Roehn, P and Kranczioch, C},
title = {Performance comparison of systemic activity correction in functional near-infrared spectroscopy for methods with and without short distance channels.},
journal = {Neurophotonics},
volume = {10},
number = {1},
pages = {013503},
pmid = {36248616},
issn = {2329-423X},
abstract = {Significance: Functional near-infrared spectroscopy (fNIRS) is a promising tool for neurofeedback (NFB) or brain-computer interfaces (BCIs). However, fNIRS signals are typically highly contaminated by systemic activity (SA) artifacts, and, if not properly corrected, NFB or BCIs run the risk of being based on noise instead of brain activity. This risk can likely be reduced by correcting for SA, in particular when short-distance channels (SDCs) are available. Literature comparing correction methods with and without SDCs is still sparse, specifically comparisons considering single trials are lacking. Aim: This study aimed at comparing the performance of SA correction methods with and without SDCs. Approach: Semisimulated and real motor task data of healthy older adults were used. Correction methods without SDCs included a simple and a more advanced spatial filter. Correction methods with SDCs included a regression approach considering only the closest SDC and two GLM-based methods, one including all eight SDCs and one using only two a priori selected SDCs as regressors. All methods were compared with data uncorrected for SA and correction performance was assessed with quality measures quantifying signal improvement and spatial specificity at single trial level. Results: All correction methods were found to improve signal quality and enhance spatial specificity as compared with the uncorrected data. Methods with SDCs usually outperformed methods without SDCs. Correction methods without SDCs tended to overcorrect the data. However, the exact pattern of results and the degree of differences observable between correction methods varied between semisimulated and real data, and also between quality measures. Conclusions: Overall, results confirmed that both Δ [ HbO ] and Δ [ HbR ] are affected by SA and that correction methods with SDCs outperform methods without SDCs. Nonetheless, improvements in signal quality can also be achieved without SDCs and should therefore be given priority over not correcting for SA.},
}
@article {pmid36247357,
year = {2022},
author = {Chen, G and Zhang, X and Zhang, J and Li, F and Duan, S},
title = {A novel brain-computer interface based on audio-assisted visual evoked EEG and spatial-temporal attention CNN.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {995552},
pmid = {36247357},
issn = {1662-5218},
abstract = {OBJECTIVE: Brain-computer interface (BCI) can translate intentions directly into instructions and greatly improve the interaction experience for disabled people or some specific interactive applications. To improve the efficiency of BCI, the objective of this study is to explore the feasibility of an audio-assisted visual BCI speller and a deep learning-based single-trial event related potentials (ERP) decoding strategy.<br><br>APPROACH: In this study, a two-stage BCI speller combining the motion-onset visual evoked potential (mVEP) and semantically congruent audio evoked ERP was designed to output the target characters. In the first stage, the different group of characters were presented in the different locations of visual field simultaneously and the stimuli were coded to the mVEP based on a new space division multiple access scheme. And then, the target character can be output based on the audio-assisted mVEP in the second stage. Meanwhile, a spatial-temporal attention-based convolutional neural network (STA-CNN) was proposed to recognize the single-trial ERP components. The CNN can learn 2-dimentional features including the spatial information of different activated channels and time dependence among ERP components. In addition, the STA mechanism can enhance the discriminative event-related features by adaptively learning probability weights.<br><br>MAIN RESULTS: The performance of the proposed two-stage audio-assisted visual BCI paradigm and STA-CNN model was evaluated using the Electroencephalogram (EEG) recorded from 10 subjects. The average classification accuracy of proposed STA-CNN can reach 59.6 and 77.7% for the first and second stages, which were always significantly higher than those of the comparison methods (p < 0.05).<br><br>SIGNIFICANCE: The proposed two-stage audio-assisted visual paradigm showed a great potential to be used to BCI speller. Moreover, through the analysis of the attention weights from time sequence and spatial topographies, it was proved that STA-CNN could effectively extract interpretable spatiotemporal EEG features.},
}
@article {pmid36246365,
year = {2022},
author = {Zhang, J and Wang, T and Zhang, Y and Lu, P and Shi, N and Zhu, W and Cai, C and He, N},
title = {Soft integration of a neural cells network and bionic interfaces.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {950235},
pmid = {36246365},
issn = {2296-4185},
abstract = {Both glial cells and neurons can be considered basic computational units in neural networks, and the brain-computer interface (BCI) can play a role in awakening the latency portion and being sensitive to positive feedback through learning. However, high-quality information gained from BCI requires invasive approaches such as microelectrodes implanted under the endocranium. As a hard foreign object in the aqueous microenvironment, the soft cerebral cortex's chronic inflammation state and scar tissue appear subsequently. To avoid the obvious defects caused by hard electrodes, this review focuses on the bioinspired neural interface, guiding and optimizing the implant system for better biocompatibility and accuracy. At the same time, the bionic techniques of signal reception and transmission interfaces are summarized and the structural units with functions similar to nerve cells are introduced. Multiple electrical and electromagnetic transmissions, regulating the secretion of neuromodulators or neurotransmitters via nanofluidic channels, have been flexibly applied. The accurate regulation of neural networks from the nanoscale to the cellular reconstruction of protein pathways will make BCI the extension of the brain.},
}
@article {pmid36246355,
year = {2022},
author = {Xu, H and Piao, L and Wu, Y and Liu, X},
title = {IFN-γ enhances the antitumor activity of attenuated salmonella-mediated cancer immunotherapy by increasing M1 macrophage and CD4 and CD8 T cell counts and decreasing neutrophil counts.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {996055},
pmid = {36246355},
issn = {2296-4185},
abstract = {Bacteria-mediated cancer immunotherapy (BCI) inhibits tumor progression and has a synergistic antitumor effect when combined with chemotherapy. The anti- or pro-tumorigenic effects of interferon-γ (IFN-γ) are controversial; hence, we were interested in the antitumor effects of IFN-γ/BCI combination therapy. Here, we demonstrated that IFN-γ increased the tumor cell killing efficacy of attenuated Salmonella by prolonging the survival of tumor-colonizing bacteria via blockade of tumor-infiltrating neutrophil recruitment. In addition, IFN-γ attenuated Salmonella-stimulated immune responses by stimulating tumor infiltration by M1-like macrophages and CD4[+] and CD8[+] T cells, thereby facilitating tumor eradication. Taken together, these findings suggest that combination treatment with IFN-γ boosts the therapeutic response of BCI with S. tΔppGpp, suggesting that IFN-γ/BCI is a promising approach to immunotherapy.},
}
@article {pmid36246302,
year = {2022},
author = {Collinger, JL and Krusienski, DJ},
title = {The 8[th] international brain-computer interface meeting, BCIs: the next frontier.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {9},
number = {2},
pages = {67-68},
pmid = {36246302},
issn = {2326-263X},
support = {/WT_/Wellcome Trust/United Kingdom ; R13 DC018466/DC/NIDCD NIH HHS/United States ; },
}
@article {pmid36245840,
year = {2022},
author = {Sun, M},
title = {Study on Antidepressant Emotion Regulation Based on Feedback Analysis of Music Therapy with Brain-Computer Interface.},
journal = {Computational and mathematical methods in medicine},
volume = {2022},
number = {},
pages = {7200678},
pmid = {36245840},
issn = {1748-6718},
mesh = {Aged ; Antidepressive Agents/therapeutic use ; *Brain-Computer Interfaces ; Cholinergic Antagonists ; *Emotional Regulation ; Emotions/physiology ; Feedback ; Humans ; *Music/psychology ; *Music Therapy ; },
abstract = {In today's society, people with poor mental ability are prone to neuropsychiatric diseases such as anxiety, ADHD, and depression due to long-term negative emotions. Although conventional Western medicine has certain curative effect, these drugs have significant anticholinergic side effects central toxicity as well as cardiovascular and gastrointestinal side effects which limit their application in the elderly. At present, several antidepressants used in clinic have certain limitations. According to the symptoms of depression, this paper proposes a feedback emotion regulation method of brain-computer interface music therapy. This method uses special music stimulation to regulate the release of inhibiting sex hormones in the body, reduce the influence of negative emotions on the internal environment of the body, and maintain the steady state of the body. In this method, EEG is used as the emotional control signal of depressed patients, and this biological signal is transformed into music that depressed patients can understand, so as to clarify their physiological and psychological state and realize emotional self-regulation by feedback.},
}
@article {pmid36241018,
year = {2022},
author = {Chen, J and Meng, X and Liu, Z and Shang, B and Chang, C and Ku, Y},
title = {Decoding semantics from intermodulation responses in frequency-tagged stereotactic EEG.},
journal = {Journal of neuroscience methods},
volume = {382},
number = {},
pages = {109727},
doi = {10.1016/j.jneumeth.2022.109727},
pmid = {36241018},
issn = {1872-678X},
mesh = {Humans ; *Evoked Potentials, Visual ; Semantics ; *Brain-Computer Interfaces ; Photic Stimulation ; Electroencephalography ; },
abstract = {BACKGROUND: Humans perform object recognition using holistic processing, which is different from computers. Intermodulation responses in the steady-state visual evoked potential (SSVEP) of scalp electroencephalography (EEG) have recently been used as an objective label for holistic processing.<br><br>NEW METHOD: Using stereotactic EEG (sEEG) to record SSVEP directly from inside of the brain, we aimed to decode Chinese characters from non-characters with activation from multiple brain areas including occipital, parietal, temporal, and frontal cortices.<br><br>RESULTS: Semantic categories could be decoded from responses at the intermodulation frequency with high accuracy (80%-90%), but not the base frequency. Moreover, semantic categories could be decoded with activation from multiple areas including temporal, parietal, and frontal areas.<br><br>Previous studies investigated holistic processing in faces and words with frequency-tagged scalp EEGs. The current study extended the results to stereotactic EEG signals directly recorded from the brain.<br><br>CONCLUSIONS: The human brain applies holistic processing in recognizing objects like Chinese characters. Our findings could be extended to an add-on feature in the existing SSVEP BCI speller.},
}
@article {pmid36240942,
year = {2022},
author = {Wang, W and Li, B and Wang, H},
title = {A Novel End-to-end Network Based on a bidirectional GRU and a Self-Attention Mechanism for Denoising of Electroencephalography Signals.},
journal = {Neuroscience},
volume = {505},
number = {},
pages = {10-20},
doi = {10.1016/j.neuroscience.2022.10.006},
pmid = {36240942},
issn = {1873-7544},
mesh = {*Algorithms ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) signals are nonlinear and non-stationary sequences that carry much information. However, physiological signals from other body regions may readily interfere with EEG signal capture, having a significant unfavorable influence on subsequent analysis. Therefore, signal denoising is a crucial step in EEG signal processing. This paper proposes a bidirectional gated recurrent unit (GRU) network based on a self-attention mechanism (BG-Attention) for extracting pure EEG signals from noise-contaminated EEG signals. The bidirectional GRU network can simultaneously capture past and future information while processing continuous time sequence. And by paying different levels of attention to the content of varying importance, the model can learn more significant feature of EEG signal sequences, highlighting the contribution of essential samples to denoising. The proposed model is evaluated on the EEGdenoiseNet data set. We compared the proposed model with a fully connected network (FCNN), the one-dimensional residual convolutional neural network (1D-ResCNN), and a recurrent neural network (RNN). The experimental results show that the proposed model can reconstruct a clear EEG waveform with a decent signal-to-noise ratio (SNR) and the relative root mean squared error (RRMSE) value. This study demonstrates the potential of BG-Attention in the pre-processing phase of EEG experiments, which has significant implications for medical technology and brain-computer interface (BCI) applications.},
}
@article {pmid36240727,
year = {2022},
author = {Faes, A and Hulle, MMV},
title = {Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac9a75},
pmid = {36240727},
issn = {1741-2552},
mesh = {Humans ; *Movement/physiology ; Fingers/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Electrocorticography/methods ; },
abstract = {Objective.We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.Approach.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.Main results.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.Significance.eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.},
}
@article {pmid36237407,
year = {2022},
author = {Fu, R and Xu, D and Li, W and Shi, P},
title = {Single-trial motor imagery electroencephalogram intention recognition by optimal discriminant hyperplane and interpretable discriminative rectangle mixture model.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {5},
pages = {1073-1085},
pmid = {36237407},
issn = {1871-4080},
abstract = {UNLABELLED: Spatial filtering is widely used in brain-computer interface (BCI) systems to augmented signal characteristics of electroencephalogram (EEG) signals. In this study, a spatial domain filtering based EEG feature extraction method, optimal discriminant hyperplane-common spatial subspace decomposition (ODH-CSSD) is proposed. Specifically, the multi-dimensional EEG features were extracted from the original EEG signals by common space subspace decomposition (CSSD) algorithm, and the optimal feature criterion was established to find the multi-dimensional optimal projection space. A classic method of data dimension optimizing is using the eigenvectors of a lumped covariance matrix corresponding to the maximum eigenvalues. Then, the cost function is defined as the extreme value of the discriminant criterion, and the orthogonal N discriminant vectors corresponding to the N extreme value of the criterion are solved and constructed into the N-dimensional optimal feature space. Finally, the multi-dimensional EEG features are projected into the N-dimensional optimal projection space to obtain the optimal N-dimensional EEG features. Moreover, this study involves the extraction of two-dimensional and three-dimensional optimal EEG features from motor imagery EEG datasets, and the optimal EEG features are identified using the interpretable discriminative rectangular mixture model (DRMM). Experimental results show that the accuracy of DRMM to identify two-dimensional optimal features is more than 0.91, and the highest accuracy even reaches 0.975. Meanwhile, DRMM has the most stable recognition accuracy for two-dimensional optimal features, and its average clustering accuracy reaches 0.942, the gap between the accuracy of the DRMM with the accuracy of the FCM and K-means can reach 0.26. And the optimal three-dimensional features, for most subjects, the clustering accuracy of DRMM is higher than that of FCM and K-means. In general, the decision rectangle obtained by DRMM can clearly explain the difference of each cluster, notably, the optimization of multidimensional EEG features by optimal projection is superior to Fisher's ratio, and this method provides an alternative for the application of BCI.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09768-w.},
}
@article {pmid36237403,
year = {2022},
author = {Dong, E and Zhang, H and Zhu, L and Du, S and Tong, J},
title = {A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {5},
pages = {1123-1133},
pmid = {36237403},
issn = {1871-4080},
abstract = {In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.},
}
@article {pmid36237402,
year = {2022},
author = {Bagherzadeh, S and Maghooli, K and Shalbaf, A and Maghsoudi, A},
title = {Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {5},
pages = {1087-1106},
pmid = {36237402},
issn = {1871-4080},
abstract = {Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.},
}
@article {pmid36237399,
year = {2022},
author = {Tao, Q and Jiang, L and Li, F and Qiu, Y and Yi, C and Si, Y and Li, C and Zhang, T and Yao, D and Xu, P},
title = {Dynamic networks of P300-related process.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {5},
pages = {975-985},
pmid = {36237399},
issn = {1871-4080},
abstract = {P300 as an effective biomarker to index attention and memory has been widely used for brain-computer interface, cognitive evaluation, and clinical diagnosis. To evoke clear P300, an oddball paradigm consisting of two types of stimuli, i.e., infrequent target stimuli and frequent standard stimuli, is usually used. However, to simply and quickly explore the P300-related process, previous studies predominately focused on the target condition but ignored the fusion of target and standard conditions, as well as the difference of brain networks between them. Therefore, in this study, we used the hidden Markov model to investigate the fused multi-conditional electroencephalogram dataset of P300, aiming to effectively identify the underlying brain networks and explore the difference between conditions. Specifically, the inferred networks, including their transition sequences and spatial distributions, were scrutinized first. Then, we found that the difference between target and standard conditions was mainly concentrated in two phases. One was the stimulation phase that mainly related to the cortical activities of the postcentral gyrus and superior parietal lobule, and the other corresponded to the response phase that involved the activities of superior and medial frontal gyri. This might be attributed to distinct cognitive functions, as the stimulation phase is associated with visual information integration whereas the response phase involves stimulus discrimination and behavior control. Taken together, the current work explored dynamic networks underlying the P300-related process and provided a complementary understanding of distinct P300 conditions, which may contribute to the design of P300-related brain-machine systems.},
}
@article {pmid36236694,
year = {2022},
author = {Antony, MJ and Sankaralingam, BP and Mahendran, RK and Gardezi, AA and Shafiq, M and Choi, JG and Hamam, H},
title = {Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {19},
pages = {},
pmid = {36236694},
issn = {1424-8220},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.},
}
@article {pmid36234792,
year = {2022},
author = {Barros, MT and Siljak, H and Mullen, P and Papadias, C and Hyttinen, J and Marchetti, N},
title = {Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.},
journal = {Molecules (Basel, Switzerland)},
volume = {27},
number = {19},
pages = {},
pmid = {36234792},
issn = {1420-3049},
mesh = {Humans ; Machine Learning ; *Neural Networks, Computer ; Neurons ; *Supervised Machine Learning ; Support Vector Machine ; },
abstract = {The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.},
}
@article {pmid36234564,
year = {2022},
author = {Sharifi, S and Maleki Dizaj, S and Ahmadian, E and Karimpour, A and Maleki, A and Memar, MY and Ghavimi, MA and Dalir Abdolahinia, E and Goh, KW},
title = {A Biodegradable Flexible Micro/Nano-Structured Porous Hemostatic Dental Sponge.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {12},
number = {19},
pages = {},
pmid = {36234564},
issn = {2079-4991},
abstract = {A biodegradable micro/nano-structured porous hemostatic gelatin-based sponge as a dentistry surgery foam was prepared using a freeze-drying method. In vitro function evaluation tests were performed to ensure its hemostatic effect. Biocompatibility tests were also performed to show the compatibility of the sponge on human fetal foreskin fibroblasts (HFFF2) cells and red blood cells (RBCs). Then, 10 patients who required the extraction of two teeth were selected, and after teeth extraction, for dressing, the produced sponge was placed in one of the extracavities while a commercial sponge was placed in the cavity in the other tooth as a control. The total weight of the absorbed blood in each group was compared. The results showed a porous structure with micrometric and nanometric pores, flexibility, a two-week range for degradation, and an ability to absorb blood 35 times its weight in vitro. The prepared sponge showed lower blood clotting times (BCTs) (243.33 ± 2.35 s) and a lower blood clotting index (BCI) (10.67 ± 0.004%) compared to two commercial sponges that displayed its ability for faster coagulation and good hemostatic function. It also had no toxic effects on the HFFF2 cells and RBCs. The clinical assessment showed a better ability of blood absorption for the produced sponge (p-value = 0.0015). The sponge is recommended for use in dental surgeries because of its outstanding abilities.},
}
@article {pmid36230358,
year = {2022},
author = {Miljević, M and Čabrilo, B and Budinski, I and Rajičić, M and Bajić, B and Bjelić-Čabrilo, O and Blagojević, J},
title = {Host-Parasite Relationship-Nematode Communities in Populations of Small Mammals.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {19},
pages = {},
pmid = {36230358},
issn = {2076-2615},
support = {451-03-68/2022-14/200007//Ministry of Education, Science and Technological Development of the Republic of Serbia/ ; },
abstract = {Nematode burdens and variation in morphological characteristics were assessed in eighty-eight animals from three host species (Apodemus sylvaticus, Apodemus flavicollis, and Myodes glareolus) from eight localities in Serbia. In total, 15 species of nematodes were identified, and the overall mean parasite species richness (IndPSR) was 1.61 per animal (1.98 in A. flavicollis, 1.43 in M. glareolus, and 0.83 in A. sylvaticus). Furthermore, the studied host species significantly differed in individual parasite load (IndPL) and in the following morphological characters: spleen mass, body condition index (BCI), and body mass. We aimed to analyze the relationship between the burden of intestinal nematodes, on one hand, and the body conditions of the host and its capability to develop immune defends on the other. Spleen mass was considered as a measure of immune response. In all host species, larger animals with a better condition (higher BCI) were infected with more parasites species (IndPSR), while parasite load was not related to BCI. Only in A. flavicollis were males significantly larger, but females of the same sizes were infected with more parasite species. This female-biased parasitism is contrary to the theoretical expectation that males should be more parasitized, being larger, more active, with a wider home range. Although the spleen size was significantly correlated with body condition and body mass, IndPSR was not related to spleen mass in any studied species, but in M. galareolus, we found that a smaller spleen was related to higher infection intensity (IndPL).},
}
@article {pmid36228894,
year = {2022},
author = {Brickwedde, M and Bezsudnova, Y and Kowalczyk, A and Jensen, O and Zhigalov, A},
title = {Application of rapid invisible frequency tagging for brain computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {382},
number = {},
pages = {109726},
pmid = {36228894},
issn = {1872-678X},
support = {207550/WT_/Wellcome Trust/United Kingdom ; 207550/WT_/Wellcome Trust/United Kingdom ; BB/R018723/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; 207550/Z/17/Z/WT_/Wellcome Trust/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Humans ; *Evoked Potentials, Visual ; *Brain-Computer Interfaces ; Photic Stimulation/methods ; Electroencephalography/methods ; Magnetoencephalography ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEPs/SSVEFs) are among the most commonly used BCI systems. They require participants to covertly attend to visual objects flickering at specified frequencies. The attended location is decoded online by analysing the power of neuronal responses at the flicker frequency.<br><br>NEW METHOD: We implemented a novel rapid invisible frequency-tagging technique, utilizing a state-of-the-art projector with refresh rates of up to 1440&#xa0;Hz. We flickered the luminance of visual objects at 56 and 60&#xa0;Hz, which was invisible to participants but produced strong neuronal responses measurable with magnetoencephalography (MEG). The direction of covert attention, decoded from frequency-tagging responses, was used to control an online BCI PONG game.<br><br>RESULTS: Our results show that seven out of eight participants were able to play the pong game controlled by the frequency-tagging signal, with average accuracies exceeding 60 %. Importantly, participants were able to modulate the power of the frequency-tagging response within a 1-second interval, while only seven occipital sensors were required to reliably decode the neuronal response.<br><br>In contrast to existing SSVEP-based BCI systems, rapid frequency-tagging does not produce a visible flicker. This extends the time-period participants can use it without fatigue, by avoiding distracting visual input. Furthermore, higher frequencies increase the temporal resolution of decoding, resulting in higher communication rates.<br><br>CONCLUSION: Using rapid invisible frequency-tagging opens new avenues for fundamental research and practical applications. In combination with novel optically pumped magnetometers (OPMs), it could facilitate the development of high-speed and mobile next-generation BCI systems.},
}
@article {pmid36228578,
year = {2022},
author = {Tong, J and Wei, X and Dong, E and Sun, Z and Du, S and Duan, F},
title = {Hybrid mental tasks based human computer interface via integration of pronunciation and motor imagery.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac9a01},
pmid = {36228578},
issn = {1741-2552},
mesh = {Humans ; *Brain-Computer Interfaces ; Imagination ; Electroencephalography/methods ; Imagery, Psychotherapy ; Computers ; Algorithms ; },
abstract = {Objective.Among the existing active brain-computer interfaces (BCI), the motor imagination (MI) is widely used. To operate the MI BCI effectively, subjects need to carry out trainings on corresponding imagining tasks. Here, we studied how to reduce the discomfort and fatigue of active BCI imaginary tasks and the inability to concentrate on them while improving the accuracy.Approach.This paper proposes a hybrid BCI composed of MI and pronunciation imagination (PI). The electroencephalogram signals of ten subjects are recognized by the adaptive Riemannian distance classification and the improved frequency selective filter-bank Common Spatial Pattern recognition.Main results.The results show that under the new paradigm with the combination of MI and PI, the recognition accuracy is higher than the MI alone. The highest recognition rate of the proposed hybrid system can reach more than 90%. Furthermore, through the subjects' scoring results of the operation difficulty, it is concluded that the designed hybrid paradigm is more operable than the traditional BCI paradigm.Significance.The separable tasks in the active BCI are limited and the accuracy needs to be improved. The new hybrid paradigm proposed by us improves the accuracy and operability of the active BCI system, providing a new possibility for the research direction of the active BCI.},
}
@article {pmid36226318,
year = {2022},
author = {Wang, X and Li, H},
title = {Chronic high-fat diet induces overeating and impairs synaptic transmission in feeding-related brain regions.},
journal = {Frontiers in molecular neuroscience},
volume = {15},
number = {},
pages = {1019446},
pmid = {36226318},
issn = {1662-5099},
abstract = {Obesity is linked to overeating, which can exacerbate unhealthy weight gain. However, the mechanisms for mediating such linkages are elusive. In the current study, we hypothesized that synaptic remodeling occurs in feeding-related brain regions of obese mice. To investigate this, we established a high-fat diet (HFD)-induced obese mouse model and observed that these mice consumed excessive calories. The effect of chronic HFD feeding on lipid droplet accumulation in different brain structures was also investigated. We found that lipid droplets accumulated on the ependyma of the third ventricle (3V), which is surrounded by key areas of the hypothalamus that are involved in feeding. Then, the spontaneous synaptic activity of miniature excitatory postsynaptic current (mEPSC) and miniature inhibitory postsynaptic current (mIPSC) was recorded in these hypothalamic areas. HFD induced a decreased amplitude of mEPSC in the arcuate nucleus (ARC) and the ventromedial hypothalamus (VMH), meanwhile, increased the frequency in the VMH. In addition, HFD reduced the frequency of mIPSC in the lateral hypothalamus (LH) and increased the amplitude of mIPSC in the paraventricular nucleus of the hypothalamus (PVH). Subsequently, we also measured the synaptic activity of nucleus accumbens (NAc) neurons, which play a vital role in the hedonic aspect of eating, and discovered that HFD diminished the frequency of both mEPSC and mIPSC in the NAc. These findings suggest that chronic HFD feeding leads to lipid accumulation and synaptic dysfunction in specific brain regions, which are associated with energy homeostasis and reward regulation, and these impairments may lead to the overeating of obesity.},
}
@article {pmid36222713,
year = {2022},
author = {Huang, WC and Hung, CH and Lin, YW and Zheng, YC and Lei, WL and Lu, HE},
title = {Electrically Copolymerized Polydopamine Melanin/Poly(3,4-ethylenedioxythiophene) Applied for Bioactive Multimodal Neural Interfaces with Induced Pluripotent Stem Cell-Derived Neurons.},
journal = {ACS biomaterials science &amp; engineering},
volume = {8},
number = {11},
pages = {4807-4818},
doi = {10.1021/acsbiomaterials.2c00822},
pmid = {36222713},
issn = {2373-9878},
mesh = {Humans ; *Induced Pluripotent Stem Cells ; Melanins ; Polymers/pharmacology ; Neurons/physiology ; },
abstract = {Multimodal neural interfaces include combined functions of electrical neuromodulation and synchronic monitoring of neurochemical and physiological signals in one device. The remarkable biocompatibility and electrochemical performance of polystyrene sulfonate-doped poly(3,4-ethylenedioxythiophene) (PEDOT:PSS) have made it the most recommended conductive polymer neural electrode material. However, PEDOT:PSS formed by electrochemical deposition, called PEDOT/PSS, often need multiple doping to improve structural instability in moisture, resolve the difficulties of functionalization, and overcome the poor cellular affinity. In this work, inspired by the catechol-derived adhesion and semiconductive properties of polydopamine melanin (PDAM), we used electrochemical oxidation polymerization to develop PDAM-doped PEDOT (PEDOT/PDAM) as a bioactive multimodal neural interface that permits robust electrochemical performance, structural stability, analyte-trapping capacity, and neural stem cell affinity. The use of potentiodynamic scans resolved the problem of copolymerizing 3,4-ethylenedioxythiophene (EDOT) and dopamine (DA), enabling the formation of PEDOT/PDAM self-assembled nanodomains with an ideal doping state associated with remarkable current storage and charge transfer capacity. Owing to the richness of hydrogen bond donors/acceptors provided by the hydroxyl groups of PDAM, PEDOT/PDAM presented better electrochemical and mechanical stability than PEDOT/PSS. It has also enabled high sensitivity and selectivity in the electrochemical detection of DA. Different from PEDOT/PSS, which inhibited the survival of human induced pluripotent stem cell-derived neural progenitor cells, PEDOT/PDAM maintained cell proliferation and even promoted cell differentiation into neuronal networks. Finally, PEDOT/PDAM was modified on a commercialized microelectrode array system, which resulted in the reduction of impedance by more than one order of magnitude; this significantly improved the resolution and reduced the noise of neuronal signal recording. With these advantages, PEDOT/PDAM is anticipated to be an efficient bioactive multimodal neural electrode material with potential application to brain-machine interfaces.},
}
@article {pmid36222132,
year = {2022},
author = {Zhong, D and Zhan, Z and Zhang, J and Liu, Y and He, Z},
title = {SMYD3 regulates the abnormal proliferation of non-small-cell lung cancer cells via the H3K4me3/ANO1 axis.},
journal = {Journal of biosciences},
volume = {47},
number = {},
pages = {},
pmid = {36222132},
issn = {0973-7138},
mesh = {Anoctamin-1/genetics/metabolism ; *Carcinoma, Non-Small-Cell Lung/genetics ; Cell Line, Tumor ; Cell Proliferation/genetics ; Chromatin ; Gene Expression Regulation, Neoplastic ; Histone-Lysine N-Methyltransferase/genetics ; Histones ; Humans ; *Lung Neoplasms/genetics ; Lysine/genetics ; Neoplasm Proteins/genetics/metabolism ; RNA, Messenger/genetics ; },
abstract = {Non-small-cell lung cancer (NSCLC) is the most prevalent type of lung cancer. This study evaluated the mechanism of histone methyltransferase SET and MYND domain-containing 3 (SMYD3) in the abnormal proliferation of NSCLC cells. The human bronchial epithelial cell (HBEC) line (16HBE) and NSCLC cell lines (H1299, A549, H460, and H1650) were collected. A549 and H1650 cells were transfected with si-SMYD3 and Anoctamin-1 (ANO1) and their negative controls or treated with BCI-121, or A549 cells were treated with CPI-455. SMYD3, H3 lysine 4 tri-methylation (H3K4me3), and ANO1 levels in the cells were detected. The proliferation ability of A549 and H1650 cells were examined. We found that SMYD3, H3K4me3, and ANO1 were highly expressed in NSCLC cell lines. Silencing SMYD3 or SMYD3 activity in A549 and H1650 cells inhibited the cell proliferation ability and decreased H3K4me3 level and ANO1 mRNA level in the cells. H3K4me3 upregulation orANO1 overexpression reversed the inhibitory effects of silencing SMYD3 on the abnormal proliferation of NSCLC cells. Chromatin-Immunoprecipitation (Ch-IP) assay detected that SMYD3 bound to and enriched in the ANO1 promoter region, and the ANO1 promoter region was enriched with H3K4me3. Collectively, SMYD3 promoted ANO1 transcription by upregulating H3K4me3 in the ANO1 promoter region, thus facilitating the abnormal proliferation of NSCLC cells.},
}
@article {pmid36220896,
year = {2022},
author = {Yang, YY and Hwang, AH and Wu, CT and Huang, TR},
title = {Person-identifying brainprints are stably embedded in EEG mindprints.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {17031},
pmid = {36220896},
issn = {2045-2322},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Twins, Dizygotic ; Twins, Monozygotic ; },
abstract = {Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.},
}
@article {pmid36219654,
year = {2022},
author = {Shen, X and Zhang, X and Huang, Y and Chen, S and Yu, Z and Wang, Y},
title = {Intermediate Sensory Feedback Assisted Multi-Step Neural Decoding for Reinforcement Learning Based Brain-Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2834-2844},
doi = {10.1109/TNSRE.2022.3210700},
pmid = {36219654},
issn = {1558-0210},
mesh = {Animals ; Rats ; *Brain-Computer Interfaces ; Feedback, Sensory ; Reinforcement, Psychology ; Learning ; Movement ; },
abstract = {Reinforcement-learning (RL)-based brain-machine interfaces (BMIs) interpret dynamic neural activity into movement intention without patients' real limb movements, which is promising for clinical applications. A movement task generally requires the subjects to reach the target within one step and rewards the subjects instantaneously. However, a real BMI scenario involves tasks that require multiple steps, during which sensory feedback is provided to indicate the status of the prosthesis, and the reward is only given at the end of the trial. Actually, subjects internally evaluate the sensory feedback to adjust motor activity. Existing RL-BMI tasks have not fully utilized the internal evaluation from the brain upon the sensory feedback to guide the decoder training, and there lacks an effective tool to assign credit for the multi-step decoding task. We propose first to extract intermediate guidance from the medial prefrontal cortex (mPFC) to assist the learning of multi-step decoding in an RL framework. To effectively explore the neural-action mapping in a large state-action space, a temporal difference (TD) method is incorporated into quantized attention-gated kernel reinforcement learning (QAGKRL) to assign the credit over the temporal sequence of movement, but also discriminate spatially in the Reproducing Kernel Hilbert Space (RKHS). We test our approach on the data collected from the primary motor cortex (M1) and the mPFC of rats when they brain control the cursor to reach the target within multiple steps. Compared with the models which only utilize the final reward, the intermediate evaluation interpreted from the mPFC can help improve the prediction accuracy by 10.9% on average across subjects, with faster convergence and more stability. Moreover, our proposed algorithm further increases 18.2% decoding accuracy compared with existing TD-RL methods. The results reveal the possibility of achieving better multi-step decoding performance for more complicated BMI tasks.},
}
@article {pmid36216898,
year = {2023},
author = {Wang, QQ and Hussain, L and Yu, PH and Yang, C and Zhu, CY and Ma, YF and Wang, SC and Yang, T and Kang, YY and Yu, WJ and Maimaitiyiming, Y and Naranmandura, H},
title = {Hyperthermia promotes degradation of the acute promyelocytic leukemia driver oncoprotein ZBTB16/RARα.},
journal = {Acta pharmacologica Sinica},
volume = {44},
number = {4},
pages = {822-831},
pmid = {36216898},
issn = {1745-7254},
mesh = {Humans ; Antineoplastic Agents/pharmacology ; Arsenic Trioxide/therapeutic use ; HeLa Cells ; *Hyperthermia, Induced ; *Leukemia, Promyelocytic, Acute/therapy/drug therapy ; Oncogene Proteins, Fusion/genetics/metabolism/therapeutic use ; Promyelocytic Leukemia Zinc Finger Protein/genetics ; Tretinoin/pharmacology/therapeutic use ; },
abstract = {The acute promyelocytic leukemia (APL) driver ZBTB16/RARα is generated by the t(11;17) (q23;q21) chromosomal translocation, which is resistant to combined treatment of all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) or conventional chemotherapy, resulting in extremely low survival rates. In the current study, we investigated the effects of hyperthermia on the oncogenic fusion ZBTB16/RARα protein to explore a potential therapeutic approach for this variant APL. We showed that Z/R fusion protein expressed in HeLa cells was resistant to ATO, ATRA, and conventional chemotherapeutic agents. However, mild hyperthermia (42 °C) rapidly destabilized the ZBTB16/RARα fusion protein expressed in HeLa, 293T, and OCI-AML3 cells, followed by robust ubiquitination and proteasomal degradation. In contrast, hyperthermia did not affect the normal (i.e., unfused) ZBTB16 and RARα proteins, suggesting a specific thermal sensitivity of the ZBTB16/RARα fusion protein. Importantly, we found that the destabilization of ZBTB16/RARα was the initial step for oncogenic fusion protein degradation by hyperthermia, which could be blocked by deletion of nuclear receptor corepressor (NCoR) binding sites or knockdown of NCoRs. Furthermore, SIAH2 was identified as the E3 ligase participating in hyperthermia-induced ubiquitination of ZBTB16/RARα. In short, these results demonstrate that hyperthermia could effectively destabilize and subsequently degrade the ZBTB16/RARα fusion protein in an NCoR-dependent manner, suggesting a thermal-based therapeutic strategy that may improve the outcome in refractory ZBTB16/RARα-driven APL patients in the clinic.},
}
@article {pmid36215972,
year = {2022},
author = {Merken, L and Schelles, M and Ceyssens, F and Kraft, M and Janssen, P},
title = {Thin flexible arrays for long-term multi-electrode recordings in macaque primary visual cortex.},
journal = {Journal of neural engineering},
volume = {19},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac98e2},
pmid = {36215972},
issn = {1741-2552},
mesh = {Animals ; Humans ; Microelectrodes ; *Macaca ; *Primary Visual Cortex ; Electrodes, Implanted ; Neurons/physiology ; },
abstract = {Objective.Basic, translational and clinical neuroscience are increasingly focusing on large-scale invasive recordings of neuronal activity. However, in large animals such as nonhuman primates and humans-in which the larger brain size with sulci and gyri imposes additional challenges compared to rodents, there is a huge unmet need to record from hundreds of neurons simultaneously anywhere in the brain for long periods of time. Here, we tested the electrical and mechanical properties of thin, flexible multi-electrode arrays (MEAs) inserted into the primary visual cortex of two macaque monkeys, and assessed their magnetic resonance imaging (MRI) compatibility and their capacity to record extracellular activity over a period of 1 year.Approach.To allow insertion of the floating arrays into the visual cortex, the 20 by 100µm[2]shafts were temporarily strengthened by means of a resorbable poly(lactic-co-glycolic acid) coating.Main results. After manual insertion of the arrays, theex vivoandin vivoMRI compatibility of the arrays proved to be excellent. We recorded clear single-unit activity from up to 50% of the electrodes, and multi-unit activity (MUA) on 60%-100% of the electrodes, which allowed detailed measurements of the receptive fields and the orientation selectivity of the neurons. Even 1 year after insertion, we obtained significant MUA responses on 70%-100% of the electrodes, while the receptive fields remained remarkably stable over the entire recording period.Significance.Thus, the thin and flexible MEAs we tested offer several crucial advantages compared to existing arrays, most notably in terms of brain tissue compliance, scalability, and brain coverage. Future brain-machine interface applications in humans may strongly benefit from this new generation of chronically implanted MEAs.},
}
@article {pmid36213754,
year = {2022},
author = {Mitskopoulos, L and Amvrosiadis, T and Onken, A},
title = {Mixed vine copula flows for flexible modeling of neural dependencies.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {910122},
pmid = {36213754},
issn = {1662-4548},
abstract = {Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications.},
}
@article {pmid36213753,
year = {2022},
author = {Sebastián-Romagosa, M and Udina, E and Ortner, R and Dinarès-Ferran, J and Cho, W and Murovec, N and Matencio-Peralba, C and Sieghartsleitner, S and Allison, BZ and Guger, C},
title = {Corrigendum: EEG biomarkers related with the functional state of stroke patients.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {1032959},
doi = {10.3389/fnins.2022.1032959},
pmid = {36213753},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2020.00582.].},
}
@article {pmid36213545,
year = {2022},
author = {Kwon, J and Hwang, J and Nam, H and Im, CH},
title = {Novel hybrid visual stimuli incorporating periodic motions into conventional flickering or pattern-reversal visual stimuli for steady-state visual evoked potential-based brain-computer interfaces.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {997068},
pmid = {36213545},
issn = {1662-5196},
abstract = {In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.},
}
@article {pmid36213341,
year = {2023},
author = {Du, R and Zhu, S and Ni, H and Mao, T and Li, J and Wei, R},
title = {Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students.},
journal = {Multimedia tools and applications},
volume = {82},
number = {10},
pages = {15439-15456},
pmid = {36213341},
issn = {1380-7501},
abstract = {During the COVID-19 pandemic, young people are using multimedia content more frequently to communicate with each other on Internet platforms. Among them, music, as psychological support for a lonely life in this special period, is a powerful tool for emotional self-regulation and getting rid of loneliness. More and more attention has been paid to the music recommender system based on emotion. In recent years, Chinese music has tended to be considered an independent genre. Chinese ancient-style music is one of the new folk music styles in Chinese music and is becoming more and more popular among young people. The complexity of Chinese-style music brings significant challenges to the quantitative calculation of music. To effectively solve the problem of emotion classification in music information search, emotion is often characterized by valence and arousal. This paper focuses on the valence and arousal classification of Chinese ancient-style music-evoked emotion. It proposes a hybrid one-dimensional convolutional neural network and bidirectional and unidirectional long short-term memory model (1D-CNN-BiLSTM). And a self-acquisition EEG dataset for Chinese college students was designed to classify music-induced emotion by valence-arousal based on EEG. In addition to that, the proposed 1D-CNN-BILSTM model verified the performance of public datasets DEAP and DREAMER, as well as the self-acquisition dataset DESC. The experimental results show that, compared with traditional LSTM and 1D-CNN-LSTM models, the proposed method has the highest accuracy in the valence classification task of music-induced emotion, reaching 94.85%, 98.41%, and 99.27%, respectively. The accuracy of the arousal classification task also gained 93.40%, 98.23%, and 99.20%, respectively. In addition, compared with the positive valence classification results of emotion, this method has obvious advantages in negative valence classification. This study provides a computational classification model for a music recommender system with emotion. It also provides some theoretical support for the brain-computer interactive (BCI) application products of Chinese ancient-style music which is popular among young people.},
}
@article {pmid36211589,
year = {2022},
author = {Jangwan, NS and Ashraf, GM and Ram, V and Singh, V and Alghamdi, BS and Abuzenadah, AM and Singh, MF},
title = {Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects.},
journal = {Frontiers in systems neuroscience},
volume = {16},
number = {},
pages = {1000495},
pmid = {36211589},
issn = {1662-5137},
abstract = {Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of the many neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts.},
}
@article {pmid36211127,
year = {2022},
author = {Kennedy, P and Cervantes, AJ},
title = {Recruitment and Differential Firing Patterns of Single Units During Conditioning to a Tone in a Mute Locked-In Human.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {864983},
pmid = {36211127},
issn = {1662-5161},
abstract = {Single units that are not related to the desired task can become related to the task by conditioning their firing rates. We theorized that, during conditioning of firing rates to a tone, (a) unrelated single units would be recruited to the task; (b) the recruitment would depend on the phase of the task; (c) tones of different frequencies would produce different patterns of single unit recruitment. In our mute locked-in participant, we conditioned single units using tones of different frequencies emitted from a tone generator. The conditioning task had three phases: Listen to the tone for 20 s, then silently sing the tone for 10 s, with a prior control period of resting for 10 s. Twenty single units were recorded simultaneously while feedback of one of the twenty single units was made audible to the mute locked-in participant. The results indicate that (a) some of the non-audible single units were recruited during conditioning, (b) some were recruited differentially depending on the phase of the paradigm (listen, rest, or silent sing), and (c) single unit firing patterns were specific for different tone frequencies such that the tone could be recognized from the pattern of single unit firings. These data are important when conditioning single unit firings in brain-computer interfacing tasks because they provide evidence that increased numbers of previously unrelated single units can be incorporated into the task. This incorporation expands the bandwidth of the recorded single unit population and thus enhances the brain-computer interface. This is the first report of conditioning of single unit firings in a human participant with a brain to computer implant.},
}
@article {pmid36211121,
year = {2022},
author = {Floreani, ED and Orlandi, S and Chau, T},
title = {A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {938708},
pmid = {36211121},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8-14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.},
}
@article {pmid36209529,
year = {2022},
author = {Hsieh, JC and Alawieh, H and Li, Y and Iwane, F and Zhao, L and Anderson, R and Abdullah, SI and Kevin Tang, KW and Wang, W and Pyatnitskiy, I and Jia, Y and Millán, JDR and Wang, H},
title = {A highly stable electrode with low electrode-skin impedance for wearable brain-computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {218},
number = {},
pages = {114756},
doi = {10.1016/j.bios.2022.114756},
pmid = {36209529},
issn = {1873-4235},
mesh = {Humans ; *Brain-Computer Interfaces ; Silver ; Electric Impedance ; Chlorides ; *Biosensing Techniques ; Electrodes ; *Wearable Electronic Devices ; Hydrogels ; Polymers ; },
abstract = {To date, brain-computer interfaces (BCIs) have proved to play a key role in many medical applications, for example, the rehabilitation of stroke patients. For post-stroke rehabilitation, the BCIs require the EEG electrodes to precisely translate the brain signals of patients into intended movements of the paralyzed limb for months. However, the gold standard silver/silver-chloride electrodes cannot satisfy the requirements for long-term stability and preparation-free recording capability in wearable EEG devices, thus limiting the versatility of EEG in wearable BCI applications over time outside the rehabilitation center. Here, we design a long-term stable and low electrode-skin interfacial impedance conductive polymer-hydrogel EEG electrode that maintains a lower impedance value than gel-based electrodes for 29 days. With this technology, EEG-based long-term and wearable BCIs could be realized in the near future. To demonstrate this, our designed electrode is applied for a wireless single-channel EEG device that detects changes in alpha rhythms in eye-open/eye-close conditions. In addition, we validate that the designed electrodes could capture oscillatory rhythms in motor imagery protocols as well as low-frequency time-locked event-related potentials from healthy subjects, with similar or better performance than gel-based electrodes. Finally, we demonstrate the use of the designed electrode in online BCI-based functional electrical stimulation, which could be used for post-stroke rehabilitation.},
}
@article {pmid36209298,
year = {2022},
author = {Lu, CY and Dong, L and Wang, D and Li, S and Fang, BZ and Han, MX and Liu, F and Jiang, HC and Ahmed, I and Li, WJ},
title = {Dongia deserti sp. nov., Isolated from the Gurbantunggut Desert Soil.},
journal = {Current microbiology},
volume = {79},
number = {11},
pages = {342},
pmid = {36209298},
issn = {1432-0991},
support = {32000005//National Natural Science Foundation of China/ ; 32061143043//National Natural Science Foundation of China/ ; 2022xjkk1200//the Third Xinjiang Scientific Expedition Program/ ; 2021qntd26//Fundamental Research Funds for Central Universities of the Central South University/ ; },
mesh = {Agar ; Bacterial Typing Techniques ; DNA, Bacterial/genetics ; Fatty Acids/chemistry ; *Phosphatidylethanolamines ; Phospholipids/chemistry ; Phylogeny ; Quinones ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; Sodium Chloride ; Soil ; *Soil Microbiology ; },
abstract = {A Gram-stain-negative, aerobic, short rod-shaped strain, designated as SYSU D60009[T], was isolated from a dry sandy soil sample collected from the Gurbantunggut Desert in Xinjiang, northwest China. Strain SYSU D60009[T] was observed to grow at 15-42 °C (optimum at 37 °C), pH 4.0-10.0 (optimum at 7.0), and with 0-0.5% (w/v) NaCl (optimum, 0%). The strain grew well on R2A agar, and colonies were smooth, white-pigmented, and circular with low convexity. The polar lipids consisted of phosphatidylethanolamine, aminolipid, aminophospholipid, and unknown lipids. The major cellular fatty acid (> 10%) was C16:0 and the predominant respiratory quinone was Q-10. Whole genome sequencing of strain SYSU D60009[T] revealed 6,132,710 bp with a DNA G + C content of 63.6%. The ANI and dDDH values of strains SYSU D60009[T] to Dongia mobilis CGMCC 1.7660[ T] were 72.8% and 19.0%, respectively. Based on the phenotypic, phylogenetic, and chemotaxonomic properties, strain SYSU D60009[T] represents a novel species of the genus Dongia, for which the name Dongia deserti sp. nov. is proposed, the type strain is SYSU D60009[T] (= CGMCC 1.16441[ T] = KCTC 52790[ T]).},
}
@article {pmid36208730,
year = {2022},
author = {Blanco-Díaz, CF and Guerrero-Méndez, CD and Bastos-Filho, T and Jaramillo-Isaza, S and Ruiz-Olaya, AF},
title = {Effects of the concentration level, eye fatigue and coffee consumption on the performance of a BCI system based on visual ERP-P300.},
journal = {Journal of neuroscience methods},
volume = {382},
number = {},
pages = {109722},
doi = {10.1016/j.jneumeth.2022.109722},
pmid = {36208730},
issn = {1872-678X},
mesh = {Humans ; *Brain-Computer Interfaces ; Coffee ; Electroencephalography/methods ; *Asthenopia ; Event-Related Potentials, P300/physiology ; Photic Stimulation ; },
abstract = {BACKGROUND: A widely used paradigm for Brain-Computer Interfaces (BCI) is based on detecting P300 Event-Related Potentials (ERPs) in response to stimulation and concentration tasks. An open challenge corresponds to maximizing the performance of a BCI by considering artifacts arising from the user's cognitive and physical conditions during task execution.<br><br>NEW METHOD: In this study, an analysis of the performance of a visual BCI-P300 system was performed under the metrics of Sensitivity (Sen), Specificity (Spe), Accuracy (Acc), and Area-Under the ROC Curve (AUC), considering the main reported factors affecting the neurophysiological behavior of the P300 signal: Concentration Level, Eye Fatigue, and Coffee Consumption.<br><br>We compared the performance of three P300 signal detection methods (MA-LDA, CCA-RLR, and MA+CCA-RLR) using a public database (GigaScience) in different groups. Data were segmented according to three factors of interest: high and low levels of concentration, high and low eye fatigue, and coffee consumption at different times.<br><br>RESULTS: The results showed a significant improvement between 3% and 6% for the metrics evaluated for identifying the P300 signal in relation to concentration levels and coffee consumption.<br><br>CONCLUSION: P300 signal can be influenced by physical and mental factors during the execution of ERPs evocation tasks, which could be controlled to maximize the interface's capacity to detect the individual's intention.},
}
@article {pmid36206939,
year = {2022},
author = {Huang, G and Hu, Z and Chen, W and Zhang, S and Liang, Z and Li, L and Zhang, L and Zhang, Z},
title = {M[3]CV: A multi-subject, multi-session, and multi-task database for EEG-based biometrics challenge.},
journal = {NeuroImage},
volume = {264},
number = {},
pages = {119666},
doi = {10.1016/j.neuroimage.2022.119666},
pmid = {36206939},
issn = {1095-9572},
mesh = {Humans ; *Electroencephalography/methods ; Algorithms ; Machine Learning ; Databases, Factual ; *Brain-Computer Interfaces ; },
abstract = {EEG signals exhibit commonality and variability across subjects, sessions, and tasks. But most existing EEG studies focus on mean group effects (commonality) by averaging signals over trials and subjects. The substantial intra- and inter-subject variability of EEG have often been overlooked. The recently significant technological advances in machine learning, especially deep learning, have brought technological innovations to EEG signal application in many aspects, but there are still great challenges in cross-session, cross-task, and cross-subject EEG decoding. In this work, an EEG-based biometric competition based on a large-scale M[3]CV (A Multi-subject, Multi-session, and Multi-task Database for investigation of EEG Commonality and Variability) database was launched to better characterize and harness the intra- and inter-subject variability and promote the development of machine learning algorithm in this field. In the M[3]CV database, EEG signals were recorded from 106 subjects, of which 95 subjects repeated two sessions of the experiments on different days. The whole experiment consisted of 6 paradigms, including resting-state, transient-state sensory, steady-state sensory, cognitive oddball, motor execution, and steady-state sensory with selective attention with 14 types of EEG signals, 120000 epochs. Two learning tasks (identification and verification), performance metrics, and baseline methods were introduced in the competition. In general, the proposed M[3]CV dataset and the EEG-based biometric competition aim to provide the opportunity to develop advanced machine learning algorithms for achieving an in-depth understanding of the commonality and variability of EEG signals across subjects, sessions, and tasks.},
}
@article {pmid36206725,
year = {2022},
author = {Lo, YT and Premchand, B and Libedinsky, C and So, RQY},
title = {Neural correlates of learning in a linear discriminant analysis brain-computer interface paradigm.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac985f},
pmid = {36206725},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Discriminant Analysis ; Movement/physiology ; Learning ; Neurons ; Haplorhini ; Macaca ; Electroencephalography ; },
abstract = {Objective.With practice, the control of brain-computer interfaces (BCI) would improve over time; the neural correlate for such learning had not been well studied. We demonstrated here that monkeys controlling a motor BCI using a linear discriminant analysis (LDA) decoder could learn to make the firing patterns of the recorded neurons more distinct over a short period of time for different output classes to improve task performance.Approach.Using an LDA decoder, we studied two Macaque monkeys implanted with microelectrode arrays as they controlled the movement of a mobile robotic platform. The LDA decoder mapped high-dimensional neuronal firing patterns linearly onto a lower-dimensional linear discriminant (LD) space, and we studied the changes in the spatial coordinates of these neural signals in the LD space over time, and their correspondence to trial performance. Direction selectivity was quantified with permutation feature importance (FI).Main results.We observed that, within individual sessions, there was a tendency for the points in the LD space encoding different directions to diverge, leading to fewer misclassification errors, and, hence, improvement in task accuracy. Accuracy was correlated with the presence of channels with strong directional preference (i.e. high FI), as well as a varied population code (i.e. high variance in FI distribution).Significance.We emphasized the importance of studying the short-term/intra-sessional variations in neural representations during the use of BCI. Over the course of individual sessions, both monkeys could modulate their neural activities to create increasingly distinct neural representations.},
}
@article {pmid36206723,
year = {2022},
author = {Xiao, X and Xu, L and Yue, J and Pan, B and Xu, M and Ming, D},
title = {Fixed template network and dynamic template network: novel network designs for decoding steady-state visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac9861},
pmid = {36206723},
issn = {1741-2552},
mesh = {*Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation/methods ; *Brain-Computer Interfaces ; },
abstract = {Objective. Decomposition methods are efficient to decode steady-state visual evoked potentials (SSVEPs). In recent years, the brain-computer interface community has also been developing deep learning networks for decoding SSVEPs. However, there is no clear evidence that current deep learning models outperform decomposition methods on the SSVEP decoding tasks. Many studies lacked the comparison with state-of-the-art decomposition methods in a fair environment.Approach. This study proposed a novel network design motivated by the works of decomposition methods. Fixed template network (FTN) and dynamic template network (DTN) are two novel networks combining the advantages of fixed templates and subject-specific templates. This study also proposed a data augmentation method for SSVEPs. This study compared the intra-subject classification performance of DTN and FTN with that of state-of-the-art decomposition methods on three public SSVEP datasets.Main results. The results show that both FTN and DTN achieved the suboptimal classification performance compared with state-of-the-art decomposition methods.Significance. Both network designs could enhance the decoding performance of SSVEPs, making them promising networks for improving the practicality of SSVEP-based applications.},
}
@article {pmid36205739,
year = {2022},
author = {Yoshida, M and Gotoh, M and Yokoyama, O and Kakizaki, H and Yamanishi, T and Yamaguchi, O},
title = {Efficacy of TAC-302 for patients with detrusor underactivity and overactive bladder: a randomized, double-blind, placebo-controlled phase 2 study.},
journal = {World journal of urology},
volume = {40},
number = {11},
pages = {2799-2805},
pmid = {36205739},
issn = {1433-8726},
mesh = {Male ; Female ; Humans ; *Urinary Bladder, Overactive/drug therapy/complications ; *Urinary Bladder, Underactive/complications ; Urodynamics ; Urination ; Double-Blind Method ; Treatment Outcome ; },
abstract = {PURPOSE: This multicenter, randomized, double-blind, placebo-controlled phase 2 study evaluated the efficacy and safety of TAC-302, a novel drug that restores neurite outgrowth, in patients with detrusor underactivity (DU) and overactive bladder (OAB).<br><br>METHODS: After 2-4&#xa0;weeks of observation, patients were randomized 2:1 to receive oral TAC-302 200&#xa0;mg or placebo twice daily for 12&#xa0;weeks. The primary endpoint was detrusor contraction strength, estimated by bladder contractility index (BCI) for males and projected isovolumetric pressure 1 (PIP1) for females. Secondary endpoints included changes in bladder voiding efficiency (BVE) and safety.<br><br>RESULTS: Seventy-six patients were included (TAC-302, n&#x2009;=&#x2009;52; placebo, n&#x2009;=&#x2009;24). The mean (standard deviation [SD]) BCI for males was 64.6 (16.6) at baseline and 75.2 (21.1) at week 12 (p&#x2009;<&#x2009;0.001) with TAC-302 (n&#x2009;=&#x2009;27), and 61.3 (16.6) and 60.5 (16.7) (p&#x2009;=&#x2009;0.82) with placebo (n&#x2009;=&#x2009;11). The respective mean (SD) PIP1 for females was 18.8 (6.6) and 29.4 (9.4) (p&#x2009;<&#x2009;0.001) with TAC-302 (n&#x2009;=&#x2009;15), and 20.6 (7.5) and 25.5 (9.6) (p&#x2009;=&#x2009;0.14) with placebo (n&#x2009;=&#x2009;7). TAC-302 significantly increased BCI in males and BVE in both sexes. TAC-302 efficacy on OAB was not clearly shown. The incidences of adverse events (AEs), serious AEs, and AEs leading to dose interruption were similar between groups; no adverse drug reactions occurred.<br><br>CONCLUSION: Considering the significant effects on BCI in males and BVE in both sexes, TAC-302 may benefit patients with DU.<br><br>REGISTRATION: ClinicalTrials.gov Identifier NCT03175029 registered 6/5/2017.},
}
@article {pmid36204719,
year = {2022},
author = {Huggins, JE and Karlsson, P and Warschausky, SA},
title = {Challenges of brain-computer interface facilitated cognitive assessment for children with cerebral palsy.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {977042},
pmid = {36204719},
issn = {1662-5161},
support = {R41 DC015142/DC/NIDCD NIH HHS/United States ; R42 DC015142/DC/NIDCD NIH HHS/United States ; SB1 DC015142/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) have been successfully used by adults, but little information is available on BCI use by children, especially children with severe multiple impairments who may need technology to facilitate communication. Here we discuss the challenges of using non-invasive BCI with children, especially children who do not have another established method of communication with unfamiliar partners. Strategies to manage these challenges require consideration of multiple factors related to accessibility, cognition, and participation. These factors include decisions regarding where (home, clinic, or lab) participation will take place, the number of sessions involved, and the degree of participation necessary for success. A strategic approach to addressing the unique challenges inherent in BCI use by children with disabilities will increase the potential for successful BCI calibration and adoption of BCI as a valuable access method for children with the most significant impairments in movement and communication.},
}
@article {pmid36204424,
year = {2022},
author = {Knierim, MT and Schemmer, M and Bauer, N},
title = {A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform.},
journal = {HardwareX},
volume = {12},
number = {},
pages = {e00357},
pmid = {36204424},
issn = {2468-0672},
abstract = {We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton &amp; Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assembly. Besides describing how to replicate and use the system, we highlight promising application scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common measurement artefacts and methods for removing them are provided, introducing a prototypical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system's capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the system could, for instance, be used to devise applications that reduce pathological jaw clenching and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing systems and a low-cost alternative to current commercial alternatives.},
}
@article {pmid36198278,
year = {2022},
author = {Awasthi, P and Lin, TH and Bae, J and Miller, LE and Danziger, ZC},
title = {Validation of a non-invasive, real-time, human-in-the-loop model of intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
pmid = {36198278},
issn = {1741-2552},
support = {R01 NS109257/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Humans ; *Motor Cortex/physiology ; Movement ; },
abstract = {Objective. Despite the tremendous promise of invasive brain-computer interfaces (iBCIs), the associated study costs, risks, and ethical considerations limit the opportunity to develop and test the algorithms that decode neural activity into a user's intentions. Our goal was to address this challenge by designing an iBCI model capable of testing many human subjects in closed-loop.Approach. We developed an iBCI model that uses artificial neural networks (ANNs) to translate human finger movements into realistic motor cortex firing patterns, which can then be decoded in real time. We call the model the joint angle BCI, or jaBCI. jaBCI allows readily recruited, healthy subjects to perform closed-loop iBCI tasks using any neural decoder, preserving subjects' control-relevant short-latency error correction and learning dynamics.Main results. We validated jaBCI offline through emulated neuron firing statistics, confirming that emulated neural signals have firing rates, low-dimensional PCA geometry, and rotational jPCA dynamics that are quite similar to the actual neurons (recorded in monkey M1) on which we trained the ANN. We also tested jaBCI in closed-loop experiments, our single study examining roughly as many subjects as have been tested world-wide with iBCIs (n= 25). Performance was consistent with that of the paralyzed, human iBCI users with implanted intracortical electrodes. jaBCI allowed us to imitate the experimental protocols (e.g. the same velocity Kalman filter decoder and center-out task) and compute the same seven behavioral measures used in three critical studies.Significance. These encouraging results suggest the jaBCI's real-time firing rate emulation is a useful means to provide statistically robust sample sizes for rapid prototyping and optimization of decoding algorithms, the study of bi-directional learning in iBCIs, and improving iBCI control.},
}
@article {pmid36197872,
year = {2023},
author = {Sun, Y and Liang, L and Sun, J and Chen, X and Tian, R and Chen, Y and Zhang, L and Gao, X},
title = {A Binocular Vision SSVEP Brain-Computer Interface Paradigm for Dual-Frequency Modulation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {4},
pages = {1172-1181},
doi = {10.1109/TBME.2022.3212192},
pmid = {36197872},
issn = {1558-2531},
mesh = {Humans ; *Evoked Potentials, Visual ; Vision, Binocular ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Photic Stimulation ; Algorithms ; },
abstract = {OBJECTIVE: This study presents a novel brain-computer interface paradigm of dual-frequency modulated steady-state visual evoked potential (SSVEP), aiming to suppress the unpredictable intermodulation components in current applications. This paradigm is especially suitable for training-free scenarios.<br><br>APPROACH: This study built a dual-frequency binocular vision SSVEP brain-computer interface system using circularly polarized light technology. Two experiments, including a 6-target offline experiment and a 40-target online experiment, were taken with this system. Meanwhile, an improved algorithm filter bank dual-frequency canonical correlation analysis (FBDCCA) was presented for the dual-frequency SSVEP paradigm.<br><br>MAIN RESULTS: Energy analysis was conducted for 9 subjects in the 6-target dual-frequency offline experiment, among which the signal-to-noise ratio of target frequency components have increased by 2 dB compared to the one of unpredictable intermodulation components. Subsequently, the online experiment with 40 targets was conducted with 12 subjects. With this new dual-frequency paradigm, the online training-free experiment's average information transmission rate (ITR) reached 104.56 &#xb1; 15.74 bits/min, which was almost twice as fast as the current best dual-frequency paradigm. And the average information transfer rate for offline training analysis of this new paradigm was 180.87 &#xb1; 17.88 bits/min.<br><br>SIGNIFICANCE: These results demonstrate that this new dual-frequency SSVEP brain-computer interface paradigm can suppress the unpredictable intermodulation harmonics and generate higher quality responses while completing dual-frequency encoding. Moreover, its performance shows high ITR in applications both with and without training. It is thus believed that this paradigm is competent for achieving large target numbers in brain-computer interface systems and has more possible practices.},
}
@article {pmid36197639,
year = {2022},
author = {Wu, C and Liu, Y and Guo, X and Zhu, T and Bao, Z},
title = {Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {60},
number = {12},
pages = {3447-3460},
pmid = {36197639},
issn = {1741-0444},
mesh = {Humans ; *Electroencephalography/methods ; Feasibility Studies ; *COVID-19 ; Neural Networks, Computer ; Cognition ; },
abstract = {The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.},
}
@article {pmid36196121,
year = {2022},
author = {Nicolelis, MAL},
title = {Brain-machine-brain interfaces as the foundation for the next generation of neuroprostheses.},
journal = {National science review},
volume = {9},
number = {10},
pages = {nwab206},
pmid = {36196121},
issn = {2053-714X},
}
@article {pmid36196114,
year = {2022},
author = {Chen, Y and Zhang, G and Guan, L and Gong, C and Ma, B and Hao, H and Li, L},
title = {Progress in the development of a fully implantable brain-computer interface: the potential of sensing-enabled neurostimulators.},
journal = {National science review},
volume = {9},
number = {10},
pages = {nwac099},
pmid = {36196114},
issn = {2053-714X},
abstract = {This perspective article investigates the performance of using a sensing-enabled neurostimulator as a motor brain-computer interface.},
}
@article {pmid36195625,
year = {2022},
author = {Lotun, S and Lamarche, VM and Samothrakis, S and Sandstrom, GM and Matran-Fernandez, A},
title = {Parasocial relationships on YouTube reduce prejudice towards mental health issues.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {16565},
pmid = {36195625},
issn = {2045-2322},
mesh = {*Attitude ; Humans ; Interpersonal Relations ; Mental Health ; Prejudice ; *Social Media ; },
abstract = {Intergroup contact has long been established as a way to reduce prejudice among society, but in-person interventions can be resource intensive and limited in reach. Parasocial relationships (PSRs) might navigate these problems by reaching large audiences with minimal resources and have been shown to help reduce prejudice in an extended version of contact theory. However, previous studies have shown inconsistent success. We assessed whether parasocial interventions reduce prejudice towards people with mental health issues by first creating a new PSR with a YouTube creator disclosing their experiences with borderline personality disorder. Our intervention successfully reduced explicit prejudice and intergroup anxiety. We corroborated these effects through causal analyses, where lower prejudice levels were mediated by the strength of parasocial bond. Preliminary findings suggest that this lower prejudice is sustained over time. Our results support the parasocial contact hypothesis and provide an organic method to passively reduce prejudice on a large scale.},
}
@article {pmid36194720,
year = {2022},
author = {Chen, P and Wang, H and Sun, X and Li, H and Grebogi, C and Gao, Z},
title = {Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2866-2875},
doi = {10.1109/TNSRE.2022.3211881},
pmid = {36194720},
issn = {1558-0210},
mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Machine Learning ; Recognition, Psychology ; Imagination ; },
abstract = {Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.},
}
@article {pmid36194480,
year = {2022},
author = {Chen, L and Hu, Y and Wang, S and Cao, K and Mai, W and Sha, W and Ma, H and Zeng, LH and Xu, ZZ and Gao, YJ and Duan, S and Wang, Y and Gao, Z},
title = {mTOR-neuropeptide Y signaling sensitizes nociceptors to drive neuropathic pain.},
journal = {JCI insight},
volume = {7},
number = {22},
pages = {},
pmid = {36194480},
issn = {2379-3708},
mesh = {Animals ; Mice ; Ganglia, Spinal/metabolism ; *Neuralgia/drug therapy/metabolism ; *Neuropeptide Y/metabolism ; Nociceptors/metabolism ; Receptors, G-Protein-Coupled/metabolism ; TOR Serine-Threonine Kinases/metabolism ; },
abstract = {Neuropathic pain is a refractory condition that involves de novo protein synthesis in the nociceptive pathway. The mTOR is a master regulator of protein translation; however, mechanisms underlying its role in neuropathic pain remain elusive. Using the spared nerve injury-induced neuropathic pain model, we found that mTOR was preferentially activated in large-diameter dorsal root ganglion (DRG) neurons and spinal microglia. However, selective ablation of mTOR in DRG neurons, rather than microglia, alleviated acute neuropathic pain in mice. We show that injury-induced mTOR activation promoted the transcriptional induction of neuropeptide Y (Npy), likely via signal transducer and activator of transcription 3 phosphorylation. NPY further acted primarily on Y2 receptors (Y2R) to enhance neuronal excitability. Peripheral replenishment of NPY reversed pain alleviation upon mTOR removal, whereas Y2R antagonists prevented pain restoration. Our findings reveal an unexpected link between mTOR and NPY/Y2R in promoting nociceptor sensitization and neuropathic pain.},
}
@article {pmid36191111,
year = {2022},
author = {Wang, J and Bi, L and Fei, W and Tian, K},
title = {EEG-Based Continuous Hand Movement Decoding Using Improved Center-Out Paradigm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2845-2855},
doi = {10.1109/TNSRE.2022.3211276},
pmid = {36191111},
issn = {1558-0210},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Hand ; Movement ; Upper Extremity ; },
abstract = {The continuous decoding of human movement intention based on electroencephalogram (EEG) signals is valuable for developing a more natural motor augmented or assistive system instead of its discrete classifications. The classic center-out paradigm has been widely used to study discrete and continuous hand movement parameter decoding. However, when applying it in studying continuous movement decoding, the classic paradigm needs to be improved to increase the decoding performance, especially generalization performance. In this paper, we first discuss the limitations of the classic center-out paradigm in exploring the hand movement's continuous decoding. Then, an improved paradigm is proposed to enhance the continuous decoding performance. Besides, an adaptive decoder-ensemble framework is developed for continuous kinematic parameter decoding. Finally, with the improved center-out paradigm and the ensemble decoding framework, the average Pearson's correlation coefficients between the predicted and recorded movement kinematic parameters improve significantly by about 75 percent for the directional parameters and about 10 percent for the non-directional parameters. Furthermore, its generalization performance improves significantly by about 20 percent for the directional parameters. This study indicates the advantage of the improved paradigm in predicting the hand movement's kinematic information from low-frequency scalp EEG signals. It can advance the applications of the noninvasive motor brain-computer interface (BCI) in rehabilitation, daily assistance, and human augmentation areas.},
}
@article {pmid36188944,
year = {2022},
author = {McGeady, C and Vučković, A and Singh Tharu, N and Zheng, YP and Alam, M},
title = {Brain-Computer Interface Priming for Cervical Transcutaneous Spinal Cord Stimulation Therapy: An Exploratory Case Study.},
journal = {Frontiers in rehabilitation sciences},
volume = {3},
number = {},
pages = {896766},
pmid = {36188944},
issn = {2673-6861},
abstract = {Loss of arm and hand function is one of the most devastating consequences of cervical spinal cord injury (SCI). Although some residual functional neurons often pass the site of injury, recovery after SCI is extremely limited. Recent efforts have aimed to augment traditional rehabilitation by combining exercise-based training with techniques such as transcutaneous spinal cord stimulation (tSCS), and movement priming. Such methods have been linked with elevated corticospinal excitability, and enhanced neuroplastic effects following activity-based therapy. In the present study, we investigated the potential for facilitating tSCS-based exercise-training with brain-computer interface (BCI) motor priming. An individual with chronic AIS A cervical SCI with both sensory and motor complete tetraplegia participated in a two-phase cross-over intervention whereby they engaged in 15 sessions of intensive tSCS-mediated hand training for 1 h, 3 times/week, followed by a two week washout period, and a further 15 sessions of tSCS training with bimanual BCI motor priming preceding each session. We found using the Graded Redefined Assessment for Strength, Sensibility, and Prehension that the participant's arm and hand function improved considerably across each phase of the study: from 96/232 points at baseline, to 117/232 after tSCS training alone, and to 131/232 points after BCI priming with tSCS training, reflecting improved strength, sensation, and gross and fine motor skills. Improved motor scores and heightened perception to sharp sensations improved the neurological level of injury from C4 to C5 following training and improvements were generally maintained four weeks after the final training session. Although functional improvements were similar regardless of the presence of BCI priming, there was a moderate improvement of bilateral strength only when priming preceded tSCS training, perhaps suggesting a benefit of motor priming for tSCS training.},
}
@article {pmid36188803,
year = {2021},
author = {Hekmatmanesh, A and Wu, H and Handroos, H},
title = {Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study.},
journal = {Frontiers in rehabilitation sciences},
volume = {2},
number = {},
pages = {802070},
pmid = {36188803},
issn = {2673-6861},
abstract = {This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.},
}
@article {pmid36188181,
year = {2022},
author = {Liang, S and Su, L and Fu, Y and Wu, L},
title = {Multi-source joint domain adaptation for cross-subject and cross-session emotion recognition from electroencephalography.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {921346},
pmid = {36188181},
issn = {1662-5161},
abstract = {As an important component to promote the development of affective brain-computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task.},
}
@article {pmid36188180,
year = {2022},
author = {Kostoglou, K and Müller-Putz, GR},
title = {Using linear parameter varying autoregressive models to measure cross frequency couplings in EEG signals.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {915815},
pmid = {36188180},
issn = {1662-5161},
abstract = {For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques.},
}
@article {pmid36188178,
year = {2022},
author = {Annaheim, C and Hug, K and Stumm, C and Messerli, M and Simon, Y and Hund-Georgiadis, M},
title = {Neurofeedback in patients with frontal brain lesions: A randomized, controlled double-blind trial.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {979723},
pmid = {36188178},
issn = {1662-5161},
abstract = {BACKGROUND: Frontal brain dysfunction is a major challenge in neurorehabilitation. Neurofeedback (NF), as an EEG-based brain training method, is currently applied in a wide spectrum of mental health conditions, including traumatic brain injury.<br><br>OBJECTIVE: This study aimed to explore the capacity of Infra-Low Frequency Neurofeedback (ILF-NF) to promote the recovery of brain function in patients with frontal brain injury.<br><br>MATERIALS AND METHODS: Twenty patients hospitalized at a neurorehabilitation clinic in Switzerland with recently acquired, frontal and optionally other brain lesions were randomized to either receive NF or sham-NF. Cognitive improvement was assessed using the Frontal Assessment Battery (FAB) and the Test of Attentional Performance (TAP) tasks regarding intrinsic alertness, phasic alertness and impulse control.<br><br>RESULTS: With respect to cognitive improvements, there was no significant difference between the two groups after 20 sessions of either NF or sham-NF. However, in a subgroup of patients with predominantly frontal brain lesions, the improvements measured by the FAB and intrinsic alertness were significantly higher in the NF-group.<br><br>CONCLUSION: This is the first double-blind controlled study using NF in recovery from brain injury, and thus also the first such study of ILF NF. Although the result of the subgroup has limited significance because of the small number of participants, it accentuates the trend seen in the whole group regarding the FAB and intrinsic alertness (p = 0.068, p = 0.079, respectively). We therefore conclude that NF could be a promising candidate promoting the recoveryfrom frontal brain lesions. Further studies with larger numbers of patients and less lesion heterogeneity are needed to verify the usefulness of NF in the neurorehabilitation of patients with frontal brain injury (NCT02957695 ClinicalTrials.gov).},
}
@article {pmid36186339,
year = {2022},
author = {Gao, Y and Kassymova, RT and Luo, Y},
title = {Application of virtual simulation situational model in Russian spatial preposition teaching.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {985887},
pmid = {36186339},
issn = {1664-1078},
abstract = {The purpose is to improve the teaching quality of Russian spatial prepositions in colleges. This work takes teaching Russian spatial prepositions as an example to study the key technologies in 3D Virtual Simulation (VS) teaching. 3D VS situational teaching is a high-end visual teaching technology. VS situation construction focuses on Human-Computer Interaction (HCI) to explore and present a realistic language teaching scene. Here, the Steady State Visual Evoked Potential (SSVEP) is used to control Brain-Computer Interface (BCI). An SSVEP-BCI system is constructed through the Hybrid Frequency-Phase Modulation (HFPM). The acquisition system can obtain the current SSVEP from the user's brain to know which module the user is watching to complete instructions encoded by the module. Experiments show that the recognition accuracy of the proposed SSVEP-BCI system based on HFPM increases with data length. When the data length is 0.6-s, the Information Transfer Rate (ITR) reaches the highest: 242.21 ± 46.88 bits/min. Therefore, a high-speed BCI character input system based on SSVEP is designed using HFPM. The main contribution of this work is to build a SSVEP-BCI system based on joint frequency phase modulation. It is better than the currently-known brain computer interface character input system, and is of great value to optimize the performance of the virtual simulation situation system for Russian spatial preposition teaching.},
}
@article {pmid36186085,
year = {2022},
author = {Lim, J and Lee, J and Moon, E and Barrow, M and Atzeni, G and Letner, JG and Costello, JT and Nason, SR and Patel, PR and Sun, Y and Patil, PG and Kim, HS and Chestek, CA and Phillips, J and Blaauw, D and Sylvester, D and Jang, T},
title = {A Light-Tolerant Wireless Neural Recording IC for Motor Prediction With Near-Infrared-Based Power and Data Telemetry.},
journal = {IEEE journal of solid-state circuits},
volume = {57},
number = {4},
pages = {1061-1074},
pmid = {36186085},
issn = {0018-9200},
support = {R21 EY029452/EY/NEI NIH HHS/United States ; },
abstract = {Miniaturized and wireless near-infrared (NIR) based neural recorders with optical powering and data telemetry have been introduced as a promising approach for safe long-term monitoring with the smallest physical dimension among state-of-the-art standalone recorders. However, a main challenge for the NIR based neural recording ICs is to maintain robust operation in the presence of light-induced parasitic short circuit current from junction diodes. This is especially true when the signal currents are kept small to reduce power consumption. In this work, we present a light-tolerant and low-power neural recording IC for motor prediction that can fully function in up to 300 μW/mm[2] of light exposure. It achieves best-in-class power consumption of 0.57 μW at 38° C with a 4.1 NEF pseudo-resistorless amplifier, an on-chip neural feature extractor, and individual mote level gain control. Applying the 20-channel pre-recorded neural signals of a monkey, the IC predicts finger position and velocity with correlation coefficient up to 0.870 and 0.569, respectively, with individual mote level gain control enabled. In addition, wireless measurement is demonstrated through optical power and data telemetry using a custom PV/LED GaAs chip wire bonded to the proposed IC.},
}
@article {pmid36182360,
year = {2022},
author = {Cai, Q and An, JP and Li, HY and Guo, JY and Gao, ZK},
title = {Cross-subject emotion recognition using visibility graph and genetic algorithm-based convolution neural network.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {32},
number = {9},
pages = {093110},
doi = {10.1063/5.0098454},
pmid = {36182360},
issn = {1089-7682},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Humans ; Neural Networks, Computer ; },
abstract = {An efficient emotion recognition model is an important research branch in electroencephalogram (EEG)-based brain-computer interfaces. However, the input of the emotion recognition model is often a whole set of EEG channels obtained by electrodes placed on subjects. The unnecessary information produced by redundant channels affects the recognition rate and depletes computing resources, thereby hindering the practical applications of emotion recognition. In this work, we aim to optimize the input of EEG channels using a visibility graph (VG) and genetic algorithm-based convolutional neural network (GA-CNN). First, we design an experiment to evoke three types of emotion states using movies and collect the multi-channel EEG signals of each subject under different emotion states. Then, we construct VGs for each EEG channel and derive nonlinear features representing each EEG channel. We employ the genetic algorithm (GA) to find the optimal subset of EEG channels for emotion recognition and use the recognition results of the CNN as fitness values. The experimental results show that the recognition performance of the proposed method using a subset of EEG channels is superior to that of the CNN using all channels for each subject. Last, based on the subset of EEG channels searched by the GA-CNN, we perform cross-subject emotion recognition tasks employing leave-one-subject-out cross-validation. These results demonstrate the effectiveness of the proposed method in recognizing emotion states using fewer EEG channels and further enrich the methods of EEG classification using nonlinear features.},
}
@article {pmid36179659,
year = {2022},
author = {Valeriani, D and O'Flynn, LC and Worthley, A and Sichani, AH and Simonyan, K},
title = {Multimodal collaborative brain-computer interfaces aid human-machine team decision-making in a pandemic scenario.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac96a5},
pmid = {36179659},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Magnetic Resonance Imaging ; Pandemics ; Parietal Lobe ; },
abstract = {Objective.Critical decisions are made by effective teams that are characterized by individuals who trust each other and know how to best integrate their opinions. Here, we introduce a multimodal brain-computer interface (BCI) to help collaborative teams of humans and an artificial agent achieve more accurate decisions in assessing danger zones during a pandemic scenario.Approach.Using high-resolution simultaneous electroencephalography/functional MRI (EEG/fMRI), we first disentangled the neural markers of decision-making confidence and trust and then employed machine-learning to decode these neural signatures for BCI-augmented team decision-making. We assessed the benefits of BCI on the team's decision-making process compared to the performance of teams of different sizes using the standard majority or weighing individual decisions.Main results.We showed that BCI-assisted teams are significantly more accurate in their decisions than traditional teams, as the BCI is capable of capturing distinct neural correlates of confidence on a trial-by-trial basis. Accuracy and subjective confidence in the context of collaborative BCI engaged parallel, spatially distributed, and temporally distinct neural circuits, with the former being focused on incorporating perceptual information processing and the latter involving action planning and executive operations during decision making. Among these, the superior parietal lobule emerged as a pivotal region that flexibly modulated its activity and engaged premotor, prefrontal, visual, and subcortical areas for shared spatial-temporal control of confidence and trust during decision-making.Significance.Multimodal, collaborative BCIs that assist human-artificial agent teams may be utilized in critical settings for augmented and optimized decision-making strategies.},
}
@article {pmid36177890,
year = {2023},
author = {Chen, X and Dong, D and Zhou, F and Gao, X and Liu, Y and Wang, J and Qin, J and Tian, Y and Xiao, M and Xu, X and Li, W and Qiu, J and Feng, T and He, Q and Lei, X and Chen, H},
title = {Connectome-based prediction of eating disorder-associated symptomatology.},
journal = {Psychological medicine},
volume = {53},
number = {12},
pages = {5786-5799},
doi = {10.1017/S0033291722003026},
pmid = {36177890},
issn = {1469-8978},
mesh = {Humans ; *Connectome/methods ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging ; Cognition ; *Binge-Eating Disorder/psychology ; },
abstract = {BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).<br><br>METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.<br><br>RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.<br><br>CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.},
}
@article {pmid36177358,
year = {2022},
author = {Li, R and Liu, D and Li, Z and Liu, J and Zhou, J and Liu, W and Liu, B and Fu, W and Alhassan, AB},
title = {A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {988535},
pmid = {36177358},
issn = {1662-4548},
abstract = {Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain-computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.},
}
@article {pmid36176162,
year = {2022},
author = {Patwardhan, S and Schofield, J and Joiner, WM and Sikdar, S},
title = {Sonomyography shows feasibility as a tool to quantify joint movement at the muscle level.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2022},
number = {},
pages = {1-5},
pmid = {36176162},
issn = {1945-7901},
support = {U01 EB027601/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Feasibility Studies ; Humans ; *Movement/physiology ; Muscles ; },
abstract = {Several methods have been used to quantify human movement at different levels, from coordinated multi joint movements to those taking place at the single muscle level. These methods are developed either in order to allow us to interact with computers and machines, or to use such technologies for aiding rehabilitation among those with mobility impairments or movement disorders. Human machine interfaces typically rely on some existing human movement ability and measure it using motion tracking or inertial measurement units, while the rehabilitation applications may require us to measure human motor intent. Surface or implanted electrodes, electromyography, electroencephalography, and brain computer interfaces are beneficial in this regard, but have their own shortcomings. We have previously shown feasibility of using ultrasound imaging (Sonomyography) to infer human motor intent and allow users to control external biomechatronic devices such as prosthetics. Here, we asked users to freely move their hand in three different movement patterns, measuring their actual joint angles and passively computing their Sonomyographic output signal. We found a high correlation between these two signals, demonstrating that the Sonomyography signal is not only user-controlled and stable, but it is closely linked with the user's actual movement level. These results could help design wearable rehabilitation or human computer interaction devices based on Sonomyography to decode human motor intent.},
}
@article {pmid36176154,
year = {2022},
author = {Cardoso, ASS and Andreasen Struijk, LNS and Kaeseler, RL and Jochumsen, M},
title = {Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2022},
number = {},
pages = {1-6},
doi = {10.1109/ICORR55369.2022.9896588},
pmid = {36176154},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; *Robotics ; },
abstract = {Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.},
}
@article {pmid36176143,
year = {2022},
author = {Behboodi, A and Lee, WA and Bulea, TC and Damiano, DL},
title = {Evaluation of Multi-layer Perceptron Neural Networks in Predicting Ankle Dorsiflexion in Healthy Adults using Movement-related Cortical Potentials for BCI-Neurofeedback Applications.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2022},
number = {},
pages = {1-5},
pmid = {36176143},
issn = {1945-7901},
support = {ZIA CL090084/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Ankle ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement/physiology ; Neural Networks, Computer ; *Neurofeedback/physiology ; Reproducibility of Results ; Young Adult ; },
abstract = {Brain computer interface (BCI) systems were initially developed to replace lost function; however, they are being increasingly utilized in rehabilitation to restore motor functioning after brain injury. In such BCI-mediated neurofeedback training (BCI-NFT), the brain-state associated with movement attempt or intention is used to activate an external device which assists the movement while providing sensory feedback to enhance neuroplasticity. A critical element in the success of BCI-NFT is accurate timing of the feedback within the active period of the brain state. The overarching goal of this work was to develop a reliable deep learning model that can predict motion before its onset, and thereby deliver the sensory stimuli in a timely manner for BCI-NFT applications. To this end, the main objective of the current study was to design and evaluate a Multi-layer Perceptron Neural Network (MLP-NN). Movement-related cortical potentials (MRCP) during planning and execution of ankle dorsiflexion was used to train the model to classify dorsiflexion planning vs. rest. The accuracy and reliability of the model was evaluated offline using data from eight healthy individuals (age: 26.3 ± 7.6 years). First, we evaluated three different epoching strategies for defining our 2 classes, to identify the one which best discriminated rest from dorsiflexion. The best model accuracy for predicting ankle dorsiflexion from EEG before movement execution was 84.7%. Second, the effect of various spatial filters on the model accuracy was evaluated, demonstrating that the spatial filtering had minimal effect on model accuracy and reliability.},
}
@article {pmid36176084,
year = {2022},
author = {Jo, S and Jung, JH and Yang, MJ and Lee, Y and Jang, SJ and Feng, J and Heo, SH and Kim, J and Shin, JH and Jeong, J and Park, HS},
title = {EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2022},
number = {},
pages = {1-6},
doi = {10.1109/ICORR55369.2022.9896592},
pmid = {36176084},
issn = {1945-7901},
mesh = {Activities of Daily Living ; Electroencephalography/methods ; Hand ; Hand Strength ; Humans ; Intention ; *Stroke ; *Stroke Rehabilitation/methods ; },
abstract = {Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.},
}
@article {pmid36172602,
year = {2022},
author = {Song, M and Jeong, H and Kim, J and Jang, SH and Kim, J},
title = {An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {971547},
pmid = {36172602},
issn = {1662-5218},
abstract = {Many studies have used motor imagery-based brain-computer interface (MI-BCI) systems for stroke rehabilitation to induce brain plasticity. However, they mainly focused on detecting motor imagery but did not consider the effect of false positive (FP) detection. The FP could be a threat to patients with stroke as it can induce wrong-directed brain plasticity that would result in adverse effects. In this study, we proposed a rehabilitative MI-BCI system that focuses on rejecting the FP. To this end, we first identified numerous electroencephalogram (EEG) signals as the causes of the FP, and based on the characteristics of the signals, we designed a novel two-phase classifier using a small number of EEG channels, including the source of the FP. Through experiments with eight healthy participants and nine patients with stroke, our proposed MI-BCI system showed 71.76% selectivity and 13.70% FP rate by using only four EEG channels in the patient group with stroke. Moreover, our system can compensate for day-to-day variations for prolonged session intervals by recalibration. The results suggest that our proposed system, a practical approach for the clinical setting, could improve the therapeutic effect of MI-BCI by reducing the adverse effect of the FP.},
}
@article {pmid36171871,
year = {2022},
author = {Duan, J and Li, S and Ling, L and Zhang, N and Meng, J},
title = {Exploring the effects of head movements and accompanying gaze fixation switch on steady-state visual evoked potential.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {943070},
pmid = {36171871},
issn = {1662-5161},
abstract = {In a realistic steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) application like driving a car or controlling a quadrotor, observing the surrounding environment while simultaneously gazing at the stimulus is necessary. This kind of application inevitably could cause head movements and variation of the accompanying gaze fixation point, which might affect the SSVEP and BCI's performance. However, few papers studied the effects of head movements and gaze fixation switch on SSVEP response, and the corresponding BCI performance. This study aimed to explore these effects by designing a new ball tracking paradigm in a virtual reality (VR) environment with two different moving tasks, i.e., the following and free moving tasks, and three moving patterns, pitch, yaw, and static. Sixteen subjects were recruited to conduct a BCI VR experiment. The offline data analysis showed that head moving patterns [F(2, 30) = 9.369, p = 0.001, effect size = 0.384] resulted in significantly different BCI decoding performance but the moving tasks had no effect on the results [F(1, 15) = 3.484, p = 0.082, effect size = 0.188]. Besides, the canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) accuracy were better than the PSDA and MEC methods in all of the conditions. These results implied that head movement could significantly affect the SSVEP performance but it was possible to switch gaze fixation to interact with the surroundings in a realistic BCI application.},
}
@article {pmid36171602,
year = {2022},
author = {Behboodi, A and Lee, WA and Hinchberger, VS and Damiano, DL},
title = {Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {19},
number = {1},
pages = {104},
pmid = {36171602},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Child ; Electroencephalography/methods ; Humans ; *Neurofeedback ; *Neurological Rehabilitation ; *Stroke ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications?<br><br>METHODS: We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes.<br><br>RESULTS: From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n&#xa0;=&#xa0;13) and the Action Research Arm Test (ARAT; n&#xa0;=&#xa0;6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose.<br><br>CONCLUSION: This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.},
}
@article {pmid36171400,
year = {2022},
author = {Mansour, S and Giles, J and Ang, KK and Nair, KPS and Phua, KS and Arvaneh, M},
title = {Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {16223},
pmid = {36171400},
issn = {2045-2322},
support = {MC_PC_19051/MRC_/Medical Research Council/United Kingdom ; MC-PC-19051/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Stroke ; *Stroke Rehabilitation/methods ; Survivors ; Upper Extremity ; },
abstract = {Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.},
}
@article {pmid36170408,
year = {2023},
author = {Yan, T and Suzuki, K and Kameda, S and Kuratomi, T and Mihara, M and Maeda, M and Hirata, M},
title = {Intracranial EEG Recordings of High-Frequency Activity From a Wireless Implantable BMI Device in Awake Nonhuman Primates.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {4},
pages = {1107-1113},
doi = {10.1109/TBME.2022.3210286},
pmid = {36170408},
issn = {1558-2531},
mesh = {Animals ; Humans ; Electrocorticography ; Body Mass Index ; *Persons with Disabilities ; *Ketamine/pharmacology ; *Motor Disorders ; Wakefulness ; Macaca ; },
abstract = {OBJECTIVE: Wireless implantable brain machine interfaces (BMIs) are a promising tool to restore communication and motor functions for individuals with severe motor disability. Prior to clinical application, recording performance must be sufficiently confirmed by animal experiments. In this paper, we aimed to evaluate the performance of a novel BMI wireless device for recording brain activity in two nonhuman primates.<br><br>METHOD: We customized a wireless device for implantable BMIs for clinical application. We used a battery instead of a wireless power charging system. Thirty-two electrodes were subdurally implanted over the left temporoparietal cortex. We evaluated the recording performance of the wireless device by auditory steady-state responses (ASSRs) and ketamine-induced responses.<br><br>RESULT: The devices successfully recorded broadband oscillatory activities up to the high-frequency band from the temporal cortex in two awake macaque monkeys. Spectral analysis of raw signals demonstrated that the devices detected characteristic results of a 40-Hz ASSR and prominent high-frequency band activity induced by ketamine injection.<br><br>CONCLUSION: We confirmed the functionality of the wireless device in recording and transmitting electrocorticography (ECoG) signals with both millisecond precision and recording stability.<br><br>SIGNIFICANCE: These results provide confidence that this wireless device can be a translational tool for other fundamental neuroscientific studies in free-moving models.},
}
@article {pmid36170151,
year = {2023},
author = {Lu, X and Chen, L and Jiang, C and Cao, K and Gao, Z and Wang, Y},
title = {Microglia and macrophages contribute to the development and maintenance of sciatica in lumbar disc herniation.},
journal = {Pain},
volume = {164},
number = {2},
pages = {362-374},
doi = {10.1097/j.pain.0000000000002708},
pmid = {36170151},
issn = {1872-6623},
mesh = {Rats ; Mice ; Animals ; *Sciatica ; *Intervertebral Disc Displacement/complications ; Microglia ; Rats, Sprague-Dawley ; Macrophages ; Ganglia, Spinal ; },
abstract = {Lumbar disc herniation (LDH) is a major cause of sciatica. Emerging evidence indicated that inflammation induced by the herniated nucleus pulposus (NP) tissues plays a major role in the pathogenesis of sciatica. However, the underlying mechanisms are still elusive. Although microglia and macrophages have been implicated in nerve injury-induced neuropathic pain, their roles in LDH-induced sciatica largely remain unknown. This study successfully established and modified a mouse model of LDH. We found that nerve root compression using degenerated NP tissues can initiate remarkable and persistent sciatica, with increased and prolonged macrophage infiltration in dorsal root ganglia (DRG) and significant activation of microglia in the spinal dorsal horn. Instead, compression of the nerve root with nondegenerated NP tissues only led to transient sciatica, with transient infiltration and activation of macrophages and microglia. Moreover, continuous treatment of PLX5622, a specific colony-stimulating factor 1 receptor antagonist, ablated both macrophages and microglia, which effectively alleviated LDH-induced sciatica. However, mechanical allodynia reoccurred along with the repopulation of macrophages and microglia after the withdrawal of PLX5622. Using RNA sequencing analysis, the current study depicted transcriptional profile changes of DRG after LDH and identified several macrophage-related potential target candidates. Our results suggested that microglia and macrophages may play an essential role in the development and maintenance of LDH-induced sciatica. Targeting microglia and macrophages may be a promising treatment for chronic LDH-induced sciatica.},
}
@article {pmid36168224,
year = {2022},
author = {He, X and Wu, M and Li, H and Liu, S and Liu, B and Qi, H},
title = {Real-time regulation of room temperature based on individual thermal sensation using an online brain-computer interface.},
journal = {Indoor air},
volume = {32},
number = {9},
pages = {e13106},
doi = {10.1111/ina.13106},
pmid = {36168224},
issn = {1600-0668},
mesh = {*Air Pollution, Indoor ; *Brain-Computer Interfaces ; Humans ; Skin Temperature ; Temperature ; Thermosensing/physiology ; },
abstract = {Regulation of indoor temperature based on neurophysiological and psychological signals is one of the most promising technologies for intelligent buildings. In this study, we developed a system for closed-loop control of indoor temperature based on brain-computer interface (BCI) technology for the first time. Electroencephalogram (EEG) signals were collected from subjects for two room temperature categories (cool comfortable and hot uncomfortable) and used to build a thermal-sensation discrimination model (TSDM) with an ensemble learning method. Then, an online BCI system was developed based on the TSDM. In the online room temperature control experiment, when the TSDM detected that the subjects felt hot and uncomfortable, BCI would automatically turn on the air conditioner, and when the TSDM detected that the subjects felt cool and comfortable, BCI would automatically turn off the air conditioner. The results of online experiments in a hot environment showed that a BCI could significantly improve the thermal comfort of subjects (the subjective thermal comfort score decreased from 2.45 (hot uncomfortable) to 0.55 (cool comfortable), p < 0.001). A parallel experiment further showed that if the subjects wore thicker clothes during the experiment, the BCI would turn on the air conditioner for a longer time to ensure the thermal comfort of the subjects. This has further confirmed the effectiveness of TSDM model in evaluating thermal sensation under the dynamic change of room temperature and showed the model's good robustness. This study proposed a new paradigm of human-building interaction, which is expected to play a promising role in the development of human-centered intelligent buildings.},
}
@article {pmid36166987,
year = {2022},
author = {Tang, Z and Wang, X and Wu, J and Ping, Y and Guo, X and Cui, Z},
title = {A BCI painting system using a hybrid control approach based on SSVEP and P300.},
journal = {Computers in biology and medicine},
volume = {150},
number = {},
pages = {106118},
doi = {10.1016/j.compbiomed.2022.106118},
pmid = {36166987},
issn = {1879-0534},
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Brain/physiology ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Photic Stimulation ; },
abstract = {Brain-computer interfaces (BCIs) can help people with disabilities to communicate with others, express themselves, and even create art. In this paper, a BCI painting system using a hybrid control approach based on steady-state visual evoked potential (SSVEP) and P300 was developed, which can enable simple painting through brain-controlled painting tools. The BCI painting system is composed of two parts: a hybrid stimulus interface and a hybrid electroencephalogram (EEG) signal processing module. The user selects the menus and tools through the SSVEP and P300 stimulus matrices, respectively, and the paintings are displayed in the canvas area of the hybrid stimulus interface in real time. Twenty subjects participated in this study. An offline training experiment was performed to construct the P300 and SSVEP recognition models for each subject; an online painting experiment, which included a copy-painting task and a free-painting task, was performed to evaluate the BCI painting system. The results of the online painting experiment showed that the average tool selection accuracy (88.92 ± 3.94%) of the BCI painting system using the hybrid stimulus interface was slightly higher than that of the traditional brain painting system based on the P300 stimulus interface; the average information transfer rate (ITR) (74.20 ± 5.28 bpm, 71.80 ± 5.15 bpm) in the copy-painting and free-painting tasks of the BCI painting system was significantly higher than that of the traditional brain painting system. Our BCI painting system can effectively help users express their artistic creativity and improve their painting efficiency, and can provide new methods and new ideas for developing BCI-controlled applications.},
}
@article {pmid36166895,
year = {2022},
author = {Zhang, H and Fu, P and Liu, Y and Zheng, Z and Zhu, L and Wang, M and Abdellah, M and He, M and Qian, J and Roe, AW and Xi, W},
title = {Large-depth three-photon fluorescence microscopy imaging of cortical microvasculature on nonhuman primates with bright AIE probe In vivo.},
journal = {Biomaterials},
volume = {289},
number = {},
pages = {121809},
doi = {10.1016/j.biomaterials.2022.121809},
pmid = {36166895},
issn = {1878-5905},
mesh = {Animals ; *Cerebral Cortex ; *Fluorescent Dyes/chemistry ; Humans ; Macaca ; Mice ; Microscopy, Fluorescence ; Microscopy, Fluorescence, Multiphoton/methods ; Microvessels ; Optical Imaging ; },
abstract = {Multiphoton microscopy has been a powerful tool in brain research, three-photon fluorescence microscopy is increasingly becoming an emerging technique for neurological research of the cortex in depth. Nonhuman primates play important roles in the study of brain science because of their neural and vascular similarity to humans. However, there are few research results of three-photon fluorescence microscopy on the brain of nonhuman primates due to the lack of optimized imaging systems and excellent fluorescent probes. Here we introduced a bright aggregation-induced emission (AIE) probe with excellent three-photon fluorescence efficiency as well as facile synthesis process and we validated its biocompatibility in the macaque monkey. We achieved a large-depth vascular imaging of approximately 1 mm in the cerebral cortex of macaque monkey with our lab-modified three-photon fluorescence microscopy system and the AIE probe. Functional measurement of blood velocity in deep cortex capillaries was also performed. Furthermore, the comparison of cortical deep vascular structure parameters across species was presented on the monkey and mouse cortex. This work is the first in vivo three-photon fluorescence microscopic imaging research on the macaque monkey cortex reaching the imaging depth of ∼1 mm with the bright AIE probe. The results demonstrate the potential of three-photon microscopy as primate-compatible method for imaging fine vascular networks and will advance our understanding of vascular function in normal and disease in humans.},
}
@article {pmid36163326,
year = {2022},
author = {Wang, Q and Liu, Y and Wang, H and Jiang, P and Qian, W and You, M and Han, Y and Zeng, X and Li, J and Lu, H and Jiang, L and Zhu, M and Li, S and Huang, K and Tang, M and Wang, X and Yan, L and Xiong, Z and Shi, X and Bai, G and Liu, H and Li, Y and Zhao, Y and Chen, C and Qian, P},
title = {Graphdiyne oxide nanosheets display selective anti-leukemia efficacy against DNMT3A-mutant AML cells.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {5657},
pmid = {36163326},
issn = {2041-1723},
mesh = {Actins/genetics ; CD18 Antigens ; DNA ; *DNA (Cytosine-5-)-Methyltransferases/genetics ; DNA Methyltransferase 3A ; DNA Modification Methylases/genetics ; Graphite ; Humans ; *Leukemia, Myeloid, Acute/drug therapy/genetics ; Mutation ; Oxides ; },
abstract = {DNA methyltransferase 3 A (DNMT3A) is the most frequently mutated gene in acute myeloid leukemia (AML). Although chemotherapy agents have improved outcomes for DNMT3A-mutant AML patients, there is still no targeted therapy highlighting the need for further study of how DNMT3A mutations affect AML phenotype. Here, we demonstrate that cell adhesion-related genes are predominantly enriched in DNMT3A-mutant AML cells and identify that graphdiyne oxide (GDYO) display an anti-leukemia effect specifically against these mutated cells. Mechanistically, GDYO directly interacts with integrin β2 (ITGB2) and c-type mannose receptor (MRC2), which facilitate the attachment and cellular uptake of GDYO. Furthermore, GDYO binds to actin and prevents actin polymerization, thus disrupting the actin cytoskeleton and eventually leading to cell apoptosis. Finally, we validate the in vivo safety and therapeutic potential of GDYO against DNMT3A-mutant AML cells. Collectively, these findings demonstrate that GDYO is an efficient and specific drug candidate against DNMT3A-mutant AML.},
}
@article {pmid36162641,
year = {2023},
author = {Zhu, Z and Wang, H and Bi, H and Lv, J and Zhang, X and Wang, S and Zou, L},
title = {Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder.},
journal = {Behavioural brain research},
volume = {437},
number = {},
pages = {114121},
doi = {10.1016/j.bbr.2022.114121},
pmid = {36162641},
issn = {1872-7549},
mesh = {Humans ; Child ; *Attention Deficit Disorder with Hyperactivity/diagnostic imaging ; Brain Mapping/methods ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Neural Pathways/diagnostic imaging ; },
abstract = {Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.},
}
@article {pmid36161174,
year = {2022},
author = {Loriette, C and Amengual, JL and Ben Hamed, S},
title = {Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {811736},
pmid = {36161174},
issn = {1662-4548},
abstract = {One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain-computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.},
}
@article {pmid36158550,
year = {2022},
author = {Zhang, X and Jiang, Y and Hou, W and Jiang, N},
title = {Age-related differences in the transient and steady state responses to different visual stimuli.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {1004188},
pmid = {36158550},
issn = {1663-4365},
abstract = {OBJECTIVE: Brain-computer interface (BCI) has great potential in geriatric applications. However, most BCI studies in the literature used data from young population, and dedicated studies investigating the feasibility of BCIs among senior population are scarce. The current study, we analyzed the age-related differences in the transient electroencephalogram (EEG) response used in visual BCIs, i.e., visual evoked potential (VEP)/motion onset VEP (mVEP), and steady state-response, SSVEP/SSMVEP, between the younger group (age ranges from 22 to 30) and senior group (age ranges from 60 to 75).<br><br>METHODS: The visual stimulations, including flicker, checkerboard, and action observation (AO), were designed with a periodic frequency. Videos of several hand movement, including grasping, dorsiflexion, the thumb opposition, and pinch were utilized to generate the AO stimuli. Eighteen senior and eighteen younger participants were enrolled in the experiments. Spectral-temporal characteristics of induced EEG were compared. Three EEG algorithms, canonical correlation analysis (CCA), task-related component analysis (TRCA), and extended CCA, were utilized to test the performance of the respective BCI systems.<br><br>RESULTS: In the transient response analysis, the motion checkerboard and AO stimuli were able to elicit prominent mVEP with a specific P1 peak and N2 valley, and the amplitudes of P1 elicited in the senior group were significantly higher than those in the younger group. In the steady-state analysis, SSVEP/SSMVEP could be clearly elicited in both groups. The CCA accuracies of SSVEPs/SSMVEPs in the senior group were slightly lower than those in the younger group in most cases. With extended CCA, the performance of both groups improved significantly. However, for AO targets, the improvement of the senior group (from 63.1 to 71.9%) was lower than that of the younger group (from 63.6 to 83.6%).<br><br>CONCLUSION: Compared with younger subjects, the amplitudes of P1 elicited by motion onset is significantly higher in the senior group, which might be a potential advantage for seniors if mVEP-based BCIs is used. This study also shows for the first time that AO-based BCI is feasible for the senior population. However, new algorithms for senior subjects, especially in identifying AO targets, are needed.},
}
@article {pmid36156934,
year = {2022},
author = {Moreno, J and Gross, ML and Becker, J and Hereth, B and Shortland, ND and Evans, NG},
title = {The ethics of AI-assisted warfighter enhancement research and experimentation: Historical perspectives and ethical challenges.},
journal = {Frontiers in big data},
volume = {5},
number = {},
pages = {978734},
pmid = {36156934},
issn = {2624-909X},
abstract = {The military applications of AI raise myriad ethical challenges. Critical among them is how AI integrates with human decision making to enhance cognitive performance on the battlefield. AI applications range from augmented reality devices to assist learning and improve training to implantable Brain-Computer Interfaces (BCI) to create bionic "super soldiers." As these technologies mature, AI-wired warfighters face potential affronts to cognitive liberty, psychological and physiological health risks and obstacles to integrating into military and civil society during their service and upon discharge. Before coming online and operational, however, AI-assisted technologies and neural interfaces require extensive research and human experimentation. Each endeavor raises additional ethical concerns that have been historically ignored thereby leaving military and medical scientists without a cogent ethics protocol for sustainable research. In this way, this paper is a "prequel" to the current debate over enhancement which largely considers neuro-technologies once they are already out the door and operational. To lay the ethics foundation for AI-assisted warfighter enhancement research, we present an historical overview of its technological development followed by a presentation of salient ethics research issues (ICRC, 2006). We begin with a historical survey of AI neuro-enhancement research highlighting the ethics lacunae of its development. We demonstrate the unique ethical problems posed by the convergence of several technologies in the military research setting. Then we address these deficiencies by emphasizing how AI-assisted warfighter enhancement research must pay particular attention to military necessity, and the medical and military cost-benefit tradeoffs of emerging technologies, all attending to the unique status of warfighters as experimental subjects. Finally, our focus is the enhancement of friendly or compatriot warfighters and not, as others have focused, enhancements intended to pacify enemy warfighters.},
}
@article {pmid36155481,
year = {2022},
author = {Zhang, S and Ang, KK and Zheng, D and Hui, Q and Chen, X and Li, Y and Tang, N and Chew, E and Lim, RY and Guan, C},
title = {Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2824-2833},
doi = {10.1109/TNSRE.2022.3209155},
pmid = {36155481},
issn = {1558-0210},
mesh = {Humans ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; *Stroke ; },
abstract = {Although brain-computer interface (BCI) shows promising prospects to help post-stroke patients recover their motor function, its decoding accuracy is still highly dependent on feature extraction methods. Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. This approach can enhance the decoding accuracy by learning a low dimensional embedding to extract distance-based representations from pair-wise EEG data. To enhance training efficiency and algorithm performance, a temporal-spectral distance weighted sampling method is proposed to select more informative input samples. In addition, an adaptive training strategy is adopted to address the session-to-session non-stationarity by progressively updating the subject-specific model. The proposed method is applied on both upper limb and lower limb neurorehabilitation datasets acquired from 33 stroke patients, with a total of 358 sessions. Results indicate that using k-Nearest Neighbor as the classification algorithm, the proposed method yielded 72.8% and 66.0% accuracies for the two datasets respectively, significantly better than the other state-of-the-arts (). Without losing generality, we also evaluated the proposed method on two publicly available datasets acquired from healthy subjects, wherein the proposed algorithm demonstrated superior performance at most cases as well. Our results support, for the first time, the use of a metric learning based feature extractor to learn representations from non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.},
}
@article {pmid36153356,
year = {2022},
author = {Zippi, EL and You, AK and Ganguly, K and Carmena, JM},
title = {Selective modulation of cortical population dynamics during neuroprosthetic skill learning.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {15948},
pmid = {36153356},
issn = {2045-2322},
support = {T32 NS095939/NS/NINDS NIH HHS/United States ; Award R01NS106094/NH/NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; Macaca ; *Motor Cortex/physiology ; Neurons/physiology ; Population Dynamics ; },
abstract = {Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.},
}
@article {pmid36152398,
year = {2023},
author = {Nathwani, JN and Baucom, MR and Salvator, A and Makley, AT and Tsuei, BJ and Droege, CA and Goodman, MD and Nomellini, V},
title = {Evaluating the Utility of High Sensitivity Troponin in Blunt Cardiac Injury.},
journal = {The Journal of surgical research},
volume = {281},
number = {},
pages = {104-111},
doi = {10.1016/j.jss.2022.08.030},
pmid = {36152398},
issn = {1095-8673},
mesh = {Humans ; Troponin I ; Sensitivity and Specificity ; *Thoracic Injuries ; Electrocardiography ; *Myocardial Contusions ; Biomarkers ; },
abstract = {INTRODUCTION: Screening for blunt cardiac injury (BCI) includes obtaining a serum troponin level and an electrocardiogram for patients diagnosed with a sternal fracture. Our institution has transitioned to the use of a high sensitivity troponin I (hsTnI). The aim of this study was to determine whether hsTnI is comparable to troponin I (TnI) in identifying clinically significant BCI.<br><br>MATERIALS AND METHODS: Trauma patients presenting to a level I trauma center over a 24-mo period with the diagnosis of sternal fracture were screened for BCI. Any initial TnI more than 0.04&#xa0;ng/mL or hsTnI more than 18&#xa0;ng/L was considered positive for potential BCI. Clinically significant BCI was defined as a new-bundle branch block, ST wave change, echocardiogram change, or need for cardiac catheterization.<br><br>RESULTS: Two hundred sixty five patients with a sternal fracture were identified, 161 underwent screening with TnI and 104 with hsTnI. For TnI, the sensitivity and specificity for detection of clinically significant BCI was 0.80 and 0.79, respectively. For hsTnI, the sensitivity and specificity for detection of clinically significant BCI was 0.71 and 0.69, respectively. A multivariate analysis demonstrated the odds ratio for significant BCI with a positive TnI was 14.4 (95% confidence interval, 3.9-55.8, P&#xa0;<&#xa0;0.0001) versus an odds ratio of 5.48 (95% confidence interval 1.9-15.7, P&#xa0;=&#xa0;0.002) in the hsTnI group.<br><br>CONCLUSIONS: The sensitivity of hsTnI is comparable to TnI for detection of significant BCI. Additional investigation is needed to determine the necessity and interval for repeat testing and the need for additional diagnostic testing.},
}
@article {pmid36151487,
year = {2022},
author = {Kapgate, D},
title = {Effective 2-D cursor control system using hybrid SSVEP + P300 visual brain computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {60},
number = {11},
pages = {3243-3254},
pmid = {36151487},
issn = {1741-0444},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; User-Computer Interface ; },
abstract = {A cursor control system based on brain-computer interface (BCI) provides efficient computer access. These systems operate without any muscular activity from the user. Conventional BCI-based cursor control systems have several limitations. Therefore, hybrid SSVEP + P300 visual BCI (VBCI)-based cursor control is needed to overcome these limitations. This paper explores the feasibility of using noninvasive hybrid SSVEP + P300 VBCI for cursor control as a universal form of computer access. The proposed cursor control system has a graphical user interface (GUI) design that simultaneously evokes both SSVEP and P300 signals in the human cortex. The performance metrics of the proposed system are compared with conventional SSVEP VBCI and P300 VBCI-based cursor control systems. The proposed hybrid SSVEP + P300 BCI-based cursor control system achieves a maximum accuracy of 97.51% with a 27.15 bit/min information transfer rate (ITR). The results proved that the proposed system performed more efficiently than other systems. The proposed system was tested in a noisy environment and found to be suitable for real-world applications.},
}
@article {pmid36150969,
year = {2022},
author = {Elston, TW and Wallis, JD},
title = {Decoding cognition in real-time.},
journal = {Trends in cognitive sciences},
volume = {26},
number = {12},
pages = {1073-1075},
doi = {10.1016/j.tics.2022.08.005},
pmid = {36150969},
issn = {1879-307X},
support = {R01 MH117763/MH/NIMH NIH HHS/United States ; R01 MH121448/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; *Cognition ; *Cognitive Science ; },
abstract = {How can we study unobservable cognitive processes that cannot be measured directly? This has been an enduring challenge for cognitive scientists. In this essay we discuss advances in neurotechnology that could allow cognitive processes to be decoded in real-time and the implications that this may have for cognitive science and the treatment of neuropsychiatric disease.},
}
@article {pmid36147561,
year = {2022},
author = {Wang, TY and Xia, FY and Gong, JW and Xu, XK and Lv, MC and Chatoo, M and Shamsi, BH and Zhang, MC and Liu, QR and Liu, TX and Zhang, DD and Lu, XJ and Zhao, Y and Du, JZ and Chen, XQ},
title = {CRHR1 mediates the transcriptional expression of pituitary hormones and their receptors under hypoxia.},
journal = {Frontiers in endocrinology},
volume = {13},
number = {},
pages = {893238},
pmid = {36147561},
issn = {1664-2392},
mesh = {Animals ; Hormones/metabolism ; *Hypothalamo-Hypophyseal System/metabolism ; Hypoxia/genetics/metabolism ; Pituitary-Adrenal System/metabolism ; Pro-Opiomelanocortin/genetics ; RNA, Messenger/genetics ; Rats ; *Receptors, Corticotropin-Releasing Hormone/genetics/metabolism ; Receptors, Cytokine/metabolism ; Transcription Factors/metabolism ; CRF Receptor, Type 1 ; },
abstract = {Hypothalamus-pituitary-adrenal (HPA) axis plays critical roles in stress responses under challenging conditions such as hypoxia, via regulating gene expression and integrating activities of hypothalamus-pituitary-targets cells. However, the transcriptional regulatory mechanisms and signaling pathways of hypoxic stress in the pituitary remain to be defined. Here, we report that hypoxia induced dynamic changes in the transcription factors, hormones, and their receptors in the adult rat pituitary. Hypoxia-inducible factors (HIFs), oxidative phosphorylation, and cAMP signaling pathways were all differentially enriched in genes induced by hypoxic stress. In the pituitary gene network, hypoxia activated c-Fos and HIFs with specific pituitary transcription factors (Prop1), targeting the promoters of hormones and their receptors. HIF and its related signaling pathways can be a promising biomarker during acute or constant hypoxia. Hypoxia stimulated the transcription of marker genes for microglia, chemokines, and cytokine receptors of the inflammatory response. Corticotropin-releasing hormone receptor 1 (CRHR1) mediated the transcription of Pomc, Sstr2, and Hif2a, and regulated the function of HPA axis. Together with HIF, c-Fos initiated and modulated dynamic changes in the transcription of hormones and their receptors. The receptors were also implicated in the regulation of functions of target cells in the pituitary network under hypoxic stress. CRHR1 played an integrative role in the hypothalamus-pituitary-target axes. This study provides new evidence for CRHR1 involved changes of hormones, receptors, signaling molecules and pathways in the pituitary induced by hypoxia.},
}
@article {pmid36146323,
year = {2022},
author = {Shah, U and Alzubaidi, M and Mohsen, F and Abd-Alrazaq, A and Alam, T and Househ, M},
title = {The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {18},
pages = {},
pmid = {36146323},
issn = {1424-8220},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Quality of Life ; Speech ; },
abstract = {Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain-computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.},
}
@article {pmid36146175,
year = {2022},
author = {Cousens, GA and Fotis, MM and Bradshaw, CM and Ramirez-Alvarado, YM and McKittrick, CR},
title = {Characterization of Retronasal Airflow Patterns during Intraoral Fluid Discrimination Using a Low-Cost, Open-Source Biosensing Platform.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {18},
pages = {},
pmid = {36146175},
issn = {1424-8220},
mesh = {Humans ; *Odorants ; *Smell/physiology ; },
abstract = {Nasal airflow plays a critical role in olfactory processes, and both retronasal and orthonasal olfaction involve sensorimotor processes that facilitate the delivery of volatiles to the olfactory epithelium during odor sampling. Although methods are readily available for monitoring nasal airflow characteristics in laboratory and clinical settings, our understanding of odor sampling behavior would be enhanced by the development of inexpensive wearable technologies. Thus, we developed a method of monitoring nasal air pressure using a lightweight, open-source brain-computer interface (BCI) system and used the system to characterize patterns of retronasal airflow in human participants performing an oral fluid discrimination task. Participants exhibited relatively sustained low-rate retronasal airflow during sampling punctuated by higher-rate pulses often associated with deglutition. Although characteristics of post-deglutitive pulses did not differ across fluid conditions, the cumulative duration, probability, and estimated volume of retronasal airflow were greater during discrimination of perceptually similar solutions. These findings demonstrate the utility of a consumer-grade BCI system in assessing human olfactory behavior. They suggest further that sensorimotor processes regulate retronasal airflow to optimize the delivery of volatiles to the olfactory epithelium and that discrimination of perceptually similar oral fluids may be accomplished by varying the duration of optimal airflow rate.},
}
@article {pmid36144152,
year = {2022},
author = {Ide, K and Takahashi, S},
title = {A Review of Neurologgers for Extracellular Recording of Neuronal Activity in the Brain of Freely Behaving Wild Animals.},
journal = {Micromachines},
volume = {13},
number = {9},
pages = {},
pmid = {36144152},
issn = {2072-666X},
support = {19H01131 (ST), 19K12768 (KI), and 21H05296 (ST)//Japanese Society for the Promotion of Science Kakenhi/ ; },
abstract = {Simultaneous monitoring of animal behavior and neuronal activity in the brain enables us to examine the neural underpinnings of behaviors. Conventionally, the neural activity data are buffered, amplified, multiplexed, and then converted from analog to digital in the head-stage amplifier, following which they are transferred to a storage server via a cable. Such tethered recording systems, intended for indoor use, hamper the free movement of animals in three-dimensional (3D) space as well as in large spaces or underwater, making it difficult to target wild animals active under natural conditions; it also presents challenges in realizing its applications to humans, such as the Brain-Machine Interfaces (BMI). Recent advances in micromachine technology have established a wireless logging device called a neurologger, which directly stores neural activity on ultra-compact memory media. The advent of the neurologger has triggered the examination of the neural correlates of 3D flight, underwater swimming of wild animals, and translocation experiments in the wild. Examples of the use of neurologgers will provide an insight into understanding the neural underpinnings of behaviors in the natural environment and contribute to the practical application of BMI. Here we outline the monitoring of the neural underpinnings of flying and swimming behaviors using neurologgers. We then focus on neuroethological findings and end by discussing their future perspectives.},
}
@article {pmid36144108,
year = {2022},
author = {Zhang, J and Liu, D and Chen, W and Pei, Z and Wang, J},
title = {Deep Convolutional Neural Network for EEG-Based Motor Decoding.},
journal = {Micromachines},
volume = {13},
number = {9},
pages = {},
pmid = {36144108},
issn = {2072-666X},
support = {No. 61773042 and No. 51675018//National Natural Science Foundation of China/ ; No. 2016YFE0105000//National Key R&amp;D Program of China/ ; },
abstract = {Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.},
}
@article {pmid36144107,
year = {2022},
author = {Wang, M and Zhang, Y and Bin, J and Niu, L and Zhang, J and Liu, L and Wang, A and Tao, J and Liang, J and Zhang, L and Kang, X},
title = {Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface.},
journal = {Micromachines},
volume = {13},
number = {9},
pages = {},
pmid = {36144107},
issn = {2072-666X},
support = {no. 61904038 and no. U1913216//National Natural Science Foundation of China/ ; no. 2021YFC0122702//National Key Research and Development Program of China/ ; no. 19YF1403600//Shanghai Sailing Program/ ; no. 19441907600, no.19441908200, and no. 19511132000//Shanghai Municipal Science and Technology Commission/ ; no.FC2019-002//Fudan University-CIOMP Joint Fund/ ; no. KEH2310024//Opening Project of Shanghai Robot Industry R&amp;D and Transformation Functional Platform/ ; no. 2021MC0AB01//Opening Project of Zhejiang Lab/ ; no. X190021TB190//Ji Hua Laboratory/ ; no. 2021SHZDZX0103 and no. 2018SHZDZX01//Shanghai Municipal Science and Technology Major Project/ ; },
abstract = {PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain-computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface. This study investigated and optimized the effect of excimer laser cutting parameters on the electrochemical impedance of a neural electrode by using orthogonal experiment design. Electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Furthermore, the parameters of the equivalent circuit model are fitted, which reveals details about the electrochemical property of neural electrode using PDMS as an encapsulation layer. Our experimental findings suggest the promising application of excimer lasers in the micro-machining of implantable neural interface.},
}
@article {pmid36141685,
year = {2022},
author = {Pap, IA and Oniga, S},
title = {A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {18},
pages = {},
pmid = {36141685},
issn = {1660-4601},
mesh = {Artificial Intelligence ; *COVID-19/epidemiology ; Delivery of Health Care ; Humans ; Pandemics ; *Telemedicine ; },
abstract = {Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.},
}
@article {pmid36141073,
year = {2022},
author = {Li, Q and Liu, Y and Shang, Y and Zhang, Q and Yan, F},
title = {Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {9},
pages = {},
pmid = {36141073},
issn = {1099-4300},
support = {20200401095GX//Yunqing Liu/ ; },
abstract = {Recently, emotional electroencephalography (EEG) has been of great importance in brain-computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method.},
}
@article {pmid36140136,
year = {2022},
author = {Muñoz, D and Barria, P and Cifuentes, CA and Aguilar, R and Baleta, K and Azorín, JM and Múnera, M},
title = {EEG Evaluation in a Neuropsychological Intervention Program Based on Virtual Reality in Adults with Parkinson's Disease.},
journal = {Biosensors},
volume = {12},
number = {9},
pages = {},
pmid = {36140136},
issn = {2079-6374},
mesh = {Adult ; Cognition/physiology ; Electroencephalography ; Humans ; *Parkinson Disease ; *Virtual Reality ; },
abstract = {Nowadays, several strategies for treating neuropsychologic function loss in Parkinson’s disease (PD) have been proposed, such as physical activity performance and developing games to exercise the mind. However, few studies illustrate the incidence of these therapies in neuronal activity. This work aims to study the feasibility of a virtual reality-based program oriented to the cognitive functions’ rehabilitation of PD patients. For this, the study was divided into intervention with the program, acquisition of signals, data processing, and results analysis. The alpha and beta bands’ power behavior was determined by evaluating the electroencephalography (EEG) signals obtained during the execution of control tests and games of the “Hand Physics Lab” Software, from which five games related to attention, planning, and sequencing, concentration, and coordination were taken. Results showed the characteristic performance of the cerebral bands during resting states and activity states. In addition, it was determined that the beta band increased its activity in all the cerebral lobes in all the tested games (p-value < 0.05). On the contrary, just one game exhibited an adequate performance of the alpha band activity of the temporal and frontal lobes (p-value < 0.02). Furthermore, the visual attention and the capacity to process and interpret the information given by the surroundings was favored during the execution of trials (p-value < 0.05); thus, the efficacy of the virtual reality program to recover cognitive functions was verified. The study highlights implementing new technologies to rehabilitate people with neurodegenerative diseases.},
}
@article {pmid36139033,
year = {2022},
author = {Jiang, L and Ding, X and Wang, W and Yang, X and Li, T and Lei, P},
title = {Head-to-Head Comparison of Different Blood Collecting Tubes for Quantification of Alzheimer's Disease Biomarkers in Plasma.},
journal = {Biomolecules},
volume = {12},
number = {9},
pages = {},
pmid = {36139033},
issn = {2218-273X},
mesh = {Adult ; *Alzheimer Disease/diagnosis ; Amyloid beta-Peptides ; Biomarkers ; Edetic Acid ; Female ; Heparin ; Humans ; Lithium ; Male ; Peptide Fragments ; Young Adult ; tau Proteins ; },
abstract = {To examine whether the type of blood collection tubes affects the quantification of plasma biomarkers for Alzheimer's disease analyzed with a single-molecule array (Simoa), we recruited a healthy cohort (n = 34, 11 males, mean age = 28.7 ± 7.55) and collected plasma in the following tubes: dipotassium ethylenediaminetetraacetic acid (K2-EDTA), heparin lithium (Li-Hep), and heparin sodium (Na-Hep). Plasma tau, phosphorylated tau 181 (p-tau181), amyloid β (1-40) (Aβ40), and amyloid β (1-42) (Aβ42) were quantified using Simoa. We compared the value of plasma analytes, as well as the effects of sex on the measurements. We found that plasma collected in Li-Hep and Na-Hep tubes yielded significantly higher tau and p-tau181 levels compared to plasma collected in K2-EDTA tubes from the same person, but there was no difference in the measured values of the Aβ40, Aβ42, and Aβ42/40 ratio. Therefore, the type of blood collecting tubes should be considered when planning studies that measure plasma tau.},
}
@article {pmid36138969,
year = {2022},
author = {Gao, S and Yang, J and Shen, T and Jiang, W},
title = {A Parallel Feature Fusion Network Combining GRU and CNN for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {12},
number = {9},
pages = {},
pmid = {36138969},
issn = {2076-3425},
abstract = {In recent years, deep-learning-based motor imagery (MI) electroencephalography (EEG) decoding methods have shown great potential in the field of the brain-computer interface (BCI). The existing literature is relatively mature in decoding methods for two classes of MI tasks. However, with the increase in MI task classes, decoding studies for four classes of MI tasks need to be further explored. In addition, it is difficult to obtain large-scale EEG datasets. When the training data are limited, deep-learning-based decoding models are prone to problems such as overfitting and poor robustness. In this study, we design a data augmentation method for MI-EEG. The original EEG is slid along the time axis and reconstructed to expand the size of the dataset. Second, we combine the gated recurrent unit (GRU) and convolutional neural network (CNN) to construct a parallel-structured feature fusion network to decode four classes of MI tasks. The parallel structure can avoid temporal, frequency and spatial features interfering with each other. Experimenting on the well-known four-class MI dataset BCI Competition IV 2a shows a global average classification accuracy of 80.7% and a kappa value of 0.74. The proposed method improves the robustness of deep learning to decode small-scale EEG datasets and alleviates the overfitting phenomenon caused by insufficient data. The method can be applied to BCI systems with a small amount of daily recorded data.},
}
@article {pmid36138897,
year = {2022},
author = {Zhao, R and Zhang, T and Zhou, S and Huang, L},
title = {Emotional Brain Network Community Division Study Based on an Improved Immunogenetic Algorithm.},
journal = {Brain sciences},
volume = {12},
number = {9},
pages = {},
pmid = {36138897},
issn = {2076-3425},
support = {61977039//National Natural Science Foundation of China/ ; },
abstract = {Emotion analysis has emerged as one of the most prominent study areas in the field of Brain Computer Interface (BCI) due to the critical role that the human brain plays in the creation of human emotions. In this study, a Multi-objective Immunogenetic Community Division Algorithm Based on Memetic Framework (MFMICD) was suggested to study different emotions from the perspective of brain networks. To improve convergence and accuracy, MFMICD incorporates the unique immunity operator based on the traditional genetic algorithm and combines it with the taboo search algorithm. Based on this approach, we examined how the structure of people's brain networks alters in response to different emotions using the electroencephalographic emotion database. The findings revealed that, in positive emotional states, more brain regions are engaged in emotion dominance, the information exchange between local modules is more frequent, and various emotions cause more varied patterns of brain area interactions than in negative brain states. A brief analysis of the connections between different emotions and brain regions shows that MFMICD is reliable in dividing emotional brain functional networks into communities.},
}
@article {pmid36138888,
year = {2022},
author = {Liu, K and Yu, Y and Zeng, LL and Liang, X and Liu, Y and Chu, X and Lu, G and Zhou, Z},
title = {Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {12},
number = {9},
pages = {},
pmid = {36138888},
issn = {2076-3425},
support = {62006239//National Natural Science Foundation of China/ ; 62036013//National Natural Science Foundation of China/ ; 61722313//National Natural Science Foundation of China/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; U19A2083//joint Funds of National Natural Science Fundation of China/ ; },
abstract = {Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user’s mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.},
}
@article {pmid36137226,
year = {2023},
author = {Xavier Macedo de Azevedo, F and Heimgärtner, R and Nebe, K},
title = {Development of a metric to evaluate the ergonomic principles of assistive systems, based on the DIN 92419.},
journal = {Ergonomics},
volume = {66},
number = {6},
pages = {821-848},
doi = {10.1080/00140139.2022.2127920},
pmid = {36137226},
issn = {1366-5847},
mesh = {Humans ; Reproducibility of Results ; Canada ; *Self-Help Devices ; *Wheelchairs ; Ergonomics ; Surveys and Questionnaires ; },
abstract = {The DIN 92419 defines six principles for assistive systems' ergonomic design. There is, however, a lack of measurement tools to evaluate assistive systems considering these principles. Consequently, this study developed a measurement tool for the quantitative evaluation of the fulfilment of each principle for assistive systems. A systematic literature review was performed to identify dimensions belonging to the principles, identify how previous research evaluated these dimensions, and develop a measurement tool for assistive systems. Findings show that scales commonly used for evaluating assistive systems disregard several aspects highlighted as relevant by research, implying the need for considering the DIN 92419 principles. Based on established scales and theoretical findings, a questionnaire, and a checklist for evaluating assistive systems were developed. The work provides a grounding for measuring relevant aspects of assistive systems. Further development is needed to substantiate the reliability and validity of the proposed questionnaire scales and items. Practitioner Summary: Responding to the gap of a holistic measurement tool to evaluate assistive systems, a systematic literature review was performed considering the DIN 92419 principles. This resulted in a comprehensive summary of relevant aspects of assistive systems that were made numerically measurable, which proposes better criteria to assess assistive systems. Abbreviations: IoT: internet of things; RQ: research question; TAM: technology acceptance model; UTAUT: unified theory of acceptance and use of technology; AaaS: adaptivity as a service; SAR: socially assistive robots; SEEV: salience, effort, expectancy, and value; PRISMA: preferred reporting items for systematic reviews and meta-analyses; HMI: human-machine interaction; HRI: human-robot interaction; BCI: brain-computer interface; QUEST: Quebec user evaluation of satisfaction with assistive technology; SUS: system usability scale; NASA-TLX: NASA task load index; ATD PA: assistive technology device predisposition assessment; Wheel Con: wheelchair use confidence scale; CATOM: caregiver assistive technology outcome measure; CBI: caregiver burden inventory; RoSAS: robotic social attributes scale; WheelCon: wheelchair use confidence scale; IMI: intrinsic motivation inventory; ATD PA: assistive technology device predisposition assessment; UEQ: User experience questionnaire; USEUQ: usefulness satisfaction and ease of use questionnaire; USPW: usability scale for power wheelchairs; UES: user engagement scale; SUTAQ: service user technology acceptability questionnaire; QUEAD: questionnaire for the evaluation of physical assistive devices; FATCAT: functional assessment tool for cognitive assistive technology; SE-HRI: human-robot interaction scale; SART: situation awareness rating technique; TSQ;WT: tele-healthcare satisfaction questionnaire-wearable technology; PAIF: participants' assessment of the intervention's feasibility; SWAT: subjective workload assessment technique; MARS-HA: measure of audiologic rehabilitation self-efficacy for hearing aids; IOI-HA: International outcome inventory for hearing aids; FMA: functional mobility assessment; FBIS: familiarity and behavioural intention survey; CSQ: client satisfaction questionnaire; COPM: canadian occupational performance measure; ATCS: assistive technology confidence scale; ACC: acceptance; SSP: safety, security and privacy; OPT: optimisation of resultant internal load; CTRL: controllability; ADAPT: adaptability; P&amp;I: perceptibility and identifiability; AAL: ambient assisted living; VR: virtual reality; AS: assistive system; WEIRD: Western, educated, industrialised, rich, and democratic; HEART: horizontal european activities of rehabilitation technology; AAATE: advancement of assistive technology in Europe's; GATE: global collaboration on assistive technology; ATA-C: assistive technology assessment toolkit.},
}
@article {pmid36136927,
year = {2022},
author = {Chen, X and Liu, B and Wang, Y and Gao, X},
title = {A Spectrally-Dense Encoding Method for Designing a High-Speed SSVEP-BCI With 120 Stimuli.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2764-2772},
doi = {10.1109/TNSRE.2022.3208717},
pmid = {36136927},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {The practical functionality of a brain-computer interface (BCI) is critically affected by the number of stimuli, especially for steady-state visual evoked potential based BCI (SSVEP-BCI), which shows promise for the implementation of a multi-target system for real-world applications. Joint frequency-phase modulation (JFPM) is an effective and widely used method in modulating SSVEPs. However, the ability of JFPM to implement an SSVEP-BCI system with a large number of stimuli, e.g., over 100 stimuli, remains unclear. To address this issue, a spectrally-dense JPFM (sJFPM) method is proposed to encode a broad array of stimuli, which modulates the low- and medium-frequency SSVEPs with a frequency interval of 0.1 Hz and triples the number of stimuli in conventional SSVEP-BCI to 120. To validate the effectiveness of the proposed 120-target BCI system, an offline experiment and a subsequent online experiment testing 18 healthy subjects in total were conducted. The offline experiment verified the feasibility of using sJFPM in designing an SSVEP-BCI system with 120 stimuli. Furthermore, the online experiment demonstrated that the proposed system achieved an average performance of 92.47±1.83% in online accuracy and 213.23±6.60 bits/min in online information transfer rate (ITR), where more than 75% of the subjects attained the accuracy above 90% and the ITR above 200 bits/min. This present study demonstrates the effectiveness of sJFPM in elevating the number of stimuli to more than 100 and extends our understanding of encoding a large number of stimuli by means of finer frequency division.},
}
@article {pmid36136926,
year = {2022},
author = {Tao, Y and Xu, W and Wang, G and Yuan, Z and Wang, M and Houston, M and Zhang, Y and Chen, B and Yan, X and Wang, G},
title = {Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2754-2763},
doi = {10.1109/TNSRE.2022.3208710},
pmid = {36136926},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Movement ; Neural Networks, Computer ; },
abstract = {Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.},
}
@article {pmid36133917,
year = {2022},
author = {Tao, S and Zhang, Y and Wang, Q and Qiao, C and Deng, W and Liang, S and Wei, J and Wei, W and Yu, H and Li, X and Li, M and Guo, W and Ma, X and Zhao, L and Li, T},
title = {Identifying transdiagnostic biological subtypes across schizophrenia, bipolar disorder, and major depressive disorder based on lipidomics profiles.},
journal = {Frontiers in cell and developmental biology},
volume = {10},
number = {},
pages = {969575},
pmid = {36133917},
issn = {2296-634X},
abstract = {Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.},
}
@article {pmid36131024,
year = {2022},
author = {Su, N and Zhu, A and Tao, X and Ding, ZJ and Chang, S and Ye, F and Zhang, Y and Zhao, C and Chen, Q and Wang, J and Zhou, CY and Guo, Y and Jiao, S and Zhang, S and Wen, H and Ma, L and Ye, S and Zheng, SJ and Yang, F and Wu, S and Guo, J},
title = {Publisher Correction: Structures and mechanisms of the Arabidopsis auxin transporter PIN3.},
journal = {Nature},
volume = {610},
number = {7930},
pages = {E2},
doi = {10.1038/s41586-022-05360-2},
pmid = {36131024},
issn = {1476-4687},
}
@article {pmid36130589,
year = {2022},
author = {Wen, Y and He, W and Zhang, Y},
title = {A new attention-based 3D densely connected cross-stage-partial network for motor imagery classification in BCI.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac93b4},
pmid = {36130589},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective. The challenge for motor imagery (MI) in brain-computer interface (BCI) systems is finding a reliable classification model that has high classification accuracy and excellent robustness. Currently, one of the main problems leading to degraded classification performance is the inaccuracy caused by nonstationarities and low signal-to-noise ratio in electroencephalogram (EEG) signals.Approach. This study proposes a novel attention-based 3D densely connected cross-stage-partial network (DCSPNet) model to achieve efficient EEG-based MI classification. This is an end-to-end classification model framework based on the convolutional neural network (CNN) architecture. In this framework, to fully utilize the complementary features in each dimension, the optimal features are extracted adaptively from the EEG signals through the spatial-spectral-temporal (SST) attention mechanism. The 3D DCSPNet is introduced to reduce the gradient loss by segmenting the extracted feature maps to strengthen the network learning capability. Additionally, the design of the densely connected structure increases the robustness of the network.Main results. The performance of the proposed method was evaluated using the BCI competition IV 2a and the high gamma dataset, achieving an average accuracy of 84.45% and 97.88%, respectively. Our method outperformed most state-of-the-art classification algorithms, demonstrating its effectiveness and strong generalization ability.Significance.The experimental results show that our method is promising for improving the performance of MI-BCI. As a general framework based on time-series classification, it can be applied to BCI-related fields.},
}
@article {pmid36129927,
year = {2022},
author = {Shimizu, H and Srinivasan, R},
title = {Improving classification and reconstruction of imagined images from EEG signals.},
journal = {PloS one},
volume = {17},
number = {9},
pages = {e0274847},
pmid = {36129927},
issn = {1932-6203},
mesh = {Algorithms ; Attention ; Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; },
abstract = {Decoding brain activity related to specific tasks, such as imagining something, is important for brain computer interface (BCI) control. While decoding of brain signals, such as functional magnetic resonance imaging (fMRI) signals and electroencephalography (EEG) signals, during observing visual images and while imagining images has been previously reported, further development of methods for improving training, performance, and interpretation of brain data was the goal of this study. We applied a Sinc-EEGNet to decode brain activity during perception and imagination of visual stimuli, and added an attention module to extract the importance of each electrode or frequency band. We also reconstructed images from brain activity by using a generative adversarial network (GAN). By combining the EEG recorded during a visual task (perception) and an imagination task, we have successfully boosted the accuracy of classifying EEG data in the imagination task and improved the quality of reconstruction by GAN. Our result indicates that the brain activity evoked during the visual task is present in the imagination task and can be used for better classification of the imagined image. By using the attention module, we can derive the spatial weights in each frequency band and contrast spatial or frequency importance between tasks from our model. Imagination tasks are classified by low frequency EEG signals over temporal cortex, while perception tasks are classified by high frequency EEG signals over occipital and frontal cortex. Combining data sets in training results in a balanced model improving classification of the imagination task without significantly changing performance in the visual task. Our approach not only improves performance and interpretability but also potentially reduces the burden on training since we can improve the accuracy of classifying a relatively hard task with high variability (imagination) by combining with the data of the relatively easy task, observing visual images.},
}
@article {pmid36129854,
year = {2022},
author = {Zhang, W and Song, A and Zeng, H and Xu, B and Miao, M},
title = {The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2732-2742},
doi = {10.1109/TNSRE.2022.3208312},
pmid = {36129854},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand/physiology ; Humans ; Imagination/physiology ; *Stroke Rehabilitation/methods ; Vibration ; },
abstract = {Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closed-loop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closed-loop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phase-dependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closed-loop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.},
}
@article {pmid36126733,
year = {2022},
author = {Liang, Z and Wang, X and Zhao, J and Li, X},
title = {Comparative study of attention-related features on attention monitoring systems with a single EEG channel.},
journal = {Journal of neuroscience methods},
volume = {382},
number = {},
pages = {109711},
doi = {10.1016/j.jneumeth.2022.109711},
pmid = {36126733},
issn = {1872-678X},
mesh = {Humans ; *Electroencephalography/methods ; *Attention ; Entropy ; Monitoring, Physiologic ; Computers ; Algorithms ; },
abstract = {The easy-to-use attention monitoring systems usually detect the participant's attentional status via processing electroencephalogram (EEG) data recorded from a single FPz channel. But due to the influence of noises and artifacts, the attention-monitoring performance needs to be further improved to suit different individuals and devices. This paper compared the attention-related features extracted using four state-of-the-art methods including delta/beta1 (D/B1), α + β + δ + θ + R, entropy and optimized complex network (OCN). The classification performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curves (AUC) on two EEG data acquisition devices, i.e., a BrainAmp device with high precision and a Sichiray device with low cost, respectively. Considering the varied performance on different individuals and devices, this paper proposed a novel Mutual information-based feature fusion (MIFF) method, selecting the optimal combinations of the attention-related features for classification, to enhance the attention detection performance. The experimental results showed that the proposed MIFF method outperformed the state-of-the-art methods regardless of data length on both devices. Especially, the proposed method with data length of 2.5 s achieved an average AUC of 0.8505 on the low-cost Sichiray device, which is 56.08 % higher than that of D/B1, 27.28 % higher than that of α + β + δ + θ + R, 17.42 % higher than that of entropy, and 15.48 % higher than that of OCN.},
}
@article {pmid36126643,
year = {2022},
author = {Qu, T and Jin, J and Xu, R and Wang, X and Cichocki, A},
title = {Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and Riemannian tangent space for MI-BCIs.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac9338},
pmid = {36126643},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination/physiology ; Support Vector Machine ; },
abstract = {Objective.Motor imagery-based brain computer interfaces (MI-BCIs) have been widely researched because they do not demand external stimuli and have a high degree of maneuverability. In most scenarios, superabundant selected channels, fixed time windows, and frequency bands would certainly affect the performance of MI-BCIs due to the neurophysiological diversities among different individuals. In this study, we attempt to effectively use the Riemannian geometry of spatial covariance matrix to extract more robust features and thus enhance the decoding efficiency.Approach.First, we utilize a Riemannian distance-based electroencephalography (EEG) channel selection method, which preliminarily reduces the information redundancy in the first stage. Second, we extract discriminative Riemannian tangent space features of EEG signals of selected channels from the most discriminant time-frequency bands to further enhance decoding accuracy for MI-BCIs. Finally, we train a support vector machine model with a linear kernel to classify our extracted discriminative Riemannian features, and evaluate our proposed method using publicly available BCI Competition IV dataset Ⅰ (DS1) and Competition Ⅲ dataset Ⅲa (DS2).Main results.The experimental results show that the average classification accuracy with the selected 16-channel EEG signals of our method is 90.0% and 89.4% in DS1 and DS2 respectively. The average improvements are 20.0% and 21.2% on DS1, 9.4% and 7.2% on DS2 for 8 and 16 selected channels, respectively.Significance.These results show that our proposed method is a promising candidate for the performance improvement of MI-BCIs.},
}
@article {pmid36126617,
year = {2022},
author = {Guo, L and Qi, YJ and Tan, H and Dai, D and Balesar, R and Sluiter, A and van Heerikhuize, J and Hu, SH and Swaab, DF and Bao, AM},
title = {Different oxytocin and corticotropin-releasing hormone system changes in bipolar disorder and major depressive disorder patients.},
journal = {EBioMedicine},
volume = {84},
number = {},
pages = {104266},
pmid = {36126617},
issn = {2352-3964},
mesh = {Animals ; *Bipolar Disorder ; Corticotropin-Releasing Hormone/genetics/metabolism ; *Depressive Disorder, Major ; Female ; Male ; Mice ; Oxytocin ; RNA, Messenger/genetics ; },
abstract = {BACKGROUND: Oxytocin (OXT) and corticotropin-releasing hormone (CRH) are both produced in hypothalamic paraventricular nucleus (PVN). Central CRH may cause depression-like symptoms, while peripheral higher OXT plasma levels were proposed to be a trait marker for bipolar disorder (BD). We aimed to investigate differential OXT and CRH expression in the PVN and their receptors in prefrontal cortex of major depressive disorder (MDD) and BD patients. In addition, we investigated mood-related changes by stimulating PVN-OXT in mice.<br><br>METHODS: Quantitative immunocytochemistry and in situ hybridization were performed in the PVN for OXT and CRH on 6 BD and 6 BD-controls, 9 MDD and 9 MDD-controls. mRNA expressions of their receptors (OXTR, CRHR1 and CRHR2) were determined in anterior cingulate cortex and dorsolateral prefrontal cortex (DLPFC) of 30 BD and 34 BD-controls, and 24 MDD and 12 MDD-controls. PVN of 41 OXT-cre mice was short- or long-term activated by chemogenetics, and mood-related behavior was compared with 26 controls.<br><br>FINDINGS: Significantly increased OXT-immunoreactivity (ir), OXT-mRNA in PVN and increased OXTR-mRNA in DLPFC, together with increased ratios of OXT-ir/CRH-ir and OXTR-mRNA/CRHR-mRNA were observed in BD, at least in male BD patients, but not in MDD patients. PVN-OXT stimulation induced depression-like behaviors in male mice, and mixed depression/mania-like behaviors in female mice in a time-dependent way.<br><br>INTERPRETATION: Increased PVN-OXT and DLPFC-OXTR expression are characteristic for BD, at least for male BD patients. Stimulation of PVN-OXT neurons induced mood changes in mice, in a pattern different from BD.<br><br>FUNDING: National Natural Science Foundation of China (81971268, 82101592).},
}
@article {pmid36125443,
year = {2023},
author = {Matsukawa, Y and Naito, Y and Ishida, S and Matsuo, K and Majima, T and Gotoh, M},
title = {Two types of detrusor underactivity in men with nonneurogenic lower urinary tract symptoms.},
journal = {Neurourology and urodynamics},
volume = {42},
number = {1},
pages = {73-79},
doi = {10.1002/nau.25044},
pmid = {36125443},
issn = {1520-6777},
mesh = {Male ; Humans ; *Urinary Bladder, Underactive ; Urinary Bladder ; Retrospective Studies ; *Lower Urinary Tract Symptoms ; *Urinary Bladder Neck Obstruction ; *Urinary Incontinence ; Urodynamics ; },
abstract = {AIMS: To clarify the clinical features of men with nonneurogenic detrusor underactivity (DU) by focusing on storage dysfunction (SD).<br><br>METHODS: We retrospectively reviewed the clinical and urodynamic data of men with nonneurogenic DU. Patients were divided into two groups according to the presence or absence of SD, such as detrusor overactivity (DO) and reduced bladder compliance (BC). Patient characteristics, lower urinary tract symptoms (LUTS), and urodynamic parameters were compared. DU was defined as bladder contractility index (BCI)&#x2009;&#x2264;&#x2009;100 and bladder outlet obstruction index&#xa0;(BOOI)&#x2009;&#x2264;&#x2009;40.<br><br>RESULTS: Of 212 men with DU, 123 (58.0%) had concomitant SD (SD&#x2009;+&#x2009;DU group), and 89 (42.0%) had only DU (DU-only group). Age, prostate volume, and severity of storage symptoms were significantly higher in the SD&#x2009;+&#x2009;DU group. Particularly, >80% of men in the SD&#x2009;+&#x2009;DU group met the diagnostic criteria for overactive bladder in Japan, which was significantly higher than the 26% of men in the DU-only group. The frequency of urinary urgency incontinence (UUI) was also significantly higher in the SD&#x2009;+&#x2009;DU group (65% vs. 12% in DU-only group). In contrast, voiding symptoms, including straining, were more severe in the DU-only group. Regarding the urodynamic parameters, compared to the DU-only group, bladder capacity was significantly smaller and BOOI and BCI were significantly higher in the SD&#x2009;+&#x2009;DU group. However, there was no significant difference in the maximum flow rate and bladder voiding efficiency.<br><br>CONCLUSIONS: Approximately 60% of men with DU had SD, such as DO and/or reduced BC, whereas the remaining 40% had increased bladder capacity without an increase in detrusor pressure during the storage phase. There were significant differences in the storage and voiding symptoms between the groups. It is important to divide patients with DU based on SD to accurately clarify the clinical picture of DU.},
}
@article {pmid36125116,
year = {2022},
author = {Guan, C and Aflalo, T and Zhang, CY and Amoruso, E and Rosario, ER and Pouratian, N and Andersen, RA},
title = {Stability of motor representations after paralysis.},
journal = {eLife},
volume = {11},
number = {},
pages = {},
pmid = {36125116},
issn = {2050-084X},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Fingers/physiology ; Humans ; Magnetic Resonance Imaging/methods ; *Motor Cortex/diagnostic imaging/physiology ; Movement/physiology ; Paralysis ; },
abstract = {Neural plasticity allows us to learn skills and incorporate new experiences. What happens when our lived experiences fundamentally change, such as after a severe injury? To address this question, we analyzed intracortical population activity in the posterior parietal cortex (PPC) of a tetraplegic adult as she controlled a virtual hand through a brain-computer interface (BCI). By attempting to move her fingers, she could accurately drive the corresponding virtual fingers. Neural activity during finger movements exhibited robust representational structure similar to fMRI recordings of able-bodied individuals' motor cortex, which is known to reflect able-bodied usage patterns. The finger representational structure was consistent throughout multiple sessions, even though the structure contributed to BCI decoding errors. Within individual BCI movements, the representational structure was dynamic, first resembling muscle activation patterns and then resembling the anticipated sensory consequences. Our results reveal that motor representations in PPC reflect able-bodied motor usage patterns even after paralysis, and BCIs can re-engage these stable representations to restore lost motor functions.},
}
@article {pmid36124975,
year = {2022},
author = {Vansteensel, MJ and Branco, MP and Leinders, S and Freudenburg, ZF and Schippers, A and Geukes, SH and Gaytant, MA and Gosselaar, PH and Aarnoutse, EJ and Ramsey, NF},
title = {Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome.},
journal = {Neurorehabilitation and neural repair},
volume = {36},
number = {10-11},
pages = {666-677},
pmid = {36124975},
issn = {1552-6844},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Locked-In Syndrome ; Communication ; Brain ; Electroencephalography ; },
abstract = {Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.},
}
@article {pmid36124150,
year = {2022},
author = {Pabba, K and Widmer, RJ and Nguyen, V and Martinez, MW},
title = {Cardiac Contusion Complicated by Heart Failure in a Young Athlete.},
journal = {JACC. Case reports},
volume = {4},
number = {17},
pages = {1124-1128},
pmid = {36124150},
issn = {2666-0849},
abstract = {Chest trauma is a relatively common injury in athletes. Here, we report a case of a cardiac contusion in a football player that led to hemodynamically significant low-output state. Early invasive management was critical in treatment with imaging playing an important role in diagnosis. (Level of Difficulty: Advanced.).},
}
@article {pmid36122609,
year = {2023},
author = {Caglar, HO and Duzgun, Z},
title = {Identification of upregulated genes in glioblastoma and glioblastoma cancer stem cells using bioinformatics analysis.},
journal = {Gene},
volume = {848},
number = {},
pages = {146895},
doi = {10.1016/j.gene.2022.146895},
pmid = {36122609},
issn = {1879-0038},
mesh = {Adult ; *Brain Neoplasms/pathology ; Computational Biology/methods ; ErbB Receptors/genetics ; Gene Expression Profiling/methods ; Gene Expression Regulation, Neoplastic ; *Glioblastoma/metabolism ; Humans ; Molecular Docking Simulation ; Neoplastic Stem Cells/metabolism ; },
abstract = {Glioblastoma (GBM) is the most common malignant brain tumor among adults. Cancer stem cells (CSCs) are known to drive treatment resistance and recurrence. However, a few CSC markers have been identified as therapeutic targets for GBM. This study aimed to show highly coexpressed genes in GBM CSCs and TCGA GBM samples and to identify possible therapeutic targets for GBM. The gene expression profiles of GBM CSCs were obtained from Gene Expression Omnibus database. After the differentially upregulated genes were screened, functional enrichment analyses were performed using DAVID and Reactome databases. For upregulated genes, biological processes were mainly associated with the regulation of transcription. Subsequently, a protein-protein interaction network was constructed for upregulated genes through STRING, in which DUSP6, FGFR3, EGFR, SOX2, NES, and PLP1 were further identified as hub genes via MCC and MNC methods. Expression profiles of hub genes and their association with survival were examined in TCGA GBM dataset using GEPIA2 platform. The expression levels of four hub genes were found to be increased in TCGA GBM samples. Of these, DUSP6 and SOX2 had prognostic value for patients with GBM. Molecular compounds targeting DUSP6 were searched through PubChem database. (E/Z)-BCI and BCI were found to be inhibitors of DUSP6. The molecular docking was performed using Autodock vina 1.02. The compounds showed strong binding capacities by forming various interactions with the ERK2 binding domain of DUSP6. Hence, the current study unravels the potential of (E/Z)-BCI and BCI compounds as possible anti-cancer molecules for GBM treatment.},
}
@article {pmid36121939,
year = {2022},
author = {Stuart, M and Lesaja, S and Shih, JJ and Schultz, T and Manic, M and Krusienski, DJ},
title = {An Interpretable Deep Learning Model for Speech Activity Detection Using Electrocorticographic Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2783-2792},
doi = {10.1109/TNSRE.2022.3207624},
pmid = {36121939},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; *Deep Learning ; Electrocorticography ; Humans ; Speech ; },
abstract = {Numerous state-of-the-art solutions for neural speech decoding and synthesis incorporate deep learning into the processing pipeline. These models are typically opaque and can require significant computational resources for training and execution. A deep learning architecture is presented that learns input bandpass filters that capture task-relevant spectral features directly from data. Incorporating such explainable feature extraction into the model furthers the goal of creating end-to-end architectures that enable automated subject-specific parameter tuning while yielding an interpretable result. The model is implemented using intracranial brain data collected during a speech task. Using raw, unprocessed timesamples, the model detects the presence of speech at every timesample in a causal manner, suitable for online application. Model performance is comparable or superior to existing approaches that require substantial signal preprocessing and the learned frequency bands were found to converge to ranges that are supported by previous studies.},
}
@article {pmid36120787,
year = {2022},
author = {Li, XY and Bao, YF and Xie, JJ and Qian, SX and Gao, B and Xu, M and Dong, Y and Burgunder, JM and Wu, ZY},
title = {The Chinese Version of UHDRS in Huntington's Disease: Reliability and Validity Assessment.},
journal = {Journal of Huntington's disease},
volume = {11},
number = {4},
pages = {407-413},
doi = {10.3233/JHD-220542},
pmid = {36120787},
issn = {1879-6400},
mesh = {Humans ; *Huntington Disease ; Reproducibility of Results ; East Asian People ; Neuropsychological Tests ; },
abstract = {BACKGROUND: The Unified Huntington's Disease Rating Scale (UHDRS) is a universal scale assessing disease severity of Huntington's disease (HD). However, the English version cannot be widely used in China, and the reliability and validity of the Chinese UHDRS have not yet been confirmed.<br><br>OBJECTIVE: To test the reliability and validity of Chinse UHDRS in patients with HD.<br><br>METHODS: Between August 2013 and August 2021, 159 HD patients, 40 premanifest HD, and 64 healthy controls were consecutively recruited from two medical centers in China and assessed by Chinese UHDRS. Internal consistency and interrater reliability of the scale were examined. Intercorrelation was performed to analyze the convergent and divergent validity of the scale. A receiver operating characteristic analysis was conducted to explore the optimal cutoff point of each cognitive test.<br><br>RESULTS: High internal consistency was found in Chinese UHDRS, and its Cronbach's alpha values of the motor, cognitive, behavioral and functional subscales were 0.954, 0.826, 0.804, and 0.954, respectively. The interrater reliability of the total motor score was 0.960. The convergent and divergent validity revealed that motor, cognitive and functional subscales strongly related to each other except for Problem Behavior Assessment. Furthermore, we not only provided the normal level of each cognitive test in controls, but also gave the optimal cutoff points of cognitive tests between controls and HD patients.<br><br>CONCLUSION: We demonstrate for the first time that the translated version of UHDRS is reliable for assessing HD patients in China. This can promote the universal use of UHDRS in clinical practice.},
}
@article {pmid36120085,
year = {2022},
author = {Triana-Guzman, N and Orjuela-Cañon, AD and Jutinico, AL and Mendoza-Montoya, O and Antelis, JM},
title = {Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {961089},
pmid = {36120085},
issn = {1662-5196},
abstract = {Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.},
}
@article {pmid36119719,
year = {2022},
author = {Mughal, NE and Khan, MJ and Khalil, K and Javed, K and Sajid, H and Naseer, N and Ghafoor, U and Hong, KS},
title = {EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {873239},
pmid = {36119719},
issn = {1662-5218},
abstract = {The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain-computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.},
}
@article {pmid36118975,
year = {2022},
author = {Hasslinger, J and Meregalli, M and Bölte, S},
title = {How standardized are "standard protocols"? Variations in protocol and performance evaluation for slow cortical potential neurofeedback: A systematic review.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {887504},
pmid = {36118975},
issn = {1662-5161},
abstract = {UNLABELLED: Neurofeedback (NF) aims to alter neural activity by enhancing self-regulation skills. Over the past decade NF has received considerable attention as a potential intervention option for many somatic and mental conditions and ADHD in particular. However, placebo-controlled trials have demonstrated insufficient superiority of NF compared to treatment as usual and sham conditions. It has been argued that the reason for limited NF effects may be attributable to participants' challenges to self-regulate the targeted neural activity. Still, there is support of NF efficacy when only considering so-called "standard protocols," such as Slow Cortical Potential NF training (SCP-NF). This PROSPERO registered systematic review following PRISMA criteria searched literature databases for studies applying SCP-NF protocols. Our review focus concerned the operationalization of self-regulatory success, and protocol-details that could influence the evaluation of self-regulation. Such details included; electrode placement, number of trials, length per trial, proportions of training modalities, handling of artifacts and skill-transfer into daily-life. We identified a total of 63 eligible reports published in the year 2000 or later. SCP-NF protocol-details varied considerably on most variables, except for electrode placement. However, due to the increased availability of commercial systems, there was a trend to more uniform protocol-details. Although, token-systems are popular in SCP-NF for ADHD, only half reported a performance-based component. Also, transfer exercises have become a staple part of SCP-NF. Furthermore, multiple operationalizations of regulatory success were identified, limiting comparability between studies, and perhaps usefulness of so-called transfer-exercises, which purpose is to facilitate the transfer of the self-regulatory skills into every-day life. While studies utilizing SCP as Brain-Computer-Interface mainly focused on the acquisition of successful self-regulation, clinically oriented studies often neglected this. Congruently, rates of successful regulators in clinical studies were mostly low (<50%). The relation between SCP self-regulation and behavior, and how symptoms in different disorders are affected, is complex and not fully understood. Future studies need to report self-regulation based on standardized measures, in order to facilitate both comparability and understanding of the effects on symptoms. When applied as treatment, future SCP-NF studies also need to put greater emphasis on the acquisition of self-regulation (before evaluating symptom outcomes).<br><br>https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021260087, Identifier: CRD42021260087.},
}
@article {pmid36112563,
year = {2022},
author = {Zhang, W and Wang, Z and Wu, D},
title = {Multi-Source Decentralized Transfer for Privacy-Preserving BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2710-2720},
doi = {10.1109/TNSRE.2022.3207494},
pmid = {36112563},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Privacy ; },
abstract = {Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra- and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacy-protection; then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.},
}
@article {pmid36111058,
year = {2022},
author = {Yang, J and Zhao, W and Liao, Y and Wu, S and Li, J and Jin, L and Liu, Q and Huang, F and Liang, L},
title = {Ocular surface disease index questionnaire as a sensitive test for primary screening of chronic ocular graft-versus-host disease.},
journal = {Annals of translational medicine},
volume = {10},
number = {16},
pages = {855},
pmid = {36111058},
issn = {2305-5839},
abstract = {BACKGROUND: After allogeneic hematopoietic stem cell transplantation (allo-HSCT), patients are followed up by transplant clinicians. Finding an effective primary screening method that transplant clinicians or patients can master is essential in the early referral of suspected chronic ocular graft-versus-host disease (coGVHD) to an ophthalmologist. This study investigated if the ocular surface disease index (OSDI) questionnaire could be used for coGVHD primary screening.<br><br>METHODS: This case-controlled, cross-sectional study enrolled 161 allo-HSCT patients. All participants completed an OSDI questionnaire and underwent a silt-lamp examination. Bulbar conjunctival injection (BCI) was assessed using torchlight, while tear volume was measured via the Schirmer test (ST). The receiver operating characteristic curve was used to evaluate the sensitivity, specificity, and cutoff values of OSDI, ST, and BCI grading. Performance comparisons of the 3 tests applied in isolation, parallel, and series were made.<br><br>RESULTS: There were 84 patients with and 77 patients without coGVHD. Compared to those without coGVHD, patients with coGVHD had significantly higher median values of OSDI, corneal fluorescein staining, conjunctival injection, conjunctival fibrosis, and meibum quality, but lower ST scores (All P values <0.001). The cutoff values for OSDI, ST, and BCI grade in the diagnosis of coGVHD were 19.4 points, 7 mm, and grade 0, respectively. The sensitivity and specificity of the tests based on the cutoff values were, respectively, 89.3% and 89.6% for OSDI, 91.7% and 59.7% for ST, and 78.6% and 70.1% for BCI. The area under the curve (AUC) value of OSDI was significantly higher than that of ST (0.931 vs. 0.826; P=0.010) and BCI grade (0.931 vs. 0.781; P<0.001). The AUC values of the combinations were lower than that of OSDI alone.<br><br>CONCLUSIONS: The OSDI questionnaire can be used as a simple screening test for coGVHD as demonstrated by its high sensitivity and specificity in the transplant clinic and patients' self-monitoring. An OSDI greater than 19.4 could be considered an ophthalmology referral criterion.},
}
@article {pmid36110124,
year = {2022},
author = {Zhang, W and Yang, H and Gao, M and Zhang, H and Shi, L and Yu, X and Zhao, R and Song, J and Du, G},
title = {Edaravone Dexborneol Alleviates Cerebral Ischemic Injury via MKP-1-Mediated Inhibition of MAPKs and Activation of Nrf2.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {4013707},
pmid = {36110124},
issn = {2314-6141},
mesh = {Animals ; *Brain Injuries ; *Dual Specificity Phosphatase 1/metabolism ; *Edaravone/pharmacology ; Extracellular Signal-Regulated MAP Kinases/metabolism ; JNK Mitogen-Activated Protein Kinases/metabolism ; Malondialdehyde ; NF-E2-Related Factor 2 ; *NF-kappa B ; Nitric Oxide ; Peroxidase ; Rats ; p38 Mitogen-Activated Protein Kinases/metabolism ; },
abstract = {The edaravone and dexborneol concentrated solution for injection (edaravone-dexborneol) is a medication used clinically to treat neurological impairment induced by ischemic stroke. This study was aimed at investigating the preventive effects and the underlying mechanisms of edaravone-dexborneol on cerebral ischemic injury. A rat four-vessel occlusion (4-VO) model was established, and the neuronal injury and consequent neurological impairment of rats was investigated. Brain tissue malondialdehyde (MDA), myeloperoxidase (MPO), and nitric oxide (NO) levels were determined. The levels of proteins in mitogen-activated protein kinases (MAPKs), nuclear factor erythroid 2-related factor 2 (Nrf2), and nuclear factor-κB (NF-κB) signaling pathways were determined by western immunoblotting. The function of mitogen-activated protein kinase phosphatase 1 (MKP-1) was investigated using both western blot and immunofluorescence methods, and the effect of the MKP-1 inhibitor, (2E)-2-benzylidene-3-(cyclohexylamino)-3H-inden-1-one (BCI), was investigated. The results indicated that edaravone-dexborneol alleviated neurological deficiency symptoms and decreased apoptosis and neuron damage in the hippocampal CA1 area of the ischemic rats. Edaravone-dexborneol increased the MKP-1 level; decreased the phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (p38 MAPK); inhibited NF-κB p65 activation; and boosted Nrf2 activation, all of which were partially reversed by the MKP-1 inhibitor, BCI. The above results indicated that the upregulation of MKP-1 contributed to the protective effects of edaravone-dexborneol against ischemic brain injury. Our findings support the hypothesis that edaravone-dexborneol can alleviate cerebral ischemic injury via the upregulation of MKP-1, which inhibits MAPKs and activates Nrf2.},
}
@article {pmid36108535,
year = {2023},
author = {El-Qawaqzeh, K and Anand, T and Richards, J and Hosseinpour, H and Nelson, A and Akl, MN and Obaid, O and Ditillo, M and Friese, R and Joseph, B},
title = {Predictors of Mortality in Blunt Cardiac Injury: A Nationwide Analysis.},
journal = {The Journal of surgical research},
volume = {281},
number = {},
pages = {22-32},
doi = {10.1016/j.jss.2022.07.047},
pmid = {36108535},
issn = {1095-8673},
mesh = {Adult ; Male ; Humans ; United States/epidemiology ; Middle Aged ; Aged ; Female ; Hemothorax ; *Thoracic Injuries/complications/diagnosis ; *Myocardial Contusions/complications/epidemiology ; *Wounds, Nonpenetrating/complications/diagnosis ; Injury Severity Score ; *Heart Injuries/etiology ; Retrospective Studies ; },
abstract = {INTRODUCTION: Blunt thoracic injury (BTI) is one of the most common causes of trauma admission in the United States and is uncommonly associated with cardiac injuries. Blunt cardiac injury (BCI) after blunt thoracic trauma is infrequent but carries a substantial risk of morbidity and sudden mortality. Our study aims to identify predictors of concomitant cardiac contusion among BTI patients and the predictors of mortality among patients presenting with BCI on a national level.<br><br>MATERIALS AND METHODS: We performed a 1-y (2017) analysis of the American College of Surgeons Trauma Quality Improvement Program. We included all adults (aged &#x2265; 18&#xa0;y) with the diagnosis of BTI. We excluded patients who were transferred, had a penetrating mechanism of injury, and who were dead on arrival. Our primary outcomes were the independent predictors of concomitant cardiac contusions among BTI patients and the predictors of mortality among BCI patients. Our secondary outcome measures were in-hospital complications, differences in injury patterns, and injury severity between the survivors and nonsurvivors of BCI.<br><br>RESULTS: A total of 125,696 patients with BTI were identified, of which 2368 patients had BCI. Mean age was 52&#xa0;&#xb1;&#xa0;20&#xa0;y, 67% were male, and median injury severity score was 14 [9-21]. The most common type of cardiac injury was cardiac contusion (43%). Age &#x2265; 65&#xa0;y, higher 4-h packed red blood cell requirements, motor vehicle collision mechanism of injury, and concomitant thoracic injuries (hemothorax, flail chest, lung contusion, sternal fracture, diaphragmatic injury, and thoracic aortic injuries) were independently associated with concomitant cardiac contusion among BTI patients (P value < 0.05). Age &#x2265; 65&#xa0;y, thoracic aortic injury, diaphragmatic injury, hemothorax, and a history of congestive heart failure were independently associated with mortality in BCI patients (P value < 0.05).<br><br>CONCLUSIONS: Predictors of concomitant cardiac contusion among BTI patients and mortality among BCI patients were identified. Guidelines on the management of BCI should incorporate these predictors for timely identification of high-risk patients.},
}
@article {pmid36108415,
year = {2022},
author = {Shoeibi, A and Moridian, P and Khodatars, M and Ghassemi, N and Jafari, M and Alizadehsani, R and Kong, Y and Gorriz, JM and Ramírez, J and Khosravi, A and Nahavandi, S and Acharya, UR},
title = {An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works.},
journal = {Computers in biology and medicine},
volume = {149},
number = {},
pages = {106053},
doi = {10.1016/j.compbiomed.2022.106053},
pmid = {36108415},
issn = {1879-0534},
mesh = {Algorithms ; *Deep Learning ; Electroencephalography/methods ; *Epilepsy/diagnostic imaging ; Humans ; Neuroimaging ; Seizures/diagnostic imaging ; },
abstract = {Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.},
}
@article {pmid36108075,
year = {2022},
author = {Keller, L and Stelzle, D and Schmidt, V and Carabin, H and Reinhold, AK and Keller, C and Welte, TM and Richter, V and Amos, A and Boeckman, L and Harrison, W and Winkler, AS},
title = {Community-level prevalence of epilepsy and of neurocysticercosis among people with epilepsy in the Balaka district of Malawi: A cross-sectional study.},
journal = {PLoS neglected tropical diseases},
volume = {16},
number = {9},
pages = {e0010675},
pmid = {36108075},
issn = {1935-2735},
mesh = {Bayes Theorem ; Cross-Sectional Studies ; *Epilepsy/complications/epidemiology ; Humans ; Malawi/epidemiology ; *Neurocysticercosis/complications/diagnosis/epidemiology ; Prevalence ; Seizures/epidemiology ; },
abstract = {BACKGROUND: Epilepsy and neurocysticercosis (NCC) prevalence estimates in sub-Saharan Africa are still scarce but show important variation due to the population studied and different screening and diagnosis strategies used. The aims of this study were to estimate the prevalence of epileptic seizures and epilepsy in the sampled population, and the proportion of NCC among people with epilepsy (PWE) in a large cross-sectional study in a rural district of southern Malawi.<br><br>METHODS: We conducted a community-based door-to-door screening study for epileptic seizures in Balaka, Malawi between October and December 2012. Past epileptic seizures were reported through a 15-item questionnaire answered by at least one person per household generating five major criteria. People who screened positive were further examined by a neurologist to establish diagnosis. Patients diagnosed with epilepsy were examined and offered Taenia solium cyst antigen and antibody serological tests, and a CT scan for the diagnosis of NCC.<br><br>RESULTS: In total, screening information on 69,595 individuals was obtained for lifetime occurrence of epileptic seizures. 3,100 (4.5%) participants screened positive, of whom 1,913 (62%) could be followed-up and underwent further assessment. Lifetime prevalence was 3.0% (95% Bayesian credible interval [CI] 2.8 to 3.1%) and 1.2% (95%BCI 0.9 to 1.6%) for epileptic seizures and epilepsy, respectively. NCC prevalence among PWE was estimated to be 4.4% (95%BCI 0.8 to 8.5%). A diagnosis of epilepsy was ultimately reached for 455 participants.<br><br>CONCLUSION: The results of this large community-based study contribute to the evaluation and understanding of the burden of epilepsy in the population and of NCC among PWE in sub-Saharan Africa.},
}
@article {pmid36106568,
year = {2023},
author = {Yu, X and Qi, X and Wei, L and Zhao, L and Deng, W and Guo, W and Wang, Q and Ma, X and Hu, X and Ni, P and Li, T},
title = {Fingolimod ameliorates schizophrenia-like cognitive impairments induced by phencyclidine in male rats.},
journal = {British journal of pharmacology},
volume = {180},
number = {2},
pages = {161-173},
doi = {10.1111/bph.15954},
pmid = {36106568},
issn = {1476-5381},
support = {//Science and Technology Department of Zhejiang Province/ ; 2022C03096//Key Technologies R &amp; D Program of Zhejiang Province/ ; //Hangzhou Municipal Health Commission, Project for Hangzhou Medical Disciplines of Excellence &amp; Key Project for Hangzhou Medical Disciplines/ ; LY22H090009//Natural Science Foundation of Zhejiang Province/ ; 81920108018//National Natural Science Foundation of China/ ; 82001412//National Natural Science Foundation of China/ ; 81901356//National Natural Science Foundation of China/ ; },
mesh = {Animals ; Rats ; Male ; Phencyclidine/toxicity ; *Schizophrenia/chemically induced/drug therapy/metabolism ; Fingolimod Hydrochloride/pharmacology/therapeutic use ; Brain-Derived Neurotrophic Factor ; Rats, Sprague-Dawley ; *Cognitive Dysfunction/chemically induced/drug therapy ; Cytokines ; Disease Models, Animal ; },
abstract = {BACKGROUND AND PURPOSE: Improvement of cognitive deficits in schizophrenia remains an unmet need owing to the lack of new therapies and drugs. Recent studies have reported that fingolimod, an immunomodulatory drug for treating multiple sclerosis, demonstrates anti-inflammatory and neuroprotective effects in several neurological disease models. This suggests its usefulness for ameliorating cognitive dysfunction in schizophrenia. Herein, we assessed the efficacy profile and mechanism of fingolimod in a rat model of phencyclidine (PCP)-induced schizophrenia.<br><br>EXPERIMENTAL APPROACH: Male Sprague-Dawley rats were treated with PCP for 14&#x2009;days. The therapeutic effect of fingolimod on cognitive function was assessed using the Morris water maze and fear conditioning tests. Hippocampal neurogenesis and the expression of astrocytes and microglia were evaluated using immunostaining. Cytokine expression was quantified using multiplexed flow cytometry. Brain-derived neurotrophic factor expression and phosphorylation of extracellular signal-regulated kinase were determined using western blot analysis.<br><br>KEY RESULTS: Fingolimod attenuated cognitive deficits and restored hippocampal neurogenesis in a dose-dependent manner in PCP-treated rats. Fingolimod treatment exerted anti-inflammatory effects by inhibiting microglial activation and IL-6 and IL-1&#x3b2; pro-inflammatory cytokine expression. The underlying mechanism involves the upregulation of brain-derived neurotrophic factor protein expression and activation of the ERK signalling pathway.<br><br>CONCLUSION AND IMPLICATIONS: This is the first preclinical assessment of the effects of fingolimod on cognitive function in a model for schizophrenia. Our results suggest the immune system plays an crucial role in cognitive alterations in schizophrenia and highlight the potential of immunomodulatory strategies to improve cognitive deficits in schizophrenia.},
}
@article {pmid36104988,
year = {2022},
author = {Zhang, S and Wang, S and Liu, R and Dong, H and Zhang, X and Tai, X},
title = {A bibliometric analysis of research trends of artificial intelligence in the treatment of autistic spectrum disorders.},
journal = {Frontiers in psychiatry},
volume = {13},
number = {},
pages = {967074},
pmid = {36104988},
issn = {1664-0640},
abstract = {OBJECTIVE: Autism Spectrum Disorder (ASD) is a serious neurodevelopmental disorder that has become the leading cause of disability in children. Artificial intelligence (AI) is a potential solution to this issue. This study objectively analyzes the global research situation of AI in the treatment of ASD from 1995 to 2022, aiming to explore the global research status and frontier trends in this field.<br><br>METHODS: Web of Science (WoS) and PubMed databese were searched for Literature related to AI on ASD from 1995 to April 2022. CiteSpace, VOSviewer, Pajek and Scimago Graphica were used to analyze the collaboration between countries/institutions/authors, clusters and bursts of keywords, as well as analyses on references.<br><br>RESULTS: A total of 448 literature were included, the total number of literature has shown an increasing trend. The most productive country and institution were the USA, and Vanderbilt University. The authors with the greatest contributions were Warren, Zachary, Sakar, Nilanjan and Swanson, Amy. the most prolific and cited journal is Journal of Autism and Developmental Disorders, the highest cited and co-cited articles were Dautenhahn (Socially intelligent robots: dimensions of human-robot interaction 2007) and Scassellati B (Robots for Use in Autism Research 2012). "Artificial Intelligence", "Brain Computer Interface" and "Humanoid Robot" were the hotspots and frontier trends of AI on ASD.<br><br>CONCLUSION: The application of AI in the treatment of ASD has attracted the attention of researchers all over the world. The education, social function and joint attention of children with ASD are the most concerned issues for global researchers. Robots shows gratifying advantages in these issues and have become the most commonly used technology. Wearable devices and brain-computer interface (BCI) were emerging AI technologies in recent years, which is the direction of further exploration. Restoring social function in individuals with ASD is the ultimate aim and driving force of research in the future.},
}
@article {pmid36103781,
year = {2022},
author = {Savya, SP and Li, F and Lam, S and Wellman, SM and Stieger, KC and Chen, K and Eles, JR and Kozai, TDY},
title = {In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation.},
journal = {Biomaterials},
volume = {289},
number = {},
pages = {121784},
pmid = {36103781},
issn = {1878-5905},
support = {R01 NS105691/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; F99 NS124186/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Astrocytes/metabolism ; Electrodes, Implanted ; *Gliosis/metabolism ; Mice ; Microelectrodes ; Microglia ; Neuroglia ; },
abstract = {Brain computer interfaces (BCIs), including penetrating microelectrode arrays, enable both recording and stimulation of neural cells. However, device implantation inevitably causes injury to brain tissue and induces a foreign body response, leading to reduced recording performance and stimulation efficacy. Astrocytes in the healthy brain play multiple roles including regulating energy metabolism, homeostatic balance, transmission of neural signals, and neurovascular coupling. Following an insult to the brain, they are activated and gather around the site of injury. These reactive astrocytes have been regarded as one of the main contributors to the formation of a glial scar which affects the performance of microelectrode arrays. This study investigates the dynamics of astrocytes within the first 2 weeks after implantation of an intracortical microelectrode into the mouse brain using two-photon microscopy. From our observation astrocytes are highly dynamic during this period, exhibiting patterns of process extension, soma migration, morphological activation, and device encapsulation that are spatiotemporally distinct from other glial cells, such as microglia or oligodendrocyte precursor cells. This detailed characterization of astrocyte reactivity will help to better understand the tissue response to intracortical devices and lead to the development of more effective intervention strategies to improve the functional performance of neural interfacing technology.},
}
@article {pmid36099220,
year = {2024},
author = {Hou, Y and Jia, S and Lun, X and Hao, Z and Shi, Y and Li, Y and Zeng, R and Lv, J},
title = {GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {35},
number = {6},
pages = {7312-7323},
doi = {10.1109/TNNLS.2022.3202569},
pmid = {36099220},
issn = {2162-2388},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Imagination/physiology ; Signal Processing, Computer-Assisted ; Deep Learning ; Brain/physiology ; Algorithms ; Adult ; },
abstract = {Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.},
}
@article {pmid36099009,
year = {2022},
author = {Athanasiou, A and Mitsopoulos, K and Praftsiotis, A and Astaras, A and Antoniou, P and Pandria, N and Petronikolou, V and Kasimis, K and Lyssas, G and Terzopoulos, N and Fiska, V and Kartsidis, P and Savvidis, T and Arvanitidis, A and Chasapis, K and Moraitopoulos, A and Nizamis, K and Kalfas, A and Iakovidis, P and Apostolou, T and Magras, I and Bamidis, P},
title = {Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.},
journal = {JMIR research protocols},
volume = {11},
number = {9},
pages = {e41152},
pmid = {36099009},
issn = {1929-0748},
abstract = {BACKGROUND: Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system.<br><br>OBJECTIVE: This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine.<br><br>METHODS: A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023.<br><br>RESULTS: A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024.<br><br>CONCLUSIONS: Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486.<br><br>PRR1-10.2196/41152.},
}
@article {pmid36097316,
year = {2023},
author = {Taniguchi, A and Yunoki, T and Oiwake, T and Hayashi, A},
title = {Association between tear meniscus dimensions and higher-order aberrations in patients with surgically treated lacrimal passage obstruction.},
journal = {International ophthalmology},
volume = {43},
number = {4},
pages = {1135-1141},
pmid = {36097316},
issn = {1573-2630},
mesh = {Humans ; Tears ; *Dry Eye Syndromes ; *Lacrimal Apparatus ; Cornea ; Tomography, Optical Coherence/methods ; *Meniscus ; },
abstract = {PURPOSE: To analyze the relationship between tear meniscus dimensions and higher-order aberrations (HOAs) in patients with lacrimal passage obstruction using anterior segment optical coherence tomography (AS-OCT).<br><br>METHODS: This study was a retrospective observational study of 71 eyes of 49 patients with lacrimal passage obstruction. These patients received sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) at Toyama University Hospital between August 2020 and October 2021. Using AS-OCT, tear meniscus height (TMH), tear meniscus area (TMA), and total corneal HOAs values were measured before and after surgery.<br><br>RESULTS: Surgical success was achieved in 69 eyes (97.1%). At the final observation, 62 eyes showed lacrimal patency (89.8%). The preoperative TMH, TMA, and HOAs values were 1.55&#x2009;&#xb1;&#x2009;0.96&#xa0;mm, 0.11&#x2009;&#xb1;&#x2009;0.14&#xa0;mm[2], and 0.37&#x2009;&#xb1;&#x2009;0.27&#xa0;&#xb5;m, respectively, and the final postoperative TMH, TMA, and HOAs values were 0.97&#x2009;&#xb1;&#x2009;0.74&#xa0;mm (p&#x2009;<&#x2009;0.0001), 0.06&#x2009;&#xb1;&#x2009;0.11&#xa0;mm[2] (p&#x2009;=&#x2009;0.02), and 0.29&#x2009;&#xb1;&#x2009;0.16&#xa0;&#xb5;m (p&#x2009;=&#x2009;0.001), respectively. The results showed a significant improvement. The changes in HOAs before and after surgery were positively correlated with the changes in TMH (r&#x2009;=&#x2009;0.3476, p&#x2009;=&#x2009;0.0241) and TMA (r&#x2009;=&#x2009;0.3653, p&#x2009;=&#x2009;0.0174).<br><br>CONCLUSION: SG-BCI for lacrimal passage obstruction resulted in a significant decrease in measured HOAs. The decrease in HOAs was correlated with decreases in tear meniscus dimensions.},
}
@article {pmid36092985,
year = {2022},
author = {Hou, S and Fan, D and Wang, Q},
title = {Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal.},
journal = {Applied mathematics and mechanics},
volume = {43},
number = {9},
pages = {1399-1414},
pmid = {36092985},
issn = {1573-2754},
abstract = {In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D 1 neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D 1, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D 1-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.},
}
@article {pmid36092645,
year = {2022},
author = {Pereira, JA and Ray, A and Rana, M and Silva, C and Salinas, C and Zamorano, F and Irani, M and Opazo, P and Sitaram, R and Ruiz, S},
title = {A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {933559},
pmid = {36092645},
issn = {1662-5161},
abstract = {Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.},
}
@article {pmid36090260,
year = {2022},
author = {Wang, J and Zhang, J and Yu, H and Shi, B},
title = {Editorial: Human machine interface-based neuromodulation solutions for neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {987455},
doi = {10.3389/fnins.2022.987455},
pmid = {36090260},
issn = {1662-4548},
}
@article {pmid36090185,
year = {2022},
author = {Girdler, B and Caldbeck, W and Bae, J},
title = {Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review.},
journal = {Frontiers in systems neuroscience},
volume = {16},
number = {},
pages = {836778},
pmid = {36090185},
issn = {1662-5137},
abstract = {Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL's applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm's learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.},
}
@article {pmid36089460,
year = {2022},
author = {Shah, S and Shaing, C and Khatib, J and Lodrigues, W and Dreadin-Pulliam, J and Anderson, BB and Unni, N and Farr, D and Li, HC and Sadeghi, N and Syed, S},
title = {The Utility of Breast Cancer Index (BCI) Over Clinical Prognostic Tools for Predicting the Need for Extended Endocrine Therapy: A Safety Net Hospital Experience.},
journal = {Clinical breast cancer},
volume = {22},
number = {8},
pages = {823-827},
doi = {10.1016/j.clbc.2022.08.003},
pmid = {36089460},
issn = {1938-0666},
mesh = {Humans ; Female ; Middle Aged ; Prognosis ; Tamoxifen/therapeutic use ; *Breast Neoplasms/diagnosis/drug therapy/genetics ; Antineoplastic Agents, Hormonal/adverse effects ; Retrospective Studies ; Receptors, Estrogen ; Safety-net Providers ; *Brain-Computer Interfaces ; Neoplasm Recurrence, Local/pathology ; Recurrence ; },
abstract = {INTRODUCTION: Extended endocrine therapy (EET) benefits select patients with early-stage hormone-receptor positive (HR+) breast cancer (BC) but also incurs side effects and cost. The Clinical Treatment Score at Five Years (CTS5) is a free tool that estimates risks of late relapse in estrogen-receptor positive (ER+) BC using clinicopathologic factors. The Breast Cancer Index (BCI) incorporates 2 genomic assays to estimate late relapse risk and likelihood of benefit from EET. This retrospective study assesses the utility of BCI in selecting EET candidates in a safety net hospital.<br><br>MATERIALS AND METHODS: We performed a retrospective chart review on 69 women with early-stage HR+, HER2- BC diagnosed at our institution from December 2009 to February 2016 on whom BCI was submitted. The CTS5 score was also calculated to assess clinical risk of late relapse.<br><br>RESULTS: Median age was 53 years. All patients included in our analysis had early ER+ HER2-negative BC. Roughly half of the patients (55%) were postmenopausal and 61% were of Hispanic origin. A total of 34 patients (49%) were deemed high-risk (>5%) for late relapse by CTS5, compared to 42 (61%) by BCI. BCI identified 31 (45%) patients that would benefit from EET and of those, 74%% were advised EET. 16 (47%) clinical high-risk patients were advised against EET due to low benefit predicted by BCI. In the clinical low risk group, 9 (26%) were recommended EET based on high benefit predicted by BCI.<br><br>CONCLUSION: BCI is reasonable to consider in early-stage HR+ BC and offered clinically relevant information over clinical pathologic information alone.},
}
@article {pmid36086771,
year = {2022},
author = {Kim, MY and Park, JY and Leigh, JH and Kim, YJ and Nam, HS and Seo, HG and Oh, BM and Kim, S and Bang, MS},
title = {Exploring user perspectives on a robotic arm with brain-machine interface: A qualitative focus group study.},
journal = {Medicine},
volume = {101},
number = {36},
pages = {e30508},
pmid = {36086771},
issn = {1536-5964},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Focus Groups ; Humans ; Quadriplegia ; *Robotic Surgical Procedures ; },
abstract = {Brain-machine Interface (BMI) is a system that translates neuronal data into an output variable to control external devices such as a robotic arm. A robotic arm can be used as an assistive living device for individuals with tetraplegia. To reflect users' needs in the development process of the BMI robotic arm, our team followed an interactive approach to system development, human-centered design, and Human Activity Assistive Technology model. This study aims to explore the perspectives of people with tetraplegia about activities they want to participate in, their opinions, and the usability of the BMI robotic arm. Eight people with tetraplegia participated in a focus group interview in a semistructured interview format. A general inductive analysis method was used to analyze the qualitative data. The 3 overarching themes that emerged from this analysis were: 1) activities, 2) acceptance, and 3) usability. Activities that the users wanted to do using the robotic arm were categorized into the following 5 activity domains: activities of daily living (ADL), instrumental ADL, health management, education, and leisure. Participants provided their opinions on the needs and acceptance of the BMI technology. Participants answered usability and expected standards of the BMI robotic arm within 7 categories such as accuracy, setup, cost, etc. Participants with tetraplegia have a strong interest in the robotic arm and BMI technology to restore their mobility and independence. Creating BMI features appropriate to users' needs, such as safety and high accuracy, will be the key to acceptance. These findings from the perspectives of potential users should be taken into account when developing the BMI robotic arm.},
}
@article {pmid36086657,
year = {2022},
author = {Koorathota, S and Khan, Z and Lapborisuth, P and Sajda, P},
title = {Multimodal Neurophysiological Transformer for Emotion Recognition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3563-3567},
doi = {10.1109/EMBC48229.2022.9871421},
pmid = {36086657},
issn = {2694-0604},
mesh = {*Arousal/physiology ; Attention ; *Emotions/physiology ; Endoscopy ; Neurophysiology ; },
abstract = {Understanding neural function often requires multiple modalities of data, including electrophysiogical data, imaging techniques, and demographic surveys. In this paper, we introduce a novel neurophysiological model to tackle major challenges in modeling multimodal data. First, we avoid non-alignment issues between raw signals and extracted, frequency-domain features by addressing the issue of variable sampling rates. Second, we encode modalities through "cross-attention" with other modalities. Lastly, we utilize properties of our parent transformer architecture to model long-range dependencies between segments across modalities and assess intermediary weights to better understand how source signals affect prediction. We apply our Multimodal Neurophysiological Transformer (MNT) to predict valence and arousal in an existing open-source dataset. Experiments on non-aligned multimodal time-series show that our model performs similarly and, in some cases, outperforms existing methods in classification tasks. In addition, qualitative analysis suggests that MNT is able to model neural influences on autonomic activity in predicting arousal. Our architecture has the potential to be fine-tuned to a variety of downstream tasks, including for BCI systems.},
}
@article {pmid36086641,
year = {2022},
author = {Lee, KW and Lee, DH and Kim, SJ and Lee, SW},
title = {Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {1977-1980},
doi = {10.1109/EMBC48229.2022.9871721},
pmid = {36086641},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Language ; Speech ; Speech Disorders ; *Speech Perception ; },
abstract = {Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructing the neural signals of speech production. However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio. In this paper, we investigated the neural signals for two groups of native speakers with two tasks with different languages, English and Chinese. Our assumption was that English, a non-tonal and phonogram-based language, would have spectral differences in neural computation compared to Chinese, a tonal and ideogram-based language. The results showed the significant difference in the relative power spectral density between English and Chinese in specific frequency band groups. Also, the spatial evaluation of Chinese native speakers in the theta band was distinctive during the imagination task. Hence, this paper would suggest the key spectral and spatial information of word imagination with specialized language while decoding the neural signals of speech. Clinical Relevance- Imagined speech-related studies lead to the development of assistive communication technology especially for patients with speech disorders such as aphasia due to brain damage. This study suggests significant spectral features by analyzing cross-language differences of EEG-based imagined speech using two widely used languages.},
}
@article {pmid36086599,
year = {2022},
author = {Liao, G and Wang, S and Wei, Z and Liu, B and Okubo, R and Hernandez, ME},
title = {Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {381-384},
doi = {10.1109/EMBC48229.2022.9871843},
pmid = {36086599},
issn = {2694-0604},
mesh = {Anxiety/diagnosis ; Anxiety Disorders ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Virtual Reality ; },
abstract = {Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.},
}
@article {pmid36086535,
year = {2022},
author = {Musellim, S and Han, DK and Jeong, JH and Lee, SW},
title = {Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {711-714},
doi = {10.1109/EMBC48229.2022.9871434},
pmid = {36086535},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Humans ; *Neurofeedback ; },
abstract = {Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance. Clinical relevance-This study suggests a strategy to improve the performance of the subject-independent BCI systems. Our framework can help to reduce the need for further calibration and can be utilized for a range of mental state monitoring tasks (e.g. neurofeedback, identification of epileptic seizures, and sleep disorders).},
}
@article {pmid36086493,
year = {2022},
author = {Uyanik, C and Khan, MA and Brunner, IC and Hansen, JP and Puthusserypady, S},
title = {Machine Learning for Motor Imagery Wrist Dorsiflexion Prediction in Brain-Computer Interface Assisted Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {715-719},
doi = {10.1109/EMBC48229.2022.9871600},
pmid = {36086493},
issn = {2694-0604},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Humans ; Machine Learning ; *Stroke/diagnosis ; *Stroke Rehabilitation ; Wrist ; },
abstract = {Stroke is a life-changing event that can affect the survivors' physical, cognitive and emotional state. Stroke care focuses on helping the survivors to regain their strength; recover as much functionality as possible and return to independent living through rehabilitation therapies. Automated training protocols have been reported to improve the efficiency of the rehabilitation process. These protocols also decrease the dependency of the process on a professional trainer. Brain-Computer Interface (BCI) based systems are examples of such systems where they make use of the motor imagery (MI) based electroencephalogram (EEG) signals to drive the rehabilitation protocols. In this paper, we have proposed the use of well-known machine learning (ML) algorithms, such as, the decision tree (DT), Naive Bayesian (NB), linear discriminant analysis (LDA), support vector machine (SVM), ensemble learning classifier (ELC), and artificial neural network (ANN) for MI wrist dorsiflexion prediction in a BCI assisted stroke rehabilitation study conducted on eleven stroke survivors with either the left or right paresis. The doubling sub-band selection filter bank common spatial pattern (DSBS-FBCSP) has been proposed as feature extractor and it is observed that the ANN based classifier produces the best results.},
}
@article {pmid36086482,
year = {2022},
author = {Ayoobi, N and Sadeghian, EB},
title = {A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {218-221},
doi = {10.1109/EMBC48229.2022.9871590},
pmid = {36086482},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Humans ; Imagination ; },
abstract = {A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents naive users from using the system immediately. Developing a subject-independent MI-BCI system to reduce the calibration phase is still challenging due to the subject-dependent characteristics of the MI signals. Many algorithms based on machine learning and deep learning have been developed to extract high-level features from the MI signals to improve the subject-to-subject generalization of a BCI system. However, these methods are based on supervised learning and extract features useful for discriminating various MI signals. Hence, these approaches cannot find the common underlying patterns in the MI signals and their generalization level is limited. This paper proposes a subject-independent MI-BCI based on a supervised autoencoder (SAE) to circumvent the calibration phase. The suggested framework is validated on dataset 2a from BCI competition IV. The simulation results show that our SISAE model outperforms the conventional and widely used BCI algorithms, common spatial and filter bank common spatial patterns, in terms of the mean Kappa value, in eight out of nine subjects.},
}
@article {pmid36086404,
year = {2022},
author = {Song, Z and Zhang, X and Wang, Y},
title = {Cluster Kernel Reinforcement Learning-based Kalman Filter for Three-Lever Discrimination Task in Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {690-693},
doi = {10.1109/EMBC48229.2022.9871669},
pmid = {36086404},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Humans ; Learning ; Neural Networks, Computer ; Rats ; Reinforcement, Psychology ; },
abstract = {Brain-Machine Interface (BMI) translates paralyzed people's neural activity into control commands of the prosthesis so that their lost motor functions could be restored. The neural activities represent brain states that change continuously over time which brings the challenge to the online decoder. Reinforcement Learning (RL) has the advantage to construct the dynamic neural-kinematic mapping during the interaction. However, existing RL decoders output discrete actions as a classification problem and cannot provide continuous estimation. Previous work has combined Kalman Filter (KF) with RL for BMI, which achieves a continuous motor state estimation. However, this method adopts a neural network structure, which might get stuck in local optimum and cannot provide an efficient online update for the neural-kinematic mapping. In this paper, we propose a Cluster Kernel Reinforcement Learning-based Kalman Filter (CKRL-based KF) to avoid the local optimum problem for online neural-kinematic updating. The neural patterns are projected into Reproducing Kernel Hilbert Space (RKHS), which builds a universal approximation to guarantee the global optimum. We compare our proposed algorithm with the existing method on rat data collected during a brain control three-lever discrimination task. Our preliminary results show that the proposed method has a higher trial accuracy with lower variance across data segments, which shows its potential to improve the performance for online BMI control. Clinical Relevance- This paper provides a more stable decoding method for adaptive and continuous neural decoding. It is promising for clinical applications in BMI.},
}
@article {pmid36086336,
year = {2022},
author = {Ferrero, L and Quiles, V and Ortiz, M and Ianez, E and Megia, A and Gil-Agudo, AM and Azorin, JM},
title = {Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4064-4067},
doi = {10.1109/EMBC48229.2022.9870982},
pmid = {36086336},
issn = {2694-0604},
mesh = {Body Mass Index ; *Brain-Computer Interfaces ; *Exoskeleton Device ; Humans ; Lower Extremity ; *Spinal Cord Injuries/rehabilitation ; },
abstract = {Spinal Cord Injury (SCI) refers to damage to the spinal cord that can affect different body functionalities. Recovery after SCI depends on multiple factors, being the rehabilitation therapy one of them. New approaches based on robot-assisted training offer the possibility to make training sessions longer and with a reproducible pattern of movements. The control of these robotic devices by means of Brain-Machine Interfaces (BMIs) based on Motor Imagery (MI) favors the patient cognitive engagement during the rehabilitation, promoting mechanisms of neuroplasticity. This research evaluates the acceptance and feedback received from patients with incomplete SCI about the usage of a MI-based BMI with a lower-limb exoskeleton. Clinical Relevance- Patients experienced satisfaction when using the exoskeleton and levels of mental and physical workload were withing reasonable limits. In addition results from the BMI were promising for the inclusion of this type of systems in rehabilitation programs.},
}
@article {pmid36086333,
year = {2022},
author = {Wu, X and Chan, RHM},
title = {Does Meta-Learning Improve EEG Motor Imagery Classification?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4048-4051},
doi = {10.1109/EMBC48229.2022.9871035},
pmid = {36086333},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Imagination ; Neural Networks, Computer ; },
abstract = {Deep learning has been applied to enhance the performance of EEG-based brain-computer interface applications. However, the cross-subject variations in EEG signals cause domain shifts and negatively affect the model performance and generalization. Meta-learning algorithms have shown fast new domain adaption in various fields, which may help solve the domain shift problems in EEG. Reptile, with satisfactory performance and low computational costs, stands out from other existing meta-learning algorithms. We integrated Reptile with a deep neural network as Reptile-EEG for the EEG motor imagery tasks, and compared Reptile-EEG with other state-of-the-art models in three motor imagery BCI benchmark datasets. Results show that Reptile-EEGdoes not outperform simple training of deep neural networks in motor imagery BCI tasks.},
}
@article {pmid36086332,
year = {2022},
author = {See, BA and Francis, JT},
title = {High Classification Accuracy of Touch Locations from S1 LFPs Using CNNs and Fastai.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {342-345},
doi = {10.1109/EMBC48229.2022.9871856},
pmid = {36086332},
issn = {2694-0604},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; R01 NS124222/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Neural Networks, Computer ; *Touch ; },
abstract = {The primary somatosensory cortex (S1) is a region often targeted for input via somatosensory neuroprosthesis as tactile and proprioception are represented in S1. How this information is represented is an ongoing area of research. Neural signals are high-dimensional, making accurate models for decoding a significant challenge. Artificial neural networks (ANNs) have proven efficient at classification tasks in multiple fields. Moreover, ANNs allow for transfer learning, which exploits feature extraction trained on a large and more general dataset than may be available for a particular problem. In this work, convolutional neural networks (CNN), used for image recognition, were fine-tuned with somatosensory cortical recordings during experiments with naturalistic touch stimuli. We created a highly accurate (correct) classifier for cutaneous stimulation locations as part of a somatosensory neuroprosthesis pipeline. Here we present the classifier results. Clinical Relevance- Our work provides a method for classifying cortical activity in brain-machine interface applications, specifically towards somatosensory neuroprosthetics.},
}
@article {pmid36086292,
year = {2022},
author = {de Seta, V and Colamarino, E and Cincotti, F and Mattia, D and Mongiardini, E and Pichiorri, F and Toppi, J},
title = {Cortico-Muscular Coupling Allows to Discriminate Different Types of Hand Movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {2324-2327},
doi = {10.1109/EMBC48229.2022.9871383},
pmid = {36086292},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand/physiology ; Humans ; Movement/physiology ; *Stroke/diagnosis ; },
abstract = {Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.},
}
@article {pmid36086283,
year = {2022},
author = {Alexandre, H and Stephane, B},
title = {Blinking characterization for each eye from EEG analysis using wavelets.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4274-4277},
doi = {10.1109/EMBC48229.2022.9871044},
pmid = {36086283},
issn = {2694-0604},
mesh = {*Blinking ; *Brain-Computer Interfaces ; Electroencephalography ; Eye Movements ; Wakefulness ; },
abstract = {Eye blinks can be used to perform monitoring tasks such as drowsiness detection, attention measurement or other biological measurement mainly using video data. With the developement of brain computer interfaces (BCI) eye movements and blinks could be used to perform control tasks such as pointer activation or communications. This work aims to prove that it is possible to characterize eye blinks for each eye separately using only electroencephalography (EEG) signal acquired through non invasive portable device and dry electroencephalography.},
}
@article {pmid36086257,
year = {2022},
author = {Tan, J and Shen, X and Zhang, X and Song, Z and Wang, Y},
title = {Estimating Reward Function from Medial Prefrontal Cortex Cortical Activity using Inverse Reinforcement Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3346-3349},
doi = {10.1109/EMBC48229.2022.9871194},
pmid = {36086257},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Learning ; Prefrontal Cortex ; Rats ; *Reinforcement, Psychology ; Reward ; },
abstract = {Reinforcement learning (RL)-based brain-machine interfaces (BMIs) learn the mapping from neural signals to subjects' intention using a reward signal. External rewards (water or food) or internal rewards extracted from neural activity are leveraged to update the parameters of decoders in the existing RL-based BMI framework. However, for complex tasks, the design of external reward could be difficult, which may not fully reflect the subject's own evaluation internally. It is important to obtain an internal reward model from neural activity to access subject's internal evaluation when the subject is performing the task through trial and error. In this paper, we propose to use an inverse reinforcement learning (IRL) method to estimate the internal reward function interpreted from the brain to assist the update of the decoders. Specifically, the inverse Q-learning (IQL) algorithm is applied to extract internal reward information from real data collected from medial prefrontal cortex (mPFC) when a rat was learning a two-lever-press discrimination task. Such an internal reward information is validated by checking whether it can guide the training of the RL decoder to complete movement task. Compared with the RL decoder trained with the external reward, our approach achieves a similar decoding performance. This preliminary result validates the effectiveness of using IRL to obtain the internal reward model. It reveals the potential of estimating internal reward model to improve the design of autonomous learning BMIs.},
}
@article {pmid36086250,
year = {2022},
author = {Carretero, A and Araujo, A},
title = {Analysis of Simple Algorithms for Motion Detection in Wearable Devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {2410-2415},
doi = {10.1109/EMBC48229.2022.9871070},
pmid = {36086250},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Motion ; *Wearable Electronic Devices ; },
abstract = {Brain Computer Interfaces are used to obtain relevant information from the electroencephalogram (EEG) with a concrete objective. The evoked potentials related to movement are much demanded nowadays, in particular the ones associated to imagery movement. The objective of this work is to develop simple algorithms to imagery motion detection that can be included in a non-invasive wearable that everybody can use in a comfortable way for new services and applications. A wearable implies low resources, which is the most important requirement that the algorithms have. A public database with 105 subjects doing an upper-limb imagery movement is used. We have developed two algorithms (FBA and BLA) based on three characteristics of the signal (correlation, wavelet energy per segment and wavelet energy per electrode). They are tested for different number of electrodes and frequency bands. The best performance is found for 6 electrodes. The beta band is not the only band who achieves good performances. In fact, in this study the range between 25 Hz - 30 Hz has obtained the best performance using 6 electrodes. The conclusions show that these simple algorithms not fit well with the wearable requirements. However, it shows the need of adaptive algorithms to bypass the differences between subjects. Also, it affirms that more electrodes not lead to a better information, as well as, less electrodes not lead to a worse information. The same goes for frequency, where not only the beta band have the information required that fits our needs.},
}
@article {pmid36086248,
year = {2022},
author = {Mongiardini, E and Colamarino, E and Toppi, J and de Seta, V and Pichiorri, F and Mattia, D and Cincotti, F},
title = {Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {226-229},
doi = {10.1109/EMBC48229.2022.9871772},
pmid = {36086248},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; *Stroke/diagnosis ; },
abstract = {Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.},
}
@article {pmid36086246,
year = {2022},
author = {Micheli, A and Consoli, D and Merlini, A and Ricci, P and Andriulli, FP},
title = {Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {222-225},
doi = {10.1109/EMBC48229.2022.9870831},
pmid = {36086246},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Imagination ; Quality of Life ; },
abstract = {Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices that provide the frequency stimuli required by the technique. This limits the scenarios in which they can be applied, especially when compared to other BCI approaches. In this work, we have investigated the possibility of obtaining frequency responses in the EEG output based on the pure visual imagination of SSVEP-eliciting stimuli. Our results show that not only that EEG signals present frequency-specific peaks related to the frequency the user is focusing on, but also that promising classification accuracy can be achieved, paving the way for a robust and reliable visual imagery BCI modality. Clinical relevance-Brain computer interfaces play a fundamental role in enhancing the quality of life of patients with severe motor impairments. Strategies based on purely imagined stimuli, like the one presented here, are particularly impacting, especially in the most severe cases.},
}
@article {pmid36086236,
year = {2022},
author = {Thapa, BR and Tangarife, DR and Bae, J},
title = {Kernel Temporal Differences for EEG-based Reinforcement Learning Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3327-3333},
doi = {10.1109/EMBC48229.2022.9871862},
pmid = {36086236},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Learning ; Reinforcement, Psychology ; },
abstract = {Kernel temporal differences (KTD) (λ) algorithm integrated in Q-learning (Q-KTD) has shown its applicability and feasibility for reinforcement learning brain machine interfaces (RLBMIs). RLBMI with its unique learning strategy based on trial-error allows continuous learning and adaptation in BMIs. Q-KTD has shown good performance in both open and closed-loop experiments for finding a proper mapping from neural intention to control commands of an external device. However, previous studies have been limited to intracortical BMIs where monkey's firing rates from primary motor cortex were used as inputs to the neural decoder. This study provides the first attempt to investigate Q-KTD algorithm's applicability in EEG-based RLBMIs. Two different publicly available EEG data sets are considered, we refer to them as Data set A and Data set B. EEG motor imagery tasks are integrated in a single step center-out reaching task, and we observe the open-loop RLBMI experiments reach 100% average success rates after sufficient learning experience. Data set A converges after approximately 20 epochs for raw features and Data set B shows convergence after approximately 40 epochs for both raw and Fourier transform features. Although there still exist challenges to overcome in EEG-based RLBMI using Q-KTD, including increasing the learning speed, and optimization of a continuously growing number of kernel units, the results encourage further investigation of Q-KTD in closed-loop RLBMIs using EEG. Clinical Relevance- This study supports feasibility of noninvasive EEG-based RLBMI implementations and addresses benefits and challenges of RLBMI using EEG.},
}
@article {pmid36086221,
year = {2022},
author = {Vaghei, Y and Park, EJ and Arzanpour, S},
title = {Decoding Brain Signals to Classify Gait Direction Anticipation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {309-312},
doi = {10.1109/EMBC48229.2022.9871566},
pmid = {36086221},
issn = {2694-0604},
mesh = {Adult ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Gait ; Humans ; Movement ; },
abstract = {The use of brain-computer interface (BCI) technology has emerged as a promising rehabilitation approach for patients with motor function and motor-related disorders. BCIs provide an augmentative communication platform for controlling advanced assistive robots such as a lower-limb exoskeleton. Brain recordings collected by an electroencephalography (EEG) system have been employed in the BCI platform to command the exoskeleton. To date, the literature on this topic is limited to the prediction of gait intention and gait variations from EEG signals. This study, however, aims to predict the anticipated gait direction using a stream of EEG signals collected from the brain cortex. Three healthy participants (age range: 29-31, 2 female) were recruited. While wearing the EEG device, the participants were instructed to initiate gait movement toward the direction of the arrow triggers (pointing forward, backward, left, or right) being shown on a screen with a blank white background. Collected EEG data was then epoched around the trigger timepoints. These epochs were then converted to the time-frequency domain using event- related synchronization (ERS) and event-related desynchronization (ERD) methods. Finally, the classification pipeline was constructed using logistic regression (LR), support vector machine (SVM), and convolutional neural network (CNN). A ten-fold cross-validation scheme was used to evaluate the classification performance. The results revealed that the CNN classifier outperforms the other two classifiers with an accuracy of 0.75. Clinical Relevance - The outcome of this study has the potential to be ultimately used for interactive navigation of the lower-limb exoskeletons during robotic rehabilitation therapy and enhance neurodegeneration and neuroplasticity in a wide range of individuals with lower-limb motor function disabilities.},
}
@article {pmid36086209,
year = {2022},
author = {Rimbert, S and Lotte, F},
title = {ERD modulations during motor imageries relate to users' traits and BCI performances.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {203-207},
doi = {10.1109/EMBC48229.2022.9871411},
pmid = {36086209},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand/physiology ; Humans ; Imagery, Psychotherapy ; Neurophysiology ; },
abstract = {Improving user performances is one of the major issues for Motor Imagery (MI) - based BCI control. MI-BCIs exploit the modulation of sensorimotor rhythms (SMR) over the motor and sensorimotor cortices to discriminate several mental states and enable user interaction. Such modulations are known as Event-Related Desynchronization (ERD) and Synchronization (ERS), coming from the mu (7-13 Hz) and beta (15-30 Hz) frequency bands. This kind of BCI opens up promising fields, particularly to control assistive technologies, for sport training or even for post-stroke motor rehabilitation. However, MI - BCIs remain barely used outside laboratories, notably due to their lack of robustness and usability (15 to 30% of users seem unable to gain control of an MI-BCI). One way to increase user performance would be to better understand the relationships between user traits and ERD/ERS modulations underlying BCI performance. Therefore, in this article we analyzed how cerebral motor patterns underlying MI tasks (i.e., ERDs and ERSs) are modulated depending (i) on nature of the task (i.e., right-hand MI and left-hand MI), (ii) the session during which the task was performed (i.e., calibration or user training) and (iii) on the characteristics of the user (e.g., age, gender, manual activity, personality traits) on a large MI-BCI data base of N=75 participants. One of the originality of this study is to combine the investigation of human factors related to the user's traits and the neurophysiological ERD modulations during the MI task. Our study revealed for the first time an association between ERD and self-control from the 16PF5 questionnaire.},
}
@article {pmid36086186,
year = {2022},
author = {Sweet, T and Thompson, DE},
title = {Applying Big Transfer-based classifiers to the DEAP dataset.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {406-409},
pmid = {36086186},
issn = {2694-0604},
support = {P20 GM113109/GM/NIGMS NIH HHS/United States ; },
mesh = {*Arousal/physiology ; Emotions/physiology ; Humans ; *Neural Networks, Computer ; },
abstract = {Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.},
}
@article {pmid36086170,
year = {2022},
author = {Ajra, Z and Xu, B and Dray, G and Montmain, J and Perrey, S},
title = {Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {52-55},
doi = {10.1109/EMBC48229.2022.9870887},
pmid = {36086170},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Machine Learning ; Neural Networks, Computer ; },
abstract = {In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classification accuracy of 90.68%. It's also more robust to deal with cross-subject classification issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.},
}
@article {pmid36086167,
year = {2022},
author = {Pan, C and Liu, H and Zheng, D and Chen, F},
title = {Neural Entrainment to Rhythms of Imagined Syllables.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4040-4043},
doi = {10.1109/EMBC48229.2022.9871767},
pmid = {36086167},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Speech ; },
abstract = {Imagined speech based brain-computer interface (BCI) is of great interest due to its efficiency and user-friendliness for patients with speech impairment. The aim of this work was to study whether different rhythms of imagined syllables could elicit corresponding frequency components on EEG amplitude spectra. Seventeen participants were recruited to take part in the experiments, and performed a control task and four imagery tasks with the presence of periodic pure tones while their EEG signals were recorded. The four imagery tasks included imagining the syllable' /a/' every time, every two times, and every three times the periodic pure tones occurred, and imagined twice every three times the periodic pure tones occurred. The experimental results analyzed by Fourier transform indicated that neural entrainment to rhythmic speech imagery can be notably reflected on the EEG amplitude spectra. Clinical Relevance- This work manifested that different rhythms of imagined syllables could be identified from EEG amplitude spectra, which may be beneficial to the development of imagined speech based BCIs.},
}
@article {pmid36086139,
year = {2022},
author = {Ayoobi, N and Sadeghian, EB},
title = {Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4817-4820},
doi = {10.1109/EMBC48229.2022.9871906},
pmid = {36086139},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy/methods ; Imagination ; },
abstract = {Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the common spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The results indicate that the proposed method can effectively prune MI-EEG signals and significantly enhance the performance of the CSP algorithm in terms of classification accuracy.},
}
@article {pmid36086125,
year = {2022},
author = {Floreani, ED and Kelly, D and Rowley, D and Irvine, B and Kinney-Lang, E and Kirton, A},
title = {Iterative Development of a Software to Facilitate Independent Home Use of BCI Technologies for Children with Quadriplegic Cerebral Palsy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3361-3364},
doi = {10.1109/EMBC48229.2022.9871105},
pmid = {36086125},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Cerebral Palsy ; Child ; *Persons with Disabilities ; Electroencephalography ; Humans ; Software ; },
abstract = {Brain-computer interfaces (BCIs) are emerging as a new solution for children with severe disabilities to interact with the world. However, BCI technologies have yet to reach end-users in their daily lives due to significant translational gaps. To address these gaps, we applied user-centered design principles to establish a home BCI program for children with quadriplegic cerebral palsy. This work describes the technical development of the software we designed to facilitate BCI use at home. Children and their families were involved at each design stage to evaluate and provide feedback. Since deployment, seven families have successfully used the system independently at home and continue to use BCI at home to further enable participation and independence for their children. Clinical relevance- The design and successful implementation of user-centered software for home use will both inform on the feasibility of BCI as a long-term access solution for children with neurological disabilities as well as decrease barriers of accessibility and availability of BCI technologies for end-users.},
}
@article {pmid36086091,
year = {2022},
author = {Earley, EJ and Mastinu, E and Ortiz-Catalan, M},
title = {Cross-Channel Impedance Measurement for Monitoring Implanted Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4880-4883},
doi = {10.1109/EMBC48229.2022.9871954},
pmid = {36086091},
issn = {2694-0604},
mesh = {*Cochlear Implantation ; *Cochlear Implants ; Electric Impedance ; Electrodes, Implanted ; Monitoring, Physiologic ; },
abstract = {Implanted electrodes, such as those used for cochlear implants, brain-computer interfaces, and prosthetic limbs, rely on particular electrical conditions for optimal operation. Measurements of electrical impedance can be a diagnostic tool to monitor implanted electrodes for changing conditions arising from glial scarring, encapsulation, and shorted or broken wires. Such measurements provide information about the electrical impedance between a single electrode and its electrical reference, but offer no insights into the overall network of impedances between electrodes. Other solutions generally rely on geometrical assumptions of the arrangement of the electrodes and may not generalize to other electrode networks. Here, we propose a linear algebra-based approach, Cross-Channel Impedance Measurement (CCIM), for measuring a network of impedances between electrodes which all share a common electrical reference. This is accomplished by measuring the voltage response from all electrodes to a known current applied between each electrode and the shared reference, and is agnostic to the number and arrangement of electrodes. The approach is validated using a simulated 8-electrode network, demonstrating direct impedance measurements between electrodes and the reference with 96.6% ±0.2% accuracy, and cross-channel impedance measurements with 93.3% ±0.6% accuracy in a typical system. Subsequent analyses on randomized systems demonstrate the sensitivity of the model to impedance range and measurement noise. Clinical Relevance- CCIM provides a system-agnostic diagnostic test for implanted electrode networks, which may aid in the longitudinal tracking of electrode performance and early identification of electronics failures.},
}
@article {pmid36086084,
year = {2022},
author = {Li, C and Sheng, Y and Wang, H and Niu, M and Jing, P and Zhao, Z and Schuller, BW},
title = {EEG Emotion Recognition Based on Self-attention Dynamic Graph Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {292-296},
doi = {10.1109/EMBC48229.2022.9871072},
pmid = {36086084},
issn = {2694-0604},
mesh = {Attention ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Emotions/physiology ; Neural Networks, Computer ; },
abstract = {In recent years, due to the fundamental role played by the central nervous system in emotion expression, electroencephalogram (EEG) signals have emerged as the most robust signals for use in emotion recognition and inference. Current emotion recognition methods mainly employ deep learning technology to learn the spatial or temporal representation of each channel, then obtain complementary information from different EEG channels by adopting a multi-modal fusion strategy. However, emotional expression is usually accompanied by the dynamic spatio-temporal evolution of functional connections in the brain. Therefore, the effective learning of more robust long-term dynamic representations for the brain's functional connection networks is a key to improving the EEG-based emotion recognition system. To address these issues, we propose a brain network representation learning method that employs self-attention dynamic graph neural networks to obtain the spatial structure information and temporal evolution characteristics of brain networks. Experimental results on the AMIGOS dataset show that the proposed method is superior to the state-of-the-art methods.},
}
@article {pmid36086083,
year = {2022},
author = {Teversham, J and Wong, SS and Hsieh, B and Rapeaux, A and Troiani, F and Savolainen, O and Zhang, Z and Maslik, M and Constandinou, TG},
title = {Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {208-213},
doi = {10.1109/EMBC48229.2022.9871064},
pmid = {36086083},
issn = {2694-0604},
support = {UKDRI-7004/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; },
abstract = {This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.},
}
@article {pmid36086076,
year = {2022},
author = {Brandt, TM and Sweet, T and Thompson, DE},
title = {BCI Accuracy Using Classifier-Based Latency Estimation and the Optimal Interstimulus Interval.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4097-4100},
doi = {10.1109/EMBC48229.2022.9872003},
pmid = {36086076},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {PURPOSE: Detection of event-related potentials (ERPs) in brain-computer interfaces (BCIs) allow for communication by individuals with neuromuscular disorders. Enhancing BCI accuracy may be possible through the exploration of the optimal interstimulus interval (ISI). Our objective is to investigate the relationship between BCI accuracy and the optimal ISI value for an individual.<br><br>APPROACH: Using the previously developed classifier-based latency estimation (CBLE) [1], we investigated the relationship between the interstimulus interval (ISI) and P3 Speller BCI accuracy. Participants underwent two consecutive sessions in one day. The first session had a default ISI value of 120ms. An optimal ISI value calculated from the first session was used in the second.<br><br>RESULTS: Ten subjects participated in the study. Of the ten, half received an optimal ISI value of 120ms and half 160ms. Accuracy differences after implementing the adjusted ISI ranged from -26.1 percent to 4.35 percent. Suggestions for additional experimental design adjustments are highlighted under the discussion portion of this manuscript.},
}
@article {pmid36086071,
year = {2022},
author = {Soroush, PZ and Herff, C and Ries, S and Shih, JJ and Schultz, T and Krusienski, DJ},
title = {Contributions of Stereotactic EEG Electrodes in Grey and White Matter to Speech Activity Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4789-4792},
doi = {10.1109/EMBC48229.2022.9871464},
pmid = {36086071},
issn = {2694-0604},
mesh = {Electrodes, Implanted ; Electroencephalography ; Gray Matter/diagnostic imaging ; Speech ; *White Matter/diagnostic imaging ; },
abstract = {Recent studies have shown it is possible to decode and synthesize speech directly using brain activity recorded from implanted electrodes. While this activity has been extensively examined using electrocorticographic (ECoG) recordings from cortical surface grey matter, stereotactic electroen-cephalography (sEEG) provides comparatively broader coverage and access to deeper brain structures including both grey and white matter. The present study examines the relative and joint contributions of grey and white matter electrodes for speech activity detection in a brain-computer interface.},
}
@article {pmid36086033,
year = {2022},
author = {Bethge, D and Hallgarten, P and Ozdenizci, O and Mikut, R and Schmidt, A and Grosse-Puppendahl, T},
title = {Exploiting Multiple EEG Data Domains with Adversarial Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3154-3158},
doi = {10.1109/EMBC48229.2022.9871743},
pmid = {36086033},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Emotions ; Humans ; Machine Learning ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are highly subject-dependent, and are bound to the equipment and experimental setup used, (i.e. domain). This leads to machine learning models often suffer from poor generalization ability, where they perform significantly worse on real-world data than on the exploited training data. Recent research heavily focuses on cross-subject and cross-session transfer learning frameworks to reduce domain calibration efforts for EEG signals. We argue that multi-source learning via learning domain-invariant representations from multiple data-sources is a viable alternative, as the available data from different EEG data-source domains (e.g., subjects, sessions, experimental setups) grow massively. We propose an adversarial inference approach to learn data-source invariant representations in this context, enabling multi-source learning for EEG-based brain- computer interfaces. We unify EEG recordings from different source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP, DREAMER), and demonstrate the feasibility of our invariant representation learning approach in suppressing data- source-relevant information leakage by 35% while still achieving stable EEG-based emotion classification performance.},
}
@article {pmid36086028,
year = {2022},
author = {Yadav, T and Tellez, OM and Francis, JT},
title = {Reward-dependent Graded Suppression of Sensorimotor Beta-band Local Field Potentials During an Arm Reaching Task in NHP.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3123-3126},
doi = {10.1109/EMBC48229.2022.9871212},
pmid = {36086028},
issn = {2694-0604},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Arm ; Female ; Macaca mulatta ; *Motor Cortex ; Movement ; Reward ; },
abstract = {A better understanding of reward signaling in the sensorimotor cortices can aid in developing Reinforcement Learning-based Brain-Computer Interfaces (RLBCI) for restoration of movement functions with fewer implants. Brain-computer interfaces (BCIs) using local field potentials (LFPs) have recently achieved performance comparable to spike-BCIs [1]. With superior stability over time, LFPs may be the preferred signal for BCIs. We show that sensorimotor LFPs can provide reward level information (R1 - R3) like spikes[2]. We used a cued reward-level reaching task in which reward information was temporally dissociated from movement information. This allowed the study of reward- and movement-related modulations in LFPs. We recorded simultaneously from contralateral primary -somatosensory (S1), -motor (M1), and the dorsal premotor (PMd) cortices in a female Macaca Mulatta. We found that all three cortices' average beta band (14-30 Hz) amplitude showed robust modulation with reward levels during the cue presentation period. Such modulation was consistently observed after controlling for cue color, differences in behavioral variables, and electromyogram (EMG) activity. Statistical amplitude analysis showed that reward level could be extracted from the simple LFP feature of beta band amplitude, even before a reaching target appeared, and no specific reach plan could be developed. Clinical Relevance - The availability of reward-related signals in the sensorimotor cortical (S1, M1,and PMd) LFPs' prior to movement planning opens new avenues to build RLBCIs with fewer implants recording fewer sites among different cortices Reward and motivational representations derived from LFPs compared to spikes allow the development of long-term clinical applications given LFP's stability and ease of recording over long periods.},
}
@article {pmid36086005,
year = {2022},
author = {Jeong, JH and Kim, KT and Kim, DJ and Lee, SJ and Kim, H},
title = {Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {48-51},
doi = {10.1109/EMBC48229.2022.9871463},
pmid = {36086005},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; },
abstract = {Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.},
}
@article {pmid36085918,
year = {2022},
author = {Mu, J and Liu, PC and Grayden, DB and Tan, Y and Oetomo, D},
title = {Does Real-Time Feedback Improve User Performance in SSVEP-based Brain-Computer Interfaces?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {694-697},
doi = {10.1109/EMBC48229.2022.9871535},
pmid = {36085918},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Feedback ; Humans ; },
abstract = {Offline and online experiments are both widely used in SSVEP-based BCI research and development for different purposes. One of the major differences between offline and online experiments is the existence of real-time feedback to the user while they are using the interface. However, the role of feedback in SSVEP-based BCIs has not yet been well studied. This work focuses on understanding the effect of feedback in SSVEP-based BCIs and if there exists any relationship between offline and online BCI performance. An experiment was designed to compare directly the accuracies of the BCI with and without feedback for participants. Results showed that feedback can improve performance in a complex task, but no clear improvement was observed in a simple task.},
}
@article {pmid36085892,
year = {2022},
author = {Hong, J and Shamsi, F and Najafizadeh, L},
title = {A Deep Learning Framework Based on Dynamic Channel Selection for Early Classification of Left and Right Hand Motor Imagery Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3550-3553},
doi = {10.1109/EMBC48229.2022.9871446},
pmid = {36085892},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Hand ; Humans ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Ideal brain-computer interfaces (BCIs) need to be efficient and accurate, demanding for classifiers that can work across subjects while providing high classification accu-racy results from recordings with short duration. To address this problem, we present a new deep learning framework for discriminating motor imagery (MI) tasks from electroen-cephalography (EEG) signals. The framework consists of a 1D convolutional neural network-long short-term memory (CNN-LSTM), combined with a dynamic channel selection approach based on Davies-Bouldin index (DBI). Using data from BCI competition IV-IIa data, the proposed framework reports an average classification accuracy of 70.17% and 76.18% when using only 800 ms and 1500 ms of the EEG data after the task onset, respectively. The proposed framework dynamically balances individual differences, achieves comparable or better performance compared to existing work, while using short duration of EEG.},
}
@article {pmid36085842,
year = {2022},
author = {Zhang, J and Li, K},
title = {A Pruned Deep Learning Approach for Classification of Motor Imagery Electroencephalography Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4072-4075},
doi = {10.1109/EMBC48229.2022.9871078},
pmid = {36085842},
issn = {2694-0604},
mesh = {Algorithms ; *Deep Learning ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {The Deep Learning (DL) approach has been gaining much popularity in recent years in the development of electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) systems, aiming to improve the performance of existing stroke rehabilitation strategies. A complex deep neural network structure has lots of neurons with thousands of parameters to optimize, and a great deal of data is often required to train the network and the training process can take an extremely long time. High training costs and high model complexity not only have negative impacts on the performance of the BCI system but also on its applicability to meet the real-time requirement to support the rehabilitation exercises of patients. To tackle the challenge, a contribution-based neuron selection method is proposed in this paper. A Convolutional Neural Network (CNN) based motor imagery classification framework is implemented, and a neuron pruning approach is developed and applied. The temporal and spatial features of EEG signals are captured by the CNN layers, and then the fast recursive algorithm (FRA) is applied to prune redundant parameters in the fully connected layers which reduces the computation cost of the CNN model without affecting its performance. The experimental results show that the proposed method can achieve up to 50% model size reduction and 67.09% computation savings.},
}
@article {pmid36085779,
year = {2022},
author = {Berezutskaya, J and Ambrogioni, L and Ramsey, NF and van Gerven, MAJ},
title = {Towards Naturalistic Speech Decoding from Intracranial Brain Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3100-3104},
doi = {10.1109/EMBC48229.2022.9871301},
pmid = {36085779},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Communication ; Humans ; Neural Networks, Computer ; *Speech ; },
abstract = {Speech decoding from brain activity can enable development of brain-computer interfaces (BCIs) to restore naturalistic communication in paralyzed patients. Previous work has focused on development of decoding models from isolated speech data with a clean background and multiple repetitions of the material. In this study, we describe a novel approach to speech decoding that relies on a generative adversarial neural network (GAN) to reconstruct speech from brain data recorded during a naturalistic speech listening task (watching a movie). We compared the GAN-based approach, where reconstruction was done from the compressed latent representation of sound decoded from the brain, with several baseline models that reconstructed sound spectrogram directly. We show that the novel approach provides more accurate reconstructions compared to the baselines. These results underscore the potential of GAN models for speech decoding in naturalistic noisy environments and further advancing of BCIs for naturalistic communication. Clinical Relevance - This study presents a novel speech decoding paradigm that combines advances in deep learning, speech synthesis and neural engineering, and has the potential to advance the field of BCI for severely paralyzed individuals.},
}
@article {pmid36085749,
year = {2022},
author = {Premchand, B and Toe, KK and Wang, C and Libedinsky, C and Ang, KK and So, RQ},
title = {Information sparseness in cortical microelectrode channels while decoding movement direction using an artificial neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3534-3537},
doi = {10.1109/EMBC48229.2022.9870896},
pmid = {36085749},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Microelectrodes ; Movement ; *Neural Networks, Computer ; Upper Extremity ; },
abstract = {Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.},
}
@article {pmid36085743,
year = {2022},
author = {Li, M and Chen, S and Liu, X and Song, Z and Wang, Y},
title = {Modeling Neural Connectivity in a Point-Process Analogue of Kalman Filter.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {768-771},
doi = {10.1109/EMBC48229.2022.9871283},
pmid = {36085743},
issn = {2694-0604},
mesh = {Bayes Theorem ; Brain ; *Brain-Computer Interfaces ; *Neurons ; Normal Distribution ; },
abstract = {A neural encoding model describes how single neuron tunes to external stimuli as well as its connectivity with other neurons. The connectivity illustrates the neuronal interaction within populations in response to the shared latent brain states. Understanding these interactions is crucial to computationally predict the neural activity, which elucidates the neural encoding mechanism A computational analysis on the neural connectivity also facilitates developing more point process decoding model to interpret movement state from neural spike observations for brain machine interfaces (BMI). Most of the previous point process models only consider single neural tuning property and assumes conditional independence among multiple neurons. The connectivity among neurons is not considered in such a Bayesian approach to derive the state. In this work, we propose a point-process analogue of Kalman Filter to model the neural connectivity in a closed-form Bayesian filter. Neural connectivity corrects the posterior of the state given the multi-dimension observation, and a Gaussian distribution is used to approximate the updated posterior distribution. We validate the proposed method on simulation data and compared with traditional point process filtering with conditional independent assumption. The result shows that our method models the neural connectivity information and the single neuronal tuning property at the same time and achieves a better performance of the state decoding. Clinical Relevance - This paper proposes a closed-form derivation of a point process filter based on Gaussian approximations. It can model both single neuronal tuning property and the neural connectivity, which is potential to understanding the neural connectivity computationally.},
}
@article {pmid36085700,
year = {2022},
author = {Kumar, JNA and Francis, JT},
title = {Improved Grip Force Prediction Using a Loss Function that Penalizes Reward Related Neural Information.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {2336-2339},
doi = {10.1109/EMBC48229.2022.9871920},
pmid = {36085700},
issn = {2694-0604},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Hand Strength ; Kinetics ; Reward ; *Sensorimotor Cortex ; },
abstract = {Neural activity in the sensorimotor cortices has been previously shown to correlate with kinematics, kinetics, and non-sensorimotor variables, such as reward. In this work, we compare the grip force offline Brain Machine Interface (BMI) prediction performance, of a simple artificial neural network (ANN), under two loss functions: the standard mean squared error (MSE) and a modified reward penalized mean squared error (RP_MSE), which penalizes for correlation between reward and grip force. Our results show that the ANN performs significantly better under the RP_MSE loss function in three brain regions: dorsal premotor cortex (PMd), primary motor cortex (M1) and the primary somatosensory cortex (S1) by approximately 6%.},
}
@article {pmid36085697,
year = {2022},
author = {Favero, P and Berezutskaya, J and Ramsey, NF and Nazarov, A and Freudenburg, ZV},
title = {Mapping Acoustics to Articulatory Gestures in Dutch: Relating Speech Gestures, Acoustics and Neural Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {802-806},
doi = {10.1109/EMBC48229.2022.9871909},
pmid = {36085697},
issn = {2694-0604},
mesh = {Acoustics ; Chromosome Inversion ; *Gestures ; Humans ; Language ; Paralysis ; Quality of Life ; *Speech ; },
abstract = {Completely locked-in patients suffer from paralysis affecting every muscle in their body, reducing their communication means to brain-computer interfaces (BCIs). State-of-the-art BCIs have a slow spelling rate, which inevitably places a burden on patients' quality of life. Novel techniques address this problem by following a bio-mimetic approach, which consists of decoding sensory-motor cortex (SMC) activity that underlies the movements of the vocal tract's articulators. As recording articulatory data in combination with neural recordings is often unfeasible, the goal of this study was to develop an acoustic-to-articulatory inversion (AAI) model, i.e. an algorithm that generates articulatory data (speech gestures) from acoustics. A fully convolutional neural network was trained to solve the AAI mapping, and was tested on an unseen acoustic set, recorded simultaneously with neural data. Representational similarity analysis was then used to assess the relationship between predicted gestures and neural responses. The network's predictions and targets were significantly correlated. Moreover, SMC neural activity was correlated to the vocal tract gestural dynamics. The present AAI model has the potential to further our understanding of the relationship between neural, gestural and acoustic signals and lay the foundations for the development of a bio-mimetic speech BCI. Clinical Relevance- This study investigates the relationship between articulatory gestures during speech and the underlying neural activity. The topic is central for development of brain-computer interfaces for severely paralysed individuals.},
}
@article {pmid36085679,
year = {2022},
author = {Lin, Y and Shu, IW and Hsu, SH and Pineda, JA and Granholm, EL and Singh, F},
title = {Novel EEG-Based Neurofeedback System Targeting Frontal Gamma Activity of Schizophrenia Patients to Improve Working Memory.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4031-4035},
doi = {10.1109/EMBC48229.2022.9870878},
pmid = {36085679},
issn = {2694-0604},
mesh = {Cognition ; Electroencephalography/methods ; Humans ; Memory Disorders ; Memory, Short-Term ; *Neurofeedback/methods ; *Schizophrenia/therapy ; },
abstract = {Patients with schizophrenia (SCZ) exhibit working memory (WM) deficits that are associated with deficient dorsal-lateral prefrontal cortical activity, including decreased frontal gamma power. We thus hypothesized that training SCZ patients to increase frontal gamma activity would improve their WM performance. We administered electroencephalographic (EEG) neurofeedback (NFB) to 31 participants with SCZ for 12 weeks (24 sessions), which provides real-time visual and auditory feedback related to frontal gamma activity. The EEG-NFB training significantly improved EEG markers of optimal working memory, e.g., frontal P3 amplitude and gamma power. Based on these promising results, we developed a novel, EEGLAB/MATLAB-based brain-computer interface (BCI) that delivers F3-F4 gamma coherence NFB with a dynamic threshold to SCZ patients randomized in a double-blind, placebo-controlled clinical trial. The BCI significantly increased F3-F4 gamma coherence after 12 weeks (24 sessions) of training, according to data from the first 12 subjects (n=6 /group) who completed gamma- or placebo-NFB training.},
}
@article {pmid36085621,
year = {2022},
author = {Chen, J and Wang, D and Hu, B and Yi, W and Xu, M and Chen, D and Zhao, Q},
title = {MCFHNet: Multi-Channel Fusion Hybrid Network for Efficient EEG-fNIRS Multi-modal Motor Imagery Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {4821-4825},
doi = {10.1109/EMBC48229.2022.9871385},
pmid = {36085621},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Medicine ; Spectrum Analysis ; },
abstract = {Motor Imagery-based Brain Computer Interface (MI-BCI) is a typical active BCI with a main focus on motor intention identification. Hybrid motor imagery (MI) decoding methods that based on multi-modal fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), especially deep learning-based methods, become popular in recent MI-BCI studies. However, the fusion strategy and network design in deep learning-based methods are complex. To solve this problem, we proposed the multi-channel fusion method (MCF) to simplify current fusion methods, and we designed a multi-channel fusion hybrid network (MCFHNet) based on MCF. MCFHNet combines depthwise convolutional layers, channel attention mechanism, and Bidirectional Long Short Term Memory (Bi-LSTM) layers, which enables strong capability of feature extraction in spatial and temporal domain. The comparison between MCFHNet and representative deep learning-based methods was performed on an open EEG-fNIRS dataset. We found the proposed method can yield superior performance (mean accuracy of 99.641 % in 5-fold cross validation of an intra-subject experiment). This work provides a new option for multi-modal MI decoding, which can be applied in the rehabilitation field based on hybrid BCI systems.},
}
@article {pmid36085617,
year = {2022},
author = {Schmitz, C and Sweet, T and Thompson, DE},
title = {The Effects of Word Priming on Emotion Classification from Neurological Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {410-413},
pmid = {36085617},
issn = {2694-0604},
support = {P20 GM113109/GM/NIGMS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Emotions ; Humans ; Motor Activity ; Research Design ; },
abstract = {Affective states play an important role in human behavior and decision-making. In recent years, several affective brain-computer interface (aBCI) studies have focused on developing an emotion classifier based on elicited emotions within the user. However, it is difficult to achieve consistency in elicited emotions across populations, which can lead to dataset imbalances. The experimental design presented in this paper seeks to avoid consistency issues by asking the participant to classify the emotion portrayed in images of facial expressions, rather than their own emotions. Priming is also a common technique used in psychology studies that is known to influence emotional perception. To improve participant accuracy, we investigated matching and mis-matched word priming for the facial expression images. Electro-encephalogram (EEG) data were used to generate images fed into a classifier based on the Big Transfer model, BiT-M R101x1. The primed images resulted in higher classification accuracy overall. Further, by building different classifier models for both mis-matched primed images and matching primed images, we were able to achieve classification accuracies above 90%. We also provided the classifier with the true labels of the photographs instead of the labels generated by the participants and achieved similar results. The experimental paradigm of measuring brain activity during the emotional classification of another individual provides consistently high, balanced classification accuracies.},
}
@article {pmid36085604,
year = {2022},
author = {Yamamoto, MS and Lotte, F and Yger, F and Chevallier, S},
title = {Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3690-3693},
doi = {10.1109/EMBC48229.2022.9871820},
pmid = {36085604},
issn = {2694-0604},
mesh = {*Algorithms ; Humans ; Imagery, Psychotherapy ; *Physical Therapy Modalities ; },
abstract = {Considering user-specific settings is known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band for oscillatory activity classification is highly user-dependent and many frequency band selection methods have been developed in the past two decades. However, it is not well studied whether those conventional methods can be efficiently applied to the Riemannian BCIs, a recent family of BCI systems that utilize the non-Euclidean nature of the data unlike conventional BCI pipelines. In this paper, we proposed a novel frequency band selection method working on the Riemannian manifold. The frequency band is selected considering the class distinctiveness as quantified based on the inter-class distance and the intra-class variance on the manifold. An advantage of this method is that the frequency bandwidth can be adjusted for each individual without intensive optimization steps. In a comparative experiment using a public dataset of motor imagery-based BCI, our method showed a substantial improvement in average accuracy over both a fixed broad frequency band and a popular conventional frequency band selection method. In particular, our method substantially improved performances for subjects with initially low accuracies. This preliminary result suggests the importance of developing new user-specific setting algorithms considering the manifold properties, rather than directly applying methods developed prior to the rise of the Riemannian BCIs.},
}
@article {pmid36083918,
year = {2022},
author = {Fang, T and Song, Z and Mu, W and Le, S and Zhang, Y and Zhang, X and Zhan, G and Wang, P and Wang, J and Bin, J and Zhang, F and Zhang, L and Kang, X},
title = {Comparison of MI-EEG Decoding in Source to Sensor Domain.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2022},
number = {},
pages = {3586-3589},
doi = {10.1109/EMBC48229.2022.9871186},
pmid = {36083918},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Imagination ; },
abstract = {Brain-computer interface (BCI) system based on sensorimotor rhythm (SMR) is a more natural brain-computer interaction system. In this paper, we propose a new multi-task motor imagery EEG (MI-EEG) classification framework. Unlike traditional EEG decoding algorithms, we perform the decoding task in the source domain rather than the sensor domain. In the proposed algorithm, we first build a conduction model of the signal using the public ICBM152 head model and the boundary element method (BEM). The sensor domain EEG was then mapped to the selected cortex region using standardized low-resolution electromagnetic tomography (sLORETA) technology, which benefit to address volume conduction effects problem. Finally, the source domain features are extracted and classified by combining FBCSP and simple LDA. The results show that the classification-decoding algorithm performed in the source domain can well solve the classification task of MI-EEG. In addition, we found that the source imaging method can significantly increase the number of available EEG channels, which can be expanded at least double. The preliminary results of this study encourage the implementation of EEG decoding algorithms in the source domain. Clinical Relevance- This confirms that better results can be obtained by performing MI-EEG decoding in the source domain than in the sensor domain.},
}
@article {pmid36081031,
year = {2022},
author = {Tao, L and Cao, T and Wang, Q and Liu, D and Sun, J},
title = {Distribution Adaptation and Classification Framework Based on Multiple Kernel Learning for Motor Imagery BCI Illiteracy.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {17},
pages = {},
pmid = {36081031},
issn = {1424-8220},
support = {61471140//National Natural Science Foundation of China/ ; IR2021222//Fundamental Research Funds for the Central Universities/ ; 2016RALGJ001//Sci-tech Innovation Foundation of Harbin/ ; 216506//Future Science and Technology Innovation Team project of HIT/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Learning ; Literacy ; },
abstract = {A brain-computer interface (BCI) translates a user's thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of BCI illiterate subjects are low classification rates and poor repeatability. To address the problem of MI-BCI illiteracy, we propose a distribution adaptation method based on multi-kernel learning to make the distribution of features between the source domain and target domain become even closer to each other, while the divisibility of categories is maximized. Inspired by the kernel trick, we adopted a multiple-kernel-based extreme learning machine to train the labeled source-domain data to find a new high-dimensional subspace that maximizes data divisibility, and then use multiple-kernel-based maximum mean discrepancy to conduct distribution adaptation to eliminate the difference in feature distribution between domains in the new subspace. In light of the high dimension of features of MI-BCI illiteracy, random forest, which can effectively handle high-dimensional features without additional cross-validation, was employed as a classifier. The proposed method was validated on an open dataset. The experimental results show that that the method we proposed suits MI-BCI illiteracy and can reduce the inter-domain differences, resulting in a reduction in the performance degradation of both cross-subjects and cross-sessions.},
}
@article {pmid36081018,
year = {2022},
author = {Aydin, M and Carpenelli, AL and Lucia, S and Di Russo, F},
title = {The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory-Motor Tasks.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {17},
pages = {},
pmid = {36081018},
issn = {1424-8220},
support = {CDR2.BANDO2020DRF//Foro Italico University of Rome/ ; },
mesh = {*Contingent Negative Variation/physiology ; Cues ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; *Motor Cortex ; },
abstract = {Anticipatory event-related potentials (ERPs) precede upcoming events such as stimuli or actions. These ERPs are usually obtained in cued sensory-motor tasks employing a warning stimulus that precedes a probe stimulus as in the contingent negative variation (CNV) paradigms. The CNV wave has been widely studied, from clinical to brain-computer interface (BCI) applications, and has been shown to emerge in medial frontoparietal areas, localized in the cingulate and supplementary motor areas. Several dated studies also suggest the existence of a prefrontal CNV, although this component was not confirmed by later studies due to the contamination of ocular artifacts. Another lesser-known anticipatory ERP is the prefrontal negativity (pN) that precedes the uncued probe stimuli in discriminative response tasks and has been localized in the inferior frontal gyrus. This study aimed to characterize the pN by comparing it with the CNV in cued and uncued tasks and test if the pN could be associated with event preparation, temporal preparation, or both. To achieve these aims, high-density electroencephalographic recording and advanced ERP analysis controlling for ocular activity were obtained in 25 volunteers who performed 4 different visuomotor tasks. Our results showed that the pN amplitude was largest in the condition requiring both time and event preparation, medium in the condition requiring event preparation only, and smallest in the condition requiring temporal preparation only. We concluded that the prefrontal CNV could be associated with the pN, and this activity emerges in complex tasks requiring the anticipation of both the category and timing of the upcoming stimulus. The proposed method can be useful in BCI studies investigating the endogenous neural signatures triggered by different sensorimotor paradigms.},
}
@article {pmid36080217,
year = {2022},
author = {Lee, JH and Choi, ME and An, H and Moon, JW and Yeo, HJ and Song, Y and Chang, SE},
title = {BCI-215, a Dual-Specificity Phosphatase Inhibitor, Reduces UVB-Induced Pigmentation in Human Skin by Activating Mitogen-Activated Protein Kinase Pathways.},
journal = {Molecules (Basel, Switzerland)},
volume = {27},
number = {17},
pages = {},
pmid = {36080217},
issn = {1420-3049},
support = {2020R1A4A4079708//National Research Foundation of Korea/ ; },
mesh = {*Brain-Computer Interfaces ; Cell Line, Tumor ; *Dual-Specificity Phosphatases/antagonists &amp; inhibitors ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Humans ; *Hyperpigmentation/metabolism ; Melanins ; Melanocytes/metabolism ; Monophenol Monooxygenase ; Pigmentation ; },
abstract = {BACKGROUND: The dysregulation of melanin production causes skin-disfiguring ultraviolet (UV)-associated hyperpigmented spots. Previously, we found that the activation of c-Jun N-terminal kinase (JNK), a mitogen-activated protein kinase (MAPK), inhibited melanogenesis.<br><br>METHODS: We selected BCI-215 as it may modify MAPK expression via a known function of a dual-specificity phosphatase (DUSP) 1/6 inhibitor. B16F10 melanoma cells, Mel-ab cells, human melanocytes, and a coculture were used to assess the anti-melanogenic activity of BCI-215. The molecular mechanisms were deciphered by assaying the melanin content and cellular tyrosinase activity via immunoblotting and RT-PCR.<br><br>RESULTS: BCI-215 was found to suppress basal and cAMP-stimulated melanin production and cellular tyrosinase activity in vitro through the downregulation of microphthalmia-associated transcription factor (MITF) protein and its downstream enzymes. The reduction in MITF expression caused by BCI-215 was found to be due to all three types of MAPK activation, including extracellular signal-regulated kinase (ERK), JNK, and p38. The degree of activation was greater in ERK. A phosphorylation of the &#x3b2;-catenin pathway was also demonstrated. The melanin index, expression of MITF, and downstream enzymes were well-reduced in UVB-irradiated ex vivo human skin by BCI-215.<br><br>CONCLUSIONS: As BCI-215 potently inhibits UV-stimulated melanogenesis, small molecules of DUSP-related signaling modulators may provide therapeutic benefits against pigmentation disorders.},
}
@article {pmid36080117,
year = {2022},
author = {Ding, R and Xuan, W and Dong, S and Zhang, B and Gao, F and Liu, G and Zhang, Z and Jin, H and Luo, J},
title = {The 3.4 GHz BAW RF Filter Based on Single Crystal AlN Resonator for 5G Application.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {12},
number = {17},
pages = {},
pmid = {36080117},
issn = {2079-4991},
support = {No.2021C05004//Zhejiang Province Key R &amp; D programs/ ; No. U1909212//NSFC Zhejiang Joint Fund for the Integration of Industrialization and information/ ; },
abstract = {To meet the stringent requirements of 5G communication, we proposed a high-performance bulk acoustic wave (BAW) filter based on single crystal AlN piezoelectric films on a SiC substrate. The fabrication of the BAW filter is compatible with the GaN high electron mobility transistor (HEMT) process, enabling the implementation of the integration of the BAW device and high-performance monolithic microwave integrated circuit (MMIC). The single crystal AlN piezoelectric film with 650-nm thickness was epitaxially grown on the SiC substrate by Metal Organic Chemical Vapor Deposition (MOCVD). After wafer bonding and substrate removal, the single crystal AlN film with electrode layers was transferred to another SiC wafer to form an air gap type BAW. Testing results showed that the fabricated resonators have a maximum Q-factor up to 837 at 3.3 GHz resonant frequency and electromechanical coupling coefficient up to 7.2%. Ladder-type filters were developed to verify the capabilities of the BAW and process, which has a center frequency of 3.38 GHz with 160 MHz 3 dB bandwidth. The filter achieved a minimum 1.5 dB insertion loss and more than 31 dB out-of-band rejection. The high performance of the filters is attributed to the high crystallinity and low defects of epitaxial single crystal AlN films.},
}
@article {pmid36071712,
year = {2022},
author = {Kohl, SH and Mehler, DMA and Lührs, M and Thibault, RT and Konrad, K and Sorger, B},
title = {Corrigendum: The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {907941},
pmid = {36071712},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2020.00594.].},
}
@article {pmid36068570,
year = {2022},
author = {Korik, A and McCreadie, K and McShane, N and Du Bois, N and Khodadadzadeh, M and Stow, J and McElligott, J and Carroll, &#xc1; and Coyle, D},
title = {Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {19},
number = {1},
pages = {95},
pmid = {36068570},
issn = {1743-0003},
mesh = {*Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Quadriplegia ; },
abstract = {BACKGROUND: The brain-computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events.<br><br>METHODS: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot's performance is presented for two Cybathlon competition training periods-spanning 20 sessions over 5&#xa0;weeks prior to the 2019 competition, and 25 sessions over 5&#xa0;weeks in the run up to the 2020 competition.<br><br>RESULTS: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy&#x2009;>&#x2009;90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274-156&#xa0;s (255&#x2009;&#xb1;&#x2009;24&#xa0;s to 191&#x2009;&#xb1;&#x2009;14&#xa0;s mean&#x2009;&#xb1;&#x2009;std), over 17&#xa0;days (10 sessions) in 2019, and from 230-168&#xa0;s (214&#x2009;&#xb1;&#x2009;14&#xa0;s to 181&#x2009;&#xb1;&#x2009;4&#xa0;s), over 18&#xa0;days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly.<br><br>CONCLUSIONS: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered.},
}
@article {pmid36067595,
year = {2022},
author = {Chen, Y and Li, S and Wu, F and Zou, L and Zhang, J},
title = {Altered functional and directed connectivity in propofol-induced loss of consciousness: A source-space resting-state EEG study.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {142},
number = {},
pages = {209-219},
doi = {10.1016/j.clinph.2022.08.003},
pmid = {36067595},
issn = {1872-8952},
mesh = {Consciousness ; Electroencephalography ; Humans ; *Propofol/adverse effects ; Unconsciousness/chemically induced ; Wakefulness ; },
abstract = {OBJECTIVE: General anesthesia might disrupt neuronal network communications measured by functional connectivity (FC; undirected connectivity) and directional information flow (directed connectivity). We sought to characterize the state-dependent effects of propofol on cortico-cortical undirected and directed FC.<br><br>METHODS: We collected 256-channel high-density EEGs from 14 patients undergoing surgery while awake (AWA) or in propofol-induced moderate sedation (SED) or loss of consciousness (LOC) states. Using source-space EEG, we estimated neuronal oscillatory activity for 68 cortical regions of interest. FC was analyzed using the weighted phase lag index. Directed connectivity was computed using directed phase transfer entropy (dPTE) as a measure of information flow in the bilateral prefrontal, frontal, parietal, and occipital areas.<br><br>RESULTS: FC strength evidently reduced during LOC compared with those during the AWA and SED states. The dPTE analysis showed significant propofol-induced changes in directed connectivity. In the alpha band, the prefrontal-to-frontal information flow was significantly stronger in the AWA than in the SED (p&#xa0;=&#xa0;0.033) and LOC states (p&#xa0;=&#xa0;0.033). The parietal-to-frontal dPTE was significantly weaker during LOC than during the AWA (p&#xa0;=&#xa0;0.033) and SED states (p&#xa0;=&#xa0;0.007). Finally, a loss of occipital-to-frontal connectivity occurred during LOC but not the AWA state (p&#xa0;=&#xa0;0.001). In the beta band, the dominant occipital-to-frontal direction of information flow in the AWA state was gradually converted to a frontal-to-occipital direction during LOC.<br><br>CONCLUSIONS: Propofol-induced unconsciousness is marked by a decrease in FC and posterior-to-anterior (feedforward) directed connectivity, which may be useful as a measure to discriminate different conscious states caused by propofol administration.<br><br>SIGNIFICANCE: The study demonstrates that propofol produces state-dependent effects on cortico-cortical undirected and directed FC, supporting the idea that propofol induces loss of consciousness may through disrupting network interactions and cortical coordination.},
}
@article {pmid36061607,
year = {2022},
author = {Liang, J and Song, Y and Belkacem, AN and Li, F and Liu, S and Chen, X and Wang, X and Wang, Y and Wan, C},
title = {Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {968928},
pmid = {36061607},
issn = {1662-4548},
abstract = {Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients' secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.},
}
@article {pmid36061606,
year = {2022},
author = {Pan, C and Yu, H and Fei, X and Zheng, X and Yu, R},
title = {Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {965937},
pmid = {36061606},
issn = {1662-4548},
abstract = {With the development of resting-state functional magnetic resonance imaging (rs-fMRI) technology, the functional connectivity network (FCN) which reflects the statistical similarity of temporal activity between brain regions has shown promising results for the identification of neuropsychiatric disorders. Alteration in FCN is believed to have the potential to locate biomarkers for classifying or predicting schizophrenia (SZ) from healthy control. However, the traditional FCN analysis with stationary assumption, i.e., static functional connectivity network (SFCN) at the time only measures the simple functional connectivity among brain regions, ignoring the dynamic changes of functional connectivity and the high-order dynamic interactions. In this article, the dynamic functional connectivity network (DFCN) is constructed to delineate the characteristic of connectivity variation across time. A high-order functional connectivity network (HFCN) designed based on DFCN, could characterize more complex spatial interactions across multiple brain regions with the potential to reflect complex functional segregation and integration. Specifically, the temporal variability and the high-order network topology features, which characterize the brain FCNs from region and connectivity aspects, are extracted from DFCN and HFCN, respectively. Experiment results on SZ identification prove that our method is more effective (i.e., obtaining a significantly higher classification accuracy, 81.82%) than other competing methods. Post hoc inspection of the informative features in the individualized classification task further could serve as the potential biomarkers for identifying associated aberrant connectivity in SZ.},
}
@article {pmid36061506,
year = {2022},
author = {Shin, H and Suma, D and He, B},
title = {Closed-loop motor imagery EEG simulation for brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {951591},
pmid = {36061506},
issn = {1662-5161},
abstract = {In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI. As such, a number of studies have attempted to address this cost-wise middle ground between offline and live experimentation with real-time neural activity simulators. We present one such system which generates motor imagery electroencephalography (EEG) via forward modeling and novel motor intention encoding models for conducting sensorimotor rhythm (SMR)-based continuous cursor control experiments in a closed-loop setting. We use the proposed simulator with 10 healthy human subjects to test the effect of three decoder and task parameters across 10 different values. Our simulated approach produces similar statistical conclusions to those produced during parallel, paired, online experimentation, but in 55% of the time. Notably, both online and simulated experimentation expressed a positive effect of cursor velocity limit on performance regardless of subject average performance, supporting the idea of relaxing constraints on cursor gain in online continuous cursor control. We demonstrate the merits of our closed-loop motor imagery EEG simulation, and provide an open-source framework to the community for closed-loop SMR-based BCI studies in the future. All code including the simulator have been made available on GitHub.},
}
@article {pmid36060998,
year = {2022},
author = {George, O and Smith, R and Madiraju, P and Yahyasoltani, N and Ahamed, SI},
title = {Data augmentation strategies for EEG-based motor imagery decoding.},
journal = {Heliyon},
volume = {8},
number = {8},
pages = {e10240},
pmid = {36060998},
issn = {2405-8440},
abstract = {The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to its characteristic ability to generate discriminatory signals for communication and control. In recent times, deep learning techniques have increasingly been explored, in motor imagery decoding. While deep learning techniques are promising, a major challenge limiting their wide adoption is the amount of data available for decoding. To combat this challenge, data augmentation can be performed, to enhance decoding performance. In this study, we performed data augmentation by synthesizing motor imagery (MI) electroencephalography (EEG) trials, following six approaches. Data generated using these methods were evaluated based on four criteria, namely - the accuracy of prediction, the Frechet Inception distance (FID), the t-distributed Stochastic Neighbour Embedding (t-SNE) plots and topographic head plots. We show, based on these, that the synthesized data exhibit similar characteristics with real data, gaining up to 3% and 12% increases in mean accuracies across two public datasets. Finally, we believe these approaches should be utilized in applying deep learning techniques, as they not only have the potential to improve prediction performances, but also to save time spent on subject data collection.},
}
@article {pmid36060374,
year = {2022},
author = {Pervaiz, A and Ather, MH and Bashir, A and Aziz, W},
title = {Urdu Translation and Linguistic Validation of the Bladder Cancer Index Questionnaire.},
journal = {Cureus},
volume = {14},
number = {7},
pages = {e27487},
pmid = {36060374},
issn = {2168-8184},
abstract = {Background This study aimed to translate the Bladder Cancer Index (BCI) questionnaire to Urdu and validate it to assess the quality of life of patients with bladder cancer. Material and methods After forward and backward translation of the BCI questionnaire into Urdu, content validity was calculated using the content validity index (CVI) based on input from five health experts regarding the clarity and relevance of the questionnaire. Construct validity was measured by comparing the inter-scale domains and subdomains of BCI and by comparing BCI with Short Form 36 (SF-36) using correlations. For assessment of reliability, Cronbach's alpha was calculated to measure internal consistency and for test-retest reliability, the questionnaire was re-administered four weeks later and the correlation of responses at baseline and at a four-week time point was evaluated. Results The questionnaire has good content validity for clarity (0.91) and relevance (0.87). The construct validity of BCI was also adequately displayed by moderate to high correlation between different subdomains of BCI (Pearson's r: urinary - 0.62, bowel - 0.78, sexual function - 0.43) and low to moderate correlation between responses of BCI compared with SF-36 (Pearson's r mostly >0.45). Test-retest reliability was excellent (Pearson's r 0.90-0.98), and there was good internal consistency (Cronbach's alpha 0.79-0.92) in the different domains of the questionnaire. Conclusion The Urdu-translated BCI is a valid and reliable tool to measure the impact of bladder cancer on the quality of life of patients.},
}
@article {pmid36060141,
year = {2022},
author = {Kim, KT and Choi, J and Jeong, JH and Kim, H and Lee, SJ},
title = {High-Frequency Vibrating Stimuli Using the Low-Cost Coin-Type Motors for SSSEP-Based BCI.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {4100381},
pmid = {36060141},
issn = {2314-6141},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Support Vector Machine ; },
abstract = {Steady-state somatosensory-evoked potential- (SSSEP-) based brain-computer interfaces (BCIs) have been applied for assisting people with physical disabilities since it does not require gaze fixation or long-time training. Despite the advancement of various noninvasive electroencephalogram- (EEG-) based BCI paradigms, researches on SSSEP with the various frequency range and related classification algorithms are relatively unsettled. In this study, we investigated the feasibility of classifying the SSSEP within high-frequency vibration stimuli induced by a versatile coin-type eccentric rotating mass (ERM) motor. Seven healthy subjects performed selective attention (SA) tasks with vibration stimuli attached to the left and right index fingers. Three EEG feature extraction methods, followed by a support vector machine (SVM) classifier, have been tested: common spatial pattern (CSP), filter-bank CSP (FBCSP), and mutual information-based best individual feature (MIBIF) selection after the FBCSP. Consequently, the FBCSP showed the highest performance at 71.5 ± 2.5% for classifying the left and right-hand SA tasks than the other two methods (i.e., CSP and FBCSP-MIBIF). Based on our findings and approach, the high-frequency vibration stimuli using low-cost coin motors with the FBCSP-based feature selection can be potentially applied to developing practical SSSEP-based BCI systems.},
}
@article {pmid36053476,
year = {2023},
author = {Arici, C and Oto, BB},
title = {Nasal endoscopy-guided primary nasolacrimal duct intubation for congenital nasolacrimal duct obstruction in children older than 4 years.},
journal = {International ophthalmology},
volume = {43},
number = {3},
pages = {1005-1011},
pmid = {36053476},
issn = {1573-2630},
mesh = {Male ; Female ; Humans ; Child ; Infant ; Child, Preschool ; *Lacrimal Duct Obstruction/diagnosis/therapy ; *Nasolacrimal Duct/surgery ; *Dacryocystorhinostomy ; Endoscopy ; *Lacrimal Apparatus ; Treatment Outcome ; Retrospective Studies ; },
abstract = {PURPOSE: To evaluate the clinical outcomes of endoscopic guided primary bicanalicular intubation (BCI) for congenital nasolacrimal duct obstruction (CNLDO) in children older than 4 years.<br><br>METHODS: A total of 40 eyes from 33 children (18 males, 15 females) with CNLDO who underwent bicanalicular intubation were evaluated. The type of CNLDO was determined by endonasal endoscopic visualisation. The mean silicone tube removal time was 4.3&#x2009;&#xb1;&#x2009;0.9&#xa0;months (ranging from 3 to 6&#xa0;months). The children were followed up for 6&#xa0;months after the removal of tubes. Therapeutic success was defined as the normal result of the fluorescein dye disappearance test and complete resolution of previous lacrimal symptoms and signs.<br><br>RESULTS: The median age was 80 [48] (range 48-156) months. Treatment success was achieved in 32 of 40 eyes (80.0%). A statistically significant correlation was observed between the age and success rate (p&#x2009;=&#x2009;0.006). The success rate was lower in older children. Membranous type of CNLDO was observed in 47.5% (19/40) of the cases. The median age of patients with a membranous and complex type of CNLDO were 60 [30] months and 96 [53] months, respectively. Surgical success was 100% in the membranous type of CNLDO and 61.9% in the complex CNLDO group.<br><br>CONCLUSIONS: Primary BCI using nasal endoscopic visualisation has a favourably high success rate for treating CNLDO in children aged 4 to 13&#xa0;years. Treatment success was found to be related to both the type of CNLDO and age.},
}
@article {pmid36051649,
year = {2022},
author = {Zhang, Z and Koike, Y},
title = {Clustered event related spectral perturbation (ERSP) feature in right hand motor imagery classification.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {867480},
pmid = {36051649},
issn = {1662-4548},
abstract = {A technology that allows humans to interact with machines more directly and efficiently would be desirable. Research on brain-computer interfaces (BCIs) provides the possibility for computers to understand human thoughts in a straightforward manner thereby facilitating communication. As a branch of BCI research, motor imagery (MI) techniques analyze the brain signals and help people in many aspects such as rehabilitation, clinical applications, entertainment, and system controlling. In this study, an imagery experiment consisting of four kinds of right-hand movements (gripping, opening, pronation, and supination) was designed. Then a novel feature, namely, clustered feature was proposed based on the event-related spectral perturbation (ERSP) calculated from the EEG signal. Based on the selected features, two classical classifiers (support vector machine and linear discriminant classifier) were trained, achieving an acceptable accurate result (80%, on average).},
}
@article {pmid36051201,
year = {2022},
author = {Yoon, YS and Drew, C},
title = {Effects of the intensified frequency and time ranges on consonant enhancement in bilateral cochlear implant and hearing aid users.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {918914},
pmid = {36051201},
issn = {1664-1078},
support = {R15 DC019240/DC/NIDCD NIH HHS/United States ; },
abstract = {A previous study demonstrated that consonant recognition improved significantly in normal hearing listeners when useful frequency and time ranges were intensified by 6 dB. The goal of this study was to determine whether bilateral cochlear implant (BCI) and bilateral hearing aid (BHA) users experienced similar enhancement on consonant recognition with these intensified spectral and temporal cues in noise. In total, 10 BCI and 10 BHA users participated in a recognition test using 14 consonants. For each consonant, we used the frequency and time ranges that are critical for its recognition (called "target frequency and time range"), identified from normal hearing listeners. Then, a signal processing tool called the articulation-index gram (AI-Gram) was utilized to add a 6 dB gain to target frequency and time ranges. Consonant recognition was monaurally and binaurally measured under two signal processing conditions, unprocessed and intensified target frequency and time ranges at +5 and +10 dB signal-to-noise ratio and in quiet conditions. We focused on three comparisons between the BCI and BHA groups: (1) AI-Gram benefits (i.e., before and after intensifying target ranges by 6 dB), (2) enhancement in binaural benefits (better performance with bilateral devices compared to the better ear alone) via the AI-Gram processing, and (3) reduction in binaural interferences (poorer performance with bilateral devices compared to the better ear alone) via the AI-Gram processing. The results showed that the mean AI-Gram benefit was significantly improved for the BCI (max 5.9%) and BHA (max 5.2%) groups. However, the mean binaural benefit was not improved after AI-Gram processing. Individual data showed wide ranges of the AI-Gram benefit (max -1 to 23%) and binaural benefit (max -7.6 to 13%) for both groups. Individual data also showed a decrease in binaural interference in both groups after AI-Gram processing. These results suggest that the frequency and time ranges, intensified by the AI-Gram processing, contribute to consonant enhancement for monaural and binaural listening and both BCI and BHA technologies. The intensified frequency and time ranges helped to reduce binaural interference but contributed less to the synergistic binaural benefit in consonant recognition for both groups.},
}
@article {pmid36050394,
year = {2022},
author = {Ma, J and Yang, B and Qiu, W and Li, Y and Gao, S and Xia, X},
title = {A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {531},
pmid = {36050394},
issn = {2052-4463},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; },
abstract = {In building a practical and robust brain-computer interface (BCI), the classification of motor imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing challenge due to the large variability of the EEG signals. We collected a large dataset of MI from 5 different days with 25 subjects, the first open-access dataset to address BCI issues across 5 different days with a large number of subjects. The dataset includes 5 session data from 5 different days (2-3 days apart) for each subject. Each session contains 100 trials of left-hand and right-hand MI. In this report, we provide the benchmarking classification accuracy for three conditions, namely, within-session classification (WS), cross-session classification (CS), and cross-session adaptation (CSA), with subject-specific models. WS achieves an average classification accuracy of up to 68.8%, while CS degrades the accuracy to 53.7% due to the cross-session variability. However, by adaptation, CSA improves the accuracy to 78.9%. We anticipate this new dataset will significantly push further progress in MI BCI research in addressing the cross-session and cross-subject challenge.},
}
@article {pmid36047007,
year = {2023},
author = {Bekteshi, S and Konings, M and Karlsson, P and Criekinge, TV and Dan, B and Monbaliu, E},
title = {Teleintervention for users of augmentative and alternative communication devices: A systematic review.},
journal = {Developmental medicine and child neurology},
volume = {65},
number = {2},
pages = {171-184},
doi = {10.1111/dmcn.15387},
pmid = {36047007},
issn = {1469-8749},
mesh = {Humans ; Cohort Studies ; *Communication Disorders/etiology/therapy ; *Autistic Disorder ; Language Therapy/methods ; Communication ; },
abstract = {AIM: To synthesize existing evidence on the effectiveness of speech-language teleinterventions delivered via videoconferencing to users of augmentative and alternative communication (AAC) devices.<br><br>METHOD: A systematic literature search was conducted in 10 electronic databases, from inception until August 2021. Included were speech-language teleinterventions delivered by researchers and/or clinicians via videoconferencing to users of AAC devices, without restrictions on chronological age and clinical diagnosis. The quality of the studies included in the review was appraised using the Downs and Black checklist and the Single-Case Experimental Design Scale; risk of bias was assessed using the Risk Of Bias In Non-Randomized Studies - of Interventions and the single-case design risk of bias tools.<br><br>RESULTS: Six teleinterventions including 25 participants with a variety of conditions, such as Down syndrome, autism, Rett syndrome, and amyotrophic lateral sclerosis met the inclusion criteria. Five studies used a single-case experimental design and one was a cohort study. Teleinterventions included active consultation (n&#xa0;=&#xa0;2), functional communication training (n&#xa0;=&#xa0;2), brain-computer interface (n&#xa0;=&#xa0;1), and both teleintervention and in-person intervention (n&#xa0;=&#xa0;1). All teleinterventions reported an increase in participants' independent use of AAC devices during the training sessions compared to baseline, as well as an overall high satisfaction and treatment acceptability.<br><br>INTERPRETATION: Speech-language teleinterventions for users of AAC devices show great potential for a successful method of service delivery. Future telehealth studies with larger sample sizes and more robust methodology are strongly encouraged to allow the generalization of results across different populations.<br><br>WHAT THIS PAPER ADDS: Individuals can learn to use augmentative and alternative communication (AAC) devices independently during tele-AAC interventions. Service providers and recipients reported an overall high satisfaction and acceptability for AAC services delivered via teleinterventions. Speech-language teleinterventions may be an effective method of providing AAC intervention services.},
}
@article {pmid36046950,
year = {2023},
author = {Fu, JX and Wei, Q and Chen, YL and Li, HF},
title = {Novel stop-gain RNF170 variation detected in a Chinese family with adolescent-onset hereditary spastic paraplegia.},
journal = {Clinical genetics},
volume = {103},
number = {1},
pages = {87-92},
doi = {10.1111/cge.14219},
pmid = {36046950},
issn = {1399-0004},
mesh = {Adolescent ; Humans ; East Asian People ; *Neurodegenerative Diseases ; *Spastic Paraplegia, Hereditary/genetics ; *Ubiquitin-Protein Ligases/genetics ; },
abstract = {Hereditary spastic paraplegia (HSP) is a heterogeneous group of inherited neurodegenerative disease characterized by progressive lower limb spasticity. Recent studies revealed that biallelic variants in RNF170 gene cause autosomal recessive complicated HSP with infancy onset. Here, we report an adolescent-onset HSP patient from a consanguineous Chinese family, with lower extremity stiffness, spastic gait, and unstable straight-line walking as the main manifestations. Whole-exome sequencing identifies a novel RNF170 mutation c.190C>T (p.R64*), which co-segregates with the disease in this pedigree. Functional analysis, including quantitative real-time PCR (RT-qPCR) and Western blot, indicates that both the mRNA and protein levels of mutant RNF170 are significantly reduced, which confirms the loss-of-function mechanism. Our study expands the spectrum of RNF170-associated HSP, while the RNF170 protein-involved degradation of the inositol 1,4,5-trisphosphate receptor in neurodegenerative motor neuron disorders deserves further investigation.},
}
@article {pmid36046470,
year = {2022},
author = {Su, Y and Zhang, Z and Li, X and Zhang, B and Ma, H},
title = {The multiscale 3D convolutional network for emotion recognition based on electroencephalogram.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {872311},
pmid = {36046470},
issn = {1662-4548},
abstract = {Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.},
}
@article {pmid36043718,
year = {2023},
author = {Huang, H and Sharma, HS and Chen, L and Chen, D},
title = {Neurorestoratology: New Advances in Clinical Therapy.},
journal = {CNS &amp; neurological disorders drug targets},
volume = {22},
number = {7},
pages = {1031-1038},
doi = {10.2174/1871527321666220827093805},
pmid = {36043718},
issn = {1996-3181},
mesh = {Humans ; *Quality of Life ; *Nervous System Diseases/therapy ; Central Nervous System/physiology ; Nerve Regeneration/physiology ; Neuronal Plasticity ; },
abstract = {Neurorestorative treatments have been able to improve the quality of life for patients suffering from neurological diseases and damages since the concept of Neurorestoratology was proposed. The discipline of Neurorestoratology focuses on restoring impaired neurological functions and/or structures through varying neurorestorative mechanisms including neurostimulation or neuromodulation, neuroprotection, neuroplasticity, neuroreplacement, loop reconstruction, remyelination, immunoregulation, angiogenesis or revascularization, neuroregeneration or neurogenesis and others. The neurorestorative strategies of Neurorestoratology include all therapeutic methods which can restore dysfunctions for patients with neurological diseases and improve their quality of life. Neurorestoratology is different from regenerative medicine in the nervous system, which mainly focuses on the neuroregeneration. It also is different from Neurorehabilitation. Neurorestoratology and Neurorehabilitation share some functional recovering mechanisms, such as neuroplasticity, especially in the early phase of neurological diseases; but generally Neurorehabilitation mainly focuses on recovering neurological functions through making the best use of residual neurological functions, replacing lost neurological functions in the largest degree, and preventing and treating varying complications. Recently, there have been more advances in restoring damaged nerves by cell therapy, neurostimulation/neuromodulation and braincomputer interface (BCI), neurorestorative surgery, neurorestorative pharmaceutics, and other clinic strategies. Simultaneously related therapeutic guidelines and standards are set up in succession. Based on those advances, clinicians should consider injured and degenerated nervous disorders or diseases in the central nervous system as treatable or neurorestorative disorders. Extending and encouraging further neurorestorative explorations and achieving better clinical efficacy with stronger evidence regarding neurorestoratology will shed new light and discover superior benefits for patients with neurological disorders.},
}
@article {pmid36042463,
year = {2022},
author = {Bai, L and Tu, WY and Xiao, Y and Zhang, K and Shen, C},
title = {Motoneurons innervation determines the distinct gene expressions in multinucleated myofibers.},
journal = {Cell &amp; bioscience},
volume = {12},
number = {1},
pages = {140},
pmid = {36042463},
issn = {2045-3701},
support = {31871203//National Natural Science Foundation of China/ ; 32071032//National Natural Science Foundation of China/ ; 31671040//National Natural Science Foundation of China/ ; 31701036//National Natural Science Foundation of China/ ; 2021YFA1101100//National Key Research and Development Program of China/ ; LZ22C110002//Zhejiang Provincial Natural Science Foundation/ ; },
abstract = {BACKGROUND: Neuromuscular junctions (NMJs) are peripheral synapses connecting motoneurons and skeletal myofibers. At the postsynaptic side in myofibers, acetylcholine receptor (AChR) proteins are clustered by the neuronal agrin signal. Meanwhile, several nuclei in each myofiber are specially enriched around the NMJ for postsynaptic gene transcription. It remains mysterious that how gene expressions in these synaptic nuclei are systematically regulated, especially by motoneurons.<br><br>RESULTS: We found that synaptic nuclei have a distinctive chromatin structure and gene expression profiling. Synaptic nuclei are formed during NMJ development and maintained by motoneuron innervation. Transcriptome analysis revealed that motoneuron innervation determines the distinct expression patterns in the synaptic region and non-synaptic region in each multinucleated myofiber, probably through epigenetic regulation. Myonuclei in synaptic and non-synaptic regions have different responses to denervation. Weighted gene co-expression network analysis revealed that the histone lysine demethylases Kdm1a is a negative regulator of synaptic gene expression. Inhibition of Kdm1a promotes AChR expression but impairs motor functions.<br><br>CONCLUSION: These results demonstrate that motoneurons innervation determines the distinct gene expressions in multinucleated myofibers. Thus, dysregulation of nerve-controlled chromatin structure and muscle gene expression might cause muscle weakness and atrophy in motoneuron degenerative disorders.},
}
@article {pmid36042184,
year = {2022},
author = {Louka, AM and Tsagkaris, C and Christoforou, P and Khan, A and Alexiou, F and Simou, P and Haranas, I and Gkigkitzis, I and Zouganelis, G and Jha, NK and Uddin, MS and Shen, B and Kamal, MA and Ashraf, GM and Alexiou, A},
title = {Current Trends of Computational Tools in Geriatric Medicine and Frailty Management.},
journal = {Frontiers in bioscience (Landmark edition)},
volume = {27},
number = {8},
pages = {232},
doi = {10.31083/j.fbl2708232},
pmid = {36042184},
issn = {2768-6698},
mesh = {Aged ; Artificial Intelligence ; Frail Elderly ; *Frailty/diagnosis ; Geriatric Assessment ; *Geriatrics ; Humans ; },
abstract = {While frailty corresponds to a multisystem failure, geriatric assessment can recognize multiple pathophysiological lesions and age changes. Up to now, a few frailty indexes have been introduced, presenting definitions of psychological problems, dysregulations in nutritional intake, behavioral abnormalities, and daily functions, genetic, environmental, and cardiovascular comorbidities. The geriatric evaluation includes a vast range of health professionals; therefore, we describe a broad range of applications and frailty scales-biomarkers to investigate and formulate the relationship between frailty lesions, diagnosis, monitoring, and treatment. Additionally, artificial intelligence applications and computational tools are presented, targeting a more efficacy individualized geriatric management of healthy aging.},
}
@article {pmid36041426,
year = {2022},
author = {Pan, Y and Chen, J and Zhang, Y and Zhang, Y},
title = {An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac8dc5},
pmid = {36041426},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Neural Networks, Computer ; Photic Stimulation/methods ; },
abstract = {Objective. Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received great interests owing to the high information transfer rate and available large number of targets. However, the performance of frequency recognition methods heavily depends on the amount of the calibration data for intra-subject classification. Some research adopted the deep learning (DL) algorithm to conduct the inter-subject classification, which could reduce the calculation procedure, but the performance still has large room to improve compared with the intra-subject classification.Approach. To address these issues, we proposed an efficient SSVEP DL NETwork (termed SSVEPNET) based on one-dimensional convolution and long short-term memory (LSTM) module. To enhance the performance of SSVEPNET, we adopted the spectral normalization and label smoothing technologies during implementing the network architecture. We evaluated the SSVEPNET and compared it with other methods for the intra- and inter-subject classification under different conditions, i.e. two datasets, two time-window lengths (1 s and 0.5 s), three sizes of training data.Main results. Under all the experimental settings, the proposed SSVEPNET achieved the highest average accuracy for the intra- and inter-subject classification on the two SSVEP datasets, when compared with other traditional and DL baseline methods.Significance. The extensive experimental results demonstrate that the proposed DL model holds promise to enhance frequency recognition performance in SSVEP-based BCIs. Besides, the mixed network structures with convolutional neural network and LSTM, and the spectral normalization and label smoothing could be useful optimization strategies to design efficient models for electroencephalography data.},
}
@article {pmid36041272,
year = {2022},
author = {Wu, Y and Xia, M and Nie, L and Zhang, Y and Fan, A},
title = {Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition.},
journal = {Computers in biology and medicine},
volume = {149},
number = {},
pages = {106002},
doi = {10.1016/j.compbiomed.2022.106002},
pmid = {36041272},
issn = {1879-0534},
mesh = {Algorithms ; Arousal ; *Electroencephalography/methods ; Emotions ; *Neural Networks, Computer ; },
abstract = {In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.},
}
@article {pmid36039116,
year = {2023},
author = {Xu, DQ and Li, MA},
title = {A dual alignment-based multi-source domain adaptation framework for motor imagery EEG classification.},
journal = {Applied intelligence (Dordrecht, Netherlands)},
volume = {53},
number = {9},
pages = {10766-10788},
pmid = {36039116},
issn = {1573-7497},
abstract = {Domain adaptation, as an important branch of transfer learning, can be applied to cope with data insufficiency and high subject variabilities in motor imagery electroencephalogram (MI-EEG) based brain-computer interfaces. The existing methods generally focus on aligning data and feature distribution; however, aligning each source domain with the informative samples of the target domain and seeking the most appropriate source domains to enhance the classification effect has not been considered. In this paper, we propose a dual alignment-based multi-source domain adaptation framework, denoted DAMSDAF. Based on continuous wavelet transform, all channels of MI-EEG signals are converted respectively and the generated time-frequency spectrum images are stitched to construct multi-source domains and target domain. Then, the informative samples close to the decision boundary are found in the target domain by using entropy, and they are employed to align and reassign each source domain with normalized mutual information. Furthermore, a multi-branch deep network (MBDN) is designed, and the maximum mean discrepancy is embedded in each branch to realign the specific feature distribution. Each branch is separately trained by an aligned source domain, and all the single branch transfer accuracies are arranged in descending order and utilized for weighted prediction of MBDN. Therefore, the most suitable number of source domains with top weights can be automatically determined. Extensive experiments are conducted based on 3 public MI-EEG datasets. DAMSDAF achieves the classification accuracies of 92.56%, 69.45% and 89.57%, and the statistical analysis is performed by the kappa value and t-test. Experimental results show that DAMSDAF significantly improves the transfer effects compared to the present methods, indicating that dual alignment can sufficiently use the different weighted samples and even source domains at different levels as well as realizing optimal selection of multi-source domains.},
}
@article {pmid36037456,
year = {2023},
author = {Zhang, L and Chen, L and Wang, Z and Zhang, X and Liu, X and Ming, D},
title = {Enhancing Visual-Guided Motor Imagery Performance via Sensory Threshold Somatosensory Electrical Stimulation Training.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {2},
pages = {756-765},
doi = {10.1109/TBME.2022.3202189},
pmid = {36037456},
issn = {1558-2531},
mesh = {Somatosensory Cortex ; *Electric Stimulation/instrumentation/methods ; Humans ; Young Adult ; Adult ; Vision, Ocular ; Sensory Thresholds ; },
abstract = {OBJECTIVE: Motor imagery (MI) based brain- computer interface (BCI) has been widely studied as an effective way to enhance motor learning and promote motor recovery. However, the accuracy of MI-BCI heavily depends on whether subjects can perform MI tasks correctly, which largely limits the general application of MI-BCI. To overcome this limitation, a training strategy based on the combination of MI and sensory threshold somatosensory electrical stimulation (MI+st-SES) is proposed in this study.<br><br>METHODS: Thirty healthy subjects were recruited and randomly divided into SES group and control group. Both groups performed left-hand and right-hand MI tasks in three consecutive blocks. The main difference between two groups lies in the second block, where subjects in SES group received the st-SES during MI tasks whereas the control group performed MI tasks only.<br><br>RESULTS: The results showed that the SES group had a significant improvement in event-related desynchronization (ERD) of alpha rhythm after the training session of MI+st-SES (left-hand: F(2,27) = 9.98, p<0.01; right-hand: F(2, 27) = 10.43, p<0.01). The classification accuracy between left- and right-hand MI in the SES group was also significantly improved following MI+st-SES training (F(2,27) = 6.46, p<0.01). In contrary, there was no significant difference between the first and third blocks in the control group (F(2,27) = 0.18, p = 0.84). The functional connectivity based on weighted pairwise phase consistency (wPPC) over the sensorimotor area also showed an increase after the MI+st-SES training.<br><br>CONCLUSION AND SIGNIFICANCE: Our findings indicate that training based on MI+st-SES is a promising way to foster MI performance and assist subjects in achieving efficient BCI control.},
}
@article {pmid36036300,
year = {2023},
author = {Xie, JJ and Li, XY and Dong, Y and Chen, C and Qu, BY and Wang, S and Xu, H and Roe, AW and Lai, HY and Wu, ZY},
title = {Local and Global Abnormalities in Pre-symptomatic Huntington's Disease Revealed by 7T Resting-state Functional MRI.},
journal = {Neuroscience bulletin},
volume = {39},
number = {1},
pages = {94-98},
pmid = {36036300},
issn = {1995-8218},
mesh = {Humans ; *Huntington Disease/diagnostic imaging ; Brain/diagnostic imaging ; Brain Mapping ; Magnetic Resonance Imaging ; },
}
@article {pmid36034935,
year = {2022},
author = {Han, Z and Chang, H and Zhou, X and Wang, J and Wang, L and Shao, Y},
title = {E2ENNet: An end-to-end neural network for emotional brain-computer interface.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {942979},
pmid = {36034935},
issn = {1662-5188},
abstract = {OBJECTVE: Emotional brain-computer interface can recognize or regulate human emotions for workload detection and auxiliary diagnosis of mental illness. However, the existing EEG emotion recognition is carried out step by step in feature engineering and classification, resulting in high engineering complexity and limiting practical applications in traditional EEG emotion recognition tasks. We propose an end-to-end neural network, i.e., E2ENNet.<br><br>METHODS: Baseline removal and sliding window slice used for preprocessing of the raw EEG signal, convolution blocks extracted features, LSTM network obtained the correlations of features, and the softmax function classified emotions.<br><br>RESULTS: Extensive experiments in subject-dependent experimental protocol are conducted to evaluate the performance of the proposed E2ENNet, achieves state-of-the-art accuracy on three public datasets, i.e., 96.28% of 2-category experiment on DEAP dataset, 98.1% of 2-category experiment on DREAMER dataset, and 41.73% of 7-category experiment on MPED dataset.<br><br>CONCLUSION: Experimental results show that E2ENNet can directly extract more discriminative features from raw EEG signals.<br><br>SIGNIFICANCE: This study provides a methodology for implementing a plug-and-play emotional brain-computer interface system.},
}
@article {pmid36034124,
year = {2022},
author = {Yang, H and Paller, KA and van Vugt, M},
title = {The steady state visual evoked potential (SSVEP) tracks "sticky" thinking, but not more general mind-wandering.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {892863},
pmid = {36034124},
issn = {1662-5161},
abstract = {For a large proportion of our daily lives, spontaneously occurring thoughts tend to disengage our minds from goal-directed thinking. Previous studies showed that EEG features such as the P3 and alpha oscillations can predict mind-wandering to some extent, but only with accuracies of around 60%. A potential candidate for improving prediction accuracy is the Steady-State Visual Evoked Potential (SSVEP), which is used frequently in single-trial contexts such as brain-computer interfaces as a marker of the direction of attention. In this study, we modified the sustained attention to response task (SART) that is usually employed to measure spontaneous thought to incorporate the SSVEP elicited by a 12.5-Hz flicker. We then examined whether the SSVEP could track and allow for the prediction of the stickiness and task-relatedness dimensions of spontaneous thought. Our results show that the SSVEP evoked by flickering words was able to distinguish between more and less sticky thinking but not between whether a participant was on- or off-task. This suggests that the SSVEP is able to track spontaneous thinking when it is strongly disengaged from the task (as in the sticky form of off-task thinking) but not off-task thought in general. Future research should determine the exact dimensions of spontaneous thought to which the SSVEP is most sensitive.},
}
@article {pmid36034123,
year = {2022},
author = {Krogmeier, C and Coventry, BS and Mousas, C},
title = {Frontal alpha asymmetry interaction with an experimental story EEG brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {883467},
pmid = {36034123},
issn = {1662-5161},
abstract = {Although interest in brain-computer interfaces (BCIs) from researchers and consumers continues to increase, many BCIs lack the complexity and imaginative properties thought to guide users toward successful brain activity modulation. We investigate the possibility of using a complex BCI by developing an experimental story environment with which users interact through cognitive thought strategies. In our system, the user's frontal alpha asymmetry (FAA) measured with electroencephalography (EEG) is linearly mapped to the color saturation of the main character in the story. We implemented a user-friendly experimental design using a comfortable EEG device and short neurofeedback (NF) training protocol. In our system, seven out of 19 participants successfully increased FAA during the course of the study, for a total of ten successful blocks out of 152. We detail our results concerning left and right prefrontal cortical activity contributions to FAA in both successful and unsuccessful story blocks. Additionally, we examine inter-subject correlations of EEG data, and self-reported questionnaire data to understand the user experience of BCI interaction. Results suggest the potential of imaginative story BCI environments for engaging users and allowing for FAA modulation. Our data suggests new research directions for BCIs investigating emotion and motivation through FAA.},
}
@article {pmid36034117,
year = {2022},
author = {Hou, Y and Gu, Z and Yu, ZL and Xie, X and Tang, R and Xu, J and Qi, F},
title = {Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {975410},
pmid = {36034117},
issn = {1662-5161},
abstract = {Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, β and γ rhythms in lower-limb movement imagery tasks.},
}
@article {pmid36034109,
year = {2022},
author = {Li, W and Zhang, W and Jiang, Z and Zhou, T and Xu, S and Zou, L},
title = {Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {960784},
pmid = {36034109},
issn = {1662-5161},
abstract = {BACKGROUND: The neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.<br><br>OBJECTIVES: We proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.<br><br>METHODS: EEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal task. Fused priori weighted minimum norm estimation (FWMNE) with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index (PLI) was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.<br><br>RESULTS: The functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.<br><br>CONCLUSION: The proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders.},
}
@article {pmid36033632,
year = {2022},
author = {Spataro, R and Xu, Y and Xu, R and Mandalà, G and Allison, BZ and Ortner, R and Heilinger, A and La Bella, V and Guger, C},
title = {How brain-computer interface technology may improve the diagnosis of the disorders of consciousness: A comparative study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {959339},
pmid = {36033632},
issn = {1662-4548},
abstract = {OBJECTIVE: Clinical assessment of consciousness relies on behavioural assessments, which have several limitations. Hence, disorder of consciousness (DOC) patients are often misdiagnosed. In this work, we aimed to compare the repetitive assessment of consciousness performed with a clinical behavioural and a Brain-Computer Interface (BCI) approach.<br><br>MATERIALS AND METHODS: For 7 weeks, sixteen DOC patients participated in weekly evaluations using both the Coma Recovery Scale-Revised (CRS-R) and a vibrotactile P300 BCI paradigm. To use the BCI, patients had to perform an active mental task that required detecting specific stimuli while ignoring other stimuli. We analysed the reliability and the efficacy in the detection of command following resulting from the two methodologies.<br><br>RESULTS: Over repetitive administrations, the BCI paradigm detected command following before the CRS-R in seven patients. Four clinically unresponsive patients consistently showed command following during the BCI assessments.<br><br>CONCLUSION: Brain-Computer Interface active paradigms might contribute to the evaluation of the level of consciousness, increasing the diagnostic precision of the clinical bedside approach.<br><br>SIGNIFICANCE: The integration of different diagnostic methods leads to a better knowledge and care for the DOC.},
}
@article {pmid36033287,
year = {2022},
author = {Klomchitcharoen, S and Tangwattanasirikun, T and Gallup, S and Smerwong, N and Arunwiriyakit, P and Tachavises, P and Tangkijngamwong, J and Phatthanaanukun, P and Jirapanyalerd, B and Chattanupakorn, S and Rungpongvanich, V and Nangsue, N and Meemon, K and Wongtrakoonkate, P and Hongeng, S and Wongsawat, Y},
title = {MINERVA: A CubeSat for demonstrating DNA damage mitigation against space radiation in C. elegans by using genetic modification.},
journal = {Heliyon},
volume = {8},
number = {8},
pages = {e10267},
pmid = {36033287},
issn = {2405-8440},
abstract = {The ideas of deep-space human exploration, interplanetary travel, and space civilizations are becoming a reality. However, numerous hindrances remain standing in the way of accomplishing these feats, one of which is space ionizing radiation. Space ionizing radiation has become the most hazardous health risk for long-term human space exploration, as it can induce chromosomal damage and epigenetic changes. The Minerva mission aims to demonstrate cutting-edge technology to inhibit DNA damage against deep-space radiation exposure by using genetic modification. The concept of the experiment is to transform a creature with radiation intolerance into a transgenic organism that is radiation-tolerant. In this mission, Caenorhabditis elegans (C. elegans) will be genetically engineered with a protein-coding gene associated with DNA damage protection called damage suppressor (Dsup). Dsup is a nucleosome-binding protein from the tardigrade Ramazzottius varieornatus that has a unique ability to prevent DNA damage. This paper describes the feasibility of Minerva CubeSat, which will venture out to cis-lunar orbit with a biosensor payload capable of sustaining and culturing C. elegans under space environment conditions for 4 months. The mission will set in motion a paradigm shift corresponding to future space medicines and how they will be developed in the future, introducing a platform suitable for future experiments in the fields of space biology. Ultimately, the paramount objective of Minerva will be to test the limits of genetic engineering and incorporate it into the arduous journey of human perseverance to overcome the boundaries of space exploration-a vital step in making Mars colonization safe.},
}
@article {pmid36031182,
year = {2022},
author = {Chuang, CH and Chang, KY and Huang, CS and Jung, TP},
title = {IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal.},
journal = {NeuroImage},
volume = {263},
number = {},
pages = {119586},
doi = {10.1016/j.neuroimage.2022.119586},
pmid = {36031182},
issn = {1095-9572},
mesh = {Humans ; *Artifacts ; *Signal Processing, Computer-Assisted ; Eye Movements ; Blinking ; Electroencephalography/methods ; Algorithms ; },
abstract = {Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end-to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://github.com/roseDwayane/AIEEG.},
}
@article {pmid36030608,
year = {2022},
author = {Al-Khouja, F and Paladugu, A and Ruiz, A and Prentice, K and Kirby, K and Santos, J and Rockne, W and Nahmias, J},
title = {Evaluating the Need for Prolonged Telemetry Monitoring in Patients With Isolated Sternal Fractures.},
journal = {The Journal of surgical research},
volume = {280},
number = {},
pages = {320-325},
doi = {10.1016/j.jss.2022.07.031},
pmid = {36030608},
issn = {1095-8673},
mesh = {Humans ; Retrospective Studies ; Prospective Studies ; *Thoracic Injuries/complications ; Sternum/injuries ; *Rib Fractures/complications ; Arrhythmias, Cardiac/diagnosis/etiology ; Telemetry ; Troponin ; *Wounds, Nonpenetrating/diagnosis ; },
abstract = {INTRODUCTION: Isolated sternal fractures (ISFs) often result from deceleration or chest wall trauma. Current guidelines recommend screening ISF patients for blunt cardiac injury (BCI) with electrocardiogram (ECG) and troponin. If either is abnormal, 24-h telemetry monitoring is recommended. This study sought to determine if ISF patients with abnormal ECG will manifest any cardiac-related complications within 6 h of hospital arrival.<br><br>METHODS: A retrospective study was performed at a single level I trauma center. Patients with diagnosed sternal fracture and an Abbreviated Injury Scale <2 for head/neck, face, abdomen, and extremities were included. Patients with multiple rib fractures or hemopneumothorax were excluded. Demographic data, ECG, troponin, and echocardiogram results were collected. The primary outcome was cardiac-related complications or procedures. Complications included hypotension, arrhythmia, and hemodynamic instability. Procedures included sternal stabilization, cardiac catheterization, or sternotomy/thoracotomy. Descriptive statistics were performed.<br><br>RESULTS: One hundred twenty-nine ISF patients were evaluated, 68 (52.7%) had an ECG abnormality. Eight patients had elevated troponin (6.2%). One patient (0.78%) suffered a cardiac-related complication (arrhythmia); however, this was 82&#xa0;h into hospitalization. Two patients suffered noncardiac complications (urinary tract infection and acute kidney injury) (1.55%). Three patients had echocardiogram abnormality (2.33%), but no patients sustained a BCI or underwent a BCI-related procedure.<br><br>CONCLUSIONS: After ISF, <1% of patients suffered a cardiac-related complication and none had BCI. These findings suggest 24-h monitoring for patients with ISF and abnormal ECG may be unnecessarily long. A prospective multicenter study to evaluate the validity of these results is needed prior to change of practice.},
}
@article {pmid36026601,
year = {2022},
author = {Quimby, AE and Park, J and Brant, JA and Eliades, SJ and Kaufman, HS and Bigelow, DC and Ruckenstein, MJ},
title = {Audiometric and Surgical Outcomes of a Novel Bone-Conduction Hearing Aid.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {43},
number = {9},
pages = {995-999},
doi = {10.1097/MAO.0000000000003668},
pmid = {36026601},
issn = {1537-4505},
mesh = {Adult ; Bone Conduction/physiology ; *Hearing Aids ; Hearing Loss, Conductive/surgery ; Humans ; Retrospective Studies ; Treatment Outcome ; },
abstract = {OBJECTIVE: To report the audiometric and surgical outcomes of a series of patients having undergone implantation of a novel transcutaneous bone conduction implant (t-BCI).<br><br>STUDY DESIGN: Retrospective case series.<br><br>SETTING: Single academic tertiary referral center.<br><br>PATIENTS: Adults (&#x2265;18 yr) implanted between December 1, 2019, and August 1, 2021, with audiometric data available before and after device implantation and a minimum of 4 weeks follow-up.<br><br>INTERVENTIONS: Surgical t-BCI.<br><br>MAIN OUTCOME MEASURES: Change in aided pure tone average (PTA) after implantation. Secondary outcomes include average operative time, and adverse events.<br><br>RESULTS: Twenty-three patients underwent implantation of the t-BCI via either a conventional or minimally invasive surgical approach. The most common indication for implantation was unilateral conductive hearing loss with a history of chronic otitis media. The mean operative time was 59 minutes. The mean preimplantation unaided air conduction PTA was 65 dB, and mean postimplantation was 27.2 dB. The mean change in PTA was 37.8 dB, which was significant (p < 0.0001). There were 30.4% of the patients that suffered from adverse events, the most common of which were pain (8.7%) and device-related complications (13%). One major adverse event occurred, involving magnet displacement that impaired device activation and required reoperation for replacement.<br><br>CONCLUSION: Forming the largest series of patients implanted with this t-BCI in the published literature, our data demonstrate that implantation of the device is feasible via either a traditional or minimally invasive surgical approach, with good audiometric benefit and a favorable safety profile.},
}
@article {pmid36015974,
year = {2022},
author = {Li, J and Cheng, S and Tao, Y and Liu, H and Zhou, J and Zhang, J},
title = {Dual-Branch Discrimination Network Using Multiple Sparse Priors for Image Deblurring.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {16},
pages = {},
pmid = {36015974},
issn = {1424-8220},
support = {Q20F020062//the Natural Science Foundation of Zhejiang Provincial/ ; 62002089//the National Natural Science Foundation of China/ ; 2020C04009//the Key Research and Development Project of Zhejiang Province/ ; },
mesh = {*Image Processing, Computer-Assisted/methods ; *Neural Networks, Computer ; },
abstract = {Blind image deblurring is a challenging problem in computer vision, aiming to restore the sharp image from blurred observation. Due to the incompatibility between the complex unknown degradation and the simple synthetic model, directly training a deep convolutional neural network (CNN) usually cannot sufficiently handle real-world blurry images. An existed generative adversarial network (GAN) can generate more detailed and realistic images, but the game between generator and discriminator is unbalancing, which leads to the training parameters not being able to converge to the ideal Nash equilibrium points. In this paper, we propose a GAN with a dual-branch discriminator using multiple sparse priors for image deblurring (DBSGAN) to overcome this limitation. By adding the multiple sparse priors into the other branch of the discriminator, the task of the discriminator is more complex. It can balance the game between the generator and the discriminator. Extensive experimental results on both synthetic and real-world blurry image datasets demonstrate the superior performance of our method over the state of the art in terms of quantitative metrics and visual quality. Especially for the GOPRO dataset, the averaged PSNR improves 1.7% over others.},
}
@article {pmid36015922,
year = {2022},
author = {Vortmann, LM and Weidenbach, P and Putze, F},
title = {AtAwAR Translate: Attention-Aware Language Translation Application in Augmented Reality for Mobile Phones.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {16},
pages = {},
pmid = {36015922},
issn = {1424-8220},
mesh = {Attention ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Language ; Smartphone ; },
abstract = {As lightweight, low-cost EEG headsets emerge, the feasibility of consumer-oriented brain-computer interfaces (BCI) increases. The combination of portable smartphones and easy-to-use EEG dry electrode headbands offers intriguing new applications and methods of human-computer interaction. In previous research, augmented reality (AR) scenarios have been identified to profit from additional user state information-such as that provided by a BCI. In this work, we implemented a system that integrates user attentional state awareness into a smartphone application for an AR written language translator. The attentional state of the user is classified in terms of internally and externally directed attention by using the Muse 2 electroencephalography headband with four frontal electrodes. The classification results are used to adapt the behavior of the translation app, which uses the smartphone's camera to display translated text as augmented reality elements. We present the first mobile BCI system that uses a smartphone and a low-cost EEG device with few electrodes to provide attention awareness to an AR application. Our case study with 12 participants did not fully support the assumption that the BCI improves usability. However, we are able to show that the classification accuracy and ease of setup are promising paths toward mobile consumer-oriented BCI usage. For future studies, other use cases, applications, and adaptations will be tested for this setup to explore the usability.},
}
@article {pmid36015862,
year = {2022},
author = {Reichert, C and Klemm, L and Mushunuri, RV and Kalyani, A and Schreiber, S and Kuehn, E and Azañón, E},
title = {Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {16},
pages = {},
pmid = {36015862},
issn = {1424-8220},
support = {ZS/2016/04/78113//European Regional Development Fund/ ; SFB-1436 (project-ID 425899996)//Deutsche Forschungsgemeinschaft/ ; ZS/2016/04/78120//European Regional Development Fund/ ; },
mesh = {Aged ; Algorithms ; *Brain-Computer Interfaces ; Hand ; Humans ; Movement ; Neural Networks, Computer ; *Support Vector Machine ; },
abstract = {Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.},
}
@article {pmid36015854,
year = {2022},
author = {Lomelin-Ibarra, VA and Gutierrez-Rodriguez, AE and Cantoral-Ceballos, JA},
title = {Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {16},
pages = {},
pmid = {36015854},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Machine Learning ; Movement ; Neural Networks, Computer ; },
abstract = {Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain-computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques.},
}
@article {pmid36015803,
year = {2022},
author = {Choi, H and Park, J and Yang, YM},
title = {Whitening Technique Based on Gram-Schmidt Orthogonalization for Motor Imagery Classification of Brain-Computer Interface Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {16},
pages = {},
pmid = {36015803},
issn = {1424-8220},
support = {2020R1A2C4001606//National Research Foundation of Korea/ ; 2021//Kumoh National Institute of Technology/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {A novel whitening technique for motor imagery (MI) classification is proposed to reduce the accuracy variance of brain-computer interfaces (BCIs). This method is intended to improve the electroencephalogram eigenface analysis performance for the MI classification of BCIs. In BCI classification, the variance of the accuracy among subjects is sensitive to the accuracy itself for superior classification results. Hence, with the help of Gram-Schmidt orthogonalization, we propose a BCI channel whitening (BCICW) scheme to minimize the variance among subjects. The newly proposed BCICW method improved the variance of the MI classification in real data. To validate and verify the proposed scheme, we performed an experiment on the BCI competition 3 dataset IIIa (D3D3a) and the BCI competition 4 dataset IIa (D4D2a) using the MATLAB simulation tool. The variance data when using the proposed BCICW method based on Gram-Schmidt orthogonalization was much lower (11.21) than that when using the EFA method (58.33) for D3D3a and decreased from (17.48) to (9.38) for D4D2a. Therefore, the proposed method could be effective for MI classification of BCI applications.},
}
@article {pmid36014141,
year = {2022},
author = {Qi, W and Wang, S},
title = {Sequential Covariance Intersection Fusion Robust Time-Varying Kalman Filters with Uncertainties of Noise Variances for Advanced Manufacturing.},
journal = {Micromachines},
volume = {13},
number = {8},
pages = {},
pmid = {36014141},
issn = {2072-666X},
support = {No. LH2021E100//the Natural Science Foundation of Heilongjiang Province/ ; },
abstract = {This paper addresses the robust Kalman filtering problem for multisensor time-varying systems with uncertainties of noise variances. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, the robust local time-varying Kalman filters are presented. Further, the batch covariance intersection (BCI) fusion and a fast sequential covariance intersection (SCI) fusion robust time-varying Kalman filters are presented. They have the robustness that the actual filtering error variances or their traces are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. Their robustness is proved based on the proposed Lyapunov equations approach. The concepts of the robust and actual accuracies are presented, and the robust accuracy relations are proved. It is also proved that the robust accuracies of the BCI and SCI fusers are higher than that of each local Kalman filter, the robust accuracy of the BCI fuser is higher than that of the SCI fuser, and the actual accuracies of each robust Kalman filter are higher than its robust accuracy for all admissible uncertainties of noise variances. The corresponding steady-state robust local and fused Kalman filters are also presented for multisensor time-invariant systems, and the convergence in a realization between the local and fused time-varying and steady-state Kalman filters is proved by the dynamic error system analysis (DESA) method and dynamic variance error system analysis (DVESA) method. A simulation example is given to verify the robustness and the correctness of the robust accuracy relations.},
}
@article {pmid36012152,
year = {2022},
author = {Man, YKS and Aguirre-Hernandez, C and Fernandez, A and Martin-Duque, P and González-Pastor, R and Halldén, G},
title = {Complexing the Oncolytic Adenoviruses Ad∆∆ and Ad-3∆-A20T with Cationic Nanoparticles Enhances Viral Infection and Spread in Prostate and Pancreatic Cancer Models.},
journal = {International journal of molecular sciences},
volume = {23},
number = {16},
pages = {},
pmid = {36012152},
issn = {1422-0067},
support = {BCI CRUK Centre Grant [grant number C16420/A18066/CRUK_/Cancer Research UK/United Kingdom ; Pancreatic Cancer Research Fund (PCRF) to Gunnel Hallden/PANCREATICCANUK_/Pancreatic Cancer UK/United Kingdom ; RIA16-ST2-026/PCUK_/Prostate Cancer UK/United Kingdom ; },
mesh = {Adenoviridae/physiology ; Cell Line, Tumor ; Gold/metabolism ; Humans ; Male ; *Metal Nanoparticles ; *Oncolytic Virotherapy/methods ; *Oncolytic Viruses/physiology ; *Pancreatic Neoplasms/pathology ; Prostate/pathology ; *Virus Diseases ; Virus Replication ; Xenograft Model Antitumor Assays ; },
abstract = {Oncolytic adenoviruses (OAd) can be employed to efficiently eliminate cancer cells through multiple mechanisms of action including cell lysis and immune activation. Our OAds, AdΔΔ and Ad-3∆-A20T, selectively infect, replicate in, and kill adenocarcinoma cells with the added benefit of re-sensitising drug-resistant cells in preclinical models. Further modifications are required to enable systemic delivery in patients due to the rapid hepatic elimination and neutralisation by blood factors and antibodies. Here, we show data that support the use of coating OAds with gold nanoparticles (AuNPs) as a possible new method of virus modification to help augment tumour uptake. The pre-incubation of cationic AuNPs with AdΔΔ, Ad-3∆-A20T and wild type adenovirus (Ad5wt) was performed prior to infection of prostate/pancreatic cancer cell lines (22Rv, PC3, Panc04.03, PT45) and a pancreatic stellate cell line (PS1). Levels of viral infection, replication and cell viability were quantified 24-72 h post-infection in the presence and absence of AuNPs. Viral spread was assessed in organotypic cultures. The presence of AuNPs significantly increased the uptake of Ad∆∆, Ad-3∆-A20T and Ad5wt in all the cell lines tested (ranging from 1.5-fold to 40-fold), compared to virus alone, with the greatest uptake observed in PS1, a usually adenovirus-resistant cell line. Pre-coating the AdΔΔ and Ad-3∆-A20T with AuNPs also increased viral replication, leading to enhanced cell killing, with maximal effect in the most virus-insensitive cells (from 1.4-fold to 5-fold). To conclude, the electrostatic association of virus with cationic agents provides a new avenue to increase the dose in tumour lesions and potentially protect the virus from detrimental blood factor binding. Such an approach warrants further investigation for clinical translation.},
}
@article {pmid36010782,
year = {2022},
author = {Zhu, J and Chen, M and Lu, J and Zhao, K and Cui, E and Zhang, Z and Wan, H},
title = {A Fast and Efficient Ensemble Transfer Entropy and Applications in Neural Signals.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {8},
pages = {},
pmid = {36010782},
issn = {1099-4300},
support = {61673353//National Natural Science Foundation of China/ ; 62173310//National Natural Science Foundation of China/ ; },
abstract = {The ensemble transfer entropy (TEensemble) refers to the transfer entropy estimated from an ensemble of realizations. Due to its time-resolved analysis, it is adapted to analyze the dynamic interaction between brain regions. However, in the traditional TEensemble, multiple sets of surrogate data should be used to construct the null hypothesis distribution, which dramatically increases the computational complexity. To reduce the computational cost, a fast, efficient TEensemble with a simple statistical test method is proposed here, in which just one set of surrogate data is involved. To validate the improved efficiency, the simulated neural signals are used to compare the characteristics of the novel TEensemble with those of the traditional TEensemble. The results show that the time consumption is reduced by two or three magnitudes in the novel TEensemble. Importantly, the proposed TEensemble could accurately track the dynamic interaction process and detect the strength and the direction of interaction robustly even in the presence of moderate noises. The novel TEensemble reaches its steady state with the increased samples, which is slower than the traditional method. Furthermore, the effectiveness of the novel TEensemble was verified in the actual neural signals. Accordingly, the TEensemble proposed in this work may provide a suitable way to investigate the dynamic interactions between brain regions.},
}
@article {pmid36009146,
year = {2022},
author = {Gao, W and Cui, Z and Yu, Y and Mao, J and Xu, J and Ji, L and Kan, X and Shen, X and Li, X and Zhu, S and Hong, Y},
title = {Application of a Brain-Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report.},
journal = {Brain sciences},
volume = {12},
number = {8},
pages = {},
pmid = {36009146},
issn = {2076-3425},
support = {2021zhyx-C50//Anhui Medical University/ ; },
abstract = {(1) Objective: To investigate the feasibility, safety, and effectiveness of a brain-computer interface (BCI) system with visual and motor feedback in limb and brain function rehabilitation after stroke. (2) Methods: First, we recruited three hemiplegic stroke patients to perform rehabilitation training using a BCI system with visual and motor feedback for two consecutive days (four sessions) to verify the feasibility and safety of the system. Then, we recruited five other hemiplegic stroke patients for rehabilitation training (6 days a week, lasting for 12-14 days) using the same BCI system to verify the effectiveness. The mean and Cohen's w were used to compare the changes in limb motor and brain functions before and after training. (3) Results: In the feasibility verification, the continuous motor state switching time (CMSST) of the three patients was 17.8 ± 21.0s, and the motor state percentages (MSPs) in the upper and lower limb training were 52.6 ± 25.7% and 72.4 ± 24.0%, respectively. The effective training revolutions (ETRs) per minute were 25.8 ± 13.0 for upper limb and 24.8 ± 6.4 for lower limb. There were no adverse events during the training process. Compared with the baseline, the motor function indices of the five patients were improved, including sitting balance ability, upper limb Fugel-Meyer assessment (FMA), lower limb FMA, 6 min walking distance, modified Barthel index, and root mean square (RMS) value of triceps surae, which increased by 0.4, 8.0, 5.4, 11.4, 7.0, and 0.9, respectively, and all had large effect sizes (Cohen's w ≥ 0.5). The brain function indices of the five patients, including the amplitudes of the motor evoked potentials (MEP) on the non-lesion side and lesion side, increased by 3.6 and 3.7, respectively; the latency of MEP on the non-lesion side was shortened by 2.6 ms, and all had large effect sizes (Cohen's w ≥ 0.5). (4) Conclusions: The BCI system with visual and motor feedback is applicable in active rehabilitation training of stroke patients with hemiplegia, and the pilot results show potential multidimensional benefits after a short course of treatment.},
}
@article {pmid36009099,
year = {2022},
author = {Wang, CC and Cheng, PK and Wang, TH},
title = {Measurement of Extraneous and Germane Cognitive Load in the Mathematics Addition Task: An Event-Related Potential Study.},
journal = {Brain sciences},
volume = {12},
number = {8},
pages = {},
pmid = {36009099},
issn = {2076-3425},
support = {N/A//Yin Shu-Tien Educational Foundation in Taiwan/ ; },
abstract = {Cognitive load significantly influences learning effectiveness. All the three types of cognitive load-intrinsic, extraneous, and germane-are important for guiding teachers in preparing effective instructional designs for students. However, the techniques used to assess the relationship between brain activity and cognitive load during learning activities require further investigation. This study preliminarily examined cognitive load during mathematics computations based on cognitive-load theory. We used event-related potentials to compare carryover and without carryover additions under three types of stimuli (uncoloured Arabic numerals, colourful Arabic numerals, and Chinese numerals) to measure learners' cognitive load. According to the concept and rationale of cognitive-load theory, the design defined the extraneous and germane cognitive load to measure the N1 and P2 components and the relevant behavioural data. The highest P2 amplitude was observed in the Chinese numerals condition as extraneous cognitive load, and the N1 component was observed in the colourful Arabic numerals condition as germane cognitive load. Thus, both components may play an important role in extraneous and germane cognitive load. Additionally, these exhibit negative correlations during mathematical computations. This study's findings and implications offer insights into future ways for assessing cognitive load using brain imaging techniques and potential applications for brain-computer interfaces.},
}
@article {pmid36007627,
year = {2023},
author = {Qin, R and An, C and Chen, W},
title = {Physical-Chemical Regulation of Membrane Receptors Dynamics in Viral Invasion and Immune Defense.},
journal = {Journal of molecular biology},
volume = {435},
number = {1},
pages = {167800},
pmid = {36007627},
issn = {1089-8638},
mesh = {Humans ; Receptors, Antigen, T-Cell/metabolism ; SARS-CoV-2/physiology ; Spike Glycoprotein, Coronavirus/chemistry ; *Receptors, Immunologic/chemistry ; *Virus Internalization ; *Single Molecule Imaging ; Microscopy, Atomic Force ; Immunity ; },
abstract = {Mechanical cues dynamically regulate membrane receptors functions to trigger various physiological and pathological processes from viral invasion to immune defense. These cues mainly include various types of dynamic mechanical forces and the spatial confinement of plasma membrane. However, the molecular mechanisms of how they couple with biochemical cues in regulating membrane receptors functions still remain mysterious. Here, we review recent advances in methodologies of single-molecule biomechanical techniques and in novel biomechanical regulatory mechanisms of critical ligand recognition of viral and immune receptors including SARS-CoV-2 spike protein, T cell receptor (TCR) and other co-stimulatory immune receptors. Furthermore, we provide our perspectives of the general principle of how force-dependent kinetics determine the dynamic functions of membrane receptors and of biomechanical-mechanism-driven SARS-CoV-2 neutralizing antibody design and TCR engineering for T-cell-based therapies.},
}
@article {pmid36001921,
year = {2022},
author = {Varkevisser, F and Costa, TL and Serdijn, WA},
title = {Energy efficiency of pulse shaping in electrical stimulation: the interdependence of biophysical effects and circuit design losses.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ac8c47},
pmid = {36001921},
issn = {2057-1976},
mesh = {Biophysics ; *Conservation of Energy Resources ; Electric Stimulation ; *Electric Stimulation Therapy ; Neurons/physiology ; },
abstract = {Power efficiency in electrical stimulator circuits is crucial for developing large-scale multichannel applications like bidirectional brain-computer interfaces and neuroprosthetic devices. Many state-of-the-art papers have suggested that some non-rectangular pulse shapes are more energy-efficient for exciting neural excitation than the conventional rectangular shape. However, additional losses in the stimulator circuit, which arise from employing such pulses, were not considered. In this work, we analyze the total energy efficiency of a stimulation system featuring non-rectangular stimuli, taking into account the losses in the stimulator circuit. To this end, activation current thresholds for different pulse shapes and durations in cortical neurons are modeled, and the energy required to generate the pulses from a constant voltage supply is calculated. The proposed calculation reveals an energy increase of 14%-51% for non-rectangular pulses compared to the conventional rectangular stimuli, instead of the decrease claimed in previous literature. This result indicates that a rectangular stimulation pulse is more power-efficient than the tested alternative shapes in large-scale multichannel electrical stimulation systems.},
}
@article {pmid36001601,
year = {2022},
author = {Al-Taie, I and Di Giuseppantonio Di Franco, P and Tymkiw, M and Williams, D and Daly, I},
title = {Sonic enhancement of virtual exhibits.},
journal = {PloS one},
volume = {17},
number = {8},
pages = {e0269370},
pmid = {36001601},
issn = {1932-6203},
mesh = {Attention ; Emotions ; Humans ; Learning ; *Museums ; *Sound ; },
abstract = {Museums have widely embraced virtual exhibits. However, relatively little attention is paid to how sound may create a more engaging experience for audiences. To begin addressing this lacuna, we conducted an online experiment to explore how sound influences the interest level, emotional response, and engagement of individuals who view objects within a virtual exhibit. As part of this experiment, we designed a set of different soundscapes, which we presented to participants who viewed museum objects virtually. We then asked participants to report their felt affect and level of engagement with the exhibits. Our results show that soundscapes customized to exhibited objects significantly enhance audience engagement. We also found that more engaged audience members were more likely to want to learn additional information about the object(s) they viewed and to continue viewing these objects for longer periods of time. Taken together, our findings suggest that virtual museum exhibits can improve visitor engagement through forms of customized soundscape design.},
}
@article {pmid36001509,
year = {2023},
author = {Zhong, Y and Yao, L and Wang, J and Wang, Y},
title = {Tactile Sensation Assisted Motor Imagery Training for Enhanced BCI Performance: A Randomized Controlled Study.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {2},
pages = {694-702},
doi = {10.1109/TBME.2022.3201241},
pmid = {36001509},
issn = {1558-2531},
mesh = {Humans ; *Electroencephalography ; *Brain-Computer Interfaces ; Imagination/physiology ; Touch/physiology ; Hand/physiology ; },
abstract = {OBJECTIVE: Independent of conventional neurofeedback training, in this study, we propose a tactile sensation assisted motor imagery training (SA-MI Training) approach to improve the performance of MI-based BCI.<br><br>METHODS: Twenty-six subjects were recruited and randomly divided into a Training-Group and a Control-Group. All subjects were required to perform three blocks of MI tasks. In the Training-Group, during the second block (SA-MI Training block), tactile stimulation was applied to the left or right wrist while the subjects were performing the left or right-hand MI task, while during the first block (Pre-Training block) and the third block (Post-Training block), subjects performed pure MI tasks without the tactile sensation assistance. In contrast, in the Control-Group, subjects performed the left and right-hand MI tasks in all three blocks.<br><br>RESULTS: The performance of the Post-Training block (83.2 &#xb1; 11.4%) was significantly (p = 0.0014) higher than that of the Pre-Training block (73.2 &#xb1; 16.3%). By contrast, in the Control-Group, no significant difference was found among the three blocks. Moreover, after the SA-MI Training, the motor-related cortex activation (i.e., ERD/ERS) and the R [2] coefficient in the alpha-beta band were enhanced, while no training effect was found in the Control-Group.<br><br>CONCLUSION: The proposed SA-MI Training approach can significantly improve the performance of MI, which provides a novel training framework for MI-based BCI.<br><br>SIGNIFICANCE: It may be especially beneficial to those who are with difficulty in convention neurofeedback training or performing pure MI mental tasks to gain BCI control.},
}
@article {pmid36001366,
year = {2022},
author = {Fang, BZ and Gao, L and Jiao, JY and Zhang, ZT and Li, MM and Mohamad, OAA and Ahmed, I and Li, L and Liu, YH and Li, WJ},
title = {Agromyces cavernae sp. nov., a novel member of the genus Agromyces isolated from a karstic cave in Shaoguan.},
journal = {International journal of systematic and evolutionary microbiology},
volume = {72},
number = {8},
pages = {},
doi = {10.1099/ijsem.0.005503},
pmid = {36001366},
issn = {1466-5034},
mesh = {*Actinomycetales ; Bacterial Typing Techniques ; Base Composition ; China ; DNA, Bacterial/genetics ; Fatty Acids/chemistry ; Phospholipids/chemistry ; Phylogeny ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; *Soil Microbiology ; },
abstract = {A novel actinobacterial strain, designated SYSU K20354[T], was isolated from a soil sample collected from a karst cave in Shaoguan city, Guangdong province, southern China. The taxonomic position of the strain was investigated using a polyphasic approach. Cells of the strain were aerobic, Gram-stain-positive and non-motile. On the basis of 16S rRNA gene sequence similarities and phylogenetic analysis, strain SYSU K20354[T] was most closely related to Agromyces humatus JCM 14319[T], and shared the highest sequence identity of 98.3 % based on NCBI database. In addition, 2,4-diaminobutyric acid was the diagnostic diamino acid in cell-wall peptidoglycan. The whole-cell sugars were galactose, glucose, mannose and ribose. The major isoprenoid quinone was MK-12, while the major fatty acids (>10 %) were iso-C16 : 0, anteiso-C15 : 0 and anteiso-C17 : 0. The polar lipids contained diphosphatidylglycerol, phosphatidylglycerol, three unknown glycolipids, three unknown phospholipids and two unknown lipids. The draft genome size of strain SYSU K20354[T] was 3.96 Mbp with G+C content of 69.7 mol%. Furthermore, the average nucleotide identity and digital DNA-DNA hybridization values between strain SYSU K20354[T] and A. humatus JCM 14319[T] were 90.3 and 55.6 %, respectively. On the basis of phenotypic, genotypic and phylogenetic data, strain SYSU K20354[T] represents a novel species of the genus Agromyces, for which the name Agromyces cavernae sp. nov. is proposed. The type strain is SYSU K20354[T] (=KCTC 49499[T]= CGMCC 4.7691[T]).},
}
@article {pmid35999349,
year = {2022},
author = {Singh, MK and Altameemi, S and Lares, M and Newton, MA and Setaluri, V},
title = {Role of dual specificity phosphatases (DUSPs) in melanoma cellular plasticity and drug resistance.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {14395},
pmid = {35999349},
issn = {2045-2322},
support = {I01BX004921; W81XWH-18-PRCRP-IASF (CA181014); P30AR066524/VA/VA/United States ; P30 AR066524/AR/NIAMS NIH HHS/United States ; },
mesh = {Cell Line, Tumor ; *Cell Plasticity/genetics ; Drug Resistance, Neoplasm/genetics ; Dual-Specificity Phosphatases/genetics ; Humans ; *Melanoma/drug therapy/genetics/pathology ; Protein Kinase Inhibitors/pharmacology ; },
abstract = {Melanoma cells exhibit phenotypic plasticity that allows transition from a proliferative and differentiated phenotype to a more invasive and undifferentiated or transdifferentiated phenotype often associated with drug resistance. The mechanisms that control melanoma phenotype plasticity and its role in drug resistance are not fully understood. We previously demonstrated that emergence of MAPK inhibitor (MAPKi)-resistance phenotype is associated with decreased expression of stem cell proliferation genes and increased expression of MAPK inactivation genes, including dual specificity phosphatases (DUSPs). Several members of the DUSP family genes, specifically DUSP1, -3, -8 and -9, are expressed in primary and metastatic melanoma cell lines and pre-and post BRAFi treated melanoma cells. Here, we show that knockdown of DUSP1 or DUSP8 or treatment with BCI, a pharmacological inhibitor of DUSP1/6 decrease the survival of MAPKi-resistant cells and sensitizes them to BRAFi and MEKi. Pharmacological inhibition of DUSP1/6 upregulated nestin, a neural crest stem cell marker, in both MAPKi-sensitive cells and cells with acquired MAPKi-resistance. In contrast, treatment with BCI resulted in upregulation of MAP2, a neuronal differentiation marker, only in MAPKi-sensitive cells but caused downregulation of both MAP2 and GFAP, a glial marker, in all MAPKi-resistant cell lines. These data suggest that DUSP proteins are involved in the regulation of cellular plasticity cells and melanoma drug resistance and are potential targets for treatment of MAPKi-resistant melanoma.},
}
@article {pmid35999245,
year = {2022},
author = {Du, Y and Xu, Y and Wang, X and Liu, L and Ma, P},
title = {EEG temporal-spatial transformer for person identification.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {14378},
pmid = {35999245},
issn = {2045-2322},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; },
abstract = {An increasing number of studies have been devoted to electroencephalogram (EEG) identity recognition since EEG signals are not easily stolen. Most of the existing studies on EEG person identification have only addressed brain signals in a single state, depending upon specific and repetitive sensory stimuli. However, in reality, human states are diverse and rapidly changing, which limits their practicality in realistic settings. Among many potential solutions, transformer is widely used and achieves an excellent performance in natural language processing, which demonstrates the outstanding ability of the attention mechanism to model temporal signals. In this paper, we propose a transformer-based approach for the EEG person identification task that extracts features in the temporal and spatial domains using a self-attention mechanism. We conduct an extensive study to evaluate the generalization ability of the proposed method among different states. Our method is compared with the most advanced EEG biometrics techniques and the results show that our method reaches state-of-the-art results. Notably, we do not need to extract any features manually.},
}
@article {pmid35995855,
year = {2022},
author = {Arami, H and Kananian, S and Khalifehzadeh, L and Patel, CB and Chang, E and Tanabe, Y and Zeng, Y and Madsen, SJ and Mandella, MJ and Natarajan, A and Peterson, EE and Sinclair, R and Poon, ASY and Gambhir, SS},
title = {Remotely controlled near-infrared-triggered photothermal treatment of brain tumours in freely behaving mice using gold nanostars.},
journal = {Nature nanotechnology},
volume = {17},
number = {9},
pages = {1015-1022},
pmid = {35995855},
issn = {1748-3395},
support = {T32 CA009695/CA/NCI NIH HHS/United States ; R01 CA199656/CA/NCI NIH HHS/United States ; K99 EB031178/EB/NIBIB NIH HHS/United States ; R00 CA234208/CA/NCI NIH HHS/United States ; U54 CA199075/CA/NCI NIH HHS/United States ; T32 CA196585/CA/NCI NIH HHS/United States ; R00 EB031178/EB/NIBIB NIH HHS/United States ; R01 CA222836/CA/NCI NIH HHS/United States ; K99 CA234208/CA/NCI NIH HHS/United States ; P30 CA124435/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; *Brain Neoplasms/drug therapy ; Cell Line, Tumor ; Gold/therapeutic use ; Mice ; *Nanoparticles ; *Photochemotherapy ; Theranostic Nanomedicine ; },
abstract = {Current clinical brain tumour therapy practices are based on tumour resection and post-operative chemotherapy or X-ray radiation. Resection requires technically challenging open-skull surgeries that can lead to major neurological deficits and, in some cases, death. Treatments with X-ray and chemotherapy, on the other hand, cause major side-effects such as damage to surrounding normal brain tissues and other organs. Here we report the development of an integrated nanomedicine-bioelectronics brain-machine interface that enables continuous and on-demand treatment of brain tumours, without open-skull surgery and toxicological side-effects on other organs. Near-infrared surface plasmon characteristics of our gold nanostars enabled the precise treatment of deep brain tumours in freely behaving mice. Moreover, the nanostars' surface coating enabled their selective diffusion in tumour tissues after intratumoral administration, leading to the exclusive heating of tumours for treatment. This versatile remotely controlled and wireless method allows the adjustment of nanoparticles' photothermal strength, as well as power and wavelength of the therapeutic light, to target tumours in different anatomical locations within the brain.},
}
@article {pmid35994092,
year = {2022},
author = {Grobet-Jeandin, E and Benamran, D and Pinar, U and Beirnaert, J and Parra, J and Vaessen, C and Seisen, T and Rouprêt, M and Phé, V},
title = {Urodynamic assessment and quality of life outcomes of robot-assisted totally intracorporeal radical cystectomy and orthotopic neobladder for bladder cancer: a preliminary study.},
journal = {World journal of urology},
volume = {40},
number = {10},
pages = {2535-2541},
pmid = {35994092},
issn = {1433-8726},
mesh = {Cystectomy/methods ; *Erectile Dysfunction/etiology ; Humans ; Male ; Middle Aged ; Quality of Life ; *Robotic Surgical Procedures/methods ; *Robotics ; Treatment Outcome ; *Urinary Bladder Neoplasms/etiology/surgery ; *Urinary Diversion/methods ; *Urinary Incontinence/epidemiology/etiology/surgery ; Urodynamics ; },
abstract = {PURPOSE: Few data exist regarding the functional outcomes of robot-assisted radical cystectomy (RARC) with intracorporeal orthotopic neobladder. The aim of this study was to evaluate the urodynamic and functional outcomes in patients undergoing RARC and totally intracorporeal orthotopic neobladder for bladder cancer.<br><br>METHODS: In this monocentric, observational study carried out between 2016 and 2020, consecutive patients undergoing RARC and intracorporeal orthotopic neobladder in the Department of Urology, Piti&#xe9;-Salp&#xea;tri&#xe8;re Hospital, were included. Reconstruction was totally intracorporeal Y-shaped neobladder. Main outcomes were urodynamic findings 6&#xa0;months post-surgery, continence and quality of life (QoL). Continence was defined by no pad or one safety pad. International Consultation on Incontinence Questionnaire (ICIQ), International Index of Erectile Function questionnaire (IIEF-5) and Bladder Cancer Index (BCI) scores were recorded.<br><br>RESULTS: Fourteen male patients were included (median age: 64&#xa0;years [IQR 54-67]. Median maximal neobladder cystometric capacity was 495&#xa0;ml [IQR 410-606] and median compliance was 35.5&#xa0;ml/cm H2O [IQR 28-62]. All patients had post-void residual volume&#x2009;<&#x2009;30&#xa0;ml, except for three (22%) who required clean intermittent-self catheterisation. Daytime continence was achieved in 10 patients (71%) and night-time continence in two (14.3%). Median ICIQ score was 7 [IQR 5-11]. Postoperative erectile function was present in 7% of patients (mean IIEF-5&#x2009;=&#x2009;5 [IQR 2-7]). Thirteen patients (93%) were satisfied with their choice of neobladder.<br><br>CONCLUSION: RARC with totally intracorporeal orthotopic neobladder for bladder cancer provides satisfactory urodynamic results and good QoL. These findings should be confirmed long-term.},
}
@article {pmid35992956,
year = {2022},
author = {Qiu, L and Zhong, Y and He, Z and Pan, J},
title = {Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {973959},
pmid = {35992956},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface (BCI) is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery (MI) and mental arithmetic (MA) tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS.},
}
@article {pmid35992948,
year = {2022},
author = {Schnakers, C and Bauer, C and Formisano, R and Noé, E and Llorens, R and Lejeune, N and Farisco, M and Teixeira, L and Morrissey, AM and De Marco, S and Veeramuthu, V and Ilina, K and Edlow, BL and Gosseries, O and Zandalasini, M and De Bellis, F and Thibaut, A and Estraneo, A},
title = {What names for covert awareness? A systematic review.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {971315},
pmid = {35992948},
issn = {1662-5161},
abstract = {BACKGROUND: With the emergence of Brain Computer Interfaces (BCI), clinicians have been facing a new group of patients with severe acquired brain injury who are unable to show any behavioral sign of consciousness but respond to active neuroimaging or electrophysiological paradigms. However, even though well documented, there is still no consensus regarding the nomenclature for this clinical entity.<br><br>OBJECTIVES: This systematic review aims to 1) identify the terms used to indicate the presence of this entity through the years, and 2) promote an informed discussion regarding the rationale for these names and the best candidates to name this fascinating disorder.<br><br>METHODS: The Disorders of Consciousness Special Interest Group (DoC SIG) of the International Brain Injury Association (IBIA) launched a search on Pubmed and Google scholar following PRISMA guidelines to collect peer-reviewed articles and reviews on human adults (>18 years) published in English between 2006 and 2021.<br><br>RESULTS: The search launched in January 2021 identified 4,089 potentially relevant titles. After screening, 1,126 abstracts were found relevant. Finally, 161 manuscripts were included in our analyses. Only 58% of the manuscripts used a specific name to discuss this clinical entity, among which 32% used several names interchangeably throughout the text. We found 25 different names given to this entity. The five following names were the ones the most frequently used: covert awareness, cognitive motor dissociation, functional locked-in, non-behavioral MCS (MCS[*]) and higher-order cortex motor dissociation.<br><br>CONCLUSION: Since 2006, there has been no agreement regarding the taxonomy to use for unresponsive patients who are able to respond to active neuroimaging or electrophysiological paradigms. Developing a standard taxonomy is an important goal for future research studies and clinical translation. We recommend a Delphi study in order to build such a consensus.},
}
@article {pmid35992923,
year = {2022},
author = {Xie, YL and Yang, YX and Jiang, H and Duan, XY and Gu, LJ and Qing, W and Zhang, B and Wang, YX},
title = {Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {949575},
pmid = {35992923},
issn = {1662-4548},
abstract = {BACKGROUND: Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As an emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals and provide feedback to form a closed-loop rehabilitation, which is currently being studied for functional restoration after stroke. However, there is no reliable medical evidence to support the effect of BMI-based training on upper extremity function after stroke. This review aimed to evaluate the efficacy and safety of BMI-based training for improving upper extremity function after stroke, as well as potential differences in efficacy of different external devices.<br><br>METHODS: English-language literature published before April 1, 2022, was searched in five electronic databases using search terms including "brain-computer/machine interface", "stroke" and "upper extremity." The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.4.1 software. The GRADE method was used to assess the quality of the evidence.<br><br>RESULTS: A total of 17 studies with 410 post-stroke patients were included. Meta-analysis showed that BMI-based training significantly improved upper extremity motor function [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I [2] = 38%; p < 0.0001; n = 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated that BMI-based training significantly improves upper extremity motor function in both chronic [SMD = 0.68; 95% CI (0.32, 1.03), I [2] = 46%; p = 0.0002, random-effects model] and subacute [SMD = 1.11; 95%CI (0.22, 1.99); I [2] = 76%; p = 0.01; random-effects model] stroke patients compared with control interventions, and using functional electrical stimulation (FES) [SMD = 1.11; 95% CI (0.67, 1.54); I [2] = 11%; p < 0.00001; random-effects model]or visual feedback [SMD = 0.66; 95% CI (0.2, 1.12); I [2] = 4%; p = 0.005; random-effects model;] as the feedback devices in BMI training was more effective than using robot. In addition, BMI-based training was more effective in improving patients' activities of daily living (ADL) than control interventions [SMD = 1.12; 95% CI (0.65, 1.60); I [2] = 0%; p < 0.00001; n = 80; random-effects model]. There was no statistical difference in the dropout rate and adverse effects between the BMI-based training group and the control group.<br><br>CONCLUSION: BMI-based training improved upper limb motor function and ADL in post-stroke patients. BMI combined with FES or visual feedback may be a better combination for functional recovery than robot. BMI-based trainings are well-tolerated and associated with mild adverse effects.},
}
@article {pmid35992902,
year = {2022},
author = {Liu, N and Yücel, MA and Tong, Y and Minagawa, Y and Tian, F and Li, X},
title = {Editorial: FNIRS in neuroscience and its emerging applications.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {960591},
pmid = {35992902},
issn = {1662-4548},
}
@article {pmid35992349,
year = {2022},
author = {Spanu, A and Martines, L and Tedesco, M and Martinoia, S and Bonfiglio, A},
title = {Simultaneous recording of electrical and metabolic activity of cardiac cells in vitro using an organic charge modulated field effect transistor array.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {945575},
pmid = {35992349},
issn = {2296-4185},
abstract = {In vitro electrogenic cells monitoring is an important objective in several scientific and technological fields, such as electrophysiology, pharmacology and brain machine interfaces, and can represent an interesting opportunity in other translational medicine applications. One of the key aspects of cellular cultures is the complexity of their behavior, due to the different kinds of bio-related signals, both chemical and electrical, that characterize these systems. In order to fully understand and exploit this extraordinary complexity, specific devices and tools are needed. However, at the moment this important scientific field is characterized by the lack of easy-to-use, low-cost devices for the sensing of multiple cellular parameters. To the aim of providing a simple and integrated approach for the study of in vitro electrogenic cultures, we present here a new solution for the monitoring of both the electrical and the metabolic cellular activity. In particular, we show here how a particular device called Micro Organic Charge Modulated Array (MOA) can be conveniently engineered and then used to simultaneously record the complete cell activity using the same device architecture. The system has been tested using primary cardiac rat myocytes and allowed to detect the metabolic and electrical variations thar occur upon the administration of different drugs. This first example could lay the basis for the development of a new generation of multi-sensing tools that can help to efficiently probe the multifaceted in vitro environment.},
}
@article {pmid35990886,
year = {2022},
author = {Chen, L and Yu, Z and Yang, J},
title = {SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {958052},
pmid = {35990886},
issn = {1662-5218},
abstract = {The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification accuracy in the cross-subject learning scenario. Convolutional neural networks (CNNs) have been extensively applied to EEG-based Brain-Computer Interfaces (BCIs) by virtue of the capability of performing automatic feature extraction and classification. However, they have been mainly applied to the within-subject classification which would consume lots of time for training and calibration. Thus, it limits the further applications of CNNs in BCIs. In order to build a robust classification algorithm for a calibration-less BCI system, we propose an end-to-end model that transforms the EEG signals into symmetric positive definite (SPD) matrices and captures the features of SPD matrices by using a CNN. To avoid the time-consuming calibration and ensure the application of the proposed model, we use the meta-transfer-learning (MTL) method to learn the essential features from different subjects. We validate our model by making extensive experiments on three public motor-imagery datasets. The experimental results demonstrate the effectiveness of our proposed method in the cross-subject learning scenario.},
}
@article {pmid35990018,
year = {2022},
author = {Lazarou, I and Nikolopoulos, S and Georgiadis, K and Oikonomou, VP and Mariakaki, A and Kompatsiaris, I},
title = {Exploring the Connection of Brain Computer Interfaces and Multimedia Use With the Social Integration of People With Various Motor Disabilities: A Questionnaire-Based Usability Study.},
journal = {Frontiers in digital health},
volume = {4},
number = {},
pages = {846963},
pmid = {35990018},
issn = {2673-253X},
abstract = {We have designed a platform to aid people with motor disabilities to be part of digital environments, in order to create digitally and socially inclusive activities that promote their quality of life. To evaluate in depth the impact of the platform on social inclusion indicators across patients with various motor disabilities, we constructed a questionnaire in which the following indicators were assessed: (i) Well Being, (ii) Empowerment, (iii) Participation, (iv) Social Capital, (v) Education, and (vi) Employment. In total 30 participants (10 with Neuromuscular Disorders-NMD, 10 with Spinal Cord Injury-SCI, and 10 with Parkinson's Disease-PD) used the platform for ~1 month, and its impact on social inclusion indicators was measured before and after the usage. Moreover, monitoring mechanisms were used to track computer usage as well as an online social activity. Finally, testimonials and experimenter input were collected to enrich the study with qualitative understanding. All participants were favorable to use the suggested platform, while they would prefer it for longer periods of time in order to become "re-awakened" to possibilities of expanded connection and inclusion, while it became clear that the platform has to offer them further the option to use it in a reclining position. The present study has clearly shown that the challenge of social inclusion cannot be tackled solely with technology and it needs to integrate persuasive design elements that foster experimentation and discovery.},
}
@article {pmid35989578,
year = {2022},
author = {Chen, BW and Yang, SH and Kuo, CH and Chen, JW and Lo, YC and Kuo, YT and Lin, YC and Chang, HC and Lin, SH and Yu, X and Qu, B and Ro, SV and Lai, HY and Chen, YY},
title = {Neuro-Inspired Reinforcement Learning to Improve Trajectory Prediction in Reward-Guided Behavior.},
journal = {International journal of neural systems},
volume = {32},
number = {9},
pages = {2250038},
doi = {10.1142/S0129065722500381},
pmid = {35989578},
issn = {1793-6462},
mesh = {Animals ; Learning/physiology ; Neurons/physiology ; Reinforcement, Psychology ; *Reward ; *Spatial Navigation ; },
abstract = {Hippocampal pyramidal cells and interneurons play a key role in spatial navigation. In goal-directed behavior associated with rewards, the spatial firing pattern of pyramidal cells is modulated by the animal's moving direction toward a reward, with a dependence on auditory, olfactory, and somatosensory stimuli for head orientation. Additionally, interneurons in the CA1 region of the hippocampus monosynaptically connected to CA1 pyramidal cells are modulated by a complex set of interacting brain regions related to reward and recall. The computational method of reinforcement learning (RL) has been widely used to investigate spatial navigation, which in turn has been increasingly used to study rodent learning associated with the reward. The rewards in RL are used for discovering a desired behavior through the integration of two streams of neural activity: trial-and-error interactions with the external environment to achieve a goal, and the intrinsic motivation primarily driven by brain reward system to accelerate learning. Recognizing the potential benefit of the neural representation of this reward design for novel RL architectures, we propose a RL algorithm based on [Formula: see text]-learning with a perspective on biomimetics (neuro-inspired RL) to decode rodent movement trajectories. The reward function, inspired by the neuronal information processing uncovered in the hippocampus, combines the preferred direction of pyramidal cell firing as the extrinsic reward signal with the coupling between pyramidal cell-interneuron pairs as the intrinsic reward signal. Our experimental results demonstrate that the neuro-inspired RL, with a combined use of extrinsic and intrinsic rewards, outperforms other spatial decoding algorithms, including RL methods that use a single reward function. The new RL algorithm could help accelerate learning convergence rates and improve the prediction accuracy for moving trajectories.},
}
@article {pmid35989311,
year = {2023},
author = {Wang, G and Lei, J and Wang, Y and Yu, J and He, Y and Zhao, W and Hu, Z and Xu, Z and Jin, Y and Gu, Y and Guo, X and Yang, B and Gao, Z and Wang, Z},
title = {The ZSWIM8 ubiquitin ligase regulates neurodevelopment by guarding the protein quality of intrinsically disordered Dab1.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {7},
pages = {3866-3881},
doi = {10.1093/cercor/bhac313},
pmid = {35989311},
issn = {1460-2199},
mesh = {Animals ; Female ; Pregnancy ; Cell Adhesion Molecules, Neuronal/metabolism ; Extracellular Matrix Proteins/metabolism ; Ligases ; Mammals/metabolism ; *Reelin Protein ; Serine Endopeptidases/metabolism ; *Ubiquitin/metabolism ; Ubiquitin-Protein Ligases/metabolism ; Adaptor Proteins, Signal Transducing/metabolism ; Nerve Tissue Proteins/metabolism ; },
abstract = {Protein quality control (PQC) is essential for maintaining protein homeostasis and guarding the accuracy of neurodevelopment. Previously, we found that a conserved EBAX-type CRL regulates the protein quality of SAX-3/ROBO guidance receptors in Caenorhabditis elegans. Here, we report that ZSWIM8, the mammalian homolog of EBAX-1, is essential for developmental stability of mammalian brains. Conditional deletion of Zswim8 in the embryonic nervous system causes global cellular stress, partial perinatal lethality and defective migration of neural progenitor cells. CRISPR-mediated knockout of ZSWIM8 impairs spine formation and synaptogenesis in hippocampal neurons. Mechanistic studies reveal that ZSWIM8 controls protein quality of Disabled 1 (Dab1), a key signal molecule for brain development, thus protecting the signaling strength of Dab1. As a ubiquitin ligase enriched with intrinsically disordered regions (IDRs), ZSWIM8 specifically recognizes IDRs of Dab1 through a "disorder targets misorder" mechanism and eliminates misfolded Dab1 that cannot be properly phosphorylated. Adult survivors of ZSWIM8 CKO show permanent hippocampal abnormality and display severely impaired learning and memory behaviors. Altogether, our results demonstrate that ZSWIM8-mediated PQC is critical for the stability of mammalian brain development.},
}
@article {pmid35986984,
year = {2022},
author = {Liu, M and Chen, C and Gao, K and Gao, F and Qin, C and Yuan, Q and Zhang, H and Zhuang, L and Wang, P},
title = {Neuronal network-based biomimetic chip for long-term detection of olfactory dysfunction model in early-stage Alzheimer's disease.},
journal = {Biosensors &amp; bioelectronics},
volume = {216},
number = {},
pages = {114619},
doi = {10.1016/j.bios.2022.114619},
pmid = {35986984},
issn = {1873-4235},
mesh = {*Alzheimer Disease/metabolism ; Amyloid beta-Peptides/metabolism ; Biomimetics ; *Biosensing Techniques ; Dizocilpine Maleate/metabolism ; Humans ; Memantine/metabolism ; *Neurodegenerative Diseases ; Neurons/metabolism ; *Olfaction Disorders/etiology/metabolism/pathology ; Olfactory Bulb ; Smell ; },
abstract = {Olfactory dysfunction is an early symptom of neurodegenerative disease. Amyloid-beta oligomers (AβOs), the pathologic protein of Alzheimer's disease (AD), have been confirmed to be firstly deposited in olfactory bulb (OB), causing smell to malfunction. However, the detailed mechanisms underlying pathogenic nature of AβOs-induced olfactory neuronal degeneration in AD are not completely realized. Here, an early-stage olfactory dysfunction pathological model of AD in vitro based on biomimetic OB neuronal network chip was established for dynamic multi-site detection of neuronal electrical activity and network connection. We found both spike firing and correlation of overall neuronal network change regularly displayed gradually active state and then rapidly decay state after AβOs induction. Moreover, MK-801 and memantine were administrated at early-stage to detect alteration of OB neurons simulating nasal administration for AD treatment, which showed an almost recovery through the intermittent firing pattern. Together, this neuronal network-on-chip has revealed synaptic impairment and network neurodegeneration of olfactory dysfunction in AD, providing potential mechanisms information for early-stage progressive olfactory amyloidogenic pathology.},
}
@article {pmid35986097,
year = {2022},
author = {Xie, X and Liu, X and Zhu, J and Xu, Y and Li, X and Zheng, Y and Gong, S and Xiao, X and Chen, Y and Zhang, J and Gong, W and Si, K},
title = {Long-term microglial phase-specific dynamics during single vessel occlusion and recanalization.},
journal = {Communications biology},
volume = {5},
number = {1},
pages = {841},
pmid = {35986097},
issn = {2399-3642},
mesh = {*Brain ; *Microglia/metabolism ; },
abstract = {Vascular occlusion leading to brain dysfunctions is usually considered evoking microglia-induced inflammation response. However, it remains unclear how microglia interact with blood vessels in the development of vascular occlusion-related brain disorders. Here, we illuminate long-term spatiotemporal dynamics of microglia during single vessel occlusion and recanalization. Microglia display remarkable response characteristics in different phases, including acute reaction, rapid diffusion, transition and chronic effect. Fibrinogen-induced microglial cluster promotes major histocompatibility complex II (MHCII) expression. Microglial soma represents a unique filament-shape migration and has slower motility compared to the immediate reaction of processes to occlusion. We capture proliferative microglia redistribute territory. Microglial cluster resolves gradually and microglia recover to resting state both in the morphology and function in the chronic effect phase. Therefore, our study offers a comprehensive analysis of spatiotemporal dynamics of microglia and potential mechanisms to both vessel occlusion and recanalization. Microglial phase-specific response suggests the morphological feature-oriented phased intervention would be an attractive option for vascular occlusion-related diseases treatments.},
}
@article {pmid35985293,
year = {2022},
author = {Jang, SJ and Yang, YJ and Ryun, S and Kim, JS and Chung, CK and Jeong, J},
title = {Decoding trajectories of imagined hand movement using electrocorticograms for brain-machine interface.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac8b37},
pmid = {35985293},
issn = {1741-2552},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Movement ; },
abstract = {Objective. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.Approach. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.Main results. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively.Significance. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.},
}
@article {pmid35985292,
year = {2022},
author = {Żygierewicz, J and Janik, RA and Podolak, IT and Drozd, A and Malinowska, U and Poziomska, M and Wojciechowski, J and Ogniewski, P and Niedbalski, P and Terczynska, I and Rogala, J},
title = {Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac8b38},
pmid = {35985292},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Machine Learning ; Memory, Short-Term ; Neural Networks, Computer ; },
abstract = {Objective.Extracting reliable information from electroencephalogram (EEG) is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem.Approach.The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention. We designed four neural network models differing in architecture, training strategies, and input representation to classify single experimental trials of a working memory task.Main results.Our best models achieved an accuracy (ACC) of 65.29 ± 0.76 and Matthews correlation coefficient of 0.288 ± 0.018, outperforming the reference model trained on the same data. The highest correlation between classification score and behavioral performance was 0.36 (p= 0.0007). Using analysis of input perturbation, we estimated the importance of EEG channels and frequency bands in the task at hand. The set of essential features identified for each network varies. We identified a subset of features common to all models that identified brain regions and frequency bands consistent with current neurophysiological knowledge of the processes critical to attention and working memory. Finally, we proposed sanity checks to examine further the robustness of each model's set of features.Significance.Our results indicate that explainable deep learning is a powerful tool for decoding information from EEG signals. It is crucial to train and analyze a range of models to identify stable and reliable features. Our results highlight the need for explainable modeling as the model with the highest ACC appeared to use residual artifactual activity.},
}
@article {pmid35983766,
year = {2022},
author = {Xu, C and Xie, Y and Zhong, T and Liang, S and Guan, H and Long, Z and Cao, H and Xing, L and Xue, X and Zhan, Y},
title = {A self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature.},
journal = {Nanoscale},
volume = {14},
number = {34},
pages = {12483-12490},
doi = {10.1039/d2nr03115a},
pmid = {35983766},
issn = {2040-3372},
mesh = {Animals ; Body Temperature ; Brain/physiology ; *Brain-Computer Interfaces ; *Heat Stroke ; Mice ; *Wearable Electronic Devices ; },
abstract = {Heat stroke that may cause acute central nervous system dysfunction, multiple organ dysfunction and even death has become a typical health problem in tropical developing countries. The primary goal of heat stroke treatment is to lower core body temperature, which necessitates physical or medical cooling in time. Here, we design a new self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature. This system can monitor body temperature in real time and transmit neural electrical stimulation signals into specific brain regions to lower the body temperature. The whole system can work without an external power supply and be powered by the body itself through the piezoelectric effect. The system comprises a temperature detecting unit, a power supply unit, a data processing module, and a brain stimulator. Demonstration of the system with stimulation electrodes implanted in the median preoptic nucleus brain region in mice reveals an evident decrease in body temperature (1.0 °C within 15 min). This self-powered strategy provides a new concept for future treatment of heat stroke and can extend the application of brain-machine-interface systems in medical care.},
}
@article {pmid35982520,
year = {2023},
author = {Valzolgher, C and Gatel, J and Bouzaid, S and Grenouillet, S and Todeschini, M and Verdelet, G and Salemme, R and Gaveau, V and Truy, E and Farnè, A and Pavani, F},
title = {Reaching to Sounds Improves Spatial Hearing in Bilateral Cochlear Implant Users.},
journal = {Ear and hearing},
volume = {44},
number = {1},
pages = {189-198},
pmid = {35982520},
issn = {1538-4667},
mesh = {Humans ; Auditory Perception/physiology ; *Cochlear Implantation/methods ; *Cochlear Implants ; Hearing/physiology ; Hearing Tests/methods ; *Sound Localization/physiology ; Cross-Over Studies ; },
abstract = {OBJECTIVES: We assessed if spatial hearing training improves sound localization in bilateral cochlear implant (BCI) users and whether its benefits can generalize to untrained sound localization tasks.<br><br>DESIGN: In 20 BCI users, we assessed the effects of two training procedures (spatial versus nonspatial control training) on two different tasks performed before and after training (head-pointing to sound and audiovisual attention orienting). In the spatial training, participants identified sound position by reaching toward the sound sources with their hand. In the nonspatial training, comparable reaching movements served to identify sound amplitude modulations. A crossover randomized design allowed comparison of training procedures within the same participants. Spontaneous head movements while listening to the sounds were allowed and tracked to correlate them with localization performance.<br><br>RESULTS: During spatial training, BCI users reduced their sound localization errors in azimuth and adapted their spontaneous head movements as a function of sound eccentricity. These effects generalized to the head-pointing sound localization task, as revealed by greater reduction of sound localization error in azimuth and more accurate first head-orienting response, as compared to the control nonspatial training. BCI users benefited from auditory spatial cues for orienting visual attention, but the spatial training did not enhance this multisensory attention ability.<br><br>CONCLUSIONS: Sound localization in BCI users improves with spatial reaching-to-sound training, with benefits to a nontrained sound localization task. These findings pave the way to novel rehabilitation procedures in clinical contexts.},
}
@article {pmid35981433,
year = {2022},
author = {Skarzynski, PH and Krol, B and Skarzynski, H and Cywka, KB},
title = {Implantation of two generations of Bonebridge after mastoid obliteration with bioactive glass S53P4.},
journal = {American journal of otolaryngology},
volume = {43},
number = {5},
pages = {103601},
doi = {10.1016/j.amjoto.2022.103601},
pmid = {35981433},
issn = {1532-818X},
mesh = {Anti-Bacterial Agents ; Audiometry, Pure-Tone ; Bone Conduction ; *Cholesteatoma/surgery ; Glass ; *Hearing Aids ; Humans ; Mastoid/surgery ; Mastoidectomy/methods ; Treatment Outcome ; },
abstract = {PURPOSE: After radical surgery for chronic cholesteatoma (CWD mastoidectomy), patients have the option to have the posterior wall of their external auditory canal surgically reconstructed with S53P4 bioactive glass. The procedure eliminates some of the restrictions related to having a postoperative cavity and extends the options for a hearing prosthesis. If classic reconstruction is not possible and a hearing aid is not used, we suggest use of a Bonebridge implant.<br><br>METHODS: This study describes, over 18&#xa0;months of follow-up, 16 patients after a two-stage surgical procedure: obliteration of the mastoid cavity with bioactive glass followed by Bonebridge implantation. There were 7 patients who received the first generation implant (BCI 601) and 9 who used the second (BCI 602). Before and after implantation, pure tone audiometry, sound field thresholds, and free-field audiometry were performed. Speech reception thresholds in noise were assessed using the Polish Sentence Matrix Test. Subjective assessment of benefits was done using the APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire.<br><br>RESULTS: During the observation period, no serious complications were found. The study demonstrated the safety and validity of the procedures and confirmed the safety of using S53P4 bioactive glass in otosurgery (antibacterial effect, nonrecurrence of cholesteatoma, and no effect on the inner ear). The audiological benefits expected from using the Bonebridge implant processor were also confirmed.<br><br>CONCLUSION: It is concluded that, after reconstructing the posterior wall of the external auditory canal with bioactive glass, two-stage implantation of a Bonebridge implant in a typical site is a safe solution for patients who have difficult anatomical conditions following their CWD mastoidectomy.},
}
@article {pmid35978888,
year = {2022},
author = {Lin, R and Dong, C and Ma, P and Ma, S and Chen, X and Liu, H},
title = {A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {7609196},
pmid = {35978888},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree feature selection (FMCM-ETFS) is proposed for discerning motor imagery EEG tasks. First, the EEG signal was filtered by a Butterworth filter for preprocessing. Second, C3, C4, and CZ channels were selected to extract time-frequency domain and spatial domain features using autoregressive (AR), common spatial pattern (CSP), and discrete wavelet transform (DWT). The extracted features were fused for a further feature elimination. Then, the features were selected using three feature selection methods: recursive feature elimination (RFE), principal component analysis method (PCA), and extreme trees (ET). The selected feature vectors were classified using support vector machines (SVM). Finally, a total of twelve subjects' EEG data from Inner Mongolia University of Technology (IMUT data), the 2nd BCI competition in 2003, and the 4th BCI competition in 2008 were employed to show the effectiveness of this proposed FMCM-ETFS method. The results show that the classification accuracy using the multidimensional fused feature extraction (AR + CSP + DWT) is 3%-20% higher than those using the aforementioned three single feature extractions (AR, CSP, and DWT). Extreme trees (ET), which is a sort of tree-based model method, outperforms RFE and PCA by 1%-9% in term of classification accuracies, when these three methods were applied to the procedure of feature extraction, respectively.},
}
@article {pmid35978082,
year = {2022},
author = {Eidel, M and Kübler, A},
title = {Identifying potential training factors in a vibrotactile P300-BCI.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {14006},
pmid = {35978082},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Humans ; Photic Stimulation ; Touch/physiology ; },
abstract = {Brain-computer interfaces (BCI) often rely on visual stimulation and feedback. Potential end-users with impaired vision, however, cannot use these BCIs efficiently and require a non-visual alternative. Both auditory and tactile paradigms have been developed but are often not sufficiently fast or accurate. Thus, it is particularly relevant to investigate if and how users can train and improve performance. We report data from 29 healthy participants who trained with a 4-choice tactile P300-BCI during five sessions. To identify potential training factors, we pre-post assessed the robustness of the BCI performance against increased workload in a dual task condition and determined the participants' somatosensory sensitivity thresholds with a forced-choice intensity discrimination task. Accuracy (M = 79.2% to 92.0%) and tactually evoked P300 amplitudes increased significantly, confirming successful training. Pre-post somatosensory sensitivity increased, and workload decreased significantly, but results of the dual task condition remained inconclusive. The present study confirmed the previously reported feasibility and trainability of our tactile BCI paradigm within a multi-session design. Importantly, we provide first evidence of improvement in the somatosensory system as a potential mediator for the observed training effects.},
}
@article {pmid35976835,
year = {2022},
author = {He, Y and Lu, Z and Wang, J and Ying, S and Shi, J},
title = {A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2406-2417},
doi = {10.1109/TNSRE.2022.3199363},
pmid = {35976835},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; Supervised Machine Learning ; },
abstract = {Convolutional Neural Network (CNN) is commonly used for the Electroencephalogram (EEG) based motor-imagery (MI) decoding. However, its performance is generally limited due to the small size sample problem. An alternative way to address such issue is to segment EEG trials into small slices for data augmentation, but this approach usually inevitably loses the valuable long-range dependencies of temporal information in EEG signals. To this end, we propose a novel self-supervised learning (SSL) based channel attention MLP-Mixer network (S-CAMLP-Net) for MI decoding with EEG. Specifically, a new EEG slice prediction task is designed as the pretext task to capture the long-range information of EEG trials in the time domain. In the downstream task, a newly proposed MLP-Mixer is applied to the classification task for signals rather than for images. Moreover, in order to effectively learn the discriminative spatial representations in EEG slices, an attention mechanism is integrated into MLP-Mixer to adaptively estimate the importance of each EEG channel without any prior information. Thus, the proposed S-CAMLP-Net can effectively learn more long-range temporal information and global spatial features of EEG signals. Extensive experiments are conducted on the public MI-2 dataset and the BCI Competition IV Dataset 2A. The experimental results indicate that our proposed S-CAMLP-Net achieves superior classification performance over all the compared algorithms.},
}
@article {pmid35976542,
year = {2022},
author = {Dlamini, SN and Fall, IS and Mabaso, SD},
title = {Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini.},
journal = {Journal of epidemiology and global health},
volume = {12},
number = {3},
pages = {340-361},
pmid = {35976542},
issn = {2210-6014},
support = {001/WHO_/World Health Organization/International ; },
mesh = {Bayes Theorem ; Eswatini ; Humans ; Incidence ; *Malaria/epidemiology/prevention &amp; control ; Seasons ; },
abstract = {Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion - 0.25 (BCI: - 0.46, - 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.},
}
@article {pmid35973644,
year = {2022},
author = {Zhou, W and Liu, A and Wu, L and Chen, X},
title = {A L1 normalization enhanced dynamic window method for SSVEP-based BCIs.},
journal = {Journal of neuroscience methods},
volume = {380},
number = {},
pages = {109688},
doi = {10.1016/j.jneumeth.2022.109688},
pmid = {35973644},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Pattern Recognition, Automated/methods ; Photic Stimulation ; },
abstract = {BACKGROUND: Filter bank canonical correlation analysis (FBCCA) has been widely applied to detect the frequency components of steady-state visual evoked potential (SSVEP). FBCCA with dynamic window (FBCCA-DW) is recently proposed to improve its performance. FBCCA-DW adaptively chooses a proper window length based on the signal-to-noise ratio (SNR) of SSVEP signals. It takes the output of FBCCA to evaluate the SNR of SSVEP signals, by using the softmax function and cost function. In practice, SSVEP signals always contain task-unrelated electroencephalogram (EEG), which degrades the SSVEP task. When the power of task-unrelated EEG changes, there would be an offset in the output of FBCCA. However, due to the insensitivity of softmax function to the offset, the SNR in FBCCA-DW ignores the interference of the task-unrelated EEG. Therefore, FBCCA-DW will analyze SSVEP signals at an inappropriate window length.<br><br>NEW METHOD: To solve the issue, we replace the softmax function with the L1 normalization, which could respond a reasonable SNR to the offset. Since the proposed method takes task-unrelated EEG into account, it could choose a more appropriate window length.<br><br>RESULTS: We comprehensively validate the proposed method on three publicly available SSVEP datasets. The results indicate that the proposed method could improve the performance significantly.<br><br>The proposed method outperforms FBCCA and FBCCA-DW in terms of information transfer rate (ITR).<br><br>CONCLUSIONS: The proposed method enhances the correlation between the window length and the credibility of the recognition result. It shows its potential for practical applications in complex environments.},
}
@article {pmid35972891,
year = {2022},
author = {Wanar, A and Isley, BC and Saia, K and Field, TA},
title = {False-positive Fentanyl Urine Detection after Initiation of Labetalol Treatment for Hypertension in Pregnancy: A Case Report.},
journal = {Journal of addiction medicine},
volume = {16},
number = {6},
pages = {e417-e419},
doi = {10.1097/ADM.0000000000001010},
pmid = {35972891},
issn = {1935-3227},
mesh = {Pregnancy ; Female ; Humans ; *Labetalol/therapeutic use ; Fentanyl ; *Hypertension/drug therapy ; Substance Abuse Detection ; Postpartum Period ; *Substance-Related Disorders/drug therapy ; },
abstract = {BACKGROUND: Labetalol hydrochloride (LH) is a pharmacologic treatment for hypertensive disease (HD) in pregnancy. However, for pregnant persons with substance use disorders (SUDs), LH may interfere with urine drug testing.<br><br>CASE SUMMARY: We present 3 pregnant or postpregnant persons with SUDs who experienced presumptive positive urine immunoassays for fentanyl while prescribed LH for perinatal HD.<br><br>DISCUSSION: Labetalol hydrochloride treatment for HD in pregnancy can result in presumptive positive urine immunoassays for fentanyl. Unrecognized or misinterpreted, this phenomenon can lead to significant consequences for pregnant and postpartum persons with co-occurring substance use and hypertensive disorders. Clinicians caring for pregnant persons with SUDs must be aware of this phenomenon and its sequelae when ordering and interpreting urine immunoassays for fentanyl.},
}
@article {pmid35971518,
year = {2022},
author = {Nie, R and Abdelrahman, Z and Liu, Z and Wang, X},
title = {Evaluation of the role of vaccination in the COVID-19 pandemic based on the data from the 50 U.S. States.},
journal = {Computational and structural biotechnology journal},
volume = {20},
number = {},
pages = {4138-4145},
pmid = {35971518},
issn = {2001-0370},
abstract = {Vaccination is considered as the ultimate weapon to end the pandemic. However, the role of vaccines in the pandemic remains controversial. To explore the impact of vaccination on the COVID-19 pandemic, we used logistic regression models to predict numbers of population-adjusted confirmed cases, deaths, intensive care unit (ICU) cases, case fatality rates and ICU admission rates of COVID-19 in the 50 U.S. states, based on 17 related variables. The logistic regression analysis showed that percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths and case fatality rates but showed no significant correlation with numbers of confirmed cases or ICU cases, or ICU admission rates. The Spearman correlation analysis showed that the percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths, ICU cases, ICU case rates, and case fatality rates but showed no significant correlation with numbers of confirmed cases. The number of deaths and mortality in the group after the vaccine usage were significantly lower than those in the group before the vaccine usage. However, after delta became the dominant strain, there were no longer significant differences in the number of deaths and the mortality rate between before and after delta became the dominant strain, although vaccines were used in both periods. Vaccination can significantly reduce COVID-19 deaths and mortality, while it cannot reduce the risk of COVID-19 infection. In addition to vaccination, other measures, such as social distancing, remain important in containing COVID-19 transmission and lower the risk of COVID-19 severe outcomes.},
}
@article {pmid35969565,
year = {2023},
author = {Wang, Z and Wong, CM and Rosa, A and Qian, T and Jung, TP and Wan, F},
title = {Stimulus-Stimulus Transfer Based on Time-Frequency-Joint Representation in SSVEP-Based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {2},
pages = {603-615},
doi = {10.1109/TBME.2022.3198639},
pmid = {35969565},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Photic Stimulation ; Recognition, Psychology ; Algorithms ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) require extensive and costly calibration to achieve high performance. Using transfer learning to re-use existing calibration data from old stimuli is a promising strategy, but finding commonalities in the SSVEP signals across different stimuli remains a challenge.<br><br>METHOD: This study presents a new perspective, namely time-frequency-joint representation, in which SSVEP signals corresponding to different stimuli can be synchronized, and thus can emphasize common components. According to this time-frequency-joint representation, an adaptive decomposition technique based on the multi-channel adaptive Fourier decomposition (MAFD) is proposed to adaptively decompose SSVEP signals of different stimuli simultaneously. Then, common components can be identified and transferred across stimuli.<br><br>RESULTS: A simulation study on public SSVEP datasets demonstrates that the proposed stimulus-stimulus transfer method has the ability to extract and transfer these common components across stimuli. By using calibration data from eight source stimuli, the proposed stimulus-stimulus transfer method can generate SSVEP templates of other 32 target stimuli. It boosts the ITR of the stimulus-stimulus transfer based recognition method from 95.966 bits/min to 123.684 bits/min.<br><br>CONCLUSION: By extracting and transfer common components across stimuli in the proposed time-frequency-joint representation, the proposed stimulus-stimulus transfer method produces good classification performance without requiring calibration data of target stimuli.<br><br>SIGNIFICANCE: This study provides a synchronization standpoint to analyze and model SSVEP signals. In addition, the proposed stimulus-stimulus method shortens the calibration time and thus improve comfort, which could facilitate real-world applications of SSVEP-based BCIs.},
}
@article {pmid35969547,
year = {2022},
author = {Cao, L and Wang, W and Huang, C and Xu, Z and Wang, H and Jia, J and Chen, S and Dong, Y and Fan, C and de Albuquerque, VHC},
title = {An Effective Fusing Approach by Combining Connectivity Network Pattern and Temporal-Spatial Analysis for EEG-Based BCI Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2264-2274},
doi = {10.1109/TNSRE.2022.3198434},
pmid = {35969547},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Spatial Analysis ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {Motor-modality-based brain computer interface (BCI) could promote the neural rehabilitation for stroke patients. Temporal-spatial analysis was commonly used for pattern recognition in this task. This paper introduced a novel connectivity network analysis for EEG-based feature selection. The network features of connectivity pattern not only captured the spatial activities responding to motor task, but also mined the interactive pattern among these cerebral regions. Furthermore, the effective combination between temporal-spatial analysis and network analysis was evaluated for improving the performance of BCI classification (81.7%). And the results demonstrated that it could raise the classification accuracies for most of patients (6 of 7 patients). This proposed method was meaningful for developing the effective BCI training program for stroke rehabilitation.},
}
@article {pmid35969546,
year = {2022},
author = {Yao, L and Jiang, N and Mrachacz-Kersting, N and Zhu, X and Farina, D and Wang, Y},
title = {Performance Variation of a Somatosensory BCI Based on Imagined Sensation: A Large Population Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2486-2493},
doi = {10.1109/TNSRE.2022.3198970},
pmid = {35969546},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/physiology ; Somatosensory Cortex/physiology ; Touch/physiology ; },
abstract = {A proportion of users cannot achieve adequate brain-computer interface (BCI) control. The diversity of BCI modalities provides a way to solve this emerging issue. Here, we investigate the accuracy of a somatosensory BCI based on sensory imagery (SI). During the SI tasks, subjects were instructed to imagine a tactile sensation and to maintain the attention on the corresponding hand, as if there was tactile stimulus on the skin of the wrist. The performance across 106 healthy subjects in left- and right-hand SI discrimination was 78.9±13.2%. In 70.7% of the subjects the performance was above 70%. The SI task induced a contralateral cortical activation, and high-density EEG source localization showed that the real tactile stimulation and imagined tactile stimulation shared similar cortical activations within the somatosensory cortex. The somatosensory BCI based on SI provides a new signal modality for independent BCI development. Moreover, a combination of SI and other BCI modalities, such as motor imagery, may provide new avenues for further improving BCI usage and applicability, especially in those subjects unable to attain adequate BCI control with conventional BCI modalities.},
}
@article {pmid35969153,
year = {2022},
author = {Han, JX and Wen, CX and Sun, R and Tang, MY and Li, XM and Lian, H},
title = {The dorsal hippocampal CA3 regulates spatial reference memory through the CtBP2/GluR2 pathway.},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
volume = {36},
number = {9},
pages = {e22456},
doi = {10.1096/fj.202101609RR},
pmid = {35969153},
issn = {1530-6860},
mesh = {Animals ; *CA3 Region, Hippocampal/metabolism ; Calcium/metabolism ; *Eye Proteins/genetics/metabolism ; Rats ; Rats, Sprague-Dawley ; *Receptors, AMPA/genetics/metabolism ; *Spatial Memory ; },
abstract = {The dorsal hippocampus plays a pivotal role in spatial memory. However, the role of subregion-specific molecular pathways in spatial cognition remains unclear. We observed that the transcriptional coregulator C-terminal binding protein 2 (CtBP2) presented CA3-specific enrichment in expression. RNAi interference of CtBP2 in the dorsal CA3 (dCA3) neurons, but not the ventral CA3 (vCA3), specifically impaired spatial reference memory and reduced the expression of GluR2, the calcium permeability determinant subunit of AMPA receptors. Application of an antagonist for GluR2-absent calcium permeable AMPA receptors rescued spatial memory deficits in dCA3 CtBP2 knockdown animals. Transcriptomic analysis suggest that CtBP2 may regulate GluR2 protein level through post-translational mechanisms, especially by the endocytosis pathway which regulates AMPA receptor sorting. Consistently, CtBP2 deficiency altered the mRNA expression of multiple endocytosis-regulatory genes, and CtBP2 knockdown in primary hippocampal neurons enhanced GluR2-containing AMPA receptor endocytosis. Together, our results provide evidence that the dCA3 regulates spatial reference memory by the CtBP2/GluR2 pathway through the modulation of calcium permeable AMPA receptors.},
}
@article {pmid35966998,
year = {2022},
author = {Warschausky, S and Huggins, JE and Alcaide-Aguirre, RE and Aref, AW},
title = {Preliminary psychometric properties of a standard vocabulary test administered using a non-invasive brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {930433},
pmid = {35966998},
issn = {1662-5161},
abstract = {OBJECTIVE: To examine measurement agreement between a vocabulary test that is administered in the standardized manner and a version that is administered with a brain-computer interface (BCI).<br><br>METHOD: The sample was comprised of 21 participants, ages 9-27, mean age 16.7 (5.4) years, 61.9% male, including 10 with congenital spastic cerebral palsy (CP), and 11 comparison peers. Participants completed both standard and BCI-facilitated alternate versions of the Peabody Picture Vocabulary Test - 4 (PPVT&#x2122;-4). The BCI-facilitated PPVT-4 uses items identical to the unmodified PPVT-4, but each quadrant forced-choice item is presented on a computer screen for use with the BCI.<br><br>RESULTS: Measurement agreement between instruments was excellent, including an intra-class correlation coefficient of 0.98, and Bland-Altman plots and tests indicating adequate limits of agreement and no systematic test version bias. The mean standard score difference between test versions was 2.0 points (SD 6.3).<br><br>CONCLUSION: These results demonstrate that BCI-facilitated quadrant forced-choice vocabulary testing has the potential to measure aspects of language without requiring any overt physical or communicative response. Thus, it may be possible to identify the language capabilities and needs of many individuals who have not had access to standardized clinical and research instruments.},
}
@article {pmid35966997,
year = {2022},
author = {Schönau, A and Goering, S and Versalovic, E and Montes, N and Brown, T and Dasgupta, I and Klein, E},
title = {Asking questions that matter - Question prompt lists as tools for improving the consent process for neurotechnology clinical trials.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {983226},
pmid = {35966997},
issn = {1662-5161},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
abstract = {Implantable neurotechnology devices such as Brain Computer Interfaces (BCIs) and Deep Brain Stimulators (DBS) are an increasing part of treating or exploring potential treatments for neurological and psychiatric disorders. While only a few devices are approved, many promising prospects for future devices are under investigation. The decision to participate in a clinical trial can be challenging, given a variety of risks to be taken into consideration. During the consent process, prospective participants might lack the language to consider those risks, feel unprepared, or simply not know what questions to ask. One tool to help empower participants to play a more active role during the consent process is a Question Prompt List (QPL). QPLs are communication tools that can prompt participants and patients to articulate potential concerns. They offer a structured list of disease, treatment, or research intervention-specific questions that research participants can use as support for question asking. While QPLs have been studied as tools for improving the consent process during cancer treatment, in this paper, we suggest they would be helpful in neurotechnology research, and offer an example of a QPL as a template for an informed consent tool in neurotechnology device trials.},
}
@article {pmid35966996,
year = {2022},
author = {Dillen, A and Lathouwers, E and Miladinović, A and Marusic, U and Ghaffari, F and Romain, O and Meeusen, R and De Pauw, K},
title = {A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {949224},
pmid = {35966996},
issn = {1662-5161},
abstract = {Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.},
}
@article {pmid35966991,
year = {2022},
author = {Yue, Z and Wu, Q and Ren, SY and Li, M and Shi, B and Pan, Y and Wang, J},
title = {A novel multiple time-frequency sequential coding strategy for hybrid brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {859259},
pmid = {35966991},
issn = {1662-5161},
abstract = {BACKGROUND: For brain-computer interface (BCI) communication, electroencephalography provides a preferable choice due to its high temporal resolution and portability over other neural recording techniques. However, current BCIs are unable to sufficiently use the information from time and frequency domains simultaneously. Thus, we proposed a novel hybrid time-frequency paradigm to investigate better ways of using the time and frequency information.<br><br>METHOD: We adopt multiple omitted stimulus potential (OSP) and steady-state motion visual evoked potential (SSMVEP) to design the hybrid paradigm. A series of pre-experiments were undertaken to study factors that would influence the feasibility of the hybrid paradigm and the interaction between multiple features. After that, a novel Multiple Time-Frequencies Sequential Coding (MTFSC) strategy was introduced and explored in experiments.<br><br>RESULTS: Omissions with multiple short and long durations could effectively elicit time and frequency features, including the multi-OSP, ERP, and SSVEP in this hybrid paradigm. The MTFSC was feasible and efficient. The preliminary online analysis showed that the accuracy and the ITR of the nine-target stimulator over thirteen subjects were 89.04% and 36.37 bits/min.<br><br>SIGNIFICANCE: This study first combined the SSMVEP and multi-OSP in a hybrid paradigm to produce robust and abundant time features for coding BCI. Meanwhile, the MTFSC proved feasible and showed great potential in improving performance, such as expanding the number of BCI targets by better using time information in specific stimulated frequencies. This study holds promise for designing better BCI systems with a novel coding method.},
}
@article {pmid35966988,
year = {2022},
author = {Peters, B and Eddy, B and Galvin-McLaughlin, D and Betz, G and Oken, B and Fried-Oken, M},
title = {A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {952380},
pmid = {35966988},
issn = {1662-5161},
abstract = {UNLABELLED: Augmentative and alternative communication brain-computer interface (AAC-BCI) systems are intended to offer communication access to people with severe speech and physical impairment (SSPI) without requiring volitional movement. As the field moves toward clinical implementation of AAC-BCI systems, research involving participants with SSPI is essential. Research has demonstrated variability in AAC-BCI system performance across users, and mixed results for comparisons of performance for users with and without disabilities. The aims of this systematic review were to (1) describe study, system, and participant characteristics reported in BCI research, (2) summarize the communication task performance of participants with disabilities using AAC-BCI systems, and (3) explore any differences in performance for participants with and without disabilities. Electronic databases were searched in May, 2018, and March, 2021, identifying 6065 records, of which 73 met inclusion criteria. Non-experimental study designs were common and sample sizes were typically small, with approximately half of studies involving five or fewer participants with disabilities. There was considerable variability in participant characteristics, and in how those characteristics were reported. Over 60% of studies reported an average selection accuracy ≤70% for participants with disabilities in at least one tested condition. However, some studies excluded participants who did not reach a specific system performance criterion, and others did not state whether any participants were excluded based on performance. Twenty-nine studies included participants both with and without disabilities, but few reported statistical analyses comparing performance between the two groups. Results suggest that AAC-BCI systems show promise for supporting communication for people with SSPI, but they remain ineffective for some individuals. The lack of standards in reporting outcome measures makes it difficult to synthesize data across studies. Further research is needed to demonstrate efficacy of AAC-BCI systems for people who experience SSPI of varying etiologies and severity levels, and these individuals should be included in system design and testing. Consensus in terminology and consistent participant, protocol, and performance description will facilitate the exploration of user and system characteristics that positively or negatively affect AAC-BCI use, and support innovations that will make this technology more useful to a broader group of people.<br><br>CLINICAL TRIAL REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018095345, PROSPERO: CRD42018095345.},
}
@article {pmid35966838,
year = {2022},
author = {Chi, YC and Lai, CC},
title = {Endoscopic dacryocystorhinostomy with short-term, pushed-type bicanalicular intubation vs. pulled-type monocanalicular intubation for primary acquired nasolacrimal duct obstruction.},
journal = {Frontiers in medicine},
volume = {9},
number = {},
pages = {946083},
pmid = {35966838},
issn = {2296-858X},
abstract = {Dacryocystorhinostomy (DCR) has been a primary treatment for adults with nasolacrimal duct obstruction, while the optimal approach and technique remain controversial. With the advancement of endoscopic DCR and the silicone stents, an update of the surgical outcomes and preferable approaches is required. This study aims at comparing the surgical outcomes of endoscopic DCR using pushed bicanalicular intubation (BCI) to pulled monocanalicular intubation (MCI) in adults with primary acquired nasolacrimal duct obstruction (PANDO). Forty five eyes of 45 patients were enrolled, including 22 eyes of 22 patients treated with endoscopic DCR with pulled MCI and 23 eyes of 23 patients with pushed BCI from January 2014 to June 2021. The success rates at stent removal, 1 month and 3 months after removal were 95, 91, and 82%, respectively, in the MCI group, and 100, 87, and 87% in the BCI group. The BCI group had better success rates but failed to reach a significant difference (p = 0.49, p = 0.67, p = 0.24, respectively). After analyzing with binary logistic regression, the implant material was demonstrated as the predictive of surgical success (p = 0.045). There was no significant difference in success rates between patients with dacryocystitis and those without dacryocystitis. We conclude that endoscopic DCR with pushed BCI is easily manipulated and has a promising surgical outcome over pulled MCI. Stent indwelling duration as well as history of dacryocystitis have less influence on the success rates.},
}
@article {pmid35961121,
year = {2022},
author = {Tang, R and Zhang, C and Liu, B and Jiang, C and Wang, L and Zhang, X and Huang, Q and Liu, J and Li, L},
title = {Towards an artificial peripheral nerve: Liquid metal-based fluidic cuff electrodes for long-term nerve stimulation and recording.},
journal = {Biosensors &amp; bioelectronics},
volume = {216},
number = {},
pages = {114600},
doi = {10.1016/j.bios.2022.114600},
pmid = {35961121},
issn = {1873-4235},
mesh = {Animals ; *Biosensing Techniques ; Electric Stimulation ; Electrodes ; Electrodes, Implanted ; *Gallium ; *Nerve Tissue ; Peripheral Nerves/physiology ; Rats ; },
abstract = {Nerve cuff electrodes have been used for decades as peripheral nerve interfacing devices in the fields of neural science, neural disease, and brain-machine interfacing. The currently-used cuff electrode is commonly based on rigid materials whose flexibility and tensile properties are far different from those of biological nervous tissue. Herein, a fluidic cuff electrode using a gallium-based liquid metal (LM) conductor is developed as a prototype artificial peripheral nerve. The results indicate that the LM cuff electrode has high flexibility and maintains outstanding conductivity. After implanted and connected to the sciatic nerve, the LM electrodes within the freely moving rats' bodies survive repeated body stretching and retain their long-term effectiveness in transmitting sciatic nerve signals with a high signal-to-noise ratio during two-week experiments. The LM electrodes are also proven capable of transmitting neural stimuli to the peripheral nerve on a long-term basis by triggering clear event-related potentials (ERPs) in terms of both the cortical potential and sciatic signal. These tests demonstrate that the LM electrodes meet the requirements of peripheral nerve signal recording and stimulation for long-term implantation, and have the potential to become a new generation of artificial peripheral nerve devices to interface with, supplement, or even enhance and replace the real peripheral nerve.},
}
@article {pmid35959402,
year = {2022},
author = {González-Zacarías, C and Choi, S and Vu, C and Xu, B and Shen, J and Joshi, AA and Leahy, RM and Wood, JC},
title = {Chronic anemia: The effects on the connectivity of white matter.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {894742},
pmid = {35959402},
issn = {1664-2295},
support = {UL1 TR001855/TR/NCATS NIH HHS/United States ; },
abstract = {Chronic anemia is commonly observed in patients with hemoglobinopathies, mainly represented by disorders of altered hemoglobin (Hb) structure (sickle cell disease, SCD) and impaired Hb synthesis (e.g. thalassemia syndromes, non-SCD anemia). Both hemoglobinopathies have been associated with white matter (WM) alterations. Novel structural MRI research in our laboratory demonstrated that WM volume was diffusely lower in deep, watershed areas proportional to anemia severity. Furthermore, diffusion tensor imaging analysis has provided evidence that WM microstructure is disrupted proportionally to Hb level and oxygen saturation. SCD patients have been widely studied and demonstrate lower fractional anisotropy (FA) in the corticospinal tract and cerebellum across the internal capsule and corpus callosum. In the present study, we compared 19 SCD and 15 non-SCD anemia patients with a wide range of Hb values allowing the characterization of the effects of chronic anemia in isolation of sickle Hb. We performed a tensor analysis to quantify FA changes in WM connectivity in chronic anemic patients. We calculated the volumetric mean of FA along the pathway of tracks connecting two regions of interest defined by BrainSuite's BCI-DNI atlas. In general, we found lower FA values in anemic patients; indicating the loss of coherence in the main diffusion direction that potentially indicates WM injury. We saw a positive correlation between FA and hemoglobin in these same regions, suggesting that decreased WM microstructural integrity FA is highly driven by chronic hypoxia. The only connection that did not follow this pattern was the connectivity within the left middle-inferior temporal gyrus. Interestingly, more reductions in FA were observed in non-SCD patients (mainly along with intrahemispheric WM bundles and watershed areas) than the SCD patients (mainly interhemispheric).},
}
@article {pmid35959164,
year = {2022},
author = {Queiroz, CMM and da Silva, GM and Walter, S and Peres, LB and Luiz, LMD and Costa, SC and de Faria, KC and Pereira, AA and Vieira, MF and Cabral, AM and Andrade, AO},
title = {Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {822987},
pmid = {35959164},
issn = {1662-5188},
abstract = {Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single-channel approaches are scarce. In this context, this study proposed a single-channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.},
}
@article {pmid35958998,
year = {2022},
author = {Li, P and Su, J and Belkacem, AN and Cheng, L and Chen, C},
title = {Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {971039},
pmid = {35958998},
issn = {1662-4548},
abstract = {OBJECTIVE: The conventional single-person brain-computer interface (BCI) systems have some intrinsic deficiencies such as low signal-to-noise ratio, distinct individual differences, and volatile experimental effect. To solve these problems, a centralized steady-state visually evoked potential collaborative BCI system (SSVEP-cBCI), which characterizes multi-person electroencephalography (EEG) feature fusion was constructed in this paper. Furthermore, three different feature fusion methods compatible with this new system were developed and applied to EEG classification, and a comparative analysis of their classification accuracy was performed with transfer learning-based convolutional neural network (TL-CNN) approach.<br><br>APPROACH: An EEG-based SSVEP-cBCI system was set up to merge different individuals' EEG features stimulated by the instructions for the same task, and three feature fusion methods were adopted, namely parallel connection, serial connection, and multi-person averaging. The fused features were then input into CNN for classification. Additionally, transfer learning (TL) was applied first to a Tsinghua University (THU) benchmark dataset, and then to a collected dataset, so as to meet the CNN training requirement with a much smaller size of collected dataset and increase the classification accuracy. Ten subjects were recruited for data collection, and both datasets were used to gauge the three fusion algorithms' performance.<br><br>MAIN RESULTS: The results predicted by TL-CNN approach in single-person mode and in multi-person mode with the three feature fusion methods were compared. The experimental results show that each multi-person mode is superior to single-person mode. Within the 3 s time window, the classification accuracy of the single-person CNN is only 90.6%, while the same measure of the two-person parallel connection fusion method can reach 96.6%, achieving better classification effect.<br><br>SIGNIFICANCE: The results show that the three multi-person feature fusion methods and the deep learning classification algorithm based on TL-CNN can effectively improve the SSVEP-cBCI classification performance. The feature fusion method of multi -person parallel feature connection achieves better classification results. Different feature fusion methods can be selected in different application scenarios to further optimize cBCI.},
}
@article {pmid35958996,
year = {2022},
author = {Park, W and Kim, L and Ball, T and Atashzar, SF},
title = {Editorial: NeuroHaptics: From Human Touch to Neuroscience.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {964014},
doi = {10.3389/fnins.2022.964014},
pmid = {35958996},
issn = {1662-4548},
}
@article {pmid35957420,
year = {2022},
author = {Choi, H and Park, J and Yang, YM},
title = {A Novel Quick-Response Eigenface Analysis Scheme for Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957420},
issn = {1424-8220},
support = {GCU- 202110180001//Gachon University research fund/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Imagination/physiology ; },
abstract = {The brain-computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications.},
}
@article {pmid35957419,
year = {2022},
author = {Borirakarawin, M and Punsawad, Y},
title = {Event-Related Potential-Based Brain-Computer Interface Using the Thai Vowels' and Numerals' Auditory Stimulus Pattern.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957419},
issn = {1424-8220},
support = {//Walailak University/ ; },
mesh = {Acoustic Stimulation ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Photic Stimulation ; Thailand ; },
abstract = {Herein, we developed an auditory stimulus pattern for an event-related potential (ERP)-based brain-computer interface (BCI) system to improve control and communication in quadriplegia with visual impairment. Auditory stimulus paradigms for multicommand electroencephalogram (EEG)-based BCIs and audio stimulus patterns were examined. With the proposed auditory stimulation, using the selected Thai vowel, similar to the English vowel, and Thai numeral sounds, as simple target recognition, we explored the ERPs' response and classification efficiency from the suggested EEG channels. We also investigated the use of single and multi-loudspeakers for auditory stimuli. Four commands were created using the proposed paradigm. The experimental paradigm was designed to observe ERP responses and verify the proposed auditory stimulus pattern. The conventional classification method produced four commands using the proposed auditory stimulus pattern. The results established that the proposed auditory stimulation with 20 to 30 trials of stream stimuli could produce a prominent ERP response from Pz channels. The vowel stimuli could achieve higher accuracy than the proposed numeral stimuli for two auditory stimuli intervals (100 and 250 ms). Additionally, multi-loudspeaker patterns through vowel and numeral sound stimulation provided an accuracy greater than 85% of the average accuracy. Thus, the proposed auditory stimulation patterns can be implemented as a real-time BCI system to aid in the daily activities of quadratic patients with visual and tactile impairments. In future, practical use of the auditory ERP-based BCI system will be demonstrated and verified in an actual scenario.},
}
@article {pmid35957360,
year = {2022},
author = {Padfield, N and Camilleri, K and Camilleri, T and Fabri, S and Bugeja, M},
title = {A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957360},
issn = {1424-8220},
support = {ERDF.01.124//European Union/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.},
}
@article {pmid35957329,
year = {2022},
author = {Tobón-Henao, M and Álvarez-Meza, A and Castellanos-Domínguez, G},
title = {Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957329},
issn = {1424-8220},
support = {50835//National University of Colombia/ ; },
mesh = {Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Signal Processing, Computer-Assisted ; },
abstract = {The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.},
}
@article {pmid35957264,
year = {2022},
author = {Zhvansky, DS and Sylos-Labini, F and Dewolf, A and Cappellini, G and d'Avella, A and Lacquaniti, F and Ivanenko, Y},
title = {Evaluation of Spatiotemporal Patterns of the Spinal Muscle Coordination Output during Walking in the Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957264},
issn = {1424-8220},
support = {sub-project PEPATO//H2020-779963 EUROBENCH FSTP-1/ ; Ricerca Corrente, IRCCS Fondazione Santa Lucia//Italian Ministry of Health/ ; Ricerca Finalizzata RF-2019-12370232//Italian Ministry of Health/ ; grant I/006/06/0 and grant 2019-11-U.0//Italian Space Agency/ ; PRIN grant 2017CBF8NJ_005 and 2020EM9A8X_003//Italian University Ministry/ ; },
mesh = {*Exoskeleton Device ; Gait/physiology ; Humans ; Motor Neurons/physiology ; Muscle, Skeletal/physiology ; Walking/physiology ; },
abstract = {Recent advances in the performance and evaluation of walking in exoskeletons use various assessments based on kinematic/kinetic measurements. While such variables provide general characteristics of gait performance, only limited conclusions can be made about the neural control strategies. Moreover, some kinematic or kinetic parameters are a consequence of the control implemented on the exoskeleton. Therefore, standard indicators based on kinematic variables have limitations and need to be complemented by performance measures of muscle coordination and control strategy. Knowledge about what happens at the spinal cord output level might also be critical for rehabilitation since an abnormal spatiotemporal integration of activity in specific spinal segments may result in a risk for abnormalities in gait recovery. Here we present the PEPATO software, which is a benchmarking solution to assess changes in the spinal locomotor output during walking in the exoskeleton with respect to reference data on normal walking. In particular, functional and structural changes at the spinal cord level can be mapped into muscle synergies and spinal maps of motoneuron activity. A user-friendly software interface guides the user through several data processing steps leading to a set of performance indicators as output. We present an example of the usage of this software for evaluating walking in an unloading exoskeleton that allows a person to step in simulated reduced (the Moon's) gravity. By analyzing the EMG activity from lower limb muscles, the algorithms detected several performance indicators demonstrating differential adaptation (shifts in the center of activity, prolonged activation) of specific muscle activation modules and spinal motor pools and increased coactivation of lumbar and sacral segments. The software is integrated at EUROBENCH facilities to benchmark the performance of walking in the exoskeleton from the point of view of changes in the spinal locomotor output.},
}
@article {pmid35957188,
year = {2022},
author = {Wu, J and Wang, Z and Xu, T and Sun, C},
title = {Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {15},
pages = {},
pmid = {35957188},
issn = {1424-8220},
support = {NO. 21KJB470027//the Basic Science (Natural Science) Research Project of Jiangsu Province Colleges and Universities/ ; },
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {BACKGROUND: The brain-computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers.<br><br>METHODS: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system.<br><br>CONCLUSION: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.},
}
@article {pmid35955890,
year = {2022},
author = {Tokutake, K and Takeuchi, M and Kurimoto, S and Saeki, S and Asami, Y and Onaka, K and Saeki, M and Aoyama, T and Hasegawa, Y and Hirata, H},
title = {A Therapeutic Strategy for Lower Motor Neuron Disease and Injury Integrating Neural Stem Cell Transplantation and Functional Electrical Stimulation in a Rat Model.},
journal = {International journal of molecular sciences},
volume = {23},
number = {15},
pages = {},
pmid = {35955890},
issn = {1422-0067},
support = {A114//Japan Agency for Medical Research and Development/ ; 20K17997//Japan Society for the Promotion of Science/ ; },
mesh = {Animals ; Electric Stimulation ; *Motor Neuron Disease/therapy ; Motor Neurons/physiology ; Muscle, Skeletal/innervation ; Nerve Regeneration/physiology ; *Neural Stem Cells ; Rats ; Rats, Inbred F344 ; Sciatic Nerve/physiology ; Stem Cell Transplantation ; },
abstract = {Promising treatments for upper motor neuron disease are emerging in which motor function is restored by brain-computer interfaces and functional electrical stimulation. At present, such technologies and procedures are not applicable to lower motor neuron disease. We propose a novel therapeutic strategy for lower motor neuron disease and injury integrating neural stem cell transplantation with our new functional electrical stimulation control system. In a rat sciatic nerve transection model, we transplanted embryonic spinal neural stem cells into the distal stump of the peripheral nerve to reinnervate denervated muscle, and subsequently demonstrated that highly responsive limb movement similar to that of a healthy limb could be attained with a wirelessly powered two-channel neurostimulator that we developed. This unique technology, which can reinnervate and precisely move previously denervated muscles that were unresponsive to electrical stimulation, contributes to improving the condition of patients suffering from intractable diseases of paralysis and traumatic injury.},
}
@article {pmid35954882,
year = {2022},
author = {Rodriguez, J and Del-Valle-Soto, C and Gonzalez-Sanchez, J},
title = {Affective States and Virtual Reality to Improve Gait Rehabilitation: A Preliminary Study.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {15},
pages = {},
pmid = {35954882},
issn = {1660-4601},
mesh = {Child ; Humans ; Emotions ; *Exercise Therapy/methods ; Gait ; *Virtual Reality ; },
abstract = {Over seven million people suffer from an impairment in Mexico; 64.1% are gait-related, and 36.2% are children aged 0 to 14 years. Furthermore, many suffer from neurological disorders, which limits their verbal skills to provide accurate feedback. Robot-assisted gait therapy has shown significant benefits, but the users must make an active effort to accomplish muscular memory, which usually is only around 30% of the time. Moreover, during therapy, the patients' affective state is mostly unsatisfied, wide-awake, and powerless. This paper proposes a method for increasing the efficiency by combining affective data from an Emotiv Insight, an Oculus Go headset displaying an immersive interaction, and a feedback system. Our preliminary study had eight patients during therapy and eight students analyzing the footage using the self-assessment Manikin. It showed that it is possible to use an EEG headset and identify the affective state with a weighted average precision of 97.5%, recall of 87.9%, and F1-score of 92.3% in general. Furthermore, using a VR device could boost efficiency by 16% more. In conclusion, this method allows providing feedback to the therapist in real-time even if the patient is non-verbal and has a limited amount of facial and body expressions.},
}
@article {pmid35954192,
year = {2022},
author = {Song, S and Regan, B and Ereifej, ES and Chan, ER and Capadona, JR},
title = {Neuroinflammatory Gene Expression Analysis Reveals Pathways of Interest as Potential Targets to Improve the Recording Performance of Intracortical Microelectrodes.},
journal = {Cells},
volume = {11},
number = {15},
pages = {},
pmid = {35954192},
issn = {2073-4409},
support = {T32 EB004314/EB/NIBIB NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; T32 GM007250/GM/NIGMS NIH HHS/United States ; TL1 TR002549/TR/NCATS NIH HHS/United States ; IK2 RX002628/RX/RRD VA/United States ; },
mesh = {Animals ; Electrodes, Implanted ; Gene Expression ; *Inflammation/genetics/pathology ; Mice ; Microelectrodes ; },
abstract = {Intracortical microelectrodes are a critical component of brain-machine interface (BMI) systems. The recording performance of intracortical microelectrodes used for both basic neuroscience research and clinical applications of BMIs decreases over time, limiting the utility of the devices. The neuroinflammatory response to the microelectrode has been identified as a significant contributing factor to its performance. Traditionally, pathological assessment has been limited to a dozen or so known neuroinflammatory proteins, and only a few groups have begun to explore changes in gene expression following microelectrode implantation. Our initial characterization of gene expression profiles of the neuroinflammatory response to mice implanted with non-functional intracortical probes revealed many upregulated genes that could inform future therapeutic targets. Emphasis was placed on the most significant gene expression changes and genes involved in multiple innate immune sets, including Cd14, C3, Itgam, and Irak4. In previous studies, inhibition of Cluster of Differentiation 14 (Cd14) improved microelectrode performance for up to two weeks after electrode implantation, suggesting CD14 can be explored as a potential therapeutic target. However, all measures of improvements in signal quality and electrode performance lost statistical significance after two weeks. Therefore, the current study investigated the expression of genes in the neuroinflammatory pathway at the tissue-microelectrode interface in Cd14[-/-] mice to understand better how Cd14 inhibition was connected to temporary improvements in recording quality over the initial 2-weeks post-surgery, allowing for the identification of potential co-therapeutic targets that may work synergistically with or after CD14 inhibition to improve microelectrode performance.},
}
@article {pmid35952864,
year = {2022},
author = {Kober, SE and Ninaus, M and Witte, M and Buchrieser, F and Grössinger, D and Fischmeister, FPS and Neuper, C and Wood, G},
title = {Triathletes are experts in self-regulating physical activity - But what about self-regulating neural activity?.},
journal = {Biological psychology},
volume = {173},
number = {},
pages = {108406},
doi = {10.1016/j.biopsycho.2022.108406},
pmid = {35952864},
issn = {1873-6246},
mesh = {Brain/diagnostic imaging/physiology ; Brain Mapping/methods ; Exercise ; Humans ; Magnetic Resonance Imaging/methods ; *Neurofeedback/physiology ; },
abstract = {Regular exercise improves cognitive control abilities and successful self-regulation of physical activity. However, it is not clear whether exercising also improves the ability to self-regulate one's own brain activity. We investigated this in 26 triathletes and 25 control participants who did not exercise regularly. Within each group half of the participants performed one session of sensorimotor rhythm (SMR, 12-15 Hz) upregulation neurofeedback training, the other half received a sham neurofeedback training. The neurofeedback training session took about 45 min. In a separate session, participants underwent structural magnetic resonance imaging (MRI) to investigate possible differences in brain structure between triathletes and controls. Triathletes and controls were able to voluntarily upregulate their SMR activity during neurofeedback when receiving real feedback. Triathletes showed a stronger increase in SMR activity in the second half of the training compared to controls, suggesting that triathletes are able to self-regulate their own brain activity over a longer period of time. Further, triathletes and controls showed differences in brain structure as reflected by larger gray and white matter volumes in the inferior frontal gyrus and insula compared to controls. These brain areas are generally involved in cognitive control mechanisms. Our results provide new evidence regarding self-regulation abilities of people who exercise regularly and might impact the practical application of neurofeedback.},
}
@article {pmid35952544,
year = {2022},
author = {Abenna, S and Nahid, M and Bouyghf, H and Ouacha, B},
title = {EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing.},
journal = {Computers in biology and medicine},
volume = {148},
number = {},
pages = {105931},
doi = {10.1016/j.compbiomed.2022.105931},
pmid = {35952544},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This work aims to improve EEG signal binary and multiclass classification for real-time BCI applications. Therefore, our paper discusses the results of a new real-time approach that was integrated into a complete prediction system, where we proposed a new trick to eliminate the effect of EEG's non-stationarity nature. This improvement can increase the accuracy from 50% using raw EEG to the order of 90% after preprocessing step in the binary case and from 28% to 78% in the multiclass case. Then, we chose to filter all signals by the proposed bandpass filter automatically optimized using the sine cosine algorithm (SCA) to find the optimal bandwidth that contains the entire EEG characteristics in beta waves. Moreover, we used a common spatial pattern (CSP) filter to eliminate the correlation between all extracted features. Then, the light gradient boosting machine (LGBM) classifier is also combined with SCA algorithm to build better prediction models. As a result, the outcome system was applied on UCI and PhysioNet datasets to get excellent accuracy values of higher than 99% and 95%, respectively, using the data acquired only from three channels. On the other hand, the related works used all the data acquired from 14 channels to find an accuracy value between 70% and 98.5%, which shows the robustness of our method to improve EEG signal prediction quality.},
}
@article {pmid35951573,
year = {2022},
author = {Zhu, H and Forenzo, D and He, B},
title = {On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2283-2291},
pmid = {35951573},
issn = {1558-0210},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; RF1 MH114233/MH/NIMH NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Imagination ; },
abstract = {Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and resulted in multiple models. Finding the best performing models among them would be beneficial for designing better BCI systems and classifiers going forward. However, it is difficult to directly compare performance of various models through the original publications, since the datasets used to test the models are different from each other, too small, or even not publicly available. In this work, we selected five MI-EEG deep classification models proposed recently: EEGNet, Shallow &amp; Deep ConvNet, MB3D and ParaAtt, and tested them on two large, publicly available, databases with 42 and 62 human subjects. Our results show that the models performed similarly on one dataset while EEGNet performed the best on the second with a relatively small training cost using the parameters that we evaluated.},
}
@article {pmid35950925,
year = {2022},
author = {Miskowiak, KW and Yalin, N and Seeberg, I and Burdick, KE and Balanzá-Martínez, V and Bonnin, CDM and Bowie, CR and Carvalho, AF and Dols, A and Douglas, K and Gallagher, P and Hasler, G and Kessing, LV and Lafer, B and Lewandowski, KE and López-Jaramillo, C and Martinez-Aran, A and McIntyre, RS and Porter, RJ and Purdon, SE and Schaffer, A and Sumiyoshi, T and Torres, IJ and Van Rheenen, TE and Yatham, LN and Young, AH and Vieta, E and Stokes, PRA},
title = {Can magnetic resonance imaging enhance the assessment of potential new treatments for cognitive impairment in mood disorders? A systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force.},
journal = {Bipolar disorders},
volume = {24},
number = {6},
pages = {615-636},
pmid = {35950925},
issn = {1399-5618},
mesh = {*Bipolar Disorder/diagnostic imaging/drug therapy ; Cognition ; *Cognitive Dysfunction/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; Mood Disorders/diagnostic imaging/drug therapy ; },
abstract = {BACKGROUND: Developing treatments for cognitive impairment is key to improving the functioning of people with mood disorders. Neuroimaging may assist in identifying brain-based efficacy markers. This systematic review and position paper by the International Society for Bipolar Disorders Targeting Cognition Task Force examines the evidence from neuroimaging studies of pro-cognitive interventions.<br><br>METHODS: We included magnetic resonance imaging (MRI) studies of candidate interventions in people with mood disorders or healthy individuals, following the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis 2020 statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE, Cochrane Library, and Clinicaltrials.gov from inception to 30th April 2021. Two independent authors reviewed the studies using the National Heart, Lung, Blood Institutes of Health Quality Assessment Tool for Controlled Intervention Studies and the quality of neuroimaging methodology assessment checklist.<br><br>RESULTS: We identified 26 studies (N&#xa0;=&#xa0;702). Six investigated cognitive remediation or pharmacological treatments in mood disorders (N&#xa0;=&#xa0;190). In healthy individuals, 14 studies investigated pharmacological interventions (N&#xa0;=&#xa0;319), 2 cognitive training (N&#xa0;=&#xa0;73) and 4 neuromodulatory treatments (N&#xa0;=&#xa0;120). Methodologies were mostly rated as 'fair'. 77% of studies investigated effects with task-based fMRI. Findings varied but most consistently involved treatment-associated cognitive control network (CCN) activity increases with cognitive improvements, or CCN activity decreases with no cognitive change, and increased functional connectivity. In mood disorders, treatment-related default mode network suppression occurred.<br><br>CONCLUSIONS: Modulation of CCN and DMN activity is a putative efficacy biomarker. Methodological recommendations are to pre-declare intended analyses and use task-based fMRI, paradigms probing the CCN, longitudinal assessments, mock scanning, and out-of-scanner tests.},
}
@article {pmid35948840,
year = {2022},
author = {Rathi, N and Singla, R and Tiwari, S},
title = {A comparative study of classification methods for designing a pictorial P300-based authentication system.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {60},
number = {10},
pages = {2899-2916},
pmid = {35948840},
issn = {1741-0444},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Event-Related Potentials, P300/physiology ; Humans ; Support Vector Machine ; },
abstract = {The response of the P300-based speller is associated with factors like matrix size, inter-stimulus interval, and flashing period. This study proposes the comparison of the novel 2 × 2 image-based speller with the traditional 6 × 6 character-based speller to analyze the effects of the stimulus on the accuracy and information transfer rates. To determine the best classification methodology for the approach suggested, a comparative study was performed using linear and quadratic discrimination analysis, K-nearest neighbor, and support vector machine. In the proposed paradigm, four pictures (objects, special symbols, geometrical shapes, and colors) were randomly placed at four corners of the monitor. Subjects were asked to focus on the target image while ignoring all other images. The proposed method outperformed the traditional method, with an average accuracy of 96.99 ± 1.64% and 86.74 ± 5.19%, respectively, and information transfer rates of 33.82 ± 0.57 bits/min and 23.35 ± 0.79 bits/min, respectively. Results show that a modified speller can play a significant role in optimizing brain-computer interface-driven applications. A repeated measure ANOVA test was performed, which concluded that the improved results are obtained using QDA classifiers in terms of mean accuracy, speed, and error rates.},
}
@article {pmid35947962,
year = {2022},
author = {Xiao, P and Liu, X},
title = {Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac88a0},
pmid = {35947962},
issn = {1741-2552},
mesh = {Action Potentials ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Models, Neurological ; Monte Carlo Method ; },
abstract = {Objective.Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment.Approach.To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the variational Bayesian inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters.Main results.Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved.Significance.Our work can be applied to rapidly decode large-scale multichannel neural spike trains in BMIs.},
}
@article {pmid35947562,
year = {2022},
author = {Martin-Chinea, K and Gomez-Gonzalez, JF and Acosta, L},
title = {A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2275-2282},
doi = {10.1109/TNSRE.2022.3198021},
pmid = {35947562},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI.<br><br>METHODS: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage.<br><br>RESULTS: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification.<br><br>CONCLUSION: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.},
}
@article {pmid35947366,
year = {2022},
author = {Simonyan, K and Ehrlich, SK and Andersen, R and Brumberg, J and Guenther, F and Hallett, M and Howard, MA and Millán, JDR and Reilly, RB and Schultz, T and Valeriani, D},
title = {Brain-Computer Interfaces for Treatment of Focal Dystonia.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {37},
number = {9},
pages = {1798-1802},
pmid = {35947366},
issn = {1531-8257},
support = {R01 DC012545/DC/NIDCD NIH HHS/United States ; R01 DC019353/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Dystonic Disorders/diagnosis/therapy ; Humans ; },
abstract = {Task-specificity in isolated focal dystonias is a powerful feature that may successfully be targeted with therapeutic brain-computer interfaces. While performing a symptomatic task, the patient actively modulates momentary brain activity (disorder signature) to match activity during an asymptomatic task (target signature), which is expected to translate into symptom reduction.},
}
@article {pmid35947168,
year = {2022},
author = {Guimaraes, JS and Lemos, NAM and Freire, MAM and Pereira, A and Ribeiro, S},
title = {Microelectrode implants, inflammatory response and long-lasting effects on NADPH diaphorase neurons in the rat frontal cortex.},
journal = {Experimental brain research},
volume = {240},
number = {10},
pages = {2569-2580},
pmid = {35947168},
issn = {1432-1106},
support = {01.06.1092.00//Financiadora de Estudos e Projetos/ ; },
mesh = {Animals ; Frontal Lobe ; Humans ; Microelectrodes ; NADP ; *NADPH Dehydrogenase/metabolism ; Neurons/metabolism ; Paralysis ; Rats ; *Tungsten ; },
abstract = {At present, one of the main therapeutic challenges comprises the development of technologies to improve the life quality of people suffering from different types of body paralysis, through the reestablishment of sensory and motor functions. In this regard, brain-machine interfaces (BMI) offer hope to effectively mitigate body paralysis through the control of paralyzed body parts by brain activity. Invasive BMI use chronic multielectrode implants to record neural activity directly from the brain tissue. While such invasive devices provide the highest amount of usable neural activity for BMI control, they also involve direct damage to the nervous tissue. In the cerebral cortex, high levels of the enzyme NADPH diaphorase (NADPH-d) characterize a particular class of interneurons that regulates neuronal excitability and blood supply. To gain insight into the biocompatibility of invasive BMI, we assessed the impact of chronic implanted tungsten multielectrode bundles on the distribution and morphology of NADPH-d-reactive neurons in the rat frontal cortex. NADPH-d neuronal labeling was correlated with glial response markers and with indices of healthy neuronal activity measured by electrophysiological recordings performed up to 3 months after multielectrode implantation. Chronic electrode arrays caused a small and quite localized structural disturbance on the implanted site, with neuronal loss and glial activation circumscribed to the site of implant. Electrodes remained viable during the entire period of implantation. Moreover, neither the distribution nor the morphology of NADPH-d neurons was altered. Overall, our findings provide additional evidence that tungsten multielectrodes can be employed as a viable element for long-lasting therapeutic BMI applications.},
}
@article {pmid35941403,
year = {2023},
author = {He, Q and He, J and Yang, Y and Zhao, J},
title = {Brain-Computer Interfaces in Disorders of Consciousness.},
journal = {Neuroscience bulletin},
volume = {39},
number = {2},
pages = {348-352},
pmid = {35941403},
issn = {1995-8218},
mesh = {Humans ; *Brain-Computer Interfaces ; Consciousness Disorders ; Consciousness ; Electroencephalography ; Brain ; },
}
@article {pmid35940874,
year = {2022},
author = {Khademi, F and Naros, G and Nicksirat, A and Kraus, D and Gharabaghi, A},
title = {Rewiring Cortico-Muscular Control in the Healthy and Poststroke Human Brain with Proprioceptive β-Band Neurofeedback.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {36},
pages = {6861-6877},
pmid = {35940874},
issn = {1529-2401},
abstract = {In severely affected stroke survivors, cortico-muscular control is disturbed and volitional upper limb movements often absent. Mental rehearsal of the impaired movement in conjunction with sensory feedback provision are suggested as promising rehabilitation exercises. Knowledge about the underlying neural processes, however, remains vague. In male and female chronic stroke patients with hand paralysis, a brain-computer interface controlled a robotic orthosis and turned sensorimotor β-band desynchronization during motor imagery (MI) of finger extension into contingent hand opening. Healthy control subjects performed the same task and received the same proprioceptive feedback with a robotic orthosis or visual feedback only. Only when proprioceptive feedback was provided, cortico-muscular coherence (CMC) increased with a predominant information flow from the sensorimotor cortex to the finger extensors. This effect (1) was specific to the β frequency band, (2) transferred to a motor task (MT), (3) was proportional to subsequent corticospinal excitability (CSE) and correlated with behavioral changes in the (4) healthy and (5) poststroke condition; notably, MI-related enhancement of β-band CMC in the ipsilesional premotor cortex correlated with motor improvements after the intervention. In the healthy and injured human nervous system, synchronized activation of motor-related cortical and spinal neural pools facilitates, in accordance with the communication-through-coherence hypothesis, cortico-spinal communication and may, thereby, be therapeutically relevant for functional restoration after stroke, when voluntary movements are no longer possible.SIGNIFICANCE STATEMENT This study provides insights into the neural processes that transfer effects of brain-computer interface neurofeedback to subsequent motor behavior. Specifically, volitional control of cortical oscillations and proprioceptive feedback enhances both cortical activity and behaviorally relevant connectivity to the periphery in a topographically circumscribed and frequency-specific way. This enhanced cortico-muscular control can be induced in the healthy and poststroke brain. Thereby, activating the motor cortex with mental rehearsal of the impaired movement and closing the loop by robot-assisted feedback synchronizes ipsilesional premotor cortex and spinal neural pools in the β frequency band. This facilitates, in accordance with the communication-through-coherence hypothesis, cortico-spinal communication and may, thereby, be therapeutically relevant for functional restoration after stroke, when voluntary movements are no longer possible.},
}
@article {pmid35939991,
year = {2022},
author = {van Weelden, E and Alimardani, M and Wiltshire, TJ and Louwerse, MM},
title = {Aviation and neurophysiology: A systematic review.},
journal = {Applied ergonomics},
volume = {105},
number = {},
pages = {103838},
doi = {10.1016/j.apergo.2022.103838},
pmid = {35939991},
issn = {1872-9126},
mesh = {Humans ; Neurophysiology ; *Aviation ; Workload/psychology ; *Virtual Reality ; Ergonomics ; Electroencephalography ; },
abstract = {This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.},
}
@article {pmid35937679,
year = {2022},
author = {Jeong, JH and Cho, JH and Lee, YE and Lee, SH and Shin, GH and Kweon, YS and Millán, JDR and Müller, KR and Lee, SW},
title = {2020 International brain-computer interface competition: A review.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {898300},
pmid = {35937679},
issn = {1662-5161},
abstract = {The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.},
}
@article {pmid35936295,
year = {2022},
author = {Peng, Y and Xu, Q and Lin, S and Wang, X and Xiang, G and Huang, S and Zhang, H and Fan, C},
title = {The Application of Electroencephalogram in Driving Safety: Current Status and Future Prospects.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {919695},
pmid = {35936295},
issn = {1664-1078},
abstract = {The driver is one of the most important factors in the safety of the transportation system. The driver's perceptual characteristics are closely related to driving behavior, while electroencephalogram (EEG) as the gold standard for evaluating human perception is non-deceptive. It is essential to study driving characteristics by analyzing the driver's brain activity pattern, effectively acquiring driver perceptual characteristics, creating a direct connection between the driver's brain and external devices, and realizing information interchange. This paper first introduces the theories related to EEG, then reviews the applications of EEG in scenarios such as fatigue driving, distracted driving, and emotional driving. The limitations of existing research have been identified and the prospect of EEG application in future brain-computer interface automotive assisted driving systems have been proposed. This review provides guidance for researchers to use EEG to improve driving safety. It also offers valuable suggestions for future research.},
}
@article {pmid35933960,
year = {2022},
author = {Ran, X and Chen, W and Yvert, B and Zhang, S},
title = {A hybrid autoencoder framework of dimensionality reduction for brain-computer interface decoding.},
journal = {Computers in biology and medicine},
volume = {148},
number = {},
pages = {105871},
doi = {10.1016/j.compbiomed.2022.105871},
pmid = {35933960},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Electroencephalography ; Least-Squares Analysis ; },
abstract = {OBJECTIVE: As the scale of neural recording increases, Brain-computer interfaces (BCIs) are restrained by high-dimensional neural features, so dimensionality reduction is required as a preprocess of neural features. In this context, we propose a novel framework based on deep learning to reduce the dimensionality of neural features that are typically extracted from electrocorticography (ECoG) or local field potential (LFP).<br><br>APPROACH: A high-performance autoencoder was implemented by chaining convolutional layers to deal with spatial and frequency dimensions with bottleneck long short-term memory (LSTM) layers to deal with the temporal dimension of the features. Furthermore, this autoencoder is combined with a fully connected layer to regularize the training.<br><br>MAIN RESULTS: By applying the proposed method to two different datasets, we found that this dimensionality reduction method largely outperforms kernel principal component analysis (KPCA), partial least square (PLS), preferential subspace identification (PSID), and latent factor analysis via dynamical systems (LFADS). Besides, the new features obtained by our method can be applied to various BCI decoders, without significant differences in decoding performance.<br><br>SIGNIFICANCE: A novel method is proposed as a reliable tool for efficient dimensionality reduction of neural signals. Its high performance and robustness are promising to enhance the decoding accuracy and long-term stability of online BCI systems based on large-scale neural recordings.},
}
@article {pmid35933840,
year = {2022},
author = {Sustronck, B and Hoflack, G and Lebrun, M and Vertenten, G},
title = {Bayesian latent class analysis of the characteristics of diagnostic tests to assess the passive immunity transfer status in neonatal Belgian Blue beef calves.},
journal = {Preventive veterinary medicine},
volume = {207},
number = {},
pages = {105729},
doi = {10.1016/j.prevetmed.2022.105729},
pmid = {35933840},
issn = {1873-1716},
mesh = {Animals ; Animals, Newborn ; Bayes Theorem ; Belgium ; Cattle ; Colostrum ; *Diagnostic Tests, Routine ; Female ; *Immunity, Maternally-Acquired ; Immunoglobulin G ; Latent Class Analysis ; Pregnancy ; Sensitivity and Specificity ; },
abstract = {The aim of the current study was to assess the diagnostic characteristics of radial immunodiffusion (RID), capillary electrophoresis (CE) and digital brix refractometry (Bx) for the diagnosis of failure of passive transfer (FPT) of immunity in neonatal Belgian Blue beef calves in the absence of a gold standard using a Bayesian latent class model. Belgian blue beef calves (n = 202) from a large farm in the south of Belgium were blood-sampled at 48-72 h of age and tested for FPT. The true prevalence of FPT in this population of calves was 34.5 % (95 % BCI: 26.1-44.3) using a FPT cut-off point of 10 g IgG/L. This true prevalence increased to 66.3 (95 % BCI: 56.9-74.8) and 88.9 % (95 % BCI: 83.1-93.2) at FPT cut-off points of respectively 18 and 25 g IgG/L serum. The Bland-Altman plot comparing the RID and CE methods, revealed that the serum IgG concentrations obtained by RID were on average 2.25 (95 % CI 1.62-2.88) g/L higher than those measured by CE. Optimal cut-off values for CE, corresponding to the FPT values as measured by RID of 10, 18, and 25 g IgG/L serum, were respectively 10, 15, and 20 g IgG/L. The overall diagnostic accuracy of the three diagnostic tests was comparable at the FPT cut-off point of 10 g IgG/L serum (i.e. 85 %). At higher cut-off points for FPT, the RID and CE assays presumably performed better that the Bx method. In conclusion, we demonstrated that: (1) the CE method is a good alternative for the RID assay, the latter having important constraints when considering its practicality, and (2) the Bx method is a cheap and user-friendly indirect method to evaluate the FPT in new-born Belgian Blue beef calves.},
}
@article {pmid35932992,
year = {2022},
author = {Yanagisawa, T and Fukuma, R and Seymour, B and Tanaka, M and Yamashita, O and Hosomi, K and Kishima, H and Kamitani, Y and Saitoh, Y},
title = {Neurofeedback Training without Explicit Phantom Hand Movements and Hand-Like Visual Feedback to Modulate Pain: A Randomized Crossover Feasibility Trial.},
journal = {The journal of pain},
volume = {23},
number = {12},
pages = {2080-2091},
doi = {10.1016/j.jpain.2022.07.009},
pmid = {35932992},
issn = {1528-8447},
support = {MR/W027593/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Humans ; *Neurofeedback ; *Phantom Limb/therapy ; Feedback, Sensory ; Cross-Over Studies ; Single-Blind Method ; Feasibility Studies ; Movement ; Hand ; },
abstract = {Phantom limb pain is attributed to abnormal sensorimotor cortical representations, although the causal relationship between phantom limb pain and sensorimotor cortical representations suffers from the potentially confounding effects of phantom hand movements. We developed neurofeedback training to change sensorimotor cortical representations without explicit phantom hand movements or hand-like visual feedback. We tested the feasibility of neurofeedback training in fourteen patients with phantom limb pain. Neurofeedback training was performed in a single-blind, randomized, crossover trial using two decoders constructed using motor cortical currents measured during phantom hand movements; the motor cortical currents contralateral or ipsilateral to the phantom hand (contralateral and ipsilateral training) were estimated from magnetoencephalograms. Patients were instructed to control the size of a disk, which was proportional to the decoding results, but to not move their phantom hands or other body parts. The pain assessed by the visual analogue scale was significantly greater after contralateral training than after ipsilateral training. Classification accuracy of phantom hand movements significantly increased only after contralateral training. These results suggested that the proposed neurofeedback training changed phantom hand representation and modulated pain without explicit phantom hand movements or hand-like visual feedback, thus showing the relation between the phantom hand representations and pain. PERSPECTIVE: Our work demonstrates the feasibility of using neurofeedback training to change phantom hand representation and modulate pain perception without explicit phantom hand movements and hand-like visual feedback. The results enhance the mechanistic understanding of certain treatments, such as mirror therapy, that change the sensorimotor cortical representation.},
}
@article {pmid35932270,
year = {2023},
author = {Li, L and Ibayashi, K and Piscopo, A and Deifelt Streese, C and Chen, H and Greenlee, JDW and Hasan, DM},
title = {Intraarterial encephalography from an acutely implanted aneurysm embolization device in awake humans.},
journal = {Journal of neurosurgery},
volume = {138},
number = {3},
pages = {785-792},
doi = {10.3171/2022.6.JNS22932},
pmid = {35932270},
issn = {1933-0693},
mesh = {Humans ; *Intracranial Aneurysm/therapy ; Wakefulness ; Treatment Outcome ; *Endovascular Procedures ; Retrospective Studies ; *Embolization, Therapeutic ; },
abstract = {OBJECTIVE: Endovascular electroencephalography (evEEG) uses the cerebrovascular system to record electrical activity from adjacent neural structures. The safety, feasibility, and efficacy of using the Woven EndoBridge Aneurysm Embolization System (WEB) for evEEG has not been investigated.<br><br>METHODS: Seventeen participants undergoing awake WEB endovascular treatment of unruptured cerebral aneurysms were included. After WEB deployment and before detachment, its distal deployment wire was connected to an EEG receiver, and participants performed a decision-making task for 10 minutes. WEB and scalp recordings were captured.<br><br>RESULTS: All patients underwent successful embolization and evEEG with no complications. Event-related potentials were detected on scalp EEG in 9/17 (53%) patients. Of these 9 patients, a task-related low-gamma (30-70 Hz) response on WEB channels was captured in 8/9 (89%) cases. In these 8 patients, the WEB was deployed in 2 middle cerebral arteries, 3 anterior communicating arteries, the terminal internal carotid artery, and 2 basilar tip aneurysms. Electrocardiogram artifact on WEB channels was present in 12/17 cases.<br><br>CONCLUSIONS: The WEB implanted within cerebral aneurysms of awake patients is capable of capturing task-specific brain electrical activities. Future studies are warranted to establish the efficacy of and support for evEEG as a tool for brain recording, brain stimulation, and brain-machine interface applications.},
}
@article {pmid35931455,
year = {2022},
author = {Lu, B and Xiao, T and Zhang, C and Jiang, J and Wang, Y and Diao, X and Zhai, J},
title = {Brain Wave-Like Signal Modulator by Ionic Nanochannel Rectifier Bridges.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {18},
number = {35},
pages = {e2203104},
doi = {10.1002/smll.202203104},
pmid = {35931455},
issn = {1613-6829},
mesh = {*Brain Waves ; *Ion Channels ; Ion Transport ; Ions ; Light ; },
abstract = {Smart modulation of bioelectric signals is of great significance for the development of brain-computer interfaces, bio-computers, and other technologies. The regulation and transmission of bioelectrical signals are realized through the synergistic action of various ion channels in organisms. The bionic nanochannels, which have similar physiological working environment and ion rectification as their biological counterparts, can be used to construct ion rectifier bridges to modulate the bioelectric signals. Here, the artificial smart ionic rectifier bridge with light response is constructed by anodic aluminum oxide (AAO)/poly (spiropyran acrylate) (PSP) nanochannels. The output ion current of the rectifier bridge can be switched between "ON" and "OFF" states by irradiation with UV and visible (Vis) light, and the conversion efficiency (η) of the system in "ON" state is ≈70.5%. The controllable modulation of brain wave-like signal can be realized by ionic rectifier bridge. The ion transport properties and processes of ion rectifier bridges are explained using theoretical calculations based on Poisson-Nernst-Planck (PNP) equations. These findings have significant implications for the understanding of the intelligent ionic circuit and combination of artificial smart ionic channels to organisms, which provide new avenues for development of intelligent ion devices.},
}
@article {pmid35931055,
year = {2022},
author = {Fahimi Hnazaee, M and Verwoert, M and Freudenburg, ZV and van der Salm, SMA and Aarnoutse, EJ and Leinders, S and Van Hulle, MM and Ramsey, NF and Vansteensel, MJ},
title = {Towards predicting ECoG-BCI performance: assessing the potential of scalp-EEG.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac8764},
pmid = {35931055},
issn = {1741-2552},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electrocorticography/methods ; Electroencephalography/methods ; Humans ; Movement ; Scalp ; },
abstract = {Objective. Implanted brain-computer interfaces (BCIs) employ neural signals to control a computer and may offer an alternative communication channel for people with locked-in syndrome (LIS). Promising results have been obtained using signals from the sensorimotor (SM) area. However, in earlier work on home-use of an electrocorticography (ECoG)-based BCI by people with LIS, we detected differences in ECoG-BCI performance, which were related to differences in the modulation of low frequency band (LFB) power in the SM area. For future clinical implementation of ECoG-BCIs, it will be crucial to determine whether reliable performance can be predicted before electrode implantation. To assess if non-invasive scalp-electroencephalography (EEG) could serve such prediction, we here investigated if EEG can detect the characteristics observed in the LFB modulation of ECoG signals.Approach. We included three participants with LIS of the earlier study, and a control group of 20 healthy participants. All participants performed a Rest task, and a Movement task involving actual (healthy) or attempted (LIS) hand movements, while their EEG signals were recorded.Main results.Data of the Rest task was used to determine signal-to-noise ratio, which showed a similar range for LIS and healthy participants. Using data of the Movement task, we selected seven EEG electrodes that showed a consistent movement-related decrease in beta power (13-30 Hz) across healthy participants. Within the EEG recordings of this subset of electrodes of two LIS participants, we recognized the phenomena reported earlier for the LFB in their ECoG recordings. Specifically, strong movement-related beta band suppression was observed in one, but not the other, LIS participant, and movement-related alpha band (8-12 Hz) suppression was practically absent in both. Results of the third LIS participant were inconclusive due to technical issues with the EEG recordings.Significance. Together, these findings support a potential role for scalp EEG in the presurgical assessment of ECoG-BCI candidates.},
}
@article {pmid35930533,
year = {2022},
author = {Cinel, C and Fernandez-Vargas, J and Tremmel, C and Citi, L and Poli, R},
title = {Enhancing performance with multisensory cues in a realistic target discrimination task.},
journal = {PloS one},
volume = {17},
number = {8},
pages = {e0272320},
pmid = {35930533},
issn = {1932-6203},
mesh = {*Attention/physiology ; Auditory Perception/physiology ; *Cues ; Discrimination, Psychological/physiology ; Humans ; Reaction Time/physiology ; Visual Perception/physiology ; },
abstract = {Making decisions is an important aspect of people's lives. Decisions can be highly critical in nature, with mistakes possibly resulting in extremely adverse consequences. Yet, such decisions have often to be made within a very short period of time and with limited information. This can result in decreased accuracy and efficiency. In this paper, we explore the possibility of increasing speed and accuracy of users engaged in the discrimination of realistic targets presented for a very short time, in the presence of unimodal or bimodal cues. More specifically, we present results from an experiment where users were asked to discriminate between targets rapidly appearing in an indoor environment. Unimodal (auditory) or bimodal (audio-visual) cues could shortly precede the target stimulus, warning the users about its location. Our findings show that, when used to facilitate perceptual decision under time pressure, and in condition of limited information in real-world scenarios, spoken cues can be effective in boosting performance (accuracy, reaction times or both), and even more so when presented in bimodal form. However, we also found that cue timing plays a critical role and, if the cue-stimulus interval is too short, cues may offer no advantage. In a post-hoc analysis of our data, we also show that congruency between the response location and both the target location and the cues, can interfere with the speed and accuracy in the task. These effects should be taken in consideration, particularly when investigating performance in realistic tasks.},
}
@article {pmid35930511,
year = {2022},
author = {Song, X and Su, X and Chen, X and Xu, M and Ming, D},
title = {In Vivo Transcranial Acoustoelectric Brain Imaging of Different Steady-State Visual Stimulation Paradigms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2233-2241},
doi = {10.1109/TNSRE.2022.3196828},
pmid = {35930511},
issn = {1558-0210},
mesh = {Brain/physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {OBJECTIVE: Based on the acoustoelectric (AE) effect, transcranial acoustoelectric brain imaging (tABI) is of potential for brain functional imaging with high temporal and spatial resolution. With nonlinear and non-steady-state, brain electrical signal is microvolt level which makes the development of tABI more difficult. This study demonstrates for the first time in vivo tABI of different steady-state visual stimulation paradigms.<br><br>METHOD: To obtain different brain activation maps, we designed three steady-state visual stimulation paradigms, including binocular, left eye and right eye stimulations. Then, tABI was implemented with one fixed recording electrode. And, based on decoded signal power spectrum (tABI-power) and correlation coefficient between steady-state visual evoked potential (SSVEP) and decoded signal (tABI-cc) respectively, two imaging methods were investigated. To quantitatively evaluate tABI spatial resolution performance, ECoG was implemented at the same time. Finally, we explored the performance of tABI transient imaging.<br><br>RESULTS: Decoded AE signal of activation region is consistent with SSVEP in both time and frequency domains, while that of the nonactivated region is noise. Besides, with transcranial measurement, tABI has a millimeter-level spatial resolution (< 3mm). Meanwhile, it can achieve millisecond-level (125ms) transient brain activity imaging.<br><br>CONCLUSION: Experiment results validate tABI can realize brain functional imaging under complex paradigms and is expected to develop into a brain functional imaging method with high spatiotemporal resolution.},
}
@article {pmid35927998,
year = {2022},
author = {Zhou, Q and Lin, J and Yao, L and Wang, Y and Han, Y and Xu, K},
title = {Corrigendum: Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {977379},
doi = {10.3389/fnhum.2022.977379},
pmid = {35927998},
issn = {1662-5161},
abstract = {[This corrects the article DOI: 10.3389/fnhum.2021.701091.].},
}
@article {pmid35922600,
year = {2023},
author = {Yang, J and Xie, S and Zhu, S and Xu, ZZ},
title = {Specialized Microglia Resolve Neuropathic Pain in the Spinal Cord.},
journal = {Neuroscience bulletin},
volume = {39},
number = {1},
pages = {173-175},
pmid = {35922600},
issn = {1995-8218},
mesh = {Humans ; *Microglia ; Spinal Cord ; *Neuralgia ; Hyperalgesia ; },
}
@article {pmid35921802,
year = {2022},
author = {Huang, Y and Zhang, X and Shen, X and Chen, S and Principe, JC and Wang, Y},
title = {Extracting synchronized neuronal activity from local field potentials based on a marked point process framework.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac86a3},
pmid = {35921802},
issn = {1741-2552},
mesh = {Action Potentials/physiology ; Animals ; Humans ; Macaca mulatta ; *Motor Cortex/physiology ; Neurons ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Objective.Brain-machine interfaces (BMIs) translate neural activity into motor commands to restore motor functions for people with paralysis. Local field potentials (LFPs) are promising for long-term BMIs, since the quality of the recording lasts longer than single neuronal spikes. Inferring neuronal spike activity from population activities such as LFPs is challenging, because LFPs stem from synaptic currents flowing in the neural tissue produced by various neuronal ensembles and reflect neural synchronization. Existing studies that combine LFPs with spikes leverage the spectrogram of the former, which can neither detect the transient characteristics of LFP features (here, neuromodulation in a specific frequency band) with high accuracy, nor correlate them with relevant neuronal activity with a sufficient time resolution.Approach.We propose a feature extraction and validation framework to directly extract LFP neuromodulations related to synchronized spike activity using recordings from the primary motor cortex of six Sprague Dawley rats during a lever-press task. We first select important LFP frequency bands relevant to behavior, and then implement a marked point process (MPP) methodology to extract transient LFP neuromodulations. We validate the LFP feature extraction by examining the correlation with the pairwise synchronized firing probability of important neurons, which are selected according to their contribution to behavioral decoding. The highly correlated synchronized firings identified by the LFP neuromodulations are fed into a decoder to check whether they can serve as a reliable neural data source for movement decoding.Main results.We find that the gamma band (30-80 Hz) LFP neuromodulations demonstrate significant correlation with synchronized firings. Compared with traditional spectrogram-based method, the higher-temporal resolution MPP method captures the synchronized firing patterns with fewer false alarms, and demonstrates significantly higher correlation than single neuron spikes. The decoding performance using the synchronized neuronal firings identified by the LFP neuromodulations can reach 90% compared to the full recorded neuronal ensembles.Significance.Our proposed framework successfully extracts the sparse LFP neuromodulations that can identify temporal synchronized neuronal spikes with high correlation. The identified neuronal spike pattern demonstrates high decoding performance, which suggest LFP can be used as an effective modality for long-term BMI decoding.},
}
@article {pmid35918334,
year = {2022},
author = {Pei, X and Xu, G and Zhou, Y and Tao, L and Cui, X and Wang, Z and Xu, B and Wang, AL and Zhao, X and Dong, H and An, Y and Cao, Y and Li, R and Hu, H and Yu, Y},
title = {A simultaneous electroencephalography and eye-tracking dataset in elite athletes during alertness and concentration tasks.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {465},
pmid = {35918334},
issn = {2052-4463},
support = {202001012//Shanghai Municipal Commission of Economy and Informatization (Shanghai Municipal Working Committee of Economy and Informatization)/ ; U20A20221//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Athletes ; Attention ; *Electroencephalography ; Eye Movements ; *Eye-Tracking Technology ; Humans ; },
abstract = {The dataset of simultaneous 64-channel electroencephalography (EEG) and high-speed eye-tracking (ET) recordings was collected from 31 professional athletes and 43 college students during alertness behavior task (ABT) and concentration cognitive task (CCT). The CCT experiment lasting 1-2 hours included five sessions for groups of the Shooting, Archery and Modern Pentathlon elite athletes and the controls. Concentration targets included shooting target and combination target with or without 24 different directions of visual distractors and 2 types of music distractors. Meditation and Schulte Grid trainings were done as interventions. Analysis of the dataset aimed to extract effective biological markers of eye movement and EEG that can assess the concentration level of talented athletes compared with same-aged controls. Moreover, this dataset is useful for the research of related visual brain-computer interfaces.},
}
@article {pmid35917926,
year = {2022},
author = {Su, N and Zhu, A and Tao, X and Ding, ZJ and Chang, S and Ye, F and Zhang, Y and Zhao, C and Chen, Q and Wang, J and Zhou, CY and Guo, Y and Jiao, S and Zhang, S and Wen, H and Ma, L and Ye, S and Zheng, SJ and Yang, F and Wu, S and Guo, J},
title = {Structures and mechanisms of the Arabidopsis auxin transporter PIN3.},
journal = {Nature},
volume = {609},
number = {7927},
pages = {616-621},
pmid = {35917926},
issn = {1476-4687},
mesh = {Apoproteins/chemistry/metabolism/ultrastructure ; *Arabidopsis/chemistry/metabolism/ultrastructure ; *Arabidopsis Proteins/antagonists &amp; inhibitors/chemistry/metabolism/ultrastructure ; Biological Transport/drug effects ; Cryoelectron Microscopy ; *Indoleacetic Acids/chemistry/metabolism ; Phthalimides/chemistry/pharmacology ; Protein Domains ; Protein Multimerization ; Protein Subunits/chemistry/metabolism ; },
abstract = {The PIN-FORMED (PIN) protein family of auxin transporters mediates polar auxin transport and has crucial roles in plant growth and development[1,2]. Here we present cryo-electron microscopy structures of PIN3 from Arabidopsis thaliana in the apo state and in complex with its substrate indole-3-acetic acid and the inhibitor N-1-naphthylphthalamic acid (NPA). A. thaliana PIN3 exists as a homodimer, and its transmembrane helices 1, 2 and 7 in the scaffold domain are involved in dimerization. The dimeric PIN3 forms a large, joint extracellular-facing cavity at the dimer interface while each subunit adopts an inward-facing conformation. The structural and functional analyses, along with computational studies, reveal the structural basis for the recognition of indole-3-acetic acid and NPA and elucidate the molecular mechanism of NPA inhibition on PIN-mediated auxin transport. The PIN3 structures support an elevator-like model for the transport of auxin, whereby the transport domains undergo up-down rigid-body motions and the dimerized scaffold domains remain static.},
}
@article {pmid35917402,
year = {2022},
author = {Chen, D and Ou, Z and Zhu, J and Wang, H and Ding, P and Luo, L and Ding, X and Sun, C and Lan, T and Sahu, SK and Wu, W and Yuan, Y and Wu, W and Qiu, J and Zhu, Y and Yue, Q and Jia, Y and Wei, Y and Qin, Q and Li, R and Zhao, W and Lv, Z and Pu, M and Lv, B and Yang, S and Chang, A and Wei, X and Chen, F and Yang, T and Wei, Z and Yang, F and Zhang, P and Guo, G and Li, Y and Hua, Y and Liu, H},
title = {Screening of cell-virus, cell-cell, gene-gene crosstalk among animal kingdom at single cell resolution.},
journal = {Clinical and translational medicine},
volume = {12},
number = {8},
pages = {e886},
pmid = {35917402},
issn = {2001-1326},
mesh = {Animals ; *COVID-19/genetics ; Host Specificity ; Humans ; Receptors, Virus/metabolism ; SARS-CoV-2/genetics ; *Spike Glycoprotein, Coronavirus/metabolism ; },
abstract = {BACKGROUND: The exact animal origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains obscure and understanding its host range is vital for preventing interspecies transmission.<br><br>METHODS: Herein, we applied single-cell sequencing to multiple tissues of 20 species (30 data sets) and integrated them with public resources (45 data sets covering 26 species) to expand the virus receptor distribution investigation. While the binding affinity between virus and receptor is essential for viral infectivity, understanding the receptor distribution could predict the permissive organs and tissues when infection occurs.<br><br>RESULTS: Based on the transcriptomic data, the expression profiles of receptor or associated entry factors for viruses capable of causing respiratory, blood, and brain diseases were described in detail. Conserved cellular connectomes and regulomes were also identified, revealing fundamental cell-cell and gene-gene cross-talks from reptiles to humans.<br><br>CONCLUSIONS: Overall, our study provides a resource of the single-cell atlas of the animal kingdom which could help to identify the potential host range and tissue tropism of viruses and reveal the host-virus co-evolution.},
}
@article {pmid35915173,
year = {2022},
author = {Cheng, J and Ma, X and Li, C and Ullah, R and Wang, X and Long, J and Yuan, Z and Liu, S and Fu, J and Chen, Z and Shen, Y and Zhou, YD},
title = {Diet-induced inflammation in the anterior paraventricular thalamus induces compulsive sucrose-seeking.},
journal = {Nature neuroscience},
volume = {25},
number = {8},
pages = {1009-1013},
pmid = {35915173},
issn = {1546-1726},
mesh = {Animals ; *Anterior Thalamic Nuclei ; Compulsive Behavior ; Cues ; Diet, High-Fat/adverse effects ; Inflammation/chemically induced ; Mice ; Reward ; *Sucrose/metabolism ; },
abstract = {Overconsumption of palatable food may initiate neuroadaptive responses in brain reward circuitry that may contribute to eating disorders. Here we report that high-fat diet (HFD) consumption impedes threat-cue-induced suppression of sucrose-seeking in mice. This compulsive sucrose-seeking was due to enhanced cue-triggered neuronal activity in the anterior paraventricular thalamus (aPVT) resulting from HFD-induced microglia activation. Thus, metabolic inflammation in the aPVT produces an adaptive response to threat cues, leading to compulsive food-seeking.},
}
@article {pmid35914075,
year = {2022},
author = {Tao, Z and Deng, H and Chu, H and Wiederhold, M and Wiederhold, BK and Zhong, H and Kang, Z and Zhao, J and Xiong, M and Zhu, M and Lin, Z and Wang, J},
title = {Exploring the Relationship Between Binocular Imbalance and Myopia: Refraction with a Virtual Reality Platform.},
journal = {Cyberpsychology, behavior and social networking},
volume = {25},
number = {10},
pages = {672-677},
pmid = {35914075},
issn = {2152-2723},
mesh = {Adolescent ; Child ; Humans ; Cross-Sectional Studies ; Refraction, Ocular ; *Myopia/complications ; Visual Acuity ; *Virtual Reality ; },
abstract = {To explore the relationship between binocular imbalance (BI) and the abnormal development of binocular refraction. BI data were collected by enrolling the first 1,000 adolescents and children aged 6-18 years in Shenzhen Eye Hospital from April 2020 to January 2021. In this cross-sectional study, the imbalance value (IV) did not show a statistical correlation with the spherical equivalent (SE) (oculus dexter [OD]: r = 0.022, p = 0.586; oculus sinister [OS]: r = -0.021, p = 0.606), and had little correlation with the uncorrected visual acuity (VA) (OD: r = -0.084, p = 0.039; OS: r = -0.034, p = 0.408). The proportion of binocular contrast imbalance (BCI) (the absolute value) maintained the highest level (from 54.42 to 79.17 percent) with the increase of bilateral SE difference in the four subcategories (binocular balance, monocular suppression, binocular rivalry, and BCI). From -100 to +100 of IV, the SE of the left eye tends to increase negatively when compared with the right eye (from -95 < IV ≦ -80, SE difference = -0.83 ± 1.58, to -20 < IV ≦ -10, SE difference = -0.14 ± 0.61; from 10 ≦ IV <20, SE difference = -0.05 ± 0.80, to 80 ≦ IV <95, SE difference = 1.48 ± 2.77). BI widely exists within the general pediatric population. The BI did not show significant correlation with the unilateral eye refractive state and the VA. However, the BI may be accompanied by imbalanced development of the eye refractive system. Furthermore, the SE of the dominant eye (from the prospective of BI) tends to be more negative than that of the opposite eye as the value increases. Clinical Trial Registration number: ChiCTR2100045457.},
}
@article {pmid35914032,
year = {2022},
author = {Xie, J and Zhang, J and Sun, J and Ma, Z and Qin, L and Li, G and Zhou, H and Zhan, Y},
title = {A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2126-2136},
doi = {10.1109/TNSRE.2022.3194600},
pmid = {35914032},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Imagination ; Movement ; },
abstract = {The attention mechanism of the Transformer has the advantage of extracting feature correlation in the long-sequence data and visualizing the model. As time-series data, the spatial and temporal dependencies of the EEG signals between the time points and the different channels contain important information for accurate classification. So far, Transformer-based approaches have not been widely explored in motor-imagery EEG classification and visualization, especially lacking general models based on cross-individual validation. Taking advantage of the Transformer model and the spatial-temporal characteristics of the EEG signals, we designed Transformer-based models for classifications of motor imagery EEG based on the PhysioNet dataset. With 3s EEG data, our models obtained the best classification accuracy of 83.31%, 74.44%, and 64.22% on two-, three-, and four-class motor-imagery tasks in cross-individual validation, which outperformed other state-of-the-art models by 0.88%, 2.11%, and 1.06%. The inclusion of the positional embedding modules in the Transformer could improve the EEG classification performance. Furthermore, the visualization results of attention weights provided insights into the working mechanism of the Transformer-based networks during motor imagery tasks. The topography of the attention weights revealed a pattern of event-related desynchronization (ERD) which was consistent with the results from the spectral analysis of Mu and beta rhythm over the sensorimotor areas. Together, our deep learning methods not only provide novel and powerful tools for classifying and understanding EEG data but also have broad applications for brain-computer interface (BCI) systems.},
}
@article {pmid35914031,
year = {2022},
author = {Ziafati, A and Maleki, A},
title = {Boosting the Evoked Response of Brain to Enhance the Reference Signals of CCA Method.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2107-2115},
doi = {10.1109/TNSRE.2022.3192413},
pmid = {35914031},
issn = {1558-0210},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Brain-computer interface (BCI) systems can be used to communicate and express desires from people with severe nervous system damage. Among BCI systems based on evoked responses, steady state visual evoked potential (SSVEP) responses are the most widely used. Canonical correlation analysis (CCA)-based methods have been widely used in SSVEP-based online BCIs due to their low computation and high speed, and many methods have been introduced to improve the results. In this research, a method for constructing reference signals used in CCA based on the amplified evoked response of brain is introduced. In the proposed method, after removing the latency in the training signals, to construct reference signals, multilayer perceptron neural networks of the fitting type are used instead of the usual sine/cosine signals. The results show the success of this method in boosting the evoked responses of brain. The detection accuracy in 100-second time windows was 100%, and the information transfer rate in the same period was 240 bits per minute. Making reference signals similar to the recorded electroencephalogram allowed us to make more similarities in the CCA between the signals under consideration, and the reference signals, and to dramatically improve the results.},
}
@article {pmid35912081,
year = {2022},
author = {Li, X and Chen, P and Yu, X and Jiang, N},
title = {Analysis of the Relationship Between Motor Imagery and Age-Related Fatigue for CNN Classification of the EEG Data.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {909571},
pmid = {35912081},
issn = {1663-4365},
abstract = {BACKGROUND: The aging of the world population poses a major health challenge, and brain-computer interface (BCI) technology has the potential to provide assistance and rehabilitation for the elderly.<br><br>OBJECTIVES: This study aimed to investigate the electroencephalogram (EEG) characteristics during motor imagery by comparing young and elderly, and study Convolutional Neural Networks (CNNs) classification for the elderly population in terms of fatigue analysis in both frontal and parietal regions.<br><br>METHODS: A total of 20 healthy individuals participated in the study, including 10 young and 10 older adults. All participants completed the left- and right-hand motor imagery experiment. The energy changes in the motor imagery process were analyzed using time-frequency graphs and quantified event-related desynchronization (ERD) values. The fatigue level of the motor imagery was assessed by two indicators: (&#x3b8; + &#x3b1;)/&#x3b2; and &#x3b8;/&#x3b2;, and fatigue-sensitive channels were distinguished from the parietal region of the brain. Then, rhythm entropy was introduced to analyze the complexity of the cognitive activity. The phase-lock values related to the parietal and frontal lobes were calculated, and their temporal synchronization was discussed. Finally, the motor imagery EEG data was classified by CNNs, and the accuracy was discussed based on the analysis results.<br><br>RESULT: For the young and elderly, ERD was observed in C3 and C4 channels, and their fatigue-sensitive channels in the parietal region were slightly different. During the experiment, the rhythm entropy of the frontal lobe showed a decreasing trend with time for most of the young subjects, while there was an increasing trend for most of the older ones. Using the CNN classification method, the elderly achieved around 70% of the average classification accuracy, which is almost the same for the young adults.<br><br>CONCLUSION: Compared with the young adults, the elderly are less affected by the level of cognitive fatigue during motor imagery, but the classification accuracy of motor imagery data in the elderly may be slightly lower than that in young persons. At the same time, the deep learning method also provides a potentially feasible option for the application of motor-imagery BCI (MI-BCI) in the elderly by considering the ERD and fatigue phenomenon together.},
}
@article {pmid35911982,
year = {2022},
author = {Ponce, H and Martínez-Villaseñor, L and Chen, Y},
title = {Editorial: Artificial intelligence in brain-computer interfaces and neuroimaging for neuromodulation and neurofeedback.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {974269},
pmid = {35911982},
issn = {1662-4548},
}
@article {pmid35911603,
year = {2022},
author = {Sciaraffa, N and Di Flumeri, G and Germano, D and Giorgi, A and Di Florio, A and Borghini, G and Vozzi, A and Ronca, V and Babiloni, F and Aricò, P},
title = {Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {901387},
pmid = {35911603},
issn = {1662-5161},
abstract = {Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.},
}
@article {pmid35911600,
year = {2022},
author = {Zhang, S and Gao, X and Chen, X},
title = {Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {908050},
pmid = {35911600},
issn = {1662-5161},
abstract = {The application study of robot control based brain-computer interface (BCI) not only helps to promote the practicality of BCI but also helps to promote the advancement of robot technology, which is of great significance. Among the many obstacles, the importability of the stimulator brings much inconvenience to the robot control task. In this study, augmented reality (AR) technology was employed as the visual stimulator of steady-state visual evoked potential (SSVEP)-BCI and the robot walking experiment in the maze was designed to testify the applicability of the AR-BCI system. The online experiment was designed to complete the robot maze walking task and the robot walking commands were sent out by BCI system, in which human intentions were decoded by Filter Bank Canonical Correlation Analysis (FBCCA) algorithm. The results showed that all the 12 subjects could complete the robot walking task in the maze, which verified the feasibility of the AR-SSVEP-NAO system. This study provided an application demonstration for the robot control base on brain-computer interface, and further provided a new method for the future portable BCI system.},
}
@article {pmid35911598,
year = {2022},
author = {Layne, CS and Malaya, CA and Ravindran, AS and John, I and Francisco, GE and Contreras-Vidal, JL},
title = {Distinct Kinematic and Neuromuscular Activation Strategies During Quiet Stance and in Response to Postural Perturbations in Healthy Individuals Fitted With and Without a Lower-Limb Exoskeleton.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {942551},
pmid = {35911598},
issn = {1662-5161},
abstract = {Many individuals with disabling conditions have difficulty with gait and balance control that may result in a fall. Exoskeletons are becoming an increasingly popular technology to aid in walking. Despite being a significant aid in increasing mobility, little attention has been paid to exoskeleton features to mitigate falls. To develop improved exoskeleton stability, quantitative information regarding how a user reacts to postural challenges while wearing the exoskeleton is needed. Assessing the unique responses of individuals to postural perturbations while wearing an exoskeleton provides critical information necessary to effectively accommodate a variety of individual response patterns. This report provides kinematic and neuromuscular data obtained from seven healthy, college-aged individuals during posterior support surface translations with and without wearing a lower limb exoskeleton. A 2-min, static baseline standing trial was also obtained. Outcome measures included a variety of 0 dimensional (OD) measures such as center of pressure (COP) RMS, peak amplitude, velocities, pathlength, and electromyographic (EMG) RMS, and peak amplitudes. These measures were obtained during epochs associated with the response to the perturbations: baseline, response, and recovery. T-tests were used to explore potential statistical differences between the exoskeleton and no exoskeleton conditions. Time series waveforms (1D) of the COP and EMG data were also analyzed. Statistical parametric mapping (SPM) was used to evaluate the 1D COP and EMG waveforms obtained during the epochs with and without wearing the exoskeleton. The results indicated that during quiet stance, COP velocity was increased while wearing the exoskeleton, but the magnitude of sway was unchanged. The OD COP measures revealed that wearing the exoskeleton significantly reduced the sway magnitude and velocity in response to the perturbations. There were no systematic effects of wearing the exoskeleton on EMG. SPM analysis revealed that there was a range of individual responses; both behaviorally (COP) and among neuromuscular activation patterns (EMG). Using both the OD and 1D measures provided a more comprehensive representation of how wearing the exoskeleton impacts the responses to posterior perturbations. This study supports a growing body of evidence that exoskeletons must be personalized to meet the specific capabilities and needs of each individual end-user.},
}
@article {pmid35911593,
year = {2022},
author = {Zhou, Q and Cheng, R and Yao, L and Ye, X and Xu, K},
title = {Corrigendum: Neurofeedback training of alpha relative power improves the performance of motor imagery brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {977387},
doi = {10.3389/fnhum.2022.977387},
pmid = {35911593},
issn = {1662-5161},
abstract = {[This corrects the article DOI: 10.3389/fnhum.2022.831995.].},
}
@article {pmid35911589,
year = {2022},
author = {Le Franc, S and Herrera Altamira, G and Guillen, M and Butet, S and Fleck, S and Lécuyer, A and Bougrain, L and Bonan, I},
title = {Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {917909},
pmid = {35911589},
issn = {1662-5161},
abstract = {Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain-computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient's clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient's context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients' needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.},
}
@article {pmid35909799,
year = {2022},
author = {Wilson, EM and Minja, IK and Machibya, FM and Jonathan, A and Makani, J and Ruggajo, P and Balandya, E},
title = {Oxygen Saturation in Primary Teeth of Individuals With Sickle Cell Disease and Sickle Cell Trait.},
journal = {Journal of blood medicine},
volume = {13},
number = {},
pages = {407-412},
pmid = {35909799},
issn = {1179-2736},
support = {U01 HL156853/HL/NHLBI NIH HHS/United States ; U24 HL135881/HL/NHLBI NIH HHS/United States ; },
abstract = {PURPOSE: To determine oxygen saturation in the pulp of primary teeth in children with sickle cell disease (SCD) and sickle cell trait (SCT) for establishing the usefulness of pulse oximetry in screening and monitoring of SCD or therapy.<br><br>MATERIALS AND METHODS: A cross-sectional study among 30-60 months children with sickle cell disease (SCD) and sickle cell trait (SCT) compared with healthy children (HbAA). A pulse oximeter (BCI 3301) recorded oxygen saturation on six anterior primary maxillary teeth and on index fingers. Data were analyzed using SPSS version 20.0. Mean oxygen saturation for teeth and fingers was calculated. Comparison of Mean across groups was done using post hoc analysis in one-way ANOVA (Bonferroni test). Pearson correlation coefficient was calculated for mean oxygen saturation on fingers and teeth. Level of significance was set at 0.05.<br><br>RESULTS: Altogether 360, 102, and 96 teeth were examined from children with SCD, SCT, and HbAA respectively. 53% of participants were girls. The mean age of participants was 46.3 months &#xb1; 9.4 SD. Low mean oxygen saturation (77.5%) was recorded from teeth of children with SCD relative to those with SCT and HbAA (>86%; P = 0.00). There was no statistically significant difference in oxygen saturation on teeth between children with SCT and HbAA. The mean oxygen saturation on fingers was found to be above 97.2% regardless of sickle cell status. There was no correlation between oxygen saturation on teeth and fingers.<br><br>CONCLUSION: Pulse oximeter detected a lower oxygen saturation in dental pulp of primary teeth of participants with SCD (HbSS) relative to those with SCT (HbAS) and HbAA. Oxygen saturation on fingers remained unaffected regardless of sickle cell disease status. Although more studies are needed, our study shows that when other conditions affecting peripheral tissue oxygen delivery are ruled out, the low pulse oximetry in primary teeth may be indicative of SCD. The oximeter may also be useful in monitoring response to SCD therapy targeted at improving oxygen carrying capacity and delivery.},
}
@article {pmid35909579,
year = {2022},
author = {Mumtaz, W and Amin, HU and Qayyum, A and Subhani, AR},
title = {Editorial: EEG-based assistive robotics for rehabilitation.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {952495},
pmid = {35909579},
issn = {1662-5218},
}
@article {pmid35907491,
year = {2022},
author = {Cooney, C and Folli, R and Coyle, D},
title = {Opportunities, pitfalls and trade-offs in designing protocols for measuring the neural correlates of speech.},
journal = {Neuroscience and biobehavioral reviews},
volume = {140},
number = {},
pages = {104783},
doi = {10.1016/j.neubiorev.2022.104783},
pmid = {35907491},
issn = {1873-7528},
mesh = {Brain ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Linguistics ; *Speech ; },
abstract = {Decoding speech and speech-related processes directly from the human brain has intensified in studies over recent years as such a decoder has the potential to positively impact people with limited communication capacity due to disease or injury. Additionally, it can present entirely new forms of human-computer interaction and human-machine communication in general and facilitate better neuroscientific understanding of speech processes. Here, we synthesize the literature on neural speech decoding pertaining to how speech decoding experiments have been conducted, coalescing around a necessity for thoughtful experimental design aimed at specific research goals, and robust procedures for evaluating speech decoding paradigms. We examine the use of different modalities for presenting stimuli to participants, methods for construction of paradigms including timings and speech rhythms, and possible linguistic considerations. In addition, novel methods for eliciting naturalistic speech and validating imagined speech task performance in experimental settings are presented based on recent research. We also describe the multitude of terms used to instruct participants on how to produce imagined speech during experiments and propose methods for investigating the effect of these terms on imagined speech decoding. We demonstrate that the range of experimental procedures used in neural speech decoding studies can have unintended consequences which can impact upon the efficacy of the knowledge obtained. The review delineates the strengths and weaknesses of present approaches and poses methodological advances which we anticipate will enhance experimental design, and progress toward the optimal design of movement independent direct speech brain-computer interfaces.},
}
@article {pmid35907174,
year = {2022},
author = {Hosni, SMI and Borgheai, SB and McLinden, J and Zhu, S and Huang, X and Ostadabbas, S and Shahriari, Y},
title = {A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.},
journal = {Neuroinformatics},
volume = {20},
number = {4},
pages = {1169-1189},
pmid = {35907174},
issn = {1559-0089},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Imagination/physiology ; Nonlinear Dynamics ; Electroencephalography/methods ; Support Vector Machine ; },
abstract = {Decoding neural responses from multimodal information sources, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has the transformative potential to advance hybrid brain-computer interfaces (hBCIs). However, existing modest performance improvement of hBCIs might be attributed to the lack of computational frameworks that exploit complementary synergistic properties in multimodal features. This study proposes a multimodal data fusion framework to represent and decode synergistic multimodal motor imagery (MI) neural responses. We hypothesize that exploiting EEG nonlinear dynamics adds a new informative dimension to the commonly combined EEG-fNIRS features and will ultimately increase the synergy between EEG and fNIRS features toward an enhanced hBCI. The EEG nonlinear dynamics were quantified by extracting graph-based recurrence quantification analysis (RQA) features to complement the commonly used spectral features for an enhanced multimodal configuration when combined with fNIRS. The high-dimensional multimodal features were further given to a feature selection algorithm relying on the least absolute shrinkage and selection operator (LASSO) for fused feature selection. Linear support vector machine (SVM) was then used to evaluate the framework. The mean hybrid classification performance improved by up to 15% and 4% compared to the unimodal EEG and fNIRS, respectively. The proposed graph-based framework substantially increased the contribution of EEG features for hBCI classification from 28.16% up to 52.9% when introduced the nonlinear dynamics and improved the performance by approximately 2%. These findings suggest that graph-based nonlinear dynamics can increase the synergy between EEG and fNIRS features for an enhanced MI response representation that is not dominated by a single modality.},
}
@article {pmid35905809,
year = {2022},
author = {Engemann, DA and Mellot, A and Höchenberger, R and Banville, H and Sabbagh, D and Gemein, L and Ball, T and Gramfort, A},
title = {A reusable benchmark of brain-age prediction from M/EEG resting-state signals.},
journal = {NeuroImage},
volume = {262},
number = {},
pages = {119521},
doi = {10.1016/j.neuroimage.2022.119521},
pmid = {35905809},
issn = {1095-9572},
mesh = {Algorithms ; *Benchmarking ; Brain ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; },
abstract = {Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R[2] scores between 0.60-0.74. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.},
}
@article {pmid35905727,
year = {2022},
author = {Peterson, SM and Rao, RPN and Brunton, BW},
title = {Learning neural decoders without labels using multiple data streams.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac857c},
pmid = {35905727},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Learning ; Movement/physiology ; Supervised Machine Learning ; Walking ; },
abstract = {Objective.Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding.Approach.We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models.Main results.We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models.Significance.We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.},
}
@article {pmid35901779,
year = {2022},
author = {Sagila, GK and Vinod, AP},
title = {Direction decoding of imagined hand movements using subject-specific features from parietal EEG.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac8501},
pmid = {35901779},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Movement ; },
abstract = {Objective.Research on the decoding of brain signals to control external devices is rapidly emerging due to its versatile potential applications, including neuroprosthetic control and neurorehabilitation. Electroencephalogram (EEG)-based non-invasive brain-computer interface (BCI) systems decode brain signals to establish an augmented communication and control pathway between the brain and the computer. The development of an efficient BCI system requires accurate decoding of neural activity underlying the user's intentions. This study investigates the directional tuning of EEG characteristics from the posterior parietal region, associated with bidirectional hand movement imagination or motor imagery (MI) in left and right directions.Approach. The imagined movement directions of the chosen hand were decoded using a combination of envelope and phase features derived from parietal EEGs of both hemispheres. The proposed algorithm uses wavelets for spectral decomposition, and discriminative subject-specific subband levels are identified based on Fisher analysis of envelope and phase features. The selected features from the discriminative subband levels are used to classify left and right MI directions of the hand using a support vector machine classifier. Furthermore, the performance of the proposed algorithm is evaluated by incorporating a maximum-variance-based EEG time bin selection algorithm.Main results.With the time bin selection approach using subject-specific features, the proposed algorithm yielded an average left vs right MI direction decoding accuracy of 73.33% across 15 healthy subjects. In addition, the decoding accuracy offered by the phase features was higher than that of the envelope features, indicating the importance of phase features in MI kinematics decoding.Significance.The results reveal the significance of the parietal EEG in decoding of imagined kinematics and open new possibilities for future BCI research.},
}
@article {pmid35901114,
year = {2022},
author = {Gyamfi, J and Vieira, D and Iwelunmor, J and Watkins, BX and Williams, O and Peprah, E and Ogedegbe, G and Allegrante, JP},
title = {Assessing descriptions of scalability for hypertension control interventions implemented in low-and middle-income countries: A systematic review.},
journal = {PloS one},
volume = {17},
number = {7},
pages = {e0272071},
pmid = {35901114},
issn = {1932-6203},
mesh = {Cost-Benefit Analysis ; *Developing Countries ; Humans ; *Hypertension/epidemiology/prevention &amp; control ; Income ; },
abstract = {BACKGROUND: The prevalence of hypertension continues to rise in low- and middle-income- countries (LMICs) where scalable, evidence-based interventions (EBIs) that are designed to reduce morbidity and mortality attributed to hypertension have yet to be fully adopted or disseminated. We sought to evaluate evidence from published randomized controlled trials using EBIs for hypertension control implemented in LMICs, and identify the WHO/ExpandNet scale-up components that are relevant for consideration during "scale-up" implementation planning.<br><br>METHODS: Systematic review of RCTs reporting EBIs for hypertension control implemented in LMICs that stated "scale-up" or a variation of scale-up; using the following data sources PubMed/Medline, Web of Science Biosis Citation Index (BCI), CINAHL, EMBASE, Global Health, Google Scholar, PsycINFO; the grey literature and clinicaltrials.gov from inception through June 2021 without any restrictions on publication date. Two reviewers independently assessed studies for inclusion, conducted data extraction using the WHO/ExpandNet Scale-up components as a guide and assessed the risk of bias using the Cochrane risk-of-bias tool. We provide intervention characteristics for each EBI, BP results, and other relevant scale-up descriptions.<br><br>MAIN RESULTS: Thirty-one RCTs were identified and reviewed. Studies reported clinically significant differences in BP, with 23 studies reporting statistically significant mean differences in BP (p < .05) following implementation. Only six studies provided descriptions that captured all of the nine WHO/ExpandNet components. Multi-component interventions, including drug therapy and health education, provided the most benefit to participants. The studies were yet to be scaled and we observed limited reporting on translation of the interventions into existing institutional policy (n = 11), cost-effectiveness analyses (n = 2), and sustainability measurements (n = 3).<br><br>CONCLUSION: This study highlights the limited data on intervention scalability for hypertension control in LMICs and demonstrates the need for better scale-up metrics and processes for this setting.<br><br>TRIAL REGISTRATION: Registration PROSPERO (CRD42019117750).},
}
@article {pmid35898959,
year = {2022},
author = {Perez-Valero, E and Morillas, C and Lopez-Gordo, MA and Carrera-Muñoz, I and López-Alcalde, S and Vílchez-Carrillo, RM},
title = {An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {924547},
pmid = {35898959},
issn = {1662-5196},
abstract = {Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.},
}
@article {pmid35898631,
year = {2022},
author = {Zeng, H and Jin, Y and Wu, Q and Pan, D and Xu, F and Zhao, Y and Hu, H and Kong, W},
title = {EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation.},
journal = {Frontiers in psychiatry},
volume = {13},
number = {},
pages = {928781},
pmid = {35898631},
issn = {1664-0640},
abstract = {Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.},
}
@article {pmid35896097,
year = {2022},
author = {Ai, Q and Zhao, M and Chen, K and Zhao, X and Ma, L and Liu, Q},
title = {Flexible coding scheme for robotic arm control driven by motor imagery decoding.},
journal = {Journal of neural engineering},
volume = {19},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac84a9},
pmid = {35896097},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Imagination/physiology ; Neural Networks, Computer ; *Robotic Surgical Procedures ; },
abstract = {Objective. Brain computer interface (BCI) technology is an innovative way of information exchange, which can effectively convert physiological signals into control instructions of machines. Due to its spontaneity and device independence, the motor imagery (MI) electroencephalography (EEG) signal is used as a common BCI signal source to achieve direct control of external devices. Several online MI EEG-based systems have shown potential for rehabilitation. However, the generalization ability of the current classification model of MI tasks is still limited and the real-time prototype is far from widespread in practice.Approach. To solve these problems, this paper proposes an optimized neural network architecture based on our previous work. Firstly, the artifact components in the MI-EEG signal are removed by using the threshold and threshold function related to the artifact removal evaluation index, and then the data is augmented by the empirical mode decomposition (EMD) algorithm. Furthermore, the ensemble learning (EL) method and fine-tuning strategy in transfer learning (TL) are used to optimize the classification model. Finally, combined with the flexible binary encoding strategy, the EEG signal recognition results are mapped to the control commands of the robotic arm, which realizes multiple degrees of freedom control of the robotic arm.Main results. The results show that EMD has an obvious data amount enhancement effect on a small dataset, and the EL and TL can improve intra-subject and inter-subject model evaluation performance, respectively. The use of a binary coding method realizes the expansion of control instructions, i.e. four kinds of MI-EEG signals are used to complete the control of 7 degrees of freedom of the robotic arm.Significance. Our work not only improves the classification accuracy of the subject and the generality of the classification model while also extending the BCI control instruction set.},
}
@article {pmid35893997,
year = {2022},
author = {Zhang, Y and Zhang, J and Le, S and Niu, L and Tao, J and Liang, J and Zhang, L and Kang, X},
title = {Parylene C as an Insulating Polymer for Implantable Neural Interfaces: Acute Electrochemical Impedance Behaviors in Saline and Pig Brain In Vitro.},
journal = {Polymers},
volume = {14},
number = {15},
pages = {},
pmid = {35893997},
issn = {2073-4360},
support = {61904038//National Natural Science Foundation of China/ ; U1913216//National Natural Science Foundation of China/ ; 2021YFC0122702//National Key R&amp;D Program of China/ ; 2018YFC1705800//National Key R&amp;D Program of China/ ; 19YF1403600//Shanghai Sailing Program/ ; 19441907600//Shanghai Municipal Science and Technology Commission/ ; 19441908200//Shanghai Municipal Science and Technology Commission/ ; 19511132000//Shanghai Municipal Science and Technology Commission/ ; 2021MC0AB01//Opening Project of Zhejiang Lab/ ; FC2019-002//Fudan University-CIOMP Joint Fund/ ; KEH2310024//Opening Project of Shanghai Robot R&amp;D and Transformation Functional Platform/ ; X190021TB190//Ji Hua Laboratory/ ; X190021TB193//Ji Hua Laboratory/ ; 2021SHZDZX0103//Shanghai Municipal Science and Technology Major Project/ ; 2018SHZDZX01//Shanghai Municipal Science and Technology Major Project/ ; },
abstract = {Parylene is used as encapsulating material for medical devices due to its excellent biocompatibility and insulativity. Its performance as the insulating polymer of implantable neural interfaces has been studied in electrolyte solutions and in vivo. Biological tissue in vitro, as a potential environment for characterization and application, is convenient to access in the fabrication lab of polymer and neural electrodes, but there has been little study investigating the behaviors of Parylene in the tissue in vitro. Here, we investigated the electrochemical impedance behaviors of Parylene C polymer coating both in normal saline and in a chilled pig brain in vitro by performing electrochemical impedance spectroscopy (EIS) measurements of platinum (Pt) wire neural electrodes. The electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Statistical analysis of fitted parameters of the equivalent circuit model showed good reliability of Parylene C as an insulating polymer in both electrolyte models. The electrochemical impedance measured in pig brain in vitro shows marked differences from that of saline.},
}
@article {pmid35892452,
year = {2022},
author = {Quiles, V and Ferrero, L and Iáñez, E and Ortiz, M and Azorín, JM},
title = {Decoding of Turning Intention during Walking Based on EEG Biomarkers.},
journal = {Biosensors},
volume = {12},
number = {8},
pages = {},
pmid = {35892452},
issn = {2079-6374},
support = {RTI2018-096677-B-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; },
mesh = {Algorithms ; Biomarkers ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Intention ; Walking ; },
abstract = {In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.},
}
@article {pmid35892435,
year = {2022},
author = {Cha, TH and Hwang, HS},
title = {Rehabilitation Interventions Combined with Noninvasive Brain Stimulation on Upper Limb Motor Function in Stroke Patients.},
journal = {Brain sciences},
volume = {12},
number = {8},
pages = {},
pmid = {35892435},
issn = {2076-3425},
abstract = {(1) Background: This systematic review aimed to focus on the effects of rehabilitation interventions combined with noninvasive brain stimulation on upper limb motor function in stroke patients. (2) Methods: PubMed, MEDLINE, and CINAHL were used for the literature research. Articles were searched using the following terms: "Stroke OR CVA OR cerebrovascular accident" AND "upper limb OR upper extremity" AND "NIBS OR Non-Invasive Brain Stimulation" OR "rTMS" OR "repetitive transcranial magnetic stimulation" OR "tDCS" OR "transcranial direct current stimulation" AND "RCT" OR randomized control trial." In total, 12 studies were included in the final analysis. (3) Results: Analysis using the Physiotherapy Evidence Database scale for qualitative evaluation of the literature rated eight articles as "excellent" and four as "good." Combined rehabilitation interventions included robotic therapy, motor imagery using brain-computer interaction, sensory control, occupational therapy, physiotherapy, task-oriented approach, task-oriented mirror therapy, neuromuscular electrical stimulation, and behavior observation therapy. (4) Conclusions: Although it is difficult to estimate the recovery of upper limb motor function in stroke patients treated with noninvasive brain stimulation alone, a combination of a task-oriented approach, occupational therapy, action observation, wrist robot-assisted rehabilitation, and physical therapy can be effective.},
}
@article {pmid35892418,
year = {2022},
author = {Dai, J and Xi, X and Li, G and Wang, T},
title = {EEG-Based Emotion Classification Using Improved Cross-Connected Convolutional Neural Network.},
journal = {Brain sciences},
volume = {12},
number = {8},
pages = {},
pmid = {35892418},
issn = {2076-3425},
support = {61971169//National Natural Science Foundation of China/ ; U20B2074//National Natural Science Foundation of China/ ; 2021C03031//Zhejiang Provincial Key Research and Development Program of China/ ; 2021ZD0113204//National Key R&amp;D Program of China/ ; },
abstract = {The use of electroencephalography to recognize human emotions is a key technology for advancing human-computer interactions. This study proposes an improved deep convolutional neural network model for emotion classification using a non-end-to-end training method that combines bottom-, middle-, and top-layer convolution features. Four sets of experiments using 4500 samples were conducted to verify model performance. Simultaneously, feature visualization technology was used to extract the three-layer features obtained by the model, and a scatterplot analysis was performed. The proposed model achieved a very high accuracy of 93.7%, and the extracted features exhibited the best separability among the tested models. We found that adding redundant layers did not improve model performance, and removing the data of specific channels did not significantly reduce the classification effect of the model. These results indicate that the proposed model allows for emotion recognition with a higher accuracy and speed than the previously reported models. We believe that our approach can be implemented in various applications that require the quick and accurate identification of human emotions.},
}
@article {pmid35891842,
year = {2022},
author = {Jadavji, Z and Zewdie, E and Kelly, D and Kinney-Lang, E and Robu, I and Kirton, A},
title = {Establishing a Clinical Brain-Computer Interface Program for Children With Severe Neurological Disabilities.},
journal = {Cureus},
volume = {14},
number = {6},
pages = {e26215},
pmid = {35891842},
issn = {2168-8184},
abstract = {BACKGROUND: Children with severe motor impairment but intact cognition are deprived of fundamental human rights. Quadriplegic cerebral palsy is the most common scenario where rehabilitation options remain limited. Brain-computer interfaces (BCI) represent a potential solution, but pediatric populations have been neglected. Direct engagement of children and families could provide meaningful opportunities while informing program development. We describe a patient-centered, clinical, non-invasive pediatric BCI program.<br><br>METHODS: Eligible children were identified within a population-based, tertiary care children's hospital. Criteria included 1) age six to 18 years, 2) severe physical disability (non-ambulatory, minimal hand use), 3) severely limited speech, and 4) evidence of grade 1 cognitive capacity. After initial screening for BCI competency, participants attended regular sessions, attempting commercially available and customized systems to play computer games, control devices, and attempt communication.<br><br>RESULTS: We report the first 10 participants (median 11 years, range 6-16, 60% male). Over 334 hours of participation, there were no serious adverse events. BCI training was well tolerated, with favorable feedback from children and parents. All but one participant demonstrated the ability to perform BCI tasks. The majority performed well, using motor imagery based tasks for games and entertainment. Difficulties were most significant&#xa0;using P300, visual evoked potential based paradigms where maintenance of attention was challenging. Children and families expressed interest in continuing and informing program development.<br><br>CONCLUSIONS: Patient-centered clinical BCI programs are feasible for children with severe disabilities. Carefully selected participants can often learn quickly to perform meaningful tasks on readily available systems. Patient and family motivation and engagement appear high.},
}
@article {pmid35891088,
year = {2022},
author = {Erdoğan, SB and Yükselen, G},
title = {Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {14},
pages = {},
pmid = {35891088},
issn = {1424-8220},
mesh = {Adolescent ; Bayes Theorem ; Biomarkers ; Discriminant Analysis ; Humans ; Reproducibility of Results ; *Spectroscopy, Near-Infrared/methods ; *Support Vector Machine ; },
abstract = {Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.},
}
@article {pmid35891029,
year = {2022},
author = {Pei, D and Olikkal, P and Adali, T and Vinjamuri, R},
title = {Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {14},
pages = {},
pmid = {35891029},
issn = {1424-8220},
support = {HCC-2053498//National Science Foundation/ ; IUCRC-2042203//National Science Foundation/ ; },
mesh = {*Activities of Daily Living ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Hand Strength ; Humans ; Movement ; },
abstract = {Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.},
}
@article {pmid35886624,
year = {2022},
author = {De Miguel-Rubio, A and Muñoz-Pérez, L and Alba-Rueda, A and Arias-Avila, M and Rodrigues-de-Souza, DP},
title = {A Therapeutic Approach Using the Combined Application of Virtual Reality with Robotics for the Treatment of Patients with Spinal Cord Injury: A Systematic Review.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {14},
pages = {},
pmid = {35886624},
issn = {1660-4601},
mesh = {Humans ; *Neurological Rehabilitation ; *Robotics ; *Spinal Cord Injuries/complications ; Upper Extremity ; *Virtual Reality ; },
abstract = {Spinal cord injury (SCI) has been associated with high mortality rates. Thanks to the multidisciplinary vision and approach of SCI, including the application of new technologies in the field of neurorehabilitation, people with SCI can survive and prosper after injury. The main aim of this systematic review was to analyze the effectiveness of the combined use of VR and robotics in the treatment of patients with SCI. The literature search was performed between May and July 2021 in the Cochrane Central Register of Controlled Trials, Physiotherapy Evidence Database (PEDro), PubMed, and Web of Science. The methodological quality of each study was assessed using the SCIRE system and the PEDro scale, whereas the risk of bias was analyzed using the Cochrane Collaboration's tool. A total of six studies, involving 63 participants, were included in this systematic review. Relevant changes were found in the upper limbs, with improvements of shoulder and upper arm mobility, as well as the strengthening of weaker muscles. Combined rehabilitation may be a valuable approach to improve motor function in SCI patients. Nonetheless, further research is necessary, with a larger patient sample and a longer duration.},
}
@article {pmid35884732,
year = {2022},
author = {Gannouni, S and Belwafi, K and Al-Sulmi, MR and Al-Farhood, MD and Al-Obaid, OA and Al-Awadh, AM and Aboalsamh, H and Belghith, A},
title = {A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer.},
journal = {Brain sciences},
volume = {12},
number = {7},
pages = {},
pmid = {35884732},
issn = {2076-3425},
support = {14-INF3139-02//National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology/ ; },
abstract = {There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts.},
}
@article {pmid35884640,
year = {2022},
author = {Hayta, &#xdc; and Irimia, DC and Guger, C and Erkutlu, &#x130; and Güzelbey, &#x130;H},
title = {Optimizing Motor Imagery Parameters for Robotic Arm Control by Brain-Computer Interface.},
journal = {Brain sciences},
volume = {12},
number = {7},
pages = {},
pmid = {35884640},
issn = {2076-3425},
support = {PN-III-P1-1.1-TE-2019-1753//Unitatea Executiva Pentru Finantarea Invatamantului Superior a Cercetarii Dezvoltarii si Inovarii/ ; },
abstract = {Brain-Computer Interface (BCI) technology has been shown to provide new communication possibilities, conveying brain information externally. BCI-based robot control has started to play an important role, especially in medically assistive robots but not only there. For example, a BCI-controlled robotic arm can provide patients diagnosed with neurodegenerative diseases such as Locked-in syndrome (LIS), Amyotrophic lateral sclerosis (ALS), and others with the ability to manipulate different objects. This study presents the optimization of the configuration parameters of a three-class Motor Imagery (MI) -based BCI for controlling a six Degrees of Freedom (DOF) robotic arm in a plane. Electroencephalography (EEG) signals are recorded from 64 positions on the scalp according to the International 10-10 System. In terms of the resulting classification of error rates, we investigated twelve time windows for the spatial filter and classifier calculation and three time windows for the variance smoothing time. The lowest error rates were achieved when using a 3 s time window for creating the spatial filters and classifier, for a variance time window of 1.5 s.},
}
@article {pmid35884626,
year = {2022},
author = {Li, H and Lin, H and Wang, Y and Wang, H and Zhang, M and Gao, H and Ai, Q and Luo, Z and Li, G},
title = {Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language.},
journal = {Brain sciences},
volume = {12},
number = {7},
pages = {},
pmid = {35884626},
issn = {2076-3425},
support = {JCKY2018204B053//the Science Foundation of Chinese Aerospace Industry/ ; No. ICT2021A13//the Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China/ ; },
abstract = {Silent speech decoding (SSD), based on articulatory neuromuscular activities, has become a prevalent task of brain-computer interfaces (BCIs) in recent years. Many works have been devoted to decoding surface electromyography (sEMG) from articulatory neuromuscular activities. However, restoring silent speech in tonal languages such as Mandarin Chinese is still difficult. This paper proposes an optimized sequence-to-sequence (Seq2Seq) approach to synthesize voice from the sEMG-based silent speech. We extract duration information to regulate the sEMG-based silent speech using the audio length. Then, we provide a deep-learning model with an encoder-decoder structure and a state-of-the-art vocoder to generate the audio waveform. Experiments based on six Mandarin Chinese speakers demonstrate that the proposed model can successfully decode silent speech in Mandarin Chinese and achieve a character error rate (CER) of 6.41% on average with human evaluation.},
}
@article {pmid35883345,
year = {2022},
author = {Huang, S and Niu, X and Wang, J and Wang, Z and Xu, H and Shi, L},
title = {Visual Responses to Moving and Flashed Stimuli of Neurons in Domestic Pigeon (Columba livia domestica) Optic Tectum.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {14},
pages = {},
pmid = {35883345},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; 62173309//National Natural Science Foundation of China/ ; 20A413009//Key Scientific Research Projects of Colleges and Universities in Henan province/ ; XKZDQY201905//Key Discipline Construction Project of Zhengzhou University in 2019/ ; },
abstract = {Birds can rapidly and accurately detect moving objects for better survival in complex environments. This visual ability may be attributed to the response properties of neurons in the optic tectum. However, it is unknown how neurons in the optic tectum respond differently to moving objects compared to static ones. To address this question, neuronal activities were recorded from domestic pigeon (Columba livia domestica) optic tectum, responsible for orienting to moving objects, and the responses to moving and flashed stimuli were compared. An encoding model based on the Generalized Linear Model (GLM) framework was established to explain the difference in neuronal responses. The experimental results showed that the first spike latency to moving stimuli was smaller than that to flashed ones and firing rate was higher. The model further implied the faster and stronger response to a moving target result from spatiotemporal integration process, corresponding to the spatially sequential activation of tectal neurons and the accumulation of information in time. This study provides direct electrophysiological evidence about the different tectal neuron responses to moving objects and flashed ones. The findings of this investigation increase our understanding of the motion detection mechanism of tectal neurons.},
}
@article {pmid35883092,
year = {2022},
author = {Liu, K and Yu, Y and Liu, Y and Tang, J and Liang, X and Chu, X and Zhou, Z},
title = {A novel brain-controlled wheelchair combined with computer vision and augmented reality.},
journal = {Biomedical engineering online},
volume = {21},
number = {1},
pages = {50},
pmid = {35883092},
issn = {1475-925X},
support = {62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 62006239//National Natural Science Foundation of China/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; 2018YFB1305101//National Key Research and Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; JCKY2020550B003//Defense industrial Technology Development Program/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; U19A2083//the joint Funds of National Natural Science Foundation of China/ ; },
mesh = {Artificial Intelligence ; *Augmented Reality ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Humans ; *Wheelchairs ; },
abstract = {BACKGROUND: Brain-controlled wheelchairs (BCWs) are important applications of brain-computer interfaces (BCIs). Currently, most BCWs are semiautomatic. When users want to reach a target of interest in their immediate environment, this semiautomatic interaction strategy is slow.<br><br>METHODS: To this end, we combined computer vision (CV) and augmented reality (AR) with a BCW and proposed the CVAR-BCW: a BCW with a novel automatic interaction strategy. The proposed CVAR-BCW uses a translucent head-mounted display (HMD) as the user interface, uses CV to automatically detect environments, and shows the detected targets through AR technology. Once a user has chosen a target, the CVAR-BCW can automatically navigate to it. For a few scenarios, the semiautomatic strategy might be useful. We integrated a semiautomatic interaction framework into the CVAR-BCW. The user can switch between the automatic and semiautomatic strategies.<br><br>RESULTS: We recruited 20 non-disabled subjects for this study and used the accuracy, information transfer rate (ITR), and average time required for the CVAR-BCW to reach each designated target as performance metrics. The experimental results showed that our CVAR-BCW performed well in indoor environments: the average accuracies across all subjects were 83.6% (automatic) and 84.1% (semiautomatic), the average ITRs were 8.2 bits/min (automatic) and 8.3 bits/min (semiautomatic), the average times required to reach a target were 42.4&#xa0;s (automatic) and 93.4&#xa0;s (semiautomatic), and the average workloads and degrees of fatigue for the two strategies were both approximately 20.<br><br>CONCLUSIONS: Our CVAR-BCW provides a user-centric interaction approach and a good framework for integrating more advanced artificial intelligence technologies, which may be useful in the field of disability assistance.},
}
@article {pmid35882224,
year = {2022},
author = {Putri, F and Susnoschi Luca, I and Garcia Pedro, JA and Ding, H and Vučković, A},
title = {Winners and losers in brain computer interface competitive gaming: directional connectivity analysis.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac8451},
pmid = {35882224},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Female ; Humans ; Linear Models ; Male ; Regression Analysis ; *Video Games ; },
abstract = {Objective. To characterize the direction within and between brain connectivity in winning and losing players in a competitive brain-computer interface game.Approach. Ten dyads (26.9 ± 4.7 yr old, eight females and 12 males) participated in the study. In a competitive game based on neurofeedback, they used their relative alpha (RA) band power from the electrode location Pz, to control a virtual seesaw. The players in each pair were separated into winners (W) and losers (L) based on their scores. Intrabrain connectivity was analyzed using multivariate Granger causality (GC) and directed transfer function, while interbrain connectivity was analyzed using bivariate GC.Main results. Linear regression analysis revealed a significant relationship (p< 0.05) between RA and individual scores. During the game, W players maintained a higher RA than L players, although it was not higher than their baseline RA. The analysis of intrabrain GC indicated that both groups engaged in general social interactions, but only the W group succeeded in controlling their brain activity at Pz. Group L applied an inappropriate metal strategy, characterized by strong activity in the left frontal cortex, indicative of collaborative gaming. Interbrain GC showed a larger flow of information from the L to the W group, suggesting a higher capability of the W group to monitor the activity of their opponent.Significance. Both innate neurological indices and gaming mental strategies contribute to game outcomes. Future studies should investigate whether there is a causal relationship between these two factors.},
}
@article {pmid35881017,
year = {2022},
author = {Sánchez-Reolid, R and Martínez-Sáez, MC and García-Martínez, B and Fernández-Aguilar, L and Ros, L and Latorre, JM and Fernández-Caballero, A},
title = {Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment.},
journal = {International journal of neural systems},
volume = {32},
number = {10},
pages = {2250041},
doi = {10.1142/S0129065722500411},
pmid = {35881017},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Humans ; Neural Networks, Computer ; },
abstract = {The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain-computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F[Formula: see text]-score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions.},
}
@article {pmid35881016,
year = {2022},
author = {Xu, F and Dong, G and Li, J and Yang, Q and Wang, L and Zhao, Y and Yan, Y and Zhao, J and Pang, S and Guo, D and Zhang, Y and Leng, J},
title = {Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.},
journal = {International journal of neural systems},
volume = {32},
number = {9},
pages = {2250039},
doi = {10.1142/S0129065722500393},
pmid = {35881016},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Deep Learning ; Electroencephalography/methods ; Humans ; Imagination ; Stroke/complications/*physiopathology ; *Stroke Rehabilitation/instrumentation/methods ; },
abstract = {The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.},
}
@article {pmid35877796,
year = {2022},
author = {Zou, J and Zhang, Q},
title = {eyeSay: Brain Visual Dynamics Decoding With Deep Learning &amp; Edge Computing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2217-2224},
doi = {10.1109/TNSRE.2022.3193714},
pmid = {35877796},
issn = {1558-0210},
mesh = {*Amyotrophic Lateral Sclerosis ; Brain ; *Brain-Computer Interfaces ; *Deep Learning ; Electrooculography/methods ; Humans ; },
abstract = {Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so- called 'lock-in' patients to 'speak' with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients' intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eye-word recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations.},
}
@article {pmid35877374,
year = {2022},
author = {Altuwaijri, GA and Muhammad, G},
title = {Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {9},
number = {7},
pages = {},
pmid = {35877374},
issn = {2306-5354},
support = {RSP-2021/34//Researchers Supporting Project number (RSP-2021/34), King Saud University, Riyadh, Saudi Arabia/ ; },
abstract = {Brain signals can be captured via electroencephalogram (EEG) and be used in various brain-computer interface (BCI) applications. Classifying motor imagery (MI) using EEG signals is one of the important applications that can help a stroke patient to rehabilitate or perform certain tasks. Dealing with EEG-MI signals is challenging because the signals are weak, may contain artefacts, are dependent on the patient's mood and posture, and have low signal-to-noise ratio. This paper proposes a multi-branch convolutional neural network model called the Multi-Branch EEGNet with Convolutional Block Attention Module (MBEEGCBAM) using attention mechanism and fusion techniques to classify EEG-MI signals. The attention mechanism is applied both channel-wise and spatial-wise. The proposed model is a lightweight model that has fewer parameters and higher accuracy compared to other state-of-the-art models. The accuracy of the proposed model is 82.85% and 95.45% using the BCI-IV2a motor imagery dataset and the high gamma dataset, respectively. Additionally, when using the fusion approach (FMBEEGCBAM), it achieves 83.68% and 95.74% accuracy, respectively.},
}
@article {pmid35877267,
year = {2022},
author = {Pauls, M and Chia, S},
title = {Clinical Utility of Genomic Assay in Node-Positive Early-Stage Breast Cancer.},
journal = {Current oncology (Toronto, Ont.)},
volume = {29},
number = {7},
pages = {5139-5149},
pmid = {35877267},
issn = {1718-7729},
mesh = {*Breast Neoplasms/pathology ; Chemotherapy, Adjuvant ; Female ; Gene Expression Profiling ; Genomics ; Humans ; Neoplasm Recurrence, Local/drug therapy ; },
abstract = {Breast cancer (BC) is the most common malignancy among women in Canada. Adjuvant treatment in early BC can reduce the risk of BC recurrence. Historically, the decision for adjuvant chemotherapy for early BC was made only based on clinical and tumour characteristics. In recent years, there has been an effort toward developing genomic assays as a predictive and prognostic tool to improve precision in estimating disease recurrence, sensitivity to systemic treatment and ultimately with clinical utility for guidance regarding adjuvant systemic treatment(s). There are various commercial genomic tests available for early-stage ER+/HER-2 negative BC. This paper will review the Oncotype DX 21-gene Recurrence Score (RS), MammaPrint, EndoPredict, Prosigna[®], and Breast Cancer Index (BCI) genomic assays. We will also focus on these genomic assays' clinical application and utility in node-positive early-stage BC based on the most recent evidence and guidance recommendations.},
}
@article {pmid35874933,
year = {2022},
author = {Eastmond, C and Subedi, A and De, S and Intes, X},
title = {Deep learning in fNIRS: a review.},
journal = {Neurophotonics},
volume = {9},
number = {4},
pages = {041411},
pmid = {35874933},
issn = {2329-423X},
support = {R01 EB010037/EB/NIBIB NIH HHS/United States ; R01 EB005807/EB/NIBIB NIH HHS/United States ; R01 EB019443/EB/NIBIB NIH HHS/United States ; R01 EB014305/EB/NIBIB NIH HHS/United States ; R01 EB009362/EB/NIBIB NIH HHS/United States ; },
abstract = {Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional near-infrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields. Aim: We aim to review the emerging DL applications in fNIRS studies. Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery. Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.},
}
@article {pmid35874164,
year = {2022},
author = {Heskebeck, F and Bergeling, C and Bernhardsson, B},
title = {Multi-Armed Bandits in Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {931085},
pmid = {35874164},
issn = {1662-5161},
abstract = {The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.},
}
@article {pmid35874158,
year = {2022},
author = {Remsik, AB and van Kan, PLE and Gloe, S and Gjini, K and Williams, L and Nair, V and Caldera, K and Williams, JC and Prabhakaran, V},
title = {BCI-FES With Multimodal Feedback for Motor Recovery Poststroke.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {725715},
pmid = {35874158},
issn = {1662-5161},
support = {R01 NS105646/NS/NINDS NIH HHS/United States ; R01 NS117568/NS/NINDS NIH HHS/United States ; },
abstract = {An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.},
}
@article {pmid35873764,
year = {2022},
author = {Bigoni, C and Zandvliet, SB and Beanato, E and Crema, A and Coscia, M and Espinosa, A and Henneken, T and Hervé, J and Oflar, M and Evangelista, GG and Morishita, T and Wessel, MJ and Bonvin, C and Turlan, JL and Birbaumer, N and Hummel, FC},
title = {A Novel Patient-Tailored, Cumulative Neurotechnology-Based Therapy for Upper-Limb Rehabilitation in Severely Impaired Chronic Stroke Patients: The AVANCER Study Protocol.},
journal = {Frontiers in neurology},
volume = {13},
number = {},
pages = {919511},
pmid = {35873764},
issn = {1664-2295},
abstract = {Effective, patient-tailored rehabilitation to restore upper-limb motor function in severely impaired stroke patients is still missing. If suitably combined and administered in a personalized fashion, neurotechnologies offer a large potential to assist rehabilitative therapies to enhance individual treatment effects. AVANCER (clinicaltrials.gov NCT04448483) is a two-center proof-of-concept trial with an individual based cumulative longitudinal intervention design aiming at reducing upper-limb motor impairment in severely affected stroke patients with the help of multiple neurotechnologies. AVANCER will determine feasibility, safety, and effectivity of this innovative intervention. Thirty chronic stroke patients with a Fugl-Meyer assessment of the upper limb (FM-UE) <20 will be recruited at two centers. All patients will undergo the cumulative personalized intervention within two phases: the first uses an EEG-based brain-computer interface to trigger a variety of patient-tailored movements supported by multi-channel functional electrical stimulation in combination with a hand exoskeleton. This phase will be continued until patients do not improve anymore according to a quantitative threshold based on the FM-UE. The second interventional phase will add non-invasive brain stimulation by means of anodal transcranial direct current stimulation to the motor cortex to the initial approach. Each phase will last for a minimum of 11 sessions. Clinical and multimodal assessments are longitudinally acquired, before the first interventional phase, at the switch to the second interventional phase and at the end of the second interventional phase. The primary outcome measure is the 66-point FM-UE, a significant improvement of at least four points is hypothesized and considered clinically relevant. Several clinical and system neuroscience secondary outcome measures are additionally evaluated. AVANCER aims to provide evidence for a safe, effective, personalized, adjuvant treatment for patients with severe upper-extremity impairment for whom to date there is no efficient treatment available.},
}
@article {pmid35869451,
year = {2022},
author = {Esfandiari, H and Troxler, P and Hodel, S and Suter, D and Farshad, M and , and Fürnstahl, P},
title = {Introducing a brain-computer interface to facilitate intraoperative medical imaging control - a feasibility study.},
journal = {BMC musculoskeletal disorders},
volume = {23},
number = {1},
pages = {701},
pmid = {35869451},
issn = {1471-2474},
mesh = {*Brain-Computer Interfaces ; Feasibility Studies ; Humans ; Software ; Tomography, X-Ray Computed ; User-Computer Interface ; },
abstract = {BACKGROUND: Safe and accurate execution of surgeries to date mainly rely on preoperative plans generated based on preoperative imaging. Frequent intraoperative interaction with such patient images during the intervention is needed, which is currently a cumbersome process given that such images are generally displayed on peripheral two-dimensional (2D) monitors and controlled through interface devices that are outside the sterile filed. This study proposes a new medical image control concept based on a Brain Computer Interface (BCI) that allows for hands-free and direct image manipulation without relying on gesture recognition methods or voice commands.<br><br>METHOD: A software environment was designed for displaying three-dimensional (3D) patient images onto external monitors, with the functionality of hands-free image manipulation based on the user's brain signals detected by the BCI device (i.e., visually evoked signals). In a user study, ten orthopedic surgeons completed a series of standardized image manipulation tasks to navigate and locate predefined 3D points in a Computer Tomography (CT) image using the developed interface. Accuracy was assessed as the mean error between the predefined locations (ground truth) and the navigated locations by the surgeons. All surgeons rated the performance and potential intraoperative usability in a standardized survey using a five-point Likert scale (1&#x2009;=&#x2009;strongly disagree to 5&#x2009;=&#x2009;strongly agree).<br><br>RESULTS: When using the developed interface, the mean image control error was 15.51&#xa0;mm (SD: 9.57). The user's acceptance was rated with a Likert score of 4.07 (SD: 0.96) while the overall impressions of the interface was rated as 3.77 (SD: 1.02) by the users. We observed a significant correlation between the users' overall impression and the calibration score they achieved.<br><br>CONCLUSIONS: The use of the developed BCI, that allowed for a purely brain-guided medical image control, yielded promising results, and showed its potential for future intraoperative applications. The major limitation to overcome was noted as the interaction delay.},
}
@article {pmid35869138,
year = {2022},
author = {Verwoert, M and Ottenhoff, MC and Goulis, S and Colon, AJ and Wagner, L and Tousseyn, S and van Dijk, JP and Kubben, PL and Herff, C},
title = {Dataset of Speech Production in intracranial.Electroencephalography.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {434},
pmid = {35869138},
issn = {2052-4463},
mesh = {Electrocorticography ; Electroencephalography ; Humans ; Reading ; *Speech/physiology ; },
abstract = {Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.},
}
@article {pmid35867703,
year = {2022},
author = {Tibrewal, N and Leeuwis, N and Alimardani, M},
title = {Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.},
journal = {PloS one},
volume = {17},
number = {7},
pages = {e0268880},
pmid = {35867703},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers' performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.},
}
@article {pmid35867371,
year = {2023},
author = {Liu, K and Yang, M and Yu, Z and Wang, G and Wu, W},
title = {FBMSNet: A Filter-Bank Multi-Scale Convolutional Neural Network for EEG-Based Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {2},
pages = {436-445},
doi = {10.1109/TBME.2022.3193277},
pmid = {35867371},
issn = {1558-2531},
mesh = {*Imagination ; Neural Networks, Computer ; Machine Learning ; Brain ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {OBJECT: Motor imagery (MI) is a mental process widely utilized as the experimental paradigm for brain-computer interfaces (BCIs) across a broad range of basic science and clinical studies. However, decoding intentions from MI remains challenging due to the inherent complexity of brain patterns relative to the small sample size available for machine learning.<br><br>APPROACH: This paper proposes an end-to-end Filter-Bank Multiscale Convolutional Neural Network (FBMSNet) for MI classification. A filter bank is first employed to derive a multiview spectral representation of the EEG data. Mixed depthwise convolution is then applied to extract temporal features at multiple scales, followed by spatial filtering to mitigate volume conduction. Finally, with the joint supervision of cross-entropy and center loss, FBMSNet obtains features that maximize interclass dispersion and intraclass compactness.<br><br>MAIN RESULTS: We compare FBMSNet with several state-of-the-art EEG decoding methods on two MI datasets: the BCI Competition IV 2a dataset and the OpenBMI dataset. FBMSNet significantly outperforms the benchmark methods by achieving 79.17% and 70.05% for four-class and two-class hold-out classification accuracy, respectively.<br><br>SIGNIFICANCE: These results demonstrate the efficacy of FBMSNet in improving EEG decoding performance toward more robust BCI applications. The FBMSNet source code is available at https://github.com/Want2Vanish/FBMSNet.},
}
@article {pmid35866239,
year = {2022},
author = {Zubair, H and Shamas, S and Ullah, H and Nabi, G and Huma, T and Ullah, R and Hussain, R and Shahab, M},
title = {Morphometric and Myelin Basic Protein Expression Changes in Arcuate Nucleus Kisspeptin Neurons Underlie Activation of Hypothalamic Pituitary Gonadal-axis in Monkeys (Macaca Mulatta) during the Breeding Season.},
journal = {Endocrine research},
volume = {47},
number = {3-4},
pages = {113-123},
doi = {10.1080/07435800.2022.2102649},
pmid = {35866239},
issn = {1532-4206},
mesh = {Animals ; *Arcuate Nucleus of Hypothalamus/metabolism ; Gonadotropin-Releasing Hormone/metabolism ; *Kisspeptins/metabolism ; Macaca mulatta/metabolism ; Male ; Myelin Basic Protein/metabolism ; Neurons/metabolism ; Seasons ; Sheep ; Testosterone ; },
abstract = {INTRODUCTION: Kisspeptin is involved in the hypothalamic pituitary gonadal-axis' seasonal regulation in rodents and sheep. Studies of kisspeptin signaling in regulating the transition between breeding and nonbreeding seasons have focused on kisspeptin expression, myelin basic protein (MBP) expression around kisspeptin-ir cells, and quantifying the synaptic connections between kisspeptin and gonadotropin-releasing hormone (GnRH) neurons in various animal models; however, the role of kisspeptin in regulating the seasonal breeding of primates has not been explored yet.<br><br>OBJECTIVE: This study investigated changes in kisspeptin signaling during breeding and a non-breeding season in a non-human primate model, the rhesus monkey.<br><br>METHODS: Three adult male monkeys (n&#xa0;=&#xa0;3) from the breeding season and two monkeys (n&#xa0;=&#xa0;2) from the non-breeding season were used in this study. After measuring the testicular volume and collecting a single blood sample, all animals were humanely euthanized under controlled conditions, and their hypothalami were collected and processed. Two 20&#xa0;&#xb5;m thick hypothalamic sections (mediobasal hypothalamus) from each animal were processed for kisspeptin-MBP and kisspeptin-GnRH immunohistochemistry (IHC). One section from each animal was used as a primary antibody omitted control to check the nonspecific binding in each IHC.<br><br>RESULTS: Compared to the non-breeding season, plasma testosterone levels and testicular volumes were significantly higher in monkeys during the breeding season. Furthermore, compared to the non-breeding season, increased kisspeptin expression and a higher number of synaptic contacts between kisspeptin fibers and GnRH cell bodies were observed in the arcuate nucleus of the breeding season monkeys. In contrast, enlarged kisspeptin soma and higher MBP expression were observed in non-breeding monkeys.<br><br>CONCLUSION: Our results indicated enhanced kisspeptin signaling in primate hypothalamus during the breeding season. These findings support the idea that kisspeptin acts as a mediator for the seasonal regulation of the reproductive axis in higher primates.},
}
@article {pmid35863346,
year = {2022},
author = {Bi, Q and Wang, C and Cheng, G and Chen, N and Wei, B and Liu, X and Li, L and Lu, C and He, J and Weng, Y and Yin, C and Lin, Y and Wan, S and Zhao, L and Xu, J and Wang, Y and Gu, Y and Shen, XZ and Shi, P},
title = {Microglia-derived PDGFB promotes neuronal potassium currents to suppress basal sympathetic tonicity and limit hypertension.},
journal = {Immunity},
volume = {55},
number = {8},
pages = {1466-1482.e9},
doi = {10.1016/j.immuni.2022.06.018},
pmid = {35863346},
issn = {1097-4180},
mesh = {Animals ; *Hypertension/metabolism ; Mice ; *Microglia ; Neurons/physiology ; Potassium/metabolism ; Proto-Oncogene Proteins c-sis/metabolism ; Receptor, Platelet-Derived Growth Factor alpha/metabolism ; },
abstract = {Although many studies have addressed the regulatory circuits affecting neuronal activities, local non-synaptic mechanisms that determine neuronal excitability remain unclear. Here, we found that microglia prevented overactivation of pre-sympathetic neurons in the hypothalamic paraventricular nucleus (PVN) at steady state. Microglia constitutively released platelet-derived growth factor (PDGF) B, which signaled via PDGFRα on neuronal cells and promoted their expression of Kv4.3, a key subunit that conducts potassium currents. Ablation of microglia, conditional deletion of microglial PDGFB, or suppression of neuronal PDGFRα expression in the PVN elevated the excitability of pre-sympathetic neurons and sympathetic outflow, resulting in a profound autonomic dysfunction. Disruption of the PDGFB[MG]-Kv4.3[Neuron] pathway predisposed mice to develop hypertension, whereas central supplementation of exogenous PDGFB suppressed pressor response when mice were under hypertensive insult. Our results point to a non-immune action of resident microglia in maintaining the balance of sympathetic outflow, which is important in preventing cardiovascular diseases.},
}
@article {pmid35863124,
year = {2022},
author = {Kaongoen, N and Choi, J and Jo, S},
title = {A novel online BCI system using speech imagery and ear-EEG for home appliances control.},
journal = {Computer methods and programs in biomedicine},
volume = {224},
number = {},
pages = {107022},
doi = {10.1016/j.cmpb.2022.107022},
pmid = {35863124},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Online Systems ; Probability ; Speech ; },
abstract = {BACKGROUND AND OBJECTIVE: This paper investigates a novel way to interact with home appliances via a brain-computer interface (BCI), using electroencephalograph (EEG) signals acquired from around the user's ears with a custom-made wearable BCI headphone.<br><br>METHODS: The users engage in speech imagery (SI), a type of mental task where they imagine speaking out a specific word without producing any sound, to control an interactive simulated home appliance. In this work, multiple models are employed to improve the performance of the system. Temporally-stacked multi-band covariance matrix (TSMBC) method is used to represent the neural activities during SI tasks with spatial, temporal, and spectral information included. To further increase the usability of our proposed system in daily life, a calibration session, where the pre-trained models are fine-tuned, is added to maintain performance over time with minimal training. Eleven participants were recruited to evaluate our method over three different sessions: a training session, a calibration session, and an online session where users were given the freedom to achieve a given goal on their own.<br><br>RESULTS: In the offline experiment, all participants were able to achieve a classification accuracy significantly higher than the chance level. In the online experiments, a few participants were able to use the proposed system to freely control the home appliance with high accuracy and relatively fast command delivery speed. The best participant achieved an average true positive rate and command delivery time of 0.85 and 3.79&#x202f;s/command, respectively.<br><br>CONCLUSION: Based on the positive experimental results and user surveys, the novel ear-EEG-SI-based BCI paradigm is a promising approach for the wearable BCI system for daily life.},
}
@article {pmid35860640,
year = {2022},
author = {Zafar, R and Javvad Ur Rehman, M and Alam, S and Arslan Khan, M and Hussain, A and Ahmad, RF and Reza, F and Jahan, R},
title = {A Cumulants-Based Human Brain Decoding.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {6474515},
pmid = {35860640},
issn = {1687-5273},
mesh = {*Brain ; *Brain-Computer Interfaces ; Cognition ; Humans ; Likelihood Functions ; Magnetic Resonance Imaging/methods ; },
abstract = {Human cognition is influenced by the way the nervous system processes information and is linked to this mechanical explanation of the human body's cognitive function. Accuracy is the key emphasis in neuroscience which may be enhanced by utilising new hardware, mathematical, statistical, and computational methodologies. Feature extraction and feature selection also play a crucial function in gaining improved accuracy since the proper characteristics can identify brain states efficiently. However, both feature extraction and selection procedures are dependent on mathematical and statistical techniques which implies that mathematical and statistical techniques have a direct or indirect influence on prediction accuracy. The forthcoming challenges of the brain-computer interface necessitate a thorough critical understanding of the complicated structure and uncertain behavior of the brain. It is impossible to upgrade hardware periodically, and thus, an option is necessary to collect maximum information from the brain against varied actions. The mathematical and statistical combination could be the ideal answer for neuroscientists which can be utilised for feature extraction, feature selection, and classification. That is why in this research a statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy. A score fusion function is changed utilising an enhanced cumulants-driven likelihood ratio test employing multivariate pattern analysis. Functional MRI data were acquired from 12 patients versus a visual test that comprises of pictures from five distinct categories. After cleaning the data, feature extraction and selection were done using mathematical approaches, and lastly, the best match of the projected class was established using the likelihood ratio test. To validate the suggested approach, it is compared with the current methods reported in recent research.},
}
@article {pmid35858343,
year = {2022},
author = {Ma, L and Yang, F and Wu, X and Mao, C and Guo, L and Miao, T and Zang, SK and Jiang, X and Shen, DD and Wei, T and Zhou, H and Wei, Q and Li, S and Shu, Q and Feng, S and Jiang, C and Chu, B and Du, L and Sun, JP and Yu, X and Zhang, Y and Zhang, P},
title = {Structural basis and molecular mechanism of biased GPBAR signaling in regulating NSCLC cell growth via YAP activity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {119},
number = {29},
pages = {e2117054119},
pmid = {35858343},
issn = {1091-6490},
mesh = {Bile Acids and Salts/metabolism ; *Carcinoma, Non-Small-Cell Lung/metabolism/pathology ; *Cell Cycle Proteins/metabolism ; Cholic Acids/pharmacology ; Cryoelectron Microscopy ; Humans ; *Lung Neoplasms/metabolism/pathology ; *Receptors, G-Protein-Coupled/agonists/chemistry/metabolism ; *Transcription Factors/metabolism ; beta-Arrestin 1/metabolism ; },
abstract = {The G protein-coupled bile acid receptor (GPBAR) is the membrane receptor for bile acids and a driving force of the liver-bile acid-microbiota-organ axis to regulate metabolism and other pathophysiological processes. Although GPBAR is an important therapeutic target for a spectrum of metabolic and neurodegenerative diseases, its activation has also been found to be linked to carcinogenesis, leading to potential side effects. Here, via functional screening, we found that two specific GPBAR agonists, R399 and INT-777, demonstrated strikingly different regulatory effects on the growth and apoptosis of non-small cell lung cancer (NSCLC) cells both in vitro and in vivo. Further mechanistic investigation showed that R399-induced GPBAR activation displayed an obvious bias for β-arrestin 1 signaling, thus promoting YAP signaling activation to stimulate cell proliferation. Conversely, INT-777 preferentially activated GPBAR-Gs signaling, thus inactivating YAP to inhibit cell proliferation and induce apoptosis. Phosphorylation of GPBAR by GRK2 at S310/S321/S323/S324 sites contributed to R399-induced GPBAR-β-arrestin 1 association. The cryoelectron microscopy (cryo-EM) structure of the R399-bound GPBAR-Gs complex enabled us to identify key interaction residues and pivotal conformational changes in GPBAR responsible for the arrestin signaling bias and cancer cell proliferation. In summary, we demonstrate that different agonists can regulate distinct functions of cell growth and apoptosis through biased GPBAR signaling and control of YAP activity in a NSCLC cell model. The delineated mechanism and structural basis may facilitate the rational design of GPBAR-targeting drugs with both metabolic and anticancer benefits.},
}
@article {pmid35858095,
year = {2022},
author = {Jiang, Y and Sheng, F and Belkaya, N and Platt, ML},
title = {Oxytocin and testosterone administration amplify viewing preferences for sexual images in male rhesus macaques.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {377},
number = {1858},
pages = {20210133},
pmid = {35858095},
issn = {1471-2970},
support = {R01 MH095894/MH/NIMH NIH HHS/United States ; R01 MH108627/MH/NIMH NIH HHS/United States ; R37 MH109728/MH/NIMH NIH HHS/United States ; R21 AG073958/AG/NIA NIH HHS/United States ; R01 MH118203/MH/NIMH NIH HHS/United States ; R56 MH122819/MH/NIMH NIH HHS/United States ; R01 NS123054/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Eye ; Female ; Macaca mulatta/physiology ; Male ; *Oxytocin/pharmacology ; Social Behavior ; *Testosterone ; },
abstract = {Social stimuli, like faces, and sexual stimuli, like genitalia, spontaneously attract visual attention in both human and non-human primates. Social orienting behaviour is thought to be modulated by neuropeptides as well as sex hormones. Using a free viewing task in which paired images of monkey faces and anogenital regions were presented simultaneously, we found that male rhesus macaques overwhelmingly preferred to view images of anogenital regions over faces. They were more likely to make an initial gaze shift towards, and spent more time viewing, anogenital regions compared with faces, and this preference was accompanied by relatively constricted pupils. On face images, monkeys mostly fixated on the forehead and eyes. These viewing preferences were found for images of both males and females. Both oxytocin (OT), a neuropeptide linked to social bonding and affiliation, and testosterone (TE), a sex hormone implicated in mating and aggression, amplified the pre-existing orienting bias for female genitalia over female faces; neither treatment altered the viewing preference for male anogenital regions over male faces. Testosterone but not OT increased the probability of monkeys making the first gaze shift towards female anogenital rather than face pictures, with the strongest effects on anogenital images of young and unfamiliar females. Finally, both OT and TE promoted viewing of the forehead region of both female and male faces, which display sexual skins, but decreased the relative salience of the eyes of older males. Together, these results invite the hypothesis that both OT and TE regulate reproductive behaviours by acting as a gain control on the visual orienting network to increase attention to mating-relevant signals in the environment. This article is part of the theme issue 'Interplays between oxytocin and other neuromodulators in shaping complex social behaviours'.},
}
@article {pmid35857723,
year = {2022},
author = {Sun, Y and Shen, A and Sun, J and Du, C and Chen, X and Wang, Y and Pei, W and Gao, X},
title = {Minimally Invasive Local-Skull Electrophysiological Modification With Piezoelectric Drill.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2042-2051},
doi = {10.1109/TNSRE.2022.3192543},
pmid = {35857723},
issn = {1558-0210},
mesh = {Animals ; Electrocorticography ; *Electroencephalography/methods ; Electrophysiological Phenomena ; Rats ; Rats, Sprague-Dawley ; *Skull ; },
abstract = {The research on non-invasive BCI is nowadays hitting the bottleneck due to the humble quality of scalp EEG signals. Whereas invasive solutions that offer higher signal quality in contrast are suffocated in their spreading because of the potential surgical complication and health risks caused by electrode implantation. Therefore, it puts forward a necessity to explore a scheme that could both collect high-quality EEG signals and guarantee high-level operation safety.This study proposed a Minimally Invasive Local-skull Electrophysiological Modification method to improve scalp EEG signals qualities at specific brain regions. Six eight-month-old SD rats were used for in vivo verification experiment. A hole with a diameter of about 500 micrometers was drilled in the skull above the visual cortex of rats. Significant changes in rsEEG and SSVEP signals before and after modification were observed. After modification, the skull impedance of rats decreases by about 84 %, the average maximum bandwidth of rsEEG increase by 57 %, and the broadband SNR of SSVEP is increased by 5.13 dB. The time of piezoelectric drilling operation is strictly controlled under 30 seconds for each rat to prevent possible brain damage from overheating. Compared with traditional invasive procedures such as ECoG, Minimally Invasive Local-skull Electrophysiological Modification operation time is shorter and no electrode implantation is needed while it remarkably boosts the scalp EEG signal quality. This technical solution has the potential to replace the use of ECoG in certain application scenarios and further invigorate studies in the field of scalp EEG in the future.},
}
@article {pmid35857453,
year = {2022},
author = {Cui, Y and Huang, X and Huang, P and Huang, L and Feng, Z and Xiang, X and Chen, X and Li, A and Ren, C and Li, H},
title = {Reward ameliorates depressive-like behaviors via inhibition of the substantia innominata to the lateral habenula projection.},
journal = {Science advances},
volume = {8},
number = {27},
pages = {eabn0193},
pmid = {35857453},
issn = {2375-2548},
abstract = {The lateral habenula (LHb) is implicated in emotional processing, especially depression. Recent studies indicate that the basal forebrain (BF) transmits reward or aversive signals to the LHb. However, the contribution of the BF-LHb circuit to the pathophysiology of depression still needs to be determined. Here, we find that the excitatory projection to the LHb from the substantia innominata (SI), a BF subregion, is activated by aversive stimuli and inhibited by reward stimuli. Furthermore, chronic activation of the SI-LHb circuit is sufficient to induce depressive-like behaviors, whereas inhibition of the circuit alleviates chronic stress-induced depressive-like phenotype. We also find that reward consumption buffers depressive-like behaviors induced by chronic activation of the SI-LHb circuit. In summary, we systematically define the function and mechanism of the SI-LHb circuit in modulating depressive-like behaviors, thus providing important insights to better decipher LHb processing in the pathophysiology of depression.},
}
@article {pmid35853776,
year = {2022},
author = {Bex, A and Bex, V and Carpentier, A and Mathon, B},
title = {Therapeutic ultrasound: The future of epilepsy surgery?.},
journal = {Revue neurologique},
volume = {178},
number = {10},
pages = {1055-1065},
doi = {10.1016/j.neurol.2022.03.015},
pmid = {35853776},
issn = {0035-3787},
mesh = {Adult ; Child ; Humans ; *Drug Resistant Epilepsy ; *Radiosurgery ; Quality of Life ; *Epilepsy/surgery ; *Ultrasonic Therapy ; },
abstract = {Epilepsy is one of the leading neurological diseases in both adults and children and in spite of advancement in medical treatment, 20 to 30% of patients remain refractory to current medical treatment. Medically intractable epilepsy has a real impact on a patient's quality of life, neurologic morbidity and even mortality. Actual therapy options are an increase in drug dosage, radiosurgery, resective surgery and non-resective neuromodulatory treatments (deep brain stimulation, vagus nerve stimulation). Resective, thermoablative or neuromodulatory surgery in the treatment of epilepsy are invasive procedures, sometimes requiring long stay-in for the patients, risks of permanent neurological deficit, general anesthesia and other potential surgery-related complications such as a hemorrhage or an infection. Radiosurgical approaches can trigger radiation necrosis, brain oedema and transient worsening of epilepsy. With technology-driven developments and pursuit of minimally invasive neurosurgery, transcranial MR-guided focused ultrasound has become a valuable treatment for neurological diseases. In this critical review, we aim to give the reader a better understanding of current advancement for ultrasound in the treatment of epilepsy. By outlining the current understanding gained from both preclinical and clinical studies, this article explores the different mechanisms and potential applications (thermoablation, blood brain barrier disruption for drug delivery, neuromodulation and cortical stimulation) of high and low intensity ultrasound and compares the various possibilities available to patients with intractable epilepsy. Technical limitations of therapeutic ultrasound for epilepsy surgery are also detailed and discussed.},
}
@article {pmid35853669,
year = {2022},
author = {Mane, R and Wu, Z and Wang, D},
title = {Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: a perspective review.},
journal = {Stroke and vascular neurology},
volume = {7},
number = {6},
pages = {541-549},
pmid = {35853669},
issn = {2059-8696},
abstract = {Brain-computer interface (BCI) technology translates brain activity into meaningful commands to establish a direct connection between the brain and the external world. Neuroscientific research in the past two decades has indicated a tremendous potential of BCI systems for the rehabilitation of patients suffering from poststroke impairments. By promoting the neuronal recovery of the damaged brain networks, BCI systems have achieved promising results for the recovery of poststroke motor, cognitive, and language impairments. Also, several assistive BCI systems that provide alternative means of communication and control to severely paralysed patients have been proposed to enhance patients' quality of life. In this article, we present a perspective review of the recent advances and challenges in the BCI systems used in the poststroke rehabilitation of motor, cognitive, and communication impairments.},
}
@article {pmid35853509,
year = {2022},
author = {Gharib, T and Eldakhakhny, A and Alazaby, H and Khalil, M and Elgamal, K and Alhefnawy, M},
title = {Evaluation of Storage Symptoms Improvement and Factors Affecting, After Relief of Obstruction in Patients With Benign Prostatic Enlargement.},
journal = {Urology},
volume = {169},
number = {},
pages = {180-184},
doi = {10.1016/j.urology.2022.07.005},
pmid = {35853509},
issn = {1527-9995},
mesh = {Male ; Humans ; Aged ; Middle Aged ; *Transurethral Resection of Prostate ; Quality of Life ; *Prostatic Hyperplasia/complications/surgery ; Urodynamics ; *Urinary Bladder, Overactive/complications ; Treatment Outcome ; },
abstract = {OBJECTIVES: To evaluate the improvement of storage symptoms in accordance with voiding symptoms and assess the prognostic factors that influence the relief of storage symptoms after transurethral resection of the prostate (TURP).<br><br>METHODS: Between August 2017 and November 2019, 75 patients indicated for TURP were included in the study, we assessed the improvement of storage symptoms and factors that may influence storage symptoms persistence after TURP such as Age, Overactive bladder symptoms (OABS) score (Blaivas 2007) and Urodynamic parameters such as maximum flow rate (Q MAX), maximum cystometric capacity (MCC), bladder contractility index (BCI), phasic and terminal detrusor overactivity (DO). Assessment of patients was done before and 6 months after TURP by international prostate symptom score (IPSS), quality of life score (QLSS), OABSS (Blaivas score 2007), and urodynamic studies.<br><br>RESULTS: Mean age of the patients was 67.88&#xb1;7.82 years. The patients with persistence of storage symptoms were significantly older 70.43&#xb1;8.32 vs 67.04&#xb1;7.49 respectively P-value&#xa0;=&#xa0;0.022, also IPSS score was significantly higher in patients with resolution of symptoms (26.83&#xb1;3.91 vs 24.35&#xb1;3.68 P&#xa0;=&#xa0;.017). Terminal D.O and Q max were significantly higher in patients with persistence of storage symptoms (26.3% and 8.1 vs 8.9% and 6 respectively). MCC was significantly higher in a patient with resolution vs persistence of storage symptoms (345.18&#xb1;90.89 mL vs 242.16&#xb1;72.73) respectively P&#xa0;=&#xa0;0.001 There was no significant difference between both groups regarding duration of symptoms, prostate size, prostatic specific antigen (PSA), QOL score, OABS score, and maximum detrusor pressure CONCLUSION: more elderly patients with MCC less than 250 ccs and terminal DO were associated with worse outcomes and persistence of storage symptoms post TURP.},
}
@article {pmid35853437,
year = {2022},
author = {Zhang, X and Qiu, S and Zhang, Y and Wang, K and Wang, Y and He, H},
title = {Bidirectional Siamese correlation analysis method for enhancing the detection of SSVEPs.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac823e},
pmid = {35853437},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted increasing attention due to their high information transfer rate. To improve the performance of SSVEP detection, we propose a bidirectional Siamese correlation analysis (bi-SiamCA) model.Approach. In this model, an long short-term memory-based Siamese architecture is designed to measure the similarity between the SSVEP signal and the template in each frequency and obtain the probability that the SSVEP signal belongs to each frequency. Additionally, a maximize agreement module with a designed contrastive loss is adopted in the Siamese architecture to increase the similarity between the SSVEP signal and the reference signal in the same frequency. Moreover, a two-way signal processing mechanism is built to effectively integrate complementary information from two temporal directions of the input signals. Our model uses raw SSVEPs as inputs and can be trained end-to-end.Main results.Experimental results on a 40-class dataset and a 12-class dataset indicate that bi-SiamCA can significantly improve the classification accuracy compared with the prominent traditional and deep learning methods, especially under short data lengths. Feature visualizations show that the similarity between the SSVEP signal and the reference signal in the same frequency gradually improved in our model.Conclusion.The proposed bi-SiamCA model enhances the performance of SSVEP detection and outperforms the compared methods.Significance.Due to its high decoding accuracy under short signals, our approach has great potential to implement a high-speed SSVEP-based BCI.},
}
@article {pmid35853431,
year = {2022},
author = {Zheng, Y and Tian, B and Zhuang, Z and Zhang, Y and Wang, D},
title = {fNIRS-based adaptive visuomotor task improves sensorimotor cortical activation.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac823f},
pmid = {35853431},
issn = {1741-2552},
mesh = {Brain Mapping/methods ; Feedback, Sensory ; Humans ; Prefrontal Cortex/physiology ; *Sensorimotor Cortex ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {Objective. Investigating how to promote the functional activation of the central sensorimotor system is an important goal in the neurorehabilitation research domain. We aim to validate the effectiveness of facilitating cortical excitability using a closed-loop visuomotor task, in which the task difficulty is adaptively adjusted based on an individual's sensorimotor cortical activation.Approach. We developed a novel visuomotor task, in which subjects moved a handle of a haptic device along a specific path while exerting a constant force against a virtual surface under visual feedback. The difficulty levels of the task were adapted with the aim of increasing the activation of sensorimotor areas, measured non-invasively by functional near-infrared spectroscopy. The changes in brain activation of the bilateral prefrontal cortex, sensorimotor cortex, and the occipital cortex obtained during the adaptive visuomotor task (adaptive group), were compared to the brain activation pattern elicited by the same duration of task with random difficulties in a control group.Main results.During one intervention session, the adaptive group showed significantly increased activation in the bilateral sensorimotor cortex, also enhanced effective connectivity between the prefrontal and sensorimotor areas compared to the control group.Significance.Our findings demonstrated that the functional near-infrared spectroscopy-based adaptive visuomotor task with high ecological validity can facilitate the neural activity in sensorimotor areas and thus has the potential to improve hand motor functions.},
}
@article {pmid35853068,
year = {2022},
author = {Wei, Y and Li, J and Ji, H and Jin, L and Liu, L and Bai, Z and Ye, C},
title = {A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {2067-2076},
doi = {10.1109/TNSRE.2022.3192448},
pmid = {35853068},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Humans ; Imagination ; Supervised Machine Learning ; },
abstract = {Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.},
}
@article {pmid35851798,
year = {2023},
author = {Wang, X and Zhang, Y and Zhu, L and Bai, S and Li, R and Sun, H and Qi, R and Cai, R and Li, M and Jia, G and Cao, X and Schriver, KE and Li, X and Gao, L},
title = {Selective corticofugal modulation on sound processing in auditory thalamus of awake marmosets.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {7},
pages = {3372-3386},
pmid = {35851798},
issn = {1460-2199},
mesh = {Animals ; *Callithrix ; Wakefulness ; *Auditory Cortex/physiology ; Acoustic Stimulation ; Thalamus/physiology ; Geniculate Bodies/physiology ; Auditory Perception/physiology ; Auditory Pathways/physiology ; },
abstract = {Cortical feedback has long been considered crucial for the modulation of sensory perception and recognition. However, previous studies have shown varying modulatory effects of the primary auditory cortex (A1) on the auditory response of subcortical neurons, which complicate interpretations regarding the function of A1 in sound perception and recognition. This has been further complicated by studies conducted under different brain states. In the current study, we used cryo-inactivation in A1 to examine the role of corticothalamic feedback on medial geniculate body (MGB) neurons in awake marmosets. The primary effects of A1 inactivation were a frequency-specific decrease in the auditory response of most MGB neurons coupled with an increased spontaneous firing rate, which together resulted in a decrease in the signal-to-noise ratio. In addition, we report for the first time that A1 robustly modulated the long-lasting sustained response of MGB neurons, which changed the frequency tuning after A1 inactivation, e.g. some neurons are sharper with corticofugal feedback and some get broader. Taken together, our results demonstrate that corticothalamic modulation in awake marmosets serves to enhance sensory processing in a manner similar to center-surround models proposed in visual and somatosensory systems, a finding which supports common principles of corticothalamic processing across sensory systems.},
}
@article {pmid35851156,
year = {2022},
author = {Lemprière, S},
title = {Brain-machine interface treats pain in rats.},
journal = {Nature reviews. Neurology},
volume = {18},
number = {9},
pages = {510},
pmid = {35851156},
issn = {1759-4766},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Pain ; Rats ; },
}
@article {pmid35850094,
year = {2022},
author = {Yan, W and Wu, Y and Du, C and Xu, G},
title = {An improved cross-subject spatial filter transfer method for SSVEP-based BCI.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac81ee},
pmid = {35850094},
issn = {1741-2552},
mesh = {Algorithms ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Objective.Steady-state visual evoked potential (SSVEP) training feature recognition algorithms utilize user training data to reduce the interference of spontaneous electroencephalogram activities on SSVEP response for improved recognition accuracy. The data collection process can be tedious, increasing the mental fatigue of users and also seriously affecting the practicality of SSVEP-based brain-computer interface (BCI) systems.Approach. As an alternative, a cross-subject spatial filter transfer (CSSFT) method to transfer an existing user data model with good SSVEP response to new user test data has been proposed. The CSSFT method uses superposition averages of data for multiple blocks of data as transfer data. However, the amplitude and pattern of brain signals are often significantly different across trials. The goal of this study was to improve superposition averaging for the CSSFT method and propose anEnsemblescheme based on ensemble learning, and anExpansionscheme based on matrix expansion.Main results. The feature recognition performance was compared for CSSFT and the proposed improved CSSFT method using two public datasets. The results demonstrated that the improved CSSFT method can significantly improve the recognition accuracy and information transmission rate of existing methods.Significance.This strategy avoids a tedious data collection process, and promotes the potential practical application of BCI systems.},
}
@article {pmid35849678,
year = {2022},
author = {Chen, Y and Yang, R and Huang, M and Wang, Z and Liu, X},
title = {Single-Source to Single-Target Cross-Subject Motor Imagery Classification Based on Multisubdomain Adaptation Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1992-2002},
doi = {10.1109/TNSRE.2022.3191869},
pmid = {35849678},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; },
abstract = {In the electroencephalography (EEG) based cross-subject motor imagery (MI) classification task, the device and subject problems can cause the time-related data distribution shift problem. In a single-source to single-target (STS) MI classification task, such a shift problem will certainly provoke an increase in the overall data distribution difference between the source and target domains, giving rise to poor classification accuracy. In this paper, a novel multi-subdomain adaptation method (MSDAN) is proposed to solve the shift problem and improve the classification accuracy of the traditional approaches. In the proposed MSDAN, the adaptation losses in both class-related and time-related subdomains (that are divided by different data labels and session labels) are obtained by measuring the distribution differences between the source and target subdomains. Then, the adaptation and classification losses in the loss function of MSDAN are minimized concurrently. To illustrate the application value of the proposed method, our method is applied to solve the STS MI classification task about data analysis with respect to the brain-computer interface (BCI) competition III-IVa dataset. The resultant experiment results demonstrate that compared with other well-known domain adaptation and deep learning methods, the proposed method is capable of solving the time-related data distribution problem at higher classification accuracy.},
}
@article {pmid35849623,
year = {2022},
author = {Huang, SY and Lai, YS and Fang, YY},
title = {The spatial-temporal distribution of soil-transmitted helminth infections in Guangdong Province, China: A geostatistical analysis of data derived from the three national parasitic surveys.},
journal = {PLoS neglected tropical diseases},
volume = {16},
number = {7},
pages = {e0010622},
pmid = {35849623},
issn = {1935-2735},
mesh = {Aged ; Ancylostomatoidea ; Animals ; Ascaris lumbricoides ; Bayes Theorem ; Child ; China/epidemiology ; Feces/parasitology ; Female ; *Helminthiasis/epidemiology/parasitology ; *Helminths ; *Hookworm Infections/epidemiology ; Humans ; Male ; Middle Aged ; *Parasitic Diseases ; Prevalence ; Soil/parasitology ; },
abstract = {BACKGROUND: The results of the latest national survey on important human parasitic diseases in 2015-2016 showed Guangdong Province is still a moderately endemic area, with the weighted prevalence of soil-transmitted helminths (STHs) higher than the national average. High-resolution age- and gender-specific spatial-temporal risk maps can support the prevention and control of STHs, but not yet available in Guangdong.<br><br>METHODOLOGY: Georeferenced age- and gender-specific disease data of STH infections in Guangdong Province was derived from three national surveys on important human parasitic diseases, conducted in 1988-1992, 2002-2003, and 2015-2016, respectively. Potential influencing factors (e.g., environmental and socioeconomic factors) were collected from open-access databases. Bayesian geostatistical models were developed to analyze the above data, based on which, high-resolution maps depicting the STH infection risk were produced in the three survey years in Guangdong Province.<br><br>PRINCIPAL FINDINGS: There were 120, 31, 71 survey locations in the first, second, and third national survey in Guangdong, respectively. The overall population-weighted prevalence of STH infections decreased significantly over time, from 68.66% (95% Bayesian credible interval, BCI: 64.51-73.06%) in 1988-1992 to 0.97% (95% BCI: 0.69-1.49%) in 2015-2016. In 2015-2016, only low to moderate infection risk were found across Guangdong, with hookworm becoming the dominant species. Areas with relatively higher risk (>5%) were mostly distributed in the western region. Females had higher infection risk of STHs than males. The infection risk of A. lumbricoides and T. trichiura were higher in children, while middle-aged and elderly people had higher infection risk of hookworm. Precipitation, elevation, land cover, and human influence index (HII) were significantly related with STH infection risk.<br><br>CONCLUSIONS/SIGNIFICANCE: We produced the high-resolution, age- and gender-specific risk maps of STH infections in the three national survey periods across nearly 30 years in Guangdong Province, which can provide important information assisting the control and prevention strategies.},
}
@article {pmid35849615,
year = {2022},
author = {Shrestha, H and McCulloch, K and Hedtke, SM and Grant, WN},
title = {Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination.},
journal = {PLoS neglected tropical diseases},
volume = {16},
number = {7},
pages = {e0010620},
pmid = {35849615},
issn = {1935-2735},
mesh = {Animals ; Bayes Theorem ; Ethiopia/epidemiology ; Humans ; *Intestinal Volvulus ; Ivermectin ; Onchocerca ; *Onchocerca volvulus ; *Onchocerciasis/epidemiology/prevention &amp; control ; Prevalence ; Retrospective Studies ; },
abstract = {BACKGROUND: Onchocerciasis is a neglected tropical filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus nodule prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus nodule prevalence, and many districts are still unmapped despite their potential for onchocerciasis transmission.<br><br>A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus nodules across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia's central and eastern parts. Distance to the nearest river (RR: 0.9850, 95% BCI: 0.9751-0.995), precipitation seasonality (RR: 0.9837, 95% BCI: 0.9681-0.9995), and flow accumulation (RR: 0.9586, 95% BCI: 0.9321-0.9816) were negatively associated with O. volvulus nodule prevalence, while soil moisture (RR: 1.0218, 95% BCI: 1.0135-1.0302) was positively associated. The model estimated the number of pre-intervention cases of O. volvulus nodules in Ethiopia to be around 6.48 million (95% BCI: 3.53-13.04 million).<br><br>CONCLUSIONS/SIGNIFICANCE: Nodule prevalence distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priorities for elimination mapping in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.},
}
@article {pmid35848165,
year = {2024},
author = {Jalalkamali, H and Tajik, A and Hatami, R and Nezamabadipour, H},
title = {Detecting how time is subjectively perceived based on event-related potentials (ERPs): a machine learning approach.},
journal = {The International journal of neuroscience},
volume = {134},
number = {4},
pages = {372-380},
doi = {10.1080/00207454.2022.2103413},
pmid = {35848165},
issn = {1563-5279},
mesh = {Humans ; Male ; Female ; *Evoked Potentials, Auditory/physiology ; *Evoked Potentials/physiology ; Brain/physiology ; Auditory Perception ; Electroencephalography ; Reaction Time/physiology ; },
abstract = {Background and objective: Time perception is essential for the precise performance of many of our activities and the coordination between different modalities. But it is distorted in many diseases and disorders. Event-related potentials (ERP) have long been used to understand better how the human brain perceives time, but machine learning methods have rarely been used to detect a person's time perception from his/her ERPs. Methods: In this study, EEG signals of the individuals were recorded while performing an auditory oddball time discrimination task. After features were extracted from ERPs, data balancing, and feature selection, machine learning models were used to distinguish between the oddball durations of 400 ms and 600 ms from standard durations of 500 ms. ERP results showed that the P3 evoked by the 600 ms oddball stimuli appeared about 200 ms later than that of the 400 ms oddball tones. Classification performance results indicated that support vector machine (SVM) outperformed K-nearest neighbors (KNN), Random Forest, and Logistic regression models. Results: The accuracy of SVM was 91.24, 92.96, and 89.9 for the three used labeling modes, respectively. Another important finding was that most features selected for classification were in the P3 component range, supporting the observed significant effect of duration on the P3. Although all N1, P2, N2, and P3 components contributed to detecting the desired durations. Conclusion: Therefore, results of this study suggest the P3 component as a potential candidate to detect sub-second periods in future researches on brain-computer interface (BCI) applications.},
}
@article {pmid35847664,
year = {2022},
author = {Xie, P and Wang, Z and Li, Z and Wang, Y and Wang, N and Liang, Z and Wang, J and Chen, X},
title = {Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {892178},
pmid = {35847664},
issn = {1663-4365},
abstract = {It is difficult for stroke patients with flaccid paralysis to receive passive rehabilitation training. Therefore, virtual rehabilitation technology that integrates the motor imagery brain-computer interface and virtual reality technology has been applied to the field of stroke rehabilitation and has evolved into a physical rehabilitation training method. This virtual rehabilitation technology can enhance the initiative and adaptability of patient rehabilitation. To maximize the deep activation of the subjects motor nerves and accelerate the remodeling mechanism of motor nerve function, this study designed a brain-computer interface rehabilitation training strategy using different virtual scenes, including static scenes, dynamic scenes, and VR scenes. Including static scenes, dynamic scenes, and VR scenes. We compared and analyzed the degree of neural activation and the recognition rate of motor imagery in stroke patients after motor imagery training using stimulation of different virtual scenes, The results show that under the three scenarios, The order of degree of neural activation and the recognition rate of motor imagery from high to low is: VR scenes, dynamic scenes, static scenes. This paper provided the research basis for a virtual rehabilitation strategy that could integrate the motor imagery brain-computer interface and virtual reality technology.},
}
@article {pmid35847542,
year = {2022},
author = {Zhu, L and Cui, G and Li, Y and Zhang, J and Kong, W and Cichocki, A and Li, J},
title = {Attention allocation on mobile app interfaces when human interacts with them.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {4},
pages = {859-870},
pmid = {35847542},
issn = {1871-4080},
abstract = {With the popularity of smartphones and the pervasion of mobile apps, people spend more and more time to interact with a diversity of apps on their smartphones, especially for young population. This raises a question: how people allocate attention to interfaces of apps during using them. To address this question, we, in this study, designed an experiment with two sessions (i.e., Session1: browsing original interfaces; Session 2: browsing interfaces after removal of colors and background) integrating with an eyetracking system. Attention fixation durations were recorded by an eye-tracker while participants browsed app interfaces. The whole screen of smartphone was divided into four even regions to explore fixation durations. The results revealed that participants gave significantly longer total fixation duration on the bottom left region compared to other regions in the session (1) Longer total fixation duration on the bottom was preserved, but there is no significant difference between left side and right side in the session2. Similar to the finding of total fixation duration, first fixation duration is also predominantly paid on the bottom area of the interface. Moreover, the skill in the use of mobile phone was quantified by assessing familiarity and accuracy of phone operation and was investigated in the association with the fixation durations. We found that first fixation duration of the bottom left region is significantly negatively correlated with the smartphone operation level in the session 1, but there is no significant correlation between them in the session (2) According to the results of ratio exploration, the ratio of the first fixation duration to the total fixation duration is not significantly different between areas of interest for both sessions. The findings of this study provide insights into the attention allocation during browsing app interfaces and are of implications on the design of app interfaces and advertisements as layout can be optimized according to the attention allocation to maximally deliver information.},
}
@article {pmid35847541,
year = {2022},
author = {Xiao, R and Huang, Y and Xu, R and Wang, B and Wang, X and Jin, J},
title = {Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {4},
pages = {791-803},
pmid = {35847541},
issn = {1871-4080},
abstract = {In the motor-imagery (MI) based brain computer interface (BCI), multi-channel electroencephalogram (EEG) is often used to ensure the complete capture of physiological phenomena. With the redundant information and noise, EEG signals cannot be easily converted into separable features through feature extraction algorithms. Channel selection algorithms are proposed to address the issue, in which the filtering technique is widely used with the advantages of low computational cost and strong practicability. In this study, we proposed several improved methods for filtering channel selection algorithm. Specifically, based on the coefficient of variation and inter-class distance, a novel channel classification method was designed, which divided channels into different categories based on their contribution to feature extraction process. Then a filtering channel selection algorithm was proposed according to the previous classification method. Moreover, a new testing framework for filtering channel selection algorithms was proposed, which can better reflect the generalization ability of the algorithm. Experimental results indicated that the proposed channel classification method is effective, and the proposed testing framework is better than the original one. Meanwhile, the proposed channel selection algorithm achieved the accuracy of 87.7% and 81.7% in two BCI competition datasets, respectively, which was superior to competing algorithms.},
}
@article {pmid35847538,
year = {2022},
author = {Xiao, G and Shi, M and Ye, M and Xu, B and Chen, Z and Ren, Q},
title = {4D attention-based neural network for EEG emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {4},
pages = {805-818},
pmid = {35847538},
issn = {1871-4080},
abstract = {Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the weights of different brain regions and frequency bands, and a convolutional neural network (CNN) is utilized to deal with the spectral and spatial information of the 4D representations. Moreover, a temporal attention mechanism is integrated into a bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of the 4D representations. Our model achieves state-of-the-art performances on both DEAP, SEED and SEED-IV datasets under intra-subject splitting. The experimental results have shown the effectiveness of the attention mechanisms in different domains for EEG emotion recognition.},
}
@article {pmid35846606,
year = {2022},
author = {Liang, S and Yin, M and Huang, Y and Dai, X and Wang, Q},
title = {Nuclear Norm Regularized Deep Neural Network for EEG-Based Emotion Recognition.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {924793},
pmid = {35846606},
issn = {1664-1078},
abstract = {Electroencephalography (EEG) based emotion recognition enables machines to perceive users' affective states, which has attracted increasing attention. However, most of the current emotion recognition methods neglect the structural information among different brain regions, which can lead to the incorrect learning of high-level EEG feature representation. To mitigate possible performance degradation, we propose a novel nuclear norm regularized deep neural network framework (NRDNN) that can capture the structural information among different brain regions in EEG decoding. The proposed NRDNN first utilizes deep neural networks to learn high-level feature representations of multiple brain regions, respectively. Then, a set of weights indicating the contributions of each brain region can be automatically learned using a region-attention layer. Subsequently, the weighted feature representations of multiple brain regions are stacked into a feature matrix, and the nuclear norm regularization is adopted to learn the structural information within the feature matrix. The proposed NRDNN method can learn the high-level representations of EEG signals within multiple brain regions, and the contributions of them can be automatically adjusted by assigning a set of weights. Besides, the structural information among multiple brain regions can be captured in the learning procedure. Finally, the proposed NRDNN can perform in an efficient end-to-end manner. We conducted extensive experiments on publicly available emotion EEG dataset to evaluate the effectiveness of the proposed NRDNN. Experimental results demonstrated that the proposed NRDNN can achieve state-of-the-art performance by leveraging the structural information.},
}
@article {pmid35845896,
year = {2022},
author = {Lu, Y and Chen, H and Yan, H},
title = {E-Sports Competition Analysis Based on Intelligent Analysis System.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4855550},
pmid = {35845896},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Sports ; },
abstract = {To improve the analysis effect of e-sports competitions, this paper studies the intelligent analysis methods of e-sports, compares the advantages and disadvantages of various multi-classification methods, and innovatively designs a two-layer SVM classifier structure for four types of motor imagery EEG signals. Moreover, this paper uses the competition data set to test the classification accuracy of the designed double-layer SVM classifier structure and compares it with the DAG-SVM multi-classification method. The research results show that the e-sports competition analysis system based on the intelligent analysis system proposed in this paper has a good e-sports competition analysis effect, and has a good effect in e-sports competition prediction.},
}
@article {pmid35845759,
year = {2022},
author = {Chen, B and Chen, C and Hu, J and Nguyen, T and Qi, J and Yang, B and Chen, D and Alshahrani, Y and Zhou, Y and Tsai, A and Frush, T and Goitz, H},
title = {A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {880073},
pmid = {35845759},
issn = {1662-5218},
abstract = {The signals from electromyography (EMG) have been used for volitional control of robotic assistive devices with the challenges of performance improvement. Currently, the most common method of EMG signal processing for robot control is RMS (root mean square)-based algorithm, but system performance accuracy can be affected by noise or artifacts. This study hypothesized that the frequency bandwidths of noise and artifacts are beyond the main EMG signal frequency bandwidth, hence the fixed-bandwidth frequency-domain signal processing methods can filter off the noise and artifacts only by processing the main frequency bandwidth of EMG signals for robot control. The purpose of this study was to develop a cost-effective embedded system and short-time Fourier transform (STFT) method for an EMG-controlled robotic hand. Healthy volunteers were recruited in this study to identify the optimal myoelectric signal frequency bandwidth of muscle contractions. The STFT embedded system was developed using the STM32 microcontroller unit (MCU). The performance of the STFT embedded system was compared with RMS embedded system. The results showed that the optimal myoelectric signal frequency band responding to muscle contractions was between 60 and 80 Hz. The STFT embedded system was more stable than the RMS embedded system in detecting muscle contraction. Onsite calibration was required for RMS embedded system. The average accuracy of the STFT embedded system is 91.55%. This study presents a novel approach for developing a cost-effective and less complex embedded myoelectric signal processing system for robot control.},
}
@article {pmid35845476,
year = {2022},
author = {Liu, X and Chen, P and Ding, X and Liu, A and Li, P and Sun, C and Guan, H},
title = {A narrative review of cortical visual prosthesis systems: the latest progress and significance of nanotechnology for the future.},
journal = {Annals of translational medicine},
volume = {10},
number = {12},
pages = {716},
pmid = {35845476},
issn = {2305-5839},
abstract = {BACKGROUND AND OBJECTIVE: We sought to review the latest developments in cortical visual prosthesis (CVP) systems and the significance of nanotechnology for the future. Over the past century, CVP systems have been researched and developed, resulting in various unique surgical and mechanical techniques. Research findings indicate that partial vision recovery is possible, with improvements in coarse target functions and performance in routine activities.<br><br>METHODS: This review discusses the architecture and physiology of the visual cortex, the neuroplasticity of the blind brain, and the history of CVP development, and also provides an update on the CVP systems currently being examined in research and clinical trials. Due to advances in nanotechnology, it is possible to make CVPs that are smaller, more efficient, and more biocompatible than ever before.<br><br>KEY CONTENT AND FINDINGS: Currently, 3 CVPs have entered clinical trials, and several additional systems are undergoing preclinical reviews to determine the safety of the devices for chronic implantation. This development provides the first indication that the area of cortical vision restoration medication may be able to meaningfully benefit blind people. However, several significant technical and biological challenges need to be solved before the gap between artificial and natural eyesight can be reconciled. Rapid breakthroughs in nanotechnology have considerably increased its use in biological domains.<br><br>CONCLUSIONS: This paper summarizes the recent progress of CVP in recent years and its future development direction. It is forecasted that nanotechnology can provide better technical support for the development of CVP.},
}
@article {pmid35845249,
year = {2022},
author = {Tavakkoli, H and Motie Nasrabadi, A},
title = {A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP-STSA) for Emotion Recognition Using EEG Signals.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {936393},
pmid = {35845249},
issn = {1662-5161},
abstract = {Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method's effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.},
}
@article {pmid35845246,
year = {2022},
author = {Plucknett, W and Sanchez Giraldo, LG and Bae, J},
title = {Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {902183},
pmid = {35845246},
issn = {1662-5161},
abstract = {Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio. Metric learning allows finding a representation of data in a way that captures a desired notion of similarity between data points. In this study, we investigate how metric learning can help finding a representation of the data to efficiently classify EEG movement and pre-movement intentions. We evaluate the effectiveness of the obtained representation by comparing classification the performance of a Support Vector Machine (SVM) as a classifier when trained on the original representation, called Euclidean, and representations obtained with three different metric learning algorithms, including Conditional Entropy Metric Learning (CEML), Neighborhood Component Analysis (NCA), and the Entropy Gap Metric Learning (EGML) algorithms. We examine different types of features, such as time and frequency components, which input to the metric learning algorithm, and both linear and non-linear SVM are applied to compare the classification accuracies on a publicly available EEG data set for two subjects (Subject B and C). Although metric learning algorithms do not increase the classification accuracies, their interpretability using an importance measure we define here, helps understanding data organization and how much each EEG channel contributes to the classification. In addition, among the metric learning algorithms we investigated, EGML shows the most robust performance due to its ability to compensate for differences in scale and correlations among variables. Furthermore, from the observed variations of the importance maps on the scalp and the classification accuracy, selecting an appropriate feature such as clipping the frequency range has a significant effect on the outcome of metric learning and subsequent classification. In our case, reducing the range of the frequency components to 0-5 Hz shows the best interpretability in both Subject B and C and classification accuracy for Subject C. Our experiments support potential benefits of using metric learning algorithms by providing visual explanation of the data projections that explain the inter class separations, using importance. This visualizes the contribution of features that can be related to brain function.},
}
@article {pmid35845243,
year = {2022},
author = {Hosseini, SM and Shalchyan, V},
title = {Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {901285},
pmid = {35845243},
issn = {1662-5161},
abstract = {The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.},
}
@article {pmid35843369,
year = {2022},
author = {Pindi, P and Houenou, J and Piguet, C and Favre, P},
title = {Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: A meta-analysis.},
journal = {Progress in neuro-psychopharmacology &amp; biological psychiatry},
volume = {119},
number = {},
pages = {110605},
doi = {10.1016/j.pnpbp.2022.110605},
pmid = {35843369},
issn = {1878-4216},
mesh = {Brain/diagnostic imaging ; Emotions/physiology ; Humans ; Magnetic Resonance Imaging/methods ; *Mental Disorders/diagnostic imaging/therapy ; *Neurofeedback/methods ; Randomized Controlled Trials as Topic ; },
abstract = {Neurofeedback using real-time functional MRI (RT-fMRI-NF) is an innovative technique that allows to voluntarily modulate a targeted brain response and its associated behavior. Despite promising results in the current literature, its effectiveness on symptoms management in psychiatric disorders is not yet clearly demonstrated. Here, we provide 1) a state-of-art qualitative review of RT-fMRI-NF studies aiming at alleviating clinical symptoms in a psychiatric population; 2) a quantitative evaluation (meta-analysis) of RT-fMRI-NF effectiveness on various psychiatric disorders and 3) methodological suggestions for future studies. Thirty-one clinical trials focusing on psychiatric disorders were included and categorized according to standard diagnostic categories. Among the 31 identified studies, 22 consisted of controlled trials, of which only eight showed significant clinical improvement in the experimental vs. control group after the training. Nine studies found an effect at follow-up on ADHD symptoms, emotion dysregulation, facial emotion processing, depressive symptoms, hallucinations, psychotic symptoms, and specific phobia. Within-group meta-analysis revealed large effects of the NF training on depressive symptoms right after the training (g = 0.81, p < 0.01) and at follow-up (g = 1.19, p < 0.01), as well as medium effects on anxiety (g = 0.44, p = 0.01) and emotion regulation (g = 0.48, p < 0.01). Between-group meta-analysis showed a medium effect on depressive symptoms (g = 0.49, p < 0.01) and a large effect on anxiety (g = 0.77, p = 0.01). However, the between-studies heterogeneity is very high. The use of RT-fMRI-NF as a treatment for psychiatric symptoms is promising, however, further double-blind, multicentric, randomized-controlled trials are warranted.},
}
@article {pmid35843026,
year = {2022},
author = {Elsohaby, I and Arango-Sabogal, JC and Selim, A and Attia, KA and Alsubki, RA and Mohamed, AM and Megahed, A},
title = {Bayesian estimation of sensitivity and specificity of fecal culture, fecal PCR and serum ELISA for diagnosis of Mycobacterium avium subsp. paratuberculosis infections in sheep.},
journal = {Preventive veterinary medicine},
volume = {206},
number = {},
pages = {105712},
doi = {10.1016/j.prevetmed.2022.105712},
pmid = {35843026},
issn = {1873-1716},
mesh = {Animals ; Bayes Theorem ; Cattle ; *Cattle Diseases/microbiology ; Enzyme-Linked Immunosorbent Assay/veterinary ; Feces/microbiology ; *Mycobacterium avium subsp. paratuberculosis/genetics ; *Paratuberculosis/diagnosis/epidemiology/microbiology ; Polymerase Chain Reaction/veterinary ; Sensitivity and Specificity ; Sheep ; },
abstract = {The objective of the present study was to evaluate the diagnostic accuracy of the individual fecal culture (IFC), fecal PCR (FPCR), and serum ELISA for the detection of Mycobacterium avium subsp. paratuberculosis (MAP) infections in sheep from four governorates in Egypt, using a latent class model (LCM) fitted within a Bayesian framework. Furthermore, the within-governorate prevalence of MAP infection in sheep was estimated as a secondary objective. Fecal and blood samples were collected from 370 sheep in four Egyptian governorates. Fecal samples were analyzed by IFC and RT-PCR based on ISMav2 gene, while ELISA was performed on serum samples. The sensitivity (Se) and specificity (Sp) of the three diagnostic tests were estimated using a three-tests-four-populations Bayesian LCM to obtain posterior estimates [medians and 95% Bayesian credible intervals (95% BCI)] for each parameter. The median Se estimates (95% BCI) for IFC, FPCR, and serum ELISA were 31.8% (22.8-41.4), 49.7% (31.8-79.9), and 61.2% (39.8-81.4), respectively. The median Sp estimates (95% BCI) for IFC, FPCR, and serum ELISA were 97.7% (96.1-98.9), 97.7% (95.6-99.5), and 98.4% (96.9-99.3), respectively. The median within-governorate paratuberculosis prevalence (95% BCI) was 5.2% (1.1-13.6), 8.4% (2.9-17.7), 9.4% (3.0-20.7), and 18.2% (10.5-29.5) for the Gharbia, Menoufia, Qalyubia, and Kafr El-Sheikh governorates, respectively. In conclusion, at a ratio of the optical density (OD) sample/OD positive control threshold of > 45%, ELISA showed the highest Se among the three tests and comparable Sp to IFC and FPCR. The test ELISA evaluated in this study is an interesting alternative for detecting MAP in sheep due to its higher Se, lower cost, and shorter turnaround laboratory time compared to IFC and FPCR.},
}
@article {pmid35842523,
year = {2022},
author = {Kasahara, K and DaSalla, CS and Honda, M and Hanakawa, T},
title = {Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans.},
journal = {Communications biology},
volume = {5},
number = {1},
pages = {712},
pmid = {35842523},
issn = {2399-3642},
mesh = {*Basal Ganglia ; Brain/physiology ; Corpus Striatum ; Electroencephalography ; Humans ; *Self-Control ; },
abstract = {Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology.},
}
@article {pmid35842416,
year = {2022},
author = {Chen, K and Wang, R and Huang, J and Gao, F and Yuan, Z and Qi, Y and Wu, H},
title = {A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {416},
pmid = {35842416},
issn = {2052-4463},
mesh = {Adolescent ; Adult ; *Brain/physiology ; Brain Mapping ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {We present a dataset combining high-density Electroencephalography (HD-EEG, 128-channels) and mouse-tracking intended as a resource for examining the dynamic decision process of semantics and preference choices in the human brain. The dataset includes resting-state and task-related (food preference choices and semantic judgments) EEG acquired from 31 individuals (ages: 18-33). Along with the dataset, we also provided the preliminary microstate analysis of resting-state EEG and the ERPs, topomap, and time-frequency maps of the task-related EEG. We believe that the simultaneous mouse-tracking and EEG recording would crack the core components of binary choices and further index the temporal dynamics of decision making and response hesitation. This publicly available dataset could support the development of neural signal processing methods in motor EEG, thus advancing research in both the decision neuroscience and brain-computer interface (BCI) applications.},
}
@article {pmid35842015,
year = {2022},
author = {Yao, H and Liu, K and Deng, X and Tang, X and Yu, H},
title = {FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection.},
journal = {Journal of neuroscience methods},
volume = {379},
number = {},
pages = {109674},
doi = {10.1016/j.jneumeth.2022.109674},
pmid = {35842015},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neural Networks, Computer ; Photic Stimulation ; },
abstract = {BACKGROUND: Steady-state visual evoked potential (SSVEP) is a prevalent paradigm of brain-computer interface (BCI). Recently, deep neural networks (DNNs) have been employed for SSVEP target recognition. However, current DNN models can not fully extract information from SSVEP harmonic components, and ignore the influence of non-target stimuli.<br><br>NEW METHOD: To employ information of multiple sub-bands and non-target stimulus data, we propose a DNN model for SSVEP target detection, i.e., FB-EEGNet, which fuses features of multiple neural networks. Additionally, we design a multi-label for each sample and optimize the parameters of FB-EEGNet across multi-stimulus to incorporate the information from non-target stimuli.<br><br>RESULTS: Under the subject-specific condition, FB-EEGNet achieves the average classification accuracies (information transfer rate (ITR)) of 76.75&#xa0;% (50.70 bits/min) and 89.14&#xa0;% (70.45 bits/min) in a time widow of 0.7&#xa0;s under the public 12-target dataset and our experimental 9-target dataset, respectively. Under the cross-subject condition, FB-EEGNet achieved mean accuracies (ITRs) of 81.72&#xa0;% (67.99 bits/min) and 92.15&#xa0;% (76.12 bits/min) on the public and experimental datasets in a time window of 1&#xa0;s, respectively.<br><br>FB-EEGNet shows superior performance than CCNN, EEGNet, CCA and FBCCA both for subject-dependent and subject-independent SSVEP target recognition.<br><br>CONCLUSION: FB-EEGNet can effectively extract information from multiple sub-bands and cross-stimulus targets, providing a promising way for extracting deep features in SSVEP using neural networks.},
}
@article {pmid35840800,
year = {2022},
author = {Pan, L and Zheng, L and Wu, X and Zhu, Z and Wang, S and Lu, Y and He, Y and Yang, Q and Ma, X and Wang, X and Yang, H and Zhan, L and Luo, Y and Li, X and Zhou, Y and Wang, X and Luo, J and Wang, L and Duan, S and Wang, H},
title = {A short period of early life oxytocin treatment rescues social behavior dysfunction via suppression of hippocampal hyperactivity in male mice.},
journal = {Molecular psychiatry},
volume = {27},
number = {10},
pages = {4157-4171},
pmid = {35840800},
issn = {1476-5578},
mesh = {Animals ; Male ; Mice ; Fragile X Mental Retardation Protein ; *Fragile X Syndrome ; Hippocampus/metabolism ; *Oxytocin/pharmacology ; Receptors, Oxytocin/genetics/metabolism ; Social Behavior ; },
abstract = {Early sensory experiences interact with genes to shape precise neural circuits during development. This process is vital for proper brain function in adulthood. Neurological dysfunctions caused by environmental alterations and/or genetic mutation may share the same molecular or cellular mechanisms. Here, we show that early life bilateral whisker trimming (BWT) subsequently affects social discrimination in adult male mice. Enhanced activation of the hippocampal dorsal CA3 (dCA3) in BWT mice was observed during social preference tests. Optogenetic activation of dCA3 in naive mice impaired social discrimination, whereas chemogenetic silencing of dCA3 rescued social discrimination deficit in BWT mice. Hippocampal oxytocin (OXT) is reduced after whisker trimming. Neonatal intraventricular compensation of OXT relieved dCA3 over-activation and prevented social dysfunction. Neonatal knockdown of OXT receptor in dCA3 mimics the effects of BWT, and cannot be rescued by OXT treatment. Social behavior deficits in a fragile X syndrome mouse model (Fmr1 KO mice) could also be recovered by early life OXT treatment, through negating dCA3 over-activation. Here, a possible avenue to prevent social dysfunction is uncovered.},
}
@article {pmid35840656,
year = {2022},
author = {Ye, H and Chen, V and Hendee, J},
title = {Cellular mechanisms underlying state-dependent neural inhibition with magnetic stimulation.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {12131},
pmid = {35840656},
issn = {2045-2322},
mesh = {Action Potentials/physiology ; Electric Stimulation ; Magnetic Phenomena ; *Models, Neurological ; *Neural Inhibition ; Neurons/physiology ; Potassium Channels/physiology ; },
abstract = {Novel stimulation protocols for neuromodulation with magnetic fields are explored in clinical and laboratory settings. Recent evidence suggests that the activation state of the nervous system plays a significant role in the outcome of magnetic stimulation, but the underlying cellular and molecular mechanisms of state-dependency have not been completely investigated. We recently reported that high frequency magnetic stimulation could inhibit neural activity when the neuron was in a low active state. In this paper, we investigate state-dependent neural modulation by applying a magnetic field to single neurons, using the novel micro-coil technology. High frequency magnetic stimulation suppressed single neuron activity in a state-dependent manner. It inhibited neurons in slow-firing states, but spared neurons from fast-firing states, when the same magnetic stimuli were applied. Using a multi-compartment NEURON model, we found that dynamics of voltage-dependent sodium and potassium channels were significantly altered by the magnetic stimulation in the slow-firing neurons, but not in the fast-firing neurons. Variability in neural activity should be monitored and explored to optimize the outcome of magnetic stimulation in basic laboratory research and clinical practice. If selective stimulation can be programmed to match the appropriate neural state, prosthetic implants and brain-machine interfaces can be designed based on these concepts to achieve optimal results.},
}
@article {pmid35839739,
year = {2022},
author = {Wu, S and Qian, C and Shen, X and Zhang, X and Huang, Y and Chen, S and Wang, Y},
title = {Spike prediction on primary motor cortex from medial prefrontal cortex during task learning.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac8180},
pmid = {35839739},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Learning/physiology ; *Motor Cortex/physiology ; Prefrontal Cortex/physiology ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Objectives. Brain-machine interfaces (BMIs) aim to help people with motor disabilities by interpreting brain signals into motor intentions using advanced signal processing methods. Currently, BMI users require intensive training to perform a pre-defined task, not to mention learning a new task. Thus, it is essential to understand neural information pathways among the cortical areas in task learning to provide principles for designing BMIs with learning abilities. We propose to investigate the relationship between the medial prefrontal cortex (mPFC) and primary motor cortex (M1), which are actively involved in motor control and task learning, and show how information is conveyed in spikes between the two regions on a single-trial basis by computational models.Approach. We are interested in modeling the functional relationship between mPFC and M1 activities during task learning. Six Sprague Dawley rats were trained to learn a new behavioral task. Neural spike data was recorded from mPFC and M1 during learning. We then implement the generalized linear model, the second-order generalized Laguerre-Volterra model, and the staged point-process model to predict M1 spikes from mPFC spikes across multiple days during task learning. The prediction performance is compared across different models or learning stages to reveal the relationship between mPFC and M1 spike activities.Main results. We find that M1 neural spikes can be well predicted from mPFC spikes on the single-trial level, which indicates a highly correlated relationship between mPFC and M1 activities during task learning. By comparing the performance across models, we find that models with higher nonlinear capacity perform significantly better than linear models. This indicates that predicting M1 activity from mPFC activity requires the model to consider higher-order nonlinear interactions beyond pairwise interactions. We also find that the correlation coefficient between the mPFC and M1 spikes increases during task learning. The spike prediction models perform the best when the subjects become well trained on the new task compared with the early and middle stages. The results suggest that the co-activation between mPFC and M1 activities evolves during task learning, and becomes stronger as subjects become well trained.Significance. This study demonstrates that the dynamic patterns of M1 spikes can be predicted from mPFC spikes during task learning, and this will further help in the design of adaptive BMI decoders for task learning.},
}
@article {pmid35839731,
year = {2022},
author = {Phogat, R and Parmananda, P and Prasad, A},
title = {Intensity dependence of sub-harmonics in cortical response to photic stimulation.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac817f},
pmid = {35839731},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; Photic Stimulation/methods ; },
abstract = {Objective. Periodic photic stimulation of human volunteers at 10 Hz is known to entrain their electroencephalography (EEG) signals. This entrainment manifests as an increment in power at 10, 20, 30 Hz. We observed that this entrainment is accompanied by the emergence of sub-harmonics, but only at specific frequencies and higher intensities of the stimulating signal. Thereafter, we describe our results and explain them using the physiologically inspired Jansen and Rit neural mass model (NMM).Approach. Four human volunteers were separately exposed to both high and low intensity 10 Hz and 6 Hz stimulation. A total of four experiments per subject were therefore performed. Simulations and bifurcation analysis of the NMM were carried out and compared with the experimental findings.Main results.High intensity 10 Hz stimulation led to an increment in power at 5 Hz across all the four subjects. No increment of power was observed with low intensity stimulation. However, when the same protocol was repeated with a 6 Hz photic stimulation, neither high nor low intensity stimulation were found to cause a discernible change in power at 3 Hz. We found that the NMM was able to recapitulate these results. A further numerical analysis indicated that this arises from the underlying bifurcation structure of the NMM.Significance. The excellent match between theory and experiment suggest that the bifurcation properties of the NMM are mirroring similar features possessed by the actual neural masses producing the EEG dynamics. NMMs could thus be valuable for understanding properties and pathologies of EEG dynamics, and may contribute to the engineering of brain-computer interface technologies.},
}
@article {pmid35837250,
year = {2022},
author = {Handelman, DA and Osborn, LE and Thomas, TM and Badger, AR and Thompson, M and Nickl, RW and Anaya, MA and Wormley, JM and Cantarero, GL and McMullen, D and Crone, NE and Wester, B and Celnik, PA and Fifer, MS and Tenore, FV},
title = {Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {918001},
pmid = {35837250},
issn = {1662-5218},
abstract = {Advances in intelligent robotic systems and brain-machine interfaces (BMI) have helped restore functionality and independence to individuals living with sensorimotor deficits; however, tasks requiring bimanual coordination and fine manipulation continue to remain unsolved given the technical complexity of controlling multiple degrees of freedom (DOF) across multiple limbs in a coordinated way through a user input. To address this challenge, we implemented a collaborative shared control strategy to manipulate and coordinate two Modular Prosthetic Limbs (MPL) for performing a bimanual self-feeding task. A human participant with microelectrode arrays in sensorimotor brain regions provided commands to both MPLs to perform the self-feeding task, which included bimanual cutting. Motor commands were decoded from bilateral neural signals to control up to two DOFs on each MPL at a time. The shared control strategy enabled the participant to map his four-DOF control inputs, two per hand, to as many as 12 DOFs for specifying robot end effector position and orientation. Using neurally-driven shared control, the participant successfully and simultaneously controlled movements of both robotic limbs to cut and eat food in a complex bimanual self-feeding task. This demonstration of bimanual robotic system control via a BMI in collaboration with intelligent robot behavior has major implications for restoring complex movement behaviors for those living with sensorimotor deficits.},
}
@article {pmid35835448,
year = {2022},
author = {Pan, J and Xiao, J and Wang, J and Wang, F and Li, J and Qiu, L and Di, H and Li, Y},
title = {Brain-Computer Interfaces for Awareness Detection, Auxiliary Diagnosis, Prognosis, and Rehabilitation in Patients with Disorders of Consciousness.},
journal = {Seminars in neurology},
volume = {42},
number = {3},
pages = {363-374},
pmid = {35835448},
issn = {1098-9021},
mesh = {*Brain-Computer Interfaces ; Consciousness ; Consciousness Disorders/diagnosis ; Electroencephalography ; Humans ; Prognosis ; },
abstract = {In recent years, neuroimaging studies have remarkably demonstrated the presence of cognitive motor dissociation in patients with disorders of consciousness (DoC). These findings accelerated the development of brain-computer interfaces (BCIs) as clinical tools for behaviorally unresponsive patients. This article reviews the recent progress of BCIs in patients with DoC and discusses the open challenges. In view of the practical application of BCIs in patients with DoC, four aspects of the relevant literature are introduced: consciousness detection, auxiliary diagnosis, prognosis, and rehabilitation. For each aspect, the paradigm design, brain signal processing methods, and experimental results of representative BCI systems are analyzed. Furthermore, this article provides guidance for BCI design for patients with DoC and discusses practical challenges for future research.},
}
@article {pmid35832876,
year = {2022},
author = {Zhan, G and Chen, S and Ji, Y and Xu, Y and Song, Z and Wang, J and Niu, L and Bin, J and Kang, X and Jia, J},
title = {EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {909610},
pmid = {35832876},
issn = {1662-5161},
abstract = {Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain-computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI-FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI-FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl-Meyer assessment scale (FMA) score was significantly improved in the BCI-FES group (p < 0.05), and there was no significant difference in the FES group. (ii) Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI-FES group were improved after rehabilitation training. (iii) The node strength in the contralesional hemisphere and central region of patients in the BCI-FES group was significantly higher than that in the FES group after the intervention (p < 0.05), and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI-FES group (p < 0.05). These results suggest that BCI-FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI-FES rehabilitation training.},
}
@article {pmid35830404,
year = {2022},
author = {Wang, Z and Zhang, J and Xia, Y and Chen, P and Wang, B},
title = {A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1982-1991},
doi = {10.1109/TNSRE.2022.3190431},
pmid = {35830404},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Fingers ; Humans ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and brain-computer interfaces (BCIs). Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers. We apply more appropriate evaluation methods to three open-access datasets to solve the first two barriers. For domain barriers, we propose a general and scalable vision fNIRS framework that converts multi-channel fNIRS signals into multi-channel virtual images using the Gramian angular difference field (GADF). We use the framework to train state-of-the-art visual models from computer vision (CV) within a few minutes, and the classification performance is competitive with the latest fNIRS models. In cross-validation experiments, visual models achieve the highest average classification results of 78.68% and 73.92% on mental arithmetic and word generation tasks, respectively. Although visual models are slightly lower than the fNIRS models on unilateral finger- and foot-tapping tasks, the F1-score and kappa coefficient indicate that these differences are insignificant in subject-independent experiments. Furthermore, we study fNIRS signal representations and the classification performance of sequence-to-image methods. We hope to introduce rich achievements from the CV domain to improve fNIRS classification research.},
}
@article {pmid35820400,
year = {2022},
author = {Valencia, D and Mercier, PP and Alimohammad, A},
title = {In vivoneural spike detection with adaptive noise estimation.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac8077},
pmid = {35820400},
issn = {1741-2552},
mesh = {Action Potentials ; *Algorithms ; Animals ; Neurons/physiology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Objective.The ability to reliably detect neural spikes from a relatively large population of neurons contaminated with noise is imperative for reliable decoding of recorded neural information.Approach.This article first analyzes the accuracy and feasibility of various potential spike detection techniques forin vivorealizations. Then an accurate and computationally-efficient spike detection module that can autonomously adapt to variations in recording channels' statistics is presented.Main results.The accuracy of the chosen candidate spike detection technique is evaluated using both synthetic and real neural recordings. The designed detector also offers the highest decoding performance over two animal behavioral datasets among alternative detection methods.Significance.The implementation results of the designed 128-channel spike detection module in a standard 180 nm CMOS process is among the most area and power-efficient spike detection ASICs and operates within the tissue-safe constraints for brain implants, while offering adaptive noise estimation.},
}
@article {pmid35818313,
year = {2022},
author = {Hudson, AL and Wattiez, N and Navarro-Sune, X and Chavez, M and Similowski, T},
title = {Combined head accelerometry and EEG improves the detection of respiratory-related cortical activity during inspiratory loading in healthy participants.},
journal = {Physiological reports},
volume = {10},
number = {13},
pages = {e15383},
pmid = {35818313},
issn = {2051-817X},
mesh = {Accelerometry ; *Electroencephalography/methods ; Healthy Volunteers ; Humans ; *Respiration ; Respiratory Rate ; },
abstract = {Mechanical ventilation is a highly utilized life-saving tool, particularly in the current era. The use of EEG in a brain-ventilator interface (BVI) to detect respiratory discomfort (due to sub-optimal ventilator settings) would improve treatment in mechanically ventilated patients. This concept has been realized via development of an EEG covariance-based classifier that detects respiratory-related cortical activity associated with respiratory discomfort. The aim of this study was to determine if head movement, detected by an accelerometer, can detect and/or improve the detection of respiratory-related cortical activity compared to EEG alone. In 25 healthy participants, EEG and acceleration of the head were recorded during loaded and quiet breathing in the seated and lying postures. Detection of respiratory-related cortical activity using an EEG covariance-based classifier was improved by inclusion of data from an Accelerometer-based classifier, i.e. classifier 'Fusion'. In addition, 'smoothed' data over 50s, rather than one 5 s window of EEG/Accelerometer signals, improved detection. Waveform averages of EEG and head acceleration showed the incidence of pre-inspiratory potentials did not differ between loaded and quiet breathing, but head movement was greater in loaded breathing. This study confirms that compared to event-related analysis with >5 min of signal acquisition, an EEG-based classifier is a clinically valuable tool with rapid processing, detection times, and accuracy. Data smoothing would introduce a small delay (<1 min) but improves detection results. As head acceleration improved detection compared to EEG alone, the number of EEG signals required to detect respiratory discomfort with future BVIs could be reduced if head acceleration is included.},
}
@article {pmid35817814,
year = {2022},
author = {Salimpour, S and Kalbkhani, H and Seyyedi, S and Solouk, V},
title = {Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {11773},
pmid = {35817814},
issn = {2045-2322},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Support Vector Machine ; },
abstract = {Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time-frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.},
}
@article {pmid35817307,
year = {2022},
author = {Pires, G and Barbosa, S and Nunes, UJ and Gonçalves, E},
title = {Visuo-auditory stimuli with semantic, temporal and spatial congruence for a P300-based BCI: An exploratory test with an ALS patient in a completely locked-in state.},
journal = {Journal of neuroscience methods},
volume = {379},
number = {},
pages = {109661},
doi = {10.1016/j.jneumeth.2022.109661},
pmid = {35817307},
issn = {1872-678X},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Semantics ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are a promising tool for communication with completely locked-in state (CLIS) patients. Despite the great efforts already made by the BCI research community, the cases of success are still very few, very exploratory, limited in time, and based on simple 'yes/no' paradigms.<br><br>NEW METHOD: A P300-based BCI is proposed comparing two conditions, one corresponding to purely spatial auditory stimuli (AU-S) and the other corresponding to hybrid visual and spatial auditory stimuli (HVA-S). In the HVA-S condition, there is a semantic, temporal, and spatial congruence between visual and auditory stimuli. The stimuli comprise a lexicon of 7 written and spoken words. Spatial sounds are generated through the head-related transfer function. Given the good results obtained with 10 able-bodied participants, we investigated whether a patient entering CLIS could use the proposed BCI.<br><br>RESULTS: The able-bodied group achieved 71.3 % and 90.5 % online classification accuracy for the auditory and hybrid BCIs respectively, while the patient achieved 30 % and chance level accuracies, for the same conditions. Notwithstanding, the patient's event-related potentials (ERPs) showed statistical discrimination between target and non-target events in different time windows.<br><br>The results of the control group compare favorably with the state-of-the-art, considering a 7-class BCI controlled visual-covertly and with auditory stimuli. The integration of visual and auditory stimuli has not been tested before with CLIS patients.<br><br>CONCLUSIONS: The semantic, temporal, and spatial congruence of the stimuli increased the performance of the control group, but not of the CLIS patient, which can be due to impaired attention and cognitive function. The patient's unique ERP patterns make interpretation difficult, requiring further tests/paradigms to decouple patients' responses at different levels (reflexive, perceptual, cognitive). The ERPs discrimination found indicates that a simplification of the proposed approaches may be feasible.},
}
@article {pmid35815432,
year = {2024},
author = {Wang, T and Wang, C and Chen, K and Yang, D and Xi, X and Kong, W},
title = {Evaluating stroke rehabilitation using brain functional network and corticomuscular coupling.},
journal = {The International journal of neuroscience},
volume = {134},
number = {3},
pages = {234-242},
doi = {10.1080/00207454.2022.2099386},
pmid = {35815432},
issn = {1563-5279},
mesh = {Humans ; *Stroke Rehabilitation ; Muscle, Skeletal ; Hemiplegia/etiology ; *Stroke/complications ; Brain ; *Motor Cortex ; },
abstract = {Objective: Stroke is the leading cause of disability worldwide. Traditionally, doctors assess stroke rehabilitation assessment, which can be subjective. Therefore, an objective assessment method is required. Methods: In this context, we investigated the changes in brain functional connectivity patterns and corticomuscular coupling in stroke patients during rehabilitation. In this study, electroencephalogram (EEG) and electromyogram (EMG) of stroke patients were collected synchronously at baseline(BL), two weeks after BL, and four weeks after BL. A brain functional network was established, and the corticomuscular coupling relationship was calculated using phase transfer entropy (PTE). Results: We found that during the rehabilitation of stroke patients, the overall connection of the brain functional network was strengthened, and the network characteristic value increased. The average corticomuscular PTE appeared to first decrease and subsequently increase, and the PTE increase in the frontal lobe was significant. Value: In this study, PTE was used for the first time to analyze the relationship between EEG signals in patients with hemiplegia. We believe that our findings contribute to evaluating the rehabilitation of stroke patients with hemiplegia.},
}
@article {pmid35814961,
year = {2022},
author = {Xavier Fidêncio, A and Klaes, C and Iossifidis, I},
title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {806517},
pmid = {35814961},
issn = {1662-5161},
abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},
}
@article {pmid35814562,
year = {2022},
author = {Rocha-Herrera, CA and Díaz-Manríquez, A and Barron-Zambrano, JH and Elizondo-Leal, JC and Saldivar-Alonso, VP and Martínez-Angulo, JR and Nuño-Maganda, MA and Polanco-Martagon, S},
title = {EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {7571208},
pmid = {35814562},
issn = {1687-5273},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.},
}
@article {pmid35812226,
year = {2022},
author = {Chen, J and Min, C and Wang, C and Tang, Z and Liu, Y and Hu, X},
title = {Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {878146},
pmid = {35812226},
issn = {1662-4548},
abstract = {In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject's EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications.},
}
@article {pmid35812211,
year = {2022},
author = {Riha, C and Güntensperger, D and Kleinjung, T and Meyer, M},
title = {Recovering Hidden Responder Groups in Individuals Receiving Neurofeedback for Tinnitus.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {867704},
pmid = {35812211},
issn = {1662-4548},
abstract = {The widespread understanding that chronic tinnitus is a heterogeneous phenomenon with various neural oscillatory profiles has spurred investigations into individualized approaches in its treatment. Neurofeedback, as a non-invasive tool for altering neural activity, has become increasingly popular in the personalized treatment of a wide range of neuropsychological disorders. Despite the success of neurofeedback on the group level, the variability in the treatment efficacy on the individual level is high, and evidence from recent studies shows that only a small number of people can effectively modulate the desired aspects of neural activity. To reveal who may be more suitable, and hence benefit most from neurofeedback treatment, we classified individuals into unobserved subgroups with similar oscillatory trajectories during the treatment and investigated how subgroup membership was predicted by a series of characteristics. Growth mixture modeling was used to identify distinct latent subgroups with similar oscillatory trajectories among 50 individuals suffering from chronic subjective tinnitus (38 male, 12 female, mean age = 47.1 ± 12.84) across 15 neurofeedback training sessions. Further, the impact of characteristics and how they predicted the affiliation in the identified subgroups was evaluated by including measures of demographics, tinnitus-specific (Tinnitus Handicap Inventory) and depression variables, as well as subjective quality of life subscales (World Health Organization-Quality of Life Questionnaire), and health-related quality of life subscales (Short Form-36) in a logistic regression analysis. A latent class model could be fitted to the longitudinal data with a high probability of correctly classifying distinct oscillatory patterns into 3 different groups: non-responder (80%), responder (16%), and decliner (4%). Further, our results show that the health-related wellbeing subscale of the Short Form-36 questionnaire was differentially associated with the groups. However, due to the small sample size in the Responder group, we are not able to provide sufficient evidence for a distinct responder profile. Nevertheless, the identification of oscillatory change-rate differences across distinct groups of individuals provides the groundwork from which to tease apart the complex and heterogeneous oscillatory processes underlying tinnitus and the attempts to modify these through neurofeedback. While more research is needed, our results and the analytical approach presented may bring clarity to contradictory past findings in the field of tinnitus research, and eventually influence clinical practice.},
}
@article {pmid35808498,
year = {2022},
author = {Quiles, E and Dadone, J and Chio, N and García, E},
title = {Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {13},
pages = {},
pmid = {35808498},
issn = {1424-8220},
support = {20220266 AYUDA UPV PUBL-SSVEP//Universitat Polit&#xe8;cnica de Val&#xe8;ncia/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Robotics ; Software ; },
abstract = {Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.},
}
@article {pmid35808434,
year = {2022},
author = {Ouyang, D and Yuan, Y and Li, G and Guo, Z},
title = {The Effect of Time Window Length on EEG-Based Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {13},
pages = {},
pmid = {35808434},
issn = {1424-8220},
support = {JCYJ20190808142613246//Shenzhen Fundamental Research Fund/ ; 51805332 and 52072320//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography/methods ; Emotions ; Humans ; Recognition, Psychology ; *Signal Processing, Computer-Assisted ; },
abstract = {Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.},
}
@article {pmid35808414,
year = {2022},
author = {Belkhiria, C and Boudir, A and Hurter, C and Peysakhovich, V},
title = {EOG-Based Human-Computer Interface: 2000-2020 Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {13},
pages = {},
pmid = {35808414},
issn = {1424-8220},
support = {ANR-18-ASTR-0026//Agence Nationale de la Recherche/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Electrooculography ; Eye Movements ; Humans ; *User-Computer Interface ; },
abstract = {Electro-oculography (EOG)-based brain-computer interface (BCI) is a relevant technology influencing physical medicine, daily life, gaming and even the aeronautics field. EOG-based BCI systems record activity related to users' intention, perception and motor decisions. It converts the bio-physiological signals into commands for external hardware, and it executes the operation expected by the user through the output device. EOG signal is used for identifying and classifying eye movements through active or passive interaction. Both types of interaction have the potential for controlling the output device by performing the user's communication with the environment. In the aeronautical field, investigations of EOG-BCI systems are being explored as a relevant tool to replace the manual command and as a communicative tool dedicated to accelerating the user's intention. This paper reviews the last two decades of EOG-based BCI studies and provides a structured design space with a large set of representative papers. Our purpose is to introduce the existing BCI systems based on EOG signals and to inspire the design of new ones. First, we highlight the basic components of EOG-based BCI studies, including EOG signal acquisition, EOG device particularity, extracted features, translation algorithms, and interaction commands. Second, we provide an overview of EOG-based BCI applications in the real and virtual environment along with the aeronautical application. We conclude with a discussion of the actual limits of EOG devices regarding existing systems. Finally, we provide suggestions to gain insight for future design inquiries.},
}
@article {pmid35803976,
year = {2022},
author = {Won, K and Kwon, M and Ahn, M and Jun, SC},
title = {EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {388},
pmid = {35803976},
issn = {2052-4463},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Reproducibility of Results ; },
abstract = {As attention to deep learning techniques has grown, many researchers have attempted to develop ready-to-go brain-computer interfaces (BCIs) that include automatic processing pipelines. However, to do so, a large and clear dataset is essential to increase the model's reliability and performance. Accordingly, our electroencephalogram (EEG) dataset for rapid serial visual representation (RSVP) and P300 speller may contribute to increasing such BCI research. We validated our dataset with respect to features and accuracy. For the RSVP, the participants (N = 50) achieved about 92% mean target detection accuracy. At the feature level, we observed notable ERPs (at 315 ms in the RSVP; at 262 ms in the P300 speller) during target events compared to non-target events. Regarding P300 speller performance, the participants (N = 55) achieved about 92% mean accuracy. In addition, P300 speller performance over trial repetitions up to 15 was explored. The presented dataset could potentially improve P300 speller applications. Further, it may be used to evaluate feature extraction and classification algorithm effectively, such as for cross-subjects/cross-datasets, and even for the cross-paradigm BCI model.},
}
@article {pmid35801178,
year = {2022},
author = {Liu, C},
title = {The Role of Mesenchymal Stem Cells in Regulating Astrocytes-Related Synapse Dysfunction in Early Alzheimer's Disease.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {927256},
pmid = {35801178},
issn = {1662-4548},
abstract = {Alzheimer's disease (AD), a neurodegenerative disease, is characterized by the presence of extracellular amyloid-β (Aβ) aggregates and intracellular neurofibrillary tangles formed by hyperphosphorylated tau as pathological features and the cognitive decline as main clinical features. An important cellular correlation of cognitive decline in AD is synapse loss. Soluble Aβ oligomer has been proposed to be a crucial early event leading to synapse dysfunction in AD. Astrocytes are crucial for synaptic formation and function, and defects in astrocytic activation and function have been suggested in the pathogenesis of AD. Astrocytes may contribute to synapse dysfunction at an early stage of AD by participating in Aβ metabolism, brain inflammatory response, and synaptic regulation. While mesenchymal stem cells can inhibit astrogliosis, and promote non-reactive astrocytes. They can also induce direct regeneration of neurons and synapses. This review describes the role of mesenchymal stem cells and underlying mechanisms in regulating astrocytes-related Aβ metabolism, neuroinflammation, and synapse dysfunction in early AD, exploring the open questions in this field.},
}
@article {pmid35800256,
year = {2022},
author = {Minguillon, J and Volosyak, I and Guger, C and Tangermann, M and Lopez, MA},
title = {Editorial: Brain-Computer Interfaces: Novel Applications and Interactive Technologies.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {939202},
doi = {10.3389/fncom.2022.939202},
pmid = {35800256},
issn = {1662-5188},
}
@article {pmid35796537,
year = {2022},
author = {Hayashi, M and Okuyama, K and Mizuguchi, N and Hirose, R and Okamoto, T and Kawakami, M and Ushiba, J},
title = {Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition.},
journal = {eLife},
volume = {11},
number = {},
pages = {},
pmid = {35796537},
issn = {2050-084X},
mesh = {Electroencephalography/methods ; Functional Laterality/physiology ; Humans ; *Motor Cortex/physiology ; *Neurofeedback ; Transcranial Magnetic Stimulation/methods ; },
abstract = {Human behavior requires inter-regional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Here, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, e.g., in patients suffering from a stroke.},
}
@article {pmid35794953,
year = {2022},
author = {Du, X and Jiang, X and Kuriki, I and Takahata, T and Zhou, T and Roe, AW and Tanigawa, H},
title = {Representation of Cone-Opponent Color Space in Macaque Early Visual Cortices.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {891247},
pmid = {35794953},
issn = {1662-4548},
abstract = {In primate vision, the encoding of color perception arises from three types of retinal cone cells (L, M, and S cones). The inputs from these cones are linearly integrated into two cone-opponent channels (cardinal axes) before the lateral geniculate nucleus. In subsequent visual cortical stages, color-preferring neurons cluster into functional domains within "blobs" in V1, "thin/color stripes" in V2, and "color bands" in V4. Here, we hypothesize that, with increasing cortical hierarchy, the functional organization of hue representation becomes more balanced and less dependent on cone opponency. To address this question, we used intrinsic signal optical imaging in macaque V1, V2, and V4 cortices to examine the domain-based representation of specific hues (here referred to as "hue domains") in cone-opponent color space (4 cardinal and 4 intermediate hues). Interestingly, we found that in V1, the relative size of S-cone hue preference domain was significantly smaller than that for other hues. This notable difference was less prominent in V2, and, in V4 was virtually absent, resulting in a more balanced representation of hues. In V2, hue clusters contained sequences of shifting preference, while in V4 the organization of hue clusters was more complex. Pattern classification analysis of these hue maps showed that accuracy of hue classification improved from V1 to V2 to V4. These results suggest that hue representation by domains in the early cortical hierarchy reflects a transformation away from cone-opponency and toward a full-coverage representation of hue.},
}
@article {pmid35794496,
year = {2022},
author = {Chengaiyan, S and Anandan, K},
title = {Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures.},
journal = {Cognitive processing},
volume = {23},
number = {4},
pages = {593-618},
pmid = {35794496},
issn = {1612-4790},
mesh = {Algorithms ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Articulation imagery, a form of mental imagery, refers to the activity of imagining or speaking to oneself mentally without an articulation movement. It is an effective domain of research in speech impaired neural disorders, as speech imagination has high similarity to real voice communication. This work employs electroencephalography (EEG) signals acquired from articulation and articulation imagery in identifying the vowel being imagined during different tasks. EEG signals from chosen electrodes are decomposed using the empirical mode decomposition (EMD) method into a series of intrinsic mode functions. Brain connectivity estimators and entropy measures have been computed to analyze the functional cooperation and causal dependence between different cortical regions as well as the regularity in the signals. Using machine learning techniques such as multiclass support vector machine (MSVM) and random forest (RF), the vowels have been classified. Three different training and testing protocols (Articulation-AR, Articulation imagery-AI and Articulation vs Articulation imagery-AR vs AI) were employed for identifying the vowel being imagined of articulating. An overall classification accuracy of 80% was obtained for articulation imagery protocol which was found to be higher than the other two protocols. Also, MSVM techniques outperformed the RF technique in terms of the classification accuracy. The effect of brain connectivity estimators and machine learning techniques seems to be reliable in identifying the vowel from the subjects' thought and thereby assisting the people with speech impairment.},
}
@article {pmid35794417,
year = {2023},
author = {Guttmann-Flury, E and Sheng, X and Zhu, X},
title = {Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms.},
journal = {Behavior research methods},
volume = {55},
number = {4},
pages = {1980-2003},
pmid = {35794417},
issn = {1554-3528},
support = {2020YFC2007800//National Key R&amp;D Program of China/ ; 51905339//National Natural Science Foundation of China/ ; 18JC1410400//Science and Technology Commission of Shanghai Municipality/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography/methods ; Algorithms ; Brain/physiology ; },
abstract = {Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.},
}
@article {pmid35792474,
year = {2022},
author = {Zarei, A and Mohammadzadeh Asl, B},
title = {Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals.},
journal = {Computers in biology and medicine},
volume = {147},
number = {},
pages = {105771},
doi = {10.1016/j.compbiomed.2022.105771},
pmid = {35792474},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; Support Vector Machine ; },
abstract = {BACKGROUND AND OBJECTIVE: Over the last years, code-modulated visual evoked potentials (cVEP)-based brain-computer interfaces (BCIs) have been developed as robust and non-invasive tools to construct high-speed communication systems. Recently, beamforming-based algorithms have extensively been used in cVEP-based BCI systems because of the need for short-time stimulation and less training data. One of the main drawbacks of the beamforming-based approaches is that their performance highly depends on estimating data covariance matrix and calculating activation patterns.<br><br>METHODS: In the present study, two novel covariance estimators (i.e., the modified convex combination (MCC) and the maximum likelihood (ML) techniques) are proposed to estimate a robust and more reliable covariance matrix. In the ML method, a new sparsity constraint is considered to express the specific eigendecomposition of the covariance matrix as a sparse matrix transform (SMT). Then, the SMT is calculated using the product of pairwise coordinate rotations. These rotations can be constructed by a cross-validation method. Two stimulation presentation rates of 60 and 120 Hz are used for the coding sequence.<br><br>RESULTS: Both of the suggested approaches (i.e., the MCC and SMT-based techniques) can efficiently improve the performance of the conventional spatiotemporal beamforming-based methods by providing a robust estimate of the covariance matrix in short stimulation times. Based on the experimental results, it can be concluded that the proposed SMT and MCC methods achieve the best results for the 60 and 120 Hz stimulus presentation rates, respectively. However, for both stimulus presentation rates, the proposed SMT and MCC-based methods remarkably outperform other state-of-the-art methods in cVEP-based BCI, such as conventional spatiotemporal beamforming and optimized support vector machines (SVM). Also, the results showed that the 120 Hz stimulus presentation rate provided faster communication. This procedure is performed by obtaining a maximal Information Transfer Rate (ITR) of 187.38 bits/minute.<br><br>CONCLUSION: Finally, the present study suggested that the proposed MCC and SMT-based techniques could automatically detect the gazed targets. Also, these methods could be used as non-invasive alternatives over conventional methods.},
}
@article {pmid35790978,
year = {2022},
author = {Tortora, S and Beraldo, G and Bettella, F and Formaggio, E and Rubega, M and Del Felice, A and Masiero, S and Carli, R and Petrone, N and Menegatti, E and Tonin, L},
title = {Neural correlates of user learning during long-term BCI training for the Cybathlon competition.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {19},
number = {1},
pages = {69},
pmid = {35790978},
issn = {1743-0003},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Machine Learning ; Reproducibility of Results ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.<br><br>METHODS: We present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.<br><br>RESULTS: First, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot's neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.<br><br>CONCLUSION: We firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.},
}
@article {pmid35788530,
year = {2022},
author = {Liu, Y and Gong, A and Ding, P and Zhao, L and Qian, Q and Zhou, J and Su, L and Fu, Y},
title = {[Key technology of brain-computer interaction based on speech imagery].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {3},
pages = {596-611},
pmid = {35788530},
issn = {1001-5515},
mesh = {Brain ; Computers ; Humans ; *Imagery, Psychotherapy ; *Speech ; Technology ; },
abstract = {Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.},
}
@article {pmid35788518,
year = {2022},
author = {Li, H and Yin, F and Zhang, R and Ma, X and Chen, H},
title = {[Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {3},
pages = {488-497},
pmid = {35788518},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.},
}
@article {pmid35788284,
year = {2023},
author = {Jia, T and Li, C and Mo, L and Qian, C and Li, W and Xu, Q and Pan, Y and Liu, A and Ji, L},
title = {Tailoring brain-machine interface rehabilitation training based on neural reorganization: towards personalized treatment for stroke patients.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {33},
number = {6},
pages = {3043-3052},
pmid = {35788284},
issn = {1460-2199},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Precision Medicine ; *Stroke/therapy ; Brain ; },
abstract = {Electroencephalogram (EEG)-based brain-machine interface (BMI) has the potential to enhance rehabilitation training efficiency, but it still remains elusive regarding how to design BMI training for heterogeneous stroke patients with varied neural reorganization. Here, we hypothesize that tailoring BMI training according to different patterns of neural reorganization can contribute to a personalized rehabilitation trajectory. Thirteen stroke patients were recruited in a 2-week personalized BMI training experiment. Clinical and behavioral measurements, as well as cortical and muscular activities, were assessed before and after training. Following treatment, significant improvements were found in motor function assessment. Three types of brain activation patterns were identified during BMI tasks, namely, bilateral widespread activation, ipsilesional focusing activation, and contralesional recruitment activation. Patients with either ipsilesional dominance or contralesional dominance can achieve recovery through personalized BMI training. Results indicate that personalized BMI training tends to connect the potentially reorganized brain areas with event-contingent proprioceptive feedback. It can also be inferred that personalization plays an important role in establishing the sensorimotor loop in BMI training. With further understanding of neural rehabilitation mechanisms, personalized treatment strategy is a promising way to improve the rehabilitation efficacy and promote the clinical use of rehabilitation robots and other neurotechnologies.},
}
@article {pmid35788233,
year = {2022},
author = {Xiaojun, Z and Xinrui, K and Xupeng, L},
title = {The influence of learning mode and learning sharing behavior on the synchronicity of attention of sharers and learners.},
journal = {BMC psychology},
volume = {10},
number = {1},
pages = {166},
pmid = {35788233},
issn = {2050-7283},
support = {BBA200031//This study was supported from Education General Project of National Social Science Foundation of China in 2020 "The Research on Cognitive and Emotional Mechanism of Augmented Reality (AR) Multimedia Learning and Its Promotion to Primary School Students' Efficient Learning"/ ; },
mesh = {Humans ; *Learning ; Students ; *Virtual Reality ; },
abstract = {Attention is the concentration of mental activities to a certain object, and students' inattentiveness in class directly affects their learning efficiency. As an emerging technology of educational application, augmented reality (AR) technology combines virtual reality and three-dimensional reconstruction to bring multisensory stimulation to students, enhancing immersion and attention in learning. A quantitative study was conducted on third-grade pupils. Study 1 examined whether learning mode and learning sharing behavior affect the synchronization of sharers' and learners' attention. Study 2 examined the impact of learning mode and sharing role on sharer and shared. The results showed that compared with learning alone, when sharing, the attention score of AR group is higher than that of text group. Whether it is the sharer or the shared, the attention score of AR group is higher than that of text group. AR has more advantages than text in terms of learning attention. In future research, it is optional to diversify AR learning materials and further use near-infrared spectroscopy technology to study interactive learning in AR mode.},
}
@article {pmid35787028,
year = {2022},
author = {Wu, J and Ma, H and Zhong, C and Wei, M and Sun, C and Ye, Y and Xu, Y and Tang, B and Luo, Y and Sun, B and Jian, J and Dai, H and Lin, H and Li, L},
title = {Waveguide-Integrated PdSe2 Photodetector over a Broad Infrared Wavelength Range.},
journal = {Nano letters},
volume = {22},
number = {16},
pages = {6816-6824},
doi = {10.1021/acs.nanolett.2c02099},
pmid = {35787028},
issn = {1530-6992},
mesh = {Equipment Design ; Optics and Photonics ; Photons ; Silicon/chemistry ; *Telecommunications ; },
abstract = {Hybrid integration of van der Waals materials on a photonic platform enables diverse exploration of novel active functions and significant improvement in device performance for next-generation integrated photonic circuits, but developing waveguide-integrated photodetectors based on conventionally investigated transition metal dichalcogenide materials at the full optical telecommunication bands and mid-infrared range is still a challenge. Here, we integrate PdSe2 with silicon waveguide for on-chip photodetection with a high responsivity from 1260 to 1565 nm, a low noise-equivalent power of 4.0 pW·Hz[-0.5], a 3-dB bandwidth of 1.5 GHz, and a measured data rate of 2.5 Gbit·s[-1]. The achieved PdSe2 photodetectors provide new insights to explore the integration of novel van der Waals materials with integrated photonic platforms and exhibit great potential for diverse applications over a broad infrared range of wavelengths, such as on-chip sensing and spectroscopy.},
}
@article {pmid35786558,
year = {2022},
author = {Mak, J and Kocanaogullari, D and Huang, X and Kersey, J and Shih, M and Grattan, ES and Skidmore, ER and Wittenberg, GF and Ostadabbas, S and Akcakaya, M},
title = {Detection of Stroke-Induced Visual Neglect and Target Response Prediction Using Augmented Reality and Electroencephalography.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1840-1850},
doi = {10.1109/TNSRE.2022.3188184},
pmid = {35786558},
issn = {1558-0210},
support = {IK2 RX002420/RX/RRD VA/United States ; },
mesh = {*Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Perceptual Disorders/diagnosis/etiology ; *Stroke/complications/diagnosis ; },
abstract = {We aim to build a system incorporating electroencephalography (EEG) and augmented reality (AR) that is capable of identifying the presence of visual spatial neglect (SN) and mapping the estimated neglected visual field. An EEG-based brain-computer interface (BCI) was used to identify those spatiospectral features that best detect participants with SN among stroke survivors using their EEG responses to ipsilesional and contralesional visual stimuli. Frontal-central delta and alpha, frontal-parietal theta, Fp1 beta, and left frontal gamma were found to be important features for neglect detection. Additionally, temporal analysis of the responses shows that the proposed model is accurate in detecting potentially neglected targets. These targets were predicted using common spatial patterns as the feature extraction algorithm and regularized discriminant analysis combined with kernel density estimation for classification. With our preliminary results, our system shows promise for reliably detecting the presence of SN and predicting visual target responses in stroke patients with SN.},
}
@article {pmid35784856,
year = {2022},
author = {Chen, L and Zhang, L and Wang, Z and Gu, B and Zhang, X and Ming, D},
title = {The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {909434},
pmid = {35784856},
issn = {1662-4548},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) has been largely studied to improve motor learning and promote motor recovery. However, the difficulty in performing MI limits the widespread application of MI-BCI. It has been suggested that the usage of sensory threshold somatosensory electrical stimulation (st-SES) is a promising way to guide participants on MI tasks, but it is still unclear whether st-SES is effective for all users. In the present study, we aimed to examine the effects of st-SES on the MI-BCI performance in two BCI groups (High Performers and Low Performers). Twenty healthy participants were recruited to perform MI and resting tasks with EEG recordings. These tasks were modulated with or without st-SES. We demonstrated that st-SES improved the performance of MI-BCI in the Low Performers, but led to a decrease in the accuracy of MI-BCI in the High Performers. Furthermore, for the Low Performers, the combination of st-SES and MI resulted in significantly greater event-related desynchronization (ERD) and sample entropy of sensorimotor rhythm than MI alone. However, the ERD and sample entropy values of MI did not change significantly during the st-SES intervention in the High Performers. Moreover, we found that st-SES had an effect on the functional connectivity of the fronto-parietal network in the alpha band of Low Performers and the beta band of High Performers, respectively. Our results demonstrated that somatosensory input based on st-SES was only beneficial for sensorimotor cortical activation and MI-BCI performance in the Low Performers, but not in the High Performers. These findings help to optimize guidance strategies to adapt to different categories of users in the practical application of MI-BCI.},
}
@article {pmid35784835,
year = {2022},
author = {Kunigk, NG and Urdaneta, ME and Malone, IG and Delgado, F and Otto, KJ},
title = {Reducing Behavioral Detection Thresholds per Electrode via Synchronous, Spatially-Dependent Intracortical Microstimulation.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {876142},
pmid = {35784835},
issn = {1662-4548},
support = {T32 HL134621/HL/NHLBI NIH HHS/United States ; },
abstract = {Intracortical microstimulation (ICMS) has shown promise in restoring quality of life to patients suffering from paralysis, specifically when used in the primary somatosensory cortex (S1). However, these benefits can be hampered by long-term degradation of electrode performance due to the brain's foreign body response. Advances in microfabrication techniques have allowed for the development of neuroprostheses with subcellular electrodes, which are characterized by greater versatility and a less detrimental immune response during chronic use. These probes are hypothesized to enable more selective, higher-resolution stimulation of cortical tissue with long-term implants. However, microstimulation using physiologically relevant charges with these smaller-scale devices can damage electrode sites and reduce the efficacy of the overall device. Studies have shown promise in bypassing this limitation by spreading the stimulation charge between multiple channels in an implanted electrode array, but to our knowledge the usefulness of this strategy in laminar arrays with electrode sites spanning each layer of the cortex remains unexplored. To investigate the efficacy of simultaneous multi-channel ICMS in electrode arrays with stimulation sites spanning cortical depth, we implanted laminar electrode arrays in the primary somatosensory cortex of rats trained in a behavioral avoidance paradigm. By measuring detection thresholds, we were able to quantify improvements in ICMS performance using a simultaneous multi-channel stimulation paradigm. The charge required per site to elicit detection thresholds was halved when stimulating from two adjacent electrode sites, although the overall charge used by the implant was increased. This reduction in threshold charge was more pronounced when stimulating with more than two channels and lessened with greater distance between stimulating channels. Our findings suggest that these improvements are based on the synchronicity and polarity of each stimulus, leading us to conclude that these improvements in stimulation efficiency per electrode are due to charge summation as opposed to a summation of neural responses to stimulation. Additionally, the per-site charge reductions are seen regardless of the cortical depth of each utilized channel. This evocation of physiological detection thresholds with lower stimulation currents per electrode site has implications for the feasibility of stimulation regimes in future advanced neuroprosthetic devices, which could benefit from reducing the charge output per site.},
}
@article {pmid35784834,
year = {2022},
author = {Wang, Z and Cao, C and Chen, L and Gu, B and Liu, S and Xu, M and He, F and Ming, D},
title = {Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {884420},
pmid = {35784834},
issn = {1662-4548},
abstract = {Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation.},
}
@article {pmid35784188,
year = {2022},
author = {Massé, E and Bartheye, O and Fabre, L},
title = {Classification of Electrophysiological Signatures With Explainable Artificial Intelligence: The Case of Alarm Detection in Flight Simulator.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {904301},
pmid = {35784188},
issn = {1662-5196},
abstract = {Relevant sounds such as alarms are sometimes involuntarily ignored, a phenomenon called inattentional deafness. This phenomenon occurs under specific conditions including high workload (i.e., multitasking) and/or cognitive fatigue. In the context of aviation, such an error can have drastic consequences on flight safety. This study uses an oddball paradigm in which participants had to detect rare sounds in an ecological context of simulated flight. Cognitive fatigue and cognitive load were manipulated to trigger inattentional deafness, and brain activity was recorded via electroencephalography (EEG). Our results showed that alarm omission and alarm detection can be classified based on time-frequency analysis of brain activity. We reached a maximum accuracy of 76.4% when the algorithm was trained on all participants and a maximum of 90.5%, on one participant, when the algorithm was trained individually. This method can benefit from explainable artificial intelligence to develop efficient and understandable passive brain-computer interfaces, improve flight safety by detecting such attentional failures in real time, and give appropriate feedback to pilots, according to our ambitious goal, providing them with reliable and rich human/machine interactions.},
}
@article {pmid35782381,
year = {2022},
author = {Chen, D and Cheng, H and Liu, S and Al-Sheikh, U and Fan, Y and Duan, D and Zou, W and Zhu, L and Kang, L},
title = {The Voltage-Gated Calcium Channel EGL-19 Acts on Glia to Drive Olfactory Adaptation.},
journal = {Frontiers in molecular neuroscience},
volume = {15},
number = {},
pages = {907064},
pmid = {35782381},
issn = {1662-5099},
abstract = {Calcium channelopathies have been strongly linked to cardiovascular, muscular, neurological and psychiatric disorders. The voltage-gated calcium channels (VGCC) are vital transducers of membrane potential changes to facilitate the dynamics of calcium ions and release of neurotransmitter. Whether these channels function in the glial cell to mediate calcium variations and regulate behavioral outputs, is poorly understood. Our results showed that odorant and mechanical stimuli evoked robust calcium increases in the amphid sheath (AMsh) glia from C. elegans, which were largely dependent on the L-Type VGCC EGL-19. Moreover, EGL-19 modulates the morphologies of both ASH sensory neurons and AMsh glia. Tissue-specific knock-down of EGL-19 in AMsh glia regulated sensory adaptability of ASH neurons and promoted olfactory adaptation. Our results reveal a novel role of glial L-Type VGCC EGL-19 on olfaction, lead to improved understanding of the functions of VGCCs in sensory transduction.},
}
@article {pmid35782087,
year = {2022},
author = {Nazneen, T and Islam, IB and Sajal, MSR and Jamal, W and Amin, MA and Vaidyanathan, R and Chau, T and Mamun, KA},
title = {Recent Trends in Non-invasive Neural Recording Based Brain-to-Brain Synchrony Analysis on Multidisciplinary Human Interactions for Understanding Brain Dynamics: A Systematic Review.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {875282},
pmid = {35782087},
issn = {1662-5188},
abstract = {The study of brain-to-brain synchrony has a burgeoning application in the brain-computer interface (BCI) research, offering valuable insights into the neural underpinnings of interacting human brains using numerous neural recording technologies. The area allows exploring the commonality of brain dynamics by evaluating the neural synchronization among a group of people performing a specified task. The growing number of publications on brain-to-brain synchrony inspired the authors to conduct a systematic review using the PRISMA protocol so that future researchers can get a comprehensive understanding of the paradigms, methodologies, translational algorithms, and challenges in the area of brain-to-brain synchrony research. This review has gone through a systematic search with a specified search string and selected some articles based on pre-specified eligibility criteria. The findings from the review revealed that most of the articles have followed the social psychology paradigm, while 36% of the selected studies have an application in cognitive neuroscience. The most applied approach to determine neural connectivity is a coherence measure utilizing phase-locking value (PLV) in the EEG studies, followed by wavelet transform coherence (WTC) in all of the fNIRS studies. While most of the experiments have control experiments as a part of their setup, a small number implemented algorithmic control, and only one study had interventional or a stimulus-induced control experiment to limit spurious synchronization. Hence, to the best of the authors' knowledge, this systematic review solely contributes to critically evaluating the scopes and technological advances of brain-to-brain synchrony to allow this discipline to produce more effective research outcomes in the remote future.},
}
@article {pmid35782086,
year = {2022},
author = {Li, Q and Wu, Y and Song, Y and Zhao, D and Sun, M and Zhang, Z and Wu, J},
title = {A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {909553},
pmid = {35782086},
issn = {1662-5188},
abstract = {BACKGROUND: Electroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR).<br><br>MATERIALS AND METHODS: We proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy.<br><br>RESULTS: We compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants.<br><br>CONCLUSION: The accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems.},
}
@article {pmid35779694,
year = {2022},
author = {Liu, C and Yang, TQ and Zhou, YD and Shen, Y},
title = {Reduced astrocytic mGluR5 in the hippocampus is associated with stress-induced depressive-like behaviors in mice.},
journal = {Neuroscience letters},
volume = {784},
number = {},
pages = {136766},
doi = {10.1016/j.neulet.2022.136766},
pmid = {35779694},
issn = {1872-7972},
mesh = {Animals ; *Astrocytes/metabolism ; *Depressive Disorder, Major ; Hippocampus/metabolism ; Humans ; Mice ; Neurogenesis ; Neurons ; },
abstract = {Major depressive disorder (MDD) is one of the most common and disabling mental disorders that characterized by profound disturbances in emotional regulation, motivation, cognition, and the physiology of affected individuals. Although MDD was initially thought to be primarily triggered through neuronal dysfunction, the pathological alterations in astrocytic function have been previously reported in MDD. We report that chronic restraint stress (CRS) induces astrocyte activation and decreases expression of astrocytic mGluR5 in the hippocampal CA1 of susceptible mice exhibited depressive-like behaviors. Reducing expression of astrocytic mGluR5 in dorsal CA1 simulates CRS-induced depressive-like behaviors and impairs excitatory synaptic function in mice, while overexpression of astrocytic mGluR5 in dorsal CA1 rescues CRS-induced depressive-like traits and excitatory synaptic dysfunction. Thus, we provide direct evidence for an important role of astrocytic mGluR5 in producing the behavioral phenotypes of MDD, supporting astrocytic mGluR5 may serve as an effective therapeutic target for MDD.},
}
@article {pmid35777634,
year = {2022},
author = {Ru, X and He, K and Lyu, B and Li, D and Xu, W and Gu, W and Ma, X and Liu, J and Li, C and Li, T and Zheng, F and Yan, X and Yin, Y and Duan, H and Na, S and Wan, S and Qin, J and Sheng, J and Gao, JH},
title = {Multimodal neuroimaging with optically pumped magnetometers: A simultaneous MEG-EEG-fNIRS acquisition system.},
journal = {NeuroImage},
volume = {259},
number = {},
pages = {119420},
doi = {10.1016/j.neuroimage.2022.119420},
pmid = {35777634},
issn = {1095-9572},
mesh = {Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Magnetoencephalography/methods ; Neuroimaging ; },
abstract = {Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.},
}
@article {pmid35775751,
year = {2022},
author = {Pino, O and Romano, G},
title = {Engagement and Arousal effects in predicting the increase of cognitive functioning following a neuromodulation program.},
journal = {Acta bio-medica : Atenei Parmensis},
volume = {93},
number = {3},
pages = {e2022248},
pmid = {35775751},
issn = {2531-6745},
mesh = {Arousal/physiology ; *Cognition ; *Electroencephalography ; Humans ; },
abstract = {BACKGROUND AND AIM: Research in the field of Brain-Computer Interfaces (BCIs) has increased exponentially over the past few years, demonstrating their effectiveness and application in several areas. The main purpose of the present paper was to explore the relevance of user engagement during interaction with a BCI prototype (Neuro-Upper, NU), which aimed at brainwave synchronization through audio-visual entrainment, in the improvement of cognitive performance.<br><br>METHODS: This paper presents findings on data collected from a sample of 18 subjects with clinical disorders who completed about 55 consecutive sessions of 30 min of audio-visual stimulation. The relationship between engagement and improvement of cognitive function (measured through the Intelligence Quotient - IQ) during NU neuromodulation was evaluated through the Index of Cognitive Engagement (ICE) measured by the Pope ratio (Beta / (Alpha + Theta), and Arousal [(High Beta + Low Beta) / (High Alpha + Low Alpha)].<br><br>RESULTS: A significant correlation between engagement and IQ improvement, but no correlation between arousal and IQ improvement emerged, as expected.<br><br>CONCLUSIONS: Future research aiming at clarifying the role of arousal in psychological disorders and related symptoms will be essential.},
}
@article {pmid35772393,
year = {2022},
author = {Shi, B and Yue, Z and Yin, S and Wang, W and Yu, H and Huang, Z and Wang, J},
title = {Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac7d73},
pmid = {35772393},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.Multi-channel electroencephalogram data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery (MI)-based brain-computer interface (BCI) systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of MI-based BCI.Approach.In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive cross-subject generalization model (ACGM) is proposed. Three public MI datasets were used to validate the effectiveness of the proposed method.Main results.The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p< 0.001), the C3-Cz-C4 channels (p< 0.001), and 20 channels (p< 0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher's linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by common spatial pattern-Rank and correlation-based channel selection algorithm. Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32.Significance.The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.},
}
@article {pmid35770131,
year = {2022},
author = {},
title = {Correction to: Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury.},
journal = {Brain communications},
volume = {4},
number = {3},
pages = {fcac143},
doi = {10.1093/braincomms/fcac143},
pmid = {35770131},
issn = {2632-1297},
abstract = {[This corrects the article DOI: 10.1093/braincomms/fcab248.].},
}
@article {pmid35769707,
year = {2022},
author = {Urdaneta, ME and Kunigk, NG and Currlin, S and Delgado, F and Fried, SI and Otto, KJ},
title = {The Long-Term Stability of Intracortical Microstimulation and the Foreign Body Response Are Layer Dependent.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {908858},
pmid = {35769707},
issn = {1662-4548},
abstract = {Intracortical microstimulation (ICMS) of the somatosensory cortex (S1) can restore sensory function in patients with paralysis. Studies assessing the stability of ICMS have reported heterogeneous responses across electrodes and over time, potentially hindering the implementation and translatability of these technologies. The foreign body response (FBR) and the encapsulating glial scar have been associated with a decay in chronic performance of implanted electrodes. Moreover, the morphology, intrinsic properties, and function of cells vary across cortical layers, each potentially affecting the sensitivity to ICMS as well as the degree of the FBR across cortical depth. However, layer-by-layer comparisons of the long-term stability of ICMS as well as the extent of the astrocytic glial scar change across cortical layers have not been well explored. Here, we implanted silicon microelectrodes with electrode sites spanning all the layers of S1 in rats. Using a behavioral paradigm, we obtained ICMS detection thresholds from all cortical layers for up to 40 weeks. Our results showed that the sensitivity and long-term performance of ICMS is indeed layer dependent. Overall, detection thresholds decreased during the first 7 weeks post-implantation (WPI). This was followed by a period in which thresholds remained stable or increased depending on the interfacing layer: thresholds in L1 and L6 exhibited the most consistent increases over time, while those in L4 and L5 remained the most stable. Furthermore, histological investigation of the tissue surrounding the electrode showed a biological response of microglia and macrophages which peaked at L1, while the area of the astrocytic glial scar peaked at L2/3. Interestingly, the biological response of these FBR markers is less exacerbated at L4 and L5, suggesting a potential link between the FBR and the long-term stability of ICMS. These findings suggest that interfacing depth can play an important role in the design of chronically stable implantable microelectrodes.},
}
@article {pmid35767651,
year = {2022},
author = {Sun, G and Zeng, F and McCartin, M and Zhang, Q and Xu, H and Liu, Y and Chen, ZS and Wang, J},
title = {Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents.},
journal = {Science translational medicine},
volume = {14},
number = {651},
pages = {eabm5868},
pmid = {35767651},
issn = {1946-6242},
support = {R01 GM115384/GM/NIGMS NIH HHS/United States ; RF1 NS121776/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Chronic Pain/therapy ; *Deep Brain Stimulation ; Gyrus Cinguli ; Prefrontal Cortex ; Rats ; Rodentia ; },
abstract = {Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.},
}
@article {pmid35767500,
year = {2022},
author = {Yao, L and Jiang, N and Mrachacz-Kersting, N and Zhu, X and Farina, D and Wang, Y},
title = {Reducing the Calibration Time in Somatosensory BCI by Using Tactile ERD.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1870-1876},
doi = {10.1109/TNSRE.2022.3184402},
pmid = {35767500},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; Imagination/physiology ; Touch/physiology ; },
abstract = {OBJECTIVE: We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI).<br><br>METHODS: Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm.<br><br>RESULTS: The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset ([Formula: see text]=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy.<br><br>CONCLUSION: The proposed approach of using tactile ERD from the sensory cortex provides an effective way of reducing the calibration time in a somatosensory BCI system.<br><br>SIGNIFICANCE: The tactile stimulation would be specifically useful before BCI usage, avoiding excessive fatigue when the mental task is difficult to perform. The tactile ERD approach may find BCI applications for patients or users with preserved afferent pathways.},
}
@article {pmid35767496,
year = {2023},
author = {Fei, W and Bi, L and Wang, J and Xia, S and Fan, X and Guan, C},
title = {Effects of Cognitive Distraction on Upper Limb Movement Decoding From EEG Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {70},
number = {1},
pages = {166-174},
doi = {10.1109/TBME.2022.3187085},
pmid = {35767496},
issn = {1558-2531},
mesh = {Humans ; Bayes Theorem ; *Brain-Computer Interfaces ; Upper Extremity ; Electroencephalography/methods ; Movement ; Cognition ; },
abstract = {OBJECTIVE: Hand movement decoding from electroencephalograms (EEG) signals is vital to the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on hand movement decoding from EEG signals consider any distractions. However, in practice, patients can be distracted while using the hand movement decoding systems in real life. In this paper, we aim to investigate the effects of cognitive distraction on movement decoding performance.<br><br>METHODS: We first propose a robust decoding method of hand movement directions to cognitive distraction from EEG signals by using the Riemannian Manifold to extract affine invariant features and Gaussian Naive Bayes classifier (named RM-GNBC). Then, we use the experimental and simulated EEG data under conditions without and with distraction to compare the decoding performance of three decoding methods (including the proposed method, tangent space linear discriminant analysis (TSLDA), and baseline method)).<br><br>RESULTS: The simulation and experimental results show that the Riemannian-based methods (i.e., RM-GNBC and TSLDA) have higher accuracy under the conditions without and with cognitive distraction and smaller decreases in decoding accuracy between the conditions without and with cognitive distraction than the baseline method. Furthermore, the RM-GNBC method has 6% (paired t-test, p = 0.026) and 5% (paired t-test, p = 0.137) higher accuracies than the TSLDA method under the conditions without and with cognitive distraction, respectively.<br><br>CONCLUSION: The results show that the Riemannian-based methods have higher robustness to cognitive distraction.<br><br>SIGNIFICANCE: This work contributes to developing a brain-computer interface (BCI) to improve the rehabilitation and assistance of hand-impaired patients in real life and open an avenue to the studies on the effects of distraction on other BCI paradigms.},
}
@article {pmid35764160,
year = {2022},
author = {Jia, T and Li, C and Mo, L and Qian, C and Ji, L and Liu, A},
title = {Recognizing the individualized sensorimotor loop of stroke patients during BMI-supported rehabilitation training based on brain functional connectivity analysis.},
journal = {Journal of neuroscience methods},
volume = {378},
number = {},
pages = {109658},
doi = {10.1016/j.jneumeth.2022.109658},
pmid = {35764160},
issn = {1872-678X},
mesh = {Body Mass Index ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) based brain-machine interaction training can facilitate rehabilitation by closing the sensorimotor loop. However, it remains unclear how to evaluate whether the loop is closed, especially for stroke patients whose brain regions of motor control and sensorimotor feedback could be altered. Our hypothesis is that motor recovery depends on whether sensorimotor loop is established poststroke. This study aims to explore how to evaluate the establishment of sensorimotor loop based on the evolving neural reorganization patterns after stroke.<br><br>NEW METHOD: 14 stroke patients participated in the experiment and EEG were recorded during three specific tasks: Movement Imagery (MI), Passive Movement (PM) and Movement Execution (ME). Activated brain regions correlated with movement intention expression and sensorimotor feedback were detected respectively during MI and PM. In ME, local-averaged Phase Lag Index (PLI) was analyzed to represent the functional connectivity between activated brain regions of MI and PM.<br><br>RESULTS: Individualized cortical activation was found both in MI and PM. The overlapping brain activation during PM and MI did not correlate with patient's Fugl-Meyer Upper Extremity Motor Score (FMU) . However, we found that FMU of the group with higher local-averaged PLI was statistically higher than FMU of the group with lower local-averaged PLI compared with global-averaged PLI (p&#xa0;<&#xa0;0.05).<br><br>CONCLUSIONS: The findings demonstrate functional connectivity between activated brain regions of motor control and sensorimotor feedback may imply if the individualized sensorimotor loop is established poststroke. The successful formation of the closed loop can indicate stroke patients' motor recovery.},
}
@article {pmid35760828,
year = {2022},
author = {Zurita, F and Del Duca, F and Teshima, T and Hiendlmeier, L and Gebhardt, M and Luksch, H and Wolfrum, B},
title = {In vivo closed-loop control of a locust's leg using nerve stimulation.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {10864},
pmid = {35760828},
issn = {2045-2322},
mesh = {Algorithms ; Animals ; Electric Stimulation ; Feedback ; *Grasshoppers/physiology ; *Neurons/physiology ; },
abstract = {Activity of an innervated tissue can be modulated based on an acquired biomarker through feedback loops. How to convert this biomarker into a meaningful stimulation pattern is still a topic of intensive research. In this article, we present a simple closed-loop mechanism to control the mean angle of a locust's leg in real time by modulating the frequency of the stimulation on its extensor motor nerve. The nerve is interfaced with a custom-designed cuff electrode and the feedback loop is implemented online with a proportional control algorithm, which runs solely on a microcontroller without the need of an external computer. The results show that the system can be controlled with a single-input, single-output feedback loop. The model described in this article can serve as a primer for young researchers to learn about neural control in biological systems before applying these concepts in advanced systems. We expect that the approach can be advanced to achieve control over more complex movements by increasing the number of recorded biomarkers and selective stimulation units.},
}
@article {pmid35760181,
year = {2022},
author = {Qin, Y and Yang, B and Li, H and Ma, J},
title = {Immobilized BiOCl0.75I0.25/g-C3N4 nanocomposites for photocatalytic degradation of bisphenol A in the presence of effluent organic matter.},
journal = {The Science of the total environment},
volume = {842},
number = {},
pages = {156828},
doi = {10.1016/j.scitotenv.2022.156828},
pmid = {35760181},
issn = {1879-1026},
mesh = {Benzhydryl Compounds/chemistry ; Catalysis ; *Light ; *Nanocomposites/chemistry ; Phenols/chemistry ; },
abstract = {The BiOCl0.75I0.25/g-C3N4 nanosheet (BCI-CN) was successfully immobilized on polyolefin polyester fiber (PPF) through the hydrothermal method. The novel immobilized BiOCl0.75I0.25/g-C3N4 nanocomposites (BCI-CN-PPF) were characterized by scanning electron microscope (SEM), energy dispersive spectroscopy EDS, X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and UV-vis diffuse reflectance spectroscopy (UV-vis DRS) to confirm that BCI-CN was successfully immobilized on PPF with abundant oxygen vacancies reserved. Under simulated solar light irradiation, 100 % of bisphenol A (BPA) with an initial concentration of 10 mg·L[-1] was degraded by BCI-CN-PPF (0.2 g·L[-1] of BCI-CN immobilized) after 60 min. A similar photocatalytic efficiency of BPA was obtained in the presence of effluent organic matter (EfOM). The photocatalytic degradation of BPA was not affected by EfOM <5 mg-C/L. In comparison, the photocatalytic performance was considerably inhibited by EfOM with a concentration of 10 mg-C/L. Furthermore, photogenerated holes and superoxide radicals predominated in the photocatalytic degradation processes of BPA. The total organic carbon (TOC) removal efficiencies of BPA and EfOM were 75.2 % and 50 % in the BCI-CN-PPF catalytic system. The BPA removal efficiency of 94.9 % was still achieved in the eighth cycle of repeated use. This study provides a promising immobilized nanocomposite with high photocatalytic activity and excellent recyclability and reusability for practical application in wastewater treatment.},
}
@article {pmid35759821,
year = {2022},
author = {Mirfathollahi, A and Ghodrati, MT and Shalchyan, V and Daliri, MR},
title = {Decoding locomotion speed and slope from local field potentials of rat motor cortex.},
journal = {Computer methods and programs in biomedicine},
volume = {223},
number = {},
pages = {106961},
doi = {10.1016/j.cmpb.2022.106961},
pmid = {35759821},
issn = {1872-7565},
mesh = {Action Potentials ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; Locomotion ; *Motor Cortex ; Rats ; },
abstract = {BACKGROUND AND OBJECTIVE: Local Field Potentials (LFPs) recorded from the primary motor cortex (M1) have been shown to be very informative for decoding movement parameters, and these signals can be used to decode forelimb kinematic and kinetic parameters accurately. Although locomotion is one of the most basic and important motor abilities of humans and animals, the potential of LFPs in decoding abstract hindlimb locomotor parameters has not been investigated. This study investigates the feasibility of decoding speed and slope of locomotion, as two important abstract parameters of walking, using the LFP signals.<br><br>METHODS: Rats were trained to walk smoothly on a treadmill with different speeds and slopes. The brain signals were recorded using the microwire arrays chronically implanted in the hindlimb area of M1 while rats walked on the treadmill. LFP channels were spatially filtered using optimal common spatial patterns to increase the discriminability of speeds and slopes of locomotion. Logarithmic&#xa0;wavelet band powers were extracted as basic features, and the best features were selected using the statistical dependency criterion before classification.<br><br>RESULTS: Using 5&#xa0;s LFP trials, the average classification accuracies of four different speeds and seven different slopes reached 90.8% and 86.82%, respectively. The high-frequency LFP band (250-500&#xa0;Hz) was the most informative band about these parameters and contributed more than other frequency bands in the final decoder model.<br><br>CONCLUSIONS: Our results show that the LFP signals in M1 accurately decode locomotion speed and slope, which can be considered as abstract walking parameters needed for designing long-term brain-computer interfaces for hindlimb locomotion control.},
}
@article {pmid35759578,
year = {2022},
author = {Perez-Velasco, S and Santamaria-Vazquez, E and Martinez-Cagigal, V and Marcos-Martinez, D and Hornero, R},
title = {EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1766-1775},
doi = {10.1109/TNSRE.2022.3186442},
pmid = {35759578},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated to affect 10-50% of the population. This convolutional neural network includes the use of inception modules, residual connections and a design that introduces the symmetry of the brain through the mid-sagittal plane into the network architecture. It is complemented with a data augmentation technique that improves the generalization of the model and with the use of transfer learning across different datasets. We compare EEGSym's performance on inter-subject MI classification with ShallowConvNet, DeepConvNet, EEGNet and EEG-Inception. This comparison is performed on 5 publicly available datasets that include left or right hand motor imagery of 280 subjects. This population is the largest that has been evaluated in similar studies to date. EEGSym significantly outperforms the baseline models reaching accuracies of 88.6±9.0 on Physionet, 83.3±9.3 on OpenBMI, 85.1±9.5 on Kaya2018, 87.4±8.0 on Meng2019 and 90.2±6.5 on Stieger2021. At the same time, it allows 95.7% of the tested population (268 out of 280 users) to reach BCI control (≥70% accuracy). Furthermore, these results are achieved using only 16 electrodes of the more than 60 available on some datasets. Our implementation of EEGSym, which includes new advances for EEG processing with DL, outperforms previous state-of-the-art approaches on inter-subject MI classification.},
}
@article {pmid35757542,
year = {2022},
author = {Semprini, M and Arnulfo, G and Delis, I and Siebenhühner, F and Susi, G},
title = {Editorial: Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {937801},
pmid = {35757542},
issn = {1662-4548},
}
@article {pmid35756677,
year = {2022},
author = {Puttanawarut, C and Sirirutbunkajorn, N and Tawong, N and Khachonkham, S and Pattaranutaporn, P and Wongsawat, Y},
title = {Impact of Interfractional Error on Dosiomic Features.},
journal = {Frontiers in oncology},
volume = {12},
number = {},
pages = {726896},
pmid = {35756677},
issn = {2234-943X},
abstract = {OBJECTIVES: The purpose of this study was to investigate the stability of dosiomic features under random interfractional error. We investigated the differences in the values of features with different fractions and the error in the values of dosiomic features under interfractional error.<br><br>MATERIAL AND METHODS: The isocenters of the treatment plans of 15 lung cancer patients were translated by a maximum of &#xb1;3 mm in each axis with a mean of (0, 0, 0) and a standard deviation of (1.2, 1.2, 1.2) mm in the x, y, and z directions for each fraction. A total of 81 dose distributions for each patient were then calculated considering four fraction number groups (2, 10, 20, and 30). A total of 93 dosiomic features were extracted from each dose distribution in four different regions of interest (ROIs): gross tumor volume (GTV), planning target volume (PTV), heart, and both lungs. The stability of dosiomic features was analyzed for each fraction number group by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The agreements in the means of dosiomic features among the four fraction number groups were tested by ICC. The percent differences (PD) between the dosiomic features extracted from the original dose distribution and the dosiomic features extracted from the dose distribution with interfractional error were calculated.<br><br>RESULTS: Eleven out of 93 dosiomic features demonstrated a large CV (CV &#x2265; 20%). Overall CV values were highest in GTV ROIs and lowest in lung ROIs. The stability of dosiomic features decreased as the total number of fractions decreased. The ICC results showed that five out of 93 dosiomic features had an ICC lower than 0.75, which indicates intermediate or poor stability under interfractional error. The mean dosiomic feature values were shown to be consistent with different numbers of fractions (ICC &#x2265; 0.9). Some of the dosiomic features had PD greater than 50% and showed different PD values with different numbers of fractions.<br><br>CONCLUSION: Some dosiomic features have low stability under interfractional error. The stability and values of the dosiomic features were affected by the total number of fractions. The effect of interfractional error on dosiomic features should be considered in further studies regarding dosiomics for reproducible results.},
}
@article {pmid35754975,
year = {2022},
author = {Galvin-McLaughlin, D and Klee, D and Memmott, T and Peters, B and Wiedrick, J and Fried-Oken, M and Oken, B and , },
title = {Methodology and preliminary data on feasibility of a neurofeedback protocol to improve visual attention to letters in mild Alzheimer's disease.},
journal = {Contemporary clinical trials communications},
volume = {28},
number = {},
pages = {100950},
pmid = {35754975},
issn = {2451-8654},
support = {P30 AG066518/AG/NIA NIH HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) systems are controlled by users through neurophysiological input for a variety of applications, including communication, environmental control, and motor rehabilitation. Although individuals with severe speech and physical impairment are the primary users of this technology, BCIs have emerged as a potential tool for broader populations, including delivering cognitive training/interventions with neurofeedback (NFB).<br><br>METHODS: This paper describes the development and preliminary testing of a protocol for use of a BCI system with NFB as an intervention for people with mild Alzheimer's disease (AD). The intervention focused on training visual attention and language skills, as AD is often associated with functional impairments in both. This funded pilot study called for enrolling five participants with mild AD in a six-week BCI EEG-based NFB intervention that followed a four-to-seven-week baseline phase. While two participants completed the study, the remaining three participants could not complete the intervention phase because of COVID-19 restrictions.<br><br>RESULTS: Preliminary pilot results suggested: (1) participants with mild AD were able to participate in a study with multiple assessments per week and complete all outcome measures, (2) most outcome measures were reliable during the baseline phase, and (3) all participants with mild AD learned to operate a BCI spelling system with training.<br><br>CONCLUSIONS: Although preliminary results demonstrate practical feasibility to deliver NFB intervention using a BCI to adults with AD, completion of the protocol in its entirety with more participants is needed to further assess whether implementing NFB-based cognitive intervention is justified by functional treatment outcomes.<br><br>TRIAL REGISTRATION: This study was registered with ClinicalTrials.gov (NCT03790774).},
}
@article {pmid35754766,
year = {2022},
author = {Zhao, X and Jin, J and Xu, R and Li, S and Sun, H and Wang, X and Cichocki, A},
title = {A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {875851},
pmid = {35754766},
issn = {1662-5161},
abstract = {The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.},
}
@article {pmid35753202,
year = {2022},
author = {Wu, D and Jiang, X and Peng, R},
title = {Transfer learning for motor imagery based brain-computer interfaces: A tutorial.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {153},
number = {},
pages = {235-253},
doi = {10.1016/j.neunet.2022.06.008},
pmid = {35753202},
issn = {1879-2782},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Learning ; Machine Learning ; },
abstract = {A brain-computer interface (BCI) enables a user to communicate directly with an external device, e.g., a computer, using brain signals. It can be used to research, map, assist, augment, or repair human cognitive or sensory-motor functions. A closed-loop BCI system performs signal acquisition, temporal filtering, spatial filtering, feature engineering and classification, before sending out the control signal to an external device. Transfer learning (TL) has been widely used in motor imagery (MI) based BCIs to reduce the calibration effort for a new subject, greatly increasing their utility. This tutorial describes how TL can be considered in as many components of a BCI system as possible, and introduces a complete TL pipeline for MI-based BCIs. Examples on two MI datasets demonstrated the advantages of considering TL in multiple components of MI-based BCIs. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort.},
}
@article {pmid35752622,
year = {2022},
author = {Jeon, BB and Fuchs, T and Chase, SM and Kuhlman, SJ},
title = {Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {3638},
pmid = {35752622},
issn = {2041-1723},
support = {R21 NS115036/NS/NINDS NIH HHS/United States ; R01 EY024678/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Discrimination, Psychological/physiology ; Mice ; Photic Stimulation/methods ; Primary Visual Cortex ; *Visual Cortex/physiology ; Visual Perception/physiology ; },
abstract = {Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.},
}
@article {pmid35751949,
year = {2022},
author = {Prakash, SS and Mayo, JP and Ray, S},
title = {Decoding of attentional state using local field potentials.},
journal = {Current opinion in neurobiology},
volume = {76},
number = {},
pages = {102589},
pmid = {35751949},
issn = {1873-6882},
support = {/WT_/Wellcome Trust/United Kingdom ; P30 EY008098/EY/NEI NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Brain ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Movement/physiology ; },
abstract = {We review recent efforts to decode visual spatial attention from different types of brain signals, such as spikes and local field potentials (LFPs). Combining signals from more electrodes improves decoding, but the pattern of improvement varies considerably depending on the signal as well as the task (for example, decoding of sensory stimulus/motor intention versus location of attention). We argue that this pattern of results conveys important information not only about the usefulness of a particular brain signal for decoding attention, but also about the spatial scale over which attention operates in the brain. The spatial scale, in turn, likely depends on the extent of underlying mechanisms such as normalization, gain control via excitation-inhibition interactions, and neuromodulatory regulation of attention.},
}
@article {pmid35751052,
year = {2022},
author = {Florescu, AM and Sørensen, ALT and Nielsen, HV and Tolnai, D and Sjö, LD and Larsen, KL and Al-Karagholi, MA},
title = {Blastic plasmacytoid dendritic cell neoplasm and cerebral toxoplasmosis: a case report.},
journal = {BMC neurology},
volume = {22},
number = {1},
pages = {233},
pmid = {35751052},
issn = {1471-2377},
mesh = {Dendritic Cells/pathology ; Female ; *Hematologic Neoplasms/diagnosis/pathology ; Humans ; Male ; Middle Aged ; *Myeloproliferative Disorders ; *Skin Neoplasms/diagnosis/pathology ; *Toxoplasmosis, Cerebral/complications/diagnosis/pathology ; },
abstract = {BACKGROUND: The present case contributes to the limited literature on central nervous system involvement of blastic plasmacytoid dendritic cell neoplasm (BPDCN). CASE PRESENTATION : A 63-year-old male presented to the department of neurology with a three-day history of rapidly progressing headache, fatigue, and confusion. Physical examination revealed multiple bruise-like skin lesions. Initial laboratory workup raised suspicion of acute leukemia, and a brain computer tomography identified several hyperdense processes. A bone marrow biopsy gave the diagnosis BPDCN, a rare and aggressive hematologic malignancy derived from plasmacytoid dendritic cells with a poor prognosis. Lumbar puncture showed not only signs of BPDCN, but also cerebral toxoplasmosis, thus providing a differential diagnosis. Despite intensive systemic and intrathecal chemotherapy, the patient died 25 days later due to multi-organ failure.<br><br>DISCUSSION: The exact incidence of BPDCN is unknown and perhaps underestimated but may account for 0.5 - 1% of all hematological malignancies. The median age at onset is 60 to 70&#xa0;years, and most patients are men. Cutaneous lesions are the most frequent clinical manifestation at diagnosis. Other symptoms present at time of diagnosis or during disease progression include lymphadenopathy, splenomegaly and cytopenia caused by bone marrow involvement. Although the majority of BPDCN patients have no symptoms or signs of central nervous system involvement, plasmacytoid dendritic cells have been detected in the cerebrospinal fluid in more than 50%.<br><br>CONCLUSIONS: This case highlights the importance of considering hematological malignancies as a differential diagnosis in patients developing acute neurological symptoms and raises suspicion of a possible association between toxoplasmosis and hematological malignancies.},
}
@article {pmid35750930,
year = {2022},
author = {Rubinos, C and Kwon, SB and Megjhani, M and Terilli, K and Wong, B and Cespedes, L and Ford, J and Reyes, R and Kirsch, H and Alkhachroum, A and Velazquez, A and Roh, D and Agarwal, S and Claassen, J and Connolly, ES and Park, S},
title = {Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.},
journal = {Neurocritical care},
volume = {37},
number = {3},
pages = {670-677},
pmid = {35750930},
issn = {1556-0961},
support = {20POST35210653/AHA/American Heart Association-American Stroke Association/United States ; R21 NS113055/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Retrospective Studies ; Prospective Studies ; Ventriculoperitoneal Shunt ; *Hydrocephalus/surgery ; Cerebrospinal Fluid Leak ; *Subarachnoid Hemorrhage/surgery ; Drainage/methods ; Cerebrospinal Fluid Shunts ; },
abstract = {BACKGROUND: Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH.<br><br>METHODS: This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (&#x394;BCI). We analyzed the relationship of &#x394;BCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between &#x394;BCI and CSF output was computed to classify VPS.<br><br>RESULTS: Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p&#x2009;<&#x2009;0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80.<br><br>CONCLUSIONS: The correlation of &#x394;BCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between &#x394;BCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).},
}
@article {pmid35750532,
year = {2022},
author = {Tang, CC and Huang, JF and Kuo, LW and Cheng, CT and Liao, CH and Hsieh, CH and Fu, CY},
title = {The highest troponin I level during admission is associated with mortality in blunt cardiac injury patients.},
journal = {Injury},
volume = {53},
number = {9},
pages = {2960-2966},
doi = {10.1016/j.injury.2022.06.010},
pmid = {35750532},
issn = {1879-0267},
mesh = {Biomarkers ; Humans ; Intensive Care Units ; *Myocardial Contusions ; Prognosis ; *Thoracic Injuries ; Troponin I ; *Wounds, Nonpenetrating ; },
abstract = {BACKGROUND: Cardiac troponin I (cTnI) levels are usually measured in primary evaluations of blunt cardiac injury (BCI) patients. We evaluated the associations of cTnI levels with the outcomes of BCI patients at different times.<br><br>METHODS: From 2015 to 2019, blunt chest trauma patients with elevated cTnI levels were compared with patients without elevated cTnI levels using propensity score matching (PSM) to minimize selection bias. The cTnI levels at different times in the survivors and nonsurvivors were compared.<br><br>RESULTS: A total of 2,287 blunt chest trauma patients were included, and 57 (2.5%) of the patients had BCIs. PSM showed that patients with and without elevated cTnI levels had similar mortality rates (13.0% vs. 11.1%, p-value&#xa0;=&#xa0;0.317], hospital lengths of stay (LOSs) [17.3 (14.4) vs. 15.5 (22.2) days, p-value&#xa0;=&#xa0;0.699] and intensive care unit (ICU) LOSs [7.7 (12.1) vs. 6.4 (15.4) days, p-value&#xa0;=&#xa0;0.072]. Among the BCI patients, nonsurvivors had a significantly higher highest cTnI level during the observation period than survivors. Additionally, patients who needed surgical intervention had significantly higher highest cTnI levels than patients who did not.<br><br>CONCLUSIONS: An elevated cTnI level is insufficient for the evaluation of BCI and the determination of the need for further treatment. The highest cTnI level during the observation period may be related to mortality and the need for surgery in BCI patients.},
}
@article {pmid35750100,
year = {2022},
author = {Wang, Q and Luo, T and Xu, X and Han, Q and Xu, X and Zhang, X and Liu, X and Shi, Q},
title = {Chitosan-based composites reinforced with antibacterial flexible wood membrane for rapid hemostasis.},
journal = {International journal of biological macromolecules},
volume = {215},
number = {},
pages = {450-464},
doi = {10.1016/j.ijbiomac.2022.06.074},
pmid = {35750100},
issn = {1879-0003},
mesh = {Alginates/chemistry ; Anti-Bacterial Agents/chemistry/pharmacology ; *Chitosan/chemistry/pharmacology ; Hemostasis ; *Hemostatics/pharmacology ; Humans ; *Metal Nanoparticles/chemistry ; Silver/chemistry/pharmacology ; Wood ; },
abstract = {Irregular hemorrhagic traumas always threaten the health of patients due to uncontrollable bleeding and wound infections. The traditional hemostatic materials show dissatisfactory hemostatic efficiency and antibacterial activity in solving these potential bleeding dangers. Herein, we proposed a kind of composites based on flexible wood membrane (FWM) loaded with chitosan/alginate derivative for accelerating rapid hemostasis and preventing infection. FWM was removed part of hemicellulose and lignin by using NaOH/Na2SO3 mixture to obtain excellent flexibility while retaining the original porous structure, followed by loading silver nanoparticles on the FWM surface to prepare AgNPs-FWM as an antibacterial bio-carrier. Then, AgNPs-FWM was coated with polyoxyethylene stearate-modified chitosan and multi-aldehyde sodium alginate to fabricate the composites of chitosan/alginate/AgNPs-FWM (CSA/AgNPs-FWM) using in-situ Schiff base reaction. Furthermore, in vitro and in vivo experiments showed that the CSA/AgNPs-FWM composites exhibited lower BCI value (2.6 ± 1.3 %), more rapid hemostasis (26 s) and lower blood loss (67.8 mg) than that of the traditional materials. The possible mechanism for the hemostasis process was not only the high blood absorption capacity, but also the synergistic interaction between hydrophobic alkane chains, amino groups, aldehydes, hydroxyl groups and blood cells. Moreover, CSA/AgNPs-FWM showed exceptional superiorities in mechanical properties and antibacterial activity, which endowed composites high potential in hemostasis application for irregular external wound.},
}
@article {pmid35750042,
year = {2022},
author = {Hu, Z and Ma, J and Yue, H and Luo, Y and Li, X and Wang, C and Wang, L and Sun, B and Chen, Z and Wang, L and Gu, Y},
title = {Involvement of LIN28A in Wnt-dependent regulation of hippocampal neurogenesis in the aging brain.},
journal = {Stem cell reports},
volume = {17},
number = {7},
pages = {1666-1682},
pmid = {35750042},
issn = {2213-6711},
mesh = {Aging/physiology ; Animals ; Brain ; Dentate Gyrus/metabolism ; Hippocampus/metabolism ; Mice ; *Neural Stem Cells/metabolism ; *Neurogenesis/physiology ; },
abstract = {Hippocampal neurogenesis declines with aging. Wnt ligands and antagonists within the hippocampal neurogenic niche regulate the proliferation of neural progenitor cells and the development of new neurons, and the changes of their levels in the niche mediate aging-associated decline of neurogenesis. We found that RNA-binding protein LIN28A remained existent in neural progenitor cells and granule neurons in the adult hippocampus and that it decreased with aging. Lin28a knockout inhibited the responsiveness of neural progenitor cells to niche Wnt agonists and reduced neurogenesis, thus impairing pattern separation. Overexpression of Lin28a increased the proliferation of neural progenitor cells, promoted the functional integration of newborn neurons, restored neurogenesis in Wnt-deficient dentate gyrus, and rescued the impaired pattern separation in aging mice. Our data suggest that LIN28A regulates adult hippocampal neurogenesis as an intracellular mechanism by responding to niche Wnt signals, and its decrease is involved in aging-associated decline of hippocampal neurogenesis and related cognitive functions.},
}
@article {pmid35749326,
year = {2023},
author = {Ju, C and Guan, C},
title = {Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {34},
number = {12},
pages = {10955-10969},
doi = {10.1109/TNNLS.2022.3172108},
pmid = {35749326},
issn = {2162-2388},
abstract = {Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have been particularly successful in visual images. However, since the statistical characteristics of visual images depart radically from EEG signals, a natural question arises whether an alternative network architecture exists apart from CNNs. To address this question, we propose a novel geometric DL (GDL) framework called Tensor-CSPNet, which characterizes spatial covariance matrices derived from EEG signals on symmetric positive definite (SPD) manifolds and fully captures the temporospatiofrequency patterns using existing deep neural networks on SPD manifolds, integrating with experiences from many successful MI-EEG classifiers to optimize the framework. In the experiments, Tensor-CSPNet attains or slightly outperforms the current state-of-the-art performance on the cross-validation and holdout scenarios in two commonly used MI-EEG datasets. Moreover, the visualization and interpretability analyses also exhibit the validity of Tensor-CSPNet for the MI-EEG classification. To conclude, in this study, we provide a feasible answer to the question by generalizing the DL methodologies on SPD manifolds, which indicates the start of a specific GDL methodology for the MI-EEG classification.},
}
@article {pmid35748712,
year = {2022},
author = {Needham, JF and Arellano, G and Davies, SJ and Fisher, RA and Hammer, V and Knox, RG and Mitre, D and Muller-Landau, HC and Zuleta, D and Koven, CD},
title = {Tree crown damage and its effects on forest carbon cycling in a tropical forest.},
journal = {Global change biology},
volume = {28},
number = {18},
pages = {5560-5574},
doi = {10.1111/gcb.16318},
pmid = {35748712},
issn = {1365-2486},
mesh = {Biomass ; Carbon ; *Ecosystem ; Forests ; *Trees ; Tropical Climate ; },
abstract = {Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade-off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.},
}
@article {pmid35746109,
year = {2022},
author = {Sapari, L and Hout, S and Chung, JY},
title = {Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {12},
pages = {},
pmid = {35746109},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; Equipment Design ; Prostheses and Implants ; *Wireless Technology ; },
abstract = {An end-fire radiating implantable antenna with a small footprint and broadband operation at the frequency range of 3-5 GHz is proposed for high-data-rate wireless communication in a brain-machine interface. The proposed Vivaldi antenna was implanted vertically along the height of the skull to avoid deformation in the radiation pattern and to compensate for a gain-loss caused by surrounding lossy brain tissues. It was shown that the vertically implanted end-fire antenna had a 3 dB higher antenna gain than a horizontally implanted broadside radiating antenna discussed in recent literature. Additionally, comb-shaped slot arrays imprinted on the Vivaldi antenna lowered the resonant frequency by approximately 2 GHz and improved the antenna gain by more than 2 dB compared to an ordinary Vivaldi antenna. An antenna prototype was fabricated and then tested for verification inside a seven-layered semi-solid brain phantom where each layer had similar electromagnetic material properties as actual brain tissues. The measured data showed that the antenna radiated toward the end-fire direction with an average gain of -15.7 dBi under the frequency of interest, 3-5 GHz. A link budget analysis shows that reliable wireless communication can be achieved over a distance of 10.8 cm despite the electromagnetically harsh environment.},
}
@article {pmid35744539,
year = {2022},
author = {Chang, Z and Zhang, C and Li, C},
title = {Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network.},
journal = {Micromachines},
volume = {13},
number = {6},
pages = {},
pmid = {35744539},
issn = {2072-666X},
support = {19YF1437200//Shanghai Sailing Program/ ; },
abstract = {For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.},
}
@article {pmid35738232,
year = {2022},
author = {Tremmel, C and Fernandez-Vargas, J and Stamos, D and Cinel, C and Pontil, M and Citi, L and Poli, R},
title = {A meta-learning BCI for estimating decision confidence.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac7ba8},
pmid = {35738232},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Mental Processes ; Neural Networks, Computer ; },
abstract = {Objective.We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of brain-computer interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.Approach.We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from electroencephalography (EEG) and electro-oculogram (EOG) data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called domain adversarial neural networks, a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm.Main results.The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period.Significance.Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.},
}
@article {pmid35737671,
year = {2022},
author = {Bodda, S and Diwakar, S},
title = {Exploring EEG spectral and temporal dynamics underlying a hand grasp movement.},
journal = {PloS one},
volume = {17},
number = {6},
pages = {e0270366},
pmid = {35737671},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Hand Strength ; Humans ; Movement ; },
abstract = {For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.},
}
@article {pmid35737622,
year = {2022},
author = {Li, R and Wang, L and Suganthan, PN and Sourina, O},
title = {Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {10},
pages = {4996-5003},
doi = {10.1109/JBHI.2022.3185587},
pmid = {35737622},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Humans ; Imagination/physiology ; },
abstract = {Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective representations without human supervision, which has the potential to improve the electroencephalogram-based recognition performance with limited labeled data. However, heavy data augmentation is a key ingredient of contrastive learning. In view of the limited number of sample-based data augmentation in electroencephalogram processing, three methods, performance-measure-based time warp, frequency noise addition and frequency masking, are proposed based on the characteristics of electroencephalogram signal. These methods are parameter learning free, easy to implement, and can be applied to individual samples. In the experiment, the proposed data augmentation methods are evaluated on three electroencephalogram-based classification tasks, including situation awareness recognition, motor imagery classification and brain-computer interface steady-state visually evoked potentials speller system. Results demonstrated that the convolutional models trained with the proposed data augmentation methods yielded significantly improved performance over baselines. In overall, this work provides more potential methods to cope with the problem of limited data and boost the classification performance in electroencephalogram processing.},
}
@article {pmid35736909,
year = {2022},
author = {Marzuki, I and Septiningsih, E and Kaseng, ES and Herlinah, H and Sahrijanna, A and Sahabuddin, S and Asaf, R and Athirah, A and Isnawan, BH and Samidjo, GS and Rumagia, F and Hamidah, E and Santi, IS and Nisaa, K},
title = {Investigation of Global Trends of Pollutants in Marine Ecosystems around Barrang Caddi Island, Spermonde Archipelago Cluster: An Ecological Approach.},
journal = {Toxics},
volume = {10},
number = {6},
pages = {},
pmid = {35736909},
issn = {2305-6304},
support = {315/E4.1/AK.04.PT/2021//Ministry of Research, Technology and Higher Education/ ; },
abstract = {High-quality marine ecosystems are free from global trending pollutants' (GTP) contaminants. Accuracy and caution are needed during the exploitation of marine resources during marine tourism to prevent future ecological hazards that cause chain effects on aquatic ecosystems and humans. This article identifies exposure to GTP: microplastic (MP); polycyclic aromatic hydrocarbons (PAH); pesticide residue (PR); heavy metal (HM); and medical waste (MW), in marine ecosystems in the marine tourism area (MTA) area and Barrang Caddi Island (BCI) waters. A combination of qualitative and quantitative analysis methods were used with analytical instruments and mathematical formulas. The search results show the average total abundance of MPs in seawater (5.47 units/m[3]) and fish samples (7.03 units/m[3]), as well as in the sediment and sponge samples (8.18 units/m[3]) and (8.32 units/m[3]). Based on an analysis of the polymer structure, it was identified that the dominant light group was MPs: polyethylene (PE); polypropylene (PP); polystyrene (PS); followed by polyamide-nylon (PA); and polycarbonate (PC). Several PAH pollutants were identified in the samples. In particular, naphthalene (NL) types were the most common pollutants in all of the samples, followed by pyrene (PN), and azulene (AZ). Pb[+2] and Cu[+2] pollutants around BCI were successfully calculated, showing average concentrations in seawater of 0.164 ± 0.0002 mg/L and 0.293 ± 0.0007 mg/L, respectively, while in fish, the concentrations were 1.811 ± 0.0002 µg/g and 4.372 ± 0.0003 µg/g, respectively. Based on these findings, the BCI area is not recommended as a marine tourism destination.},
}
@article {pmid35735532,
year = {2022},
author = {Wang, J and Chen, YH and Yang, J and Sawan, M},
title = {Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern.},
journal = {Biosensors},
volume = {12},
number = {6},
pages = {},
pmid = {35735532},
issn = {2079-6374},
support = {041030080118//Westlake University/ ; 2021C03002//Zhejiang Key R&amp;D Program/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Follow-Up Studies ; Hand/*physiology ; Humans ; Intelligence ; Logistic Models ; Paralysis/rehabilitation ; Rehabilitation/methods ; },
abstract = {To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a "follow-up" pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.},
}
@article {pmid35733147,
year = {2022},
author = {Zhang, C and Qin, F and Li, X and Du, X and Li, T},
title = {Identification of novel proteins for lacunar stroke by integrating genome-wide association data and human brain proteomes.},
journal = {BMC medicine},
volume = {20},
number = {1},
pages = {211},
pmid = {35733147},
issn = {1741-7015},
mesh = {Bayes Theorem ; Brain ; Genome-Wide Association Study ; Humans ; Proteome/genetics ; Proteomics ; *Stroke/complications/genetics ; *Stroke, Lacunar/complications/genetics ; },
abstract = {BACKGROUND: Previous genome-wide association studies (GWAS) have identified numerous risk genes for lacunar stroke, but it is challenging to decipher how they confer risk for the disease. We employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for lacunar stroke.<br><br>METHODS: We systematically integrated lacunar stroke genome-wide association study (GWAS) (N=7338) with human brain proteomes (N=376) to perform proteome-wide association studies (PWAS), Mendelian randomization (MR), and Bayesian colocalization. We also used an independent human brain proteomic dataset (N=152) to annotate the new genes.<br><br>RESULTS: We found that the protein abundance of seven genes (ICA1L, CAND2, ALDH2, MADD, MRVI1, CSPG4, and PTPN11) in the brain was associated with lacunar stroke. These seven genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and astrocytes. Three genes (ICA1L, CAND2, ALDH2) were causal in lacunar stroke (P < 0.05/proteins identified for PWAS; posterior probability of hypothesis 4 &#x2265; 75 % for Bayesian colocalization), and they were linked with lacunar stroke in confirmatory PWAS and independent MR. We also found that ICA1L is related to lacunar stroke at the brain transcriptome level.<br><br>CONCLUSIONS: Our present proteomic findings have identified ICA1L, CAND2, and ALDH2 as compelling genes that may give key hints for future functional research and possible therapeutic targets for lacunar stroke.},
}
@article {pmid35732141,
year = {2022},
author = {Steele, AG and Manson, GA and Horner, PJ and Sayenko, DG and Contreras-Vidal, JL},
title = {Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: a proof-of-concept EEG study.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac7b4b},
pmid = {35732141},
issn = {1741-2552},
mesh = {Electroencephalography ; Humans ; Movement/physiology ; *Spinal Cord Injuries ; *Spinal Cord Stimulation/methods ; },
abstract = {Objective.Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity.Approach. Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components (ICs) were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. ICs of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks.Main results. IC analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions.Significance.The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.},
}
@article {pmid35732136,
year = {2022},
author = {Zhang, XN and Meng, QH and Zeng, M},
title = {A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac7b4a},
pmid = {35732136},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; },
abstract = {Objective.The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels.Approach.In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search algorithm, including an opposition-based learning strategy for generating high-quality initial population, an adaptive parameter strategy for improving search capability, and a bitwise operation strategy for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels.Main results.With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy.Significance.The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.},
}
@article {pmid35731756,
year = {2022},
author = {Guo, N and Wang, X and Duanmu, D and Huang, X and Li, X and Fan, Y and Li, H and Liu, Y and Yeung, EHK and To, MKT and Gu, J and Wan, F and Hu, Y},
title = {SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1737-1744},
doi = {10.1109/TNSRE.2022.3185262},
pmid = {35731756},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials ; Humans ; Recovery of Function/physiology ; *Robotics/methods ; *Stroke/complications ; *Stroke Rehabilitation/methods ; Treatment Outcome ; Upper Extremity ; },
abstract = {Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05 ± 8.03, p = 0.001), FMA shoulder/elbow (6.2 ± 5.94, p = 0.0004) and FMA wrist/hand (4.3 ± 2.83, p = 0.007), and WMFT (5.1 ± 5.53, p = 0.037). The improvement of FMA was significantly correlated with BCI accuracy (r = 0.714, p = 0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.},
}
@article {pmid35730476,
year = {2022},
author = {Li, M and Wu, L and Xu, G and Duan, F and Zhu, C},
title = {A Robust 3D-Convolutional Neural Network-Based Electroencephalogram Decoding Model for the Intra-Individual Difference.},
journal = {International journal of neural systems},
volume = {32},
number = {7},
pages = {2250034},
doi = {10.1142/S0129065722500344},
pmid = {35730476},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Individuality ; Neural Networks, Computer ; },
abstract = {The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in the event-related potential-based brain-computer interface (ERP-BCI). However, the intra-individual difference of ERP makes the traditional learning models trained on static EEG data hard to decode when the EEG features vary along the time, which limits the long-time performance of the model. Addressing this problem, this study proposes a three-dimension CNN (3D-CNN)-based model to decode the ERPs dynamically. As input, the EEG is transformed into a brain topographic map stream along time. Then the 3D-CNN applies three-dimension kernels to capture the dynamical characteristic of spatial feature at several time points. Ten subjects participated in a cross-time task for 6 or 12[Formula: see text]h. The 3D-CNN shows higher accuracies and shorter computational cost than the baseline models of the 2D-CNN, the long short term memory (LSTM), the back propagation (BP), and the fisher linear discriminant analysis (FLDA) when detecting the ERPs. In addition, four schemes of the 3D-CNN are compared to explore the influence of the structure on the performance. This result demonstrates advanced robustness of the 3D-CNN kernel to the intra-individual EEG difference, helping to launch a more practical EEG decoding model for a long-time use.},
}
@article {pmid35730288,
year = {2022},
author = {Ou, Y and Sun, S and Gan, H and Zhou, R and Yang, Z},
title = {An improved self-supervised learning for EEG classification.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {19},
number = {7},
pages = {6907-6922},
doi = {10.3934/mbe.2022325},
pmid = {35730288},
issn = {1551-0018},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Supervised Machine Learning ; },
abstract = {Motor Imagery EEG (MI-EEG) classification plays an important role in different Brain-Computer Interface (BCI) systems. Recently, deep learning has been widely used in the MI-EEG classification tasks, however this technology requires a large number of labeled training samples which are difficult to obtain, and insufficient labeled training samples will result in a degradation of the classification performance. To address the degradation problem, we investigate a Self-Supervised Learning (SSL) based MI-EEG classification method to reduce the dependence on a large number of labeled training samples. The proposed method includes a pretext task and a downstream classification one. In the pretext task, each MI-EEG is rearranged according to the temporal characteristic. A network is pre-trained using the original and rearranged MI-EEGs. In the downstream task, a MI-EEG classification network is firstly initialized by the network learned in the pretext task and then trained using a small number of the labeled training samples. A series of experiments are conducted on Data sets 1 and 2b of BCI competition IV and IVa of BCI competition III. In the case of one third of the labeled training samples, the proposed method can obtain an obvious improvement compared to the baseline network without using SSL. In the experiments under different percentages of the labeled training samples, the results show that the designed SSL strategy is effective and beneficial to improving the classification performance.},
}
@article {pmid35725741,
year = {2022},
author = {Nickl, RW and Anaya, MA and Thomas, TM and Fifer, MS and Candrea, DN and McMullen, DP and Thompson, MC and Osborn, LE and Anderson, WS and Wester, BA and Tenore, FV and Crone, NE and Cantarero, GL and Celnik, PA},
title = {Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {10353},
pmid = {35725741},
issn = {2045-2322},
mesh = {Electromyography ; *Forearm/physiology ; Humans ; Movement/physiology ; *Muscle, Skeletal/physiology ; Quadriplegia ; },
abstract = {Understanding the cortical representations of movements and their stability can shed light on improved brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs). We built representation maps by recording bilateral multiunit activity (MUA) and surface electromyography (EMG) as the participant executed voluntary contractions of the extensor carpi radialis (ECR), and attempted motions in the flexor carpi radialis (FCR), which was paralytic. To assess stability, we repeatedly mapped and compared left- and right-wrist-extensor-related activity throughout several sessions, comparing somatotopy of active electrodes, as well as neural signals both at the within-electrode (multiunit) and cross-electrode (network) levels. Wrist motions showed significant activation in motor and sensory cortical electrodes. Within electrodes, firing strength stability diminished as the time increased between consecutive measurements (hours within a session, or days across sessions), with higher stability observed in sensory cortex than in motor, and in the contralateral hemisphere than in the ipsilateral. However, we observed no differences at network level, and no evidence of decoding instabilities for wrist EMG, either across timespans of hours or days, or across recording area. While map stability differs between brain area and hemisphere at multiunit/electrode level, these differences are nullified at ensemble level.},
}
@article {pmid35724770,
year = {2022},
author = {Liu, D and Li, S and Ren, L and Li, X and Wang, Z},
title = {The Superior Colliculus/Lateral Posterior Thalamic Nuclei in Mice Rapidly Transmit Fear Visual Information Through the Theta Frequency Band.},
journal = {Neuroscience},
volume = {496},
number = {},
pages = {230-240},
doi = {10.1016/j.neuroscience.2022.06.021},
pmid = {35724770},
issn = {1873-7544},
mesh = {Animals ; Fear/physiology ; Lateral Thalamic Nuclei/physiology ; Mice ; *Posterior Thalamic Nuclei/physiology ; *Superior Colliculi/physiology ; Thalamic Nuclei/physiology ; Visual Pathways/physiology ; },
abstract = {Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential (LFP) signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.},
}
@article {pmid35724287,
year = {2022},
author = {Fan, L and Shen, H and Xie, F and Su, J and Yu, Y and Hu, D},
title = {DC-tCNN: A Deep Model for EEG-Based Detection of Dim Targets.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1727-1736},
doi = {10.1109/TNSRE.2022.3184725},
pmid = {35724287},
issn = {1558-0210},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Scalp ; },
abstract = {OBJECTIVE: Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficiency enhancement.<br><br>METHODS: We develop a BCI paradigm named Asynchronous Visual Evoked Paradigm (AVEP), in which subjects are required to search the dim targets within satellite images when their scalp electroencephalography (EEG) signals are simultaneously recorded. In the paradigm, stimulus onset time and target onset time are asynchronous because subjects need enough time to confirm whether there are targets of interest in the presented serial images. We further propose a Domain adaptive and Channel-wise attention-based Time-domain Convolutional Neural Network (DC-tCNN) to solve the single-trial EEG classification problem for the AVEP task. In this model, we design a multi-scale CNN module combined with a channel-wise attention module to effectively extract event-related brain responses underlying EEG signals. Meanwhile, domain adaptation is proposed to mitigate cross-subject distribution discrepancy.<br><br>RESULTS: The results demonstrate the superior performance and better generalizability of this model in classifying the single-trial EEG data of AVEP task in contrast to typical EEG deep learning networks. Visualization analyses of spatiotemporal features also illustrate the effectiveness and interpretability of our proposed paradigm and learning model.<br><br>CONCLUSION: The proposed paradigm and model can effectively explore ambiguous event-related brain responses on EEG-based dim target detection tasks.<br><br>SIGNIFICANCE: Our work can provide a valuable reference for BCI-based image detection of dim targets.},
}
@article {pmid35721361,
year = {2022},
author = {Riccio, A and Schettini, F and Galiotta, V and Giraldi, E and Grasso, MG and Cincotti, F and Mattia, D},
title = {Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {868419},
pmid = {35721361},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication "Grid 3." The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (± 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers' daily practice.},
}
@article {pmid35721351,
year = {2022},
author = {Al Boustani, G and Weiß, LJK and Li, H and Meyer, SM and Hiendlmeier, L and Rinklin, P and Menze, B and Hemmert, W and Wolfrum, B},
title = {Influence of Auditory Cues on the Neuronal Response to Naturalistic Visual Stimuli in a Virtual Reality Setting.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {809293},
pmid = {35721351},
issn = {1662-5161},
abstract = {Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli - an exploding and a burning box - interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern - a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by -1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.},
}
@article {pmid35720904,
year = {2022},
author = {Hu, X and Liu, Y and Zhang, HL and Wang, W and Li, Y and Meng, C and Fu, Z},
title = {Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1635672},
pmid = {35720904},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; *Robotic Surgical Procedures ; *Robotics/methods ; User-Computer Interface ; },
abstract = {The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs "Mapping Knowledge Domains" (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.},
}
@article {pmid35720721,
year = {2022},
author = {Liu, X and Liu, B and Dong, G and Gao, X and Wang, Y},
title = {Facilitating Applications of SSVEP-Based BCIs by Within-Subject Information Transfer.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {863359},
pmid = {35720721},
issn = {1662-4548},
abstract = {The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) can provide high-speed alternative and augmentative communication in real-world applications. For individuals using a long-term BCI, within-subject (i.e., cross-day and cross-electrode) transfer learning could improve the BCI performance and reduce the calibration burden. To validate the within-subject transfer learning scheme, this study designs a 40-target SSVEP-BCI. Sixteen subjects are recruited, each of whom has performed experiments on three different days and has undergone the experiments of the SSVEP-BCIs based on the dry and wet electrodes. Several transfer directions, including the cross-day directions in parallel with the cross-electrode directions, are analyzed, and it is found that the transfer learning-based approach can maintain stable performance by zero training. Compared with the fully calibrated approaches, the transfer learning-based approach can achieve significantly better or comparable performance in different transfer directions. This result verifies that the transfer learning-based scheme is well suited for implementing a high-speed zero-training SSVEP-BCI, especially the dry electrode-based SSVEP-BCI system. A validation experiment of the cross-day wet-to-dry transfer, involving nine subjects, has shown that the average accuracy is 85.97 ± 5.60% for the wet-to-dry transfer and 77.69 ± 6.42% for the fully calibrated method with dry electrodes. By leveraging the electroencephalography data acquired on different days by different electrodes via transfer learning, this study lays the foundation for facilitating the long-term usage of the SSVEP-BCI and advancing the frontier of the dry electrode-based SSVEP-BCI in real-world applications.},
}
@article {pmid35720707,
year = {2022},
author = {Lu, R and Zeng, Y and Zhang, R and Yan, B and Tong, L},
title = {SAST-GCN: Segmentation Adaptive Spatial Temporal-Graph Convolutional Network for P3-Based Video Target Detection.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {913027},
pmid = {35720707},
issn = {1662-4548},
abstract = {Detecting video-induced P3 is crucial to building the video target detection system based on the brain-computer interface. However, studies have shown that the brain response patterns corresponding to video-induced P3 are dynamic and determined by the interaction of multiple brain regions. This paper proposes a segmentation adaptive spatial-temporal graph convolutional network (SAST-GCN) for P3-based video target detection. To make full use of the dynamic characteristics of the P3 signal data, the data is segmented according to the processing stages of the video-induced P3, and the brain network connections are constructed correspondingly. Then, the spatial-temporal feature of EEG data is extracted by adaptive spatial-temporal graph convolution to discriminate the target and non-target in the video. Especially, a style-based recalibration module is added to select feature maps with higher contributions and increase the feature extraction ability of the network. The experimental results demonstrate the superiority of our proposed model over the baseline methods. Also, the ablation experiments indicate that the segmentation of data to construct the brain connection can effectively improve the recognition performance by reflecting the dynamic connection relationship between EEG channels more accurately.},
}
@article {pmid35720691,
year = {2022},
author = {Wang, XY and Li, C and Zhang, R and Wang, L and Tan, JL and Wang, H},
title = {Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {921642},
pmid = {35720691},
issn = {1662-4548},
abstract = {At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications.},
}
@article {pmid35718470,
year = {2022},
author = {Liu, M and Ushiba, J},
title = {Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis.},
journal = {The Keio journal of medicine},
volume = {71},
number = {4},
pages = {82-92},
doi = {10.2302/kjm.2022-0002-OA},
pmid = {35718470},
issn = {1880-1293},
mesh = {Humans ; *Stroke Rehabilitation ; *Brain-Computer Interfaces ; Body Mass Index ; *Stroke/complications/therapy ; Upper Extremity ; *Neurological Rehabilitation ; Hemiplegia ; Recovery of Function ; },
abstract = {Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.},
}
@article {pmid35717542,
year = {2022},
author = {Asadzadeh, S and Yousefi Rezaii, T and Beheshti, S and Meshgini, S},
title = {Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {10282},
pmid = {35717542},
issn = {2045-2322},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Emotions ; Humans ; Neural Networks, Computer ; },
abstract = {Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli-Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1-2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.},
}
@article {pmid35716819,
year = {2022},
author = {Maÿe, A and Mutz, M and Engel, AK},
title = {Training the spatially-coded SSVEP BCI on the fly.},
journal = {Journal of neuroscience methods},
volume = {378},
number = {},
pages = {109652},
doi = {10.1016/j.jneumeth.2022.109652},
pmid = {35716819},
issn = {1872-678X},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {BACKGROUND: The spatially-coded SSVEP BCI employs the retinotopic map in the human visual pathway to infer the gaze direction of the operator relative to a flicker stimulus inducing steady-state visual evoked potentials (SSVEPs) in the brain. It has been shown that with this method, up to 16 channels can be encoded using only a single flicker stimulus. Another advantage over conventional frequency-coded SSVEP BCIs, in which channels are encoded by different combinations of frequencies and phases, is that the operator does not have to gaze directly at flickering lights. This can reduce visual fatigue and improve user comfort. Whereas the frequency of the SSVEP response is well predictable, which has enabled the development of frequency-coded SSVEP BCIs which do not require training data, the spatial distribution of the SSVEP response over the scalp differs much more between different people. This requires collecting a substantial amount of training data before the spatially-coded BCI could be put into operation.<br><br>NEW METHOD: In this study we address this issue by combining the spatially-coded BCI with a feedback channel which the operator uses to flag classification errors, and which allows the system to accumulate valid training data while the BCI is used to solve a spatial navigation task.<br><br>RESULTS: Starting from the minimal number of samples required by the classification method, the approach achieved an average accuracy of 69&#xa0;&#xb1;&#xa0;15 %, corresponding to an ITR of 31&#xa0;&#xb1;&#xa0;17 bits/min, in solving the task for the first time. This accuracy improved to 87&#xa0;&#xb1;&#xa0;9 % (ITR: 54&#xa0;&#xb1;&#xa0;14 bits/min) after completing the task 2 more times. Further we show that participants with a stable SSVEP topography over repeated stimulation enable the BCI to achieve higher accuracies.<br><br>Compared to a similar system with separate training and application phases, the time to achieve the same output is reduced by more than 50 %.<br><br>CONCLUSIONS: Evaluating the approach in 17 participants suggests that the performance of the spatially-coded BCI with a minimal set of training samples is sufficient to be operational, and that performance keeps improving in the course of its application.},
}
@article {pmid35716435,
year = {2022},
author = {Zheng, Y and Ma, Y and Cammon, J and Zhang, S and Zhang, J and Zhang, Y},
title = {A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm.},
journal = {Computers in biology and medicine},
volume = {147},
number = {},
pages = {105718},
doi = {10.1016/j.compbiomed.2022.105718},
pmid = {35716435},
issn = {1879-0534},
mesh = {Algorithms ; *Automobile Driving ; *Electroencephalography/methods ; Entropy ; Machine Learning ; },
abstract = {This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.},
}
@article {pmid35715209,
year = {2022},
author = {Luis-Islas, J and Luna, M and Floran, B and Gutierrez, R},
title = {Optoception: Perception of Optogenetic Brain Perturbations.},
journal = {eNeuro},
volume = {9},
number = {3},
pages = {},
pmid = {35715209},
issn = {2373-2822},
mesh = {Animals ; *Brain/physiology ; Head ; Mice ; *Optogenetics ; Perception ; Reward ; },
abstract = {How do animals experience brain manipulations? Optogenetics has allowed us to manipulate selectively and interrogate neural circuits underlying brain function in health and disease. However, little is known about whether mice can detect and learn from arbitrary optogenetic perturbations from a wide range of brain regions to guide behavior. To address this issue, mice were trained to report optogenetic brain perturbations to obtain rewards and avoid punishments. Here, we found that mice can perceive optogenetic manipulations regardless of the perturbed brain area, rewarding effects, or the stimulation of glutamatergic, GABAergic, and dopaminergic cell types. We named this phenomenon optoception, a perceptible signal internally generated from perturbing the brain, as occurs with interoception. Using optoception, mice can learn to execute two different sets of instructions based on the laser frequency. Importantly, optoception can occur either activating or silencing a single cell type. Moreover, stimulation of two brain regions in a single mouse uncovered that the optoception induced by one brain region does not necessarily transfer to a second not previously stimulated area, suggesting a different sensation is experienced from each site. After learning, they can indistinctly use randomly interleaved perturbations from both brain regions to guide behavior. Collectively taken, our findings revealed that mice's brains could "monitor" perturbations of their self-activity, albeit indirectly, perhaps via interoception or as a discriminative stimulus, opening a new way to introduce information to the brain and control brain-computer interfaces.},
}
@article {pmid35714614,
year = {2022},
author = {Huang, S and Xu, P and Shen, DD and Simon, IA and Mao, C and Tan, Y and Zhang, H and Harpsøe, K and Li, H and Zhang, Y and You, C and Yu, X and Jiang, Y and Zhang, Y and Gloriam, DE and Xu, HE},
title = {GPCRs steer Gi and Gs selectivity via TM5-TM6 switches as revealed by structures of serotonin receptors.},
journal = {Molecular cell},
volume = {82},
number = {14},
pages = {2681-2695.e6},
doi = {10.1016/j.molcel.2022.05.031},
pmid = {35714614},
issn = {1097-4164},
mesh = {GTP-Binding Proteins/metabolism ; Ligands ; *Receptors, G-Protein-Coupled/metabolism ; Receptors, Serotonin/genetics/metabolism ; *Serotonin ; },
abstract = {Serotonin (or 5-hydroxytryptamine, 5-HT) is an important neurotransmitter that activates 12 different G protein-coupled receptors (GPCRs) through selective coupling of Gs, Gi, or Gq proteins. The structural basis for G protein subtype selectivity by these GPCRs remains elusive. Here, we report the structures of the serotonin receptors 5-HT4, 5-HT6, and 5-HT7 with Gs, and 5-HT4 with Gi1. The structures reveal that transmembrane helices TM5 and TM6 alternate lengths as a macro-switch to determine receptor's selectivity for Gs and Gi, respectively. We find that the macro-switch by the TM5-TM6 length is shared by class A GPCR-G protein structures. Furthermore, we discover specific residues within TM5 and TM6 that function as micro-switches to form specific interactions with Gs or Gi. Together, these results present a common mechanism of Gs versus Gi protein coupling selectivity or promiscuity by class A GPCRs and extend the basis of ligand recognition at serotonin receptors.},
}
@article {pmid35714087,
year = {2022},
author = {Ye, X and Yang, C and Chen, Y and Wang, Y and Gao, X and Zhang, H},
title = {Multisymbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1693-1704},
doi = {10.1109/TNSRE.2022.3183087},
pmid = {35714087},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {The optimization of coding stimulus is a crucial factor in the study of steady-state visual evoked potential (SSVEP)-based brain-computer interface(BCI).This study proposed an encoding approach named Multi-Symbol Time Division Coding (MSTDC). This approach is based on a protocol of maximizing the distance between neural responses, which aims to encode stimulation systems implementing any number of targets with finite stimulations of different frequencies and phases. Firstly, this study designed an SSVEP-based BCI system containing forty targets with this approach. The stimulation encoding of this system was achieved with four temporal-divided stimuli that adopt the same frequency of 30 Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of 96.77 ±2.47 % and an average information transfer rate (ITR) of 119.05 ± 6.11 bits/min. This study also devised an SSVEP-based BCI system containing 72 targets and proposed a Template Splicing task-related component analysis (TRCA) algorithm that utilized the dataset of the previous system containing forty targets as the training dataset. The subjects acquired an average accuracy of 86.23 ± 7.75% and an average ITR of 95.68 ± 14.19 bits/min. It can be inferred that MSTDC can encode multiple targets with limited frequencies and phases of stimuli. Meanwhile, this protocol can be effortlessly expanded into other systems and sufficiently reduce the cost of collecting training data. This study provides a feasible technique for obtaining a comfortable SSVEP-based BCI with multiple targets while maintaining high information transfer rate.},
}
@article {pmid35714085,
year = {2022},
author = {Tsai, BY and Diddi, SVS and Ko, LW and Wang, SJ and Chang, CY and Jung, TP},
title = {Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2022.3174528},
pmid = {35714085},
issn = {2162-2388},
abstract = {Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.},
}
@article {pmid35713603,
year = {2023},
author = {Venkatesh, S and Miranda, ER and Braund, E},
title = {SSVEP-based brain-computer interface for music using a low-density EEG system.},
journal = {Assistive technology : the official journal of RESNA},
volume = {35},
number = {5},
pages = {378-388},
doi = {10.1080/10400435.2022.2084182},
pmid = {35713603},
issn = {1949-3614},
mesh = {Female ; Humans ; *Brain-Computer Interfaces ; *Music ; Evoked Potentials, Visual ; Electroencephalography ; Evoked Potentials ; Photic Stimulation ; Algorithms ; },
abstract = {In this paper, we present a bespoke brain-computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min[-1] and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min[-1] and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.},
}
@article {pmid35712448,
year = {2022},
author = {Birch, N and Graham, J and Ozolins, C and Kumarasinghe, K and Almesfer, F},
title = {Home-Based EEG Neurofeedback Intervention for the Management of Chronic Pain.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {3},
number = {},
pages = {855493},
pmid = {35712448},
issn = {2673-561X},
abstract = {BACKGROUND: Chronic pain and associated symptoms often cause significant disability and reduced quality of life (QoL). Neurofeedback (NFB) as part of a Brain Computer Interface can help some patients manage chronic pain by normalising maladaptive brain activity measured with electroencephalography (EEG).<br><br>OBJECTIVES: This study was designed to assess the efficacy and safety of a novel home-based NFB device for managing chronic pain by modifying specific EEG activity.<br><br>METHODS: A prospective, single-arm, proof-of-concept study was conducted between June 2020 and March 2021 among adults with chronic pain (registered with ClinicalTrials.gov NCT04418362). Axon EEG NFB systems for home use were provided to each, and 32-48 NFB training sessions were completed by the participants over 8-weeks. The primary outcome was self-reported pain. Assessment of central sensitisation, sleep quality, affective symptoms, change in QoL, adverse events during use and EEG correlations with symptoms were secondary outcomes.<br><br>RESULTS: Sixteen participants were enrolled. Eleven reported pain relief following NFB training, eight reporting clinically significant improvements. Central sensitisation symptoms improved by a third (p < 0.0001), sleep quality by almost 50% (p < 0.001), anxiety reduced by 40% (p = 0.015), and QoL improved at final follow-up for 13 participants. The majority (69%) of participants who upregulated relative alpha reported improved pain, and those who downregulated relative hi-beta reported improved pain, reduced anxiety and depression scores. There were no adverse events during the trial.<br><br>CONCLUSIONS: Home-based NFB training is well-tolerated and may provide relief for sufferers of chronic pain and its associated symptoms.<br><br>SUMMARY: Axon, a home-based NFB training device, can positively influence pain and associated symptoms in a proportion of people with chronic pain.},
}
@article {pmid35707519,
year = {2022},
author = {Tao, J and Li, Z and Liu, Y and Li, J and Bai, R},
title = {Performance Comparison of Different Neuroimaging Methods for Predicting Upper Limb Motor Outcomes in Patients after Stroke.},
journal = {Neural plasticity},
volume = {2022},
number = {},
pages = {4203698},
pmid = {35707519},
issn = {1687-5443},
mesh = {*Diffusion Tensor Imaging ; Humans ; Pyramidal Tracts ; Recovery of Function ; *Stroke ; Upper Extremity ; },
abstract = {Several neuroimaging methods have been proposed to assess the integrity of the corticospinal tract (CST) for predicting recovery of motor function after stroke, including conventional structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI). In this study, we aimed to compare the predicative performance of these methods using different neuroimaging modalities and optimize the prediction protocol for upper limb motor function after stroke in a clinical environment. We assessed 28 first-ever stroke patients with upper limb motor impairment. We used the upper extremity module of the Fugl-Meyer assessment (UE-FM) within 1 month of onset (baseline) and again 3 months poststroke. sMRI (T1- and T2-based) was used to measure CST-weighted lesion load (CST-wLL), and DTI was used to measure the fractional anisotropy asymmetry index (FAAI) and the ratio of fractional anisotropy (rFA). The CST-wLL within 1 month poststroke was closely correlated with upper limb motor outcomes and recovery potential. CST-wLL ≥ 2.068 cc indicated serious CST damage and a poor outcome (100%). CST-wLL < 1.799 cc was correlated with a considerable rate (>70%) of upper limb motor function recovery. CST-wLL showed a comparable area under the curve (AUC) to that of the CST-FAAI (p = 0.71). Inclusion of extra-CST-FAAI did not significantly increase the AUC (p = 0.58). Our findings suggest that sMRI-derived CST-wLL is a precise predictor of upper limb motor outcomes 3 months poststroke. We recommend this parameter as a predictive imaging biomarker for classifying patients' recovery prognosis in clinical practice. Conversely, including DTI appeared to induce no significant benefits.},
}
@article {pmid35705556,
year = {2022},
author = {Luo, X},
title = {Directly wireless communication of human minds via mind-controlled programming metasurface.},
journal = {Light, science &amp; applications},
volume = {11},
number = {1},
pages = {182},
pmid = {35705556},
issn = {2047-7538},
abstract = {An concept of electromagnetic brain-computer-metasurface (EBCM), and remotely mindcontrolled metasurface (RMCM) via brainwaves is reported in eLight. Rather than DC voltage from power supply or AC voltages from signal generators, such metasurfaces are controlled by brainwaves collected in real time and can transmit information wirelessly between human brains. Such platforms can lead to a promising approach for the service of disabled people.},
}
@article {pmid35704992,
year = {2022},
author = {Borra, D and Magosso, E and Castelo-Branco, M and Simões, M},
title = {A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism.},
journal = {Journal of neural engineering},
volume = {19},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac7908},
pmid = {35704992},
issn = {1741-2552},
mesh = {Algorithms ; *Autism Spectrum Disorder ; *Autistic Disorder ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Neural Networks, Computer ; },
abstract = {Objective.P300 can be analyzed in autism spectrum disorder (ASD) to derive biomarkers and can be decoded in brain-computer interfaces to reinforce ASD impaired skills. Convolutional neural networks (CNNs) have been proposed for P300 decoding, outperforming traditional algorithms but they (a) do not investigate optimal designs in different training conditions; (b) lack in interpretability. To overcome these limitations, an interpretable CNN (ICNN), that we recently proposed for motor decoding, has been modified and adopted here, with its optimal design searched via Bayesian optimization.Approach.The ICNN provides a straightforward interpretation of spectral and spatial features learned to decode P300. The Bayesian-optimized (BO) ICNN design was investigated separately for different training strategies (within-subject, within-session, and cross-subject) and BO models were used for the subsequent analyses. Specifically, transfer learning (TL) potentialities were investigated by assessing how pretrained cross-subject BO models performed on a new subject vs. random-initialized models. Furthermore, within-subject BO-derived models were combined with an explanation technique (ICNN + ET) to analyze P300 spectral and spatial features.Main results.The ICNN resulted comparable or even outperformed existing CNNs, at the same time being lighter. BO ICNN designs differed depending on the training strategy, needing more capacity as the training set variability increased. Furthermore, TL provided higher performance than networks trained from scratch. The ICNN + ET analysis suggested the frequency range [2, 5.8] Hz as the most relevant, and spatial features showed a right-hemispheric parietal asymmetry. The ICNN + ET-derived features, but not ERP-derived features, resulted significantly and highly correlated to autism diagnostic observation schedule clinical scores.Significance.This study substantiates the idea that a CNN can be designed both accurate and interpretable for P300 decoding, with an optimized design depending on the training condition. The novel ICNN-based analysis tool was able to better capture ASD neural signatures than traditional event-related potential analysis, possibly paving the way for identifying novel biomarkers.},
}
@article {pmid35704211,
year = {2023},
author = {Xie, X and Gong, S and Sun, N and Zhu, J and Xu, X and Xu, Y and Li, X and Du, Z and Liu, X and Zhang, J and Gong, W and Si, K},
title = {Contextual Fear Learning and Extinction in the Primary Visual Cortex of Mice.},
journal = {Neuroscience bulletin},
volume = {39},
number = {1},
pages = {29-40},
pmid = {35704211},
issn = {1995-8218},
mesh = {Mice ; Animals ; *Primary Visual Cortex ; *Extinction, Psychological/physiology ; Learning/physiology ; Fear/physiology ; Hippocampus/physiology ; },
abstract = {Fear memory contextualization is critical for selecting adaptive behavior to survive. Contextual fear conditioning (CFC) is a classical model for elucidating related underlying neuronal circuits. The primary visual cortex (V1) is the primary cortical region for contextual visual inputs, but its role in CFC is poorly understood. Here, our experiments demonstrated that bilateral inactivation of V1 in mice impaired CFC retrieval, and both CFC learning and extinction increased the turnover rate of axonal boutons in V1. The frequency of neuronal Ca[2+] activity decreased after CFC learning, while CFC extinction reversed the decrease and raised it to the naïve level. Contrary to control mice, the frequency of neuronal Ca[2+] activity increased after CFC learning in microglia-depleted mice and was maintained after CFC extinction, indicating that microglial depletion alters CFC learning and the frequency response pattern of extinction-induced Ca[2+] activity. These findings reveal a critical role of microglia in neocortical information processing in V1, and suggest potential approaches for cellular-based manipulation of acquired fear memory.},
}
@article {pmid35702730,
year = {2022},
author = {Rustamov, N and Humphries, J and Carter, A and Leuthardt, EC},
title = {Theta-gamma coupling as a cortical biomarker of brain-computer interface-mediated motor recovery in chronic stroke.},
journal = {Brain communications},
volume = {4},
number = {3},
pages = {fcac136},
pmid = {35702730},
issn = {2632-1297},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R21 NS102696/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
abstract = {Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain-computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain-computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain-computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain-computer interface therapy are associated with alterations in phase-amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain-computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain-computer interface therapy and once every 4 weeks after the therapy. Changes in phase-amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta-gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain-computer interface therapy sessions. Importantly, an increase in theta-gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta-gamma coupling increase following brain-computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta-gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha-gamma coupling was not altered by brain-computer interface therapy. Power spectra did not change significantly over the course of the brain-computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain-computer interface therapy was strongly correlated with increased theta-gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.},
}
@article {pmid35701505,
year = {2022},
author = {Ying, J and Wei, Q and Zhou, X},
title = {Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {9818},
pmid = {35701505},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Machine Learning ; },
abstract = {One of the main problems that a brain-computer interface (BCI) face is that a training stage is required for acquiring training data to calibrate its classification model just before every use. Transfer learning is a promising method for addressing the problem. In this paper, we propose a Riemannian geometry-based transfer learning algorithm for code modulated visual evoked potential (c-VEP)-based BCIs, which can effectively reduce the calibration time without sacrificing the classification accuracy. The algorithm includes the main procedures of log-Euclidean data alignment (LEDA), super-trial construction, covariance matrix estimation, training accuracy-based subject selection (TSS) and minimum distance to mean classification. Among them, the LEDA reduces the difference in data distribution between subjects, whereas the TSS promotes the similarity between a target subject and the source subjects. The resulting performance of transfer learning is improved significantly. Sixteen subjects participated in a c-VEP BCI experiment and the recorded data were used in offline analysis. Leave-one subject-out (LOSO) cross-validation was used to evaluate the proposed algorithm on the data set. The results showed that the algorithm achieved much higher classification accuracy than the subject-specific (baseline) algorithm with the same number of training trials. Equivalently, the algorithm reduces the training time of the BCI at the same performance level and thus facilitates its application in real world.},
}
@article {pmid35700633,
year = {2022},
author = {Ni, P and Zhou, C and Meng, Y and Xue, R and Yang, X and Li, L and Zhao, L and Wei, J and Ni, R and Wang, Y and Ma, X and Guo, W and Wang, Q and Li, T},
title = {Generation and characterization of human-derived iPSC lines from two cousins with schizophrenia and bipolar disorder and their unaffected cousin.},
journal = {Stem cell research},
volume = {63},
number = {},
pages = {102832},
doi = {10.1016/j.scr.2022.102832},
pmid = {35700633},
issn = {1876-7753},
mesh = {Adolescent ; *Bipolar Disorder ; Cell Differentiation ; Family ; Female ; Humans ; *Induced Pluripotent Stem Cells ; Leukocytes, Mononuclear ; *Schizophrenia ; },
abstract = {Schizophrenia (SCZ) and bipolar disorder (BD) are debilitating neurodevelopmental disorders with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were donated by three females. An adolescent female was clinically diagnosed as first-episode SCZ. One of her cousins was clinically diagnosed as BD and another one was unaffected control. Induced pluripotent stem cells (iPSCs) were established with reprograming factors Oct4, Sox2, Nanog, Lin28, c-myc, Klf4, and SV40LT. All lines presented normal karyotype and highly expressed pluripotency markers in vitro. All iPSCs were capable to differentiate into derivatives of three germ layers in vivo.},
}
@article {pmid35700258,
year = {2022},
author = {Qi, Y and Zhu, X and Xu, K and Ren, F and Jiang, H and Zhu, J and Zhang, J and Pan, G and Wang, Y},
title = {Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {12},
pages = {3825-3835},
doi = {10.1109/TBME.2022.3182588},
pmid = {35700258},
issn = {1558-2531},
mesh = {Humans ; *Brain-Computer Interfaces ; Bayes Theorem ; *Artificial Limbs ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs.<br><br>METHOD: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.<br><br>RESULTS: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days).<br><br>CONCLUSION: Experimental results demonstrate the superiority of DyEnsemble in online BMI control.<br><br>SIGNIFICANCE: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.},
}
@article {pmid35700255,
year = {2023},
author = {Tang, Y and Chen, D and Liu, H and Cai, C and Li, X},
title = {Deep EEG Superresolution via Correlating Brain Structural and Functional Connectivities.},
journal = {IEEE transactions on cybernetics},
volume = {53},
number = {7},
pages = {4410-4422},
doi = {10.1109/TCYB.2022.3178370},
pmid = {35700255},
issn = {2168-2275},
mesh = {Humans ; *Autism Spectrum Disorder ; Brain ; Electroencephalography/methods ; Emotions ; *Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by 1%-6% and the improvement of signal-to-noise ratio (SNR) up to 1.2 dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR.},
}
@article {pmid35700243,
year = {2022},
author = {Cai, S and Li, H and Wu, Q and Liu, J and Zhang, Y},
title = {Motor Imagery Decoding in the Presence of Distraction Using Graph Sequence Neural Networks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1716-1726},
doi = {10.1109/TNSRE.2022.3183023},
pmid = {35700243},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {In this study, we propose a graph sequence neural network (GSNN) to accurately decode patterns of motor imagery from electroencephalograms (EEGs) in the presence of distractions. GSNN aims to build subgraphs by exploiting biological topologies among brain regions to capture local and global relationships across characteristic channels. Specifically, we model the similarity between pairwise EEG channels by the adjacency matrix of the graph sequence neural network. In addition, we propose a node domain attention selection network in which the connection and sparsity of the adjacency matrix can be adjusted dynamically according to the EEG signals acquired from different subjects. Extensive experiments on the public Berlin-distraction dataset show that in most experimental settings, our model performs considerably better than the state-of-the-art models. Moreover, comparative experiments indicate that our proposed node domain attention selection network plays a crucial role in improving the sensibility and adaptability of the GSNN model. The results show that the GSNN algorithm obtained superior classification accuracy (The average value of Recall, Precision, and F-score were 80.44%, 81.07% and 80.54%) compared to the state-of-the-art models. Finally, in the process of extracting the intermediate results, the relationships between important brain regions and channels were revealed to different influences in distraction themes.},
}
@article {pmid35699080,
year = {2022},
author = {Yao, C and Cao, Y and Wang, D and Lv, Y and Liu, Y and Gu, X and Wang, Y and Wang, X and Yu, B},
title = {Single-cell sequencing reveals microglia induced angiogenesis by specific subsets of endothelial cells following spinal cord injury.},
journal = {FASEB journal : official publication of the Federation of American Societies for Experimental Biology},
volume = {36},
number = {7},
pages = {e22393},
doi = {10.1096/fj.202200337R},
pmid = {35699080},
issn = {1530-6860},
mesh = {Animals ; Endothelial Cells/metabolism ; Macrophages/metabolism ; *Microglia/metabolism ; Rats ; Spinal Cord/metabolism ; *Spinal Cord Injuries/metabolism ; },
abstract = {Spinal cord injury (SCI) results in dynamic alterations of the microenvironment at the lesion site, which inevitably leads to neuronal degeneration and functional impairment. The destruction of the spinal vascular system leads to a significant deterioration of the milieu, which exacerbates inflammatory response and deprives cells of nutrient support in the lesion. Limited endogenous angiogenesis occurs after SCI, but the cellular events at the lesion site during this process are unclear so far. Here, we performed single-cell RNA sequencing (scRNA-seq) on spinal cord tissues of rats at different time points after SCI. After clustering and cell-type identification, we focused on vascular endothelial cells (ECs), which play a pivotal role in angiogenesis, and drew the cellular and molecular atlas for angiogenesis after SCI. We found that microglia and macrophages promote endogenous angiogenesis by regulating EC subsets through SPP1 and IGF signaling pathways. Our results indicate that immune cells promote angiogenesis by regulating specific subsets of vascular ECs, which provides new clues for exploring SCI intervention.},
}
@article {pmid35698421,
year = {2022},
author = {Yang, E and Hou, W and Liu, K and Yang, H and Wei, W and Kang, H and Dai, H},
title = {A multifunctional chitosan hydrogel dressing for liver hemostasis and infected wound healing.},
journal = {Carbohydrate polymers},
volume = {291},
number = {},
pages = {119631},
doi = {10.1016/j.carbpol.2022.119631},
pmid = {35698421},
issn = {1879-1344},
mesh = {Anhydrides ; Anti-Bacterial Agents/chemistry/pharmacology ; Bandages ; *Chitosan/chemistry ; Escherichia coli ; Hemostasis ; Humans ; Hydrogels/chemistry/pharmacology ; Liver ; Methacrylates/pharmacology ; Staphylococcus aureus ; Wound Healing ; *Wound Infection ; },
abstract = {For the treatment of infected bleeding wounds, we compounded methacrylate anhydride dopamine (DAMA) and Zn-doped whitlockite nanoparticles (Zn-nWH) into methacrylate anhydride quaternized chitosan (QCSMA) to obtain a multifunctional hydrogel dressing (QCSMA/DAMA/Zn-nWH) with hemostasis, disinfection and wound healing promotion. QCSMA/DAMA/Zn-nWH exhibited good adhesion (0.031 MPa) and DPPH scavenging ability (94%), favorable biocompatibility (hemolysis ratio < 2%, no cytotoxicity), and showed a low BCI value (< 13%) in vitro coagulation test and could activate coagulation pathway. In addition, QCSMA/DAMA/Zn-nWH had excellent hemostatic effect (129 ± 22 s, 27 ± 5 mg) in vivo compared with the control (571 ± 15 s, 147 ± 31 mg) and CCS (354 ± 27 s, 110 ± 46 mg). Meanwhile, QCSMA/DAMA/Zn-nWH showed excellent antibacterial properties (> 90% against S. aureus and E. coli) and could promote collagen deposition, reduce inflammatory expression and promote wound healing. All results indicate that these multifunctional hydrogel dressings have great potential in clinical hemostasis and anti-infection healing.},
}
@article {pmid35697741,
year = {2022},
author = {Renton, AI and Painter, DR and Mattingley, JB},
title = {Optimising the classification of feature-based attention in frequency-tagged electroencephalography data.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {296},
pmid = {35697741},
issn = {2052-4463},
support = {CE140100007//Department of Education and Training | ARC | Centre of Excellence for Integrative Brain Function, Australian Research Council (ARC Centre of Excellence for Integrative Brain Function)/ ; CE140100007//Department of Education and Training | ARC | Centre of Excellence for Integrative Brain Function, Australian Research Council (ARC Centre of Excellence for Integrative Brain Function)/ ; GNT2010141//Department of Health | National Health and Medical Research Council (NHMRC)/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.},
}
@article {pmid35694207,
year = {2022},
author = {Dingle, AM and Moxon, K and Shokur, S and Strauss, I},
title = {Editorial: Getting Neuroprosthetics Out of the Lab: Improving the Human-Machine Interactions to Restore Sensory-Motor Functions.},
journal = {Frontiers in robotics and AI},
volume = {9},
number = {},
pages = {928383},
pmid = {35694207},
issn = {2296-9144},
support = {R01 NS096971/NS/NINDS NIH HHS/United States ; },
}
@article {pmid35693955,
year = {2022},
author = {Li, S and Zhang, R and Hu, S and Lai, J},
title = {Plasma Orexin-A Levels in Patients With Schizophrenia: A Systematic Review and Meta-Analysis.},
journal = {Frontiers in psychiatry},
volume = {13},
number = {},
pages = {879414},
pmid = {35693955},
issn = {1664-0640},
abstract = {BACKGROUND: Orexins are polypeptides regulating appetite, sleep-wake cycle, and cognition functions, which are commonly disrupted in patients with schizophrenia. Patients with schizophrenia show a decreased connectivity between the prefrontal cortex and midline-anterior thalamus, and orexin can directly activate the axon terminal of cells within the prefrontal cortex and selectively depolarize neurons in the midline intralaminar nuclei of the thalamus. To address the relationship between orexin and schizophrenia, this study performed a meta-analysis on the alteration of plasma orexin-A levels in patients with schizophrenia.<br><br>METHOD: We searched eligible studies in PubMed, Embase, Cochrane, and China National Knowledge Infrastructure (CNKI) from 1998 to September 3, 2021. A total of 8 case-control studies were included in the meta-analyses, providing data on 597 patients with schizophrenia and 370 healthy controls. The Stata version 16.0 software was used to calculate the Hedges's adjusted g with 95% confidence intervals (CI).<br><br>RESULTS: The plasma orexin-A levels were not altered in subjects with schizophrenia (n = 597) when compared to healthy controls (n = 370). Subgroup analyses of gender (male and female vs. only male), country (China vs. other countries), medication (medication vs. non-medication), and the measurement of plasma orexin-A (Enzyme-linked immunosorbent assay vs. radioimmunoassay) revealed heterogeneity ranging from 30.15 to 98.15%, but none showed a significant alteration of plasma orexin-A levels in patients with schizophrenia. Heterogeneity was lower in the other countries and radioimmunoassay subgroup, while other subgroups remained to be highly heterogeneous. No significant evidence of publication bias was found either in Begg's test or the Egger's test.<br><br>CONCLUSION: The present meta-analysis indicated that patients with schizophrenia did not show abnormal plasma levels of orexin-A.<br><br>https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021283455, identifier: CRD42021283455.},
}
@article {pmid35693537,
year = {2022},
author = {Mashrur, FR and Rahman, KM and Miya, MTI and Vaidyanathan, R and Anwar, SF and Sarker, F and Mamun, KA},
title = {BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {861270},
pmid = {35693537},
issn = {1662-5161},
abstract = {Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.},
}
@article {pmid35692628,
year = {2022},
author = {Ortega-Martinez, A and Von Lühmann, A and Farzam, P and Rogers, D and Mugler, EM and Boas, DA and Yücel, MA},
title = {Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.},
journal = {Neurophotonics},
volume = {9},
number = {2},
pages = {025003},
pmid = {35692628},
issn = {2329-423X},
support = {R24 NS104096/NS/NINDS NIH HHS/United States ; U01 EB029856/EB/NIBIB NIH HHS/United States ; },
abstract = {Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.},
}
@article {pmid35692622,
year = {2021},
author = {Ma, T and Huggins, JE and Kang, J},
title = {Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem.},
journal = {Proceedings. IEEE International Conference on Bioinformatics and Biomedicine},
volume = {2021},
number = {},
pages = {3648-3655},
pmid = {35692622},
issn = {2156-1125},
support = {R01 DA048993/DA/NIDA NIH HHS/United States ; R01 GM124061/GM/NIGMS NIH HHS/United States ; R01 MH105561/MH/NIMH NIH HHS/United States ; },
abstract = {A Brain-Computer Interface (BCI) is a device that interprets brain activity to help people with disabilities communicate. The P300 ERP-based BCI speller displays a series of events on the screen and searches the elicited electroencephalogram (EEG) data for target P300 event-related potential (ERP) responses among a series of non-target events. The Checkerboard (CB) paradigm is a common stimulus presentation paradigm. Although a few studies have proposed data-driven methods for stimulus selection, they suffer from intractable decision rules, large computation complexity, or error propagation for participants who perform poorly under the static paradigm. In addition, none of the methods have been applied to the CB paradigm directly. In this work, we propose a sequence-based adaptive stimulus selection method using Thompson Sampling in the multi-bandit problem with multiple actions. During each sequence, the algorithm selects a random subset of stimuli with fixed size, aiming to identify all target stimuli and to improve the spelling speed by reducing the number of unnecessary non-target stimuli. We compute "clean" stimulus-specific rewards from raw classifier scores via the Bayes rule. We perform extensive simulation studies to compare our algorithm to the static CB paradigm. We show the robustness of our algorithm by considering the constraints of practical use. For scenarios where simulated data resemble the real data the most, the spelling efficiency of our algorithm increases by more than 70%, compared to the static CB paradigm.},
}
@article {pmid35692430,
year = {2022},
author = {Perry Fordson, H and Xing, X and Guo, K and Xu, X},
title = {Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {865201},
pmid = {35692430},
issn = {1662-4548},
abstract = {Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.},
}
@article {pmid35690333,
year = {2022},
author = {Wu, M and Hu, S and Wei, B and Lv, Z},
title = {A novel deep learning model based on the ICA and Riemannian manifold for EEG-based emotion recognition.},
journal = {Journal of neuroscience methods},
volume = {378},
number = {},
pages = {109642},
doi = {10.1016/j.jneumeth.2022.109642},
pmid = {35690333},
issn = {1872-678X},
mesh = {Algorithms ; *Deep Learning ; *Electroencephalography/methods ; Emotions ; Humans ; Neural Networks, Computer ; },
abstract = {BACKGROUND: The EEG-based emotion recognition is one of the primary research orientations in the field of emotional intelligence and human-computer interaction (HCI).<br><br>NEW METHOD: We proposed a novel model, denoted as ICRM-LSTM, for EEG-based emotion recognition by combining the independent component analysis (ICA), the Riemannian manifold (RM), and the long short-term memory network (LSTM). The SEED and MAHNOB-HCI dataset were employed to verify the performance of the proposed model. Firstly, ICA was used to perform blind source separation (BSS) for the preprocessed EEG signals provided by the two datasets. Then, a series of the covariance matrices according to time order were extracted from the blind source signals, and the symmetric positive definiteness of covariance matrix allowed us to project them from RM space to Euclid space by logarithmic mapping. Finally, the transformed covariance matrices were taken as inputs of the LSTM network to perform the emotion recognition.<br><br>RESULTS: To validate the effect of the ICRM method on the performance of the proposed model, we designed three groups of comparative experiments. The average accuracy of the ICRM-LSTM model were 96.75 % and 98.09 % in SEED and MAHNOB-HCI, respectively. Then we compared our model with the models didn't perform the ICA or RM method, where we found that the proposed model had better performance. Furthermore, to verify the robustness, we added three groups of Gaussian noise with different signal-to-noise ratios (SNR) to the preprocessed EEG signals, and the proposed model achieved a good performance in both the two datasets.<br><br>The performance of our model was superior to most of existing methods which also employed the SEED or MAHNOB-HCI dataset.<br><br>CONCLUSION: This paper demonstrated that the ICA and RM were helpful for EEG-based emotion recognition, and provided the evidence that the RM method could effectively alleviate the problem of the uncertain ordering of ICA.},
}
@article {pmid35690117,
year = {2022},
author = {Pitt, KM and Mansouri, A and Wang, Y and Zosky, J},
title = {Toward P300-brain-computer interface access to contextual scene displays for AAC: An initial exploration of context and asymmetry processing in healthy adults.},
journal = {Neuropsychologia},
volume = {173},
number = {},
pages = {108289},
doi = {10.1016/j.neuropsychologia.2022.108289},
pmid = {35690117},
issn = {1873-3514},
mesh = {Adult ; *Brain-Computer Interfaces ; Communication ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Female ; Humans ; Male ; },
abstract = {UNLABELLED: Brain-computer interfaces for augmentative and alternative communication (BCI-AAC) may help overcome physical barriers to AAC access. Traditionally, visually based P300-BCI-AAC displays utilize a symmetrical grid layout. Contextual scene displays are composed of context-rich images (e.g., photographs) and may support AAC success. However, contextual scene displays contrast starkly with the standard P300-grid approach. Understanding the neurological processes from which BCI-AAC devices function is crucial to human-centered computing for BCI-AAC. Therefore, the aim of this multidisciplinary investigation is to provide an initial exploration of contextual scene use for BCI-AAC.<br><br>METHODS: Participants completed three experimental conditions to evaluate the effects of item arrangement asymmetry and context on P300-based BCI-AAC signals and offline BCI-AAC accuracy, including 1) the full contextual scene condition, 2) asymmetrical item arraignment without context condition and 3) the grid condition. Following each condition, participants completed task-evaluation ratings (e.g., engagement). Offline BCI-AAC accuracy for each condition was evaluated using cross-validation.<br><br>RESULTS: Display asymmetry significantly decreased P300 latency in the centro-parietal cluster. P300 amplitudes in the frontal cluster were decreased, though nonsignificantly. Display context significantly increased N170 amplitudes in the occipital cluster, and N400 amplitudes in the centro-parietal and occipital clusters. Scenes were rated as more visually appealing and engaging, and offline BCI-AAC performance for the scene condition was not statistically different from the grid standard.<br><br>CONCLUSION: Findings support the feasibility of incorporating scene-based displays for P300-BCI-AAC development to help provide communication for individuals with minimal or emerging language and literacy skills.},
}
@article {pmid35688315,
year = {2022},
author = {Liu, D and Xu, X and Li, D and Li, J and Yu, X and Ling, Z and Hong, B},
title = {Intracranial brain-computer interface spelling using localized visual motion response.},
journal = {NeuroImage},
volume = {258},
number = {},
pages = {119363},
doi = {10.1016/j.neuroimage.2022.119363},
pmid = {35688315},
issn = {1095-9572},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Language ; },
abstract = {Intracranial brain-computer interfaces (BCIs) can assist severely disabled persons in text communication and environmental control with high precision and speed. Nevertheless, sustainable BCI implants require minimal invasiveness. One of the implantation strategies is to adopt localized and robust cortical activities to drive BCI communication and to make a precise presurgical planning. The visual motion response is a good candidate for inclusion in this strategy because of its focal activity over the middle temporal visual area (MT). Here, we developed an intracranial BCI for spelling, utilizing only three electrodes over the MT area. The best recording electrodes were decided by preoperative functional magnetic resonance imaging (MRI) localization of the MT, and local neural activities were further enhanced by differential rereferencing of these electrodes. The BCI spelling system was validated both offline and online by five epilepsy patients, achieving the fastest speed of 62 bits/min, i.e., 12 characters/min. Moreover, the response patterns of dual-directional visual motion stimuli provided an additional dimension of BCI target encoding and paved the way for a higher information transfer rate of intracranial BCI spelling.},
}
@article {pmid35688127,
year = {2022},
author = {Niu, J and Jiang, N},
title = {Pseudo-online detection and classification for upper-limb movements.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac77be},
pmid = {35688127},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Movement ; Signal Processing, Computer-Assisted ; Upper Extremity ; },
abstract = {Objective. This study analyzed detection (movement vs. non-movement) and classification (different types of movements) to decode upper-limb movement volitions in a pseudo-online fashion.Approach. Nine healthy subjects executed four self-initiated movements: left wrist extension, right wrist extension, left index finger extension, and right index finger extension. For detection, we investigated the performance of three individual classifiers (support vector machine (SVM), EEGNET, and Riemannian geometry featured SVM) on three frequency bands (0.05-5 Hz, 5-40 Hz, 0.05-40 Hz). The best frequency band and the best classifier combinations were constructed to realize an ensemble processing pipeline using majority voting. For classification, we used adaptive boosted Riemannian geometry model to differentiate contra-lateral and ipsilateral movements.Main results. The ensemble model achieved 79.6 ± 8.8% true positive rate and 3.1 ± 1.2 false positives per minute with 75.3 ± 112.6 ms latency on a pseudo-online detection task. The following classification gave around 67% accuracy to differentiate contralateral movements.Significance. The newly proposed ensemble method and pseudo-online testing procedure could provide a robust brain-computer interface design for movement decoding.},
}
@article {pmid35684700,
year = {2022},
author = {Tian, P and Xu, G and Han, C and Zheng, X and Zhang, K and Du, C and Wei, F and Zhang, S},
title = {Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {11},
pages = {},
pmid = {35684700},
issn = {1424-8220},
support = {2021GXLH-Z-008//the Key Projects in Shaanxi Province/ ; 20KYPT0001-10//the Science and Technology Plan Project of Xi'an/ ; Not applicable//the Xi'an Key Laboratory of Brain Computer Interaction &amp; Neuroregulation Digital Medicine/ ; },
mesh = {*Asthenopia ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Pupil/physiology ; Visual Perception ; },
abstract = {Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain−computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index—the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.},
}
@article {pmid35684626,
year = {2022},
author = {Varandas, R and Lima, R and Bermúdez I Badia, S and Silva, H and Gamboa, H},
title = {Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {11},
pages = {},
pmid = {35684626},
issn = {1424-8220},
support = {PD/BDE/150304/2019//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; 2020.06024.BD//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cognition ; Humans ; Machine Learning ; Spectroscopy, Near-Infrared/methods ; *Wearable Electronic Devices ; },
abstract = {Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.},
}
@article {pmid35675795,
year = {2022},
author = {Kim, MG and Lim, H and Lee, HS and Han, IJ and Ku, J and Kang, YJ},
title = {Brain-computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac76e0},
pmid = {35675795},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Healthy Volunteers ; Humans ; *Stroke ; Transcranial Magnetic Stimulation/methods ; },
abstract = {Objective.Action observation (AO) combined with brain-computer interface (BCI) technology enhances cortical activation. Peripheral electrical stimulation (PES) increases corticospinal excitability, thereby activating brain plasticity. To maximize motor recovery, we assessed the effects of BCI-AO combined with PES on corticospinal plasticity.Approach.Seventeen patients with chronic hemiplegic stroke and 17 healthy subjects were recruited. The participants watched a video of repetitive grasping actions with four different tasks for 15 min: (A) AO alone; (B) AO + PES; (C) BCI-AO + continuous PES; and (D) BCI-AO + triggered PES. PES was applied at the ulnar nerve of the wrist. The tasks were performed in a random order at least three days apart. We assessed the latency and amplitude of motor evoked potentials (MEPs). We examined changes in MEP parameters pre-and post-exercise across the four tasks in the first dorsal interosseous muscle of the dominant hand (healthy subjects) and affected hand (stroke patients).Main results.The decrease in MEP latency and increase in MEP amplitude after the four tasks were significant in both groups. The increase in MEP amplitude was sustained for 20 min after tasks B, C, and D in both groups. The increase in MEP amplitude was significant between tasks A vs. B, B vs. C, and C vs. D. The estimated mean difference in MEP amplitude post-exercise was the highest for A and D in both groups.Significance.The results indicate that BCI-AO combined with PES is superior to AO alone or AO + PES for facilitating corticospinal plasticity in both healthy subjects and patients with stroke. Furthermore, this study supports the idea that synchronized activation of cortical and peripheral networks can enhance neuroplasticity after stroke. We suggest that the BCI-AO paradigm and PES could provide a novel neurorehabilitation strategy for patients with stroke.},
}
@article {pmid35672283,
year = {2022},
author = {Ding, Y and Zhang, H and Liao, YY and Chen, LN and Ji, SY and Qin, J and Mao, C and Shen, DD and Lin, L and Wang, H and Zhang, Y and Li, XM},
title = {Structural insights into human brain-gut peptide cholecystokinin receptors.},
journal = {Cell discovery},
volume = {8},
number = {1},
pages = {55},
pmid = {35672283},
issn = {2056-5968},
abstract = {The intestinal hormone and neuromodulator cholecystokinin (CCK) receptors CCK1R and CCK2R act as a signaling hub in brain-gut axis, mediating digestion, emotion, and memory regulation. CCK receptors exhibit distinct preferences for ligands in different posttranslational modification (PTM) states. CCK1R couples to Gs and Gq, whereas CCK2R primarily couples to Gq. Here we report the cryo-electron microscopy (cryo-EM) structures of CCK1R-Gs signaling complexes liganded either by sulfated cholecystokinin octapeptide (CCK-8) or a CCK1R-selective small-molecule SR146131, and CCK2R-Gq complexes stabilized by either sulfated CCK-8 or a CCK2R-selective ligand gastrin-17. Our structures reveal a location-conserved yet charge-distinct pocket discriminating the effects of ligand PTM states on receptor subtype preference, the unique pocket topology underlying selectivity of SR146131 and gastrin-17, the conformational changes in receptor activation, and key residues contributing to G protein subtype specificity, providing multiple structural templates for drug design targeting the brain-gut axis.},
}
@article {pmid35671947,
year = {2022},
author = {Hughes, CL and Flesher, SN and Gaunt, RA},
title = {Effects of stimulus pulse rate on somatosensory adaptation in the human cortex.},
journal = {Brain stimulation},
volume = {15},
number = {4},
pages = {987-995},
pmid = {35671947},
issn = {1876-4754},
support = {U01 NS108922/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Electric Stimulation/methods ; Heart Rate ; Humans ; *Somatosensory Cortex/physiology ; *Touch/physiology ; },
abstract = {BACKGROUND: Intracortical microstimulation (ICMS) of the somatosensory cortex can restore sensation to people with neurological diseases. However, many aspects of ICMS are poorly understood, including the effect of stimulation on percept intensity over time.<br><br>OBJECTIVE: Here, we evaluate how tactile percepts evoked by ICMS in the somatosensory cortex of a human participant adapt over time.<br><br>METHODS: We delivered continuous and intermittent ICMS to the somatosensory cortex and assessed the reported intensity of tactile percepts over time in a human participant. Experiments were conducted over approximately one year and linear mixed effects models were used to assess significance.<br><br>RESULTS: Continuous stimulation at high frequencies led to rapid decreases in intensity, while low frequency stimulation maintained percept intensity for longer periods. Burst-modulated stimulation extended the time before the intensity began to decrease, but all protocols ultimately resulted in complete sensation loss within 1&#xa0;min. Intermittent stimulation paradigms with several seconds between stimulus trains evoked intermittent percepts and also led to decreases in intensity on many electrodes, but never resulted in extinction of the sensation after over 3&#xa0;min of stimulation. Longer breaks between each pulse train resulted in some recovery in the intensity of the stimulus-evoked percepts. For several electrodes, intermittent stimulation had almost no effect on the perceived intensity.<br><br>CONCLUSIONS: Intermittent ICMS paradigms were more effective at maintaining percepts. Given that transient neural activity dominates the response in somatosensory cortex during mechanical contact onsets and offsets, providing brief stimulation trains at these times may more closely represent natural cortical activity and have the additional benefit of prolonging the ability to evoke sensations over longer time periods.},
}
@article {pmid35671575,
year = {2022},
author = {Doya, K and Ema, A and Kitano, H and Sakagami, M and Russell, S},
title = {Social impact and governance of AI and neurotechnologies.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {152},
number = {},
pages = {542-554},
doi = {10.1016/j.neunet.2022.05.012},
pmid = {35671575},
issn = {1879-2782},
mesh = {*Artificial Intelligence ; Humans ; Social Change ; *Social Media ; },
abstract = {Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries. We then consider possible risks from those technologies, including intentional abuse, autonomous weapons, cognitive enhancement by brain-computer interfaces, insidious effects of social media, inequity, and enfeeblement. We also discuss practical ways to bring ethical principles into practice. One proposal is to stop giving explicit goals to AI agents and to enable them to keep learning human preferences. Another is to learn from democratic mechanisms that evolved in human society to avoid over-consolidation of power. Finally, we emphasize the importance of open discussions not only by experts, but also including a diverse array of lay opinions.},
}
@article {pmid35669388,
year = {2022},
author = {Fujiwara, Y and Ushiba, J},
title = {Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {882290},
pmid = {35669388},
issn = {1662-5188},
abstract = {Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.},
}
@article {pmid35669387,
year = {2022},
author = {Liu, Y and Höllerer, T and Sra, M},
title = {SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {803384},
pmid = {35669387},
issn = {1662-5188},
abstract = {Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.},
}
@article {pmid35669203,
year = {2022},
author = {Pais-Vieira, C and Gaspar, P and Matos, D and Alves, LP and da Cruz, BM and Azevedo, MJ and Gago, M and Poleri, T and Perrotta, A and Pais-Vieira, M},
title = {Embodiment Comfort Levels During Motor Imagery Training Combined With Immersive Virtual Reality in a Spinal Cord Injury Patient.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {909112},
pmid = {35669203},
issn = {1662-5161},
abstract = {Brain-machine interfaces combining visual, auditory, and tactile feedback have been previously used to generate embodiment experiences during spinal cord injury (SCI) rehabilitation. It is not known if adding temperature to these modalities can result in discomfort with embodiment experiences. Here, comfort levels with the embodiment experiences were investigated in an intervention that required a chronic pain SCI patient to generate lower limb motor imagery commands in an immersive environment combining visual (virtual reality -VR), auditory, tactile, and thermal feedback. Assessments were made pre-/ post-, throughout the intervention (Weeks 0-5), and at 7 weeks follow up. Overall, high levels of embodiment in the adapted three-domain scale of embodiment were found throughout the sessions. No significant adverse effects of VR were reported. Although sessions induced only a modest reduction in pain levels, an overall reduction occurred in all pain scales (Faces, Intensity, and Verbal) at follow up. A high degree of comfort in the comfort scale for the thermal-tactile sleeve, in both the thermal and tactile feedback components of the sleeve was reported. This study supports the feasibility of combining multimodal stimulation involving visual (VR), auditory, tactile, and thermal feedback to generate embodiment experiences in neurorehabilitation programs.},
}
@article {pmid35666347,
year = {2022},
author = {Tan, X and Zhou, Z and Gao, J and Yu, Y and Wei, R and Luo, B and Zhang, X},
title = {White matter connectometry in patients with disorders of consciousness revealed by 7-Tesla magnetic resonance imaging.},
journal = {Brain imaging and behavior},
volume = {16},
number = {5},
pages = {1983-1991},
pmid = {35666347},
issn = {1931-7565},
support = {2018YFA0701400//National Key Research and Development Program of China/ ; 81701774//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 81870817//National Natural Science Foundation of China/ ; 2018EB0ZX01//Zhejiang Lab/ ; 2018B030333001//Key-Area Research and Development Program of Guangdong Province/ ; 202007030005//Guangzhou Key R&amp;D Program of China/ ; LGF22H090004//Zhejiang Provincial Natural Science Foundation of China/ ; MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration at Zhejiang University//MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration at Zhejiang University/ ; J-202224//Scientific Research Foundation of Zhejiang University City College/ ; },
mesh = {Humans ; *White Matter/diagnostic imaging/pathology ; Consciousness ; Magnetic Resonance Imaging ; Persistent Vegetative State ; Anisotropy ; Consciousness Disorders/diagnostic imaging ; },
abstract = {White matter disruption plays an important role in disorders of consciousness (DOC). The aim of this study was to analyze the connectometry between DOC patients and healthy controls and to explore the relationship between diffusion connectometry and levels of consciousness. Fourteen patients with DOC and 13 sex- and age-matched controls were included in this study. The participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 Tesla. Diffusion MRI connectometry was performed to investigate the differences between groups, and to subsequently study the correlation between Coma Recovery Scale-Revised (CRS-R) indexes and white matter integrity. In DOC patients, the quantitative anisotropy (QA) was significantly reduced in deep white matter tracts, whereas significantly higher QA values were found in the bilateral cerebellum compared with healthy controls. Moreover, the QA values in many tracts within the right hemisphere were higher in patients in a minimally conscious state compared to those in vegetative state/unresponsive wakefulness syndrome, which was reflected by the correlation between diffusion connectometry and CRS-R indexes. These results indicate that the cerebellum may play an important role in DOC, and the lateralization of the cerebral hemisphere in affected patients may suggest neural compensation.},
}
@article {pmid35664916,
year = {2022},
author = {Israsena, P and Pan-Ngum, S},
title = {A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {868642},
pmid = {35664916},
issn = {1662-5188},
abstract = {This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.},
}
@article {pmid35664638,
year = {2022},
author = {Oralhan, Z and Oralhan, B and Khayyat, MM and Abdel-Khalek, S and Mansour, RF},
title = {3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User.},
journal = {Computational and mathematical methods in medicine},
volume = {2022},
number = {},
pages = {8452002},
pmid = {35664638},
issn = {1748-6718},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; },
abstract = {This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.},
}
@article {pmid35664345,
year = {2022},
author = {Ahn, M and Jun, SC and Yeom, HG and Cho, H},
title = {Editorial: Deep Learning in Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {927567},
pmid = {35664345},
issn = {1662-5161},
}
@article {pmid35662242,
year = {2022},
author = {Zhao, C and Xie, Y and Xu, L and Ye, F and Xu, X and Yang, W and Yang, F and Guo, J},
title = {Structures of a mammalian TRPM8 in closed state.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {3113},
pmid = {35662242},
issn = {2041-1723},
mesh = {Animals ; Cold Temperature ; Ligands ; Mammals/metabolism ; Menthol/pharmacology ; Mice ; *TRPM Cation Channels/metabolism ; Thermosensing ; *Transient Receptor Potential Channels/metabolism ; },
abstract = {Transient receptor potential melastatin 8 (TRPM8) channel is a Ca[2+]-permeable non-selective cation channel that acts as the primary cold sensor in humans. TRPM8 is also activated by ligands such as menthol, icilin, and phosphatidylinositol 4,5-bisphosphate (PIP2), and desensitized by Ca[2+]. Here we have determined electron cryo-microscopy structures of mouse TRPM8 in the absence of ligand, and in the presence of Ca[2+] and icilin at 2.5-3.2 Å resolution. The ligand-free state TRPM8 structure represents the full-length structure of mammalian TRPM8 channels with a canonical S4-S5 linker and the clearly resolved selectivity filter and outer pore loop. TRPM8 has a short but wide selectivity filter which may account for its permeability to hydrated Ca[2+]. Ca[2+] and icilin bind in the cytosolic-facing cavity of the voltage-sensing-like domain of TRPM8 but induce little conformational change. All the ligand-bound TRPM8 structures adopt the same closed conformation as the ligand-free structure. This study reveals the overall architecture of mouse TRPM8 and the structural basis for its ligand recognition.},
}
@article {pmid35662188,
year = {2022},
author = {Zhang, M and Li, C and Liu, SY and Zhang, FS and Zhang, PX},
title = {An electroencephalography-based human-machine interface combined with contralateral C7 transfer in the treatment of brachial plexus injury.},
journal = {Neural regeneration research},
volume = {17},
number = {12},
pages = {2600-2605},
pmid = {35662188},
issn = {1673-5374},
abstract = {Transferring the contralateral C7 nerve root to the median or radial nerve has become an important means of repairing brachial plexus nerve injury. However, outcomes have been disappointing. Electroencephalography (EEG)-based human-machine interfaces have achieved promising results in promoting neurological recovery by controlling a distal exoskeleton to perform functional limb exercises early after nerve injury, which maintains target muscle activity and promotes the neurological rehabilitation effect. This review summarizes the progress of research in EEG-based human-machine interface combined with contralateral C7 transfer repair of brachial plexus nerve injury. Nerve transfer may result in loss of nerve function in the donor area, so only nerves with minimal impact on the donor area, such as the C7 nerve, should be selected as the donor. Single tendon transfer does not fully restore optimal joint function, so multiple functions often need to be reestablished simultaneously. Compared with traditional manual rehabilitation, EEG-based human-machine interfaces have the potential to maximize patient initiative and promote nerve regeneration and cortical remodeling, which facilitates neurological recovery. In the early stages of brachial plexus injury treatment, the use of an EEG-based human-machine interface combined with contralateral C7 transfer can facilitate postoperative neurological recovery by making full use of the brain's computational capabilities and actively controlling functional exercise with the aid of external machinery. It can also prevent disuse atrophy of muscles and target organs and maintain neuromuscular junction effectiveness. Promoting cortical remodeling is also particularly important for neurological recovery after contralateral C7 transfer. Future studies are needed to investigate the mechanism by which early movement delays neuromuscular junction damage and promotes cortical remodeling. Understanding this mechanism should help guide the development of neurological rehabilitation strategies for patients with brachial plexus injury.},
}
@article {pmid35659259,
year = {2022},
author = {Davis, KC and Meschede-Krasa, B and Cajigas, I and Prins, NW and Alver, C and Gallo, S and Bhatia, S and Abel, JH and Naeem, JA and Fisher, L and Raza, F and Rifai, WR and Morrison, M and Ivan, ME and Brown, EN and Jagid, JR and Prasad, A},
title = {Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {19},
number = {1},
pages = {53},
pmid = {35659259},
issn = {1743-0003},
support = {T32 GM112601/GM/NIGMS NIH HHS/United States ; R25 NS108937/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Cervical Cord ; Electroencephalography ; Hand ; Humans ; Imagery, Psychotherapy ; *Spinal Cord Injuries ; User-Computer Interface ; },
abstract = {OBJECTIVE: The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A).<br><br>BACKGROUND: BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home.<br><br>METHODS: The BCI platform implementation consisted of an Activa PC&#x2009;+&#x2009;S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use.<br><br>RESULTS: Average bluetooth data transmission delay between the minicomputer and mobile App was 23&#x2009;&#xb1;&#x2009;0.014&#xa0;ms. The average setup time for the subject's caregiver was 5.6&#x2009;&#xb1;&#x2009;0.83&#xa0;min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1&#xa0;ms and 1.02&#xa0;ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5&#x2009;&#xb1;&#x2009;4.71% without retraining.<br><br>CONCLUSIONS: The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015.},
}
@article {pmid35657949,
year = {2022},
author = {Huang, X and Liang, S and Li, Z and Lai, CYY and Choi, KS},
title = {EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review.},
journal = {PloS one},
volume = {17},
number = {6},
pages = {e0269001},
pmid = {35657949},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory ; Humans ; },
abstract = {Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.},
}
@article {pmid35657833,
year = {2022},
author = {Chi, X and Wan, C and Wang, C and Zhang, Y and Chen, X and Cui, H},
title = {A Novel Hybrid Brain-Computer Interface Combining Motor Imagery and Intermodulation Steady-State Visual Evoked Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1525-1535},
doi = {10.1109/TNSRE.2022.3179971},
pmid = {35657833},
issn = {1558-0210},
mesh = {Attention ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Movement/physiology ; Photic Stimulation/methods ; },
abstract = {The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP.},
}
@article {pmid35655835,
year = {2022},
author = {Yao, L and Li, H and Liu, K and Zhang, Z and Li, P},
title = {Endoscopic optical coherence tomography angiography using inverse SNR-amplitude decorrelation features and electrothermal micro-electro-mechanical system raster scan.},
journal = {Quantitative imaging in medicine and surgery},
volume = {12},
number = {6},
pages = {3078-3091},
pmid = {35655835},
issn = {2223-4292},
abstract = {BACKGROUND: Angiogenesis is closely associated with tumor development and progression. Endoscopic optical coherence tomography angiography (OCTA) enables rapid inspection of mucosal 3D vasculature of inner organs in the early-stage tumor diagnosis; however, it is limited by instabilities of the optical signal and beam scanning.<br><br>METHODS: In the phase-unstable swept source OCTA (SS-OCTA), amplitude decorrelation was used to compute the motion-induced changes as motion contrast. The influence of the random noise-induced amplitude fluctuations on decorrelation was characterized as a function of inverse signal-to-noise ratio (SNR) with a multi-variate time series (MVTS) model and statistical analysis. Then, the noise-induced decorrelation artifacts in static tissue regions were eliminated by applying a flow mask based on the statistical relation between inverse SNR (iSNR) and amplitude decorrelation (IDa), which was named IDa-OCTA. In addition, a distal stepwise raster scan was realized with a low-voltage electrothermal micro-electro-mechanical system (ET-MEMS)-based catheter for endoscopic imaging, whereby the stable and repeatable B-scans at each step suppressed the decorrelation noise induced by the spatial mismatch between paired scans.<br><br>RESULTS: The derived IDa relation was validated through numerical simulation and flow phantom experiments. In vivo human buccal mucosa imaging was performed to demonstrate the endoscopic IDa-OCTA imaging. In this, the subsurface structure and vasculature were visualized in a rapid and depth-resolved manner.<br><br>CONCLUSIONS: The rapid 3D vasculature visualization realized by the endoscopic IDa-OCTA improves the diagnosis of early tumors in internal organs.},
}
@article {pmid35654019,
year = {2022},
author = {Edmondson, LR and Saal, HP},
title = {Getting a grasp on BMIs: Decoding prehension and speech signals.},
journal = {Neuron},
volume = {110},
number = {11},
pages = {1743-1745},
doi = {10.1016/j.neuron.2022.05.004},
pmid = {35654019},
issn = {1097-4199},
mesh = {*Brain-Computer Interfaces ; Hand Strength ; Humans ; Parietal Lobe ; Somatosensory Cortex ; *Speech ; },
abstract = {Wandelt et al. (2022) show that different grasps can be decoded from neural activity in the human supramarginal gyrus (SMG), ventral premotor cortex, and somatosensory cortex during motor imagery and speech, highlighting the attractiveness of higher-level areas such as the SMG for brain-machine interface applications.},
}
@article {pmid35652562,
year = {2022},
author = {Enz, N and Schmidt, J and Nolan, K and Mitchell, M and Alvarez Gomez, S and Alkayyali, M and Cambay, P and Gippert, M and Whelan, R and Ruddy, K},
title = {Self-regulation of the brain's right frontal Beta rhythm using a brain-computer interface.},
journal = {Psychophysiology},
volume = {59},
number = {11},
pages = {e14115},
pmid = {35652562},
issn = {1469-8986},
mesh = {Alpha Rhythm/physiology ; Beta Rhythm/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurofeedback ; *Self-Control ; },
abstract = {Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n = 44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different.},
}
@article {pmid35644516,
year = {2022},
author = {Christie, B and Osborn, LE and McMullen, DP and Pawar, AS and Thomas, TM and Bensmaia, SJ and Celnik, PA and Fifer, MS and Tenore, FV},
title = {Perceived timing of cutaneous vibration and intracortical microstimulation of human somatosensory cortex.},
journal = {Brain stimulation},
volume = {15},
number = {3},
pages = {881-888},
doi = {10.1016/j.brs.2022.05.015},
pmid = {35644516},
issn = {1876-4754},
mesh = {Electric Stimulation ; Humans ; Male ; Microelectrodes ; *Somatosensory Cortex/physiology ; Touch/physiology ; *Vibration ; },
abstract = {BACKGROUND: Intracortical microstimulation (ICMS) of somatosensory cortex can partially restore the sense of touch. Though ICMS bypasses much of the neuraxis, prior studies have found that conscious detection of touch elicited by ICMS lags behind the detection of cutaneous vibration. These findings may have been influenced by mismatched stimulus intensities, which can impact temporal perception.<br><br>OBJECTIVE: Evaluate the relative latency at which intensity-matched vibration and ICMS are perceived by a human participant.<br><br>METHODS: One person implanted with microelectrode arrays in somatosensory cortex performed reaction time and temporal order judgment (TOJ) tasks. To measure reaction time, the participant reported when he perceived vibration or ICMS. In the TOJ task, vibration and ICMS were sequentially presented and the participant reported which stimulus occurred first. To verify that the participant could distinguish between stimuli, he also performed a modality discrimination task, in which he indicated if he felt vibration, ICMS, or both.<br><br>RESULTS: When vibration was matched in perceived intensity to high-amplitude ICMS, vibration was perceived, on average, 48&#x202f;ms faster than ICMS. However, in the TOJ task, both sensations arose at comparable latencies, with points of subjective simultaneity not significantly different from zero. The participant could discriminate between tactile modalities above chance level but was more inclined to report feeling vibration than ICMS.<br><br>CONCLUSIONS: The latencies of ICMS-evoked percepts are slower than their mechanical counterparts. However, differences in latencies are small, particularly when stimuli are matched for intensity, implying that ICMS-based somatosensory feedback is rapid enough to be effective in neuroprosthetic applications.},
}
@article {pmid35641547,
year = {2022},
author = {Liu, B and Wang, Y and Gao, X and Chen, X},
title = {eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {252},
pmid = {35641547},
issn = {2052-4463},
mesh = {Aged ; Aging ; Benchmarking ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; },
abstract = {Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large eldercare-oriented BEnchmark database of SSVEP-BCI for The Aging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder participants, each of whom performed seven blocks of 9-target SSVEP-BCI task. The quality and characteristics of the eldBETA database were validated by a series of analyses followed by a classification analysis of thirteen frequency recognition methods. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders. The eldBETA database is open-access for research and can be downloaded from the website https://doi.org/10.6084/m9.figshare.18032669 .},
}
@article {pmid35640554,
year = {2022},
author = {Arpaia, P and Esposito, A and Natalizio, A and Parvis, M},
title = {How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac74e0},
pmid = {35640554},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Movement ; Reproducibility of Results ; },
abstract = {Objective.Processing strategies are analyzed with respect to the classification of electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor imagery (MI). A review of literature is carried out to understand the achievements in MI classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility.Approach.The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery-based BCIs. Article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses standard and 89 studies were included.Main results.Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85%-100% range for the binary case and in the 83%-93% range for multi-class one. Associated uncertainties are up to 6% while repeatability for a predetermined dataset is up to 8%. Reproducibility assessment was instead prevented by lack of standardization in experiments.Significance.By relying on the analyzed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a BCI. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of the results reproducibility.},
}
@article {pmid35635834,
year = {2022},
author = {Dinh, TH and Singh, AK and Linh Trung, N and Nguyen, DN and Lin, CT},
title = {EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1548-1556},
doi = {10.1109/TNSRE.2022.3179255},
pmid = {35635834},
issn = {1558-0210},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Cluster Analysis ; Cognition ; Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).},
}
@article {pmid35635610,
year = {2022},
author = {Kulkarni, V and Joshi, Y and Manthalkar, R and Elamvazuthi, I},
title = {Band decomposition of asynchronous electroencephalogram signal for upper limb movement classification.},
journal = {Physical and engineering sciences in medicine},
volume = {45},
number = {2},
pages = {643-656},
pmid = {35635610},
issn = {2662-4737},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Movement ; Upper Extremity ; },
abstract = {Decoding asynchronous electroencephalogram (A-EEG) signals is a crucial challenge in the emerging field of EEG based brain-computer interface. In the case of A-EEG signals, the time markers of motor activity are absent. The paper proposes a method to decompose the A-EEG signals using gabor elementary function designed with Gabor frames. The scale-space analysis extracts Gabor dominant frequencies from A-EEG signals. Statistical and temporal moment dependent features are used to create the feature vector for each estimated gabor band. The statistical significance of the features is tested with the Kruskal-Wallis test. The deep neural network is implemented with bi-directional long short-term memory block to classify the upper limb movement. The EEG data of healthy volunteers have been collected using the Enobio-20 electrode system and ArmeoSpring rehabilitation device. The proposed methodology has achieved an average classification accuracy of 96.83%, precision 0.96, recall 0.96, and F1-score of 0.93 on the acquired data set. The designed framework for decoding upper limb movement outperforms the existing state-of-the-art methods. In the future, the proposed framework could increase classification performance by incorporating multiple types of biological inputs for investigating various brain functions.},
}
@article {pmid35634676,
year = {2022},
author = {Zhang, LN and DU, XX and Zhang, YT and Guo, X and Hao, N and Zhao, X and Zhang, Y},
title = {[A comparative study of microwire electrode array with built-in and external reference electrodes].},
journal = {Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology},
volume = {38},
number = {1},
pages = {85-90},
doi = {10.12047/j.cjap.6193.2022.016},
pmid = {35634676},
issn = {1000-6834},
mesh = {Action Potentials/physiology ; Animals ; Brain ; *Electrophysiological Phenomena ; Microelectrodes ; *Neurons ; Rats ; },
abstract = {Objective: To compare the difference between the built-in and external reference electrode of microwire electrode array in the process of recording rat brain neuron firings, optimizing the production and embedding of the microwire electrode array, and providing a more affordable and excellent media tool for multi-channel electrophysiological real-time recording system. Methods: A 16 channel microwire electrode array was made by using nickel chromium alloy wires, circuit board, electrode pin and ground wires (silver wires). The reference electrode of the microwire electrode array was built-in (the reference electrode and electrode array were arranged in parallel) or external (the reference electrode and ground wire were welded at both ends of one side of the electrode), and the difference between the two electrodes was observed and compared in recording neuronal discharges in ACC brain area of rats. Experimental rats were divided into built-in group and external group, n=8-9. The test indicators included signal-to-noise ratio (n=8), discharge amplitude (n=380) and discharge frequency (n=54). Results: The microwire electrode array with both built-in and external reference electrodes successfully recorded the electrical signals of neurons in the ACC brain region of rats. Compared with the external group, the electrical signals of neurons in built-in group had the advantages of a higher signal-to-noise ratio (P＜0.05), a smaller amplitude of background signals and less noise interference, and a larger discharge amplitude(P＜0.05); there was no significant difference in spike discharge frequency recorded by these two types of electrodes (P＞0.05). Conclusion: When recording the electrical activity of neurons in the ACC brain region of rats, the microwire electrode array with built-in reference electrode recorded electrical signals with higher signal-to-noise ratio and larger discharge amplitude, providing a more reliable tool for multi-channel electrophysiology technology.},
}
@article {pmid35634118,
year = {2022},
author = {Al-Nafjan, A},
title = {Feature selection of EEG signals in neuromarketing.},
journal = {PeerJ. Computer science},
volume = {8},
number = {},
pages = {e944},
pmid = {35634118},
issn = {2376-5992},
abstract = {Brain-computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain's responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers.},
}
@article {pmid35625045,
year = {2022},
author = {Jiang, Q and Zhang, Y and Zheng, K},
title = {Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold.},
journal = {Brain sciences},
volume = {12},
number = {5},
pages = {},
pmid = {35625045},
issn = {2076-3425},
support = {51905065//the National Nature Science Foundation of China/ ; BYJS201910//Doctoral Program of Chongqing University of Posts and Telecommunications/ ; },
abstract = {BACKGROUND: Recording the calibration data of a brain-computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target.<br><br>METHODS: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors.<br><br>RESULTS: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain-computer interfaces.},
}
@article {pmid35623610,
year = {2022},
author = {Williams, SC and Horsfall, HL and Funnell, JP and Hanrahan, JG and Schaefer, AT and Muirhead, W and Marcus, HJ},
title = {Neurosurgical Team Acceptability of Brain-Computer Interfaces: A Two-Stage International Cross-Sectional Survey.},
journal = {World neurosurgery},
volume = {164},
number = {},
pages = {e884-e898},
pmid = {35623610},
issn = {1878-8769},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Cross-Sectional Studies ; Electroencephalography/methods ; Humans ; *Stroke Rehabilitation ; Surveys and Questionnaires ; },
abstract = {OBJECTIVE: Invasive brain-computer interfaces (BCIs) require neurosurgical implantation, which confers a range of risks. Despite this situation, no studies have assessed the acceptability of invasive BCIs among the neurosurgical team. This study aims to establish baseline knowledge of BCIs within the neurosurgical team and identify attitudes toward different applications of invasive BCI.<br><br>METHODS: A 2-stage cross-sectional international survey of the neurosurgical team (neurosurgeons, anesthetists, and operating room nurses) was conducted. Results from the first, qualitative, survey were used to guide the second-stage quantitative survey, which assessed acceptability of invasive BCI applications. Five-part Likert scales were used to collect quantitative data. Surveys were distributed internationally via social media and collaborators.<br><br>RESULTS: A total of 108 qualitative responses were collected. Themes included the promise of BCIs positively affecting disease targets, concerns regarding stability, and an overall positive emotional reaction to BCI technology. The quantitative survey generated 538 responses from 32 countries. Baseline knowledge of BCI technology was poor, with 9% claiming to have a good or expert knowledge of BCIs. Acceptability of invasive BCI for rehabilitative purposes was >80%. Invasive BCI for augmentation in healthy populations divided opinion.<br><br>CONCLUSIONS: The neurosurgical team's view of the acceptability of invasive BCI was divided across a range of indications. Some applications (e.g., stroke rehabilitation) were viewed as more appropriate than other applications (e.g., augmentation for military use). This range in views highlights the need for stakeholder consultation on acceptable use cases along with regulation and guidance to govern initial BCI implantations if patients are to realize the potential benefits.},
}
@article {pmid35623447,
year = {2022},
author = {Goldway, N and Jalon, I and Keynan, JN and Hellrung, L and Horstmann, A and Paret, C and Hendler, T},
title = {Feasibility and utility of amygdala neurofeedback.},
journal = {Neuroscience and biobehavioral reviews},
volume = {138},
number = {},
pages = {104694},
doi = {10.1016/j.neubiorev.2022.104694},
pmid = {35623447},
issn = {1873-7528},
mesh = {Amygdala/diagnostic imaging/physiology ; Brain/diagnostic imaging ; Brain Mapping ; Feasibility Studies ; Humans ; Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; },
abstract = {Amygdala NeuroFeedback (NF) have the potential of being a valuable non-invasive intervention tool in many psychiatric disporders. However, the feasibility and best practices of this method have not been systematically examined. The current article presents a review of amygdala-NF studies, an analytic summary of study design parameters, and examination of brain mechanisms related to successful amygdala-NF performance. A meta-analysis of 33 publications showed that real amygdala-NF facilitates learned modulation compared to control conditions. In addition, while variability in study dsign parameters is high, these design choices are implicitly organized by the targeted valence domain (positive or negative). However, in most cases the neuro-behavioral effects of targeting such domains were not directly assessed. Lastly, re-analyzing six data sets of amygdala-fMRI-NF revealed that successful amygdala down-modulation is coupled with deactivation of the posterior insula and nodes in the Default-Mode-Network. Our findings suggest that amygdala self-modulation can be acquired using NF. Yet, additional controlled studies, relevant behavioral tasks before and after NF intervention, and neural 'target engagement' measures are critically needed to establish efficacy and specificity. In addition, the fMRI analysis presented here suggest that common accounts regarding the brain network involved in amygdala NF might reflect unsuccessful modulation attempts rather than successful modulation.},
}
@article {pmid35623347,
year = {2022},
author = {Hu, JM and Roe, AW},
title = {Functionally specific and sparse domain-based micro-networks in monkey V1 and V2.},
journal = {Current biology : CB},
volume = {32},
number = {13},
pages = {2797-2809.e3},
doi = {10.1016/j.cub.2022.04.095},
pmid = {35623347},
issn = {1879-0445},
mesh = {Animals ; Brain Mapping/methods ; Haplorhini ; Humans ; Photic Stimulation/methods ; *Visual Cortex/physiology ; *Visual Pathways/physiology ; },
abstract = {The cerebral cortices of human and nonhuman primate brains are characterized by submillimeter functional domains. However, little is known about the connections of single functional domains. Here, in macaque monkey visual cortex, we have developed a targeted focal electrical stimulation method, coupled with functional optical imaging, to map cortical networks with submillimeter precision in vivo. We find that single functional domains are a part of highly specific and sparse intra-areal and inter-areal micro-networks. Across color-related and orientation-related functionalities, these micro-networks exhibit parallel connection patterns, suggesting a common domain-based architecture. Moreover, these micro-networks shift topographically at a submillimeter scale, suggesting that they serve as a fundamental unit for cortical information processing. Our findings establish a domain-based connectional architecture in the primate brain and present new constraints for cortical map representation.},
}
@article {pmid35621994,
year = {2022},
author = {Merk, T and Peterson, V and Lipski, WJ and Blankertz, B and Turner, RS and Li, N and Horn, A and Richardson, RM and Neumann, WJ},
title = {Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease.},
journal = {eLife},
volume = {11},
number = {},
pages = {},
pmid = {35621994},
issn = {2050-084X},
support = {R01 NS110424/NS/NINDS NIH HHS/United States ; },
mesh = {Brain ; *Deep Brain Stimulation ; Electrocorticography ; Humans ; Movement ; *Parkinson Disease ; },
abstract = {Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.},
}
@article {pmid35619967,
year = {2022},
author = {Tonin, L and Beraldo, G and Tortora, S and Menegatti, E},
title = {ROS-Neuro: An Open-Source Platform for Neurorobotics.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {886050},
pmid = {35619967},
issn = {1662-5218},
abstract = {The growing interest in neurorobotics has led to a proliferation of heterogeneous neurophysiological-based applications controlling a variety of robotic devices. Although recent years have seen great advances in this technology, the integration between human neural interfaces and robotics is still limited, making evident the necessity of creating a standardized research framework bridging the gap between neuroscience and robotics. This perspective paper presents Robot Operating System (ROS)-Neuro, an open-source framework for neurorobotic applications based on ROS. ROS-Neuro aims to facilitate the software distribution, the repeatability of the experimental results, and support the birth of a new community focused on neuro-driven robotics. In addition, the exploitation of Robot Operating System (ROS) infrastructure guarantees stability, reliability, and robustness, which represent fundamental aspects to enhance the translational impact of this technology. We suggest that ROS-Neuro might be the future development platform for the flourishing of a new generation of neurorobots to promote the rehabilitation, the inclusion, and the independence of people with disabilities in their everyday life.},
}
@article {pmid35617933,
year = {2022},
author = {Wu, D and Yang, J and Sawan, M},
title = {Bridging the gap between patient-specific and patient-independent seizure prediction via knowledge distillation.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac73b3},
pmid = {35617933},
issn = {1741-2552},
mesh = {Algorithms ; Child ; *Electroencephalography/methods ; *Epilepsy ; Humans ; Neural Networks, Computer ; Seizures/diagnosis ; },
abstract = {Objective.Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. Therefore, only a limited number of labeled recordings from each subject can be used for training. As a consequence, current DNN based methods demonstrate poor generalization ability to some extent due to the insufficiency of training data. On the other hand, patient-independent models attempt to utilize more patient data to train a universal model for all patients by pooling patient data together. Despite different techniques applied, results show that patient-independent models perform worse than patient-specific models due to high individual variation across patients. A substantial gap thus exists between patient-specific and patient-independent models.Approach. In this paper, we propose a novel training scheme based on knowledge distillation which makes use of a large amount of data from multiple subjects. It first distills informative features from signals of all available subjects with a pre-trained general model. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data.Main results. Four state-of-the-art seizure prediction methods are trained on the Children's Hospital of Boston-MIT sEEG database with our proposed scheme. The resulting accuracy, sensitivity, and false prediction rate show that our proposed training scheme consistently improves the prediction performance of state-of-the-art methods by a large margin.Significance.The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors.},
}
@article {pmid35615273,
year = {2022},
author = {Lun, X and Liu, J and Zhang, Y and Hao, Z and Hou, Y},
title = {A Motor Imagery Signals Classification Method via the Difference of EEG Signals Between Left and Right Hemispheric Electrodes.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {865594},
pmid = {35615273},
issn = {1662-4548},
abstract = {Brain-computer interface (BCI) based on motor imagery (MI) can help patients with limb movement disorders in their normal life. In order to develop an efficient BCI system, it is necessary to decode high-accuracy motion intention by electroencephalogram (EEG) with low signal-to-noise ratio. In this article, a MI classification approach is proposed, combining the difference in EEG signals between the left and right hemispheric electrodes with a dual convolutional neural network (dual-CNN), which effectively improved the decoding performance of BCI. The positive and inverse problems of EEG were solved by the boundary element method (BEM) and weighted minimum norm estimation (WMNE), and then the scalp signals were mapped to the cortex layer. We created nine pairs of new electrodes on the cortex as the region of interest. The time series of the nine electrodes on the left and right hemispheric are respectively used as the input of the dual-CNN model to classify four MI tasks. The results show that this method has good results in both group-level subjects and individual subjects. On the Physionet database, the averaged accuracy on group-level can reach 96.36%, while the accuracies of four MI tasks reach 98.54, 95.02, 93.66, and 96.19%, respectively. As for the individual subject, the highest accuracy is 98.88%, and its four MI accuracies are 99.62, 99.68, 98.47, and 97.73%, respectively.},
}
@article {pmid35614546,
year = {2022},
author = {Costa-García, &#xc1; and Iáñez, E and Yokoyama, M and Ueda, S and Okajima, S and Shimoda, S},
title = {Quantification of high and low sEMG spectral components during sustained isometric contraction.},
journal = {Physiological reports},
volume = {10},
number = {10},
pages = {e15296},
pmid = {35614546},
issn = {2051-817X},
mesh = {Electromyography ; *Isometric Contraction/physiology ; Muscle Contraction ; Muscle Fatigue/physiology ; *Muscle, Skeletal/physiology ; },
abstract = {Superficial Electromyography (sEMG) spectrum contains aggregated information from several underlying physiological processes. Due to technological limitations, the isolation of these processes is challenging, and therefore, the interpretation of changes in muscle activity frequency is still controversial. Recent studies showed that the spectrum of sEMG signals recorded from isotonic and short-term isometric contractions can be decomposed into independent components whose spectral features recall those of motor unit action potentials. In this paper sEMG spectral decomposition is tested during muscle fatigue induced by long-term isometric contraction where sEMG spectral changes have been widely studied. The main goals of this work are to validate spectral component extraction during long-term isometric muscle activation and the quantification of energy exchange between the low- and high-frequency bands of sEMG signals during muscle fatigue.},
}
@article {pmid35614453,
year = {2022},
author = {Liebl, S and Tischendorf, T and Winterlich, J and Schaal, T},
title = {Technical innovations in stroke rehabilitation - a survey for development of a non-invasive, brainwave-guided, functional muscle stimulation.},
journal = {BMC neurology},
volume = {22},
number = {1},
pages = {194},
pmid = {35614453},
issn = {1471-2377},
support = {100370687//This measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament./ ; 100370687//This measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament./ ; 100370687//This measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament./ ; 100370687//This measure is co-financed with tax funds on the basis of the budget passed by the Saxon state parliament./ ; },
mesh = {*Brain Waves ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; Electroencephalography/methods ; Humans ; Muscles ; *Stroke/therapy ; *Stroke Rehabilitation/methods ; },
abstract = {BACKGROUND: Stroke is one of the most frequent causes of death in Germany and the developed countries. After a stroke, those affected often suffer particularly from functional motor restrictions of the upper extremities. Newer techniques such as the BCI-FES systems aim to establish a communication channel between the brain and external devices with a neuromuscular intervention. The electrical activity of the brain is measured, processed, translated into control signals and can then be used to control an application.<br><br>METHODS: As a mixed-methods design (exploratory design), eight guideline-based expert interviews were conducted first. For the quantitative expert survey, 95 chief physicians from the field of neuromedicine in rehabilitation facilities nationwide were subsequently invited to participate in an online survey.<br><br>RESULTS: In our data analysis, we found that doctors are largely open-minded towards new technical rehabilitation systems. In addition to the proper functioning of the system, they consider the understanding of the functionality and the meaningfulness of the system to be particularly important. In addition, the system should be motivating for individuals, generate meaningful movements, be easy to use, evidence-based and quick to set up. Concerns were expressed regarding the understanding of the system's processes, especially in the acute phase after a stroke, as well as the excessive expectation of results from the system on the part of the persons. The experts named stroke patients in rehabilitation phase C, which is about mobilization and recovery, as well as all persons who can understand the language requirements as benefiting groups of people.<br><br>CONCLUSION: The present study shows that more research should and must be done in the field of BCI-FES interfaces, and various development trends have been identified. The system has the potential to play a leading role in the rehabilitation of stroke patients in the future. Nevertheless, more work should be done on the improvement and implementation as well as the system's susceptibility to interference in everyday patient life.},
}
@article {pmid35614168,
year = {2022},
author = {Ladouce, S and Darmet, L and Torre Tresols, JJ and Velut, S and Ferraro, G and Dehais, F},
title = {Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {8865},
pmid = {35614168},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Photic Stimulation/methods ; },
abstract = {Steady-States Visually Evoked Potentials (SSVEP) refer to the sustained rhythmic activity observed in surface electroencephalography (EEG) in response to the presentation of repetitive visual stimuli (RVS). Due to their robustness and rapid onset, SSVEP have been widely used in Brain Computer Interfaces (BCI). However, typical SSVEP stimuli are straining to the eyes and present risks of triggering epileptic seizures. Reducing visual stimuli contrast or extending their frequency range both appear as relevant solutions to address these issues. It however remains sparsely documented how BCI performance is impacted by these features and to which extent user experience can be improved. We conducted two studies to systematically characterize the effects of frequency and amplitude depth reduction on SSVEP response. The results revealed that although high frequency stimuli improve visual comfort, their classification performance were not competitive enough to design a reliable/responsive BCI. Importantly, we found that the amplitude depth reduction of low frequency RVS is an effective solution to improve user experience while maintaining high classification performance. These findings were further validated by an online T9 SSVEP-BCI in which stimuli with 40% amplitude depth reduction achieved comparable results (>90% accuracy) to full amplitude stimuli while significantly improving user experience.},
}
@article {pmid35613546,
year = {2022},
author = {Costello, JT and Nason-Tomaszewski, SR and An, H and Lee, J and Mender, MJ and Temmar, H and Wallace, DM and Lim, J and Willsey, MS and Patil, PG and Jang, T and Phillips, JD and Kim, HS and Blaauw, D and Chestek, CA},
title = {A low-power communication scheme for wireless, 1000 channel brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac7352},
pmid = {35613546},
issn = {1741-2552},
support = {R21 EY029452/EY/NEI NIH HHS/United States ; F31 HD098804/HD/NICHD NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Communication ; Electrodes ; Primates ; Wireless Technology ; },
abstract = {Objective. Brain-machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating 'motes' which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption.Approach. To test PIM's ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption.Main results. We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels.Significance.These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.},
}
@article {pmid35605612,
year = {2022},
author = {Lenarz, T and Büchner, A and Illg, A},
title = {Cochlear Implantation: Concept, Results Outcomes and Quality of Life.},
journal = {Laryngo- rhino- otologie},
volume = {101},
number = {S 01},
pages = {S36-S78},
doi = {10.1055/a-1731-9321},
pmid = {35605612},
issn = {1438-8685},
mesh = {Child ; *Cochlear Implantation ; *Cochlear Implants ; *Deafness/rehabilitation/surgery ; Hearing Loss, High-Frequency ; Humans ; Quality of Life ; *Speech Perception/physiology ; },
abstract = {Cochlear implant today are an essential method of auditory rehabilitation in patients with severe to profound hearing loss. Due to the rapid development of implant technology the results have been markedly improved. Today about 80 % of patients can use the telephone and children achieve near to normal hearing and speech development. In consequence, more patients are candidates for a cochlear implant today including those with high frequency deafness and single sided deafness. However, today only 60,000 out of 1 Million CI-candidates in Germany have been implanted so far. In future multi modal universal auditory implants will provide combined electric-mechanical stimulation to make best use of the residual auditory hearing and the electrical stimulation of the auditory nerve. They allow a continuous adaptation of the stimulation strategy onto the given functional status of haircells and auditory nerve fibers especially in cases of progressive hearing loss. Brain computer interfaces will allow the automated fitting and adaptation to the acoustic scene by optimizing the signal processing for best possible auditory performance. Binaural hearing systems will improve directional hearing and speech perception in noise. Advanced implants are composed of individualized electrodes by additive manufacturing which can be inserted atraumaticly by a computer and robot assisted surgery. After insertion they automatically adept to the anatomy of the individual cochlear. These advanced implants are composed with additional integrated biological components for the preservation of residual hearing and regeneration of neural elements to improve the electrode nerve interface. This will allow to increase the number of electrical contacts as a major step towards the bionic ear. This will allow overcoming the principal limits of today's cochlear implant technology. Advanced care models will allow an easy way for the patient towards hearing preservation cochlear implantation under local anesthesia using minimal invasive high precision cochlear implant surgery. These implant systems will become a personal communicator with improved connectivity. Remote care and self-fitting will empower the patient to optimize his own hearing.},
}
@article {pmid35605585,
year = {2022},
author = {Du, Y and Liu, J},
title = {IENet: a robust convolutional neural network for EEG based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac7257},
pmid = {35605585},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {Objective.Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop into novel application areas with more complex scenarios, which put forward higher requirements for the robustness of EEG signal processing algorithms. Deep learning can automatically extract discriminative features and potential dependencies via deep structures, demonstrating strong analytical capabilities in numerous domains such as computer vision and natural language processing. Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper.Approach.Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field (RF) gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost. We compare with the current state-of-the-art methods across five EEG-BCI paradigms: steady-state visual evoked potentials (VEPs), epilepsy EEG, overt attention P300 VEPs, covert attention P300 visual-EPs and movement-related cortical potentials.Main results.The classification results show that the generalizability of IENet is on par with the state-of-the-art paradigm-agnostic models on test datasets. Furthermore, the feature explainability analysis of IENet illustrates its capability to extract neurophysiologically interpretable features for different BCI paradigms, ensuring the reliability of algorithm.Significance.It can be seen from our results that IENet can generalize to different BCI paradigms. And it is essential for deep CNNs to increase the RF size using average RF gain.},
}
@article {pmid35604492,
year = {2022},
author = {Jiang, H and Wang, R and Zheng, Z and Zhu, J and Qi, Y and Xu, K and Zhang, J},
title = {Short report: surgery for implantable brain-computer interface assisted by robotic navigation system.},
journal = {Acta neurochirurgica},
volume = {164},
number = {9},
pages = {2299-2302},
pmid = {35604492},
issn = {0942-0940},
mesh = {Aged ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Movement ; Quadriplegia ; *Robotic Surgical Procedures ; *Robotics ; },
abstract = {We present an implantable brain-computer interface surgical case assisted by robotic navigation system in an elderly patient with tetraplegia caused by cervical spinal cord injury. Left primary motor cortex was selected for implantation of microelectrode arrays based on fMRI location of motor imagery. Robotic navigation system was used during this procedure for precise and stable manipulation. A design of bipartite incision was made to reduce the risk of surgery-related infection and facilitate BCI training. At 1-year follow-up, the neural signals were robust, and this patient was able to control three-dimensional movement of a prosthetic limb without any complications.},
}
@article {pmid35603289,
year = {2022},
author = {Serruya, MD and Napoli, A and Satterthwaite, N and Kardine, J and McCoy, J and Grampurohit, N and Talekar, K and Middleton, DM and Mohamed, F and Kogan, M and Sharan, A and Wu, C and Rosenwasser, RH},
title = {Neuromotor prosthetic to treat stroke-related paresis: N-of-1 trial.},
journal = {Communications medicine},
volume = {2},
number = {},
pages = {37},
pmid = {35603289},
issn = {2730-664X},
abstract = {BACKGROUND: Functional recovery of arm movement typically plateaus following a stroke, leaving chronic motor deficits. Brain-computer interfaces (BCI) may be a potential treatment for post-stroke deficits.<br><br>METHODS: In this n-of-1 trial (NCT03913286), a person with chronic subcortical stroke with upper-limb motor impairment used a powered elbow-wrist-hand orthosis that opened and closed the affected hand using cortical activity, recorded from a percutaneous BCI comprised of four microelectrode arrays implanted in the ipsilesional precentral gyrus, based on decoding of spiking patterns and high frequency field potentials generated by imagined hand movements. The system was evaluated in a home setting for 12 weeks.<br><br>RESULTS: Robust single unit activity, modulating with attempted or imagined movement, was present throughout the precentral gyrus. The participant acquired voluntary control over a hand-orthosis, achieving 10 points on the Action Research Arm Test using the BCI, compared to 0 without any device, and 5 using myoelectric control. Strength, spasticity, the Fugl-Meyer scores improved.<br><br>CONCLUSIONS: We demonstrate in a human being that ensembles of individual neurons in the cortex overlying a chronic supratentorial, subcortical stroke remain active and engaged in motor representation and planning and can be used to electrically bypass the stroke and promote limb function. The participant's ability to rapidly acquire control over otherwise paralyzed hand opening, more than 18 months after a stroke, may justify development of a fully implanted movement restoration system to expand the utility of fully implantable BCI to a clinical population that numbers in the tens of millions worldwide.},
}
@article {pmid35603051,
year = {2022},
author = {Zhang, L and Zhang, R and Guo, Y and Zhao, D and Li, S and Chen, M and Shi, L and Yao, D and Gao, J and Wang, X and Hu, Y},
title = {Assessing residual motor function in patients with disorders of consciousness by brain network properties of task-state EEG.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {3},
pages = {609-620},
pmid = {35603051},
issn = {1871-4080},
abstract = {UNLABELLED: Recent achievements in evaluating the residual consciousness of patients with disorders of consciousness (DOCs) have demonstrated that spontaneous or evoked electroencephalography (EEG) could be used to improve consciousness state diagnostic classification. Recent studies showed that the EEG signal of the task-state could better characterize the conscious state and cognitive ability of the brain, but it has rarely been used in consciousness assessment. A cue-guide motor task experiment was designed, and task-state EEG were collected from 18 patients with unresponsive wakefulness syndrome (UWS), 29 patients in a minimally conscious state (MCS), and 19 healthy controls. To obtain the markers of residual motor function in patients with DOC, the event-related potential (ERP), scalp topography, and time-frequency maps were analyzed. Then the coherence (COH) and debiased weighted phase lag index (dwPLI) networks in the delta, theta, alpha, beta, and gamma bands were constructed, and the correlations of network properties and JFK Coma Recovery Scale-Revised (CRS-R) motor function scores were calculated. The results showed that there was an obvious readiness potential (RP) at the Cz position during the motor preparation process in the MCS group, but no RP was observed in the UWS group. Moreover, the node degree properties of the COH network in the theta and alpha bands and the global efficiency properties of the dwPLI network in the theta band were significantly greater in the MCS group compared to the UWS group. The above network properties and CRS-R motor function scores showed a strong linear correlation. These findings demonstrated that the brain network properties of task-state EEG could be markers of residual motor function of DOC patients.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09741-7.},
}
@article {pmid35601907,
year = {2022},
author = {Wang, F and Liu, H and Zhao, L and Su, L and Zhou, J and Gong, A and Fu, Y},
title = {Improved Brain-Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {880304},
pmid = {35601907},
issn = {1662-5161},
abstract = {Common spatial pattern (CSP) is an effective algorithm for extracting electroencephalogram (EEG) features of motor imagery (MI); however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor-even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels (three channels), wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time-frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time-frequency components and the original EEG signal. Furthermore, phase space reconstruction (PSR) was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster-Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II (left- and right-hand MI), which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.},
}
@article {pmid35601357,
year = {2021},
author = {Watters, PA and Ivanov, PC and Ning, X and Wang, W},
title = {Editorial: Neural Dynamics - Models and Complexity.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {841077},
doi = {10.3389/fnins.2021.841077},
pmid = {35601357},
issn = {1662-4548},
}
@article {pmid35599834,
year = {2022},
author = {Peterson, V and Galván, C and Hernández, H and Saavedra, MP and Spies, R},
title = {A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware.},
journal = {Data in brief},
volume = {42},
number = {},
pages = {108225},
pmid = {35599834},
issn = {2352-3409},
abstract = {The data consist of electroencephalography (EEG) signals acquired by means of low-cost consumer-grade devices from 10 participants (four females, right-handed, mean age ± SD = 26.1 ± 4.0 years) without any previous experience in Brain-Computer Interfaces (BCIs) usage. The BCI protocol consisted of two conditions, namely the kinesthetic imagination of grasping movement (motor imagery, MI) of the dominant hand and a rest/idle condition. Five protocol runs were required to be performed by each participant in a single-day session, of about 1.5 h. The first run, called RUN0, involved 5 trials of real grasping movement together with the same number of trials in a rest condition. This first run was done to both better explain the protocol and to encourage the participant to focus on the sensation of executing the movement. The rest of the runs (RUN1-RUN4) were identical, consisting of 20 trials for each condition presented in a random order. The electrical brain activity was registered from 15 electrodes covering the sensorimotor area, at a sampling frequency of 125 Hz. Muscle activity of the dominant hand was controlled via the electromyography (EMG) activity by two electrodes placed at two antagonist muscles involved in the flexion/extension of the wrist. The recordings were performed in a non-shielded office, by means of low-cost consumer grade devices and free multi-platform open source software. The EMG corruption level was analyzed and EEG trials for which the EMG activity was higher than a prescribed threshold value, were discarded. During acquisition, EEG data was digitally band-pass filtered between 0.5 and 45 Hz. These data provide a motor imagery vs. rest EEG dataset, relevant for BCI for motor rehabilitation applications. Since the recordings were performed by means of low-cost consumer grade devices in a non-controlled environment, this dataset provides an excellent source for exploring robust brain decoding techniques for future in-home BCI usage.},
}
@article {pmid35599368,
year = {2022},
author = {Zhang, H and Zhu, L and Gao, DS and Liu, Y and Zhang, J and Yan, M and Qian, J and Xi, W},
title = {Imaging the Deep Spinal Cord Microvascular Structure and Function with High-Speed NIR-II Fluorescence Microscopy.},
journal = {Small methods},
volume = {6},
number = {8},
pages = {e2200155},
doi = {10.1002/smtd.202200155},
pmid = {35599368},
issn = {2366-9608},
support = {81961128029//National Natural Science Foundation of China/ ; 91632105//National Natural Science Foundation of China/ ; 82071227//National Natural Science Foundation of China/ ; Zhejiang2022C03096//Zhejiang Health Leading Talent, Zhejiang Health and Health Committee Office/ ; 226-2022-00083//Fundamental Research Funds for the Central Universities/ ; },
mesh = {*Indocyanine Green ; Microscopy, Fluorescence/methods ; *Optical Imaging/methods ; Photons ; Spinal Cord/diagnostic imaging ; },
abstract = {The spinal cord (SC) is crucial for a myriad of somatosensory, autonomic signal processing, and transductions. Understanding the SC vascular structure and function thus plays an integral part in neuroscience and clinical research. However, the dense layers of myelinated ascending axons on the dorsal side inconveniently grant the SC tissue with high optical scattering property, which significantly hinders the imaging depth of the SC vasculature in vivo. Commonly used antiscattering techniques such as multiphoton fluorescence microscopy have low imaging speed and cannot capture the rapid vascular particle flow without significant motion blur. Here, advantage of the high penetration of near-infrared (NIR)-II fluorescence is taken to demonstrate a deep SC vascular structural image stack up to 350 µm, comparable to two-photon microscopy. Furthermore, the red blood cells are labelled with the clinically approved NIR dye indocyanine. The combination of a fast NIR camera and indocyanine green-red blood cells (RBCs) makes it possible to attain high-speed 100 frame-per-second NIR-II imaging to identify the corresponding changes in RBC velocity during the external hind leg stimulus. For the first time, it is established that the NIR-II region would be a promising spectral window for SC imaging. NIR-II fluorescence microscopy has excellent potential for clinical and basic science research on SC.},
}
@article {pmid35598127,
year = {2022},
author = {Sharma, B and Paul, M and Panagariya, A and Dubey, P},
title = {Neuroborreliosis in India - A Diagnostic Challenge and a Great Mimicker: A Case Series.},
journal = {The Journal of the Association of Physicians of India},
volume = {70},
number = {5},
pages = {11-12},
pmid = {35598127},
issn = {0004-5772},
mesh = {Adult ; Aged ; Antibodies, Bacterial/cerebrospinal fluid ; Female ; Humans ; Immunoglobulin G ; Immunoglobulin M ; *Lyme Neuroborreliosis/diagnosis/drug therapy/epidemiology ; Male ; Middle Aged ; *Nervous System Diseases ; *Tick Bites ; },
abstract = {OBJECTIVES: Neuroborreliosis is generally known to be a disease confined to the Western part of the globe. It is not commonly encountered in this part of the world. Interestingly, we recently came across a series of cases of Lyme's disease with a plethora of neurological presentations. Most of the cases were a diagnostic dilemma, with poor response to immunotherapy and on subsequent evaluation all were found to have positive Borrelia antibodies.<br><br>MATERIALS AND METHODS: Eight cases were selected from the tertiary care hospital in North western India. Patients were suspected to have Neuroborreliosis whose neurological presentations were atypical for other classical neurological disorders, who had a progressive or relapsing clinical course and had responded poorly to the initial treatment given for the previous neurological diagnosis. Skin lesions were present in some cases. The patients underwent a detailed clinical assessment which comprised of an elaborate history including history of travel, any insect bite or skin rashes along with a complete systemic and neurological examination. All the required blood investigations, Magnetic Resonance Imaging (MRI) Brain, Computer Tomography Angiography (CT), Nerve conduction study (NCS) and Electromyographic (EMG) studies and Cerebrospinal fluid (CSF) studies were done as indicated in each case. Borrelia antibody titre was done in all the patients using immunoblot technique.<br><br>RESULTS: Among the 8 patients, 6 were male and 2 were females. The age group was between 25-70 years. The clinical presentation was acute, subacute or chronic. One patient gave a clear history of tick bite. Two patients had skin lesions and one had the pathognomic "eschar". All the suspected 8 patients had either IgG or IgM or both IgG and IgM Borrelia antibodies positive. Almost all the patients had previously received either steroids or intravenous immunoglobulins, but had not adequately responded to immunotherapy. These patients were given a trial of injectable Ceftriaxone and oral Doxycycline. Most of them either showed partial or complete clinical improvement.<br><br>CONCLUSION: Lyme's disease, a common disease of the west does exist in the Indian subcontinent as well. Because of increasing global travel and migration and change in vector habitat the disease seems to have percolated in the non endemic areas too. Proper history of travel or exposure to tick bite is important. We want to emphasize, Neuroborreliosis, a great mimicker may have diverse and varied neurological presentations and has a potential for reversibility with appropriate treatment even after a significant delay in diagnosis.},
}
@article {pmid35594931,
year = {2022},
author = {Mashrur, FR and Rahman, KM and Miya, MTI and Vaidyanathan, R and Anwar, SF and Sarker, F and Mamun, KA},
title = {An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals.},
journal = {Physiology &amp; behavior},
volume = {253},
number = {},
pages = {113847},
doi = {10.1016/j.physbeh.2022.113847},
pmid = {35594931},
issn = {1873-507X},
mesh = {*Consumer Behavior ; *Electroencephalography ; Frontal Lobe ; Marketing ; Support Vector Machine ; },
abstract = {Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, Fz achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.},
}
@article {pmid35594216,
year = {2022},
author = {Chen, X and Hu, N and Gao, X},
title = {Development of a Brain-Computer Interface-Based Symbol Digit Modalities Test and Validation in Healthy Elderly Volunteers and Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1433-1440},
doi = {10.1109/TNSRE.2022.3176615},
pmid = {35594216},
issn = {1558-0210},
mesh = {Aged ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Healthy Volunteers ; Humans ; Neuropsychological Tests ; Photic Stimulation ; *Stroke ; },
abstract = {Standard cognitive assessment tools often involve motor or verbal responses, making them impossible for severely motor-disabled individuals. Brain-computer interfaces (BCIs) are expected to help severely motor-impaired individuals to perform cognitive assessment because BCIs can circumvent motor and verbal requirements. Currently, the field of research to develop cognitive tasks based on BCI is still in its nascent stage and needs further development. This study explored the possibility of developing a BCI version of symbol digit modalities test (BCI-SDMT). Steady-state visual evoked potential (SSVEP) was adopted to build the BCI and a 9-target SSVEP-BCI was realized to send examinees' responses. A training-free algorithm (i.e., filter bank canonical correlation analysis) was used for SSVEP identification. Thus, examinees are able to start the proposed BCI-SDMT immediately. Eighty-nine healthy elderly volunteers and 9 stroke patients were enrolled to validate the technical feasibility of the developed BCI-SDMT. For all participants, the average recognition accuracies of the developed BCI and BCI-SDMT were 93.89 ± 8.48% and 92.58 ± 10.52%, respectively, were considerably above the chance level (i.e., 11.11%). These results indicated that both healthy elderly volunteers and stroke patients could elicit sufficient SSVEPs to control the BCI. Furthermore, patient use of the developed BCI-SDMT was unaffected by the presence of motor impairment. They could understand instructions, pair numbers with specific symbols, and send commands using the BCI. The proposed BCI-SDMT can be used as a complement to the existing versions of the SDMT and has the potential to evaluate cognitive abilities in individuals with severe motor disabilities.},
}
@article {pmid35594208,
year = {2022},
author = {An, H and Nason-Tomaszewski, SR and Lim, J and Kwon, K and Willsey, MS and Patil, PG and Kim, HS and Sylvester, D and Chestek, CA and Blaauw, D},
title = {A Power-Efficient Brain-Machine Interface System With a Sub-mw Feature Extraction and Decoding ASIC Demonstrated in Nonhuman Primates.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {16},
number = {3},
pages = {395-408},
pmid = {35594208},
issn = {1940-9990},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; R01 GM111293/GM/NIGMS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Amplifiers, Electronic ; Animals ; *Brain-Computer Interfaces ; Humans ; Microelectrodes ; Paralysis ; Primates ; },
abstract = {Intracortical brain-machine interfaces have shown promise for restoring function to people with paralysis, but their translation to portable and implantable devices is hindered by their high power consumption. Recent devices have drastically reduced power consumption compared to standard experimental brain-machine interfaces, but still require wired or wireless connections to computing hardware for feature extraction and inference. Here, we introduce a Neural Recording And Decoding (NeuRAD) application specific integrated circuit (ASIC) in 180 nm CMOS that can extract neural spiking features and predict two-dimensional behaviors in real-time. To reduce amplifier and feature extraction power consumption, the NeuRAD has a hardware accelerator for extracting spiking band power (SBP) from intracortical spiking signals and includes an M0 processor with a fixed-point Matrix Acceleration Unit (MAU) for efficient and flexible decoding. We validated device functionality by recording SBP from a nonhuman primate implanted with a Utah microelectrode array and predicting the one- and two-dimensional finger movements the monkey was attempting to execute in closed-loop using a steady-state Kalman filter (SSKF). Using the NeuRAD's real-time predictions, the monkey achieved 100% success rate and 0.82 s mean target acquisition time to control one-dimensional finger movements using just 581 μW. To predict two-dimensional finger movements, the NeuRAD consumed 588 μW to enable the monkey to achieve a 96% success rate and 2.4 s mean acquisition time. By employing SBP, ASIC brain-machine interfaces can close the gap to enable fully implantable therapies for people with paralysis.},
}
@article {pmid35591103,
year = {2022},
author = {Masud, U and Saeed, T and Akram, F and Malaikah, H and Akbar, A},
title = {Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
pmid = {35591103},
issn = {1424-8220},
mesh = {*Algorithms ; Humans ; Lasers ; Physical Phenomena ; *Unmanned Aerial Devices ; },
abstract = {Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain-computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person's health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity.},
}
@article {pmid35591021,
year = {2022},
author = {Värbu, K and Muhammad, N and Muhammad, Y},
title = {Past, Present, and Future of EEG-Based BCI Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
pmid = {35591021},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.},
}
@article {pmid35590938,
year = {2022},
author = {Topic, A and Russo, M and Stella, M and Saric, M},
title = {Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
pmid = {35590938},
issn = {1424-8220},
support = {KK.01.1.1.01//European Regional Development Fund - the Competitiveness and Cohesion Operational Programme/ ; },
mesh = {Arousal ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Female ; *Holography ; Humans ; Male ; },
abstract = {An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.},
}
@article {pmid35590823,
year = {2022},
author = {Zapała, D and Augustynowicz, P and Tokovarov, M},
title = {Recognition of Attentional States in VR Environment: An fNIRS Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {9},
pages = {},
pmid = {35590823},
issn = {1424-8220},
support = {CPP-2021/10/4//Cortivision sp. z o.o./ ; },
mesh = {Attention ; Humans ; Recognition, Psychology ; *Smart Glasses ; *Virtual Reality ; },
abstract = {An improvement in ecological validity is one of the significant challenges for 21st-century neuroscience. At the same time, the study of neurocognitive processes in real-life situations requires good control of all variables relevant to the results. One possible solution that combines the capability of creating realistic experimental scenarios with adequate control of the test environment is virtual reality. Our goal was to develop an integrative research workspace involving a CW-fNIRS and head-mounted-display (HMD) technology dedicated to offline and online cognitive experiments. We designed an experimental study in a repeated-measures model on a group of BCI-naïve participants to verify our assumptions. The procedure included a 3D environment-adapted variant of the classic n-back task (2-back version). Tasks were divided into offline (calibration) and online (feedback) sessions. In both sessions, the signal was recorded during the cognitive task for within-group comparisons of changes in oxy-Hb concentration in the regions of interest (the dorsolateral prefrontal cortex-DLPFC and middle frontal gyrus-MFG). In the online session, the recorded signal changes were translated into real-time feedback. We hypothesized that it would be possible to obtain significantly higher than the level-of-chance threshold classification accuracy for the enhanced attention engagement (2-back task) vs. relaxed state in both conditions. Additionally, we measured participants' subjective experiences of the BCI control in terms of satisfaction. Our results confirmed hypotheses regarding the offline condition. In accordance with the hypotheses, combining fNIRS and HMD technologies enables the effective transfer of experimental cognitive procedures to a controlled VR environment. This opens the new possibility of creating more ecologically valid studies and training procedures.},
}
@article {pmid35589391,
year = {2022},
author = {Rubin, DB and Hosman, T and Kelemen, JN and Kapitonava, A and Willett, FR and Coughlin, BF and Halgren, E and Kimchi, EY and Williams, ZM and Simeral, JD and Hochberg, LR and Cash, SS},
title = {Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {25},
pages = {5007-5020},
pmid = {35589391},
issn = {1529-2401},
support = {R25 NS065743/NS/NINDS NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces ; Cervical Vertebrae ; Electroencephalography/methods ; Humans ; Learning/*physiology ; Male ; Motor Cortex/*physiology ; Pilot Projects ; Quadriplegia/etiology/physiopathology ; Sleep/*physiology ; Spinal Cord Injuries/complications/physiopathology ; },
abstract = {Consolidation of memory is believed to involve offline replay of neural activity. While amply demonstrated in rodents, evidence for replay in humans, particularly regarding motor memory, is less compelling. To determine whether replay occurs after motor learning, we sought to record from motor cortex during a novel motor task and subsequent overnight sleep. A 36-year-old man with tetraplegia secondary to cervical spinal cord injury enrolled in the ongoing BrainGate brain-computer interface pilot clinical trial had two 96-channel intracortical microelectrode arrays placed chronically into left precentral gyrus. Single- and multi-unit activity was recorded while he played a color/sound sequence matching memory game. Intended movements were decoded from motor cortical neuronal activity by a real-time steady-state Kalman filter that allowed the participant to control a neurally driven cursor on the screen. Intracortical neural activity from precentral gyrus and 2-lead scalp EEG were recorded overnight as he slept. When decoded using the same steady-state Kalman filter parameters, intracortical neural signals recorded overnight replayed the target sequence from the memory game at intervals throughout at a frequency significantly greater than expected by chance. Replay events occurred at speeds ranging from 1 to 4 times as fast as initial task execution and were most frequently observed during slow-wave sleep. These results demonstrate that recent visuomotor skill acquisition in humans may be accompanied by replay of the corresponding motor cortex neural activity during sleep.SIGNIFICANCE STATEMENT Within cortex, the acquisition of information is often followed by the offline recapitulation of specific sequences of neural firing. Replay of recent activity is enriched during sleep and may support the consolidation of learning and memory. Using an intracortical brain-computer interface, we recorded and decoded activity from motor cortex as a human research participant performed a novel motor task. By decoding neural activity throughout subsequent sleep, we find that neural sequences underlying the recently practiced motor task are repeated throughout the night, providing direct evidence of replay in human motor cortex during sleep. This approach, using an optimized brain-computer interface decoder to characterize neural activity during sleep, provides a framework for future studies exploring replay, learning, and memory.},
}
@article {pmid35588948,
year = {2022},
author = {Xing, M and Hu, S and Wei, B and Lv, Z},
title = {Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition.},
journal = {Journal of neuroscience methods},
volume = {376},
number = {},
pages = {109624},
doi = {10.1016/j.jneumeth.2022.109624},
pmid = {35588948},
issn = {1872-678X},
mesh = {Attention ; *Electroencephalography ; Emotions ; Humans ; *Neural Networks, Computer ; },
abstract = {BACKGROUND: Multimedia stimulation of brain activity is important for emotion induction. Based on brain activity, emotion recognition using EEG signals has become a hot issue in the field of affective computing.<br><br>NEW METHOD: In this paper, we develop a noval odor-video elicited physiological signal database (OVPD), in which we collect the EEG signals from eight participants in positive, neutral and negative emotional states when they are stimulated by synchronizing traditional video content with the odors. To make full use of the EEG features from different domains, we design a 3DCNN-BiLSTM model combining convolutional neural network (CNN) and bidirectional long short term memory (BiLSTM) for EEG emotion recognition. First, we transform EEG signals into 4D representations that retain spatial, frequency and temporal information. Then, the representations are fed into the 3DCNN-BiLSTM model to recognize emotions. CNN is applied to learn spatial and frequency information from the 4D representations. BiLSTM is designed to extract forward and backward temporal dependences.<br><br>RESULTS: We conduct 5-fold cross validation experiments five times on the OVPD dataset to evaluate the performance of the model. The experimental results show that our presented model achieves an average accuracy of 98.29% with the standard deviation of 0.72% under the olfactory-enhanced video stimuli, and an average accuracy of 98.03% with the standard deviation of 0.73% under the traditional video stimuli on the OVPD dataset in the three-class classification of positive, neutral and negative emotions. To verify the generalisability of our proposed model, we also evaluate this approach on the public EEG emotion dataset (SEED).<br><br>Compared with other baseline methods, our designed model achieves better recognition performance on the OVPD dataset. The average accuracy of positive, neutral and negative emotions is better in response to the olfactory-enhanced videos than the pure videos for the 3DCNN-BiLSTM model and other baseline methods.<br><br>CONCLUSION: The proposed 3DCNN-BiLSTM model is effective by fusing the spatial-frequency-temporal features of EEG signals for emotion recognition. The provided olfactory stimuli can induce stronger emotions than traditional video stimuli and improve the accuracy of emotion recognition to a certain extent. However, superimposing odors unrelated to the video scenes may distract participants' attention, and thus reduce the final accuracy of EEG emotion recognition.},
}
@article {pmid35588679,
year = {2022},
author = {Gao, Y and Sun, X and Meng, M and Zhang, Y},
title = {EEG emotion recognition based on enhanced SPD matrix and manifold dimensionality reduction.},
journal = {Computers in biology and medicine},
volume = {146},
number = {},
pages = {105606},
doi = {10.1016/j.compbiomed.2022.105606},
pmid = {35588679},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Emotions ; Humans ; Support Vector Machine ; },
abstract = {Recently, Riemannian geometry-based pattern recognition has been widely employed to brain computer interface (BCI) researches, providing new idea for emotion recognition based on electroencephalogram (EEG) signals. Although the symmetric positive definite (SPD) matrix manifold constructed from the traditional covariance matrix contains large amount of spatial information, these methods do not perform well to classify and recognize emotions, and the high dimensionality problem still unsolved. Therefore, this paper proposes a new strategy for EEG emotion recognition utilizing Riemannian geometry with the aim of achieving better classification performance. The emotional EEG signals of 32 healthy subjects were from an open-source dataset (DEAP). The wavelet packets were first applied to extract the time-frequency features of the EEG signals, and then the features were used to construct the enhanced SPD matrix. A supervised dimensionality reduction algorithm was then designed on the Riemannian manifold to reduce the high dimensionality of the SPD matrices, gather samples of the same labels together, and separate samples of different labels as much as possible. Finally, the samples were mapped to the tangent space, and the K-nearest neighbors (KNN), Random Forest (RF) and Support Vector Machine (SVM) method were employed for classification. The proposed method achieved an average accuracy of 91.86%, 91.84% on the valence and arousal recognition tasks. Furthermore, we also obtained the superior accuracy of 86.71% on the four-class recognition task, demonstrated the superiority over state-of-the-art emotion recognition methods.},
}
@article {pmid35588044,
year = {2022},
author = {Qian, S and Kumar, P and Testai, FD},
title = {Bilirubin Encephalopathy.},
journal = {Current neurology and neuroscience reports},
volume = {22},
number = {7},
pages = {343-353},
pmid = {35588044},
issn = {1534-6293},
mesh = {Bilirubin ; *Brain Diseases ; Humans ; Infant, Newborn ; *Kernicterus/diagnosis/epidemiology/etiology ; Phototherapy/adverse effects ; },
abstract = {PURPOSE OF REVIEW: Hyperbilirubinemia is commonly seen in neonates. Though hyperbilirubinemia is typically asymptomatic, severe elevation of bilirubin levels can lead to acute bilirubin encephalopathy and progress to kernicterus spectrum disorder, a chronic condition characterized by hearing loss, extrapyramidal dysfunction, ophthalmoplegia, and enamel hypoplasia. Epidemiological data show that the implementation of universal pre-discharge bilirubin screening programs has reduced the rates of hyperbilirubinemia-associated complications. However, acute bilirubin encephalopathy and kernicterus spectrum disorder are still particularly common in low- and middle-income countries.<br><br>RECENT FINDINGS: The understanding of the genetic and biochemical processes that increase the susceptibility of defined anatomical areas of the central nervous system to the deleterious effects of bilirubin may facilitate the development of effective treatments for acute bilirubin encephalopathy and kernicterus spectrum disorder. Scoring systems are available for the diagnosis and severity grading of these conditions. The treatment of hyperbilirubinemia in newborns relies on the use of phototherapy and exchange transfusion. However, novel therapeutic options including deep brain stimulation, brain-computer interface, and stem cell transplantation may alleviate the heavy disease burden associated with kernicterus spectrum disorder. Despite improved screening and treatment options, the prevalence of acute bilirubin encephalopathy and kernicterus spectrum disorder remains elevated in low- and middle-income countries. The continued presence and associated long-term disability of these conditions warrant further research to improve their prevention and management.},
}
@article {pmid35584066,
year = {2022},
author = {Xiao, J and He, Y and Yu, T and Pan, J and Xie, Q and Cao, C and Zheng, H and Huang, W and Gu, Z and Yu, Z and Li, Y},
title = {Toward Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1422-1432},
doi = {10.1109/TNSRE.2022.3176354},
pmid = {35584066},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Coma/diagnosis ; Consciousness ; Consciousness Disorders/diagnosis ; *Persons with Disabilities ; Electroencephalography ; Female ; Humans ; *Motor Disorders ; *Sound Localization ; },
abstract = {Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.},
}
@article {pmid35580764,
year = {2022},
author = {Oliveira, LJC and Amorim, LC and Megid, TBC and de Resende, CAA and Mano, MS},
title = {Gene expression signatures in early breast cancer: Better together with clinicopathological features.},
journal = {Critical reviews in oncology/hematology},
volume = {175},
number = {},
pages = {103708},
doi = {10.1016/j.critrevonc.2022.103708},
pmid = {35580764},
issn = {1879-0461},
mesh = {*Breast Neoplasms/diagnosis/genetics/therapy ; Female ; Gene Expression Profiling ; Humans ; Neoplasm Recurrence, Local/genetics/pathology/therapy ; Prognosis ; Receptor, ErbB-2/genetics/metabolism ; Transcriptome ; },
abstract = {Breast cancer (BC) is a highly heterogeneous disease, characterized by a variety of subtypes with distinct biological, molecular, and clinical behavior. Standard clinicopathological and tumor biology information (as assessed by gene expression signatures-GES), have provided enhanced prognostic and predictive information in both node-negative(N0) and positive(N +), hormonal receptor positive/human epidermal growth factor 2 negative (HR+/HER2-) early breast cancer (EBC). Herein, we comprehensively review the clinical data of 5 commonly used GES, namely, Oncotype DX(ODX)®; MammaPrint (MP)®; Prosigna®; Breast Cancer Index (BCI)® and Endopredict® - with sections specifically addressing the role of GES in special histologic subtypes, premenopausal women, late recurrence and adjuvant treatment de-escalation.},
}
@article {pmid35580046,
year = {2022},
author = {Lei, Y and Fei, P and Song, B and Shi, W and Luo, C and Luo, D and Li, D and Chen, W and Zheng, J},
title = {A loosened gating mechanism of RIG-I leads to autoimmune disorders.},
journal = {Nucleic acids research},
volume = {50},
number = {10},
pages = {5850-5863},
pmid = {35580046},
issn = {1362-4962},
mesh = {*Autoimmune Diseases/genetics ; DEAD Box Protein 58/chemistry/genetics ; DEAD-box RNA Helicases/chemistry/genetics ; Humans ; Immunity, Innate/genetics ; Metacarpus ; *Odontodysplasia ; RNA/chemistry ; },
abstract = {DDX58 encodes RIG-I, a cytosolic RNA sensor that ensures immune surveillance of nonself RNAs. Individuals with RIG-IE510V and RIG-IQ517H mutations have increased susceptibility to Singleton-Merten syndrome (SMS) defects, resulting in tissue-specific (mild) and classic (severe) phenotypes. The coupling between RNA recognition and conformational changes is central to RIG-I RNA proofreading, but the molecular determinants leading to dissociated disease phenotypes remain unknown. Herein, we employed hydrogen/deuterium exchange mass spectrometry (HDX-MS) and single molecule magnetic tweezers (MT) to precisely examine how subtle conformational changes in the helicase insertion domain (HEL2i) promote impaired ATPase and erroneous RNA proofreading activities. We showed that the mutations cause a loosened latch-gate engagement in apo RIG-I, which in turn gradually dampens its self RNA (Cap2 moiety:m7G cap and N1-2-2'-O-methylation RNA) proofreading ability, leading to increased immunopathy. These results reveal HEL2i as a unique checkpoint directing two specialized functions, i.e. stabilizing the CARD2-HEL2i interface and gating the helicase from incoming self RNAs; thus, these findings add new insights into the role of HEL2i in the control of antiviral innate immunity and autoimmunity diseases.},
}
@article {pmid35578209,
year = {2022},
author = {Hu, J and Li, Y and Li, Z and Chen, J and Cao, Y and Xu, D and Zheng, L and Bai, R and Wang, L},
title = {Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease.},
journal = {BMC neurology},
volume = {22},
number = {1},
pages = {179},
pmid = {35578209},
issn = {1471-2377},
support = {81870910//National Natural Science Foundation of China/ ; Y18H090007//Natural Science Foundation of Zhejiang Province/ ; },
mesh = {Brain/diagnostic imaging ; Brain Mapping ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; *Motor Cortex/diagnostic imaging ; *Moyamoya Disease/diagnostic imaging ; Prefrontal Cortex ; },
abstract = {BACKGROUND: Disruption of brain functional connectivity has been detected after stroke, but whether it also occurs in moyamoya disease (MMD) is unknown. Impaired functional connectivity is always correlated with abnormal white matter fibers. Herein, we used multimodal imaging techniques to explore the changes in brain functional and structural connectivity in MMD patients.<br><br>METHODS: We collected structural images, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging for each subject. Cognitive functions of MMD patients were evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Trail Making Test parts A and B (TMT-A/-B). We calculated the functional connectivity for every paired region using 90 regions of interest from the Anatomical Automatic Labeling Atlas and then determined the differences between MMD patients and HCs. We extracted the functional connectivity of paired brain regions with significant differences between the two groups. Correlation analyses were then performed between the functional connectivity and variable cognitive functions. To explore whether the impaired functional connectivity and cognitive performances were attributed to the destruction of white matter fibers, we further analyzed fiber integrity using tractography between paired regions that were correlated with cognition.<br><br>RESULTS: There was lower functional connectivity in MMD patients as compared to HCs between the bilateral inferior frontal gyrus, between the bilateral supramarginal gyrus, between the left supplementary motor area (SMA) and the left orbital part of the inferior frontal gyrus (IFGorb), and between the left SMA and the left middle temporal gyrus (P&#x2009;<&#x2009;0.01, FDR corrected). The decreased functional connectivity between the left SMA and the left IFGorb was significantly correlated with the MMSE (r&#x2009;=&#x2009;0.52, P&#x2009;=&#x2009;0.024), MoCA (r&#x2009;=&#x2009;0.60, P&#x2009;=&#x2009;0.006), and TMT-B (r&#x2009;=&#x2009;-0.54, P&#x2009;=&#x2009;0.048) in MMD patients. White matter fibers were also injured between the SMA and IFGorb in the left hemisphere and were positively correlated with reduced functional connectivity.<br><br>CONCLUSIONS: Brain functional and structural connectivity between the supplementary motor area and inferior frontal gyrus in the left hemisphere are damaged in MMD. These findings could be useful in the evaluation of disease progression and prognosis of MMD.},
}
@article {pmid35578093,
year = {2022},
author = {Qiu, L and Zhang, B and Gao, Z},
title = {Lighting Up Neural Circuits by Viral Tracing.},
journal = {Neuroscience bulletin},
volume = {38},
number = {11},
pages = {1383-1396},
pmid = {35578093},
issn = {1995-8218},
mesh = {*Synapses/physiology ; Prospective Studies ; *Neurons/physiology ; Genetic Vectors ; Brain/physiology ; Neural Pathways/physiology ; },
abstract = {Neurons are highly interwoven to form intricate neural circuits that underlie the diverse functions of the brain. Dissecting the anatomical organization of neural circuits is key to deciphering how the brain processes information, produces thoughts, and instructs behaviors. Over the past decades, recombinant viral vectors have become the most commonly used tracing tools to define circuit architecture. In this review, we introduce the current categories of viral tools and their proper application in circuit tracing. We further discuss some advances in viral tracing strategy and prospective innovations of viral tools for future study.},
}
@article {pmid35578016,
year = {2022},
author = {Chen, LN and Wang, WW and Dong, YJ and Shen, DD and Guo, J and Yu, X and Qin, J and Ji, SY and Zhang, H and Shen, Q and He, Q and Yang, B and Zhang, Y and Li, Q and Mao, C},
title = {Structures of the endogenous peptide- and selective non-peptide agonist-bound SSTR2 signaling complexes.},
journal = {Cell research},
volume = {32},
number = {8},
pages = {785-788},
pmid = {35578016},
issn = {1748-7838},
mesh = {*Peptides/pharmacology ; *Signal Transduction ; },
}
@article {pmid35576912,
year = {2022},
author = {Fang, H and Yang, Y},
title = {Designing and validating a robust adaptive neuromodulation algorithm for closed-loop control of brain states.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac7005},
pmid = {35576912},
issn = {1741-2552},
mesh = {*Algorithms ; Brain/physiology ; *Brain Diseases ; Humans ; Monte Carlo Method ; Nonlinear Dynamics ; },
abstract = {Objective. Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications.Approach. We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity.Main results. The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms.Significance. Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.},
}
@article {pmid35576911,
year = {2022},
author = {Flint, RD and Li, Y and Wang, PT and Vaidya, M and Barry, A and Ghassemi, M and Tomic, G and Brkic, N and Ripley, D and Liu, C and Kamper, D and Do, AH and Slutzky, MW},
title = {Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
pmid = {35576911},
issn = {1741-2552},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; R01 NS099210/NS/NINDS NIH HHS/United States ; R01 NS112942/NS/NINDS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {*Brain Injuries ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Feedback ; Humans ; *Neurological Rehabilitation/methods ; },
abstract = {Objective.Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain-machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ(70-115 Hz) information through a unique post-traumatic brain injury (TBI) hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force.Approach. We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with TBI. The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers.Main results. All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γmodulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γcontrol significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control).Significance. These proof-of-concept results show that high-γnrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like electrocorticography). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γsignals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.},
}
@article {pmid35576428,
year = {2022},
author = {Zhou, Y and Yang, B and Guan, C},
title = {Task-Related Component Analysis Combining Paired Character Decoding for Miniature Asymmetric Visual Evoked Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1331-1339},
doi = {10.1109/TNSRE.2022.3175307},
pmid = {35576428},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Reproducibility of Results ; },
abstract = {Brain-computer interface (BCI) technology based on event-related potentials (ERP) of electroencephalography (EEG) is widely used in daily life and medical treatment. However, the research of identifying the miniature and more informative asymmetric visual evoked potentials (aVEPs), which belongs to ERP, needs further exploration. Herein, a task-related component analysis combining paired character decoding (TRCA-PCD) method, which can enhance reproducibility of aVEPs in multiple trials and strengthen the features of different samples, was designed to realize fast decoding of aVEPs. The BCI performance and the influence of repetition times between the TRCA-PCD method, the discriminative canonical pattern matching (DCPM) method and traditional task-related component analysis (TRCA) method were compared using a 32-class aVEPs dataset recorded from 32 subjects. The highest average recognition accuracy and information transfer rate (ITR) of TRCA-PCD after parameter selection were 70.37 ± 2.49 % (DCPM: 64.91 ± 2.81 %, TRCA: 44.01 ± 3.25 %) with the peak value of 97.92% and 28.90 ± 3.83 bits/min (DCPM: 21.29 ± 3.35 bits/min, TRCA: 11.54 ± 2.81 bits/min) with the peak value of 94.55 bits/min respectively. Statistical analysis indicated that the highest average recognition rate could be obtained when the repetition time was six, and the highest ITR could be obtained when the repetition time was one. Overall, the results verified the effectiveness and superiority of TRCA-PCD in recognition of aVEPs and provided a reference for parameter selection. Therefore, the TRCA-PCD method can promote the further application of aVEPs in the BCI speller field.},
}
@article {pmid35574291,
year = {2022},
author = {Loizidou, P and Rios, E and Marttini, A and Keluo-Udeke, O and Soetedjo, J and Belay, J and Perifanos, K and Pouratian, N and Speier, W},
title = {Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {9},
number = {1},
pages = {36-48},
pmid = {35574291},
issn = {2326-263X},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCI) such as the P300 speller have the potential to restore communication to advanced-stage neuromuscular disease patients. Research has improved typing speed and accuracy through innovations including the use of language models. While significant advances have been made, implementations have largely been restricted to a single language, primarily English. It is unclear whether these improvements would extend to other languages that present potential technical hurdles due to different alphabets and grammatical structures. Here, we adapt a language model-based classifier designed for English to two other languages, Spanish and Greek, to demonstrate the generalizability of these methods. Online experimental trials with 30 healthy native English, Spanish, and Greek speakers showed no significant difference between performances using the different versions of the system (66.20 vs. 61.97 vs. 60.89 bits/minute). Extending these methods across languages allows for expanding access to BCI systems to other populations, particularly in the developing world.},
}
@article {pmid35573313,
year = {2022},
author = {Fan, C and Hu, J and Huang, S and Peng, Y and Kwong, S},
title = {EEG-TNet: An End-To-End Brain Computer Interface Framework for Mental Workload Estimation.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {869522},
pmid = {35573313},
issn = {1662-4548},
abstract = {The mental workload (MWL) of different occupational groups' workers is the main and direct factor of unsafe behavior, which may cause serious accidents. One of the new and useful technologies to estimate MWL is the Brain computer interface (BCI) based on EEG signals, which is regarded as the gold standard of cognitive status. However, estimation systems involving handcrafted EEG features are time-consuming and unsuitable to apply in real-time. The purpose of this study was to propose an end-to-end BCI framework for MWL estimation. First, a new automated data preprocessing method was proposed to remove the artifact without human interference. Then a new neural network structure named EEG-TNet was designed to extract both the temporal and frequency information from the original EEG. Furthermore, two types of experiments and ablation studies were performed to prove the effectiveness of this model. In the subject-dependent experiment, the estimation accuracy of dual-task estimation (No task vs. TASK) and triple-task estimation (Lo vs. Mi vs. Hi) reached 99.82 and 99.21%, respectively. In contrast, the accuracy of different tasks reached 82.78 and 66.83% in subject-independent experiments. Additionally, the ablation studies proved that preprocessing method and network structure had significant contributions to estimation MWL. The proposed method is convenient without any human intervention and outperforms other related studies, which becomes an effective way to reduce human factor risks.},
}
@article {pmid35573306,
year = {2022},
author = {Vasko, JL and Aume, L and Tamrakar, S and Colachis, SCI and Dunlap, CF and Rich, A and Meyers, EC and Gabrieli, D and Friedenberg, DA},
title = {Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {858377},
pmid = {35573306},
issn = {1662-4548},
abstract = {For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.},
}
@article {pmid35572780,
year = {2022},
author = {Wei, C and Wang, Y and Pei, W and Han, X and Lin, L and Liu, Z and Ming, G and Chen, R and Wu, P and Yang, X and Zheng, L and Wang, Y},
title = {Distributed implantation of a flexible microelectrode array for neural recording.},
journal = {Microsystems &amp; nanoengineering},
volume = {8},
number = {},
pages = {50},
pmid = {35572780},
issn = {2055-7434},
abstract = {Flexible multichannel electrode arrays (fMEAs) with multiple filaments can be flexibly implanted in various patterns. It is necessary to develop a method for implanting the fMEA in different locations and at various depths based on the recording demands. This study proposed a strategy for reducing the microelectrode volume with integrated packaging. An implantation system was developed specifically for semiautomatic distributed implantation. The feasibility and convenience of the fMEA and implantation platform were verified in rodents. The acute and chronic recording results provied the effectiveness of the packaging and implantation methods. These methods could provide a novel strategy for developing fMEAs with more filaments and recording sites to measure functional interactions across multiple brain regions.},
}
@article {pmid35571721,
year = {2022},
author = {Li, L and Sun, N},
title = {Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {8187009},
pmid = {35571721},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Neural Networks, Computer ; },
abstract = {With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG) is still the bottleneck restricting the development of MI-BCI. In order to improve the accuracy of EEG classification, a DSC-ConvLSTM model based on the attention mechanism is proposed for the multi-classification of motor imagery EEG signals. To address the problem of the small sample size of well-labeled and accurate EEG data, the preprocessing uses sliding windows for data augmentation, and the average prediction loss of each sliding window is used as the final prediction loss for that trial. This not only increases the training sample size and is beneficial to train complex neural network models, but also the network no longer extracts the global features of the whole trial so as to avoid learning the difference features among trials, which can effectively eliminate the influence of individual specificity. In the aspect of feature extraction and classification, the overall network structure is designed according to the characteristics of the EEG signals in this paper. Firstly, depth separable convolution (DSC) is used to extract spatial features of EEG signals. On the one hand, this reduces the number of parameters and improves the response speed of the system. On the other hand, the network structure we designed is more conducive to extract directly the direct extraction of spatial features of EEG signals. Secondly, the internal structure of the Long Short-Term Memory (LSTM) unit is improved by using convolution and attention mechanism, and a novel bidirectional convolution LSTM (ConvLSTM) structure is proposed by comparing the effects of embedding convolution and attention mechanism in the input and different gates, respectively. In the ConvLSTM module, the convolutional structure is only introduced into the input-to-state transition, while the gates still remain the original fully connected mechanism, and the attention mechanism is introduced into the input to further improve the overall decoding performance of the model. This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. The experimental results show that the average classification accuracy of the model reaches 73.7% and 92.6% on two datasets, BCI Competition IV Dataset 2a and High Gamma Dataset, respectively, which proves the robustness and effectiveness of the model we proposed. It can be seen that the model in this paper can deeply excavate significant EEG features from the original EEG signals, show good performance in different subjects and different datasets, and improve the influence of individual variability on the classification performance, which is of practical significance for promoting the development of brain-computer interface technology towards a practical and marketable direction.},
}
@article {pmid35571670,
year = {2022},
author = {Zeng, C and Zhang, J},
title = {A narrative review of five multigenetic assays in breast cancer.},
journal = {Translational cancer research},
volume = {11},
number = {4},
pages = {897-907},
pmid = {35571670},
issn = {2219-6803},
abstract = {BACKGROUND AND OBJECTIVE: Breast cancer is a highly heterogeneous disease. Its incidence rate is increasing year by year and the mortality rate is the highest in female malignant tumors. Even patients with the same clinical stage and pathological grade have different response to treatment and postoperative recurrence risk. Although the prognosis of breast cancer in China has been gradually improved, there is still a certain gap compared with the 5-year survival rate as high as 89% in developed countries. In recent years, with the continuous enrichment of molecular sequencing data of breast cancer, gene detection technology has important reference value in prognosis judgement and guiding treatment of early breast cancer. This article reviews the current application and latest progress of genetic tests in comprehensive treatment for breast cancer, with a view to promote the precise treatment of breast cancer in clinical practice.<br><br>METHODS: We conducted searches using the MeSH terms 'breast neoplasms' and 'genetic testing' in the PubMed databases from root to 22 January 2021. We conducted an additional search in the National Comprehensive Cancer Network (NCCN) and American Society of Clinical Oncology (ASCO) guidelines to obtain additional information. The search was limited to English, Dutch, French and German articles and research involving humans. Out of the references screened, 51 articles were found eligible for inclusion finally.<br><br>KEY CONTENT AND FINDINGS: The article reviews the mechanisms and clinical trials of five genetic tests including Oncotype Dx, Mammaprint, Endopredict, mRNA expression of 50 genes (PAM50) and breast cancer index (BCI) in comprehensive treatment for breast cancer. All these tools have been proved to have prognosis value, but only two of them, Oncotype Dx and Mammaprint, are recommended as predictive tools for chemotherapy by National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology (ASCO).<br><br>CONCLUSIONS: In order to promote the comprehensive treatment of breast cancer to "precision" and "individualization" for further development, people have extensively researched on multigene testing technology represented by Oncotype Dx, Mammaprint, Endopredict and mRNA expression of 50 genes (PAM50) and breast cancer index (BCI). Each of these five tools has its advantages and limitation, which must be weighed in a wise application.},
}
@article {pmid35570218,
year = {2022},
author = {Shao, Z and Tan, Y and Shen, Q and Hou, L and Yao, B and Qin, J and Xu, P and Mao, C and Chen, LN and Zhang, H and Shen, DD and Zhang, C and Li, W and Du, X and Li, F and Chen, ZH and Jiang, Y and Xu, HE and Ying, S and Ma, H and Zhang, Y and Shen, H},
title = {Molecular insights into ligand recognition and activation of chemokine receptors CCR2 and CCR3.},
journal = {Cell discovery},
volume = {8},
number = {1},
pages = {44},
pmid = {35570218},
issn = {2056-5968},
support = {81930003//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31770796//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81870007//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81920108001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81922071//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019SHZDZX02//Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission)/ ; LR19H310001//Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation)/ ; },
abstract = {Chemokine receptors are a family of G-protein-coupled receptors with key roles in leukocyte migration and inflammatory responses. Here, we present cryo-electron microscopy structures of two human CC chemokine receptor-G-protein complexes: CCR2 bound to its endogenous ligand CCL2, and CCR3 in the apo state. The structure of the CCL2-CCR2-G-protein complex reveals that CCL2 inserts deeply into the extracellular half of the transmembrane domain, and forms substantial interactions with the receptor through the most N-terminal glutamine. Extensive hydrophobic and polar interactions are present between both two chemokine receptors and the Gα-protein, contributing to the constitutive activity of these receptors. Notably, complemented with functional experiments, the interactions around intracellular loop 2 of the receptors are found to be conserved and play a more critical role in G-protein activation than those around intracellular loop 3. Together, our findings provide structural insights into chemokine recognition and receptor activation, shedding lights on drug design targeting chemokine receptors.},
}
@article {pmid35569239,
year = {2022},
author = {Zarei, A and Mohammadzadeh Asl, B},
title = {Classification of code-modulated visual evoked potentials using adaptive modified covariance beamformer and EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {221},
number = {},
pages = {106859},
doi = {10.1016/j.cmpb.2022.106859},
pmid = {35569239},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; },
abstract = {OBJECTIVE: In general, brain computer interface (BCI) studies based on code-modulated Visual Evoked Potentials (c-VEP) use m-sequences to decode EEG responses to visual stimuli. BCI systems based on the c-VEP paradigm can simultaneously present a large number of commands, which results in a significantly high information transfer rate (ITR). Spatiotemporal beamforming (STB) is one of the commonly used approaches in c-VEP-based BCI systems.<br><br>APPROACH: In the current work, a novel STB-based technique is proposed to detect the gazed targets. The proposed method improves the performance of conventional STB-based techniques by providing a robust estimation of the covariance matrix in short stimulation times. Different user parameter-free methods, including the convex combination (CC), the general linear combination (GLC), and the modified versions of these techniques, are used to estimate a reliable and robust covariance matrix when a small number of repetitions are available.<br><br>MAIN RESULTS: The stimulus presentation rate of 120 Hz is used to assess the performance of the proposed structures. Our proposed methods improved the classification accuracy by an average of 20% compared to the conventional STB method at the shortest stimulation time. The proposed method achieves an average ITR of 157.07 bits/min by using only two repetitions of the m-sequences.<br><br>SIGNIFICANCE: The results show that our proposed methods perform significantly better than the conventional STB technique in all stimulation times.},
}
@article {pmid35565569,
year = {2022},
author = {Wang, J and Qian, L and Wang, S and Shi, L and Wang, Z},
title = {Directional Preference in Avian Midbrain Saliency Computing Nucleus Reflects a Well-Designed Receptive Field Structure.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {9},
pages = {},
pmid = {35565569},
issn = {2076-2615},
abstract = {Neurons responding sensitively to motions in several rather than all directions have been identified in many sensory systems. Although this directional preference has been demonstrated by previous studies to exist in the isthmi pars magnocellularis (Imc) of pigeon (Columba livia), which plays a key role in the midbrain saliency computing network, the dynamic response characteristics and the physiological basis underlying this phenomenon are unclear. Herein, dots moving in 16 directions and a biologically plausible computational model were used. We found that pigeon Imc's significant responses for objects moving in preferred directions benefit the long response duration and high instantaneous firing rate. Furthermore, the receptive field structures predicted by a computational model, which captures the actual directional tuning curves, agree with the real data collected from population Imc units. These results suggested that directional preference in Imc may be internally prebuilt by elongating the vertical axis of the receptive field, making predators attack from the dorsal-ventral direction and conspecifics flying away in the ventral-dorsal direction, more salient for avians, which is of great ecological and physiological significance for survival.},
}
@article {pmid35558735,
year = {2022},
author = {Lopez-Bernal, D and Balderas, D and Ponce, P and Molina, A},
title = {A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {867281},
pmid = {35558735},
issn = {1662-5161},
abstract = {Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.},
}
@article {pmid35552976,
year = {2022},
author = {He, W and Yang, J and Gao, M and Liu, H and Li, J and Hu, J and Zhang, Y and Zhong, G and Li, K and Dong, W and Huang, H and Lin, T and Huang, J},
title = {Pelvic reconstruction and lateral prostate capsule sparing techniques improve early continence of robot-assisted radical cystectomy with orthotopic ileal neobladder.},
journal = {International urology and nephrology},
volume = {54},
number = {7},
pages = {1537-1543},
pmid = {35552976},
issn = {1573-2584},
support = {2018YFA0902803//National Key Research and Development Program of China/ ; 2017YFC1308600//National Key Research and Development Program of China/ ; 81825016//National Natural Science Foundation of China/ ; 81802530//National Natural Science Foundation of China/ ; 81830082//National Natural Science Foundation of China/ ; 81672395//National Natural Science Foundation of China/ ; 81871945//National Natural Science Foundation of China/ ; 81772719//National Natural Science Foundation of China/ ; 81772728//National Natural Science Foundation of China/ ; 2072639//National Natural Science Foundation of China/ ; 91740119//National Natural Science Foundation of China/ ; 81472381//National Natural Science Foundation of China/ ; 81972385//National Natural Science Foundation of China/ ; 82173266//National Natural Science Foundation of China/ ; 81802552//National Natural Science Foundation of China/ ; 2020A1515010815//the Key Areas Research and Development Program of Guangdong/ ; 2018B010109006//the Key Areas Research and Development Program of Guangdong/ ; 2017A020215072//the Key Areas Research and Development Program of Guangdong/ ; 202002030388//Science and Technology Planning Project of Guangdong Province/ ; 201803010049//Science and Technology Planning Project of Guangdong Province/ ; 2017B020227007//Science and Technology Planning Project of Guangdong Province/ ; 201704020097//Science and Technology Planning Project of Guangdong Province/ ; 2020B1111170006//Guangdong Clinical Research Center for Urological Diseases/ ; YXQH201812//Yixian Youth project of Sun Yat-sen Memorial Hospital/ ; 19ykzd21//Young Teacher Training Funding of Sun Yat-sen University/ ; 19ykpy121//Young Teacher Training Funding of Sun Yat-sen University/ ; 201904010004//Science and Technology Program of Guangzhou, China/ ; 2018A030313545//Natural Science Foundation of Guangdong Province, China/ ; },
mesh = {Cystectomy/adverse effects/methods ; Humans ; Male ; Prostate/pathology ; Retrospective Studies ; *Robotics ; Treatment Outcome ; *Urinary Bladder Neoplasms/pathology/surgery ; *Urinary Diversion/methods ; },
abstract = {PURPOSE: To evaluate urinary outcomes of pelvic construction and lateral capsule sparing techniques in robot-assisted radical cystectomy with orthotopic ileal neobladder (RARC-OIN).<br><br>METHODS: A total of 107 male patients who underwent RARC-OIN during January 2017 and February 2021 in Sun Yat-sen Memorial Hospital were analyzed retrospectively. Standard RARC-OIN with or without nerve sparing technique was performed in 44 patients (standard group), lateral prostate capsule sparing technique was performed in 20 patients (LCS group), combined pelvic reconstruction (CPR) technique including anterior suspension and posterior reconstruction were performed in 43 patients (CPR group). The urinary function was assessed by the use of pads and the Bladder Cancer Index (BCI). Continence was defined as the use of 0-1 pad during daytime or night-time.<br><br>RESULTS: There was no statistical difference between the three groups regarding demographic, perioperative, and pathological data. Continence rates were 6.8, 50.0 and 34.9% for daytime, 4.6, 40.0 and 32.6% for night-time in the standard group, LCS group and CPR group at 1&#xa0;month post-operation, respectively. Continence rates were 34.1, 80.0 and 69.8% for daytime, 27.3, 75.0 and 65.1% for night-time in the standard group, LCS group and CPR group at 3&#xa0;month post-operation, respectively. No statistically significant difference was observed in the daytime and night-time continence rates at 12&#xa0;months.<br><br>CONCLUSIONS: Lateral capsule-sparing and combined pelvic reconstruction techniques are feasible to improve early daytime and night-time continence rates in RARC with orthotopic neobladder.<br><br>CLINICAL TRIAL REGISTRATION: The trial registration number: ChiCTR2100047606.},
}
@article {pmid35552154,
year = {2022},
author = {Da, I and Dui, LG and Ferrante, S and Pedrocchi, A and Antonietti, A},
title = {Leveraging Deep Learning Techniques to Improve P300-Based Brain Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {10},
pages = {4892-4902},
doi = {10.1109/JBHI.2022.3174771},
pmid = {35552154},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Humans ; Neural Networks, Computer ; },
abstract = {Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from electroencephalography (EEG) recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs. We propose a novel BCI classifier, called P3CNET, that improved P300 classification accuracy performances of the best state-of-the-art classifier. In addition, we explored pre-processing and training choices that improved the usability of BCI systems. For the pre-processing of EEG data, we explored the optimal signal interval that would improve classification accuracies. Then, we explored the minimum number of calibration sessions to balance higher accuracy and shorter calibration time. To improve the explainability of deep learning architectures, we analyzed the saliency maps of the input EEG signal leading to a correct P300 classification, and we observed that the elimination of less informative electrode channels from the data did not result in better accuracy. All the methodologies and explorations were performed and validated on two different CNN classifiers, demonstrating the generalizability of the obtained results. Finally, we showed the advantages given by transfer learning when using the proposed novel architecture on other P300 datasets. The presented architectures and practical suggestions can be used by BCI practitioners to improve its effectiveness.},
}
@article {pmid35550813,
year = {2022},
author = {Lubianiker, N and Paret, C and Dayan, P and Hendler, T},
title = {Neurofeedback through the lens of reinforcement learning.},
journal = {Trends in neurosciences},
volume = {45},
number = {8},
pages = {579-593},
doi = {10.1016/j.tins.2022.03.008},
pmid = {35550813},
issn = {1878-108X},
mesh = {Humans ; Learning/physiology ; *Neurofeedback/methods/physiology ; Reward ; },
abstract = {Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.},
}
@article {pmid35548781,
year = {2022},
author = {Kumar, A and Gao, L and Li, J and Ma, J and Fu, J and Gu, X and Mahmoud, SS and Fang, Q},
title = {Error-Related Negativity-Based Robot-Assisted Stroke Rehabilitation System: Design and Proof-of-Concept.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {837119},
pmid = {35548781},
issn = {1662-5218},
abstract = {Conventional rehabilitation systems typically execute a fixed set of programs that most motor-impaired stroke patients undergo. In these systems, the brain, which is embodied in the body, is often left out. Including the brains of stroke patients in the control loop of a rehabilitation system can be worthwhile as the system can be tailored to each participant and, thus, be more effective. Here, we propose a novel brain-computer interface (BCI)-based robot-assisted stroke rehabilitation system (RASRS), which takes inputs from the patient's intrinsic feedback mechanism to adapt the assistance level of the RASRS. The proposed system will utilize the patients' consciousness about their performance decoded through their error-related negativity signals. As a proof-of-concept, we experimented on 12 healthy people in which we recorded their electroencephalogram (EEG) signals while performing a standard rehabilitation exercise. We set the performance requirements beforehand and observed participants' neural responses when they failed/met the set requirements and found a statistically significant (p < 0.05) difference in their neural responses in the two conditions. The feasibility of the proposed BCI-based RASRS was demonstrated through a use-case description with a timing diagram and meeting the crucial requirements for developing the proposed rehabilitation system. The use of a patient's intrinsic feedback mechanism will have significant implications for the development of human-in-the-loop stroke rehabilitation systems.},
}
@article {pmid35547770,
year = {2022},
author = {Zhang, Y and Lu, S and Huang, S and Yu, Z and Xia, T and Li, M and Yang, C and Mao, Y and Xu, B and Wang, L and Xu, L and Shi, J and Zhu, X and Zhu, S and Zhang, S and Qian, H and Hu, Y and Li, W and Tu, Y and Wu, W},
title = {Optic chiasmatic potential by endoscopically implanted skull base microinvasive biosensor: a brain-machine interface approach for anterior visual pathway assessment.},
journal = {Theranostics},
volume = {12},
number = {7},
pages = {3273-3287},
pmid = {35547770},
issn = {1838-7640},
support = {R01 EY023295/EY/NEI NIH HHS/United States ; R01 EY024932/EY/NEI NIH HHS/United States ; R01 EY028106/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Biosensing Techniques ; *Brain-Computer Interfaces ; Optic Chiasm ; Skull Base/anatomy &amp; histology/surgery ; Visual Pathways ; },
abstract = {Background: Visually evoked potential (VEP) is widely used to detect optic neuropathy in basic research and clinical practice. Traditionally, VEP is recorded non-invasively from the surface of the skull over the visual cortex. However, its trace amplitude is highly variable, largely due to intracranial modulation and artifacts. Therefore, a safe test with a strong and stable signal is highly desirable to assess optic nerve function, particularly in neurosurgical settings and animal experiments. Methods: Minimally invasive trans-sphenoidal endoscopic recording of optic chiasmatic potential (OCP) was carried out with a titanium screw implanted onto the sphenoid bone beneath the optic chiasm in the goat, whose sphenoidal anatomy is more human-like than non-human primates. Results: The implantation procedure was swift (within 30 min) and did not cause any detectable abnormality in fetching/moving behaviors, skull CT scans and ophthalmic tests after surgery. Compared with traditional VEP, the amplitude of OCP was 5-10 times stronger, more sensitive to weak light stimulus and its subtle changes, and was more repeatable, even under extremely low general anesthesia. Moreover, the OCP signal relied on ipsilateral light stimulation, and was abolished immediately after complete optic nerve (ON) transection. Through proof-of-concept experiments, we demonstrated several potential applications of the OCP device: (1) real-time detector of ON function, (2) detector of region-biased retinal sensitivity, and (3) therapeutic electrical stimulator for the optic nerve with low and thus safe excitation threshold. Conclusions: OCP developed in this study will be valuable for both vision research and clinical practice. This study also provides a safe endoscopic approach to implant skull base brain-machine interface, and a feasible in vivo testbed (goat) for evaluating safety and efficacy of skull base brain-machine interface.},
}
@article {pmid35546894,
year = {2022},
author = {Yang, J and Liu, L and Yu, H and Ma, Z and Shen, T},
title = {Multi-Hierarchical Fusion to Capture the Latent Invariance for Calibration-Free Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {824471},
pmid = {35546894},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCI) based motor imagery (MI) has become a research hotspot for establishing a flexible communication channel for patients with apoplexy or degenerative pathologies. Accurate decoding of motor imagery electroencephalography (MI-EEG) signals, while essential for effective BCI systems, is still challenging due to the significant noise inherent in the EEG signals and the lack of informative correlation between the signals and brain activities. The application of deep learning for EEG feature representation has been rarely investigated, nevertheless bringing improvements to the performance of motor imagery classification. This paper proposes a deep learning decoding method based on multi-hierarchical representation fusion (MHRF) on MI-EEG. It consists of a concurrent framework constructed of bidirectional LSTM (Bi-LSTM) and convolutional neural network (CNN) to fully capture the contextual correlations of MI-EEG and the spectral feature. Also, the stacked sparse autoencoder (SSAE) is employed to concentrate these two domain features into a high-level representation for cross-session and subject training guidance. The experimental analysis demonstrated the efficacy and practicality of the proposed approach using a public dataset from BCI competition IV and a private one collected by our MI task. The proposed approach can serve as a robust and competitive method to improve inter-session and inter-subject transferability, adding anticipation and prospective thoughts to the practical implementation of a calibration-free BCI system.},
}
@article {pmid35546879,
year = {2022},
author = {Shishkin, SL},
title = {Active Brain-Computer Interfacing for Healthy Users.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {859887},
pmid = {35546879},
issn = {1662-4548},
}
@article {pmid35545899,
year = {2022},
author = {Jiang, H and Kokkinos, V and Ye, S and Urban, A and Bagić, A and Richardson, M and He, B},
title = {Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {9},
number = {18},
pages = {e2200887},
pmid = {35545899},
issn = {2198-3844},
support = {RF1 MH114233/MH/NIMH NIH HHS/United States ; NS124564/NS/NINDS NIH HHS/United States ; EB029354/EB/NIBIB NIH HHS/United States ; MH114233/MH/NIMH NIH HHS/United States ; AT009263/GF/NIH HHS/United States ; NS096761/NS/NINDS NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; MH114233/GF/NIH HHS/United States ; NS124564/GF/NIH HHS/United States ; NS096761/GF/NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; EB029354/GF/NIH HHS/United States ; U18 EB029354/EB/NIBIB NIH HHS/United States ; AT009263/AT/NCCIH NIH HHS/United States ; EB021027/EB/NIBIB NIH HHS/United States ; R01 NS124564/NS/NINDS NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; EB021027/GF/NIH HHS/United States ; },
mesh = {Brain Mapping/methods ; Cohort Studies ; *Drug Resistant Epilepsy ; *Epilepsy ; Humans ; Seizures ; },
abstract = {Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.},
}
@article {pmid35545672,
year = {2022},
author = {Chen, P and Wang, W and Liu, R and Lyu, J and Zhang, L and Li, B and Qiu, B and Tian, A and Jiang, W and Ying, H and Jing, R and Wang, Q and Zhu, K and Bai, R and Zeng, L and Duan, S and Liu, C},
title = {Olfactory sensory experience regulates gliomagenesis via neuronal IGF1.},
journal = {Nature},
volume = {606},
number = {7914},
pages = {550-556},
pmid = {35545672},
issn = {1476-4687},
mesh = {Animals ; *Carcinogenesis ; *Glioma/metabolism/pathology ; *Insulin-Like Growth Factor I ; Mice ; Neural Pathways ; Olfactory Bulb/pathology ; *Olfactory Receptor Neurons/physiology ; *Smell/physiology ; },
abstract = {Animals constantly receive various sensory stimuli, such as odours, sounds, light and touch, from the surrounding environment. These sensory inputs are essential for animals to search for food and avoid predators, but they also affect their physiological status, and may cause diseases such as cancer. Malignant gliomas-the most lethal form of brain tumour[1]-are known to intimately communicate with neurons at the cellular level[2,3]. However, it remains unclear whether external sensory stimuli can directly affect the development of malignant glioma under normal living conditions. Here we show that olfaction can directly regulate gliomagenesis. In an autochthonous mouse model that recapitulates adult gliomagenesis[4-6] originating in oligodendrocyte precursor cells (OPCs), gliomas preferentially emerge in the olfactory bulb-the first relay of brain olfactory circuitry. Manipulating the activity of olfactory receptor neurons (ORNs) affects the development of glioma. Mechanistically, olfaction excites mitral and tufted (M/T) cells, which receive sensory information from ORNs and release insulin-like growth factor 1 (IGF1) in an activity-dependent manner. Specific knockout of Igf1 in M/T cells suppresses gliomagenesis. In addition, knocking out the IGF1 receptor in pre-cancerous mutant OPCs abolishes the ORN-activity-dependent mitogenic effects. Our findings establish a link between sensory experience and gliomagenesis through their corresponding sensory neuronal circuits.},
}
@article {pmid35533644,
year = {2022},
author = {Andersen, RA and Aflalo, T},
title = {Preserved cortical somatotopic and motor representations in tetraplegic humans.},
journal = {Current opinion in neurobiology},
volume = {74},
number = {},
pages = {102547},
pmid = {35533644},
issn = {1873-6882},
support = {R01 EY005522/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex/physiology ; Parietal Lobe/physiology ; Somatosensory Cortex/physiology ; },
abstract = {A rich literature has documented changes in cortical representations of the body in somatosensory and motor cortex. Recent clinical studies of brain-machine interfaces designed to assist paralyzed patients have afforded the opportunity to record from and stimulate human somatosensory, motor, and action-related areas of the posterior parietal cortex. These studies show considerable preserved structure in the cortical somato-motor system. Motor cortex can immediately control assistive devices, stimulation of somatosensory cortex produces sensations in an orderly somatotopic map, and the posterior parietal cortex shows a high-dimensional representation of cognitive action variables. These results are strikingly similar to what would be expected in a healthy subject, demonstrating considerable stability of adult cortex even after severe injury and despite potential plasticity-induced new activations within the same region of cortex. Clinically, these results emphasize the importance of targeting cortical areas for BMI control signals that are consistent with their normal functional role.},
}
@article {pmid35533168,
year = {2022},
author = {Szlawski, J and Feleppa, T and Mohan, A and Wong, YT and Lowery, AJ},
title = {A Model for Assessing the Electromagnetic Safety of an Inductively Coupled, Modular Brain-Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1267-1276},
doi = {10.1109/TNSRE.2022.3173682},
pmid = {35533168},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electromagnetic Fields/adverse effects ; Humans ; *Radiation Protection/methods ; Radio Waves/adverse effects ; },
abstract = {Brain-Machine Interfaces (BMI) offer the potential to modulate dysfunctional neurological networks by electrically stimulating the cerebral cortex via chronically-implanted microelectrodes. Wireless transmitters worn by BMI recipients must operate within electromagnetic emission and tissue heating limits, such as those prescribed by the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP), to ensure that radiofrequency emissions of BMI systems are safe. Here, we describe an approach to generating pre-compliance safety data by simulating the Specific Absorption Rate (SAR) and tissue heating of a multi-layered human head model containing a system of wireless, modular BMIs powered and controlled by an externally worn telemetry unit. We explore a number of system configurations such that our approach can be utilized for similar BMI systems, and our results provide a benchmark for the electromagnetic emissions of similar telemetry units. Our results show that the volume-averaged SAR per 10g of tissue exposed to our telemetry field complies with ICNIRP and IEEE reference levels, and that the maximum temperature increase in tissues was within permissible limits. These results were unaffected by the number of implants in the system model, and therefore we conclude that the electromagnetic emissions our BMI in any configuration are safe.},
}
@article {pmid35533152,
year = {2023},
author = {Pancholi, S and Giri, A and Jain, A and Kumar, L and Roy, S},
title = {Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.},
journal = {IEEE transactions on cybernetics},
volume = {53},
number = {7},
pages = {4094-4106},
doi = {10.1109/TCYB.2022.3166604},
pmid = {35533152},
issn = {2168-2275},
mesh = {Algorithms ; *Deep Learning ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; },
abstract = {The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multivariable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving a maximum correlation of up to 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) for CNN-LSTM are presented. In addition, novelty in the work includes the utilization of brain source localization (BSL) [using standardized low-resolution brain electromagnetic tomography (sLORETA)] for the reliable decoding of motor intention. The information is utilized for channel selection and accurate EEG time segment selection. The performance of the proposed models is compared with the traditionally utilized mLR technique on the reach, grasp, and lift (GAL) dataset. The effectiveness of the proposed framework is established using the Pearson correlation coefficient (PCC) and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with the state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real-time BCI implementation.},
}
@article {pmid35533008,
year = {2022},
author = {Li, T and Su, Y and Chen, F and Zheng, H and Meng, W and Liu, Z and Ai, Q and Liu, Q and Tan, Y and Zhou, Z},
title = {Bioinspired Stretchable Fiber-Based Sensor toward Intelligent Human-Machine Interactions.},
journal = {ACS applied materials &amp; interfaces},
volume = {14},
number = {19},
pages = {22666-22677},
doi = {10.1021/acsami.2c05823},
pmid = {35533008},
issn = {1944-8252},
mesh = {Bionics ; *Biosensing Techniques/classification/methods ; *Brain-Computer Interfaces/standards ; Electronics ; Humans ; Lamiales/chemistry ; Motion ; Optical Fibers/classification/standards ; Virtual Reality ; *Wearable Electronic Devices ; },
abstract = {Wearable integrated sensing devices with flexible electronic elements exhibit enormous potential in human-machine interfaces (HMI), but they have limitations such as complex structures, poor waterproofness, and electromagnetic interference. Herein, inspired by the profile of Lindernia nummularifolia (LN), a bionic stretchable optical strain (BSOS) sensor composed of an LN-shaped optical fiber incorporated with a stretchable substrate is developed for intelligent HMI. Such a sensor enables large strain and bending angle measurements with temperature self-compensation by the intensity difference of two fiber Bragg gratings' (FBGs') center wavelength. Such configurations enable an excellent tensile strain range of up to 80%, moreover, leading to ultrasensitivity, durability (≥20,000 cycles), and waterproofness. The sensor is also capable of measuring different human activities and achieving HMI control, including immersive virtual reality, robot remote interactive control, and personal hands-free communication. Combined with the machine learning technique, gesture classification can be achieved using muscle activity signals captured from the BSOS sensor, which can be employed to obtain the motion intention of the prosthetic. These merits effectively indicate its potential as a solution for medical care HMI and show promise in smart medical and rehabilitation medicine.},
}
@article {pmid35530175,
year = {2022},
author = {Kondo, T and Saito, R and Sato, Y and Sato, K and Uchida, A and Yoshino-Saito, K and Shinozaki, M and Tashiro, S and Nagoshi, N and Nakamura, M and Ushiba, J and Okano, H},
title = {Treadmill Training for Common Marmoset to Strengthen Corticospinal Connections After Thoracic Contusion Spinal Cord Injury.},
journal = {Frontiers in cellular neuroscience},
volume = {16},
number = {},
pages = {858562},
pmid = {35530175},
issn = {1662-5102},
abstract = {Spinal cord injury (SCI) leads to locomotor dysfunction. Locomotor rehabilitation promotes the recovery of stepping ability in lower mammals, but it has limited efficacy in humans with a severe SCI. To explain this discrepancy between different species, a nonhuman primate rehabilitation model with a severe SCI would be useful. In this study, we developed a rehabilitation model of paraplegia caused by a severe traumatic SCI in a nonhuman primate, common marmoset (Callithrix jacchus). The locomotor rating scale for marmosets was developed to accurately assess the recovery of locomotor functions in marmosets. All animals showed flaccid paralysis of the hindlimb after a thoracic contusive SCI, but the trained group showed significant locomotor recovery. Kinematic analysis revealed significantly improved hindlimb stepping patterns in trained marmosets. Furthermore, intracortical microstimulation (ICMS) of the motor cortex evoked the hindlimb muscles in the trained group, suggesting the reconnection between supraspinal input and the lumbosacral network. Because rehabilitation may be combined with regenerative interventions such as medicine or cell therapy, this primate model can be used as a preclinical test of therapies that can be used in human clinical trials.},
}
@article {pmid35529778,
year = {2022},
author = {Chandler, JA and Van der Loos, KI and Boehnke, S and Beaudry, JS and Buchman, DZ and Illes, J},
title = {Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {841035},
pmid = {35529778},
issn = {1662-5161},
abstract = {A brain-computer interface technology that can decode the neural signals associated with attempted but unarticulated speech could offer a future efficient means of communication for people with severe motor impairments. Recent demonstrations have validated this approach. Here we assume that it will be possible in future to decode imagined (i.e., attempted but unarticulated) speech in people with severe motor impairments, and we consider the characteristics that could maximize the social utility of a BCI for communication. As a social interaction, communication involves the needs and goals of both speaker and listener, particularly in contexts that have significant potential consequences. We explore three high-consequence legal situations in which neurally-decoded speech could have implications: Testimony, where decoded speech is used as evidence; Consent and Capacity, where it may be used as a means of agency and participation such as consent to medical treatment; and Harm, where such communications may be networked or may cause harm to others. We then illustrate how design choices might impact the social and legal acceptability of these technologies.},
}
@article {pmid35529775,
year = {2022},
author = {Klee, D and Memmott, T and Smedemark-Margulies, N and Celik, B and Erdogmus, D and Oken, BS},
title = {Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {882557},
pmid = {35529775},
issn = {1662-5161},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.},
}
@article {pmid35529345,
year = {2022},
author = {Valencia, D and Alimohammad, A},
title = {Towards in vivo neural decoding.},
journal = {Biomedical engineering letters},
volume = {12},
number = {2},
pages = {185-195},
pmid = {35529345},
issn = {2093-985X},
abstract = {Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computing device, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain's activity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in vivo neural decoding. This article explores the feasibility and efficient realization of in vivo decoding, with and without spike sorting. The efficiency of neural network-based models for reliable motor decoding is presented and the performance of candidate neural decoding schemes on sorted single-unit activity and unsorted multi-unit activity are evaluated. A programmable processor with a custom instruction set architecture, for the first time to the best of our knowledge, is designed and implemented for executing neural network operations in a standard 180-nm CMOS process. The processor's layout is estimated to occupy 49 mm 2 of silicon area and to dissipate 12 mW of power from a 1.8 V supply, which is within the tissue-safe operation of the brain.},
}
@article {pmid35527849,
year = {2022},
author = {Wang, CH and Tsai, KY},
title = {Optimization of machine learning method combined with brain-computer interface rehabilitation system.},
journal = {Journal of physical therapy science},
volume = {34},
number = {5},
pages = {379-385},
pmid = {35527849},
issn = {0915-5287},
abstract = {[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to allow the nervous system to trigger and restore its function. Traditional practice is to use electrode caps to extract brain wave features and combine them with assistive devices. However, there are problems that the electrode cap is not easy to wear, and the potential recognition is not good, and different extraction methods will affect the accuracy of the Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and Methods] The brainwave headphones used in this experiment do not must a conductive gel to get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8 stroke patients and 200 normal participants were invited for a 4-week rehabilitation training. The effectiveness of the training was determined using Fast Fourier Transform (FFT), Magnitude squared coherence (MSC) feature extraction methods, and five machine learning techniques that induced flicker frequencies. [Results] The results show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the identification rate of FFT is about 5.2% higher than that of the MSC method. Using an optimized model for different feature extraction methods can improve the recognition rate by 1.3%-9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance Imaging (fMRI) show that the sensory region of brain movement has become a concentrated activation phenomenon. Besides strengthening the feature extraction method also lets the elbow has an obvious recovery effect.},
}
@article {pmid35527273,
year = {2022},
author = {Zhang, C and Li, X and Zhao, L and Liang, R and Deng, W and Guo, W and Wang, Q and Hu, X and Du, X and Sham, PC and Luo, X and Li, T},
title = {Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene.},
journal = {BMC medicine},
volume = {20},
number = {1},
pages = {169},
pmid = {35527273},
issn = {1741-7015},
support = {P50 MH096891/MH/NIMH NIH HHS/United States ; R37 MH057881/MH/NIMH NIH HHS/United States ; R01 MH075916/MH/NIMH NIH HHS/United States ; P50 MH084051/MH/NIMH NIH HHS/United States ; F31 AG051381/AG/NIA NIH HHS/United States ; P50 MH084053/MH/NIMH NIH HHS/United States ; HHSN271201300031C/DA/NIDA NIH HHS/United States ; P50 MH066392/MH/NIMH NIH HHS/United States ; R01 MH080405/MH/NIMH NIH HHS/United States ; R01 MH085542/MH/NIMH NIH HHS/United States ; R01 MH097276/MH/NIMH NIH HHS/United States ; P01 AG002219/AG/NIA NIH HHS/United States ; R01 MH093725/MH/NIMH NIH HHS/United States ; P50 AG005138/AG/NIA NIH HHS/United States ; },
mesh = {Bayes Theorem ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; *Mixed Function Oxygenases/genetics ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; *Schizophrenia/diagnostic imaging/genetics ; },
abstract = {BACKGROUND: Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.<br><br>METHODS: We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.<br><br>RESULTS: Both Sherlock (P = 3. 38 &#xd7; 10[-6]) and SMR (P = 1. 90 &#xd7; 10[-8]) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.<br><br>CONCLUSIONS: We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.},
}
@article {pmid35525241,
year = {2022},
author = {Sun, L and Jiang, RH and Ye, WJ and Rosbash, M and Guo, F},
title = {Recurrent circadian circuitry regulates central brain activity to maintain sleep.},
journal = {Neuron},
volume = {110},
number = {13},
pages = {2139-2154.e5},
doi = {10.1016/j.neuron.2022.04.010},
pmid = {35525241},
issn = {1097-4199},
support = {/HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; Brain/physiology ; Circadian Rhythm/physiology ; Drosophila/metabolism ; *Drosophila Proteins/metabolism ; *Drosophila melanogaster/physiology ; Sleep/physiology ; },
abstract = {Animal brains have discrete circadian neurons, but little is known about how they are coordinated to influence and maintain sleep. Here, through a systematic optogenetic screening, we identified a subtype of uncharacterized circadian DN3 neurons that is strongly sleep promoting in Drosophila. These anterior-projecting DN3s (APDN3s) receive signals from DN1 circadian neurons and then output to newly identified noncircadian "claw" neurons (CLs). CLs have a daily Ca[2+] cycle, which peaks at night and correlates with DN1 and DN3 Ca[2+] cycles. The CLs feedback onto a subset of DN1s to form a positive recurrent loop that maintains sleep. Using trans-synaptic photoactivatable green fluorescent protein (PA-GFP) tracing and functional in vivo imaging, we demonstrated that the CLs drive sleep by interacting with and releasing acetylcholine onto the mushroom body γ lobe. Taken together, the data identify a novel self-reinforcing loop within the circadian network and a new sleep-promoting neuropile that are both essential for maintaining normal sleep.},
}
@article {pmid35525171,
year = {2022},
author = {Mahmood, M and Kim, N and Mahmood, M and Kim, H and Kim, H and Rodeheaver, N and Sang, M and Yu, KJ and Yeo, WH},
title = {VR-enabled portable brain-computer interfaces via wireless soft bioelectronics.},
journal = {Biosensors &amp; bioelectronics},
volume = {210},
number = {},
pages = {114333},
doi = {10.1016/j.bios.2022.114333},
pmid = {35525171},
issn = {1873-4235},
mesh = {*Biosensing Techniques ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods ; Humans ; *Virtual Reality ; },
abstract = {Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes - and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. This results in unique asynchronous stimulus patterns measurable with as few as four EEG electrodes, as demonstrated with improved wireless soft electronics for portable BCI. This VR-embedded SEAS paradigm demonstrates potential for improved throughput with a greater number of unique stimuli. A wearable soft platform featuring dry needle electrodes and shielded stretchable interconnects enables high throughput decoding of steady-state visually evoked potentials (SSVEP) for a text spelling interface. A combination of skin-conformal electrodes and soft materials offers high-quality recordings of SSVEP with minimal motion artifacts, validated by comparing the performance with a conventional wearable system. A deep-learning algorithm provides real-time classification, with an accuracy of 78.93% for 0.8 s and 91.73% for 2 s with 33 classes from nine human subjects, allowing for a successful demonstration of VR text spelling and navigation of a real-world environment. With as few as only four data recording channels, the system demonstrates a highly competitive information transfer rate (243.6 bit/min). Collectively, the VR-enabled soft system offers unique advantages in wireless, real-time monitoring of brain signals for portable BCI, neurological rehabilitation, and disease diagnosis.},
}
@article {pmid35524069,
year = {2022},
author = {Irmer, C and Volkenstein, S and Dazert, S and Neumann, A},
title = {The bone conduction implant BONEBRIDGE increases quality of life and social life satisfaction.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {279},
number = {12},
pages = {5555-5563},
pmid = {35524069},
issn = {1434-4726},
mesh = {Adult ; Humans ; Bone Conduction ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery/rehabilitation ; Quality of Life ; Retrospective Studies ; Personal Satisfaction ; *Hearing Aids ; Hearing Loss, Conductive/surgery/rehabilitation ; *Speech Perception ; Treatment Outcome ; },
abstract = {PURPOSE: Transcutaneous active bone conduction hearing aids represent an alternative approach to middle ear surgery and conventional hearing aids for patients with conductive or mixed hearing loss. The aim of this study was to determine quality of life, subjective hearing experience and patients' satisfaction after implantation of a bone conduction hearing aid.<br><br>METHODS: This monocentric and retrospective study included twelve adult patients who received a bone conduction hearing aid (Bonebridge, MedEL) consisting of an extracorporeal audio processor and a bone conduction implant (BCI) between 2013 and 2017. On average 40&#xa0;months after implantation, the patients were asked to answer three questionnaires regarding quality of life (AqoL-8D), self-reported auditory disability (SSQ-12-B) and user's satisfaction (APSQ) after implantation of the Bonebridge (BB). A descriptive statistical analysis of the questionnaires followed.<br><br>RESULTS: 12 patients aged 26-85&#xa0;years (sex: m&#x2009;=&#x2009;7, w&#x2009;=&#x2009;5) were recruited. The quality of life of all patients after implantation of the BB (AqoL 8D) averaged an overall utility score of 0.76 (SD&#x2009;&#xb1;&#x2009;0.17). The mean for 'speech hearing' in the SSQ-12-B was&#x2009;+&#x2009;2.43 (SD&#x2009;&#xb1;&#x2009;2.03),&#x2009;+&#x2009;1.94 (SD&#x2009;&#xb1;&#x2009;1.48) for 'spatial hearing' and&#x2009;+&#x2009;2.28 (SD&#x2009;&#xb1;&#x2009;2.32) for 'qualities of hearing'. 11 out of 12 patients reported an improvement in their overall hearing. The APSQ score for the subsection 'wearing comfort' was 3.50 (SD&#x2009;&#xb1;&#x2009;0.87), 'social life' attained a mean of 4.17 (SD&#x2009;&#xb1;&#x2009;1.06). The 'device inconveniences' reached 4.02 (SD&#x2009;&#xb1;&#x2009;0.71) and 'usability' of the device was measured at 4.23 (SD&#x2009;&#xb1;&#x2009;1.06). The average wearing time of the audio processor in the cohort was 11&#xa0;h per day, with 8 of 12 patients reporting the maximum length of 12&#xa0;h per day.<br><br>CONCLUSION: BB implantation results in a gain in the perceived quality of life (AqoL 8D). The SSQ-12-B shows an improvement in subjective hearing. According to the APSQ, it can be assumed that the BB audio processor, although in an extracorporeal position, is rated as a useful instrument with positive impact on social life. The majority stated that they had subjectively benefited from BB implantation and that there were no significant physical or sensory limitations after implantation.},
}
@article {pmid35523564,
year = {2022},
author = {Yang, M and Jung, TP and Han, J and Xu, M and Ming, D},
title = {[A review of researches on decoding algorithms of steady-state visual evoked potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {2},
pages = {416-425},
pmid = {35523564},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.},
}
@article {pmid35523563,
year = {2022},
author = {Luo, J and Ding, P and Gong, A and Tian, G and Xu, H and Zhao, L and Fu, Y},
title = {[Applications, industrial transformation and commercial value of brain-computer interface technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {2},
pages = {405-415},
pmid = {35523563},
issn = {1001-5515},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Technology ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.},
}
@article {pmid35523129,
year = {2022},
author = {Ji, Y and Li, F and Fu, B and Li, Y and Zhou, Y and Niu, Y and Zhang, L and Chen, Y and Shi, G},
title = {Spatial-temporal network for fine-grained-level emotion EEG recognition.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6d7d},
pmid = {35523129},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Emotions ; Humans ; Intention ; },
abstract = {Electroencephalogram (EEG)-based affective computing brain-computer interfaces provide the capability for machines to understand human intentions. In practice, people are more concerned with the strength of a certain emotional state over a short period of time, which was called as fine-grained-level emotion in this paper. In this study, we built a fine-grained-level emotion EEG dataset that contains two coarse-grained emotions and four corresponding fine-grained-level emotions. To fully extract the features of the EEG signals, we proposed a corresponding fine-grained emotion EEG network (FG-emotionNet) for spatial-temporal feature extraction. Each feature extraction layer is linked to raw EEG signals to alleviate overfitting and ensure that the spatial features of each scale can be extracted from the raw signals. Moreover, all previous scale features are fused before the current spatial-feature layer to enhance the scale features in the spatial block. Additionally, long short-term memory is adopted as the temporal block to extract the temporal features based on spatial features and classify the category of fine-grained emotions. Subject-dependent and cross-session experiments demonstrated that the performance of the proposed method is superior to that of the representative methods in emotion recognition and similar structure methods with proposed method.},
}
@article {pmid35523120,
year = {2022},
author = {Farabbi, A and Aloia, V and Mainardi, L},
title = {ARX-based EEG data balancing for error potential BCI.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6d7f},
pmid = {35523120},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; },
abstract = {Objective.Deep learning algorithms employed in brain computer interfaces (BCIs) need large electroencephalographic (EEG) datasets to be trained. These datasets are usually unbalanced, particularly when error potential (ErrP) experiment are considered, being ErrP's epochs much rarer than non-ErrP ones. To face the issue of unbalance of rare epochs, this paper presents a novel, data balancing methods based on ARX-modelling.Approach.AutoRegressive with eXogenous input (ARX)-models are identified on the EEG data of the 'Monitoring error-related potentials' dataset of the BNCI Horizon 2020 and then employed to generate new synthetic data of the minority class of ErrP epochs. The balanced dataset is used to train a classifier of non-ErrP vs. ErrP epochs based on EEGNet.Main results.Compared to classical techniques (e.g. class weights, CW) for data balancing, the new method outperforms the others in terms of resulting accuracy (i.e. ARX91.5%vs CW88.3%), F1-score (i.e. ARX78.3%vs CW73.7%) and balanced accuracy (i.e. ARX87.0%vs CW81.1%) and also reduces the number of false positive detection (i.e. ARX 51 vs CW 104). Moreover, the ARX-based method shows a better generalization capability of the whole model to classify and predict new data.Significance.The results obtained suggest that the proposed method can be used in BCI application for tackling the issue of data unbalance and obtain more reliable and robust performances.},
}
@article {pmid35523023,
year = {2022},
author = {Perez-Valero, E and Lopez-Gordo, M&#xc1; and Gutiérrez, CM and Carrera-Muñoz, I and Vílchez-Carrillo, RM},
title = {A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {220},
number = {},
pages = {106841},
doi = {10.1016/j.cmpb.2022.106841},
pmid = {35523023},
issn = {1872-7565},
mesh = {*Alzheimer Disease/diagnostic imaging ; *Cognitive Dysfunction/diagnosis ; Electroencephalography/methods ; Humans ; Machine Learning ; *Wearable Electronic Devices ; },
abstract = {Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.},
}
@article {pmid35519260,
year = {2022},
author = {Wojtkiewicz, S and Bejm, K and Liebert, A},
title = {Lock-in functional near-infrared spectroscopy for measurement of the haemodynamic brain response.},
journal = {Biomedical optics express},
volume = {13},
number = {4},
pages = {1869-1887},
pmid = {35519260},
issn = {2156-7085},
abstract = {Here we show a method of the lock-in amplifying near-infrared signals originating within a human brain. It implies using two 90-degree rotated source-detector pairs fixed on a head surface. Both pairs have a joint sensitivity region located towards the brain. A direct application of the lock-in technique on both signals results in amplifying common frequency components, e.g. related to brain cortex stimulation and attenuating the rest, including all components not related to the stimulation: e.g. pulse, instrumental and biological noise or movement artefacts. This is a self-driven method as no prior assumptions are needed and the noise model is provided by the interfering signals themselves. We show the theory (classical modified Beer-Lambert law and diffuse optical tomography approaches), the algorithm implementation and tests on a finite element mathematical model and in-vivo on healthy volunteers during visual cortex stimulation. The proposed hardware and algorithm complexity suit the entire spectrum of (continuous wave, frequency domain, time-resolved) near-infrared spectroscopy systems featuring real-time, direct, robust and low-noise brain activity registration tool. As such, this can be of special interest in optical brain computer interfaces and high reliability/stability monitors of tissue oxygenation.},
}
@article {pmid35519153,
year = {2022},
author = {Haider, S and Saleem, F and Ahmad, N and Iqbal, Q and Bashaar, M},
title = {Translation, Validation, and Psychometric Evaluation of the Diabetes Quality-of-Life Brief Clinical Inventory: The Urdu Version.},
journal = {Journal of multidisciplinary healthcare},
volume = {15},
number = {},
pages = {955-966},
pmid = {35519153},
issn = {1178-2390},
abstract = {PURPOSE: The study is aimed to examine the psychometric properties of the Urdu version of the Diabetes Quality-of-Life Brief Clinical Inventory.<br><br>METHODS: We adopted the forward-backward procedure to translate the Diabetes Quality-of-Life Brief Clinical Inventory (DQoL-BCI) into the Urdu language (lingua franca of Pakistan). The intraclass correlation (ICC) confirmed the consistency of retaining the items, and Cronbach's alpha established the test-re-test reliability. The confirmatory factor analysis (principal axis factoring extraction and oblique rotation with Kaiser normalization) validated the DQoL-BCI in Urdu.<br><br>RESULTS: A two-time point with an interval of 2 weeks was used, and the Urdu version of DQoL-BCI was piloted accordingly. The 15-item translated version (DQoL-BCI-U) exhibited a satisfactory Cronbach's value of 0.866 (test) at week 1 and 0.850 at week 3 (re-test). Using the one-way random model with single measurements, the ICC for all 15 items exhibited coefficient values of >0.80. The Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's Test of Sphericity revealed relationships of the data and suitability of CFA (0.899, p<0.05). Seven factors explaining the total variance of 69% were extracted. With acceptable communalities, all 15 items of DQoL-BCI-U were retained.<br><br>CONCLUSION: The study concludes that the translated version of DQoL-BCI-U is a valid instrument in regions, where Urdu is a communal language of communication and can examine quality-of-life issues during the typical patient-provider encounter.},
}
@article {pmid35513171,
year = {2022},
author = {Zhang, R and Zeng, Y and Tong, L and Shu, J and Lu, R and Yang, K and Li, Z and Yan, B},
title = {ERP-WGAN: A data augmentation method for EEG single-trial detection.},
journal = {Journal of neuroscience methods},
volume = {376},
number = {},
pages = {109621},
doi = {10.1016/j.jneumeth.2022.109621},
pmid = {35513171},
issn = {1872-678X},
mesh = {Brain ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; Research Design ; },
abstract = {Brain computer interaction based on EEG presents great potential and becomes the research hotspots. However, the insufficient scale of EEG database limits the BCI system performance, especially the positive and negative sample imbalance caused by oddball paradigm. To alleviate the bottleneck problem of scarce EEG sample, we propose a data augmentation method based on generative adversarial network to improve the performance of EEG signal classification. Taking the characteristics of EEG into account in wasserstein generative adversarial networks (WGAN), the problems of model collapse and poor quality of artificial data were solved by using resting noise, smoothing and random amplitude. The quality of artificial data was comprehensively evaluated from verisimilitude, diversity and accuracy. Compared with the three artificial data methods and two data sampling methods, the proposed ERP-WGAN framework significantly improve the performance of both subject and general classifiers, especially the accuracy of general classifiers trained by less than 5 dimensional features is improved by 20-25%. Moreover, we evaluate the training sets performance with different mixing ratios of artificial and real samples. ERP-WGAN can reduced at least 73% of the real subject data and acquisition cost, which greatly saves the test cycle and research cost.},
}
@article {pmid35511858,
year = {2022},
author = {Beavers, DP and Hsieh, KL and Kitzman, DW and Kritchevsky, SB and Messier, SP and Neiberg, RH and Nicklas, BJ and Rejeski, WJ and Beavers, KM},
title = {Estimating heterogeneity of physical function treatment response to caloric restriction among older adults with obesity.},
journal = {PloS one},
volume = {17},
number = {5},
pages = {e0267779},
pmid = {35511858},
issn = {1932-6203},
support = {R21 AG061344/AG/NIA NIH HHS/United States ; R01 AG020583/AG/NIA NIH HHS/United States ; R01 AG018915/AG/NIA NIH HHS/United States ; R01 HL093713/HL/NHLBI NIH HHS/United States ; R01 HL076441/HL/NHLBI NIH HHS/United States ; P30 AG021332/AG/NIA NIH HHS/United States ; T32 AG033534/AG/NIA NIH HHS/United States ; R01 AR052528/AR/NIAMS NIH HHS/United States ; },
mesh = {Aged ; *Caloric Restriction ; Exercise/physiology ; Female ; Humans ; *Interleukin-6 ; Male ; Obesity/therapy ; Walking Speed ; },
abstract = {Clinical trials conventionally test aggregate mean differences and assume homogeneous variances across treatment groups. However, significant response heterogeneity may exist. The purpose of this study was to model treatment response variability using gait speed change among older adults participating in caloric restriction (CR) trials. Eight randomized controlled trials (RCTs) with five- or six-month assessments were pooled, including 749 participants randomized to CR and 594 participants randomized to non-CR (NoCR). Statistical models compared means and variances by CR assignment and exercise assignment or select subgroups, testing for treatment differences and interactions for mean changes and standard deviations. Continuous equivalents of dichotomized variables were also fit. Models used a Bayesian framework, and posterior estimates were presented as means and 95% Bayesian credible intervals (BCI). At baseline, participants were 67.7 (SD = 5.4) years, 69.8% female, and 79.2% white, with a BMI of 33.9 (4.4) kg/m2. CR participants reduced body mass [CR: -7.7 (5.8) kg vs. NoCR: -0.9 (3.5) kg] and increased gait speed [CR: +0.10 (0.16) m/s vs. NoCR: +0.07 (0.15) m/s] more than NoCR participants. There were no treatment differences in gait speed change standard deviations [CR-NoCR: -0.002 m/s (95% BCI: -0.013, 0.009)]. Significant mean interactions between CR and exercise assignment [0.037 m/s (95% BCI: 0.004, 0.070)], BMI [0.034 m/s (95% BCI: 0.003, 0.066)], and IL-6 [0.041 m/s (95% BCI: 0.009, 0.073)] were observed, while variance interactions were observed between CR and exercise assignment [-0.458 m/s (95% BCI: -0.783, -0.138)], age [-0.557 m/s (95% BCI: -0.900, -0.221)], and gait speed [-0.530 m/s (95% BCI: -1.018, -0.062)] subgroups. Caloric restriction plus exercise yielded the greatest gait speed benefit among older adults with obesity. High BMI and IL-6 subgroups also improved gait speed in response to CR. Results provide a novel statistical framework for identifying treatment heterogeneity in RCTs.},
}
@article {pmid35511846,
year = {2022},
author = {Tang, Z and Zhang, L and Chen, X and Ying, J and Wang, X and Wang, H},
title = {Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1298-1309},
doi = {10.1109/TNSRE.2022.3172974},
pmid = {35511846},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; *Robotic Surgical Procedures ; *Wearable Electronic Devices ; },
abstract = {Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient's motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects' MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% ± 2.36 %) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.},
}
@article {pmid35511845,
year = {2022},
author = {Salvatore, C and Valeriani, D and Piccialli, V and Bianchi, L},
title = {Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1223-1232},
doi = {10.1109/TNSRE.2022.3173079},
pmid = {35511845},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Facial Recognition ; Humans ; Language ; Support Vector Machine ; },
abstract = {The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.},
}
@article {pmid35509047,
year = {2022},
author = {Xu, H and Piao, L and Liu, X and Jiang, SN},
title = {Ursolic acid-enriched kudingcha extract enhances the antitumor activity of bacteria-mediated cancer immunotherapy.},
journal = {BMC complementary medicine and therapies},
volume = {22},
number = {1},
pages = {123},
pmid = {35509047},
issn = {2662-7671},
mesh = {Animals ; Bacteria ; Disease Models, Animal ; *Ilex ; Immunotherapy ; Mice ; *Neoplasms ; Plant Extracts/pharmacology ; Triterpenes ; Ursolic Acid ; },
abstract = {BACKGROUND: Bacteria-mediated cancer immunotherapy (BCI) robustly stimulates the immune system and represses angiogenesis, but tumor recurrence and metastasis commonly occur after BCI. The natural product Ilex kudingcha C. J Tseng enriched with ursolic acid has anti-cancer activity and could potentially augment the therapeutic effects of BCI. The objective of the present study was to determine potential additive effects of these modalities.<br><br>METHODS: We investigated the anti-cancer activity of KDCE (Kudingcha extract) combined with S.t&#x25b3;ppGpp in the mice colon cancer models.<br><br>RESULTS: In the present study, KDCE combined with S.t&#x25b3;ppGpp BCI improved antitumor therapeutic efficacy compared to S.t&#x25b3;ppGpp or KDCE alone. KDCE did not prolong bacterial tumor-colonizing time, but enhanced the antiangiogenic effect of S.t&#x25b3;ppGpp by downregulatingVEGFR2. We speculated that KDCE-induced VEGFR2 downregulation is associated with FAK/MMP9/STAT3 axis but not AKT or ERK.<br><br>CONCLUSIONS: Ursolic acid-enriched KDCE enhances the antitumor activity of BCI, which could be mediated by VEGFR2 downregulation and subsequent suppression of angiogenesis. Therefore, combination therapy with S.t&#x25b3;ppGpp and KDCE is a potential cancer therapeutic strategy.},
}
@article {pmid35508113,
year = {2022},
author = {Sujatha Ravindran, A and Malaya, CA and John, I and Francisco, GE and Layne, C and Contreras-Vidal, JL},
title = {Decoding neural activity preceding balance loss during standing with a lower-limb exoskeleton using an interpretable deep learning model.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6ca9},
pmid = {35508113},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Electromyography ; *Exoskeleton Device ; Humans ; },
abstract = {Objective:Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall.Approach:To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from seven healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials.Main results:We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼180 ms) and the COP (∼350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3%. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06.Significance:Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.},
}
@article {pmid35507442,
year = {2022},
author = {Wang, RM and Xu, WQ and Zheng, ZW and Yang, GM and Zhang, MY and Ke, HZ and Xia, N and Dong, Y and Wu, ZY},
title = {Serum Neurofilament Light Chain in Wilson's Disease: A Promising Indicator but Unparallel to Real-Time Treatment Response.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {37},
number = {7},
pages = {1531-1535},
doi = {10.1002/mds.29039},
pmid = {35507442},
issn = {1531-8257},
mesh = {Biomarkers ; *Hepatolenticular Degeneration/diagnosis/therapy ; Humans ; Intermediate Filaments ; Magnetic Resonance Imaging ; Treatment Outcome ; },
abstract = {BACKGROUND: Wilson's disease (WD) currently lacks a promising indicator that could reflect neurological impairment and monitor treatment outcome. We aimed to investigate whether serum neurofilament light chain (sNfL) functions as a candidate for disease assessment and treatment monitoring of WD.<br><br>METHODS: We assessed preclinical and manifested WD patients' sNfL levels compared to controls and analyzed the differences between patients with various clinical symptoms. We then explored the correlation between clinical scales and sNfL levels. And repeated measurements were performed in 34 patients before and after treatment.<br><br>RESULTS: WD patients with neurological involvement had significantly higher sNfL levels than both hepatic patients and controls. Positive correlations were found between Unified Wilson's Disease Rating Scale scores and sNfL and between semiquantitative magnetic resonance imaging scales and sNfL levels in WD patients. However, in the treatment follow-up analysis, the trend of sNfL before and after treatment disaccorded with clinical response.<br><br>CONCLUSION: These findings suggest that sNfL levels can be an ideal indicator for the severity of neurological involvement but fail to evaluate change in disease condition after treatment. &#xa9; 2022 International Parkinson and Movement Disorder Society.},
}
@article {pmid35503097,
year = {2022},
author = {Qin, C and Chen, C and Yuan, Q and Jiang, N and Liu, M and Duan, Y and Wan, H and Li, R and Zhuang, L and Wang, P},
title = {Biohybrid Tongue for Evaluation of Taste Interaction between Sweetness and Sourness.},
journal = {Analytical chemistry},
volume = {94},
number = {19},
pages = {6976-6985},
doi = {10.1021/acs.analchem.1c05384},
pmid = {35503097},
issn = {1520-6882},
mesh = {Animals ; Mammals ; *Sweetening Agents ; *Taste/physiology ; Tongue ; },
abstract = {The past decade has witnessed tremendous progress achieved in taste research, while few studies focus on interactions among taste compounds. Indeed, sweeteners and acidulants are commonly used food additives, and sweet-sour mixtures always provide improved tastes. For example, sensory studies have shown that sourness suppresses sweetness. However, the degree of sweetness suppression by sourness is difficult to evaluate quantitatively and objectively. Therefore, we propose a biohybrid tongue that is constructed by integrating mammalian gustatory epithelium with a microelectrode array chip. The taste quality and intensity information is coded in time-frequency patterns of local field potential. Different response patterns evoked by sweet and sour stimuli are observed, and the response is dose-dependent. Then, interaction effects of sourness against sweetness are quantified. The results indicate that suppression of sweetness by sourness occurs by increasing sourness concentrations. In summary, this study provides a powerful new tool for quantitative evaluation of sweet, sour, and their binary taste interactions that mimic the mammalian taste system.},
}
@article {pmid35500376,
year = {2022},
author = {Yuan, X and Zhang, L and Sun, Q and Lin, X and Li, C},
title = {A novel command generation method for SSVEP-based BCI by introducing SSVEP blocking response.},
journal = {Computers in biology and medicine},
volume = {146},
number = {},
pages = {105521},
doi = {10.1016/j.compbiomed.2022.105521},
pmid = {35500376},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; },
abstract = {Increasing the number of commands in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) by increasing the number of visual stimuli has been widely studied. This paper proposes a novel BCI paradigm based on SSVEP and SSVEP blocking responses (defined as the disappearance or attenuation of the ongoing SSVEP) to increase the number of BCI commands with limited visual stimuli, in which the duration of SSVEP blocking response can be voluntarily controlled by users. Besides, this paper also proposes a frequency-specific threshold method and a unified threshold method to identify SSVEP blocking response. The paradigm includes a frequency recognition phase and an SSVEP blocking response identification phase. Filter bank canonical correlation analysis is used to detect the stimulation frequency, and the proposed threshold method is used to identify the SSVEP blocking response and calculate the blocking duration. The experimental results show that the two proposed threshold methods can effectively identify the SSVEP blocking response with different blocking duration and alternative stimulation frequencies. When there are Nf stimulation frequencies, the number of commands can be increased to Nf×Nt using the proposed paradigm, where Nt blocking durations correspond to each stimulus. This study demonstrates that the proposed paradigm based on SSVEP and SSVEP blocking responses is effective in increasing the number of BCI commands and has great potential for practical applications.},
}
@article {pmid35499947,
year = {2025},
author = {AlFarraj, A and AlIbrahim, M and AlHajjaj, H and Khater, F and AlGhamdi, A and Fayad, J},
title = {Transcutaneous Bone Conduction Implants in Patients With Single-Sided Deafness: Objective and Subjective Evaluation.},
journal = {Ear, nose, &amp; throat journal},
volume = {104},
number = {2},
pages = {NP92-NP100},
doi = {10.1177/01455613221099996},
pmid = {35499947},
issn = {1942-7522},
mesh = {Humans ; *Bone Conduction/physiology ; Retrospective Studies ; Male ; Female ; Middle Aged ; *Hearing Aids ; Adult ; Treatment Outcome ; *Hearing Loss, Unilateral/surgery ; Patient Satisfaction ; Audiometry, Pure-Tone ; Speech Reception Threshold Test ; Speech Perception ; Aged ; },
abstract = {OBJECTIVES: This study aimed to investigate the audiological outcomes and subjective benefits of transcutaneous bone conduction implants (BCIs) in patients with single-sided deafness (SSD).<br><br>METHODS: This retrospective study was conducted on 11 patients with SSD implantations between 2015 and 2018 at a tertiary center. Pure-tone audiometry, speech reception threshold (SRT), and speech-in-noise (SPIN) tests were performed. Preoperative and postoperative performances were compared. Subjective satisfaction level was assessed using validated questionnaires. A PubMed search was conducted to identify the relevant studies published to date.<br><br>RESULTS: All patients demonstrated significant audiological improvements compared with their preoperative condition. The mean SRT improved significantly (p = 0.001) from 109&#xa0;dB to 23&#xa0;dB after implantation. The mean SPIN score improved significantly after implantation. The questionnaires showed an overall positive benefit of transcutaneous bone conduction devices (BCDs). A literature search revealed 21 articles, of which 14 reported the use of BCIs in patients with SSD. Our results agree with the published evidence showing the overall benefit of BCI in patients with SSD.<br><br>CONCLUSIONS: Transcutaneous BCDs could be considered as an alternative treatment option for patients with SSD, it could show good audiological outcomes and high satisfaction levels. Further studies should be conducted on patients with SSD to determine the most appropriate hearing solutions.},
}
@article {pmid35496073,
year = {2022},
author = {Kostick-Quenet, K and Kalwani, L and Koenig, B and Torgerson, L and Sanchez, C and Munoz, K and Hsu, RL and Sierra-Mercado, D and Robinson, JO and Outram, S and Pereira, S and McGuire, A and Zuk, P and Lazaro-Munoz, G},
title = {Researchers' Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {813922},
pmid = {35496073},
issn = {1662-5161},
support = {R01 MH114854/MH/NIMH NIH HHS/United States ; UH3 NS100549/NS/NINDS NIH HHS/United States ; },
abstract = {The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g. transcranial stimulation) currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to (1) safety and security; (2) enhancement as unnecessary, unnatural or aberrant; and (3) fairness, equality, and distributive justice. Most (70%) researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening (particularly for military purposes). However, researchers unanimously (100%) felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood.},
}
@article {pmid35495034,
year = {2022},
author = {Petschenig, H and Bisio, M and Maschietto, M and Leparulo, A and Legenstein, R and Vassanelli, S},
title = {Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {838054},
pmid = {35495034},
issn = {1662-4548},
abstract = {Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data-multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state-their performance is similar during UP and DOWN states.},
}
@article {pmid35492507,
year = {2022},
author = {King, JT and John, AR and Wang, YK and Shih, CK and Zhang, D and Huang, KC and Lin, CT},
title = {Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {10},
number = {},
pages = {2100408},
pmid = {35492507},
issn = {2168-2372},
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy/methods ; *Motor Cortex ; Movement ; },
abstract = {Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.},
}
@article {pmid35490934,
year = {2022},
author = {Ding, P and Wang, H and Zhu, J and An, F and Xu, J and Ding, X and Luo, L and Wu, W and Qin, Q and Wei, Y and Zhao, W and Lv, Z and Li, H and Zhu, Y and Li, M and Zhang, W and Zhang, Y and Ou, Z and Liu, H and Hua, Y},
title = {Viral receptor profiles of masked palm civet revealed by single-cell transcriptomics.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {49},
number = {11},
pages = {1072-1075},
doi = {10.1016/j.jgg.2022.04.009},
pmid = {35490934},
issn = {1673-8527},
mesh = {Animals ; *Viverridae/genetics ; *Transcriptome/genetics ; Phylogeny ; },
}
@article {pmid35490057,
year = {2022},
author = {Reinfeldt, S and Eeg-Olofsson, M and Fredén Jansson, KJ and Persson, AC and Håkansson, B},
title = {Long-term follow-up and review of the Bone Conduction Implant.},
journal = {Hearing research},
volume = {421},
number = {},
pages = {108503},
doi = {10.1016/j.heares.2022.108503},
pmid = {35490057},
issn = {1878-5891},
mesh = {Audiometry ; Bone Conduction/physiology ; Follow-Up Studies ; *Hearing Aids ; *Hearing Loss ; Hearing Loss, Conductive ; *Hearing Loss, Mixed Conductive-Sensorineural/diagnosis/therapy ; Humans ; *Speech Perception ; Treatment Outcome ; },
abstract = {Active transcutaneous bone conduction devices are a type of bone conduction device developed to keep the skin intact and provide direct bone conduction stimulation. The Bone Conduction Implant (BCI) is such a device and has been implanted in 16 patients. The objective of this paper is to give a broad overview of the BCI development to the final results of 13 patients at 5-year follow-up. Follow-up of these patients included audiological performance investigations, questionnaires, as well as safety evaluation and objective functionality testing of the device. Among those audiological measurements were sound field warble tone thresholds, speech recognition threshold (SRT), speech recognition score (SRS) and signal to noise ratio threshold (SNR-threshold). The accumulated implant time for all 16 patients was 113 years in February 2022. During this time, no serious adverse events have occurred. The functional improvement for the 13 patients reported in this paper was on average 29.5 dB (average over 0.5, 1, 2 and 4 kHz), while the corresponding effective gain was -12.4 dB. The SRT improvement was 24.5 dB and the SRS improvement was 38.1%, while the aided SNR-threshold was on average -6.4 dB. It was found that the BCI can give effective and safe hearing rehabilitation for patients with conductive and mild-to-moderate mixed hearing loss.},
}
@article {pmid35488791,
year = {2022},
author = {Niazi, IK and Navid, MS and Rashid, U and Amjad, I and Olsen, S and Haavik, H and Alder, G and Kumari, N and Signal, N and Taylor, D and Farina, D and Jochumsen, M},
title = {Associative cued asynchronous BCI induces cortical plasticity in stroke patients.},
journal = {Annals of clinical and translational neurology},
volume = {9},
number = {5},
pages = {722-733},
pmid = {35488791},
issn = {2328-9503},
mesh = {*Brain-Computer Interfaces ; Cues ; Evoked Potentials, Motor/physiology ; Humans ; *Stroke/therapy ; Transcranial Magnetic Stimulation/methods ; },
abstract = {OBJECTIVE: We propose a novel cue-based asynchronous brain-computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.<br><br>METHODS: The proposed BCI detects the intention to move from single-trial EEG signals in real time, but, contrary to classic asynchronous-BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue-based asynchronous-BCI was compared with two traditional BCI modes (asynchronous-BCI and offline synchronous-BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor-evoked potentials in the tibialis-anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30&#x2009;min after the intervention.<br><br>RESULTS: The proposed cue-based asynchronous-BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous-BCI. Linear-mixed-models showed that motor-evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p&#x2009;<0.05). The proposed cue-based asynchronous-BCI resulted in the largest relative increase in peak-to-peak motor-evoked potential amplitudes(141%&#x2009;&#xb1;&#x2009;33%) among all interventions and sustained it for 30&#x2009;min(111%&#x2009;&#xb1;&#x2009;33%).<br><br>INTERPRETATION: These findings prove the high performance of a newly proposed cue-based asynchronous-BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.},
}
@article {pmid35483505,
year = {2022},
author = {Ouyang, R and Jin, Z and Tang, S and Fan, C and Wu, X},
title = {Low-quality training data detection method of EEG signals for motor imagery BCI system.},
journal = {Journal of neuroscience methods},
volume = {376},
number = {},
pages = {109607},
doi = {10.1016/j.jneumeth.2022.109607},
pmid = {35483505},
issn = {1872-678X},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination/physiology ; Online Systems ; },
abstract = {BACKGROUND: The design and implementation of high-performance motor imagery-based brain computer interface (MI-BCI) requires high-quality training samples. However, fluctuation in subjects' physiological and mental states as well as artifacts can produce the low-quality motor imagery electroencephalogram (EEG) signal, which will damage the performance of MI-BCI system.<br><br>NEW METHOD: In order to select high-quality MI-EEG training data, this paper proposes a low-quality training data detection method combining independent component analysis (ICA) and weak classifier cluster. we also design and implement a new online BCI system based on motor imagery to verify the online processing performance of the proposed method.<br><br>RESULT: In order to verify the effectiveness of the proposed method, we conducted offline experiments on the public dataset called BCI Competition IV Data Set 2b. Furthermore, in order to verify the processing performance of the online system, we designed 60 groups of online experiments on 12 subjects. The online experimental results show that the twelve subjects can complete the system task efficiently (the best experiment is 135.6&#xa0;s with 9 trials of subject S1).<br><br>CONCLUSION: This paper demonstrated that the proposed low-quality training data detection method can effectively screen out low-quality training samples, so as to improve the performance of the MI-BCI system.},
}
@article {pmid35483331,
year = {2022},
author = {Yan, W and Wu, Y and Du, C and Xu, G},
title = {Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6b57},
pmid = {35483331},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Objective.Steady-state visual evoked potential (SSVEP) is an important control method of the brain-computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system.Approach.A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed.Main results.Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities.Significance.The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.},
}
@article {pmid35482705,
year = {2022},
author = {Maÿe, A and Rauterberg, R and Engel, AK},
title = {Instant classification for the spatially-coded BCI.},
journal = {PloS one},
volume = {17},
number = {4},
pages = {e0267548},
pmid = {35482705},
issn = {1932-6203},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; },
abstract = {The spatially-coded SSVEP BCI exploits changes in the topography of the steady-state visual evoked response to visual flicker stimulation in the extrafoveal field of view. In contrast to frequency-coded SSVEP BCIs, the operator does not gaze into any flickering lights; therefore, this paradigm can reduce visual fatigue. Other advantages include high classification accuracies and a simplified stimulation setup. Previous studies of the paradigm used stimulation intervals of a fixed duration. For frequency-coded SSVEP BCIs, it has been shown that dynamically adjusting the trial duration can increase the system's information transfer rate (ITR). We therefore investigated whether a similar increase could be achieved for spatially-coded BCIs by applying dynamic stopping methods. To this end we introduced a new stopping criterion which combines the likelihood of the classification result and its stability across larger data windows. Whereas the BCI achieved an average ITR of 28.4±6.4 bits/min with fixed intervals, dynamic intervals increased the performance to 81.1±44.4 bits/min. Users were able to maintain performance up to 60 minutes of continuous operation. We suggest that the dynamic response time might have worked as a kind of temporal feedback which allowed operators to optimize their brain signals and compensate fatigue.},
}
@article {pmid35480157,
year = {2022},
author = {Cai, X and Pan, J},
title = {Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.},
journal = {Journal of healthcare engineering},
volume = {2022},
number = {},
pages = {6894392},
pmid = {35480157},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Electrooculography/methods ; Humans ; *Internet of Things ; Movement ; },
abstract = {This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.},
}
@article {pmid35478847,
year = {2022},
author = {Chen, Z and Ye, N and Teng, C and Li, X},
title = {Alternations and Applications of the Structural and Functional Connectome in Gliomas: A Mini-Review.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {856808},
pmid = {35478847},
issn = {1662-4548},
abstract = {In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural-functional coupling of glioma. Additionally, the brain-computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.},
}
@article {pmid35478300,
year = {2022},
author = {Thompson, EM and Patel, V and Rajeeve, V and Cutillas, PR and Stoker, AW},
title = {The cytotoxic action of BCI is not dependent on its stated DUSP1 or DUSP6 targets in neuroblastoma cells.},
journal = {FEBS open bio},
volume = {12},
number = {7},
pages = {1388-1405},
pmid = {35478300},
issn = {2211-5463},
support = {20008/LLR_/Blood Cancer UK/United Kingdom ; },
mesh = {*Antineoplastic Agents/pharmacology ; Cell Line, Tumor ; *Dual Specificity Phosphatase 1/genetics ; *Dual Specificity Phosphatase 6/genetics ; Humans ; *Neuroblastoma/drug therapy/genetics ; Phosphorylation ; Signal Transduction ; },
abstract = {Neuroblastoma (NB) is a heterogeneous cancer of the sympathetic nervous system, which accounts for 7-10% of paediatric malignancies worldwide. Due to the lack of targetable molecular aberrations in NB, most treatment options remain relatively nonspecific. Here, we investigated the therapeutic potential of BCI, an inhibitor of DUSP1 and DUSP6, in cultured NB cells. BCI was cytotoxic in a range of NB cell lines and induced a short-lived activation of the AKT and stress-inducible MAP kinases, although ERK phosphorylation was unaffected. Furthermore, a phosphoproteomic screen identified significant upregulation of JNK signalling components and suppression in mTOR and R6K signalling. To assess the specificity of BCI, CRISPR-Cas9 was employed to introduce insertions and deletions in the DUSP1 and DUSP6 genes. Surprisingly, BCI remained fully cytotoxic in NB cells with complete loss of DUSP6 and partial depletion of DUSP1, suggesting that BCI exerts cytotoxicity in NB cells through a complex mechanism that is unrelated to these phosphatases. Overall, these data highlight the risk of using an inhibitor such as BCI as supposedly specific DUSP1/6, without understanding its full range of targets in cancer cells.},
}
@article {pmid35477130,
year = {2022},
author = {Zhang, R and Xu, Z and Zhang, L and Cao, L and Hu, Y and Lu, B and Shi, L and Yao, D and Zhao, X},
title = {The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP-BCI in augmented reality.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6ae5},
pmid = {35477130},
issn = {1741-2552},
mesh = {Algorithms ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; *Retinal Diseases ; },
abstract = {Objective. The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP-BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP-BCI. However, whether the advantages of SSVEP-BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP-BCI in an AR context.Approach.We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR-SSVEP and CS-SSVEP.Main results.The amplitude spectrum and signal-to-noise ratio of AR-SSVEP were not significantly different from CS-SSVEP at the fundamental frequency but were significantly lower than CS-SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR-SSVEP but not in CS-SSVEP. When the stimulus number increased, the maximum ITR of CS-SSVEP also increased, but not for AR-SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits min[-1]) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR-SSVEP and CS-SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value.Significance. Our study indicates that the conclusions based on CS-SSVEP cannot be simply applied to AR-SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.},
}
@article {pmid35475424,
year = {2022},
author = {van Velthoven, EAM and van Stuijvenberg, OC and Haselager, DRE and Broekman, M and Chen, X and Roelfsema, P and Bredenoord, AL and Jongsma, KR},
title = {Ethical implications of visual neuroprostheses-a systematic review.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac65b2},
pmid = {35475424},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neural Prostheses ; },
abstract = {Objective. The aim of this review was to systematically identify the ethical implications of visual neuroprostheses.Approach. A systematic search was performed in both PubMed and Embase using a search string that combined synonyms for visual neuroprostheses, brain-computer interfaces (BCIs), cochlear implants (CIs), and ethics. We chose to include literature on BCIs and CIs, because of their ethically relavant similarities and functional parallels with visual neuroprostheses.Main results. We included 84 articles in total. Six focused specifically on visual prostheses. The other articles focused more broadly on neurotechnologies, on BCIs or CIs. We identified 169 ethical implications that have been categorized under seven main themes: (a) benefits for health and well-being; (b) harm and risk; (c) autonomy; (d) societal effects; (e) clinical research; (f) regulation and governance; and (g) involvement of experts, patients and the public.Significance. The development and clinical use of visual neuroprostheses is accompanied by ethical issues that should be considered early in the technological development process. Though there is ample literature on the ethical implications of other types of neuroprostheses, such as motor neuroprostheses and CIs, there is a significant gap in the literature regarding the ethical implications of visual neuroprostheses. Our findings can serve as a starting point for further research and normative analysis.},
}
@article {pmid35475302,
year = {2021},
author = {Jaipuria, J and Karimi, AM and Singh, A and Thapa, BB and Gupta, S and Sadasukhi, N and Venkatasubramaniyan, M and Pathak, P and Kasaraneni, P and Khanna, A and Narayan, TA and Sharma, G and Rawal, S},
title = {Pitcher pot neourethral modification of ileal orthotopic neobladder achieves satisfactory long-term functional and quality of life outcomes with low clean intermittent self-catheterization rate.},
journal = {BJUI compass},
volume = {2},
number = {4},
pages = {292-299},
pmid = {35475302},
issn = {2688-4526},
abstract = {OBJECTIVE: To describe a decade of our experience with a neo-urethral modification of ileal orthotopic neobladder (pitcher pot ONB). Multiple investigators have reported similar modifications. However, long-term longitudinal functional and quality of life (QOL) outcomes are lacking.<br><br>METHODS: Prospectively maintained hospital registry for 238 ONB patients comprising a mix of open and robotic surgery cohorts from 2007 to 2017, and minimum of 2&#xa0;years of follow-up was retrospectively queried. QOL was evaluated using Bladder Cancer Index (BCI). Longitudinal trends of QOL domain parameters were analysed. List of perioperative variables that have a biologically plausible association with continence, potency, and post-operative BCI QOL sexual, urinary, and bowel domain scores was drawn. Variables included surgery type, Body Mass Index (BMI), T and N stage, neurovascular bundle (NVB) sparing, age, and related pre-operative BCI QOL domain score. Prognostic associations were analysed using multivariable Cox proportional hazard models and multilevel mixed-effects modeling.<br><br>RESULTS: The study comprised 80 and 158 patients who underwent open and robotic sandwich technique cohorts, respectively. Open surgery was associated with significantly higher "any" complication (40% vs 27%, P-value .050) and "major" complication rate (15% vs 11%, P-value .048). All patients developed a bladder capacity&#xa0;>400&#xa0;cc with negligible post-void residual urine, and all but one patient achieved spontaneous voiding by the end of study period (<1% clean intermittent self-catheterization [CISC] rate). By 15&#xa0;months, QOL for all three domains had recovered to reach a plateau. About 45% of patients achieved potency, and the median time to achieve day and night time continence was 9 and 12&#xa0;months respectively. Lower age and NVBs spared during surgery were found to be significantly associated with the earlier achievement of potency, day and night time continence, as well as better urinary and sexual summary QOL scores.<br><br>CONCLUSIONS: Pitcher pot neobladder achieves satisfactory long-term functional and QOL outcomes with negligible CISC rate. Results were superior with incremental nerves spared during surgery.},
}
@article {pmid35473959,
year = {2022},
author = {Jalilpour, S and Müller-Putz, G},
title = {Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {6802},
pmid = {35473959},
issn = {2045-2322},
mesh = {*Automobile Driving ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; },
abstract = {Neuroimaging studies have provided proof that loss of balance evokes specific neural transient wave complexes in electroencephalography (EEG), called perturbation evoked potentials (PEPs). Online decoding of balance perturbations from ongoing EEG signals can establish the possibility of implementing passive brain-computer interfaces (pBCIs) as a part of aviation/driving assistant systems. In this study, we investigated the feasibility of identifying the existence and expression of perturbations in four different conditions by using EEG signals. Fifteen healthy participants experienced four various postural changes while they sat in a glider cockpit. Sudden perturbations were exposed by a robot connected to a glider and moved to the right and left directions with tilting angles of 5 and 10 degrees. Perturbations occurred in an oddball paradigm in which participants were not aware of the time and expression of the perturbations. We employed a hierarchical approach to separate the perturbation and rest, and then discriminate the expression of perturbations. The performance of the BCI system was evaluated by using classification accuracy and F1 score. Asynchronously, we achieved average accuracies of 89.83 and 73.64% and average F1 scores of 0.93 and 0.60 for binary and multiclass classification, respectively. These results manifest the practicality of pBCI for the detection of balance disturbances in a realistic situation.},
}
@article {pmid35467033,
year = {2022},
author = {Han, JJ},
title = {A man in a completely locked-in state produces intelligible sentences using a brain-computer interface.},
journal = {Artificial organs},
volume = {46},
number = {6},
pages = {985-986},
doi = {10.1111/aor.14249},
pmid = {35467033},
issn = {1525-1594},
mesh = {*Amyotrophic Lateral Sclerosis/therapy ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Male ; },
abstract = {Patients with amyotrophic lateral sclerosis may enter into a completely locked-in state without any capability for communication using neuromuscular output. Using an auditory-guided neurofeedback-based strategy with implantable sensors in the motor cortex, scientists were able to help a patient in this state produce intelligible sentences.},
}
@article {pmid35465540,
year = {2022},
author = {Wang, Y and Yang, Z and Ji, H and Li, J and Liu, L and Zhuang, J},
title = {Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {833007},
pmid = {35465540},
issn = {1664-1078},
abstract = {The brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) has received more and more attention due to its vast application potential in emotion recognition. However, the relatively insufficient investigation of the feature extraction algorithms limits its use in practice. In this article, to improve the performance of fNIRS-based BCI, we proposed a method named R-CSP-E, which introduces EEG signals when computing fNIRS signals' features based on transfer learning and ensemble learning theory. In detail, we used the Independent Component Analysis (ICA) algorithm for the correspondence between the sources of the two signals. We then introduced the EEG signals when computing the spatial filter based on a modified Common Spatial Pattern (CSP) algorithm. Experimental results on public datasets show that the proposed method in this paper outperforms traditional methods without transfer. In general, the mean classification accuracy can be increased by up to 5%. To our knowledge, it is an innovation that we tried to apply transfer learning between EEG and fNIRS. Our study's findings not only prove the potential of the transfer learning algorithm in cross-model brain-computer interface, but also offer a new and innovative perspective to research the hybrid brain-computer interface.},
}
@article {pmid35464315,
year = {2022},
author = {Liu, L and Jin, M and Zhang, L and Zhang, Q and Hu, D and Jin, L and Nie, Z},
title = {Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {809657},
pmid = {35464315},
issn = {1662-4548},
abstract = {INTRODUCTION: We evaluated the efficacy of brain-computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.<br><br>METHODS: In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.<br><br>RESULTS: Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (&#x394;WMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (&#x394;WMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (&#x394;FMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (&#x394;FMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (&#x394;FMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P < 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P < 0.05).<br><br>CONCLUSION: The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.<br><br>CLINICAL TRIAL REGISTRATION: www.chictr.org.cn, identifier: ChiCTR2100046301.},
}
@article {pmid35463935,
year = {2022},
author = {Zhou, Q and Cheng, R and Yao, L and Ye, X and Xu, K},
title = {Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {831995},
pmid = {35463935},
issn = {1662-5161},
abstract = {Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.},
}
@article {pmid35463924,
year = {2022},
author = {Belkacem, AN and Falk, TH and Yanagisawa, T and Guger, C},
title = {Editorial: Cognitive and Motor Control Based on Brain-Computer Interfaces for Improving the Health and Well-Being in Older Age.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {881922},
doi = {10.3389/fnhum.2022.881922},
pmid = {35463924},
issn = {1662-5161},
}
@article {pmid35463262,
year = {2022},
author = {Pan, J and Yang, F and Qiu, L and Huang, H},
title = {Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {3854513},
pmid = {35463262},
issn = {1687-5273},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions/physiology ; Fear ; Humans ; },
abstract = {At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.},
}
@article {pmid35463256,
year = {2022},
author = {Huang, Z and Cheng, L and Liu, Y},
title = {Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {6752067},
pmid = {35463256},
issn = {1687-5273},
mesh = {Algorithms ; *Artificial Intelligence ; Athletes ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.},
}
@article {pmid35462690,
year = {2022},
author = {Li, L and Zhang, Y and Huang, L and Zhao, J and Wang, J and Liu, T},
title = {Robot Assisted Treatment of Hand Functional Rehabilitation Based on Visual Motor Imagination.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {870871},
pmid = {35462690},
issn = {1663-4365},
abstract = {This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.},
}
@article {pmid35462356,
year = {2022},
author = {Gehrke, L and Lopes, P and Klug, M and Akman, S and Gramann, K},
title = {Neural sources of prediction errors detect unrealistic VR interactions.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac69bc},
pmid = {35462356},
issn = {1741-2552},
mesh = {*Augmented Reality ; Electroencephalography ; Evoked Potentials ; Movement ; User-Computer Interface ; *Virtual Reality ; },
abstract = {Objective. Neural interfaces hold significant promise to implicitly track user experience. Their application in virtual and augmented reality (VR/AR) simulations is especially favorable as it allows user assessment without breaking the immersive experience. In VR, designing immersion is one key challenge. Subjective questionnaires are the established metrics to assess the effectiveness of immersive VR simulations. However, administering such questionnaires requires breaking the immersive experience they are supposed to assess.Approach. We present a complimentary metric based on a event-related potentials. For the metric to be robust, the neural signal employed must be reliable. Hence, it is beneficial to target the neural signal's cortical origin directly, efficiently separating signal from noise. To test this new complementary metric, we designed a reach-to-tap paradigm in VR to probe electroencephalography (EEG) and movement adaptation to visuo-haptic glitches. Our working hypothesis was, that these glitches, or violations of the predicted action outcome, may indicate a disrupted user experience.Main results. Using prediction error negativity features, we classified VR glitches with 77% accuracy. We localized the EEG sources driving the classification and found midline cingulate EEG sources and a distributed network of parieto-occipital EEG sources to enable the classification success.Significance. Prediction error signatures from these sources reflect violations of user's predictions during interaction with AR/VR, promising a robust and targeted marker for adaptive user interfaces.},
}
@article {pmid35459070,
year = {2022},
author = {Butt, AM and Alsaffar, H and Alshareef, M and Qureshi, KK},
title = {AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35459070},
issn = {1424-8220},
support = {SR 191027//Deanship of Research, Oversight, and Coordination, KFUPM/ ; },
mesh = {Artificial Intelligence ; Bayes Theorem ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Gait ; Humans ; },
abstract = {Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87-93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10-20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065-0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.},
}
@article {pmid35458962,
year = {2022},
author = {Algarni, M and Saeed, F and Al-Hadhrami, T and Ghabban, F and Al-Sarem, M},
title = {Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35458962},
issn = {1424-8220},
support = {77 /442//The Deputyship for Research &amp; Innovation, Ministry of Education in Saudi Arabia/ ; },
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Emotions ; Neural Networks, Computer ; },
abstract = {Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine interactions. Several studies have been conducted in the field of emotion recognition. However, one of the most important issues facing the emotion recognition process, using EEG signals, is the accuracy of recognition. This paper proposes a deep learning-based approach for emotion recognition through EEG signals, which includes data selection, feature extraction, feature selection and classification phases. This research serves the medical field, as the emotion recognition model helps diagnose psychological and behavioral disorders. The research contributes to improving the performance of the emotion recognition model to obtain more accurate results, which, in turn, aids in making the correct medical decisions. A standard pre-processed Database of Emotion Analysis using Physiological signaling (DEAP) was used in this work. The statistical features, wavelet features, and Hurst exponent were extracted from the dataset. The feature selection task was implemented through the Binary Gray Wolf Optimizer. At the classification stage, the stacked bi-directional Long Short-Term Memory (Bi-LSTM) Model was used to recognize human emotions. In this paper, emotions are classified into three main classes: arousal, valence and liking. The proposed approach achieved high accuracy compared to the methods used in past studies, with an average accuracy of 99.45%, 96.87% and 99.68% of valence, arousal, and liking, respectively, which is considered a high performance for the emotion recognition model.},
}
@article {pmid35458940,
year = {2022},
author = {Phadikar, S and Sinha, N and Ghosh, R and Ghaderpour, E},
title = {Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {8},
pages = {},
pmid = {35458940},
issn = {1424-8220},
support = {1-5770264050//Ministry of Human Resource Development/ ; },
mesh = {Algorithms ; *Artifacts ; Electroencephalography/methods ; Muscles ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain-computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.},
}
@article {pmid35458297,
year = {2022},
author = {Afzali, M and Boateng, JS},
title = {Composite Fish Collagen-Hyaluronate Based Lyophilized Scaffolds Modified with Sodium Alginate for Potential Treatment of Chronic Wounds.},
journal = {Polymers},
volume = {14},
number = {8},
pages = {},
pmid = {35458297},
issn = {2073-4360},
abstract = {Chronic wounds are characterized by both decreased collagen deposition and increased collagen breakdown. It is reasonable to hypothesize that exogenous collagen can potentially promote wound healing by reducing degradation enzymes in the wound environment and disrupting the cycle of chronicity. Therefore, this study aimed to develop an optimal combination of fish collagen (FCOL), sodium alginate (SA), and hyaluronic acid (HA) loaded with bovine serum albumin (BSA) as a model protein fabricated as lyophilized scaffolds. The effects of sodium alginate (SA#) with higher mannuronic acid (M) were compared to sodium alginate (SA*) with higher guluronic acid (G). The SA* with higher G resulted in elegant scaffolds with hardness ranging from 3.74 N−4.29 N that were able to withstand the external force due to the glycosidic bonds in guluronic acid. Furthermore, the high G content also had a significant effect on the pore size, pore shape, and porosity. The water absorption (WA) ranged from 380−1382 (%) and equilibrium water content (EWC) 79−94 (%) after 24 h incubation at 37 °C. The SA* did not affect the water vapor transmission rate (WVTR) but incorporating BSA significantly increased the WVTR making these wound dressing scaffolds capable of absorbing about 50% exudate from a heavily exuding chronic wound. The protein released from the composite systems was best explained by the Korsmeyer−Peppas model with regression R2 values ranging from 0.896 to 0.971 and slope or n < 0.5 indicating that the BSA release mechanism was governed by quasi-Fickian diffusion. Cell viability assay showed that the scaffolds did not inhibit the proliferation of human dermal fibroblasts and human epidermal keratinocytes, and are therefore biocompatible. In vitro blood analysis using human whole blood confirmed that the BSA-loaded SA*:FCOL:HA scaffolds reduced the blood clotting index (BCI) by up to 20% compared to a commercially available sponge for chronic wounds. These features confirm that SA*:FCOL:HA scaffolds could be applied as a multifunctional wound dressing.},
}
@article {pmid35457787,
year = {2022},
author = {Yamatsu, K and Narazaki, K},
title = {Feasibility of the Remote Physical Activity Follow-Up Intervention after the Face-to-Face Program for Healthy Middle-Aged Adults: A Randomized Trial Using ICT and Mobile Technology.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {8},
pages = {},
pmid = {35457787},
issn = {1660-4601},
mesh = {Adult ; *Exercise ; Feasibility Studies ; Follow-Up Studies ; Humans ; Middle Aged ; *Motor Activity ; Technology ; },
abstract = {Although the effectiveness of face-to-face and remote intervention for increasing and maintaining physical activity (PA) have been compared, the effect of combining the two forms of intervention is unknown. The purpose of this study was to examine the feasibility of the remote PA follow-up intervention after the face-to-face PA program on changing PA behaviors and some health outcomes in healthy middle-aged adults. As a secondary analysis, we also attempted a preliminary analysis of the difference in the number of behavior change interviews in the remote PA follow-up intervention. After the face-to-face intervention, 30 healthy subjects were randomly divided into four behavior change coaching interviews (BCI4 group) or three BCI (BCI3 group). The results of this study showed that body weight, body fat mass, and waist circumference were significantly reduced after face-to-face intervention, and were further reduced after remote PA follow-up intervention. However, the difference in the number of BCI affected only body fat mass. The remote PA follow-up intervention may have potential to maintain the effects of face-to-face intervention. In the future, it is necessary to refine the research design and conduct a full-scale intervention study.},
}
@article {pmid35454265,
year = {2022},
author = {Li, M and Fan, J and Lin, L and Shang, Z and Wan, H},
title = {Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {8},
pages = {},
pmid = {35454265},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {Previous studies showed that spatial navigation depends on a local network including multiple brain regions with strong interactions. However, it is still not fully understood whether and how the neural patterns in avian nidopallium caudolaterale (NCL), which is suggested to play a key role in navigation as a higher cognitive structure, are modulated by the behaviors during spatial navigation, especially involved path adjustment needs. Hence, we examined neural activity in the NCL of pigeons and explored the local field potentials' (LFPs) spectral and functional connectivity patterns in a goal-directed spatial cognitive task with the detour paradigm. We found the pigeons progressively learned to solve the path adjustment task when the learned path was blocked suddenly. Importantly, the behavioral changes during the adjustment were accompanied by the modifications in neural patterns in the NCL. Specifically, the spectral power in lower bands (1-4 Hz and 5-12 Hz) decreased as the pigeons were tested during the adjustment. Meanwhile, an elevated gamma (31-45 Hz and 55-80 Hz) connectivity in the NCL was also detected. These results and the partial least square discriminant analysis (PLS-DA) modeling analysis provide insights into the neural activities in the avian NCL during the spatial path adjustment, contributing to understanding the potential mechanism of avian spatial encoding. This study suggests the important role of the NCL in spatial learning, especially path adjustment in avian navigation.},
}
@article {pmid35454043,
year = {2022},
author = {Altuwaijri, GA and Muhammad, G and Altaheri, H and Alsulaiman, M},
title = {A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
pmid = {35454043},
issn = {2075-4418},
support = {RSP-2021/34//Researchers Supporting Project number (RSP-2021/34), King Saud University, Riyadh, Saudi Ara-bia/ ; },
abstract = {Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data's high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.},
}
@article {pmid35452895,
year = {2022},
author = {Filippini, M and Borra, D and Ursino, M and Magosso, E and Fattori, P},
title = {Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {151},
number = {},
pages = {276-294},
doi = {10.1016/j.neunet.2022.03.044},
pmid = {35452895},
issn = {1879-2782},
mesh = {Action Potentials/physiology ; Animals ; Bayes Theorem ; Macaca fascicularis ; Movement/physiology ; Neural Networks, Computer ; *Parietal Lobe/physiology ; *Psychomotor Performance/physiology ; },
abstract = {Despite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey's PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional Neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons' firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient's intentions.},
}
@article {pmid35451963,
year = {2022},
author = {Andrews, A},
title = {Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.},
journal = {JMIR formative research},
volume = {6},
number = {4},
pages = {e18222},
pmid = {35451963},
issn = {2561-326X},
abstract = {BACKGROUND: Augmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.<br><br>OBJECTIVE: The described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.<br><br>METHODS: The study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.<br><br>RESULTS: The study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.<br><br>CONCLUSIONS: As one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.},
}
@article {pmid35450498,
year = {2024},
author = {Qu, H and Zeng, F and Tang, Y and Shi, B and Wang, Z and Chen, X and Wang, J},
title = {The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {19},
number = {1},
pages = {30-41},
doi = {10.1080/17483107.2022.2060354},
pmid = {35450498},
issn = {1748-3115},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Robotics ; Upper Extremity ; Recovery of Function ; },
abstract = {PURPOSE: Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and improvement in motor function. In this review, we performed a systematic review and meta-analysis to evaluate the clinical effects of BCI-robot systems.<br><br>METHODS: The articles published from January 2010 to December 2020 have been searched by using the databases (EMBASE, PubMed, CINAHL, EBSCO, Web of Science and manual search). The single-group studies were qualitatively described, and only the controlled-trial studies were included for the meta-analysis. The mean difference (MD) of Fugl-Meyer Assessment (FMA) scores were pooled and the random-effects model method was used to perform the meta-analysis. The PRISMA criteria were followed in current review.<br><br>RESULTS: A total of 897 records were identified, eight single-group studies and 11 controlled-trial studies were included in our review. The systematic analysis indicated that the BCI-robot systems had a significant improvement on motor function recovery. The meta-analysis showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: The use of BCI-robot systems has significant improvement on the motor function recovery of hemiparetic upper-limb, and there is a sustaining effect. The meta-analysis showed no statistical difference between the experimental group (BCI-robot) and the control group (robot). However, there are a few shortcomings in the experimental design of existing studies, more clinical trials need to be conducted, and the experimental design needs to be more rigorous.Implications for RehabilitationIn this review, we evaluated the clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation. After we screened the databases, 19 articles were included in this review. These articles all clinical trial research, they all used non-invasive brain-computer interfaces and upper-limb robot.We conducted the systematic review with nine articles, the result indicated that the BCI-robot system had a significant improvement on motor function recovery. Eleven articles were included for the meta-analysis, the result showed there were no statistic differences between BCI-robot groups and robot groups, neither in the immediate effects nor long-term effects.We thought the result of meta-analysis which showed no statistic difference was probably caused by the heterogenicity of clinical trial designs of these articles.We thought the BCI-robot systems are promising strategies for post-stroke rehabilitation. And we gave several suggestions for further research: (1) The experimental design should be more rigorous, and describe the experimental designs in detail, especially the control group intervention, to make the experiment replicability. (2) New evaluation criteria need to be established, more objective assessment such as biomechanical assessment, fMRI should be utilised as the primary outcome. (3) More clinical studies with larger sample size, novel external devices, and BCI systems need to be conducted to investigate the differences between BCI-robot system and other interventions. (4) Further research could shift the focus to the patients who are in subacute stage, to explore if the early BCI training can make a positive impact on cerebral cortical recovery.},
}
@article {pmid35447619,
year = {2022},
author = {Atherton, E and Hu, Y and Brown, S and Papiez, E and Ling, V and Colvin, VL and Borton, DA},
title = {A 3Din vitromodel of the device-tissue interface: functional and structural symptoms of innate neuroinflammation are mitigated by antioxidant ceria nanoparticles.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6908},
pmid = {35447619},
issn = {1741-2552},
support = {R01 NS044834/NS/NINDS NIH HHS/United States ; S10 OD025181/OD/NIH HHS/United States ; },
mesh = {*Antioxidants/pharmacology/therapeutic use ; Brain ; Humans ; Inflammation/drug therapy ; *Nanoparticles ; Neuroinflammatory Diseases ; },
abstract = {Objective.The recording instability of neural implants due to neuroinflammation at the device-tissue interface is a primary roadblock to broad adoption of brain-machine interfaces. While a multiphasic immune response, marked by glial scaring, oxidative stress (OS), and neurodegeneration, is well-characterized, the independent contributions of systemic and local 'innate' immune responses are not well-understood. We aimed to understand and mitigate the isolated the innate neuroinflammatory response to devices.Approach.Three-dimensional primary neural cultures provide a unique environment for studying the drivers of neuroinflammation by decoupling the innate and systemic immune systems, while conserving an endogenous extracellular matrix and structural and functional network complexity. We created a three-dimensionalin vitromodel of the device-tissue interface by seeding primary cortical cells around microwires. Live imaging of both dye and Adeno-Associated Virus (AAV) - mediated functional, structural, and lipid peroxidation fluorescence was employed to characterize the neuroinflammatory response.Main results.Live imaging of microtissues over time revealed independent innate neuroinflammation, marked by increased OS, decreased neuronal density, and increased functional connectivity. We demonstrated the use of this model for therapeutic screening by directly applying drugs to neural tissue, bypassing low bioavailability through thein vivoblood brain barrier. As there is growing interest in long-acting antioxidant therapies, we tested efficacy of 'perpetual' antioxidant ceria nanoparticles, which reduced OS, increased neuronal density, and protected functional connectivity.Significance.Our three-dimensionalin vitromodel of the device-tissue interface exhibited symptoms of OS-mediated innate neuroinflammation, indicating a significant local immune response to devices. The dysregulation of functional connectivity of microcircuits surround implants suggests the presence of an observer effect, in which the process of recording neural activity may fundamentally change the neural signal. Finally, the demonstration of antioxidant ceria nanoparticle treatment exhibited substantial promise as a neuroprotective and anti-inflammatory treatment strategy.},
}
@article {pmid35444515,
year = {2022},
author = {Singanamalla, SKR and Lin, CT},
title = {Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {792318},
pmid = {35444515},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.},
}
@article {pmid35444255,
year = {2022},
author = {Li, Z and Lai, J and Zhang, P and Ding, J and Jiang, J and Liu, C and Huang, H and Zhen, H and Xi, C and Sun, Y and Wu, L and Wang, L and Gao, X and Li, Y and Fu, Y and Jie, Z and Li, S and Zhang, D and Chen, Y and Zhu, Y and Lu, S and Lu, J and Wang, D and Zhou, H and Yuan, X and Li, X and Pang, L and Huang, M and Yang, H and Zhang, W and Brix, S and Kristiansen, K and Song, X and Nie, C and Hu, S},
title = {Multi-omics analyses of serum metabolome, gut microbiome and brain function reveal dysregulated microbiota-gut-brain axis in bipolar depression.},
journal = {Molecular psychiatry},
volume = {27},
number = {10},
pages = {4123-4135},
pmid = {35444255},
issn = {1476-5578},
mesh = {Humans ; *Gastrointestinal Microbiome/genetics ; *Bipolar Disorder/metabolism ; Brain-Gut Axis ; Metabolome ; *Microbiota ; Brain/metabolism ; },
abstract = {The intricate processes of microbiota-gut-brain communication in modulating human cognition and emotion, especially in the context of mood disorders, have remained elusive. Here we performed faecal metagenomic, serum metabolomics and neuroimaging studies on a cohort of 109 unmedicated patients with depressed bipolar disorder (BD) patients and 40 healthy controls (HCs) to characterise the microbial-gut-brain axis in BD. Across over 12,000 measured metabolic features, we observed a large discrepancy (73.54%) in the serum metabolome between BD patients and HCs, spotting differentially abundant microbial-derived neuroactive metabolites including multiple B-vitamins, kynurenic acid, gamma-aminobutyric acid and short-chain fatty acids. These metabolites could be linked to the abundance of gut microbiota presented with corresponding biosynthetic potentials, including Akkermansia muciniphila, Citrobacter spp. (Citrobacter freundii and Citrobacter werkmanii), Phascolarctobacterium spp., Yersinia spp. (Yersinia frederiksenii and Yersinia aleksiciae), Enterobacter spp. (Enterobacter cloacae and Enterobacter kobei) and Flavobacterium spp. Based on functional neuroimaging, BD-related neuroactive microbes and metabolites were discovered as potential markers associated with BD-typical features of functional connectivity of brain networks, hinting at aberrant cognitive function, emotion regulation, and interoception. Our study combines gut microbiota and neuroactive metabolites with brain functional connectivity, thereby revealing potential signalling pathways from the microbiota to the gut and the brain, which may have a role in the pathophysiology of BD.},
}
@article {pmid35444245,
year = {2022},
author = {Darbin, O and Hatanaka, N and Takara, S and Kaneko, N and Chiken, S and Naritoku, D and Martino, A and Nambu, A},
title = {Subthalamic nucleus deep brain stimulation driven by primary motor cortex γ2 activity in parkinsonian monkeys.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {6493},
pmid = {35444245},
issn = {2045-2322},
mesh = {Animals ; *Deep Brain Stimulation/methods ; Haplorhini ; *Motor Cortex/physiology ; *Parkinsonian Disorders ; *Subthalamic Nucleus/physiology ; },
abstract = {In parkinsonism, subthalamic nucleus (STN) electrical deep brain stimulation (DBS) improves symptoms, but may be associated with side effects. Adaptive DBS (aDBS), which enables modulation of stimulation, may limit side effects, but limited information is available about clinical effectiveness and efficaciousness. We developed a brain-machine interface for aDBS, which enables modulation of stimulation parameters of STN-DBS in response to γ2 band activity (80-200 Hz) of local field potentials (LFPs) recorded from the primary motor cortex (M1), and tested its effectiveness in parkinsonian monkeys. We trained two monkeys to perform an upper limb reaching task and rendered them parkinsonian with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Bipolar intracortical recording electrodes were implanted in the M1, and a recording chamber was attached to access the STN. In aDBS, the M1 LFPs were recorded, filtered into the γ2 band, and discretized into logic pulses by a window discriminator, and the pulses were used to modulate the interval and amplitude of DBS pulses. In constant DBS (cDBS), constant stimulus intervals and amplitudes were used. Reaction and movement times during the task were measured and compared between aDBS and cDBS. The M1-γ2 activities were increased before and during movements in parkinsonian monkeys and these activities modulated the aDBS pulse interval, amplitude, and dispersion. With aDBS and cDBS, reaction and movement times were significantly decreased in comparison to DBS-OFF. The electric charge delivered was lower with aDBS than cDBS. M1-γ2 aDBS in parkinsonian monkeys resulted in clinical benefits that did not exceed those from cDBS. However, M1-γ2 aDBS achieved this magnitude of benefit for only two thirds of the charge delivered by cDBS. In conclusion, M1-γ2 aDBS is an effective therapeutic approach which requires a lower electrical charge delivery than cDBS for comparable clinical benefits.},
}
@article {pmid35443233,
year = {2022},
author = {Pulferer, HS and Ásgeirsdóttir, B and Mondini, V and Sburlea, AI and Müller-Putz, GR},
title = {Continuous 2D trajectory decoding from attempted movement: across-session performance in able-bodied and feasibility in a spinal cord injured participant.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac689f},
pmid = {35443233},
issn = {1741-2552},
support = {681231/ERC_/European Research Council/International ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Feasibility Studies ; Humans ; Movement ; *Spinal Cord Injuries ; },
abstract = {Objective. In people with a cervical spinal cord injury (SCI) or degenerative diseases leading to limited motor function, restoration of upper limb movement has been a goal of the brain-computer interface field for decades. Recently, research from our group investigated non-invasive and real-time decoding of continuous movement in able-bodied participants from low-frequency brain signals during a target-tracking task. To advance our setup towards motor-impaired end users, we consequently chose a new paradigm based on attempted movement.Approach. Here, we present the results of two studies. During the first study, data of ten able-bodied participants completing a target-tracking/shape-tracing task on-screen were investigated in terms of improvements in decoding performance due to user training. In a second study, a spinal cord injured participant underwent the same tasks. To investigate the merit of employing attempted movement in end users with SCI, data of the spinal cord injured participant were recorded twice; once within an observation-only condition, and once while simultaneously attempting movement.Main results. We observed mean correlations well above chance level for continuous motor decoding based on attempted movement in able-bodied participants. Additionally, no global improvement over three sessions within five days, both in sensor and in source space, could be observed across all participants and movement parameters. In the participant with SCI, decoding performance well above chance was found.Significance. No presence of a learning effect in continuous attempted movement decoding in able-bodied participants could be observed. In contrast, non-significantly varying decoding patterns may promote the use of source space decoding in terms of generalized decoders utilizing transfer learning. Furthermore, above-chance correlations for attempted movement decoding ranging between those of observation only and executed movement were seen in one spinal cord injured participant, suggesting attempted movement decoding as a possible link between feasibility studies in able-bodied and actual applications in motor impaired end users.},
}
@article {pmid35442890,
year = {2022},
author = {Lim, H and Kim, S and Ku, J},
title = {Distraction Classification During Target Tracking Tasks Involving Target and Cursor Flickering Using EEGNet.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1113-1119},
doi = {10.1109/TNSRE.2022.3168829},
pmid = {35442890},
issn = {1558-0210},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Keeping patients from being distracted while performing motor rehabilitation is important. An EEG-based biofeedback strategy has been introduced to help encourage participants to focus their attention on rehabilitation tasks. Here, we suggest a BCI-based monitoring method using a flickering cursor and target that can evoke a steady-state visually evoked potential (SSVEP) using the fact that the SSVEP is modulated by a patient's attention. Fifteen healthy individuals performed a tracking task where the target and cursor flickered. There were two tracking sessions, one with and one without flickering stimuli, and each session had four conditions in which each had no distractor (non-D), a visual (vis-D) or cognitive distractor (cog-D), and both distractors (both-D). An EEGNet was trained as a classifier using only non-D and both-D conditions to classify whether it was distracted and validated with a leave-one-subject-out scheme. The results reveal that the proposed classifier demonstrates superior performance when using data from the task with the flickering stimuli compared to the case without the flickering stimuli. Furthermore, the observed classification likelihood was between those corresponding to the non-D and both-D when using the trained EEGNet. This suggests that the classifier trained for the two conditions could also be used to measure the level of distraction by windowing and averaging the outcomes. Therefore, the proposed method is advantageous because it can reveal a robust and continuous level of patient distraction. This facilitates its successful application to the rehabilitation systems that use computerized technology, such as virtual reality to encourage patient engagement.},
}
@article {pmid35441936,
year = {2022},
author = {Wilson, BS and Tucci, DL and Moses, DA and Chang, EF and Young, NM and Zeng, FG and Lesica, NA and Bur, AM and Kavookjian, H and Mussatto, C and Penn, J and Goodwin, S and Kraft, S and Wang, G and Cohen, JM and Ginsburg, GS and Dawson, G and Francis, HW},
title = {Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.},
journal = {Journal of the Association for Research in Otolaryngology : JARO},
volume = {23},
number = {3},
pages = {319-349},
pmid = {35441936},
issn = {1438-7573},
support = {R03 CA253212/CA/NCI NIH HHS/United States ; R21 DC016069/DC/NIDCD NIH HHS/United States ; },
mesh = {*Artificial Intelligence ; Communication ; Humans ; *Otolaryngology ; },
abstract = {Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.},
}
@article {pmid35439750,
year = {2022},
author = {Gu, H and Yao, Q and Chen, H and Ding, Z and Zhao, X and Liu, H and Feng, Y and Li, C and Li, X},
title = {The effect of mental schema evolution on mental workload measurement: an EEG study with simulated quadrotor UAV operation.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac6828},
pmid = {35439750},
issn = {1741-2552},
mesh = {*Electroencephalography/methods ; Humans ; Mental Processes ; *Workload/psychology ; },
abstract = {Objective. Mental workload is the result of the interactions between the demands of an operation task, the environment in which the task is performed, and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions, judgments and motor actions while operating an unmanned aerial vehicle (UAV). However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing.Approach. We recruited 51 students to participate in a 10-day simulated quadrotor UAV flight training exercise. The EEG power spectral density (PSD)-based metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution.Main results. It was found that the mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD, which are EEG indicators of mental workload. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed.Significance. Our results describe the relationship between the EEG PSD and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG activity can be used to identify the mental schema and mental workload experienced by operators while performing a task, which can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.},
}
@article {pmid35439124,
year = {2022},
author = {Xia, K and Deng, L and Duch, W and Wu, D},
title = {Privacy-Preserving Domain Adaptation for Motor Imagery-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {11},
pages = {3365-3376},
doi = {10.1109/TBME.2022.3168570},
pmid = {35439124},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Privacy ; Electroencephalography/methods ; Imagination ; Algorithms ; },
abstract = {OBJECTIVE: Electroencephalogram (EEG) is one of the most widely used signals in motor imagery (MI) based brain-computer interfaces (BCIs). Domain adaptation has been frequently used to improve the accuracy of EEG-based BCIs for a new user (target domain), by making use of labeled data from a previous user (source domain). However, this raises privacy concerns, as EEG contains sensitive health and mental information. It is very important to perform privacy-preserving domain adaptation, which simultaneously improves the classification accuracy for a new user and protects the privacy of a previous user.<br><br>METHODS: We propose augmentation-based source-free adaptation (ASFA), which consists of two parts: 1) source model training, where a novel data augmentation approach is proposed for MI EEG signals to improve the cross-subject generalization performance of the source model; and, 2) target model training, which simultaneously considers uncertainty reduction for domain adaptation and consistency regularization for robustness. ASFA only needs access to the source model parameters, instead of the raw EEG data, thus protecting the privacy of the source domain. We further extend ASFA to a stricter privacy-preserving scenario, where the source model's parameters are also inaccessible.<br><br>RESULTS: Experimental results on four MI datasets demonstrated that ASFA outperformed 15 classical and state-of-the-art MI classification approaches.<br><br>SIGNIFICANCE: This is the first work on completely source-free domain adaptation for EEG-based BCIs. Our proposed ASFA achieves high classification accuracy and strong privacy protection simultaneously, important for the commercial applications of EEG-based BCIs.},
}
@article {pmid35439025,
year = {2022},
author = {Andre, F and Ismaila, N and Allison, KH and Barlow, WE and Collyar, DE and Damodaran, S and Henry, NL and Jhaveri, K and Kalinsky, K and Kuderer, NM and Litvak, A and Mayer, EL and Pusztai, L and Raab, R and Wolff, AC and Stearns, V},
title = {Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update.},
journal = {Journal of clinical oncology : official journal of the American Society of Clinical Oncology},
volume = {40},
number = {16},
pages = {1816-1837},
doi = {10.1200/JCO.22.00069},
pmid = {35439025},
issn = {1527-7755},
support = {U10 CA180819/CA/NCI NIH HHS/United States ; U10 CA180888/CA/NCI NIH HHS/United States ; P30 CA008748/CA/NCI NIH HHS/United States ; },
mesh = {Biomarkers, Tumor/genetics ; *Breast Neoplasms/drug therapy/genetics/metabolism ; Chemotherapy, Adjuvant/methods ; Female ; Humans ; Middle Aged ; Prospective Studies ; Receptor, ErbB-2/genetics ; Retrospective Studies ; *Triple Negative Breast Neoplasms/drug therapy ; },
abstract = {PURPOSE: To update recommendations on appropriate use of breast cancer biomarker assay results to guide adjuvant endocrine and chemotherapy decisions in early-stage breast cancer.<br><br>METHODS: An updated literature search identified randomized clinical trials and prospective-retrospective studies published from January 2016 to October 2021. Outcomes of interest included overall survival and disease-free or recurrence-free survival. Expert Panel members used informal consensus to develop evidence-based recommendations.<br><br>RESULTS: The search identified 24 studies informing the evidence base.<br><br>RECOMMENDATIONS: Clinicians may use Oncotype DX, MammaPrint, Breast Cancer Index (BCI), and EndoPredict to guide adjuvant endocrine and chemotherapy in patients who are postmenopausal or age > 50 years with early-stage estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+ and HER2-) breast cancer that is node-negative or with 1-3 positive nodes. Prosigna and BCI may be used in postmenopausal patients with node-negative ER+ and HER2- breast cancer. In premenopausal patients, clinicians may use Oncotype in patients with node-negative ER+ and HER2- breast cancer. Current data suggest that premenopausal patients with 1-3 positive nodes benefit from chemotherapy regardless of genomic assay result. There are no data on use of genomic tests to guide adjuvant chemotherapy in patients with &#x2265; 4 positive nodes. Ki67 combined with other parameters or immunohistochemistry 4 score may be used in postmenopausal patients without access to genomic tests to guide adjuvant therapy decisions. BCI may be offered to patients with 0-3 positive nodes who received 5 years of endocrine therapy without evidence of recurrence to guide decisions about extended endocrine therapy. None of the assays are recommended for treatment guidance in individuals with HER2-positive or triple-negative breast cancer. Treatment decisions should also consider disease stage, comorbidities, and patient preferences.Additional information is available at www.asco.org/breast-cancer-guidelines.},
}
@article {pmid35434211,
year = {2022},
author = {du Bois, N and Bigirimana, AD and Korik, A and Kéthina, LG and Rutembesa, E and Mutabaruka, J and Mutesa, L and Prasad, G and Jansen, S and Coyle, D},
title = {Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity.},
journal = {Data in brief},
volume = {42},
number = {},
pages = {108066},
pmid = {35434211},
issn = {2352-3409},
abstract = {The datasets described here comprise electroencephalography (EEG) data and psychometric data freely available on data.mendeley.com. The EEG data is available in .mat formatted files containing the EEG signal values structured in two-dimensional (2D) matrices, with channel data and trigger information in rows, and samples in columns (having a sampling rate of 250Hz). Twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, underwent a psychological assessment before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The pre- and post- intervention psychometric data were analysed using non-parametric statistical methods and the post-intervention data were further evaluated according to diagnostic assessment rules to determine clinically relevant improvements for each group. The participants were assigned to a control group (CG, n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received Brain-Computer Interface (BCI) based training as a treatment intervention over a sixteen-day period, between the pre- and post- clinical interviews. The training involved presenting feedback visually via a videogame, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.},
}
@article {pmid35433527,
year = {2022},
author = {Maghsoudi, A and Shalbaf, A},
title = {Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {12},
number = {2},
pages = {161-170},
pmid = {35433527},
issn = {2251-7200},
abstract = {BACKGROUND: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered.<br><br>OBJECTIVE: This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively.<br><br>MATERIAL AND METHODS: In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal-Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification.<br><br>RESULTS: The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8-12 Hz) - Beta1 (12 - 15 Hz) frequency band using GPDC method.<br><br>CONCLUSION: This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.},
}
@article {pmid35431850,
year = {2022},
author = {Ren, B and Yang, K and Zhu, L and Hu, L and Qiu, T and Kong, W and Zhang, J},
title = {Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {785397},
pmid = {35431850},
issn = {1662-5188},
abstract = {Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500-800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.},
}
@article {pmid35431792,
year = {2022},
author = {Lin, Q and Zhang, Y and Zhang, Y and Zhuang, W and Zhao, B and Ke, X and Peng, T and You, T and Jiang, Y and Yilifate, A and Huang, W and Hou, L and You, Y and Huai, Y and Qiu, Y and Zheng, Y and Ou, H},
title = {The Frequency Effect of the Motor Imagery Brain Computer Interface Training on Cortical Response in Healthy Subjects: A Randomized Clinical Trial of Functional Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {810553},
pmid = {35431792},
issn = {1662-4548},
abstract = {BACKGROUND: The motor imagery brain computer interface (MI-BCI) is now available in a commercial product for clinical rehabilitation. However, MI-BCI is still a relatively new technology for commercial rehabilitation application and there is limited prior work on the frequency effect. The MI-BCI has become a commercial product for clinical neurological rehabilitation, such as rehabilitation for upper limb motor dysfunction after stroke. However, the formulation of clinical rehabilitation programs for MI-BCI is lack of scientific and standardized guidance, especially limited prior work on the frequency effect. Therefore, this study aims at clarifying how frequency effects on MI-BCI training for the plasticity of the central nervous system.<br><br>METHODS: Sixteen young healthy subjects (aged 22.94 &#xb1; 3.86 years) were enrolled in this randomized clinical trial study. Subjects were randomly assigned to a high frequency group (HF group) and low frequency group (LF group). The HF group performed MI-BCI training once per day while the LF group performed once every other day. All subjects performed 10 sessions of MI-BCI training. functional near-infrared spectroscopy (fNIRS) measurement, Wolf Motor Function Test (WMFT) and brain computer interface (BCI) performance were assessed at baseline, mid-assessment (after completion of five BCI training sessions), and post-assessment (after completion of 10 BCI training sessions).<br><br>RESULTS: The results from the two-way ANOVA of beta values indicated that GROUP, TIME, and GROUP &#xd7; TIME interaction of the right primary sensorimotor cortex had significant main effects [GROUP: F (1,14) = 7.251, P = 0.010; TIME: F (2,13) = 3.317, P = 0.046; GROUP &#xd7; TIME: F (2,13) = 5.676, P = 0.007]. The degree of activation was affected by training frequency, evaluation time point and interaction. The activation of left primary sensory motor cortex was also affected by group (frequency) (P = 0.003). Moreover, the TIME variable was only significantly different in the HF group, in which the beta value of the mid-assessment was higher than that of both the baseline assessment (P = 0.027) and post-assessment (P = 0.001), respectively. Nevertheless, there was no significant difference in the results of WMFT between HF group and LF group.<br><br>CONCLUSION: The major results showed that more cortical activation and better BCI performance were found in the HF group relative to the LF group. Moreover, the within-group results also showed more cortical activation after five sessions of BCI training and better BCI performance after 10 sessions in the HF group, but no similar effects were found in the LF group. This pilot study provided an essential reference for the formulation of clinical programs for MI-BCI training in improvement for upper limb dysfunction.},
}
@article {pmid35431612,
year = {2022},
author = {Prasad, DS and Chanamallu, SR and Prasad, KS},
title = {Optimized deformable convolution network for detection and mitigation of ocular artifacts from EEG signal.},
journal = {Multimedia tools and applications},
volume = {81},
number = {21},
pages = {30841-30879},
pmid = {35431612},
issn = {1380-7501},
abstract = {Electroencephalogram (EEG) is the key component in the field of analyzing brain activity and behavior. EEG signals are affected by artifacts in the recorded electrical activity; thereby it affects the analysis of EGG. To extract the clean data from EEG signals and to improve the efficiency of detection during encephalogram recordings, a developed model is required. Although various methods have been proposed for the artifacts removal process, sill the research on this process continues. Even if, several types of artifacts from both the subject and equipment interferences are highly contaminated the EEG signals, the most common and important type of interferences is known as Ocular artifacts. Many applications like Brain-Computer Interface (BCI) need online and real-time processing of EEG signals. Hence, it is best if the removal of artifacts is performed in an online fashion. The main intention of this proposal is to accomplish the new deep learning-based ocular artifacts detection and prevention model. In the detection phase, the 5-level Discrete Wavelet Transform (DWT), and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the Principle Component Analysis (PCA) and Independent Component Analysis (ICA) are adopted as the techniques for extracting the features. With the collected features, the development of optimized Deformable Convolutional Networks (DCN) is used for the detection of ocular artifacts from the input EEG signal. Here, the optimized DCN is developed by optimizing or tuning some significant parameters by Distance Sorted-Electric Fish Optimization (DS-EFO). If the artifacts are detected, the mitigation process is performed by applying the Empirical Mean Curve Decomposition (EMCD), and then, the optimized DCN is used for denoising the signals. Finally, the clean signal is generated by applying inverse EMCD. Based on the EEG data collected from diverse subjects, the proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular-artifact reduction by the proposed method.},
}
@article {pmid35430825,
year = {2022},
author = {Liu, H and Gao, Y and Huang, W and Li, R and Houston, M and Benoit, JS and Roh, J and Zhang, Y},
title = {Inter-muscular coherence and functional coordination in the human upper extremity after stroke.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {19},
number = {5},
pages = {4506-4525},
doi = {10.3934/mbe.2022208},
pmid = {35430825},
issn = {1551-0018},
mesh = {Electromyography ; Humans ; *Muscle, Skeletal ; Shoulder ; *Stroke ; Upper Extremity ; },
abstract = {Muscle coordination and motor function of stroke patients are weakened by stroke-related motor impairments. Our earlier studies have determined alterations in inter-muscular coordination patterns (muscle synergies). However, the functional connectivity of these synergistically paired or unpaired muscles is still unclear in stroke patients. The goal of this study is to quantify the alterations of inter-muscular coherence (IMC) among upper extremity muscles that have been shown to be synergistically or non-synergistically activated in stroke survivors. In a three-dimensional isometric force matching task, surface EMG signals are collected from 6 age-matched, neurologically intact healthy subjects and 10 stroke patients, while the target force space is divided into 8 subspaces. According to the results of muscle synergy identification with non-negative matrix factorization algorithm, muscle pairs are classified as synergistic and non-synergistic. In both control and stroke groups, IMC is then calculated for all available muscle pairs. The results show that synergistic muscle pairs have higher coherence in both groups. Furthermore, anterior and middle deltoids, identified as synergistic muscles in both groups, exhibited significantly weaker IMC at alpha band in stroke patients. The anterior and posterior deltoids, identified as synergistic muscles only in stroke patients, revealed significantly higher IMC in stroke group at low gamma band. On the contrary, anterior deltoid and pectoralis major, identified as synergistic muscles in control group only, revealed significantly higher IMC in control group in alpha band. The results of muscle synergy and IMC analyses provide congruent and complementary information for investigating the mechanism that underlies post-stroke motor recovery.},
}
@article {pmid35430495,
year = {2022},
author = {Zhang, P and Tang, A and Geng, Y and Lai, J and Gao, X and Pan, Y and Huang, H and Jiang, J and Zhang, D and Xi, C and Wu, L and Hu, S},
title = {Gut microbial trajectory in patients with bipolar depression: A longitudinal study.},
journal = {Asian journal of psychiatry},
volume = {73},
number = {},
pages = {103098},
doi = {10.1016/j.ajp.2022.103098},
pmid = {35430495},
issn = {1876-2026},
mesh = {*Bipolar Disorder ; Depression ; *Gastrointestinal Microbiome ; Humans ; Longitudinal Studies ; },
}
@article {pmid35427686,
year = {2022},
author = {Sun, Q and Zheng, L and Pei, W and Gao, X and Wang, Y},
title = {A 120-target brain-computer interface based on code-modulated visual evoked potentials.},
journal = {Journal of neuroscience methods},
volume = {375},
number = {},
pages = {109597},
doi = {10.1016/j.jneumeth.2022.109597},
pmid = {35427686},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation/methods ; },
abstract = {BACKGROUND: In recent years, numerous studies on the brain-computer interface (BCI) have been published. However, the number of targets in most of the existing studies was not enough for many practical applications.<br><br>NEW METHOD: To achieve highly efficient communications, this study proposed a 120-target BCI system based on code-modulated visual evoked potentials (c-VEPs). Four 31-bit pseudorandom codes were used, and each code generated 30 targets by cyclic shift with a lag of 1&#xa0;bit.<br><br>RESULTS: In the online experiments, subjects could select one target in 1.04&#xa0;s (0.52&#xa0;s for stimulation and 0.52&#xa0;s for gaze shifting) with an average information transfer rate (ITR) of 265.74 bits/min.<br><br>The proposed system achieved more targets and higher ITR than other recent c-VEP based studies. which attributes to the optimal code combination and the 1-bit lag.<br><br>CONCLUSION: The results illustrate that the proposed BCI system can achieve a high ITR with a short stimulation time. In addition, the c-VEP paradigm can shorten the training time, which ensures practicality in real applications.},
}
@article {pmid35426262,
year = {2022},
author = {Lin, X and Sun, T and Tang, M and Yang, A and Yan-Do, R and Chen, D and Gao, Y and Duan, X and Kai, JJ and Wang, F and Shi, P},
title = {3D Upconversion Barcodes for Combinatory Wireless Neuromodulation in Behaving Animals.},
journal = {Advanced healthcare materials},
volume = {11},
number = {13},
pages = {e2200304},
doi = {10.1002/adhm.202200304},
pmid = {35426262},
issn = {2192-2659},
mesh = {Animals ; Brain/physiology ; *Deep Brain Stimulation ; Neurons/physiology ; *Optogenetics/methods ; Wireless Technology ; },
abstract = {Upconversion techniques offer all-optical wireless alternatives to modulate targeted neurons in behaving animals, but most existing upconversion-based optogenetic devices show prefixed emission that is used to excite just one channelrhodopsin at a restricted brain region. Here, a hierarchical upconversion device is reported to enable spatially selective and combinatory optogenetics in behaving rodent animals. The device assumes a multiarrayed optrode format containing engineered upconversion nanoparticles (UCNPs) to deliver dynamic light palettes as a function of excitation wavelength. Three primary emissions at 477, 540, and 654 nm are selected to match the absorption of different channelrhodopsins. The UCNPs are barcode assembled to multiple nanomachined optical pinholes in a microscale pipette device to allow remotely addressable, spectrum programmable, and spatially selective optical interrogation of complex brain circuits. Using the unique device, the basolateral amygdala and caudoputamen circuits are selectively modulated and the associated fear or anxiety behavior in freely behaving rodents is successfully differentiated. It is believed that the 3D barcode upconversion device would be a great supplement to current optogenetic toolsets and opens up new possibilities for sophisticated neural control.},
}
@article {pmid35422693,
year = {2022},
author = {Peng, Y and Wang, J and Liu, Z and Zhong, L and Wen, X and Wang, P and Gong, X and Liu, H},
title = {The Application of Brain-Computer Interface in Upper Limb Dysfunction After Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {798883},
pmid = {35422693},
issn = {1662-5161},
abstract = {OBJECTIVE: This study aimed to examine the effectiveness and safety of the Brain-computer interface (BCI) in treatment of upper limb dysfunction after stroke.<br><br>METHODS: English and Chinese electronic databases were searched up to July 2021. Randomized controlled trials (RCTs) were eligible. The methodological quality was assessed using Cochrane's risk-of-bias tool. Meta-analysis was performed using RevMan 5.4.<br><br>RESULTS: A total of 488 patients from 16 RCTs were included. The results showed that (1) the meta-analysis of BCI-combined treatment on the improvement of the upper limb function showed statistical significance [standardized mean difference (SMD): 0.53, 95% CI: 0.26-0.80, P < 0.05]; (2) BCI treatment can improve the abilities of daily living of patients after stroke, and the analysis results are statistically significant (SMD: 1.67, 95% CI: 0.61-2.74, P < 0.05); and (3) the BCI-combined therapy was not statistically significant for the analysis of the Modified Ashworth Scale (MAS) (SMD: -0.10, 95% CI: -0.50 to 0.30, P = 0.61).<br><br>CONCLUSION: The meta-analysis indicates that the BCI therapy or BCI combined with other therapies such as conventional rehabilitation training and motor imagery training can improve upper limb dysfunction after stroke and enhance the quality of daily life.},
}
@article {pmid35421857,
year = {2022},
author = {Hammer, J and Schirrmeister, RT and Hartmann, K and Marusic, P and Schulze-Bonhage, A and Ball, T},
title = {Interpretable functional specialization emerges in deep convolutional networks trained on brain signals.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ac6770},
pmid = {35421857},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; },
abstract = {Objective.Functional specialization is fundamental to neural information processing. Here, we study whether and how functional specialization emerges in artificial deep convolutional neural networks (CNNs) during a brain-computer interfacing (BCI) task.Approach.We trained CNNs to predict hand movement speed from intracranial electroencephalography (iEEG) and delineated how units across the different CNN hidden layers learned to represent the iEEG signal.Main results.We show that distinct, functionally interpretable neural populations emerged as a result of the training process. While some units became sensitive to either iEEG amplitude or phase, others showed bimodal behavior with significant sensitivity to both features. Pruning of highly sensitive units resulted in a steep drop of decoding accuracy not observed for pruning of less sensitive units, highlighting the functional relevance of the amplitude- and phase-specialized populations.Significance.We anticipate that emergent functional specialization as uncovered here will become a key concept in research towards interpretable deep learning for neuroscience and BCI applications.},
}
@article {pmid35421817,
year = {2022},
author = {Zhao, S and Guan, W and Qi, G and Li, P},
title = {Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task.},
journal = {Accident; analysis and prevention},
volume = {171},
number = {},
pages = {106665},
doi = {10.1016/j.aap.2022.106665},
pmid = {35421817},
issn = {1879-2057},
mesh = {*Accidents, Traffic ; *Automobile Driving ; Electroencephalography ; Fatigue ; Humans ; },
abstract = {Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed &amp; strong-oscillation type, high-speed &amp; strong-steering type, and high-speed &amp; close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.},
}
@article {pmid35420985,
year = {2022},
author = {Sadatnejad, K and Lotte, F},
title = {Riemannian Channel Selection for BCI With Between-Session Non-Stationarity Reduction Capabilities.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1158-1171},
doi = {10.1109/TNSRE.2022.3167262},
pmid = {35420985},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; },
abstract = {OBJECTIVE: Between-session non-stationarity is a major challenge of current Brain-Computer Interfaces (BCIs) that affects system performance. In this paper, we investigate the use of channel selection for reducing between-session non-stationarity with Riemannian BCI classifiers. We use the Riemannian geometry framework of covariance matrices due to its robustness and promising performances. Current Riemannian channel selection methods do not consider between-session non-stationarity and are usually tested on a single session. Here, we propose a new channel selection approach that specifically considers non-stationarity effects and is assessed on multi-session BCI data sets.<br><br>METHODS: We remove the least significant channels using a sequential floating backward selection search strategy. Our contributions include: 1) quantifying the non-stationarity effects on brain activity in multi-class problems by different criteria in a Riemannian framework and 2) a method to predict whether BCI performance can improve using channel selection.<br><br>RESULTS: We evaluate the proposed approaches on three multi-session and multi-class mental tasks (MT)-based BCI datasets. They could lead to significant improvements in performance as compared to using all channels for datasets affected by between-session non-stationarity and to significant superiority to the state-of-the-art Riemannian channel selection methods over all datasets, notably when selecting small channel set sizes.<br><br>CONCLUSION: Reducing non-stationarity by channel selection could significantly improve Riemannian BCI classification accuracy.<br><br>SIGNIFICANCE: Our proposed channel selection approach contributes to make Riemannian BCI classifiers more robust to between-session non-stationarities.},
}
@article {pmid35420003,
year = {2022},
author = {Fu, Y and Wang, F and Li, Y and Gong, A and Qian, Q and Su, L and Zhao, L},
title = {Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {67},
number = {3},
pages = {173-183},
doi = {10.1515/bmt-2021-0422},
pmid = {35420003},
issn = {1862-278X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; *Imagination ; Movement ; Spectroscopy, Near-Infrared ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.},
}
@article {pmid35419919,
year = {2022},
author = {Pu, J and Zhou, X and Ullah, R and Dong, G and Wu, W and Huang, K and Chen, X and Fu, J},
title = {Optimized simplified pediatric diabetes severity warning system for the early identification of diabetic ketoacidosis in children.},
journal = {Pediatric diabetes},
volume = {23},
number = {5},
pages = {569-577},
doi = {10.1111/pedi.13345},
pmid = {35419919},
issn = {1399-5448},
mesh = {Child ; Cohort Studies ; *Diabetes Mellitus, Type 1/complications/diagnosis/epidemiology ; *Diabetic Ketoacidosis/diagnosis/epidemiology/etiology ; Hospitalization ; Humans ; Incidence ; Retrospective Studies ; },
abstract = {OBJECTIVE: Diabetic ketoacidosis (DKA) is the leading cause of mortality in children with type 1 diabetes. Diagnosis of DKA is difficult in resource-limited areas owing to the unavailability of blood gas test, the gold standard for DKA diagnosis. The Simplified Pediatric Diabetes Severity Warning System (SPDSWS) has been developed to identify high-risk DKA patients with limited resources in China. Here we optimized and validated this system.<br><br>METHODS: This study included 835 children admitted between January 2011 and June 2020 with the principal diagnosis of type 1 diabetes. Data were collected based on demographic and clinical characteristics. DKA and its severity were defined according to the criteria of ISPAD. SPDSWS was optimized based on logistic regression analyses and then was validated in a validation cohort.<br><br>RESULTS: The 20-point optimized SPDSWS included strong positive urine ketone, young age, dehydration, fatigue, anorexia, vomiting, abdominal pain, abnormal pulse, and high blood glucose. The optimized SPDSWS predicted DKA with an AUC value of 0.882 in the derivation cohort. When the cut-point score&#x2009;&#x2265;7 was used, the sensitivity and specificity were 75.5% and 86.0%, respectively, in the derivation cohort and were 90.0% and 85.8%, respectively, in the validation cohort. The optimized SPDSWS also predicted the moderate/severe DKA with an AUC value of 0.911 in the derivation cohort and 0.937 in the validation cohort. A score&#x2009;>&#x2009;11 was associated with an extremely high incidence of DKA.<br><br>CONCLUSIONS: The optimized SPDSWS could assist health care practitioners in underdeveloped remote areas to identify the children at high risk of DKA as early as on admission.},
}
@article {pmid35418848,
year = {2022},
author = {Jiang, Y and Jessee, W and Hoyng, S and Borhani, S and Liu, Z and Zhao, X and Price, LK and High, W and Suhl, J and Cerel-Suhl, S},
title = {Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?.},
journal = {Frontiers in aging neuroscience},
volume = {14},
number = {},
pages = {780817},
pmid = {35418848},
issn = {1663-4365},
support = {I21 RX003173/RX/RRD VA/United States ; R56 AG060608/AG/NIA NIH HHS/United States ; },
abstract = {Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.},
}
@article {pmid35417714,
year = {2022},
author = {Li, KT and He, X and Zhou, G and Yang, J and Li, T and Hu, H and Ji, D and Zhou, C and Ma, H},
title = {Rational designing of oscillatory rhythmicity for memory rescue in plasticity-impaired learning networks.},
journal = {Cell reports},
volume = {39},
number = {2},
pages = {110678},
doi = {10.1016/j.celrep.2022.110678},
pmid = {35417714},
issn = {2211-1247},
mesh = {Animals ; *Hippocampus/physiology ; Humans ; *Interneurons/physiology ; Learning ; Long-Term Potentiation/physiology ; Mice ; Periodicity ; Synapses/physiology ; },
abstract = {In the brain, oscillatory strength embedded in network rhythmicity is important for processing experiences, and this process is disrupted in certain psychiatric disorders. The use of rhythmic network stimuli can change these oscillations and has shown promise in terms of improving cognitive function, although the underlying mechanisms are poorly understood. Here, we combine a two-layer learning model, with experiments involving genetically modified mice, that provides precise control of experience-driven oscillations by manipulating long-term potentiation of excitatory synapses onto inhibitory interneurons (LTPE→I). We find that, in the absence of LTPE→I, impaired network dynamics and memory are rescued by activating inhibitory neurons to augment the power in theta and gamma frequencies, which prevents network overexcitation with less inhibitory rebound. In contrast, increasing either theta or gamma power alone was less effective. Thus, inducing network changes at dual frequencies is involved in memory encoding, indicating a potentially feasible strategy for optimizing network-stimulating therapies.},
}
@article {pmid35415314,
year = {2023},
author = {Jin, L and Zhu, Z and Hong, L and Qian, Z and Wang, F and Mao, Z},
title = {ROS-responsive 18β-glycyrrhetic acid-conjugated polymeric nanoparticles mediate neuroprotection in ischemic stroke through HMGB1 inhibition and microglia polarization regulation.},
journal = {Bioactive materials},
volume = {19},
number = {},
pages = {38-49},
pmid = {35415314},
issn = {2452-199X},
abstract = {Ischemic stroke is an acute and serious cerebral vascular disease, which greatly affects people's health and brings huge economic burden to society. Microglia, as important innate immune components in central nervous system (CNS), are double-edged swords in the battle of nerve injury, considering their polarization between pro-inflammatory M1 or anti-inflammatory M2 phenotypes. High mobility group box 1 (HMGB1) is one of the potent pro-inflammatory mediators that promotes the M1 polarization of microglia. 18β-glycyrrhetinic acid (GA) is an effective intracellular inhibitor of HMGB1, but of poor water solubility and dose-dependent toxicity. To overcome the shortcomings of GA delivery and to improve the efficacy of cerebral ischemia therapy, herein, we designed reactive oxygen species (ROS) responsive polymer-drug conjugate nanoparticles (DGA) to manipulate microglia polarization by suppressing the translocation of nuclear HMGB1. DGA presented excellent therapeutic efficacy in stroke mice, as evidenced by the reduction of infarct volume, recovery of motor function, suppressed of M1 microglia activation and enhanced M2 activation, and induction of neurogenesis. Altogether, our work demonstrates a close association between HMGB1 and microglia polarization, suggesting potential strategies for coping with inflammatory microglia-related diseases.},
}
@article {pmid35413050,
year = {2022},
author = {Korkmaz, OE and Aydemir, O and Oral, EA and Ozbek, IY},
title = {An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation.},
journal = {PloS one},
volume = {17},
number = {4},
pages = {e0265904},
pmid = {35413050},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.},
}
@article {pmid35408306,
year = {2022},
author = {Montoya, D and Barria, P and Cifuentes, CA and Aycardi, LF and Morís, A and Aguilar, R and Azorín, JM and Múnera, M},
title = {Biomechanical Assessment of Post-Stroke Patients' Upper Limb before and after Rehabilitation Therapy Based on FES and VR.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408306},
issn = {1424-8220},
mesh = {Electric Stimulation/methods ; Humans ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation/methods ; Upper Extremity ; *Virtual Reality ; },
abstract = {Stroke is a medical condition characterized by the rapid loss of focal brain function. Post-stroke patients attend rehabilitation training to prevent the degeneration of physical function and improve upper limb movements and functional status after stroke. Promising rehabilitation therapies include functional electrical stimulation (FES), exergaming, and virtual reality (VR). This work presents a biomechanical assessment of 13 post-stroke patients with hemiparesis before and after rehabilitation therapy for two months with these three methods. Patients performed two tests (Maximum Forward Reach and Apley Scratching) where maximum angles, range of motion, angular velocities, and execution times were measured. A Wilcoxon test was performed (p = 0.05) to compare the variables before and after the therapy for paretic and non-paretic limbs. Significant differences were found in range of motion in flexion-extension, adduction-abduction, and internal-external rotation of the shoulder. Increases were found in flexion-extension, 17.98%, and internal-external rotation, 18.12%, after therapy in the Maximum Forward Reach Test. For shoulder adduction-abduction, the increase found was 20.23% in the Apley Scratching Test, supporting the benefits of rehabilitation therapy that combines FES, exergaming, and VR in the literature.},
}
@article {pmid35408190,
year = {2022},
author = {Gulraiz, A and Naseer, N and Nazeer, H and Khan, MJ and Khan, RA and Shahbaz Khan, U},
title = {LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408190},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Spectroscopy, Near-Infrared/methods ; Support Vector Machine ; Walking ; },
abstract = {Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.},
}
@article {pmid35408182,
year = {2022},
author = {Karimi, R and Mohammadi, A and Asif, A and Benali, H},
title = {DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {7},
pages = {},
pmid = {35408182},
issn = {1424-8220},
support = {RGPIN-2016-04988//Natural Sciences and Engineering Research Council/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation/methods ; Rotation ; },
abstract = {Recent advancements in Electroencephalographic (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications, ranging from rehabilitation systems to smart consumer technologies. When it comes to Signal Processing (SP) for BCI, there has been a surge of interest on Steady-State motion Visual Evoked Potentials (SSmVEP), where motion stimulation is used to address key issues associated with conventional light flashing/flickering. Such benefits, however, come with the price of being less accurate and having a lower Information Transfer Rate (ITR). From this perspective, this paper focuses on the design of a novel SSmVEP paradigm without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, called the Dual Frequency Aggregated Steady-State motion Visual Evoked Potential (DF-SSmVEP), by concurrently integrating "Radial Zoom" and "Rotation" motions in a single target without increasing the trial length. Compared to conventional SSmVEPs, the proposed DF-SSmVEP framework consists of two motion modes integrated and shown simultaneously each modulated by a specific target frequency. The paper also develops a specific unsupervised classification model, referred to as the Bifold Canonical Correlation Analysis (BCCA), based on two motion frequencies per target. The corresponding covariance coefficients are used as extra features improving the classification accuracy. The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. The proposed DF-SSmVEP outperforms its counterparts and achieved an average ITR of 30.7 ± 1.97 and an average accuracy of 92.5 ± 2.04, while the Radial Zoom and Rotation result in average ITRs of 18.35 ± 1 and 20.52 ± 2.5, and average accuracies of 68.12 ± 3.5 and 77.5 ± 3.5, respectively.},
}
@article {pmid35406445,
year = {2022},
author = {Asuthkar, S and Venkataraman, S and Avilala, J and Shishido, K and Vibhakar, R and Veo, B and Purvis, IJ and Guda, MR and Velpula, KK},
title = {SMYD3 Promotes Cell Cycle Progression by Inducing Cyclin D3 Transcription and Stabilizing the Cyclin D1 Protein in Medulloblastoma.},
journal = {Cancers},
volume = {14},
number = {7},
pages = {},
pmid = {35406445},
issn = {2072-6694},
abstract = {Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Maximum safe resection, postoperative craniospinal irradiation, and chemotherapy are the standard of care for MB patients. MB is classified into four subgroups: Shh, Wnt, Group 3, and Group 4. Of these subgroups, patients with Myc+ Group 3 MB have the worst prognosis, necessitating alternative therapies. There is increasing interest in targeting epigenetic modifiers for treating pediatric cancers, including MB. Using an RNAi functional genomic screen, we identified the lysine methyltransferase SMYD3, as a crucial epigenetic regulator that drives the growth of Group 3 Myc+ MB cells. We demonstrated that SMYD3 directly binds to the cyclin D3 promoter to activate its transcription. Further, SMYD3 depletion significantly reduced MB cell proliferation and led to the downregulation of cyclin D3, cyclin D1, pRBSer795, with concomitant upregulations in RB in vitro. Similar results were obtained following pharmacological inhibition of SMYD3 using BCI-121 ex vivo. SMYD3 knockdown also promoted cyclin D1 ubiquitination, indicating that SMYD3 plays a vital role in stabilizing the cyclin D1 protein. Collectively, our studies demonstrate that SMYD3 drives cell cycle progression in Group 3 Myc+ MB cells and that targeting SMYD3 has the potential to improve clinical outcomes for high-risk patients.},
}
@article {pmid35405471,
year = {2022},
author = {Sun, B and Wu, Z and Hu, Y and Li, T},
title = {Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {151},
number = {},
pages = {111-120},
doi = {10.1016/j.neunet.2022.03.025},
pmid = {35405471},
issn = {1879-2782},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Electroencephalographic measurement of cortical activity subserving motor behavior varies among different individuals, restricting the potential of brain computer interfaces (BCIs) based on motor imagery (MI). How to deal with this variability and thereby improve the accuracy of BCI classification remains a key issue. This paper proposes a deep learning-based approach to transfer the data distribution from BCI-friendly - "golden subjects" to the data from more typical BCI-illiterate users. In this work, we use the perceptual loss to align the dimensionality-reduced BCI-illiterate data with the data of golden subjects in low dimensions, by which a subject transfer neural network (STNN) is proposed. The network consists of two parts: 1) a generator, which generates the transferred BCI-illiterate features, and 2) a CNN classifier, which is used for the classification of the transferred features, thus outperforming traditional classification methods both in terms of accuracy and robustness. Electroencephalography (EEG) signals from 25 healthy subjects performing MI of the right hand and foot were classified with an average accuracy of 88.2%±5.1%. The proposed model was further validated on the BCI Competition IV dataset 2b, and was demonstrated to be robust to inter-subject variations. The advantages of STNN allow it to bridge the gap between the golden subjects and the BCI-illiterate ones, paving the way to real-time BCI applications.},
}
@article {pmid35405146,
year = {2022},
author = {Campos-Arteaga, G and Araneda, A and Ruiz, S and Rodríguez, E and Sitaram, R},
title = {Classifying brain states and pupillary responses associated with the processing of old and new information.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {176},
number = {},
pages = {129-141},
doi = {10.1016/j.ijpsycho.2022.04.004},
pmid = {35405146},
issn = {1872-7697},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; Mental Recall ; },
abstract = {Memory retrieval of consolidated memories has been extensively studied using "old-new tasks", meaning tasks in which participants are instructed to discriminate between stimuli they have experienced before and new ones. Significant differences in the neural processing of old and new elements have been demonstrated using different techniques, such as electroencephalography and pupillometry. In this work, using the data from a previously published study (Campos-Arteaga, Forcato et al. 2020), we investigated whether machine learning methods can classify, based on single trials, the brain activity and pupil responses associated with the processing of old and new information. Specifically, we used the EEG and pupillary information of 39 participants who completed an associative recall old-new task in which they had to discriminate between previously seen or new pictures and, for the old ones, to recall an associated word. Our analyses corroborated the differences in neural processing of old and new items reported in previous studies. Based on these results, we hypothesized that the application of machine learning methods would allow an optimal classification of old and new conditions. Using a Windowed Means approach (WM) and two different machine learning algorithms - Logistic Regression (WM-LR) and Linear Discriminant Analysis (WM-LDA) - mean classification performances of 0.75 and 0.74 (AUC) were achieved when EEG and pupillary signals were combined to train the models, respectively. In both cases, when the EEG and pupillary data were merged, the performance was significantly better than when they were used separately. In addition, our results showed similar classification performances when fused classification models (i.e., models created with the concatenated information of 38 participants) were applied to individuals whose EEG and pupillary information was not considered for the model training. Similar results were found when alternative preprocessing methods were used. Taken together, these findings show that it is possible to classify the neurophysiological activity associated with the processing of experienced and new stimuli using machine learning techniques. Future research is needed to determine how this knowledge might have potential implications for memory research and clinical practice.},
}
@article {pmid35404821,
year = {2022},
author = {Wang, Q and Liu, F and Wan, G and Chen, Y},
title = {Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {1081-1091},
doi = {10.1109/TNSRE.2022.3166517},
pmid = {35404821},
issn = {1558-0210},
mesh = {Algorithms ; *Anesthesia ; *Anesthetics ; Brain ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Neural Networks, Computer ; },
abstract = {Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We use a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.},
}
@article {pmid35401871,
year = {2022},
author = {Xu, S and Zhu, L and Kong, W and Peng, Y and Hu, H and Cao, J},
title = {A novel classification method for EEG-based motor imagery with narrow band spatial filters and deep convolutional neural network.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {2},
pages = {379-389},
pmid = {35401871},
issn = {1871-4080},
abstract = {UNLABELLED: The Common Spatial Pattern (CSP) algorithm is the most widely used method for decoding Electroencephalography (EEG) signals from motor imagery (MI) paradigm. However, due to the inter-subject variability, the CSP algorithm heavily relies on the selection of filter bands and extensive analytical processing time required to build an effective model, which has been a challenge in current research. In this paper, we propose a narrow filter bank CSP (NFBCSP) algorithm, which automatically determines the optimal narrow band for two-class motor imagery by band search tree, and a high-performance classification model dedicated to each subject can be obtained in a short time for online processing or further offline analysis. The optimal narrow band is combined with the CSP algorithm to extract the dynamic features in the EEG signals. For the multi-class motor imagery task, it is first transformed into multiple One-Versus-Rest (OVR) tasks and determines the corresponding optimal narrow bands. After extracting the features of each optimal narrow band separately, the Deep Convolutional Neural Network (DCNN) is used for the fusion of band features and classification of multi-class motor imagery. Finally, we verified our method using two different motor imagery datasets, the BCI-VR dataset with two classes of motor imagery and the BCI Competition IV dataset 2a with four classes of motor imagery. The experimental results show that the proposed method achieves an average classification accuracy of 86.43% for the two-class motor imagery task, and 76.87% for the four-class motor imagery task, which outperforms other recent methods.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09721-x.},
}
@article {pmid35401095,
year = {2022},
author = {Zhang, P and Kong, L and Huang, H and Pan, Y and Zhang, D and Jiang, J and Shen, Y and Xi, C and Lai, J and Ng, CH and Hu, S},
title = {Gut Microbiota - A Potential Contributor in the Pathogenesis of Bipolar Disorder.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {830748},
pmid = {35401095},
issn = {1662-4548},
abstract = {Bipolar disorder (BD) is one of the major psychiatric disorders that is characterized by recurrent episodes of depression and mania (or hypomania), leading to seriously adverse outcomes with unclear pathogenesis. There is an underlying relationship between bacterial communities residing in the gut and brain function, which together form the gut-brain axis (GBA). Recent studies have shown that changes in the gut microbiota have been observed in a large number of BD patients, so the axis may play a role in the pathogenesis of BD. This review summarizes briefly the relationship between the GBA and brain function, the composition and changes of gut microbiota in patients with BD, and further explores the potential role of GBA-related pathway in the pathogenesis of BD as well as the limitations in this field at present in order to provide new ideas for the future etiology research and drug development.},
}
@article {pmid35401092,
year = {2022},
author = {Choi, SI and Lee, JY and Lim, KM and Hwang, HJ},
title = {Evaluation of Real-Time Endogenous Brain-Computer Interface Developed Using Ear-Electroencephalography.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {842635},
pmid = {35401092},
issn = {1662-4548},
abstract = {While previous studies have demonstrated the feasibility of using ear-electroencephalography (ear-EEG) for the development of brain-computer interfaces (BCIs), most of them have been performed using exogenous paradigms in offline environments. To verify the reliable feasibility of constructing ear-EEG-based BCIs, the feasibility of using ear-EEG should be further demonstrated using another BCI paradigm, namely the endogenous paradigm, in real-time online environments. Exogenous and endogenous BCIs are to use the EEG evoked by external stimuli and induced by self-modulation, respectively. In this study, we investigated whether an endogenous ear-EEG-based BCI with reasonable performance can be implemented in online environments that mimic real-world scenarios. To this end, we used three different mental tasks, i.e., mental arithmetic, word association, and mental singing, and performed BCI experiments with fourteen subjects on three different days to investigate not only the reliability of a real-time endogenous ear-EEG-based BCI, but also its test-retest reliability. The mean online classification accuracy was almost 70%, which was equivalent to a marginal accuracy for a practical two-class BCI (70%), demonstrating the feasibility of using ear-EEG for the development of real-time endogenous BCIs, but further studies should follow to improve its performance enough to be used for practical ear-EEG-based BCI applications.},
}
@article {pmid35399917,
year = {2022},
author = {Pan, K and Li, L and Zhang, L and Li, S and Yang, Z and Guo, Y},
title = {A Noninvasive BCI System for 2D Cursor Control Using a Spectral-Temporal Long Short-Term Memory Network.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {799019},
pmid = {35399917},
issn = {1662-5188},
abstract = {Two-dimensional cursor control is an important and challenging problem in the field of electroencephalography (EEG)-based brain computer interfaces (BCIs) applications. However, most BCIs based on categorical outputs are incapable of generating accurate and smooth control trajectories. In this article, a novel EEG decoding framework based on a spectral-temporal long short-term memory (stLSTM) network is proposed to generate control signals in the horizontal and vertical directions for accurate cursor control. Precisely, the spectral information is used to decode the subject's motor imagery intention, and the error-related P300 information is used to detect a deviation in the movement trajectory. The concatenated spectral and temporal features are fed into the stLSTM network and mapped to the velocities in vertical and horizontal directions of the 2D cursor under the velocity-constrained (VC) strategy, which enables the decoding network to fit the velocity in the imaginary direction and simultaneously suppress the velocity in the non-imaginary direction. This proposed framework was validated on a public real BCI control dataset. Results show that compared with the state-of-the-art method, the RMSE of the proposed method in the non-imaginary directions on the testing sets of 2D control tasks is reduced by an average of 63.45%. Besides, the visualization of the actual trajectories distribution of the cursor also demonstrates that the decoupling of velocity is capable of yielding accurate cursor control in complex path tracking tasks and significantly improves the control accuracy.},
}
@article {pmid35399355,
year = {2022},
author = {Müller-Putz, GR and Coyle, D and Lotte, F and Jin, J and Steyrl, D},
title = {Editorial: Long Term User Training and Preparation to Succeed in a Closed-Loop BCI Competition.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {869700},
pmid = {35399355},
issn = {1662-5161},
}
@article {pmid35398087,
year = {2022},
author = {Liu, C and Jin, J and Daly, I and Sun, H and Huang, Y and Wang, X and Cichocki, A},
title = {Bispectrum-based hybrid neural network for motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {375},
number = {},
pages = {109593},
doi = {10.1016/j.jneumeth.2022.109593},
pmid = {35398087},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND: The performance of motor imagery electroencephalogram (MI-EEG) decoding systems is easily affected by noise. As a higher-order spectra (HOS), the bispectrum is capable of suppressing Gaussian noise and increasing the signal-to-noise ratio of signals. However, the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from the bispectrum only use the numerical values of the bispectrum, ignoring the related information between different frequency bins.<br><br>NEW METHOD: In this study, we proposed a novel framework, termed a bispectrum-based hybrid neural network (BHNN), to make full use of bispectrum for improving the performance of the MI-based brain-computer interface (BCI). Specifically, the BHNN consisted of a convolutional neural network (CNN), gated recurrent units (GRU), and squeeze-and-excitation (SE) modules. The SE modules and CNNs are first used to learn the deep relation between frequency bins of the bispectrum estimated from different time window segmentations of the MI-EEG. Then, we used GRU to seek the overlooked sequential information of the bispectrum.<br><br>RESULTS: To validate the effectiveness of the proposed BHNN, three public BCI competition datasets were used in this study. The results demonstrated that the BHNN can achieve promising performance in decoding MI-EEG.<br><br>The statistical test results demonstrated that the proposed BHNN can significantly outperform other competing methods (p&#xa0;<&#xa0;=0.05).<br><br>CONCLUSION: The proposed BHNN is a novel bispectrum-based neural network, which can enhance the decoding performance of MI-based BCIs.},
}
@article {pmid35396325,
year = {2022},
author = {Liza, K and Ray, S},
title = {Local Interactions between Steady-State Visually Evoked Potentials at Nearby Flickering Frequencies.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {19},
pages = {3965-3974},
pmid = {35396325},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; IA/S/18/2/504003/WTDBT_/DBT-Wellcome Trust India Alliance/India ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation/methods ; },
abstract = {Steady-state visually evoked potentials (SSVEPs) are widely used to index top-down cognitive processing in human electroencephalogram (EEG) studies. Typically, two stimuli flickering at different temporal frequencies (TFs) are presented, each producing a distinct response in the EEG at its flicker frequency. However, how SSVEP responses in EEGs are modulated in the presence of a competing flickering stimulus just because of sensory interactions is not well understood. We have previously shown in local field potentials (LFPs) recorded from awake monkeys that when two overlapping full-screen gratings are counterphased at different TFs, there is an asymmetric SSVEP response suppression, with greater suppression from lower TFs, which further depends on the relative orientations of the gratings (stronger suppression and asymmetry for parallel compared with orthogonal gratings). Here, we first confirmed these effects in both male and female human EEG recordings. Then, we mapped the response suppression of one stimulus (target) by a competing stimulus (mask) over a much wider range than the previous study. Surprisingly, we found that the suppression was not stronger at low frequencies in general, but systematically varied depending on the target TF, indicating local interactions between the two competing stimuli. These results were confirmed in both human EEG and monkey LFP and electrocorticogram (ECoG) data. Our results show that sensory interactions between multiple SSVEPs are more complex than shown previously and are influenced by both local and global factors, underscoring the need to cautiously interpret the results of studies involving SSVEP paradigms.SIGNIFICANCE STATEMENT Steady-state visually evoked potentials (SSVEPs) are extensively used in human cognitive studies and brain-computer interfacing applications where multiple stimuli flickering at distinct frequencies are concurrently presented in the visual field. We recently characterized interactions between competing flickering stimuli in animal recordings and found that stimuli flickering slowly produce larger suppression. Here, we confirmed these in human EEGs, and further characterized the interactions by using a much wider range of target and competing (mask) frequencies in both human EEGs and invasive animal recordings. These revealed a new "local" component, whereby the suppression increased when competing stimuli flickered at nearby frequencies. Our results highlight the complexity of sensory interactions among multiple SSVEPs and underscore the need to cautiously interpret studies involving SSVEP paradigms.},
}
@article {pmid35395645,
year = {2022},
author = {Wu, X and Li, G and Jiang, S and Wellington, S and Liu, S and Wu, Z and Metcalfe, B and Chen, L and Zhang, D},
title = {Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac65b1},
pmid = {35395645},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Hand Strength ; Humans ; Linear Models ; Neural Networks, Computer ; },
abstract = {Objective.Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates.Approach.Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network).Main results.The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates.Significance.This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.},
}
@article {pmid35395403,
year = {2022},
author = {Yu, W and Sun, W and Ding, N},
title = {Asymmetrical cross-modal influence on neural encoding of auditory and visual features in natural scenes.},
journal = {NeuroImage},
volume = {255},
number = {},
pages = {119182},
doi = {10.1016/j.neuroimage.2022.119182},
pmid = {35395403},
issn = {1095-9572},
mesh = {Acoustic Stimulation ; *Auditory Perception/physiology ; Electroencephalography ; Humans ; Photic Stimulation ; *Visual Perception/physiology ; },
abstract = {Natural scenes contain multi-modal information, which is integrated to form a coherent perception. Previous studies have demonstrated that cross-modal information can modulate neural encoding of low-level sensory features. These studies, however, mostly focus on the processing of single sensory events or rhythmic sensory sequences. Here, we investigate how the neural encoding of basic auditory and visual features is modulated by cross-modal information when the participants watch movie clips primarily composed of non-rhythmic events. We presented audiovisual congruent and audiovisual incongruent movie clips, and since attention can modulate cross-modal interactions, we separately analyzed high- and low-arousal movie clips. We recorded neural responses using electroencephalography (EEG), and employed the temporal response function (TRF) to quantify the neural encoding of auditory and visual features. The neural encoding of sound envelope is enhanced in the audiovisual congruent condition than the incongruent condition, but this effect is only significant for high-arousal movie clips. In contrast, audiovisual congruency does not significantly modulate the neural encoding of visual features, e.g., luminance or visual motion. In summary, our findings demonstrate asymmetrical cross-modal interactions during the processing of natural scenes that lack rhythmicity: Congruent visual information enhances low-level auditory processing, while congruent auditory information does not significantly modulate low-level visual processing.},
}
@article {pmid35390744,
year = {2022},
author = {Soni, S and Seal, A and Yazidi, A and Krejcar, O},
title = {Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression.},
journal = {Computers in biology and medicine},
volume = {145},
number = {},
pages = {105420},
doi = {10.1016/j.compbiomed.2022.105420},
pmid = {35390744},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Depression/diagnosis ; *Depressive Disorder, Major/diagnosis ; Electroencephalography ; Humans ; },
abstract = {Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.},
}
@article {pmid35390282,
year = {2022},
author = {Aflalo, T and Zhang, C and Revechkis, B and Rosario, E and Pouratian, N and Andersen, RA},
title = {Implicit mechanisms of intention.},
journal = {Current biology : CB},
volume = {32},
number = {9},
pages = {2051-2060.e6},
pmid = {35390282},
issn = {1879-0445},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Intention ; Movement/physiology ; Parietal Lobe ; Retrospective Studies ; },
abstract = {High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions. Here, we study the timing of human posterior parietal cortex single-neuron activity recorded from implanted microelectrode arrays relative to the explicit urge to initiate movement. Participants were free to choose when to move, whether to move, and what to move, and they retrospectively reported the time they felt the urge to move. We replicate prior studies by showing that posterior parietal cortex (PPC) neural activity sharply rises hundreds of milliseconds before the reported urge. However, we find that this "preconscious" activity is part of a dynamic neural population response that initiates much earlier, when the participant first chooses to perform the task. Together with details of neural timing, our results suggest that PPC encodes an internal model of the motor planning network that transforms high-level task objectives into appropriate motor behavior. These new data challenge traditional interpretations of early neural activity and offer a more holistic perspective on the interplay between choice, behavior, and their neural underpinnings. Our results have important implications for translating BMIs into more complex real-world environments. We find that early neural dynamics are sufficient to drive BMI movements before the participant intends to initiate movement. Appropriate algorithms ensure that BMI movements align with the subject's awareness of choice.},
}
@article {pmid35387250,
year = {2022},
author = {Kalashami, MP and Pedram, MM and Sadr, H},
title = {EEG Feature Extraction and Data Augmentation in Emotion Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {7028517},
pmid = {35387250},
issn = {1687-5273},
mesh = {Arousal ; *Electroencephalography/methods ; Emotions ; *Neural Networks, Computer ; Support Vector Machine ; },
abstract = {Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of brain signals in comparison to facial expression, tone of voice, or speech in emotion recognition tasks. However, the lack of EEG data and high dimensional EEG recordings lead to difficulties in building effective classifiers with high accuracy. In this study, data augmentation and feature extraction techniques are proposed to solve the lack of data problem and high dimensionality of data, respectively. In this study, the proposed method is based on deep generative models and a data augmentation strategy called Conditional Wasserstein GAN (CWGAN), which is applied to the extracted features to regenerate additional EEG features. DEAP dataset is used to evaluate the effectiveness of the proposed method. Finally, a standard support vector machine and a deep neural network with different tunes were implemented to build effective models. Experimental results show that using the additional augmented data enhances the performance of EEG-based emotion recognition models. Furthermore, the mean accuracy of classification after data augmentation is increased 6.5% for valence and 3.0% for arousal, respectively.},
}
@article {pmid35386387,
year = {2022},
author = {Reece, AS and Hulse, GK},
title = {Geospatiotemporal and causal inference study of cannabis and other drugs as risk factors for female breast cancer USA 2003-2017.},
journal = {Environmental epigenetics},
volume = {8},
number = {1},
pages = {dvac006},
pmid = {35386387},
issn = {2058-5888},
abstract = {Breast cancer (BC) is the commonest human cancer and its incidence (BC incidence, BCI) is rising worldwide. Whilst both tobacco and alcohol have been linked to BCI genotoxic cannabinoids have not been investigated. Age-adjusted state-based BCI 2003-2017 was taken from the Surveillance Epidemiology and End Results database of the Centers for Disease Control. Drug use from the National Survey of Drug Use and Health, response rate 74.1%. Median age, median household income and ethnicity were from US census. Inverse probability weighted (ipw) multivariable regression conducted in R. In bivariate analysis BCI was shown to be significantly linked with rising cannabis exposure {β-est. = 3.93 [95% confidence interval 2.99, 4.87], P = 1.10 × 10[-15]}
. At 8 years lag cigarettes:cannabis [β-est. = 2660 (2150.4, 3169.3), P = 4.60 × 10[-22]] and cannabis:alcoholism [β-est. = 7010 (5461.6, 8558.4), P = 1.80 × 10[-17]] were significant in ipw-panel regression. Terms including cannabidiol [CBD; β-est. = 16.16 (0.39, 31.93), P = 0.446] and cannabigerol [CBG; β-est. = 6.23 (2.06, 10.39), P = 0.0034] were significant in spatiotemporal models lagged 1:2 years, respectively. Cannabis-liberal paradigms had higher BCI [67.50 ± 0.26 v. 65.19 ± 0.21/100 000 (mean ± SEM), P = 1.87 × 10[-11]; β-est. = 2.31 (1.65, 2.96), P = 9.09 × 10[-12]]. 55/58 expected values >1.25 and 13/58 >100. Abortion was independently and causally significant in space-time models. Data show that exposure to cannabis and the cannabinoids Δ9-tetrahydrocannabinol, CBD, CBG and alcoholism fulfil quantitative causal criteria for BCI across space and time. Findings are robust to adjustment for age and several known sociodemographic, socio-economic and hormonal risk factors and establish cannabinoids as an additional risk factor class for breast carcinogenesis. BCI is higher under cannabis-liberal legal paradigms.},
}
@article {pmid35386356,
year = {2022},
author = {Yang, B and Liang, C and Chen, D and Cheng, F and Zhang, Y and Wang, S and Shu, J and Huang, X and Wang, J and Xia, K and Ying, L and Shi, K and Wang, C and Wang, X and Li, F and Zhao, Q and Chen, Q},
title = {A conductive supramolecular hydrogel creates ideal endogenous niches to promote spinal cord injury repair.},
journal = {Bioactive materials},
volume = {15},
number = {},
pages = {103-119},
pmid = {35386356},
issn = {2452-199X},
abstract = {The current effective method for treatment of spinal cord injury (SCI) is to reconstruct the biological microenvironment by filling the injured cavity area and increasing neuronal differentiation of neural stem cells (NSCs) to repair SCI. However, the method is characterized by several challenges including irregular wounds, and mechanical and electrical mismatch of the material-tissue interface. In the current study, a unique and facile agarose/gelatin/polypyrrole (Aga/Gel/PPy, AGP3) hydrogel with similar conductivity and modulus as the spinal cord was developed by altering the concentration of Aga and PPy. The gelation occurred through non-covalent interactions, and the physically crosslinked features made the AGP3 hydrogels injectable. In vitro cultures showed that AGP3 hydrogel exhibited excellent biocompatibility, and promoted differentiation of NSCs toward neurons whereas it inhibited over-proliferation of astrocytes. The in vivo implanted AGP3 hydrogel completely covered the tissue defects and reduced injured cavity areas. In vivo studies further showed that the AGP3 hydrogel provided a biocompatible microenvironment for promoting endogenous neurogenesis rather than glial fibrosis formation, resulting in significant functional recovery. RNA sequencing analysis further indicated that AGP3 hydrogel significantly modulated expression of neurogenesis-related genes through intracellular Ca[2+] signaling cascades. Overall, this supramolecular strategy produces AGP3 hydrogel that can be used as favorable biomaterials for SCI repair by filling the cavity and imitating the physiological properties of the spinal cord.},
}
@article {pmid35385340,
year = {2022},
author = {Xie, YK and Luo, H and Zhang, SX and Chen, XY and Guo, R and Qiu, XY and Liu, S and Wu, H and Chen, WB and Zhen, XH and Ma, Q and Tian, JL and Li, S and Chen, X and Han, Q and Duan, S and Shen, C and Yang, F and Xu, ZZ},
title = {GPR177 in A-fiber sensory neurons drives diabetic neuropathic pain via WNT-mediated TRPV1 activation.},
journal = {Science translational medicine},
volume = {14},
number = {639},
pages = {eabh2557},
doi = {10.1126/scitranslmed.abh2557},
pmid = {35385340},
issn = {1946-6242},
mesh = {Animals ; *Diabetes Mellitus/metabolism ; *Diabetic Neuropathies/metabolism ; Ganglia, Spinal/metabolism ; Humans ; *Intracellular Signaling Peptides and Proteins/metabolism ; Mice ; *Neuralgia/metabolism ; *Receptors, G-Protein-Coupled/metabolism ; Sensory Receptor Cells/metabolism ; *TRPV Cation Channels/metabolism ; *Wnt-5a Protein/metabolism ; },
abstract = {Diabetic neuropathic pain (DNP) is a common and devastating complication in patients with diabetes. The mechanisms mediating DNP are not completely elucidated, and effective treatments are lacking. A-fiber sensory neurons have been shown to mediate the development of mechanical allodynia in neuropathic pain, yet the molecular basis underlying the contribution of A-fiber neurons is still unclear. Here, we report that the orphan G protein-coupled receptor 177 (GPR177) in A-fiber neurons drives DNP via WNT5a-mediated activation of transient receptor potential vanilloid receptor-1 (TRPV1) ion channel. GPR177 is mainly expressed in large-diameter A-fiber dorsal root ganglion (DRG) neurons and required for the development of DNP in mice. Mechanistically, we found that GPR177 mediated the secretion of WNT5a from A-fiber DRG neurons into cerebrospinal fluid (CSF), which was necessary for the maintenance of DNP. Extracellular perfusion of WNT5a induced rapid currents in both TRPV1-expressing heterologous cells and nociceptive DRG neurons. Computer simulations revealed that WNT5a has the potential to bind the residues at the extracellular S5-S6 loop of TRPV1. Using a peptide able to disrupt the predicted WNT5a/TRPV1 interaction suppressed DNP- and WNT5a-induced neuropathic pain symptoms in rodents. We confirmed GPR177/WNT5A coexpression in human DRG neurons and WNT5A secretion in CSF from patients with DNP. Thus, our results reveal a role for WNT5a as an endogenous and potent TRPV1 agonist, and the GPR177-WNT5a-TRPV1 axis as a driver of DNP pathogenesis in rodents. Our findings identified a potential analgesic target that might relieve neuropathic pain in patients with diabetes.},
}
@article {pmid35382585,
year = {2022},
author = {Lamarre, GPA and Pardikes, NA and Segar, S and Hackforth, CN and Laguerre, M and Vincent, B and Lopez, Y and Perez, F and Bobadilla, R and Silva, JAR and Basset, Y},
title = {More winners than losers over 12 years of monitoring tiger moths (Erebidae: Arctiinae) on Barro Colorado Island, Panama.},
journal = {Biology letters},
volume = {18},
number = {4},
pages = {20210519},
pmid = {35382585},
issn = {1744-957X},
mesh = {Animals ; Climate Change ; Colorado ; Ecology ; *Moths/physiology ; Trees ; *Tropical Climate ; },
abstract = {Understanding the causes and consequences of insect declines has become an important goal in ecology, particularly in the tropics, where most terrestrial diversity exists. Over the past 12 years, the ForestGEO Arthropod Initiative has systematically monitored multiple insect groups on Barro Colorado Island (BCI), Panama, providing baseline data for assessing long-term population trends. Here, we estimate the rates of change in abundance among 96 tiger moth species on BCI. Population trends of most species were stable (n = 20) or increasing (n = 62), with few (n = 14) declining species. Our analysis of morphological and climatic sensitivity traits associated with population trends shows that species-specific responses to climate were most strongly linked with trends. Specifically, tiger moth species that are more abundant in warmer and wetter years are more likely to show population increases. Our study contrasts with recent findings indicating insect decline in tropical and temperate regions. These results highlight the significant role of biotic responses to climate in determining long-term population trends and suggest that future climate changes are likely to impact tropical insect communities.},
}
@article {pmid35382584,
year = {2022},
author = {Bujan, J and Nottingham, AT and Velasquez, E and Meir, P and Kaspari, M and Yanoviak, SP},
title = {Tropical ant community responses to experimental soil warming.},
journal = {Biology letters},
volume = {18},
number = {4},
pages = {20210518},
pmid = {35382584},
issn = {1744-957X},
mesh = {Animals ; *Ants/physiology ; Climate Change ; Global Warming ; Soil ; *Thermotolerance ; },
abstract = {Climate change is one of the primary agents of the global decline in insect abundance. Because of their narrow thermal ranges, tropical ectotherms are predicted to be most threatened by global warming, yet tests of this prediction are often confounded by other anthropogenic disturbances. We used a tropical forest soil warming experiment to directly test the effect of temperature increase on litter-dwelling ants. Two years of continuous warming led to a change in ant community between warming and control plots. Specifically, six ant genera were recorded only on warming plots, and one genus only on control plots. Wasmannia auropuctata, a species often invasive elsewhere but native to this forest, was more abundant in warmed plots. Ant recruitment at baits was best predicted by soil surface temperature and ant heat tolerance. These results suggest that heat tolerance is useful for predicting changes in daily foraging activity, which is directly tied to colony fitness. We show that a 2-year increase in temperature (of 2-4°C) can have a profound effect on the most abundant insects, potentially favouring species with invasive traits and moderate heat tolerances.},
}
@article {pmid35382179,
year = {2022},
author = {Fokin, AA and Wycech Knight, J and Yoshinaga, K and Abid, AT and Grady, R and Alayon, AL and Puente, I},
title = {Blunt Cardiac Injury in Patients With Sternal Fractures.},
journal = {Cureus},
volume = {14},
number = {3},
pages = {e22841},
pmid = {35382179},
issn = {2168-8184},
abstract = {Background Blunt cardiac injury (BCI) is a possible consequence of sternal fractures (SF). There is a scarcity of studies addressing BCI in patients with different types of SF and with pre-existing cardiac conditions. The goal of this study was to delineate diagnostic patterns of BCI in different cohorts of SF patients. Methods This retrospective cohort study included 380 blunt trauma patients admitted to two level 1 trauma centers between January 2015 and March 2020 with radiologically confirmed SF. Electrocardiography, cardiac enzymes and echocardiography were evaluated for BCI diagnosis. Analyzed variables included: age, comorbidities, injury severity score, Glasgow coma score, type of SF (isolated, combined, displaced), incidence of traumatic brain injury, co-injuries, retrosternal hematoma, intensive care unit admissions, hospital lengths of stay, and mortality. Results In 380 SF patients there were 250 (66%) females and 130 (34%) males and the mean age was 63 years old. Electrocardiography was done in all patients, cardiac enzymes in 234 (62%) and echocardiography in 181 (48%). BCI was diagnosed in 19 (5%) of patients, all having combined SF. BCI patients had higher injury severity score (mean 18.4) and 14 (74%) had pulmonary co-injuries. Multivariable analysis confirmed pulmonary co-injuries as a statistically significant predictor of BCI (p<0.001). BCI patients compared to no BCI patients had all three tests (electrocardiography, cardiac enzymes and echocardiography) performed statistically more often (90% vs 36%, p<0.001). SF patients with pre-injury cardiac comorbidities had similar incidence of BCI as without cardiac comorbidities (5% vs 6%, p=0.6). In SF patients with traumatic brain injury, cardiac enzymes (troponin, creatine kinase) were elevated significantly more often compared to patients without traumatic brain injury (58% vs 38%, p=0.02). SF displacement or retrosternal hematoma presence were not associated with BCI. Mortality in SF patients with BCI versus without was not statistically different (16 vs 9%, p=0.4). Conclusions Blunt cardiac injury is rare in patients with SF. Higher degree of BCI suspicion must be applied in combined SF patients, especially those with pulmonary co-injuries. Cardiac comorbidities did not affect the rate of BCI. Echocardiography for BCI diagnosis is essential in SF patients with traumatic brain injury, as cardiac enzymes may be less informative, however is less important in isolated SF patients. Performing all three diagnostic tests in combined SF patients improves the accuracy of BCI diagnosis.},
}
@article {pmid35381455,
year = {2022},
author = {Khan, A and Rasool, S},
title = {Game induced emotion analysis using electroencephalography.},
journal = {Computers in biology and medicine},
volume = {145},
number = {},
pages = {105441},
doi = {10.1016/j.compbiomed.2022.105441},
pmid = {35381455},
issn = {1879-0534},
mesh = {Algorithms ; *Electroencephalography/methods ; *Emotions/physiology ; Machine Learning ; Support Vector Machine ; },
abstract = {Organizations vie to develop insights into the psychological aspects of consumer decision-making to enhance their products accordingly. Understanding how emotions and personality traits influence the choices we make is an integral part of product design. In this paper, we have employed machine learning algorithms to profile discrete emotions, in response to video games stimuli, based on features extracted from recorded electroencephalography (EEG) and to understand certain characteristics of personality. Four video games from different genres have been used for emotion elicitation and players' EEG signals are recorded. EEG being a non-stationary, non-linear and extremely noisy signal has been cleaned using a Savitzky-Golay filter which is found to be suitable for single-channel EEG devices. Seven out of sixteen features from time, frequency and time-frequency domains have been selected using Random Forest and used to classify emotions. Support Vector Machine, k-Nearest Neighbour and Gradient Boosted Trees classifiers have been used where the highest classification accuracy 82.26% is achieved with Boosted Trees classifier. Our findings propagate that for a single-channel EEG device, only four discrete emotions (happy, bored, relaxed, stressed) can be classified where two emotions happy and bored achieved the highest individual accuracy of 88.89% and 85.29% respectively with the Gradient Boosted Trees Classifier. In this study, we have also identified personality traits, extroversion and neuroticism influence players' perception of video games. The results indicate that players with low extroversion prefer relatively slow and strategy games as compared to highly extroverted. It has also been identified that puzzle and racing games are well-liked irrespective of the levels of the two personality traits.},
}
@article {pmid35379771,
year = {2022},
author = {Wu, C and Gong, Q and Xu, X and Fang, P and Wang, C and Yu, JY and Wang, XX and Fang, SH and Chen, WJ and Lou, HF and Liu, YH and Wang, L and Liu, YJ and Chen, W and Wang, XD},
title = {Disrupted presynaptic nectin1-based neuronal adhesion in the entorhinal-hippocampal circuit contributes to early-life stress-induced memory deficits.},
journal = {Translational psychiatry},
volume = {12},
number = {1},
pages = {141},
pmid = {35379771},
issn = {2158-3188},
mesh = {Animals ; Male ; Mice ; Hippocampus/metabolism ; *Memory Disorders/etiology/metabolism ; *Pyramidal Cells/metabolism ; Spatial Memory/physiology ; *Stress, Psychological ; Nectins ; Cell Adhesion ; },
abstract = {The cell adhesion molecule nectin3 and its presynaptic partner nectin1 have been linked to early-life stress-related cognitive disorders, but how the nectin1-nectin3 system contributes to stress-induced neuronal, circuit, and cognitive abnormalities remains to be studied. Here we show that in neonatally stressed male mice, temporal order and spatial working memories, which require the medial entorhinal cortex (MEC)-CA1 pathway, as well as the structural integrity of CA1 pyramidal neurons were markedly impaired in adulthood. These cognitive and structural abnormalities in stressed mice were associated with decreased nectin levels in entorhinal and hippocampal subregions, especially reduced nectin1 level in the MEC and nectin3 level in the CA1. Postnatal suppression of nectin1 but not nectin3 level in the MEC impaired spatial memory, whereas conditional inactivation of nectin1 from MEC excitatory neurons reproduced the adverse effects of early-life stress on MEC-dependent memories and neuronal plasticity in CA1. Our data suggest that early-life stress disrupts presynaptic nectin1-mediated interneuronal adhesion in the MEC-CA1 pathway, which may in turn contribute to stress-induced synaptic and cognitive deficits.},
}
@article {pmid35378515,
year = {2022},
author = {Sombeck, JT and Heye, J and Kumaravelu, K and Goetz, SM and Peterchev, AV and Grill, WM and Bensmaia, S and Miller, LE},
title = {Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
pmid = {35378515},
issn = {1741-2552},
support = {F31 NS115478/NS/NINDS NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; T32 HD007418/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/methods ; Electrodes, Implanted ; Macaca mulatta ; Microelectrodes ; *Somatosensory Cortex/physiology ; },
abstract = {Objective.Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. ICMS that mimics naturally occurring neural activity, may allow afferent interfaces that are more informative and easier to learn than stimulation evoking unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS.Approach.Using a Utah multi-electrode array, we recorded activity evoked by both single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings.Main results.After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ∼0.7 ms. This was immediately followed by suppressed neural activity lasting 10-150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ∼100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels.Significance.The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.},
}
@article {pmid35378341,
year = {2022},
author = {Kumari, R and Janković, MM and Costa, A and Savić, AM and Konstantinović, L and Djordjević, O and Vucković, A},
title = {Short term priming effect of brain-actuated muscle stimulation using bimanual movements in stroke.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {138},
number = {},
pages = {108-121},
doi = {10.1016/j.clinph.2022.03.002},
pmid = {35378341},
issn = {1872-8952},
mesh = {Adult ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement/physiology ; Muscles ; *Stroke ; },
abstract = {OBJECTIVE: Brain-computer interface triggered-functional electrical stimulation (BCI-FES) is an emerging neurorehabilitation therapy post stroke, mostly for the affected hand. We explored the feasibility of a bimanual BCI-FES and its short-term priming effects, i.e. stimuli-induced behaviour change. We compared EEG parameters between unimanual and bimanual movements and differentiated the effect of age from the effect of stroke.<br><br>METHODS: Ten participants with subacute stroke, ten age-matched older healthy adults, and ten younger healthy adults underwent unimanual and bimanual BCI-FES sessions. Delta alpha ratio (DAR) and brain symmetry index (BSI) were derived from the pre- and post- resting-state EEG. Event-related desynchronization (ERD) and laterality index were derived from movement- EEG.<br><br>RESULTS: Participants were able to control bimanual BCI-FES. ERD was predominantly contralateral for unimanual movements and bilateral for bimanual movements. DAR and BSI only changed in healthy controls. Baseline values indicated that DAR was affected by stroke while BSI was affected by both age and stroke.<br><br>CONCLUSIONS: Bimanual BCI control offers a larger repertoire of movements, while causing the same short-term changes as unimanual BCI-FES. Prolonged practice may be required to achieve a measurable effect on DAR and BSI for stroke.<br><br>SIGNIFICANCE: Bimanual BCI-FES is feasible in people affected by stroke.},
}
@article {pmid35372429,
year = {2022},
author = {Huang, X and Jin, K and Zhu, J and Xue, Y and Si, K and Zhang, C and Meng, S and Gong, W and Ye, J},
title = {A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field Grading.},
journal = {Frontiers in medicine},
volume = {9},
number = {},
pages = {832920},
pmid = {35372429},
issn = {2296-858X},
abstract = {PURPOSE: Glaucoma is the main cause of irreversible blindness worldwide. However, the diagnosis and treatment of glaucoma remain difficult because of the lack of an effective glaucoma grading measure. In this study, we aimed to propose an artificial intelligence system to provide adequate assessment of glaucoma patients.<br><br>METHODS: A total of 16,356 visual fields (VFs) measured by Octopus perimeters and Humphrey Field Analyzer (HFA) were collected, from three hospitals in China and the public Harvard database. We developed a fine-grained grading deep learning system, named FGGDL, to evaluate the VF loss, compared to ophthalmologists. Subsequently, we discuss the relationship between structural and functional damage for the comprehensive evaluation of glaucoma level. In addition, we developed an interactive interface and performed a cross-validation study to test its auxiliary ability. The performance was valued by F1 score, overall accuracy and area under the curve (AUC).<br><br>RESULTS: The FGGDL achieved a high accuracy of 85 and 90%, and AUC of 0.93 and 0.90 for HFA and Octopus data, respectively. It was significantly superior (p < 0.01) to that of medical students and nearly equal (p = 0.614) to that of ophthalmic clinicians. For the cross-validation study, the diagnosis accuracy was almost improved (p < 0.05).<br><br>CONCLUSION: We proposed a deep learning system to grade VF of glaucoma with a high detection accuracy, for effective and adequate assessment for glaucoma patients. Besides, with the convenient and credible interface, this system can promote telemedicine and be used as a self-assessment tool for patients with long-duration diseases.},
}
@article {pmid35371255,
year = {2022},
author = {Zeng, C and Mu, Z and Wang, Q},
title = {Classifying Driving Fatigue by Using EEG Signals.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1885677},
pmid = {35371255},
issn = {1687-5273},
mesh = {Accidents, Traffic/prevention &amp; control ; *Automobile Driving ; Brain ; *Electroencephalography/methods ; Fatigue ; Humans ; },
abstract = {Fatigue driving is one of the main reasons for the occurrence of traffic accidents. Brain-computer interface, as a human-computer interaction method based on EEG signals, can communicate with the outside world and move freely through brain signals without relying on the peripheral neuromuscular system. In this paper, a simulation driving platform composed of driving simulation equipment and driving simulation software is used to simulate the real driving process. The EEG signals of the subjects are collected through simulated driving, and the EEG of five subjects is selected as the training sample, and the remaining one is the subject. As a test sample, perform feature extraction and classification experiments, select any set of normal signals and fatigue signals recorded in the driving fatigue experiment for data analysis, and then study the classification of driver fatigue levels. Experiments have proved that the PSO-H-ELM algorithm has only about 4% advantage compared with the average accuracy of the KNN algorithm and the SVM algorithm. The gap is not as big as expected, but as a new algorithm, it is applied to the detection of fatigue EEG. The two traditional algorithms are indeed more suitable. It shows that the driver fatigue level can be judged by detecting EEG, which will provide a basis for the development of on-board, real-time driving fatigue alarm devices. It will lay the foundation for traffic management departments to intervene in driving fatigue reasonably and provide a reliable basis for minimizing traffic accidents.},
}
@article {pmid35370596,
year = {2022},
author = {Peng, F and Li, M and Zhao, SN and Xu, Q and Xu, J and Wu, H},
title = {Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI.},
journal = {Frontiers in neurorobotics},
volume = {16},
number = {},
pages = {855825},
pmid = {35370596},
issn = {1662-5218},
abstract = {Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.},
}
@article {pmid35370578,
year = {2022},
author = {Gao, D and Zheng, W and Wang, M and Wang, L and Xiao, Y and Zhang, Y},
title = {A Zero-Padding Frequency Domain Convolutional Neural Network for SSVEP Classification.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {815163},
pmid = {35370578},
issn = {1662-5161},
abstract = {The brain-computer interface (BCI) of steady-state visual evoked potential (SSVEP) is one of the fundamental ways of human-computer communication. The main challenge is that there may be a nonlinear relationship between different SSVEP in other states. For improving the performance of SSVEP BCI, a novel CNN algorithm model is proposed in this study. Based on the discrete Fourier transform to calculate the signal's power spectral density (PSD), we perform zero-padding in the signal's time domain to improve its performance on the PSD and make it more refined. In this way, the frequency point interval in the PSD of the SSVEP is consistent with the minimum gap between the stimulation frequency. Combining the nonlinear transformation capabilities of CNN in deep learning, a zero-padding frequency domain convolutional neural network (ZPFDCNN) model is proposed. Extensive experiments based on the SSVEP dataset validate the effectiveness of our method. The study verifies that the proposed ZPFDCNN method can improve the effectiveness of the SSVEP-based high-speed BCI ITR. It has massive potential in the application of BCI.},
}
@article {pmid35368960,
year = {2022},
author = {Malibari, AA and Al-Wesabi, FN and Obayya, M and Alkhonaini, MA and Hamza, MA and Motwakel, A and Yaseen, I and Zamani, AS},
title = {Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface.},
journal = {Journal of healthcare engineering},
volume = {2022},
number = {},
pages = {3987494},
pmid = {35368960},
issn = {2040-2309},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain Computer Interface (BCI) technology commonly used to enable communication for the person with movement disability. It allows the person to communicate and control assistive robots by the use of electroencephalogram (EEG) or other brain signals. Though several approaches have been available in the literature for learning EEG signal feature, the deep learning (DL) models need to further explore for generating novel representation of EEG features and accomplish enhanced outcomes for MI classification. With this motivation, this study designs an arithmetic optimization with RetinaNet based deep learning model for MI classification (AORNDL-MIC) technique on BCIs. The proposed AORNDL-MIC technique initially exploits Multiscale Principal Component Analysis (MSPCA) approach for the EEG signal denoising and Continuous Wavelet Transform (CWT) is exploited for the transformation of 1D-EEG signal into 2D time-frequency amplitude representation, which enables to utilize the DL model via transfer learning approach. In addition, the DL based RetinaNet is applied for extracting of feature vectors from the EEG signal which are then classified with the help of ID3 classifier. In order to optimize the classification efficiency of the AORNDL-MIC technique, arithmetical optimization algorithm (AOA) is employed for hyperparameter tuning of the RetinaNet. The experimental analysis of the AORNDL-MIC algorithm on the benchmark data sets reported its promising performance over the recent state of art methodologies.},
}
@article {pmid35368726,
year = {2021},
author = {Chang, H and Zong, Y and Zheng, W and Tang, C and Zhu, J and Li, X},
title = {Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.},
journal = {Frontiers in psychiatry},
volume = {12},
number = {},
pages = {837149},
pmid = {35368726},
issn = {1664-0640},
abstract = {The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.},
}
@article {pmid35368269,
year = {2022},
author = {Ye, H and Fan, Z and Li, G and Wu, Z and Hu, J and Sheng, X and Chen, L and Zhu, X},
title = {Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {818214},
pmid = {35368269},
issn = {1662-4548},
abstract = {As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60-140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.},
}
@article {pmid35366653,
year = {2022},
author = {Libert, A and Van Den Kerchove, A and Wittevrongel, B and Van Hulle, MM},
title = {Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac636a},
pmid = {35366653},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Support Vector Machine ; },
abstract = {Objective.While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation.Approach.We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof.Main results.When using phase- and combined phase/magnitude information extracted by ABT, we show significant improvements in accuracy of population-trained classifiers applied to individual users (p< 0.001). We also show that 450 epochs are needed for a correct functioning of ABT, which corresponds to 2 min of paradigm stimulation.Significance.We have shown that ABT can be used to create population-trained mVEP classifiers using a limited number of epochs. We expect this to pertain to other ERPs or synchronous stimulation paradigms, allowing for a more effective, population-based training of visual BCIs. Finally, as ABT renders recordings across subjects more structurally invariant, it could be used for transfer learning purposes in view of plug-and-play BCI applications.},
}
@article {pmid35366649,
year = {2022},
author = {Wimalasena, LN and Braun, JF and Keshtkaran, MR and Hofmann, D and Gallego, J&#xc1; and Alessandro, C and Tresch, MC and Miller, LE and Pandarinath, C},
title = {Estimating muscle activation from EMG using deep learning-based dynamical systems models.},
journal = {Journal of neural engineering},
volume = {19},
number = {3},
pages = {},
pmid = {35366649},
issn = {1741-2552},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; R01 NS086973/NS/NINDS NIH HHS/United States ; DP2 NS127291/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; RF1 DA055667/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Bayes Theorem ; *Deep Learning ; Electromyography/methods ; Locomotion ; Muscle, Skeletal/physiology ; Rats ; },
abstract = {Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation.Main results. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches.Significance.This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and its control by upstream brain areas, and for improving brain-machine interfaces that rely on myoelectric control signals.},
}
@article {pmid35365737,
year = {2022},
author = {Yang, J and Tang, C and Jin, R and Liu, B and Wang, P and Chen, Y and Zeng, C},
title = {Molecular mechanisms of Huanglian jiedu decoction on ulcerative colitis based on network pharmacology and molecular docking.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {5526},
pmid = {35365737},
issn = {2045-2322},
mesh = {*Colitis, Ulcerative/drug therapy/genetics ; *Drugs, Chinese Herbal/chemistry ; Humans ; Molecular Docking Simulation ; Network Pharmacology ; },
abstract = {Huanglian jiedu decoction (HLJDD) is a heat-clearing and detoxifying agent composed of four kinds of Chinese herbal medicine. Previous studies have shown that HLJDD can improve the inflammatory response of ulcerative colitis (UC) and maintain intestinal barrier function. However, its molecular mechanism is not completely clear. In this study, we verified the bioactive components (BCI) and potential targets of HLJDD in the treatment of UC using network pharmacology and molecular docking, and constructed the pharmacological network and PPI network. Then the core genes were enriched by GO and KEGG. Finally, the bioactive components were docked with the key targets to verify the binding ability between them. A total of 54 active components related to UC were identified. Ten genes are very important to the PPI network. Functional analysis showed that these target genes were mainly involved in the regulation of cell response to different stimuli, IL-17 signal pathway and TNF signal pathway. The results of molecular docking showed that the active components of HLJDD had a good binding ability with the Hub gene. This study systematically elucidates the "multi-component, multi-target, multi-pathway" mechanism of anti-UC with HLJDD for the first time, suggesting that HLJDD or its active components may be candidate drugs for the treatment of ulcerative colitis.},
}
@article {pmid35364014,
year = {2022},
author = {Wandelt, SK and Kellis, S and Bjånes, DA and Pejsa, K and Lee, B and Liu, C and Andersen, RA},
title = {Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human.},
journal = {Neuron},
volume = {110},
number = {11},
pages = {1777-1787.e3},
pmid = {35364014},
issn = {1097-4199},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Hand Strength/physiology ; Humans ; *Motor Cortex/physiology ; Parietal Lobe ; Psychomotor Performance/physiology ; Speech ; },
abstract = {The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.},
}
@article {pmid35362243,
year = {2022},
author = {Liang, Q and Xia, X and Sun, X and Yu, D and Huang, X and Han, G and Mugo, SM and Chen, W and Zhang, Q},
title = {Highly Stretchable Hydrogels as Wearable and Implantable Sensors for Recording Physiological and Brain Neural Signals.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {9},
number = {16},
pages = {e2201059},
pmid = {35362243},
issn = {2198-3844},
support = {20210509036RQ//Jilin Province Science and Technology Development Plan/ ; 2021SYHZ0038//Jilin Province Science and Technology Development Plan/ ; 20200801008GH//Jilin Province Science and Technology Development Plan/ ; 2018YFD1100503//National Key Research and Development Program of China/ ; 2020YFA0713601//National Key Research and Development Program of China/ ; CGZHYD202012-010//Transformation Program of Scientific and Technological Achievement of the First Hospital of Jilin University and Changchun Institute of Applied Chemistry/ ; },
mesh = {Animals ; Brain ; Electric Conductivity ; *Foreign Bodies ; Hydrogels ; Rats ; *Wearable Electronic Devices ; },
abstract = {Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. However, foreign body response and performance loss over time are major challenges stemming from the chemomechanical mismatch between sensors and tissues. Herein, microgels are utilized as large crosslinking centers in hydrogel networks to modulate the tradeoff between modulus and fatigue resistance/stretchability for producing hydrogels that closely match chemomechanical properties of neural tissues. The hydrogels exhibit notably different characteristics compared to nanoparticles reinforced hydrogels. The hydrogels exhibit relatively low modulus, good stretchability, and outstanding fatigue resistance. It is demonstrated that the hydrogels are well suited for fashioning into wearable and implantable sensors that can obtain physiological pressure signals, record the local field potentials in rat brains, and transmit signals through the injured peripheral nerves of rats. The hydrogels exhibit good chemomechanical match to tissues, negligible foreign body response, and minimal signal attenuation over an extended time, and as such is successfully demonstrated for use as long-term implantable sensory devices. This work facilitates a deeper understanding of biohybrid interfaces, while also advancing the technical design concepts for implantable neural probes that efficiently obtain physiological information.},
}
@article {pmid35360527,
year = {2022},
author = {Yu, H and Zhao, Q and Li, S and Li, K and Liu, C and Wang, J},
title = {Decoding Digital Visual Stimulation From Neural Manifold With Fuzzy Leaning on Cortical Oscillatory Dynamics.},
journal = {Frontiers in computational neuroscience},
volume = {16},
number = {},
pages = {852281},
pmid = {35360527},
issn = {1662-5188},
abstract = {A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8-15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined via two manifold learning methods. We connect temporally consecutive brain states in the t distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain-computer interface (BCI) control, brain function research, and neural rehabilitation.},
}
@article {pmid35360289,
year = {2022},
author = {Müller-Putz, GR and Kobler, RJ and Pereira, J and Lopes-Dias, C and Hehenberger, L and Mondini, V and Martínez-Cagigal, V and Srisrisawang, N and Pulferer, H and Batistić, L and Sburlea, AI},
title = {Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {841312},
pmid = {35360289},
issn = {1662-5161},
abstract = {Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project "Feel Your Reach". In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the "Feel Your Reach" framework to people with cervical spinal cord injury and evaluate the decoders' performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.},
}
@article {pmid35360158,
year = {2022},
author = {Li, X and Wang, L and Miao, S and Yue, Z and Tang, Z and Su, L and Zheng, Y and Wu, X and Wang, S and Wang, J and Dou, Z},
title = {Sensorimotor Rhythm-Brain Computer Interface With Audio-Cue, Motor Observation and Multisensory Feedback for Upper-Limb Stroke Rehabilitation: A Controlled Study.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {808830},
pmid = {35360158},
issn = {1662-4548},
abstract = {Several studies have shown the positive clinical effect of brain computer interface (BCI) training for stroke rehabilitation. This study investigated the efficacy of the sensorimotor rhythm (SMR)-based BCI with audio-cue, motor observation and multisensory feedback for post-stroke rehabilitation. Furthermore, we discussed the interaction between training intensity and training duration in BCI training. Twenty-four stroke patients with severe upper limb (UL) motor deficits were randomly assigned to two groups: 2-week SMR-BCI training combined with conventional treatment (BCI Group, BG, n = 12) and 2-week conventional treatment without SMR-BCI intervention (Control Group, CG, n = 12). Motor function was measured using clinical measurement scales, including Fugl-Meyer Assessment-Upper Extremities (FMA-UE; primary outcome measure), Wolf Motor Functional Test (WMFT), and Modified Barthel Index (MBI), at baseline (Week 0), post-intervention (Week 2), and follow-up week (Week 4). EEG data from patients allocated to the BG was recorded at Week 0 and Week 2 and quantified by mu suppression means event-related desynchronization (ERD) in mu rhythm (8-12 Hz). All functional assessment scores (FMA-UE, WMFT, and MBI) significantly improved at Week 2 for both groups (p < 0.05). The BG had significantly higher FMA-UE and WMFT improvement at Week 4 compared to the CG. The mu suppression of bilateral hemisphere both had a positive trend with the motor function scores at Week 2. This study proposes a new effective SMR-BCI system and demonstrates that the SMR-BCI training with audio-cue, motor observation and multisensory feedback, together with conventional therapy may promote long-lasting UL motor improvement. Clinical Trial Registration: [http://www.chictr.org.cn], identifier [ChiCTR2000041119].},
}
@article {pmid35358959,
year = {2022},
author = {Bassi, PRAS and Attux, R},
title = {FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {3},
pages = {},
doi = {10.1088/2057-1976/ac6300},
pmid = {35358959},
issn = {2057-1976},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Neural Networks, Computer ; },
abstract = {Objective.To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.Approach.We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI.Results.The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins.Conclusion and significance.Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable, economical, fast and low-latency BCIs.},
}
@article {pmid35358265,
year = {2022},
author = {Donoso, DA and Basset, Y and Shik, JZ and Forrister, DL and Uquillas, A and Salazar-Méndez, Y and Arizala, S and Polanco, P and Beckett, S and Dominguez G, D and Barrios, H},
title = {Male ant reproductive investment in a seasonal wet tropical forest: Consequences of future climate change.},
journal = {PloS one},
volume = {17},
number = {3},
pages = {e0266222},
pmid = {35358265},
issn = {1932-6203},
mesh = {Animals ; *Ants ; Climate Change ; Forests ; Male ; Rain ; Seasons ; Trees ; *Tropical Climate ; },
abstract = {Tropical forests sustain many ant species whose mating events often involve conspicuous flying swarms of winged gynes and males. The success of these reproductive flights depends on environmental variables and determines the maintenance of local ant diversity. However, we lack a strong understanding of the role of environmental variables in shaping the phenology of these flights. Using a combination of community-level analyses and a time-series model on male abundance, we studied male ant phenology in a seasonally wet lowland rainforest in the Panama Canal. The male flights of 161 ant species, sampled with 10 Malaise traps during 58 consecutive weeks (from August 2014 to September 2015), varied widely in number (mean = 9.8 weeks, median = 4, range = 1 to 58). Those species abundant enough for analysis (n = 97) flew mainly towards the end of the dry season and at the start of the rainy season. While litterfall, rain, temperature, and air humidity explained community composition, the time-series model estimators elucidated more complex patterns of reproductive investment across the entire year. For example, male abundance increased in weeks when maximum daily temperature increased and in wet weeks during the dry season. On the contrary, male abundance decreased in periods when rain receded (e.g., at the start of the dry season), in periods when rain fell daily (e.g., right after the beginning of the wet season), or when there was an increase in the short-term rate of litterfall (e.g., at the end of the dry season). Together, these results suggest that the BCI ant community is adapted to the dry/wet transition as the best timing of reproductive investment. We hypothesize that current climate change scenarios for tropical regions with higher average temperature, but lower rainfall, may generate phenological mismatches between reproductive flights and the adequate conditions needed for a successful start of the colony.},
}
@article {pmid35356767,
year = {2022},
author = {Liu, J and Lin, S and Li, W and Zhao, Y and Liu, D and He, Z and Wang, D and Lei, M and Hong, B and Wu, H},
title = {Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes.},
journal = {Research (Washington, D.C.)},
volume = {2022},
number = {},
pages = {9830457},
pmid = {35356767},
issn = {2639-5274},
abstract = {Noninvasive brain-computer interface (BCI) has been extensively studied from many aspects in the past decade. In order to broaden the practical applications of BCI technique, it is essential to develop electrodes for electroencephalogram (EEG) collection with advanced characteristics such as high conductivity, long-term effectiveness, and biocompatibility. In this study, we developed a silver-nanowire/PVA hydrogel/melamine sponge (AgPHMS) semidry EEG electrode for long-lasting monitoring of EEG signal. Benefiting from the water storage capacity of PVA hydrogel, the electrolyte solution can be continuously released to the scalp-electrode interface during used. The electrolyte solution can infiltrate the stratum corneum and reduce the scalp-electrode impedance to 10 kΩ-15 kΩ. The flexible structure enables the electrode with mechanical stability, increases the wearing comfort, and reduces the scalp-electrode gap to reduce contact impedance. As a result, a long-term BCI application based on measurements of motion-onset visual evoked potentials (mVEPs) shows that the 3-hour BCI accuracy of the new electrode (77% to 100%) is approximately the same as that of conventional electrodes supported by a conductive gel during the first hour. Furthermore, the BCI system based on the new electrode can retain low contact impedance for 10 hours on scalp, which greatly improved the ability of BCI technique.},
}
@article {pmid35356539,
year = {2022},
author = {Gao, Y and Chen, X and Liu, A and Liang, D and Wu, L and Qian, R and Xie, H and Zhang, Y},
title = {Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {10},
number = {},
pages = {4900209},
pmid = {35356539},
issn = {2168-2372},
mesh = {Child ; Electroencephalography/methods ; Humans ; Neural Networks, Computer ; *Quality of Life ; *Scalp ; Seizures/diagnosis ; },
abstract = {Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.},
}
@article {pmid35356325,
year = {2022},
author = {Lu, S and Yu, H},
title = {Research on Digital Business Model Innovation Based on Emotion Regulation Lens.},
journal = {Frontiers in psychology},
volume = {13},
number = {},
pages = {842076},
pmid = {35356325},
issn = {1664-1078},
abstract = {Digital technologies, such as artificial intelligence, brain-computer interfaces technology and big data, enable many firms to innovate their business model. It is clearly an emotional process due to its complex and uncertain nature, and involves individuals' emotion regulation, yet the current research lacks an effective conversion path from emotion to digital business model innovation (BMI). Drawing on theories and research on emotion regulation and business model innovation, we investigate how emotion regulation of entrepreneurs (i.e., cognitive reappraisal and expressive suppression) influence digital BMI. Data from 126 new ventures show that entrepreneurs' reappraisal positively affects digital BMI, while entrepreneurs' suppression exerts opposite effects on digital BMI. Moreover, we find that environmental dynamism moderates this relationship. The findings explain the emotional complexity in digital technology empowerment, which has implications for the development and design of brain computer interface applications and the literature on emotions and business model innovation.},
}
@article {pmid35355586,
year = {2022},
author = {Dreyer, AM and Heikkinen, BLA and Herrmann, CS},
title = {The Influence of the Modulation Index on Frequency-Modulated Steady-State Visual Evoked Potentials.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {859519},
pmid = {35355586},
issn = {1662-5161},
abstract = {Based on increased user experience during stimulation, frequency-modulated steady-state visual evoked potentials (FM-SSVEPs) have been suggested as an improved stimulation method for brain-computer interfaces. Adapting such a novel stimulation paradigm requires in-depth analyses of all different stimulation parameters and their influence on brain responses as well as the user experience during the stimulation. In the current manuscript, we assess the influence of different values for the modulation index, which determine the spectral distribution in the stimulation signal on FM-SSVEPs. We visually stimulated 14 participants at different target frequencies with four different values for the modulation index. Our results reveal that changing the modulation index in a way that elevates the stimulation power in the targeted sideband leads to increased FM-SSVEP responses. There is, however, a tradeoff with user experience as increased modulation indices also lead to increased perceived flicker intensity as well as decreased stimulation comfort in our participants. Our results can guide the choice of parameters in future FM-SSVEP implementations.},
}
@article {pmid35354131,
year = {2022},
author = {Yan, T and Suzuki, K and Kameda, S and Maeda, M and Mihara, T and Hirata, M},
title = {Electrocorticographic effects of acute ketamine on non-human primate brains.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac6293},
pmid = {35354131},
issn = {1741-2552},
mesh = {Animals ; Brain/metabolism ; Electrocorticography ; *Ketamine/pharmacology ; Primates ; Receptors, N-Methyl-D-Aspartate/metabolism ; },
abstract = {Objective. Acute blockade of glutamate N-methyl-D-aspartate receptors by ketamine induces symptoms and electrophysiological changes similar to schizophrenia. Previous studies have shown that ketamine elicits aberrant gamma oscillations in several cortical areas and impairs coupling strength between the low-frequency phase and fast frequency amplitude, which plays an important role in integrating functional information.Approach. This study utilized a customized wireless electrocorticography (ECoG) recording device to collect subdural signals from the somatosensory and primary auditory cortices in two monkeys. Ketamine was administered at a dose of 3 mg kg[-1](intramuscular) or 0.56 mg kg[-1](intravenous) to elicit brain oscillation reactions. We analyzed the raw data using methods such as power spectral density, time-frequency spectra, and phase-amplitude coupling (PAC).Main results. Acute ketamine triggered broadband gamma and high gamma oscillation power and decreased lower frequencies. The effect was stronger in the primary auditory cortex than in the somatosensory area. The coupling strength between the low phase of theta and the faster amplitude of gamma/high gamma bands was increased by a lower dose (0.56 mg kg[-1]iv) and decreased with a higher dose (3 mg kg[-1]im) ketamine.Significance. Our results showed that lower and higher doses of ketamine elicited differential effects on theta-gamma PAC. These findings support the utility of ECoG models as a translational platform for pharmacodynamic research in future research.},
}
@article {pmid35352525,
year = {2022},
author = {Yoldas, M},
title = {Non-invasive diagnosis of under active bladder: A pilot study.},
journal = {Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica},
volume = {94},
number = {1},
pages = {51-56},
doi = {10.4081/aiua.2022.1.51},
pmid = {35352525},
issn = {2282-4197},
mesh = {Aged, 80 and over ; Humans ; Male ; Pilot Projects ; Retrospective Studies ; *Urinary Bladder ; *Urinary Bladder Neck Obstruction/diagnosis ; Urodynamics ; },
abstract = {OBJECTIVE: We assessed the efficacy of voiding efficiency (VE) to distinguish between underactive bladder (UB) and bladder outlet obstruction (BO) without using pressure flow studies (PFS).<br><br>MATERIALS AND METHODS: in male patients, uroflowmetry and post-void residual (PVR) urine data and subsequent pressure flow studies (PFS) data were examined retrospectively. Bladder outlet obstruction index (BOI) and bladder contractility index (BCI) were calculated from patients' PFS values. Patients with BCI < 100 and BOI < 40 were grouped as UB group and patients with BCI > 100 and BOI > 40 were grouped as BOO group. VE was computed as a percentage of volume voided compared to the pre-void bladder volume.<br><br>RESULTS: In total we examined 93 patients, 44 in UB and 49 in BO group. There was no statistically significant difference between the two groups in relation to Qmax value (p = 0.38). However, total voiding time, time to reach the maximum urinary flow rate and voided volume showed statistically significant difference between the two groups (p < 0.001). Average VE was 63.6 + 2.43% and 46.2 + 2.63%) for UB and BO groups respectively and the difference was statistically significant (p < 0.001). UB can be diagnosed with at least 95% sensitivity and 88% specificity in men over age 80.<br><br>CONCLUSIONS: Non-invasive uroflowmetry and VE measurements were able to differentiate between UB and BOO patients, presenting with identical clinic features, but different findings of PFS.},
}
@article {pmid35349268,
year = {2022},
author = {Surendranath, M and Rajalekshmi, R and Ramesan, RM and Nair, P and Parameswaran, R},
title = {UV-Crosslinked Electrospun Zein/PEO Fibroporous Membranes for Wound Dressing.},
journal = {ACS applied bio materials},
volume = {5},
number = {4},
pages = {1538-1551},
doi = {10.1021/acsabm.1c01293},
pmid = {35349268},
issn = {2576-6422},
mesh = {Bandages ; Collagen ; Fibroblasts ; Humans ; Wound Healing ; *Zein/chemistry ; },
abstract = {Electrospun zein membranes are suitable for various biomedical applications. A UV-crosslinked electrospun membrane of a zein/PEO blend for wound healing application was explored in this work. The improvement in mechanical properties of the membrane after UV crosslinking was attributed to the change in protein conformation from an α-helix to a β-sheet. The circular dichroism (CD) spectra and FTIR spectra confirmed this conformational change. XRD analysis was shown to prove the amorphous nature of polymer blends with specific broad peaks at 2θ = 9° and 20°. The water vapor transmission rate (WVTR) of the membrane was found to be in the range of 1500-2000 g m[-2] day[-1], which was well suited with that of commercially available wound dressing material. Enough number of available functional groups like thiol, amino, and hydroxyl groups supplement a blood clotting index (BCI) to the matrix, causing 99% BCI within 4 min. A 91% cell viability result in the MTT assay with human dermal fibroblast cells confirmed the noncytotoxicity of the membrane. Tripeptides produced after the thermolysin-based hydrolysis of zein caused inhibition of TGF β1 expression and thus increased fibroblast and collagen production. The membrane stimulated 54% more collagen production compared to control cells at day 2 and caused 84% wound closure in human dermal fibroblast cells, which were desirable index markers of a potential wound care material.},
}
@article {pmid35346456,
year = {2022},
author = {Yaeger, K and Mocco, J},
title = {Future Directions of Endovascular Neurosurgery.},
journal = {Neurosurgery clinics of North America},
volume = {33},
number = {2},
pages = {233-239},
doi = {10.1016/j.nec.2021.11.007},
pmid = {35346456},
issn = {1558-1349},
mesh = {Humans ; *Neurosurgery ; Neurosurgical Procedures/methods ; },
abstract = {In the last few decades, endovascular neurosurgery has progressed from treating conventional cerebrovascular pathology to expanding outside the realm of vascular neurosurgery. As technologies, techniques, and devices are developed and refined, more patients with neurologic conditions can be treated with a less-invasive endovascular approach. For pathologies such as neurodegenerative diseases or hydrocephalus, the surgical treatment paradigm is starting to change with novel endovascular innovations. We anticipate more pathologies treatable by endovascular means, as more technological progress is made.},
}
@article {pmid35344941,
year = {2022},
author = {Rybář, M and Daly, I},
title = {Neural decoding of semantic concepts: a systematic literature review.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac619a},
pmid = {35344941},
issn = {1741-2552},
mesh = {*Brain/diagnostic imaging ; Machine Learning ; Magnetic Resonance Imaging ; Neuroimaging ; *Semantics ; },
abstract = {Objective.Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding.Approach.We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity.Main results.Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area.Significance.Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.},
}
@article {pmid35336455,
year = {2022},
author = {Rácz, M and Noboa, E and Détár, B and Nemes, &#xc1; and Galambos, P and Szűcs, L and Márton, G and Eigner, G and Haidegger, T},
title = {PlatypOUs-A Mobile Robot Platform and Demonstration Tool Supporting STEM Education.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336455},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electromyography ; Humans ; *Robotics/methods ; Software ; Support Vector Machine ; },
abstract = {Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform-named PlatypOUs-which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain-computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform. Along with the physical mobile robot platform, a virtual environment was implemented using Gazebo (an open-source 3D robotic simulator) inside the Robot Operating System (ROS) framework, which has the same capabilities as the real-world device. This can be used for development and test purposes. The main goal of the PlatypOUs project is to create a tool for STEM education and extracurricular activities, particularly laboratory practices and demonstrations. With the physical robot, the aim is to improve awareness of STEM outside and beyond the scope of regular education programmes. It implies several disciplines, including system design, control engineering, mobile robotics and machine learning with several application aspects in each. Using the PlatypOUs platform and the simulator provides students and self-learners with a firsthand exercise, and teaches them to deal with complex engineering problems in a professional, yet intriguing way.},
}
@article {pmid35336418,
year = {2022},
author = {Wang, X and Yang, R and Huang, M},
title = {An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336418},
issn = {1424-8220},
support = {61603223//National Natural Science Foundation of China/ ; 2021//Jiangsu Provincial Qinglan Project/ ; SYG202106//Suzhou Science and Technology Programme/ ; RDF-18-02-30//Research Development Fund of XJTLU/ ; RDF-20-01-18//Research Development Fund of XJTLU/ ; KSF-E-34//Key Program Special Fund in XJTLU/ ; 20KJB520034//The Natural Science Foundation of the Jiangsu Higher Education Institutions of China/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Machine Learning ; },
abstract = {Brain-computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain's electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods.},
}
@article {pmid35336276,
year = {2022},
author = {Sodhro, AH and Sennersten, C and Ahmad, A},
title = {Towards Cognitive Authentication for Smart Healthcare Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {35336276},
issn = {1424-8220},
mesh = {*Biometric Identification/methods ; Biometry/methods ; Cognition ; Delivery of Health Care ; Humans ; Privacy ; },
abstract = {Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals' biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events.},
}
@article {pmid35332120,
year = {2022},
author = {Luo, W and Yun, D and Hu, Y and Tian, M and Yang, J and Xu, Y and Tang, Y and Zhan, Y and Xie, H and Guan, JS},
title = {Acquiring new memories in neocortex of hippocampal-lesioned mice.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {1601},
pmid = {35332120},
issn = {2041-1723},
mesh = {Animals ; Entorhinal Cortex/physiology ; Hippocampus/physiology ; Memory/physiology ; Mental Recall/physiology ; Mice ; *Neocortex/physiology ; },
abstract = {The hippocampus interacts with the neocortical network for memory retrieval and consolidation. Here, we found the lateral entorhinal cortex (LEC) modulates learning-induced cortical long-range gamma synchrony (20-40 Hz) in a hippocampal-dependent manner. The long-range gamma synchrony, which was coupled to the theta (7-10 Hz) rhythm and enhanced upon learning and recall, was mediated by inter-cortical projections from layer 5 neurons of the LEC to layer 2 neurons of the sensory and association cortices. Artificially induced cortical gamma synchrony across cortical areas improved memory encoding in hippocampal lesioned mice for originally hippocampal-dependent tasks. Mechanistically, we found that activities of cortical c-Fos labeled neurons, which showed egocentric map properties, were modulated by LEC-mediated gamma synchrony during memory recall, implicating a role of cortical synchrony to generate an integrative memory representation from disperse features. Our findings reveal the hippocampal mediated organization of cortical memories and suggest brain-machine interface approaches to improve cognitive function.},
}
@article {pmid35330803,
year = {2022},
author = {Wang, YJ and Li, X and Ng, CH and Xu, DW and Hu, S and Yuan, TF},
title = {Risk factors for non-suicidal self-injury (NSSI) in adolescents: A meta-analysis.},
journal = {EClinicalMedicine},
volume = {46},
number = {},
pages = {101350},
pmid = {35330803},
issn = {2589-5370},
abstract = {BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents is a significant mental health problem around the world. Here, we performed a meta-analysis to systematically delineate the risk factors for NSSI.<br><br>METHOD: We searched Medline, Embase, Web of Science and Cochrane for relevant articles and abstracts published prior to 12 November 2021. Pooled odds ratios (ORs) and 95% confident intervals (CIs) were used to assess various risk factors, and publication bias was assessed by Egger's test, the trim and fill method and meta-regression. This study is registered with PROSPERO, CRD42021265885.<br><br>RESULTS: A total of 25 articles were eventually included in the analysis. Eighty risk factors were identified and classified into 7 categories: mental disorders (ORs, 1&#xb7;89; 95% CI, 1&#xb7;60-2&#xb7;24), bullying (ORs, 1&#xb7;98; 95% CI, 1&#xb7;32-2&#xb7;95), low health literacy (ORs, 2&#xb7;20; 95% CI, 1&#xb7;63-2&#xb7;96), problem behaviours (ORs, 2&#xb7;36; 95% CI, 2&#xb7;00-2&#xb7;77), adverse childhood experiences (ORs, 2&#xb7;49; 95% CI, 1&#xb7;85-3.34), physical symptoms (ORs, 2&#xb7;85; 95% CI, 1&#xb7;36-5&#xb7;97) and the female gender (ORs, 2&#xb7;89; 95% CI, 2&#xb7;43-3&#xb7;43). The range of heterogeneity (I[2]) was from 20&#xb7;3% to 99&#xb7;2%.<br><br>CONCLUSION: This meta-analysis found that mental disorders, low health literacy, adverse childhood experiences, bullying, problem behaviours, the female gender and physical symptoms appear to be risk factors for NSSI.},
}
@article {pmid35328418,
year = {2022},
author = {Ousingsawat, J and Centeio, R and Schreiber, R and Kunzelmann, K},
title = {Expression of SLC26A9 in Airways and Its Potential Role in Asthma.},
journal = {International journal of molecular sciences},
volume = {23},
number = {6},
pages = {},
pmid = {35328418},
issn = {1422-0067},
support = {KU756/14-1//Deutsche Forschungsgemeinschaft/ ; Mucus//Gilead Sciences (Germany)/ ; SRC013//CF-trust UK/ ; },
mesh = {Antiporters/metabolism ; *Asthma/metabolism ; Chlorides/metabolism ; *Cystic Fibrosis Transmembrane Conductance Regulator/metabolism ; Epithelial Cells/metabolism ; Humans ; Membrane Transport Proteins/metabolism ; Sulfate Transporters/genetics/metabolism ; },
abstract = {SLC26A9 is an epithelial anion transporter with a poorly defined function in airways. It is assumed to contribute to airway chloride secretion and airway surface hydration. However, immunohistochemistry showing precise localization of SLC26A9 in airways is missing. Some studies report localization near tight junctions, which is difficult to reconcile with a chloride secretory function of SLC26A9. We therefore performed immunocytochemistry of SLC26A9 in sections of human and porcine lungs. Obvious apical localization of SLC26A9 was detected in human and porcine superficial airway epithelia, whereas submucosal glands did not express SLC26A9. The anion transporter was located exclusively in ciliated epithelial cells. Highly differentiated BCi-NS1 human airway epithelial cells grown on permeable supports also expressed SLC26A9 in the apical membrane of ciliated epithelial cells. BCi-NS1 cells expressed the major Cl[-] transporting proteins CFTR, TMEM16A and SLC26A9 in about equal proportions and produced short-circuit currents activated by increases in intracellular cAMP or Ca[2+]. Both CFTR and SLC26A9 contribute to basal chloride currents in non-stimulated BCi-NS1 airway epithelia, with CFTR being the dominating Cl[-] conductance. In wtCFTR-expressing CFBE human airway epithelial cells, SLC26A9 was partially located in the plasma membrane, whereas CFBE cells expressing F508del-CFTR showed exclusive cytosolic localization of SLC26A9. Membrane localization of SLC26A9 and basal chloride currents were augmented by interleukin 13 in wild-type CFTR-expressing cells, but not in cells expressing the most common disease-causing mutant F508del-CFTR. The data suggest an upregulation of SLC26A9-dependent chloride secretion in asthma, but not in the presence of F508del-CFTR.},
}
@article {pmid35327887,
year = {2022},
author = {Yang, J and Gao, S and Shen, T},
title = {A Two-Branch CNN Fusing Temporal and Frequency Features for Motor Imagery EEG Decoding.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {35327887},
issn = {1099-4300},
abstract = {With the development of technology and the rise of the meta-universe concept, the brain-computer interface (BCI) has become a hotspot in the research field, and the BCI based on motor imagery (MI) EEG has been widely concerned. However, in the process of MI-EEG decoding, the performance of the decoding model needs to be improved. At present, most MI-EEG decoding methods based on deep learning cannot make full use of the temporal and frequency features of EEG data, which leads to a low accuracy of MI-EEG decoding. To address this issue, this paper proposes a two-branch convolutional neural network (TBTF-CNN) that can simultaneously learn the temporal and frequency features of EEG data. The structure of EEG data is reconstructed to simplify the spatio-temporal convolution process of CNN, and continuous wavelet transform is used to express the time-frequency features of EEG data. TBTF-CNN fuses the features learned from the two branches and then inputs them into the classifier to decode the MI-EEG. The experimental results on the BCI competition IV 2b dataset show that the proposed model achieves an average classification accuracy of 81.3% and a kappa value of 0.63. Compared with other methods, TBTF-CNN achieves a better performance in MI-EEG decoding. The proposed method can make full use of the temporal and frequency features of EEG data and can improve the decoding accuracy of MI-EEG.},
}
@article {pmid35325878,
year = {2022},
author = {Xu, W and Gao, P and He, F and Qi, H},
title = {Improving the performance of a gaze independent P300-BCI by using the expectancy wave.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac60c8},
pmid = {35325878},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Evoked Potentials ; Eye Movements ; Humans ; Photic Stimulation/methods ; },
abstract = {Objective.A P300-brain computer interface (P300-BCI) conveys a subject's intention through recognition of their event-related potentials (ERPs). However, in the case of visual stimuli, its performance depends strongly on eye gaze. When eye movement is impaired, it becomes difficult to focus attention on a target stimulus, and the quality of the ERP declines greatly, thereby affecting recognition efficiency.Approach.In this paper, the expectancy wave (E-wave) is proposed to improve signal quality and thereby improve identification of visual targets under the covert attention. The stimuli of the P300-BCI described here are presented in a fixed sequence, so the subjects can predict the next target stimulus and establish a stable expectancy effect of the target stimulus through training. Features from the E-wave that occurred 0 ∼ 300 ms before a stimulus were added to the post-stimulus ERP components for intention recognition.Main results.Comparisons of ten healthy subjects before and after training demonstrated that the expectancy wave generated before target stimulus could be used with the P300 component to improve character recognition accuracy (CRA) from 85% to 92.4%. In addition, CRA using only the expectancy component can reach 68.2%, which is significantly greater than random probability (16.7%). The results of this study indicate that the expectancy wave can be used to improve recognition efficiency for a gaze-independent P300-BCI, and that training contributes to induction and recognition of the potential.Significance.This study proposes an effective approach to an efficient gaze-independent P300-BCI system.},
}
@article {pmid35325875,
year = {2022},
author = {Fry, A and Chan, HW and Harel, NY and Spielman, LA and Escalon, MX and Putrino, DF},
title = {Evaluating the clinical benefit of brain-computer interfaces for control of a personal computer.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac60ca},
pmid = {35325875},
issn = {1741-2552},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Microcomputers ; Paralysis ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) enabling the control of a personal computer could provide myriad benefits to individuals with disabilities including paralysis. However, to realize this potential, these BCIs must gain regulatory approval and be made clinically available beyond research participation. Therefore, a transition from engineering-oriented to clinically oriented outcome measures will be required in the evaluation of BCIs. This review examined how to assess the clinical benefit of BCIs for the control of a personal computer. We report that: (a) a variety of different patient-reported outcome measures can be used to evaluate improvements inhow a patient feels, and we offer some considerations that should guide instrument selection. (b) Activities of daily living can be assessed to demonstrate improvements inhow a patient functions, however, new instruments that are sensitive to increases in functional independence via the ability to perform digital tasks may be needed. (c) Benefits tohow a patient surviveshas not previously been evaluated but establishing patient-initiated communication channels using BCIs might facilitate quantifiable improvements in health outcomes.},
}
@article {pmid35324445,
year = {2022},
author = {Huang, J and Yang, P and Xiong, B and Wan, B and Su, K and Zhang, ZQ},
title = {Latency Aligning Task-Related Component Analysis Using Wave Propagation for Enhancing SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {851-859},
doi = {10.1109/TNSRE.2022.3162029},
pmid = {35324445},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {Due to the high robustness to artifacts, steady-state visual evoked potential (SSVEP) has been widely applied to construct high-speed brain-computer interfaces (BCIs). Thus far, many spatial filtering methods have been proposed to enhance the target identification performance for SSVEP-based BCIs, and task-related component analysis (TRCA) is among the most effective ones. In this paper, we further extend TRCA and propose a new method called Latency Aligning TRCA (LA-TRCA), which aligns visual latencies on channels to obtain accurate phase information from task-related signals. Based on the SSVEP wave propagation theory, SSVEP spreads from posterior occipital areas over the cortex with a fixed phase velocity. Via estimation of the phase velocity using phase shifts of channels, the visual latencies on different channels can be determined for inter-channel alignment. TRCA is then applied to aligned data epochs for target recognition. For the validation purpose, the classification performance comparison between the proposed LA-TRCA and TRCA-based expansions were performed on two different SSVEP datasets. The experimental results illustrated that the proposed LA-TRCA method outperformed the other TRCA-based expansions, which thus demonstrated the effectiveness of the proposed approach for enhancing the SSVEP detection performance.},
}
@article {pmid35324444,
year = {2022},
author = {Pei, W and Wu, X and Zhang, X and Zha, A and Tian, S and Wang, Y and Gao, X},
title = {A Pre-Gelled EEG Electrode and Its Application in SSVEP-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {843-850},
doi = {10.1109/TNSRE.2022.3161989},
pmid = {35324444},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Electroencephalogram (EEG) electrodes are critical devices for brain-computer interface and neurofeedback. A pre-gelled (PreG) electrode was developed in this paper for EEG signal acquisition with a short installation time and good comfort. A hydrogel probe was placed in advance on the Ag/AgCl electrode before wearing the EEG headband instead of a time-consuming gel injection after wearing the headband. The impedance characteristics were compared between the PreG electrode and the wet electrode. The PreG electrode and the wet electrode performed the Brain-Computer Interface (BCI) application experiment to evaluate their performance. The average impedance of the PreG electrode can be decreased to 43 [Formula: see text] or even lower, which is higher than the wet electrode with an impedance of 8 [Formula: see text]. However, there is no significant difference in classification accuracy and information transmission rate (ITR) between the PreG electrode and the wet electrode in a 40 target BCI system based on Steady State Visually Evoked Potential (SSVEP). This study validated the efficiency of the proposed PreG electrode in the SSVEP-based BCI. The proposed PreG electrode will be an excellent substitute for wet electrodes in an actual application with convenience and good comfort.},
}
@article {pmid35324277,
year = {2022},
author = {Servick, K},
title = {Brain implant enables man in locked-in state to communicate.},
journal = {Science (New York, N.Y.)},
volume = {375},
number = {6587},
pages = {1327-1328},
doi = {10.1126/science.abq1706},
pmid = {35324277},
issn = {1095-9203},
mesh = {Adult ; *Amyotrophic Lateral Sclerosis/physiopathology/therapy ; *Brain ; *Brain-Computer Interfaces ; Communication ; Female ; Humans ; Male ; *Neural Prostheses ; *Neurofeedback/methods ; *Quadriplegia/therapy ; },
abstract = {Despite complete paralysis from amyotrophic lateral sclerosis, person used neural signals to spell out thoughts.},
}
@article {pmid35318316,
year = {2022},
author = {Chaudhary, U and Vlachos, I and Zimmermann, JB and Espinosa, A and Tonin, A and Jaramillo-Gonzalez, A and Khalili-Ardali, M and Topka, H and Lehmberg, J and Friehs, GM and Woodtli, A and Donoghue, JP and Birbaumer, N},
title = {Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {1236},
pmid = {35318316},
issn = {2041-1723},
mesh = {*Amyotrophic Lateral Sclerosis/therapy ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Language ; Male ; *Neurofeedback ; },
abstract = {Patients with amyotrophic lateral sclerosis (ALS) can lose all muscle-based routes of communication as motor neuron degeneration progresses, and ultimately, they may be left without any means of communication. While others have evaluated communication in people with remaining muscle control, to the best of our knowledge, it is not known whether neural-based communication remains possible in a completely locked-in state. Here, we implanted two 64 microelectrode arrays in the supplementary and primary motor cortex of a patient in a completely locked-in state with ALS. The patient modulated neural firing rates based on auditory feedback and he used this strategy to select letters one at a time to form words and phrases to communicate his needs and experiences. This case study provides evidence that brain-based volitional communication is possible even in a completely locked-in state.},
}
@article {pmid35310583,
year = {2022},
author = {Song, Z and Zhan, G and Lin, Y and Fang, T and Niu, L and Zhang, X and Wang, H and Zhang, L and Jia, J and Kang, X},
title = {Electroacupuncture Alters BCI-Based Brain Network in Stroke Patients.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {8112375},
pmid = {35310583},
issn = {1687-5273},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroacupuncture ; Electroencephalography/methods ; Humans ; *Stroke/therapy ; },
abstract = {Goal. Stroke patients are usually accompanied by motor dysfunction, which greatly affects daily life. Electroacupuncture is a kind of nondrug therapy that can effectively improve motor function. However, the effect of electroacupuncture is hard to be measured immediately in clinic. This paper is aimed to reveal the instant changes in brain activity of three groups of stroke patients before, during, and after the electroacupuncture treatment by the EEG analysis in the alpha band and beta band. Methods. Seven different functional connectivity indicators including Pearson correlation coefficient, spectral coherence, mutual information, phase locking value, phase lag index, partial directed coherence, and directed transfer function were used to build the BCI-based brain network in stroke patients. Results and Conclusion. The results showed that the brain activity based on the alpha band of EEG decreased after the electroacupuncture treatment, while in the beta band of EEG, the brain activity decreased only in the first two groups. Significance. This method could be used to evaluate the effect of electroacupuncture instantly and quantitatively. The study will hopefully provide some neurophysiological evidence of the relationship between changes in brain activity and the effects of electroacupuncture. The study of BCI-based brain network changes in the alpha and beta bands before, during, and after electroacupuncture in stroke patients of different periods is helpful in adjusting and selecting the electroacupuncture regimens for different patients. The trial was registered on the Chinese clinical trial registry (ChiCTR2000036959).},
}
@article {pmid35310091,
year = {2022},
author = {Huang, R and Zeng, L and Cheng, H and Guo, X},
title = {Editorial: Neural Interface for Cognitive Human-Robot Interaction and Collaboration.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {830877},
doi = {10.3389/fnins.2022.830877},
pmid = {35310091},
issn = {1662-4548},
}
@article {pmid35308879,
year = {2022},
author = {Ding, Y and Liu, J and Zhang, X and Yang, Z},
title = {Dynamic Tracking of State Anxiety via Multi-Modal Data and Machine Learning.},
journal = {Frontiers in psychiatry},
volume = {13},
number = {},
pages = {757961},
pmid = {35308879},
issn = {1664-0640},
abstract = {Anxiety induction is widely used in the investigations of the mechanism and treatment of state anxiety. State anxiety is accompanied by immediate psychological and physiological responses. However, the existing state anxiety measurement, such as the commonly used state anxiety subscale of the State-Trait Anxiety Inventory, mainly relies on questionnaires with low temporal resolution. This study aims to develop a tracking model of state anxiety with high temporal resolution. To capture the dynamic changes of state anxiety levels, we induced the participants' state anxiety through exposure to aversive pictures or the risk of electric shocks and simultaneously recorded multi-modal data, including dimensional emotion ratings, electrocardiogram, and galvanic skin response. Using the paired self-reported state anxiety levels and multi-modal measures, we trained and validated machine learning models to predict state anxiety based on psychological and physiological features extracted from the multi-modal data. The prediction model achieved a high correlation between the predicted and self-reported state anxiety levels. This quantitative model provides fine-grained and sensitive measures of state anxiety levels for future affective brain-computer interaction and anxiety modulation studies.},
}
@article {pmid35306036,
year = {2022},
author = {Joshi, AA and Choi, S and Liu, Y and Chong, M and Sonkar, G and Gonzalez-Martinez, J and Nair, D and Wisnowski, JL and Haldar, JP and Shattuck, DW and Damasio, H and Leahy, RM},
title = {A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI.},
journal = {Journal of neuroscience methods},
volume = {374},
number = {},
pages = {109566},
pmid = {35306036},
issn = {1872-678X},
support = {K23 HD099309/HD/NICHD NIH HHS/United States ; R01 EB026299/EB/NIBIB NIH HHS/United States ; R01 NS074980/NS/NINDS NIH HHS/United States ; U54 MH091657/MH/NIMH NIH HHS/United States ; F31 NS106828/NS/NINDS NIH HHS/United States ; T32 MH111360/MH/NIMH NIH HHS/United States ; R01 NS089212/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/anatomy &amp; histology/diagnostic imaging ; Cerebral Cortex/anatomy &amp; histology/diagnostic imaging ; *Connectome/methods ; Humans ; Image Processing, Computer-Assisted/methods ; *Magnetic Resonance Imaging/methods ; Reproducibility of Results ; Rest ; },
abstract = {We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.},
}
@article {pmid35304652,
year = {2022},
author = {Li, M and Zhang, N},
title = {A dynamic directed transfer function for brain functional network-based feature extraction.},
journal = {Brain informatics},
volume = {9},
number = {1},
pages = {7},
pmid = {35304652},
issn = {2198-4018},
support = {62173010//National Natural Science Foundation of China/ ; 11832003//National Natural Science Foundation of China/ ; },
abstract = {Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8-13 Hz) and β band [13-30 Hz, with [Formula: see text](13-21 Hz) and [Formula: see text](21-30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, [Formula: see text] [Formula: see text]) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.},
}
@article {pmid35303579,
year = {2022},
author = {Dar, MN and Akram, MU and Yuvaraj, R and Gul Khawaja, S and Murugappan, M},
title = {EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning.},
journal = {Computers in biology and medicine},
volume = {144},
number = {},
pages = {105327},
doi = {10.1016/j.compbiomed.2022.105327},
pmid = {35303579},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; Neural Networks, Computer ; *Parkinson Disease ; },
abstract = {Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic emotional state, resulting in more reliable, natural, and meaningful human-computer interaction with applications in entertainment consumption behavior, interactive brain-computer interface, and monitoring of psychological health of patients in the domain of e-healthcare. Challenges of EEG-based emotion recognition in real-world applications are variations among experimental settings and cognitive health conditions. Parkinson's Disease (PD) is the second most common neurodegenerative disorder, resulting in impaired recognition and expression of emotions. The deficit of emotional expression poses challenges for the healthcare services provided to PD patients. This study proposes 1D-CRNN-ELM architecture, which combines one-dimensional Convolutional Recurrent Neural Network (1D-CRNN) with an Extreme Learning Machine (ELM), robust for the emotion detection of PD patients, also available for cross dataset learning with various emotions and experimental settings. In the proposed framework, after EEG preprocessing, the trained CRNN can use as a feature extractor with ELM as the classifier, and again this trained CRNN can be used for learning of new emotions set with fine-tuning of other datasets. This paper also applied cross dataset learning of emotions by training with PD patients datasets and fine-tuning with publicly available datasets of AMIGOS and SEED-IV, and vice versa. Random splitting of train and test data with 80 - 20 ratio resulted in an accuracy of 97.75% for AMIGOS, 83.20% for PD, and 86.00% for HC with six basic emotion classes. Fine-tuning of trained architecture with four emotions of the SEED-IV dataset results in 92.5% accuracy. To validate the generalization of our results, leave one subject (patient) out cross-validation is also incorporated with mean accuracies of 95.84% for AMIGOS, 75.09% for PD, 77.85% for HC, and 84.97% for SEED-IV is achieved. Only a 1 - sec segment of EEG signal from 14 channels is enough to detect emotions with this performance. The proposed method outperforms state-of-the-art studies to classify EEG-based emotions with publicly available datasets, provide cross dataset learning, and validate the robustness of the deep learning framework for real-world application of psychological healthcare monitoring of Parkinson's disease patients.},
}
@article {pmid35301366,
year = {2022},
author = {Ko, W and Jeon, E and Yoon, JS and Suk, HI},
title = {Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {4587},
pmid = {35301366},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discrimination Learning ; Electroencephalography/methods ; Imagination/physiology ; },
abstract = {Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. Our framework learns the distributional characteristics of EEG signals in an embedding space using a generative model. By using artificially generated and real EEG signals, our framework finds class-discriminative spatio-temporal feature representations that help to correctly discriminate input EEG signals. It is noteworthy that the framework facilitates the exploitation of real, unlabeled samples to better uncover the underlying patterns inherent in a user's EEG signals. To validate our framework, we conducted experiments comparing our method with conventional linear models by utilizing variants of three existing CNN architectures as generator networks and measuring the performance on three public datasets. Our framework exhibited statistically significant improvements over the competing methods. We investigated the learned network via activation pattern maps and visualized generated artificial samples to empirically justify the stability and neurophysiological plausibility of our model.},
}
@article {pmid35299166,
year = {2022},
author = {Wei, W and Qiu, S and Zhang, Y and Mao, J and He, H},
title = {ERP prototypical matching net: a meta-learning method for zero-calibration RSVP-based image retrieval.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac5eb7},
pmid = {35299166},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Learning ; },
abstract = {Objective.A rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is an efficient information detection technology through detecting event-related potentials (ERPs) evoked by target visual stimuli. The BCI system requires a time-consuming calibration process to build a reliable decoding model for a new user. Therefore, zero-calibration has become an important topic in BCI research.Approach.In this paper, we construct an RSVP dataset that includes 31 subjects, and propose a zero-calibration method based on a metric-based meta-learning: ERP prototypical matching net (EPMN). EPMN learns a metric space where the distance between electroencephalography (EEG) features and ERP prototypes belonging to the same category is smaller than that of different categories. Here, we employ prototype learning to learn a common representation from ERP templates of different subjects as ERP prototypes. Additionally, a metric-learning loss function is proposed for maximizing the distance between different classes of EEG and ERP prototypes and minimizing the distance between the same classes of EEG and ERP prototypes in the metric space.Main results.The experimental results showed that EPMN achieved a balanced-accuracy of 86.34% and outperformed the comparable methods.Significance.Our EPMN can realize zero-calibration for an RSVP-based BCI system.},
}
@article {pmid35298779,
year = {2022},
author = {Wang, T and Chen, Y and Cui, H},
title = {From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.},
journal = {Neuroscience bulletin},
volume = {38},
number = {7},
pages = {796-808},
pmid = {35298779},
issn = {1995-8218},
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Neurons/physiology ; },
abstract = {In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preference for kinetics and kinematics, a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution. In this review, we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view. Here, we aim to reconcile the above perspectives, and evaluate their theoretical impact, future direction, and potential applications in brain-machine interfaces.},
}
@article {pmid35294887,
year = {2022},
author = {Muret, D and Root, V and Kieliba, P and Clode, D and Makin, TR},
title = {Beyond body maps: Information content of specific body parts is distributed across the somatosensory homunculus.},
journal = {Cell reports},
volume = {38},
number = {11},
pages = {110523},
pmid = {35294887},
issn = {2211-1247},
support = {/WT_/Wellcome Trust/United Kingdom ; 215575/Z/19/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain Mapping/methods ; Hand ; *Human Body ; Magnetic Resonance Imaging/methods ; Somatosensory Cortex ; },
abstract = {The homunculus in primary somatosensory cortex (S1) is famous for its body part selectivity, but this dominant feature may eclipse other representational features, e.g., information content, also relevant for S1 organization. Using multivariate fMRI analysis, we ask whether body part information content can be identified in S1 beyond its primary region. Throughout S1, we identify significant representational dissimilarities between body parts but also subparts in distant non-primary regions (e.g., between the hand and the lips in the foot region and between different face parts in the foot region). Two movements performed by one body part (e.g., the hand) could also be dissociated well beyond its primary region (e.g., in the foot and face regions), even within Brodmann area 3b. Our results demonstrate that information content is more distributed across S1 than selectivity maps suggest. This finding reveals underlying information contents in S1 that could be harnessed for rehabilitation and brain-machine interfaces.},
}
@article {pmid35293735,
year = {2022},
author = {Wang, T and Song, J and Liu, R and Chan, SY and Wang, K and Su, Y and Li, P and Huang, W},
title = {Motion Detecting, Temperature Alarming, and Wireless Wearable Bioelectronics Based on Intrinsically Antibacterial Conductive Hydrogels.},
journal = {ACS applied materials &amp; interfaces},
volume = {14},
number = {12},
pages = {14596-14606},
doi = {10.1021/acsami.2c00713},
pmid = {35293735},
issn = {1944-8252},
mesh = {Anti-Bacterial Agents/pharmacology ; Electric Conductivity ; Humans ; *Hydrogels/pharmacology ; Temperature ; *Wearable Electronic Devices ; },
abstract = {Hydrogels have attracted considerable interest in developing flexible bioelectronics such as wearable devices, brain-machine interface products, and health-monitoring sensors. However, these bioelectronics are always challenged by microbial contamination, which frequently reduces their service life and durability due to a lack of antibacterial property. Herein, we report a class of inherently antibacterial conductive hydrogels (ACGs) as bioelectronics for motion and temperature detection. The ACGs were composed of poly(N-isopropylacrylamide) (pNIPAM) and silver nanowires (AgNWs) via a two-step polymerization strategy, which increased the crosslink density for enhanced mechanical properties. The introduction of AgNWs improved the conductivity of ACGs and endowed them with excellent antibacterial activity against both Gram-positive and -negative bacteria. Meanwhile, pNIPAM existed in ACGs and exhibited a thermal responsive behavior, thereby inducing sharp changes in their conductivity around body temperature, which was successfully employed to assemble a temperature alarm. Moreover, ACG-based sensors exhibited excellent sensitivity (within a small strain of 5%) and the capability of capturing various motion signals (finger bending, elbow bending, and even throat vibrating). Benefiting from the superiority of ACG-based sensors, we further demonstrated a wearable wireless system for the remote control of a vehicle, which is expected to help disabled people in the future.},
}
@article {pmid35293319,
year = {2022},
author = {Li, M and Cheng, S and Fan, J and Shang, Z and Wan, H},
title = {Many heads are better than one: A multiscale neural information feature fusion framework for spatial route selections decoding from multichannel neural recordings of pigeons.},
journal = {Brain research bulletin},
volume = {184},
number = {},
pages = {1-12},
doi = {10.1016/j.brainresbull.2022.03.007},
pmid = {35293319},
issn = {1873-2747},
mesh = {Animals ; *Columbidae ; *Hippocampus ; },
abstract = {The neural information at different scales exhibits spatial representations and the corresponding features are believed to be conducive for neural encoding. However, existing neural decoding studies on multiscale feature fusion have rarely been investigated. In this study, a multiscale neural information feature fusion framework is presented and we integrate these features to decode spatial routes from multichannel recordings. We design a goal-directed spatial cognitive experiment in which the pigeons need to perform a route selection task. Multichannel neural activities including spike and local field potential (LFP) recordings in the hippocampus are recorded and analyzed. The multiscale neural information features including spike firing rate features, LFP time-frequency energy features, and functional network connectivity features are extracted for spatial route decoding. Finally, we fuse the multiscale feature to solve the neural decoding problem and the results indicate that feature fusion operation improves the decoding performance significantly. Ten-fold cross-validation result analysis shows a promising improvement in the decoding performance using fusing multiscale features by an average of 0.04-0.11 at least than using any individual feature set alone. The proposed framework investigates the possibility of route decoding based on multiscale features, providing an effective way to solve the neural information decoding problems.},
}
@article {pmid35292680,
year = {2022},
author = {Duan, J and Shen, DD and Zhao, T and Guo, S and He, X and Yin, W and Xu, P and Ji, Y and Chen, LN and Liu, J and Zhang, H and Liu, Q and Shi, Y and Cheng, X and Jiang, H and Eric Xu, H and Zhang, Y and Xie, X and Jiang, Y},
title = {Molecular basis for allosteric agonism and G protein subtype selectivity of galanin receptors.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {1364},
pmid = {35292680},
issn = {2041-1723},
mesh = {*GTP-Binding Proteins/metabolism ; *Galanin/metabolism ; Protein Binding ; Receptors, G-Protein-Coupled/metabolism ; Receptors, Galanin/metabolism ; Signal Transduction ; },
abstract = {Peptide hormones and neuropeptides are complex signaling molecules that predominately function through G protein-coupled receptors (GPCRs). Two unanswered questions remaining in the field of peptide-GPCR signaling systems pertain to the basis for the diverse binding modes of peptide ligands and the specificity of G protein coupling. Here, we report the structures of a neuropeptide, galanin, bound to its receptors, GAL1R and GAL2R, in complex with their primary G protein subtypes Gi and Gq, respectively. The structures reveal a unique binding pose of galanin, which almost 'lays flat' on the top of the receptor transmembrane domain pocket in an α-helical conformation, and acts as an 'allosteric-like' agonist via a distinct signal transduction cascade. The structures also uncover the important features of intracellular loop 2 (ICL2) that mediate specific interactions with Gq, thus determining the selective coupling of Gq to GAL2R. ICL2 replacement in Gi-coupled GAL1R, μOR, 5-HT1AR, and Gs-coupled β2AR and D1R with that of GAL2R promotes Gq coupling of these receptors, highlighting the dominant roles of ICL2 in Gq selectivity. Together our results provide insights into peptide ligand recognition and allosteric activation of galanin receptors and uncover a general structural element for Gq coupling selectivity.},
}
@article {pmid35291556,
year = {2022},
author = {Willey, B and Mimmack, K and Gagliardi, G and Dossett, ML and Wang, S and Udeogu, OJ and Donovan, NJ and Gatchel, JR and Quiroz, YT and Amariglio, R and Liu, CH and Hyun, S and ElTohamy, A and Rentz, D and Sperling, RA and Marshall, GA and Vannini, P},
title = {Racial and socioeconomic status differences in stress, posttraumatic growth, and mental health in an older adult cohort during the COVID-19 pandemic.},
journal = {EClinicalMedicine},
volume = {45},
number = {},
pages = {101343},
pmid = {35291556},
issn = {2589-5370},
support = {K23 AG058805/AG/NIA NIH HHS/United States ; P01 AG036694/AG/NIA NIH HHS/United States ; },
abstract = {BACKGROUND: The COVID-19 pandemic has disproportionately impacted the most vulnerable and widened the health disparity gap in both physical and mental well-being. Consequentially, it is vital to understand how to best support elderly individuals, particularly Black Americans and people of low socioeconomic status, in navigating stressful situations during the COVID-19 pandemic and beyond. The aim of this study was to investigate perceived levels of stress, posttraumatic growth, coping strategies, socioeconomic status, and mental health between Black and non-Hispanic, White older adults, the majority over the age of 70. Additionally, we investigated which variables, if any, were associated with posttraumatic growth in these populations.<br><br>METHODS: One hundred seventy-six community dwelling older adults (mean age&#xa0;=&#xa0;76.30 &#xb1;8.94), part of two observational studies (The Harvard Aging Brain Study and Instrumental Activities of Daily Living Study) in Massachusetts, US, were included in this cross-sectional study. The survey, conducted from March 23, 2021 to May 13, 2021, measured perceived stress, behavioral coping strategies, posttraumatic growth, and mental health during the COVID-19 pandemic. We investigated associations with post-traumatic growth in a multiple linear regression model and examined their differences by race with t-tests, Wilcoxon rank-sum tests, and Fisher's exact tests. A second multiple linear regression model was used to examine which coping strategies were associated with posttraumatic growth.<br><br>FINDINGS: Our results indicated no significant difference between the groups in terms of mental health or stress. However, Black participants showed significantly greater posttraumatic growth compared to non-Hispanic, White participants. Additionally, the coping strategies of religion and positive reframing were found to be significantly associated with posttraumatic growth. Furthermore, even with the effects of stress and coping strategies controlled for, race remained significantly associated with posttraumatic growth.<br><br>INTERPRETATION: The COVID-19 pandemic has differentially impacted Black and non-Hispanic White older adults. These results may help encourage further analysis on geriatric psychiatry as well as understanding how cultural values and adaptations impact posttraumatic growth and mental health in diverse populations.<br><br>FUNDING: The Harvard Aging Brain Study (HABS) has been funded by NIH-NIA P01 AG036694 (PI: Reisa Sperling). The IADL study is funded by the National Institute on Aging (R01 AG053184, PI: Gad A. Marshall).},
}
@article {pmid35290589,
year = {2022},
author = {He, X and Wang, Y and Zhou, G and Yang, J and Li, J and Li, T and Hu, H and Ma, H},
title = {A Critical Role for γCaMKII in Decoding NMDA Signaling to Regulate AMPA Receptors in Putative Inhibitory Interneurons.},
journal = {Neuroscience bulletin},
volume = {38},
number = {8},
pages = {916-926},
pmid = {35290589},
issn = {1995-8218},
mesh = {Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism ; Hippocampus/metabolism ; Interneurons/physiology ; Long-Term Potentiation/physiology ; *N-Methylaspartate/metabolism ; *Receptors, AMPA/physiology ; Receptors, N-Methyl-D-Aspartate/metabolism ; Synapses/physiology ; },
abstract = {CaMKII is essential for long-term potentiation (LTP), a process in which synaptic strength is increased following the acquisition of information. Among the four CaMKII isoforms, γCaMKII is the one that mediates the LTP of excitatory synapses onto inhibitory interneurons (LTPE→I). However, the molecular mechanism underlying how γCaMKII mediates LTPE→I remains unclear. Here, we show that γCaMKII is highly enriched in cultured hippocampal inhibitory interneurons and opts to be activated by higher stimulating frequencies in the 10-30 Hz range. Following stimulation, γCaMKII is translocated to the synapse and becomes co-localized with the postsynaptic protein PSD-95. Knocking down γCaMKII prevents the chemical LTP-induced phosphorylation and trafficking of AMPA receptors (AMPARs) in putative inhibitory interneurons, which are restored by overexpression of γCaMKII but not its kinase-dead form. Taken together, these data suggest that γCaMKII decodes NMDAR-mediated signaling and in turn regulates AMPARs for expressing LTP in inhibitory interneurons.},
}
@article {pmid35290187,
year = {2022},
author = {Kaeseler, RL and Johansson, TW and Struijk, LNSA and Jochumsen, M},
title = {Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {678-687},
doi = {10.1109/TNSRE.2022.3157959},
pmid = {35290187},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Movement ; Tongue ; },
abstract = {Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten individuals without a disability) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.},
}
@article {pmid35287119,
year = {2022},
author = {Śliwowski, M and Martin, M and Souloumiac, A and Blanchart, P and Aksenova, T},
title = {Decoding ECoG signal into 3D hand translation using deep learning.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac5d69},
pmid = {35287119},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electrocorticography/methods ; Electroencephalography/methods ; Hand ; Humans ; },
abstract = {Objective.Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise between invasiveness of the recording device and good spatial and temporal resolution of the recorded signal. However, most ECoG signal decoders used to predict continuous hand movements are linear models. These models have a limited representational capacity and may fail to capture the relationship between ECoG signal features and continuous hand movements. Deep learning (DL) models, which are state-of-the-art in many problems, could be a solution to better capture this relationship.Approach.In this study, we tested several DL-based architectures to predict imagined 3D continuous hand translation using time-frequency features extracted from ECoG signals. The dataset used in the analysis is a part of a long-term clinical trial (ClinicalTrials.gov identifier: NCT02550522) and was acquired during a closed-loop experiment with a tetraplegic subject. The proposed architectures include multilayer perceptron, convolutional neural networks (CNNs), and long short-term memory networks (LSTM). The accuracy of the DL-based and multilinear models was compared offline using cosine similarity.Main results.Our results show that CNN-based architectures outperform the current state-of-the-art multilinear model. The best architecture exploited the spatial correlation between neighboring electrodes with CNN and benefited from the sequential character of the desired hand trajectory by using LSTMs. Overall, DL increased the average cosine similarity, compared to the multilinear model, by up to 60%, from 0.189 to 0.302 and from 0.157 to 0.249 for the left and right hand, respectively.Significance.This study shows that DL-based models could increase the accuracy of BCI systems in the case of 3D hand translation prediction in a tetraplegic subject.},
}
@article {pmid35281718,
year = {2022},
author = {Ha, J and Park, S and Im, CH},
title = {Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.},
journal = {Frontiers in neuroinformatics},
volume = {16},
number = {},
pages = {758537},
pmid = {35281718},
issn = {1662-5196},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.},
}
@article {pmid35276242,
year = {2022},
author = {Liang, D and Liu, A and Li, C and Liu, J and Chen, X},
title = {A novel consistency-based training strategy for seizure prediction.},
journal = {Journal of neuroscience methods},
volume = {372},
number = {},
pages = {109557},
doi = {10.1016/j.jneumeth.2022.109557},
pmid = {35276242},
issn = {1872-678X},
mesh = {Algorithms ; Electrocorticography ; Electroencephalography/methods ; *Epilepsy ; Humans ; *Seizures/diagnosis ; },
abstract = {BACKGROUND: Early prediction of epilepsy seizures can warn the patients to take precautions and improve their lives significantly. In recent years, deep learning has become increasingly predominant in seizure prediction for its outstanding performance. With the aim of predicting unseen seizures, it is essential to guarantee the generalization ability of the model, especially considering the non-stationary nature of EEG and the scarcity of seizure events in EEG recordings. Stability training against extra perturbations is an intuitive and effective way to improve the model's ability to generalize. Though a great number of deep learning methods have been developed for seizure prediction, their strategies to increase generalization performance focus on improving the model's architecture itself, and few of them pay attention to the stability of the model against small perturbations.<br><br>NEW METHOD: In this study, we propose a novel consistency-based training strategy to address this issue. The proposed strategy underlines that a robust model should maintain consistent results for the same input under extra perturbations. Specifically, during training, we use stochastic augmentations to make the input vary from iteration to iteration and consider the output as a stochastic variable. Then a consistency constraint is constructed to penalize the difference between the current output and previous outputs. In this way, the generalization ability of the model will be fully enhanced.<br><br>RESULTS: To better verify the effectiveness of our proposed strategy, we implement it in two state-of-the-art models with public-available codes, including STFT CNN and Multi-view CNN. Notably, we compare with the first baseline on a scalp EEG dataset and the other on an intracranial EEG dataset. The results show that our strategy could improve the performance significantly for both of them.<br><br>Our strategy has increased the sensitivity by 7.1% and reduced the false prediction rate by 0.12/h on the first baseline while improving the AUC by 0.020 on the second baseline.<br><br>CONCLUSIONS: This study is easy to implement, providing a new solution to enhance the performance of seizure prediction.},
}
@article {pmid35274925,
year = {2022},
author = {Tomba, C and Migdal, C and Fuard, D and Villard, C and Nicolas, A},
title = {Poly-l-lysine/Laminin Surface Coating Reverses Glial Cell Mechanosensitivity on Stiffness-Patterned Hydrogels.},
journal = {ACS applied bio materials},
volume = {5},
number = {4},
pages = {1552-1563},
doi = {10.1021/acsabm.1c01295},
pmid = {35274925},
issn = {2576-6422},
mesh = {Adhesives ; Fibronectins/pharmacology ; *Hydrogels/pharmacology ; *Laminin/pharmacology ; Neuroglia ; Polylysine/pharmacology ; },
abstract = {Brain tissues demonstrate heterogeneous mechanical properties, which evolve with aging and pathologies. The observation in these tissues of smooth to sharp rigidity gradients raises the question of brain cell responses to both different values of rigidity and their spatial variations, in dependence on the surface chemistry they are exposed to. Here, we used recent techniques of hydrogel photopolymerization to achieve stiffness texturing down to micrometer resolution in polyacrylamide hydrogels. We investigated primary neuron adhesion and orientation as well as glial cell proliferative properties on these rigidity-textured hydrogels for two adhesive coatings: fibronectin or poly-l-lysine/laminin. Our main observation is that glial cell adhesion and proliferation is favored on the stiffer regions when the adhesive coating is fibronectin and on the softer ones when it consists of poly-l-lysine/laminin. This behavior was unchanged by the presence or the absence of neuronal cells. In addition, glial cells were not confined by sharp, micron-scaled gradients of rigidity. Our observations suggest that rigidity sensing could involve adhesion-related pathways that profoundly depend on surface chemistry.},
}
@article {pmid35273310,
year = {2022},
author = {Ali, O and Saif-Ur-Rehman, M and Dyck, S and Glasmachers, T and Iossifidis, I and Klaes, C},
title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {4245},
pmid = {35273310},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Fourier Analysis ; Humans ; *Imagination ; Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs are pivotal. Here, we propose a feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a data augmentation method derived from l2-norm fast gradient sign method (FGSM), called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms the current state-of-the-art methods and yields classification accuracies of 90.7% on BCI competition II dataset III and 89.5%, 81.8%, 76.0% and 85.4%, 69.1%, 80.9% on different data distributions of BCI Competition IV dataset 2b and 2a, respectively.},
}
@article {pmid35271875,
year = {2022},
author = {Zaidi, SMT and Kocatürk, S and Baykaş, T and Kocatürk, M},
title = {A behavioral paradigm for cortical control of a robotic actuator by freely moving rats in a one-dimensional two-target reaching task.},
journal = {Journal of neuroscience methods},
volume = {373},
number = {},
pages = {109555},
doi = {10.1016/j.jneumeth.2022.109555},
pmid = {35271875},
issn = {1872-678X},
mesh = {Animals ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Movement/physiology ; Neurons/physiology ; Rats ; *Robotic Surgical Procedures ; *Robotics ; },
abstract = {BACKGROUND: Controlling the trajectory of a neuroprosthesis to reach distant targets is a commonly used brain-machine interface (BMI) task in primates and has not been available for rodents yet.<br><br>NEW METHOD: Here, we describe a novel, fine-tuned behavioral paradigm and setup which enables this task for rats in one-dimensional space for reaching two distant targets depending on their limited cognitive and visual capabilities compared to those of primates. An online transform was used to convert the activity of a pair of primary motor cortex (M1) units into two robotic actions. The rats were shaped to adapt to the transform and direct the robotic actuator toward the selected target by modulating the activity of the M1 neurons.<br><br>RESULTS: All three rats involved in the study were capable of achieving randomly selected targets with at least 78% accuracy. A total of 9 out of 16 pairs of units examined were eligible for exceeding this success criterion. Two out of three rats were capable of reversal learning, where the mapping between the activity of the M1 units and the robotic actions were reversed.<br><br>The present work is the first demonstration of trajectory-based control of a neuroprosthetic device by rodents to reach two distant targets using visual feedback.<br><br>CONCLUSION: The behavioral paradigm and setup introduced here can be used as a cost-effective platform for elucidating the information processing principles in the neural circuits related to neuroprosthetic control and for studying the performance of novel BMI technologies using freely moving rats.},
}
@article {pmid35271175,
year = {2022},
author = {Asanza, V and Peláez, E and Loayza, F and Lorente-Leyva, LL and Peluffo-Ordóñez, DH},
title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35271175},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Lower Extremity ; Pattern Recognition, Automated ; Quality of Life ; },
abstract = {Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},
}
@article {pmid35271077,
year = {2022},
author = {Hamid, H and Naseer, N and Nazeer, H and Khan, MJ and Khan, RA and Shahbaz Khan, U},
title = {Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35271077},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Gait ; Humans ; Neural Networks, Computer ; Spectroscopy, Near-Infrared/methods ; },
abstract = {This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.},
}
@article {pmid35270895,
year = {2022},
author = {An, Y and Lam, HK and Ling, SH},
title = {Auto-Denoising for EEG Signals Using Generative Adversarial Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {35270895},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects' data for training, it can still apply to the new subjects' data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.},
}
@article {pmid35265732,
year = {2022},
author = {Wyser, DG and Kanzler, CM and Salzmann, L and Lambercy, O and Wolf, M and Scholkmann, F and Gassert, R},
title = {Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy.},
journal = {Neurophotonics},
volume = {9},
number = {1},
pages = {015004},
pmid = {35265732},
issn = {2329-423X},
abstract = {Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level (ICC single = 0.81 and correlation r = 0.81). SCR increased the reproducibility from 0.64 to 0.81 (ICC single) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.},
}
@article {pmid35265464,
year = {2022},
author = {Mohammadi, E and Daneshmand, PG and Khorzooghi, SMSM},
title = {Electroencephalography-Based Brain-Computer Interface Motor Imagery Classification.},
journal = {Journal of medical signals and sensors},
volume = {12},
number = {1},
pages = {40-47},
pmid = {35265464},
issn = {2228-7477},
abstract = {BACKGROUND: Advances in the medical applications of brain-computer interface, like the motor imagery systems, are highly contributed to making the disabled live better. One of the challenges with such systems is to achieve high classification accuracy.<br><br>METHODS: A highly accurate classification algorithm with low computational complexity is proposed here to classify different motor imageries and execution tasks. An experimental study is performed on two electroencephalography datasets (Iranian Brain-Computer Interface competition [iBCIC] dataset and the world BCI Competition IV dataset 2a) to validate the effectiveness of the proposed method. For lower complexity, the common spatial pattern is applied to decrease the 64 channel signal to four components, in addition to increase the class separability. From these components, first, some features are extracted in the time and time-frequency domains, and next, the best linear combination of these is selected by adopting the stepwise linear discriminant analysis (LDA) method, which are then applied in training and testing the classifiers including LDA, random forest, support vector machine, and K nearest neighbors. The classification strategy is of majority voting among the results of the binary classifiers.<br><br>RESULTS: The experimental results indicate that the proposed algorithm accuracy is much higher than that of the winner of the first iBCIC. As to dataset 2a of the world BCI competition IV, the obtained results for subjects 6 and 9 outperform their counterparts. Moreover, this algorithm yields a mean kappa value of 0.53, which is higher than that of the second winner of the competition.<br><br>CONCLUSION: The results indicate that this method is able to classify motor imagery and execution tasks in both effective and automatic manners.},
}
@article {pmid35265111,
year = {2022},
author = {Ma, S and Dong, C and Jia, T and Ma, P and Xiao, Z and Chen, X and Zhang, L},
title = {A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4496992},
pmid = {35265111},
issn = {1687-5273},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; },
abstract = {Aiming at the feature extraction of left- and right-hand movement imagination EEG signals, this paper proposes a multichannel correlation analysis method and employs the Directed Transfer Function (DTF) to identify the connectivity between different channels of EEG signals, construct a brain network, and extract the characteristics of the network information flow. Since the network information flow identified by DTF can also reflect indirect connectivity of the EEG signal networks, the newly extracted DTF features are incorporated into the traditional AR model parameter features and extend the scope of feature sets. Classifications are carried out through the Support Vector Machine (SVM). The classification results show the enlarged feature set can significantly improve the classification accuracy of the left- and right-hand motor imagery EEG signals compared to the traditional AR feature set. Finally, the EEG signals of 2 channels, 10 channels, and 32 channels were selected for comparing their different effects of classifications. The classification results showed that the multichannel analysis method was more effective. Compared with the parameter features of the traditional AR model, the network information flow features extracted by the DTF method also achieve a higher classification effect, which verifies the effectiveness of the multichannel correlation analysis method.},
}
@article {pmid35262127,
year = {2022},
author = {Xiang, W and Xie, Y and Han, Y and Long, Z and Zhang, W and Zhong, T and Liang, S and Xing, L and Xue, X and Zhan, Y},
title = {A self-powered wearable brain-machine-interface system for ceasing action.},
journal = {Nanoscale},
volume = {14},
number = {12},
pages = {4671-4678},
doi = {10.1039/d1nr08168c},
pmid = {35262127},
issn = {2040-3372},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Electric Power Supplies ; Electrodes ; Mice ; *Wearable Electronic Devices ; },
abstract = {A self-powered wearable brain-machine-interface system with pulse detection and brain stimulation for ceasing action has been realized. The system is composed of (1) a power supply unit that employs a piezoelectric generator and converts the mechanical energy of human daily activities into electricity; (2) a neck pulse biosensor that allows continuous measurements of carotid pulse by using a piezoelectric polyvinylidene fluoride film; (3) a data analysis module that enables a coordinated brain-machine-interface system to output brain stimulation signals; and (4) brain stimulating electrodes linked to the brain that implement behavioral intervention. Demonstration of the system with stimulating electrodes implanted in the periaqueductal gray (PAG) in running mice reveals the great effect of forced ceasing action. The mice stop their running within several seconds when the stimulation signals are sent into the PAG brain region (inducing fear). This self-powered scheme for neural stimulation realizes specific behavioral intervention without any external power supply, thus providing a new concept for future behavior intervention.},
}
@article {pmid35260273,
year = {2022},
author = {Apra, C and Serra, M and Robert, H and Carpentier, A},
title = {Early rehabilitation using gait exoskeletons is possible in the neurosurgical setting, even in patients with cognitive impairment.},
journal = {Neuro-Chirurgie},
volume = {68},
number = {4},
pages = {458-460},
doi = {10.1016/j.neuchi.2021.12.010},
pmid = {35260273},
issn = {1773-0619},
mesh = {*Cognitive Dysfunction ; *Exoskeleton Device ; Gait ; Humans ; *Robotics ; },
}
@article {pmid35259107,
year = {2022},
author = {Chen, X and Yu, Y and Tang, J and Zhou, L and Liu, K and Liu, Z and Chen, S and Wang, J and Zeng, LL and Liu, J and Hu, D},
title = {Clinical Validation of BCI-Controlled Wheelchairs in Subjects With Severe Spinal Cord Injury.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {579-589},
doi = {10.1109/TNSRE.2022.3156661},
pmid = {35259107},
issn = {1558-0210},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Humans ; *Spinal Cord Injuries ; *Wheelchairs ; },
abstract = {Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a "locked-in" state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients' average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients' average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient's physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.},
}
@article {pmid35257802,
year = {2022},
author = {Varela-Moreira, A and van Leur, H and Krijgsman, D and Ecker, V and Braun, M and Buchner, M and Fens, MHAM and Hennink, WE and Schiffelers, RM},
title = {Utilizing in vitro drug release assays to predict in vivo drug retention in micelles.},
journal = {International journal of pharmaceutics},
volume = {618},
number = {},
pages = {121638},
doi = {10.1016/j.ijpharm.2022.121638},
pmid = {35257802},
issn = {1873-3476},
mesh = {Albumins ; Animals ; *Drug Carriers/chemistry ; Drug Liberation ; Mice ; *Micelles ; Octoxynol ; Polyethylene Glycols/chemistry ; Polymers/chemistry ; },
abstract = {In the present work, we aim at developing an in vitro release assay to predict circulation times of hydrophobic drugs loaded into polymeric micelles (PM), upon intravenous (i.v.) administration. PM based on poly (ethylene glycol)-b-poly (N-2-benzoyloxypropyl methacrylamide) (mPEG-b-p(HPMA-Bz)) block copolymer were loaded with a panel of hydrophobic anti-cancer drugs and characterized for size, loading efficiency and release profile in different release media. Circulation times in mice of two selected drugs loaded in PM were evaluated and compared to the in vitro release profile. Release of drugs from PM was evaluated over 7 days in PBS containing Triton X-100 and in PBS containing albumin at physiological concentration (40 g/L). The results were utilized to identify crucial molecular features of the studied hydrophobic drugs leading to better micellar retention. For the best and the worst retained drugs in the in vitro assays (ABT-737 and BCI, respectively), the circulation of free and entrapped drugs into PM was examined after i.v. administration in mice. We found in vivo drug retention at 24 h post-injection similar to the retention found in the in vitro assays. This demonstrates that in vitro release assay in buffers supplemented with albumin, and to a lesser degree Triton X-100, can be employed to predict the in vivo circulation kinetics of drugs loaded in PM. Utilizing media containing acceptor molecules for hydrophobic compounds, provide a first screen to understand the stability of drug-loaded PM in the circulation and, therefore, can contribute to the reduction of animals used for circulation kinetics studies.},
}
@article {pmid35256748,
year = {2022},
author = {Hua, J and Wolff, A and Zhang, J and Yao, L and Zang, Y and Luo, J and Ge, X and Liu, C and Northoff, G},
title = {Alpha and theta peak frequency track on- and off-thoughts.},
journal = {Communications biology},
volume = {5},
number = {1},
pages = {209},
pmid = {35256748},
issn = {2399-3642},
support = {//CIHR/Canada ; },
mesh = {*Cognition ; *Electroencephalography ; },
abstract = {Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics.},
}
@article {pmid35255123,
year = {2022},
author = {Lee, LO and Grodstein, F and Trudel-Fitzgerald, C and James, P and Okuzono, SS and Koga, HK and Schwartz, J and Spiro, A and Mroczek, DK and Kubzansky, LD},
title = {Optimism, Daily Stressors, and Emotional Well-Being Over Two Decades in a Cohort of Aging Men.},
journal = {The journals of gerontology. Series B, Psychological sciences and social sciences},
volume = {77},
number = {8},
pages = {1373-1383},
pmid = {35255123},
issn = {1758-5368},
support = {R01 AG018436/AG/NIA NIH HHS/United States ; R01 AG053273/AG/NIA NIH HHS/United States ; RF1 AG064006/AG/NIA NIH HHS/United States ; //U.S. Department of Veterans Affairs/ ; K08 AG048221/AG/NIA NIH HHS/United States ; //Epidemiological Research Centers/ ; //Clinical Science Research and Development Service/ ; //Veterans Affairs Cooperative Studies Program/ ; R01 AG067622/AG/NIA NIH HHS/United States ; },
mesh = {*Affect/physiology ; Aged ; Aging/psychology ; Bayes Theorem ; Emotions/physiology ; Humans ; Male ; Optimism ; *Stress, Psychological/psychology ; },
abstract = {OBJECTIVES: Growing evidence supports optimism as a health asset, yet how optimism influences well-being and health remains uncertain. We evaluated 1 potential pathway-the association of optimism with daily stress processes-and tested 2 hypotheses. The stressor exposure hypothesis posits that optimism would preserve emotional well-being by limiting exposure to daily stressors. The buffering hypothesis posits that higher optimism would be associated with lower emotional reactivity to daily stressors and more effective emotional recovery from them.<br><br>METHODS: Participants were 233 men from the Veterans Affairs Normative Aging Study who completed the Minnesota Multiphasic Personality Inventory-2 Revised Optimism-Pessimism scale in 1986/1991 and participated in up to three 8-day daily diary bursts in 2002-2010 (age at first burst: M = 76.7, SD = 6.5). Daily stressor occurrence, positive affect (PA), and negative affect (NA) were assessed nightly. We evaluated the hypotheses using multilevel structural equation models.<br><br>RESULTS: Optimism was unrelated to emotional reactivity to or recovery from daily stressors. Higher optimism was associated with higher average daily PA (B = 2.31, 95% Bayesian credible interval [BCI]: 1.24, 3.38) but not NA, independent of stressor exposure. Lower stressor exposure mediated the association of higher optimism with lower daily NA (indirect effect: B = -0.27, 95% BCI: -0.50, -0.09), supporting the stressor exposure hypothesis.<br><br>DISCUSSION: Findings from a sample of older men suggest that optimism may be associated with more favorable emotional well-being in later life through differences in stressor exposure rather than emotional stress response. Optimism may preserve emotional well-being among older adults by engaging emotion regulation strategies that occur relatively early in the emotion-generative process.},
}
@article {pmid35254424,
year = {2022},
author = {Dzianok, P and Antonova, I and Wojciechowski, J and Dreszer, J and Kublik, E},
title = {The Nencki-Symfonia electroencephalography/event-related potential dataset: Multiple cognitive tasks and resting-state data collected in a sample of healthy adults.},
journal = {GigaScience},
volume = {11},
number = {},
pages = {},
pmid = {35254424},
issn = {2047-217X},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; *Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Young Adult ; },
abstract = {BACKGROUND: One of the goals of neuropsychology is to understand the brain mechanisms underlying aspects of attention and cognitive control. Several tasks have been developed as a part of this body of research, however their results are not always consistent. A reliable comparison of the data and a synthesis of study conclusions has been precluded by multiple methodological differences. Here, we describe a publicly available, high-density electroencephalography (EEG) dataset obtained from 42 healthy young adults while they performed 3 cognitive tasks: (i) an extended multi-source interference task; (ii) a 3-stimuli oddball task; (iii) a control, simple reaction task; and (iv) a resting-state protocol. Demographic and psychometric information are included within the dataset.<br><br>DATASET VALIDATION: First, data validation confirmed acceptable quality of the obtained EEG signals. Typical event-related potential (ERP) waveforms were obtained, as expected for attention and cognitive control tasks (i.e., N200, P300, N450). Behavioral results showed the expected progression of reaction times and error rates, which confirmed the effectiveness of the applied paradigms.<br><br>CONCLUSIONS: This dataset is well suited for neuropsychological research regarding common and distinct mechanisms involved in different cognitive tasks. Using this dataset, researchers can compare a wide range of classical EEG/ERP features across tasks for any selected subset of electrodes. At the same time, 128-channel EEG recording allows for source localization and detailed connectivity studies. Neurophysiological measures can be correlated with additional psychometric data obtained from the same participants. This dataset can also be used to develop and verify novel analytical and classification approaches that can advance the field of deep/machine learning algorithms, recognition of single-trial ERP responses to different task conditions, and detection of EEG/ERP features for use in brain-computer interface applications.},
}
@article {pmid35253675,
year = {2022},
author = {Villamil, V and Wolbring, G},
title = {Influencing discussions and use of neuroadvancements as professionals and citizens: Perspectives of Canadian speech-language pathologists and audiologists.},
journal = {Work (Reading, Mass.)},
volume = {71},
number = {3},
pages = {565-584},
doi = {10.3233/WOR-205104},
pmid = {35253675},
issn = {1875-9270},
mesh = {*Audiologists ; Canada ; Humans ; Pathologists ; Speech ; *Speech-Language Pathology ; },
abstract = {BACKGROUND: Early involvement of stakeholders in neuroethics and neurogovernance discourses of neuroscientific and neurotechnological advancements is seen as essential to curtail negative consequences. Speech-language pathologists (SLPs) and audiologists (AUs) make use of neuroadvancements including cochlear implants, brain-computer interfaces, and deep-brain stimulation. Although they have a stake in neuroethics and neurogovernance discussions, they are rarely mentioned in having a role, whether as professionals or as citizens.<br><br>OBJECTIVE: The objective of the study was to explore the role of SLPs and AUs as professionals and citizens in neuroethics and neurogovernance discussions and examine the utility of lifelong learning mechanisms to learn about the implications of neuroadvancements to contribute in a meaningful way to these discussions.<br><br>METHODS: Semi-structured interviews conducted with 7 SLPs and 3 AUs were analyzed using thematic analysis.<br><br>RESULTS: Participants stated that their roles expected from them as professionals and as citizens indicate the importance to be knowledgeable on ethical, legal, and social implications of neuroadvancements and that lifelong learning is not used to learn about these implications.<br><br>CONCLUSION: More must be done to facilitate the participation of SLPs and AUs in neuroethics and neurogovernance discussions, which would enrich the neuroethics and neurogovernance discourses benefitting patients, professionals, and the public.},
}
@article {pmid35252458,
year = {2022},
author = {Sun, X and Li, M and Li, Q and Yin, H and Jiang, X and Li, H and Sun, Z and Yang, T},
title = {Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {9935192},
pmid = {35252458},
issn = {2314-6141},
mesh = {*Brain-Computer Interfaces ; *Cognitive Dysfunction/complications ; Electroencephalography/methods ; Humans ; Recovery of Function/physiology ; *Stroke ; *Stroke Rehabilitation/methods ; },
abstract = {Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions. This technique has been widely used in the rehabilitation of stroke patients in recent years and appears to promote motor function recovery after stroke. At present, the application of BCI in poststroke cognitive impairment is increasing, which is a common complication that also affects the rehabilitation process. This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.},
}
@article {pmid35251959,
year = {2022},
author = {Puttanawarut, C and Sirirutbunkajorn, N and Tawong, N and Jiarpinitnun, C and Khachonkham, S and Pattaranutaporn, P and Wongsawat, Y},
title = {Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer.},
journal = {Frontiers in oncology},
volume = {12},
number = {},
pages = {768152},
pmid = {35251959},
issn = {2234-943X},
abstract = {PURPOSE: The aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.<br><br>MATERIALS AND METHODS: A dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.<br><br>RESULT: The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 &#xb1; 0.11 and 0.75 &#xb1; 0.10, 0.71 &#xb1; 0.10 and 0.77 &#xb1; 0.09, 0.71 &#xb1; 0.11 and 0.79 &#xb1; 0.09, and 0.75 &#xb1; 0.10 and 0.81 &#xb1; 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 &#xb1; 0.18 and 0.37 &#xb1; 0.20, 0.67 &#xb1; 0.17 and 0.37 &#xb1; 0.20, 0.67 &#xb1; 0.16 and 0.45 &#xb1; 0.23, and 0.68 &#xb1; 0.16 and 0.44 &#xb1; 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.<br><br>CONCLUSION: The results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.},
}
@article {pmid35251147,
year = {2022},
author = {Thilagaraj, M and Ramkumar, S and Arunkumar, N and Durgadevi, A and Karthikeyan, K and Hariharasitaraman, S and Rajasekaran, MP and Govindan, P},
title = {Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4487254},
pmid = {35251147},
issn = {1687-5273},
mesh = {Adult ; Age Factors ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Machine Learning ; Neural Networks, Computer ; User-Computer Interface ; Young Adult ; },
abstract = {Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.},
}
@article {pmid35250454,
year = {2022},
author = {Francis, JT and Rozenboym, A and von Kraus, L and Xu, S and Chhatbar, P and Semework, M and Hawley, E and Chapin, J},
title = {Similarities Between Somatosensory Cortical Responses Induced via Natural Touch and Microstimulation in the Ventral Posterior Lateral Thalamus in Macaques.},
journal = {Frontiers in neuroscience},
volume = {16},
number = {},
pages = {812837},
pmid = {35250454},
issn = {1662-4548},
abstract = {Lost sensations, such as touch, could be restored by microstimulation (MiSt) along the sensory neural substrate. Such neuroprosthetic sensory information can be used as feedback from an invasive brain-machine interface (BMI) to control a robotic arm/hand, such that tactile and proprioceptive feedback from the sensorized robotic arm/hand is directly given to the BMI user. Microstimulation in the human somatosensory thalamus (Vc) has been shown to produce somatosensory perceptions. However, until recently, systematic methods for using thalamic stimulation to evoke naturalistic touch perceptions were lacking. We have recently presented rigorous methods for determining a mapping between ventral posterior lateral thalamus (VPL) MiSt, and neural responses in the somatosensory cortex (S1), in a rodent model (Choi et al., 2016; Choi and Francis, 2018). Our technique minimizes the difference between S1 neural responses induced by natural sensory stimuli and those generated via VPL MiSt. Our goal is to develop systems that know what neural response a given MiSt will produce and possibly allow the development of natural "sensation." To date, our optimization has been conducted in the rodent model and simulations. Here, we present data from simple non-optimized thalamic MiSt during peri-operative experiments, where we used MiSt in the VPL of macaques, which have a somatosensory system more like humans, as compared to our previous rat work (Li et al., 2014; Choi et al., 2016). We implanted arrays of microelectrodes across the hand area of the macaque S1 cortex as well as in the VPL. Multi and single-unit recordings were used to compare cortical responses to natural touch and thalamic MiSt in the anesthetized state. Post-stimulus time histograms were highly correlated between the VPL MiSt and natural touch modalities, adding support to the use of VPL MiSt toward producing a somatosensory neuroprosthesis in humans.},
}
@article {pmid35248817,
year = {2022},
author = {Xu, F and Xu, X and Sun, Y and Li, J and Dong, G and Wang, Y and Li, H and Wang, L and Zhang, Y and Pang, S and Yin, S},
title = {A framework for motor imagery with LSTM neural network.},
journal = {Computer methods and programs in biomedicine},
volume = {218},
number = {},
pages = {106692},
doi = {10.1016/j.cmpb.2022.106692},
pmid = {35248817},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND AND OBJECTIVE: How to learn robust representations from brain activities and to improve algorithm performance are the most significant issues for brain-computer interface systems.<br><br>METHODS: This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for implementing an effective motor imagery-based brain-computer interface system. The unique information processing mechanism of the long short-term memory network characterizes spatio-temporal dynamics in time sequences. This study evaluates the proposed method using publically available electroencephalogram/electrocorticogram datasets.<br><br>RESULTS: The decoded features coupled with a gradient boosting classifier could obtain high recognition accuracies of 99% for electroencephalogram and 100% for electrocorticogram, respectively.<br><br>CONCLUSIONS: The results demonstrated that the proposed model can estimate robust spatial-temporal features and obtain significant performance improvement for motor imagery-based brain-computer interface systems. Further, the proposed method is of low computational complexity.},
}
@article {pmid35241365,
year = {2022},
author = {Girges, C and Vijiaratnam, N and Zrinzo, L and Ekanayake, J and Foltynie, T},
title = {Volitional Control of Brain Motor Activity and Its Therapeutic Potential.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {25},
number = {8},
pages = {1187-1196},
doi = {10.1016/j.neurom.2022.01.007},
pmid = {35241365},
issn = {1525-1403},
mesh = {Humans ; *Neurofeedback/methods ; Brain ; *Brain-Computer Interfaces ; Learning ; Motor Activity ; },
abstract = {BACKGROUND: Neurofeedback training is a closed-loop neuromodulatory technique in which real-time feedback of brain activity and connectivity is provided to the participant for the purpose of volitional neural control. Through practice and reinforcement, such learning has been shown to facilitate measurable changes in brain function and behavior.<br><br>OBJECTIVES: In this review, we examine how neurofeedback, coupled with motor imagery training, has the potential to improve or normalize motor function in neurological diseases such as Parkinson disease and chronic stroke. We will also explore neurofeedback in the context of brain-machine interfaces (BMIs), discussing both noninvasive and invasive methods which have been used to power external devices (eg, robot hand orthosis or exoskeleton) in the context of motor neurorehabilitation.<br><br>CONCLUSIONS: The published literature provides mounting high-quality evidence that neurofeedback and BMI control may lead to clinically relevant changes in brain function and behavior.},
}
@article {pmid35237140,
year = {2022},
author = {Rahman, ML and Files, BT and Oiknine, AH and Pollard, KA and Khooshabeh, P and Song, C and Passaro, AD},
title = {Combining Neural and Behavioral Measures Enhances Adaptive Training.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {787576},
pmid = {35237140},
issn = {1662-5161},
abstract = {Adaptive training adjusts a training task with the goal of improving learning outcomes. Adaptive training has been shown to improve human performance in attention, working memory capacity, and motor control tasks. Additionally, correlations have been observed between neural EEG spectral features (4-13 Hz) and the performance of some cognitive tasks. This relationship suggests some EEG features may be useful in adaptive training regimens. Here, we anticipated that adding a neural measure into a behavioral-based adaptive training system would improve human performance on a subsequent transfer task. We designed, developed, and conducted a between-subjects study of 44 participants comparing three training regimens: Single Item Fixed Difficulty (SIFD), Behaviorally Adaptive Training (BAT), and Combined Adaptive Training (CAT) using both behavioral and EEG measures. Results showed a statistically significant transfer task performance advantage of the CAT-based system relative to SIFD and BAT systems of 6 and 9 percentage points, respectively. Our research shows a promising pathway for designing closed-loop BCI systems based on both users' behavioral performance and neural signals for augmenting human performance.},
}
@article {pmid35235804,
year = {2022},
author = {Ruan, Y and Li, KY and Zheng, R and Yan, YQ and Wang, ZX and Chen, Y and Liu, Y and Tian, J and Zhu, LY and Lou, HF and Yu, YQ and Pu, JL and Zhang, BR},
title = {Cholinergic neurons in the pedunculopontine nucleus guide reversal learning by signaling the changing reward contingency.},
journal = {Cell reports},
volume = {38},
number = {9},
pages = {110437},
doi = {10.1016/j.celrep.2022.110437},
pmid = {35235804},
issn = {2211-1247},
mesh = {Cholinergic Agents ; Cholinergic Neurons ; *Intralaminar Thalamic Nuclei ; *Reversal Learning ; Reward ; },
abstract = {Cognitive flexibility enables effective switching between mental processes to generate appropriate responses. Cholinergic neurons (CNs) within the pedunculopontine nucleus (PPN) are associated with many functions, but their contribution to cognitive flexibility remains poorly understood. Here we measure PPN cholinergic activities using calcium indicators during the attentional set-shifting task. We find that PPN CNs exhibit increasing activities correlated with rewards during each stage and error trials in reversal stages, indicating sensitivity to rule switching. Inhibition of PPN cholinergic activity selectively impairs reversal learning, which improves with PPN CN activation. Activation of PPN CNs projecting to the substantia nigra pars compacta, mediodorsal thalamus, and parafascicular nucleus in a time-locked manner with reward improves reversal learning. Therefore, PPN CNs may encode not only reward signals but also the information of changing reward contingency that contributes to guiding reversal learning through output projections to multiple nuclei that participate in flexibility.},
}
@article {pmid35235515,
year = {2022},
author = {Liu, C and Jin, J and Daly, I and Li, S and Sun, H and Huang, Y and Wang, X and Cichocki, A},
title = {SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {540-549},
doi = {10.1109/TNSRE.2022.3156076},
pmid = {35235515},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs. First, raw EEG is segmented into different time windows and mapped into the CSP feature space. Then, SincNets are used as filter bank band-pass filters to automatically filter the data. Next, we used squeeze-and-excitation modules to learn a sparse representation of the filtered data. The resulting sparse data were fed into convolutional neural networks to learn deep feature representations. Finally, these deep features were fed into a gated recurrent unit module to seek sequential relations, and a fully connected layer was used for classification. We used the BCI competition IV datasets 2a and 2b to verify the effectiveness of our SHNN method. The mean classification accuracies (kappa values) of our SHNN method are 0.7426 (0.6648) on dataset 2a and 0.8349 (0.6697) on dataset 2b, respectively. The statistical test results demonstrate that our SHNN can significantly outperform other state-of-the-art methods on these datasets.},
}
@article {pmid35235180,
year = {2022},
author = {Shao, Y and Ge, Q and Yang, J and Wang, M and Zhou, Y and Guo, JX and Zhu, M and Shi, J and Hu, Y and Shen, L and Chen, Z and Li, XM and Zhu, JM and Zhang, J and Duan, S and Chen, J},
title = {Pathological Networks Involving Dysmorphic Neurons in Type II Focal Cortical Dysplasia.},
journal = {Neuroscience bulletin},
volume = {38},
number = {9},
pages = {1007-1024},
pmid = {35235180},
issn = {1995-8218},
mesh = {Animals ; *Drug Resistant Epilepsy/surgery ; *Epilepsy/pathology ; *Malformations of Cortical Development/pathology ; Malformations of Cortical Development, Group I ; Mice ; Neurons/pathology ; Seizures/pathology ; },
abstract = {Focal cortical dysplasia (FCD) is one of the most common causes of drug-resistant epilepsy. Dysmorphic neurons are the major histopathological feature of type II FCD, but their role in seizure genesis in FCD is unclear. Here we performed whole-cell patch-clamp recording and morphological reconstruction of cortical principal neurons in postsurgical brain tissue from drug-resistant epilepsy patients. Quantitative analyses revealed distinct morphological and electrophysiological characteristics of the upper layer dysmorphic neurons in type II FCD, including an enlarged soma, aberrant dendritic arbors, increased current injection for rheobase action potential firing, and reduced action potential firing frequency. Intriguingly, the upper layer dysmorphic neurons received decreased glutamatergic and increased GABAergic synaptic inputs that were coupled with upregulation of the Na[+]-K[+]-Cl[-] cotransporter. In addition, we found a depolarizing shift of the GABA reversal potential in the CamKII-cre::PTEN[flox/flox] mouse model of drug-resistant epilepsy, suggesting that enhanced GABAergic inputs might depolarize dysmorphic neurons. Thus, imbalance of synaptic excitation and inhibition of dysmorphic neurons may contribute to seizure genesis in type II FCD.},
}
@article {pmid35234668,
year = {2022},
author = {Li, P and Li, C and Bore, JC and Si, Y and Li, F and Cao, Z and Zhang, Y and Wang, G and Zhang, Z and Yao, D and Xu, P},
title = {L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac59a4},
pmid = {35234668},
issn = {1741-2552},
mesh = {*Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent.Approach.In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments.Main results.A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.Significance.The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.},
}
@article {pmid35234665,
year = {2022},
author = {Moly, A and Costecalde, T and Martel, F and Martin, M and Larzabal, C and Karakas, S and Verney, A and Charvet, G and Chabardes, S and Benabid, AL and Aksenova, T},
title = {An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic.},
journal = {Journal of neural engineering},
volume = {19},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ac59a0},
pmid = {35234665},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Clinical Studies as Topic ; Electrocorticography/methods ; Epidural Space ; *Exoskeleton Device ; Humans ; Linear Models ; },
abstract = {Objective.The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration.Approach.Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a 'gating' model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action.Main results.Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated.Significance.Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.},
}
@article {pmid35231982,
year = {2022},
author = {Xu, H and Gong, A and Ding, P and Luo, J and Chen, C and Fu, Y},
title = {[Key technologies for intelligent brain-computer interaction based on magnetoencephalography].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {198-206},
pmid = {35231982},
issn = {1001-5515},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; *Magnetoencephalography ; Technology ; },
abstract = {Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.},
}
@article {pmid35231981,
year = {2022},
author = {Zhang, Y and Xia, M and Chen, K and Xu, P and Yao, D},
title = {[Progresses and prospects on frequency recognition methods for steady-state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {192-197},
pmid = {35231981},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.},
}
@article {pmid35231964,
year = {2022},
author = {Cui, Y and Xie, S and Xie, X and Duan, X and Gao, C},
title = {[A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {39-46},
pmid = {35231964},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Rapid serial visual presentation-brain computer interface (RSVP-BCI) is the most popular technology in the early discover task based on human brain. This algorithm can obtain the rapid perception of the environment by human brain. Decoding brain state based on single-trial of multichannel electroencephalogram (EEG) recording remains a challenge due to the low signal-to-noise ratio (SNR) and nonstationary. To solve the problem of low classification accuracy of single-trial in RSVP-BCI, this paper presents a new feature extraction algorithm which uses principal component analysis (PCA) and common spatial pattern (CSP) algorithm separately in spatial domain and time domain, creating a spatial-temporal hybrid CSP-PCA (STHCP) algorithm. By maximizing the discrimination distance between target and non-target, the feature dimensionality was reduced effectively. The area under the curve (AUC) of STHCP algorithm is higher than that of the three benchmark algorithms (SWFP, CSP and PCA) by 17.9%, 22.2% and 29.2%, respectively. STHCP algorithm provides a new method for target detection.},
}
@article {pmid35231963,
year = {2022},
author = {Xu, D and Li, M},
title = {[Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {39},
number = {1},
pages = {28-38},
pmid = {35231963},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Machine Learning ; },
abstract = {Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.},
}
@article {pmid35231052,
year = {2022},
author = {Fossi, LD and Debien, C and Demarty, AL and Vaiva, G and Messiah, A},
title = {Loss to follow-up in a population-wide brief contact intervention to prevent suicide attempts - The VigilanS program, France.},
journal = {PloS one},
volume = {17},
number = {3},
pages = {e0263379},
pmid = {35231052},
issn = {1932-6203},
mesh = {Humans ; *Suicide, Attempted/statistics &amp; numerical data ; Male ; Female ; Adult ; France/epidemiology ; Middle Aged ; *Lost to Follow-Up ; Follow-Up Studies ; Suicide Prevention ; Young Adult ; },
abstract = {BACKGROUND: Brief Contact Interventions (BCIs) after a suicide attempt (SA) are an important element of prevention against SA and suicide. VigilanS generalizes to a whole French region a BCI combining resource cards, telephone calls and sending postcards, according to a predefined algorithm. However, a major obstacle to such real-life intervention is the loss of contact during follow-up. Here, we analyze the occurrence of loss of follow-up (LFU) and compare characteristics of patients LFU with follow-up completers.<br><br>METHODS: The study concerned patients included in VigilanS over the period from 1st January 2015 to 31 December 2018, with an end of follow-up on 1st July 2019. We performed a series of descriptive analysis and logistic regressions. The outcome was the loss to follow-up, relative to the 6th month call marking the end of the follow-up; the predictive variables were the characteristics of the patient at entry and during follow-up. Age and sex were considered as adjustment variables.<br><br>RESULTS: 11879 inclusions occurred during the study period, corresponding to 10666 different patients. The mean age was 40.6 &#xb1; 15 years. More than a third were non-first suicide attempters (46.6%) and the most frequent means of suicide was by voluntary drug intoxication (83.2%). 8335 patients were LFU. After simple and multiple regression, a significant relationship with loss to follow-up was identified among non-first suicide attempters, alcohol consumers, patients having no companion on arrival at the emergency room, patients who didn't make or receive any calls. An increased stay in hospital after a SA was a protective factor against loss of follow-up.<br><br>CONCLUSION: A majority of patients were lost to follow-up by the expected surveillance time of 6 months. Characteristics of lost patients will help focusing efforts to improve retention in the VigilanS program and might give insights for BCI implemented elsewhere.},
}
@article {pmid35227374,
year = {2022},
author = {Merrick, CM and Dixon, TC and Breska, A and Lin, J and Chang, EF and King-Stephens, D and Laxer, KD and Weber, PB and Carmena, J and Thomas Knight, R and Ivry, RB},
title = {Left hemisphere dominance for bilateral kinematic encoding in the human brain.},
journal = {eLife},
volume = {11},
number = {},
pages = {},
pmid = {35227374},
issn = {2050-084X},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; Brain ; Electrocorticography ; *Functional Laterality/physiology ; Humans ; *Movement/physiology ; Psychomotor Performance/physiology ; },
abstract = {Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.},
}
@article {pmid35226599,
year = {2022},
author = {Corsi, MC and Chevallier, S and Fallani, FV and Yger, F},
title = {Functional Connectivity Ensemble Method to Enhance BCI Performance (FUCONE).},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {9},
pages = {2826-2838},
doi = {10.1109/TBME.2022.3154885},
pmid = {35226599},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination/physiology ; },
abstract = {OBJECTIVE: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery.<br><br>METHODS: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets.<br><br>RESULTS: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods.<br><br>CONCLUSION: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter- and intra-subject variability.<br><br>SIGNIFICANCE: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.},
}
@article {pmid35223807,
year = {2021},
author = {Hou, Y and Jia, S and Lun, X and Zhang, S and Chen, T and Wang, F and Lv, J},
title = {Deep Feature Mining via the Attention-Based Bidirectional Long Short Term Memory Graph Convolutional Neural Network for Human Motor Imagery Recognition.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {9},
number = {},
pages = {706229},
pmid = {35223807},
issn = {2296-4185},
abstract = {Recognition accuracy and response time are both critically essential ahead of building the practical electroencephalography (EEG)-based brain-computer interface (BCI). However, recent approaches have compromised either the classification accuracy or the responding time. This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional long short-term memory (BiLSTM) with the attention mechanism is employed, and the graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. Particularly, this method is trained and tested on the short EEG recording with only 0.4 s in length, and the result has shown effective and efficient prediction based on individual and groupwise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw and almost-instant EEG signals, which paves the road to translate the EEG-based MI recognition to practical BCI systems.},
}
@article {pmid35223798,
year = {2022},
author = {Hu, Y and Liu, N and Chen, K and Liu, M and Wang, F and Liu, P and Zhang, Y and Zhang, T and Xiao, X},
title = {Resilient and Self-Healing Hyaluronic Acid/Chitosan Hydrogel With Ion Conductivity, Low Water Loss, and Freeze-Tolerance for Flexible and Wearable Strain Sensor.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {10},
number = {},
pages = {837750},
pmid = {35223798},
issn = {2296-4185},
abstract = {Conductive hydrogel is a vital candidate for the fabrication of flexible and wearable electric sensors due to its good designability and biocompatibility. These well-designed conductive hydrogel-based flexible strain sensors show great potential in human motion monitoring, artificial skin, brain computer interface (BCI), and so on. However, easy drying and freezing of conductive hydrogels with high water content greatly limited their further application. Herein, we proposed a natural polymer-based conductive hydrogel with excellent mechanical property, low water loss, and freeze-tolerance. The main hydrogel network was formed by the Schiff base reaction between the hydrazide-grafted hyaluronic acid and the oxidized chitosan, and the added KCl worked as the conductive filler. The reversible crosslinking in the prepared hydrogel resulted in its resilience and self-healing feature. At the same time, the synthetic effect of KCl and glycerol endowed our hydrogel with outstanding anti-freezing property, while glycerol also endowed this hydrogel with anti-drying property. When this hydrogel was assembled as a flexible strain sensor, it showed good sensitivity (GF = 2.64), durability, and stability even under cold condition (-37°C).},
}
@article {pmid35221886,
year = {2021},
author = {Liu, Y and Huang, S and Wang, Z and Ji, F and Ming, D},
title = {Functional Reorganization After Four-Week Brain-Computer Interface-Controlled Supernumerary Robotic Finger Training: A Pilot Study of Longitudinal Resting-State fMRI.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {766648},
pmid = {35221886},
issn = {1662-4548},
abstract = {Humans have long been fascinated by the opportunities afforded through motor augmentation provided by the supernumerary robotic fingers (SRFs) and limbs (SRLs). However, the neuroplasticity mechanism induced by the motor augmentation equipment still needs further investigation. This study focused on the resting-state brain functional reorganization during longitudinal brain-computer interface (BCI)-controlled SRF training in using the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC) metrics. Ten right-handed subjects were enrolled for 4 weeks of BCI-controlled SRF training. The behavioral data and the neurological changes were recorded at baseline, training for 2 weeks, training for 4 weeks immediately after, and 2 weeks after the end of training. One-way repeated-measure ANOVA was used to investigate long-term motor improvement [F(2.805,25.24) = 43.94, p < 0.0001] and neurological changes. The fALFF values were significantly modulated in Cerebelum_6_R and correlated with motor function improvement (r = 0.6887, p < 0.0402) from t0 to t2. Besides, Cerebelum_9_R and Vermis_3 were also significantly modulated and showed different trends in longitudinal SRF training in using ReHo metric. At the same time, ReHo values that changed from t0 to t1 in Vermis_3 was significantly correlated with motor function improvement (r = 0.7038, p < 0.0344). We conclude that the compensation and suppression mechanism of the cerebellum existed during BCI-controlled SRF training, and this current result provided evidence to the neuroplasticity mechanism brought by the BCI-controlled motor-augmentation devices.},
}
@article {pmid35219429,
year = {2022},
author = {Vendrell-Llopis, N and Fang, C and Qü, AJ and Costa, RM and Carmena, JM},
title = {Diverse operant control of different motor cortex populations during learning.},
journal = {Current biology : CB},
volume = {32},
number = {7},
pages = {1616-1622.e5},
pmid = {35219429},
issn = {1879-0445},
support = {U19 NS104649/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning/physiology ; Mice ; *Motor Cortex/physiology ; Motor Neurons/physiology ; Pyramidal Tracts/physiology ; },
abstract = {During motor learning,[1] as well as during neuroprosthetic learning,[2-4] animals learn to control motor cortex activity in order to generate behavior. Two different populations of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations,[5][,][6] it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a calcium-imaging-based brain-machine interface (CaBMI)[3] and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that the animals learned to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that the motor cortex is more efficient at controlling the activity of pyramidal tract neurons, which are embedded deep in the cortex, and relaying motor commands outside the telencephalon.},
}
@article {pmid35219212,
year = {2022},
author = {Ambika, and Kumar, V and Jamwal, A and Kumar, V and Singh, D},
title = {Green bioprocess for degradation of synthetic dyes mixture using consortium of laccase-producing bacteria from Himalayan niches.},
journal = {Journal of environmental management},
volume = {310},
number = {},
pages = {114764},
doi = {10.1016/j.jenvman.2022.114764},
pmid = {35219212},
issn = {1095-8630},
mesh = {Azo Compounds/metabolism ; Bacteria/metabolism ; Biodegradation, Environmental ; *Coloring Agents/metabolism ; *Laccase/metabolism ; Spectroscopy, Fourier Transform Infrared ; },
abstract = {Microbial remediation of synthetic dyes from industrial effluents offers a sustainable and eco-friendly alternative. Herein, laccase-producing bacteria were isolated from decaying wood niches in the Himalayan region. A bacterial consortium (BC-I) was developed to decolorize synthetic dyes cocktail of three major groups (azo, anthraquinone, and triphenylmethane). BC-I consisted of Klebsiella sp. PCH427, Enterobacter sp. PCH428, and Pseudomonas sp. PCH429 can decolorize 77% of 240 mg/L dyes cocktail in 44 h at 37 °C. BC-I works under wide pH (4.0-10.0), a high salt concentration (NaCl, 10%), and low nutrients. Further, FT-IR and LC-MS validated the dyes cocktail degradation and identified the degraded products. Additionally, phytotoxicity analysis of BC-I treated dyes cocktail significantly reduced the toxicity to Vigna radiata and Cicer arietinum compared to untreated dyes cocktail. The present study has simulated environmental challenges of acidic, alkaline, and saline industrial dyes effluents, which are significant to bioremediation.},
}
@article {pmid35214978,
year = {2022},
author = {Mgharbel, A and Migdal, C and Bouchonville, N and Dupenloup, P and Fuard, D and Lopez-Soler, E and Tomba, C and Courçon, M and Gulino-Debrac, D and Delanoë-Ayari, H and Nicolas, A},
title = {Cells on Hydrogels with Micron-Scaled Stiffness Patterns Demonstrate Local Stiffness Sensing.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {12},
number = {4},
pages = {},
pmid = {35214978},
issn = {2079-4991},
support = {ANR-12- JSVE5-0008//Agence Nationale de la Recherche/ ; },
abstract = {Cell rigidity sensing-a basic cellular process allowing cells to adapt to mechanical cues-involves cell capabilities exerting force on the extracellular environment. In vivo, cells are exposed to multi-scaled heterogeneities in the mechanical properties of the surroundings. Here, we investigate whether cells are able to sense micron-scaled stiffness textures by measuring the forces they transmit to the extracellular matrix. To this end, we propose an efficient photochemistry of polyacrylamide hydrogels to design micron-scale stiffness patterns with kPa/µm gradients. Additionally, we propose an original protocol for the surface coating of adhesion proteins, which allows tuning the surface density from fully coupled to fully independent of the stiffness pattern. This evidences that cells pull on their surroundings by adjusting the level of stress to the micron-scaled stiffness. This conclusion was achieved through improvements in the traction force microscopy technique, e.g., adapting to substrates with a non-uniform stiffness and achieving a submicron resolution thanks to the implementation of a pyramidal optical flow algorithm. These developments provide tools for enhancing the current understanding of the contribution of stiffness alterations in many pathologies, including cancer.},
}
@article {pmid35214576,
year = {2022},
author = {Usama, N and Niazi, IK and Dremstrup, K and Jochumsen, M},
title = {Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214576},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {*Amputees ; Brain ; *Brain-Computer Interfaces ; *Cerebral Palsy ; Electroencephalography ; Humans ; *Stroke/diagnosis ; },
abstract = {Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.},
}
@article {pmid35214468,
year = {2022},
author = {Mandekar, S and Holland, A and Thielen, M and Behbahani, M and Melnykowycz, M},
title = {Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214468},
issn = {1424-8220},
mesh = {Ear ; Electrodes ; *Electroencephalography/methods ; *Forehead ; Humans ; Scalp ; },
abstract = {Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.},
}
@article {pmid35214341,
year = {2022},
author = {Siribunyaphat, N and Punsawad, Y},
title = {Steady-State Visual Evoked Potential-Based Brain-Computer Interface Using a Novel Visual Stimulus with Quick Response (QR) Code Pattern.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {4},
pages = {},
pmid = {35214341},
issn = {1424-8220},
support = {//Walailak University/ ; },
mesh = {Algorithms ; *Asthenopia ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.},
}
@article {pmid35210460,
year = {2022},
author = {Khalil, K and Asgher, U and Ayaz, Y},
title = {Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {3198},
pmid = {35210460},
issn = {2045-2322},
abstract = {The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.},
}
@article {pmid35208323,
year = {2022},
author = {Wang, M and Fan, Y and Li, L and Wen, F and Guo, B and Jin, M and Xu, J and Zhou, Y and Kang, X and Ji, B and Cheng, Y and Wang, G},
title = {Flexible Neural Probes with Optical Artifact-Suppressing Modification and Biofriendly Polypeptide Coating.},
journal = {Micromachines},
volume = {13},
number = {2},
pages = {},
pmid = {35208323},
issn = {2072-666X},
support = {2018YFE0120000//National Key R&amp;D Program of China/ ; GK199900X001//Fundamental Research Funds for the Provincial Universities of Zhejiang/ ; LQ21F010010//Zhejiang Provincial Natural Science Foundation of China/ ; 2019C04003//Zhejiang Provincial Key Research &amp; Development Project/ ; 62141409//National Natural Science Foundation of China/ ; 62104056//National Natural Science Foundation of China/ ; 2020TQ0246, 2021M692638//China Postdoctoral Science Foundation/ ; 21YF1451000//Shanghai Sailing Program/ ; 31020200QD013//the Fundamental Research Funds for the Central Universities/ ; },
abstract = {The advent of optogenetics provides a well-targeted tool to manipulate neurons because of its high time resolution and cell-type specificity. Recently, closed-loop neural manipulation techniques consisting of optical stimulation and electrical recording have been widely used. However, metal microelectrodes exposed to light radiation could generate photoelectric noise, thus causing loss or distortion of neural signal in recording channels. Meanwhile, the biocompatibility of neural probes remains to be improved. Here, five kinds of neural interface materials are deposited on flexible polyimide-based neural probes and illuminated with a series of blue laser pulses to study their electrochemical performance and photoelectric noises for single-unit recording. The results show that the modifications can not only improve the electrochemical performance, but can also reduce the photoelectric artifacts. In particular, the double-layer composite consisting of platinum-black and conductive polymer has the best comprehensive performance. Thus, a layer of polypeptide is deposited on the entire surface of the double-layer modified neural probes to further improve their biocompatibility. The results show that the biocompatible polypeptide coating has little effect on the electrochemical performance of the neural probe, and it may serve as a drug carrier due to its special micromorphology.},
}
@article {pmid35206341,
year = {2022},
author = {Jung, D and Choi, J and Kim, J and Cho, S and Han, S},
title = {EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {4},
pages = {},
pmid = {35206341},
issn = {1660-4601},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions/physiology ; *Gene-Environment Interaction ; Humans ; Support Vector Machine ; },
abstract = {Classifying emotional states is critical for brain-computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.},
}
@article {pmid35205490,
year = {2022},
author = {Jiang, L and Liu, S and Ma, Z and Lei, W and Chen, C},
title = {Regularized RKHS-Based Subspace Learning for Motor Imagery Classification.},
journal = {Entropy (Basel, Switzerland)},
volume = {24},
number = {2},
pages = {},
pmid = {35205490},
issn = {1099-4300},
support = {61773022//National Natural Science Foundation of China/ ; 68000-42050001//Science and Technology Program of Guangzhou/ ; },
abstract = {Brain-computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject's signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2-9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.},
}
@article {pmid35204415,
year = {2022},
author = {Jana, GC and Agrawal, A and Pattnaik, PK and Sain, M},
title = {DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {12},
number = {2},
pages = {},
pmid = {35204415},
issn = {2075-4418},
support = {Research Fund of 2021 (DSU-20210004).//This work was supported by Dongseo University, "Dongseo Cluster Project"/ ; },
abstract = {Brain Computer Interface technology enables a pathway for analyzing EEG signals for seizure detection. EEG signal decomposition, features extraction and machine learning techniques are more familiar in seizure detection. However, selecting decomposition technique and concatenation of their features for seizure detection is still in the state-of-the-art phase. This work proposes DWT-EMD Feature level Fusion-based seizure detection approach over multi and single channel EEG signals and studied the usability of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) feature fusion with respect to individual DWT and EMD features over classifiers SVM, SVM with RBF kernel, decision tree and bagging classifier for seizure detection. All classifiers achieved an improved performance over DWT-EMD feature level fusion for two benchmark seizure detection EEG datasets. Detailed quantification results have been mentioned in the Results section.},
}
@article {pmid35204011,
year = {2022},
author = {Quiles, V and Ferrero, L and Iáñez, E and Ortiz, M and Azorín, JM},
title = {Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35204011},
issn = {2076-3425},
support = {RTI2018-096677-B-I00//MCIN/AEI/10.13039/501100011033/ ; },
abstract = {This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.},
}
@article {pmid35203998,
year = {2022},
author = {Stawicki, P and Volosyak, I},
title = {cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35203998},
issn = {2076-3425},
abstract = {This paper investigates the effects of the repetitive block-wise training process on the classification accuracy for a code-modulated visual evoked potentials (cVEP)-based brain-computer interface (BCI). The cVEP-based BCIs are popular thanks to their autocorrelation feature. The cVEP-based stimuli are generated by a specific code pattern, usually the m-sequence, which is phase-shifted between the individual targets. Typically, the cVEP classification requires a subject-specific template (individually created from the user's own pre-recorded EEG responses to the same stimulus target), which is compared to the incoming electroencephalography (EEG) data, using the correlation algorithms. The amount of the collected user training data determines the accuracy of the system. In this offline study, previously recorded EEG data collected during an online experiment with 10 participants from multiple sessions were used. A template matching target identification, with similar models as the task-related component analysis (TRCA), was used for target classification. The spatial filter was generated by the canonical correlation analysis (CCA). When comparing the training models from one session with the same session's data (intra-session) and the model from one session with the data from the other session (inter-session), the accuracies were (94.84%, 94.53%) and (76.67%, 77.34%) for intra-sessions and inter-sessions, respectively. In order to investigate the most reliable configuration for accurate classification, the training data blocks from different sessions (days) were compared interchangeably. In the best training set composition, the participants achieved an average accuracy of 82.66% for models based only on two training blocks from two different sessions. Similarly, at least five blocks were necessary for the average accuracy to exceed 90%. The presented method can further improve cVEP-based BCI performance by reusing previously recorded training data.},
}
@article {pmid35203991,
year = {2022},
author = {Du, B and Cheng, X and Duan, Y and Ning, H},
title = {fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey.},
journal = {Brain sciences},
volume = {12},
number = {2},
pages = {},
pmid = {35203991},
issn = {2076-3425},
support = {KC2019SZ11, 54896055//University of Science and Technology Course Fund/ ; U1633121//the National Nature Science Foundation of China/ ; },
abstract = {Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.},
}
@article {pmid35203185,
year = {2022},
author = {Niu, X and Huang, S and Zhu, M and Wang, Z and Shi, L},
title = {Surround Modulation Properties of Tectal Neurons in Pigeons Characterized by Moving and Flashed Stimuli.},
journal = {Animals : an open access journal from MDPI},
volume = {12},
number = {4},
pages = {},
pmid = {35203185},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; 20A413009//Key Scientific Research Projects of Colleges and Universities in Henan province/ ; XKZDQY201905//the Key Discipline Construction Project of Zhengzhou University in 2019/ ; },
abstract = {Surround modulation has been abundantly studied in several mammalian brain areas, including the primary visual cortex, lateral geniculate nucleus, and superior colliculus (SC), but systematic analysis is lacking in the avian optic tectum (OT, homologous to mammal SC). Here, multi-units were recorded from pigeon (Columba livia) OT, and responses to different sizes of moving, flashed squares, and bars were compared. The statistical results showed that most tectal neurons presented suppressed responses to larger stimuli in both moving and flashed paradigms, and suppression induced by flashed squares was comparable with moving ones when the stimuli center crossed the near classical receptive field (CRF) center, which corresponded to the full surrounding condition. Correspondingly, the suppression grew weaker when the stimuli center moved across the CRF border, equivalent to partially surrounding conditions. Similarly, suppression induced by full surrounding flashed squares was more intense than by partially surrounding flashed bars. These results suggest that inhibitions performed on tectal neurons appear to be full surrounding rather than locally lateral. This study enriches the understanding of surround modulation properties of avian tectum neurons and provides possible hypotheses about the arrangement of inhibitions from other nuclei, both of which are important for clarifying the mechanism of target detection against clutter background performed by avians.},
}
@article {pmid35202615,
year = {2022},
author = {Han, J and Liu, C and Chu, J and Xiao, X and Chen, L and Xu, M and Ming, D},
title = {Effects of inter-stimulus intervals on concurrent P300 and SSVEP features for hybrid brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {372},
number = {},
pages = {109535},
doi = {10.1016/j.jneumeth.2022.109535},
pmid = {35202615},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Recently, we have implemented a high-speed brain-computer interface (BCI) system with a large instruction set using the concurrent P300 and steady-state visual evoked potential (SSVEP) features (also known as hybrid features). However, it remains unclear how to select inter-stimulus interval (ISI) for the proposed BCI system to balance the encoding efficiency and decoding performance.<br><br>NEW METHOD: This study developed a 6&#xa0;*&#xa0;9 hybrid P300-SSVEP BCI system and investigated a series of ISIs ranged from -175-0&#xa0;ms with a step of 25&#xa0;ms. The influence of ISI on the hybrid features was analyzed from several aspects, including the amplitude of the induced features, classification accuracy, information transfer rate (ITR). Twelve naive subjects were recruited for the experiment.<br><br>RESULTS: The results showed the ISI factor had a significant impact on the hybrid features. Specifically, as the values of ISI decreased, the amplitudes of the induced features and accuracies decreased gradually, while the ITRs increased rapidly. It's achieved the highest ITR of 158.50 bits/min when ISI equal to -&#xa0;175&#xa0;ms.<br><br>The optimal ISI in this study achieved superior performance in comparison with the one we used in the previous study.<br><br>CONCLUSIONS: The ISI can exert an important influence on the P300-SSVEP BCI system and its optimal value is -&#xa0;175&#xa0;ms in this study, which is significant for developing the high-speed BCI system with larger instruction sets in the future.},
}
@article {pmid35201989,
year = {2022},
author = {Goel, R and Nakagome, S and Paloski, WH and Contreras-Vidal, JL and Parikh, PJ},
title = {Assessment of Biomechanical Predictors of Occurrence of Low-Amplitude N1 Potentials Evoked by Naturally Occurring Postural Instabilities.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {476-485},
pmid = {35201989},
issn = {1558-0210},
support = {P2C HD086844/HD/NICHD NIH HHS/United States ; },
mesh = {Acceleration ; Aged ; Biomechanical Phenomena ; Electroencephalography ; *Evoked Potentials/physiology ; Humans ; *Postural Balance/physiology ; Young Adult ; },
abstract = {Naturally occurring postural instabilities that occur while standing and walking elicit specific cortical responses in the fronto-central regions (N1 potentials) followed by corrective balance responses to prevent falling. However, no framework could simultaneously track different biomechanical parameters preceding N1s, predict N1s, and assess their predictive power. Here, we propose a framework and show its utility by examining cortical activity (through electroencephalography [EEG]), ground reaction forces, and head acceleration in the anterior-posterior (AP) direction. Ten healthy young adults carried out a balance task of standing on a support surface with or without sway referencing in the AP direction, amplifying, or dampening natural body sway. Using independent components from the fronto-central cortical region obtained from subject-specific head models, we first robustly validated a prior approach on identifying low-amplitude N1 potentials before early signs of balance corrections. Then, a machine learning algorithm was used to evaluate different biomechanical parameters obtained before N1 potentials, to predict the occurrence of N1s. When different biomechanical parameters were directly compared, the time to boundary (TTB) was found to be the best predictor of the occurrence of upcoming low-amplitude N1 potentials during a balance task. Based on these findings, we confirm that the spatio-temporal characteristics of the center of pressure (COP) might serve as an essential parameter that can facilitate the early detection of postural instability in a balance task. Extending our framework to identify such biomarkers in dynamic situations like walking might improve the implementation of corrective balance responses through brain-machine-interfaces to reduce falls in the elderly.},
}
@article {pmid35201988,
year = {2022},
author = {Ma, X and Qiu, S and He, H},
title = {Time-Distributed Attention Network for EEG-Based Motor Imagery Decoding From the Same Limb.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {496-508},
doi = {10.1109/TNSRE.2022.3154369},
pmid = {35201988},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination/physiology ; Upper Extremity ; },
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) from the same limb can provide an intuitive control pathway but has received limited attention. It is still a challenge to classify multiple MI tasks from the same limb. The goal of this study is to propose a novel decoding method to classify the MI tasks of four joints of the same upper limb and the resting state. EEG signals were collected from 20 participants. A time-distributed attention network (TD-Atten) was proposed to adaptively assign different weights to different classes and frequency bands of the input multiband Common Spatial Pattern (CSP) features. The long short-term memory (LSTM) and dense layers were then used to learn sequential information from the reweight features and perform the classification. Our proposed method outperformed other baseline and deep learning-based methods and obtained the accuracies of 46.8% in the 5-class scenario and 53.4% in the 4-class scenario. The visualization results of attention weights indicated that the proposed framework can adaptively pay attention to alpha-band related features in MI tasks, which was consistent with the analysis of brain activation patterns. These results demonstrated the feasibility and interpretability of the attention mechanism in MI decoding and the potential of this fine MI paradigm to be applied for the control of a robotic arm or a neural prosthesis.},
}
@article {pmid35200389,
year = {2022},
author = {Yang, N and Liu, F and Zhang, X and Chen, C and Xia, Z and Fu, S and Wang, J and Xu, J and Cui, S and Zhang, Y and Yi, M and Wan, Y and Li, Q and Xu, S},
title = {A Hybrid Titanium-Softmaterial, High-Strength, Transparent Cranial Window for Transcranial Injection and Neuroimaging.},
journal = {Biosensors},
volume = {12},
number = {2},
pages = {},
pmid = {35200389},
issn = {2079-6374},
support = {2017YFA0701302//National Key R&amp;D Program of China/ ; 32171002//National Natural Science Foundation of China/ ; 81974166//National Natural Science Foundation of China/ ; 31872774//National Natural Science Foundation of China/ ; BMU2021MX002//Interdisciplinary Medicine Seed Fund of Peking University/ ; },
mesh = {Animals ; Mice ; Neuroimaging/methods ; Photons ; Printing, Three-Dimensional/instrumentation ; *Skull/diagnostic imaging ; *Titanium ; },
abstract = {A transparent and penetrable cranial window is essential for neuroimaging, transcranial injection and comprehensive understanding of cortical functions. For these applications, cranial windows made from glass coverslip, polydimethylsiloxane (PDMS), polymethylmethacrylate, crystal and silicone hydrogel have offered remarkable convenience. However, there is a lack of high-strength, high-transparency, penetrable cranial window with clinical application potential. We engineer high-strength hybrid Titanium-PDMS (Ti-PDMS) cranial windows, which allow large transparent area for in vivo two-photon imaging, and provide a soft window for transcranial injection. Laser scanning and 3D printing techniques are used to match the hybrid cranial window to different skull morphology. A multi-cycle degassing pouring process ensures a good combination of PDMS and Ti frame. Ti-PDMS cranial windows have a high fracture strength matching human skull bone, excellent light transmittance up to 94.4%, and refractive index close to biological tissue. Ti-PDMS cranial windows show excellent bio-compatibility during 21-week implantation in mice. Dye injection shows that the PDMS window has a "self-sealing" to keep liquid from leaking out. Two-photon imaging for brain tissues could be achieved up to 450 µm in z-depth. As a novel brain-computer-interface, this Ti-PDMS device offers an alternative choice for in vivo drug delivery, optical experiments, ultrasonic treatment and electrophysiology recording.},
}
@article {pmid35199034,
year = {2022},
author = {Cheng, H and Al-Sheikh, U and Chen, D and Duan, D and Kang, L},
title = {Protocol for glial Ca[2+] imaging in C. elegans following chemical, mechanical, or optogenetic stimulation.},
journal = {STAR protocols},
volume = {3},
number = {1},
pages = {101169},
pmid = {35199034},
issn = {2666-1667},
mesh = {Animals ; *Caenorhabditis elegans/genetics ; Calcium ; Neuroglia ; Neurons ; *Optogenetics ; },
abstract = {Caenorhabditis elegans is an exceptionally transparent model to analyze calcium (Ca[2+]) signals, but available protocols for neuronal Ca2[+] imaging may not be suitable for studying glial cells. Here, we present a detailed protocol for glial Ca[2+] imaging in C. elegans following three different approaches including chemical, mechanical, and optogenetic stimulation. We also provide the details for imaging analysis using Image-J. For complete details on the use and execution of this protocol, please refer to Duan et al. (2020).},
}
@article {pmid35197817,
year = {2021},
author = {Yang, W and Zhang, X and Li, Z and Zhang, Q and Xue, C and Huai, Y},
title = {The Effect of Brain-Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {766879},
pmid = {35197817},
issn = {1662-4548},
abstract = {BACKGROUND: Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain-computer interface (BCI)-based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued.<br><br>OBJECTIVE: We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies.<br><br>METHODS: The search terms used to retrieve related articles included "brain-computer interface," "stroke," and "upper extremity." A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis.<br><br>RESULTS: The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29-0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20-2.01) and 0.51 (95% CI: 0.09-0.92), respectively (p = 0.24).<br><br>CONCLUSION: Brain-computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.},
}
@article {pmid35196360,
year = {2022},
author = {Halme, HL and Parkkonen, L},
title = {The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training.},
journal = {PloS one},
volume = {17},
number = {2},
pages = {e0264354},
pmid = {35196360},
issn = {1932-6203},
mesh = {Adult ; Brain Waves ; Brain-Computer Interfaces ; *Feedback, Sensory ; Humans ; *Proprioception ; Sensorimotor Cortex/*physiology ; *Visual Perception ; },
abstract = {Brain-computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40-60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4-40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24-40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.},
}
@article {pmid35195458,
year = {2023},
author = {Zerrouki, F and Haddab, S},
title = {Experimental Validation of the Cumulative MDRM in theP300 Speller Machine.},
journal = {Clinical EEG and neuroscience},
volume = {54},
number = {3},
pages = {238-246},
doi = {10.1177/15500594221078166},
pmid = {35195458},
issn = {2169-5202},
mesh = {Humans ; *Electroencephalography/methods ; Event-Related Potentials, P300 ; *Brain-Computer Interfaces ; Algorithms ; },
abstract = {The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the n repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the n repetitions.},
}
@article {pmid35194026,
year = {2022},
author = {Harikesh, PC and Yang, CY and Tu, D and Gerasimov, JY and Dar, AM and Armada-Moreira, A and Massetti, M and Kroon, R and Bliman, D and Olsson, R and Stavrinidou, E and Berggren, M and Fabiano, S},
title = {Organic electrochemical neurons and synapses with ion mediated spiking.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {901},
pmid = {35194026},
issn = {2041-1723},
mesh = {*Brain-Computer Interfaces ; Neuronal Plasticity ; Neurons ; *Robotics ; Silicon ; Synapses/physiology ; },
abstract = {Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.},
}
@article {pmid35191214,
year = {2022},
author = {Chalmers, T and Eaves, S and Lees, T and Lin, CT and Newton, PJ and Clifton-Bligh, R and McLachlan, CS and Gustin, SM and Lal, S},
title = {The relationship between neurocognitive performance and HRV parameters in nurses and non-healthcare participants.},
journal = {Brain and behavior},
volume = {12},
number = {3},
pages = {e2481},
pmid = {35191214},
issn = {2162-3279},
mesh = {Autonomic Nervous System ; *Autonomic Nervous System Diseases ; *Electrocardiography ; Heart Rate/physiology ; Humans ; Prospective Studies ; },
abstract = {Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non-invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non-nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini-Mental State Examination and Cognistat psychometric questionnaires. ECG-derived HRV parameters were acquired during the Trier Social Stress Test. Between-group differences were found in domain-specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain-specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain-specific cognitive performance in the non-nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.},
}
@article {pmid35185500,
year = {2022},
author = {Jiang, L and Li, X and Pei, W and Gao, X and Wang, Y},
title = {A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.},
journal = {Frontiers in human neuroscience},
volume = {16},
number = {},
pages = {834959},
pmid = {35185500},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and "BCI illiteracy." To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8-2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects' feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.},
}
@article {pmid35185454,
year = {2021},
author = {Chen, Y and Liu, A and Fu, X and Wen, J and Chen, X},
title = {An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {828512},
pmid = {35185454},
issn = {1662-4548},
abstract = {Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center data due to limited feature representation ability and insufficient interpretability. Given that Graph Convolutional Network (GCN) has demonstrated superiority in learning discriminative representations of brain connectivity networks, in this paper, we propose an invertible dynamic GCN model to identify ASD and investigate the alterations of connectivity patterns associated with the disease. In order to select explainable features from the model, invertible blocks are introduced in the whole network, and we are able to reconstruct the input dynamic features from the network's output. A pre-screening of connectivity features is adopted to reduce the redundancy of the input information, and a fully-connected layer is added to perform classification. The experimental results on 867 subjects show that our proposed method achieves superior disease classification performance. It provides an interpretable deep learning model for brain connectivity analysis and is of great potential in studying brain-related disorders.},
}
@article {pmid35182860,
year = {2022},
author = {Ni, P and Wang, H and Cai, J and Ran, J and Jiang, Y and Zhao, L and Wei, J and Ni, R and Wang, Y and Ma, X and Wang, Q and Guo, W and Li, T},
title = {Generation and characterization of human-derived iPSC lines from one pair of dizygotic twins discordant for schizophrenia.},
journal = {Stem cell research},
volume = {60},
number = {},
pages = {102710},
doi = {10.1016/j.scr.2022.102710},
pmid = {35182860},
issn = {1876-7753},
mesh = {Cell Differentiation ; Female ; Humans ; *Induced Pluripotent Stem Cells ; Kruppel-Like Factor 4 ; Leukocytes, Mononuclear ; Male ; *Schizophrenia/genetics ; Twins, Dizygotic ; },
abstract = {Schizophrenia (SCZ) is a debilitating neurodevelopmental disorder with a high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were donated by a pair of dizygotic twins. The female was clinically diagnosed as SCZ by Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria, and her unaffected male sibling was healthy control. Induced pluripotent stem cells (iPSCs) were established using Episomal vectors carrying reprograming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. These lines with normal karyotype highly expressed pluripotency markers and are capable to differentiate into derivatives of three germ layers. Both lines are negative of mycoplasma.},
}
@article {pmid35182185,
year = {2022},
author = {Cywka, KB and Skarzynski, PH and Krol, B and Hatzopoulos, S and Skarzynski, H},
title = {Evaluation of the Bonebridge BCI 602 active bone conductive implant in adults: efficacy and stability of audiological, surgical, and functional outcomes.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {279},
number = {7},
pages = {3525-3534},
pmid = {35182185},
issn = {1434-4726},
mesh = {Adult ; Audiometry, Pure-Tone ; Bone Conduction ; *Brain-Computer Interfaces ; *Deafness ; *Hearing Aids ; *Hearing Loss/surgery ; Hearing Loss, Conductive/rehabilitation/surgery ; *Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Humans ; *Speech Perception ; Treatment Outcome ; },
abstract = {PURPOSE: (1) To assess the effectiveness and safety of a bone-conduction implant, the Bonebridge BCI 602, in adults with conductive or mixed hearing loss. (2) To investigate whether the Bonebridge BCI 602 is at least as effective as the Bonebridge BCI 601 in such patients.<br><br>METHODS: The study group included 42 adults who had either conductive or mixed hearing loss. All patients underwent Bonebridge BCI 602 implant surgery. Before and after implantation, pure-tone audiometry, speech recognition tests (in quiet and noise), and free-field audiometry were performed. Word recognition scores were evaluated using the Polish Monosyllabic Word Test. Speech reception thresholds in noise were assessed using the Polish Sentence Matrix Test. Subjective assessment of benefits was done using the APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire.<br><br>RESULTS: The APHAB questionnaire showed that difficulties in hearing decreased after BCI 602 implantation. Both word recognition in quiet and speech reception threshold in noise were significantly better after BCI 602 implantation and remained stable for at least 12&#xa0;months. A significant advantage of the device is a reduced time for surgery while maintaining safety. In this study, the mean time for BCI 602 implantation was 28.3&#xa0;min&#x2009;&#xb1;&#x2009;9.4.<br><br>CONCLUSIONS: The second-generation Bonebridge BCI 602 implant is an effective hearing rehabilitation device for patients with conductive or mixed hearing loss. Patient satisfaction and audiological results confirm its efficacy and safety. Its new shape and dimensions allow it to be used in patients previously excluded due to insufficient or difficult anatomical conditions. The new BCI 602 implant is as effective as its predecessor, the BCI 601.},
}
@article {pmid35181552,
year = {2022},
author = {Banville, H and Wood, SUN and Aimone, C and Engemann, DA and Gramfort, A},
title = {Robust learning from corrupted EEG with dynamic spatial filtering.},
journal = {NeuroImage},
volume = {251},
number = {},
pages = {118994},
doi = {10.1016/j.neuroimage.2022.118994},
pmid = {35181552},
issn = {1095-9572},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels. This need is particularly great when working with sparse EEG montages (1-6 channels), often encountered in consumer-grade or mobile EEG devices. Neither classical machine learning models nor deep neural networks trained end-to-end on EEG are typically designed or tested for robustness to corruption, and especially to randomly missing channels. While some studies have proposed strategies for using data with missing channels, these approaches are not practical when sparse montages are used and computing power is limited (e.g., wearables, cell phones). To tackle this problem, we propose dynamic spatial filtering (DSF), a multi-head attention module that can be plugged in before the first layer of a neural network to handle missing EEG channels by learning to focus on good channels and to ignore bad ones. We tested DSF on public EEG data encompassing ∼4000 recordings with simulated channel corruption and on a private dataset of ∼100 at-home recordings of mobile EEG with natural corruption. Our proposed approach achieves the same performance as baseline models when no noise is applied, but outperforms baselines by as much as 29.4% accuracy when significant channel corruption is present. Moreover, DSF outputs are interpretable, making it possible to monitor the effective channel importance in real-time. This approach has the potential to enable the analysis of EEG in challenging settings where channel corruption hampers the reading of brain signals.},
}
@article {pmid35181199,
year = {2022},
author = {Kasprzyk-Hordern, B and Proctor, K and Jagadeesan, K and Edler, F and Standerwick, R and Barden, R},
title = {Human population as a key driver of biochemical burden in an inter-city system: Implications for One Health concept.},
journal = {Journal of hazardous materials},
volume = {429},
number = {},
pages = {127882},
doi = {10.1016/j.jhazmat.2021.127882},
pmid = {35181199},
issn = {1873-3336},
mesh = {Cities ; Drug Resistance, Microbial ; Humans ; *One Health ; *Pesticides/analysis ; Wastewater/analysis ; },
abstract = {This paper tests the hypothesis that human population and city function are key drivers of biochemical burden in an inter-city system, which can be used to inform One Health actions as it enables a holistic understanding of city's metabolism encompassing all of the activities of a city in a single model: from lifestyle choices, through to health status and exposure to harmful chemicals as well as effectiveness of implemented management strategies. Chemical mining of wastewater for biophysico-chemical indicators (BCIs) was undertaken to understand speciation of BCIs in the context of geographical as well as community-wide socioeconomic factors. Spatiotemporal variabilities in chemical and biological target groups in the studied inter-city system were observed. A linear relationship (R[2] > 0.99) and a strong positive correlation between most BCIs and population size (r > 0.998, p < 0.001) were observed which provides a strong evidence for the population size as a driver of BCI burden. BCI groups that are strongly correlated with population size and are intrinsic to humans' function include mostly high usage pharmaceuticals that are linked with long term non-communicable conditions (NSAIDs, analgesics, cardiovascular, mental health and antiepileptics) and lifestyle chemicals. These BCIs can be used as population size markers. BCIs groups that are produced as a result of a specific city's function (e.g. industry presence and occupational exposure or agriculture) and as such are not correlated with population size include: pesticides, PCPs and industrial chemicals. These BCIs can be used to assess city's function, such as occupational exposure, environmental or food exposure, and as a proxy of community-wide health. This study confirmed a strong positive correlation between antibiotics (ABs), population size and antibiotic resistance genes (ARGs). This confirms the population size and AB usage as the main driver of AB and ARG levels and provides an opportunity for interventions aimed at the reduction of AB usage to reduce AMR. Holistic evaluation of biophysicochemical fingerprints (BCI burden) of the environment and data triangulation with socioeconomic fingerprints (indices) of tested communities are required to fully embrace One Health concept.},
}
@article {pmid35178518,
year = {2022},
author = {Musso, M and Hübner, D and Schwarzkopf, S and Bernodusson, M and LeVan, P and Weiller, C and Tangermann, M},
title = {Aphasia recovery by language training using a brain-computer interface: a proof-of-concept study.},
journal = {Brain communications},
volume = {4},
number = {1},
pages = {fcac008},
pmid = {35178518},
issn = {2632-1297},
abstract = {Aphasia, the impairment to understand or produce language, is a frequent disorder after stroke with devastating effects. Conventional speech and language therapy include each formal intervention for improving language and communication abilities. In the chronic stage after stroke, it is effective compared with no treatment, but its effect size is small. We present a new language training approach for the rehabilitation of patients with aphasia based on a brain-computer interface system. The approach exploits its capacity to provide feedback time-locked to a brain state. Thus, it implements the idea that reinforcing an appropriate language processing strategy may induce beneficial brain plasticity. In our approach, patients perform a simple auditory target word detection task whilst their EEG was recorded. The constant decoding of these signals by machine learning models generates an individual and immediate brain-state-dependent feedback. It indicates to patients how well they accomplish the task during a training session, even if they are unable to speak. Results obtained from a proof-of-concept study with 10 stroke patients with mild to severe chronic aphasia (age range: 38-76 years) are remarkable. First, we found that the high-intensity training (30 h, 4 days per week) was feasible, despite a high-word presentation speed and unfavourable stroke-induced EEG signal characteristics. Second, the training induced a sustained recovery of aphasia, which generalized to multiple language aspects beyond the trained task. Specifically, all tested language assessments (Aachen Aphasia Test, Snodgrass &amp; Vanderwart, Communicative Activity Log) showed significant medium to large improvements between pre- and post-training, with a standardized mean difference of 0.63 obtained for the Aachen Aphasia Test, and five patients categorized as non-aphasic at post-training assessment. Third, our data show that these language improvements were accompanied neither by significant changes in attention skills nor non-linguistic skills. Investigating possible modes of action of this brain-computer interface-based language training, neuroimaging data (EEG and resting-state functional MRI) indicates a training-induced faster word processing, a strengthened language network and a rebalancing between the language- and default mode networks.},
}
@article {pmid35174594,
year = {2022},
author = {Miskowiak, KW and Seeberg, I and Jensen, MB and Balanzá-Martínez, V and Del Mar Bonnin, C and Bowie, CR and Carvalho, AF and Dols, A and Douglas, K and Gallagher, P and Hasler, G and Lafer, B and Lewandowski, KE and López-Jaramillo, C and Martinez-Aran, A and McIntyre, RS and Porter, RJ and Purdon, SE and Schaffer, A and Stokes, P and Sumiyoshi, T and Torres, IJ and Van Rheenen, TE and Yatham, LN and Young, AH and Kessing, LV and Burdick, KE and Vieta, E},
title = {Randomised controlled cognition trials in remitted patients with mood disorders published between 2015 and 2021: A systematic review by the International Society for Bipolar Disorders Targeting Cognition Task Force.},
journal = {Bipolar disorders},
volume = {24},
number = {4},
pages = {354-374},
pmid = {35174594},
issn = {1399-5618},
mesh = {*Bipolar Disorder/psychology/therapy ; Cognition ; *Cognitive Dysfunction/therapy ; Humans ; Lurasidone Hydrochloride ; Mood Disorders/etiology/therapy ; },
abstract = {BACKGROUND: Cognitive impairments are an emerging treatment target in mood disorders, but currently there are no evidence-based pro-cognitive treatments indicated for patients in remission. With this systematic review of randomised controlled trials (RCTs), the International Society for Bipolar Disorders (ISBD) Targeting Cognition Task force provides an update of the most promising treatments and methodological recommendations.<br><br>METHODS: The review included RCTs of candidate pro-cognitive interventions in fully or partially remitted patients with major depressive disorder or bipolar disorder. We followed the procedures of the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) 2020&#xa0;statement. Searches were conducted on PubMed/MEDLINE, PsycInfo, EMBASE and Cochrane Library from January 2015, when two prior systematic reviews were conducted, until February 2021. Two independent authors reviewed the studies with the Revised Cochrane Collaboration's Risk of Bias tool for Randomised trials.<br><br>RESULTS: We identified 16 RCTs (N&#xa0;=&#xa0;859) investigating cognitive remediation (CR; k&#xa0;=&#xa0;6; N&#xa0;=&#xa0;311), direct current or repetitive magnetic stimulation (k&#xa0;=&#xa0;3; N&#xa0;=&#xa0;127), or pharmacological interventions (k&#xa0;=&#xa0;7; N&#xa0;=&#xa0;421). CR showed most consistent cognitive benefits, with two trials showing improvements on primary outcomes. Neuromodulatory interventions revealed no clear efficacy. Among pharmacological interventions, modafinil and lurasidone showed early positive results. Sources of bias included small samples, lack of pre-screening for objective cognitive impairment, no primary outcome and no information on allocation sequence masking.<br><br>CONCLUSIONS: Evidence for pro-cognitive treatments in mood disorders is emerging. Recommendations are to increase sample sizes, pre-screen for impairment in targeted domain(s), select one primary outcome, aid transfer to real-world functioning, investigate multimodal interventions and include neuroimaging.},
}
@article {pmid35174449,
year = {2022},
author = {Zhao, CG and Ju, F and Sun, W and Jiang, S and Xi, X and Wang, H and Sun, XL and Li, M and Xie, J and Zhang, K and Xu, GH and Zhang, SC and Mou, X and Yuan, H},
title = {Effects of Training with a Brain-Computer Interface-Controlled Robot on Rehabilitation Outcome in Patients with Subacute Stroke: A Randomized Controlled Trial.},
journal = {Neurology and therapy},
volume = {11},
number = {2},
pages = {679-695},
pmid = {35174449},
issn = {2193-8253},
support = {81100932//National Natural Science Foundation of China/ ; 82072534//National Natural Science Foundation of China/ ; 91420301//National Natural Science Foundation of China/ ; 2021JM-232//Shaanxi Provincial Science and Technology Department/ ; 2020KW-050//Shaanxi Provincial Science and Technology Department/ ; 2018ZDCXL-GY-06-01//Shaanxi Provincial Science and Technology Department/ ; },
abstract = {INTRODUCTION: Stroke is always associated with a difficult functional recovery process. A brain-computer interface (BCI) is a technology which provides a direct connection between the human brain and external devices. The primary aim of this study was to determine whether training with a BCI-controlled robot can improve functions in patients with subacute stroke.<br><br>METHODS: Subacute stroke patients aged 32-68&#xa0;years with a course of 2&#xa0;weeks to 3&#xa0;months were randomly assigned to the BCI group or to the sham group for a 4-week course. The primary outcome measures were Loewenstein Occupational Therapy Cognitive Assessment (LOCTA) and Fugl-Meyer Assessment for Lower Extremity (FMA-LE). Secondary outcome measures included Fugl-Meyer Assessment for Balance (FMA-B), Functional Ambulation Category (FAC), Modified Barthel Index (MBI), serum brain-derived neurotrophic factor (BDNF) levels and motor-evoked potential (MEP).<br><br>RESULTS: A total of 28 patients completed the study. Both groups showed a significant increase in mean LOCTA (sham: P&#xa0;<&#xa0;0.001, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;2.972; BCI: P&#xa0;<&#xa0;0.001, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;4.266) and FMA-LE (sham: P&#xa0;<&#xa0;0.001, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;3.178; BCI: P&#xa0;<&#xa0;0.001, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;3.063) scores. The LOCTA scores in the BCI group were 14.89% higher than in the sham group (P&#xa0;=&#xa0;0.049, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;0.580). There were no significant differences between the two groups in terms of FMA-B (P&#xa0;=&#xa0;0.363, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;0.252), FAC (P&#xa0;=&#xa0;0.363), or MBI (P&#xa0;=&#xa0;0.493, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;0.188) scores. The serum levels of BDNF were significantly higher within the BCI group (P&#xa0;<&#xa0;0.001, Cohen's d&#xa0;=&#xa0;&#xa0;-&#xa0;1.167), and the MEP latency decreased by 3.75% and 4.71% in the sham and BCI groups, respectively.<br><br>CONCLUSION: Training with a BCI-controlled robot combined with traditional physiotherapy promotes cognitive function recovery, and enhances motor functions of the lower extremity in patients with subacute stroke. These patients also showed increased secretion of BDNF.<br><br>TRIAL REGISTRATION: Chinese clinical trial registry: ChiCTR-INR-17012874.},
}
@article {pmid35171745,
year = {2022},
author = {DePass, M and Falaki, A and Quessy, S and Dancause, N and Cos, I},
title = {A machine learning approach to characterize sequential movement-related states in premotor and motor cortices.},
journal = {Journal of neurophysiology},
volume = {127},
number = {5},
pages = {1348-1362},
doi = {10.1152/jn.00368.2021},
pmid = {35171745},
issn = {1522-1598},
support = {389886//Gouvernement du Canada | Canadian Institutes of Health Research (CIHR)/ ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Machine Learning ; Microelectrodes ; *Motor Cortex/physiology ; Movement/physiology ; },
abstract = {Nonhuman primate (NHP) movement kinematics have been decoded from spikes and local field potentials (LFPs) recorded during motor tasks. However, the potential of LFPs to provide network-like characterizations of neural dynamics during planning and execution of sequential movements requires further exploration. Is the aggregate nature of LFPs suitable to construct informative brain state descriptors of movement preparation and execution? To investigate this, we developed a framework to process LFPs based on machine-learning classifiers and analyzed LFP from a primate, implanted with several microelectrode arrays covering the premotor cortex in both hemispheres and the primary motor cortex on one side. The monkey performed a reach-to-grasp task, consisting of five consecutive states, starting from rest until a rewarding target (food) was attained. We use this five-state task to characterize neural activity within eight frequency bands, using spectral amplitude and pairwise correlations across electrodes as features. Our results show that we could best distinguish all five movement-related states using the highest frequency band (200-500 Hz), yielding an 87% accuracy with spectral amplitude, and 60% with pairwise electrode correlation. Further analyses characterized each movement-related state, showing differential neuronal population activity at above-γ frequencies during the various stages of movement. Furthermore, the topological distribution for the high-frequency LFPs allowed for a highly significant set of pairwise correlations, strongly suggesting a concerted distribution of movement planning and execution function is distributed across premotor and primary motor cortices in a specific fashion, and is most significant in the low ripple (100-150 Hz), high ripple (150-200 Hz), and multiunit frequency bands. In summary, our results show that the concerted use of novel machine-learning techniques with coarse grained queue broad signals such as LFPs may be successfully used to track and decode fine movement aspects involving preparation, reach, grasp, and reward retrieval across several brain regions.NEW &amp; NOTEWORTHY Local field potentials (LFPs), despite lower spatial resolution compared to single-neuron recordings, can be used with machine learning classifiers to decode sequential movements involving motor preparation, execution, and reward retrieval. Our results revealed heterogeneity of neural activity on small spatial scales, further evidencing the utility of micro-electrode array recordings for complex movement decoding. With further advancement, high-dimensional LFPs may become the gold standard for brain-computer interfaces such as neural prostheses in the near future.},
}
@article {pmid35169837,
year = {2022},
author = {Nagata, K and Kunii, N and Shimada, S and Fujitani, S and Takasago, M and Saito, N},
title = {Spatiotemporal target selection for intracranial neural decoding of abstract and concrete semantics.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {32},
number = {24},
pages = {5544-5554},
pmid = {35169837},
issn = {1460-2199},
mesh = {Humans ; *Semantics ; *Speech ; Language ; Electrocorticography/methods ; Brain ; },
abstract = {Decoding the inner representation of a word meaning from human cortical activity is a substantial challenge in the development of speech brain-machine interfaces (BMIs). The semantic aspect of speech is a novel target of speech decoding that may enable versatile communication platforms for individuals with impaired speech ability; however, there is a paucity of electrocorticography studies in this field. We decoded the semantic representation of a word from single-trial cortical activity during an imageability-based property identification task that required participants to discriminate between the abstract and concrete words. Using high gamma activity in the language-dominant hemisphere, a support vector machine classifier could discriminate the 2-word categories with significantly high accuracy (73.1 ± 7.5%). Activities in specific time components from two brain regions were identified as significant predictors of abstract and concrete dichotomy. Classification using these feature components revealed that comparable prediction accuracy could be obtained based on a spatiotemporally targeted decoding approach. Our study demonstrated that mental representations of abstract and concrete word processing could be decoded from cortical high gamma activities, and the coverage of implanted electrodes and time window of analysis could be successfully minimized. Our findings lay the foundation for the future development of semantic-based speech BMIs.},
}
@article {pmid35169437,
year = {2022},
author = {Park, A and Principe, DR},
title = {Blunt cardiac injury presenting as a left-sided coronary artery dissection.},
journal = {Journal of surgical case reports},
volume = {2022},
number = {2},
pages = {rjac008},
pmid = {35169437},
issn = {2042-8812},
support = {F30 CA236031/CA/NCI NIH HHS/United States ; },
abstract = {The presentation of blunt cardiac injuries (BCIs) following thoracic trauma is extremely varied, classically affecting the right-sided chambers of the heart. In extremely rare cases, BCIs can affect the coronary arteries. Diagnosing a traumatic coronary dissection can be challenging, as not only is presentation highly variable, but dissections are often masked by concomitant injuries. Here, we present the unusual case of a patient presenting to the emergency department following blunt thoracic trauma from an automobile accident. He demonstrated diffuse S and T wave segment elevations on electrocardiogram, and coronary angiography was significant for occlusion of the apical left anterior descending artery and stenosis of the second obtuse marginal artery. The patient was diagnosed with a BCI causing a left-sided coronary artery dissection. This serves as an important reminder that BCIs can manifest in any part of the cardiac anatomy, and should be considered in any patient with a history of thoracic trauma.},
}
@article {pmid35168083,
year = {2022},
author = {Khademi, Z and Ebrahimi, F and Kordy, HM},
title = {A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105288},
doi = {10.1016/j.compbiomed.2022.105288},
pmid = {35168083},
issn = {1879-0534},
abstract = {In the Motor Imagery (MI)-based Brain Computer Interface (BCI), users' intention is converted into a control signal through processing a specific pattern in brain signals reflecting motor characteristics. There are such restrictions as the limited size of the existing datasets and low signal to noise ratio in the classification of MI Electroencephalogram (EEG) signals. Machine learning (ML) methods, particularly Deep Learning (DL), have overcome these limitations relatively. In this study, three hybrid models were proposed to classify the EEG signal in the MI-based BCI. The proposed hybrid models consist of the convolutional neural networks (CNN) and the Long-Short Term Memory (LSTM). In the first model, the CNN with different number of convolutional-pooling blocks (from shallow to deep CNN) was examined; a two-block CNN model not affected by the vanishing gradient descent and yet able to extract desirable features employed; the second and third models contained pre-trained CNNs conducing to the exploration of more complex features. The transfer learning strategy and data augmentation methods were applied to overcome the limited size of the datasets by transferring learning from one model to another. This was achieved by employing two powerful pre-trained convolutional neural networks namely ResNet-50 and Inception-v3. The continuous wavelet transform (CWT) was used to generate images for the CNN. The performance of the proposed models was evaluated on the BCI Competition IV dataset 2a. The mean accuracy vlaues of 86%, 90%, and 92%, and mean Kappa values of 81%, 86%, and 88% were obtained for the hybrid neural network with the customized CNN, the hybrid neural network with ResNet-50 and the hybrid neural network with Inception-v3, respectively. Despite the promising performance of the three proposed models, the hybrid neural network with Inception-v3 outperformed the two other models. The best obtained result in the present study improved the previous best result in the literature by 7% in terms of classification accuracy. From the findings, it can be concluded that transfer learning based on a pre-trained CNN in combination with LSTM is a novel method in MI-based BCI. The study also has implications for the discrimination of motor imagery tasks in each EEG recording channel and in different brain regions which can reduce computational time in future works by only selecting the most effective channels.},
}
@article {pmid35167839,
year = {2022},
author = {Lin, G and Zhang, J and Liu, Y and Gao, T and Kong, W and Lei, X and Qiu, T},
title = {Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109498},
doi = {10.1016/j.jneumeth.2022.109498},
pmid = {35167839},
issn = {1872-678X},
mesh = {Algorithms ; *Artifacts ; *Ballistocardiography/methods ; Electroencephalography/methods ; Magnetic Resonance Imaging/methods ; },
abstract = {Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, and has been widely used in various research fields of brain science. However, during the fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate the EEG. As an unpaired problem, BCG artifact removal now remains a considerable challenge. Aiming to provide a solution, this paper proposed a novel modular generative adversarial network (GAN) and corresponding training strategy to improve the network performance by optimizing the parameters of each module. In this manner, we hope to improve the local representation ability of the network model, thereby improving its overall performance and obtaining a reliable generator for BCG artifact removal. Moreover, the proposed method does not rely on additional reference signal or complex hardware equipment. Experimental results show that, compared with multiple methods, the technique presented in this paper can remove the BCG artifact more effectively while retaining essential EEG information.},
}
@article {pmid35165308,
year = {2022},
author = {Nieto, N and Peterson, V and Rufiner, HL and Kamienkowski, JE and Spies, R},
title = {Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition.},
journal = {Scientific data},
volume = {9},
number = {1},
pages = {52},
pmid = {35165308},
issn = {2052-4463},
mesh = {Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Speech Perception ; },
abstract = {Surface electroencephalography is a standard and noninvasive way to measure electrical brain activity. Recent advances in artificial intelligence led to significant improvements in the automatic detection of brain patterns, allowing increasingly faster, more reliable and accessible Brain-Computer Interfaces. Different paradigms have been used to enable the human-machine interaction and the last few years have broad a mark increase in the interest for interpreting and characterizing the "inner voice" phenomenon. This paradigm, called inner speech, raises the possibility of executing an order just by thinking about it, allowing a "natural" way of controlling external devices. Unfortunately, the lack of publicly available electroencephalography datasets, restricts the development of new techniques for inner speech recognition. A ten-participant dataset acquired under this and two others related paradigms, recorded with an acquisition system of 136 channels, is presented. The main purpose of this work is to provide the scientific community with an open-access multiclass electroencephalography database of inner speech commands that could be used for better understanding of the related brain mechanisms.},
}
@article {pmid35161473,
year = {2022},
author = {Sattar, NY and Kausar, Z and Usama, SA and Farooq, U and Shah, MF and Muhammad, S and Khan, R and Badran, M},
title = {fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {3},
pages = {},
pmid = {35161473},
issn = {1424-8220},
mesh = {*Amputees ; *Artificial Limbs ; Humans ; Intention ; Neural Networks, Computer ; Upper Extremity ; },
abstract = {Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.},
}
@article {pmid35158119,
year = {2022},
author = {Liu, G and Tian, L and Zhou, W},
title = {Multiscale time-frequency method for multiclass Motor Imagery Brain Computer Interface.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105299},
doi = {10.1016/j.compbiomed.2022.105299},
pmid = {35158119},
issn = {1879-0534},
abstract = {Motor Imagery Brain Computer Interface (MI-BCI) has become a promising technology in the field of neurorehabilitation. However, the performance and computational complexity of the current multiclass MI-BCI have not been fully optimized, and the intuitive interpretation of individual differences on motor imagery tasks is seldom investigated. In this paper, a well-designed multiscale time-frequency segmentation scheme is first applied to multichannel EEG recordings to obtain Time-Frequency Segments (TFSs). Then, the TFS selection based on a specific wrapper feature selection rule is utilized to determine optimum TFSs. Next, One-Versus-One (OvO)-divCSP implemented in divergence framework is used to extract discriminative features. Finally, One-Versus-Rest (OvR)-SVM is utilized to predict the class label based on selected multiclass MI features. Experimental results indicate our method yields a superior performance on two publicly available multiclass MI datasets with a mean accuracy of 80.00% and a mean kappa of 0.73. Meanwhile, the proposed TFS selection method can significantly alleviate the computational burden with little accuracy reduction, demonstrating the feasibility of real-time multiclass MI-BCI. Furthermore, the Motor Imagery Time-Frequency Reaction Map (MI-TFRM) is visualized, contributing to analyzing and interpreting the performance differences between different subjects.},
}
@article {pmid35158052,
year = {2022},
author = {Albani, S and Stolfo, D and Venkateshvaran, A and Chubuchny, V and Biondi, F and De Luca, A and Lo Giudice, F and Pasanisi, EM and Petersen, C and Airò, E and Bauleo, C and Ciardetti, M and Coceani, M and Formichi, B and Spiesshoefer, J and Savarese, G and Lund, LH and Emdin, M and Sinagra, G and Manouras, A and Giannoni, A and , },
title = {Echocardiographic Biventricular Coupling Index to Predict Precapillary Pulmonary Hypertension.},
journal = {Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography},
volume = {35},
number = {7},
pages = {715-726},
doi = {10.1016/j.echo.2022.02.003},
pmid = {35158052},
issn = {1097-6795},
mesh = {Cardiac Catheterization ; Echocardiography ; Heart Ventricles/diagnostic imaging ; Humans ; *Hypertension, Pulmonary/diagnostic imaging ; Retrospective Studies ; Ventricular Function, Right ; },
abstract = {BACKGROUND: Pulmonary hypertension (PH) is a frequent and detrimental condition. Right heart catheterization (RHC) is the gold standard to identify PH subtype (precapillary from postcapillary PH) and is key for treatment allocation. In this study, the novel echocardiographic biventricular coupling index (BCI), based on the ratio between right ventricular stroke work index and left ventricular E/E' ratio, was tested for the discrimination of PH subtype using RHC as the comparator.<br><br>METHODS: BCI was derived in 334 consecutive patients who underwent transthoracic echocardiography and RHC for all indications. BCI was then tested in a validation cohort of 1,349 patients.<br><br>RESULTS: The accuracy of BCI to identify precapillary PH was high in the derivation cohort (area under the curve, 0.82; 95% CI, 0.78-0.88; P&#xa0;<&#xa0;.001; optimal cut point, 1.9). BCI identified patients with precapillary PH with high accuracy also in the validation cohort (area under the curve, 0.87 [95% CI, 0.85-0.89; P&#xa0;<&#xa0;.001]; subgroup with PH: area under the curve, 0.91 [95% CI, 0.89-0.93; P&#xa0;<&#xa0;.001]; cut point, 1.9; sensitivity, 82%; specificity, 89%; positive predictive value, 77%; negative predictive value, 92%). BCI outperformed both the D'Alto score (Z&#xa0;=&#xa0;3.56; difference between areas&#xa0;=&#xa0;0.05; 95% CI, 0.02-0.07; P&#xa0;<&#xa0;.001) and the echocardiographic pulmonary-to-left atrial ratio index (Z&#xa0;=&#xa0;2.88; difference between areas&#xa0;=&#xa0;0.02; 95% CI, 0.01-0.04; P&#xa0;=&#xa0;.004).<br><br>CONCLUSIONS: BCI is a novel, noninvasive index based on routinely available echocardiographic parameters that identifies with high accuracy patients with precapillary PH. BCI may be of value in the screening workup of patients with PH.},
}
@article {pmid35157605,
year = {2022},
author = {Yu, X and Aziz, MZ and Sadiq, MT and Jia, K and Fan, Z and Xiao, G},
title = {Computerized Multidomain EEG Classification System: A New Paradigm.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {8},
pages = {3626-3637},
doi = {10.1109/JBHI.2022.3151570},
pmid = {35157605},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Epilepsy/diagnosis ; Humans ; Imagination ; Machine Learning ; Neural Networks, Computer ; },
abstract = {The recent advancements in electroencepha- logram (EEG) signals classification largely center around the domain-specific solutions that hinder the algorithm cross-discipline adaptability. This study introduces a computer-aided broad learning EEG system (CABLES) for the classification of six distinct EEG domains under a unified sequential framework. Specifically, this paper proposes three novel modules namely, complex variational mode de- composition (CVMD), ensemble optimization-based featu- res selection (EOFS), and t-distributed stochastic neighbor embedding-based samples reduction (tSNE-SR) methods respectively for the realization of CABLES. Extensive expe- riments are carried out on seven different datasets from diverse disciplines using different variants of the neural network, extreme learning machine, and machine learning classifiers employing a 10-fold cross-validation strategy. Results compared with existing studies reveal that the highest classification accuracy of 99.1%, 97.8%, 94.3%, 91.5%, 98.9%, 95.3%, and 92% is achieved for the motor imagery dataset A, dataset B, slow cortical potentials, epilepsy, alcoholic, and schizophrenia EEG datasets res- pectively. The overall empirical analysis authenticates that the proposed CABLES framework outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus can be endorsed as an effective automated neural rehabilitation system.},
}
@article {pmid35157588,
year = {2023},
author = {Shaeri, M and Sodagar, AM},
title = {Data Transformation in the Processing of Neuronal Signals: A Powerful Tool to Illuminate Informative Contents.},
journal = {IEEE reviews in biomedical engineering},
volume = {16},
number = {},
pages = {611-626},
doi = {10.1109/RBME.2022.3151340},
pmid = {35157588},
issn = {1941-1189},
mesh = {Humans ; Algorithms ; Signal Processing, Computer-Assisted ; *Data Compression ; *Brain-Computer Interfaces ; Neurophysiology ; },
abstract = {Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today's technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.},
}
@article {pmid35153708,
year = {2021},
author = {Haruvi, A and Kopito, R and Brande-Eilat, N and Kalev, S and Kay, E and Furman, D},
title = {Measuring and Modeling the Effect of Audio on Human Focus in Everyday Environments Using Brain-Computer Interface Technology.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {760561},
pmid = {35153708},
issn = {1662-5188},
abstract = {The goal of this study was to investigate the effect of audio listened to through headphones on subjectively reported human focus levels, and to identify through objective measures the properties that contribute most to increasing and decreasing focus in people within their regular, everyday environment. Participants (N = 62, 18-65 years) performed various tasks on a tablet computer while listening to either no audio (silence), popular audio playlists designed to increase focus (pre-recorded music arranged in a particular sequence of songs), or engineered soundscapes that were personalized to individual listeners (digital audio composed in real-time based on input parameters such as heart rate, time of day, location, etc.). Audio stimuli were delivered to participants through headphones while their brain signals were simultaneously recorded by a portable electroencephalography headband. Participants completed four 1-h long sessions at home during which different audio played continuously in the background. Using brain-computer interface technology for brain decoding and based on an individual's self-report of their focus, we obtained individual focus levels over time and used this data to analyze the effects of various properties of the sounds contained in the audio content. We found that while participants were working, personalized soundscapes increased their focus significantly above silence (p = 0.008), while music playlists did not have a significant effect. For the young adult demographic (18-36 years), all audio tested was significantly better than silence at producing focus (p = 0.001-0.009). Personalized soundscapes increased focus the most relative to silence, but playlists of pre-recorded songs also increased focus significantly during specific time intervals. Ultimately we found it is possible to accurately predict human focus levels a priori based on physical properties of audio content. We then applied this finding to compare between music genres and revealed that classical music, engineered soundscapes, and natural sounds were the best genres for increasing focus, while pop and hip-hop were the worst. These insights can enable human and artificial intelligence composers to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research will include real-time adaptation of audio for other functional objectives beyond affecting focus, such as affecting listener enjoyment, drowsiness, stress and memory.},
}
@article {pmid35151668,
year = {2022},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Daliri, MR},
title = {Spatio-Spectral CCA (SS-CCA): A novel approach for frequency recognition in SSVEP-based BCI.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109499},
doi = {10.1016/j.jneumeth.2022.109499},
pmid = {35151668},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {BACKGROUND: Steady-state visually evoked potentials (SSVEP) are one of the most important paradigms in the BCI Domain. Among the best methods for detecting frequency in the SSVEP-based BCI is the Canonical Correlation Analysis (CCA), which calculates canonical correlation between two sets of multidimensional variables, the electroencephalogram (EEG) and reference signals. Despite its efficiency and widespread application, CCA algorithm has some limitations. One major limitation of CCA is to only consider the spatial domain information of the signal.<br><br>NEW METHOD: However, regarding frequency of signal as another critical feature of the signals, combining both spatial and frequency domain information can significantly improve the performance of frequency recognition. Although several previous studies about CCA algorithm, could improve its performance, they have not addressed CCA algorithm's limitation. To address this concern, in the current study, we presented Spatio-Spectral CCA (SS-CCA) algorithm, which is inspired from Common Spatio-Spectral Patterns (CSSP) algorithm. In the SS-CCA algorithm, we added a time delay to the EEG signal, in order to simultaneously optimize spatial and frequency information and obtain the canonical variables. Accordingly, for correlation coefficient's calculations, more information from EEG signal is utilized.<br><br>RESULTS: Finally, SS-CCA algorithm which is used as the base model of Filter Bank CCA (FBCCA), and Filter Bank SS-CCA algorithms, can help increase the frequency recognition performance. In order to evaluate the proposed method, 35-subject benchmark dataset were used. Proposed algorithm yielded mean accuracy 98.33 across all subjects.<br><br>Our classification accuracy and Information Transfer Rate (ITR) results showed that the performance of the above-mentioned method improves in comparison to the CCA.<br><br>CONCLUSIONS: In conclusion, using the proposed SS-CCA algorithm instead of the CCA, in all our experiments the CCA-based methods were improved.},
}
@article {pmid35151667,
year = {2022},
author = {Li, M and Zhang, P and Yang, G and Xu, G and Guo, M and Liao, W},
title = {A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109496},
doi = {10.1016/j.jneumeth.2022.109496},
pmid = {35151667},
issn = {1872-678X},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; },
abstract = {BACKGROUND: An asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states.<br><br>NEW METHODS: This study proposes a fisher linear discriminant analysis classification algorithm fused with na&#xef;ve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects' intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion.<br><br>RESULTS: The accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance.<br><br>Ten subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods.<br><br>CONCLUSIONS: It indicates that the na&#xef;ve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.},
}
@article {pmid35151665,
year = {2022},
author = {Ma, P and Dong, C and Lin, R and Ma, S and Jia, T and Chen, X and Xiao, Z and Qi, Y},
title = {A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109502},
doi = {10.1016/j.jneumeth.2022.109502},
pmid = {35151665},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Support Vector Machine ; },
abstract = {BACKGROUND: In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time.<br><br>NEW METHOD: For targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used.<br><br>RESULTS: This fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets.<br><br>Through the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2&#xa0;s and an ITR of 48.92&#xa0;bits/min, which are 10.88% and 13.18&#xa0;bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69&#xa0;bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91&#xa0;bit/min ITR in a time window of 2&#xa0;s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4&#xa0;bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.},
}
@article {pmid35150764,
year = {2022},
author = {Blanco-Diaz, CF and Antelis, JM and Ruiz-Olaya, AF},
title = {Comparative analysis of spectral and temporal combinations in CSP-based methods for decoding hand motor imagery tasks.},
journal = {Journal of neuroscience methods},
volume = {371},
number = {},
pages = {109495},
doi = {10.1016/j.jneumeth.2022.109495},
pmid = {35150764},
issn = {1872-678X},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: A widely used paradigm for brain-computer interfaces (BCI) is based on the detection of event-related (des)synchronization (ERD/S) in response to hand motor imagery (MI) tasks. The common spatial pattern (CSP) has been recognized as a powerful algorithm to design spatial filters for ERD/ERS detection. However, a limitation of CSP focus on identification only of discriminative spatial information but not the spectral one.<br><br>NEW METHOD: An open problem remains in literature related to extracting the most discriminative brain patterns in MI-based BCIs using an optimal time segment and spectral information that accounts for intersubject variability. In recent years, different variants of CSP-based methods have been proposed to address the problem of decoding motor imagery tasks under the intersubject variability of frequency bands related to ERD/ERS events, including Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatio-Spectral Patterns (FBCSSP).<br><br>We performed a comparative study of different combinations of time segments and filter banks for three methods (CSP, FBCSP, and FBCSSP) to decode hand (right and left) motor imagery tasks using two different EEG datasets (Gigascience and BCI IVa competition).<br><br>RESULTS: The best configuration corresponds to a filter bank with 3 filters (8-15&#xa0;Hz, 15-22&#xa0;Hz and 22-29&#xa0;Hz) using a time window of 1.5&#xa0;s after the trigger, which provide accuracies of approximately 74% and an estimated ITRs of approximately 7 bits/min.<br><br>CONCLUSION: Discriminative information in time and spectral domains could be obtained using a convenient filter bank and a time segment configuration, to enhance the classification rate and ITR for detection of hand motor imagery tasks with CSP-related methods, to be used in the implementation of a real-time BCI system.},
}
@article {pmid35147515,
year = {2022},
author = {Basti, A and Chella, F and Guidotti, R and Ermolova, M and D'Andrea, A and Stenroos, M and Romani, GL and Pizzella, V and Marzetti, L},
title = {Looking through the windows: a study about the dependency of phase-coupling estimates on the data length.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac542f},
pmid = {35147515},
issn = {1741-2552},
mesh = {Brain/physiology ; *Brain Mapping/methods ; Electroencephalography/methods ; *Magnetoencephalography/methods ; Reproducibility of Results ; },
abstract = {Objective. Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes.Approach. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio (SNR), the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (iPLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence.Main results. Our findings show that, for a SNR of at least 10 dB, a data window that contains 5-8 cycles of the oscillation of interest (e.g. a 500-800 ms window at 10 Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required.Significance. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.},
}
@article {pmid35146564,
year = {2022},
author = {Carino-Escobar, RI and Rodriguez-Barragan, MA and Carrillo-Mora, P and Cantillo-Negrete, J},
title = {Brain-computer interface as complementary therapy for hemiparesis in an astrocytoma patient.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {43},
number = {4},
pages = {2879-2881},
pmid = {35146564},
issn = {1590-3478},
support = {SALUD-2018-02-B-S-45803//Consejo Nacional de Ciencia y Tecnolog&#xed;a/ ; },
mesh = {*Astrocytoma/complications/diagnostic imaging/therapy ; *Brain Neoplasms/complications/diagnostic imaging/therapy ; *Brain-Computer Interfaces ; *Complementary Therapies ; Humans ; Paresis/etiology/therapy ; },
}
@article {pmid35145386,
year = {2021},
author = {Gonzalez-Navarro, P and Celik, B and Moghadamfalahi, M and Akcakaya, M and Fried-Oken, M and Erdoğmuş, D},
title = {Feedback Related Potentials for EEG-Based Typing Systems.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {788258},
pmid = {35145386},
issn = {1662-5161},
abstract = {Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.},
}
@article {pmid35144966,
year = {2022},
author = {Bartlett, JMS and Sgroi, DC and Treuner, K and Zhang, Y and Piper, T and Salunga, RC and Ahmed, I and Doos, L and Thornber, S and Taylor, KJ and Brachtel, EF and Pirrie, SJ and Schnabel, CA and Rea, DW},
title = {Breast Cancer Index Is a Predictive Biomarker of Treatment Benefit and Outcome from Extended Tamoxifen Therapy: Final Analysis of the Trans-aTTom Study.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {28},
number = {9},
pages = {1871-1880},
pmid = {35144966},
issn = {1557-3265},
support = {12125/CRUK_/Cancer Research UK/United Kingdom ; 25354/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Antineoplastic Agents, Hormonal/therapeutic use ; Biomarkers ; *Breast Neoplasms/diagnosis/drug therapy ; Chemotherapy, Adjuvant/methods ; Disease-Free Survival ; Female ; Humans ; Neoplasm Recurrence, Local/drug therapy ; Prognosis ; Tamoxifen/therapeutic use ; Treatment Outcome ; },
abstract = {PURPOSE: The Breast Cancer Index (BCI) HOXB13/IL17BR (H/I) ratio predicts benefit from extended endocrine therapy in hormone receptor-positive (HR+) early-stage breast cancer. Here, we report the final analysis of the Trans-aTTom study examining BCI (H/I)'s predictive performance.<br><br>EXPERIMENTAL DESIGN: BCI results were available for 2,445 aTTom trial patients. The primary endpoint of recurrence-free interval (RFI) and secondary endpoints of disease-free interval (DFI) and disease-free survival (DFS) were examined using Cox proportional hazards regression and log-rank test.<br><br>RESULTS: Final analysis of the overall study population (N = 2,445) did not show a significant improvement in RFI with extended tamoxifen [HR, 0.90; 95% confidence interval (CI), 0.69-1.16; P = 0.401]. Both the overall study population and N0 group were underpowered due to the low event rate in the N0 group. In a pre-planned analysis of the N+ subset (N = 789), BCI (H/I)-High patients derived significant benefit from extended tamoxifen (9.7% absolute benefit: HR, 0.33; 95% CI, 0.14-0.75; P = 0.016), whereas BCI (H/I)-Low patients did not (-1.2% absolute benefit; HR, 1.11; 95% CI, 0.76-1.64; P = 0.581). A significant treatment-to-biomarker interaction was demonstrated on the basis of RFI, DFI, and DFS (P = 0.037, 0.040, and 0.025, respectively). BCI (H/I)-High patients remained predictive of benefit from extended tamoxifen in the N+/HER2- subgroup (9.4% absolute benefit: HR, 0.35; 95% CI, 0.15-0.81; P = 0.047). A three-way interaction evaluating BCI (H/I), treatment, and HER2 status was not statistically significant (P = 0.849).<br><br>CONCLUSIONS: Novel findings demonstrate that BCI (H/I) significantly predicts benefit from extended tamoxifen in HR+ N+ patients with HER2- disease. Moreover, BCI (H/I) demonstrates significant treatment to biomarker interaction across survival outcomes.},
}
@article {pmid35143920,
year = {2022},
author = {Rosipal, R and Rošťáková, Z and Trejo, LJ},
title = {Tensor decomposition of human narrowband oscillatory brain activity in frequency, space and time.},
journal = {Biological psychology},
volume = {169},
number = {},
pages = {108287},
doi = {10.1016/j.biopsycho.2022.108287},
pmid = {35143920},
issn = {1873-6246},
mesh = {Algorithms ; *Brain/physiology ; *Electroencephalography/methods ; Humans ; },
abstract = {Many brain processes in health and disease are associated with modulation of narrowband brain oscillations (NBOs) in the scalp-recorded EEG, which exhibit specific frequency spectra and scalp topography. Isolating and tracking NBOs over time using algorithms is useful in domains such as brain-computer interfaces or when measuring the EEG effects of experimental manipulations. Previously, we successfully applied modified tensor methods for identifying and tracking NBO activity over time or conditions. We introduced frequency and spatial constraints that greatly improved their physiological plausibility. In this paper we rigorously demonstrate the power and precision of tensor methods to separate, isolate and track NBOs using sources simulated with an anatomical forward model. This allows us to control the attributes of NBOs and validate tensor solutions. We find that tensor methods can accurately identify, separate and track NBOs over time, using realistic sources either alone or in combination, and compare favorably to well-known spatio-spectral decomposition methods for NBO estimation.},
}
@article {pmid35141974,
year = {2022},
author = {Nemmi, F and Levardon, M and Péran, P},
title = {Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction.},
journal = {Human brain mapping},
volume = {43},
number = {7},
pages = {2365-2376},
pmid = {35141974},
issn = {1097-0193},
mesh = {Aged, 80 and over ; Brain/diagnostic imaging ; Diffusion Magnetic Resonance Imaging/methods ; *Diffusion Tensor Imaging/methods ; Humans ; Male ; Water ; *White Matter/diagnostic imaging ; },
abstract = {Although free-water diffusion reconstruction for diffusion-weighted imaging (DWI) data can be applied to both single-shell and multishell data, recent finding in synthetic data suggests that the free-water indices from single-shell acquisition should be interpreted with care, as they are heavily influenced by initialization parameters and cannot discriminate between free-water and mean diffusivity modifications. However, whether using a longer multishell acquisition protocol significantly improve reconstruction for real human MRI data is still an open question. In this study, we compare canonical diffusion tensor imaging (DTI), single-shell and multishell free-water imaging (FW) indices derived from a short, clinical compatible diffusion protocol (b = 500 s/mm[2] , b = 1,000 s/mm[2] , 32 directions each) on their power to predict brain age. Age was chosen as it is well-known to be related to widespread modification of the white matter and because brain-age estimation has recently been found to be relevant to several neurodegenerative diseases. We used a previously developed and validated data-driven whole-brain machine learning pipeline to directly compare the precision of brain-age estimates in a sample of 89 healthy males between 20 and 85 years old. We found that multishell FW outperform DTI indices in estimating brain age and that multishell FW, even when using low (500 ms[2]) b-values secondary shell, outperform single-shell FW. Single-shell FW led to lower brain-age estimation accuracy even of canonical DTI indices, suggesting that single-shell FW indices should be used with caution. For all considered reconstruction algorithms, the most discriminant indices were those measuring free diffusion of water in the white matter.},
}
@article {pmid35141969,
year = {2022},
author = {Huang, S and Huang, F and Zhang, H and Yang, Y and Lu, J and Chen, J and Shen, L and Pei, G},
title = {In vivo development and single-cell transcriptome profiling of human brain organoids.},
journal = {Cell proliferation},
volume = {55},
number = {3},
pages = {e13201},
pmid = {35141969},
issn = {1365-2184},
support = {XDA16010309//Strategic Priority Research Program" of the Chinese Academy of Sciences/ ; 2018YFA0108003//National Key Research and Development Programs of China/ ; 81901094//National Science Foundation for Young Scientists of China/ ; 32022023//National Natural Science Foundation of China/ ; },
mesh = {Animals ; Brain/*cytology ; Cell Differentiation/physiology ; *Gene Expression Profiling/methods ; Humans ; Mice, SCID ; Neurogenesis/physiology ; Neurons/cytology ; Organoids/*cytology ; Pluripotent Stem Cells/*cytology ; Mice ; },
abstract = {OBJECTIVES: Human brain organoids can provide not only promising models for physiological and pathological neurogenesis but also potential therapies in neurological diseases. However, technical issues such as surgical lesions due to transplantation still limit their applications.<br><br>MATERIALS AND METHODS: Instead of applying mature organoids, we innovatively developed human brain organoids in vivo by injecting small premature organoids into corpus striatum of adult SCID mice. Two months after injection, single-cell transcriptome analysis was performed on 6131 GFP-labeled human cells from transplanted mouse brains.<br><br>RESULTS: Eight subsets of cells (including neuronal cells expressing striatal markers) were identified in these in vivo developed organoids (IVD-organoids) by unbiased clustering. Compared with in vitro cultured human cortical organoids, we found that IVD-organoids developed more supporting cells including pericyte-like and choroid plexus cells, which are important for maintaining organoid homeostasis. Furthermore, IVD-organoids showed lower levels of cellular stress and apoptosis.<br><br>CONCLUSIONS: Our study thus provides a novel method to generate human brain organoids, which is promising in various applications of disease models and therapies.},
}
@article {pmid35140763,
year = {2022},
author = {Fu, Y and Li, Z and Gong, A and Qian, Q and Su, L and Zhao, L},
title = {Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {1038901},
pmid = {35140763},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Support Vector Machine ; },
abstract = {The traditional imagery task for brain-computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery-visual imagery (VI)-in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert-Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14 ± 3.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29 ± 2.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40 ± 2.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.},
}
@article {pmid35140593,
year = {2021},
author = {Meng, J and Wu, Z and Li, S and Zhu, X},
title = {Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {773603},
pmid = {35140593},
issn = {1662-5161},
abstract = {Motor imagery-based brain-computer interfaces (BCIs) have been studied without controlling subjects' gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects' performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects' BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance.},
}
@article {pmid35139061,
year = {2022},
author = {Du, Q and Luo, J and Chu, C and Wang, Y and Cheng, Q and Guo, S},
title = {The brain state of motor imagery is reflected in the causal information of functional near-infrared spectroscopy.},
journal = {Neuroreport},
volume = {33},
number = {3},
pages = {137-144},
doi = {10.1097/WNR.0000000000001765},
pmid = {35139061},
issn = {1473-558X},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination/physiology ; Quality of Life ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a promising neurorehabilitation strategy for ameliorating post-stroke function disorders. Physiological changes in the brain, such as functional near-infrared spectroscopy (fNIRS) dedicated to exploring cerebral circulatory responses during neurological rehabilitation tasks, are essential for gaining insights into neurorehabilitation mechanisms. However, the relationship between the neurovascular responses in different brain regions under rehabilitation tasks remains unknown.<br><br>OBJECTIVE: The present study explores the fNIRS interactions between brain regions under different motor imagery (MI) tasks, emphasizing functional characteristics of brain network patterns and BCI motor task classification.<br><br>METHODS: Granger causality analysis (GCA) is carried out for oxyhemoglobin data from 29 study participants in left- and right-hand MI tasks.<br><br>RESULTS: According to research findings, homozygous and heterozygous states in the two brain connectivity modes reveal one and nine channel pairs, respectively, with significantly different (P < 0.05) GC values under the left- and right-hand MI tasks in the population. With reference to the total 10 channel pairs of causality differences between the two brain working states, a support vector machine is used to classify the two tasks with an overall accuracy of 83% for five-fold cross-validation.<br><br>CONCLUSION: As demonstrated in the present study, fNIRS offers causality patterns in different brain states of MIBCI motor tasks. The research findings show that fNIRS causality can be used to assess different states of the brain, providing theoretical support for its application to neurorehabilitation assessment protocols to ultimately improve patients' quality of life.Video Abstract: http://links.lww.com/WNR/A653.},
}
@article {pmid35138249,
year = {2022},
author = {Zheng, R and Du, Y and Wang, X and Liao, T and Zhang, Z and Wang, N and Li, X and Shen, Y and Shi, L and Luo, J and Xia, J and Wang, Z and Xu, J},
title = {KIF2C regulates synaptic plasticity and cognition in mice through dynamic microtubule depolymerization.},
journal = {eLife},
volume = {11},
number = {},
pages = {},
pmid = {35138249},
issn = {2050-084X},
mesh = {Animals ; Cognition ; Female ; HEK293 Cells ; Humans ; Kinesins/genetics/*metabolism ; Long-Term Potentiation ; Male ; Mice ; Mice, Inbred C57BL ; Microtubules/genetics/*metabolism ; Neuronal Plasticity/*genetics ; Neurons/metabolism ; Protein Transport ; Receptors, AMPA/*metabolism ; Synapses/*metabolism ; },
abstract = {Dynamic microtubules play a critical role in cell structure and function. In nervous system, microtubules are the major route for cargo protein trafficking and they specially extend into and out of synapses to regulate synaptic development and plasticity. However, the detailed depolymerization mechanism that regulates dynamic microtubules in synapses and dendrites is still unclear. In this study, we find that KIF2C, a dynamic microtubule depolymerization protein without known function in the nervous system, plays a pivotal role in the structural and functional plasticity of synapses and regulates cognitive function in mice. Through its microtubule depolymerization capability, KIF2C regulates microtubule dynamics in dendrites, and regulates microtubule invasion of spines in neurons in a neuronal activity-dependent manner. Using RNAi knockdown and conditional knockout approaches, we showed that KIF2C regulates spine morphology and synaptic membrane expression of AMPA receptors. Moreover, KIF2C deficiency leads to impaired excitatory transmission, long-term potentiation, and altered cognitive behaviors in mice. Collectively, our study explores a novel function of KIF2C in the nervous system and provides an important regulatory mechanism on how activity-dependent microtubule dynamic regulates synaptic plasticity and cognition behaviors.},
}
@article {pmid35134085,
year = {2022},
author = {Xiong, W and Wei, Q},
title = {Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition.},
journal = {PloS one},
volume = {17},
number = {2},
pages = {e0263641},
pmid = {35134085},
issn = {1932-6203},
mesh = {Algorithms ; Brain-Computer Interfaces/psychology/*trends ; Calibration ; Discriminant Analysis ; Electroencephalography/methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Logistic Models ; Models, Theoretical ; Signal Processing, Computer-Assisted/instrumentation ; Visual Perception/physiology ; },
abstract = {One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available. The artificial EEG data are obtained by first performing empirical mode decomposition (EMD) and then mixing resulting intrinsic mode functions (IMFs). The original training trials are aligned to common reference point with Euclidean alignment (EA) method prior to EMD and pooled together with artificial trials as the expended training set, which is input into a linear discriminant analysis (LDA) classifier or a logistic regression (LR) classifier. The performance of the proposed algorithm is evaluated on two motor imagery (MI) data sets and compared with that of the algorithm trained with only real EEG data (Baseline) and the algorithm trained with expanded EEG data by EMD without data alignment. The experimental results showed that the proposed algorithm can significantly reduce the amount of training data needed to achieve a given performance level and thus is expected to facilitate the real-world applications of MI-based BCIs.},
}
@article {pmid35133967,
year = {2022},
author = {Chen, C and Ma, Z and Liu, Z and Zhou, L and Wang, G and Li, Y and Zhao, J},
title = {An Energy-Efficient Wearable Functional Near-infrared Spectroscopy System Employing Dual-level Adaptive Sampling Technique.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {16},
number = {1},
pages = {119-128},
doi = {10.1109/TBCAS.2022.3149766},
pmid = {35133967},
issn = {1940-9990},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Humans ; Signal-To-Noise Ratio ; Spectroscopy, Near-Infrared/methods ; *Wearable Electronic Devices ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a powerful medical imaging tool in brain science and psychology, it can also be employed in brain-computer interface (BCI) due to its noninvasive and artifact-less-sensitive characteristics. Conventional ways to detect large-area brain activity using near-infrared (NIR) technology are based on Time-division or Frequency-division modulation technique, which traverses all physical sensory channels in a specific period. To achieve higher imaging resolution or brain-tasks classification accuracy, the NIRS system require higher density and more channels, which conflict with the limited battery capacity. Inspired by the functional atlas of the human brain, this paper proposes a spatial adaptive sampling (SAS) method. It can change the active channel pattern of the fNIRS system to match with the real-time brain activity, to increase the energy efficiency without significant reduction on the brain imaging quality or the accuracy of brain activity classification. Therefore, the number of the averaging enabled channels will be dramatically reduced in practice. To verify the proposed SAS technique, a wearable and flexible NIRS system has been implemented, in which each channel of light-emitting diode (LED) drive circuits and photodiode (PD) detection circuits can be power gated independently. Brain task experiments have been conducted to validate the proposed method, the power consumption of the LED drive module is reduced by 46.58% compared to that without SAS technology while maintaining an average brain imaging PSNR (Peak Signal to Noise Ratio) of 35 dB. The brain-task classification accuracy is 80.47%, which has a 2.67% reduction compared to that without the SAS technique.},
}
@article {pmid35133966,
year = {2022},
author = {Ni, Z and Xu, J and Wu, Y and Li, M and Xu, G and Xu, B},
title = {Improving Cross-State and Cross-Subject Visual ERP-Based BCI With Temporal Modeling and Adversarial Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {369-379},
doi = {10.1109/TNSRE.2022.3150007},
pmid = {35133966},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; },
abstract = {Brain-computer interface (BCI) is a useful device for people without relying on peripheral nerves and muscles. However, the performance of the event-related potential (ERP)-based BCI declines when applying it to real environments, especially in cross-state and cross-subject conditions. Here we employ temporal modeling and adversarial training to improve the visual ERP-based BCI under different mental workload states and to alleviate the problems above. The rationality of our method is that the ERP-based BCI is based on electroencephalography (EEG) signals recorded from the scalp's surface, continuously changing with time and somewhat stochastic. In this paper, we propose a hierarchical recurrent network to encode all ERP signals in each repetition at the same time and model them with a temporal manner to predict which visual event elicited an ERP. The hierarchical architecture is a simple yet effective method for organizing recurrent layers in a deep structure to model long sequence signals. Taking a cue from recent advances in adversarial training, we further applied dynamic adversarial perturbations to create adversarial examples to enhance the model performance. We conduct our experiments on one published visual ERP-based BCI task with 15 subjects and 3 different auditory workload states. The results indicate that our hierarchical method can effectively model the long sequence EEG raw data, outperform the baselines on most conditions, including cross-state and cross-subject conditions. Finally, we show how deep learning-based methods with limited EEG data can improve ERP-based BCI with adversarial training. Our code is available at https://github.com/aispeech-lab/VisBCI.},
}
@article {pmid35133292,
year = {2022},
author = {Basti, A and Chella, F and Guidotti, R and Ermolova, M and D'Andrea, A and Stenroos, M and Romani, GL and Pizzella, V and Marzetti, L},
title = {Looking through the windows: a study about the dependency of phase-coupling estimates on the data length.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac52d3},
pmid = {35133292},
issn = {1741-2552},
abstract = {OBJECTIVE: Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes.<br><br>APPROACH: We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence.<br><br>MAIN RESULTS: Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required.<br><br>SIGNIFICANCE: The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.},
}
@article {pmid35130536,
year = {2022},
author = {Zhang, Z and Savolainen, OW and Constandinou, TG},
title = {Algorithm and hardware considerations for real-time neural signal on-implant processing.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac5268},
pmid = {35130536},
issn = {1741-2552},
support = {/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Computers ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.Various on-workstation neural-spike-based brain machine interface (BMI) systems have reached the point of in-human trials, but on-node and on-implant BMI systems are still under exploration. Such systems are constrained by the area and battery. Researchers should consider the algorithm complexity, available resources, power budgets, CMOS technologies, and the choice of platforms when designing BMI systems. However, the effect of these factors is currently still unclear.Approaches.Here we have proposed a novel real-time 128 channel spike detection algorithm and optimised it on microcontroller (MCU) and field programmable gate array (FPGA) platforms towards consuming minimal power and memory/resources. It is presented as a use case to explore the different considerations in system design.Main results.The proposed spike detection algorithm achieved over 97% sensitivity and a smaller than 3% false detection rate. The MCU implementation occupies less than 3 KB RAM and consumes 31.5 µW ch[-1]. The FPGA platform only occupies 299 logic cells and 3 KB RAM for 128 channels and consumes 0.04 µW ch[-1].Significance.On the spike detection algorithm front, we have eliminated the processing bottleneck by reducing the dynamic power consumption to lower than the hardware static power, without sacrificing detection performance. More importantly, we have explored the considerations in algorithm and hardware design with respect to scalability, portability, and costs. These findings can facilitate and guide the future development of real-time on-implant neural signal processing platforms.},
}
@article {pmid35130163,
year = {2022},
author = {Kwak, Y and Song, WJ and Kim, SE},
title = {FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {329-339},
doi = {10.1109/TNSRE.2022.3149899},
pmid = {35130163},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; },
abstract = {Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitations of EEG- and fNIRS-standalone BCI systems. However, most hybrid EEG-fNIRS BCI studies have focused on late fusion because of discrepancies in their temporal resolutions and recording locations. Despite the enhanced performance of hybrid BCIs, late fusion methods have difficulty in extracting correlated features in both EEG and fNIRS signals. Therefore, in this study, we proposed a deep learning-based early fusion structure, which combines two signals before the fully-connected layer, called the fNIRS-guided attention network (FGANet). First, 1D EEG and fNIRS signals were converted into 3D EEG and fNIRS tensors to spatially align EEG and fNIRS signals at the same time point. The proposed fNIRS-guided attention layer extracted a joint representation of EEG and fNIRS tensors based on neurovascular coupling, in which the spatially important regions were identified from fNIRS signals, and detailed neural patterns were extracted from EEG signals. Finally, the final prediction was obtained by weighting the sum of the prediction scores of the EEG and fNIRS-guided attention features to alleviate performance degradation owing to delayed fNIRS response. In the experimental results, the FGANet significantly outperformed the EEG-standalone network. Furthermore, the FGANet has 4.0% and 2.7% higher accuracy than the state-of-the-art algorithms in mental arithmetic and motor imagery tasks, respectively.},
}
@article {pmid35130161,
year = {2022},
author = {Liu, G and Wang, J},
title = {EEGG: An Analytic Brain-Computer Interface Algorithm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {643-655},
doi = {10.1109/TNSRE.2022.3149654},
pmid = {35130161},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination/physiology ; },
abstract = {OBJECTIVE: Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity.<br><br>APPROACH: Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery (MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain.<br><br>MAIN RESULTS: (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain.<br><br>SIGNIFICANCE: EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.},
}
@article {pmid35126800,
year = {2021},
author = {Paek, AY and Brantley, JA and Evans, BJ and Contreras-Vidal, JL},
title = {Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology.},
journal = {IEEE systems journal},
volume = {15},
number = {2},
pages = {3069-3080},
pmid = {35126800},
issn = {1932-8184},
support = {F99 NS105210/NS/NINDS NIH HHS/United States ; K00 NS105210/NS/NINDS NIH HHS/United States ; },
abstract = {Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.},
}
@article {pmid35126771,
year = {2022},
author = {Ketu, S and Mishra, PK},
title = {Hybrid classification model for eye state detection using electroencephalogram signals.},
journal = {Cognitive neurodynamics},
volume = {16},
number = {1},
pages = {73-90},
pmid = {35126771},
issn = {1871-4080},
abstract = {The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.},
}
@article {pmid35125142,
year = {2022},
author = {Del Campo-Vera, RM and Tang, AM and Gogia, AS and Chen, KH and Sebastian, R and Gilbert, ZD and Nune, G and Liu, CY and Kellis, S and Lee, B},
title = {Neuromodulation in Beta-Band Power Between Movement Execution and Inhibition in the Human Hippocampus.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {25},
number = {2},
pages = {232-244},
pmid = {35125142},
issn = {1525-1403},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; *Electroencephalography ; *Epilepsy/therapy ; Female ; Hippocampus ; Humans ; Male ; Middle Aged ; Movement ; Young Adult ; },
abstract = {INTRODUCTION: The hippocampus is thought to be involved in movement, but its precise role in movement execution and inhibition has not been well studied. Previous work with direct neural recordings has found beta-band (13-30 Hz) modulation in both movement execution and inhibition throughout the motor system, but the role of beta-band modulation in the hippocampus during movement inhibition is not well understood. Here, we perform a Go/No-Go reaching task in ten patients with medically refractory epilepsy to study human hippocampal beta-power changes during movement.<br><br>MATERIALS AND METHODS: Ten epilepsy patients (5 female; ages 21-46) were implanted with intracranial depth electrodes for seizure monitoring and localization. Local field potentials were sampled at 2000 Hz during a Go/No-Go movement task. Comparison of beta-band power between Go and No-Go conditions was conducted using Wilcoxon signed-rank hypothesis testing for each patient. Sub-analyses were conducted to assess differences in the anterior vs posterior contacts, ipsilateral vs contralateral contacts, and male vs female beta-power values.<br><br>RESULTS: Eight out of ten patients showed significant beta-power decreases during the Go movement response (p&#x2009;<&#x2009;0.05) compared to baseline. Eight out of ten patients also showed significant beta-power increases in the No-Go condition, occurring in the absence of movement. No significant differences were noted between ipsilateral vs contralateral contacts nor in anterior vs posterior hippocampal contacts. Female participants had a higher task success rate than males and had significantly greater beta-power increases in the No-Go condition (p&#x2009;<&#x2009;0.001).<br><br>CONCLUSION: These findings indicate that increases in hippocampal beta power are associated with movement inhibition. To the best of our knowledge, this study is the first to report this phenomenon in the human hippocampus. The beta band may represent a state-change signal involved in motor processing. Future focus on the beta band in understanding human motor and impulse control will be vital.},
}
@article {pmid35124225,
year = {2022},
author = {Li, G and Jiang, S and Meng, J and Chai, G and Wu, Z and Fan, Z and Hu, J and Sheng, X and Zhang, D and Chen, L and Zhu, X},
title = {Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.},
journal = {NeuroImage},
volume = {250},
number = {},
pages = {118969},
doi = {10.1016/j.neuroimage.2022.118969},
pmid = {35124225},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cues ; Drug Resistant Epilepsy/physiopathology ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; Stereotaxic Techniques ; },
abstract = {Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.},
}
@article {pmid35114101,
year = {2022},
author = {Zheng, Z and Guo, C and Li, M and Yang, L and Liu, P and Zhang, X and Liu, Y and Guo, X and Cao, S and Dong, Y and Zhang, C and Chen, M and Xu, J and Hu, H and Cui, Y},
title = {Hypothalamus-habenula potentiation encodes chronic stress experience and drives depression onset.},
journal = {Neuron},
volume = {110},
number = {8},
pages = {1400-1415.e6},
doi = {10.1016/j.neuron.2022.01.011},
pmid = {35114101},
issn = {1097-4199},
mesh = {Animals ; Depression/etiology ; *Habenula/physiology ; Hypothalamic Area, Lateral ; Hypothalamus ; Mice ; Synapses/physiology ; },
abstract = {Chronic stress is a major risk factor for depression onset. However, it remains unclear how repeated stress sculpts neural circuits and finally elicits depression. Given the essential role of lateral habenula (LHb) in depression, here, we attempt to clarify how LHb-centric neural circuitry integrates stress-related information. We identify lateral hypothalamus (LH) as the most physiologically relevant input to LHb under stress. LH neurons fire with a unique pattern that efficiently drives postsynaptic potential summation and a closely followed LHb bursting (EPSP-burst pairing) in response to various stressors. We found that LH-LHb synaptic potentiation is determinant in stress-induced depression. Mimicking this repeated EPSP-burst pairings at LH-LHb synapses by photostimulation, we artificially induced an "emotional status" merely by potentiating this pathway in mice. Collectively, these results delineate the spatiotemporal dynamics of chronic stress processing from forebrain onto LHb in a pathway-, cell-type-, and pattern-specific manner, shedding light on early interventions before depression onset.},
}
@article {pmid35108199,
year = {2021},
author = {Shen, X and Zhang, X and Huang, Y and Chen, S and Wang, Y},
title = {Corrections to "Task Learning Over Multi-Day Recording via Internally Rewarded Reinforcement Learning Based Brain Machine Interfaces".},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2776},
doi = {10.1109/TNSRE.2021.3080405},
pmid = {35108199},
issn = {1558-0210},
abstract = {In the above article [1], in the second paragraph of Section II-A, the randomly selected low-pitch audio cue was 1.5 kHz (not 4 kHz). Thus, the article should state "the two-lever-press discrimination task trials were initialized by a high-pitch (10 kHz) or low-pitch (1.5 kHz) audio cue, which was randomly generated."},
}
@article {pmid35106254,
year = {2022},
author = {Cripe, CT and Mikulecky, P and Sucher, M and Huang, JH and Hack, D},
title = {Improved Sobriety Rates After Brain-Computer Interface-Based Cognitive Remediation Training.},
journal = {Cureus},
volume = {14},
number = {1},
pages = {e21429},
pmid = {35106254},
issn = {2168-8184},
abstract = {Up to 80% of individuals seeking treatment fail in their attempts at sobriety. This study investigated whether 1) a cognitive remediation therapy (CRT) program augmented with a brain-computer interface (BCI) to influence brain performance metrics would increase participants' self-agency by restoring cognitive control performance; and 2) that ability increase would produce increased sobriety rates, greater than published treatment rates. The study employed a retrospective chart review structured to replicate a switching replication methodology (i.e., waitlist group) using a pre-test and post-test profile analysis quasi-experimental design. Participants' records were organized into treatment and non-treatment groups. Adult poly-substance users were recruited from alcohol and other drugs (AOD) use outpatient programs and AOD use treatment centers in the United States. Participants volunteered for pre- and post-testing without treatment (n = 121) or chose to enter the treatment program (n = 200). The treatment group engaged in a 48-session BCI/CRT augmented treatment program. Pre- and post-treatment measures comprised 14 areas from the Woodcock-Johnson Cognitive Abilities III Assessment Battery. An 18-month follow-up assessment measured maintenance of sobriety. After testing the difference for all variables across time between test groups, a significant multivariate effect was found. In addition, at 18 months post-treatment, 89% of the treatment group maintained sobriety, compared to 31% of the non-treatment group. Consistent with addiction neurobehavioral imbalance models, traditional treatment programs augmented with BCI/CRT training, focused on improving cognitive control abilities, may strengthen self-control and improve sobriety rates.},
}
@article {pmid35104529,
year = {2022},
author = {Jiménez, J and Godinho, R and Pinto, D and Lopes, S and Castro, D and Cubero, D and Osorio, MA and Piqué, J and Moreno-Opo, R and Quiros, P and González-Nuevo, D and Hernandez-Palacios, O and Kéry, M},
title = {The Cantabrian capercaillie: A population on the edge.},
journal = {The Science of the total environment},
volume = {821},
number = {},
pages = {153523},
doi = {10.1016/j.scitotenv.2022.153523},
pmid = {35104529},
issn = {1879-1026},
mesh = {Animals ; DNA ; Female ; *Galliformes ; Humans ; Male ; Population Density ; Population Dynamics ; Spain ; },
abstract = {The capercaillie Tetrao urogallus - the world's largest grouse- is a circumboreal forest species, which only two remaining populations in Spain: one in the Cantabrian mountains in the west and the other in the Pyrenees further east. Both have shown severe declines, especially in the Cantabrian population, which has recently been classified as "Critically Endangered". To develop management plans, information on demographic parameters is necessary to understand and forecast population dynamics. We used spatial capture-recapture (SCR) modeling and non-invasive DNA samples to estimate the current population size in the whole Cantabrian mountain range. In addition, for the assessment of population status, we analyzed the population trajectory over the last 42 years (1978-2019) at 196 leks on the Southern slope of the range, using an integrated population model with a Dail-Madsen model at its core, combined with a multistate capture-recapture model for survival and a Poisson regression for productivity. For 2019, we estimate the size of the entire population at 191 individuals (95% BCI 165-222) for an estimated 60 (48-78) females and 131 (109-157) males. Since the 1970s, our study estimates a shrinkage of the population range by 83%. The population at the studied leks in 2019 was at about 10% of the size estimated for 1978. Apparent annual survival was estimated at 0.707 (0.677-0.735), and per-capita recruitment at 0.233 (0.207-0.262), and insufficient to maintain a stable population. We suggest work to improve the recruitment (and survival) and manage these mountain forests for capercaillie conservation. Also, in the future, management should assess the genetic viability of this population.},
}
@article {pmid35104499,
year = {2022},
author = {Merk, T and Peterson, V and Köhler, R and Haufe, S and Richardson, RM and Neumann, WJ},
title = {Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation.},
journal = {Experimental neurology},
volume = {351},
number = {},
pages = {113993},
pmid = {35104499},
issn = {1090-2430},
support = {R01 NS110424/NS/NINDS NIH HHS/United States ; R01 NS117058/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Deep Brain Stimulation ; Machine Learning ; },
abstract = {Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.},
}
@article {pmid35103873,
year = {2022},
author = {Li, Y and Zhong, N and Taniar, D and Zhang, H},
title = {MCGNet[+]: an improved motor imagery classification based on cosine similarity.},
journal = {Brain informatics},
volume = {9},
number = {1},
pages = {3},
pmid = {35103873},
issn = {2198-4018},
support = {21A13022003//Ministry of Education of the People's Republic of China/ ; LY19F030010//Natural Science Foundation of Zhejiang Province/ ; 20NDJC216YB//Zhejiang Provincial Social Science Fund/ ; 2019A610083//Natural Science Foundation of Ningbo/ ; GH2021642//Zhejiang Provincial Educational Science Scheme 2021/ ; 72071049//National Natural Science Foundation of China/ ; },
abstract = {It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet[+], which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet[+] is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.},
}
@article {pmid35099768,
year = {2022},
author = {Luo, S and Rabbani, Q and Crone, NE},
title = {Brain-Computer Interface: Applications to Speech Decoding and Synthesis to Augment Communication.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {19},
number = {1},
pages = {263-273},
pmid = {35099768},
issn = {1878-7479},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; UH3 NS114439/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Communication ; Electrocorticography ; Humans ; Quality of Life ; Speech/physiology ; },
abstract = {Damage or degeneration of motor pathways necessary for speech and other movements, as in brainstem strokes or amyotrophic lateral sclerosis (ALS), can interfere with efficient communication without affecting brain structures responsible for language or cognition. In the worst-case scenario, this can result in the locked in syndrome (LIS), a condition in which individuals cannot initiate communication and can only express themselves by answering yes/no questions with eye blinks or other rudimentary movements. Existing augmentative and alternative communication (AAC) devices that rely on eye tracking can improve the quality of life for people with this condition, but brain-computer interfaces (BCIs) are also increasingly being investigated as AAC devices, particularly when eye tracking is too slow or unreliable. Moreover, with recent and ongoing advances in machine learning and neural recording technologies, BCIs may offer the only means to go beyond cursor control and text generation on a computer, to allow real-time synthesis of speech, which would arguably offer the most efficient and expressive channel for communication. The potential for BCI speech synthesis has only recently been realized because of seminal studies of the neuroanatomical and neurophysiological underpinnings of speech production using intracranial electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery. These studies have shown that cortical areas responsible for vocalization and articulation are distributed over a large area of ventral sensorimotor cortex, and that it is possible to decode speech and reconstruct its acoustics from ECoG if these areas are recorded with sufficiently dense and comprehensive electrode arrays. In this article, we review these advances, including the latest neural decoding strategies that range from deep learning models to the direct concatenation of speech units. We also discuss state-of-the-art vocoders that are integral in constructing natural-sounding audio waveforms for speech BCIs. Finally, this review outlines some of the challenges ahead in directly synthesizing speech for patients with LIS.},
}
@article {pmid35095410,
year = {2021},
author = {Papadopoulos, S and Bonaiuto, J and Mattout, J},
title = {An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {824759},
pmid = {35095410},
issn = {1662-4548},
abstract = {The development of reliable assistive devices for patients that suffer from motor impairments following central nervous system lesions remains a major challenge in the field of non-invasive Brain-Computer Interfaces (BCIs). These approaches are predominated by electroencephalography and rely on advanced signal processing and machine learning methods to extract neural correlates of motor activity. However, despite tremendous and still ongoing efforts, their value as effective clinical tools remains limited. We advocate that a rather overlooked research avenue lies in efforts to question neurophysiological markers traditionally targeted in non-invasive motor BCIs. We propose an alternative approach grounded by recent fundamental advances in non-invasive neurophysiology, specifically subject-specific feature extraction of sensorimotor bursts of activity recorded via (possibly magnetoencephalography-optimized) electroencephalography. This path holds promise in overcoming a significant proportion of existing limitations, and could foster the wider adoption of online BCIs in rehabilitation protocols.},
}
@article {pmid35094982,
year = {2022},
author = {Wu, X and Zheng, WL and Li, Z and Lu, BL},
title = {Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac49a7},
pmid = {35094982},
issn = {1741-2552},
mesh = {Arousal ; Brain ; *Electroencephalography ; Emotions ; *Neural Networks, Computer ; },
abstract = {Objective.Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality.Approach.After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP.Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are95.08±6.42%on the SEED dataset,84.51±5.11%on the SEED-V dataset, and85.34±2.90%and86.61±3.76%for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios.Significance.The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.},
}
@article {pmid35093844,
year = {2022},
author = {Sadiq, MT and Aziz, MZ and Almogren, A and Yousaf, A and Siuly, S and Rehman, AU},
title = {Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework.},
journal = {Computers in biology and medicine},
volume = {143},
number = {},
pages = {105242},
doi = {10.1016/j.compbiomed.2022.105242},
pmid = {35093844},
issn = {1879-0534},
abstract = {Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI systems with small and ample samples of motor and mental imagery EEG training data. The framework is explored by investigating the implications of different limiting factors, such as learning rates and optimizers, processed versus unprocessed scalograms, and features derived from untuned pretrained models in small, medium, and large pretrained CNN models. The experiments were performed on three public datasets obtained from BCI Competition III. The datasets were denoised with multiscale principal component analysis, and time-frequency scalograms were obtained by employing a continuous wavelet transform. The scalograms were fed into several variants of ten pretrained models for feature extraction and identification of different EEG tasks. The experimental results showed that ShuffleNet yielded the maximum average classification accuracy of 99.52% using an RMSProp optimizer with a learning rate of 0.000 1. It was observed that low learning rates converge to more optimal performances compared to high learning rates. Moreover, noisy scalograms and features extracted from untuned networks resulted in slightly lower performance than denoised scalograms and tuned networks, respectively. The overall results suggest that pretrained models are robust when identifying EEG signals because of their ability to preserve the time-frequency structure of EEG signals and promising classification outcomes.},
}
@article {pmid35092360,
year = {2022},
author = {Kiba, K and Akashi, Y and Yamamoto, Y and Hirayama, A and Fujimoto, K and Uemura, H},
title = {Clinical features of detrusor underactivity in elderly men without neurological disorders.},
journal = {Lower urinary tract symptoms},
volume = {14},
number = {3},
pages = {193-198},
doi = {10.1111/luts.12424},
pmid = {35092360},
issn = {1757-5672},
mesh = {Aged ; Female ; Humans ; *Lower Urinary Tract Symptoms/diagnosis ; Male ; *Nervous System Diseases/complications ; Retrospective Studies ; *Urinary Bladder Neck Obstruction/diagnosis ; *Urinary Bladder, Underactive/complications/diagnosis ; Urodynamics ; },
abstract = {OBJECTIVES: To investigate the clinical features of detrusor underactivity (DU) in elderly men without neurological disorders.<br><br>METHODS: A total of 336 men aged &#x2265;50&#x2009;years without neurogenic disorders who underwent pressure flow studies and who had DU or bladder outlet obstruction (BOO) were reviewed retrospectively. According to the bladder contractility index (BCI) and the BOO index (BOOI), the subjects were classified into the following three groups: (a) pure DU group, BCI&#x2009;<&#x2009;100 and BOOI&#x2009;<&#x2009;40; (b) DU&#x2009;+&#x2009;BOO group, BCI&#x2009;<&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;40; and (c) pure BOO group, BCI&#x2009;&#x2265;&#x2009;100 and BOOI&#x2009;&#x2265;&#x2009;40. Subjective and objective parameters were compared among the three groups, and the predictors for pure DU were evaluated by multivariate analysis.<br><br>RESULTS: Of the 336 patients, 205 who met the study criteria were included in the analysis: 63 (30.7%) with pure DU, 48 (23.4%) with DU&#x2009;+&#x2009;BOO, and 94 (45.9%) with pure BOO. The proportion of the pure DU group increased with increasing age. Prostate volume was the lowest in the pure DU group. Frequency, urgency on the International Prostate Symptom Score (IPSS), and the IPSS storage subscore were the lowest in the pure DU group. Multivariate analysis showed that age (odds ratio [OR] 1.114 [95% CI, 1.032-1.203], P&#xa0;=&#xa0;.005), prostate volume (OR 0.968 [95% CI, 0.949-0.987], P&#xa0;=&#xa0;.001), and urgency (OR 0.623 [95% CI, 0.431-0.900], P&#xa0;=&#xa0;.012) were predictors of pure DU.<br><br>CONCLUSION: Older age, smaller prostate volume, and less urgency may be clinical features of pure DU.},
}
@article {pmid35090904,
year = {2022},
author = {Cai, Y and She, Q and Ji, J and Ma, Y and Zhang, J and Zhang, Y},
title = {Motor imagery EEG decoding using manifold embedded transfer learning.},
journal = {Journal of neuroscience methods},
volume = {370},
number = {},
pages = {109489},
doi = {10.1016/j.jneumeth.2022.109489},
pmid = {35090904},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Learning ; Machine Learning ; },
abstract = {BACKGROUND: Brain computer interface (BCI) utilizes brain signals to help users interact with external devices directly. EEG is one of the most commonly used techniques for brain signal acquisition in BCI. However, it is notoriously difficult to build a generic EEG recognition model due to significant non-stationarity and subject-to-subject variations, and the requirement for long time training. Transfer learning (TL) is particularly useful because it can alleviate the calibration requirement in EEG-based BCI applications by transferring the calibration information from existing subjects to new subject. To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding.<br><br>NEW METHOD: First, the covariance matrices of the EEG trials are first aligned on the SPD manifold. Then the features are extracted from both the symmetric positive definite (SPD) manifold and Grassmann manifold. Finally, the classification model is learned by combining the structural risk minimization (SRM) of source domain and joint distribution alignment of source and target domains.<br><br>RESULT: Experimental results on two MI EEG datasets verify the effectiveness of the proposed METL. In particular, when there are a small amount of labeled samples in the target domain, METL demonstrated a more accurate and stable classification performance than conventional methods.<br><br>Compared with several state-of-the-art methods, METL has achieved better classification accuracy, 71.81% and 69.06% in single-to-single (STS), 83.14% and 76.00% in multi-to-single (MTS) transfer tasks, respectively.<br><br>CONCLUSIONS: METL can cope with single source domain or multi-source domains and compared with single-source transfer learning, multi-source transfer learning can improve the performance effectively due to the data expansion. It is effective enough to achieve superior performance for classification of EEG signals.},
}
@article {pmid35088740,
year = {2023},
author = {Suzuki, Y and Jovanovic, LI and Fadli, RA and Yamanouchi, Y and Marquez-Chin, C and Popovic, MR and Nomura, T and Milosevic, M},
title = {Evidence That Brain-Controlled Functional Electrical Stimulation Could Elicit Targeted Corticospinal Facilitation of Hand Muscles in Healthy Young Adults.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {26},
number = {8},
pages = {1612-1621},
doi = {10.1016/j.neurom.2021.12.007},
pmid = {35088740},
issn = {1525-1403},
mesh = {Humans ; Young Adult ; *Muscle, Skeletal/physiology ; Hand ; Electroencephalography/methods ; Evoked Potentials, Motor/physiology ; *Motor Cortex/physiology ; Electric Stimulation/methods ; Transcranial Magnetic Stimulation/methods ; Electromyography ; },
abstract = {OBJECTIVES: Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) has been used in rehabilitation for improving hand motor function. However, mechanisms of improvements are still not well understood. The objective of this study was to investigate how BCI-controlled FES affects hand muscle corticospinal excitability.<br><br>MATERIALS AND METHODS: A total of 12 healthy young adults were recruited in the study. During BCI calibration, a single electroencephalography channel from the motor cortex and a frequency band were chosen to detect event-related desynchronization (ERD) of cortical oscillatory activity during kinesthetic wrist motor imagery (MI). The MI-based BCI system was used to detect active states on the basis of ERD activity in real time and produce contralateral wrist extension movements through FES of the extensor carpi radialis (ECR) muscle. As a control condition, FES was used to generate wrist extension at random intervals. The two interventions were performed on separate days and lasted 25 minutes. Motor evoked potentials (MEPs) in ECR (intervention target) and flexor carpi radialis (FCR) muscles were elicited through single-pulse transcranial magnetic stimulation of the motor cortex to compare corticospinal excitability before (pre), immediately after (post0), and 30 minutes after (post30) the interventions.<br><br>RESULTS: After the BCI-FES intervention, ECR muscle MEPs were significantly facilitated at post0 and post30 time points compared with before the intervention (pre), whereas there were no changes in the FCR muscle corticospinal excitability. Conversely, after the random FES intervention, both ECR and FCR muscle MEPs were unaffected compared with before the intervention (pre).<br><br>CONCLUSIONS: Our results demonstrated evidence that BCI-FES intervention could elicit muscle-specific short-term corticospinal excitability facilitation of the intervention targeted (ECR) muscle only, whereas randomly applied FES was ineffective in eliciting any changes. Notably, these findings suggest that associative cortical and peripheral activations during BCI-FES can effectively elicit targeted muscle corticospinal excitability facilitation, implying possible rehabilitation mechanisms.},
}
@article {pmid35088596,
year = {2022},
author = {Xu, J and Schoenfeld, MA and Rossini, PM and Tatlisumak, T and Nürnberger, A and Antal, A and He, H and Gao, Y and Sabel, BA},
title = {Adaptive and Maladaptive Brain Functional Network Reorganization After Stroke in Hemianopia Patients: An Electroencephalogram-Tracking Study.},
journal = {Brain connectivity},
volume = {12},
number = {8},
pages = {725-739},
doi = {10.1089/brain.2021.0145},
pmid = {35088596},
issn = {2158-0022},
mesh = {Humans ; *Brain ; Hemianopsia/complications ; Electroencephalography/methods ; *Stroke/complications ; Magnetic Resonance Imaging/methods ; Brain Mapping/methods ; },
abstract = {Objective: Hemianopia after occipital stroke is believed to be mainly due to local damage at or near the lesion site. However, magnetic resonance imaging studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear whether reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance. Methods: Resting-state electroencephalography (EEG) was recorded in chronic, unilateral stroke patients and healthy age-matched controls (n = 24 each). This study was approved by the local ethics committee. The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of regions of interest, and FCN graph theory metrics (degree, strength, clustering coefficient) were correlated with stimulus detection and reaction time. Results: Stroke brains showed altered FCNs in the alpha- and low beta-band in numerous occipital, temporal brain structures. On a global level, FCN had a less efficient network organization whereas on the local level node networks were reorganized especially in the intact hemisphere. Here, the occipital network was 58% more rigid (with a more "regular" network structure) whereas the temporal network was 32% more efficient (showing greater "small-worldness"), both of which correlated with worse or better visual processing, respectively. Conclusions: Occipital stroke is associated with both local and global FCN reorganization, but this can be both adaptive and maladaptive. We propose that the more "regular" FCN structure in the intact visual cortex indicates maladaptive plasticity, where less processing efficacy with reduced signal/noise ratio may cause the perceptual deficits in the intact visual field (VF). In contrast, reorganization in intact temporal brain regions is presumably adaptive, possibly supporting enhanced peripheral movement perception.},
}
@article {pmid35087920,
year = {2021},
author = {Jiang, Y and Idikuda, V and Chanda, B},
title = {Preparation of Giant Escherichia coli spheroplasts for Electrophysiological Recordings.},
journal = {Bio-protocol},
volume = {11},
number = {24},
pages = {e4261},
pmid = {35087920},
issn = {2331-8325},
support = {R01 NS081293/NS/NINDS NIH HHS/United States ; R01 NS101723/NS/NINDS NIH HHS/United States ; R21 GM131662/GM/NIGMS NIH HHS/United States ; R35 NS116850/NS/NINDS NIH HHS/United States ; },
abstract = {Prokaryotic ion channels have been instrumental in furthering our understanding of many fundamental aspects of ion channels' structure and function. However, characterizing the biophysical properties of a prokaryotic ion channel in a native membrane system using patch-clamp electrophysiology is technically challenging. Patch-clamp is regarded as a gold standard technique to study ion channel properties in both native and heterologous expression systems. The presence of a cell wall and the small size of bacterial cells makes it impossible to directly patch clamp using microelectrodes. Here, we describe a method for the preparation of giant E. coli spheroplasts in order to investigate the electrophysiological properties of bacterial cell membranes. Spheroplasts are formed by first inhibiting bacterial cell wall synthesis, followed by enzymatic digestion of the outer cell wall in the presence of a permeabilizing agent. This protocol can be used to characterize the function of any heterologous ion channels or ion transporters expressed in E. coli membranes.},
}
@article {pmid35087788,
year = {2021},
author = {Xue, X and Yang, X and Deng, Z and Tu, H and Kong, D and Li, N and Xu, F},
title = {Global Trends and Hotspots in Research on Rehabilitation Robots: A Bibliometric Analysis From 2010 to 2020.},
journal = {Frontiers in public health},
volume = {9},
number = {},
pages = {806723},
pmid = {35087788},
issn = {2296-2565},
mesh = {Artificial Intelligence ; Bibliometrics ; Databases, Factual ; Humans ; Reproducibility of Results ; *Robotics ; United States ; },
abstract = {Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research. Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words. Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention. Conclusions: At present, the brain-computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.},
}
@article {pmid35087580,
year = {2022},
author = {Song, X and Zeng, Y and Tong, L and Shu, J and Yang, Q and Kou, J and Sun, M and Yan, B},
title = {A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4752450},
pmid = {35087580},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Learning ; },
abstract = {The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.},
}
@article {pmid35087399,
year = {2021},
author = {Wen, D and Xu, J and Wu, Z and Liu, Y and Zhou, Y and Li, J and Wang, S and Dong, X and Saripan, MI and Song, H},
title = {The Effective Cognitive Assessment and Training Methods for COVID-19 Patients With Cognitive Impairment.},
journal = {Frontiers in aging neuroscience},
volume = {13},
number = {},
pages = {827273},
pmid = {35087399},
issn = {1663-4365},
}
@article {pmid35087375,
year = {2021},
author = {Zummo, F and Esposito, P and Hou, H and Wetzl, C and Rius, G and Tkatchenko, R and Guimera, A and Godignon, P and Prato, M and Prats-Alfonso, E and Criado, A and Scaini, D},
title = {Bidirectional Modulation of Neuronal Cells Electrical and Mechanical Properties Through Pristine and Functionalized Graphene Substrates.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {811348},
pmid = {35087375},
issn = {1662-4548},
abstract = {In recent years, the quest for surface modifications to promote neuronal cell interfacing and modulation has risen. This course is justified by the requirements of emerging technological and medical approaches attempting to effectively interact with central nervous system cells, as in the case of brain-machine interfaces or neuroprosthetic. In that regard, the remarkable cytocompatibility and ease of chemical functionalization characterizing surface-immobilized graphene-based nanomaterials (GBNs) make them increasingly appealing for these purposes. Here, we compared the (morpho)mechanical and functional adaptation of rat primary hippocampal neurons when interfaced with surfaces covered with pristine single-layer graphene (pSLG) and phenylacetic acid-functionalized single-layer graphene (fSLG). Our results confirmed the intrinsic ability of glass-supported single-layer graphene to boost neuronal activity highlighting, conversely, the downturn inducible by the surface insertion of phenylacetic acid moieties. fSLG-interfaced neurons showed a significant reduction in spontaneous postsynaptic currents (PSCs), coupled to reduced cell stiffness and altered focal adhesion organization compared to control samples. Overall, we have here demonstrated that graphene substrates, both pristine and functionalized, could be alternatively used to intrinsically promote or depress neuronal activity in primary hippocampal cultures.},
}
@article {pmid35085095,
year = {2022},
author = {Fang, H and Jin, J and Daly, I and Wang, X},
title = {Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {6},
pages = {2504-2514},
doi = {10.1109/JBHI.2022.3146274},
pmid = {35085095},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference. Furthermore, the performance of CSP is not satisfactory when addressing multi-category classification problems. In this work, we propose a fusion method combining Filter Banks and Riemannian Tangent Space (FBRTS) in multiple time windows. FBRTS uses multiple filter banks to overcome the problem of variance in the operational frequency band. It also applies the Riemannian method to the covariance matrix extracted by the spatial filter to obtain more robust features in order to overcome the problem of noise interference. In addition, we use a One-Versus-Rest support vector machine (OVR-SVM) model to classify multi-category features. We evaluate our FBRTS method using BCI competition IV dataset 2a and 2b. The experimental results show that the average classification accuracy of our FBRTS method is 77.7% and 86.9% in datasets 2a and 2b respectively. By analyzing the influence of the different numbers of filter banks and time windows on the performance of our FBRTS method, we can identify the optimal number of filter banks and time windows. Additionally, our FBRTS method can obtain more distinctive features than the filter banks common spatial pattern (FBCSP) method in two-dimensional embedding space. These results show that our proposed method can improve the performance of MI-BCIs.},
}
@article {pmid35083968,
year = {2022},
author = {Meechan, CF and Laws, KR and Young, AH and McLoughlin, DM and Jauhar, S},
title = {A critique of narrative reviews of the evidence-base for ECT in depression.},
journal = {Epidemiology and psychiatric sciences},
volume = {31},
number = {},
pages = {e10},
pmid = {35083968},
issn = {2045-7979},
support = {//National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London./ ; },
mesh = {Depression/therapy ; *Electroconvulsive Therapy ; Humans ; Narration ; },
abstract = {There has been recent debate regarding the efficacy of electroconvulsive therapy in the treatment of depression. This has been based on narrative reviews that contradict existing systematic reviews and meta-analyses. In this special article, we highlight the mistakes that occur when interpreting evidence using narrative reviews, as opposed to conventional systematic reviews and meta-analyses.},
}
@article {pmid35083329,
year = {2022},
author = {Sahoo, SK and Mohapatra, SK},
title = {Recognition of Ocular Artifacts in EEG Signal through a Hybrid Optimized Scheme.},
journal = {BioMed research international},
volume = {2022},
number = {},
pages = {4875399},
pmid = {35083329},
issn = {2314-6141},
mesh = {Algorithms ; *Artifacts ; Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {Brain computer interface (BCI) requires an online and real-time processing of EEG signals. Hence, the accuracy of the recording system is improved by nullifying the developed artifacts. The goal of this proposal is to develop a hybrid model for recognizing and minimizing ocular artifacts through an improved deep learning scheme. The discrete wavelet transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then, the features are extracted by principal component analysis (PCA) and independent component analysis (ICA) techniques. After collecting the features, an optimized deformable convolutional network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying the empirical mean curve decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods, which demonstrates a better ocular artifact reduction by the proposed method.},
}
@article {pmid35080731,
year = {2022},
author = {Zheng, D and Fu, JY and Tang, MY and Yu, XD and Zhu, Y and Shen, CJ and Li, CY and Xie, SZ and Lin, S and Luo, M and Li, XM},
title = {A Deep Mesencephalic Nucleus Circuit Regulates Licking Behavior.},
journal = {Neuroscience bulletin},
volume = {38},
number = {6},
pages = {565-575},
pmid = {35080731},
issn = {1995-8218},
mesh = {Animals ; Behavior, Animal ; *Central Amygdaloid Nucleus ; GABAergic Neurons/physiology ; Mesencephalon/metabolism ; Vesicular Glutamate Transport Protein 2/metabolism ; },
abstract = {Licking behavior is important for water intake. The deep mesencephalic nucleus (DpMe) has been implicated in instinctive behaviors. However, whether the DpMe is involved in licking behavior and the precise neural circuit behind this behavior remains unknown. Here, we found that the activity of the DpMe decreased during water intake. Inhibition of vesicular glutamate transporter 2-positive (VGLUT2[+]) neurons in the DpMe resulted in increased water intake. Somatostatin-expressing (SST[+]), but not protein kinase C-δ-expressing (PKC-δ[+]), GABAergic neurons in the central amygdala (CeA) preferentially innervated DpMe VGLUT2[+] neurons. The SST[+] neurons in the CeA projecting to the DpMe were activated at the onset of licking behavior. Activation of these CeA SST[+] GABAergic neurons, but not PKC-δ[+] GABAergic neurons, projecting to the DpMe was sufficient to induce licking behavior and promote water intake. These findings redefine the roles of the DpMe and reveal a novel CeA[SST]-DpMe[VGLUT2] circuit that regulates licking behavior and promotes water intake.},
}
@article {pmid35080572,
year = {2022},
author = {Lin, L and He, Z and Zhang, T and Zuo, Y and Chen, X and Abdelrahman, Z and Chen, F and Wei, Z and Si, K and Gong, W and Wang, X and He, S and Chen, Z},
title = {Correction: A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging.},
journal = {Journal of materials chemistry. B},
volume = {10},
number = {6},
pages = {977},
doi = {10.1039/d2tb90014a},
pmid = {35080572},
issn = {2050-7518},
abstract = {Correction for 'A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging' by Lingmin Lin et al., J. Mater. Chem. B, 2022, DOI: 10.1039/d1tb02040d.},
}
@article {pmid35078639,
year = {2022},
author = {Gallego, JA and Makin, TR and McDougle, SD},
title = {Going beyond primary motor cortex to improve brain-computer interfaces.},
journal = {Trends in neurosciences},
volume = {45},
number = {3},
pages = {176-183},
doi = {10.1016/j.tins.2021.12.006},
pmid = {35078639},
issn = {1878-108X},
support = {715022/ERC_/European Research Council/International ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Brain-computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent.},
}
@article {pmid35078158,
year = {2022},
author = {Fang, T and Song, Z and Zhan, G and Zhang, X and Mu, W and Wang, P and Zhang, L and Kang, X},
title = {Decoding motor imagery tasks using ESI and hybrid feature CNN.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4ed0},
pmid = {35078158},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination/physiology ; Neural Networks, Computer ; Wavelet Analysis ; },
abstract = {Objective.Brain-computer interface (BCI) based on motor imaging electroencephalogram (MI-EEG) can be useful in a natural interaction system. In this paper, a new framework is proposed to solve the MI-EEG binary classification problem.Approach.Electrophysiological source imaging (ESI) technology is used to solve the influence of volume conduction effect and improve spatial resolution. Continuous wavelet transform and best time of interest (TOI) are combined to extract the optimal discriminant spatial-frequency features. Finally, a convolutional neural network with seven convolution layers is used to classify the features. In addition, we also validated several new data augment methods to solve the problem of small data sets and reduce network over-fitting.Main results.The model achieved an average classification accuracy of 93.2% and 95.4% on the BCI Competition III IVa and high-gamma data sets, which is better than most of the published advanced algorithms. By selecting the best TOI for each subject, the classification accuracy rate increased by about 2%. The effects of four data augment methods on the classification results were also verified. Among them, the noise addition and overlap methods are better than the other two, and the classification accuracy is improved by at least 4%. On the contrary, the rotation and flip data augment methods reduced the classification accuracy.Significance.Decoding MI tasks can benefit from combing the ESI technology and the data augment technology, which is used to solve the problem of low spatial resolution and small samples of EEG signals, respectively. Based on the results, the model proposed has higher accuracy and application potential in the task of MI-EEG binary classification.},
}
@article {pmid35530739,
year = {2021},
author = {Zhu, L and Hu, Q and Yang, J and Zhang, J and Xu, P and Ying, N},
title = {EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6668859},
pmid = {35530739},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Imagination ; Normal Distribution ; Signal Processing, Computer-Assisted ; },
abstract = {In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.},
}
@article {pmid35402968,
year = {2021},
author = {Chavarriaga, R and Carey, C and Luis Contreras-Vidal, J and McKinney, Z and Bianchi, L},
title = {Standardization of Neurotechnology for Brain-Machine Interfacing: State of the Art and Recommendations.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {2},
number = {},
pages = {71-73},
pmid = {35402968},
issn = {2644-1276},
}
@article {pmid35402986,
year = {2021},
author = {Paek, AY and Brantley, JA and Sujatha Ravindran, A and Nathan, K and He, Y and Eguren, D and Cruz-Garza, JG and Nakagome, S and Wickramasuriya, DS and Chang, J and Rashed-Al-Mahfuz, M and Amin, MR and Bhagat, NA and Contreras-Vidal, JL},
title = {A Roadmap Towards Standards for Neurally Controlled End Effectors.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {2},
number = {},
pages = {84-90},
pmid = {35402986},
issn = {2644-1276},
abstract = {The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.},
}
@article {pmid35402984,
year = {2021},
author = {Easttom, C and Bianchi, L and Valeriani, D and Nam, CS and Hossaini, A and Zapala, D and Roman-Gonzalez, A and Singh, AK and Antonietti, A and Sahonero-Alvarez, G and Balachandran, P},
title = {A Functional Model for Unifying Brain Computer Interface Terminology.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {2},
number = {},
pages = {91-96},
pmid = {35402984},
issn = {2644-1276},
abstract = {Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.},
}
@article {pmid36275194,
year = {2020},
author = {Gerginov, V and Pomponio, M and Knappe, S},
title = {Scalar Magnetometry Below 100 fT/Hz[1/2] in a Microfabricated Cell.},
journal = {IEEE sensors journal},
volume = {20},
number = {21},
pages = {12684-12690},
pmid = {36275194},
issn = {1530-437X},
support = {R01 EB027004/EB/NIBIB NIH HHS/United States ; R01 NS094604/NS/NINDS NIH HHS/United States ; },
abstract = {Zero-field optically-pumped magnetometers are a room-temperature alternative to traditionally used super-conducting sensors detecting extremely weak magnetic fields. They offer certain advantages such as small size, flexible arrangement, reduced sensitivity in ambient fields offering the possibility for telemetry. Devices based on microfabricated technology are nowadays commercially available. The limited dynamic range and vector nature of the zero-field magnetometers restricts their use to environments heavily shielded against magnetic noise. Total-field (or scalar) magnetometers based on microfabricated cells have demonstrated subpicotesla sensitivities only recently. This work demonstrates a scalar magnetometer based on a single optical axis, 18 (3 × 3 × 2) mm[3] microfabricated cell, with a noise floor of 70 fT/Hz[1/2]. The magnetometer operates in a large static magnetic field range, and and is based on a simple optical and electronic configuration that allows the development of dense sensor arrays. Different methods of magnetometer interrogation are demonstrated. The features of this magnetic field sensor hold promise for applications of miniature sensors in nonzero field environments such as unshielded magnetoencephalography (MEG) and brain-computer interfaces (BCI).},
}
@article {pmid35663261,
year = {2020},
author = {Wang, X and Weltman Hirschberg, A and Xu, H and Slingsby-Smith, Z and Lecomte, A and Scholten, K and Song, D and Meng, E},
title = {A Parylene Neural Probe Array for Multi-Region Deep Brain Recordings.},
journal = {Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems},
volume = {29},
number = {4},
pages = {499-513},
pmid = {35663261},
issn = {1057-7157},
support = {U01 NS099703/NS/NINDS NIH HHS/United States ; },
abstract = {A Parylene C polymer neural probe array with 64 electrodes purposefully positioned across 8 individual shanks to anatomically match specific regions of the hippocampus was designed, fabricated, characterized, and implemented in vivo for enabling recording in deep brain regions in freely moving rats. Thin film polymer arrays were fabricated using surface micromachining techniques and mechanically braced to prevent buckling during surgical implantation. Importantly, the mechanical bracing technique developed in this work involves a novel biodegradable polymer brace that temporarily reduces shank length and consequently, increases its stiffness during implantation, therefore enabling access to deeper brain regions while preserving a low original cross-sectional area of the shanks. The resulting mechanical properties of braced shanks were evaluated at the benchtop. Arrays were then implemented in vivo in freely moving rats, achieving both acute and chronic recordings from the pyramidal cells in the cornu ammonis (CA) 1 and CA3 regions of the hippocampus which are responsible for memory encoding. This work demonstrated the potential for minimally invasive polymer-based neural probe arrays for multi-region recording in deep brain structures.},
}
@article {pmid35516202,
year = {2020},
author = {Kim, YJ and Yoon, S and Cho, YH and Kim, G and Kim, HK},
title = {Paintable and writable electrodes using black conductive ink on traditional Korean paper (Hanji).},
journal = {RSC advances},
volume = {10},
number = {41},
pages = {24631-24641},
pmid = {35516202},
issn = {2046-2069},
abstract = {We demonstrate black conductive ink (BCI) that is writable and paintable on traditional handmade Korean paper (Hanji) for application as a high performing electrode. By optimal mixing of Ag nanowire (Ag NW) suspension and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS) solution in standard charcoal-based blank ink, we synthesized BCI suitable for writing and painting on Hanji with a normal paintbrush. Due to the shear stress induced by the paintbrush bristles, the Ag NW and PEDOT:PSS mixture was uniformly coated on the porous cellulose structure of Hanji and showed a low sheet resistance of 11.7 Ohm per square even after repeated brush strokes. Moreover, the brush-painted electrodes on Hanji showed a constant resistance during tests of inner/outer bending and folding due to the outstanding flexibility of the Ag NW and PEDOT:PSS mixture that filled the porous cellulose structure of Hanji. Therefore, the pictures drawn in the BCI on Hanji exhibited a level of flexibility and conductivity sufficiently high to enable the BCI to function as an effective electrode even when the paper substrate is wrinkled or crumpled. The successful operation of the paintable interconnector and heater on Hanji indicates the high potential of the brush-painted electrodes that can be used in various social and cultural fields, including fine art, fashion, interior design, architecture, and heating industry.},
}
@article {pmid35498243,
year = {2020},
author = {Mouli, S and Palaniappan, R},
title = {DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset.},
journal = {HardwareX},
volume = {8},
number = {},
pages = {e00113},
pmid = {35498243},
issn = {2468-0672},
abstract = {A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P300 precisely with reduced fatigue and improved classification performance. The system comprises of four independent radial green visual stimuli controlled individually by a 32-bit microcontroller platform to evoke SSVEP and four red LEDs flashing at random intervals to generate P300 events. The system can also record the P300 event timestamps that can be used in classification, to improve the accuracy and reliability. The hybrid stimulus was tested for real-time classification accuracy by controlling a LEGO robot to move in four directions.},
}
@article {pmid35402943,
year = {2020},
author = {Bevilacqua, M and Perdikis, S and Millan, JDR},
title = {On Error-Related Potentials During Sensorimotor-Based Brain-Computer Interface: Explorations With a Pseudo-Online Brain-Controlled Speller.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {1},
number = {},
pages = {17-22},
pmid = {35402943},
issn = {2644-1276},
abstract = {Objective: Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy. Nevertheless, few works have studied the elicitation of ErrPs during engagement with other BCI tasks, especially when BCI feedback is provided continuously. Methods: Here, we test the possibility of correcting errors during pseudo-online Motor Imagery (MI) BCI spelling through ErrPs, and investigate whether BCI feedback hinders their generation. Ten subjects performed a series of MI spelling tasks with and without observing BCI feedback. Results: The average pseudo-online ErrP detection accuracy was found to be significantly above the chance level in both conditions and did not significantly differ between the two (74% with, and 78% without feedback). Conclusions: Our results support the possibility to detect ErrPs during MI-BCI spelling and suggest the absence of any BCI feedback-related interference.},
}
@article {pmid35078156,
year = {2022},
author = {Yao, L and Zhu, B and Shoaran, M},
title = {Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4ed1},
pmid = {35078156},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electrocorticography/methods ; Electroencephalography/methods ; Fingers ; Humans ; Machine Learning ; Movement ; },
abstract = {Objective.Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers.Approach.We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art ML algorithms on the brain-computer interface (BCI) competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, nine subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p < 0.01) and regression tasks (p < 0.01).Main results.Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (six-class task, including rest state), improving over the state-of-the-art conditional random fields by 11.7% on the three BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson's correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN + LSTM). Furthermore, our proposed method features a low time complexity, with only<17.2 s required for training and<50 ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance.Significance.The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.},
}
@article {pmid35075067,
year = {2022},
author = {Pino, O},
title = {A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.},
journal = {Acta bio-medica : Atenei Parmensis},
volume = {92},
number = {6},
pages = {e2021408},
pmid = {35075067},
issn = {2531-6745},
mesh = {Adaptation, Psychological ; Anxiety ; Emotions ; Humans ; *Mental Disorders ; Middle Aged ; },
abstract = {BACKGROUND AND AIM: Although many mental disorders have relevant proud in neurobiological dysfunctions, most intervention approaches neglect neurophysiological features or use pharmacological intervention alone. Non-invasive Brain-Computer Interfaces (BCIs), providing natural ways of modulating mood states, can be promoted as an alternative intervention to cope with neurobiological dysfunction.<br><br>METHODS: A BCI prototype was proposed to feedback a person's affective state such that a closed-loop interaction between the participant's brain responses and the musical stimuli is established. It feedbacks in real-time flickering lights matching with the individual's brain rhythms undergo to auditory stimuli. A RCT was carried out on 15 individuals of both genders (mean age = 49.27 years) with anxiety and depressive spectrum disorders randomly assigned to 2 groups (experimental vs. active control).<br><br>RESULTS: Outcome measures revealed either a significant decrease in Hamilton Rating Scale for Depression (HAM-D) scores and gains in cognitive functions only for participants who undergone to the experimental treatment. Variability in HAM-D scores seems explained by the changes in beta 1, beta 2 and delta bands. Conversely, the rise in cognitive function scores appear associated with theta variations.<br><br>CONCLUSIONS: Future work needs to validate the relationship proposed here between music and brain responses. Findings of the present study provided support to a range of research examining BCI brain modulation and contributes to the understanding of this technique as instruments to alternative therapies We believe that Neuro-Upper can be used as an effective new tool for investigating affective responses, and emotion regulation (www.actabiomedica.it).},
}
@article {pmid35073648,
year = {2022},
author = {, and , },
title = {[Chinese expert consensus on multigene testing for postoperatively adjuvant treatment of hormone receptor-positive, HER2-negative early breast cancer].},
journal = {Zhonghua zhong liu za zhi [Chinese journal of oncology]},
volume = {44},
number = {1},
pages = {54-59},
doi = {10.3760/cma.j.cn112152-20211108-00822},
pmid = {35073648},
issn = {0253-3766},
support = {82172650//National Natural Science Foundation of China/ ; 12019XK320071//Foundation for Clinical Translational and Medical Research, Chinese Academy of Medical Sciences/ ; YXJL-2020-0941-0763//Beijing Medical Award Foundatin/ ; },
mesh = {*Breast Neoplasms/drug therapy/genetics ; Chemotherapy, Adjuvant ; China ; Consensus ; Female ; Hormones/therapeutic use ; Humans ; Prognosis ; Receptor, ErbB-2/genetics ; },
abstract = {Breast cancer is the most common malignant tumor in women, of which early-stage (stages Ⅰ-Ⅱ) breast cancer (EBC) accounts for 73.1%. The strategy of postoperative adjuvant treatment relies mainly on the clinicopathologic characteristics of patients, but there are certain deficiencies in the assessment of treatment benefits and disease prognosis. Multigene testing tools can evaluate the prognosis and predict therapeutic effects of breast cancer patients to guide the clinical decision-making on whether to use adjuvant chemotherapy, radiotherapy, and endocrine therapy by detecting the expression levels of specific genes. The consensus-writing expert group, based on the characteristics, validation results, and accessibility of the multigene testing tools and combined with clinical practice, described the result interpretation and clinical application of OncotypeDx(®) (21-gene), MammaPrint(®) (70-gene), RecurIndex(®) (28-gene), and BreastCancerIndex(®) (BCI, 7-gene) for hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC. The development and validation process of each tool was also briefly introduced. It is expected that the consensus will help to guide and standardize the clinical application of multigene testing tools and further improve the level of precise treatment for EBC.},
}
@article {pmid35073267,
year = {2022},
author = {Xu, M and Chen, Y and Wang, Y and Wang, D and Liu, Z and Zhang, L},
title = {BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {251-263},
doi = {10.1109/TNSRE.2022.3145515},
pmid = {35073267},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Humans ; },
abstract = {In the rapid serial visual presentation (RSVP) classification task, the data from the target and non-target classes are incredibly imbalanced. These class imbalance problems (CIPs) can hinder the classifier from achieving better performance, especially in deep learning. This paper proposed a novel data augmentation method called balanced Wasserstein generative adversarial network with gradient penalty (BWGAN-GP) to generate RSVP minority class data. The model learned useful features from majority classes and used them to generate minority-class artificial EEG data. It combines generative adversarial network (GAN) with autoencoder initialization strategy enables this method to learn an accurate class-conditioning in the latent space to drive the generation process towards the minority class. We used RSVP datasets from nine subjects to evaluate the classification performance of our proposed generated model and compare them with those of other methods. The average AUC obtained with BWGAN-GP on EEGNet was 94.43%, an increase of 3.7% over the original data. We also used different amounts of original data to investigate the effect of the generated EEG data on the calibration phase. Only 60% of original data were needed to achieve acceptable classification performance. These results show that the BWGAN-GP could effectively alleviate CIPs in the RSVP task and obtain the best performance when the two classes of data are balanced. The findings suggest that data augmentation techniques could generate artificial EEG to reduce calibration time in other brain-computer interfaces (BCI) paradigms similar to RSVP.},
}
@article {pmid35072767,
year = {2022},
author = {Talon, E and Wimmer, W and Hakim, A and Kiefer, C and Pastore-Wapp, M and Anschuetz, L and Mantokoudis, G and Caversaccio, MD and Wagner, F},
title = {Influence of head orientation and implantation site of a novel transcutaneous bone conduction implant on MRI metal artifact reduction sequence.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {279},
number = {10},
pages = {4793-4799},
pmid = {35072767},
issn = {1434-4726},
mesh = {*Artifacts ; *Bone Conduction ; Humans ; Image Enhancement/methods ; Magnetic Resonance Imaging/methods ; Metals ; Prostheses and Implants ; },
abstract = {PURPOSE: The use of magnetic resonance imaging (MRI) is often limited in patients with auditory implants because of the presence of metallic components and magnets. The aim of this study was to evaluate the clinical usefulness of a customized MRI sequence for metal artifact suppression in patients with BONEBRIDGETM BCI 602 implants (MED-EL, Innsbruck, Austria), the successor of the BCI 601 model.<br><br>METHODS: Using our in-house developed and customized metal artifact reduction sequence (SEMAC-VAT WARP), MRI artifacts were evaluated qualitatively and quantitatively. MRI sequences were performed with and without artifact reduction on two whole head specimens with and without the BCI 602 implant. In addition, the influence of two different implantation sites (mastoid versus retrosigmoid) and head orientation on artifact presence was investigated.<br><br>RESULTS: Artifact volume was reduced by more than the 50%. Results were comparable with those obtained with the BCI 601, showing no significant differences in the dimensions of artifacts caused by the implant.<br><br>CONCLUSION: SEMAC-VAT WARP was once more proved to be efficient at reducing metal artifacts on MR images. The dimensions of artifacts associated with the BCI 602 are not smaller than those caused by the BCI 601.},
}
@article {pmid35069721,
year = {2022},
author = {Abdi Alkareem Alyasseri, Z and Alomari, OA and Al-Betar, MA and Awadallah, MA and Hameed Abdulkareem, K and Abed Mohammed, M and Kadry, S and Rajinikanth, V and Rho, S},
title = {EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {5974634},
pmid = {35069721},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Delivery of Health Care ; Electrodes ; *Electroencephalography ; Humans ; },
abstract = {Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.},
}
@article {pmid35069099,
year = {2021},
author = {Salari, V and Rodrigues, S and Saglamyurek, E and Simon, C and Oblak, D},
title = {Are Brain-Computer Interfaces Feasible With Integrated Photonic Chips?.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {780344},
pmid = {35069099},
issn = {1662-4548},
abstract = {The present paper examines the viability of a radically novel idea for brain-computer interface (BCI), which could lead to novel technological, experimental, and clinical applications. BCIs are computer-based systems that enable either one-way or two-way communication between a living brain and an external machine. BCIs read-out brain signals and transduce them into task commands, which are performed by a machine. In closed loop, the machine can stimulate the brain with appropriate signals. In recent years, it has been shown that there is some ultraweak light emission from neurons within or close to the visible and near-infrared parts of the optical spectrum. Such ultraweak photon emission (UPE) reflects the cellular (and body) oxidative status, and compelling pieces of evidence are beginning to emerge that UPE may well play an informational role in neuronal functions. In fact, several experiments point to a direct correlation between UPE intensity and neural activity, oxidative reactions, EEG activity, cerebral blood flow, cerebral energy metabolism, and release of glutamate. Therefore, we propose a novel skull implant BCI that uses UPE. We suggest that a photonic integrated chip installed on the interior surface of the skull may enable a new form of extraction of the relevant features from the UPE signals. In the current technology landscape, photonic technologies are advancing rapidly and poised to overtake many electrical technologies, due to their unique advantages, such as miniaturization, high speed, low thermal effects, and large integration capacity that allow for high yield, volume manufacturing, and lower cost. For our proposed BCI, we are making some very major conjectures, which need to be experimentally verified, and therefore we discuss the controversial parts, feasibility of technology and limitations, and potential impact of this envisaged technology if successfully implemented in the future.},
}
@article {pmid35069096,
year = {2021},
author = {Han, C and Xu, G and Zheng, X and Tian, P and Zhang, K and Yan, W and Jia, Y and Chen, X},
title = {Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {757679},
pmid = {35069096},
issn = {1662-4548},
abstract = {The refresh rate is one of the important parameters of visual presentation devices, and assessing the effect of the refresh rate of a device on motion perception has always been an important direction in the field of visual research. This study examined the effect of the refresh rate of a device on the motion perception response at different stimulation frequencies and provided an objective visual electrophysiological assessment method for the correct selection of display parameters in a visual perception experiment. In this study, a flicker-free steady-state motion visual stimulation with continuous scanning frequency and different forms (sinusoidal or triangular) was presented on a low-latency LCD monitor at different refresh rates. Seventeen participants were asked to observe the visual stimulation without head movement or eye movement, and the effect of the refresh rate was assessed by analyzing the changes in the intensity of their visual evoked potentials. The results demonstrated that an increased refresh rate significantly improved the intensity of motion visual evoked potentials at stimulation frequency ranges of 7-28 Hz, and there was a significant interaction between the refresh rate and motion frequency. Furthermore, the increased refresh rate also had the potential to enhance the ability to perceive similar motion. Therefore, we recommended using a refresh rate of at least 120 Hz in motion visual perception experiments to ensure a better stimulation effect. If the motion frequency or velocity is high, a refresh rate of≥240 Hz is also recommended.},
}
@article {pmid35064439,
year = {2022},
author = {Sun, J and Wei, M and Luo, N and Li, Z and Wang, H},
title = {Euler common spatial patterns for EEG classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {60},
number = {3},
pages = {753-767},
pmid = {35064439},
issn = {1741-0444},
support = {61773114//National Natural Science Foundation of China/ ; GXXT-2020-015//the university synergy innovation program of anhui province/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {The technique of common spatial patterns (CSP) is a widely used method in the field of feature extraction of electroencephalogram (EEG) signals. Motivated by the fact that a cosine distance can enlarge the distance between samples of different classes, we propose the Euler CSP (e-CSP) for the feature extraction of EEG signals, and it is then used for EEG classification. The e-CSP is essentially the conventional CSP with the Euler representation. It includes the following two stages: each sample value is first mapped into a complex space by using the Euler representation, and then the conventional CSP is performed in the Euler space. Thus, the e-CSP is equivalent to applying the Euler representation as a kernel function to the input of the CSP. It is computationally as straightforward as the CSP. However, it extracts more discriminative features from the EEG signals. Extensive experimental results illustrate the discrimination ability of the e-CSP.},
}
@article {pmid35062641,
year = {2022},
author = {Kim, S and Shin, DY and Kim, T and Lee, S and Hyun, JK and Park, SM},
title = {Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {35062641},
issn = {1424-8220},
support = {2020R1A6A1A03047902//National Research Foundation of Korea/ ; 2020R1A2C2005385, 2020R1A2C2004764//Korea government (Ministry of Science and ICT, MSIT)/ ; 202017D01//Korean government (MSIT, the Ministry of Trade, Industry and Energy, the Ministry of Health &amp; Welfare, and the Ministry of Food and Drug Safety)/ ; },
mesh = {Algorithms ; *Amputees ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Electromyography ; Humans ; Wrist ; },
abstract = {Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18-4.35% in the control group, and by 2.51-3.00% in the patient group.},
}
@article {pmid35062495,
year = {2022},
author = {Bagheri, M and Power, SD},
title = {Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {35062495},
issn = {1424-8220},
support = {RGPIN-2016-04210//Natural Sciences and Engineering Research Council/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Risk ; Workload ; },
abstract = {Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user's mental state considered. However, in real-life situations, different aspects of the user's state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI-for example both mental workload and stress level might be related to an aircraft pilot's risk of error-and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.},
}
@article {pmid35062460,
year = {2022},
author = {Zhang, H and Hu, X and Gou, R and Zhang, L and Zheng, B and Shen, Z},
title = {Rich Structural Index for Stereoscopic Image Quality Assessment.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {35062460},
issn = {1424-8220},
support = {61471150//the National Natural Science Foundation of China/ ; },
mesh = {*Algorithms ; Databases, Factual ; Humans ; *Support Vector Machine ; Vision, Ocular ; },
abstract = {The human visual system (HVS), affected by viewing distance when perceiving the stereo image information, is of great significance to study of stereoscopic image quality assessment. Many methods of stereoscopic image quality assessment do not have comprehensive consideration for human visual perception characteristics. In accordance with this, we propose a Rich Structural Index (RSI) for Stereoscopic Image objective Quality Assessment (SIQA) method based on multi-scale perception characteristics. To begin with, we put the stereo pair into the image pyramid based on Contrast Sensitivity Function (CSF) to obtain sensitive images of different resolution. Then, we obtain local Luminance and Structural Index (LSI) in a locally adaptive manner on gradient maps which consider the luminance masking and contrast masking. At the same time we use Singular Value Decomposition (SVD) to obtain the Sharpness and Intrinsic Structural Index (SISI) to effectively capture the changes introduced in the image (due to distortion). Meanwhile, considering the disparity edge structures, we use gradient cross-mapping algorithm to obtain Depth Texture Structural Index (DTSI). After that, we apply the standard deviation method for the above results to obtain contrast index of reference and distortion components. Finally, for the loss caused by the randomness of the parameters, we use Support Vector Machine Regression based on Genetic Algorithm (GA-SVR) training to obtain the final quality score. We conducted a comprehensive evaluation with state-of-the-art methods on four open databases. The experimental results show that the proposed method has stable performance and strong competitive advantage.},
}
@article {pmid35060847,
year = {2022},
author = {Dong, L and Li, S and Lian, WH and Wei, QC and Mohamad, OAA and Hozzein, WN and Ahmed, I and Li, WJ},
title = {Sphingomonas arenae sp. nov., isolated from desert soil.},
journal = {International journal of systematic and evolutionary microbiology},
volume = {72},
number = {1},
pages = {},
doi = {10.1099/ijsem.0.005195},
pmid = {35060847},
issn = {1466-5034},
mesh = {Bacterial Typing Techniques ; Base Composition ; China ; DNA, Bacterial/genetics ; Desert Climate ; Fatty Acids/chemistry ; Phospholipids/chemistry ; *Phylogeny ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; *Soil Microbiology ; *Sphingomonas/classification/isolation &amp; purification ; },
abstract = {Two bacterial strains, designated as SYSU D00720[T] and SYSU D00722, were isolated from a desert sandy soil sample collected from Gurbantunggut Desert in Xinjiang, north-west China. Cells were Gram-stain-negative, aerobic, non-motile, rod-shaped, oxidase-positive and catalase-negative. Colonies were circular, opaque, convex, smooth, orange on Reasoner's 2A (R2A) agar. The isolates were found to grow at 4-45 °C (optimum, 28-30 °C), at pH 6.0-7.0 (optimum, 7.0) and with 0-1.5 % (w/v) NaCl (optimum, 0%). Growth was observed on R2A agar, Luria-Bertani agar and nutrient agar, but not on trypticase soy agar. The polar lipids consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine, sphingoglycolipid, two unidentified aminolipids, one unidentified glycolipid, one unidentified aminoglycolipid, one unidentified aminophospholipid, one unidentified phospholipid and two unidentified lipids. The main fatty acids (>10%) were C17 : 1 ω6c, summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c) and C16 : 0. The major respiratory quinone was ubiquinone-10 and the major polyamine was sym-homospermidine. The genomic DNA G+C content was 66.0 mol%. Strains SYSU D00720[T] and SYSU D00722 were nearly identical with a 16S rRNA gene sequence similarity of 99.6 %, and 100.0 % average nucleotide identity (ANI), average amino acid identity (AAI) and digital DNA-DNA hybridization (dDDH) values. Phylogenetic analyses clearly demonstrated that these two strains belonged to the same species of the genus Sphingomonas, and had highest sequence similarity to Sphingomonas lutea KCTC 23642[T] (97.3 %). The ANI, AAI and dDDH values of strains SYSU D00720[T] and SYSU D00722 to S. lutea KCTC 23642[T] were both 73.2, 69.9 and 19.2 %, respectively. Based on phylogenetic, phenotypic and chemotaxonomic distinctiveness, strains SYSU D00720[T] and SYSU D00722 represent a novel species of the genus Sphingomonas, for which the name Sphingomonas arenae sp. nov. is proposed. The type strain is SYSU D00720[T] (=MCCC 1K05154[T]=NBRC 115061[T]).},
}
@article {pmid35059816,
year = {2022},
author = {Pugliese, R and Sala, R and Regondi, S and Beltrami, B and Lunetta, C},
title = {Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces.},
journal = {Journal of neurology},
volume = {269},
number = {6},
pages = {2910-2921},
pmid = {35059816},
issn = {1432-1459},
mesh = {*Amyotrophic Lateral Sclerosis/therapy ; *COVID-19 ; Humans ; Motor Neurons ; Pandemics ; *Telemedicine ; },
abstract = {Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and subsequently paralysis. It begins subtly with focal weakness but spreads relentlessly to involve most muscles, thus proving to be effectively incurable. Typically, death due to respiratory paralysis occurs in 3-5 years. To date, it has been shown that the management of ALS patients is best achieved with a multidisciplinary approach, and with the help of emerging technologies ranging from multidisciplinary teleconsults (for monitoring the dysphagia, respiratory function, and nutritional status) to brain-computer interfaces and eye tracking for alternative augmentative communication, until robotics, it may increase effectiveness. The COVID-19 pandemic created a spasmodic need to accelerate the development and implementation of such technologies in clinical practice, to improve the daily lives of both ALS patients and caregivers. However, despite the remarkable strides that have been made in the field, there are still issues to be addressed. This review will be discussed on the eureka moment of emerging technologies for ALS, used as a blueprint not only for neurodegenerative diseases, examining the current technologies already in place or being evaluated, highlighting the pros and cons for future clinical applications.},
}
@article {pmid35058858,
year = {2021},
author = {Liu, M},
title = {An EEG Neurofeedback Interactive Model for Emotional Classification of Electronic Music Compositions Considering Multi-Brain Synergistic Brain-Computer Interfaces.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {799132},
pmid = {35058858},
issn = {1664-1078},
abstract = {This paper presents an in-depth study and analysis of the emotional classification of EEG neurofeedback interactive electronic music compositions using a multi-brain collaborative brain-computer interface (BCI). Based on previous research, this paper explores the design and performance of sound visualization in an interactive format from the perspective of visual performance design and the psychology of participating users with the help of knowledge from various disciplines such as psychology, acoustics, aesthetics, neurophysiology, and computer science. This paper proposes a specific mapping model for the conversion of sound to visual expression based on people's perception and aesthetics of sound based on the phenomenon of audiovisual association, which provides a theoretical basis for the subsequent research. Based on the mapping transformation pattern between audio and visual, this paper investigates the realization path of interactive sound visualization, the visual expression form and its formal composition, and the aesthetic style, and forms a design expression method for the visualization of interactive sound, to benefit the practice of interactive sound visualization. In response to the problem of neglecting the real-time and dynamic nature of the brain in traditional brain network research, dynamic brain networks proposed for analyzing the EEG signals induced by long-time music appreciation. During prolonged music appreciation, the connectivity of the brain changes continuously. We used mutual information on different frequency bands of EEG signals to construct dynamic brain networks, observe changes in brain networks over time and use them for emotion recognition. We used the brain network for emotion classification and achieved an emotion recognition rate of 67.3% under four classifications, exceeding the highest recognition rate available.},
}
@article {pmid35058768,
year = {2021},
author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y},
title = {EEG Delta/Theta Ratio and Microstate Analysis Originating Novel Biomarkers for Malnutrition-Inflammation Complex Syndrome in ESRD Patients.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {795237},
pmid = {35058768},
issn = {1662-5161},
abstract = {The Malnutrition-Inflammation Score (MIS) was initially proposed to evaluate malnutrition-inflammation complex syndrome (MICS) in end-stage renal disease (ESRD) patients. Although MICS should be routinely evaluated to reduce the hospitalization and mortality rate of ESRD patients, the inconvenience of the MIS might limit its use. Cerebral complications in ESRD, possibly induced by MICS, were previously assessed by using spectral electroencephalography (EEG) via the delta/theta ratio and microstate analysis. Correspondingly, EEG could be used to directly assess MICS in ESRD patients, but the relationships among MICS and these EEG features remain inconclusive. Thus, we aimed to investigate the delta/theta ratio and microstates in ESRD patients with high and low risks of MICS. We also attempted to identify the correlation among the MIS, delta/theta ratio, and microstate parameters, which might clarify their relationships. To achieve these objectives, a total of forty-six ESRD subjects were willingly recruited. We collected their blood samples, MIS, and EEGs after receiving written informed consent. Sixteen women and seven men were allocated to low risk group (MIS ≤ 5, age 57.57 ± 14.88 years). Additionally, high risk group contains 15 women and 8 men (MIS > 5, age 59.13 ± 11.77 years). Here, we discovered that delta/theta ratio (p < 0.041) and most microstate parameters (p < 0.001) were significantly different between subject groups. We also found that the delta/theta ratio was not correlated with MIS but was strongly with the average microstate duration (ρ = 0.708, p < 0.001); hence, we suggested that the average microstate duration might serve as an alternative encephalopathy biomarker. Coincidentally, we noticed positive correlations for most parameters of microstates A and B (0.54 ≤ ρ ≤ 0.68, p < 0.001) and stronger negative correlations for all microstate C parameters (-0.75 ≤ ρ ≤ -0.61, p < 0.001). These findings unveiled a novel EEG biomarker, the MIC index, that could efficiently distinguish ESRD patients at high and low risk of MICS when utilized as a feature in a binary logistic regression model (accuracy of train-test split validation = 1.00). We expected that the average microstate duration and MIC index might potentially contribute to monitor ESRD patients in the future.},
}
@article {pmid35058514,
year = {2022},
author = {Kumar, N and Michmizos, KP},
title = {A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity.},
journal = {Scientific reports},
volume = {12},
number = {1},
pages = {1101},
pmid = {35058514},
issn = {2045-2322},
support = {K12HD093427//National Center for Medical Rehabilitation Research/ ; },
mesh = {Algorithms ; Brain/diagnostic imaging/*physiology ; Brain-Computer Interfaces ; Data Collection/methods ; Electroencephalography/methods ; Female ; Forecasting/methods ; Humans ; Male ; Movement/*physiology ; Nervous System Physiological Phenomena ; Neural Networks, Computer ; Neurological Rehabilitation/methods ; Neurophysiology/*methods ; Reaction Time ; Research Design ; Young Adult ; },
abstract = {The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.},
}
@article {pmid35053846,
year = {2022},
author = {Vekety, B and Logemann, A and Takacs, ZK},
title = {Mindfulness Practice with a Brain-Sensing Device Improved Cognitive Functioning of Elementary School Children: An Exploratory Pilot Study.},
journal = {Brain sciences},
volume = {12},
number = {1},
pages = {},
pmid = {35053846},
issn = {2076-3425},
support = {LP-2018-21/2018//E&#xf6;tv&#xf6;s Lor&#xe1;nd Research Network/ ; K131635//Hungarian National Research, Development and Innovation Office/ ; },
abstract = {This is the first pilot study with children that has assessed the effects of a brain-computer interface-assisted mindfulness program on neural mechanisms and associated cognitive performance. The participants were 31 children aged 9-10 years who were randomly assigned to either an eight-session mindfulness training with EEG-feedback or a passive control group. Mindfulness-related brain activity was measured during the training, while cognitive tests and resting-state brain activity were measured pre- and post-test. The within-group measurement of calm/focused brain states and mind-wandering revealed a significant linear change. Significant positive changes were detected in children's inhibition, information processing, and resting-state brain activity (alpha, theta) compared to the control group. Elevated baseline alpha activity was associated with less reactivity in reaction time on a cognitive test. Our exploratory findings show some preliminary support for a potential executive function-enhancing effect of mindfulness supplemented with EEG-feedback, which may have some important implications for children's self-regulated learning and academic achievement.},
}
@article {pmid35053801,
year = {2021},
author = {Ferracuti, F and Iarlori, S and Mansour, Z and Monteriù, A and Porcaro, C},
title = {Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition.},
journal = {Brain sciences},
volume = {12},
number = {1},
pages = {},
pmid = {35053801},
issn = {2076-3425},
abstract = {The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.},
}
@article {pmid35049650,
year = {2022},
author = {Altuwaijri, GA and Muhammad, G},
title = {A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.},
journal = {Biosensors},
volume = {12},
number = {1},
pages = {},
pmid = {35049650},
issn = {2079-6374},
support = {RSP-2021/34//King Saud University, Riyadh, Saudi Arabia/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; *Neural Networks, Computer ; },
abstract = {Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method's promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.},
}
@article {pmid35049411,
year = {2022},
author = {Xie, P and Hao, S and Zhao, J and Liang, Z and Li, X},
title = {A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task.},
journal = {International journal of neural systems},
volume = {32},
number = {3},
pages = {2250010},
doi = {10.1142/S0129065722500101},
pmid = {35049411},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.},
}
@article {pmid35047569,
year = {2021},
author = {Cifuentes, CA and Veneman, JF and Rocon, E and Rodriguez-Guerrero, C},
title = {Editorial: Interfacing Humans and Machines for Rehabilitation and Assistive Devices.},
journal = {Frontiers in robotics and AI},
volume = {8},
number = {},
pages = {796431},
doi = {10.3389/frobt.2021.796431},
pmid = {35047569},
issn = {2296-9144},
}
@article {pmid35047035,
year = {2022},
author = {Ramalingam, P and Mehbodniya, A and Webber, JL and Shabaz, M and Gopalakrishnan, L},
title = {Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning.},
journal = {Computational intelligence and neuroscience},
volume = {2022},
number = {},
pages = {4886586},
pmid = {35047035},
issn = {1687-5273},
mesh = {Algorithms ; *Data Compression ; *Deep Learning ; Neural Networks, Computer ; Telemetry ; },
abstract = {Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.},
}
@article {pmid35046522,
year = {2022},
author = {Serino, A and Bockbrader, M and Bertoni, T and Colachis Iv, S and Solcà, M and Dunlap, C and Eipel, K and Ganzer, P and Annetta, N and Sharma, G and Orepic, P and Friedenberg, D and Sederberg, P and Faivre, N and Rezai, A and Blanke, O},
title = {Sense of agency for intracortical brain-machine interfaces.},
journal = {Nature human behaviour},
volume = {6},
number = {4},
pages = {565-578},
pmid = {35046522},
issn = {2397-3374},
mesh = {*Brain-Computer Interfaces ; Humans ; Movement ; },
abstract = {Intracortical brain-machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain-machine interfaces, the neural mechanisms involved and its clinical relevance are currently unknown. By experimentally manipulating the coherence between decoded motor commands and sensory feedback in a tetraplegic individual using a brain-machine interface, we provide evidence that primary motor cortex processes sensory feedback, sensorimotor conflicts and subjective states of actions generated via the brain-machine interface. Neural signals processing the sense of agency affected the proficiency of the brain-machine interface, underlining the clinical potential of the present approach. These findings show that primary motor cortex encodes information related to action and sensing, but also sensorimotor and subjective agency signals, which in turn are relevant for clinical applications of brain-machine interfaces.},
}
@article {pmid35045797,
year = {2022},
author = {Hinvest, NS and Ashwin, C and Carter, F and Hook, J and Smith, LGE and Stothart, G},
title = {An Empirical Evaluation of Methodologies Used for Emotion Recognition via EEG Signals.},
journal = {Social neuroscience},
volume = {17},
number = {1},
pages = {1-12},
doi = {10.1080/17470919.2022.2029558},
pmid = {35045797},
issn = {1747-0927},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; },
abstract = {A goal of brain-computer-interface (BCI) research is to accurately classify participants' emotional status via objective measurements. While there has been a growth in EEG-BCI literature tackling this issue, there exist methodological limitations that undermine its ability to reach conclusions. These include both the nature of the stimuli used to induce emotions and the steps used to process and analyze the data. To highlight and overcome these limitations we appraised whether previous literature using commonly used, widely available, datasets is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional artifacts. Subsequently, we propose new methods based on empirically driven, scientifically rigorous, foundations. We close by providing guidance to any researcher involved or wanting to work within this dynamic research field.},
}
@article {pmid35044788,
year = {2022},
author = {Tchoe, Y and Bourhis, AM and Cleary, DR and Stedelin, B and Lee, J and Tonsfeldt, KJ and Brown, EC and Siler, DA and Paulk, AC and Yang, JC and Oh, H and Ro, YG and Lee, K and Russman, SM and Ganji, M and Galton, I and Ben-Haim, S and Raslan, AM and Dayeh, SA},
title = {Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.},
journal = {Science translational medicine},
volume = {14},
number = {628},
pages = {eabj1441},
pmid = {35044788},
issn = {1946-6242},
support = {F32 MH120886/MH/NIMH NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; UG3 NS123723/NS/NINDS NIH HHS/United States ; R01 NS123655/NS/NINDS NIH HHS/United States ; DP2 EB029757/EB/NIBIB NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Brain/physiology ; *Brain Mapping ; Electric Stimulation ; *Epilepsy ; Humans ; Rats ; Wakefulness ; },
abstract = {Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively used for research in brain-machine interfaces. However, the existing clinical and experimental devices are often limited in either spatial resolution or cortical coverage. Here, we developed scalable manufacturing processes with a dense electrical connection scheme to achieve reconfigurable thin-film, multithousand-channel neurophysiological recording grids using platinum nanorods (PtNRGrids). With PtNRGrids, we have achieved a multithousand-channel array of small (30 μm) contacts with low impedance, providing high spatial and temporal resolution over a large cortical area. We demonstrated that PtNRGrids can resolve submillimeter functional organization of the barrel cortex in anesthetized rats that captured the tissue structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. In addition, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1-mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrated the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces.},
}
@article {pmid35044123,
year = {2021},
author = {Borisova, VA and Isakova, EV and Kotov, SV},
title = {[Cognitive rehabilitation after stroke using non-pharmacological approaches].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {121},
number = {12. Vyp. 2},
pages = {26-32},
doi = {10.17116/jnevro202112112226},
pmid = {35044123},
issn = {1997-7298},
mesh = {Brain ; Cognition ; Humans ; *Stroke/therapy ; *Stroke Rehabilitation ; Transcranial Magnetic Stimulation ; },
abstract = {Cerebral stroke is one of the leading causes of disability in the modern world. Despite the high efficacy of high-tech treatment methods, the issue of rehabilitation is extremely relevant for post-stroke patients. Much attention is paid to non-pharmacological approaches; their use in the treatment process seems to be very promising. The review presents therapeutic approaches aimed at increasing the plasticity of the brain, including rhythmic transcranial magnetic stimulation, direct current stimulation, training with a speech therapist-neuropsychologist, computerized cognitive training, biofeedback techniques for support response, electroencephalogram, high-tech approaches using virtual reality, interfaces brain-computer, art therapy, music therapy, various complexes of physical exercises.},
}
@article {pmid35043274,
year = {2022},
author = {Colamarino, E and Pichiorri, F and Toppi, J and Mattia, D and Cincotti, F},
title = {Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm.},
journal = {Brain topography},
volume = {35},
number = {2},
pages = {182-190},
pmid = {35043274},
issn = {1573-6792},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination/physiology ; *Stroke ; *Stroke Rehabilitation/methods ; },
abstract = {Sensorimotor rhythms-based Brain-Computer Interfaces (BCIs) have successfully been employed to address upper limb motor rehabilitation after stroke. In this context, becomes crucial the choice of features that would enable an appropriate electroencephalographic (EEG) sensorimotor activation/engagement underlying the favourable motor recovery. Here, we present a novel feature selection algorithm (GUIDER) designed and implemented to integrate specific requirements related to neurophysiological knowledge and rehabilitative principles. The GUIDER algorithm was tested on an EEG dataset collected from 13 subacute stroke participants. The comparison between the automatic feature selection procedure by means of GUIDER algorithm and the manual feature selection executed by an expert neurophysiologist returned similar performance in terms of both feature selection and classification. Our preliminary findings suggest that the choices of experienced neurophysiologists could be reproducible by an automatic approach. The proposed automatic algorithm could be apt to support the professional end-users not expert in BCI such as therapist/clinicians and, to ultimately foster a wider employment of the BCI-based rehabilitation after stroke.},
}
@article {pmid35041605,
year = {2022},
author = {Lee, DY and Jeong, JH and Lee, BH and Lee, SW},
title = {Motor Imagery Classification Using Inter-Task Transfer Learning via a Channel-Wise Variational Autoencoder-Based Convolutional Neural Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {226-237},
doi = {10.1109/TNSRE.2022.3143836},
pmid = {35041605},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Highly sophisticated control based on a brain-computer interface (BCI) requires decoding kinematic information from brain signals. The forearm is a region of the upper limb that is often used in everyday life, but intuitive movements within the same limb have rarely been investigated in previous BCI studies. In this study, we focused on various forearm movement decoding from electroencephalography (EEG) signals using a small number of samples. Ten healthy participants took part in an experiment and performed motor execution (ME) and motor imagery (MI) of the intuitive movement tasks (Dataset I). We propose a convolutional neural network using a channel-wise variational autoencoder (CVNet) based on inter-task transfer learning. We approached that training the reconstructed ME-EEG signals together will also achieve more sufficient classification performance with only a small amount of MI-EEG signals. The proposed CVNet was validated on our own Dataset I and a public dataset, BNCI Horizon 2020 (Dataset II). The classification accuracies of various movements are confirmed to be 0.83 (±0.04) and 0.69 (±0.04) for Dataset I and II, respectively. The results show that the proposed method exhibits performance increases of approximately 0.09~0.27 and 0.08~0.24 compared with the conventional models for Dataset I and II, respectively. The outcomes suggest that the training model for decoding imagined movements can be performed using data from ME and a small number of data samples from MI. Hence, it is presented the feasibility of BCI learning strategies that can sufficiently learn deep learning with a few amount of calibration dataset and time only, with stable performance.},
}
@article {pmid35038681,
year = {2022},
author = {Ancau, DM and Ancau, M and Ancau, M},
title = {Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {2},
pages = {},
doi = {10.1088/2057-1976/ac4c28},
pmid = {35038681},
issn = {2057-1976},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; },
abstract = {Objective.Brain-computer interfaces (BCIs) allow subjects with sensorimotor disability to interact with the environment. Non-invasive BCIs relying on EEG signals such as event-related potentials (ERPs) have been established as a reliable compromise between spatio-temporal resolution and patient impact, but limitations due to portability and versatility preclude their broad application. Here we describe a deep-learning augmented error-related potential (ErrP) discriminating BCI using a consumer-grade portable headset EEG, the Emotiv EPOC[+].Approach.We recorded and discriminated ErrPs offline and online from 14 subjects during a visual feedback task.Main results:We achieved online discrimination accuracies of up to 81%, comparable to those obtained with professional 32/64-channel EEG devices via deep-learning using either a generative-adversarial network or an intrinsic-mode function augmentation of the training data and minimalistic computing resources.Significance.Our BCI model has the potential of expanding the spectrum of BCIs to more portable, artificial intelligence-enhanced, efficient interfaces accelerating the routine deployment of these devices outside the controlled environment of a scientific laboratory.},
}
@article {pmid35038244,
year = {2022},
author = {Zhang, Y and Zhang, T and Huang, Z and Yang, J},
title = {A New Class of Electronic Devices Based on Flexible Porous Substrates.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {9},
number = {7},
pages = {e2105084},
pmid = {35038244},
issn = {2198-3844},
support = {ORF-RE #09-078//Ontario Research Fund-Research Excellence/ ; RGPIN-2016-05198//Natural Sciences and Engineering Research Council of Canada/ ; JCYJ20210324115412035//Shenzhen Science and Technology Program/ ; //China Scholarship Council/ ; //Ministry of Education of P. R. China/ ; },
mesh = {Electronics ; Humans ; *Nanofibers ; Polymers/chemistry ; Porosity ; *Wearable Electronic Devices ; },
abstract = {With the advent of the Internet of Things era, the connection between electronic devices and humans is getting closer and closer. New-concept electronic devices including e-skins, nanogenerators, brain-machine interfaces, and implantable medical devices, can work on or inside human bodies, calling for wearing comfort, super flexibility, biodegradability, and stability under complex deformations. However, conventional electronics based on metal and plastic substrates cannot effectively meet these new application requirements. Therefore, a series of advanced electronic devices based on flexible porous substrates (e.g., paper, fabric, electrospun nanofibers, wood, and elastic polymer sponge) is being developed to address these challenges by virtue of their superior biocompatibility, breathability, deformability, and robustness. The porous structure of these substrates can not only improve device performance but also enable new functions, but due to their wide variety, choosing the right porous substrate is crucial for preparing high-performance electronics for specific applications. Herein, the properties of different flexible porous substrates are summarized and their basic principles of design, manufacture, and use are highlighted. Subsequently, various functionalization methods of these porous substrates are briefly introduced and compared. Then, the latest advances in flexible porous substrate-based electronics are demonstrated. Finally, the remaining challenges and future directions are discussed.},
}
@article {pmid35036688,
year = {2022},
author = {Li, B and Sun, H and Shu, H and Wang, X},
title = {Applying Neuromorphic Computing Simulation in Band Gap Prediction and Chemical Reaction Classification.},
journal = {ACS omega},
volume = {7},
number = {1},
pages = {168-175},
pmid = {35036688},
issn = {2470-1343},
abstract = {The rapidly developing artificial intelligence (AI) requires revolutionary computing architectures to break the energy efficiency bottleneck caused by the traditional von Neumann computing architecture. In addition, the emerging brain-machine interface also requires computational circuitry that can conduct large parallel computational tasks with low energy cost and good biocompatibility. Neuromorphic computing, a novel computational architecture emulating human brains, has drawn significant interest for the aforementioned applications due to its low energy cost, capability to parallelly process large-scale data, and biocompatibility. Most efforts in the domain of neuromorphic computing focus on addressing traditional AI problems, such as handwritten digit recognition and file classification. Here, we demonstrate for the first time that current neuromorphic computing techniques can be used to solve key machine learning questions in cheminformatics. We predict the band gaps of small-molecule organic semiconductors and classify chemical reaction types with a simulated neuromorphic circuitry. Our work can potentially guide the design and fabrication of elementary devices and circuitry for neuromorphic computing specialized for chemical purposes.},
}
@article {pmid35036481,
year = {2022},
author = {Ghosh, R and Deb, N and Sengupta, K and Phukan, A and Choudhury, N and Kashyap, S and Phadikar, S and Saha, R and Das, P and Sinha, N and Dutta, P},
title = {SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task.},
journal = {Data in brief},
volume = {40},
number = {},
pages = {107772},
pmid = {35036481},
issn = {2352-3409},
abstract = {This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.},
}
@article {pmid35034741,
year = {2022},
author = {Pozzi, NG and Isaias, IU},
title = {Adaptive deep brain stimulation: Retuning Parkinson's disease.},
journal = {Handbook of clinical neurology},
volume = {184},
number = {},
pages = {273-284},
doi = {10.1016/B978-0-12-819410-2.00015-1},
pmid = {35034741},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; *Deep Brain Stimulation ; Humans ; *Parkinson Disease/therapy ; },
abstract = {A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.},
}
@article {pmid35030477,
year = {2022},
author = {Santamaría-Vázquez, E and Martínez-Cagigal, V and Pérez-Velasco, S and Marcos-Martínez, D and Hornero, R},
title = {Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.},
journal = {Computer methods and programs in biomedicine},
volume = {215},
number = {},
pages = {106623},
doi = {10.1016/j.cmpb.2022.106623},
pmid = {35030477},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Evoked Potentials ; Humans ; Neural Networks, Computer ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features.<br><br>METHODS: The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user's control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach.<br><br>RESULTS: Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system's performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54&#xa0;bpm, a suitable value for high-speed communication.<br><br>CONCLUSIONS: The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.},
}
@article {pmid35030232,
year = {2022},
author = {Simar, C and Petit, R and Bozga, N and Leroy, A and Cebolla, AM and Petieau, M and Bontempi, G and Cheron, G},
title = {Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans.},
journal = {PloS one},
volume = {17},
number = {1},
pages = {e0262417},
pmid = {35030232},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; Signal Processing, Computer-Assisted ; Visual Perception/physiology ; },
abstract = {OBJECTIVE: Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field.<br><br>APPROACH: We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA.<br><br>MAIN RESULTS AND SIGNIFICANCE: We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.},
}
@article {pmid35027551,
year = {2022},
author = {Qin, J and Cai, Y and Xu, Z and Ming, Q and Ji, SY and Wu, C and Zhang, H and Mao, C and Shen, DD and Hirata, K and Ma, Y and Yan, W and Zhang, Y and Shao, Z},
title = {Molecular mechanism of agonism and inverse agonism in ghrelin receptor.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {300},
pmid = {35027551},
issn = {2041-1723},
mesh = {Cryoelectron Microscopy ; Crystallography, X-Ray ; Ghrelin/agonists/genetics ; Humans ; Ligands ; Mutation ; Protein Binding ; Receptors, Ghrelin/*agonists/*antagonists &amp; inhibitors/genetics ; },
abstract = {Much effort has been invested in the investigation of the structural basis of G protein-coupled receptors (GPCRs) activation. Inverse agonists, which can inhibit GPCRs with constitutive activity, are considered useful therapeutic agents, but the molecular mechanism of such ligands remains insufficiently understood. Here, we report a crystal structure of the ghrelin receptor bound to the inverse agonist PF-05190457 and a cryo-electron microscopy structure of the active ghrelin receptor-Go complex bound to the endogenous agonist ghrelin. Our structures reveal a distinct binding mode of the inverse agonist PF-05190457 in the ghrelin receptor, different from the binding mode of agonists and neutral antagonists. Combining the structural comparisons and cellular function assays, we find that a polar network and a notable hydrophobic cluster are required for receptor activation and constitutive activity. Together, our study provides insights into the detailed mechanism of ghrelin receptor binding to agonists and inverse agonists, and paves the way to design specific ligands targeting ghrelin receptors.},
}
@article {pmid35026688,
year = {2022},
author = {Stirner, M and Gurevitch, G and Lubianiker, N and Hendler, T and Schmahl, C and Paret, C},
title = {An Investigation of Awareness and Metacognition in Neurofeedback with the Amygdala Electrical Fingerprint.},
journal = {Consciousness and cognition},
volume = {98},
number = {},
pages = {103264},
doi = {10.1016/j.concog.2021.103264},
pmid = {35026688},
issn = {1090-2376},
mesh = {Amygdala/physiology ; Brain Mapping ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; *Metacognition ; *Neurofeedback/physiology ; },
abstract = {Awareness theory posits that individuals connected to a brain-computer interface can learn to estimate and discriminate their brain states. We used the amygdala Electrical Fingerprint (amyg-EFP) - a functional Magnetic Resonance Imaging-inspired Electroencephalogram surrogate of deep brain activation - to investigate whether participants could accurately estimate their own brain activation. Ten participants completed up to 20 neurofeedback runs and estimated their amygdala-EFP activation (depicted as a thermometer) and confidence in this rating during each trial. We analysed data using multilevel models, predicting the real thermometer position with participant rated position and adjusted for activation during the previous trial. Hypotheses on learning regulation and improvement of estimation were not confirmed. However, participant ratings were significantly associated with the amyg-EFP signal. Higher rating accuracy also predicted higher subjective confidence in the rating. This proof-of-concept study introduces an approach to study awareness with fMRI-informed neurofeedback and provides initial evidence for metacognition in neurofeedback.},
}
@article {pmid35026286,
year = {2022},
author = {Yi, GL and Zhu, MZ and Cui, HC and Yuan, XR and Liu, P and Tang, J and Li, YQ and Zhu, XH},
title = {A hippocampus dependent neural circuit loop underlying the generation of auditory mismatch negativity.},
journal = {Neuropharmacology},
volume = {206},
number = {},
pages = {108947},
doi = {10.1016/j.neuropharm.2022.108947},
pmid = {35026286},
issn = {1873-7064},
mesh = {Animals ; Auditory Cortex/drug effects/*physiology ; Auditory Perception/drug effects/*physiology ; CA1 Region, Hippocampal/drug effects/physiology ; Discrimination, Psychological/drug effects/physiology ; Entorhinal Cortex/drug effects/*physiology ; Evoked Potentials, Auditory/drug effects/*physiology ; Excitatory Amino Acid Antagonists/*pharmacology ; Fear/physiology ; Hippocampus/drug effects/*physiology ; Ketamine/*pharmacology ; Mice ; Nerve Net/drug effects/*physiology ; },
abstract = {Extracting relevant information and transforming it into appropriate behavior, is a fundamental brain function, and requires the coordination between the sensory and cognitive systems, however, the underlying mechanisms of interplay between sensory and cognition systems remain largely unknown. Here, we developed a mouse model for mimicking human auditory mismatch negativity (MMN), a well-characterized translational biomarker for schizophrenia, and an index of early auditory information processing. We found that a subanesthetic dose of ketamine decreased the amplitude of MMN in adult mice. Using pharmacological and chemogenetic approaches, we identified an auditory cortex-entorhinal cortex-hippocampus neural circuit loop that is required for the generation of MMN. In addition, we found that inhibition of dCA1→MEC circuit impaired the auditory related fear discrimination. Moreover, we found that ketamine induced MMN deficiency by inhibition of long-range GABAergic projection from the CA1 region of the dorsal hippocampus to the medial entorhinal cortex. These results provided circuit insights for ketamine effects and early auditory information processing. As the entorhinal cortex is the interface between the neocortex and hippocampus, and the hippocampus is critical for the formation, consolidation, and retrieval of episodic memories and other cognition, our results provide a neural mechanism for the interplay between the sensory and cognition systems.},
}
@article {pmid35025745,
year = {2022},
author = {Yan, W and Xu, G and Du, Y and Chen, X},
title = {SSVEP-EEG Feature Enhancement Method Using an Image Sharpening Filter.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {115-123},
doi = {10.1109/TNSRE.2022.3142736},
pmid = {35025745},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potential (SSVEP) is widely used in brain computer interface (BCI), medical detection, and neuroscience, so there is significant interest in enhancing SSVEP features via signal processing for better performance. In this study, an image processing method was combined with brain signal analysis and a sharpening filter was used to extract image details and features for the enhancement of SSVEP features. The results demonstrated that sharpening filter could eliminate the SSVEP signal trend term and suppress its low-frequency component. Meanwhile, sharpening filter effectively enhanced the signal-to-noise ratios (SNRs) of the single-channel and multi-channel fused signals. Image sharpening filter also significantly improved the recognition accuracy of canonical correlation analysis (CCA), filter bank canonical correlation analysis (FBCCA), and task-related component analysis (TRCA). The tools developed here effectively enhanced the SSVEP signal features, suggesting that image processing methods can be considered for improved brain signal analysis.},
}
@article {pmid35024784,
year = {2022},
author = {Provansal, M and Marazova, K and Sahel, JA and Picaud, S},
title = {Vision Restoration by Optogenetic Therapy and Developments Toward Sonogenetic Therapy.},
journal = {Translational vision science &amp; technology},
volume = {11},
number = {1},
pages = {18},
pmid = {35024784},
issn = {2164-2591},
support = {P30 EY008098/EY/NEI NIH HHS/United States ; },
mesh = {Humans ; *Optogenetics ; *Retinitis Pigmentosa ; Vision Disorders ; Vision, Ocular ; Visual Acuity ; },
abstract = {After revolutionizing neuroscience, optogenetic therapy has entered successfully in clinical trials for restoring vision to blind people with degenerative eye diseases, such as retinitis pigmentosa. These clinical trials still have to evaluate the visual acuity achieved by patients and to determine if it reaches its theoretical limit extrapolated from ex vivo experiments. Different strategies are developed in parallel to reduce required light levels and improve information processing by targeting various cell types. For patients with vision loss due to optic atrophy, as in the case of glaucoma, optogenetic cortical stimulation is hampered by light absorption and scattering by the brain tissue. By contrast, ultrasound waves can diffuse widely through the dura mater and the brain tissue as indicated by ultrasound imaging. Based on our recent results in rodents, we propose the sonogenetic therapy relying on activation of the mechanosensitive channel as a very promising vision restoration strategy with a suitable spatiotemporal resolution. Genomic approaches may thus provide efficient brain machine interfaces for sight restoration.},
}
@article {pmid35023812,
year = {2022},
author = {Cai, Z and Wang, L and Guo, M and Xu, G and Guo, L and Li, Y},
title = {From Intricacy to Conciseness: A Progressive Transfer Strategy for EEG-Based Cross-Subject Emotion Recognition.},
journal = {International journal of neural systems},
volume = {32},
number = {3},
pages = {2250005},
doi = {10.1142/S0129065722500058},
pmid = {35023812},
issn = {1793-6462},
mesh = {*Algorithms ; Databases, Factual ; *Electroencephalography/methods ; Emotions ; Humans ; Learning ; },
abstract = {Emotion plays a significant role in human daily activities, and it can be effectively recognized from EEG signals. However, individual variability limits the generalization of emotion classifiers across subjects. Domain adaptation (DA) is a reliable method to solve the issue. Due to the nonstationarity of EEG, the inferior-quality source domain data bring negative transfer in DA procedures. To solve this problem, an auto-augmentation joint distribution adaptation (AA-JDA) method and a burden-lightened and source-preferred JDA (BLSP-JDA) approach are proposed in this paper. The methods are based on a novel transfer idea, learning the specific knowledge of the target domain from the samples that are appropriate for transfer, which reduces the difficulty of transfer between two domains. On multiple emotion databases, our model shows state-of-the-art performance.},
}
@article {pmid35022643,
year = {2022},
author = {Lin, L and He, Z and Zhang, T and Zuo, Y and Chen, X and Abdelrahman, Z and Chen, F and Wei, Z and Si, K and Gong, W and Wang, X and He, S and Chen, Z},
title = {A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging.},
journal = {Journal of materials chemistry. B},
volume = {10},
number = {6},
pages = {887-898},
doi = {10.1039/d1tb02040d},
pmid = {35022643},
issn = {2050-7518},
mesh = {Animals ; *Fluorescent Dyes ; Mitochondria ; Optical Imaging/methods ; Organelles ; *Photons ; },
abstract = {Mitochondria, key organelles which keep in tune with energy demands for eukaryotic cells, are firmly associated with neurological conditions and post-traumatic rehabilitation. In vivo fluorescence imaging of mitochondria, especially with deep tissue penetration, would open a window to investigate the actual context of the brain. However, the depth of traditional two-photon mitochondrial fluorescence imaging is still limited due to the poor biological compatibility or low two-photon absorption cross-sections. A biocompatible mitochondria-targeted two-photon fluorescent dye (FO2) with an excellent two-photon absorption cross-section (the maximum of 1184 GM at 790 nm) and low cellular toxicity was designed and synthesized to overcome this problem. With this dye, we reached an imaging depth of ca. 640 μm during mitochondrial imaging of cortical cells in live animals. FO2 could be an excellent mitochondrial probe for live animal neural imaging to investigate the function and dysfunction of mitochondria in the brain.},
}
@article {pmid35017540,
year = {2022},
author = {Martins, A and Contreras-Martel, C and Janet-Maitre, M and Miyachiro, MM and Estrozi, LF and Trindade, DM and Malospirito, CC and Rodrigues-Costa, F and Imbert, L and Job, V and Schoehn, G and Attrée, I and Dessen, A},
title = {Publisher Correction: Self-association of MreC as a regulatory signal in bacterial cell wall elongation.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {329},
doi = {10.1038/s41467-022-28008-1},
pmid = {35017540},
issn = {2041-1723},
}
@article {pmid35016752,
year = {2021},
author = {Riglietti, G and Avatefipour, A and Trucco, P},
title = {The impact of business continuity management on the components of supply chain resilience: A quantitative analysis.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {15},
number = {2},
pages = {182-195},
pmid = {35016752},
issn = {1749-9216},
mesh = {*Disaster Planning ; },
abstract = {This study investigates the mitigating influence of business continuity management (BCM) with respect to supply chain disruptions. Using a dataset from the 2017 BCI Supply Chain Resilience Report, the authors conduct partial least square-based structural equation modelling with reflective constructs for both exogenous and endogenous variables. The results demonstrate that BCM reduces vulnerability and mitigates the impact of supply chain disruptions on operational performance. The study highlights BCM's contribution to such important components of supply chain resilience as visibility, collaboration and agility. In addition to demonstrating the impact of BCM on supply chain resilience, the paper explains the role of top management in the BCM process, and provides a list of measures that organisations can take to protect themselves from external threats. This is the first study to use statistical analysis to provide empirical validation in this field, while employing a clear definition of BCM in line with international best practices.},
}
@article {pmid35016160,
year = {2022},
author = {Zhang, S and Chen, X and Wang, Y and Liu, B and Gao, X},
title = {Visual field inhomogeneous in brain-computer interfaces based on rapid serial visual presentation.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4a3e},
pmid = {35016160},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Photic Stimulation/methods ; Visual Fields ; },
abstract = {Objective. Visual attention is not homogeneous across the visual field, while how to mine the effective electroencephalogram (EEG) characteristics that are sensitive to the inhomogeneous of visual attention and further explore applications such as the performance of brain-computer interface (BCI) are still distressing explorative scientists.Approach. Images were encoded into a rapid serial visual presentation (RSVP) paradigm, and were presented in three visuospatial patterns (central, left/right, upper/lower) at the stimulation frequencies of 10, 15 and 20 Hz. The comparisons among different visual fields were conducted in the dimensions of subjective behavioral and EEG characteristics. Furthermore, the effective features (e.g. steady-state visual evoked potential (SSVEP), N2-posterior-contralateral (N2pc) and P300) that sensitive to visual-field asymmetry were also explored.Main results. The visual fields had significant influences on the performance of RSVP target detection, in which the performance of central was better than that of peripheral visual field, the performance of horizontal meridian was better than that of vertical meridian, the performance of left visual field was better than that of right visual field, and the performance of upper visual field was better than that of lower visual field. Furthermore, stimuli of different visual fields had significant effects on the spatial distributions of EEG, in which N2pc and P300 showed left-right asymmetry in occipital and frontal regions, respectively. In addition, the evidences of SSVEP characteristics indicated that there was obvious overlap of visual fields on the horizontal meridian, but not on the vertical meridian.Significance. The conclusions of this study provide insights into the relationship between visual field inhomogeneous and EEG characteristics. In addition, this study has the potential to achieve precise positioning of the target's spatial orientation in RSVP-BCIs.},
}
@article {pmid35014529,
year = {2021},
author = {He, C and Ke, M and Zhong, Z and Ye, Q and He, L and Chen, Y and Zhou, J},
title = {Effect of the Degree of Acetylation of Chitin Nonwoven Fabrics for Promoting Wound Healing.},
journal = {ACS applied bio materials},
volume = {4},
number = {2},
pages = {1833-1842},
doi = {10.1021/acsabm.0c01536},
pmid = {35014529},
issn = {2576-6422},
mesh = {Acetylation ; Animals ; Biocompatible Materials/chemical synthesis/chemistry/*pharmacology ; Chitin/chemical synthesis/chemistry/*pharmacology ; Female ; Materials Testing ; Particle Size ; Rats ; Rats, Sprague-Dawley ; Skin/*drug effects/pathology ; *Textiles ; Wound Healing/*drug effects ; },
abstract = {Chitin and chitosan have been extensively used as wound dressings because of their special functions to promote wound healing. However, there was little focus on the effects of the degree of acetylation (DA) on wound healing. In this work, the regenerated chitin nonwoven fabrics with DA values of 90, 71, 60, and 42% were prepared, and the morphology and physical performances of the fabrics were characterized. Moreover, the effects of DA of the chitin nonwoven fabrics on wound recovery were studied with a full-thickness skin defect model in rats. In vitro experiments indicated that the chitin nonwoven fabrics exhibited good biocompatibility and blood compatibility and a low blood-clotting index (BCI). In vivo experiments revealed that the chitin nonwoven fabrics could accelerate wound healing more effectively than gauze by promoting re-epithelialization and collagen deposition as well as by stimulating neovascularization. The results of the wound healing process showed that DA of the chitin nonwoven fabrics had a profound effect on promoting wound healing. Notably, the regenerated chitin nonwoven fabrics with 71% DA significantly improved the wound healing compared to the commercial wound dressing Algoplaque film. Therefore, the regenerated chitin nonwoven fabrics are promising candidates for wound healing.},
}
@article {pmid35013268,
year = {2022},
author = {Proix, T and Delgado Saa, J and Christen, A and Martin, S and Pasley, BN and Knight, RT and Tian, X and Poeppel, D and Doyle, WK and Devinsky, O and Arnal, LH and Mégevand, P and Giraud, AL},
title = {Imagined speech can be decoded from low- and cross-frequency intracranial EEG features.},
journal = {Nature communications},
volume = {13},
number = {1},
pages = {48},
pmid = {35013268},
issn = {2041-1723},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/diagnostic imaging ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrocorticography ; Electrodes ; Female ; Humans ; Imagination ; *Language ; Male ; Middle Aged ; Phonetics ; *Speech ; Young Adult ; },
abstract = {Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.},
}
@article {pmid35010288,
year = {2021},
author = {Abuín-Porras, V and Martinez-Perez, C and Romero-Morales, C and Cano-de-la-Cuerda, R and Martín-Casas, P and Palomo-López, P and Sánchez-Tena, M&#xc1;},
title = {Citation Network Study on the Use of New Technologies in Neurorehabilitation.},
journal = {International journal of environmental research and public health},
volume = {19},
number = {1},
pages = {},
pmid = {35010288},
issn = {1660-4601},
mesh = {Bibliometrics ; *Brain-Computer Interfaces ; Databases, Factual ; Humans ; *Neurological Rehabilitation ; *Neurosciences ; },
abstract = {New technologies in neurorehabilitation is a wide concept that intends to find solutions for individual and collective needs through technical systems. Analysis through citation networks is used to search scientific literature related to a specific topic. On the one hand, the main countries, institutions, and authors researching this topic have been identified, as well as their evolution over time. On the other hand, the links between the authors, the countries, and the topics under research have been analyzed. The publications analysis was performed through the Web of Science database using the search terms "new technolog*," "neurorehabilitation," "physical therapy*," and "occupational therapy*." The selected interval of publication was from 1992 to December 2020. The results were analyzed using CitNetExplorer software. After a Web of Science search, a total of 454 publications and 135 citation networks were found, 1992 being the first year of publication. An exponential increase was detected from the year 2009. The largest number was detected in 2020. The main areas are rehabilitation and neurosciences and neurology. The most cited article was from Perry et al. in 2007, with a citation index of 460. The analysis of the top 20 most cited articles shows that most approach the use of robotic devices and brain-computer interface systems. In conclusion, the main theme was found to be the use of robotic devices to address neuromuscular rehabilitation goals and brain-computer interfaces and their applications in neurorehabilitation.},
}
@article {pmid35009860,
year = {2022},
author = {Palumbo, A and Ielpo, N and Calabrese, B},
title = {An FPGA-Embedded Brain-Computer Interface System to Support Individual Autonomy in Locked-In Individuals.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {35009860},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCI) can detect specific EEG patterns and translate them into control signals for external devices by providing people suffering from severe motor disabilities with an alternative/additional channel to communicate and interact with the outer world. Many EEG-based BCIs rely on the P300 event-related potentials, mainly because they require training times for the user relatively short and provide higher selection speed. This paper proposes a P300-based portable embedded BCI system realized through an embedded hardware platform based on FPGA (field-programmable gate array), ensuring flexibility, reliability, and high-performance features. The system acquires EEG data during user visual stimulation and processes them in a real-time way to correctly detect and recognize the EEG features. The BCI system is designed to allow to user to perform communication and domotic controls.},
}
@article {pmid35009639,
year = {2021},
author = {McDermott, EJ and Raggam, P and Kirsch, S and Belardinelli, P and Ziemann, U and Zrenner, C},
title = {Artifacts in EEG-Based BCI Therapies: Friend or Foe?.},
journal = {Sensors (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {35009639},
issn = {1424-8220},
support = {13GW0213A//Federal Ministry of Education and Research/ ; },
mesh = {Algorithms ; Artifacts ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; *Neurofeedback ; Signal Processing, Computer-Assisted ; },
abstract = {EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.},
}
@article {pmid35008333,
year = {2021},
author = {Montegut, C and Correard, F and Nouguerède, E and Rey, D and Chevalier, T and Meurer, M and Deville, JL and Baciuchka, M and Pradel, V and Greillier, L and Villani, P and Couderc, AL},
title = {Prognostic Value of the B12/CRP Index in Older Systemically Treatable Cancer Patients.},
journal = {Cancers},
volume = {14},
number = {1},
pages = {},
pmid = {35008333},
issn = {2072-6694},
abstract = {BACKGROUND: While comprehensive geriatric assessment (CGA) in older patients treated for cancer assesses several related domains, it does not include standardized biological tests. The present study aimed to: (1) assess the prognosis value of the B12/CRP index (BCI) in a population of systemically treatable older patients with cancer and (2) analyze the association between BCI value and pre-existing geriatric frailty.<br><br>METHOD: We conducted a retrospective observational study between January 2016 and June 2020 at Marseille University Hospital. All consecutive cancer patients aged 70 years and over before initiating systemic therapy were included.<br><br>RESULTS: Of the 863 patients included, 60.5% were men and 42.5% had metastatic stage cancer. Mean age was 81 years. The low-BCI group (&#x2264;10,000) had a significantly longer survival time than the mid-BCI (10,000 < BCI &#x2264; 40,000) and high-BCI (BCI > 40,000) groups (HR = 0.327, CI95% [0.26-0.42], p-value = 0.0001). Mid- and high-BCI (BCI > 40,000) values were associated with impaired functional status and malnutrition.<br><br>CONCLUSION: A BCI > 10,000 would appear to be a good biological prognostic factor for poor survival times and pre-existing geriatric impairment in older cancer patients before they initiate systemic treatment.},
}
@article {pmid35008079,
year = {2022},
author = {Liu, Y and Wang, Z and Huang, S and Wang, W and Ming, D},
title = {EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac49a6},
pmid = {35008079},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Objective.Supernumerary robotic limbs are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel motor imagery (MI)-based brain-computer interface (BCI) paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the electromyographic (EEG) characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).Approach.Fourteen subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation was adopted to analyze EEG spatial features and key-channel time-frequency features. Common spatial patterns were used for feature extraction and classification was implemented by support vector machine. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4 weeks EEG control experiment based on the new paradigm.Main results.Event-related desynchronization (ERD) was found in the supplementary motor area and primary motor area with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal eight-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal eight-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random eight-channel (p< 0.01).Significance.This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.},
}
@article {pmid35007548,
year = {2022},
author = {Moon, J and Orlandi, S and Chau, T},
title = {A comparison and classification of oscillatory characteristics in speech perception and covert speech.},
journal = {Brain research},
volume = {1781},
number = {},
pages = {147778},
doi = {10.1016/j.brainres.2022.147778},
pmid = {35007548},
issn = {1872-6240},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Speech ; *Speech Perception ; Wavelet Analysis ; },
abstract = {Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, 'blue' and 'orange', and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes (tasks) were conducted to determine statistical differences in frequency and time (t-CWT). In the current experiment, a task comprised speech perception or covert rehearsal of a word while a condition was the discrimination between tasks. Features were extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within tasks and across conditions between perception and covert activities (i.e. cross-task). All binary classifications accuracies (80-90%) significantly exceeded chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception condition dynamically invoked all frequencies with more prominent θ and α activity, the covert condition favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, possibly corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, seems to support the notion that the γ- and θ-bands reflect, respectively, shared and unique encoding processes across tasks.},
}
@article {pmid35006823,
year = {2021},
author = {Mathur, V and Talapatra, S and Kar, S and Hennighausen, Z},
title = {In Vivo Partial Restoration of Neural Activity across Severed Mouse Spinal Cord Bridged with Ultralong Carbon Nanotubes.},
journal = {ACS applied bio materials},
volume = {4},
number = {5},
pages = {4071-4078},
doi = {10.1021/acsabm.1c00248},
pmid = {35006823},
issn = {2576-6422},
mesh = {Animals ; Biocompatible Materials/*chemistry ; Materials Testing ; Mice ; Nanotubes, Carbon/*chemistry ; Neurons/*metabolism ; Particle Size ; Spinal Cord/*metabolism ; },
abstract = {Electrically bridging severed nerves in vivo has transformative healthcare implications, but current materials are inadequate. Carbon nanotubes (CNTs) are promising, with low impedance, high charge injection capacity, high flexibility, are chemically inert, and can electrically couple to neurons. Ultralong CNTs are unexplored for neural applications. Using only ultralong CNTs in saline, without neuroregeneration or rehabilitation, we partially restored neural activity across a severed mouse spinal cord, recovering 23.8% of the intact amplitude, while preserving signal shape. Neural signals are preferentially facilitated over artifact signals by a factor of ×5.2, suggesting ultralong CNTs are a promising material for neural-scaffolding and neural-electronics applications.},
}
@article {pmid35006782,
year = {2021},
author = {Li, X and Song, Y and Xiao, G and He, E and Xie, J and Dai, Y and Xing, Y and Wang, Y and Wang, Y and Xu, S and Wang, M and Tao, TH and Cai, X},
title = {PDMS-Parylene Hybrid, Flexible Micro-ECoG Electrode Array for Spatiotemporal Mapping of Epileptic Electrophysiological Activity from Multicortical Brain Regions.},
journal = {ACS applied bio materials},
volume = {4},
number = {11},
pages = {8013-8022},
doi = {10.1021/acsabm.1c00923},
pmid = {35006782},
issn = {2576-6422},
mesh = {Brain/physiology ; Dimethylpolysiloxanes ; Electrodes ; *Epilepsy/diagnosis ; Humans ; *Nanotubes, Carbon ; Polymers ; Xylenes ; },
abstract = {Epilepsy detection and focus location are urgent issues that need to be solved in epilepsy research. A cortex conformable and fine spatial accuracy electrocorticogram (ECoG) sensor array, especially for real-time detection of multicortical functional regions and delineating epileptic focus remains a challenge. Here, we fabricated a polydimethylsiloxane (PDMS)-parylene hybrid, flexible micro-ECoG electrode array. The multiwalled carbon nanotubes (MWCNTs)/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) nanocomposite-modified electrode interface significantly improved the sensing performance with low impedance (20.68 ± 6.65 kΩ), stable phase offset, and high sensitivity. The electrophysiological activities of multicortical brain regions (somatosensory cortex, parietal association cortex, and visual cortex) were simultaneously monitored during normal and epileptic statuses. The epileptic ECoG activities spread spatiotemporally from the starting point toward the adjacent cortex. Significant variations of the waveform, power, and frequency band were observed. The ECoG potential (123 ± 23 μV) at normal status was prominently up to 417 ± 87 μV at the spike wave stage. Besides, the power for epileptic activity (11.049 ± 4.513 μW) was 10 times higher than that (1.092 ± 0.369 μW) for normal activity. In addition, the theta frequency band was found to be a characteristic frequency band of epileptic signals. These joint analysis results of multicortical regions indicated that the active micron-scale region on the parietal association cortex was more likely to be the epileptogenic focus. Cortical mapping with high spatial detail provides the accurate delineation of lesions. The flexible micro-ECoG electrode array is a powerful tool for constructing a spatiotemporal map of the cortex. It provides a technical platform for epileptic focus location, biomedical diagnosis, and brain-computer interaction.},
}
@article {pmid35002899,
year = {2021},
author = {Ziadeh, H and Gulyas, D and Nielsen, LD and Lehmann, S and Nielsen, TB and Kjeldsen, TKK and Hougaard, BI and Jochumsen, M and Knoche, H},
title = {"Mine Works Better": Examining the Influence of Embodiment in Virtual Reality on the Sense of Agency During a Binary Motor Imagery Task With a Brain-Computer Interface.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {806424},
pmid = {35002899},
issn = {1664-1078},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCI) have been proposed as a means for stroke rehabilitation, which combined with virtual reality allows for introducing game-based interactions into rehabilitation. However, the control of the MI-BCI may be difficult to obtain and users may face poor performance which frustrates them and potentially affects their motivation to use the technology. Decreases in motivation could be reduced by increasing the users' sense of agency over the system. The aim of this study was to understand whether embodiment (ownership) of a hand depicted in virtual reality can enhance the sense of agency to reduce frustration in an MI-BCI task. Twenty-two healthy participants participated in a within-subject study where their sense of agency was compared in two different embodiment experiences: 1) avatar hand (with body), or 2) abstract blocks. Both representations closed with a similar motion for spatial congruency and popped a balloon as a result. The hand/blocks were controlled through an online MI-BCI. Each condition consisted of 30 trials of MI-activation of the avatar hand/blocks. After each condition a questionnaire probed the participants' sense of agency, ownership, and frustration. Afterwards, a semi-structured interview was performed where the participants elaborated on their ratings. Both conditions supported similar levels of MI-BCI performance. A significant correlation between ownership and agency was observed (r = 0.47, p = 0.001). As intended, the avatar hand yielded much higher ownership than the blocks. When controlling for performance, ownership increased sense of agency. In conclusion, designers of BCI-based rehabilitation applications can draw on anthropomorphic avatars for the visual mapping of the trained limb to improve ownership. While not While not reducing frustration ownership can improve perceived agency given sufficient BCI performance. In future studies the findings should be validated in stroke patients since they may perceive agency and ownership differently than able-bodied users.},
}
@article {pmid35002658,
year = {2021},
author = {Chang, Y and He, C and Tsai, BY and Ko, LW},
title = {Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {785562},
pmid = {35002658},
issn = {1662-5161},
abstract = {Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.},
}
@article {pmid34997274,
year = {2022},
author = {Perrault, JR and Page-Karjian, A and Morgan, AN and Burns, LK and Stacy, NI},
title = {Morphometrics and blood analytes of leatherback sea turtle hatchlings (Dermochelys coriacea) from Florida: reference intervals, temporal trends with clutch deposition date, and body size correlations.},
journal = {Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology},
volume = {192},
number = {2},
pages = {313-324},
pmid = {34997274},
issn = {1432-136X},
mesh = {Animals ; Body Size ; Florida ; Reference Values ; Seasons ; *Turtles/physiology ; },
abstract = {The northwest Atlantic leatherback sea turtle (Dermochelys coriacea) population is exhibiting decreasing trends along numerous nesting beaches. Since population health and viability are inherently linked, it is important to establish species- and life-stage class-specific blood analyte reference intervals (RIs) so that effects of future disturbances on organismal health can be better understood. For hatchling leatherbacks, the objectives of this study were to (1) establish RIs for morphometrics and blood analytes; (2) evaluate correlations between hatchling morphometrics, blood analytes, and hatching success; and (3) determine temporal trends in hatchling morphometrics and blood analytes across nesting season. Blood samples were collected from 176 naturally emerging leatherback hatchlings from 18 clutches. Reference intervals were established for morphometrics and blood analytes. Negative relationships were noted between hatchling mass and packed cell volume, total white blood cells, heterophils, lymphocytes, and total protein and between body condition index (BCI) and immature red blood cells (RBC), RBC polychromasia and anisocytosis, and total protein. Clutch deposition date showed positive relationships with lymphocytes and total protein, and negative relationships with hatchling mass and BCI. Hatching success was positively correlated with mass, and negatively with total protein and glucose, suggesting that nutritional provisions in eggs, incubation time, and/or metabolic rates could change later in the season and affect survivorship. These various observed correlations provide evidence for increased physiological stress (e.g., inflammation, subclinical dehydration) in hatchlings emerging later in nesting season, presumably due to increased nest temperatures or other environmental factors (e.g., moisture/rainfall). Data reported herein provide morphometric and blood analyte data for leatherback hatchlings and will allow for future investigations into spatiotemporal trends and responses to various stressors.},
}
@article {pmid34996515,
year = {2022},
author = {Endris, BS and Dinant, GJ and Gebreyesus, SH and Spigt, M},
title = {Risk factors of anemia among preschool children in Ethiopia: a Bayesian geo-statistical model.},
journal = {BMC nutrition},
volume = {8},
number = {1},
pages = {2},
pmid = {34996515},
issn = {2055-0928},
abstract = {BACKGROUND: The etiology and risk factors of anemia are multifactorial and varies across context. Due to the geospatial clustering of anemia, identifying risk factors for anemia should account for the geographic variability. Failure to adjust for spatial dependence whilst identifying risk factors of anemia could give spurious association. We aimed to identify risk factors of anemia using a Bayesian geo-statistical model.<br><br>METHODS: We analyzed the Ethiopian Demographic and Health Survey (EDHS) 2016 data. The sample was selected using a stratified, two- stage cluster sampling design. In this survey, 9268 children had undergone anemia testing. Hemoglobin level was measured using a HemoCue photometer and the results were recorded onsite. Based on the World Health Organization's cut-off points, a child was considered anaemic if their altitude adjusted haemoglobin (Hb) level was less than 11&#x2009;g/dL. Risk factors for anemia were identified using a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data. Posterior means and 95% credible interval (BCI) were used to report our findings. We used a statistically significant level at 0.05.<br><br>RESULT: The 9267 children in our study were between 6 and 59&#x2009;months old. Fifty two percent (52%) of children were males. Thirteen percent (13%) of children were from the highest wealth quintile whereas 23% from the lowest wealth quintile. Most of them lived in rural areas (90%). The overall prevalence of anemia among preschool children was 57% (95% CI: 54.4-59.4). We found that child stunting (OR&#x2009;=&#x2009;1.26, 95% BCI (1.14-1.39), wasting (OR&#x2009;=&#x2009;1.35, 95% BCI (1.15-1.57), maternal anemia (OR&#x2009;=&#x2009;1.61, 95% BCI (1.44-1.79), mothers having two under five children (OR&#x2009;=&#x2009;1.2, 95% BCI (1.08-1.33) were risk factors associated with anemia among preschool children. Children from wealthy households had lower risk of anemia (AOR&#x2009;=&#x2009;0.73, 95% BCI (0.62-0.85).<br><br>CONCLUSION: Using the Bayesian geospatial statistical modeling, we were able to account for spatial dependent structure in the data, which minimize spurious association. Childhood Malnutrition, maternal anemia, increased fertility, and poor wealth status were risk factors of anemia among preschool children in Ethiopia. The existing anaemia control programs such as IFA supplementation during pregnancy should be strengthened to halt intergenerational effect of anaemia. Furthermore, routine childhood anaemia screening and intervention program should be part of the Primary health care in Ethiopia.},
}
@article {pmid34996051,
year = {2022},
author = {Zheng, L and Pei, W and Gao, X and Zhang, L and Wang, Y},
title = {A high-performance brain switch based on code-modulated visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac494f},
pmid = {34996051},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Objective.Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR).Approach.In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series.Main results.The online experiments achieved an averageRTof 1.49 s when the averageIDLEfor eachFPwas 68.57 min (1.46 × 10[-2]FP min[-1]) or an averageRTof 1.67 s withoutFPs. Significance.This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.},
}
@article {pmid34995902,
year = {2022},
author = {Wang, X and Wang, M and Sheng, H and Zhu, L and Zhu, J and Zhang, H and Liu, Y and Zhan, L and Wang, X and Zhang, J and Wu, X and Suo, Z and Xi, W and Wang, H},
title = {Subdural neural interfaces for long-term electrical recording, optical microscopy and magnetic resonance imaging.},
journal = {Biomaterials},
volume = {281},
number = {},
pages = {121352},
doi = {10.1016/j.biomaterials.2021.121352},
pmid = {34995902},
issn = {1878-5905},
mesh = {*Brain/diagnostic imaging ; Elastomers ; Electrodes, Implanted ; Hydrogels ; Magnetic Resonance Imaging ; Metals ; *Microscopy ; },
abstract = {Though commonly used, metal electrodes are incompatible with brain tissues, often leading to injury and failure to achieve long-term implantation. Here we report a subdural neural interface of hydrogel functioning as an ionic conductor, and elastomer as a dielectric. We demonstrate that it incurs a far less glial reaction and less cerebrovascular destruction than a metal electrode. Using a cat model, the hydrogel electrode was able to record electrical signals comparably in quality to a metal electrode. The hydrogel-elastomer neural interface also readily facilitated multimodal functions. Both the hydrogel and elastomer are transparent, enabling in vivo optical microscopy. For imaging, cerebral vessels and calcium signals were imaged using two-photon microscopy. The new electrode is compatible with magnetic resonance imaging and does not cause artifact images. Such a new multimodal neural interface could represent immediate opportunity for use in broad areas of application in neuroscience research and clinical neurology.},
}
@article {pmid34994497,
year = {2022},
author = {Nafees, M and Ullah, S and Ahmed, I},
title = {Modulation of drought adversities in Vicia faba by the application of plant growth promoting rhizobacteria and biochar.},
journal = {Microscopy research and technique},
volume = {85},
number = {5},
pages = {1856-1869},
doi = {10.1002/jemt.24047},
pmid = {34994497},
issn = {1097-0029},
mesh = {Charcoal ; *Droughts ; Ecosystem ; *Vicia faba ; },
abstract = {Drought is the greatest threat to world food security, seen as the catalyst for the great famines of the past. Given that the world's water supply is limited, it is likely that future demand of food for increasing population will further exacerbate the drought effects. Therefore, the present study was aimed to investigate the effect of biochar and plant growth promoting rhizobacteria (PGPR) Sphingobacterium pakistanensis (NCCP246) and Cellulomonas pakistanensis (NCCP11) on agronomic and physiological attributes of Vicia faba two varieties Desi (V1) and Pulista (V2) under induced drought stress. The seeds were sown in earthen pots filled with 3 kg sand and soil (1:2), and biochar (0 and 5% w/w) in triplicate arranged in complete randomized design. Analysis of biochar possessed 0.49 g cm[-3] bulk density, 9.6 pH; 5.4 cmol kg[-1] cation exchange capacity, 3.64% organic carbon and EC 6.7 ds/m. Agronomic attributes including seed LAI, LAR, SVI, %PHSI and RWC were improved by 30.4-180.4%, 14.37-47.20%, 37.64-50.91%, 18.21-30.80, and 35.82-54.34% in both varieties by the co-application of biochar and PGPR. Stomatal physiology and epidermal vigor was successfully improved by the application of PGPR and biochar as analyzed by scanning electron microscopy (SEM). Photosynthetic pigments, flavonoids, phenols, proline and glycine betaine were amplified by 58.33-173.8%, 50.59-130.33%, 46.58-86.62%, 46.66-109.30%, 35.74-56.10%, and 21.96-77.22% in both varieties by the co-application of biochar and PGPR. So, the present work concluded that, combined application of biochar and PGPR could be an effective strategy to alleviate the adversities of drought in V. faba growing in drastic ecosystems.},
}
@article {pmid34992606,
year = {2021},
author = {Lai, J and Zhang, P and Jiang, J and Mou, T and Li, Y and Xi, C and Wu, L and Gao, X and Zhang, D and Chen, Y and Huang, H and Li, H and Cai, X and Li, M and Zheng, P and Hu, S},
title = {New Evidence of Gut Microbiota Involvement in the Neuropathogenesis of Bipolar Depression by TRANK1 Modulation: Joint Clinical and Animal Data.},
journal = {Frontiers in immunology},
volume = {12},
number = {},
pages = {789647},
pmid = {34992606},
issn = {1664-3224},
mesh = {Adolescent ; Adult ; Animals ; Bipolar Disorder/blood/*immunology/microbiology/pathology ; Brain-Gut Axis/*immunology ; Case-Control Studies ; Cell Line ; Cytokines/analysis/*metabolism ; Depression/blood/*immunology/microbiology/pathology ; Disease Models, Animal ; Fecal Microbiota Transplantation ; Female ; Gastrointestinal Microbiome/*immunology ; Healthy Volunteers ; Hippocampus/immunology/metabolism/pathology ; Humans ; Lipopolysaccharides/immunology ; Male ; Mice ; Microglia/immunology/metabolism ; Neurons/immunology/metabolism ; Prefrontal Cortex/immunology/metabolism/pathology ; Primary Cell Culture ; Young Adult ; },
abstract = {Tetratricopeptide repeat and ankyrin repeat containing 1 (TRANK1) is a robust risk gene of bipolar disorder (BD). However, little is known on the role of TRANK1 in the pathogenesis of BD and whether the gut microbiota is capable of regulating TRANK1 expression. In this study, we first investigated the serum mRNA level of TRANK1 in medication-free patients with a depressive episode of BD, then a mice model was constructed by fecal microbiota transplantation (FMT) to explore the effects of gut microbiota on brain TRANK1 expression and neuroinflammation, which was further verified by in vitro Lipopolysaccharide (LPS) treatment in BV-2 microglial cells and neurons. 22 patients with a depressive episode and 28 healthy individuals were recruited. Serum level of TRANK1 mRNA was higher in depressed patients than that of healthy controls. Mice harboring 'BD microbiota' following FMT presented depression-like phenotype. mRNA levels of inflammatory cytokines and TRANK1 were elevated in mice hippocampus and prefrontal cortex. In vitro, LPS treatment activated the secretion of pro-inflammatory factors in BV-2 cells, which was capable of upregulating the neuronal expression of TRANK1 mRNA. Moreover, primary cortical neurons transfected with plasmid Cytomegalovirus DNA (pcDNA3.1(+)) vector encoding human TRANK1 showed decreased dendritic spine density. Together, these findings add new evidence to the microbiota-gut-brain regulation in BD, indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.},
}
@article {pmid34991734,
year = {2022},
author = {Kong, LZ and Zhang, RL and Hu, SH and Lai, JB},
title = {Military traumatic brain injury: a challenge straddling neurology and psychiatry.},
journal = {Military Medical Research},
volume = {9},
number = {1},
pages = {2},
pmid = {34991734},
issn = {2054-9369},
mesh = {*Brain Injuries, Traumatic/complications ; Humans ; *Military Personnel ; *Neurology ; *Psychiatry ; },
abstract = {Military psychiatry, a new subcategory of psychiatry, has become an invaluable, intangible effect of the war. In this review, we begin by examining related military research, summarizing the related epidemiological data, neuropathology, and the research achievements of diagnosis and treatment technology, and discussing its comorbidity and sequelae. To date, advances in neuroimaging and molecular biology have greatly boosted the studies on military traumatic brain injury (TBI). In particular, in terms of pathophysiological mechanisms, several preclinical studies have identified abnormal protein accumulation, blood-brain barrier damage, and brain metabolism abnormalities involved in the development of TBI. As an important concept in the field of psychiatry, TBI is based on organic injury, which is largely different from many other mental disorders. Therefore, military TBI is both neuropathic and psychopathic, and is an emerging challenge at the intersection of neurology and psychiatry.},
}
@article {pmid34990366,
year = {2022},
author = {Ravi, A and Lu, J and Pearce, S and Jiang, N},
title = {Enhanced System Robustness of Asynchronous BCI in Augmented Reality Using Steady-State Motion Visual Evoked Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {85-95},
doi = {10.1109/TNSRE.2022.3140772},
pmid = {34990366},
issn = {1558-0210},
mesh = {Algorithms ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; },
abstract = {This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.},
}
@article {pmid34987165,
year = {2022},
author = {Wang, Y and Zhou, S and Qi, X and Yang, F and Maurer, MJ and Habermann, TM and Witzig, TE and Wang, ML and Nowakowski, GS},
title = {Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials.},
journal = {Blood cancer journal},
volume = {12},
number = {1},
pages = {1},
pmid = {34987165},
issn = {2044-5385},
support = {P50 CA097274/CA/NCI NIH HHS/United States ; },
mesh = {Antineoplastic Combined Chemotherapy Protocols/*therapeutic use ; Bayes Theorem ; Cyclophosphamide/therapeutic use ; Doxorubicin/therapeutic use ; Humans ; *Immunotherapy/methods ; Lymphoma, Follicular/*therapy ; Maintenance Chemotherapy ; Prednisone/therapeutic use ; Progression-Free Survival ; Randomized Controlled Trials as Topic ; Rituximab/therapeutic use ; Treatment Outcome ; Vincristine/therapeutic use ; },
abstract = {Front-line treatment for follicular lymphoma has evolved with the introduction of maintenance therapy, bendamustine (Benda), obinutuzumab (G), and lenalidomide (Len). We conducted a random-effects Bayesian network meta-analysis (NMA) of phase 3 randomized controlled trials (RCTs) to identify the regimens with superior efficacy. Progression-free survival (PFS) was compared between 11 modern regimens with different immunochemotherapy and maintenance strategies. G-Benda-G resulted in with the best PFS, with an HR of 0.41 compared to R-Benda, a surface under the cumulative ranking curve (SUCRA) of 0.97, a probability of being the best treatment (PbBT) of 72%, and a posterior ranking distribution (PoRa) of 1 (95% BCI 1-3). This was followed by R-Benda-R4 (HR = 0.49, PbBT = 25%, PoRa = 2) and R-Benda-R (HR = 0.60, PbBT = 3%, PoRa = 3). R-CHOP-R (HR = 0.96) and R-Len-R (HR = 0.97) had similar efficacy to R-Benda. Bendamustine was a better chemotherapy backbone than CHOP either with maintenance (R-Benda-R vs R-CHOP-R, HR = 0.62; G-Benda-G vs G-CHOP-G, HR = 0.55) or without maintenance therapy (R-Benda vs R-CHOP, HR = 0.68). Rituximab maintenance improved PFS following R-CHOP (R-CHOP-R vs R-CHOP, HR = 0.65) or R-Benda (R-Benda-R vs R-Benda, HR = 0.60; R-Benda-R4 vs R-Benda, HR = 0.49). In the absence of multi-arm RCTs that include all common regimens, this NMA provides an important and useful guide to inform treatment decisions.},
}
@article {pmid34986475,
year = {2022},
author = {Chen, J and Yi, W and Wang, D and Du, J and Fu, L and Li, T},
title = {FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac4852},
pmid = {34986475},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding.Approach.A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure.Main results.The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment.Significance.This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.},
}
@article {pmid34986110,
year = {2022},
author = {Wang, Z and Zhang, J and Zhang, X and Chen, P and Wang, B},
title = {Transformer Model for Functional Near-Infrared Spectroscopy Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {6},
pages = {2559-2569},
doi = {10.1109/JBHI.2022.3140531},
pmid = {34986110},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Humans ; Movement ; Neural Networks, Computer ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classification network based on Transformer, named fNIRS-T. We explore the spatial-level and channel-level representation of fNIRS signals to improve data utilization and network representation capacity. Besides, a preprocessing module, which consists of one-dimensional average pooling and layer normalization, is designed to replace filtering and baseline correction of data preprocessing. It makes fNIRS-T an end-to-end network, called fNIRS-PreT. Compared with traditional machine learning classifiers, convolutional neural network (CNN), and long short-term memory (LSTM), the proposed models obtain the best accuracy on three open-access datasets. Specifically, in the most extensive ternary classification task (30 subjects) that includes three types of overt movements, fNIRS-T, CNN, and LSTM obtain 75.49%, 72.89%, and 61.94% on test sets, respectively. Compared to traditional classifiers, fNIRS-T is at least 27.41% higher than statistical features and 6.79% higher than well-designed features. In the individual subject experiment of the ternary classification task, fNIRS-T achieves an average subject accuracy of 78.22% and surpasses CNN and LSTM by a large margin of +4.75% and +11.33%. fNIRS-PreT using raw data also achieves competitive performance to fNIRS-T. Therefore, the proposed models improve the performance of fNIRS-based BCI significantly.},
}
@article {pmid34982594,
year = {2022},
author = {Andersen, RA and Aflalo, T and Bashford, L and Bjånes, D and Kellis, S},
title = {Exploring Cognition with Brain-Machine Interfaces.},
journal = {Annual review of psychology},
volume = {73},
number = {},
pages = {131-158},
doi = {10.1146/annurev-psych-030221-030214},
pmid = {34982594},
issn = {1545-2085},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Cerebral Cortex ; Cognition ; Humans ; Parietal Lobe ; },
abstract = {Traditional brain-machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.},
}
@article {pmid34979193,
year = {2022},
author = {Wang, Y and Luo, Z and Zhao, S and Xie, L and Xu, M and Ming, D and Yin, E},
title = {Spatial localization in target detection based on decoding N2pc component.},
journal = {Journal of neuroscience methods},
volume = {369},
number = {},
pages = {109440},
doi = {10.1016/j.jneumeth.2021.109440},
pmid = {34979193},
issn = {1872-678X},
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; Electroencephalography/methods ; Humans ; Photic Stimulation/methods ; Recognition, Psychology ; },
abstract = {BACKGROUND: The Gaze-independent BCI system is used to restore communication in patients with eye movement disorders. One available control mechanism is the utilization of spatial attention. However, spatial information is mostly used to simply answer the "True/False" target recognition question and is seldom used to improve the efficiency of target detection. Therefore, it is necessary to utilize the potential advantages of spatial attention to improving the target detection efficiency.<br><br>NEW METHOD: We found that N2pc could be used to assess spatial attention shift and determine target position. It was a negative wave in the posterior brain on the contralateral target stimulus. From this, we designed a novel spatial coding paradigm to achieve two main purposes at each stimulus presentation: target recognition and spatial localization.<br><br>We used a two-step classification framework to decode the P300 and N2pc components.<br><br>RESULTS: The average decoding accuracy of fourteen subjects was 84.43% (&#x3c3;&#xa0;=&#xa0;1.14%), and the classification accuracy of six subjects was more than 85%. The information transfer rate of the spatial coding paradigm could reach 60.52 bits/min. Compared with the single stimulus paradigm, the target detection efficiency was successfully improved by approximately 10%.<br><br>CONCLUSIONS: The spatial coding paradigm proposed in this paper answered both "True/False" and "Left/Right" questions by decoding spatial attention information. This method could significantly improve image detection efficiencies, such as visual search tasks, Internet image screening, or military target determination.},
}
@article {pmid34977321,
year = {2022},
author = {Sykes, AL and Larrieu, E and Poggio, TV and Céspedes, MG and Mujica, GB and Basáñez, MG and Prada, JM},
title = {Modelling diagnostics for Echinococcus granulosus surveillance in sheep using Latent Class Analysis: Argentina as a case study.},
journal = {One health (Amsterdam, Netherlands)},
volume = {14},
number = {},
pages = {100359},
pmid = {34977321},
issn = {2352-7714},
support = {MR/R015600/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%-58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%-68%) and 68% (95%BCI: 63%-92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation.},
}
@article {pmid34976324,
year = {2021},
author = {Liu, Y and Chen, C and Belkacem, AN and Wang, Z and Cheng, L and Wang, C and Chang, Y and Li, P},
title = {Motor Imagination of Lower Limb Movements at Different Frequencies.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {4073739},
pmid = {34976324},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Lower Extremity ; Movement ; },
abstract = {Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by "enhancing the difficulty of imagination tasks," conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (-1.827) is less than that of LFMI (-1.3487) in the alpha band, so did (-3.4756 < -2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p=0.0074 < 0.01; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different (p=0.0034 < 0.01; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.},
}
@article {pmid34976039,
year = {2021},
author = {Zhang, S and Sun, L and Mao, X and Hu, C and Liu, P},
title = {Review on EEG-Based Authentication Technology.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {5229576},
pmid = {34976039},
issn = {1687-5273},
mesh = {*Biometric Identification ; Biometry ; *Brain-Computer Interfaces ; Electroencephalography ; Technology ; },
abstract = {With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.},
}
@article {pmid34975434,
year = {2021},
author = {He, C and Liu, J and Zhu, Y and Du, W},
title = {Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {765525},
pmid = {34975434},
issn = {1662-5161},
abstract = {Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.},
}
@article {pmid34972343,
year = {2021},
author = {Shang, B and Shang, P},
title = {Multivariate synchronization curve: A measure of synchronization in different multivariate signals.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {31},
number = {12},
pages = {123121},
doi = {10.1063/5.0064807},
pmid = {34972343},
issn = {1089-7682},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; },
abstract = {As a method to measure the synchronization between two different sets of signals, the multivariate synchronization index (MSI) has played an irreplaceable role in the field of frequency recognition of brain-computer interface since it was proposed. On this basis, we make a generalization of MSI by using the escort distribution to replace the original distribution. In this way, MSI can be converted from a determined value to the multivariate synchronization curve, which will vary as the parameter q of the escort distribution changes. Numerical experiments are carried out on both simulated and real-world data to confirm the effectiveness of this new method. Compared with the case of MSI (i.e., q = 1), the extended form of MSI proposed in this article can obviously capture the relationship between signals more comprehensively, implying that it is a more perfect method to describe the synchronization between them. The results reveal that this method can not only effectively extract the important information contained in different signals, but also has the potential to become a practical synchronization measurement method of multivariate signals.},
}
@article {pmid34971597,
year = {2022},
author = {Zhou, L and Zhu, Q and Wu, B and Qin, B and Hu, H and Qian, Z},
title = {A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation.},
journal = {Brain research},
volume = {1777},
number = {},
pages = {147769},
doi = {10.1016/j.brainres.2021.147769},
pmid = {34971597},
issn = {1872-6240},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Connectome ; Electroencephalography ; Female ; Functional Laterality/*physiology ; Hand/physiology ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Male ; Motor Cortex ; Movement/*physiology ; Nervous System Physiological Phenomena ; },
abstract = {Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.},
}
@article {pmid34970111,
year = {2021},
author = {Zhou, Y and Hu, L and Yu, T and Li, Y},
title = {A BCI-Based Study on the Relationship Between the SSVEP and Retinal Eccentricity in Overt and Covert Attention.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {746146},
pmid = {34970111},
issn = {1662-4548},
abstract = {Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them.},
}
@article {pmid34969148,
year = {2022},
author = {Ruiz, S and Virseda-Chamorro, M and Salinas, J and Queissert, F and Arance, I and Angulo, JC},
title = {Influence of ATOMS implant on the voiding phase of patients with post-prostatectomy urinary incontinence.},
journal = {Neurourology and urodynamics},
volume = {41},
number = {2},
pages = {609-615},
doi = {10.1002/nau.24856},
pmid = {34969148},
issn = {1520-6777},
mesh = {Humans ; Male ; Prospective Studies ; Prostatectomy/adverse effects ; *Urinary Bladder Neck Obstruction/etiology/surgery ; *Urinary Incontinence/complications ; Urination ; Urodynamics ; },
abstract = {OBJECTIVE: To assess changes in voiding phase, especially urethral resistance after post-prostatectomy urinary incontinence (PPI) treatment with the Adjustable TransObturator Male System (ATOMS).<br><br>MATERIAL AND METHODS: A longitudinal prospective study was performed on 45 men treated with ATOMS for PPI, with the intention to evaluate the changes produced by the implant on the voiding phase. Patients with preoperative urodynamic study were offered postoperative urodynamic evaluation, and both studies were compared. The following urodynamic date were evaluated: maximum voiding detrusor pressure, detrusor pressure at maximum flow rate, maximum flow rate (Qmax), voiding volume, post-void residue, bladder outlet obstruction index (BOOI), urethral resistance factor (URA), and bladder contractility index (BCI). The statistical analysis used were the mean comparison test for dependent groups (Student's t test) for parametric variables and the Wilcoxon test for non-parametric variables. The signification level was set at 95% bilateral.<br><br>RESULTS: A total of 37 patients (82.2%) used zero pads/day at the time of urodynamic postoperative evaluation and pad-test evolved from 592&#x2009;&#xb1;&#x2009;289&#x2009;ml baseline to 25&#x2009;&#xb1;&#x2009;40&#x2009;ml (p&#x2009;=&#x2009;0.0001). Significant differences were observed in Qmax (15&#x2009;&#xb1;&#x2009;8.3 before and 11&#x2009;&#xb1;&#x2009;8.3 after surgery; p&#x2009;=&#x2009;0.008), voiding volume (282&#x2009;&#xb1;&#x2009;130.7 before and 184&#x2009;&#xb1;&#x2009;99.92 after surgery). BOOI (-12&#x2009;&#xb1;&#x2009;23.9 before and -2&#x2009;&#xb1;&#x2009;21.4 after surgery; p&#x2009;=&#x2009;0.025) and BCI (93&#x2009;&#xb1;&#x2009;46.4 before and 76&#x2009;&#xb1;&#x2009;46.0 after surgery; p&#x2009;=&#x2009;0.044). In no case did we observe postoperative bladder outlet obstruction, according to URA parameter below 29&#x2009;cm H2 O in all cases. There was not a significant variation either in post-void urinary residual volume (15&#x2009;&#xb1;&#x2009;47.4 before and 14&#x2009;&#xb1;&#x2009;24.2 after surgery, p&#x2009;=&#x2009;0.867).<br><br>CONCLUSIONS: The ATOMS implant induces a decrease of Qmax, voided volume, and bladder contractility and an increase of BOOI. However, our findings suggest that ATOMS device does not cause bladder outlet obstruction.},
}
@article {pmid34969088,
year = {2022},
author = {Bibián, C and Irastorza-Landa, N and Schönauer, M and Birbaumer, N and López-Larraz, E and Ramos-Murguialday, A},
title = {On the extraction of purely motor EEG neural correlates during an upper limb visuomotor task.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {32},
number = {19},
pages = {4243-4254},
doi = {10.1093/cercor/bhab479},
pmid = {34969088},
issn = {1460-2199},
mesh = {Brain/physiology ; *Electroencephalography/methods ; Eye Movements ; Humans ; Movement/physiology ; *Upper Extremity ; },
abstract = {Deciphering and analyzing the neural correlates of different movements from the same limb using electroencephalography (EEG) would represent a notable breakthrough in the field of sensorimotor neurophysiology. Functional movements involve concurrent posture co-ordination and head and eye movements, which create electrical activity that affects EEG recordings. In this paper, we revisit the identification of brain signatures of different reaching movements using EEG and present, test, and validate a protocol to separate the effect of head and eye movements from a reaching task-related visuomotor brain activity. Ten healthy participants performed reaching movements under two different conditions: avoiding head and eye movements and moving with no constrains. Reaching movements can be identified from EEG with unconstrained eye and head movement, whereas the discriminability of the signals drops to chance level otherwise. These results show that neural patterns associated with different arm movements could only be extracted from EEG if the eye and head movements occurred concurrently with the task, polluting the recordings. Although these findings do not imply that brain correlates of reaching directions cannot be identified from EEG, they show the consequences that ignoring these events can have in any EEG study that includes a visuomotor task.},
}
@article {pmid34966976,
year = {2021},
author = {Mederos, A and Galarraga, D and van der Graaf-van Bloois, L and Buczinski, S},
title = {Performance of bovine genital campylobacteriosis diagnostic tests in bulls from Uruguay: a Bayesian latent class model approach.},
journal = {Tropical animal health and production},
volume = {54},
number = {1},
pages = {32},
pmid = {34966976},
issn = {1573-7438},
support = {FSSA_X_2014_1_105894//agencia nacional de investigaci&#xf3;n e innovaci&#xf3;n de uruguay/ ; CL_37//instituto nacional de investigaci&#xf3;n agropecuaria/ ; },
mesh = {Animals ; Bayes Theorem ; *Campylobacter Infections/diagnosis/epidemiology/veterinary ; Campylobacter fetus/genetics ; Cattle ; *Cattle Diseases/diagnosis/epidemiology ; Diagnostic Tests, Routine ; Genitalia ; Latent Class Analysis ; Male ; Real-Time Polymerase Chain Reaction/veterinary ; Sensitivity and Specificity ; Uruguay ; },
abstract = {The sensitivity (Se) and specificity (Sp) of three diagnostic tests for the detection of Campylobacter fetus venerealis (Cfv) using field samples were estimated using a Bayesian latent class model (BLCM), accounting for the absence of a gold standard. The tests included in this study were direct immunofluorescence antibody test (IFAT), polymerase chain reaction (PCR), and real-time PCR (RT-PCR). Twelve farms from two different populations were selected and bull prepuce samples were collected. The IFAT was performed according to the OIE Manual. The conventional PCR was performed as multiplex, targeting the gene nahE for C. fetus species identification and insertion element ISCfe1 for Cfv identification. The RT-PCR was performed as uniplex: one targeting the gene nahE for C. fetus and the other targeting the insertion ISCfe1 (ISC2) for Cfv. Results from the BLCM showed a median Se of 11.7% (Bayesian credibility interval (BCI): 1.93-29.79%), 53.7% (BCI: 23.1-95.0%), and 36.1% (BCI: 14.5-71.7%) for IFAT, PCR, and RT-PCR respectively. The Sp were 94.5% (BCI: 90.1-97.9%), 97.0% (BCI: 92.9-99.3%), and 98.4% (BCI: 95.3-99.7%) for IFAT, PCR, and RT-PCR respectively. The correlation between PCR and RT-PCR was positive and low in samples from both sampled population (0.63% vs 8.47%). These results suggest that diagnostic sensitivity of the studied tests is lower using field samples than using pure Cfv strains.},
}
@article {pmid34966524,
year = {2021},
author = {Li, C and Wei, J and Huang, X and Duan, Q and Zhang, T},
title = {Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {4710044},
pmid = {34966524},
issn = {2040-2309},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Diffusion Tensor Imaging ; Humans ; Lower Extremity ; Recovery of Function ; *Robotics ; *Stroke/diagnostic imaging ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {PURPOSE: To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms.<br><br>METHODS: Subacute-phase stroke patients were randomly divided into two groups. In addition to the routine intervention, patients in the treatment group trained on the BCI-LLRR and underwent the lower limb pedal training in the control group, both for the same time (30&#x2009;min/day). All patients underwent assessment by instruments such as the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer upper and lower limb motor function and balance tests, at 2 and 4 weeks of treatment and at 3 months after the end of treatment. Patients were also tested before treatment and after 4 weeks by leg motor evoked potential (MEP) and diffusion tensor imaging/tractography (DTI/DTT) of the head.<br><br>RESULTS: After 4&#x2009;weeks, the Fugl-Meyer leg function and NIHSS scores were significantly improved in the treatment group vs. controls (P < 0.01). At 3 months, further significant improvement was observed. The MEP amplitude and latency of the treatment group were significantly improved vs. controls. The effect of treatment on fractional anisotropy values was not significant.<br><br>CONCLUSIONS: The BCI-LLRR promoted leg functional recovery after stroke and improved activities of daily living, possibly by improving cerebral-cortex excitability and white matter connectivity.},
}
@article {pmid34964297,
year = {2021},
author = {Becsei, &#xc1; and Solymosi, N and Csabai, I and Magyar, D},
title = {Detection of antimicrobial resistance genes in urban air.},
journal = {MicrobiologyOpen},
volume = {10},
number = {6},
pages = {e1248},
pmid = {34964297},
issn = {2045-8827},
mesh = {*Air Microbiology ; Bacteria/*genetics ; Cities ; Drug Resistance, Bacterial/*genetics ; *Genes, Bacterial ; Metagenome ; *Microbiota ; Sensitivity and Specificity ; },
abstract = {To understand antibiotic resistance in pathogenic bacteria, we need to monitor environmental microbes as reservoirs of antimicrobial resistance genes (ARGs). These bacteria are present in the air and can be investigated with the whole metagenome shotgun sequencing approach. This study aimed to investigate the feasibility of a method for metagenomic analysis of microbial composition and ARGs in the outdoor air. Air samples were collected with a Harvard impactor in the PM10 range at 50 m from a hospital in Budapest. From the DNA yielded from samples of PM10 fraction single-end reads were generated with an Ion Torrent sequencer. During the metagenomic analysis, reads were classified taxonomically. The core bacteriome was defined. Reads were assembled to contigs and the ARG content was analyzed. The dominant genera in the core bacteriome were Bacillus, Acinetobacter, Leclercia and Paenibacillus. Among the identified ARGs best hits were vanRA, Bla1, mphL, Escherichia coli EF-Tu mutants conferring resistance to pulvomycin; BcI, FosB, and mphM. Despite the low DNA content of the samples of PM10 fraction, the number of detected airborne ARGs was surprisingly high.},
}
@article {pmid34962871,
year = {2022},
author = {Jin, J and Sun, H and Daly, I and Li, S and Liu, C and Wang, X and Cichocki, A},
title = {A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {20-29},
doi = {10.1109/TNSRE.2021.3139095},
pmid = {34962871},
issn = {1558-0210},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; },
abstract = {The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory. Specifically, motifs describing local network structures in the brain are extracted from functional connectivity graphs. A graph embedding model called Ego-CNNs is then used to build a classifier, which can convert the graph from a structural representation to a fixed-dimensional vector for detecting critical structure in the graph. We validate our proposed method on four datasets, and the results show that our proposed method produces high classification accuracies in two-class classification tasks (92.8% for dataset 1, 93.4% for dataset 2, 96.5% for dataset 3, and 80.2% for dataset 4) and multiclass classification tasks (90.33% for dataset 1). Our proposed method achieves a mean Kappa value of 0.88 across nine participants, which is superior to other methods we compared it to. These results indicate that there is a local structural difference in functional connectivity graphs extracted under different motor imagery tasks. Our proposed method has great potential for motor imagery classification in future studies.},
}
@article {pmid34961366,
year = {2022},
author = {Aydemir, O and Saka, K and Ozturk, M},
title = {Investigating the effects of stimulus duration and inter-stimulus interval parameters on P300 based BCI application performance.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {25},
number = {14},
pages = {1545-1553},
doi = {10.1080/10255842.2021.2022127},
pmid = {34961366},
issn = {1476-8259},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Humans ; Wavelet Analysis ; },
abstract = {The main goal of electroencephalography (EEG) based brain-computer interface (BCI) research is to develop a fast and higher classification accuracy (CA) rate method than those of existing ones. Generally, in BCI applications, either motor imagery or event-related P300 based techniques are used for data recording. The stimulus duration (SD) and the inter-stimulus interval (ISI) are crucial two parameters directly affecting the decision speed of the BCI system. In this study, we investigated the performance of the P300 based application in terms of speed and CA for three kinds of protocols which are called fast, medium, and slow included different SD and the ISI values. The training and test data sets were recorded in one week of delay from 8 subjects. The features were extracted by standard deviation, variance, mean, Wavelet Transform and Fourier Transform techniques. Afterwards, they were classified by the k-nearest neighbor algorithm. We obtained 87.08%, 85.41% and 83.95% average CA rate for the fast, medium, and slow protocols, respectively. The obtained results showed that the proposed fast protocol method achieved CA rate between 78.33% and 93.33%. Based on the obtained results, it can be concluded that the fast protocol values can be used for establishing a more accurate and faster P300 based BCI.},
}
@article {pmid34960597,
year = {2021},
author = {Rossi, F and Savi, F and Prestia, A and Mongardi, A and Demarchi, D and Buccino, G},
title = {Combining Action Observation Treatment with a Brain-Computer Interface System: Perspectives on Neurorehabilitation.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960597},
issn = {1424-8220},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Child ; Female ; Humans ; Male ; *Neurological Rehabilitation ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain-computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson's disease patients, and children with cerebral palsy.},
}
@article {pmid34960469,
year = {2021},
author = {Hag, A and Handayani, D and Altalhi, M and Pillai, T and Mantoro, T and Kit, MH and Al-Shargie, F},
title = {Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960469},
issn = {1424-8220},
support = {funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project number. TURSP-2020 / 300), Taif University,Taif, Saudi Arabia//Taif University/ ; },
mesh = {*Algorithms ; *Electroencephalography ; Recognition, Psychology ; Stress, Psychological/diagnosis ; Support Vector Machine ; },
abstract = {In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.},
}
@article {pmid34960399,
year = {2021},
author = {Covantes-Osuna, C and López, JB and Paredes, O and Vélez-Pérez, H and Romo-Vázquez, R},
title = {Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960399},
issn = {1424-8220},
support = {This work was supported by the Consejo Nacional de Ciencia y Tecnolog&#xed;a - CONACyT [Scholarship to C.C.O scholarship 480527, O.P. with CVU 713526 and J.B.L. with CVU 745514]//Consejo Nacional de Ciencia y Tecnolog&#xed;a/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu's version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.},
}
@article {pmid34960373,
year = {2021},
author = {Mezzina, G and Annese, VF and De Venuto, D},
title = {A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960373},
issn = {1424-8220},
mesh = {Algorithms ; Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {In a progressively interconnected world where the Internet of Things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user's physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.},
}
@article {pmid34960261,
year = {2021},
author = {Ascari, L and Marchenkova, A and Bellotti, A and Lai, S and Moro, L and Koshmak, K and Mantoan, A and Barsotti, M and Brondi, R and Avveduto, G and Sechi, D and Compagno, A and Avanzini, P and Ambeck-Madsen, J and Vecchiato, G},
title = {Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {24},
pages = {},
pmid = {34960261},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Ergonomics ; Humans ; Systems Analysis ; *Wearable Electronic Devices ; },
abstract = {Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.},
}
@article {pmid34959509,
year = {2021},
author = {Zheng, S and Wu, J and Hu, Z and Gan, M and Liu, L and Song, C and Lei, Y and Wang, H and Liao, L and Feng, Y and Shao, Y and Ruan, Y and Xing, H},
title = {Epidemiology and Molecular Transmission Characteristics of HIV in the Capital City of Anhui Province in China.},
journal = {Pathogens (Basel, Switzerland)},
volume = {10},
number = {12},
pages = {},
pmid = {34959509},
issn = {2076-0817},
support = {2017ZX10201101002-004//National Science and Technology Major Project/ ; 11971479//National Natural Science Foundation of China/ ; },
abstract = {Hefei, Anhui province, is one of the cities in the Yangtze River Delta, where many people migrate to Jiangsu, Zhejiang and Shanghai. High migration also contributes to the HIV epidemic. This study explored the HIV prevalence in Hefei to provide a reference for other provinces and assist in the prevention and control of HIV in China. A total of 816 newly reported people with HIV in Hefei from 2017 to 2020 were recruited as subjects. HIV subtypes were identified by a phylogenetic tree. The most prevalent subtypes were CRF07_BC (41.4%), CRF01_AE (38.1%) and CRF55_01B (6.3%). Molecular networks were inferred using HIV-TRACE. The largest and most active transmission cluster was CRF55_01B in Hefei's network. A Chinese national database (50,798 sequences) was also subjected to molecular network analysis to study the relationship between patients in Hefei and other provinces. CRF55_01B and CRF07_BC-N had higher clustered and interprovincial transmission rates in the national molecular network. People with HIV in Hefei mainly transmitted the disease within the province. Finally, we displayed the epidemic trend of HIV in Hefei in recent years with the dynamic change of effective reproductive number (Re). The weighted overall Re increased rapidly from 2012 to 2015, with a peak value of 3.20 (95% BCI, 2.18-3.85). After 2015, Re began to decline and remained stable at around 1.80. In addition, the Re of CRF55_01B was calculated to be between 2.0 and 4.0 in 2018 and 2019. More attention needs to be paid to the rapid spread of CRF55_01B and CRF07_BC-N strains among people with HIV and the high Re in Hefei. These data provide necessary support to guide the targeted prevention and control of HIV.},
}
@article {pmid34958737,
year = {2022},
author = {Pitt, KM and Dietz, A},
title = {Applying Implementation Science to Support Active Collaboration in Noninvasive Brain-Computer Interface Development and Translation for Augmentative and Alternative Communication.},
journal = {American journal of speech-language pathology},
volume = {31},
number = {1},
pages = {515-526},
doi = {10.1044/2021_AJSLP-21-00152},
pmid = {34958737},
issn = {1558-9110},
mesh = {*Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Humans ; Implementation Science ; },
abstract = {PURPOSE: The purpose of this article is to consider how, alongside engineering advancements, noninvasive brain-computer interface (BCI) for augmentative and alternative communication (AAC; BCI-AAC) developments can leverage implementation science to increase the clinical impact of this technology. We offer the Consolidated Framework for Implementation Research (CFIR) as a structure to help guide future BCI-AAC research. Specifically, we discuss CFIR primary domains that include intervention characteristics, the outer and inner settings, the individuals involved in the intervention, and the process of implementation, alongside pertinent subdomains including adaptability, cost, patient needs and recourses, implementation climate, other personal attributes, and the process of engaging. The authors support their view with current citations from both the AAC and BCI-AAC fields.<br><br>CONCLUSIONS: The article aimed to provide thoughtful considerations for how future research may leverage the CFIR to support meaningful BCI-AAC translation for those with severe physical impairments. We believe that, although significant barriers to BCI-AAC development still exist, incorporating implementation research may be timely for the field of BCI-AAC and help account for diversity in end users, navigate implementation obstacles, and support a smooth and efficient translation of BCI-AAC technology. Moreover, the sooner clinicians, individuals who use AAC, their support networks, and engineers collectively improve BCI-AAC outcomes and the efficiency of translation, the sooner BCI-AAC may become an everyday tool in the AAC arsenal.},
}
@article {pmid34958261,
year = {2022},
author = {Nojima, I and Sugata, H and Takeuchi, H and Mima, T},
title = {Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis.},
journal = {Neurorehabilitation and neural repair},
volume = {36},
number = {2},
pages = {83-96},
doi = {10.1177/15459683211062895},
pmid = {34958261},
issn = {1552-6844},
mesh = {*Brain-Computer Interfaces ; Humans ; Motor Activity/*physiology ; Recovery of Function/*physiology ; Stroke/physiopathology/*therapy ; *Stroke Rehabilitation ; Upper Extremity/*physiopathology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a procedure involving brain activity in which neural status is provided to the participants for self-regulation. The current review aims to evaluate the effect sizes of clinical studies investigating the use of BCI-based rehabilitation interventions in restoring upper extremity function and effective methods to detect brain activity for motor recovery.<br><br>METHODS: A computerized search of MEDLINE, CENTRAL, Web of Science, and PEDro was performed to identify relevant articles. We selected clinical trials that used BCI-based training for post-stroke patients and provided motor assessment scores before and after the intervention. The pooled standardized mean differences of BCI-based training were calculated using the random-effects model.<br><br>RESULTS: We initially identified 655 potentially relevant articles; finally, 16 articles fulfilled the inclusion criteria, involving 382 participants. A significant effect of neurofeedback intervention for the paretic upper limb was observed (standardized mean difference = .48, [.16-.80], P = .006). However, the effect estimates were moderately heterogeneous among the studies (I[2] = 45%, P = .03). Subgroup analysis of the method of measurement of brain activity indicated the effectiveness of the algorithm focusing on sensorimotor rhythm.<br><br>CONCLUSION: This meta-analysis suggested that BCI-based training was superior to conventional interventions for motor recovery of the upper limbs in patients with stroke. However, the results are not conclusive because of a high risk of bias and a large degree of heterogeneity due to the differences in the BCI interventions and the participants; therefore, further studies involving larger cohorts are required to confirm these results.},
}
@article {pmid34958014,
year = {2022},
author = {Massetti, N and Russo, M and Franciotti, R and Nardini, D and Mandolini, GM and Granzotto, A and Bomba, M and Delli Pizzi, S and Mosca, A and Scherer, R and Onofrj, M and Sensi, SL and , and , },
title = {A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {85},
number = {4},
pages = {1639-1655},
doi = {10.3233/JAD-210573},
pmid = {34958014},
issn = {1875-8908},
support = {U01 AG024904/AG/NIA NIH HHS/United States ; //CIHR/Canada ; R01 AG046171/AG/NIA NIH HHS/United States ; RF1 AG051550/AG/NIA NIH HHS/United States ; },
mesh = {Aged ; Algorithms ; Alzheimer Disease/*diagnosis ; Biomarkers/cerebrospinal fluid ; Brain/pathology ; Cognitive Dysfunction/diagnosis ; Databases, Factual ; *Disease Progression ; Female ; Humans ; *Machine Learning ; Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.<br><br>OBJECTIVE: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.<br><br>METHODS: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.<br><br>RESULTS: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.<br><br>CONCLUSION: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.},
}
@article {pmid34956344,
year = {2021},
author = {Rui, Z and Gu, Z},
title = {A Review of EEG and fMRI Measuring Aesthetic Processing in Visual User Experience Research.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {2070209},
pmid = {34956344},
issn = {1687-5273},
mesh = {*Brain/diagnostic imaging ; Electroencephalography ; Esthetics ; Humans ; *Magnetic Resonance Imaging ; User-Computer Interface ; },
abstract = {In human-computer interaction, the visual interaction of user experience (UX) and user interface (UI) plays an important role in enriching the quality of daily life. The purpose of our study analyzes the use of brain-computer interface (BCI), wearable technology, and functional magnetic resonance imaging (fMRI) to explore the aesthetic processing of visual neural response to UI and UX designs. Specifically, this review aims to understand neuroaesthetic processing knowledge, aesthetic appreciation models, and the ways in which visual brain studies can improve the quality of current and future UI and UX designs. Recent research has found that subjective evaluations of aesthetic appreciation produce different results for objective evaluations of brain research analysis. We applied SWOT analysis and examined the advantages and disadvantages of both evaluation methods. Furthermore, we conducted a traditional literature review on topics pertaining to the use of aesthetic processing knowledge in the visual interaction field in terms of art therapy, information visualization, website or mobile applications, and other interactive platforms. Our main research findings from current studies have helped and motivated researchers and designers to use convincing scientific knowledge of brain event-related potential, electroencephalography, and fMRI to understand aesthetic judgment. The key trend finds that many designers, artists, and engineers use artistic BCI technology in the visual interaction experience. Herein, the scientific methods applied in the aesthetic appreciation to human-computer interface are summarized, and the influence of the latest wearable brain technology on visual interaction design is discussed. Furthermore, current possible research entry points for aesthetics, usability, and creativity in UI and UX designs are explicated. The study results have implications for the visual user experience research domain as well as for interaction industries, which produce interactive projects to improve people's daily lives.},
}
@article {pmid34954026,
year = {2022},
author = {Mirjalili, S and Powell, P and Strunk, J and James, T and Duarte, A},
title = {Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG.},
journal = {NeuroImage},
volume = {247},
number = {},
pages = {118851},
pmid = {34954026},
issn = {1095-9572},
support = {T32 AG000175/AG/NIA NIH HHS/United States ; },
mesh = {Adult ; Aged ; Bayes Theorem ; Brain-Computer Interfaces ; Datasets as Topic ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Memory, Episodic ; Mental Recall ; Middle Aged ; },
abstract = {Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.},
}
@article {pmid34951857,
year = {2022},
author = {Wang, K and Zhai, DH and Xiong, Y and Hu, L and Xia, Y},
title = {An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {5},
pages = {2159-2167},
doi = {10.1109/TNNLS.2021.3135696},
pmid = {34951857},
issn = {2162-2388},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; Photic Stimulation ; *Robotics ; },
abstract = {This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.},
}
@article {pmid34950640,
year = {2021},
author = {Woeppel, K and Hughes, C and Herrera, AJ and Eles, JR and Tyler-Kabara, EC and Gaunt, RA and Collinger, JL and Cui, XT},
title = {Explant Analysis of Utah Electrode Arrays Implanted in Human Cortex for Brain-Computer-Interfaces.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {9},
number = {},
pages = {759711},
pmid = {34950640},
issn = {2296-4185},
abstract = {Brain-computer interfaces are being developed to restore movement for people living with paralysis due to injury or disease. Although the therapeutic potential is great, long-term stability of the interface is critical for widespread clinical implementation. While many factors can affect recording and stimulation performance including electrode material stability and host tissue reaction, these factors have not been investigated in human implants. In this clinical study, we sought to characterize the material integrity and biological tissue encapsulation via explant analysis in an effort to identify factors that influence electrophysiological performance. We examined a total of six Utah arrays explanted from two human participants involved in intracortical BCI studies. Two platinum (Pt) arrays were implanted for 980 days in one participant (P1) and two Pt and two iridium oxide (IrOx) arrays were implanted for 182 days in the second participant (P2). We observed that the recording quality followed a similar trend in all six arrays with an initial increase in peak-to-peak voltage during the first 30-40 days and gradual decline thereafter in P1. Using optical and two-photon microscopy we observed a higher degree of tissue encapsulation on both arrays implanted for longer durations in participant P1. We then used scanning electron microscopy and energy dispersive X-ray spectroscopy to assess material degradation. All measures of material degradation for the Pt arrays were found to be more prominent in the participant with a longer implantation time. Two IrOx arrays were subjected to brief survey stimulations, and one of these arrays showed loss of iridium from most of the stimulated sites. Recording performance appeared to be unaffected by this loss of iridium, suggesting that the adhesion of IrOx coating may have been compromised by the stimulation, but the metal layer did not detach until or after array removal. In summary, both tissue encapsulation and material degradation were more pronounced in the arrays that were implanted for a longer duration. Additionally, these arrays also had lower signal amplitude and impedance. New biomaterial strategies that minimize fibrotic encapsulation and enhance material stability should be developed to achieve high quality recording and stimulation for longer implantation periods.},
}
@article {pmid34949983,
year = {2021},
author = {Chen, H and Jin, M and Li, Z and Fan, C and Li, J and He, H},
title = {MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {778488},
pmid = {34949983},
issn = {1662-4548},
abstract = {As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.},
}
@article {pmid34949982,
year = {2021},
author = {Arlotti, M and Colombo, M and Bonfanti, A and Mandat, T and Lanotte, MM and Pirola, E and Borellini, L and Rampini, P and Eleopra, R and Rinaldo, S and Romito, L and Janssen, MLF and Priori, A and Marceglia, S},
title = {A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {763235},
pmid = {34949982},
issn = {1662-4548},
abstract = {Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson's disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBS[R] System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson's disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.},
}
@article {pmid34949837,
year = {2022},
author = {Shao, Z and Shen, Q and Yao, B and Mao, C and Chen, LN and Zhang, H and Shen, DD and Zhang, C and Li, W and Du, X and Li, F and Ma, H and Chen, ZH and Xu, HE and Ying, S and Zhang, Y and Shen, H},
title = {Identification and mechanism of G protein-biased ligands for chemokine receptor CCR1.},
journal = {Nature chemical biology},
volume = {18},
number = {3},
pages = {264-271},
pmid = {34949837},
issn = {1552-4469},
mesh = {Chemokines/metabolism/pharmacology ; *GTP-Binding Proteins/metabolism ; Ligands ; *Receptors, Chemokine/agonists/metabolism ; beta-Arrestins/metabolism ; },
abstract = {Biased signaling of G protein-coupled receptors describes an ability of different ligands that preferentially activate an alternative downstream signaling pathway. In this work, we identified and characterized different N-terminal truncations of endogenous chemokine CCL15 as balanced or biased agonists targeting CCR1, and presented three cryogenic-electron microscopy structures of the CCR1-Gi complex in the ligand-free form or bound to different CCL15 truncations with a resolution of 2.6-2.9 Å, illustrating the structural basis of natural biased signaling that initiates an inflammation response. Complemented with pharmacological and computational studies, these structures revealed it was the conformational change of Tyr291 (Y291[7.43]) in CCR1 that triggered its polar network rearrangement in the orthosteric binding pocket and allosterically regulated the activation of β-arrestin signaling. Our structure of CCL15-bound CCR1 also exhibited a critical site for ligand binding distinct from many other chemokine-receptor complexes, providing new insights into the mode of chemokine recognition.},
}
@article {pmid34945880,
year = {2021},
author = {Marcos-Martínez, D and Martínez-Cagigal, V and Santamaría-Vázquez, E and Pérez-Velasco, S and Hornero, R},
title = {Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {12},
pages = {},
pmid = {34945880},
issn = {1099-4300},
support = {RTC2019-007350-1//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades de Espa&#xf1;a/ ; 0702_MIGRAINEE_2_E//European Comission/ ; },
abstract = {Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI-NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject's scores from Luria tests performed before and after MI-NFT. We found that MI-NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI-NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI-NFT.},
}
@article {pmid34945371,
year = {2021},
author = {Yuan, H and Li, Y and Yang, J and Li, H and Yang, Q and Guo, C and Zhu, S and Shu, X},
title = {State of the Art of Non-Invasive Electrode Materials for Brain-Computer Interface.},
journal = {Micromachines},
volume = {12},
number = {12},
pages = {},
pmid = {34945371},
issn = {2072-666X},
support = {51672173//National Natural Science Foundation of China/ ; 51801121//National Natural Science Foundation of China/ ; 51572169//National Natural Science Foundation of China/ ; 51902200//National Natural Science Foundation of China/ ; 52005321//National Natural Science Foundation of China/ ; 2017YFE0113000//Key Program for International S&amp;T Cooperation Program of China/ ; 17JC1400700//Science and Technology Commission of Shanghai Municipality/ ; 18JC1410500//Science and Technology Commission of Shanghai Municipality/ ; 18ZR1421000//Science and Technology Commission of Shanghai Municipality/ ; 18520744700//Science and Technology Commission of Shanghai Municipality/ ; 19ZR1425300//Science and Technology Commission of Shanghai Municipality/ ; 2016A010103018//Science and Technology Planning Project of Guangdong Province/ ; 2016XCWZK15//Shanghai Research Institute of Criminal Science and Technology/ ; YG2017QN11//Medical-Engineering Cross Research Funding of SJTU/ ; 2016YFA0202900//National Key R&amp;D Program of China/ ; },
abstract = {The brain-computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.},
}
@article {pmid34945296,
year = {2021},
author = {Kim, Y and Ereifej, ES and Schwartzman, WE and Meade, SM and Chen, K and Rayyan, J and Feng, H and Aluri, V and Mueller, NN and Bhambra, R and Bhambra, S and Taylor, DM and Capadona, JR},
title = {Investigation of the Feasibility of Ventricular Delivery of Resveratrol to the Microelectrode Tissue Interface.},
journal = {Micromachines},
volume = {12},
number = {12},
pages = {},
pmid = {34945296},
issn = {2072-666X},
support = {RX002628-01A1//United States Department of Veterans Affairs/ ; GRANT12418820//United States Department of Veterans Affairs/ ; GRANT12647351//United States Department of Veterans Affairs/ ; GRANT12635707//United States Department of Veterans Affairs/ ; GRANT12635723/NS/NINDS NIH HHS/United States ; T32EB004314//National Institute for Biomedical Imaging and Bioengineering/ ; },
abstract = {(1) Background: Intracortical microelectrodes (IMEs) are essential to basic brain research and clinical brain-machine interfacing applications. However, the foreign body response to IMEs results in chronic inflammation and an increase in levels of reactive oxygen and nitrogen species (ROS/RNS). The current study builds on our previous work, by testing a new delivery method of a promising antioxidant as a means of extending intracortical microelectrodes performance. While resveratrol has shown efficacy in improving tissue response, chronic delivery has proven difficult because of its low solubility in water and low bioavailability due to extensive first pass metabolism. (2) Methods: Investigation of an intraventricular delivery of resveratrol in rats was performed herein to circumvent bioavailability hurdles of resveratrol delivery to the brain. (3) Results: Intraventricular delivery of resveratrol in rats delivered resveratrol to the electrode interface. However, intraventricular delivery did not have a significant impact on electrophysiological recordings over the six-week study. Histological findings indicated that rats receiving intraventricular delivery of resveratrol had a decrease of oxidative stress, yet other biomarkers of inflammation were found to be not significantly different from control groups. However, investigation of the bioavailability of resveratrol indicated a decrease in resveratrol accumulation in the brain with time coupled with inconsistent drug elution from the cannulas. Further inspection showed that there may be tissue or cellular debris clogging the cannulas, resulting in variable elution, which may have impacted the results of the study. (4) Conclusions: These results indicate that the intraventricular delivery approach described herein needs further optimization, or may not be well suited for this application.},
}
@article {pmid34945210,
year = {2021},
author = {Cywka, KB and Skarżyński, H and Król, B and Skarżyński, PH},
title = {The Bonebridge BCI 602 Active Transcutaneous Bone Conduction Implant in Children: Objective and Subjective Benefits.},
journal = {Journal of clinical medicine},
volume = {10},
number = {24},
pages = {},
pmid = {34945210},
issn = {2077-0383},
abstract = {BACKGROUND: the Bonebridge hearing implant is an active transcutaneous bone conduction implant suitable for various types of hearing loss. It was first launched in 2012 as the BCI 601, with a newer internal part (BCI 602) released in 2019. With the new size and shape, the BCI 602 can be used in patients previously excluded due to insufficient anatomical conditions, especially in patients with congenital defects of the outer and middle ear.<br><br>OBJECTIVES: the purpose of this study is to evaluate the objective and subjective benefits of the new Bonebridge BCI 602 in children who have hearing impairment due to conductive or mixed hearing loss. Safety and effectiveness of the device was assessed.<br><br>METHODS: the study group included 22 children aged 8-18 years (mean age 14.7 years) who had either conductive or mixed hearing loss. All patients were implanted unilaterally with the new Bonebridge BCI 602 implant. Pure tone audiometry, speech recognition tests (in quiet and noise), and free-field audiometry were performed before and after implantation. Word recognition scores were evaluated using the Demenko and Pruszewicz Polish Monosyllabic Word Test, and speech reception thresholds in noise were assessed using the Polish Sentence Matrix Test. The subjective assessment of benefits was carried outusing the APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire.<br><br>RESULTS: after implantation of the Bonebridge BCI 602 all patients showed a statistically significant improvement in hearing and speech understanding. The mean word recognition score (WRS) changed from 12.1% before implantation to 87.3% after 6 months. Mean speech reception threshold (SRT) before implantation was +4.79 dB SNR and improved to -1.29 dB SNR after 6 months. All patients showed stable postoperative results. The APHAB questionnaire showed that difficulties in hearing decreased after implantation, with a statistically significant improvement in global score. Pre-operative scores (M = 35.7) were significantly worse than post-operative scores at 6 months (M = 25.7).<br><br>CONCLUSIONS: the present study confirms that the Bonebridge BCI 602 is an innovative and effective solution, especially for patients with conductive and mixed hearing loss due to anatomical ear defects. The Bonebridge BCI 602 system provides valuable and stable audiological and surgical benefits. Subjective assessment also confirms the effectiveness of the BCI 602. The BCI 602 offers the same amplification as the BCI601, but with a smaller size. The smaller dimensions make it an effective treatment option for a wider group of patients, especially children with congenital defects of the outer and middle ear.},
}
@article {pmid34942608,
year = {2022},
author = {Niu, X and Lu, N and Kang, J and Cui, Z},
title = {Knowledge-driven feature component interpretable network for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {19},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ac463a},
pmid = {34942608},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination/physiology ; Neural Networks, Computer ; },
abstract = {Objective. The end-to-end convolutional neural network (CNN) has achieved great success in motor imagery (MI) classification without a manual feature design. However, all the existing deep network solutions are purely datadriven and lack interpretability, which makes it impossible to discover insightful knowledge from the learned features, not to mention to design specific network structures. The heavy computational cost of CNN also makes it challenging for real-time application along with high classification performance.Approach. To address these problems, a novel knowledge-driven feature component interpretable network (KFCNet) is proposed, which combines spatial and temporal convolution in analogy to independent component analysis and a power spectrum pipeline. Prior frequency band knowledge of sensory-motor rhythms has been formulated as band-pass linear-phase digital finite impulse response filters to initialize the temporal convolution kernels to enable the knowledge-driven mechanism. To avoid signal distortion and achieve a linear phase and unimodality of filters, a symmetry loss is proposed, which is used in combination with the cross-entropy classification loss for training. Besides the general prior knowledge, subject-specific time-frequency property of event-related desynchronization and synchronization has been employed to construct and initialize the network with significantly fewer parameters.Main results.Comparison of experiments on two public datasets has been performed. Interpretable feature components could be observed in the trained model. The physically meaningful observation could efficiently assist the design of the network structure. Excellent classification performance on MI has been obtained.Significance. The performance of KFCNet is comparable to the state-of-the-art methods but with much fewer parameters and makes real-time applications possible.},
}
@article {pmid34939217,
year = {2022},
author = {Zabcikova, M and Koudelkova, Z and Jasek, R and Lorenzo Navarro, JJ},
title = {Recent advances and current trends in brain-computer interface research and their applications.},
journal = {International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience},
volume = {82},
number = {2},
pages = {107-123},
doi = {10.1002/jdn.10166},
pmid = {34939217},
issn = {1873-474X},
support = {IGA/CebiaTech/2021/005//Internal Grant Agency/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, 100 most cited articles from the WOS database were selected over the last 4 years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.},
}
@article {pmid34937017,
year = {2022},
author = {Lapborisuth, P and Koorathota, S and Wang, Q and Sajda, P},
title = {Integrating neural and ocular attention reorienting signals in virtual reality.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac4593},
pmid = {34937017},
issn = {1741-2552},
support = {R01 MH112267/MH/NIMH NIH HHS/United States ; },
mesh = {*Electroencephalography/methods ; Eye ; Fixation, Ocular ; Humans ; *Virtual Reality ; },
abstract = {Objective.Reorienting is central to how humans direct attention to different stimuli in their environment. Previous studies typically employ well-controlled paradigms with limited eye and head movements to study the neural and physiological processes underlying attention reorienting. Here, we aim to better understand the relationship between gaze and attention reorienting using a naturalistic virtual reality (VR)-based target detection paradigm.Approach.Subjects were navigated through a city and instructed to count the number of targets that appeared on the street. Subjects performed the task in a fixed condition with no head movement and in a free condition where head movements were allowed. Electroencephalography (EEG), gaze and pupil data were collected. To investigate how neural and physiological reorienting signals are distributed across different gaze events, we used hierarchical discriminant component analysis (HDCA) to identify EEG and pupil-based discriminating components. Mixed-effects general linear models (GLM) were used to determine the correlation between these discriminating components and the different gaze events time. HDCA was also used to combine EEG, pupil and dwell time signals to classify reorienting events.Main results.In both EEG and pupil, dwell time contributes most significantly to the reorienting signals. However, when dwell times were orthogonalized against other gaze events, the distributions of the reorienting signals were different across the two modalities, with EEG reorienting signals leading that of the pupil reorienting signals. We also found that the hybrid classifier that integrates EEG, pupil and dwell time features detects the reorienting signals in both the fixed (AUC = 0.79) and the free (AUC = 0.77) condition.Significance.We show that the neural and ocular reorienting signals are distributed differently across gaze events when a subject is immersed in VR, but nevertheless can be captured and integrated to classify target vs. distractor objects to which the human subject orients.},
}
@article {pmid34932486,
year = {2021},
author = {Wang, X and Cavigelli, L and Schneider, T and Benini, L},
title = {Sub-100 μW Multispectral Riemannian Classification for EEG-Based Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {15},
number = {6},
pages = {1149-1160},
doi = {10.1109/TBCAS.2021.3137290},
pmid = {34932486},
issn = {1940-9990},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery (MI) brain-machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198 μJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85 μW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.},
}
@article {pmid34932480,
year = {2021},
author = {Wang, P and Zhou, Y and Li, Z and Huang, S and Zhang, D},
title = {Neural Decoding of Chinese Sign Language With Machine Learning for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2721-2732},
doi = {10.1109/TNSRE.2021.3137340},
pmid = {34932480},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; China ; Electroencephalography ; Humans ; Imagination ; Machine Learning ; Movement ; Sign Language ; },
abstract = {Limb motion decoding is an important part of brain-computer interface (BCI) research. Among the limb motion, sign language not only contains rich semantic information and abundant maneuverable actions but also provides different executable commands. However, many researchers focus on decoding the gross motor skills, such as the decoding of ordinary motor imagery or simple upper limb movements. Here we explored the neural features and decoding of Chinese sign language from electroencephalograph (EEG) signal with motor imagery and motor execution. Sign language not only contains rich semantic information, but also has abundant maneuverable actions, and provides us with more different executable commands. In this paper, twenty subjects were instructed to perform movement execution and movement imagery based on Chinese sign language. Seven classifiers are employed to classify the selected features of sign language EEG. L1 regularization is used to learn and select features that contain more information from the mean, power spectral density, sample entropy, and brain network connectivity. The best average classification accuracy of the classifier is 89.90% (imagery sign language is 83.40%). These results have shown the feasibility of decoding between different sign languages. The source location reveals that the neural circuits involved in sign language are related to the visual contact area and the pre-movement area. Experimental evaluation shows that the proposed decoding strategy based on sign language can obtain outstanding classification results, which provides a certain reference value for the subsequent research of limb decoding based on sign language.},
}
@article {pmid34932469,
year = {2022},
author = {Autthasan, P and Chaisaen, R and Sudhawiyangkul, T and Rangpong, P and Kiatthaveephong, S and Dilokthanakul, N and Bhakdisongkhram, G and Phan, H and Guan, C and Wilaiprasitporn, T},
title = {MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {6},
pages = {2105-2118},
doi = {10.1109/TBME.2021.3137184},
pmid = {34932469},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Imagination/physiology ; Learning ; },
abstract = {OBJECTIVE: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite significant advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner.<br><br>METHODS: To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.<br><br>RESULTS: This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72% and 2.23% on the SMR-BCI and OpenBMI datasets, respectively.<br><br>CONCLUSION: We demonstrate that MIN2Net improves discriminative information in the latent representation.<br><br>SIGNIFICANCE: This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.},
}
@article {pmid34932468,
year = {2022},
author = {Libert, A and Wittevrongel, B and Camarrone, F and Van Hulle, MM},
title = {Phase-Spatial Beamforming Renders a Visual Brain Computer Interface Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target Responses.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {5},
pages = {1802-1812},
doi = {10.1109/TBME.2021.3136938},
pmid = {34932468},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Motion ; Photic Stimulation ; },
abstract = {OBJECTIVE: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG potential over the parieto-occipital scalp area called the motion-onset visual evoked potential (mVEP).<br><br>METHODS: We recruited 21 healthy subjects for an experiment in which motion-onset stimulations translating leftwards (LT) or rightwards (RT) were encoding 9 displayed targets. We propose a novel algorithm that exploits the phase-shift between EEG electrodes to improve target decoding performance. We hereto extend the spatiotemporal beamformer (stBF) with a phase extracting procedure, leading to the phase-spatial beamformer (psBF).<br><br>RESULTS: we show that psBF performs significantly better than the stBF (p < 0.001 for 1 and 2 stimulus repetitions and p < 0.01 for 3 to 5 stimulus repetitions), as well as the previously validated linear support-vector machines (p < 0.001 for 5 stimulus repetitions and p < 0.01 for 1,2 and 6 stimulus repetitions) and stepwise linear discriminant analysis decoders (p < 0.001 for all repetitions) when simultaneously addressing timing and translation direction.<br><br>CONCLUSION: We provide evidence of decodability of joint direction and target in mVEP responses.<br><br>SIGNIFICANCE: the described methods can aid in the development of a faster and more comfortable BCI based on mVEPs.},
}
@article {pmid34930915,
year = {2021},
author = {Lee, YE and Shin, GH and Lee, M and Lee, SW},
title = {Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running.},
journal = {Scientific data},
volume = {8},
number = {1},
pages = {315},
pmid = {34930915},
issn = {2052-4463},
support = {2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2017-0-00451//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2015-0-00185//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; 2019-0-00079//MSIP | Institute for Information and communications Technology Promotion (Institute for Information &amp; communications Technology Promotion)/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Ear ; Electroencephalography ; *Evoked Potentials ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Running/*physiology ; Scalp ; *Standing Position ; Walking/*physiology ; Young Adult ; },
abstract = {We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.},
}
@article {pmid34925636,
year = {2021},
author = {Schönau, A},
title = {The spectrum of responsibility ascription for end users of neurotechnologies.},
journal = {Neuroethics},
volume = {14},
number = {3},
pages = {423-435},
pmid = {34925636},
issn = {1874-5490},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
abstract = {Invasive neural devices offer novel prospects for motor rehabilitation on different levels of agentive behavior. From a functional perspective, they interact with, support, or enable human intentional actions in such a way that movement capabilities are regained. However, when there is a technical malfunction resulting in an unintended movement, the complexity of the relationship between the end user and the device sometimes makes it difficult to determine who is responsible for the outcome - a circumstance that has been coined as "responsibility gap" in the literature. So far, recent accounts frame this issue around the theme of control but more work is needed to explore the complicated terrain of assigning responsibility for neural device-mediated actions from this control perspective. This paper aims at contributing to this tendency by offering more fine-grained distinctions of how that control capacity is facilitated by the machine and how it can be exercised by the end user. This results in a novel framework that depicts an in-depth exploration of the control aspect of responsibility in a way that incorporates the diversity of relationships between neurotechnologies, the various conditions they treat, and the individual end user's experience.},
}
@article {pmid34924942,
year = {2021},
author = {Li, S and Duan, J and Sun, Y and Sheng, X and Zhu, X and Meng, J},
title = {Exploring Fatigue Effects on Performance Variation of Intensive Brain-Computer Interface Practice.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {773790},
pmid = {34924942},
issn = {1662-4548},
abstract = {Motor imagery (MI) is an endogenous mental process and is commonly used as an electroencephalogram (EEG)-based brain-computer interface (BCI) strategy. Previous studies of P300 and MI-based (without online feedback) BCI have shown that mental states like fatigue can negatively affect participants' EEG signatures. However, exogenous stimuli cause visual fatigue, which might have a different mechanism than endogenous tasks do. Furthermore, subjects could adjust themselves if online feedback is provided. In this sense, it is still unclear how fatigue affects online MI-based BCI performance. With this question, 12 healthy subjects are recruited to investigate this issue, and an MI-based online BCI experiment is performed for four sessions on different days. The first session is for training, and the other three sessions differ in rest condition and duration-no rest, 16-min eyes-open rest, and 16-min eyes-closed rest-arranged in a pseudo-random order. Multidimensional fatigue inventory (MFI) and short stress state questionnaire (SSSQ) reveal that general fatigue, mental fatigue, and distress have increased, while engagement has decreased significantly within certain sessions. However, the BCI performances, including percent valid correct (PVC) and information transfer rate (ITR), show no significant change across 400 trials. The results suggest that although the repetitive MI task has affected subjects' mental states, their BCI performances and feature separability within a session are not affected by the task significantly. Further electrophysiological analysis reveals that the alpha-band power in the sensorimotor area has an increasing tendency, while event-related desynchronization (ERD) modulation level has a decreasing trend. During the rest time, no physiological difference has been found in the eyes-open rest condition; on the contrary, the alpha-band power increase and subsequent decrease appear in the eyes-closed rest condition. In summary, this experiment shows evidence that mental states can change dramatically in the intensive MI-BCI practice, but BCI performances could be maintained.},
}
@article {pmid34924927,
year = {2021},
author = {Zhang, P and Min, C and Zhang, K and Xue, W and Chen, J},
title = {Hierarchical Spatiotemporal Electroencephalogram Feature Learning and Emotion Recognition With Attention-Based Antagonism Neural Network.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {738167},
pmid = {34924927},
issn = {1662-4548},
abstract = {Inspired by the neuroscience research results that the human brain can produce dynamic responses to different emotions, a new electroencephalogram (EEG)-based human emotion classification model was proposed, named R2G-ST-BiLSTM, which uses a hierarchical neural network model to learn more discriminative spatiotemporal EEG features from local to global brain regions. First, the bidirectional long- and short-term memory (BiLSTM) network is used to obtain the internal spatial relationship of EEG signals on different channels within and between regions of the brain. Considering the different effects of various cerebral regions on emotions, the regional attention mechanism is introduced in the R2G-ST-BiLSTM model to determine the weight of different brain regions, which could enhance or weaken the contribution of each brain area to emotion recognition. Then a hierarchical BiLSTM network is again used to learn the spatiotemporal EEG features from regional to global brain areas, which are then input into an emotion classifier. Especially, we introduce a domain discriminator to work together with the classifier to reduce the domain offset between the training and testing data. Finally, we make experiments on the EEG data of the DEAP and SEED datasets to test and compare the performance of the models. It is proven that our method achieves higher accuracy than those of the state-of-the-art methods. Our method provides a good way to develop affective brain-computer interface applications.},
}
@article {pmid34922702,
year = {2021},
author = {Pimenta, T and Rocha, JA},
title = {Cardiac rehabilitation and improvement of chronotropic incompetence: Is it the exercise or just the beta blockers?.},
journal = {Revista portuguesa de cardiologia},
volume = {40},
number = {12},
pages = {947-953},
doi = {10.1016/j.repce.2021.11.013},
pmid = {34922702},
issn = {2174-2049},
mesh = {Adrenergic beta-Antagonists/therapeutic use ; *Cardiac Rehabilitation ; Exercise Test ; Heart Rate ; Humans ; Retrospective Studies ; },
abstract = {INTRODUCTION: Clinical use of chronotropic response has been limited due to lack of consensus on the appropriate formula for chronotropic index (Ci) calculation and the definition of chronotropic incompetence.<br><br>OBJECTIVES: To assess the effects of cardiac rehabilitation programs (CRP) on Ci, irrespective of betablockers (BB) use and dosage. Assess the relative contribution of change in Ci on improvement in functional capacity.<br><br>METHODS: Retrospective analysis of a sample of patients admitted to a CRP after acute coronary syndrome, with at least 12 months of follow-up. Ci was calculated using the conventional (CCi) and the Brawner formula (BCi) for age-predicted maximum heart rate. Ci and functional capacity were estimated at three time points: T1 and T2, before and at the end of the CRP, and T3, at 12 months. The sample was categorized according to BB dosage modification between T1 and T3: G1 - reduced; G2 - no change; G3 - increased.<br><br>RESULTS: In G1, CCi increased from 63.5% in T1 to 77.9% in T3; in G2, from 67.3% to 77.9%; in G3, from 71.2% to 75.4%. In G1, BCi increased from 110.4% to 140.0%; in G2, from 122.8% to 140.1%; in G3, from 133.3% to 139.2%. An average increase in 1.0% in CCi was associated with an average increase in functional capacity of 0.37 METS.<br><br>CONCLUSIONS: Chronotropic index significantly improves with CRP, irrespective of BB dose changes. CCi is more closely related with improvement in functional capacity than BCi. Improvement of Ci is an important predictor of functional capacity and prognosis in cardiovascular disease patients.},
}
@article {pmid34920443,
year = {2022},
author = {Mattioli, F and Porcaro, C and Baldassarre, G},
title = {A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac4430},
pmid = {34920443},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; *Machine Learning ; *Movement ; *Neural Networks, Computer ; Sensorimotor Cortex/physiology ; },
abstract = {Objective.Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing.Approach.We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data.Main results.The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a99.38%accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of99.46%.Significance.The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.},
}
@article {pmid34916905,
year = {2021},
author = {Xu, B and Deng, L and Zhang, D and Xue, M and Li, H and Zeng, H and Song, A},
title = {Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {797990},
pmid = {34916905},
issn = {1662-4548},
abstract = {Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain-computer interface.},
}
@article {pmid34916587,
year = {2021},
author = {Batzianoulis, I and Iwane, F and Wei, S and Correia, CGPR and Chavarriaga, R and Millán, JDR and Billard, A},
title = {Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.},
journal = {Communications biology},
volume = {4},
number = {1},
pages = {1406},
pmid = {34916587},
issn = {2399-3642},
mesh = {Adult ; Humans ; *Learning ; Male ; *Reinforcement, Psychology ; Robotics/*methods ; },
abstract = {Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user's error expectation of the robot's current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user's preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user's preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal.},
}
@article {pmid34915046,
year = {2022},
author = {Tan, Y and Lin, Y and Zang, B and Gao, X and Yong, Y and Yang, J and Li, S},
title = {An autonomous hybrid brain-computer interface system combined with eye-tracking in virtual environment.},
journal = {Journal of neuroscience methods},
volume = {368},
number = {},
pages = {109442},
doi = {10.1016/j.jneumeth.2021.109442},
pmid = {34915046},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Eye-Tracking Technology ; Fixation, Ocular ; Humans ; Photic Stimulation ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) has become an effective human-machine interactive way. However, the performance of the traditional BCI system needs to be further improved, such as flexibility, robustness, and accuracy. We aim to develop an autonomous hybrid BCI system combined with eye-tracking for the control tasks in the virtual environment.<br><br>NEW METHOD: This work developed an autonomous control strategy and proposed an effective fusion method for electroencephalogram (EEG) and eye tracking. For the autonomous control, the sliding window method was adopted to analyze the user's eye-gaze data. When the variance of eye-gaze data was less than the threshold, target recognition was triggered. EEG and eye-gaze data were synchronously collected and fused for classification. In addition, a fusion method based on particle swarm optimization (PSO) was proposed, which can find the best fusion weights to adapt to the differences of single modalities.<br><br>RESULTS: EEG data and eye-gaze data of 15&#xa0;subjects in steady-state visual evoked potentials (SSVEP) tasks were collected to evaluate the effectiveness of the hybrid BCI system. The results showed that the PSO fusion method performed best in all fusion methods. And the proposed hybrid BCI system obtained higher accuracy and information transfer rate (ITR) than the single-modality.<br><br>The PSO fusion method was compared with average weighting fusion, prior weighting fusion, support vector machine, decision tree, random forest, and extreme random tree.<br><br>CONCLUSION: The proposed methods of autonomous control and dual-modal fusion can improve the flexibility, robustness and classification performance of the hybrid BCI system.},
}
@article {pmid34913508,
year = {2022},
author = {Fan, J and Shi, J and Zhang, Y and Liu, J and An, C and Zhu, H and Wu, P and Hu, W and Qin, R and Yao, D and Shou, X and Xu, Y and Tong, Z and Wen, X and Xu, J and Zhang, J and Fang, W and Lou, J and Yin, W and Chen, W},
title = {NKG2D discriminates diverse ligands through selectively mechano-regulated ligand conformational changes.},
journal = {The EMBO journal},
volume = {41},
number = {2},
pages = {e107739},
pmid = {34913508},
issn = {1460-2075},
mesh = {Binding Sites ; Cells, Cultured ; Histocompatibility Antigens Class I/chemistry/metabolism ; Humans ; K562 Cells ; Ligands ; Mechanical Phenomena ; Molecular Dynamics Simulation ; NK Cell Lectin-Like Receptor Subfamily K/*chemistry/metabolism ; Protein Binding ; Single Molecule Imaging ; },
abstract = {Stimulatory immune receptor NKG2D binds diverse ligands to elicit differential anti-tumor and anti-virus immune responses. Two conflicting degeneracy recognition models based on static crystal structures and in-solution binding affinities have been considered for almost two decades. Whether and how NKG2D recognizes and discriminates diverse ligands still remain unclear. Using live-cell-based single-molecule biomechanical assay, we characterized the in situ binding kinetics of NKG2D interacting with different ligands in the absence or presence of mechanical force. We found that mechanical force application selectively prolonged NKG2D interaction lifetimes with the ligands MICA and MICB, but not with ULBPs, and that force-strengthened binding is much more pronounced for MICA than for other ligands. We also integrated steered molecular dynamics simulations and mutagenesis to reveal force-induced rotational conformational changes of MICA, involving formation of additional hydrogen bonds on its binding interface with NKG2D, impeding MICA dissociation under force. We further provided a kinetic triggering model to reveal that force-dependent affinity determines NKG2D ligand discrimination and its downstream NK cell activation. Together, our results demonstrate that NKG2D has a discrimination power to recognize different ligands, which depends on selective mechanical force-induced ligand conformational changes.},
}
@article {pmid34912883,
year = {2021},
author = {Simões, FB and Kmit, A and Amaral, MD},
title = {Cross-talk of inflammatory mediators and airway epithelium reveals the cystic fibrosis transmembrane conductance regulator as a major target.},
journal = {ERJ open research},
volume = {7},
number = {4},
pages = {},
pmid = {34912883},
issn = {2312-0541},
abstract = {Airway inflammation, mucus hyperproduction and epithelial remodelling are hallmarks of many chronic airway diseases, including asthma, COPD and cystic fibrosis. While several cytokines are dysregulated in these diseases, most studies focus on the response of airways to interleukin (IL)-4 and IL-13, which have been shown to induce mucus hyperproduction and shift the airway epithelium towards a hypersecretory phenotype. We hypothesised that other cytokines might induce the expression of chloride (Cl[-]) channels/transporters, and regulate epithelial differentiation and mucus production. To this end, fully differentiated human airway basal cells (BCi-NS1.1) were treated with cytokines identified as dysregulated in those diseases, namely IL-8, IL-1β, IL-4, IL-17A, IL-10 and IL-22, and tumour necrosis factor-α. Our results show that the cystic fibrosis transmembrane conductance regulator (CFTR) is the main Cl[-] channel modulated by inflammation, in contrast to transmembrane protein 16A (TMEM16A), whose levels only changed with IL-4. Furthermore, we identified novel roles for IL-10 and IL-22 by influencing epithelial differentiation towards ciliated cells and away from pulmonary ionocytes. In contrast, IL-1β and IL-4 reduced the number of ciliated cells while increasing club cells. Interestingly, while IL-1β, IL-4 and IL-10 upregulated CFTR expression, IL-4 was the only cytokine that increased both its function and the number of CFTR-expressing club cells, suggesting that this cell type may be the main contributor for CFTR function. Additionally, all cytokines assessed increased mucus production through a differential upregulation of MUC5AC and MUC5B transcript levels. This study reveals a novel insight into differentiation resulting from the cross-talk of inflammatory mediators and airway epithelial cells, which is particularly relevant for chronic airway diseases.},
}
@article {pmid34912203,
year = {2021},
author = {Wei, CS and Keller, CJ and Li, J and Lin, YP and Nakanishi, M and Wagner, J and Wu, W and Zhang, Y and Jung, TP},
title = {Editorial: Inter- and Intra-subject Variability in Brain Imaging and Decoding.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {791129},
doi = {10.3389/fncom.2021.791129},
pmid = {34912203},
issn = {1662-5188},
support = {R01 MH126639/MH/NIMH NIH HHS/United States ; },
}
@article {pmid34906019,
year = {2022},
author = {Cuomo, G and Maglianella, V and Ghanbari Ghooshchy, S and Zoccolotti, P and Martelli, M and Paolucci, S and Morone, G and Iosa, M},
title = {Motor imagery and gait control in Parkinson's disease: techniques and new perspectives in neurorehabilitation.},
journal = {Expert review of neurotherapeutics},
volume = {22},
number = {1},
pages = {43-51},
doi = {10.1080/14737175.2022.2018301},
pmid = {34906019},
issn = {1744-8360},
mesh = {Gait ; Humans ; Imagery, Psychotherapy/methods ; *Neurological Rehabilitation ; *Parkinson Disease ; *Virtual Reality ; },
abstract = {INTRODUCTION: Motor imagery (MI), defined as the ability to mentally represent an action without actual movement, has been used to improve motor function in athletes and, more recently, in neurological disorders such as Parkinson's disease (PD). Several studies have investigated the neural correlates of motor imagery, which change also depending on the action imagined.<br><br>AREAS COVERED: This review focuses on locomotion, which is a crucial activity in everyday life and is often impaired by neurological conditions. After a general discussion on the neural correlates of motor imagery and locomotion, we review the evidence highlighting the abnormalities in gait control and gait imagery in PD patients. Next, new perspectives and techniques for PD patients' rehabilitation are discussed, namely Brain Computer Interfaces (BCIs), neurofeedback, and virtual reality (VR).<br><br>EXPERT OPINION: Despite the few studies, the literature review supports the potential beneficial effects of motor imagery interventions in PD focused on locomotion. The development of new technologies could empower the administration of training based on motor imagery locomotor tasks, and their application could lead to new rehabilitation protocols aimed at improving walking ability in patients with PD.},
}
@article {pmid34903005,
year = {2022},
author = {Liu, H and Gao, Y and Zhang, J and Zhang, J},
title = {Epilepsy EEG classification method based on supervised locality preserving canonical correlation analysis.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {19},
number = {1},
pages = {624-642},
doi = {10.3934/mbe.2022028},
pmid = {34903005},
issn = {1551-0018},
mesh = {Algorithms ; Canonical Correlation Analysis ; *Electroencephalography ; *Epilepsy/diagnosis ; Humans ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {Existing epileptic seizure automatic detection systems are often troubled by high-dimensional electroencephalogram (EEG) features. High-dimensional features will not only bring redundant information and noise, but also reduce the response speed of the system. In order to solve this problem, supervised locality preserving canonical correlation analysis (SLPCCA), which can effectively use both sample category information and nonlinear relationships between features, is introduced. And an epileptic signal classification method based on SLPCCA is proposed. Firstly, the power spectral density and the fluctuation index of the frequency slice wavelet transform are extracted as features from the EEG fragments. Next, SLPCCA obtains the optimal projection direction by maximizing the weight correlation between the paired samples in the class and their neighbors. And the projection combination of original features in the optimal direction is the fusion feature. The fusion features are then input into LS-SVM for training and testing. This method is verified on the Bonn dataset and the CHB-MIT dataset and gets good results. On various classification tasks of Bonn data set, the proposed method achieves an average classification accuracy of 99.16%. On the binary classification task of the inter-seizure and seizure epileptic EEG of the CHB-MIT dataset, the proposed method achieves an average accuracy of 97.18%. The experimental results show that the algorithm achieves excellent results compared with several state-of-the-art methods. In addition, the parameter sensitivity of SLPCCA and the relationship between the dimension of the fusion features and the classification results are discussed. Therefore, the stability and effectiveness of the method are further verified.},
}
@article {pmid34902850,
year = {2021},
author = {Li, S and Jin, J and Daly, I and Liu, C and Cichocki, A},
title = {Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac42b4},
pmid = {34902850},
issn = {1741-2552},
abstract = {Brain-computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal. A room for improvement should be considered. We propose a novel hybrid feature selection method for the P300-based BCI system to address the problem of feature redundancy, which combines the Menger curvature and linear discriminant analysis. First, selected strategies are applied separately to a given dataset to estimate the gain for application to each feature. Then, each generated value set is ranked in descending order and judged by a predefined criterion to be suitable in classification models. The intersection of the two approaches is then evaluated to identify an optimal feature subset. The proposed method is evaluated using three public datasets, i.e., BCI Competition III dataset II, BNCI Horizon dataset, and EPFL dataset. Experimental results indicate that compared with other typical feature selection and classification methods, our proposed method has better or comparable performance. Additionally, our proposed method can achieve the best classification accuracy after all epochs in three datasets. In summary, our proposed method provides a new way to enhance the performance of the P300-based BCI speller.},
}
@article {pmid34902609,
year = {2022},
author = {Li, R and Ren, C and Zhang, X and Hu, B},
title = {A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition.},
journal = {Computers in biology and medicine},
volume = {140},
number = {},
pages = {105080},
doi = {10.1016/j.compbiomed.2021.105080},
pmid = {34902609},
issn = {1879-0534},
abstract = {Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%.},
}
@article {pmid34902364,
year = {2022},
author = {Shen, L and Xia, Y and Li, Y and Sun, M},
title = {A multiscale siamese convolutional neural network with cross-channel fusion for motor imagery decoding.},
journal = {Journal of neuroscience methods},
volume = {367},
number = {},
pages = {109426},
doi = {10.1016/j.jneumeth.2021.109426},
pmid = {34902364},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Recently, convolutional neural networks (CNN) are widely applied in motor imagery electroencephalography (MI-EEG) signal classification tasks. However, a simple CNN framework is challenging to satisfy the complex MI-EEG signal decoding.<br><br>NEW METHOD: In this study, we propose a multiscale Siamese convolutional neural network with cross-channel fusion (MSCCF-Net) for MI-EEG classification tasks. The proposed network consists of three parts: Siamese cross-channel fusion streams, similarity module and classification module. Each Siamese cross-channel fusion stream contains multiple branches, and each branch is supplemented by cross-channel fusion modules to improve multiscale temporal feature representation capability. The similarity module is adopted to measure the feature similarity between multiple branches. At the same time, the classification module provides a strong constraint to classify the features from all Siamese cross-channel fusion streams. The combination of the similarity module and classification module constitutes a new joint training strategy to further optimize the network performance.<br><br>RESULTS: The experiment is conducted on the public BCI Competition IV 2a and 2b datasets, and the results show that the proposed network achieves an average accuracy of 87.36% and 87.33%, respectively.<br><br>The proposed network adopts cross-channel fusion to learn multiscale temporal characteristics and joint training strategy to optimize the training process. Therefore, the performance outperforms other state-of-the-art MI-EEG signal classification methods.},
}
@article {pmid34901837,
year = {2021},
author = {Yang, J and Wang, YK and Yao, X and Lin, CT},
title = {Adaptive Initialization Method for K-Means Algorithm.},
journal = {Frontiers in artificial intelligence},
volume = {4},
number = {},
pages = {740817},
pmid = {34901837},
issn = {2624-8212},
abstract = {The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.},
}
@article {pmid34900346,
year = {2021},
author = {Zhou, Z and Gong, A and Qian, Q and Su, L and Zhao, L and Fu, Y},
title = {A novel strategy for driving car brain-computer interfaces: Discrimination of EEG-based visual-motor imagery.},
journal = {Translational neuroscience},
volume = {12},
number = {1},
pages = {482-493},
pmid = {34900346},
issn = {2081-3856},
abstract = {A brain-computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert-Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.},
}
@article {pmid34899223,
year = {2021},
author = {Nagels-Coune, L and Riecke, L and Benitez-Andonegui, A and Klinkhammer, S and Goebel, R and De Weerd, P and Lührs, M and Sorger, B},
title = {See, Hear, or Feel - to Speak: A Versatile Multiple-Choice Functional Near-Infrared Spectroscopy-Brain-Computer Interface Feasible With Visual, Auditory, or Tactile Instructions.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {784522},
pmid = {34899223},
issn = {1662-5161},
abstract = {Severely motor-disabled patients, such as those suffering from the so-called "locked-in" syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.},
}
@article {pmid34899220,
year = {2021},
author = {Gutierrez-Martinez, J and Mercado-Gutierrez, JA and Carvajal-Gámez, BE and Rosas-Trigueros, JL and Contreras-Martinez, AE},
title = {Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {772837},
pmid = {34899220},
issn = {1662-5161},
abstract = {Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.},
}
@article {pmid34896845,
year = {2022},
author = {Lai, J and Li, A and Jiang, J and Yuan, X and Zhang, P and Xi, C and Wu, L and Wang, Z and Chen, J and Lu, J and Lu, S and Mou, T and Zhou, H and Wang, D and Huang, M and Dong, F and Li, MD and Xu, Y and Song, X and Hu, S},
title = {Metagenomic analysis reveals gut bacterial signatures for diagnosis and treatment outcome prediction in bipolar depression.},
journal = {Psychiatry research},
volume = {307},
number = {},
pages = {114326},
doi = {10.1016/j.psychres.2021.114326},
pmid = {34896845},
issn = {1872-7123},
mesh = {*Bipolar Disorder/diagnosis/drug therapy ; Dysbiosis ; *Gastrointestinal Microbiome/genetics ; Humans ; Metagenomics ; Treatment Outcome ; },
abstract = {BACKGROUND: We aimed to characterize gut microbial alterations in depressed patients with bipolar disorder (BD) following quetiapine monotherapy and explored its potential for disease diagnosis and outcome prediction.<br><br>METHODS: Fecal samples were obtained from 60 healthy individuals and 62 patients in acute depressive episodes. All patients received one-month quetiapine treatment after enrollment. The structure of gut microbiota was measured with metagenomic sequencing, and its correlation with clinical profiles and brain function as indicated by resting-state functional magnetic resonance imaging was analyzed. Random forest models based on bacterial species were constructed to distinguish patients from controls, and responders from non-responders, respectively.<br><br>RESULTS: BD patients displayed specific alterations in gut microbial diversity and composition. Quetiapine treatment increased the diversity of microbial communities and changed the composition. The abundance of Clostridium bartlettii was negatively associated with age, baseline depression severity, while positively associated with spontaneous neural oscillation in the hippocampus. Tree-based classification models for (1) patients and controls and (2) responders and non-responders showed an area under the curve of 0.733 and 0.800, respectively.<br><br>CONCLUSION: Our findings add new evidence to the existing literature regarding gut dysbiosis in BD and reveal the potential of microbe-based biomarkers for disease diagnosis and treatment outcome prediction.},
}
@article {pmid34895161,
year = {2021},
author = {Argante, L and Abbing-Karahagopian, V and Vadivelu, K and Rappuoli, R and Medini, D},
title = {A re-assessment of 4CMenB vaccine effectiveness against serogroup B invasive meningococcal disease in England based on an incidence model.},
journal = {BMC infectious diseases},
volume = {21},
number = {1},
pages = {1244},
pmid = {34895161},
issn = {1471-2334},
mesh = {Bayes Theorem ; England/epidemiology ; Humans ; Incidence ; Infant ; *Meningococcal Infections/epidemiology/prevention &amp; control ; *Meningococcal Vaccines ; *Neisseria meningitidis, Serogroup B ; Serogroup ; Vaccine Efficacy ; },
abstract = {BACKGROUND: The four-component serogroup B meningococcal 4CMenB vaccine (Bexsero, GSK) has been routinely given to all infants in the United Kingdom at 2, 4 and 12 months of age since September 2015. After 3 years, Public Health England (PHE) reported a 75% [95% confidence interval 64%; 81%] reduction in the incidence of serogroup B invasive meningococcal disease (IMD) in age groups eligible to be fully vaccinated. In contrast, vaccine effectiveness (VE) evaluated in the same immunization program applying the screening method was not statistically significant. We re-analyzed the data using an incidence model.<br><br>METHODS: Aggregate data-stratified by age, year and doses received-were provided by PHE: serogroup B IMD case counts for the entire population of England (years 2011-2018) and 4CMenB vaccine uptake in infants. We combined uptake with national population estimates to obtain counts of vaccinated and unvaccinated person-time by age and time. We re-estimated VE comparing incidence rates in vaccinated and non-vaccinated subjects using a Bayesian Poisson model for case counts with person-time data as an offset. The model was adjusted for age, time and number of doses received.<br><br>RESULTS: The incidence model showed that cases decreased until 2013-2014, followed by an increasing trend that continued in the non-vaccinated population during the immunization program. VE in fully vaccinated subjects (three doses) was 80.1% [95% Bayesian credible interval (BCI): 70.3%; 86.7%]. After a single dose, VE was 33.5% [12.4%; 49.7%]95%BCI and after two doses, 78.7% [71.5%; 84.5%]95%BCI. We estimated that vaccination averted 312 cases [252; 368]95%BCI between 2015 and 2018. VE was in line with the previously reported incidence reduction.<br><br>CONCLUSIONS: Our estimates of VE had higher precision than previous estimates based on the screening method, which were statistically not significant, and in line with the 75% incidence reduction previously reported by PHE. When disease incidence is low and vaccine uptake is high, the screening method applied to cases exclusively from the population eligible for vaccination may not be precise enough and may produce misleading point-estimates. Precise and accurate VE estimates are fundamental to inform public health decision making. VE assessment can be enhanced using models that leverage data on subjects not eligible for vaccination.},
}
@article {pmid34892854,
year = {2021},
author = {Xu, T and Wang, X and Wang, J and Zhou, Y},
title = {From Textbook to Teacher: an Adaptive Intelligent Tutoring System Based on BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {7621-7624},
doi = {10.1109/EMBC46164.2021.9629483},
pmid = {34892854},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Learning ; Students ; },
abstract = {In this work, we propose FT[3], an adaptive intelligent tutoring system based on Brain Computer Interface(BCI). It can automatically generate different difficulty levels of lecturing video with teachers from textbook adapting to student engagement measured by BCI. Most current studies employ animated images to create pedagogical agents in such adaptive learning environments. However, evidence suggests that human teacher video brings a better learning experience than animated images. We design a virtual teacher generation engine consisting of text-to-speech (TTS) and lip synthesis method, being able to generate high-quality adaptive lecturing clips of talking teachers with accurate lip sync merely based on a textbook and teacher's photo. We propose a BCI to measure engagement, serving as an indicator for adaptively generating appropriate lecturing videos. We conduct a preliminary study to build and evaluate FT[3]. Results verify that FT3 can generate synced lecturing videos, and provide proper levels of learning content with an accuracy of 73.33%.},
}
@article {pmid34892761,
year = {2021},
author = {Chuang, J},
title = {Neural Dynamics of a Single Human with Long-Term, High Temporal Density Electroencephalography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {7199-7205},
doi = {10.1109/EMBC46164.2021.9630280},
pmid = {34892761},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Cluster Analysis ; *Electroencephalography ; Humans ; Longitudinal Studies ; Neurophysiology ; },
abstract = {We undertake a longitudinal study with high temporal recording density, capturing daily electroencephalograms (EEG) of an individual in an in-situ setting for 370 consecutive days. Resting-state EEG retains a high level of stability over the course of the year, and inter-session variability remains unchanged, whether the sessions are one day, one week, or one month apart. On the other hand, EEG for certain cognitive tasks experience a steady decline in similarity over the same time period. Clustering analysis reveals that days with low similarity scores should not be considered as outliers, but instead are part of a cluster of days with a consistent alternate spectral signature. This has methodological and design implications for the selection of baseline references or templates in fields ranging from neurophysiology to brain-computer interfaces (BCI) and neurobiometrics.},
}
@article {pmid34892650,
year = {2021},
author = {Shen, X and Zhang, X and Wang, Y},
title = {Kernel Temporal Difference based Reinforcement Learning for Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6721-6724},
doi = {10.1109/EMBC46164.2021.9631086},
pmid = {34892650},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Movement ; Reinforcement, Psychology ; Reward ; },
abstract = {Brain-machine interfaces (BMIs) enable people with disabilities to control external devices with their motor intentions through a decoder. Compared with supervised learning, reinforcement learning (RL) is more promising for the disabled because it can assist them to learn without actual limb movement. Current RL decoders deal with tasks with immediate reward delivery. But for tasks where the reward is only given by the end of the trial, existing RL methods may take a long time to train and are prone to becoming trapped in the local minima. In this paper, we propose to embed temporal difference method (TD) into Quantized Attention-Gated Kernel Reinforcement Learning (QAGKRL) to solve this temporal credit assignment problem. This algorithm utilizes a kernel network to ensure the global linear structure and adopts a softmax policy to efficiently explore the state-action mapping through TD error. We simulate a center-out task where the agent needs several steps to first reach a periphery target and then return to the center to get the external reward. Our proposed algorithm is tested on simulated data and compared with two state-of-the-art models. We find that introducing the TD method to QAGKRL achieves a prediction accuracy of 96.2% ± 0.77% (mean ± std), which is significantly better the other two methods.Clinical Relevance-This paper proposes a novel kernel temporal difference RL method for the multi-step task with delayed reward delivery, which potentially enables BMI online continuous decoding.},
}
@article {pmid34892645,
year = {2021},
author = {Tan, J and Shen, X and Zhang, X and Wang, Y},
title = {Multivariate Encoding Analysis of Medial Prefrontal Cortex Cortical Activity during Task Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6699-6702},
doi = {10.1109/EMBC46164.2021.9630322},
pmid = {34892645},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; *Neurons ; Prefrontal Cortex ; Rats ; Reward ; },
abstract = {Studies have shown that medial prefrontal cortex (mPFC) is responsible for outcome evaluation. Some recent studies also suggest that mPFC may play an important role in goal planning and action execution when performing a task. If the information encoded in mPFC can be accurately extracted and identified, it can improve the design of brain-machine interfaces by better reconstructing subjects' motion intention guided by reward information. In this paper, we investigate whether mPFC neural signals simultaneously encode information of goal planning, action execution and outcome evaluation. Linear-nonlinear-Poisson (LNP) model is applied for encoding analysis on mPFC neural spike data when a rat is learning a two-lever-press discrimination task. We use the L[2]-norm of tuning parameter in LNP model to indicate the importance of the encoded information and compare the spike train prediction performance of LNP model using all information, the most significant information and reward information only. The preliminary results indicate that mPFC activity can encode simultaneously the information of goal planning, action execution and outcome evaluation and that all the relevant information could be reconstructed from mPFC spike trains on a single trial basis.},
}
@article {pmid34892638,
year = {2021},
author = {Meng, J and Liu, J and Wang, H and Xu, M and Ming, D},
title = {Prediction Deviants with Varying Degrees Induce Separable Error-related EEG Features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6671-6674},
doi = {10.1109/EMBC46164.2021.9630218},
pmid = {34892638},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Error-related potential (ErrP) usually emerges in the brain when human perceives errors and is believed to be a promising signal for optimizing brain-computer interface (BCI) system. However, most of the ErrP studies only focus on how to distinguish the correct and wrong conditions, which is not enough for the BCI application in real scenarios. Therefore, it is necessary to study the ErrPs induced by the prediction deviants with varying degrees, concurrently test the separability of such EEG features. To this end, electroencephalogram (EEG) data of twelve healthy subjects were recorded when they participated in a direction prediction experiment. There are three prediction -deviant conditions in it, i.e., correct prediction, 90°deviant, 180° deviant. Event-related potential and inter-trial coherence were analyzed. Consequently, the error-related negativity (ERN) and N450 component in FCZ were significantly modulated by the degrees of prediction deviants, especially in the low-frequency band (<13Hz). Moreover, single-trial classification was adopted to test the separability of these features; the averaged accuracies between any two conditions were 87.75%, 85.25%, 64.79%. This study demonstrates the prediction deviants with varying degrees can induce separable ErrP features, which provide a deeper understanding of the ErrP signatures for developing BCIs.},
}
@article {pmid34892635,
year = {2021},
author = {Montag, M and Paschall, C and Ojemann, J and Rao, R and Herron, J},
title = {A Platform for Virtual Reality Task Design with Intracranial Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6659-6662},
doi = {10.1109/EMBC46164.2021.9630231},
pmid = {34892635},
issn = {2694-0604},
mesh = {Electrodes ; Electroencephalography ; Humans ; Movement ; Software ; *Virtual Reality ; },
abstract = {Research with human intracranial electrodes has traditionally been constrained by the limitations of the inpatient clinical setting. Immersive virtual reality (VR), however, can transcend setting and enable novel task design with precise control over visual and auditory stimuli. This control over visual and auditory feedback makes VR an exciting platform for new in-patient, human electrocorticography (ECOG) and stereo-electroencephalography (sEEG) research. The integration of intracranial electrode recording and stimulation with VR task dynamics required foundational systems engineering. In this work, we present a custom API that bridges Unity, the leading VR game development engine, and Synapse, the proprietary software of the Tucker Davis Technologies (TDT) neural recording and stimulation platform. To demonstrate the functionality and efficiency of our API, we developed a closed-loop brain-computer interface (BCI) task in which filtered neural signals controlled the movement of a virtual object and virtual object dynamics triggered neural stimulation. This closed-loop VR-BCI task confirmed the utility, safety, and efficacy of our API and its readiness for human task deployment.},
}
@article {pmid34892627,
year = {2021},
author = {Moslehi, AH and Davies, TC},
title = {EEG Electrode Selection for a Two-Class Motor Imagery Task in a BCI Using fNIRS Prior Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6627-6630},
doi = {10.1109/EMBC46164.2021.9630786},
pmid = {34892627},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Spectroscopy, Near-Infrared ; },
abstract = {This study investigated the possibility of using functional near infrared spectroscopy (fNIRS) during right- and left-hand motor imagery tasks to select an optimum set of electroencephalography (EEG) electrodes for a brain computer interface. fNIRS has better spatial resolution allowing areas of brain activity to more readily be identified. The ReliefF algorithm was used to identify the most reliable fNIRS channels. Then, EEG electrodes adjacent to those channels were selected for classification. This study used three different classifiers of linear and quadratic discriminant analyses, and support vector machine to examine the proposed method.Clinical Relevance- Reducing the number of sensors in a BCI makes the system more usable for patients with severe disabilities.},
}
@article {pmid34892625,
year = {2021},
author = {Zhang, X and Song, Z and Wang, Y},
title = {Reinforcement Learning-based Kalman Filter for Adaptive Brain Control in Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6619-6622},
doi = {10.1109/EMBC46164.2021.9629511},
pmid = {34892625},
issn = {2694-0604},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Learning ; Rats ; Reinforcement, Psychology ; Reward ; },
abstract = {Brain-Machine Interfaces (BMIs) convert paralyzed people's neural signals into the command of the neuro-prosthesis. During the subject's brain control (BC) process, the neural patterns might change across time, making it crucial and challenging for the decoder to co-adapt with the dynamic neural patterns. Kalman Filter (KF) is commonly used for continuous control in BC. However, if the neural patterns become quite different compared with the training data, KF needs a re-calibration session to maintain its performance. On the other hand, Reinforcement Learning (RL) has the advantage of adaptive updating by the reward signal. But it is not very suitable for generating continuous motor states in BC due to the discrete action selection. In this paper, we propose a reinforcement learning-based Kalman filter. We maintain the state transition model of KF for a continuous motor state prediction. At the same time, we use RL to generate the action from the corresponding neural pattern, which is then used as a correction for the state prediction. The RL's parameters are continuously adjusted by the reward signal in BC. In this way, we could achieve a continuous motor state prediction when the neural patterns have drifted across time. The proposed algorithm is tested on a simulated rat lever-pressing experiment, where the rat's neural patterns have drifted across days. Compared with pure KF without re-calibration, our algorithm could follow the neural pattern drift in an online fashion and maintain good performance.Clinical Relevance- The proposed method bridges the gap between the online parameter adaptation and the continuous control of the neuro-prosthesis. It is promising to be used in adaptive brain control applications during clinical usage.},
}
@article {pmid34892618,
year = {2021},
author = {An, WW and Pei, A and Noyce, AL and Shinn-Cunningham, B},
title = {Decoding auditory attention from EEG using a convolutional neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6586-6589},
doi = {10.1109/EMBC46164.2021.9630484},
pmid = {34892618},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate directly with a device using their brain. BCI devices leveraging electroencephalography (EEG) signals as a means of communication typically use manual feature engineering on the data to perform decoding. This approach is time intensive, requires substantial domain knowledge, and does not translate well, even to similar tasks. To combat this issue, we designed a convolutional neural network (CNN) model to perform decoding on EEG data collected from an auditory attention paradigm. Our CNN model not only bypasses the need for manual feature engineering, but additionally improves decoding accuracy (∼77%) and efficiency (∼11 bits/min) compared to a support vector machine (SVM) baseline. The results demonstrate the potential for the use of CNN in auditory BCI designs.},
}
@article {pmid34892617,
year = {2021},
author = {Alothman, A and Gilja, V},
title = {Unsupervised Channel Compression Methods in Motor Prostheses Design.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6581-6585},
doi = {10.1109/EMBC46164.2021.9630343},
pmid = {34892617},
issn = {2694-0604},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; *Data Compression ; Learning ; Neurons ; },
abstract = {The development of high performance brain machine interfaces (BMIs) requires scaling recording channel count to enable simultaneous recording from large populations of neurons. Unfortunately, proposed implantable neural interfaces have power requirements that scale linearly with channel count. To facilitate the design of interfaces with reduced power requirements, we propose and evaluate an unsupervised-learning-based compressed sensing strategy. This strategy suggests novel neural interface architectures which compress neural data by methodically combining channels of spiking activity. We develop an entropy-based compression strategy that models the population of neurons as being generated from a lower dimensional set of latent variables and aims to minimize the loss of information in the latent variables due to compression. We evaluate compressed features by inferring the latent variables from these features and measuring the accuracy with which the activity of held out neurons and arm movements can be estimated. We apply these methods to different cortical regions (PMd and M1) and compare the proposed compression methods to a random projections strategy often employed for compressed sensing and to a supervised regression based channel dropping strategy traditionally applied in BMI applications.},
}
@article {pmid34892608,
year = {2021},
author = {Dash, D and Ferrari, P and Babajani-Feremi, A and Borna, A and Schwindt, PDD and Wang, J},
title = {Magnetometers vs Gradiometers for Neural Speech Decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6543-6546},
doi = {10.1109/EMBC46164.2021.9630489},
pmid = {34892608},
issn = {2694-0604},
support = {R03 DC013990/DC/NIDCD NIH HHS/United States ; R01 DC016621/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Magnetoencephalography ; Neuroimaging ; *Speech ; *Wearable Electronic Devices ; },
abstract = {Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.},
}
@article {pmid34892589,
year = {2021},
author = {Hosni, SMI and Borgheai, SB and McLinden, J and Zhu, S and Huang, X and Ostadabbas, S and Shahriari, Y},
title = {Graph-based Recurrence Quantification Analysis of EEG Spectral Dynamics for Motor Imagery-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6453-6457},
doi = {10.1109/EMBC46164.2021.9630068},
pmid = {34892589},
issn = {2694-0604},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Support Vector Machine ; },
abstract = {UNLABELLED: Despite continuous research, communication approaches based on brain-computer interfaces (BCIs) are not yet an efficient and reliable means that severely disabled patients can rely on. To date, most motor imagery (MI)-based BCI systems use conventional spectral analysis methods to extract discriminative features and classify the associated electroencephalogram (EEG)-based sensorimotor rhythms (SMR) dynamics that results in relatively low performance. In this study, we investigated the feasibility of using recurrence quantification analysis (RQA) and complex network theory graph-based feature extraction methods as a novel way to improve MI-BCIs performance. Rooted in chaos theory, these features explore the nonlinear dynamics underlying the MI neural responses as a new informative dimension in classifying MI.<br><br>METHOD: EEG time series recorded from six healthy participants performing MI-Rest tasks were projected into multidimensional phase space trajectories in order to construct the corresponding recurrence plots (RPs). Eight nonlinear graph-based RQA features were extracted from the RPs then compared to the classical spectral features through a 5-fold nested cross-validation procedure for parameter optimization using a linear support vector machine (SVM) classifier.<br><br>RESULTS: Nonlinear graph-based RQA features were able to improve the average performance of MI-BCI by 5.8% as compared to the classical features.<br><br>SIGNIFICANCE: These findings suggest that RQA and complex network analysis could represent new informative dimensions for nonlinear characteristics of EEG signals in order to enhance the MI-BCI performance.},
}
@article {pmid34892587,
year = {2021},
author = {Zhang, Y and Wan, Z and Wan, G and Zheng, Q and Chen, W and Zhang, S},
title = {Changes in Modulation Characteristics of Neurons in Different Modes of Motion Control Using Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6445-6448},
doi = {10.1109/EMBC46164.2021.9630212},
pmid = {34892587},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motion ; Neurons ; },
abstract = {In the research of motion control using brain-machine interface (BMI), analysis is usually conducted on one ensemble of neurons whose activity serves as direct input to the BMI decoder (control units). The number of control units is diverse in different control modes. That is to say, the size of dimensions of neural signals used in motion control is diverse. However, how will the behavioral performance change with this kind of diversity? What effects does this diversity have on modulation characteristics of control units? To answer these questions, we designed three modes of motion tasks using neural signals with different dimension sizes to control. Our results imply that as the dimension reduces, some deviations appear in behavioral performance. At the same time, the control units tend to have a directional division of control, then enhance their stability and increase modulations after division.},
}
@article {pmid34892582,
year = {2021},
author = {Krana, M and Farmaki, C and Pediaditis, M and Sakkalis, V},
title = {SSVEP based Wheelchair Navigation in Outdoor Environments.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6424-6427},
doi = {10.1109/EMBC46164.2021.9629516},
pmid = {34892582},
issn = {2694-0604},
mesh = {Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Wheelchairs ; },
abstract = {A promising application of Brain Computer Interfaces (BCIs), and in particular of Steady-State Visually Evoked Potentials (SSVEP) is wheelchair navigation which can facilitate the daily life of patients suffering from severe paralysis. However, the outdoor performance of such a system is highly affected by uncontrolled environmental factors. In this paper, we present an SSVEP-based wheelchair navigation system and propose incremental learning as a method of adapting the system to changing environmental conditions.},
}
@article {pmid34892577,
year = {2021},
author = {Liu, C and Li, M and Wang, R and Cui, X and Jung, H and Halin, K and You, H and Yang, X and Chen, W},
title = {Online Decoding System with Calcium Image From Mice Primary Motor Cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6402-6405},
doi = {10.1109/EMBC46164.2021.9630138},
pmid = {34892577},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Calcium ; Mice ; *Motor Cortex ; Online Systems ; Signal Processing, Computer-Assisted ; },
abstract = {With the development of calcium imaging, neuroscientists have been able to study neural activity with a higher spatial resolution. However, the real-time processing of calcium imaging is still a big challenge for future experiments and applications. Most neuroscientists have to process their imaging data offline due to the time-consuming of most existing calcium imaging analysis methods. We proposed a novel online neural signal processing framework for calcium imaging and established an Optical Brain-Computer Interface System (OBCIs) for decoding neural signals in real-time. We tested and evaluated this system by classifying the calcium signals obtained from the primary motor cortex of mice when the mice were performing a lever-pressing task. The performance of our online system could achieve above 80% in the average decoding accuracy. Our preliminary results show that the online neural processing framework could be applied to future closed-loop OBCIs studies.},
}
@article {pmid34892563,
year = {2021},
author = {Chen, S and Liu, X and Wang, Y},
title = {Considering Neural Connectivity in Point Process Decoder for Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6341-6344},
doi = {10.1109/EMBC46164.2021.9630383},
pmid = {34892563},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Neurons ; },
abstract = {Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.Clinical Relevance-This paper proposes a decoder that can model the neural connectivity and the single neuronal tuning property at the same time, which is potential to explain the neural adaptation computationally.},
}
@article {pmid34892562,
year = {2021},
author = {Nagarajan, A and Robinson, N and Guan, C},
title = {Investigation on Robustness of EEG-based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6334-6340},
doi = {10.1109/EMBC46164.2021.9630031},
pmid = {34892562},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Machine Learning ; },
abstract = {Electroencephalogram (EEG)-based brain-computer interface (BCI) systems tend to suffer from performance degradation due to the presence of noise and artifacts in EEG data. This study is aimed at systematically investigating the robustness of state-of-the-art machine learning and deep learning based EEG-BCI models for motor imagery classification against simulated channel-specific noise in EEG data, at various low values of signal-to-noise ratio (SNR). Our results illustrate higher robustness of deep learning based MI classification models compared to the traditional machine learning based model, while identifying a set of channels with large sensitivity to simulated channel-specific noise. The EEGNet is relatively more robust towards channel-specific noise than Shallow ConvNet and FBCSP. We propose a preliminary solution, based on activation function, to improve the robustness of the deep learning models. By using saturating nonlinearities, the percentage drop in classification accuracy for SNR of -18 dB had reduced from 10.99% to 6.53% for EEGNet and 14.05% to 3.57% for Shallow ConvNet. Through this study, we emphasize the need for a more precise solution for enhancing the robustness, and thereby usability of EEG-BCI systems.},
}
@article {pmid34892544,
year = {2021},
author = {Osborn, LE and Christie, BP and McMullen, DP and Nickl, RW and Thompson, MC and Pawar, AS and Thomas, TM and Alejandro Anaya, M and Crone, NE and Wester, BA and Bensmaia, SJ and Celnik, PA and Cantarero, GL and Tenore, FV and Fifer, MS},
title = {Intracortical microstimulation of somatosensory cortex enables object identification through perceived sensations.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6259-6262},
doi = {10.1109/EMBC46164.2021.9630450},
pmid = {34892544},
issn = {2694-0604},
mesh = {Electric Stimulation ; *Hand ; Humans ; Microelectrodes ; *Somatosensory Cortex ; Touch ; },
abstract = {Advances in brain-machine interfaces have helped restore function and independence for individuals with sensorimotor deficits; however, providing efficient and effective sensory feedback remains challenging. Intracortical microstimulation (ICMS) of sensorimotor brain regions is a promising technique for providing bioinspired sensory feedback. In a human participant with chronically-implanted microelectrode arrays, we provided ICMS to the primary somatosensory cortex to generate tactile percepts in his hand. In a 3-choice object identification task, the participant identified virtual objects using tactile sensory feedback and no visual information. We evaluated three different stimulation paradigms, each with a different weighting of the grip force and its derivative, to explore the potential benefits of a more bioinspired stimulation strategy. In all paradigms, the participant's ability to identify the objects was above-chance, with object identification accuracy reaching 80% correct when using only sustained grip force feedback and 76.7% when using equal weighting of both sustained grip force and its derivative. These results demonstrate that bioinspired ICMS can provide sensory feedback that is functionally beneficial in sensorimotor tasks. Designing more efficient stimulation paradigms is important because it will allow us to 1) provide safer stimulation delivery methods that reduce overall injected charge without sacrificing function and 2) more effectively transmit sensory information to promote intuitive integration and usage by the human body.},
}
@article {pmid34892532,
year = {2021},
author = {Meyer, SM and Rao Mangalore, A and Ehrlich, SK and Berberich, N and Nassour, J and Cheng, G},
title = {A Comparative Pilot Study on ErrPs for Different Usage Conditions of an Exoskeleton with a Mobile EEG Device.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6203-6206},
doi = {10.1109/EMBC46164.2021.9630465},
pmid = {34892532},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Exoskeleton Device ; Pilot Projects ; },
abstract = {Exoskeletons and prosthetic devices controlled using brain-computer interfaces (BCIs) can be prone to errors due to inconsistent decoding. In recent years, it has been demonstrated that error-related potentials (ErrPs) can be used as a feedback signal in electroencephalography (EEG) based BCIs. However, modern BCIs often take large setup times and are physically restrictive, making them impractical for everyday use. In this paper, we use a mobile and easy-to-setup EEG device to investigate whether an erroneously functioning 1-DOF exoskeleton in different conditions, namely, visually observing and wearing the exoskeleton, elicits a brain response that can be classified. We develop a pipeline that can be applied to these two conditions and observe from our experiments that there is evidence for neural responses from electrodes near regions associated with ErrPs in an environment that resembles the real world. We found that these error-related responses can be classified as ErrPs with accuracies ranging from 60% to 71%, depending on the condition and the subject. Our pipeline could be further extended to detect and correct erroneous exoskeleton behavior in real-world settings.},
}
@article {pmid34892528,
year = {2021},
author = {Chin, ZY and Zhang, Z and Wang, C and Ang, KK},
title = {An Affective Interaction System using Virtual Reality and Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6183-6186},
doi = {10.1109/EMBC46164.2021.9630045},
pmid = {34892528},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Support Vector Machine ; *Virtual Reality ; },
abstract = {Affective Computing is a multidisciplinary area of research that allows computers to perform human emotion recognition, with potential applications in areas such as healthcare, gaming and intuitive human computer interface design. Hence, this paper proposes an affective interaction system using dry EEG-based Brain-Computer Interface and Virtual Reality (BCI-VR). The proposed BCI-VR system integrates existing low-cost consumer devices such as an EEG headband with frontal and temporal dry electrodes for brain signal acquisition, and a low-cost VR headset that houses an Android handphone. The handphone executes an in-house developed software that connects wirelessly to the headband, processes the acquired EEG signals, and displays VR content to elicit emotional responses. The proposed BCI-VR system was used to collect EEG data from 13 subjects while they watched VR content that elicits positive or negative emotional responses. EEG bandpower features were extracted to train Linear Discriminant and Support Vector Machine classifiers. The classification performances of these classifiers on this dataset and the results of a public dataset (SEED-IV) are then evaluated. The results in classifying positive vs negative emotions in both datasets (~66% for 2-class) show promise that positive and negative emotions can be detected by the proposed low cost BCIVR system, yielding nearly the same performance on the public dataset that used wet EEG electrodes. Hence the results show promise of the proposed BCI-VR system for real-time affective interaction applications in future.},
}
@article {pmid34892524,
year = {2021},
author = {Nasrollahpour, M and Zaeimbashi, M and Khalifa, A and Liang, X and Chen, H and Sun, N and Abrishami, SMS and Martos-Repath, I and Emam, S and Cash, S and Sun, NX},
title = {Magnetoelectric (ME) Antenna for On-chip Implantable Energy Harvesting.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6167-6170},
doi = {10.1109/EMBC46164.2021.9629823},
pmid = {34892524},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Prostheses and Implants ; *Wireless Technology ; },
abstract = {A novel magnetoelectric (ME) antenna is fabricated to be integrated to the on-chip energy harvesting circuit for brain-computer interface applications. The proposed ME antenna resonates at the frequency of 2.57 GHz while providing a bandwidth of 3.37 MHz. The proposed rectangular ME antenna wireless power transfer efficiency is 0.304 %, which is considerably higher than that of micro-coils.Clinical Relevance- This provides a suitable energy harvesting efficiency for wirelessly powering up the brain implant devices.},
}
@article {pmid34892520,
year = {2021},
author = {Mu, J and Tan, Y and Grayden, DB and Oetomo, D},
title = {Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6151-6154},
doi = {10.1109/EMBC46164.2021.9629669},
pmid = {34892520},
issn = {2694-0604},
mesh = {Algorithms ; Canonical Correlation Analysis ; *Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the purpose of increasing the number of targets that can be presented simultaneously. However, there is no unified decoding algorithm that can be used without training for each individual users or cases, and applied to a large class of multi-frequency stimulated SSVEP settings. This paper extends the widely used canonical correlation analysis (CCA) decoder to explicitly accommodate multi-frequency SSVEP by exploiting the interactions between the multiple stimulation frequencies. A concept of order, defined as the sum of absolute value of the coefficients in the linear combination of the input frequencies, was introduced to assist the design of Multi-Frequency CCA (MFCCA). The probability distribution of the order in the resulting SSVEP response was then used to improve decoding accuracy. Results show that, compared to the standard CCA formulation, the proposed MFCCA has a 20% improvement in decoding accuracy on average at order 2, while keeping its generality and training-free characteristics.},
}
@article {pmid34892518,
year = {2021},
author = {Zhang, Z and Guan, K and Wang, L and Chai, X and Ma, Y and Gao, X and Liu, T and Niu, H},
title = {Effects of Jaw Clench Actions on Steady-State Visual Evoked Potential Detection at Some Typical Frequencies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6142-6145},
doi = {10.1109/EMBC46164.2021.9629729},
pmid = {34892518},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {More and more hybrid brain-computer interfaces (BCI) supplement traditional single-modality BCI in practical applications. Combinations based on steady-state visual evoked potential (SSVEP) and electromyography (EMG) are the widely used hybrid BCIs. The EMG of jaw clench is commonly used together with SSVEP. This article explored the interference with SSVEP from occipital electrodes by the jaw clench-related EMG so that SSVEP with specific frequency can be identified even during occlusal movements. The experiment was divided into three sets base on the jaw clench patterns (no clenches, chew, and long clench). In each set, the subjects used the same visual stimuli, which were realized by the three flashing targets at different frequencies (6.2Hz, 9.8Hz, and 14.6Hz). After collecting the SSVEP at 4 sites in the occipital region, the SSVEP response spectrum of each stimulus was observed under the three jaw clench patterns. Then, the SSVEP signal was identified by the canonical correlation analysis method for accuracy statistics. Spectrum responses showed that the interference of the jaw clench EMG on SSVEP could be avoided when the stimulation frequency is lower than 20Hz. SSVEP could be identified based on the frequency domain characteristics of these signals. During steady-state visual stimulation with jaw clenches, the recognition rate of SSVEP was still high (no clenches: 100.0%, chew: 94.7%, and long clench: 100.0%). Through reasonable frequency selecting and signal processing, the influence of the jaw clench movement on the SSVEP could be reduced and a high recognition accuracy could be achieved, even the jaw clench actions and the SSVEP stimulation occur simultaneously.},
}
@article {pmid34892512,
year = {2021},
author = {Jia, T and Mo, L and Li, C and Liu, A and Li, Z and Ji, L},
title = {5 Hz rTMS improves motor-imagery based BCI classification performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6116-6120},
doi = {10.1109/EMBC46164.2021.9630102},
pmid = {34892512},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Transcranial Magnetic Stimulation ; },
abstract = {Brain-computer interface (BCI) based rehabilitation has been proven a promising method facilitating motor recovery. Recognizing motor intention is crucial for realizing BCI rehabilitation training. Event-related desynchronization (ERD) is a kind of electroencephalogram (EEG) inherent characteristics associated with motor intention. However, due to brain deficits poststroke, some patients are not able to generate ERD, which discourages them to be involved in BCI rehabilitation training. To boost ERD during motor imagery (MI), this paper investigates the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) on BCI classification performance. Eleven subjects participated in this study. The experiment consisted of two conditions: rTMS + MI versus sham rTMS + MI, which were arranged on different days. MI tests with 64-channel EEG recording were arranged immediately before and after rTMS and sham rTMS. Time-frequency analysis were utilized to measure ERD changes. Common spatial pattern was used to extract features and linear discriminant analysis was used to calculate offline classification accuracies. Paired-sample t-test and Wilcoxon signed rank tests with post-hoc analysis were used to compare performance before and after stimulation. Statistically stronger ERD (-13.93±12.99%) was found after real rTMS compared with ERD (-5.71±21.25%) before real rTMS (p<0.05). Classification accuracy after real rTMS (70.71±10.32%) tended to be higher than that before real rTMS (66.50±8.48%) (p<0.1). However, no statistical differences were found after sham stimulation. This research provides an effective method in improving BCI performance by utilizing neural modulation.Clinical Relevance- This study offers a promising treatment for patients who cannot be recruited in BCI rehabilitation training due to poor BCI classification performance.},
}
@article {pmid34892509,
year = {2021},
author = {Pals, M and Belizon, RJP and Berberich, N and Ehrlich, SK and Nassour, J and Cheng, G},
title = {Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6102-6105},
doi = {10.1109/EMBC46164.2021.9629621},
pmid = {34892509},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of decoding accuracy, their power consumption is relatively high for mobile applications. Neuromorphic hardware arises as a promising solution to tackle this problem since it can run massive spiking neural networks with energy consumption orders of magnitude lower than traditional hardware. Herein, we show the viability of directly mapping a continuous-valued convolutional neural network for motor imagery EEG classification to a spiking neural network. The converted network, able to run on the SpiNNaker neuromorphic chip, only shows a 1.91% decrease in accuracy after conversion. Thus, we take full advantage of the benefits of both deep learning accuracies and low-power neuro-inspired hardware, properties that are key for the development of wearable BCI devices.},
}
@article {pmid34892508,
year = {2021},
author = {Ottenhoff, MC and Goulis, S and Wagner, L and Tousseyn, S and Colon, A and Kubben, P and Herff, C},
title = {Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6098-6101},
doi = {10.1109/EMBC46164.2021.9629639},
pmid = {34892508},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Hand Strength ; Humans ; Movement ; },
abstract = {Brain-Computer Interfaces (BCIs) that decode a patient's movement intention to control a prosthetic device could restore some independence to paralyzed patients. An important step on the road towards naturalistic prosthetic control is to decode movement continuously with low-latency. BCIs based on intracortical micro-arrays provide continuous control of robotic arms, but require a minor craniotomy. Surface recordings of neural activity using EEG have made great advances over the last years, but suffer from high noise levels and large intra-session variance. Here, we investigate the use of minimally invasive recordings using stereotactically implanted EEG (sEEG). These electrodes provide a sparse sampling across many brain regions. So far, promising decoding results have been presented using data measured from the subthalamic nucleus or trial-to-trial based methods using depth electrodes. In this work, we demonstrate that grasping movements can continuously be decoded using sEEG electrodes, as well. Beta and high-gamma activity was extracted from eight participants performing a grasping task. We demonstrate above chance level decoding of movement vs rest and left vs right, from both frequency bands with accuracies up to 0.94 AUC. The vastly different electrode locations between participants lead to large variability. In the future, we hope that sEEG recordings will provide additional information for the decoding process in neuroprostheses.},
}
@article {pmid34892505,
year = {2021},
author = {Sato, H and Yoshida, A and Shimada, T and Fukami, T},
title = {Performance Improvement of EEG-Based BCI Using Visual Feedback Based on Evaluation Scores Calculated by a Computer.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6086-6089},
doi = {10.1109/EMBC46164.2021.9630801},
pmid = {34892505},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Feedback, Sensory ; Humans ; },
abstract = {In the study of an electroencephalography (EEG)-based brain computer interface (BCI) using the P300, there have been many reports on computer algorithms that identify the target intended by a user from multiple candidates. However, because the P300 amplitude depends on the subject's condition and is attenuated by physical and mental factors, such as fatigue and motivation, the performance of the BCI is low. Therefore, we aim to improve performance by introducing a feedback mechanism that provides the user with an evaluation calculated by the computer during EEG measurement. In this study, we conducted an experiment in which the user input one character from four characters on the display. By changing the character size according to the evaluation score calculated by the computer, the computer's current evaluation was fed back to the user. This is expected to change the consciousness of the user to enable them to execute a task by knowing the evaluation; that is, if the evaluation is high, the user needs to maintain the current state, and if the evaluation is low, a behavioral change, such as increasing attention, is required to improve the evaluation.As a result of comparing 10 subjects with and without feedback, accuracy improved for seven subjects that were given feedback.},
}
@article {pmid34892495,
year = {2021},
author = {Angrick, M and Ottenhoff, M and Goulis, S and Colon, AJ and Wagner, L and Krusienski, DJ and Kubben, PL and Schultz, T and Herff, C},
title = {Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6045-6048},
doi = {10.1109/EMBC46164.2021.9629711},
pmid = {34892495},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Electrodes, Implanted ; Humans ; Neural Networks, Computer ; *Speech ; },
abstract = {Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.},
}
@article {pmid34892491,
year = {2021},
author = {Zhang, Y and Zhang, L and Wang, G and Lyu, W and Ran, Y and Su, S and Xu, P and Yao, D},
title = {Noise-assisted Multivariate Empirical Mode Decomposition based Causal Decomposition for Detecting Upper Limb Movement in EEG-EMG Hybrid Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {6029-6032},
doi = {10.1109/EMBC46164.2021.9630384},
pmid = {34892491},
issn = {2694-0604},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Upper Extremity ; },
abstract = {EEG-EMG based hybrid Brain Computer Interface (hBCI) utilizes the brain-muscle physiological system to interpret and identify motor behaviors, and transmit human intelligence to automated machines in AI applications such as neurorehabilitations and brain-like intelligence. The study introduces a hBCI method for motor behaviors, where multiple time series of the brain neuromuscular network are introduced to indicate brain-muscle causal interactions, and features are extracted based on Relative Causal Strengths (RCSs) derived by Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition. The complex process in brain neuromuscular interactions is specifically investigated towards a monitoring task of upper limb movement, whose 63-channel EEGs and 2-channel EMGs are composed of data inputs. The energy and frequency factors counted from RCSs were extracted as Core Features (CFs). Results showed accuracies of 91.4% and 81.4% with CFs for identifying cascaded (No Movement and Movement Execution) and 3-class (No Movement, Right Movement, and Left Movement) using Naive Bayes classifier, respectively. Moreover, those reached 100% and 94.3% when employing CFs combined with eigenvalues processed by Common Spatial Pattern (CSP). This initial work implies a novel causality inference based hBCI solution for the detection of human upper limb movement.},
}
@article {pmid34892481,
year = {2021},
author = {Alfeo, AL and Catrambone, V and Cimino, MGCA and Vaglini, G and Valenza, G},
title = {Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5983-5986},
doi = {10.1109/EMBC46164.2021.9629900},
pmid = {34892481},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability.},
}
@article {pmid34892479,
year = {2021},
author = {Wang, K and Qiu, S and Wei, W and Zhang, C and He, H and Xu, M and Ming, D},
title = {Vigilance Estimating in SSVEP-Based BCI Using Multimodal Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5974-5978},
doi = {10.1109/EMBC46164.2021.9629736},
pmid = {34892479},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; *Evoked Potentials, Visual ; Humans ; },
abstract = {Brain-computer interface (BCI) is a communication system that allows a direct connection between the human brain and external devices. With the application of BCI, it is important to estimate vigilance for BCI users. In order to investigate the vigilance changes of the subjects during BCI tasks and develop a multimodal method to estimate the vigilance level, a high-speed 4-target BCI system for cursor control was built based on steady-state visual evoked potential (SSVEP). 18 participants were recruited and underwent a 90-min continuous cursor-control BCI task, when electroencephalogram (EEG), electrooculogram (EOG), electrocardiography (ECG), and electrodermal activity (EDA) were recorded simultaneously. Then, we extracted features from the multimodal signals and applied regression models to estimate vigilance. Experimental results showed that the differential entropy (DE) feature could effectively reflect the change of vigilance. The vigilance estimation method, which integrates DE and EOG features into the support vector regression (SVR) model, achieved a better performance than the compared methods. These results demonstrate the feasibility of our methods for estimating vigilance levels in BCI.},
}
@article {pmid34892469,
year = {2021},
author = {Nur Chowdhury, MS and Dutta, A and Robison, MK and Blais, C and Brewer, G and Bliss, DW},
title = {3D CNN to Estimate Reaction Time from Multi-Channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5932-5935},
doi = {10.1109/EMBC46164.2021.9630748},
pmid = {34892469},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Machine Learning ; Neural Networks, Computer ; Reaction Time ; },
abstract = {The study of human reaction time (RT) is invaluable not only to understand the sensory-motor functions but also to translate brain signals into machine comprehensible commands that can facilitate augmentative and alternative communication using brain-computer interfaces (BCI). Recent developments in sensor technologies, hardware computational capabilities, and neural network models have significantly helped advance biomedical signal processing research. This study is an attempt to utilize state-of-the-art resources to explore the relationship between human behavioral responses during perceptual decision-making and corresponding brain signals in the form of electroencephalograms (EEG). In this paper, a generalized 3D convolutional neural network (CNN) architecture is introduced to estimate RT for a simple visual task using single-trial multi-channel EEG. Earlier comparable studies have also employed a number of machine learning and deep learning-based models, but none of them considered inter-channel relationships while estimating RT. On the contrary, the use of 3D convolutional layers enabled us to consider the spatial relationship among adjacent channels while simultaneously utilizing spectral information from individual channels. Our model can predict RT with a root mean square error of 91.5 ms and a correlation coefficient of 0.83. These results surpass all the previous results attained from different studies.Clinical relevance Novel approaches to decode brain signals can facilitate research on brain-computer interfaces (BCIs), psychology, and neuroscience, enabling people to utilize assistive devices by root-causing psychological or neuromuscular disorders.},
}
@article {pmid34892467,
year = {2021},
author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D},
title = {Frequency Superposition - A Multi-Frequency Stimulation Method in SSVEP-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5924-5927},
doi = {10.1109/EMBC46164.2021.9630511},
pmid = {34892467},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages. However, the existence of harmonics and the limited range of responsive frequencies in SSVEP make it challenging to further expand the number of targets without sacrificing other aspects of the interface or putting additional constraints on the system. This paper introduces a novel multi-frequency stimulation method for SSVEP and investigates its potential to effectively and efficiently increase the number of targets presented. The proposed stimulation method, obtained by the superposition of the stimulation signals at different frequencies, is size-efficient, allows single-step target identification, puts no strict constraints on the usable frequency range, can be suited to self-paced BCIs, and does not require specific light sources. In addition to the stimulus frequencies and their harmonics, the evoked SSVEP waveforms include frequencies that are integer linear combinations of the stimulus frequencies. Results of decoding SSVEPs collected from nine subjects using canonical correlation analysis (CCA) with only the frequencies and harmonics as reference, also demonstrate the potential of using such a stimulation paradigm in SSVEP-based BCIs.},
}
@article {pmid34892464,
year = {2021},
author = {Kobler, RJ and Hirayama, JI and Hehenberger, L and Lopes-Dias, C and Muller-Putz, GR and Kawanabe, M},
title = {On the interpretation of linear Riemannian tangent space model parameters in M/EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5909-5913},
doi = {10.1109/EMBC46164.2021.9630144},
pmid = {34892464},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Magnetoencephalography ; Space Simulation ; },
abstract = {Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signal. In simulations and two real MEG and EEG datasets, we demonstrate the validity of the proposed approach and investigate its behavior when the model assumptions are violated. Our results confirm that Riemannian tangent space methods are robust to differences in the source patterns across observations. We found that this robustness property also transfers to the associated patterns.},
}
@article {pmid34892463,
year = {2021},
author = {Tang, Y and Zhang, JJ and Corballis, PM and Hallum, LE},
title = {Towards the Classification of Error-Related Potentials using Riemannian Geometry.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5905-5908},
doi = {10.1109/EMBC46164.2021.9629583},
pmid = {34892463},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Feedback ; Humans ; },
abstract = {The error-related potential (ErrP) is an event-related potential (ERP) evoked by an experimental participant's recognition of an error during task performance. ErrPs, originally described by cognitive psychologists, have been adopted for use in brain-computer interfaces (BCIs) for the detection and correction of errors, and the online refinement of decoding algorithms. Riemannian geometry-based feature extraction and classification is a new approach to BCI which shows good performance in a range of experimental paradigms, but has yet to be applied to the classification of ErrPs. Here, we describe an experiment that elicited ErrPs in seven normal participants performing a visual discrimination task. Audio feedback was provided on each trial. We used multi-channel electroencephalogram (EEG) recordings to classify ErrPs (success/failure), comparing a Riemannian geometry-based method to a traditional approach that computes time-point features. Overall, the Riemannian approach outperformed the traditional approach (78.2% versus 75.9% accuracy, p <0.05); this difference was statistically significant (p <0.05) in three of seven participants. These results indicate that the Riemannian approach better captured the features from feedback-elicited ErrPs, and may have application in BCI for error detection and correction.},
}
@article {pmid34892460,
year = {2021},
author = {Mussabayeva, A and Jamwal, PK and Tahir Akhtar, M},
title = {Ensemble Learning Approach for Subject-Independent P300 Speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5893-5896},
doi = {10.1109/EMBC46164.2021.9629679},
pmid = {34892460},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Support Vector Machine ; },
abstract = {P300 speller is a brain-computer interface (BCI) speller system, used for enabling human with different paralyzing disorders, such as amyotrophic lateral sclerosis (ALS), to communicate with the outer world by processing electroencephalography (EEG) signals. Different people have different latency and amplitude of the P300 event-related potential (ERP) component, which is used as the main feature for detecting the target character. In order to achieve robust results for different subjects using generic training (GT), the ensemble learning classifiers are proposed based on linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and convolutional neural network (CNN). The proposed models are trained using data from healthy subjects and tested on both healthy subjects and ALS patients. The results show that the fusion of LDA, kNN and SVM provides the most accurate results, achieving the accuracy of 99% for healthy subjects and about 85% for ALS patients.},
}
@article {pmid34892456,
year = {2021},
author = {Venot, T and Corsi, MC and Saint-Bauzel, L and Vico Fallani, F},
title = {Towards multimodal BCIs: the impact of peripheral control on motor cortex activity and sense of agency.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5876-5879},
doi = {10.1109/EMBC46164.2021.9630021},
pmid = {34892456},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Motor Cortex ; Reproducibility of Results ; },
abstract = {In the recent years, brain computer interfaces (BCI) using motor imagery have shown some limitations regarding the quality of control. In an effort to improve this promising technology, some studies intended to develop hybrid BCI with other technologies such as eye tracking which shows more reliability. However, the use of an eye tracker in the control of a robot might affect by itself the sense of agency (SoA) and the brain activity in the regions used for motor imagery (MI). Here, we explore the link between the sense of agency and the activity of the motor cortex. For this purpose, we used of a virtual arm projected on a surface which is either controlled by motion capture or controlled by gaze using an eye tracker. We found out that there is an activity in the motor cortex during the task of control by gaze and that having control over a projected robotic arm presents significant differences with the situation of observing the robot moving.},
}
@article {pmid34892453,
year = {2021},
author = {Floreani, ED and Rowley, D and Khan, N and Kelly, D and Robu, I and Kirton, A and Kinney-Lang, E},
title = {Unlocking Independence: Exploring Movement with Brain-Computer Interface for Children with Severe Physical Disabilities.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5864-5867},
doi = {10.1109/EMBC46164.2021.9630578},
pmid = {34892453},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Child ; Electroencephalography ; Humans ; Movement ; Pilot Projects ; *Self-Help Devices ; },
abstract = {Children with severe physical disabilities are often unable to independently explore their environments, further contributing to complex developmental delays. Brain-computer interfaces (BCIs) could be a novel access method to power mobility for children who struggle to use existing alternate access technologies, allowing them to reap the developmental, social, and psychological benefits of independent mobility. In this pilot study we demonstrated that children with quadriplegic cerebral palsy can use a simple BCI system to explore movement with a power mobility device. Four children were able to use the BCI to drive forward at least 7m, although more practice is needed to achieve more efficient driving skills through sustained BCI activations.},
}
@article {pmid34892452,
year = {2021},
author = {Lu, HY and Bollimunta, A and Eaton, RW and Morrison, JH and Moxon, KA and Carmena, JM and Nassi, JJ and Santacruz, SR},
title = {Short-training Algorithm for Online Brain-machine Interfaces Using One-photon Microendoscopic Calcium Imaging.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5860-5863},
doi = {10.1109/EMBC46164.2021.9629838},
pmid = {34892452},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Calcium ; Photons ; },
abstract = {Calcium imaging has great potential to be applied to online brain-machine interfaces (BMIs). As opposed to two-photon imaging settings, a one-photon microendoscopic imaging device can be chronically implanted and is subject to little motion artifacts. Traditionally, one-photon microendoscopic calcium imaging data are processed using the constrained nonnegative matrix factorization (CNMFe) algorithm, but this batched processing algorithm cannot be applied in real-time. An online analysis of calcium imaging data algorithm (or OnACIDe) has been proposed, but OnACIDe updates the neural components by repeatedly performing neuron identification frame-by-frame, which may decelerate the update speed if applying to online BMIs. For BMI applications, the ability to track a stable population of neurons in real-time has a higher priority over accurately identifying all the neurons in the field of view. By leveraging the fact that 1) microendoscopic recordings are rather stable with little motion artifacts and 2) the number of neurons identified in a short training period is sufficient for potential online BMI tasks such as cursor movements, we proposed the short-training CNMFe algorithm (stCNMFe) that skips motion correction and neuron identification processes to enable a more efficient BMI training program in a one-photon microendoscopic setting.},
}
@article {pmid34892450,
year = {2021},
author = {Marjaninejad, A and Klaes, C and Valero-Cuevas, FJ},
title = {Data-efficient Causal Decoding of Spiking Neural Activity using Weighted Voting.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5850-5855},
doi = {10.1109/EMBC46164.2021.9631022},
pmid = {34892450},
issn = {2694-0604},
support = {R21 HD048566/HD/NICHD NIH HHS/United States ; R21 NS113613/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Parietal Lobe ; Politics ; },
abstract = {Brain-Computer Interface systems can contribute to a vast set of applications such as overcoming physical disabilities in people with neural injuries or hands-free control of devices in healthy individuals. However, having systems that can accurately interpret intention online remains a challenge in this field. Robust and data-efficient decoding-despite the dynamical nature of cortical activity and causality requirements for physical function-is among the most important challenges that limit the widespread use of these devices for real-world applications. Here, we present a causal, data-efficient neural decoding pipeline that predicts intention by first classifying recordings in short sliding windows. Next, it performs weighted voting over initial predictions up to the current point in time to report a refined final prediction. We demonstrate its utility by classifying spiking neural activity collected from the human posterior parietal cortex for a cue, delay, imaginary motor task. This pipeline provides higher classification accuracy than state-of-the-art time windowed spiking activity based causal methods, and is robust to the choice of hyper-parameters.},
}
@article {pmid34892447,
year = {2021},
author = {Chen, J and Yi, W and Wang, D},
title = {Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5837-5841},
doi = {10.1109/EMBC46164.2021.9629728},
pmid = {34892447},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery-based brain computer interface (MI-BCI) is a representative active BCI paradigm which is widely employed in the rehabilitation field. In MI-BCI, a classification model is built to identify the target limb from MI-based EEG signals, but the performance of models cannot meet the demand for practical use. Lightweight neural networks in deep learning methods are used to build high performance models in MI-BCI. Small sample sizes and the lack of multi-scale information extraction in frequency domain limit the performance improvement of lightweight neural networks. To solve these problems, the Filter Bank Sinc-ShallowNet (FB-Sinc-ShallowNet) algorithm combined with the mixed noise adding method based on empirical mode decomposition (EMD) was proposed. The FB-Sinc-ShallowNet algorithm improves a lightweight neural network Sinc-ShallowNet with a filter bank structure corresponding to four sensory motor rhythms. The mixed noise adding method employs the EMD method to improve the quality of generated data. The proposed method was evaluated on the BCI competition IV IIa dataset and can achieve highest average accuracy of 77.2%, about 6.34% higher than state-of-the-art method Sinc-ShallowNet. This work implies the effectiveness of filter bank structure in lightweight neural networks and provides a novel option for data augmentation and classification of MI-based EEG signals, which can be applied in the rehabilitation field for decoding MI-EEG with few samples.},
}
@article {pmid34892439,
year = {2021},
author = {Su, E and Cai, S and Li, P and Xie, L and Li, H},
title = {Auditory Attention Detection with EEG Channel Attention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5804-5807},
doi = {10.1109/EMBC46164.2021.9630508},
pmid = {34892439},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; Speech ; *Speech Perception ; },
abstract = {Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin across all test cases.},
}
@article {pmid34892438,
year = {2021},
author = {Xu, L and Ma, Z and Meng, J and Xu, M and Jung, TP and Ming, D},
title = {Improving Transfer Performance of Deep Learning with Adaptive Batch Normalization for Brain-computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5800-5803},
doi = {10.1109/EMBC46164.2021.9629529},
pmid = {34892438},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Recently, transfer learning and deep learning have been introduced to solve intra- and inter-subject variability problems in Brain-Computer Interfaces. However, the generalization ability of these BCIs is still to be further verified in a cross-dataset scenario. This study compared the transfer performance of manifold embedded knowledge transfer and pre-trained EEGNet with three preprocessing strategies. This study also introduced AdaBN for target domain adaptation. The results showed that EEGNet with Riemannian alignment and AdaBN could achieve the best transfer accuracy about 65.6% on the target dataset. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.},
}
@article {pmid34892437,
year = {2021},
author = {Chen, XJ and Collins, LM and Mainsah, BO},
title = {Mitigating the Impact of Psychophysical Effects During Adaptive Stimulus Selection in the P300 Speller Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5796-5799},
pmid = {34892437},
issn = {2694-0604},
support = {R21 DC018347/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; },
abstract = {Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using sensory stimuli to elicit specific neural signal components called event-related potentials (ERPs) to control external devices. However, psychophysical factors, such as refractory effects and adjacency distractions, may negatively impact ERP elicitation and BCI performance. Although conventional BCI stimulus presentation paradigms usually design stimulus presentation schedules in a pseudo-random manner, recent studies have shown that controlling the stimulus selection process can enhance ERP elicitation. In prior work, we developed an algorithm to adaptively select BCI stimuli using an objective criterion that maximizes the amount of information about the user's intent that can be elicited with the presented stimuli given current data conditions. Here, we enhance this adaptive BCI stimulus selection algorithm to mitigate adjacency distractions and refractory effects by modeling temporal dependencies of ERP elicitation in the objective function and imposing spatial restrictions in the stimulus search space. Results from simulations using synthetic data and human data from a BCI study show that the enhanced adaptive stimulus selection algorithm can improve spelling speeds relative to conventional BCI stimulus presentation paradigms.Clinical relevance-Increased communication rates with our enhanced adaptive stimulus selection algorithm can potentially facilitate the translation of BCIs as viable communication alternatives for individuals with severe neuromuscular limitations.},
}
@article {pmid34892436,
year = {2021},
author = {Phyo Wai, AA and Ern Tchen, J and Guan, C},
title = {A Study of Visual Search based Calibration Protocol for EEG Attention Detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5792-5795},
doi = {10.1109/EMBC46164.2021.9631083},
pmid = {34892436},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; Cognition ; *Electroencephalography ; Humans ; Recognition, Psychology ; },
abstract = {Attention, a multi-faceted cognitive process, is essential in our daily lives. We can measure visual attention using an EEG Brain-Computer Interface for detecting different levels of attention in gaming, performance training, and clinical applications. In attention calibration, we use Flanker task to capture EEG data for attentive class. For EEG data belonging to inattentive class calibration, we instruct subject not focusing on a specific position on screen. We then classify attention levels using binary classifier trained with these surrogate ground-truth classes. However, subjects may not be in desirable attention conditions when performing repetitive boring activities over a long experiment duration. We propose attention calibration protocols in this paper that use simultaneous visual search with an audio directional change paradigm and static white noise as 'attentive' and 'inattentive' conditions, respectively. To compare the performance of proposed calibrations against baselines, we collected data from sixteen healthy subjects. For a fair comparison of classification performance; we used six basic EEG band-power features with a standard binary classifier. With the new calibration protocol, we achieved 74.37 ± 6.56% mean subject accuracy, which is about 3.73 ± 2.49% higher than the baseline, but there were no statistically significant differences. According to post-experiment survey results, new calibrations are more effective in inducing desired perceived attention levels. We will improve calibration protocols with reliable attention classifier modeling to enable better attention recognition based on these promising results.},
}
@article {pmid34892433,
year = {2021},
author = {Malekzadeh-Arasteh, O and Pu, H and Danesh, AR and Lim, J and Wang, PT and Liu, CY and Do, AH and Nenadic, Z and Heydari, P},
title = {A Fully-Integrated 1µW/Channel Dual-Mode Neural Data Acquisition System for Implantable Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5780-5783},
doi = {10.1109/EMBC46164.2021.9630058},
pmid = {34892433},
issn = {2694-0604},
mesh = {Amplifiers, Electronic ; *Brain-Computer Interfaces ; Humans ; Prostheses and Implants ; },
abstract = {This paper presents an ultra-low power mixed-signal neural data acquisition (MSN-DAQ) system that enables a novel low-power hybrid-domain neural decoding architecture for implantable brain-machine interfaces with high channel count. Implemented in 180nm CMOS technology, the 32-channel custom chip operates at 1V supply voltage and achieves excellent performance including 1.07µW/channel, 2.37/5.62 NEF/PEF and 88dB common-mode rejection ratio (CMRR) with significant back-end power-saving advantage compared to prior works. The fabricated prototype was further evaluated with in vivo human tests at bedside, and its performance closely follows that of a commercial recording system.},
}
@article {pmid34892432,
year = {2021},
author = {Chang, Y and Saritac, M},
title = {Decoding Brain Activity Features to Recognize Distorted Objects.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5776-5779},
doi = {10.1109/EMBC46164.2021.9630360},
pmid = {34892432},
issn = {2694-0604},
mesh = {Brain/diagnostic imaging ; *Brain Mapping ; Humans ; *Magnetic Resonance Imaging ; Neural Networks, Computer ; Visual Perception ; },
abstract = {Brain decoding is able to make human interact with an external machine or robot for assisting patient's rehabilitation. Brain generic object recognition ability can be decoded through multiple neuroimaging modalities like functional magnetic resonance imaging (fMRI). On the other hand, external machine may wrongly recognize objects due to distorted noisy or blurring images caused by many factors, and therefore deteriorate performance of brain-machine interaction. In order to create better machine, generalization capability of human brain is transferred to classifier for enhancing classification accuracy of distorted images. Since homology existing between human and machine vision has been demonstrated, through decoding neural activity features of fMRI signals into feature units of convolutional neural network layers, an enhanced object recognition method is proposed to integrate brain activity into classifier for increasing classification accuracy. Experimental results show that the proposed method is able to enhance generalization capability of distorted object recognition.},
}
@article {pmid34892421,
year = {2021},
author = {Adama, S and Bogdan, M},
title = {Yes/No Classification of EEG data from CLIS patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5727-5732},
doi = {10.1109/EMBC46164.2021.9629716},
pmid = {34892421},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; *Electroencephalography ; Humans ; Support Vector Machine ; },
abstract = {The goal of this research is to evaluate the usability of new features to classify EEG data from several completely locked-in patients (CLIS), and eventually build a more reliable communication system for them. Patients in such state are completely paralyzed, preventing them to be able to talk, but they retain their cognitive abilities.The data were obtained from four CLIS patients and recorded during an auditory paradigm task during which they were asked yes/no questions. Spectral measures such as the relative power of δ, θ, α, β and γ frequency bands, spectral edge frequencies (SEF50 and SEF95), complexity measure obtained from Poincaré plots and connectivity measures such as the imaginary part of coherency and the weighted Symbolic Mutual Information (wSMI) were used as features. The data was classified using Random Forest and Support Vector Machine, two methods successfully used to classify mental states in both healthy subjects and patients. Additionally, two cases were studied. The first case uses data recorded when the patient is answering questions, while in the second case it also includes data recorded when the experimenter is asking the questions.The classification accuracy during training varies between 51.73 to 67.72% in the first case, and from 50.41 to 67.94% for the second case. Overall, wSMI with a time lag of 64 ms gave the best classification accuracy and in general, Random Forest appears to be the best classification method.Clinical relevance This case study investigates the usability of new features based on EEG complexity and connectivity to classify CLIS patients brain signal, what results in a further step toward the demand of more effective EEG-based Brain-Computer Interface communication systems for CLIS patients.},
}
@article {pmid34892414,
year = {2021},
author = {Armengol-Urpi, A and Salazar-Gomez, AF and Sarma, SE},
title = {A Novel Approach to Decode Covert Spatial Attention Using SSVEP and Single-Frequency Phase-Coded Stimuli.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5694-5699},
doi = {10.1109/EMBC46164.2021.9630688},
pmid = {34892414},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Visual Cortex ; },
abstract = {This paper investigates for the first time the use of single-frequency phase-coded stimuli to detect covert visuo-spatial attention (CVSA) with steady-state visual evoked potentials (SSVEP). Two 15Hz pattern-onset stimulations were encoded with opposite phases and simultaneously presented on a LCD monitor. The effects of attending each stimulus on the amplitudes and phases of the evoked SSVEPs across the visual cortex are explored. A real-time CVSA classification experiment was simulated offline with 9 BCI-naive subjects, achieving an average classification accuracy of 88.4 ± 8% SE. Our results are, to our knowledge, the first report that CVSA can be decoded with SSVEP using single-frequency phase-coded stimuli. This opens opportunities for attention-tracking applications with largely increased number of targets.},
}
@article {pmid34892412,
year = {2021},
author = {Shiels, TA and Oxley, TJ and Fitzgerald, PB and Opie, NL and Wong, YT and Grayden, DB and John, SE},
title = {Feasibility of using discrete Brain Computer Interface for people with Multiple Sclerosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {5686-5689},
doi = {10.1109/EMBC46164.2021.9629518},
pmid = {34892412},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Feasibility Studies ; Humans ; *Multiple Sclerosis ; Quality of Life ; },
abstract = {AIM: Brain-Computer Interfaces (BCIs) hold promise to provide people with partial or complete paralysis, the ability to control assistive technology. This study reports offline classification of imagined and executed movements of the upper and lower limb in one participant with multiple sclerosis and people with no limb function deficits.<br><br>METHODS: We collected neural signals using electroencephalography (EEG) while participants performed executed and imagined motor tasks as directed by prompts shown on a screen.<br><br>RESULTS: Participants with no limb function attained >70% decoding accuracy on their best-imagined task compared to rest and on at-least one task comparison. The participant with multiple sclerosis also achieved accuracies within the range of participants with no limb function loss.Clinical Relevance - While only one case study is provided it was promising that the participant with MS was able to achieve comparable classification to that of the seven healthy controls. Further studies are needed to assess whether people suffering from MS may be able to use a BCI to improve their quality of life.},
}
@article {pmid34892323,
year = {2021},
author = {Yu, H and Qi, Y and Wang, H and Pan, G},
title = {Secure typing via BCI system with encrypted feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {4969-4973},
doi = {10.1109/EMBC46164.2021.9630645},
pmid = {34892323},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Feedback ; },
abstract = {Information transmission security is an important issue in many scenarios such as password input. Traditional approaches such as typing or voice input are prone to peep, leading to a risk of information leakage. Brain computer interface (BCI) can read information directly from the brain, which is confidential inherently, thus it may be an ideal way for secure information input. This paper proposes a novel BCI-based secure input approach with encrypted feedback. The encrypted feedback is specially designed to notify users and confuse peepers at the same time. We give the theoretical guarantee of accuracy and evaluate the system with both simulation and experiments. The results show that our method can transmit messages effectively.},
}
@article {pmid34891729,
year = {2021},
author = {Panachakel, JT and G, RA},
title = {Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {2226-2229},
doi = {10.1109/EMBC46164.2021.9630699},
pmid = {34891729},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Speech ; },
abstract = {Phonological categories in articulated speech are defined based on the place and manner of articulation. In this work, we investigate whether the phonological categories of the prompts imagined during speech imagery lead to differences in phase synchronization in various cortical regions that can be discriminated from the EEG captured during the imagination. Nasal and bilabial consonant are the two phonological categories considered due to their differences in both place and manner of articulation. Mean phase coherence (MPC) is used for measuring the phase synchronization and shallow neural network (NN) is used as the classifier. As a benchmark, we have also designed another NN based on statistical parameters extracted from imagined speech EEG. The NN trained on MPC values in the beta band gives classification results superior to NN trained on alpha band MPC values, gamma band MPC values and statistical parameters extracted from the EEG.Clinical relevance: Brain-computer interface (BCI) is a promising tool for aiding differently-abled people and for neurorehabilitation. One of the challenges in designing speech imagery based BCI is the identification of speech prompts that can lead to distinct neural activations. We have shown that nasal and blilabial consonants lead to dissimilar activations. Hence prompts orthogonal in these phonological categories are good choices as speech imagery prompts.},
}
@article {pmid34891708,
year = {2021},
author = {Yoshioka, N and Araki, N and Ohsuga, M},
title = {Importance of the Features of Event-Related Potentials Used for a Machine Learning-Based Model Applied to Single-Trial Data during Oddball Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {2123-2126},
doi = {10.1109/EMBC46164.2021.9629947},
pmid = {34891708},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials ; Humans ; Machine Learning ; },
abstract = {In this study, a method for assessing the human state and brain-machine interface (BMI) has been developed using event-related potentials (ERPs). Most of these algorithms are classified based on the ERP characteristics. To observe the characteristics of ERPs, an averaging method using electroencephalography (EEG) signals cut out by time-locking to the event for each condition is required. To date, several classification methods using only single-trial EEG signals have been studied. In some cases, the machine learning models were used for the classifications; however, the relationship between the constructed model and the characteristics of ERPs remains unclear. In this study, the LightGBM model was constructed for each individual to classify a single-trial waveform and visualize the relationship between these features and the characteristics of ERPs. The features used in the model were the average values and standard deviation of the EEG amplitude with a time width of 10 ms. The best area under the curve (AUC) score was 0.92, but, in some cases, the AUC scores were low. Large individual differences in AUC scores were observed. In each case, on checking the importance of the features, high importance was shown at the 10-ms time width section, where a large difference was observed in ERP waveforms between the target and the non-target. Since the model constructed in this study was found to reflect the characteristics of ERP, as the next step, we would like to try to improve the discrimination performance by using stimuli that the participants can concentrate on with interest.},
}
@article {pmid34891478,
year = {2021},
author = {Kocanaogullari, D and Huang, X and Mak, J and Shih, M and Skidmore, E and Wittenberg, GF and Ostadabbas, S and Akcakaya, M},
title = {Fine-tuning and Personalization of EEG-based Neglect Detection in Stroke Patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {1096-1099},
doi = {10.1109/EMBC46164.2021.9630794},
pmid = {34891478},
issn = {2694-0604},
mesh = {Electroencephalography ; Functional Laterality ; Humans ; *Perceptual Disorders/diagnosis ; *Stroke/diagnosis ; Visual Fields ; },
abstract = {Spatial neglect (SN) is a neurological disorder that causes inattention to visual stimuli in the contralesional visual field, stemming from unilateral brain injury such as stroke. The current gold standard method of SN assessment, the conventional Behavioral Inattention Test (BIT-C), is highly variable and inconsistent in its results. In our previous work, we built an augmented reality (AR)-based BCI to overcome the limitations of the BIT-C and classified between neglected and non-neglected targets with high accuracy. Our previous approach included personalization of the neglect detection classifier but the process required rigorous retraining from scratch and time-consuming feature selection for each participant. Future steps of our work will require rapid personalization of the neglect classifier; therefore, in this paper, we investigate fine-tuning of a neural network model to hasten the personalization process.},
}
@article {pmid34891468,
year = {2021},
author = {Ran, X and Zhang, Y and Shen, C and Yvert, B and Chen, W and Zhang, S},
title = {Dimensionality Reduction of Local Field Potential Features with Convolution Neural Network in Neural Decoding: A Pilot Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {1047-1050},
doi = {10.1109/EMBC46164.2021.9630630},
pmid = {34891468},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Motor Cortex ; Neural Networks, Computer ; Pilot Projects ; Principal Component Analysis ; },
abstract = {Local field potentials (LFPs) have better long-term stability compared with spikes in brain-machine interfaces (BMIs). Many studies have shown promising results of LFP decoding, but the high-dimensional feature of LFP still hurdle the development of the BMIs to low-cost. In this paper, we proposed a framework of a 1D convolution neural network (CNN) to reduce the dimensionality of the LFP features. For evaluating the performance of this architecture, the reduced LFP features were decoded to cursor position (Center-out task) by a Kalman filter. The Principal components analysis (PCA) was also performed as a comparison. The results showed that the CNN model could reduce the dimensionality of LFP features to a smaller size without significant performance loss. The decoding result based on the CNN features outperformed that based on the PCA features. Moreover, the reduced features by CNN also showed robustness across different sessions. These results demonstrated that the LFP features reduced by the CNN model achieved low cost without sacrificing high-performance and robustness, suggesting that this method could be used for portable BMI systems in the future.},
}
@article {pmid34891458,
year = {2021},
author = {Solorzano-Espindola, CE and Zamora, E and Sossa, H},
title = {Multi-subject classification of Motor Imagery EEG signals using transfer learning in neural networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {1006-1009},
doi = {10.1109/EMBC46164.2021.9630155},
pmid = {34891458},
issn = {2694-0604},
mesh = {Algorithms ; *Electroencephalography ; Humans ; *Imagination ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Brain-Computer Interfaces are new technologies with a fast development due to their possible usages, which still require overcoming some challenges to be readily usable. The paradigm of motor imagery is among the ones in these types of systems where the pipeline is tuned to work with only one person as it fails to classify the signals of a different person. Deep Learning methods have been gaining attention for tasks involving high-dimensional unstructured data, like EEG signals, but fail to generalize when trained on small datasets. In this work, to acquire a benchmark, we evaluate the performance of several classifiers while decoding signals from a new subject using a leave-one-out approach. Then we test the classifiers on the previous experiment and a method based on transfer learning in neural networks to classify the signals of multiple persons at a time. The resulting neural network classifier achieves a classification accuracy of 73% on the evaluation sessions of four subjects at a time and 74% on three at a time on the BCI competition IV 2a dataset.},
}
@article {pmid34891448,
year = {2021},
author = {Zerafa, R and Camilleri, T and Camilleri, KP},
title = {SAT: A Switch-And-Train Framework for Real-Time Training of SSVEP-based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {959-962},
doi = {10.1109/EMBC46164.2021.9629488},
pmid = {34891448},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Reducing the training time for brain computer interfaces based on steady state evoked potentials, is essential to develop practical applications. We propose to eliminate the training required by the user before using the BCI with a switch-and-train (SAT) framework. Initially the BCI uses a training-free detection algorithm, and once sufficient training data is collected online, the BCI switches to a subject-specific training-based algorithm. Furthermore, the training-based algorithm is continuously re-trained in real-time. The performance of the SAT framework reached that of training-based algorithms for 8 out of 10 subjects after an average of 179 s ±33 s, an overall improvement over the training-free algorithm of 8.06%.},
}
@article {pmid34891447,
year = {2021},
author = {Gomez-Orozco, V and Martinez, CB and Augusto Cardenas-Pena, D and Herrera, PM and Angel Orozco-Guitierrez, A},
title = {EEG representation approach based on Kernel Canonical Correlation Analysis highlighting discriminative patterns for BCI applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {955-958},
doi = {10.1109/EMBC46164.2021.9630538},
pmid = {34891447},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) is applied in the study of different cognitive processes or clinical conditions as enhancing cognitive skills, motor rehabilitation, and control. However, many approaches focus on using a robust classifier instead of providing a better feature space. This work develops a feature representation methodology through the kernel canonical correlation analysis to reveal nonlinear relations between filter-banked common spatial patterns (CSP) extracted. Our approach reveals nonlinear relations between ranked filter-banked multi-class CSP features and the labels in a finite-dimensional canonical space. We tested the performance of our methodology on the BCI Competition IV dataset 2a. The introduced feature representation using a classic linear SVM achieves accuracy rates competitive with the state-of-the-art BCI strategies. Besides, the processing pipeline allows identifying the spatial and spectral features driven by the underlying brain activity and best modeling the motor imagery intentions.Clinical relevance- This BCI strategy assesses the nonlinear relationships between time series to improve the interpretation of brain electrical activity, taking into account the spatial and spectral features driven by the underlying brain dynamic.},
}
@article {pmid34891438,
year = {2021},
author = {Kainolda, Y and Abibullaev, B and Sameni, R and Zollanvari, A},
title = {Is Riemannian Geometry Better than Euclidean in Averaging Covariance Matrices for CSP-based Subject-Independent Classification of Motor Imagery?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {910-914},
doi = {10.1109/EMBC46164.2021.9629816},
pmid = {34891438},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagery, Psychotherapy ; },
abstract = {Common Spatial Pattern (CSP) is a popular feature extraction algorithm used for electroencephalogram (EEG) data classification in brain-computer interfaces. One of the critical operations used in CSP is taking the average of trial covariance matrices for each class. In this regard, the arithmetic mean, which minimizes the sum of squared Euclidean distances to the data points, is conventionally used; however, this operation ignores the Riemannian geometry in the manifold of covariance matrices. To alleviate this problem, Fréchet mean determined using different Riemannian distances have been used. In this paper, we are primarily concerned with the following question: Does using the Fréchet mean with Riemannian distances instead of arithmetic mean in averaging CSP covariance matrices improve the subject-independent classification of motor imagery (MI)? To answer this question we conduct a comparative study using the largest MI dataset to date, with 54 subjects and a total of 21,600 trials of left-and right-hand MI. The results indicate a general trend of having a statistically significant better performance when the Riemannian geometry is used.},
}
@article {pmid34891437,
year = {2021},
author = {Sciaraffa, N and Germano, D and Giorgi, A and Ronca, V and Vozzi, A and Borghini, G and Di Flumeri, G and Babiloni, F and Arico, P},
title = {Mental Effort Estimation by Passive BCI: A Cross-Subject Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {906-909},
doi = {10.1109/EMBC46164.2021.9630613},
pmid = {34891437},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; },
abstract = {Despite the technological advancements, the employment of passive brain computer interface (BCI) out of the laboratory context is still challenging. This is largely due to methodological reasons. On the one hand, machine learning methods have shown their potential in maximizing performance for user mental states classification. On the other hand, the issues related to the necessary and frequent calibration of algorithms and to the temporal resolution of the measurement (i.e. how long it takes to have a reliable state measure) are still unsolved. This work explores the performances of a passive BCI system for mental effort monitoring consisting of three frontal electroencephalographic (EEG) channels. In particular, three calibration approaches have been tested: an intra-subject approach, a cross-subject approach, and a free-calibration procedure based on the simple average of theta activity over the three employed channels. A Random Forest model has been employed in the first two cases. The results obtained during multi-tasking have shown that the cross-subject approach allows the classification of low and high mental effort with an AUC higher than 0.9, with a related time resolution of 45 seconds. Moreover, these performances are not significantly different from the intra-subject approach although they are significantly higher than the calibration-free approach. In conclusion, these results suggest that a light (three EEG channels) passive BCI system based on a Random Forest algorithm and cross-subject calibration could be a simple and reliable tool for out-of-the-lab employment.},
}
@article {pmid34891434,
year = {2021},
author = {Mendes, BV and Tome, AM and Santos, IM and Bem-Haja, P},
title = {Analysis of eyewitness testimony using electroencephalogram signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {894-897},
doi = {10.1109/EMBC46164.2021.9630054},
pmid = {34891434},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Support Vector Machine ; },
abstract = {Face recognition and related psychological phenomenon have been the subject of neurocognitive studies during last decades. More recently the problem of face identification is also addressed to test the possibility of finding markers on the electroencephalogram signals. To this end, this work presents an experimental study where Brain Computer Interface strategies were implemented to find features on the signals that could discriminate between culprit and innocent. The feature extraction block comprises time domain and frequency domain characteristics of single-trial signals. The classification block is based on a support vector machine and its performance for the best ranked features. The data analysis comprises the signals of a cohort of 28 participants.},
}
@article {pmid34891430,
year = {2021},
author = {Zhu, S and Hosni, SI and Huang, X and Borgheai, SB and McLinden, J and Shahriari, Y and Ostadabbas, S},
title = {A Graph-Based Feature Extraction Algorithm Towards a Robust Data Fusion Framework for Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {878-881},
doi = {10.1109/EMBC46164.2021.9630804},
pmid = {34891430},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Support Vector Machine ; },
abstract = {OBJECTIVE: The topological information hidden in the EEG spectral dynamics is often ignored in the majority of the existing brain-computer interface (BCI) systems. Moreover, a systematic multimodal fusion of EEG with other informative brain signals such as functional near-infrared spectroscopy (fNIRS) towards enhancing the performance of the BCI systems is not fully investigated. In this study, we present a robust EEG-fNIRS data fusion framework utilizing a series of graph-based EEG features to investigate their performance on a motor imaginary (MI) classification task.<br><br>METHOD: We first extract the amplitude and phase sequences of users' multi-channel EEG signals based on the complex Morlet wavelet time-frequency maps, and then convert them into an undirected graph to extract EEG topological features. The graph-based features from EEG are then selected by a thresholding method and fused with the temporal features from fNIRS signals after each being selected by the least absolute shrinkage and selection operator (LASSO) algorithm. The fused features were then classified as MI task vs. baseline by a linear support vector machine (SVM) classifier.<br><br>RESULTS: The time-frequency graphs of EEG signals improved the MI classification accuracy by &#x223c;5% compared to the graphs built on the band-pass filtered temporal EEG signals. Our proposed graph-based method also showed comparable performance to the classical EEG features based on power spectral density (PSD), however with a much smaller standard deviation, showing its robustness for potential use in a practical BCI system. Our fusion analysis revealed a considerable improvement of &#x223c;17% as opposed to the highest average accuracy of EEG only and &#x223c;3% compared with the highest fNIRS only accuracy demonstrating an enhanced performance when modality fusion is used relative to single modal outcomes.<br><br>SIGNIFICANCE: Our findings indicate the potential use of the proposed data fusion framework utilizing the graph-based features in the hybrid BCI systems by making the motor imaginary inference more accurate and more robust.},
}
@article {pmid34891414,
year = {2021},
author = {Qiao, J and Tang, J and Yang, J and Xu, M and Ming, D},
title = {Basic Graphic Shape Decoding for EEG-based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {812-815},
doi = {10.1109/EMBC46164.2021.9630661},
pmid = {34891414},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {Image decoding using electroencephalogram (EEG) has became a new topic for brain-computer interface (BCI) studies in recent years. Previous studies often tried to decode EEG signals modulated by a picture of complex object. However, it's still unclear how a simple image with different positions and orientations influence the EEG signals. To this end, this study used a same white bar with eight different spatial patterns as visual stimuli. Convolutional neural network (CNN) combined with long short-term memory (LSTM) was employed to decode the corresponding EEG signals. Four subjects were recruited in this study. As a result, the highest binary classification accuracy could reach 97.2%, 95.7%, 90.2%, and 88.3% for the four subjects, respectively. Almost all subjects could achieve more than 70% for 4-class classification. The results demonstrate basic graphic shapes are decodable from EEG signals, which hold promise for image decoding of EEG-based BCIs.},
}
@article {pmid34891410,
year = {2021},
author = {R, V and Robinson, N and Reddy M, R and Guan, C},
title = {Performance Evaluation of Compressed Deep CNN for Motor Imagery Classification using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {795-799},
doi = {10.1109/EMBC46164.2021.9631018},
pmid = {34891410},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Recently, deep learning and convolutional neural networks (CNNs) have reported several promising results in the classification of Motor Imagery (MI) using Electroencephalography (EEG). With the gaining popularity of CNN-based BCI, the challenges in deploying it in a real-world mobile and embedded device with limited computational and memory resources need to be explored. Towards this objective, we investigate the impact of the magnitude-based weight pruning technique to reduce the number of parameters of the pre-trained CNN-based classifier while maintaining its performance. We evaluated the proposed method on an open-source Korea University dataset which consists of 54 healthy subjects' EEG, recorded while performing right-and left-hand MI. Experimental results demonstrate that the subject-independent model can be maximumly pruned to 90% sparsity, with a compression ratio of 4.77× while retaining classification accuracy at 84.44% with minimal loss of 0.02% when compared to the baseline model's performance. Therefore, the proposed method can be used to design more compact deep CNN- based BCIs without compromising on their performance.},
}
@article {pmid34891409,
year = {2021},
author = {Samuel, OW and Asogbon, MG and Ejay, E and Geng, Y and Lopez-Delis, A and Jarrah, YA and Idowu, OP and Chen, S and Fang, P and Li, G},
title = {A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL's Motor Imagery Characterization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {791-794},
doi = {10.1109/EMBC46164.2021.9629547},
pmid = {34891409},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; },
abstract = {Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for brain-computer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method.Clinical Relevance- This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.},
}
@article {pmid34891407,
year = {2021},
author = {Tun, NN and Sanuki, F and Iramina, K},
title = {EEG-EMG Correlation Analysis with Linear and Nonlinear Coupling Methods Across Four Motor Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {783-786},
doi = {10.1109/EMBC46164.2021.9629969},
pmid = {34891407},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; *Motor Cortex ; Movement ; },
abstract = {Correlation between brain and muscle signal is referred to as functional coupling. The amount of correlation between two signals greatly depends on the motor task performance. In this study, we designed the experimental paradigm with four types of motor tasks such as real hand grasping movement (RM), movement intention (Inten), motor imagery (MI) and only looking at virtual hand in three dimensional head mounted display (OL). We aimed to investigate EEG-EMG correlation with linear and nonlinear coupling methods. The results proved that high correlation could be occurred in RM and Inten tasks rather than MI and OL tasks in both linear and nonlinear methods. High coherence occurred in beta and gamma bands of RM and Inten tasks whereas no coherence was detected in MI and OL tasks. In terms of nonlinear correlation, the high mutual information was detected in RM and Inten tasks. There was slight mutual information in MI and OL tasks. The results showed that the coherence in the contralateral brain cortex was higher than in the ipsilateral motor cortex during motor tasks. Furthermore, the amount of EEG-EMG functional coupling changed according to the motor task executed.},
}
@article {pmid34891403,
year = {2021},
author = {Hernandez-Gonzalez, E and Gomez-Gil, P and Bojorges-Valdez, E and Ramirez-Cortes, M},
title = {Bi-dimensional representation of EEGs for BCI classification using CNN architectures.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {767-770},
doi = {10.1109/EMBC46164.2021.9629958},
pmid = {34891403},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {An important challenge when designing Brain Computer Interfaces (BCI) is to create a pipeline (signal conditioning, feature extraction and classification) requiring minimal parameter adjustments for each subject and each run. On the other hand, Convolutional Neural Networks (CNN) have shown outstanding to automatically extract features from images, which may help when distribution of input data is unknown and irregular. To obtain full benefits of a CNN, we propose two meaningful image representations built from multichannel EEG signals. Images are built from spectrograms and scalograms. We evaluated two kinds of classifiers: one based on a CNN-2D and the other built using a CNN-2D combined with a LSTM. Our experiments showed that this pipeline allows to use the same channels and architectures for all subjects, getting competitive accuracy using different datasets: 71.3 ± 11.9% for BCI IV-2a (four classes); 80.7 ± 11.8 % for BCI IV-2a (two classes); 73.8 ± 12.1% for BCI IV-2b; 83.6 ± 1.0% for BCI II-III and 82.10% ± 6.9% for a private database based on mental calculation.},
}
@article {pmid34891372,
year = {2021},
author = {Dias, C and Costa, DM and Sousa, T and Castelhano, J and Figueiredo, V and Pereira, AC and Castelo-Branco, M},
title = {Classification of erroneous actions using EEG frequency features: implications for BCI performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {629-632},
doi = {10.1109/EMBC46164.2021.9630509},
pmid = {34891372},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Several studies have demonstrated that error-related neuronal signatures can be successfully detected and used to improve the performance of brain-computer interfaces. However, this has been tested mainly in well-controlled environments and based on temporal features, such as the amplitude of event-related potentials. In this study, we propose a classification algorithm combining frequency features and a weighted SVM to detect the neuronal signatures of errors committed in a complex saccadic go/no-go task. We follow the hypothesis that frequency features yield better discrimination performance in complex tasks, generalize better, and require fewer pre-processing steps. When combining temporal and frequency features, we achieved a balanced classification accuracy of 75% - almost the same as using only frequency features. On the other hand, when using only temporal features, the balanced accuracy decreased to 66%. These findings show that subjects' performance can be automatically detected based on frequency features of error-related neuronal signatures. Additionally, our results revealed that features computed in the pre-response time contribute to the discrimination between correct and erroneous responses, which suggests the existence of error-related patterns even before response execution.},
}
@article {pmid34891358,
year = {2021},
author = {Sartipi, S and Torkamani-Azar, M and Cetin, M},
title = {EEG Emotion Recognition via Graph-based Spatio-Temporal Attention Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {571-574},
doi = {10.1109/EMBC46164.2021.9629628},
pmid = {34891358},
issn = {2694-0604},
mesh = {Arousal ; *Brain-Computer Interfaces ; *Electroencephalography ; Emotions ; Neural Networks, Computer ; },
abstract = {Emotion recognition based on electroencephalography (EEG) signals has been receiving significant attention in the domains of affective computing and brain-computer interfaces (BCI). Although several deep learning methods have been proposed dealing with the emotion recognition task, developing methods that effectively extract and use discriminative features is still a challenge. In this work, we propose the novel spatio-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of multi-column convolutional neural network and attention-based bidirectional long-short term memory. Moreover, we explore the inter-channel relationships of EEG signals via graph signal processing (GSP) tools. Our experimental analysis demonstrates that the proposed network improves the state-of-the-art results in subject-wise, binary classification of valence and arousal levels as well as four-class classification in the valence-arousal emotion space when raw EEG signals or their graph representations, in an architecture coined as GFT-STANN, are used as model inputs.},
}
@article {pmid34891328,
year = {2021},
author = {Igasaki, T and Kuramura, Y and Takemoto, J},
title = {Motor Imagery, Execution, and Observation Classification using Small Amount of EEG Data with Multiple Two-Class CNNs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {443-446},
doi = {10.1109/EMBC46164.2021.9629942},
pmid = {34891328},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; },
abstract = {This study attempted to classify a small amount of electroencephalogram (EEG) data on five states: four tasks involving right index-finger flexion (kinesthetic motor imagery, visual motor imagery, motor execution, and motor observation) and resting with eyes open. We employed a convolutional neural network (CNN) as a classifier and compared the classification accuracies of two types of CNNs: 1) a "single five-class CNN," which classified the aforementioned states with a single CNN and 2) "multiple two-class CNNs," wherein ten CNNs that classify pairs of states were combined. In addition, the classification accuracies were compared between two scenarios: one wherein the EEGs from all 19 scalp probe electrodes (19-channel EEG) were adopted as input data for the CNN, and the other wherein the EEGs of four regions closely related to the motor execution and observation of the index finger (4-channel EEG) were adopted. The classification accuracies of the single five-class CNN with 19- and 4-channel EEGs were 48.2 ± 5.9% and 46.6 ± 6.9%, respectively, and those of the multiple two-class CNNs with 19- and 4-channel EEGs were 52.8 ± 9.7% and 47.5 ± 9.4%, respectively. These results indicate the effectiveness of multiple two-class CNNs that utilize the EEGs of all scalp electrodes as input data for classifying motor imagery, execution, and observation, even in the case of the marginal dataset.},
}
@article {pmid34891323,
year = {2021},
author = {Pradeepkumar, J and Anandakumar, M and Kugathasan, V and Lalitharatne, TD and De Silva, AC and Kappel, SL},
title = {Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {420-423},
doi = {10.1109/EMBC46164.2021.9630958},
pmid = {34891323},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Gestures ; Humans ; Neural Networks, Computer ; Principal Component Analysis ; },
abstract = {Hand gesture decoding is a key component of controlling prosthesis in the area of Brain Computer Interface (BCI). This study is concerned with classification of hand gestures, based on Electrocorticography (ECoG) recordings. Recent studies have utilized the temporal information in ECoG signals for robust hand gesture decoding. In our preliminary analysis on ECoG recordings of hand gestures, we observed different power variations in six frequency bands ranging from 4 to 200 Hz. Therefore, the current trend of including temporal information in the classifier was extended to provide equal importance to power variations in each of these frequency bands. Statistical and Principal Component Analysis (PCA) based feature reduction was implemented for each frequency band separately, and classification was performed with a Long Short-Term Memory (LSTM) based neural network to utilize both temporal and spatial information of each frequency band. The proposed architecture along with each feature reduction method was tested on ECoG recordings of five finger flexions performed by seven subjects from the publicly available 'fingerflex' dataset. An average classification accuracy of 82.4% was achieved with the statistical based channel selection method which is an improvement compared to state-of-the-art methods.},
}
@article {pmid34891304,
year = {2021},
author = {Bolanos, MC and Barrado Ballestero, S and Puthusserypady, S},
title = {Filter bank approach for enhancement of supervised Canonical Correlation Analysis methods for SSVEP-based BCI spellers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {337-340},
doi = {10.1109/EMBC46164.2021.9630838},
pmid = {34891304},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; },
abstract = {Canonical correlation analysis (CCA) is one of the most used algorithms in the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) systems due to its simplicity, efficiency, and robustness. Researchers have proposed modifications to CCA to improve its speed, allowing high-speed spelling and thus a more natural communication. In this work, we combine two approaches, the filter-bank (FB) approach to extract more information from the harmonics, and a range of different supervised methods which optimize the reference signals to improve the SSVEP detection. The proposed models are tested on the publicly available benchmark dataset for SSVEP-based BCIs and the results show improved performance compared to the state-of-the-art methods and, in particular, the proposed FBMwayCCA approach achieves the best results with an information transfer rate (ITR) of 134.8±8.4 bits/minute. This study indeed suggests the feasibility of combining the fundamental and harmonic SSVEP components with supervised methods in target identification to develop high-speed BCI spellers.},
}
@article {pmid34891303,
year = {2021},
author = {Kumaravel, VP and Kartsch, V and Benatti, S and Vallortigara, G and Farella, E and Buiatti, M},
title = {Efficient Artifact Removal from Low-Density Wearable EEG using Artifacts Subspace Reconstruction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {333-336},
doi = {10.1109/EMBC46164.2021.9629771},
pmid = {34891303},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; Electroencephalography ; Signal Processing, Computer-Assisted ; *Wearable Electronic Devices ; },
abstract = {Light-weight, minimally-obtrusive mobile EEG systems with a small number of electrodes (i.e., low-density) allow for convenient monitoring of the brain activity in out-of-the-lab conditions. However, they pose a higher risk for signal contamination with non-stereotypical artifacts due to hardware limitations and the challenging environment where signals are collected. A promising solution is Artifacts Subspace Reconstruction (ASR), a component-based approach that can automatically remove non-stationary transient-like artifacts in EEG data. Since ASR has only been validated with high-density systems, it is unclear whether it is equally efficient on low-density portable EEG. This paper presents a complete analysis of ASR performance based on clean and contaminated datasets acquired with BioWolf, an Ultra-Low-Power system featuring only eight channels, during SSVEP sessions recorded from six adults. Empirical results show that even with such few channels, ASR efficiently corrects artifacts, enabling an overall enhancement of up to 40% in SSVEP response. Furthermore, by choosing the optimal ASR parameters on a single-subject basis, SSVEP response can be further increased to more than 45%. These results suggest that ASR is a viable and robust method for online automatic artifact correction with low-density BCI systems in real-life scenarios.},
}
@article {pmid34891300,
year = {2021},
author = {Dolzhikova, I and Abibullaev, B and Sameni, R and Zollanvari, A},
title = {An Ensemble CNN for Subject-Independent Classification of Motor Imagery-based EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {319-324},
doi = {10.1109/EMBC46164.2021.9630419},
pmid = {34891300},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; },
abstract = {Deep learning methods, and in particular Convolutional Neural Networks (CNNs), have shown breakthrough performance in a wide variety of classification applications, including electroencephalogram-based Brain Computer Interfaces (BCIs). Despite the advances in the field, BCIs are still far from the subject-independent decoding of brain activities, primarily due to substantial inter-subject variability. In this study, we examine the potential application of an ensemble CNN classifier to integrate the capabilities of CNN architectures and ensemble learning for decoding EEG signals collected in motor imagery experiments. The results prove the superiority of the proposed ensemble CNN in comparison with the average base CNN classifiers, with an improvement up to 9% in classification accuracy depending on the test subject. The results also show improvement with respect to the performance of a number of state-of-the-art methods that have been previously used for subject-independent classification in the same datasets used here (i.e., BCI Competition IV 2A and 2B datasets).},
}
@article {pmid34891272,
year = {2021},
author = {Correia, JR and Miguel Sanches, J and Mainardi, L},
title = {Error perception classification in Brain-Computer Interfaces using CNN.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {204-207},
doi = {10.1109/EMBC46164.2021.9631080},
pmid = {34891272},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Data Collection ; Electroencephalography ; Humans ; Neural Networks, Computer ; Perception ; },
abstract = {Capturing the error perception of a human interacting with a Brain-Computer Interface (BCI) is a key piece in improving the accuracy of these systems and making the interaction more seamless. Convolutional Neural Networks (CNN) have recently been applied for this task rendering the model free of feature-selection. We propose a new model with shorter temporal input trying to approximate its usability to that of a real-time BCI application. We evaluate and compare our model with some other recent CNN models using the Monitoring Error-Related Potential dataset, obtaining an accuracy of 80% with a sensitivity and specificity of 76% and 85%, respectively. These results outperform previous models. All models are made available online for reproduction and peer review.},
}
@article {pmid34891260,
year = {2021},
author = {Qiu, W and Yang, B and Ma, J and Gao, S and Zhu, Y and Wang, W},
title = {The Paradigm Design of a Novel 2-class Unilateral Upper Limb Motor Imagery Tasks and its EEG Signal Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {152-155},
doi = {10.1109/EMBC46164.2021.9630837},
pmid = {34891260},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagery, Psychotherapy ; Upper Extremity ; },
abstract = {Multitasking motor imagery (MI) of the unilateral upper limb is potentially more valuable in stroke rehabilitation than the current conventional MI in both hands. In this paper, a novel experimental paradigm was designed to imagine two motions of unilateral upper limb, which is hand gripping and releasing, and elbow reciprocating left and right. During this experiment, the electroencephalogram (EEG) signals were collected from 10 subjects. The time and frequency domains of the EEG signals were analyzed and visualized, indicating the presence of different Event-Related Desynchronization (ERD) or Event-Related Synchronization (ERS) for the two tasks. Then the two tasks were classified through three different EEG decoding methods, in which the optimized convolutional neural network (CNN) based on FBCNet achieved an average accuracy of 67.8%, obtaining a good recognition result. This work not only can advance the studies of MI decoding of unilateral upper limb, but also can provide a basis for better upper limb stroke rehabilitation in MI-BCI.},
}
@article {pmid34891255,
year = {2021},
author = {Pan, C and Lai, YH and Chen, F},
title = {The Effects of Classification Method and Electrode Configuration on EEG-based Silent Speech Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {131-134},
doi = {10.1109/EMBC46164.2021.9629709},
pmid = {34891255},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Humans ; Records ; *Speech ; },
abstract = {The effective classification for imagined speech and intended speech is of great help to the development of speech-based brain-computer interfaces (BCIs). This work distinguished imagined speech and intended speech by employing the cortical EEG signals recorded from scalp. EEG signals from eleven subjects were recorded when they produced Mandarin-Chinese monosyllables in imagined speech and intended speech, and EEG features were classified by the common spatial pattern, time-domain, frequency-domain and Riemannian manifold based methods. The classification results indicated that the Riemannian manifold based method yielded the highest classification accuracy of 85.9% among the four classification methods. Moreover, the classification accuracy with the left-only brain electrode configuration was close to that with the whole brain electrode configuration. The findings of this work have potential to extend the output commands of silent speech interfaces.},
}
@article {pmid34891254,
year = {2021},
author = {Tao, Y and Sun, T and Muhamed, A and Genc, S and Jackson, D and Arsanjani, A and Yaddanapudi, S and Li, L and Kumar, P},
title = {Gated Transformer for Decoding Human Brain EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2021},
number = {},
pages = {125-130},
doi = {10.1109/EMBC46164.2021.9630210},
pmid = {34891254},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {In this work, we propose to use a deep learning framework for decoding the electroencephalogram (EEG) signals of human brain activities. More specifically, we learn an end-to-end model that recognizes natural images or motor imagery by the EEG data that is collected from the corresponding human neural activities. In order to capture the temporal information encoded in the long EEG sequences, we first employ an enhanced version of Transformer, i.e., gated Transformer, on EEG signals to learn the feature representation along a sequence of embeddings. Then a fully-connected Softmax layer is used to predict the classification results of the decoded representations. To demonstrate the effectiveness of the gated Transformer approach, we conduct experiments on the image classification task for a human brain-visual dataset and the classification task for a motor imagery dataset. The experimental results show that our method achieves new state-of-the-art performance compared to multiple existing methods that are widely used for EEG classification.},
}
@article {pmid34891146,
year = {2021},
author = {Parashiva, PK and Vinod, AP},
title = {Single-trial detection of EEG error-related potentials in serial visual presentation paradigm.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ac4200},
pmid = {34891146},
issn = {2057-1976},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; },
abstract = {When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP).Objective-In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis.Method-The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features.Results -The ErrP data recorded from 11 subjects' have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is69.09±4.70%and63.33±4.56%for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods.Significance -The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.},
}
@article {pmid34888902,
year = {2022},
author = {Zhang, Y and Gao, Y and Fang, K and Ye, J and Ruan, Y and Yang, X and Zhang, Y and Thompson, G and Chen, G and Zhang, X},
title = {Proton/Deuterium Magnetic Resonance Imaging of Rodents at 9.4T Using Birdcage Coils.},
journal = {Bioelectromagnetics},
volume = {43},
number = {1},
pages = {40-46},
doi = {10.1002/bem.22382},
pmid = {34888902},
issn = {1521-186X},
support = {2018YFA0701400//National Key Research and Development Program of China/ ; 61771423//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; //MOE Frontier Science Center for Brain Science &amp; Brain-machine Integration at Zhejiang University/ ; 2018EB0ZX01//Zhejiang Lab/ ; 2018B030333001//Key-Area Research and Development Program of Guangdong Province/ ; },
mesh = {Animals ; Deuterium ; Equipment Design ; Magnetic Resonance Imaging ; Phantoms, Imaging ; *Protons ; *Rodentia ; },
abstract = {The purpose of the present study was to fabricate a volume coil for proton/deuterium magnetic resonance imaging (MRI) in rodents at 9.4 T. Two birdcage radiofrequency (RF) coils have been designed for proton/deuterium MRI: the rungs of two concentric birdcages were azimuthally interleaved with each other for better decoupling, and the two coils were tuned to 400.3 and 61.4 MHz for [1] H/[2] H resonance at 9.4 T. Compared to a commercially available coil, the proposed [1] H/[2] H RF coil provides reasonable transmission efficiency and imaging signal-to-noise ratio (SNR); the relationships among imaging parameters such as SNR, voxel size, and deuterium oxide concentrations have been quantitatively studied, and the linear correlation results together with the spectroscopic data in vivo indicate its feasibility in deuterium metabolic imaging (DMI) in vivo. Our study indicates that using the birdcage design for MRI signal excitation combined with surface coil array for signal reception can facilitate DMI investigations more effectively towards future pre-clinical and clinical applications. As a noninvasive method by measuring nonhydrogen nuclear deuterium signals to reflect metabolite information, DMI will feature prominently in future precision medicine through the whole process of diagnosis, treatment, and prognosis. © 2021 Bioelectromagnetics Society.},
}
@article {pmid34888758,
year = {2021},
author = {Roh, H and Yoon, YJ and Park, JS and Kang, DH and Kwak, SM and Lee, BC and Im, M},
title = {Fabrication of High-Density Out-of-Plane Microneedle Arrays with Various Heights and Diverse Cross-Sectional Shapes.},
journal = {Nano-micro letters},
volume = {14},
number = {1},
pages = {24},
pmid = {34888758},
issn = {2150-5551},
abstract = {Out-of-plane microneedle structures are widely used in various applications such as transcutaneous drug delivery and neural signal recording for brain machine interface. This work presents a novel but simple method to fabricate high-density silicon (Si) microneedle arrays with various heights and diverse cross-sectional shapes depending on photomask pattern designs. The proposed fabrication method is composed of a single photolithography and two subsequent deep reactive ion etching (DRIE) steps. First, a photoresist layer was patterned on a Si substrate to define areas to be etched, which will eventually determine the final location and shape of each individual microneedle. Then, the 1st DRIE step created deep trenches with a highly anisotropic etching of the Si substrate. Subsequently, the photoresist was removed for more isotropic etching; the 2nd DRIE isolated and sharpened microneedles from the predefined trench structures. Depending on diverse photomask designs, the 2nd DRIE formed arrays of microneedles that have various height distributions, as well as diverse cross-sectional shapes across the substrate. With these simple steps, high-aspect ratio microneedles were created in the high density of up to 625 microneedles mm[-2] on a Si wafer. Insertion tests showed a small force as low as ~ 172 µN/microneedle is required for microneedle arrays to penetrate the dura mater of a mouse brain. To demonstrate a feasibility of drug delivery application, we also implemented silk microneedle arrays using molding processes. The fabrication method of the present study is expected to be broadly applicable to create microneedle structures for drug delivery, neuroprosthetic devices, and so on.},
}
@article {pmid34883949,
year = {2021},
author = {Khan, H and Noori, FM and Yazidi, A and Uddin, MZ and Khan, MNA and Mirtaheri, P},
title = {Classification of Individual Finger Movements from Right Hand Using fNIRS Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {23},
pages = {},
pmid = {34883949},
issn = {1424-8220},
support = {273599//The Research Council of Norway/ ; },
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Humans ; Movement ; *Spectroscopy, Near-Infrared ; Support Vector Machine ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky-Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were 0.75±0.04, 0.75±0.05, and 0.77±0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.},
}
@article {pmid34882542,
year = {2022},
author = {Wong, CM and Wang, Z and Nakanishi, M and Wang, B and Rosa, A and Chen, CLP and Jung, TP and Wan, F},
title = {Online Adaptation Boosts SSVEP-Based BCI Performance.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {6},
pages = {2018-2028},
doi = {10.1109/TBME.2021.3133594},
pmid = {34882542},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {OBJECTIVE: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs.<br><br>METHODS: An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method.<br><br>RESULTS: A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning.<br><br>CONCLUSION: By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms.<br><br>SIGNIFICANCE: This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.},
}
@article {pmid34878977,
year = {2021},
author = {Zhang, X and Hou, W and Wu, X and Feng, S and Chen, L},
title = {A Novel Online Action Observation-Based Brain-Computer Interface That Enhances Event-Related Desynchronization.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2605-2614},
doi = {10.1109/TNSRE.2021.3133853},
pmid = {34878977},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Hand ; Humans ; Imagination ; Movement ; },
abstract = {Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient' motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)-based BCI was proposed. The visual stimuli of four types of hand movements were designed to simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital region and to activate the sensorimotor region. Task-related component analysis was performed to identify the SSMVEP. Results showed that the amplitude of the induced frequency in the SSMVEP had a negative relationship with the stimulus frequency. The classification accuracy in the four-class scenario reached 72.81 ± 13.55% within 2.5s. Importantly, the AO-based closed-loop BCI, which provided visual feedback based on the SSMVEP, could enhance ERD compared with AO-alone. The increased attentiveness might be one key factor for the enhancement of the ERD in the designed AO-based BCI. In summary, the proposed AO-based BCI provides a new insight for BCI-based rehabilitation.},
}
@article {pmid34875637,
year = {2021},
author = {Liang, L and Bin, G and Chen, X and Wang, Y and Gao, S and Gao, X},
title = {Optimizing a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac40a1},
pmid = {34875637},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Visual Fields ; },
abstract = {Objective.Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has the characteristics of fast communication speed, high stability, and wide applicability, thus it has been widely studied. With the rapid development in paradigm, algorithm, and system design, SSVEP-BCI is gradually applied in clinical and real-life scenarios. In order to improve the ease of use of the SSVEP-BCI system, many studies have been focusing on developing it on the hairless area, but due to the lack of redesigning the stimulation paradigm to better adapt to the new area, the electroencephalography response in the hairless area is worse than occipital region.Approach. This study first proposed a phase difference estimation method based on stimulating the left and right visual field separately, then developed and optimized a left and right visual field biphasic stimulation paradigm for SSVEP-based BCIs with hairless region behind the ear.Main results.In the 12-target online experiment, after a short model estimation training, all 16 subjects used their best stimulus condition. The paradigm designed in this study can increase the proportion of applicable subjects for the behind-ear SSVEP-BCI system from 58.3% to 75% and increase the accuracy from 74.6 ± 20.0% (the existing best SSVEP stimulus with hairless region behind the ear) to 84.2±14.7%, and the information transfer rate from 14.2±6.4 bits min[-1]to 17.8±5.7 bits min[-1].Significance.These results demonstrated that the proposed paradigm can effectively improve the BCI performance using the signal from the hairless region behind the ear, compared with the standard SSVEP stimulation paradigm.},
}
@article {pmid34874850,
year = {2022},
author = {Cooney, C and Folli, R and Coyle, D},
title = {A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {6},
pages = {1983-1994},
doi = {10.1109/TBME.2021.3132861},
pmid = {34874850},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Neural Networks, Computer ; Speech ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes of multiple signal modalities simultaneously. Bimodal data acquisition protocols combining the temporal resolution of electroencephalography (EEG) with the spatial resolution of functional near-infrared spectroscopy (fNIRS) require novel approaches to decoding.<br><br>METHODS: We present an EEG-fNIRS Hybrid BCI that employs a new bimodal deep neural network architecture consisting of two convolutional sub-networks (subnets) to decode overt and imagined speech. Features from each subnet are fused before further feature extraction and classification. Nineteen participants performed overt and imagined speech in a novel cue-based paradigm enabling investigation of stimulus and linguistic effects on decoding.<br><br>RESULTS: Using the hybrid approach, classification accuracies (46.31% and 34.29% for overt and imagined speech, respectively (chance: 25%)) indicated a significant improvement on EEG used independently for imagined speech (p = 0.020) while tending towards significance for overt speech (p = 0.098). In comparison with fNIRS, significant improvements for both speech-types were achieved with bimodal decoding (p<0.001). There was a mean difference of &#x223c;12.02% between overt and imagined speech with accuracies as high as 87.18% and 53%. Deeper subnets enhanced performance while stimulus effected overt and imagined speech in significantly different ways.<br><br>CONCLUSION: The bimodal approach was a significant improvement on unimodal results for several tasks. Results indicate the potential of multi-modal deep learning for enhancing neural signal decoding.<br><br>SIGNIFICANCE: This novel architecture can be used to enhance speech decoding from bimodal neural signals.},
}
@article {pmid34874291,
year = {2021},
author = {Faisal, SN and Amjadipour, M and Izzo, K and Singer, JA and Bendavid, A and Lin, CT and Iacopi, F},
title = {Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac4085},
pmid = {34874291},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; *Graphite ; Reproducibility of Results ; },
abstract = {Objective. Brain-machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion.Approach.Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity.Main results and significance.This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.},
}
@article {pmid34871742,
year = {2022},
author = {Iliopoulos, AC and Papasotiriou, I},
title = {Functional Complex Networks Based on Operational Architectonics: Application on EEG-based Brain-computer Interface for Imagined Speech.},
journal = {Neuroscience},
volume = {484},
number = {},
pages = {98-118},
doi = {10.1016/j.neuroscience.2021.11.045},
pmid = {34871742},
issn = {1873-7544},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Speech ; },
abstract = {A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEG signals, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.},
}
@article {pmid34871175,
year = {2021},
author = {Yuan, Z and Peng, Y and Wang, L and Song, S and Chen, S and Yang, L and Liu, H and Wang, H and Shi, G and Han, C and Cammon, JA and Zhang, Y and Qiao, J and Wang, G},
title = {Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2569-2577},
doi = {10.1109/TNSRE.2021.3132944},
pmid = {34871175},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Cognition ; Electroencephalography ; Feedback ; Humans ; Recovery of Function ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation interventions after stroke. Although BCIs bring many benefits to the rehabilitation process, their effects are limited since many patients cannot concentrate during training. Despite this outcome post-stroke motor-attention dual-task training using BCIs has remained mostly unexplored. This study was a randomized placebo-controlled blinded-endpoint clinical trial to investigate the effects of a BCI-controlled pedaling training system (BCI-PT) on the motor and cognitive function of stroke patients during rehabilitation. A total of 30 early subacute ischemic stroke patients with hemiplegia and cognitive impairment were randomly assigned to the BCI-PT or traditional pedaling training. We used single-channel Fp1 to collect electroencephalography data and analyze the attention index. The BCI-PT system timely provided visual, auditory, and somatosensory feedback to enhance the patient's participation to pedaling based on the real-time attention index. After 24 training sessions, the attention index of the experimental group was significantly higher than that of the control group. The lower limbs motor function (FMA-L) increased by an average of 4.5 points in the BCI-PT group and 2.1 points in the control group (P = 0.022) after treatments. The difference was still significant after adjusting for the baseline indicators (β = 2.41 , 95%CI: 0.48-4.34, P = 0.024). We found that BCI-PT significantly improved the patient's lower limb motor function by increasing the patient's participation. (clinicaltrials.gov: NCT04612426).},
}
@article {pmid34870643,
year = {2021},
author = {Yuan, Q and Qin, C and Duan, Y and Jiang, N and Liu, M and Wan, H and Zhuang, L and Wang, P},
title = {An in vivo bioelectronic nose for possible quantitative evaluation of odor masking using M/T cell spatial response patterns.},
journal = {The Analyst},
volume = {147},
number = {1},
pages = {178-186},
doi = {10.1039/d1an01569a},
pmid = {34870643},
issn = {1364-5528},
mesh = {Animals ; *Odorants ; *Olfactory Bulb ; Rats ; T-Lymphocytes ; },
abstract = {Odor masking is a prominent phenomenon in the biological olfactory perception system. It has been applied in industry and daily life to develop masking agents to reduce or even eliminate the adverse effects of unpleasant odors. However, it is challenging to assess the odor masking efficiency with traditional gas sensors. Here, we took advantage of the olfactory perception system of an animal to develop a system for the evaluation and quantification of odor masking based on an in vivo bioelectronic nose. The linear decomposition method was used to extract the features of the spatial response pattern of the mitral/tufted (M/T) cell population of the olfactory bulb of a rat to monomolecular odorants and their binary mixtures. Finally, the masking intensity was calculated to quantitatively measure the degree of interference of one odor to another in the biological olfactory system. Compared with the human sensory evaluation reported in a previous study, the trend of masking intensity obtained with this system positively correlated with the human olfactory system. The system could quantitatively analyze the masking efficiency of masking agents, as well as assist in the development of new masking agents or flavored food in odor or food companies.},
}
@article {pmid34870202,
year = {2021},
author = {Cajigas, I and Davis, KC and Meschede-Krasa, B and Prins, NW and Gallo, S and Naeem, JA and Palermo, A and Wilson, A and Guerra, S and Parks, BA and Zimmerman, L and Gant, K and Levi, AD and Dietrich, WD and Fisher, L and Vanni, S and Tauber, JM and Garwood, IC and Abel, JH and Brown, EN and Ivan, ME and Prasad, A and Jagid, J},
title = {Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury.},
journal = {Brain communications},
volume = {3},
number = {4},
pages = {fcab248},
pmid = {34870202},
issn = {2632-1297},
support = {F32 AG064886/AG/NIA NIH HHS/United States ; R25 NS108937/NS/NINDS NIH HHS/United States ; T32 EB019940/EB/NIBIB NIH HHS/United States ; T32 GM112601/GM/NIGMS NIH HHS/United States ; },
abstract = {Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78-93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80-98.95%) and an accuracy of 88.3% (range 77.6-95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp.},
}
@article {pmid34867760,
year = {2021},
author = {Angerhöfer, C and Colucci, A and Vermehren, M and Hömberg, V and Soekadar, SR},
title = {Post-stroke Rehabilitation of Severe Upper Limb Paresis in Germany - Toward Long-Term Treatment With Brain-Computer Interfaces.},
journal = {Frontiers in neurology},
volume = {12},
number = {},
pages = {772199},
pmid = {34867760},
issn = {1664-2295},
abstract = {Severe upper limb paresis can represent an immense burden for stroke survivors. Given the rising prevalence of stroke, restoration of severe upper limb motor impairment remains a major challenge for rehabilitation medicine because effective treatment strategies are lacking. Commonly applied interventions in Germany, such as mirror therapy and impairment-oriented training, are limited in efficacy, demanding for new strategies to be found. By translating brain signals into control commands of external devices, brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) represent promising, neurotechnology-based alternatives for stroke patients with highly restricted arm and hand function. In this mini-review, we outline perspectives on how BCI-based therapy can be integrated into the different stages of neurorehabilitation in Germany to meet a long-term treatment approach: We found that it is most appropriate to start therapy with BCI-based neurofeedback immediately after early rehabilitation. BCI-driven functional electrical stimulation (FES) and BMI robotic therapy are well suited for subsequent post hospital curative treatment in the subacute stage. BCI-based hand exoskeleton training can be continued within outpatient occupational therapy to further improve hand function and address motivational issues in chronic stroke patients. Once the rehabilitation potential is exhausted, BCI technology can be used to drive assistive devices to compensate for impaired function. However, there are several challenges yet to overcome before such long-term treatment strategies can be implemented within broad clinical application: 1. developing reliable BCI systems with better usability; 2. conducting more research to improve BCI training paradigms and 3. establishing reliable methods to identify suitable patients.},
}
@article {pmid34867220,
year = {2021},
author = {Latash, ML},
title = {Understanding and Synergy: A Single Concept at Different Levels of Analysis?.},
journal = {Frontiers in systems neuroscience},
volume = {15},
number = {},
pages = {735406},
pmid = {34867220},
issn = {1662-5137},
abstract = {Biological systems differ from the inanimate world in their behaviors ranging from simple movements to coordinated purposeful actions by large groups of muscles, to perception of the world based on signals of different modalities, to cognitive acts, and to the role of self-imposed constraints such as laws of ethics. Respectively, depending on the behavior of interest, studies of biological objects based on laws of nature (physics) have to deal with different salient sets of variables and parameters. Understanding is a high-level concept, and its analysis has been linked to other high-level concepts such as "mental model" and "meaning". Attempts to analyze understanding based on laws of nature are an example of the top-down approach. Studies of the neural control of movements represent an opposite, bottom-up approach, which starts at the interface with classical physics of the inanimate world and operates with traditional concepts such as forces, coordinates, etc. There are common features shared by the two approaches. In particular, both assume organizations of large groups of elements into task-specific groups, which can be described with only a handful of salient variables. Both assume optimality criteria that allow the emergence of families of solutions to typical tasks. Both assume predictive processes reflected in anticipatory adjustments to actions (motor and non-motor). Both recognize the importance of generating dynamically stable solutions. The recent progress in studies of the neural control of movements has led to a theory of hierarchical control with spatial referent coordinates for the effectors. This theory, in combination with the uncontrolled manifold hypothesis, allows quantifying the stability of actions with respect to salient variables. This approach has been used in the analysis of motor learning, changes in movements with typical and atypical development and with aging, and impaired actions by patients with various neurological disorders. It has been developed to address issues of kinesthetic perception. There seems to be hope that the two counter-directional approaches will meet and result in a single theoretical scheme encompassing biological phenomena from figuring out the best next move in a chess position to activating motor units appropriate for implementing that move on the chessboard.},
}
@article {pmid34867218,
year = {2021},
author = {Kuc, A and Korchagin, S and Maksimenko, VA and Shusharina, N and Hramov, AE},
title = {Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification.},
journal = {Frontiers in systems neuroscience},
volume = {15},
number = {},
pages = {716897},
pmid = {34867218},
issn = {1662-5137},
abstract = {Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.},
}
@article {pmid34867174,
year = {2021},
author = {Huang, JS and Liu, WS and Yao, B and Wang, ZX and Chen, SF and Sun, WF},
title = {Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {774857},
pmid = {34867174},
issn = {1662-4548},
abstract = {The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.},
}
@article {pmid34867163,
year = {2021},
author = {Soon, SXY and Kumar, AA and Tan, AJL and Lo, YT and Lock, C and Kumar, S and Kwok, J and Keong, NC},
title = {The Impact of Multimorbidity Burden, Frailty Risk Scoring, and 3-Directional Morphological Indices vs. Testing for CSF Responsiveness in Normal Pressure Hydrocephalus.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {751145},
pmid = {34867163},
issn = {1662-4548},
abstract = {Objective: Multimorbidity burden across disease cohorts and variations in clinico-radiographic presentations within normal pressure hydrocephalus (NPH) confound its diagnosis, and the assessment of its amenability to interventions. We hypothesized that novel imaging techniques such as 3-directional linear morphological indices could help in distinguishing between hydrocephalus vs. non-hydrocephalus and correlate with responsiveness to external lumbar drainage (CSF responsiveness) within NPH subtypes. Methodology: Twenty-one participants with NPH were recruited and age-matched to 21 patients with Alzheimer's Disease (AD) and 21 healthy controls (HC) selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Patients with NPH underwent testing via the NPH programme with external lumbar drainage (ELD); pre- and post-ELD MRI scans were obtained. The modified Frailty Index (mFI-11) was used to stratify the NPH cohort, including Classic and Complex subtypes, by their comorbidity and frailty risks. The quantitative imaging network tool 3D Slicer was used to derive traditional 2-dimensional (2d) linear measures; Evans Index (EI), Bicaudate Index (BCI) and Callosal Angle (CA), along with novel 3-directional (3d) linear measures; z-Evans Index and Brain per Ventricle Ratio (BVR). 3-Dimensional (3D) ventricular volumetry was performed as an independent correlate of ventriculomegaly to CSF responsiveness. Results: Mean age for study participants was 71.14 ± 6.3 years (18, 85.7% males). The majority (15/21, 71.4%) of participants with NPH comprised the Complex subtype (overlay from vascular risk burden and AD); 12/21 (57.1%) were Non-Responders to ELD. Frailty alone was insufficient in distinguishing between NPH subtypes. By contrast, 3d linear measures distinguished NPH from both AD and HC cohorts, but also correlated to CSF responsiveness. The z-Evans Index was the most sensitive volumetric measure of CSF responsiveness (p = 0.012). Changes in 3d morphological indices across timepoints distinguished between Responders vs. Non-Responders to lumbar testing. There was a significant reduction of indices, only in Non-Responders and across multiple measures (z-Evans Index; p = 0.001, BVR at PC; p = 0.024). This was due to a significant decrease in ventricular measurement (p = 0.005) that correlated to independent 3D volumetry (p = 0.008). Conclusion. In the context of multimorbidity burden, frailty risks and overlay from neurodegenerative disease, 3d morphological indices demonstrated utility in distinguishing hydrocephalus vs. non-hydrocephalus and degree of CSF responsiveness. Further work may support the characterization of patients with Complex NPH who would best benefit from the risks of interventions.},
}
@article {pmid34867150,
year = {2021},
author = {Zhang, X and Lu, Z and Zhang, T and Li, H and Wang, Y and Tao, Q},
title = {Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {727394},
pmid = {34867150},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.},
}
@article {pmid34864003,
year = {2022},
author = {Aubinet, C and Chatelle, C and Gosseries, O and Carrière, M and Laureys, S and Majerus, S},
title = {Residual implicit and explicit language abilities in patients with disorders of consciousness: A systematic review.},
journal = {Neuroscience and biobehavioral reviews},
volume = {132},
number = {},
pages = {391-409},
doi = {10.1016/j.neubiorev.2021.12.001},
pmid = {34864003},
issn = {1873-7528},
mesh = {*Consciousness/physiology ; Consciousness Disorders/diagnosis ; Humans ; *Language ; Persistent Vegetative State/diagnosis ; Wakefulness ; },
abstract = {Language assessment in post-comatose patients is difficult due to their limited behavioral repertoire; yet associated language deficits might lead to an underestimation of consciousness levels in unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS; -/+) diagnoses. We present a systematic review of studies from 2002 assessing residual language abilities with neuroimaging, electrophysiological or behavioral measures in patients with severe brain injury. Eighty-five articles including a total of 2278 patients were assessed for quality. The median percentages of patients showing residual implicit language abilities (i.e., cortical responses to specific words/sentences) were 33 % for UWS, 50 % for MCS- and 78 % for MCS + patients, whereas explicit language abilities (i.e., command-following using brain-computer interfaces) were reported in 20 % of UWS, 33 % of MCS- and 50 % of MCS + patients. Cortical responses to verbal stimuli increased along with consciousness levels and the progressive recovery of consciousness after a coma was paralleled by the reappearance of both implicit and explicit language processing. This review highlights the importance of language assessment in patients with disorders of consciousness.},
}
@article {pmid34858491,
year = {2021},
author = {Xiong, X and Yu, H and Wang, H and Jiang, J},
title = {Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {1462369},
pmid = {34858491},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Intention ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good.<br><br>METHOD: To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns.<br><br>RESULTS: The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects.<br><br>CONCLUSIONS: The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.},
}
@article {pmid34858156,
year = {2021},
author = {Nakayashiki, K and Tojiki, H and Hayashi, Y and Yano, S and Kondo, T},
title = {Brain Processes Involved in Motor Planning Are a Dominant Factor for Inducing Event-Related Desynchronization.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {764281},
pmid = {34858156},
issn = {1662-5161},
abstract = {Event-related desynchronization (ERD) is a relative attenuation in the spectral power of an electroencephalogram (EEG) observed over the sensorimotor area during motor execution and motor imagery. It is a well-known EEG feature and is commonly employed in brain-computer interfaces. However, its underlying neural mechanisms are not fully understood, as ERD is a single variable correlated with external events involving numerous pathways, such as motor intention, planning, and execution. In this study, we aimed to identify a dominant factor for inducing ERD. Participants were instructed to grasp their right hand with three different (10, 25, or 40%MVF: maximum voluntary force) levels under two distinct experimental conditions: a closed-loop condition involving real-time visual force feedback (VF) or an open-loop condition in a feedforward (FF) manner. In each condition, participants were instructed to repeat the grasping task a certain number of times with a timeline of Rest (10.0 s), Preparation (1.0 s), and Motor Execution (4.0 s) periods, respectively. EEG signals were recorded simultaneously with the motor task to evaluate the time-course of the event-related spectrum perturbation for each condition and dissect the modulation of EEG power. We performed statistical analysis of mu and beta-ERD under the instructed grasping force levels and the feedback conditions. In the FF condition (i.e., no force feedback), mu and beta-ERD were significantly attenuated in the contralateral motor cortex during the middle of the motor execution period, while ERD in the VF condition was maintained even during keep grasping. Only mu-ERD at the somatosensory cortex tended to be slightly stronger in high load conditions. The results suggest that the extent of ERD reflects neural activity involved in the motor planning process for changing virtual equilibrium point rather than the motor control process for recruiting motor neurons to regulate grasping force.},
}
@article {pmid34858136,
year = {2021},
author = {Valeriani, D and Ayaz, H and Kosmyna, N and Poli, R and Maes, P},
title = {Editorial: Neurotechnologies for Human Augmentation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {789868},
pmid = {34858136},
issn = {1662-4548},
}
@article {pmid34857289,
year = {2021},
author = {Xu, X and Sui, B and Liu, X and Sun, J},
title = {Superior low-immunogenicity of tilapia type I collagen based on unique secondary structure with single calcium binding motif over terrestrial mammals by inhibiting activation of DC intracellular Ca[2+]-mediated STIM1-Orai1/NF-кB pathway.},
journal = {Materials science &amp; engineering. C, Materials for biological applications},
volume = {131},
number = {},
pages = {112503},
doi = {10.1016/j.msec.2021.112503},
pmid = {34857289},
issn = {1873-0191},
mesh = {Animals ; Calcium/metabolism ; Calcium Channels ; Cattle ; Collagen Type I ; Mammals/metabolism ; Mice ; NF-kappa B/metabolism ; ORAI1 Protein ; *Percutaneous Coronary Intervention ; Stromal Interaction Molecule 1 ; Swine ; *Tilapia/metabolism ; },
abstract = {The reason for low- or non-immunogenicity of fish collagens is still in doubt, which, to some extent, bottlenecks their production and clinical application as biomaterials. Employing bovine or porcine type I collagens (BCI or PCI) as controls in this paper, we intensively investigate the influence of tilapia type I collagens (TCI) on the function of dendritic cells (DCs) and T cells. From bio-informatic analyses, as well as data obtained in vitro and in vivo, we find the variations in amino acid sequences lead to only one calcium binding motif in the secondary structure of TCI, compared with three in BCI or PCI. So when TCI (together with the minor amount of Ca[2+] they take) are uptaken, intracellular [Ca[2+]] remains stable and DCs maintain immature. On the contrary, those that have uptaken PCI or BCI experience not only increased [Ca[2+]] in the plasma but also phosphorylation of p65, resulting in activation of STIM1-Orai1/NF-кB signaling pathway and DC maturation. We fully prove our results on mice models, with no obvious cellular and humoral immune reactions. Our study primarily reveal the underlying mechanisms why TCI, different from BCI or PCI, show almost non-immunogenicity. Our findings are of great importance for the promotion and wide application of TCI in biomedicine.},
}
@article {pmid34855777,
year = {2021},
author = {Dreyer, AM and Rieger, JW},
title = {High-gamma mirror activity patterns in the human brain during reach-to-grasp movement observation, retention, and execution-An MEG study.},
journal = {PloS one},
volume = {16},
number = {12},
pages = {e0260304},
pmid = {34855777},
issn = {1932-6203},
mesh = {Humans ; *Magnetoencephalography/methods ; Male ; Adult ; Female ; *Movement/physiology ; *Mirror Neurons/physiology ; *Hand Strength/physiology ; *Brain/physiology ; Young Adult ; Psychomotor Performance/physiology ; Brain Mapping/methods ; },
abstract = {While the existence of a human mirror neuron system is evident, the involved brain areas and their exact functional roles remain under scientific debate. A number of functionally different mirror neuron types, neurons that selectively respond to specific grasp phases and types for example, have been reported with single cell recordings in monkeys. In humans, spatially limited, intracranially recorded electrophysiological signals in the high-gamma (HG) range have been used to investigate the human mirror system, as they are associated with spiking activity in single neurons. Our goal here is to complement previous intracranial HG studies by using magnetoencephalography to record HG activity simultaneously from the whole head. Participants performed a natural reach-to-grasp movement observation and delayed imitation task with different everyday objects and grasp types. This allowed us to characterize the spatial organization of cortical areas that show HG-activation modulation during movement observation (mirroring), retention (mnemonic mirroring), and execution (motor control). Our results show mirroring related HG modulation patterns over bilateral occipito-parietal as well as sensorimotor areas. In addition, we found mnemonic mirroring related HG modulation over contra-lateral fronto-temporal areas. These results provide a foundation for further human mirror system research as well as possible target areas for brain-computer interface and neurorehabilitation approaches.},
}
@article {pmid34852330,
year = {2021},
author = {Sunny, MSH and Hossain, S and Afroze, N and Hasan, MK and Hossain, E and Rahman, MH},
title = {Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination.},
journal = {Biomedical physics &amp; engineering express},
volume = {8},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ac3f17},
pmid = {34852330},
issn = {2057-1976},
mesh = {*Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.},
}
@article {pmid34852103,
year = {2021},
author = {Papini, CB and Campos, L and Nakamura, PM and Brito, BTG and Kokubun, E},
title = {Cost-analysis and cost-effectiveness of physical activity interventions in Brazilian primary health care: a randomised feasibility study.},
journal = {Ciencia &amp; saude coletiva},
volume = {26},
number = {11},
pages = {5711-5726},
doi = {10.1590/1413-812320212611.27142020},
pmid = {34852103},
issn = {1678-4561},
mesh = {Brazil ; Cost-Benefit Analysis ; *Exercise ; Feasibility Studies ; Humans ; *Primary Health Care ; Quality of Life ; },
abstract = {Physical exercise programs have been carried out in primary health care in Brazil and have provided good results in terms of effectiveness, their economic contribution has not been investigated yet. The aim of the study is to verify the feasibility of brief counseling physical activity intervention and to compare its economic cost and cost-effectiveness with supervised physical exercise intervention in primary care. A multi-arm parallel feasibility trial, with equal randomization [1:1:1] was conducted in Basic Health Units in Brazil. 61 participants were randomized in Brief Counseling Intervention (BCI), Supervised Physical Exercise Intervention (SPEI) and Control Group (CG). Interventions lasted one year. The BCI is more economical than the SPEI, costing around 50% less in the economic comparisons (session cost, annual cost and cost per participant annually). At leisure time, the cost to move one person to the physically active category at 12 months is estimated in R$369.00 for BCI and R$426.21 for the SPEI. The Incremental Cost-effectiveness Ratio (ICER) is R$310.32. The BCI is feasible and more economic, however, the cost effective is not that different. Thus, it is strongly recommended that the two interventions be offered at primary care in Brazil.},
}
@article {pmid34851830,
year = {2021},
author = {Ding, W and Shan, J and Fang, B and Wang, C and Sun, F and Li, X},
title = {Filter Bank Convolutional Neural Network for Short Time-Window Steady-State Visual Evoked Potential Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2615-2624},
doi = {10.1109/TNSRE.2021.3132162},
pmid = {34851830},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Canonical Correlation Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {Convolutional neural network (CNN) has been gradually applied to steady-state visual evoked potential (SSVEP) of the brain-computer interface (BCI). Frequency-domain features extracted by fast Fourier Transform (FFT) or time-domain signals are used as network input. In the frequency-domain diagram, the features at the short time-window are not obvious and the phase information of each electrode channel may be ignored as well. Hence we propose a time-domain-based CNN method (tCNN), using the time-domain signal as network input. And the filter bank tCNN (FB-tCNN) is further proposed to improve its performance in the short time-window. We compare FB-tCNN with the canonical correlation analysis (CCA) methods and other CNN methods in our dataset and public dataset. And FB-tCNN shows superior performance at the short time-window in the intra-individual test. At the 0.2 s time-window, the accuracy of our method reaches 88.36 ± 4.89 % in our dataset, 77.78 ± 2.16 % and 79.21 ± 1.80 % respectively in the two sessions of the public dataset, which is higher than other methods. The impacts of training-subject number and data length in inter-individual or cross-individual are studied. FB-tCNN shows the potential in implementing inter-individual BCI. Further analysis shows that the deep learning method is easier in terms of the implementation of the asynchronous BCI system than the training data-driven CCA. The code is available for reproducibility at https://github.com/DingWenl/FB-tCNN.},
}
@article {pmid34847547,
year = {2021},
author = {Sponheim, C and Papadourakis, V and Collinger, JL and Downey, J and Weiss, J and Pentousi, L and Elliott, K and Hatsopoulos, NG},
title = {Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
pmid = {34847547},
issn = {1741-2552},
support = {R01 NS111982/NS/NINDS NIH HHS/United States ; R01 NS045853/NS/NINDS NIH HHS/United States ; R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electrodes, Implanted ; *Longevity ; Macaca mulatta ; Microelectrodes ; Reproducibility of Results ; Utah ; },
abstract = {Objective.Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset.Approach.We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time.Main results.Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization.Significance.Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.},
}
@article {pmid34847047,
year = {2022},
author = {Miao, M and Hu, W and Xu, B and Zhang, J and Rodrigues, JJPC and de Albuquerque, VHC},
title = {Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {8},
pages = {3607-3617},
doi = {10.1109/JBHI.2021.3131186},
pmid = {34847047},
issn = {2168-2208},
mesh = {Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Electrooculography/methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people's emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.},
}
@article {pmid34847036,
year = {2021},
author = {Hsu, HT and Shyu, KK and Hsu, CC and Lee, LH and Lee, PL},
title = {Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2754-2764},
doi = {10.1109/TNSRE.2021.3131779},
pmid = {34847036},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; *Virtual Reality ; },
abstract = {Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.},
}
@article {pmid34840072,
year = {2022},
author = {Patel, KM and Kumar, NS and Desai, RG and Mitrev, L and Trivedi, K and Krishnan, S},
title = {Blunt Trauma to the Heart: A Review of Pathophysiology and Current Management.},
journal = {Journal of cardiothoracic and vascular anesthesia},
volume = {36},
number = {8 Pt A},
pages = {2707-2718},
doi = {10.1053/j.jvca.2021.10.018},
pmid = {34840072},
issn = {1532-8422},
mesh = {Accidents, Traffic ; Echocardiography ; *Heart Injuries/diagnostic imaging/etiology ; Humans ; *Thoracic Injuries/complications ; *Wounds, Nonpenetrating/diagnostic imaging/therapy ; },
abstract = {Blunt cardiac injury (BCI), defined as an injury to the heart from blunt force trauma, ranges from minor to life-threatening. The majority of BCIs are due to motor vehicle accidents; however, injuries caused by falls, blasts, and sports-related injuries also can be sources of BCI. A significant proportion of patients with BCI do not survive long enough to receive medical care, succumbing to their injuries at the scene of the accident. Additionally, patients with blunt trauma often have coexisting injuries (brain, spine, orthopedic) that can obscure the clinical picture; therefore, a high degree of suspicion often is required to diagnose BCI. Traditionally, hemodynamically stable injuries suspicious for BCI have been evaluated with electrocardiograms and chest radiographs, whereas hemodynamically unstable BCIs have received operative intervention. More recently, computed tomography and echocardiography increasingly have been utilized to identify injuries more rapidly in hemodynamically unstable patients. Transesophageal echocardiography can play an important role in the diagnosis and management of several BCIs that require operative repair. Close communication with the surgical team and access to blood products for potentially massive transfusion also play key roles in maintaining hemodynamic stability. With proper surgical and anesthetic care, survival in cases involving urgent cardiac repair can reach 66%-to-75%. This narrative review focuses on the types of cardiac injuries that are caused by blunt chest trauma, the modalities and techniques currently used to diagnose BCI, and the perioperative management of injuries that require surgical correction.},
}
@article {pmid34838951,
year = {2022},
author = {Thenmozhi, T and Helen, R},
title = {Feature Selection Using Extreme Gradient Boosting Bayesian Optimization to upgrade the Classification Performance of Motor Imagery signals for BCI.},
journal = {Journal of neuroscience methods},
volume = {366},
number = {},
pages = {109425},
doi = {10.1016/j.jneumeth.2021.109425},
pmid = {34838951},
issn = {1872-678X},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: A motor imagery (MI) based brain computer interface (BCI) is a challenging nonmuscular connection system used to independently perform movement-related tasks. It is gaining increasing prominence in helping paralyzed individuals communicate with the real world. Achieving better classification accuracy is the major concern in the field of motor imagery-based BCI. To upgrade the classification performance, relevant features play a vital role. The relevant features can be selected by the extreme gradient Bayesian optimization (XGBO) method.<br><br>METHODS: In this paper, a combination of time-, frequency-, and spatial-related MI features are extracted to design a reliable MI-BCI system. The proposed method incorporates the XGBO algorithm for feature selection and the random forest for the classification of EEG signals. The potency of the proposed system was investigated using two public EEG datasets (BCI Competition III dataset IIIa and dataset IVa). A novel XGBO algorithm increases the accuracy and reduces the time consumption by reducing the dimensionality of features. The proposed algorithm selects the minimum number of features that increase the computational efficacy for MI-based BCI systems.<br><br>The proposed method is compared with ANOVA, sequential forward selection, recursive feature elimination, and LASSO methods and the accuracy rate is increased with the lowest computation time.<br><br>RESULTS: The proposed method achieves mean accuracies of 94.44% and 88.72% and classification errors of 5.56% and 11.28% for Datasets IIIa and IVa, respectively. It outperforms four state-of-art methods with 0.87% and 0.59% increases in the accuracy for Datasets IIIa and IVa, respectively.},
}
@article {pmid34833681,
year = {2021},
author = {Lech, M and Czyżewski, A and Kucewicz, MT},
title = {CyberEye: New Eye-Tracking Interfaces for Assessment and Modulation of Cognitive Functions beyond the Brain.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {22},
pages = {},
pmid = {34833681},
issn = {1424-8220},
support = {POIR.04.04.00-00-4379/17//Foundation for Polish Science/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; Eye Movements ; *Eye-Tracking Technology ; },
abstract = {The emergence of innovative neurotechnologies in global brain projects has accelerated research and clinical applications of BCIs beyond sensory and motor functions. Both invasive and noninvasive sensors are developed to interface with cognitive functions engaged in thinking, communication, or remembering. The detection of eye movements by a camera offers a particularly attractive external sensor for computer interfaces to monitor, assess, and control these higher brain functions without acquiring signals from the brain. Features of gaze position and pupil dilation can be effectively used to track our attention in healthy mental processes, to enable interaction in disorders of consciousness, or to even predict memory performance in various brain diseases. In this perspective article, we propose the term 'CyberEye' to encompass emerging cognitive applications of eye-tracking interfaces for neuroscience research, clinical practice, and the biomedical industry. As CyberEye technologies continue to develop, we expect BCIs to become less dependent on brain activities, to be less invasive, and to thus be more applicable.},
}
@article {pmid34827524,
year = {2021},
author = {Saeidi, M and Karwowski, W and Farahani, FV and Fiok, K and Taiar, R and Hancock, PA and Al-Juaid, A},
title = {Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.},
journal = {Brain sciences},
volume = {11},
number = {11},
pages = {},
pmid = {34827524},
issn = {2076-3425},
support = {TURSP-2020/229//Taif University/ ; },
abstract = {Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.},
}
@article {pmid34827420,
year = {2021},
author = {Zhang, Y and Liao, Y and Zhang, Y and Huang, L},
title = {Emergency Braking Intention Detect System Based on K-Order Propagation Number Algorithm: A Network Perspective.},
journal = {Brain sciences},
volume = {11},
number = {11},
pages = {},
pmid = {34827420},
issn = {2076-3425},
support = {61977039//National Natural Science Foundation of China/ ; },
abstract = {In order to avoid erroneous braking responses when vehicle drivers are faced with a stressful setting, a K-order propagation number algorithm-Feature selection-Classification System (KFCS) is developed in this paper to detect emergency braking intentions in simulated driving scenarios using electroencephalography (EEG) signals. Two approaches are employed in KFCS to extract EEG features and to improve classification performance: the K-Order Propagation Number Algorithm is the former, calculating the node importance from the perspective of brain networks as a novel approach; the latter uses a set of feature extraction algorithms to adjust the thresholds. Working with the data collected from seven subjects, the highest classification accuracy of a single trial can reach over 90%, with an overall accuracy of 83%. Furthermore, this paper attempts to investigate the mechanisms of brain activeness under two scenarios by using a topography technique at the sensor-data level. The results suggest that the active regions at two states is different, which leaves further exploration for future investigations.},
}
@article {pmid34827392,
year = {2021},
author = {Bhattacharyya, S and Hayashibe, M},
title = {An Optimal Transport Based Transferable System for Detection of Erroneous Somato-Sensory Feedback from Neural Signals.},
journal = {Brain sciences},
volume = {11},
number = {11},
pages = {},
pmid = {34827392},
issn = {2076-3425},
abstract = {This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain-computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it.},
}
@article {pmid34827391,
year = {2021},
author = {Hua, Y and Zhong, X and Zhang, B and Yin, Z and Zhang, J},
title = {Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition.},
journal = {Brain sciences},
volume = {11},
number = {11},
pages = {},
pmid = {34827391},
issn = {2076-3425},
support = {61703277//National Natural Science Foundation of China/ ; 17YF1427000//Shanghai Sailing Program/ ; },
abstract = {Affective computing systems can decode cortical activities to facilitate emotional human-computer interaction. However, personalities exist in neurophysiological responses among different users of the brain-computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50-0.48 (DEAP) and 0.46-0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models.},
}
@article {pmid34826300,
year = {2022},
author = {Sawangjai, P and Trakulruangroj, M and Boonnag, C and Piriyajitakonkij, M and Tripathy, RK and Sudhawiyangkul, T and Wilaiprasitporn, T},
title = {EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks.},
journal = {IEEE journal of biomedical and health informatics},
volume = {26},
number = {10},
pages = {4913-4924},
doi = {10.1109/JBHI.2021.3131104},
pmid = {34826300},
issn = {2168-2208},
mesh = {Algorithms ; *Artifacts ; Blinking ; *Electroencephalography/methods ; Electrooculography/methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.},
}
@article {pmid34826293,
year = {2022},
author = {Li, F and Wang, C and Li, Y and Wu, H and Fu, B and Ji, Y and Niu, Y and Shi, G},
title = {Phase Preservation Neural Network for Electroencephalography Classification in Rapid Serial Visual Presentation Task.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {6},
pages = {1931-1942},
doi = {10.1109/TBME.2021.3130917},
pmid = {34826293},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/methods ; Evoked Potentials ; Learning ; Neural Networks, Computer ; },
abstract = {Neuroscience studies have demonstrated the phase-locked characteristics of some early event-related potential (ERP) components evoked by stimuli. In this study, we propose a phase preservation neural network (PPNN) to learn phase information to improve the Electroencephalography (EEG) classification in a rapid serial visual presentation (RSVP) task. The PPNN consists of three major modules that can produce spatial and temporal representations with the high discriminative ability of the EEG features for classification. We first adopt a stack of dilated temporal convolution layers to extract temporal dynamics while avoiding the loss of phase information. Considering the intrinsic channel dependence of the EEG data, a spatial convolution layer is then applied to obtain the spatial-temporal representation of the input EEG signal. Finally, a fully connected layer is adopted to extract higher-level features for the final classification. The experiments are conducted on two public and one collected EEG datasets from the RSVP task, in which we evaluated the performance and explored the capability of phase preservation of our PPNN model and visualized the extracted features. The experimental results indicate the superiority of the proposed PPNN when compared with previous methods, suggesting the PPNN is a robust model for EEG classification in RSVP task.},
}
@article {pmid34825850,
year = {2022},
author = {Sweeti, },
title = {Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface.},
journal = {Journal of medical engineering &amp; technology},
volume = {46},
number = {1},
pages = {69-77},
doi = {10.1080/03091902.2021.1992519},
pmid = {34825850},
issn = {1464-522X},
mesh = {Attention ; *Brain-Computer Interfaces ; Cognition ; Electroencephalography ; Humans ; *Support Vector Machine ; },
abstract = {Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-computer interface (BCI) however existing cBCI systems currently offer lower accuracy than the motor BCI. Since attention is one of the cognitive signals that can be used to realise the cBCI, this work uses the multiple object tracking (MOT) task to acquire the desired electroencephalograph (EEG) signal from healthy subjects. The main objective of the paper is to explore the preliminary applications of support vector machine (SVM) classifier to classify the attentional load in multiple object tracking task. Results show that the attentional load can be classified using SVM with sensitivity, specificity, and accuracy of 94.03%, 92.50%, and 93.28%, respectively using the spectral entropy EEG feature. The classification performance promises the potential application of the current approach in the cognitive brain-computer interface for neurorehabilitation.},
}
@article {pmid34814281,
year = {2021},
author = {Meng, M and Dai, L and She, Q and Ma, Y and Kong, W},
title = {Crossing time windows optimization based on mutual information for hybrid BCI.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {18},
number = {6},
pages = {7919-7935},
doi = {10.3934/mbe.2021392},
pmid = {34814281},
issn = {1551-0018},
mesh = {*Brain-Computer Interfaces ; Data Collection ; Electroencephalography ; Research Design ; Spectroscopy, Near-Infrared ; },
abstract = {Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.},
}
@article {pmid34814272,
year = {2021},
author = {Gan, H and Yang, Z and Wang, J and Li, B},
title = {ℓ1-norm based safe semi-supervised learning.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {18},
number = {6},
pages = {7727-7742},
doi = {10.3934/mbe.2021383},
pmid = {34814272},
issn = {1551-0018},
mesh = {*Machine Learning ; *Supervised Machine Learning ; },
abstract = {In the past few years, Safe Semi-Supervised Learning (S3L) has received considerable attentions in machine learning field. Different researchers have proposed many S3L methods for safe exploitation of risky unlabeled samples which result in performance degradation of Semi-Supervised Learning (SSL). Nevertheless, there exist some shortcomings: (1) Risk degrees of the unlabeled samples are in advance defined by analyzing prediction differences between Supervised Learning (SL) and SSL; (2) Negative impacts of labeled samples on learning performance are not investigated. Therefore, it is essential to design a novel method to adaptively estimate importance and risk of both unlabeled and labeled samples. For this purpose, we present ℓ1-norm based S3L which can simultaneously reach the safe exploitation of the labeled and unlabeled samples in this paper. In order to solve the proposed ptimization problem, we utilize an effective iterative approach. In each iteration, one can adaptively estimate the weights of both labeled and unlabeled samples. The weights can reflect the importance or risk of the labeled and unlabeled samples. Hence, the negative effects of the labeled and unlabeled samples are expected to be reduced. Experimental performance on different datasets verifies that the proposed S3L method can obtain comparable performance with the existing SL, SSL and S3L methods and achieve the expected goal.},
}
@article {pmid34814257,
year = {2021},
author = {Gao, Y and Cao, Z and Liu, J and Zhang, J},
title = {A novel dynamic brain network in arousal for brain states and emotion analysis.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {18},
number = {6},
pages = {7440-7463},
doi = {10.3934/mbe.2021368},
pmid = {34814257},
issn = {1551-0018},
mesh = {*Arousal ; Brain ; Electroencephalography ; *Emotions ; Entropy ; Humans ; },
abstract = {BACKGROUND: Brain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals. New Method: Time factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain.<br><br>RESULTS: The frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal. Comparison with Existing Method(s): Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis.<br><br>CONCLUSIONS: The proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.},
}
@article {pmid34814029,
year = {2022},
author = {Im, C and Shin, J and Lee, WR and Kim, JM},
title = {Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb.},
journal = {Biosensors &amp; bioelectronics},
volume = {197},
number = {},
pages = {113782},
doi = {10.1016/j.bios.2021.113782},
pmid = {34814029},
issn = {1873-4235},
mesh = {Animals ; *Biosensing Techniques ; Hemodynamics ; Machine Learning ; Odorants ; *Olfactory Bulb ; Rats ; Smell ; },
abstract = {Rodents have a well-developed sense of smell and are used to detect explosives, mines, illegal substances, hidden currency, and contraband, but it is impossible to keep their concentration constantly. Therefore, there is an ongoing effort to infer odors detected by animals without behavioral readings with brain-computer interface (BCI) technology. However, the invasive BCI technique has the disadvantage that long-term studies are limited by the immune response and electrode movement. On the other hand, near-infrared spectroscopy (NIRS)-based BCI technology is a non-invasive method that can measure neuronal activity without worrying about the immune response or electrode movement. This study confirmed that the NIRS-based BCI technology can be used as an odor detection and identification from the rat olfactory system. In addition, we tried to present features optimized for machine learning models by extracting six features, such as slopes, peak, variance, mean, kurtosis, and skewness, from the hemodynamic response, and analyzing the importance of individuals or combinations. As a result, the feature with the highest F1-Score was indicated as slopes, and it was investigated that the combination of the features including slopes and mean was the most important for odor inference. On the other hand, the inclusion of other features with a low correlation with slopes had a positive effect on the odor inference, but most of them resulted in insignificant or rather poor performance. The results presented in this paper are expected to serve as a basis for suggesting the development direction of the hemodynamic response-based bionic nose in the future.},
}
@article {pmid34813127,
year = {2022},
author = {Lalay, G and Ullah, S and Ahmed, I},
title = {Physiological and biochemical responses of Brassica napus L. to drought-induced stress by the application of biochar and Plant Growth Promoting Rhizobacteria.},
journal = {Microscopy research and technique},
volume = {85},
number = {4},
pages = {1267-1281},
doi = {10.1002/jemt.23993},
pmid = {34813127},
issn = {1097-0029},
mesh = {*Brassica napus ; Charcoal ; Chlorophyll/pharmacology ; *Droughts ; Stress, Physiological ; },
abstract = {Climate change induces biotic and abiotic stress conditions, which badly affect the yield of crops with leading to the biochemical and physiological damages to plants. Biochar and plant growth promoting rhizobacteria (PGPR) alleviate the effect of drought condition therefore a field study was conducted to examine the single and combine application of drought tolerant Pseudomonas sp. and Staphylococcus sp. with biochar of Morus alba L. wood to mitigate the adverse effects of drought stress in two genotypes of Brassica napus L. including Punjab sarson and westar. Physioco-chemical analysis of biochar showed 5.4 cmol/kg cation exchange capacity, 6.9 ds/m electrical conductivity, pH of 9.6, 0.50 g/cm[3] bulk density, and organic carbon 3.64%. Synergistic application of PGPR and biochar developed the plant antioxidant enzyme including catalase (CAT) and ascorbate peroxidase (APX) and also enhanced the content of photosynthetic pigments like chlorophyll pigments, carotenoids content, and anthocyanin content. Scanning electron microscope (SEM) study revealed that biochar and PGPR improved epidermal vigor and stomatal physiology. Malondialdehyde (MDA), hydrogen peroxide (H2 O2), APX, and osmolyte content including proline increased in drought stress, which were then decreased by these growth promoters. These results are very important as they illustrate the potential of PGPR and biochar to alleviate the adverse consequences of drought stress and offer a way of increasing the tolerance of B. napus L. plant grown under induced drought stress.},
}
@article {pmid34812967,
year = {2022},
author = {Wang, R and Zhu, J and Zhang, J and Ma, Y and Jiang, H},
title = {Psychological assessments of a senile patient with tetraplegia who received brain-computer interface implantation: a case report.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {43},
number = {2},
pages = {1427-1430},
pmid = {34812967},
issn = {1590-3478},
support = {2017YFC1308500//National Key R&amp;D Plan of China/ ; },
mesh = {Aged ; *Brain-Computer Interfaces ; Humans ; Male ; Quadriplegia ; Quality of Life ; Sleep ; },
abstract = {OBJECTIVE: Research on individuals with brain-computer interface (BCI) presents not only technological challenges but ethical challenges (e.g., psychological aspects) as well. We assessed the mental health of a senile patient with tetraplegia after an invasive implantation of BCI and a long-term daily training, in order to provide new experience about the ethical impact of BCI on users and inform future clinical applications of such devices.<br><br>METHODS: This case was a 71-year-old man with tetraplegia for 2&#xa0;years. Prior to the implant surgery of BCI, and 1&#xa0;month, 2&#xa0;months, 3&#xa0;months, and 9&#xa0;months after training, a series of tests for cognition, emotion, social support, sleep, and quality of life were performed to evaluate the patient's mental health.<br><br>RESULTS: Compared with baseline before surgery, the patients' cognition, emotion, social support, sleep, and quality of life improved after the surgery and the long-term daily training. At 3&#xa0;months post-training, the patient's cognitive score measured by Mini-mental State Examination reached the cutoff point for cognitive impairment in the elderly. Subjective well-being and quality of life showed a slight decline at 9&#xa0;months post-training compared with that 3&#xa0;months post-training but remained above the baseline.<br><br>CONCLUSION: This study shows the psychological benefits in a senile patient after an invasive BCI implantation and a long-term daily training. BCI ethics is still in its early stages, and further research is needed to understand emerging psychological states of this specific population.},
}
@article {pmid34812596,
year = {2022},
author = {Tian, Y and Zhang, Y and Zhang, X and Pan, H and Zhang, L and Liu, S and Chen, Y and Su, L and Zhao, P and Chang, J and Wang, H},
title = {"Magnetism-Optogenetic" System for Wireless and Highly Sensitive Neuromodulation.},
journal = {Advanced healthcare materials},
volume = {11},
number = {3},
pages = {e2102023},
doi = {10.1002/adhm.202102023},
pmid = {34812596},
issn = {2192-2659},
mesh = {Animals ; *Brain-Computer Interfaces ; *Optogenetics/methods ; Rats ; Wireless Technology ; },
abstract = {Neuromodulation is becoming more and more important in studying brain function, disease treatment, and brain-computer interfaces. However, traditional regulation methods cannot effectively achieve both wireless regulation and highly sensitive response, which are essential factors in neuromodulation. In this paper, a "magnetism-optogenetic" system is constructed, which uses a magnetic field to drive mechanoluminescent materials (ZnS:Cu) to generate light, thus stimulating photogenetic proteins. This system effectively combines the wireless magnetic regulation with the high sensitivity of optogenetics. The results show that the luminous intensity of this system changes with the power of an external magnetic field. In addition, under the continuous stimulation of the wireless magnetic field, this system can activate hippocampal-related neural responses and induce the expression of C-fos. In the end, this system can further regulate the movement behavior of rats with C1V1 protein expression in the primary motor cortex. This new magnetism-optogenetic system will provide an excellent reference for wireless and highly sensitive neuromodulation.},
}
@article {pmid34806939,
year = {2022},
author = {Karakullukcu, N and Yilmaz, B},
title = {Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform.},
journal = {International journal of neural systems},
volume = {32},
number = {1},
pages = {2150059},
doi = {10.1142/S0129065721500593},
pmid = {34806939},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Intention ; Movement ; },
abstract = {Patients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.},
}
@article {pmid34805123,
year = {2021},
author = {Habelt, B and Wirth, C and Afanasenkau, D and Mihaylova, L and Winter, C and Arvaneh, M and Minev, IR and Bernhardt, N},
title = {A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {9},
number = {},
pages = {770274},
pmid = {34805123},
issn = {2296-4185},
abstract = {Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.},
}
@article {pmid34803714,
year = {2021},
author = {Cai, C and Hu, W and Zhang, Y and Hu, X and Yang, S and Qiu, H and Wang, R and Ma, M and Qiu, Y and Chu, T},
title = {BCI Suppresses RANKL-Mediated Osteoclastogenesis and Alleviates Ovariectomy-Induced Bone Loss.},
journal = {Frontiers in pharmacology},
volume = {12},
number = {},
pages = {772540},
pmid = {34803714},
issn = {1663-9812},
abstract = {Osteoporosis is a common aging-related metabolic disease that mainly occurs in older adults and postmenopausal women. Despite advances in anti-osteoporosis treatment, outcomes remain unsatisfactory due to detrimental side effects. BCI hydrochloride (BCI), a selective dual-specificity phosphatase 6 (DUSP6) inhibitor, is associated with multiple cellular functions, including inhibiting tumor growth and macrophage inflammation; however, its role in regulating osteoclast differentiation remains unknown. Here, we revealed that treatment with BCI attenuated RANKL-mediated osteoclast differentiation in vitro and alleviated ovariectomy-induced osteoporosis without obvious toxicity. Specifically, BCI disrupted F-actin ring formation and bone-resorption activity and decreased osteoclast-specific gene and protein levels in a dose-dependent manner. KEGG pathway analysis, GSEA based on transcriptome sequencing, and western blot results suggested that BCI inhibited RANKL-induced osteoclastogenesis by restraining STAT3 and NF-κB signaling and attenuating NF-κB/p65 interaction with NFATc1. These results revealed that BCI treatment prevented postmenopausal osteoporosis and might represent an effective approach for treating osteoporosis.},
}
@article {pmid34803647,
year = {2021},
author = {Chen, S and Shu, X and Wang, H and Ding, L and Fu, J and Jia, J},
title = {The Differences Between Motor Attempt and Motor Imagery in Brain-Computer Interface Accuracy and Event-Related Desynchronization of Patients With Hemiplegia.},
journal = {Frontiers in neurorobotics},
volume = {15},
number = {},
pages = {706630},
pmid = {34803647},
issn = {1662-5218},
abstract = {Background: Motor attempt and motor imagery (MI) are two common motor tasks used in brain-computer interface (BCI). They are widely researched for motor rehabilitation in patients with hemiplegia. The differences between the motor attempt (MA) and MI tasks of patients with hemiplegia can be used to promote BCI application. This study aimed to explore the accuracy of BCI and event-related desynchronization (ERD) between the two tasks. Materials and Methods: We recruited 13 patients with stroke and 3 patients with traumatic brain injury, to perform MA and MI tasks in a self-control design. The BCI accuracies from the bilateral, ipsilesional, and contralesional hemispheres were analyzed and compared between different tasks. The cortical activation patterns were evaluated with ERD and laterality index (LI). Results: The study showed that the BCI accuracies of MA were significantly (p < 0.05) higher than MI in the bilateral, ipsilesional, and contralesional hemispheres in the alpha-beta (8-30 Hz) frequency bands. There was no significant difference in ERD and LI between the MA and MI tasks in the 8-30 Hz frequency bands. However, in the MA task, there was a negative correlation between the ERD values in the channel CP1 and ipsilesional hemispheric BCI accuracies (r = -0.552, p = 0.041, n = 14) and a negative correlation between the ERD values in channel CP2 and bilateral hemispheric BCI accuracies (r = -0.543, p = 0.045, n = 14). While in the MI task, there were negative correlations between the ERD values in channel C4 and bilateral hemispheric BCI accuracies (r = -0.582, p = 0.029, n = 14) as well as the contralesional hemispheric BCI accuracies (r = -0.657, p = 0.011, n = 14). As for motor dysfunction, there was a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 8-13 Hz (r = 0.565, p = 0.035, n = 14) in the MA task and a significant positive correlation between the ipsilesional BCI accuracies and FMA scores of the hand part in 13-30 Hz (r = 0.558, p = 0.038, n = 14) in the MI task. Conclusion: The MA task may achieve better BCI accuracy but have similar cortical activations with the MI task. Cortical activation (ERD) may influence the BCI accuracy, which should be carefully considered in the BCI motor rehabilitation of patients with hemiplegia.},
}
@article {pmid34803606,
year = {2021},
author = {Cheng, H and Liu, Y and Xue, Y and Shao, J and Tan, Z and Liu, S and Duan, S and Kang, L},
title = {Molecular Strategies for Intensity-Dependent Olfactory Processing in Caenorhabditis elegans.},
journal = {Frontiers in molecular neuroscience},
volume = {14},
number = {},
pages = {748214},
pmid = {34803606},
issn = {1662-5099},
abstract = {Various odorants trigger complex animal behaviors across species in both quality- and quantity-dependent manners. However, how the intensity of olfactory input is encoded remains largely unknown. Here we report that isoamyl alcohol (IAA) induces bi-directional currents through a Gα- guanylate cyclase (GC)- cGMP signaling pathway in Caenorhabditis elegans olfactory neuron amphid wing "C" cell (AWC), while two opposite cGMP signaling pathways are responsible for odor-sensing in olfactory neuron amphid wing "B" cell (AWB): (1) a depolarizing Gα (GPA-3)- phosphodiesterase (PDE) - cGMP pathway which can be activated by low concentrations of isoamyl alcohol (IAA), and (2) a hyperpolarizing Gα (ODR-3)- GC- cGMP pathway sensing high concentrations of IAA. Besides, IAA induces Gα (ODR-3)-TRPV(OSM-9)-dependent currents in amphid wing "A" cell (AWA) and amphid neuron "H" cell with single ciliated sensory ending (ASH) neurons with different thresholds. Our results demonstrate that an elaborate combination of multiple signaling machineries encode the intensity of olfactory input, shedding light on understanding the molecular strategies on sensory transduction.},
}
@article {pmid34803601,
year = {2021},
author = {Ivanenko, Y and Ferris, DP and Lee, K and Sakurai, Y and Beloozerova, IN and Lebedev, M},
title = {Editorial: Neural Prostheses for Locomotion.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {788021},
doi = {10.3389/fnins.2021.788021},
pmid = {34803601},
issn = {1662-4548},
}
@article {pmid34803600,
year = {2021},
author = {Xu, Y and Huang, X and Lan, Q},
title = {Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {779231},
pmid = {34803600},
issn = {1662-4548},
abstract = {A motor imagery (MI) brain-computer interface (BCI) plays an important role in the neurological rehabilitation training for stroke patients. Electroencephalogram (EEG)-based MI BCI has high temporal resolution, which is convenient for real-time BCI control. Therefore, we focus on EEG-based MI BCI in this paper. The identification of MI EEG signals is always quite challenging. Due to high inter-session/subject variability, each subject should spend long and tedious calibration time in collecting amounts of labeled samples for a subject-specific model. To cope with this problem, we present a supervised selective cross-subject transfer learning (sSCSTL) approach which simultaneously makes use of the labeled samples from target and source subjects based on Riemannian tangent space. Since the covariance matrices representing the multi-channel EEG signals belong to the smooth Riemannian manifold, we perform the Riemannian alignment to make the covariance matrices from different subjects close to each other. Then, all aligned covariance matrices are converted into the Riemannian tangent space features to train a classifier in the Euclidean space. To investigate the role of unlabeled samples, we further propose semi-supervised and unsupervised versions which utilize the total samples and unlabeled samples from target subject, respectively. Sequential forward floating search (SFFS) method is executed for source selection. All our proposed algorithms transfer the labeled samples from most suitable source subjects into the feature space of target subject. Experimental results on two publicly available MI datasets demonstrated that our algorithms outperformed several state-of-the-art algorithms using small number of the labeled samples from target subject, especially for good target subjects.},
}
@article {pmid34803594,
year = {2021},
author = {Xu, X and Drougard, N and Roy, RN},
title = {Topological Data Analysis as a New Tool for EEG Processing.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {761703},
pmid = {34803594},
issn = {1662-4548},
abstract = {Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a different angle than traditionally used methods. As a higher dimensional analogy of graph analysis, TDA can model rich interactions beyond pairwise relations. It also distinguishes different dynamics of EEG time series. TDA remains largely unknown to the EEG processing community while it fits well the heterogeneous nature of EEG signals. This short review aims to give a quick introduction to TDA and how it can be applied to EEG analysis in various applications including brain-computer interfaces (BCIs). After introducing the objective of the article, the main concepts and ideas of TDA are explained. Next, how to implement it for EEG processing is detailed, and lastly the article discusses the benefits and limitations of the method.},
}
@article {pmid34803582,
year = {2021},
author = {Park, S and Ha, J and Kim, DH and Kim, L},
title = {Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {732545},
pmid = {34803582},
issn = {1662-4548},
abstract = {The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (p < 0.05). In poor performers, the classification performance in SMI was 10.73% larger than in the MI condition (62.18% vs. 51.45%). However, good performers showed a slight performance decrement (0.86%) in the SMI condition compared to the MI condition (80.93% vs. 81.79%). Combining the brain signals from the motor and somatosensory cortex, the proposed hybrid MI-BCI system demonstrated improved classification performance, this phenomenon was predominant in poor performers (eight out of nine subjects). Hybrid MI-BCI systems may significantly contribute to reducing the proportion of BCI-inefficiency users and closing the performance gap with other BCI systems.},
}
@article {pmid34802710,
year = {2022},
author = {Zhao, X and Du, Y and Zhang, R},
title = {A CNN-based multi-target fast classification method for AR-SSVEP.},
journal = {Computers in biology and medicine},
volume = {141},
number = {},
pages = {105042},
doi = {10.1016/j.compbiomed.2021.105042},
pmid = {34802710},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Neural Networks, Computer ; Photic Stimulation ; },
abstract = {Because an augmented-reality-based brain-computer interface (AR-BCI) is easily disturbed by external factors, the traditional electroencephalograph (EEG) classification algorithms fail to meet the real-time processing requirements with a large number of stimulus targets or in a real environment. We propose a multi-target fast classification method for augmented-reality-based steady-state visual evoked potential (AR-SSVEP), using a convolutional neural network (CNN). To explore the availability and accuracy of high-efficiency multi-target classification methods in AR-SSVEP with a short stimulation duration, a similar stimulus layout was used for a computer screen (PC) and an optical see-through head-mounted display (OST-HMD) device (HoloLens). The experiment included nine flicker stimuli of different frequencies, and a multi-target fast classification method based on a CNN was constructed to complete nine classification tasks, for which the average accuracy of AR-BCI in our CNN model at 0.5- and 1-s stimulus duration was 67.93% and 80.83%, respectively. These results verified the efficacy of the proposed model for processing multi-target classification in AR-BCI.},
}
@article {pmid34797365,
year = {2021},
author = {Xu, B and Pei, J and Feng, L and Zhang, XD},
title = {Graphene and graphene-related materials as brain electrodes.},
journal = {Journal of materials chemistry. B},
volume = {9},
number = {46},
pages = {9485-9496},
doi = {10.1039/d1tb01795k},
pmid = {34797365},
issn = {2050-7518},
mesh = {Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; *Graphite ; Humans ; },
abstract = {Neural electrodes are used for acquiring neuron signals in brain-machine interfaces, and they are crucial for next-generation neuron engineering and related medical applications. Thus, developing flexible, stable and high-resolution neural electrodes will play an important role in stimulation, acquisition, recording and analysis of signals. Compared with traditional metallic electrodes, electrodes based on graphene and other two-dimensional materials have attracted wide attention in electrophysiological recording and stimulation due to their excellent physical properties such as unique flexibility, low resistance, and high optical transparency. In this review, we have reviewed the recent progress of electrodes based on graphene, graphene/polymer compounds and graphene-related materials for neuron signal recording, stimulation, and related optical signal coupling technology, which provides an outlook on the role of electrodes in the nanotechnology-neuron interface as well as medical diagnosis.},
}
@article {pmid34795879,
year = {2021},
author = {Subasi, A and Mian Qaisar, S},
title = {The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {1970769},
pmid = {34795879},
issn = {2040-2309},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Machine Learning ; },
abstract = {The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.},
}
@article {pmid34795394,
year = {2023},
author = {Wen, S and Yin, A and Furlanello, T and Perich, MG and Miller, LE and Itti, L},
title = {Rapid adaptation of brain-computer interfaces to new neuronal ensembles or participants via generative modelling.},
journal = {Nature biomedical engineering},
volume = {7},
number = {4},
pages = {546-558},
pmid = {34795394},
issn = {2157-846X},
support = {F31 NS092356/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; T32 HD007418/HD/NICHD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Neurons ; Biomechanical Phenomena ; },
abstract = {For brain-computer interfaces (BCIs), obtaining sufficient training data for algorithms that map neural signals onto actions can be difficult, expensive or even impossible. Here we report the development and use of a generative model-a model that synthesizes a virtually unlimited number of new data distributions from a learned data distribution-that learns mappings between hand kinematics and the associated neural spike trains. The generative spike-train synthesizer is trained on data from one recording session with a monkey performing a reaching task and can be rapidly adapted to new sessions or monkeys by using limited additional neural data. We show that the model can be adapted to synthesize new spike trains, accelerating the training and improving the generalization of BCI decoders. The approach is fully data-driven, and hence, applicable to applications of BCIs beyond motor control.},
}
@article {pmid34793704,
year = {2021},
author = {Al-Sheikh, U and Kang, L},
title = {Mechanosensation: Alpha-7 nAChR transduces sound signals in earless C. elegans.},
journal = {Neuron},
volume = {109},
number = {22},
pages = {3539-3541},
doi = {10.1016/j.neuron.2021.10.026},
pmid = {34793704},
issn = {1097-4199},
mesh = {Animals ; Caenorhabditis elegans/metabolism ; *Caenorhabditis elegans Proteins/genetics ; Mechanotransduction, Cellular ; *Receptors, Nicotinic/metabolism ; alpha7 Nicotinic Acetylcholine Receptor ; },
abstract = {How do organisms without specialized auditory systems perceive and transduce sound? In this issue of Neuron, Iliff et al. (2021) investigate the functional mechanism of airborne sound sensation in Caenorhabditis elegans and highlight the crucial role of alpha-7 nicotinic acetylcholine receptor subunits in mechanotransduction.},
}
@article {pmid34793692,
year = {2022},
author = {Zhang, C and Zhu, H and Ni, Z and Xin, Q and Zhou, T and Wu, R and Gao, G and Gao, Z and Ma, H and Li, H and He, M and Zhang, J and Cheng, H and Hu, H},
title = {Dynamics of a disinhibitory prefrontal microcircuit in controlling social competition.},
journal = {Neuron},
volume = {110},
number = {3},
pages = {516-531.e6},
doi = {10.1016/j.neuron.2021.10.034},
pmid = {34793692},
issn = {1097-4199},
mesh = {Animals ; *Interneurons/physiology ; Mice ; *Parvalbumins/metabolism ; Prefrontal Cortex/physiology ; Pyramidal Cells/physiology ; Vasoactive Intestinal Peptide/metabolism ; },
abstract = {Social competition plays a pivotal role in determining individuals' social status. While the dorsomedial prefrontal cortex (dmPFC) is essential in regulating social competition, it remains unclear how information is processed within its local networks. Here, by applying optogenetic and chemogenetic manipulations in a dominance tube test, we reveal that, in accordance with pyramidal (PYR) neuron activation, excitation of the vasoactive intestinal polypeptide (VIP) or inhibition of the parvalbumin (PV) interneurons induces winning. The winning behavior is associated with sequential calcium activities initiated by VIP and followed by PYR and PV neurons. Using miniature two-photon microscopic (MTPM) and optrode recordings in awake mice, we show that VIP stimulation directly leads to a two-phased activity pattern of both PYR and PV neurons-rapid suppression followed by activation. The delayed activation of PV implies an embedded feedback tuning. This disinhibitory VIP-PV-PYR motif forms the core of a dmPFC microcircuit to control social competition.},
}
@article {pmid34792128,
year = {2021},
author = {Nair, J and Basha Syed, S and Mahaddalkar, T and Ketkar, M and Thorat, R and Sastri Goda, J and Dutt, S},
title = {DUSP6 regulates radiosensitivity in glioblastoma by modulating the recruitment of phosphorylated DNAPKcs at DNA double-strand breaks.},
journal = {Journal of cell science},
volume = {134},
number = {24},
pages = {},
doi = {10.1242/jcs.259520},
pmid = {34792128},
issn = {1477-9137},
mesh = {Animals ; *Brain Neoplasms/genetics ; Cell Line, Tumor ; DNA ; DNA Breaks, Double-Stranded ; *DNA-Activated Protein Kinase ; Dual Specificity Phosphatase 6 ; *Glioblastoma/genetics/radiotherapy ; Humans ; Mice ; Radiation Tolerance/genetics ; },
abstract = {Glioblastoma (GBM) has poor median survival due to its resistance to chemoradiotherapy, which results in tumor recurrence. Recurrent GBMs currently lack effective treatments. DUSP6 is known to be pro-tumorigenic and is upregulated in GBM. We show that DUSP6 expression is significantly higher in recurrent GBM patient biopsies compared to expression levels in primary GBM biopsies. Importantly, although it has been reported to be a cytoplasmic protein, we found nuclear localization of DUSP6 in primary and recurrent patient samples and in parent and relapse populations of GBM cell lines generated from an in vitro radiation survival model. DUSP6 inhibition using BCI resulted in decreased proliferation and clonogenic survival of parent and relapse cells. Pharmacological or genetic inhibition of DUSP6 catalytic activity radiosensitized primary and, importantly, relapse GBM cells by inhibiting the recruitment of phosphorylated DNAPKcs (also known as PRKDC), subsequently downregulating the recruitment of phosphorylated histone H2AX (γH2AX) and 53BP1 (also known as TP53BP1). This resulted in decreased cell survival and prolonged growth arrest upon irradiation in vitro and significantly increased the progression-free survival in orthotopic mouse models of GBM. Our study highlights a non-canonical function of DUSP6, emphasizing the potential application of DUSP6 inhibitors in the treatment of recurrent GBM.},
}
@article {pmid34790263,
year = {2021},
author = {Xie, J and Jiang, L and Li, Y and Chen, B and Li, F and Jiang, Y and Gao, D and Deng, L and Lv, X and Ma, X and Yin, G and Yao, D and Xu, P},
title = {Rehabilitation of motor function in children with cerebral palsy based on motor imagery.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {6},
pages = {939-948},
pmid = {34790263},
issn = {1871-4080},
abstract = {To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.},
}
@article {pmid34790157,
year = {2021},
author = {Yan, W and Liu, X and Shan, B and Zhang, X and Pu, Y},
title = {Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {771591},
pmid = {34790157},
issn = {1664-1078},
abstract = {This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.},
}
@article {pmid34788177,
year = {2023},
author = {Pitt, KM and McCarthy, JW},
title = {Strategies for highlighting items within visual scene displays to support augmentative and alternative communication access for those with physical impairments.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {18},
number = {8},
pages = {1319-1329},
doi = {10.1080/17483107.2021.2003455},
pmid = {34788177},
issn = {1748-3115},
mesh = {Humans ; *Brain-Computer Interfaces ; Fixation, Ocular ; *Communication Devices for People with Disabilities ; *Mobile Applications ; Communication ; *Communication Disorders ; },
abstract = {PURPOSE: In contrast to the traditional grid-based display, visual scene displays (VSDs) offer a new paradigm for aided communication. For individuals who cannot select items from an AAC display by direct selection due to physical impairments, AAC access can be supported via methods such as item scanning. Item scanning sequentially highlights items on a display until the individual signals for selection. How items are highlighted or scanned for AAC access can impact performance outcomes. Further, the effectiveness of a VSD interface may be enhanced through consultation with experts in visual communication. Therefore, to support AAC access for those with physical impairments, the aim of this study was to evaluate the perspectives of experts in visual communication regarding effective methods for highlighting VSD elements.<br><br>METHODS: Thirteen participants with expertise related to visual communication (e.g., photographers, artists) completed semi-structured interviews regarding techniques for item highlighting.<br><br>RESULTS: Study findings identified four main themes to inform how AAC items may be highlighted or scanned, including (1) use of contrast related to light and dark, (2) use of contrast as it relates to colour, (3) outline highlighting, and (4) use of scale and motion.<br><br>CONCLUSION: By identifying how compositional techniques can be utilized to highlight VSD elements, study findings may inform current practice for scanning-based AAC access, along with other selection techniques where feedback or highlighting is used (e.g., eye-gaze, brain-computer interface). Further, avenues for just-in-time programming are discussed to support effective implementation for those with physical impairments.IMPLICATIONS FOR REHABILITATIONFindings identify multiple potential techniques to improve scanning through items in a photograph for individuals with severe motor impairments using alternative access strategies.Study findings inform current practice for scanning-based AAC access, along with other selection techniques where feedback or highlighting is used (e.g., eye-gaze, brain-computer interface).Avenues for just in time programming of AAC displays are discussed to decrease programming demands and support effective implementation of study findings.},
}
@article {pmid34788156,
year = {2021},
author = {Ting, JE and Del Vecchio, A and Sarma, D and Verma, N and Colachis, SC and Annetta, NV and Collinger, JL and Farina, D and Weber, DJ},
title = {Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array.},
journal = {Journal of neurophysiology},
volume = {126},
number = {6},
pages = {2104-2118},
pmid = {34788156},
issn = {1522-1598},
support = {U01 NS108922/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Electromyography ; Feasibility Studies ; Hand/*physiopathology ; Humans ; Machine Learning ; Male ; Muscle, Skeletal/*physiopathology ; Neurological Rehabilitation/*instrumentation/methods ; *Quadriplegia/etiology/physiopathology/rehabilitation ; Recruitment, Neurophysiological/*physiology ; *Spinal Cord Injuries/complications/physiopathology/rehabilitation ; *Wearable Electronic Devices ; },
abstract = {Motor neurons convey information about motor intent that can be extracted and interpreted to control assistive devices. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use that can be mitigated by instead using noninvasive interfaces. The objective of this study was to evaluate the feasibility of deriving motor control signals from a wearable sensor that can detect residual motor unit activity in paralyzed muscles after chronic cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor units below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the electromyogram (EMG) power [root-mean-square (RMS)] or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real-time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor units below the level of injury in a person with motor complete SCI.NEW &amp; NOTEWORTHY A wearable electrode array and machine learning methods were used to record and decode myoelectric signals and motor unit firing in paralyzed muscles of a person with motor complete tetraplegia. The myoelectric activity and motor unit firing rates were task specific, even in the absence of visible motion, enabling accurate classification of attempted single-digit movements. This wearable system has the potential to enable people with tetraplegia to control assistive devices through movement intent.},
}
@article {pmid34786009,
year = {2021},
author = {Krobisch, V and Gebert, P and Gül, K and Schenk, L},
title = {Women bear a burden: gender differences in health of older migrants from Turkey.},
journal = {European journal of ageing},
volume = {18},
number = {4},
pages = {467-478},
pmid = {34786009},
issn = {1613-9372},
abstract = {Studies show that older migrants have poorer health than native populations in Western Europe. To date, little systematic research has explored the differences between men and women within older populations with migration backgrounds. This article examines gender-specific aspects and mediating mechanisms of self-reported health among older migrants from Turkey. Using a mixed method approach, data and results from a quantitative survey and a qualitative study conducted in Berlin, Germany, are analysed and integrated at the interpretive level. Standardised face-to-face interviews were carried out with the help of a network approach with 194 older migrants from Turkey (93 women, 101 men, mean age: 68). Potential mediators showing significant gender differences are included in a parallel multiple mediation analysis. The documentary method is used to analyse 11 semi-structured narrative interviews with first-generation labour migrants from Turkey. Women reported significantly worse subjective health than men (c = 0.443, bCI [0.165-0.736]), conveyed through greater functional limitations (ab = 0.183, bCI [0.056-0.321]) and emotional loneliness (ab = 0.057, bCI [0.008-0.128]). Respondents to the qualitative study perceived that women age earlier and have poorer health due to the burden of performing a greater variety of social roles. Higher levels of emotional loneliness among women could be caused by their experiences of negatively assessed partnerships. Our results show that as a group, older female migrants have an elevated health vulnerability. A broader scientific foundation regarding gender differences in the health of older migrants and their causes is needed to promote gender-sensitive prevention and care for this group.},
}
@article {pmid34781272,
year = {2021},
author = {Carrere, LC and Taborda, M and Ballario, C and Tabernig, C},
title = {Effects of brain-computer interface with functional electrical stimulation for gait rehabilitation in multiple sclerosis patients: preliminary findings in gait speed and event-related desynchronization onset latency.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac39b8},
pmid = {34781272},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; *Electric Stimulation Therapy/methods ; Gait/physiology ; Humans ; *Multiple Sclerosis/rehabilitation ; Quality of Life ; Walking Speed ; },
abstract = {Objective.Brain-computer Interfaces (BCI) with functional electrical stimulation (FES) as a feedback device might promote neuroplasticity and hence improve motor function. Novel findings suggested that neuroplasticity could be possible in people with multiple sclerosis (pwMS). This preliminary study explores the effects of using a BCI-FES in therapeutic intervention, as an emerging methodology for gait rehabilitation in pwMS.Approach.People with relapsing-remitting, primary progressive or secondary progressive MS were evaluated with the inclusion criteria to enroll the nine participants required by the statistically computed sample size. Each patient trained with a BCI-FES during 24 sessions distributed in eight weeks. The effects were evaluated on gait speed (Timed 25 Foot Walk), walking ability (12-item Multiple Sclerosis Walking Scale), quality of life measures, the true positive rate as the BCI-FES performance metric and the event-related desynchronization (ERD) onset latency of the sensorimotor rhythms.Main results.Seven patients completed the therapeutic intervention. A statistically and clinically significant post-treatment improvement was observed in gait speed, as a result of a reduction in the time to walk 25 feet (-1.99 s,p= 0.018), and walking ability (-31.25 score points,p= 0.028). The true positive rate showed a statistically significant improvement (+15.87 score points,p= 0.018). An earlier ERD onset latency (-180 ms) after treatment was found.Significance.This is the first study that explored gait rehabilitation using BCI-FES in pwMS. The results showed improvement in gait which might have been promoted by changes in functional brain connections involved in sensorimotor rhythm modulation. Although more studies with a larger sample size and control group are required to validate the efficacy of this approach, these results suggest that BCI-FES technology could have a positive effect on MS gait rehabilitation.},
}
@article {pmid34779740,
year = {2021},
author = {Jovanovic, LI and Popovic, MR and Marquez-Chin, C},
title = {KITE-BCI: A brain-computer interface system for functional electrical stimulation therapy.},
journal = {The journal of spinal cord medicine},
volume = {44},
number = {sup1},
pages = {S203-S214},
pmid = {34779740},
issn = {2045-7723},
mesh = {*Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Humans ; Movement ; *Spinal Cord Injuries/therapy ; Upper Extremity ; },
abstract = {CONTEXT/OBJECTIVE: Integrating brain-computer interface (BCI) technology with functional electrical stimulation therapy (FEST) is an emerging strategy for upper limb motor rehabilitation after spinal cord injury (SCI). Despite promising results, the combined use of these technologies (BCI-FEST) in clinical practice is minimal. To address this issue, we developed KITE-BCI, a BCI system specifically designed for clinical application and integration with dynamic FEST. In this paper, we report its technical features and performance. In addition, we discuss the differences in distributions of the BCI- and therapist-triggered stimulation latencies.<br><br>DESIGN: Two single-arm 40-session interventional studies to test the feasibility of BCI-controlled FEST for upper limb motor rehabilitation in individuals with cervical SCI.<br><br>SETTING: Rehabilitation programs within the University and Lyndhurst Centres of the Toronto Rehabilitation Institute - University Health Network, Toronto, Canada.<br><br>PARTICIPANTS: Five individuals with sub-acute (< 6 months post-injury) SCI at the C4-C5 level, AIS B-D, and three individuals with chronic (> 24 months post-injury) SCI at C4 level, AIS B-C.<br><br>OUTCOME MEASURES: We measured BCI setup duration, and to characterize the performance of KITE-BCI, we recorded BCI sensitivity, defined as the percentage of successful BCI activations out of the total number of cued movements.<br><br>RESULTS: The overall BCI sensitivities were 74.46% and 79.08% for the sub-acute and chronic groups, respectively. The average KITE-BCI setup duration across the two studies was 11 min and 13 s.<br><br>CONCLUSION: KITE-BCI demonstrates a clinically viable single-channel BCI system for integration with FEST resulting in a versatile technology-enhanced upper limb motor rehabilitation strategy after SCI.},
}
@article {pmid34777531,
year = {2021},
author = {Hu, YQ and Gao, TH and Li, J and Tao, JC and Bai, YL and Lu, RR},
title = {Motor Imagery-Based Brain-Computer Interface Combined with Multimodal Feedback to Promote Upper Limb Motor Function after Stroke: A Preliminary Study.},
journal = {Evidence-based complementary and alternative medicine : eCAM},
volume = {2021},
number = {},
pages = {1116126},
pmid = {34777531},
issn = {1741-427X},
abstract = {BACKGROUND: Recently, the brain-computer interface (BCI) has seen rapid development, which may promote the recovery of motor function in chronic stroke patients.<br><br>METHODS: Twelve stroke patients with severe upper limb and hand motor impairment were enrolled and randomly assigned into two groups: motor imagery (MI)-based BCI training with multimodal feedback (BCI group, n&#x2009;=&#x2009;7) and classical motor imagery training (control group, n&#x2009;=&#x2009;5). Motor function and electrophysiology were evaluated before and after the intervention. The Fugl-Meyer assessment-upper extremity (FMA-UE) is the primary outcome measure. Secondary outcome measures include an increase in wrist active extension or surface electromyography (the amplitude and cocontraction of extensor carpi radialis during movement), the action research arm test (ARAT), the motor status scale (MSS), and Barthel index (BI). Time-frequency analysis and power spectral analysis were used to reflect the electroencephalogram (EEG) change before and after the intervention.<br><br>RESULTS: Compared with the baseline, the FMA-UE score increased significantly in the BCI group (p&#x2009;=&#x2009;0.006). MSS scores improved significantly in both groups, while ARAT did not improve significantly. In addition, before the intervention, all patients could not actively extend their wrists or just had muscle contractions. After the intervention, four patients regained the ability to extend their paretic wrists (two in each group). The amplitude and area under the curve of extensor carpi radialis improved to some extent, but there was no statistical significance between the groups.<br><br>CONCLUSION: MI-based BCI combined with sensory and visual feedback might improve severe upper limb and hand impairment in chronic stroke patients, showing the potential for application in rehabilitation medicine.},
}
@article {pmid34776915,
year = {2021},
author = {Ding, P and Wang, F and Li, S and Zhang, W and Li, H and Chen, Z and Zhao, L and Gong, A and Fu, Y},
title = {Monitoring and Evaluation of Emotion Regulation by Aerobic Exercise and Motor Imagery Based on Functional Near-Infrared Spectroscopy.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {759360},
pmid = {34776915},
issn = {1662-5188},
abstract = {Objective: We sought to effectively alleviate the emotion of individuals with anxiety and depression, and explore the effects of aerobic exercise on their emotion regulation. Functional near-infrared spectroscopy (fNIRS) brain imaging technology is used to monitor and evaluate the process of aerobic exercise and imagination that regulates emotion. Approach:Thirty participants were scored by the state-trait anxiety inventory (STAI) and profile of mood states (POMS), and fNIRS images were collected before, after, and during aerobic exercise and motor imagery. Then, the oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) concentrations and their average value were calculated, and the ratio of HbO concentration in the left and right frontal lobes was determined. Spearman's correlation coefficient was used to calculate the correlation between variations in the average scores of the two scales and in blood oxygen concentrations. Results: In comparison with motor imagery, STAI, and POMS scores decreased after 20 min of aerobic exercise. The prefrontal cortex had asymmetry and laterality (with the left side being dominant in emotion regulation). The increase in hemoglobin concentration recorded by fNIRS was negatively correlated with STAI and POMS scores. Aerobic exercise has a good effect on emotion regulation. Significance:The study showed that portable fNIRS could be effectively used for monitoring and evaluating emotion regulation by aerobic exercise. This study is expected to provide ideas for constructing fNIRS-based online real-time monitoring and evaluation of emotion regulation by aerobic exercise.},
}
@article {pmid34776904,
year = {2021},
author = {Le Bars, S and Chokron, S and Balp, R and Douibi, K and Waszak, F},
title = {Theoretical Perspective on an Ideomotor Brain-Computer Interface: Toward a Naturalistic and Non-invasive Brain-Computer Interface Paradigm Based on Action-Effect Representation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {732764},
pmid = {34776904},
issn = {1662-5161},
abstract = {Recent years have been marked by the fulgurant expansion of non-invasive Brain-Computer Interface (BCI) devices and applications in various contexts (medical, industrial etc.). This technology allows agents "to directly act with thoughts," bypassing the peripheral motor system. Interestingly, it is worth noting that typical non-invasive BCI paradigms remain distant from neuroscientific models of human voluntary action. Notably, bidirectional links between action and perception are constantly ignored in BCI experiments. In the current perspective article, we proposed an innovative BCI paradigm that is directly inspired by the ideomotor principle, which postulates that voluntary actions are driven by the anticipated representation of forthcoming perceptual effects. We believe that (1) adapting BCI paradigms could allow simple action-effect bindings and consequently action-effect predictions and (2) using neural underpinnings of those action-effect predictions as features of interest in AI methods, could lead to more accurate and naturalistic BCI-mediated actions.},
}
@article {pmid34776902,
year = {2021},
author = {Remsik, AB and Gjini, K and Williams, L and van Kan, PLE and Gloe, S and Bjorklund, E and Rivera, CA and Romero, S and Young, BM and Nair, VA and Caldera, KE and Williams, JC and Prabhakaran, V},
title = {Ipsilesional Mu Rhythm Desynchronization Correlates With Improvements in Affected Hand Grip Strength and Functional Connectivity in Sensorimotor Cortices Following BCI-FES Intervention for Upper Extremity in Stroke Survivors.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {725645},
pmid = {34776902},
issn = {1662-5161},
support = {T32 GM008692/GM/NIGMS NIH HHS/United States ; T32 GM140935/GM/NIGMS NIH HHS/United States ; },
abstract = {Stroke is a leading cause of acquired long-term upper extremity motor disability. Current standard of care trajectories fail to deliver sufficient motor rehabilitation to stroke survivors. Recent research suggests that use of brain-computer interface (BCI) devices improves motor function in stroke survivors, regardless of stroke severity and chronicity, and may induce and/or facilitate neuroplastic changes associated with motor rehabilitation. The present sub analyses of ongoing crossover-controlled trial NCT02098265 examine first whether, during movements of the affected hand compared to rest, ipsilesional Mu rhythm desynchronization of cerebral cortical sensorimotor areas [Brodmann's areas (BA) 1-7] is localized and tracks with changes in grip force strength. Secondly, we test the hypothesis that BCI intervention results in changes in frequency-specific directional flow of information transmission (direct path functional connectivity) in BA 1-7 by measuring changes in isolated effective coherence (iCoh) between cerebral cortical sensorimotor areas thought to relate to electrophysiological signatures of motor actions and motor learning. A sample of 16 stroke survivors with right hemisphere lesions (left hand motor impairment), received a maximum of 18-30 h of BCI intervention. Electroencephalograms were recorded during intervention sessions while outcome measures of motor function and capacity were assessed at baseline and completion of intervention. Greater desynchronization of Mu rhythm, during movements of the impaired hand compared to rest, were primarily localized to ipsilesional sensorimotor cortices (BA 1-7). In addition, increased Mu desynchronization in the ipsilesional primary motor cortex, Post vs. Pre BCI intervention, correlated significantly with improvements in hand function as assessed by grip force measurements. Moreover, the results show a significant change in the direction of causal information flow, as measured by iCoh, toward the ipsilesional motor (BA 4) and ipsilesional premotor cortices (BA 6) during BCI intervention. Significant iCoh increases from ipsilesional BA 4 to ipsilesional BA 6 were observed in both Mu [8-12 Hz] and Beta [18-26 Hz] frequency ranges. In summary, the present results are indicative of improvements in motor capacity and behavior, and they are consistent with the view that BCI-FES intervention improves functional motor capacity of the ipsilesional hemisphere and the impaired hand.},
}
@article {pmid34776898,
year = {2021},
author = {Schleim, S},
title = {Neurorights in History: A Contemporary Review of José M. R. Delgado's "Physical Control of the Mind" (1969) and Elliot S. Valenstein's "Brain Control" (1973).},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {703308},
pmid = {34776898},
issn = {1662-5161},
abstract = {Scholars from various disciplines discuss the ethical, legal, and social implications of neurotechnology. Some have proposed four concrete "neurorights". This review presents the research of two pioneers in brain stimulation from the 1950s to 1970s, José M. R. Delgado and Elliot S. Valenstein, who also reflected upon the ethical, legal, and social aspects of their and other scientists' related research. Delgado even formulated the vision "toward a psychocivilized society" where brain stimulation is used to control, in particular, citizens' aggressive and violent behavior. Valenstein, by contrast, believed that the brain is not organized in such a way to allow the control or even removal of only negative processes without at the same time diminishing desirable ones. The paper also describes how animal and human experimentation on brain stimulation was carried out in that time period. It concludes with a contemporary perspective on the relevance of neurotechnology for neuroethics, neurolaw, and neurorights, including two recent examples for brain-computer interfaces.},
}
@article {pmid34776860,
year = {2021},
author = {Tan, JL and Liang, ZF and Zhang, R and Dong, YQ and Li, GH and Zhang, M and Wang, H and Xu, N},
title = {Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {780373},
pmid = {34776860},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG) plays an important role in brain disease diagnosis and research of brain-computer interface (BCI). However, the measurements of EEG are often exposed to strong interference of power line artifact (PLA). Digital notch filters (DNFs) can be applied to remove the PLA effectively, but it also results in severe signal distortions in the time domain. To address this problem, spectrum correction (SC) based methods can be utilized. These methods estimate harmonic parameters of the PLA such that compensation signals are produced to remove the noise. In order to ensure high accuracy during harmonic parameter estimations, a novel approach is proposed in this paper. This novel approach is based on the combination of sparse representation (SR) and SC. It can deeply mine the information of PLA in the frequency domain. Firstly, a ratio-based spectrum correction (RBSC) using rectangular window is employed to make rough estimation of the harmonic parameters of PLA. Secondly, the two spectral line closest to the estimated frequency are calculated. Thirdly, the two spectral lines with high amplitudes can be utilized as input of RBSC to make finer estimations of the harmonic parameters. Finally, a compensation signal, based on the extracted harmonic parameters, is generated to suppress PLA. Numerical simulations and actual EEG signals with PLA were used to evaluate the effectiveness of the improved approach. It is verified that this approach can effectively suppress the PLA without distorting the time-domain waveform of the EEG signal.},
}
@article {pmid34775975,
year = {2021},
author = {Puttanawarut, C and Sirirutbunkajorn, N and Khachonkham, S and Pattaranutaporn, P and Wongsawat, Y},
title = {Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients.},
journal = {Radiation oncology (London, England)},
volume = {16},
number = {1},
pages = {220},
pmid = {34775975},
issn = {1748-717X},
mesh = {Adult ; Aged ; Aged, 80 and over ; Esophageal Neoplasms/pathology/*radiotherapy ; Female ; Humans ; Male ; Middle Aged ; Organs at Risk/*radiation effects ; Prognosis ; Radiation Pneumonitis/etiology/*pathology ; Radiotherapy Dosage ; Radiotherapy, Intensity-Modulated/*adverse effects ; Retrospective Studies ; },
abstract = {OBJECTIVE: The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2).<br><br>MATERIALS AND METHODS: DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade&#x2009;&#x2265;&#x2009;1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs).<br><br>RESULT: The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC&#x2009;=&#x2009;0.70) and without correction of the dose to EQD2 (AUC&#x2009;=&#x2009;0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2.<br><br>CONCLUSION: Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.},
}
@article {pmid34770554,
year = {2021},
author = {Kubacki, A},
title = {Use of Force Feedback Device in a Hybrid Brain-Computer Interface Based on SSVEP, EOG and Eye Tracking for Sorting Items.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {21},
pages = {},
pmid = {34770554},
issn = {1424-8220},
support = {0614/SBAD/1547//Pozna&#x144; University of Technology/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; Evoked Potentials, Visual ; Eye-Tracking Technology ; Feedback ; Humans ; Photic Stimulation ; },
abstract = {Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EEG signal recording devices and ever-lower set prices. Unfortunately, such systems are substantially limited in terms of the number of generated commands. This especially applies to sets that are not medical devices. This article proposes a hybrid brain-computer system based on the Steady-State Visual Evoked Potential (SSVEP), EOG, eye tracking, and force feedback system. Such an expanded system eliminates many of the particular system shortcomings and provides much better results. The first part of the paper presents information on the methods applied in the hybrid brain-computer system. The presented system was tested in terms of the ability of the operator to place the robot's tip to a designated position. A virtual model of an industrial robot was proposed, which was used in the testing. The tests were repeated on a real-life industrial robot. Positioning accuracy of system was verified with the feedback system both enabled and disabled. The results of tests conducted both on the model and on the real object clearly demonstrate that force feedback improves the positioning accuracy of the robot's tip when controlled by the operator. In addition, the results for the model and the real-life industrial model are very similar. In the next stage, research was carried out on the possibility of sorting items using the BCI system. The research was carried out on a model and a real robot. The results show that it is possible to sort using bio signals from the human body.},
}
@article {pmid34770553,
year = {2021},
author = {Milanés-Hermosilla, D and Trujillo Codorniú, R and López-Baracaldo, R and Sagaró-Zamora, R and Delisle-Rodriguez, D and Villarejo-Mayor, JJ and Núñez-Álvarez, JR},
title = {Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {21},
pages = {},
pmid = {34770553},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Uncertainty ; },
abstract = {Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition.},
}
@article {pmid34770272,
year = {2021},
author = {Wang, H and Lu, D and Liu, L and Gao, H and Wu, R and Zhou, Y and Ai, Q and Wang, Y and Li, G},
title = {Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {21},
pages = {},
pmid = {34770272},
issn = {1424-8220},
support = {JCKY2018204B053//the Science Foundation of Chinese Aerospace Industry/ ; No. ICT2021A13//the Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China/ ; },
mesh = {Electromyography ; Humans ; Sodium Chloride ; Sodium Glutamate ; *Sucrose ; *Taste ; },
abstract = {A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model's performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.},
}
@article {pmid34764279,
year = {2021},
author = {Yu, H and Xiang, X and Chen, Z and Wang, X and Dai, J and Wang, X and Huang, P and Zhao, ZD and Shen, WL and Li, H},
title = {Periaqueductal gray neurons encode the sequential motor program in hunting behavior of mice.},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {6523},
pmid = {34764279},
issn = {2041-1723},
mesh = {Animals ; Behavior, Animal/*physiology ; Electromyography ; Hypothalamic Area, Lateral/metabolism/physiology ; Immunohistochemistry ; Male ; Mice ; Neurons/*metabolism/physiology ; Open Field Test ; Periaqueductal Gray/*metabolism/physiology ; Zona Incerta/metabolism/physiology ; },
abstract = {Sequential encoding of motor programs is essential for behavior generation. However, whether it is critical for instinctive behavior is still largely unknown. Mouse hunting behavior typically contains a sequential motor program, including the prey search, chase, attack, and consumption. Here, we reveal that the neuronal activity in the lateral periaqueductal gray (LPAG) follows a sequential pattern and is time-locked to different hunting actions. Optrode recordings and photoinhibition demonstrate that LPAG[Vgat] neurons are required for the prey detection, chase and attack, while LPAG[Vglut2] neurons are selectively required for the attack. Ablation of inputs that could trigger hunting, including the central amygdala, the lateral hypothalamus, and the zona incerta, interrupts the activity sequence pattern and substantially impairs hunting actions. Therefore, our findings reveal that periaqueductal gray neuronal ensembles encode the sequential hunting motor program, which might provide a framework for decoding complex instinctive behaviors.},
}
@article {pmid34763331,
year = {2021},
author = {Martínez-Cagigal, V and Thielen, J and Santamaría-Vázquez, E and Pérez-Velasco, S and Desain, P and Hornero, R},
title = {Brain-computer interfaces based on code-modulated visual evoked potentials (c-VEP): a literature review.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac38cf},
pmid = {34763331},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Language ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain-computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines.Approach.The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc.Main results.The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development.Significance.Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines of research are suggested to contribute to the development of reliable plug-and-play c-VEP-based BCIs.},
}
@article {pmid34758941,
year = {2022},
author = {Karasneh, J and Christoforou, J and Walker, JS and Dios, PD and Lockhart, PB and Patton, LL},
title = {World Workshop on Oral Medicine VII: Bleeding control interventions for invasive dental procedures in patients with inherited functional platelet disorders: A systematic review.},
journal = {Oral surgery, oral medicine, oral pathology and oral radiology},
volume = {133},
number = {4},
pages = {412-431},
doi = {10.1016/j.oooo.2021.08.003},
pmid = {34758941},
issn = {2212-4411},
mesh = {*Antifibrinolytic Agents/therapeutic use ; Dentistry ; Humans ; Platelet Transfusion ; Postoperative Hemorrhage/prevention &amp; control ; },
abstract = {OBJECTIVES: The objective of this study was to determine bleeding control interventions (BCIs) that were reported to be effective in controlling postoperative bleeding in patients with inherited functional platelet disorders (IFPDs) undergoing invasive dental procedures.<br><br>STUDY DESIGN: We searched MEDLINE/PubMed, Embase, Cochrane Library (Wiley), and Scopus from 1960 through April 2020 for studies on patients with IFPD undergoing invasive dental procedures. Two reviewers conducted assessments independently.<br><br>RESULTS: We found a total of 620 nonduplicate published articles, of which 32 studies met our inclusion criteria. Management with BCI in patients with IFPD included in this systematic review was effective in 80.7% of treatment sessions. Local measures used intraoperatively were found to be effective. Three different protocols of BCI were noted; the most effective protocol consisted of antifibrinolytics, scaffold/matrix agents, and sutures (P < .01). An adjunct protocol consisting of a tissue sealant was also effective (P < .01). A third protocol of platelet transfusion and antifibrinolytics was ineffective in controlling postoperative bleeding in 4 of 6 dental sessions.<br><br>CONCLUSIONS: This systematic review supports the use of local measures intraoperatively and antifibrinolytics postoperatively. It also supports making decision regarding platelet transfusion based on the clinician's clinical judgment and medical history of the individual patient.},
}
@article {pmid34753099,
year = {2021},
author = {Nasr, AO and Al-Harbi, TM and AlRamadan, FS},
title = {Case report; successful treatment of traumatic ischaemic hemiplegia secondary to blunt carotid injury associating high grade liver trauma.},
journal = {International journal of surgery case reports},
volume = {88},
number = {},
pages = {106547},
pmid = {34753099},
issn = {2210-2612},
abstract = {INTRODUCTION AND IMPORTANCE: Blunt carotid injury (BCI) injury is a rare sequel of trauma and could result in ischemic complication if not detected and treated early. The presence of high-grade solid organ injury with ongoing bleeding represents additional challenge in treating BCI.<br><br>CASE PRESENTATION: A 25-year-old victim of motor vehicle collision resulted in grade IV liver, grade III left kidney and grade I spleen injury. He underwent an urgent laparotomy with transient liver packing at local hospital. A full body Contrast-Enhanced Computer Tomography (CECT) upon arrival revealed right internal carotid intimal tear with intra and extra-cranial thrombosis and a 3&#xa0;cm aneurysm. With a decreased level of consciousness, the patient showed a GCS of 13 and left-sided hemiplegia. After complex multidisciplinary treatment sessions, patient recovered with a partial regain of left-sided muscle power.<br><br>CLINICAL DISCUSSION: Selective embolization of active liver bleeding was a turning point in the management of our patient as it deferred the need for a second operative intervention. It was a necessary step before endovascular stenting and recanalization of the BCI to restore the circulation to the right cerebral hemisphere. Dual anti-platelet therapy (DAPT) was necessary to prevent thrombosis of the stent and continuity of carotid recanalization.<br><br>CONCLUSION: BCI with traumatic ischaemic hemiplegia associating a sum of life-threatening multiple injuries including high grade liver trauma with ongoing bleeding could still be managed non-operatively with acceptable outcome in the presence of a comprehensive specialized multidisciplinary service.},
}
@article {pmid34751918,
year = {2022},
author = {Wang, J and Zhao, W and Zhao, Q and Zhou, J and Li, X and He, Y and Gong, Z},
title = {Drosophila Larval Light-Avoidance is Negatively Regulated by Temperature Through Two Pairs of Central Brain Neurons.},
journal = {Neuroscience bulletin},
volume = {38},
number = {2},
pages = {200-204},
pmid = {34751918},
issn = {1995-8218},
mesh = {Animals ; Brain/metabolism ; *Drosophila/physiology ; *Drosophila Proteins/metabolism ; Drosophila melanogaster/physiology ; Interneurons/metabolism ; Larva/physiology ; Temperature ; },
}
@article {pmid34750918,
year = {2022},
author = {Yang, D and Cen, Z and Wang, L and Chen, X and Liu, P and Wang, H and Ouyang, Z and Chen, Y and Zhang, F and Xie, F and Wang, B and Wu, S and Yin, H and Jiang, B and Wang, Z and Ji, J and Luo, W},
title = {Neuronal intranuclear inclusion disease tremor-dominant subtype: A mimicker of essential tremor.},
journal = {European journal of neurology},
volume = {29},
number = {2},
pages = {450-458},
doi = {10.1111/ene.15169},
pmid = {34750918},
issn = {1468-1331},
mesh = {*Essential Tremor/diagnosis/genetics ; Humans ; *Intranuclear Inclusion Bodies/genetics/pathology ; Neurodegenerative Diseases ; Tremor/diagnosis/genetics ; Trinucleotide Repeat Expansion/genetics ; },
abstract = {BACKGROUND AND PURPOSE: The GGC repeat expansion in the NOTCH2NLC gene has been identified as the genetic cause of neuronal intranuclear inclusion disease (NIID). Recently, this repeat expansion was also reported to be associated with essential tremor (ET). However, some patients with this repeat expansion, initially diagnosed with ET, were eventually diagnosed with NIID. Therefore, controversy remains regarding the clinical diagnosis of these expansion-positive patients presenting with tremor-dominant symptoms. This study aimed to clarify the clinical phenotype in tremor-dominant patients who have the GGC repeat expansion in the NOTCH2NLC gene.<br><br>METHODS: We screened for pathogenic GGC repeat expansions in 602 patients initially diagnosed with ET and systematically re-evaluated the clinical features of the expansion-positive probands and their family members.<br><br>RESULTS: Pathogenic GGC repeat expansion in the NOTCH2NLC gene was detected in 10 probands (1.66%). Seven of these probands were re-evaluated and found to have systemic areflexia, cognitive impairment, and abnormal nerve conduction, which prompted a change of diagnosis from ET to NIID. Three of the probands had typical hyperintensity in the corticomedullary junction on diffusion-weighted imaging. Intranuclear inclusions were detected in all four probands who underwent skin biopsy.<br><br>CONCLUSIONS: The NIID tremor-dominant subtype can be easily misdiagnosed as ET. We should take NIID into account for differential diagnosis of ET. Systemic areflexia could be an important clinical clue suggesting that cranial magnetic resonance imaging examination, or even further genetic testing and skin biopsy examination, should be used to confirm the diagnosis of NIID.},
}
@article {pmid34749248,
year = {2021},
author = {Rapeaux, AB and Constandinou, TG},
title = {Implantable brain machine interfaces: first-in-human studies, technology challenges and trends.},
journal = {Current opinion in biotechnology},
volume = {72},
number = {},
pages = {102-111},
doi = {10.1016/j.copbio.2021.10.001},
pmid = {34749248},
issn = {1879-0429},
support = {UKDRI-7004/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Prostheses and Implants ; Technology ; },
abstract = {Implantable brain machine interfaces (BMIs) are now on a trajectory to go mainstream, wherein what was once considered last resort will progressively become elective at earlier stages in disease treatment. First-in-human successes have demonstrated the ability to decode highly dexterous motor skills such as handwriting, and speech from human cortical activity. These have been used for cursor and prosthesis control, direct-to-text communication and speech synthesis. Along with these breakthrough studies, technology advancements have enabled the observation of more channels of neural activity through new concepts for centralised/distributed implant architectures. This is complemented by research in flexible substrates, packaging, surgical workflows and data processing. New regulatory guidance and funding has galvanised the field. This culmination of resource, efforts and capability is now attracting significant investment for BMI commercialisation. This paper reviews recent developments and describes the paradigm shift in BMI development that is leading to new innovations, insights and BMI translation.},
}
@article {pmid34748509,
year = {2022},
author = {Cho, JH and Jeong, JH and Lee, SW},
title = {NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.},
journal = {IEEE transactions on cybernetics},
volume = {52},
number = {12},
pages = {13279-13292},
doi = {10.1109/TCYB.2021.3122969},
pmid = {34748509},
issn = {2168-2275},
mesh = {Humans ; *Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Movement ; Hand ; },
abstract = {Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.},
}
@article {pmid34746899,
year = {2021},
author = {Ng, OT and Koh, V and Chiew, CJ and Marimuthu, K and Thevasagayam, NM and Mak, TM and Chua, JK and Ong, SSH and Lim, YK and Ferdous, Z and Johari, AKB and Chen, MI and Maurer-Stroh, S and Cui, L and Lin, RTP and Tan, KB and Cook, AR and Leo, PY and Lee, PVJ},
title = {Impact of Delta Variant and Vaccination on SARS-CoV-2 Secondary Attack Rate Among Household Close Contacts.},
journal = {The Lancet regional health. Western Pacific},
volume = {17},
number = {},
pages = {100299},
pmid = {34746899},
issn = {2666-6065},
abstract = {BACKGROUND: Impact of the Delta variant and vaccination on SARS-CoV-2 transmission remains unclear. In Singapore, quarantine of all close contacts, including entry and exit PCR testing, provided the opportunity to determine risk of infection by the Delta variant compared to other variants, vaccine efficacy against SARS-CoV-2 acquisition, symptomatic or severe COVID-19, and risk factors associated with SARS-CoV-2 acquisition and symptomatic disease.<br><br>METHODS: This retrospective cohort study included all close contacts between September 1, 2020 and May 31, 2021. Regardless of symptoms, all were quarantined for 14 days with entry and exit PCR testing. Household contacts were defined as individuals who shared a residence with a Covid-19 index case. Secondary attack rates among household close contacts of Delta variant-infected indexes and other variant-infected indexes were derived from prevalence of diagnosed cases among contacts. Relative risk ratios and bootstrapping at the cluster level was used to determine risk of infection by the Delta variant compared to other variants and vaccine efficacy against SARS-CoV-2 acquisition, symptomatic or severe COVID-19. Logistic regression using generalized estimating equations was used to determine risk factors associated with SARS-CoV-2 acquisition and symptomatic disease.<br><br>FINDINGS: Of 1024 household contacts linked to 301 PCR-confirmed index cases, 753 (73.5%) were linked to Delta-infected indexes and 248 (24.2%) were exposed to indexes with other variants. Household secondary attack rate among unvaccinated Delta-exposed contacts was 25.8% (95% boostrap confidence interval [BCI] 20.6-31.5%) compared with 12.9% (95%BCI 7.0-20.0%) among other variant-exposed contacts. Unvaccinated Delta-exposed contacts were more likely to be infected than those exposed to other variants (Relative risk 2.01, 95%CI 1.24-3.84). Among Delta-exposed contacts, complete vaccination had a vaccine effectiveness of 56.4% (95%BCI 32.6-75.8%) against acquisition, 64.1% (95%BCI 37.8-85.4%) against symptomatic disease and 100% against severe disease. Among Delta-exposed contacts, vaccination status (adjusted odds ratio [aOR] 0.33, 95% robust confidence interval [RCI] 0.17-0.63) and older age of the index (aOR 1.20 per decade, 95%RCI 1.03-1.39) was associated with increased risk of SARS-CoV-2 acquisition by the contact. Vaccination status of the index was not associated with a statistically-significant difference for contact SARS-CoV-2 acquisition (aOR 0.73, 95%RCI 0.38-1.40).<br><br>INTERPRETATION: Increased risk of SARS-CoV-2 Delta acquisition compared with other variants was reduced with vaccination. Close-contacts of vaccinated Delta-infected indexes did not have statistically significant reduced risk of acquisition compared with unvaccinated Delta-infected indexes.},
}
@article {pmid34745714,
year = {2021},
author = {Afzal Khan, MN and Hong, KS},
title = {Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI.},
journal = {Biomedical optics express},
volume = {12},
number = {10},
pages = {5939-5954},
pmid = {34745714},
issn = {2156-7085},
abstract = {One of the primary objectives of the brain-computer interface (BCI) is to obtain a command with higher classification accuracy within the shortest possible time duration. Therefore, this study evaluates several stimulation durations to propose a duration that can yield the highest classification accuracy. Furthermore, this study aims to address the inherent delay in the hemodynamic responses (HRs) for the command generation time. To this end, HRs in the sensorimotor cortex were evaluated for the functional near-infrared spectroscopy (fNIRS)-based BCI. To evoke brain activity, right-hand-index finger poking and tapping tasks were used. In this study, six different stimulation durations (i.e., 1, 3, 5, 7, 10, and 15 s) were tested on 10 healthy male subjects. Upon stimulation, different temporal features and multiple time windows were utilized to extract temporal features. The extracted features were then classified using linear discriminant analysis. The classification results using the main HR showed that a 5 s stimulation duration could yield the highest classification accuracy, i.e., 74%, with a combination of the mean and maximum value features. However, the results were not significantly different from the classification accuracy obtained using the 15 s stimulation. To further validate the results, a classification using the initial dip was performed. The results obtained endorsed the finding with an average classification accuracy of 73.5% using the features of minimum peak and skewness in the 5 s window. The results based on classification using the initial dip for 5 s were significantly different from all other tested stimulation durations (p < 0.05) for all feature combinations. Moreover, from the visual inspection of the HRs, it is observed that the initial dip occurred as soon as the task started, but the main HR had a delay of more than 2 s. Another interesting finding is that impulsive stimulation in the sensorimotor cortex can result in the generation of a clearer initial dip phenomenon. The results reveal that the command for the fNIRS-based BCI can be generated using the 5 s stimulation duration. In conclusion, the use of the initial dip can reduce the time taken for the generation of commands and can be used to achieve a higher classification accuracy for the fNIRS-BCI within a 5 s task duration rather than relying on longer durations.},
}
@article {pmid34745488,
year = {2021},
author = {Almarzouki, HZ and Alsulami, H and Rizwan, A and Basingab, MS and Bukhari, H and Shabaz, M},
title = {An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {1233166},
pmid = {34745488},
issn = {2040-2309},
mesh = {Forecasting ; Humans ; Internet ; Pilot Projects ; *Stroke/diagnosis ; *Wearable Electronic Devices ; },
abstract = {In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.},
}
@article {pmid34745070,
year = {2021},
author = {Wallner, A and Moulin, L and Busset, N and Rimbault, I and Béna, G},
title = {Genetic Diversity of Type 3 Secretion System in Burkholderia s.l. and Links With Plant Host Adaptation.},
journal = {Frontiers in microbiology},
volume = {12},
number = {},
pages = {761215},
pmid = {34745070},
issn = {1664-302X},
abstract = {Burkholderia sensu lato species are prominent for their diversity of hosts. The type 3 secretion system (T3SS) is a major mechanism impacting the interactions between bacteria and eukaryotic hosts. Besides the human pathogenic species Burkholderia pseudomallei and closely affiliated species, the T3SS has received little attention in this genus as in taxonomically and evolutionary close genera Paraburkholderia, Caballeronia, Trinickia, and Mycetohabitans. We proceeded to identify and characterize the diversity of T3SS types using the genomic data from a subset of 145 strains representative of the species diversity found in the Burkholderia s.l. group. Through an analysis of their phylogenetic distribution, we identified two new T3SS types with an atypical chromosomal organization and which we propose to name BCI (Burkholderia cepacia complex Injectisome) and PSI (Paraburkholderia Short Injectisome). BCI is the dominant T3SS type found in Burkholderia sensu stricto (s.s.) species and PSI is mostly restricted to the Paraburkholderia genus. By correlating their distribution with the ecology of their strains of origin, we propose a role in plant interaction for these T3SS types. Experimentally, we demonstrated that a BCI deficient B. vietnamiensis LMG10929 mutant was strongly affected in its rice colonization capacity.},
}
@article {pmid34744677,
year = {2021},
author = {Kim, H and Im, CH},
title = {Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.},
journal = {Frontiers in neuroinformatics},
volume = {15},
number = {},
pages = {750839},
pmid = {34744677},
issn = {1662-5196},
abstract = {There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1-3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.},
}
@article {pmid34744622,
year = {2021},
author = {Huang, X and Zhou, N and Choi, KS},
title = {A Generalizable and Discriminative Learning Method for Deep EEG-Based Motor Imagery Classification.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {760979},
pmid = {34744622},
issn = {1662-4548},
abstract = {Convolutional neural networks (CNNs) have been widely applied to the motor imagery (MI) classification field, significantly improving the state-of-the-art (SoA) performance in terms of classification accuracy. Although innovative model structures are thoroughly explored, little attention was drawn toward the objective function. In most of the available CNNs in the MI area, the standard cross-entropy loss is usually performed as the objective function, which only ensures deep feature separability. Corresponding to the limitation of current objective functions, a new loss function with a combination of smoothed cross-entropy (with label smoothing) and center loss is proposed as the supervision signal for the model in the MI recognition task. Specifically, the smoothed cross-entropy is calculated by the entropy between the predicted labels and the one-hot hard labels regularized by a noise of uniform distribution. The center loss learns a deep feature center for each class and minimizes the distance between deep features and their corresponding centers. The proposed loss tries to optimize the model in two learning objectives, preventing overconfident predictions and increasing deep feature discriminative capacity (interclass separability and intraclass invariant), which guarantee the effectiveness of MI recognition models. We conduct extensive experiments on two well-known benchmarks (BCI competition IV-2a and IV-2b) to evaluate our method. The result indicates that the proposed approach achieves better performance than other SoA models on both datasets. The proposed learning scheme offers a more robust optimization for the CNN model in the MI classification task, simultaneously decreasing the risk of overfitting and increasing the discriminative power of deeply learned features.},
}
@article {pmid34744619,
year = {2021},
author = {Bhattacharyya, S and Konar, A and Raza, H and Khasnobish, A},
title = {Editorial: Brain-Computer Interfaces for Perception, Learning, and Motor Control.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {758104},
pmid = {34744619},
issn = {1662-4548},
}
@article {pmid34742297,
year = {2021},
author = {Johnson, S and Marshall, A and Hughes, D and Holmes, E and Henrich, F and Nurmikko, T and Sharma, M and Frank, B and Bassett, P and Marshall, A and Magerl, W and Goebel, A},
title = {Mechanistically informed non-invasive peripheral nerve stimulation for peripheral neuropathic pain: a randomised double-blind sham-controlled trial.},
journal = {Journal of translational medicine},
volume = {19},
number = {1},
pages = {458},
pmid = {34742297},
issn = {1479-5876},
support = {PB-PG-0215-36039PB//Research for Patient Benefit Programme/ ; },
mesh = {*Chronic Pain ; Double-Blind Method ; Humans ; *Neuralgia/therapy ; Pain Measurement ; Peripheral Nerves ; *Transcutaneous Electric Nerve Stimulation ; },
abstract = {BACKGROUND: Induction of long-term synaptic depression (LTD) is proposed as a treatment mechanism for chronic pain but remains untested in clinical populations. Two interlinked studies; (1) A patient-assessor blinded, randomised, sham-controlled clinical trial and (2) an open-label mechanistic study, sought to examine therapeutic LTD for persons with chronic peripheral nerve injury pain.<br><br>METHODS: (1) Patients were randomised using a concealed, computer-generated schedule to either active or sham non-invasive low-frequency nerve stimulation (LFS), for 3&#xa0;months (minimum 10&#xa0;min/day). The primary outcome was average pain intensity (0-10 Likert scale) recorded over 1&#xa0;week, at 3&#xa0;months, compared between study groups. (2) On trial completion, consenting subjects entered a mechanistic study assessing somatosensory changes in response to LFS.<br><br>RESULTS: (1) 76 patients were randomised (38 per group), with 65 (31 active, 34 sham) included in the intention to treat analysis. The primary outcome was not significant, pain scores were 0.3 units lower in active group (95% CI -&#x2009;1.0, 0.3; p&#x2009;=&#x2009;0.30) giving an effect size of 0.19 (Cohen's D). Two non-device related serious adverse events were reported. (2) In the mechanistic study (n&#x2009;=&#x2009;19) primary outcomes of mechanical pain sensitivity (p&#x2009;=&#x2009;0.006) and dynamic mechanical allodynia (p&#x2009;=&#x2009;0.043) significantly improved indicating reduced mechanical hyperalgesia.<br><br>CONCLUSIONS: Results from the RCT failed to reach significance. Results from the mechanistic study provide new evidence for effective induction of LTD in a clinical population. Taken together results add to mechanistic understanding of LTD and help inform future study design and approaches to treatment. Trial registration ISRCTN53432663.},
}
@article {pmid34735990,
year = {2021},
author = {Yoo, J and Shoaran, M},
title = {Neural interface systems with on-device computing: machine learning and neuromorphic architectures.},
journal = {Current opinion in biotechnology},
volume = {72},
number = {},
pages = {95-101},
doi = {10.1016/j.copbio.2021.10.012},
pmid = {34735990},
issn = {1879-0429},
support = {R01 MH123634/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developments target pervasive implantables. Along with advances in electrodes, neural recording, and neurostimulation circuits, integration of disease biomarkers and machine learning algorithms enables real-time and on-site processing of neural activity with no need for power-demanding telemetry. This recent trend on combining artificial intelligence and machine learning with modern neural interfaces will lead to a new generation of low-power, smart, and miniaturized therapeutic devices for a wide range of neurological and psychiatric disorders. This paper reviews the recent development of the 'on-chip' machine learning and neuromorphic architectures, which is one of the key puzzles in devising next-generation clinically viable neural interface systems.},
}
@article {pmid34735347,
year = {2022},
author = {Pei, Y and Luo, Z and Zhao, H and Xu, D and Li, W and Yan, Y and Yan, H and Xie, L and Xu, M and Yin, E},
title = {A Tensor-Based Frequency Features Combination Method for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {30},
number = {},
pages = {465-475},
doi = {10.1109/TNSRE.2021.3125386},
pmid = {34735347},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Generalization, Psychological ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% (). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.},
}
@article {pmid34733134,
year = {2021},
author = {Vasconcelos, B and Fiedler, P and Machts, R and Haueisen, J and Fonseca, C},
title = {The Arch Electrode: A Novel Dry Electrode Concept for Improved Wearing Comfort.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {748100},
pmid = {34733134},
issn = {1662-4548},
abstract = {Electroencephalography (EEG) is increasingly used for repetitive and prolonged applications like neurofeedback, brain computer interfacing, and long-term intermittent monitoring. Dry-contact electrodes enable rapid self-application. A common drawback of existing dry electrodes is the limited wearing comfort during prolonged application. We propose a novel dry Arch electrode. Five semi-circular arches are arranged parallelly on a common baseplate. The electrode substrate material is a flexible thermoplastic polyurethane (TPU) produced by additive manufacturing. A chemical coating of Silver/Silver-Chloride (Ag/AgCl) is applied by electroless plating using a novel surface functionalization method. Arch electrodes were manufactured and validated in terms of mechanical durability, electrochemical stability, in vivo applicability, and signal characteristics. We compare the results of the dry arch electrodes with dry pin-shaped and conventional gel-based electrodes. 21-channel EEG recordings were acquired on 10 male and 5 female volunteers. The tests included resting state EEG, alpha activity, and a visual evoked potential. Wearing comfort was rated by the subjects directly after application, as well as at 30 min and 60 min of wearing. Our results show that the novel plating technique provides a well-adhering electrically conductive and electrochemically stable coating, withstanding repetitive strain and bending tests. The signal quality of the Arch electrodes is comparable to pin-shaped dry electrodes. The average channel reliability of the Arch electrode setup was 91.9 ± 9.5%. No considerable differences in signal characteristics have been observed for the gel-based, dry pin-shaped, and arch-shaped electrodes after the identification and exclusion of bad channels. The comfort was improved in comparison to pin-shaped electrodes and enabled applications of over 60 min duration. Arch electrodes required individual adaptation of the electrodes to the orientation and hairstyle of the volunteers. This initial preparation time of the 21-channel cap increased from an average of 5 min for pin-like electrodes to 15 min for Arch electrodes and 22 min for gel-based electrodes. However, when re-applying the arch electrode cap on the same volunteer, preparation times of pin-shaped and arch-shaped electrodes were comparable. In summary, our results indicate the applicability of the novel Arch electrode and coating for EEG acquisition. The novel electrode enables increased comfort for prolonged dry-contact measurement.},
}
@article {pmid34732522,
year = {2021},
author = {Derosier, K and Veuthey, TL and Ganguly, K},
title = {Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {41},
number = {49},
pages = {10120-10129},
pmid = {34732522},
issn = {1529-2401},
support = {I01 RX001640/RX/RRD VA/United States ; K02 NS093014/NS/NINDS NIH HHS/United States ; R01 MH111871/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning/*physiology ; Male ; Motor Cortex/*physiology ; Neurons/*physiology ; Rats ; Rats, Long-Evans ; },
abstract = {How does the brain integrate signals with different timescales to drive purposeful actions? Brain-machine interfaces (BMIs) offer a powerful tool to causally test how distributed neural networks achieve specific neural patterns. During neuroprosthetic learning, actuator movements are causally linked to primary motor cortex (M1) neurons, i.e., "direct" neurons that project to the decoder and whose firing is required to successfully perform the task. However, it is unknown how such direct M1 neurons interact with both "indirect" local (in M1 but not part of the decoder) and across area neural populations (e.g., in premotor cortex/M2), all of which are embedded in complex biological recurrent networks. Here, we trained male rats to perform a M1-BMI task and simultaneously recorded the activity of indirect neurons in both M2 and M1. We found that both M2 and M1 indirect neuron populations could be used to predict the activity of the direct neurons (i.e., "BMI-potent activity"). Interestingly, compared with M1 indirect activity, M2 neural activity was correlated with BMI-potent activity across a longer set of time lags, and the timescale of population activity patterns evolved more slowly. M2 units also predicted the activity of both M1 direct and indirect neural populations, suggesting that M2 population dynamics provide a continuous modulatory influence on M1 activity as a whole, rather than a moment-by-moment influence solely on neurons most relevant to a task. Together, our results indicate that longer timescale M2 activity provides modulatory influence over extended time lags on shorter-timescale control signals in M1.SIGNIFICANCE STATEMENT A central question in the study of motor control is whether primary motor cortex (M1) and premotor cortex (M2) interact through task-specific subpopulations of neurons, or whether tasks engage broader correlated networks. Brain-machine interfaces (BMIs) are powerful tools to study cross-area interactions. Here, we performed simultaneous recordings of M1 and M2 in a BMI task using a subpopulation of M1 neurons (direct neurons). We found that activity outside of direct neurons in M1 and M2 was predictive of M1-BMI task activity, and that M2 activity evolved at slower timescales than M1. These findings suggest that M2 provides a continuous modulatory influence on M1 as a whole, supporting a model of interactions through broad correlated networks rather than task-specific neural subpopulations.},
}
@article {pmid34728040,
year = {2021},
author = {Chen, KY and Liu, SY and Ji, X and Zhang, H and Li, T},
title = {[Research Progress on the Application of Artificial Intelligence in Rehabilitation Medicine in China].},
journal = {Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae},
volume = {43},
number = {5},
pages = {773-784},
doi = {10.3881/j.issn.1000-503X.13926},
pmid = {34728040},
issn = {1000-503X},
mesh = {*Artificial Intelligence ; China ; Humans ; *Robotics ; },
abstract = {The development of science and technology and the increasing demand of rehabilitation have driven the integration between artificial intelligence and rehabilitation medicine.In this study,statistical methods,document visualization tools,and other analysis methods were used in the Citespace software to analyze China's research status of artificial intelligence in the field of rehabilitation medicine with the key words of co-occurrence,emergence,and clustering.The relevant research hot spots were then classified and expounded.The results demonstrated that the current hot spots of artificial intelligence related to rehabilitation medicine included robots,brain-computer interfaces,human-computer interaction,and motor imagery.According to the clustering of key words and literature analysis,the five themes of artificial intelligence in rehabilitation medicine were determined as robot,brain-computer interface,intelligent rehabilitation training system,human-computer interaction,and assisted diagnosis and remote rehabilitation.Robotics and human-computer interaction would still be the research hot spots in the long future,and brain-computer interfaces,motor imagery,and remote rehabilitation would be new ones.This study analyzed the current hot spots,predicted the development trends,discussed the limitations,and proposed suggestions,aiming to provide reference for other scholars focusing on the application of artificial intelligence in rehabilitation medicine.},
}
@article {pmid34727532,
year = {2021},
author = {Formento, E and Botros, P and Carmena, JM},
title = {Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac35ac},
pmid = {34727532},
issn = {1741-2552},
support = {R01 NS106094/NS/NINDS NIH HHS/United States ; },
mesh = {Arm/physiology ; *Brain-Computer Interfaces ; Humans ; Isometric Contraction/physiology ; *Motor Neurons/physiology ; Muscle, Skeletal/physiology ; },
abstract = {Objective.Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance.Approach.Here, we hypothesized that a non-invasive neuromuscular-machine interface providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications.Main results.Over 6 days of training, eight participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a 2D center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task.Significance.These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.},
}
@article {pmid34725311,
year = {2021},
author = {Petit, J and Rouillard, J and Cabestaing, F},
title = {EEG-based brain-computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2fc4},
pmid = {34725311},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory ; Signal Processing, Computer-Assisted ; },
abstract = {A brain-computer interface (BCI) aims to derive commands from the user's brain activity in order to relay them to an external device. To do so, it can either detect a spontaneous change in the mental state, in the so-called 'active' BCIs, or a transient or sustained change in the brain response to an external stimulation, in 'reactive' BCIs. In the latter, external stimuli are perceived by the user through a sensory channel, usually sight or hearing. When the stimulation is sustained and periodical, the brain response reaches an oscillatory steady-state that can be detected rather easily. We focus our attention on electroencephalography-based BCIs (EEG-based BCI) in which a periodical signal, either mechanical or electrical, stimulates the user skin. This type of stimulus elicits a steady-state response of the somatosensory system that can be detected in the recorded EEG. The oscillatory and phase-locked voltage component characterising this response is called a steady-state somatosensory-evoked potential (SSSEP). It has been shown that the amplitude of the SSSEP is modulated by specific mental tasks, for instance when the user focuses their attention or not to the somatosensory stimulation, allowing the translation of this variation into a command. Actually, SSSEP-based BCIs may benefit from straightforward analysis techniques of EEG signals, like reactive BCIs, while allowing self-paced interaction, like active BCIs. In this paper, we present a survey of scientific literature related to EEG-based BCI exploiting SSSEP. Firstly, we endeavour to describe the main characteristics of SSSEPs and the calibration techniques that allow the tuning of stimulation in order to maximise their amplitude. Secondly, we present the signal processing and data classification algorithms implemented by authors in order to elaborate commands in their SSSEP-based BCIs, as well as the classification performance that they evaluated on user experiments.},
}
@article {pmid34724132,
year = {2022},
author = {Zheng, J and Liu, N and Xu, H},
title = {Pathway Matters: Prefrontal Control of Negative Emotions via Distinct Downstream Regions.},
journal = {Neuroscience bulletin},
volume = {38},
number = {2},
pages = {226-228},
pmid = {34724132},
issn = {1995-8218},
mesh = {*Brain Mapping ; *Emotions ; Magnetic Resonance Imaging ; Prefrontal Cortex ; },
}
@article {pmid34722403,
year = {2021},
author = {Jalalvandi, M and Riyahi Alam, N and Sharini, H and Hashemi, H and Nadimi, M},
title = {Brain Cortical Activation during Imagining of the Wrist Movement Using Functional Near-Infrared Spectroscopy (fNIRS).},
journal = {Journal of biomedical physics &amp; engineering},
volume = {11},
number = {5},
pages = {583-594},
pmid = {34722403},
issn = {2251-7200},
abstract = {BACKGROUND: fNIRS is a useful tool designed to record the changes in the density of blood's oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) molecules during brain activity. This method has made it possible to evaluate the hemodynamic changes of the brain during neuronal activity in a completely non-aggressive manner.<br><br>OBJECTIVE: The present study has been designed to investigate and evaluate the brain cortex activities during imagining of the execution of wrist motor tasks by comparing fMRI and fNIRS imaging methods.<br><br>MATERIAL AND METHODS: This novel observational Optical Imaging study aims to investigate the brain motor cortex activity during imagining of the right wrist motor tasks in vertical and horizontal directions. To perform the study, ten healthy young right-handed volunteers were asked to think about right-hand movements in different directions according to the designed movement patterns. The required data were collected in two wavelengths, including 845 and 763 nanometers using a 48 channeled fNIRS machine.<br><br>RESULTS: Analysis of the obtained data showed the brain activity patterns during imagining of the execution of a movement are formed in various points of the motor cortex in terms of location. Moreover, depending on the direction of the movement, activity plans have distinguishable patterns. The results showed contralateral M1 was mainly activated during imagining of the motor cortex (p<0.05).<br><br>CONCLUSION: The results of our study showed that in brain imaging, it is possible to distinguish between patterns of activities during wrist motion in different directions using the recorded signals obtained through near-infrared Spectroscopy. The findings of this study can be useful in further studies related to movement control and BCI.},
}
@article {pmid34722130,
year = {2021},
author = {Maslen, H and Rainey, S},
title = {Control and Ownership of Neuroprosthetic Speech.},
journal = {Philosophy &amp; technology},
volume = {34},
number = {3},
pages = {425-445},
pmid = {34722130},
issn = {2210-5433},
support = {/WT_/Wellcome Trust/United Kingdom ; 104848/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; },
abstract = {Implantable brain-computer interfaces (BCIs) are being developed to restore speech capacity for those who are unable to speak. Patients with locked-in syndrome or amyotrophic lateral sclerosis could be able to use covert speech - vividly imagining saying something without actual vocalisation - to trigger neural controlled systems capable of synthesising speech. User control has been identified as particularly pressing for this type of BCI. The incorporation of machine learning and statistical language models into the decoding process introduces a contribution to (or 'shaping of') the output that is beyond the user's control. Whilst this type of 'shared control' of BCI action is not unique to speech BCIs, the automated shaping of what a user 'says' has a particularly acute ethical dimension, which may differ from parallel concerns surrounding automation in movement BCIs. This paper provides an analysis of the control afforded to the user of a speech BCI of the sort under development, as well as the relationships between accuracy, control, and the user's ownership of the speech produced. Through comparing speech BCIs with BCIs for movement, we argue that, whilst goal selection is the more significant locus of control for the user of a movement BCI, control over process will be more significant for the user of the speech BCI. The design of the speech BCI may therefore have to trade off some possible efficiency gains afforded by automation in order to preserve sufficient guidance control necessary for users to express themselves in ways they prefer. We consider the implications for the speech BCI user's responsibility for produced outputs and their ownership of token outputs. We argue that these are distinct assessments. Ownership of synthetic speech concerns whether the content of the output sufficiently represents the user, rather than their morally relevant, causal role in producing that output.},
}
@article {pmid34721724,
year = {2021},
author = {Postan, E},
title = {Narrative Devices: Neurotechnologies, Information, and Self-Constitution.},
journal = {Neuroethics},
volume = {14},
number = {2},
pages = {231-251},
pmid = {34721724},
issn = {1874-5490},
support = {/WT_/Wellcome Trust/United Kingdom ; },
abstract = {This article provides a conceptual and normative framework through which we may understand the potentially ethically significant roles that information generated by neurotechnologies about our brains and minds may play in our construction of our identities. Neuroethics debates currently focus disproportionately on the ways that third parties may (ab)use these kinds of information. These debates occlude interests we may have in whether and how we ourselves encounter information about our own brains and minds. This gap is not yet adequately addressed by most allusions in the literature to potential identity impacts. These lack the requisite conceptual or normative foundations to explain why we should be concerned about such effects or how they might be addressed. This article seeks to fill this gap by presenting a normative account of identity as constituted by embodied self-narratives. It proposes that information generated by neurotechnologies can play significant content-supplying and interpretive roles in our construction of our self-narratives. It argues, to the extent that these roles support and detract from the coherence and inhabitability of these narratives, access to information about our brains and minds engages non-trivial identity-related interests. These claims are illustrated using examples drawn from empirical literature reporting reactions to information generated by implantable predictive BCIs and psychiatric neuroimaging. The article concludes by highlighting ways in which information generated by neurotechnologies might be governed so as to protect information subjects' interests in developing and inhabiting their own identities.},
}
@article {pmid34721654,
year = {2021},
author = {Wen, T and Du, Y and Pan, T and Huang, C and Zhang, Z},
title = {A Deep Learning-Based Classification Method for Different Frequency EEG Data.},
journal = {Computational and mathematical methods in medicine},
volume = {2021},
number = {},
pages = {1972662},
pmid = {34721654},
issn = {1748-6718},
mesh = {Algorithms ; Brain-Computer Interfaces ; Computational Biology ; Databases, Factual ; *Deep Learning ; Diagnosis, Computer-Assisted/statistics &amp; numerical data ; Electroencephalography/classification/*statistics &amp; numerical data ; Epilepsy/classification/*diagnosis ; Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets.},
}
@article {pmid34720907,
year = {2021},
author = {Leeuwis, N and Yoon, S and Alimardani, M},
title = {Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {732946},
pmid = {34720907},
issn = {1662-5161},
abstract = {Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.},
}
@article {pmid34720871,
year = {2021},
author = {Liu, XR and Xu, XX and Lin, SM and Fan, CY and Ye, TT and Tang, B and Shi, YW and Su, T and Li, BM and Yi, YH and Luo, JH and Liao, WP},
title = {GRIN2A Variants Associated With Idiopathic Generalized Epilepsies.},
journal = {Frontiers in molecular neuroscience},
volume = {14},
number = {},
pages = {720984},
pmid = {34720871},
issn = {1662-5099},
abstract = {Objective: The objective of this study is to explore the role of GRIN2A gene in idiopathic generalized epilepsies and the potential underlying mechanism for phenotypic variation. Methods: Whole-exome sequencing was performed in a cohort of 88 patients with idiopathic generalized epilepsies. Electro-physiological alterations of the recombinant N-methyl-D-aspartate receptors (NMDARs) containing GluN2A mutants were examined using two-electrode voltage-clamp recordings. The alterations of protein expression were detected by immunofluorescence staining and biotinylation. Previous studies reported that epilepsy related GRIN2A missense mutations were reviewed. The correlation among phenotypes, functional alterations, and molecular locations was analyzed. Results: Three novel heterozygous missense GRIN2A mutations (c.1770A > C/p.K590N, c.2636A > G/p.K879R, and c.3199C > T/p.R1067W) were identified in three unrelated cases. Electrophysiological analysis demonstrated R1067W significantly increased the current density of GluN1/GluN2A NMDARs. Immunofluorescence staining indicated GluN2A mutants had abundant distribution in the membrane and cytoplasm. Western blotting showed the ratios of surface and total expression of the three GluN2A-mutants were significantly increased comparing to the wild type. Further analysis on the reported missense mutations demonstrated that mutations with severe gain-of-function were associated with epileptic encephalopathy, while mutations with mild gain of function were associated with mild phenotypes, suggesting a quantitative correlation between gain-of-function and phenotypic severity. The mutations located around transmembrane domains were more frequently associated with severe phenotypes and absence seizure-related mutations were mostly located in carboxyl-terminal domain, suggesting molecular sub-regional effects. Significance: This study revealed GRIN2A gene was potentially a candidate pathogenic gene of idiopathic generalized epilepsies. The functional quantitative correlation and the molecular sub-regional implication of mutations helped in explaining the relatively mild clinical phenotypes and incomplete penetrance associated with GRIN2A variants.},
}
@article {pmid34720854,
year = {2021},
author = {Qi, F and Wang, W and Xie, X and Gu, Z and Yu, ZL and Wang, F and Li, Y and Wu, W},
title = {Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {715855},
pmid = {34720854},
issn = {1662-4548},
abstract = {Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l 2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.},
}
@article {pmid34719347,
year = {2021},
author = {Barios, JA and Ezquerro, S and Bertomeu-Motos, A and Catalan, JM and Sanchez-Aparicio, JM and Donis-Barber, L and Fernandez, E and Garcia-Aracil, N},
title = {Movement-Related EEG Oscillations of Contralesional Hemisphere Discloses Compensation Mechanisms of Severely Affected Motor Chronic Stroke Patients.},
journal = {International journal of neural systems},
volume = {31},
number = {12},
pages = {2150053},
doi = {10.1142/S0129065721500532},
pmid = {34719347},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography ; Humans ; *Motor Disorders ; Movement ; *Stroke/therapy ; *Stroke Rehabilitation ; },
abstract = {Conventional rehabilitation strategies for stroke survivors become difficult when voluntary movements are severely disturbed. Combining passive limb mobilization, robotic devices and EEG-based brain-computer interfaces (BCI) systems might improve treatment and clinical follow-up of these patients, but detailed knowledge of neurophysiological mechanisms involved in functional recovery, which might help for tailoring stroke treatment strategies, is lacking. Movement-related EEG changes (EEG event-related desynchronization (ERD) in [Formula: see text] and [Formula: see text] bands, an indicator of motor cortex activation traditionally used for BCI systems), were evaluated in a group of 23 paralyzed chronic stroke patients in two unilateral motor tasks alternating paretic and healthy hands ((i) passive movement, using a hand exoskeleton, and (ii) voluntary movement), and compared to nine healthy subjects. In tasks using unaffected hand, we observed an increase of contralesional hemisphere activation for stroke patients group. Unexpectedly, when using paralyzed hand, motor cortex activation was reduced or absent in severely affected group of patients, while patients with moderate motor deficit showed an activation greater than control group. Cortical activation was reduced or absent in damaged hemisphere of all the patients in both tasks. Significant differences related to severity of motor deficit were found in the time course of [Formula: see text]-[Formula: see text] bands power ratio in EEG of contralesional hemisphere while moving affected hand. These findings suggest the presence of different compensation mechanisms in contralesional hemisphere of stroke patients related to the grade of motor disability, that might turn quantitative EEG during a movement task, obtained from a BCI system controlling a robotic device included in a rehabilitation task, into a valuable tool for monitoring clinical progression, evaluating recovery, and tailoring treatment of stroke patients.},
}
@article {pmid34717820,
year = {2022},
author = {Reis, G and Dos Santos Moreira-Silva, EA and Silva, DCM and Thabane, L and Milagres, AC and Ferreira, TS and Dos Santos, CVQ and de Souza Campos, VH and Nogueira, AMR and de Almeida, APFG and Callegari, ED and de Figueiredo Neto, AD and Savassi, LCM and Simplicio, MIC and Ribeiro, LB and Oliveira, R and Harari, O and Forrest, JI and Ruton, H and Sprague, S and McKay, P and Glushchenko, AV and Rayner, CR and Lenze, EJ and Reiersen, AM and Guyatt, GH and Mills, EJ and , },
title = {Effect of early treatment with fluvoxamine on risk of emergency care and hospitalisation among patients with COVID-19: the TOGETHER randomised, platform clinical trial.},
journal = {The Lancet. Global health},
volume = {10},
number = {1},
pages = {e42-e51},
pmid = {34717820},
issn = {2214-109X},
mesh = {Adult ; Aged ; Aged, 80 and over ; Brazil ; Double-Blind Method ; Emergency Medical Services/*statistics &amp; numerical data ; Female ; Fluvoxamine/adverse effects/*therapeutic use ; Hospitalization/*statistics &amp; numerical data ; Humans ; Male ; Middle Aged ; SARS-CoV-2 ; Selective Serotonin Reuptake Inhibitors/adverse effects/therapeutic use ; Treatment Outcome ; *COVID-19 Drug Treatment ; },
abstract = {BACKGROUND: Recent evidence indicates a potential therapeutic role of fluvoxamine for COVID-19. In the TOGETHER trial for acutely symptomatic patients with COVID-19, we aimed to assess the efficacy of fluvoxamine versus placebo in preventing hospitalisation defined as either retention in a COVID-19 emergency setting or transfer to a tertiary hospital due to COVID-19.<br><br>METHODS: This placebo-controlled, randomised, adaptive platform trial done among high-risk symptomatic Brazilian adults confirmed positive for SARS-CoV-2 included eligible patients from 11 clinical sites in Brazil with a known risk factor for progression to severe disease. Patients were randomly assigned (1:1) to either fluvoxamine (100 mg twice daily for 10 days) or placebo (or other treatment groups not reported here). The trial team, site staff, and patients were masked to treatment allocation. Our primary outcome was a composite endpoint of hospitalisation defined as either retention in a COVID-19 emergency setting or transfer to tertiary hospital due to COVID-19 up to 28 days post-random assignment on the basis of intention to treat. Modified intention to treat explored patients receiving at least 24 h of treatment before a primary outcome event and per-protocol analysis explored patients with a high level adherence (>80%). We used a Bayesian analytic framework to establish the effects along with probability of success of intervention compared with placebo. The trial is registered at ClinicalTrials.gov (NCT04727424) and is ongoing.<br><br>FINDINGS: The study team screened 9803 potential participants for this trial. The trial was initiated on June 2, 2020, with the current protocol reporting randomisation to fluvoxamine from Jan 20 to Aug 5, 2021, when the trial arms were stopped for superiority. 741 patients were allocated to fluvoxamine and 756 to placebo. The average age of participants was 50 years (range 18-102 years); 58% were female. The proportion of patients observed in a COVID-19 emergency setting for more than 6 h or transferred to a teritary hospital due to COVID-19 was lower for the fluvoxamine group compared with placebo (79 [11%] of 741 vs 119 [16%] of 756); relative risk [RR] 0&#xb7;68; 95% Bayesian credible interval [95% BCI]: 0&#xb7;52-0&#xb7;88), with a probability of superiority of 99&#xb7;8% surpassing the prespecified superiority threshold of 97&#xb7;6% (risk difference 5&#xb7;0%). Of the composite primary outcome events, 87% were hospitalisations. Findings for the primary outcome were similar for the modified intention-to-treat analysis (RR 0&#xb7;69, 95% BCI 0&#xb7;53-0&#xb7;90) and larger in the per-protocol analysis (RR 0&#xb7;34, 95% BCI, 0&#xb7;21-0&#xb7;54). There were 17 deaths in the fluvoxamine group and 25 deaths in the placebo group in the primary intention-to-treat analysis (odds ratio [OR] 0&#xb7;68, 95% CI: 0&#xb7;36-1&#xb7;27). There was one death in the fluvoxamine group and 12 in the placebo group for the per-protocol population (OR 0&#xb7;09; 95% CI 0&#xb7;01-0&#xb7;47). We found no significant differences in number of treatment emergent adverse events among patients in the fluvoxamine and placebo groups.<br><br>INTERPRETATION: Treatment with fluvoxamine (100 mg twice daily for 10 days) among high-risk outpatients with early diagnosed COVID-19 reduced the need for hospitalisation defined as retention in a COVID-19 emergency setting or transfer to a tertiary hospital.<br><br>FUNDING: FastGrants and The Rainwater Charitable Foundation.<br><br>TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.},
}
@article {pmid34717124,
year = {2021},
author = {Prominski, A and Tian, B},
title = {Bridging the gap - biomimetic design of bioelectronic interfaces.},
journal = {Current opinion in biotechnology},
volume = {72},
number = {},
pages = {69-75},
doi = {10.1016/j.copbio.2021.10.005},
pmid = {34717124},
issn = {1879-0429},
mesh = {*Biomimetics ; },
abstract = {Applied bioelectronic interfaces have an enormous potential for their application in personalized medicine and brain-machine interfaces. While significant progress has been made in the translational applications, there are still concerns about the safety and compliance of artificial devices interacting with cells and tissues. Applying biomimetic design principles enables developing new devices with improved properties in terms of their signal transduction efficiency and biocompatibility. Learning from the paradigms of biological architecture, we can define four cornerstones of biomimetics, which can guide designing new bioelectronic devices or providing improved solutions to challenging biomedical problems. Recent progress shows how these paradigms were successfully employed, for example, to create neuron-like electronics and assemble electronic materials in situ onto the cell membranes using genetic targeting.},
}
@article {pmid34716229,
year = {2022},
author = {Schroeder, KE and Perkins, SM and Wang, Q and Churchland, MM},
title = {Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {42},
number = {2},
pages = {220-239},
pmid = {34716229},
issn = {1529-2401},
support = {T32 NS064929/NS/NINDS NIH HHS/United States ; U19 NS104649/NS/NINDS NIH HHS/United States ; R01 NS100066/NS/NINDS NIH HHS/United States ; P30 EY019007/EY/NEI NIH HHS/United States ; K99 NS115919/NS/NINDS NIH HHS/United States ; DP2 NS083037/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Muscle, Skeletal/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track. Unlike during reaching, we found no high-variance dimensions that directly correlated with to-be-decoded variables. This was due to no neurons having consistent correlations between their responses and kinematic variables. Yet we could decode a single variable-self-motion-by nonlinearly leveraging structure that spanned multiple high-variance neural dimensions. Resulting online BMI-control success rates approached those during manual control. These findings make two broad points regarding how to build decode algorithms that harmonize with the empirical structure of neural activity in motor cortex. First, even when decoding from the same cortical region (e.g., arm-related motor cortex), different movement classes may need to employ very different strategies. Although correlations between neural activity and hand velocity are prominent during reaching tasks, they are not a fundamental property of motor cortex and cannot be counted on to be present in general. Second, although one generally desires a low-dimensional readout, it can be beneficial to leverage a multidimensional high-variance subspace. Fully embracing this approach requires highly nonlinear approaches tailored to the task at hand, but can produce near-native levels of performance.SIGNIFICANCE STATEMENT Many brain-machine interface decoders have been constructed for controlling movements normally performed with the arm. Yet it is unclear how these will function beyond the reach-like scenarios where they were developed. Existing decoders implicitly assume that neural covariance structure, and correlations with to-be-decoded kinematic variables, will be largely preserved across tasks. We find that the correlation between neural activity and hand kinematics, a feature typically exploited when decoding reach-like movements, is essentially absent during another task performed with the arm: cycling through a virtual environment. Nevertheless, the use of a different strategy, one focused on leveraging the highest-variance neural signals, supported high performance real-time brain-machine interface control.},
}
@article {pmid34714417,
year = {2021},
author = {Micoulaud-Franchi, JA and Jeunet, C and Pelissolo, A and Ros, T},
title = {EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy.},
journal = {Current psychiatry reports},
volume = {23},
number = {12},
pages = {84},
pmid = {34714417},
issn = {1535-1645},
mesh = {Anxiety Disorders/therapy ; Brain ; Electroencephalography ; Humans ; *Neurofeedback ; *Stress Disorders, Post-Traumatic/therapy ; },
abstract = {PURPOSE OF REVIEW: This review provides an overview of current knowledge and understanding of EEG neurofeedback for anxiety disorders and post-traumatic stress disorders.<br><br>RECENT FINDINGS: The manifestations of anxiety disorders and post-traumatic stress disorders (PTSD) are associated with dysfunctions of neurophysiological stress axes and brain arousal circuits, which are important dimensions of the research domain criteria (RDoC). Even if the pathophysiology of these disorders is complex, one of its defining signatures is behavioral and physiological over-arousal. Interestingly, arousal-related brain activity can be modulated by electroencephalogram-based neurofeedback (EEG NF), a non-pharmacological and non-invasive method that involves neurocognitive training through a brain-computer interface (BCI). EEG NF is characterized by a simultaneous learning process where both patient and computer are involved in modifying neuronal activity or connectivity, thereby improving associated symptoms of anxiety and/or over-arousal. Positive effects of EEG NF have been described for both anxiety disorders and PTSD, yet due to a number of methodological issues, it remains unclear whether symptom improvement is the direct result of neurophysiological changes targeted by EEG NF. Thus, in this work we sought to bridge current knowledge on brain mechanisms of arousal with past and present EEG NF therapies for anxiety and PTSD. In a nutshell, we discuss the neurophysiological mechanisms underlying the effects of EEG NF in anxiety disorder and PTSD, the methodological strengths/weaknesses of existing EEG NF randomized controlled trials for these disorders, and the neuropsychological factors that may impact NF training success.},
}
@article {pmid34714181,
year = {2024},
author = {Hasan, MA and Sattar, P and Qazi, SA and Fraser, M and Vuckovic, A},
title = {Brain Networks With Modified Connectivity in Patients With Neuropathic Pain and Spinal Cord Injury.},
journal = {Clinical EEG and neuroscience},
volume = {55},
number = {1},
pages = {88-100},
doi = {10.1177/15500594211051485},
pmid = {34714181},
issn = {2169-5202},
support = {G0902257/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Humans ; Electroencephalography ; Quality of Life ; *Spinal Cord Injuries/complications ; Brain ; *Neuralgia ; },
abstract = {Background. Neuropathic pain (NP) following spinal cord injury (SCI) affects the quality of life of almost 40% of the injured population. The modified brain connectivity was reported under different NP conditions. Therefore, brain connectivity was studied in the SCI population with and without NP with the aim to identify networks that are altered due to injury, pain, or both. Methods. The study cohort is classified into 3 groups, SCI patients with NP, SCI patients without NP, and able-bodied. EEG of each participant was recorded during motor imagery (MI) of paralyzed and painful, and nonparalyzed and nonpainful limbs. Phased locked value was calculated using Hilbert transform to study altered functional connectivity between different regions. Results. The posterior region connectivity with frontal, fronto-central, and temporal regions is strongly decreased mainly during MI of dominant upper limb (nonparalyzed and nonpainful limbs) in SCI no pain group. This modified connectivity is prominent in the alpha and high-frequency bands (beta and gamma). Moreover, oscillatory modified global connectivity is observed in the pain group during MI of painful and paralyzed limb which is more evident between fronto-posterior, frontocentral-posterior, and within posterior and within frontal regions in the theta and SMR frequency bands. Cluster coefficient and local efficiency values are reduced in patients with no reported pain group while increased in the PWP group. Conclusion. The altered theta band connectivity found in the fronto-parietal network along with a global increase in local efficiency is a consequence of pain only, while altered connectivity in the beta and gamma bands along with a decrease in cluster coefficient values observed in the sensory-motor network is dominantly a consequence of injury only. The outcomes of this study may be used as a potential diagnostic biomarker for the NP. Further, the expected insight holds great clinical relevance in the design of neurofeedback-based neurorehabilitation and connectivity-based brain-computer interfaces for SCI patients.},
}
@article {pmid34713668,
year = {2021},
author = {Liu, T and Ye, Y and Wang, K and Xu, L and Yi, W and Xu, M and Ming, D},
title = {[Progress of classification algorithms for motor imagery electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {5},
pages = {995-1002},
pmid = {34713668},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Machine Learning ; },
abstract = {Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.},
}
@article {pmid34713069,
year = {2020},
author = {Saba-Sadiya, S and Chantland, E and Alhanai, T and Liu, T and Ghassemi, MM},
title = {Unsupervised EEG Artifact Detection and Correction.},
journal = {Frontiers in digital health},
volume = {2},
number = {},
pages = {608920},
pmid = {34713069},
issn = {2673-253X},
abstract = {Electroencephalography (EEG) is used in the diagnosis, monitoring, and prognostication of many neurological ailments including seizure, coma, sleep disorders, brain injury, and behavioral abnormalities. One of the primary challenges of EEG data is its sensitivity to a breadth of non-stationary noises caused by physiological-, movement-, and equipment-related artifacts. Existing solutions to artifact detection are deficient because they require experts to manually explore and annotate data for artifact segments. Existing solutions to artifact correction or removal are deficient because they assume that the incidence and specific characteristics of artifacts are similar across both subjects and tasks (i.e., "one-size-fits-all"). In this paper, we describe a novel EEG noise-reduction method that uses representation learning to perform patient- and task-specific artifact detection and correction. More specifically, our method extracts 58 clinically relevant features and applies an ensemble of unsupervised outlier detection algorithms to identify EEG artifacts that are unique to a given task and subject. The artifact segments are then passed to a deep encoder-decoder network for unsupervised artifact correction. We compared the performance of classification models trained with and without our method and observed a 10% relative improvement in performance when using our approach. Our method provides a flexible end-to-end unsupervised framework that can be applied to novel EEG data without the need for expert supervision and can be used for a variety of clinical decision tasks, including coma prognostication and degenerative illness detection. By making our method, code, and data publicly available, our work provides a tool that is of both immediate practical utility and may also serve as an important foundation for future efforts in this domain.},
}
@article {pmid34712879,
year = {2021},
author = {Mistry, R and Falland, RL and Wheeler, M and Sidebotham, D},
title = {Traumatic Injury to Both Atrioventricular Valves.},
journal = {CASE (Philadelphia, Pa.)},
volume = {5},
number = {5},
pages = {329-334},
pmid = {34712879},
issn = {2468-6441},
abstract = {• BCI encompasses a spectrum of cardiac injuries. • BCI should be considered in all patients with major thoracoabdominal trauma. • TEE is an excellent imaging method for evaluating BCI, including atrioventricular valve injury.},
}
@article {pmid34710584,
year = {2021},
author = {Bhat, S and Lührs, M and Goebel, R and Senden, M},
title = {Extremely fast pRF mapping for real-time applications.},
journal = {NeuroImage},
volume = {245},
number = {},
pages = {118671},
doi = {10.1016/j.neuroimage.2021.118671},
pmid = {34710584},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/methods ; Male ; Models, Neurological ; Movement Disorders ; Neuroimaging ; Normal Distribution ; Photic Stimulation/methods ; Visual Cortex ; Visual Fields ; },
abstract = {Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.},
}
@article {pmid34710045,
year = {2021},
author = {Xu, F and Rong, F and Leng, J and Sun, T and Zhang, Y and Siddharth, S and Jung, TP},
title = {Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2417-2424},
doi = {10.1109/TNSRE.2021.3123969},
pmid = {34710045},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; *Stroke ; },
abstract = {Acquiring Electroencephalography (EEG) data is often time-consuming, laborious, and costly, posing practical challenges to train powerful but data-demanding deep learning models. This study proposes a surrogate EEG data-generation system based on cycle-consistent adversarial networks (CycleGAN) that can expand the number of training data. This study used EEG2Image based on a modified S-transform (MST) to convert EEG data into EEG-topography. This method retains the frequency-domain characteristics and spatial information of the EEG signals. Then, the CycleGAN is used to learn and generate motor-imagery EEG data of stroke patients. From the visual inspection, there is no difference between the EEG topographies of the generated and original EEG data collected from the stroke patients. Finally, we used convolutional neural networks (CNN) to evaluate and analyze the generated EEG data. The experimental results show that the generated data effectively improved the classification accuracy.},
}
@article {pmid34710035,
year = {2021},
author = {Binkley, CE and Politz, MS and Green, BP},
title = {Who, If Not the FDA, Should Regulate Implantable Brain-Computer Interface Devices?.},
journal = {AMA journal of ethics},
volume = {23},
number = {9},
pages = {E745-749},
doi = {10.1001/amajethics.2021.745},
pmid = {34710035},
issn = {2376-6980},
mesh = {*Brain-Computer Interfaces ; Humans ; United States ; United States Food and Drug Administration ; },
abstract = {Implantable brain-computer interface (BCI) and other devices with potential for both therapeutic purposes and human enhancement are being rapidly developed. The distinction between therapeutic and enhancement uses of these devices is not well defined. While the US Food and Drug Administration (FDA) rightly determines what is safe and effective, this article argues that the FDA should not make subjective, value-laden assessments about risks and benefits when it comes to approval of BCIs for therapy and enhancement. This article also argues that determining BCIs' benefits to society requires deliberations on values that the FDA is neither accustomed to making nor qualified to make. Given the inadequacy of the FDA's safe-and-effective standard to judge devices spanning the spectrum of therapy to enhancement, this article argues that BCI regulation should not be overseen by the FDA.},
}
@article {pmid34709462,
year = {2022},
author = {Qian, Q and Li, Y and Song, M and Feng, Y and Fu, Y and Shinomori, K},
title = {Interactive modulations between congruency sequence effects and validity sequence effects.},
journal = {Psychological research},
volume = {86},
number = {6},
pages = {1944-1957},
pmid = {34709462},
issn = {1430-2772},
support = {32060193//National Natural Science Foundation of China/ ; 61962031//National Natural Science Foundation of China/ ; 62062047//National Natural Science Foundation of China/ ; 61763022//National Natural Science Foundation of China/ ; 82172058//National Natural Science Foundation of China/ ; 61872231//National Natural Science Foundation of China/ ; 202101AT070082//Yunnan Fundamental Research Projects/ ; KAKNHI:18H03323//Japan society for the promotion of science/ ; },
mesh = {Conditioning, Classical ; *Cues ; *Executive Function ; Humans ; Reaction Time ; },
abstract = {Sequential modulations have been found in both conflict and spatial orienting tasks. The former is called congruency sequence effects (CSE) and the latter is called validity sequence effects (VSE). Although the two effects have similar phenomenon descriptions, the relationship of the cognitive control mechanisms under the two effects is still unclear. Using a modified attentional network test (ANT), a flanker task and an arrow cueing task are integrated into a single task, which enables the test of the possible interactions between CSE and VSE. Since a confound-minimized design is used, the observed sequence effects cannot be attributed to the feature integration of low-level stimulus features or the contingency learning. It was found that the CSE are only significant when the arrow cue in preceding trial is invalid, and the VSE are only significant when the target letter in preceding trial is congruent with the distractor letters. The findings suggest that the sequential modulations during orienting and executive control of attention networks are highly interacted with each other, and the sequence effects in these networks are possibly controlled by a complex and multifaceted adaptive control mechanism.},
}
@article {pmid34706446,
year = {2021},
author = {du Bois, N and Bigirimana, AD and Korik, A and Kéthina, LG and Rutembesa, E and Mutabaruka, J and Mutesa, L and Prasad, G and Jansen, S and Coyle, DH},
title = {Neurofeedback with low-cost, wearable electroencephalography (EEG) reduces symptoms in chronic Post-Traumatic Stress Disorder.},
journal = {Journal of affective disorders},
volume = {295},
number = {},
pages = {1319-1334},
doi = {10.1016/j.jad.2021.08.071},
pmid = {34706446},
issn = {1573-2517},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurofeedback ; *Stress Disorders, Post-Traumatic/therapy ; *Wearable Electronic Devices ; },
abstract = {BACKGROUND: The study examines the effectiveness of both neurofeedback and motor-imagery brain-computer interface (BCI) training, which promotes self-regulation of brain activity, using low-cost electroencephalography (EEG)-based wearable neurotechnology outside a clinical setting, as a potential treatment for post-traumatic stress disorder (PTSD) in Rwanda.<br><br>METHODS: Participants received training/treatment sessions along with a pre- and post- intervention clinical assessment, (N&#xa0;=&#xa0;29; control n&#xa0;=&#xa0;9, neurofeedback (NF, 7 sessions) n&#xa0;=&#xa0;10, and motor-imagery (MI, 6 sessions) n&#xa0;=&#xa0;10). Feedback was presented visually via a videogame. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.<br><br>RESULTS: The NF group demonstrated an increase in resting-state alpha 8-12&#xa0;Hz bandpower following individual training sessions, termed alpha 'rebound' (Pz channel, p&#xa0;=&#xa0;0.025, all channels, p&#xa0;=&#xa0;0.024), consistent with previous research findings. This alpha 'rebound', unobserved in the MI group, produced a clinically relevant reduction in symptom severity in NF group, as revealed in three of seven clinical outcome measures: PCL-5 (p&#xa0;=&#xa0;0.005), PTSD screen (p&#xa0;=&#xa0;0.005), and HTQ (p&#xa0;=&#xa0;0.005).<br><br>LIMITATIONS: Data collection took place in environments that posed difficulties in controlling environmental factors. Nevertheless, this limitation improves ecological validity, as neurotechnology treatments must be deployable outside controlled environments, to be a feasible technological treatment.<br><br>CONCLUSIONS: The study produced the first evidence to support a low-cost, neurotechnological solution for neurofeedback as an effective treatment of PTSD for victims of acute trauma in conflict zones in a developing country.},
}
@article {pmid34706357,
year = {2021},
author = {Chuang, CH and Lu, SW and Chao, YP and Peng, PH and Hsu, HC and Hung, CC and Chang, CL and Jung, TP},
title = {Near-zero phase-lag hyperscanning in a novel wireless EEG system.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac33e6},
pmid = {34706357},
issn = {1741-2552},
mesh = {Amplifiers, Electronic ; *Brain ; Brain Mapping/methods ; *Electroencephalography/methods ; Humans ; Interpersonal Relations ; },
abstract = {Objective. Hyperscanning is an emerging technology that concurrently scans the neural dynamics of multiple individuals to study interpersonal interactions. In particular, hyperscanning with electroencephalography (EEG) is increasingly popular owing to its mobility and its ability to allow studying social interactions in naturalistic settings at the millisecond scale.Approach.To align multiple EEG time series with sophisticated event markers in a single time domain, a precise and unified timestamp is required for stream synchronization. This study proposes a clock-synchronized method that uses a custom-made RJ45 cable to coordinate the sampling between wireless EEG amplifiers to prevent incorrect estimation of interbrain connectivity due to asynchronous sampling. In this method, analog-to-digital converters are driven by the same sampling clock. Additionally, two clock-synchronized amplifiers leverage additional radio frequency channels to keep the counter of their receiving dongles updated, which guarantees that binding event markers received by the dongle with the EEG time series have the correct timestamp.Main results.The results of two simulation experiments and one video gaming experiment reveal that the proposed method ensures synchronous sampling in a system with multiple EEG devices, achieving near-zero phase lag and negligible amplitude difference between the signals.Significance.According to all of the signal-similarity metrics, the suggested method is a promising option for wireless EEG hyperscanning and can be utilized to precisely assess the interbrain couplings underlying social-interaction behaviors.},
}
@article {pmid34706356,
year = {2021},
author = {Liu, X and Ren, C and Huang, Z and Wilson, M and Kim, JH and Lu, Y and Ramezani, M and Komiyama, T and Kuzum, D},
title = {Decoding of cortex-wide brain activity from local recordings of neural potentials.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
pmid = {34706356},
issn = {1741-2552},
support = {U19 NS123717/NS/NINDS NIH HHS/United States ; R01 DC014690/DC/NIDCD NIH HHS/United States ; R21 NS109722/NS/NINDS NIH HHS/United States ; R01 NS091010/NS/NINDS NIH HHS/United States ; R21 EY029466/EY/NEI NIH HHS/United States ; R21 EB026180/EB/NIBIB NIH HHS/United States ; DP2 EB030992/EB/NIBIB NIH HHS/United States ; P30 EY022589/EY/NEI NIH HHS/United States ; R01 EY025349/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Evoked Potentials ; Mice ; Neural Networks, Computer ; Wakefulness ; },
abstract = {Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions.Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings.Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials.Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.},
}
@article {pmid34705651,
year = {2021},
author = {Xiong, D and Zhang, D and Zhao, X and Chu, Y and Zhao, Y},
title = {Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2271-2280},
doi = {10.1109/TNSRE.2021.3123630},
pmid = {34705651},
issn = {1558-0210},
mesh = {*Deep Learning ; Electromyography ; Gait ; Humans ; Muscle, Skeletal ; Walking ; },
abstract = {Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit decomposition-based method, this work presents a novel neural interface for human gait tracking based on muscle synergy, the high-level neural control information to collaborate muscle groups for performing movements. Three classical synergy extraction approaches include Principle Component Analysis (PCA), Factor Analysis (FA), and Nonnegative Matrix Factorization (NMF), are employed for muscle synergy extraction. A deep regression neural network based on the bidirectional gated recurrent unit (BGRU) is used to extract temporal information from the synergy matrix to estimate joint angles of the lower limb. Eight subjects participated in the experiment while walking at four types of speed: 0.5km/h, 1.0km/h, 2.0km/h, and 3.0km/h. Two machine learning methods based on linear regression (LR) and multilayer perceptron (MLP) are set as the contrast group. The result shows that the synergy-based approach's performance outperforms two contrast methods with Rvar[2] scores of 0.83~0.88. PCA reaches the highest performance of 0.871±0.029, corresponding to RMSE of 3.836°, 6.278°, 2.197° for hip, knee, and ankle, respectively. The effect of walking speed, synergy number, and joint location will be analyzed. The performance shows that muscle synergy has a good correlation will joint angles which can be unearthed by deep learning. The proposed method explores a new way for gait analysis and contributes to building a novel neural interface with muscle synergy and deep learning.},
}
@article {pmid34699367,
year = {2021},
author = {Deng, B and Fan, Y and Wang, J and Yang, S},
title = {Reconstruction of a Fully Paralleled Auditory Spiking Neural Network and FPGA Implementation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {15},
number = {6},
pages = {1320-1331},
doi = {10.1109/TBCAS.2021.3122549},
pmid = {34699367},
issn = {1940-9990},
mesh = {Bayes Theorem ; Brain ; Computers ; Humans ; *Neural Networks, Computer ; *Neurons ; },
abstract = {This paper presents a field-programmable gate array (FPGA) implementation of an auditory system, which is biologically inspired and has the advantages of robustness and anti-noise ability. We propose an FPGA implementation of an eleven-channel hierarchical spiking neuron network (SNN) model, which has a sparsely connected architecture with low power consumption. According to the mechanism of the auditory pathway in human brain, spiking trains generated by the cochlea are analyzed in the hierarchical SNN, and the specific word can be identified by a Bayesian classifier. Modified leaky integrate-and-fire (LIF) model is used to realize the hierarchical SNN, which achieves both high efficiency and low hardware consumption. The hierarchical SNN implemented on FPGA enables the auditory system to be operated at high speed and can be interfaced and applied with external machines and sensors. A set of speech from different speakers mixed with noise are used as input to test the performance our system, and the experimental results show that the system can classify words in a biologically plausible way with the presence of noise. The method of our system is flexible and the system can be modified into desirable scale. These confirm that the proposed biologically plausible auditory system provides a better method for on-chip speech recognition. Compare to the state-of-the-art, our auditory system achieves a higher speed with a maximum frequency of 65.03 MHz and a lower energy consumption of 276.83 μJ for a single operation. It can be applied in the field of brain-computer interface and intelligent robots.},
}
@article {pmid34697401,
year = {2021},
author = {Brydges, CR and Fiehn, O and Mayberg, HS and Schreiber, H and Dehkordi, SM and Bhattacharyya, S and Cha, J and Choi, KS and Craighead, WE and Krishnan, RR and Rush, AJ and Dunlop, BW and Kaddurah-Daouk, R and , },
title = {Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {21011},
pmid = {34697401},
issn = {2045-2322},
support = {P50-MH077083/NH/NIH HHS/United States ; R01MH108348/NH/NIH HHS/United States ; R01 AG046171/AG/NIA NIH HHS/United States ; U01 AG061359/AG/NIA NIH HHS/United States ; R01 MH080880/MH/NIMH NIH HHS/United States ; UL1 RR025008/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Aged ; Anxiety/blood/*diagnosis/*etiology ; Biomarkers ; Brain/diagnostic imaging/physiopathology ; Disease Susceptibility ; Female ; Functional Neuroimaging/methods ; *Gastrointestinal Microbiome ; Humans ; Indican/*adverse effects/biosynthesis ; *Magnetic Resonance Imaging/methods ; Male ; Metabolic Networks and Pathways ; Metabolome ; Metabolomics/methods ; Middle Aged ; Symptom Assessment ; Uremic Toxins/*adverse effects/biosynthesis ; Young Adult ; },
abstract = {It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies.Clinical trial NCT00360399 "Predictors of Antidepressant Treatment Response: The Emory CIDAR" https://clinicaltrials.gov/ct2/show/NCT00360399 .},
}
@article {pmid34696014,
year = {2021},
author = {Perez-Ortiz, CX and Gordillo, JL and Mendoza-Montoya, O and Antelis, JM and Caraza, R and Martinez, HR},
title = {Functional Connectivity and Frequency Power Alterations during P300 Task as a Result of Amyotrophic Lateral Sclerosis.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {20},
pages = {},
pmid = {34696014},
issn = {1424-8220},
mesh = {*Amyotrophic Lateral Sclerosis/diagnosis ; Electroencephalography ; Humans ; *Neurodegenerative Diseases ; },
abstract = {Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p < 0.05) were: an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success.},
}
@article {pmid34695956,
year = {2021},
author = {Vorontsova, D and Menshikov, I and Zubov, A and Orlov, K and Rikunov, P and Zvereva, E and Flitman, L and Lanikin, A and Sokolova, A and Markov, S and Bernadotte, A},
title = {Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {20},
pages = {},
pmid = {34695956},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; Speech ; *Speech Perception ; },
abstract = {In this work, we focus on silent speech recognition in electroencephalography (EEG) data of healthy individuals to advance brain-computer interface (BCI) development to include people with neurodegeneration and movement and communication difficulties in society. Our dataset was recorded from 270 healthy subjects during silent speech of eight different Russia words (commands): 'forward', 'backward', 'up', 'down', 'help', 'take', 'stop', and 'release', and one pseudoword. We began by demonstrating that silent word distributions can be very close statistically and that there are words describing directed movements that share similar patterns of brain activity. However, after training one individual, we achieved 85% accuracy performing 9 words (including pseudoword) classification and 88% accuracy on binary classification on average. We show that a smaller dataset collected on one participant allows for building a more accurate classifier for a given subject than a larger dataset collected on a group of people. At the same time, we show that the learning outcomes on a limited sample of EEG-data are transferable to the general population. Thus, we demonstrate the possibility of using selected command-words to create an EEG-based input device for people on whom the neural network classifier has not been trained, which is particularly important for people with disabilities.},
}
@article {pmid34695942,
year = {2021},
author = {Jung, W and Lim, S and Kwak, Y and Sim, J and Park, J and Jang, D},
title = {The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {20},
pages = {},
pmid = {34695942},
issn = {1424-8220},
support = {NRF-2019M3C7A1031278 and 2016M3C7A1904987//the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT/ ; },
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Electrocorticography ; Electroencephalography ; *Motor Cortex ; },
abstract = {Numerous brain-machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.},
}
@article {pmid34695815,
year = {2021},
author = {Tankus, A and Solomon, L and Aharony, Y and Faust-Socher, A and Strauss, I},
title = {Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac3315},
pmid = {34695815},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Machine Learning ; Speech/physiology ; *Subthalamic Nucleus/physiology ; },
abstract = {Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces (BMI).Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns.Main results. In an extensive comparison of algorithms, our sparse decoder ('SpaDe'), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery.Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards BMIs for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ('locked-in') patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech BMI.},
}
@article {pmid34695809,
year = {2021},
author = {Choi, H and Lim, S and Min, K and Ahn, KH and Lee, KM and Jang, DP},
title = {Non-human primate epidural ECoG analysis using explainable deep learning technology.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac3314},
pmid = {34695809},
issn = {1741-2552},
mesh = {Animals ; *Artificial Intelligence ; *Deep Learning ; Neural Networks, Computer ; Primates ; Technology ; },
abstract = {Objective.With the development in the field of neural networks,explainable AI(XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results.Approach. We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment.Main results. The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements.Significance.As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.},
}
@article {pmid34695649,
year = {2021},
author = {Arango-Sabogal, JC and Fecteau, G and Doré, E and Côté, G and Roy, JP and Wellemans, V and Buczinski, S},
title = {Bayesian accuracy estimates of environmental sampling for determining herd paratuberculosis infection status and its association with the within-herd individual fecal culture prevalence in Québec dairies.},
journal = {Preventive veterinary medicine},
volume = {197},
number = {},
pages = {105510},
doi = {10.1016/j.prevetmed.2021.105510},
pmid = {34695649},
issn = {1873-1716},
mesh = {Animals ; Bayes Theorem ; Cattle ; *Cattle Diseases/diagnosis/epidemiology ; Dairying ; Enzyme-Linked Immunosorbent Assay/veterinary ; Feces ; *Paratuberculosis/diagnosis/epidemiology ; Prevalence ; Quebec/epidemiology ; Retrospective Studies ; },
abstract = {The objectives of this retrospective analysis were to: 1) estimate the diagnostic sensitivity (Se) and specificity (Sp) of bacterial culture of environmental samples for determining Mycobacterium avium subsp. paratuberculosis (MAP) infection status in Québec dairies, using a Bayesian Latent Class Model (BLCM); and 2) determine the association between the number of positive environmental samples and the individual fecal culture (IFC) apparent and true MAP within-herd prevalence. Environmental and individual fecal samples were collected from 87 commercial dairy herds participating in previous research projects. Environmental samples included two composite samples of 20 g collected from different locations within each of the following sites: an area where manure from the majority of adult cattle accumulates, a manure storage area and another site of manure accumulation chosen by the veterinarian. Samples were cultured using the MGIT Para TB culture liquid media and the BACTEC MGIT 960 system. The Se and Sp of environmental sampling were estimated using a one-test-one-population BLCM. Herds were considered positive for environmental sampling if at least one out of the six samples collected was positive. The apparent and true IFC within-herd MAP prevalence estimates for each herd were obtained using a two-stage cluster BLCM, then merged in a single dataset with the environmental sample results. The association between the within-herd MAP prevalence results (apparent and true), and the number of positive environmental samples was assessed using a zero-inflated negative binomial (ZINB) model. In all BLCMs, median posterior estimates and 95 % Bayesian credible intervals (BCI) were obtained with OpenBUGS statistical freeware. Se and Sp of environmental sampling were 43.7 % (95 % BCI: 32.5-55.5) and 96.2 % (95 % BCI: 84.2-99.8), respectively. Overall, the number of positive environmental samples increased with the apparent and true MAP within-herd prevalence. The true prevalence was higher than the apparent prevalence for a given number of positive environmental samples. The probability of not observing a positive environmental sample decreased with the prevalence. Despite its imperfect accuracy, environmental sampling is an inexpensive and non-invasive sampling method to determine MAP infection status in tie-stall herds that can be used as a proxy to estimate the true within-herd prevalence. The absence of positive environmental samples in a single sampling visit is likely an indicator of a very low within-herd prevalence rather than being MAP exempt.},
}
@article {pmid34695455,
year = {2022},
author = {Mohammed, M and Ivica, N and Bjartmarz, H and Thorbergsson, PT and Pettersson, LME and Thelin, J and Schouenborg, J},
title = {Microelectrode clusters enable therapeutic deep brain stimulation without noticeable side-effects in a rodent model of Parkinson's disease.},
journal = {Journal of neuroscience methods},
volume = {365},
number = {},
pages = {109399},
doi = {10.1016/j.jneumeth.2021.109399},
pmid = {34695455},
issn = {1872-678X},
mesh = {Animals ; *Deep Brain Stimulation/methods ; Microelectrodes ; *Parkinson Disease/therapy ; Rats ; Rodentia ; *Subthalamic Nucleus/physiology ; },
abstract = {BACKGROUND: Deep Brain Stimulation (DBS) is an established treatment for motor symptoms in Parkinson's disease (PD). However, side effects often limit the usefulness of the treatment.<br><br>NEW METHOD: To mitigate this problem, we developed a novel cluster of ultrathin platinum-iridium microelectrodes (n&#xa0;=&#xa0;16) embedded in a needle shaped gelatin vehicle. In an established rodent PD-model (6-OHDA unilateral lesion), the clusters were implanted in the subthalamic area for up to 8 weeks. In an open field setting, combinations of microelectrodes yielding therapeutic effects were identified using statistical methods. Immunofluorescence techniques were used for histological assessments of biocompatibility.<br><br>RESULTS: In all rats tested (n&#xa0;=&#xa0;5), we found subsets of 3-4 microelectrodes which, upon stimulation (160&#xa0;Hz, 60&#xa0;&#x3bc;s pulse width, 25-40 &#x3bc;A/microelectrode), prompted normal movements without noticeable side effects. Other microelectrode subsets often caused side effects such as rotation, dyskinesia and tremor. The threshold (per microelectrode) to elicit normal movements strongly depended on the number of activated microelectrodes in the selected subset. The histological analysis revealed viable neurons close to the electrode contacts, minor microglial and astrocytic reactions and no major changes in the vasculature, indicating high biocompatibility.<br><br>By contrast to the continuous and relatively large stimulation fields produced by existing DBS electrodes, the developed microelectrode cluster enables a fine-tuned granular and individualized microstimulation. This granular type of stimulation pattern provided powerful and specific therapeutic effects, free of noticeable side effects, in a PD animal model.},
}
@article {pmid34693880,
year = {2021},
author = {Feng, N and Hu, F and Wang, H and Zhou, B},
title = {Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity.},
journal = {International journal of neural systems},
volume = {31},
number = {12},
pages = {2150047},
doi = {10.1142/S0129065721500477},
pmid = {34693880},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Intention ; Upper Extremity ; },
abstract = {Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.},
}
@article {pmid34691612,
year = {2021},
author = {Zhang, X and Wu, D and Ding, L and Luo, H and Lin, CT and Jung, TP and Chavarriaga, R},
title = {Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers.},
journal = {National science review},
volume = {8},
number = {4},
pages = {nwaa233},
pmid = {34691612},
issn = {2053-714X},
abstract = {An electroencephalogram (EEG)-based brain-computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.},
}
@article {pmid34690724,
year = {2021},
author = {Jatupornpoonsub, T and Thimachai, P and Supasyndh, O and Wongsawat, Y},
title = {Background Activity Findings in End-Stage Renal Disease With and Without Comorbid Diabetes: An Electroencephalogram Study.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {741446},
pmid = {34690724},
issn = {1662-5161},
abstract = {Renal failure and diabetes can induce cerebral complications, including encephalopathy, for which attentional and cognitive impairment are common symptoms. It is possible that renal failure with comorbid diabetes may induce more severe encephalopathy due to multiple pathogenic mechanisms. This concept was supported by the main findings of this study, which showed that EEG background activity between end-stage renal disease with and without comorbid diabetes was significantly different in relative power of delta in the eyes-open condition in frontoparietal regions; theta in the eyes-closed condition in all regions; beta in the parieto-occipital regions in both eye conditions; the delta/theta ratio in both eye conditions in frontoparietal regions; and the theta/beta ratio in all regions in the eyes-closed condition. These findings may increase awareness of comorbid cerebral complications in clinical practice. Moreover, the delta/theta ratio is recommended as an optimal feature to possibly determine the severity of encephalopathy.},
}
@article {pmid34690716,
year = {2021},
author = {Xie, L and Lu, C and Liu, Z and Yan, L and Xu, T},
title = {Study of Auditory Brain Cognition Laws-Based Recognition Method of Automobile Sound Quality.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {663049},
pmid = {34690716},
issn = {1662-5161},
abstract = {The research shows that subjective feelings of people, such as emotions and fatigue, can be objectively reflected by electroencephalography (EEG) physiological signals Thus, an evaluation method based on EEG, which is used to explore auditory brain cognition laws, is introduced in this study. The brain cognition laws are summarized by analyzing the EEG power topographic map under the stimulation of three kinds of automobile sound, namely, quality of comfort, powerfulness, and acceleration. Then, the EEG features of the subjects are classified through a machine learning algorithm, by which the recognition of diversified automobile sound is realized. In addition, the Kalman smoothing and minimal redundancy maximal relevance (mRMR) algorithm is used to improve the recognition accuracy. The results show that there are differences in the neural characteristics of diversified automobile sound quality, with a positive correlation between EEG energy and sound intensity. Furthermore, by using the Kalman smoothing and mRMR algorithm, recognition accuracy is improved, and the amount of calculation is reduced. The novel idea and method to explore the cognitive laws of automobile sound quality from the field of brain-computer interface technology are provided in this study.},
}
@article {pmid34690673,
year = {2021},
author = {Liu, S and Li, G and Jiang, S and Wu, X and Hu, J and Zhang, D and Chen, L},
title = {Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {725384},
pmid = {34690673},
issn = {1662-4548},
abstract = {Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain-computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray-white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.},
}
@article {pmid34689073,
year = {2021},
author = {Lopez-Sola, E and Moreno-Bote, R and Arsiwalla, XD},
title = {Sense of agency for mental actions: Insights from a belief-based action-effect paradigm.},
journal = {Consciousness and cognition},
volume = {96},
number = {},
pages = {103225},
doi = {10.1016/j.concog.2021.103225},
pmid = {34689073},
issn = {1090-2376},
mesh = {*Cues ; Humans ; *Psychomotor Performance ; },
abstract = {A substantial body of research has converged on the idea that the sense of agency arises from the integration of multiple sources of information. In this study, we investigated whether a measurable sense of agency can be detected for mental actions, without the contribution of motor components. We used a fake action-effect paradigm, where participants were led to think that a motor action or a particular thought could trigger a sound. Results showed that the sense of agency, when measured through explicit reports, was of comparable strength for motor and mental actions. The intentional binding effect, a phenomenon typically associated with the experience of agency, was also observed for both motor and mental actions. Taken together, our results provide novel insights into the specific role of intentional cues in instantiating a sense of agency, even in the absence of motor signals.},
}
@article {pmid34686346,
year = {2021},
author = {Duan, S and Moro, L and Qu, R and Simoneschi, D and Cho, H and Jiang, S and Zhao, H and Chang, Q and de Stanchina, E and Arbini, AA and Pagano, M},
title = {Loss of FBXO31-mediated degradation of DUSP6 dysregulates ERK and PI3K-AKT signaling and promotes prostate tumorigenesis.},
journal = {Cell reports},
volume = {37},
number = {3},
pages = {109870},
pmid = {34686346},
issn = {2211-1247},
support = {P30 CA008748/CA/NCI NIH HHS/United States ; R01 CA076584/CA/NCI NIH HHS/United States ; R35 GM136250/GM/NIGMS NIH HHS/United States ; T32 CA009161/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; Antineoplastic Agents/pharmacology ; Cell Line, Tumor ; Cullin Proteins/genetics/metabolism ; Cyclohexylamines/pharmacology ; Dual Specificity Phosphatase 6/antagonists &amp; inhibitors/genetics/*metabolism ; Enzyme Activation ; Enzyme Inhibitors/pharmacology ; Enzyme Stability ; Extracellular Signal-Regulated MAP Kinases/*metabolism ; F-Box Proteins/genetics/*metabolism ; Gene Expression Regulation, Neoplastic ; HEK293 Cells ; Humans ; Indenes/pharmacology ; Male ; Mice, Inbred NOD ; Mice, SCID ; Phosphatidylinositol 3-Kinase/*metabolism ; Prostatic Neoplasms/drug therapy/*enzymology/genetics/pathology ; Proteolysis ; Proto-Oncogene Proteins c-akt/*metabolism ; Signal Transduction ; Tumor Suppressor Proteins/genetics/*metabolism ; Xenograft Model Antitumor Assays ; Mice ; },
abstract = {FBXO31 is the substrate receptor of one of many CUL1-RING ubiquitin ligase (CRL1) complexes. Here, we show that low FBXO31 mRNA levels are associated with high pre-operative prostate-specific antigen (PSA) levels and Gleason grade in human prostate cancer. Mechanistically, the ubiquitin ligase CRL1[FBXO31] promotes the ubiquitylation-mediated degradation of DUSP6, a dual specificity phosphatase that dephosphorylates and inactivates the extracellular-signal-regulated kinase-1 and -2 (ERK1/2). Depletion of FBXO31 stabilizes DUSP6, suppresses ERK signaling, and activates the PI3K-AKT signaling cascade. Moreover, deletion of FBXO31 promotes tumor development in a mouse orthotopic model of prostate cancer. Treatment with BCI, a small molecule inhibitor of DUSP6, suppresses AKT activation and prevents tumor formation, suggesting that the FBXO31 tumor suppressor activity is dependent on DUSP6. Taken together, our studies highlight the relevance of the FBXO31-DUSP6 axis in the regulation of ERK- and PI3K-AKT-mediated signaling pathways, as well as its therapeutic potential in prostate cancer.},
}
@article {pmid34678801,
year = {2021},
author = {Norton, JJS and DiRisio, GF and Carp, JS and Norton, AE and Kochan, NS and Wolpaw, JR},
title = {Brain-computer interface-based assessment of color vision.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
pmid = {34678801},
issn = {1741-2552},
support = {I01 CX001812/CX/CSRD VA/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; R01 NS110577/NS/NINDS NIH HHS/United States ; R01 EB026439/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R25 HD088157/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Color Vision ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Light ; Photic Stimulation/methods ; Research Design ; },
abstract = {Objective.Present methods for assessing color vision require the person's active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person's participation.Approach.This method uses steady-state visual evoked potentials to identify metamers-two light sources that have different spectral distributions but appear to the person to be the same color.Main results.We demonstrate that: minimization of the visual evoked potential elicited by two flickering light sources identifies the metamer; this approach can distinguish people with color-vision deficits from those with normal color vision; and this metamer-identification process can be automated.Significance.This new method has numerous potential clinical, scientific, and industrial applications.},
}
@article {pmid34678794,
year = {2021},
author = {Marceglia, S and Guidetti, M and Harmsen, IE and Loh, A and Meoni, S and Foffani, G and Lozano, AM and Volkmann, J and Moro, E and Priori, A},
title = {Deep brain stimulation: is it time to change gears by closing the loop?.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac3267},
pmid = {34678794},
issn = {1741-2552},
mesh = {Biofeedback, Psychology ; *Deep Brain Stimulation/methods ; Humans ; *Nervous System Diseases ; },
abstract = {Objective.Adaptive deep brain stimulation (aDBS) is a form of invasive stimulation that was conceived to overcome the technical limitations of traditional DBS, which delivers continuous stimulation of the target structure without considering patients' symptoms or status in real-time. Instead, aDBS delivers on-demand, contingency-based stimulation. So far, aDBS has been tested in several neurological conditions, and will be soon extensively studied to translate it into clinical practice. However, an exhaustive description of technical aspects is still missing.Approach.in this topical review, we summarize the knowledge about the current (and future) aDBS approach and control algorithms to deliver the stimulation, as reference for a deeper undestending of aDBS model.Main results.We discuss the conceptual and functional model of aDBS, which is based on the sensing module (that assesses the feedback variable), the control module (which interpretes the variable and elaborates the new stimulation parameters), and the stimulation module (that controls the delivery of stimulation), considering both the historical perspective and the state-of-the-art of available biomarkers.Significance.aDBS modulates neuronal circuits based on clinically relevant biofeedback signals in real-time. First developed in the mid-2000s, many groups have worked on improving closed-loop DBS technology. The field is now at a point in conducting large-scale randomized clinical trials to translate aDBS into clinical practice. As we move towards implanting brain-computer interfaces in patients, it will be important to understand the technical aspects of aDBS.},
}
@article {pmid34677345,
year = {2021},
author = {Paulmurugan, K and Vijayaragavan, V and Ghosh, S and Padmanabhan, P and Gulyás, B},
title = {Brain-Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS).},
journal = {Biosensors},
volume = {11},
number = {10},
pages = {},
pmid = {34677345},
issn = {2079-6374},
mesh = {*Brain ; Computers ; Neurons ; *Spectroscopy, Near-Infrared ; },
abstract = {Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain-computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain-computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.},
}
@article {pmid34676208,
year = {2021},
author = {Zhang, X and Wang, X and Wang, S and Peng, W and Ullah, R and Fu, J and Zhou, Y and Shen, Y},
title = {Trilogy Development of Proopiomelanocortin Neurons From Embryonic to Adult Stages in the Mice Retina.},
journal = {Frontiers in cell and developmental biology},
volume = {9},
number = {},
pages = {718851},
pmid = {34676208},
issn = {2296-634X},
abstract = {Proopiomelanocortin-positive amacrine cells (POMC ACs) were first discovered in adult mouse retinas in 2010; however, the development of POMC-ACs has not been studied. We bred POMC-EGFP mice to label POMC-positive cells and investigated the development of POMC neurons from embryonic to adult stages. We found that POMC neuron development is mainly divided into three stages: the embryonic stage, the closed-eye stage, and the open-eye stage. Each stage has unique characteristics. In the embryonic stage, POMC neurons appeared in the retina at about E13. There was a cell number developmental peak at E15, followed by a steep decline at E16. POMC neurons showed a large soma and increased spine numbers at the closed-eye stage, and two dendritic sublaminas formed in the inner plexiform layer (IPL). The appearance and increased soma size and dendrite numbers did not occur continuously in space. We found that the soma number was asymmetric between the superior and inferior retinas according to the developmental topographic map. Density peaked in the superior retina, which existed persistently in the retinal ganglion cell layer (GCL), but disappeared from the inner nuclear layer (INL) at about P6. At the same time, the soma distribution in the INL was the most regular. At the open-eye stage, the development of POMC neurons was nearly stable only with only an increase in the IPL width, which increased the soma-dendrite distance.},
}
@article {pmid34675771,
year = {2021},
author = {Liu, T and Xu, Z and Cao, L and Tan, G},
title = {Evolutionary Multitasking-Based Multiobjective Optimization Algorithm for Channel Selection in Hybrid Brain Computer Interfacing Systems.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {749232},
pmid = {34675771},
issn = {1662-4548},
abstract = {Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.},
}
@article {pmid34674879,
year = {2021},
author = {Loriette, C and Ziane, C and Ben Hamed, S},
title = {Neurofeedback for cognitive enhancement and intervention and brain plasticity.},
journal = {Revue neurologique},
volume = {177},
number = {9},
pages = {1133-1144},
doi = {10.1016/j.neurol.2021.08.004},
pmid = {34674879},
issn = {0035-3787},
mesh = {Brain ; Brain Mapping ; Cognition ; Humans ; Magnetic Resonance Imaging ; *Neurofeedback ; Neuronal Plasticity ; },
abstract = {In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.},
}
@article {pmid34674118,
year = {2021},
author = {Wang, T and Du, S and Dong, E},
title = {A novel method to reduce the motor imagery BCI illiteracy.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {11-12},
pages = {2205-2217},
pmid = {34674118},
issn = {1741-0444},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Literacy ; },
abstract = {To reduce the motor imagery brain-computer interface (MI-BCI) illiteracy phenomenon and improve the classification accuracy, this paper proposed a novel method combining paradigm selection and Riemann distance classification. Firstly, a novel sensitivity-based paradigm selection (SPS) algorithm is designed for the optimization of classification to find the best classification pattern through a sensitive indicator. Then, a generalized Riemann minimum distance mean (GRMDM) classifier is proposed by introducing a weight factor to fuse the Log-Euclidean Metric classifier and the Riemannian Stein divergence classifier. The experimental results show that the proposed method achieves a better performance for multi-class motor imagery tasks. The average classification accuracy on the BCI competition IV dataset2a is 80.98%, which is 11.04% higher than Stein divergence classifier on the original two-class paradigm. Furthermore, the proposed method demonstrates its capacity on reducing MI-BCI illiteracy. Graphical abstract Here we investigate whether the BCI illiteracy phenomenon can be reduced through sensitivity-based paradigm selection (SPS) method and generalized Riemann minimum distance mean (GRMDM) classifier when performing motor imagery tasks.},
}
@article {pmid34671415,
year = {2021},
author = {Pan, B and Zheng, W},
title = {Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network.},
journal = {Computational and mathematical methods in medicine},
volume = {2021},
number = {},
pages = {2520394},
pmid = {34671415},
issn = {1748-6718},
mesh = {Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Databases, Factual ; Deep Learning ; Electroencephalography/*statistics &amp; numerical data ; Emotions/classification/*physiology ; Humans ; *Neural Networks, Computer ; },
abstract = {Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.},
}
@article {pmid34666418,
year = {2021},
author = {Bruurmijn, MLCM and Raemaekers, M and Branco, MP and Ramsey, NF and Vansteensel, MJ},
title = {Distinct representation of ipsilateral hand movements in sensorimotor areas.},
journal = {The European journal of neuroscience},
volume = {54},
number = {10},
pages = {7599-7608},
pmid = {34666418},
issn = {1460-9568},
mesh = {Brain Mapping ; Functional Laterality ; Hand ; Humans ; Magnetic Resonance Imaging ; *Motor Cortex ; Movement ; *Sensorimotor Cortex ; },
abstract = {There is ample evidence that the contralateral sensorimotor areas play an important role in movement generation, with the primary motor cortex and the primary somatosensory cortex showing a detailed spatial organization of the representation of contralateral body parts. Interestingly, there are also indications for a role of the motor cortex in controlling the ipsilateral side of the body. However, the precise function of ipsilateral sensorimotor cortex in unilateral movement control is still unclear. Here, we show hand movement representation in the ipsilateral sensorimotor hand area, in which hand gestures can be distinguished from each other and from contralateral hand gestures. High-field functional magnetic resonance imaging (fMRI) data acquired during the execution of six left- and six right-hand gestures by healthy volunteers showed ipsilateral activation mainly in the anterior section of precentral gyrus and the posterior section of the postcentral gyrus. Despite the lower activation in ipsilateral areas closer to the central sulcus, activity patterns for the 12 hand gestures could be mutually distinguished in these areas. The existence of a unique representation of ipsilateral hand movements in the human sensorimotor cortex favours the notion of transcallosal integrative processes that support optimal coordination of hand movements.},
}
@article {pmid34665741,
year = {2022},
author = {Xu, Q and Shen, J and Ran, X and Tang, H and Pan, G and Liu, JK},
title = {Robust Transcoding Sensory Information With Neural Spikes.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {5},
pages = {1935-1946},
doi = {10.1109/TNNLS.2021.3107449},
pmid = {34665741},
issn = {2162-2388},
mesh = {Action Potentials/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Models, Neurological ; *Neural Networks, Computer ; Neurons/physiology ; },
abstract = {Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies only aim at dealing with the continuous signal of neural systems, while lacking a unique feature of biological neurons, termed spike, which is the fundamental information unit for neural computation as well as a building block for brain-machine interface. Aiming at these limitations, we propose a transcoding framework to encode multi-modal sensory information into neural spikes and then reconstruct stimuli from spikes. Sensory information can be compressed into 10% in terms of neural spikes, yet re-extract 100% of information by reconstruction. Our framework can not only feasibly and accurately reconstruct dynamical visual and auditory scenes, but also rebuild the stimulus patterns from functional magnetic resonance imaging (fMRI) brain activities. More importantly, it has a superb ability of noise immunity for various types of artificial noises and background signals. The proposed framework provides efficient ways to perform multimodal feature representation and reconstruction in a high-throughput fashion, with potential usage for efficient neuromorphic computing in a noisy environment.},
}
@article {pmid34663975,
year = {2021},
author = {Zhang, X and Li, N and Zhang, J and Zhang, Y and Yang, X and Luo, Y and Zhang, B and Xu, Z and Zhu, Z and Yang, X and Yan, Y and Lin, B and Wang, S and Chen, D and Ye, C and Ding, Y and Lou, M and Wu, Q and Hou, Z and Zhang, K and Liang, Z and Wei, A and Wang, B and Wang, C and Jiang, N and Zhang, W and Xiao, G and Ma, C and Ren, Y and Qi, X and Han, W and Wang, C and Rao, F},
title = {5-IP7 is a GPCR messenger mediating neural control of synaptotagmin-dependent insulin exocytosis and glucose homeostasis.},
journal = {Nature metabolism},
volume = {3},
number = {10},
pages = {1400-1414},
pmid = {34663975},
issn = {2522-5812},
mesh = {Animals ; *Exocytosis ; Inositol Phosphates/*metabolism ; Insulin/*metabolism ; Mice ; Phosphorylation ; Receptors, G-Protein-Coupled/*metabolism ; Signal Transduction ; Synaptotagmins/*metabolism ; },
abstract = {5-diphosphoinositol pentakisphosphate (5-IP7) is a signalling metabolite linked to various cellular processes. How extracellular stimuli elicit 5-IP7 signalling remains unclear. Here we show that 5-IP7 in β cells mediates parasympathetic stimulation of synaptotagmin-7 (Syt7)-dependent insulin release. Mechanistically, vagal stimulation and activation of muscarinic acetylcholine receptors triggers Gαq-PLC-PKC-PKD-dependent signalling and activates IP6K1, the 5-IP7 synthase. Whereas both 5-IP7 and its precursor IP6 compete with PIP2 for binding to Syt7, Ca[2+] selectively binds 5-IP7 with high affinity, freeing Syt7 to enable fusion of insulin-containing vesicles with the cell membrane. β-cell-specific IP6K1 deletion diminishes insulin secretion and glucose clearance elicited by muscarinic stimulation, whereas mice carrying a phosphorylation-mimicking, hyperactive IP6K1 mutant display augmented insulin release, congenital hyperinsulinaemia and obesity. These phenotypes are absent in mice lacking Syt7. Our study proposes a new conceptual framework for inositol pyrophosphate physiology in which 5-IP7 acts as a GPCR second messenger at the interface between peripheral nervous system and metabolic organs, transmitting Gq-coupled GPCR stimulation to unclamp Syt7-dependent, and perhaps other, exocytotic events.},
}
@article {pmid34663771,
year = {2021},
author = {Li, Y and Qi, Y and Wang, Y and Wang, Y and Xu, K and Pan, G},
title = {Robust neural decoding by kernel regression with Siamese representation learning.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2c4e},
pmid = {34663771},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Neurons ; },
abstract = {Objective. Brain-machine interfaces (BMIs) provide a direct pathway between the brain and external devices such as computer cursors and prosthetics, which have great potential in motor function restoration. One critical limitation of current BMI systems is the unstable performance, partly due to the variability of neural signals. Studies showed that neural activities exhibit trial-to-trial variability, and the preferred direction of neurons frequently changes under different conditions. Therefore, a fixed decoding function does not work well.Approach. To deal with the problems, we propose a novel kernel regression framework. The nonparametric kernel regression is used to fit diverse decoding functions by finding similar neural patterns to handle neural variations caused by varying tuning functions. Further, the representations of raw neural signals are learned by Siamese networks and constrained by kinematic parameters, which can alleviate neural variations caused by intrinsic noises and task-irrelevant information. The representations are jointly learned with the kernel regression framework in an end-to-end manner so that neural variations can be tackled effectively.Main results. Experiments on two datasets demonstrate that our approach outperforms most existing methods and significantly improves the robustness in challenging situations such as limited samples and missing channels.Significance. The proposed approach demonstrates robust performance with different conditions and provides a new and inspiring perspective toward robust BMI control.},
}
@article {pmid34660704,
year = {2021},
author = {Loeb, GE and Richmond, FJ},
title = {Turning Neural Prosthetics Into Viable Products.},
journal = {Frontiers in robotics and AI},
volume = {8},
number = {},
pages = {754114},
pmid = {34660704},
issn = {2296-9144},
abstract = {Academic researchers concentrate on the scientific and technological feasibility of novel treatments. Investors and commercial partners, however, understand that success depends even more on strategies for regulatory approval, reimbursement, marketing, intellectual property protection and risk management. These considerations are critical for technologically complex and highly invasive treatments that entail substantial costs and risks in small and heterogeneous patient populations. Most implanted neural prosthetic devices for novel applications will be in FDA Device Class III, for which guidance documents have been issued recently. Less invasive devices may be eligible for the recently simplified "de novo" submission routes. We discuss typical timelines and strategies for integrating the regulatory path with approval for reimbursement, securing intellectual property and funding the enterprise, particularly as they might apply to implantable brain-computer interfaces for sensorimotor disabilities that do not yet have a track record of approved products.},
}
@article {pmid34658814,
year = {2021},
author = {Zhao, Y and Dai, G and Borghini, G and Zhang, J and Li, X and Zhang, Z and Aricò, P and Di Flumeri, G and Babiloni, F and Zeng, H},
title = {Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {706270},
pmid = {34658814},
issn = {1662-5161},
abstract = {Accurate detection of driving fatigue is helpful in significantly reducing the rate of road traffic accidents. Electroencephalogram (EEG) based methods are proven to be efficient to evaluate mental fatigue. Due to its high non-linearity, as well as significant individual differences, how to perform EEG fatigue mental state evaluation across different subjects still keeps challenging. In this study, we propose a Label-based Alignment Multi-Source Domain Adaptation (LA-MSDA) for cross-subject EEG fatigue mental state evaluation. Specifically, LA-MSDA considers the local feature distributions of relevant labels between different domains, which efficiently eliminates the negative impact of significant individual differences by aligning label-based feature distributions. In addition, the strategy of global optimization is introduced to address the classifier confusion decision boundary issues and improve the generalization ability of LA-MSDA. Experimental results show LA-MSDA can achieve remarkable results on EEG-based fatigue mental state evaluation across subjects, which is expected to have wide application prospects in practical brain-computer interaction (BCI), such as online monitoring of driver fatigue, or assisting in the development of on-board safety systems.},
}
@article {pmid34658770,
year = {2021},
author = {Daly, I and Matran-Fernandez, A and Valeriani, D and Lebedev, M and Kübler, A},
title = {Editorial: Datasets for Brain-Computer Interface Applications.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {732165},
pmid = {34658770},
issn = {1662-4548},
}
@article {pmid34658162,
year = {2021},
author = {Aierken, A and Xie, YK and Dong, W and Apaer, A and Lin, JJ and Zhao, Z and Yang, S and Xu, ZZ and Yang, F},
title = {Rational Design of a Modality-Specific Inhibitor of TRPM8 Channel against Oxaliplatin-Induced Cold Allodynia.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {8},
number = {22},
pages = {e2101717},
pmid = {34658162},
issn = {2198-3844},
support = {31971040//National Natural Science Foundation of China/ ; 31800990//National Natural Science Foundation of China/ ; 3212200006//National Natural Science Foundation of China/ ; 32022010//National Natural Science Foundation of China/ ; 31770835//National Natural Science Foundation of China/ ; 81971050//National Natural Science Foundation of China/ ; LR20C050002//Zhejiang Provincial Natural Science Foundation of China/ ; LZ18C090002//Zhejiang Provincial Natural Science Foundation of China/ ; 2020132610//Grassland Administration/ ; },
mesh = {Animals ; Antineoplastic Agents/*adverse effects/metabolism ; Cold Temperature ; Hyperalgesia/chemically induced/metabolism/*prevention &amp; control ; Male ; Mice ; Oxaliplatin/*adverse effects/metabolism ; TRPM Cation Channels/*antagonists &amp; inhibitors/*genetics/metabolism ; },
abstract = {Platinum-based compounds in chemotherapy such as oxaliplatin often induce peripheral neuropathy and neuropathic pain such as cold allodynia in patients. Transient Receptor Potential Melastatin 8 (TRPM8) ion channel is a nociceptor critically involved in such pathological processes. Direct blockade of TRPM8 exhibits significant analgesic effects but also incurs severe side effects such as hypothermia. To selectively target TRPM8 channels against cold allodynia, a cyclic peptide DeC-1.2 is de novo designed with the optimized hot-spot centric approach. DeC-1.2 modality specifically inhibited the ligand activation of TRPM8 but not the cold activation as measured in single-channel patch clamp recordings. It is further demonstrated that DeC-1.2 abolishes cold allodynia in oxaliplatin treated mice without altering body temperature, indicating DeC-1.2 has the potential for further development as a novel analgesic against oxaliplatin-induced neuropathic pain.},
}
@article {pmid34654000,
year = {2021},
author = {Chen, L and Chen, P and Zhao, S and Luo, Z and Chen, W and Pei, Y and Zhao, H and Jiang, J and Xu, M and Yan, Y and Yin, E},
title = {Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {6},
pages = {},
doi = {10.1088/1741-2552/ac3044},
pmid = {34654000},
issn = {1741-2552},
mesh = {*Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Objective. Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application.Approach. In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively.Main results. Experimental results of this study found that the high-frequency SSVEP-based brain-computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97%, whereas the average information translate rate was 67.37 ± 14.27 bits·min[-1]. The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task.Significance. The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.},
}
@article {pmid34652546,
year = {2021},
author = {Pham, TD},
title = {Recurrence eigenvalues of movements from brain signals.},
journal = {Brain informatics},
volume = {8},
number = {1},
pages = {22},
pmid = {34652546},
issn = {2198-4018},
abstract = {The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain-computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson's disease, Huntington's disease, or amyotrophic lateral sclerosis.},
}
@article {pmid34650399,
year = {2021},
author = {Olsen, S and Alder, G and Williams, M and Chambers, S and Jochumsen, M and Signal, N and Rashid, U and Niazi, IK and Taylor, D},
title = {Electroencephalographic Recording of the Movement-Related Cortical Potential in Ecologically Valid Movements: A Scoping Review.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {721387},
pmid = {34650399},
issn = {1662-4548},
abstract = {The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson's Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting.},
}
@article {pmid34650398,
year = {2021},
author = {Xu, B and Zhang, D and Wang, Y and Deng, L and Wang, X and Wu, C and Song, A},
title = {Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {684547},
pmid = {34650398},
issn = {1662-4548},
abstract = {Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3-3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human-machine interaction system, such as rehabilitation robot.},
}
@article {pmid34648459,
year = {2023},
author = {Jin, J and Wang, Z and Xu, R and Liu, C and Wang, X and Cichocki, A},
title = {Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {34},
number = {8},
pages = {4096-4105},
doi = {10.1109/TNNLS.2021.3118468},
pmid = {34648459},
issn = {2162-2388},
abstract = {The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has received extensive attention in research for the less training time, excellent recognition performance, and high information translate rate. At present, most of the powerful SSVEPs detection methods are similarity measurements based on spatial filters and Pearson's correlation coefficient. Among them, the task-related component analysis (TRCA)-based method and its variant, the ensemble TRCA (eTRCA)-based method, are two methods with high performance and great potential. However, they have a defect, that is, they can only suppress certain kinds of noise, but not more general noises. To solve this problem, a novel time filter was designed by introducing the temporally local weighting into the objective function of the TRCA-based method and using the singular value decomposition. Based on this, the time filter and (e)TRCA-based similarity measurement methods were proposed, which can perform a robust similarity measure to enhance the detection ability of SSVEPs. A benchmark dataset recorded from 35 subjects was used to evaluate the proposed methods and compare them with the (e)TRCA-based methods. The results indicated that the proposed methods performed significantly better than the (e)TRCA-based methods. Therefore, it is believed that the proposed time filter and the similarity measurement methods have promising potential for SSVEPs detection.},
}
@article {pmid34646112,
year = {2021},
author = {Sinha, AM and Nair, VA and Prabhakaran, V},
title = {Brain-Computer Interface Training With Functional Electrical Stimulation: Facilitating Changes in Interhemispheric Functional Connectivity and Motor Outcomes Post-stroke.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {670953},
pmid = {34646112},
issn = {1662-4548},
abstract = {While most survivors of stroke experience some spontaneous recovery and receive treatment in the subacute setting, they are often left with persistent impairments in upper limb sensorimotor function which impact autonomy in daily life. Brain-Computer Interface (BCI) technology has shown promise as a form of rehabilitation that can facilitate motor recovery after stroke, however, we have a limited understanding of the changes in functional connectivity and behavioral outcomes associated with its use. Here, we investigate the effects of EEG-based BCI intervention with functional electrical stimulation (FES) on resting-state functional connectivity (rsFC) and motor outcomes in stroke recovery. 23 patients post-stroke with upper limb motor impairment completed BCI intervention with FES. Resting-state functional magnetic resonance imaging (rs-fMRI) scans and behavioral data were collected prior to intervention, post- and 1-month post-intervention. Changes in rsFC within the motor network and behavioral measures were investigated to identify brain-behavior correlations. At the group-level, there were significant increases in interhemispheric and network rsFC in the motor network after BCI intervention, and patients significantly improved on the Action Research Arm Test (ARAT) and SIS domains. Notably, changes in interhemispheric rsFC from pre- to both post- and 1 month post-intervention correlated with behavioral improvements across several motor-related domains. These findings suggest that BCI intervention with FES can facilitate interhemispheric connectivity changes and upper limb motor recovery in patients after stroke.},
}
@article {pmid34644693,
year = {2021},
author = {Johnson, T and Taylor, D},
title = {Improving reaching with functional electrical stimulation by incorporating stiffness modulation.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
pmid = {34644693},
issn = {1741-2552},
support = {I01 RX001296/RX/RRD VA/United States ; R01 NS119160/NS/NINDS NIH HHS/United States ; T32 AR007505/AR/NIAMS NIH HHS/United States ; },
mesh = {Electric Stimulation ; Hand ; *Movement ; Muscle, Skeletal ; *Upper Extremity ; },
abstract = {Objective.Intracortical recordings have now been combined with functional electrical stimulation (FES) of arm/hand muscles to demonstrate restoration of upper-limb function after spinal cord injury. However, for each desired limb position decoded from the brain, there are multiple combinations of muscle stimulation levels that can produce that position. The objective of this simulation study is to explore how modulating the amount of coactivation of antagonist muscles during FES can impact reaching performance and energy usage. Stiffening the limb by cocontracting antagonist muscles makes the limb more resistant to perturbation. Minimizing cocontraction saves energy and reduces fatigue.Approach.Prior demonstrations of reaching via FES used a fixed empirically-derived lookup table for each joint that defined the muscle stimulation levels that would position the limb at the desired joint angle decoded from the brain at each timestep. This study expands on that previous work by using simulations to: (a) test the feasibility of controlling arm reaching using asuiteof lookup tables with varying levels of cocontraction instead of a single fixed lookup table for each joint, (b) optimize a simple function for automatically switching between these different cocontraction tables using only the desired kinematic information already being decoded from the brain, and (c) compare energy savings and movement performance when using the optimized function to automatically modulate cocontraction during reaching versus using the best fixed level of cocontraction.Main results.Our data suggests energy usage and/or movement performance can be significantly improved by dynamically modulating limb stiffness using our multi-table method and a simple function that determines cocontraction level based on decoded endpoint speed and its derivative.Significance.By demonstrating how modulating cocontraction can reduce energy usage while maintaining or even improving movement performance, this study makes brain-controlled FES a more viable option for restoration of reaching after paralysis.},
}
@article {pmid34644480,
year = {2021},
author = {Moses, DA and Liu, PM and Chang, EF},
title = {Decoding Speech from Cortical Surface Electrical Activity. Reply.},
journal = {The New England journal of medicine},
volume = {385},
number = {16},
pages = {e55},
doi = {10.1056/NEJMc2113384},
pmid = {34644480},
issn = {1533-4406},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Nervous System Physiological Phenomena ; Speech ; },
}
@article {pmid34644479,
year = {2021},
author = {Brenner, MJ},
title = {Decoding Speech from Cortical Surface Electrical Activity.},
journal = {The New England journal of medicine},
volume = {385},
number = {16},
pages = {e55},
doi = {10.1056/NEJMc2113384},
pmid = {34644479},
issn = {1533-4406},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Nervous System Physiological Phenomena ; Speech ; },
}
@article {pmid34640992,
year = {2021},
author = {Jeong, JH and Choi, JH and Kim, KT and Lee, SJ and Kim, DJ and Kim, HM},
title = {Multi-Domain Convolutional Neural Networks for Lower-Limb Motor Imagery Using Dry vs. Wet Electrodes.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640992},
issn = {1424-8220},
support = {2017-0-00432//Institute of Information and Communications Technology Planning and Evaluation (IITP)/ ; 2019R1A2C1003399//National Research Foundation of Korea/ ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {Motor imagery (MI) brain-computer interfaces (BCIs) have been used for a wide variety of applications due to their intuitive matching between the user's intentions and the performance of tasks. Applying dry electroencephalography (EEG) electrodes to MI BCI applications can resolve many constraints and achieve practicality. In this study, we propose a multi-domain convolutional neural networks (MD-CNN) model that learns subject-specific and electrode-dependent EEG features using a multi-domain structure to improve the classification accuracy of dry electrode MI BCIs. The proposed MD-CNN model is composed of learning layers for three domain representations (time, spatial, and phase). We first evaluated the proposed MD-CNN model using a public dataset to confirm 78.96% classification accuracy for multi-class classification (chance level accuracy: 30%). After that, 10 healthy subjects participated and performed three classes of MI tasks related to lower-limb movement (gait, sitting down, and resting) over two sessions (dry and wet electrodes). Consequently, the proposed MD-CNN model achieved the highest classification accuracy (dry: 58.44%; wet: 58.66%; chance level accuracy: 43.33%) with a three-class classifier and the lowest difference in accuracy between the two electrode types (0.22%, d = 0.0292) compared with the conventional classifiers (FBCSP, EEGNet, ShallowConvNet, and DeepConvNet) that used only a single domain. We expect that the proposed MD-CNN model could be applied for developing robust MI BCI systems with dry electrodes.},
}
@article {pmid34640913,
year = {2021},
author = {McGeady, C and Vučković, A and Zheng, YP and Alam, M},
title = {EEG Monitoring Is Feasible and Reliable during Simultaneous Transcutaneous Electrical Spinal Cord Stimulation.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640913},
issn = {1424-8220},
mesh = {Electroencephalography ; Humans ; Muscle, Skeletal ; *Spinal Cord Injuries ; *Spinal Cord Stimulation ; },
abstract = {Transcutaneous electrical spinal cord stimulation (tSCS) is a non-invasive neuromodulatory technique that has in recent years been linked to improved volitional limb control in spinal-cord injured individuals. Although the technique is growing in popularity there is still uncertainty regarding the neural mechanisms underpinning sensory and motor recovery. Brain monitoring techniques such as electroencephalography (EEG) may provide further insights to the changes in coritcospinal excitability that have already been demonstrated using other techniques. It is unknown, however, whether intelligible EEG can be extracted while tSCS is being applied, owing to substantial high-amplitude artifacts associated with stimulation-based therapies. Here, for the first time, we characterise the artifacts that manifest in EEG when recorded simultaneously with tSCS. We recorded multi-channel EEG from 21 healthy volunteers as they took part in a resting state and movement task across two sessions: One with tSCS delivered to the cervical region of the neck, and one without tSCS. An offline analysis in the time and frequency domain showed that tSCS manifested as narrow, high-amplitude peaks with a spectral density contained at the stimulation frequency. We quantified the altered signals with descriptive statistics-kurtosis, root-mean-square, complexity, and zero crossings-and applied artifact-suppression techniques-superposition of moving averages, adaptive, median, and notch filtering-to explore whether the effects of tSCS could be suppressed. We found that the superposition of moving averages filter was the most successful technique at returning contaminated EEG to levels statistically similar to that of normal EEG. In the frequency domain, however, notch filtering was more effective at reducing the spectral power contribution of stimulation from frontal and central electrodes. An adaptive filter was more appropriate for channels closer to the stimulation site. Lastly, we found that tSCS posed no detriment the binary classification of upper-limb movements from sensorimotor rhythms, and that adaptive filtering resulted in poorer classification performance. Overall, we showed that, depending on the analysis, EEG monitoring during transcutaneous electrical spinal cord stimulation is feasible. This study supports future investigations using EEG to study the activity of the sensorimotor cortex during tSCS, and potentially paves the way to brain-computer interfaces operating in the presence of spinal stimulation.},
}
@article {pmid34640888,
year = {2021},
author = {Awais, MA and Yusoff, MZ and Khan, DM and Yahya, N and Kamel, N and Ebrahim, M},
title = {Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640888},
issn = {1424-8220},
support = {er 015ME0-2//Iqra University/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.},
}
@article {pmid34640870,
year = {2021},
author = {Li, L and Bai, R and Zhang, S and Chang, CC and Shi, M},
title = {Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640870},
issn = {1424-8220},
support = {No.LGG19F020016//Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department/ ; No. 62172132//National Natural Science Foundation of China/ ; },
mesh = {*Algorithms ; *Image Interpretation, Computer-Assisted ; Neural Networks, Computer ; },
abstract = {This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.},
}
@article {pmid34640824,
year = {2021},
author = {Sarmiento, LC and Villamizar, S and López, O and Collazos, AC and Sarmiento, J and Rodríguez, JB},
title = {Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640824},
issn = {1424-8220},
support = {contract RC 838/2017, code: 110177758402//the Ministry of Science, Technology and Innovation -MINCIENCIAS-, Colombia/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Speech ; },
abstract = {The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.},
}
@article {pmid34640750,
year = {2021},
author = {Barria, P and Pino, A and Tovar, N and Gomez-Vargas, D and Baleta, K and Díaz, CAR and Múnera, M and Cifuentes, CA},
title = {BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640750},
issn = {1424-8220},
support = {801-2017//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; 845-2020//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; },
mesh = {Ankle ; *Brain-Computer Interfaces ; *Exoskeleton Device ; Humans ; *Stroke ; Survivors ; },
abstract = {Brain-computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex's electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55-63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient's perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient's performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject's MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.},
}
@article {pmid34640699,
year = {2021},
author = {Yang, SH and Huang, JW and Huang, CJ and Chiu, PH and Lai, HY and Chen, YY},
title = {Selection of Essential Neural Activity Timesteps for Intracortical Brain-Computer Interface Based on Recurrent Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {19},
pages = {},
pmid = {34640699},
issn = {1424-8220},
support = {110-2636-E-006-010 (Young Scholar Fellowship Program), 110-2321-B-010-006, 109-2314-B-303-016, and 109-2221-E-010-004-MY2//Ministry of Science and Technology, Taiwan/ ; 2018YFA0701400//National Key R&amp;D Program of China/ ; 61673346//National Natural Science Foundation of China/ ; 2019XZZX001-01-21//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Animals ; Awareness ; *Brain-Computer Interfaces ; Learning ; Movement ; Neural Networks, Computer ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, excessively long or short input neural activity for an RNN may decrease its decoding performance. Based on the temporal attention module exploiting relations in features over time, we propose a temporal attention-aware timestep selection (TTS) method that improves the interpretability of the salience of each timestep in an input neural activity. Furthermore, TTS determines the appropriate input neural activity length for accurate neural decoding. Experimental results show that the proposed TTS efficiently selects 28 essential timesteps for RNN-based neural decoders, outperforming state-of-the-art neural decoders on two nonhuman primate datasets (R2=0.76±0.05 for monkey Indy and CC=0.91±0.01 for monkey N). In addition, it reduces the computation time for offline training (reducing 5-12%) and online prediction (reducing 16-18%). When visualizing the attention mechanism in TTS, the preparatory neural activity is consecutively highlighted during arm movement, and the most recent neural activity is highlighted during the resting state in nonhuman primates. Selecting only a few essential timesteps for an RNN-based neural decoder provides sufficient decoding performance and requires only a short computation time.},
}
@article {pmid34638106,
year = {2021},
author = {Gao, P and Huang, Y and He, F and Qi, H},
title = {Improve P300-speller performance by online tuning stimulus onset asynchrony (SOA).},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2f04},
pmid = {34638106},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; Photic Stimulation ; },
abstract = {Objective. The P300-Speller is a classic brain-computer interface paradigm that has been subjected to numerous clinical trials. Some studies have reported that the performance of the P300-Speller is closely related to stimulus onset asynchrony (SOA), but very few studies have attempted to improve the performance of the P300-Speller by optimizing SOA.Approach.In this paper, we designed a P300-Speller system based on a variable SOA and dynamic stop strategy, which can automatically adjust SOA according to real-time operational performance.Main results.The online experiment results of 18 subjects showed that the event-related potential classifier and the dynamic stop algorithm established at 200 ms SOA can maintain the performance at a certain level among 50-300 ms SOA. The system can then reduce the SOA from an initial 200 ms to an average of about 98.5 ms while maintaining letter output accuracy. The average theoretical information transfer rate was significantly improved from 42.4 to 85 bit min[-1](the maximum was 232 bit min[-1]).Significance.These results demonstrate that the system established in this paper can automatically optimize the SOA settings, and this personalized SOA adjustment can effectively improve the performance of the P300-Speller.},
}
@article {pmid34635035,
year = {2021},
author = {Liu, Y and Wang, W and Xu, W and Cheng, Q and Ming, D},
title = {Quantifying the Generation Process of Multi-Level Tactile Sensations via ERP Component Investigation.},
journal = {International journal of neural systems},
volume = {31},
number = {12},
pages = {2150049},
doi = {10.1142/S0129065721500490},
pmid = {34635035},
issn = {1793-6462},
mesh = {*Electroencephalography ; Evoked Potentials ; Humans ; Touch ; *Touch Perception ; },
abstract = {Humans obtain characteristic information such as texture and weight of external objects, relying on the brain's integration and classification of tactile information; however, the decoding mechanism of multi-level tactile information is relatively elusive from the temporal sequence. In this paper, nonvariant frequency, along with the variant pulse width of electrotactile stimulus, was performed to generate multi-level pressure sensation. Event-related potentials (ERPs) were measured to investigate the mechanism of whole temporal tactile processing. Five ERP components, containing P100-N140-P200-N200-P300, were observed. By establishing the relationship between stimulation parameters and ERP component amplitudes, we found the following: (1) P200 is the most significant component for distinguishing multi-level tactile sensations; (2) P300 is correlated well with the subjective judgment of tactile sensation. The temporal sequence of brain topographies was implemented to clarify the spatiotemporal characteristics of the tactile process, which conformed to the serial processing model in neurophysiology and cortical network response area described by fMRI. Our results can help further clarify the mechanism of tactile sequential processing, which can be applied to improve the tactile BCI performance, sensory enhancement, and clinical diagnosis for doctors to evaluate the tactile process disorders by examining the temporal ERP components.},
}
@article {pmid34633650,
year = {2022},
author = {Li, LX and Li, YL and Wu, JT and Song, JZ and Li, XM},
title = {Glutamatergic Neurons in the Caudal Zona Incerta Regulate Parkinsonian Motor Symptoms in Mice.},
journal = {Neuroscience bulletin},
volume = {38},
number = {1},
pages = {1-15},
pmid = {34633650},
issn = {1995-8218},
mesh = {Animals ; Mice ; Neurons ; *Parkinson Disease ; *Parkinsonian Disorders ; Substantia Nigra ; *Zona Incerta ; },
abstract = {Parkinson's disease (PD) is the second most common and fastest-growing neurodegenerative disorder. In recent years, it has been recognized that neurotransmitters other than dopamine and neuronal systems outside the basal ganglia are also related to PD pathogenesis. However, little is known about whether and how the caudal zona incerta (ZIc) regulates parkinsonian motor symptoms. Here, we showed that specific glutamatergic but not GABAergic ZIc[VgluT2] neurons regulated these symptoms. ZIc[VgluT2] neuronal activation induced time-locked parkinsonian motor symptoms. In mouse models of PD, the ZIc[VgluT2] neurons were hyperactive and inhibition of their activity ameliorated the motor deficits. ZIc[VgluT2] neurons monosynaptically projected to the substantia nigra pars reticulata. Incerta-nigral circuit activation induced parkinsonian motor symptoms. Together, our findings provide a direct link between the ZIc, its glutamatergic neurons, and parkinsonian motor symptoms for the first time, help to better understand the mechanisms of PD, and supply a new important potential therapeutic target for PD.},
}
@article {pmid34630308,
year = {2021},
author = {Foldes, ST and Chandrasekaran, S and Camerone, J and Lowe, J and Ramdeo, R and Ebersole, J and Bouton, CE},
title = {Case Study: Mapping Evoked Fields in Primary Motor and Sensory Areas via Magnetoencephalography in Tetraplegia.},
journal = {Frontiers in neurology},
volume = {12},
number = {},
pages = {739693},
pmid = {34630308},
issn = {1664-2295},
abstract = {Devices interfacing with the brain through implantation in cortical or subcortical structures have great potential for restoration and rehabilitation in patients with sensory or motor dysfunction. Typical implantation surgeries are planned based on maps of brain activity generated from intact function. However, mapping brain activity for planning implantation surgeries is challenging in the target population due to abnormal residual function and, increasingly often, existing MRI-incompatible implanted hardware. Here, we present methods and results for mapping impaired somatosensory and motor function in an individual with paralysis and an existing brain-computer interface (BCI) device. Magnetoencephalography (MEG) was used to directly map the neural activity evoked during transcutaneous electrical stimulation and attempted movement of the impaired hand. Evoked fields were found to align with the expected anatomy and somatotopic organization. This approach may be valuable for guiding implants in other applications, such as cortical stimulation for pain and to improve implant targeting to help reduce the craniotomy size.},
}
@article {pmid34630055,
year = {2021},
author = {Hehenberger, L and Batistic, L and Sburlea, AI and Müller-Putz, GR},
title = {Directional Decoding From EEG in a Center-Out Motor Imagery Task With Visual and Vibrotactile Guidance.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {687252},
pmid = {34630055},
issn = {1662-5161},
abstract = {Motor imagery is a popular technique employed as a motor rehabilitation tool, or to control assistive devices to substitute lost motor function. In both said areas of application, artificial somatosensory input helps to mirror the sensorimotor loop by providing kinesthetic feedback or guidance in a more intuitive fashion than via visual input. In this work, we study directional and movement-related information in electroencephalographic signals acquired during a visually guided center-out motor imagery task in two conditions, i.e., with and without additional somatosensory input in the form of vibrotactile guidance. Imagined movements to the right and forward could be discriminated in low-frequency electroencephalographic amplitudes with group level peak accuracies of 70% with vibrotactile guidance, and 67% without vibrotactile guidance. The peak accuracies with and without vibrotactile guidance were not significantly different. Furthermore, the motor imagery could be classified against a resting baseline with group level accuracies between 76 and 83%, using either low-frequency amplitude features or μ and β power spectral features. On average, accuracies were higher with vibrotactile guidance, while this difference was only significant in the latter set of features. Our findings suggest that directional information in low-frequency electroencephalographic amplitudes is retained in the presence of vibrotactile guidance. Moreover, they hint at an enhancing effect on motor-related μ and β spectral features when vibrotactile guidance is provided.},
}
@article {pmid34626685,
year = {2022},
author = {Huang, Y and Jin, J and Xu, R and Miao, Y and Liu, C and Cichocki, A},
title = {Multi-view optimization of time-frequency common spatial patterns for brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {365},
number = {},
pages = {109378},
doi = {10.1016/j.jneumeth.2021.109378},
pmid = {34626685},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Common spatial pattern (CSP) is a prevalent method applied to feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs) recorded by electroencephalogram (EEG). The selection of time windows and frequency bands prominently affects the performance of CSP algorithms. Concerning the joint optimization of these two parameters, several studies have utilized a unified framework based on different feature selection strategies and achieved considerable improvement. However, during the feature selection process, useful information could be discarded inevitably and the underlying internal structure of features could be neglected.<br><br>NEW METHOD: In this paper, we proposed a novel framework termed time window filter bank common spatial pattern with multi-view optimization (TWFBCSP-MVO) to further boost the decoding of MI tasks. Concretely, after extracting CSP features from different time-frequency decompositions of EEG signals, a preliminary screening strategy based on variance ratio was devised to filter out the unrelated spatial patterns. We then introduced a multi-view learning strategy for the simultaneous optimization of time windows and frequency bands. A support vector machine classifier was trained to determine the output of the brain.<br><br>RESULTS: An experimental study was conducted on two public datasets to verify the effectiveness of TWFBCSP-MVO. Results showed that the proposed TWFBCSP-MVO could help improve the performance of MI classification.<br><br>In comparison to other competing methods, the proposed method performed significantly better (p<0.01).<br><br>CONCLUSIONS: The proposed method is a promising contestant to improve the performance of practical MI-based BCIs.},
}
@article {pmid34624799,
year = {2022},
author = {Shor, E and Herrero-Vidal, P and Dewan, A and Uguz, I and Curto, VF and Malliaras, GG and Savin, C and Bozza, T and Rinberg, D},
title = {Sensitive and robust chemical detection using an olfactory brain-computer interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {195},
number = {},
pages = {113664},
doi = {10.1016/j.bios.2021.113664},
pmid = {34624799},
issn = {1873-4235},
support = {R90 DA043849/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; *Biosensing Techniques ; *Brain-Computer Interfaces ; Mice ; Odorants ; Olfactory Bulb ; *Olfactory Receptor Neurons ; Smell ; },
abstract = {When it comes to detecting volatile chemicals, biological olfactory systems far outperform all artificial chemical detection devices in their versatility, speed, and specificity. Consequently, the use of trained animals for chemical detection in security, defense, healthcare, agriculture, and other applications has grown astronomically. However, the use of animals in this capacity requires extensive training and behavior-based communication. Here we propose an alternative strategy, a bio-electronic nose, that capitalizes on the superior capability of the mammalian olfactory system, but bypasses behavioral output by reading olfactory information directly from the brain. We engineered a brain-computer interface that captures neuronal signals from an early stage of olfactory processing in awake mice combined with machine learning techniques to form a sensitive and selective chemical detector. We chronically implanted a grid electrode array on the surface of the mouse olfactory bulb and systematically recorded responses to a large battery of odorants and odorant mixtures across a wide range of concentrations. The bio-electronic nose has a comparable sensitivity to the trained animal and can detect odors on a variable background. We also introduce a novel genetic engineering approach that modifies the relative abundance of particular olfactory receptors in order to improve the sensitivity of our bio-electronic nose for specific chemical targets. Our recordings were stable over months, providing evidence for robust and stable decoding over time. The system also works in freely moving animals, allowing chemical detection to occur in real-world environments. Our bio-electronic nose outperforms current methods in terms of its stability, specificity, and versatility, setting a new standard for chemical detection.},
}
@article {pmid34623400,
year = {2021},
author = {Patel, K and Katz, CN and Kalia, SK and Popovic, MR and Valiante, TA},
title = {Volitional control of individual neurons in the human brain.},
journal = {Brain : a journal of neurology},
volume = {144},
number = {12},
pages = {3651-3663},
pmid = {34623400},
issn = {1460-2156},
support = {U01 NS103792/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electrocorticography ; Female ; Humans ; Learning/*physiology ; Male ; Middle Aged ; Neurofeedback/*methods ; Neurons/*physiology ; Volition/*physiology ; },
abstract = {Brain-machine interfaces allow neuroscientists to causally link specific neural activity patterns to a particular behaviour. Thus, in addition to their current clinical applications, brain-machine interfaces can also be used as a tool to investigate neural mechanisms of learning and plasticity in the brain. Decades of research using such brain-machine interfaces have shown that animals (non-human primates and rodents) can be operantly conditioned to self-regulate neural activity in various motor-related structures of the brain. Here, we ask whether the human brain, a complex interconnected structure of over 80 billion neurons, can learn to control itself at the most elemental scale-a single neuron. We used the unique opportunity to record single units in 11 individuals with epilepsy to explore whether the firing rate of a single (direct) neuron in limbic and other memory-related brain structures can be brought under volitional control. To do this, we developed a visual neurofeedback task in which participants were trained to move a block on a screen by modulating the activity of an arbitrarily selected neuron from their brain. Remarkably, participants were able to volitionally modulate the firing rate of the direct neuron in these previously uninvestigated structures. We found that a subset of participants (learners), were able to improve their performance within a single training session. Successful learning was characterized by (i) highly specific modulation of the direct neuron (demonstrated by significantly increased firing rates and burst frequency); (ii) a simultaneous decorrelation of the activity of the direct neuron from the neighbouring neurons; and (iii) robust phase-locking of the direct neuron to local alpha/beta-frequency oscillations, which may provide some insights in to the potential neural mechanisms that facilitate this type of learning. Volitional control of neuronal activity in mnemonic structures may provide new ways of probing the function and plasticity of human memory without exogenous stimulation. Furthermore, self-regulation of neural activity in these brain regions may provide an avenue for the development of novel neuroprosthetics for the treatment of neurological conditions that are commonly associated with pathological activity in these brain structures, such as medically refractory epilepsy.},
}
@article {pmid34621062,
year = {2021},
author = {Greenberg, A and Cohen, A and Grewal, M},
title = {Patent landscape of brain-machine interface technology.},
journal = {Nature biotechnology},
volume = {39},
number = {10},
pages = {1194-1199},
pmid = {34621062},
issn = {1546-1696},
mesh = {*Brain-Computer Interfaces/classification/statistics &amp; numerical data ; Humans ; Industrial Development/statistics &amp; numerical data ; Patents as Topic/*statistics &amp; numerical data ; },
}
@article {pmid34616530,
year = {2021},
author = {Mohdiwale, S and Sahu, M and Sinha, GR and Nisar, H},
title = {Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {3928470},
pmid = {34616530},
issn = {2040-2309},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; },
abstract = {Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.},
}
@article {pmid34613968,
year = {2021},
author = {Amare, HH and Lindtjorn, B},
title = {Risk factors for scabies, tungiasis, and tinea infections among schoolchildren in southern Ethiopia: A cross-sectional Bayesian multilevel model.},
journal = {PLoS neglected tropical diseases},
volume = {15},
number = {10},
pages = {e0009816},
pmid = {34613968},
issn = {1935-2735},
mesh = {Adolescent ; Animals ; Child ; Cross-Sectional Studies ; Ethiopia/epidemiology ; Female ; Humans ; Hygiene ; Male ; Prevalence ; Risk Factors ; Rural Population ; Scabies/*epidemiology ; Schools/statistics &amp; numerical data ; Tinea/*epidemiology ; Tungiasis/*epidemiology ; },
abstract = {BACKGROUND: Skin problems cause significant sickness in communities with poor living conditions, but they have received less attention in national or global health studies because of their low mortality rates. In many developing regions, the prevalence of parasitic skin diseases among schoolchildren is not reported. Previous studies thus have attempted to identify risk factors for these conditions using the frequentist approach. This study aimed to assess the occurrence and risk factors of skin infections among rural schoolchildren in southern Ethiopia by combining a frequentist and a Bayesian approach.<br><br>Using three-stage random sampling, we assessed 864 schoolchildren aged 7-14 years from the Wonago district in southern Ethiopia. We detected potential risk factors for scabies, tungiasis, and tinea infections and recorded their hygienic practices and socio-demographic information. The frequentist model revealed a clustering effect of 8.8% at the classroom level and an insignificant effect at the school level. The Bayesian model revealed a clustering effect of 16% at the classroom level and 5.3% at the school level. Almost three-fourths of the sample had at least one type of skin problem, and boys were at higher overall risk than girls (adjusted odds ratio [aOR] 1.55 [95% Bayesian credible interval [BCI] 1.01, 2.28). Risk factors included unclean fingernails (aOR 1.85 [95% BCI 1.08, 2.97]); not washing the body (aOR 1.90 [95% BCI 1.21, 2.85]) and hair (aOR 3.07 [95% BCI 1.98, 4.57]) with soap every week; sharing a bed (aOR 1.97 [95% BCI 1.27, 2.89]), clothes (aOR 5.65 [95% BCI 3.31, 9.21]), or combs (aOR 3.65 [95% BCI 2.28, 5.53]); and living in a poor household (aOR 1.76 [95% BCI 1.03, 2.83]). Washing legs and feet with soap daily was identified as a protective factor for each of the three skin diseases (aOR 0.23 [95% BCI 0.15, 0.33]).<br><br>CONCLUSIONS/SIGNIFICANCE: We observed high variation in skin problems at the classroom level, indicating the presence of shared risk factors in these locations. The findings suggest the need to improve children's personal hygiene via health education by schoolteachers and health workers.},
}
@article {pmid34611209,
year = {2021},
author = {Xu, F and Miao, Y and Sun, Y and Guo, D and Xu, J and Wang, Y and Li, J and Li, H and Dong, G and Rong, F and Leng, J and Zhang, Y},
title = {A transfer learning framework based on motor imagery rehabilitation for stroke.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {19783},
pmid = {34611209},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Data Analysis ; Deep Learning ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Models, Theoretical ; Stroke Rehabilitation/*methods ; *Transfer, Psychology ; },
abstract = {Deep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced 'fine-tune' to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and 'fine-tune' transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.},
}
@article {pmid34610476,
year = {2021},
author = {Song, F and Kong, Y and Shao, C and Cheng, Y and Lu, J and Tao, Y and Du, J and Wang, H},
title = {Chitosan-based multifunctional flexible hemostatic bio-hydrogel.},
journal = {Acta biomaterialia},
volume = {136},
number = {},
pages = {170-183},
doi = {10.1016/j.actbio.2021.09.056},
pmid = {34610476},
issn = {1878-7568},
mesh = {Animals ; Bandages ; *Chitosan/pharmacology ; Hemostasis ; *Hemostatics/pharmacology ; Hydrogels/pharmacology ; Mice ; },
abstract = {Realizing the potential application of chitosan as an effective biomedical hemostatic agent has become an emerging research hotspot. However, fabricating a flexible chitosan-based hemostatic bio-hydrogel with self-adhesion feature in humid conditions and rapid hemostasis capability remains a challenge. Herein, we reported the development of chitosan-based hydrogels (DCS-PEGSH gels) with typical multilevel pore structures, which were cross-linked by 3-(3,4-dihydroxyphenyl) propionic acid-modified chitosan (DCS) and sebacic acid-terminated polyethylene glycol modified by p-hydroxybenzaldehyde (PEGSH). By precisely regulating the proportion of PEGSH, the fabricated bio-hydrogels displayed favorable cytocompatibility, suitable stretchability (∼780%), and blood absorbability (1300% ± 50%). Moreover, the strong adhesion (∼68.5 kPa) of the assembled bio-hydrogel ensured its firm adherence on pigskin and on bleeding wound in both static and dynamic humid environments without shedding, thus providing a long service life. The fabricated hydrogels exhibited shorter blood clotting time (50 s) and lower blood clotting index (BCI, 41) than the commercial chitosan sponge (288 s, BCI 65). Notably, the amount of blood loss from the liver in mice was reduced by almost 90% as compared to that for the control group. This study paves a solid way for developing a chitosan-based hydrogel with self-adhesive, self-healing, stretchability, biocompatibility, and antibacterial and antioxidant properties through molecular design and structural regulation, which will enable the biomedical application of chitosan in emergency hemostasis, particularly in joints and extremities. STATEMENT OF SIGNIFICANCE: The design and preparation of multifunctional integrated green adhesive bio-hydrogels while avoiding the use of organic solvents and toxic chemical reagents has been an emerging challenge. Herein, a flexible chitosan-based hemostatic bio-hydrogel that integrates multifunctional properties was successfully synthesized. The bio-hydrogel displayed suitable stretchability (780%) and blood absorbability (1300% ± 50%). Moreover, the strong adhesion (68.5 kPa) ensured firm adherence of the assembled hydrogel on pigskin and on the bleeding wound site in both static and dynamic humid environments without shedding, thus providing a long service life. In addition, the designed hydrogel showed good compatibility and antibacterial performance. The dynamic Schiff base endowed the bio-hydrogel with excellent self-healing performance without any external stimuli.},
}
@article {pmid34609639,
year = {2021},
author = {Patel, P and R, R and Annavarapu, RN},
title = {EEG-based human emotion recognition using entropy as a feature extraction measure.},
journal = {Brain informatics},
volume = {8},
number = {1},
pages = {20},
pmid = {34609639},
issn = {2198-4018},
abstract = {Many studies on brain-computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.},
}
@article {pmid34607320,
year = {2021},
author = {Zhang, D and Liu, S and Wang, K and Zhang, J and Chen, D and Zhang, Y and Nie, L and Yang, J and Shinntarou, F and Wu, J and Yan, T},
title = {Machine-vision fused brain machine interface based on dynamic augmented reality visual stimulation.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2c9e},
pmid = {34607320},
issn = {1741-2552},
mesh = {*Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Objective.Brain-machine interfaces (BMIs) interpret human intent into machine reactions, and the visual stimulation (VS) paradigm is one of the most widely used of these approaches. Although VS-based BMIs have a relatively high information transfer rate (ITR), it is still difficult for BMIs to control machines in dynamic environments (for example, grabbing a dynamic object or targeting a walking person).Approach.In this study, we utilized a BMI based on augmented reality (AR) VS (AR-VS). The proposed VS was dynamically generated based on machine vision, and human intent was interpreted by a dynamic decision time interval approach. A robot based on the coordination of a task and self-motion system was controlled by the proposed paradigm in a fast and flexible state.Methods.Objects in scenes were first recognized by machine vision and tracked by optical flow. AR-VS was generated based on the objects' parameters. The number and distribution of VS was confirmed by the recognized objects. Electroencephalogram (EEG) features corresponding to VS and human intent were collected by a dry-electrode EEG cap and determined by the filter bank canonical correlation analysis method. Key parameters in the AR-VS, including the effect of VS size, frequency, dynamic object moving speed, ITR and the performance of the BMI-controlled robot, were analyzed.Conclusion and significance.The ITR of the proposed AR-VS paradigm for nine healthy subjects was 36.3 ± 20.1 bits min[-1]. In the online robot control experiment, brain-controlled hybrid tasks including self-moving and grabbing objects were 64% faster than when using the traditional steady-state visual evoked potential paradigm. The proposed paradigm based on AR-VS could be optimized and adopted in other kinds of VS-based BMIs, such as P300, omitted stimulus potential, and miniature event-related potential paradigms, for better results in dynamic environments.},
}
@article {pmid34607318,
year = {2021},
author = {Verwoert, M and Vansteensel, MJ and Freudenburg, ZV and Aarnoutse, EJ and Leijten, FSS and Ramsey, NF and Branco, MP},
title = {Decoding four hand gestures with a single bipolar pair of electrocorticography electrodes.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
pmid = {34607318},
issn = {1741-2552},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Electrodes ; Electrodes, Implanted ; Electroencephalography ; Gestures ; Hand ; Humans ; },
abstract = {Objective.Electrocorticography (ECoG) based brain-computer interfaces (BCIs) can be used to restore communication in individuals with locked-in syndrome. In motor-based BCIs, the number of degrees-of-freedom, and thus the speed of the BCI, directly depends on the number of classes that can be discriminated from the neural activity in the sensorimotor cortex. When considering minimally invasive BCI implants, the size of the subdural ECoG implant must be minimized without compromising the number of degrees-of-freedom.Approach.Here we investigated if four hand gestures could be decoded using a single ECoG strip of four consecutive electrodes spaced 1 cm apart and compared the performance between a unipolar and a bipolar montage. For that we collected data of seven individuals with intractable epilepsy implanted with ECoG grids, covering the hand region of the sensorimotor cortex. Based on the implanted grids, we generated virtual ECoG strips and compared the decoding accuracy between (a) a single unipolar electrode (Unipolar Electrode), (b) a combination of four unipolar electrodes (Unipolar Strip), (c) a single bipolar pair (Bipolar Pair) and (d) a combination of six bipolar pairs (Bipolar Strip).Main results.We show that four hand gestures can be equally well decoded using 'Unipolar Strips' (mean 67.4 ± 11.7%), 'Bipolar Strips' (mean 66.6 ± 12.1%) and 'Bipolar Pairs' (mean 67.6 ± 9.4%), while 'Unipolar Electrodes' (61.6 ± 5.9%) performed significantly worse compared to 'Unipolar Strips' and 'Bipolar Pairs'.Significance.We conclude that a single bipolar pair is a potential candidate for minimally invasive motor-based BCIs and encourage the use of ECoG as a robust and reliable BCI platform for multi-class movement decoding.},
}
@article {pmid34606773,
year = {2021},
author = {Miah, MO and Muhammod, R and Mamun, KAA and Farid, DM and Kumar, S and Sharma, A and Dehzangi, A},
title = {CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data.},
journal = {Journal of neuroscience methods},
volume = {364},
number = {},
pages = {109373},
doi = {10.1016/j.jneumeth.2021.109373},
pmid = {34606773},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography ; Imagination ; Movement ; },
abstract = {BACKGROUND: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data.<br><br>PROPOSED METHOD: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices.<br><br>RESULTS: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.},
}
@article {pmid34605976,
year = {2022},
author = {Pan, H and Song, H and Zhang, Q and Mi, W and Sun, J},
title = {Auxiliary controller design and performance comparative analysis in closed-loop brain-machine interface system.},
journal = {Biological cybernetics},
volume = {116},
number = {1},
pages = {23-32},
pmid = {34605976},
issn = {1432-0770},
mesh = {Brain ; *Brain-Computer Interfaces ; Feedback ; },
abstract = {Brain-machine interface (BMI) can realize information interaction between the brain and external devices, and yet the control accuracy is limited by the change of electroencephalogram signals. The introduction of auxiliary controller can overcome the above problems, but the performance of different auxiliary controllers is quite different. Hence, in this paper, we comprehensively compare and analyze the performance of different auxiliary controllers to provide a theoretical basis for designing BMI system. The main work includes: (1) designing four kinds of auxiliary controllers based on simultaneous perturbation stochastic approximation-function approximator (SPSA-FA), iterative feedback tuning-PID (IFT-PID), model predictive control (MPC) and model-free control (MFC); (2) based on the model of improved single-joint information transmission, constructing the closed-loop BMI systems with the decoder-based Wiener filter; and (3) comparing their performance in the constructed closed-loop BMI systems for dynamic motion restoration. The results show that the order of tracking accuracy is MPC, IFT-PID, SPSA-FA, MFC, and the order of time consumed is opposite. A good control effectiveness is achieved at the expense of time, so a suitable auxiliary controller should be selected according to the actual requirements.},
}
@article {pmid34603552,
year = {2021},
author = {Xu, L and Xu, M and Jung, TP and Ming, D},
title = {Correction to: Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {5},
pages = {921},
doi = {10.1007/s11571-021-09686-x},
pmid = {34603552},
issn = {1871-4080},
abstract = {[This corrects the article DOI: 10.1007/s11571-021-09676-z.].},
}
@article {pmid34603548,
year = {2021},
author = {Zhang, X and Jin, J and Li, S and Wang, X and Cichocki, A},
title = {Evaluation of color modulation in visual P300-speller using new stimulus patterns.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {5},
pages = {873-886},
pmid = {34603548},
issn = {1871-4080},
abstract = {Objective The stimulus color of P300-BCI systems has been successfully modified. However, the effects of different color combinations have not been widely investigated. In this study, we designed new stimulus patterns to evaluate the influence of color modulation on the BCI performance and waveforms of the evoked related potential (ERP).Methods Comparison was performed for three new stimulus patterns consisting of red face and colored block-shape, namely, red face with a white rectangle (RFW), red face with a blue rectangle (RFB), and red face with a red rectangle (RFR). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Results The RFW pattern obtained the highest average online accuracy with 96.94%, and those of RFR and RFB patterns were 93.61% and of 92.22% respectively. Significant differences in online accuracy and information transfer rate (ITR) were found between RFW and RFR patterns (p < 0.05). Conclusion Compared with RFR and RFB patterns, RFW yielded the best performance in P300-BCI. These new stimulus patterns with different color combinations have considerable importance to BCI applications and user-friendliness.},
}
@article {pmid34603543,
year = {2021},
author = {Rathi, N and Singla, R and Tiwari, S},
title = {A novel approach for designing authentication system using a picture based P300 speller.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {5},
pages = {805-824},
pmid = {34603543},
issn = {1871-4080},
abstract = {Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain-Computer Interface (BCI) is a tool that provides direct human-computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.},
}
@article {pmid34602001,
year = {2022},
author = {Orhanbulucu, F and Latifoğlu, F},
title = {Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {25},
number = {8},
pages = {840-851},
doi = {10.1080/10255842.2021.1983803},
pmid = {34602001},
issn = {1476-8259},
mesh = {*Amyotrophic Lateral Sclerosis/diagnosis ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials ; Humans ; },
abstract = {This study, it was aimed to contribute to the literature on Amyotrophic lateral sclerosis (ALS) diagnosis and Brain-Computer Interface (BCI) technologies by analyzing the electroencephalography (EEG) signals obtained as a result of visual stimuli and attention from ALS patients and healthy controls. It was observed that the success rate significantly increased both in the occipital and central regions in all classifiers, especially in the entropy features. The most successful classification was obtained with the Naïve Bayes (NB) classifier using the Morphological Features (MF) + Variational Mode Decomposition (VMD) -Entropy features at 88.89% in the occipital region and 94.44% in the central region.},
}
@article {pmid34598502,
year = {2021},
author = {Wang, F and Ping, J and Xu, Z and Bi, J},
title = {Classification of motor imagery using multisource joint transfer learning.},
journal = {The Review of scientific instruments},
volume = {92},
number = {9},
pages = {094106},
doi = {10.1063/5.0054912},
pmid = {34598502},
issn = {1089-7623},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Machine Learning ; },
abstract = {As an important way for human-computer interaction, the motor imagery brain-computer interface (MI-BCI) can decode personal motor intention directly by analyzing electroencephalogram (EEG) signals. However, a large amount of labeled data has to be collected for each new subject since EEG patterns vary between individuals. The long calibration phase severely limits the further development of MI-BCI. To tackle this problem, multi-source joint domain adaption (MJDA) and multi-source joint Riemannian adaption (MJRA) algorithms are proposed in this paper. Both methods aim to transfer knowledge from other subjects to the current subject who has only a small amount of labeled data. First, the common spatial pattern with Euclidean alignment is used to select source subjects who have similar spatial patterns to the target subject. Second, the covariance matrices of EEG trials are aligned in Riemannian space by removing subject-specific baselines. These two steps are shared by MJDA and MJRA. In the last step, MJDA attempts to minimize the feature distribution mismatch in the Riemannian tangent space, while MJRA attempts to find an adaptive Riemannian classifier. Finally, the proposed methods are validated on two datasets: BCI Competition IV 2a and online event-related desynchronization (ERD)-BCI. The experimental results demonstrate that both MJDA and MJRA outperform the state-of-the-art approaches. The MJDA provides a new idea for the offline analysis of MI-BCI, while MJRA could make a big difference to the online calibration of MI-BCI.},
}
@article {pmid34598440,
year = {2021},
author = {Runnova, A and Selskii, A and Emelyanova, E and Zhuravlev, M and Popova, M and Kiselev, A and Shamionov, R},
title = {Modification of Joint Recurrence Quantification Analysis (JRQA) for assessing individual characteristics from short EEG time series.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {31},
number = {9},
pages = {093116},
doi = {10.1063/5.0055550},
pmid = {34598440},
issn = {1089-7682},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neoplasms ; },
abstract = {This article proposes a modification of joint recurrence quantification analysis for identifying individual characteristics applied to human electroencephalography (EEG) using short time series. Statistical analysis of EEG characteristics facilitated the clarification of the spatial localization of identified individual characteristics. The method can be adapted for use as a stage of a rapid automatic configuration of brain-computer interface devices, which is especially relevant when working with children, due to limited opportunities for their long-term monitoring.},
}
@article {pmid34595055,
year = {2021},
author = {Rabah, H and Khalaf, Z and Rabah, A},
title = {Dopamine in Idiopathic Polymorphic Ventricular Tachycardia/Ventricular Fibrillation.},
journal = {The Journal of innovations in cardiac rhythm management},
volume = {12},
number = {9},
pages = {4699-4703},
pmid = {34595055},
issn = {2156-3977},
abstract = {The role of medical therapy in the treatment of idiopathic polymorphic ventricular tachycardia (IPMVT) and idiopathic ventricular fibrillation (IVF) is not well established. Current medications in use include amiodarone, lidocaine, isoproterenol, verapamil, and quinidine. However, the use of dopamine for controlling such arrhythmias has never been described. We present an interesting case of IPMVT/IVF storm induced by short-coupled premature ventricular contractions. The arrhythmia was terminated acutely using dopamine infusion and was suppressed chronically using verapamil.},
}
@article {pmid34593916,
year = {2021},
author = {Castelblanco-Martínez, DN and Slone, DH and Landeo-Yauri, SS and Ramos, EA and Alvarez-Alemán, A and Attademo, FLN and Beck, CA and Bonde, RK and Butler, SM and Cabrias-Contreras, LJ and Caicedo-Herrera, D and Galves, J and Gómez-Camelo, IV and González-Socoloske, D and Jiménez-Domínguez, D and Luna, FO and Mona-Sanabria, Y and Morales-Vela, JB and Olivera-Gómez, LD and Padilla-Saldívar, JA and Powell, J and Reid, JP and Rieucau, G and Mignucci-Giannoni, AA},
title = {Analysis of body condition indices reveals different ecotypes of the Antillean manatee.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {19451},
pmid = {34593916},
issn = {2045-2322},
mesh = {Animals ; Biometry ; *Body Size ; *Ecotype ; Female ; Male ; Trichechus manatus/*anatomy &amp; histology ; },
abstract = {Assessing the body condition of wild animals is necessary to monitor the health of the population and is critical to defining a framework for conservation actions. Body condition indices (BCIs) are a non-invasive and relatively simple means to assess the health of individual animals, useful for addressing a wide variety of ecological, behavioral, and management questions. The Antillean manatee (Trichechus manatus manatus) is an endangered subspecies of the West Indian manatee, facing a wide variety of threats from mostly human-related origins. Our objective was to define specific BCIs for the subspecies that, coupled with additional health, genetic and demographic information, can be valuable to guide management decisions. Biometric measurements of 380 wild Antillean manatees captured in seven different locations within their range of distribution were obtained. From this information, we developed three BCIs (BCI1 = UG/SL, BCI2 = W/SL[3], BCI3 = W/(SL*UG[2])). Linear models and two-way ANCOVA tests showed significant differences of the BCIs among sexes and locations. Although our three BCIs are suitable for Antillean manatees, BCI1 is more practical as it does not require information about weight, which can be a metric logistically difficult to collect under particular circumstances. BCI1 was significantly different among environments, revealing that the phenotypic plasticity of the subspecies have originated at least two ecotypes-coastal marine and riverine-of Antillean manatees.},
}
@article {pmid34592716,
year = {2021},
author = {Chen, R and Xu, G and Zhang, X and Han, C and Zhang, S},
title = {Multi-scale noise transfer and feature frequency detection in SSVEP based on FitzHugh-Nagumo neuron system.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2bb7},
pmid = {34592716},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neurons ; Photic Stimulation ; },
abstract = {Objective. The steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals for brain-computer interfaces (BCIs) due to its excellent interactive potential, such as high tolerance to noises and robust performance across users. In addition, it has a stable cycle, obvious characteristics and minimal training requirements. However, the SSVEP is extremely weak and companied with strong and multi-scale noise, resulting in a poor signal-to-noise ratio in practice. Common algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of SSVEP detection under the multi-scale noise. Therefore, using linear methods to extract SSVEP with obvious nonlinear and non-stationary characteristics, the useful signal will be attenuated or lost.Approach.To address this issue, two novel frameworks based on a two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract feature frequency of SSVEP.Results.In order to evaluate the effectiveness of the proposed methods, this research recruit 22 subjects to participate the experiment. Experimental results show that nonlinear FHN neuron model can force the energy of noise to be transferred into SSVEP and hence amplifying the amplitude of the target frequency. Compared with the traditional methods, the FHN and FHNCCA methods can achieve higher classification accuracy and faster processing speed, which effectively improves the information transmission rate of SSVEP-based BCI.},
}
@article {pmid34590990,
year = {2021},
author = {Colamarino, E and de Seta, V and Masciullo, M and Cincotti, F and Mattia, D and Pichiorri, F and Toppi, J},
title = {Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2150052},
doi = {10.1142/S0129065721500520},
pmid = {34590990},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Hand ; Humans ; *Motor Cortex ; Movement ; Muscle, Skeletal ; },
abstract = {Hybrid Brain-Computer Interfaces (BCIs) for upper limb rehabilitation after stroke should enable the reinforcement of "more normal" brain and muscular activity. Here, we propose the combination of corticomuscular coherence (CMC) and intermuscular coherence (IMC) as control features for a novel hybrid BCI for rehabilitation purposes. Multiple electroencephalographic (EEG) signals and surface electromyography (EMG) from 5 muscles per side were collected in 20 healthy participants performing finger extension (Ext) and grasping (Grasp) with both dominant and non-dominant hand. Grand average of CMC and IMC patterns showed a bilateral sensorimotor area as well as multiple muscles involvement. CMC and IMC values were used as features to classify each task versus rest and Ext versus Grasp. We demonstrated that a combination of CMC and IMC features allows for classification of both movements versus rest with better performance (Area Under the receiver operating characteristic Curve, AUC) for the Ext movement (0.97) with respect to Grasp (0.88). Classification of Ext versus Grasp also showed high performances (0.99). All in all, these preliminary findings indicate that the combination of CMC and IMC could provide for a comprehensive framework for simple hand movements to eventually be employed in a hybrid BCI system for post-stroke rehabilitation.},
}
@article {pmid34589965,
year = {2020},
author = {Szlosarek, PW and Phillips, MM and Pavlyk, I and Steele, J and Shamash, J and Spicer, J and Kumar, S and Pacey, S and Feng, X and Johnston, A and Bomalaski, J and Moir, G and Lau, K and Ellis, S and Sheaff, M},
title = {Expansion Phase 1 Study of Pegargiminase Plus Pemetrexed and Cisplatin in Patients With Argininosuccinate Synthetase 1-Deficient Mesothelioma: Safety, Efficacy, and Resistance Mechanisms.},
journal = {JTO clinical and research reports},
volume = {1},
number = {4},
pages = {100093},
pmid = {34589965},
issn = {2666-3643},
abstract = {INTRODUCTION: Pegargiminase (ADI-PEG 20; ADI) degrades arginine and potentiates pemetrexed (Pem) cytotoxicity in argininosuccinate synthetase 1 (ASS1)-deficient malignant pleural mesothelioma (MPM). We conducted a phase 1 dose-expansion study at the recommended phase 2 dose of ADI-PEG 20 with Pem and cisplatin (ADIPemCis), to further evaluate arginine-lowering therapy in ASS1-deficient MPM and explore the mechanisms of resistance.<br><br>METHODS: A total of 32 patients with ASS1-deficient MPM (11 epithelioid; 10 biphasic;11 sarcomatoid) who were chemonaive received weekly intramuscular pegargiminase (36 mg/m[2]) with Pem (500 mg/m[2]) and cisplatin (75 mg/m[2]) intravenously, every 3 weeks (six cycles maximum). Maintenance pegargiminase was permitted until disease progression or withdrawal. Safety, pharmacodynamics, immunogenicity, and efficacy were determined. Biopsies were performed in progressing patients to explore the mechanisms of resistance to pegargiminase.<br><br>RESULTS: The treatment was well tolerated. Most adverse events were of grade 1/2, whereas four nonhematologic grade 3/4 adverse events related to pegargiminase were reversible. Plasma arginine decreased whereas citrulline increased; this was maintained by 18 weeks of ADIPemCis therapy. The disease control rate in 31 assessed patients was 93.5% (n&#xa0;= 29 of 31; 95% confidence interval [CI]: 78.6%-99.2%), with a partial response rate of 35.5% (n&#xa0;= 11 of 31; 95% CI: 19.2%-54.6%). The median progression-free and overall survivals were 5.6 (95% CI: 4.0-6.0) and 10.1 (95% CI: 6.1-11.1) months, respectively. Progression biopsies on pegargiminase revealed a statistically significant influx of macrophages (n&#xa0;= 6; p&#xa0;= 0.0255) and patchy tumoral ASS1 reexpression (n&#xa0;= 2 of 6). In addition, we observed increased tumoral programmed death-ligand 1-an ADI-PEG 20 inducible gene-and the formation of CD3-positive T lymphocyte aggregates on disease progression (n&#xa0;= 2 of 5).<br><br>CONCLUSIONS: The dose expansion of ADIPemCis confirmed the high clinical activity and good tolerability in ASS1-deficient poor-prognosis mesothelioma, underpinning an ongoing phase 3 study (ClinicalTrials.govNCT02709512). Notably, resistance to pegargiminase correlated with marked macrophage recruitment and-along with the tumor immune microenvironment-warrants further study to optimize arginine deprivation for the treatment of mesothelioma.},
}
@article {pmid34588951,
year = {2021},
author = {Salahuddin, U and Gao, PX},
title = {Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {728178},
pmid = {34588951},
issn = {1662-4548},
abstract = {Brain machine interfaces (BMIs), or brain computer interfaces (BCIs), are devices that act as a medium for communications between the brain and the computer. It is an emerging field with numerous applications in domains of prosthetic devices, robotics, communication technology, gaming, education, and security. It is noted in such a multidisciplinary field, many reviews have surveyed on various focused subfields of interest, such as neural signaling, microelectrode fabrication, and signal classification algorithms. A unified review is lacking to cover and link all the relevant areas in this field. Herein, this review intends to connect on the relevant areas that circumscribe BMIs to present a unified script that may help enhance our understanding of BMIs. Specifically, this article discusses signal generation within the cortex, signal acquisition using invasive, non-invasive, or hybrid techniques, and the signal processing domain. The latest development is surveyed in this field, particularly in the last decade, with discussions regarding the challenges and possible solutions to allow swift disruption of BMI products in the commercial market.},
}
@article {pmid34588627,
year = {2021},
author = {Hu, W and Zhang, Y and Fei, P and Zhang, T and Yao, D and Gao, Y and Liu, J and Chen, H and Lu, Q and Mudianto, T and Zhang, X and Xiao, C and Ye, Y and Sun, Q and Zhang, J and Xie, Q and Wang, PH and Wang, J and Li, Z and Lou, J and Chen, W},
title = {Author Correction: Mechanical activation of spike fosters SARS-CoV-2 infection.},
journal = {Cell research},
volume = {31},
number = {11},
pages = {1223},
doi = {10.1038/s41422-021-00576-9},
pmid = {34588627},
issn = {1748-7838},
}
@article {pmid34587854,
year = {2021},
author = {Chaudhary, U and Chander, BS and Ohry, A and Jaramillo-Gonzalez, A and Lulé, D and Birbaumer, N},
title = {Brain Computer Interfaces for Assisted Communication in Paralysis and Quality of Life.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2130003},
doi = {10.1142/S0129065721300035},
pmid = {34587854},
issn = {1793-6462},
mesh = {*Amyotrophic Lateral Sclerosis ; Brain ; *Brain-Computer Interfaces ; Communication ; Electroencephalography ; Humans ; Paralysis ; Quality of Life ; },
abstract = {The rapid evolution of Brain-Computer Interface (BCI) technology and the exponential growth of BCI literature during the past 20 years is a consequence of increasing computational power and the achievements of statistical learning theory and machine learning since the 1960s. Despite this rapid scientific progress, the range of successful clinical and societal applications remained limited, with some notable exceptions in the rehabilitation of chronic stroke and first steps towards BCI-based assisted verbal communication in paralysis. In this contribution, we focus on the effects of noninvasive and invasive BCI-based verbal communication on the quality of life (QoL) of patients with amyotrophic lateral sclerosis (ALS) in the locked-in state (LIS) and the completely locked-in state (CLIS). Despite a substantial lack of replicated scientific data, this paper complements the existing methodological knowledge and focuses future investigators' attention on (1) Social determinants of QoL and (2) Brain reorganization and behavior. While it is not documented in controlled studies that the good QoL in these patients is a consequence of BCI-based neurorehabilitation, the proposed determinants of QoL might become the theoretical background needed to develop clinically more useful BCI systems and to evaluate the effects of BCI-based communication on QoL for advanced ALS patients and other forms of severe paralysis.},
}
@article {pmid34587606,
year = {2021},
author = {Vargas, P and Sitaram, R and Sepúlveda, P and Montalba, C and Rana, M and Torres, R and Tejos, C and Ruiz, S},
title = {Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2b7e},
pmid = {34587606},
issn = {1741-2552},
mesh = {Cerebellum ; Humans ; Magnetic Resonance Imaging ; *Motor Cortex ; *Neurofeedback ; *Self-Control ; },
abstract = {Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.},
}
@article {pmid34586607,
year = {2022},
author = {Nieto, N and Ibarrola, FJ and Peterson, V and Rufiner, HL and Spies, R},
title = {Extreme Learning Machine Design for Dealing with Unrepresentative Features.},
journal = {Neuroinformatics},
volume = {20},
number = {3},
pages = {641-650},
pmid = {34586607},
issn = {1559-0089},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Research Design ; },
abstract = {Extreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes. While most traditional approaches dictate that this parameter should be chosen smaller than the number of available training examples, in this article we argue that, in the case of problems in which the data contain unrepresentative features, such as in EEG classification problems, it is beneficial to choose a much larger number of hidden nodes. We characterize this phenomenon, explain why this happens and exhibit several concrete examples to illustrate how ELMs behave. Furthermore, as searching for the optimal number of hidden nodes could be time consuming in enlarged ELMs, we propose a new training scheme, including a novel pruning method. This scheme provides an efficient way of finding the optimal number of nodes, making ELMs more suitable for dealing with real time EEG classification problems. Experimental results using synthetic data and real EEG data show a major improvement in the training time with respect to most traditional and state of the art ELM approaches, without jeopardising classification performance and resulting in more compact networks.},
}
@article {pmid34586477,
year = {2021},
author = {Rakhmatulin, I and Parfenov, A and Traylor, Z and Nam, CS and Lebedev, M},
title = {Low-cost brain computer interface for everyday use.},
journal = {Experimental brain research},
volume = {239},
number = {12},
pages = {3573-3583},
pmid = {34586477},
issn = {1432-1106},
mesh = {Brain ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user. The device is compact, having a circular shape with a radius of only 25 mm, which would allow for comfortable daily use during both daytime and nighttime. Our solution is also very cost effective, approximately $350 for 24 electrodes. The built-in noise suppression capability improves the accuracy of recordings with a peak input noise below 0.35 μV. Here, we provide the results of the tests for the developed device. On our GitHub page, we provide detailed specification of the steps involved in building this EEG device which should be helpful to readers designing similar devices for their needs https://github.com/Ildaron/ironbci .},
}
@article {pmid34584019,
year = {2021},
author = {Noorbasha, SK and Florence Sudha, G},
title = {Novel approach to remove Electrical Shift and Linear Trend artifact from single channel EEG.},
journal = {Biomedical physics &amp; engineering express},
volume = {7},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ac2aee},
pmid = {34584019},
issn = {2057-1976},
mesh = {Algorithms ; *Artifacts ; Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) signals are crucial to Brain-Computer Interfacing (BCI). However, these are vulnerable to a variety of unintended artifacts that could negatively impact the precise brain function assessment. This paper provides a new algorithm to eliminate Electrical Shift and Linear Trend artifact (ESLT) in EEG using Singular Spectrum Analysis (SSA) and Enhanced local Polynomial (LP) Approximation-based Total Variation (EPATV). The contaminated single channel EEG is subdivided into multiple bands of frequency components by SSA. In order to acquire all LP and TV components, EPATV filtering is applied over the contaminated component frequency band. Filtered sub-signal is collected by subtracting both the LP and TV components from the component contaminated frequency band. Then, the addition of filtered sub-signal and remaining SSA frequency band components yield the final denoised EEG signal. The effectiveness of the proposed method in this paper is evaluated using the data obtained from three databases and compared with the existing methods. From the extensive simulation results, it is inferred that the algorithm discussed in the paper is effective when compared the existing methods, exhibiting a highest averaged Correlation Coefficient (CC) of 0.9534, averaged Signal to Noise Ratio (SNR) of 10.2208dB, lowest averaged Relative Root Mean Square Error (RRMSE) value 0.2787 and averaged Mean absolute Error (MAE) inαband value of 0.0557. The algorithm presented in this paper may be a viable choice for extracting ESLT artifact from a small streaming section of the EEG without requirement of the initial calibration or enormous EEG data.},
}
@article {pmid34582344,
year = {2022},
author = {Feng, Z and Sun, Y and Qian, L and Qi, Y and Wang, Y and Guan, C and Sun, Y},
title = {Design a Novel BCI for Neurorehabilitation Using Concurrent LFP and EEG Features: A Case Study.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {5},
pages = {1554-1563},
doi = {10.1109/TBME.2021.3115799},
pmid = {34582344},
issn = {1558-2531},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Neurological Rehabilitation ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) that enables people with severe motor disabilities to use their brain signals for direct control of objects have attracted increased interest in rehabilitation. To date, no study has investigated feasibility of the BCI framework incorporating both intracortical and scalp signals.<br><br>METHODS: Concurrent local field potential (LFP) from the hand-knob area and scalp EEG were recorded in a paraplegic patient undergoing a spike-based close-loop neurorehabilitation training. Based upon multimodal spatio-spectral feature extraction and Na&#xef;ve Bayes classification, we developed, for the first time, a novel LFP-EEG-BCI for motor intention decoding. A transfer learning (TL) approach was employed to further improve the feasibility. The performance of the proposed LFP-EEG-BCI for four-class upper-limb motor intention decoding was assessed.<br><br>RESULTS: Using a decision fusion strategy, we showed that the LFP-EEG-BCI significantly (p 0.05) outperformed single modal BCI (LFP-BCI and EEG-BCI) in terms of decoding accuracy with the best performance achieved using regularized common spatial pattern features. Interrogation of feature characteristics revealed discriminative spatial and spectral patterns, which may lead to new insights for better understanding of brain dynamics during different motor imagery tasks and promote development of efficient decoding algorithms. Moreover, we showed that similar classification performance could be obtained with few training trials, therefore highlighting the efficacy of TL.<br><br>CONCLUSION: The present findings demonstrated the superiority of the novel LFP-EEG-BCI in motor intention decoding.<br><br>SIGNIFICANCE: This work introduced a novel LFP-EEG-BCI that may lead to new directions for developing practical neurorehabilitation systems with high detection accuracy and multi-paradigm feasibility in clinical applications.},
}
@article {pmid34577493,
year = {2021},
author = {Palumbo, A and Gramigna, V and Calabrese, B and Ielpo, N},
title = {Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {18},
pages = {},
pmid = {34577493},
issn = {1424-8220},
support = {CUP B69G14000180008//SIMpLE (Smart solutIons for health Monitoring and independent mobiLity for Elderly and disable people) project/ ; },
mesh = {*Brain-Computer Interfaces ; *COVID-19 ; Electroencephalography ; Humans ; Movement ; SARS-CoV-2 ; *Wheelchairs ; },
abstract = {The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.},
}
@article {pmid34577481,
year = {2021},
author = {Usama, N and Niazi, IK and Dremstrup, K and Jochumsen, M},
title = {Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {18},
pages = {},
pmid = {34577481},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; Reproducibility of Results ; *Stroke/diagnosis ; },
abstract = {Error-related potentials (ErrPs) have been proposed as a means for improving brain-computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test-retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test-retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63-72% with LDA performing the best. There was no association between the individuals' impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.},
}
@article {pmid34577225,
year = {2021},
author = {Khramova, MV and Kuc, AK and Maksimenko, VA and Frolov, NS and Grubov, VV and Kurkin, SA and Pisarchik, AN and Shusharina, NN and Fedorov, AA and Hramov, AE},
title = {Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {18},
pages = {},
pmid = {34577225},
issn = {1424-8220},
support = {19-29-14101//Russian Foundation for Basic Research/ ; 19-32-60033//Russian Foundation for Basic Research/ ; MK-1760.2020.2//President Program/ ; },
mesh = {Adult ; Attention ; *Brain ; Child ; Cognition ; *Electroencephalography ; Humans ; Memory, Short-Term ; },
abstract = {In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top-down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain-computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.},
}
@article {pmid34573795,
year = {2021},
author = {Dai, Y and Duan, F and Feng, F and Sun, Z and Zhang, Y and Caiafa, CF and Marti-Puig, P and Solé-Casals, J},
title = {A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {9},
pages = {},
pmid = {34573795},
issn = {1099-4300},
abstract = {An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.},
}
@article {pmid34573253,
year = {2021},
author = {Vasilyev, AN and Nuzhdin, YO and Kaplan, AY},
title = {Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice?.},
journal = {Brain sciences},
volume = {11},
number = {9},
pages = {},
pmid = {34573253},
issn = {2076-3425},
support = {19-315-60011//Russian Foundation for Basic Research/ ; },
abstract = {BACKGROUND: Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain's involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or-even better-to turn them into actions through a technology called the brain-computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training.<br><br>METHODS: Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling.<br><br>RESULTS: The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson's &#x3c1; > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength.<br><br>CONCLUSION: The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.},
}
@article {pmid34573213,
year = {2021},
author = {Hu, D and Wang, S and Li, B and Liu, H and He, J},
title = {Spinal Cord Injury-Induced Changes in Encoding and Decoding of Bipedal Walking by Motor Cortical Ensembles.},
journal = {Brain sciences},
volume = {11},
number = {9},
pages = {},
pmid = {34573213},
issn = {2076-3425},
support = {2018YFB1307301//The National Key R&amp;D Program of China/ ; 91648207//National Natural Science Foundation of China/ ; 61673068//National Natural Science Foundation of China/ ; },
abstract = {Recent studies have shown that motor recovery following spinal cord injury (SCI) is task-specific. However, most consequential conclusions about locomotor functional recovery from SCI have been derived from quadrupedal locomotion paradigms. In this study, two monkeys were trained to perform a bipedal walking task, mimicking human walking, before and after T8 spinal cord hemisection. Importantly, there is no pharmacological therapy with nerve growth factor for monkeys after SCI; thus, in this study, the changes that occurred in the brain were spontaneous. The impairment of locomotion on the ipsilateral side was more severe than that on the contralateral side. We used information theory to analyze single-cell activity from the left primary motor cortex (M1), and results show that neuronal populations in the unilateral primary motor cortex gradually conveyed more information about the bilateral hindlimb muscle activities during the training of bipedal walking after SCI. We further demonstrated that, after SCI, progressively expanded information from the neuronal population reconstructed more accurate control of muscle activity. These results suggest that, after SCI, the unilateral primary motor cortex could gradually regain control of bilateral coordination and motor recovery and in turn enhance the performance of brain-machine interfaces.},
}
@article {pmid34571497,
year = {2021},
author = {Zhang, K and Xu, G and Du, C and Liang, R and Han, C and Zheng, X and Zhang, S and Wang, J and Tian, P and Jia, Y},
title = {Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2a6f},
pmid = {34571497},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Mirror Neurons ; },
abstract = {Objective.Motor imagery (MI), based on the theory of mirror neurons and neuroplasticity, can promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface (BCI) has been used in rehabilitation training and daily assistance for patients with hemiplegia in recent years. However, it is difficult to maintain the consistency and timeliness of receiving external stimulation to neural activation in most subjects owing to the high variability of electroencephalogram (EEG) representation across trials/subjects. Moreover, in practical application, MI-BCI cannot highly activate the motor cortex and provide stable interaction owing to the weakness of the EEG feature and lack of an effective mode of activation.Approach.In this study, a novel hybrid BCI paradigm based on MI and vestibular stimulation motor imagery (VSMI) was proposed to enhance the capability of feature response for MI. Twelve subjects participated in a group of controlled experiments containing VSMI and MI. Three indicators, namely, activation degree, timeliness, and classification accuracy, were adopted to evaluate the performance of the task.Main results.Vestibular stimulation could significantly strengthen the suppression ofαandβbands of contralateral brain regions during MI, that is, enhance the activation degree of the motor cortex (p< 0.01). Compared with MI, the timeliness of EEG feature-response achieved obvious improvements in VSMI experiments. Moreover, the averaged classification accuracy of VSMI and MI was 80.56% and 69.38%, respectively.Significance.The experimental results indicate that specific vestibular activity contributes to the oscillations of the motor cortex and has a positive effect on spontaneous imagery, which provides a novel MI paradigm and enables the preliminary exploration of sensorimotor integration of MI.},
}
@article {pmid34566617,
year = {2021},
author = {Park, S and Kim, DW and Han, CH and Im, CH},
title = {Estimation of Emotional Arousal Changes of a Group of Individuals During Movie Screening Using Steady-State Visual-Evoked Potential.},
journal = {Frontiers in neuroinformatics},
volume = {15},
number = {},
pages = {731236},
pmid = {34566617},
issn = {1662-5196},
abstract = {Neurocinematics is an emerging discipline in neuroscience, which aims to provide new filmmaking techniques by analyzing the brain activities of a group of audiences. Several neurocinematics studies attempted to track temporal changes in mental states during movie screening; however, it is still needed to develop efficient and robust electroencephalography (EEG) features for tracking brain states precisely over a long period. This study proposes a novel method for estimating emotional arousal changes in a group of individuals during movie screening by employing steady-state visual evoked potential (SSVEP), which is a widely used EEG response elicited by the presentation of periodic visual stimuli. Previous studies have reported that the emotional arousal of each individual modulates the strength of SSVEP responses. Based on this phenomenon, movie clips were superimposed on a background, eliciting an SSVEP response with a specific frequency. Two emotionally arousing movie clips were presented to six healthy male participants, while EEG signals were recorded from the occipital channels. We then investigated whether the movie scenes that elicited higher SSVEP responses coincided well with those rated as the most impressive scenes by 37 viewers in a separate experimental session. Our results showed that the SSVEP response averaged across six participants could accurately predict the overall impressiveness of each movie, evaluated with a much larger group of individuals.},
}
@article {pmid34566614,
year = {2021},
author = {Li, S and Lyu, X and Zhao, L and Chen, Z and Gong, A and Fu, Y},
title = {Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {732763},
pmid = {34566614},
issn = {1662-5188},
abstract = {Emotional brain-computer interface based on electroencephalogram (EEG) is a hot issue in the field of human-computer interaction, and is also an important part of the field of emotional computing. Among them, the recognition of EEG induced by emotion is a key problem. Firstly, the preprocessed EEG is decomposed by tunable-Q wavelet transform. Secondly, the sample entropy, second-order differential mean, normalized second-order differential mean, and Hjorth parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf optimization algorithm is used to optimize the feature matrix. Finally, support vector machine is used to train the classifier. The five types of emotion signal samples of 32 subjects in the database for emotion analysis using physiological signal dataset is identified by the proposed algorithm. After 6-fold cross-validation, the maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and the Kappa coefficient is 0.603. The results show that the proposed method has good performance indicators in the recognition of multiple types of EEG emotion signals, and has a better performance improvement compared with the traditional methods.},
}
@article {pmid34566563,
year = {2021},
author = {Saldanha, RL and Urdaneta, ME and Otto, KJ},
title = {The Role of Electrode-Site Placement in the Long-Term Stability of Intracortical Microstimulation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {712578},
pmid = {34566563},
issn = {1662-4548},
abstract = {Intracortical microelectrodes are neuroprosthetic devices used in brain-machine interfaces to both record and stimulate neural activity in the brain. These technologies have been improved by advances in microfabrication, which have led to the creation of subcellular and high-density microelectrodes. The greater number of independent stimulation channels in these devices allows for improved neuromodulation selectivity, compared to single-site microelectrodes. Elements of electrode design such as electrode-site placement can influence the long-term performance of neuroprostheses. Previous studies have shown that electrode-sites placed on the edge of a planar microelectrode have greater chronic recording functionality than sites placed in the center. However, the effect of electrode-site placement on long-term intracortical microstimulation (ICMS) is still unknown. Here, we show that, in rats chronically implanted with custom-made planar silicon microelectrodes, electrode-sites on the tip of the device outperformed those on both the edge and center in terms of the effect per charge delivered, though there is still a slight advantage to using edge sites over center sites for ICMS. Longitudinal analysis of ICMS detection thresholds over a 16-week period revealed that while all sites followed a similar trend over time, the tip and edge sites consistently elicited the behavioral response with less charge compared to center sites. Furthermore, we quantified channel activity over time and found that edge sites remained more active than center sites over time, though the rate of decay of active sites for center and edge sites was comparable. Our results demonstrate that electrode-site placement plays an important role in the long-term stability of intracortical microstimulation and could be a potential factor to consider in the design of future intracortical electrodes.},
}
@article {pmid34565550,
year = {2021},
author = {Zheng, M and Yang, B},
title = {A deep neural network with subdomain adaptation for motor imagery brain-computer interface.},
journal = {Medical engineering &amp; physics},
volume = {96},
number = {},
pages = {29-40},
doi = {10.1016/j.medengphy.2021.08.006},
pmid = {34565550},
issn = {1873-4030},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {BACKGROUND: The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI).<br><br>OBJECTIVE: In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time.<br><br>METHODS: We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the distance within each class (DWC) and maximizing the distance between classes within each domain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets.<br><br>RESULTS: The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm.<br><br>CONCLUSION: Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.},
}
@article {pmid34563599,
year = {2021},
author = {Aellen, FM and Göktepe-Kavis, P and Apostolopoulos, S and Tzovara, A},
title = {Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features.},
journal = {Journal of neuroscience methods},
volume = {364},
number = {},
pages = {109367},
doi = {10.1016/j.jneumeth.2021.109367},
pmid = {34563599},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Deep learning has revolutionized the field of computer vision, where convolutional neural networks (CNNs) extract complex patterns of information from large datasets. The use of deep networks in neuroscience is mainly focused to neuroimaging or brain computer interface -BCI- applications. In electroencephalography (EEG) research, multivariate pattern analysis (MVPA) mainly relies on linear algorithms, which require a homogeneous dataset and assume that discriminant features appear at consistent latencies and electrodes across trials. However, neural responses may shift in time or space during an experiment, resulting in under-estimation of discriminant features. Here, we aimed at using CNNs to classify EEG responses to external stimuli, by taking advantage of time- and space- unlocked neural activity, and at examining how discriminant features change over the course of an experiment, on a trial by trial basis.<br><br>NEW METHOD: We present a novel pipeline, consisting of data augmentation, CNN training, and feature visualization techniques, fine-tuned for MVPA on EEG data.<br><br>RESULTS: Our pipeline provides high classification performance and generalizes to new datasets. Additionally, we show that the features identified by the CNN for classification are electrophysiologically interpretable and can be reconstructed at the single-trial level to study trial-by-trial evolution of class-specific discriminant activity.<br><br>The developed pipeline was compared to commonly used MVPA algorithms like logistic regression and support vector machines, as well as to shallow and deep convolutional neural networks. Our approach yielded significantly higher classification performance than existing MVPA techniques (p&#xa0;=&#xa0;0.006) and comparable results to other CNNs for EEG data.<br><br>CONCLUSION: In summary, we present a novel deep learning pipeline for MVPA of EEG data, that can extract trial-by-trial discriminative activity in a data-driven way.},
}
@article {pmid34561503,
year = {2021},
author = {Wen, S and Yin, A and Tseng, PH and Itti, L and Lebedev, MA and Nicolelis, M},
title = {Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {19020},
pmid = {34561503},
issn = {2045-2322},
mesh = {Animals ; *Brain-Computer Interfaces ; Haplorhini ; Locomotion/physiology ; Machine Learning ; Motor Cortex/*physiology ; Neurons/physiology ; Wavelet Analysis ; },
abstract = {Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description-wavelet average coefficients (WAC)-to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders (Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.},
}
@article {pmid34559657,
year = {2021},
author = {Bi, L and Xia, S and Fei, W},
title = {Hierarchical Decoding Model of Upper Limb Movement Intention From EEG Signals Based on Attention State Estimation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2008-2016},
doi = {10.1109/TNSRE.2021.3115490},
pmid = {34559657},
issn = {1558-0210},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Intention ; Movement ; Upper Extremity ; },
abstract = {Decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decoding models are built under the attended state while subjects perform motion tasks. In practice, people are often distracted by other tasks or environmental factors, which may impair decoding performance. To address this problem, in this paper, we propose a hierarchical decoding model of human upper limb motion intention from EEG signals based on attention state estimation. The proposed decoding model includes two components. First, the attention state detection (ASD) component estimates the attention state during the upper limb movement. Next, the motion intention recognition (MIR) component decodes the motion intention by using the decoding models built under the attended and distracted states. The experimental results show that the proposed hierarchical decoding model performs well under the attended and distracted states. This work can advance the application of human movement intention decoding and provides new insights into the study of brain-machine interfaces.},
}
@article {pmid34557078,
year = {2021},
author = {Ingel, A and Vicente, R},
title = {Information Bottleneck as Optimisation Method for SSVEP-Based BCI.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {675091},
pmid = {34557078},
issn = {1662-5161},
abstract = {In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottleneck. This approach is particularly suitable for BCIs since the information bottleneck optimises the amount of information transferred by the BCI. Steady-state visual evoked potential-based BCIs often classify targets using very simple rules like choosing the class corresponding to the largest feature value. We call this classifier the arg max classifier. It is unlikely that this approach is optimal, and in this study, we propose a classification method specifically designed to optimise the performance measure of BCIs. This approach gives an advantage over standard machine learning methods, which aim to optimise different measures. The performance of the proposed algorithm is tested on two publicly available datasets in offline experiments. We use the standard power spectral density analysis (PSDA) and canonical correlation analysis (CCA) feature extraction methods on one dataset and show that the current approach outperforms most of the related studies on this dataset. On the second dataset, we use the task-related component analysis (TRCA) method and demonstrate that the proposed method outperforms the standard argmax classification rule in terms of information transfer rate when using a small number of classes. To our knowledge, this is the first time the information bottleneck is used in the context of SSVEP-based BCIs. The approach is unique in the sense that optimisation is done over the space of classification functions. It potentially improves the performance of BCIs and makes it easier to calibrate the system for different subjects.},
}
@article {pmid34556793,
year = {2021},
author = {Angrick, M and Ottenhoff, MC and Diener, L and Ivucic, D and Ivucic, G and Goulis, S and Saal, J and Colon, AJ and Wagner, L and Krusienski, DJ and Kubben, PL and Schultz, T and Herff, C},
title = {Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.},
journal = {Communications biology},
volume = {4},
number = {1},
pages = {1055},
pmid = {34556793},
issn = {2399-3642},
mesh = {*Brain-Computer Interfaces ; Electrodes, Implanted/*statistics &amp; numerical data ; Female ; Humans ; Neural Prostheses/*statistics &amp; numerical data ; *Quality of Life ; *Speech ; Young Adult ; },
abstract = {Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.},
}
@article {pmid34554373,
year = {2021},
author = {Maziero, D and Stenger, VA and Carmichael, DW},
title = {Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion.},
journal = {Brain topography},
volume = {34},
number = {6},
pages = {745-761},
pmid = {34554373},
issn = {1573-6792},
support = {R01EB028627//Foundation for the National Institutes of Health/ ; WT203148/Z/16/Z//Medical Engineering Centre, King's College London/ ; },
mesh = {*Artifacts ; Electroencephalography ; Humans ; Magnetic Fields ; *Magnetic Resonance Imaging ; Prospective Studies ; Retrospective Studies ; },
abstract = {The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.},
}
@article {pmid34552239,
year = {2021},
author = {Duan, J and Xu, P and Cheng, X and Mao, C and Croll, T and He, X and Shi, J and Luan, X and Yin, W and You, E and Liu, Q and Zhang, S and Jiang, H and Zhang, Y and Jiang, Y and Xu, HE},
title = {Structures of full-length glycoprotein hormone receptor signalling complexes.},
journal = {Nature},
volume = {598},
number = {7882},
pages = {688-692},
pmid = {34552239},
issn = {1476-4687},
support = {209407/Z/17/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Chorionic Gonadotropin/chemistry ; Cryoelectron Microscopy ; GTP-Binding Protein alpha Subunits, Gs/chemistry ; Humans ; Models, Molecular ; Molecular Dynamics Simulation ; Protein Binding ; Protein Domains ; Protein Structure, Secondary ; Receptors, LH/*chemistry ; },
abstract = {Luteinizing hormone and chorionic gonadotropin are glycoprotein hormones that are related to follicle-stimulating hormone and thyroid-stimulating hormone[1,2]. Luteinizing hormone and chorionic gonadotropin are essential to human reproduction and are important therapeutic drugs[3-6]. They activate the same G-protein-coupled receptor, luteinizing hormone-choriogonadotropin receptor (LHCGR), by binding to the large extracellular domain[3]. Here we report four cryo-electron microscopy structures of LHCGR: two structures of the wild-type receptor in the inactive and active states; and two structures of the constitutively active mutated receptor. The active structures are bound to chorionic gonadotropin and the stimulatory G protein (Gs), and one of the structures also contains Org43553, an allosteric agonist[7]. The structures reveal a distinct 'push-and-pull' mechanism of receptor activation, in which the extracellular domain is pushed by the bound hormone and pulled by the extended hinge loop next to the transmembrane domain. A highly conserved 10-residue fragment (P10) from the hinge C-terminal loop at the interface between the extracellular domain and the transmembrane domain functions as a tethered agonist to induce conformational changes in the transmembrane domain and G-protein coupling. Org43553 binds to a pocket of the transmembrane domain and interacts directly with P10, which further stabilizes the active conformation. Together, these structures provide a common model for understanding the signalling of glycoprotein hormone receptors and a basis for drug discovery for endocrine diseases.},
}
@article {pmid34551675,
year = {2021},
author = {Zweerings, J and Sarasjärvi, K and Mathiak, KA and Iglesias-Fuster, J and Cong, F and Zvyagintsev, M and Mathiak, K},
title = {Data-Driven Approach to the Analysis of Real-Time FMRI Neurofeedback Data: Disorder-Specific Brain Synchrony in PTSD.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2150043},
doi = {10.1142/S012906572150043X},
pmid = {34551675},
issn = {1793-6462},
mesh = {Amygdala ; Brain/diagnostic imaging ; Brain Mapping ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; *Neurofeedback ; *Stress Disorders, Post-Traumatic ; },
abstract = {Brain-computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.},
}
@article {pmid34551403,
year = {2021},
author = {Giulia, L and Adolfo, V and Julie, C and Quentin, D and Simon, B and Fleury, M and Leveque-Le Bars, E and Bannier, E and Lécuyer, A and Barillot, C and Bonan, I},
title = {The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac291e},
pmid = {34551403},
issn = {1741-2552},
mesh = {Bayes Theorem ; Humans ; Magnetic Resonance Imaging ; *Motor Cortex ; *Neurofeedback ; *Stroke/diagnostic imaging/therapy ; },
abstract = {Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.},
}
@article {pmid34550888,
year = {2021},
author = {Kim, MK and Sohn, JW and Kim, SP},
title = {Finding Kinematics-Driven Latent Neural States From Neuronal Population Activity for Motor Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {2027-2036},
doi = {10.1109/TNSRE.2021.3114367},
pmid = {34550888},
issn = {1558-0210},
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex ; Neurons ; },
abstract = {While intracortical brain-machine interfaces (BMIs) demonstrate feasibility to restore mobility to people with paralysis, it is still challenging to maintain high-performance decoding in clinical BMIs. One of the main obstacles for high-performance BMI is the noise-prone nature of traditional decoding methods that connect neural response explicitly with physical quantity, such as velocity. In contrast, the recent development of latent neural state model enables a robust readout of large-scale neuronal population activity contents. However, these latent neural states do not necessarily contain kinematic information useful for decoding. Therefore, this study proposes a new approach to finding kinematics-dependent latent factors by extracting latent factors' kinematics-dependent components using linear regression. We estimated these components from the population activity through nonlinear mapping. The proposed kinematics-dependent latent factors generate neural trajectories that discriminate latent neural states before and after the motion onset. We compared the decoding performance of the proposed analysis model with the results from other popular models. They are factor analysis (FA), Gaussian process factor analysis (GPFA), latent factor analysis via dynamical systems (LFADS), preferential subspace identification (PSID), and neuronal population firing rates. The proposed analysis model results in higher decoding accuracy than do the others (% improvement on average). Our approach may pave a new way to extract latent neural states specific to kinematic information from motor cortices, potentially improving decoding performance for online intracortical BMIs.},
}
@article {pmid34550551,
year = {2021},
author = {Singh, SA and Meitei, TG and Devi, ND and Majumder, S},
title = {A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images.},
journal = {Physical and engineering sciences in medicine},
volume = {44},
number = {4},
pages = {1221-1230},
pmid = {34550551},
issn = {2662-4737},
support = {REF: NECBH/2019-20/177, BT/COE/34/SP28408/2018//Department of Biotechnology , Ministry of Science and Technology/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; Wavelet Analysis ; },
abstract = {Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.},
}
@article {pmid34548098,
year = {2021},
author = {Chandrasekaran, S and Fifer, M and Bickel, S and Osborn, L and Herrero, J and Christie, B and Xu, J and Murphy, RKJ and Singh, S and Glasser, MF and Collinger, JL and Gaunt, R and Mehta, AD and Schwartz, A and Bouton, CE},
title = {Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications.},
journal = {Bioelectronic medicine},
volume = {7},
number = {1},
pages = {14},
pmid = {34548098},
issn = {2332-8886},
abstract = {Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.},
}
@article {pmid34544060,
year = {2021},
author = {Ming, G and Pei, W and Chen, H and Gao, X and Wang, Y},
title = {Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac284a},
pmid = {34544060},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Objective.Low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems with high performance are prone to cause visual discomfort and fatigue. High-frequency SSVEP-based BCI systems can alleviate the discomfort, but always obtain lower performance. This study optimized the spatial properties of a proposed checkerboard-like visual stimulus toward high-performance and user-friendly SSVEP-based BCI systems.Approach.On the one hand, two checkerboard-like stimuli with distinct spatial contrasts (the black- and white-background) were designed to balance the tradeoff between BCI performance and user experience and compared with the traditional flickering stimulus. On the other hand, the impacts of the spatial frequency of the new checkerboard-like stimulus on the flicker perception and the intensity of the elicited SSVEP were clarified. The SSVEP-based BCI systems were implemented based on the checkerboard-like stimuli under low-frequency and high-frequency conditions. The user experience for each stimulation pattern was estimated by questionnaires for subjective evaluation.Main results.The comparison results indicate that the black-background checkerboard-like stimulus with an optimized spatial frequency achieved comparable performance and enhanced visual comfort compared with the flickering stimulus. Furthermore, the online nine-target BCI system using the black-background checkerboard-like stimuli achieved averaged information transfer rates of 124.0 ± 2.3 and 109.0 ± 20.4 bits min[-1]with low-frequency and high-frequency stimulation respectively.Significance.The new checkerboard-like stimuli with optimized properties show superiority of system performance and user experience in implementing SSVEP-based BCI, which will promote its practical applications in communication and control.},
}
@article {pmid34543200,
year = {2021},
author = {Liu, B and Chen, X and Shi, N and Wang, Y and Gao, S and Gao, X},
title = {Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1998-2007},
doi = {10.1109/TNSRE.2021.3114340},
pmid = {34543200},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.},
}
@article {pmid34543189,
year = {2022},
author = {Mladenovic, J and Frey, J and Pramij, S and Mattout, J and Lotte, F},
title = {Towards Identifying Optimal Biased Feedback for Various User States and Traits in Motor Imagery BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {3},
pages = {1101-1110},
doi = {10.1109/TBME.2021.3113854},
pmid = {34543189},
issn = {1558-2531},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Feedback ; Humans ; Imagination/physiology ; Learning/physiology ; },
abstract = {OBJECTIVE: Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process especially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from a biased feedback.<br><br>METHODS: We conduct an experiment (n = 30 for 2 sessions). The feedback provided to each group (n = 10) is either positively, negatively or not biased.<br><br>RESULTS: Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR = 2%) as during the second session it severely drops (LR = -1%).<br><br>CONCLUSION: We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions.<br><br>SIGNIFICANCE: This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning.},
}
@article {pmid34541898,
year = {2022},
author = {Pandarinath, C and Bensmaia, SJ},
title = {The science and engineering behind sensitized brain-controlled bionic hands.},
journal = {Physiological reviews},
volume = {102},
number = {2},
pages = {551-604},
pmid = {34541898},
issn = {1522-1210},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; R35 NS122333/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; R01 NS095251/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Bionics ; Brain/physiology ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Hand/*physiology ; Humans ; Movement/*physiology ; Touch Perception/physiology ; },
abstract = {Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.},
}
@article {pmid34539326,
year = {2021},
author = {Yan, Y and Zhou, H and Huang, L and Cheng, X and Kuang, S},
title = {A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {657540},
pmid = {34539326},
issn = {1662-4548},
abstract = {Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain-computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.},
}
@article {pmid34539168,
year = {2021},
author = {Liu, X and Bibineyshvili, Y and Robles, DA and Boreland, AJ and Margolis, DJ and Shreiber, DI and Zahn, JD},
title = {Fabrication of a Multilayer Implantable Cortical Microelectrode Probe to Improve Recording Potential.},
journal = {Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems},
volume = {30},
number = {4},
pages = {569-581},
pmid = {34539168},
issn = {1057-7157},
support = {P41 RR012408/RR/NCRR NIH HHS/United States ; T32 GM008339/GM/NIGMS NIH HHS/United States ; T32 GM135141/GM/NIGMS NIH HHS/United States ; TL1 TR003019/TR/NCATS NIH HHS/United States ; },
abstract = {Intracortical neural probes are a key enabling technology for acquiring high fidelity neural signals within the cortex. They are viewed as a crucial component of brain-computer interfaces (BCIs) in order to record electrical activities from neurons within the brain. Smaller, more flexible, polymer-based probes have been investigated for their potential to limit the acute and chronic neural tissue response. Conventional methods of patterning electrodes and connecting traces on a single supporting layer can limit the number of recording sites which can be defined, particularly when designing narrower probes. We present a novel strategy of increasing the number of recording sites without proportionally increasing the size of the probe by using a multilayer fabrication process to vertically layer recording traces on multiple Parylene support layers, allowing more recording traces to be defined on a smaller probe width. Using this approach, we are able to define 16 electrodes on 4 supporting layers (4 electrodes per layer), each with a 30 μm diameter recording window and 5 μm wide connecting trace defined by conventional LWUV lithography, on an 80 μm wide by 9 μm thick microprobe. Prior to in vitro and in vivo validation, the multilayer probes are electrically characterized via impedance spectroscopy and evaluating crosstalk between adjacent layers. Demonstration of acute in vitro recordings in a cerebral organoid model and in vivo recordings in a murine model indicate the probe's capability for single unit recordings. This work demonstrates the ability to fabricate smaller, more compliant neural probes without sacrificing electrode density.},
}
@article {pmid34533381,
year = {2022},
author = {Wang, X and Lu, H and Shen, X and Ma, L and Wang, Y},
title = {Prosthetic control system based on motor imagery.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {25},
number = {7},
pages = {764-771},
doi = {10.1080/10255842.2021.1977800},
pmid = {34533381},
issn = {1476-8259},
mesh = {Activities of Daily Living ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; *Imagination ; },
abstract = {A brain-computer interface (BCI) can be used for function replacement through the control of devices, such as prostheses, by identifying the subject's intent from brain activity. We process electroencephalography (EEG) signals related to motor imagery to improve the accuracy of intent classification. The original signals are decomposed into three layers based on db4 wavelet basis. The wavelet soft threshold denoising method is used to improve the signal-to-noise ratio. The sample entropy algorithm is used to extract the features of the signal after noise reduction and reconstruction. Combined with event-related synchronisation/desynchronisation (ERS/ERD) phenomenon, the sample entropy in the motor imagery time periods of C3, C4 and Cz is selected as the feature value. Feature vectors are then used as the input of three classifiers. From the evaluated classifiers, the backpropagation (BP) neural network provides the best EEG signal classification (93% accuracy). BP neural network is thus selected as the final classifier and used to design a prosthetic control system based on motor imagery. The classification results are wirelessly transmitted to control a prosthesis successfully via commands of hand opening, fist clenching, and external wrist rotation. Such functionality may allow amputees to complete simple activities of daily living. Thus, this study is valuable for subsequent developments in rehabilitation.},
}
@article {pmid34531923,
year = {2021},
author = {Goering, S and Brown, T and Klein, E},
title = {Neurotechnology ethics and relational agency.},
journal = {Philosophy compass},
volume = {16},
number = {4},
pages = {},
pmid = {34531923},
issn = {1747-9991},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
abstract = {Novel neurotechnologies, like deep brain stimulation and brain-computer interface, offer great hope for treating, curing, and preventing disease, but raise important questions about effects these devices may have on human identity, authenticity, and autonomy. After briefly assessing recent narrative work in these areas, we show that agency is a phenomenon key to all three goods and highlight the ways in which neural devices can help to draw attention to the relational nature of our agency. Drawing on insights from disability theory, we argue that neural devices provide a kind of agential assistance, similar to that provided by caregivers, family, and others. As such, users and devices participate in a kind of co-agency. We conclude by suggesting the need for developing relational agency-competencies-skills for reflecting on the influence of devices on agency, for adapting to novel circumstances ushered in by devices, and for incorporating the feedback of loved ones and others about device effects on agency.},
}
@article {pmid34528400,
year = {2022},
author = {Wang, JH and Wu, C and Lian, YN and Liu, L and Li, XY},
title = {Targeting long-term depression of excitatory synaptic transmission for the treatment of neuropathic pain.},
journal = {The FEBS journal},
volume = {289},
number = {23},
pages = {7334-7342},
doi = {10.1111/febs.16200},
pmid = {34528400},
issn = {1742-4658},
mesh = {Humans ; *Synaptic Transmission ; Neuronal Plasticity ; *Neuralgia/drug therapy ; },
abstract = {Injury or disease in the somatosensory nervous system may cause broad molecular changes and lead to neuropathic pain. Excitatory synaptic transmission in somatosensory pathways conveys the somatosensory information from the peripheral to the central nervous system. Long-term effects of excitatory synaptic transmission on the pain pathway contribute to neuropathic pain hypersensitivity. Synaptic strength is dynamically regulated and undergoes bidirectional changes, manifested by two primary forms of synaptic plasticity, long-term potentiation and long-term depression (LTD), which are mediated by insertion and endocytosis of amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), respectively. Molecular mechanisms of LTP have been extensively studied; on the other hand, the role of AMPAR endocytosis in the pain-related synaptic enhancement is less well known. Recent research in the anterior cingulate cortex reveals that loss of LTD contributes to the maintenance of neuropathic pain, which provides the novel perspective of the mechanism of LTD also being critical for maintaining neuropathic pain. More importantly, exploring the molecular mechanism of LTD may help with the development of novel analgesic strategies to manage neuropathic pain.},
}
@article {pmid34517940,
year = {2021},
author = {Alfiero, CJ and Brooks, SJ and Bideganeta, HM and Contreras, C and Brown, AF},
title = {Protein Supplementation Does Not Improve Aerobic and Anaerobic Fitness in Collegiate Dancers Performing Cycling Based High Intensity Interval Training.},
journal = {Journal of dance medicine &amp; science : official publication of the International Association for Dance Medicine &amp; Science},
volume = {25},
number = {4},
pages = {249-260},
doi = {10.12678/1089-313X.121521d},
pmid = {34517940},
issn = {2374-8060},
mesh = {Anaerobiosis ; *Dancing ; Dietary Supplements ; *High-Intensity Interval Training ; Humans ; Physical Fitness ; },
abstract = {The effects of a 6-week cycling high-intensity interval training (HIIT) concurrently with protein supplementation on aerobic and anaerobic fitness and body composition in collegiate dancers was investigated. Eighteen participants enrolled in a collegiate dance program were matched into three groups: high-protein (HP; 90 g˙d[-1]), moderate-protein (MP; 40 g˙d[-1]), and control (C; 0 g˙d[-1]). All participants performed a 6-week HIIT intervention. Participants completed a graded exercise test, Wingate anaerobic test (Wingate), and dual energy x-ray absorptiometry scan before and after the intervention. Peak heart rate (HRpeak), peak oxygen uptake (VOpeak), peak power output (PPO), lactate threshold (LT), and ventilatory thresholds 1 (VT1) and 2 (VT2) were assessed during the graded exercise test. Peak power output, mean power output (MPO), and fatigue index (FI) were assessed during the Wingate. Lean mass (LM), fat mass (FM), visceral adipose tissue, appendicular skeletal muscle mass, and appendicular skeletal muscle mass index were assessed during dual energy x-ray absorptiometry. Body composition index (BCI) was calculated from pre and post LM and FM. Habitual diet was recorded weekly. Significance was set at p ≤ 0.05. No significant differences in VO2peak and percent fat mass (%FM) were observed between groups prior to the intervention. Significant main effects for time were observed for HRpeak (p = 0.02), VO2peak (p < 0.001), PPO (p < 0.01), LT (p < 0.001), VT1 (p < 0.001), and VT2 (p < 0.001) during the graded exercise test, and PPO (p < 0.01) and FI (p < 0.01) during the Wingate. Significant main effects for time were observed for LM (kg; p = 0.01) and FM (kg; p < 0.01). Body composition index was improved for all groups, however, no significant differences by group were observed. No significant differences were observed between groups for the measured outcomes (p > 0.05). Therefore, there was no effect of protein supplementation in the short 6-week intervention. This cycling based HIIT routine increased physical fitness, optimized aesthetics, and was a simple addition to an existing collegiate dance curriculum.},
}
@article {pmid34517346,
year = {2021},
author = {Zhang, S and Yan, X and Wang, Y and Liu, B and Gao, X},
title = {Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2628},
pmid = {34517346},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Fractals ; },
abstract = {Objective. Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.Approach. The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.Main results. The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the steady-state visual evoked potential amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.Significance. The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.},
}
@article {pmid34516749,
year = {2021},
author = {Silvernagel, MP and Ling, AS and Nuyujukian, P and , },
title = {A markerless platform for ambulatory systems neuroscience.},
journal = {Science robotics},
volume = {6},
number = {58},
pages = {eabj7045},
doi = {10.1126/scirobotics.abj7045},
pmid = {34516749},
issn = {2470-9476},
mesh = {Algorithms ; Animals ; Behavior ; Behavior, Animal/physiology ; Biomechanical Phenomena ; Brain/*physiology ; Brain-Computer Interfaces ; Equipment Design ; Macaca mulatta/*physiology ; Man-Machine Systems ; Motion ; Movement ; Neurons/*physiology ; *Neurosciences ; User-Computer Interface ; Walking ; },
abstract = {Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.},
}
@article {pmid34516378,
year = {2021},
author = {Lin, PJ and Jia, T and Li, C and Li, T and Qian, C and Li, Z and Pan, Y and Ji, L},
title = {CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1936-1943},
doi = {10.1109/TNSRE.2021.3112167},
pmid = {34516378},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R[2] 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.},
}
@article {pmid34513337,
year = {2021},
author = {Guan, S and Li, J and Wang, F and Yuan, Z and Kang, X and Lu, B},
title = {Discriminating three motor imagery states of the same joint for brain-computer interface.},
journal = {PeerJ},
volume = {9},
number = {},
pages = {e12027},
pmid = {34513337},
issn = {2167-8359},
abstract = {The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.},
}
@article {pmid34510321,
year = {2021},
author = {Rubio-Tomás, T},
title = {Novel insights into SMYD2 and SMYD3 inhibitors: from potential anti-tumoural therapy to a variety of new applications.},
journal = {Molecular biology reports},
volume = {48},
number = {11},
pages = {7499-7508},
pmid = {34510321},
issn = {1573-4978},
mesh = {Enzyme Inhibitors/*therapeutic use ; *Histone-Lysine N-Methyltransferase/antagonists &amp; inhibitors/metabolism ; Humans ; *Neoplasm Proteins/antagonists &amp; inhibitors/metabolism ; *Neoplasms/drug therapy/enzymology ; },
abstract = {The revelance of the epigenetic regulation of cancer led to the design and testing of many drugs targeting epigenetic modifiers. The Su(Var)3-9, Enhancer-of-zeste and Trithorax (SET) and myeloid, Nervy, and DEAF-1 (MYND) domain-containing protein 2 (SMYD2) and 3 (SMYD3) are methyltransferases which act on histone and non-histone proteins to promote tumorigenesis in many cancer types. In addition to their oncogenic roles, SMYD2 and SMYD3 are involved in many other physiopathological conditions. In this review we will focus on the advances made in the last five years in the field of pharmacology regarding drugs targeting SMYD2 (such as LLY-507 or AZ505) and SMYD3 (such as BCI-121 or EPZ031686) and their potential cellular and molecular mechanisms of action and application in anti-tumoural therapy and/or against other diseases.},
}
@article {pmid34510300,
year = {2021},
author = {Pang, J and Bachmatiuk, A and Yang, F and Liu, H and Zhou, W and Rümmeli, MH and Cuniberti, G},
title = {Applications of Carbon Nanotubes in the Internet of Things Era.},
journal = {Nano-micro letters},
volume = {13},
number = {1},
pages = {191},
pmid = {34510300},
issn = {2150-5551},
abstract = {The post-Moore's era has boosted the progress in carbon nanotube-based transistors. Indeed, the 5G communication and cloud computing stimulate the research in applications of carbon nanotubes in electronic devices. In this perspective, we deliver the readers with the latest trends in carbon nanotube research, including high-frequency transistors, biomedical sensors and actuators, brain-machine interfaces, and flexible logic devices and energy storages. Future opportunities are given for calling on scientists and engineers into the emerging topics.},
}
@article {pmid34508756,
year = {2022},
author = {Hou, Y and Chen, T and Lun, X and Wang, F},
title = {A novel method for classification of multi-class motor imagery tasks based on feature fusion.},
journal = {Neuroscience research},
volume = {176},
number = {},
pages = {40-48},
doi = {10.1016/j.neures.2021.09.002},
pmid = {34508756},
issn = {1872-8111},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery based brain computer interface (MI-BCI) has the advantage of strong independence that can rely on the spontaneous brain activity of the user to operate external devices. However, MI-BCI still has the problem of poor control effect, which requires more effective feature extraction algorithms and classification methods to extract distinctly separable features from electroencephalogram (EEG) signals. This paper proposes a novel framework based on Bispectrum, Entropy and common spatial pattern (BECSP). Here we use three methods of bispectrum in higher order spectra, entropy and CSP to extract MI-EEG signal features, and then select the most contributing features through tree-based feature selection algorithm. By comparing the classification results of SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost and Adaboost, we finally decide to use the SVM algorithm based on RBF kernel function which obtained the best result among them for classification. The proposed method is applied to the BCI competition IV data set 2a and BCI competition III data set IVa. On data set 2a, the highest accuracy on the evaluation data set reaches 85%. The experiment on data set IVa can also achieve good results. Compared with other algorithms that use the same data set, the performance of our algorithm has also been improved.},
}
@article {pmid34507311,
year = {2021},
author = {Si, X and Li, S and Xiang, S and Yu, J and Ming, D},
title = {Imagined speech increases the hemodynamic response and functional connectivity of the dorsal motor cortex.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac25d9},
pmid = {34507311},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Hemodynamics ; Humans ; *Motor Cortex ; *Sensorimotor Cortex ; Speech ; },
abstract = {Objective. Decoding imagined speech from brain signals could provide a more natural, user-friendly way for developing the next generation of the brain-computer interface (BCI). With the advantages of non-invasive, portable, relatively high spatial resolution and insensitivity to motion artifacts, the functional near-infrared spectroscopy (fNIRS) shows great potential for developing the non-invasive speech BCI. However, there is a lack of fNIRS evidence in uncovering the neural mechanism of imagined speech. Our goal is to investigate the specific brain regions and the corresponding cortico-cortical functional connectivity features during imagined speech with fNIRS.Approach. fNIRS signals were recorded from 13 subjects' bilateral motor and prefrontal cortex during overtly and covertly repeating words. Cortical activation was determined through the mean oxygen-hemoglobin concentration changes, and functional connectivity was calculated by Pearson's correlation coefficient.Main results. (a) The bilateral dorsal motor cortex was significantly activated during the covert speech, whereas the bilateral ventral motor cortex was significantly activated during the overt speech. (b) As a subregion of the motor cortex, sensorimotor cortex (SMC) showed a dominant dorsal response to covert speech condition, whereas a dominant ventral response to overt speech condition. (c) Broca's area was deactivated during the covert speech but activated during the overt speech. (d) Compared to overt speech, dorsal SMC(dSMC)-related functional connections were enhanced during the covert speech.Significance. We provide fNIRS evidence for the involvement of dSMC in speech imagery. dSMC is the speech imagery network's key hub and is probably involved in the sensorimotor information processing during the covert speech. This study could inspire the BCI community to focus on the potential contribution of dSMC during speech imagery.},
}
@article {pmid34507098,
year = {2021},
author = {Zhang, Y and Le, S and Li, H and Ji, B and Wang, MH and Tao, J and Liang, JQ and Zhang, XY and Kang, XY},
title = {MRI magnetic compatible electrical neural interface: From materials to application.},
journal = {Biosensors &amp; bioelectronics},
volume = {194},
number = {},
pages = {113592},
doi = {10.1016/j.bios.2021.113592},
pmid = {34507098},
issn = {1873-4235},
mesh = {Artifacts ; *Biosensing Techniques ; Brain/diagnostic imaging ; Electricity ; Magnetic Resonance Imaging ; },
abstract = {Neural electrical interfaces are important tools for local neural stimulation and recording, which potentially have wide application in the diagnosis and treatment of neural diseases, as well as in the transmission of neural activity for brain-computer interface (BCI) systems. At the same time, magnetic resonance imaging (MRI) is one of the effective and non-invasive techniques for recording whole-brain signals, providing details of brain structures and also activation pattern maps. Simultaneous recording of extracellular neural signals and MRI combines two expressions of the same neural activity and is believed to be of great importance for the understanding of brain function. However, this combination makes requests on the magnetic and electronic performance of neural interface devices. MRI-compatibility refers here to a technological approach to simultaneous MRI and electrode recording or stimulation without artifacts in imaging. Trade-offs between materials magnetic susceptibility selection and electrical function should be considered. Herein, prominent trends in selecting materials of suitable magnetic properties are analyzed and material design, function and application of neural interfaces are outlined together with the remaining challenge to fabricate MRI-compatible neural interface.},
}
@article {pmid34506866,
year = {2021},
author = {Zhang, XN and Meng, QH and Zeng, M and Hou, HR},
title = {Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature.},
journal = {Journal of neuroscience methods},
volume = {363},
number = {},
pages = {109355},
doi = {10.1016/j.jneumeth.2021.109355},
pmid = {34506866},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Odorants ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {BACKGROUND: Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy.<br><br>NEW METHODS: By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF.<br><br>RESULTS: To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%.<br><br>Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance.<br><br>CONCLUSIONS: The proposed WSDF is a promising candidate for decoding olfactory EEG signals.},
}
@article {pmid34502655,
year = {2021},
author = {Kim, S and Lee, S and Kang, H and Kim, S and Ahn, M},
title = {P300 Brain-Computer Interface-Based Drone Control in Virtual and Augmented Reality.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {17},
pages = {},
pmid = {34502655},
issn = {1424-8220},
support = {2019R1F1A1058844//National Research Foundation of Korea/ ; 2021R1I1A3060828//National Research Foundation of Korea/ ; 2017-0-00130//National Program for Excellence in Software at Handong Global University/ ; },
mesh = {*Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; User-Computer Interface ; *Virtual Reality ; },
abstract = {Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain-computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.},
}
@article {pmid34502636,
year = {2021},
author = {Mridha, MF and Das, SC and Kabir, MM and Lima, AA and Islam, MR and Watanobe, Y},
title = {Brain-Computer Interface: Advancement and Challenges.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {17},
pages = {},
pmid = {34502636},
issn = {1424-8220},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.},
}
@article {pmid34502629,
year = {2021},
author = {Appriou, A and Pillette, L and Trocellier, D and Dutartre, D and Cichocki, A and Lotte, F},
title = {BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {17},
pages = {},
pmid = {34502629},
issn = {1424-8220},
mesh = {Algorithms ; Animals ; *Boidae ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.},
}
@article {pmid34501373,
year = {2021},
author = {Monini, S and Filippi, C and De Luca, A and Salerno, G and Barbara, M},
title = {On the Effect of Bimodal Rehabilitation in Asymmetric Hearing Loss.},
journal = {Journal of clinical medicine},
volume = {10},
number = {17},
pages = {},
pmid = {34501373},
issn = {2077-0383},
abstract = {BACKGROUND: Bone conductive implants (BCI) have been reported to provide greater beneficial effects for the auditory and perceptual functions of the contralateral ear in patients presenting with asymmetric hearing loss (AHL) compared to those with single-sided deafness (SSD). The aim of the study was to assess the effects of wearing a conventional hearing aid in the contralateral ear on BCI in terms of an improved overall auditory performance.<br><br>METHODS: eleven AHL subjects wearing a BCI in their worse hearing ear underwent an auditory evaluation by pure tone and speech audiometry in free field. This study group was obtained by adding to the AHL patients those SSD subjects that, during the follow-up, showed deterioration of the hearing threshold of the contralateral ear, thus presenting with the features of AHL. Four different conditions were tested and compared: unaided, with BCI only, with contralateral hearing aid (CHA) only and with BCI combined with CHA.<br><br>RESULTS: all of the prosthetic conditions caused a significant improvement with respect to the unaided condition. When a CHA was adopted, its combination with the BCI showed significantly better auditory performances than those achieved with the BCI only.<br><br>CONCLUSIONS: the present study suggests the beneficial role of a CHA in BCI-implanted AHL subjects in terms of overall auditory performance.},
}
@article {pmid34501246,
year = {2021},
author = {Sampedro Baena, L and Fuente, GAC and Martos-Cabrera, MB and Gómez-Urquiza, JL and Albendín-García, L and Romero-Bejar, JL and Suleiman-Martos, N},
title = {Effects of Neurofeedback in Children with Attention-Deficit/Hyperactivity Disorder: A Systematic Review.},
journal = {Journal of clinical medicine},
volume = {10},
number = {17},
pages = {},
pmid = {34501246},
issn = {2077-0383},
abstract = {Attention deficit/hyperactivity disorder (ADHD) is one of the most frequent neurodevelopmental disorders in childhood and adolescence. Choosing the right treatment is critical to controlling and improving symptoms. An innovative ADHD treatment is neurofeedback (NF) that trains participants to self-regulate brain activity. The aim of the study was to analyze the effects of NF interventions in children with ADHD. A systematic review was carried out in the CINAHL, Medline (PubMed), Proquest, and Scopus databases, following the PRISMA recommendations. Nine articles were found. The NF improved behavior, allowed greater control of impulsivity, and increased sustained attention. In addition, it improved motor control, bimanual coordination and was associated with a reduction in theta waves. NF combined with other interventions such as medication, physical activity, behavioral therapy training, or attention training with brain-computer interaction, reduced primary ADHD symptoms. Furthermore, more randomized controlled trials would be necessary to determine the significant effects.},
}
@article {pmid34499856,
year = {2021},
author = {Nason, SR and Mender, MJ and Vaskov, AK and Willsey, MS and Ganesh Kumar, N and Kung, TA and Patil, PG and Chestek, CA},
title = {Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface.},
journal = {Neuron},
volume = {109},
number = {19},
pages = {3164-3177.e8},
pmid = {34499856},
issn = {1097-4199},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; R01 GM111293/GM/NIGMS NIH HHS/United States ; R01 NS105132/NS/NINDS NIH HHS/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Fingers/innervation/*physiology ; Forecasting ; Linear Models ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/physiology ; Movement/*physiology ; *Neural Prostheses ; Posture/physiology ; Prosthesis Design ; Psychomotor Performance ; },
abstract = {Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.},
}
@article {pmid34498776,
year = {2022},
author = {Chen, J and Ma, XL and Zhao, H and Wang, XY and Xu, MX and Wang, H and Yang, TQ and Peng, C and Liu, SS and Huang, M and Zhou, YD and Shen, Y},
title = {Increasing astrogenesis in the developing hippocampus induces autistic-like behavior in mice via enhancing inhibitory synaptic transmission.},
journal = {Glia},
volume = {70},
number = {1},
pages = {106-122},
pmid = {34498776},
issn = {1098-1136},
mesh = {Animals ; *Autism Spectrum Disorder/genetics ; *Autistic Disorder ; Hippocampus/physiology ; Mice ; Mice, Inbred C57BL ; Pyramidal Cells/physiology ; Synaptic Transmission ; },
abstract = {Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized primarily by impaired social communication and rigid, repetitive, and stereotyped behaviors. Many studies implicate abnormal synapse development and the resultant abnormalities in synaptic excitatory-inhibitory (E/I) balance may underlie many features of the disease, suggesting aberrant neuronal connections and networks are prone to occur in the developing autistic brain. Astrocytes are crucial for synaptic formation and function, and defects in astrocytic activation and function during a critical developmental period may also contribute to the pathogenesis of ASD. Here, we report that increasing hippocampal astrogenesis during development induces autistic-like behavior in mice and a concurrent decreased E/I ratio in the hippocampus that results from enhanced GABAergic transmission in CA1 pyramidal neurons. Suppressing the aberrantly elevated GABAergic synaptic transmission in hippocampal CA1 area rescues autistic-like behavior and restores the E/I balance. Thus, we provide direct evidence for a developmental role of astrocytes in driving the behavioral phenotypes of ASD, and our results support that targeting the altered GABAergic neurotransmission may represent a promising therapeutic strategy for ASD.},
}
@article {pmid34497500,
year = {2021},
author = {Singh, AK and Sahonero-Alvarez, G and Mahmud, M and Bianchi, L},
title = {Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data.},
journal = {Frontiers in neuroinformatics},
volume = {15},
number = {},
pages = {699840},
pmid = {34497500},
issn = {1662-5196},
}
@article {pmid34496393,
year = {2021},
author = {Liu, X and Shen, X and Chen, S and Zhang, X and Huang, Y and Wang, Y and Wang, Y},
title = {Hierarchical Dynamical Model for Multiple Cortical Neural Decoding.},
journal = {Neural computation},
volume = {33},
number = {5},
pages = {1372-1401},
doi = {10.1162/neco_a_01380},
pmid = {34496393},
issn = {1530-888X},
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; *Motor Cortex ; Movement ; Rats ; Reward ; },
abstract = {Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a prosthesis. As the subject adapts to control the neural prosthesis, the medial prefrontal cortex (mPFC), upstream of the primary motor cortex (M1), is heavily involved in reward-guided motor learning. Thus, considering mPFC and M1 functionality within a hierarchical structure could potentially improve the effectiveness of BMI decoding while subjects are learning. The commonly used Kalman decoding method with only one simple state model may not be able to represent the multiple brain states that evolve over time as well as along the neural pathway. In addition, the performance of Kalman decoders degenerates in heavy-tailed nongaussian noise, which is usually generated due to the nonlinear neural system or influences of movement-related noise in online neural recording. In this letter, we propose a hierarchical model to represent the brain states from multiple cortical areas that evolve along the neural pathway. We then introduce correntropy theory into the hierarchical structure to address the heavy-tailed noise existing in neural recordings. We test the proposed algorithm on in vivo recordings collected from the mPFC and M1 of two rats when the subjects were learning to perform a lever-pressing task. Compared with the classic Kalman filter, our results demonstrate better movement decoding performance due to the hierarchical structure that integrates the past failed trial information over multisite recording and the combination with correntropy criterion to deal with noisy heavy-tailed neural recordings.},
}
@article {pmid34495825,
year = {2022},
author = {Guney, OB and Oblokulov, M and Ozkan, H},
title = {A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {2},
pages = {932-944},
doi = {10.1109/TBME.2021.3110440},
pmid = {34495825},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {OBJECTIVE: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture.<br><br>METHOD: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities.<br><br>RESULTS: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI.<br><br>CONCLUSION: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets.<br><br>SIGNIFICANCE: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.},
}
@article {pmid34492637,
year = {2021},
author = {Chen, R and Xu, G and Zheng, Y and Yao, P and Zhang, S and Yan, L and Zhang, K},
title = {Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2459},
pmid = {34492637},
issn = {1741-2552},
mesh = {*Evoked Potentials, Visual ; Humans ; Signal-To-Noise Ratio ; *Visual Pathways ; },
abstract = {Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.},
}
@article {pmid34492547,
year = {2021},
author = {Zhang, Y and Cai, H and Nie, L and Xu, P and Zhao, S and Guan, C},
title = {An end-to-end 3D convolutional neural network for decoding attentive mental state.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {144},
number = {},
pages = {129-137},
doi = {10.1016/j.neunet.2021.08.019},
pmid = {34492547},
issn = {1879-2782},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain-computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.},
}
@article {pmid34492399,
year = {2021},
author = {Ali, A and Tariq, H and Abbas, S and Arshad, M and Li, S and Dong, L and Li, L and Li, WJ and Ahmed, I},
title = {Draft genome sequence of a multidrug-resistant novel candidate Pseudomonas sp. NCCP-436[T] isolated from faeces of a bovine host in Pakistan.},
journal = {Journal of global antimicrobial resistance},
volume = {27},
number = {},
pages = {91-94},
doi = {10.1016/j.jgar.2021.08.011},
pmid = {34492399},
issn = {2213-7173},
mesh = {Animals ; Cattle ; Feces ; *Genome, Bacterial ; Humans ; Pakistan ; *Pseudomonas/genetics ; beta-Lactams ; },
abstract = {OBJECTIVES: Here we describe the first draft genome analysis of a CRISPR-carrying, multidrug-resistant, candidate novel Pseudomonas sp. NCCP-436[T] isolated from faeces of a neonatal diarrhoeic calf.<br><br>METHODS: The genome of strain NCCP-436[T] was sequenced using an Illumina NovaSeq PE150 platform and analysed using various bioinformatic tools. The virulence factors and resistome were identified using PATRIC and CARD servers, while CGView Server was used to construct a circular genome map. Antimicrobial susceptibility was determined by the disk diffusion technique.<br><br>RESULTS: The draft genome of strain NCCP-436[T] contains 43 contigs with a total genome size of 3,683,517 bp (61.4% GC content). There are 3,452 predicted genes, including 60 tRNAs, 7 rRNAs and 12 sRNAs. CRISPR analysis revealed two CRISPR arrays with lengths of 1103 bp and 867 bp. Strain NCCP-436[T] was highly resistant to fluoroquinolone, &#x3b2;-lactam, cephalosporin, aminoglycoside, penicillin, rifamycin, macrolide, glycopeptide, trimethoprim/sulfonamide and tetracycline antibiotic classes. Additionally, 22 antibiotic resistance genes, 313 virulence genes and 253 pathogen-host interactor genes were predicted. Comparison of the average nucleotide identity and digital DNA-DNA hybridisation values with the closely-related strain Pseudomonas khazarica (TBZ2) was found to be 82.08% and 34.90%, respectively, illustrating strain NCCP-436[T] as a potentially new species of Pseudomonas.<br><br>CONCLUSION: Substantial number of antibiotic resistance and virulence genes and homology with human pathogens were predicted, exposing the pathogenic and zoonotic potential of strain NCCP-436[T] to public health. These findings may be used to better understand the genomic epidemiological features and drug resistance mechanisms of pathogenic Pseudomonas spp. in Pakistan.},
}
@article {pmid34489813,
year = {2021},
author = {Shan, B and Pu, Y and Chen, B and Lu, S},
title = {New Technologies' Commercialization: The Roles of the Leader's Emotion and Incubation Support.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {710122},
pmid = {34489813},
issn = {1664-1078},
abstract = {New technologies, such as brain-computer interfaces technology, advanced artificial intelligence, cloud computing, and virtual reality technology, have a strong influence on our daily activities. The application and commercialization of these technologies are prevailing globally, such as distance education, health monitoring, smart home devices, and robots. However, we still know little about the roles of individual emotion and the external environment on the commercialization of these new technologies. Therefore, we focus on the emotional factor of the leader, which is their passion for work, and discuss its effect on technology commercialization. We also analyzed the moderating role of incubation support in the relationship between the leader's emotion and technology commercialization. The results contribute to the application of emotion in improving the commercialization of new technologies.},
}
@article {pmid34489637,
year = {2021},
author = {Jiang, Y and Zhang, YD and Khosravi, M},
title = {Editorial: Advanced Deep-Transfer-Leveraged Studies on Brain-Computer Interfacing.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {733732},
doi = {10.3389/fnins.2021.733732},
pmid = {34489637},
issn = {1662-4548},
}
@article {pmid34489636,
year = {2021},
author = {Huang, X and Xu, Y and Hua, J and Yi, W and Yin, H and Hu, R and Wang, S},
title = {A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {733546},
pmid = {34489636},
issn = {1662-4548},
abstract = {In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.},
}
@article {pmid34489634,
year = {2021},
author = {Shupe, LE and Miles, FP and Jones, G and Yun, R and Mishler, J and Rembado, I and Murphy, RL and Perlmutter, SI and Fetz, EE},
title = {Neurochip3: An Autonomous Multichannel Bidirectional Brain-Computer Interface for Closed-Loop Activity-Dependent Stimulation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {718465},
pmid = {34489634},
issn = {1662-4548},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; R01 NS099872/NS/NINDS NIH HHS/United States ; },
abstract = {Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.},
}
@article {pmid34485946,
year = {2021},
author = {Zhang, B and Qiu, L and Long, C and Gao, Z},
title = {Protocol for targeting the magnocellular neuroendocrine cell ensemble via retrograde tracing from the posterior pituitary.},
journal = {STAR protocols},
volume = {2},
number = {3},
pages = {100787},
pmid = {34485946},
issn = {2666-1667},
mesh = {Animals ; Animals, Genetically Modified ; Hypothalamus/*cytology ; Male ; Median Eminence/cytology ; Nerve Net/cytology/physiology ; *Neuroendocrine Cells/cytology/physiology ; Optogenetics/*methods ; Pituitary Gland, Posterior/*cytology ; Rats ; Rats, Sprague-Dawley ; },
abstract = {The hypothalamic magnocellular neuroendocrine cells (MNCs) project to the posterior pituitary (PPi), regulating reproduction and fluid homeostasis. It has been challenging to selectively label and manipulate MNCs, as they are intermingled with parvocellular neuroendocrine cells projecting to the median eminence. Here, we provide a step-by-step protocol for specifically targeting the MNCs by infusing retrograde viral tracers into the PPi. When combined with optogenetics, chemogenetics, and transgenic animals, this approach allows cell-type-specific manipulation of MNCs in multiple sites for functional dissection. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2021) and Tang et al. (2020).},
}
@article {pmid34484029,
year = {2021},
author = {Hao, Y and Yao, L and Evans, GW},
title = {Neural Responses During Emotion Transitions and Emotion Regulation.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {666284},
pmid = {34484029},
issn = {1664-1078},
abstract = {Why are some people more susceptible to interference from previous emotional stimuli? Neural mechanisms underlying emotion regulation are typically studied with one-off positive or negative stimuli. Less is known about how they operate during dynamic emotional experiences, which more closely resemble how emotions occur in real life. Therefore, we investigated the interaction among temporal context, stimulus content, and regulatory strategy. Image sequences included either neutral to negative emotion or negative to neutral emotion. Participants were instructed to either passively watch the emotional stimuli or apply cognitive reappraisal during the image sequences presentation. Participants also reported their habitual use of cognitive reappraisal in their daily lives on a standard scale. We measured functional connectivity (FC) with electroencephalography (EEG) source localization. A three-way interaction suggested that, in addition to momentary emotional content and regulatory effort, the temporal context of stimuli impacts the FC between the ventromedial prefrontal cortex (vmPFC) and the ventral anterior cingulate cortex (ACC) in both alpha and beta frequency bands. In the reappraisal condition-but not the passive watch conditions-, individual differences in habitual reappraisal were manifested in the FC of vmPFC-ACC in alpha band. Emotion transitions may be more demanding because prefrontal-posterior FC in the beta band decreased during emotion transitions regardless of emotional content or regulation efforts. Flexible emotion regulation enables the recruiting of neural activities in response to the content of dynamic, ever-changing experiences encountered in daily life. Studying brain responses to dynamic emotional stimuli may shed light on individual differences in adaptation and psychological health. It also provides a more ecologically valid assessment of emotion regulation.},
}
@article {pmid34483868,
year = {2021},
author = {Douibi, K and Le Bars, S and Lemontey, A and Nag, L and Balp, R and Breda, G},
title = {Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {705064},
pmid = {34483868},
issn = {1662-5161},
abstract = {In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.},
}
@article {pmid34483866,
year = {2021},
author = {Zhou, Q and Lin, J and Yao, L and Wang, Y and Han, Y and Xu, K},
title = {Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {701091},
pmid = {34483866},
issn = {1662-5161},
abstract = {One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.},
}
@article {pmid34483823,
year = {2021},
author = {Bouton, C and Bhagat, N and Chandrasekaran, S and Herrero, J and Markowitz, N and Espinal, E and Kim, JW and Ramdeo, R and Xu, J and Glasser, MF and Bickel, S and Mehta, A},
title = {Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {699631},
pmid = {34483823},
issn = {1662-4548},
abstract = {Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.},
}
@article {pmid34483816,
year = {2021},
author = {Kang, JH and Youn, J and Kim, SH and Kim, J},
title = {Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {663101},
pmid = {34483816},
issn = {1662-4548},
abstract = {Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain-computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.},
}
@article {pmid34479383,
year = {2021},
author = {Senathirajah, Y and Hribar, M and , },
title = {Human Factors and Organizational Issues Section Synopsis IMIA Yearbook 2021.},
journal = {Yearbook of medical informatics},
volume = {30},
number = {1},
pages = {100-104},
pmid = {34479383},
issn = {2364-0502},
mesh = {Burnout, Professional ; *Electronic Health Records ; *Health Equity ; Humans ; Medical Informatics/*organization &amp; administration ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2020.<br><br>METHODS: A rigorous extraction process based on queries from Web of Science&#xae; and PubMed/Medline was conducted to identify the scientific contributions published in 2020 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 1,562 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.<br><br>RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance in the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Emerging themes included several which discussed physician burnout, mobile health, and health equity. Those concerning the Corona Virus Disease 2019 (Covid-19) were included as part of that section.<br><br>CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.},
}
@article {pmid34479222,
year = {2021},
author = {Erdoğan, SB and Yükselen, G and Yegül, MM and Usanmaz, R and Kıran, E and Derman, O and Akın, A},
title = {Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac23bb},
pmid = {34479222},
issn = {1741-2552},
mesh = {Adolescent ; Algorithms ; Humans ; *Impulsive Behavior ; Neural Networks, Computer ; *Spectroscopy, Near-Infrared ; Support Vector Machine ; },
abstract = {Background.The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.Objective.The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.Approach. Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.Main results.Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (92.2% and 90.16%, respectively,p= 0.005).Significance.Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.},
}
@article {pmid34479215,
year = {2021},
author = {Haddix, C and Al-Bakri, AF and Sunderam, S},
title = {Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac23c0},
pmid = {34479215},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Female ; Hand ; *Hand Strength ; Humans ; Male ; Movement ; },
abstract = {Objective. Brain-computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject's focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal.Approach.Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force.Main results.We found that event-related desynchronization (ERD) of the 8-30 Hz mu-beta sensorimotor rhythm of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to pass through a trough that hinted at non-monotonic behavior.Significance.Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.},
}
@article {pmid34478941,
year = {2021},
author = {Huang, W and Yan, H and Cheng, K and Wang, C and Li, J and Wang, Y and Li, C and Li, C and Li, Y and Zuo, Z and Chen, H},
title = {A neural decoding algorithm that generates language from visual activity evoked by natural images.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {144},
number = {},
pages = {90-100},
doi = {10.1016/j.neunet.2021.08.006},
pmid = {34478941},
issn = {1879-2782},
mesh = {Algorithms ; Artificial Intelligence ; *Brain Mapping ; Humans ; *Language ; Magnetic Resonance Imaging ; Semantics ; },
abstract = {Transforming neural activities into language is revolutionary for human-computer interaction as well as functional restoration of aphasia. Present rapid development of artificial intelligence makes it feasible to decode the neural signals of human visual activities. In this paper, a novel Progressive Transfer Language Decoding Model (PT-LDM) is proposed to decode visual fMRI signals into phrases or sentences when natural images are being watched. The PT-LDM consists of an image-encoder, a fMRI encoder and a language-decoder. The results showed that phrases and sentences were successfully generated from visual activities. Similarity analysis showed that three often-used evaluation indexes BLEU, ROUGE and CIDEr reached 0.182, 0.197 and 0.680 averagely between the generated texts and the corresponding annotated texts in the testing set respectively, significantly higher than the baseline. Moreover, we found that higher visual areas usually had better performance than lower visual areas and the contribution curve of visual response patterns in language decoding varied at successively different time points. Our findings demonstrate that the neural representations elicited in visual cortices when scenes are being viewed have already contained semantic information that can be utilized to generate human language. Our study shows potential application of language-based brain-machine interfaces in the future, especially for assisting aphasics in communicating more efficiently with fMRI signals.},
}
@article {pmid34478922,
year = {2021},
author = {Al-Qazzaz, NK and Alyasseri, ZAA and Abdulkareem, KH and Ali, NS and Al-Mhiqani, MN and Guger, C},
title = {EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation.},
journal = {Computers in biology and medicine},
volume = {137},
number = {},
pages = {104799},
doi = {10.1016/j.compbiomed.2021.104799},
pmid = {34478922},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.},
}
@article {pmid34476364,
year = {2020},
author = {Lee, SK and Jeakins, GS and Tukiainen, A and Hewage, E and Armitage, OE},
title = {Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.},
journal = {Bioelectricity},
volume = {2},
number = {4},
pages = {321-327},
pmid = {34476364},
issn = {2576-3113},
abstract = {Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to address conditions with unmet needs, including heart failure, hypertension, inflammation, arthritis, asthma, Alzheimer's disease, and diabetes. Neural therapies, which target the brain, spinal cord, or peripheral nerves, are already being applied to conditions such as epilepsy, Parkinson's, and chronic pain. While today's therapies have made exciting advancements, their open-loop design-where stimulation is administered without collecting feedback-means that results can be variable and devices do not work for everyone. Stimulation effects are sensitive to changes in neural tissue, nerve excitability, patient position, and more. Closing the loop by providing neural or non-neural biomarkers to the system can guide therapy by providing additional insights into stimulation effects and overall patient condition. Devices currently on the market use recorded biomarkers to close the loop and improve therapy. The future of bioelectric medicine is more holistically personalized. Collected data will be used for increasingly precise application of neural stimulations to achieve therapeutic effects. To achieve this future, advances are needed in device design, implanted and computational technologies, and scientific/medical interpretation of neural activity. Research and commercial devices are enabling the development of multiple levels of responsiveness to neural, physiological, and environmental changes. This includes developing suitable implanted technologies for high bandwidth brain/machine interfaces and addressing the challenge of neural or state biomarker decoding. Consistent progress is being made in these challenges toward the long-term vision of automatically and holistically personalized care for chronic health conditions.},
}
@article {pmid34475393,
year = {2021},
author = {Zhang, B and Yuan, P and Xu, G and Chen, Z and Li, Z and Ye, H and Wang, J and Shi, P and Sun, X},
title = {DUSP6 expression is associated with osteoporosis through the regulation of osteoclast differentiation via ERK2/Smad2 signaling.},
journal = {Cell death &amp; disease},
volume = {12},
number = {9},
pages = {825},
pmid = {34475393},
issn = {2041-4889},
support = {81902232//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; Bone Resorption/complications/pathology ; Bone and Bones/drug effects/pathology ; *Cell Differentiation/drug effects/genetics ; Disease Models, Animal ; Down-Regulation/drug effects/genetics ; Dual Specificity Phosphatase 6/*genetics/metabolism ; Extracellular Signal-Regulated MAP Kinases/*metabolism ; Humans ; Macrophages/drug effects/metabolism ; Mice, Inbred C57BL ; Osteoclasts/drug effects/*pathology ; Osteogenesis/drug effects/genetics ; Osteoporosis/complications/enzymology/genetics/*pathology ; RANK Ligand/antagonists &amp; inhibitors/pharmacology ; *Signal Transduction/drug effects ; Smad2 Protein/*metabolism ; Tartrate-Resistant Acid Phosphatase/metabolism ; Mice ; },
abstract = {Osteoporosis-related fractures, such as femoral neck and vertebral fractures, are common in aged people, resulting in increased disability rate and health-care costs. Thus, it is of great importance to clarify the mechanism of osteoclast-related osteoporosis and find effective ways to avoid its complication. In this study, gene expression profile analysis and real-time polymerase chain reaction revealed that DUSP6 expression was suppressed in human and mice osteoporosis cases. In vitro experiments confirmed that DUSP6 overexpression prevented osteoclastogenesis, whereas inhibition of DUSP6 by small interference RNA or with a chemical inhibitor, (E/Z)-BCI, had the opposite effect. (E/Z)-BCl significantly accelerated the bone loss process in vivo by enhancing osteoclastogenesis. Bioinformatics analyses and in vitro experiments indicated that miR-181a was an upstream regulator of DUSP6. Moreover, miR-181a positively induced the differentiation and negatively regulated the apoptosis of osteoclasts via DUSP6. Furthermore, downstream signals by ERK2 and SMAD2 were also found to be involved in this process. Evaluation of ERK2-deficiency bone marrow-derived macrophages confirmed the role of ERK2 signaling in the DUSP6-mediated osteoclastogenesis. Additionally, immunoprecipitation assays confirmed that DUSP6 directly modified the phosphorylation status of SMAD2 and the subsequent nuclear transportation of NFATC1 to regulate osteoclast differentiation. Altogether, this study demonstrated for the first time the role of miRNA-181a/DUSP6 in the progression of osteoporosis via the ERK2 and SMAD2 signaling pathway. Hence, DUSP6 may represent a novel target for the treatment of osteoclast-related diseases in the future.},
}
@article {pmid34474428,
year = {2022},
author = {Xu, R and Spataro, R and Allison, BZ and Guger, C},
title = {Brain-Computer Interfaces in Acute and Subacute Disorders of Consciousness.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {39},
number = {1},
pages = {32-39},
doi = {10.1097/WNP.0000000000000810},
pmid = {34474428},
issn = {1537-1603},
mesh = {*Brain-Computer Interfaces ; Coma ; *Consciousness ; Consciousness Disorders ; Humans ; Persistent Vegetative State ; },
abstract = {Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients. The application of brain-computer interfaces in disorders of consciousness can be divided into four fields: assessment, communication, prediction, and rehabilitation. The operational theoretical model of consciousness that brain-computer interfaces explore was reviewed in this article, with a focus on studies with acute and subacute patients. We then proposed a clinically friendly guideline, which could contribute to the implementation of brain-computer interfaces in neurorehabilitation settings. Finally, we discussed limitations and future directions, including major challenges and possible solutions.},
}
@article {pmid34474046,
year = {2021},
author = {Zhang, H and Zhu, L and Xu, S and Cao, J and Kong, W},
title = {Two brains, one target: Design of a multi-level information fusion model based on dual-subject RSVP.},
journal = {Journal of neuroscience methods},
volume = {363},
number = {},
pages = {109346},
doi = {10.1016/j.jneumeth.2021.109346},
pmid = {34474046},
issn = {1872-678X},
mesh = {*Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Memory, Short-Term ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Rapid serial visual presentation (RSVP) based brain-computer interface (BCI) is widely used to categorize the target and non-target images. The available information limits the prediction accuracy of single-trial using single-subject electroencephalography (EEG) signals. New Method. Hyperscanning is a new manner to record two or more subjects' signals simultaneously. So we designed a multi-level information fusion model for target image detection based on dual-subject RSVP, namely HyperscanNet. The two modules of this model fuse the data and features of the two subjects at the data and feature layers. A chunked long and short-term memory artificial neural network (LSTM) was used in the time dimension to extract features at different periods separately, completing fine-grained underlying feature extraction. While the feature layer is fused, some plain operations are used to complete the fusion of the data layer to ensure that important information is not missed.<br><br>RESULTS: Experimental results show that the F1-score (the harmonic mean of precision and recall) of this method with best group of channels and segment length is 82.76%. Comparison with existing methods. This method improves the F1-score by at least 5% compared to single-subject target detection.<br><br>CONCLUSIONS: Target detection can be accomplished by the two subjects' collaboration to achieve a higher and more stable F1-score than a single subject.},
}
@article {pmid34473788,
year = {2021},
author = {Le Franc, S and Fleury, M and Jeunet, C and Butet, S and Barillot, C and Bonan, I and Cogné, M and Lécuyer, A},
title = {Influence of the visuo-proprioceptive illusion of movement and motor imagery of the wrist on EEG cortical excitability among healthy participants.},
journal = {PloS one},
volume = {16},
number = {9},
pages = {e0256723},
pmid = {34473788},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; Cortical Excitability/*physiology ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Hand/innervation/physiology ; Healthy Volunteers ; Humans ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Male ; Middle Aged ; Movement/*physiology ; Proprioception/*physiology ; Sensorimotor Cortex/diagnostic imaging/*physiology ; Wrist Joint/innervation/physiology ; },
abstract = {INTRODUCTION: Motor Imagery (MI) is a powerful tool to stimulate sensorimotor brain areas and is currently used in motor rehabilitation after a stroke. The aim of our study was to evaluate whether an illusion of movement induced by visuo-proprioceptive immersion (VPI) including tendon vibration (TV) and Virtual moving hand (VR) combined with MI tasks could be more efficient than VPI alone or MI alone on cortical excitability assessed using Electroencephalography (EEG).<br><br>METHODS: We recorded EEG signals in 20 healthy participants in 3 different conditions: MI tasks involving their non-dominant wrist (MI condition); VPI condition; and VPI with MI tasks (combined condition). Each condition lasted 3 minutes, and was repeated 3 times in randomized order. Our main judgment criterion was the Event-Related De-synchronization (ERD) threshold in sensori-motor areas in each condition in the brain motor area.<br><br>RESULTS: The combined condition induced a greater change in the ERD percentage than the MI condition alone, but no significant difference was found between the combined and the VPI condition (p = 0.07) and between the VPI and MI condition (p = 0.20).<br><br>CONCLUSION: This study demonstrated the interest of using a visuo-proprioceptive immersion with MI rather than MI alone in order to increase excitability in motor areas of the brain. Further studies could test this hypothesis among patients with stroke to provide new perspectives for motor rehabilitation in this population.},
}
@article {pmid34472399,
year = {2024},
author = {Waqar, M and Mohamed, S and Dulhanty, L and Khan, H and Omar, A and Hulme, S and Parry Jones, AR and Patel, HC},
title = {Radiologically defined acute hydrocephalus in aneurysmal subarachnoid haemorrhage.},
journal = {British journal of neurosurgery},
volume = {38},
number = {4},
pages = {805-810},
doi = {10.1080/02688697.2021.1973367},
pmid = {34472399},
issn = {1360-046X},
mesh = {Humans ; *Subarachnoid Hemorrhage/diagnostic imaging/complications/surgery ; *Hydrocephalus/diagnostic imaging/surgery/etiology ; Middle Aged ; Male ; Female ; Aged ; Tomography, X-Ray Computed ; Adult ; Prospective Studies ; Registries ; Acute Disease ; },
abstract = {BACKGROUND: Ventriculomegaly is common in aneurysmal subarachnoid haemorrhage (aSAH). An imaging measure to predict the need for cerebrospinal fluid (CSF) diversion may be useful. The bicaudate index (BCI) has been previously applied to aSAH. Our aim was to derive and test a threshold BCI above which CSF diversion may be required.<br><br>METHODS: Review of prospective registry. The derivation group (2009-2015) included WFNS grade 1-2 aSAH patients who deteriorated clinically, had a repeat CT brain and underwent CSF diversion. BCI was measured on post-deterioration CTs and the lower limit of the 95% confidence interval (95%CI) was the hydrocephalus threshold. In a separate test group (2016), in WFNS &#x2265; 2 patients, we compared BCI on diagnostic CTs with CSF diversion within 24&#x2009;hours.<br><br>RESULTS: The derivation group (n&#x2009;=&#x2009;62) received an external ventricular (n&#x2009;=&#x2009;57, 92%) or lumbar drain (n&#x2009;=&#x2009;5, 8%). Mean post-deterioration BCI was 0.19 (95%CI 0.17-0.22) for age &#x2264;49 years, 0.22 (95%CI 0.20-0.23) for age 50-64 years and 0.24 (95%CI 0.22-0.27) for age &#x2265;65 years. Hydrocephalus thresholds were therefore 0.17, 0.20 and 0.22, respectively. In the test group (n&#x2009;=&#x2009;105), there was no significant difference in BCI on the diagnostic CT between good and poor grade patients aged &#x2264;49 years (p&#x2009;=&#x2009;0.31) and &#x2265;65 years (p&#x2009;=&#x2009;0.96). 30/66 WFNS &#x2265; 2 patients underwent CSF diversion, although only 15/30 (50%) exceeded BCI thresholds for hydrocephalus.<br><br>CONCLUSION: A significant proportion of aSAH patients may undergo CSF diversion without objective evidence of hydrocephalus. Our threshold values require further testing but may provide an objective measure to aid clinical decision making in aSAH.},
}
@article {pmid34471852,
year = {2020},
author = {Tarasenko, A and Oganesyan, M and Ivaskevych, D and Tukaiev, S and Toleukhanov, D and Vysokov, N},
title = {Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020.},
journal = {Bioelectricity},
volume = {2},
number = {3},
pages = {310-313},
pmid = {34471852},
issn = {2576-3113},
abstract = {In this report, we give an overview of the proceedings from the online Imperial College London Neurotechnology Symposium 2020. The first part deals with the fundamentals of how artificial intelligence (AI) can be used to inform research frameworks used in the field of neurotechnology. The second part goes a level higher and shows how AI can be used in cutting-edge cellular and molecular methodologies and their applications. The final part focuses on the efforts to "decode" neural systems in brain-computer interfaces to advance neuroprosthetics.},
}
@article {pmid34465913,
year = {2021},
author = {Hu, W and Zhang, Y and Fei, P and Zhang, T and Yao, D and Gao, Y and Liu, J and Chen, H and Lu, Q and Mudianto, T and Zhang, X and Xiao, C and Ye, Y and Sun, Q and Zhang, J and Xie, Q and Wang, PH and Wang, J and Li, Z and Lou, J and Chen, W},
title = {Mechanical activation of spike fosters SARS-CoV-2 viral infection.},
journal = {Cell research},
volume = {31},
number = {10},
pages = {1047-1060},
pmid = {34465913},
issn = {1748-7838},
support = {11772348//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Angiotensin-Converting Enzyme 2/chemistry/metabolism ; Antibodies, Neutralizing/immunology ; Binding Sites ; COVID-19/*diagnosis/therapy/virology ; Humans ; Hydrogen-Ion Concentration ; Immunization, Passive ; Molecular Dynamics Simulation ; Protein Binding ; Protein Domains/immunology ; Protein Subunits/chemistry/immunology/metabolism ; SARS-CoV-2/isolation &amp; purification/*metabolism ; Spike Glycoprotein, Coronavirus/chemistry/immunology/*metabolism ; *Tensile Strength ; Virus Internalization ; COVID-19 Serotherapy ; },
abstract = {The outbreak of SARS-CoV-2 (SARS2) has caused a global COVID-19 pandemic. The spike protein of SARS2 (SARS2-S) recognizes host receptors, including ACE2, to initiate viral entry in a complex biomechanical environment. Here, we reveal that tensile force, generated by bending of the host cell membrane, strengthens spike recognition of ACE2 and accelerates the detachment of spike's S1 subunit from the S2 subunit to rapidly prime the viral fusion machinery. Mechanistically, such mechano-activation is fulfilled by force-induced opening and rotation of spike's receptor-binding domain to prolong the bond lifetime of spike/ACE2 binding, up to 4 times longer than that of SARS-S binding with ACE2 under 10 pN force application, and subsequently by force-accelerated S1/S2 detachment which is up to ~10[3] times faster than that in the no-force condition. Interestingly, the SARS2-S D614G mutant, a more infectious variant, shows 3-time stronger force-dependent ACE2 binding and 35-time faster force-induced S1/S2 detachment. We also reveal that an anti-S1/S2 non-RBD-blocking antibody that was derived from convalescent COVID-19 patients with potent neutralizing capability can reduce S1/S2 detachment by 3 × 10[6] times under force. Our study sheds light on the mechano-chemistry of spike activation and on developing a non-RBD-blocking but S1/S2-locking therapeutic strategy to prevent SARS2 invasion.},
}
@article {pmid34460942,
year = {2022},
author = {Jovanovic, LI and Popovic, MR and Marquez-Chin, C},
title = {Characterizing the stimulation interference in electroencephalographic signals during brain-computer interface-controlled functional electrical stimulation therapy.},
journal = {Artificial organs},
volume = {46},
number = {3},
pages = {398-411},
doi = {10.1111/aor.14059},
pmid = {34460942},
issn = {1525-1594},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Signal-To-Noise Ratio ; Spinal Cord Injuries/rehabilitation ; },
abstract = {INTRODUCTION: The integration of brain-computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI-controlled FES therapy or BCI-FEST. During BCI-FEST, the stimulation is triggered by the patient's brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI-FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI-FEST. In this study, we evaluated the effectiveness of band-pass filters, used to extract the BCI-relevant EEG components, in simultaneously reducing stimulation interference.<br><br>METHODS: We used EEG data from eight participants recorded during BCI-FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal-to-noise ratio (SNR) values before and after band-pass filtering, for two types of movements practiced during BCI-FEST: reaching and grasping.<br><br>RESULTS: The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31 dB and 36.3 dB. Similarly, for grasping movements, mean SNR values increased between 2.82 dB and 40.16 dB, after filtering.<br><br>CONCLUSIONS: Band-pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of the selected stimulation pulse frequency, FSTIM , and the EEG sampling rate, FS .},
}
@article {pmid34458199,
year = {2021},
author = {Sharini, H and Zolghadriha, S and Riyahi Alam, N and Jalalvandi, M and Khabiri, H and Arabalibeik, H and Nadimi, M},
title = {Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement Using Functional MRI.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {11},
number = {4},
pages = {515-526},
pmid = {34458199},
issn = {2251-7200},
abstract = {BACKGROUND: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions.<br><br>OBJECTIVE: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.<br><br>MATERIAL AND METHODS: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software.<br><br>RESULTS: The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p< 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions.<br><br>CONCLUSION: Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.},
}
@article {pmid34456696,
year = {2021},
author = {Chakraborty, S and Saetta, G and Simon, C and Lenggenhager, B and Ruddy, K},
title = {Could Brain-Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {699830},
pmid = {34456696},
issn = {1662-5161},
abstract = {Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person's anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain-computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.},
}
@article {pmid34455934,
year = {2021},
author = {Pan, Y and Zhou, G and Li, W and He, X and Li, C and Li, Y and Li, T and Hu, H and Ma, H},
title = {Excitation-transcription coupling via synapto-nuclear signaling triggers autophagy for synaptic turnover and long-lasting synaptic depression.},
journal = {Autophagy},
volume = {17},
number = {11},
pages = {3887-3888},
pmid = {34455934},
issn = {1554-8635},
mesh = {Animals ; *Autophagy/physiology ; Brain/metabolism/physiology ; Cyclic AMP Response Element-Binding Protein/metabolism ; Humans ; *Long-Term Synaptic Depression/physiology ; Signal Transduction ; Synapses/*metabolism/physiology ; Transcription Factors/metabolism ; },
abstract = {For network rewiring and information storage in the brain, late phase long-term synaptic depression (L-LTD) requires the long-lasting reorganization of cellular resources. We found that activation of GRIN/NMDAR recruits transcription-dependent autophagy for synaptic turnover to support L-LTD. Activity-dependent CRTC1 synapto-nuclear translocation increases nuclear CRTC1 that competes with FXR for binding to CREB; this in turn enhances the direct binding between CRTC1-CREB and macroautophagy/autophagy gene promoters. Synergistic actions of CRTC1-CREB are preferentially turned on by LTD-inducing stimuli and switched off by genetic knockdown of CREB or CRTC1, or acutely activating FXR. Disrupted CRTC1-CREB signaling impairs activity-driven loss of surface GRIA/AMPARs and DLG4/PSD-95, and selectively prevents GRIN/NMDAR-dependent L-LTD, which are rescued by enhancing MTOR-regulated autophagy. These findings suggest a novel mechanism in L-LTD, in which brief synaptic activities recruit long-lasting autophagy through excitation-transcription coupling for ensuing synaptic remodeling.},
}
@article {pmid34454954,
year = {2021},
author = {Kline, A and Forkert, ND and Felfeliyan, B and Pittman, D and Goodyear, B and Ronsky, J},
title = {fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface.},
journal = {Journal of neuroscience methods},
volume = {363},
number = {},
pages = {109339},
doi = {10.1016/j.jneumeth.2021.109339},
pmid = {34454954},
issn = {1872-678X},
support = {//CIHR/Canada ; },
mesh = {Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Feasibility Studies ; Humans ; Lower Extremity/diagnostic imaging ; Magnetic Resonance Imaging ; Male ; },
abstract = {BACKGROUND: EEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement.<br><br>NEW METHOD: EEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement.<br><br>RESULTS: Results showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach. Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability.<br><br>CONCLUSIONS: The developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.},
}
@article {pmid34454267,
year = {2021},
author = {Verbaarschot, C and Tump, D and Lutu, A and Borhanazad, M and Thielen, J and van den Broek, P and Farquhar, J and Weikamp, J and Raaphorst, J and Groothuis, JT and Desain, P},
title = {A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {132},
number = {10},
pages = {2404-2415},
doi = {10.1016/j.clinph.2021.07.012},
pmid = {34454267},
issn = {1872-8952},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*physiopathology/*therapy ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Middle Aged ; Young Adult ; },
abstract = {OBJECTIVE: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants.<br><br>METHODS: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers.<br><br>RESULTS: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%.<br><br>CONCLUSIONS: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers.<br><br>SIGNIFICANCE: These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home.},
}
@article {pmid34454264,
year = {2021},
author = {Daly, I},
title = {Removal of physiological artifacts from simultaneous EEG and fMRI recordings.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {132},
number = {10},
pages = {2371-2383},
doi = {10.1016/j.clinph.2021.05.036},
pmid = {34454264},
issn = {1872-8952},
mesh = {Acoustic Stimulation/methods/standards ; Adult ; *Artifacts ; Brain/*diagnostic imaging/*physiology ; Electroencephalography/methods/*standards ; Female ; Head Movements/physiology ; Humans ; Magnetic Resonance Imaging/methods/*standards ; Male ; Young Adult ; },
abstract = {OBJECTIVE: Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.<br><br>METHODS: We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG.<br><br>RESULTS: We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts.<br><br>CONCLUSIONS: Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods.<br><br>SIGNIFICANCE: Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.},
}
@article {pmid34450924,
year = {2021},
author = {Molina-Cantero, AJ and Castro-García, JA and Gómez-Bravo, F and López-Ahumada, R and Jiménez-Naharro, R and Berrazueta-Alvarado, S},
title = {Controlling a Mouse Pointer with a Single-Channel EEG Sensor.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {16},
pages = {},
pmid = {34450924},
issn = {1424-8220},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement ; },
abstract = {(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user's attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor's speed. The influence of the attention level on performance was studied. Additionally, Fitts' model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor's initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system's usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain-computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.},
}
@article {pmid34450878,
year = {2021},
author = {Won, K and Kwon, M and Ahn, M and Jun, SC},
title = {Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {16},
pages = {},
pmid = {34450878},
issn = {1424-8220},
support = {2017-0-00451//Institute of Information &amp; Communications Technology (IITP)/ ; 2019-0-01842//Institute of Information &amp; Communications Technology (IITP)/ ; 2021-0-01537//Institute of Information &amp; Communications Technology (IITP)/ ; },
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.},
}
@article {pmid34450751,
year = {2021},
author = {Ikeda, A and Washizawa, Y},
title = {Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {16},
pages = {},
pmid = {34450751},
issn = {1424-8220},
support = {17H01760//Japan Society for the Promotion of Science/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neural Networks, Computer ; Photic Stimulation ; },
abstract = {The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain-computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.},
}
@article {pmid34450750,
year = {2021},
author = {De la Cruz-Guevara, DR and Alfonso-Morales, W and Caicedo-Bravo, E},
title = {Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {16},
pages = {},
pmid = {34450750},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Multivariate Analysis ; Photic Stimulation ; },
abstract = {This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.},
}
@article {pmid34450713,
year = {2021},
author = {Zhang, X and Hou, W and Wu, X and Chen, L and Jiang, N},
title = {Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {16},
pages = {},
pmid = {34450713},
issn = {1424-8220},
support = {31771069//National Natural Science Foundation of China/ ; 31800824//National Natural Science Foundation of China/ ; cstc2018jcyjAX0390//Chongqing Science and Technology Foundation/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Motion ; Photic Stimulation ; },
abstract = {Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.},
}
@article {pmid34447933,
year = {2021},
author = {Yoshimura, N and Umetsu, K and Tonin, A and Maruyama, Y and Harada, K and Rana, A and Ganesh, G and Chaudhary, U and Koike, Y and Birbaumer, N},
title = {Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals.},
journal = {Cerebral cortex communications},
volume = {2},
number = {3},
pages = {tgab046},
pmid = {34447933},
issn = {2632-7376},
abstract = {To develop a more reliable brain-computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of "yes" and "no" by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking "yes" or "no" is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.},
}
@article {pmid34446245,
year = {2021},
author = {Nason, SR and Mender, MJ and Letner, JG and Chestek, CA and Patil, PG},
title = {Restoring upper extremity function with brain-machine interfaces.},
journal = {International review of neurobiology},
volume = {159},
number = {},
pages = {153-186},
doi = {10.1016/bs.irn.2021.06.001},
pmid = {34446245},
issn = {2162-5514},
mesh = {*Brain-Computer Interfaces ; Humans ; Recovery of Function ; *Upper Extremity/physiology ; },
abstract = {One of the most exciting advances to emerge in neural interface technologies has been the development of real-time brain-machine interface (BMI) neuroprosthetic devices to restore upper extremity function. BMI neuroprostheses, made possible by synergistic advances in neural recording technologies, high-speed computation and signal processing, and neuroscience, have permitted the restoration of volitional movement to patients suffering the loss of upper-extremity function. In this chapter, we review the scientific and technological advances underlying these remarkable devices. After presenting an introduction to the current state of the field, we provide an accessible technical discussion of the two fundamental requirements of a successful neuroprosthesis: signal extraction from the brain and signal decoding that results in robust prosthetic control. We close with a presentation of emerging technologies that are likely to substantially advance the field.},
}
@article {pmid34445294,
year = {2021},
author = {Abend, A and Steele, C and Jahnke, HG and Zink, M},
title = {Adhesion of Neurons and Glial Cells with Nanocolumnar TiN Films for Brain-Machine Interfaces.},
journal = {International journal of molecular sciences},
volume = {22},
number = {16},
pages = {},
pmid = {34445294},
issn = {1422-0067},
support = {100331685 (MUDIPlex)//Saxon Ministry of Science and the Fine Arts (SMWK)/ ; },
mesh = {Actin Cytoskeleton/drug effects/metabolism ; *Brain-Computer Interfaces ; Cell Adhesion/drug effects ; Cell Line, Tumor ; Cell Proliferation/drug effects ; Coated Materials, Biocompatible/chemistry/pharmacology ; Extracellular Matrix/chemistry ; Gold/chemistry/pharmacology ; Humans ; Materials Testing ; Nanostructures/chemistry ; Neurites/drug effects/physiology ; Neuroglia/*drug effects/physiology ; Neurons/*drug effects/physiology ; Tin Compounds/chemistry/pharmacology ; Titanium/chemistry/*pharmacology ; },
abstract = {Coupling of cells to biomaterials is a prerequisite for most biomedical applications; e.g., neuroelectrodes can only stimulate brain tissue in vivo if the electric signal is transferred to neurons attached to the electrodes' surface. Besides, cell survival in vitro also depends on the interaction of cells with the underlying substrate materials; in vitro assays such as multielectrode arrays determine cellular behavior by electrical coupling to the adherent cells. In our study, we investigated the interaction of neurons and glial cells with different electrode materials such as TiN and nanocolumnar TiN surfaces in contrast to gold and ITO substrates. Employing single-cell force spectroscopy, we quantified short-term interaction forces between neuron-like cells (SH-SY5Y cells) and glial cells (U-87 MG cells) for the different materials and contact times. Additionally, results were compared to the spreading dynamics of cells for different culture times as a function of the underlying substrate. The adhesion behavior of glial cells was almost independent of the biomaterial and the maximum growth areas were already seen after one day; however, adhesion dynamics of neurons relied on culture material and time. Neurons spread much better on TiN and nanocolumnar TiN and also formed more neurites after three days in culture. Our designed nanocolumnar TiN offers the possibility for building miniaturized microelectrode arrays for impedance spectroscopy without losing detection sensitivity due to a lowered self-impedance of the electrode. Hence, our results show that this biomaterial promotes adhesion and spreading of neurons and glial cells, which are important for many biomedical applications in vitro and in vivo.},
}
@article {pmid34440942,
year = {2021},
author = {Sánchez-Cuesta, FJ and Arroyo-Ferrer, A and González-Zamorano, Y and Vourvopoulos, A and Badia, SBI and Figuereido, P and Serrano, JI and Romero, JP},
title = {Clinical Effects of Immersive Multimodal BCI-VR Training after Bilateral Neuromodulation with rTMS on Upper Limb Motor Recovery after Stroke. A Study Protocol for a Randomized Controlled Trial.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {57},
number = {8},
pages = {},
pmid = {34440942},
issn = {1648-9144},
mesh = {*Brain-Computer Interfaces ; Humans ; Randomized Controlled Trials as Topic ; Recovery of Function ; Single-Blind Method ; *Stroke/therapy ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity ; *Virtual Reality ; },
abstract = {Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain-Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.},
}
@article {pmid34437542,
year = {2021},
author = {Almarri, B and Rajasekaran, S and Huang, CH},
title = {Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.},
journal = {PloS one},
volume = {16},
number = {8},
pages = {e0253383},
pmid = {34437542},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; *Emotions ; Humans ; Support Vector Machine ; },
abstract = {The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.},
}
@article {pmid34437487,
year = {2021},
author = {Benatti, HR and Luz, HR and Lima, DM and Gonçalves, VD and Costa, FB and Ramos, VN and Aguiar, DM and Pacheco, RC and Piovezan, U and Szabó, MPJ and Ferraz, KMPMB and Labruna, MB},
title = {Morphometric Patterns and Blood Biochemistry of Capybaras (Hydrochoerus hydrochaeris) from Human-Modified Landscapes and Natural Landscapes in Brazil.},
journal = {Veterinary sciences},
volume = {8},
number = {8},
pages = {},
pmid = {34437487},
issn = {2306-7381},
support = {2013/18046-7//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa do Estado de S&#xe3;o Paulo/ ; },
abstract = {The capybara, Hydrochoerus hydrochaeris, is the largest extant rodent of the world. To better understand the correlation between size and body mass, and biochemical parameters of capybaras from areas with different degrees of anthropization (i.e., different food supplies), we sampled free-ranging capybaras from areas of natural landscapes (NLs) and human-modified landscapes (HMLs) in Brazil. Analyses of biometrical and biochemical parameters of capybaras showed that animals from HMLs were heavier (higher body mass) than those from NL, a condition possibly related to fat deposit rather than body length, as indicated by Body Condition Index (BCI) analyses. Biochemical parameters indicated higher serum levels of albumin, creatine kinase, cholesterol, fructosamine and total protein among capybaras from HMLs than from NLs; however, when all adult capybaras were analyzed together only cholesterol and triglycerides were positively correlated with body mass. We propose that the biochemical profile differences between HMLs and NLs are related to the obesity condition of capybaras among HMLs. Considering that heavier animals might live longer and reproduce more often, our results could have important implications in the population dynamics of capybaras among HMLs, where this rodent species is frequently represented by overgrowth populations that generate several levels of conflicts with human beings.},
}
@article {pmid34437130,
year = {2021},
author = {Servick, K},
title = {Brain signals 'speak' for person with paralysis.},
journal = {Science (New York, N.Y.)},
volume = {373},
number = {6552},
pages = {263},
doi = {10.1126/science.373.6552.263},
pmid = {34437130},
issn = {1095-9203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Deep Learning ; Humans ; Male ; Paralysis ; Sensorimotor Cortex/*physiology ; *Speech ; },
}
@article {pmid34436077,
year = {2021},
author = {Mian, SY and Honey, JR and Carnicer-Lombarte, A and Barone, DG},
title = {Large Animal Studies to Reduce the Foreign Body Reaction in Brain-Computer Interfaces: A Systematic Review.},
journal = {Biosensors},
volume = {11},
number = {8},
pages = {},
pmid = {34436077},
issn = {2079-6374},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Cats ; Electrodes, Implanted ; Electroencephalography ; *Foreign-Body Reaction ; Mice ; Rabbits ; Swine ; Swine, Miniature ; },
abstract = {Brain-computer interfaces (BCI) are reliant on the interface between electrodes and neurons to function. The foreign body reaction (FBR) that occurs in response to electrodes in the brain alters this interface and may pollute detected signals, ultimately impeding BCI function. The size of the FBR is influenced by several key factors explored in this review; namely, (a) the size of the animal tested, (b) anatomical location of the BCI, (c) the electrode morphology and coating, (d) the mechanics of electrode insertion, and (e) pharmacological modification (e.g., drug eluting electrodes). Trialing methods to reduce FBR in vivo, particularly in large models, is important to enable further translation in humans, and we systematically reviewed the literature to this effect. The OVID, MEDLINE, EMBASE, SCOPUS and Scholar databases were searched. Compiled results were analysed qualitatively. Out of 8388 yielded articles, 13 were included for analysis, with most excluded studies experimenting on murine models. Cats, rabbits, and a variety of breeds of minipig/marmoset were trialed. On average, over 30% reduction in inflammatory cells of FBR on post mortem histology was noted across intervention groups. Similar strategies to those used in rodent models, including tip modification and flexible and sinusoidal electrode configurations, all produced good effects in histology; however, a notable absence of trials examining the effect on BCI end-function was noted. Future studies should assess whether the reduction in FBR correlates to an improvement in the functional effect of the intended BCI.},
}
@article {pmid34434096,
year = {2021},
author = {Song, X and Zeng, Y and Tong, L and Shu, J and Bao, G and Yan, B},
title = {P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {685173},
pmid = {34434096},
issn = {1662-5161},
abstract = {Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.},
}
@article {pmid34434085,
year = {2021},
author = {Fekri Azgomi, H and Hahn, JO and Faghih, RT},
title = {Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {695975},
pmid = {34434085},
issn = {1662-4548},
abstract = {Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.},
}
@article {pmid34433901,
year = {2021},
author = {Zhang, H and Chen, LN and Yang, D and Mao, C and Shen, Q and Feng, W and Shen, DD and Dai, A and Xie, S and Zhou, Y and Qin, J and Sun, JP and Scharf, DH and Hou, T and Zhou, T and Wang, MW and Zhang, Y},
title = {Structural insights into ligand recognition and activation of the melanocortin-4 receptor.},
journal = {Cell research},
volume = {31},
number = {11},
pages = {1163-1175},
pmid = {34433901},
issn = {1748-7838},
mesh = {Amino Acid Sequence ; Humans ; Ligands ; *Obesity ; *Receptor, Melanocortin, Type 4/chemistry ; },
abstract = {Melanocortin-4 receptor (MC4R) plays a central role in the regulation of energy homeostasis. Its high sequence similarity to other MC receptor family members, low agonist selectivity and the lack of structural information concerning MC4R-specific activation have hampered the development of MC4R-seletive therapeutics to treat obesity. Here, we report four high-resolution structures of full-length MC4R in complex with the heterotrimeric Gs protein stimulated by the endogenous peptide ligand α-MSH, FDA-approved drugs afamelanotide (Scenesse™) and bremelanotide (Vyleesi™), and a selective small-molecule ligand THIQ, respectively. Together with pharmacological studies, our results reveal the conserved binding mode of peptidic agonists, the distinctive molecular details of small-molecule agonist recognition underlying receptor subtype selectivity, and a distinct activation mechanism for MC4R, thereby offering new insights into G protein coupling. Our work may facilitate the discovery of selective therapeutic agents targeting MC4R.},
}
@article {pmid34433158,
year = {2021},
author = {Bruurmijn, LCM and Raemaekers, M and Branco, MP and Vansteensel, MJ and Ramsey, NF},
title = {Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac20e4},
pmid = {34433158},
issn = {1741-2552},
mesh = {Amputation, Surgical ; Hand ; Humans ; *Motor Cortex ; Movement ; *Sensorimotor Cortex ; },
abstract = {Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.},
}
@article {pmid34433031,
year = {2021},
author = {Shi, X and Zhang, Q and Li, J and Liu, X and Zhang, Y and Huang, M and Fang, W and Xu, J and Yuan, T and Xiao, L and Tang, YQ and Wang, XD and Luo, J and Yang, W},
title = {Disrupting phosphorylation of Tyr-1070 at GluN2B selectively produces resilience to depression-like behaviors.},
journal = {Cell reports},
volume = {36},
number = {8},
pages = {109612},
doi = {10.1016/j.celrep.2021.109612},
pmid = {34433031},
issn = {2211-1247},
mesh = {Animals ; Antidepressive Agents/*pharmacology ; Behavior, Animal/*drug effects ; Depression/*drug therapy ; Depressive Disorder, Major/drug therapy/metabolism ; Hippocampus/drug effects/metabolism ; Mice ; Neurons/metabolism ; Receptors, N-Methyl-D-Aspartate/drug effects/metabolism ; Synapses/drug effects/metabolism ; Tyrosine/*drug effects/metabolism ; },
abstract = {Drugs targeting N-methyl-D-aspartate receptors (NMDARs) have been approved to treat major depressive disorder (MDD); however, the presence of undesirable psychotomimetic and cognitive side effects may limit their utility. In this study, we show that the phosphorylation levels of the GluN2B subunit at tyrosine (Y) 1070 increase in mice after both acute and chronic restraint stress (CRS) exposure. Preventing GluN2B-Y1070 phosphorylation via Y1070F mutation knockin produces effects similar to those of antidepressants but does not affect cognitive or anxiety-related behaviors in subject mice. Mechanistically, the Y1070F mutation selectively reduces non-synaptic NMDAR currents and increases the number of excitatory synapses in the layer 5 pyramidal neurons of medial prefrontal cortex (mPFC) but not in the hippocampus. Altogether, our study identifies phosphorylation levels of GluN2B-Y1070 in the mPFC as a dynamic, master switch guarding depressive behaviors, suggesting that disrupting the Y1070 phosphorylation of GluN2B subunit has the potential for developing new antidepressants.},
}
@article {pmid34431480,
year = {2021},
author = {Tabernig, CB and Carrere, LC and Manresa, JB and Spaich, EG},
title = {Does feedback based on FES-evoked nociceptive withdrawal reflex condition event-related desynchronization? An exploratory study with brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {7},
number = {6},
pages = {},
doi = {10.1088/2057-1976/ac2077},
pmid = {34431480},
issn = {2057-1976},
mesh = {Brain-Computer Interfaces ; *Electric Stimulation ; *Feedback ; Humans ; Nociception ; Reflex ; Stroke ; },
abstract = {Introduction.Event-related desynchronization (ERD) is used in brain-computer interfaces (BCI) to detect the user's motor intention (MI) and convert it into a command for an actuator to provide sensory feedback or mobility, for example by means of functional electrical stimulation (FES). Recent studies have proposed to evoke the nociceptive withdrawal reflex (NWR) using FES, in order to evoke synergistic movements of the lower limb and to facilitate the gait rehabilitation of stroke patients. The use of NWR to provide sensorimotor feedback in ERD-based BCI is novel; thererfore, the conditioning effect that nociceptive stimuli might have on MI is still unknown.Objetive.To assess the ERD produced during the MI after FES-evoked NWR, in order to evaluate if nociceptive stimuli condition subsequent ERDs.Methods. Data from 528 electroencephalography trials of 8 healthy volunteers were recorded and analyzed. Volunteers used an ERD-based BCI, which provided two types of feedback: intrisic by the FES-evoked NWR and extrinsic by virtual reality. The electromyogram of the tibialis anterior muscle was also recorded. The main outcome variables were the normalized root mean square of the evoked electromyogram (RMSnorm), the average electroencephalogram amplitude at the ERD frequency during MI (A¯MI) and the percentage decrease ofA¯MIrelative to rest (ERD%) at the first MI subsequent to the activation of the BCI.Results.No evidence of changes of theRMSnormon both theA¯MI(p = 0.663) and theERD%(p = 0.252) of the subsequent MI was detected. A main effect of the type of feedback was found in the subsequentA¯MI(p < 0.001), with intrinsic feedback resulting in a largerA¯MI.Conclusions.No evidence of ERD conditioning was observed using BCI feedback based on FES-evoked NWR .Significance.FES-evoked NWR could constitute a potential feedback modality in an ERD-based BCI to facilitate motor recovery of stroke people.},
}
@article {pmid34428769,
year = {2021},
author = {Hampel, P and Stachow, R and Wienert, J},
title = {Mediating Effects of Mental Health Problems in a Clinical Sample of Adolescents with Obesity.},
journal = {Obesity facts},
volume = {14},
number = {5},
pages = {471-480},
pmid = {34428769},
issn = {1662-4033},
mesh = {Adolescent ; Body Mass Index ; Child ; Cross-Sectional Studies ; Humans ; Mental Health ; *Pediatric Obesity/epidemiology ; *Quality of Life ; },
abstract = {INTRODUCTION: The prevalence rates of obesity have increased in recent decades; despite leveling off in recent German studies among children and adolescents, obesity rates remain high. Psychosocial factors have an adverse impact on the maintenance of obesity. Hence, this study examined the mediating effects of mental health problems on the relation between the body mass index standard deviation score (BMI-SDS) and global health-related quality of life (HRQoL) among adolescent inpatients with obesity while controlling for gender and age-group.<br><br>METHODS: Three simple mediation analyses with gender and age-group as covariates were conducted for n = 313 adolescents with obesity (nfemale = 193, 61.7%) aged 12-17 (M = 14.19, SD = 1.52; BMI-SDS: M = 2.67, SD = 0.52). The adolescents were asked to report their global HRQoL at admission, and their parents estimated the children's mental health problems at home prior to inpatient rehabilitation.<br><br>RESULTS: Emotional, peer-related, and conduct problems mediated the unfavorable effects of BMI-SDS on global HRQoL, showing high, moderate, and small effect sizes, respectively (completely standardized indirect effect of emotional problems: &#x3b2; = -0.09, SE = 0.03, 95% B-CI = -0.15 to -0.04; conduct problems: &#x3b2; = -0.03, SE = 0.02, 95% B-CI = -0.06 to -0.01; peer-related problems: &#x3b2; = -0.10, SE = 0.03, 95% B-CI = -0.16 to -0.05).<br><br>CONCLUSION: Mental health problems may be one salient pathway through which BMI-SDS impairs global HRQoL among adolescents with obesity. Hence, inpatient rehabilitation programs for adolescents with obesity should increase their focus more on the development of psychosocial skills. Thus, the promotion of emotion regulation and social-emotional competencies is suggested.},
}
@article {pmid34428554,
year = {2021},
author = {Neumann, WJ and Memarian Sorkhabi, M and Benjaber, M and Feldmann, LK and Saryyeva, A and Krauss, JK and Contarino, MF and Sieger, T and Jech, R and Tinkhauser, G and Pollo, C and Palmisano, C and Isaias, IU and Cummins, DD and Little, SJ and Starr, PA and Kokkinos, V and Gerd-Helge, S and Herrington, T and Brown, P and Richardson, RM and Kühn, AA and Denison, T},
title = {The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces.},
journal = {Brain stimulation},
volume = {14},
number = {5},
pages = {1301-1306},
pmid = {34428554},
issn = {1876-4754},
support = {K23 NS099380/NS/NINDS NIH HHS/United States ; MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electrocardiography ; *Essential Tremor ; Humans ; },
abstract = {BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability.<br><br>OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity.<br><br>METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.<br><br>RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination.<br><br>CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.},
}
@article {pmid34428205,
year = {2021},
author = {Bodenham, RF and Mazeri, S and Cleaveland, S and Crump, JA and Fasina, FO and de Glanville, WA and Haydon, DT and Kazwala, RR and Kibona, TJ and Maro, VP and Maze, MJ and Mmbaga, BT and Mtui-Malamsha, NJ and Shirima, GM and Swai, ES and Thomas, KM and Bronsvoort, BMD and Halliday, JEB},
title = {Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania.},
journal = {PLoS neglected tropical diseases},
volume = {15},
number = {8},
pages = {e0009630},
pmid = {34428205},
issn = {1935-2735},
support = {R01 TW009237/TW/FIC NIH HHS/United States ; BBS/E/D/20002172/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; BB/L018926/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; BB/J010367/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; BB/L018845/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; /MRC_/Medical Research Council/United Kingdom ; BB/J010367/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; BB/S013857/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; R01 AI121378/AI/NIAID NIH HHS/United States ; },
mesh = {Animals ; Bayes Theorem ; Brucella/*immunology ; Brucellosis/epidemiology/transmission/*veterinary ; Cattle ; Cattle Diseases/*epidemiology/transmission ; Enzyme-Linked Immunosorbent Assay ; Female ; Goat Diseases/*epidemiology/transmission ; Goats ; Latent Class Analysis ; Male ; Rose Bengal ; Seroepidemiologic Studies ; Serologic Tests ; Sheep ; Sheep Diseases/*epidemiology/transmission ; Tanzania ; },
abstract = {BACKGROUND: Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities.<br><br>METHODS AND FINDINGS: Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570-0.894), 0.893 (0.636-0.989), and 0.807 (0.575-0.966), and for cELISA were: 0.623 (0.443-0.790), 0.409 (0.241-0.644), and 0.561 (0.376-0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980-0.998) and 0.995 (0.985-0.999), and cELISA between 0.984 (0.974-0.995) and 0.996 (0.988-1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045-0.090), 0.033 (0.018-0.049), and 0.051 (0.034-0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were &#x2265;0.920 (0.760-0.986) for sensitivity and &#x2265;0.973 (0.955-0.992) for specificity, for all species models.<br><br>CONCLUSIONS: Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.},
}
@article {pmid34428142,
year = {2021},
author = {Xu, B and Wang, Y and Deng, L and Wu, C and Zhang, W and Li, H and Song, A},
title = {Decoding Hand Movement Types and Kinematic Information From Electroencephalogram.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1744-1755},
doi = {10.1109/TNSRE.2021.3106897},
pmid = {34428142},
issn = {1558-0210},
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography ; *Hand ; Humans ; Movement ; },
abstract = {Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.},
}
@article {pmid34428141,
year = {2021},
author = {Habibzadeh, H and Norton, JJS and Vaughan, TM and Soyata, T and Zois, DS},
title = {A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1766-1773},
pmid = {34428141},
issn = {1558-0210},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.},
}
@article {pmid34426357,
year = {2021},
author = {Ali, A and Liaqat, S and Tariq, H and Abbas, S and Arshad, M and Li, WJ and Ahmed, I},
title = {Neonatal calf diarrhea: A potent reservoir of multi-drug resistant bacteria, environmental contamination and public health hazard in Pakistan.},
journal = {The Science of the total environment},
volume = {799},
number = {},
pages = {149450},
doi = {10.1016/j.scitotenv.2021.149450},
pmid = {34426357},
issn = {1879-1026},
mesh = {Animals ; Anti-Bacterial Agents/pharmacology ; Diarrhea/epidemiology ; Drug Resistance, Multiple, Bacterial/genetics ; Microbial Sensitivity Tests ; Pakistan ; *Pharmaceutical Preparations ; Phylogeny ; Public Health ; RNA, Ribosomal, 16S/genetics ; *beta-Lactamases/genetics ; },
abstract = {Though emergence of multi-drug resistant bacteria in the environment is a demonstrated worldwide phenomenon, limited research is reported about the prevalence of resistant bacteria in fecal ecology of neonatal calf diarrhea (NCD) animals in Pakistan. The present study aimed to identify and assess the prevalence of bacterial pathogens and their resistance potential in the fecal ecology of NCD diseased animals of Pakistan. The presence of antibiotic resistance genes (blaTEM, blaNDM-1, blaCTX-M, qnrS) was also investigated. A total of 51 bacterial isolates were recovered from feces of young diarrheic animals (n = 11), collected from 7 cities of Pakistan and identified on the basis of 16S rRNA gene sequence and phylogenetic analysis. Selected isolates were subjected to antimicrobial susceptibility by disc diffusion method while polymerase chain reaction (PCR) was used to characterize the blaTEM, blaNDM-1, blaCTX-M, qnrS and mcr-1 antibiotic resistance genes. Based on the 16S rRNA gene sequences (Accession numbers: LC488898 to LC488948), all isolates were identified that belonged to seventeen genera with the highest prevalence rate for phylum Proteobacteria and genus Bacillus (23%). Antibiotic susceptibility explained the prevalence of resistance in isolates ciprofloxacin (100%), ampicillin (100%), sulfamethoxazole-trimethoprim (85%), tetracycline (75%), amoxicillin (55%), ofloxacin (50%), ceftazidime (45%), amoxicillin/clavulanic acid (45%), levofloxacin (30%), cefpodoxime (25%), cefotaxime (25%), cefotaxime/clavulanic acid (20%), and imipenem (10%). MICs demonstrated that almost 90% isolates were multi-drug resistant (against at least three antibiotics), specially against ciprofloxacin, and tetracycline with the highest resistance levels for Shigella sp. (NCCP-421) (MIC-CIP up to 75 μg mL[-1]) and Escherichia sp. (NCCP-432) (MIC-TET up to 250 μg mL[-1]). PCR-assisted detection of antibiotic resistance genes showed that 54% isolates were positive for blaTEM gene, 7% isolates were positive for blaCTX-M gene, 23% isolates were positive for each of qnrS and mcr-1 genes, 23% isolates were co-positive in combinations of qnrS and mcr-1 genes and blaTEM and mcr-1 genes, whereas none of the isolate showed presence of blaNDM-1 gene.},
}
@article {pmid34425566,
year = {2021},
author = {Larzabal, C and Bonnet, S and Costecalde, T and Auboiroux, V and Charvet, G and Chabardes, S and Aksenova, T and Sauter-Starace, F},
title = {Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac2003},
pmid = {34425566},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Epidural Space ; Humans ; Wireless Technology ; },
abstract = {Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.},
}
@article {pmid34425160,
year = {2021},
author = {Sun, J and He, J and Gao, X},
title = {Neurofeedback Training of the Control Network in Children Improves Brain Computer Interface Performance.},
journal = {Neuroscience},
volume = {478},
number = {},
pages = {24-38},
doi = {10.1016/j.neuroscience.2021.08.010},
pmid = {34425160},
issn = {1873-7544},
mesh = {*Brain-Computer Interfaces ; Child ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; *Neurofeedback ; Photic Stimulation ; },
abstract = {In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and in neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, there has been little research aiming to improve the performance of brain-computer interfaces (BCIs) using neuromodulation. The present study presents a novel design for a neurofeedback training (NFT) method to improve the operation of a steady-state visual evoked potential (SSVEP)-based BCI and further explores its underlying mechanisms. The use of NFT to upregulate alpha-band power in the user's parietal lobe is presented in this study as a new neuromodulation method to improve SSVEP-based BCI in this study. After users completed this NFT intervention, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of the SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6%, respectively. However, no improvement was observed in the control group in which the subjects did not participate in NFT. Moreover, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was observed. Evidence from a network analysis and an attention test further indicates that NFT improves attention by developing the control capacity of the parietal lobe and then enhances the above SSVEP indicators. Upregulating the amplitude of parietal alpha oscillations using NFT significantly improves the SSVEP-based BCI performance by modulating the control network. The study validates an effective neuromodulation method and possibly contributes to explaining the function of the parietal lobe in the control network.},
}
@article {pmid34424453,
year = {2021},
author = {Li, MA and Han, JF and Yang, JF},
title = {Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {10},
pages = {2037-2050},
pmid = {34424453},
issn = {1741-0444},
mesh = {Algorithms ; Automation ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an active brain computer interface (BCI) system, and it has been a popular field to recognize MI-EEG images via convolutional neural network (CNN), which poses a potential problem for maintaining the integrity of the time-frequency-space information in MI-EEG images and exploring the feature fusion mechanism in the CNN. However, information is excessively compressed in the present MI-EEG image, and the sequential CNN is unfavorable for the comprehensive utilization of local features. In this paper, a multidimensional MI-EEG imaging method is proposed, which is based on time-frequency analysis and the Clough-Tocher (CT) interpolation algorithm. The time-frequency matrix of each electrode is generated via continuous wavelet transform (WT), and the relevant section of frequency is extracted and divided into nine submatrices, the longitudinal sums and lengths of which are calculated along the directions of frequency and time successively to produce a 3 × 3 feature matrix for each electrode. Then, feature matrix of each electrode is interpolated to coincide with their corresponding coordinates, thereby yielding a WT-based multidimensional image, called WTMI. Meanwhile, a multilevel and multiscale feature fusion convolutional neural network (MLMSFFCNN) is designed for WTMI, which has dense information, low signal-to-noise ratio, and strong spatial distribution. Extensive experiments are conducted on the BCI Competition IV 2a and 2b datasets, and accuracies of 92.95% and 97.03% are yielded based on 10-fold cross-validation, respectively, which exceed those of the state-of-the-art imaging methods. The kappa values and p values demonstrate that our method has lower class skew and error costs. The experimental results demonstrate that WTMI can fully represent the time-frequency-space features of MI-EEG and that MLMSFFCNN is beneficial for improving the collection of multiscale features and the fusion recognition of general and abstract features for WTMI.},
}
@article {pmid34421565,
year = {2021},
author = {Trinh, TT and Tsai, CF and Hsiao, YT and Lee, CY and Wu, CT and Liu, YH},
title = {Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {700467},
pmid = {34421565},
issn = {1662-5188},
abstract = {Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.},
}
@article {pmid34421512,
year = {2021},
author = {Di Liberto, GM and Marion, G and Shamma, SA},
title = {Accurate Decoding of Imagined and Heard Melodies.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {673401},
pmid = {34421512},
issn = {1662-4548},
abstract = {Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRF env). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.},
}
@article {pmid34417494,
year = {2021},
author = {Bhattacharyya, S and Valeriani, D and Cinel, C and Citi, L and Poli, R},
title = {Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {17008},
pmid = {34417494},
issn = {2045-2322},
mesh = {Adult ; *Brain-Computer Interfaces ; *Decision Making ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Neurons/physiology ; Perception/*physiology ; Reaction Time/physiology ; Task Performance and Analysis ; },
abstract = {In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.},
}
@article {pmid34416760,
year = {2022},
author = {Chen, H and Lu, F and Guo, X and Pang, Y and He, C and Han, S and Duan, X and Chen, H},
title = {Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {32},
number = {6},
pages = {1307-1317},
doi = {10.1093/cercor/bhab296},
pmid = {34416760},
issn = {1460-2199},
mesh = {*Bipolar Disorder/diagnostic imaging ; Brain/diagnostic imaging ; Depression ; *Depressive Disorder, Major/diagnostic imaging ; Humans ; Magnetic Resonance Imaging/methods ; },
abstract = {Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.},
}
@article {pmid34414575,
year = {2021},
author = {Han, JJ},
title = {Synchron receives FDA approval to begin early feasibility study of their endovascular, brain-computer interface device.},
journal = {Artificial organs},
volume = {45},
number = {10},
pages = {1134-1135},
doi = {10.1111/aor.14049},
pmid = {34414575},
issn = {1525-1594},
mesh = {*Brain-Computer Interfaces ; *Device Approval ; Endovascular Procedures/instrumentation ; Humans ; Stents ; United States ; United States Food and Drug Administration ; },
abstract = {Stentrode by Synchron is an endovascularly implanted brain computer interface platform that can relay the activities from the motor cortices of paralyzed patients, potentially offering a relatively noninvasive option for more than five million people in the United States alone.},
}
@article {pmid34413969,
year = {2021},
author = {Quiles Pérez, M and Martínez Beltrán, ET and López Bernal, S and Huertas Celdrán, A and Martínez Pérez, G},
title = {Breaching Subjects' Thoughts Privacy: A Study with Visual Stimuli and Brain-Computer Interfaces.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {5517637},
pmid = {34413969},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; Humans ; Privacy ; },
abstract = {Brain-computer interfaces (BCIs) started being used in clinical scenarios, reaching nowadays new fields such as entertainment or learning. Using BCIs, neuronal activity can be monitored for various purposes, with the study of the central nervous system response to certain stimuli being one of them, being the case of evoked potentials. However, due to the sensitivity of these data, the transmissions must be protected, with blockchain being an interesting approach to ensure the integrity of the data. This work focuses on the visual sense, and its relationship with the P300 evoked potential, where several open challenges related to the privacy of subjects' information and thoughts appear when using BCI. The first and most important challenge is whether it would be possible to extract sensitive information from evoked potentials. This aspect becomes even more challenging and dangerous if the stimuli are generated when the subject is not aware or conscious that they have occurred. There is an important gap in this regard in the literature, with only one work existing dealing with subliminal stimuli and BCI and having an unclear methodology and experiment setup. As a contribution of this paper, a series of experiments, five in total, have been created to study the impact of visual stimuli on the brain tangibly. These experiments have been applied to a heterogeneous group of ten subjects. The experiments show familiar visual stimuli and gradually reduce the sampling time of known images, from supraliminal to subliminal. The study showed that supraliminal visual stimuli produced P300 potentials about 50% of the time on average across all subjects. Reducing the sample time between images degraded the attack, while the impact of subliminal stimuli was not confirmed. Additionally, younger subjects generally presented a shorter response latency. This work corroborates that subjects' sensitive data can be extracted using visual stimuli and P300.},
}
@article {pmid34411268,
year = {2021},
author = {Kim, YJ and Brackbill, N and Batty, E and Lee, J and Mitelut, C and Tong, W and Chichilnisky, EJ and Paninski, L},
title = {Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings.},
journal = {Neural computation},
volume = {33},
number = {7},
pages = {1719-1750},
doi = {10.1162/neco_a_01395},
pmid = {34411268},
issn = {1530-888X},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca ; Neural Networks, Computer ; Retina ; *Retinal Ganglion Cells ; },
abstract = {Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.},
}
@article {pmid34410924,
year = {2021},
author = {Chen, S and Zhang, X and Shen, X and Huang, Y and Wang, Y},
title = {Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1690-1700},
doi = {10.1109/TNSRE.2021.3105968},
pmid = {34410924},
issn = {1558-0210},
mesh = {Action Potentials ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Movement ; Neurons ; Rats ; },
abstract = {Brain-machine interfaces (BMIs) help the disabled restore body functions by translating neural activity into digital commands to control external devices. Neural adaptation, where the brain signals change in response to external stimuli or movements, plays an important role in BMIs. When subjects purely use neural activity to brain-control a prosthesis, some neurons will actively explore a new tuning property to accomplish the movement task. The prediction of this neural tuning property can help subjects adapt more efficiently to brain control and maintain a good decoding performance. Existing prediction methods track the slow change of the tuning property in the manual control, which is not suitable for the fast neural adaptation in brain control. In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework. We implement the method on real data from rats performing a two-lever discrimination task under manual control and brain control. The results show our method successfully predicts the neural modulation state and identifies the neurons that become active in brain control. Compared to the existing method, ours tracks the fast changes of the hyper preferred direction from manual control to brain control more accurately and efficiently and reconstructs the kinematics better and faster.},
}
@article {pmid34408621,
year = {2021},
author = {Charlebois, CM and Caldwell, DJ and Rampersad, SM and Janson, AP and Ojemann, JG and Brooks, DH and MacLeod, RS and Butson, CR and Dorval, AD},
title = {Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {691701},
pmid = {34408621},
issn = {1662-4548},
abstract = {Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.},
}
@article {pmid34408337,
year = {2021},
author = {Chandler, JA and Van der Loos, KI and Boehnke, SE and Beaudry, JS and Buchman, DZ and Illes, J},
title = {Building communication neurotechnology for high stakes communications.},
journal = {Nature reviews. Neuroscience},
volume = {22},
number = {10},
pages = {587-588},
pmid = {34408337},
issn = {1471-0048},
mesh = {Animals ; *Brain-Computer Interfaces/trends ; *Communication ; *Communication Devices for People with Disabilities/trends ; Humans ; Nanotechnology/*methods/trends ; },
}
@article {pmid34407527,
year = {2021},
author = {Liu, J and Ye, F and Xiong, H},
title = {Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1ed0},
pmid = {34407527},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Individuality ; Neural Networks, Computer ; },
abstract = {Objective.Most current methods of classifying different patterns for motor imagery EEG signals require complex pre-processing and feature extraction steps, which consume time and lack adaptability, ignoring individual differences in EEG signals. It is essential to improve algorithm performance with the increased classes and diversity of subjects.Approach.This study introduces deep learning method for end-to-end learning to complete the classification of four-class MI tasks, aiming to improve the recognition rate and balance the classification accuracy among different subjects. A new one-dimensional input data representation method is proposed. This representation method can increase the number of samples and ignore the influence of channel correlation. In addition, a cascade network of convolutional neural network and gated recurrent unit is designed to learn time-frequency information from EEG data without extracting features manually, this model can capture the hidden representations related to different MI mode of each people.Main results. Experiments on BCI Competition 2a dataset and actual collected dataset achieve high accuracy near 99.40% and 92.56%, and the standard deviation is 0.34 and 1.35 respectively. Results demonstrate that the proposed method outperforms the advanced methods and baseline models.Significance.Experimental results show that the proposed method improves the accuracy of multi-classification and overcomes the impact of individual differences on classification by training neural network subject-dependent, which promotes the development of actual brain-computer interface systems.},
}
@article {pmid34407522,
year = {2021},
author = {Xu, L and Xu, M and Ma, Z and Wang, K and Jung, TP and Ming, D},
title = {Enhancing transfer performance across datasets for brain-computer interfaces using a combination of alignment strategies and adaptive batch normalization.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1ed2},
pmid = {34407522},
issn = {1741-2552},
mesh = {Adaptation, Physiological ; Algorithms ; *Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Objective. Recently, transfer learning (TL) and deep learning (DL) have been introduced to solve intra- and inter-subject variability problems in brain-computer interfaces (BCIs). However, current TL and DL algorithms are usually validated within a single dataset, assuming that data of the test subjects are acquired under the same condition as that of training (source) subjects. This assumption is generally violated in practice because of different acquisition systems and experimental settings across studies and datasets. Thus, the generalization ability of these algorithms needs further validations in a cross-dataset scenario, which is closer to the actual situation. This study compared the transfer performance of pre-trained deep-learning models with different preprocessing strategies in a cross-dataset scenario.Approach. This study used four publicly available motor imagery datasets, each was successively selected as a source dataset, and the others were used as target datasets. EEGNet and ShallowConvNet with four preprocessing strategies, namely channel normalization, trial normalization, Euclidean alignment, and Riemannian alignment, were trained with the source dataset. The transfer performance of pre-trained models was validated on the target datasets. This study also used adaptive batch normalization (AdaBN) for reducing interval covariate shift across datasets. This study compared the transfer performance of using the four preprocessing strategies and that of a baseline approach based on manifold embedded knowledge transfer (MEKT). This study also explored the possibility and performance of fusing MEKT and EEGNet.Main results. The results show that DL models with alignment strategies had significantly better transfer performance than the other two preprocessing strategies. As an unsupervised domain adaptation method, AdaBN could also significantly improve the transfer performance of DL models. The transfer performance of DL models that combined AdaBN and alignment strategies significantly outperformed MEKT. Moreover, the generalizability of EEGNet models that combined AdaBN and alignment strategies could be further improved via the domain adaptation step in MEKT, achieving the best generalization ability among multiple datasets (BNCI2014001: 0.788, PhysionetMI: 0.679, Weibo2014: 0.753, Cho2017: 0.650).Significance. The combination of alignment strategies and AdaBN could easily improve the generalizability of DL models without fine-tuning. This study may provide new insights into the design of transfer neural networks for BCIs by separating source and target batch normalization layers in the domain adaptation process.},
}
@article {pmid34406935,
year = {2022},
author = {Peterson, V and Nieto, N and Wyser, D and Lambercy, O and Gassert, R and Milone, DH and Spies, RD},
title = {Transfer Learning Based on Optimal Transport for Motor Imagery Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {2},
pages = {807-817},
doi = {10.1109/TBME.2021.3105912},
pmid = {34406935},
issn = {1558-2531},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Learning ; Machine Learning ; },
abstract = {OBJECTIVE: This paper tackles the cross-sessions variability of electroencephalography-based brain-computer interfaces (BCIs) in order to avoid the lengthy recalibration step of the decoding method before every use.<br><br>METHODS: We develop a new approach of domain adaptation based on optimal transport to tackle brain signal variability between sessions of motor imagery BCIs. We propose a backward method where, unlike the original formulation, the data from a new session are transported to a calibration session, and thereby avoiding model retraining. Several domain adaptation approaches are evaluated and compared. We simulated two possible online scenarios: i) block-wise adaptation and ii) sample-wise adaptation. In this study, we collect a dataset of 10 subjects performing a hand motor imagery task in 2 sessions. A publicly available dataset is also used.<br><br>RESULTS: For the first scenario, results indicate that classifier retraining can be avoided by means of our backward formulation yielding to equivalent classification performance as compared to retraining solutions. In the second scenario, classification performance rises up to 90.23% overall accuracy when the label of the indicated mental task is used to learn the transport. Adaptive time is between 10 and 80 times faster than the other methods.<br><br>CONCLUSIONS: The proposed method is able to mitigate the cross-session variability in motor imagery BCIs.<br><br>SIGNIFICANCE: The backward formulation is an efficient retraining-free approach built to avoid lengthy calibration times. Thus, the BCI can be actively used after just a few minutes of setup. This is important for practical applications such as BCI-based motor rehabilitation.},
}
@article {pmid34406934,
year = {2022},
author = {Liu, B and Chen, X and Li, X and Wang, Y and Gao, X and Gao, S},
title = {Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {2},
pages = {795-806},
doi = {10.1109/TBME.2021.3105331},
pmid = {34406934},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; },
abstract = {OBJECTIVE: The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented in dry electrodes is a promising paradigm for alternative and augmentative communication in real-world applications. To improve its performance and reduce the calibration effort for dry-electrode systems, we utilize cross-device transfer learning by exploiting auxiliary individual wet-electrode electroencephalogram (EEG).<br><br>METHODS: We proposed a novel transfer learning framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the spatial pattern and the covariance for domain adaptation. To evaluate its efficacy, 75 subjects performed an experiment of 2 sessions involving a 12-target SSVEP-BCI task.<br><br>RESULTS: ALPHA significantly outperformed a baseline approach (canonical correlation analysis, CCA) and two competing transfer learning approaches (transfer template CCA, ttCCA and least square transformation, LST) in two transfer directions. When transferring from wet to dry EEG headsets, ALPHA significantly outperformed the fully-calibrated approach of task-related component analysis (TRCA).<br><br>CONCLUSION: ALPHA advances the frontier of recalibration-free cross-device transfer learning for SSVEP-BCIs and boosts the performance of dry electrode based systems.<br><br>SIGNIFICANCE: ALPHA has methodological and practical implications and pushes the boundary of dry electrode based SSVEP-BCI toward real-world applications.},
}
@article {pmid34406435,
year = {2021},
author = {Krishnan, A and Sharma, G and Devana, SK and Zohmangaihi, D and Mavuduru, RS and Mandal, AK and Sharma, AP and Bora, GS},
title = {Urinary adenosine triphosphate and nitric oxide levels in patients with underactive bladder: a preliminary study.},
journal = {World journal of urology},
volume = {39},
number = {12},
pages = {4421-4425},
pmid = {34406435},
issn = {1433-8726},
mesh = {Adenosine Triphosphate/*urine ; Adolescent ; Adult ; Aged ; Case-Control Studies ; Humans ; Male ; Middle Aged ; Nitric Oxide/*urine ; Prospective Studies ; Urinary Bladder, Underactive/*urine ; Young Adult ; },
abstract = {INTRODUCTION: Various in vitro and in vivo animal studies have shown that adenosine triphosphate (ATP) has a stimulatory role and nitric oxide (NO) has an inhibitory role in modulating bladder contractility. However, it is not known what happens to the urinary levels of ATP and NO in humans with underactive bladder (UAB).<br><br>METHODS: In this prospective case-control study, we compared ATP and NO levels in twenty six male patients of UAB with a bladder contractility index (BCI) of&#x2009;<&#x2009;100 and 18 healthy male volunteers without any lower urinary tract symptoms (LUTS).<br><br>RESULTS: The mean urinary ATP levels were significantly lower in cases compared to controls (546.1&#x2009;&#xb1;&#x2009;37.3&#xa0;pg/&#xb5;l vs. 610.7&#x2009;&#xb1;&#x2009;24.9&#xa0;pg/&#xb5;l, p value&#x2009;<&#x2009;0.001) and the mean NO levels were significantly higher in cases compared to controls (1233.4&#x2009;&#xb1;&#x2009;91.2&#xa0;pg/&#xb5;l vs. 1126.3&#x2009;&#xb1;&#x2009;91.3.4&#xa0;pg/&#xb5;l, p value&#x2009;<&#x2009;0.001). The mean NO/ATP ratio in cases was significantly higher than that of controls (2.26&#x2009;&#xb1;&#x2009;0.2 vs. 1.84&#x2009;&#xb1;&#x2009;0.18, p value&#x2009;<&#x2009;0.000). Using receiver operating curve (ROC) analysis, we noted the area under the curve (AUC) for NO/ATP ratio to be 0.91 in the diagnosis of cases. A cut-off value of 2.06 for NO/ATP ratio had sensitivity, specificity and diagnostic accuracy of 88.5%, 88.9% and 88.6%, respectively, in diagnosing patients with UAB.<br><br>CONCLUSION: Patients with UAB have significantly higher levels of urinary NO and decreased levels of urinary ATP. Urinary NO/ATP levels can be considered as a noninvasive alternate test for diagnosing bladder underactivity.},
}
@article {pmid34403422,
year = {2021},
author = {Ravindran, A and Rieke, JD and Zapata, JDA and White, KD and Matarasso, A and Yusufali, MM and Rana, M and Gunduz, A and Modarres, M and Sitaram, R and Daly, JJ},
title = {Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI.},
journal = {PloS one},
volume = {16},
number = {8},
pages = {e0254338},
pmid = {34403422},
issn = {1932-6203},
support = {I21 RX002192/RX/RRD VA/United States ; },
mesh = {Adult ; Aged ; *Brain/diagnostic imaging/physiopathology ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; *Movement ; Range of Motion, Articular ; *Stroke/diagnostic imaging/physiopathology ; *Stroke Rehabilitation ; Wrist ; Wrist Joint/*physiopathology ; },
abstract = {OBJECTIVE: In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.<br><br>APPROACH: We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance.<br><br>RESULTS: With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.<br><br>SIGNIFICANCE: We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.},
}
@article {pmid34400771,
year = {2021},
author = {Huang, L and Chen, Y and Jin, S and Lin, L and Duan, S and Si, K and Gong, W and Julius Zhu, J},
title = {Organizational principles of amygdalar input-output neuronal circuits.},
journal = {Molecular psychiatry},
volume = {26},
number = {12},
pages = {7118-7129},
pmid = {34400771},
issn = {1476-5578},
support = {R01 NS092548/NS/NINDS NIH HHS/United States ; R01 NS104670/NS/NINDS NIH HHS/United States ; },
mesh = {*Amygdala/physiology ; Hippocampus ; Neural Pathways/physiology ; *Neurons/physiology ; Nucleus Accumbens ; Prefrontal Cortex/physiology ; },
abstract = {The amygdala, one of the most studied brain structures, integrates brain-wide heterogeneous inputs and governs multidimensional outputs to control diverse behaviors central to survival, yet how amygdalar input-output neuronal circuits are organized remains unclear. Using a simplified cell-type- and projection-specific retrograde transsynaptic tracing technique, we scrutinized brain-wide afferent inputs of four major output neuronal groups in the amygdalar basolateral complex (BLA) that project to the bed nucleus of the stria terminals (BNST), ventral hippocampus (vHPC), medial prefrontal cortex (mPFC) and nucleus accumbens (NAc), respectively. Brain-wide input-output quantitative analysis unveils that BLA efferent neurons receive a diverse array of afferents with varied input weights and predominant contextual representation. Notably, the afferents received by BNST-, vHPC-, mPFC- and NAc-projecting BLA neurons exhibit virtually identical origins and input weights. These results indicate that the organization of amygdalar BLA input-output neuronal circuits follows the input-dependent and output-independent principles, ideal for integrating brain-wide diverse afferent stimuli to control parallel efferent actions. The data provide the objective basis for improving the virtual reality exposure therapy for anxiety disorders and validate the simplified cell-type- and projection-specific retrograde transsynaptic tracing method.},
}
@article {pmid34398754,
year = {2021},
author = {Yan, W and Du, C and Wu, Y and Zheng, X and Xu, G},
title = {SSVEP-EEG Denoising via Image Filtering Methods.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1634-1643},
doi = {10.1109/TNSRE.2021.3104825},
pmid = {34398754},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal-To-Noise Ratio ; },
abstract = {Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.},
}
@article {pmid34398660,
year = {2021},
author = {de Kerckhove, D},
title = {The personal digital twin, ethical considerations.},
journal = {Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
volume = {379},
number = {2207},
pages = {20200367},
doi = {10.1098/rsta.2020.0367},
pmid = {34398660},
issn = {1471-2962},
abstract = {The personal digital twin extends to individual persons, a concept that originated in engineering to twin complex machines with a digital simulation containing a model of its functions to monitor its past and present behaviour, and repair, correct, improve or otherwise ensure its optimal operation. Several independent trends in technological developments are seen to converge towards the elaboration of the digital replication of individual human data and life history, notably in health industries. Among the main ones, we consider the ubiquitous distribution of digital assistants, the rapid progress of machine learning concurrent with the exponential growth of 'personal' Big Data and the incipient interest in developing lifelogs. The core hypothesis here is that among the psychological effects of the digital transformation, the externalization of cognitive faculties such as memory, planning and judgement, the decision-making processes located within the human person are also emigrating to digital functions, perhaps as a prelude to a later re-integration within the person via brain-computer interfaces. The paper concludes with ethical considerations about these ongoing developments. This article is part of the theme issue 'Towards symbiotic autonomous systems'.},
}
@article {pmid34396711,
year = {2021},
author = {Liu, R and Jia, Y and Guo, P and Jiang, W and Bai, R and Liu, C},
title = {In Vivo Clonal Analysis Reveals Development Heterogeneity of Oligodendrocyte Precursor Cells Derived from Distinct Germinal Zones.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {8},
number = {20},
pages = {e2102274},
pmid = {34396711},
issn = {2198-3844},
support = {2016YFA0101201//National Key Research and Development Program of China Stem Cell and Translational Research/ ; 81673035//National Natural Science Foundation of China/ ; 81972915//National Natural Science Foundation of China/ ; LR17H160001//Science Foundation for Distinguished Young Scientists of Zhejiang Province/ ; 2016QNA7023//Fundamental Research Funds for the Central Universities/ ; 2017QNA7028//Fundamental Research Funds for the Central Universities/ ; //Thousand Talent Program for Young Outstanding Scientists, China/ ; },
mesh = {Animals ; Animals, Newborn ; Brain/growth &amp; development/*metabolism ; CRISPR-Cas Systems/genetics ; Cell Count ; Cell Differentiation/genetics ; Cell Lineage/*genetics ; Cells, Cultured ; Clonal Evolution/*genetics ; DNA Transposable Elements/genetics ; Electroporation ; *Genetic Heterogeneity ; Humans ; Mice ; Oligodendrocyte Precursor Cells/*metabolism ; Oligodendroglia/metabolism ; Stem Cells/metabolism ; },
abstract = {Mounting evidence supports that oligodendrocyte precursor cells (OPCs) play important roles in maintaining the integrity of normal brains, and that their dysfunction is the etiology of numerous severe neurological diseases. OPCs exhibit diverse heterogeneity in the adult brain, and distinct germinal zones of the embryonic brain contribute to OPC genesis. However, it remains obscure whether developmental origins shape OPC heterogeneity in the adult brain. Here, an in vivo clonal analysis approach is developed to address this. By combining OPC-specific transgenes, in utero electroporation, and the PiggyBac transposon system, the lineages of individual neonatal OPCs derived from either dorsal or ventral embryonic germinal zones are traced, and the landscape of their trajectories is comprehensively described throughout development. Surprisingly, despite behaving indistinguishably in the brain before weaning, dorsally derived OPCs continuously expand throughout life, but ventrally derived OPCs eventually diminish. Importantly, clonal analysis supports the existence of an intrinsic cellular "clock" to control OPC expansion. Moreover, knockout of NF1 could circumvent the distinction of ventrally derived OPCs in the adult brain. Together, this work shows the importance of in vivo clonal analysis in studying stem/progenitor cell heterogeneity, and reveals that developmental origins play a role in determining OPC fate.},
}
@article {pmid34396092,
year = {2021},
author = {Wickramaratne, SD and Mahmud, MS},
title = {Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data.},
journal = {Frontiers in big data},
volume = {4},
number = {},
pages = {659146},
pmid = {34396092},
issn = {2624-909X},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology's economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject's task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.},
}
@article {pmid34393958,
year = {2021},
author = {Ni, T and Ni, Y and Xue, J and Wang, S},
title = {A Domain Adaptation Sparse Representation Classifier for Cross-Domain Electroencephalogram-Based Emotion Classification.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {721266},
pmid = {34393958},
issn = {1664-1078},
abstract = {The brain-computer interface (BCI) interprets the physiological information of the human brain in the process of consciousness activity. It builds a direct information transmission channel between the brain and the outside world. As the most common non-invasive BCI modality, electroencephalogram (EEG) plays an important role in the emotion recognition of BCI; however, due to the individual variability and non-stationary of EEG signals, the construction of EEG-based emotion classifiers for different subjects, different sessions, and different devices is an important research direction. Domain adaptation utilizes data or knowledge from more than one domain and focuses on transferring knowledge from the source domain (SD) to the target domain (TD), in which the EEG data may be collected from different subjects, sessions, or devices. In this study, a new domain adaptation sparse representation classifier (DASRC) is proposed to address the cross-domain EEG-based emotion classification. To reduce the differences in domain distribution, the local information preserved criterion is exploited to project the samples from SD and TD into a shared subspace. A common domain-invariant dictionary is learned in the projection subspace so that an inherent connection can be built between SD and TD. In addition, both principal component analysis (PCA) and Fisher criteria are exploited to promote the recognition ability of the learned dictionary. Besides, an optimization method is proposed to alternatively update the subspace and dictionary learning. The comparison of CSFDDL shows the feasibility and competitive performance for cross-subject and cross-dataset EEG-based emotion classification problems.},
}
@article {pmid34390757,
year = {2021},
author = {Xi, X and Tao, Q and Li, J and Kong, W and Zhao, YB and Wang, H and Wang, J},
title = {Emotion-movement relationship: A study using functional brain network and cortico-muscular coupling.},
journal = {Journal of neuroscience methods},
volume = {362},
number = {},
pages = {109320},
doi = {10.1016/j.jneumeth.2021.109320},
pmid = {34390757},
issn = {1872-678X},
mesh = {*Brain ; *Electroencephalography ; Electromyography ; Emotions ; Humans ; Movement ; },
abstract = {BACKGROUND: Emotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC).<br><br>NEW METHODS: In this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory.<br><br>RESULTS: We found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different.<br><br>Much of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship.<br><br>CONCLUSIONS: Different emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC.},
}
@article {pmid34388531,
year = {2021},
author = {Ullah, R and Rauf, N and Nabi, G and Yi, S and Yu-Dong, Z and Fu, J},
title = {Mechanistic insight into high-fat diet-induced metabolic inflammation in the arcuate nucleus of the hypothalamus.},
journal = {Biomedicine &amp; pharmacotherapy = Biomedecine &amp; pharmacotherapie},
volume = {142},
number = {},
pages = {112012},
doi = {10.1016/j.biopha.2021.112012},
pmid = {34388531},
issn = {1950-6007},
mesh = {Animals ; Arcuate Nucleus of Hypothalamus/pathology ; Cytokines/metabolism ; Diet, High-Fat/*adverse effects ; Female ; Humans ; Inflammation/etiology/*pathology ; Inflammation Mediators/metabolism ; Male ; Neurons/metabolism ; Obesity/etiology/*physiopathology/therapy ; Sex Factors ; },
abstract = {A high-fat diet (HFD) is linked with cytokines production by non-neuronal cells within the hypothalamus, which mediates metabolic inflammation. These cytokines then activate different inflammatory mediators in the arcuate nucleus of the hypothalamus (ARC), a primary hypothalamic area accommodating proopiomelanocortin (POMC) and agouti-related peptide (AGRP) neurons, first-order neurons that sense and integrate peripheral metabolic signals and then respond accordingly. These mediators, such as inhibitor of κB kinase-β (IKKβ), suppression of cytokine signaling 3 (SOCS3), c-Jun N-terminal kinases (JNKs), protein kinase C (PKC), etc., cause insulin and leptin resistance in POMC and AGRP neurons and support obesity and related metabolic complications. On the other hand, inhibition of these mediators has been shown to counteract the impaired metabolism. Therefore, it is important to discuss the contribution of neuronal and non-neuronal cells in HFD-induced hypothalamic inflammation. Furthermore, understanding few other questions, such as the diets causing hypothalamic inflammation, the gender disparity in response to HFD feeding, and how hypothalamic inflammation affects ARC neurons to cause impaired metabolism, will be helpful for the development of therapeutic approaches to prevent or treat HFD-induced obesity.},
}
@article {pmid34388395,
year = {2021},
author = {Yu, LM and Bafadhel, M and Dorward, J and Hayward, G and Saville, BR and Gbinigie, O and Van Hecke, O and Ogburn, E and Evans, PH and Thomas, NPB and Patel, MG and Richards, D and Berry, N and Detry, MA and Saunders, C and Fitzgerald, M and Harris, V and Shanyinde, M and de Lusignan, S and Andersson, MI and Barnes, PJ and Russell, REK and Nicolau, DV and Ramakrishnan, S and Hobbs, FDR and Butler, CC and , },
title = {Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.},
journal = {Lancet (London, England)},
volume = {398},
number = {10303},
pages = {843-855},
pmid = {34388395},
issn = {1474-547X},
support = {/WT_/Wellcome Trust/United Kingdom ; MC_PC_19079/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Administration, Inhalation ; Aged ; Bayes Theorem ; Budesonide/*administration &amp; dosage ; COVID-19/mortality ; Female ; Glucocorticoids/*administration &amp; dosage ; Hospitalization ; Humans ; Male ; Middle Aged ; Prospective Studies ; Risk Factors ; SARS-CoV-2 ; Treatment Outcome ; *COVID-19 Drug Treatment ; },
abstract = {BACKGROUND: A previous efficacy trial found benefit from inhaled budesonide for COVID-19 in patients not admitted to hospital, but effectiveness in high-risk individuals is unknown. We aimed to establish whether inhaled budesonide reduces time to recovery and COVID-19-related hospital admissions or deaths among people at high risk of complications in the community.<br><br>METHODS: PRINCIPLE is a multicentre, open-label, multi-arm, randomised, controlled, adaptive platform trial done remotely from a central trial site and at primary care centres in the UK. Eligible participants were aged 65 years or older or 50 years or older with comorbidities, and unwell for up to 14 days with suspected COVID-19 but not admitted to hospital. Participants were randomly assigned to usual care, usual care plus inhaled budesonide (800 &#x3bc;g twice daily for 14 days), or usual care plus other interventions, and followed up for 28 days. Participants were aware of group assignment. The coprimary endpoints are time to first self-reported recovery and hospital admission or death related to COVID-19, within 28 days, analysed using Bayesian models. The primary analysis population included all eligible SARS-CoV-2-positive participants randomly assigned to budesonide, usual care, and other interventions, from the start of the platform trial until the budesonide group was closed. This trial is registered at the ISRCTN registry (ISRCTN86534580) and is ongoing.<br><br>FINDINGS: The trial began enrolment on April 2, 2020, with randomisation to budesonide from Nov 27, 2020, until March 31, 2021, when the prespecified time to recovery superiority criterion was met. 4700 participants were randomly assigned to budesonide (n=1073), usual care alone (n=1988), or other treatments (n=1639). The primary analysis model includes 2530 SARS-CoV-2-positive participants, with 787 in the budesonide group, 1069 in the usual care group, and 974 receiving other treatments. There was a benefit in time to first self-reported recovery of an estimated 2&#xb7;94 days (95% Bayesian credible interval [BCI] 1&#xb7;19 to 5&#xb7;12) in the budesonide group versus the usual care group (11&#xb7;8 days [95% BCI 10&#xb7;0 to 14&#xb7;1] vs 14&#xb7;7 days [12&#xb7;3 to 18&#xb7;0]; hazard ratio 1&#xb7;21 [95% BCI 1&#xb7;08 to 1&#xb7;36]), with a probability of superiority greater than 0&#xb7;999, meeting the prespecified superiority threshold of 0&#xb7;99. For the hospital admission or death outcome, the estimated rate was 6&#xb7;8% (95% BCI 4&#xb7;1 to 10&#xb7;2) in the budesonide group versus 8&#xb7;8% (5&#xb7;5 to 12&#xb7;7) in the usual care group (estimated absolute difference 2&#xb7;0% [95% BCI -0&#xb7;2 to 4&#xb7;5]; odds ratio 0&#xb7;75 [95% BCI 0&#xb7;55 to 1&#xb7;03]), with a probability of superiority 0&#xb7;963, below the prespecified superiority threshold of 0&#xb7;975. Two participants in the budesonide group and four in the usual care group had serious adverse events (hospital admissions unrelated to COVID-19).<br><br>INTERPRETATION: Inhaled budesonide improves time to recovery, with a chance of also reducing hospital admissions or deaths (although our results did not meet the superiority threshold), in people with COVID-19 in the community who are at higher risk of complications.<br><br>FUNDING: National Institute of Health Research and United Kingdom Research Innovation.},
}
@article {pmid34388175,
year = {2021},
author = {Sime, MM and Bissoli, ALC and Lavino-Júnior, D and Bastos-Filho, TF},
title = {Usability, occupational performance and satisfaction evaluation of a smart environment controlled by infrared oculography by people with severe motor disabilities.},
journal = {PloS one},
volume = {16},
number = {8},
pages = {e0256062},
pmid = {34388175},
issn = {1932-6203},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/pathology/psychology/*rehabilitation ; Brain-Computer Interfaces/*standards ; Disability Evaluation ; Persons with Disabilities/*psychology/rehabilitation ; Environment ; Female ; Humans ; Independent Living/*standards ; Male ; Middle Aged ; Occupational Therapy/*methods ; Personal Satisfaction ; Self-Help Devices/*statistics &amp; numerical data ; Spinal Cord Injuries/pathology/psychology/*rehabilitation ; },
abstract = {A smart environment is an assistive technology space that can enable people with motor disabilities to control their equipment (TV, radio, fan, etc.) through a human-machine interface activated by different inputs. However, assistive technology resources are not always considered useful, reaching quite high abandonment rate. This study aims to evaluate the effectiveness of a smart environment controlled through infrared oculography by people with severe motor disabilities. The study sample was composed of six individuals with motor disabilities. Initially, sociodemographic data forms, the Functional Independence Measure (FIMTM), and the Canadian Occupational Performance Measure (COPM) were applied. The participants used the system in their domestic environment for a week. Afterwards, they were reevaluated with regards to occupational performance (COPM), satisfaction with the use of the assistive technology resource (QUEST 2.0), psychosocial impact (PIADS) and usability of the system (SUS), as well as through semi-structured interviews for suggestions or complaints. The most common demand from the participants of this research was 'control of the TV'. Two participants did not use the system. All participants who used the system (four) presented positive results in all assessment protocols, evidencing greater independence in the control of the smart environment equipment. In addition, they evaluated the system as useful and with good usability. Non-acceptance of disability and lack of social support may have influenced the results.},
}
@article {pmid34384910,
year = {2021},
author = {Mihelj, E and Bächinger, M and Kikkert, S and Ruddy, K and Wenderoth, N},
title = {Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback.},
journal = {NeuroImage},
volume = {242},
number = {},
pages = {118463},
doi = {10.1016/j.neuroimage.2021.118463},
pmid = {34384910},
issn = {1095-9572},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Evoked Potentials, Motor ; Female ; Fingers ; Humans ; Imagination/*physiology ; Individuation ; Male ; Motor Activity/*physiology ; Motor Cortex/physiology ; Movement ; Muscle, Skeletal/physiology ; Neurofeedback/*methods ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronization of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.},
}
@article {pmid34384417,
year = {2021},
author = {Fushimi, Y and Kamei, S and Tatsumi, F and Sanada, J and Shimoda, M and Kimura, T and Obata, A and Nakanishi, S and Kaku, K and Mune, T and Kaneto, H},
title = {Multiple endocrine neoplasia type 1 with a frameshift mutation in its gene accompanied by a giant cervical lipoma and multiple fatty deposits in the pancreas: case report.},
journal = {BMC endocrine disorders},
volume = {21},
number = {1},
pages = {164},
pmid = {34384417},
issn = {1472-6823},
mesh = {Adult ; *Frameshift Mutation ; Humans ; Lipoma/complications/genetics/*pathology/surgery ; Male ; Multiple Endocrine Neoplasia Type 1/complications/genetics/*pathology/surgery ; Pancreatic Diseases/complications/genetics/*pathology/surgery ; Parathyroidectomy ; Prognosis ; Proto-Oncogene Proteins/*genetics ; },
abstract = {BACKGROUND: Multiple endocrine neoplasia type 1 (MEN1) is a syndrome characterized by pituitary neoplasia, primary hyperparathyroidism and pancreatic endocrine tumor. Here we show a case of MEN1 with a germline frameshift mutation in its gene accompanied by a giant cervical lipoma and multiple fatty deposits in the pancreas.<br><br>CASE PRESENTATION: A 28-year-old man noticed the decreased visual acuity of both eyes and visited our institution. Since he was diagnosed as visual disturbance and brain computer tomography (CT) showed a mass in the pituitary fossa, he was hospitalized in our institution. Endoscopic trans-sphenoidal hypophysectomy and total parathyroidectomy with auto-transplantation were performed, and a giant cervical lipoma was resected. Furthermore, in genetic search, we found a germline frameshift mutation in MEN1 gene leading to the appearance of a new stop codon.<br><br>CONCLUSIONS: We should bear in m ind that giant skin lipoma and multiple abnormal fatty deposits in the pancreas could be complicated with MEN1.},
}
@article {pmid34384059,
year = {2021},
author = {Liu, C and Jin, J and Xu, R and Li, S and Zuo, C and Sun, H and Wang, X and Cichocki, A},
title = {Distinguishable spatial-spectral feature learning neural network framework for motor imagery-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1d36},
pmid = {34384059},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Spatial and spectral features extracted from electroencephalogram (EEG) are critical for the classification of motor imagery (MI) tasks. As prevalently used methods, the common spatial pattern (CSP) and filter bank CSP (FBCSP) can effectively extract spatial-spectral features from MI-related EEG. To further improve the separability of the CSP features, we proposed a distinguishable spatial-spectral feature learning neural network (DSSFLNN) framework for MI-based brain-computer interfaces (BCIs) in this study.Approach.The first step of the DSSFLNN framework was to extract FBCSP features from raw EEG signals. Then two squeeze-and-excitation modules were used to re-calibrate CSP features along the band-wise axis and the class-wise axis, respectively. Next, we used a parallel convolutional neural network module to learn distinguishable spatial-spectral features. Finally, the distinguishable spatial-spectral features were fed to a fully connected layer for classification. To verify the effectiveness of the proposed framework, we compared it with the state-of-the-art methods on BCI competition IV datasets 2a and 2b.Main results.The results showed that the DSSFLNN framework can achieve a mean Cohen's kappa value of 0.7 on two datasets, which outperformed the state-of-the-art methods. Moreover, two additional experiments were conducted and they proved that the combination of band-wise feature learning and class-wise feature learning can achieve significantly better performance than only using either one of them.Significance.The proposed DSSFLNN can effectively improve the decoding performance of MI-based BCIs.},
}
@article {pmid34384052,
year = {2021},
author = {Kumar, A and Pirogova, E and Mahmoud, SS and Fang, Q},
title = {Classification of error-related potentials evoked during stroke rehabilitation training.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac1d32},
pmid = {34384052},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Stroke/diagnosis ; *Stroke Rehabilitation ; Support Vector Machine ; },
abstract = {Objective.Error-related potentials (ErrPs) are elicited in the human brain following an error's perception. Recently, ErrPs have been observed in a novel task situation, i.e. when stroke patients perform upper-limb rehabilitation exercises. These ErrPs can be used to developassist-as-needed(AAN) robotic stroke rehabilitation systems. However, to date, there is no reported research on assessing the feasibility of using the ErrPs to implement the AAN approach. Hence, in this study, we evaluated and compared the single-trial classification of novel ErrPs using various classical machine learning and deep learning approaches.Approach.Electroencephalogram data of 13 stroke patients recorded while performing an upper-limb physical rehabilitation exercise were used. Two classification approaches, one combining the xDAWN spatial filtering and support vector machines, and the other using a convolutional neural network-based double transfer learning, were utilized.Main results.Results showed that the ErrPs could be detected with a mean area under the receiver operating characteristics curve of 0.838, and a mean accuracy of 0.842, 0.257 above the chance level (p< 0.05), for a within-subject classification. The results indicated the feasibility of using ErrP signals in real-time AAN robot therapy with evidence from the conducted latency analysis, cross-subject classification, and three-class asynchronous classification.Significance.The findings presented support our proposed approach of using ErrPs as a measure to trigger and/or modulate as required the robotic assistance in a real-timehuman-in-the-looprobotic stroke rehabilitation system.},
}
@article {pmid34383613,
year = {2023},
author = {Branco, MP and Pels, EGM and Nijboer, F and Ramsey, NF and Vansteensel, MJ},
title = {Brain-Computer interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {18},
number = {6},
pages = {963-973},
pmid = {34383613},
issn = {1748-3115},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Locked-In Syndrome ; Caregivers ; Prospective Studies ; Communication ; },
abstract = {OBJECTIVES: The development of Brain-Computer Interfaces to restore communication (cBCIs) in people with severe motor impairment ideally relies on a close collaboration between end-users and other stakeholders, such as caregivers and researchers. Awareness about potential differences in opinion between these groups is crucial for development of usable cBCIs and access technology (AT) in general. In this study, we compared the opinions of prospective cBCI users, their caregivers and cBCI researchers regarding: (1) what applications would users like to control with a cBCI; (2) what mental strategies would users prefer to use for cBCI control; and (3) at what stage of their clinical trajectory would users like to be informed about AT and cBCIs.<br><br>METHODS: We collected data from 28 individuals with locked-in syndrome, 29 of their caregivers and 28 cBCI researchers. The questionnaire was supported with animation videos to explain different cBCI concepts, the utility of which was also assessed.<br><br>RESULTS: Opinions of the three groups were aligned with respect to the most desired cBCI applications, but diverged regarding mental strategies and the timing of being informed about cBCIs. Animation videos were regarded as clear and useful tools to explain cBCIs and mental strategies to end-users and other stakeholders.<br><br>CONCLUSIONS: Disagreements were clear between stakeholders regarding which mental strategies users prefer to use and when they would like to be informed about cBCIs. To move forward in the development and clinical implementation of cBCIs, it will be necessary to align the research agendas with the needs of the end-users and caregivers.IMPLICATIONS FOR REHABILITATIONBrain-Computer Interfaces may offer people with severe motor impairment a brain-based and muscle-independent approach to control communication-technology. The successful development of communication BCIs (cBCIs) relies on a close collaboration between end-users and other stakeholders, such as caregivers and researchers.Our work reveals that people with locked-in syndrome (end-users), their caregivers and researchers developing cBCIs agree that direct and private forms of communication are the most desired cBCI applications, but disagree regarding the preferred mental strategies for cBCI control and when to be informed about cBCIs.Animation videos are an effective tool for providing information to individuals, independent of their level of health literacy, regarding the concept of cBCIs and mental strategies for control.The misalignment in opinions of different groups of stakeholders about cBCIs strengthens the argument for a user-centered design approach in the development of cBCIs and access technology designed for daily life usage.},
}
@article {pmid34382365,
year = {2021},
author = {Szlosarek, PW and Wimalasingham, AG and Phillips, MM and Hall, PE and Chan, PY and Conibear, J and Lim, L and Rashid, S and Steele, J and Wells, P and Shiu, CF and Kuo, CL and Feng, X and Johnston, A and Bomalaski, J and Ellis, S and Grantham, M and Sheaff, M},
title = {Phase 1, pharmacogenomic, dose-expansion study of pegargiminase plus pemetrexed and cisplatin in patients with ASS1-deficient non-squamous non-small cell lung cancer.},
journal = {Cancer medicine},
volume = {10},
number = {19},
pages = {6642-6652},
pmid = {34382365},
issn = {2045-7634},
support = {//Higher Education Funding Council for England/ ; //Queen Mary University of London/ ; //Polaris Pharmaceuticals, Inc./ ; },
mesh = {Adult ; Aged ; Carcinoma, Non-Small-Cell Lung/*drug therapy ; Cisplatin/pharmacology/*therapeutic use ; Cohort Studies ; Female ; Humans ; Hydrolases/pharmacology/*therapeutic use ; Lung Neoplasms/*drug therapy ; Male ; Middle Aged ; Pemetrexed/pharmacology/*therapeutic use ; Polyethylene Glycols/pharmacology/*therapeutic use ; },
abstract = {INTRODUCTION: We evaluated the arginine-depleting enzyme pegargiminase (ADI-PEG20; ADI) with pemetrexed (Pem) and cisplatin (Cis) (ADIPemCis) in ASS1-deficient non-squamous non-small cell lung cancer (NSCLC) via a phase 1 dose-expansion trial with exploratory biomarker analysis.<br><br>METHODS: Sixty-seven chemona&#xef;ve patients with advanced non-squamous NSCLC were screened, enrolling 21 ASS1-deficient subjects from March 2015 to July 2017 onto weekly pegargiminase (36&#xa0;mg/m[2]) with Pem (500&#xa0;mg/m[2]) and Cis (75&#xa0;mg/m[2]), every 3&#xa0;weeks (four cycles maximum), with maintenance Pem or pegargiminase. Safety, pharmacodynamics, immunogenicity, and efficacy were determined; molecular biomarkers were annotated by next-generation sequencing and PD-L1 immunohistochemistry.<br><br>RESULTS: ADIPemCis was well-tolerated. Plasma arginine and citrulline were differentially modulated; pegargiminase antibodies plateaued by week 10. The disease control rate was 85.7% (n&#xa0;=&#xa0;18/21; 95% CI 63.7%-97%), with a partial response rate of 47.6% (n&#xa0;=&#xa0;10/21; 95% CI 25.7%-70.2%). The median progression-free and overall survivals were 4.2 (95% CI 2.9-4.8) and 7.2 (95% CI 5.1-18.4) months, respectively. Two PD-L1-expressing (&#x2265;1%) patients are alive following subsequent pembrolizumab immunotherapy (9.5%). Tumoral ASS1 deficiency enriched for p53 (64.7%) mutations, and numerically worse median overall survival as compared to ASS1-proficient disease (10.2&#xa0;months; n&#xa0;=&#xa0;29). There was no apparent increase in KRAS mutations (35.3%) and PD-L1 (<1%) expression (55.6%). Re-expression of tumoral ASS1 was detected in one patient at progression (n&#xa0;=&#xa0;1/3).<br><br>CONCLUSIONS: ADIPemCis was safe and highly active in patients with ASS1-deficient non-squamous NSCLC, however, survival was poor overall. ASS1&#xa0;loss was co-associated with p53&#xa0;mutations. Therapies incorporating pegargiminase merit further evaluation in ASS1-deficient and treatment-refractory NSCLC.},
}
@article {pmid34376532,
year = {2021},
author = {Nunes, R and Sella, T and Treuner, K and Atkinson, JM and Wong, J and Zhang, Y and Exman, P and Dabbs, D and Richardson, AL and Schnabel, CA and Sgroi, DC and Oesterreich, S and Cimino-Mathews, A and Metzger, O},
title = {Prognostic Utility of Breast Cancer Index to Stratify Distant Recurrence Risk in Invasive Lobular Carcinoma.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {27},
number = {20},
pages = {5688-5696},
pmid = {34376532},
issn = {1557-3265},
support = {P30 CA047904/CA/NCI NIH HHS/United States ; P50 CA168504/CA/NCI NIH HHS/United States ; },
mesh = {Breast Neoplasms/epidemiology/*pathology ; Carcinoma, Lobular/epidemiology/*pathology ; Cohort Studies ; Female ; Humans ; Middle Aged ; Neoplasm Invasiveness ; Neoplasm Recurrence, Local/epidemiology/*pathology ; Prognosis ; Retrospective Studies ; Risk Assessment ; },
abstract = {PURPOSE: The prognostic utility of Breast Cancer Index (BCI) for risk assessment of overall (0-10 years), early (0-5 years), and late (5-10 years) distant recurrence (DR) in hormone receptor-positive (HR+) invasive lobular carcinoma (ILC) was evaluated.<br><br>EXPERIMENTAL DESIGN: BCI gene expression analysis was performed blinded to clinical outcome utilizing tumor specimens from patients with HR+ ILC from a multi-institutional cohort. The primary endpoint was time to DR. Kaplan-Meier analyses of overall, early, and late DR risk were performed, and statistical significance was evaluated by log-rank test and Cox proportional hazards regression. The prognostic contribution of BCI in addition to clinicopathologic factors was evaluated by likelihood ratio analysis.<br><br>RESULTS: Analysis of 307 patients (99% ER+, 53% T1, 42% N+, 70% grade II) showed significant differences in DR over 10 years based on BCI risk categories. BCI low- and intermediate-risk patients demonstrated similar DR rates of 7.6% and 8.0%, respectively, compared with 27.0% for BCI high-risk patients. BCI was a significant independent prognostic factor for overall 10-year DR [HR = 4.09; 95% confidence interval (CI), 2.00-8.34; P = 0.0001] as well as for both early (HR = 8.19; 95% CI, 1.85-36.30; P = 0.0042) and late (HR = 3.04; 95% CI, 1.32-7.00; P = 0.0224) DR. In multivariate analysis, BCI remained the only statistically significant prognostic factor for DR (HR = 3.49; 95% CI, 1.28-9.54; P = 0.0150).<br><br>CONCLUSIONS: BCI is an independent prognostic factor for ILC and significantly stratified patients for cumulative risk of 10-year, early, and late DR. BCI added prognostic value beyond clinicopathologic characteristics in this distinct subtype of breast cancer.},
}
@article {pmid34376278,
year = {2022},
author = {Jacob-Brassard, J and Al-Omran, M and Haas, B and Nathens, AB and Gomez, D and Dueck, AD and Forbes, TL and de Mestral, C},
title = {A multicenter retrospective cohort study of blunt traumatic injury to the common or internal carotid arteries.},
journal = {Injury},
volume = {53},
number = {1},
pages = {152-159},
doi = {10.1016/j.injury.2021.07.035},
pmid = {34376278},
issn = {1879-0267},
mesh = {Adult ; *Carotid Artery Injuries/epidemiology/therapy ; Carotid Artery, Internal ; Hospital Mortality ; Humans ; Retrospective Studies ; *Wounds, Nonpenetrating/complications/therapy ; },
abstract = {OBJECTIVE: Current EAST guidelines recommend against routine carotid intervention for patients with blunt carotid artery injury (BCI), but offer limited information on its role for BCI patients presenting with neurological deficit. Our goal was to describe the contemporary management and outcomes of patients presenting with BCI and neurological deficit unrelated to head injury.<br><br>METHODS: We identified all adults who sustained a BCI between 2010 and 2017 in the American College of Surgeons Trauma Quality Improvement Program. We extracted patient demographics, injury characteristics (carotid and non-carotid), as well as the frequency, timing and approach of carotid intervention. Presence of neurological deficit unrelated to head injury at presentation was determined using Abbreviated Injury Scale codes. The main outcomes were in-hospital mortality and home discharge. Patients with and without neurological deficit at presentation were compared through multivariable logistic regression modeling. Among those with neurological deficit at presentation, the associations between carotid intervention (open or endovascular) and the outcomes were also assessed through multivariable logistic regression.<br><br>RESULTS: We identified 5,788 patients with BCI of whom 383 (7%) presented with neurological deficit unrelated to head injury. Among the 296 patients (5%) who underwent carotid intervention, 36 (12%) had presented with neurological deficit unrelated to head injury. Interventions were most often endovascular (68% [200/296]) and within a median time of 32&#xa0;h (IQR 5-203). In-hospital mortality was 16% (918/5,788), and in-hospital stroke prevalence was 6% (336/5,788). When comparing patients with and without neurological deficit at presentation, those with deficits were more frequently managed with an intervention. After adjustment, the likelihood of mortality was higher (OR [95% CI]&#xa0;=&#xa0;2.16 [1.63-2.85]) and the likelihood of home discharge lower (OR [95% CI]&#xa0;=&#xa0;0.29 [0.21-0.40]) among patients presenting with neurological deficit. Among those with neurological deficit, carotid intervention was positively associated with home discharge (OR [95% CI]&#xa0;=&#xa0;2.96 [1.21-7.23]), but not with in-hospital mortality (OR [95% CI]&#xa0;=&#xa0;0.87 [0.36-2.10]). Results were similar in the subgroup of patients with isolated BCI (2,971/5,788).<br><br>CONCLUSIONS: Intervention in BCI patients presenting with neurological deficit may contribute to a greater likelihood of home discharge but not reduced in-hospital mortality.},
}
@article {pmid34376215,
year = {2021},
author = {Wittevrongel, B and Holmes, N and Boto, E and Hill, R and Rea, M and Libert, A and Khachatryan, E and Van Hulle, MM and Bowtell, R and Brookes, MJ},
title = {Practical real-time MEG-based neural interfacing with optically pumped magnetometers.},
journal = {BMC biology},
volume = {19},
number = {1},
pages = {158},
pmid = {34376215},
issn = {1741-7007},
support = {/WT_/Wellcome Trust/United Kingdom ; 203257/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; 203257/B/16/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain ; Electroencephalography ; Humans ; *Magnetoencephalography ; },
abstract = {BACKGROUND: Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware. The recently developed optically pumped magnetometers (OPMs) promise to overcome this limitation, but are currently untested in the context of neural interfacing.<br><br>RESULTS: In this work, we show that OPM-MEG allows robust single-trial analysis which we exploited in a real-time 'mind-spelling' application yielding an average accuracy of 97.7%.<br><br>CONCLUSIONS: This shows that OPM-MEG can be used to exploit neuro-magnetic brain responses in a practical and flexible manner, and opens up new avenues for a wide range of new neural interface applications in the future.},
}
@article {pmid34376123,
year = {2021},
author = {Shi, Y and Ganesh, G and Ando, H and Koike, Y and Yoshida, E and Yoshimura, N},
title = {Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2150034},
doi = {10.1142/S0129065721500349},
pmid = {34376123},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Intention ; },
abstract = {A significant problem in brain-computer interface (BCI) research is decoding - obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front/back MI task was validated. A test decoding accuracy of 77.83-78.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.},
}
@article {pmid34376122,
year = {2021},
author = {Sun, H and Jin, J and Xu, R and Cichocki, A},
title = {Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain-Computer Interfaces.},
journal = {International journal of neural systems},
volume = {31},
number = {9},
pages = {2150040},
doi = {10.1142/S0129065721500404},
pmid = {34376122},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.},
}
@article {pmid34376121,
year = {2021},
author = {Ieracitano, C and Morabito, FC and Hussain, A and Mammone, N},
title = {A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources.},
journal = {International journal of neural systems},
volume = {31},
number = {9},
pages = {2150038},
doi = {10.1142/S0129065721500386},
pmid = {34376121},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Machine Learning ; Neural Networks, Computer ; },
abstract = {In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of [Formula: see text]%.},
}
@article {pmid34374293,
year = {2021},
author = {Yang, J and Ma, T and Yu, H and Yin, M and Qiao, Y and Niu, L and Yang, F and He, J and Li, Z},
title = {Alternations of N-glycans recognized by Phaseolus vulgaris leucoagglutinin in the saliva of patients with breast cancer.},
journal = {Neoplasma},
volume = {68},
number = {5},
pages = {994-1004},
doi = {10.4149/neo_2021_210301N264},
pmid = {34374293},
issn = {0028-2685},
mesh = {*Breast Neoplasms ; Female ; Humans ; Phytohemagglutinins ; Polysaccharides ; *Saliva ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ; },
abstract = {Breast cancer is the most frequently diagnosed cancer in most countries. Early diagnosis of breast disease is necessary for its prognosis and treatment. Altered protein glycosylation has been shown to be expressed in precursor lesions of breast cancer, making them powerful early diagnostic biomarkers. The present study validated alterations of the N-glycan profiles of their salivary glycoproteins isolated by the Phaseolus vulgaris leucoagglutinin (PHA-E+L)-magnetic particle conjugates from 141 female subjects (66 healthy volunteers (HV), and 75 patients with breast disease including breast benign cyst (BB) or breast cancer in stage I/II (BC-I/II)) were analyzed and annotated by MALDI-TOF/TOF-MS. The results showed that there were 11, 20, 16, and 17 N-glycans recognized by PHA-E+L identified and annotated from the pooled salivary samples of HV, BB, BC-I, and BC-II, respectively. There were 3 N-glycans peaks (m/z 2459.8799, 2507.9139, and 2954.0547), 2 N-glycans peaks (m/z 1957.7265 and 2794.0427), and 2 N-glycans peaks (m/z 1866.6608 and 2240.8056) recognized by PHA-E+L that existed only in BB, BC-I, and BC-II, respectively. The present study compared the alternations of N-glycans from the salivary proteins isolated by PHA-E+L-magnetic particle conjugates among HV, BB, BC-I, and BC-II, which could provide information on N-glycans during the development of breast cancer in saliva to promote the study of its biomarkers.},
}
@article {pmid34373644,
year = {2021},
author = {Yang, H and de Jong, JW and Cerniauskas, I and Peck, JR and Lim, BK and Gong, H and Fields, HL and Lammel, S},
title = {Pain modulates dopamine neurons via a spinal-parabrachial-mesencephalic circuit.},
journal = {Nature neuroscience},
volume = {24},
number = {10},
pages = {1402-1413},
pmid = {34373644},
issn = {1546-1726},
support = {R01 DA042889/DA/NIDA NIH HHS/United States ; R01 MH107742/MH/NIMH NIH HHS/United States ; R01 MH112721/MH/NIMH NIH HHS/United States ; U01 MH114829/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal ; Brain Mapping ; *Dopaminergic Neurons ; Male ; Mesencephalon/*physiopathology ; Mice ; Mice, Inbred C57BL ; Neural Pathways/*physiopathology ; Neurons ; Nociception ; Optogenetics ; Pain/*physiopathology/psychology ; Pain Management ; Parabrachial Nucleus/*physiopathology ; Spinal Cord/*physiopathology ; Substantia Nigra/physiopathology ; Ventral Tegmental Area/physiopathology ; },
abstract = {Pain decreases the activity of many ventral tegmental area (VTA) dopamine (DA) neurons, yet the underlying neural circuitry connecting nociception and the DA system is not understood. Here we show that a subpopulation of lateral parabrachial (LPB) neurons is critical for relaying nociceptive signals from the spinal cord to the substantia nigra pars reticulata (SNR). SNR-projecting LPB neurons are activated by noxious stimuli and silencing them blocks pain responses in two different models of pain. LPB-targeted and nociception-recipient SNR neurons regulate VTA DA activity directly through feed-forward inhibition and indirectly by inhibiting a distinct subpopulation of VTA-projecting LPB neurons thereby reducing excitatory drive onto VTA DA neurons. Correspondingly, ablation of SNR-projecting LPB neurons is sufficient to reduce pain-mediated inhibition of DA release in vivo. The identification of a neural circuit conveying nociceptive input to DA neurons is critical to our understanding of how pain influences learning and behavior.},
}
@article {pmid34373025,
year = {2021},
author = {Tang, AM and Chen, KH and Gogia, AS and Del Campo-Vera, RM and Sebastian, R and Gilbert, ZD and Lee, Y and Nune, G and Liu, CY and Kellis, S and Lee, B},
title = {Amygdaloid theta-band power increases during conflict processing in humans.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {91},
number = {},
pages = {183-192},
doi = {10.1016/j.jocn.2021.07.001},
pmid = {34373025},
issn = {1532-2653},
mesh = {Adult ; *Conflict, Psychological ; Electroencephalography ; Emotions ; Female ; Humans ; Male ; Middle Aged ; Reaction Time ; Stroop Test ; Young Adult ; },
abstract = {The amygdala is a medial temporal lobe structure known to be involved in processing emotional conflict. However, its role in processing non-emotional conflict is not well understood. Previous studies have utilized the Stroop Task to examine brain modulation of humans under the color-word conflict scenario, which is non-emotional conflict processing, and found hippocampal theta-band (4-7 Hz) modulation. This study aims to survey amygdaloid theta power changes during non-emotional conflict processing using intracranial depth electrodes in nine epileptic patients (3 female; age 20-62). All patients were asked to perform a modified Stroop task. During task performance, local field potential (LFP) data was recorded from macro contacts sampled at 2 K Hz and used for analysis. Mean theta power change from baseline was compared between the incongruent and congruent task condition groups using a paired sample t-test. Seven patients were available for analysis after artifact exclusion. In five out of seven patients, statistically significant increases in theta-band power from baseline were noted during the incongruent task condition (paired sample t-test p < 0.001), including one patient exhibiting theta power increases in both task conditions. Average response time was 1.07 s (failure trials) and 1.04 s (success trials). No speed-accuracy tradeoff was noted in this analysis. These findings indicate that human amygdaloid theta-band modulation may play a role in processing non-emotional conflict. It builds directly upon work suggesting that the amygdala processes emotional conflict and provides a neurophysiological mechanism for non-emotional conflict processing as well.},
}
@article {pmid34372448,
year = {2021},
author = {He, C and Chikara, RK and Yeh, CL and Ko, LW},
title = {Neural Dynamics of Target Detection via Wireless EEG in Embodied Cognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {15},
pages = {},
pmid = {34372448},
issn = {1424-8220},
support = {108-2221-E-009-045-MY3;110-2622-E-A49 -009 -;110-2321-B- 410 010 -005 -//Ministry of Science and Technology, Taiwan/ ; },
mesh = {Brain ; Brain Mapping ; *Cognition ; *Electroencephalography ; Humans ; Reaction Time ; },
abstract = {Embodied cognitive attention detection is important for many real-world applications, such as monitoring attention in daily driving and studying. Exploring how the brain and behavior are influenced by visual sensory inputs becomes a major challenge in the real world. The neural activity of embodied mind cognitive states can be understood through simple symbol experimental design. However, searching for a particular target in the real world is more complicated than during a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations for investigating the neural dynamics of subjects during real-world environments is critical. This study designed a novel military-inspired target detection task for investigating the neural activities of performing embodied cognition tasks in the real-world setting. We adopted independent component analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study the participant's event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power spectral density (PSD) during the target detection task using a wireless EEG system, which is more convenient for real-life use. Behavioral results showed that the response time in the congruent condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms) conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which are associated with attention ERPs. Furthermore, delta (1-4 Hz) and theta (4-7 Hz) band powers in the right frontal lobe, as well as alpha (8-12 Hz) and beta (13-30 Hz) band powers in the left motor cortex were suppressed, whereas the theta (4-7 Hz) band powers in the middle occipital lobe were increased considerably in the attention task. Experimental results showed that the embodied body function influences human mental states and psychological performance under cognition attention tasks. These neural markers will be also feasible to implement in the real-time brain computer interface. Novel findings in this study can be helpful for humans to further understand the interaction between the brain and behavior in multiple target detection conditions in real life.},
}
@article {pmid34372338,
year = {2021},
author = {Collazos-Huertas, DF and Velasquez-Martinez, LF and Perez-Nastar, HD and Alvarez-Meza, AM and Castellanos-Dominguez, G},
title = {Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {15},
pages = {},
pmid = {34372338},
issn = {1424-8220},
support = {727//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; 785//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Machine Learning ; Surveys and Questionnaires ; },
abstract = {Motor imagery (MI) promotes motor learning and encourages brain-computer interface systems that entail electroencephalogram (EEG) decoding. However, a long period of training is required to master brain rhythms' self-regulation, resulting in users with MI inefficiency. We introduce a parameter-based approach of cross-subject transfer-learning to improve the performances of poor-performing individuals in MI-based BCI systems, pooling data from labeled EEG measurements and psychological questionnaires via kernel-embedding. To this end, a Deep and Wide neural network for MI classification is implemented to pre-train the network from the source domain. Then, the parameter layers are transferred to initialize the target network within a fine-tuning procedure to recompute the Multilayer Perceptron-based accuracy. To perform data-fusion combining categorical features with the real-valued features, we implement stepwise kernel-matching via Gaussian-embedding. Finally, the paired source-target sets are selected for evaluation purposes according to the inefficiency-based clustering by subjects to consider their influence on BCI motor skills, exploring two choosing strategies of the best-performing subjects (source space): single-subject and multiple-subjects. Validation results achieved for discriminant MI tasks demonstrate that the introduced Deep and Wide neural network presents competitive performance of accuracy even after the inclusion of questionnaire data.},
}
@article {pmid34372256,
year = {2021},
author = {Chen, YJ and Chen, PC and Chen, SC and Wu, CM},
title = {Denoising Autoencoder-Based Feature Extraction to Robust SSVEP-Based BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {15},
pages = {},
pmid = {34372256},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {For subjects with amyotrophic lateral sclerosis (ALS), the verbal and nonverbal communication is greatly impaired. Steady state visually evoked potential (SSVEP)-based brain computer interfaces (BCIs) is one of successful alternative augmentative communications to help subjects with ALS communicate with others or devices. For practical applications, the performance of SSVEP-based BCIs is severely reduced by the effects of noises. Therefore, developing robust SSVEP-based BCIs is very important to help subjects communicate with others or devices. In this study, a noise suppression-based feature extraction and deep neural network are proposed to develop a robust SSVEP-based BCI. To suppress the effects of noises, a denoising autoencoder is proposed to extract the denoising features. To obtain an acceptable recognition result for practical applications, the deep neural network is used to find the decision results of SSVEP-based BCIs. The experimental results showed that the proposed approaches can effectively suppress the effects of noises and the performance of SSVEP-based BCIs can be greatly improved. Besides, the deep neural network outperforms other approaches. Therefore, the proposed robust SSVEP-based BCI is very useful for practical applications.},
}
@article {pmid34372231,
year = {2021},
author = {Wang, Y and Wang, H and Li, H and Ullah, A and Zhang, M and Gao, H and Hu, R and Li, G},
title = {Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {15},
pages = {},
pmid = {34372231},
issn = {1424-8220},
support = {JCKY2018204B053//Science Foundation of Chinese Aerospace Industry/ ; No. ICT2021A13//Autonomous Research Project of the State Key Laboratory of Industrial Control Technology, China/ ; },
mesh = {*Algorithms ; Electrodes ; Electromyography ; Humans ; *Taste ; },
abstract = {Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.},
}
@article {pmid34370840,
year = {2021},
author = {Shriver, AJ and John, TM},
title = {Neuroethics and Animals: Report and Recommendations From the University of Pennsylvania Animal Research Neuroethics Workshop.},
journal = {ILAR journal},
volume = {60},
number = {3},
pages = {424-433},
pmid = {34370840},
issn = {1930-6180},
mesh = {*Animal Experimentation ; Animals ; Bioethical Issues ; Morals ; *Neurosciences ; Pain ; },
abstract = {Growing awareness of the ethical implications of neuroscience in the early years of the 21st century led to the emergence of the new academic field of "neuroethics," which studies the ethical implications of developments in the neurosciences. However, despite the acceleration and evolution of neuroscience research on nonhuman animals, the unique ethical issues connected with neuroscience research involving nonhuman animals remain underdiscussed. This is a significant oversight given the central place of animal models in neuroscience. To respond to these concerns, the Center for Neuroscience and Society and the Center for the Interaction of Animals and Society at the University of Pennsylvania hosted a workshop on the "Neuroethics of Animal Research" in Philadelphia, Pennsylvania. At the workshop, expert speakers and attendees discussed ethical issues arising from neuroscience research involving nonhuman animals, including the use of animal models in the study of pain and psychiatric conditions, animal brain-machine interfaces, animal-animal chimeras, cerebral organoids, and the relevance of neuroscience to debates about personhood. This paper highlights important emerging ethical issues based on the discussions at the workshop. This paper includes recommendations for research in the United States from the authors based on the discussions at the workshop, loosely following the format of the 2 Gray Matters reports on neuroethics published by the Presidential Commission for the Study of Bioethical Issues.},
}
@article {pmid34368396,
year = {2021},
author = {Moon, E and Barrow, M and Lim, J and Lee, J and Nason, SR and Costello, J and Kim, HS and Chestek, C and Jang, T and Blaauw, D and Phillips, JD},
title = {Bridging the"Last Millimeter" Gap of Brain-Machine Interfaces via Near-Infrared Wireless Power Transfer and Data Communications.},
journal = {ACS photonics},
volume = {8},
number = {5},
pages = {1430-1438},
pmid = {34368396},
issn = {2330-4022},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; R21 EY029452/EY/NEI NIH HHS/United States ; },
abstract = {Arrays of floating neural sensors with high channel count that cover an area of square centimeters and larger would be transformative for neural engineering and brain-machine interfaces. Meeting the power and wireless data communications requirements within the size constraints for each neural sensor has been elusive due to the need to incorporate sensing, computing, communications, and power functionality in a package of approximately 100 micrometers on a side. In this work, we demonstrate a near infrared optical power and data communication link for a neural recording system that satisfies size requirements to achieve dense arrays and power requirements to prevent tissue heating. The optical link is demonstrated using an integrated optoelectronic device consisting of a tandem photovoltaic cell and microscale light emitting diode. End-to-end functionality of a wireless neural link within system constraints is demonstrated using a pre-recorded neural signal between a self-powered CMOS integrated circuit and single photon avalanche photodiode.},
}
@article {pmid34368390,
year = {2021},
author = {Cheng, X and Sie, EJ and Naufel, S and Boas, DA and Marsili, F},
title = {Measuring neuronal activity with diffuse correlation spectroscopy: a theoretical investigation.},
journal = {Neurophotonics},
volume = {8},
number = {3},
pages = {035004},
pmid = {34368390},
issn = {2329-423X},
abstract = {Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS. Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales. Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes. Results: We found that neuronal cell motion induces a DCS signal variation of ∼ 10 - 5 . We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays. Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain-computer interfaces.},
}
@article {pmid34367361,
year = {2021},
author = {Xu, L and Xu, M and Jung, TP and Ming, D},
title = {Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {4},
pages = {569-584},
pmid = {34367361},
issn = {1871-4080},
abstract = {A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.},
}
@article {pmid34366808,
year = {2021},
author = {Wang, Y and Luo, J and Guo, Y and Du, Q and Cheng, Q and Wang, H},
title = {Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {627100},
pmid = {34366808},
issn = {1662-5161},
abstract = {BACKGROUND: In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns' changes after a short-term rehabilitation training.<br><br>MATERIALS AND METHODS: Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal's Mu band power's attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG's Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain.<br><br>RESULTS: Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group's ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group's Mu band power's attenuation increased but not significantly (fitting line slope = 0.2, t-test value p > 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = -0.4, t-test value p > 0.05). In the ERP-based brain network analysis, the neurofeedback group's network parameters were attenuated in all scales significantly (t-test value: p < 0.01); while the non-feedback group's most network parameters didn't change significantly (t-test value: p > 0.05).<br><br>CONCLUSION: The MI-BCI training's short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.},
}
@article {pmid34365114,
year = {2021},
author = {Rommelfanger, NJ and Keck, CH and Chen, Y and Hong, G},
title = {Learning from the brain's architecture: bioinspired strategies towards implantable neural interfaces.},
journal = {Current opinion in biotechnology},
volume = {72},
number = {},
pages = {8-12},
pmid = {34365114},
issn = {1879-0429},
support = {R00 AG056636/AG/NIA NIH HHS/United States ; },
mesh = {Biomimetics ; *Brain/surgery ; Electrophysiological Phenomena ; *Neurons ; Prostheses and Implants ; },
abstract = {While early neural interfaces consisted of rigid, monolithic probes, recent implantable technologies include meshes, gels, and threads that imitate various properties of the neural tissue itself. Such mimicry brings new capabilities to the traditional electrophysiology toolbox, with benefits for both neuroscience studies and clinical treatments. Specifically, by matching the multi-dimensional mechanical properties of the brain, neural implants can preserve the endogenous environment while functioning over chronic timescales. Further, topological mimicry of neural structures enables seamless integration into the tissue and provides proximal access to neurons for high-quality recordings. Ultimately, we envision that neuromorphic devices incorporating functional, mechanical, and topological mimicry of the brain may facilitate stable operation of advanced brain machine interfaces with minimal disruption of the native tissue.},
}
@article {pmid34364850,
year = {2021},
author = {Benyamini, M and Demchenko, I and Zacksenhouse, M},
title = {Error related EEG potentials evoked by visuo-motor rotations.},
journal = {Brain research},
volume = {1769},
number = {},
pages = {147606},
doi = {10.1016/j.brainres.2021.147606},
pmid = {34364850},
issn = {1872-6240},
mesh = {Adult ; Biomechanical Phenomena ; *Electroencephalography ; Eye Movements ; Female ; Humans ; Male ; Movement/*physiology ; Parietal Lobe/physiology ; Psychomotor Performance/*physiology ; *Rotation ; Saccades ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Electroencephalographic (EEG) correlates of errors, known as error-related potentials (ErrPs), provide promising tools to investigate error processing in the brain and to detect and correct errors induced by brain-computer interfaces (BCIs). Visuo-motor rotation (VMR) is a well-known experimental paradigm to introduce visuo-motor errors that closely mimics directional errors induced by BCIs. However, investigations of ErrPs during VMR experiments are limited and reveals different ErrPs depending on task and synchronization. We conducted VMR experiments with 5 randomly selected conditions (no-rotation, small, ±22.5°, or large, ±45° rotations) to hamper adaptation and facilitate investigation of the effect of error size. We tracked eye movements so EEG was synchronized not only to onset of movement correction (OMC) but also to saccadic movement onset (SMO). Kinematic analysis indicated that maximum deviations from a straight line to the target were larger in trials with large rotations compared to small or no rotations, but there was a large overlap. Thus, we also compared ErrPs generated by trials with different maximum deviations. Our results reveal that trials with large rotations and especially trials with large maximum deviations evoke a significant positive ErrP component. The positive peak appeared 380 msec after SMO and 240 msec after OMC. Furthermore, the positive peak was associated with activity in Brodmann areas 5 and 7, in agreement with other studies and with the role of posterior parietal cortex in reaching movements. The observed ErrP may facilitate further investigation of error processing in the brain and error detection and correction in BCIs.},
}
@article {pmid34359131,
year = {2021},
author = {Li, MM and Fan, JT and Cheng, SG and Yang, LF and Yang, L and Wang, LF and Shang, ZG and Wan, H},
title = {Enhanced Hippocampus-Nidopallium Caudolaterale Connectivity during Route Formation in Goal-Directed Spatial Learning of Pigeons.},
journal = {Animals : an open access journal from MDPI},
volume = {11},
number = {7},
pages = {},
pmid = {34359131},
issn = {2076-2615},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.},
}
@article {pmid34358704,
year = {2021},
author = {Chandrasekaran, S and Bickel, S and Herrero, JL and Kim, JW and Markowitz, N and Espinal, E and Bhagat, NA and Ramdeo, R and Xu, J and Glasser, MF and Bouton, CE and Mehta, AD},
title = {Evoking highly focal percepts in the fingertips through targeted stimulation of sulcal regions of the brain for sensory restoration.},
journal = {Brain stimulation},
volume = {14},
number = {5},
pages = {1184-1196},
pmid = {34358704},
issn = {1876-4754},
support = {R01 MH060974/MH/NIMH NIH HHS/United States ; },
mesh = {Electric Stimulation ; Electrocorticography ; Electrodes, Implanted ; *Hand ; Humans ; *Somatosensory Cortex ; Touch ; },
abstract = {BACKGROUND: Paralysis and neuropathy, affecting millions of people worldwide, can be accompanied by significant loss of somatosensation. With tactile sensation being central to achieving dexterous movement, brain-computer interface (BCI) researchers have used intracortical and cortical surface electrical stimulation to restore somatotopically-relevant sensation to the hand. However, these approaches are restricted to stimulating the gyral areas of the brain. Since representation of distal regions of the hand extends into the sulcal regions of human primary somatosensory cortex (S1), it has been challenging to evoke sensory percepts localized to the fingertips.<br><br>OBJECTIVE/HYPOTHESIS: Targeted stimulation of sulcal regions of S1, using stereoelectroencephalography (SEEG) depth electrodes, can evoke focal sensory percepts in the fingertips.<br><br>METHODS: Two participants with intractable epilepsy received cortical stimulation both at the gyri via high-density electrocorticography (HD-ECoG) grids and in the sulci via SEEG depth electrode leads. We characterized the evoked sensory percepts localized to the hand.<br><br>RESULTS: We show that highly focal percepts can be evoked in the fingertips of the hand through sulcal stimulation. fMRI, myelin content, and cortical thickness maps from the Human Connectome Project elucidated specific cortical areas and sub-regions within S1 that evoked these focal percepts. Within-participant comparisons showed that percepts evoked by sulcal stimulation via SEEG electrodes were significantly more focal (80% less area; p&#xa0;=&#xa0;0.02) and localized to the fingertips more often, than by gyral stimulation via HD-ECoG electrodes. Finally, sulcal locations with consistent modulation of high-frequency neural activity during mechanical tactile stimulation of the fingertips showed the same somatotopic correspondence as cortical stimulation.<br><br>CONCLUSIONS: Our findings indicate minimally invasive sulcal stimulation via SEEG electrodes could be a clinically viable approach to restoring sensation.},
}
@article {pmid34357871,
year = {2023},
author = {Jeon, E and Ko, W and Yoon, JS and Suk, HI},
title = {Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {34},
number = {2},
pages = {739-749},
doi = {10.1109/TNNLS.2021.3100583},
pmid = {34357871},
issn = {2162-2388},
mesh = {Humans ; *Neural Networks, Computer ; Machine Learning ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Calibration ; Algorithms ; },
abstract = {In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no concern of its practical use, hampered by time-consuming steps and a large data requirement. To this end, recent studies adopted a transfer learning strategy, especially domain adaptation techniques. Among those, we have witnessed the potential of adversarial learning-based transfer learning in BCIs. In the meantime, it is known that adversarial learning-based domain adaptation methods are prone to negative transfer that disrupts learning generalized feature representations, applicable to diverse domains, for example, subjects or sessions in BCIs. In this article, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning. To be specific, we devise two operational components in a deep network that explicitly estimate mutual information between feature representations: 1) to decompose features in an intermediate layer into class-relevant and class-irrelevant ones and 2) to enrich class-discriminative feature representation. On two large EEG datasets, we validated the effectiveness of our proposed framework by comparing with several comparative methods in performance. Furthermore, we conducted rigorous analyses by performing an ablation study in regard to the components in our network, explaining our model's decision on input EEG signals via layer-wise relevance propagation, and visualizing the distribution of learned features via t-SNE.},
}
@article {pmid34354213,
year = {2021},
author = {Atique, MMU and Francis, JT},
title = {Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv).},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {15959},
pmid = {34354213},
issn = {2045-2322},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Brain-Computer Interfaces ; Cues ; Female ; Hand Strength/physiology ; Macaca mulatta/physiology ; Macaca radiata/physiology ; Male ; Mirror Neurons/metabolism/*physiology ; Motivation/*physiology ; Motor Cortex/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Reward ; Sensorimotor Cortex/*physiology ; },
abstract = {Mirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1-2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1-2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.},
}
@article {pmid34352736,
year = {2021},
author = {Colachis, SC and Dunlap, CF and Annetta, NV and Tamrakar, SM and Bockbrader, MA and Friedenberg, DA},
title = {Long-term intracortical microelectrode array performance in a human: a 5 year retrospective analysis.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1add},
pmid = {34352736},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Microelectrodes ; Primates ; Retrospective Studies ; },
abstract = {Objective. Brain-computer interfaces (BCIs) that record neural activity using intracortical microelectrode arrays (MEAs) have shown promise for mitigating disability associated with neurological injuries and disorders. While the chronic performance and failure modes of MEAs have been well studied and systematically described in non-human primates, there is far less reported about long-term MEA performance in humans. Our group has collected one of the largest neural recording datasets from a Utah MEA in a human subject, spanning over 5 years (2014-2019). Here we present both long-term signal quality and BCI performance as well as highlight several acute signal disruption events observed during the clinical study.Approach. Long-term Utah array performance was evaluated by analyzing neural signal metric trends and decoding accuracy for tasks regularly performed across 448 clinical recording sessions. For acute signal disruptions, we identify or hypothesize the root cause of the disruption, show how the disruption manifests in the collected data, and discuss potential identification and mitigation strategies for the disruption.Main results. Neural signal quality metrics deteriorated rapidly within the first year, followed by a slower decline through the remainder of the study. Nevertheless, BCI performance remained high 5 years after implantation, which is encouraging for the translational potential of this technology as an assistive device. We also present examples of unanticipated signal disruptions during chronic MEA use, which are critical to detect as BCI technology progresses toward home usage.Significance. Our work fills a gap in knowledge around long-term MEA performance in humans, providing longevity and efficacy data points to help characterize the performance of implantable neural sensors in a human population. The trial was registered on ClinicalTrials.gov (Identifier NCT01997125) and conformed to institutional requirements for the conduct of human subjects research.},
}
@article {pmid34352734,
year = {2021},
author = {Lashgari, E and Ott, J and Connelly, A and Baldi, P and Maoz, U},
title = {An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1ade},
pmid = {34352734},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing.Approach.To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain-computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community.Main results.Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning.Significance.Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.},
}
@article {pmid34350839,
year = {2021},
author = {Ortega, P and Faisal, AA},
title = {Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1ab3},
pmid = {34350839},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Hand Strength ; Humans ; Spectroscopy, Near-Infrared ; },
abstract = {Objective.Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unimanual case, controlling forces from both hands would enable BMI-users to perform a greater range of interactions. We here investigate the decoding of hand-specific forces.Approach.We maximise cortical information by using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and developing a deep-learning architecture with attention and residual layers (cnnatt) to improve their fusion. Our task required participants to generate hand-specific force profiles on which we trained and tested our deep-learning and linear decoders.Main results.The use of EEG and fNIRS improved the decoding of bimanual force and the deep-learning models outperformed the linear model. In both cases, the greatest gain in performance was due to the detection of force generation. In particular, the detection of forces was hand-specific and better for the right dominant hand andcnnattwas better at fusing EEG and fNIRS. Consequently, the study ofcnnattrevealed that forces from each hand were differently encoded at the cortical level.Cnnattalso revealed traces of the cortical activity being modulated by the level of force which was not previously found using linear models.Significance.Our results can be applied to avoid hand-cross talk during hand force decoding to improve the robustness of BMI robotic devices. In particular, we improve the fusion of EEG and fNIRS signals and offer hand-specific interpretability of the encoded forces which are valuable during motor rehabilitation assessment.},
}
@article {pmid34349630,
year = {2021},
author = {Woo, S and Lee, J and Kim, H and Chun, S and Lee, D and Gwon, D and Ahn, M},
title = {An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {647839},
pmid = {34349630},
issn = {1662-5161},
abstract = {Brain-computer interfaces can provide a new communication channel and control functions to people with restricted movements. Recent studies have indicated the effectiveness of brain-computer interface (BCI) applications. Various types of applications have been introduced so far in this field, but the number of those available to the public is still insufficient. Thus, there is a need to expand the usability and accessibility of BCI applications. In this study, we introduce a BCI application for users to experience a virtual world tour. This software was built on three open-source environments and is publicly available through the GitHub repository. For a usability test, 10 healthy subjects participated in an electroencephalography (EEG) experiment and evaluated the system through a questionnaire. As a result, all the participants successfully played the BCI application with 96.6% accuracy with 20 blinks from two sessions and gave opinions on its usability (e.g., controllability, completeness, comfort, and enjoyment) through the questionnaire. We believe that this open-source BCI world tour system can be used in both research and entertainment settings and hopefully contribute to open science in the BCI field.},
}
@article {pmid34349626,
year = {2021},
author = {Benyamini, M and Zacksenhouse, M},
title = {Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation.},
journal = {Frontiers in systems neuroscience},
volume = {15},
number = {},
pages = {677688},
pmid = {34349626},
issn = {1662-5137},
abstract = {Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.},
}
@article {pmid34343888,
year = {2021},
author = {Onishi, A},
title = {Brain-computer interface with rapid serial multimodal presentation using artificial facial images and voice.},
journal = {Computers in biology and medicine},
volume = {136},
number = {},
pages = {104685},
doi = {10.1016/j.compbiomed.2021.104685},
pmid = {34343888},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {Electroencephalography (EEG) signals elicited by multimodal stimuli can drive brain-computer interfaces (BCIs), and research has demonstrated that visual and auditory stimuli can be employed simultaneously to improve BCI performance. However, no studies have investigated the effect of multimodal stimuli in rapid serial visual presentation (RSVP) BCIs. The present study proposed a rapid serial multimodal presentation (RSMP) BCI that incorporates artificial facial images and artificial voice stimuli. To clarify the effect of audiovisual stimuli on the RSMP BCI, scrambled images and masked sounds were applied instead of visual and auditory stimuli, respectively. The findings indicated that the audiovisual stimuli improved performance of the RSMP BCI, and that P300 at Pz contributed to classification accuracy. Online accuracy of the BCI reached 85.7 ± 11.5 %. Taken together, these findings may aid in the development of better gaze-independent BCI systems.},
}
@article {pmid34343575,
year = {2021},
author = {Li, S and Jin, J and Daly, I and Wang, X and Lam, HK and Cichocki, A},
title = {Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method.},
journal = {Journal of neuroscience methods},
volume = {362},
number = {},
pages = {109300},
doi = {10.1016/j.jneumeth.2021.109300},
pmid = {34343575},
issn = {1872-678X},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Language ; *Recognition, Psychology ; },
abstract = {BACKGROUND: P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability.<br><br>NEW METHODS: In this study, we propose a novel multi-feature subset fuzzy fusion (MSFF) framework for the P300 speller to recognize the users' spelling intention. This method includes two parts: 1) feature selection by the Lasso algorithm and feature division; 2) the construction of ensemble LDA classifiers and the fuzzy fusion of those classifiers to recognize user intention.<br><br>RESULTS: The proposed framework is evaluated in three public datasets and achieves an average accuracy of 100% after 4 epochs for BCI Competition II Dataset IIb, 96% for BCI Competition III dataset II and 98.3% for the BNCI Horizon Dataset. It indicates that the proposed MSFF method can make use of temporal information of signals and helps to enhance classification performance.<br><br>The proposed MSFF method yields better or comparable performance than previously reported machine learning algorithms.<br><br>CONCLUSIONS: The proposed MSFF method is able to improve the performance of P300-based BCIs.},
}
@article {pmid34343094,
year = {2021},
author = {Grigorev, NA and Savosenkov, AO and Lukoyanov, MV and Udoratina, A and Shusharina, NN and Kaplan, AY and Hramov, AE and Kazantsev, VB and Gordleeva, S},
title = {A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1583-1592},
doi = {10.1109/TNSRE.2021.3102304},
pmid = {34343094},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; *Cortical Excitability ; Electroencephalography ; Humans ; Imagination ; *Neurofeedback ; },
abstract = {In this study, we address the issue of whether vibrotactile feedback can enhance the motor cortex excitability translated into the plastic changes in local cortical areas during motor imagery (MI) BCI-based training. For this purpose, we focused on two of the most notable neurophysiological effects of MI - the event-related desynchronization (ERD) level and the increase in cortical excitability assessed with navigated transcranial magnetic stimulation (nTMS). For TMS navigation, we used individual high-resolution 3D brain MRIs. Ten BCI-naive and healthy adults participated in this study. The MI (rest or left/right hand imagery using Graz-BCI paradigm) tasks were performed separately in the presence and absence of feedback. To investigate how much the presence/absence of vibrotactile feedback in MI BCI-based training could contribute to the sensorimotor cortical activations, we compared the MEPs amplitude during MI after training with and without feedback. In addition, the ERD levels during MI BCI-based training were investigated. Our findings provide evidence that applying vibrotactile feedback during MI training leads to (i) an enhancement of the desynchronization level of mu-rhythm EEG patterns over the contralateral motor cortex area corresponding to the MI of the non-dominant hand; (ii) an increase in motor cortical excitability in hand muscle representation corresponding to a muscle engaged by the MI.},
}
@article {pmid34341881,
year = {2021},
author = {Kazim, SF and Bowers, CA and Cole, CD and Varela, S and Karimov, Z and Martinez, E and Ogulnick, JV and Schmidt, MH},
title = {Corticospinal Motor Circuit Plasticity After Spinal Cord Injury: Harnessing Neuroplasticity to Improve Functional Outcomes.},
journal = {Molecular neurobiology},
volume = {58},
number = {11},
pages = {5494-5516},
pmid = {34341881},
issn = {1559-1182},
mesh = {Animals ; Brain-Computer Interfaces ; Combined Modality Therapy ; Electric Stimulation Therapy ; Humans ; Locomotion/physiology ; Low-Level Light Therapy ; Motor Cortex/physiopathology ; Nerve Regeneration ; Neuronal Outgrowth ; *Neuronal Plasticity ; Neuroprotective Agents/therapeutic use ; Photochemotherapy ; Pyramidal Tracts/*physiopathology ; Quality of Life ; Recovery of Function ; Riluzole/therapeutic use ; Spinal Cord/physiopathology ; Spinal Cord Diseases/rehabilitation ; Spinal Cord Injuries/*physiopathology/therapy ; Stem Cell Transplantation ; Transcranial Direct Current Stimulation ; Transcutaneous Electric Nerve Stimulation ; },
abstract = {Spinal cord injury (SCI) is a devastating condition that affects approximately 294,000 people in the USA and several millions worldwide. The corticospinal motor circuitry plays a major role in controlling skilled movements and in planning and coordinating movements in mammals and can be damaged by SCI. While axonal regeneration of injured fibers over long distances is scarce in the adult CNS, substantial spontaneous neural reorganization and plasticity in the spared corticospinal motor circuitry has been shown in experimental SCI models, associated with functional recovery. Beneficially harnessing this neuroplasticity of the corticospinal motor circuitry represents a highly promising therapeutic approach for improving locomotor outcomes after SCI. Several different strategies have been used to date for this purpose including neuromodulation (spinal cord/brain stimulation strategies and brain-machine interfaces), rehabilitative training (targeting activity-dependent plasticity), stem cells and biological scaffolds, neuroregenerative/neuroprotective pharmacotherapies, and light-based therapies like photodynamic therapy (PDT) and photobiomodulation (PMBT). This review provides an overview of the spontaneous reorganization and neuroplasticity in the corticospinal motor circuitry after SCI and summarizes the various therapeutic approaches used to beneficially harness this neuroplasticity for functional recovery after SCI in preclinical animal model and clinical human patients' studies.},
}
@article {pmid34339002,
year = {2021},
author = {Fiaz, M and Ahmed, I and Riaz, R and Nawaz, U and Arshad, M},
title = {Prevalence of antibiotic-resistant bacterial strains in wastewater streams: molecular characterization and relative abundance.},
journal = {Folia microbiologica},
volume = {66},
number = {6},
pages = {1023-1037},
pmid = {34339002},
issn = {1874-9356},
mesh = {*Anti-Bacterial Agents/pharmacology ; Bacteria/genetics ; Levofloxacin ; Microbial Sensitivity Tests ; Prevalence ; RNA, Ribosomal, 16S/genetics ; *Wastewater ; },
abstract = {Bacteria from wastewater discharged to the sewerage near three hospitals of Islamabad, Rawalpindi, and Faisalabad were examined for resistance to the most commonly prescribed antibiotics in Pakistan. From the selected sites, a total of 109 isolates from 40 different species were identified based on 16S rRNA gene sequence and phylogeny. The isolates were tested for their resistance to ciprofloxacin, levofloxacin, ofloxacin, amoxicillin, and ampicillin. The results indicated that the isolates were resistant with the highest percentage to ampicillin and the lowest percentage to ciprofloxacin. Among the resistant isolates, 91.7% were found resistant to ampicillin, 83.5% to amoxicillin, 67.0% to ofloxacin, whereas only 27.5% to ciprofloxacin and 21.1% to levofloxacin. Among three sampled locations, the most of resistance was observed in Escherichia and Acinetobacter species. More than 30% isolated microorganisms were found resistant to more than three antibiotics. These findings concluded the presence of predominant microbial population resistant to antibiotics in wastewater channels near hospitals and its surroundings, which requires further investigation regarding their existence and spread in other environmental media having potential community health implications.},
}
@article {pmid34335210,
year = {2021},
author = {Hu, M and Cheng, HJ and Ji, F and Chong, JSX and Lu, Z and Huang, W and Ang, KK and Phua, KS and Chuang, KH and Jiang, X and Chew, E and Guan, C and Zhou, JH},
title = {Brain Functional Changes in Stroke Following Rehabilitation Using Brain-Computer Interface-Assisted Motor Imagery With and Without tDCS: A Pilot Study.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {692304},
pmid = {34335210},
issn = {1662-5161},
abstract = {Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been proven effective in post-stroke motor function enhancement, yet whether the combination of MI-BCI and tDCS may further benefit the rehabilitation of motor functions remains unknown. This study investigated brain functional activity and connectivity changes after a 2 week MI-BCI and tDCS combined intervention in 19 chronic subcortical stroke patients. Patients were randomized into MI-BCI with tDCS group and MI-BCI only group who underwent 10 sessions of 20 min real or sham tDCS followed by 1 h MI-BCI training with robotic feedback. We derived amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) from resting-state functional magnetic resonance imaging (fMRI) data pre- and post-intervention. At baseline, stroke patients had lower ALFF in the ipsilesional somatomotor network (SMN), lower ReHo in the contralesional insula, and higher ALFF/Reho in the bilateral posterior default mode network (DMN) compared to age-matched healthy controls. After the intervention, the MI-BCI only group showed increased ALFF in contralesional SMN and decreased ALFF/Reho in the posterior DMN. In contrast, no post-intervention changes were detected in the MI-BCI + tDCS group. Furthermore, higher increases in ALFF/ReHo/FC measures were related to better motor function recovery (measured by the Fugl-Meyer Assessment scores) in the MI-BCI group while the opposite association was detected in the MI-BCI + tDCS group. Taken together, our findings suggest that brain functional re-normalization and network-specific compensation were found in the MI-BCI only group but not in the MI-BCI + tDCS group although both groups gained significant motor function improvement post-intervention with no group difference. MI-BCI and tDCS may exert differential or even opposing impact on brain functional reorganization during post-stroke motor rehabilitation; therefore, the integration of the two strategies requires further refinement to improve efficacy and effectiveness.},
}
@article {pmid34335203,
year = {2021},
author = {Orlandi, S and House, SC and Karlsson, P and Saab, R and Chau, T},
title = {Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {643294},
pmid = {34335203},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) represent a new frontier in the effort to maximize the ability of individuals with profound motor impairments to interact and communicate. While much literature points to BCIs' promise as an alternative access pathway, there have historically been few applications involving children and young adults with severe physical disabilities. As research is emerging in this sphere, this article aims to evaluate the current state of translating BCIs to the pediatric population. A systematic review was conducted using the Scopus, PubMed, and Ovid Medline databases. Studies of children and adolescents that reported BCI performance published in English in peer-reviewed journals between 2008 and May 2020 were included. Twelve publications were identified, providing strong evidence for continued research in pediatric BCIs. Research evidence was generally at multiple case study or exploratory study level, with modest sample sizes. Seven studies focused on BCIs for communication and five on mobility. Articles were categorized and grouped based on type of measurement (i.e., non-invasive and invasive), and the type of brain signal (i.e., sensory evoked potentials or movement-related potentials). Strengths and limitations of studies were identified and used to provide requirements for clinical translation of pediatric BCIs. This systematic review presents the state-of-the-art of pediatric BCIs focused on developing advanced technology to support children and youth with communication disabilities or limited manual ability. Despite a few research studies addressing the application of BCIs for communication and mobility in children, results are encouraging and future works should focus on customizable pediatric access technologies based on brain activity.},
}
@article {pmid34330113,
year = {2021},
author = {Sellers, KK and Chung, JE and Zhou, J and Triplett, MG and Dawes, HE and Haque, R and Chang, EF},
title = {Thin-film microfabrication and intraoperative testing ofµECoG and iEEG depth arrays for sense and stimulation.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34330113},
issn = {1741-2552},
support = {R01 DC012379/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Electrodes, Implanted ; Humans ; Microtechnology ; Subdural Space ; },
abstract = {Objective.Intracranial neural recordings and electrical stimulation are tools used in an increasing range of applications, including intraoperative clinical mapping and monitoring, therapeutic neuromodulation, and brain computer interface control and feedback. However, many of these applications suffer from a lack of spatial specificity and localization, both in terms of sensed neural signal and applied stimulation. This stems from limited manufacturing processes of commercial-off-the-shelf (COTS) arrays unable to accommodate increased channel density, higher channel count, and smaller contact size.Approach.Here, we describe a manufacturing and assembly approach using thin-film microfabrication for 32-channel high density subdural micro-electrocorticography (µECoG) surface arrays (contacts 1.2 mm diameter, 2 mm pitch) and intracranial electroencephalography (iEEG) depth arrays (contacts 0.5 mm × 1.5 mm, pitch 0.8 mm × 2.5 mm). Crucially, we tackle the translational hurdle and test these arrays during intraoperative studies conducted in four humans under regulatory approval.Main results.We demonstrate that the higher-density contacts provide additional unique information across the recording span compared to the density of COTS arrays which typically have electrode pitch of 8 mm or greater; 4 mm in case of specially ordered arrays. Our intracranial stimulation study results reveal that refined spatial targeting of stimulation elicits evoked potentials with differing spatial spread.Significance.Thin-film,μECoG and iEEG depth arrays offer a promising substrate for advancing a number of clinical and research applications reliant on high-resolution neural sensing and intracranial stimulation.},
}
@article {pmid34329624,
year = {2021},
author = {Butler, CC and Yu, LM and Dorward, J and Gbinigie, O and Hayward, G and Saville, BR and Van Hecke, O and Berry, N and Detry, MA and Saunders, C and Fitzgerald, M and Harris, V and Djukanovic, R and Gadola, S and Kirkpatrick, J and de Lusignan, S and Ogburn, E and Evans, PH and Thomas, NPB and Patel, MG and Hobbs, FDR and , },
title = {Doxycycline for community treatment of suspected COVID-19 in people at high risk of adverse outcomes in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.},
journal = {The Lancet. Respiratory medicine},
volume = {9},
number = {9},
pages = {1010-1020},
pmid = {34329624},
issn = {2213-2619},
support = {/WT_/Wellcome Trust/United Kingdom ; MC_PC_19079/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Age Factors ; Aged ; Aged, 80 and over ; Anti-Bacterial Agents/*administration &amp; dosage/adverse effects ; COVID-19/diagnosis/mortality/virology ; Doxycycline/*administration &amp; dosage/adverse effects ; Female ; Hospitalization/statistics &amp; numerical data ; Humans ; Intention to Treat Analysis ; Male ; Middle Aged ; Minimal Clinically Important Difference ; Risk Factors ; SARS-CoV-2/isolation &amp; purification ; Self Report/statistics &amp; numerical data ; Treatment Outcome ; United Kingdom/epidemiology ; *COVID-19 Drug Treatment ; },
abstract = {BACKGROUND: Doxycycline is often used for treating COVID-19 respiratory symptoms in the community despite an absence of evidence from clinical trials to support its use. We aimed to assess the efficacy of doxycycline to treat suspected COVID-19 in the community among people at high risk of adverse outcomes.<br><br>METHODS: We did a national, open-label, multi-arm, adaptive platform randomised trial of interventions against COVID-19 in older people (PRINCIPLE) across primary care centres in the UK. We included people aged 65 years or older, or 50 years or older with comorbidities (weakened immune system, heart disease, hypertension, asthma or lung disease, diabetes, mild hepatic impairment, stroke or neurological problem, and self-reported obesity or body-mass index of 35 kg/m[2] or greater), who had been unwell (for &#x2264;14 days) with suspected COVID-19 or a positive PCR test for SARS-CoV-2 infection in the community. Participants were randomly assigned using response adaptive randomisation to usual care only, usual care plus oral doxycycline (200 mg on day 1, then 100 mg once daily for the following 6 days), or usual care plus other interventions. The interventions reported in this manuscript are usual care plus doxycycline and usual care only; evaluations of other interventions in this platform trial are ongoing. The coprimary endpoints were time to first self-reported recovery, and hospitalisation or death related to COVID-19, both measured over 28 days from randomisation and analysed by intention to treat. This trial is ongoing and is registered with ISRCTN, 86534580.<br><br>FINDINGS: The trial opened on April 2, 2020. Randomisation to doxycycline began on July 24, 2020, and was stopped on Dec 14, 2020, because the prespecified futility criterion was met; 2689 participants were enrolled and randomised between these dates. Of these, 2508 (93&#xb7;3%) participants contributed follow-up data and were included in the primary analysis: 780 (31&#xb7;1%) in the usual care plus doxycycline group, 948 in the usual care only group (37&#xb7;8%), and 780 (31&#xb7;1%) in the usual care plus other interventions group. Among the 1792 participants randomly assigned to the usual care plus doxycycline and usual care only groups, the mean age was 61&#xb7;1 years (SD 7&#xb7;9); 999 (55&#xb7;7%) participants were female and 790 (44&#xb7;1%) were male. In the primary analysis model, there was little evidence of difference in median time to first self-reported recovery between the usual care plus doxycycline group and the usual care only group (9&#xb7;6 [95% Bayesian Credible Interval [BCI] 8&#xb7;3 to 11&#xb7;0] days vs 10&#xb7;1 [8&#xb7;7 to 11&#xb7;7] days, hazard ratio 1&#xb7;04 [95% BCI 0&#xb7;93 to 1&#xb7;17]). The estimated benefit in median time to first self-reported recovery was 0&#xb7;5 days [95% BCI -0&#xb7;99 to 2&#xb7;04] and the probability of a clinically meaningful benefit (defined as &#x2265;1&#xb7;5 days) was 0&#xb7;10. Hospitalisation or death related to COVID-19 occurred in 41 (crude percentage 5&#xb7;3%) participants in the usual care plus doxycycline group and 43 (4&#xb7;5%) in the usual care only group (estimated absolute percentage difference -0&#xb7;5% [95% BCI -2&#xb7;6 to 1&#xb7;4]); there were five deaths (0&#xb7;6%) in the usual care plus doxycycline group and two (0&#xb7;2%) in the usual care only group.<br><br>INTERPRETATION: In patients with suspected COVID-19 in the community in the UK, who were at high risk of adverse outcomes, treatment with doxycycline was not associated with clinically meaningful reductions in time to recovery or hospital admissions or deaths related to COVID-19, and should not be used as a routine treatment for COVID-19.<br><br>FUNDING: UK Research and Innovation, Department of Health and Social Care, National Institute for Health Research.},
}
@article {pmid34327294,
year = {2021},
author = {Portillo-Lara, R and Tahirbegi, B and Chapman, CAR and Goding, JA and Green, RA},
title = {Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces.},
journal = {APL bioengineering},
volume = {5},
number = {3},
pages = {031507},
pmid = {34327294},
issn = {2473-2877},
abstract = {Brain-computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human-machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.},
}
@article {pmid34326401,
year = {2021},
author = {Geravanchizadeh, M and Roushan, H},
title = {Dynamic selective auditory attention detection using RNN and reinforcement learning.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {15497},
pmid = {34326401},
issn = {2045-2322},
abstract = {The cocktail party phenomenon describes the ability of the human brain to focus auditory attention on a particular stimulus while ignoring other acoustic events. Selective auditory attention detection (SAAD) is an important issue in the development of brain-computer interface systems and cocktail party processors. This paper proposes a new dynamic attention detection system to process the temporal evolution of the input signal. The proposed dynamic SAAD is modeled as a sequential decision-making problem, which is solved by recurrent neural network (RNN) and reinforcement learning methods of Q-learning and deep Q-learning. Among different dynamic learning approaches, the evaluation results show that the deep Q-learning approach with RNN as agent provides the highest classification accuracy (94.2%) with the least detection delay. The proposed SAAD system is advantageous, in the sense that the detection of attention is performed dynamically for the sequential inputs. Also, the system has the potential to be used in scenarios, where the attention of the listener might be switched in time in the presence of various acoustic events.},
}
@article {pmid34325128,
year = {2021},
author = {Li, H and Guo, W and Zhang, R and Xiu, C},
title = {Variable length particle swarm optimization and multi-feature deep fusion for motor imagery EEG classification.},
journal = {Biochemical and biophysical research communications},
volume = {571},
number = {},
pages = {131-136},
doi = {10.1016/j.bbrc.2021.07.064},
pmid = {34325128},
issn = {1090-2104},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; },
abstract = {Brain-computer interfaces are a new pathway for communication between human body and the external environment. High classification accuracy for motor imagery electroencephalogram (EEG) signals is desirable by improving the algorithm of feature extraction and classification. A novel algorithm (VLPSO-MFDF) based on the variable length particle swarm optimization (VLPSO) and multi-feature deep fusion (MFDF) is proposed. First, each layer of the deep forest is reconstructed into two same classification modules. Then, several different features are extracted for the motor imagery EEG signal to feed separately to the classification modules. The VLPSO is used to search for the optimal weights for the probability vectors output by each classification module, which can continuously optimize the classification performance. Experimental results demonstrate that the VLPSO-MFDF algorithm can achieve higher classification accuracy for four classifications of motor imagery EEG signals compared with the traditional deep forest algorithm. The proposed method fused multi-domain features and corrected the prediction difference. It was of great significance for improving the performance of the classifier.},
}
@article {pmid34320481,
year = {2021},
author = {Hughes, CL and Flesher, SN and Weiss, JM and Downey, JE and Boninger, M and Collinger, JL and Gaunt, RA},
title = {Neural stimulation and recording performance in human sensorimotor cortex over 1500 days.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34320481},
issn = {1741-2552},
support = {U01 NS108922/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Electric Stimulation ; Electrodes, Implanted ; *Hand ; Humans ; Microelectrodes ; *Somatosensory Cortex ; Touch ; },
abstract = {Objective.Intracortical microstimulation (ICMS) in somatosensory cortex can restore sensation to people with spinal cord injury. However, the recording quality from implanted microelectrodes can degrade over time and limitations in stimulation longevity have been considered a potential barrier to the clinical use of ICMS. Our objective was to evaluate recording stability of intracortical electrodes implanted in the motor and somatosensory cortex of one person. The electrodes in motor cortex had platinum tips and were not stimulated, while the electrodes in somatosensory cortex had sputtered iridium oxide film (SIROF) tips and were stimulated. Additionally, we measured how well ICMS was able to evoke sensations over time.Approach. We implanted microelectrode arrays with SIROF tips in the somatosensory cortex (SIROF-sensory) of a human participant with a cervical spinal cord injury. We regularly stimulated these electrodes to evoke tactile sensations on the hand. Here, we quantify the stability of these electrodes in comparison to non-stimulated platinum electrodes implanted in the motor cortex (platinum-motor) over 1500 days with recorded signal quality and electrode impedances. Additionally, we quantify the stability of ICMS-evoked sensations using detection thresholds.Main results. We found that recording quality, as assessed by the number of electrodes with high-amplitude waveforms (>100µV peak-to-peak), peak-to-peak voltage, noise, and signal-to-noise ratio, decreased over time on SIROF-sensory and platinum-motor electrodes. However, SIROF-sensory electrodes were more likely to continue to record high-amplitude signals than platinum-motor electrodes. Interestingly, the detection thresholds for stimulus-evoked sensations decreased over time from a median of 31.5μA at day 100-10.4μA at day 1500, with the largest changes occurring between day 100 and 500.Significance. These results demonstrate that ICMS in human somatosensory cortex can be provided over long periods of time without deleterious effects on recording or stimulation capabilities. In fact, the sensitivity to stimulation improved over time.},
}
@article {pmid34314384,
year = {2021},
author = {Szymanski, LJ and Kellis, S and Liu, CY and Jones, KT and Andersen, RA and Commins, D and Lee, B and McCreery, DB and Miller, CA},
title = {Neuropathological effects of chronically implanted, intracortical microelectrodes in a tetraplegic patient.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac127e},
pmid = {34314384},
issn = {1741-2552},
mesh = {Animals ; *Cerebral Cortex ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Male ; Microelectrodes ; *Somatosensory Cortex ; },
abstract = {Objective.Intracortical microelectrode arrays (MEA) can be used as part of a brain-machine interface system to provide sensory feedback control of an artificial limb to assist persons with tetraplegia. Variability in functionality of electrodes has been reported but few studies in humans have examined the impact of chronic brain tissue responses revealed postmortem on electrode performancein vivo. Approach.In a tetraplegic man, recording MEAs were implanted into the anterior intraparietal area and Brodmann's area 5 (BA5) of the posterior parietal cortex and a recording and stimulation array was implanted in BA1 of the primary somatosensory cortex (S1). The participant expired from unrelated causes seven months after MEA implantation. The underlying tissue of two of the three devices was processed for histology and electrophysiological recordings were assessed.Main results.Recordings of neuronal activity were obtained from all three MEAs despite meningeal encapsulation. However, the S1 array had a greater encapsulation, yielded lower signal quality than the other arrays and failed to elicit somatosensory percepts with electrical stimulation. Histological examination of tissues underlying S1 and BA5 implant sites revealed localized leptomeningeal proliferation and fibrosis, lymphocytic infiltrates, astrogliosis, and foreign body reaction around the electrodes. The BA5 recording site showed focal cerebral microhemorrhages and leptomeningeal vascular ectasia. The S1 site showed focal tissue damage including vascular recanalization, neuronal loss, and extensive subcortical white matter necrosis. The tissue response at the S1 site included hemorrhagic-induced injury suggesting a likely mechanism for reduced function of the S1 implant.Significance.Our findings are similar to those from animal studies with chronic intracortical implants and suggest that vascular disruption and microhemorrhage during device implantation are important contributors to overall array and individual electrode performance and should be a topic for future device development to mitigate tissue responses. Neurosurgical considerations are also discussed.},
}
@article {pmid34314088,
year = {2021},
author = {Huang, W and Yan, H and Cheng, K and Wang, Y and Wang, C and Li, J and Li, C and Li, C and Zuo, Z and Chen, H},
title = {A dual-channel language decoding from brain activity with progressive transfer training.},
journal = {Human brain mapping},
volume = {42},
number = {15},
pages = {5089-5100},
pmid = {34314088},
issn = {1097-0193},
mesh = {Adult ; *Artificial Intelligence ; *Brain Mapping ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; *Psycholinguistics ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {When we view a scene, the visual cortex extracts and processes visual information in the scene through various kinds of neural activities. Previous studies have decoded the neural activity into single/multiple semantic category tags which can caption the scene to some extent. However, these tags are isolated words with no grammatical structure, insufficiently conveying what the scene contains. It is well-known that textual language (sentences/phrases) is superior to single word in disclosing the meaning of images as well as reflecting people's real understanding of the images. Here, based on artificial intelligence technologies, we attempted to build a dual-channel language decoding model (DC-LDM) to decode the neural activities evoked by images into language (phrases or short sentences). The DC-LDM consisted of five modules, namely, Image-Extractor, Image-Encoder, Nerve-Extractor, Nerve-Encoder, and Language-Decoder. In addition, we employed a strategy of progressive transfer to train the DC-LDM for improving the performance of language decoding. The results showed that the texts decoded by DC-LDM could describe natural image stimuli accurately and vividly. We adopted six indexes to quantitatively evaluate the difference between the decoded texts and the annotated texts of corresponding visual images, and found that Word2vec-Cosine similarity (WCS) was the best indicator to reflect the similarity between the decoded and the annotated texts. In addition, among different visual cortices, we found that the text decoded by the higher visual cortex was more consistent with the description of the natural image than the lower one. Our decoding model may provide enlightenment in language-based brain-computer interface explorations.},
}
@article {pmid34313221,
year = {2021},
author = {Hughes, CL and Flesher, SN and Weiss, JM and Boninger, M and Collinger, JL and Gaunt, RA},
title = {Perception of microstimulation frequency in human somatosensory cortex.},
journal = {eLife},
volume = {10},
number = {},
pages = {},
pmid = {34313221},
issn = {2050-084X},
support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; U01 NS108922/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Electric Stimulation ; Electrodes, Implanted ; Feedback, Physiological ; Humans ; Male ; Microelectrodes ; Somatosensory Cortex/*physiology ; Touch ; *Touch Perception ; },
abstract = {Microstimulation in the somatosensory cortex can evoke artificial tactile percepts and can be incorporated into bidirectional brain-computer interfaces (BCIs) to restore function after injury or disease. However, little is known about how stimulation parameters themselves affect perception. Here, we stimulated through microelectrode arrays implanted in the somatosensory cortex of two human participants with cervical spinal cord injury and varied the stimulus amplitude, frequency, and train duration. Increasing the amplitude and train duration increased the perceived intensity on all tested electrodes. Surprisingly, we found that increasing the frequency evoked more intense percepts on some electrodes but evoked less-intense percepts on other electrodes. These different frequency-intensity relationships were divided into three groups, which also evoked distinct percept qualities at different stimulus frequencies. Neighboring electrode sites were more likely to belong to the same group. These results support the idea that stimulation frequency directly controls tactile perception and that these different percepts may be related to the organization of somatosensory cortex, which will facilitate principled development of stimulation strategies for bidirectional BCIs.},
}
@article {pmid34311452,
year = {2021},
author = {Yang, L and Song, Y and Jia, X and Ma, K and Xie, L},
title = {Two-branch 3D convolutional neural network for motor imagery EEG decoding.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac17d6},
pmid = {34311452},
issn = {1741-2552},
mesh = {*Algorithms ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; Research Design ; },
abstract = {Objective.The original motor imagery electroencephalography (MI-EEG) data contains not only temporal features but also a large number of spatial features related to the distribution of electrodes on the brain. However, in the process of MI-EEG decoding, most of the current convolutional neural network (CNN) based methods do not make the utmost of the spatial features related to electrode distribution.Approach.In this study, we adopt a concise 3D representation for the MI-EEG data to take full advantage of the spatial features and propose a two-branch 3D CNN (TB-3D CNN) for the 3D representation of MI-EEG data. First, the spatial and temporal features of the input 3D samples are extracted by the spatial and temporal feature learning branches, respectively, to avoid the mutual interference between the temporal and spatial features. Then, the central loss is introduced into the TB-3D CNN framework to further improve the MI-EEG decoding accuracy. And a 3D data augmentation method based on the cyclic translation of time dimension is proposed for the 3D representation method to alleviate the overfitting problem.Main results.Some experiments are conducted on the famous BCI competition IV 2a dataset to evaluate the effectiveness of the proposed MI-EEG decoding method. The experimental results comparison with some state-of-the-art methods demonstrates that the average accuracy of our method is 4.42% higher than that of the best of the comparative methods.Significance.The proposed MI-EEG decoding method has great promise to improve the performance of motor imagery brain-computer interface system.},
}
@article {pmid34310314,
year = {2021},
author = {Li, Y and Guo, L and Liu, Y and Liu, J and Meng, F},
title = {A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1534-1545},
doi = {10.1109/TNSRE.2021.3099908},
pmid = {34310314},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; },
abstract = {Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with the outside world via external devices. Recent deep learning methods, which fail to fully explore both deep-temporal characterizations in EEGs itself and multi-spectral information in different rhythms, generally ignore the temporal or spectral dependencies in MI-EEG. Also, the lack of effective feature fusion probably leads to redundant or irrelative information and thus fails to achieve the most discriminative features, resulting in the limited MI-EEG decoding performance. To address these issues, in this paper, a MI-EEG decoding framework is proposed, which uses a novel temporal-spectral-based squeeze-and-excitation feature fusion network (TS-SEFFNet). First, the deep-temporal convolution block (DT-Conv block) implements convolutions in a cascade architecture, which extracts high-dimension temporal representations from raw EEG signals. Second, the multi-spectral convolution block (MS-Conv block) is then conducted in parallel using multi-level wavelet convolutions to capture discriminative spectral features from corresponding clinical subbands. Finally, the proposed squeeze-and-excitation feature fusion block (SE-Feature-Fusion block) maps the deep-temporal and multi-spectral features into comprehensive fused feature maps, which highlights channel-wise feature responses by constructing interdependencies among different domain features. Competitive experimental results on two public datasets demonstrate that our method is able to achieve promising decoding performance compared with the state-of-the-art methods.},
}
@article {pmid34306590,
year = {2021},
author = {Fu, Y and Chen, R and Gong, A and Qian, Q and Ding, N and Zhang, W and Su, L and Zhao, L},
title = {Recognition of Flexion and Extension Imagery Involving the Right and Left Arms Based on Deep Belief Network and Functional Near-Infrared Spectroscopy.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {5533565},
pmid = {34306590},
issn = {2040-2309},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Motor Cortex ; Spectroscopy, Near-Infrared ; },
abstract = {Brain-computer interaction based on motor imagery (MI) is an important brain-computer interface (BCI). Most methods for MI classification are based on electroencephalogram (EEG), and few studies have investigated signal processing based on MI-Functional Near-Infrared Spectroscopy (fNIRS). In addition, there is a need to improve the classification accuracy for MI fNIRS methods. In this study, a deep belief network (DBN) based on a restricted Boltzmann machine (RBM) was used to classify fNIRS signals of flexion and extension imagery involving the left and right arms. fNIRS signals from 16 channels covering the motor cortex area were recorded for each of 10 subjects executing or imagining flexion and extension involving the left and right arms. Oxygenated hemoglobin (HbO) concentration was used as a feature to train two RBMs that were subsequently stacked with an additional softmax regression output layer to construct DBN. We also explored the DBN model classification accuracy for the test dataset from one subject using training dataset from other subjects. The average DBN classification accuracy for flexion and extension movement and imagery involving the left and right arms was 84.35 ± 3.86% and 78.19 ± 3.73%, respectively. For a given DBN model, better classification results are obtained for test datasets for a given subject when the model is trained using dataset from the same subject than when the model is trained using datasets from other subjects. The results show that the DBN algorithm can effectively identify flexion and extension imagery involving the right and left arms using fNIRS. This study is expected to serve as a reference for constructing online MI-BCI systems based on DBN and fNIRS.},
}
@article {pmid34305558,
year = {2021},
author = {Abdalmalak, A and Milej, D and Norton, L and Debicki, DB and Owen, AM and Lawrence, KS},
title = {The Potential Role of fNIRS in Evaluating Levels of Consciousness.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {703405},
pmid = {34305558},
issn = {1662-5161},
abstract = {Over the last few decades, neuroimaging techniques have transformed our understanding of the brain and the effect of neurological conditions on brain function. More recently, light-based modalities such as functional near-infrared spectroscopy have gained popularity as tools to study brain function at the bedside. A recent application is to assess residual awareness in patients with disorders of consciousness, as some patients retain awareness albeit lacking all behavioural response to commands. Functional near-infrared spectroscopy can play a vital role in identifying these patients by assessing command-driven brain activity. The goal of this review is to summarise the studies reported on this topic, to discuss the technical and ethical challenges of working with patients with disorders of consciousness, and to outline promising future directions in this field.},
}
@article {pmid34305550,
year = {2021},
author = {Borra, D and Fantozzi, S and Magosso, E},
title = {A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {655840},
pmid = {34305550},
issn = {1662-5161},
abstract = {Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales (i.e., multi-scale, MS) in an efficient and optimized (in terms of trainable parameters) way, and was validated on three P300 datasets. The CNN was trained using different strategies (within-participant and within-session, within-participant and cross-session, leave-one-subject-out, transfer learning) and was compared with several state-of-the-art (SOA) algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs.},
}
@article {pmid34302743,
year = {2021},
author = {Foo, C and Lozada, A and Aljadeff, J and Li, Y and Wang, JW and Slesinger, PA and Kleinfeld, D},
title = {Reinforcement learning links spontaneous cortical dopamine impulses to reward.},
journal = {Current biology : CB},
volume = {31},
number = {18},
pages = {4111-4119.e4},
pmid = {34302743},
issn = {1879-0445},
support = {R35 NS097265/NS/NINDS NIH HHS/United States ; R01 DC009597/DC/NIDCD NIH HHS/United States ; R01 DA050159/DA/NIDA NIH HHS/United States ; R01 MH111499/MH/NIMH NIH HHS/United States ; U19 NS107466/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Dopamine/physiology ; Dopaminergic Neurons/physiology ; Learning/physiology ; Mice ; Reinforcement, Psychology ; *Reward ; },
abstract = {In their pioneering study on dopamine release, Romo and Schultz speculated "...that the amount of dopamine released by unmodulated spontaneous impulse activity exerts a tonic, permissive influence on neuronal processes more actively engaged in preparation of self-initiated movements...."[1] Motivated by the suggestion of "spontaneous impulses," as well as by the "ramp up" of dopaminergic neuronal activity that occurs when rodents navigate to a reward,[2-5] we asked two questions. First, are there spontaneous impulses of dopamine that are released in cortex? Using cell-based optical sensors of extrasynaptic dopamine, [DA]ex,[6] we found that spontaneous dopamine impulses in cortex of naive mice occur at a rate of ∼0.01 per second. Next, can mice be trained to change the amplitude and/or timing of dopamine events triggered by internal brain dynamics, much as they can change the amplitude and timing of dopamine impulses based on an external cue?[7-9] Using a reinforcement learning paradigm based solely on rewards that were gated by feedback from real-time measurements of [DA]ex, we found that mice can volitionally modulate their spontaneous [DA]ex. In particular, by only the second session of daily, hour-long training, mice increased the rate of impulses of [DA]ex, increased the amplitude of the impulses, and increased their tonic level of [DA]ex for a reward. Critically, mice learned to reliably elicit [DA]ex impulses prior to receiving a reward. These effects reversed when the reward was removed. We posit that spontaneous dopamine impulses may serve as a salient cognitive event in behavioral planning.},
}
@article {pmid34300492,
year = {2021},
author = {Jamil, N and Belkacem, AN and Ouhbi, S and Lakas, A},
title = {Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {14},
pages = {},
pmid = {34300492},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.},
}
@article {pmid34300386,
year = {2021},
author = {Chen, Z and Wang, Y and Song, Z},
title = {Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {14},
pages = {},
pmid = {34300386},
issn = {1424-8220},
support = {61540022//National Natural Science Foundation of China/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; },
abstract = {In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.},
}
@article {pmid34295217,
year = {2021},
author = {Bu, J and Liu, C and Gou, H and Gan, H and Cheng, Y and Liu, M and Ni, R and Liang, Z and Cui, G and Zeng, GQ and Zhang, X},
title = {A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {647844},
pmid = {34295217},
issn = {1662-4548},
abstract = {Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain-computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants' smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.},
}
@article {pmid34292239,
year = {2022},
author = {Dryden, SC and Meador, AG and Awh, C and Smith, BD and Eder, AE and Bohn, SN and Hoehn, ME and Fowler, B and Fleming, JC},
title = {Effect of Premature Tube Extrusion for Simple Congenital Nasolacrimal Duct Obstruction: A Comparison of Monocanalicular and Bicanalicular Intubation.},
journal = {The Journal of craniofacial surgery},
volume = {33},
number = {1},
pages = {211-213},
doi = {10.1097/SCS.0000000000007915},
pmid = {34292239},
issn = {1536-3732},
mesh = {Child ; *Dacryocystorhinostomy ; Humans ; Infant ; Intubation ; *Lacrimal Duct Obstruction/therapy ; *Nasolacrimal Duct/surgery ; Retrospective Studies ; Treatment Outcome ; },
abstract = {The objective of this article is to compare the incidence of premature dislocation of silicone tubes and the effect on treatment success between monocanalicular (MCI) and bicanalicular (BCI) intubation in pediatric patients with simple congenital nasolacrimal duct obstruction. Retrospective comparative case series of 108 eyes of 78 pediatric patients with simple congenital nasolacrimal duct obstruction who underwent probing with either BCI (n = 38 eyes) or MCI (n = 70 eyes) from 2017 to 2020. Premature tube extrusion was defined as any tube removed prior to the 3 month postoperative appointment. Success was defined as resolution of tearing 3 months post tube removal. Ages ranged from 10 months to 5.35 years (mean, 1.95 years; Standard deviation (SD), 0.91). Premature tube extrusion occurred in 15 eyes with BCI and 29 eyes with MCI. Success rates were not significantly different regardless of intubation type between the planned tube removal (90.6%) and the premature tube extrusion cohorts (84.1%), P = 0.89. There was no significant difference in treatment success between the planned tube removal (92.7% MCI, 87% BCI) and the premature tube extrusion cohorts (86.2% MCI, 80% BCI). Complications included 2 infections (1 MCI, 1 BCI) and 2 cases of tube related keratopathy (1 MCI, 1 BCI) that all resolved with tube removal. There were 2 BCI patients that presented to the emergency department for premature tube extrusion. Silicone intubation regardless of stent type is an effective treatment for simple congenital nasolacrimal duct obstruction. There was no significant difference in treatment success between tubes that extrude prematurely, and tubes removed at term based on type of intubation.},
}
@article {pmid34289456,
year = {2021},
author = {Dekleva, BM and Weiss, JM and Boninger, ML and Collinger, JL},
title = {Generalizable cursor click decoding using grasp-related neural transients.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34289456},
issn = {1741-2552},
support = {U01 NS108922/NS/NINDS NIH HHS/United States ; UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Hand Strength ; Humans ; *Motor Cortex ; Movement ; Quadriplegia ; },
abstract = {Objective.Intracortical brain-computer interfaces (iBCI) have the potential to restore independence for individuals with significant motor or communication impairments. One of the most realistic avenues for clinical translation of iBCI technology is enabling control of a computer cursor-i.e. movement-related neural activity is interpreted (decoded) and used to drive cursor function. Here we aim to improve cursor click decoding to allow for both point-and-click and click-and-drag control.Approach.Using chronic microelectrode arrays implanted in the motor cortex of two participants with tetraplegia, we identified prominent neural responses related to attempted hand grasp. We then developed a new approach for decoding cursor click (hand grasp) based on the most salient responses.Main results.We found that the population-wide response contained three dominant components related to hand grasp: an onset transient response, a sustained response, and an offset transient response. The transient responses were larger in magnitude-and thus more reliably detected-than the sustained response, and a click decoder based on these transients outperformed the standard approach of binary state classification.Significance.A transient-based approach for identifying hand grasp can provide a high degree of cursor click control for both point-and-click and click-and-drag applications. This generalized click functionality is an important step toward high-performance cursor control and eventual clinical translation of iBCI technology.},
}
@article {pmid34289350,
year = {2021},
author = {Pan, Y and He, X and Li, C and Li, Y and Li, W and Zhang, H and Wang, Y and Zhou, G and Yang, J and Li, J and Qu, J and Wang, H and Gao, Z and Shen, Y and Li, T and Hu, H and Ma, H},
title = {Neuronal activity recruits the CRTC1/CREB axis to drive transcription-dependent autophagy for maintaining late-phase LTD.},
journal = {Cell reports},
volume = {36},
number = {3},
pages = {109398},
doi = {10.1016/j.celrep.2021.109398},
pmid = {34289350},
issn = {2211-1247},
mesh = {Animals ; Autophagy/*genetics ; Cell Nucleus/metabolism ; Cyclic AMP Response Element-Binding Protein/*metabolism ; HEK293 Cells ; Humans ; Long-Term Synaptic Depression/*genetics ; Mice, Inbred C57BL ; Neurons/*metabolism ; Protein Subunits/metabolism ; Protein Transport ; Receptors, AMPA/metabolism ; Receptors, N-Methyl-D-Aspartate/metabolism ; Signal Transduction ; Time Factors ; Transcription Factors/*metabolism ; *Transcription, Genetic ; Mice ; },
abstract = {Cellular resources must be reorganized for long-term synaptic plasticity during brain information processing, in which coordinated gene transcription and protein turnover are required. However, the mechanism underlying this process remains elusive. Here, we report that activating N-methyl-d-aspartate receptors (NMDARs) induce transcription-dependent autophagy for synaptic turnover and late-phase long-term synaptic depression (L-LTD), which invokes cytoplasm-to-nucleus signaling mechanisms known to be required for late-phase long-term synaptic potentiation (L-LTP). Mechanistically, LTD-inducing stimuli specifically dephosphorylate CRTC1 (CREB-regulated transcription coactivator 1) at Ser-151 and are advantaged in recruiting CRTC1 from cytoplasm to the nucleus, where it competes with FXR (fed-state sensing nuclear receptor) for binding to CREB (cAMP response element-binding protein) and drives autophagy gene expression. Disrupting synergistic actions of CREB and CRTC1 (two essential L-LTP transcription factors) impairs transcription-dependent autophagy induction and prevents NMDAR-dependent L-LTD, which can be rescued by constitutively inducing mechanistic target of rapamycin (mTOR)-dependent autophagy. Together, these findings uncover mechanistic commonalities between L-LTP and L-LTD, suggesting that synaptic activity can tune excitation-transcription coupling for distinct long-lasting synaptic remodeling.},
}
@article {pmid34289222,
year = {2022},
author = {Hashem, A and Abdellutif, MM and Laymon, M and Abdullateef, M and Abdelhamid, A and Mosbah, A and Abol-Enein, H},
title = {Clinical efficacy of mebeverine for persistent nocturnal enuresis after orthotopic W-neobladder.},
journal = {BJU international},
volume = {129},
number = {3},
pages = {387-393},
doi = {10.1111/bju.15555},
pmid = {34289222},
issn = {1464-410X},
mesh = {Adult ; Cystectomy ; Female ; Humans ; Male ; *Nocturnal Enuresis/drug therapy ; Phenethylamines ; Quality of Life ; Treatment Outcome ; *Urinary Bladder Neoplasms/surgery ; *Urinary Reservoirs, Continent ; },
abstract = {OBJECTIVES: To investigate the efficacy of mebeverine for nocturnal incontinence in male patients with an ileal orthotopic bladder substitute (OBS).<br><br>PATIENTS AND METHODS: A randomised controlled trial was carried out for adult male patients who were nocturnal incontinent. Patients were allocated to receive mebeverine 200&#x2009;mg or placebo once a day in the evening for 3&#x2009;months. The primary outcome was to compare the continence status between groups, assessed by the urinary domain of the Bladder Cancer Index (BCI) and pad usage. The secondary outcomes were to assess the safety of mebeverine.<br><br>RESULTS: There were 55 patients in the placebo group and 58 in mebeverine group who completed the follow-up. The median (interquartile range) interval between OBS surgery and starting treatment was 9&#x2009;(4-13)&#x2009;years in the placebo group and 9&#x2009;(6-13)&#x2009;years in the mebeverine group. The mean (SD) 3-month urinary domain score of the BCI was 70.8&#x2009;(5.6) and 86.4&#x2009;(14.2) in the placebo and mebeverine groups, respectively (P&#x2009;<&#x2009;0.001). At 3&#x2009;months, 54 (98.2%) and 26 (44.8%) patients required the use of a night-time pad in the placebo and mebeverine groups, respectively. Mebeverine reduced the risk of pad use by 53.4% (95% confidence interval 40.1-66.6; P&#x2009;<&#x2009;0.001). Constipation occurred in one (2.1%) and three (5.8%) patients in the placebo and mebeverine groups, respectively; abdominal distention occurred in two (3.8%) of the patients in the mebeverine group (P&#x2009;=&#x2009;0.25).<br><br>CONCLUSION: Mebeverine decreases night-time pad use and improves the quality of life in male patients with an ileal OBS and is associated with minimal adverse events.},
}
@article {pmid34286308,
year = {2021},
author = {Haslacher, D and Nasr, K and Soekadar, SR},
title = {Advancing sensory neuroprosthetics using artificial brain networks.},
journal = {Patterns (New York, N.Y.)},
volume = {2},
number = {7},
pages = {100304},
pmid = {34286308},
issn = {2666-3899},
abstract = {Implementation of effective brain or neural stimulation protocols for restoration of complex sensory perception, e.g., in the visual domain, is an unresolved challenge. By leveraging the capacity of deep learning to model the brain's visual system, optic nerve stimulation patterns could be derived that are predictive of neural responses of higher-level cortical visual areas in silico. This novel approach could be generalized to optimize different types of neuroprosthetics or bidirectional brain-computer interfaces (BCIs).},
}
@article {pmid34284361,
year = {2021},
author = {Li, G and Jiang, S and Paraskevopoulou, SE and Chai, G and Wei, Z and Liu, S and Wang, M and Xu, Y and Fan, Z and Wu, Z and Chen, L and Zhang, D and Zhu, X},
title = {Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG).},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac160e},
pmid = {34284361},
issn = {1741-2552},
mesh = {Cerebral Cortex ; Electroencephalography ; Gray Matter/diagnostic imaging ; Humans ; Movement ; *White Matter/diagnostic imaging ; },
abstract = {Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.},
}
@article {pmid34283397,
year = {2021},
author = {Zheng, J and Tian, Y and Xu, H and Gu, L and Xu, H},
title = {A Standardized Protocol for the Induction of Specific Social Fear in Mice.},
journal = {Neuroscience bulletin},
volume = {37},
number = {12},
pages = {1708-1712},
pmid = {34283397},
issn = {1995-8218},
mesh = {Animals ; *Anxiety ; *Fear ; Mice ; Mice, Inbred C57BL ; Social Behavior ; },
}
@article {pmid34283150,
year = {2021},
author = {Koh, DW and Kwon, JK and Lee, SG},
title = {Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {13},
pages = {},
pmid = {34283150},
issn = {1424-8220},
mesh = {Accidents, Traffic ; Adolescent ; Adult ; Aged ; *Automobile Driving ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers' recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20-30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group's correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group's data were not. On the other hand, the elderly group's data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.},
}
@article {pmid34283122,
year = {2021},
author = {Ha, J and Park, S and Im, CH and Kim, L},
title = {A Hybrid Brain-Computer Interface for Real-Life Meal-Assist Robot Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {13},
pages = {},
pmid = {34283122},
issn = {1424-8220},
support = {2017-0-00432//Institute for Information and Communications Technology Promotion/ ; },
mesh = {Aged ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Quality of Life ; *Robotics ; },
abstract = {Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain-computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user's intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.},
}
@article {pmid34282770,
year = {2021},
author = {Roy, R and Mahadevappa, M and Nazarpour, K},
title = {An Electro-Oculogram Based Vision System for Grasp Assistive Devices-A Proof of Concept Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {13},
pages = {},
pmid = {34282770},
issn = {1424-8220},
support = {EP/M025977/1//Engineering and Physical Sciences Research Council/ ; EP/R004242/1//Engineering and Physical Sciences Research Council/ ; DST/ INSPIRE Fellow-432ship/ 2014/[249]//INSPIRE Fellowship, Ministry of Science and Technology, Government of India./ ; },
mesh = {Electrooculography ; Hand ; *Hand Strength ; Humans ; Movement ; Proof of Concept Study ; *Self-Help Devices ; },
abstract = {Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple 'signature' extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.},
}
@article {pmid34280907,
year = {2021},
author = {Hayat, H and Nukala, A and Nyamira, A and Fan, J and Wang, P},
title = {A concise review: the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine.},
journal = {Biomedical materials (Bristol, England)},
volume = {16},
number = {5},
pages = {},
doi = {10.1088/1748-605X/ac15b2},
pmid = {34280907},
issn = {1748-605X},
mesh = {Algorithms ; Animals ; *Artificial Intelligence ; *Biocompatible Materials ; Diabetes Mellitus, Type 1/diagnostic imaging/drug therapy ; Humans ; Mice ; Nanomedicine ; *Nanostructures ; Neoplasms/diagnostic imaging/drug therapy ; *Theranostic Nanomedicine ; },
abstract = {Nanomedicine has recently experienced unprecedented growth and development. However, the complexity of operations at the nanoscale introduces a layer of difficulty in the clinical translation of nanodrugs and biomedical nanotechnology. This problem is further exacerbated when engineering and optimizing nanomaterials for biomedical purposes. To navigate this issue, artificial intelligence (AI) algorithms have been applied for data analysis and inference, allowing for a more applicable understanding of the complex interaction amongst the abundant variables in a system involving the synthesis or use of nanomedicine. Here, we report on the current relationship and implications of nanomedicine and AI. Particularly, we explore AI as a tool for enabling nanomedicine in the context of nanodrug screening and development, brain-machine interfaces and nanotoxicology. We also report on the current state and future direction of nanomedicine and AI in cancer, diabetes, and neurological disorder therapy.},
}
@article {pmid34280906,
year = {2021},
author = {Tang, S and Liu, C and Zhang, Q and Gu, H and Li, X and Li, Z},
title = {Mental workload classification based on ignored auditory probes and spatial covariance.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac15e5},
pmid = {34280906},
issn = {1741-2552},
mesh = {*Electroencephalography ; *Evoked Potentials ; Humans ; Support Vector Machine ; Workload ; },
abstract = {Objective.Estimation of mental workload (MWL) levels by electroencephalography (EEG)-based mental state monitoring systems has been widely explored. Using event-related potentials (ERPs), elicited by ignored auditory probes, minimizes intrusiveness and has shown high performance for estimating MWL level when tested in laboratory situations. However, when facing real-world applications, the characteristics of ERP waveforms, like latency and amplitude, are often affected by noise, which leads to a decrease in classification performance. One approach to mitigating this is using spatial covariance, which is less sensitive to latency and amplitude distortion. In this study, we used ignored auditory probes in a single-stimulus paradigm and tested Riemannian processed covariance-based features for MWL level estimation in a realistic flight-control task.Approach.We recorded EEG data with an eight-channel system from participants while they performed a simulated drone-control task and manipulated MWL levels (high and low) by task difficulty. We compared support vector machine classification performance based on frequency band power features versus features generated via the Riemannian log map operator from spatial covariance matrices. We also compared accuracy of using data segmented as auditory ERPs versus non-ERPs, for which data windows did not overlap with the ERPs.Main results.Classification accuracy of both types of features showed no significant difference between ERPs and non-ERPs. When we ignore auditory stimuli to perform continuous decoding, covariance-based features in the gamma band had area under the receiver-operating-characteristic curve (AUC) of 0.883, which was significantly higher than band power features (AUC = 0.749).Significance.This study demonstrates that Riemannian-processed covariance features are viable for MWL classification under a realistic experimental scenario.},
}
@article {pmid34280899,
year = {2021},
author = {Aliakbaryhosseinabadi, S and Dosen, S and Savic, AM and Blicher, J and Farina, D and Mrachacz-Kersting, N},
title = {Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/ac15e3},
pmid = {34280899},
issn = {1741-2552},
mesh = {*Amyotrophic Lateral Sclerosis/diagnosis ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Movement ; },
abstract = {Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.},
}
@article {pmid34278616,
year = {2021},
author = {Cui, Y and Zhang, F and Chen, G and Yao, L and Zhang, N and Liu, Z and Li, Q and Zhang, F and Cui, Z and Zhang, K and Li, P and Cheng, Y and Zhang, S and Chen, X},
title = {A Stretchable and Transparent Electrode Based on PEGylated Silk Fibroin for In Vivo Dual-Modal Neural-Vascular Activity Probing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {33},
number = {34},
pages = {e2100221},
doi = {10.1002/adma.202100221},
pmid = {34278616},
issn = {1521-4095},
mesh = {Animals ; Brain-Computer Interfaces ; Disease Models, Animal ; Elastic Modulus ; Electric Conductivity ; *Electrodes ; Electrodes, Implanted ; Electronics ; Electrophysiology/*instrumentation/methods ; Epoxy Resins/chemistry ; Fibroins/*chemistry ; Hydrogels/*chemistry ; Polystyrenes/*chemistry ; Pressure ; Rats ; Silk/*metabolism ; Spectroscopy, Fourier Transform Infrared ; Stress, Mechanical ; Stroke/physiopathology ; Thiophenes/*chemistry ; },
abstract = {Transparent electrodes that form seamless contact and enable optical interrogation at the electrode-brain interface are potentially of high significance for neuroscience studies. Silk hydrogels can offer an ideal platform for transparent neural interfaces owing to their superior biocompatibility. However, conventional silk hydrogels are too weak and have difficulties integrating with highly conductive and stretchable electronics. Here, a transparent and stretchable hydrogel electrode based on poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) and PEGylated silk protein is reported. PEGylated silk protein with poly(ethylene glycol) diglycidyl ether (PEGDE) improves the Young's modulus to 1.51-10.73 MPa and the stretchability to ≈400% from conventional silk hydrogels (<10 kPa). The PEGylated silk also helps form a robust interface with PEDOT:PSS thin film, making the hydrogel electrode synergistically incorporate superior stretchability (≈260%), stable electrical performance (≈4 months), and a low sheet resistance (≈160 ± 56 Ω sq[-1]). Finally, the electrode facilitates efficient electrical recording, and stimulation with unobstructed optical interrogation and rat-brain imaging are demonstrated. The highly transparent and stretchable hydrogel electrode offers a practical tool for neuroscience and paves the way for a harmonized tissue-electrode interface.},
}
@article {pmid34277828,
year = {2021},
author = {Zhu, H and Zhang, C and Zhao, W and Xu, X and Shi, Y and Zhao, G},
title = {A rare survival case of blunt left ventricular rupture caused by a low-energy pedestrian collision with a stationary forklift: a case report.},
journal = {Annals of translational medicine},
volume = {9},
number = {12},
pages = {1028},
pmid = {34277828},
issn = {2305-5839},
abstract = {Blunt cardiac rupture (BCR) is a rare injury with a high mortality rate. It is usually caused by high-energy traumatic accidents, such as motor vehicle collisions. For the first time, we report a rare case of BCR caused by a pedestrian collision with a stationary motor vehicle, which is a low-energy traumatic accident. This is also the first surgical survival BCR case to be reported of a contralateral ventricular rupture at the direct stress site. A 45-year-old formerly healthy Chinese woman, with no family history of heart disease, was walking in a hurry when she accidentally hit a forklift that was parked on the side of the road. The patient gradually lost consciousness, and was admitted to Hwa Mei Hospital Emergency Center 1 hour later. An ultrasound revealed a pericardial effusion about 1 cm deep and a small amount of peritoneal -35 effusion. Emergency computed tomography (CT) scans revealed a small amount of fluid accumulation in the right thoracic cavity, fractures of the 5th and 6th ribs on the right side, and pericardial effusion. The patient's blood pressure remained unstable after 1 hour of endotracheal intubation, B-ultrasound-guided pericardiocentesis, and antishock therapy; thus, open-heart surgery was deemed necessary. A large amount of blood accumulation was found in the intact pericardium. There was a small blood clot at the apex of the left ventricle near the interventricular septum. The removal of the clot revealed a tear about 1 cm in diameter. The patient's BCR was successfully repaired in the surgery. By the end of the 18-month follow-up period, the patient was found to have recovered well without significant complications. The internal mechanism of the case report was deceleration. Prompt diagnosis and emergency thoracotomy when BCR is suspected are key to rescuing patients, regardless of whether the accident is high energy or low energy, or if there is evidence of a direct force acting on the precordium, or the presence of pericardial rupture.},
}
@article {pmid34276299,
year = {2021},
author = {Simon, C and Bolton, DAE and Kennedy, NC and Soekadar, SR and Ruddy, KL},
title = {Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {699428},
pmid = {34276299},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.},
}
@article {pmid34276295,
year = {2021},
author = {Dan, Y and Tao, J and Fu, J and Zhou, D},
title = {Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {690044},
pmid = {34276295},
issn = {1662-4548},
abstract = {The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition.},
}
@article {pmid34276292,
year = {2021},
author = {Gu, B and Xu, M and Xu, L and Chen, L and Ke, Y and Wang, K and Tang, J and Ming, D},
title = {Optimization of Task Allocation for Collaborative Brain-Computer Interface Based on Motor Imagery.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {683784},
pmid = {34276292},
issn = {1662-4548},
abstract = {OBJECTIVE: Collaborative brain-computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy's.<br><br>APPROACH: This study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject's instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called "2 Tasks" &#x2026; "5 Tasks." To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI.<br><br>MAIN RESULTS: Taking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at "4 Tasks," which means each subset contained four instructions. It outperformed the common-work strategy ("6 Tasks") in classification accuracy (72.29 &#xb1; 4.43 vs. 58.53 &#xb1; 4.36%).<br><br>SIGNIFICANCE: The results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload.},
}
@article {pmid34273721,
year = {2021},
author = {Orkan Olcay, B and Özgören, M and Karaçalı, B},
title = {On the characterization of cognitive tasks using activity-specific short-lived synchronization between electroencephalography channels.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {143},
number = {},
pages = {452-474},
doi = {10.1016/j.neunet.2021.06.022},
pmid = {34273721},
issn = {1879-2782},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Cognition ; Electroencephalography ; Imagination ; },
abstract = {Accurate characterization of brain activity during a cognitive task is challenging due to the dynamically changing and the complex nature of the brain. The majority of the proposed approaches assume stationarity in brain activity and disregard the systematic timing organization among brain regions during cognitive tasks. In this study, we propose a novel cognitive activity recognition method that captures the activity-specific timing parameters from training data that elicits maximal average short-lived pairwise synchronization between electroencephalography signals. We evaluated the characterization power of the activity-specific timing parameter triplets in a motor imagery activity recognition framework. The activity-specific timing parameter triplets consist of latency of the maximally synchronized signal segments from activity onset Δt, the time lag between maximally synchronized signal segments τ, and the duration of the maximally synchronized signal segments w. We used cosine-based similarity, wavelet bi-coherence, phase-locking value, phase coherence value, linearized mutual information, and cross-correntropy to calculate the channel synchronizations at the specific timing parameters. Recognition performances as well as statistical analyses on both BCI Competition-III dataset IVa and PhysioNet Motor Movement/Imagery dataset, indicate that the inter-channel short-lived synchronization calculated using activity-specific timing parameter triplets elicit significantly distinct synchronization profiles for different motor imagery tasks and can thus reliably be used for cognitive task recognition purposes.},
}
@article {pmid34272934,
year = {2021},
author = {Mahmood, M and Kwon, S and Kim, H and Kim, YS and Siriaraya, P and Choi, J and Otkhmezuri, B and Kang, K and Yu, KJ and Jang, YC and Ang, CS and Yeo, WH},
title = {Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery-Based Brain-Machine Interfaces.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {8},
number = {19},
pages = {e2101129},
pmid = {34272934},
issn = {2198-3844},
support = {R21 AG064309/AG/NIA NIH HHS/United States ; NRF-2018M3A7B4071109//National Research Foundation of Korea/ ; NIH R21AG064309/NH/NIH HHS/United States ; NRF-2019R1A2C2086085//National Research Foundation of Korea/ ; //Georgia Tech Institute for Electronics and Nanotechnology/ ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Humans ; Scalp ; *User-Computer Interface ; *Virtual Reality ; },
abstract = {Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain-machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals is introduced. This low-profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network-machine learning provides a real-time, continuous motor imagery-based brain-machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real-time control of a virtual reality game.},
}
@article {pmid34269189,
year = {2022},
author = {Flack, JA and Sharma, KD and Xie, JY},
title = {Delving into the recent advancements of spinal cord injury treatment: a review of recent progress.},
journal = {Neural regeneration research},
volume = {17},
number = {2},
pages = {283-291},
pmid = {34269189},
issn = {1673-5374},
abstract = {Spinal cord injury (SCI) research is a very complex field lending to why reviews of SCI literatures can be beneficial to current and future researchers. This review focuses on recent articles regarding potential modalities for the treatment and management of SCI. The modalities were broken down into four categories: neuroprotection-pharmacologic, neuroprotection-non-pharmacologic, neuroregeneration-pharmacologic, neuroregeneration-non-pharmacologic. Peer-reviewed articles were found using PubMed with search terms: "spinal cord injury", "spinal cord injury neuroregeneration", "olfactory ensheathing cells spinal cord injury", "rho-rock inhibitors spinal cord injury", "neural stem cell", "scaffold", "neural stem cell transplantation", "exosomes and SCI", "epidural stimulation SCI", "brain-computer interfaces and SCI". Most recent articles spanning two years were chosen for their relevance to the categories of SCI management and treatment. There has been a plethora of pre-clinical studies completed with their results being difficult to replicate in clinical studies. Therefore, scientists should focus on understanding and applying the results of previous research to develop more efficacious preclinical studies and clinical trials.},
}
@article {pmid34267633,
year = {2021},
author = {Hildt, E},
title = {Affective Brain-Computer Music Interfaces-Drivers and Implications.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {711407},
pmid = {34267633},
issn = {1662-5161},
}
@article {pmid34266505,
year = {2021},
author = {Fossi, LD and Debien, C and Demarty, AL and Vaiva, G and Messiah, A},
title = {Suicide reattempt in a population-wide brief contact intervention to prevent suicide attempts: The VigilanS program, France.},
journal = {European psychiatry : the journal of the Association of European Psychiatrists},
volume = {64},
number = {1},
pages = {e57},
pmid = {34266505},
issn = {1778-3585},
mesh = {Algorithms ; France/epidemiology ; Humans ; *Suicide ; *Suicide, Attempted ; },
abstract = {OBJECTIVE: Among the postcrisis suicide prevention programmes, brief contact interventions (BCIs) have been proven to be efficient. VigilanS generalizes to a whole French region a BCI combining resource cards, telephone calls, and sending postcards, according to a predefined algorithm. However, a major problem in suicide prevention is the suicide reattempt, which can lead to final suicide. Here, we analyze the suicide reattempt in VigilanS.<br><br>METHODS: The study concerned patients included in VigilanS over the period from January 1, 2015 to December 31, 2018, with an end of follow-up on July 1, 2019. We performed a series of descriptive analyses, survival curves, and regressions. The outcome was the suicide reattempt, and the predictive variables were the characteristics of the patient at entry and during follow-up in VigilanS. Age and sex were considered as adjustment variables.<br><br>RESULTS: A total of 11,879 inclusions occurred during the study period, corresponding to 10,666 different patients, among which 905 reattempted suicide. More than half were primary suicide attempters (53.4%). A significant relationship with suicide reattempt was identified for the following characteristics: being a non-primary suicide attempter, having attempted suicide by voluntary drug intoxication and phlebotomy, alcohol consumption among primary suicide attempters, and having no companion at the emergency room visit among non-primary suicide attempters. Hanging (as suicide method), having made no call to VigilanS were protective factors.<br><br>CONCLUSION: This study provides us with a valuable insight into the profiles of patients repeating a suicide attempts, which is important for suicide prevention in general.},
}
@article {pmid34262515,
year = {2021},
author = {Gu, X and Fan, Y and Zhou, J and Zhu, J},
title = {Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {705528},
pmid = {34262515},
issn = {1664-1078},
abstract = {Electroencephalogram (EEG)-based emotion recognition (ER) has drawn increasing attention in the brain-computer interface (BCI) due to its great potentials in human-machine interaction applications. According to the characteristics of rhythms, EEG signals usually can be divided into several different frequency bands. Most existing methods concatenate multiple frequency band features together and treat them as a single feature vector. However, it is often difficult to utilize band-specific information in this way. In this study, an optimized projection and Fisher discriminative dictionary learning (OPFDDL) model is proposed to efficiently exploit the specific discriminative information of each frequency band. Using subspace projection technology, EEG signals of all frequency bands are projected into a subspace. The shared dictionary is learned in the projection subspace such that the specific discriminative information of each frequency band can be utilized efficiently, and simultaneously, the shared discriminative information among multiple bands can be preserved. In particular, the Fisher discrimination criterion is imposed on the atoms to minimize within-class sparse reconstruction error and maximize between-class sparse reconstruction error. Then, an alternating optimization algorithm is developed to obtain the optimal solution for the projection matrix and the dictionary. Experimental results on two EEG-based ER datasets show that this model can achieve remarkable results and demonstrate its effectiveness.},
}
@article {pmid34260835,
year = {2021},
author = {Moses, DA and Metzger, SL and Liu, JR and Anumanchipalli, GK and Makin, JG and Sun, PF and Chartier, J and Dougherty, ME and Liu, PM and Abrams, GM and Tu-Chan, A and Ganguly, K and Chang, EF},
title = {Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria.},
journal = {The New England journal of medicine},
volume = {385},
number = {3},
pages = {217-227},
pmid = {34260835},
issn = {1533-4406},
support = {U01 DC018671/DC/NIDCD NIH HHS/United States ; U01 NS098971-01/NH/NIH HHS/United States ; Sponsored Academic Research Agreement//Facebook/ ; },
mesh = {Adult ; Brain Stem Infarctions/*complications ; *Brain-Computer Interfaces ; *Deep Learning ; Dysarthria/etiology/*rehabilitation ; Electrocorticography ; Electrodes, Implanted ; Humans ; Male ; Natural Language Processing ; *Neural Prostheses ; Quadriplegia/etiology ; Sensorimotor Cortex/physiology ; *Speech ; },
abstract = {BACKGROUND: Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication.<br><br>METHODS: We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them.<br><br>RESULTS: We decoded sentences from the participant's cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period.<br><br>CONCLUSIONS: In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.).},
}
@article {pmid34259623,
year = {2021},
author = {Yu, X and Creamer, MS and Randi, F and Sharma, AK and Linderman, SW and Leifer, AM},
title = {Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training.},
journal = {eLife},
volume = {10},
number = {},
pages = {},
pmid = {34259623},
issn = {2050-084X},
support = {P30 EY026877/EY/NEI NIH HHS/United States ; P40 OD010440/OD/NIH HHS/United States ; R01 NS113119/NS/NINDS NIH HHS/United States ; R21 NS101629/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Brain/physiology ; Caenorhabditis elegans/*physiology ; *Deep Learning ; Hand ; Humans ; Machine Learning ; Neural Networks, Computer ; Neurons/*physiology ; },
abstract = {We present an automated method to track and identify neurons in C. elegans, called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL (Yemini et al., 2021). Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group (Chaudhary et al., 2021). Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10 ms making it suitable for future real-time applications.},
}
@article {pmid34258927,
year = {2021},
author = {Turovsky, YA and Gureev, AP and Vitkalova, IY and Popov, VN and Vakhtin, AA},
title = {The connection between rs6265 polymorphism in the BDNF gene and successful mastering of the video-oculographic interface.},
journal = {Journal of integrative neuroscience},
volume = {20},
number = {2},
pages = {287-296},
doi = {10.31083/j.jin2002028},
pmid = {34258927},
issn = {0219-6352},
support = {17-29-02505 ofi_m//Russian Foundation for Basic Research to AAV/ ; 19-29-01156 mk//Russian Foundation for Basic Research to YAT/ ; NSh 2535.2020.11//President grant for support of leading scientific school to VNP/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Brain-Derived Neurotrophic Factor/genetics/*physiology ; *Eye-Tracking Technology ; Female ; Humans ; Male ; Polymorphism, Single Nucleotide ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {A video-oculographic interface is a system for controlling objects using eye movements. The video-oculographic interface differs from other brain-computer interfaces regarding its improved accuracy, simplicity, and ergonomics. Despite these advantages, all users are not equally successful in mastering these various devices. It has been suggested that the genetic characteristics of the operators may determine the efficiency of video-oculographic interface mastery. We recruited healthy users with rs6313, rs2030324, rs429358, rs10119, rs457062, rs4290270, and rs6265 polymorphisms and analyzed the relationships between these polymorphisms and values of success in video-oculographic interface mastery. We found that carriers of the G/G genotype of the rs6265 polymorphism (BDNF gene) demonstrated the best results in video-oculographic interface mastery. In contrast, carriers of the A/A genotype were characterized by large standard deviations in the average amplitude of eye movement and the range of eye movement negatively correlated with goal achievement. This can be explained through the fact that carriers of the A/A genotype demonstrate lower synaptic plasticity due to reduced expression of BDNF when compared to carriers of the G/G genotype. These results expand our understanding of the genetic predictors of successful video-oculographic interface management, which will help to optimize device management training for equipment operators and people with disabilities.},
}
@article {pmid34257636,
year = {2021},
author = {Fu, Y and Zhou, Z and Gong, A and Qian, Q and Su, L and Zhao, L},
title = {Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG-Based Brain Network.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {5565824},
pmid = {34257636},
issn = {1687-5273},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Lower Extremity ; },
abstract = {Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery (p < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke.},
}
@article {pmid34256357,
year = {2021},
author = {Li, F and Chao, W and Li, Y and Fu, B and Ji, Y and Wu, H and Shi, G},
title = {Decoding imagined speech from EEG signals using hybrid-scale spatial-temporal dilated convolution network.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac13c0},
pmid = {34256357},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Speech ; },
abstract = {Objective.Directly decoding imagined speech from electroencephalogram (EEG) signals has attracted much interest in brain-computer interface applications, because it provides a natural and intuitive communication method for locked-in patients. Several methods have been applied to imagined speech decoding, but how to construct spatial-temporal dependencies and capture long-range contextual cues in EEG signals to better decode imagined speech should be considered.Approach.In this study, we propose a novel model called hybrid-scale spatial-temporal dilated convolution network (HS-STDCN) for EEG-based imagined speech recognition. HS-STDCN integrates feature learning from temporal and spatial information into a unified end-to-end model. To characterize the temporal dependencies of the EEG sequences, we adopted a hybrid-scale temporal convolution layer to capture temporal information at multiple levels. A depthwise spatial convolution layer was then designed to construct intrinsic spatial relationships of EEG electrodes, which can produce a spatial-temporal representation of the input EEG data. Based on the spatial-temporal representation, dilated convolution layers were further employed to learn long-range discriminative features for the final classification.Main results.To evaluate the proposed method, we compared the HS-STDCN with other existing methods on our collected dataset. The HS-STDCN achieved an averaged classification accuracy of 54.31% for decoding eight imagined words, which is significantly better than other methods at a significance level of 0.05.Significance.The proposed HS-STDCN model provided an effective approach to make use of both the temporal and spatial dependencies of the input EEG signals for imagined speech recognition. We also visualized the word semantic differences to analyze the impact of word semantics on imagined speech recognition, investigated the important regions in the decoding process, and explored the use of fewer electrodes to achieve comparable performance.},
}
@article {pmid34255630,
year = {2021},
author = {Lee, DY and Lee, M and Lee, SW},
title = {Decoding Imagined Speech Based on Deep Metric Learning for Intuitive BCI Communication.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1363-1374},
doi = {10.1109/TNSRE.2021.3096874},
pmid = {34255630},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Speech ; },
abstract = {Imagined speech is a highly promising paradigm due to its intuitive application and multiclass scalability in the field of brain-computer interfaces. However, optimal feature extraction and classifiers have not yet been established. Furthermore, retraining still requires a large number of trials when new classes are added. The aim of this study is (i) to increase the classification performance for imagined speech and (ii) to apply a new class using a pretrained classifier with a small number of trials. We propose a novel framework based on deep metric learning that learns the distance by comparing the similarity between samples. We also applied the instantaneous frequency and spectral entropy used for speech signals to electroencephalography signals during imagined speech. The method was evaluated on two public datasets (6-class Coretto DB and 5-class BCI Competition DB). We achieved a 6-class accuracy of 45.00 ± 3.13% and a 5-class accuracy of 48.10 ± 3.68% using the proposed method, which significantly outperformed state-of-the-art methods. Additionally, we verified that the new class could be detected through incremental learning with a small number of trials. As a result, the average accuracy is 44.50 ± 0.26% for Coretto DB and 47.12 ± 0.27% for BCI Competition DB, which shows similar accuracy to baseline accuracy without incremental learning. Our results have shown that the accuracy can be greatly improved even with a small number of trials by selecting appropriate features from imagined speech. The proposed framework could be directly used to help construct an extensible intuitive communication system based on brain-computer interfaces.},
}
@article {pmid34255197,
year = {2021},
author = {Sundaresan, A and Penchina, B and Cheong, S and Grace, V and Valero-Cabré, A and Martel, A},
title = {Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI.},
journal = {Brain informatics},
volume = {8},
number = {1},
pages = {13},
pmid = {34255197},
issn = {2198-4018},
support = {OSCILOSCOPUS//Agence Nationale de la Recherche/ ; EU project 898813-CLONESA-DLV-898813//H2020 Marie Sklodowska-Curie Actions/ ; Flag-era-JTC-2017//Agence Nationale de la Recherche/ ; },
abstract = {Mental stress is a major individual and societal burden and one of the main contributing factors that lead to pathologies such as depression, anxiety disorders, heart attacks, and strokes. Given that anxiety disorders are one of the most common comorbidities in youth with autism spectrum disorder (ASD), this population is particularly vulnerable to mental stress, severely limiting overall quality of life. To prevent this, early stress quantification with machine learning (ML) and effective anxiety mitigation with non-pharmacological interventions are essential. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals for stress assessment by comparing several ML classifiers, namely support vector machine (SVM) and deep learning methods. We trained a total of eleven subject-dependent models-four with conventional brain-computer interface (BCI) methods and seven with deep learning approaches-on the EEG of neurotypical (n=5) and ASD (n=8) participants performing alternating blocks of mental arithmetic stress induction, guided and unguided breathing. Our results show that a multiclass two-layer LSTM RNN deep learning classifier is capable of identifying mental stress from ongoing EEG with an overall accuracy of 93.27%. Our study is the first to successfully apply an LSTM RNN classifier to identify stress states from EEG in both ASD and neurotypical adolescents, and offers promise for an EEG-based BCI for the real-time assessment and mitigation of mental stress through a closed-loop adaptation of respiration entrainment.},
}
@article {pmid34253312,
year = {2021},
author = {Vaskov, AK and Chestek, CA},
title = {Brain-Machine Interfaces: Lessons for Prosthetic Hand Control.},
journal = {Hand clinics},
volume = {37},
number = {3},
pages = {391-399},
doi = {10.1016/j.hcl.2021.04.003},
pmid = {34253312},
issn = {1558-1969},
support = {R01 NS105132/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Artificial Limbs ; *Brain-Computer Interfaces ; Hand ; Humans ; *Spinal Cord Injuries ; Upper Extremity ; },
abstract = {Brain-machine interfaces (BMI) are being developed to restore upper limb function for persons with spinal cord injury or other motor degenerative conditions. BMI and implantable sensors for myoelectric prostheses directly extract information from the central or peripheral nervous system to provide users with high fidelity control of their prosthetic device. Control algorithms have been highly transferable between the 2 technologies but also face common issues. In this review of the current state of the art in each field, the authors point out similarities and differences between the 2 technologies that may guide the implementation of common solutions to these challenges.},
}
@article {pmid34250608,
year = {2021},
author = {Viana, PF and Remvig, LS and Duun-Henriksen, J and Glasstetter, M and Dümpelmann, M and Nurse, ES and Martins, IP and Schulze-Bonhage, A and Freestone, DR and Brinkmann, BH and Kjaer, TW and Richardson, MP},
title = {Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG.},
journal = {Epilepsia},
volume = {62},
number = {8},
pages = {1820-1828},
doi = {10.1111/epi.16969},
pmid = {34250608},
issn = {1528-1167},
support = {MR/N026063/1/MRC_/Medical Research Council/United Kingdom ; /DH_/Department of Health/United Kingdom ; },
mesh = {*Electroencephalography ; *Epilepsy/diagnosis ; Humans ; Seizures/diagnosis ; Spectrum Analysis ; Subcutaneous Tissue ; },
abstract = {OBJECTIVE: Ultra long-term subcutaneous electroencephalography (sqEEG) monitoring is a new modality with great potential for both health and disease, including epileptic seizure detection and forecasting. However, little is known about the long-term quality and consistency of the sqEEG signal, which is the objective of this study.<br><br>METHODS: The largest multicenter cohort of sqEEG was analyzed, including 14 patients with epilepsy and 12 healthy subjects, implanted with a sqEEG device (24/7 EEG[&#x2122;] SubQ), and recorded from 23 to 230&#xa0;days (median 42&#xa0;days), with a median data capture rate of 75% (17.9&#xa0;hours/day). Median power spectral density plots of each subject were examined for physiological peaks, including at diurnal and nocturnal periods. Long-term temporal trends in signal impedance and power spectral features were investigated with subject-specific linear regression models and group-level linear mixed-effects models.<br><br>RESULTS: sqEEG spectrograms showed an approximate 1/f power distribution. Diurnal peaks in the alpha range (8-13Hz) and nocturnal peaks in the sigma range (12-16Hz) were seen in the majority of subjects. Signal impedances remained low, and frequency band powers were highly stable throughout the recording periods.<br><br>SIGNIFICANCE: The spectral characteristics of minimally invasive, ultra long-term sqEEG are similar to scalp EEG, whereas the signal is highly stationary. Our findings reinforce the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.},
}
@article {pmid34248521,
year = {2021},
author = {Kostas, D and Aroca-Ouellette, S and Rudzicz, F},
title = {BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {653659},
pmid = {34248521},
issn = {1662-5161},
abstract = {Deep neural networks (DNNs) used for brain-computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive electroencephalography (EEG) datasets. We consider how to adapt techniques and architectures used for language modeling (LM) that appear capable of ingesting awesome amounts of data toward the development of encephalography modeling with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a variety of downstream BCI and EEG classification tasks, outperforming prior work in more task-specific (sleep stage classification) self-supervision.},
}
@article {pmid34248478,
year = {2021},
author = {Araujo, RS and Silva, CR and Netto, SPN and Morya, E and Brasil, FL},
title = {Development of a Low-Cost EEG-Controlled Hand Exoskeleton 3D Printed on Textiles.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {661569},
pmid = {34248478},
issn = {1662-4548},
abstract = {Stroke survivors can be affected by motor deficits in the hand. Robotic equipment associated with brain-machine interfaces (BMI) may aid the motor rehabilitation of these patients. BMIs involving orthotic control by motor imagery practices have been successful in restoring stroke patients' movements. However, there is still little acceptance of the robotic devices available, either by patients and clinicians, mainly because of the high costs involved. Motivated by this context, this work aims to design and construct the Hand Exoskeleton for Rehabilitation Objectives (HERO) to recover extension and flexion movements of the fingers. A three-dimensional (3D) printing technique in association with textiles was used to produce a lightweight and wearable device. 3D-printed actuators have also been designed to reduce equipment costs. The actuator transforms the torque of DC motors into linear force transmitted by Bowden cables to move the fingers passively. The exoskeleton was controlled by neuroelectric signal-electroencephalography (EEG). Concept tests were performed to evaluate control performance. A healthy volunteer was submitted to a training session with the exoskeleton, according to the Graz-BCI protocol. Ergonomy was evaluated with a two-dimensional (2D) tracking software and correlation analysis. HERO can be compared to ordinary clothing. The weight over the hand was around 102 g. The participant was able to control the exoskeleton with a classification accuracy of 91.5%. HERO project resulted in a lightweight, simple, portable, ergonomic, and low-cost device. Its use is not restricted to a clinical setting. Thus, users will be able to execute motor training with the HERO at hospitals, rehabilitation clinics, and at home, increasing the rehabilitation intervention time. This may support motor rehabilitation and improve stroke survivors life quality.},
}
@article {pmid34248477,
year = {2021},
author = {Silva, EMGS and Holanda, LJ and Coutinho, GKB and Andrade, FS and Nascimento, GIS and Nagem, DAP and Valentim, RAM and Lindquist, AR},
title = {Effects of Active Upper Limb Orthoses Using Brain-Machine Interfaces for Rehabilitation of Patients With Neurological Disorders: Protocol for a Systematic Review and Meta-Analysis.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {661494},
pmid = {34248477},
issn = {1662-4548},
abstract = {Introduction: The field of brain-machine interfaces (BMI) for upper limb (UL) orthoses is growing exponentially due to improvements in motor performance, quality of life, and functionality of people with neurological diseases. Considering this, we planned a systematic review to investigate the effects of BMI-controlled UL orthoses for rehabilitation of patients with neurological disorders. Methods: This systematic review and meta-analysis protocol was elaborated according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P 2015) and Cochrane Handbook for Systematic Reviews of Interventions. A search will be conducted on Pubmed, IEEE Xplore Digital Library, Medline, and Web of Science databases without language and year restrictions, and Patents Scope, Patentlens, and Google Patents websites in English, Spanish, French, German, and Portuguese between 2011 and 2021. Two independent reviewers will include randomized controlled trials and quasi-experimental studies using BMI-controlled active UL orthoses to improve human movement. Studies must contain participants aged >18 years, diagnosed with neurological disorders, and with impaired UL movement. Three independent reviewers will conduct the same procedure for patents. Evidence quality and risk of bias will be evaluated following the Cochrane collaboration by two review authors. Meta-analysis will be conducted in case of homogeneity between groups. Otherwise, a narrative synthesis will be performed. Data will be inserted into a table containing physical description, UL orthoses control system, and effect of BMI-controlled orthoses. Discussion: BMI-controlled orthoses can assist individuals in several routine activities and provide functional independence and sense of overcoming limitations imposed by the underlying disease. These benefits will also be associated with orthoses descriptions, safety, portability, adverse events, and tools used to assess UL motor performance in patients with neurological disorders. PROSPERO Registration Number: CRD42020182195.},
}
@article {pmid34247553,
year = {2021},
author = {Alzahrani, SI and Anderson, CW},
title = {A Comparison of Conventional and Tri-Polar EEG Electrodes for Decoding Real and Imaginary Finger Movements from One Hand.},
journal = {International journal of neural systems},
volume = {31},
number = {9},
pages = {2150036},
doi = {10.1142/S0129065721500362},
pmid = {34247553},
issn = {1793-6462},
mesh = {Electrodes ; *Electroencephalography ; Fingers ; *Hand ; Humans ; Movement ; },
abstract = {The representations of different fingers in the sensorimotor cortex are largely overlapped, which necessitate a good signal-to-noise ratio (SNR) and high spatial resolution to classify individual finger movements from one hand. Electroencephalography (EEG) recorded with disc electrodes has low SNR and poor spatial resolution. The surface Laplacian has been applied to EEG to improve the spatial resolution and selectivity of the surface electrical activity recording. Tri-polar concentric ring electrodes (TCREs) were shown to estimate the Laplacian automatically with better spatial resolution than disc electrodes. For this work, movement-related potentials (MRPs) were recorded from four TCREs and disc electrodes while 13 subjects performed real and imaginary finger movements. The MRP signals recorded with the TCREs have significantly less mutual information and coherence between neighboring locations compared to disc electrodes. The results also show that signals from TCREs generated higher accuracy compared to disc electrodes. It further shows that TCREs using temporal EEG data as features yield an average accuracy of [Formula: see text]% and [Formula: see text]% for real and imaginary finger movements, respectively, which is significantly higher than utilizing EEG spectral power changes in [Formula: see text] and [Formula: see text] bands as features. Similarly, with the disc electrodes, it achieved highest accuracy of [Formula: see text]% and [Formula: see text]% for real and imaginary finger movements, respectively, with temporal EEG data feature.},
}
@article {pmid34247451,
year = {2021},
author = {Xu, L and Zhang, H and Wang, Y and Lu, X and Zhao, Z and Ma, C and Yang, S and Yarov-Yarovoy, V and Tian, Y and Zheng, J and Yang, F},
title = {De Novo Design of Peptidic Positive Allosteric Modulators Targeting TRPV1 with Analgesic Effects.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {8},
number = {17},
pages = {e2101716},
pmid = {34247451},
issn = {2198-3844},
support = {31800990//National Science Foundation of China/ ; 31971040//National Science Foundation of China/ ; R01 NS072377/NS/NINDS NIH HHS/United States ; R01 NS103954/NS/NINDS NIH HHS/United States ; R01NS072377/NH/NIH HHS/United States ; R01 GM132110/GM/NIGMS NIH HHS/United States ; R01NS103954/NH/NIH HHS/United States ; R01NS072377/NH/NIH HHS/United States ; R01NS103954/NH/NIH HHS/United States ; },
mesh = {Allosteric Regulation/drug effects ; Analgesics/*pharmacology ; Animals ; Disease Models, Animal ; Male ; Nociceptors/*drug effects ; Pain/*drug therapy ; Peptides ; Rats ; TRPV Cation Channels/*drug effects ; },
abstract = {Transient receptor potential vanilloid 1 (TRPV1) ion channel is a nociceptor critically involved in pain sensation. Direct blockade of TRPV1 exhibits significant analgesic effects but also incurs severe side effects such as hyperthermia, causing failures of TRPV1 inhibitors in clinical trials. In order to selectively target TRPV1 channels that are actively involved in pain-sensing, peptidic positive allosteric modulators (PAMs) based on the high-resolution structure of the TRPV1 intracellular ankyrin-repeat like domain are de novo designed. The hotspot centric approach is optimized for protein design; its usage in Rosetta increases the success rate in protein binder design. It is demonstrated experimentally, with a combination of fluorescence resonance energy transfer (FRET) imaging, surface plasmon resonance, and patch-clamp recording, that the designed PAMs bind to TRPV1 with nanomolar affinity and allosterically enhance its response to ligand activation as it is designed. It is further demonstrated that the designed PAM exhibits long-lasting in vivo analgesic effects in rats without changing their body temperature, suggesting that they have potentials for developing into novel analgesics.},
}
@article {pmid34244727,
year = {2021},
author = {Xia, LP and Luo, H and Ma, Q and Xie, YK and Li, W and Hu, H and Xu, ZZ},
title = {GPR151 in nociceptors modulates neuropathic pain via regulating P2X3 function and microglial activation.},
journal = {Brain : a journal of neurology},
volume = {144},
number = {11},
pages = {3405-3420},
doi = {10.1093/brain/awab245},
pmid = {34244727},
issn = {1460-2156},
mesh = {Animals ; Ganglia, Spinal/metabolism ; Humans ; Male ; Mice ; Mice, Inbred C57BL ; Microglia/*metabolism ; Neuralgia/*metabolism ; Nociceptors/*metabolism ; Receptors, G-Protein-Coupled/*metabolism ; Receptors, Purinergic P2X3/*metabolism ; },
abstract = {Neuropathic pain is a major health problem that affects up to 7-10% of the population worldwide. Currently, neuropathic pain is difficult to treat because of its elusive mechanisms. Here we report that orphan G protein-coupled receptor 151 (GPR151) in nociceptive sensory neurons controls neuropathic pain induced by nerve injury. GPR151 was mainly expressed in non-peptidergic C-fibre dorsal root ganglion neurons and highly upregulated after nerve injury. Importantly, conditional knockout of Gpr151 in adult nociceptive sensory neurons significantly alleviated chronic constriction injury-induced neuropathic pain-like behaviour but did not affect basal nociception. Moreover, GPR151 in DRG neurons was required for chronic constriction injury-induced neuronal hyperexcitability and upregulation of colony-stimulating factor 1 (CSF1), which is necessary for microglial activation in the spinal cord after nerve injury. Mechanistically, GPR151 coupled with P2X3 ion channels and promoted their functional activities in neuropathic pain-like hypersensitivity. Knockout of Gpr151 suppressed P2X3-mediated calcium elevation and spontaneous pain behaviour in chronic constriction injury mice. Conversely, overexpression of Gpr151 significantly enhanced P2X3-mediated calcium elevation and dorsal root ganglion neuronal excitability. Furthermore, knockdown of P2X3 in dorsal root ganglia reversed chronic constriction injury-induced CSF1 upregulation, spinal microglial activation and neuropathic pain-like behaviour. Finally, the coexpression of GPR151 and P2X3 was confirmed in small-diameter human dorsal root ganglion neurons, indicating the clinical relevance of our findings. Together, our results indicate that GPR151 in nociceptive dorsal root ganglion neurons plays a key role in the pathogenesis of neuropathic pain and could be a potential target for treating neuropathic pain.},
}
@article {pmid34243738,
year = {2021},
author = {Stasinaki, A and Büchter, D and Shih, CI and Heldt, K and Güsewell, S and Brogle, B and Farpour-Lambert, N and Kowatsch, T and l'Allemand, D},
title = {Effects of a novel mobile health intervention compared to a multi-component behaviour changing program on body mass index, physical capacities and stress parameters in adolescents with obesity: a randomized controlled trial.},
journal = {BMC pediatrics},
volume = {21},
number = {1},
pages = {308},
pmid = {34243738},
issn = {1471-2431},
mesh = {Adolescent ; Body Mass Index ; Child ; Humans ; Overweight ; *Pediatric Obesity/therapy ; Switzerland ; *Telemedicine ; },
abstract = {BACKGROUND: Less than 2% of overweight children and adolescents in Switzerland can participate in multi-component behaviour changing interventions (BCI), due to costs and lack of time. Stress often hinders positive health outcomes in youth with obesity. Digital health interventions, with fewer on-site visits, promise health care access in remote regions; however, evidence for their effectiveness is scarce.<br><br>METHODS: This randomized controlled not blinded trial (1:1) was conducted in a childhood obesity center in Switzerland. Forty-one youth aged 10-18&#x2009;years with body mass index (BMI)&#x2009;>&#x2009;P.90 with risk factors or co-morbidities or BMI&#x2009;>&#x2009;P.97 were recruited. During 5.5&#x2009;months, the PathMate2 group (PM) received daily conversational agent counselling via mobile app, combined with standardized counselling (4 on-site visits). Controls (CON) participated in a BCI (7 on-site visits). We compared the outcomes of both groups after 5.5 (T1) and 12 (T2) months. Primary outcome was reduction in BMI-SDS (BMI standard deviation score: BMI adjusted for age and sex). Secondary outcomes were changes in body fat and muscle mass (bioelectrical impedance analysis), waist-to-height ratio, physical capacities (modified Dordel-Koch-Test), blood pressure and pulse. Additionally, we hypothesized that less stressed children would lose more weight. Thus, children performed biofeedback relaxation exercises while stress parameters (plasma cortisol, stress questionnaires) were evaluated.<br><br>RESULTS: At intervention start median BMI-SDS of all patients (18&#x2009;PM, 13 CON) was 2.61 (obesity >&#x2009;+&#x2009;2SD). BMI-SDS decreased significantly in CON at T1, but not at T2, and did not decrease in PM during the study. Muscle mass, strength and agility improved significantly in both groups at T2; only PM reduced significantly their body fat at T1 and T2. Average daily PM app usage rate was 71.5%. Cortisol serum levels decreased significantly after biofeedback but with no association between stress parameters and BMI-SDS. No side effects were observed.<br><br>CONCLUSIONS: Equally to BCI, PathMate2 intervention resulted in significant and lasting improvements of physical capacities and body composition, but not in sustained BMI-SDS decrease. This youth-appealing mobile health intervention provides an interesting approach for youth with obesity who have limited access to health care. Biofeedback reduces acute stress and could be an innovative adjunct to usual care.},
}
@article {pmid34242704,
year = {2021},
author = {Sheng, T and Xing, D and Wu, Y and Wang, Q and Li, X and Lu, W},
title = {A novel 3D-printed multi-driven system for large-scale neurophysiological recordings in multiple brain regions.},
journal = {Journal of neuroscience methods},
volume = {361},
number = {},
pages = {109286},
doi = {10.1016/j.jneumeth.2021.109286},
pmid = {34242704},
issn = {1872-678X},
mesh = {Animals ; *Brain ; Electrodes, Implanted ; Memory ; *Neurophysiology ; Printing, Three-Dimensional ; },
abstract = {BACKGROUND: Electrical probes have been widely used for recording single-unit spike activity and local field potentials (LFPs) in brain regions. However, setting up an easily-assembled large-scale recording in multiple brain regions for long-term and stable neural activity monitoring is still a hard task.<br><br>NEW METHOD: We established a novel 3D-printed multi-drive system with high-density (up to 256 channels) tetrodes/grid electrodes that enables us to record cortical and subcortical brain regions in freely behaving animals.<br><br>RESULTS: In this paper, we described the design and fabrication of this system in detail. By using this system, we obtained successful recording on both spikes and LFPs from seven distinct brain regions that are related to memory function.<br><br>The low cost, large-scale electrodes with small size and flexible 3D-printed design of the system allow us to implant assembled tetrodes or grid electrodes into multiple target brain areas.<br><br>CONCLUSIONS: The 3D-printed large-scale multi-drive platform we described here may serve as a powerful new tool for future studies of brain circuitry functions.},
}
@article {pmid34239936,
year = {2021},
author = {Yang, S and Li, R and Li, H and Xu, K and Shi, Y and Wang, Q and Yang, T and Sun, X},
title = {Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation.},
journal = {BioMed research international},
volume = {2021},
number = {},
pages = {9967348},
pmid = {34239936},
issn = {2314-6141},
mesh = {*Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Equipment Design ; Humans ; Neurological Rehabilitation/*methods ; Orthotic Devices ; Robotics ; Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; Wheelchairs ; },
abstract = {With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabilitation. As a result, brain-computer interfaces (BCIs) have been included in many countries' strategic plans for innovating this field, and subsequently, major funding and talent have been invested in this technology. In neurological rehabilitation for stroke patients, the use of BCIs opens up a new chapter in "top-down" rehabilitation. In our study, we first reviewed the latest BCI technologies, then presented recent research advances and landmark findings in BCI-based neurorehabilitation for stroke patients. Neurorehabilitation was focused on the areas of motor, sensory, speech, cognitive, and environmental interactions. Finally, we summarized the shortcomings of BCI use in the field of stroke neurorehabilitation and the prospects for BCI technology development for rehabilitation.},
}
@article {pmid34239430,
year = {2021},
author = {Kim, S and Emory, C and Choi, I},
title = {Neurofeedback Training of Auditory Selective Attention Enhances Speech-In-Noise Perception.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {676992},
pmid = {34239430},
issn = {1662-5161},
support = {P50 DC000242/DC/NIDCD NIH HHS/United States ; },
abstract = {Selective attention enhances cortical responses to attended sensory inputs while suppressing others, which can be an effective strategy for speech-in-noise (SiN) understanding. Emerging evidence exhibits a large variance in attentional control during SiN tasks, even among normal-hearing listeners. Yet whether training can enhance the efficacy of attentional control and, if so, whether the training effects can be transferred to performance on a SiN task has not been explicitly studied. Here, we introduce a neurofeedback training paradigm designed to reinforce the attentional modulation of auditory evoked responses. Young normal-hearing adults attended one of two competing speech streams consisting of five repeating words ("up") in a straight rhythm spoken by a female speaker and four straight words ("down") spoken by a male speaker. Our electroencephalography-based attention decoder classified every single trial using a template-matching method based on pre-defined patterns of cortical auditory responses elicited by either an "up" or "down" stream. The result of decoding was provided on the screen as online feedback. After four sessions of this neurofeedback training over 4 weeks, the subjects exhibited improved attentional modulation of evoked responses to the training stimuli as well as enhanced cortical responses to target speech and better performance during a post-training SiN task. Such training effects were not found in the Placebo Group that underwent similar attention training except that feedback was given only based on behavioral accuracy. These results indicate that the neurofeedback training may reinforce the strength of attentional modulation, which likely improves SiN understanding. Our finding suggests a potential rehabilitation strategy for SiN deficits.},
}
@article {pmid34237711,
year = {2021},
author = {Yang, C and Yan, X and Wang, Y and Chen, Y and Zhang, H and Gao, X},
title = {Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac127f},
pmid = {34237711},
issn = {1741-2552},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Objective.Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data.Approach.The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the 'non-control state detection' methods, this algorithm was based on the 'statistical inspection-rejection decision' mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates.Main results.Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of97.2±2.6%and the average information transfer rate (ITR) of106.3±32.0 bitsmin-1. At the same time, the average false alarm rate in the 240 s resting state test was0.607±0.602 min-1. In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min[-1]in two free spelling experiments.Significance.This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.},
}
@article {pmid34236615,
year = {2022},
author = {Tian, S and Lin, G and Piao, L and Liu, X},
title = {Del-1 enhances therapeutic efficacy of bacterial cancer immunotherapy by blocking recruitment of tumor-infiltrating neutrophils.},
journal = {Clinical &amp; translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico},
volume = {24},
number = {2},
pages = {244-253},
pmid = {34236615},
issn = {1699-3055},
mesh = {Animals ; Biological Therapy/*methods ; Calcium-Binding Proteins/*physiology ; Cell Adhesion Molecules/*physiology ; Colonic Neoplasms/*immunology/*therapy ; Disease Models, Animal ; Male ; Mice ; Mice, Inbred BALB C ; *Neutrophil Infiltration ; *Salmonella typhimurium ; Treatment Outcome ; },
abstract = {BACKGROUND: Bacterial-mediated cancer immunotherapy (BCI) elicits a more robust initial immune response than conventional immunotherapy, but does not prevent tumor recurrence and metastasis. BCI is associated with recruitment of tumor-infiltrating neutrophils, which could suppress the therapeutic efficacy of this modality. Development endothelial locus 1 (Del-1), a potent inhibitor of neutrophil recruitment, antagonizes lymphocyte function-associated antigen-1 on the vascular endothelium. Here, we aimed to determine the effect of Del-1-secreting S.t△ppGpp on anti-tumor activity and tumor-infiltrating neutrophil recruitment in a mouse model of colon cancer.<br><br>METHODS: We investigated the anti-cancer activity of Del-1-secreting engineered Salmonella (&#x25b3;ppGpp S. Typhimurium) in the mice colon cancer models.<br><br>RESULTS: In the present study, we identified that Del-1-secreting engineered Salmonella had more potent anti-cancer activity compared with normal S.t&#x25b3;ppGpp without Del-1 secretion. We postulated that Del-1 expression increased M1 macrophage recruitment to tumors by decreasing tumor-infiltrating neutrophils. This approach could enhance the anti-cancer effects of S.t&#x25b3;ppGpp.<br><br>CONCLUSIONS: Collectively, the approach of using engineered bacteria that deliver Del-1 to block tumor-infiltrating neutrophil recruitment is a potential therapeutic approach.},
}
@article {pmid34235225,
year = {2021},
author = {Wang, Q and Sun, W and Qu, Y and Feng, C and Wang, D and Yin, H and Li, C and Sun, Z and Sun, D},
title = {Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury.},
journal = {BioMed research international},
volume = {2021},
number = {},
pages = {9962905},
pmid = {34235225},
issn = {2314-6141},
mesh = {*Artificial Intelligence ; Biomedical Engineering/methods ; Brain Injuries, Traumatic/*rehabilitation/therapy ; *Brain-Computer Interfaces ; Deep Brain Stimulation ; Humans ; Rehabilitation/*instrumentation/*methods ; Robotics ; Software ; Telemedicine ; Transcranial Magnetic Stimulation ; Virtual Reality ; Wearable Electronic Devices ; },
abstract = {The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At present, these techniques has been widely used in the rehabilitation of TBI. In this review article, we summarizes the progress of the combination of medicine and industry in the rehabilitation of traumatic brain injury in recent years, mainly from the following aspects: artificial intelligence (AI), brain-computer interfaces (BCI), noninvasive brain stimulation (NIBS), and wearable-assisted devices. We believe the summary of this article can improve insight into the combination of medicine and industry in the rehabilitation of traumatic brain injury.},
}
@article {pmid34233305,
year = {2021},
author = {Vidaurre, C and Jorajuría, T and Ramos-Murguialday, A and Müller, KR and Gómez, M and Nikulin, VV},
title = {Improving motor imagery classification during induced motor perturbations.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac123f},
pmid = {34233305},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Movement ; Reproducibility of Results ; },
abstract = {Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.},
}
@article {pmid34229308,
year = {2021},
author = {Nann, M and Haslacher, D and Colucci, A and Eskofier, B and von Tscharner, V and Soekadar, SR},
title = {Heart rate variability predicts decline in sensorimotor rhythm control.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac1177},
pmid = {34229308},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Heart Rate ; Humans ; Imagery, Psychotherapy ; },
abstract = {Objective.Voluntary control of sensorimotor rhythms (SMRs, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme.Approach.Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased.Main results.While control performance (p= .18) and HRV (p= .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: -0.61%/10 s,p= 0; HRV: -0.007 ms/10 s,p< .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level (p< .001).Significance.These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.},
}
@article {pmid34229027,
year = {2021},
author = {Meng, M and Yin, X and She, Q and Gao, Y and Kong, W and Luo, Z},
title = {Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition.},
journal = {Journal of neuroscience methods},
volume = {361},
number = {},
pages = {109274},
doi = {10.1016/j.jneumeth.2021.109274},
pmid = {34229027},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Sparse representation-based classification (SRC) has more advantages in motor imagery EEG pattern recognition, and the quality of dictionary construction directly determines the performance of SRC. In this paper, we proposed a two-dimensional dictionary optimization (TDDO) method to directly improve the performance of SRC.<br><br>NEW METHOD: Firstly, an initial dictionary was constructed with multi-band features extracted by filter band common spatial pattern (FBCSP). Then Lasso regression is used to select significant features in each atom synchronously in the horizontal direction, and the KNN-based method is used to clean up noise atoms in the vertical direction. Finally, an SRC method by training samples linearly representing test samples was implemented in classification.<br><br>RESULTS: The results show the necessity and rationality of TDDO-SRC method. The highest average classification accuracy of 86.5% and 92.4% is obtained on two public datasets.<br><br>The proposed method has more superior classification accuracy compared to traditional methods and existing winners' methods.<br><br>CONCLUSIONS: The quality of dictionary construction has a great impact on the robustness of SRC. And compared with the original SRC, the classification accuracy of the optimized TDDO-SRC is greatly improved.},
}
@article {pmid34228765,
year = {2021},
author = {Maneerat, P and Niwitpong, SA and Niwitpong, S},
title = {Simultaneous confidence intervals for all pairwise comparisons of the means of delta-lognormal distributions with application to rainfall data.},
journal = {PloS one},
volume = {16},
number = {7},
pages = {e0253935},
pmid = {34228765},
issn = {1932-6203},
mesh = {Bayes Theorem ; Computer Simulation ; Confidence Intervals ; *Models, Statistical ; Probability ; *Rain ; Thailand ; },
abstract = {Natural disasters such as flooding and landslides are important unexpected events during the rainy season in Thailand, and how to direct action to avoid their impacts is the motivation behind this study. The differences between the means of natural rainfall datasets in different areas can be estimated using simultaneous confidence intervals (SCIs) for pairwise comparisons of the means of delta-lognormal distributions. Our proposed methods are based on a parametric bootstrap (PB), a fiducial generalized confidence interval (FGCI), the method of variance estimates recovery (MOVER), and Bayesian credible intervals based on mixed (BCI-M) and uniform (BCI-U) priors. Their coverage probabilities, lower and upper error probabilities, and relative average lengths were used to evaluate and compare their SCI performances through Monte Carlo simulation. The results show that BCI-U and PB work well in different situations, even with large differences in variances [Formula: see text]. All of the methods were applied to estimate pairwise differences between the means of natural rainfall data from five areas in Thailand during the rainy season to determine their abilities to predict occurrences of flooding and landslides.},
}
@article {pmid34225320,
year = {2022},
author = {Coudert, A and Gaveau, V and Gatel, J and Verdelet, G and Salemme, R and Farne, A and Pavani, F and Truy, E},
title = {Spatial Hearing Difficulties in Reaching Space in Bilateral Cochlear Implant Children Improve With Head Movements.},
journal = {Ear and hearing},
volume = {43},
number = {1},
pages = {192-205},
pmid = {34225320},
issn = {1538-4667},
mesh = {Adolescent ; Child ; *Cochlear Implantation/methods ; *Cochlear Implants ; Head Movements ; Hearing ; *Hearing Loss ; Humans ; *Sound Localization ; *Speech Perception ; },
abstract = {OBJECTIVES: The aim of this study was to assess three-dimensional (3D) spatial hearing abilities in reaching space of children and adolescents fitted with bilateral cochlear implants (BCI). The study also investigated the impact of spontaneous head movements on sound localization abilities.<br><br>DESIGN: BCI children (N = 18, aged between 8 and 17) and age-matched normal-hearing (NH) controls (N = 18) took part in the study. Tests were performed using immersive virtual reality equipment that allowed control over visual information and initial eye position, as well as real-time 3D motion tracking of head and hand position with subcentimeter accuracy. The experiment exploited these technical features to achieve trial-by-trial exact positioning in head-centered coordinates of a single loudspeaker used for real, near-field sound delivery, which was reproducible across trials and participants. Using this novel approach, broadband sounds were delivered at different azimuths within the participants' arm length, in front and back space, at two different distances from their heads. Continuous head-monitoring allowed us to compare two listening conditions: "head immobile" (no head movements allowed) and "head moving" (spontaneous head movements allowed). Sound localization performance was assessed by computing the mean 3D error (i.e. the difference in space between the X-Y-Z position of the loudspeaker and the participant's final hand position used to indicate the localization of the sound's source), as well as the percentage of front-back and left-right confusions in azimuth, and the discriminability between two nearby distances. Several clinical factors (i.e. age at test, interimplant interval, and duration of binaural experience) were also correlated with the mean 3D error. Finally, the Speech Spatial and Qualities of Hearing Scale was administered to BCI participants and their parents.<br><br>RESULTS: Although BCI participants distinguished well between left and right sound sources, near-field spatial hearing remained challenging, particularly under the " head immobile" condition. Without visual priors of the sound position, response accuracy was lower than that of their NH peers, as evidenced by the mean 3D error (BCI: 55&#x2009;cm, NH: 24&#x2009;cm, p = 0.008). The BCI group mainly pointed along the interaural axis, corresponding to the position of their CI microphones. This led to important front-back confusions (44.6%). Distance discrimination also remained challenging for BCI users, mostly due to sound compression applied by their processor. Notably, BCI users benefitted from head movements under the "head moving" condition, with a significant decrease of the 3D error when pointing to front targets (p < 0.001). Interimplant interval was correlated with 3D error (p < 0.001), whereas no correlation with self-assessment of spatial hearing difficulties emerged (p = 0.9).<br><br>CONCLUSIONS: In reaching space, BCI children and adolescents are able to extract enough auditory cues to discriminate sound side. However, without any visual cues or spontaneous head movements during sound emission, their localization abilities are substantially impaired for front-back and distance discrimination. Exploring the environment with head movements was a valuable strategy for improving sound localization within individuals with different clinical backgrounds. These novel findings could prompt new perspectives to better understand sound localization maturation in BCI children, and more broadly in patients with hearing loss.},
}
@article {pmid34225263,
year = {2021},
author = {Mylavarapu, R and Prins, NW and Pohlmeyer, EA and Shoup, AM and Debnath, S and Geng, S and Sanchez, JC and Schwartz, O and Prasad, A},
title = {Chronic recordings from the marmoset motor cortex reveals modulation of neural firing and local field potentials overlap with macaques.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34225263},
issn = {1741-2552},
support = {DP2 EB022357/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Callithrix ; Macaca ; Male ; *Motor Cortex ; Movement ; },
abstract = {Objective.The common marmoset has been increasingly used in neural interfacing studies due to its smaller size, easier handling, and faster breeding compared to Old World non-human primate (NHP) species. While assessment of cortical anatomy in marmosets has shown strikingly similar layout to macaques, comprehensive assessment of electrophysiological properties underlying forelimb reaching movements in this bridge species does not exist. The objective of this study is to characterize electrophysiological properties of signals recorded from the marmoset primary motor cortex (M1) during a reach task and compare with larger NHP models such that this smaller NHP model can be used in behavioral neural interfacing studies.Approach and main results.Neuronal firing rates and local field potentials (LFPs) were chronically recorded from M1 in three adult, male marmosets. Firing rates, mu + beta and high gamma frequency bands of LFPs were evaluated for modulation with respect to movement. Firing rate and regularity of neurons of the marmoset M1 were similar to that reported in macaques with a subset of neurons showing selectivity to movement direction. Movement phases (rest vs move) was classified from both neural spiking and LFPs. Microelectrode arrays provide the ability to sample small regions of the motor cortex to drive brain-machine interfaces (BMIs). The results demonstrate that marmosets are a robust bridge species for behavioral neuroscience studies with motor cortical electrophysiological signals recorded from microelectrode arrays that are similar to Old World NHPs.Significance. As marmosets represent an interesting step between rodent and macaque models, successful demonstration that neuron modulation in marmoset motor cortex is analogous to reports in macaques illustrates the utility of marmosets as a viable species for BMI studies.},
}
@article {pmid34221725,
year = {2021},
author = {Cardenas, CR and Luo, AR and Jones, TH and Schultz, TR and Adams, RMM},
title = {Using an integrative taxonomic approach to delimit a sibling species, Mycetomoellerius mikromelanos sp. nov. (Formicidae: Attini: Attina).},
journal = {PeerJ},
volume = {9},
number = {},
pages = {e11622},
pmid = {34221725},
issn = {2167-8359},
abstract = {The fungus-growing ant Mycetomoellerius (previously Trachymyrmex) zeteki (Weber 1940) has been the focus of a wide range of studies examining symbiotic partners, garden pathogens, mating frequencies, and genomics. This is in part due to the ease of collecting colonies from creek embankments and its high abundance in the Panama Canal region. The original description was based on samples collected on Barro Colorado Island (BCI), Panama. However, most subsequent studies have sampled populations on the mainland 15 km southeast of BCI. Herein we show that two sibling ant species live in sympatry on the mainland: Mycetomoellerius mikromelanos Cardenas, Schultz, &amp; Adams and M. zeteki. This distinction was originally based on behavioral differences of workers in the field and on queen morphology (M. mikromelanos workers and queens are smaller and black while those of M. zeteki are larger and red). Authors frequently refer to either species as "M. cf. zeteki," indicating uncertainty about identity. We used an integrative taxonomic approach to resolve this, examining worker behavior, chemical profiles of worker volatiles, molecular markers, and morphology of all castes. For the latter, we used conventional taxonomic indicators from nine measurements, six extrapolated indices, and morphological characters. We document a new observation of a Diapriinae (Hymenoptera: Diapriidae) parasitoid wasp parasitizing M. zeteki. Finally, we discuss the importance of vouchering in dependable, accessible museum collections and provide a table of previously published papers to clarify the usage of the name T. zeteki. We found that most reports of M. zeteki or M. cf. zeteki-including a genome-actually refer to the new species M. mikromelanos.},
}
@article {pmid34221675,
year = {2021},
author = {Chen, T and Zhao, C and Pan, X and Qu, J and Wei, J and Li, C and Liang, Y and Zhang, X},
title = {Decoding different working memory states during an operation span task from prefrontal fNIRS signals.},
journal = {Biomedical optics express},
volume = {12},
number = {6},
pages = {3495-3511},
pmid = {34221675},
issn = {2156-7085},
abstract = {We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.},
}
@article {pmid34211672,
year = {2021},
author = {Fakhri, M and Abdan, M and Ramezanpour, M and Dehkordi, AH and Sarikhani, D},
title = {Systematic Review and Meta-Analysis on Quality of Life in Diabetic Patients in Iran.},
journal = {International journal of preventive medicine},
volume = {12},
number = {},
pages = {41},
pmid = {34211672},
issn = {2008-7802},
abstract = {BACKGROUND: Diabetes is the fifth leading cause of death in the world, which reduces the patients' quality of life (QOL) and is considered as an important subject especially in medicine and medical community. The present study aimed at investigating the QOL of diabetic patients in Iran through meta-analysis.<br><br>METHODS: The search was conducted using relevant keywords in national and international databases including Iranmedex, SID, Magiran, IranDoc, Medlib, Science Direct, PubMed, Scopus, Cochrane, Embase, Web of Science. Questionnaires WHOQOL, SF-36, SF-20, DQOL, QOL, PedsQL, ADDQOL, D-39, DQOL-BCI, SWED-QUAL, IRDQOL, PHG-2, EQ-5D, and IDQOL-BCI were used to assess the QOL. Heterogeneity of studies was assessed using I[2] index. Data were analyzed using STATA version 11.<br><br>RESULTS: In 96 studies of 17,994 people, the mean score of QOL in diabetic patients was based on the questionnaires WHOQOL [66.55 (95% CI: 45.83, 87.26)], D-39 [129.43 (95%CI: 88.77, 170.10)], SF-36 [65.64 (95% CI: 59.82, 71.46)], SF-20 [46.50 (95% CI: 37.19, 55.81], DQOL [61.19 (95% CI: 35.73, 86.66)], QOL [117.91 (95% CI: -62.97, 298.79)], PedsQL [34.36 (95% CI: -31.49, 100.22)], ADDQOL [41.76 (95% CI: 12.01-71.50)], SWED-QUAL [59.19 (95% CI: 21.15, 97.23)], IRDQOL [105.92 (95% CI: 102.73, 109.10)], PHG-2 [61.00 (95%CI: 59.63, 62.37)], EQ-5D [0.62 (95% CI: 0.61, 0.64)], DQOL-BCI [3.40 (95% CI: 3.31, 3.49)], and IDQOL-BCI [22.63 (95% CI: -2.38, 47.64)].<br><br>CONCLUSIONS: The QOL of diabetic patients was evaluated according to different types of questionnaires and the QOL of diabetic patients was found to be lower than normal population.},
}
@article {pmid34211549,
year = {2021},
author = {Chen, C and Yuan, K and Wang, X and Khan, A and Chu, WC and Tong, RK},
title = {Neural Correlates of Motor Recovery after Robot-Assisted Training in Chronic Stroke: A Multimodal Neuroimaging Study.},
journal = {Neural plasticity},
volume = {2021},
number = {},
pages = {8866613},
pmid = {34211549},
issn = {1687-5443},
support = {MC_UU_00005/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Aged ; Chronic Disease ; Dominance, Cerebral ; *Electroencephalography ; Female ; Hand/physiology ; Humans ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; *Motor Activity ; Multimodal Imaging/*methods ; Neuroimaging/*methods ; Recovery of Function ; Robotics ; Stroke Rehabilitation/*methods ; },
abstract = {Stroke is a leading cause of motor disability worldwide, and robot-assisted therapies have been increasingly applied to facilitate the recovery process. However, the underlying mechanism and induced neuroplasticity change remain partially understood, and few studies have investigated this from a multimodality neuroimaging perspective. The current study adopted BCI-guided robot hand therapy as the training intervention and combined multiple neuroimaging modalities to comprehensively understand the potential association between motor function alteration and various neural correlates. We adopted EEG-informed fMRI technique to understand the functional regions sensitive to training intervention. Additionally, correlation analysis among training effects, nonlinear property change quantified by fractal dimension (FD), and integrity of M1-M1 (M1: primary motor cortex) anatomical connection were performed. EEG-informed fMRI analysis indicated that for iM1 (iM1: ipsilesional M1) regressors, regions with significantly increased partial correlation were mainly located in contralesional parietal, prefrontal, and sensorimotor areas and regions with significantly decreased partial correlation were mainly observed in the ipsilesional supramarginal gyrus and superior temporal gyrus. Pearson's correlations revealed that the interhemispheric asymmetry change significantly correlated with the training effect as well as the integrity of M1-M1 anatomical connection. In summary, our study suggested that multiple functional brain regions not limited to motor areas were involved during the recovery process from multimodality perspective. The correlation analyses suggested the essential role of interhemispheric interaction in motor rehabilitation. Besides, the underlying structural substrate of the bilateral M1-M1 connection might relate to the interhemispheric change. This study might give some insights in understanding the neuroplasticity induced by the integrated BCI-guided robot hand training intervention and further facilitate the design of therapies for chronic stroke patients.},
}
@article {pmid34211380,
year = {2021},
author = {Robinson, N and Chouhan, T and Mihelj, E and Kratka, P and Debraine, F and Wenderoth, N and Guan, C and Lehner, R},
title = {Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {648275},
pmid = {34211380},
issn = {1662-5161},
abstract = {Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.},
}
@article {pmid34207559,
year = {2021},
author = {Angiolillo, A and Gandaglia, A and Arcaro, A and Carpi, A and Gentile, F and Naso, F and Di Costanzo, A},
title = {Altered Blood Levels of Anti-Gal Antibodies in Alzheimer's Disease: A New Clue to Pathogenesis?.},
journal = {Life (Basel, Switzerland)},
volume = {11},
number = {6},
pages = {},
pmid = {34207559},
issn = {2075-1729},
abstract = {Alzheimer's disease is a neurodegenerative disorder whose pathological mechanisms, despite recent advances, are not fully understood. However, the deposition of beta amyloid -peptide and neuroinflammation, which is probably aggravated by dysbiotic microbiota, seem to play a key role. Anti-Gal are the most abundant xenoreactive natural antibodies. They are supposed to stem from immunization against the gut microbiota and have been implicated in the pathogenesis of several diseases, including multiple sclerosis. These antibodies target the alpha-Gal epitope, expressed on the terminal sugar units of glycoprotein or glycolipid of all mammals except apes, Old World monkeys and humans. The alpha-Gal is constitutively expressed in several bacteria constituting the brain microbiota, and alpha-Gal-like epitopes have been detected in gray matter, amyloid plaque, neurofibrillary tangles and corpora amylacea of the human brain, suggesting a potential link between anti-Gal and Alzheimer's disease etiopathogenesis. For the first time, our study searched for possible alterations of anti-Gal immunoglobulin levels in Alzheimer's disease patients. IgG and IgM blood levels were significantly lower, and IgA significantly higher in patients than in healthy subjects. These results suggest that such immunoglobulins might be implicated in Alzheimer's disease pathogenesis and open new scenarios in the research for new biomarkers and therapeutic strategies.},
}
@article {pmid34204814,
year = {2021},
author = {Liu, X and Chen, S and Shen, X and Zhang, X and Wang, Y},
title = {A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {6},
pages = {},
pmid = {34204814},
issn = {1099-4300},
support = {SGDX2019081623021543//Shenzhen-Hong Kong Innovation Circle (Category D)/ ; ITS/092/17//Hong Kong Innovation and Technology Fund/ ; 61836003//National Natural Science Foundation of China/ ; R9051//Chau Hoi Shuen Foundation's Donation/ ; FP902//sponsorship scheme for targeted strategic partnership/ ; },
abstract = {Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.},
}
@article {pmid34202546,
year = {2021},
author = {Camargo-Vargas, D and Callejas-Cuervo, M and Mazzoleni, S},
title = {Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {13},
pages = {},
pmid = {34202546},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography ; Lower Extremity ; Upper Extremity ; *Neurological Rehabilitation/methods ; },
abstract = {In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.},
}
@article {pmid34202413,
year = {2021},
author = {Bobrova, EV and Reshetnikova, VV and Vershinina, EA and Grishin, AA and Bobrov, PD and Frolov, AA and Gerasimenko, YP},
title = {Success of Hand Movement Imagination Depends on Personality Traits, Brain Asymmetry, and Degree of Handedness.},
journal = {Brain sciences},
volume = {11},
number = {7},
pages = {},
pmid = {34202413},
issn = {2076-3425},
support = {20-31-70001//Russian Foundation for Basic Research/ ; },
abstract = {Brain-computer interfaces (BCIs), based on motor imagery, are increasingly used in neurorehabilitation. However, some people cannot control BCI, predictors of this are the features of brain activity and personality traits. It is not known whether the success of BCI control is related to interhemispheric asymmetry. The study was conducted on 44 BCI-naive subjects and included one BCI session, EEG-analysis, 16PF Cattell Questionnaire, estimation of latent left-handedness, and of subjective complexity of real and imagery movements. The success of brain states recognition during imagination of left hand (LH) movement compared to the rest is higher in reserved, practical, skeptical, and not very sociable individuals. Extraversion, liveliness, and dominance are significant for the imagination of right hand (RH) movements in "pure" right-handers, and sensitivity in latent left-handers. Subjective complexity of real LH and of imagery RH movements correlates with the success of brain states recognition in the imagination of movement of LH compared to RH and depends on the level of handedness. Thus, the level of handedness is the factor influencing the success of BCI control. The data are supposed to be connected with hemispheric differences in motor control, lateralization of dopamine, and may be important for rehabilitation of patients after a stroke.},
}
@article {pmid34201788,
year = {2021},
author = {Belwafi, K and Gannouni, S and Aboalsamh, H},
title = {Embedded Brain Computer Interface: State-of-the-Art in Research.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {13},
pages = {},
pmid = {34201788},
issn = {1424-8220},
support = {RG-1441-524//Deanship of Scientific Research, King Saud University/ ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.},
}
@article {pmid34201381,
year = {2021},
author = {De Venuto, D and Mezzina, G},
title = {A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {12},
pages = {},
pmid = {34201381},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Neural Networks, Computer ; },
abstract = {In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain-computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.},
}
@article {pmid34198440,
year = {2021},
author = {Chen, X and Ma, Y and Liu, X and Kong, W and Xi, X},
title = {Analysis of corticomuscular connectivity during walking using vine copula.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {18},
number = {4},
pages = {4341-4357},
doi = {10.3934/mbe.2021218},
pmid = {34198440},
issn = {1551-0018},
mesh = {Electroencephalography ; Electromyography ; Humans ; Movement ; *Muscle, Skeletal ; *Walking ; },
abstract = {Corticomuscular connectivity plays an important role in the neural control of human motion. This study recorded electroencephalography (EEG) and surface electromyography (sEMG) signals from subjects performing specific tasks (walking on level ground and on stairs) based on metronome instructions. This study presents a novel method based on vine copula to jointly model EEG and sEMG signals. The advantage of vine copula is its applicability in the construction of dependency structures to describe the connectivity between the cortex and muscles during different movements. A corticomuscular function network was also constructed by analyzing the dependence of each channel sample. The successfully constructed network shows information transmission between different divisions of the cortex, between muscles, and between the cortex and muscles when the body performs lower limb movements. Additionally, it highlights the potential of the vine copula concept used in this study, indicating that significant changes in the corticomuscular network under lower limb movements can be quantified by effective connectivity values.},
}
@article {pmid34198435,
year = {2021},
author = {Yin, X and Meng, M and She, Q and Gao, Y and Luo, Z},
title = {Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {18},
number = {4},
pages = {4247-4263},
doi = {10.3934/mbe.2021213},
pmid = {34198435},
issn = {1551-0018},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Common spatial pattern (CSP) as a spatial filtering method has been most widely applied to electroencephalogram (EEG) feature extraction to classify motor imagery (MI) in brain-computer interface (BCI) applications. The effectiveness of CSP is determined by the quality of interception in a specific time window and frequency band. Although numerous algorithms have been designed to optimize CSP by splitting the EEG data with a sliding time window and dividing the frequency bands with a set of band-pass filters, simultaneously. However, they did not consider the drawbacks of the rapid increase in data volume and feature dimensions brought about by this method, which would reduce the classification accuracy and calculation efficiency of the model. Therefore, we propose an optimal channel-based sparse time-frequency blocks common spatial pattern (OCSB-CSP) feature extraction method to improve the model classification accuracy and computational efficiency. Comparative experiments on two public EEG datasets show that the proposed method can quickly select significant time-frequency blocks and improve classification performance. The average classification accuracies are higher than those of other winners' methods, providing a new idea for the improvement of BCI applications.},
}
@article {pmid34196062,
year = {2021},
author = {Nafees, M and Ullah, S and Ahmed, I},
title = {Morphological and elemental evaluation of biochar through analytical techniques and its combined effect along with plant growth promoting rhizobacteria on Vicia faba L. under induced drought stress.},
journal = {Microscopy research and technique},
volume = {84},
number = {12},
pages = {2947-2959},
doi = {10.1002/jemt.23854},
pmid = {34196062},
issn = {1097-0029},
mesh = {Cellulomonas ; Charcoal ; *Droughts ; Sphingobacterium ; *Vicia faba ; },
abstract = {Drought is a persistent and complex natural vulnerability whose rate and extent of recurrence are expected to increase with climate change. Regardless of the progress made in responding and adapting to water scarcity, drought stress causes severe afflictions. Therefore, the present study has been accomplished in Department of Botany, University of Peshawar to investigate the effect of biochar and plant growth promoting rhizobacteria (PGPR) Cellulomonas pakistanensis (NCCP11) and Sphingobacterium pakistanensis (NCCP246) on Vicia faba under drought stress. Two varieties of seeds Desi (V1) and Pulista (V2) were obtained from Cereal Crop Research Institute (CCRI) Nowshera, sown in earthen pots in triplicate filled with 3 kg soil and sand (2:1) and biochar (0 and 5% w/w). Scanning electron microscopy of biochar showed porous nature and energy dispersive x-ray spectroscopy spectroscopy showed C, Ca, Mg, and Na elemental composition. Germination parameters including germination energy (GE), Timson germination index (TGI), germination index (GI), and water use efficiency (WUE) were amplified to 28.04, 19.17, 25.72, and 43.62% in V1, respectively, and 14.38, 16.66, 19.79, and 41.50% in V2 respectively, by the co-application of biochar and PGPR. Agronomical attributes including, fresh and dry weight of leaves, root, and shoot were significantly reduced, which were positively ameliorated by 28.57, 36.36, 16, 10.47, 14.28, and 10%, respectively, by the application of biochar and PGPR especially by NCCP246 in combination as well as individually. It has been concluded that, adversities of drought significantly condensed with the application of biochar and PGPR, which may be important in agricultural practices carried out in water-deficient regions.},
}
@article {pmid34191781,
year = {2021},
author = {Sprinzl, GM and Schoerg, P and Ploder, M and Edlinger, SH and Magele, A},
title = {Surgical Experience and Early Audiological Outcomes With New Active Transcutaneous Bone Conduction Implant.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {42},
number = {8},
pages = {1208-1215},
doi = {10.1097/MAO.0000000000003230},
pmid = {34191781},
issn = {1537-4505},
mesh = {Adolescent ; Adult ; Bone Conduction ; Child ; Child, Preschool ; *Hearing Aids ; Hearing Loss, Conductive ; Humans ; Middle Aged ; Prostheses and Implants ; Retrospective Studies ; *Speech Perception ; Treatment Outcome ; Young Adult ; },
abstract = {OBJECTIVES: Our objective was to report the very first surgical experiences, audiologic benefits, and satisfaction with the new active transcutaneous bone conduction implant, generation 602 (BCI602), in patients with mixed/conductive hearing loss (M/CHL) and single-sided deafness.<br><br>METHODS: A retrospective chart review from patients who underwent BCI602 surgery was performed.<br><br>RESULTS: Twelve subjects were implanted (mean age 33.17&#x200a;&#xb1;&#x200a;21.67&#x200a;yrs). Mean surgery time was 29.89&#x200a;&#xb1;&#x200a;8.59&#x200a;minutes, with the longest being a difficult passive-BCI explantation due to excessive osseointegration. No surgical nor post-surgical complications occurred. No pre-operative surgical planning for device placement was necessary, no BCI-lifts were used and complete transmastoid implantation was possible. The mean functional gain in the M/CHL cohort significantly increased after 3&#x200a;months (&#x3c1;&#x200a;<&#x200a;.0001). The mean word recognition score (%) in quiet for the M/CHL group significantly improved at activation and 3&#x200a;months post-surgery (&#x3c1;&#x200a;=&#x200a;.0002; &#x3c1;&#x200a;<&#x200a;.0001). At the 3&#x200a;months follow-up the subjects reported high satisfaction with the device accompanied with a mean wearing time of 10.13&#x200a;hours per day (range 18-6&#x200a;h/d) resulting in a reported battery change of every 8.29&#x200a;&#xb1;&#x200a;0.49&#x200a;days.<br><br>CONCLUSIONS: These early results of the new BCI602 showed significantly improved audiological performance, no limitations during surgery (youngest subject 2&#x200a;yrs at surgery), no prior surgical planning necessary, accompanied by high patient satisfaction and increased wearing time. Based on these results, the BCI602 can be highly recommended and especially for difficult anatomical and surgical cases and the given indication for children older than 5&#x200a;years should probably be revaluated.Level of Evidence: Level 4.},
}
@article {pmid34190425,
year = {2021},
author = {Woeppel, KM and Cui, XT},
title = {Nanoparticle and Biomolecule Surface Modification Synergistically Increases Neural Electrode Recording Yield and Minimizes Inflammatory Host Response.},
journal = {Advanced healthcare materials},
volume = {10},
number = {16},
pages = {e2002150},
pmid = {34190425},
issn = {2192-2659},
support = {R01 NS089688/NS/NINDS NIH HHS/United States ; R21 DA049592/DA/NIDA NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; U01 NS113279/NS/NINDS NIH HHS/United States ; R01 NS110564/NS/NINDS NIH HHS/United States ; },
mesh = {Electrodes, Implanted ; Humans ; Microelectrodes ; *Nanoparticles ; *Neural Cell Adhesion Molecule L1 ; Neurons ; },
abstract = {Due to their ability to interface with neural tissues, neural electrodes are the key tool used for neurophysiological studies, electrochemical detection, brain computer interfacing, and countless neuromodulation therapies and diagnostic procedures. However, the long-term applications of neural electrodes are limited by the inflammatory host tissue response, decreasing detectable electrical signals, and insulating the device from the native environment. Surface modification methods are proposed to limit these detrimental responses but each has their own limitations. Here, a combinatorial approach is presented toward creating a stable interface between the electrode and host tissues. First, a thiolated nanoparticle (TNP) coating is utilized to increase the surface area and roughness. Next, the neural adhesion molecule L1 is immobilized to the nanoparticle modified substrate. In vitro, the combined nanotopographical and bioactive modifications (TNP+L1) elevate the bioactivity of L1, which is maintained for 28 d. In vivo, TNP+L1 modification improves the recording performance of the neural electrode arrays compared to TNP or L1 modification alone. Postmortem histology reveals greater neural cell density around the TNP+L1 coating while eliminating any inflammatory microglial encapsulation after 4 weeks. These results demonstrate that nanotopographical and bioactive modifications synergistically produce a seamless neural tissue interface for chronic neural implants.},
}
@article {pmid34189124,
year = {2021},
author = {Shojaedini, SV and Morabbi, S and Keyvanpour, MR},
title = {A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {11},
number = {3},
pages = {357-366},
pmid = {34189124},
issn = {2251-7200},
abstract = {BACKGROUND: Deep neural networks have been widely used in detection of P300 signal in Brain Machine Interface (BMI) systems which are rely on Event-Related Potentials (ERPs) (i.e. P300 signals). Such networks have high curvature variation in their error surface hampering their favorable performance. Therefore, the variations in curvature of the error surface must be minimized to improve the performance of these networks in detecting P300 signals.<br><br>OBJECTIVE: The aim of this paper is to introduce a method for minimizing the curvature of the error surface during training Convolutional Neural Network (CNN). The curvature variation of the error surface is highly dependent on model parameters of deep neural network; therefore, we try to minimize this curvature by optimizing the model parameters.<br><br>MATERIAL AND METHODS: In this experimental study an attempt is made to tune the CNN parameters affecting the curvature of its error surface in order to obtain the best possible learning. For achieving this goal, Genetic Algorithm is utilized to optimize the above parameters in order to minimize the curvature variations.<br><br>RESULTS: The performance of the proposed algorithm was evaluated on EPFL dataset. The obtained results demonstrated that the proposed method detected the P300 signals with maximally 98.91% classification accuracy and 98.54% True Positive Ratio (TPR).<br><br>CONCLUSION: The obtained results showed that using genetic algorithm for minimizing curvature of the error surface in CNN increased its accuracy in parallel with decreasing the variance of the results. Consequently, it may be concluded that the proposed method has considerable potential to be used as P300 detection module in BMI applications.},
}
@article {pmid34184974,
year = {2023},
author = {Pitt, KM and Brumberg, JS},
title = {Evaluating the perspectives of those with severe physical impairments while learning BCI control of a commercial augmentative and alternative communication paradigm.},
journal = {Assistive technology : the official journal of RESNA},
volume = {35},
number = {1},
pages = {74-82},
pmid = {34184974},
issn = {1949-3614},
support = {R01 DC016343/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Learning ; Communication ; Fatigue ; },
abstract = {Augmentative and alternative communication (AAC) techniques can provide access to communication for individuals with severe physical impairments. Brain-computer interface (BCI) access techniques may serve alongside existing AAC access methods to provide communication device control. However, there is limited information available about how individual perspectives change with motor-based BCI-AAC learning. Four individuals with ALS completed 12 BCI-AAC training sessions in which they made letter selections during an automatic row-column scanning pattern via a motor-based BCI-AAC. Recurring measures were taken before and after each BCI-AAC training session to evaluate changes associated with BCI-AAC performance, and included measures of fatigue, frustration, mental effort, physical effort, device satisfaction, and overall ease of device control. Levels of pre- to post-fatigue were low for use of the BCI-AAC system. However, participants indicated different perceptions of the term fatigue, with three participants discussing fatigue to be generally synonymous with physical effort, and one mental effort. Satisfaction with the BCI-AAC system was related to BCI-AAC performance for two participants, and levels of frustration for two participants. Considering a range of person-centered measures in future clinical BCI-AAC applications is important for optimizing and standardizing BCI-AAC assessment procedures.},
}
@article {pmid34184601,
year = {2021},
author = {Singh, HP and Kumar, P},
title = {Developments in the human machine interface technologies and their applications: a review.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {7},
pages = {552-573},
doi = {10.1080/03091902.2021.1936237},
pmid = {34184601},
issn = {1464-522X},
mesh = {Humans ; Man-Machine Systems ; *Self-Help Devices ; Technology ; *User-Computer Interface ; },
abstract = {Human-machine interface (HMI) techniques use bioelectrical signals to gain real-time synchronised communication between the human body and machine functioning. HMI technology not only provides a real-time control access but also has the ability to control multiple functions at a single instance of time with modest human inputs and increased efficiency. The HMI technologies yield advanced control access on numerous applications such as health monitoring, medical diagnostics, development of prosthetic and assistive devices, automotive and aerospace industry, robotic controls and many more fields. In this paper, various physiological signals, their acquisition and processing techniques along with their respective applications in different HMI technologies have been discussed.},
}
@article {pmid34182408,
year = {2021},
author = {van den Boom, M and Miller, KJ and Gregg, NM and Ojeda Valencia, G and Lee, KH and Richner, TJ and Ramsey, NF and Worrell, GA and Hermes, D},
title = {Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm: An ECoG case study.},
journal = {NeuroImage. Clinical},
volume = {31},
number = {},
pages = {102728},
pmid = {34182408},
issn = {2213-1582},
support = {KL2 TR002379/TR/NCATS NIH HHS/United States ; R01 MH122258/MH/NIMH NIH HHS/United States ; R01 NS112144/NS/NINDS NIH HHS/United States ; R01 MH111417/MH/NIMH NIH HHS/United States ; R01 NS092882/NS/NINDS NIH HHS/United States ; },
mesh = {*Arm ; Electrocorticography ; Electroencephalography ; Hand ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.},
}
@article {pmid34181585,
year = {2021},
author = {Liu, Z and Meng, L and Zhang, X and Fang, W and Wu, D},
title = {Universal adversarial perturbations for CNN classifiers in EEG-based BCIs.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0f4c},
pmid = {34181585},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; },
abstract = {Objective. Multiple convolutional neural network (CNN) classifiers have been proposed for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN models have been found vulnerable to universal adversarial perturbations (UAPs), which are small and example-independent, yet powerful enough to degrade the performance of a CNN model, when added to a benign example.Approach. This paper proposes a novel total loss minimization (TLM) approach to generate UAPs for EEG-based BCIs.Main results. Experimental results demonstrated the effectiveness of TLM on three popular CNN classifiers for both target and non-target attacks. We also verified the transferability of UAPs in EEG-based BCI systems.Significance. To our knowledge, this is the first study on UAPs of CNN classifiers in EEG-based BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a potentially critical security concern of BCIs.},
}
@article {pmid34180194,
year = {2021},
author = {Huang, H and Cai, Y and Feng, X and Li, Y},
title = {[An electroencephalogram-based study of resting-state spectrogram and attention in tinnitus patients].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {492-497},
pmid = {34180194},
issn = {1001-5515},
mesh = {Attention ; Brain ; Electroencephalography ; Humans ; Parietal Lobe ; *Tinnitus ; },
abstract = {The incidence of tinnitus is very high, which can affect the patient's attention, emotion and sleep, and even cause serious psychological distress and suicidal tendency. Currently, there is no uniform and objective method for tinnitus detection and therapy, and the mechanism of tinnitus is still unclear. In this study, we first collected the resting state electroencephalogram (EEG) data of tinnitus patients and healthy subjects. Then the power spectrum topology diagrams were compared of in the band of δ (0.5-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (14-30 Hz) and γ (31-50 Hz) to explore the central mechanism of tinnitus. A total of 16 tinnitus patients and 16 healthy subjects were recruited to participate in the experiment. The results of resting state EEG experiments found that the spectrum power value of tinnitus patients was higher than that of healthy subjects in all concerned frequency bands. The t-test results showed that the significant difference areas were mainly concentrated in the right temporal lobe of the θ and α band, and the temporal lobe, parietal lobe and forehead area of the β and γ band. In addition, we designed an attention-related task experiment to further study the relationship between tinnitus and attention. The results showed that the classification accuracy of tinnitus patients was significantly lower than that of healthy subjects, and the highest classification accuracies were 80.21% and 88.75%, respectively. The experimental results indicate that tinnitus may cause the decrease of patients' attention.},
}
@article {pmid34180193,
year = {2021},
author = {Chen, X and Li, K},
title = {[Robotic arm control system based on augmented reality brain-computer interface and computer vision].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {483-491},
pmid = {34180193},
issn = {1001-5515},
mesh = {*Augmented Reality ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Robotic Surgical Procedures ; },
abstract = {Brain-computer interface (BCI) has great potential to replace lost upper limb function. Thus, there has been great interest in the development of BCI-controlled robotic arm. However, few studies have attempted to use noninvasive electroencephalography (EEG)-based BCI to achieve high-level control of a robotic arm. In this paper, a high-level control architecture combining augmented reality (AR) BCI and computer vision was designed to control a robotic arm for performing a pick and place task. A steady-state visual evoked potential (SSVEP)-based BCI paradigm was adopted to realize the BCI system. Microsoft's HoloLens was used to build an AR environment and served as the visual stimulator for eliciting SSVEPs. The proposed AR-BCI was used to select the objects that need to be operated by the robotic arm. The computer vision was responsible for providing the location, color and shape information of the objects. According to the outputs of the AR-BCI and computer vision, the robotic arm could autonomously pick the object and place it to specific location. Online results of 11 healthy subjects showed that the average classification accuracy of the proposed system was 91.41%. These results verified the feasibility of combing AR, BCI and computer vision to control a robotic arm, and are expected to provide new ideas for innovative robotic arm control approaches.},
}
@article {pmid34180192,
year = {2021},
author = {Zhang, R and Liu, J and Chen, M and Zhang, L and Hu, Y},
title = {[Research on automatic removal of ocular artifacts from single channel electroencephalogram signals based on wavelet transform and ensemble empirical mode decomposition].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {473-482},
pmid = {34180192},
issn = {1001-5515},
mesh = {Algorithms ; *Artifacts ; Computer Simulation ; Electroencephalography ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the 'clean' EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.},
}
@article {pmid34180191,
year = {2021},
author = {Sun, J and Jung, TP and Xiao, X and Meng, J and Xu, M and Ming, D},
title = {[Classification algorithms of error-related potentials in brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {463-472},
pmid = {34180191},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Support Vector Machine ; },
abstract = {Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.},
}
@article {pmid34180190,
year = {2021},
author = {Cai, Z and Guo, M and Yang, X and Chen, X and Xu, G},
title = {[Cross-subject electroencephalogram emotion recognition based on maximum classifier discrepancy].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {455-462},
pmid = {34180190},
issn = {1001-5515},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; Reproducibility of Results ; },
abstract = {Affective brain-computer interfaces (aBCIs) has important application value in the field of human-computer interaction. Electroencephalogram (EEG) has been widely concerned in the field of emotion recognition due to its advantages in time resolution, reliability and accuracy. However, the non-stationary characteristics and individual differences of EEG limit the generalization of emotion recognition model in different time and different subjects. In this paper, in order to realize the recognition of emotional states across different subjects and sessions, we proposed a new domain adaptation method, the maximum classifier difference for domain adversarial neural networks (MCD_DA). By establishing a neural network emotion recognition model, the shallow feature extractor was used to resist the domain classifier and the emotion classifier, respectively, so that the feature extractor could produce domain invariant expression, and train the decision boundary of classifier learning task specificity while realizing approximate joint distribution adaptation. The experimental results showed that the average classification accuracy of this method was 88.33% compared with 58.23% of the traditional general classifier. It improves the generalization ability of emotion brain-computer interface in practical application, and provides a new method for aBCIs to be used in practice.},
}
@article {pmid34180189,
year = {2021},
author = {Wang, J and Wang, Y and Yao, L},
title = {[Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {447-454},
pmid = {34180189},
issn = {1001-5515},
mesh = {Arousal ; Electroencephalography ; Emotions ; Humans ; *Neural Networks, Computer ; },
abstract = {Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.},
}
@article {pmid34180188,
year = {2021},
author = {Tian, G and Chen, J and Ding, P and Gong, A and Wang, F and Luo, J and Dong, Y and Zhao, L and Dang, C and Fu, Y},
title = {[Execution, assessment and improvement methods of motor imagery for brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {434-446},
pmid = {34180188},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects' MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system's performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.},
}
@article {pmid34180187,
year = {2021},
author = {Li, J and Zhao, L and Bian, Y and Li, M and Jia, Z},
title = {[Research on feature classification of lower limb motion imagination based on electrical stimulation to enhance rehabilitation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {425-433},
pmid = {34180187},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination ; Lower Extremity ; Movement ; },
abstract = {Motor imaging therapy is of great significance to the rehabilitation of patients with stroke or motor dysfunction, but there are few studies on lower limb motor imagination. When electrical stimulation is applied to the posterior tibial nerve of the ankle, the steady-state somatosensory evoked potentials (SSSEP) can be induced at the electrical stimulation frequency. In order to better realize the classification of lower extremity motor imagination, improve the classification effect, and enrich the instruction set of lower extremity motor imagination, this paper designs two experimental paradigms: Motor imaging (MI) paradigm and Hybrid paradigm. The Hybrid paradigm contains electrical stimulation assistance. Ten healthy college students were recruited to complete the unilateral movement imagination task of left and right foot in two paradigms. Through time-frequency analysis and classification accuracy analysis, it is found that compared with MI paradigm, Hybrid paradigm could get obvious SSSEP and ERD features. The average classification accuracy of subjects in the Hybrid paradigm was 78.61%, which was obviously higher than the MI paradigm. It proves that electrical stimulation has a positive role in promoting the classification training of lower limb motor imagination.},
}
@article {pmid34180186,
year = {2021},
author = {Zuo, C and Mao, Y and Liu, Q and Wang, X and Jin, J},
title = {[Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character patterns].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {417-424},
pmid = {34180186},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; China ; Electroencephalography ; Evoked Potentials ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {The traditional paradigm of motor-imagery-based brain-computer interface (BCI) is abstract, which cannot effectively guide users to modulate brain activity, thus limiting the activation degree of the sensorimotor cortex. It was found that the motor imagery task of Chinese characters writing was better accepted by users and helped guide them to modulate their sensorimotor rhythms. However, different Chinese characters have different writing complexity (number of strokes), and the effect of motor imagery tasks of Chinese characters with different writing complexity on the performance of motor-imagery-based BCI is still unclear. In this paper, a total of 12 healthy subjects were recruited for studying the effects of motor imagery tasks of Chinese characters with two different writing complexity (5 and 10 strokes) on the performance of motor-imagery-based BCI. The experimental results showed that, compared with Chinese characters with 5 strokes, motor imagery task of Chinese characters writing with 10 strokes obtained stronger sensorimotor rhythm and better recognition performance (P < 0.05). This study indicated that, appropriately increasing the complexity of the motor imagery task of Chinese characters writing can obtain stronger motor imagery potential and improve the recognition accuracy of motor-imagery-based BCI, which provides a reference for the design of the motor-imagery-based BCI paradigm in the future.},
}
@article {pmid34180185,
year = {2021},
author = {Zhang, L and Chen, X and Chen, L and Gu, B and Wang, Z and Ming, D},
title = {[Research progress and prospect of collaborative brain-computer interface for group brain collaboration].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {409-416},
pmid = {34180185},
issn = {1001-5515},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31% vs. 56.34%), and was significantly higher than that of the average single user (77.31% vs. 44.90%). The research in this paper proves that the cBCI collaboration strategy can effectively improve the MI-BCI classification performance, which lays the foundation for MI-cBCI research and its future application.},
}
@article {pmid34180184,
year = {2021},
author = {Yao, D},
title = {[Brain-computer interface: from lab to real scene].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {3},
pages = {405-408},
pmid = {34180184},
issn = {1001-5515},
mesh = {Brain ; *Brain-Computer Interfaces ; China ; Electroencephalography ; Laboratories ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) can be summarized as a system that uses online brain information to realize communication between brain and computer. BCI has experienced nearly half a century of development, although it now has a high degree of awareness in the public, but the application of BCI in the actual scene is still very limited. This collection invited some BCI teams in China to report their efforts to promote BCI from laboratory to real scene. This paper summarizes the main contents of the invited papers, and looks forward to the future of BCI.},
}
@article {pmid34177767,
year = {2021},
author = {Wu, Q and Ge, Y and Ma, D and Pang, X and Cao, Y and Zhang, X and Pan, Y and Zhang, T and Dou, W},
title = {Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study.},
journal = {Frontiers in neurology},
volume = {12},
number = {},
pages = {661816},
pmid = {34177767},
issn = {1664-2295},
abstract = {Objective: Upper limb (UL) motor function recovery, especially distal function, is one of the main goals of stroke rehabilitation as this function is important to perform activities of daily living (ADL). The efficacy of the motor-imagery brain-computer interface (MI-BCI) has been demonstrated in patients with stroke. Most patients with stroke receive comprehensive rehabilitation, including MI-BCI and routine training. However, most aspects of MI-BCI training for patients with subacute stroke are based on routine training. Risk factors for inadequate distal UL functional recovery in these patients remain unclear; therefore, it is more realistic to explore the prognostic factors of this comprehensive treatment based on clinical practice. The present study aims to investigate the independent risk factors that might lead to inadequate distal UL functional recovery in patients with stroke after comprehensive rehabilitation including MI-BCI (CRIMI-BCI). Methods: This prospective study recruited 82 patients with stroke who underwent CRIMI-BCI. Motor-imagery brain-computer interface training was performed for 60 min per day, 5 days per week for 4 weeks. The primary outcome was improvement of the wrist and hand dimensionality of Fugl-Meyer Assessment (δFMA-WH). According to the improvement score, the patients were classified into the efficient group (EG, δFMA-WH > 2) and the inefficient group (IG, δFMA-WH ≤ 2). Binary logistic regression was used to analyze clinical and demographic data, including aphasia, spasticity of the affected hand [assessed by Modified Ashworth Scale (MAS-H)], initial UL function, age, gender, time since stroke (TSS), lesion hemisphere, and lesion location. Results: Seventy-three patients completed the study. After training, all patients showed significant improvement in FMA-UL (Z = 7.381, p = 0.000[**]), FMA-SE (Z = 7.336, p = 0.000[**]), and FMA-WH (Z = 6.568, p = 0.000[**]). There were 35 patients (47.9%) in the IG group and 38 patients (52.1%) in the EG group. Multivariate analysis revealed that presence of aphasia [odds ratio (OR) 4.617, 95% confidence interval (CI) 1.435-14.860; p < 0.05], initial FMA-UL score ≤ 30 (OR 5.158, 95% CI 1.150-23.132; p < 0.05), and MAS-H ≥ level I+ (OR 3.810, 95% CI 1.231-11.790; p < 0.05) were the risk factors for inadequate distal UL functional recovery in patients with stroke after CRIMI-BCI. Conclusion: We concluded that CRIMI-BCI improved UL function in stroke patients with varying effectiveness. Inferior initial UL function, significant hand spasticity, and presence of aphasia were identified as independent risk factors for inadequate distal UL functional recovery in stroke patients after CRIMI-BCI.},
}
@article {pmid34176450,
year = {2021},
author = {Jin, J and Fang, H and Daly, I and Xiao, R and Miao, Y and Wang, X and Cichocki, A},
title = {Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI.},
journal = {International journal of neural systems},
volume = {31},
number = {7},
pages = {2150030},
doi = {10.1142/S0129065721500301},
pmid = {34176450},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.},
}
@article {pmid34176416,
year = {2021},
author = {Jafar, MR and Nagesh, DS},
title = {Literature review on assistive devices available for quadriplegic people: Indian context.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/17483107.2021.1938708},
pmid = {34176416},
issn = {1748-3115},
abstract = {PURPOSE: This literature review aims to find the current state of the art in self-help devices (SHD) available for people with quadriplegia.<br><br>MATERIALS AND METHODS: We searched original articles, technical and case studies, conference articles, and literature reviews published between 2014 to 2019 with the keywords ("Self-help devices" OR "Assistive Devices" OR "Assistive Product" OR "Assistive Technology") AND "Quadriplegia" in Science Direct, Pubmed, IEEE Xplore digital library and Web of Science.<br><br>RESULTS: Total 222 articles were found. After removing duplicates and screening these articles based on their title and abstracts 80 articles remained. After this, we reviewed the full text, and articles unrelated to SHD development or about the patients who require mechanical ventilation or where the upper limb is functional (C2 or above and T2 or below injuries) were discarded. After the exclusion of articles using the above-mentioned criterion 75 articles were used for further review.<br><br>CONCLUSION: The abandonment rate of SHD currently available in the literature is very high. The major requirement of the people was independence and improved quality of life. The situation in India is very bad as compared to the developed countries. The people with spinal cord injury in India are uneducated and very poor, with an average income of 3000 &#x20b9; (41$). They require SHDs and training specially designed for them, keeping their needs in mind.Implications for rehabilitationPeople with quadriplegia are totally dependent on caregivers. Assistive devices not only help these people to do day-to-day tasks but also provides them self-confidence.Even though there are a lot of self-help devices currently available, still they are not able to fulfil the requirements of people with quadriplegia, hence there is a very high abandonment rate of such devices.This study provides an evidence that developing devices after understanding the functional and non-functional requirements of these subjects will decrease the abandonment rate and increase the effectiveness of the device.The results of this study can be used for planning and developing assistive devices which are more focussed on fulfilling the requirements of people with quadriplegia.},
}
@article {pmid34163342,
year = {2021},
author = {Cantillo-Negrete, J and Carino-Escobar, RI and Carrillo-Mora, P and Rodriguez-Barragan, MA and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Hernandez-Sanchez, IR and Galicia-Alvarado, MA and Miguel-Puga, A and Arias-Carrion, O},
title = {Brain-Computer Interface Coupled to a Robotic Hand Orthosis for Stroke Patients' Neurorehabilitation: A Crossover Feasibility Study.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {656975},
pmid = {34163342},
issn = {1662-5161},
abstract = {Brain-Computer Interfaces (BCI) coupled to robotic assistive devices have shown promise for the rehabilitation of stroke patients. However, little has been reported that compares the clinical and physiological effects of a BCI intervention for upper limb stroke rehabilitation with those of conventional therapy. This study assesses the feasibility of an intervention with a BCI based on electroencephalography (EEG) coupled to a robotic hand orthosis for upper limb stroke rehabilitation and compares its outcomes to conventional therapy. Seven subacute and three chronic stroke patients (M = 59.9 ± 12.8) with severe upper limb impairment were recruited in a crossover feasibility study to receive 1 month of BCI therapy and 1 month of conventional therapy in random order. The outcome measures were comprised of: Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), motor evoked potentials elicited by transcranial magnetic stimulation (TMS), hand dynamometry, and EEG. Additionally, BCI performance and user experience were measured. All measurements were acquired before and after each intervention. FMA-UE and ARAT after BCI (23.1 ± 16; 8.4 ± 10) and after conventional therapy (21.9 ± 15; 8.7 ± 11) were significantly higher (p < 0.017) compared to baseline (17.5 ± 15; 4.3 ± 6) but were similar between therapies (p > 0.017). Via TMS, corticospinal tract integrity could be assessed in the affected hemisphere of three patients at baseline, in five after BCI, and four after conventional therapy. While no significant difference (p > 0.05) was found in patients' affected hand strength, it was higher after the BCI therapy. EEG cortical activations were significantly higher over motor and non-motor regions after both therapies (p < 0.017). System performance increased across BCI sessions, from 54 (50, 70%) to 72% (56, 83%). Patients reported moderate mental workloads and excellent usability with the BCI. Outcome measurements implied that a BCI intervention using a robotic hand orthosis as feedback has the potential to elicit neuroplasticity-related mechanisms, similar to those observed during conventional therapy, even in a group of severely impaired stroke patients. Therefore, the proposed BCI system could be a suitable therapy option and will be further assessed in clinical trials.},
}
@article {pmid34163337,
year = {2021},
author = {Duan, X and Xie, S and Xie, X and Obermayer, K and Cui, Y and Wang, Z},
title = {An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {625983},
pmid = {34163337},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect.},
}
@article {pmid34162936,
year = {2021},
author = {Zapała, D and Iwanowicz, P and Francuz, P and Augustynowicz, P},
title = {Handedness effects on motor imagery during kinesthetic and visual-motor conditions.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {13112},
pmid = {34162936},
issn = {2045-2322},
mesh = {Adult ; Electroencephalography ; Female ; Functional Laterality/*physiology ; Humans ; Imagination/*physiology ; Kinesthesis/*physiology ; Male ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Recent studies show that during a simple movement imagery task, the power of sensorimotor rhythms differs according to handedness. However, the effects of motor imagery perspectives on these differences have not been investigated yet. Our study aimed to check how handedness impacts the activity of alpha (8-13 Hz) and beta (15-30 Hz) oscillations during creating a kinesthetic (KMI) or visual-motor (VMI) representation of movement. Forty subjects (20 right-handed and 20 left-handed) who participated in the experiment were tasked with imagining sequential finger movement from a visual or kinesthetic perspective. Both the electroencephalographic (EEG) activity and behavioral correctness of the imagery task performance were measured. After the registration, we used independent component analysis (ICA) on EEG data to localize visual- and motor-related EEG sources of activity shared by both motor imagery conditions. Significant differences were obtained in the visual cortex (the occipital ICs cluster) and the right motor-related area (right parietal ICs cluster). In comparison to right-handers who, regardless of the task, demonstrated the same pattern in the visual area, left-handers obtained higher power in the alpha waves in the VMI task and better performance in this condition. On the other hand, only the right-handed showed different patterns in the alpha waves in the right motor cortex during the KMI condition. The results indicate that left-handers imagine movement differently than right-handers, focusing on visual experience. This provides new empirical evidence on the influence of movement preferences on imagery processes and has possible future implications for research in the area of neurorehabilitation and motor imagery-based brain-computer interfaces (MI-BCIs).},
}
@article {pmid34159536,
year = {2021},
author = {Chen, L and Gu, B and Wang, Z and Zhang, L and Xu, M and Liu, S and He, F and Ming, D},
title = {EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application.},
journal = {Frontiers of medicine},
volume = {15},
number = {5},
pages = {740-749},
pmid = {34159536},
issn = {2095-0225},
mesh = {Electric Stimulation ; *Electric Stimulation Therapy ; Electroencephalography ; Humans ; Recovery of Function ; *Stroke/therapy ; *Stroke Rehabilitation ; },
abstract = {Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain-computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r = 0.6093, P = 0.012), which provides theoretical basis for exploring novel objective evaluation methods.},
}
@article {pmid34159434,
year = {2021},
author = {Amin, A and Ahmed, I and Khalid, N and Schumann, P and Busse, HJ and Khan, IU and Ali, A and Li, S and Li, WJ},
title = {Correction to: Zafaria cholistanensis gen. nov. sp. nov., a moderately thermotolerant and halotolerant actinobacterium isolated from Cholistan desert soil of Pakistan.},
journal = {Archives of microbiology},
volume = {203},
number = {7},
pages = {4753-4754},
doi = {10.1007/s00203-021-02427-y},
pmid = {34159434},
issn = {1432-072X},
}
@article {pmid34156104,
year = {2021},
author = {Serruya, MD and Rosenwasser, RH},
title = {An artificial nervous system to treat chronic stroke.},
journal = {Artificial organs},
volume = {45},
number = {8},
pages = {804-812},
doi = {10.1111/aor.13998},
pmid = {34156104},
issn = {1525-1594},
mesh = {Animals ; Brain-Computer Interfaces ; Chronic Disease ; Humans ; Nerve Transfer ; Neurofeedback ; Neuronal Plasticity ; Prostheses and Implants ; Recovery of Function ; Stroke Rehabilitation/*methods ; Wearable Electronic Devices ; },
abstract = {Despite remarkable advances in the treatment of numerous medical conditions, neurological disease and injury remains an outstanding challenge and cause of disability worldwide. The decreased regenerative capacity and extreme complexity and heterogeneity of nervous tissue, in particular the brain, and the fact that the brain remains the least understood organ, have hampered our ability to provide definitive treatments for prevalent conditions such as stroke. Stroke is the second-leading cause of death worldwide, and the nervous system is intimately involved in other prevalent conditions including ischemic heart disease, diabetes mellitus, and hypertension. Advances in neuromodulation, electroceuticals, microsurgical techniques, optogenetics, brain-computer interfaces, and autologous constructs offer potential solutions to address the otherwise permanent neurological deficits of stroke and other conditions. Here we review these various approaches to build an "artificial nervous system" that could restore function and independence in people living with these conditions. We focus on stroke both because it is the leading cause of neurological disability worldwide and because we anticipate that advances in the reversal of stroke-related deficits will have ripple effects benefiting people with other neurological conditions including spinal cord injury, traumatic brain injury, ALS, and muscular dystrophy.},
}
@article {pmid34155480,
year = {2021},
author = {Benitez-Andonegui, A and Lührs, M and Nagels-Coune, L and Ivanov, D and Goebel, R and Sorger, B},
title = {Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength.},
journal = {Neurophotonics},
volume = {8},
number = {2},
pages = {025012},
pmid = {34155480},
issn = {2329-423X},
abstract = {Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.},
}
@article {pmid34155354,
year = {2023},
author = {Zhang, Q and Hu, S and Talay, R and Xiao, Z and Rosenberg, D and Liu, Y and Sun, G and Li, A and Caravan, B and Singh, A and Gould, JD and Chen, ZS and Wang, J},
title = {A prototype closed-loop brain-machine interface for the study and treatment of pain.},
journal = {Nature biomedical engineering},
volume = {7},
number = {4},
pages = {533-545},
pmid = {34155354},
issn = {2157-846X},
support = {R01 GM115384/GM/NIGMS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; },
mesh = {Rats ; Animals ; *Brain-Computer Interfaces ; Rats, Sprague-Dawley ; Pain/psychology ; Gyrus Cinguli ; },
abstract = {Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top-down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.},
}
@article {pmid34151469,
year = {2021},
author = {Yeiser, JM and Morgan, JJ and Baxley, DL and Chandler, RB and Martin, JA},
title = {Optimizing conservation in species-specific agricultural landscapes.},
journal = {Conservation biology : the journal of the Society for Conservation Biology},
volume = {35},
number = {6},
pages = {1871-1881},
doi = {10.1111/cobi.13750},
pmid = {34151469},
issn = {1523-1739},
support = {#1012498//National Institute of Food and Agriculture McIntire-Stennis Project/ ; //University of Georgia/ ; //Federal Aid to Wildlife Restoration Act (Pittman-Robertson)/ ; //Kentucky Department of Fish and Wildlife Resources/ ; },
mesh = {Agriculture ; Animals ; Bayes Theorem ; Birds ; *Conservation of Natural Resources ; *Ecosystem ; },
abstract = {Recovery of grassland birds in agricultural landscapes is a global imperative. Agricultural landscapes are complex, and the value of resource patches may vary substantially among species. The spatial extent at which landscape features affect populations (i.e., scale of effect) may also differ among species. There is a need for regional-scale conservation planning that considers landscape-scale and species-specific responses of grassland birds to environmental change. We developed a spatially explicit approach to optimizing grassland conservation in the context of species-specific landscapes and prioritization of species recovery and applied it to a conservation program in Kentucky (USA). We used a hierarchical distance-sampling model with an embedded scale of effect predictor to estimate the relationship between landscape structure and abundance of eastern meadowlarks (Sturnella magna), field sparrows (Spizella pusilla), and northern bobwhites (Colinus virginianus). We used a novel spatially explicit optimization procedure rooted in multi-attribute utility theory to design alternative conservation strategies (e.g., prioritize only northern bobwhite recovery or assign equal weight to each species' recovery). Eastern meadowlarks and field sparrows were more likely to respond to landscape-scale resource patch adjacencies than landscape-scale patch densities. Northern bobwhite responded to both landscape-scale resource patch adjacencies and densities and responded strongly to increased grassland density. Effects of landscape features on local abundance decreased as distance increased and had negligible influence at 0.8 km for eastern meadowlarks (0.7-1.2 km 95% Bayesian credibility intervals [BCI]), 2.5 km for field sparrows (1.5-5.8 km 95% BCI), and 8.4 km for bobwhite (6.4-26 km 95% BCI). Northern bobwhites were predicted to benefit greatly from future grassland conservation regardless of conservation priorities, but eastern meadowlark and field sparrow were not. Our results suggest similar species can respond differently to broad-scale conservation practices because of species-specific, distance-dependent relationships with landscape structure. Our framework is quantitative, conceptually simple, customizable, and predictive and can be used to optimize conservation in heterogeneous ecosystems while considering landscape-scale processes and explicit prioritization of species recovery.},
}
@article {pmid34147756,
year = {2021},
author = {Al-Saegh, A and Dawwd, SA and Abdul-Jabbar, JM},
title = {CutCat: An augmentation method for EEG classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {141},
number = {},
pages = {433-443},
doi = {10.1016/j.neunet.2021.05.032},
pmid = {34147756},
issn = {1879-2782},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Fourier Analysis ; Humans ; Neural Networks, Computer ; },
abstract = {The non-invasive electroencephalogram (EEG) signals enable humans to communicate with devices and have control over them, this process requires precise classification and identification of those signals. The recent revolution of deep learning has empowered both feature extraction and classification in a joint manner of different data types. However, deep learning is a data learning approach that demands a large number of training samples. Whilst, the EEG research field lacks a large amount of data which restricts the use of deep learning within this field. This paper proposes a novel augmentation method for enlarging EEG datasets. Our CutCat augmentation method generates trials from inter- and intra-subjects and trials. The method relies on cutting a specific period from an EEG trial and concatenating it with a period from another trial from the same subject or different subjects. The method has been tested on shallow and deep convolutional neural networks (CNN) for the classification of motor imagery (MI) EEG data. Two input formulation types images and time-series have been used as input to the neural networks. Short-time Fourier transform (STFT) is used for generating training images from the time-series signals. The experimental results demonstrate that the proposed augmentation method is a promising strategy for handling the classification of small-scale datasets. Classification results on two EEG datasets show advancement in comparison with the results of state-of-the-art researches.},
}
@article {pmid34145245,
year = {2021},
author = {Cong, Z and Chen, LN and Ma, H and Zhou, Q and Zou, X and Ye, C and Dai, A and Liu, Q and Huang, W and Sun, X and Wang, X and Xu, P and Zhao, L and Xia, T and Zhong, W and Yang, D and Eric Xu, H and Zhang, Y and Wang, MW},
title = {Molecular insights into ago-allosteric modulation of the human glucagon-like peptide-1 receptor.},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {3763},
pmid = {34145245},
issn = {2041-1723},
mesh = {Animals ; Binding Sites/physiology ; CHO Cells ; Cell Line ; Cricetulus ; Cryoelectron Microscopy ; Diabetes Mellitus, Type 2/drug therapy ; Enzyme Activation/drug effects ; Glucagon-Like Peptide 1/*analogs &amp; derivatives/metabolism/*pharmacology ; Glucagon-Like Peptide-1 Receptor/genetics/metabolism ; Glucagon-Like Peptides/pharmacology ; HEK293 Cells ; Humans ; Molecular Dynamics Simulation ; Protein Conformation ; Sf9 Cells ; Spodoptera ; *Glucagon-Like Peptide-1 Receptor Agonists ; },
abstract = {The glucagon-like peptide-1 (GLP-1) receptor is a validated drug target for metabolic disorders. Ago-allosteric modulators are capable of acting both as agonists on their own and as efficacy enhancers of orthosteric ligands. However, the molecular details of ago-allosterism remain elusive. Here, we report three cryo-electron microscopy structures of GLP-1R bound to (i) compound 2 (an ago-allosteric modulator); (ii) compound 2 and GLP-1; and (iii) compound 2 and LY3502970 (a small molecule agonist), all in complex with heterotrimeric Gs. The structures reveal that compound 2 is covalently bonded to C347 at the cytoplasmic end of TM6 and triggers its outward movement in cooperation with the ECD whose N terminus penetrates into the GLP-1 binding site. This allows compound 2 to execute positive allosteric modulation through enhancement of both agonist binding and G protein coupling. Our findings offer insights into the structural basis of ago-allosterism at GLP-1R and may aid the design of better therapeutics.},
}
@article {pmid34144929,
year = {2021},
author = {Zhang, L and Zhu, J and Wang, H and Xia, J and Liu, P and Chen, F and Jiang, H and Miao, Q and Wu, W and Zhang, L and Luo, L and Jiang, X and Bai, Y and Sun, C and Chen, D and Zhang, X},
title = {A high-resolution cell atlas of the domestic pig lung and an online platform for exploring lung single-cell data.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {48},
number = {5},
pages = {411-425},
doi = {10.1016/j.jgg.2021.03.012},
pmid = {34144929},
issn = {1673-8527},
mesh = {Animals ; Biomarkers ; Computational Biology/methods ; Conserved Sequence ; Databases, Genetic ; Disease Susceptibility/immunology ; Evolution, Molecular ; *Gene Expression Profiling ; Genetic Predisposition to Disease ; Host-Pathogen Interactions/genetics ; Humans ; Lung/*metabolism ; Molecular Sequence Annotation ; RNA-Seq ; Single-Cell Analysis/*methods ; Sus scrofa/*genetics ; Swine ; *Transcriptome ; Web Browser ; },
abstract = {The genetically engineered pig is regarded as an optimal source of organ transplantation for humans and an excellent model for human disease research, given its comparable physiology to human beings. A myriad of single-cell RNA sequencing (scRNA-seq) data on humans has been reported, but such data on pigs are scarce. Here, we apply scRNA-seq technology to study the cellular heterogeneity of 3-month-old pig lungs, generating the single-cell atlas of 13,580 cells covering 16 major cell types. Based on these data, we systematically characterize the similarities and differences in the cellular cross-talk and expression patterns of respiratory virus receptors in each cell type of pig lungs compared with human lungs. Furthermore, we analyze pig lung xenotransplantation barriers and reported the cell-type expression patterns of 10 genes associated with pig-to-human immunobiological incompatibility and coagulation dysregulation. We also investigate the conserved transcription factors (TFs) and their candidate target genes and constructed five conserved TF regulatory networks in the main cell types shared by pig and human lungs. Finally, we present a comprehensive and openly accessible online platform, ScdbLung. Our scRNA-seq atlas of the domestic pig lung and ScdbLung database can guide pig lung research and clinical applicability.},
}
@article {pmid34144268,
year = {2021},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Daliri, MR},
title = {Ensemble Regularized Common Spatio-Spectral Pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification.},
journal = {Computers in biology and medicine},
volume = {135},
number = {},
pages = {104546},
doi = {10.1016/j.compbiomed.2021.104546},
pmid = {34144268},
issn = {1879-0534},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {The Brain-Computer interface system provides a communication path among the brain and computer, and recently, it is the subject of increasing attention. One of the most common paradigms of BCI systems is motor imagery. Currently, to classify motor imagery EEG signals, Common Spatial Patterns (CSP) are extensively used. Generally, the recorded motor imagery EEG signals in BCI are noisy, non-stationary, thus significantly reducing the BCI system's performance. It is shown that the CSP algorithm has a good performance in the classification of various types of motor imagery data. However, once the number of trials is low, or the data are noisy, overfitting will probably occur, which precludes extracting an appropriate spatial filter. Another drawback of the CSP is that it only extracts spatial-based filters. Therefore, the current study attempts to decrease the probability of overfitting in the CSP algorithm by presenting an improved method called Ensemble Regularized Common Spatio-Spectral Pattern (Ensemble RCSSP). Compared with other CSP and improved versions of CSP algorithms, our proposed models indicate a better accuracy, robustness, and reliability for motor imagery EEG data. The performance of the proposed Ensemble RCSSP has been tested for BCI Competition IV, Dataset 1, and BCI Competition III, Dataset Iva. Compared with other methods, performance is improved, and on average, the accuracy for all subjects is reached to 82.64% and 86.91% for the first and second datasets, respectively.},
}
@article {pmid34140885,
year = {2021},
author = {Wan, W and Cui, X and Gao, Z and Gu, Z},
title = {Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {673955},
pmid = {34140885},
issn = {1662-5161},
abstract = {Measuring and identifying the specific level of sustained attention during continuous tasks is essential in many applications, especially for avoiding the terrible consequences caused by reduced attention of people with special tasks. To this end, we recorded EEG signals from 42 subjects during the performance of a sustained attention task and obtained resting state and three levels of attentional states using the calibrated response time. EEG-based dynamical complexity features and Extreme Gradient Boosting (XGBoost) classifier were proposed as the classification model, Complexity-XGBoost, to distinguish multi-level attention states with improved accuracy. The maximum average accuracy of Complexity-XGBoost were 81.39 ± 1.47% for four attention levels, 80.42 ± 0.84% for three attention levels, and 95.36 ± 2.31% for two attention levels in 5-fold cross-validation. The proposed method is compared with other models of traditional EEG features and different classification algorithms, the results confirmed the effectiveness of the proposed method. We also found that the frontal EEG dynamical complexity measures were related to the changing process of response during sustained attention task. The proposed dynamical complexity approach could be helpful to recognize attention status during important tasks to improve safety and efficiency, and be useful for further brain-computer interaction research in clinical research or daily practice, such as the cognitive assessment or neural feedback treatment of individuals with attention deficit hyperactivity disorders, Alzheimer's disease, and other diseases which affect the sustained attention function.},
}
@article {pmid34140883,
year = {2021},
author = {Ko, W and Jeon, E and Jeong, S and Phyo, J and Suk, HI},
title = {A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {643386},
pmid = {34140883},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.},
}
@article {pmid34140486,
year = {2021},
author = {Trautmann, EM and O'Shea, DJ and Sun, X and Marshel, JH and Crow, A and Hsueh, B and Vesuna, S and Cofer, L and Bohner, G and Allen, W and Kauvar, I and Quirin, S and MacDougall, M and Chen, Y and Whitmire, MP and Ramakrishnan, C and Sahani, M and Seidemann, E and Ryu, SI and Deisseroth, K and Shenoy, KV},
title = {Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface.},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {3689},
pmid = {34140486},
issn = {2041-1723},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; F31 NS089376/NS/NINDS NIH HHS/United States ; R01 MH086373/MH/NIMH NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Calcium/*metabolism ; Calcium-Binding Proteins/metabolism ; Dendrites/metabolism/*physiology ; Green Fluorescent Proteins/metabolism ; Implants, Experimental ; Intravital Microscopy/*instrumentation/*methods ; Macaca mulatta ; Male ; Models, Neurological ; Motor Activity/physiology ; Motor Cortex/*diagnostic imaging/physiology ; Multimodal Imaging/*methods ; Neurons/physiology ; Photons ; },
abstract = {Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon imaging of neuronal calcium signals from macaques engaged in a motor task. By imaging apical dendrites, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signalswhich successfully decoded movement direction online. By fusing two-photon functional imaging with CLARITY volumetric imaging, we verified that many imaged dendrites which contributed to oBCI decoding originated from layer 5 output neurons, including a putative Betz cell. This approach establishes new opportunities for studying motor control and designing BCIs via two photon imaging.},
}
@article {pmid34139268,
year = {2021},
author = {Islam, MK and Ghorbanzadeh, P and Rastegarnia, A},
title = {Probability mapping based artifact detection and removal from single-channel EEG signals for brain-computer interface applications.},
journal = {Journal of neuroscience methods},
volume = {360},
number = {},
pages = {109249},
doi = {10.1016/j.jneumeth.2021.109249},
pmid = {34139268},
issn = {1872-678X},
mesh = {Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Probability ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {BACKGROUND: Different types of artifacts in the electroencephalogram (EEG) signals can considerably reduce the performance of the later-stage EEG analysis algorithms for making decisions, such as those for brain-computer interfacing (BCI) classification. In this paper, we address the problem of artifact detection and removal from single-channel EEG signals.<br><br>NEW METHOD: We propose a novel approach that maps the probability of an EEG epoch to be artifactual based on four different statistical measures: entropy (a measure of uncertainty), kurtosis (a measure of peakedness), skewness (a measure of asymmetry), and periodic waveform index (a measure of periodicity). Then, a stationary wavelet transform based artifact removal is proposed that employs a particular probability threshold provided by the user.<br><br>RESULTS: We have executed our experiments with both synthetic and real EEG data. It is observed that the proposed method exhibits a superior performance for suppressing the artifact contaminated from EEG with minimum distortion. Moreover, evaluation of the algorithm using EEG dataset for BCI experiments reveals that artifact removal can considerably improve the BCI output in both event-related potential and motor-imagery based BCI applications.<br><br>The proposed algorithm has been applied to both real and synthesized data testing and compared with other state-of-the-art automated artifact removal methods. Its superior performance is verified in terms of various performance metrics including computational complexity for justifying its use in BCI-like real-time applications.<br><br>CONCLUSION: Our work is expected to be useful for future research EEG signal processing and eventually to develop more accurate real-time EEG-based BCI applications.},
}
@article {pmid34139192,
year = {2021},
author = {Arneodo, EM and Chen, S and Brown, DE and Gilja, V and Gentner, TQ},
title = {Neurally driven synthesis of learned, complex vocalizations.},
journal = {Current biology : CB},
volume = {31},
number = {15},
pages = {3419-3425.e5},
pmid = {34139192},
issn = {1879-0445},
support = {R01 DC018446/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; Brain ; *Learning ; *Songbirds ; *Vocalization, Animal ; },
abstract = {Brain machine interfaces (BMIs) hold promise to restore impaired motor function and serve as powerful tools to study learned motor skill. While limb-based motor prosthetic systems have leveraged nonhuman primates as an important animal model,[1-4] speech prostheses lack a similar animal model and are more limited in terms of neural interface technology, brain coverage, and behavioral study design.[5-7] Songbirds are an attractive model for learned complex vocal behavior. Birdsong shares a number of unique similarities with human speech,[8-10] and its study has yielded general insight into multiple mechanisms and circuits behind learning, execution, and maintenance of vocal motor skill.[11-18] In addition, the biomechanics of song production bear similarity to those of humans and some nonhuman primates.[19-23] Here, we demonstrate a vocal synthesizer for birdsong, realized by mapping neural population activity recorded from electrode arrays implanted in the premotor nucleus HVC onto low-dimensional compressed representations of song, using simple computational methods that are implementable in real time. Using a generative biomechanical model of the vocal organ (syrinx) as the low-dimensional target for these mappings allows for the synthesis of vocalizations that match the bird's own song. These results provide proof of concept that high-dimensional, complex natural behaviors can be directly synthesized from ongoing neural activity. This may inspire similar approaches to prosthetics in other species by exploiting knowledge of the peripheral systems and the temporal structure of their output.},
}
@article {pmid34138712,
year = {2021},
author = {Samejima, S and Khorasani, A and Ranganathan, V and Nakahara, J and Tolley, NM and Boissenin, A and Shalchyan, V and Daliri, MR and Smith, JR and Moritz, CT},
title = {Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1233-1242},
doi = {10.1109/TNSRE.2021.3090269},
pmid = {34138712},
issn = {1558-0210},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Computers ; Rats ; Spinal Cord ; *Spinal Cord Injuries ; Upper Extremity ; },
abstract = {Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.},
}
@article {pmid34137498,
year = {2021},
author = {Nie, A and Li, M},
title = {Professional discrepancies of doctors and lawyers in episodic memory: Modulations of professional morality and warning.},
journal = {PsyCh journal},
volume = {10},
number = {5},
pages = {707-731},
doi = {10.1002/pchj.457},
pmid = {34137498},
issn = {2046-0260},
support = {//Fundamental Research Funds for the Central Universities/ ; //MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; 17YJA190010//Humanities and Social Sciences, Ministry of Education, China/ ; 31300831//National Natural Science Foundation of China/ ; LY21C090002//Zhejiang Provincial Natural Science Foundation of China/ ; 2021N78//Zhejiang Federation of Humanities and Social Sciences Circles/ ; },
mesh = {Humans ; *Lawyers ; *Memory, Episodic ; Morals ; },
abstract = {Past investigations have consistently demonstrated the robust stereotype-consistent effect in the circumstance of source memory but not always in item memory, including the case of professional stereotype. However, it remains unclear whether the effect still occurs in professional stereotype when considering the attributes of negative (or bad) or positive (or good); besides, it has not been concerned about how does warning work in remembering the professional stereotypical stimuli. The current experiments aimed to address these issues by adopting descriptive sentences as stimuli, which were related or unrelated to doctors and lawyers, and with different professional moral valences (negative, neutral, or positive). Item memory and source memory were tested successively. Experiment 1 without the explicit warning confirmed the reliable stereotype-consistent effect solely in source memory; the modulation of professional morality on memory behaved differently between doctor and lawyer, that is, negativity bias versus positivity bias. When giving an explicit warning (Experiment 2), the stereotype-consistent effect attenuated in the lawyer case, and the occurrence of negativity bias was sensitive to the memory task. Thus, our findings further reinforce the dual-process model; both professional morality and warning work in memory of professional stereotype, depending upon the nature of the profession, the concerned memory task, and also the presence of warning. Implications are made for future research to consider more perspectives.},
}
@article {pmid34135952,
year = {2021},
author = {Cai, Q and Yan, J and Han, H and Gong, W and Wang, H},
title = {Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6634672},
pmid = {34135952},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Fingers ; Movement ; },
abstract = {The discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not make full use of the information in frequency domain. The paper presents multilinear discriminative spatial patterns (MDSP) to derive multiple interrelated lower dimensional discriminative subspaces of low frequency movement-related cortical potential (MRCP). Experimental results on two finger movement tasks' EEG datasets demonstrate the effectiveness of the proposed MDSP method.},
}
@article {pmid34134091,
year = {2021},
author = {Chen, Y and Yang, C and Ye, X and Chen, X and Wang, Y and Gao, X},
title = {Implementing a calibration-free SSVEP-based BCI system with 160 targets.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0bfa},
pmid = {34134091},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Objective.Steady-state visual evoked potential (SSVEP) is an essential paradigm of electroencephalogram based brain-computer interface (BCI). Previous studies in the BCI research field mostly focused on enhancing classification accuracy and reducing stimuli duration. This study, however, concentrated on increasing the number of available targets in the BCI systems without calibration.Approach. Motivated by the idea of multiple frequency sequential coding, we developed a calibration-free SSVEP-BCI system implementing 160 targets by four continuous sinusoidal stimuli that lasted four seconds in total. Taking advantage of the benchmark dataset of SSVEP-BCI, this study optimized an arrangement of stimuli sequences, maximizing the response distance between different stimuli. We proposed an effective classification algorithm based on filter bank canonical correlation analysis. To evaluate the performance of this system, we conducted offline and online experiments using cue-guided selection tasks. Eight subjects participated in the offline experiments, and 12 subjects participated in the online experiments with real-time feedbacks.Mainresults. Offline experiments indicated the feasibility of the stimulation selection and detection algorithms. Furthermore, the online system achieved an average accuracy of 87.16 ± 11.46% and an information transfer rate of 78.84 ± 15.59 bits min[-1]. Specifically, seven of 12 subjects accomplished online experiments with accuracy higher than 90%. This study proposed an intact solution of applying numerous targets to SSVEP-based BCIs. Results of experiments confirmed the utility and efficiency of the system.Significance. This study firstly provides a calibration-free SSVEP-BCI speller system that enables more than 100 commands. This system could significantly expand the application scenario of SSVEP-based BCI. Meanwhile, the design criterion can hopefully enhance the overall performance of the BCI system. The demo video can be found in the supplementary material available online atstacks.iop.org/JNE/18/046094/mmedia.},
}
@article {pmid34130614,
year = {2021},
author = {Delfino, E and Pastore, A and Zucchini, E and Cruz, MFP and Ius, T and Vomero, M and D'Ausilio, A and Casile, A and Skrap, M and Stieglitz, T and Fadiga, L},
title = {Prediction of Speech Onset by Micro-Electrocorticography of the Human Brain.},
journal = {International journal of neural systems},
volume = {31},
number = {7},
pages = {2150025},
doi = {10.1142/S0129065721500258},
pmid = {34130614},
issn = {1793-6462},
mesh = {Brain/diagnostic imaging/surgery ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes ; Humans ; *Speech ; },
abstract = {Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient's intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic ([Formula: see text]ECoG) devices. First, we observed that, at the smallest investigated pitch (600[Formula: see text][Formula: see text]m), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70-150[Formula: see text]Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.},
}
@article {pmid34130268,
year = {2021},
author = {Zhou, X and Xu, M and Xiao, X and Wang, Y and Jung, TP and Ming, D},
title = {Detection of fixation points using a small visual landmark for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0b51},
pmid = {34130268},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Retina ; },
abstract = {Objective.The speed of visual brain-computer interfaces (v-BCIs) has been greatly improved in recent years. However, the traditional v-BCI paradigms require users to directly gaze at the intensive flickering items, which would cause severe problems such as visual fatigue and excessive visual resource consumption in practical applications. Therefore, it is imperative to develop a user-friendly v-BCI.Approach.According to the retina-cortical relationship, this study developed a novel BCI paradigm to detect the fixation point of eyes using a small visual stimulus that subtended only 0.6° in visual angle and was out of the central visual field. Specifically, the visual stimulus was treated as a landmark to judge the eccentricity and polar angle of the fixation point. Sixteen different fixation points were selected around the visual landmark, i.e. different combinations of two eccentricities (2° and 4°) and eight polar angles (0,π4,π2,3π4,π,5π4,3π2and7π4). Twelve subjects participated in this study, and they were asked to gaze at one out of the 16 points for each trial. A multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm was proposed to decode the user's fixation point.Main results.We found the visual stimulation landmark elicited different spatial event-related potential patterns for different fixation points. Multi-DCPM could achieve an average accuracy of 66.2% with a standard deviation of 15.8% for the classification of the sixteen fixation points, which was significantly higher than traditional algorithms (p⩽0.001). Experimental results of this study demonstrate the feasibility of using a small visual stimulus as a landmark to track the relative position of the fixation point.Significance.The proposed new paradigm provides a potential approach to alleviate the problem of irritating stimuli in v-BCIs, which can broaden the applications of BCIs.},
}
@article {pmid34130267,
year = {2021},
author = {Pereira, J and Kobler, R and Ofner, P and Schwarz, A and Müller-Putz, GR},
title = {Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0b52},
pmid = {34130267},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Hand Strength ; Humans ; Movement ; },
abstract = {Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.},
}
@article {pmid34129500,
year = {2021},
author = {Zhang, G and Etemad, A},
title = {Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1138-1149},
doi = {10.1109/TNSRE.2021.3089594},
pmid = {34129500},
issn = {1558-0210},
mesh = {*Automobile Driving ; *Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; Humans ; Wakefulness ; },
abstract = {Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art. We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.},
}
@article {pmid34127850,
year = {2021},
author = {Kirton, A and Metzler, MJ and Craig, BT and Hilderley, A and Dunbar, M and Giuffre, A and Wrightson, J and Zewdie, E and Carlson, HL},
title = {Perinatal stroke: mapping and modulating developmental plasticity.},
journal = {Nature reviews. Neurology},
volume = {17},
number = {7},
pages = {415-432},
pmid = {34127850},
issn = {1759-4766},
mesh = {Brain/diagnostic imaging/*growth &amp; development ; Brain Mapping/*methods/trends ; Brain-Computer Interfaces/trends ; Cerebral Palsy/diagnostic imaging/etiology/therapy ; Female ; Humans ; Infant, Newborn ; Neuroimaging/methods/trends ; Neuronal Plasticity/*physiology ; Perinatal Care/*methods/trends ; Pregnancy ; Pregnancy Complications/diagnostic imaging/therapy ; Robotics/methods/trends ; Stroke/diagnostic imaging/etiology/*therapy ; Stroke Rehabilitation/*methods/trends ; },
abstract = {Most cases of hemiparetic cerebral palsy are caused by perinatal stroke, resulting in lifelong disability for millions of people. However, our understanding of how the motor system develops following such early unilateral brain injury is increasing. Tools such as neuroimaging and brain stimulation are generating informed maps of the unique motor networks that emerge following perinatal stroke. As a focal injury of defined timing in an otherwise healthy brain, perinatal stroke represents an ideal human model of developmental plasticity. Here, we provide an introduction to perinatal stroke epidemiology and outcomes, before reviewing models of developmental plasticity after perinatal stroke. We then examine existing therapeutic approaches, including constraint, bimanual and other occupational therapies, and their potential synergy with non-invasive neurostimulation. We end by discussing the promise of exciting new therapies, including novel neurostimulation, brain-computer interfaces and robotics, all focused on improving outcomes after perinatal stroke.},
}
@article {pmid34126595,
year = {2021},
author = {Misaki, M and Bodurka, J},
title = {The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0b33},
pmid = {34126595},
issn = {1741-2552},
support = {R01 MH098099/MH/NIMH NIH HHS/United States ; },
mesh = {*Artifacts ; Brain/diagnostic imaging ; Brain Mapping ; Image Processing, Computer-Assisted ; *Magnetic Resonance Imaging ; Retrospective Studies ; },
abstract = {Objective. Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.Approach.We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main results.All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.Significance.The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.},
}
@article {pmid34124247,
year = {2021},
author = {Ma, Y and Zhu, Z and Dong, Z and Shen, T and Sun, M and Kong, W},
title = {Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network.},
journal = {BioMed research international},
volume = {2021},
number = {},
pages = {5561125},
pmid = {34124247},
issn = {2314-6141},
mesh = {*Algorithms ; *Databases, Factual ; Humans ; *Image Processing, Computer-Assisted ; Retinal Vessels/*diagnostic imaging ; },
abstract = {Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.},
}
@article {pmid34121989,
year = {2021},
author = {Wairagkar, M and Hayashi, Y and Nasuto, SJ},
title = {Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {660032},
pmid = {34121989},
issn = {1662-4548},
abstract = {Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms-our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain-computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.},
}
@article {pmid34119819,
year = {2021},
author = {Qi, G and Zhao, S and Ceder, AA and Guan, W and Yan, X},
title = {Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis.},
journal = {Accident; analysis and prevention},
volume = {159},
number = {},
pages = {106223},
doi = {10.1016/j.aap.2021.106223},
pmid = {34119819},
issn = {1879-2057},
mesh = {Accidents, Traffic ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Noninvasive EEG signals provide neural activity information at high resolution, of which human mental status can be properly detected. However, artefacts always exist in brain oscillatory EEG signals and thus impede the accuracy and reliability of relevant analysis, especially in real-world tasks. Moreover, the use of a mature artefact identification method cannot assure impeccable artefact separation; this leads to a trade-off between removing contaminated information and losing valuable information. This study addresses this problem by investigating a simulator-based driving behaviour analysis using a car-following scenario to correlate the EEG-based mental features with behavioural responses. The study develops an architecture for an artefact composition pool and proposes three integrated prediction models to evaluate the removal compositions of the EEG artefacts. Three errors (mean absolute, root mean square, mean absolute percentage) and R-squared index are considered for measuring the performance of the models. The results show that the best-performing composition outperformed the no-removal and all-removal cases by 11.75% and 4.28% improvements, respectively. Specifically, we investigate different common artefacts including eye blinks, horizontal eye movements, vertical eye movements, generic discontinuities and muscle artefacts. The gained knowledge on artefact removal, EEG spectral features and stimuli-response patterns can be further applied to properly manipulate real-world EEG signals and develop an effective brain-computer interface.},
}
@article {pmid34116918,
year = {2021},
author = {Gao, X and Wang, Y and Chen, X and Gao, S},
title = {Interface, interaction, and intelligence in generalized brain-computer interfaces.},
journal = {Trends in cognitive sciences},
volume = {25},
number = {8},
pages = {671-684},
doi = {10.1016/j.tics.2021.04.003},
pmid = {34116918},
issn = {1879-307X},
mesh = {Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Intelligence ; User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.},
}
@article {pmid34116449,
year = {2021},
author = {Goelz, C and Mora, K and Rudisch, J and Gaidai, R and Reuter, E and Godde, B and Reinsberger, C and Voelcker-Rehage, C and Vieluf, S},
title = {Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {142},
number = {},
pages = {363-374},
doi = {10.1016/j.neunet.2021.04.029},
pmid = {34116449},
issn = {1879-2782},
mesh = {Aged ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Movement ; },
abstract = {Classification of physiological data provides a data driven approach to study central aspects of motor control, which changes with age. To implement such results in real-life applications for elderly it is important to identify age-specific characteristics of movement classification. We compared task-classification based on EEG derived activity patterns related to brain network characteristics between older and younger adults performing force tracking with two task characteristics (sinusoidal; constant) with the right or left hand. We extracted brain network patterns with dynamic mode decomposition (DMD) and classified the tasks on an individual level using linear discriminant analysis (LDA). Next, we compared the models' performance between the groups. Studying brain activity patterns, we identified signatures of altered motor network function reflecting dedifferentiated and compensational brain activation in older adults. We found that the classification performance of the body side was lower in older adults. However, classification performance with respect to task characteristics was better in older adults. This may indicate a higher susceptibility of brain network mechanisms to task difficulty in elderly. Signatures of dedifferentiation and compensation refer to an age-related reorganization of functional brain networks, which suggests that classification of visuomotor tracking tasks is influenced by age-specific characteristics of brain activity patterns. In addition to insights into central aspects of fine motor control, the results presented here are relevant in application-oriented areas such as brain computer interfaces.},
}
@article {pmid34115596,
year = {2022},
author = {Yang, S and Wang, J and Deng, B and Azghadi, MR and Linares-Barranco, B},
title = {Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {12},
pages = {7126-7140},
doi = {10.1109/TNNLS.2021.3084250},
pmid = {34115596},
issn = {2162-2388},
mesh = {*Neural Networks, Computer ; *Algorithms ; Neurons/physiology ; Computers ; Brain/physiology ; },
abstract = {Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning.},
}
@article {pmid34115589,
year = {2021},
author = {Cattai, T and Colonnese, S and Corsi, MC and Bassett, DS and Scarano, G and De Vico Fallani, F},
title = {Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1168-1177},
doi = {10.1109/TNSRE.2021.3088637},
pmid = {34115589},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; },
abstract = {In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and we investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and we compared the discriminant power with respect to standard power spectrum features. At the group level, results showed that while spectral-coherence based network features were increasing in the sensorimotor areas, those based on imaginary-coherence were significantly decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we eventually assessed the potential of these network connectivity features in a simple off-line classification scenario. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.},
}
@article {pmid34115016,
year = {2021},
author = {Xue, X and Tu, H and Deng, Z and Zhou, L and Li, N and Wang, X},
title = {Effects of brain-computer interface training on upper limb function recovery in stroke patients: A protocol for systematic review and meta-analysis.},
journal = {Medicine},
volume = {100},
number = {23},
pages = {e26254},
pmid = {34115016},
issn = {1536-5964},
support = {2020-A001//The Key Laboratory of Sports Medicine of Sichuan Province/ ; },
mesh = {Brain-Computer Interfaces/psychology/*standards ; *Clinical Protocols ; Humans ; Meta-Analysis as Topic ; Recovery of Function ; Stroke/complications ; Stroke Rehabilitation/methods/*standards ; Systematic Reviews as Topic ; Upper Extremity/*physiopathology ; },
abstract = {BACKGROUND: In recent years, with the development of medical technology and the increase of inter-disciplinary cooperation technology, new methods in the field of artificial intelligence medicine emerge in an endless stream. Brain-computer interface (BCI), as a frontier technology of multidisciplinary integration, has been widely used in various fields. Studies have shown that BCI-assisted training can improve upper limb function in stroke patients, but its effect is still controversial and lacks evidence-based evidence, which requires further exploration and confirmation. Therefore, the main purpose of this paper is to systematically evaluate the efficacy of different BCI-assisted training on upper limb function recovery in stroke patients, to provide a reference for the application of BCI-assisted technology in stroke rehabilitation.<br><br>METHODS: We will search PubMed, Web of Science, The Cochrane Library, Chinese National Knowledge Infrastructure Database, Wanfang Data, Weipu Electronics, and other databases (from the establishment to February 2021) for full text in Chinese and English. Randomized controlled trials were collected to examine the effect of BCI-assisted training on upper limb functional recovery in stroke patients. We will consider inclusion, select high-quality articles for data extraction and analysis, and summarize the intervention effect of BCI-assisted training on the upper limb function of stroke patients. Two reviewers will screen titles, abstracts, and full texts independently according to inclusion criteria; Data extraction and risk of bias assessment were performed in the included studies. We will use a hierarchy of recommended assessment, development, and assessment methods to assess the overall certainty of the evidence and report findings accordingly. Endnote X8 will be applied in selecting the study, Review Manager 5.3 will be applied in analyzing and synthesizing.<br><br>RESULTS: The results will provide evidence for judging whether BCI is effective and safe in improving upper limb function in patients with stroke.<br><br>CONCLUSION: Our study will provide reliable evidence for the effect of BCI technology on the improvement of upper limb function in stroke patients.<br><br>PROSPERO REGISTRATION NUMBER: CRD42021250378.},
}
@article {pmid34112826,
year = {2021},
author = {Richter, CP and La Faire, P and Tan, X and Fiebig, P and Landsberger, DM and Micco, AG},
title = {Listening to speech with a guinea pig-to-human brain-to-brain interface.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {12231},
pmid = {34112826},
issn = {2045-2322},
support = {R01 DC011855/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; *Auditory Perception ; *Brain-Computer Interfaces ; Electrophysiological Phenomena ; Guinea Pigs ; *Hearing ; Humans ; *Models, Biological ; Reproducibility of Results ; *Speech ; *Speech Perception ; },
abstract = {Nicolelis wrote in his 2003 review on brain-machine interfaces (BMIs) that the design of a successful BMI relies on general physiological principles describing how neuronal signals are encoded. Our study explored whether neural information exchanged between brains of different species is possible, similar to the information exchange between computers. We show for the first time that single words processed by the guinea pig auditory system are intelligible to humans who receive the processed information via a cochlear implant. We recorded the neural response patterns to single-spoken words with multi-channel electrodes from the guinea inferior colliculus. The recordings served as a blueprint for trains of biphasic, charge-balanced electrical pulses, which a cochlear implant delivered to the cochlear implant user's ear. Study participants completed a four-word forced-choice test and identified the correct word in 34.8% of trials. The participants' recognition, defined by the ability to choose the same word twice, whether right or wrong, was 53.6%. For all sessions, the participants received no training and no feedback. The results show that lexical information can be transmitted from an animal to a human auditory system. In the discussion, we will contemplate how learning from the animals might help developing novel coding strategies.},
}
@article {pmid34111849,
year = {2021},
author = {Asogbon, MG and Samuel, OW and Li, X and Nsugbe, E and Scheme, E and Li, G},
title = {A linearly extendible multi-artifact removal approach for improved upper extremity EEG-based motor imagery decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ac0a55},
pmid = {34111849},
issn = {1741-2552},
abstract = {BACKGROUND AND OBJECTIVE: Non-invasive multichannel Electroencephalography (EEG) recordings provide an alternative source of neural information from which motor imagery (MI) patterns associated with limb movement intent can be decoded for use as control inputs for rehabilitation robots. The presence of multiple inherent dynamic artifacts in EEG signals, however, poses processing challenges for brain-computer interface (BCI) systems. A large proportion of the existing EEG signal preprocessing methods focus on isolating single artifact per time from an ensemble of EEG trials and require calibration and/or reference electrodes, resulting in increased complexity of their application to MI-EEG controlled rehabilitation devices in practical settings. Also, a few existing multi-artifacts removal methods though explored in other domains, they have rarely been investigated in the space of MI-EEG signals for multiple artifacts cancellation in a simultaneous manner.<br><br>APPROACH: Building on the premise of previous works, this study propose a semi-automatic EEG preprocessing method that combines Generalized Eigenvalue Decomposition driven by low-rank approximation and a Multi-channel Wiener Filter (GEVD-MWF) that employs a learning technique for simultaneous elimination of multiple artifacts from MI-EEG signals. The proposed method is applied to remove multiple artifacts from 64-channel EEG signals recorded from transhumeral amputees while they performed distinct classes of upper limb MI tasks before decoding their movement intent using a selection of features and machine learning algorithms.<br><br>MAIN RESULTS: Experimental results show that the proposed GEVD-MWF method yields significant improvements in MI decoding accuracies, in the range of 13.23%-41.21% compared to four existing popular artifact removal algorithms. Further investigation revealed that the GEVD-MWF approach enabled accuracies in the range of 90.44% - 99.67% using "single trial" EEG recordings, which could eliminate the need to record and process large ensembles of EEG trials as commonly required in some existing approaches. Additionally, using a variant of the sequential forward floating selection algorithm, a subset of 9 channels was used to obtain a decoding accuracy of 93.73%&#xb1;1.58%.<br><br>SIGNIFICANCE: Given its improved performance, reduced data requirements, and feasibility with few channels, the proposed GEVD-MWF could potentially spur the development of effective real-time control strategies for multi-degree of freedom EEG-based miniaturized rehabilitation robotic interfaces.},
}
@article {pmid34103545,
year = {2021},
author = {Saeedinia, SA and Jahed-Motlagh, MR and Tafakhori, A and Kasabov, N},
title = {Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {12064},
pmid = {34103545},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; Brain/diagnostic imaging ; Brain-Computer Interfaces ; Child ; Computer Simulation ; Electroencephalography/*methods ; Female ; Humans ; Imaging, Three-Dimensional ; *Learning ; Magnetic Resonance Imaging/*methods ; Male ; Models, Neurological ; Nerve Net ; *Neural Networks, Computer ; Neurons ; Signal Processing, Computer-Assisted ; },
abstract = {This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.},
}
@article {pmid34101595,
year = {2021},
author = {Lee, M and Jeong, JH and Kim, YH and Lee, SW},
title = {Decoding Finger Tapping With the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1099-1109},
doi = {10.1109/TNSRE.2021.3087506},
pmid = {34101595},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; Movement ; *Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {In stroke rehabilitation, motor imagery based on a brain-computer interface is an extremely useful method to control an external device and utilize neurofeedback. Many studies have reported on the classification performance of motor imagery to decode individual fingers in stroke patients compared with healthy controls. However, classification performance for a given limb is still low because the differences between patients owing to brain reorganization after stroke are not considered. We used electroencephalography signals from eleven healthy controls and eleven stroke patients in this study. The subjects performed a finger tapping task during motor execution, and motor imagery was performed with the dominant and affected hands in the healthy controls and stroke patients, respectively. All fingers except for the thumb were classified using the proposed framework based on a voting module. The averaged four-class accuracies during motor execution and motor imagery were 53.16 ± 8.42% and 46.94 ± 5.99% for the healthy controls and 53.17 ± 14.09% and 66.00 ± 14.96% for the stroke patients, respectively. Importantly, the classification accuracies in the stroke patients were statistically higher than those in healthy controls during motor imagery. However, there was no significant difference between the accuracies of motor execution and motor imagery. These findings show the potential for high classification performance for a given limb during motor imagery in stroke patients. These results can also provide insights into controlling an external device on the basis of a brain-computer interface.},
}
@article {pmid34101579,
year = {2022},
author = {Song, X and Chen, X and Guo, J and Xu, M and Ming, D},
title = {Living Rat SSVEP Mapping With Acoustoelectric Brain Imaging.},
journal = {IEEE transactions on bio-medical engineering},
volume = {69},
number = {1},
pages = {75-82},
doi = {10.1109/TBME.2021.3087177},
pmid = {34101579},
issn = {1558-2531},
mesh = {Animals ; Brain/diagnostic imaging ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; Rats ; },
abstract = {OBJECTIVE: Acoustoelectric Brain Imaging (ABI) is a potential method for mapping brain electrical activity with high spatial resolution (millimeter). To resolve the key issue for eventual realization of ABI, testing that recorded acoustoelectric (AE) signal can be used to decode intrinsic brain electrical activity, the experiment of living rat SSVEP measurement with ABI is implemented.<br><br>METHOD: A 1-MHz ultrasound transducer is focused on the visual cortex of anesthetized rat. With visual stimulus, the electroencephalogram and AE signal are simultaneously recorded with Pt electrode. Besides, with FUS transducer scanning at the visual cortex, corresponding AE signals at different spatial positions are decoded and imaged.<br><br>RESULTS: Consistent with the directly measured SSVEP, decoded AE signal presents a clear event-related spectral perturbation (ERSP). And, decoded AE signal is of high amplitude response at the base and harmonics of the visual stimulus frequency. What's more, for timing signal, a significant positive amplitude correlation is observed between decoded AE signal and simultaneously measured SSVEP. In addition, the mean SNRs of SSVEP and decoded AE signal are both significantly higher than that of background EEG. Finally, with one fixed recording electrode, the active area with an inner diameter of 1mm is located within the 4 mm&#xd7;4 mm measurement region.<br><br>CONCLUSION: Experimental results demonstrate that the millimeter-level spatial resolution SSVEP measurement of living rat is achieved through ABI for the first time.<br><br>SIGNIFICANCE: This study confirms that ABI should shed light on high spatiotemporal resolution neuroimaging.},
}
@article {pmid34099912,
year = {2021},
author = {Wood, H},
title = {Bidirectional brain-computer interface aids robotic arm control.},
journal = {Nature reviews. Neurology},
volume = {17},
number = {8},
pages = {462},
pmid = {34099912},
issn = {1759-4766},
}
@article {pmid34099907,
year = {2021},
author = {Pfotenhauer, SM and Frahm, N and Winickoff, D and Benrimoh, D and Illes, J and Marchant, G},
title = {Mobilizing the private sector for responsible innovation in neurotechnology.},
journal = {Nature biotechnology},
volume = {39},
number = {6},
pages = {661-664},
pmid = {34099907},
issn = {1546-1696},
support = {171583;03027 IC-127354//CIHR/Canada ; },
mesh = {*Brain-Computer Interfaces ; Ethics, Institutional ; Humans ; *Organizational Innovation ; *Private Sector ; },
}
@article {pmid34098023,
year = {2021},
author = {Ye, F and Sun, Z and Yang, D and Wang, H and Xi, X},
title = {Corticomuscular coupling analysis based on improved LSTM and transfer entropy.},
journal = {Neuroscience letters},
volume = {760},
number = {},
pages = {136012},
doi = {10.1016/j.neulet.2021.136012},
pmid = {34098023},
issn = {1872-7972},
mesh = {Canonical Correlation Analysis ; Electroencephalography ; Electromyography ; Entropy ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Movement Disorders/physiopathology/rehabilitation ; Muscle, Skeletal/*physiology ; Neural Networks, Computer ; },
abstract = {The study of functional corticomuscular coupling can reflect the interaction between the cerebral cortex and muscle tissue, thereby helping to understand how the brain controls muscle tissue and the effect of muscle movement on brain function. This study proposes a detection model of the coupling strength between the cortex and muscles. The detection model uses an adaptive selector to choose the optimal long short-term memory network, uses this network to extract the features of electroencephalography and electromyography, and finally transforms time characteristics into the frequency domain. The transfer entropy is used to represent the interaction intensity of signals in different frequency bands. Using this model, we analyze the coupling relationship between the cortex and muscles in the three movements of wrist flexion, wrist extension, and clench fist, and compare the model with traditional wavelet coherence analysis and deep canonical correlation analysis. The experimental results show that our model can not only express the bidirectional coupling relationship between different frequency bands but also suppress the possible false coupling that traditional methods may detect. Our research shows that the proposed model has great potential in medical rehabilitation, movement decoding, and other fields.},
}
@article {pmid34097619,
year = {2022},
author = {Zhang, Z and Chen, G and Yang, S},
title = {Ensemble Support Vector Recurrent Neural Network for Brain Signal Detection.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {11},
pages = {6856-6866},
doi = {10.1109/TNNLS.2021.3083710},
pmid = {34097619},
issn = {2162-2388},
mesh = {Humans ; *Support Vector Machine ; Event-Related Potentials, P300 ; Neural Networks, Computer ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Brain ; },
abstract = {The brain-computer interface (BCI) P300 speller analyzes the P300 signals from the brain to achieve direct communication between humans and machines, which can assist patients with severe disabilities to control external machines or robots to complete expected tasks. Therefore, the classification method of P300 signals plays an important role in the development of BCI systems and technologies. In this article, a novel ensemble support vector recurrent neural network (E-SVRNN) framework is proposed and developed to acquire more accurate and efficient electroencephalogram (EEG) signal classification results. First, we construct a support vector machine (SVM) to formulate EEG signals recognizing model. Second, the SVM formulation is transformed into a standard convex quadratic programming (QP) problem. Third, the convex QP problem is solved by combining a varying parameter recurrent neural network (VPRNN) with a penalty function. Experimental results on BCI competition II and BCI competition III datasets demonstrate that the proposed E-SVRNN framework can achieve accuracy rates as high as 100% and 99%, respectively. In addition, the results of comparison experiments verify that the proposed E-SVRNN possesses the best recognition accuracy and information transfer rate (ITR) compared with most of the state-of-the-art algorithms.},
}
@article {pmid34096888,
year = {2021},
author = {Xiao, X and Xu, M and Han, J and Yin, E and Liu, S and Zhang, X and Jung, TP and Ming, D},
title = {Enhancement for P300-speller classification using multi-window discriminative canonical pattern matching.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac028b},
pmid = {34096888},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; },
abstract = {Objective.P300s are one of the most studied event-related potentials (ERPs), which have been widely used for brain-computer interfaces (BCIs). Thus, fast and accurate recognition of P300s is an important issue for BCI study. Recently, there emerges a lot of novel classification algorithms for P300-speller. Among them, discriminative canonical pattern matching (DCPM) has been proven to work effectively, in which discriminative spatial pattern (DSP) filter can significantly enhance the spatial features of P300s. However, the pattern of ERPs in space varies with time, which was not taken into consideration in the traditional DCPM algorithm.Approach.In this study, we developed an advanced version of DCPM, i.e. multi-window DCPM, which contained a series of time-dependent DSP filters to fine-tune the extraction of spatial ERP features. To verify its effectiveness, 25 subjects were recruited and they were asked to conduct the typical P300-speller experiment.Main results.As a result, multi-window DCPM achieved the character recognition accuracy of 91.84% with only five training characters, which was significantly better than the traditional DCPM algorithm. Furthermore, it was also compared with eight other popular methods, including SWLDA, SKLDA, STDA, BLDA, xDAWN, HDCA, sHDCA and EEGNet. The results showed multi-window DCPM preformed the best, especially using a small calibration dataset. The proposed algorithm was applied to the BCI Controlled Robot Contest of P300 paradigm in 2019 World Robot Conference, and won the first place.Significance.These results demonstrate that multi-window DCPM is a promising method for improving the performance and enhancing the practicability of P300-speller.},
}
@article {pmid34094699,
year = {2021},
author = {Shoar, S and Hosseini, FS and Naderan, M and Khavandi, S and Tabibzadeh, E and Khavandi, S and Shoar, N},
title = {Cardiac injury following blunt chest trauma: diagnosis, management, and uncertainty.},
journal = {International journal of burns and trauma},
volume = {11},
number = {2},
pages = {80-89},
pmid = {34094699},
issn = {2160-2026},
abstract = {Due to the evolving nature of injuries caused by high-speed motor vehicle accidents, the incidence rate of blunt chest trauma is continuously increasing. Blunt cardiac injury (BCI) is a potentially lethal entity as a result of trauma to the chest. Due to its indistinct clinical presentation and heterogeneous definition, BCI might be missed during the initial survey of trauma patients in the acute care setting. Additionally, unnecessary operation in hemodynamically stable patients in whom the extent of cardiac injury has not been thoroughly evaluated might result in adverse clinical outcome. Due to ongoing advances in the diagnostic modalities and minimally invasive procedures in the acute care and trauma setting, patients with blunt trauma to the chest, who are also suspected of having a BCI, can be monitored with more confidence and managed accordingly as the clinical scenario evolves. While low-yield diagnostics such as chest X ray, electrocardiogram, and a bedside ultrasonography are still routinely performed in patients with suspected BCI, high-yield modalities such as computed tomography, highly sensitive cardiac biomarkers, and transesophageal echocardiography are all a next step in the management approach. In either case, the clinical judgment of the medical team plays a pivotal role in transition to the next step with adequate resuscitation remaining an inevitable part.},
}
@article {pmid34093495,
year = {2021},
author = {Song, N and Cui, GL and Zeng, QL},
title = {Genomic Epidemiology of SARS-CoV-2 From Mainland China With Newly Obtained Genomes From Henan Province.},
journal = {Frontiers in microbiology},
volume = {12},
number = {},
pages = {673855},
pmid = {34093495},
issn = {1664-302X},
abstract = {Even though the COVID-19 epidemic in China has been successfully put under control within a few months, it is still very important to infer the origin time and genetic diversity from the perspective of the whole genome sequence of its agent, SARS-CoV-2. Yet, the sequence of the entire virus genome from China in the current public database is very unevenly distributed with reference to time and place of collection. In particular, only one sequence was obtained in Henan province, adjacent to China's worst-case province, Hubei Province. Herein, we used high-throughput sequencing techniques to get 19 whole-genome sequences of SARS-CoV-2 from 18 severe patients admitted to the First Affiliated Hospital of Zhengzhou University, a provincial designated hospital for the treatment of severe COVID-19 cases in Henan province. The demographic, baseline, and clinical characteristics of these patients were described. To investigate the molecular epidemiology of SARS-CoV-2 of the current COVID-19 outbreak in China, 729 genome sequences (including 19 sequences from this study) sampled from Mainland China were analyzed with state-of-the-art comprehensive methods, including likelihood-mapping, split network, ML phylogenetic, and Bayesian time-scaled phylogenetic analyses. We estimated that the evolutionary rate and the time to the most recent common ancestor (TMRCA) of SARS-CoV-2 from Mainland China were 9.25 × 10[-4] substitutions per site per year (95% BCI: 6.75 × 10[-4] to 1.28 × 10[-3]) and October 1, 2019 (95% BCI: August 22, 2019 to November 6, 2019), respectively. Our results contribute to studying the molecular epidemiology and genetic diversity of SARS-CoV-2 over time in Mainland China.},
}
@article {pmid34087844,
year = {2021},
author = {Li, D and Zhou, Y and Cui, H and Kong, L and Zhu, W and Chai, X and Zhuo, H},
title = {Analysis of the curative effect of percutaneous kyphoplasty in the treatment of osteoporotic vertebral compression fracture with intravertebral clefts.},
journal = {Medicine},
volume = {100},
number = {22},
pages = {e25996},
pmid = {34087844},
issn = {1536-5964},
mesh = {Aged ; Aged, 80 and over ; Bone Cements ; Bone Density ; Female ; Fractures, Compression/*surgery ; Humans ; Kyphoplasty/*methods ; Laparoscopy ; Male ; Middle Aged ; Osteoporotic Fractures/*surgery ; Pain Measurement ; Postoperative Complications/epidemiology ; Retrospective Studies ; Spinal Fractures/*surgery ; },
abstract = {Kummell's disease is a delayed vertebral collapse fracture caused by posttraumatic osteonecrosis. It is a special type of osteoporotic vertebral fracture in the elderly. This study compares and analyzes the difference in the curative effect of 2 kinds of osteoporotic vertebral compression fracture (OVCF) in the presence of fracture or not in the vertebral body, and provides a clinical reference for the application of percutaneous kyphoplasty (PKP).This research is a kind of retrospective analysis from January 2012 to January 2015, PKP was used to treat 165 patients with osteoporotic vertebral compression fracture. The patients were divided into 2 groups: Intravertebral clefts group (group A) and none-intravertebral clefts group in vertebral body (group B). Bone mineral density (BMD), bone cement injection (BCI), Visual analogue scale (VAS) score before and after surgery, anterior, central and posterior height of vertebral body (before and after surgery) and Cobb angle of injured vertebra (before and after surgery) were compared between the 2 groups.Surgeries for 165 patients in the 2 groups were successfully completed, and 226 fractured vertebrae were performed through bilateral puncture approach to strengthen the vertebral body. Intraoperative injection of bone cement (ml) was 4.25 + 1.29 (range: 2.6-7.8). There were statistically significant differences in bone cement injection quantity between the 2 groups (P < .05), and in bone cement leakage (P > .05) as well as the Postoperative VAS score (P < .05). However, There was no statistical difference in VAS score before surgery between the 2 groups (P > .05). The results indicated that the pain relief degree of OVCF patients without intravertebral clefts is better than that in the vertebral body. No statistical difference was found in Cobb Angle before and after surgery (P > .05), as well as the correction rate of the injured vertebrae before and after surgery (P > .05). There was no statistical difference in the degree of recovery of the anterior, middle and posterior margins of the injured vertebrae after surgery (P > .05).PKP treatment led to better degree of pain relief in OVCF patients without intravertebral clefts, and less bone cement was injected into the surgery.},
}
@article {pmid34085789,
year = {2021},
author = {Rubilotta, E and Balzarro, M and Trabacchin, N and Righetti, R and D'Amico, A and Blaivas, JG and Antonelli, A},
title = {Post-void residual urine ratio: A novel clinical approach to the post-void residual urine in the assessment of males with lower urinary tract symptoms.},
journal = {Investigative and clinical urology},
volume = {62},
number = {4},
pages = {470-476},
pmid = {34085789},
issn = {2466-054X},
mesh = {Adult ; Aged ; Humans ; Lower Urinary Tract Symptoms/etiology/*physiopathology/urine ; Male ; Middle Aged ; Muscle, Smooth/physiopathology ; Organ Size ; Predictive Value of Tests ; Urinary Bladder/pathology/*physiopathology ; Urinary Bladder Neck Obstruction/complications/*physiopathology ; Urinary Retention/etiology/*physiopathology/urine ; Urination ; Urine ; Urodynamics ; },
abstract = {PURPOSE: To assess the correlation between post-void residual urine ratio (PVR-R) and pathological bladder emptying diagnosed by pressure-flow studies (PFS) in males with lower urinary tract symptoms (LUTS).<br><br>MATERIALS AND METHODS: PVR-R and PVR urine were evaluated in 410 males underwent PFS for LUTS. PVR-R was the percentage of PVR to bladder volume (voided volume+PVR). Schafer and International Continence Society (ICS) nomograms, Bladder Contractility Index (BCI) were used to diagnose bladder outlet obstruction (BOO) and detrusor underactivity (DUA). We subdivided the cohort in 4 groups: Group I, BOO+/DUA+; Group II, BOO-/DUA+; Group III, BOO+/DUA-; Group IV, BOO-/DUA- (control group). We subdivided the 4 groups according to PVR-R strata: (1) 0%-20%; (2) 21%-40%; (3) 41%-60%; (4) 61%-80%; (5) 81%-100%.<br><br>RESULTS: Group I had a greater median PVR-R (50%) with a >40% in 61.4% of the cohort. Median PVR-R was 16.6% in Group II, 24% in Group III, and 0% in the control Group. According to ICS nomograms and BCI, median PVR-R and PVR were significantly higher (p<0.001) in obstructed and underactive males. PVR-R threshold of 20% allowed to recognize males with voiding disorders with high sensibility, specificity, PPV, and NPV. A PVR-R cut-off of 40% identified males with associated BOO and DUA and more severe voiding dysfunction.<br><br>CONCLUSIONS: A higher PVR-R is related to a more severe pathological bladder emptying, and to the association of BOO and DUA. PVR-R may have a clinical role in first assessment of males with LUTS and severe voiding dysfunction.},
}
@article {pmid34085402,
year = {2021},
author = {Lee, Y and Shin, H and Lee, D and Choi, S and Cho, IJ and Seo, J},
title = {A Lubricated Nonimmunogenic Neural Probe for Acute Insertion Trauma Minimization and Long-Term Signal Recording.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {8},
number = {15},
pages = {e2100231},
pmid = {34085402},
issn = {2198-3844},
support = {NRF-2019R1C1C1006720//National Research Foundation of Korea/ ; NRF-2021M3H4A1A03048648//National Research Foundation of Korea/ ; 9991006804//Korea Medical Device Development Fund/ ; KMDF_PR_20200901_0131//Korea Medical Device Development Fund/ ; 9991007124//Korea Medical Device Development Fund/ ; KMDF_PR_20200901_0039//Korea Medical Device Development Fund/ ; NRF-2017M3C7A1028854//Brain Research Program of the National Research Foundation/ ; },
mesh = {Animals ; Brain/*physiology ; Brain-Computer Interfaces ; Disease Models, Animal ; *Electrodes, Implanted ; Equipment Design/*methods ; Foreign-Body Reaction/prevention &amp; control ; Gliosis/prevention &amp; control ; Lubrication ; Male ; Mice ; Mice, Inbred C57BL ; *Signal Processing, Computer-Assisted ; Wounds and Injuries/*prevention &amp; control ; },
abstract = {Brain-machine interfaces (BMIs) that link the brain to a machine are promising for the treatment of neurological disorders through the bi-directional translation of neural information over extended periods. However, the longevity of such implanted devices remains limited by the deterioration of their signal sensitivity over time due to acute inflammation from insertion trauma and chronic inflammation caused by the foreign body reaction. To address this challenge, a lubricated surface is fabricated to minimize friction during insertion and avoid immunogenicity during neural signal recording. Reduced friction force leads to 86% less impulse on the brain tissue, and thus immediately increases the number of measured signal electrodes by 102% upon insertion. Furthermore, the signal measurable period increases from 8 to 16 weeks due to the prevention of gliosis. By significantly reducing insertion damage and the foreign body reaction, the lubricated immune-stealthy probe surface (LIPS) can maximize the longevity of implantable BMIs.},
}
@article {pmid34084601,
year = {2021},
author = {Rabadán, AT},
title = {Neurochips: Considerations from a neurosurgeon's standpoint.},
journal = {Surgical neurology international},
volume = {12},
number = {},
pages = {173},
pmid = {34084601},
issn = {2229-5097},
abstract = {A neurochip comprises a small device based on the brain-machine interfaces that emulate the functioning synapses. Its implant in the human body allows the interaction of the brain with a computer. Although the data-processing speed is still slower than that of the human brain, they are being developed. There is no ethical conflict as long as it is used for neural rehabilitation or to supply impaired or missing neurological functions. However, other applications emerge as controversial. To the best of our knowledge, there have no been publications about the neurosurgical role in the application of this neurotechnological advance. Deliberation on neurochips is primarily limited to a small circle of scholars such as neurotechnological engineers, artists, philosophers, and bioethicists. Why do we address neurosurgeons? They will be directly involved as they could be required to perform invasive procedures. Future neurosurgeons will have to be a different type of neurosurgeon. They will be part of interdisciplinary teams interacting with computer engineers, neurobiologist, and ethicists. Although a neurosurgeon is not expected to be an expert in all areas, they have to be familiar with them; they have to be prepared to determine indications, contraindications and risks of the procedures, participating in the decision-making processes, and even collaborating in the design of devices to preserve anatomic structures. Social, economic, and legal aspects are also inherent to the neurosurgical activity; therefore, these aspects should also be considered.},
}
@article {pmid34082405,
year = {2021},
author = {Yokoyama, H and Kaneko, N and Watanabe, K and Nakazawa, K},
title = {Neural decoding of gait phases during motor imagery and improvement of the decoding accuracy by concurrent action observation.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac07bd},
pmid = {34082405},
issn = {1741-2552},
mesh = {Electroencephalography ; *Gait ; *Imagery, Psychotherapy ; Imagination ; Walking ; },
abstract = {Objective. Brain decoding of motor imagery (MI) not only is crucial for the control of neuroprosthesis but also provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous knowledge on decoding the MI of gait is limited to simple information (e.g. the classification of 'walking' and 'rest').Approach. Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy.Main results. We demonstrated that the stance and swing phases could be decoded from EEGs during MI or AO alone. We also demonstrated the decoding accuracy during MI was improved by concurrent AO. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations related to sensorimotor, visual, and action understanding systems associated with MI and AO.Significance. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO + MI of walking.Novelty and significanceBrain decoding of detailed gait-related information during motor imagery (MI) is important for brain-computer interfaces (BCIs) for gait rehabilitation. This study is the first to show the feasibility of EEG decoding of the stance versus swing phases during MI. We also demonstrated that the combined use of MI and action observation (AO) improves decoding accuracy, which is facilitated by the concurrent and synergistic involvement of the cortical activations for MI and AO. These findings extend the current understanding of neural activity and the combined effects of AO and MI and provide a basis for effective techniques for walking rehabilitation.},
}
@article {pmid34081417,
year = {2021},
author = {Heersmink, R},
title = {Varieties of Artifacts: Embodied, Perceptual, Cognitive, and Affective.},
journal = {Topics in cognitive science},
volume = {13},
number = {4},
pages = {573-596},
doi = {10.1111/tops.12549},
pmid = {34081417},
issn = {1756-8765},
mesh = {*Algorithms ; *Artifacts ; Cognition ; Humans ; },
abstract = {The primary goal of this essay is to provide a comprehensive overview and analysis of the various relations between material artifacts and the embodied mind. A secondary goal of this essay is to identify some of the trends in the design and use of artifacts. First, based on their functional properties, I identify four categories of artifacts co-opted by the embodied mind, namely (a) embodied artifacts, (b) perceptual artifacts, (c) cognitive artifacts, and (d) affective artifacts. These categories can overlap and so some artifacts are members of more than one category. I also identify some of the techniques (or skills) we use when interacting with artifacts. Identifying these categories of artifacts and techniques allows us to map the landscape of relations between embodied minds and the artifactual world. Second, having identified categories of artifacts and techniques, this essay then outlines some of the trends in the design and use of artifacts, focusing on neuroprosthetics, brain-computer interfaces, and personalization algorithms nudging their users toward particular epistemic paths of information consumption.},
}
@article {pmid34078274,
year = {2021},
author = {Kumar, S and Tsunoda, T and Sharma, A},
title = {SPECTRA: a tool for enhanced brain wave signal recognition.},
journal = {BMC bioinformatics},
volume = {22},
number = {Suppl 6},
pages = {195},
pmid = {34078274},
issn = {1471-2105},
support = {JPMJCR1412//Advanced Science Institute/ ; },
mesh = {Algorithms ; Brain ; *Brain Waves ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP).<br><br>RESULTS: The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively.<br><br>CONCLUSIONS: Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.},
}
@article {pmid34077914,
year = {2021},
author = {Tao, X and Yi, W and Wang, K and He, F and Qi, H},
title = {Inter-stimulus phase coherence in steady-state somatosensory evoked potentials and its application in improving the performance of single-channel MI-BCI.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0767},
pmid = {34077914},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Somatosensory ; Hand ; Humans ; Imagination ; },
abstract = {Objective. With the development of clinical applications of motor imagery-based brain-computer interfaces (MI-BCIs), a single-channel MI-BCI system that can be easily assembled is an attractive goal. However, due to the low quality of the spectral power features in the traditional MI-BCI paradigm, the recognition performance of current single-channel systems is far lower than that of multi-channel systems, impeding their use in clinical applications.Approach.In this study, the subjects' right and left hands were stimulated simultaneously at different frequencies to induce steady-state somatosensory evoked potentials (SSSEP). Subjects then performed motor imagery (MI) tasks. A new electroencephalography (EEG) index, inter-stimulus phase coherence (ISPC), was built to measure phase desynchronization of SSSEP caused by MI. Then, ISPC is introduced as a feature into left-hand and right-hand MI recognition.Main results.ISPC analysis found that left-handed MI can cause a significant decrease in phase synchronization in contralateral sensorimotor SSSEP, while right-handed MI has little effect on it, and vice versa. Combining ISPC features with traditional spectral power features, the single-channel left-hand versus right-hand MI recognition accuracy reaches 81.0%, which is much higher than that observed with traditional MI paradigms (about 60%).Significance.This work shows that the hybrid MI-SSSEP paradigm can provide more sensitive EEG features to decode motor intentions, demonstrating its potential for clinical applications.},
}
@article {pmid34077363,
year = {2021},
author = {Song, and Borton, and Park, S and Patterson, and Bull, and Laiwalla, F and Mislow, J and Simeral, and Donoghue, and Nurmikko, },
title = {Active Microelectronic Neurosensor Arrays for Implantable Brain Communication Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2009.2029493},
pmid = {34077363},
issn = {1558-0210},
support = {R01 EB007401/EB/NIBIB NIH HHS/United States ; },
abstract = {We have built a wireless implantable microelectronic device for transmitting cortical signals transcutaneously. The device is aimed at interfacing a microelectrode array cortical to an external computer for neural control applications. Our implantable microsystem enables presently 16-channel broadband neural recording in a nonhuman primate brain by converting these signals to a digital stream of infrared light pulses for transmission through the skin. The implantable unit employs a flexible polymer substrate onto which we have integrated ultra-low power amplification with analog multiplexing, an analog-to-digital converter, a low power digital controller chip, and infrared telemetry. The scalable 16-channel microsystem can employ any of several modalities of power supply, including via radio frequency by induction, or infrared light via a photovoltaic converter. As of today, the implant has been tested as a sub-chronic unit in non-human primates (~ 1 month), yielding robust spike and broadband neural data on all available channels.},
}
@article {pmid34073602,
year = {2021},
author = {Velasco-Álvarez, F and Fernández-Rodríguez, &#xc1; and Vizcaíno-Martín, FJ and Díaz-Estrella, A and Ron-Angevin, R},
title = {Brain-Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {11},
pages = {},
pmid = {34073602},
issn = {1424-8220},
support = {RTI2018-100912-B-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; RTI2018-100912-B-I00//European Regional Development Fund/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Smartphone ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.},
}
@article {pmid34072895,
year = {2021},
author = {Saikia, MJ and Besio, WG and Mankodiya, K},
title = {The Validation of a Portable Functional NIRS System for Assessing Mental Workload.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {11},
pages = {},
pmid = {34072895},
issn = {1424-8220},
support = {1539068//National Science Foundation/ ; },
mesh = {Humans ; Memory, Short-Term ; *Prefrontal Cortex ; *Spectroscopy, Near-Infrared ; Task Performance and Analysis ; Workload ; },
abstract = {Portable functional near-infrared spectroscopy (fNIRS) systems have the potential to image the brain in naturalistic settings. Experimental studies are essential to validate such fNIRS systems. Working memory (WM) is a short-term active memory that is associated with the temporary storage and manipulation of information. The prefrontal cortex (PFC) brain area is involved in the processing of WM. We assessed the PFC brain during n-back WM tasks in a group of 25 college students using our laboratory-developed portable fNIRS system, WearLight. We designed an experimental protocol with 32 n-back WM task blocks with four different pseudo-randomized task difficulty levels. The hemodynamic response of the brain was computed from the experimental data and the evaluated brain responses due to these tasks. We observed the incremental mean hemodynamic activation induced by the increasing WM load. The left-PFC area was more activated in the WM task compared to the right-PFC. The task performance was seen to be related to the hemodynamic responses. The experimental results proved the functioning of the WearLight system in cognitive load imaging. Since the portable fNIRS system was wearable and operated wirelessly, it was possible to measure the cognitive load in the naturalistic environment, which could also lead to the development of a user-friendly brain-computer interface system.},
}
@article {pmid34071982,
year = {2021},
author = {Al-Quraishi, MS and Elamvazuthi, I and Tang, TB and Al-Qurishi, M and Adil, SH and Ebrahim, M},
title = {Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements.},
journal = {Brain sciences},
volume = {11},
number = {6},
pages = {},
pmid = {34071982},
issn = {2076-3425},
support = {015LC0-243//Universiti Teknologi Petronas/ ; },
abstract = {Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.},
}
@article {pmid34066492,
year = {2021},
author = {Serrano-Barroso, A and Siugzdaite, R and Guerrero-Cubero, J and Molina-Cantero, AJ and Gomez-Gonzalez, IM and Lopez, JC and Vargas, JP},
title = {Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {9},
pages = {},
pmid = {34066492},
issn = {1424-8220},
support = {PSI2015-65500-P//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PID2019-110739GB-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PSI2015-65500-P//European Regional Development Fund/ ; },
mesh = {*Attention Deficit Disorder with Hyperactivity/diagnosis ; Brain ; *Brain-Computer Interfaces ; Child ; Humans ; *Video Games ; },
abstract = {Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 12 years old. The analyses revealed that the GokEvolution was valuable in measuring attention through its use of EEG-BCI technology. The ADHD group showed lower levels of attention and more variability in brain attentional responses when compared to the control group. The application was able to map the low attention profiles of the ADHD group when compared to the control group and could distinguish between participants who completed the task and those who did not. Therefore, this system could potentially be used in clinical settings as a screening tool for early detection of attentional traits in order to prevent their development.},
}
@article {pmid34066250,
year = {2021},
author = {Centeio, R and Ousingsawat, J and Schreiber, R and Kunzelmann, K},
title = {CLCA1 Regulates Airway Mucus Production and Ion Secretion Through TMEM16A.},
journal = {International journal of molecular sciences},
volume = {22},
number = {10},
pages = {},
pmid = {34066250},
issn = {1422-0067},
support = {KU756/14-1//Deutsche Forschungsgemeinschaft/ ; project number 387509280, SFB 1350 (project A3)//Deutsche Forschungsgemeinschaft/ ; UK CF Trust SRC013//Cystic Fibrosis Trust/ ; Mucus//Gilead Sciences/ ; },
mesh = {Animals ; Anoctamin-1/genetics/*metabolism ; Asthma/chemically induced/metabolism/*pathology ; Chloride Channels/genetics/*metabolism ; Goblet Cells/metabolism/*pathology ; Metaplasia/chemically induced/metabolism/*pathology ; Mice ; Mucus/*metabolism ; Ovalbumin/toxicity ; Respiratory Mucosa/metabolism/*pathology ; },
abstract = {TMEM16A, a Ca[2+]-activated chloride channel (CaCC), and its regulator, CLCA1, are associated with inflammatory airway disease and goblet cell metaplasia. CLCA1 is a secreted protein with protease activity that was demonstrated to enhance membrane expression of TMEM16A. Expression of CLCA1 is particularly enhanced in goblet cell metaplasia and is associated with various lung diseases. However, mice lacking expression of CLCA1 showed the same degree of mucous cell metaplasia and airway hyperreactivity as asthmatic wild-type mice. To gain more insight into the role of CLCA1, we applied secreted N-CLCA1, produced in vitro, to mice in vivo using intratracheal instillation. We observed no obvious upregulation of TMEM16A membrane expression by CLCA1 and no differences in ATP-induced short circuit currents (Iscs). However, intraluminal mucus accumulation was observed by treatment with N-CLCA1 that was not seen in control animals. The effects of N-CLCA1 were augmented in ovalbumin-sensitized mice. Mucus production induced by N-CLCA1 in polarized BCi-NS1 human airway epithelial cells was dependent on TMEM16A expression. IL-13 upregulated expression of CLCA1 and enhanced mucus production, however, without enhancing purinergic activation of Isc. In contrast to polarized airway epithelial cells and mouse airways, which express very low levels of TMEM16A, nonpolarized airway cells express large amounts of TMEM16A protein and show strong CaCC. The present data show an only limited contribution of TMEM16A to airway ion secretion but suggest a significant role of both CLCA1 and TMEM16A for airway mucus secretion.},
}
@article {pmid34065035,
year = {2021},
author = {Zero, E and Bersani, C and Sacile, R},
title = {Identification of Brain Electrical Activity Related to Head Yaw Rotations.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {10},
pages = {},
pmid = {34065035},
issn = {1424-8220},
mesh = {Algorithms ; Brain ; *Electroencephalography ; *Hand ; Humans ; Neural Networks, Computer ; },
abstract = {Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant's head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant's left/right hand side. This identification process is based on "Levenberg-Marquardt" backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.},
}
@article {pmid34064847,
year = {2021},
author = {Zhang, B and Chai, C and Yin, Z and Shi, Y},
title = {Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism.},
journal = {Brain sciences},
volume = {11},
number = {5},
pages = {},
pmid = {34064847},
issn = {2076-3425},
support = {62007024//National Natural Science Foundation of China/ ; 17YF1428400//Shanghai Sailing Program/ ; },
abstract = {Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners' actual learning styles, designing a method to effectively stimulate different learners' internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.},
}
@article {pmid34063778,
year = {2021},
author = {Toma, FM and Miyakoshi, M},
title = {Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market.},
journal = {Brain sciences},
volume = {11},
number = {6},
pages = {},
pmid = {34063778},
issn = {2076-3425},
support = {5R01NS047293-16/NH/NIH HHS/United States ; Gift//The Swartz Foundation/ ; },
abstract = {Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1-4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.},
}
@article {pmid34062630,
year = {2021},
author = {Savareh, BA and Bashiri, A and Hatef, MM and Hatef, B},
title = {Prediction of salivary cortisol level by electroencephalography features.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {66},
number = {3},
pages = {275-284},
doi = {10.1515/bmt-2020-0005},
pmid = {34062630},
issn = {1862-278X},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/methods ; Humans ; Hydrocortisone/*chemistry ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Change in cortisol affects brain EEG signals. So, the identification of the significant EEG features which are sensitized to cortisol concentration was the aim of the present study. From 468 participated healthy subjects, the salivary samples were taken to test the cortisol concentration and EEG signal recording was done simultaneously. Then, the subjects were categorized into three classes based on the salivary cortisol concentration (<5, 5-15 and >15 nmol/l). Some linear and nonlinear features extracted and finally, in order to investigate the relationship between cortisol level and EEG features, the following steps were taken on features in sequence: Genetic Algorithm, Neighboring Component Analysis, polyfit, artificial neural network and support vector machine classification. Two classifications were considered as following: state 1 categorized the subjects into three groups (three classes) and the second state put them into two groups (group 1: class 1 and 3, group 2: class 2). The best classification was done using ANN in the second state with the accuracy=94.1% while it was 92.7% in the first state. EEG features carefully predicted the cortisol level. This result is applicable to design the intelligence brain computer machines to control stress and brain performance.},
}
@article {pmid34062601,
year = {2021},
author = {Brkic, FF and Riss, D and Arnoldner, C and Liepins, R and Gstöttner, W and Baumgartner, WD and Vyskocil, E},
title = {Safety and Efficacy of Implantation of the Bonebridge Active Transcutaneous Bone-Conduction Device Using Implant Lifts.},
journal = {Journal of the American Academy of Audiology},
volume = {32},
number = {5},
pages = {290-294},
doi = {10.1055/s-0041-1723038},
pmid = {34062601},
issn = {2157-3107},
mesh = {Bone Conduction ; Hearing ; *Hearing Aids/adverse effects ; Hearing Loss, Conductive ; Humans ; Retrospective Studies ; Treatment Outcome ; },
abstract = {BACKGROUND: Implant lifts were recently introduced to facilitate implantation of the Bonebridge and to reduce the risk of uncovering the sigmoid sinus and/or dura.<br><br>PURPOSE: The current study analyzed medical, technical, and audiological outcomes of implantation with the Bonebridge implant using lifts.<br><br>RESEARCH DESIGN: This was a retrospective study on all consecutive patients implanted with a bone-conduction hearing implant at a tertiary medical referral center between March 2012 and October 2018. Outcome measures were complications, explantations, and revisions and the mean time of implant use. Audiological results were assessed as well. Outcomes were evaluated for devices implanted with BCI Lifts and compared with those implanted without lifts.<br><br>RESULTS: In the study period, 13 out of a total of 54 implantations were conducted using one or two 1- to 4-mm BCI Lifts. During the follow-up period, two complications occurred and both in patients implanted without lifts (2/41; 4.9%). All patients in the lifts group were using the implant at the end of observation period. No statistically significant difference was observed in functional hearing gain or word-recognition improvement at 65&#x2009;dB between two groups.<br><br>CONCLUSIONS: The use of BCI Lifts in Bonebridge implantations was not associated with adverse events during the observation period. The clinical follow-up revealed no complications in implantations requiring lifts. Furthermore, the functional hearing gain and the word-recognition improvement did not differ from those of devices implanted without lifts. Data indicate safety and efficacy for Bonebridge implantations using lifts.},
}
@article {pmid34054410,
year = {2021},
author = {Ting, WK and Fadul, FA and Fecteau, S and Ethier, C},
title = {Neurostimulation for Stroke Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {649459},
pmid = {34054410},
issn = {1662-4548},
abstract = {Neurological injuries such as strokes can lead to important loss in motor function. Thanks to neuronal plasticity, some of the lost functionality may be recovered over time. However, the recovery process is often slow and incomplete, despite the most effective conventional rehabilitation therapies. As we improve our understanding of the rules governing activity-dependent plasticity, neuromodulation interventions are being developed to harness neural plasticity to achieve faster and more complete recovery. Here, we review the principles underlying stimulation-driven plasticity as well as the most commonly used stimulation techniques and approaches. We argue that increased spatiotemporal precision is an important factor to improve the efficacy of neurostimulation and drive a more useful neuronal reorganization. Consequently, closed-loop systems and optogenetic stimulation hold theoretical promise as interventions to promote brain repair after stroke.},
}
@article {pmid34053728,
year = {2021},
author = {Cajigas, I and Vedantam, A},
title = {Brain-Computer Interface, Neuromodulation, and Neurorehabilitation Strategies for Spinal Cord Injury.},
journal = {Neurosurgery clinics of North America},
volume = {32},
number = {3},
pages = {407-417},
doi = {10.1016/j.nec.2021.03.012},
pmid = {34053728},
issn = {1558-1349},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neurological Rehabilitation ; Spinal Cord ; *Spinal Cord Injuries/therapy ; },
abstract = {As neural bypass interfacing, neuromodulation, and neurorehabilitation continue to evolve, there is growing recognition that combination therapies may achieve superior results. This article briefly introduces these broad areas of active research and lays out some of the current evidence for their use for patients with spinal cord injury.},
}
@article {pmid34052184,
year = {2021},
author = {Zhong, J and Tang, G and Zhu, J and Wu, W and Li, G and Lin, X and Liang, L and Chai, C and Zeng, Y and Wang, F and Luo, L and Li, J and Chen, F and Huang, Z and Zhang, X and Zhang, Y and Liu, H and Qiu, X and Tang, S and Chen, D},
title = {Single-cell brain atlas of Parkinson's disease mouse model.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {48},
number = {4},
pages = {277-288},
doi = {10.1016/j.jgg.2021.01.003},
pmid = {34052184},
issn = {1673-8527},
mesh = {Animals ; Brain/*metabolism/pathology/ultrastructure ; Cerebellum/metabolism/pathology/ultrastructure ; Corpus Striatum/metabolism/pathology/ultrastructure ; Disease Models, Animal ; Humans ; Intermediate Filament Proteins/*genetics ; Mesencephalon/metabolism/pathology/ultrastructure ; Mice ; Muscle Proteins/*genetics ; NF-kappa B/genetics ; Parkinson Disease/*genetics/pathology ; RNA-Seq ; Single-Cell Analysis/trends ; Transcriptome/*genetics ; },
abstract = {Parkinson's disease (PD) is a neurodegenerative disease, leading to the impairment of movement execution. PD pathogenesis has been largely investigated, either limited to bulk transcriptomic levels or at certain cell types, which failed to capture the cellular heterogeneity and intrinsic interplays among distinct cell types. Here, we report the application of single-nucleus RNA-seq on midbrain, striatum, and cerebellum of the α-syn-A53T mouse, a well-established PD mouse model, and matched controls, generating the first single cell transcriptomic atlas for the PD model mouse brain composed of 46,174 individual cells. Additionally, we comprehensively depicte the dysfunctions in PD pathology, covering the elevation of NF-κB activity, the alteration of ion channel components, the perturbation of protein homeostasis network, and the dysregulation of glutamatergic signaling. Notably, we identify a variety of cell types closely associated with PD risk genes. Taken together, our study provides valuable resources to systematically dissect the molecular mechanism of PD pathogenesis at the single-cell resolution, which facilitates the development of novel approaches for diagnosis and therapies against PD.},
}
@article {pmid34050691,
year = {2021},
author = {Cho, Y and Park, S and Lee, J and Yu, KJ},
title = {Emerging Materials and Technologies with Applications in Flexible Neural Implants: A Comprehensive Review of Current Issues with Neural Devices.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {33},
number = {47},
pages = {e2005786},
pmid = {34050691},
issn = {1521-4095},
support = {NRF-2018M3A7B4071109//National Research Foundation of Korea/ ; NRF-2019R1A2C2086085//National Research Foundation of Korea/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Animals ; Electrodes, Implanted ; Biocompatible Materials/chemistry ; Prostheses and Implants ; },
abstract = {Neuroscience is an essential field of investigation that reveals the identity of human beings, with a comprehensive understanding of advanced mental activities, through the study of neurobiological structures and functions. Fully understanding the neurotransmission system that allows for connectivity among neuronal circuits has paved the way for the development of treatments for neurodegenerative diseases such as Parkinson's disease, Alzheimer's disease, and depression. The field of flexible implants has attracted increasing interest mainly to overcome the mechanical mismatch between rigid electrode materials and soft neural tissues, enabling precise measurements of neural signals from conformal contact. Here, the current issues of flexible neural implants (chronic device failure, non-bioresorbable electronics, low-density electrode arrays, among others are summarized) by presenting material candidates and designs to address each challenge. Furthermore, the latest investigations associated with the aforementioned issues are also introduced, including suggestions for ideal neural implants. In terms of the future direction of these advances, designing flexible devices would provide new opportunities for the study of brain-machine interfaces or brain-computer interfaces as part of locomotion through brain signals, and for the treatment of neurodegenerative diseases.},
}
@article {pmid34045948,
year = {2021},
author = {Larrivee, D},
title = {Values Evolution in Human Machine Relations: Grounding Computationalism and Neural Dynamics in a Physical a Priorism of Nature.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {649544},
pmid = {34045948},
issn = {1662-5161},
}
@article {pmid34045742,
year = {2021},
author = {O'Leary, K},
title = {Handwriting with a brain implant.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
doi = {10.1038/d41591-021-00036-2},
pmid = {34045742},
issn = {1546-170X},
}
@article {pmid34041712,
year = {2021},
author = {Xie, YK and Luo, H and Qiu, XY and Xu, ZZ},
title = {Resolution of Inflammatory Pain by Endogenous Chemerin and G Protein-Coupled Receptor ChemR23.},
journal = {Neuroscience bulletin},
volume = {37},
number = {9},
pages = {1351-1356},
pmid = {34041712},
issn = {1995-8218},
mesh = {*Chemokines ; Humans ; *Pain ; },
}
@article {pmid34040666,
year = {2021},
author = {Gao, Z and Dang, W and Wang, X and Hong, X and Hou, L and Ma, K and Perc, M},
title = {Complex networks and deep learning for EEG signal analysis.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {3},
pages = {369-388},
pmid = {34040666},
issn = {1871-4080},
abstract = {Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.},
}
@article {pmid34039029,
year = {2021},
author = {Jadavji, Z and Zhang, J and Paffrath, B and Zewdie, E and Kirton, A},
title = {Can Children With Perinatal Stroke Use a Simple Brain Computer Interface?.},
journal = {Stroke},
volume = {52},
number = {7},
pages = {2363-2370},
doi = {10.1161/STROKEAHA.120.030596},
pmid = {34039029},
issn = {1524-4628},
mesh = {Adolescent ; *Brain-Computer Interfaces ; Cerebral Palsy/*diagnostic imaging/etiology/*rehabilitation ; Child ; Electroencephalography/methods ; Female ; Fetal Diseases/diagnostic imaging/rehabilitation ; Humans ; Infant, Newborn ; Infant, Newborn, Diseases/diagnostic imaging/rehabilitation ; Magnetic Resonance Imaging/methods ; Male ; Stroke/complications/*diagnostic imaging/therapy ; Stroke Rehabilitation/*methods ; },
abstract = {BACKGROUND AND PURPOSE: Perinatal stroke is the leading cause of hemiparetic cerebral palsy resulting in lifelong disability for millions of people worldwide. Options for motor rehabilitation are limited, especially for the most severely affected children. Brain computer interfaces (BCIs) sample brain activity to allow users to control external devices. Functional electrical stimulation enhances motor recovery after stroke, and BCI-activated functional electrical stimulation was recently shown to improve upper extremity function in adult stroke. We aimed to determine the ability of children with perinatal stroke to operate a simple BCI.<br><br>METHODS: Twenty-one children with magnetic resonance imaging&#x2013;confirmed perinatal stroke (57% male, mean [SD] 13.5 [2.6] years, range 9&#x2013;18) were compared with 24 typically developing controls (71% male, mean age [SD] 13.7 [3.7] years, range 6&#x2013;18). Participants trained on a simple EEG-based BCI over 2 sessions (10 trials each) utilizing 2 different mental imagery strategies: (1) motor imagery (imagine opening and closing of hands) and (2) goal oriented (imagine effector object moving toward target) to complete 2 tasks: (1) drive a remote controlled car to a target and (2) move a computer cursor to a target. Primary outcome was Cohen Kappa with a score >0.40 suggesting BCI competence.<br><br>RESULTS: BCI performance was comparable between stroke and control participants. Mean scores were 0.39 (0.18) for stroke versus 0.42 (0.18) for controls (t[42]=0.478, P=0.94). No difference in performance between venous (M=0.45, SD=0.29) and arterial (M=0.34, SD=0.22) stroke (t[82]=1.89, P=0.090) was observed. No effect of task or strategy was observed in the stroke participants. Over 90% of stroke participants demonstrated competency on at least one of the 4 task-strategy combinations.<br><br>CONCLUSIONS: Children with perinatal stroke can achieve proficiency in basic tasks using simple BCI systems. Future directions include exploration of BCI-functional electrical stimulation systems for rehabilitation for children with hemiparesis and other forms of cerebral palsy.},
}
@article {pmid34038875,
year = {2021},
author = {Boergens, KM and Tadić, A and Hopper, MS and McNamara, I and Fell, D and Sahasrabuddhe, K and Kong, Y and Straka, M and Sohal, HS and Angle, MR},
title = {Laser ablation of the pia mater for insertion of high-density microelectrode arrays in a translational sheep model.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0585},
pmid = {34038875},
issn = {1741-2552},
mesh = {Animals ; Cerebral Cortex ; Electrodes, Implanted ; *Laser Therapy ; Microelectrodes ; *Pia Mater ; Sheep ; },
abstract = {Objective. The safe insertion of high density intracortical electrode arrays has been a long-standing practical challenge for neural interface engineering and applications such as brain-computer interfaces (BCIs). However, the pia mater can be difficult to penetrate and causes deformation of underlying cortical tissue during insertion of high-density intracortical arrays. This can lead to neuron damage or failed insertions. The development of a method to ease insertion through the pia mater would represent a significant step toward inserting high density intracortical arrays.Approach. Here we describe a surgical procedure, inspired by laser corneal ablation, that can be used in translational models to thin the pia mater.Main results. We demonstrate that controlled pia removal with laser ablation over a small area of cortex allows for microelectrode arrays to be inserted into the cortex with less force, thus reducing deformation of underlying tissue during placement of the microelectrodes. This procedure allows for insertion of high-density electrode arrays and subsequent acute recordings of spiking neuron activity in sheep cortex. We also show histological and electrophysiological evidence that laser removal of the pia does not acutely affect neuronal viability in the region.Significance. Laser ablation of the pia reduces insertion forces of high-density arrays with minimal to no acute damage to cortical neurons. This approach suggests a promising new path for clinical BCI with high-density microelectrode arrays.},
}
@article {pmid34038874,
year = {2021},
author = {Xi, X and Wu, X and Zhao, YB and Wang, J and Kong, W and Luo, Z},
title = {Cortico-muscular functional network: an exploration of cortico-muscular coupling in hand movements.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0586},
pmid = {34038874},
issn = {1741-2552},
mesh = {Electroencephalography ; Electromyography ; *Hand ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Objective. The main objective of this research was to study cortico-muscular, intra-cortical, and inter-muscular coupling. Herein, we established a cortico-muscular functional network (CMFN) to assess the network differences associated with making a fist, opening the hand, and wrist flexion.Approach. We used transfer entropy (TE) to calculate the causality between electroencephalographic and electromyographic data and established the TE connection matrix. We then applied graph theory to analyze the clustering coefficient, global efficiency, and small-world attributes of the CMFN. We also used Relief-F to extract the features of the TE connection matrix of the beta2 band for the different hand movements and observed high accuracy when this feature was used for action recognition.Main results. We found that the CMFN of the three actions in the beta band had small-world attributes, among which the beta2 band's small-world was stronger. Moreover, we found that the extracted features were mainly concentrated in the left frontal area, left motor area, occipital lobe, and related muscles, suggesting that the CMFN could be used to assess the coupling differences between the cortex and the muscles that are associated with different hand movements. Overall, our results showed that the beta2 (21-35 Hz) wave is the main information carrier between the cortex and the muscles, and the CMFN can be used in the beta2 band to assess cortico-muscular coupling.Significance. Our study preliminarily explored the CMFN associated with hand movements, providing additional insights regarding the transmission of information between the cortex and the muscles, thereby laying a foundation for future rehabilitation therapy targeting pathological cortical areas in stroke patients.},
}
@article {pmid34038873,
year = {2021},
author = {Stieger, JR and Engel, SA and Suma, D and He, B},
title = {Benefits of deep learning classification of continuous noninvasive brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34038873},
issn = {1741-2552},
support = {RF1 MH114233/MH/NIMH NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective. Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. Prior work has suggested that EEG motor imagery based BCI can benefit from increased decoding accuracy through the application of deep learning methods, such as convolutional neural networks (CNNs).Approach. Here, we examine whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages), and the existing challenges to the practical implementation of deep-learning based continuous BCI control.Main results. We report that: (1) deep learning methods significantly increase offline performance compared to standard methods on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2D feedback; (2) our results suggest that a variety of neural biomarkers for BCI, including those outside the motor cortex, can be detected and used to improve performance through deep learning methods, and (3) tuning neural network output will be an important step in optimizing online BCI control, as we found the CNN models trained with full scalp EEG also significantly reduce the average trial length in a simulated online cursor control environment.Significance. This work demonstrates the benefits of CNNs classification during BCI control while providing evidence that electrode montage selection and the mapping of CNN output to device control will be important design choices in CNN based BCIs.},
}
@article {pmid34038871,
year = {2021},
author = {Yu, J and Yu, ZL},
title = {Cross-correlation based discriminant criterion for channel selection in motor imagery BCI systems.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0583},
pmid = {34038871},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Objective.Many electroencephalogram (EEG)-based brain-computer interface (BCI) systems use a large amount of channels for higher performance, which is time-consuming to set up and inconvenient for practical applications. Finding an optimal subset of channels without compromising the performance is a necessary and challenging task.Approach.In this article, we proposed a cross-correlation based discriminant criterion (XCDC) which assesses the importance of a channel for discriminating the mental states of different motor imagery (MI) tasks. Channels are ranked and selected according to the proposed criterion. The efficacy of XCDC is evaluated on two MI EEG datasets.Main results.On the two datasets, the proposed method reduces the channel number from 71 and 15 to under 18 and 11 respectively without compromising the classification accuracy on unseen data. Under the same constraint of accuracy, the proposed method requires fewer channels than existing channel selection methods based on Pearson's correlation coefficient and common spatial pattern. Visualization of XCDC shows consistent results with neurophysiological principles.Significance.This work proposes a quantitative criterion for assessing and ranking the importance of EEG channels in MI tasks and provides a practical method for selecting the ranked channels in the calibration phase of MI BCI systems, which alleviates the computational complexity and configuration difficulty in the subsequent steps, leading to real-time and more convenient BCI systems.},
}
@article {pmid34038363,
year = {2021},
author = {Angjelichinoski, M and Soltani, M and Choi, J and Pesaran, B and Tarokh, V},
title = {Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1058-1067},
doi = {10.1109/TNSRE.2021.3083755},
pmid = {34038363},
issn = {1558-0210},
mesh = {Animals ; *Brain-Computer Interfaces ; Eye Movements ; Intention ; *Movement ; Neural Networks, Computer ; },
abstract = {Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely on simple classification methods, circumventing deep neural networks (DNNs) due to limited training data. This paper leverages the robustness of several important results in non-parametric regression to harness the potentials of deep learning in limited data setups. We consider a solution that combines Pinsker's theorem as well as its adaptively optimal counterpart due to James-Stein for feature extraction from LFPs, followed by a DNN for classifying motor intentions. We apply our approach to the problem of decoding eye movement intentions from LFPs collected in macaque cortex while the animals perform memory-guided visual saccades to one of eight target locations. The results demonstrate that a DNN classifier trained over the Pinsker features outperforms the benchmark method based on linear discriminant analysis (LDA) trained over the same features.},
}
@article {pmid34036941,
year = {2021},
author = {Pei, L and Ouyang, G},
title = {Online recognition of handwritten characters from scalp-recorded brain activities during handwriting.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac01a0},
pmid = {34036941},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Handwriting ; Humans ; *Scalp ; },
abstract = {Objective.Brain-computer interfaces aim to build an efficient communication with the world using neural signals, which may bring great benefits to human society, especially to people with physical impairments. To date, the ability to translate brain signals to effective communication outcome remains low. This work explores whether the handwriting process could serve as a potential interface with high performance. To this end, we first examined how much the scalp-recorded brain signals encode information related to handwriting and whether it is feasible to precisely retrieve the handwritten content solely from the scalp-recorded electrical data.Approach.Five participants were instructed to write the sentence 'HELLO, WORLD!' repeatedly on a tablet while their brain signals were simultaneously recorded by electroencephalography (EEG). The EEG signals were first decomposed by independent component analysis for extracting features to be used to train a convolutional neural network (CNN) to recognize the written symbols.Main results.The accuracy of the CNN-based classifier trained and applied on the same participant (training and test data separated) ranged from 76.8% to 97.0%. The accuracy of cross-participant application was more diverse, ranging from 14.7% to 58.7%. These results showed the possibility of recognizing the handwritten content directly from the scalp level brain signal. A demonstration of the recognition system in an online mode was presented. The major factor that grounded the recognition was the close association between the rich dynamics of electroencephalogram source activities and the kinematic information during the handwriting movements.Significance.This work revealed an explicit and precise mapping between scalp-level electrophysiological signals and linguistic information conveyed by handwriting, which provided a novel approach to developing brain computer interfaces that focus on semantic communication.},
}
@article {pmid34035865,
year = {2021},
author = {Secco, A and Tonin, A and Rana, A and Jaramillo-Gonzalez, A and Khalili-Ardali, M and Birbaumer, N and Chaudhary, U},
title = {EEG power spectral density in locked-in and completely locked-in state patients: a longitudinal study.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {3},
pages = {473-480},
pmid = {34035865},
issn = {1871-4080},
abstract = {Persons with their eye closed and without any means of communication is said to be in a completely locked-in state (CLIS) while when they could still open their eyes actively or passively and have some means of communication are said to be in locked-in state (LIS). Two patients in CLIS without any means of communication, and one patient in the transition from LIS to CLIS with means of communication, who have Amyotrophic Lateral Sclerosis were followed at a regular interval for more than 1 year. During each visit, resting-state EEG was recorded before the brain-computer interface (BCI) based communication sessions. The resting-state EEG of the patients was analyzed to elucidate the evolution of their EEG spectrum over time with the disease's progression to provide future BCI-research with the relevant information to classify changes in EEG evolution. Comparison of power spectral density (PSD) of these patients revealed a significant difference in the PSD's of patients in CLIS without any means of communication and the patient in the transition from LIS to CLIS with means of communication. The EEG of patients without any means of communication is devoid of alpha, beta, and higher frequencies than the patient in transition who still had means of communication. The results show that the change in the EEG frequency spectrum may serve as an indicator of the communication ability of such patients.},
}
@article {pmid34034581,
year = {2022},
author = {Sharifiaghdas, F and Khoiniha, MR and Basiri, A and Bonakdar Hashemi, M and Borumandnia, N and Dadpour, M},
title = {Benign prostatic hyperplasia: Who will benefit from surgical intervention? A single center experience.},
journal = {Urologia},
volume = {89},
number = {3},
pages = {371-377},
doi = {10.1177/03915603211019987},
pmid = {34034581},
issn = {1724-6075},
mesh = {Humans ; *Lower Urinary Tract Symptoms/etiology/surgery ; Male ; Prospective Studies ; Prostate ; *Prostatic Hyperplasia/complications/surgery ; Urodynamics ; },
abstract = {BACKGROUND: To evaluate the pre-operative factors affecting clinical response to prostate surgery in men with benign prostatic hyperplasia (BPH).<br><br>MATERIALS AND METHODS: In this prospective cohort study, 172 patients who underwent surgical intervention for BPH (either as open prostatectomy (n&#x2009;=&#x2009;78) or monopolar-trans-urethral resection of prostate (n&#x2009;=&#x2009;94) from February 2017 to October 2019 were consecutively enrolled. Pre-operative conventional three-lumen urodynamic study and transabdominal sonography were performed for all patients to determine peak flow rate (Qmax), detrusor pressure at the peak flow rate (PdetQmax), post-void residual volume (PVR), presence of detrusor overactivity (DO), prostate volume and median lobe size, and bladder wall thickness with empty and full bladder. Uroflowmetry and cystoscopy were performed during follow-up, whenever indicated. Successful surgical outcome was defined as subjective satisfaction of the patient and a Qmax of more than 15&#x2009;ml/s on post-operative uroflowmetry.<br><br>RESULTS: At 1-year follow-up, complete resolution of lower urinary tract syndrome (LUTS) was detected in 138 (80.2%) patients; however, 21 (12.2%) still had pure obstructive LUTS, 9 (5.2%) had pure storage LUTS, and 4 (2.3%) were still suffering from both storage and obstructive LUTS. After performing multivariable analysis, shorter duration of pre-operative medical treatment and higher pre-operative bladder contractility index (BCI) were found to be independent predictors of successful surgery (p&#x2009;=&#x2009;0.012 and p&#x2009;<&#x2009;0.001, respectively). Results of the ROC curve analysis showed that a preoperative BCI level more than 90.95 and pre-surgical medical treatment duration less than 14.45&#x2009;months have the most specificity and sensitivity to predict the success of surgical outcome. We also observed that the probability of recovery decreased considerably over time following surgery.<br><br>CONCLUSION: Shorter duration of pre-operative medical treatment and increased pre-operative BCI can independently predict favorable outcome of BPH surgery. These factors could be used for better patient management and appropriate planning and consultation before BPH surgery.},
}
@article {pmid34034032,
year = {2021},
author = {Zhang, X and She, Q and Chen, Y and Kong, W and Mei, C},
title = {Sub-band target alignment common spatial pattern in brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {207},
number = {},
pages = {106150},
doi = {10.1016/j.cmpb.2021.106150},
pmid = {34034032},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND AND OBJECTIVE: In the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited.<br><br>METHODS: This paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification.<br><br>RESULTS: Our method is evaluated on Datasets &#x2161;a and &#x2161;b of the BCI Competition &#x2163;. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets &#x2161;a and &#x2161;b respectively.<br><br>CONCLUSION: Therefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.},
}
@article {pmid34033571,
year = {2022},
author = {Fumanal-Idocin, J and Wang, YK and Lin, CT and Fernandez, J and Sanz, JA and Bustince, H},
title = {Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.},
journal = {IEEE transactions on cybernetics},
volume = {52},
number = {8},
pages = {7944-7955},
doi = {10.1109/TCYB.2021.3073210},
pmid = {34033571},
issn = {2168-2275},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.},
}
@article {pmid34033543,
year = {2021},
author = {Gao, W and Yu, T and Yu, JG and Gu, Z and Li, K and Huang, Y and Yu, ZL and Li, Y},
title = {Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {1047-1057},
doi = {10.1109/TNSRE.2021.3083548},
pmid = {34033543},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration.},
}
@article {pmid34033513,
year = {2023},
author = {Amiri, S and Hassani-Abharian, P and Vaseghi, S and Kazemi, R and Nasehi, M},
title = {Effect of RehaCom cognitive rehabilitation software on working memory and processing speed in chronic ischemic stroke patients.},
journal = {Assistive technology : the official journal of RESNA},
volume = {35},
number = {1},
pages = {41-47},
doi = {10.1080/10400435.2021.1934608},
pmid = {34033513},
issn = {1949-3614},
mesh = {Humans ; Memory, Short-Term ; *Ischemic Stroke ; Processing Speed ; Cognitive Training ; Hemiplegia ; *Stroke/complications/psychology ; Software ; *Stroke Rehabilitation/methods ; },
abstract = {Stroke survivors need assistance to overcome cognitive impairments. Working memory (WM) and processing speed (PS) as two critical cognitive functions are disrupted by stroke. The goal of this study was to investigate the effect of RehaCom rehabilitation software on WM and PS in participants with chronic ischemic stroke with hemiplegia (right/left side). Participants were selected among stroke patients who were referred to our special rehabilitation clinic. Fifty participants were assigned to control (n = 25) and experimental (n = 25) groups. The results of the experimental group were compared with the control group before and after the treatment with RehaCom (ten 45-min sessions across five weeks, two sessions per week). The results showed a significant improvement in WM and PS in the experimental group in comparison with the control group after a 5-week training with RehaCom. In conclusion, our findings indicate that treatment with RehaCom software improves WM and PS in chronic ischemic stroke participants with hemiplegia. The exact mechanism of RehaCom is largely unknown and further studies are needed, but its effects on the function of brain regions involved in modulating cognitive functions such as the prefrontal cortex, cingulate cortex, and parietal cortex may be mechanisms of interest.},
}
@article {pmid34031942,
year = {2021},
author = {Wolfgang, JD and White, BT and Long, TE},
title = {Non-isocyanate Polyurethanes from 1,1'-Carbonyldiimidazole: A Polycondensation Approach.},
journal = {Macromolecular rapid communications},
volume = {42},
number = {13},
pages = {e2100163},
doi = {10.1002/marc.202100163},
pmid = {34031942},
issn = {1521-3927},
support = {418967//Honeywell Federal Manufacturing and Technologies, LLC/ ; },
mesh = {Catalysis ; Imidazoles ; *Isocyanates ; Polymerization ; *Polyurethanes ; },
abstract = {1,1'-Carbonyldiimidazole (CDI) provides a platform to generate high molecular weight polyurethanes from industrially relevant diols and diamines. CDI, which is described in the literature for its use in amidation and functionalization reactions, enables the production of well-defined and stable polyurethane precursors, thus eliminating the need for isocyanates. Herein, the functionalization of 1,4-butanediol with CDI yields an electrophilic biscarbamate, bis-carbonylimidazolide (BCI), which is suitable for further step-growth polymerization in the presence of amines. Elevated reaction temperatures enable the solvent-, catalyst-, and isocyanate-free polycondensation reaction between the BCI monomer and various diamines. The thermoplastic polyurethanes produced from this reaction demonstrate high thermal stability, tunable glass transition temperatures based on incorporation of flexible polyether segments, and mechanically ductile thin films. CDI functionalized diols will allow the preparation of diverse polyurethanes without the use of isocyanate-containing monomers.},
}
@article {pmid34031436,
year = {2021},
author = {Liu, T and Yang, D},
title = {A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {10758},
pmid = {34031436},
issn = {2045-2322},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; Hand/*physiology ; Humans ; Movement ; Neural Networks, Computer ; },
abstract = {Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-to-use EEG representation method that can preserve both temporal information as well as spatial information. To further utilize the spatial and temporal features of EEG signals, we proposed a 3D representation of EEG and an end-to-end EEG three-branch 3D convolutional neural network, to address the class imbalance problem (dataset show unequal distribution among their classes), we proposed a class balance cropped strategy. Experimental results indicated that there are also a problem of the different classification difficulty for different classes in motor stages classification tasks, we introduce focal loss to address problem of 'easy-hard' examples, when trained with the focal loss, the three-branch 3D-CNN network achieve good performance (relatively more balanced classification accuracy of binary classifications) on the WAY-EEG-GAL data set. Experimental results show that the proposed method is a good method, which can improve classification effect of different motor stages classification.},
}
@article {pmid34030144,
year = {2021},
author = {Xu, M and Chen, Y and Wang, D and Wang, Y and Zhang, L and Wei, X},
title = {Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0489},
pmid = {34030144},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; Electroencephalography ; Humans ; },
abstract = {Objective.Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience. Furthermore, cross-subject generalization has always been one of major challenges in electroencephalography channel reduction, especially in the RSVP paradigm. Most search-based channel selection method presented in the literature are single-objective methods, the classification accuracy (ACC) is usually chosen as the only criterion.Approach.In this article, the idea of multi-objective optimization was introduced into the RSVP channel selection to minimize two objectives: classification error and the number of channels. By combining a multi-objective evolutionary algorithm for solving large-scale sparse problems and hierarchical discriminant component analysis (HDCA), a novel channel selection method for RSVP was proposed. After that, the cross-subject generalization validation through the proposed channel selection method.Main results.The proposed method achieved an average ACC of 95.41% in a public dataset, which is 3.49% higher than HDCA. The ACC was increased by 2.73% and 2.52%, respectively. Besides, the cross-subject generalization models in channel selection, namely special-16 and special-32, on untrained subjects show that the classification performance is better than the Hoffmann empirical channels.Significance.The proposed channel selection method could reduce the calibration time in the experimental preparation phase and obtain a better accuracy, which is promising application in the RSVP scenario that requires low-density electrodes.},
}
@article {pmid34030137,
year = {2021},
author = {Savić, AM and Aliakbaryhosseinabadi, S and Blicher, JU and Farina, D and Mrachacz-Kersting, N and Došen, S},
title = {Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ac0488},
pmid = {34030137},
issn = {1741-2552},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Imagination ; },
abstract = {Objective.A brain-computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS).Approach.A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results.Main results.The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G).Significance.The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.},
}
@article {pmid34029333,
year = {2021},
author = {Schneider, AJ and Grimes, M and Lyon, W and Kemper, A and Wang, S and Bushman, W},
title = {Cluster analysis of men undergoing surgery for BPH/LUTS reveals prominent roles of both bladder outlet obstruction and diminished bladder contractility.},
journal = {PloS one},
volume = {16},
number = {5},
pages = {e0251721},
pmid = {34029333},
issn = {1932-6203},
support = {I01 BX003454/BX/BLRD VA/United States ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; Aging/*physiology ; Cluster Analysis ; Disease Progression ; Humans ; Lower Urinary Tract Symptoms/*etiology/physiopathology/surgery ; Male ; Middle Aged ; Prostate/pathology/surgery ; Prostatectomy/methods ; Prostatic Hyperplasia/*complications/physiopathology/surgery ; Retrospective Studies ; Treatment Outcome ; Urinary Bladder/*physiopathology ; Urinary Bladder Neck Obstruction/*complications/etiology/physiopathology/surgery ; Urodynamics/physiology ; },
abstract = {Lower urinary tract symptoms (LUTS) in aging men are commonly attributed to bladder outlet obstruction from benign prostatic hyperplasia (BPH) but BPH/LUTS often reflects a confluence of many factors. We performed a hierarchical cluster analysis using four objective patient characteristics (age, HTN, DM, and BMI), and five pre-operative urodynamic variables (volume at first uninhibited detrusor contraction, number of uninhibited contractions, Bladder Outlet Obstruction Index (BOOI), Bladder Contractility Index (BCI) and Bladder Power at Qmax) to identify meaningful subgroups within a cohort of 94 men undergoing surgery for BPH/LUTS. Two meaningful subgroups (clusters) were identified. Significant differences between the two clusters included Prostate Volume (95 vs 53 cc; p-value = 0.001), BOOI (mean 70 vs 49; p-value = 0.001), BCI (mean 129 vs 83; p-value <0.001), Power (689 vs 236; p-value <0.001), Qmax (8.3 vs 4.9 cc/sec; p-value <0.001) and post-void residual (106 vs 250 cc; p-value = 0.001). One cluster is distinguished by larger prostate volume, greater outlet resistance and better bladder contractility. The other is distinguished by smaller prostate volume, lower outlet resistance and worse bladder contractility. Remarkably, the second cluster exhibited greater impairment of urine flow and bladder emptying. Surgery improved flow and emptying for patients in both clusters. These findings reveal important roles for both outlet obstruction and diminished detrusor function in development of diminished urine flow and impaired bladder emptying in patients with BPH/LUTS.},
}
@article {pmid34029000,
year = {2021},
author = {Ye, J and Jin, S and Cai, W and Chen, X and Zheng, H and Zhang, T and Lu, W and Li, X and Liang, C and Chen, Q and Wang, Y and Gu, X and Yu, B and Chen, Z and Wang, X},
title = {Rationally Designed, Self-Assembling, Multifunctional Hydrogel Depot Repairs Severe Spinal Cord Injury.},
journal = {Advanced healthcare materials},
volume = {10},
number = {13},
pages = {e2100242},
doi = {10.1002/adhm.202100242},
pmid = {34029000},
issn = {2192-2659},
mesh = {Axons ; Humans ; *Hydrogels ; Nerve Regeneration ; Recovery of Function ; Spinal Cord ; *Spinal Cord Injuries/drug therapy ; },
abstract = {Following severe spinal cord injury (SCI), dysregulated neuroinflammation causes neuronal and glial apoptosis, resulting in scar and cystic cavity formation during wound healing and ultimately the formation of an atrophic microenvironment that inhibits nerve regrowth. Because of this complex and dynamic pathophysiology, a systemic solution for scar- and cavity-free wound healing with microenvironment remodeling to promote nerve regrowth has rarely been explored. A one-step solution is proposed through a self-assembling, multifunctional hydrogel depot that punctually releases the anti-inflammatory drug methylprednisolone sodium succinate (MPSS) and growth factors (GFs) locally according to pathophysiology to repair severe SCI. Synergistically releasing the anti-inflammatory drug MPSS and GFs in the hydrogel depot throughout SCI pathophysiology protects spared tissues/axons from secondary injury, promotes scar boundary- and cavity-free wound healing, and results in permissive bridges for remarkable axonal regrowth. Behavioral and electrophysiological studies indicate that remnants of spared axons, not regenerating axons, mediate functional recovery, strongly suggesting that additional interventions are still required to render the rebuilt neuronal circuits functional. These findings pave the way for the development of a systemic solution to treat acute SCI.},
}
@article {pmid34028306,
year = {2022},
author = {Chen, S and Shu, X and Jia, J and Wang, H and Ding, L and He, Z and Brauer, S and Zhu, X},
title = {Relation Between Sensorimotor Rhythm During Motor Attempt/Imagery and Upper-Limb Motor Impairment in Stroke.},
journal = {Clinical EEG and neuroscience},
volume = {53},
number = {3},
pages = {238-247},
doi = {10.1177/15500594211019917},
pmid = {34028306},
issn = {2169-5202},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; *Motor Disorders ; *Stroke/complications ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Motor attempt (MA)/motor imagery (MI)-based brain-computer interface (BCI) is a newly developing rehabilitation technology for motor impairment. This study aims to explore the relationship between electroencephalography sensorimotor rhythm and motor impairment to provide reference for a BCI design. Twenty-eight stroke survivors with varying levels of motor dysfunction and spasticity status in the subacute or chronic stage were enrolled in the study to perform MA and MI tasks. Event-related desynchronization (ERD)/event-related synchronization (ERS) during and immediately after motor tasks were calculated. The Fugl-Meyer assessment scale (FMA) and the modified Ashworth scale (MAS) were applied to characterize upper-limb motor dysfunction and spasticity. There was a positive correlation between FMA total scores and ERS in the contralesional hemisphere in the MI task (P < .05) and negative correlations between FMA total scores and ERD in both hemispheres in the MA task (P < .05). Negative correlations were found between MAS scores of wrist flexors and ERD in the ipsilesional hemisphere (P < .05) in the MA task. It suggests that motor dysfunction may be more correlated to ERS in the MI task and to ERD in the MA task while spasticity may be more correlated to ERD in the MA task.},
}
@article {pmid34025383,
year = {2021},
author = {Nam, CS and Traylor, Z and Chen, M and Jiang, X and Feng, W and Chhatbar, PY},
title = {Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface.},
journal = {Frontiers in neurorobotics},
volume = {15},
number = {},
pages = {656943},
pmid = {34025383},
issn = {1662-5218},
abstract = {This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.},
}
@article {pmid34025381,
year = {2021},
author = {López-Hernández, JL and González-Carrasco, I and López-Cuadrado, JL and Ruiz-Mezcua, B},
title = {Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals.},
journal = {Frontiers in neuroinformatics},
volume = {15},
number = {},
pages = {642766},
pmid = {34025381},
issn = {1662-5196},
abstract = {Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person's behavior and emotions based on brain signals is the brain-computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.},
}
@article {pmid34025347,
year = {2021},
author = {Gu, Y and Hua, L},
title = {A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {685119},
pmid = {34025347},
issn = {1662-4548},
abstract = {The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user's intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.},
}
@article {pmid34025305,
year = {2021},
author = {Souza, IG and Souza, RF and Barbosa, FDS and Scipioni, KRDDS and Aidar, FJ and Zanona, AF},
title = {Protocols Used by Occupational Therapists on Shoulder Pain after Stroke: Systematic Review and Meta-Analysis.},
journal = {Occupational therapy international},
volume = {2021},
number = {},
pages = {8811721},
pmid = {34025305},
issn = {1557-0703},
mesh = {*Clinical Protocols ; Humans ; Occupational Therapists ; *Occupational Therapy ; Range of Motion, Articular ; Shoulder Pain/etiology/therapy ; *Stroke/complications ; },
abstract = {INTRODUCTION: Shoulder pain as a consequence after a stroke has multifactorial causes and can prevent the functional return of the upper limb. In addition, the effectiveness of clinical protocols applied by occupational therapists remains uncertain.<br><br>OBJECTIVE: To identify the main treatments currently used by occupational therapists for pain in the shoulder after a stroke.<br><br>METHOD: Articles in English published between 2015 and 2019, of the randomized clinical trial type, with populations that stroke survivors a stroke and sequelae of shoulder pain were selected. The terms and combinations used were "shoulder pain and stroke and occupational therapy," in the electronic databases, Directory of Open Access Journals (DOAJ), Occupational Therapy Systematic Evaluation of Evidence (OTseeker), and PubMed. Statistical Review Manager (version 5.3) established the significance level P &#x2264; 0.05.<br><br>RESULTS: Thirty-nine articles were found, but only four met the inclusion criteria. Electrical stimulation, therapeutic bandaging, and dry needling were eventually employed. For the meta-analysis, pain was the primary outcome, and range of motion (ROM) and upper limb function were secondary. Pain, ROM (external rotation, abduction, and flexion), and manual function were compared, and the meta-analysis showed improvement in the treatment group in clinical trials: pain (MD -2.08; 95% CI -3.23, -0.93; P = 0.0004), ROM (MD 4.67; 95% CI 1.54, 7.79; P = 0.0003), and manual function (MD 1.84; 95% CI 0.52, 3.16; P = 0.006).<br><br>CONCLUSION: Dry needling, California tripull taping (CTPT), and functional electrical stimulation controlled by brain-machine interface (BCI-FES) are proved effective in shoulder pain and functionality.},
}
@article {pmid34023412,
year = {2021},
author = {Yang, K and Xi, X and Wang, T and Wang, J and Kong, W and Zhao, YB and Zhang, Q},
title = {Effects of transcranial direct current stimulation on brain network connectivity and complexity in motor imagery.},
journal = {Neuroscience letters},
volume = {757},
number = {},
pages = {135968},
doi = {10.1016/j.neulet.2021.135968},
pmid = {34023412},
issn = {1872-7972},
mesh = {Adult ; Connectome ; Double-Blind Method ; Electroencephalography ; Evoked Potentials, Motor/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Motor Cortex/*physiology ; *Transcranial Direct Current Stimulation ; Young Adult ; },
abstract = {Related experiments have shown that transcranial direct current stimulation (tDCS) anodal stimulation of the brain's primary motor cortex (M1) and supplementary motor area (SMA) can improve the motor control and clinical manifestations of stroke patients with aphasia and dyskinesia. In this study, to explore the different effects of tDCS on the M1 and SMA in motor imagery, 35 healthy volunteers participated in a double-blind randomized controlled experiment. Five subjects underwent sham stimulation (control), 15 subjects underwent tDCS anode stimulation of the M1, and the remaining 15 subjects underwent tDCS anode stimulation of the SMA. The electroencephalogram data of the subjects' left- and right-hand motor imagery under different stimulation paradigms were recorded. We used a functional brain network and sample entropy to examine the different complexities and functional connectivities in subjects undergoing sham-tDCS and the two stimulation paradigms. The results show that tDCS anodal stimulation of the SMA produces less obvious differences in the motor preparation phase, while tDCS anodal stimulation of the M1 produces significant differences during the motor imaging task execution phase. The effect of tDCS on the motor area of the brain is significant, especially in the M1.},
}
@article {pmid34021043,
year = {2021},
author = {Mishra, J and Lowenstein, M and Campusano, R and Hu, Y and Diaz-Delgado, J and Ayyoub, J and Jain, R and Gazzaley, A},
title = {Closed-Loop Neurofeedback of α Synchrony during Goal-Directed Attention.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {41},
number = {26},
pages = {5699-5710},
pmid = {34021043},
issn = {1529-2401},
support = {R01 MH096861/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Alpha Rhythm/*physiology ; Attention/*physiology ; *Attention Deficit Disorder with Hyperactivity/physiopathology ; Cerebral Cortex/*physiology ; Child ; Double-Blind Method ; Female ; Goals ; Humans ; Male ; Neurofeedback/*methods/physiology ; Neuronal Plasticity/physiology ; Reaction Time/physiology ; },
abstract = {α Oscillations in sensory cortex, under frontal control, desynchronize during attentive preparation. Here, in a selective attention study with simultaneous EEG in humans of either sex, we first demonstrate that diminished anticipatory α synchrony between the mid-frontal region of the dorsal attention network and ventral visual sensory cortex [frontal-sensory synchrony (FSS)] significantly correlates with greater task performance. Then, in a double-blind, randomized controlled study in healthy adults, we implement closed-loop neurofeedback (NF) of the anticipatory α FSS signal over 10 d of training. We refer to this closed-loop experimental approach of rapid NF integrated within a cognitive task as cognitive NF (cNF). We show that cNF results in significant trial-by-trial modulation of the anticipatory α FSS measure during training, concomitant plasticity of stimulus-evoked α/θ responses, as well as transfer of benefits to response time (RT) improvements on a standard test of sustained attention. In a third study, we implement cNF training in children with attention deficit hyperactivity disorder (ADHD), replicating trial-by-trial modulation of the anticipatory α FSS signal as well as significant improvement of sustained attention RTs. These first findings demonstrate the basic mechanisms and translational utility of rapid cognitive-task-integrated NF.SIGNIFICANCE STATEMENT When humans prepare to attend to incoming sensory information, neural oscillations in the α band (8-14 Hz) undergo desynchronization under the control of prefrontal cortex. Here, in an attention study with electroencephalography, we first show that frontal-sensory synchrony (FSS) of α oscillations during attentive preparation significantly correlates with task performance. Then, in a randomized controlled study in healthy adults, we show that neurofeedback (NF) training of this α FSS signal within the attention task is feasible. We show that this rapid cognitive NF (cNF) approach engenders plasticity of stimulus-evoked neural responses, and improves performance on a standard test of sustained attention. In a final study, we implement cNF in children with attention deficit hyperactivity disorder (ADHD), replicating the improvement of sustained attention found in adults.},
}
@article {pmid34016967,
year = {2021},
author = {Martins, A and Contreras-Martel, C and Janet-Maitre, M and Miyachiro, MM and Estrozi, LF and Trindade, DM and Malospirito, CC and Rodrigues-Costa, F and Imbert, L and Job, V and Schoehn, G and Attrée, I and Dessen, A},
title = {Self-association of MreC as a regulatory signal in bacterial cell wall elongation.},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {2987},
pmid = {34016967},
issn = {2041-1723},
mesh = {Amino Acid Sequence/genetics ; Bacterial Proteins/genetics/isolation &amp; purification/*metabolism/ultrastructure ; Cell Wall/*metabolism/ultrastructure ; Conserved Sequence/genetics ; Cryoelectron Microscopy ; Crystallography, X-Ray ; Mutagenesis ; Phylogeny ; Protein Conformation, alpha-Helical/genetics ; Protein Conformation, beta-Strand/genetics ; Protein Domains/genetics ; Protein Multimerization ; Pseudomonas aeruginosa/cytology/genetics/*metabolism/ultrastructure ; Recombinant Proteins/genetics/isolation &amp; purification/metabolism/ultrastructure ; },
abstract = {The elongasome, or Rod system, is a protein complex that controls cell wall formation in rod-shaped bacteria. MreC is a membrane-associated elongasome component that co-localizes with the cytoskeletal element MreB and regulates the activity of cell wall biosynthesis enzymes, in a process that may be dependent on MreC self-association. Here, we use electron cryo-microscopy and X-ray crystallography to determine the structure of a self-associated form of MreC from Pseudomonas aeruginosa in atomic detail. MreC monomers interact in head-to-tail fashion. Longitudinal and lateral interfaces are essential for oligomerization in vitro, and a phylogenetic analysis of proteobacterial MreC sequences indicates the prevalence of the identified interfaces. Our results are consistent with a model where MreC's ability to alternate between self-association and interaction with the cell wall biosynthesis machinery plays a key role in the regulation of elongasome activity.},
}
@article {pmid34016775,
year = {2021},
author = {Flesher, SN and Downey, JE and Weiss, JM and Hughes, CL and Herrera, AJ and Tyler-Kabara, EC and Boninger, ML and Collinger, JL and Gaunt, RA},
title = {A brain-computer interface that evokes tactile sensations improves robotic arm control.},
journal = {Science (New York, N.Y.)},
volume = {372},
number = {6544},
pages = {831-836},
pmid = {34016775},
issn = {1095-9203},
support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Arm/innervation/*physiology ; *Artificial Limbs ; *Brain-Computer Interfaces ; Hand Strength/physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement ; Quadriplegia/*therapy ; *Robotics ; Somatosensory Cortex/physiology ; Touch/*physiology ; },
abstract = {Prosthetic arms controlled by a brain-computer interface can enable people with tetraplegia to perform functional movements. However, vision provides limited feedback because information about grasping objects is best relayed through tactile feedback. We supplemented vision with tactile percepts evoked using a bidirectional brain-computer interface that records neural activity from the motor cortex and generates tactile sensations through intracortical microstimulation of the somatosensory cortex. This enabled a person with tetraplegia to substantially improve performance with a robotic limb; trial times on a clinical upper-limb assessment were reduced by half, from a median time of 20.9 to 10.2 seconds. Faster times were primarily due to less time spent attempting to grasp objects, revealing that mimicking known biological control principles results in task performance that is closer to able-bodied human abilities.},
}
@article {pmid34016768,
year = {2021},
author = {Faisal, AA},
title = {Putting touch into action.},
journal = {Science (New York, N.Y.)},
volume = {372},
number = {6544},
pages = {791-792},
doi = {10.1126/science.abi7262},
pmid = {34016768},
issn = {1095-9203},
mesh = {*Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Touch ; *Touch Perception ; },
}
@article {pmid34013040,
year = {2021},
author = {Alhudhaif, A},
title = {An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals.},
journal = {PeerJ. Computer science},
volume = {7},
number = {},
pages = {e537},
pmid = {34013040},
issn = {2376-5992},
abstract = {BACKGROUND: The brain-computer interface (BCI) is a relatively new but highly promising special field that is actively used in basic neuroscience. BCI includes interfaces for human-computer communication based directly on neural activity concerning mental processes. Fundamental BCI components consist of different units. In the first stage, the EEG and NIRS signals obtained from the individuals are preprocessed, and the signals are brought to a certain standard.<br><br>METHODS: In order to realize proposed framework, a dataset containing Motor Imaginary and Mental Activity tasks are prepared with Electroencephalography (EEG) and Near-Infrared Spectroscopy (NIRS) signal. First of all, HbO and HbR curves are obtained from NIRS signals. Hbo, HbR, HbO+HbR, EEG, EEG+HbO and EEG+HbR features tables are created with the features obtained by using HbO, HbR, and EEG signals, and feature weighted is carried out with the k-Means clustering centers based attribute weighting method (KMCC-based) and the k-Means clustering centers difference based attribute weighting method (KMCCD-based). Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbors algorithm (kNN) classifiers are used to see the classifier differences in the study.<br><br>RESULTS: As a result of this study, an accuracy rate of 99.7% (with kNN classifier and KMCCD-based weighting) is obtained in the data set of Motor Imaginary. Similarly, an accuracy rate of 99.9% (with SVM and kNN classifier and KMCCD-based weighting) is obtained in the Mental Activity dataset. The weighting method is used to increase the classification accuracy, and it has been shown that it will contribute to the classification of EEG and NIRS BCI systems. The results show that the proposed method increases classifiers' performance, offering less processing power and ease of application. In the future, studies could be carried out by combining the k-Means clustering center-based weighted hybrid BCI method with deep learning architectures. Further improved classifier performances can be achieved by combining both systems.},
}
@article {pmid34010815,
year = {2021},
author = {Chiang, CH and Wang, C and Barth, K and Rahimpour, S and Trumpis, M and Duraivel, S and Rachinskiy, I and Dubey, A and Wingel, KE and Wong, M and Witham, NS and Odell, T and Woods, V and Bent, B and Doyle, W and Friedman, D and Bihler, E and Reiche, CF and Southwell, DG and Haglund, MM and Friedman, AH and Lad, SP and Devore, S and Devinsky, O and Solzbacher, F and Pesaran, B and Cogan, G and Viventi, J},
title = {Flexible, high-resolution thin-film electrodes for human and animal neural research.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {34010815},
issn = {1741-2552},
support = {U01 NS099697/NS/NINDS NIH HHS/United States ; UL1 TR002553/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; *Biocompatible Materials ; *Brain ; Electric Impedance ; Electrodes ; Electrodes, Implanted ; Humans ; Neurons ; },
abstract = {Objective.Brain functions such as perception, motor control, learning, and memory arise from the coordinated activity of neuronal assemblies distributed across multiple brain regions. While major progress has been made in understanding the function of individual neurons, circuit interactions remain poorly understood. A fundamental obstacle to deciphering circuit interactions is the limited availability of research tools to observe and manipulate the activity of large, distributed neuronal populations in humans. Here we describe the development, validation, and dissemination of flexible, high-resolution, thin-film (TF) electrodes for recording neural activity in animals and humans.Approach.We leveraged standard flexible printed-circuit manufacturing processes to build high-resolution TF electrode arrays. We used biocompatible materials to form the substrate (liquid crystal polymer; LCP), metals (Au, PtIr, and Pd), molding (medical-grade silicone), and 3D-printed housing (nylon). We designed a custom, miniaturized, digitizing headstage to reduce the number of cables required to connect to the acquisition system and reduce the distance between the electrodes and the amplifiers. A custom mechanical system enabled the electrodes and headstages to be pre-assembled prior to sterilization, minimizing the setup time required in the operating room. PtIr electrode coatings lowered impedance and enabled stimulation. High-volume, commercial manufacturing enables cost-effective production of LCP-TF electrodes in large quantities.Main Results. Our LCP-TF arrays achieve 25× higher electrode density, 20× higher channel count, and 11× reduced stiffness than conventional clinical electrodes. We validated our LCP-TF electrodes in multiple human intraoperative recording sessions and have disseminated this technology to >10 research groups. Using these arrays, we have observed high-frequency neural activity with sub-millimeter resolution.Significance.Our LCP-TF electrodes will advance human neuroscience research and improve clinical care by enabling broad access to transformative, high-resolution electrode arrays.},
}
@article {pmid34007905,
year = {2021},
author = {Hanna, CR and Lemmon, E and Ennis, H and Jones, RJ and Hay, J and Halliday, R and Clark, S and Morris, E and Hall, P},
title = {Creation of the first national linked colorectal cancer dataset in Scotland: prospects for future research and a reflection on lessons learned.},
journal = {International journal of population data science},
volume = {6},
number = {1},
pages = {1654},
pmid = {34007905},
issn = {2399-4908},
support = {15960/CRUK_/Cancer Research UK/United Kingdom ; C23434/A23706/CRUK_/Cancer Research UK/United Kingdom ; C61974/A2429/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {*Colorectal Neoplasms/diagnosis ; Costs and Cost Analysis ; Forecasting ; Humans ; Prospective Studies ; Scotland/epidemiology ; },
abstract = {INTRODUCTION: Current understanding of cancer patients, their treatment pathways and outcomes relies mainly on information from clinical trials and prospective research studies representing a selected sub-set of the patient population. Whole-population analysis is necessary if we are to assess the true impact of new interventions or policy in a real-world setting. Accurate measurement of geographic variation in healthcare use and outcomes also relies on population-level data. Routine access to such data offers efficiency in research resource allocation and a basis for policy that addresses inequalities in care provision.<br><br>OBJECTIVE: Acknowledging these benefits, the objective of this project was to create a population level dataset in Scotland of patients with a diagnosis of colorectal cancer (CRC).<br><br>METHODS: This paper describes the process of creating a novel, national dataset in Scotland.<br><br>RESULTS: In total, thirty two separate healthcare administrative datasets have been linked to provide a comprehensive resource to investigate the management pathways and outcomes for patients with CRC in Scotland, as well as the costs of providing CRC treatment. This is the first time that chemotherapy prescribing and national audit datasets have been linked with the Scottish Cancer Registry on a national scale.<br><br>CONCLUSIONS: We describe how the acquired dataset can be used as a research resource and reflect on the data access challenges relating to its creation. Lessons learned from this process and the policy implications for future studies using administrative cancer data are highlighted.},
}
@article {pmid34007777,
year = {2021},
author = {Cripe, CT and Cooper, R and Mikulecky, P and Huang, JH and Hack, DC},
title = {Improved Mild Closed Head Traumatic Brain Injury Outcomes With a Brain-Computer Interface Amplified Cognitive Remediation Training.},
journal = {Cureus},
volume = {13},
number = {5},
pages = {e14996},
pmid = {34007777},
issn = {2168-8184},
abstract = {This study is a retrospective chart review of 200 clients who participated in a non-verbal restorative cognitive remediation training (rCRT) program between 2012 and 2020. Each client participated in the program for about 16 weeks, and the study as a whole occurred over a five-year period. The program was applied to effect proper neural functional remodeling needed to support resilient, flexible, and adaptable behaviors after encountering a mild closed head traumatic brain injury (mTBI). The rCRT program focused on improving functional performance in executive cognitive control networks as defined by fMRI studies. All rCRT activities were delivered in a semi-game-like manner, incorporating a brain-computer interface (BCI) that provided in-the-moment neural network performance integrity metrics (nPIMs) used to adjust the level of play required to properly engage long-term potentiation (LTP) and long-term depression (LTD) network learning rules. This study reports on t-test and Reliable Change Index (RCI) changes found within individual cognitive abilities' performance metrics derived from the Woodcock-Johnson Cognitive Abilities III Test. We compared pre- and post-scores from seven cognitive abilities considered dependent on executive cognitive control networks against seven non-executive control abilities. We observed significant improvements (p < 10[-4]) with large Cohen's deffect sizes (0.78-1.20) across 13 of 14 cognitive ability domains with a medium effect size (0.49) on the remaining one. The mean percent change for the pooled trained domain was double that observed for the pooled untrained domain, at 17.2% versus 8.3%, respectively. To further adjust for practice effects, practice effect RCI values were computed and further supported the effectiveness of the rCRT (trained RCI 1.4-4.8; untrained RCI 0.-08-0.75).},
}
@article {pmid34007432,
year = {2021},
author = {Frikha, T and Abdennour, N and Chaabane, F and Ghorbel, O and Ayedi, R and Shahin, OR and Cheikhrouhou, O},
title = {Source Localization of EEG Brainwaves Activities via Mother Wavelets Families for SWT Decomposition.},
journal = {Journal of healthcare engineering},
volume = {2021},
number = {},
pages = {9938646},
pmid = {34007432},
issn = {2040-2309},
mesh = {Algorithms ; Brain ; *Brain Waves ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {A Brain-Computer Interface (BCI) is a system used to communicate with an external world through the brain activity. The brain activity is measured by electroencephalography (EEG) signal and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEG based brain-computer interface (BCI). The source localization of the human brain activities can be an important resource for the recognition of the cognitive state, medical disorders, and a better understanding of the brain in general. In this study, we have compared 51 mother wavelets taken from 7 different wavelet families, which are applied to a Stationary Wavelet Transform (SWT) decomposition of an EEG signal. This process includes Haar, Symlets, Daubechies, Coiflets, Discrete Meyer, Biorthogonal, and reverse Biorthogonal wavelet families in extracting five different brainwave subbands for source localization. For this process, we used the Independent Component Analysis (ICA) for feature extraction followed by the Boundary Element Model (BEM) and the Equivalent Current Dipole (ECD) for the forward and inverse problem solutions. The evaluation results in investigating the optimal mother wavelet for source localization eventually identified the sym20 mother wavelet as the best choice followed by bior6.8 and coif5.},
}
@article {pmid34004544,
year = {2021},
author = {Li, P and Yin, C and Li, M and Li, H and Yang, B},
title = {A dry electroencephalogram electrode for applications in steady-state visual evoked potential-based brain-computer interface systems.},
journal = {Biosensors &amp; bioelectronics},
volume = {187},
number = {},
pages = {113326},
doi = {10.1016/j.bios.2021.113326},
pmid = {34004544},
issn = {1873-4235},
mesh = {*Biosensing Techniques ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; },
abstract = {High-efficiency electroencephalogram (EEG) dry electrodes are a key component of brain-computer interface (BCI) technology because of their direct contact with the scalp. In this study, a semi-flexible polydopamine (PDA)/Pt-TiO2 electrode is prepared for the dry-contact acquisition of EEG signals. The PDA biofilm adheres strongly to the scalp and maintains a dynamic balance of water and ions. The Pt nanoparticles and TiO2 nanotube array together result in fast electron transfer. Therefore, the interface impedance between the dry PDA/Pt-TiO2 electrode and scalp is as low as 19.63-24.53 kΩ. The spontaneous EEG signal collected simultaneously using the dry PDA/Pt-TiO2 and wet Ag/AgCl electrodes had a correlation coefficient of up to 99.9%. In a steady-state visual evoked potential (SSVEP)-based BCI system, the dry electrode was used to collect EEG feedback signals for stimulations at 27 different frequencies in the range of 7-19.25 Hz. For these feedback signals, O1, Oz, and O2 channels in the occipital area exhibited high signal-to-noise ratios of 11.3, 11.8, and 11 dB, respectively. A volunteer wore an EEG headband with three PDA/Pt-TiO2 dry electrodes and successfully controlled the robotic arm of the SSVEP-BCI system in the untrained mode. The dry PDA/Pt-TiO2 electrode-based EEG cap is comfortable to wear, the identification signals of the SSVEP paradigm are accurate, and it is suitable for controlling external devices including a keyboard in the SSVEP-BCI system.},
}
@article {pmid34003186,
year = {2021},
author = {Collecchia, G},
title = {[Neurotechnologies and neurorights: mental privacy.].},
journal = {Recenti progressi in medicina},
volume = {112},
number = {5},
pages = {343-346},
doi = {10.1701/3608.35871},
pmid = {34003186},
issn = {2038-1840},
mesh = {*Brain-Computer Interfaces ; Humans ; *Privacy ; },
abstract = {The therapeutic and rehabilitative use of neurotechnologies against disabling diseases for which common treatments are partially or completely ineffective is developing more and more rapidly. Projects for the installation of brain-computer interfaces capable of enhancing perceptions, saving memories, amplifying and canceling them selectively are now a reality. The mental domain, which has always been considered the private sphere par excellence, the last territory inaccessible even to the rampant intrusiveness of "datism", is in serious danger. The ethical implications of such possible derives are not addressable by existing protections and rules. New specific rights are needed for the neural domain, real neuro rights, to define and realize the extent of change, one of the greatest challenges of our time, in an ethically and socially shareable way, in order not to allow violating the last bulwark of the subjective identity of humanity, the mental privacy.},
}
@article {pmid34002096,
year = {2021},
author = {Mullins, N and Forstner, AJ and O'Connell, KS and Coombes, B and Coleman, JRI and Qiao, Z and Als, TD and Bigdeli, TB and Børte, S and Bryois, J and Charney, AW and Drange, OK and Gandal, MJ and Hagenaars, SP and Ikeda, M and Kamitaki, N and Kim, M and Krebs, K and Panagiotaropoulou, G and Schilder, BM and Sloofman, LG and Steinberg, S and Trubetskoy, V and Winsvold, BS and Won, HH and Abramova, L and Adorjan, K and Agerbo, E and Al Eissa, M and Albani, D and Alliey-Rodriguez, N and Anjorin, A and Antilla, V and Antoniou, A and Awasthi, S and Baek, JH and Bækvad-Hansen, M and Bass, N and Bauer, M and Beins, EC and Bergen, SE and Birner, A and Bøcker Pedersen, C and Bøen, E and Boks, MP and Bosch, R and Brum, M and Brumpton, BM and Brunkhorst-Kanaan, N and Budde, M and Bybjerg-Grauholm, J and Byerley, W and Cairns, M and Casas, M and Cervantes, P and Clarke, TK and Cruceanu, C and Cuellar-Barboza, A and Cunningham, J and Curtis, D and Czerski, PM and Dale, AM and Dalkner, N and David, FS and Degenhardt, F and Djurovic, S and Dobbyn, AL and Douzenis, A and Elvsåshagen, T and Escott-Price, V and Ferrier, IN and Fiorentino, A and Foroud, TM and Forty, L and Frank, J and Frei, O and Freimer, NB and Frisén, L and Gade, K and Garnham, J and Gelernter, J and Giørtz Pedersen, M and Gizer, IR and Gordon, SD and Gordon-Smith, K and Greenwood, TA and Grove, J and Guzman-Parra, J and Ha, K and Haraldsson, M and Hautzinger, M and Heilbronner, U and Hellgren, D and Herms, S and Hoffmann, P and Holmans, PA and Huckins, L and Jamain, S and Johnson, JS and Kalman, JL and Kamatani, Y and Kennedy, JL and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Koromina, M and Kranz, TM and Kranzler, HR and Kubo, M and Kupka, R and Kushner, SA and Lavebratt, C and Lawrence, J and Leber, M and Lee, HJ and Lee, PH and Levy, SE and Lewis, C and Liao, C and Lucae, S and Lundberg, M and MacIntyre, DJ and Magnusson, SH and Maier, W and Maihofer, A and Malaspina, D and Maratou, E and Martinsson, L and Mattheisen, M and McCarroll, SA and McGregor, NW and McGuffin, P and McKay, JD and Medeiros, H and Medland, SE and Millischer, V and Montgomery, GW and Moran, JL and Morris, DW and Mühleisen, TW and O'Brien, N and O'Donovan, C and Olde Loohuis, LM and Oruc, L and Papiol, S and Pardiñas, AF and Perry, A and Pfennig, A and Porichi, E and Potash, JB and Quested, D and Raj, T and Rapaport, MH and DePaulo, JR and Regeer, EJ and Rice, JP and Rivas, F and Rivera, M and Roth, J and Roussos, P and Ruderfer, DM and Sánchez-Mora, C and Schulte, EC and Senner, F and Sharp, S and Shilling, PD and Sigurdsson, E and Sirignano, L and Slaney, C and Smeland, OB and Smith, DJ and Sobell, JL and Søholm Hansen, C and Soler Artigas, M and Spijker, AT and Stein, DJ and Strauss, JS and Świątkowska, B and Terao, C and Thorgeirsson, TE and Toma, C and Tooney, P and Tsermpini, EE and Vawter, MP and Vedder, H and Walters, JTR and Witt, SH and Xi, S and Xu, W and Yang, JMK and Young, AH and Young, H and Zandi, PP and Zhou, H and Zillich, L and , and Adolfsson, R and Agartz, I and Alda, M and Alfredsson, L and Babadjanova, G and Backlund, L and Baune, BT and Bellivier, F and Bengesser, S and Berrettini, WH and Blackwood, DHR and Boehnke, M and Børglum, AD and Breen, G and Carr, VJ and Catts, S and Corvin, A and Craddock, N and Dannlowski, U and Dikeos, D and Esko, T and Etain, B and Ferentinos, P and Frye, M and Fullerton, JM and Gawlik, M and Gershon, ES and Goes, FS and Green, MJ and Grigoroiu-Serbanescu, M and Hauser, J and Henskens, F and Hillert, J and Hong, KS and Hougaard, DM and Hultman, CM and Hveem, K and Iwata, N and Jablensky, AV and Jones, I and Jones, LA and Kahn, RS and Kelsoe, JR and Kirov, G and Landén, M and Leboyer, M and Lewis, CM and Li, QS and Lissowska, J and Lochner, C and Loughland, C and Martin, NG and Mathews, CA and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Melle, I and Michie, P and Milani, L and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Mowry, B and Müller-Myhsok, B and Myers, RM and Neale, BM and Nievergelt, CM and Nordentoft, M and Nöthen, MM and O'Donovan, MC and Oedegaard, KJ and Olsson, T and Owen, MJ and Paciga, SA and Pantelis, C and Pato, C and Pato, MT and Patrinos, GP and Perlis, RH and Posthuma, D and Ramos-Quiroga, JA and Reif, A and Reininghaus, EZ and Ribasés, M and Rietschel, M and Ripke, S and Rouleau, GA and Saito, T and Schall, U and Schalling, M and Schofield, PR and Schulze, TG and Scott, LJ and Scott, RJ and Serretti, A and Shannon Weickert, C and Smoller, JW and Stefansson, H and Stefansson, K and Stordal, E and Streit, F and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Waldman, ID and Weickert, TW and Werge, T and Wray, NR and Zwart, JA and Biernacka, JM and Nurnberger, JI and Cichon, S and Edenberg, HJ and Stahl, EA and McQuillin, A and Di Florio, A and Ophoff, RA and Andreassen, OA},
title = {Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.},
journal = {Nature genetics},
volume = {53},
number = {6},
pages = {817-829},
pmid = {34002096},
issn = {1546-1718},
support = {U01 MH085520/MH/NIMH NIH HHS/United States ; R01 MH104964/MH/NIMH NIH HHS/United States ; MR/L010305/1/MRC_/Medical Research Council/United Kingdom ; G0801418/MRC_/Medical Research Council/United Kingdom ; G1000708/MRC_/Medical Research Council/United Kingdom ; R01 MH085548/MH/NIMH NIH HHS/United States ; R01 MH123451/MH/NIMH NIH HHS/United States ; MR/S015132/1/MRC_/Medical Research Council/United Kingdom ; U01 MH094421/MH/NIMH NIH HHS/United States ; 001/WHO_/World Health Organization/International ; U01 MH109528/MH/NIMH NIH HHS/United States ; R01 MH119243/MH/NIMH NIH HHS/United States ; S10 OD018522/OD/NIH HHS/United States ; S10 OD026880/OD/NIH HHS/United States ; MR/T04604X/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Bipolar Disorder/*genetics ; Case-Control Studies ; Chromosomes, Human/genetics ; Genetic Predisposition to Disease ; Genome, Human ; *Genome-Wide Association Study ; Humans ; Major Histocompatibility Complex/genetics ; Multifactorial Inheritance/genetics ; Phenotype ; Quantitative Trait Loci/genetics ; Risk Factors ; },
abstract = {Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.},
}
@article {pmid34001423,
year = {2021},
author = {Hespanhol, L and Vallio, CS and van Mechelen, W and Verhagen, E},
title = {Can we explain running-related injury preventive behavior? A path analysis.},
journal = {Brazilian journal of physical therapy},
volume = {25},
number = {5},
pages = {601-609},
pmid = {34001423},
issn = {1809-9246},
mesh = {*Athletic Injuries/prevention &amp; control ; Bayes Theorem ; Humans ; Prospective Studies ; *Running ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Behavioral and social science theories/models have been gaining attention in sports injury prevention.<br><br>OBJECTIVE: To investigate the potential of the Theory of Planned Behavior in explaining running-related injury preventive behavior.<br><br>METHODS: Six-month prospective cohort study based on data gathered from a randomized controlled trial. From a total of 1512 invited trail runners, 232 were included in this study. Preventive behaviors and their determinants were assessed at baseline and two and six months after baseline. Five-point Likert scales were used to assess the determinants of preventive behavior. A Bayesian path analysis was conducted applying mixed models and mediation analysis.<br><br>RESULTS: A 1-point increase in intention, attitude, subjective norm, and perceived behavioral control predicted an increase of 54% (95% Bayesian credible interval [BCI]: 38, 71) in the rate of performing running-related injury preventive behavior, explaining 49% (R[2] 0.49; 95% BCI: 0.41, 0.56) of the variance around preventive behavior. Intention and perceived behavioral control predicted running-related injury preventive behavior directly, while 40% (95% BCI: 21, 61) and 44% (95% BCI: 20, 69) of the total effect of attitude was mediated by intention and perceived behavioral control, respectively. Attitude, subjective norm, and perceived behavioral control predicted intention.<br><br>CONCLUSIONS: The Theory of Planned Behavior may have the potential to explain half of the variance around running-related injury preventive behavior and intention. Therefore, such theory may be considered a relevant and useful tool in developing, investigating, and/or implementing programs aimed at preventing running-related injuries.},
}
@article {pmid34000400,
year = {2021},
author = {Min, BK and Kim, HS and Ko, W and Ahn, MH and Suk, HI and Pantazis, D and Knight, RT},
title = {Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex.},
journal = {NeuroImage},
volume = {237},
number = {},
pages = {118165},
pmid = {34000400},
issn = {1095-9572},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Color Perception/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Prefrontal Cortex/*physiology ; Space Perception/*physiology ; Young Adult ; },
abstract = {The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized linear discriminant analysis to human scalp EEG data and were able to distinguish a mental-rotation task versus a color-perception task with 87% decoding accuracy. Dorsal and ventral areas in lateral PFC provided the dominant features dissociating the two tasks. Our findings show that EEG can reliably decode two independent task states from PFC and emphasize the PFC dorsal/ventral functional specificity in processing the where rotation task versus the what color task.},
}
@article {pmid33994983,
year = {2021},
author = {Arif, S and Khan, MJ and Naseer, N and Hong, KS and Sajid, H and Ayaz, Y},
title = {Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {658444},
pmid = {33994983},
issn = {1662-5161},
abstract = {A passive brain-computer interface (BCI) based upon functional near-infrared spectroscopy (fNIRS) brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex (DPFC) of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation (CORE) state was continuously measured. Vector phase analysis (VPA) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features (mean oxyhemoglobin, signal peak, and the sum of peaks) along with six VPA features (trajectory slopes of VPA indices) were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects' data. Trajectory slopes of CORE vector magnitude and angle: m(|R|) and m(∠R) are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification.},
}
@article {pmid33994975,
year = {2021},
author = {Nguyen, HS and Luu, TP},
title = {Tremor-Suppression Orthoses for the Upper Limb: Current Developments and Future Challenges.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {622535},
pmid = {33994975},
issn = {1662-5161},
abstract = {Introduction: Pathological tremor is the most common motor disorder in adults and characterized by involuntary, rhythmic muscular contraction leading to shaking movements in one or more parts of the body. Functional Electrical Stimulation (FES) and biomechanical loading using wearable orthoses have emerged as effective and non-invasive methods for tremor suppression. A variety of upper-limb orthoses for tremor suppression have been introduced; however, a systematic review of the mechanical design, algorithms for tremor extraction, and the experimental design is still missing. Methods: To address this gap, we applied a standard systematic review methodology to conduct a literature search in the PubMed and PMC databases. Inclusion criteria and full-text access eligibility were used to filter the studies from the search results. Subsequently, we extracted relevant information, such as suppression mechanism, system weights, degrees of freedom (DOF), algorithms for tremor estimation, experimental settings, and the efficacy. Results: The results show that the majority of tremor-suppression orthoses are active with 47% prevalence. Active orthoses are also the heaviest with an average weight of 561 ± 467 g, followed by semi-active 486 ± 395 g, and passive orthoses 191 ± 137 g. Most of the orthoses only support one DOF (54.5%). Two-DOF and three-DOF orthoses account for 33 and 18%, respectively. The average efficacy of tremor suppression using wearable orthoses is 83 ± 13%. Active orthoses are the most efficient with an average efficacy of 83 ± 8%, following by the semi-active 77 ± 19%, and passive orthoses 75 ± 12%. Among different experimental setups, bench testing shows the highest efficacy at 95 ± 5%, this value dropped to 86 ± 8% when evaluating with tremor-affected subjects. The majority of the orthoses (92%) measured voluntary and/or tremorous motions using biomechanical sensors (e.g., IMU, force sensor). Only one system was found to utilize EMG for tremor extraction. Conclusions: Our review showed an improvement in efficacy of using robotic orthoses in tremor suppression. However, significant challenges for the translations of these systems into clinical or home use remain unsolved. Future challenges include improving the wearability of the orthoses (e.g., lightweight, aesthetic, and soft structure), and user control interfaces (i.e., neural machine interface). We also suggest addressing non-technical challenges (e.g., regulatory compliance, insurance reimbursement) to make the technology more accessible.},
}
@article {pmid33994922,
year = {2021},
author = {Panachakel, JT and Ramakrishnan, AG},
title = {Decoding Covert Speech From EEG-A Comprehensive Review.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {642251},
pmid = {33994922},
issn = {1662-4548},
abstract = {Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.},
}
@article {pmid33987221,
year = {2021},
author = {Schmitz, S},
title = {TechnoBrainBodies-in-Cultures: An Intersectional Case.},
journal = {Frontiers in sociology},
volume = {6},
number = {},
pages = {651486},
pmid = {33987221},
issn = {2297-7775},
abstract = {The cyborgization of brainbodies with computer hardware and software today ranges in scope from the realization of Brain-Computer Interfaces (BCIs) to visions of mind upload to silicon, the latter being targeted toward a transhuman future. Refining posthumanist concepts to formulate a posthumanities perspective, and contrasting those approaches with transhumanist trajectories, I explore the intersectional dimension of realizations and visions of neuro-technological developments, which I name TechnoBrainBodies-in-Cultures. In an intersectional analysis, I investigate the embedding and legitimation of transhumanist visions brought about by neuroscientific research and neuro-technological development based on a concept of modern neurobiological determinism. The conjoined trajectories of BCI research and development and transhumanist visions perpetuate the inscription of intersectional norms, with the concomitant danger of producing discriminatory effects. This culminates in normative capacity being seen as a conflation of the abled, successful, white masculinized techno-brain with competition. My deeper analysis, however, also enables displacements within recent BCI research and development to be characterized: from ''thought-translation" to affective conditioning and from controllability to obstinacy within the BCI, going so far as to open the closed loop. These realizations challenge notions about the BCI's actor status and agency and foster questions about shifts in the corresponding subject-object relations. Based on these analyses, I look at the effects of neuro-technological and transhumanist governmentality on the question of whose lives are to be improved and whose lives should be excluded from these developments. Within the framework of political feminist materialisms, I combine the concept of posthumanities with my concept of TechnoBrainBodies-in-Cultures to envision and discuss a material-discursive strategy, encompassing dimensions of affect, sociality, resistance, compassion, cultural diversity, ethnic diversity, multiple sexes/sexualities, aging, dis/abilities-in short, all of this "intersectional stuff"-as well as obstinate techno-brain agencies and contumacies foreseen in these cyborgian futures.},
}
@article {pmid33983885,
year = {2021},
author = {Chen, J and Wang, Y and Maye, A and Hong, B and Gao, X and Engel, AK and Zhang, D},
title = {A Spatially-Coded Visual Brain-Computer Interface for Flexible Visual Spatial Information Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {926-933},
doi = {10.1109/TNSRE.2021.3080045},
pmid = {33983885},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Online Systems ; Photic Stimulation ; },
abstract = {Conventional visual BCIs, in which control channels are tagged with stimulation patterns to elicit distinguishable brain patterns, has made impressive progress in terms of the information transfer rates (ITRs). However, less development has been seen with respect to user experience and complexity of the technical setup. The requirement to tag each of targets by a unique stimulus substantially limits the flexibility of conventional visual BCI systems. A method for decoding the targets in the environment flexibly was therefore proposed in the present study. A BCI speller with thirteen symbols drawn on paper was developed. The symbols were interspersed with four flickers with distinct frequencies, but the user did not have to gaze at flickers. Rather, subjects could spell a sequence by looking at the symbols on the paper. In a cue-guided spelling task, the average offline and online accuracies reached 89.3± 7.3% and 90.3± 6.9% for 13 subjects, corresponding to ITRs of 43.0± 7.4 bit/min and 43.8± 6.8 bit/min. In an additional free-spelling task for seven out of thirteen subjects, an accuracy of 92.3± 3.1% and an ITR of 45.6± 3.3 bit/min were achieved. Analysis of a simulated online system showed the possibility to reach an average ITR of 105.8 bit/min by reducing the epoch duration from 4 to 1 second. Reliable BCI control is possible by gazing at targets in the environment instead of dedicated stimuli which encode control channels. The proposed method can drastically reduce the technical effort for visual BCIs and thereby advance their applications outside the laboratory.},
}
@article {pmid33983605,
year = {2021},
author = {Wang, P and Zhao, Z and Bu, L and Kudulaiti, N and Shan, Q and Zhou, Y and Farrukh Hameed, NU and Zhu, Y and Jin, L and Zhang, J and Lu, J and Wu, J},
title = {Clinical applications of neurolinguistics in neurosurgery.},
journal = {Frontiers of medicine},
volume = {15},
number = {4},
pages = {562-574},
pmid = {33983605},
issn = {2095-0225},
mesh = {Brain Mapping ; *Brain Neoplasms ; Humans ; Language ; *Neurosurgery ; Neurosurgical Procedures ; },
abstract = {The protection of language function is one of the major challenges of brain surgery. Over the past century, neurosurgeons have attempted to seek the optimal strategy for the preoperative and intraoperative identification of language-related brain regions. Neurosurgeons have investigated the neural mechanism of language, developed neurolinguistics theory, and provided unique evidence to further understand the neural basis of language functions by using intraoperative cortical and subcortical electrical stimulation. With the emergence of modern neuroscience techniques and dramatic advances in language models over the last 25 years, novel language mapping methods have been applied in the neurosurgical practice to help neurosurgeons protect the brain and reduce morbidity. The rapid advancements in brain-computer interface have provided the perfect platform for the combination of neurosurgery and neurolinguistics. In this review, the history of neurolinguistics models, advancements in modern technology, role of neurosurgery in language mapping, and modern language mapping methods (including noninvasive neuroimaging techniques and invasive cortical electroencephalogram) are presented.},
}
@article {pmid33981047,
year = {2021},
author = {Willett, FR and Avansino, DT and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {High-performance brain-to-text communication via handwriting.},
journal = {Nature},
volume = {593},
number = {7858},
pages = {249-254},
pmid = {33981047},
issn = {1476-4687},
support = {I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Communication ; *Handwriting ; Humans ; Neural Networks, Computer ; Spinal Cord Injuries ; Time Factors ; },
abstract = {Brain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping[1-5] or point-and-click typing with a computer cursor[6,7]. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute)[8]. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.},
}
@article {pmid33980875,
year = {2021},
author = {Smith, R and Moutoussis, M and Bilek, E},
title = {Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: insights from active inference.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {10128},
pmid = {33980875},
issn = {2045-2322},
mesh = {Avoidance Learning ; Behavior Therapy ; Brain-Computer Interfaces ; Choice Behavior ; *Cognition ; *Cognitive Behavioral Therapy ; Humans ; Learning ; *Models, Theoretical ; },
abstract = {Cognitive-behavioral therapy (CBT) leverages interactions between thoughts, feelings, and behaviors. To deepen understanding of these interactions, we present a computational (active inference) model of CBT that allows formal simulations of interactions between cognitive interventions (i.e., cognitive restructuring) and behavioral interventions (i.e., exposure) in producing adaptive behavior change (i.e., reducing maladaptive avoidance behavior). Using spider phobia as a concrete example of maladaptive avoidance more generally, we show simulations indicating that when conscious beliefs about safety/danger have strong interactions with affective/behavioral outcomes, behavioral change during exposure therapy is mediated by changes in these beliefs, preventing generalization. In contrast, when these interactions are weakened, and cognitive restructuring only induces belief uncertainty (as opposed to strong safety beliefs), behavior change leads to generalized learning (i.e., "over-writing" the implicit beliefs about action-outcome mappings that directly produce avoidance). The individual is therefore equipped to face any new context, safe or dangerous, remaining in a content state without the need for avoidance behavior-increasing resilience from a CBT perspective. These results show how the same changes in behavior during CBT can be due to distinct underlying mechanisms; they predict lower rates of relapse when cognitive interventions focus on inducing uncertainty and on reducing the effects of automatic negative thoughts on behavior.},
}
@article {pmid33979288,
year = {2021},
author = {Paulo, JR and Pires, G and Nunes, UJ},
title = {Cross-Subject Zero Calibration Driver's Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {905-915},
doi = {10.1109/TNSRE.2021.3079505},
pmid = {33979288},
issn = {1558-0210},
mesh = {*Automobile Driving ; Calibration ; *Electroencephalography ; Humans ; Neural Networks, Computer ; Wakefulness ; },
abstract = {This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness. Advanced driver assistance systems that could detect mental states which are associated with hazardous situations, such as drowsiness, are of critical importance. EEG signals are used widely for brain-computer interfaces, as well as mental state recognition. However, these systems are still difficult to design due to very low signal-to-noise ratios and cross-subject disparities, requiring individual calibration cycles. To tackle this research domain, here, we explore drowsiness detection based on EEG signals' spatiotemporal image encoding representations in the form of either recurrence plots or gramian angular fields for deep convolutional neural network (CNN) classification. Results comparing both techniques using a public dataset of 27 subjects show a superior balanced accuracy of up to 75.87% for leave-one-out cross-validation, using both techniques, against works in the literature, demonstrating the possibility to pursue cross-subject zero calibration design.},
}
@article {pmid33978758,
year = {2021},
author = {Wang, Y and Lin, Y and Fu, C and Huang, Z and Xiao, S and Yu, R},
title = {Effortless retaliation: the neural dynamics of interpersonal intentions in the Chicken Game using brain-computer interface.},
journal = {Social cognitive and affective neuroscience},
volume = {16},
number = {11},
pages = {1138-1149},
pmid = {33978758},
issn = {1749-5024},
mesh = {Aggression ; Animals ; *Brain-Computer Interfaces ; *Chickens ; Electroencephalography/methods ; Humans ; Intention ; },
abstract = {The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by a low-level response-related brain activity. Using an Electroencephalogram (EEG)-based brain-computer interface combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response-related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (alpha-ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.},
}
@article {pmid33978599,
year = {2021},
author = {Olsen, S and Zhang, J and Liang, KF and Lam, M and Riaz, U and Kao, JC},
title = {An artificial intelligence that increases simulated brain-computer interface performance.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfaaa},
pmid = {33978599},
issn = {1741-2552},
support = {DP2 NS122037/NS/NINDS NIH HHS/United States ; },
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Humans ; Movement ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {Objective.Brain-computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard.Approach.Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI's trajectories.Main results.We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to 'dial in' on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control.Significance.This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.},
}
@article {pmid33976348,
year = {2021},
author = {Curtis, JR and Robinson, WD and Rompré, G and Moore, RP and McCune, B},
title = {Erosion of tropical bird diversity over a century is influenced by abundance, diet and subtle climatic tolerances.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {10045},
pmid = {33976348},
issn = {2045-2322},
mesh = {Animals ; *Biodiversity ; *Birds ; Cluster Analysis ; Diet ; *Extinction, Biological ; Panama ; Population Density ; *Rainforest ; Tropical Climate ; },
abstract = {Human alteration of landscapes leads to attrition of biodiversity. Recommendations for maximizing retention of species richness typically focus on protection and preservation of large habitat patches. Despite a century of protection from human disturbance, 27% of the 228 bird species initially detected on Barro Colorado Island (BCI), Panama, a large hilltop forest fragment isolated by waters of Gatun Lake, are now absent. Lost species were more likely to be initially uncommon and terrestrial insectivores. Analyses of the regional avifauna, exhaustively inventoried and mapped across 24 subregions, identified strong geographical discontinuities in species distributions associated with a steep transisthmian rainfall gradient. Having lost mostly species preferring humid forests, the BCI species assemblage continues to shift from one originally typical of wetter forests toward one now resembling bird communities in drier forests. Even when habitat remnants are large and protected for 100 years, altered habitat characteristics resulting from isolation produce non-random loss of species linked with their commonness, dietary preferences and subtle climatic sensitivities.},
}
@article {pmid33975291,
year = {2021},
author = {Bakas, S and Adamos, DA and Laskaris, N},
title = {On the estimate of music appraisal from surface EEG: a dynamic-network approach based on cross-sensor PAC measurements.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abffe6},
pmid = {33975291},
issn = {1741-2552},
mesh = {Auditory Perception ; Brain ; Brain Mapping ; Electroencephalography ; Humans ; *Music ; },
abstract = {Objective.The aesthetic evaluation of music is strongly dependent on the listener and reflects manifold brain processes that go well beyond the perception of incident sound. Being a high-level cognitive reaction, it is difficult to predict merely from the acoustic features of the audio signal and this poses serious challenges to contemporary music recommendation systems. We attempted to decode music appraisal from brain activity, recorded via wearable EEG, during music listening.Approach.To comply with the dynamic nature of music stimuli, cross-frequency coupling measurements were employed in a time-evolving manner to capture the evolving interactions between distinct brain-rhythms during music listening. Brain response to music was first represented as a continuous flow of functional couplings referring to both regional and inter-regional brain dynamics and then modelled as an ensemble of time-varying (sub)networks. Dynamic graph centrality measures were derived, next, as the final feature-engineering step and, lastly, a support-vector machine was trained to decode the subjective music appraisal. A carefully designed experimental paradigm provided the labeled brain signals.Main results.Using data from 20 subjects, dynamic programming to tailor the decoder to each subject individually and cross-validation, we demonstrated highly satisfactory performance (MAE= 0.948,R[2]= 0.63) that can be attributed, mostly, to interactions of left frontal gamma rhythm. In addition, our music-appraisal decoder was also employed in a part of the DEAP dataset with similar success. Finally, even a generic version of the decoder (common for all subjects) was found to perform sufficiently.Significance.A novel brain signal decoding scheme was introduced and validated empirically on suitable experimental data. It requires simple operations and leaves room for real-time implementation. Both the code and the experimental data are publicly available.},
}
@article {pmid33973143,
year = {2021},
author = {Fang, F and Hu, H},
title = {Recent progress on mechanisms of human cognition and brain disorders.},
journal = {Science China. Life sciences},
volume = {64},
number = {6},
pages = {843-846},
pmid = {33973143},
issn = {1869-1889},
mesh = {Brain Diseases/*physiopathology ; Cognition/*physiology ; Humans ; },
}
@article {pmid33969531,
year = {2021},
author = {Hu, YT and Boonstra, J and McGurran, H and Stormmesand, J and Sluiter, A and Balesar, R and Verwer, R and Swaab, D and Bao, AM},
title = {Sex differences in the neuropathological hallmarks of Alzheimer's disease: focus on cognitively intact elderly individuals.},
journal = {Neuropathology and applied neurobiology},
volume = {47},
number = {7},
pages = {958-966},
pmid = {33969531},
issn = {1365-2990},
mesh = {Aged ; Aged, 80 and over ; Aging/*physiology ; Alzheimer Disease/*pathology ; Amyloid beta-Peptides/*metabolism ; Brain/*pathology ; Entorhinal Cortex/metabolism ; Female ; Humans ; Male ; Neurofibrillary Tangles/*pathology ; Sex Characteristics ; tau Proteins/metabolism ; },
abstract = {AIMS: Women are more vulnerable to Alzheimer's disease (AD) than men. We investigated (i) whether and at what age the AD hallmarks, that is, β-amyloid (Aβ) and hyperphosphorylated Tau (p-Tau) show sex differences; and (ii) whether such sex differences may occur in cognitively intact elderly individuals.<br><br>METHODS: We first analysed the entire post-mortem brain collection of all non-demented 'controls' and AD donors from our Brain Bank (245 men and 403 women), for the presence of sex differences in AD hallmarks. Second, we quantitatively studied possible sex differences in A&#x3b2;, A&#x3b2;42 and p-Tau in the entorhinal cortex of well-matched female (n&#xa0;=&#xa0;31) and male (n&#xa0;=&#xa0;21) clinically cognitively intact elderly individuals.<br><br>RESULTS: Women had significantly higher Braak stages for tangles and amyloid scores than men, after 80&#xa0;years. In the cognitively intact elderly, women showed higher levels of p-Tau, but not A&#x3b2; or A&#x3b2;42, in the entorhinal cortex than men, and a significant interaction of sex with age was found only for p-Tau but not A&#x3b2; or A&#x3b2;42.<br><br>CONCLUSIONS: Enhanced p-Tau in the entorhinal cortex may play a major role in the vulnerability to AD in women.},
}
@article {pmid33967397,
year = {2021},
author = {Zhu, M and Chen, J and Li, H and Liang, F and Han, L and Zhang, Z},
title = {Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.},
journal = {Neural computing &amp; applications},
volume = {33},
number = {20},
pages = {13965-13980},
pmid = {33967397},
issn = {0941-0643},
abstract = {Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.},
}
@article {pmid33964319,
year = {2021},
author = {Relav, L and Estienne, A and Price, CA},
title = {Dual-specificity phosphatase 6 (DUSP6) mRNA and protein abundance is regulated by fibroblast growth factor 2 in sheep granulosa cells and inhibits c-Jun N-terminal kinase (MAPK8) phosphorylation.},
journal = {Molecular and cellular endocrinology},
volume = {531},
number = {},
pages = {111297},
doi = {10.1016/j.mce.2021.111297},
pmid = {33964319},
issn = {1872-8057},
mesh = {Animals ; Cyclohexylamines/pharmacology ; Dual Specificity Phosphatase 6/*genetics/*metabolism ; Female ; Fibroblast Growth Factor 2/*metabolism ; Gene Expression Regulation/drug effects ; Granulosa Cells/*cytology/drug effects/metabolism ; Indenes/pharmacology ; Mitogen-Activated Protein Kinase 8/*metabolism ; Phosphorylation/drug effects ; Sheep ; Signal Transduction/drug effects ; },
abstract = {Growth factors regulate ovarian follicle development and they signal through intracellular pathways including mitogen-activated protein kinase (MAPK) phosphorylation, which is negatively regulated by a subfamily of 23 dual-specificity phosphatases (DUSP). Using sheep granulosa cells as a model, we detected mRNA encoding 16 DUSPs in vivo and in vitro. Stimulation of cells in vitro with FGF2 increased (p < 0.05) abundance of DUSP1, DUSP2, DUSP5 and DUSP6 mRNA, and abundance of DUSP1 and DUSP6 proteins (p < 0.05). In contrast, neither FGF8b nor FGF18 had any major effect on DUSP mRNA abundance. Inhibition of DUSP6 action with the inhibitor BCI significantly increased (p < 0.05) MAPK8 (JNK) phosphorylation but not phosphoMAPK14 (p38) or MAPK3/1 (ERK1/2) abundance. This study suggests that FGFs stimulate DUSP protein abundance, that DUSP6 regulates MAPK8 phosphorylation in granulosa cells, and DUSPs are involved in the differential MAPK signaling of individual FGF ligands.},
}
@article {pmid33963226,
year = {2021},
author = {Taesler, P and Rose, M},
title = {The modulation of neural insular activity by a brain computer interface differentially affects pain discrimination.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {9795},
pmid = {33963226},
issn = {2045-2322},
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiopathology ; Female ; Humans ; Male ; Pain/*physiopathology ; },
abstract = {The experience of pain is generated by activations throughout a complex pain network with the insular cortex as a central processing area. The state of ongoing oscillatory activity can influence subsequent processing throughout this network. In particular the ongoing theta-band power can be relevant for later pain processing, however a direct functional relation to post-stimulus processing or behaviour is missing. Here, we used a non-invasive brain-computer interface to either increase or decrease ongoing theta-band power originating in the insular cortex. Our results show a differential modulation of oscillatory power and even more important a transfer to independently measured pain processing and sensation. Pain evoked neural power and subjective pain discrimination were differentially affected by the induced modulations of the oscillatory state. The results demonstrate a functional relevance of insular based theta-band oscillatory states for the processing and subjective discrimination of nociceptive stimuli and offer the perspective for clinical applications.},
}
@article {pmid33960895,
year = {2021},
author = {Kostick, K and Zuk, P and Lázaro-Muñoz, G},
title = {Operationalizing Agency in Brain Computer Interface (BCI) Research.},
journal = {AJOB neuroscience},
volume = {12},
number = {2-3},
pages = {203-205},
pmid = {33960895},
issn = {2150-7759},
support = {R01 MH114854/MH/NIMH NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; User-Computer Interface ; },
}
@article {pmid33960081,
year = {2021},
author = {Tajaddini, A and Roshanravan, N and Mobasseri, M and Aeinehchi, A and Sefid-Mooye Azar, P and Hadi, A and Ostadrahimi, A},
title = {Saffron improves life and sleep quality, glycaemic status, lipid profile and liver function in diabetic patients: A double-blind, placebo-controlled, randomised clinical trial.},
journal = {International journal of clinical practice},
volume = {75},
number = {8},
pages = {e14334},
doi = {10.1111/ijcp.14334},
pmid = {33960081},
issn = {1742-1241},
mesh = {Blood Glucose ; *Crocus ; *Diabetes Mellitus, Type 2/complications/drug therapy ; Double-Blind Method ; Humans ; Lipids ; Liver ; Quality of Life ; Sleep ; },
abstract = {BACKGROUND: Type 2 diabetes (T2D) is a metabolic disorder that is related to hyperglycaemia, hyperlipidaemia and liver dysfunction and has detrimental effects on a patient's mental health. Hence, the current study investigated the effects of saffron supplementation on dietary intake, anthropometric measures, mood, sleep quality and metabolic biomarkers in overweight/obese patients with T2D.<br><br>METHODS: In a double-blind, randomised controlled trial, 70 overweight/obese patients with T2D were randomly allocated to two groups and received 100&#xa0;mg/day saffron or placebo for 8&#xa0;weeks. Participants completed the Beck depression inventory-II (BDI-II), Hurlbert index of sexual desire (HISD), Pittsburgh Sleep Quality Index (PSQI) and Diabetes-specific Quality-of-Life&#xa0;Brief Clinical Inventory questionnaires (DQOL-BCI). Dietary intake, anthropometric measures, fasting plasma glucose (FPG), haemoglobin A1C (HbA1C), insulin, lipid profile and liver enzymes were determined at baseline and the end of the study.<br><br>RESULTS: At the end of the eighth week, saffron supplementation significantly decreased FPG, triglyceride (TG), insulin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) (P&#xa0;<&#xa0;.001). Moreover, significant improvements in BDI-II scores and total quality of life were observed in the intervention group (P&#xa0;<&#xa0;.001). The saffron group showed more significant improvements in PSQI scores than the placebo group, such that at the post-intervention analysis, only the saffron group achieved a "good" sleep band. At this relatively high dose, saffron supplementation improved glycaemic status, lipid profile and liver enzyme measures in patients with T2D while also improving sleep and overall quality of life.<br><br>CONCLUSION: Our results indicate that saffron notably reduced hyperglycaemia and hyperlipidaemia and improved liver function in patients with T2D in an 8-week randomised clinical trial. Saffron also significantly improved depression, sleep quality and overall quality of life in diabetic patients. However, further investigation is necessary to confirm whether saffron is an effective complementary therapy for T2D.},
}
@article {pmid33957716,
year = {2021},
author = {Mytilekas, KV and Oeconomou, A and Sokolakis, I and Kalaitzi, M and Mouzakitis, G and Nakopoulou, E and Apostolidis, A},
title = {Defining Voiding Dysfunction in Women: Bladder Outflow Obstruction Versus Detrusor Underactivity.},
journal = {International neurourology journal},
volume = {25},
number = {3},
pages = {244-251},
pmid = {33957716},
issn = {2093-4777},
support = {//Astellas Pharma/ ; },
abstract = {PURPOSE: We aimed to develop urodynamic criteria to improve the accuracy of the diagnosis of bladder outlet obstruction (BOO) and detrusor underactivity (DU) in women with lower urinary tract symptoms (LUTS).<br><br>METHODS: Initially, in a group of 68 consecutive women with LUTS and increased postvoid residual (PVR) who had undergone urodynamic investigations, we examined the level of agreement between the operating physician's diagnosis of BOO or DU and the diagnosis according to urodynamic nomograms/indices, including the Blaivas-Groutz (B-G) nomogram, urethral resistance factor (URA), bladder outlet obstruction index (BOOI), and bladder contractility index (BCI). Based on the initial results, we categorized 160 women into 4 groups using the B-G nomogram and URA (group 1, severe-moderate BOO; group 2, mild BOO and URA&#x2265;20; group 3, mild BOO and URA<20; group 4, nonobstructed) and compared the urodynamic parameters. Finally, we redefined women as obstructed (groups 1+2) and nonobstructed (groups 3+4) for subanalysis.<br><br>RESULTS: The agreement between the B-G nomogram and physician's diagnosis was poor in the mild obstruction zone (&#x3ba;=0.308, P=0.01). By adding URA (cutoff value=20), excellent agreement was reached (&#x3ba;=0.856, P<0.001). Statistically significant differences were found among the 4 groups (analysis of variance) in maximum flow rate (Qmax) (P<0.0001), voided volume (VV) (P=0.042), PVR (P=0.032), BOOI (P<0.0001), and BCI (P<0.0001), with a positive linear trend for Qmax and VV and a negative linear trend for PVR and BOOI moving from groups 1 to 4. In the subanalysis, all parameters showed statistically significant differences between obstructed and nonobstructed women, except BCI (Qmax, P=0.0001; VV, P=0.0091; PVR, P=0.0005; BOOI, P=0.0001).<br><br>CONCLUSION: The combination of the B-G nomogram with URA increased the accuracy of diagnosing BOO among women with LUTS. Based on this combination, most women in the mild obstruction zone of the B-G nomogram would be considered underactive rather than obstructed.},
}
@article {pmid33957606,
year = {2021},
author = {Wang, L and Wu, EX and Chen, F},
title = {EEG-based auditory attention decoding using speech-level-based segmented computational models.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfeba},
pmid = {33957606},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Attention ; Computer Simulation ; Electroencephalography ; Humans ; *Speech ; *Speech Perception ; },
abstract = {Objective.Auditory attention in complex scenarios can be decoded by electroencephalography (EEG)-based cortical speech-envelope tracking. The relative root-mean-square (RMS) intensity is a valuable cue for the decomposition of speech into distinct characteristic segments. To improve auditory attention decoding (AAD) performance, this work proposed a novel segmented AAD approach to decode target speech envelopes from different RMS-level-based speech segments.Approach.Speech was decomposed into higher- and lower-RMS-level speech segments with a threshold of -10 dB relative RMS level. A support vector machine classifier was designed to identify higher- and lower-RMS-level speech segments, using clean target and mixed speech as reference signals based on corresponding EEG signals recorded when subjects listened to target auditory streams in competing two-speaker auditory scenes. Segmented computational models were developed with the classification results of higher- and lower-RMS-level speech segments. Speech envelopes were reconstructed based on segmented decoding models for either higher- or lower-RMS-level speech segments. AAD accuracies were calculated according to the correlations between actual and reconstructed speech envelopes. The performance of the proposed segmented AAD computational model was compared to those of traditional AAD methods with unified decoding functions.Main results.Higher- and lower-RMS-level speech segments in continuous sentences could be identified robustly with classification accuracies that approximated or exceeded 80% based on corresponding EEG signals at 6 dB, 3 dB, 0 dB, -3 dB and -6 dB signal-to-mask ratios (SMRs). Compared with unified AAD decoding methods, the proposed segmented AAD approach achieved more accurate results in the reconstruction of target speech envelopes and in the detection of attentional directions. Moreover, the proposed segmented decoding method had higher information transfer rates (ITRs) and shorter minimum expected switch times compared with the unified decoder.Significance.This study revealed that EEG signals may be used to classify higher- and lower-RMS-level-based speech segments across a wide range of SMR conditions (from 6 dB to -6 dB). A novel finding was that the specific information in different RMS-level-based speech segments facilitated EEG-based decoding of auditory attention. The significantly improved AAD accuracies and ITRs of the segmented decoding method suggests that this proposed computational model may be an effective method for the application of neuro-controlled brain-computer interfaces in complex auditory scenes.},
}
@article {pmid33957375,
year = {2021},
author = {Idowu, OP and Ilesanmi, AE and Li, X and Samuel, OW and Fang, P and Li, G},
title = {An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees.},
journal = {Computer methods and programs in biomedicine},
volume = {206},
number = {},
pages = {106121},
doi = {10.1016/j.cmpb.2021.106121},
pmid = {33957375},
issn = {1872-7565},
mesh = {*Amputees ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Intention ; Upper Extremity ; },
abstract = {BACKGROUND AND OBJECTIVE: Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions.<br><br>METHODS: The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition.<br><br>RESULTS: The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space.<br><br>CONCLUSION: This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.},
}
@article {pmid33956845,
year = {2021},
author = {Matarasso, AK and Rieke, JD and White, K and Yusufali, MM and Daly, JJ},
title = {Combined real-time fMRI and real time fNIRS brain computer interface (BCI): Training of volitional wrist extension after stroke, a case series pilot study.},
journal = {PloS one},
volume = {16},
number = {5},
pages = {e0250431},
pmid = {33956845},
issn = {1932-6203},
support = {IK6 RX002661/RX/RRD VA/United States ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; *Brain-Computer Interfaces ; Pilot Projects ; Male ; Middle Aged ; Female ; *Wrist/physiopathology/diagnostic imaging ; *Stroke/physiopathology/diagnostic imaging/therapy ; *Stroke Rehabilitation/methods ; Aged ; Spectroscopy, Near-Infrared/methods ; Adult ; Brain/diagnostic imaging/physiopathology ; Range of Motion, Articular ; },
abstract = {OBJECTIVE: Pilot testing of real time functional magnetic resonance imaging (rt-fMRI) and real time functional near infrared spectroscopy (rt-fNIRS) as brain computer interface (BCI) neural feedback systems combined with motor learning for motor recovery in chronic severely impaired stroke survivors.<br><br>APPROACH: We enrolled a four-case series and administered three sequential rt-fMRI and ten rt-fNIRS neural feedback sessions interleaved with motor learning sessions. Measures were: Arm Motor Assessment Tool, functional domain (AMAT-F; 13 complex functional tasks), Fugl-Meyer arm coordination scale (FM); active wrist extension range of motion (ROM); volume of activation (fMRI); and fNIRS HbO concentration. Performance during neural feedback was assessed, in part, using percent successful brain modulations during rt-fNIRS.<br><br>MAIN RESULTS: Pre-/post-treatment mean clinically significant improvement in AMAT-F (.49 &#xb1; 0.22) and FM (10.0 &#xb1; 3.3); active wrist ROM improvement ranged from 20&#xb0; to 50&#xb0;. Baseline to follow-up change in brain signal was as follows: fMRI volume of activation was reduced in almost all ROIs for three subjects, and for one subject there was an increase or no change; fNIRS HbO was within normal range, except for one subject who increased beyond normal at post-treatment. During rt-fNIRS neural feedback training, there was successful brain signal modulation (42%-78%).<br><br>SIGNIFICANCE: Severely impaired stroke survivors successfully engaged in spatially focused BCI systems, rt-fMRI and rt-fNIRS, to clinically significantly improve motor function. At the least, equivalency in motor recovery was demonstrated with prior long-duration motor learning studies (without neural feedback), indicating that no loss of motor improvement resulted from substituting neural feedback sessions for motor learning sessions. Given that the current neural feedback protocol did not prevent the motor improvements observed in other long duration studies, even in the presence of fewer sessions of motor learning in the current work, the results support further study of neural feedback and its potential for recovery of motor function in stroke survivors. In future work, expanding the sophistication of either or both rt-fMRI and rt-fNIRS could hold the potential for further reducing the number of hours of training needed and/or the degree of recovery. ClinicalTrials.gov ID: NCT02856035.},
}
@article {pmid33956801,
year = {2021},
author = {Schaefer, LV and Bittmann, FN},
title = {Paired personal interaction reveals objective differences between pushing and holding isometric muscle action.},
journal = {PloS one},
volume = {16},
number = {5},
pages = {e0238331},
pmid = {33956801},
issn = {1932-6203},
mesh = {Humans ; Male ; Female ; *Isometric Contraction/physiology ; *Muscle, Skeletal/physiology ; Adult ; Young Adult ; Muscle Fatigue/physiology ; },
abstract = {In sports and movement sciences isometric muscle function is usually measured by pushing against a stable resistance. However, subjectively one can hold or push isometrically. Several investigations suggest a distinction of those forms. The aim of this study was to investigate whether these two forms of isometric muscle action can be distinguished by objective parameters in an interpersonal setting. 20 subjects were grouped in 10 same sex pairs, in which one partner should perform the pushing isometric muscle action (PIMA) and the other partner executed the holding isometric muscle action (HIMA). The partners had contact at the distal forearms via an interface, which included a strain gauge and an acceleration sensor. The mechanical oscillations of the triceps brachii (MMGtri) muscle, its tendon (MTGtri) and the abdominal muscle (MMGobl) were recorded by a piezoelectric-sensor-based measurement system. Each partner performed three 15s (80% MVIC) and two fatiguing trials (90% MVIC) during PIMA and HIMA, respectively. Parameters to compare PIMA and HIMA were the mean frequency, the normalized mean amplitude, the amplitude variation, the power in the frequency range of 8 to 15 Hz, a special power-frequency ratio and the number of task failures during HIMA or PIMA (partner who quit the task). A "HIMA failure" occurred in 85% of trials (p < 0.001). No significant differences between PIMA and HIMA were found for the mean frequency and normalized amplitude. The MMGobl showed significantly higher values of amplitude variation (15s: p = 0.013; fatiguing: p = 0.007) and of power-frequency-ratio (15s: p = 0.040; fatiguing: p = 0.002) during HIMA and a higher power in the range of 8 to 15 Hz during PIMA (15s: p = 0.001; fatiguing: p = 0.011). MMGtri and MTGtri showed no significant differences. Based on the findings it is suggested that a holding and a pushing isometric muscle action can be distinguished objectively, whereby a more complex neural control is assumed for HIMA.},
}
@article {pmid33950345,
year = {2021},
author = {Zhuang, X and Ma, J and Xu, S and Zhang, M and Xu, G and Sun, Z},
title = {All-Trans Retinoic Acid Attenuates Blue Light-Induced Apoptosis of Retinal Photoreceptors by Upregulating MKP-1 Expression.},
journal = {Molecular neurobiology},
volume = {58},
number = {8},
pages = {4157-4168},
pmid = {33950345},
issn = {1559-1182},
support = {2013CB967503//National Key Basic Research Program of China/ ; 81700851//National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (CN)/ ; },
mesh = {Animals ; Apoptosis/*drug effects/physiology ; Dual Specificity Phosphatase 1/*biosynthesis/genetics ; Light/*adverse effects ; Male ; Photoreceptor Cells, Vertebrate/*metabolism ; Rats ; Rats, Sprague-Dawley ; Tretinoin/*administration &amp; dosage ; Up-Regulation/*drug effects/physiology ; },
abstract = {The study investigated the antiapoptotic effects of all-trans retinoic acid (RA) on retinal degeneration caused by exposure to blue light. Sprague-Dawley rats received intraperitoneal injections of RA and, if necessary, the mitogen-activated protein kinase phosphotase-1(MKP-1) inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2, 3-dihydro-1H-inden-1-one (BCI), or the retinoic acid receptor (RAR) antagonist, AGN 193109. Retinal damage was induced by 24 h of continuous exposure to blue light. Haematoxylin and eosin staining and electroretinography were performed to measure retinal thickness and retinal function before and at 3 days and 7 days after light exposure. The retinal protein expression levels of phosphorylated c-Jun N-terminal kinase (JNK), phosphorylated nuclear factor-κB, MKP-1, Bim, Bax, and cleaved caspase-3 were also measured. Terminal-deoxynucleotidyl-transferase-mediated deoxyuridine triphosphate-biotin nick end labelling (TUNEL) staining and immunofluorescent staining of cleaved caspase-3 were also performed to evaluate photoreceptor apoptosis. The administration of RA significantly mitigated retinal dysfunction and the decrease in the outer nuclear layer (ONL) thickness at 3 days and 7 days after light exposure. RA also reduced the percentage of TUNEL-positive nuclei in the ONL and cleaved caspase-3 immunofluorescence intensity at 3 days after light exposure. Light exposure increased the retinal expression of proapoptotic proteins (Bim, Bax, and cleaved caspase-3), which was attenuated by RA. Moreover, RA enhanced the expression of MKP-1 and inhibited the phosphorylation of JNK, which were attenuated by the inhibition of RAR. The inhibitory effects of RA on blue light-induced photoreceptor apoptosis were abrogated by the MKP-1inhibitor. Our results indicate that RA alleviates photoreceptor loss following blue light exposure, at least partly, by the MKP-1/JNK pathway, which may serve as a therapeutic target for relieving retinal degeneration.},
}
@article {pmid33946051,
year = {2021},
author = {Sun, Q and Chen, M and Zhang, L and Li, C and Kang, W},
title = {Similarity-constrained task-related component analysis for enhancing SSVEP detection.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfdfa},
pmid = {33946051},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; Reproducibility of Results ; },
abstract = {Objective. Task-related component analysis (TRCA) is a representative subject-specific training algorithm in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces. Task-related components (TRCs), extracted by the TRCA-based spatial filtering from electroencephalogram (EEG) signals through maximizing the reproducibility across trials, may contain some task-related inherent noise that is still trial-reproducible.Approach. To address this problem, this study proposed a similarity-constrained TRCA (scTRCA) algorithm to remove the task-related noise and extract TRCs maximally correlated with SSVEPs for enhancing SSVEP detection. Similarity constraints, which were created by introducing covariance matrices between EEG training data and an artificial SSVEP template, were added to the objective function of TRCA. Therefore, a better spatial filter was obtained by maximizing not only the reproducibility across trials but also the similarity between TRCs and SSVEPs. The proposed scTRCA was compared with TRCA, multi-stimulus TRCA, and sine-cosine reference signal based on two public datasets.Main results. The performance of TRCA in target identification of SSVEPs is improved by introducing similarity constraints. The proposed scTRCA significantly outperformed the other three methods, and the improvement was more significant especially with insufficient training data.Significance. The proposed scTRCA algorithm is promising for enhancing SSVEP detection considering its better performance and robustness against insufficient calibration.},
}
@article {pmid33945156,
year = {2021},
author = {Kleih-Dahms, SC and Botrel, L and Kübler, A},
title = {The influence of motivation and emotion on sensorimotor rhythm-based brain-computer interface performance.},
journal = {Psychophysiology},
volume = {58},
number = {8},
pages = {e13832},
doi = {10.1111/psyp.13832},
pmid = {33945156},
issn = {1469-8986},
mesh = {Adult ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Emotions/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; Motion Pictures ; Motivation/*physiology ; Music ; *Reward ; Young Adult ; },
abstract = {While decades of research have investigated and technically improved brain-computer interface (BCI)-controlled applications, relatively little is known about the psychological aspects of brain-computer interfacing. In 35 healthy students, we investigated whether extrinsic motivation manipulated via monetary reward and emotional state manipulated via video and music would influence behavioral and psychophysiological measures of performance with a sensorimotor rhythm (SMR)-based BCI. We found increased task-related brain activity in extrinsically motivated (rewarded) as compared with nonmotivated participants but no clear effect of emotional state manipulation. Our experiment investigated the short-term effect of motivation and emotion manipulation in a group of young healthy subjects, and thus, the significance for patients in the locked-in state, who may be in need of a BCI, remains to be investigated.},
}
@article {pmid33945075,
year = {2021},
author = {Ullah, S and Halim, Z},
title = {Imagined character recognition through EEG signals using deep convolutional neural network.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {5},
pages = {1167-1183},
pmid = {33945075},
issn = {1741-0444},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user's intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)-based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets. Overall working of the proposed solution for imagined character recognition through EEG signals.},
}
@article {pmid33942016,
year = {2021},
author = {Goering, S and Klein, E and Specker Sullivan, L and Wexler, A and Agüera Y Arcas, B and Bi, G and Carmena, JM and Fins, JJ and Friesen, P and Gallant, J and Huggins, JE and Kellmeyer, P and Marblestone, A and Mitchell, C and Parens, E and Pham, M and Rubel, A and Sadato, N and Teicher, M and Wasserman, D and Whittaker, M and Wolpaw, J and Yuste, R},
title = {Recommendations for Responsible Development and Application of Neurotechnologies.},
journal = {Neuroethics},
volume = {14},
number = {3},
pages = {365-386},
pmid = {33942016},
issn = {1874-5490},
support = {I01 CX001812/CX/CSRD VA/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
abstract = {Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including "Neurorights," for data privacy, security, and consent to empower neurotechnology users' control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.},
}
@article {pmid33941201,
year = {2021},
author = {Wen, C and Huang, X and Feng, L and Chen, L and Hu, W and Lai, Y and Hao, Y},
title = {High-resolution age-specific mapping of the two-week illness prevalence rate based on the National Health Services Survey and geostatistical analysis: a case study in Guangdong province, China.},
journal = {International journal of health geographics},
volume = {20},
number = {1},
pages = {20},
pmid = {33941201},
issn = {1476-072X},
mesh = {Age Factors ; Bayes Theorem ; China/epidemiology ; Humans ; Infant, Newborn ; Prevalence ; *State Medicine ; },
abstract = {BACKGROUND: The two-week illness prevalence rate is an important and comparable indicator of health service needs. High-spatial-resolution, age-specific risk mapping of this indicator can provide valuable information for health resource allocation. The age-prevalence relationships may be different among areas of the study region, but previous geostatistical models usually ignored the spatial-age interaction.<br><br>METHODS: We took Guangdong province, the province with the largest population and economy in China, as a study case. We collected two-week illness data and other potential influencing predictors from the fifth National Health Services Survey in 2013 and other open-access databases. Bayesian geostatistical binary regression models were developed with spatial-age structured random effect, based on which, high-resolution, age-specific two-week illness prevalence rates, as well as number of people reporting two-week illness, were estimated. The equality of health resource distribution was further evaluated based on the two-week illness mapping results and the health supply data.<br><br>RESULTS: The map across all age groups revealed that the highest risk was concentrated in the central (i.e., Pearl River Delta) and northern regions of the province. These areas had a two-week illness prevalence&#x2009;>&#x2009;25.0%, compared with 10.0-20.0% in other areas. Age-specific maps revealed significant differences in prevalence between age groups, and the age-prevalence relationships also differed across locations. In most areas, the prevalence rates decrease from age 0 to age 20, and then increase gradually. Overall, the estimated age- and population-adjusted prevalence was 16.5% [95% Bayesian credible interval (BCI): 14.5-18.6%], and the estimated total number of people reporting illness within the two-week period was 17.5 million (95% BCI: 15.5-19.8 million) in Guangdong Province. The Lorenz curve and the Gini coefficient (resulted in 0.3526) showed a moderate level of inequality in health resource distribution.<br><br>CONCLUSIONS: We developed a Bayesian geostatistical modeling framework with spatial-age structured effect to produce age-specific, high-resolution maps of the two-week illness prevalence rate and the numbers of people reporting two-week illness in Guangdong province. The methodology developed in this study can be generalized to other global regions with available relevant survey data. The mapping results will support plans for health resource allocation.},
}
@article {pmid33936901,
year = {2021},
author = {Fiani, B and Reardon, T and Ayres, B and Cline, D and Sitto, SR},
title = {An Examination of Prospective Uses and Future Directions of Neuralink: The Brain-Machine Interface.},
journal = {Cureus},
volume = {13},
number = {3},
pages = {e14192},
pmid = {33936901},
issn = {2168-8184},
abstract = {The human brain is one of the most mystifying biological structures in nature. Overwhelming research, technology, and innovations in neuroscience have augmented clinical assessments, diagnosis, and treatment capabilities. Nonetheless, there is still much to be discovered about nervous system disorders and defects. Neuralink, a neurotechnology company, is advancing the field of neuroscience and neuroengineering. The company's initial aim is to develop an implantable brain-machine interface device that will enhance the lives of people with severe brain and spinal cord injuries. Here, we provide insight into Neuralink's design, early testing, and future applications in neurosurgery. While early testing with small and large animals show promising results, no clinical trials have been conducted to date. Additionally, a term search for "Neuralink" was performed in PubMed. The literature search yielded only 28 references, of which most indirectly mentioned the device but not in direct testing. In order to conclude the safety and viability of the Neuralink device, further research studies are needed to move forward beyond speculation.},
}
@article {pmid33936191,
year = {2021},
author = {Lekova, A and Chavdarov, I},
title = {A Fuzzy Shell for Developing an Interpretable BCI Based on the Spatiotemporal Dynamics of the Evoked Oscillations.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6685672},
pmid = {33936191},
issn = {1687-5273},
mesh = {Attention ; Brain ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; },
abstract = {Researchers in neuroscience computing experience difficulties when they try to carry out neuroanalysis in practice or when they need to design an explainable brain-computer interface (BCI) with quick setup and minimal training phase. There is a need of interpretable computational intelligence techniques and new brain states decoding for more understandable interpretation of the sensory, cognitive, and motor brain processing. We propose a general-purpose fuzzy software system shell for developing a custom EEG BCI system. It relies on the bursts of the ongoing EEG frequency power synchronization/desynchronization at scalp level and supports quick BCI setup by linguistic features, ad hoc fuzzy membership construction, explainable IF-THEN rules, and the concept of the Internet of Things (IoT), which makes the BCI system device and service independent. It has a potential for designing both passive and event-related BCIs with options for visual representation at scalp-source level in response to time. The feasibility of the proposed system has been proven by real experiments and bursts for β and γ frequency power have been detected in real time in response to evoked visuospatial selective attention. The presence of the proposed new brain state decoding can be used as a feasible metric for interpretation of the spatiotemporal dynamics of the passive or evoked neural oscillations.},
}
@article {pmid33935672,
year = {2021},
author = {Chu, C and Luo, J and Tian, X and Han, X and Guo, S},
title = {A P300 Brain-Computer Interface Paradigm Based on Electric and Vibration Simple Command Tactile Stimulation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {641357},
pmid = {33935672},
issn = {1662-5161},
abstract = {This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.},
}
@article {pmid33931006,
year = {2021},
author = {Johnston, R and Doucet, G and Boulay, C and Miller, K and Martinez-Trujillo, J and Sachs, A},
title = {Decoding Saccade Intention From Primate Prefrontal Cortical Local Field Potentials Using Spectral, Spatial, and Temporal Dimensionality Reduction.},
journal = {International journal of neural systems},
volume = {31},
number = {6},
pages = {2150023},
doi = {10.1142/S0129065721500234},
pmid = {33931006},
issn = {1793-6462},
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Intention ; Prefrontal Cortex ; Primates ; *Saccades ; },
abstract = {Most invasive Brain Computer Interfaces (iBCIs) use spike and Local Field Potentials (LFPs) from the motor or parietal cortices to decode movement intentions. It has been debated whether harvesting signals from other brain areas that encode global cognitive variables, such as the allocation of attention and eye movement goals in a variety of spatial reference frames, may improve the outcome of iBCIs. Here, we explore the ability of LFP signals, sampled from the lateral prefrontal cortex (LPFC) of macaque monkeys, to encode eye-movement intention during the pre-movement fixation period of a delayed saccade task. We use spectral dimensionality reduction to examine the spatiotemporal properties of the extracted non-rhythmic broadband activity and explore its usefulness in decoding saccade goals. The dynamics of the broadband signal in low spatial dimensions across the pre-movement fixation period uncovered saccade target separation; its discriminative potential was confirmed using support vector machine classifications. These findings reveal that broadband LFP from the LPFC can be used to decode intended saccade target location during pre-movement periods. We further provide a general workflow that can be implemented in iBCIs and it is relatively robust to the loss of spikes in individual electrodes.},
}
@article {pmid33929315,
year = {2021},
author = {Vandecappelle, S and Deckers, L and Das, N and Ansari, AH and Bertrand, A and Francart, T},
title = {EEG-based detection of the locus of auditory attention with convolutional neural networks.},
journal = {eLife},
volume = {10},
number = {},
pages = {},
pmid = {33929315},
issn = {2050-084X},
mesh = {Acoustic Stimulation ; *Attention ; Electroencephalography ; Humans ; Male ; Neural Networks, Computer ; Sound ; Speech ; *Speech Perception ; },
abstract = {In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker someone is attending by relating the neural activity to the speech signals. However, classifying auditory attention within a short time interval remains the main challenge. We present a convolutional neural network-based approach to extract the locus of auditory attention (left/right) without knowledge of the speech envelopes. Our results show that it is possible to decode the locus of attention within 1-2 s, with a median accuracy of around 81%. These results are promising for neuro-steered noise suppression in hearing aids, in particular in scenarios where per-speaker envelopes are unavailable.},
}
@article {pmid33928657,
year = {2021},
author = {Farina, D and Mrachacz-Kersting, N},
title = {Brain-computer interfaces and plasticity of the human nervous system.},
journal = {The Journal of physiology},
volume = {599},
number = {9},
pages = {2349-2350},
doi = {10.1113/JP279845},
pmid = {33928657},
issn = {1469-7793},
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; Neuronal Plasticity ; },
}
@article {pmid33927204,
year = {2021},
author = {Rathee, D and Raza, H and Roy, S and Prasad, G},
title = {A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface.},
journal = {Scientific data},
volume = {8},
number = {1},
pages = {120},
pmid = {33927204},
issn = {2052-4463},
support = {ES/S007156/1//RCUK | Economic and Social Research Council (ESRC)/ ; DST-UKIERI-2016-17-0128//UK-India Education and Research Initiative (UKIERI)/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Cognition ; Female ; Humans ; Machine Learning ; *Magnetoencephalography ; Male ; *Motor Activity ; *Neuroimaging ; Pattern Recognition, Automated ; Young Adult ; },
abstract = {Recent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.},
}
@article {pmid33925209,
year = {2021},
author = {Sciaraffa, N and Borghini, G and Di Flumeri, G and Cincotti, F and Babiloni, F and Aricò, P},
title = {Joint Analysis of Eye Blinks and Brain Activity to Investigate Attentional Demand during a Visual Search Task.},
journal = {Brain sciences},
volume = {11},
number = {5},
pages = {},
pmid = {33925209},
issn = {2076-3425},
support = {950998//Horizon 2020/ ; 894238//Horizon 2020/ ; 826232//Horizon 2020/ ; 723386//Horizon 2020/ ; 814961//Horizon 2020/ ; PGR05730//Ministero dell'Istruzione, dell'Universit&#xe0; e della Ricerca/ ; },
abstract = {In several fields, the need for a joint analysis of brain activity and eye activity to investigate the association between brain mechanisms and manifest behavior has been felt. In this work, two levels of attentional demand, elicited through a conjunction search task, have been modelled in terms of eye blinks, brain activity, and brain network features. Moreover, the association between endogenous neural mechanisms underlying attentional demand and eye blinks, without imposing a time-locked structure to the analysis, has been investigated. The analysis revealed statistically significant spatial and spectral modulations of the recorded brain activity according to the different levels of attentional demand, and a significant reduction in the number of eye blinks when a higher amount of attentional investment was required. Besides, the integration of information coming from high-density electroencephalography (EEG), brain source localization, and connectivity estimation allowed us to merge spectral and causal information between brain areas, characterizing a comprehensive model of neurophysiological processes behind attentional demand. The analysis of the association between eye and brain-related parameters revealed a statistically significant high correlation (R > 0.7) of eye blink rate with anterofrontal brain activity at 8 Hz, centroparietal brain activity at 12 Hz, and a significant moderate correlation with the participation of right Intra Parietal Sulcus in alpha band (R = -0.62). Due to these findings, this work suggests the possibility of using eye blinks measured from one sensor placed on the forehead as an unobtrusive measure correlating with neural mechanisms underpinning attentional demand.},
}
@article {pmid33922456,
year = {2021},
author = {Valentin, O and Viallet, G and Delnavaz, A and Cretot-Richert, G and Ducharme, M and Monsarat-Chanon, H and Voix, J},
title = {Custom-Fitted In- and Around-the-Ear Sensors for Unobtrusive and On-the-Go EEG Acquisitions: Development and Validation.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {9},
pages = {},
pmid = {33922456},
issn = {1424-8220},
support = {IT07737//Mitacs/ ; },
mesh = {*Brain-Computer Interfaces ; Ear ; Ear Canal ; Electrodes ; *Electroencephalography ; Evoked Potentials, Auditory ; Humans ; },
abstract = {OBJECTIVES: This paper aims to validate the performance and physical design of a wearable, unobtrusive ear-centered electroencephalography (EEG) device, dubbed "EARtrodes", using early and late auditory evoked responses. Results would also offer a proof-of-concept for the device to be used as a concealed brain-computer interface (BCI).<br><br>DESIGN: The device is composed of a custom-fitted earpiece and an ergonomic behind-the-ear piece with embedded electrodes made of a soft and flexible combination of silicone rubber and carbon fibers. The location of the conductive silicone electrodes inside the ear canal and the optimal geometry of the behind-the-ear piece were obtained through morphological and geometrical analysis of the human ear canal and the region around-the-ear. An entirely conductive generic earpiece was also developed to assess the potential of a universal, more affordable solution.<br><br>RESULTS: Early latency results illustrate the conductive silicone electrodes' capability to record quality EEG signals, comparable to those obtained with traditional gold-plated electrodes. Additionally, late latency results demonstrate EARtrodes' capacity to reliably detect decision-making processes from the ear.<br><br>CONCLUSIONS: EEG results validate the performance of EARtrodes as a circum-aural and intra-aural EEG recording system adapted for a wide range of applications in audiology, neuroscience, clinical research, and as an unobtrusive BCI.},
}
@article {pmid33921929,
year = {2021},
author = {Król, B and Cywka, KB and Skarżyńska, MB and Skarżyński, PH},
title = {Implantation of the Bonebridge BCI 602 after Mastoid Obliteration with S53P4 Bioactive Glass: A Safe Method of Treating Difficult Anatomical Conditions-Preliminary Results.},
journal = {Life (Basel, Switzerland)},
volume = {11},
number = {5},
pages = {},
pmid = {33921929},
issn = {2075-1729},
abstract = {This study presents the preliminary results of a new otosurgical method in patients after canal wall down (CWD) surgery; it involves the implantation of the Bonebridge BCI 602 implant after obliteration of the mastoid cavity with S53P4 bioactive glass. The study involved eight adult patients who had a history of chronic otitis media with cholesteatoma in one or both ears and who had had prior radical surgery. The mean follow-up period was 12 months, with routine follow-up visits according to the schedule. The analysis had two aspects: a surgical aspect in terms of healing, development of bacterial flora, the impact on the inner ear or labyrinth, recurrence of cholesteatoma, and possible postoperative complications (firstly, after obliteration of the mastoid cavity with S53P4 bioactive glass, then after implantation). The second was an audiological aspect which assessed audiometric results and the patient's satisfaction based on questionnaires. During the follow-up period, we did not notice any serious postoperative complications. Studies demonstrated significantly improved hearing thresholds and speech recognition in quiet and noise using the Bonebridge BCI 602. Data collected after six months of use showed improved audiological thresholds and patient satisfaction. Based on the preliminary results, we believe that the proposed two-stage surgical method using bioactive glass S53P4 is a safe and effective way of implanting the Bonebridge BCI 602 in difficult anatomical conditions. This makes it possible to treat a larger group of patients with the device.},
}
@article {pmid33918833,
year = {2021},
author = {Liang, H and Maedono, S and Yu, Y and Liu, C and Ueda, N and Li, P and Zhu, C},
title = {Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {4},
pages = {},
pmid = {33918833},
issn = {1099-4300},
support = {JP15K00362//Japan Society for the Promotion of Science/ ; JP24500240//Japan Society for the Promotion of Science/ ; },
abstract = {Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12-40 Hz) power increased after the NFB training for both the elbow and the shoulder joints' movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.},
}
@article {pmid33918223,
year = {2021},
author = {Kuc, A and Grubov, VV and Maksimenko, VA and Shusharina, N and Pisarchik, AN and Hramov, AE},
title = {Sensor-Level Wavelet Analysis Reveals EEG Biomarkers of Perceptual Decision-Making.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {7},
pages = {},
pmid = {33918223},
issn = {1424-8220},
support = {19-72-10121//Russian Science Foundation/ ; 19-32-60042//Russian Foundation for Basic Research/ ; },
mesh = {Biomarkers ; *Decision Making ; Electroencephalography ; *Wavelet Analysis ; },
abstract = {Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3-0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI).},
}
@article {pmid33918116,
year = {2021},
author = {Park, J and Park, J and Shin, D and Choi, Y},
title = {A BCI Based Alerting System for Attention Recovery of UAV Operators.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {7},
pages = {},
pmid = {33918116},
issn = {1424-8220},
support = {2020-0410//National Research Foundation of Korea/ ; },
mesh = {Cognition ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {As unmanned aerial vehicles have become popular, the number of accidents caused by an operator's inattention have increased. To prevent such accidents, the operator should maintain an attention status. However, limited research has been conducted on the brain-computer interface (BCI)-based system with an alerting module for the operator's attention recovery of unmanned aerial vehicles. Therefore, we introduce a detection and alerting system that prevents an unmanned aerial vehicle operator from falling into inattention status by using the operator's electroencephalogram signal. The proposed system consists of the following three components: a signal processing module, which collects and preprocesses an electroencephalogram signal of an operator, an inattention detection module, which determines whether an inattention status occurred based on the preprocessed signal, and, lastly, an alert providing module that presents stimulus to an operator when inattention is detected. As a result of evaluating the performance with a real-world dataset, it was shown that the proposed system successfully contributed to the recovery of operator attention in the evaluating dataset, although statistical significance could not be established due to the small number of subjects.},
}
@article {pmid33916189,
year = {2021},
author = {Li, M and He, D and Li, C and Qi, S},
title = {Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.},
journal = {Brain sciences},
volume = {11},
number = {4},
pages = {},
pmid = {33916189},
issn = {2076-3425},
support = {82072008//National Natural Science Foundation of China/ ; N2024005-2//Fundamental Research Funds for the Central Universities/ ; },
abstract = {The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain-computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.},
}
@article {pmid33913351,
year = {2022},
author = {Mansour, S and Ang, KK and Nair, KPS and Phua, KS and Arvaneh, M},
title = {Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials.},
journal = {Clinical EEG and neuroscience},
volume = {53},
number = {1},
pages = {79-90},
pmid = {33913351},
issn = {2169-5202},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Randomized Controlled Trials as Topic ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.},
}
@article {pmid33913280,
year = {2021},
author = {Lu, X and Ding, P and Li, S and Gong, A and Zhao, L and Qian, Q and Su, L and Fu, Y},
title = {[Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {2},
pages = {210-223},
pmid = {33913280},
issn = {1001-5515},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Ergonomics ; Humans ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.},
}
@article {pmid33912122,
year = {2021},
author = {Drane, DL and Pedersen, NP and Sabsevitz, DS and Block, C and Dickey, AS and Alwaki, A and Kheder, A},
title = {Cognitive and Emotional Mapping With SEEG.},
journal = {Frontiers in neurology},
volume = {12},
number = {},
pages = {627981},
pmid = {33912122},
issn = {1664-2295},
support = {K08 NS105929/NS/NINDS NIH HHS/United States ; R01 MH118514/MH/NIMH NIH HHS/United States ; R01 NS088748/NS/NINDS NIH HHS/United States ; R01 NS110347/NS/NINDS NIH HHS/United States ; },
abstract = {Mapping of cortical functions is critical for the best clinical care of patients undergoing epilepsy and tumor surgery, but also to better understand human brain function and connectivity. The purpose of this review is to explore existing and potential means of mapping higher cortical functions, including stimulation mapping, passive mapping, and connectivity analyses. We examine the history of mapping, differences between subdural and stereoelectroencephalographic approaches, and some risks and safety aspects, before examining different types of functional mapping. Much of this review explores the prospects for new mapping approaches to better understand other components of language, memory, spatial skills, executive, and socio-emotional functions. We also touch on brain-machine interfaces, philosophical aspects of aligning tasks to brain circuits, and the study of consciousness. We end by discussing multi-modal testing and virtual reality approaches to mapping higher cortical functions.},
}
@article {pmid33912002,
year = {2021},
author = {Chen, Y and Ma, B and Hao, H and Li, L},
title = {Removal of Electrocardiogram Artifacts From Local Field Potentials Recorded by Sensing-Enabled Neurostimulator.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {637274},
pmid = {33912002},
issn = {1662-4548},
abstract = {Sensing-enabled neurostimulators are an advanced technology for chronic observation of brain activities, and show great potential for closed-loop neuromodulation and as implantable brain-computer interfaces. However, local field potentials (LFPs) recorded by sensing-enabled neurostimulators can be contaminated by electrocardiogram (ECG) signals due to complex recording conditions and limited common-mode-rejection-ratio (CMRR). In this study, we propose a solution for removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulator. A synchronized monopolar channel was added as an ECG reference, and two pre-existing methods, i.e., template subtraction and adaptive filtering, were then applied. ECG artifacts were successfully removed and the performance of the method was insensitive to residual stimulation artifacts. This approach to removal of ECG artifacts broadens the range of applications of sensing-enabled neurostimulators.},
}
@article {pmid33911284,
year = {2021},
author = {Shen, C and Mao, C and Xu, C and Jin, N and Zhang, H and Shen, DD and Shen, Q and Wang, X and Hou, T and Chen, Z and Rondard, P and Pin, JP and Zhang, Y and Liu, J},
title = {Structural basis of GABAB receptor-Gi protein coupling.},
journal = {Nature},
volume = {594},
number = {7864},
pages = {594-598},
pmid = {33911284},
issn = {1476-4687},
mesh = {Cryoelectron Microscopy ; GTP-Binding Proteins/*chemistry ; Humans ; Protein Binding ; Protein Domains ; Protein Multimerization ; Protein Structure, Tertiary ; Receptors, GABA-B/*chemistry ; },
abstract = {G-protein-coupled receptors (GPCRs) have central roles in intercellular communication[1,2]. Structural studies have revealed how GPCRs can activate G proteins. However, whether this mechanism is conserved among all classes of GPCR remains unknown. Here we report the structure of the class-C heterodimeric GABAB receptor, which is activated by the inhibitory transmitter GABA, in its active form complexed with Gi1 protein. We found that a single G protein interacts with the GB2 subunit of the GABAB receptor at a site that mainly involves intracellular loop 2 on the side of the transmembrane domain. This is in contrast to the G protein binding in a central cavity, as has been observed with other classes of GPCR. This binding mode results from the active form of the transmembrane domain of this GABAB receptor being different from that of other GPCRs, as it shows no outside movement of transmembrane helix 6. Our work also provides details of the inter- and intra-subunit changes that link agonist binding to G-protein activation in this heterodimeric complex.},
}
@article {pmid33910024,
year = {2021},
author = {Singh, SH and Peterson, SM and Rao, RPN and Brunton, BW},
title = {Mining naturalistic human behaviors in long-term video and neural recordings.},
journal = {Journal of neuroscience methods},
volume = {358},
number = {},
pages = {109199},
doi = {10.1016/j.jneumeth.2021.109199},
pmid = {33910024},
issn = {1872-678X},
mesh = {Algorithms ; Brain ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrocorticography ; Humans ; Movement ; },
abstract = {BACKGROUND: Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term.<br><br>NEW METHOD: Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video.<br><br>RESULTS: Our pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7-9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain-computer interfacing applications, and we show decoding results from a movement initiation detection task.<br><br>Spontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution.<br><br>CONCLUSIONS: Our work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.},
}
@article {pmid33909570,
year = {2021},
author = {Hao, H and Chen, J and Richardson, A and Van der Spiegel, J and Aflatouni, F},
title = {A 10.8 µW Neural Signal Recorder and Processor With Unsupervised Analog Classifier for Spike Sorting.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {15},
number = {2},
pages = {351-364},
doi = {10.1109/TBCAS.2021.3076147},
pmid = {33909570},
issn = {1940-9990},
mesh = {Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Software ; },
abstract = {Implantable brain machine interfaces for treatment of neurological disorders require on-chip, real-time signal processing of action potentials (spikes). In this work, we present the first spike sorting SoC with integrated neural recording front-end and analog unsupervised classifier. The event-driven, low power spike sorter features a novel hardware-optimized, K-means based algorithm that effectively eliminates duplicate clusters and is implemented using a novel clockless and ADC-less analog architecture. The 1.4 mm[2] chip is fabricated in a 180-nm CMOS SOI process. The analog front-end achieves a 3.3 μVrms noise floor over the spike bandwidth (400 - 5000 Hz) and consumes 6.42 μW from a 1.5 V supply. The analog spike sorter consumes 4.35 μW and achieves 93.2% classification accuracy on a widely used synthetic test dataset. In addition, higher than 93% agreement between the chip classification result and that of a standard spike sorting software is observed using pre-recorded real neural signals. Simulations of the implemented spike sorter show robust performance under process-voltage-temperature variations.},
}
@article {pmid33909559,
year = {2022},
author = {Kocanaogullari, A and Akcakaya, M and Erdogmus, D},
title = {Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {44},
number = {9},
pages = {5590-5601},
doi = {10.1109/TPAMI.2021.3075915},
pmid = {33909559},
issn = {1939-3539},
mesh = {*Algorithms ; Bayes Theorem ; *Brain/diagnostic imaging ; },
abstract = {Systems that are based on recursive Bayesian updates for classification limit the cost of evidence collection through certain stopping/termination criteria and accordingly enforce decision making. Conventionally, two termination criteria based on pre-defined thresholds over (i) the maximum of the state posterior distribution; and (ii) the state posterior uncertainty are commonly used. In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria. For example, through the proposed geometric interpretation we show that confidence thresholds defined over maximum of the state posteriors suffer from stiffness that results in unnecessary evidence collection whereas uncertainty based thresholding methods are fragile to number of categories and terminate prematurely if some state candidates are already discovered to be unfavorable. Moreover, both types of termination methods neglect the evolution of posterior updates. We then propose a new stopping/termination criterion with a geometrical insight to overcome the limitations of these conventional methods and provide a comparison in terms of decision accuracy and speed. We validate our claims using simulations and using real experimental data obtained through a brain computer interfaced typing system.},
}
@article {pmid33909252,
year = {2021},
author = {Carvalho, SN and Vargas, GV and da Silva Costa, TB and de Arruda Leite, HM and Coradine, L and Boccato, L and Soriano, DC and Attux, R},
title = {Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {5},
pages = {1133-1150},
pmid = {33909252},
issn = {1741-0444},
support = {01.16.0067.00//FINEP/ ; 2013/07559-3//FAPESP/ ; 2019/09512-0//FAPESP/ ; 305616/2016-1//CNPq/ ; 308811/2019-4//CNPq/ ; 001//CAPES/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.},
}
@article {pmid33909193,
year = {2021},
author = {Ahkami, B and Ghassemi, F},
title = {Adding Tactile Feedback and Changing ISI to Improve BCI Systems' Robustness: An Error-Related Potential Study.},
journal = {Brain topography},
volume = {34},
number = {4},
pages = {467-477},
pmid = {33909193},
issn = {1573-6792},
support = {6401//Cognitive Sciences and Technologies Council/ ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Humans ; Touch ; },
abstract = {Nowadays, the brain-computer interface (BCI) systems attract much more attention than before, yet they have not found their ways into our lives since their accuracy is not satisfying. Error Related Potential (ErRP) is a potential that occurs in human brain signals when an unintended event happens, against ones' will and thoughts. An example is the occurrence of an error in BCI systems. Investigation of the ErRP could enable researchers to increase the accuracy of BCI systems by detecting instances of inaccuracy in the system. In this research the effects of two parameters on the ErRP are studied: (1) The Motor Imagery Time, also known as Inter-Stimulus Interval (ISI) and (2) different types of feedback (Visual and Tactile). The statistical analysis of the ErRP characteristics showed that feedback type meaningfully affects the ErRP in a cue-paced BCI system and it will affect the time of occurrence of this potential. To validate the proposed idea, different feature extraction, and classification techniques were used for the classification of the BCI system responses. It was shown that by proper selection of the parameters and features, the accuracy of the system could be improved. Tactile feedback together with higher ISI could increase the accuracy of finding erroneous trials up to 90%. The proposed method's accuracy was significantly higher (p-value < 0.05) compared to other methods of feature extraction.},
}
@article {pmid33906163,
year = {2021},
author = {Carino-Escobar, RI and Valdés-Cristerna, R and Carrillo-Mora, P and Rodriguez-Barragan, MA and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Arias-Carrión, O and Cantillo-Negrete, J},
title = {Prognosis of stroke upper limb recovery with physiological variables using regression tree ensembles.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfc1e},
pmid = {33906163},
issn = {1741-2552},
mesh = {Humans ; Prognosis ; Recovery of Function ; *Stroke/diagnosis ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Objective.This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery.Approach.Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n= 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT).Main results.There were no significant differences between predicted and actual outcomes with FMA-UE (p= 0.29) and ARAT (p= 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The best features for FMA-UE prediction included mostly beta activity over the sensorimotor cortex. Best ARAT prediction features were cortical beta activity, corticospinal tract integrity of the unaffected hemisphere, and upper limb strength.Significance.Results highlighted the importance of measuring cortical activity related to motor control processes, the unaffected hemisphere's integrity, and upper limb strength for prognosis. It was also implied that stroke upper limb recovery prediction is feasible using solely physiological variables with a Regression Tree Ensemble, which can also be used to analyze physiological relationships with recovery.},
}
@article {pmid33904819,
year = {2021},
author = {Yeo, PS and Nguyen, TN and Ng, MPE and Choo, RWM and Yap, PLK and Ng, TP and Wee, SL},
title = {Evaluation of the Implementation and Effectiveness of Community-Based Brain-Computer Interface Cognitive Group Training in Healthy Community-Dwelling Older Adults: Randomized Controlled Implementation Trial.},
journal = {JMIR formative research},
volume = {5},
number = {4},
pages = {e25462},
pmid = {33904819},
issn = {2561-326X},
abstract = {BACKGROUND: Cognitive training can improve cognition in healthy older adults.<br><br>OBJECTIVE: The objectives are to evaluate the implementation of community-based computerized cognitive training (CCT) and its effectiveness on cognition, gait, and balance in healthy older adults.<br><br>METHODS: A single-blind randomized controlled trial with baseline and follow-up assessments was conducted at two community centers in Singapore. Healthy community-dwelling adults aged 55 years and older participated in a 10-week CCT program with 2-hour instructor-led group classes twice a week. Participants used a mobile app to play games targeting attention, memory, decision making, visuospatial abilities, and cognitive flexibility. Implementation was assessed at the participant, provider, and community level (eg, reach, implementation, and facilitators and barriers). Effectiveness measures were the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Color Trails Test 2 (CTT-2), Berg Balance Scale, and GAITRite walkway measures (single and dual task gait speed, dual task cost, and single and dual task gait variability index [GVI]).<br><br>RESULTS: A total of 94 healthy community-dwelling adults participated in the CCT program (mean age 68.8 [SD 6.3] years). Implementation measures revealed high reach (125/155, 80.6%) and moderate adherence but poor penetration of sedentary older adults (43/125, 34.4%). The effectiveness data were based on intention-to-treat (ITT) and per-protocol (PP) analysis. In the ITT analysis, single task GVI increased (b=2.32, P=.02, 95% CI [0.30 to 4.35]) and RBANS list recognition subtest deteriorated (b=-0.57, P=.01, 95% CI [-1.00 to -0.14]) in both groups. In the PP analysis, time taken to complete CTT-2 (b=-13.5, P=.01, 95% CI [-23.95 to -3.14]; Cohen d effect size = 0.285) was faster in the intervention group. Single task gait speed was not statistically significantly maintained in the intervention group (b=5.38, P=.06, 95% CI [-0.30 to 11.36]) and declined in the control group (Cohen d effect size = 0.414). PP analyses also showed interaction terms for RBANS list recall subtest (b=-0.36, P=.08, 95% CI [-0.75 to 0.04]) and visuospatial domain (b=0.46, P=.08, 95% CI [-0.05 to 0.96]) that were not statistically significant.<br><br>CONCLUSIONS: CCT can be implemented in community settings to improve attention and executive function among healthy older adults. Findings help to identify suitable healthy aging programs that can be implemented on a larger scale within communities.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT04439591; https://clinicaltrials.gov/ct2/show/NCT04439591.},
}
@article {pmid33899422,
year = {2021},
author = {Chu, Y and Zhu, B and Zhao, X and Zhao, Y},
title = {[Convolutional neural network based on temporal-spatial feature learning for motor imagery electroencephalogram signal decoding].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {38},
number = {1},
pages = {1-9},
pmid = {33899422},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {With the advantage of providing more natural and flexible control manner, brain-computer interface systems based on motor imagery electroencephalogram (EEG) have been widely used in the field of human-machine interaction. However, due to the lower signal-noise ratio and poor spatial resolution of EEG signals, the decoding accuracy is relative low. To solve this problem, a novel convolutional neural network based on temporal-spatial feature learning (TSCNN) was proposed for motor imagery EEG decoding. Firstly, for the EEG signals preprocessed by band-pass filtering, a temporal-wise convolution layer and a spatial-wise convolution layer were respectively designed, and temporal-spatial features of motor imagery EEG were constructed. Then, 2-layer two-dimensional convolutional structures were adopted to learn abstract features from the raw temporal-spatial features. Finally, the softmax layer combined with the fully connected layer were used to perform decoding task from the extracted abstract features. The experimental results of the proposed method on the open dataset showed that the average decoding accuracy was 80.09%, which is approximately 13.75% and 10.99% higher than that of the state-of-the-art common spatial pattern (CSP) + support vector machine (SVM) and filter bank CSP (FBCSP) + SVM recognition methods, respectively. This demonstrates that the proposed method can significantly improve the reliability of motor imagery EEG decoding.},
}
@article {pmid33895272,
year = {2021},
author = {Xu, X and Xiang, S and Zhang, Q and Yin, T and Kong, W and Zhang, T},
title = {rTMS alleviates cognitive and neural oscillatory deficits induced by hindlimb unloading in mice via maintaining balance between glutamatergic and GABAergic systems.},
journal = {Brain research bulletin},
volume = {172},
number = {},
pages = {98-107},
doi = {10.1016/j.brainresbull.2021.04.013},
pmid = {33895272},
issn = {1873-2747},
mesh = {Animals ; Cognition/*physiology ; Glutamic Acid/*metabolism ; Hindlimb Suspension ; Hippocampus/*metabolism ; Male ; Mice ; *Transcranial Magnetic Stimulation ; gamma-Aminobutyric Acid/*metabolism ; },
abstract = {Microgravity, as a part of the stress of space flight, has several negative effects on cognitive functions. Repetitive transcranial magnetic stimulation (rTMS), as a novel non-invasive technique, could be an effective approach to alleviated cognitive decline, applied in both preclinical and clinical studies. Neural oscillations and their interactions are involved in cognitive functions and support the communication of neural information. The neural oscillation could be a window from which we may understand what happens in the brain. The current study aimed to explore if 15 Hz rTMS plays a neural modulation role in a mouse model of hindlimb unloading. We hypothezed that rTMS can improve the cognitive and neural oscillatory deficits induced by hindlimb unloading via maintaining the balance between glutamatergic and GABAergic systems. Our data show that rTMS can significantly alleviate behavior deficits, modulate theta oscillation, improve the disturbed power distribution of theta oscillation and the decreased strength of Cross-Frequency Coupling in the dentate gyrus region, and effectively mitigated the blocked communication of neural information in the perforant pathway (PP)-dentate gyrus (DG) neural pathway in Hu mice. Furthermore, biochemical analysis using high-performance liquid chromatography and Western blot assay confirmed that rTMS increases the low expression of glutamate (Glu) and N-Methyl d-Aspartate receptor subtype 2B (NR2B) and decreases the high expression of γ-aminobutyric acid (GABA), 67 KDa isoform of glutamate decarboxylase (GAD67), and GABA type A receptor subunit alpha1 (GABAARα1) in the hippocampus of Hu mice. Taken together, the results suggest that rTMS plays a significant neural modulation role in the hippocampal neural activity disorders induced by Hu, which possibly depends on rTMS maintaining the balance of glutamatergic and gamma-aminobutyric acidergic (GABAergic) systems.},
}
@article {pmid33889080,
year = {2021},
author = {Leeuwis, N and Paas, A and Alimardani, M},
title = {Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {634748},
pmid = {33889080},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.},
}
@article {pmid33887707,
year = {2021},
author = {Zheng, X and Xu, G and Du, C and Yan, W and Tian, P and Zhang, K and Liang, R and Han, C and Zhang, S},
title = {Real-time, precise, rapid and objective visual acuity assessment by self-adaptive step SSVEPs.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfaab},
pmid = {33887707},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; Visual Acuity ; },
abstract = {Objective. This study aimed to explore an online, real-time, and precise method to assess steady-state visual evoked potential (SSVEP)-based visual acuity more rapidly and objectively with self-adaptive spatial frequency steps.Approach. Taking the vertical sinusoidal reversal gratings with different spatial frequencies and temporal frequencies as the visual stimuli, according to the psychometric function for visual acuity assessment, a self-adaptive procedure, the best parameter estimation by sequential testing algorithm, was used to calculate the spatial frequency sequence based on all the previous spatial frequencies and their significance of the SSVEP response. Simultaneously, the canonical correlation analysis (CCA) method with a signal-to-noise ratio (SNR) significance detection criterion was used to judge the significance of the SSVEP response.Main results.After 18 iterative trails, the spatial frequency to be presented converged to a value, which was exactly defined as the SSVEP visual acuity threshold. Our results indicated that this SSVEP acuity had a good agreement and correlation with subjective Freiburg Visual Acuity and Contrast Test acuity, and the test-retest repeatability was also good.Significance. The self-adaptive step SSVEP procedure combined with the CCA method and SNR significance detection criterion appears to be an alternative method in the real-time SSVEP acuity test to obtain objective visual acuity more rapidly and precisely.},
}
@article {pmid33887702,
year = {2021},
author = {Ramírez Torres, JA and Daly, I},
title = {How to build a fast and accurate code-modulated brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfaac},
pmid = {33887702},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Objective.In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs.Approach.We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods. Then, we compared numerous different setups to assess their performance and identify the best configurations. We also evaluated the dependability of our simulated evaluation approach.Main results.Our results show that Golay, almost perfect, and deBruijn sequence-based visual stimulus modulations provide the best results, significantly outperforming the commonly used m-sequences in all cases. We conclude that artificial neural network processing algorithms offer the best processing pipeline for this type of BCI, achieving a maximum classification accuracy of 94.7% on real EEG data while obtaining a maximum ITR of 127.2 bits min[-1]in a simulated 64-target system.Significance.We used a simulated framework that demonstrated previously unattainable flexibility and convenience while staying reasonably realistic. Furthermore, our findings suggest several new considerations which can be used to guide further code-based BCI development.},
}
@article {pmid33887382,
year = {2021},
author = {Suzuki, Y and Kaneko, N and Sasaki, A and Tanaka, F and Nakazawa, K and Nomura, T and Milosevic, M},
title = {Muscle-specific movement-phase-dependent modulation of corticospinal excitability during upper-limb motor execution and motor imagery combined with virtual action observation.},
journal = {Neuroscience letters},
volume = {755},
number = {},
pages = {135907},
doi = {10.1016/j.neulet.2021.135907},
pmid = {33887382},
issn = {1872-7972},
mesh = {Adult ; Electromyography/methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Photic Stimulation/methods ; Pyramidal Tracts/*physiology ; Random Allocation ; Transcranial Magnetic Stimulation/methods ; Upper Extremity/physiology ; Young Adult ; },
abstract = {Corticospinal excitability in humans can be facilitated during imagination and/or observation of upper-limb motor tasks. However, it remains unclear to what extent facilitation levels may differ from those elicited during execution of the same tasks. Twelve able-bodied individuals were recruited in this study. Motor evoked potentials (MEPs) in extensor carpi radialis (ECR) and flexor carpi radialis (FCR) muscles were elicited through transcranial magnetic stimulation of the primary motor cortex during: (i) rest; (ii) wrist extension; and (iii) wrist flexion. Responses were compared between: (1) motor imagery combined with virtual action observation (MI + AO; first-person virtual wrist movements shown on a computer display, while participants remained at rest and imagined these movements); and (2) motor execution (ME; participants extended or flexed their wrist). During MI + AO, ECR MEPs were facilitated during the extension phase but not the flexion phase, while FCR MEPs were facilitated during the flexion phase but not extension phase, compared to rest. During the ME condition, same, but greater, modulations were shown as those during MI + AO, while background muscle activities were similar in the rest phase as during extension and flexion phase in the MI + AO condition. Our results demonstrated that kinesthetic MI that included imagination and observation of virtual hands can elicit phase-dependent muscles-specific corticospinal facilitation of wrist muscles, consistent to those during actual hand extension and flexion. Moreover, we showed that MI + AO can contribute considerably to the overall corticospinal facilitation (∼20 % of ME) even without muscle contractions. These findings support utility of computer graphics-based motor imagery, which may have implications for rehabilitation and development of brain-computer interfaces.},
}
@article {pmid33882461,
year = {2021},
author = {Iwane, F and Iturrate, I and Chavarriaga, R and Millán, JDR},
title = {Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abfa70},
pmid = {33882461},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Humans ; },
abstract = {Objective.When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols. The objective of the present study is to better understand the differences between two types of ErrPs elicited during continuous feedback protocols, where errors may occur either at predicted or unpredicted states. We hypothesize that ErrPs of the unpredicted state is associated with longer latency as it requires higher cognitive workload to evaluate actions compared to the predicted states.Approach.Participants monitored the trajectory of an autonomous cursor that occasionally made erroneous actions on its way to the target in two conditions, namely, predicted or unpredicted states. After characterizing the ErrP waveform elicited by erroneous actions in the two conditions, we performed single-trial decoding of ErrPs in both synchronous (i.e. time-locked to the onset of the erroneous action) and asynchronous manner. Furthermore, we explored the possibility to transfer decoders built with data of one of the conditions to the other condition.Main results.As hypothesized, erroneous actions at unpredicted states gave rise to ErrPs with higher latency than erroneous actions at predicted states, a correlate of higher cognitive effort in the former condition. Moreover, ErrP decoders trained in a given condition successfully transferred to the other condition with a slight loss of classification performance. This was the case for synchronous as well as asynchronous ErrP decoding, showing the invariability of ErrPs across conditions.Significance.These results advance the characterization of ErrPs during continuous feedback protocols, enlarging the potential use of ErrPs during natural operation of brain-controlled devices as it is not necessary to have different decoders for each kind of erroneous conditions.},
}
@article {pmid33875691,
year = {2021},
author = {Cheng, HJ and Ng, KK and Qian, X and Ji, F and Lu, ZK and Teo, WP and Hong, X and Nasrallah, FA and Ang, KK and Chuang, KH and Guan, C and Yu, H and Chew, E and Zhou, JH},
title = {Task-related brain functional network reconfigurations relate to motor recovery in chronic subcortical stroke.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {8442},
pmid = {33875691},
issn = {2045-2322},
mesh = {Brain/physiopathology ; Brain-Computer Interfaces ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Activity ; Motor Cortex/*physiopathology ; Neural Pathways/physiopathology ; Neuronal Plasticity/physiology ; Recovery of Function/*physiology ; Stroke/*physiopathology ; Stroke Rehabilitation ; Transcranial Direct Current Stimulation ; Upper Extremity/physiopathology ; },
abstract = {Stroke leads to both regional brain functional disruptions and network reorganization. However, how brain functional networks reconfigure as task demand increases in stroke patients and whether such reorganization at baseline would facilitate post-stroke motor recovery are largely unknown. To address this gap, brain functional connectivity (FC) were examined at rest and motor tasks in eighteen chronic subcortical stroke patients and eleven age-matched healthy controls. Stroke patients underwent a 2-week intervention using a motor imagery-assisted brain computer interface-based (MI-BCI) training with or without transcranial direct current stimulation (tDCS). Motor recovery was determined by calculating the changes of the upper extremity component of the Fugl-Meyer Assessment (FMA) score between pre- and post-intervention divided by the pre-intervention FMA score. The results suggested that as task demand increased (i.e., from resting to passive unaffected hand gripping and to active affected hand gripping), patients showed greater FC disruptions in cognitive networks including the default and dorsal attention networks. Compared to controls, patients had lower task-related spatial similarity in the somatomotor-subcortical, default-somatomotor, salience/ventral attention-subcortical and subcortical-subcortical connections, suggesting greater inefficiency in motor execution. Importantly, higher baseline network-specific FC strength (e.g., dorsal attention and somatomotor) and more efficient brain network reconfigurations (e.g., somatomotor and subcortical) from rest to active affected hand gripping at baseline were related to better future motor recovery. Our findings underscore the importance of studying functional network reorganization during task-free and task conditions for motor recovery prediction in stroke.},
}
@article {pmid33874995,
year = {2021},
author = {Qian, K and Liu, J and Cao, Y and Yang, J and Qiu, S},
title = {Intraperitoneal injection of lithium chloride induces lateralized activation of the insular cortex in adult mice.},
journal = {Molecular brain},
volume = {14},
number = {1},
pages = {71},
pmid = {33874995},
issn = {1756-6606},
mesh = {Aging/drug effects/*physiology ; Animals ; Brain Mapping ; Female ; Imaging, Three-Dimensional ; Injections, Intraperitoneal ; Insular Cortex/diagnostic imaging/drug effects/*physiology ; Lithium Chloride/*administration &amp; dosage/*pharmacology ; Male ; Mice, Inbred C57BL ; Physical Stimulation ; Proto-Oncogene Proteins c-fos/metabolism ; Staining and Labeling ; Vagotomy ; Mice ; },
abstract = {Insular cortex is a critical brain region that participates in the interoceptive sensations. Here, we combined the iDISCO + method and Fos immunostaining to confirm that the middle part of the right-side, but not the left-side, insular cortex in adult male mice is activated by intraperitoneal injection of lithium chloride. Lateralized activation of the insular cortex is also observed in adult female mice, but not in young or aged male mice. Furthermore, asymmetrical activation of the insular cortex was completely blocked when both sides of the vagal nerve are transected, whereas intravenous injection of lithium chloride has no effect on the insular activation. Combined together, these results indicate that the insular cortex unilaterally responds to aversive visceral stimuli in an age-dependent way and this process depends on the vagal afferent pathways.},
}
@article {pmid33872837,
year = {2021},
author = {Tang, AM and Chen, KH and Del Campo-Vera, RM and Sebastian, R and Gogia, AS and Nune, G and Liu, CY and Kellis, S and Lee, B},
title = {Hippocampal and Orbitofrontal Theta Band Coherence Diminishes During Conflict Resolution.},
journal = {World neurosurgery},
volume = {152},
number = {},
pages = {e32-e44},
pmid = {33872837},
issn = {1878-8769},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; *Conflict, Psychological ; Drug Resistant Epilepsy/diagnostic imaging/psychology/surgery ; Electrodes, Implanted ; Electroencephalography/methods/trends ; Female ; Hippocampus/diagnostic imaging/*physiology ; Humans ; Male ; Middle Aged ; Negotiating/*psychology ; Prefrontal Cortex/diagnostic imaging/*physiology ; Theta Rhythm/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Coherence between the hippocampus and other brain structures has been shown with the theta frequency (3-8 Hz). Cortical decreases in theta coherence are believed to reflect response accuracy efficiency. However, the role of theta coherence during conflict resolution is poorly understood in noncortical areas. In this study, coherence between the hippocampus and orbitofrontal cortex (OFC) was measured during a conflict resolution task. Although both brain areas have been previously implicated in the Stroop task, their interactions are not well understood.<br><br>METHODS: Nine patients were implanted with stereotactic electroencephalography contacts in the hippocampus and OFC. Local field potential data were sampled throughout discrete phases of a Stroop task. Coherence was calculated for hippocampal and OFC contact pairs, and coherence spectrograms were constructed for congruent and incongruent conditions. Coherence changes during cue processing were identified using a nonparametric cluster-permutation t test. Group analysis was conducted to compare overall theta coherence changes among conditions.<br><br>RESULTS: In 6 of 9 patients, decreased theta coherence was observed only during the incongruent condition (P < 0.05). Congruent theta coherence did not change from baseline. Group analysis showed lower theta coherence for the incongruent condition compared with the congruent condition (P < 0.05).<br><br>CONCLUSIONS: Theta coherence between the hippocampus and OFC decreased during conflict. This finding supports existing theories that theta coherence desynchronization contributes to improved response accuracy and processing efficiency during conflict resolution. The underlying theta coherence observed between the hippocampus and OFC during conflict may be distinct from its previously observed role in memory.},
}
@article {pmid33872671,
year = {2021},
author = {Ma, Q and Cao, Z and Li, H and Wang, W and Tian, Y and Yan, L and Liao, Y and Chen, X and Chen, Y and Shi, Y and Tang, S and Zhou, N},
title = {Two naturally occurring mutations of human GPR103 define distinct G protein selection bias.},
journal = {Biochimica et biophysica acta. Molecular cell research},
volume = {1868},
number = {7},
pages = {119046},
doi = {10.1016/j.bbamcr.2021.119046},
pmid = {33872671},
issn = {1879-2596},
mesh = {China ; GTP-Binding Protein alpha Subunits/metabolism ; GTP-Binding Proteins/metabolism ; HEK293 Cells ; Humans ; Ligands ; MAP Kinase Signaling System/genetics ; Male ; Mutation/genetics ; Neuropeptides/*metabolism/physiology ; Protein Conformation ; Receptors, G-Protein-Coupled/*genetics/metabolism ; Signal Transduction ; },
abstract = {The neuropeptide 26RFa plays important roles in the regulation of many physiological functions. 26RFa has been recognized as an endogenous ligand for receptor GPR103. In the present study, we demonstrate that GPR103 dually couples to Gαq and Gαi/o proteins. However, two naturally occurring missense mutations were identified from a young male patient. In the first, Y68H, induction of Ca[2+] mobilization was noted without detection of ERK1/2 activation. In the second, R371W, the potential to activate ERK1/2 signaling was retained but with failure to evoke Ca[2+] mobilization. Further analysis provides evidence that Gαq, L-type Ca[2+] channel and PKCβI and βII are involved in the Y68H-mediated signaling pathway, whereas Gαi/o, Gβγ, and PKCζ are implicated in the R371W-induced signaling. Our results demonstrate that two point mutations, Y68H and R371W, affect the equilibrium between the different receptor conformations, leading to alteration of G protein-coupling preferences. Importantly, these findings provide a foundation for future elucidation of GPCR-mediated biased signaling and the physiological implications of their bias.},
}
@article {pmid33872154,
year = {2021},
author = {Zhang, K and Xu, G and Du, C and Wu, Y and Zheng, X and Zhang, S and Han, C and Liang, R and Chen, R},
title = {Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {862-871},
doi = {10.1109/TNSRE.2021.3073918},
pmid = {33872154},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Technology ; },
abstract = {Brain computer interface (BCI) is a novel communication method that does not rely on the normal neural pathway between the brain and muscle of human. It can transform mental activities into relevant commands to control external equipment and establish direct communication pathway. Among different paradigms, steady-state visual evoked potential (SSVEP) is widely used due to its certain periodicity and stability of control. However, electroencephalogram (EEG) of SSVEP is extremely weak and companied with multi-scale and strong noise. Existing algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of feature extraction under the multi-scale noise. Especially for the subjects produce weak response for external stimuli in EEG representation, i.e., BCI-Illiteracy subject, traditional algorithms are difficult to recognize the internal patterns of brain. To address this issue, a novel method based on Chaos theory is proposed to extract feature of SSVEP. The rule of this method is applying the peculiarity of nonlinear dynamics system to detect feature of SSVEP by judging the state changes of chaotic systems after adding weak EEG. To evaluate the validity of proposed method, this research recruit 32 subjects to participate the experiment. All subjects are divided into two groups according to the preliminary classification accuracy (mean acc >70% or < 70%) by canonical correlation analysis and we define the accuracy above 70% as group A (normal subjects), below 70% as group B (BCI-Illiteracy). Then, the classification accuracy and information transmission rate of two groups are verified using Chaotic theory. Experimental results show that all classification methods using in our study achieve good performance for normal subjects while chaos obtain excellent performance and significant improvements than traditional methods for BCI-Illiteracy.},
}
@article {pmid33867928,
year = {2021},
author = {Singanamalla, SKR and Lin, CT},
title = {Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {651762},
pmid = {33867928},
issn = {1662-4548},
abstract = {With the advent of advanced machine learning methods, the performance of brain-computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.},
}
@article {pmid33867912,
year = {2021},
author = {Leuthardt, EC and Moran, DW and Mullen, TR},
title = {Defining Surgical Terminology and Risk for Brain Computer Interface Technologies.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {599549},
pmid = {33867912},
issn = {1662-4548},
abstract = {With the emergence of numerous brain computer interfaces (BCI), their form factors, and clinical applications the terminology to describe their clinical deployment and the associated risk has been vague. The terms "minimally invasive" or "non-invasive" have been commonly used, but the risk can vary widely based on the form factor and anatomic location. Thus, taken together, there needs to be a terminology that best accommodates the surgical footprint of a BCI and their attendant risks. This work presents a semantic framework that describes the BCI from a procedural standpoint and its attendant clinical risk profile. We propose extending the common invasive/non-invasive distinction for BCI systems to accommodate three categories in which the BCI anatomically interfaces with the patient and whether or not a surgical procedure is required for deployment: (1) Non-invasive-BCI components do not penetrate the body, (2) Embedded-components are penetrative, but not deeper than the inner table of the skull, and (3) Intracranial -components are located within the inner table of the skull and possibly within the brain volume. Each class has a separate risk profile that should be considered when being applied to a given clinical population. Optimally, balancing this risk profile with clinical need provides the most ethical deployment of these emerging classes of devices. As BCIs gain larger adoption, and terminology becomes standardized, having an improved, more precise language will better serve clinicians, patients, and consumers in discussing these technologies, particularly within the context of surgical procedures.},
}
@article {pmid33864836,
year = {2021},
author = {Singh, A and Gumaste, A},
title = {Decoding Imagined Speech and Computer Control using Brain Waves.},
journal = {Journal of neuroscience methods},
volume = {358},
number = {},
pages = {109196},
doi = {10.1016/j.jneumeth.2021.109196},
pmid = {33864836},
issn = {1872-678X},
mesh = {*Brain Waves ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Speech ; },
abstract = {BACKGROUND: In this work, we explore the possibility of decoding Imagined Speech (IS) brain waves using machine learning techniques.<br><br>APPROACH: We design two finite state machines to create an interface for controlling a computer system using an IS-based brain-computer interface. To decode IS signals, we propose a covariance matrix of Electroencephalogram channels as input features, covariance matrices projection to tangent space for obtaining vectors from matrices, principal component analysis for dimension reduction of vectors, an artificial neural network (ANN) as a classification model, and bootstrap aggregation for creating an ensemble of ANN models.<br><br>RESULT: Based on these findings, we are first to use an IS-based system to operate a computer and obtain an information transfer rate of 21-bits-per-minute. The proposed approach can decode the IS signal with a mean classification accuracy of 85% on classifying one long vs. short word. Our proposed approach can also differentiate between IS and rest state brain signals with a mean classification accuracy of 94%.<br><br>COMPARISON: After comparison, we show that our approach performs equivalent to the state-of-the-art approach (SOTA) on decoding long vs. short word classification task. We also show that the proposed method outperforms SOTA significantly on decoding three short words and vowels with an average margin of 11% and 9%, respectively.<br><br>CONCLUSION: These results show that the proposed approach can decode a wide variety of IS signals and is practically applicable in a real-time environment.},
}
@article {pmid33863725,
year = {2021},
author = {Kim, JY and Yun, YJ and Jeong, J and Kim, CY and Müller, KR and Lee, SW},
title = {Leaf-inspired homeostatic cellulose biosensors.},
journal = {Science advances},
volume = {7},
number = {16},
pages = {},
pmid = {33863725},
issn = {2375-2548},
mesh = {*Biosensing Techniques ; *Cellulose ; Homeostasis ; Plant Leaves ; Sweat ; },
abstract = {An incompatibility between skin homeostasis and existing biosensor interfaces inhibits long-term electrophysiological signal measurement. Inspired by the leaf homeostasis system, we developed the first homeostatic cellulose biosensor with functions of protection, sensation, self-regulation, and biosafety. Moreover, we find that a mesoporous cellulose membrane transforms into homeostatic material with properties that include high ion conductivity, excellent flexibility and stability, appropriate adhesion force, and self-healing effects when swollen in a saline solution. The proposed biosensor is found to maintain a stable skin-sensor interface through homeostasis even when challenged by various stresses, such as a dynamic environment, severe detachment, dense hair, sweat, and long-term measurement. Last, we demonstrate the high usability of our homeostatic biosensor for continuous and stable measurement of electrophysiological signals and give a showcase application in the field of brain-computer interfacing where the biosensors and machine learning together help to control real-time applications beyond the laboratory at unprecedented versatility.},
}
@article {pmid33862607,
year = {2021},
author = {Cao, L and Li, G and Xu, Y and Zhang, H and Shu, X and Zhang, D},
title = {A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf8cb},
pmid = {33862607},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; *Robotic Surgical Procedures ; },
abstract = {Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands. Based on the inference confidence and user's characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm.Main results.Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately.Significance.In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.},
}
@article {pmid33854643,
year = {2021},
author = {Zhao, S and Shang, Y and Yang, Z and Xiao, X and Zhang, J and Zhang, T},
title = {Application of expert system and LSTM in extracting index of synaptic plasticity.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {2},
pages = {253-263},
pmid = {33854643},
issn = {1871-4080},
abstract = {The indexes of synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), can usually be measured by evaluating the slope and/or magnitude of field excitatory postsynaptic potentials (fEPSPs). So far, the process depends on manually labeling the linear portion of fEPSPs one by one, which is not only a subjective procedure but also a time-consuming job. In the present study, a novel approach has been developed in order to objectively and effectively evaluate the index of synaptic plasticity. Firstly, we introduced an expert system applying symbolic rules to discard the contaminated waveform in an interpretable way, and further generate supervisory signals for subsequent seq 2seq model based on neural networks. For the propose of enhancing the system generalization ability to deal with the contaminated data of fEPSPs, we employed long short-term memory (LSTM) networks. Finally, the comparison was performed between the automatically labeling system and manually labeling system. These results show that the expert system achieves an accuracy of 96.22% on Type-I labels, and the LSTM supervised by the expert system obtains an accuracy of 96.73% on Type-II labels. Compared to the manually labeling system, the hybrids system is able to measure the index of synaptic plasticity more objectively and efficiently. The new system can reach the level of the human expert ability, and accurately produce the index of synaptic plasticity in a fast way.},
}
@article {pmid33852389,
year = {2021},
author = {Qin, K and Wang, R and Zhang, Y},
title = {Filter Bank-Driven Multivariate Synchronization Index for Training-Free SSVEP BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {934-943},
doi = {10.1109/TNSRE.2021.3073165},
pmid = {33852389},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {In recent years, multivariate synchronization index (MSI) algorithm, as a novel frequency detection method, has attracted increasing attentions in the study of brain-computer interfaces (BCIs) based on steady state visual evoked potential (SSVEP). However, MSI algorithm is hard to fully exploit SSVEP-related harmonic components in the electroencephalogram (EEG), which limits the application of MSI algorithm in BCI systems. In this paper, we propose a novel filter bank-driven MSI algorithm (FBMSI) to overcome the limitation and further improve the accuracy of SSVEP recognition. We evaluate the efficacy of the FBMSI method by developing a 6-command SSVEP-NAO robot system with extensive experimental analyses. An offline experimental study is first performed with EEG collected from nine subjects to investigate the effects of varying parameters on the model performance. Offline results show that the proposed method has achieved a stable improvement effect. We further conduct an online experiment with six subjects to assess the efficacy of the developed FBMSI algorithm in a real-time BCI application. The online experimental results show that the FBMSI algorithm yields a promising average accuracy of 83.56% using a data length of even only one second, which was 12.26% higher than the standard MSI algorithm. These extensive experimental results confirmed the effectiveness of the FBMSI algorithm in SSVEP recognition and demonstrated its potential application in the development of improved BCI systems.},
}
@article {pmid33852388,
year = {2021},
author = {Ge, S and Jiang, Y and Zhang, M and Wang, R and Iramina, K and Lin, P and Leng, Y and Wang, H and Zheng, W},
title = {SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {760-769},
doi = {10.1109/TNSRE.2021.3073134},
pmid = {33852388},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.},
}
@article {pmid33850667,
year = {2021},
author = {Mahendra Kumar, JL and Rashid, M and Muazu Musa, R and Mohd Razman, MA and Sulaiman, N and Jailani, R and P P Abdul Majeed, A},
title = {The classification of EEG-based winking signals: a transfer learning and random forest pipeline.},
journal = {PeerJ},
volume = {9},
number = {},
pages = {e11182},
pmid = {33850667},
issn = {2167-8359},
abstract = {Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.},
}
@article {pmid33849003,
year = {2021},
author = {de Cheveigné, A and Slaney, M and Fuglsang, SA and Hjortkjaer, J},
title = {Auditory stimulus-response modeling with a match-mismatch task.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf771},
pmid = {33849003},
issn = {1741-2552},
mesh = {Attention ; Auditory Perception ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; },
abstract = {Objective.An auditory stimulus can be related to the brain response that it evokes by a stimulus-response model fit to the data. This offers insight into perceptual processes within the brain and is also of potential use for devices such as brain computer interfaces (BCIs). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance.Approach.Here we focus on amatch-mismatch(MM) task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it.Main results. Using these metrics, we describe a range of models of increasing complexity that we compare to methods in the literature, showing state-of-the-art performance. We document in detail one particular implementation, calibrated on a publicly-available database, that can serve as a robust reference to evaluate future developments.Significance.The MM task allows stimulus-response models to be evaluated in the limit of very high model accuracy, making it an attractive alternative to the more commonly used task of auditory attention detection. The MM task does not require class labels, so it is immune to mislabeling, and it is applicable to data recorded in listening scenarios with only one sound source, thus it is cheap to obtain large quantities of training and testing data. Performance metrics from this task, associated with regression accuracy, provide complementary insights into the relation between stimulus and response, as well as information about discriminatory power directly applicable to BCI applications.},
}
@article {pmid33847221,
year = {2021},
author = {Rahman, MKM and Haque, T},
title = {Classification of motor imagery using a time-localised approach.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {5},
pages = {361-374},
doi = {10.1080/03091902.2021.1906966},
pmid = {33847221},
issn = {1464-522X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) is getting increasing attention where classification of motor imagery (MI) using electroencephalography (EEG) signal plays a vital role. In traditional EEG-based BCI setup, after applying pre-processing like band-pass filtering and spatial filtering, features are extracted and are fed to the classifier. However, most of the traditional features are extracted from a single time window, which is usually the full-time frame of a cue-based MI signal. Such features are usually statistical characteristics like log-variance of the whole-time signal. Thus, the information, which is localised in time and crucial for subject-specific MI classification, is not best captured. In this work, a new time-localised approach is proposed where multiple time windows are used for feature extraction. We have developed a number of feature representations using those time windows. Our experimental results corroborate that the proposed approach can achieve higher accuracy of classification when compared to methods using conventional features using the same platform.},
}
@article {pmid33845469,
year = {2021},
author = {Thielen, B and Meng, E},
title = {A comparison of insertion methods for surgical placement of penetrating neural interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {33845469},
issn = {1741-2552},
support = {U01 NS099703/NS/NINDS NIH HHS/United States ; },
mesh = {Electrodes, Implanted ; Mechanical Phenomena ; Microelectrodes ; Neuroglia ; *Neurons ; *Polymers ; },
abstract = {Many implantable electrode arrays exist for the purpose of stimulating or recording electrical activity in brain, spinal, or peripheral nerve tissue, however most of these devices are constructed from materials that are mechanically rigid. A growing body of evidence suggests that the chronic presence of these rigid probes in the neural tissue causes a significant immune response and glial encapsulation of the probes, which in turn leads to gradual increase in distance between the electrodes and surrounding neurons. In recording electrodes, the consequence is the loss of signal quality and, therefore, the inability to collect electrophysiological recordings long term. In stimulation electrodes, higher current injection is required to achieve a comparable response which can lead to tissue and electrode damage. To minimize the impact of the immune response, flexible neural probes constructed with softer materials have been developed. These flexible probes, however, are often not strong enough to be inserted on their own into the tissue, and instead fail via mechanical buckling of the shank under the force of insertion. Several strategies have been developed to allow the insertion of flexible probes while minimizing tissue damage. It is critical to keep these strategies in mind during probe design in order to ensure successful surgical placement. In this review, existing insertion strategies will be presented and evaluated with respect to surgical difficulty, immune response, ability to reach the target tissue, and overall limitations of the technique. Overall, the majority of these insertion techniques have only been evaluated for the insertion of a single probe and do not quantify the accuracy of probe placement. More work needs to be performed to evaluate and optimize insertion methods for accurate placement of devices and for devices with multiple probes.},
}
@article {pmid33844633,
year = {2021},
author = {Deo, DR and Rezaii, P and Hochberg, LR and M Okamura, A and Shenoy, KV and Henderson, JM},
title = {Effects of Peripheral Haptic Feedback on Intracortical Brain-Computer Interface Control and Associated Sensory Responses in Motor Cortex.},
journal = {IEEE transactions on haptics},
volume = {14},
number = {4},
pages = {762-775},
pmid = {33844633},
issn = {2329-4051},
support = {I50 RX002864/RX/RRD VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Feedback ; Feedback, Sensory ; Haptic Technology ; Humans ; *Motor Cortex ; Quadriplegia ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) provide people with paralysis a means to control devices with signals decoded from brain activity. Despite recent impressive advances, these devices still cannot approach able-bodied levels of control. To achieve naturalistic control and improved performance of neural prostheses, iBCIs will likely need to include proprioceptive feedback. With the goal of providing proprioceptive feedback via mechanical haptic stimulation, we aim to understand how haptic stimulation affects motor cortical neurons and ultimately, iBCI control. We provided skin shear haptic stimulation as a substitute for proprioception to the back of the neck of a person with tetraplegia. The neck location was determined via assessment of touch sensitivity using a monofilament test kit. The participant was able to correctly report skin shear at the back of the neck in 8 unique directions with 65% accuracy. We found motor cortical units that exhibited sensory responses to shear stimuli, some of which were strongly tuned to the stimuli and well modeled by cosine-shaped functions. In this article, we also demonstrated online iBCI cursor control with continuous skin-shear feedback driven by decoded command signals. Cursor control performance increased slightly but significantly when the participant was given haptic feedback, compared to the purely visual feedback condition.},
}
@article {pmid33844158,
year = {2021},
author = {Tang, W and Zhou, D and Wang, S and Hao, S and Wang, X and Helmy, M and Zhu, J and Wang, H},
title = {CRH Neurons in the Laterodorsal Tegmentum Mediate Acute Stress-induced Anxiety.},
journal = {Neuroscience bulletin},
volume = {37},
number = {7},
pages = {999-1004},
pmid = {33844158},
issn = {1995-8218},
mesh = {Anxiety/etiology ; Corticotropin-Releasing Hormone/metabolism ; *Neurons/metabolism ; *Tegmentum Mesencephali ; },
}
@article {pmid33843427,
year = {2021},
author = {Pio-Lopez, L and Poulkouras, R and Depannemaecker, D},
title = {Visual cortical prosthesis: an electrical perspective.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {5},
pages = {394-407},
doi = {10.1080/03091902.2021.1907468},
pmid = {33843427},
issn = {1464-522X},
mesh = {Electric Stimulation ; Humans ; Phosphenes ; Vision, Ocular ; *Visual Cortex ; Visual Perception ; *Visual Prosthesis ; },
abstract = {The electrical stimulation of the visual cortices has the potential to restore vision to blind individuals. Until now, the results of visual cortical prosthetics have been limited as no prosthesis has restored a full working vision but the field has shown a renewed interest these last years, thanks to wireless and technological advances. However, several scientific and technical challenges are still open to achieve the therapeutic benefit expected by these new devices. One of the main challenges is the electrical stimulation of the brain itself. In this review, we analyse the results in electrode-based visual cortical prosthetics from the electrical point of view. We first describe what is known about the electrode-tissue interface and safety of electrical stimulation. Then we focus on the psychophysics of prosthetic vision and the state-of-the-art on the interplay between the electrical stimulation of the visual cortex and the phosphene perception. Lastly, we discuss the challenges and perspectives of visual cortex electrical stimulation and electrode array design to develop the new generation implantable cortical visual prostheses.},
}
@article {pmid33841120,
year = {2021},
author = {Turi, F and Clerc, M and Papadopoulo, T},
title = {Long Multi-Stage Training for a Motor-Impaired User in a BCI Competition.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {647908},
pmid = {33841120},
issn = {1662-5161},
abstract = {In a Mental Imagery Brain-Computer Interface the user has to perform a specific mental task that generates electroencephalography (EEG) components, which can be translated in commands to control a BCI system. The development of a high-performance MI-BCI requires a long training, lasting several weeks or months, in order to improve the ability of the user to manage his/her mental tasks. This works aims to present the design of a MI-BCI combining mental imaginary and cognitive tasks for a severely motor impaired user, involved in the BCI race of the Cybathlon event, a competition of people with severe motor disability. In the BCI-race, the user becomes a pilot in a virtual race game against up to three other pilots, in which each pilot has to control his/her virtual car by his/her mental tasks. We present all the procedures followed to realize an effective MI-BCI, from the user's first contact with a BCI technology to actually controlling a video-game through her EEG. We defined a multi-stage user-centered training protocol in order to successfully control a BCI, even in a stressful situation, such as that of a competition. We put a specific focus on the human aspects that influenced the long training phase of the system and the participation to the competition.},
}
@article {pmid33841094,
year = {2021},
author = {Xiao, X and Fang, Y},
title = {Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {655599},
pmid = {33841094},
issn = {1662-4548},
abstract = {Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.},
}
@article {pmid33836516,
year = {2021},
author = {Li, MA and Ruan, ZW},
title = {A novel decoding method for motor imagery tasks with 4D data representation and 3D convolutional neural networks.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf68b},
pmid = {33836516},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective. Motor imagery electroencephalography (MI-EEG) produces one of the most commonly used biosignals in intelligent rehabilitation systems. The newly developed 3D convolutional neural network (3DCNN) is gaining increasing attention for its ability to recognize MI tasks. The key to successful identification of movement intention is dependent on whether the data representation can faithfully reflect the cortical activity induced by MI. However, the present data representation, which is often generated from partial source signals with time-frequency analysis, contains incomplete information. Therefore, it would be beneficial to explore a new type of data representation using raw spatiotemporal dipole information as well as the possible development of a matching 3DCNN.Approach.Based on EEG source imaging and 3DCNN, a novel decoding method for identifying MI tasks is proposed, called ESICNND. MI-EEG is mapped to the cerebral cortex by the standardized low resolution electromagnetic tomography algorithm, and the optimal sampling points of the dipoles are selected as the time of interest to best reveal the difference between any two MI tasks. Then, the initial subject coordinate system is converted to a magnetic resonance imaging coordinate system, followed by dipole interpolation and volume down-sampling; the resulting 3D dipole amplitude matrices are merged at the selected sampling points to obtain 4D dipole feature matrices (4DDFMs). These matrices are augmented by sliding window technology and input into a 3DCNN with a cascading architecture of three modules (3M3DCNN) to perform the extraction and classification of comprehensive features.Main results.Experiments are carried out on two public datasets; the average ten-fold CV classification accuracies reach 88.73% and 96.25%, respectively, and the statistical analysis demonstrates outstanding consistency and stability.Significance.The 4DDFMs reveals the variation of cortical activation in a 3D spatial cube with a temporal dimension and matches the 3M3DCNN well, making full use of the high-resolution spatiotemporal information from all dipoles.},
}
@article {pmid33836507,
year = {2021},
author = {Sheth, J and Tankus, A and Tran, M and Pouratian, N and Fried, I and Speier, W},
title = {Generalizing neural signal-to-text brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {7},
number = {3},
pages = {},
doi = {10.1088/2057-1976/abf6ab},
pmid = {33836507},
issn = {2057-1976},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Language ; Speech ; Translating ; },
abstract = {Objective:Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text.Approach:In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word.Main result:This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches.Significance:Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.},
}
@article {pmid33836168,
year = {2021},
author = {Cheng, S and Li, M and Fan, J and Shang, Z and Wan, H},
title = {Decoding route selection of pigeon during goal-directed behavior: A joint spike-LFP study.},
journal = {Behavioural brain research},
volume = {409},
number = {},
pages = {113289},
doi = {10.1016/j.bbr.2021.113289},
pmid = {33836168},
issn = {1872-7549},
mesh = {Animals ; Behavior, Animal/*physiology ; Columbidae ; Decision Making/*physiology ; Electrocorticography ; *Goals ; Hippocampus/*physiology ; Spatial Navigation/*physiology ; },
abstract = {How to reach the goal is one of the core problems that animals must solve to complete goal-directed behavior. Studies have proved the important role of hippocampus (Hp) in spatial navigation and shown that hippocampal neural activities can represent the current location and goal location. However, for the different routes linking these two locations, the neural representation mechanism of the route selection in Hp is not clear. Here, we addressed this question using neural recordings of Hp ensembles and decoding analyses in pigeons performing a goal-directed route selection task known to require Hp participation. The hippocampal spike trains and local field potentials (LFPs) of five pigeons performing the task were acquired and analyzed. We found that the neuron firing rates and power spectrum characteristics in Hp could encode the animal's route selection during goal-directed behavior, suggesting that the representation of route selection was coherent for hippocampal spike and LFP signals. Decoding results further indicated that joint spike-LFP features resulted in a significant improvement in the representation accuracy of the route selection. These findings of this study will help to understand the encoding mechanism of route selection in goal-directed behavior.},
}
@article {pmid33834704,
year = {2021},
author = {Passarelli, L and Gamberini, M and Fattori, P},
title = {The superior parietal lobule of primates: a sensory-motor hub for interaction with the environment.},
journal = {Journal of integrative neuroscience},
volume = {20},
number = {1},
pages = {157-171},
doi = {10.31083/j.jin.2021.01.334},
pmid = {33834704},
issn = {0219-6352},
support = {2017KZNZLN//Ministero dell'Universit&#xe0; e della Ricerca/ ; //Fondazione Cassa di Risparmio di Bologna (Bando Internazionalizzazione)/ ; 952026//European Commission/ ; H2020-MSCA-734227-PLATYPUS//European Commission/ ; H2020-EIC-FETPROACT-2019-951910-MAIA//European Commission/ ; },
mesh = {Animals ; *Cerebral Cortex/anatomy &amp; histology/physiology ; Humans ; Macaca ; *Mental Processes/physiology ; *Motor Activity/physiology ; *Nerve Net/anatomy &amp; histology/physiology ; *Parietal Lobe/anatomy &amp; histology/physiology ; *Thalamus/anatomy &amp; histology/physiology ; },
abstract = {The superior parietal lobule of the macaque monkey occupies the postero-medial part of the parietal lobe and plays a crucial role in the integration of different sources of information (from visual, motor and somatosensory brain regions) for the purpose of high-level cognitive functions, as perception for action. This region encompasses the intraparietal sulcus and the parieto-occipital sulcus and includes also the precuneate cortex in the mesial surface of the hemisphere. It hosts several areas extensively studied in the macaque: PE, PEip, PEci anteriorly and PEc, MIP, PGm and V6A posteriorly. Recently studies based on functional MRI have suggested putative human homologue of some of the areas of the macaque superior parietal lobule. Here we review the anatomical subdivision, the cortico-cortical and thalamo-cortical connections of the macaque superior parietal lobule compared with their functional properties and the homology with human organization in physiological and lesioned situations. The knowledge of this part of the macaque brain could help in understanding pathological conditions that in humans affect the normal behaviour of arm-reaching actions and can inspire brain computer interfaces performing in more accurate ways the sensorimotor transformations needed to interact with the surrounding environment.},
}
@article {pmid33834111,
year = {2021},
author = {Huang, W and Peng, Y and Ge, Y and Kong, W},
title = {A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation.},
journal = {PeerJ. Computer science},
volume = {7},
number = {},
pages = {e450},
pmid = {33834111},
issn = {2376-5992},
abstract = {The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clustering and spectral clustering, we propose a new Kmeans formulation by joint spectral embedding and spectral rotation which is an effective postprocessing approach to perform the discretization, termed KMSR. Further, instead of directly using the dot-product data similarity measure, we make generalization on KMSR by incorporating more advanced data similarity measures and call this generalized model as KMSR-G. An efficient optimization method is derived to solve the KMSR (KMSR-G) model objective whose complexity and convergence are provided. We conduct experiments on extensive benchmark datasets to validate the performance of our proposed models and the experimental results demonstrate that our models perform better than the related methods in most cases.},
}
@article {pmid33831852,
year = {2021},
author = {Ko, LW and Su, CH and Liao, PL and Liang, JT and Tseng, YH and Chen, SH},
title = {Flexible graphene/GO electrode for gel-free EEG.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf609},
pmid = {33831852},
issn = {1741-2552},
mesh = {Electrodes ; Electroencephalography ; *Graphite ; },
abstract = {Objective.Developments in electroencephalography (EEG) technology have allowed the use of the brain-computer interface (BCI) outside dedicated labratories. In order to achieve long-term monitoring and detection of EEG signals for BCI application, dry electrodes with good signal quality and high bio compatibility are essential. In 2016, we proposed a flexible dry electrode made of silicone gel and Ag flakes, which showed good signal quality and mechanical robustness. However, the Ag components used in our previous design made the electrode too expensive for commercial adaptation.Approach.In this study, we developed an affordable dry electrode made of silicone gel, metal flakes and graphene/GO based on our previous design. Two types of electrodes with different graphene/GO proportions were produced to explore how the amount of graphene/GO affects the electrode.Main results.During our tests, the electrodes showed low impedance and had good signal correlation to conventional wet electrodes in both the time and frequency domains. The graphene/GO electrode also showed good signal quality in eyes-open EEG recording. We also found that the electrode with more graphene/GO had an uneven surface and worse signal quality. This suggests that adding too much graphene/GO may reduce the electrods' performance. Furthermore, we tested the proposed dry electrodes' capability in detecting steady state visually evoked potential. We found that the dry electrodes can reliably detect evoked potential changes even in the hairy occipital area.Significance.Our results showed that the proposed electrode has good signal quality and is ready for BCI applications.},
}
@article {pmid33830927,
year = {2022},
author = {Sun, B and Mu, C and Wu, Z and Zhu, X},
title = {Training-Free Deep Generative Networks for Compressed Sensing of Neural Action Potentials.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {10},
pages = {5190-5199},
doi = {10.1109/TNNLS.2021.3069436},
pmid = {33830927},
issn = {2162-2388},
mesh = {Action Potentials/physiology ; *Neural Networks, Computer ; },
abstract = {Energy consumption is an important issue for resource-constrained wireless neural recording applications with limited data bandwidth. Compressed sensing (CS) is a promising framework for addressing this challenge because it can compress data in an energy-efficient way. Recent work has shown that deep neural networks (DNNs) can serve as valuable models for CS of neural action potentials (APs). However, these models typically require impractically large datasets and computational resources for training, and they do not easily generalize to novel circumstances. Here, we propose a new CS framework, termed APGen, for the reconstruction of APs in a training-free manner. It consists of a deep generative network and an analysis sparse regularizer. We validate our method on two in vivo datasets. Even without any training, APGen outperformed model-based and data-driven methods in terms of reconstruction accuracy, computational efficiency, and robustness to AP overlap and misalignment. The computational efficiency of APGen and its ability to perform without training make it an ideal candidate for long-term, resource-constrained, and large-scale wireless neural recording. It may also promote the development of real-time, naturalistic brain-computer interfaces.},
}
@article {pmid33828507,
year = {2021},
author = {Mencel, J and Jaskólska, A and Marusiak, J and Kamiński, &#x141; and Kurzyński, M and Wołczowski, A and Jaskólski, A and Kisiel-Sajewicz, K},
title = {Motor Imagery Training of Reaching-to-Grasp Movement Supplemented by a Virtual Environment in an Individual With Congenital Bilateral Transverse Upper-Limb Deficiency.},
journal = {Frontiers in psychology},
volume = {12},
number = {},
pages = {638780},
pmid = {33828507},
issn = {1664-1078},
abstract = {This study explored the effect of kinesthetic motor imagery training on reaching-to-grasp movement supplemented by a virtual environment in a patient with congenital bilateral transverse upper-limb deficiency. Based on a theoretical assumption, it is possible to conduct such training in this patient. The aim of this study was to evaluate whether cortical activity related to motor imagery of reaching and motor imagery of grasping of the right upper limb was changed by computer-aided imagery training (CAIT) in a patient who was born without upper limbs compared to a healthy control subject, as characterized by multi-channel electroencephalography (EEG) signals recorded before and 4, 8, and 12 weeks after CAIT. The main task during CAIT was to kinesthetically imagine the execution of reaching-to-grasp movements without any muscle activation, supplemented by computer visualization of movements provided by a special headset. Our experiment showed that CAIT can be conducted in the patient with higher vividness of imagery for reaching than grasping tasks. Our results confirm that CAIT can change brain activation patterns in areas related to motor planning and the execution of reaching and grasping movements, and that the effect was more pronounced in the patient than in the healthy control subject. The results show that CAIT has a different effect on the cortical activity related to the motor imagery of a reaching task than on the cortical activity related to the motor imagery of a grasping task. The change observed in the activation patterns could indicate CAIT-induced neuroplasticity, which could potentially be useful in rehabilitation or brain-computer interface purposes for such patients, especially before and after transplantation. This study was part of a registered experiment (ID: NCT04048083).},
}
@article {pmid33823492,
year = {2021},
author = {Liu, Q and Zheng, W and Chen, K and Ma, L and Ai, Q},
title = {Online detection of class-imbalanced error-related potentials evoked by motor imagery.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf522},
pmid = {33823492},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; },
abstract = {Objective.Error-related potentials (ErrPs) are spontaneous electroencephalogram signals related to the awareness of erroneous responses within brain domain. ErrPs-based correction mechanisms can be applied to motor imagery-brain-computer interface (MI-BCI) to prevent incorrect actions and ultimately improve the performance of the hybrid BCI. Many studies on ErrPs detection are mostly conducted under offline conditions with poor classification accuracy and the error rates of ErrPs are preset in advance, which is too ideal to apply in realistic applications. In order to solve these problems, a novel method based on adaptive autoregressive (AAR) model and common spatial pattern (CSP) is proposed for ErrPs feature extraction. In addition, an adaptive threshold classification method based spectral regression discriminant analysis (SRDA) is suggested for class-unbalanced ErrPs data to reduce the false positives and false negatives.Approach.As for ErrPs feature extraction, the AAR coefficients in the temporal domain and CSP in the spatial domain are fused. Given that the performance of different subjects' MI tasks is different but stable, and the samples of ErrPs are class-imbalanced, an adaptive threshold based SRDA is suggested for classification. Two datasets are used in this paper. The open public clinical neuroprosthetics and brain interaction (CNBI) dataset is used to validate the performance of the proposed feature extraction algorithm and the real-time data recorded in our self-designed system is used to validate the performance of the proposed classification algorithm under class-imbalanced situations. Different from the pseudo-random paradigm, the ErrPs signals collected in our experiments are all elicited by four-class of online MI-BCI tasks, and the sample distribution is more natural and suitable for practical tests.Main results.The experimental results on the CNBI dataset show that the average accuracy and false positive rate for ErrPs detection are 94.1% and 8.1%, which outperforms methods using features extracted from a single domain. What's more, although the ErrPs induction rate is affected by the performance of subjects' MI-BCI tasks, experimental results on data recorded in the self-designed system prove that the ErrPs classification algorithm based on an adaptive threshold is robust under different ErrPs data distributions. Compared with two other methods, the proposed algorithm has advantages in all three measures which are accuracy, F1-score and false positive rate. Finally, ErrPs detection results were used to prevent wrong actions in a MI-BCI experiment, and it leads to a reduction of the hybrid BCI error rate from 48.9% to 24.3% in online tests.Significance.Both the AAR-CSP fused feature extraction and the adaptive threshold based SRDA classification methods suggested in our work are efficient in improving the ErrPs detection accuracy and reducing the false positives. In addition, by introducing ErrPs to multi-class MI-BCIs, the MI decoding results can be corrected after ErrPs are detected to avoid executing wrong instructions, thereby improving the BCI accuracy and lays the foundation for using MI-BCIs in practical applications.},
}
@article {pmid33821808,
year = {2021},
author = {Xie, J and Chen, S and Zhang, Y and Gao, D and Liu, T},
title = {Combining generative adversarial networks and multi-output CNN for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {046026},
doi = {10.1088/1741-2552/abecc5},
pmid = {33821808},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; },
abstract = {OBJECTIVE: Motor imagery (MI) classification is an important task in the brain-computer interface (BCI) field. MI data exhibit highly dynamic characteristics and are difficult to obtain. Therefore, the performance of the classification model will be challenged. Recently, convolutional neural networks (CNNs) have been employed for MI classification and have demonstrated favorable performances. However, the traditional CNN model uses an end-to-end output method, and part of the feature information is discarded during the transmission process.<br><br>APPROACH: Herein, we propose a novel algorithm, that is, a combined long short-term memory generative adversarial networks (LGANs) and multi-output convolutional neural network (MoCNN) for MI classification, and an attention network for improving model performance. Specifically, the proposed method comprises three steps. First, MI data are obtained, and preprocessing is performed. Second, additional data are generated for training. Here, a data augmentation method based on a LGAN is designed. Last, the MoCNN is proposed to improve the classification performance.<br><br>MAIN RESULTS: The BCI competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed method. With multiple evaluation indicators, the proposed generative model can generate more realistic data. The expanded training set improves the performance of the classification model.<br><br>SIGNIFICANCE: The results show that the proposed method improves the classification of MI data, which facilitates motion imagination.},
}
@article {pmid33819158,
year = {2021},
author = {Miao, Y and Jin, J and Daly, I and Zuo, C and Wang, X and Cichocki, A and Jung, TP},
title = {Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {699-707},
doi = {10.1109/TNSRE.2021.3071140},
pmid = {33819158},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.},
}
@article {pmid33817022,
year = {2021},
author = {Rashid, M and Bari, BS and Hasan, MJ and Razman, MAM and Musa, RM and Ab Nasir, AF and P P Abdul Majeed, A},
title = {The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.},
journal = {PeerJ. Computer science},
volume = {7},
number = {},
pages = {e374},
pmid = {33817022},
issn = {2376-5992},
abstract = {Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.},
}
@article {pmid33815554,
year = {2021},
author = {Sun, X and Xu, K and Shi, Y and Li, H and Li, R and Yang, S and Jin, H and Feng, C and Li, B and Xing, C and Qu, Y and Wang, Q and Chen, Y and Yang, T},
title = {Discussion on the Rehabilitation of Stroke Hemiplegia Based on Interdisciplinary Combination of Medicine and Engineering.},
journal = {Evidence-based complementary and alternative medicine : eCAM},
volume = {2021},
number = {},
pages = {6631835},
pmid = {33815554},
issn = {1741-427X},
abstract = {Interdisciplinary combinations of medicine and engineering are part of the strategic plan of many universities aiming to be world-class institutions. One area in which these interactions have been prominent is rehabilitation of stroke hemiplegia. This article reviews advances in the last five years of stroke hemiplegia rehabilitation via interdisciplinary combination of medicine and engineering. Examples of these technologies include VR, RT, mHealth, BCI, tDCS, rTMS, and TCM rehabilitation. In this article, we will summarize the latest research in these areas and discuss the advantages and disadvantages of each to examine the frontiers of interdisciplinary medicine and engineering advances.},
}
@article {pmid33815261,
year = {2021},
author = {Dan, B},
title = {New Ethical Issues in Cerebral Palsy.},
journal = {Frontiers in neurology},
volume = {12},
number = {},
pages = {650653},
pmid = {33815261},
issn = {1664-2295},
abstract = {Current societal and technological changes have added to the ethical issues faced by people with cerebral palsy. These include new representations of disability, and the current International Classification of Functioning, Disability, and Health, changes in legislation and international conventions, as well as applications of possibilities offered by robotics, brain-computer interface devices, muscles and brain stimulation techniques, wearable sensors, artificial intelligence, genetics, and more for diagnostic, therapeutic, or other purposes. These developments have changed the way we approach diagnosis, set goals for intervention, and create new opportunities. This review examines those influences on clinical practice from an ethical perspective and highlights how a principled approach to clinical bioethics can help the clinician to address ethical dilemmas that occur in practice. It also points to implications of those changes on research priorities.},
}
@article {pmid33815084,
year = {2021},
author = {Asgher, U and Khan, MJ and Asif Nizami, MH and Khalil, K and Ahmad, R and Ayaz, Y and Naseer, N},
title = {Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain-Machine Interface (BMI).},
journal = {Frontiers in neurorobotics},
volume = {15},
number = {},
pages = {605751},
pmid = {33815084},
issn = {1662-5218},
abstract = {Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain-machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier-support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.},
}
@article {pmid33815081,
year = {2021},
author = {Benaroch, C and Sadatnejad, K and Roc, A and Appriou, A and Monseigne, T and Pramij, S and Mladenovic, J and Pillette, L and Jeunet, C and Lotte, F},
title = {Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {635653},
pmid = {33815081},
issn = {1662-5161},
abstract = {While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition.},
}
@article {pmid33815080,
year = {2021},
author = {Xie, J and Peng, M and Lu, J and Xiao, C and Zong, X and Wang, M and Gao, D and Qin, Y and Liu, T},
title = {Enhancement of Event-Related Desynchronization in Motor Imagery Based on Transcranial Electrical Stimulation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {635351},
pmid = {33815080},
issn = {1662-5161},
abstract = {Due to the individual differences controlling brain-computer interfaces (BCIs), the applicability and accuracy of BCIs based on motor imagery (MI-BCIs) are limited. To improve the performance of BCIs, this article examined the effect of transcranial electrical stimulation (tES) on brain activity during MI. This article designed an experimental paradigm that combines tES and MI and examined the effects of tES based on the measurements of electroencephalogram (EEG) features in MI processing, including the power spectral density (PSD) and dynamic event-related desynchronization (ERD). Finally, we investigated the effect of tES on the accuracy of MI classification using linear discriminant analysis (LDA). The results showed that the ERD of the μ and β rhythms in the left-hand MI task was enhanced after electrical stimulation with a significant effect in the tDCS group. The average classification accuracy of the transcranial alternating current stimulation (tACS) group and transcranial direct current stimulation (tDCS) group (88.19% and 89.93% respectively) were improved significantly compared to the pre-and pseudo stimulation groups. These findings indicated that tES can improve the performance and applicability of BCI and that tDCS was a potential approach in regulating brain activity and enhancing valid features during noninvasive MI-BCI processing.},
}
@article {pmid33811074,
year = {2021},
author = {Qu, C and Mao, C and Xiao, P and Shen, Q and Zhong, YN and Yang, F and Shen, DD and Tao, X and Zhang, H and Yan, X and Zhao, RJ and He, J and Guan, Y and Zhang, C and Hou, G and Zhang, PJ and Hou, G and Li, Z and Yu, X and Chai, RJ and Guan, YF and Sun, JP and Zhang, Y},
title = {Ligand recognition, unconventional activation, and G protein coupling of the prostaglandin E2 receptor EP2 subtype.},
journal = {Science advances},
volume = {7},
number = {14},
pages = {},
pmid = {33811074},
issn = {2375-2548},
abstract = {Selective modulation of the heterotrimeric G protein α S subunit-coupled prostaglandin E2 (PGE2) receptor EP2 subtype is a promising therapeutic strategy for osteoporosis, ocular hypertension, neurodegenerative diseases, and cardiovascular disorders. Here, we report the cryo-electron microscopy structure of the EP2-Gs complex with its endogenous agonist PGE2 and two synthesized agonists, taprenepag and evatanepag (CP-533536). These structures revealed distinct features of EP2 within the EP receptor family in terms of its unconventional receptor activation and G protein coupling mechanisms, including activation in the absence of a typical W[6.48] "toggle switch" and coupling to Gs via helix 8. Moreover, inspection of the agonist-bound EP2 structures uncovered key motifs governing ligand selectivity. Our study provides important knowledge for agonist recognition and activation mechanisms of EP2 and will facilitate the rational design of drugs targeting the PGE2 signaling system.},
}
@article {pmid33810122,
year = {2021},
author = {Laport, F and Iglesia, D and Dapena, A and Castro, PM and Vazquez-Araujo, FJ},
title = {Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33810122},
issn = {1424-8220},
support = {ED431G2019/01//Xunta de Galicia/ ; TEC2016-75067-C4-1-R)//Agencia Estatal de Investigaci&#xf3;n of Spain/ ; ED481A-2018/156//Xunta de Galicia/ ; },
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Electrooculography ; Humans ; User-Computer Interface ; },
abstract = {Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user's intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms-one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.},
}
@article {pmid33809721,
year = {2021},
author = {Nizamis, K and Athanasiou, A and Almpani, S and Dimitrousis, C and Astaras, A},
title = {Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33809721},
issn = {1424-8220},
support = {U10 CA016116/CA/NCI NIH HHS/United States ; },
mesh = {Artificial Intelligence ; Electromyography ; Humans ; *Robotics ; *Spinal Cord Injuries ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.},
}
@article {pmid33809317,
year = {2021},
author = {Cardoso, VF and Delisle-Rodriguez, D and Romero-Laiseca, MA and Loterio, FA and Gurve, D and Floriano, A and Valadão, C and Silva, L and Krishnan, S and Frizera-Neto, A and Freire Bastos-Filho, T},
title = {Effect of a Brain-Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33809317},
issn = {1424-8220},
support = {33361.503.19197.11092017//Funda&#xe7;&#xe3;o de Amparo &#xe0; Pesquisa e Inova&#xe7;&#xe3;o do Esp&#xed;rito Santo/ ; 88881.159029/201701//Coordena&#xe7;&#xe3;o de Aperfei&#xe7;oamento de Pessoal de N&#xed;vel Superior/ ; },
mesh = {*Brain-Computer Interfaces ; *Cortical Excitability ; Electroencephalography ; Humans ; Imagination ; *Motor Cortex ; },
abstract = {Recently, studies on cycling-based brain-computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.},
}
@article {pmid33808786,
year = {2021},
author = {Tseng, KC},
title = {Electrophysiological Correlation Underlying the Effects of Music Preference on the Prefrontal Cortex Using a Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33808786},
issn = {1424-8220},
support = {MOST 109-2410-H-027-003-MY2//Ministry of Science and Technology, Taiwan/ ; MOST 106-2628-H-027-001-MY3//Ministry of Science and Technology, Taiwan/ ; MOST 108-2410-H-027-024-MY3//Ministry of Science and Technology, Taiwan/ ; },
mesh = {Acoustic Stimulation ; Auditory Perception ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Music ; Prefrontal Cortex ; },
abstract = {This study aims to research the task of recognising brain activities in the prefrontal cortex that correspond to music at different preference levels. Since task performance regarding the effects of the subjects' favourite music can lead to better outcomes, we focus on the physical interpretation of electroencephalography (EEG) bands underlying the preference level for music. The experiment was implemented using a continuous response digital interface for the preference classification of three types of musical stimuli. The results showed that favourite songs more significantly evoked frontal theta than did the music of low and moderate preference levels. Additionally, correlations of frontal theta with cognitive state indicated that the frontal theta is associated not only with the cognitive state but also with emotional processing. These findings demonstrate that favourite songs can have more positive effects on listeners than less favourable music and suggest that theta and lower alpha in the frontal cortex are good indicators of both cognitive state and emotion.},
}
@article {pmid33805522,
year = {2021},
author = {Zeng, H and Li, X and Borghini, G and Zhao, Y and Aricò, P and Di Flumeri, G and Sciaraffa, N and Zakaria, W and Kong, W and Babiloni, F},
title = {An EEG-Based Transfer Learning Method for Cross-Subject Fatigue Mental State Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {7},
pages = {},
pmid = {33805522},
issn = {1424-8220},
support = {62076083//NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization/ ; 2017YFE0118200//National Key Research and Development Program of China/ ; 2017B01020//National International Joint Research Center for Brain-Machine Collaborative Intelligence/ ; 2020E10010//Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province/ ; GK209907299001-008//Fundamental Research Funds for the Provincial Universities of Zhejiang/ ; CXJJ2020086//Graduate Scientific Research Foundation of Hangzhou Dianzi University/ ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively "transfering" the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain-computer interaction (BCI).},
}
@article {pmid33805216,
year = {2021},
author = {Gomez-Vargas, D and Ballen-Moreno, F and Barria, P and Aguilar, R and Azorín, JM and Munera, M and Cifuentes, CA},
title = {The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke.},
journal = {Brain sciences},
volume = {11},
number = {4},
pages = {},
pmid = {33805216},
issn = {2076-3425},
support = {801-2017//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; 845-2020//Departamento Administrativo de Ciencia, Tecnolog&#xed;a e Innovaci&#xf3;n (COLCIENCIAS)/ ; 216RT0504//CYTED Ciencia y Tecnolog&#xed;a para el Desarrollo/ ; },
abstract = {Robotic devices can provide physical assistance to people who have suffered neurological impairments such as stroke. Neurological disorders related to this condition induce abnormal gait patterns, which impede the independence to execute different Activities of Daily Living (ADLs). From the fundamental role of the ankle in walking, Powered Ankle-Foot Orthoses (PAFOs) have been developed to enhance the users' gait patterns, and hence their quality of life. Ten patients who suffered a stroke used the actuation system of the T-FLEX exoskeleton triggered by an inertial sensor on the foot tip. The VICONmotion capture system recorded the users' kinematics for unassisted and assisted gait modalities. Biomechanical analysis and usability assessment measured the performance of the system actuation for the participants in overground walking. The biomechanical assessment exhibited changes in the lower joints' range of motion for 70% of the subjects. Moreover, the ankle kinematics showed a correlation with the variation of other movements analyzed. This variation had positive effects on 70% of the participants in at least one joint. The Gait Deviation Index (GDI) presented significant changes for 30% of the paretic limbs and 40% of the non-paretic, where the tendency was to decrease. The spatiotemporal parameters did not show significant variations between modalities, although users' cadence had a decrease of 70% of the volunteers. Lastly, the satisfaction with the device was positive, the comfort being the most user-selected aspect. This article presents the assessment of the T-FLEX actuation system in people who suffered a stroke. Biomechanical results show improvement in the ankle kinematics and variations in the other joints. In general terms, GDI does not exhibit significant increases, and the Movement Analysis Profile (MAP) registers alterations for the assisted gait with the device. Future works should focus on assessing the full T-FLEX orthosis in a larger sample of patients, including a stage of training.},
}
@article {pmid33805181,
year = {2021},
author = {Heo, D and Kim, M and Kim, J and Choi, YJ and Kim, SP},
title = {Effect of Static Posture on Online Performance of P300-Based BCIs for TV Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {7},
pages = {},
pmid = {33805181},
issn = {1424-8220},
support = {2017-0-00432//Ministry of Science and ICT, South Korea/ ; IITP-2020-0-01749//Ministry of Science and ICT, South Korea/ ; },
abstract = {To implement a practical brain-computer interface (BCI) for daily use, continuing changes in postures while performing daily tasks must be considered in the design of BCIs. To examine whether the performance of a BCI could depend on postures, we compared the online performance of P300-based BCIs built to select TV channels when subjects took sitting, recline, supine, and right lateral recumbent postures during BCI use. Subjects self-reported the degrees of interference, comfort, and familiarity after BCI control in each posture. We found no significant difference in the BCI performance as well as the amplitude and latency of P300 and N200 among the four postures. However, when we compared BCI accuracy outcomes normalized within individuals between two cases where subjects reported relatively more positively or more negatively about using the BCI in a particular posture, we found higher BCI accuracy in those postures for which individual subjects reported more positively. As a result, although the change of postures did not affect the overall performance of P300-based BCIs, the BCI performance varied depending on the degree of postural comfort felt by individual subjects. Our results suggest considering the postural comfort felt by individual BCI users when using a P300-based BCI at home.},
}
@article {pmid33804611,
year = {2021},
author = {Singh, A and Hussain, AA and Lal, S and Guesgen, HW},
title = {A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33804611},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; },
abstract = {Motor imagery (MI) based brain-computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.},
}
@article {pmid33802684,
year = {2021},
author = {Aquino-Brítez, D and Ortiz, A and Ortega, J and León, J and Formoso, M and Gan, JQ and Escobar, JJ},
title = {Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33802684},
issn = {1424-8220},
support = {PGC2018-098813-B-C31//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; PGC2018-098813-B-C32//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; },
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architectures include a vast number of hyperparameters on which the performance of the model relies. In this paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, but also their structure, which is able to propose solutions that consist of different architectures due to different layer combinations. The experimental results corroborate that deep architectures optimized by our method outperform the baseline approaches and result in computationally efficient models. Moreover, we demonstrate that optimized architectures improve the energy efficiency with respect to the baseline models.},
}
@article {pmid33801817,
year = {2021},
author = {Caicedo-Acosta, J and Castaño, GA and Acosta-Medina, C and Alvarez-Meza, A and Castellanos-Dominguez, G},
title = {Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33801817},
issn = {1424-8220},
support = {FP44842-213-2018//PROGRAMA DE INVESTIGACI&#xd3;N RECONSTRUCCI&#xd3;N DEL TEJIDO SOCIAL EN ZONAS DE POSCONFLICTO EN COLOMBIA C&#xf3;digo SIGP: 57579/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Motor Skills ; *Sensorimotor Cortex ; },
abstract = {Motor imaging (MI) induces recovery and neuroplasticity in neurophysical regulation. However, a non-negligible portion of users presents insufficient coordination skills of sensorimotor cortex control. Assessments of the relationship between wakefulness and tasks states are conducted to foster neurophysiological and mechanistic interpretation in MI-related applications. Thus, to understand the organization of information processing, measures of functional connectivity are used. Also, models of neural network regression prediction are becoming popular, These intend to reduce the need for extracting features manually. However, predicting MI practicing's neurophysiological inefficiency raises several problems, like enhancing network regression performance because of the overfitting risk. Here, to increase the prediction performance, we develop a deep network regression model that includes three procedures: leave-one-out cross-validation combined with Monte Carlo dropout layers, subject clustering of MI inefficiency, and transfer learning between neighboring runs. Validation is performed using functional connectivity predictors extracted from two electroencephalographic databases acquired in conditions close to real MI applications (150 users), resulting in a high prediction of pretraining desynchronization and initial training synchronization with adequate physiological interpretability.},
}
@article {pmid33801663,
year = {2021},
author = {Antoniou, E and Bozios, P and Christou, V and Tzimourta, KD and Kalafatakis, K and G Tsipouras, M and Giannakeas, N and Tzallas, AT},
title = {EEG-Based Eye Movement Recognition Using Brain-Computer Interface and Random Forests.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {7},
pages = {},
pmid = {33801663},
issn = {1424-8220},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Eye Movements ; Humans ; Movement ; Signal Processing, Computer-Assisted ; },
abstract = {Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain-computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model's prediction. The categories of the proposed random forests brain-computer interface (RF-BCI) are defined according to the position of the subject's eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects' EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.},
}
@article {pmid33801070,
year = {2021},
author = {Jeong, H and Kim, J},
title = {Development of a Guidance System for Motor Imagery Enhancement Using the Virtual Hand Illusion.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33801070},
issn = {1424-8220},
support = {2020R1F1A1076711//National Research Foundation of Korea/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; *Illusions ; },
abstract = {Motor imagery (MI) is widely used to produce input signals for brain-computer interfaces (BCI) due to the similarities between MI-BCI and the planning-execution cycle. Despite its usefulness, MI tasks can be ambiguous to users and MI produces weaker cortical signals than motor execution. Existing MI guidance systems, which have been reported to provide visual guidance for MI and enhance MI, still have limitations: insufficient immersion for MI or poor expandability to MI for another body parts. We propose a guidance system for MI enhancement that can immerse users in MI and will be easy to extend to other body parts and target motions with few physical constraints. To make easily extendable MI guidance system, the virtual hand illusion is applied to the MI guidance system with a motion tracking sensor. MI enhancement was evaluated in 11 healthy people by comparison with another guidance system and conventional motor commands for BCI. The results showed that the proposed MI guidance system produced an amplified cortical signal compared to pure MI (p < 0.017), and a similar cortical signal as those produced by both actual execution (p > 0.534) and an MI guidance system with the rubber hand illusion (p > 0.722) in the contralateral region. Therefore, we believe that the proposed MI guidance system with the virtual hand illusion is a viable alternative to existing MI guidance systems in various applications with MI-BCI.},
}
@article {pmid33799850,
year = {2021},
author = {Huang, H and Zhang, J and Zhu, L and Tang, J and Lin, G and Kong, W and Lei, X and Zhu, L},
title = {EEG-Based Sleep Staging Analysis with Functional Connectivity.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {6},
pages = {},
pmid = {33799850},
issn = {1424-8220},
support = {No.61633010//National Natural Science Foundation of China/ ; },
mesh = {Brain ; *Electroencephalography ; Sleep ; *Sleep Stages ; },
abstract = {Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.},
}
@article {pmid33799218,
year = {2021},
author = {Placidi, G and Cinque, L and Polsinelli, M},
title = {A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components.},
journal = {Computers in biology and medicine},
volume = {132},
number = {},
pages = {104347},
doi = {10.1016/j.compbiomed.2021.104347},
pmid = {33799218},
issn = {1879-0534},
mesh = {Algorithms ; *Artifacts ; Blinking ; Brain ; Electroencephalography ; Humans ; *Scalp ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND AND OBJECTIVES: Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts due to eye movements and blinking, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the Useful Brain Signals (UBS). Independent Component Analysis (ICA) is effective to split the signal into Independent Components (IC) whose re-projection on 2D topographies of the scalp (images also called Topoplots) allows to recognize/separate artifacts and UBS. Topoplot analysis, a gold standard for EEG, is usually carried out offline either visually by human experts or through automated strategies, both unenforceable when a fast response is required as in online Brain-Computer Interfaces (BCI). We present a fully automatic, effective, fast, scalable framework for artifacts recognition from EEG signals represented in IC Topoplots to be used in online BCI.<br><br>METHODS: The proposed architecture, optimized to contain three 2D Convolutional Neural Networks (CNN), divides Topoplots in 4 classes: 3 types of artifacts and UBS. The framework architecture is described and the results are presented, discussed and indirectly compared with those obtained from state-of-the-art competitive strategies.<br><br>RESULTS: Experiments on public EEG datasets showed overall accuracy, sensitivity and specificity greater than 98%, taking 1.4&#xa0;s on a standard PC for 32 Topoplots, i.e. for an EEG system with at least 32 sensors.<br><br>CONCLUSIONS: The proposed framework is faster than other automatic methods based on IC analysis and fast enough to be used in EEG-based online BCI. In addition, its scalable architecture and ease of training are necessary conditions to apply it in BCI, where difficult operating conditions caused by uncontrolled muscle spasms, eye rotations or head movements, produce specific artifacts that need to be recognized and dealt with.},
}
@article {pmid33796080,
year = {2021},
author = {Shamsi, BH and Chatoo, M and Xu, XK and Xu, X and Chen, XQ},
title = {Versatile Functions of Somatostatin and Somatostatin Receptors in the Gastrointestinal System.},
journal = {Frontiers in endocrinology},
volume = {12},
number = {},
pages = {652363},
pmid = {33796080},
issn = {1664-2392},
mesh = {Animals ; Antineoplastic Agents/pharmacology ; Cell Communication/drug effects ; Cell Proliferation ; Disease Models, Animal ; Enteric Nervous System/physiology ; Gastrointestinal Tract/*physiology ; Homeostasis ; Humans ; Inflammation ; Ligands ; Neurons/metabolism ; Parasympathetic Nervous System/physiology ; Prognosis ; Receptors, Somatostatin/metabolism/*physiology ; Somatostatin/metabolism/*physiology ; Somatostatin-Secreting Cells/metabolism ; Sympathetic Nervous System/physiology ; },
abstract = {Somatostatin (SST) and somatostatin receptors (SSTRs) play an important role in the brain and gastrointestinal (GI) system. SST is produced in various organs and cells, and the inhibitory function of somatostatin-containing cells is involved in a range of physiological functions and pathological modifications. The GI system is the largest endocrine organ for digestion and absorption, SST-endocrine cells and neurons in the GI system are a critical effecter to maintain homeostasis via SSTRs 1-5 and co-receptors, while SST-SSTRs are involved in chemo-sensory, mucus, and hormone secretion, motility, inflammation response, itch, and pain via the autocrine, paracrine, endocrine, and exoendocrine pathways. It is also a power inhibitor for tumor cell proliferation, severe inflammation, and post-operation complications, and is a first-line anti-cancer drug in clinical practice. This mini review focuses on the current function of producing SST endocrine cells and local neurons SST-SSTRs in the GI system, discusses new development prognostic markers, phosphate-specific antibodies, and molecular imaging emerging in diagnostics and therapy, and summarizes the mechanism of the SST family in basic research and clinical practice. Understanding of endocrines and neuroendocrines in SST-SSTRs in GI will provide an insight into advanced medicine in basic and clinical research.},
}
@article {pmid33795705,
year = {2021},
author = {Stieger, JR and Engel, SA and He, B},
title = {Continuous sensorimotor rhythm based brain computer interface learning in a large population.},
journal = {Scientific data},
volume = {8},
number = {1},
pages = {98},
pmid = {33795705},
issn = {2052-4463},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; RF1 MH114233/MH/NIMH NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Brain Waves ; *Brain-Computer Interfaces ; Electroencephalography Phase Synchronization ; Female ; Humans ; *Learning ; Male ; Neural Prostheses ; Sensorimotor Cortex/*physiology ; },
abstract = {Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.},
}
@article {pmid33793402,
year = {2021},
author = {Ma, R and Yu, T and Zhong, X and Yu, ZL and Li, Y and Gu, Z},
title = {Capsule Network for ERP Detection in Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {718-730},
doi = {10.1109/TNSRE.2021.3070327},
pmid = {33793402},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Neural Networks, Computer ; },
abstract = {Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism.},
}
@article {pmid33788862,
year = {2021},
author = {Darvish Ghanbar, K and Yousefi Rezaii, T and Farzamnia, A and Saad, I},
title = {Correlation-based common spatial pattern (CCSP): A novel extension of CSP for classification of motor imagery signal.},
journal = {PloS one},
volume = {16},
number = {3},
pages = {e0248511},
pmid = {33788862},
issn = {1932-6203},
mesh = {Animals ; *Brain-Computer Interfaces ; Computer Simulation ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagination ; Motor Activity/*physiology ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.},
}
@article {pmid33787456,
year = {2022},
author = {MacDuffie, KE and Ransom, S and Klein, E},
title = {Neuroethics Inside and Out: A Comparative Survey of Neural Device Industry Representatives and the General Public on Ethical Issues and Principles in Neurotechnology.},
journal = {AJOB neuroscience},
volume = {13},
number = {1},
pages = {44-54},
doi = {10.1080/21507740.2021.1896596},
pmid = {33787456},
issn = {2150-7759},
mesh = {Humans ; *Morals ; *Neurosciences ; Privacy ; Surveys and Questionnaires ; },
abstract = {Neurotechnologies are rapidly being developed with the aim of alleviating suffering caused by disease and assisting individuals with various disabilities. As the capabilities and applications of neural devices advance, potential ethical challenges related to agency, identity, privacy, equality, normality and justice have been noted. We sought to explore attitudes toward these ethical challenges in two important, but understudied groups of stakeholders-members of the neural device industry and members of the general public. Survey responses from 66 industry professionals and 1088 members of the general public who do not work with neural devices were collected. After controlling for demographic differences between the groups (industry vs. general public; age, gender, racial/ethnic background), we found a large degree of consistency between the groups in their attitudes toward the ethical topic areas and the need for guiding ethical principles, but also some differences related to privacy, consent, and confidence in the neural device industry to incorporate ethical concerns into the design process. These data have implications for industry professionals tasked with designing and disseminating new neural devices, end-users of their products, and stakeholders at each step in between who must navigate the rapidly-growing landscape of advances in neurotechnology.},
}
@article {pmid33786666,
year = {2021},
author = {Pinter, D and Kober, SE and Fruhwirth, V and Berger, L and Damulina, A and Khalil, M and Neuper, C and Wood, G and Enzinger, C},
title = {MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study.},
journal = {Journal of neurology},
volume = {268},
number = {10},
pages = {3808-3816},
pmid = {33786666},
issn = {1432-1459},
mesh = {Cognition ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; *Multiple Sclerosis/diagnostic imaging/therapy ; *Neurofeedback ; Pilot Projects ; },
abstract = {OBJECTIVE: Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients with MS (pwMS).<br><br>METHODS: Fourteen pwMS underwent ten neurofeedback training sessions within 3-4&#xa0;weeks at home using a tele-rehabilitation system. Half of the pwMS (N&#x2009;=&#x2009;7, responders) learned to self-regulate sensorimotor rhythm (SMR, 12-15&#xa0;Hz) by visual feedback and improved cognitively after training, whereas the remainder (non-responders, n&#x2009;=&#x2009;7) did not. Diffusion-tensor imaging and resting-state fMRI of the brain was performed before and after training. We analyzed fractional anisotropy (FA) and functional connectivity (FC) of the default-mode, sensorimotor (SMN) and salience network (SAL).<br><br>RESULTS: At baseline, responders and non-responders were comparable regarding sex, age, education, disease duration, physical and cognitive impairment, and MRI parameters. After training, compared to non-responders, responders showed increased FA and FC within the SAL and SMN. Cognitive improvement correlated with increased FC in SAL and a correlation trend with increased FA was observed.<br><br>CONCLUSIONS: This exploratory study suggests that successful neurofeedback training may not only lead to cognitive improvement, but also to increases in brain microstructure and functional connectivity.},
}
@article {pmid33786390,
year = {2021},
author = {Punsawad, Y and Siribunyaphat, N and Wongsawat, Y},
title = {Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems.},
journal = {Heliyon},
volume = {7},
number = {3},
pages = {e06457},
pmid = {33786390},
issn = {2405-8440},
abstract = {This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.},
}
@article {pmid33786276,
year = {2021},
author = {Benioudakis, ES and Georgiou, ED and Barouxi, ED and Armagos, AM and Koutsoumani, V and Anastasiou-Veneti, F and Koutsoumani, E and Brokalaki, M},
title = {The diabetes quality of life brief clinical inventory in combination with the management strategies in type 1 diabetes mellitus with or without the use of insulin pump.},
journal = {Diabetology international},
volume = {12},
number = {2},
pages = {217-228},
pmid = {33786276},
issn = {2190-1678},
abstract = {AIMS: The aims of this study are to evaluate any differences in the Quality of life among Continuous Subcutaneous Insulin Infusion (CSII) and Multiple Dose Injection (MDI) insulin delivery, applying the Diabetes Quality of life Brief Clinical Inventory (DQoL-BCI) questionnaire, and assess the diabetes management strategies between the two groups.<br><br>METHODS: One hundred and ten adult participants (male/female ratio 1:2.7) with type 1 diabetes were recruited in this online survey. Forty-eight of them were using CSII and the rest 62 (were using) MDI insulin delivery. A 23-item socio-demographic/diabetes management strategies questionnaire and the 15-item DQoL-BCI were administered.<br><br>RESULTS: CSII users scored statistically, significantly better at the satisfaction treatment subscale (p&#x2009;=&#x2009;0.032) of the DQoL-BCI and emerged that they were implemented more management strategies such as dietician guidance services (p&#x2009;=&#x2009;0.002), carbohydrate education seminars (p&#x2009;=&#x2009;0.03). Predictive factors were also detected regarding the HbA1c&#x2009;<&#x2009;7% (53&#xa0;mmol/mol) and &#x3b2;-coefficients in relation to DQoL-BCI questionnaire with the subscales of a negative impact and satisfaction treatment.<br><br>CONCLUSION: Diabetes self-management education plays a key role to a better compliance with the treatment. Client-centered multidisciplinary centers in T1DM education are essential so that they be applicable for all T1DM patients irrespective of the type of insulin delivery they used.},
}
@article {pmid33786162,
year = {2021},
author = {Neo, PS and Mayne, T and Fu, X and Huang, Z and Franz, EA},
title = {Crosstalk disrupts the production of motor imagery brain signals in brain-computer interfaces.},
journal = {Health information science and systems},
volume = {9},
number = {1},
pages = {13},
pmid = {33786162},
issn = {2047-2501},
abstract = {UNLABELLED: Brain-computer interfaces (BCIs) target specific brain activity for neuropsychological rehabilitation, and also allow patients with motor disabilities to control mobility and communication devices. Motor imagery of single-handed actions is used in BCIs but many users cannot control the BCIs effectively, limiting applications in the health systems. Crosstalk is unintended brain activations that interfere with bimanual actions and could also occur during motor imagery. To test if crosstalk impaired BCI user performance, we recorded EEG in 46 participants while they imagined movements in four experimental conditions using motor imagery: left hand (L), right hand (R), tongue (T) and feet (F). Pairwise classification accuracies of the tasks were compared (LR, LF, LT, RF, RT, FT), using common spatio-spectral filters and linear discriminant analysis. We hypothesized that LR classification accuracy would be lower than every other combination that included a hand imagery due to crosstalk. As predicted, classification accuracy for LR (58%) was reliably the lowest. Interestingly, participants who showed poor LR classification also demonstrated at least one good TR, TL, FR or FL classification; and good LR classification was detected in 16% of the participants. For the first time, we showed that crosstalk occurred in motor imagery, and affected BCI performance negatively. Such effects are effector-sensitive regardless of the BCI methods used; and likely not apparent to the user or the BCI developer. This means that tasks choice is crucial when designing BCI. Critically, the effects of crosstalk appear mitigatable. We conclude that understanding crosstalk mitigation is important for improving BCI applicability.<br><br>SUPPLEMENTARY INFORMATION: The online version of this article contains supplementary material available (10.1007/s13755-021-00142-y).},
}
@article {pmid33786085,
year = {2021},
author = {Sun, H and Jin, J and Kong, W and Zuo, C and Li, S and Wang, X},
title = {Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {1},
pages = {141-156},
pmid = {33786085},
issn = {1871-4080},
abstract = {Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.},
}
@article {pmid33786075,
year = {2021},
author = {Wang, R and Pan, X},
title = {Research progress of neurodynamics in China.},
journal = {Cognitive neurodynamics},
volume = {15},
number = {1},
pages = {1-2},
pmid = {33786075},
issn = {1871-4080},
}
@article {pmid33784640,
year = {2021},
author = {Zhao, X and Wang, Z and Zhang, M and Hu, H},
title = {A comfortable steady state visual evoked potential stimulation paradigm using peripheral vision.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abf397},
pmid = {33784640},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Objective. Steady-state visual evoked potential (SSVEP)-brain-computer interfaces (BCIs) can cause much visual discomfort if the users use the SSVEP-BCIs for a long time. As an alternative scheme to reduce users' visual fatigue, this study proposes a new stimulation paradigm (termed as steady state peripheral visual evoked potential, abbreviated as SSPVEP) which makes full use of peripheral vision. The electroencephalography (EEG) signals are classifiable which means this proposed stimulation paradigm can be used in BCI system with the aid of the latest hybrid signal processing approach.Approach. Under the SSPVEP stimulation paradigm, 20 targets are mounted on 20 frequencies and other targets are set between two targets with flicker stimuli coding. In order to ensure the classification accuracy of SSPVEP signal detection under the proposed stimulation paradigm, two optimization schemes are proposed for the detection stage of the conventional ensemble task-related component analysis (ETRCA) algorithm. The first optimization scheme uses nonlinear correlation coefficient at the detection part for the first time to improve the classification accuracy of the system. The second optimization scheme usesγcorrection to enhance the time domain features of the SSPVEP signals, and uses Manhattan distance for the final detection.Main results. According to the response waveforms of the EEG signals generated under the SSPVEP stimulation paradigm and the results of the questionnaire on user's comfort level to the two stimulation paradigms (SSPVEP paradigm and conventional SSVEP paradigm), the proposed stimulation paradigm brings less visual fatigue. The comparison results indicate that the proposed detection methods (ETRCA +γcorrection + Manhattan distance, ETRCA + Spearman correlation) can greatly improve the classification accuracy compared with the individual template canonical correlation analysis method and conventional ETRCA method based on Pearson correlation.Significance. The SSPVEP stimulation paradigm reduces users' visual fatigue via using peripheral vision, which provides a new design idea for SSVEP stimulation paradigm aimed at visual comfort.},
}
@article {pmid33784612,
year = {2021},
author = {Simeral, JD and Hosman, T and Saab, J and Flesher, SN and Vilela, M and Franco, B and Kelemen, JN and Brandman, DM and Ciancibello, JG and Rezaii, PG and Eskandar, EN and Rosler, DM and Shenoy, KV and Henderson, JM and Nurmikko, AV and Hochberg, LR},
title = {Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {7},
pages = {2313-2325},
pmid = {33784612},
issn = {1558-2531},
support = {I01 RX002827/RX/RRD VA/United States ; I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; I01 RX001155/RX/RRD VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Hand ; Humans ; Microelectrodes ; Quadriplegia ; },
abstract = {OBJECTIVE: Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI.<br><br>METHODS: Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control.<br><br>RESULTS: Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home.<br><br>SIGNIFICANCE: Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.},
}
@article {pmid33782622,
year = {2021},
author = {Hennig, JA and Oby, ER and Golub, MD and Bahureksa, LA and Sadtler, PT and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Chase, SM and Yu, BM},
title = {Learning is shaped by abrupt changes in neural engagement.},
journal = {Nature neuroscience},
volume = {24},
number = {5},
pages = {727-736},
pmid = {33782622},
issn = {1546-1726},
support = {R01 EB026953/EB/NIBIB NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Attention/physiology ; *Brain-Computer Interfaces ; Learning/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Neurons/*physiology ; },
abstract = {Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.},
}
@article {pmid33780916,
year = {2021},
author = {Rybář, M and Poli, R and Daly, I},
title = {Decoding of semantic categories of imagined concepts of animals and tools in fNIRS.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf2e5},
pmid = {33780916},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Semantics ; *Spectroscopy, Near-Infrared ; },
abstract = {Objective.Semantic decoding refers to the identification of semantic concepts from recordings of an individual's brain activity. It has been previously reported in functional magnetic resonance imaging and electroencephalography. We investigate whether semantic decoding is possible with functional near-infrared spectroscopy (fNIRS). Specifically, we attempt to differentiate between the semantic categories of animals and tools. We also identify suitable mental tasks for potential brain-computer interface (BCI) applications.Approach.We explore the feasibility of a silent naming task, for the first time in fNIRS, and propose three novel intuitive mental tasks based on imagining concepts using three sensory modalities: visual, auditory, and tactile. Participants are asked to visualize an object in their minds, imagine the sounds made by the object, and imagine the feeling of touching the object. A general linear model is used to extract hemodynamic responses that are then classified via logistic regression in a univariate and multivariate manner.Main results.We successfully classify all tasks with mean accuracies of 76.2% for the silent naming task, 80.9% for the visual imagery task, 72.8% for the auditory imagery task, and 70.4% for the tactile imagery task. Furthermore, we show that consistent neural representations of semantic categories exist by applying classifiers across tasks.Significance.These findings show that semantic decoding is possible in fNIRS. The study is the first step toward the use of semantic decoding for intuitive BCI applications for communication.},
}
@article {pmid33780913,
year = {2021},
author = {Fernandez-Vargas, J and Tremmel, C and Valeriani, D and Bhattacharyya, S and Cinel, C and Citi, L and Poli, R},
title = {Subject- and task-independent neural correlates and prediction of decision confidence in perceptual decision making.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abf2e4},
pmid = {33780913},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Decision Making ; *Electroencephalography ; Humans ; Neural Networks, Computer ; Reaction Time ; },
abstract = {Objective.In many real-world decision tasks, the information available to the decision maker is incomplete. To account for this uncertainty, we associate a degree of confidence to every decision, representing the likelihood of that decision being correct. In this study, we analyse electroencephalography (EEG) data from 68 participants undertaking eight different perceptual decision-making experiments. Our goals are to investigate (1) whether subject- and task-independent neural correlates of decision confidence exist, and (2) to what degree it is possible to build brain computer interfaces that can estimate confidence on a trial-by-trial basis. The experiments cover a wide range of perceptual tasks, which allowed to separate the task-related, decision-making features from the task-independent ones.Approach.Our systems train artificial neural networks to predict the confidence in each decision from EEG data and response times. We compare the decoding performance with three training approaches: (1) single subject, where both training and testing data were acquired from the same person; (2) multi-subject, where all the data pertained to the same task, but the training and testing data came from different users; and (3) multi-task, where the training and testing data came from different tasks and subjects. Finally, we validated our multi-task approach using data from two additional experiments, in which confidence was not reported.Main results.We found significant differences in the EEG data for different confidence levels in both stimulus-locked and response-locked epochs. All our approaches were able to predict the confidence between 15% and 35% better than the corresponding reference baselines.Significance.Our results suggest that confidence in perceptual decision making tasks could be reconstructed from neural signals even when using transfer learning approaches. These confidence estimates are based on the decision-making process rather than just the confidence-reporting process.},
}
@article {pmid33779576,
year = {2021},
author = {Lopes-Dias, C and Sburlea, AI and Breitegger, K and Wyss, D and Drescher, H and Wildburger, R and Müller-Putz, GR},
title = {Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {046022},
doi = {10.1088/1741-2552/abd1eb},
pmid = {33779576},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Humans ; *Spinal Cord Injuries/diagnosis ; },
abstract = {UNLABELLED: For brain-computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance.<br><br>OBJECTIVE: This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodied participants to participants with a spinal cord injury (SCI). Second, we test this generic ErrP classifier with SCI and control participants, in an online experiment without offline calibration. Third, we investigate the morphology of ErrPs in both groups of participants.<br><br>APPROACH: We used previously recorded electroencephalographic data from able-bodied participants to train an ErrP classifier. We tested the classifier asynchronously, in an online experiment with 16 new participants: 8 participants with SCI and 8 able-bodied control participants. The experiment had no offline calibration and participants received feedback regarding the ErrP detections from the start. To increase the fluidity of the experiment, feedback regarding false positive ErrP detections was not presented to the participants, but these detections were taken into account in the evaluation of the classifier. The generic classifier was not trained with the user's brain signals. However, its performance was optimized during the online experiment by the use of personalized decision thresholds. The classifier's performance was evaluated using trial-based metrics, which considered the asynchronous detection of ErrPs during the entire trial's duration.<br><br>MAIN RESULTS: Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed better than chance in participants with clear ErrP signals, independently of the SCI (11 out of 16 participants). Three out of the five participants that obtained chance level results with the generic classifier would have not benefitted from the use of a personalized classifier.<br><br>SIGNIFICANCE: This work shows the feasibility of transferring an ErrP classifier from able-bodied participants to participants with SCI, for asynchronous detection of ErrPs in an online experiment without offline calibration, which provided immediate feedback to the users.},
}
@article {pmid33778015,
year = {2021},
author = {Tonin, L and Menegatti, E and Coyle, D},
title = {Editorial: Advances in the Integration of Brain-Machine Interfaces and Robotic Devices.},
journal = {Frontiers in robotics and AI},
volume = {8},
number = {},
pages = {653615},
doi = {10.3389/frobt.2021.653615},
pmid = {33778015},
issn = {2296-9144},
}
@article {pmid33777542,
year = {2020},
author = {Abiri, R and Borhani, S and Kilmarx, J and Esterwood, C and Jiang, Y and Zhao, X},
title = {A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model.},
journal = {IEEE transactions on human-machine systems},
volume = {50},
number = {4},
pages = {287-297},
pmid = {33777542},
issn = {2168-2291},
support = {P30 AG028383/AG/NIA NIH HHS/United States ; P30 AG072946/AG/NIA NIH HHS/United States ; R56 AG060608/AG/NIA NIH HHS/United States ; UL1 TR000117/TR/NCATS NIH HHS/United States ; },
abstract = {Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.},
}
@article {pmid33776674,
year = {2021},
author = {Kwon, J and Im, CH},
title = {Subject-Independent Functional Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Based on Convolutional Neural Networks.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {646915},
pmid = {33776674},
issn = {1662-5161},
abstract = {Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in the field of brain-computer interfaces (BCIs) owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network (CNN)-based approach for implementing a subject-independent fNIRS-based BCI. A total of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication (70%) as well as that obtained using conventional shrinkage linear discriminant analysis (65.74 ± 7.68%). To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials (of approximately 12 min) were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly.},
}
@article {pmid33776673,
year = {2021},
author = {Pei, Y and Luo, Z and Yan, Y and Yan, H and Jiang, J and Li, W and Xie, L and Yin, E},
title = {Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {645952},
pmid = {33776673},
issn = {1662-5161},
abstract = {The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.},
}
@article {pmid33776653,
year = {2021},
author = {Ding, Q and Lin, T and Wu, M and Yang, W and Li, W and Jing, Y and Ren, X and Gong, Y and Xu, G and Lan, Y},
title = {Influence of iTBS on the Acute Neuroplastic Change After BCI Training.},
journal = {Frontiers in cellular neuroscience},
volume = {15},
number = {},
pages = {653487},
pmid = {33776653},
issn = {1662-5102},
abstract = {Objective: Brain-computer interface (BCI) training is becoming increasingly popular in neurorehabilitation. However, around one third subjects have difficulties in controlling BCI devices effectively, which limits the application of BCI training. Furthermore, the effectiveness of BCI training is not satisfactory in stroke rehabilitation. Intermittent theta burst stimulation (iTBS) is a powerful neural modulatory approach with strong facilitatory effects. Here, we investigated whether iTBS would improve BCI accuracy and boost the neuroplastic changes induced by BCI training. Methods: Eight right-handed healthy subjects (four males, age: 20-24) participated in this two-session study (BCI-only session and iTBS+BCI session in random order). Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) and single-pulse transcranial magnetic stimulation (TMS). In BCI-only session, fNIRS was measured at baseline and immediately after BCI training. In iTBS+BCI session, BCI training was followed by iTBS delivered on the right primary motor cortex (M1). Single-pulse TMS was measured at baseline and immediately after iTBS. fNIRS was measured at baseline, immediately after iTBS, and immediately after BCI training. Paired-sample t-tests were used to compare amplitudes of motor-evoked potentials, cortical silent period duration, oxygenated hemoglobin (HbO2) concentration and functional connectivity across time points, and BCI accuracy between sessions. Results: No significant difference in BCI accuracy was detected between sessions (p > 0.05). In BCI-only session, functional connectivity matrices between motor cortex and prefrontal cortex were significantly increased after BCI training (p's < 0.05). In iTBS+BCI session, amplitudes of motor-evoked potentials were significantly increased after iTBS (p's < 0.05), but no change in HbO2 concentration or functional connectivity was observed throughout the whole session (p's > 0.05). Conclusions: To our knowledge, this is the first study that investigated how iTBS targeted on M1 influences BCI accuracy and the acute neuroplastic changes after BCI training. Our results revealed that iTBS targeted on M1 did not influence BCI accuracy or facilitate the neuroplastic changes after BCI training. Therefore, M1 might not be an effective stimulation target of iTBS for the purpose of improving BCI accuracy or facilitate its effectiveness; other brain regions (i.e., prefrontal cortex) are needed to be further investigated as potentially effective stimulation targets.},
}
@article {pmid33775708,
year = {2021},
author = {Inker, LA and Heerspink, HJL and Tighiouart, H and Chaudhari, J and Miao, S and Diva, U and Mercer, A and Appel, GB and Donadio, JV and Floege, J and Li, PKT and Maes, BD and Locatelli, F and Praga, M and Schena, FP and Levey, AS and Greene, T},
title = {Association of Treatment Effects on Early Change in Urine Protein and Treatment Effects on GFR Slope in IgA Nephropathy: An Individual Participant Meta-analysis.},
journal = {American journal of kidney diseases : the official journal of the National Kidney Foundation},
volume = {78},
number = {3},
pages = {340-349.e1},
pmid = {33775708},
issn = {1523-6838},
support = {UL1 TR002538/TR/NCATS NIH HHS/United States ; UL1 TR002544/TR/NCATS NIH HHS/United States ; },
mesh = {Bayes Theorem ; Creatinine/*metabolism ; *Disease Management ; Disease Progression ; Glomerular Filtration Rate/*physiology ; Glomerulonephritis, IGA/physiopathology/therapy/*urine ; Humans ; Research Design ; Urinalysis ; },
abstract = {RATIONALE &amp; OBJECTIVE: An early change in proteinuria is considered a reasonably likely surrogate end point in immunoglobulin A nephropathy (IgAN) and can be used as a basis for accelerated approval of therapies, with verification in a postmarketing confirmatory trial. Glomerular filtration rate (GFR) slope is a recently validated surrogate end point for chronic kidney disease progression and may be considered as the end point used for verification. We undertook a meta-analysis of clinical trials in IgAN to compare treatment effects on change in proteinuria versus change in estimated GFR (eGFR) slope.<br><br>STUDY DESIGN: Individual patient-level meta-analysis.<br><br>Individual data of 1,037 patients from 12 randomized trials.<br><br>Randomized trials of IgAN with proteinuria measurements at baseline and 6 (range, 2.5-14) months and at least a further 1 year of follow-up for the clinical outcome.<br><br>ANALYTICAL APPROACH: For each trial, we estimated the treatment effects on proteinuria and on the eGFR slope, computed as the total slope starting at baseline or the chronic slope starting 3 months after randomization. We used a Bayesian mixed-effects analysis to relate the treatment effects on proteinuria to effects on GFR slope across these studies and developed a prediction model for the treatment effect on the GFR slope based on the effect on proteinuria.<br><br>RESULTS: Across all studies, treatment effects on proteinuria accurately predicted treatment effects on the total slope at 3 years (median R[2]&#xa0;= 0.88; 95% Bayesian credible interval [BCI], 0.06-1) and on the chronic slope (R[2]&#xa0;= 0.98; 95% BCI, 0.29-1). For future trials, an observed treatment effect of approximately 30% reduction in proteinuria would confer probabilities of at least 90% for nonzero treatment benefits on the total and chronic slopes of eGFR. We obtained similar results for proteinuria at 9 and 12 months and total slope at 2 years.<br><br>LIMITATIONS: Study population restricted to 12 trials of small sample size, leading to wide BCIs. There was heterogeneity among trials with respect to study design and interventions.<br><br>CONCLUSIONS: These results provide new evidence supporting that early reduction in proteinuria can be used as a surrogate end point for studies of chronic kidney disease progression in IgAN.},
}
@article {pmid33770779,
year = {2021},
author = {Larzabal, C and Auboiroux, V and Karakas, S and Charvet, G and Benabid, AL and Chabardes, S and Costecalde, T and Bonnet, S},
title = {The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abf291},
pmid = {33770779},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Cluster Analysis ; *Electroencephalography/methods ; Humans ; Imagination ; Movement ; },
abstract = {Objective. Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying common spatial pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian spatial pattern (RSP) method, which is based on the backward channel selection procedure.Approach. The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers.Main results.Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results.Significance.Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.},
}
@article {pmid33770760,
year = {2021},
author = {McMullen, DP and Thomas, TM and Fifer, MS and Candrea, DN and Tenore, FV and Nickl, RW and Pohlmeyer, EA and Coogan, C and Osborn, LE and Schiavi, A and Wojtasiewicz, T and Gordon, CR and Cohen, AB and Ramsey, NF and Schellekens, W and Bensmaia, SJ and Cantarero, GL and Celnik, PA and Wester, BA and Anderson, WS and Crone, NE},
title = {Novel intraoperative online functional mapping of somatosensory finger representations for targeted stimulating electrode placement: technical note.},
journal = {Journal of neurosurgery},
volume = {135},
number = {5},
pages = {1493-1500},
doi = {10.3171/2020.9.JNS202675},
pmid = {33770760},
issn = {1933-0693},
support = {R01 NS088606/NS/NINDS NIH HHS/United States ; },
abstract = {Defining eloquent cortex intraoperatively, traditionally performed by neurosurgeons to preserve patient function, can now help target electrode implantation for restoring function. Brain-machine interfaces (BMIs) have the potential to restore upper-limb motor control to paralyzed patients but require accurate placement of recording and stimulating electrodes to enable functional control of a prosthetic limb. Beyond motor decoding from recording arrays, precise placement of stimulating electrodes in cortical areas associated with finger and fingertip sensations allows for the delivery of sensory feedback that could improve dexterous control of prosthetic hands. In this study, the authors demonstrated the use of a novel intraoperative online functional mapping (OFM) technique with high-density electrocorticography to localize finger representations in human primary somatosensory cortex. In conjunction with traditional pre- and intraoperative targeting approaches, this technique enabled accurate implantation of stimulating microelectrodes, which was confirmed by postimplantation intracortical stimulation of finger and fingertip sensations. This work demonstrates the utility of intraoperative OFM and will inform future studies of closed-loop BMIs in humans.},
}
@article {pmid33767615,
year = {2021},
author = {Kim, M and Kim, J and Heo, D and Choi, Y and Lee, T and Kim, SP},
title = {Effects of Emotional Stimulations on the Online Operation of a P300-Based Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {612777},
pmid = {33767615},
issn = {1662-5161},
abstract = {Using P300-based brain-computer interfaces (BCIs) in daily life should take into account the user's emotional state because various emotional conditions are likely to influence event-related potentials (ERPs) and consequently the performance of P300-based BCIs. This study aimed at investigating whether external emotional stimuli affect the performance of a P300-based BCI, particularly built for controlling home appliances. We presented a set of emotional auditory stimuli to subjects, which had been selected for each subject based on individual valence scores evaluated a priori, while they were controlling an electric light device using a P300-based BCI. There were four conditions regarding the auditory stimuli, including high valence, low valence, noise, and no sound. As a result, subjects controlled the electric light device using the BCI in real time with a mean accuracy of 88.14%. The overall accuracy and P300 features over most EEG channels did not show a significant difference between the four auditory conditions (p > 0.05). When we measured emotional states using frontal alpha asymmetry (FAA) and compared FAA across the auditory conditions, we also found no significant difference (p > 0.05). Our results suggest that there is no clear evidence to support a hypothesis that external emotional stimuli influence the P300-based BCI performance or the P300 features while people are controlling devices using the BCI in real time. This study may provide useful information for those who are concerned with the implementation of a P300-based BCI in practice.},
}
@article {pmid33767254,
year = {2021},
author = {Jiang, H and Stieger, J and Kreitzer, MJ and Engel, S and He, B},
title = {Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {6818},
pmid = {33767254},
issn = {2045-2322},
support = {U18 EB029354/EB/NIBIB NIH HHS/United States ; RF1 MH114233/MH/NIMH NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; },
mesh = {*Alpha Rhythm ; Brain/physiology ; *Brain-Computer Interfaces ; Data Analysis ; Electroencephalography ; Frontal Lobe/*physiology ; Humans ; Limbic Lobe/*physiology ; Longitudinal Studies ; *Meditation/methods ; *Mindfulness/methods ; },
abstract = {Brain-computer interfaces (BCIs) are capable of translating human intentions into signals controlling an external device to assist patients with severe neuromuscular disorders. Prior work has demonstrated that participants with mindfulness meditation experience evince improved BCI performance, but the underlying neural mechanisms remain unclear. Here, we conducted a large-scale longitudinal intervention study by training participants in mindfulness-based stress reduction (MBSR; a standardized mind-body awareness training intervention), and investigated whether and how short-term MBSR affected sensorimotor rhythm (SMR)-based BCI performance. We hypothesize that MBSR training improves BCI performance by reducing mind wandering and enhancing self-awareness during the intentional rest BCI control, which would mainly be reflected by modulations of default-mode network and limbic network activity. We found that MBSR training significantly improved BCI performance compared to controls and these behavioral enhancements were accompanied by increased frontolimbic alpha activity (9-15 Hz) and decreased alpha connectivity among limbic network, frontoparietal network, and default-mode network. Furthermore, the modulations of frontolimbic alpha activity were positively correlated with the duration of meditation experience and the extent of BCI performance improvement. Overall, these data suggest that mindfulness allows participant to reach a state where they can modulate frontolimbic alpha power and improve BCI performance for SMR-based BCI control.},
}
@article {pmid33764258,
year = {2021},
author = {Schönau, A and Dasgupta, I and Brown, T and Versalovic, E and Klein, E and Goering, S},
title = {Mapping the Dimensions of Agency.},
journal = {AJOB neuroscience},
volume = {12},
number = {2-3},
pages = {172-186},
pmid = {33764258},
issn = {2150-7759},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
mesh = {Brain ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Humans ; Movement ; *Spinal Cord Injuries ; },
abstract = {Neural devices have the capacity to enable users to regain abilities lost due to disease or injury - for instance, a deep brain stimulator (DBS) that allows a person with Parkinson's disease to regain the ability to fluently perform movements or a Brain Computer Interface (BCI) that enables a person with spinal cord injury to control a robotic arm. While users recognize and appreciate the technologies' capacity to maintain or restore their capabilities, the neuroethics literature is replete with examples of concerns expressed about agentive capacities: A perceived lack of control over the movement of a robotic arm might result in an altered sense of feeling responsible for that movement. Clinicians or researchers being able to record and access detailed information of a person's brain might raise privacy concerns. A disconnect between previous, current, and future understandings of the self might result in a sense of alienation. The ability to receive and interpret sensory feedback might change whether someone trusts the implanted device or themselves. Inquiries into the nature of these concerns and how to mitigate them has produced scholarship that often emphasizes one issue - responsibility, privacy, authenticity, or trust - selectively. However, we believe that examining these ethical dimensions separately fails to capture a key aspect of the experience of living with a neural device. In exploring their interrelations, we argue that their mutual significance for neuroethical research can be adequately captured if they are described under a unified heading of agency. On these grounds, we propose an "Agency Map" which brings together the diverse neuroethical dimensions and their interrelations into a comprehensive framework. With this, we offer a theoretically-grounded approach to understanding how these various dimensions are interwoven in an individual's experience of agency.},
}
@article {pmid33762911,
year = {2021},
author = {Wu, F and Gong, A and Li, H and Zhao, L and Zhang, W and Fu, Y},
title = {A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {595723},
pmid = {33762911},
issn = {1662-5161},
abstract = {Objective: Tangent Space Mapping (TSM) using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery (MI). Compared with the traditional CSP method, the Riemann geometric method based on TSM takes into account the nonlinear information contained in the covariance matrix, and can extract more abundant and effective features. Moreover, the method is an unsupervised operation, which can reduce the time of feature extraction. However, EEG features induced by MI mental activities of different subjects are not the same, so selection of subject-specific discriminative EEG frequency components play a vital role in the recognition of multiclass MI. In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping (DMFBTSM) algorithm is proposed in this article to design the subject-specific Filter Bank (FB) so as to effectively recognize multiclass MI tasks. Methods: On the 4-class BCI competition IV-2a dataset, first, a non-parametric method of multivariate analysis of variance (MANOVA) based on the sum of squared distances is used to select discriminative frequency bands for a subject; next, a multi-scale FB is generated according to the range of these frequency bands, and then decompose multi-channel EEG of the subject into multiple sub-bands combined with several time windows. Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines (SVM) is used for classification. Main Results: The analysis results show that the proposed discriminative FB enhances the multi-scale TSM algorithm, improves the classification accuracy and reduces the execution time during training and testing. On the 4-class BCI competition IV-2a dataset, the average session to session classification accuracy of nine subjects reached 77.33 ± 12.3%. When the training time and the test time are similar, the average classification accuracy is 2.56% higher than the latest TSM method based on multi-scale filter bank analysis technology. When the classification accuracy is similar, the training speed is increased by more than three times, and the test speed is increased two times more. Compared with Supervised Fisher Geodesic Minimum Distance to the Mean (Supervised FGMDRM), another new variant based on Riemann geometry classifier, the average accuracy is 3.36% higher, we also compared with the latest Deep Learning method, and the average accuracy of 10-fold cross validation improved by 2.58%. Conclusion: Research shows that the proposed DMFBTSM algorithm can improve the classification accuracy of MI tasks. Significance: Compared with the MFBTSM algorithm, the algorithm proposed in this article is expected to select frequency bands with good separability for specific subject to improve the classification accuracy of multiclass MI tasks and reduce the feature dimension to reduce training time and testing time.},
}
@article {pmid33762731,
year = {2021},
author = {Xu, P and Huang, S and Zhang, H and Mao, C and Zhou, XE and Cheng, X and Simon, IA and Shen, DD and Yen, HY and Robinson, CV and Harpsøe, K and Svensson, B and Guo, J and Jiang, H and Gloriam, DE and Melcher, K and Jiang, Y and Zhang, Y and Xu, HE},
title = {Structural insights into the lipid and ligand regulation of serotonin receptors.},
journal = {Nature},
volume = {592},
number = {7854},
pages = {469-473},
pmid = {33762731},
issn = {1476-4687},
mesh = {Apoproteins/chemistry/metabolism/ultrastructure ; Aripiprazole/metabolism/pharmacology ; Binding Sites ; Cholesterol/pharmacology ; *Cryoelectron Microscopy ; Heterotrimeric GTP-Binding Proteins/chemistry/metabolism/ultrastructure ; Humans ; *Ligands ; *Lipids ; Models, Molecular ; Phosphatidylinositol Phosphates/chemistry/metabolism/pharmacology ; Receptor, Serotonin, 5-HT1A/chemistry/metabolism/ultrastructure ; Receptors, Serotonin, 5-HT1/chemistry/*metabolism/*ultrastructure ; Serotonin 5-HT1 Receptor Agonists/chemistry/metabolism/pharmacology ; Water/chemistry ; },
abstract = {Serotonin, or 5-hydroxytryptamine (5-HT), is an important neurotransmitter[1,2] that activates the largest subtype family of G-protein-coupled receptors[3]. Drugs that target 5-HT1A, 5-HT1D, 5-HT1E and other 5-HT receptors are used to treat numerous disorders[4]. 5-HT receptors have high levels of basal activity and are subject to regulation by lipids, but the structural basis for the lipid regulation and basal activation of these receptors and the pan-agonism of 5-HT remains unclear. Here we report five structures of 5-HT receptor-G-protein complexes: 5-HT1A in the apo state, bound to 5-HT or bound to the antipsychotic drug aripiprazole; 5-HT1D bound to 5-HT; and 5-HT1E in complex with a 5-HT1E- and 5-HT1F-selective agonist, BRL-54443. Notably, the phospholipid phosphatidylinositol 4-phosphate is present at the G-protein-5-HT1A interface, and is able to increase 5-HT1A-mediated G-protein activity. The receptor transmembrane domain is surrounded by cholesterol molecules-particularly in the case of 5-HT1A, in which cholesterol molecules are directly involved in shaping the ligand-binding pocket that determines the specificity for aripiprazol. Within the ligand-binding pocket of apo-5-HT1A are structured water molecules that mimic 5-HT to activate the receptor. Together, our results address a long-standing question of how lipids and water molecules regulate G-protein-coupled receptors, reveal how 5-HT acts as a pan-agonist, and identify the determinants of drug recognition in 5-HT receptors.},
}
@article {pmid33761480,
year = {2021},
author = {Ma, T and Wang, S and Xia, Y and Zhu, X and Evans, J and Sun, Y and He, S},
title = {CNN-based classification of fNIRS signals in motor imagery BCI system.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abf187},
pmid = {33761480},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; Spectroscopy, Near-Infrared ; },
abstract = {Objective. Development of a brain-computer interface (BCI) requires classification of brain neural activities to different states. Functional near-infrared spectroscopy (fNIRS) can measure the brain activities and has great potential for BCI. In recent years, a large number of classification algorithms have been proposed, in which deep learning methods, especially convolutional neural network (CNN) methods are successful. fNIRS signal has typical time series properties, we combined fNIRS data and kinds of CNN-based time series classification (TSC) methods to classify BCI task.Approach. In this study, participants were recruited for a left and right hand motor imagery experiment and the cerebral neural activities were recorded by fNIRS equipment (FOIRE-3000). TSC methods are used to distinguish the brain activities when imagining the left or right hand. We have tested the overall person, single person and overall person with single-channel classification results, and these methods achieved excellent classification results. We also compared the CNN-based TSC methods with traditional classification methods such as support vector machine.Main results. Experiments showed that the CNN-based methods have significant advantages in classification accuracy: the CNN-based methods have achieved remarkable results in the classification of left-handed and right-handed imagination tasks, reaching 98.6% accuracy on overall person, 100% accuracy on single person, and in the single-channel classification an accuracy of 80.1% has been achieved with the best-performing channel.Significance. These results suggest that using the CNN-based TSC methods can significantly improve the BCI performance and also lay the foundation for the miniaturization and portability of training rehabilitation equipment.},
}
@article {pmid33759032,
year = {2021},
author = {Racine, E and Sattler, S and Boehlen, W},
title = {Cognitive Enhancement: Unanswered Questions About Human Psychology and Social Behavior.},
journal = {Science and engineering ethics},
volume = {27},
number = {2},
pages = {19},
pmid = {33759032},
issn = {1471-5546},
mesh = {Cognition ; Humans ; Mental Health ; *Nootropic Agents ; *Social Behavior ; },
abstract = {Stimulant drugs, transcranial magnetic stimulation, brain-computer interfaces, and even genetic modifications are all discussed as forms of potential cognitive enhancement. Cognitive enhancement can be conceived as a benefit-seeking strategy used by healthy individuals to enhance cognitive abilities such as learning, memory, attention, or vigilance. This phenomenon is hotly debated in the public, professional, and scientific literature. Many of the statements favoring cognitive enhancement (e.g., related to greater productivity and autonomy) or opposing it (e.g., related to health-risks and social expectations) rely on claims about human welfare and human flourishing. But with real-world evidence from the social and psychological sciences often missing to support (or invalidate) these claims, the debate about cognitive enhancement is stalled. In this paper, we describe a set of crucial debated questions about psychological and social aspects of cognitive enhancement (e.g., intrinsic motivation, well-being) and explain why they are of fundamental importance to address in the cognitive enhancement debate and in future research. We propose studies targeting social and psychological outcomes associated with cognitive enhancers (e.g., stigmatization, burnout, mental well-being, work motivation). We also voice a call for scientific evidence, inclusive of but not limited to biological health outcomes, to thoroughly assess the impact of enhancement. This evidence is needed to engage in empirically informed policymaking, as well as to promote the mental and physical health of users and non-users of enhancement.},
}
@article {pmid33756429,
year = {2021},
author = {Fuhrimann, S and Farnham, A and Staudacher, P and Atuhaire, A and Manfioletti, T and Niwagaba, CB and Namirembe, S and Mugweri, J and Winkler, MS and Portengen, L and Kromhout, H and Mora, AM},
title = {Exposure to multiple pesticides and neurobehavioral outcomes among smallholder farmers in Uganda.},
journal = {Environment international},
volume = {152},
number = {},
pages = {106477},
doi = {10.1016/j.envint.2021.106477},
pmid = {33756429},
issn = {1873-6750},
mesh = {Agriculture ; Bayes Theorem ; Cross-Sectional Studies ; Farmers ; Humans ; *Occupational Exposure/adverse effects/analysis ; *Pesticides/toxicity ; Uganda ; },
abstract = {BACKGROUND: Multiple epidemiological studies have shown that exposure to single pesticide active ingredients or chemical groups is associated with adverse neurobehavioral outcomes in farmers. In agriculture, exposure to multiple pesticide active ingredients is the rule, rather than exception. Therefore, occupational studies on neurobehavioral effects of pesticides should account for potential co-exposure confounding.<br><br>METHODS: We conducted a cross-sectional study of 288 Ugandan smallholder farmers between September and December 2017. We collected data on self-reported use of pesticide products during the 12&#xa0;months prior to survey and estimated yearly exposure-intensity scores for 14 pesticide active ingredients using a semi-quantitative exposure algorithm. We administered 11 neurobehavioral tests to assess five neurobehavioral domains. We implemented a Bayesian Model-Averaging (BMA) approach to examine the association between exposure to multiple pesticides and neurobehavioral outcomes, while accounting for multiple testing. We applied two levels of inference to determine (1) which neurobehavioral outcomes were associated with overall pesticide exposure (marginal inclusion probability (MIP) for covariate-only models <0.5) and (2) which specific pesticide active ingredients were associated with these outcomes (MIP for models where active ingredient was included >0.5).<br><br>RESULTS: Seventy-two percent of farmers reported use of pesticide products that contained at least one of 14 active ingredients, while the applicators used in median three different active ingredients (interquartile range (IQR) 4) in the 12&#xa0;months prior to the study. The most widely used active ingredients were glyphosate (79%), cypermethrin (60%), and mancozeb (55%). We found that overall pesticide exposure was associated with impaired visual memory (Benton Visual Retention Test (BVRT)), language (semantic verbal fluency test), perceptual-motor function (Finger tapping test), and complex attention problems (Trail making A test and digit symbol test). However, when we looked at the associations for individual active ingredients, we only observed a positive association between glyphosate exposure and impaired visual memory (-0.103 [95% Bayesian Credible Interval (BCI)] [-0.24, 0] units in BVRT scores per interquartile range (IQR) increase in annual exposure to glyphosate, relative to a median [IQR] of 6 [3] units in BVRT across the entire study population).<br><br>CONCLUSIONS: We found that overall pesticide exposure was associated with several neurobehavioral outcome variables. However, when we examined individual pesticide active ingredients, we observed predominantly null associations, except for a positive association between glyphosate exposure and impaired visual memory. Additional epidemiologic studies are needed to evaluate glyphosate's neurotoxicity, while accounting for co-pollutant confounding.},
}
@article {pmid33756104,
year = {2021},
author = {Norman, SL and Maresca, D and Christopoulos, VN and Griggs, WS and Demene, C and Tanter, M and Shapiro, MG and Andersen, RA},
title = {Single-trial decoding of movement intentions using functional ultrasound neuroimaging.},
journal = {Neuron},
volume = {109},
number = {9},
pages = {1554-1566.e4},
pmid = {33756104},
issn = {1097-4199},
support = {T32 GM008042/GM/NIGMS NIH HHS/United States ; U01 NS099724/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain Mapping/methods ; Brain-Computer Interfaces ; *Intention ; Macaca mulatta ; Male ; Movement ; Neuroimaging/instrumentation/*methods ; Parietal Lobe/*physiology ; Psychomotor Performance/*physiology ; Ultrasonography/instrumentation/*methods ; },
abstract = {New technologies are key to understanding the dynamic activity of neural circuits and systems in the brain. Here, we show that a minimally invasive approach based on ultrasound can be used to detect the neural correlates of movement planning, including directions and effectors. While non-human primates (NHPs) performed memory-guided movements, we used functional ultrasound (fUS) neuroimaging to record changes in cerebral blood volume with 100 μm resolution. We recorded from outside the dura above the posterior parietal cortex, a brain area important for spatial perception, multisensory integration, and movement planning. We then used fUS signals from the delay period before movement to decode the animals' intended direction and effector. Single-trial decoding is a prerequisite to brain-machine interfaces, a key application that could benefit from this technology. These results are a critical step in the development of neuro-recording and brain interface tools that are less invasive, high resolution, and scalable.},
}
@article {pmid33752186,
year = {2021},
author = {Sosnik, R and Zheng, L},
title = {Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abf0d7},
pmid = {33752186},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Elbow ; Electroencephalography/methods ; Hand ; Humans ; Movement ; },
abstract = {Objective. Growing evidence suggests that electroencephalography (EEG) electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction, commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features.Approach. In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model.Main results. For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150, 116 and 84 ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person's correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r= 0.25 andr= 0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components.Significance. Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.},
}
@article {pmid33751098,
year = {2021},
author = {Dong, HL and Ma, Y and Yu, H and Wei, Q and Li, JQ and Liu, GL and Li, HF and Chen, L and Chen, DF and Bai, G and Wu, ZY},
title = {Bi-allelic loss of function variants in COX20 gene cause autosomal recessive sensory neuronopathy.},
journal = {Brain : a journal of neurology},
volume = {144},
number = {8},
pages = {2457-2470},
doi = {10.1093/brain/awab135},
pmid = {33751098},
issn = {1460-2156},
mesh = {Adolescent ; Adult ; Cell Proliferation/genetics ; Child ; Child, Preschool ; Cytochrome-c Oxidase Deficiency/*genetics/physiopathology ; Electron Transport Complex IV/*genetics ; Female ; Hereditary Sensory and Autonomic Neuropathies/*genetics/physiopathology ; Humans ; *Loss of Heterozygosity ; Male ; Median Nerve/physiopathology ; Mitochondria/*genetics ; Mutation ; Neural Conduction/physiology ; Pedigree ; Radial Nerve/physiopathology ; Ulnar Nerve/physiopathology ; },
abstract = {Sensory neuronopathies are a rare and distinct subgroup of peripheral neuropathies, characterized by degeneration of the dorsal root ganglia neurons. About 50% of sensory neuronopathies are idiopathic and genetic causes remain to be clarified. Through a combination of homozygosity mapping and whole exome sequencing, we linked an autosomal recessive sensory neuronopathy to pathogenic variants in the COX20 gene. We identified eight unrelated families from the eastern Chinese population carrying a founder variant c.41A>G (p.Lys14Arg) within COX20 in either a homozygous or compound heterozygous state. All patients displayed sensory ataxia with a decrease in non-length-dependent sensory potentials. COX20 encodes a key transmembrane protein implicated in the assembly of mitochondrial complex IV. We showed that COX20 variants lead to reduction of COX20 protein in patient's fibroblasts and transfected cell lines, consistent with a loss-of-function mechanism. Knockdown of COX20 expression in ND7/23 sensory neuron cells resulted in complex IV deficiency and perturbed assembly of complex IV, which subsequently compromised cell spare respiratory capacity and reduced cell proliferation under metabolic stress. Consistent with mitochondrial dysfunction in knockdown cells, reduced complex IV assembly, enzyme activity and oxygen consumption rate were also found in patients' fibroblasts. We speculated that the mechanism of COX20 was similar to other causative genes (e.g. SURF1, COX6A1, COA3 and SCO2) for peripheral neuropathies, all of which are functionally important in the structure and assembly of complex IV. Our study identifies a novel causative gene for the autosomal recessive sensory neuronopathy, whose vital function in complex IV and high expression in the proprioceptive sensory neuron further underlines loss of COX20 contributing to mitochondrial bioenergetic dysfunction as a mechanism in peripheral sensory neuron disease.},
}
@article {pmid33750903,
year = {2021},
author = {Zhuang, Y and Krumm, B and Zhang, H and Zhou, XE and Wang, Y and Huang, XP and Liu, Y and Cheng, X and Jiang, Y and Jiang, H and Zhang, C and Yi, W and Roth, BL and Zhang, Y and Xu, HE},
title = {Mechanism of dopamine binding and allosteric modulation of the human D1 dopamine receptor.},
journal = {Cell research},
volume = {31},
number = {5},
pages = {593-596},
pmid = {33750903},
issn = {1748-7838},
support = {R35 GM128641/GM/NIGMS NIH HHS/United States ; R01 MH112205/MH/NIMH NIH HHS/United States ; },
mesh = {Allosteric Regulation ; *Dopamine ; Humans ; *Receptors, Dopamine D1 ; Synaptic Transmission ; },
}
@article {pmid34567669,
year = {2020},
author = {Chen, H and Wang, L and Lu, Y and Du, X},
title = {Bioinspired microcone-array-based living biointerfaces: enhancing the anti-inflammatory effect and neuronal network formation.},
journal = {Microsystems &amp; nanoengineering},
volume = {6},
number = {},
pages = {58},
pmid = {34567669},
issn = {2055-7434},
abstract = {Implantable neural interfaces and systems have attracted much attention due to their broad applications in treating diverse neuropsychiatric disorders. However, obtaining a long-term reliable implant-neural interface is extremely important but remains an urgent challenge due to the resulting acute inflammatory responses. Here, bioinspired microcone-array-based (MA) interfaces have been successfully designed, and their cytocompatibility with neurons and the inflammatory response have been explored. Compared with smooth control samples, MA structures cultured with neuronal cells result in much denser extending neurites, which behave similar to creepers, wrapping tightly around the microcones to form complex and interconnected neuronal networks. After further implantation in mouse brains for 6 weeks, the MA probes (MAPs) significantly reduced glial encapsulation and neuron loss around the implants, suggesting better neuron viability at the implant-neural interfaces than that of smooth probes. This bioinspired strategy for both enhanced glial resistance and neuron network formation via a specific structural design could be a platform technology that not only opens up avenues for next-generation artificial neural networks and brain-machine interfaces but also provides universal approaches to biomedical therapeutics.},
}
@article {pmid35025460,
year = {2020},
author = {Liu, C and Nguyen, MA and Alvarez-Ciara, A and Franklin, M and Bennett, C and Domena, JB and Kleinhenz, NC and Blanco Colmenares, GA and Duque, S and Chebbi, AF and Bernard, B and Olivier, JH and Prasad, A},
title = {Surface Modifications of an Organic Polymer-Based Microwire Platform for Sustained Release of an Anti-Inflammatory Drug.},
journal = {ACS applied bio materials},
volume = {3},
number = {7},
pages = {4613-4625},
doi = {10.1021/acsabm.0c00506},
pmid = {35025460},
issn = {2576-6422},
abstract = {Brain machine interfaces (BMIs), introduced into the daily lives of individuals with injuries or disorders of the nervous system such as spinal cord injury, stroke, or amyotrophic lateral sclerosis, can improve the quality of life. BMIs rely on the capability of microelectrode arrays to monitor the activity of large populations of neurons. However, maintaining a stable, chronic electrode-tissue interface that can record neuronal activity with a high signal-to-noise ratio is a key challenge that has limited the translation of such technologies. An electrode implant injury leads to a chronic foreign body response that is well-characterized and shown to affect the electrode-tissue interface stability. Several strategies have been applied to modulate the immune response, including the application of immunomodulatory drugs applied both systemically and locally. While the use of passive drug release at the site of injury has been exploited to minimize neuroinflammation, this strategy has all but failed as a bolus of anti-inflammatory drugs is released at predetermined times that are often inconsistent with the ongoing innate inflammatory process. Common strategies do not focus on the proper anchorage of soft hydrogel scaffolds on electrode surfaces, which often results in delamination of the porous network from electrodes. In this study, we developed a microwire platform that features a robust yet soft biocompatible hydrogel coating, enabling long-lasting drug release via formation of drug aggregates and dismantlement of hydrophilic biodegradable three-dimensional polymer networks. Facile surface chemistry is developed to functionalize polyimide-coated electrodes with the covalently anchored porous hydrogel network bearing large numbers of highly biodegradable ester groups. Exponential long-lasting drug release is achieved using such hydrogels. We show that the initial state of dexamethasone (Dex) used to formulate the hydrogel precursor solution plays a cardinal role in engineering hydrophilic networks that enable a sustained and long-lasting release of the anti-inflammatory agent. Furthermore, utilization of a high loading ratio that exceeds the solubility of Dex leads to the encapsulation of Dex aggregates that regulate the release of this anti-inflammatory agent. To validate the anti-inflammatory effect of the hydrogel-functionalized Dex-loaded microwires, an in vivo preliminary study was performed in adult male rats (n = 10) for the acute time points of 48 h and 7 days post implant. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to assess the mRNA expression of certain inflammatory-related genes. In general, a decrease in fold-change expression was observed for all genes tested for Dex-loaded wires compared with controls (functionalized but no drug). The engineering of hybrid microwires enables a sustained release of the anti-inflammatory agent over extended periods of time, thus paving the way to fabricate neuroprosthetic devices capable of attenuating the foreign body response.},
}
@article {pmid34386243,
year = {2020},
author = {Rainey, S and McGillivray, K and Akintoye, S and Fothergill, T and Bublitz, C and Stahl, B},
title = {Is the European Data Protection Regulation sufficient to deal with emerging data concerns relating to neurotechnology?.},
journal = {Journal of law and the biosciences},
volume = {7},
number = {1},
pages = {lsaa051},
doi = {10.1093/jlb/lsaa051},
pmid = {34386243},
issn = {2053-9711},
abstract = {Research-driven technology development in the fields of the neurosciences presents interesting and potentially complicated issues around data in general and brain data specifically. The data produced from brain recordings are unlike names and addresses in that it may result from the processing of largely involuntarily brain activity, it can be processed and reprocessed for different aims, and it is highly sensitive. Consenting for brain recordings of a specific type, or for a specific purpose, is complicated by these factors. Brain data collection, retention, processing, storage, and destruction are each of high ethical importance. This leads us to ask: Is the present European Data Protection Regulation sufficient to deal with emerging data concerns relating to neurotechnology? This is pressing especially in a context of rapid advancement in the fields of brain computer interfaces (BCIs), where devices that can function via recorded brain signals are expanding from research labs, through medical treatments, and beyond into consumer markets for recreational uses. One notion we develop herein is that there may be no trivial data collection when it comes to brain recording, especially where algorithmic processing is involved. This article provides analysis and discussion of some specific data protection questions related to neurotechnology, especially BCIs. In particular, whether and how brain data used in BCI-driven applications might count as personal data in a way relevant to data protection regulations. It also investigates how the nature of BCI data, as it appears in various applications, may require different interpretations of data protection concepts. Importantly, we consider brain recordings to raise questions about data sensitivity, regardless of the purpose for which they were recorded. This has data protection implications.},
}
@article {pmid34692063,
year = {2020},
author = {Li, C and Zhao, W},
title = {Progress in the brain-computer interface: an interview with Bin He.},
journal = {National science review},
volume = {7},
number = {2},
pages = {480-483},
doi = {10.1093/nsr/nwz152},
pmid = {34692063},
issn = {2053-714X},
abstract = {What can the brain-computer interface (BCI) do? Wearing an electroencephalogram (EEG) headcap, you can control the flight of a drone in the laboratory by your thought; with electrodes inserted inside the brain, paralytic patients can drink by controlling a robotic arm with thinking. Both invasive and non-invasive BCI try to connect human brains to machines. In the past several decades, BCI technology has continued to develop, making science fiction into reality and laboratory inventions into indispensable gadgets. In July 2019, Neuralink, a company founded by Elon Musk, proposed a sewing machine-like device that can dig holes in the skull and implant 3072 electrodes onto the cortex, promising more accurate reading of what you are thinking, although many serious scientists consider the claim misleading to the public. Recently, National Science Review (NSR) interviewed Professor Bin He, the department head of Biomedical Engineering at Carnegie Mellon University, and a leading scientist in the non-invasive-BCI field. His team developed new methods for non-invasive BCI to control drones by thoughts. In 2019, Bin's team demonstrated the control of a robotic arm to follow a continuously randomly moving target on the screen. In this interview, Bin He recounted the history of BCI, as well as the opportunities and challenges of non-invasive BCI.},
}
@article {pmid33763499,
year = {2020},
author = {Geronimo, A and Simmons, Z},
title = {TeleBCI: remote user training, monitoring, and communication with an evoked-potential brain-computer interface.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {7},
number = {3-4},
pages = {57-69},
pmid = {33763499},
issn = {2326-263X},
support = {UL1 TR000127/TR/NCATS NIH HHS/United States ; UL1 TR002014/TR/NCATS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) are a movement-independent form of augmentative and alternative communication (AAC) for individuals with amyotrophic lateral sclerosis (ALS). The rare utilization of such devices in the homes of patients stems from a number of factors, one of which is the complexity of providing training and support for users. This paper describes the teleBCI interface used to train the patient and facilitator in the operation of a virtual keyboard using an evoked potential BCI. Fifteen patients with motor neuron disease and their communication partners were included in the study, participating from their homes while receiving remote support from the research team. Patient/caregiver teams completed 8 sessions each of P300 BCI training virtually with the researcher. As they participated in subsequent training sessions, participant teams required less help to complete physical, computer, and BCI-specific tasks associated with device use. A subset of users experienced improved performance over sessions, progressing to utilize the full functionality of the speller and communicate with a nurse partner over a telemedicine interface. Perceptions of device utility varied with accuracy of the BCI system. In the management of ALS, the integration of telemedicine provides new opportunities for care delivery, including how BCI-AAC are deployed and used.},
}
@article {pmid33747669,
year = {2020},
author = {Özdenizci, O and Wang, YE and Koike-Akino, T and ErdoĞmuŞ, D},
title = {Learning Invariant Representations from EEG via Adversarial Inference.},
journal = {IEEE access : practical innovations, open solutions},
volume = {8},
number = {},
pages = {27074-27085},
pmid = {33747669},
issn = {2169-3536},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG) based classification tasks is of significant interest for generalizability of decoding models across subjects or EEG recording sessions. While deep neural networks are recently emerging as generic EEG feature extractors, this transfer learning aspect usually relies on the prior assumption that deep networks naturally behave as subject- (or session-) invariant EEG feature extractors. We propose a further step towards invariance of EEG deep learning frameworks in a systemic way during model training. We introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting. We perform experimental studies using a publicly available motor imagery EEG dataset, and state-of-the-art convolutional neural network based EEG decoding models within the proposed adversarial learning framework. We present our results in cross-subject model transfer scenarios, demonstrate neurophysiological interpretations of the learned networks, and discuss potential insights offered by adversarial inference to the growing field of deep learning for EEG.},
}
@article {pmid35021444,
year = {2019},
author = {Cai, Y and Zhong, Z and He, C and Xia, H and Hu, Q and Wang, Y and Ye, Q and Zhou, J},
title = {Homogeneously Synthesized Hydroxybutyl Chitosans in Alkali/Urea Aqueous Solutions as Potential Wound Dressings.},
journal = {ACS applied bio materials},
volume = {2},
number = {10},
pages = {4291-4302},
doi = {10.1021/acsabm.9b00553},
pmid = {35021444},
issn = {2576-6422},
abstract = {Wound healing is a clinical challenge, and nontoxic, nonadherent wound dressings that promote healing are urgently needed. Herein, hydroxybutyl chitosans (HBCSs) with the degree of substitution (DS) from 0.41 to 1.38 were synthesized in alkali/urea aqueous solutions, from which sponge-like dressings were prepared by freeze-drying. The pore size of the sponges was in the range of 14.8-18.4 μm, and the porosity was about 98-99%. The compressive strength of the sponges decreased with increasing DS of HBCS. Cytocompatibility studies with normal human dermal fibroblast (NHDF) cells demonstrated that HBCSs were nontoxic and could even promote the growth of fibroblasts. Further tests revealed that HBCS-3 (DS = 0.85) and HBCS-5 (DS = 1.38) exhibited better hemocompatibility and a low blood-clotting index (BCI). Therefore, these two samples were selected as model dressings for in vivo wound-healing assessment in rats. The experiments suggested that HBCS-3 significantly shortened the wound recovery period compared with HBCS-5, chitosan, and gauze by facilitating epithelialization, collagen deposition, and neovascularization and activating the immune system. The results highlighted the potential of HBCSs as efficient dressings for promoting wound healing.},
}
@article {pmid34531937,
year = {2019},
author = {Pitt, KM and Brumberg, JS and Pitt, AR},
title = {Considering Augmentative and Alternative Communication Research for Brain-Computer Interface Practice.},
journal = {Assistive technology outcomes and benefits},
volume = {13},
number = {1},
pages = {1-20},
pmid = {34531937},
issn = {1938-7261},
support = {R01 DC016343/DC/NIDCD NIH HHS/United States ; },
abstract = {PURPOSE: Brain-computer interfaces (BCIs) aim to provide access to augmentative and alternative communication (AAC) devices via brain activity alone. However, while BCI technology is expanding in the laboratory setting there is minimal incorporation into clinical practice. Building upon established AAC research and clinical best practices may aid the clinical translation of BCI practice, allowing advancements in both fields to be fully leveraged.<br><br>METHOD: A multidisciplinary team developed considerations for how BCI products, practice, and policy may build upon existing AAC research, based upon published reports of existing AAC and BCI procedures.<br><br>OUTCOMES/BENEFITS: Within each consideration, a review of BCI research is provided, along with considerations regarding how BCI procedures may build upon existing AAC methods. The consistent use of clinical/research procedures across disciplines can help facilitate collaborative efforts, engaging a range-individuals within the AAC community in the transition of BCI into clinical practice.},
}
@article {pmid33868718,
year = {2019},
author = {Gunduz, A and Opri, E and Gilron, R and Kremen, V and Worrell, G and Starr, P and Leyde, K and Denison, T},
title = {Adding wisdom to 'smart' bioelectronic systems: a design framework for physiologic control including practical examples.},
journal = {Bioelectronics in medicine},
volume = {2},
number = {1},
pages = {29-41},
pmid = {33868718},
issn = {2059-1519},
support = {R01 NS096008/NS/NINDS NIH HHS/United States ; R01 NS090913/NS/NINDS NIH HHS/United States ; MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; UH3 NS095495/NS/NINDS NIH HHS/United States ; UH3 NS100544/NS/NINDS NIH HHS/United States ; UH3 NS095553/NS/NINDS NIH HHS/United States ; R01 NS092882/NS/NINDS NIH HHS/United States ; },
abstract = {This perspective provides an overview of how risk can be effectively considered in physiological control loops that strive for semi-to-fully automated operation. The perspective first introduces the motivation, user needs and framework for the design of a physiological closed-loop controller. Then, we discuss specific risk areas and use examples from historical medical devices to illustrate the key concepts. Finally, we provide a design overview of an adaptive bidirectional brain-machine interface, currently undergoing human clinical studies, to synthesize the design principles in an exemplar application.},
}
@article {pmid34988241,
year = {2019},
author = {Thompson, DE and Mowla, MR and Dhuyvetter, KJ and Tillman, JW and Huggins, JE},
title = {Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {6},
number = {4},
pages = {141-148},
pmid = {34988241},
issn = {2326-263X},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-Computer Interfaces (BCIs) have been used to restore communication and control to people with severe paralysis. However, non-invasive BCIs based on electroencephalogram (EEG) are particularly vulnerable to noise artifacts. These artifacts, including electro-oculogram (EOG), can be orders of magnitude larger than the signal to be detected. Many automated methods have been proposed to remove EOG and other artifacts from EEG recordings, most based on blind source separation. This work presents a performance comparison of ten different automated artifact removal methods. Unfortunately, all tested methods substantially and significantly reduced P3 Speller BCI performance, and all methods were more likely to reduce performance than increase it. The least harmful methods were titled SOBI, JADER, and EFICA, but even these methods caused an average of approximately ten percentage points drop in BCI accuracy. Possible mechanistic causes for this empirical performance deduction are proposed.},
}
@article {pmid34720566,
year = {2018},
author = {Myers, JC and Irani, F and Golob, EJ and Mock, JR and Robbins, KA},
title = {Single-Trial Classification of Disfluent Brain States in Adults Who Stutter.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2018},
number = {},
pages = {},
pmid = {34720566},
issn = {1062-922X},
support = {R21 DC016353/DC/NIDCD NIH HHS/United States ; },
abstract = {Normal human speech requires precise coordination between motor planning and sensory processing. Speech disfluencies are common when children learn to talk, but usually abate with time. About 5% of children experience stuttering. For most, this resolves within a year. However, for approximately 1% of the world population, stuttering continues into adulthood, which is termed 'persistent developmental stuttering'. Most stuttering events occur at the beginning of an utterance. So, in principle, brain activity before speaking should differ between fluent and stuttered speech. Here we present a method for classifying brain network states associated with fluent vs. stuttered speech on a single trial basis. Brain activity was recorded with EEG before people who stutter read aloud pseudo-word pairs. Offline independent component analysis (ICA) was used to identify the independent neural sources that underlie speech preparation. A time window selection algorithm extracted spectral power and coherence data from salient windows specific to each neural source. A stepwise linear discriminant analysis (sLDA) algorithm predicted fluent vs. stuttered speech for 81% of trials in two subjects. These results support the feasibility of developing a brain-computer interface (BCI) system to detect stuttering before it occurs, with potential for therapeutic application.},
}
@article {pmid33937917,
year = {2018},
author = {Dudy, S and Bedrick, S and Xu, S and Smith, DA},
title = {A Multi-Context Character Prediction Model for a Brain-Computer Interface.},
journal = {Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting},
volume = {2018},
number = {},
pages = {72-77},
pmid = {33937917},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods. In particular, the acquired signal of the EEG (electroencephalogram) signal is noisier, which, in turn, makes the user intent harder to decipher. In order to adapt to this condition, we propose to maintain ambiguous history for every time step, and to employ, apart from the character language model, word information to produce a more robust prediction system. We present preliminary results that compare this proposed Online-Context Language Model (OCLM) to current algorithms that are used in this type of setting. Evaluations on both perplexity and predictive accuracy demonstrate promising results when dealing with ambiguous histories in order to provide to the front end a distribution of the next character the user might type.},
}
@article {pmid34045935,
year = {2018},
author = {Serruya, MD and Harris, JP and Adewole, DO and Struzyna, LA and Burrell, JC and Nemes, A and Petrov, D and Kraft, RH and Chen, HI and Wolf, JA and Cullen, DK},
title = {Engineered Axonal Tracts as "Living Electrodes" for Synaptic-Based Modulation of Neural Circuitry.},
journal = {Advanced functional materials},
volume = {28},
number = {12},
pages = {},
pmid = {34045935},
issn = {1616-301X},
support = {IK2 RX001479/RX/RRD VA/United States ; I01 BX003748/BX/BLRD VA/United States ; T32 NS043126/NS/NINDS NIH HHS/United States ; U01 NS094340/NS/NINDS NIH HHS/United States ; IK2 RX002013/RX/RRD VA/United States ; },
abstract = {Brain-computer interface and neuromodulation strategies relying on penetrating non-organic electrodes/optrodes are limited by an inflammatory foreign body response that ultimately diminishes performance. A novel "biohybrid" strategy is advanced, whereby living neurons, biomaterials, and microelectrode/optical technology are used together to provide a biologically-based vehicle to probe and modulate nervous-system activity. Microtissue engineering techniques are employed to create axon-based "living electrodes", which are columnar microstructures comprised of neuronal population(s) projecting long axonal tracts within the lumen of a hydrogel designed to chaperone delivery into the brain. Upon microinjection, the axonal segment penetrates to prescribed depth for synaptic integration with local host neurons, with the perikaryal segment remaining externalized below conforming electrical-optical arrays. In this paradigm, only the biological component ultimately remains in the brain, potentially attenuating a chronic foreign-body response. Axon-based living electrodes are constructed using multiple neuronal subtypes, each with differential capacity to stimulate, inhibit, and/or modulate neural circuitry based on specificity uniquely afforded by synaptic integration, yet ultimately computer controlled by optical/electrical components on the brain surface. Current efforts are assessing the efficacy of this biohybrid interface for targeted, synaptic-based neuromodulation, and the specificity, spatial density and long-term fidelity versus conventional microelectronic or optical substrates alone.},
}
@article {pmid34336364,
year = {2015},
author = {Edelman, BJ and Johnson, N and Sohrabpour, A and Tong, S and Thakor, N and He, B},
title = {Systems Neuroengineering: Understanding and Interacting with the Brain.},
journal = {Engineering (Beijing, China)},
volume = {1},
number = {3},
pages = {292-308},
pmid = {34336364},
issn = {2095-8099},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EY023101/EY/NEI NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; U01 HL117664/HL/NHLBI NIH HHS/United States ; },
abstract = {In this paper, we review the current state-of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering: neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity (via a neuroimaging modality) through a neural interface (invasive or noninvasive) and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering-to develop neurotechniques for enhancing the understanding of whole-brain function and dysfunction, and the management of neurological and mental disorders.},
}
@article {pmid34334804,
year = {2015},
author = {He, B and Baxter, B and Edelman, BJ and Cline, CC and Ye, W},
title = {Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.},
journal = {Proceedings of the IEEE. Institute of Electrical and Electronics Engineers},
volume = {103},
number = {6},
pages = {907-925},
pmid = {34334804},
issn = {0018-9219},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EY023101/EY/NEI NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.},
}
@article {pmid33854819,
year = {2013},
author = {Clanton, ST and Rasmussen, RG and Zohny, Z and Velliste, M},
title = {Generalized Virtual Fixtures for Shared-Control Grasping in Brain-Machine Interfaces.},
journal = {Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems},
volume = {2013},
number = {},
pages = {323-328},
pmid = {33854819},
issn = {2153-0858},
support = {F30 NS060530/NS/NINDS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; RC1 NS070311/NS/NINDS NIH HHS/United States ; },
}
@article {pmid33863189,
year = {1997},
author = {Zotz, G and Ziegler, H},
title = {The occurrence of crassulacean acid metabolism among vascular epiphytes from Central Panama.},
journal = {The New phytologist},
volume = {137},
number = {2},
pages = {223-229},
doi = {10.1046/j.1469-8137.1997.00800.x},
pmid = {33863189},
issn = {1469-8137},
abstract = {The occurrence of crassulacean acid metabolism (CAM) among the epiphyte flora of the lowland forest on Barro Colorado Island (BCJ), Panama, was investigated. A total of 116 species was included, i.e. about 2/3 of the known epiphyte taxa. As judged from the carbon isotope ratios and the absence of Kranz anatomy, indications of CAM were found in 29 species of three families, Orchidaceae (20), Bromeliaceae (7), and Cactaceae (2). We estimate that about 25% of the epiphyte flora of BCI are CAM plants. CAM was most prevalent in exposed sites, but even in the understorey two epiphyte species engage in CAM.},
}
@article {pmid33747547,
year = {2021},
author = {Rebouillat, B and Leonetti, JM and Kouider, S},
title = {People confabulate with high confidence when their decisions are supported by weak internal variables.},
journal = {Neuroscience of consciousness},
volume = {2021},
number = {1},
pages = {niab004},
pmid = {33747547},
issn = {2057-2107},
abstract = {People can introspect on their internal state and report the reasons driving their decisions but choice blindness (CB) experiments suggest that this ability can sometimes be a retrospective illusion. Indeed, when presented with deceptive cues, people justify choices they did not make in the first place, suggesting that external cues largely contribute to introspective processes. Yet, it remains unclear what are the respective contributions of external cues and internal decision variables in forming introspective report. Here, using a brain-computer interface, we show that internal variables continue to be monitored but are less impactful than deceptive external cues during CB episodes. Moreover, we show that deceptive cues overturn the classical relationship between confidence and accuracy: introspective failures are associated with higher confidence than genuine introspective reports. We tracked back the origin of these overconfident confabulations by revealing their prominence when internal decision evidence is weak and variable. Thus, introspection is neither a direct reading of internal variables nor a mere retrospective illusion, but rather reflects the integration of internal decision evidence and external cues, with CB being a special instance where internal evidence is inconsistent.},
}
@article {pmid33746697,
year = {2021},
author = {Nguyen, D and Valet, M and Dégardin, J and Boucherit, L and Illa, X and de la Cruz, J and Del Corro, E and Bousquet, J and Garrido, JA and Hébert, C and Picaud, S},
title = {Novel Graphene Electrode for Retinal Implants: An in vivo Biocompatibility Study.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {615256},
pmid = {33746697},
issn = {1662-4548},
abstract = {Evaluating biocompatibility is a core essential step to introducing a new material as a candidate for brain-machine interfaces. Foreign body reactions often result in glial scars that can impede the performance of the interface. Having a high conductivity and large electrochemical window, graphene is a candidate material for electrical stimulation with retinal prosthesis. In this study, non-functional devices consisting of chemical vapor deposition (CVD) graphene embedded onto polyimide/SU-8 substrates were fabricated for a biocompatibility study. The devices were implanted beneath the retina of blind P23H rats. Implants were monitored by optical coherence tomography (OCT) and eye fundus which indicated a high stability in vivo up to 3 months before histology studies were done. Microglial reconstruction through confocal imaging illustrates that the presence of graphene on polyimide reduced the number of microglial cells in the retina compared to polyimide alone, thereby indicating a high biocompatibility. This study highlights an interesting approach to assess material biocompatibility in a tissue model of central nervous system, the retina, which is easily accessed optically and surgically.},
}
@article {pmid33746477,
year = {2020},
author = {Scholten, K and Larson, CE and Xu, H and Song, D and Meng, E},
title = {A 512-Channel Multi-Layer Polymer-Based Neural Probe Array.},
journal = {Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems},
volume = {29},
number = {5},
pages = {1054-1058},
pmid = {33746477},
issn = {1057-7157},
support = {U01 NS099703/NS/NINDS NIH HHS/United States ; },
abstract = {We present for the first time the design, fabrication, and preliminary bench-top characterization of a high-density, polymer-based penetrating microelectrode array, developed for chronic, large-scale recording in the cortices and hippocampi of behaving rats. We present two architectures for these targeted brain regions, both featuring 512 Pt recording electrodes patterned front-and-back on micromachined eight-shank arrays of thin-film Parylene C. These devices represent an order of magnitude improvement in both number and density of recording electrodes compared with prior work on polymer-based microelectrode arrays. We present enabling advances in polymer micro-machining related to lithographic resolution and a new method for back-side patterning of electrodes. In vitro electrochemical data verifies suitable electrode function and surface properties. Finally, we describe next steps toward the implementation of these arrays in chronic, large-scale recording studies in free-moving animal models.},
}
@article {pmid33745948,
year = {2021},
author = {Yang, Z and Xiao, X and Chen, R and Xu, X and Kong, W and Zhang, T},
title = {Disc1 gene down-regulation impaired synaptic plasticity and recognition memory via disrupting neural activity in mice.},
journal = {Brain research bulletin},
volume = {171},
number = {},
pages = {84-90},
doi = {10.1016/j.brainresbull.2021.03.011},
pmid = {33745948},
issn = {1873-2747},
mesh = {Animals ; Behavior, Animal/physiology ; *Down-Regulation ; Hippocampus/*metabolism ; Mice ; Nerve Tissue Proteins/*genetics/metabolism ; Neuronal Plasticity/*genetics ; Neurons/*physiology ; Recognition, Psychology/*physiology ; },
abstract = {The gene of Disrupted-in-schizophrenia 1 (Disc1) is closely related to mental diseases with cognitive deficits, but there are few studies on the changes in neural oscillations and recognition memory. Neural oscillations plays a key role in the nervous system in a dynamic form, which is closely related to advanced cognitive activities such as information processing and memory consolidation. Hence, we aimed to investigate if Disc1 knockdown disrupted the normal pattern of neural activities in the mouse hippocampus network, and determined if quantitative neural oscillation approach could be a potential diagnostic tool for mental disorders. In the study, we reported that Disc1 gene, downregulated by short-hairpin RNA (shRNA), not only induced anxiety-like behavior and sociability impairment but also damaged both synaptic plasticity and recognition memory in mice. Moreover, Disc1 knockdown mice exhibited evidently abnormal power spectral distributions, reduced phase synchronizations, and decreased phase-amplitude coupling strength compared to that of normal animals. In addition, transcriptome analyses showed that there were clearly transcriptional changes in Disc1 knockdown mice. Altogether, our findings suggest that the abnormal pattern of neural activities in the hippocampus network disrupts information processing and finally leads to the impairments of synaptic plasticity and recognition in Disc1 knockdown mice, which are possibly associated with the obstruction of neurotransmitter transmission. Importantly, the data imply that the analysis of neural oscillation pattern provides a potential diagnosis approach for mental disorders.},
}
@article {pmid33745921,
year = {2021},
author = {Teng, YD and Zafonte, RD},
title = {Prelude to the special issue on novel neurocircuit, cellular and molecular targets for developing functional rehabilitation therapies of neurotrauma.},
journal = {Experimental neurology},
volume = {341},
number = {},
pages = {113689},
doi = {10.1016/j.expneurol.2021.113689},
pmid = {33745921},
issn = {1090-2430},
mesh = {Animals ; Brain Injuries, Traumatic/pathology/physiopathology/*rehabilitation ; Humans ; Nerve Net/cytology/*physiology ; Neurological Rehabilitation/*methods/trends ; Recovery of Function/*physiology ; Spinal Cord Injuries/pathology/physiopathology/*rehabilitation ; },
abstract = {The poor endogenous recovery capacity and other impediments to reinstating sensorimotor or autonomic function after adult neurotrauma have perplexed modern neuroscientists, bioengineers, and physicians for over a century. However, despite limited improvement in options to mitigate acute pathophysiological sequalae, the past 20 years have witnessed marked progresses in developing efficacious rehabilitation strategies for chronic spinal cord and brain injuries. The achievement is mainly attributable to research advancements in elucidating neuroplastic mechanisms for the potential to enhance clinical prognosis. Innovative cross-disciplinary studies have established novel therapeutic targets, theoretical frameworks, and regiments to attain treatment efficacy. This Special Issue contained eight papers that described experimental and human data along with literature reviews regarding the essential roles of the conventionally undervalued factors in neural repair: systemic inflammation, neural-respiratory inflammasome axis, modulation of glutamatergic and monoaminergic neurotransmission, neurogenesis, nerve transfer, recovery neurobiology components, and the spinal cord learning, respiration and central pattern generator neurocircuits. The focus of this work was on how to induce functional recovery from manipulating these underpinnings through their interactions with secondary injury events, peripheral and supraspinal inputs, neuromusculoskeletal network, and interventions (i.e., activity training, pharmacological adjuncts, electrical stimulation, and multimodal neuromechanical, brain-computer interface [BCI] and robotic assistance [RA] devices). The evidence suggested that if key neurocircuits are therapeutically reactivated, rebuilt, and/or modulated under proper sensory feedback, neurological function (e.g., cognition, respiration, limb movement, locomotion, etc.) will likely be reanimated after neurotrauma. The efficacy can be optimized by individualizing multimodal rehabilitation treatments via BCI/RA-integrated drug administration and neuromechanical protheses.},
}
@article {pmid33745089,
year = {2021},
author = {Mugruza-Vassallo, CA and Potter, DD and Tsiora, S and Macfarlane, JA and Maxwell, A},
title = {Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling.},
journal = {Brain informatics},
volume = {8},
number = {1},
pages = {5},
pmid = {33745089},
issn = {2198-4018},
support = {2009-14//SINAPSE/ ; 2009-14//SINAPSE/ ; 229-2017//UNTELS/ ; },
abstract = {In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Networks (GDN) was explored through an innovative method for fMRI analysis considering all voxels in four experimental conditions: standard target (Goal; G), novel (N), neutral (Z) and noisy target (NG). First, average reaction times (RTs) for each condition were calculated. In the second-level analysis, 'distracted' participants, as indicated by slower RTs, evoked brain activations and differences in both hemispheres' neural networks for selective attention, while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions, i.e. Z.G, N.G, NG.G. Results showed different prefrontal activations dependent on prior context in the auditory modality, recruiting between 1 to 10 prefrontal areas. The higher the motor response and influence of the previous novel stimulus, the more prefrontal areas were engaged, which extends the findings of hierarchical studies of prefrontal control of attention and better explains how auditory processing interferes with movement. Also, the current study addressed how subcortical loops and models of previous motor response affected the signal processing of the novel stimulus, when this was presented laterally or simultaneously with the target. This multitasking model could enhance our understanding on how an auditory stimulus is affecting motor responses in a way that is self-induced, by taking into account prior context, as demonstrated in the standard condition and as supported by Pulvinar activations complementing visual findings. Moreover, current BCI works address some multimodal stimulus-driven systems.},
}
@article {pmid33744673,
year = {2021},
author = {Hyde, RM and Green, MJ and Hudson, C and Down, PM},
title = {Factors associated with daily weight gain in preweaned calves on dairy farms.},
journal = {Preventive veterinary medicine},
volume = {190},
number = {},
pages = {105320},
doi = {10.1016/j.prevetmed.2021.105320},
pmid = {33744673},
issn = {1873-1716},
mesh = {Animals ; *Animals, Newborn ; Cattle ; *Colostrum ; *Dairying ; Diet ; Farms ; Female ; Milk ; Pregnancy ; United Kingdom ; Weaning ; *Weight Gain ; },
abstract = {The preweaning period is vital in the development of calves on dairy farms and improving daily liveweight gain (DLWG) is important to both financial and carbon efficiency; minimising rearing costs and improving first lactation milk yields. In order to improve DLWG, veterinary advisors should provide advice that has both a large effect size as well as being consistently important on the majority of farms. Whilst a variety of factors have previously been identified as influencing the DLWG of preweaned calves, it can be challenging to determine their relative importance, which is essential for optimal on-farm management decisions. Regularised regression methods such as ridge or lasso regression provide a solution by penalising variable coefficients unless there is a proportional improvement in model performance. Elastic net regression incorporates both lasso and ridge penalties and was used in this research to provide a sparse model to accommodate strongly correlated predictors and provide robust coefficient estimates. Sixty randomly selected British dairy farms were enrolled to collect weigh tape data from preweaned calves at birth and weaning, resulting in data being available for 1014 calves from 30 farms after filtering to remove poor quality data, with a mean DLWG of 0.79 kg/d (range 0.49-1.06 kg/d, SD 0.13). Farm management practices (e.g. colostrum, feeding, hygiene protocols), building dimensions, temperature/humidity and colostrum quality/bacteriology data were collected, resulting in 293 potential variables affecting farm level DLWG. Bootstrapped elastic net regression models identified 17 variables as having both a large effect size and high stability. Increasing the maximum preweaned age within the first housing group (0.001 kg/d per 1d increase, 90 % bootstrap confidence interval (BCI): 0.000-0.002), increased mean environmental temperature within the first month of life (0.012 kg/d per 1 °C increase, 90 % BCI: 0.002-0.037) and increased mean volume of milk feeding (0.012 kg/d per 1 L increase, 90 % BCI: 0.001-0.024) were associated with increased DLWG. An increase in the number of days between the cleaning out of calving pen (-0.001 kg/d per 1d increase, 90 % BCI: -0.001-0.000) and group housing pens (-0.001 kg/d per 1d increase, 90 % BCI: -0.002-0.000) were both associated with decreased DLWG. Through bootstrapped elastic net regression, a small number of stable variables have been identified as most likely to have the largest effect size on DLWG in preweaned calves. Many of these variables represent practical aspects of management with a focus around stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature; these variables should now be tested in a randomised controlled trial to elucidate causality.},
}
@article {pmid33743301,
year = {2021},
author = {Khalili-Ardali, M and Wu, S and Tonin, A and Birbaumer, N and Chaudhary, U},
title = {Neurophysiological aspects of the completely locked-in syndrome in patients with advanced amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {132},
number = {5},
pages = {1064-1076},
doi = {10.1016/j.clinph.2021.01.013},
pmid = {33743301},
issn = {1872-8952},
mesh = {Adult ; Aged ; Alpha Rhythm ; Amyotrophic Lateral Sclerosis/complications/*physiopathology ; *Evoked Potentials, Somatosensory ; Female ; Humans ; Locked-In Syndrome/etiology/*physiopathology ; Male ; Middle Aged ; Sensorimotor Cortex/*physiopathology ; Sleep ; },
abstract = {OBJECTIVE: Amyotrophic lateral sclerosis (ALS) patients in completely locked-in syndrome (CLIS) are incapable of expressing themselves, and their state of consciousness and awareness is difficult to evaluate. Due to the complete paralysis included paralysis of eye muscles, any assessment of the perceptual and psychophysiological state can only be implemented in passive experimental paradigms with neurophysiological recordings.<br><br>METHODS: Four patients in CLIS were investigated in several experiments including resting state, visual stimulation (eyes open vs eyes closed), auditory stimulation (modified local-global paradigm), somatosensory stimulation (electrical stimulation of the median nerve), and during sleep.<br><br>RESULTS: All patients showed altered neurophysiological metrics, but a unique and common pattern could not be found between patients. However, slowing of the electroencephalography (EEG) and attenuation or absence of alpha wave activity was common in all patients. In two of the four patients, a slow dominant frequency emerged at 4&#xa0;Hz with synchronized EEG at all channels. In the other two patients slowing of EEG appears less synchronized. EEGs between eyes open and eyes closed were significantly different in all patients. The dominant slow frequency during the day changes during slow-wave sleep (supposedly sleep stage 3) to even slower frequencies below 2&#xa0;Hz. Somatosensory evoked potentials (SEPs) were absent or significantly altered in comparison to healthy subjects, similarly for auditory evoked potentials (AEPs).<br><br>CONCLUSIONS: The heterogeneity of the results underscores the fact that no single neurophysiological index is available to assess psychophysiological states in unresponsive ALS patients in CLIS. This caveat may also be valid for the assessment of cognitive processes; a functioning BCI can be the solution.<br><br>SIGNIFICANCE: Most of the studies of the neurophysiology of ALS patients focused on the early stage of the disease, and there are very few studies on the late stage when patients are completely paralyzed with no means of communication (i.e., CLIS). This study provides quantitative metrics of different neurophysiological aspects of these patients.},
}
@article {pmid33742433,
year = {2021},
author = {Young, MJ and Lin, DJ and Hochberg, LR},
title = {Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation.},
journal = {Seminars in neurology},
volume = {41},
number = {2},
pages = {206-216},
pmid = {33742433},
issn = {1098-9021},
support = {I50 RX002864/RX/RRD VA/United States ; F32 MH123001/MH/NIMH NIH HHS/United States ; IK1 RX003563/RX/RRD VA/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; },
mesh = {Activities of Daily Living ; Brain ; *Brain-Computer Interfaces ; Humans ; *Neurological Rehabilitation ; },
abstract = {Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease.},
}
@article {pmid33742353,
year = {2021},
author = {Fernández-Rodríguez, &#xc1; and Medina-Juliá, MT and Velasco-Álvarez, F and Ron-Angevin, R},
title = {Different effects of using pictures as stimuli in a P300 brain-computer interface under rapid serial visual presentation or row-column paradigm.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {4},
pages = {869-881},
pmid = {33742353},
issn = {1741-0444},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Photic Stimulation ; },
abstract = {Previous proposals for controlling a P300-based BCI speller have shown an improvement using alternative images instead of letters as target stimuli under a row-column paradigm (RCP). However, the RCP is not suitable for those patients with a lack of gaze control. To solve that, the rapid serial visual presentation (RSVP) paradigm has been proposed in previous studies. The aim of the present work is to assess if a set of alternative pictures that improved performance in RCP could also improve performance in RSVP. Sixteen participants controlled four conditions in calibration and online tasks: letters in RCP, pictures in RCP, letters in RSVP and pictures in RSVP. The effect given by pictures was greater under RCP than under RSVP, both for performance and event-related potential analyses. Indeed, pictures did not show any improvement under RSVP in comparison to letters. In addition, the condition with pictures under RCP was declared the favourite by most users (68.75%), while the condition with pictures under RSVP was not chosen as favourite by any participant. Therefore, this work shows that the improvement related to the use of pictures as alternative flashing stimuli under RCP may not be transferred to RSVP. Graphical abstract.},
}
@article {pmid33741900,
year = {2021},
author = {Jovanovic, LI and Kapadia, N and Zivanovic, V and Rademeyer, HJ and Alavinia, M and McGillivray, C and Kalsi-Ryan, S and Popovic, MR and Marquez-Chin, C},
title = {Brain-computer interface-triggered functional electrical stimulation therapy for rehabilitation of reaching and grasping after spinal cord injury: a feasibility study.},
journal = {Spinal cord series and cases},
volume = {7},
number = {1},
pages = {24},
pmid = {33741900},
issn = {2058-6124},
support = {2016-RHI-EEG-1020//Ontario Neurotrauma Foundation (ONF)/ ; 2016-RHI-EEG-1020//Ontario Neurotrauma Foundation (ONF)/ ; },
mesh = {*Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Feasibility Studies ; Hand Strength ; Humans ; *Spinal Cord Injuries/therapy ; },
abstract = {STUDY DESIGN: Feasibility and preliminary clinical efficacy analysis in a single-arm interventional study.<br><br>OBJECTIVES: We developed a brain-computer interface-triggered functional electrical stimulation therapy (BCI-FEST) system for clinical application and conducted an interventional study to (1) assess its feasibility and (2) understand its potential clinical efficacy for the rehabilitation of reaching and grasping in individuals with sub-acute spinal cord injury (SCI).<br><br>SETTING: Spinal cord injury rehabilitation hospital-Toronto Rehabilitation Institute-Lyndhurst Centre.<br><br>METHODS: Five participants with sub-acute SCI completed between 12 and 40 1-hour sessions using BCI-FEST, with up to 5 sessions a week. We assessed feasibility by measuring participants' compliance with treatment, the occurrence of adverse events, BCI sensitivity, and BCI setup duration. Clinical efficacy was assessed using Functional Independence Measure (FIM) and Spinal Cord Independence Measure (SCIM), as primary outcomes. In addition, we used two upper-limb function tests as secondary outcomes.<br><br>RESULTS: On average, participants completed 29.8 sessions with no adverse events. Only one of the 149 sessions was affected by technical challenges. The BCI sensitivity ranged between 69.5 and 80.2%, and the mean BCI setup duration was ~11&#x2009;min. In the primary outcomes, three out of five participants showed changes greater than the minimal clinically important differences (MCIDs). Additionally, the mean change in secondary outcome measures met the threshold for detecting MCID as well; four out of five participants achieved MCID.<br><br>CONCLUSIONS: The new BCI-FEST intervention is safe, feasible, and promising for the rehabilitation of reaching and grasping after SCI.},
}
@article {pmid33740417,
year = {2021},
author = {Zhu, Z and Ma, Q and Miao, L and Yang, H and Pan, L and Li, K and Zeng, LH and Zhang, X and Wu, J and Hao, S and Lin, S and Ma, X and Mai, W and Feng, X and Hao, Y and Sun, L and Duan, S and Yu, YQ},
title = {A substantia innominata-midbrain circuit controls a general aggressive response.},
journal = {Neuron},
volume = {109},
number = {9},
pages = {1540-1553.e9},
doi = {10.1016/j.neuron.2021.03.002},
pmid = {33740417},
issn = {1097-4199},
mesh = {Aggression/*physiology ; Animals ; Behavior, Animal/physiology ; Male ; Mesencephalon/*physiology ; Mice ; Neural Pathways/*physiology ; Neurons/physiology ; Substantia Innominata/*physiology ; },
abstract = {Although aggressive behaviors are universal and essential for survival, "uncontrollable" and abnormal aggressive behaviors in animals or humans may have severe adverse consequences or social costs. Neural circuits regulating specific forms of aggression under defined conditions have been described, but how brain circuits govern a general aggressive response remains unknown. Here, we found that posterior substantia innominata (pSI) neurons responded to several aggression-provoking cues with the graded activity of differential dynamics, predicting the aggressive state and the topography of aggression in mice. Activation of pSI neurons projecting to the periaqueductal gray (PAG) increased aggressive arousal and robustly initiated/promoted all the types of aggressive behavior examined in an activity-level-dependent manner. Inactivation of the pSI circuit largely blocked diverse aggressive behaviors but not mating. By encoding a general aggressive response, the pSI-PAG circuit universally drives multiple aggressive behaviors and may provide a potential target for alleviating human pathological aggression.},
}
@article {pmid33738147,
year = {2021},
author = {Li, H and Fan, K and Ma, J and Wang, B and Qiao, X and Yan, Y and Du, W and Wang, L},
title = {Massage Therapy's Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {9},
number = {},
pages = {2100320},
pmid = {33738147},
issn = {2168-2372},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Massage ; Muscle, Skeletal ; Pain ; },
abstract = {OBJECTIVE: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain.<br><br>METHOD: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification.<br><br>RESULTS: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME).<br><br>CONCLUSIONS: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.},
}
@article {pmid33737889,
year = {2021},
author = {Zhang, H and Gou, R and Shang, J and Shen, F and Wu, Y and Dai, G},
title = {Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition.},
journal = {Frontiers in physiology},
volume = {12},
number = {},
pages = {643202},
pmid = {33737889},
issn = {1664-042X},
abstract = {Speech emotion recognition (SER) is a difficult and challenging task because of the affective variances between different speakers. The performances of SER are extremely reliant on the extracted features from speech signals. To establish an effective features extracting and classification model is still a challenging task. In this paper, we propose a new method for SER based on Deep Convolution Neural Network (DCNN) and Bidirectional Long Short-Term Memory with Attention (BLSTMwA) model (DCNN-BLSTMwA). We first preprocess the speech samples by data enhancement and datasets balancing. Secondly, we extract three-channel of log Mel-spectrograms (static, delta, and delta-delta) as DCNN input. Then the DCNN model pre-trained on ImageNet dataset is applied to generate the segment-level features. We stack these features of a sentence into utterance-level features. Next, we adopt BLSTM to learn the high-level emotional features for temporal summarization, followed by an attention layer which can focus on emotionally relevant features. Finally, the learned high-level emotional features are fed into the Deep Neural Network (DNN) to predict the final emotion. Experiments on EMO-DB and IEMOCAP database obtain the unweighted average recall (UAR) of 87.86 and 68.50%, respectively, which are better than most popular SER methods and demonstrate the effectiveness of our propose method.},
}
@article {pmid33736656,
year = {2021},
author = {Lukyanenko, P and Dewald, HA and Lambrecht, J and Kirsch, RF and Tyler, DJ and Williams, MR},
title = {Stable, simultaneous and proportional 4-DoF prosthetic hand control via synergy-inspired linear interpolation: a case series.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {18},
number = {1},
pages = {50},
pmid = {33736656},
issn = {1743-0003},
support = {I01 RX001334/RX/RRD VA/United States ; I01 RX003355/RX/RRD VA/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; 5T32-EB004314-20/NH/NIH HHS/United States ; },
mesh = {Amputees/*rehabilitation ; Arm/innervation ; *Artificial Limbs ; Brain-Computer Interfaces ; *Electrodes, Implanted ; Electromyography/methods ; *Hand ; Humans ; Linear Models ; Male ; *Movement ; Muscle, Skeletal/innervation ; Patient Education as Topic/methods ; Physical Therapy Modalities/instrumentation ; Simulation Training/*methods ; Software ; *Virtual Reality ; },
abstract = {BACKGROUND: Current commercial prosthetic hand controllers limit patients' ability to fully engage high Degree-of-Freedom (DoF) prosthetic hands. Available feedforward controllers rely on large training data sets for controller setup and a need for recalibration upon prosthesis donning. Recently, an intuitive, proportional, simultaneous, regression-based 3-DoF controller remained stable for several months without retraining by combining chronically implanted electromyography (ciEMG) electrodes with a K-Nearest-Neighbor (KNN) mapping technique. The training dataset requirements for simultaneous KNN controllers increase exponentially with DoF, limiting the realistic development of KNN controllers in more than three DoF. We hypothesize that a controller combining linear interpolation, the muscle synergy framework, and a sufficient number of ciEMG channels (at least two per DoF), can allow stable, high-DoF control.<br><br>METHODS: Two trans-radial amputee subjects, S6 and S8, were implanted with percutaneously interfaced bipolar intramuscular electrodes. At the time of the study, S6 and S8 had 6 and 8 bipolar EMG electrodes, respectively. A Virtual Reality (VR) system guided users through single and paired training movements in one 3-DoF and four different 4-DoF cases. A linear model of user activity was built by partitioning EMG feature space into regions bounded by vectors of steady state movement EMG patterns. The controller evaluated online EMG signals by linearly interpolating the movement class labels for surrounding trained EMG movements. This yields a simultaneous, continuous, intuitive, and proportional controller. Controllers were evaluated in 3-DoF and 4-DoF through a target-matching task in which subjects controlled a virtual hand to match 80 targets spanning the available movement space. Match Percentage, Time-To-Target, and Path Efficiency were evaluated over a 10-month period based on subject availability.<br><br>RESULTS AND CONCLUSIONS: In 3-DoF, S6 and S8 matched most targets and demonstrated stable control after 8 and 10&#xa0;months, respectively. In 4-DoF, both subjects initially found two of four 4-DoF controllers usable, matching most targets. S8 4-DoF controllers were stable, and showed improving trends over 7-9&#xa0;months without retraining or at-home practice. S6 4-DoF controllers were unstable after 7&#xa0;months without retraining. These results indicate that the performance of the controller proposed in this study may remain stable, or even improve, provided initial viability and a sufficient number of EMG channels. Overall, this study demonstrates a controller capable of stable, simultaneous, proportional, intuitive, and continuous control in 3-DoF for up to ten months and in 4-DoF for up to nine months without retraining or at-home use with minimal training times.},
}
@article {pmid33736606,
year = {2021},
author = {Hoang, TV and Coletti, P and Kifle, YW and Kerckhove, KV and Vercruysse, S and Willem, L and Beutels, P and Hens, N},
title = {Close contact infection dynamics over time: insights from a second large-scale social contact survey in Flanders, Belgium, in 2010-2011.},
journal = {BMC infectious diseases},
volume = {21},
number = {1},
pages = {274},
pmid = {33736606},
issn = {1471-2334},
support = {682540//H2020 European Research Council/ ; 1234620N//Fonds Wetenschappelijk Onderzoek/ ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Basic Reproduction Number ; Belgium/epidemiology ; Chickenpox/epidemiology ; Child ; Child, Preschool ; Contact Tracing ; *Epidemics ; Family ; Female ; Holidays ; Humans ; Infant ; Infant, Newborn ; Male ; Middle Aged ; *Social Networking ; *Surveys and Questionnaires ; Young Adult ; },
abstract = {BACKGROUND: In 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period.<br><br>METHODS: Different data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a generalized additive models for location, scale and shape (GAMLSS) model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R0) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in sex, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data.<br><br>RESULTS: All estimated contact patterns featured strong homophily in age and sex, especially for small children and adolescents. A 30% (95% BCI [17%; 37%]) and 29% (95% BCI [14%; 40%]) reduction in R0 was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study.<br><br>CONCLUSIONS: The second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.},
}
@article {pmid33735851,
year = {2021},
author = {Huang, J and Qiu, L and Lin, Q and Xiao, J and Huang, Y and Huang, H and Zhou, X and Shi, X and Wang, F and He, Y and Pan, J},
title = {Hybrid asynchronous brain-computer interface for yes/no communication in patients with disorders of consciousness.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abf00c},
pmid = {33735851},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Communication ; Consciousness ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; },
abstract = {Objective.For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), communication is challenging. Currently, the communication methods of DOC patients are limited to behavioral responses. However, patients with DOC cannot provide sufficient behavioral responses due to motor impairments and limited attention. In this study, we proposed a hybrid asynchronous brain-computer interface (BCI) system that provides a new communication channel for patients with DOC.Approach.Seven patients with DOC (3 VS and 4 MCS) and eleven healthy subjects participated in our experiment. Each subject was instructed to focus on the square with the Chinese words 'Yes' and 'No'. Then, the BCI system determined the target square with both P300 and steady-state visual evoked potential (SSVEP) detections. For the healthy group, we tested the performance of the hybrid system and the single-modality BCI system.Main results.All healthy subjects achieved significant accuracy (ranging from 72% to 100%) in both the hybrid system and the single modality system. The hybrid asynchronous BCI system outperformed the P300-only and SSVEP-only systems. Furthermore, we employed the asynchronous approach to dynamically collect the electroencephalography signal. Compared with the synchronous system, there was a 21% reduction in the average required rounds and a reduction of 105 s in the online experiment time. This asynchronous system was applied to detect the 'yes/no' communication function of seven patients with DOC, and the results showed that three of the patients (3 MCS) not only showed significant accuracies (67 ± 3%) in the online experiment, and their Coma Recovery Scale-Revised scores were also improved compared with the scores before the experiment. This result demonstrated that 3 of 7 patients were able to communicate using our hybrid asynchronous BCI system.Significance.This hybrid asynchronous BCI system can be used as a useful auxiliary bedside tool for simple communication with DOC patients.},
}
@article {pmid33733167,
year = {2020},
author = {Chang, YC and Dostovalova, A and Lin, CT and Kim, J},
title = {Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller.},
journal = {Frontiers in artificial intelligence},
volume = {3},
number = {},
pages = {50},
pmid = {33733167},
issn = {2624-8212},
abstract = {We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research.},
}
@article {pmid33731767,
year = {2021},
author = {Saxena, A and Walters, MS and Shieh, JH and Shen, LB and Gomi, K and Downey, RJ and Crystal, RG and Moore, MAS},
title = {Extracellular vesicles from human airway basal cells respond to cigarette smoke extract and affect vascular endothelial cells.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {6104},
pmid = {33731767},
issn = {2045-2322},
support = {P30 CA008748/CA/NCI NIH HHS/United States ; R01 HL107882/HL/NHLBI NIH HHS/United States ; UL1 TR002384/TR/NCATS NIH HHS/United States ; },
mesh = {Cell Line, Transformed ; Endothelial Cells/*metabolism/pathology ; Extracellular Vesicles/*metabolism/pathology ; Humans ; *MAP Kinase Signaling System ; *Tobacco Products ; *Tobacco Smoke Pollution ; Vascular Endothelial Growth Factor A/metabolism ; Vascular Endothelial Growth Factor Receptor-2/metabolism ; },
abstract = {The human airway epithelium lining the bronchial tree contains basal cells that proliferate, differentiate, and communicate with other components of their microenvironment. One method that cells use for intercellular communication involves the secretion of exosomes and other extracellular vesicles (EVs). We isolated exosome-enriched EVs that were produced from an immortalized human airway basal cell line (BCi-NS1.1) and found that their secretion is increased by exposure to cigarette smoke extract, suggesting that this stress stimulates release of EVs which could affect signaling to other cells. We have previously shown that primary human airway basal cells secrete vascular endothelial growth factor A (VEGFA) which can activate MAPK signaling cascades in endothelial cells via VEGF receptor-2 (VEGFR2). Here, we show that exposure of endothelial cells to exosome-enriched airway basal cell EVs promotes the survival of these cells and that this effect also involves VEGFR2 activation and is, at least in part, mediated by VEGFA present in the EVs. These observations demonstrate that EVs are involved in the intercellular signaling between airway basal cells and the endothelium which we previously reported. The downstream signaling pathways involved may be distinct and specific to the EVs, however, as increased phosphorylation of Akt, STAT3, p44/42 MAPK, and p38 MAPK was not seen following exposure of endothelial cells to airway basal cell EVs.},
}
@article {pmid33729961,
year = {2021},
author = {Zhou, L and Tao, X and He, F and Zhou, P and Qi, H},
title = {Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {9},
pages = {3638-3648},
doi = {10.1109/JBHI.2021.3066610},
pmid = {33729961},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory ; Hand ; Humans ; Imagination ; },
abstract = {In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p < 0.05), but the sensitivity was not significantly different. These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.},
}
@article {pmid33728656,
year = {2021},
author = {Mrachacz-Kersting, N and Ibáñez, J and Farina, D},
title = {Towards a mechanistic approach for the development of non-invasive brain-computer interfaces for motor rehabilitation.},
journal = {The Journal of physiology},
volume = {599},
number = {9},
pages = {2361-2374},
doi = {10.1113/JP281314},
pmid = {33728656},
issn = {1469-7793},
mesh = {Humans ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Neuronal Plasticity ; *Neurological Rehabilitation/methods ; },
abstract = {Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned to induce targeted and functionally relevant plastic changes in the human motor nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve motor function in patients with lesions in the central nervous system, the neurophysiological structures and processes targeted with the BCI interventions have not been identified. In this review, we first summarize current knowledge of the changes in the central nervous system associated with learning new motor skills. Then, we propose a classification of current BCI paradigms for plasticity induction and motor rehabilitation based on the expected neural plastic changes promoted. This classification proposes four paradigms based on two criteria: the plasticity induction methods and the brain states targeted. The existing evidence regarding the brain circuits and processes targeted with these different BCIs is discussed in detail. The proposed classification aims to serve as a starting point for future studies trying to elucidate the underlying plastic changes following BCI interventions.},
}
@article {pmid33727625,
year = {2021},
author = {Eldeeb, S and Susam, BT and Akcakaya, M and Conner, CM and White, SW and Mazefsky, CA},
title = {Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {6000},
pmid = {33727625},
issn = {2045-2322},
mesh = {Adolescent ; Algorithms ; Autism Spectrum Disorder/*diagnosis/etiology/*physiopathology ; Biomarkers ; *Brain-Computer Interfaces ; Child ; Clinical Decision-Making ; Data Analysis ; Disease Management ; Disease Susceptibility ; *Electroencephalography ; Emotions ; Evoked Potentials ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; Symptom Assessment ; Young Adult ; },
abstract = {Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).},
}
@article {pmid33727073,
year = {2021},
author = {Deffieux, T and Demené, C and Tanter, M},
title = {Functional Ultrasound Imaging: A New Imaging Modality for Neuroscience.},
journal = {Neuroscience},
volume = {474},
number = {},
pages = {110-121},
doi = {10.1016/j.neuroscience.2021.03.005},
pmid = {33727073},
issn = {1873-7544},
mesh = {Animals ; Brain/diagnostic imaging ; Humans ; Neuroimaging ; *Neurosciences ; *Neurovascular Coupling ; Ultrasonography ; },
abstract = {Ultrasound sensitivity to slow blood flow motion gained two orders of magnitude in the last decade thanks to the advent of ultrafast ultrasound imaging at thousands of frames per second. In neuroscience, this access to small cerebral vessels flow led to the introduction of ultrasound as a new and full-fledged neuroimaging modality. Much as functional MRI or functional optical imaging, functional Ultrasound (fUS) takes benefit of the neurovascular coupling. Its ease of use, portability, spatial and temporal resolution makes it an attractive tool for functional imaging of brain activity in preclinical imaging. A large and fast-growing number of studies in a wide variety of small to large animal models have demonstrated its potential for neuroscience research. Beyond preclinical imaging, first proof of concept applications in humans are promising and proved a clear clinical interest in particular in human neonates, per-operative surgery, or even for the development of non-invasive brain machine interfaces.},
}
@article {pmid33725682,
year = {2021},
author = {Corsi, MC and Chavez, M and Schwartz, D and George, N and Hugueville, L and Kahn, AE and Dupont, S and Bassett, DS and De Vico Fallani, F},
title = {BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abef39},
pmid = {33725682},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Learning ; Magnetoencephalography ; },
abstract = {Objective.Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood.Approach.To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic and magnetoencephalographic data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time.Main results.We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in theαband was paralleled by a decrease of the integration of visual processing and working memory areas in theβband. Notably, only brain network properties in multilayer network correlated with future BCI scores in theα2band: positively in somatosensory and decision-making related areas and negatively in associative areas.Significance.Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.},
}
@article {pmid33722543,
year = {2021},
author = {Cao, SX and Wen, CX and Sun, R and Han, JX and Sun, YH and Xu, XX and Li, XM and Lian, H},
title = {ErbB4 regulate extracellular dopamine through the p38 MAPK signaling pathway.},
journal = {Neuroscience letters},
volume = {751},
number = {},
pages = {135830},
doi = {10.1016/j.neulet.2021.135830},
pmid = {33722543},
issn = {1872-7972},
mesh = {Animals ; Cell Line, Tumor ; Cells, Cultured ; Dopamine/*metabolism ; Extracellular Space/metabolism ; Humans ; *MAP Kinase Signaling System ; Mice ; Neurons/metabolism ; Norepinephrine/metabolism ; Receptor, ErbB-4/genetics/*metabolism ; p38 Mitogen-Activated Protein Kinases/*metabolism ; },
abstract = {ErbB4 loss-of-function in catecholaminergic neurons induces catecholamine dyshomeostasis. Despite ErbB4's significant role in neuropathology, the signaling pathways that regulate these changes are still widely unknown. In this study, we attempt to identify the downstream pathway of ErbB4 that regulates catecholamine homeostasis. The SH-SY5Y human neuroblastoma cell line was used as the in vitro model for catecholaminergic neurons. Western blotting, enzyme-linked immunosorbent assay, and pharmacological and genetic manipulations by agonist/antagonist or small interference RNA were used to investigate the relationship between ErbB4 and extracellular catecholamines. We confirmed that ErbB4 is abundantly expressed in undifferentiated and retinoic acid-differentiated catecholaminergic cells from the SH-SY5Y cell line. ErbB4 inhibition increase the ratio of phosphorylated p38 to total p38 in SH-SY5Y human neuroblastoma cells. Consistent with previous in vivo observations in mice, ErbB4 deficiency led to increases in extracellular dopamine and norepinephrine levels. However, the resulting increase in extracellular dopamine, but not norepinephrine, could be suppressed by p38 inhibitor SB202190. Our results suggest that both extracellular dopamine and norepinephrine homeostasis could be regulated by ErbB4 in human catecholaminergic cells, and ErbB4 may regulate extracellular dopamine, but not norepinephrine, through the p38 MAPK signaling pathway, thus indicating different regulatory pathways of dopamine and norepinephrine by ErbB4 in catecholaminergic neurons.},
}
@article {pmid33721847,
year = {2021},
author = {Garcia-Garcia, MG and Marquez-Chin, C and Popovic, MR},
title = {Operant conditioning reveals task-specific responses of single neurons in a brain-machine interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abeeac},
pmid = {33721847},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Conditioning, Operant/physiology ; *Motor Cortex/physiology ; Neurons/physiology ; Rats ; Volition/physiology ; },
abstract = {Objective. Volitional modulation of single cortical neurons holds great potential for the implementation of brain-machine interfaces (BMIs) because it can induce a rapid acquisition of arbitrary associations between machines and neural activity. It can also be used as a framework to study the limits of single-neuron control in BMIs.Approach. We tested the control of a one-dimensional actuator in two BMI tasks which differed only in the neural contingency that determined when a reward was dispensed. A thresholded activity task, commonly implemented in single-neuron BMI control, consisted of reaching or exceeding a neuron activity level, while the second task consisted of reaching and maintaining a narrow neuron activity level (i.e. windowed activity task).Main findings. Single neurons in layer V of the motor cortex of rats improved performance during both the thresholded activity and windowed activity BMI tasks. However, correct performance during the windowed activity task was accompanied by activation of neighboring neurons, not in direct control of the BMI. In contrast, only neurons in direct control of the BMI were active at the time of reward during the thresholded activity task.Significance. These results suggest that thresholded activity single-neuron BMI implementations are more appropriate compared to windowed activity BMI tasks to capitalize on the adaptability of cortical circuits to acquire novel arbitrary skills.},
}
@article {pmid33719696,
year = {2021},
author = {Arnone, D and Galadari, H and Rodgers, CJ and Östlundh, L and Aziz, KA and Stip, E and Young, AH},
title = {Efficacy of onabotulinumtoxinA in the treatment of unipolar major depression: Systematic review, meta-analysis and meta-regression analyses of double-blind randomised controlled trials.},
journal = {Journal of psychopharmacology (Oxford, England)},
volume = {35},
number = {8},
pages = {910-918},
pmid = {33719696},
issn = {1461-7285},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Antidepressive Agents/*administration &amp; dosage/adverse effects/pharmacology ; Botulinum Toxins, Type A/*administration &amp; dosage/adverse effects/pharmacology ; Depressive Disorder, Major/*drug therapy/physiopathology ; Dose-Response Relationship, Drug ; Drug Interactions ; Female ; Humans ; Male ; Neuromuscular Agents/administration &amp; dosage/adverse effects/pharmacology ; Randomized Controlled Trials as Topic ; Sex Factors ; },
abstract = {BACKGROUND: OnabotulinumtoxinA is a novel therapeutic intervention whose mechanism of action is believed to modify the negative facial feedback, thus abating symptoms of depression. This putative new antidepressant agent offers minimal systemic side effects and negligible risk of pharmacological interactions. We set out to examine the evidence for the use of onabotulinumtoxinA in major depression.<br><br>METHODS: A systematic search of the literature identified double-blind randomised controlled trials (RCTs) investigating the use of onabotulinumtoxinA in the treatment of major depression versus placebo. Data, reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), was combined in meta-analyses (PROSPERO registration ID: CRD42020183538).<br><br>RESULTS: The search identified five RCTs (four double-blind) comparing onabotulinumtoxinA to placebo. OnabotulinumtoxinA was more effective than placebo when administered within the 20-40&#x2009;IU dose range in double-blind RCTs. The analysis was free of publication bias and significantly heterogeneous. Meta-regression analyses indicated that onabotulinumtoxinA was more efficacious in women and in higher doses in female patients and less effective with polypharmacy, especially when an increasing number of antidepressants were prescribed. The effectiveness of onabotulinumtoxinA was higher in more recently published double-blind RCTs.<br><br>CONCLUSION: The meta-analysis supports the efficacy of the intervention with the results being highly heterogeneous across studies. In view of the heterogeneity of the findings and the significant moderators of benefit (sex, year of study completion and the interaction between sex and dose), more research is required to better understand the role of onabotulinumtoxinA in the treatment of depression.},
}
@article {pmid33716702,
year = {2021},
author = {Zhang, C and Qiu, S and Wang, S and He, H},
title = {Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data.},
journal = {Frontiers in computational neuroscience},
volume = {15},
number = {},
pages = {619508},
pmid = {33716702},
issn = {1662-5188},
abstract = {Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain-computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm. Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target. Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from others, and the separation significantly increased the ERP detection scores in the EEGNet classifier. Moreover, the visualization of the new method suggested the different underling of SVM and EEGNet classifiers in ERP detection of the RSVP experiment. Conclusion: In the RSVP experiment, the near-non-target samples contain separable ERP activity. The ERP detection scores can be increased using classifiers of the EEGNet model, by separating the non-target into near- and far-targets based on their delay against targets.},
}
@article {pmid33716698,
year = {2021},
author = {Hehenberger, L and Kobler, RJ and Lopes-Dias, C and Srisrisawang, N and Tumfart, P and Uroko, JB and Torke, PR and Müller-Putz, GR},
title = {Long-Term Mutual Training for the CYBATHLON BCI Race With a Tetraplegic Pilot: A Case Study on Inter-Session Transfer and Intra-Session Adaptation.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {635777},
pmid = {33716698},
issn = {1662-5161},
abstract = {CYBATHLON is an international championship where people with severe physical disabilities compete with the aid of state-of-the-art assistive technology. In one of the disciplines, the BCI Race, tetraplegic pilots compete in a computer game race by controlling an avatar with a brain-computer interface (BCI). This competition offers a perfect opportunity for BCI researchers to study long-term training effects in potential end-users, and to evaluate BCI performance in a realistic environment. In this work, we describe the BCI system designed by the team Mirage91 for participation in the CYBATHLON BCI Series 2019, as well as in the CYBATHLON 2020 Global Edition. Furthermore, we present the BCI's interface with the game and the main methodological strategies, along with a detailed evaluation of its performance over the course of the training period, which lasted 14 months. The developed system was a 4-class BCI relying on task-specific modulations of brain rhythms. We implemented inter-session transfer learning to reduce calibration time, and to reinforce the stability of the brain patterns. Additionally, in order to compensate for potential intra-session shifts in the features' distribution, normalization parameters were continuously adapted in an unsupervised fashion. Across the aforementioned 14 months, we recorded 26 game-based training sessions. Between the first eight sessions, and the final eight sessions leading up to the CYBATHLON 2020 Global Edition, the runtimes significantly improved from 255 ± 23 s (mean ± std) to 225 ± 22 s, respectively. Moreover, we observed a significant increase in the classifier's accuracy from 46 to 53%, driven by more distinguishable brain patterns. Compared to conventional single session, non-adaptive BCIs, the inter-session transfer learning and unsupervised intra-session adaptation techniques significantly improved the performance. This long-term study demonstrates that regular training helped the pilot to significantly increase the distance between task-specific patterns, which resulted in an improvement of performance, both with respect to class separability in the calibration data, and with respect to the game. Furthermore, it shows that our methodological approaches were beneficial in transferring the performance across sessions, and most importantly to the CYBATHLON competitions.},
}
@article {pmid33716680,
year = {2021},
author = {Saha, S and Mamun, KA and Ahmed, K and Mostafa, R and Naik, GR and Darvishi, S and Khandoker, AH and Baumert, M},
title = {Progress in Brain Computer Interface: Challenges and Opportunities.},
journal = {Frontiers in systems neuroscience},
volume = {15},
number = {},
pages = {578875},
pmid = {33716680},
issn = {1662-5137},
abstract = {Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.},
}
@article {pmid33711832,
year = {2021},
author = {Alchalabi, B and Faubert, J and Labbé, DR},
title = {A multi-modal modified feedback self-paced BCI to control the gait of an avatar.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abee51},
pmid = {33711832},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Gait ; Humans ; *Virtual Reality ; Walking ; },
abstract = {Brain-computer interfaces (BCIs) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. Some limitations of existing systems are: (a) some systems use mental imagery (MI) of movements other than gait; (b) most systems allow the user to take single steps or to walk but do not allow both; (c) most function in a single BCI mode (cue-paced or self-paced).Objective. The objective of this study was to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar.Approach. This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this study, which was comprised of four sessions across four different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated regularized linear discriminate analysis classifiers that used theµpower spectral density over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback (MDF) was presented to half of the participants, where the avatar moved correctly regardless of the classification of the participants' MI. The performance of the BCI was computed on each day, using different control modes.Main results. All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p< 0.01). The average online performance was 85.9 ± 8.4% showing that MDF enhanced BCI performance (p= 0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode.Significance. This study reports on a first BCI to use motor imagery of the lower limbs in order to control the gait of an avatar with different control modes and different control commands (single steps or forward walking). BCI performance is increased in a novel way by combining three different performance enhancement techniques, resulting in a single high performance and multi-modal BCI system. This study also showed that the improvements due to the effects of MDF lasted for more than one session.},
}
@article {pmid33711201,
year = {2021},
author = {Dong, R and Wang, L and Hang, C and Chen, Z and Liu, X and Zhong, L and Qi, J and Huang, Y and Liu, S and Wang, L and Lu, Y and Jiang, X},
title = {Printed Stretchable Liquid Metal Electrode Arrays for In Vivo Neural Recording.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {17},
number = {14},
pages = {e2006612},
doi = {10.1002/smll.202006612},
pmid = {33711201},
issn = {1613-6829},
mesh = {Brain ; Electrodes ; *Metals ; *Polymers ; },
abstract = {The adoption of neural interfacing into neurological diagnosis is severely hampered by the complex, costly, and error-prone manufacturing methods, requiring new fabrication processes and materials for flexible neural interfacing. Here a strategy for fabricating highly stretchable neural electrode arrays based on screen printing of liquid metal conductors onto polydimethylsiloxane substrates is presented. The screen-printed electrode arrays show a resolution of 50 µm, which is ideally applicable to neural interfaces. The integration of liquid metal-polymer conductor enables the neural electrode arrays to retain stable electrical properties and compliant mechanical performance under a significant (≈108%) strain. Taking advantage of its high biocompatibility, liquid metal electrode arrays exhibit excellent performance for neurite growth and long-term implantation. The stretchable electrode arrays can spontaneously conformally come in touch with the brain surface, and high-throughput electrocorticogram signals are recorded. Based on stretchable electrode arrays, real-time monitoring of epileptiform activities can be provided at different states of seizure. The method reported here offers a new fabrication strategy to manufacture stretchable neural electrodes, with additional potential utility in diagnostic brain-machine interfaces.},
}
@article {pmid33707935,
year = {2020},
author = {Landin, K and Benjaber, M and Jamshed, F and Stagg, C and Denison, T},
title = {Technology Integration Methods for Bi-directional Brain-computer Interfaces and XR-based Interventions.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2020},
number = {},
pages = {3695-3701},
pmid = {33707935},
issn = {1062-922X},
support = {/WT_/Wellcome Trust/United Kingdom ; 102584/WT_/Wellcome Trust/United Kingdom ; MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Brain stimulation therapies have been established as effective treatments for Parkinson's disease, essential tremor, and epilepsy, as well as having high diagnostic and therapeutic potential in a wide range of neurological and psychiatric conditions. Novel interventions such as extended reality (XR), video games and exergames that can improve physiological and cognitive functioning are also emerging as targets for therapeutic and rehabilitative treatments. Previous studies have proposed specific applications involving non-invasive brain stimulation (NIBS) and virtual environments, but to date these have been uni-directional and restricted to specific applications or proprietary hardware. Here, we describe technology integration methods that enable invasive and non-invasive brain stimulation devices to interface with a cross-platform game engine and development platform for creating bi-directional brain-computer interfaces (BCI) and XR-based interventions. Furthermore, we present a highly-modifiable software framework and methods for integrating deep brain stimulation (DBS) in 2D, 3D, virtual and mixed reality applications, as well as extensible applications for BCI integration in wireless systems. The source code and integrated brain stimulation applications are available online at https://github.com/oxfordbioelectronics/brain-stim-game.},
}
@article {pmid33707639,
year = {2021},
author = {George, JK and Soci, C and Miscuglio, M and Sorger, VJ},
title = {Symmetry perception with spiking neural networks.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {5776},
pmid = {33707639},
issn = {2045-2322},
support = {42614-1-CCNS21996F//Office of Naval Research/ ; },
abstract = {Mirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.},
}
@article {pmid33692611,
year = {2020},
author = {Toth, R and Zamora, M and Ottaway, J and Gillbe, T and Martin, S and Benjaber, M and Lamb, G and Noone, T and Taylor, B and Deli, A and Kremen, V and Worrell, G and Constandinou, TG and Gillbe, I and De Wachter, S and Knowles, C and Sharott, A and Valentin, A and Green, AL and Denison, T},
title = {DyNeuMo Mk-2: An Investigational Circadian-Locked Neuromodulator with Responsive Stimulation for Applied Chronobiology.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2020},
number = {},
pages = {3433-3440},
pmid = {33692611},
issn = {1062-922X},
support = {MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/6/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; UKDRI-7004/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Deep brain stimulation (DBS) for Parkinson's disease, essential tremor and epilepsy is an established palliative treatment. DBS uses electrical neuromodulation to suppress symptoms. Most current systems provide a continuous pattern of fixed stimulation, with clinical follow-ups to refine settings constrained to normal office hours. An issue with this management strategy is that the impact of stimulation on circadian, i.e. sleep-wake, rhythms is not fully considered; either in the device design or in the clinical follow-up. Since devices can be implanted in brain targets that couple into the reticular activating network, impact on wakefulness and sleep can be significant. This issue will likely grow as new targets are explored, with the potential to create entraining signals that are uncoupled from environmental influences. To address this issue, we have designed a new brain-machine-interface for DBS that combines a slow-adaptive circadian-based stimulation pattern with a fast-acting pathway for responsive stimulation, demonstrated here for seizure management. In preparation for first-in-human research trials to explore the utility of multi-timescale automated adaptive algorithms, design and prototyping was carried out in line with ISO risk management standards, ensuring patient safety. The ultimate aim is to account for chronobiology within the algorithms embedded in brain-machine-interfaces and in neuromodulation technology more broadly.},
}
@article {pmid33691299,
year = {2021},
author = {Zhang, C and Kim, YK and Eskandarian, A},
title = {EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abed81},
pmid = {33691299},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Classification of electroencephalography (EEG)-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based MI classification that outperforms the state-of-the-art methods.Approach.The proposed CNN model, namely EEG-inception, is built on the backbone of the inception-time network, which has showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing. Furthermore, this paper proposes a novel data augmentation method for EEG signals to enhance the accuracy, at least by 3%, and reduce overfitting with limited BCI datasets.Main results.The proposed model outperforms all state-of-the-art methods by achieving the average accuracy of 88.4% and 88.6% on the 2008 BCI Competition IV 2a (four-classes) and 2b datasets (binary-classes), respectively. Furthermore, it takes less than 0.025 s to test a sample suitable for real-time processing. Moreover, the classification standard deviation for nine different subjects achieves the lowest value of 5.5 for the 2b dataset and 7.1 for the 2a dataset, which validates that the proposed method is highly robust.Significance.From the experiment results, it can be inferred that the EEG-inception network exhibits a strong potential as a subject-independent classifier for EEG-based MI tasks.},
}
@article {pmid33690185,
year = {2021},
author = {Zhang, Y and Yang, Q and Zhang, L and Ran, Y and Wang, G and Celler, B and Su, S and Xu, P and Yao, D},
title = {Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abecf2},
pmid = {33690185},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Objective.Noise-assisted multivariate empirical mode decomposition (NA-MEMD) based causal decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based causal decomposition approach according to the covariation and power views traced to Hume and Kant:a prioricause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow.Approach.Based on the definition of NA-MEMD based causal decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects.Main results.The predominant methods used in neuroscience (Granger causality, empirical mode decomposition-based causal decomposition) are validated, showing the applicability of NA-MEMD based causal decomposition, particular to brain physiological processes in bivariate and multiscale time series.Significance.We point to the potential use in the causality inference analysis in a complex dynamic process.},
}
@article {pmid33690182,
year = {2021},
author = {Thielen, J and Marsman, P and Farquhar, J and Desain, P},
title = {From full calibration to zero training for a code-modulated visual evoked potentials for brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {5},
pages = {},
doi = {10.1088/1741-2552/abecef},
pmid = {33690182},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Evoked Potentials/physiology ; *Evoked Potentials, Visual ; },
abstract = {Objective.Typically, a brain-computer interface (BCI) is calibrated using user- and session-specific data because of the individual idiosyncrasies and the non-stationary signal properties of the electroencephalogram (EEG). Therefore, it is normal for BCIs to undergo a time-consuming passive training stage that prevents users from directly operating them. In this study, we systematically reduce the training data set in a stepwise fashion, to ultimately arrive at a calibration-free method for a code-modulated visually evoked potential (cVEP)-based BCI to fully eliminate the tedious training stage.Approach.In an extensive offline analysis, we compare our sophisticated encoding model with a traditional event-related potential (ERP) technique. We calibrate the encoding model in a standard way, with data limited to a single class while generalizing to all others and without any data. In addition, we investigate the feasibility of the zero-training cVEP BCI in an online setting.Main results.By adopting the encoding model, the training data can be reduced substantially, while maintaining both the classification performance as well as the explained variance of the ERP method. Moreover, with data from only one class or even no data at all, it still shows excellent performance. In addition, the zero-training cVEP BCI achieved high communication rates in an online spelling task, proving its feasibility for practical use.Significance.To date, this is the fastest zero-training cVEP BCI in the field, allowing high communication speeds without calibration while using only a few non-invasive water-based EEG electrodes. This allows us to skip the training stage altogether and spend all the valuable time on direct operation. This minimizes the session time and opens up new exciting directions for practical plug-and-play BCI. Fundamentally, these results validate that the adopted neural encoding model compresses data into event responses without the loss of explanatory power compared to using full ERPs as a template.},
}
@article {pmid33688336,
year = {2021},
author = {Li, H and Gong, A and Zhao, L and Zhang, W and Wang, F and Fu, Y},
title = {Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6614112},
pmid = {33688336},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Quality of Life ; Spectroscopy, Near-Infrared ; *Walking ; },
abstract = {OBJECTIVES: Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct fNIRS-BCI based on walking imagery.<br><br>METHODS: 15 subjects were recruited and fNIRS signals were collected during walking imagery and idle state. Firstly, band-pass filtering and baseline drift correction for HbO signal were carried out, and then the mean value, peak value, and root mean square (RMS) of HbO and their combinations were extracted as classification features; SRC was used to identify the extracted features and the result of SRC was compared with those of support vector machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR).<br><br>RESULTS: The experimental results showed that the average classification accuracy for walking imagery and idle state by SRC using three features combination was 91.55&#xb1;3.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.37&#xb1;4.42%, 85.65&#xb1;5.01%, 86.43&#xb1;4.41%, and 76.14&#xb1;5.32%, respectively), and the classification accuracy of other combined features was higher than that of single feature.<br><br>CONCLUSIONS: The study showed that introducing SRC into fNIRS-BCI can effectively identify walking imagery and idle state. It also showed that different time windows for feature extraction have an impact on the classification results, and the time window of 2-8&#x2009;s achieved a better classification accuracy (94.33&#xb1;2.60%) than other time windows. Significance. The study was expected to provide a new and optional active rehabilitation training method for patients with walking dysfunction. In addition, the experiment was also a rare study based on fNIRS-BCI using SRC to decode walking imagery and idle state.},
}
@article {pmid33687833,
year = {2021},
author = {Avilov, O and Rimbert, S and Popov, A and Bougrain, L},
title = {Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {10},
pages = {3087-3097},
doi = {10.1109/TBME.2021.3064794},
pmid = {33687833},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Imagination ; Intention ; *Intraoperative Awareness ; },
abstract = {OBJECTIVE: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia.<br><br>METHODS: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation.<br><br>RESULTS: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).<br><br>CONCLUSION: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels.<br><br>SIGNIFICANCE: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.},
}
@article {pmid33686659,
year = {2021},
author = {Xu, Z and Liu, M and Gao, C and Kuang, W and Chen, X and Liu, F and Ge, B and Yan, X and Zhou, T and Xie, S},
title = {Centrosomal protein FOR20 knockout mice display embryonic lethality and left-right patterning defects.},
journal = {FEBS letters},
volume = {595},
number = {10},
pages = {1462-1472},
doi = {10.1002/1873-3468.14071},
pmid = {33686659},
issn = {1873-3468},
mesh = {Animals ; Body Patterning/*genetics ; Cilia/pathology ; Embryo Loss/*genetics ; Embryo, Mammalian/*abnormalities/blood supply/*pathology ; Embryonic Development ; Female ; *Gene Deletion ; Heterozygote ; Homozygote ; Male ; Mice ; Mice, Knockout ; Neovascularization, Pathologic ; RNA, Messenger/genetics ; },
abstract = {Centrosomal protein FOR20 has been reported to be crucial for essential cellular processes, including ciliogenesis, cell migration, and cell cycle in vertebrates. However, the function of FOR20 during mammalian embryonic development remains unknown. To investigate the in vivo function of the For20 gene in mammals, we generated For20 homozygous knockout mice by gene targeting. Our data reveal that homozygous knockout of For20 results in significant embryonic growth arrest and lethality during gestation, while the heterozygotes show no obvious defects. The absence of For20 leads to impaired left-right patterning of embryos and reduced cilia in the embryonic node. Deletion of For20 also disrupts angiogenesis in yolk sacs and embryos. These results highlight a critical role of For20 in early mammalian embryogenesis.},
}
@article {pmid33682717,
year = {2021},
author = {Perez-Valero, E and Lopez-Gordo, MA and Morillas, C and Pelayo, F and Vaquero-Blasco, MA},
title = {A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {80},
number = {4},
pages = {1363-1376},
doi = {10.3233/JAD-201455},
pmid = {33682717},
issn = {1875-8908},
mesh = {Alzheimer Disease/*diagnosis/*physiopathology ; Brain-Computer Interfaces ; Early Diagnosis ; Electroencephalography/classification/*methods ; Humans ; *Machine Learning ; },
abstract = {In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the economic and social implications of the disease, traditional diagnosis techniques, and the fundaments of automated AD detection. Then, we present electroencephalography (EEG) as an appropriate alternative for the early detection of AD, owing to its reduced cost, portability, and non-invasiveness. We also describe the main time and frequency domain EEG features that are employed in AD detection. Subsequently, we examine some of the main studies of the last decade that aim to provide an automatic detection of AD and its previous stages by means of SP and ML. In these studies, brain data was acquired using multiple medical techniques such as magnetic resonance imaging, positron emission tomography, and EEG. The main aspects of each approach, namely feature extraction, classification model, validation approach, and performance metrics, are compiled and discussed. Lastly, a set of conclusions and recommendations for future research on AD automatic detection are drawn in the final section of the paper.},
}
@article {pmid33679965,
year = {2021},
author = {Lian, S and Xu, J and Zuo, G and Wei, X and Zhou, H},
title = {A Novel Time-Incremental End-to-End Shared Neural Network with Attention-Based Feature Fusion for Multiclass Motor Imagery Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6613105},
pmid = {33679965},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. First, the training data from all subjects is merged and enlarged through autoencoder to meet the need for massive amounts of data while reducing the bad effect on signal recognition because of randomness, instability, and individual variability of EEG data. Second, an end-to-end sharing structure with attention-based time-incremental shallow convolution neural network is proposed. Shallow convolution neural network (SCNN) and bidirectional long short-term memory (BiLSTM) network are used to extract frequency-spatial domain features and time-series features of EEG signals, respectively. Then, the attention model is introduced into the feature fusion layer to dynamically weight these extracted temporal-frequency-spatial domain features, which greatly contributes to the reduction of feature redundancy and the improvement of classification accuracy. At last, validation tests using BCI Competition IV 2a data sets show that classification accuracy and kappa coefficient have reached 82.7 ± 5.57% and 0.78 ± 0.074, which can strongly prove its advantages in improving classification accuracy and reducing individual difference among different subjects from the same network.},
}
@article {pmid33676597,
year = {2021},
author = {, },
title = {Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.},
journal = {Lancet (London, England)},
volume = {397},
number = {10279},
pages = {1063-1074},
pmid = {33676597},
issn = {1474-547X},
support = {/WT_/Wellcome Trust/United Kingdom ; MC_PC_19079/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Age Factors ; Aged ; Aged, 80 and over ; *Antimicrobial Stewardship ; Azithromycin/pharmacology/*therapeutic use ; COVID-19/complications/diagnosis/immunology ; Cytokine Release Syndrome/immunology/*prevention &amp; control/virology ; Drug Resistance, Bacterial ; Female ; Humans ; Male ; Middle Aged ; Patient Admission/statistics &amp; numerical data ; Risk Factors ; SARS-CoV-2/drug effects/isolation &amp; purification ; Time Factors ; Treatment Outcome ; United Kingdom ; *COVID-19 Drug Treatment ; },
abstract = {BACKGROUND: Azithromycin, an antibiotic with potential antiviral and anti-inflammatory properties, has been used to treat COVID-19, but evidence from community randomised trials is lacking. We aimed to assess the effectiveness of azithromycin to treat suspected COVID-19 among people in the community who had an increased risk of complications.<br><br>METHODS: In this UK-based, primary care, open-label, multi-arm, adaptive platform randomised trial of interventions against COVID-19 in people at increased risk of an adverse clinical course (PRINCIPLE), we randomly assigned people aged 65 years and older, or 50 years and older with at least one comorbidity, who had been unwell for 14 days or less with suspected COVID-19, to usual care plus azithromycin 500 mg daily for three days, usual care plus other interventions, or usual care alone. The trial had two coprimary endpoints measured within 28 days from randomisation: time to first self-reported recovery, analysed using a Bayesian piecewise exponential, and hospital admission or death related to COVID-19, analysed using a Bayesian logistic regression model. Eligible participants with outcome data were included in the primary analysis, and those who received the allocated treatment were included in the safety analysis. The trial is registered with ISRCTN, ISRCTN86534580.<br><br>FINDINGS: The first participant was recruited to PRINCIPLE on April 2, 2020. The azithromycin group enrolled participants between May 22 and Nov 30, 2020, by which time 2265 participants had been randomly assigned, 540 to azithromycin plus usual care, 875 to usual care alone, and 850 to other interventions. 2120 (94%) of 2265 participants provided follow-up data and were included in the Bayesian primary analysis, 500 participants in the azithromycin plus usual care group, 823 in the usual care alone group, and 797 in other intervention groups. 402 (80%) of 500 participants in the azithromycin plus usual care group and 631 (77%) of 823 participants in the usual care alone group reported feeling recovered within 28 days. We found little evidence of a meaningful benefit in the azithromycin plus usual care group in time to first reported recovery versus usual care alone (hazard ratio 1&#xb7;08, 95% Bayesian credibility interval [BCI] 0&#xb7;95 to 1&#xb7;23), equating to an estimated benefit in median time to first recovery of 0&#xb7;94 days (95% BCI -0&#xb7;56 to 2&#xb7;43). The probability that there was a clinically meaningful benefit of at least 1&#xb7;5 days in time to recovery was 0&#xb7;23. 16 (3%) of 500 participants in the azithromycin plus usual care group and 28 (3%) of 823 participants in the usual care alone group were hospitalised (absolute benefit in percentage 0&#xb7;3%, 95% BCI -1&#xb7;7 to 2&#xb7;2). There were no deaths in either study group. Safety outcomes were similar in both groups. Two (1%) of 455 participants in the azothromycin plus usual care group and four (1%) of 668 participants in the usual care alone group reported admission to hospital during the trial, not related to COVID-19.<br><br>INTERPRETATION: Our findings do not justify the routine use of azithromycin for reducing time to recovery or risk of hospitalisation for people with suspected COVID-19 in the community. These findings have important antibiotic stewardship implications during this pandemic, as inappropriate use of antibiotics leads to increased antimicrobial resistance, and there is evidence that azithromycin use increased during the pandemic in the UK.<br><br>FUNDING: UK Research and Innovation and UK Department of Health and Social Care.},
}
@article {pmid33676130,
year = {2021},
author = {Guerra-García, JM and Navarro-Barranco, C and Ros, M and Sedano, F and Espinar, R and Fernández-Romero, A and Martínez-Laiz, G and Cuesta, JA and Giráldez, I and Morales, E and Florido, M and Moreira, J},
title = {Ecological quality assessement of marinas: An integrative approach combining biological and environmental data.},
journal = {Journal of environmental management},
volume = {286},
number = {},
pages = {112237},
doi = {10.1016/j.jenvman.2021.112237},
pmid = {33676130},
issn = {1095-8630},
mesh = {Animals ; Biodiversity ; Biota ; *Ecosystem ; Environmental Monitoring ; Invertebrates ; *Metals, Heavy/analysis ; },
abstract = {The importance of marinas as infrastructures for recreational boating is increasing substantially. However, information on their soft-bottom benthic communities, a key tool for managing programmes, is still scarce. We combined environment features with macro- and meiofaunal soft-bottom community information for assessing the ecological status of marinas with an integrative approach. To address this issue, we focused on eight marinas of the Southern Iberian Peninsula. Macro- and meiofauna data revealed high benthic heterogeneity at a spatial scale. The environmental variables which correlated best with macrofauna were mainly phosphorus, granulometry, and total organic carbon, and secondarily important variables were faecal coliforms, the biocide Irgarol, and heavy metals; total hydrocarbon concentration was also significant for meiofauna. Annelida was the dominant phylum in terms of number of species (37%) and abundance (66%) and were better descriptors of the environmental conditions than Arthropoda and Mollusca. Although identification to the species level is desirable and mandatory for assessing biological pollution, significant differences among marinas and correlations between fauna and abiotic variables were already detected at the level of family and order. This implies that biota assessment at higher levels may still be useful in monitoring programmes limited by time and budget constraints. The major novelty of this study lies in the development of an integrative assessment method based on the following selected ecological indicators: Marinas Environmental Pollution Index (MEPI), Biocontamination Index (BCI), macrofaunal biotic indices (AMBI, M-AMBI, BENTIX, MEDOCC and BENFES), macrofaunal taxa richness and Shannon-Wiener's diversity, and nematode:copepod index. This approach was able to discriminate marinas of the Southern Iberian Peninsula based on their ecological status, which ranged from poor to good. The method can be useful to design standards for assigning "sustainable quality seals" to those marinas with better values of ecological indicators.},
}
@article {pmid33674657,
year = {2021},
author = {Apicella, A and Arpaia, P and Frosolone, M and Moccaldi, N},
title = {High-wearable EEG-based distraction detection in motor rehabilitation.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {5297},
pmid = {33674657},
issn = {2045-2322},
mesh = {Adult ; Attention/*physiology ; Brain-Computer Interfaces ; Data Accuracy ; Electrodes ; Electroencephalography/*instrumentation ; Female ; Healthy Volunteers ; Humans ; Imagination/physiology ; Male ; Motor Activity/*physiology ; Neurological Rehabilitation/*instrumentation/*methods ; Signal Processing, Computer-Assisted ; Support Vector Machine ; *Wearable Electronic Devices ; Wireless Technology/*instrumentation ; Young Adult ; },
abstract = {A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient's attention for enhancing the therapy effectiveness.},
}
@article {pmid33673137,
year = {2021},
author = {Lee, HK and Choi, YS},
title = {Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {4},
pages = {},
pmid = {33673137},
issn = {1424-8220},
support = {NRF-2019R1F1A1045607//National Research Foundation of Korea/ ; 2020-0306//Kwangwoon University/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Physical Phenomena ; },
abstract = {Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.},
}
@article {pmid33671722,
year = {2021},
author = {Ben Khelifa, MM and Lamti, HA and Hugel, V},
title = {A Muscular and Cerebral Physiological Indices Assessment for Stress Measuring during Virtual Wheelchair Guidance.},
journal = {Brain sciences},
volume = {11},
number = {2},
pages = {},
pmid = {33671722},
issn = {2076-3425},
abstract = {The work presented in this manuscript has the purpose to assess the relationship between human factors and physiological indices. We discuss the relationship between stress as human factor and cerebral and muscular signals as features. Ten male paraplegic, right-handed subjects were volunteers for the experiment (mean age 34 ±6). They drove a virtual wheelchair in an indoor environment. They filled five missions where, in each one, an environmental parameter was changed. Meanwhile, they were equipped with Electromyography (EMG) sensors and Electroencephalography (EEG). Frequency and temporal features were filtered and extracted. Principal component analysis (PCA), Fisher's tests, repeated measure Anova and post hoc Tukey test (α = 0.05) were implemented for statistics. Environmental modifications are subject to induce stress, which impacts muscular and cerebral activities. While the time pressure parameter was the most influent, the transition from static to moving obstacles (avatars), tends to have a significant impact on stress levels. However, adding more moving obstacles did not show any impact. A synchronization factor was noticed between cerebral and muscular features in higher stress levels. Further examination is needed to assess EEG reliability in these situations.},
}
@article {pmid33670698,
year = {2021},
author = {Mancini, M and Cherubino, P and Cartocci, G and Martinez, A and Borghini, G and Guastamacchia, E and di Flumeri, G and Rossi, D and Modica, E and Menicocci, S and Lupo, V and Trettel, A and Babiloni, F},
title = {Forefront Users' Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents.},
journal = {Brain sciences},
volume = {11},
number = {2},
pages = {},
pmid = {33670698},
issn = {2076-3425},
abstract = {Scents have the ability to affect peoples' mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. The present study aimed at investigating and fostering a novel approach to evaluate users' experience with respect to scents' effects through the joint employment of Virtual Reality and users' neurophysiological monitoring, in particular Electroencephalography. In particular, this study, involving 42 participants, aimed to compare the effects of lemon and lavender scents on the deployment of cognitive resources during a daily life experience consisting in a train journey carried out in virtual reality. Our findings showed a significant higher request of cognitive resources during the processing of an informative message for subjects exposed to the lavender scent with respect to the lemon exposure. No differences were found between lemon and lavender conditions on the self-reported items of pleasantness and involvement; as this study demonstrated, the employment of the lavender scent preserves the quality of the customer experience to the same extent as the more widely used lemon scent.},
}
@article {pmid33670277,
year = {2021},
author = {Georgiev, DD and Georgieva, I and Gong, Z and Nanjappan, V and Georgiev, GV},
title = {Virtual Reality for Neurorehabilitation and Cognitive Enhancement.},
journal = {Brain sciences},
volume = {11},
number = {2},
pages = {},
pmid = {33670277},
issn = {2076-3425},
support = {856998//Horizon 2020/ ; 318927//Academy of Finland/ ; TM-20-11342//EDUFI Fellowship/ ; },
abstract = {Our access to computer-generated worlds changes the way we feel, how we think, and how we solve problems. In this review, we explore the utility of different types of virtual reality, immersive or non-immersive, for providing controllable, safe environments that enable individual training, neurorehabilitation, or even replacement of lost functions. The neurobiological effects of virtual reality on neuronal plasticity have been shown to result in increased cortical gray matter volumes, higher concentration of electroencephalographic beta-waves, and enhanced cognitive performance. Clinical application of virtual reality is aided by innovative brain-computer interfaces, which allow direct tapping into the electric activity generated by different brain cortical areas for precise voluntary control of connected robotic devices. Virtual reality is also valuable to healthy individuals as a narrative medium for redesigning their individual stories in an integrative process of self-improvement and personal development. Future upgrades of virtual reality-based technologies promise to help humans transcend the limitations of their biological bodies and augment their capacity to mold physical reality to better meet the needs of a globalized world.},
}
@article {pmid33668950,
year = {2021},
author = {Li, M and Li, F and Pan, J and Zhang, D and Zhao, S and Li, J and Wang, F},
title = {The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {5},
pages = {},
pmid = {33668950},
issn = {1424-8220},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Reproducibility of Results ; },
abstract = {In addition to helping develop products that aid the disabled, brain-computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain-computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.},
}
@article {pmid33667154,
year = {2022},
author = {Pitt, KM and Brumberg, JS},
title = {Evaluating person-centered factors associated with brain-computer interface access to a commercial augmentative and alternative communication paradigm.},
journal = {Assistive technology : the official journal of RESNA},
volume = {34},
number = {4},
pages = {468-477},
pmid = {33667154},
issn = {1949-3614},
support = {R01 DC016343/DC/NIDCD NIH HHS/United States ; },
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Cognition ; Communication ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; },
abstract = {Current BCI-AAC systems largely utilize custom-made software and displays that may be unfamiliar to AAC stakeholders. Further, there is limited information available exploring the heterogenous profiles of individuals who may use BCI-AAC. Therefore, in this study, we aimed to evaluate how individuals with amyotrophic lateral sclerosis (ALS) learned to control a motor-based BCI switch in a row-column AAC scanning pattern, and person-centered factors associated with BCI-AAC performance. Four individuals with ALS completed 12 BCI-AAC training sessions, and three individuals without neurological impairment completed 3 BCI-AAC training sessions. To assess person-centered factors associated with BCI-AAC performance, participants completed both initial and recurring assessment measures including levels of cognition, motor ability, fatigue, and motivation. Three of four participants demonstrated either BCI-AAC performance in the range of neurotypical peers, or an improving BCI-AAC learning trajectory. However, BCI-AAC learning trajectories were variable. Assessment measures revealed that two participants presented with a suspicion for cognitive impairment yet achieved the highest levels of BCI-AAC accuracy with their increased levels of performance being possibly supported by largely unimpaired motor skills. Motor-based BCI switch access to a commercial AAC row-column scanning may be feasible for individuals with ALS and possibly supported by timely intervention.},
}
@article {pmid33663515,
year = {2021},
author = {Keïta, M and Doumbia, S and Sissoko, I and Touré, M and Diawara, SI and Konaté, D and Sodio, AB and Traoré, SF and Diakité, M and Doumbia, SO and Sogoba, N and Krogstad, DJ and Shaffer, JG and Coulibaly, MB},
title = {Indoor and outdoor malaria transmission in two ecological settings in rural Mali: implications for vector control.},
journal = {Malaria journal},
volume = {20},
number = {1},
pages = {127},
pmid = {33663515},
issn = {1475-2875},
support = {U19 AI129387/AI/NIAID NIH HHS/United States ; NIAID U19 AI 089696//National Institute of Allergy and Infectious Diseases/ ; U19 AI089696/AI/NIAID NIH HHS/United States ; NIAID U19 AI 129387//National Institute of Allergy and Infectious Diseases/ ; D43 TW008652/TW/FIC NIH HHS/United States ; },
mesh = {Adult ; Animals ; Anopheles/*physiology ; Biodiversity ; Environment ; Feeding Behavior ; Female ; Humans ; Malaria, Falciparum/*transmission ; Male ; Mali ; Mosquito Vectors/*physiology ; Plasmodium falciparum/*isolation &amp; purification ; Rural Population ; Sporozoites/isolation &amp; purification ; Young Adult ; },
abstract = {BACKGROUND: Implementation and upscale of effective malaria vector control strategies necessitates understanding the multi-factorial aspects of transmission patterns. The primary aims of this study are to determine the vector composition, biting rates, trophic preference, and the overall importance of distinguishing outdoor versus indoor malaria transmission through a study at two communities in rural Mali.<br><br>METHODS: Mosquito collection was carried out between July 2012 and June 2016 at two rural Mali communities (Dangassa and Ko&#xef;la Bamanan) using pyrethrum spray-catch and human landing catch approaches at both indoor and outdoor locations. Species of Anopheles gambiae complex were identified by polymerase chain reaction (PCR). Enzyme-Linked -Immuno-Sorbent Assay (ELISA) were used to determine the origin of mosquito blood meals and presence of Plasmodium falciparum sporozoite infections.<br><br>RESULTS: A total of 11,237 An. gambiae sensu lato (s.l.) were collected during the study period (5239 and 5998 from the Dangassa and Ko&#xef;la Bamanan sites, respectively). Of the 679 identified by PCR in Dangassa, Anopheles coluzzii was the predominant species with 91.4% of the catch followed by An. gambiae (8.0%) and Anopheles arabiensis (0.6%). At the same time in Ko&#xef;la Bamanan, of the 623 An. gambiae s.l., An. coluzzii accounted for 99% of the catch, An. arabiensis 0.8% and An. gambiae 0.2%. Human Blood Index (HBI) measures were significantly higher in Dangassa (79.4%; 95% Bayesian credible interval (BCI) [77.4, 81.4]) than in Ko&#xef;la Bamanan (15.9%; 95% BCI [14.7, 17.1]). The human biting rates were higher during the second half of the night at both sites. In Dangassa, the sporozoite rate was comparable between outdoor and indoor mosquito collections. For outdoor collections, the sporozoite positive rate was 3.6% (95% BCI [2.1-4.3]) and indoor collections were 3.1% (95% BCI [2.4-5.0]). In Ko&#xef;la Bamanan, the sporozoite rate was higher indoors at 4.3% (95% BCI [2.7-6.3]) compared with outdoors at 2.4% (95% BCI [1.1-4.2]). In Dangassa, corrected entomological inoculation rates (cEIRs) using HBI were 13.74 [95% BCI 9.21-19.14] infective bites/person/month (ib/p/m) at indoor, and 18.66 [95% BCI 12.55-25.81] ib/p/m at outdoor. For Ko&#xef;la Bamanan, cEIRs were 1.57 [95% BCI 2.34-2.72] ib/p/m and 0.94 [95% BCI 0.43-1.64] ib/p/m for indoor and outdoor, respectively. EIRs were significantly higher at the Dangassa site than the Ko&#xef;la Bamanan site.<br><br>CONCLUSION: The findings in this work may indicate the occurrence of active, outdoor residual malaria transmission is comparable to indoor transmission in some geographic settings. The high outdoor transmission patterns observed here highlight the need for additional strategies to combat outdoor malaria transmission to complement traditional indoor preventive approaches such as long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) which typically focus on resting mosquitoes.},
}
@article {pmid33661731,
year = {2021},
author = {Gao, H and Sun, M and Li, M and Wang, C and Yu, C and Wang, Y and Xu, K},
title = {Force Decoding of Caudal Forelimb Area and Rostral Forelimb Area in Chronic Stroke Rats.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {10},
pages = {3078-3086},
doi = {10.1109/TBME.2021.3063903},
pmid = {33661731},
issn = {1558-2531},
mesh = {Animals ; Forelimb ; *Motor Cortex ; Movement ; Rats ; *Stroke ; Upper Extremity ; },
abstract = {Brain machine interfaces (BMIs) used for movement restoration primarily rely on studies of motor decoding. It has been proved that local field potentials (LFPs) from primary motor cortex and premotor cortex of normal rodents could be used for decoding motor signals. However, few studies have explored the decoding performance of these brain areas under motor cortex damage. In this work, we focus on force decoding performance of LFPs spectrum from both ipsilesional caudal forelimb area (CFA) and rostral forelimb area (RFA) of rodents with ischemia over CFA. After three months of ischemia induced by photothrombosis over CFA, the power of high-frequency bands (>120 Hz) from both CFA and RFA can decode force signals by Kalman filters. The fair performance of CFA indicates motor reorganization over penumbra. Further exploration of RFA decoding ability proves that at least four electrodes of RFA should be used on decoding and electrodes far from CFA of stroke rats could achieve almost as good results as those close to CFA of normal rats, which indicates the motor remapping. Experimental results show the long-term stability of PM LFPs decoding performance of stroke rats as the trained Kalman model could be used to accurately decode force some days later which provides a possibility for online decoding system. In conclusion, our work shows that even under CFA ischemia, high-frequency power of LFPs from RFA is still able to accurately decode force signals and has long stability, which provides the possibility of BMIs for motor function reconstruction of chronic stroke patients.},
}
@article {pmid33659590,
year = {2021},
author = {Acampora, G and Trinchese, P and Vitiello, A},
title = {A dataset of EEG signals from a single-channel SSVEP-based brain computer interface.},
journal = {Data in brief},
volume = {35},
number = {},
pages = {106826},
pmid = {33659590},
issn = {2352-3409},
abstract = {The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users' comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction.},
}
@article {pmid33647233,
year = {2021},
author = {Chivukula, S and Zhang, CY and Aflalo, T and Jafari, M and Pejsa, K and Pouratian, N and Andersen, RA},
title = {Neural encoding of actual and imagined touch within human posterior parietal cortex.},
journal = {eLife},
volume = {10},
number = {},
pages = {},
pmid = {33647233},
issn = {2050-084X},
support = {UG1 EY032039/EY/NEI NIH HHS/United States ; R25 NS079198/NS/NINDS NIH HHS/United States ; R01EY015545/EY/NEI NIH HHS/United States ; P50MH094258//Conte Center/ ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Cognition ; Electrodes, Implanted ; Female ; Humans ; Imagination/*physiology ; Middle Aged ; Neurons/physiology ; Parietal Lobe/cytology/*physiology ; Quadriplegia ; Touch Perception/*physiology ; },
abstract = {In the human posterior parietal cortex (PPC), single units encode high-dimensional information with partially mixed representations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here, we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly engaged in the somatosensory domain. We recorded neurons within the PPC of a human clinical trial participant during actual touch presentation and during a tactile imagery task. Neurons encoded actual touch at short latency with bilateral receptive fields, organized by body part, and covered all tested regions. The tactile imagery task evoked body part-specific responses that shared a neural substrate with actual touch. Our results are the first neuron-level evidence of touch encoding in human PPC and its cognitive engagement during a tactile imagery task, which may reflect semantic processing, attention, sensory anticipation, or imagined touch.},
}
@article {pmid33645847,
year = {2021},
author = {Plata, M and Santander, J and Trujillo, CG and Bravo-Balado, A and Robledo, D and Higuera, T and Caicedo, JI},
title = {Impact of detrusor underactivity on the postoperative outcomes after benign prostatic enlargement surgery.},
journal = {Neurourology and urodynamics},
volume = {40},
number = {3},
pages = {868-875},
doi = {10.1002/nau.24637},
pmid = {33645847},
issn = {1520-6777},
mesh = {Aged ; Humans ; Male ; Middle Aged ; Postoperative Period ; Prostatic Hyperplasia/*complications/*surgery ; Treatment Outcome ; Urinary Bladder, Underactive/*complications ; Urologic Surgical Procedures/*adverse effects ; },
abstract = {INTRODUCTION AND OBJECTIVE: Previous studies suggest that men with detrusor underactivity (DUA) have less symptomatic improvement after prostate surgery than those with normal contractility, but the available data is controversial. We aim to determine the differences in functional outcomes of patients with or without DUA who underwent photovaporization of the prostate (PVP) with GreenLight™180 W XPS.<br><br>METHODS: A cohort of patients with lower urinary tract symptoms (LUTS) who underwent PVP between 2012 and 2019 was evaluated. Patients were stratified according to bladder contractility index (BCI). DUA was defined as BCI&#x2009;<&#x2009;100. Those with normal contractility (BCI&#x2009;=&#x2009;100-150) were included in Group 1, and those with DUA (BCI&#x2009;<&#x2009;100) in Group 2. Primary outcomes were symptomatic improvement defined as a reduction&#x2009;&#x2265;&#x2009;4 points in the international prostate symptom score (IPSS) and a reduction of at least 1 point in the quality of life (IPSS-QoL). Complications according to the Clavien-Dindo classification were also recorded.<br><br>RESULTS: A total of 271 patients who underwent PVP with GreenLight&#x2122; and met the inclusion criteria were assessed. Group 1 included 158 patients, while Group 2 included 113 patients. Mean follow-up was 24 months. Patients with normal contractility had a median reduction of 11 points (18.9&#x2009;&#xb1;&#x2009;8.0 to 7.1&#x2009;&#xb1;&#x2009;7.0) while patients with DUA had a median reduction of 10 points (19.3&#x2009;&#xb1;&#x2009;6.9 to 8.6&#x2009;&#xb1;&#x2009;8.4) in IPSS score; these differences were not statistically significant (p&#x2009;=&#x2009;.20). Patients in Group 1 had a 1.92 higher chance of QoL improvement (OR, 1.92; 90% CI, 1.10-3.37), compared to those in Group 2. Failure to void after PVP was most frequently reported in DUA patients (OR, 2.36; 90% CI, 1.26-4.43). Sociodemographic characteristics, intraoperative complications, conversion rates, hospital stay, and urinary catheterization time were similar between groups.<br><br>CONCLUSIONS: Patients with LUTS, regardless of their BCI, improved their symptoms after PVP according to the IPSS. However, patients with DUA were more likely not to improve their QoL after the procedure and had a higher chance of failure to void in the immediate postoperative period. An appropriate counseling process with the patient discussing possible outcomes based on these findings should be encouraged.},
}
@article {pmid33643562,
year = {2020},
author = {Khodakarami, Z and Firoozabadi, M},
title = {Psychological, Neurophysiological, and Mental Factors Associated With Gamma-Enhancing Neurofeedback Success.},
journal = {Basic and clinical neuroscience},
volume = {11},
number = {5},
pages = {701-714},
pmid = {33643562},
issn = {2008-126X},
abstract = {INTRODUCTION: Regarding the neurofeedback training process, previous studies indicate that 10%-50% of subjects cannot gain control over their brain activity even after repeated training sessions. This study is conducted to overcome this problem by investigating inter-individual differences in neurofeedback learning to propose some predictors for the trainability of subjects.<br><br>METHODS: Eight healthy female students took part in 8 (electroencephalography) EEG neurofeedback training sessions for enhancing EEG gamma power at the Oz channel. We studied participants' preexisting fluid intelligence and EEG frequency sub-bands' power during 2-min eyes-closed rest and a cognitive task as psychological and neurophysiological factors, concerning neurofeedback learning performance. We also assessed the self-reports of participants about mental strategies used by them during neurofeedback to identify the most effective successful strategies.<br><br>RESULTS: The results revealed that a significant percentage of individuals (25% in this study) cannot learn how to control their brain gamma activity using neurofeedback. Our findings suggest that fluid intelligence, gamma power during a cognitive task, and alpha power at rest can predict gamma-enhancing neurofeedback performance of individuals. Based on our study, neurofeedback learning is a form of implicit learning. We also found that learning without a user's mental efforts to find out successful mental strategies, in other words, unconscious learning, lead to more success in gamma-enhancing neurofeedback.<br><br>CONCLUSION: Our results may improve gamma neurofeedback efficacy for further clinical usage and studies by giving insight about both non-trainable individuals and effective mental strategies.},
}
@article {pmid33639224,
year = {2022},
author = {Yazmir, B and Reiner, M},
title = {Neural Signatures of Interface Errors in Remote Agent Manipulation.},
journal = {Neuroscience},
volume = {486},
number = {},
pages = {62-76},
doi = {10.1016/j.neuroscience.2021.02.022},
pmid = {33639224},
issn = {1873-7544},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials/physiology ; Feedback, Sensory/physiology ; Humans ; Movement/physiology ; Reaction Time/physiology ; User-Computer Interface ; },
abstract = {The manipulation of remote agents such as robotic arms in remote surgery or in BCI-wheelchair control are prone to errors. Some of these are related to user intent misclassification or other interface system errors, which lead to an incorrect movement. Here we focused on errors originating from unpredicted interface movements violating user intent and producing sensory conflicts. In addition, we examined effects of incongruent/congruent sensory stimuli induced by interface errors, focusing on haptic and visual cues in the system. The overarching goal was to identify the prototypical patterns of electroencephalogram (EEG) error signals associated with two types of interface errors rising when the visual and proprioceptive feedback are congruent or incongruent. For purposes of comparison validity, both types of errors were recorded in the same 3D virtual game environment. The comparison of congruent and incongruent interface errors revealed significant and marginally significant differences in EEG potentials with respect to profile, latencies, scalp distribution and sources. Different EEG time-frequency combinations had high power content. Incongruence between visual and proprioceptive feedback in interface errors not only elicited distinct EEG signal characteristics, but also produced a marginally significant Stroop effect. Incongruency in visuo-haptic feedback modalities cause a delayed user response. This effect is of major importance for the design of controlling interfaces and can provide designers with crucial information when aiming to control human response time.},
}
@article {pmid33637029,
year = {2021},
author = {Soriano-Segura, P and Iáñez, E and Ortiz, M and Quiles, V and Azorín, JM},
title = {Detection of the Intention of Direction Changes During Gait Through EEG Signals.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2150015},
doi = {10.1142/S0129065721500155},
pmid = {33637029},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Gait ; Humans ; *Intention ; Movement ; },
abstract = {Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.},
}
@article {pmid33636657,
year = {2021},
author = {Rizzoglio, F and Casadio, M and De Santis, D and Mussa-Ivaldi, FA},
title = {Building an adaptive interface via unsupervised tracking of latent manifolds.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {137},
number = {},
pages = {174-187},
doi = {10.1016/j.neunet.2021.01.009},
pmid = {33636657},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Calibration ; Computer Security/standards ; Humans ; *Unsupervised Machine Learning ; },
abstract = {In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body-machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users' task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process.},
}
@article {pmid33635816,
year = {2022},
author = {Kim, HS and Ahn, MH and Min, BK},
title = {Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces.},
journal = {IEEE transactions on cybernetics},
volume = {52},
number = {9},
pages = {8668-8680},
doi = {10.1109/TCYB.2021.3052813},
pmid = {33635816},
issn = {2168-2275},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Neural Networks, Computer ; },
abstract = {Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of channels applicable to general BMI paradigms using a compact convolutional neural network for electroencephalography (EEG)-based BMIs. For verification, three types of BMI paradigms are assessed: 1) the typical P300 auditory oddball; 2) the new top-down steady-state visually evoked potential; and 3) the endogenous motor imagery. We observe that the optimized minimal EEG-channel sets are automatically selected in all three cases. Their decoding accuracies using the minimal channels are statistically equivalent to (or even higher than) those based on all channels. The brain areas of the selected channel set are neurophysiologically interpretable for all of these cognitive task paradigms. This study shows that the minimal EEG channel set can be automatically selected, irrespective of the types of BMI paradigms or EEG input features using a deep-learning approach, which also contributes to their portability.},
}
@article {pmid33633557,
year = {2021},
author = {Ptito, M and Bleau, M and Djerourou, I and Paré, S and Schneider, FC and Chebat, DR},
title = {Brain-Machine Interfaces to Assist the Blind.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {638887},
pmid = {33633557},
issn = {1662-5161},
abstract = {The loss or absence of vision is probably one of the most incapacitating events that can befall a human being. The importance of vision for humans is also reflected in brain anatomy as approximately one third of the human brain is devoted to vision. It is therefore unsurprising that throughout history many attempts have been undertaken to develop devices aiming at substituting for a missing visual capacity. In this review, we present two concepts that have been prevalent over the last two decades. The first concept is sensory substitution, which refers to the use of another sensory modality to perform a task that is normally primarily sub-served by the lost sense. The second concept is cross-modal plasticity, which occurs when loss of input in one sensory modality leads to reorganization in brain representation of other sensory modalities. Both phenomena are training-dependent. We also briefly describe the history of blindness from ancient times to modernity, and then proceed to address the means that have been used to help blind individuals, with an emphasis on modern technologies, invasive (various type of surgical implants) and non-invasive devices. With the advent of brain imaging, it has become possible to peer into the neural substrates of sensory substitution and highlight the magnitude of the plastic processes that lead to a rewired brain. Finally, we will address the important question of the value and practicality of the available technologies and future directions.},
}
@article {pmid33633302,
year = {2021},
author = {Lienkämper, R and Dyck, S and Saif-Ur-Rehman, M and Metzler, M and Ali, O and Klaes, C},
title = {Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain-computer-interface setup.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {4614},
pmid = {33633302},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; *Feedback, Sensory ; Female ; Humans ; Male ; Middle Aged ; *Psychomotor Performance ; Robotics ; Software ; Virtual Reality ; Young Adult ; },
abstract = {Invasive brain-computer-interfaces (BCIs) aim to improve severely paralyzed patient's (e.g. tetraplegics) quality of life by using decoded movement intentions to let them interact with robotic limbs. We argue that the performance in controlling an end-effector using a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused by translation and/or rotation of the end-effector relative to the real or perceived body. Using a virtual reality (VR) model of an ideal BCI decoder with healthy participants, we found that a significant performance loss might be attributed solely to the alignment error. We used a shape-drawing task to investigate and quantify the effects of robot arm misalignment on motor performance independent from the other error sources. We found that a 90° rotation of the robot arm relative to the participant leads to the worst performance, while we did not find a significant difference between a 45° rotation and no rotation. Additionally, we compared a group of subjects with indirect haptic feedback with a group without indirect haptic feedback to investigate the feedback-error. In the group without feedback, we found a significant difference in performance only when no rotation was applied to the robot arm, supporting that a form of haptic feedback is another important factor to be considered in BCI control.},
}
@article {pmid33632810,
year = {2021},
author = {Shupe, L and Fetz, E},
title = {An Integrate-and-Fire Spiking Neural Network Model Simulating Artificially Induced Cortical Plasticity.},
journal = {eNeuro},
volume = {8},
number = {2},
pages = {},
pmid = {33632810},
issn = {2373-2822},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Models, Neurological ; Neural Networks, Computer ; *Neuronal Plasticity ; *Neurons ; },
abstract = {We describe an integrate-and-fire (IF) spiking neural network that incorporates spike-timing-dependent plasticity (STDP) and simulates the experimental outcomes of four different conditioning protocols that produce cortical plasticity. The original conditioning experiments were performed in freely moving non-human primates (NHPs) with an autonomous head-fixed bidirectional brain-computer interface (BCI). Three protocols involved closed-loop stimulation triggered from (1) spike activity of single cortical neurons, (2) electromyographic (EMG) activity from forearm muscles, and (3) cycles of spontaneous cortical beta activity. A fourth protocol involved open-loop delivery of pairs of stimuli at neighboring cortical sites. The IF network that replicates the experimental results consists of 360 units with simulated membrane potentials produced by synaptic inputs and triggering a spike when reaching threshold. The 240 cortical units produce either excitatory or inhibitory postsynaptic potentials (PSPs) in their target units. In addition to the experimentally observed conditioning effects, the model also allows computation of underlying network behavior not originally documented. Furthermore, the model makes predictions about outcomes from protocols not yet investigated, including spike-triggered inhibition, γ-triggered stimulation and disynaptic conditioning. The success of the simulations suggests that a simple voltage-based IF model incorporating STDP can capture the essential mechanisms mediating targeted plasticity with closed-loop stimulation.},
}
@article {pmid33632262,
year = {2021},
author = {Zulauf-Czaja, A and Al-Taleb, MKH and Purcell, M and Petric-Gray, N and Cloughley, J and Vuckovic, A},
title = {On the way home: a BCI-FES hand therapy self-managed by sub-acute SCI participants and their caregivers: a usability study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {18},
number = {1},
pages = {44},
pmid = {33632262},
issn = {1743-0003},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Caregivers ; Electric Stimulation Therapy/*instrumentation ; Electroencephalography/*instrumentation ; Female ; Hand/physiopathology ; Home Care Services ; Humans ; Male ; Middle Aged ; Movement/physiology ; Occupational Therapy/instrumentation ; Spinal Cord Injuries/*rehabilitation ; },
abstract = {BACKGROUND: Regaining hand function is the top priority for people with tetraplegia, however access to specialised therapy outwith clinics is limited. Here we present a system for hand therapy based on brain-computer interface (BCI) which uses a consumer grade electroencephalography (EEG) device combined with functional electrical stimulation (FES), and evaluate its usability among occupational therapists (OTs) and people with spinal cord injury (SCI) and their family members.<br><br>METHODS: Users:&#xa0;Eight people with sub-acute SCI (6&#xa0;M, 2F, age&#xa0;55.4&#x2009;&#xb1;&#x2009;15.6) and their caregivers (3&#xa0;M, 5F,&#xa0;age&#xa0;45.3&#x2009;&#xb1;&#x2009;14.3); four OTs (4F, age&#xa0;42.3&#x2009;&#xb1;&#x2009;9.8).&#xa0;User Activity:&#xa0;Researchers trained OTs; OTs subsequently taught caregivers to set up the system for the people with SCI to perform hand therapy. Hand therapy&#xa0;consisted of attempted movement&#xa0;(AM)&#xa0;of one hand to lower the power of EEG sensory-motor rhythm in the 8-12&#xa0;Hz band and thereby activate FES which induced&#xa0;wrist flexion and extension.&#xa0;Technology: Consumer grade wearable EEG, multichannel FES, custom made BCI application.<br><br>LOCATION: Research space within hospital.&#xa0;Evaluation: donning times,&#xa0;BCI accuracy, BCI and&#xa0;FES parameter repeatability,&#xa0;questionnaires, focus groups and interviews.<br><br>RESULTS: Effectiveness: The BCI accuracy was 70-90%. Efficiency: Median donning times decreased from 40.5&#xa0;min for initial session to 27&#xa0;min during last training session (N&#x2009;=&#x2009;7), dropping to 14&#xa0;min on the last self-managed session (N&#x2009;=&#x2009;3). BCI and FES parameters were stable from session to session.&#xa0;Satisfaction: Mean satisfaction with the system among SCI users and caregivers was 3.68&#x2009;&#xb1;&#x2009;0.81 (max 5) as measured by QUEST questionnaire. Main facilitators for implementing BCI-FES technology were "seeing hand moving", "doing something useful for the loved ones", good level of computer literacy (people with SCI and caregivers), "active engagement in therapy" (OT), while main barriers were technical complexity of setup (all groups) and "lack of clinical evidence" (OT).<br><br>CONCLUSION: BCI-FES has potential to be used as at home hand therapy by people with SCI or stroke, provided it is easy to use and&#xa0;support is provided.&#xa0;Transfer of knowledge of operating BCI is possible from researchers to therapists to users and caregivers. Trial registration Registered with NHS GG&amp;C on December 6th 2017; clinicaltrials.gov reference number NCT03257982, url: https://clinicaltrials.gov/ct2/show/NCT03257982 .},
}
@article {pmid33632167,
year = {2021},
author = {Kamao, T and Zheng, X and Shiraishi, A},
title = {Outcomes of bicanalicular nasal stent inserted by sheath-guided dacryoendoscope in patients with lacrimal passage obstruction: a retrospective observational study.},
journal = {BMC ophthalmology},
volume = {21},
number = {1},
pages = {103},
pmid = {33632167},
issn = {1471-2415},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Dacryocystorhinostomy ; Humans ; *Lacrimal Apparatus ; *Lacrimal Duct Obstruction/therapy ; Middle Aged ; *Nasolacrimal Duct/surgery ; Quality of Life ; Stents ; Treatment Outcome ; },
abstract = {BACKGROUND: The dacryoendoscope is the only instrument that can observe the luminal side of the lacrimal passage with minimal invasiveness. It was developed to treat lacrimal passage obstructions by inserting a bicanalicular nasal stent with sheath-guided bicanalicular intubation (SG-BCI). The purpose of this study was to determine the outcomes of SG-BCI to treat lacrimal passage obstructions. In addition, to determine the effects of SG-BCI treatment on the quality of life.<br><br>METHODS: This was a retrospective observational study of 128 patients (mean age 70.9&#x2009;&#xb1;&#x2009;11.0&#x2009;years, range 28-93&#x2009;years) diagnosed with a unilateral lacrimal passage obstruction. There were 73 patients with a nasolacrimal duct obstruction, 37 with a lacrimal canaliculus obstruction, 7 with a lacrimal punctum obstruction, and 11 with common lacrimal canaliculus and nasolacrimal duct obstructions. They were all treated with SG-BCI. The postoperative subjective outcomes were assessed by the answers to the Glasgow Benefit Inventory (GBI) questionnaire and to an ocular specific questionnaire on 6 symptoms including tearing, ocular discharges, swelling, pain, irritation, and blurred vision. The objective assessments were the surgical success rates and the patency at 6&#x2009;months after the bicanalicular nasal stent was removed. The patients were divided into those with a pre-saccal obstruction, Group 1, and with a post-saccal obstruction, Group 2. The subjective and objective outcomes were compared between the two groups.<br><br>RESULTS: One hundred twenty-four sides (96.9%) had a successful probing and intubation of the lacrimal passage obstruction by SG-BCI. Of the 124 sides, 110 sides (88.7%) retained the patency after the stent was removed for at least 6&#x2009;months. The GBI total, general subscale, social support, and physical health scores were&#x2009;+&#x2009;37.1&#x2009;&#xb1;&#x2009;29.0, +&#x2009;41.5&#x2009;&#xb1;&#x2009;30.0, +&#x2009;28.0&#x2009;&#xb1;&#x2009;39.4, and&#x2009;+&#x2009;24.1&#x2009;&#xb1;&#x2009;37.7, respectively, postoperatively. All of the 6 ocular specific symptom scores improved significantly postoperatively. The postoperative score of tearing improved in Group 1 (P&#xa0;<&#x2009;0.0001), while the postoperative scores of all symptoms improved significantly in Group 2.<br><br>CONCLUSIONS: The relatively high surgical success rates and positive GBI scores, and improved ocular symptom scores indicate that SG-BCI is a good minimally invasive method to treat lacrimal passage obstructions.},
}
@article {pmid33629666,
year = {2021},
author = {Kaongoen, N and Choi, J and Jo, S},
title = {Speech-imagery-based brain-computer interface system using ear-EEG.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {016023},
doi = {10.1088/1741-2552/abd10e},
pmid = {33629666},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Ear ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Speech ; },
abstract = {OBJECTIVE: This study investigates the efficacy of electroencephalography (EEG) centered around the user's ears (ear-EEG) for a speech-imagery-based brain-computer interface (BCI) system.<br><br>APPROACH: A wearable ear-EEG acquisition tool was developed and its performance was directly compared to that of a conventional 32-channel scalp-EEG setup in a multi-class speech imagery classification task. Riemannian tangent space projections of EEG covariance matrices were used as input features to a multi-layer extreme learning machine classifier. Ten subjects participated in an experiment consisting of six sessions spanning three days. The experiment involves imagining four speech commands ('Left,' 'Right,' 'Forward,' and 'Go back') and staying in a rest condition.<br><br>MAIN RESULTS: The classification accuracy of our system is significantly above the chance level (20%). The classification result averaged across all ten subjects is 38.2% and 43.1% with a maximum (max) of 43.8% and 55.0% for ear-EEG and scalp-EEG, respectively. According to an analysis of variance, seven out of ten subjects show no significant difference between the performance of ear-EEG and scalp-EEG.<br><br>SIGNIFICANCE: To our knowledge, this is the first study that investigates the performance of ear-EEG in a speech-imagery-based BCI. The results indicate that ear-EEG has great potential as an alternative to the scalp-EEG acquisition method for speech-imagery monitoring. We believe that the merits and feasibility of both speech imagery and ear-EEG acquisition in the proposed system will accelerate the development of the BCI system for daily-life use.},
}
@article {pmid33628331,
year = {2021},
author = {Shaban, SA and Ucan, ON and Duru, AD},
title = {Classification of Lactate Level Using Resting-State EEG Measurements.},
journal = {Applied bionics and biomechanics},
volume = {2021},
number = {},
pages = {6662074},
pmid = {33628331},
issn = {1176-2322},
abstract = {The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.},
}
@article {pmid33628220,
year = {2021},
author = {Luo, Z and Jin, R and Shi, H and Lu, X},
title = {Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network.},
journal = {Neural plasticity},
volume = {2021},
number = {},
pages = {6655430},
pmid = {33628220},
issn = {1687-5443},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography ; Humans ; Imagination/*physiology ; *Models, Neurological ; Nerve Net/*physiology ; Recognition, Psychology/*physiology ; },
abstract = {Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.},
}
@article {pmid33628218,
year = {2021},
author = {Mao, Y and Jin, J and Li, S and Miao, Y and Cichocki, A},
title = {Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6694310},
pmid = {33628218},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Friction ; Humans ; User-Computer Interface ; },
abstract = {Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.},
}
@article {pmid33628215,
year = {2021},
author = {Feng, Y and Xiao, W and Wu, T and Zhang, J and Xiang, J and Guo, H},
title = {A New Recognition Method for the Auditory Evoked Magnetic Fields.},
journal = {Computational intelligence and neuroscience},
volume = {2021},
number = {},
pages = {6645270},
pmid = {33628215},
issn = {1687-5273},
mesh = {Brain Mapping ; *Electroencephalography ; Evoked Potentials ; Evoked Potentials, Auditory ; Humans ; Magnetic Fields ; *Magnetoencephalography ; },
abstract = {Magnetoencephalography (MEG) is a persuasive tool to study the human brain in physiology and psychology. It can be employed to obtain the inference of change between the external environment and the internal psychology, which requires us to recognize different single trial event-related magnetic fields (ERFs) originated from different functional areas of the brain. Current recognition methods for the single trial data are mainly used for event-related potentials (ERPs) in the electroencephalography (EEG). Although the MEG shares the same signal sources with the EEG, much less interference from the other brain tissues may give the MEG an edge in recognition of the ERFs. In this work, we propose a new recognition method for the single trial auditory evoked magnetic fields (AEFs) through enhancing the signal. We find that the signal strength of the single trial AEFs is concentrated in the primary auditory cortex of the temporal lobe, which can be clearly displayed in the 2D images. These 2D images are then recognized by an artificial neural network (ANN) with 100% accuracy, which realizes the automatic recognition for the single trial AEFs. The method not only may be combined with the source estimation algorithm to improve its accuracy but also paves the way for the implementation of the brain-computer interface (BCI) with the MEG.},
}
@article {pmid33625987,
year = {2021},
author = {Lim, LG and Ung, WC and Chan, YL and Lu, CK and Funane, T and Kiguchi, M and Tang, TB},
title = {Optimizing Mental Workload Estimation by Detecting Baseline State Using Vector Phase Analysis Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {597-606},
doi = {10.1109/TNSRE.2021.3062117},
pmid = {33625987},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Hemodynamics ; Humans ; Mathematics ; *Spectroscopy, Near-Infrared ; Workload ; },
abstract = {Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.},
}
@article {pmid33624614,
year = {2021},
author = {Sahasrabuddhe, K and Khan, AA and Singh, AP and Stern, TM and Ng, Y and Tadić, A and Orel, P and LaReau, C and Pouzzner, D and Nishimura, K and Boergens, KM and Shivakumar, S and Hopper, MS and Kerr, B and Hanna, MS and Edgington, RJ and McNamara, I and Fell, D and Gao, P and Babaie-Fishani, A and Veijalainen, S and Klekachev, AV and Stuckey, AM and Luyssaert, B and Kozai, TDY and Xie, C and Gilja, V and Dierickx, B and Kong, Y and Straka, M and Sohal, HS and Angle, MR},
title = {The Argo: a high channel count recording system for neural recording in vivo.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {015002},
pmid = {33624614},
issn = {1741-2552},
support = {R43 MH110287/MH/NIMH NIH HHS/United States ; },
mesh = {*Amplifiers, Electronic ; Animals ; Electrodes, Implanted ; Microelectrodes ; *Neurons ; Rats ; Sheep ; },
abstract = {OBJECTIVE: Decoding neural activity has been limited by the lack of tools available to record from large numbers of neurons across multiple cortical regions simultaneously with high temporal fidelity. To this end, we developed the Argo system to record cortical neural activity at high data rates.<br><br>APPROACH: Here we demonstrate a massively parallel neural recording system based on platinum-iridium microwire electrode arrays bonded to a CMOS voltage amplifier array. The Argo system is the highest channel count in vivo neural recording system, supporting simultaneous recording from 65&#x2009;536 channels, sampled at 32 kHz and 12-bit resolution. This system was designed for cortical recordings, compatible with both penetrating and surface microelectrodes.<br><br>MAIN RESULTS: We validated this system through initial bench testing to determine specific gain and noise characteristics of bonded microwires, followed by in-vivo experiments in both rat and sheep cortex. We recorded spiking activity from 791 neurons in rats and surface local field potential activity from over 30&#x2009;000 channels in sheep.<br><br>SIGNIFICANCE: These are the largest channel count microwire-based recordings in both rat and sheep. While currently adapted for head-fixed recording, the microwire-CMOS architecture is well suited for clinical translation. Thus, this demonstration helps pave the way for a future high data rate intracortical implant.},
}
@article {pmid33622198,
year = {2021},
author = {Porcaro, C and Mayhew, SD and Bagshaw, AP},
title = {Role of the Ipsilateral Primary Motor Cortex in the Visuo-Motor Network During Fine Contractions and Accurate Performance.},
journal = {International journal of neural systems},
volume = {31},
number = {6},
pages = {2150011},
doi = {10.1142/S0129065721500118},
pmid = {33622198},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; *Motor Cortex ; Movement ; },
abstract = {It is widely recognized that continuous sensory feedback plays a crucial role in accurate motor control in everyday life. Feedback information is used to adapt force output and to correct errors. While primary motor cortex contralateral to the movement (cM1) plays a dominant role in this control, converging evidence supports the idea that ipsilateral primary motor cortex (iM1) also directly contributes to hand and finger movements. Similarly, when visual feedback is available, primary visual cortex (V1) and its interactions with the motor network also become important for accurate motor performance. To elucidate this issue, we performed and integrated behavioral and electroencephalography (EEG) measurements during isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback. We used a semi-blind approach (functional source separation (FSS)) to identify separate functional sources of mu-frequency (8-13[Formula: see text]Hz) EEG responses in cM1, iM1 and V1. Here for the first time, we have used orthogonal FSS to extract multiple sources, by using the same functional constraint, providing the ability to extract different sources that oscillate in the same frequency range but that have different topographic distributions. We analyzed the single-trial timecourses of mu power event-related desynchronization (ERD) in these sources and linked them with force measurements to understand which aspects are most important for good task performance. Whilst the amplitude of mu power was not related to contraction force in any of the sources, it was able to provide information on performance quality. We observed stronger ERDs in both contralateral and ipsilateral motor sources during trials where contraction force was most consistently maintained. This effect was most prominent in the ipsilateral source, suggesting the importance of iM1 to accurate performance. This ERD effect was sustained throughout the duration of visual feedback trials, but only present at the start of no feedback trials, consistent with more variable performance in the absence of feedback. Overall, we found that the behavior of the ERD in iM1 was the most informative aspect concerning the accuracy of the contraction performance, and the ability to maintain a steady level of contraction. This new approach of using FSS to extract multiple orthogonal sources provides the ability to investigate both contralateral and ipsilateral nodes of the motor network without the need for additional information (e.g. electromyography). The enhanced signal-to-noise ratio provided by FSS opens the possibility of extracting complex EEG features on an individual trial basis, which is crucial for a more nuanced understanding of fine motor performance, as well as for applications in brain-computer interfacing.},
}
@article {pmid33621208,
year = {2021},
author = {Dubaniewicz, M and Eles, JR and Lam, S and Song, S and Cambi, F and Sun, D and Wellman, SM and Kozai, TDY},
title = {Inhibition of Na[+]/H[+]exchanger modulates microglial activation and scar formation following microelectrode implantation.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
pmid = {33621208},
issn = {1741-2552},
support = {R01 NS048216/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; },
mesh = {*Cicatrix ; Humans ; Microelectrodes ; *Microglia ; Neuroglia ; Sodium-Hydrogen Exchangers ; },
abstract = {Objective.Intracortical microelectrodes are an important tool for neuroscience research and have great potential for clinical use. However, the use of microelectrode arrays to treat neurological disorders and control prosthetics is limited by biological challenges such as glial scarring, which can impair chronic recording performance. Microglia activation is an early and prominent contributor to glial scarring. After insertion of an intracortical microelectrode, nearby microglia transition into a state of activation, migrate, and encapsulate the device. Na[+]/H[+]exchanger isoform-1 (NHE-1) is involved in various microglial functions, including their polarity and motility, and has been implicated in pro-inflammatory responses to tissue injury. HOE-642 (cariporide) is an inhibitor of NHE-1 and has been shown to depress microglial activation and inflammatory response in brain injury models.Approach.In this study, the effects of HOE-642 treatment on microglial interactions to intracortical microelectrodes was evaluated using two-photon microscopyin vivo.Main results.The rate at which microglia processes and soma migrate in response to electrode implantation was unaffected by HOE-642 administration. However, HOE-642 administration effectively reduced the radius of microglia activation at 72 h post-implantation from 222.2µm to 177.9µm. Furthermore, treatment with HOE-642 significantly reduced microglial encapsulation of implanted devices at 5 h post-insertion from 50.7 ± 6.0% to 8.9 ± 6.1%, which suggests an NHE-1-specific mechanism mediating microglia reactivity and gliosis during implantation injury.Significance.This study implicates NHE-1 as a potential target of interest in microglial reactivity and HOE-642 as a potential treatment to attenuate the glial response and scar formation around implanted intracortical microelectrodes.},
}
@article {pmid33614976,
year = {2020},
author = {Michie, S and West, R and Finnerty, AN and Norris, E and Wright, AJ and Marques, MM and Johnston, M and Kelly, MP and Thomas, J and Hastings, J},
title = {Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology.},
journal = {Wellcome open research},
volume = {5},
number = {},
pages = {123},
pmid = {33614976},
issn = {2398-502X},
support = {/WT_/Wellcome Trust/United Kingdom ; 201524/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
abstract = {Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.},
}
@article {pmid33613212,
year = {2021},
author = {Zaer, H and Deshmukh, A and Orlowski, D and Fan, W and Prouvot, PH and Glud, AN and Jensen, MB and Worm, ES and Lukacova, S and Mikkelsen, TW and Fitting, LM and Adler, JR and Schneider, MB and Jensen, MS and Fu, Q and Go, V and Morizio, J and Sørensen, JCH and Stroh, A},
title = {An Intracortical Implantable Brain-Computer Interface for Telemetric Real-Time Recording and Manipulation of Neuronal Circuits for Closed-Loop Intervention.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {618626},
pmid = {33613212},
issn = {1662-5161},
abstract = {Recording and manipulating neuronal ensemble activity is a key requirement in advanced neuromodulatory and behavior studies. Devices capable of both recording and manipulating neuronal activity brain-computer interfaces (BCIs) should ideally operate un-tethered and allow chronic longitudinal manipulations in the freely moving animal. In this study, we designed a new intracortical BCI feasible of telemetric recording and stimulating local gray and white matter of visual neural circuit after irradiation exposure. To increase the translational reliance, we put forward a Göttingen minipig model. The animal was stereotactically irradiated at the level of the visual cortex upon defining the target by a fused cerebral MRI and CT scan. A fully implantable neural telemetry system consisting of a 64 channel intracortical multielectrode array, a telemetry capsule, and an inductive rechargeable battery was then implanted into the visual cortex to record and manipulate local field potentials, and multi-unit activity. We achieved a 3-month stability of the functionality of the un-tethered BCI in terms of telemetric radio-communication, inductive battery charging, and device biocompatibility for 3 months. Finally, we could reliably record the local signature of sub- and suprathreshold neuronal activity in the visual cortex with high bandwidth without complications. The ability to wireless induction charging combined with the entirely implantable design, the rather high recording bandwidth, and the ability to record and stimulate simultaneously put forward a wireless BCI capable of long-term un-tethered real-time communication for causal preclinical circuit-based closed-loop interventions.},
}
@article {pmid33613182,
year = {2021},
author = {Ovchinnikova, AO and Vasilyev, AN and Zubarev, IP and Kozyrskiy, BL and Shishkin, SL},
title = {MEG-Based Detection of Voluntary Eye Fixations Used to Control a Computer.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {619591},
pmid = {33613182},
issn = {1662-4548},
abstract = {Gaze-based input is an efficient way of hand-free human-computer interaction. However, it suffers from the inability of gaze-based interfaces to discriminate voluntary and spontaneous gaze behaviors, which are overtly similar. Here, we demonstrate that voluntary eye fixations can be discriminated from spontaneous ones using short segments of magnetoencephalography (MEG) data measured immediately after the fixation onset. Recently proposed convolutional neural networks (CNNs), linear finite impulse response filters CNN (LF-CNN) and vector autoregressive CNN (VAR-CNN), were applied for binary classification of the MEG signals related to spontaneous and voluntary eye fixations collected in healthy participants (n = 25) who performed a game-like task by fixating on targets voluntarily for 500 ms or longer. Voluntary fixations were identified as those followed by a fixation in a special confirmatory area. Spontaneous vs. voluntary fixation-related single-trial 700 ms MEG segments were non-randomly classified in the majority of participants, with the group average cross-validated ROC AUC of 0.66 ± 0.07 for LF-CNN and 0.67 ± 0.07 for VAR-CNN (M ± SD). When the time interval, from which the MEG data were taken, was extended beyond the onset of the visual feedback, the group average classification performance increased up to 0.91. Analysis of spatial patterns contributing to classification did not reveal signs of significant eye movement impact on the classification results. We conclude that the classification of MEG signals has a certain potential to support gaze-based interfaces by avoiding false responses to spontaneous eye fixations on a single-trial basis. Current results for intention detection prior to gaze-based interface's feedback, however, are not sufficient for online single-trial eye fixation classification using MEG data alone, and further work is needed to find out if it could be used in practical applications.},
}
@article {pmid33612236,
year = {2021},
author = {Elsohaby, I and Arango-Sabogal, JC and McClure, JT and Dufour, S and Buczinski, S and Keefe, GP},
title = {Accuracy of direct and indirect methods for assessing bovine colostrum quality using a latent class model fit within a Bayesian framework.},
journal = {Journal of dairy science},
volume = {104},
number = {4},
pages = {4703-4714},
doi = {10.3168/jds.2020-19231},
pmid = {33612236},
issn = {1525-3198},
mesh = {Animals ; Bayes Theorem ; Canada ; Cattle ; *Colostrum ; Female ; *Immunoglobulin G ; Latent Class Analysis ; Pregnancy ; },
abstract = {Feeding high-quality colostrum is essential for calf health and future productivity. Therefore, accurate assessment of colostrum quality is a key component of dairy farm management plans. Direct and indirect methods are available for assessment of colostrum quality; however, the indirect methods are rapid, inexpensive, and can be performed under field settings. A hierarchical latent class model fit within a Bayesian framework was used to estimate the sensitivity (Se) and specificity (Sp) of the radial immunodiffusion (RID) assay, transmission infrared (TIR) spectroscopy, and digital Brix refractometer for the assessment of low-quality bovine colostrum in Atlantic Canada dairy herds. The secondary objective of the study was to describe the distribution of herd prevalence of low-quality colostrum. Colostrum quality of 591 samples from 42 commercial Holstein dairy herds in 4 Atlantic Canada provinces was assessed using RID, TIR spectroscopy, and digital Brix refractometer. The accuracy of all tests at different Brix value thresholds was estimated using Bayesian latent class models to obtain posterior estimates [medians and 95% Bayesian credibility intervals (95% BCI)] for each parameter. Using a threshold of <23% for digital Brix refractometer and <50 g/L for RID and TIR spectroscopy, median (95% BCI) Se estimates were 73.2 (68.4-77.7), 86.2 (80.6-91.0), and 91.9% (89.0-94.2), respectively. Median (95% BCI) Sp estimates were 85.2% (81.0-88.9) for digital Brix refractometer, 99.4% (97.0-100) for RID, and 90.7% (87.8-93.2) for TIR spectroscopy. Median (95% BCI) within-herd low-quality colostrum prevalence was estimated at 32.5% (27.9-37.4). In conclusion, using digital Brix refractometer at a Brix threshold of <23% could reduce feeding of low-quality colostrum to calves and improve colostrum and calf management practices in Atlantic Canada dairy herds. The TIR spectroscopy showed high Se in detection of low-quality colostrum. However, the RID assay, which is used as the reference test in several studies, showed limited Se for detection of low-quality colostrum.},
}
@article {pmid33606618,
year = {2021},
author = {Bates, M},
title = {A Step Closer to Mind Control for Everyday Life.},
journal = {IEEE pulse},
volume = {12},
number = {1},
pages = {16-18},
doi = {10.1109/MPULS.2021.3052589},
pmid = {33606618},
issn = {2154-2317},
mesh = {Artificial Limbs ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Exoskeleton Device ; Humans ; Nervous System Diseases/therapy ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) technology holds promise for providing functional support systems for people with neurological disorders and other disabilities. In experimental laboratory settings, BCIs have allowed patients to communicate with researchers and control external devices-all by simply imagining the actions of different body parts.},
}
@article {pmid33601356,
year = {2021},
author = {Yan, W and Du, C and Luo, D and Wu, Y and Duan, N and Zheng, X and Xu, G},
title = {Enhancing detection of steady-state visual evoked potentials using channel ensemble method.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abe7cf},
pmid = {33601356},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Photic Stimulation ; Recognition, Psychology ; },
abstract = {Objective.This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs).Approach.Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using thesoftmaxfunction. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient.Main results.Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain-computer interface (BCI) systems.Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.},
}
@article {pmid33601354,
year = {2021},
author = {Catrambone, V and Averta, G and Bianchi, M and Valenza, G},
title = {Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates.},
journal = {Journal of neural engineering},
volume = {18},
number = {4},
pages = {},
doi = {10.1088/1741-2552/abe7b9},
pmid = {33601354},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography/methods ; Humans ; Movement/physiology ; Upper Extremity ; },
abstract = {Objective.Brain-computer interfaces (BCIs) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes.Approach.We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects.Main results.The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy.Significance.Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.},
}
@article {pmid33597855,
year = {2021},
author = {Ahn, S and Jun, SC},
title = {Corrigendum: Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces - Current Limitations and Future Directions.},
journal = {Frontiers in human neuroscience},
volume = {15},
number = {},
pages = {645869},
pmid = {33597855},
issn = {1662-5161},
abstract = {[This corrects the article DOI: 10.3389/fnhum.2017.00503.].},
}
@article {pmid33596960,
year = {2021},
author = {Shu, T and Huang, SS and Shallal, C and Herr, HM},
title = {Restoration of bilateral motor coordination from preserved agonist-antagonist coupling in amputation musculature.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {18},
number = {1},
pages = {38},
pmid = {33596960},
issn = {1743-0003},
support = {R01 HD097135/HD/NICHD NIH HHS/United States ; 1R01HD097135/NH/NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Amputation, Surgical ; *Artificial Limbs ; Biomechanical Phenomena ; Electromyography/methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Models, Neurological ; Movement/physiology ; Muscle, Skeletal/*physiopathology ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {BACKGROUND: Neuroprosthetic devices controlled by persons with standard limb amputation often lack the dexterity of the physiological limb due to limitations of both the user's ability to output accurate control signals and the control system's ability to formulate dynamic trajectories from those signals. To restore full limb functionality to persons with amputation, it is necessary to first deduce and quantify the motor performance of the missing limbs, then meet these performance requirements through direct, volitional control of neuroprosthetic devices.<br><br>METHODS: We develop a neuromuscular modeling and optimization paradigm for the agonist-antagonist myoneural interface, a novel tissue architecture and neural interface for the control of myoelectric prostheses, that enables it to generate virtual joint trajectories coordinated with an intact biological joint at full physiologically-relevant movement bandwidth. In this investigation, a baseline of performance is first established in a population of non-amputee control subjects ([Formula: see text]). Then, a neuromuscular modeling and optimization technique is advanced that allows unilateral AMI amputation subjects ([Formula: see text]) and standard amputation subjects ([Formula: see text]) to generate virtual subtalar prosthetic joint kinematics using measured surface electromyography (sEMG) signals generated by musculature within the affected leg residuum.<br><br>RESULTS: Using their optimized neuromuscular subtalar models under blindfolded conditions with only proprioceptive feedback, AMI amputation subjects demonstrate bilateral subtalar coordination accuracy not significantly different from that of the non-amputee control group (Kolmogorov-Smirnov test, [Formula: see text]) while standard amputation subjects demonstrate significantly poorer performance (Kolmogorov-Smirnov test, [Formula: see text]).<br><br>CONCLUSIONS: These results suggest that the absence of an intact biological joint does not necessarily remove the ability to produce neurophysical signals with sufficient information to reconstruct physiological movements. Further, the seamless manner in which virtual and intact biological joints are shown to coordinate reinforces the theory that desired movement trajectories are mentally formulated in an abstract task space which does not depend on physical limb configurations.},
}
@article {pmid33596460,
year = {2021},
author = {Ohgami, Y and Kotani, Y and Yoshida, N and Kunimatsu, A and Kiryu, S and Inoue, Y},
title = {Voice, rhythm, and beep stimuli differently affect the right hemisphere preponderance and components of stimulus-preceding negativity.},
journal = {Biological psychology},
volume = {160},
number = {},
pages = {108048},
doi = {10.1016/j.biopsycho.2021.108048},
pmid = {33596460},
issn = {1873-6246},
mesh = {*Attention ; Electroencephalography ; Feedback, Sensory ; Humans ; Language ; *Names ; },
abstract = {The present study investigated whether auditory stimuli with different contents affect right laterality and the components of stimulus-preceding negativity (SPN). A time-estimation task was performed under voice, rhythm, beep, and control conditions. The SPN interval during which participants anticipated the stimulus was divided into quarters to define early and late SPNs. Early and late components of SPN were also extracted using a principal component analysis. The anticipation of voice sounds enhanced the early SPN and the early component, which reflected the anticipation of language processing. Beep sounds elicited the right hemisphere preponderance of the early component, the early SPN, and the late SPN. The rhythmic sound tended to attenuate the amplitude compared with the two other stimuli. These findings further substantiate the existence of separate early and late components of the SPN. In addition, they suggest that the early component reflects selective anticipatory attention toward differing types of auditory feedback.},
}
@article {pmid33594631,
year = {2021},
author = {Katyal, EA and Singla, R},
title = {EEG-based hybrid QWERTY mental speller with high information transfer rate.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {59},
number = {3},
pages = {633-661},
pmid = {33594631},
issn = {1741-0444},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials, Visual ; Humans ; Seizures ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller.<br><br>METHODS: The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy.<br><br>RESULTS: On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F).<br><br>COMPARISON: The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller.<br><br>CONCLUSION: QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.},
}
@article {pmid33594066,
year = {2021},
author = {Wei, H and Shi, R and Sun, L and Yu, H and Gong, J and Liu, C and Xu, Z and Ni, Y and Xu, J and Xu, W},
title = {Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics.},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {1068},
pmid = {33594066},
issn = {2041-1723},
mesh = {Dendrites/drug effects/physiology ; Density Functional Theory ; Diffusion ; Graphite/*pharmacology ; Ions ; Nerve Net/drug effects/physiology ; Neuronal Plasticity ; Neurons, Efferent/drug effects/*physiology ; Signal Transduction/drug effects ; Synapses/drug effects/*physiology ; Temperature ; },
abstract = {A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain-computer interfaces.},
}
@article {pmid33591923,
year = {2021},
author = {Gao, Z and Sun, X and Liu, M and Dang, W and Ma, C and Chen, G},
title = {Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {8},
pages = {2887-2894},
doi = {10.1109/JBHI.2021.3059686},
pmid = {33591923},
issn = {2168-2208},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; },
abstract = {Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.},
}
@article {pmid33589806,
year = {2021},
author = {Whalley, K},
title = {Neuroprosthetic control of blood pressure.},
journal = {Nature reviews. Neuroscience},
volume = {22},
number = {4},
pages = {193},
pmid = {33589806},
issn = {1471-0048},
mesh = {Blood Pressure ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex ; },
}
@article {pmid33587702,
year = {2021},
author = {Hong, X and Zheng, Q and Liu, L and Chen, P and Ma, K and Gao, Z and Zheng, Y},
title = {Dynamic Joint Domain Adaptation Network for Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {556-565},
doi = {10.1109/TNSRE.2021.3059166},
pmid = {33587702},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Learning ; Reproducibility of Results ; },
abstract = {Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to its convenience and reliability. The EEG-based BCI applications are majorly limited by the time-consuming calibration procedure for discriminative feature representation and classification. Existing EEG classification methods either heavily depend on the handcrafted features or require adequate annotated samples at each session for calibration. To address these issues, we propose a novel dynamic joint domain adaptation network based on adversarial learning strategy to learn domain-invariant feature representation, and thus improve EEG classification performance in the target domain by leveraging useful information from the source session. Specifically, we explore the global discriminator to align the marginal distribution across domains, and the local discriminator to reduce the conditional distribution discrepancy between sub-domains via conditioning on deep representation as well as the predicted labels from the classifier. In addition, we further investigate a dynamic adversarial factor to adaptively estimate the relative importance of alignment between the marginal and conditional distributions. To evaluate the efficacy of our method, extensive experiments are conducted on two public EEG datasets, namely, Datasets IIa and IIb of BCI Competition IV. The experimental results demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.},
}
@article {pmid33586668,
year = {2021},
author = {Levi-Aharoni, H and Tishby, N},
title = {The value-complexity trade-off for reinforcement learning based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {066011},
doi = {10.1088/1741-2552/abc8d8},
pmid = {33586668},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Learning ; Reinforcement, Psychology ; Reproducibility of Results ; },
abstract = {OBJECTIVE: One of the recent developments in the field of brain-computer interfaces (BCI) is the reinforcement learning (RL) based BCI paradigm, which uses neural error responses as the reward feedback on the agent's action. While having several advantages over motor imagery based BCI, the reliability of RL-BCI is critically dependent on the decoding accuracy of noisy neural error signals. A principled method is needed to optimally handle this inherent noise under general conditions.<br><br>APPROACH: By determining a trade-off between the expected value and the informational cost of policies, the info-RL (IRL) algorithm provides optimal low-complexity policies, which are robust under noisy reward conditions and achieve the maximal obtainable value. In this work we utilize the IRL algorithm to characterize the maximal obtainable value under different noise levels, which in turn is used to extract the optimal robust policy for each noise level.<br><br>MAIN RESULTS: Our simulation results of a setting with Gaussian noise show that the complexity level of the optimal policy is dependent on the reward magnitude but not on the reward variance, whereas the variance determines whether a lower complexity solution is favorable or not. We show how this analysis can be utilized to select optimal robust policies for an RL-BCI and demonstrate its use on EEG data.<br><br>SIGNIFICANCE: We propose here a principled method to determine the optimal policy complexity of an RL problem with a noisy reward, which we argue is particularly useful for RL-based BCI paradigms. This framework may be used to minimize initial training time and allow for a more dynamic and robust shared control between the agent and the operator under different conditions.},
}
@article {pmid33584182,
year = {2020},
author = {Cao, L and Chen, S and Jia, J and Fan, C and Wang, H and Xu, Z},
title = {An Inter- and Intra-Subject Transfer Calibration Scheme for Improving Feedback Performance of Sensorimotor Rhythm-Based BCI Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {629572},
pmid = {33584182},
issn = {1662-4548},
abstract = {The Brain Computer Interface (BCI) system is a typical neurophysiological application which helps paralyzed patients with human-machine communication. Stroke patients with motor disabilities are able to perform BCI tasks for clinical rehabilitation. This paper proposes an effective scheme of transfer calibration for BCI rehabilitation. The inter- and intra-subject transfer learning approaches can improve the low-precision classification performance for experimental feedback. The results imply that the systematical scheme is positive in increasing the confidence of voluntary training for stroke patients. In addition, it also reduces the time consumption of classifier calibration.},
}
@article {pmid33582274,
year = {2021},
author = {De Sousa, C and Gaillard, C and Di Bello, F and Ben Hadj Hassen, S and Ben Hamed, S},
title = {Behavioral validation of novel high resolution attention decoding method from multi-units &amp; local field potentials.},
journal = {NeuroImage},
volume = {231},
number = {},
pages = {117853},
doi = {10.1016/j.neuroimage.2021.117853},
pmid = {33582274},
issn = {1095-9572},
mesh = {Animals ; Attention/*physiology ; Macaca mulatta ; *Machine Learning ; Male ; Photic Stimulation/*methods ; Prefrontal Cortex/*physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; },
abstract = {The ability to access brain information in real-time is crucial both for a better understanding of cognitive functions and for the development of therapeutic applications based on brain-machine interfaces. Great success has been achieved in the field of neural motor prosthesis. Progress is still needed in the real-time decoding of higher-order cognitive processes such as covert attention. Recently, we showed that we can track the location of the attentional spotlight using classification methods applied to prefrontal multi-unit activity (MUA) in the non-human primates. Importantly, we demonstrated that the decoded (x,y) attentional spotlight parametrically correlates with the behavior of the monkeys thus validating our decoding of attention. We also demonstrate that this spotlight is extremely dynamic. Here, in order to get closer to non-invasive decoding applications, we extend our previous work to local field potential signals (LFP). Specifically, we achieve, for the first time, high decoding accuracy of the (x,y) location of the attentional spotlight from prefrontal LFP signals, to a degree comparable to that achieved from MUA signals, and we show that this LFP content is predictive of behavior. This LFP attention-related information is maximal in the gamma band (30-250 Hz), peaking between 60 to 120 Hz. In addition, we introduce a novel two-step decoding procedure based on the labelling of maximally attention-informative trials during the decoding procedure. This procedure strongly improves the correlation between our real-time MUA and LFP based decoding and behavioral performance, thus further refining the functional relevance of this real-time decoding of the (x,y) locus of attention. This improvement is more marked for LFP signals than for MUA signals. Overall, this study demonstrates that the attentional spotlight can be accessed from LFP frequency content, in real-time, and can be used to drive high-information content cognitive brain-machine interfaces for the development of new therapeutic strategies.},
}
@article {pmid33580093,
year = {2021},
author = {Yang, YJ and Jeon, EJ and Kim, JS and Chung, CK},
title = {Characterization of kinesthetic motor imagery compared with visual motor imageries.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {3751},
pmid = {33580093},
issn = {2045-2322},
abstract = {Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.},
}
@article {pmid33579253,
year = {2021},
author = {Feng, C and Li, R and Shamim, AA and Ullah, MB and Li, M and Dev, R and Wang, Y and Zhao, T and Liao, J and Du, Z and Ling, Y and Lai, Y and Hao, Y},
title = {High-resolution mapping of reproductive tract infections among women of childbearing age in Bangladesh: a spatial-temporal analysis of the demographic and health survey.},
journal = {BMC public health},
volume = {21},
number = {1},
pages = {342},
pmid = {33579253},
issn = {1471-2458},
support = {81773543//National Natural Science Foundation of China/ ; 81703320//National Natural Science Foundation of China/ ; 13-133//China Medical Board/ ; },
mesh = {Bangladesh/epidemiology ; Bayes Theorem ; Female ; Humans ; *Infections ; Prevalence ; *Reproductive Tract Infections ; },
abstract = {BACKGROUND: Reproductive tract infections (RTIs) have become major but silent public health problems devastating women's lives in Bangladesh. Accurately and precisely identifying high-risk areas of RTIs through high-resolution risk maps is meaningful for resource-limited settings.<br><br>METHODS: We obtained data reported with RTI symptoms by women of childbearing age in the years 2007, 2011 and 2014 from Bangladesh Demographic and Health Survey. High-spatial Environmental, socio-economic and demographic layers were downloaded from different open-access data sources. We applied Bayesian spatial-temporal models to identify important influencing factors and to estimate the infection risk at 5&#x2009;km spatial resolution across survey years in Bangladesh.<br><br>RESULTS: We estimated that in Bangladesh, there were approximate 11.1% (95% Bayesian credible interval, BCI: 10.5-11.7%), 13.9% (95% BCI: 13.3-14.5%) and 13.4% (95% BCI: 12.8-14.0%) of women of childbearing age reported with RTI symptoms in 2007, 2011 and 2014, respectively. The risk of most areas shows an obvious increase from 2007 to 2011, then became stable between 2011 and 2014. High risk areas were identified in the southern coastal areas, the western Rajshahi Division, the middle of Khulna Division, and the southwestern Chittagong Division in 2014. The prevalence of Rajshahi and Nawabganj District were increasing during all the survey years.<br><br>CONCLUSION: The high-resolution risk maps of RTIs we produced can guide the control strategies targeted to priority areas cost-effectively. More than one eighth of women of childbearing age reported symptoms suggesting RTIs and the risk of RTIs varies in different geographical area, urging the government to pay more attention to the worrying situation of female RTIs in the country.},
}
@article {pmid33579017,
year = {2021},
author = {Lee, S and Lee, D and Gil, H and Oakley, I and Cho, YS and Kim, SP},
title = {Eye Fixation-Related Potentials during Visual Search on Acquaintance and Newly-Learned Faces.},
journal = {Brain sciences},
volume = {11},
number = {2},
pages = {},
pmid = {33579017},
issn = {2076-3425},
support = {2017-0-00432//Institute for Information and Communications Technology Promotion/ ; 2018R1D1A1A09082772//National Research Foundation of Korea/ ; 1.200038.01//Ulsan National Institute of Science and Technology/ ; },
abstract = {Searching familiar faces in the crowd may involve stimulus-driven attention by emotional significance, together with goal-directed attention due to task-relevant needs. The present study investigated the effect of familiarity on attentional processes by exploring eye fixation-related potentials (EFRPs) and eye gazes when humans searched for, among other distracting faces, either an acquaintance's face or a newly-learned face. Task performance and gaze behavior were indistinguishable for identifying either faces. However, from the EFRP analysis, after a P300 component for successful search of target faces, we found greater deflections of right parietal late positive potentials in response to newly-learned faces than acquaintance's faces, indicating more involvement of goal-directed attention in processing newly-learned faces. In addition, we found greater occipital negativity elicited by acquaintance's faces, reflecting emotional responses to significant stimuli. These results may suggest that finding a familiar face in the crowd would involve lower goal-directed attention and elicit more emotional responses.},
}
@article {pmid33578835,
year = {2021},
author = {Shen, F and Peng, Y and Kong, W and Dai, G},
title = {Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {4},
pages = {},
pmid = {33578835},
issn = {1424-8220},
support = {2017YFE0118200//National Key Research and Development Program of China/ ; 2017YFE0116800//National Key Research and Development Program of China/ ; 61971173//National Natural Science Foundation of China/ ; 61671193//National Natural Science Foundation of China/ ; U1909202//National Natural Science Foundation of China/ ; 2018C04012//Science and Technology Program of Zhejiang Province/ ; LY21F030005//Zhejiang Provincial Natural Science Foundation of China/ ; GK209907299001-008//Fundamental Research Funds for the Provincial Universities of Zhejiang/ ; GDSC202015//Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University/ ; },
mesh = {*Algorithms ; Brain ; *Electroencephalography ; Emotions ; Humans ; *Machine Learning ; },
abstract = {Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the "SEED IV" data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the "DEAP" data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.},
}
@article {pmid33578754,
year = {2021},
author = {Zhu, F and Jiang, L and Dong, G and Gao, X and Wang, Y},
title = {An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {4},
pages = {},
pmid = {33578754},
issn = {1424-8220},
support = {2017YFA0205903//National Key Research and Development Plan of China/ ; 61671424, 61335010, and 61634006//National Natural Science Foundation of China/ ; XDB32040200//Strategic Priority Research Program of Chinese Academy of Science/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Wearable Electronic Devices ; },
abstract = {Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.},
}
@article {pmid33575403,
year = {2021},
author = {Long, NX and Ngoc, NB and Phung, TT and Linh, DTD and Anh, TN and Hung, NV and Thang, NT and Lan, NTM and Trang, VT and Thuong, NH and Van Hieu, N and Van Minh, H},
title = {Coping strategies and social support among caregivers of patients with cancer: a cross-sectional study in Vietnam.},
journal = {AIMS public health},
volume = {8},
number = {1},
pages = {1-14},
pmid = {33575403},
issn = {2327-8994},
abstract = {Research on coping strategies and social support among Vietnamese cancer caregivers remains limited. In this study, we aim to examine the relationships between types of coping strategies utilized and social support among cancer caregivers. This was a cross-sectional study conducted in three main cancer hospitals in the Northern, Central and Southern regions of Vietnam. The 28-item Brief COPE Inventory (BCI) Scale and the Multidimensional Scale of Perceived Social Support (MSPSS) were utilized. Descriptive statistics and multivariate linear regression were performed. Active coping, acceptance and positive reframing were the most used coping strategies among participants, while substance use was the least commonly used. Level of social support was positively correlated with the utilization of coping mechanisms. Receiving high social support and utilizing positive coping strategies enables caregivers to mitigate their caregiving burden, control the situation and enhance their own quality of life.},
}
@article {pmid33575195,
year = {2020},
author = {Nataraj, SK and Paulraj, MP and Yaacob, SB and Adom, AHB},
title = {Thought-Actuated Wheelchair Navigation with Communication Assistance Using Statistical Cross-Correlation-Based Features and Extreme Learning Machine.},
journal = {Journal of medical signals and sensors},
volume = {10},
number = {4},
pages = {228-238},
pmid = {33575195},
issn = {2228-7477},
abstract = {BACKGROUND: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain-computer interface, i.e., thought-controlled wheelchair navigation system with communication assistance.<br><br>METHOD: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.<br><br>RESULTS AND CONCLUSION: The results of the classification networks were compared with each set of features, and the results indicated that &#x3bc; (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.},
}
@article {pmid33571431,
year = {2021},
author = {Zhuang, Y and Xu, P and Mao, C and Wang, L and Krumm, B and Zhou, XE and Huang, S and Liu, H and Cheng, X and Huang, XP and Shen, DD and Xu, T and Liu, YF and Wang, Y and Guo, J and Jiang, Y and Jiang, H and Melcher, K and Roth, BL and Zhang, Y and Zhang, C and Xu, HE},
title = {Structural insights into the human D1 and D2 dopamine receptor signaling complexes.},
journal = {Cell},
volume = {184},
number = {4},
pages = {931-942.e18},
pmid = {33571431},
issn = {1097-4172},
support = {R01 MH112205/MH/NIMH NIH HHS/United States ; R35 GM128641/GM/NIGMS NIH HHS/United States ; },
mesh = {2,3,4,5-Tetrahydro-7,8-dihydroxy-1-phenyl-1H-3-benzazepine/analogs &amp; derivatives/pharmacology ; Amino Acid Sequence ; Conserved Sequence ; Cryoelectron Microscopy ; Cyclic AMP/metabolism ; GTP-Binding Proteins/metabolism ; HEK293 Cells ; Humans ; Ligands ; Models, Molecular ; Mutant Proteins/chemistry/metabolism ; Receptors, Adrenergic, beta-2/metabolism ; Receptors, Dopamine D1/*chemistry/*metabolism/ultrastructure ; Receptors, Dopamine D2/*chemistry/*metabolism/ultrastructure ; *Signal Transduction ; Structural Homology, Protein ; },
abstract = {The D1- and D2-dopamine receptors (D1R and D2R), which signal through Gs and Gi, respectively, represent the principal stimulatory and inhibitory dopamine receptors in the central nervous system. D1R and D2R also represent the main therapeutic targets for Parkinson's disease, schizophrenia, and many other neuropsychiatric disorders, and insight into their signaling is essential for understanding both therapeutic and side effects of dopaminergic drugs. Here, we report four cryoelectron microscopy (cryo-EM) structures of D1R-Gs and D2R-Gi signaling complexes with selective and non-selective dopamine agonists, including two currently used anti-Parkinson's disease drugs, apomorphine and bromocriptine. These structures, together with mutagenesis studies, reveal the conserved binding mode of dopamine agonists, the unique pocket topology underlying ligand selectivity, the conformational changes in receptor activation, and potential structural determinants for G protein-coupling selectivity. These results provide both a molecular understanding of dopamine signaling and multiple structural templates for drug design targeting the dopaminergic system.},
}
@article {pmid33568981,
year = {2020},
author = {Saga, N and Doi, A and Oda, T and Kudoh, SN},
title = {Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {607706},
pmid = {33568981},
issn = {1662-5218},
abstract = {Non-invasive brain-computer interfaces (BCIs) based on common electroencephalography (EEG) are limited to specific instrumentation sites and frequency bands. These BCI induce certain targeted electroencephalographic features of cognitive tasks, identify them, and determine BCI's performance, and use machine-learning to extract these electroencephalographic features, which makes them enormously time-consuming. In addition, there is a problem in which the neurorehabilitation using BCI cannot receive ambulatory and immediate rehabilitation training. Therefore, we proposed an exploratory BCI that did not limit the targeted electroencephalographic features. This system did not determine the electroencephalographic features in advance, determined the frequency bands and measurement sites appropriate for detecting electroencephalographic features based on their target movements, measured the electroencephalogram, created each rule (template) with only large "High" or small "Low" electroencephalograms for arbitrarily determined thresholds (classification of cognitive tasks in the imaginary state of moving the feet by the size of the area constituted by the power spectrum of the EEG in each frequency band), and successfully detected the movement intention by detecting the electroencephalogram consistent with the rules during motor tasks using a fuzzy inference-based template matching method (FTM). However, the electroencephalographic features acquired by this BCI are not known, and their usefulness for patients with actual cerebral infarction is not known. Therefore, this study clarifies the electroencephalographic features captured by the heuristic BCI, as well as clarifies the effectiveness and challenges of this system by its application to patients with cerebral infarction.},
}
@article {pmid33568979,
year = {2020},
author = {Khan, H and Naseer, N and Yazidi, A and Eide, PK and Hassan, HW and Mirtaheri, P},
title = {Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {613254},
pmid = {33568979},
issn = {1662-5161},
abstract = {Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain-computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.},
}
@article {pmid33568973,
year = {2021},
author = {Zhang, W and Song, A and Zeng, H and Xu, B and Miao, M},
title = {Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance.},
journal = {Frontiers in neuroscience},
volume = {15},
number = {},
pages = {638638},
pmid = {33568973},
issn = {1662-4548},
abstract = {The motor imagery (MI) paradigm has been wildly used in brain-computer interface (BCI), but the difficulties in performing imagery tasks limit its application. Mechanical vibration stimulus has been increasingly used to enhance the MI performance, but its improvement consistence is still under debate. To develop more effective vibration stimulus methods for consistently enhancing MI, this study proposes an EEG phase-dependent closed-loop mechanical vibration stimulation method. The subject's index finger of the non-dominant hand was given 4 different vibration stimulation conditions (i.e., continuous open-loop vibration stimulus, two different phase-dependent closed-loop vibration stimuli and no stimulus) when performing two tasks of imagining movement and rest of the index finger from his/her dominant hand. We compared MI performance and brain oscillatory patterns under different conditions to verify the effectiveness of this method. The subjects performed 80 trials of each type in a random order, and the average phase-lock value of closed-loop stimulus conditions was 0.71. It was found that the closed-loop vibration stimulus applied in the falling phase helped the subjects to produce stronger event-related desynchronization (ERD) and sustain longer. Moreover, the classification accuracy was improved by about 9% compared with MI without any vibration stimulation (p = 0.012, paired t-test). This method helps to modulate the mu rhythm and make subjects more concentrated on the imagery and without negative enhancement during rest tasks, ultimately improves MI-based BCI performance. Participants reported that the tactile fatigue under closed-loop stimulation conditions was significantly less than continuous stimulation. This novel method is an improvement to the traditional vibration stimulation enhancement research and helps to make stimulation more precise and efficient.},
}
@article {pmid33564280,
year = {2021},
author = {Chen, C and Yu, X and Belkacem, AN and Lu, L and Li, P and Zhang, Z and Wang, X and Tan, W and Gao, Q and Shin, D and Wang, C and Sha, S and Zhao, X and Ming, D},
title = {EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.},
journal = {Journal of medical and biological engineering},
volume = {41},
number = {2},
pages = {155-164},
pmid = {33564280},
issn = {1609-0985},
abstract = {PURPOSE: Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals.<br><br>METHODS: We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups.<br><br>RESULTS: After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48&#xa0;&#xb1;&#xa0;&#x2009;&#x2009;1.20% and 88.60&#x2009;&#x2009;&#xa0;&#xb1;&#xa0;&#x2009;&#x2009;1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31&#xa0;&#xb1;&#xa0;&#x2009;&#x2009;1.97% and for anxiety subjects is 87.18&#xa0;&#xb1;&#xa0;&#x2009;&#x2009;3.51%.<br><br>CONCLUSIONS: The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.},
}
@article {pmid33563763,
year = {2021},
author = {Wang, J and Li, J and Yang, Q and Xie, YK and Wen, YL and Xu, ZZ and Li, Y and Xu, T and Wu, ZY and Duan, S and Xu, H},
title = {Basal forebrain mediates prosocial behavior via disinhibition of midbrain dopamine neurons.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {118},
number = {7},
pages = {},
pmid = {33563763},
issn = {1091-6490},
mesh = {Animals ; Dopaminergic Neurons/metabolism/*physiology ; GABAergic Neurons/metabolism/physiology ; Male ; Mice ; Neural Inhibition ; Prosencephalon/cytology/*physiology ; Reward ; *Social Behavior ; Somatostatin/genetics/metabolism ; Ventral Tegmental Area/cytology/*physiology ; },
abstract = {Sociability is fundamental for our daily life and is compromised in major neuropsychiatric disorders. However, the neuronal circuit mechanisms underlying prosocial behavior are still elusive. Here we identify a causal role of the basal forebrain (BF) in the control of prosocial behavior via inhibitory projections that disinhibit the midbrain ventral tegmental area (VTA) dopamine (DA) neurons. Specifically, BF somatostatin-positive (SST) inhibitory neurons were robustly activated during social interaction. Optogenetic inhibition of these neurons in BF or their axon terminals in the VTA largely abolished social preference. Electrophysiological examinations further revealed that SST neurons predominantly targeted VTA GABA neurons rather than DA neurons. Consistently, optical inhibition of SST neuron axon terminals in the VTA decreased DA release in the nucleus accumbens during social interaction, confirming a disinhibitory action. These data reveal a previously unappreciated function of the BF in prosocial behavior through a disinhibitory circuitry connected to the brain's reward system.},
}
@article {pmid33562814,
year = {2021},
author = {Valenti, A and Barsotti, M and Bacciu, D and Ascari, L},
title = {A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {8},
number = {2},
pages = {},
pmid = {33562814},
issn = {2306-5354},
support = {GA n. 871385//H2020 TEACHING/ ; },
abstract = {Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user's movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects' movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition.},
}
@article {pmid33562623,
year = {2021},
author = {Liu, T and Yang, D},
title = {A Densely Connected Multi-Branch 3D Convolutional Neural Network for Motor Imagery EEG Decoding.},
journal = {Brain sciences},
volume = {11},
number = {2},
pages = {},
pmid = {33562623},
issn = {2076-3425},
support = {2572016CB15//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Motor imagery (MI) is a classical method of brain-computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learning methods are being focused on in finding an easy-to-use EEG representation method that can preserve both temporal information and spatial information. To further utilize the spatial and temporal features of EEG signals, an improved 3D representation of the EEG and a densely connected multi-branch 3D convolutional neural network (dense M3D CNN) for MI classification are introduced in this paper. Specifically, as compared to the original 3D representation, a new padding method is proposed to pad the points without electrodes with the mean of all the EEG signals. Based on this new 3D presentation, a densely connected multi-branch 3D CNN with a novel dense connectivity is proposed for extracting the EEG signal features. Experiments were carried out on the WAY-EEG-GAL and BCI competition IV 2a datasets to verify the performance of this proposed method. The experimental results show that the proposed framework achieves a state-of-the-art performance that significantly outperforms the multi-branch 3D CNN framework, with a 6.208% improvement in the average accuracy for the BCI competition IV 2a datasets and 6.281% improvement in the average accuracy for the WAY-EEG-GAL datasets, with a smaller standard deviation. The results also prove the effectiveness and robustness of the method, along with validating its use in MI-classification tasks.},
}
@article {pmid33562006,
year = {2021},
author = {Moreno Escobar, JJ and Morales Matamoros, O and Aguilar Del Villar, EY and Tejeida Padilla, R and Lina Reyes, I and Espinoza Zambrano, B and Luna Gómez, BD and Calderón Morfín, VH},
title = {Non-Parametric Evaluation Methods of the Brain Activity of a Bottlenose Dolphin during an Assisted Therapy.},
journal = {Animals : an open access journal from MDPI},
volume = {11},
number = {2},
pages = {},
pmid = {33562006},
issn = {2076-2615},
support = {20200638, 20200324, and 20202061//Comisi&#xf3;n de Operaci&#xf3;n y Fomento de Actividades Acad&#xe9;micas, Instituto Polit&#xe9;cnico Nacional/ ; },
abstract = {Dolphin-Assisted Therapies (DAT) are alternative therapies aimed to reduce anxiety levels, stress relief and physical benefits. This paper is focused on measuring and analyzing dolphins brain activity when DAT is taking place in order to identify if there is any differences in female dolphin's neuronal signal when it is interacting with control or intervention subjects, performing our research in Delfiniti, Ixtapa, Mexico facilities. We designed a wireless and portable electroencephalographic single-channel signal capture sensor to acquire and monitor the brain activity of a female bottle-nose dolphin. This EEG sensor was able to show that dolphin activity at rest is characterized by high spectral power at slow-frequencies bands. When the dolphin participated in DAT, a 23.53% increment in the 12-30 Hz frequency band was observed, but this only occurred for patients with some disease or disorder, given that 0.5-4 Hz band keeps it at 17.91% when there is a control patient. Regarding the fractal or Self-Affine Analysis, we found for all samples studied that at the beginning the dolphin's brain activity behaved as a self-affine fractal described by a power-law until the fluctuations of voltage reached the crossovers, and after the crossovers these fluctuations left this scaling behavior. Hence, our findings validate the hypothesis that the participation in a DAT of a Patient with a certain disease or disorder modifies the usual behavior of a female bottle-nose dolphin.},
}
@article {pmid33561080,
year = {2021},
author = {González-Zamorano, Y and Fernández-Carnero, J and Sánchez-Cuesta, FJ and Arroyo-Ferrer, A and Vourvopoulos, A and Figueiredo, P and Serrano, JI and Romero, JP},
title = {New Approaches Based on Non-Invasive Brain Stimulation and Mental Representation Techniques Targeting Pain in Parkinson's Disease Patients: Two Study Protocols for Two Randomized Controlled Trials.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33561080},
issn = {2076-3425},
abstract = {Pain is an under-reported but prevalent symptom in Parkinson's Disease (PD), impacting patients' quality of life. Both pain and PD conditions cause cortical excitability reduction and non-invasive brain stimulation. Mental representation techniques are thought to be able to counteract it, also resulting effectively in chronic pain conditions. We aim to conduct two independent studies in order to evaluate the efficacy of transcranial direct current stimulation (tDCS) and mental representation protocol in the management of pain in PD patients during the ON state: (1) tDCS over the Primary Motor Cortex (M1); and (2) Action Observation (AO) and Motor Imagery (MI) training through a Brain-Computer Interface (BCI) using Virtual Reality (AO + MI-BCI). Both studies will include 32 subjects in a longitudinal prospective parallel randomized controlled trial design under different blinding conditions. The main outcomes will be score changes in King's Parkinson's Disease Pain Scale, Brief Pain Inventory, Temporal Summation, Conditioned Pain Modulation, and Pain Pressure Threshold. Assessment will be performed pre-intervention, post-intervention, and 15 days post-intervention, in both ON and OFF states.},
}
@article {pmid33556021,
year = {2022},
author = {Zhang, Y and Zhou, T and Wu, W and Xie, H and Zhu, H and Zhou, G and Cichocki, A},
title = {Improving EEG Decoding via Clustering-Based Multitask Feature Learning.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {8},
pages = {3587-3597},
doi = {10.1109/TNNLS.2021.3053576},
pmid = {33556021},
issn = {2162-2388},
mesh = {*Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography/methods ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG data sets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.},
}
@article {pmid33556014,
year = {2021},
author = {Wang, H and Sun, Y and Wang, F and Cao, L and Zhou, W and Wang, Z and Chen, S},
title = {Cross-Subject Assistance: Inter- and Intra-Subject Maximal Correlation for Enhancing the Performance of SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {517-526},
doi = {10.1109/TNSRE.2021.3057938},
pmid = {33556014},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {OBJECTIVE: The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects.<br><br>METHODS: IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition.<br><br>RESULTS: The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA).<br><br>SIGNIFICANCE: The proposed method has great potential for developing high-speed BCIs.},
}
@article {pmid33555565,
year = {2021},
author = {Fan, Y and Zou, W and Liu, J and Al-Sheikh, U and Cheng, H and Duan, D and Du Chen, and Liu, S and Chen, L and Xu, J and Ruhomutally, F and Kang, L},
title = {Polymodal Functionality of C. elegans OLL Neurons in Mechanosensation and Thermosensation.},
journal = {Neuroscience bulletin},
volume = {37},
number = {5},
pages = {611-622},
pmid = {33555565},
issn = {1995-8218},
mesh = {Animals ; *Caenorhabditis elegans ; *Caenorhabditis elegans Proteins/genetics ; Mechanotransduction, Cellular ; Sensory Receptor Cells ; Touch ; },
abstract = {Sensory modalities are important for survival but the molecular mechanisms remain challenging due to the polymodal functionality of sensory neurons. Here, we report the C. elegans outer labial lateral (OLL) sensilla sensory neurons respond to touch and cold. Mechanosensation of OLL neurons resulted in cell-autonomous mechanically-evoked Ca[2+] transients and rapidly-adapting mechanoreceptor currents with a very short latency. Mechanotransduction of OLL neurons might be carried by a novel Na[+] conductance channel, which is insensitive to amiloride. The bona fide mechano-gated Na[+]-selective degenerin/epithelial Na[+] channels, TRP-4, TMC, and Piezo proteins are not involved in this mechanosensation. Interestingly, OLL neurons also mediated cold but not warm responses in a cell-autonomous manner. We further showed that the cold response of OLL neurons is not mediated by the cold receptor TRPA-1 or the temperature-sensitive glutamate receptor GLR-3. Thus, we propose the polymodal functionality of OLL neurons in mechanosensation and cold sensation.},
}
@article {pmid33553350,
year = {2021},
author = {Lei, Y and Xu, Y and Jing, P and Xiang, B and Che, K and Shen, J and Ning, M and Chen, Y and Huang, Y},
title = {The effects of TGF-β1 on staphylococcus epidermidis biofilm formation in a tree shrew biomaterial-centered infection model.},
journal = {Annals of translational medicine},
volume = {9},
number = {1},
pages = {57},
pmid = {33553350},
issn = {2305-5839},
abstract = {BACKGROUND: Transforming growth factor-β1 (TGF-β1) has a wide range of biological functions. It antagonizes lymphocyte response, inhibits pro-inflammatory cytokines, and serves as a signal to turn off the immune response and inflammatory response. To study the correlation between TGF-β1 and T helper (Th)1/Th2 cytokine levels in tree shrews, and to explore the effects of different levels of TGF-β1 on central venous catheter (CVC)-centered Staphylococcus epidermidis biofilm formation in tree shrews.<br><br>METHODS: Tree shrews were injected with different concentrations of TGF-&#x3b2;1, and venous blood was drawn after 48 h to measure the levels of Th1 and Th2 cytokines. A CVC was placed into the femoral vein, and TGF-&#x3b2;1 at different concentrations and PIA- (ATCC12228) and PIA+ (ATCC35984) standard strains of Staphylococcus epidermidis were injected into the tree shrews to establish a biomaterial-centered infection (BCI) model. After 72 h, the CVC was removed, and biofilm formation was detected using the API bacterial identification system, semi-quantitative biofilm formation assay, and scanning electron microscopy.<br><br>RESULTS: In the groups treated with TGF-&#x3b2;1 at different concentrations, the levels of Th1 cytokines interleukin-2 (IL-2), tumor necrosis factor (TNF), and interferon-&#x3b3; (IFN-&#x3b3;) were lower than those of normal group, while the levels of Th2 cytokines IL-6, IL-4 and IL-10 were higher than those of normal group. In the TGF-&#x3b2;1 groups at different concentrations, the positive rate of Staphylococcus epidermidis ATCC35984 biofilm formation was higher than that in non-TGF-&#x3b2;1 group, while there was no significant difference in the positive rate of Staphylococcus epidermidis ATCC12228 biofilm formation compared with that of the non-TGF-&#x3b2;1 group.<br><br>CONCLUSIONS: TGF-&#x3b2;1 causes the imbalance of Th1/Th2 cytokines and Th1/Th2 shift in tree shrews, leading to Th1 cell-led decline in cellular immune function. TGF-&#x3b2;1 promotes PIA+ Staphylococcus epidermidis biofilm formation in the tree shrew BCI model, but it has no significant influence on PIA-Staphylococcus epidermidis biofilm formation on the surface of CVCs.},
}
@article {pmid33551777,
year = {2020},
author = {Lau, CCY and Yuan, K and Wong, PCM and Chu, WCW and Leung, TW and Wong, WW and Tong, RKY},
title = {Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {611064},
pmid = {33551777},
issn = {1662-5161},
abstract = {Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F[2,26] = 6.367, p = 0.006) and ARAT (F[2,26] = 7.230, p = 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training. Clinical Trial Registration: This study has been registered at ClinicalTrials.gov with clinical trial registration number NCT02323061.},
}
@article {pmid33551719,
year = {2020},
author = {Jiang, X and Lopez, E and Stieger, JR and Greco, CM and He, B},
title = {Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {584971},
pmid = {33551719},
issn = {1662-4548},
support = {RF1 MH114233/MH/NIMH NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
abstract = {Sensorimotor rhythm (SMR)-based brain-computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency-that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one's mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.},
}
@article {pmid33551716,
year = {2020},
author = {Fathima, S and Kore, SK},
title = {Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems-A Review.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {546656},
pmid = {33551716},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG) is one of the common modalities of monitoring the mental activities. Owing to the non-invasive availability of this system, its applicability has seen remarkable developments beyond medical use-cases. One such use case is brain-computer interfaces (BCI). Such systems require the usage of high resolution-based multi-channel EEG devices so that the data collection spans multiple locations of the brain like the occipital, frontal, temporal, and so on. This results in huge data (with high sampling rates) and with multiple EEG channels with inherent artifacts. Several challenges exist in analyzing data of this nature, for instance, selecting the optimal number of EEG channels or deciding what best features to rely on for achieving better performance. The selection of these variables is complicated and requires a lot of domain knowledge and non-invasive EEG monitoring, which is not feasible always. Hence, optimization serves to be an easy to access tool in deriving such parameters. Considerable efforts in formulating these issues as an optimization problem have been laid. As a result, various multi-objective and constrained optimization functions have been developed in BCI that has achieved reliable outcomes in device control like neuro-prosthetic arms, application control, gaming, and so on. This paper makes an attempt to study the usage of optimization techniques in formulating the issues in BCI. The outcomes, challenges, and major observations of these approaches are discussed in detail.},
}
@article {pmid33548201,
year = {2021},
author = {Xu, P and Huang, S and Mao, C and Krumm, BE and Zhou, XE and Tan, Y and Huang, XP and Liu, Y and Shen, DD and Jiang, Y and Yu, X and Jiang, H and Melcher, K and Roth, BL and Cheng, X and Zhang, Y and Xu, HE},
title = {Structures of the human dopamine D3 receptor-Gi complexes.},
journal = {Molecular cell},
volume = {81},
number = {6},
pages = {1147-1159.e4},
doi = {10.1016/j.molcel.2021.01.003},
pmid = {33548201},
issn = {1097-4164},
mesh = {Benzopyrans/chemistry ; *Cryoelectron Microscopy ; GTP-Binding Protein alpha Subunits, Gi-Go/*chemistry ; HEK293 Cells ; Humans ; *Models, Molecular ; Multiprotein Complexes/chemistry/*ultrastructure ; Oxazines/chemistry ; Pramipexole/chemistry ; Protein Domains ; Receptors, Dopamine D3/*chemistry ; Structure-Activity Relationship ; },
abstract = {The dopamine system, including five dopamine receptors (D1R-D5R), plays essential roles in the central nervous system (CNS), and ligands that activate dopamine receptors have been used to treat many neuropsychiatric disorders. Here, we report two cryo-EM structures of human D3R in complex with an inhibitory G protein and bound to the D3R-selective agonists PD128907 and pramipexole, the latter of which is used to treat patients with Parkinson's disease. The structures reveal agonist binding modes distinct from the antagonist-bound D3R structure and conformational signatures for ligand-induced receptor activation. Mutagenesis and homology modeling illuminate determinants of ligand specificity across dopamine receptors and the mechanisms for Gi protein coupling. Collectively our work reveals the basis of agonist binding and ligand-induced receptor activation and provides structural templates for designing specific ligands to treat CNS diseases targeting the dopaminergic system.},
}
@article {pmid33548174,
year = {2021},
author = {He, X and Li, J and Zhou, G and Yang, J and McKenzie, S and Li, Y and Li, W and Yu, J and Wang, Y and Qu, J and Wu, Z and Hu, H and Duan, S and Ma, H},
title = {Gating of hippocampal rhythms and memory by synaptic plasticity in inhibitory interneurons.},
journal = {Neuron},
volume = {109},
number = {6},
pages = {1013-1028.e9},
pmid = {33548174},
issn = {1097-4199},
support = {K99 MH118423/MH/NIMH NIH HHS/United States ; R00 MH118423/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Calcium-Calmodulin-Dependent Protein Kinase Type 2/*metabolism ; Hippocampus/*physiology ; Interneurons/*physiology ; Learning/*physiology ; Mice ; Mice, Inbred C57BL ; Neuronal Plasticity/*physiology ; },
abstract = {Mental experiences can become long-term memories if the hippocampal activity patterns that encode them are broadcast during network oscillations. The activity of inhibitory neurons is essential for generating these neural oscillations, but molecular control of this dynamic process during learning remains unknown. Here, we show that hippocampal oscillatory strength positively correlates with excitatory monosynaptic drive onto inhibitory neurons (E→I) in freely behaving mice. To establish a causal relationship between them, we identified γCaMKII as the long-sought mediator of long-term potentiation for E→I synapses (LTPE→I), which enabled the genetic manipulation of experience-dependent E→I synaptic input/plasticity. Deleting γCaMKII in parvalbumin interneurons selectively eliminated LTPE→I and disrupted experience-driven strengthening in theta and gamma rhythmicity. Behaviorally, this manipulation impaired long-term memory, for which the kinase activity of γCaMKII was required. Taken together, our data suggest that E→I synaptic plasticity, exemplified by LTPE→I, plays a gatekeeping role in tuning experience-dependent brain rhythms and mnemonic function.},
}
@article {pmid33545691,
year = {2021},
author = {Yang, L and Song, Y and Ma, K and Su, E and Xie, L},
title = {A novel motor imagery EEG decoding method based on feature separation.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abe39b},
pmid = {33545691},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagination ; Research Design ; },
abstract = {Objective. Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods.Approach. To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features.Main results. To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets.Significance. Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.},
}
@article {pmid33545580,
year = {2021},
author = {Soekadar, SR and Kohl, SH and Mihara, M and von Lühmann, A},
title = {Optical brain imaging and its application to neurofeedback.},
journal = {NeuroImage. Clinical},
volume = {30},
number = {},
pages = {102577},
pmid = {33545580},
issn = {2213-1582},
mesh = {Brain/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; *Neurofeedback ; Neuroimaging ; Spectroscopy, Near-Infrared ; },
abstract = {Besides passive recording of brain electric or magnetic activity, also non-ionizing electromagnetic or optical radiation can be used for real-time brain imaging. Here, changes in the radiation's absorption or scattering allow for continuous in vivo assessment of regional neurometabolic and neurovascular activity. Besides magnetic resonance imaging (MRI), over the last years, also functional near-infrared spectroscopy (fNIRS) was successfully established in real-time metabolic brain imaging. In contrast to MRI, fNIRS is portable and can be applied at bedside or in everyday life environments, e.g., to restore communication and movement. Here we provide a comprehensive overview of the history and state-of-the-art of real-time optical brain imaging with a special emphasis on its clinical use towards neurofeedback and brain-computer interface (BCI) applications. Besides pointing to the most critical challenges in clinical use, also novel approaches that combine real-time optical neuroimaging with other recording modalities (e.g. electro- or magnetoencephalography) are described, and their use in the context of neuroergonomics, neuroenhancement or neuroadaptive systems discussed.},
}
@article {pmid33544932,
year = {2021},
author = {Naoi, Y and Tsunashima, R and Shimazu, K and Noguchi, S},
title = {The multigene classifiers 95GC/42GC/155GC for precision medicine in ER-positive HER2-negative early breast cancer.},
journal = {Cancer science},
volume = {112},
number = {4},
pages = {1369-1375},
pmid = {33544932},
issn = {1349-7006},
mesh = {Breast Neoplasms/*genetics/pathology ; Female ; Gene Expression Regulation, Neoplastic/genetics ; Humans ; Precision Medicine/methods ; Prognosis ; RNA, Messenger/genetics ; Receptor, ErbB-2/*genetics ; Receptors, Estrogen/*genetics ; Receptors, Progesterone/genetics ; },
abstract = {In clinical decision-making, to decide the indication for adjuvant chemotherapy for estrogen receptor-positive (ER+), human epidermal growth factor receptor-2-negative (HER2-), and node-negative (n0) breast cancer patients, the accurate estimation of recurrence risk is essential. Unfortunately, conventional prognostic factors, such as tumor size, histological grade and ER, progesterone receptor (PR), and HER2 status as well as Ki67 index, are not sufficiently accurate for such estimation. Therefore, several multigene assays (MGAs) based on the mRNA expression analysis of multiple genes in tumor tissue have been developed to better predict patient prognosis. These assays include Oncotype DX, MammaPrint, PAM50, GGI, EndoPredict, and BCI. We developed Curebest™ 95-Gene Classifier Breast (95GC) classifier, which is unique in that mRNA expression data of all 20 000 human genes are secondarily obtainable, as the 95GC assay is performed using Affymetrix microarray. This can capture mRNA expression of not only 95 genes but also every gene at once, and such gene expression data can be utilized by the other MGAs that we have developed, such as the 155GC, which is used for the prognostic prediction of ER+/HER2- breast cancer patients treated with neoadjuvant chemotherapy. We also developed the 42GC for predicting late recurrence in ER+/HER2- breast cancer patients. In this mini-review, our recent attempt at the development of various MGAs, which is expected to facilitate the implementation of precision medicine in ER+/HER2- breast cancer patients, is presented with a special emphasis on 95GC.},
}
@article {pmid33544700,
year = {2021},
author = {Feulner, B and Clopath, C},
title = {Neural manifold under plasticity in a goal driven learning behaviour.},
journal = {PLoS computational biology},
volume = {17},
number = {2},
pages = {e1008621},
pmid = {33544700},
issn = {1553-7358},
support = {/WT_/Wellcome Trust/United Kingdom ; BB/N013956/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; BB/N019008/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; 200790/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Adaptation, Physiological ; Animals ; Behavior, Animal ; Brain-Computer Interfaces ; Computer Simulation ; Feedback ; Haplorhini ; *Learning ; *Models, Neurological ; Motivation ; Motor Cortex/*physiology ; Neuronal Plasticity ; Neurons ; Normal Distribution ; Principal Component Analysis ; },
abstract = {Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.},
}
@article {pmid33543823,
year = {2021},
author = {Zheng, Y and Chen, J and Wu, C and Gong, W and Si, K},
title = {Adaptive optics for structured illumination microscopy based on deep learning.},
journal = {Cytometry. Part A : the journal of the International Society for Analytical Cytology},
volume = {99},
number = {6},
pages = {622-631},
doi = {10.1002/cyto.a.24319},
pmid = {33543823},
issn = {1552-4930},
mesh = {*Deep Learning ; Image Processing, Computer-Assisted ; Lighting ; *Microscopy ; Optics and Photonics ; },
abstract = {Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from optical aberrations, leading to a serious deterioration in resolution. Therefore, it is necessary to reconstruct structured illumination patterns with high quality and efficiency in deep tissue imaging. Here we demonstrate an adaptive optics (AO) correction method based on deep learning in wide-field SIM imaging system. The mapping between the coefficients of the first 15 Zernike modes and their corresponding distorted patterns is established to train the convolution neural network (CNN). The results show that the optimized CNN can predict the aberration phase within ~10.1 ms with a personal computer. The correlation index between the aberration phases and their corresponding predicted aberration phase is up to 0.9986. This method is highly robust and effective for patterns with various spatial densities and illumination conditions and able to effectively correct the imaging distortion caused by optical aberration in SIM system.},
}
@article {pmid33540387,
year = {2021},
author = {Luo, J and Shi, W and Lu, N and Wang, J and Chen, H and Wang, Y and Lu, X and Wang, X and Hei, X},
title = {Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abe357},
pmid = {33540387},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective.Motor imagery (MI) EEG signals vary greatly among subjects, so scholarly research on motor imagery-based brain-computer interfaces (BCIs) has mainly focused on single-subject systems or subject-dependent systems. However, the single-subject model is applicable only to the target subject, and the small sample number greatly limits the performance of the model. This paper aims to study a convolutional neural network to achieve an adaptable MI-BCI that is applicable to multiple subjects.Approach.In this paper, a twin cascaded softmax convolutional neural network (TCSCNN) is proposed for multisubject MI-BCIs. The proposed TCSCNN is independent and can be applied to any single-subject MI classification convolutional neural network (CNN) model. First, to reduce the influence of individual differences, subject recognition and MI recognition are accomplished simultaneously. A cascaded softmax structure consisting of two softmax layers, related to subject recognition and MI recognition, is subsequently applied. Second, to improve the MI classification precision, a twin network structure is proposed on the basis of ensemble learning. TCSCNN is built by combining a cascaded softmax structure and twin network structure.Main results.Experiments were conducted on three popular CNN models (EEGNet and Shallow ConvNet and Deep ConvNet from EEGDecoding) and three public datasets (BCI Competition IV datasets 2a and 2b and the high-gamma dataset) to verify the performance of the proposed TCSCNN. The results show that compared with the state-of-the-art CNN model, the proposed TCSCNN obviously improves the precision and convergence of multisubject MI recognition.Significance.This study provides a promising scheme for multisubject MI-BCI, reflecting the progress made in the development and application of MI-BCIs.},
}
@article {pmid33536686,
year = {2021},
author = {Meunier, F and Verbeeck, H and Cowdery, B and Schnitzer, SA and Smith-Martin, CM and Powers, JS and Xu, X and Slot, M and De Deurwaerder, HPT and Detto, M and Bonal, D and Longo, M and Santiago, LS and Dietze, M},
title = {Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis.},
journal = {The Journal of ecology},
volume = {109},
number = {1},
pages = {519-540},
pmid = {33536686},
issn = {0022-0477},
abstract = {Despite their low contribution to forest carbon stocks, lianas (woody vines) play an important role in the carbon dynamics of tropical forests. As structural parasites, they hinder tree survival, growth and fecundity; hence, they negatively impact net ecosystem productivity and long-term carbon sequestration.Competition (for water and light) drives various forest processes and depends on the local abundance of resources over time. However, evaluating the relative role of resource availability on the interactions between lianas and trees from empirical observations is particularly challenging. Previous approaches have used labour-intensive and ecosystem-scale manipulation experiments, which are infeasible in most situations.We propose to circumvent this challenge by evaluating the uncertainty of water and light capture processes of a process-based vegetation model (ED2) including the liana growth form. We further developed the liana plant functional type in ED2 to mechanistically simulate water uptake and transport from roots to leaves, and start the model from prescribed initial conditions. We then used the PEcAn bioinformatics platform to constrain liana parameters and run uncertainty analyses.Baseline runs successfully reproduced ecosystem gas exchange fluxes (gross primary productivity and latent heat) and forest structural features (leaf area index, aboveground biomass) in two sites (Barro Colorado Island, Panama and Paracou, French Guiana) characterized by different rainfall regimes and levels of liana abundance.Model uncertainty analyses revealed that water limitation was the factor driving the competition between trees and lianas at the drier site (BCI), and during the relatively short dry season of the wetter site (Paracou). In young patches, light competition dominated in Paracou but alternated with water competition between the wet and the dry season on BCI according to the model simulations.The modelling workflow also identified key liana traits (photosynthetic quantum efficiency, stomatal regulation parameters, allometric relationships) and processes (water use, respiration, climbing) driving the model uncertainty. They should be considered as priorities for future data acquisition and model development to improve predictions of the carbon dynamics of liana-infested forests. Synthesis. Competition for water plays a larger role in the interaction between lianas and trees than previously hypothesized, as demonstrated by simulations from a process-based vegetation model.},
}
@article {pmid33531303,
year = {2023},
author = {Akan, OB and Ramezani, H and Civas, M and Cetinkaya, O and Bilgin, BA and Abbasi, NA},
title = {Information and Communication Theoretical Understanding and Treatment of Spinal Cord Injuries: State-of-The-Art and Research Challenges.},
journal = {IEEE reviews in biomedical engineering},
volume = {16},
number = {},
pages = {332-347},
doi = {10.1109/RBME.2021.3056455},
pmid = {33531303},
issn = {1941-1189},
mesh = {Humans ; *Spinal Cord Injuries/therapy ; Brain ; Technology ; *Brain-Computer Interfaces ; },
abstract = {Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio-Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.},
}
@article {pmid33531009,
year = {2021},
author = {Kumar, A and Fang, Q and Pirogova, E},
title = {The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements.},
journal = {Biomedical engineering online},
volume = {20},
number = {1},
pages = {13},
pmid = {33531009},
issn = {1475-925X},
support = {2020LKSFG03C//Li Ka Shing Foundation/ ; },
mesh = {Humans ; *Stroke Rehabilitation ; Male ; Female ; *Cognition ; *Movement ; *Electroencephalography ; Middle Aged ; *Evoked Potentials ; Adult ; Motivation ; Aged ; Signal Processing, Computer-Assisted ; Stroke/physiopathology ; },
abstract = {BACKGROUND: Recently, error-related negativity (ERN) signals are proposed to develop an assist-as-needed robotic stroke rehabilitation program. Stroke patients' state-of-mind, such as motivation to participate and active involvement in the rehabilitation program, affects their rate of recovery from motor disability. If the characteristics of the robotic stroke rehabilitation program can be altered based on the state-of-mind of the patients, such that the patients remain engaged in the program, the rate of recovery from their motor disability can be improved. However, before that, it is imperative to understand how the states-of-mind of a participant affect their ERN signal.<br><br>METHODS: This study aimed to determine the association between the ERN signal and the psychological and cognitive states of the participants. Experiments were conducted on stroke patients, which involved performing a physical rehabilitation exercise and a questionnaire to measure participants' subjective experience on four factors: motivation in participating in the experiment, perceived effort, perceived pressure, awareness of uncompleted exercise trials while performing the rehabilitation exercise. Statistical correlation analysis, EEG time-series and topographical analysis were used to assess the association between the ERN signals and the psychological and cognitive states of the participants.<br><br>RESULTS: A strong correlation between the amplitude of the ERN signal and the psychological and cognitive states of the participants was observed, which indicate the possibility of estimating the said states using the amplitudes of the novel ERN signal.<br><br>CONCLUSIONS: The findings pave the way for the development of an ERN based dynamically adaptive assist-as-needed robotic stroke rehabilitation program of which characteristics can be altered to keep the participants' motivation, effort, engagement in the rehabilitation program high. In future, the single-trial prediction ability of the novel ERN signals to predict the state-of-mind of stroke patients will be evaluated.},
}
@article {pmid33530868,
year = {2021},
author = {Branco, MP and Pels, EGM and Sars, RH and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ and Nijboer, F},
title = {Brain-Computer Interfaces for Communication: Preferences of Individuals With Locked-in Syndrome.},
journal = {Neurorehabilitation and neural repair},
volume = {35},
number = {3},
pages = {267-279},
pmid = {33530868},
issn = {1552-6844},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Disease Progression ; Female ; Health Communication ; Humans ; Locked-In Syndrome/*rehabilitation ; Male ; Middle Aged ; *Patient Preference ; Qualitative Research ; Time Factors ; *User-Computer Interface ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) have been proposed as an assistive technology (AT) allowing people with locked-in syndrome (LIS) to use neural signals to communicate. To design a communication BCI (cBCI) that is fully accepted by the users, their opinion should be taken into consideration during the research and development process.<br><br>OBJECTIVE: We assessed the preferences of prospective cBCI users regarding (1) the applications they would like to control with a cBCI, (2) the mental strategies they would prefer to use to control the cBCI, and (3) when during their clinical trajectory they would like to be informed about AT and cBCIs. Furthermore, we investigated if individuals diagnosed with progressive and sudden onset (SO) disorders differ in their opinion.<br><br>METHODS: We interviewed 28 Dutch individuals with LIS during a 3-hour home visit using multiple-choice, ranking, and open questions. During the interview, participants were informed about BCIs and the possible mental strategies.<br><br>RESULTS: Participants rated (in)direct forms of communication, computer use, and environmental control as the most desired cBCI applications. In addition, active cBCI control strategies were preferred over reactive strategies. Furthermore, individuals with progressive and SO disorders preferred to be informed about AT and cBCIs at the moment they would need it.<br><br>CONCLUSIONS: We show that individuals diagnosed with progressive and SO disorders preferred, in general, the same applications, mental strategies, and time of information. By collecting the opinion of a large sample of individuals with LIS, this study provides valuable information to stakeholders in cBCI and other AT development.},
}
@article {pmid33530064,
year = {2021},
author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T},
title = {High-density mapping of primate digit representations with a 1152-channelµECoG array.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abe245},
pmid = {33530064},
issn = {1741-2552},
mesh = {Animals ; *Brain Mapping ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes, Implanted ; Humans ; Primates ; },
abstract = {Objective.Advances in brain-machine interfaces (BMIs) are expected to support patients with movement disorders. Electrocorticogram (ECoG) measures electrophysiological activities over a large area using a low-invasive flexible sheet placed on the cortex. ECoG has been considered as a feasible signal source of the clinical BMI device. To capture neural activities more precisely, the feasibility of higher-density arrays has been investigated. However, currently, the number of electrodes is limited to approximately 300 due to wiring difficulties, device size, and system costs.Approach.We developed a high-density recording system with a large coverage (14 × 7 mm[2]) and using 1152 electrodes by directly integrating dedicated flexible arrays with the neural-recording application-specific integrated circuits and their interposers.Main results.Comparative experiments with a 128-channel array demonstrated that the proposed device could delineate the entire digit representation of a nonhuman primate. Subsampling analysis revealed that higher-amplitude signals can be measured using higher-density arrays.Significance.We expect that the proposed system that simultaneously establishes large-scale sampling, high temporal-precision of electrophysiology, and high spatial resolution comparable to optical imaging will be suitable for next-generation brain-sensing technology.},
}
@article {pmid33528676,
year = {2021},
author = {Ibáñez, A and Bletz, MC and Quezada, G and Geffers, R and Jarek, M and Vences, M and Steinfartz, S},
title = {No impact of a short-term climatic "El Niño" fluctuation on gut microbial diversity in populations of the Galápagos marine iguana (Amblyrhynchus cristatus).},
journal = {Die Naturwissenschaften},
volume = {108},
number = {1},
pages = {7},
pmid = {33528676},
issn = {1432-1904},
mesh = {Animals ; Biodiversity ; Ecuador ; *El Nino-Southern Oscillation ; Gastrointestinal Microbiome/*physiology ; Iguanas/*microbiology ; },
abstract = {Gut microorganisms are crucial for many biological functions playing a pivotal role in the host's well-being. We studied gut bacterial community structure of marine iguana populations across the Galápagos archipelago. Marine iguanas depend heavily on their specialized gut microbiome for the digestion of dietary algae, a resource whose growth was strongly reduced by severe "El Niño"-related climatic fluctuations in 2015/2016. As a consequence, marine iguana populations showed signs of starvation as expressed by a poor body condition. Body condition indices (BCI) varied between island populations indicating that food resources (i.e., algae) are affected differently across the archipelago during 'El Niño' events. Though this event impacted food availability for marine iguanas, we found that reductions in body condition due to "El Niño"-related starvation did not result in differences in bacterial gut community structure. Species richness of gut microorganisms was instead correlated with levels of neutral genetic diversity in the distinct host populations. Our data suggest that marine iguana populations with a higher level of gene diversity and allelic richness may harbor a more diverse gut microbiome than those populations with lower genetic diversity. Since low values of these diversity parameters usually correlate with small census and effective population sizes, we use our results to propose a novel hypothesis according to which small and genetically less diverse host populations might be characterized by less diverse microbiomes. Whether such genetically depauperate populations may experience additional threats from reduced dietary flexibility due to a limited intestinal microbiome is currently unclear and calls for further investigation.},
}
@article {pmid33524962,
year = {2021},
author = {Petrosyan, A and Sinkin, M and Lebedev, M and Ossadtchi, A},
title = {Decoding and interpreting cortical signals with a compact convolutional neural network.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abe20e},
pmid = {33524962},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrocorticography ; *Electroencephalography/methods ; Fingers ; Neural Networks, Computer ; },
abstract = {Objective.Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery.Approach.We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.Main results.We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task.Significance.We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.},
}
@article {pmid33524961,
year = {2021},
author = {Zuo, C and Jin, J and Xu, R and Wu, L and Liu, C and Miao, Y and Wang, X},
title = {Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abe20f},
pmid = {33524961},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Imagination ; Machine Learning ; },
abstract = {Objective. Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs.Approach. In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem.Main results.The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods.Significance. The proposed method is promising for improving the performance of MI-based BCIs.},
}
@article {pmid33523957,
year = {2021},
author = {Adewole, DO and Struzyna, LA and Burrell, JC and Harris, JP and Nemes, AD and Petrov, D and Kraft, RH and Chen, HI and Serruya, MD and Wolf, JA and Cullen, DK},
title = {Development of optically controlled "living electrodes" with long-projecting axon tracts for a synaptic brain-machine interface.},
journal = {Science advances},
volume = {7},
number = {4},
pages = {},
pmid = {33523957},
issn = {2375-2548},
support = {T32 NS091006/NS/NINDS NIH HHS/United States ; IK2 RX001479/RX/RRD VA/United States ; I01 BX003748/BX/BLRD VA/United States ; T32 NS043126/NS/NINDS NIH HHS/United States ; U01 NS094340/NS/NINDS NIH HHS/United States ; TL1 TR001880/TR/NCATS NIH HHS/United States ; IK2 RX002013/RX/RRD VA/United States ; I01 RX001097/RX/RRD VA/United States ; },
mesh = {Animals ; Axons ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Microelectrodes ; Neurons/physiology ; Rats ; },
abstract = {For implantable neural interfaces, functional/clinical outcomes are challenged by limitations in specificity and stability of inorganic microelectrodes. A biological intermediary between microelectrical devices and the brain may improve specificity and longevity through (i) natural synaptic integration with deep neural circuitry, (ii) accessibility on the brain surface, and (iii) optogenetic manipulation for targeted, light-based readout/control. Accordingly, we have developed implantable "living electrodes," living cortical neurons, and axonal tracts protected within soft hydrogel cylinders, for optobiological monitoring/modulation of brain activity. Here, we demonstrate fabrication, rapid axonal outgrowth, reproducible cytoarchitecture, and simultaneous optical stimulation and recording of these tissue engineered constructs in vitro. We also present their transplantation, survival, integration, and optical recording in rat cortex as an in vivo proof of concept for this neural interface paradigm. The creation and characterization of these functional, optically controllable living electrodes are critical steps in developing a new class of optobiological tools for neural interfacing.},
}
@article {pmid33521338,
year = {2021},
author = {Chang, CC and Wang, HC},
title = {Emboli stroke following migration of carotid foreign body: A case report.},
journal = {eNeurologicalSci},
volume = {22},
number = {},
pages = {100313},
pmid = {33521338},
issn = {2405-6502},
abstract = {Foreign body embolization can cause intracranial artery occlusion with ischemic stroke. Reported etiologies include post cerebrovascular interventions, migration of esophageal foreign body and neck trauma. We reported a case with punctured wound at left neck, X-ray and computed tomography revealed a foreign body located in the carotid region. The patient eventually developed stroke symptoms in the next day after operation. Non-contrast brain Computer tomography at that time revealed that porcelain fragment located at the suprasellar area, and infarction of the left anterior basal ganglion. Our patient is the first reported case having an embolic stroke secondary to distal migration of a foreign body from the carotid artery after neck trauma. We call attention to this rare neurologic complication of neck trauma with foreign body retention. Appropriate and prompt identification of concurrent vascular injuries with retention of foreign body is strongly advised in neck trauma patients.},
}
@article {pmid33520849,
year = {2020},
author = {Saghaee, A and Ghahari, S and Nasli-Esfahani, E and Sharifi, F and Alizadeh-Khoei, M and Rezaee, M},
title = {Evaluation of the effectiveness of Persian diabetes self-management education in older adults with type 2 diabetes at a diabetes outpatient clinic in Tehran: a pilot randomized control trial.},
journal = {Journal of diabetes and metabolic disorders},
volume = {19},
number = {2},
pages = {1491-1504},
pmid = {33520849},
issn = {2251-6581},
abstract = {PURPOSE: The effectiveness of diabetes self-management interventions has been more generally demonstrated in adults, but there is little evidence of diabetes self-management specific to older adults situated in Iran. The purpose of this study was to evaluate the effectiveness of Persian Diabetes Self-Management Education on self-efficacy, quality of life, self-care activity, depression and loneliness in older adults with type 2 diabetes.<br><br>METHODS: In pilot randomized controlled trial, a total of 34 participants &#x2265;60&#xa0;years with type 2 diabetes were randomly assigned into intervention (n&#x2009;=&#x2009;17) and control (n&#x2009;=&#x2009;17) group in an outpatient diabetes clinic in Tehran. To assess the primary outcome of participant experiences, the Diabetes Management Self-efficacy Scale (DMSES) was the method of measurement. The Diabetes Quality of Life-Basic Clinical Inventory (DQoL-BCI), Patient Health Questionnaire-9 (PHQ-9), Diabetes Self-Management Education Scale (DSMES), and adult Social-Emotional Loneliness Scale Short form (SELSA-S) were used as secondary outcomes. Participants' evaluations were completed at baseline, while measurements were conducted two and four weeks after allocation, using repeated measurements of Univariate and multivariate ANOVA (adjusted for baseline values) to analyze the data.<br><br>RESULTS: In the multivariate model, there was a significant difference between the control and intervention groups regarding reported quality of life (p&#x2009;=&#x2009;0.04) and the medical-domain's reported self-efficacy (p&#x2009;=&#x2009;0.02). However, there were no significant differences in the reported self-management, depression, loneliness, as well as the other domain of self-efficacy; as compared between the two groups before and after intervention.<br><br>CONCLUSION: The study depicts a promising impact on older adults, imparted by the pertinent program. The finding showed PDSME has a positive effect on quality of life and medical control domain of self-efficacy. This pilot study showed that the program is feasible and duly beneficial if delivered to older adults. This pilot proves appealing to begin further testing within a larger sample population.},
}
@article {pmid33519928,
year = {2021},
author = {Han, Z and Chen, M and Zhou, T and Nie, Z and Wu, Q},
title = {Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence.},
journal = {Neural plasticity},
volume = {2021},
number = {},
pages = {6639664},
pmid = {33519928},
issn = {1687-5443},
mesh = {*Aircraft ; *Algorithms ; Animals ; *Artificial Intelligence ; Bees ; *Brain-Computer Interfaces ; *Computer Simulation ; Flight, Animal/physiology ; Humans ; },
abstract = {Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.},
}
@article {pmid33519402,
year = {2020},
author = {Aldayel, M and Ykhlef, M and Al-Nafjan, A},
title = {Recognition of Consumer Preference by Analysis and Classification EEG Signals.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {604639},
pmid = {33519402},
issn = {1662-5161},
abstract = {Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.},
}
@article {pmid33519352,
year = {2020},
author = {Zhang, G and Luo, J and Han, L and Lu, Z and Hua, R and Chen, J and Che, W},
title = {A Dynamic Multi-Scale Network for EEG Signal Classification.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {578255},
pmid = {33519352},
issn = {1662-4548},
abstract = {Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task.},
}
@article {pmid33518728,
year = {2020},
author = {Erdmann, WS and Aschenbrenner, P and Giovanis, V},
title = {Modern technology assists disabled competitors: the first "Cybathlon" special competition in Zürich.},
journal = {Acta of bioengineering and biomechanics},
volume = {22},
number = {3},
pages = {69-75},
pmid = {33518728},
issn = {2450-6303},
mesh = {Artificial Limbs ; Bicycling ; Brain-Computer Interfaces ; *Persons with Disabilities ; Electric Stimulation ; Humans ; Lower Extremity/physiology ; Sports ; Switzerland ; *Technology ; Wheelchairs ; },
abstract = {PURPOSE: The purpose of the study was presentation of modern bioengineering technology in order to help people with severe disabilities.<br><br>METHODS: Bioengineering industry can offer severely disabled people several devices in order to enable them to take part in the competition different than Paralympics. The first international competition for people with disabilities supported by modern assistive technology, such as sensors, motors, displays were allowed to compete in Cybathlon held in Z&#xfc;rich in 2016. About 70 athletes and their teams from 25 countries appeared at the event.<br><br>RESULTS: There were six disciplines (races): 1) Powered Arms (Upper Extremities) Prostheses Race, 2) Powered Legs (Lower Extremities) Prostheses Race, 3) Powered Wheelchair Race, 4) Powered Exoskeleton Race, 5) Functional Electrical Stimulation Bike Race, 6) Brain-Computer Interface Race. About a quarter of the teams represented industry and the rest represented university laboratories.<br><br>CONCLUSIONS: The competition was a success. The organisers have decided for it to be organized every four years, just like the Olympic Games for able bodied competitors. The main inventor of the event professor Robert Riener from Z&#xfc;rich Polytechnic (ETHZ) said assistive technology should: a) be user-friendly b) to function well, c) be affordable, d) to be used within the barrier-free environment.},
}
@article {pmid33514914,
year = {2021},
author = {Liu, YJ and Zhang, T and Chen, S and Cheng, D and Wu, C and Wang, X and Duan, D and Zhu, L and Lou, H and Gong, Z and Wang, XD and Ho, MS and Duan, S},
title = {The noncanonical role of the protease cathepsin D as a cofilin phosphatase.},
journal = {Cell research},
volume = {31},
number = {7},
pages = {801-813},
pmid = {33514914},
issn = {1748-7838},
support = {2016YFA0501000//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 31501128//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81801330//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81761138044, 81971260//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {*Actin Depolymerizing Factors ; Actins ; *Cathepsin D ; Cofilin 1 ; Peptide Hydrolases ; Phosphoric Monoester Hydrolases ; },
abstract = {Cathepsin D (cathD) is traditionally regarded as a lysosomal protease that degrades substrates in acidic compartments. Here we report cathD plays an unconventional role as a cofilin phosphatase orchestrating actin remodeling. In neutral pH environments, the cathD precursor directly dephosphorylates and activates the actin-severing protein cofilin independent of its proteolytic activity, whereas mature cathD degrades cofilin in acidic pH conditions. During development, cathD complements the canonical cofilin phosphatase slingshot and regulates the morphogenesis of actin-based structures. Moreover, suppression of cathD phosphatase activity leads to defective actin organization and cytokinesis failure. Our findings identify cathD as a dual-function molecule, whose functional switch is regulated by environmental pH and its maturation state, and reveal a novel regulatory role of cathD in actin-based cellular processes.},
}
@article {pmid33510245,
year = {2021},
author = {Kumarasinghe, K and Kasabov, N and Taylor, D},
title = {Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {2486},
pmid = {33510245},
issn = {2045-2322},
mesh = {Brain/*physiology ; Brain Waves/*physiology ; Female ; *Hand ; Humans ; Male ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Nerve Net/*physiology ; },
abstract = {Compared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the 'NeuCube' brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain-Computer Interfaces (BCIs) that constitute a 'black box', BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.},
}
@article {pmid33509848,
year = {2021},
author = {Zhang, X and Cao, D and Liu, J and Zhang, Q and Liu, M},
title = {Effectiveness and safety of brain-computer interface technology in the treatment of poststroke motor disorders: a protocol for systematic review and meta-analysis.},
journal = {BMJ open},
volume = {11},
number = {1},
pages = {e042383},
pmid = {33509848},
issn = {2044-6055},
mesh = {*Brain-Computer Interfaces ; China ; Data Management ; Humans ; Meta-Analysis as Topic ; *Motor Disorders ; Research Design ; *Stroke/complications ; Systematic Reviews as Topic ; Technology ; },
abstract = {INTRODUCTION: About 85% of stroke survivors have upper extremity dysfunction, and more than 60% have continuing hand dysfunction and cannot live independently after treatment. Numerous recent publications have explored brain-computer interfaces technology as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Our study aims to synthesise results from randomised controlled trials to assess the effectiveness and safety of brain-computer interface technology in the treatment of poststroke motor disorders（PSMD）.<br><br>METHODS AND ANALYSIS: English and Chinese search strategies will be conducted in eight databases: the China National Knowledge Infrastructure, Chinese Scientific Journal Database, Wanfang Database, China Doctoral Dissertations Full-Text Database, China Master's Theses Full-Text Database, Cochrane Central Register of Controlled Trials, PubMed and Embase. In addition, manual retrieval of research papers, conference papers, ongoing experiments and internal reports, among others, will supplement electronic retrieval. The searches will select all eligible studies published on or before 8 June 2020. To enhance the effectiveness of the study, only randomised controlled trials related to brain-computer interface technology for poststroke motor disorders will be included. The Fugl-Meyer Motor Function score will be the primary outcome measure; the Modified Barthel Index, Modified Ashworth Score and the upper extremity freehand muscle strength assessment will be secondary outcomes. Side effects and adverse events will be included as safety evaluations. To ensure the quality of the systematic evaluation, study selection, data extraction and quality assessment will be independently performed by two authors, and a third author will handle any disagreement. Review Manager V.5.3.3 and STATA V.15.1 will be used to perform the data synthesis and subgroup analysis.<br><br>ETHICS AND DISSEMINATION: This systemic review will evaluate the efficacy and safety of brain-computer interface technology combined with routine rehabilitation treatment for treatment of poststroke motor disorders. Since all included data will be obtained from published articles,the review does not require ethical approval. The review will be published in a peer-reviewed journal.<br><br>PROSPERO REGISTRATION NUMBER: CRD42020190868.},
}
@article {pmid33505456,
year = {2020},
author = {Xue, J and Ren, F and Sun, X and Yin, M and Wu, J and Ma, C and Gao, Z},
title = {A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding.},
journal = {Neural plasticity},
volume = {2020},
number = {},
pages = {8863223},
pmid = {33505456},
issn = {1687-5443},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/psychology ; *Databases, Factual ; *Deep Learning ; Humans ; Imagination/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.},
}
@article {pmid33505259,
year = {2020},
author = {Dolmans, TC and Poel, M and van 't Klooster, JJR and Veldkamp, BP},
title = {Perceived Mental Workload Classification Using Intermediate Fusion Multimodal Deep Learning.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {609096},
pmid = {33505259},
issn = {1662-5161},
abstract = {A lot of research has been done on the detection of mental workload (MWL) using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of measurement modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify MWL. The goal of this research was to classify perceived mental workload (PMWL) using a deep neural network (DNN) that flexibly makes use of multiple modalities, in order to allow for feature sharing between modalities. To achieve this goal, an experiment was conducted in which MWL was simulated with the help of verbal logic puzzles. The puzzles came in five levels of difficulty and were presented in a random order. Participants had 1 h to solve as many puzzles as they could. Between puzzles, they gave a difficulty rating between 1 and 7, seven being the highest difficulty. Galvanic skin response, photoplethysmograms, functional near-infrared spectrograms and eye movements were collected simultaneously using LabStreamingLayer (LSL). Marker information from the puzzles was also streamed on LSL. We designed and evaluated a novel intermediate fusion multimodal DNN for the classification of PMWL using the aforementioned four modalities. Two main criteria that guided the design and implementation of our DNN are modularity and generalisability. We were able to classify PMWL within-level accurate (0.985 levels) on a seven-level workload scale using the aforementioned modalities. The model architecture allows for easy addition and removal of modalities without major structural implications because of the modular nature of the design. Furthermore, we showed that our neural network performed better when using multiple modalities, as opposed to a single modality. The dataset and code used in this paper are openly available.},
}
@article {pmid33505237,
year = {2020},
author = {Baqapuri, HI and Roes, LD and Zvyagintsev, M and Ramadan, S and Keller, M and Roecher, E and Zweerings, J and Klasen, M and Gur, RC and Mathiak, K},
title = {A Novel Brain-Computer Interface Virtual Environment for Neurofeedback During Functional MRI.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {593854},
pmid = {33505237},
issn = {1662-4548},
abstract = {Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)-based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain-computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player's avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE's objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.},
}
@article {pmid33501380,
year = {2020},
author = {Hentzschel, F and Obrova, K and Marti, M},
title = {No evidence for Ago2 translocation from the host erythrocyte into the Plasmodium parasite.},
journal = {Wellcome open research},
volume = {5},
number = {},
pages = {92},
pmid = {33501380},
issn = {2398-502X},
support = {/WT_/Wellcome Trust/United Kingdom ; 110166/Z/15/Z/WT_/Wellcome Trust/United Kingdom ; },
abstract = {Background: Plasmodium parasites rely on various host factors to grow and replicate within red blood cells (RBC). While many host proteins are known that mediate parasite adhesion and invasion, few examples of host enzymes co-opted by the parasite during intracellular development have been described. Recent studies suggested that the host protein Argonaute 2 (Ago2), which is involved in RNA interference, can translocate into the parasite and affect its development. Here, we investigated this hypothesis. Methods: We used several different monoclonal antibodies to test for Ago2 localisation in the human malaria parasite, P. falciparum and rodent P. berghei parasites. In addition, we biochemically fractionated infected red blood cells to localize Ago2. We also quantified parasite growth and sexual commitment in the presence of the Ago2 inhibitor BCI-137. Results: Ago2 localization by fluorescence microscopy produced inconclusive results across the three different antibodies, suggesting cross-reactivity with parasite targets. Biochemical separation of parasite and RBC cytoplasm detected Ago2 only in the RBC cytoplasm and not in the parasite. Inhibition of Ago2 using BCl-137 did not result in altered parasite development. Conclusion: Ago2 localization in infected RBCs by microscopy is confounded by non-specific binding of antibodies. Complementary results using biochemical fractionation and Ago2 detection by western blot did not detect the protein in the parasite cytosol, and growth assays using a specific inhibitor demonstrated that its catalytical activity is not required for parasite development. We therefore conclude that previous data localising Ago2 to parasite ring stages are due to antibody cross reactivity, and that Ago2 is not required for intracellular Plasmodium development.},
}
@article {pmid33501291,
year = {2020},
author = {Alimardani, M and Hiraki, K},
title = {Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction.},
journal = {Frontiers in robotics and AI},
volume = {7},
number = {},
pages = {125},
pmid = {33501291},
issn = {2296-9144},
abstract = {Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.},
}
@article {pmid33501255,
year = {2020},
author = {Frolov, A and Bobrov, P and Biryukova, E and Isaev, M and Kerechanin, Y and Bobrov, D and Lekin, A},
title = {Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain-Computer Interface Experiments.},
journal = {Frontiers in robotics and AI},
volume = {7},
number = {},
pages = {88},
pmid = {33501255},
issn = {2296-9144},
abstract = {In this study, the sources of EEG activity in motor imagery brain-computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the source activities and physiological plausibility. The latter was tested by estimating the dipolarity of the source topographic maps, i.e., the accuracy of approximating the map by potential distribution from a single current dipole, as well as by the specificity of the source activity for different motor imagery tasks. The decomposition methods were also compared according to the number of shared components found. The results indicate that most of the dipolar components are found by the Independent Component Analysis Methods AMICA and PWCICA, which also provided the highest information reduction. These two methods also found the most task-specific EEG patterns of the blind source separation algorithms used. They are outperformed only by non-blind Common Spatial Pattern methods in terms of pattern specificity. The components found by all of the methods were clustered using the Attractor Neural Network with Increasing Activity. The results of the cluster analysis revealed the most frequent patterns of electrical activity occurring in the experiments. The patterns reflect blinking, eye movements, sensorimotor rhythm suppression during the motor imagery, and activations in the precuneus, supplementary motor area, and premotor areas of both hemispheres. Overall, multi-method decomposition with subsequent clustering and task-specificity estimation is a viable and informative procedure for processing the recordings of electrophysiological experiments.},
}
@article {pmid33501206,
year = {2020},
author = {Kolkhorst, H and Veit, J and Burgard, W and Tangermann, M},
title = {A Robust Screen-Free Brain-Computer Interface for Robotic Object Selection.},
journal = {Frontiers in robotics and AI},
volume = {7},
number = {},
pages = {38},
pmid = {33501206},
issn = {2296-9144},
abstract = {Brain signals represent a communication modality that can allow users of assistive robots to specify high-level goals, such as the object to fetch and deliver. In this paper, we consider a screen-free Brain-Computer Interface (BCI), where the robot highlights candidate objects in the environment using a laser pointer, and the user goal is decoded from the evoked responses in the electroencephalogram (EEG). Having the robot present stimuli in the environment allows for more direct commands than traditional BCIs that require the use of graphical user interfaces. Yet bypassing a screen entails less control over stimulus appearances. In realistic environments, this leads to heterogeneous brain responses for dissimilar objects-posing a challenge for reliable EEG classification. We model object instances as subclasses to train specialized classifiers in the Riemannian tangent space, each of which is regularized by incorporating data from other objects. In multiple experiments with a total of 19 healthy participants, we show that our approach not only increases classification performance but is also robust to both heterogeneous and homogeneous objects. While especially useful in the case of a screen-free BCI, our approach can naturally be applied to other experimental paradigms with potential subclass structure.},
}
@article {pmid33499470,
year = {2021},
author = {Mark, N and Lyubin, A and Gerasi, R and Ofir, D and Tsur, AM and Chen, J and Bader, T},
title = {Comparison of the Effects of Motion and Environment Conditions on Accuracy of Handheld and Finger-Based Pulse Oximeters.},
journal = {Military medicine},
volume = {186},
number = {Suppl 1},
pages = {465-472},
doi = {10.1093/milmed/usaa314},
pmid = {33499470},
issn = {1930-613X},
mesh = {Algorithms ; *Fingers ; Heart Rate ; Humans ; *Oximetry ; Oxygen ; Reproducibility of Results ; },
abstract = {INTRODUCTION: The most common cause of preventable death on the battlefield is significant blood loss, eventually causing decrease in tissue oxygen delivery. Pulse oximeters (POs) are widely used by the Israeli Defense Forces to obtain fast and noninvasive information about peripheral oxygen saturation (SpO2). However, POs are produced by different manufacturers and therefore include different sensors and are based on distinctive algorithms. This makes them susceptible to different errors caused by factors varying from environmental conditions to the severity of injury. The objectives of this study were to compare the reliability of different devices and their accuracy under various conditions.<br><br>MATERIAL AND METHODS: Six POs underwent performance analysis. The finger-based category included: MightySat by Masimo, Onyx II by Nonin, and CMS50D by Contec. The handheld category comprised: RAD5 by Masimo, 9847 model by Nonin, and 3301 model by BCI. Several environmental and physiological parameters were altered using the ProSim8 simulator by Fluke biomedical, forming unique test cases under which the devices were tested in stationary and motion conditions.<br><br>RESULTS: All finger-based POs showed higher error rates of PO SpO2 and heart rate measurements in motion conditions, regardless of the manufacturer. However, newer devices in the handheld category were not affected. Results presented in Phase II showed that the SpO2 measurement error in all the devices was affected by pigmentation. However, the CMS50D, considered a low-cost device, had a significantly higher error size than other devices. In the devices that were influenced both by pigmentation and the finger cleanliness factors, the combined detected error size was clinically significant. The pigmentation, ambient light, and finger cleanliness also had a significant effect on the heart rate measurement in the CMS50D model, unlike the handheld devices, which were not affected. During Phase II, neither the Nonin nor the Masimo devices were deemed to have a significant advantage.<br><br>CONCLUSION: Considering measurement limitations of POs used is extremely important. Use of handheld devices should be favored for use in motion conditions. Technologically advanced and/or recently developed devices should be preferred because of evolving algorithms, which decrease or eliminate the error factors. The "dirty finger" effect on the measurement error cannot be neglected and therefore the action of finger cleaning should be considered part of the treatment protocol.},
}
@article {pmid33497337,
year = {2021},
author = {Shahbakhti, M and Beiramvand, M and Nazari, M and Broniec-Wojcik, A and Augustyniak, P and Rodrigues, AS and Wierzchon, M and Marozas, V},
title = {VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {408-417},
doi = {10.1109/TNSRE.2021.3054733},
pmid = {33497337},
issn = {1558-0210},
mesh = {Algorithms ; Artifacts ; Blinking ; Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {OBJECTIVE: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel.<br><br>METHOD: The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel.<br><br>RESULTS: The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87).<br><br>SIGNIFICANCE: The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.},
}
@article {pmid33495242,
year = {2021},
author = {Rastogi, A and Willett, FR and Abreu, J and Crowder, DC and Murphy, BA and Memberg, WD and Vargas-Irwin, CE and Miller, JP and Sweet, J and Walter, BL and Rezaii, PG and Stavisky, SD and Hochberg, LR and Shenoy, KV and Henderson, JM and Kirsch, RF and Ajiboye, AB},
title = {The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia.},
journal = {eNeuro},
volume = {8},
number = {1},
pages = {},
pmid = {33495242},
issn = {2373-2822},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; I01 RX002654/RX/RRD VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; T32 GM007250/GM/NIGMS NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; TL1 TR002549/TR/NCATS NIH HHS/United States ; },
mesh = {Hand ; Hand Strength ; Humans ; *Motor Cortex ; Quadriplegia ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) have the potential to restore hand grasping and object interaction to individuals with tetraplegia. Optimal grasping and object interaction require simultaneous production of both force and grasp outputs. However, since overlapping neural populations are modulated by both parameters, grasp type could affect how well forces are decoded from motor cortex in a closed-loop force iBCI. Therefore, this work quantified the neural representation and offline decoding performance of discrete hand grasps and force levels in two human participants with tetraplegia. Participants attempted to produce three discrete forces (light, medium, hard) using up to five hand grasp configurations. A two-way Welch ANOVA was implemented on multiunit neural features to assess their modulation to force and grasp Demixed principal component analysis (dPCA) was used to assess for population-level tuning to force and grasp and to predict these parameters from neural activity. Three major findings emerged from this work: (1) force information was neurally represented and could be decoded across multiple hand grasps (and, in one participant, across attempted elbow extension as well); (2) grasp type affected force representation within multiunit neural features and offline force classification accuracy; and (3) grasp was classified more accurately and had greater population-level representation than force. These findings suggest that force and grasp have both independent and interacting representations within cortex, and that incorporating force control into real-time iBCI systems is feasible across multiple hand grasps if the decoder also accounts for grasp type.},
}
@article {pmid33494072,
year = {2021},
author = {Aydarkhanov, R and Ušćumlić, M and Chavarriaga, R and Gheorghe, L and Del R Millán, J},
title = {Closed-loop EEG study on visual recognition during driving.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abdfb2},
pmid = {33494072},
issn = {1741-2552},
mesh = {*Automobile Driving ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Fixation, Ocular ; Humans ; },
abstract = {Objective.In contrast to the classical visual brain-computer interface (BCI) paradigms, which adhere to a rigid trial structure and restricted user behavior, electroencephalogram (EEG)-based visual recognition decoding during our daily activities remains challenging. The objective of this study is to explore the feasibility of decoding the EEG signature of visual recognition in experimental conditions promoting our natural ocular behavior when interacting with our dynamic environment.Approach.In our experiment, subjects visually search for a target object among suddenly appearing objects in the environment while driving a car-simulator. Given that subjects exhibit an unconstrained overt visual behavior, we based our study on eye fixation-related potentials (EFRPs). We report on gaze behavior and single-trial EFRP decoding performance (fixations on visually similar target vs. non-target objects). In addition, we demonstrate the application of our approach in a closed-loop BCI setup.Main results.To identify the target out of four symbol types along a road segment, the BCI system integrated decoding probabilities of multiple EFRP and achieved the average online accuracy of 0.37 ± 0.06 (12 subjects), statistically significantly above the chance level. Using the acquired data, we performed a comparative study of classification algorithms (discriminating target vs. non-target) and feature spaces in a simulated online scenario. The EEG approaches yielded similar moderate performances of at most 0.6 AUC, yet statistically significantly above the chance level. In addition, the gaze duration (dwell time) appears to be an additional informative feature in this context.Significance.These results show that visual recognition of sudden events can be decoded during active driving. Therefore, this study lays a foundation for assistive and recommender systems based on the driver's brain signals.},
}
@article {pmid33488356,
year = {2020},
author = {Heilinger, A and Ortner, R and La Bella, V and Lugo, ZR and Chatelle, C and Laureys, S and Spataro, R and Guger, C},
title = {Corrigendum: Performance Differences Using a Vibro-Tactile P300 BCI in LIS-Patients Diagnosed With Stroke and ALS.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {637905},
doi = {10.3389/fnins.2020.637905},
pmid = {33488356},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2018.00514.].},
}
@article {pmid33486660,
year = {2020},
author = {Bobrova, EV and Reshetnikova, VV and Vershinina, EA and Grishin, AA and Frolov, AA and Gerasimenko, YP},
title = {Interhemispheric Asymmetry and Personality Traits of Brain-Computer Interface Users in Hand Movement Imagination.},
journal = {Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections},
volume = {495},
number = {1},
pages = {265-267},
pmid = {33486660},
issn = {1608-3105},
mesh = {Adult ; Brain/physiology ; Brain-Computer Interfaces/*psychology/standards ; Female ; *Functional Laterality ; Hand/physiology ; Humans ; Imagination ; Male ; *Movement ; *Personality ; },
abstract = {Personality traits of users can affect the success in controlling brain-computer interfaces (BCIs), and the activity of right and left brain structures may differ depending on personality traits. Earlier, it was not known, how the success of BCI control with different personality traits is associated with interhemispheric asymmetry. In this work, the dependence of the success of imagination of movements, estimated by the success of recognition of EEG signals during imagination of hand movements compared to rest state, on the user's personal characteristics was studied. It is shown that in single control of BCI by naive subjects, recognition success in imagining right-hand (RH) movements was higher in expressive sensitive extroverts, and in imagining left-hand movements (LH) it was higher in practical, reserved, skeptical, and not very sociable persons. It is suggested that this phenomenon may be based on interhemispheric differences in dopamine level and in the way of encoding movement information.},
}
@article {pmid33485365,
year = {2021},
author = {Baniqued, PDE and Stanyer, EC and Awais, M and Alazmani, A and Jackson, AE and Mon-Williams, MA and Mushtaq, F and Holt, RJ},
title = {Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {18},
number = {1},
pages = {15},
pmid = {33485365},
issn = {1743-0003},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Female ; Hand/*physiology ; Humans ; Male ; Middle Aged ; Motor Skills/*physiology ; Robotics/*instrumentation ; Stroke Rehabilitation/*instrumentation/methods ; },
abstract = {BACKGROUND: Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.<br><br>METHODS: A search for January 2010-October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.<br><br>RESULTS: 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.<br><br>CONCLUSION: 19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.},
}
@article {pmid33484511,
year = {2021},
author = {Riaz, Q and Ali, SK and Khan, MR and Alvi, AR},
title = {Stress and coping among surgery residents in a developing country.},
journal = {JPMA. The Journal of the Pakistan Medical Association},
volume = {71},
number = {1(A)},
pages = {16-21},
doi = {10.47391/JPMA.522},
pmid = {33484511},
issn = {0030-9982},
mesh = {Adaptation, Psychological ; *Developing Countries ; Female ; Humans ; *Internship and Residency ; Male ; Pakistan ; Reproducibility of Results ; Surveys and Questionnaires ; Workload ; },
abstract = {OBJECTIVE: Stress during residency training in surgical disciplines not only hampers professional development but can also compromise patient care and personal health. The purpose of this study was to measure the stress level among the surgical residents, identify factors within the learning and work environment that cause stress, and identify different strategies that the residents use habitually to cope with these stresses.<br><br>METHODOLOGY: This mix method study was conducted in the department of Surgery at Aga Khan University, Pakistan. Residents' stress level was measured using Perceived Stress Scale (PSS); focus group discussions (FGDs) with faculty and residents explored stressors during residency training, while Brief COPE Inventory identified the residents' preferred coping strategy.<br><br>RESULTS: A total of 68 (83%) surgery residents completed the survey of which 19% had high stress scores while only one resident had perception of low stress. Females had significantly higher stress scores (25.7&#xb1;3.0; p=0.008) as compared to male counterparts. Planning (87.8%) and Self-distraction (65%) were the most commonly used adaptive and maladaptive strategies respectively. The reliability of the PSS and BCI measured by Cronbach's alpha was 0.73 and 0.82 respectively. Work-life imbalance, workload and contradicting programme and hospital policies were identified in FGDs as major stressors during residency.<br><br>CONCLUSIONS: Although surgical residency programmes are very stressful, coping strategies are not formally taught during surgical training. Academia and hospital should join hands in developing interventions to enable residents cope with the situation.},
}
@article {pmid33483431,
year = {2021},
author = {Bashford, L and Rosenthal, I and Kellis, S and Pejsa, K and Kramer, D and Lee, B and Liu, C and Andersen, RA},
title = {The Neurophysiological Representation of Imagined Somatosensory Percepts in Human Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {41},
number = {10},
pages = {2177-2185},
pmid = {33483431},
issn = {1529-2401},
support = {R25 NS099008/NS/NINDS NIH HHS/United States ; U01 NS098975/NS/NINDS NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Electric Stimulation/*methods ; Electrocorticography ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Neurophysiology/methods ; Parietal Lobe/*physiology ; Somatosensory Cortex/*physiology ; Spinal Cord Injuries/physiopathology ; },
abstract = {Intracortical microstimulation (ICMS) in human primary somatosensory cortex (S1) has been used to successfully evoke naturalistic sensations. However, the neurophysiological mechanisms underlying the evoked sensations remain unknown. To understand how specific stimulation parameters elicit certain sensations we must first understand the representation of those sensations in the brain. In this study we record from intracortical microelectrode arrays implanted in S1, premotor cortex, and posterior parietal cortex of a male human participant performing a somatosensory imagery task. The sensations imagined were those previously elicited by ICMS of S1, in the same array of the same participant. In both spike and local field potential recordings, features of the neural signal can be used to classify different imagined sensations. These features are shown to be stable over time. The sensorimotor cortices only encode the imagined sensation during the imagery task, while posterior parietal cortex encodes the sensations starting with cue presentation. These findings demonstrate that different aspects of the sensory experience can be individually decoded from intracortically recorded human neural signals across the cortical sensory network. Activity underlying these unique sensory representations may inform the stimulation parameters for precisely eliciting specific sensations via ICMS in future work.SIGNIFICANCE STATEMENT Electrical stimulation of human cortex is increasingly more common for providing feedback in neural devices. Understanding the relationship between naturally evoked and artificially evoked neurophysiology for the same sensations will be important in advancing such devices. Here, we investigate the neural activity in human primary somatosensory, premotor, and parietal cortices during somatosensory imagery. The sensations imagined were those previously elicited during intracortical microstimulation (ICMS) of the same somatosensory electrode array. We elucidate the neural features during somatosensory imagery that significantly encode different aspects of individual sensations and demonstrate feature stability over almost a year. The correspondence between neurophysiology elicited with or without stimulation for the same sensations will inform methods to deliver more precise feedback through stimulation in the future.},
}
@article {pmid33482716,
year = {2021},
author = {Kashefi, M and Daliri, MR},
title = {A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1).},
journal = {BMC bioinformatics},
volume = {22},
number = {1},
pages = {26},
pmid = {33482716},
issn = {1471-2105},
mesh = {Animals ; *Brain-Computer Interfaces ; Least-Squares Analysis ; *Motor Cortex ; Movement ; *Neural Networks, Computer ; Rats ; },
abstract = {BACKGROUND: Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal.<br><br>RESULTS: The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ([Formula: see text]) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity.<br><br>CONCLUSION: The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.},
}
@article {pmid33481888,
year = {2021},
author = {Eliseyev, A and Gonzales, IJ and Le, A and Doyle, K and Egbebike, J and Velazquez, A and Agarwal, S and Roh, D and Park, S and Connolly, ES and Claassen, J},
title = {Development of a brain-computer interface for patients in the critical care setting.},
journal = {PloS one},
volume = {16},
number = {1},
pages = {e0245540},
pmid = {33481888},
issn = {1932-6203},
support = {R01 NS106014/NS/NINDS NIH HHS/United States ; R03 NS112760/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Critical Care ; Equipment Design ; Humans ; },
abstract = {OBJECTIVE: Behaviorally unresponsive patients in intensive care units (ICU) are unable to consistently and effectively communicate their most fundamental physical needs. Brain-Computer Interface (BCI) technology has been established in the clinical context, but faces challenges in the critical care environment. Contrary to cue-based BCIs, which allow activation only during pre-determined periods of time, self-paced BCI systems empower patients to interact with others at any time. The study aims to develop a self-paced BCI for patients in the intensive care unit.<br><br>METHODS: BCI experiments were conducted in 18 ICU patients and 5 healthy volunteers. The proposed self-paced BCI system analyzes EEG activity from patients while these are asked to control a beeping tone by performing a motor task (i.e., opening and closing a hand). Signal decoding is performed in real time and auditory feedback given via headphones. Performance of the BCI system was judged based on correlation between the optimal and the observed performance.<br><br>RESULTS: All 5 healthy volunteers were able to successfully perform the BCI task, compared to chance alone (p<0.001). 5 of 14 (36%) conscious ICU patients were able to perform the BCI task. One of these 5 patients was quadriplegic and controlled the BCI system without any hand movements. None of the 4 unconscious patients were able to perform the BCI task.<br><br>CONCLUSIONS: More than one third of conscious ICU patients and all healthy volunteers were able to gain control over the self-paced BCI system. The initial 4 unconscious patients were not. Future studies will focus on studying the ability of behaviorally unresponsive patients with cognitive motor dissociation to control the self-paced BCI system.},
}
@article {pmid33479560,
year = {2020},
author = {Sorkhabi, MM and Benjaber, M and Brown, P and Denison, T},
title = {Physiological Artifacts and the Implications for Brain-Machine-Interface Design.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2020},
number = {},
pages = {1498-1504},
pmid = {33479560},
issn = {1062-922X},
support = {MC_UU_00003/2/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.},
}
@article {pmid33478534,
year = {2021},
author = {Page, DM and George, JA and Wendelken, SM and Davis, TS and Kluger, DT and Hutchinson, DT and Clark, GA},
title = {Discriminability of multiple cutaneous and proprioceptive hand percepts evoked by intraneural stimulation with Utah slanted electrode arrays in human amputees.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {18},
number = {1},
pages = {12},
pmid = {33478534},
issn = {1743-0003},
support = {N66001-15-C-4017//Defense Advanced Research Projects Agency/ ; N66001-12-C-4042//Defense Advanced Research Projects Agency/ ; 1ULTR001067/NH/NIH HHS/United States ; UL1TR002538/NH/NIH HHS/United States ; TL1TR002540/NH/NIH HHS/United States ; GRFP-1747505//National Science Foundation/ ; },
mesh = {Adult ; Amputees ; Arm ; *Artificial Limbs ; Electric Stimulation/*instrumentation ; Electrodes ; Feedback, Sensory/physiology ; Hand ; Humans ; Male ; Middle Aged ; Proprioception/*physiology ; Touch Perception/*physiology ; },
abstract = {BACKGROUND: Electrical stimulation of residual afferent nerve fibers can evoke sensations from a missing limb after amputation, and bionic arms endowed with artificial sensory feedback have been shown to confer functional and psychological benefits. Here we explore the extent to which artificial sensations can be discriminated based on location, quality, and intensity.<br><br>METHODS: We implanted Utah Slanted Electrode Arrays (USEAs) in the arm nerves of three transradial amputees and delivered electrical stimulation via different electrodes and frequencies to produce sensations on the missing hand with various locations, qualities, and intensities. Participants performed blind discrimination trials to discriminate among these artificial sensations.<br><br>RESULTS: Participants successfully discriminated cutaneous and proprioceptive sensations ranging in location, quality and intensity. Performance was significantly greater than chance for all discrimination tasks, including discrimination among up to ten different cutaneous location-intensity combinations (15/30 successes, p&#x2009;<&#x2009;0.0001) and seven different proprioceptive location-intensity combinations (21/40 successes, p&#x2009;<&#x2009;0.0001). Variations in the site of stimulation within the nerve, via electrode selection, enabled discrimination among up to five locations and qualities (35/35 successes, p&#x2009;<&#x2009;0.0001). Variations in the stimulation frequency enabled discrimination among four different intensities at the same location (13/20 successes, p&#x2009;<&#x2009;0.0005). One participant also discriminated among individual stimulation of two different USEA electrodes, simultaneous stimulation on both electrodes, and interleaved stimulation on both electrodes (20/24 successes, p&#x2009;<&#x2009;0.0001).<br><br>CONCLUSION: Electrode location, stimulation frequency, and stimulation pattern can be modulated to evoke functionally discriminable sensations with a range of locations, qualities, and intensities. This rich source of artificial sensory feedback may enhance functional performance and embodiment of bionic arms endowed with a sense of touch.},
}
@article {pmid33478449,
year = {2021},
author = {Samuel, N and So, E and Djuric, U and Diamandis, P},
title = {Consumer-grade electroencephalography devices as potential tools for early detection of brain tumors.},
journal = {BMC medicine},
volume = {19},
number = {1},
pages = {16},
pmid = {33478449},
issn = {1741-7015},
support = {//Canadian Institute of Health Research/ ; },
mesh = {Brain Neoplasms/*diagnostic imaging/pathology ; Electroencephalography/*methods ; Glioblastoma/*diagnostic imaging/pathology ; Glioma/*diagnostic imaging/pathology ; Humans ; },
}
@article {pmid33477728,
year = {2021},
author = {Lytaev, S and Vatamaniuk, I},
title = {Physiological and Medico-Social Research Trends of the Wave P300 and More Late Components of Visual Event-Related Potentials.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33477728},
issn = {2076-3425},
support = {&#x41d;&#x428;-2553.2020.8//Council for Grants of the President of the Russian Federation/ ; },
abstract = {To extend the application of the late waves of the event-related potentials (ERPs) to multiple modalities, devices and software the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined. The objective of this study was aimed to study the sensory processes of the "human-computer interaction" model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow ERPs components. 26 healthy subjects (men) aged 20-26 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analyzes (BMDP Statistical Software) were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. In brain pathology along with the pronounced asymmetry of the wave distribution, reduction of the N150 wave and lengthening of its peak latency, a line of regularities were noted. These include-a pronounced reduction in peak latency P250 and N350, an increased amplitude of N350 in the frontal and central points of registration, a decrease in the amplitude of N450 in the left frontal cortex and its increase in the occipital registration points, activation of the occipital cortex at a time interval of 400-500 ms, as well as fusion later waves. We called such phenomena of the development of cognitive ERP in brain pathology "the incongruence of ERP components". The results of the research are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology.},
}
@article {pmid33477128,
year = {2021},
author = {Ahmadi, N and Constandinou, TG and Bouganis, CS},
title = {Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abde8a},
pmid = {33477128},
issn = {1741-2552},
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; *Deep Learning ; *Motor Cortex ; Neural Networks, Computer ; },
abstract = {Objective. Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.Approach. We propose entire spiking activity (ESA)-an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique-as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks.Main results. Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long-term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data.Significance. Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.},
}
@article {pmid33475039,
year = {2021},
author = {Nsugbe, E},
title = {Brain-machine and muscle-machine bio-sensing methods for gesture intent acquisition in upper-limb prosthesis control: a review.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {2},
pages = {115-128},
doi = {10.1080/03091902.2020.1854357},
pmid = {33475039},
issn = {1464-522X},
mesh = {Arm ; *Artificial Limbs ; *Biosensing Techniques ; Brain/physiology ; Brain-Computer Interfaces ; Electrodiagnosis ; Gestures ; Humans ; Muscles/physiology ; *Pattern Recognition, Automated ; },
abstract = {This paper presents a review of a number of bio-sensing methods for gesture intent signal acquisition in control tasks for upper-limb prosthesis. The paper specifically provides a breakdown of the control task in myoelectric prosthesis, and in addition, highlights and describes the importance of the acquisition of a high-quality bio-signal. The paper also describes commonly used invasive and non-invasive brain and muscle machine interfaces such as electroencephalography, electrocorticography, electroneurography, surface electromyography, sonomyography, mechanomyography, near infra-red, force sensitive resistance/pressure, and magnetoencephalography. Each modality is reviewed based on its operating principle and limitations in gesture recognition, followed by respective advantages and disadvantages. Also described within this paper, are multimodal sensing approaches, which involve data fusion of information from various sensing modalities for an enhanced neuromuscular bio-sensing source. Using a semi-systematic review methodology, we are able to derive a novel tabular approach towards contrasting the various strengths and weaknesses of the reviewed bio-sensing methods towards gesture recognition in a prosthesis interface. This would allow for a streamlined method of down selection of an appropriate bio-sensor given specific prosthesis design criteria and requirements. The paper concludes by highlighting a number of research areas that require more work for strides to be made towards improving and enhancing the connection between man and machine as it concerns upper-limb prosthesis. Such areas include classifier augmentation for gesture recognition, filtering techniques for sensor disturbance rejection, feeling of tactile sensations with an artificial limb.},
}
@article {pmid33472027,
year = {2021},
author = {Rabinowitch, I and Upadhyaya, B and Pant, A and Galski, D and Kreines, L and Bai, J},
title = {Circumventing neural damage in a C. elegans chemosensory circuit using genetically engineered synapses.},
journal = {Cell systems},
volume = {12},
number = {3},
pages = {263-271.e4},
pmid = {33472027},
issn = {2405-4720},
support = {P40 OD010440/OD/NIH HHS/United States ; R01 GM127857/GM/NIGMS NIH HHS/United States ; R21 DC016158/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; Caenorhabditis elegans ; Electrical Synapses/*metabolism ; Genetic Engineering/*methods ; },
abstract = {Neuronal loss can considerably diminish neural circuit function, impairing normal behavior by disrupting information flow in the circuit. Here, we use genetically engineered electrical synapses to reroute the flow of information in a C. elegans damaged chemosensory circuit in order to restore organism behavior. We impaired chemotaxis by removing one pair of interneurons from the circuit then artificially coupled two other adjacent neuron pairs by ectopically expressing the gap junction protein, connexin, in them. This restored chemotaxis in the animals. We expected to observe linear and direct information flow between the connexin-coupled neurons in the recovered circuit but also revealed the formation of new potent left-right lateral electrical connections within the connexin-expressing neuron pairs. Our analysis suggests that these additional electrical synapses help restore circuit function by amplifying weakened neuronal signals in the damaged circuit in addition to emulating the wild-type circuit. A record of this paper's transparent peer review process is included in the Supplemental Information.},
}
@article {pmid33470970,
year = {2021},
author = {Velasco-Álvarez, F and Fernández-Rodríguez, &#xc1; and Medina-Juliá, MT and Ron-Angevin, R},
title = {Speech stream segregation to control an ERP-based auditory BCI.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abdd44},
pmid = {33470970},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Attention/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; *Speech ; },
abstract = {Objective. The use of natural sounds in auditory brain-computer interfaces (BCI) has been shown to improve classification results and usability. Some auditory BCIs are based on stream segregation, in which the subjects must attend one audio stream and ignore the other(s); these streams include some kind of stimuli to be detected. In this work we focus on event-related potentials (ERP) and study whether providing intelligible content to each audio stream could help the users to better concentrate on the desired stream and so to better attend the target stimuli and to ignore the non-target ones.Approach. In addition to a control condition, two experimental conditions, based on the selective attention and the cocktail party effect, were tested using two simultaneous and spatialized audio streams: (a) the condition A2 consisted of an overlap of auditory stimuli (single syllables) on a background consisting of natural speech for each stream, (b) in condition A3, brief alterations of the natural flow of each speech were used as stimuli.Main results. The two experimental proposals improved the results of the control condition (single words as stimuli without a speech background) both in a cross validation analysis of the calibration part and in the online test. The analysis of the ERP responses also presented better discriminability for the two proposals in comparison to the control condition. The results of subjective questionnaires support the better usability of the first experimental condition.Significance. The use of natural speech as background improves the stream segregation in an ERP-based auditory BCI (with significant results in the performance metrics, the ERP waveforms, and in the preference parameter in subjective questionnaires). Future work in the field of ERP-based stream segregation should study the use of natural speech in combination with easily perceived but not distracting stimuli.},
}
@article {pmid33469425,
year = {2020},
author = {Ron-Angevin, R and Medina-Juliá, MT and Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Andre, JM and Lespinet-Najib, V and Garcia, L},
title = {Performance Analysis With Different Types of Visual Stimuli in a BCI-Based Speller Under an RSVP Paradigm.},
journal = {Frontiers in computational neuroscience},
volume = {14},
number = {},
pages = {587702},
pmid = {33469425},
issn = {1662-5188},
abstract = {Brain-Computer Interface (BCI) systems enable an alternative communication channel for severely-motor disabled patients to interact with their environment using no muscular movements. In recent years, the importance of research into non-gaze dependent brain-computer interface paradigms has been increasing, in contrast to the most frequently studied BCI-based speller paradigm (i.e., row-column presentation, RCP). Several visual modifications that have already been validated under the RCP paradigm for communication purposes have not been validated under the most extended non-gaze dependent rapid serial visual presentation (RSVP) paradigm. Thus, in the present study, three different sets of stimuli were assessed under RSVP, with the following communication features: white letters (WL), famous faces (FF), neutral pictures (NP). Eleven healthy subjects participated in this experiment, in which the subjects had to go through a calibration phase, an online phase and, finally, a subjective questionnaire completion phase. The results showed that the FF and NP stimuli promoted better performance in the calibration and online phases, being slightly better in the FF paradigm. Regarding the subjective questionnaires, again both FF and NP were preferred by the participants in contrast to the WL stimuli, but this time the NP stimuli scored slightly higher. These findings suggest that the use of FF and NP for RSVP-based spellers could be beneficial to increase information transfer rate in comparison to the most frequently used letter-based stimuli and could represent a promising communication system for individuals with altered ocular-motor function.},
}
@article {pmid33467420,
year = {2021},
author = {Jochumsen, M and Janjua, TAM and Arceo, JC and Lauber, J and Buessinger, ES and Kæseler, RL},
title = {Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {2},
pages = {},
pmid = {33467420},
issn = {1424-8220},
support = {22357//VELUX FONDEN/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Exoskeleton Device ; Humans ; *Neuronal Plasticity ; *Printing, Three-Dimensional ; Wrist ; },
abstract = {Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.},
}
@article {pmid33465029,
year = {2021},
author = {Gu, X and Cao, Z and Jolfaei, A and Xu, P and Wu, D and Jung, TP and Lin, CT},
title = {EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {18},
number = {5},
pages = {1645-1666},
doi = {10.1109/TCBB.2021.3052811},
pmid = {33465029},
issn = {1557-9964},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; *Epilepsy/diagnosis/physiopathology ; Humans ; *Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.},
}
@article {pmid33462427,
year = {2021},
author = {Long, X and Zhang, SJ},
title = {A novel somatosensory spatial navigation system outside the hippocampal formation.},
journal = {Cell research},
volume = {31},
number = {6},
pages = {649-663},
pmid = {33462427},
issn = {1748-7838},
support = {31872775//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Animals ; Brain ; Entorhinal Cortex ; Hippocampus ; Models, Neurological ; Rats ; *Spatial Navigation ; },
abstract = {Spatially selective firing of place cells, grid cells, boundary vector/border cells and head direction cells constitutes the basic building blocks of a canonical spatial navigation system centered on the hippocampal-entorhinal complex. While head direction cells can be found throughout the brain, spatial tuning outside the hippocampal formation is often non-specific or conjunctive to other representations such as a reward. Although the precise mechanism of spatially selective firing activity is not understood, various studies show sensory inputs, particularly vision, heavily modulate spatial representation in the hippocampal-entorhinal circuit. To better understand the contribution of other sensory inputs in shaping spatial representation in the brain, we performed recording from the primary somatosensory cortex in foraging rats. To our surprise, we were able to detect the full complement of spatially selective firing patterns similar to that reported in the hippocampal-entorhinal network, namely, place cells, head direction cells, boundary vector/border cells, grid cells and conjunctive cells, in the somatosensory cortex. These newly identified somatosensory spatial cells form a spatial map outside the hippocampal formation and support the hypothesis that location information modulates body representation in the somatosensory cortex. Our findings provide transformative insights into our understanding of how spatial information is processed and integrated in the brain, as well as functional operations of the somatosensory cortex in the context of rehabilitation with brain-machine interfaces.},
}
@article {pmid33460382,
year = {2021},
author = {Yang, L and Song, Y and Ma, K and Xie, L},
title = {Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {368-379},
doi = {10.1109/TNSRE.2021.3051958},
pmid = {33460382},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; },
abstract = {With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt the constraint of classification loss, which hardly obtains the features with high discrimination and limits the improvement of EEG decoding accuracy. In this paper, a discriminative feature learning strategy is proposed to improve the discrimination of features, which includes the central distance loss (CD-loss), the central vector shift strategy, and the central vector update process. First, the CD-loss is proposed to make the same class of samples converge to the corresponding central vector. Then, the central vector shift strategy extends the distance between different classes of samples in the feature space. Finally, the central vector update process is adopted to avoid the non-convergence of CD-loss and weaken the influence of the initial value of central vectors on the final results. In addition, overfitting is another severe challenge for deep learning-based EEG decoding methods. To deal with this problem, a data augmentation method based on circular translation strategy is proposed to expand the experimental datasets without introducing any extra noise or losing any information of the original data. To validate the effectiveness of the proposed method, we conduct some experiments on two public motor imagery EEG datasets (BCI competition IV 2a and 2b dataset), respectively. The comparison with current state-of-the-art methods indicates that our method achieves the highest average accuracy and good stability on the two experimental datasets.},
}
@article {pmid33460328,
year = {2021},
author = {Fouad, IA},
title = {A robust and reliable online P300-based BCI system using Emotiv EPOC + headset.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {2},
pages = {94-114},
doi = {10.1080/03091902.2020.1853840},
pmid = {33460328},
issn = {1464-522X},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Male ; Online Systems ; *Signal Processing, Computer-Assisted ; Software ; Support Vector Machine ; Young Adult ; },
abstract = {Brain-computer interface (BCI) system aims to enable interaction with people and therefore the environment without muscular activation, using changes in brain signals due to the execution of cognitive tasks. The target of the presented work is to investigate the power of Emotiv EPOC + headset to detect and record the P300 wave. Moreover, the effect of preprocessing the acquired signal was studied. Five participants were asked to attend different sessions to an equivalent 6x6 matrix while the rows and columns were randomly flashed at a rate of 200 ms. The acquired EEG data were sent wirelessly to OpenViBE software, which is employed to run the P300 speller. Two classification methods were tried: Linear discriminate analysis (LDA) and support vector machine (SVM). The capability of the headset to detect the P300 signals is proven by the results. Additionally, results show that participants reached accuracy up to 90 and 70% after only two training sessions for Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers, respectively. The significance of this work is to demonstrate that such a portable and affordable headset might be useful to design and implement a robust and reliable online P300-based BCI system.},
}
@article {pmid33459818,
year = {2021},
author = {Amin, A and Ahmed, I and Khalid, N and Schumann, P and Busse, HJ and Khan, IU and Ali, A and Li, S and Li, WJ},
title = {Zafaria cholistanensis gen. nov. sp. nov., a moderately thermotolerant and halotolerant actinobacterium isolated from Cholistan desert soil of Pakistan.},
journal = {Archives of microbiology},
volume = {203},
number = {4},
pages = {1717-1729},
pmid = {33459818},
issn = {1432-072X},
support = {2017FY100300//Science &amp; Technology Basic Resources Investigation Program of China/ ; },
mesh = {Arthrobacter/genetics ; Bacterial Typing Techniques ; Base Composition/genetics ; DNA, Bacterial/*genetics ; Fatty Acids/analysis ; *Micrococcaceae/classification/genetics/isolation &amp; purification ; Nucleic Acid Hybridization/genetics ; Pakistan ; Peptidoglycan/chemistry ; Phospholipids/analysis ; Phylogeny ; RNA, Ribosomal, 16S/genetics ; Sequence Analysis, DNA ; Soil ; Soil Microbiology ; },
abstract = {A Gram-staining positive, non-spore forming, non-pigmented and non-motile bacterium, designated strain NCCP-1664[T], was isolated from Cholistan desert, Pakistan. Cells of strain NCCP-1664[T] were strictly aerobic, catalase positive and oxidase negative with a rod to coccus growth cycle and can grow at pH 6.0-9.0 (optimum pH 7-8) at 28-45 °C (optimum 37 °C) and could tolerate 0-16% NaCl (optimum 2%). Phylogenetic analyses based on 16S rRNA gene sequence revealed that strain NCCP-1664[T] belongs to the family Micrococcaceae and was related to members of the genus Arthrobacter having highest sequence similarities with Arthrobacter ginkgonis (98.9%), A. halodurans (97.7%) and A. oryzae (97.1%) and less than 97% with other related taxa. DNA-DNA relatedness values of strain NCCP-1664[T] with above mentioned type strains were found to be less than 54%, whereas digital DDH and average nucleotide identity (ANI) values with A. oryzae were 20.9 and of 74.3%, respectively. DNA G + C content of strain NCCP-1664[T] was 70.0 mol%. Chemotaxonomic data of strain NCCP-1664[T] showed the peptidoglycan type as A3α L-Lys-L -Ala; menaquinones as MK-9(H2) (67%), MK-8(H2) (32%) and MK-7(H2) (1%), major fatty acids as anteiso -C15:0 (51.2%), anteiso-C17:0 (9.6%) and C18:1ω9c (6.9%) and polar lipids profile comprising of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, digalactosyldiacylglycerol, small amounts of monogalactosyldiacylglycerol, trimannosyldiacylglycerol and three unidentified lipids. The phylogenomic analyses along with chemotaxonomic data, physiological, biochemical characteristics allowed to describe it as representative of a novel genus, for which the name Zafaria cholistanensis gen. nov. sp. nov. is proposed with the type strain NCCP-1664[T] (= DSM 29936[T] = KCTC 39549[T]).},
}
@article {pmid33459604,
year = {2021},
author = {Daudén Roquet, C and Sas, C},
title = {A Mindfulness-Based Brain-Computer Interface to Augment Mandala Coloring for Depression: Protocol for a Single-Case Experimental Design.},
journal = {JMIR research protocols},
volume = {10},
number = {1},
pages = {e20819},
pmid = {33459604},
issn = {1929-0748},
abstract = {BACKGROUND: The regular practice of mindfulness has been shown to provide benefits for mental well-being and prevent depression relapse. Technology-mediated interventions can facilitate the uptake and sustained practice of mindfulness, yet the evaluation of interactive systems, such as brain-computer interfaces, has been little explored.<br><br>OBJECTIVE: The objective of this paper is to present an interactive mindfulness-based technology to improve mental well-being in people who have experienced depression. The system, Anima, is a brain-computer interface that augments mandala coloring by providing a generative color palette based on the unfolding mindfulness states during the practice. In addition, this paper outlines a multiple-baseline, single-case experimental design methodology to evaluate training effectiveness.<br><br>METHODS: Adult participants who have experienced depression in the past, have finished treatment within the last year, and can provide informed consent will be able to be recruited. The Anima system, consisting of 2 tablets and a nonintrusive mental activity headband, will be delivered to participants to use during the study. Measures include state and trait mindfulness, depression symptoms, mental well-being, and user experience, and these measures will be taken throughout the baseline, intervention, and monitoring phases. The data collection will take place in the form of a questionnaire before and after each mandala-coloring session and a semistructured interview every 2 weeks. Trial results will be analyzed using structured visual analysis, supplemented with statistical analysis appropriate to single-case methodology.<br><br>RESULTS: Study results will offer new insights into the deployment and evaluation of novel interactive brain-computer interfaces for mindfulness training in the context of mental health. Moreover, findings will validate the effectiveness of this training protocol to improve the mental well-being of people who have had depression. Participants will be recruited locally through the National Health Service.<br><br>CONCLUSIONS: Evidence will assist in the design and evaluation of brain-computer interfaces and mindfulness technologies for mental well-being and the necessary services to support people who have experienced depression.<br><br>PRR1-10.2196/20819.},
}
@article {pmid33455378,
year = {2020},
author = {Kelly, A and Farid, N and Krukiewicz, K and Belisle, N and Groarke, J and Waters, EM and Trotier, A and Laffir, F and Kilcoyne, M and O'Connor, GM and Biggs, MJ},
title = {Laser-Induced Periodic Surface Structure Enhances Neuroelectrode Charge Transfer Capabilities and Modulates Astrocyte Function.},
journal = {ACS biomaterials science &amp; engineering},
volume = {6},
number = {3},
pages = {1449-1461},
doi = {10.1021/acsbiomaterials.9b01321},
pmid = {33455378},
issn = {2373-9878},
mesh = {Animals ; *Astrocytes ; Iridium ; Lasers ; Microelectrodes ; *Neuroglia ; Rats ; },
abstract = {The brain machine interface (BMI) describes a group of technologies capable of communicating with excitable nervous tissue within the central nervous system (CNS). BMIs have seen major advances in recent years, but these advances have been impeded because of a temporal deterioration in the signal to noise ratio of recording electrodes following insertion into the CNS. This deterioration has been attributed to an intrinsic host tissue response, namely, reactive gliosis, which involves a complex series of immune mediators, resulting in implant encapsulation via the synthesis of pro-inflammatory signaling molecules and the recruitment of glial cells. There is a clinical need to reduce tissue encapsulation in situ and improve long-term neuroelectrode functionality. Physical modification of the electrode surface at the nanoscale could satisfy these requirements by integrating electrochemical and topographical signals to modulate neural cell behavior. In this study, commercially available platinum iridium (Pt/Ir) microelectrode probes were nanotopographically functionalized using femto/picosecond laser processing to generate laser-induced periodic surface structures (LIPSS). Three different topographies and their physical properties were assessed by scanning electron microscopy and atomic force microscopy. The electrochemical properties of these interfaces were investigated using electrochemical impedance spectroscopy and cyclic voltammetry. The in vitro response of mixed cortical cultures (embryonic rat E14/E17) was subsequently assessed by confocal microscopy, ELISA, and multiplex protein array analysis. Overall LIPSS features improved the electrochemical properties of the electrodes, promoted cell alignment, and modulated the expression of multiple ion channels involved in key neuronal functions.},
}
@article {pmid33453213,
year = {2021},
author = {Pizzolato, C and Gunduz, MA and Palipana, D and Wu, J and Grant, G and Hall, S and Dennison, R and Zafonte, RD and Lloyd, DG and Teng, YD},
title = {Non-invasive approaches to functional recovery after spinal cord injury: Therapeutic targets and multimodal device interventions.},
journal = {Experimental neurology},
volume = {339},
number = {},
pages = {113612},
doi = {10.1016/j.expneurol.2021.113612},
pmid = {33453213},
issn = {1090-2430},
mesh = {Adrenergic Agonists/administration &amp; dosage ; Animals ; *Brain-Computer Interfaces/trends ; Combined Modality Therapy/methods/trends ; *Electric Stimulation Therapy/methods/trends ; Humans ; *Neural Prostheses/trends ; Neuronal Plasticity/*physiology ; Recovery of Function/*physiology ; Spinal Cord Injuries/diagnosis/physiopathology/*therapy ; },
abstract = {This paper is an interdisciplinary narrative review of efficacious non-invasive therapies that are increasingly used to restore function in people with chronic spinal cord injuries (SCI). First presented are the secondary injury cascade set in motion by the primary lesion and highlights in therapeutic development for mitigating the acute pathophysiologic process. Then summarized are current pharmacological strategies for modulation of noradrenergic, serotonergic, and dopaminergic neurotransmission to enhance recovery in bench and clinical studies of subacute and chronic SCI. Last examined is how neuromechanical devices (i.e., electrical stimulation, robotic assistance, brain-computer interface, and augmented sensory feedback) could be comprehensively engineered to engage efferent and afferent motosensory pathways to induce neuroplasticity-based neural pattern generation. Emerging evidence shows that computational models of the human neuromusculoskeletal system (i.e., human digital twins) can serve as functionalized anchors to integrate different neuromechanical and pharmacological interventions into a single multimodal prothesis. The system, if appropriately built, may cybernetically optimize treatment outcomes via coordination of heterogeneous biosensory, system output, and control signals. Overall, these rehabilitation protocols involved neuromodulation to evoke beneficial adaptive changes within spared supraspinal, intracord, and peripheral neuromuscular circuits to elicit neurological improvement. Therefore, qualitatively advancing the theoretical understanding of spinal cord neurobiology and neuromechanics is pivotal to designing new ways to reinstate locomotion after SCI. Future research efforts should concentrate on personalizing combination therapies consisting of pharmacological adjuncts, targeted neurobiological and neuromuscular repairs, and brain-computer interfaces, which follow multimodal neuromechanical principles.},
}
@article {pmid33451080,
year = {2021},
author = {Browarska, N and Kawala-Sterniuk, A and Zygarlicki, J and Podpora, M and Pelc, M and Martinek, R and Gorzelańczyk, EJ},
title = {Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33451080},
issn = {2076-3425},
support = {Project No. 296 SP2020/156//Ministry of Education of the Czech Republic/ ; },
abstract = {Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.},
}
@article {pmid33449886,
year = {2022},
author = {Bang, JS and Lee, MH and Fazli, S and Guan, C and Lee, SW},
title = {Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {33},
number = {7},
pages = {3038-3049},
doi = {10.1109/TNNLS.2020.3048385},
pmid = {33449886},
issn = {2162-2388},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Neuroimaging ; },
abstract = {Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space of EEG data is highly dimensional and signal patterns are specific to the subject, appropriate methods for feature representation are required to enhance the decoding accuracy of the CNN model. Furthermore, neural changes exhibit high variability between sessions, subjects within a single session, and trials within a single subject, resulting in major issues during the modeling stage. In addition, there are many subject-dependent factors, such as frequency ranges, time intervals, and spatial locations at which the signal occurs, which prevent the derivation of a robust model that can achieve the parameterization of these factors for a wide range of subjects. However, previous studies did not attempt to preserve the multivariate structure and dependencies of the feature space. In this study, we propose a method to generate a spatiospectral feature representation that can preserve the multivariate information of EEG data. Specifically, 3-D feature maps were constructed by combining subject-optimized and subject-independent spectral filters and by stacking the filtered data into tensors. In addition, a layer-wise decomposition model was implemented using our 3-D-CNN framework to secure reliable classification results on a single-trial basis. The average accuracies of the proposed model were 87.15% (±7.31), 75.85% (±12.80), and 70.37% (±17.09) for the BCI competition data sets IV_2a, IV_2b, and OpenBMI data, respectively. These results are better than those obtained by state-of-the-art techniques, and the decomposition model obtained the relevance scores for neurophysiologically plausible electrode channels and frequency domains, confirming the validity of the proposed approach.},
}
@article {pmid33444278,
year = {2020},
author = {Yasinzai, MN and Ider, YZ},
title = {New approach for designing cVEP BCI stimuli based on superposition of edge responses.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {4},
pages = {045018},
doi = {10.1088/2057-1976/ab98e7},
pmid = {33444278},
issn = {2057-1976},
mesh = {Adult ; Algorithms ; Brain/*diagnostic imaging ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Linear Models ; Male ; Neurologic Examination ; Photic Stimulation/methods ; Reproducibility of Results ; Vision, Ocular ; Young Adult ; },
abstract = {The purpose of this study is to develop a new methodology for designing stimulus sequences for Brain Computer Interfaces that utilize code modulated Visually Evoked Potentials (cVEP BCIs), based on experimental results regarding the behavior and the properties of the actual EEG responses of the visual system to binary-coded visual stimuli, such that training time is reduced and possible number of targets is increased. EEG from 8 occipital sites is recorded with 2000 sps, in response to visual stimuli presented on a computer monitor with 60 Hz refresh rate. EEG responses of the visual system to black-to-white and white-to-black transitions of a target area on the monitor are recorded for 500 ms, for 160 trials, and signal-averaged to obtain the onset (positive edge) and offset (negative edge) responses, respectively. It is found that both edge responses are delayed by 50 ms and wane completely within 350 ms. These edge responses are then used to generate (predict) the EEG responses to arbitrary binary stimulus sequences using the superposition principle. It is found that the generated and the measured EEG responses to certain (16) simple short sequences (16.67-350 ms) are highly correlated. These 'optimal short patterns' are then randomly combined to design the long (120 bit, 2 sec) 'Superposition Optimized Pulse (SOP)' sequences, and their EEG response templates are obtained by superposition of the edge responses. A SOP sequence-based Visual Speller BCI application yielded higher accuracy (95.9%) and Information Transfer Rate (ITR) (57.2 bpm), compared to when superposition principle is applied to conventional m-sequences and randomly generated sequences. Training for the BCI application involves only the acquisition of the edge responses and takes less than 4 min. This is the first study in which the EEG templates for cVEP BCI sequences are obtained by the superposition of edge responses.},
}
@article {pmid33444236,
year = {2020},
author = {Almulla, L and Al-Naib, I and Althobaiti, M},
title = {Hemodynamic responses during standing and sitting activities: a study toward fNIRS-BCI.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {5},
pages = {055005},
doi = {10.1088/2057-1976/aba102},
pmid = {33444236},
issn = {2057-1976},
mesh = {Adult ; *Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Female ; *Hemodynamics ; Humans ; Male ; Motor Cortex/*physiology ; Movement ; *Sitting Position ; Spectroscopy, Near-Infrared/*methods ; *Standing Position ; },
abstract = {In this paper, we utilized functional near-infrared spectroscopy (fNIRS) technology to examine the hemodynamic responses in the motor cortex for two conditions, namely standing and sitting tasks. Nine subjects performed five trials of standing and sitting (SAS) tasks with both real movements and imagery thinking of SAS. A group level of statistical parametric mapping (SPM) analysis during these tasks showed bilateral activation of oxy-hemoglobin for both real movements and imagery experiments. Interestingly, the SPM analysis clearly revealed that the sitting tasks induced a higher oxy-hemoglobin level activation compared to the standing task. Remarkably, this finding is persistent across the 22 measured channels at the individual and group levels for both experiments. Furthermore, six features were extracted from pre-processed HbO signals and the performance of four different classifiers was examined in order to test the viability of using SAS tasks in future fNIRS-brain-computer interface (fNIRS-BCI) systems. In particular, two features-combination tests revealed that the signal slope with signal variance represents one of the three best two-combined features for its consistency in providing high accuracy results for both real and imagery experiments. This study shows the potential of implementing such tasks into the fNIRS-BCI system. In the future, the results of this work could pave the way towards the application of fNIRS-BCI in lower limb rehabilitation.},
}
@article {pmid33438679,
year = {2020},
author = {Volosyak, I and Rezeika, A and Benda, M and Gembler, F and Stawicki, P},
title = {Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {3},
pages = {035034},
doi = {10.1088/2057-1976/ab87e6},
pmid = {33438679},
issn = {2057-1976},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Educational Status ; Electroencephalography/*methods ; Equipment Design ; *Evoked Potentials, Visual ; Female ; Humans ; Language ; *Literacy ; Male ; Movement ; Reproducibility of Results ; Social Class ; Software ; Surveys and Questionnaires ; *User-Computer Interface ; Vision, Ocular ; Young Adult ; },
abstract = {Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%-30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates. Recent progress in the BCIs based on the visually evoked potentials (VEPs) necessitates re-considering of this term, as very often all subjects are able to use VEP-based BCI systems. This study examines correlations among BCI performance, personal preferences, and further demographic factors for three different modern visually evoked BCI paradigms: (1) the conventional Steady-State Visual Evoked Potentials (SSVEPs) based on visual stimuli flickering at specific constant frequencies (fVEP), (2) Steady-State motion Visual Evoked Potentials (SSmVEP), and (3) code-modulated Visual Evoked Potentials (cVEP). Demographic parameters, as well as handedness, vision correction, BCI experience, etc., have no significant effect on the performance of VEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only 20 out of a total of 86 participants indicated a change in fatigue during the experiment. 83 subjects were able to successfully finish all spelling tasks with the fVEP speller, with a mean (SD) information transfer rate of 31.87 bit/min (9.83) and an accuracy of 95.28% (5.18), respectively. Compared to that, 80 subjects were able to successfully finish all spelling tasks using SSmVEP, with a mean information transfer rate of 26.44 bit/min (8.04) and an accuracy of 91.10% (6.01), respectively. Finally, all 86 subjects were able to successfully finish all spelling tasks with the cVEP speller, with a mean information transfer rate of 40.23 bit/min (7.63) and an accuracy of 97.83% (3.37).},
}
@article {pmid33438675,
year = {2020},
author = {Stefano Filho, CA and Costa, TBS and S Uribe, LF and Rodrigues, PG and Soriano, DC and Attux, R and Castellano, G},
title = {On the (in)efficacy of motor imagery training without feedback and event-related desynchronizations considerations.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {3},
pages = {035030},
doi = {10.1088/2057-1976/ab8992},
pmid = {33438675},
issn = {2057-1976},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Equipment Design ; *Feedback ; Female ; Humans ; Image Processing, Computer-Assisted ; *Imagination ; Male ; Models, Statistical ; Motor Skills ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Motor imagery (MI) constitutes a recurrent strategy for signals generation in brain-computer interfaces (BCIs) - systems that aim to control external devices by directly associating brain responses to distinct commands. Although great improvement has been achieved in MI-BCIs performance over recent years, they still suffer from inter- and intra-subject variability issues. As an attempt to cope with this, some studies have suggested that MI training should aid users to appropriately modulate their response for BCI usage: generally, this training is performed based on the sensorimotor rhythms' modulation over the primary sensorimotor cortex (PMC), with the signal being feedbacked to the user. Nonetheless, recent studies have revisited the actual involvement of the PMC into MI, and little to no attention has been devoted to understanding the participation of other cortical areas into training protocols. Therefore, in this work, our aim was to analyze the response induced by hands MI of 10 healthy subjects in the form of event-related desynchronizations (ERDs) and to assess whether features from beyond the PMC might be useful for hands MI classification. We investigated how this response occurs for distinct frequency intervals between 7-30 Hz, and ex0plored changes in their evocation pattern across 12 MI training sessions without feedback. Overall, we found that ERD patterns occur differently for the frequencies encompassed by the μ and β bands, with its evocation being favored for the first band. Over time, the no-feedback approach was inefficient to aid in enhancing ERD evocation (EO). Moreover, to some extent, EO tends to decrease over blocks within a given run, and runs within an MI session, but remains stable within an MI block. We also found that the C3/C4 pair is not necessarily optimal for data classification, and both spectral and spatial subjects' specificities should be considered when designing training protocols.},
}
@article {pmid33438668,
year = {2020},
author = {Ojeda, A and Buscher, N and Balasubramani, P and Maric, V and Ramanathan, D and Mishra, J},
title = {SimBSI: An open-source Simulink library for developing closed-loop brain signal interfaces in animals and humans.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {3},
pages = {035023},
pmid = {33438668},
issn = {2057-1976},
support = {IK2 BX003308/BX/BLRD VA/United States ; R01 MH123650/MH/NIMH NIH HHS/United States ; T32 MH018399/MH/NIMH NIH HHS/United States ; T32 MH020002/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Behavior, Animal ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Graphics ; Computers ; Electroencephalography/*instrumentation/*methods ; Humans ; Mice ; Programming Languages ; *Signal Processing, Computer-Assisted ; *Software ; Species Specificity ; User-Computer Interface ; },
abstract = {OBJECTIVE: A promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans is still lacking. The objective of this paper is to develop such a unifying neurotechnological software framework.<br><br>APPROACH: Here we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.<br><br>MAIN RESULTS: We use several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment.<br><br>SIGNIFICANCE: With the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.},
}
@article {pmid33438628,
year = {2020},
author = {Togha, MM and Salehi, MR and Abiri, E},
title = {Calibration time reduction through local activities estimation in motor imagery-based brain-computer interfaces.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {2},
pages = {025002},
doi = {10.1088/2057-1976/ab70e7},
pmid = {33438628},
issn = {2057-1976},
mesh = {*Algorithms ; Brain/*physiology ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Calibration ; Electrodes ; Electroencephalography/*methods ; Humans ; *Imagination ; Motor Activity/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {OBJECTIVE: One of the main limitations for the practical use of brain-computer interfaces (BCI) is the calibration phase. Several methods have been suggested for the truncating of this undesirable time, including various variants of the popular CSP method. In this study, we cope with the problem, using local activities estimation (LAE).<br><br>APPROACH: LAE is a spatial filtering technique that uses the EEG data of all electrodes along with their position information to emphasize the local activities. After spatial filtering by LAE, a few electrodes are selected based on physiological information. Then the features are extracted from the signal using FFT and classified by the support vector machine. In this study, the LAE is compared with CSP, RCSP, FBCSP and FBRCSP in two different electrode configurations of 118 and 64-channel.<br><br>MAIN RESULTS: The LAE outperforms CSP-based methods in all experiments using the different number of training samples. The LAE method also obtains an average classification accuracy of 84% even with a calibration time of fewer than 2 min Significance: Unlike CSP-based methods, the LAE does not use the covariance matrix, and also uses a priori physiological information. Therefore, LAE can significantly reduce the calibration time while maintaining proper accuracy. It works well even with a few training samples.},
}
@article {pmid33438531,
year = {2021},
author = {Mao, Y and Jin, J and Xu, R and Li, S and Miao, Y and Cichocki, A},
title = {The Influence of Visual Attention on The Performance of A Novel Tactile P300 Brain-Computer Interface with Cheeks-Stim Paradigm.},
journal = {International journal of neural systems},
volume = {31},
number = {4},
pages = {2150004},
doi = {10.1142/S0129065721500040},
pmid = {33438531},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Cheek ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Touch ; },
abstract = {Tactile P300 brain-computer interface (BCI) generally has a worse accuracy and information transfer rate (ITR) than the visual-based BCI. It may be due to the fact that human beings have a relatively poor tactile perception. This study investigated the influence of visual attention on the performance of a tactile P300 BCI. We designed our paradigms based on a novel cheeks-stim paradigm which attached the stimulators on the subject's cheeks. Two paradigms were designed as follows: a paradigm with no visual attention and another paradigm with visual attention to the target position. Eleven subjects were invited to perform the two paradigms. We also recorded and analyzed the eyeball movement data during the paradigm with visual attention to explore whether the eyeball movement would have an effect on the BCI classification. The average online accuracy was 89.09% for the paradigm with visual attention, which was significantly higher than that of the paradigm with no visual attention (70.45%). Significant difference in ITR was also found between the two paradigms ([Formula: see text]). The results demonstrated that visual attention was an effective method to improve the performance of tactile P300 BCI. Our findings suggested that it may be feasible to complete an efficient tactile BCI system by adding visual attention.},
}
@article {pmid33436950,
year = {2021},
author = {Geissler, CF and Schneider, J and Frings, C},
title = {Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {705},
pmid = {33436950},
issn = {2045-2322},
mesh = {Adult ; Automobile Driving/*psychology ; Cognition/*physiology ; *Computer Simulation ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Prefrontal Cortex/*physiology ; Spectroscopy, Near-Infrared/*methods ; Task Performance and Analysis ; *Workload ; Young Adult ; },
abstract = {Optimal mental workload plays a key role in driving performance. Thus, driver-assisting systems that automatically adapt to a drivers current mental workload via brain-computer interfacing might greatly contribute to traffic safety. To design economic brain computer interfaces that do not compromise driver comfort, it is necessary to identify brain areas that are most sensitive to mental workload changes. In this study, we used functional near-infrared spectroscopy and subjective ratings to measure mental workload in two virtual driving environments with distinct demands. We found that demanding city environments induced both higher subjective workload ratings as well as higher bilateral middle frontal gyrus activation than less demanding country environments. A further analysis with higher spatial resolution revealed a center of activation in the right anterior dorsolateral prefrontal cortex. The area is highly involved in spatial working memory processing. Thus, a main component of drivers' mental workload in complex surroundings might stem from the fact that large amounts of spatial information about the course of the road as well as other road users has to constantly be upheld, processed and updated. We propose that the right middle frontal gyrus might be a suitable region for the application of powerful small-area brain computer interfaces.},
}
@article {pmid33436508,
year = {2021},
author = {Trouillon, J and Ragno, M and Simon, V and Attrée, I and Elsen, S},
title = {Transcription Inhibitors with XRE DNA-Binding and Cupin Signal-Sensing Domains Drive Metabolic Diversification in Pseudomonas.},
journal = {mSystems},
volume = {6},
number = {1},
pages = {},
pmid = {33436508},
issn = {2379-5077},
abstract = {Transcription factors (TFs) are instrumental in the bacterial response to new environmental conditions. They can act as direct signal sensors and subsequently induce changes in gene expression leading to physiological adaptation. Here, by combining transcriptome sequencing (RNA-seq) and cistrome determination (DAP-seq), we studied a family of eight TFs in Pseudomonas aeruginosa This family, encompassing TFs with XRE-like DNA-binding and cupin signal-sensing domains, includes the metabolic regulators ErfA, PsdR, and PauR and five so-far-unstudied TFs. The genome-wide delineation of their regulons identified 39 regulatory interactions with genes mostly involved in metabolism. We found that the XRE-cupin TFs are inhibitors of their neighboring genes, forming local, functional units encoding proteins with functions in condition-specific metabolic pathways. Growth phenotypes of isogenic mutants highlighted new roles for PauR and PA0535 in polyamines and arginine metabolism. The phylogenetic analysis of this family of regulators across the bacterial kingdom revealed a wide diversity of such metabolic regulatory modules and identified species with potentially higher metabolic versatility. Numerous genes encoding uncharacterized XRE-cupin TFs were found near metabolism-related genes, illustrating the need of further systematic characterization of transcriptional regulatory networks in order to better understand the mechanisms of bacterial adaptation to new environments.IMPORTANCE Bacteria of the Pseudomonas genus, including the major human pathogen Pseudomonas aeruginosa, are known for their complex regulatory networks and high number of transcription factors, which contribute to their impressive adaptive ability. However, even in the most studied species, most of the regulators are still uncharacterized. With the recent advances in high-throughput sequencing methods, it is now possible to fill this knowledge gap and help the understanding of how bacteria adapt and thrive in new environments. By leveraging these methods, we provide an example of a comprehensive analysis of an entire family of transcription factors and bring new insights into metabolic and regulatory adaptation in the Pseudomonas genus.},
}
@article {pmid33435843,
year = {2021},
author = {Aeles, J and Horst, F and Lapuschkin, S and Lacourpaille, L and Hug, F},
title = {Revealing the unique features of each individual's muscle activation signatures.},
journal = {Journal of the Royal Society, Interface},
volume = {18},
number = {174},
pages = {20200770},
pmid = {33435843},
issn = {1742-5662},
mesh = {Electromyography ; Humans ; Machine Learning ; Movement ; Muscle, Skeletal ; *Muscles ; *Walking ; },
abstract = {There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.},
}
@article {pmid33432926,
year = {2021},
author = {Zhao, TT and Feng, YJ and Doanh, PN and Sayasone, S and Khieu, V and Nithikathkul, C and Qian, MB and Hao, YT and Lai, YS},
title = {Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia.},
journal = {eLife},
volume = {10},
number = {},
pages = {},
pmid = {33432926},
issn = {2050-084X},
mesh = {Cambodia ; Endemic Diseases/*statistics &amp; numerical data ; Humans ; Laos ; Models, Theoretical ; Opisthorchiasis/*epidemiology ; Prevalence ; Spatio-Temporal Analysis ; Thailand ; Vietnam/epidemiology ; },
abstract = {Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but have not been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate, and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% Bayesian credible intervals [BCI]: 10.10-15.06) people were estimated to be infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.},
}
@article {pmid33431994,
year = {2021},
author = {Hosman, T and Hynes, JB and Saab, J and Wilcoxen, KG and Buchbinder, BR and Schmansky, N and Cash, SS and Eskandar, EN and Simeral, JD and Franco, B and Kelemen, J and Vargas-Irwin, CE and Hochberg, LR},
title = {Auditory cues reveal intended movement information in middle frontal gyrus neuronal ensemble activity of a person with tetraplegia.},
journal = {Scientific reports},
volume = {11},
number = {1},
pages = {98},
pmid = {33431994},
issn = {2045-2322},
support = {T32 MH020068/MH/NIMH NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; DP2 NS111817/NS/NINDS NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; },
mesh = {*Acoustic Stimulation ; Adult ; Auditory Cortex/*physiopathology ; Brain-Computer Interfaces ; Cues ; Electrodes, Implanted ; Frontal Lobe/physiopathology ; Humans ; Male ; Microelectrodes ; Movement/*physiology ; Neurons/physiology ; Prefrontal Cortex/*physiopathology ; Quadriplegia/*physiopathology ; Self-Help Devices ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) allow people with paralysis to directly control assistive devices using neural activity associated with the intent to move. Realizing the full potential of iBCIs critically depends on continued progress in understanding how different cortical areas contribute to movement control. Here we present the first comparison between neuronal ensemble recordings from the left middle frontal gyrus (MFG) and precentral gyrus (PCG) of a person with tetraplegia using an iBCI. As expected, PCG was more engaged in selecting and generating intended movements than in earlier perceptual stages of action planning. By contrast, MFG displayed movement-related information during the sensorimotor processing steps preceding the appearance of the action plan in PCG, but only when the actions were instructed using auditory cues. These results describe a previously unreported function for neurons in the human left MFG in auditory processing contributing to motor control.},
}
@article {pmid33431874,
year = {2021},
author = {Jaramillo-Gonzalez, A and Wu, S and Tonin, A and Rana, A and Ardali, MK and Birbaumer, N and Chaudhary, U},
title = {A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state.},
journal = {Scientific data},
volume = {8},
number = {1},
pages = {8},
pmid = {33431874},
issn = {2052-4463},
support = {16SV7701 CoMiCon//Bundesministerium f&#xfc;r Bildung und Forschung (Federal Ministry of Education and Research)/International ; 16SV7701 CoMiCon//Bundesministerium f&#xfc;r Bildung und Forschung (Federal Ministry of Education and Research)/International ; 16SV7701 CoMiCon//Bundesministerium f&#xfc;r Bildung und Forschung (Federal Ministry of Education and Research)/International ; 16SV7701 CoMiCon//Bundesministerium f&#xfc;r Bildung und Forschung (Federal Ministry of Education and Research)/International ; DFG BI 195/77-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/International ; DFG BI 195/77-1//Deutsche Forschungsgemeinschaft (German Research Foundation)/International ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; },
mesh = {*Amyotrophic Lateral Sclerosis/diagnosis/physiopathology ; *Electroencephalography ; *Electrooculography ; Eye Movements ; Humans ; },
abstract = {The dataset presented here contains recordings of electroencephalogram (EEG) and electrooculogram (EOG) from four advanced locked-in state (LIS) patients suffering from ALS (amyotrophic lateral sclerosis). These patients could no longer use commercial eye-trackers, but they could still move their eyes and used the remnant oculomotor activity to select letters to form words and sentences using a novel auditory communication system. Data were recorded from four patients during a variable range of visits (from 2 to 10), each visit comprised of 3.22 ± 1.21 days and consisted of 5.57 ± 2.61 sessions recorded per day. The patients performed a succession of different sessions, namely, Training, Feedback, Copy spelling, and Free spelling. The dataset provides an insight into the progression of ALS and presents a valuable opportunity to design and improve assistive and alternative communication technologies and brain-computer interfaces. It might also help redefine the course of progression in ALS, thereby improving clinical judgement and treatment.},
}
@article {pmid33429938,
year = {2021},
author = {Alzahab, NA and Apollonio, L and Di Iorio, A and Alshalak, M and Iarlori, S and Ferracuti, F and Monteriù, A and Porcaro, C},
title = {Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33429938},
issn = {2076-3425},
abstract = {BACKGROUND: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015.<br><br>OBJECTIVES: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic.<br><br>METHODS: We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends.<br><br>RESULTS: Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency.<br><br>SIGNIFICANCE: To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.},
}
@article {pmid33425045,
year = {2021},
author = {Mahmud, M and Kaiser, MS and McGinnity, TM and Hussain, A},
title = {Deep Learning in Mining Biological Data.},
journal = {Cognitive computation},
volume = {13},
number = {1},
pages = {1-33},
pmid = {33425045},
issn = {1866-9956},
abstract = {Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures-known as deep learning (DL)-have been successfully applied to solve many complex pattern recognition problems. To investigate how DL-especially its different architectures-has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.},
}
@article {pmid33424562,
year = {2020},
author = {Cruz-Garza, JG and Sujatha Ravindran, A and Kopteva, AE and Rivera Garza, C and Contreras-Vidal, JL},
title = {Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {577651},
pmid = {33424562},
issn = {1662-5161},
abstract = {Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration, video recordings, and journal entries, recorded mobile brain-body activity of Spanish heritage students. Each student's brain-body activity was recorded as they experienced spaces in Houston, Texas ("Preparation" stage), and while they worked on their creative texts ("Generation" stage). We used Generalized Partial Directed Coherence (gPDC) to compare the functional connectivity among both stages. There was a trend of higher gPDC in the Preparation stage from right temporo-parietal (TP10) to left anterior-frontal (AF07) brain scalp areas within 1-50 Hz, not reaching statistical significance. The opposite directionality was found for the Generation stage, with statistical significant differences (p < 0.05) restricted to the delta band (1-4 Hz). There was statistically higher gPDC observed for the inter-hemispheric connections AF07-AF08 in the delta and theta bands (1-8 Hz), and AF08 to TP09 in the alpha and beta (8-30 Hz) bands. The left anterior-frontal (AF07) recordings showed higher power localized to the gamma band (32-50 Hz) for the Generation stage. An ancillary analysis of Sample Entropy did not show significant difference. The information transfer from anterior-frontal to temporal-parietal areas of the scalp may reflect multisensory interpretation during the Preparation stage, while brain signals originating at temporal-parietal toward frontal locations during the Generation stage may reflect the final decision making process to translate the multisensory experience into a creative text.},
}
@article {pmid33423082,
year = {2021},
author = {Bizzuti, BE and de Abreu Faria, L and da Costa, WS and Lima, PMT and Ovani, VS and Krüger, AM and Louvandini, H and Abdalla, AL},
title = {Potential use of cassava by-product as ruminant feed.},
journal = {Tropical animal health and production},
volume = {53},
number = {1},
pages = {108},
pmid = {33423082},
issn = {1573-7438},
support = {301059/2015-2//Conselho Nacional de Desenvolvimento Cient&#xed;fico e Tecnol&#xf3;gico/ ; },
mesh = {*Animal Feed ; Animals ; Diet/veterinary ; Dietary Fiber/metabolism ; Digestion ; *Fermentation ; *Manihot ; Rumen/*physiology ; Ruminants ; },
abstract = {Cassava (Manihot esculenta Crantz) bagasse is the by-product from industry (BCI), generated during manufacturing of cassava flour; this material has significant amounts of carbohydrates consisting in a potential energy source for ruminants. We hypothesized that the inclusion of BCI in the diets may lead to fermentation parameters equivalent to those of conventional feedstuff such as tropical grasses or grains; therefore, we aimed to evaluate ruminal fermentation parameters of BCI in in vitro conditions. Three different substrates were prepared: 100% BCI (BCI diet), 100% tifton (Cynodon spp.) hay (CTL diet), and 50% tifton hay +50% BCI (THB diet). Ruminal fermentation parameters of these diets were evaluated in in vitro gas production assays. In a 24-h incubation, increased values for total gas production, organic matter degradability, and methane production were observed for BCId and THB as compared to CTL (p < 0.05), while neutral THB showed the highest value for neutral detergent fiber degradability (p < 0.05). Fermentation profile was evaluated in a 48-h assay: shorter lag time as well as increased gas production potential and fractional fermentation rate were observed for the BCId and THB as compared to CTL (p < 0.05). Our results suggested that by-product from cassava industry is a suitable feed for ruminant production, providing desirable in vitro ruminal fermentation performance and organic matter degradability.},
}
@article {pmid33422469,
year = {2021},
author = {Mennen, AC and Turk-Browne, NB and Wallace, G and Seok, D and Jaganjac, A and Stock, J and deBettencourt, MT and Cohen, JD and Norman, KA and Sheline, YI},
title = {Cloud-Based Functional Magnetic Resonance Imaging Neurofeedback to Reduce the Negative Attentional Bias in Depression: A Proof-of-Concept Study.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {6},
number = {4},
pages = {490-497},
pmid = {33422469},
issn = {2451-9030},
support = {S10 OD023495/OD/NIH HHS/United States ; T32 MH065214/MH/NIMH NIH HHS/United States ; UL1 TR001863/TR/NCATS NIH HHS/United States ; },
mesh = {*Attentional Bias ; Cloud Computing ; Depression ; *Depressive Disorder, Major/therapy ; Humans ; Magnetic Resonance Imaging ; *Neurofeedback ; },
abstract = {Individuals with depression show an attentional bias toward negatively valenced stimuli and thoughts. In this proof-of-concept study, we present a novel closed-loop neurofeedback procedure intended to remediate this bias. Internal attentional states were detected in real time by applying machine learning techniques to functional magnetic resonance imaging data on a cloud server; these attentional states were externalized using a visual stimulus that the participant could learn to control. We trained 15 participants with major depressive disorder and 12 healthy control participants over 3 functional magnetic resonance imaging sessions. Exploratory analysis showed that participants with major depressive disorder were initially more likely than healthy control participants to get stuck in negative attentional states, but this diminished with neurofeedback training relative to controls. Depression severity also decreased from pre- to posttraining. These results demonstrate that our method is sensitive to the negative attentional bias in major depressive disorder and showcase the potential of this novel technique as a treatment that can be evaluated in future clinical trials.},
}
@article {pmid33418846,
year = {2021},
author = {Yuan, K and Chen, C and Wang, X and Chu, WC and Tong, RK},
title = {BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33418846},
issn = {2076-3425},
support = {14207617//Research Grants Council, University Grants Committee/ ; },
abstract = {Brain-computer interface (BCI)-guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement. Fourteen chronic stroke subjects received 20 sessions of BCI-guided robot hand training. Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting-state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes, and electrophysical changes derived from the two neuroimaging modalities was also investigated. This work suggested that (a) significant motor function improvement could be obtained after BCI training therapy, (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI, (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change.},
}
@article {pmid33418554,
year = {2021},
author = {Zhao, DG and Vasilyev, AN and Kozyrskiy, BL and Melnichuk, EV and Isachenko, AV and Velichkovsky, BM and Shishkin, SL},
title = {A passive BCI for monitoring the intentionality of the gaze-based moving object selection.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abda09},
pmid = {33418554},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Fixation, Ocular ; Humans ; Pursuit, Smooth ; },
abstract = {Objective.The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain-machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements.Approach.Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection.Main results.A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved.Significance.The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user's involvement during gaze-based interaction.},
}
@article {pmid33418552,
year = {2021},
author = {Peterson, SM and Steine-Hanson, Z and Davis, N and Rao, RPN and Brunton, BW},
title = {Generalized neural decoders for transfer learning across participants and recording modalities.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abda0b},
pmid = {33418552},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrocorticography/methods ; Electroencephalography/methods ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Objective. Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.Approach. We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (a) a Hilbert transform that computes spectral power at data-driven frequencies and (b) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.Main results. HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features.Significance. By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.},
}
@article {pmid33418549,
year = {2021},
author = {van den Boom, MA and Miller, KJ and Ramsey, NF and Hermes, D},
title = {Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
pmid = {33418549},
issn = {1741-2552},
support = {KL2 TR002379/TR/NCATS NIH HHS/United States ; R01 MH122258/MH/NIMH NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electrocorticography/methods ; Electrodes ; Electroencephalography ; *Gestures ; Humans ; Magnetic Resonance Imaging ; },
abstract = {Objective. In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements.Approach. High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size.Main results. Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5 × 5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3 × 3 electrodes, an inter-electrode distance of 8 mm, and an electrode size of 3 mm radius (performing at ~70% three-class classification accuracy).Significance. Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.},
}
@article {pmid33417560,
year = {2021},
author = {Georgiadis, K and Adamos, DA and Nikolopoulos, S and Laskaris, N and Kompatsiaris, I},
title = {Covariation Informed Graph Slepians for Motor Imagery Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {340-349},
doi = {10.1109/TNSRE.2021.3049998},
pmid = {33417560},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Graph signal processing (GSP) provides signal analytic tools for data defined in irregular domains, as is the case of non-invasive electroencephalography (EEG). In this work, the recently introduced technique of Graph Slepian functions is exploited for the robust decoding of motor imagery (MI) brain activity. The particular technique builds over the concept of graph Fourier transform (GFT) and provides additional flexibility in the subsequent data analysis by incorporating domain knowledge. Based on contrastive learning, we introduce an algorithmic pipeline that attains a data driven and subject specific design of Graph Slepian functions. These functions, by incorporating both the topology of the sensor array and the empirical evidence about the differential functional covariation, act as spatial filters that enhance the information conveyed by the multichannel signal and specifically relates to the participant's intention. The proposed technique for crafting Graph Slepians is incorporated in a MI-decoding scheme, in which the informed projections are fed to a support vector machine (SVM) that casts a prediction regarding the type of intended movement. The employed MI-decoder is evaluated based on two publicly available datasets and its superiority against popular alternatives in the field is established. Computational efficiency is listed among its main advantages, since it involves only simple matrix operations, allowing to consider its use in real-time implementations.},
}
@article {pmid33417535,
year = {2021},
author = {Wang, Z and Zhao, X and Zhang, M and Hu, H},
title = {A Maximum Likelihood Perspective of Spatial Filter Design in SSVEP-Based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {9},
pages = {2706-2717},
doi = {10.1109/TBME.2021.3049853},
pmid = {33417535},
issn = {1558-2531},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.},
}
@article {pmid33414824,
year = {2020},
author = {Miao, Y and Chen, S and Zhang, X and Jin, J and Xu, R and Daly, I and Jia, J and Wang, X and Cichocki, A and Jung, TP},
title = {BCI-Based Rehabilitation on the Stroke in Sequela Stage.},
journal = {Neural plasticity},
volume = {2020},
number = {},
pages = {8882764},
pmid = {33414824},
issn = {1687-5443},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Middle Aged ; Motor Activity/physiology ; Recovery of Function/*physiology ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; Upper Extremity/physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke.<br><br>METHOD: Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function.<br><br>RESULTS: The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study.<br><br>CONCLUSIONS: Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.},
}
@article {pmid33414701,
year = {2020},
author = {Xie, J and Cao, G and Xu, G and Fang, P and Cui, G and Xiao, Y and Li, G and Li, M and Xue, T and Zhang, Y and Han, X},
title = {Auditory Noise Leads to Increased Visual Brain-Computer Interface Performance: A Cross-Modal Study.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {590963},
pmid = {33414701},
issn = {1662-4548},
abstract = {Noise has been proven to have a beneficial role in non-linear systems, including the human brain, based on the stochastic resonance (SR) theory. Several studies have been implemented on single-modal SR. Cross-modal SR phenomenon has been confirmed in different human sensory systems. In our study, a cross-modal SR enhanced brain-computer interface (BCI) was proposed by applying auditory noise to visual stimuli. Fast Fourier transform and canonical correlation analysis methods were used to evaluate the influence of noise, results of which indicated that a moderate amount of auditory noise could enhance periodic components in visual responses. Directed transfer function was applied to investigate the functional connectivity patterns, and the flow gain value was used to measure the degree of activation of specific brain regions in the information transmission process. The results of flow gain maps showed that moderate intensity of auditory noise activated the brain area to a greater extent. Further analysis by weighted phase-lag index (wPLI) revealed that the phase synchronization between visual and auditory regions under auditory noise was significantly enhanced. Our study confirms the existence of cross-modal SR between visual and auditory regions and achieves a higher accuracy for recognition, along with shorter time window length. Such findings can be used to improve the performance of visual BCIs to a certain extent.},
}
@article {pmid33414699,
year = {2020},
author = {Bhutada, AS and Sepúlveda, P and Torres, R and Ossandón, T and Ruiz, S and Sitaram, R},
title = {Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {558981},
pmid = {33414699},
issn = {1662-4548},
abstract = {Electroencephalography (EEG) source reconstruction estimates spatial information from the brain's electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode's distance profile in relation to other electrodes. We then compare the subject's electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects' data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.},
}
@article {pmid33411838,
year = {2021},
author = {Caspar, EA and De Beir, A and Lauwers, G and Cleeremans, A and Vanderborght, B},
title = {How using brain-machine interfaces influences the human sense of agency.},
journal = {PloS one},
volume = {16},
number = {1},
pages = {e0245191},
pmid = {33411838},
issn = {1932-6203},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; },
abstract = {Brain-machine interfaces (BMI) allows individuals to control an external device by controlling their own brain activity, without requiring bodily or muscle movements. Performing voluntary movements is associated with the experience of agency ("sense of agency") over those movements and their outcomes. When people voluntarily control a BMI, they should likewise experience a sense of agency. However, using a BMI to act presents several differences compared to normal movements. In particular, BMIs lack sensorimotor feedback, afford lower controllability and are associated with increased cognitive fatigue. Here, we explored how these different factors influence the sense of agency across two studies in which participants learned to control a robotic hand through motor imagery decoded online through electroencephalography. We observed that the lack of sensorimotor information when using a BMI did not appear to influence the sense of agency. We further observed that experiencing lower control over the BMI reduced the sense of agency. Finally, we observed that the better participants controlled the BMI, the greater was the appropriation of the robotic hand, as measured by body-ownership and agency scores. Results are discussed based on existing theories on the sense of agency in light of the importance of BMI technology for patients using prosthetic limbs.},
}
@article {pmid33411817,
year = {2021},
author = {Trambaiolli, LR and Tossato, J and Cravo, AM and Biazoli, CE and Sato, JR},
title = {Subject-independent decoding of affective states using functional near-infrared spectroscopy.},
journal = {PloS one},
volume = {16},
number = {1},
pages = {e0244840},
pmid = {33411817},
issn = {1932-6203},
mesh = {Adult ; Affect/*physiology ; Brain/diagnostic imaging ; Brain-Computer Interfaces/psychology ; Discriminant Analysis ; Emotions/physiology ; Female ; Frontal Lobe/diagnostic imaging ; Functional Neuroimaging/*methods ; Humans ; Male ; Neurofeedback/methods ; Occipital Lobe/diagnostic imaging ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p<0.01) and negative vs. neutral (68.25 ± 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 ± 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features.},
}
@article {pmid33411320,
year = {2021},
author = {Zhao, M and Wang, Z and Yang, M and Ding, Y and Zhao, M and Wu, H and Zhang, Y and Lu, Q},
title = {The Roles of Orphan G Protein-Coupled Receptors in Autoimmune Diseases.},
journal = {Clinical reviews in allergy &amp; immunology},
volume = {60},
number = {2},
pages = {220-243},
pmid = {33411320},
issn = {1559-0267},
mesh = {Animals ; Autoimmune Diseases/*metabolism ; Humans ; Molecular Targeted Therapy ; Orphan Nuclear Receptors/*metabolism ; Receptors, G-Protein-Coupled/*metabolism ; Signal Transduction ; },
abstract = {G protein-coupled receptors (GPCRs) constitute the largest family of plasma membrane receptors in nature and mediate the effects of a variety of extracellular signals, such as hormone, neurotransmitter, odor, and light signals. Due to their involvement in a broad range of physiological and pathological processes and their accessibility, GPCRs are widely used as pharmacological targets of treatment. Orphan G protein-coupled receptors (oGPCRs) are GPCRs for which no natural ligands have been found, and they not only play important roles in various physiological functions, such as sensory perception, reproduction, development, growth, metabolism, and responsiveness, but are also closely related to many major diseases, such as central nervous system (CNS) diseases, metabolic diseases, and cancer. Recently, many studies have reported that oGPCRs play increasingly important roles as key factors in the occurrence and progression of autoimmune diseases. Therefore, oGPCRs are likely to become potential therapeutic targets and may provide a breakthrough in the study of autoimmune diseases. In this article, we focus on reviewing the recent research progress and clinical treatment effects of oGPCRs in three common autoimmune diseases: multiple sclerosis (MS), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE), shedding light on novel strategies for treatments.},
}
@article {pmid33408793,
year = {2021},
author = {Si, K and Xue, Y and Yu, X and Zhu, X and Li, Q and Gong, W and Liang, T and Duan, S},
title = {Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.},
journal = {Theranostics},
volume = {11},
number = {4},
pages = {1982-1990},
pmid = {33408793},
issn = {1838-7640},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Algorithms ; Carcinoma, Pancreatic Ductal/*diagnosis/diagnostic imaging ; *Deep Learning ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; Middle Aged ; Pancreatic Neoplasms/*diagnosis/diagnostic imaging ; ROC Curve ; Tomography, X-Ray Computed/*methods ; },
abstract = {Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been implemented for different clinical applications. Accurate diagnosis and treatment require reliable and interpretable data. For pancreatic tumor diagnosis, only 58.5% of images from the First Affiliated Hospital and the Second Affiliated Hospital, Zhejiang University School of Medicine are used, increasing labor and time costs to manually filter out images not directly used by the diagnostic model. Methods: This study used a training dataset of 143,945 dynamic contrast-enhanced CT images of the abdomen from 319 patients. The proposed model contained four stages: image screening, pancreas location, pancreas segmentation, and pancreatic tumor diagnosis. Results: We established a fully end-to-end deep-learning model for diagnosing pancreatic tumors and proposing treatment. The model considers original abdominal CT images without any manual preprocessing. Our artificial-intelligence-based system achieved an area under the curve of 0.871 and a F1 score of 88.5% using an independent testing dataset containing 107,036 clinical CT images from 347 patients. The average accuracy for all tumor types was 82.7%, and the independent accuracies of identifying intraductal papillary mucinous neoplasm and pancreatic ductal adenocarcinoma were 100% and 87.6%, respectively. The average test time per patient was 18.6 s, compared with at least 8 min for manual reviewing. Furthermore, the model provided a transparent and interpretable diagnosis by producing saliency maps highlighting the regions relevant to its decision. Conclusions: The proposed model can potentially deliver efficient and accurate preoperative diagnoses that could aid the surgical management of pancreatic tumor.},
}
@article {pmid33408414,
year = {2021},
author = {Ping, YQ and Mao, C and Xiao, P and Zhao, RJ and Jiang, Y and Yang, Z and An, WT and Shen, DD and Yang, F and Zhang, H and Qu, C and Shen, Q and Tian, C and Li, ZJ and Li, S and Wang, GY and Tao, X and Wen, X and Zhong, YN and Yang, J and Yi, F and Yu, X and Xu, HE and Zhang, Y and Sun, JP},
title = {Structures of the glucocorticoid-bound adhesion receptor GPR97-Go complex.},
journal = {Nature},
volume = {589},
number = {7843},
pages = {620-626},
pmid = {33408414},
issn = {1476-4687},
mesh = {Binding Sites ; *Cryoelectron Microscopy ; GTP-Binding Protein alpha Subunits, Gi-Go/*chemistry/*metabolism/ultrastructure ; Glucocorticoids/*chemistry/*metabolism ; Humans ; Ligands ; Lipoylation ; Models, Molecular ; Protein Binding ; Receptors, G-Protein-Coupled/*chemistry/metabolism/*ultrastructure ; },
abstract = {Adhesion G-protein-coupled receptors (GPCRs) are a major family of GPCRs, but limited knowledge of their ligand regulation or structure is available[1-3]. Here we report that glucocorticoid stress hormones activate adhesion G-protein-coupled receptor G3 (ADGRG3; also known as GPR97)[4-6], a prototypical adhesion GPCR. The cryo-electron microscopy structures of GPR97-Go complexes bound to the anti-inflammatory drug beclomethasone or the steroid hormone cortisol revealed that glucocorticoids bind to a pocket within the transmembrane domain. The steroidal core of glucocorticoids is packed against the 'toggle switch' residue W[6.53], which senses the binding of a ligand and induces activation of the receptor. Active GPR97 uses a quaternary core and HLY motif to fasten the seven-transmembrane bundle and to mediate G protein coupling. The cytoplasmic side of GPR97 has an open cavity, where all three intracellular loops interact with the Go protein, contributing to the high basal activity of GRP97. Palmitoylation at the cytosolic tail of the Go protein was found to be essential for efficient engagement with GPR97 but is not observed in other solved GPCR complex structures. Our work provides a structural basis for ligand binding to the seven-transmembrane domain of an adhesion GPCR and subsequent G protein coupling.},
}
@article {pmid33401571,
year = {2021},
author = {Kawala-Sterniuk, A and Browarska, N and Al-Bakri, A and Pelc, M and Zygarlicki, J and Sidikova, M and Martinek, R and Gorzelanczyk, EJ},
title = {Summary of over Fifty Years with Brain-Computer Interfaces-A Review.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33401571},
issn = {2076-3425},
support = {Project No. SP2020/156//Ministry of Education of the Czech Republic/ ; },
abstract = {Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.},
}
@article {pmid33401410,
year = {2021},
author = {Fernández-Rodríguez, &#xc1; and Ron-Angevin, R and Sanz-Arigita, EJ and Parize, A and Esquirol, J and Perrier, A and Laur, S and André, JM and Lespinet-Najib, V and Garcia, L},
title = {Effect of Distracting Background Speech in an Auditory Brain-Computer Interface.},
journal = {Brain sciences},
volume = {11},
number = {1},
pages = {},
pmid = {33401410},
issn = {2076-3425},
support = {RTI2018-100912-B-I00//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; RTI2018-100912-B-I00//Agencia Estatal de Investigaci&#xf3;n/ ; RTI2018-100912-B-I00//European Regional Development Fund/ ; },
abstract = {Studies so far have analyzed the effect of distractor stimuli in different types of brain-computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.},
}
@article {pmid33401114,
year = {2021},
author = {Zhang, K and Robinson, N and Lee, SW and Guan, C},
title = {Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {136},
number = {},
pages = {1-10},
doi = {10.1016/j.neunet.2020.12.013},
pmid = {33401114},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces/*classification ; Electroencephalography/*classification/methods ; Female ; Hand/physiology ; Humans ; Imagination/*physiology ; Machine Learning/classification ; Male ; *Neural Networks, Computer ; Psychomotor Performance/physiology ; Transfer, Psychology/*physiology ; Young Adult ; },
abstract = {In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject-specific data. To overcome this, many transfer-based approaches have been proposed, in which deep networks are trained using pre-existing data from other subjects and evaluated on new target subjects. This mode of transfer learning however faces the challenge of substantial inter-subject variability in brain data. Addressing this, in this paper, we propose 5 schemes for adaptation of a deep convolutional neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand motor imagery (MI). Each scheme fine-tunes an extensively trained, pre-trained model and adapt it to enhance the evaluation performance on a target subject. We report the highest subject-independent performance with an average (N=54) accuracy of 84.19% (±9.98%) for two-class motor imagery, while the best accuracy on this dataset is 74.15% (±15.83%) in the literature. Further, we obtain a statistically significant improvement (p=0.005) in classification using the proposed adaptation schemes compared to the baseline subject-independent model.},
}
@article {pmid33399499,
year = {2021},
author = {Zheng, Y and Cong, N and Gao, N and Chi, F and Huang, Y and Jia, X and Xu, X and Liu, YW and Chen, Y},
title = {Sound localization, speech and tone recognition for stimuli presented from the rear in bilateral cochlear implant users.},
journal = {International journal of audiology},
volume = {60},
number = {8},
pages = {588-597},
doi = {10.1080/14992027.2020.1866218},
pmid = {33399499},
issn = {1708-8186},
mesh = {*Cochlear Implantation ; *Cochlear Implants ; Humans ; *Sound Localization ; Speech ; *Speech Perception ; },
abstract = {OBJECTIVE: To assess any differences in spatial listening ability of cochlear implant recipients when using both or only one of two bilateral cochlear implants (BCIs) for stimuli originating from behind the subject.<br><br>DESIGN: Twelve loudspeakers were placed in the rear horizontal plane of the subjects to test the sound localisation performance of BCI users and normal-hearing listeners (NHLs) with or without interfering noise. Stimuli were presented via two rear loudspeakers simultaneously during the speech recognition test. In the tone recognition test, another anechoic chamber was used with stimuli presenting from a loudspeaker behind the participants.<br><br>STUDY SAMPLE: Twenty-seven NHLs and eleven BCI users.<br><br>RESULTS: Average root-mean-square (RMS) error for the bilateral condition was significantly lower than that for the right and left cochlear implant (CI) conditions with or without interfering noises (p&#x2009;<&#x2009;0.05). Average speech or tone recognition scores for the bilateral condition and the right and left CI conditions were not statistically significant (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSION: Sound localisation with BCIs was significantly more accurate than with either implant alone. Speech and tone recognition scores were not better with two compared to those of one activated implant. Given the small number of subjects, the results should be considered as preliminary.},
}
@article {pmid33395991,
year = {2020},
author = {Bhagat, NA and Yozbatiran, N and Sullivan, JL and Paranjape, R and Losey, C and Hernandez, Z and Keser, Z and Grossman, R and Francisco, GE and O'Malley, MK and Contreras-Vidal, JL},
title = {Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation.},
journal = {NeuroImage. Clinical},
volume = {28},
number = {},
pages = {102502},
pmid = {33395991},
issn = {2213-1582},
support = {R01 NS081854/NS/NINDS NIH HHS/United States ; },
mesh = {Brain ; *Exoskeleton Device ; Humans ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity ; },
abstract = {Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants' movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.},
}
@article {pmid33395961,
year = {2020},
author = {Liang, WD and Xu, Y and Schmidt, J and Zhang, LX and Ruddy, KL},
title = {Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback; A pilot study.},
journal = {NeuroImage. Clinical},
volume = {28},
number = {},
pages = {102465},
pmid = {33395961},
issn = {2213-1582},
mesh = {Electromyography ; Evoked Potentials, Motor ; Humans ; Muscle, Skeletal ; *Neurofeedback ; Pilot Projects ; Pyramidal Tracts ; *Stroke ; Transcranial Magnetic Stimulation ; },
abstract = {Upper limb weakness following a stroke affects 80% of survivors and is a key factor in preventing their return to independence. State-of-the art approaches to rehabilitation often require that the patient can generate some activity in the paretic limb, which is not possible for many patients in the early period following stroke. Approaches that enable more patients to engage with upper limb therapy earlier are urgently needed. Motor imagery has shown promise as a potential means to maintain activity in the brain's motor network, when the patient is incapable of generating functional movement. However, as imagery is a hidden mental process, it is impossible for individuals to gauge what impact this is having upon their neural activity. Here we used a novel brain-computer interface (BCI) approach allowing patients to gain an insight into the effect of motor imagery on their brain-muscle pathways, in real-time. Seven patients 2-26 weeks post stroke were provided with neurofeedback (NF) of their corticospinal excitability measured by the size of motor evoked potentials (MEP) in response to transcranial magnetic stimulation (TMS). The aim was to train patients to use motor imagery to increase the size of MEPs, using the BCI with a computer game displaying neurofeedback. Patients training finger muscles learned to elevate MEP amplitudes above their resting baseline values for the first dorsal interosseous (FDI) and abductor digiti minimi (ADM) muscles. By day 3 for ADM and day 4 for FDI, MEP amplitudes were sustained above baseline in all three NF blocks. Here we have described the first clinical implementation of TMS NF in a population of sub-acute stroke patients. The results show that in the context of severe upper limb paralysis, patients are capable of using neurofeedback to elevate corticospinal excitability in the affected muscles. This may provide a new training modality for early intervention following stroke.},
}
@article {pmid33395676,
year = {2021},
author = {Liu, X and Lv, L and Shen, Y and Xiong, P and Yang, J and Liu, J},
title = {Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abd82b},
pmid = {33395676},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Objective. Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain-computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal.Approach. In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimized.Results. The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications.Significance. To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalization and accuracy of subject-dependent and subject-independent methods with limited annotated data.},
}
@article {pmid33394498,
year = {2020},
author = {Sharma, M},
title = {Design of brain-computer interface-based classification model for mining mental state of COVID-19 afflicted mariner's.},
journal = {International maritime health},
volume = {71},
number = {4},
pages = {298-300},
doi = {10.5603/IMH.2020.0052},
pmid = {33394498},
issn = {2081-3252},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; COVID-19/*psychology ; Electroencephalography/methods ; Humans ; Mental Processes/*physiology ; Occupational Health/*statistics &amp; numerical data ; Oceans and Seas ; Pattern Recognition, Automated/methods ; },
abstract = {Not required for Letter to Editor.},
}
@article {pmid33391360,
year = {2020},
author = {Li, B and Zhang, W and Wang, Z and Xie, H and Yuan, X and Pei, E and Wang, T},
title = {Effects of landscape heterogeneity and breeding habitat diversity on rice frog abundance and body condition in agricultural landscapes of Yangtze River Delta, China.},
journal = {Current zoology},
volume = {66},
number = {6},
pages = {615-623},
pmid = {33391360},
issn = {1674-5507},
abstract = {Amphibians play a key role in structuring biological assemblages of agricultural landscapes, but they are threatened by global agricultural intensification. Landscape structure is an important variable influencing biodiversity in agricultural landscapes. However, in the Yangtze River Delta, where a "farmland-orchard-fishpond" agricultural pattern is common, the effects of landscape construction on anuran populations are unclear. In this study, we examined the effects of agricultural landscape parameters on the abundance and body condition of the rice frog (Fejervarya multistriata), which is a dominant anuran species in farmland in China. Employing a visual encounter method, we surveyed rice frog abundance for 3 years across 20 agricultural landscapes. We also calculated the body condition index (BCI) of 188 male frog individuals from these agricultural landscapes. Landscape variables, comprising landscape compositional heterogeneity (using the Shannon diversity index of all land cover types except buildings and roads), landscape configurational heterogeneity (using landscape edge density), breeding habitat diversity (using the number of 5 waterbody types available as breeding habitats), and areas of forest were also measured for each 1-km radius landscape. We found that the amount of forest in each agricultural landscape had a significant positive relationship with rice frog abundance, and breeding habitat diversity was positively related to the BCI of male rice frogs. However, body condition was negatively impacted by landscape configurational heterogeneity. Our results suggested the importance of nonagricultural habitats in agricultural landscapes, such as waterbodies and forest, to benefit rice frog population persistence.},
}
@article {pmid33390908,
year = {2020},
author = {Wang, J and Tian, Y and Zeng, LH and Xu, H},
title = {Prefrontal Disinhibition in Social Fear: A Vital Action of Somatostatin Interneurons.},
journal = {Frontiers in cellular neuroscience},
volume = {14},
number = {},
pages = {611732},
pmid = {33390908},
issn = {1662-5102},
abstract = {Social fear and avoidance of social partners and social situations represent the core behavioral symptom of Social Anxiety Disorder (SAD), a prevalent psychiatric disorder worldwide. The pathological mechanism of SAD remains elusive and there are no specific and satisfactory therapeutic options currently available. With the development of appropriate animal models, growing studies start to unravel neuronal circuit mechanisms underlying social fear, and underscore a fundamental role of the prefrontal cortex (PFC). Prefrontal cortical functions are implemented by a finely wired microcircuit composed of excitatory principal neurons (PNs) and diverse subtypes of inhibitory interneurons (INs). Disinhibition, defined as a break in inhibition via interactions between IN subtypes that enhances the output of excitatory PNs, has recently been discovered to serve as an efficient strategy in cortical information processing. Here, we review the rodent animal models of social fear, the prefrontal IN diversity, and their circuits with a particular emphasis on a novel disinhibitory microcircuit mediated by somatostatin-expressing INs in gating social fear behavior. The INs subtype distinct and microcircuit-based mechanism advances our understanding of the etiology of social fear and sheds light on developing future treatment of neuropsychiatric disorders associated with social fear.},
}
@article {pmid33390892,
year = {2020},
author = {Fontanillo Lopez, CA and Li, G and Zhang, D},
title = {Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {611130},
pmid = {33390892},
issn = {1662-4548},
abstract = {The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.},
}
@article {pmid33390885,
year = {2020},
author = {Charles, F and De Castro Martins, C and Cavazza, M},
title = {Prefrontal Asymmetry BCI Neurofeedback Datasets.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {601402},
pmid = {33390885},
issn = {1662-4548},
abstract = {Prefrontal cortex (PFC) asymmetry is an important marker in affective neuroscience and has attracted significant interest, having been associated with studies of motivation, eating behavior, empathy, risk propensity, and clinical depression. The data presented in this paper are the result of three different experiments using PFC asymmetry neurofeedback (NF) as a Brain-Computer Interface (BCI) paradigm, rather than a therapeutic mechanism aiming at long-term effects, using functional near-infrared spectroscopy (fNIRS) which is known to be particularly well-suited to the study of PFC asymmetry and is less sensitive to artifacts. From an experimental perspective the BCI context brings more emphasis on individual subjects' baselines, successful and sustained activation during epochs, and minimal training. The subject pool is also drawn from the general population, with less bias toward specific behavioral patterns, and no inclusion of any patient data. We accompany our datasets with a detailed description of data formats, experiment and protocol designs, as well as analysis of the individualized metrics for definitions of success scores based on baseline thresholds as well as reference tasks. The work presented in this paper is the result of several experiments in the domain of BCI where participants are interacting with continuous visual feedback following a real-time NF paradigm, arising from our long-standing research in the field of affective computing. We offer the community access to our fNIRS datasets from these experiments. We specifically provide data drawn from our empirical studies in the field of affective interactions with computer-generated narratives as well as interfacing with algorithms, such as heuristic search, which all provide a mechanism to improve the ability of the participants to engage in active BCI due to their realistic visual feedback. Beyond providing details of the methodologies used where participants received real-time NF of left-asymmetric increase in activation in their dorsolateral prefrontal cortex (DLPFC), we re-establish the need for carefully designing protocols to ensure the benefits of NF paradigm in BCI are enhanced by the ability of the real-time visual feedback to adapt to the individual responses of the participants. Individualized feedback is paramount to the success of NF in BCIs.},
}
@article {pmid33390884,
year = {2020},
author = {Guger, C and Prabhakaran, V and Spataro, R and Krusienski, DJ and Hebb, AO},
title = {Editorial: Breakthrough BCI Applications in Medicine.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {598247},
pmid = {33390884},
issn = {1662-4548},
}
@article {pmid33390877,
year = {2020},
author = {Vidaurre, C and Haufe, S and Jorajuría, T and Müller, KR and Nikulin, VV},
title = {Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {575081},
pmid = {33390877},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining "good" and "poor" BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.},
}
@article {pmid33386834,
year = {2021},
author = {Li, C and Zhu, Y and Qu, W and Sun, L},
title = {Research on blood oxygen activity in cerebral cortical motor function areas with adjustment intention during gait.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {29},
number = {4},
pages = {677-686},
doi = {10.3233/THC-202580},
pmid = {33386834},
issn = {1878-7401},
mesh = {Gait ; Humans ; Infant ; Intention ; *Motor Cortex ; Oxygen ; Walking ; },
abstract = {BACKGROUND: The study of the neural mechanism of human gait control can provide a theoretical basis for the treatment of walking disorders or the improvement of rehabilitation strategies, and further promote the functional rehabilitation of patients with movement disorders. However, the performance and changes of cerebral cortex activity corresponding to gait adjustment intentions are still not clear.<br><br>OBJECTIVE: The purpose of this study was to detect the blood oxygen activation characterization of the cerebral cortex motor function area when people have the intention to adjust gait during walking.<br><br>METHODS: Thirty young volunteers (21 &#xb1; 1 years old) performed normal walking, speed increase, speed reduction, step increase, and step reduction, during which oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total oxyhemoglobin (HbT) information in the prefrontal cortex (PFC), premotor cortex (PMC), supplementary motor area (SMA) was continuous monitored using near-infrared brain functional imaging.<br><br>RESULTS: (1) With the intention to adjust gait, the HbO concentration in the SMA increased significantly, while the HbT concentration in the medial-PFC decreased significantly. (2) In the HbO concentration, step reduction is more activated than the step increase in the left-PMC (p= 0.0130); step adjustment is more activated than speed adjustment in the right-PMC (p= 0.0067). In the HbR concentration, the speed reduction is more activated than the speed increase in the left-PFC (p= 0.0103).<br><br>CONCLUSIONS: When the intention of gait adjustment occurs, the increase of HbO concentration in the SMA indicates the initial stage of gait adjustment will increase the cognitive-locomotor demand of the brain. The left brain area meets the additional nerve needs of speed adjustment. The preliminary findings of this study can lay an important theoretical foundation for the realization of gait control based on fNIRS-BCI technology.},
}
@article {pmid33386583,
year = {2021},
author = {Zhu, Y and Zhao, YR and Zhong, P and Qiao, BM and Yang, ZQ and Niu, YJ},
title = {Detrusor underactivity influences the efficacy of TURP in patients with BPO.},
journal = {International urology and nephrology},
volume = {53},
number = {5},
pages = {835-841},
pmid = {33386583},
issn = {1573-2584},
mesh = {Adult ; Aged ; Aged, 80 and over ; Humans ; Male ; Middle Aged ; Prostatic Hyperplasia/*complications/*surgery ; Retrospective Studies ; *Transurethral Resection of Prostate ; Treatment Outcome ; Urinary Bladder Neck Obstruction/*complications/*surgery ; Urinary Bladder, Underactive/*complications ; },
abstract = {PURPOSE: To investigate the effect of detrusor underactivity on the efficacy of TURP in patients with benign prostate obstruction.<br><br>METHODS: A retrospective study of 350 patients with benign prostate obstruction who underwent TURP was carried out. Different degrees of bladder outlet obstruction were grouped by the bladder outlet obstruction index. ROC curves were used to calculate the optimal cut-off point for the bladder contractility index used to divide the DU patients into mild DU and severe DU patients. The effect of DU on the efficacy of TURP in benign prostate obstruction patients was studied by comparing the subjective and objective parameters preoperatively and 3&#xa0;months postoperatively between severe DU, mild DU and non-DU benign prostate obstruction patients in two obstruction groups (20&#x2009;&#x2264;&#x2009;BOOI&#x2009;<&#x2009;40 and BOOI&#x2009;&#x2265;&#x2009;40).<br><br>RESULTS: According to the ROC curve, the optimal cut-off point for the bladder contractility index was 82; thus, 69 patients were considered mild DU patients (82&#x2009;&#x2264;&#x2009;BCI&#x2009;<&#x2009;100), 67 patients were considered severe DU patients (BCI&#x2009;<&#x2009;82), and 214 patients were considered non-DU patients (BCI&#x2009;&#x2265;&#x2009;100). Both the postoperative subjective and objective parameters of the non-DU, mild DU and severe DU patients significantly improved in two obstruction groups. However, in the 20&#x2009;&#x2264;&#x2009;BOOI&#x2009;<&#x2009;40 group, the successful improvement rates for the IPSS, IPSS-S, IPSS-V, QoL and fQmax in the severe DU patients were only 38.2%, 38.2%, 44.1%, 41.2% and 38.2%, respectively.<br><br>CONCLUSION: Patients with varying degrees of benign prostate obstruction can benefit from TURP, but for patients with severe DU in the 20&#x2009;&#x2264;&#x2009;BOOI&#x2009;<&#x2009;40 group, TURP should be considered only after deliberation.},
}
@article {pmid33386032,
year = {2020},
author = {Bressi, F and Bravi, M and Campagnola, B and Bruno, D and Marzolla, A and Santacaterina, F and Miccinilli, S and Sterzi, S},
title = {Robotic treatment of the upper limb in chronic stroke and cerebral neuroplasticity: a systematic review.},
journal = {Journal of biological regulators and homeostatic agents},
volume = {34},
number = {5 Suppl. 3},
pages = {11-44. Technology in Medicine},
pmid = {33386032},
issn = {0393-974X},
mesh = {Humans ; Neuronal Plasticity ; Recovery of Function ; *Robotics ; *Stroke ; *Stroke Rehabilitation ; *Transcranial Direct Current Stimulation ; Upper Extremity ; },
abstract = {Stroke is the second cause of mortality and the third cause of long-term disability worldwide. Deficits in upper limb (UL) capacity persist at 6 months post-stroke in 30-66% of hemiplegic stroke patients with major limitations in activity of daily living (ADL), thus making the recovery of paretic UL function the main rehabilitation goal. Robotic rehabilitation plays a crucial role since it allows to perform a repetitive, intensive, and task-oriented treatment, adaptable to the patients' residual abilities, necessary to facilitate recovery and the rehabilitation of the paretic UL. It has been proposed that robot-mediated training may amplify neuroplasticity by providing a major interaction of proprioceptive and/or other sensory inputs with motor outputs, with significant modifications in functional connectivity (coherence) within the fronto-parietal networks (inter- and intra-hemispheric functional connectivity) related to processes of movement preparation and execution. However, the neurophysiological mechanisms underlying this reorganization are not entirely clear yet. Therefore, the aim of this study is to revise the literature, which assesses the effect of robotic treatment in the recovery of UL deficits measured in terms of neuroplasticity in patients affected by chronic stroke. This systematic review was conducted using PubMed, PEDro, Cinahl (EBSCOhost), Scopus and Cochrane databases. The research was carried out until February 2020 it included articles written in English language, published between 2009 and 2020, and the outcomes considered were neuroplasticity assessments. We included 23 studies over 6145 records identified from the preliminary research. The selected studies proposed different methods for neuroplasticity assessment (i.e. transcranial direct current stimulation (tDCS), EEG-Based Brain Computer Interface (BCI) and Neuroimaging (fMRI)), and different Robotic Rehabilitation treatments. These studies demonstrated a positive correlation between changes in central nervous circuits and post-treatment clinical outcomes. Our study has highlighted the effectiveness of robotic therapy in promoting mechanisms that facilitate re-learning and motor recovery in patients with post-stroke chronic disabilities. However, future studies should overcome the limitations of heterogeneity found in the current literature, by proposing a greater number of high-level RCTs, to better understand the mechanisms of robot-induced neuroplasticity, follow the clinical progress, estimate a prognosis of recovery of motor function, and plan a personalized rehabilitative programme for the patients.},
}
@article {pmid33385958,
year = {2021},
author = {Kadeha, C and Haule, H and Ali, MS and Alluri, P and Ponnaluri, R},
title = {Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida.},
journal = {Accident; analysis and prevention},
volume = {151},
number = {},
pages = {105963},
doi = {10.1016/j.aap.2020.105963},
pmid = {33385958},
issn = {1879-2057},
mesh = {*Accidents, Traffic ; *Automobile Driving ; Bayes Theorem ; Florida ; Humans ; Lighting ; Logistic Models ; },
abstract = {Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&amp;O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.},
}
@article {pmid33383909,
year = {2020},
author = {Liao, H and Xu, J and Yu, Z},
title = {Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection.},
journal = {Entropy (Basel, Switzerland)},
volume = {23},
number = {1},
pages = {},
pmid = {33383909},
issn = {1099-4300},
support = {61836003//the National Natural Science Foundation of China/ ; },
abstract = {In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.},
}
@article {pmid33383864,
year = {2020},
author = {Yan, T and Kameda, S and Suzuki, K and Kaiju, T and Inoue, M and Suzuki, T and Hirata, M},
title = {Minimal Tissue Reaction after Chronic Subdural Electrode Implantation for Fully Implantable Brain-Machine Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {1},
pages = {},
pmid = {33383864},
issn = {1424-8220},
support = {Research and development of technologies for high-speed wireless communication from inside to outside of the body and large-scale data analyses of brain information and their application for BMI//National Institute of Information and Communications Technology/ ; 18H04166//Japan Society for the Promotion of Science/ ; The grant for supporting researchers to establish a start-up company//Osaka University/ ; },
mesh = {Animals ; Biosensing Techniques ; *Brain-Computer Interfaces ; Dogs ; Electrocorticography ; *Electrodes, Implanted ; Head ; Neurons ; },
abstract = {There is a growing interest in the use of electrocorticographic (ECoG) signals in brain-machine interfaces (BMIs). However, there is still a lack of studies involving the long-term evaluation of the tissue response related to electrode implantation. Here, we investigated biocompatibility, including chronic tissue response to subdural electrodes and a fully implantable wireless BMI device. We implanted a half-sized fully implantable device with subdural electrodes in six beagles for 6 months. Histological analysis of the surrounding tissues, including the dural membrane and cortices, was performed to evaluate the effects of chronic implantation. Our results showed no adverse events, including infectious signs, throughout the 6-month implantation period. Thick connective tissue proliferation was found in the surrounding tissues in the epidural space and subcutaneous space. Quantitative measures of subdural reactive tissues showed minimal encapsulation between the electrodes and the underlying cortex. Immunohistochemical evaluation showed no significant difference in the cell densities of neurons, astrocytes, and microglia between the implanted sites and contralateral sites. In conclusion, we established a beagle model to evaluate cortical implantable devices. We confirmed that a fully implantable wireless device and subdural electrodes could be stably maintained with sufficient biocompatibility in vivo.},
}
@article {pmid33383170,
year = {2021},
author = {Flügel, K and Schmidt, K and Mareczek, L and Gäbe, M and Hennig, R and Thommes, M},
title = {Impact of incorporated drugs on material properties of amorphous solid dispersions.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {159},
number = {},
pages = {88-98},
doi = {10.1016/j.ejpb.2020.12.017},
pmid = {33383170},
issn = {1873-3441},
mesh = {Absorption, Physicochemical ; Drug Compounding/*methods ; Drug Development/*methods ; Drug Liberation ; Excipients/*chemistry ; Hot Melt Extrusion Technology ; Polymers/chemistry ; Solubility ; Solvents/chemistry ; Tablets/*chemistry/pharmacokinetics ; Water/chemistry ; Wettability ; },
abstract = {Formulation development of amorphous solid dispersions (ASD) still is challenging although several poorly water-soluble drugs have been marketed using this technique. During development of novel drugs, the selection of the preparation technique and polymer matrix is commonly performed for the certain drug via screening tools. However, if general trends regarding material properties are to be investigated, this approach is not beneficial, although often utilized in literature. The main component of the ASD usually is the polymer and thus it predominantly determines the material properties of the system. Therefore, to study the impact of different drugs and their drug loads on mechanical properties and wettability, three poorly soluble model drugs with drug loads ranging from 10% to 40% were incorporated into copovidone via hot-melt extrusion. The obtained extrudates were subsequently characterized regarding mechanical properties by applying diametral compression test and nanoindentation and the results were compared to the performance during tablet compression. Incorporation of all tested drugs resulted in a similar increase in brittleness of the ASDs, whereas the Young's modulus and hardness changed differently in dependence of the incorporated drug. These observations correlated well with the performance during tablet compression and it was concluded, that the brittleness seemed to be the predominant factor influencing the compression behavior of copovidone-based ASDs. Furthermore, the degree of water absorption and wettability was assessed by applying dynamic vapor sorption experiments and contact angle measurements. Here, the incorporated drugs impacted the contact angle to different degrees and a strong correlation between the contact angle and disintegration time was observable. These results highlight the importance of thorough characterization of the ASDs as it helps to predict their performance during tablet compression and thus facilitates the optimal selection of excipients.},
}
@article {pmid33382658,
year = {2020},
author = {Cheng, S and Wang, J and Zhang, L and Wei, Q},
title = {Motion Imagery-BCI Based on EEG and Eye Movement Data Fusion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2783-2793},
doi = {10.1109/TNSRE.2020.3048422},
pmid = {33382658},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Eye Movements ; Humans ; Imagination ; Movement ; },
abstract = {Existing studies have demonstrated that eye tracking can be a complementary approach to Electroencephalogram (EEG) based brain-computer interaction (BCI), especially in improving BCI performance in visual perception and cognition. In this paper, we proposed a method to fuse EEG and eye movement data extracted from motor imagery (MI) tasks. The results of the tests showed that on the feature layer, the average MI classification accuracy from the fusion of EEG and eye movement data was higher than that of pure EEG data or pure eye movement data, respectively. Besides, we also found that the average classification accuracy from the fusion on the decision layer was higher than that from the feature layer. Additionally, when EEG data were not available for the shifting of parts of electrodes, we combined EEG data collected from the rest of the electrodes (only 50% of the original) with the eye movement data, and the average MI classification accuracy was only 1.07% lower than that from all available electrodes. This result indicated that eye movement data was feasible to compensate for the loss of the EEG data in the MI scenario. Overall our approach was proved valuable and useful for augmenting MI based BCI applications.},
}
@article {pmid33381220,
year = {2020},
author = {Zheng, X and Li, J and Ji, H and Duan, L and Li, M and Pang, Z and Zhuang, J and Rongrong, L and Tianhao, G},
title = {Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {6056383},
pmid = {33381220},
issn = {1748-6718},
mesh = {*Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Electroencephalography/*classification/*statistics &amp; numerical data ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Machine Learning ; Motor Skills/physiology ; Sensorimotor Cortex/physiology ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.},
}
@article {pmid33380047,
year = {2020},
author = {Kitsunai, S and Cho, W and Sano, C and Saetia, S and Qin, Z and Koike, Y and Frasca, M and Yoshimura, N and Minati, L},
title = {Generation of diverse insect-like gait patterns using networks of coupled Rössler systems.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {30},
number = {12},
pages = {123132},
doi = {10.1063/5.0021694},
pmid = {33380047},
issn = {1089-7682},
mesh = {Animals ; *Gait ; Insecta ; *Robotics ; Walking ; },
abstract = {The generation of walking patterns is central to bio-inspired robotics and has been attained using methods encompassing diverse numerical as well as analog implementations. Here, we demonstrate the possibility of synthesizing viable gaits using a paradigmatic low-dimensional non-linear entity, namely, the Rössler system, as a dynamical unit. Through a minimalistic network wherein each instance is univocally associated with one leg, it is possible to readily reproduce the canonical gaits as well as generate new ones via changing the coupling scheme and the associated delays. Varying levels of irregularity can be introduced by rendering individual systems or the entire network chaotic. Moreover, through tailored mapping of the state variables to physical angles, adequate leg trajectories can be accessed directly from the coupled systems. The functionality of the resulting generator was confirmed in laboratory experiments by means of an instrumented six-legged ant-like robot. Owing to their simple form, the 18 coupled equations could be rapidly integrated on a bare-metal microcontroller, leading to the demonstration of real-time robot control navigating an arena using a brain-machine interface.},
}
@article {pmid33378883,
year = {2020},
author = {Sun, Z and Xi, X and Yuan, C and Yang, Y and Hua, X},
title = {Surface electromyography signal denoising via EEMD and improved wavelet thresholds.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {17},
number = {6},
pages = {6945-6962},
doi = {10.3934/mbe.2020359},
pmid = {33378883},
issn = {1551-0018},
mesh = {Algorithms ; Electromyography ; Movement ; *Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ensemble empirical mode decomposition (EEMD) and wavelet threshold is hence proposed to remove the random noise from the sEMG signal. With this method, the noised sEMG signal is first decomposed into several intrinsic mode functions (IMFs) by EEMD. The first IMF is mostly noise, coupled with a small useful component which is extracted using a wavelet transform based method by defining a peak-to-sum ratio and a noise-independent extracting threshold function. Other IMFs are processed using an improved wavelet threshold denoising method, where a noise variance estimation algorithm and an improved wavelet threshold function are combined. Key to the threshold denoising method, a threshold function is used to retain the required wavelet coefficients. Our denoising algorithm is tested for different sEMG signals produced by different muscles and motions. Experimental results show that the proposed new method performs better than other methods including the conventional wavelet threshold method and the EMD method, which guaranteed its usability in prosthetic limb control.},
}
@article {pmid33378260,
year = {2020},
author = {Santamaria-Vazquez, E and Martinez-Cagigal, V and Vaquerizo-Villar, F and Hornero, R},
title = {EEG-Inception: A Novel Deep Convolutional Neural Network for Assistive ERP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2773-2782},
doi = {10.1109/TNSRE.2020.3048106},
pmid = {33378260},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data. In particular, convolutional neural networks (CNN) showed adequate results in brain-computer interfaces (BCI) based on different control signals, including event-related potentials (ERP). In this study, we propose a novel CNN, called EEG-Inception, that improves the accuracy and calibration time of assistive ERP-based BCIs. To the best of our knowledge, EEG-Inception is the first model to integrate Inception modules for ERP detection, which combined efficiently with other structures in a light architecture, improved the performance of our approach. The model was validated in a population of 73 subjects, of which 31 present motor disabilities. Results show that EEG-Inception outperforms 5 previous approaches, yielding significant improvements for command decoding accuracy up to 16.0%, 10.7%, 7.2%, 5.7% and 5.1% in comparison to rLDA, xDAWN + Riemannian geometry, CNN-BLSTM, DeepConvNet and EEGNet, respectively. Moreover, EEG-Inception requires very few calibration trials to achieve state-of-the-art performances taking advantage of a novel training strategy that combines cross-subject transfer learning and fine-tuning to increase the feasibility of this approach for practical use in assistive applications.},
}
@article {pmid33375441,
year = {2020},
author = {Duart, X and Quiles, E and Suay, F and Chio, N and García, E and Morant, F},
title = {Evaluating the Effect of Stimuli Color and Frequency on SSVEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {21},
number = {1},
pages = {},
pmid = {33375441},
issn = {1424-8220},
abstract = {Brain-computer interfaces (BCI) can extract information about the subject's intentions by registering and processing electroencephalographic (EEG) signals to generate actions on physical systems. Steady-state visual-evoked potentials (SSVEP) are produced when the subject stares at flashing visual stimuli. By means of spectral analysis and by measuring the signal-to-noise ratio (SNR) of its harmonic contents, the observed stimulus can be identified. Stimulus color matters, and some authors have proposed red because of its ability to capture attention, while others refuse it because it might induce epileptic seizures. Green has also been proposed and it is claimed that white may generate the best signals. Regarding frequency, middle frequencies are claimed to produce the best SNR, although high frequencies have not been thoroughly studied, and might be advantageous due to the lower spontaneous cerebral activity in this frequency band. Here, we show white, red, and green stimuli, at three frequencies: 5 (low), 12 (middle), and 30 (high) Hz to 42 subjects, and compare them in order to find which one can produce the best SNR. We aim to know if the response to white is as strong as the one to red, and also if the response to high frequency is as strong as the one triggered by lower frequencies. Attention has been measured with the Conner's Continuous Performance Task version 2 (CPT-II) task, in order to search for a potential relationship between attentional capacity and the SNR previously obtained. An analysis of variance (ANOVA) shows the best SNR with the middle frequency, followed by the low, and finally the high one. White gives as good an SNR as red at 12 Hz and so does green at 5 Hz, with no differences at 30 Hz. These results suggest that middle frequencies are preferable and that using the red color can be avoided. Correlation analysis also show a correlation between attention and the SNR at low frequency, so suggesting that for the low frequencies, more attentional capacity leads to better results.},
}
@article {pmid33373308,
year = {2021},
author = {Wen, D and Liang, B and Zhou, Y and Chen, H and Jung, TP},
title = {The Current Research of Combining Multi-Modal Brain-Computer Interfaces With Virtual Reality.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {9},
pages = {3278-3287},
doi = {10.1109/JBHI.2020.3047836},
pmid = {33373308},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; User-Computer Interface ; *Virtual Reality ; },
abstract = {Combing brain-computer interfaces (BCI) and virtual reality (VR) is a novel technique in the field of medical rehabilitation and game entertainment. However, the limitations of BCI such as a limited number of action commands and low accuracy hinder the widespread use of BCI-VR. Recent studies have used hybrid BCIs that combine multiple BCI paradigms and/or the multi-modal biosensors to alleviate these issues, which may become the mainstream of BCIs in the future. The main purpose of this review is to discuss the current status of multi-modal BCI-VR. This study first reviewed the development of the BCI-VR, and explored the advantages and disadvantages of incorporating eye tracking, motor capture, and myoelectric sensing into the BCI-VR system. Then, this study discussed the development trend of the multi-modal BCI-VR, hoping to provide a pathway for further research in this field.},
}
@article {pmid33373294,
year = {2021},
author = {Huang, W and Zhang, P and Yu, T and Gu, Z and Guo, Q and Li, Y},
title = {A P300-Based BCI System Using Stereoelectroencephalography and Its Application in a Brain Mechanistic Study.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {8},
pages = {2509-2519},
doi = {10.1109/TBME.2020.3047812},
pmid = {33373294},
issn = {1558-2531},
mesh = {Bayes Theorem ; Brain ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; },
abstract = {Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs.},
}
@article {pmid33372958,
year = {2021},
author = {Livezey, JA and Glaser, JI},
title = {Deep learning approaches for neural decoding across architectures and recording modalities.},
journal = {Briefings in bioinformatics},
volume = {22},
number = {2},
pages = {1577-1591},
doi = {10.1093/bib/bbaa355},
pmid = {33372958},
issn = {1477-4054},
mesh = {Action Potentials ; Calcium/metabolism ; *Deep Learning ; Electrocorticography ; Electroencephalography ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; *Neural Networks, Computer ; Speech ; Vision, Ocular ; },
abstract = {Decoding behavior, perception or cognitive state directly from neural signals is critical for brain-computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.},
}
@article {pmid33371056,
year = {2020},
author = {Chung, E and Lee, BH and Hwang, S},
title = {Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial.},
journal = {Medicine},
volume = {99},
number = {51},
pages = {e22612},
pmid = {33371056},
issn = {1536-5964},
mesh = {*Brain-Computer Interfaces ; Chronic Disease ; Electric Stimulation Therapy/instrumentation/*methods ; Gait/physiology ; Gait Disorders, Neurologic/etiology/*rehabilitation ; Humans ; Pilot Projects ; Postural Balance ; Single-Blind Method ; Stroke Rehabilitation/instrumentation/*methods ; Walking Speed ; },
abstract = {BACKGROUND: Brain-computer interface-controlled functional electrical stimulation (BCI-FES) approaches as new feedback training is increasingly being investigated for its usefulness in improving the health of adults or partially impaired upper extremity function in individuals with stroke.<br><br>OBJECTIVE: To evaluate the effects of BCI-FES on postural control and gait performance in individuals with chronic hemiparetic stroke.<br><br>METHODS: A total of 25 individuals with chronic hemiparetic stroke (13 individuals received BCI-FES and 12 individuals received functional electrical stimulation [FES]). The BCI-FES group received BCI-FES on the tibialis anterior muscle on the more-affected side for 30&#x200a;minutes per session, 3 times per week for 5 weeks. The FES group received FES using the same methodology for the same periods. This study used the Mann-Whitney test to compare the two groups before and after training.<br><br>RESULTS: After training, gait velocity (mean value, 29.0 to 42.0&#x200a;cm/s) (P&#x200a;=&#x200a;.002) and cadence (mean value, 65.2 to 78.9&#x200a;steps/min) (P&#x200a;=&#x200a;.020) were significantly improved after BCI-FES training compared to those (mean value, 23.6 to 27.7&#x200a;cm/s, and mean value, 59.4 to 65.5&#x200a;steps/min, respectively) after FES approach. In the less-affected side, step length was significantly increased after BCI-FES (mean value, from 28.0&#x200a;cm to 34.7&#x200a;cm) more than that on FES approach (mean value, from 23.4 to 25.4&#x200a;cm) (P&#x200a;=&#x200a;.031).<br><br>CONCLUSION: The results of the BCI-FES training shows potential advantages on walking abilities in individuals with chronic hemiparetic stroke.},
}
@article {pmid33365336,
year = {2020},
author = {Hyde, RM and Green, MJ and Hudson, C and Down, PM},
title = {Quantitative Analysis of Colostrum Bacteriology on British Dairy Farms.},
journal = {Frontiers in veterinary science},
volume = {7},
number = {},
pages = {601227},
pmid = {33365336},
issn = {2297-1769},
abstract = {Total bacterial counts (TBC) and coliform counts (CC) were estimated for 328 colostrum samples from 56 British dairy farms. Samples collected directly from cows' teats had lower mean TBC (32,079) and CC (21) than those collected from both colostrum collection buckets (TBC: 327,879, CC: 13,294) and feeding equipment (TBC: 439,438, CC: 17,859). Mixed effects models were built using an automated backwards stepwise process in conjunction with repeated bootstrap sampling to provide robust estimates of both effect size and 95% bootstrap confidence intervals (BCI) as well as an estimate of the reproducibility of a variable effect within a target population (stability). Colostrum collected using parlor (2.06 log cfu/ml, 95% BCI: 0.35-3.71) or robot (3.38 log cfu/ml, 95% BCI: 1.29-5.80) milking systems, and samples collected from feeding equipment (2.36 log cfu/ml, 95% BCI: 0.77-5.45) were associated with higher TBC than those collected from the teat, suggesting interventions to reduce bacterial contamination should focus on the hygiene of collection and feeding equipment. The use of hot water to clean feeding equipment (-2.54 log cfu/ml, 95% BCI: -3.76 to -1.74) was associated with reductions in TBC, and the use of peracetic acid (-2.04 log cfu/ml, 95% BCI: -3.49 to -0.56) or hypochlorite (-1.60 log cfu/ml, 95% BCI: -3.01 to 0.27) to clean collection equipment was associated with reductions in TBC compared with water. Cleaning collection equipment less frequently than every use (1.75 log cfu/ml, 95% BCI: 1.30-2.49) was associated with increased TBC, the use of pre-milking teat disinfection prior to colostrum collection (-1.85 log cfu/ml, 95% BCI: -3.39 to 2.23) and the pasteurization of colostrum (-3.79 log cfu/ml, 95% BCI: -5.87 to -2.93) were associated with reduced TBC. Colostrum collection protocols should include the cleaning of colostrum collection and feeding equipment after every use with hot water as opposed to cold water, and hypochlorite or peracetic acid as opposed to water or parlor wash. Cows' teats should be prepared with a pre-milking teat disinfectant and wiped with a clean, dry paper towel prior to colostrum collection, and colostrum should be pasteurized where possible.},
}
@article {pmid33363459,
year = {2020},
author = {Khan, MU and Hasan, MAH},
title = {Hybrid EEG-fNIRS BCI Fusion Using Multi-Resolution Singular Value Decomposition (MSVD).},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {599802},
pmid = {33363459},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.},
}
@article {pmid33363449,
year = {2020},
author = {Parent, M and Albuquerque, I and Tiwari, A and Cassani, R and Gagnon, JF and Lafond, D and Tremblay, S and Falk, TH},
title = {PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {542934},
pmid = {33363449},
issn = {1662-4548},
abstract = {With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.},
}
@article {pmid33362490,
year = {2020},
author = {Nann, M and Peekhaus, N and Angerhöfer, C and Soekadar, SR},
title = {Feasibility and Safety of Bilateral Hybrid EEG/EOG Brain/Neural-Machine Interaction.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {580105},
pmid = {33362490},
issn = {1662-5161},
abstract = {Cervical spinal cord injuries (SCIs) often lead to loss of motor function in both hands and legs, limiting autonomy and quality of life. While it was shown that unilateral hand function can be restored after SCI using a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton (B/NHE), it remained unclear whether such hybrid paradigm also could be used for operating two hand exoskeletons, e.g., in the context of bimanual tasks such as eating with fork and knife. To test whether EEG/EOG signals allow for fluent and reliable as well as safe and user-friendly bilateral B/NHE control, eight healthy participants (six females, mean age 24.1 ± 3.2 years) as well as four chronic tetraplegics (four males, mean age 51.8 ± 15.2 years) performed a complex sequence of EEG-controlled bilateral grasping and EOG-controlled releasing motions of two exoskeletons visually presented on a screen. A novel EOG command performed by prolonged horizontal eye movements (>1 s) to the left or right was introduced as a reliable switch to activate either the left or right exoskeleton. Fluent EEG control was defined as average "time to initialize" (TTI) grasping motions below 3 s. Reliable EEG control was assumed when classification accuracy exceeded 80%. Safety was defined as "time to stop" (TTS) all unintended grasping motions within 2 s. After the experiment, tetraplegics were asked to rate the user-friendliness of bilateral B/NHE control using Likert scales. Average TTI and accuracy of EEG-controlled operations ranged at 2.14 ± 0.66 s and 85.89 ± 15.81% across healthy participants and at 1.90 ± 0.97 s and 81.25 ± 16.99% across tetraplegics. Except for one tetraplegic, all participants met the safety requirements. With 88 ± 11% of the maximum achievable score, tetraplegics rated the control paradigm as user-friendly and reliable. These results suggest that hybrid EEG/EOG B/NHE control of two assistive devices is feasible and safe, paving the way to test this paradigm in larger clinical trials performing bimanual tasks in everyday life environments.},
}
@article {pmid33362458,
year = {2020},
author = {Liu, X and Shen, Y and Liu, J and Yang, J and Xiong, P and Lin, F},
title = {Parallel Spatial-Temporal Self-Attention CNN-Based Motor Imagery Classification for BCI.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {587520},
pmid = {33362458},
issn = {1662-4548},
abstract = {Motor imagery (MI) electroencephalography (EEG) classification is an important part of the brain-computer interface (BCI), allowing people with mobility problems to communicate with the outside world via assistive devices. However, EEG decoding is a challenging task because of its complexity, dynamic nature, and low signal-to-noise ratio. Designing an end-to-end framework that fully extracts the high-level features of EEG signals remains a challenge. In this study, we present a parallel spatial-temporal self-attention-based convolutional neural network for four-class MI EEG signal classification. This study is the first to define a new spatial-temporal representation of raw EEG signals that uses the self-attention mechanism to extract distinguishable spatial-temporal features. Specifically, we use the spatial self-attention module to capture the spatial dependencies between the channels of MI EEG signals. This module updates each channel by aggregating features over all channels with a weighted summation, thus improving the classification accuracy and eliminating the artifacts caused by manual channel selection. Furthermore, the temporal self-attention module encodes the global temporal information into features for each sampling time step, so that the high-level temporal features of the MI EEG signals can be extracted in the time domain. Quantitative analysis shows that our method outperforms state-of-the-art methods for intra-subject and inter-subject classification, demonstrating its robustness and effectiveness. In terms of qualitative analysis, we perform a visual inspection of the new spatial-temporal representation estimated from the learned architecture. Finally, the proposed method is employed to realize control of drones based on EEG signal, verifying its feasibility in real-time applications.},
}
@article {pmid33360930,
year = {2021},
author = {Chen, M and Zhu, Y and Yu, R and Hu, Y and Wan, H and Zhang, R and Yao, D and Guo, D},
title = {Insights on the role of external globus pallidus in controlling absence seizures.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {135},
number = {},
pages = {78-90},
doi = {10.1016/j.neunet.2020.12.006},
pmid = {33360930},
issn = {1879-2782},
mesh = {Cerebral Cortex/*physiology ; Electroencephalography/methods ; Globus Pallidus/*physiology ; Humans ; *Neural Networks, Computer ; Neural Pathways/physiology ; Neurons/physiology ; Seizures/*physiopathology ; Thalamus/*physiology ; },
abstract = {Absence epilepsy, characterized by transient loss of awareness and bilaterally synchronous 2-4 Hz spike and wave discharges (SWDs) on electroencephalography (EEG) during absence seizures, is generally believed to arise from abnormal interactions between the cerebral cortex (Ctx) and thalamus. Recent animal electrophysiological studies suggested that changing the neural activation level of the external globus pallidus (GPe) neurons can remarkably modify firing rates of the thalamic reticular nucleus (TRN) neurons through the GABAergic GPe-TRN pathway. However, the existing experimental evidence does not provide a clear answer as to whether the GPe-TRN pathway contributes to regulating absence seizures. Here, using a biophysically based mean-field model of the GPe-corticothalamic (GCT) network, we found that both directly decreasing the strength of the GPe-TRN pathway and inactivating GPe neurons can effectively suppress absence seizures. Also, the pallido-cortical pathway and the recurrent connection of GPe neurons facilitate the regulation of absence seizures through the GPe-TRN pathway. Specifically, in the controllable situation, enhancing the coupling strength of either of the two pathways can successfully terminate absence seizures. Moreover, the competition between the GPe-TRN and pallido-cortical pathways may lead to the GPe bidirectionally controlling absence seizures, and this bidirectional control manner can be significantly modulated by the Ctx-TRN pathway. Importantly, when the strength of the Ctx-TRN pathway is relatively strong, the bidirectional control of absence seizures by changing GPe neural activities can be observed at both weak and strong strengths of the pallido-cortical pathway.These findings suggest that the GPe-TRN pathway may have crucial functional roles in regulating absence seizures, which may provide a testable hypothesis for further experimental studies and new perspectives on the treatment of absence epilepsy.},
}
@article {pmid33358729,
year = {2021},
author = {Xi, X and Pi, S and Zhao, YB and Wang, H and Luo, Z},
title = {Effect of muscle fatigue on the cortical-muscle network: A combined electroencephalogram and electromyogram study.},
journal = {Brain research},
volume = {1752},
number = {},
pages = {147221},
doi = {10.1016/j.brainres.2020.147221},
pmid = {33358729},
issn = {1872-6240},
mesh = {Adult ; Beta Rhythm ; Cerebral Cortex/*physiology ; Electroencephalography ; Electromyography ; Female ; Gamma Rhythm ; Humans ; Isometric Contraction ; Male ; Muscle Fatigue/*physiology ; Muscle, Skeletal/*physiology ; Neural Pathways/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Electroencephalogram (EEG) and electromyogram (EMG) signals during motion control reflect the interaction between the cortex and muscle. Therefore, dynamic information regarding the cortical-muscle system is of significance for the evaluation of muscle fatigue. We treated the cortex and muscle as a whole system and then applied graph theory and symbolic transfer entropy to establish an effective cortical-muscle network in the beta band (12-30 Hz) and the gamma band (30-45 Hz). Ten healthy volunteers were recruited to participate in the isometric contraction at the level of 30% maximal voluntary contraction. Pre- and post-fatigue EEG and EMG data were recorded. According to the Borg scale, only data with an index greater than 14<19 were selected as fatigue data. The results show that after muscle fatigue: (1) the decrease in the force-generating capacity leads to an increase in STE of the cortical-muscle system; (2) increases of dynamic forces in fatigue leads to a shift from the beta band to gamma band in the activity of the cortical-muscle network; (3) the areas of the frontal and parietal lobes involved in muscle activation within the ipsilateral hemibrain have a compensatory role. Classification based on support vector machine algorithm showed that the accuracy is improved compared to the brain network. These results illustrate the regulation mechanism of the cortical-muscle system during the development of muscle fatigue, and reveal the great potential of the cortical-muscle network in analyzing motor tasks.},
}
@article {pmid33357383,
year = {2021},
author = {Clancy, KB and Mrsic-Flogel, TD},
title = {The sensory representation of causally controlled objects.},
journal = {Neuron},
volume = {109},
number = {4},
pages = {677-689.e4},
pmid = {33357383},
issn = {1097-4199},
support = {/WT_/Wellcome Trust/United Kingdom ; 090843/E/09/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Exercise Test/methods/psychology ; Feedback, Sensory/*physiology ; Female ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Psychomotor Performance/*physiology ; *Reward ; Visual Cortex/*physiology ; },
abstract = {Intentional control over external objects is informed by our sensory experience of them. To study how causal relationships are learned and effected, we devised a brain machine interface (BMI) task using wide-field calcium signals. Mice learned to entrain activity patterns in arbitrary pairs of cortical regions to guide a visual cursor to a target location for reward. Brain areas that were normally correlated could be rapidly reconfigured to exert control over the cursor in a sensory-feedback-dependent manner. Higher visual cortex was more engaged when expert but not naive animals controlled the cursor. Individual neurons in higher visual cortex responded more strongly to the cursor when mice controlled it than when they passively viewed it, with the greatest response boosting as the cursor approached the target location. Thus, representations of causally controlled objects are sensitive to intention and proximity to the subject's goal, potentially strengthening sensory feedback to allow more fluent control.},
}
@article {pmid33353529,
year = {2021},
author = {Arpaia, P and Donnarumma, F and Esposito, A and Parvis, M},
title = {Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces.},
journal = {International journal of neural systems},
volume = {31},
number = {3},
pages = {2150003},
doi = {10.1142/S0129065721500039},
pmid = {33353529},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; },
abstract = {A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.},
}
@article {pmid33353467,
year = {2021},
author = {Morse, LR and Field-Fote, EC and Contreras-Vidal, J and Noble-Haeusslein, LJ and Rodreick, M and Shields, RK and Sofroniew, M and Wudlick, R and Zanca, JM and , },
title = {Meeting Proceedings for SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research.},
journal = {Journal of neurotrauma},
volume = {38},
number = {9},
pages = {1251-1266},
pmid = {33353467},
issn = {1557-9042},
support = {R01 AG056255/AG/NIA NIH HHS/United States ; R01 HD084423/HD/NICHD NIH HHS/United States ; R21 AG050640/AG/NIA NIH HHS/United States ; },
mesh = {Biomedical Research/methods/*trends ; Congresses as Topic/*trends ; Exoskeleton Device/trends ; Humans ; Maryland ; National Institute of Neurological Disorders and Stroke (U.S.)/*trends ; Spinal Cord Injuries/epidemiology/*therapy ; Transcutaneous Electric Nerve Stimulation/methods/trends ; United States/epidemiology ; },
abstract = {The spinal cord injury (SCI) research community has experienced great advances in discovery research, technology development, and promising clinical interventions in the past decade. To build upon these advances and maximize the benefit to persons with SCI, the National Institutes of Health (NIH) hosted a conference February 12-13, 2019 titled "SCI 2020: Launching a Decade of Disruption in Spinal Cord Injury Research." The purpose of the conference was to bring together a broad range of stakeholders, including researchers, clinicians and healthcare professionals, persons with SCI, industry partners, regulators, and funding agency representatives to break down existing communication silos. Invited speakers were asked to summarize the state of the science, assess areas of technological and community readiness, and build collaborations that could change the trajectory of research and clinical options for people with SCI. In this report, we summarize the state of the science in each of five key domains and identify the gaps in the scientific literature that need to be addressed to move the field forward.},
}
@article {pmid33352714,
year = {2020},
author = {Choi, J and Kim, KT and Jeong, JH and Kim, L and Lee, SJ and Kim, H},
title = {Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {24},
pages = {},
pmid = {33352714},
issn = {1424-8220},
support = {2017-0-00432//Institute of Information and Communications Technology Planning and Evaluation/ ; },
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; *Exoskeleton Device ; Humans ; Imagination ; Support Vector Machine ; },
abstract = {This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.},
}
@article {pmid33348823,
year = {2020},
author = {Zeng, H and Zhang, J and Zakaria, W and Babiloni, F and Gianluca, B and Li, X and Kong, W},
title = {InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {24},
pages = {},
pmid = {33348823},
issn = {1424-8220},
support = {62076083//National Natural Science Foundation of China/ ; 2017YFE0118200,2017YFE0116800//National Key R&amp;D Program of China/ ; GK209907299001-008//Fundamental Research Funds for the Provincial 308 Universities of Zhejiang/ ; 2020E10010//Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province/ ; },
mesh = {*Automobile Driving ; *Electroencephalography ; Fatigue/*diagnosis ; Humans ; *Machine Learning ; Neural Networks, Computer ; *Support Vector Machine ; },
abstract = {Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.},
}
@article {pmid33345005,
year = {2020},
author = {Jeunet, C and Hauw, D and Millán, JDR},
title = {Sport Psychology: Technologies Ahead.},
journal = {Frontiers in sports and active living},
volume = {2},
number = {},
pages = {10},
pmid = {33345005},
issn = {2624-9367},
}
@article {pmid33343323,
year = {2020},
author = {Wu, Y and Zhou, W and Lu, Z and Li, Q},
title = {A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance.},
journal = {Frontiers in neuroinformatics},
volume = {14},
number = {},
pages = {589169},
pmid = {33343323},
issn = {1662-5196},
abstract = {The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal-central-parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.},
}
@article {pmid33343318,
year = {2020},
author = {Kinney-Lang, E and Kelly, D and Floreani, ED and Jadavji, Z and Rowley, D and Zewdie, ET and Anaraki, JR and Bahari, H and Beckers, K and Castelane, K and Crawford, L and House, S and Rauh, CA and Michaud, A and Mussi, M and Silver, J and Tuck, C and Adams, K and Andersen, J and Chau, T and Kirton, A},
title = {Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {593883},
pmid = {33343318},
issn = {1662-5161},
abstract = {Thousands of youth suffering from acquired brain injury or other early-life neurological disease live, mature, and learn with only limited communication and interaction with their world. Such cognitively capable children are ideal candidates for brain-computer interfaces (BCI). While BCI systems are rapidly evolving, a fundamental gap exists between technological innovators and the patients and families who stand to benefit. Forays into translating BCI systems to children in recent years have revealed that kids can learn to operate simple BCI with proficiency akin to adults. BCI could bring significant boons to the lives of many children with severe physical impairment, supporting their complex physical and social needs. However, children have been neglected in BCI research and a collaborative BCI research community is required to unite and push pediatric BCI development forward. To this end, the pediatric BCI Canada collaborative network (BCI-CAN) was formed, under a unified goal to cooperatively drive forward pediatric BCI innovation and impact. This article reflects on the topics and discussions raised in the foundational BCI-CAN meeting held in Toronto, ON, Canada in November 2019 and suggests the next steps required to see BCI impact the lives of children with severe neurological disease and their families.},
}
@article {pmid33343308,
year = {2020},
author = {Moore, B and Khang, S and Francis, JT},
title = {Noise-Correlation Is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates.},
journal = {Frontiers in behavioral neuroscience},
volume = {14},
number = {},
pages = {541920},
pmid = {33343308},
issn = {1662-5153},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
abstract = {Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain-computer interfaces (BCIs; Zhao et al., 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearson's correlation coefficient to measure shared variability between simultaneously recorded units (32-112) and found significantly higher noise-correlation and positive correlation between the populations' signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted a mean matched noise-correlation analysis to decouple known interactions between event-triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however, the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning (RL) towards autonomous BCI updating.},
}
@article {pmid33343290,
year = {2020},
author = {Kirasirova, L and Bulanov, V and Ossadtchi, A and Kolsanov, A and Pyatin, V and Lebedev, M},
title = {A P300 Brain-Computer Interface With a Reduced Visual Field.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {604629},
pmid = {33343290},
issn = {1662-4548},
abstract = {A P300 brain-computer interface (BCI) is a paradigm, where text characters are decoded from event-related potentials (ERPs). In a popular implementation, called P300 speller, a subject looks at a display where characters are flashing and selects one character by attending to it. The selection is recognized as the item with the strongest ERP. The speller performs well when cortical responses to target and non-target stimuli are sufficiently different. Although many strategies have been proposed for improving the BCI spelling, a relatively simple one received insufficient attention in the literature: reduction of the visual field to diminish the contribution from non-target stimuli. Previously, this idea was implemented in a single-stimulus switch that issued an urgent command like stopping a robot. To tackle this approach further, we ran a pilot experiment where ten subjects operated a traditional P300 speller or wore a binocular aperture that confined their sight to the central visual field. As intended, visual field restriction resulted in a replacement of non-target ERPs with EEG rhythms asynchronous to stimulus periodicity. Changes in target ERPs were found in half of the subjects and were individually variable. While classification accuracy was slightly better for the aperture condition (84.3 ± 2.9%, mean ± standard error) than the no-aperture condition (81.0 ± 2.6%), this difference was not statistically significant for the entire sample of subjects (N = 10). For both the aperture and no-aperture conditions, classification accuracy improved over 4 days of training, more so for the aperture condition (from 72.0 ± 6.3% to 87.0 ± 3.9% and from 72.0 ± 5.6% to 97.0 ± 2.2% for the no-aperture and aperture conditions, respectively). Although in this study BCI performance was not substantially altered, we suggest that with further refinement this approach could speed up BCI operations and reduce user fatigue. Additionally, instead of wearing an aperture, non-targets could be removed algorithmically or with a hybrid interface that utilizes an eye tracker. We further discuss how a P300 speller could be improved by taking advantage of the different physiological properties of the central and peripheral vision. Finally, we suggest that the proposed experimental approach could be used in basic research on the mechanisms of visual processing.},
}
@article {pmid33340844,
year = {2021},
author = {Kline, A and Gaina Ghiroaga, C and Pittman, D and Goodyear, B and Ronsky, J},
title = {EEG differentiates left and right imagined Lower Limb movement.},
journal = {Gait &amp; posture},
volume = {84},
number = {},
pages = {148-154},
doi = {10.1016/j.gaitpost.2020.11.014},
pmid = {33340844},
issn = {1879-2219},
support = {//CIHR/Canada ; },
mesh = {Adult ; Electroencephalography/*methods ; Healthy Volunteers ; Humans ; Lower Extremity/*diagnostic imaging ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; Young Adult ; },
abstract = {BACKGROUND: Identifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature.<br><br>RESEARCH QUESTION: Can left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)?<br><br>METHODS: An experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively.<br><br>RESULTS: Findings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %.<br><br>SIGNIFICANCE: Our study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility.},
}
@article {pmid33339727,
year = {2021},
author = {Vucic, S},
title = {P300 jitter latency, brain-computer interface and amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {132},
number = {2},
pages = {614-615},
doi = {10.1016/j.clinph.2020.11.017},
pmid = {33339727},
issn = {1872-8952},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; },
}
@article {pmid33339105,
year = {2020},
author = {Chailloux Peguero, JD and Mendoza-Montoya, O and Antelis, JM},
title = {Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {24},
pages = {},
pmid = {33339105},
issn = {1424-8220},
support = {PN2015-873//Consejo Nacional de Ciencia y Tecnolog&#xed;a/ ; },
mesh = {*Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Humans ; *Photic Stimulation ; Reproducibility of Results ; },
abstract = {The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.},
}
@article {pmid33339011,
year = {2021},
author = {Bennett, JD and John, SE and Grayden, DB and Burkitt, AN},
title = {A neurophysiological approach to spatial filter selection for adaptive brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {18},
number = {2},
pages = {},
doi = {10.1088/1741-2552/abd51f},
pmid = {33339011},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; Imagination/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Objective. The common spatial patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain-computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI.Approach. A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated.Main results. Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relationship between these categories of CSP patterns and classification performance revealed discarding neurophysiologically improbable filters can decrease decoder performance. Further analysis revealed that the spatial orientation of EEG modulations can evolve over time, and that the features extracted from the original CSP filters can become inseparable. Importantly, it was shown through a novel adaptive CSP technique that adaptation in response to these emerging patterns can restore feature separability.Significance. These findings highlight the importance of considering and reporting on spatial filter activation patterns in both online and offline studies. They also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.},
}
@article {pmid33338542,
year = {2021},
author = {Riyad, M and Khalil, M and Adib, A},
title = {MI-EEGNET: A novel convolutional neural network for motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {353},
number = {},
pages = {109037},
doi = {10.1016/j.jneumeth.2020.109037},
pmid = {33338542},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Machine Learning ; Neural Networks, Computer ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCI) permits humans to interact with machines by decoding brainwaves to command for a variety of purposes. Convolutional neural networks (ConvNet) have improved the state-of-the-art of motor imagery decoding in an end-to-end approach. However, shallow ConvNets usually perform better than their deep counterparts. Thus, we aim to design a novel ConvNet that is deeper than the existing models, with an increase in terms of performances, and with optimal complexity.<br><br>NEW METHOD: We develop a ConvNet based on Inception and Xception architectures that uses convolutional layers to extract temporal and spatial features. We adopt separable convolutions and depthwise convolutions to enable faster and efficient ConvNet. Then, we introduce a new block that is inspired by Inception to learn more rich features to improve the classification performances.<br><br>RESULTS: The obtained results are comparable with other state-of-the-art techniques. Also, the weights of the convolutional layers give us some insights onto the learned features and reveal the most relevant ones.<br><br>We show that our model significantly outperforms Filter Bank Common Spatial Pattern (FBCSP), Riemannian Geometry (RG) approaches, and ShallowConvNet (p&#x202f;<&#x202f;0.05).<br><br>CONCLUSIONS: The obtained results prove that motor imagery decoding is possible without handcrafted features.},
}
@article {pmid33338023,
year = {2021},
author = {Shen, J and Zhang, X and Huang, X and Wu, M and Gao, J and Lu, D and Ding, Z and Hu, B},
title = {An Optimal Channel Selection for EEG-Based Depression Detection via Kernel-Target Alignment.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {7},
pages = {2545-2556},
doi = {10.1109/JBHI.2020.3045718},
pmid = {33338023},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Depression/diagnosis ; Electroencephalography ; Emotions ; Humans ; },
abstract = {Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Consequently, we propose an optimal channel selection method for EEG-based depression detection via kernel-target alignment (KTA) to effectively resolve the abovementioned issues. In this method, we consider a modified version KTA that can measure the similarity between the kernel matrix for channel selection and the target matrix as an objective function and optimize the objective function by a proposed optimal channel selection strategy. Experimental results on two EEG datasets show that channel selection can effectively increase the classification performance and that even if we rely only on a small subset of channels, the results are still acceptable. The selected channels are in line with the expected latent cortical activity patterns in depression detection. Moreover, the experimental results demonstrate that our method outperforms the state-of-the-art channel selection approaches.},
}
@article {pmid33335470,
year = {2020},
author = {Reichert, C and Tellez Ceja, IF and Sweeney-Reed, CM and Heinze, HJ and Hinrichs, H and Dürschmid, S},
title = {Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {591777},
pmid = {33335470},
issn = {1662-4548},
abstract = {Regaining communication abilities in patients who are unable to speak or move is one of the main goals in decoding brain waves for brain-computer interface (BCI) control. Many BCI approaches designed for communication rely on attention to visual stimuli, commonly applying an oddball paradigm, and require both eye movements and adequate visual acuity. These abilities may, however, be absent in patients who depend on BCI communication. We have therefore developed a response-based communication BCI, which is independent of gaze shifts but utilizes covert shifts of attention to the left or right visual field. We recorded the electroencephalogram (EEG) from 29 channels and coregistered the vertical and horizontal electrooculogram. Data-driven decoding of small attention-based differences between the hemispheres, also known as N2pc, was performed using 14 posterior channels, which are expected to reflect correlates of visual spatial attention. Eighteen healthy participants responded to 120 statements by covertly directing attention to one of two colored symbols (green and red crosses for "yes" and "no," respectively), presented in the user's left and right visual field, respectively, while maintaining central gaze fixation. On average across participants, 88.5% (std: 7.8%) of responses were correctly decoded online. In order to investigate the potential influence of stimulus features on accuracy, we presented the symbols with different visual angles, by altering symbol size and eccentricity. The offline analysis revealed that stimulus features have a minimal impact on the controllability of the BCI. Hence, we show with our novel approach that spatial attention to a colored symbol is a robust method with which to control a BCI, which has the potential to support severely paralyzed people with impaired eye movements and low visual acuity in communicating with their environment.},
}
@article {pmid33333814,
year = {2020},
author = {Wu, SJ and Nicolaou, N and Bogdan, M},
title = {Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis.},
journal = {Entropy (Basel, Switzerland)},
volume = {22},
number = {12},
pages = {},
pmid = {33333814},
issn = {1099-4300},
abstract = {Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain-computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients.},
}
@article {pmid33328950,
year = {2020},
author = {Zhu, Y and Li, Y and Lu, J and Li, P},
title = {A Hybrid BCI Based on SSVEP and EOG for Robotic Arm Control.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {583641},
pmid = {33328950},
issn = {1662-5218},
abstract = {Brain-computer interface (BCI) for robotic arm control has been studied to improve the life quality of people with severe motor disabilities. There are still challenges for robotic arm control in accomplishing a complex task with a series of actions. An efficient switch and a timely cancel command are helpful in the application of robotic arm. Based on the above, we proposed an asynchronous hybrid BCI in this study. The basic control of a robotic arm with six degrees of freedom was a steady-state visual evoked potential (SSVEP) based BCI with fifteen target classes. We designed an EOG-based switch which used a triple blink to either activate or deactivate the flash of SSVEP-based BCI. Stopping flash in the idle state can help to reduce visual fatigue and false activation rate (FAR). Additionally, users were allowed to cancel the current command simply by a wink in the feedback phase to avoid executing the incorrect command. Fifteen subjects participated and completed the experiments. The cue-based experiment obtained an average accuracy of 92.09%, and the information transfer rates (ITR) resulted in 35.98 bits/min. The mean FAR of the switch was 0.01/min. Furthermore, all subjects succeeded in asynchronously operating the robotic arm to grasp, lift, and move a target object from the initial position to a specific location. The results indicated the feasibility of the combination of EOG and SSVEP signals and the flexibility of EOG signal in BCI to complete a complicated task of robotic arm control.},
}
@article {pmid33328941,
year = {2020},
author = {Peters, B and Bedrick, S and Dudy, S and Eddy, B and Higger, M and Kinsella, M and McLaughlin, D and Memmott, T and Oken, B and Quivira, F and Spaulding, S and Erdogmus, D and Fried-Oken, M},
title = {SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {595890},
pmid = {33328941},
issn = {1662-5161},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Access to communication is critical for individuals with late-stage amyotrophic lateral sclerosis (ALS) and minimal volitional movement, but they sometimes present with concomitant visual or ocular motility impairments that affect their performance with eye tracking or visual brain-computer interface (BCI) systems. In this study, we explored the use of modified eye tracking and steady state visual evoked potential (SSVEP) BCI, in combination with the Shuffle Speller typing interface, for this population. Two participants with late-stage ALS, visual impairments, and minimal volitional movement completed a single-case experimental research design comparing copy-spelling performance with three different typing systems: (1) commercially available eye tracking communication software, (2) Shuffle Speller with modified eye tracking, and (3) Shuffle Speller with SSVEP BCI. Participant 1 was unable to type any correct characters with the commercial system, but achieved accuracies of up to 50% with Shuffle Speller eye tracking and 89% with Shuffle Speller BCI. Participant 2 also had higher maximum accuracies with Shuffle Speller, typing with up to 63% accuracy with eye tracking and 100% accuracy with BCI. However, participants' typing accuracy for both Shuffle Speller conditions was highly variable, particularly in the BCI condition. Both the Shuffle Speller interface and SSVEP BCI input show promise for improving typing performance for people with late-stage ALS. Further development of innovative BCI systems for this population is needed.},
}
@article {pmid33328931,
year = {2020},
author = {Zaer, H and Fan, W and Orlowski, D and Glud, AN and Andersen, ASM and Schneider, MB and Adler, JR and Stroh, A and Sørensen, JCH},
title = {A Perspective of International Collaboration Through Web-Based Telecommunication-Inspired by COVID-19 Crisis.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {577465},
pmid = {33328931},
issn = {1662-5161},
abstract = {The tsunami effect of the COVID-19 pandemic is affecting many aspects of scientific activities. Multidisciplinary experimental studies with international collaborators are hindered by the closing of the national borders, logistic issues due to lockdown, quarantine restrictions, and social distancing requirements. The full impact of this crisis on science is not clear yet, but the above-mentioned issues have most certainly restrained academic research activities. Sharing innovative solutions between researchers is in high demand in this situation. The aim of this paper is to share our successful practice of using web-based communication and remote control software for real-time long-distance control of brain stimulation. This solution may guide and encourage researchers to cope with restrictions and has the potential to help expanding international collaborations by lowering travel time and costs.},
}
@article {pmid33328874,
year = {2020},
author = {Chen, Y and Hang, W and Liang, S and Liu, X and Li, G and Wang, Q and Qin, J and Choi, KS},
title = {A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {606949},
pmid = {33328874},
issn = {1662-4548},
abstract = {In recent years, emerging matrix learning methods have shown promising performance in motor imagery (MI)-based brain-computer interfaces (BCIs). Nonetheless, the electroencephalography (EEG) pattern variations among different subjects necessitates collecting a large amount of labeled individual data for model training, which prolongs the calibration session. From the perspective of transfer learning, the model knowledge inherent in reference subjects incorporating few target EEG data have the potential to solve the above issue. Thus, a novel knowledge-leverage-based support matrix machine (KL-SMM) was developed to improve the classification performance when only a few labeled EEG data in the target domain (target subject) were available. The proposed KL-SMM possesses the powerful capability of a matrix learning machine, which allows it to directly learn the structural information from matrix-form EEG data. In addition, the KL-SMM can not only fully leverage few labeled EEG data from the target domain during the learning procedure but can also leverage the existing model knowledge from the source domain (source subject). Therefore, the KL-SMM can enhance the generalization performance of the target classifier while guaranteeing privacy protection to a certain extent. Finally, the objective function of the KL-SMM can be easily optimized using the alternating direction method of multipliers method. Extensive experiments were conducted to evaluate the effectiveness of the KL-SMM on publicly available MI-based EEG datasets. Experimental results demonstrated that the KL-SMM outperformed the comparable methods when the EEG data were insufficient.},
}
@article {pmid33328860,
year = {2020},
author = {Delijorge, J and Mendoza-Montoya, O and Gordillo, JL and Caraza, R and Martinez, HR and Antelis, JM},
title = {Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {589659},
pmid = {33328860},
issn = {1662-4548},
abstract = {This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.},
}
@article {pmid33328845,
year = {2020},
author = {Lindig-León, C and Rimbert, S and Bougrain, L},
title = {Multiclass Classification Based on Combined Motor Imageries.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {559858},
pmid = {33328845},
issn = {1662-4548},
abstract = {Motor imagery (MI) allows the design of self-paced brain-computer interfaces (BCIs), which can potentially afford an intuitive and continuous interaction. However, the implementation of non-invasive MI-based BCIs with more than three commands is still a difficult task. First, the number of MIs for decoding different actions is limited by the constraint of maintaining an adequate spacing among the corresponding sources, since the electroencephalography (EEG) activity from near regions may add up. Second, EEG generates a rather noisy image of brain activity, which results in a poor classification performance. Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. We recorded EEG signals from seven healthy subjects during an 8-class EEG experiment including the rest condition and all possible combinations using the left hand, right hand, and feet. The proposed multilabel approaches convert the original 8-class problem into a set of three binary problems to facilitate the use of the CSP algorithm. In the case of the MC2CMI method, each binary problem groups together in one class all the MIs engaging one of the three selected body parts, while the rest of MIs that do not engage the same body part are grouped together in the second class. In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. Finally, three sets of features are merged together to predict the user intention by applying an 8-class linear discriminant analysis. The MC2SMI method is quite similar, the only difference is that any of the combined MIs is considered during the training phase, which drastically accelerates the calibration time. For all subjects, both the MC2CMI and the MC2SMI approaches reached a higher accuracy than the classic pair-wise (PW) and one-vs.-all (OVA) methods. Our results show that, when brain activity is properly modulated, multilabel approaches represent a very interesting solution to increase the number of commands, and thus to provide a better interaction.},
}
@article {pmid33328841,
year = {2020},
author = {Shirzhiyan, Z and Keihani, A and Farahi, M and Shamsi, E and GolMohammadi, M and Mahnam, A and Haidari, MR and Jafari, AH},
title = {Toward New Modalities in VEP-Based BCI Applications Using Dynamical Stimuli: Introducing Quasi-Periodic and Chaotic VEP-Based BCI.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {534619},
pmid = {33328841},
issn = {1662-4548},
abstract = {Visual evoked potentials (VEPs) to periodic stimuli are commonly used in brain computer interfaces for their favorable properties such as high target identification accuracy, less training time, and low surrounding target interference. Conventional periodic stimuli can lead to subjective visual fatigue due to continuous and high contrast stimulation. In this study, we compared quasi-periodic and chaotic complex stimuli to common periodic stimuli for use with VEP-based brain computer interfaces (BCIs). Canonical correlation analysis (CCA) and coherence methods were used to evaluate the performance of the three stimulus groups. Subjective fatigue caused by the presented stimuli was evaluated by the Visual Analogue Scale (VAS). Using CCA with the M2 template approach, target identification accuracy was highest for the chaotic stimuli (M = 86.8, SE = 1.8) compared to the quasi-periodic (M = 78.1, SE = 2.6, p = 0.008) and periodic (M = 64.3, SE = 1.9, p = 0.0001) stimulus groups. The evaluation of fatigue rates revealed that the chaotic stimuli caused less fatigue compared to the quasi-periodic (p = 0.001) and periodic (p = 0.0001) stimulus groups. In addition, the quasi-periodic stimuli led to lower fatigue rates compared to the periodic stimuli (p = 0.011). We conclude that the target identification results were better for the chaotic group compared to the other two stimulus groups with CCA. In addition, the chaotic stimuli led to a less subjective visual fatigue compared to the periodic and quasi-periodic stimuli and can be suitable for designing new comfortable VEP-based BCIs.},
}
@article {pmid33326490,
year = {2020},
author = {Pont, S and Fraikin, N and Caspar, Y and Van Melderen, L and Attrée, I and Cretin, F},
title = {Bacterial behavior in human blood reveals complement evaders with some persister-like features.},
journal = {PLoS pathogens},
volume = {16},
number = {12},
pages = {e1008893},
pmid = {33326490},
issn = {1553-7374},
mesh = {Acinetobacter baumannii/growth &amp; development/pathogenicity ; Bacteremia/blood/immunology/microbiology ; Bacteria ; Bacterial Infections/*blood/*immunology ; Burkholderia/growth &amp; development/pathogenicity ; Complement Activation/*immunology ; Complement System Proteins/immunology ; Escherichia coli/growth &amp; development/pathogenicity ; Humans ; Klebsiella pneumoniae/growth &amp; development/pathogenicity ; Microbial Sensitivity Tests ; Pseudomonas Infections/blood/immunology/microbiology ; Pseudomonas aeruginosa/growth &amp; development/pathogenicity ; Yersinia enterocolitica/growth &amp; development/pathogenicity ; },
abstract = {Bacterial bloodstream infections (BSI) are a major health concern and can cause up to 40% mortality. Pseudomonas aeruginosa BSI is often of nosocomial origin and is associated with a particularly poor prognosis. The mechanism of bacterial persistence in blood is still largely unknown. Here, we analyzed the behavior of a cohort of clinical and laboratory Pseudomonas aeruginosa strains in human blood. In this specific environment, complement was the main defensive mechanism, acting either by direct bacterial lysis or by opsonophagocytosis, which required recognition by immune cells. We found highly variable survival rates for different strains in blood, whatever their origin, serotype, or the nature of their secreted toxins (ExoS, ExoU or ExlA) and despite their detection by immune cells. We identified and characterized a complement-tolerant subpopulation of bacterial cells that we named "evaders". Evaders shared some features with bacterial persisters, which tolerate antibiotic treatment. Notably, in bi-phasic killing curves, the evaders represented 0.1-0.001% of the initial bacterial load and displayed transient tolerance. However, the evaders are not dormant and require active metabolism to persist in blood. We detected the evaders for five other major human pathogens: Acinetobacter baumannii, Burkholderia multivorans, enteroaggregative Escherichia coli, Klebsiella pneumoniae, and Yersinia enterocolitica. Thus, the evaders could allow the pathogen to persist within the bloodstream, and may be the cause of fatal bacteremia or dissemination, in particular in the absence of effective antibiotic treatments.},
}
@article {pmid33326374,
year = {2021},
author = {Dantas, H and Hansen, TC and Warren, DJ and Mathews, VJ},
title = {Shared Prosthetic Control Based on Multiple Movement Intent Decoders.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {5},
pages = {1547-1556},
doi = {10.1109/TBME.2020.3045351},
pmid = {33326374},
issn = {1558-2531},
mesh = {Algorithms ; *Amputees ; *Artificial Limbs ; *Brain-Computer Interfaces ; Humans ; Movement ; Neural Networks, Computer ; },
abstract = {SIGNIFICANCE: A number of movement intent decoders exist in the literature that typically differ in the algorithms used and the nature of the outputs generated. Each approach comes with its own advantages and disadvantages. Combining the estimates of multiple algorithms may have better performance than any of the individual methods.<br><br>OBJECTIVE: This paper presents and evaluates a shared controller framework for prosthetic limbs based on multiple decoders of volitional movement intent.<br><br>METHODS: An algorithm to combine multiple estimates to control the prosthesis is developed in this paper. The capabilities of the approach are validated using a system that combines a Kalman filter-based decoder with a multilayer perceptron classifier-based decoder. The shared controller's performance is validated in online experiments where a virtual limb is controlled in real-time by amputee and intact-arm subjects. During the testing phase subjects controlled a virtual hand in real time to move digits to instructed positions using either a Kalman filter decoder, a multilayer perceptron decoder, or a linear combination of the two.<br><br>RESULTS: The shared controller results in statistically significant improvements over the component decoders. Specifically, certain degrees of shared control result in increases in the time-in-target metric and decreases in unintended movements.<br><br>CONCLUSION: The shared controller of this paper combines the good qualities of component decoders tested in this paper. Herein, combining a Kalman filter decoder with a classifier-based decoder inherits the flexibility of the Kalman filter decoder and the limited unwanted movements from the classifier-based decoder, resulting in a system that may be able to perform the tasks of everyday life more naturally and reliably.},
}
@article {pmid33325279,
year = {2020},
author = {Al-Sheikh, U and Kang, L},
title = {Mechano-gated channels in C. elegans.},
journal = {Journal of neurogenetics},
volume = {34},
number = {3-4},
pages = {363-368},
doi = {10.1080/01677063.2020.1832091},
pmid = {33325279},
issn = {1563-5260},
mesh = {Animals ; Caenorhabditis elegans/cytology/genetics/*physiology ; Caenorhabditis elegans Proteins/genetics/*physiology ; Genes, Helminth ; Ion Channel Gating/physiology ; Ion Channels/classification/genetics/physiology ; Mammals/physiology ; Mechanoreceptors/*physiology ; Mechanotransduction, Cellular/*physiology ; Multigene Family ; Species Specificity ; },
abstract = {Mechanosensation such as touch, hearing and proprioception, is functionally regulated by mechano-gated ion channels through the process of transduction. Mechano-gated channels are a subtype of gated ion channels engaged in converting mechanical stimuli to chemical or electrical signals thereby modulating sensation. To date, a few families of mechano-gated channels (DEG/ENaC, TRPN, K2P, TMC and Piezo) have been identified in eukaryotes. Using a tractable genetic model organism Caenorhabditis elegans, the molecular mechanism of mechanosensation have been the focus of much research to comprehend the process of mechanotransduction. Comprising of almost all metazoans classes of ion channels, transporters and receptors, C. elegans is a powerful genetic model to explore mechanosensitive behaviors such as touch sensation and proprioception. The nematode relies primarily on its sensory abilities to survive in its natural environment. Genetic screening, calcium imaging and electrophysiological analysis have established that ENaC proteins and TRPN channel (TRP-4 protein) can characterize mechano-gated channels in C. elegans. A recent study reported that TMCs are likely the pore-forming subunit of a mechano-gated channel in C. elegans. Nevertheless, it still remains unclear whether Piezo as well as other candidate proteins can form mechano-gated channels in C. elegans.},
}
@article {pmid33321915,
year = {2020},
author = {Gannouni, S and Belwafi, K and Aboalsamh, H and AlSamhan, Z and Alebdi, B and Almassad, Y and Alobaedallah, H},
title = {EEG-Based BCI System to Detect Fingers Movements.},
journal = {Brain sciences},
volume = {10},
number = {12},
pages = {},
pmid = {33321915},
issn = {2076-3425},
support = {RG-1440-109//Deanship of Scientific Research, King Saud University/ ; },
abstract = {The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.},
}
@article {pmid33321895,
year = {2020},
author = {Wosiak, A and Dura, A},
title = {Hybrid Method of Automated EEG Signals' Selection Using Reversed Correlation Algorithm for Improved Classification of Emotions.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {24},
pages = {},
pmid = {33321895},
issn = {1424-8220},
mesh = {*Algorithms ; Arousal ; *Brain-Computer Interfaces ; *Electroencephalography ; *Emotions ; Humans ; },
abstract = {Based on the growing interest in encephalography to enhance human-computer interaction (HCI) and develop brain-computer interfaces (BCIs) for control and monitoring applications, efficient information retrieval from EEG sensors is of great importance. It is difficult due to noise from the internal and external artifacts and physiological interferences. The enhancement of the EEG-based emotion recognition processes can be achieved by selecting features that should be taken into account in further analysis. Therefore, the automatic feature selection of EEG signals is an important research area. We propose a multistep hybrid approach incorporating the Reversed Correlation Algorithm for automated frequency band-electrode combinations selection. Our method is simple to use and significantly reduces the number of sensors to only three channels. The proposed method has been verified by experiments performed on the DEAP dataset. The obtained effects have been evaluated regarding the accuracy of two emotions-valence and arousal. In comparison to other research studies, our method achieved classification results that were 4.20-8.44% greater. Moreover, it can be perceived as a universal EEG signal classification technique, as it belongs to unsupervised methods.},
}
@article {pmid33320813,
year = {2020},
author = {Behboodi, M and Mahnam, A and Marateb, H and Rabbani, H},
title = {Optimization of Visual Stimulus Sequence in a Brain-Computer Interface Based on Code Modulated Visual Evoked Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2762-2772},
doi = {10.1109/TNSRE.2020.3044947},
pmid = {33320813},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {Brain-computer interfaces based on code-modulated visual evoked potentials provide high information transfer rates, which make them promising alternative communication tools. Circular shifts of a binary sequence are used as the flickering pattern of several visual stimuli, where the minimum correlation between them is critical for recognizing the target by analyzing the EEG signal. Implemented sequences have been borrowed from communication theory without considering visual system physiology and related ergonomics. Here, an approach is proposed to design optimum stimulus sequences considering physiological factors, and their superior performance was demonstrated for a 6-target c-VEP BCI system. This was achieved by defining a time-factor index on the frequency response of the sequence, while the autocorrelation index ensured a low correlation between circular shifts. A modified version of the non-dominated sorting genetic algorithm II (NSGAII) multi-objective optimization technique was implemented to find, for the first time, 63-bit sequences with simultaneously optimized autocorrelation and time-factor indexes. The selected optimum sequences for general (TFO) and 6-target (6TO) BCI systems, were then compared with m-sequence by conducting experiments on 16 participants. Friedman tests showed a significant difference in perceived eye irritation between TFO and m-sequence (p = 0.024). Generalized estimating equations (GEE) statistical test showed significantly higher accuracy for 6TO compared to m-sequence (p = 0.006). Evaluation of EEG responses showed enhanced SNR for the new sequences compared to m-sequence, confirming the proposed approach for optimizing the stimulus sequence. Incorporating physiological factors to select sequence(s) used for c-VEP BCI systems improves their performance and applicability.},
}
@article {pmid33319332,
year = {2021},
author = {Sosulski, J and Kemmer, JP and Tangermann, M},
title = {Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis.},
journal = {Neuroinformatics},
volume = {19},
number = {3},
pages = {461-476},
pmid = {33319332},
issn = {1559-0089},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Electroencephalogram data used in the domain of brain-computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this information is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit-Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decouple the time component from the covariance matrix of event-related potential data in order to further improve the estimates of the covariance matrix for linear discriminant analysis. We compare three regularized variants thereof and a feature representation based on Riemannian geometry against our proposed novel linear discriminant analysis with time-decoupled covariance estimates. Extensive evaluations on 14 electroencephalogram datasets reveal, that the novel approach increases the classification performance by up to four percentage points for small training datasets, and gracefully converges to the performance of standard shrinkage-regularized LDA for large training datasets. Given these results, practitioners in this field should consider using our proposed time-decoupled covariance estimation when they apply linear discriminant analysis to classify event-related potentials, especially when few training data points are available.},
}
@article {pmid33312775,
year = {2021},
author = {Hiwaki, O},
title = {Novel Technique for Noninvasive Detection of Localized Dynamic Brain Signals by Using Transcranial Static Magnetic Fields.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {9},
number = {},
pages = {4900106},
pmid = {33312775},
issn = {2168-2372},
mesh = {*Brain/diagnostic imaging ; Brain Mapping ; Electroencephalography ; Humans ; Magnetic Fields ; *Magnetoencephalography ; },
abstract = {The techniques for noninvasive measurement of brain function such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) have been used in diagnosing brain conditions. However, the conventional techniques have critical limitations of spatial or temporal resolution. Here, we developed a novel technique which enables the precise measurement of dynamic brain signals and localized identification of active brain regions. In this technique, termed as magnetically biased field (MBF), human brain signal is measured as the fluctuation of a transcranial static magnetic field emitted by a coil placed on the scalp. The validity of MBF was confirmed by the measurement of somatosensory evoked signals. Fast somatosensory evoked signals were successfully observed. Localized maximum positive and negative deflections appeared at the region which represents the right primary somatosensory area contralateral to the stimulated hand. The ability of MBF to detect dynamic brain activity precisely can have numerous applications such as diagnosing brain diseases and brain-machine interfaces.},
}
@article {pmid33311578,
year = {2020},
author = {Jafari, M and Aflalo, T and Chivukula, S and Kellis, SS and Salas, MA and Norman, SL and Pejsa, K and Liu, CY and Andersen, RA},
title = {The human primary somatosensory cortex encodes imagined movement in the absence of sensory information.},
journal = {Communications biology},
volume = {3},
number = {1},
pages = {757},
pmid = {33311578},
issn = {2399-3642},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; R25 NS079198/NS/NINDS NIH HHS/United States ; P50 MH094258/MH/NIMH NIH HHS/United States ; R01EY015545/EY/NEI NIH HHS/United States ; U01 NS123127/NS/NINDS NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; Animals ; Brain Mapping ; Brain Waves ; Cognition ; Humans ; *Imagination ; Magnetic Resonance Imaging/methods ; Male ; Motor Cortex/diagnostic imaging/physiology ; Neurons/physiology ; *Sensation ; Somatosensory Cortex/diagnostic imaging/*physiology ; },
abstract = {Classical systems neuroscience positions primary sensory areas as early feed-forward processing stations for refining incoming sensory information. This view may oversimplify their role given extensive bi-directional connectivity with multimodal cortical and subcortical regions. Here we show that single units in human primary somatosensory cortex encode imagined reaches in a cognitive motor task, but not other sensory-motor variables such as movement plans or imagined arm position. A population reference-frame analysis demonstrates coding relative to the cued starting hand location suggesting that imagined reaching movements are encoded relative to imagined limb position. These results imply a potential role for primary somatosensory cortex in cognitive imagery, engagement during motor production in the absence of sensation or expected sensation, and suggest that somatosensory cortex can provide control signals for future neural prosthetic systems.},
}
@article {pmid33310538,
year = {2021},
author = {Chen, Y and Rommelfanger, NJ and Mahdi, AI and Wu, X and Keene, ST and Obaid, A and Salleo, A and Wang, H and Hong, G},
title = {How is flexible electronics advancing neuroscience research?.},
journal = {Biomaterials},
volume = {268},
number = {},
pages = {120559},
pmid = {33310538},
issn = {1878-5905},
support = {K01 MH117490/MH/NIMH NIH HHS/United States ; R00 AG056636/AG/NIA NIH HHS/United States ; },
mesh = {*Brain ; *Electronics ; Neurons ; Prostheses and Implants ; },
abstract = {Innovative neurotechnology must be leveraged to experimentally answer the multitude of pressing questions in modern neuroscience. Driven by the desire to address the existing neuroscience problems with newly engineered tools, we discuss in this review the benefits of flexible electronics for neuroscience studies. We first introduce the concept and define the properties of flexible and stretchable electronics. We then categorize the four dimensions where flexible electronics meets the demands of modern neuroscience: chronic stability, interfacing multiple structures, multi-modal compatibility, and neuron-type-specific recording. Specifically, with the bending stiffness now approaching that of neural tissue, implanted flexible electronic devices produce little shear motion, minimizing chronic immune responses and enabling recording and stimulation for months, and even years. The unique mechanical properties of flexible electronics also allow for intimate conformation to the brain, the spinal cord, peripheral nerves, and the retina. Moreover, flexible electronics enables optogenetic stimulation, microfluidic drug delivery, and neural activity imaging during electrical stimulation and recording. Finally, flexible electronics can enable neuron-type identification through analysis of high-fidelity recorded action potentials facilitated by its seamless integration with the neural circuitry. We argue that flexible electronics will play an increasingly important role in neuroscience studies and neurological therapies via the fabrication of neuromorphic devices on flexible substrates and the development of enhanced methods of neuronal interpenetration.},
}
@article {pmid33307543,
year = {2021},
author = {Liu, R and Reimer, B and Song, S and Mehler, B and Solovey, E},
title = {Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abd2ca},
pmid = {33307543},
issn = {1741-2552},
mesh = {*Automobile Driving ; Brain ; *Brain-Computer Interfaces ; Cognition ; Spectroscopy, Near-Infrared/methods ; },
abstract = {Objective. Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS).Approach. We conducted a study using fNIRS in a driving simulator with theN-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS.Main results. By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30 s window were 73.25% and 47.21%, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45% for classifying two and four levels of driver cognitive load.Significance. This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.},
}
@article {pmid33302121,
year = {2021},
author = {Chen, K and Wellman, SM and Yaxiaer, Y and Eles, JR and Kozai, TD},
title = {In vivo spatiotemporal patterns of oligodendrocyte and myelin damage at the neural electrode interface.},
journal = {Biomaterials},
volume = {268},
number = {},
pages = {120526},
pmid = {33302121},
issn = {1878-5905},
support = {R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; },
mesh = {Microelectrodes ; *Myelin Sheath ; Neuroglia ; Neurons ; *Oligodendroglia ; },
abstract = {Intracortical microelectrodes with the ability to detect intrinsic electrical signals and/or deliver electrical stimulation into local brain regions have been a powerful tool to understand brain circuitry and for therapeutic applications to neurological disorders. However, the chronic stability and sensitivity of these intracortical microelectrodes are challenged by overwhelming biological responses, including severe neuronal loss and thick glial encapsulation. Unlike microglia and astrocytes whose activity have been extensively examined, oligodendrocytes and their myelin processes remain poorly studied within the neural interface field. Oligodendrocytes have been widely recognized to modulate electrical signal conductance along axons through insulating myelin segments. Emerging evidence offers an alternative perspective on neuron-oligodendrocyte coupling where oligodendrocytes provide metabolic and neurotrophic support to neurons through cytoplasmic myelin channels and monocarboxylate transporters. This study uses in vivo multi-photon microscopy to gain insights into the dynamics of oligodendrocyte soma and myelin processes in response to chronic device implantation injury over 4 weeks. We observe that implantation induces acute oligodendrocyte injury including initial deformation and substantial myelinosome formation, an early sign of myelin injury. Over chronic implantation periods, myelin and oligodendrocyte soma suffer severe degeneration proximal to the interface. Interestingly, wound healing attempts such as oligodendrogenesis are initiated over time, however they are hampered by continued degeneration near the implant. Nevertheless, this detailed characterization of oligodendrocyte spatiotemporal dynamics during microelectrode-induced inflammation may provide insights for novel intervention targets to facilitate oligodendrogenesis, enhance the integration of neural-electrode interfaces, and improve long-term functional performance.},
}
@article {pmid33300328,
year = {2021},
author = {Bignami, EG and Cozzani, F and Del Rio, P and Bellini, V},
title = {The role of artificial intelligence in surgical patient perioperative management.},
journal = {Minerva anestesiologica},
volume = {87},
number = {7},
pages = {817-822},
doi = {10.23736/S0375-9393.20.14999-X},
pmid = {33300328},
issn = {1827-1596},
mesh = {*Artificial Intelligence ; Humans ; },
abstract = {Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice that starts from the moment of contemplation of surgery until full recovery. Every perioperative phase (preoperative, intraoperative and postoperative) must be studied and planned in order to optimize the entire patient management. Perioperative optimization does not only concern a short-term outcome improvement, but it has also a strong impact on long term survival. Clinical cases variability leads to the collection and analysis of a huge amount of different data, coming from multiple sources, making perioperative management standardization very difficult. Artificial Intelligence (AI) can play a primary role in this challenge, helping human mind in perioperative practice planning and decision-making process. AI refers to the ability of a computer system to perform functions and reasoning typical of the human mind; Machine Learning (ML) could play a fundamental role in presurgical planning, during intraoperative phase and postoperative management. Perioperative medicine is the cornerstone of surgical patient management and the tools deriving from the application of AI seem very promising as a support in optimizing the management of each individual patient. Despite the increasing help that will derive from the use of AI tools, the uniqueness of the patient and the particularity of each individual clinical case will always keep the role of the human mind central in clinical and perioperative management. The role of the physician, who must analyze the outputs provided by AI by following his own experience and knowledge, remains and will always be essential.},
}
@article {pmid33299536,
year = {2020},
author = {Liu, X and Xi, X and Hua, X and Wang, H and Zhang, W},
title = {Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition.},
journal = {Journal of healthcare engineering},
volume = {2020},
number = {},
pages = {8824194},
pmid = {33299536},
issn = {2040-2309},
mesh = {*Algorithms ; Electromyography ; Entropy ; *Hand ; Humans ; Movement ; Support Vector Machine ; },
abstract = {The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control. To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE). First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment. Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set. Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements. Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set. The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.},
}
@article {pmid33299397,
year = {2020},
author = {Wang, G and Wang, R and Kong, W and Zhang, J},
title = {The Relationship between Sparseness and Energy Consumption of Neural Networks.},
journal = {Neural plasticity},
volume = {2020},
number = {},
pages = {8848901},
pmid = {33299397},
issn = {1687-5443},
mesh = {Energy Metabolism/*physiology ; Models, Neurological ; Nerve Net/*physiology ; *Neural Networks, Computer ; Neurons/*physiology ; },
abstract = {About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin's studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.},
}
@article {pmid33298517,
year = {2020},
author = {Cheng, G and Ehrlich, SK and Lebedev, M and Nicolelis, MAL},
title = {Neuroengineering challenges of fusing robotics and neuroscience.},
journal = {Science robotics},
volume = {5},
number = {49},
pages = {},
doi = {10.1126/scirobotics.abd1911},
pmid = {33298517},
issn = {2470-9476},
mesh = {Bioengineering ; Biomimetics ; *Brain-Computer Interfaces ; Humans ; Models, Neurological ; Neurosciences/*instrumentation ; Robotics/*instrumentation ; },
abstract = {Advances in neuroscience are inspiring developments in robotics and vice versa.},
}
@article {pmid33298434,
year = {2020},
author = {Liu, YJ and Zhang, T and Cheng, D and Yang, J and Chen, S and Wang, X and Li, X and Duan, D and Lou, H and Zhu, L and Luo, J and Ho, MS and Wang, XD and Duan, S},
title = {Late endosomes promote microglia migration via cytosolic translocation of immature protease cathD.},
journal = {Science advances},
volume = {6},
number = {50},
pages = {},
pmid = {33298434},
issn = {2375-2548},
mesh = {Actin Cytoskeleton/metabolism ; *Actins/metabolism ; *Cathepsin D/metabolism ; Endosomes/metabolism ; Microglia/metabolism ; Peptide Hydrolases/metabolism ; },
abstract = {Organelle transport requires dynamic cytoskeleton remodeling, but whether cytoskeletal dynamics are, in turn, regulated by organelles remains elusive. Here, we demonstrate that late endosomes, a type of prelysosomal organelles, facilitate actin-cytoskeleton remodeling via cytosolic translocation of immature protease cathepsin D (cathD) during microglia migration. After cytosolic translocation, late endosome-derived cathD juxtaposes actin filaments at the leading edge of lamellipodia. Suppressing cathD expression or blocking its cytosolic translocation impairs the maintenance but not the initiation of lamellipodial extension. Moreover, immature cathD balances the activity of the actin-severing protein cofilin to maintain globular-actin (G-actin) monomer pool for local actin recycling. Our study identifies cathD as a key lysosomal molecule that unconventionally contributes to actin cytoskeleton remodeling via cytosolic translocation during adenosine triphosphate-evoked microglia migration.},
}
@article {pmid33297516,
year = {2020},
author = {Nazeer, H and Naseer, N and Mehboob, A and Khan, MJ and Khan, RA and Khan, US and Ayaz, Y},
title = {Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {23},
pages = {},
pmid = {33297516},
issn = {1424-8220},
abstract = {A state-of-the-art brain-computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel's correlation coefficients' maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems' performance.},
}
@article {pmid33296306,
year = {2020},
author = {Delgado, JMC and Achanccaray, D and Villota, ER and Chevallier, S},
title = {Riemann-Based Algorithms Assessment for Single- and Multiple-Trial P300 Classification in Non-Optimal Environments.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2754-2761},
doi = {10.1109/TNSRE.2020.3043418},
pmid = {33296306},
issn = {1558-0210},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Electroencephalography ; Event-Related Potentials, P300 ; Humans ; },
abstract = {The P300 wave is commonly used in Brain-Computer Interface technology due to its higher bit rates when compared to other BCI paradigms. P300 classification pipelines based on Riemannian Geometry provide accuracies on par with state-of-the-art pipelines, without having the need for spatial filters, and also possess the ability to be calibrated with little data. In this study, five different P300 detection pipelines are compared, with three of them using Riemannian Geometry as either feature extraction or classification algorithms. The goal of this study is to assess the viability of Riemannian Geometry-based methods in non-optimal environments with sudden background noise changes, rather than maximizing classification accuracy values. For fifteen subjects, the average single-trial accuracy obtained for each pipeline was: 56.06% for Linear Discriminant Analysis (LDA), 72.13% for Bayesian Linear Discriminant Analysis (BLDA), 63.56% for Riemannian Minimum Distance to Mean (MDM), 69.22% for Riemannian Tangent Space with Logistic Regression (TS-LogR), and 63.30% for Riemannian Tangent Space with Support Vector Machine (TS-SVM). The results are higher for the pipelines based on BLDA and TS-LogR, suggesting that they could be viable methods for the detection of the P300 component when maximizing the bit rate is needed. For multiple-trial classification, the BLDA pipeline converged faster towards higher average values, closely followed by the TS-LogR pipeline. The two remaining Riemannian methods' accuracy also increases with the number of trials, but towards a lower value compared to the aforementioned ones. Single-stimulus detection metrics revealed that the TS-LogR pipeline can be a viable classification method, as its results are only slightly lower than those obtained with BLDA. P300 waveforms were also analyzed to check for evidence of the component being elicited. Finally, a questionnaire was used to retrieve the most intuitive focusing methods employed by the subjects.},
}
@article {pmid33291086,
year = {2021},
author = {Nakatani, S and Araki, N and Hoshino, T and Fukayama, O and Mabuchi, K},
title = {Brain-controlled cycling system for rehabilitation following paraplegia with delay-time prediction.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abd1bf},
pmid = {33291086},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; Locomotion ; Paraplegia/etiology/rehabilitation ; *Spinal Cord Injuries ; },
abstract = {Objective.Robotic rehabilitation systems have been investigated to assist with motor dysfunction recovery in patients with lower-extremity paralysis caused by central nervous system lesions. These systems are intended to provide appropriate sensory feedback associated with locomotion. Appropriate feedback is thought to cause synchronous neuron firing, resulting in the recovery of function.Approach.In this study, we designed and evaluated an ergometric cycling wheelchair, with a brain-machine interface (BMI), that can force the legs to move by including normal stepping speeds and quick responses. Experiments were conducted in five healthy subjects and one patient with spinal cord injury (SCI), who experienced the complete paralysis of the lower limbs. Event-related desynchronization in theβband (18-28 Hz) was used to detect lower-limb motor images.Main results.An ergometer-based BMI system was able to safely and easily force patients to perform leg movements, at a rate of approximately 1.6 s/step (19 rpm), with an online accuracy rate of 73.1% for the SCI participant. Mean detection time from the cue to pedaling onset was 0.83±0.31 s.Significance.This system can easily and safely maintain a normal walking speed during the experiment and be designed to accommodate the expected delay between the intentional onset and physical movement, to achieve rehabilitation effects for each participant. Similar BMI systems, implemented with rehabilitation systems, may be applicable to a wide range of patients.},
}
@article {pmid33291083,
year = {2021},
author = {Ko, LW and Sandeep Vara Sankar, D and Huang, Y and Lu, YC and Shaw, S and Jung, TP},
title = {SSVEP-assisted RSVP brain-computer interface paradigm for multi-target classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abd1c0},
pmid = {33291083},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Brain-computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.Objective.Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. Hybrid BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets.Approach.This study proposes a novel hybrid SSVEP-RSVP BCI to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimulation helps in identifying the user's focus location and RSVP stimuli that elicit event-related potentials differentiate target and non-target objects.Main results.The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalography (EEG) channels and an online (real-time) accuracy of 78.10% when only four EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects' performance is affected by physiological states for long-term use of the BCI.Significance.Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.},
}
@article {pmid33290753,
year = {2021},
author = {Asgharpour, M and Foodeh, R and Daliri, MR},
title = {Regularized Kalman filter for brain-computer interfaces using local field potential signals.},
journal = {Journal of neuroscience methods},
volume = {350},
number = {},
pages = {109022},
doi = {10.1016/j.jneumeth.2020.109022},
pmid = {33290753},
issn = {1872-678X},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Least-Squares Analysis ; *Motor Cortex ; Movement ; Rats ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) seek to establish a direct connection from brain to computer, to use in applications such as motor prosthesis control, control of a cursor on the monitor, and so on. Hence, the accuracy of movement decoding from brain signals in BCIs is crucial. The Kalman filter (KF) is often used in BCI systems to decode neural activity and estimate kinetic and kinematic parameters. To use the KF, the state transition matrix, the observation matrix and the covariance matrices of the process and measurement noises must be known in advance, however, in many applications these matrices are not known. Typically, to estimate these parameters, the ordinary least squares method and the sample covariance matrix estimator are used. Our purpose is to enhance the decoding performance of the KF in BCI systems by improving the estimation of the mentioned parameters.<br><br>NEW METHOD: Here, we propose the Regularized Kalman Filter (RKF) which implements two fundamental features: 1) Regularizing the regression estimate of the state equation to improve the estimation of the state transition matrix, and 2) Use of shrinkage method to improve the estimation of the unknown measurement noise covariance matrix. We validated the performance of the proposed method using two datasets of local field potentials obtained from motor cortex of a monkey (Estimation of kinematic parameters during hand movement) and three rats (Estimation of the amount of force applied by hand as a kinetic parameter).<br><br>RESULTS: The results demonstrate that the proposed method outperforms the conventional KF, the KF with feature selection, the Partial least squares, and the Ridge regression approaches.},
}
@article {pmid33287094,
year = {2020},
author = {Martínez-Cagigal, V and Santamaría-Vázquez, E and Hornero, R},
title = {Correction: Martínez-Cagigal, V.; Santamaría-Vázquez, E.; Hornero, R. Asynchronous Control of P300-Based Brain-Computer Interfaces Using Sample Entropy. Entropy 2019, 21, 230.},
journal = {Entropy (Basel, Switzerland)},
volume = {22},
number = {5},
pages = {},
pmid = {33287094},
issn = {1099-4300},
abstract = {Figure 5 of the original paper contains errors [...].},
}
@article {pmid33286475,
year = {2020},
author = {Velasquez-Martinez, L and Caicedo-Acosta, J and Castellanos-Dominguez, G},
title = {Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns.},
journal = {Entropy (Basel, Switzerland)},
volume = {22},
number = {6},
pages = {},
pmid = {33286475},
issn = {1099-4300},
abstract = {Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.},
}
@article {pmid33285871,
year = {2020},
author = {Tang, X and Zhang, X},
title = {Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding.},
journal = {Entropy (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {33285871},
issn = {1099-4300},
abstract = {Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding.},
}
@article {pmid33285821,
year = {2019},
author = {Kurthen, M and Enßlin, T},
title = {A Bayesian Model for Bivariate Causal Inference.},
journal = {Entropy (Basel, Switzerland)},
volume = {22},
number = {1},
pages = {},
pmid = {33285821},
issn = {1099-4300},
abstract = {We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e., X → Y or Y → X , from purely observational data. As the option to modify a potential cause is not given in many situations, only structural properties of the data can be used to solve this ill-posed problem. We briefly review a number of state-of-the-art methods for this, including very recent ones. A novel inference method is introduced, Bayesian Causal Inference (BCI) which assumes a generative Bayesian hierarchical model to pursue the strategy of Bayesian model selection. In the adopted model, the distribution of the cause variable is given by a Poisson lognormal distribution, which allows to explicitly regard the discrete nature of datasets, correlations in the parameter spaces, as well as the variance of probability densities on logarithmic scales. We assume Fourier diagonal Field covariance operators. The model itself is restricted to use cases where a direct causal relation X → Y has to be decided against a relation Y → X , therefore we compare it other methods for this exact problem setting. The generative model assumed provides synthetic causal data for benchmarking our model in comparison to existing state-of-the-art models, namely LiNGAM, ANM-HSIC, ANM-MML, IGCI, and CGNN. We explore how well the above methods perform in case of high noise settings, strongly discretized data, and very sparse data. BCI performs generally reliably with synthetic data as well as with the real world TCEP benchmark set, with an accuracy comparable to state-of-the-art algorithms. We discuss directions for the future development of BCI.},
}
@article {pmid33282474,
year = {2020},
author = {Hernandez-Martin, E and Marcano, F and Modroño, C and Janssen, N and González-Mora, JL},
title = {Diffuse optical tomography to measure functional changes during motor tasks: a motor imagery study.},
journal = {Biomedical optics express},
volume = {11},
number = {11},
pages = {6049-6067},
pmid = {33282474},
issn = {2156-7085},
abstract = {The present work shows the spatial reliability of the diffuse optical tomography (DOT) system in a group of healthy subjects during a motor imagery task. Prior to imagery task performance, the subjects executed a motor task based on the finger to thumb opposition for motor training, and to corroborate the DOT spatial localization during the motor execution. DOT technology and data treatment allows us to distinguish oxy- and deoxyhemoglobin at the cerebral gyri level unlike the cerebral activations provided by fMRI series that were processed using different approaches. Here we show the DOT reliability showing functional activations at the cerebral gyri level during motor execution and motor imagery, which provide subtler cerebral activations than the motor execution. These results will allow the use of the DOT system as a monitoring device in a brain computer interface.},
}
@article {pmid33281593,
year = {2020},
author = {Tortora, S and Tonin, L and Chisari, C and Micera, S and Menegatti, E and Artoni, F},
title = {Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {582728},
pmid = {33281593},
issn = {1662-5218},
abstract = {Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.},
}
@article {pmid33281590,
year = {2020},
author = {Gonzalez-Castillo, J and Ramot, M and Momenan, R},
title = {Editorial: Towards Expanded Utility of Real Time fMRI Neurofeedback in Clinical Applications.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {606868},
pmid = {33281590},
issn = {1662-5161},
}
@article {pmid33279436,
year = {2021},
author = {Zisk, AH and Borgheai, SB and McLinden, J and Hosni, SM and Deligani, RJ and Shahriari, Y},
title = {P300 latency jitter and its correlates in people with amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {132},
number = {2},
pages = {632-642},
doi = {10.1016/j.clinph.2020.10.022},
pmid = {33279436},
issn = {1872-8952},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*physiopathology/therapy ; Brain-Computer Interfaces/standards ; Electroencephalography/methods/standards ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; Reaction Time ; },
abstract = {OBJECTIVE: People with amyotrophic lateral sclerosis (ALS) can benefit from brain-computer interfaces (BCIs). However, users with ALS may experience significant variations in BCI performance and event-related potential (ERP) characteristics. This study investigated latency jitter and its correlates in ALS.<br><br>METHODS: Electroencephalographic (EEG) responses were recorded from six people with ALS and nine neurotypical controls. ERP amplitudes and latencies were extracted. Classifier-based latency estimation was used to calculate latency jitter. ERP components and latency jitter were compared between groups using Wilcoxon rank-sum tests. Correlations between latency jitter and each of the clinical measures, ERP features, and performance measures were investigated using Spearman and repeated measures correlations.<br><br>RESULTS: Latency jitter was significantly increased in participants with ALS and significantly negatively correlated with BCI performance in both ALS and control participants. ERP amplitudes were significantly attenuated in ALS, and significant correlations between ERP features and latency jitter were observed. There was no significant correlation between latency jitter and clinical measures.<br><br>CONCLUSIONS: Latency jitter is increased in ALS and correlates with both BCI performance and ERP features.<br><br>SIGNIFICANCE: These results highlight the associations of latency jitter with BCI performance and ERP characteristics and could inform future BCI designs for people with ALS.},
}
@article {pmid33279434,
year = {2021},
author = {Guha Vaze, P and Saha, S and Sinha, R and Banerjee, S},
title = {Urodynamics in Posterior Urethral Valve: Pursuit of prognostication or optimisation.},
journal = {Journal of pediatric urology},
volume = {17},
number = {1},
pages = {111.e1-111.e8},
doi = {10.1016/j.jpurol.2020.11.008},
pmid = {33279434},
issn = {1873-4898},
mesh = {Adult ; Child ; Humans ; ROC Curve ; Retrospective Studies ; *Urinary Bladder Neck Obstruction/diagnostic imaging/etiology ; *Urodynamics ; },
abstract = {INTRODUCTION: Detrusor dysfunction is known to persist in several patients of Posterior Urethral Valve (PUV) after successful fulguration leading to progressive deterioration of renal function. Persistent bladder outlet obstruction (BOO) in the form of bladder neck hypertrophy, residual valves or strictures may contribute to progressive detrusor dysfunction. These are assessed radiologically or cystoscopically and are managed variedly by anticholinergics, alpha-adrenergic blockers or even bladder neck incision. Unfortunately, currently we do not have any objective measures to evaluate the degree of BOO in children or follow treatment outcome of any such measures.<br><br>OBJECTIVE: To assess the feasibility of pressure flow studies in children and proposition of an age independent index to quantify outflow parameters.<br><br>STUDY DESIGN: We retrospectively studied the urodynamic data of the follow up cases of PUV who had been referred to us for urodynamic evaluation. Free flow uroflowmetries and filling and voiding cystometrogram were performed as per recommended protocol. Parameters like Adjusted Bladder Capacity (ABC = Voided volume&#xa0;+&#xa0;post void residue; expressed as percentage of expected bladder capacity {EBC}
), overactivity, compliance, Qmax and P det at Qmax were taken into consideration. Indices like Bladder Outlet Obstruction Index (BOOI) and Bladder Contractility Index (BCI) were calculated. Multivariate analysis was run to assess correlation of ABC with other parameters. Receiver Operating Characteristics (ROC) curve analysis was performed to assess predictive values of BOOI for ABC.<br><br>RESULTS: We did not find the ABC to change with age as has been classically described. Qmax and BCI were found to correlate with age. Values obtained for P det at Qmax and BOOI were not dependent on age and were in similar range as observed in adults. On multivariate analysis, small bladder was found to positively correlate with presence of overactivity, high BOOI and low BCI. ROC curve analysis showed a BOOI >29 could predict ABC to be <100% EBC with moderate sensitivity and specificity.<br><br>DISCUSSION: Pressure flow studies are the only objective means of quantifying outlet resistance, hitherto they have been considered to be unrepresentative in children. Documentation and correction of high outflow pressures may arrest the cycle of detrusor hypertrophy and dysfunction.<br><br>CONCLUSION: Quality pressure flow studies are feasible in children. Values of P det at Qmax and BOOI in children are age independent and similar to those observed in adults. BOOI can be potentially used in children to assess degree of BOO.},
}
@article {pmid33275851,
year = {2021},
author = {Lim, H and Ku, J},
title = {Superior Facilitation of an Action Observation Network by Congruent Character Movements in Brain-Computer Interface Action-Observation Games.},
journal = {Cyberpsychology, behavior and social networking},
volume = {24},
number = {8},
pages = {566-572},
doi = {10.1089/cyber.2020.0231},
pmid = {33275851},
issn = {2152-2723},
mesh = {*Brain-Computer Interfaces ; Female ; Healthy Volunteers ; Humans ; Male ; *Mirror Neurons ; *Movement ; Neurological Rehabilitation/*methods ; Software ; Young Adult ; },
abstract = {Action observation (AO) is a promising strategy for promoting motor function in neural rehabilitation. Recently, brain-computer interface (BCI)-AO game rehabilitation, which combines AO therapy with BCI technology, has been introduced to improve the effectiveness of rehabilitation. This approach can improve motor learning by providing feedback, which can be interactive in an observation task, and the game contents of the BCI-AO game paradigm can affect rehabilitation. In this study, the effects of congruent rather than incongruent feedback in a BCI-AO game on mirror neurons were investigated. Specifically, the mu suppression with congruent and incongruent BCI-AO games was measured in 17 healthy adults. The mu suppression in the central motor cortex was significantly higher with the congruent BCI-AO game than with the incongruent one. In addition, the satisfaction evaluation results were excellent for the congruent case. These results support the fact that providing feedback congruent with the motion of an action video facilitates mirror neuron activity and can offer useful guidelines for the design of BCI-AO games for rehabilitation.},
}
@article {pmid33270426,
year = {2020},
author = {Zhu, Y and Lin, L and Chen, Y and Song, Y and Lu, W and Guo, Y},
title = {Extreme Temperature-Tolerant Conductive Gel with Antibacterial Activity for Flexible Dual-Response Sensors.},
journal = {ACS applied materials &amp; interfaces},
volume = {12},
number = {50},
pages = {56470-56479},
doi = {10.1021/acsami.0c17242},
pmid = {33270426},
issn = {1944-8252},
mesh = {Acrylic Resins/chemistry ; Anti-Bacterial Agents/*chemistry/pharmacology ; Biocompatible Materials/chemistry/pharmacology ; Brain-Computer Interfaces ; Cell Line ; Cell Survival/drug effects ; Chlorides/chemistry ; Escherichia coli/drug effects ; Gelatin/chemistry ; Glycerol/chemistry ; Humans ; Hydrogels/*chemistry ; Phase Transition ; Staphylococcus aureus/drug effects ; Temperature ; Water/chemistry ; Zinc Compounds/chemistry ; },
abstract = {Flexible sensors based on conductive hydrogel show great potential in electronic skin and human-machine interface. However, pure water in hydrogel inevitably freezes or rapidly evaporates under extreme temperatures, leading to inadequate fulfillment of sensor performances. Herein, a well-designed strategy is reported for fabricating extreme temperature-tolerant gel-based sensors. By immersing a gelatin/polyacrylamide (PAAm)-clay composite (GC) hydrogel into a ZnCl2/water/glycerol system, a phase-transition-tunable gel (PTTGC gel) is obtained with outstanding antifreezing (-82 °C) and long-lasting moisture (70 °C, more than 40 days) properties. Meanwhile, the gel also presents good antibacterial activity and biocompatibility attributing to Zn[2+] and gelatin, respectively. Then, a dual-response sensor with a wide operating temperature (-60 to 60 °C) is proposed, presenting high stress and temperature sensitivities and long-term stability. The sensor will meet the needs of the human-machine interface for scientific investigation and data monitoring in polar, desert, etc.},
}
@article {pmid33264756,
year = {2021},
author = {Tidare, J and Leon, M and Astrand, E},
title = {Time-resolved estimation of strength of motor imagery representation by multivariate EEG decoding.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abd007},
pmid = {33264756},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagery, Psychotherapy ; *Imagination ; },
abstract = {Objective. Multivariate decoding enables access to information encoded in multiple brain activity features with high temporal resolution. However, whether the strength, of which this information is represented in the brain, can be extracted across time within single trials remains largely unexplored.Approach.In this study, we addressed this question by applying a support vector machine (SVM) to extract motor imagery (MI) representations, from electroencephalogram (EEG) data, and by performing time-resolved single-trial analyses of the multivariate decoding. EEG was recorded from a group of healthy participants during MI of opening and closing of the same hand.Main results.Cross-temporal decoding revealed both dynamic and stationary MI-relevant features during the task. Specifically, features representing MI evolved dynamically early in the trial and later stabilized into a stationary network of MI features. Using a hierarchical genetic algorithm for selection of MI-relevant features, we identified primarily contralateral alpha and beta frequency features over the sensorimotor and parieto-occipital cortices as stationary which extended into a bilateral pattern in the later part of the trial. During the stationary encoding of MI, by extracting the SVM prediction scores, we analyzed MI-relevant EEG activity patterns with respect to the temporal dynamics within single trials. We show that the SVM prediction score correlates to the amplitude of univariate MI-relevant features (as documented from an extensive repertoire of previous MI studies) within single trials, strongly suggesting that these are functional variations of MI strength hidden in trial averages.Significance.Our work demonstrates a powerful approach for estimating MI strength continually within single trials, having far-reaching impact for single-trial analyses. In terms of MI neurofeedback for motor rehabilitation, these results set the ground for more refined neurofeedback reflecting the strength of MI that can be provided to patients continually in time.},
}
@article {pmid33264305,
year = {2020},
author = {Raverty, S and St Leger, J and Noren, DP and Burek Huntington, K and Rotstein, DS and Gulland, FMD and Ford, JKB and Hanson, MB and Lambourn, DM and Huggins, J and Delaney, MA and Spaven, L and Rowles, T and Barre, L and Cottrell, P and Ellis, G and Goldstein, T and Terio, K and Duffield, D and Rice, J and Gaydos, JK},
title = {Pathology findings and correlation with body condition index in stranded killer whales (Orcinus orca) in the northeastern Pacific and Hawaii from 2004 to 2013.},
journal = {PloS one},
volume = {15},
number = {12},
pages = {e0242505},
pmid = {33264305},
issn = {1932-6203},
mesh = {Animals ; Cause of Death ; Hawaii ; Pacific Ocean ; Reproduction ; Skin/pathology ; Whale, Killer/anatomy &amp; histology/parasitology/*physiology ; },
abstract = {Understanding health and mortality in killer whales (Orcinus orca) is crucial for management and conservation actions. We reviewed pathology reports from 53 animals that stranded in the eastern Pacific Ocean and Hawaii between 2004 and 2013 and used data from 35 animals that stranded from 2001 to 2017 to assess association with morphometrics, blubber thickness, body condition and cause of death. Of the 53 cases, cause of death was determined for 22 (42%) and nine additional animals demonstrated findings of significant importance for population health. Causes of calf mortalities included infectious disease, nutritional, and congenital malformations. Mortalities in sub-adults were due to trauma, malnutrition, and infectious disease and in adults due to bacterial infections, emaciation and blunt force trauma. Death related to human interaction was found in every age class. Important incidental findings included concurrent sarcocystosis and toxoplasmosis, uterine leiomyoma, vertebral periosteal proliferations, cookiecutter shark (Isistius sp.) bite wounds, excessive tooth wear and an ingested fish hook. Blubber thickness increased significantly with body length (all p < 0.001). In contrast, there was no relationship between body length and an index of body condition (BCI). BCI was higher in animals that died from trauma. This study establishes a baseline for understanding health, nutritional status and causes of mortality in stranded killer whales. Given the evidence of direct human interactions on all age classes, in order to be most successful recovery efforts should address the threat of human interactions, especially for small endangered groups of killer whales that occur in close proximity to large human populations, interact with recreational and commercial fishers and transit established shipping lanes.},
}
@article {pmid33264096,
year = {2021},
author = {Yu, Y and Chen, C and Sheng, X and Zhu, X},
title = {Wrist Torque Estimation via Electromyographic Motor Unit Decomposition and Image Reconstruction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {7},
pages = {2557-2566},
doi = {10.1109/JBHI.2020.3041861},
pmid = {33264096},
issn = {2168-2208},
mesh = {Electromyography ; Humans ; *Image Processing, Computer-Assisted ; Torque ; *Wrist/diagnostic imaging ; },
abstract = {Neural interface using decomposed motor units (MUs) from surface electromyography (sEMG) has allowed non-invasive access to the neural control signals, and provided a novel approach for intuitive human-machine interaction. However, most of the existing methods based on decomposed MUs merely adopted the discharge rate (DR) as the feature representations, which may lack local information around the discharge instant and ignore the subtle interactions of different MUs. In this study, we proposed an MU-specific image-based scheme for wrist torque estimation. Specifically, the high-density sEMG signals were decoded into motor unit spike trains (MUSTs), and then MU-specific images were reconstructed with MUSTs and corresponding motor unit action potential (MUAP). A convolutional neural network was used to learn representative features from MU-specific images automatically, and further to estimate wrist torques. The results demonstrated that the proposed method outperformed three conventional and a deep-learning regression approaches using DR features, with the estimation accuracy R[2] of 0.82 ± 0.09, 0.89 ± 0.06, and nRMSE of 12.6 ± 2.5%, 11.0 ± 3.1% for pronation/supination and flexion/extension, respectively. Further, the analysis of the extracted features from MU-specific images showed a higher correlation than DR for recorded torques, indicating the effectiveness of the proposed method. The outcomes of this study provide a novel and promising perspective for the intuitive control of neural interfacing.},
}
@article {pmid33258017,
year = {2020},
author = {Scott, J and Colom, F and Young, A and Bellivier, F and Etain, B},
title = {An evidence map of actigraphy studies exploring longitudinal associations between rest-activity rhythms and course and outcome of bipolar disorders.},
journal = {International journal of bipolar disorders},
volume = {8},
number = {1},
pages = {37},
pmid = {33258017},
issn = {2194-7511},
abstract = {BACKGROUND: Evidence mapping is a structured approach used to synthesize the state-of-the-art in an emerging field of research when systematic reviews or meta-analyses are deemed inappropriate. We employed this strategy to summarise knowledge regarding longitudinal ecological monitoring of rest-activity rhythms (RAR) and disease modifiers, course of illness, treatment response or outcome in bipolar disorders (BD).<br><br>STRUCTURE: We had two key aims: (1) to determine the number and type of actigraphy studies of in BD that explored data regarding: outcome over time (e.g. relapse/recurrence according to polarity, or recovery/remission), treatment response or illness trajectories and (2) to examine the range of actigraphy metrics that can be used to estimate disruptions of RAR and describe which individual circadian rhythm or sleep-wake cycle parameters are most consistently associated with outcome over time in BD. The mapping process incorporated four steps: clarifying the project focus, describing boundaries and 'coordinates' for mapping, searching the literature and producing a brief synopsis with summary charts of the key outputs. Twenty-seven independent studies (reported in 29 publications) were eligible for inclusion in the map. Most were small-scale, with the median sample size being 15 per study and median duration of actigraphy being about 7&#xa0;days (range 1-210). Interestingly, 17 studies comprised wholly or partly of inpatients (63%). The available evidence indicated that a discrete number of RAR metrics are more consistently associated with transition between different phases of BD and/or may be predictive of longitudinal course of illness or treatment response. The metrics that show the most frequent associations represent markers of the amount, timing, or variability of RAR rather than the sleep quality metrics that are frequently targeted in contemporary studies of BD.<br><br>CONCLUSIONS: Despite 50&#xa0;years of research, use of actigraphy to assess RAR in longitudinal studies and examination of these metrics and treatment response, course and outcome of BD is under-investigated. This is in marked contrast to the extensive literature on case-control or cross-sectional studies of actigraphy, especially typical sleep analysis metrics in BD. However, given the encouraging findings on putative RAR markers, we recommend increased study of putative circadian phenotypes of BD.},
}
@article {pmid33256073,
year = {2020},
author = {Jochumsen, M and Niazi, IK and Zia Ur Rehman, M and Amjad, I and Shafique, M and Gilani, SO and Waris, A},
title = {Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {23},
pages = {},
pmid = {33256073},
issn = {1424-8220},
support = {22357//Velux Fonden/ ; },
mesh = {*Electromyography ; *Hand ; Humans ; Movement ; Reproducibility of Results ; *Stroke/diagnosis ; Wrist Joint ; },
abstract = {Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient's home.},
}
@article {pmid33255578,
year = {2020},
author = {Garcia-Moreno, FM and Bermudez-Edo, M and Garrido, JL and Rodríguez-Fórtiz, MJ},
title = {Reducing Response Time in Motor Imagery Using A Headband and Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {23},
pages = {},
pmid = {33255578},
issn = {1424-8220},
support = {TIN2016-79484-R//Ministerio de Ciencia e Innovaci&#xf3;n/ ; PID2019-109644RB-I00//Ministerio de Ciencia e Innovaci&#xf3;n/ ; FPU18/00287//Ministerio de Ciencia, Innovaci&#xf3;n y Universidades/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Reaction Time ; *Wearable Electronic Devices ; },
abstract = {Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.},
}
@article {pmid33255374,
year = {2020},
author = {Jin, L and Kim, EY},
title = {Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {23},
pages = {},
pmid = {33255374},
issn = {1424-8220},
mesh = {Arousal ; *Brain-Computer Interfaces ; China ; *Electroencephalography ; *Emotions ; Humans ; },
abstract = {Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in research on brain-computer interfaces (BCI) and health care. To recognize cross-subject emotion from EEG data accurately, a technique capable of finding an effective representation robust to the subject-specific variability associated with EEG data collection processes is necessary. In this paper, a new method to predict cross-subject emotion using time-series analysis and spatial correlation is proposed. To represent the spatial connectivity between brain regions, a channel-wise feature is proposed, which can effectively handle the correlation between all channels. The channel-wise feature is defined by a symmetric matrix, the elements of which are calculated by the Pearson correlation coefficient between two-pair channels capable of complementarily handling subject-specific variability. The channel-wise features are then fed to two-layer stacked long short-term memory (LSTM), which can extract temporal features and learn an emotional model. Extensive experiments on two publicly available datasets, the Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU (Shanghai Jiao Tong University) Emotion EEG Dataset (SEED), demonstrate the effectiveness of the combined use of channel-wise features and LSTM. Experimental results achieve state-of-the-art classification rates of 98.93% and 99.10% during the two-class classification of valence and arousal in DEAP, respectively, with an accuracy of 99.63% during three-class classification in SEED.},
}
@article {pmid33254159,
year = {2021},
author = {Yang, Y and Ahmadipour, P and Shanechi, MM},
title = {Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abcefd},
pmid = {33254159},
issn = {1741-2552},
mesh = {Algorithms ; *Brain/physiology ; *Brain-Computer Interfaces ; Learning ; Stereotaxic Techniques ; },
abstract = {Objective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network dynamics. However, brain network dynamics can be non-stationary for example due to learning, plasticity or recording instability. To enable modeling these non-stationarities, two problems need to be resolved. First, novel methods should be developed that can adaptively update the parameters of latent state models, which is difficult due to the state being latent. Second, new methods are needed to optimize the adaptation learning rate, which specifies how fast new neural observations update the model parameters and can significantly influence adaptation accuracy.Approach. We develop a Rate Optimized-adaptive Linear State-Space Modeling (RO-adaptive LSSM) algorithm that solves these two problems. First, to enable adaptation, we derive a computation- and memory-efficient adaptive LSSM fitting algorithm that updates the LSSM parameters recursively and in real time in the presence of the latent state. Second, we develop a real-time learning rate optimization algorithm. We use comprehensive simulations of a broad range of non-stationary brain network dynamics to validate both algorithms, which together constitute the RO-adaptive LSSM.Main results. We show that the adaptive LSSM fitting algorithm can accurately track the broad simulated non-stationary brain network dynamics. We also find that the learning rate significantly affects the LSSM fitting accuracy. Finally, we show that the real-time learning rate optimization algorithm can run in parallel with the adaptive LSSM fitting algorithm. Doing so, the combined RO-adaptive LSSM algorithm rapidly converges to the optimal learning rate and accurately tracks non-stationarities.Significance. These algorithms can be used to study time-varying neural dynamics underlying various brain functions and enhance future neurotechnologies such as brain-machine interfaces and closed-loop brain stimulation systems.},
}
@article {pmid33253891,
year = {2021},
author = {da Igreja, P and Erve, A and Thommes, M},
title = {Melt milling as manufacturing method for solid crystalline suspensions.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {158},
number = {},
pages = {245-253},
doi = {10.1016/j.ejpb.2020.11.020},
pmid = {33253891},
issn = {1873-3441},
mesh = {Biological Availability ; Calorimetry, Differential Scanning ; Chemistry, Pharmaceutical ; Drug Compounding/instrumentation/*methods ; Drug Liberation ; Drug Stability ; Griseofulvin/chemistry/pharmacokinetics ; Particle Size ; Solubility ; Suspensions ; Water/chemistry ; X-Ray Diffraction ; Xylitol/chemistry/pharmacokinetics ; },
abstract = {Production of submicron particles (0.1-1 μm) has been identified by the pharmaceutical industry as a key technology to enhance the bioavailability of poorly water-soluble drugs. However, nanosuspensions derived from commonly applied wet milling suffer from long-term stability issues, making further downstream processing necessary. In previous works, the formulation as a long-term stable solid crystalline suspension (SCS) was introduced, for which the crystalline drug is ground in a (molten) hydrophilic carrier matrix. The model formulation of the antimycotic Griseofulvin and the sugar alcohol Xylitol was reused for comparative purposes. Due to process limitations regarding the degree of comminution, the present work demonstrates the application of fine grinding in the framework of SCS manufacturing. A custom-built mill with annular gap geometry successfully yielded particles in the targeted submicron range. A process optimization study lead to improved energy utilization during grinding, which reduced the necessary grinding time and, thereby, the thermal exposition of the drug. Investigation of solid-state properties of the SCS, via differential scanning calorimetry and x-ray powder diffraction, showed no alteration even for extended grinding times. In dissolution experiments, the melt-milled SCS outperformed its predecessors, although mostly agglomerates were found by SEM imaging in the solidified product. In conclusion, melt milling is a valuable tool to overcome low aqueous solubility.},
}
@article {pmid33252322,
year = {2020},
author = {Jin-Ying Wong, and Yin Ng, Z and Mehta, M and Shukla, SD and Panneerselvam, J and Madheswaran, T and Gupta, G and Negi, P and Kumar, P and Pillay, V and Hsu, A and Hansbro, NG and Wark, P and Bebawy, M and Hansbro, PM and Dua, K and Chellappan, DK},
title = {Curcumin-loaded niosomes downregulate mRNA expression of pro-inflammatory markers involved in asthma: an in vitro study.},
journal = {Nanomedicine (London, England)},
volume = {15},
number = {30},
pages = {2955-2970},
doi = {10.2217/nnm-2020-0260},
pmid = {33252322},
issn = {1748-6963},
mesh = {*Asthma/drug therapy ; *Curcumin/pharmacology ; Humans ; Liposomes ; Particle Size ; RNA, Messenger/genetics ; },
abstract = {Aim: In this study, curcumin was encapsulated in niosomes (Nio-Curc) to increase its effectiveness for the treatment of asthma. Materials &amp; methods: The formulation underwent various physicochemical characterization experiments, an in vitro release study, molecular simulations and was evaluated for in vitro anti-inflammatory activity. Results: Results showed that Nio-Curc had a mean particle size of 284.93 ± 14.27 nm, zeta potential of -46.93 and encapsulation efficacy of 99.62%, which demonstrates optimized physicochemical characteristics. Curcumin release in vitro could be sustained for up to 24 h. Additionally, Nio-Curc effectively reduced mRNA transcript expression of pro-inflammatory markers; IL-6, IL-8, IL-1β and TNF-α in immortalized human airway basal cell line (BCi-NS1.1). Conclusion: In this study, we have demonstrated that Nio-Curc mitigated the mRNA expression of pro-inflammatory markers in an in vitro study, which could be applied to treatment of asthma with further studies.},
}
@article {pmid33250729,
year = {2020},
author = {Wobrock, D and Finke, A and Schack, T and Ritter, H},
title = {Using Fixation-Related Potentials for Inspecting Natural Interactions.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {579505},
pmid = {33250729},
issn = {1662-5161},
abstract = {Brain-Computer Interfaces (BCI) offer unique windows into the cognitive processes underlying human-machine interaction. Identifying and analyzing the appropriate brain activity to have access to such windows is often difficult due to technical or psycho-physiological constraints. Indeed, studying interactions through this approach frequently requires adapting them to accommodate specific BCI-related paradigms which change the functioning of their interface on both the human-side and the machine-side. The combined examination of Electroencephalography and Eyetracking recordings, mainly by means of studying Fixation-Related Potentials, can help to circumvent the necessity for these adaptations by determining interaction-relevant moments during natural manipulation. In this contribution, we examine how properties contained within the bi-modal recordings can be used to assess valuable information about the interaction. Practically, three properties are studied which can be obtained solely through data obtained from analysis of the recorded biosignals. Namely, these properties consist of relative gaze metrics, being abstractions of the gaze patterns, the amplitude variations in the early brain activity potentials and the brain activity frequency band differences between fixations. Through their observation, information about three different aspects of the explored interface are obtained. Respectively, the properties provide insights about general perceived task difficulty, locate moments of higher attentional effort and discriminate between moments of exploration and moments of active interaction.},
}
@article {pmid33249629,
year = {2021},
author = {Wadhwa, R and Paudel, KR and Chin, LH and Hon, CM and Madheswaran, T and Gupta, G and Panneerselvam, J and Lakshmi, T and Singh, SK and Gulati, M and Dureja, H and Hsu, A and Mehta, M and Anand, K and Devkota, HP and Chellian, J and Chellappan, DK and Hansbro, PM and Dua, K},
title = {Anti-inflammatory and anticancer activities of Naringenin-loaded liquid crystalline nanoparticles in vitro.},
journal = {Journal of food biochemistry},
volume = {45},
number = {1},
pages = {e13572},
doi = {10.1111/jfbc.13572},
pmid = {33249629},
issn = {1745-4514},
mesh = {A549 Cells ; Anti-Inflammatory Agents/pharmacology ; *Flavanones/pharmacology ; Humans ; *Nanoparticles ; },
abstract = {In this study, we had developed Naringenin-loaded liquid crystalline nanoparticles (LCNs) and investigated the anti-inflammatory and anticancer activities of Naringenin-LCNs against human airway epithelium-derived basal cells (BCi-NS1.1) and human lung epithelial carcinoma (A549) cell lines, respectively. The anti-inflammatory potential of Naringenin-LCNs evaluated by qPCR revealed a decreased expression of IL-6, IL-8, IL-1β, and TNF-α in lipopolysaccharide-induced BCi-NS1.1 cells. The activity of LCNs was comparable to the positive control drug Fluticasone propionate (10 nM). The anticancer activity was studied by evaluating the antiproliferative (MTT and trypan blue assays), antimigratory (scratch wound healing assay, modified Boyden chamber assay, and immunoblot), and anticolony formation activity in A549 cells. Naringenin LCNs showed promising antiproliferative, antimigratory, and anticolony formation activities in A549 cells, in vitro. Therefore, based on our observations and results, we conclude that Naringenin-LCNs may be employed as a potential therapy-based intervention to ameliorate airway inflammation and to inhibit the progression of lung cancer. PRACTICAL APPLICATIONS: Naringenin was encapsulated into liquid crystalline nanoparticles, thus, attributing to their sustained-release nature. In addition, Naringenin-loaded LCNs efficiently reduced the levels of pro-inflammatory markers, namely, IL-1β, IL-6, TNF-α, and IL-8. In addition, the Naringenin-loaded LCNs also possess potent anticancer activity, when tested in the A549 cell line, as revealed by the inhibition of proliferation and migration of cells. They also attenuated colony formation and induced apoptosis in the A549 cells. The findings from our study could form the basis for future research that may be translated into an in vivo model to validate the possible therapeutic alternative for lung cancer using Naringenin-loaded LCNs. In addition, the applications of Naringenin-loaded LCNs as an intervention would be of great interest to biological, formulation and respiratory scientists and clinicians.},
}
@article {pmid33247136,
year = {2020},
author = {Wang, C and Liu, H and Li, K and Wu, ZZ and Wu, C and Yu, JY and Gong, Q and Fang, P and Wang, XX and Duan, SM and Wang, H and Gu, Y and Hu, J and Pan, BX and Schmidt, MV and Liu, YJ and Wang, XD},
title = {Tactile modulation of memory and anxiety requires dentate granule cells along the dorsoventral axis.},
journal = {Nature communications},
volume = {11},
number = {1},
pages = {6045},
pmid = {33247136},
issn = {2041-1723},
mesh = {Animals ; Anxiety/*physiopathology ; Behavior, Animal/physiology ; Dendritic Spines/physiology ; Dentate Gyrus/*physiopathology ; Entorhinal Cortex/physiopathology ; Female ; Integrases/metabolism ; Male ; Memory/*physiology ; Mice, Inbred C57BL ; Neuronal Plasticity/physiology ; Neurons/physiology ; Synapses/physiology ; Time Factors ; Touch/*physiology ; },
abstract = {Touch can positively influence cognition and emotion, but the underlying mechanisms remain unclear. Here, we report that tactile experience enrichment improves memory and alleviates anxiety by remodeling neurons along the dorsoventral axis of the dentate gyrus (DG) in adult mice. Tactile enrichment induces differential activation and structural modification of neurons in the dorsal and ventral DG, and increases the presynaptic input from the lateral entorhinal cortex (LEC), which is reciprocally connected with the primary somatosensory cortex (S1), to tactile experience-activated DG neurons. Chemogenetic activation of tactile experience-tagged dorsal and ventral DG neurons enhances memory and reduces anxiety respectively, whereas inactivation of these neurons or S1-innervated LEC neurons abolishes the beneficial effects of tactile enrichment. Moreover, adulthood tactile enrichment attenuates early-life stress-induced memory deficits and anxiety-related behavior. Our findings demonstrate that enriched tactile experience retunes the pathway from S1 to DG and enhances DG neuronal plasticity to modulate cognition and emotion.},
}
@article {pmid33246319,
year = {2021},
author = {Shamsi, F and Haddad, A and Najafizadeh, L},
title = {Early classification of motor tasks using dynamic functional connectivity graphs from EEG.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abce70},
pmid = {33246319},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Imagery, Psychotherapy ; *Imagination ; },
abstract = {Objective. Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.Approach. The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a long short term memory classifier is employed for classification.Main results. Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.Significance. Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.},
}
@article {pmid33245696,
year = {2020},
author = {Jiao, Y and Zhou, T and Yao, L and Zhou, G and Wang, X and Zhang, Y},
title = {Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2589-2597},
doi = {10.1109/TNSRE.2020.3040984},
pmid = {33245696},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG signals is often highly dependent on the filter band selection. At the same time, optimizing the EEG channel combinations is another key issue that substantially affects the SMR feature representations. Although numerous algorithms have been developed to find channels that record important characteristics of MI, most of them select channels in a cumbersome way with low computational efficiency, thereby limiting the practicality of MI-based BCI systems. In this study, we propose the multi-scale optimization (MSO) of spatial patterns, optimizing filter bands over multiple channel sets within CSPs to further improve the performance of MI-based BCI. Specifically, several channel subsets are first heuristically predefined, and then raw EEG data specific to each of these subsets bandpass-filtered at the overlap between a set of filter bands. Further, instead of solving learning problems for each channel subset independently, we propose a multi-view learning based sparse optimization to jointly extract robust CSP features with L2,1 -norm regularization, aiming to capture the shared salient information across multiple related spatial patterns for enhanced classification performance. A support vector machine (SVM) classifier is then trained on these optimized EEG features for accurate recognition of MI tasks. Experimental results on three public EEG datasets validate the effectiveness of MSO compared to several other competing methods and their variants. These superior experimental results demonstrate that the proposed MSO method has promising potential in MI-based BCIs.},
}
@article {pmid33242850,
year = {2021},
author = {Ahmadi, N and Constandinou, TG and Bouganis, CS},
title = {Impact of referencing scheme on decoding performance of LFP-based brain-machine interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abce3c},
pmid = {33242850},
issn = {1741-2552},
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Evoked Potentials ; Haplorhini ; *Motor Cortex ; },
abstract = {Objective. There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs.Approach. To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks.Main results. Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions.Significance. Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.},
}
@article {pmid33240214,
year = {2020},
author = {Chew, E and Teo, WP and Tang, N and Ang, KK and Ng, YS and Zhou, JH and Teh, I and Phua, KS and Zhao, L and Guan, C},
title = {Corrigendum: Using Transcranial Direct Current Stimulation to Augment the Effect of Motor Imagery-Assisted Brain-Computer Interface Training in Chronic Stroke Patients-Cortical Reorganization Considerations.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {605141},
doi = {10.3389/fneur.2020.605141},
pmid = {33240214},
issn = {1664-2295},
abstract = {[This corrects the article DOI: 10.3389/fneur.2020.00948.].},
}
@article {pmid33239759,
year = {2020},
author = {Sun, W and Chen, LN and Zhou, Q and Zhao, LH and Yang, D and Zhang, H and Cong, Z and Shen, DD and Zhao, F and Zhou, F and Cai, X and Chen, Y and Zhou, Y and Gadgaard, S and van der Velden, WJC and Zhao, S and Jiang, Y and Rosenkilde, MM and Xu, HE and Zhang, Y and Wang, MW},
title = {A unique hormonal recognition feature of the human glucagon-like peptide-2 receptor.},
journal = {Cell research},
volume = {30},
number = {12},
pages = {1098-1108},
pmid = {33239759},
issn = {1748-7838},
mesh = {Amino Acid Sequence ; Binding Sites ; Cryoelectron Microscopy ; GTP-Binding Proteins/metabolism ; Glucagon-Like Peptide-2 Receptor/chemistry/*metabolism/ultrastructure ; HEK293 Cells ; Humans ; Ligands ; Models, Molecular ; Mutant Proteins/chemistry/metabolism ; Peptides/chemistry/metabolism ; Protein Conformation ; Structural Homology, Protein ; },
abstract = {Glucagon-like peptides (GLP-1 and GLP-2) are two proglucagon-derived intestinal hormones that mediate distinct physiological functions through two related receptors (GLP-1R and GLP-2R) which are important drug targets for metabolic disorders and Crohn's disease, respectively. Despite great progress in GLP-1R structure determination, our understanding on the differences of peptide binding and signal transduction between these two receptors remains elusive. Here we report the electron microscopy structure of the human GLP-2R in complex with GLP-2 and a Gs heterotrimer. To accommodate GLP-2 rather than GLP-1, GLP-2R fine-tunes the conformations of the extracellular parts of transmembrane helices (TMs) 1, 5, 7 and extracellular loop 1 (ECL1). In contrast to GLP-1, the N-terminal histidine of GLP-2 penetrates into the receptor core with a unique orientation. The middle region of GLP-2 engages with TM1 and TM7 more extensively than with ECL2, and the GLP-2 C-terminus closely attaches to ECL1, which is the most protruded among 9 class B G protein-coupled receptors (GPCRs). Functional studies revealed that the above three segments of GLP-2 are essential for GLP-2 recognition and receptor activation, especially the middle region. These results provide new insights into the molecular basis of ligand specificity in class B GPCRs and may facilitate the development of more specific therapeutics.},
}
@article {pmid33239627,
year = {2020},
author = {Sun, L and Liu, R and Guo, F and Wen, MQ and Ma, XL and Li, KY and Sun, H and Xu, CL and Li, YY and Wu, MY and Zhu, ZG and Li, XJ and Yu, YQ and Chen, Z and Li, XY and Duan, S},
title = {Parabrachial nucleus circuit governs neuropathic pain-like behavior.},
journal = {Nature communications},
volume = {11},
number = {1},
pages = {5974},
pmid = {33239627},
issn = {2041-1723},
mesh = {Animals ; Disease Models, Animal ; Excitatory Amino Acid Agonists/pharmacology ; Excitatory Postsynaptic Potentials/drug effects/physiology ; GABA Agonists/pharmacology ; Glutamic Acid/metabolism ; Humans ; Hyperalgesia/etiology/*physiopathology ; Inhibitory Postsynaptic Potentials/drug effects/physiology ; Male ; Mice ; Mice, Transgenic ; Neural Pathways/drug effects/physiology ; Neuralgia/etiology/*physiopathology ; Neurons/drug effects/*metabolism ; Nociception/*physiology ; Optogenetics ; Parabrachial Nucleus/cytology/drug effects/*physiology ; Peroneal Nerve/injuries/physiopathology ; Stereotaxic Techniques ; gamma-Aminobutyric Acid/metabolism ; },
abstract = {The lateral parabrachial nucleus (LPBN) is known to relay noxious information to the amygdala for processing affective responses. However, it is unclear whether the LPBN actively processes neuropathic pain characterized by persistent hyperalgesia with aversive emotional responses. Here we report that neuropathic pain-like hypersensitivity induced by common peroneal nerve (CPN) ligation increases nociceptive stimulation-induced responses in glutamatergic LPBN neurons. Optogenetic activation of GABAergic LPBN neurons does not affect basal nociception, but alleviates neuropathic pain-like behavior. Optogenetic activation of glutamatergic or inhibition of GABAergic LPBN neurons induces neuropathic pain-like behavior in naïve mice. Inhibition of glutamatergic LPBN neurons alleviates both basal nociception and neuropathic pain-like hypersensitivity. Repetitive pharmacogenetic activation of glutamatergic or GABAergic LPBN neurons respectively mimics or prevents the development of CPN ligation-induced neuropathic pain-like hypersensitivity. These findings indicate that a delicate balance between excitatory and inhibitory LPBN neuronal activity governs the development and maintenance of neuropathic pain.},
}
@article {pmid33239147,
year = {2020},
author = {Lindstedt, D},
title = {Estimated value realisation: A comparison between standard and adaptive business continuity approaches.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {14},
number = {2},
pages = {153-177},
pmid = {33239147},
issn = {1749-9216},
mesh = {*Commerce ; *Disaster Planning ; Industry ; Reference Standards ; },
abstract = {The adaptive business continuity (BC) approach may provide value at least 11 times faster than historical BC approaches that are modelled on existing standards such as DRI's Professional Practices and ANSI standard ISO 22301. By analysing the life cycle of standard BC practices as outlined in the BCI's 'Good Practice Guide', estimating the hours it would take a hypothetical organisation to execute those practices, and then estimating the value gained from each practice, it is possible to calculate a time to value (TTV) based on the estimates for the two approaches. As TTV calculations are relatively new to the BC industry, this article anticipates and addresses several possible objections. The resulting calculations, while potentially subject to a wide margin of error, indicate that the adaptive BC approach is significantly faster at providing value. In some BC life-cycle phases, the TTV of an adaptive BC approach may be 18-20 times faster. These results have broad and significant implications in the preparedness industry, several of which are highlighted in the conclusion of the article.},
}
@article {pmid33236859,
year = {2021},
author = {Hill, K and Huggins, J and Woodworth, C},
title = {Interprofessional Practitioners' Opinions on Features and Services for an Augmentative and Alternative Communication Brain-Computer Interface Device.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {13},
number = {10},
pages = {1111-1121},
pmid = {33236859},
issn = {1934-1563},
support = {R41 DC015142/DC/NIDCD NIH HHS/United States ; R42 DC015142/DC/NIDCD NIH HHS/United States ; SB1 DC015142/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; Reproducibility of Results ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) technology is an emerging access method to augmentative and alternative communication (AAC) devices.<br><br>OBJECTIVES: To identify, in the early stages of research and development, the perceptions and considerations of interprofessional practice (IPP) team members regarding features and functions for an AAC-BCI device.<br><br>DESIGN: Qualitative research methodology applying a grounded theory approach using focus groups with a follow-up survey of participants using NVivo analysis software supporting inductive coding of transcription data.<br><br>SETTING: Focus groups held at university, clinic, and industry conference rooms. Discussion was stimulated by a 14-minute video on an AAC-BCI device prototype. The prototype hardware and electroencephalography (EEG) gel and dry electrode headgear were on display.<br><br>PARTICIPANTS: Convenience sample of practitioners providing rehabilitation or clinical services to individuals with severe communication disorders and movement impairments who use AAC and/or other assistive technology.<br><br>INTERVENTIONS: Not applicable.<br><br>MAIN OUTCOME MEASURES: Descriptive statistics using thematic analysis of participants' opinions, input, and feedback on the ideal design for a noninvasive, EEG-based P300 AAC-BCI device.<br><br>RESULTS: Interrater and interjudge reliability were at 98% and 100%, respectively, for transcription and researcher coding. Triangulation of multiple data sources supported theme and subtheme identification that included design features, set-up and calibration, services, and effectiveness. An AAC device with BCI access was unanimously confirmed (100%) as a desirable commercial product. Participants felt that the AAC-BCI prototype appeared effective for meeting daily communication needs (75%). Results showed that participants' preference on headgear types would change based on accuracy (91%) and rate (83%) of performance. A data-logging feature was considered beneficial by 100% of participants.<br><br>CONCLUSIONS: IPP teams provided critical impressions on design, services, and features for a commercial AAC-BCI device. Expressed feature and function preferences showed dependence on communication accuracy, rate, and effectiveness. This provides vital guidance for successful clinical deployment.},
}
@article {pmid33236720,
year = {2020},
author = {Wilson, GH and Stavisky, SD and Willett, FR and Avansino, DT and Kelemen, JN and Hochberg, LR and Henderson, JM and Druckmann, S and Shenoy, KV},
title = {Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {066007},
pmid = {33236720},
issn = {1741-2552},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 EB028171/EB/NIBIB NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; U01 DC017844/DC/NIDCD NIH HHS/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electrodes ; Hand ; Humans ; Language ; *Speech ; },
abstract = {OBJECTIVE: To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.<br><br>APPROACH: Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times.<br><br>MAIN RESULTS: A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio.<br><br>SIGNIFICANCE: The ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.},
}
@article {pmid33232686,
year = {2021},
author = {Rezaei, MR and Arai, K and Frank, LM and Eden, UT and Yousefi, A},
title = {Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models.},
journal = {Journal of neuroscience methods},
volume = {348},
number = {},
pages = {109006},
pmid = {33232686},
issn = {1872-678X},
support = {R01 MH105174/MH/NIMH NIH HHS/United States ; },
mesh = {Action Potentials ; Algorithms ; Animals ; *Brain-Computer Interfaces ; *Models, Neurological ; Neurons ; Rats ; },
abstract = {There is an increasing demand for a computationally efficient and accurate point process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the point process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general point-process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of point process observation called marked point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional point-process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.},
}
@article {pmid33232243,
year = {2020},
author = {Lee, SH and Lee, M and Lee, SW},
title = {Neural Decoding of Imagined Speech and Visual Imagery as Intuitive Paradigms for BCI Communication.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2647-2659},
doi = {10.1109/TNSRE.2020.3040289},
pmid = {33232243},
issn = {1558-0210},
mesh = {*Auditory Cortex ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Intention ; Speech ; },
abstract = {Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily operate. Imagined speech and visual imagery are emerging paradigms that can directly convey a user's intention. We investigated the underlying characteristics that affect the decoding performance of these two paradigms. Twenty-two subjects performed imagined speech and visual imagery of twelve words/phrases frequently used for patients' communication. Spectral features were analyzed with thirteen-class classification (including rest class) using EEG filtered in six frequency ranges. In addition, cortical regions relevant to the two paradigms were analyzed by classification using single-channel and pre-defined cortical groups. Furthermore, we analyzed the word properties that affect the decoding performance based on the number of syllables, concrete and abstract concepts, and the correlation between the two paradigms. Finally, we investigated multiclass scalability in both paradigms. The high-frequency band displayed a significantly superior performance to that in the case of any other spectral features in the thirteen-class classification (imagined speech: 39.73 ± 5.64%; visual imagery: 40.14 ± 4.17%). Furthermore, the performance of Broca's and Wernicke's areas and auditory cortex was found to have improved among the cortical regions in both paradigms. As the number of classes increased, the decoding performance decreased moderately. Moreover, every subject exceeded the confidence level performance, implying the strength of the two paradigms in BCI inefficiency. These two intuitive paradigms were found to be highly effective for multiclass communication systems, having considerable similarities between each other. The results could provide crucial information for improving the decoding performance for practical BCI applications.},
}
@article {pmid33232242,
year = {2020},
author = {Lee, YE and Kwak, NS and Lee, SW},
title = {A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2660-2670},
doi = {10.1109/TNSRE.2020.3040264},
pmid = {33232242},
issn = {1558-0210},
mesh = {Algorithms ; Artifacts ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Movement ; Signal Processing, Computer-Assisted ; },
abstract = {Recently, practical brain-computer interfaces (BCIs) have been widely investigated for detecting human intentions in real world. However, performance differences still exist between the laboratory and the real world environments. One of the main reasons for such differences comes from the user's unstable physical states (e.g., human movements are not strictly controlled), which produce unexpected signal artifacts. Hence, to minimize the performance degradation of electroencephalography (EEG)-based BCIs, we present a novel artifact removal method named constrained independent component analysis with online learning (cIOL). The cIOL can find and reject the noise-like components related to human body movements (i.e., movement artifacts) in the EEG signals. To obtain movement information, isolated electrodes are used to block electrical signals from the brain using high-resistance materials. We estimate artifacts with movement information using constrained independent component analysis from EEG signals and then extract artifact-free signals using online learning in each sample. In addition, the cIOL is evaluated by signal processing under 16 different experimental conditions (two types of EEG devices × two BCI paradigms × four different walking speeds). The experimental results show that the cIOL has the highest accuracy in both scalp- and ear-EEG, and has the highest signal-to-noise ratio in scalp-EEG among the state-of-the-art methods, except for the case of steady-state visual evoked potential at 2.0 m/s with superposition problem.},
}
@article {pmid33232240,
year = {2020},
author = {Shen, X and Zhang, X and Huang, Y and Chen, S and Wang, Y},
title = {Task Learning Over Multi-Day Recording via Internally Rewarded Reinforcement Learning Based Brain Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {3089-3099},
doi = {10.1109/TNSRE.2020.3039970},
pmid = {33232240},
issn = {1558-0210},
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; Movement ; Rats ; Reinforcement, Psychology ; Reward ; },
abstract = {Autonomous brain machine interfaces (BMIs) aim to enable paralyzed people to self-evaluate their movement intention to control external devices. Previous reinforcement learning (RL)-based decoders interpret the mapping between neural activity and movements using the external reward for well-trained subjects, and have not investigated the task learning procedure. The brain has developed a learning mechanism to identify the correct actions that lead to rewards in the new task. This internal guidance can be utilized to replace the external reference to advance BMIs as an autonomous system. In this study, we propose to build an internally rewarded reinforcement learning-based BMI framework using the multi-site recording to demonstrate the autonomous learning ability of the BMI decoder on the new task. We test the model on the neural data collected over multiple days while the rats were learning a new lever discrimination task. The primary motor cortex (M1) and medial prefrontal cortex (mPFC) spikes are interpreted by the proposed RL framework into the discrete lever press actions. The neural activity of the mPFC post the action duration is interpreted as the internal reward information, where a support vector machine is implemented to classify the reward vs. non-reward trials with a high accuracy of 87.5% across subjects. This internal reward is used to replace the external water reward to update the decoder, which is able to adapt to the nonstationary neural activity during subject learning. The multi-cortical recording allows us to take in more cortical recordings as input and uses internal critics to guide the decoder learning. Comparing with the classic decoder using M1 activity as the only input and external guidance, the proposed system with multi-cortical recordings shows a better decoding accuracy. More importantly, our internally rewarded decoder demonstrates the autonomous learning ability on the new task as the decoder successfully addresses the time-variant neural patterns while subjects are learning, and works asymptotically as the subjects' behavioral learning progresses. It reveals the potential of endowing BMIs with autonomous task learning ability in the RL framework.},
}
@article {pmid33232238,
year = {2020},
author = {Wang, WE and Ho, RLM and Gatto, B and Der Veen, SMV and Underation, MK and Thomas, JS and Antony, AB and Coombes, SA},
title = {A Novel Method to Understand Neural Oscillations During Full-Body Reaching: A Combined EEG and 3D Virtual Reality Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {3074-3082},
doi = {10.1109/TNSRE.2020.3039829},
pmid = {33232238},
issn = {1558-0210},
mesh = {Electroencephalography ; Humans ; Movement ; Physical Therapy Modalities ; User-Computer Interface ; *Virtual Reality ; },
abstract = {Virtual reality (VR) can be used to create environments that are not possible in the real-world. Producing movements in VR holds enormous promise for rehabilitation and offers a platform from which to understand the neural control of movement. However, no study has examined the impact of a 3D fully immersive head-mounted display (HMD) VR system on the integrity of neural data. We assessed the quality of 64-channel EEG data with and without HMD VR during rest and during a full-body reaching task. We compared resting EEG while subjects completed three conditions: No HMD (EEG-only), HMD powered off (VR-off), and HMD powered on (VR-on). Within the same session, EEG were collected while subjects completed full-body reaching movements in two conditions (EEG-only, VR-on). During rest, no significant differences in data quality and power spectrum were observed between EEG-only, VR-off, and VR-on conditions. During reaching movements, the proportion of components attributed to the brain was greater in the EEG-only condition compared to the VR-on condition. Despite this difference, neural oscillations in source space were not significantly different between conditions, with both conditions associated with decreases in alpha and beta power in sensorimotor cortex during movements. Our findings demonstrate that the integrity of EEG data can be maintained while individuals execute full-body reaching movements within an immersive 3D VR environment. Clinical impact: Integrating VR and EEG is a viable approach to understanding the cortical processes of movement. Simultaneously recording movement and brain activity in combination with VR provides the foundation for neurobiologically informed rehabilitation therapies.},
}
@article {pmid33226953,
year = {2021},
author = {Huang, C and Xiao, Y and Xu, G},
title = {Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {18},
number = {5},
pages = {1722-1729},
doi = {10.1109/TCBB.2020.3039834},
pmid = {33226953},
issn = {1557-9964},
mesh = {Algorithms ; Brain/physiology ; *Electroencephalography ; Humans ; *Intention ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV. The experimental results show that the average accuracy of the proposed method is 73.9 percent, which improves the classification accuracy of 10.4, 5.5, 16.2 percent compared with the traditional methods including artificial neural network, support vector machine, and stacked auto-encoder.},
}
@article {pmid33223041,
year = {2020},
author = {Bryukhovetskiy, AS and Brusilovsky, LI and Kozhin, SP and Serafimovich, PG and Nikonorov, AV and Zhukova, M and Sharma, HS},
title = {Human mind has microwave electromagnetic nature and can be recorded and processed.},
journal = {Progress in brain research},
volume = {258},
number = {},
pages = {439-463},
doi = {10.1016/bs.pbr.2020.09.006},
pmid = {33223041},
issn = {1875-7855},
mesh = {*Brain ; Cognition ; Electromagnetic Phenomena ; Electrophysiological Phenomena ; Humans ; *Microwaves ; },
abstract = {INTRODUCTION: In 2014 and 2015 Professor of neurology Andrey Bryukhovetskiy published a novel theory of the information-commutation organization of the human brain in Russia, China and the USA. The theory posits the hypothesis that the higher nervous activity (cognitive, intellectual, mnestic) of the humans and their mind are material and have microwave electromagnetic nature. The theory perceives the human mind as a result of dynamic extracortical information-commutation relations of the super-positions of the electromagnetic waves of ultra high frequency emitted by different areas of the human brain in the inter-membrane cerebrospinal fluid space of the human head at a certain period of time. The inter-membrane cerebrospinal fluid space of the human head (the space between the dura, arachnoid and pia mater filled with the cerebrospinal fluid) of about 10mm size, has all morphological attributes to realize the holography. It is a universal natural bioprocessor for processing, analysis and synthesis of the input data and their record or reproduction on the pia as on the biological holographic membrane. The theory suggested that the processes of the mind can be recorded and digitalized with the last generation contemporary microwave receptors of the UHF band.<br><br>GOAL: The goal is to experimentally test the theory of the information-commutation organization of the human brain, particularly, the postulate that the human mind has material, and, namely, electromagnetic nature represented by the microwave bioelectric activity; it must be detected, recorded and statistically processed, i.e. its existence must be confirmed.<br><br>METHODS: On their own initiative, the team of mathematicians, radioengineers and neurologists performed the non-invasive research of the electromagnetic radiation of human brain in the broad frequency range varying from 850MHz to 26.5GHz with the last generation specialized measuring equipment with high sensitivity and recording speed, specialized measuring antennas and low noise amplifying equipment in the anechoic chamber of the 1st class of protection according to the Russian system of certification GOST R 50414-92.<br><br>RESULTS: The previously unknown microwave electromagnetic radiation of the EHF/UHF range (from 1.5GHz to 4.5GHz) with signal strength of -130dBm .. -100dBm (1e[-15] .. 1e[-13] W) are discovered. The detected electromagnetic waves have zonal variations in the different areas of the human head and are absent in other areas of the human body. The method of recording of the microwave electromagnetic activity of the human brain is patented in the Russian Federation. The microwave electromagnetic activity of the brain is billion-fold different from the bioelectric activity recorded by the encephalography.<br><br>CONCLUSION: Discovery of the phenomenon of the microwave radiation of the human brain provides evidence to the idea that thinking and mind are material. This phenomenon has the potential to become a new informational channel of the diagnostics of the functional and pathological state of the higher nervous activity of the human brain. It can provide the basis for the development of the equipment for real-time analysis of the microwave bioelectric activity of the brain in norm and pathology, for objective early diagnostics of the functional and emotional conditions as well as of the psychiatric disorders at the preclinical stage, for the biocontrol of the human brain and the artificial simulators of the human brain. It also can provide the foundation for new systems of the artificial intellect, brain-computer interface and systems of the closed-loop biomanagement of the damaged brain.},
}
@article {pmid33220643,
year = {2021},
author = {Varsehi, H and Firoozabadi, SMP},
title = {An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {133},
number = {},
pages = {193-206},
doi = {10.1016/j.neunet.2020.11.002},
pmid = {33220643},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces/trends ; Causality ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Movement/*physiology ; Neurofeedback/*methods/*physiology ; Support Vector Machine ; },
abstract = {Motor imagery (MI) brain-computer interface (BCI) and neurofeedback (NF) with electroencephalogram (EEG) signals are commonly used for motor function improvement in healthy subjects and to restore neurological functions in stroke patients. Generally, in order to decrease noisy and redundant information in unrelated EEG channels, channel selection methods are used which provide feasible BCI and NF implementations with better performances. Our assumption is that there are causal interactions between the channels of EEG signal in MI tasks that are repeated in different trials of a BCI and NF experiment. Therefore, a novel method for EEG channel selection is proposed which is based on Granger causality (GC) analysis. Additionally, the machine-learning approach is used to cluster independent component analysis (ICA) components of the EEG signal into artifact and normal EEG clusters. After channel selection, using the common spatial pattern (CSP) and regularized CSP (RCSP), features are extracted and with the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, MI tasks are classified into left and right hand MI. The goal of this study is to achieve a method resulting in lower EEG channels with higher classification performance in MI-based BCI and NF by causal constraint. The proposed method based on GC, with only eight selected channels, results in 93.03% accuracy, 92.93% sensitivity, and 93.12% specificity, with RCSP feature extractor and best classifier for each subject, after being applied on Physionet MI dataset, which is increased by 3.95%, 3.73%, and 4.13%, in comparison with correlation-based channel selection method.},
}
@article {pmid33217745,
year = {2021},
author = {Mladenović, J},
title = {Standardization of protocol design for user training in EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abcc7d},
pmid = {33217745},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Reference Standards ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are systems that enable a person to interact with a machine using only neural activity. Such interaction can be non-intuitive for the user hence user training methods are developed to increase one's understanding, confidence and motivation, which would in parallel increase system performance. To clearly address the current issues in the BCI user training protocol design, here it is divided intointroductoryperiod and BCIinteractionperiod. First, theintroductoryperiod (before BCI interaction) must be considered as equally important as the BCI interaction for user training. To support this claim, an extensive literature review demonstrates that BCI performance can depend on the methodologies presented in such introductory period. To standardize its design, the user training models from human-computer interaction field are adjusted to the BCI context. Second, during the user-BCI interaction, the interface can take a large spectrum of forms (2D, 3D, size, colour etc) and modalities (visual, auditory or haptic etc) without following any design standard or guidelines. Namely, studies that explore perceptual affordance on neural activity, show that motor neurons can be triggered from a simple observation of certain objects, and depending on objects' properties (size, location etc) neural reactions can vary greatly. Surprisingly, the effects of perceptual affordance were not investigated in the BCI context. Both inconsistent introductions to BCI as well as variable interface designs make it difficult to reproduce experiments, predict their outcomes and compare results between them. To address these issues, a protocol design standardization for BCI user training is proposed.},
}
@article {pmid33213297,
year = {2024},
author = {Wallace, B and Knudson, D},
title = {The effect of course format on student learning in introductory biomechanics courses that utilise low-tech active learning exercises.},
journal = {Sports biomechanics},
volume = {23},
number = {2},
pages = {156-165},
doi = {10.1080/14763141.2020.1830163},
pmid = {33213297},
issn = {1752-6116},
mesh = {Humans ; *Problem-Based Learning ; Biomechanical Phenomena ; *Students ; Exercise ; Universities ; Educational Measurement ; Curriculum ; },
abstract = {Low-tech active learning (AL) exercises in face-to-face (F2F) undergraduate biomechanics courses improve student learning vs. lecture alone. This study compared learning of biomechanics concepts with AL implemented in two course formats (hybrid: HB vs. F2F). Additional aims were to investigate if student perceptions of learning epistemology and learning factors were related to course format. Students (n = 110) in four introductory biomechanics courses (two F2F, two HB) completed the 24-question Biomechanics Concept Inventory (BCI) at the beginning and the end of the course to determine their learning of biomechanical concepts. An additional eight questions were given with the post-test to determine student perceptions of the AL exercises and their epistemology of learning. Learning in the HB format was equivalent to the F2F course format when both implement AL in these students. Student perceptions of AL were generally positive and learning scores consistent with previous research on AL in biomechanics. There were mixed results of the effect of course format with one significant difference of three ratings of the nature of learning biomechanics and one significant difference of four ratings of AL by students. These results should be replicated and potential interactions with student perceptions and characteristics explored.},
}
@article {pmid33212777,
year = {2020},
author = {Attallah, O and Abougharbia, J and Tamazin, M and Nasser, AA},
title = {A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs.},
journal = {Brain sciences},
volume = {10},
number = {11},
pages = {},
pmid = {33212777},
issn = {2076-3425},
abstract = {Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain-computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III-IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system's performance in controlling assistive devices such as wheelchairs or artificial limbs.},
}
@article {pmid33212012,
year = {2021},
author = {Zhang, B and Qiu, L and Xiao, W and Ni, H and Chen, L and Wang, F and Mai, W and Wu, J and Bao, A and Hu, H and Gong, H and Duan, S and Li, A and Gao, Z},
title = {Reconstruction of the Hypothalamo-Neurohypophysial System and Functional Dissection of Magnocellular Oxytocin Neurons in the Brain.},
journal = {Neuron},
volume = {109},
number = {2},
pages = {331-346.e7},
doi = {10.1016/j.neuron.2020.10.032},
pmid = {33212012},
issn = {1097-4199},
mesh = {Animals ; Basal Nucleus of Meynert/chemistry/drug effects/*metabolism ; Brain/drug effects/metabolism ; Hypothalamo-Hypophyseal System/chemistry/drug effects/*metabolism ; Male ; Neurons/chemistry/drug effects/*metabolism ; Organ Culture Techniques ; Oxytocin/administration &amp; dosage/*metabolism ; Rats ; Rats, Sprague-Dawley ; Rats, Transgenic ; },
abstract = {The hypothalamo-neurohypophysial system (HNS), comprising hypothalamic magnocellular neuroendocrine cells (MNCs) and the neurohypophysis, plays a pivotal role in regulating reproduction and fluid homeostasis by releasing oxytocin and vasopressin into the bloodstream. However, its structure and contribution to the central actions of oxytocin and vasopressin remain incompletely understood. Using viral tracing and whole-brain imaging, we reconstruct the three-dimensional architecture of the HNS and observe collaterals of MNCs within the brain. By dual viral tracing, we further uncover that subsets of MNCs collaterally project to multiple extrahypothalamic regions. Selective activation of magnocellular oxytocin neurons promote peripheral oxytocin release and facilitate central oxytocin-mediated social interactions, whereas inhibition of these neurons elicit opposing effects. Our work reveals the previously unrecognized complexity of the HNS and provides structural and functional evidence for MNCs in coordinating both peripheral and central oxytocin-mediated actions, which will shed light on the mechanistic understanding of oxytocin-related psychiatric diseases.},
}
@article {pmid33211662,
year = {2020},
author = {Benzy, VK and Vinod, AP and Subasree, R and Alladi, S and Raghavendra, K},
title = {Motor Imagery Hand Movement Direction Decoding Using Brain Computer Interface to Aid Stroke Recovery and Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {3051-3062},
doi = {10.1109/TNSRE.2020.3039331},
pmid = {33211662},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Female ; Hand ; Humans ; Imagination ; Male ; Movement ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {Motor Imagery (MI)-based Brain Computer Interface (BCI) system is a potential technology for active neurorehabilitation of stroke patients by complementing the conventional passive rehabilitation methods. Research to date mainly focused on classifying left vs. right hand/foot MI of stroke patients. Though a very few studies have reported decoding imagined hand movement directions using electroencephalogram (EEG)-based BCI, the experiments were conducted on healthy subjects. Our work analyzes MI-based brain cortical activity from EEG signals and decodes the imagined hand movement directions in stroke patients. The decoded direction (left vs. right) of hand movement imagination is used to provide control commands to a motorized arm support on which patient's affected (paralyzed) arm is placed. This enables the patient to move his/her stroke-affected hand towards the intended (imagined) direction that aids neuroplasticity in the brain. The synchronization measure called Phase Locking Value (PLV), extracted from EEG, is the neuronal signature used to decode the directional movement of the MI task. Event-related desynchronization/synchronization (ERD/ERS) analysis on Mu and Beta frequency bands of EEG is done to select the time bin corresponding to the MI task. The dissimilarities between the two directions of MI tasks are identified by selecting the most significant channel pairs that provided maximum difference in PLV features. The training protocol has an initial calibration session followed by a feedback session with 50 trials of MI task in each session. The feedback session extracts PLV features corresponding to most significant channel pairs which are identified in the calibration session and is used to predict the direction of MI task in left/right direction. An average MI direction classification accuracy of 74.44% is obtained in performing the training protocol and 68.63% from the prediction protocol during feedback session on 16 stroke patients.},
}
@article {pmid33206606,
year = {2020},
author = {Hosni, SM and Borgheai, SB and McLinden, J and Shahriari, Y},
title = {An fNIRS-Based Motor Imagery BCI for ALS: A Subject-Specific Data-Driven Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {3063-3073},
doi = {10.1109/TNSRE.2020.3038717},
pmid = {33206606},
issn = {1558-0210},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; },
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Spectroscopy, Near-Infrared ; Support Vector Machine ; },
abstract = {OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) has recently gained momentum in research on motor-imagery (MI)-based brain-computer interfaces (BCIs). However, strikingly, most of the research effort is primarily devoted to enhancing fNIRS-based BCIs for healthy individuals. The ability of patients with amyotrophic lateral sclerosis (ALS), among the main BCI end-users to utilize fNIRS-based hemodynamic responses to efficiently control an MI-based BCI, has not yet been explored. This study aims to quantify subject-specific spatio-temporal characteristics of ALS patients' hemodynamic responses to MI tasks, and to investigate the feasibility of using these responses as a means of communication to control a binary BCI.<br><br>METHODS: Hemodynamic responses were recorded using fNIRS from eight patients with ALS while performing MI-Rest tasks. The generalized linear model (GLM) analysis was conducted to statistically estimate and evaluate individualized spatial activation. Selected channel sets were statistically optimized for classification. Subject-specific discriminative features, including a proposed data-driven estimated coefficient obtained from GLM, and optimized classification parameters were identified and used to further evaluate the performance using a linear support vector machine (SVM) classifier.<br><br>RESULTS: Inter-subject variations were observed in spatio-temporal characteristics of patients' hemodynamic responses. Using optimized classification parameters and feature sets, all subjects could successfully use their MI hemodynamic responses to control a BCI with an average classification accuracy of 85.4% &#xb1; 9.8%.<br><br>SIGNIFICANCE: Our results indicate a promising application of fNIRS-based MI hemodynamic responses to control a binary BCI by ALS patients. These findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.},
}
@article {pmid33206578,
year = {2021},
author = {Bonizzato, M},
title = {Neuroprosthetics: an outlook on active challenges toward clinical adoption.},
journal = {Journal of neurophysiology},
volume = {125},
number = {1},
pages = {105-109},
doi = {10.1152/jn.00496.2020},
pmid = {33206578},
issn = {1522-1598},
mesh = {Brain-Computer Interfaces ; Congresses as Topic ; Humans ; *Neural Prostheses ; Neurological Rehabilitation/instrumentation/*methods ; },
abstract = {Neural prostheses are designed to counter the effects of neurotrauma and restore the fundamental building blocks of human experience including motor action, sensation, and meaningful communication with other individuals. Here, we present an overview of active avenues, open questions, and debated topics in neuroprosthetics, such as targeting the mechanisms of sensorimotor recovery and designing brain interfaces for scalability. We review leading opinions in this thriving field, aiming to inform translational practice toward clinical adoption.},
}
@article {pmid33205564,
year = {2021},
author = {Llerena Zambrano, B and Renz, AF and Ruff, T and Lienemann, S and Tybrandt, K and Vörös, J and Lee, J},
title = {Soft Electronics Based on Stretchable and Conductive Nanocomposites for Biomedical Applications.},
journal = {Advanced healthcare materials},
volume = {10},
number = {3},
pages = {e2001397},
doi = {10.1002/adhm.202001397},
pmid = {33205564},
issn = {2192-2659},
mesh = {Electric Conductivity ; Electronics ; *Nanocomposites ; Prostheses and Implants ; *Wearable Electronic Devices ; },
abstract = {Research on the field of implantable electronic devices that can be directly applied in the body with various functionalities is increasingly intensifying due to its great potential for various therapeutic applications. While conventional implantable electronics generally include rigid and hard conductive materials, their surrounding biological objects are soft and dynamic. The mechanical mismatch between implanted devices and biological environments induces damages in the body especially for long-term applications. Stretchable electronics with outstanding mechanical compliance with biological objects effectively improve such limitations of existing rigid implantable electronics. In this article, the recent progress of implantable soft electronics based on various conductive nanocomposites is systematically described. In particular, representative fabrication approaches of conductive and stretchable nanocomposites for implantable soft electronics and various in vivo applications of implantable soft electronics are focused on. To conclude, challenges and perspectives of current implantable soft electronics that should be considered for further advances are discussed.},
}
@article {pmid33204579,
year = {2020},
author = {Dash, D and Wisler, A and Ferrari, P and Davenport, EM and Maldjian, J and Wang, J},
title = {MEG Sensor Selection for Neural Speech Decoding.},
journal = {IEEE access : practical innovations, open solutions},
volume = {8},
number = {},
pages = {182320-182337},
pmid = {33204579},
issn = {2169-3536},
support = {R01 DC016621/DC/NIDCD NIH HHS/United States ; R03 DC013990/DC/NIDCD NIH HHS/United States ; },
abstract = {Direct decoding of speech from the brain is a faster alternative to current electroencephalography (EEG) speller-based brain-computer interfaces (BCI) in providing communication assistance to locked-in patients. Magnetoencephalography (MEG) has recently shown great potential as a non-invasive neuroimaging modality for neural speech decoding, owing in part to its spatial selectivity over other high-temporal resolution devices. Standard MEG systems have a large number of cryogenically cooled channels/sensors (200 - 300) encapsulated within a fixed liquid helium dewar, precluding their use as wearable BCI devices. Fortunately, recently developed optically pumped magnetometers (OPM) do not require cryogens, and have the potential to be wearable and movable making them more suitable for BCI applications. This design is also modular allowing for customized montages to include only the sensors necessary for a particular task. As the number of sensors bears a heavy influence on the cost, size, and weight of MEG systems, minimizing the number of sensors is critical for designing practical MEG-based BCIs in the future. In this study, we sought to identify an optimal set of MEG channels to decode imagined and spoken phrases from the MEG signals. Using a forward selection algorithm with a support vector machine classifier we found that nine optimally located MEG gradiometers provided higher decoding accuracy compared to using all channels. Additionally, the forward selection algorithm achieved similar performance to dimensionality reduction using a stacked-sparse-autoencoder. Analysis of spatial dynamics of speech decoding suggested that both left and right hemisphere sensors contribute to speech decoding. Sensors approximately located near Broca's area were found to be commonly contributing among the higher-ranked sensors across all subjects.},
}
@article {pmid33203973,
year = {2020},
author = {Garcia-Garcia, MG and Marquez-Chin, C and Popovic, MR},
title = {Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {19992},
pmid = {33203973},
issn = {2045-2322},
mesh = {Action Potentials/physiology ; Animals ; Brain-Computer Interfaces ; Conditioning, Operant/*physiology ; Male ; Motor Cortex/*physiology ; Motor Neurons/*metabolism ; Neurons, Efferent/physiology ; Rats ; Rats, Long-Evans ; Reward ; Volition/physiology ; },
abstract = {Operant conditioning is implemented in brain-machine interfaces (BMI) to induce rapid volitional modulation of single neuron activity to control arbitrary mappings with an external actuator. However, intrinsic factors of the volitional controller (i.e. the brain) or the output stage (i.e. individual neurons) might hinder performance of BMIs with more complex mappings between hundreds of neurons and actuators with multiple degrees of freedom. Improved performance might be achieved by studying these intrinsic factors in the context of BMI control. In this study, we investigated how neuron subtypes respond and adapt to a given BMI task. We conditioned single cortical neurons in a BMI task. Recorded neurons were classified into bursting and non-bursting subtypes based on their spike-train autocorrelation. Both neuron subtypes had similar improvement in performance and change in average firing rate. However, in bursting neurons, the activity leading up to a reward increased progressively throughout conditioning, while the response of non-bursting neurons did not change during conditioning. These results highlight the need to characterize neuron-subtype-specific responses in a variety of tasks, which might ultimately inform the design and implementation of BMIs.},
}
@article {pmid33203813,
year = {2021},
author = {Chiang, KJ and Wei, CS and Nakanishi, M and Jung, TP},
title = {Boosting template-based SSVEP decoding by cross-domain transfer learning.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abcb6e},
pmid = {33203813},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Machine Learning ; Photic Stimulation ; },
abstract = {Objective. This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) by leveraging cross-domain data transferring.Approach. We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and electroencephalogram montages).Main results. Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method.Significance. This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.},
}
@article {pmid33201825,
year = {2020},
author = {Liang, Z and Li, F and Hu, W and Huang, G and Oba, S and Zhang, Z and Ishii, S},
title = {A Generalized Encoding System for Alpha Oscillations Through Visual Saliency Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2731-2743},
doi = {10.1109/TNSRE.2020.3038789},
pmid = {33201825},
issn = {1558-0210},
mesh = {*Attention ; Brain ; Humans ; Occipital Lobe ; Photic Stimulation ; Reproducibility of Results ; *Visual Perception ; },
abstract = {By learning how the brain reacts to external visual stimuli and examining possible triggered brain statuses, we conduct a systematic study on an encoding problem that estimates ongoing EEG dynamics from visual information. A novel generalized system is proposed to encode the alpha oscillations modulated during video viewing by employing the visual saliency involved in the presented natural video stimuli. Focusing on the parietal and occipital lobes, the encoding effects at different alpha frequency bins and brain locations are examined by a real-valued genetic algorithm (GA), and possible links between alpha features and saliency patterns are constructed. The robustness and reliability of the proposed system are demonstrated in a 10-fold cross-validation. The results show that stimuli with different saliency levels can induce significant changes in occipito-parietal alpha oscillations and that alpha at higher frequency bins responded the most in involuntary attention related to bottom-up-based visual processing. This study provides a novel approach to understand the processing of involuntary attention in the brain dynamics and would further be beneficial to the development of brain-computer interfaces and visual design.},
}
@article {pmid33201824,
year = {2020},
author = {Li, Y and Xiang, J and Kesavadas, T},
title = {Convolutional Correlation Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2681-2690},
doi = {10.1109/TNSRE.2020.3038718},
pmid = {33201824},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; },
abstract = {Currently, most of the high-performance models for frequency recognition of steady-state visual evoked potentials (SSVEPs) are linear. However, SSVEPs collected from different channels can have non-linear relationship among each other. Linearly combining electroencephalogram (EEG) from multiple channels is not the most accurate solution in SSVEPs classification. To further improve the performance of SSVEP-based brain-computer interface (BCI), we propose a convolutional neural network-based non-linear model, i.e. convolutional correlation analysis (Conv-CA). Different from pure deep learning models, Conv-CA use convolutional neural networks (CNNs) at the top of a self-defined correlation layer. The CNNs function on how to transform multiple channel EEGs into a single EEG signal. The correlation layer calculates the correlation coefficients between the transformed single EEG signal and reference signals. The CNNs provide non-linear operations to combine EEGs in different channels and different time. And the correlation layer constrains the fitting space of the deep learning model. A comparison study between the proposed Conv-CA method and the task-related component analysis (TRCA) based methods is conducted. Both methods are validated on a 40-class SSVEP benchmark dataset recorded from 35 subjects. The study verifies that the Conv-CA method significantly outperforms the TRCA-based methods. Moreover, Conv-CA has good explainability since its inputs of the correlation layer can be analyzed for visualizing what the model learnt from the data. Conv-CA is a non-linear extension of spatial filters. Its CNN structures can be further explored and tuned for reaching a better performance. The structure of combining neural networks and unsupervised features has the potential to be applied to the classification of other signals.},
}
@article {pmid33201822,
year = {2020},
author = {Phang, CR and Ko, LW},
title = {Intralobular and Interlobular Parietal Functional Network Correlated to MI-BCI Performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2671-2680},
doi = {10.1109/TNSRE.2020.3038657},
pmid = {33201822},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; *Neurofeedback ; },
abstract = {Brain-computer interface (BCI) brings hope to patients suffering from neuromuscular diseases, by allowing the control of external devices using neural signals from the central nervous system. However, a portion of individuals was unable to operate BCI with high efficacy. This research aimed to study the brain-wide functional connectivity differences that contributed to BCI performance, and investigate the relationship between task-related connectivity strength and BCI performance. Functional connectivity was estimated using pairwise Pearson's correlation from the EEG of 48 subjects performing left or right hand motor imagery (MI) tasks. The classification accuracy of linear support vector machine (SVM) to distinguish both tasks were used to represent MI-BCI performance. The significant differences in connectivity strengths were examined using Welch's T-test. The association between accuracy and connection strength was studied using correlation model. Three intralobular and fourteen interlobular connections from the parietal lobe showed a correlation of 0.31 and -0.34 respectively. Results indicate that alpha wave connectivity from 8 Hz to 13 Hz was more related to classification performance compared to high-frequency waves. Subject-independent trial-based analysis shows that MI trials executed with stronger intralobular and interlobular parietal connections performed significantly better than trials with weaker connections. Further investigation from an independent MI dataset reveals several similar connections that were correlated with MI-BCI performance. The functional connectivity of the parietal lobe could potentially allow prediction of MI-BCI performance and enable implementation of neurofeedback training for users to improve the usability of MI-BCI.},
}
@article {pmid33197630,
year = {2021},
author = {Chari, A and Budhdeo, S and Sparks, R and Barone, DG and Marcus, HJ and Pereira, EAC and Tisdall, MM},
title = {Brain-Machine Interfaces: The Role of the Neurosurgeon.},
journal = {World neurosurgery},
volume = {146},
number = {},
pages = {140-147},
doi = {10.1016/j.wneu.2020.11.028},
pmid = {33197630},
issn = {1878-8769},
mesh = {*Brain-Computer Interfaces ; Humans ; *Neurosurgeons ; },
abstract = {Neurotechnology is set to expand rapidly in the coming years as technological innovations in hardware and software are translated to the clinical setting. Given our unique access to patients with neurologic disorders, expertise with which to guide appropriate treatments, and technical skills to implant brain-machine interfaces (BMIs), neurosurgeons have a key role to play in the progress of this field. We outline the current state and key challenges in this rapidly advancing field, including implant technology, implant recipients, implantation methodology, implant function, and ethical, regulatory, and economic considerations. Our key message is to encourage the neurosurgical community to proactively engage in collaborating with other health care professionals, engineers, scientists, ethicists, and regulators in tackling these issues. By doing so, we will equip ourselves with the skills and expertise to drive the field forward and avoid being mere technicians in an industry driven by those around us.},
}
@article {pmid33196442,
year = {2020},
author = {Chen, X and Huang, X and Wang, Y and Gao, X},
title = {Combination of Augmented Reality Based Brain- Computer Interface and Computer Vision for High-Level Control of a Robotic Arm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {3140-3147},
doi = {10.1109/TNSRE.2020.3038209},
pmid = {33196442},
issn = {1558-0210},
mesh = {*Augmented Reality ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Recent advances in robotics, neuroscience, and signal processing make it possible to operate a robot through electroencephalography (EEG)-based brain-computer interface (BCI). Although some successful attempts have been made in recent years, the practicality of the entire system still has much room for improvement. The present study designed and realized a robotic arm control system by combing augmented reality (AR), computer vision, and steady-state visual evoked potential (SSVEP)-BCI. AR environment was implemented by a Microsoft HoloLens. Flickering stimuli for eliciting SSVEPs were presented on the HoloLens, which allowed users to see both the robotic arm and the user interface of the BCI. Thus users did not need to switch attention between the visual stimulator and the robotic arm. A four-command SSVEP-BCI was built for users to choose the specific object to be operated by the robotic arm. Once an object was selected, the computer vision would provide the location and color of the object in the workspace. Subsequently, the object was autonomously picked up and placed by the robotic arm. According to the online results obtained from twelve participants, the mean classification accuracy of the proposed system was 93.96 ± 5.05%. Moreover, all subjects could utilize the proposed system to successfully pick and place objects in a specific order. These results demonstrated the potential of combining AR-BCI and computer vision to control robotic arms, which is expected to further promote the practicality of BCI-controlled robots.},
}
@article {pmid33195118,
year = {2020},
author = {Ding, L and He, J and Yao, L and Zhuang, J and Chen, S and Wang, H and Jiang, N and Jia, J},
title = {Mirror Visual Feedback Combining Vibrotactile Stimulation Promotes Embodiment Perception: An Electroencephalogram (EEG) Pilot Study.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {8},
number = {},
pages = {553270},
pmid = {33195118},
issn = {2296-4185},
abstract = {As one determinant of the efficacy of mirror visual feedback (MVF) in neurorehabilitation, the embodiment perception needs to be sustainable and enhanced. This study explored integrating vibrotactile stimulation into MVF to promote the embodiment perception and provide evidence of the potential mechanism of MVF. In the experiment, the participants were instructed to keep their dominant hand still (static side), while open and close their non-dominant hand (active side) and concentrate on the image of the hand movement in the mirror. They were asked to tap the pedal with the foot of the active side once the embodiment perception is generated. A vibrotactile stimulator was attached on the hand of the active side, and three conditions were investigated: no vibration (NV), continuous vibration (CV), and intermittent vibration (IV). The effects were analyzed on both objective data, including latency time (LT) and electroencephalogram (EEG) signals, and subjective data, including embodiment questionnaire (EQ). Results of LT and EQ suggested a stronger subjective sense of embodiment under the condition of CV and IV, comparing with NV. No significant difference was found between CV and IV. EEG analysis showed that in the hemisphere of the static side, the desynchronization of CV and IV around the central-frontal region (C3 and F3) in the alpha band (8-13 Hz) was significantly prominent compared to NV, and in the hemisphere of the active side, the desynchronization of three conditions was similar. The network analysis of EEG data indicated that there was no significant difference in the efficiency of neural communication under the three conditions. These results demonstrated that MVF combined with vibrotactile stimulation could strengthen the embodiment perception with increases in motor cortical activation, which indicated an evidence-based protocol of MVF to facilitate the recovery of patients with stroke.},
}
@article {pmid33192987,
year = {2020},
author = {Su, F and Xu, W},
title = {Enhancing Brain Plasticity to Promote Stroke Recovery.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {554089},
pmid = {33192987},
issn = {1664-2295},
abstract = {Stroke disturbs both the structural and functional integrity of the brain. The understanding of stroke pathophysiology has improved greatly in the past several decades. However, effective therapy is still limited, especially for patients who are in the subacute or chronic phase. Multiple novel therapies have been developed to improve clinical outcomes by improving brain plasticity. These approaches either focus on improving brain remodeling and restoration or on constructing a neural bypass to avoid brain injury. This review describes emerging therapies, including modern rehabilitation, brain stimulation, cell therapy, brain-computer interfaces, and peripheral nervous transfer, and highlights treatment-induced plasticity. Key evidence from basic studies on the underlying mechanisms is also briefly discussed. These insights should lead to a deeper understanding of the overall neural circuit changes, the clinical relevance of these changes in stroke, and stroke treatment progress, which will assist in the development of future approaches to enhance brain function after stroke.},
}
@article {pmid33192417,
year = {2020},
author = {Medina-Juliá, MT and Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Ron-Angevin, R},
title = {P300-Based Brain-Computer Interface Speller: Usability Evaluation of Three Speller Sizes by Severely Motor-Disabled Patients.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {583358},
pmid = {33192417},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) spellers allow severe motor-disabled patients to communicate using their brain activity without muscular mobility. Different visual configurations of the widely studied P300-based BCI speller had been assessed with healthy and motor-disabled users. However, the speller size (in terms of cm) had only been assessed for healthy subjects. We think that the speller size might be limiting for some severely motor-disabled patients with restricted head and eye movements. The usability of three speller sizes was assessed for seven patients diagnosed with amyotrophic lateral sclerosis (ALS) and a participant diagnosed with Duchenne muscular dystrophy (DMD). This is the first usability evaluation of speller size with severely motor-disabled participants. Effectiveness (in the online results) and efficiency (in the workload test) of the medium speller was remarkably better. Satisfaction was significantly the highest with the medium size speller and the lowest with the small size. These results correlate with previously described findings in healthy subjects. In conclusion, the speller size should be considered when designing a speller paradigm, especially for motor-disabled individuals, since it might affect their performance and user experience while controlling a BCI speller.},
}
@article {pmid33192282,
year = {2020},
author = {Wang, H and Su, Q and Yan, Z and Lu, F and Zhao, Q and Liu, Z and Zhou, F},
title = {Rehabilitation Treatment of Motor Dysfunction Patients Based on Deep Learning Brain-Computer Interface Technology.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {595084},
pmid = {33192282},
issn = {1662-4548},
abstract = {In recent years, brain-computer interface (BCI) is expected to solve the physiological and psychological needs of patients with motor dysfunction with great individual differences. However, the classification method based on feature extraction requires a lot of prior knowledge when extracting data features and lacks a good measurement standard, which makes the development of BCI. In particular, the development of a multi-classification brain-computer interface is facing a bottleneck. To avoid the blindness and complexity of electroencephalogram (EEG) feature extraction, the deep learning method is applied to the automatic feature extraction of EEG signals. It is necessary to design a classification model with strong robustness and high accuracy for EEG signals. Based on the research and implementation of a BCI system based on a convolutional neural network, this article aims to design a brain-computer interface system that can automatically extract features of EEG signals and classify EEG signals accurately. It can avoid the blindness and time-consuming problems caused by the machine learning method based on feature extraction of EEG data due to the lack of a large amount of prior knowledge.},
}
@article {pmid33192277,
year = {2020},
author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Murovec, N and Von Oertzen, T and Kamada, K and Allison, BZ and Guger, C},
title = {Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients-A Feasibility Study.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {591435},
pmid = {33192277},
issn = {1662-4548},
abstract = {INTRODUCTION: Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks.<br><br>METHODS: Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy.<br><br>RESULTS: The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), &#x394;FMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), &#x394;MAS-wrist = -0.72 points (SD = 0.83), P < 0.001, &#x394;MAS-fingers = -0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47-6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage.<br><br>CONCLUSION: The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.},
}
@article {pmid33192265,
year = {2020},
author = {Zheng, L and Sun, S and Zhao, H and Pei, W and Chen, H and Gao, X and Zhang, L and Wang, Y},
title = {A Cross-Session Dataset for Collaborative Brain-Computer Interfaces Based on Rapid Serial Visual Presentation.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {579469},
pmid = {33192265},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) based on rapid serial visual presentation (RSVP) have been widely used to categorize target and non-target images. However, it is still a challenge to detect single-trial event related potentials (ERPs) from electroencephalography (EEG) signals. Besides, the variability of EEG signal over time may cause difficulties of calibration in long-term system use. Recently, collaborative BCIs have been proposed to improve the overall BCI performance by fusing brain activities acquired from multiple subjects. For both individual and collaborative BCIs, feature extraction and classification algorithms that can be transferred across sessions can significantly facilitate system calibration. Although open datasets are highly efficient for developing algorithms, currently there is still a lack of datasets for a collaborative RSVP-based BCI. This paper presents a cross-session EEG dataset of a collaborative RSVP-based BCI system from 14 subjects, who were divided into seven groups. In collaborative BCI experiments, two subjects did the same target image detection tasks synchronously. All subjects participated in the same experiment twice with an average interval of ∼23 days. The results in data evaluation indicate that adequate signal processing algorithms can greatly enhance the cross-session BCI performance in both individual and collaborative conditions. Besides, compared with individual BCIs, the collaborative methods that fuse information from multiple subjects obtain significantly improved BCI performance. This dataset can be used for developing more efficient algorithms to enhance performance and practicality of a collaborative RSVP-based BCI system.},
}
@article {pmid33192253,
year = {2020},
author = {Brandl, S and Blankertz, B},
title = {Motor Imagery Under Distraction- An Open Access BCI Dataset.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {566147},
pmid = {33192253},
issn = {1662-4548},
}
@article {pmid33192238,
year = {2020},
author = {Ortega, P and Zhao, T and Faisal, AA},
title = {HYGRIP: Full-Stack Characterization of Neurobehavioral Signals (fNIRS, EEG, EMG, Force, and Breathing) During a Bimanual Grip Force Control Task.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {919},
pmid = {33192238},
issn = {1662-4548},
}
@article {pmid33191811,
year = {2021},
author = {Sharmila, A},
title = {Hybrid control approaches for hands-free high level human-computer interface-a review.},
journal = {Journal of medical engineering &amp; technology},
volume = {45},
number = {1},
pages = {6-13},
doi = {10.1080/03091902.2020.1838642},
pmid = {33191811},
issn = {1464-522X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; Wheelchairs ; },
abstract = {For more than a decade, more number of human-machine interfaces had been developed by various combination of user inputs such as speech, hand and head gestures, eye gaze and body movements, etc. And many research issues have been addressed, including facial expression recognition, human emotion analysis, speech recognition/synthesis, human-computer interaction, virtual reality and augmented reality interaction, etc. As a result, the development of a hybrid approach becomes a central issue for hands-free high-level human computer, to help elderly and disabled people. They characterise the user's preferred communication style and support user's ability to flexibly combine modes or to switch from one input mode to another that may be better suited to a particular task or setting. This review discusses the various hybrid control approaches of hands-free high level human-computer interface.},
}
@article {pmid33189934,
year = {2021},
author = {Domic-Siede, M and Irani, M and Valdés, J and Perrone-Bertolotti, M and Ossandón, T},
title = {Theta activity from frontopolar cortex, mid-cingulate cortex and anterior cingulate cortex shows different roles in cognitive planning performance.},
journal = {NeuroImage},
volume = {226},
number = {},
pages = {117557},
doi = {10.1016/j.neuroimage.2020.117557},
pmid = {33189934},
issn = {1095-9572},
mesh = {Adult ; Attention ; Cognition/*physiology ; Electroencephalography ; Female ; Frontal Lobe/*physiology ; Gyrus Cinguli/*physiology ; Humans ; Male ; Reaction Time/physiology ; Theta Rhythm/*physiology ; Thinking/*physiology ; Young Adult ; },
abstract = {Cognitive planning, the ability to develop a sequenced plan to achieve a goal, plays a crucial role in human goal-directed behavior. However, the specific role of frontal structures in planning is unclear. We used a novel and ecological task, that allowed us to separate the planning period from the execution period. The spatio-temporal dynamics of EEG recordings showed that planning induced a progressive and sustained increase of frontal-midline theta activity (FMθ) over time. Source analyses indicated that this activity was generated within the prefrontal cortex. Theta activity from the right mid-Cingulate Cortex (MCC) and the left Anterior Cingulate Cortex (ACC) were correlated with an increase in the time needed for elaborating plans. On the other hand, left Frontopolar cortex (FP) theta activity exhibited a negative correlation with the time required for executing a plan. Since reaction times of planning execution correlated with correct responses, left FP theta activity might be associated with efficiency and accuracy in making a plan. Associations between theta activity from the right MCC and the left ACC with reaction times of the planning period may reflect high cognitive demand of the task, due to the engagement of attentional control and conflict monitoring implementation. In turn, the specific association between left FP theta activity and planning performance may reflect the participation of this brain region in successfully self-generated plans.},
}
@article {pmid33186097,
year = {2021},
author = {Camarrone, F and Branco, MP and Ramsey, NF and Van Hulle, MM},
title = {Accurate Offline Asynchronous Detection of Individual Finger Movement From Intracranial Brain Signals Using a Novel Multiway Approach.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {7},
pages = {2176-2187},
doi = {10.1109/TBME.2020.3037934},
pmid = {33186097},
issn = {1558-2531},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Fingers ; Movement ; },
abstract = {Asynchronous motor Brain Computer Interfacing (BCI) is characterized by the continuous decoding of intended muscular activity from brain signals. Such applications have gained widespread interest for enabling users to issue commands volitionally. In conventional motor BCIs features extracted from brain signals are concatenated into vector- or matrix-based (or one-/two-way) representations. Nevertheless, when accounting for the original multimodal or multiway signal structure, decoding performance has been shown to improve jointly with result interpretability. However, as multiway decoders are notorious for the extensive computational cost to train them, conventional ones are still preferred. To curb this limitation, we introduce a novel multiway classifier, called Block-Term Tensor Classifier that inherits the improved accuracy of multiway methods while providing fast training. We show that it can outperform state-of-the-art multiway and two-way Linear Discriminant Analysis classifiers in asynchronous detection of individual finger movements from intracranial recordings, an essential feature to achieve a sense of dexterity with hand prosthetics and exoskeletons.},
}
@article {pmid33186068,
year = {2022},
author = {Khaliq Fard, M and Fallah, A and Maleki, A},
title = {Neural decoding of continuous upper limb movements: a meta-analysis.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {17},
number = {7},
pages = {731-737},
doi = {10.1080/17483107.2020.1842919},
pmid = {33186068},
issn = {1748-3115},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; Upper Extremity ; },
abstract = {OBJECTIVE: EEG-based motion trajectory decoding makes a promising approach for neurotechnology which can be used for neural control of motion reconstruction and neurorehabilitation tools. However, the feasibility and validity of continuous motion decoding by non-invasive brain activity are not clear. The main aim of this study was to perform a meta-analysis across studies that examined the ability of EEG-based continuous motion decoding of upper limb movements.<br><br>APPROACH: Pearson's correlation coefficient (CC) was used to evaluate the model performance of the studies and considered as an effect size. To estimate the overall effect size of neural decoding of motion trajectory across studies, characteristics of included studies were addressed and the random effect model was applied to the heterogeneous studies which estimated overall effect size distribution. Furthermore, the significant difference between the two subgroups of imagined and executed movements was analysed.<br><br>MAIN RESULTS: The mean of the overall effect size was computed 0.46 across the nonhomogeneous studies. The results showed no significant difference between imagined and executed movements (Chi[2]=0.28, df = 1, p&#x2009;=&#x2009;0.60).<br><br>SIGNIFICANCE: Meta-analysis results confirm that imagination like execution movements can be used for neural decoding of motion trajectory in neural motor control systems. Also, nonlinear compare with linear model statistically confirmed to be more beneficial for complex movements. Furthermore, a new approach of synergy-based motion decoding can be significantly effective to increase model performance and more research needs to evaluate this method for different levels of complexity of movements.IMPLICATIONS FOR REHABILITATIONNeural decoding methods base on EEG as a non-invasive brain activity, are more user friendly for neurorehabilitation than invasive methods that developing of it makes it more applicable for reconstructing activities of daily living.Neurotechnology for neural control of motion reconstruction, makes the rehabilitation tools to be more synchrony with human intentional movement that can be used to improve the brain neuroplastisity in stroke or other paralysed people.The feasibility and validity of imagined movements equal with executed movements show that amputee people also can benefit EEG-based motion decoding for controling rehabilitation tools just by imagination of their intentional movements.For neurorehabilitation tools, comparing the study outcomes illucidate that the approach of synergy-based motor control in brain activities concluded significantly high performance that highlighted the need it to more investigated in future research.},
}
@article {pmid33181505,
year = {2021},
author = {Zhang, H and Zhao, X and Wu, Z and Sun, B and Li, T},
title = {Motor imagery recognition with automatic EEG channel selection and deep learning.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abca16},
pmid = {33181505},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Objective.Modern motor imagery (MI)-based brain computer interface systems often entail a large number of electroencephalogram (EEG) recording channels. However, irrelevant or highly correlated channels would diminish the discriminatory ability, thus reducing the control capability of external devices. How to optimally select channels and extract associated features remains a big challenge. This study aims to propose and validate a deep learning-based approach to automatically recognize two different MI states by selecting the relevant EEG channels.Approach.In this work, we make use of a sparse squeeze-and-excitation module to extract the weights of EEG channels based on their contribution to MI classification, by which an automatic channel selection (ACS) strategy is developed. Further, we propose a convolutional neural network to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and robustness.Main results.We execute the experiments using EEG signals recorded at MI where 25 healthy subjects performed MI movements of the right hand and feet to generate motor commands. An average accuracy of87.2±5.0% (mean±std)is obtained, providing a 37.3% improvement with respect to a state-of-the-art channel selection approach.Significance.The proposed ACS method has been found to be significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduces computational complexity but also improves the MI classification performance. The proposed method selects EEG channels related to hand and feet movement, which paves the way to real-time and more natural interfaces between the patient and the robotic device.},
}
@article {pmid33181488,
year = {2021},
author = {Roc, A and Pillette, L and Mladenovic, J and Benaroch, C and N'Kaoua, B and Jeunet, C and Lotte, F},
title = {A review of user training methods in brain computer interfaces based on mental tasks.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abca17},
pmid = {33181488},
issn = {1741-2552},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Learning ; Reproducibility of Results ; },
abstract = {Mental-tasks based brain-computer interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training-notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.},
}
@article {pmid33178903,
year = {2020},
author = {Davidoff, EJ},
title = {Agency and Accountability: Ethical Considerations for Brain-Computer Interfaces.},
journal = {The Rutgers journal of bioethics},
volume = {11},
number = {},
pages = {9-20},
pmid = {33178903},
issn = {2475-644X},
support = {P41 RR012408/RR/NCRR NIH HHS/United States ; T32 GM135141/GM/NIGMS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) are systems in which a user's real-time brain activity is used to control an external device, such as a prosthetic limb. BCIs have great potential for restoring lost motor functions in a wide range of patients. However, this futuristic technology raises several ethical questions, especially concerning the degree of agency a BCI affords its user and the extent to which a BCI user ought to be accountable for actions undertaken via the device. This paper examines these and other ethical concerns found at each of the three major parts of the BCI system: the sensor that records neural activity, the decoder that converts raw data into usable signals, and the translator that uses these signals to control the movement of an external device.},
}
@article {pmid33178121,
year = {2020},
author = {Manzur-Valdivia, H and Alvarez-Ruf, J},
title = {Surface Electromyography in Clinical Practice. A Perspective From a Developing Country.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {578829},
pmid = {33178121},
issn = {1664-2295},
abstract = {Surface electromyography (sEMG) has long been used in research, health care, and other fields such as ergonomics and brain-machine interfaces. In health care, sEMG has been employed to diagnose as well as to treat musculoskeletal disorders, pelvic floor dysfunction, and post-stroke motor deficits, among others. Despite the extensive literature on sEMG, the clinical community has not widely adopted it. We believe that in developing countries, such as Chile, this phenomenon may be explained by several interacting barriers. First, the socioeconomics of the country creates an environment where only high cost-effective treatments are routinely applied. Second, the majority of the sEMG literature on clinical applications has not extensively translated into decisive outcomes, which interferes with its applicability in low-income contexts. Third, clinical training on rehabilitation provides inadequate instruction on sEMG. And fourth, accessibility to equipment (i.e., affordability, availability, portability) may constitute another barrier, especially among developing countries. Here, we analyze socio-economic indicators of health care in Chile and comment on current literature about the use of sEMG in rehabilitation. Then we analyze the curricula of several physical therapy schools in Chile and report some estimations of the training on sEMG. Finally, we analyze the accessibility of some available sEMG devices and show that several match predefined criteria. We conclude that in developing countries, the insufficient use of sEMG in health might be explained by a shortage of evidence showing a crucial role in specific outcomes and the lack of training in rehabilitation-related careers, which interact with local socioeconomic factors that limit the application of these techniques.},
}
@article {pmid33177971,
year = {2020},
author = {Kim, MK and Sohn, JW and Kim, SP},
title = {Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation Analysis.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {509364},
pmid = {33177971},
issn = {1662-4548},
abstract = {The control of arm movements through intracortical brain-machine interfaces (BMIs) mainly relies on the activities of the primary motor cortex (M1) neurons and mathematical models that decode their activities. Recent research on decoding process attempts to not only improve the performance but also simultaneously understand neural and behavioral relationships. In this study, we propose an efficient decoding algorithm using a deep canonical correlation analysis (DCCA), which maximizes correlations between canonical variables with the non-linear approximation of mappings from neuronal to canonical variables via deep learning. We investigate the effectiveness of using DCCA for finding a relationship between M1 activities and kinematic information when non-human primates performed a reaching task with one arm. Then, we examine whether using neural activity representations from DCCA improves the decoding performance through linear and non-linear decoders: a linear Kalman filter (LKF) and a long short-term memory in recurrent neural networks (LSTM-RNN). We found that neural representations of M1 activities estimated by DCCA resulted in more accurate decoding of velocity than those estimated by linear canonical correlation analysis, principal component analysis, factor analysis, and linear dynamical system. Decoding with DCCA yielded better performance than decoding the original FRs using LSTM-RNN (6.6 and 16.0% improvement on average for each velocity and position, respectively; Wilcoxon rank sum test, p < 0.05). Thus, DCCA can identify the kinematics-related canonical variables of M1 activities, thus improving the decoding performance. Our results may help advance the design of decoding models for intracortical BMIs.},
}
@article {pmid33177970,
year = {2020},
author = {Huang, JS and Li, Y and Chen, BQ and Lin, C and Yao, B},
title = {An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {808},
pmid = {33177970},
issn = {1662-4548},
abstract = {The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals, and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.},
}
@article {pmid33176141,
year = {2020},
author = {Wang, YJ and Liu, MG and Wang, JH and Cao, W and Wu, C and Wang, ZY and Liu, L and Yang, F and Feng, ZH and Sun, L and Zhang, F and Shen, Y and Zhou, YD and Zhuo, M and Luo, JH and Xu, TL and Li, XY},
title = {Restoration of Cingulate Long-Term Depression by Enhancing Non-apoptotic Caspase 3 Alleviates Peripheral Pain Hypersensitivity.},
journal = {Cell reports},
volume = {33},
number = {6},
pages = {108369},
doi = {10.1016/j.celrep.2020.108369},
pmid = {33176141},
issn = {2211-1247},
mesh = {Caspase 3/*metabolism ; Depression/*genetics ; Gyrus Cinguli/*physiopathology ; Humans ; Neuralgia/*physiopathology ; },
abstract = {Nerve injury in somatosensory pathways may lead to neuropathic pain, which affects the life quality of ∼8% of people. Long-term enhancement of excitatory synaptic transmission along somatosensory pathways contributes to neuropathic pain. Caspase 3 (Casp3) plays a non-apoptotic role in the hippocampus and regulates internalization of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) subunits. Whether Casp3-AMPAR interaction is involved in the maintenance of peripheral hypersensitivity after nerve injury remained unknown. Here, we show that nerve injury suppresses long-term depression (LTD) and downregulates Casp3 in the anterior cingulate cortex (ACC). Interfering with interactions between Casp3 and AMPAR subunits or reducing Casp3 activity in the ACC suppresses LTD induction and causes peripheral hypersensitivity. Overexpression of Casp3 restores LTD and reduces peripheral hypersensitivity after nerve injury. We reveal how Casp3 is involved in the maintenance of peripheral hypersensitivity. Our findings suggest that restoration of LTD via Casp3 provides a therapeutic strategy for neuropathic pain management.},
}
@article {pmid33175681,
year = {2020},
author = {Li, D and Xu, J and Wang, J and Fang, X and Ji, Y},
title = {A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2615-2626},
doi = {10.1109/TNSRE.2020.3037326},
pmid = {33175681},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) decoding helps motor-disabled patients to communicate with external devices directly, which can achieve the purpose of human-computer interaction and assisted living. MI EEG decoding has a core problem which is extracting as many multiple types of features as possible from the multi-channel time series of EEG to understand brain activity accurately. Recently, deep learning technology has been widely used in EEG decoding. However, the variability of the simple network framework is insufficient to satisfy the complex task of EEG decoding. A multi-scale fusion convolutional neural network based on the attention mechanism (MS-AMF) is proposed in this paper. The network extracts spatio temporal multi-scale features from multi-brain regions representation signals and is supplemented by a dense fusion strategy to retain the maximum information flow. The attention mechanism we added to the network has improved the sensitivity of the network. The experimental results show that the network has a better classification effect compared with the baseline method in the BCI Competition IV-2a dataset. We conducted visualization analysis in multiple parts of the network, and the results show that the attention mechanism is also convenient for analyzing the underlying information flow of EEG decoding, which verifies the effectiveness of the MS-AMF method.},
}
@article {pmid33171452,
year = {2021},
author = {Zhang, X and Yao, L and Wang, X and Monaghan, J and McAlpine, D and Zhang, Y},
title = {A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/abc902},
pmid = {33171452},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; },
abstract = {Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.},
}
@article {pmid33171450,
year = {2021},
author = {Kuang, D and Michoski, C},
title = {Dual stream neural networks for brain signal classification.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abc903},
pmid = {33171450},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Neural Networks, Computer ; },
abstract = {Objective. The primary objective of this work is to develop a neural nework classifier for arbitrary collections of functional neuroimaging signals to be used in brain-computer interfaces (BCIs).Approach. We propose a dual stream neural network (DSNN) for the classification problem. The first stream is an end-to-end classifier taking raw time-dependent signals as input and generating feature identification signatures from them. The second stream enhances the identified features from the first stream by adjoining a dynamic functional connectivity matrix aimed at incorporating nuanced multi-channel information during specified BCI tasks.Main results. The proposed DSNN classifier is benchmarked against three publicly available datasets, where the classifier demonstrates performance comparable to, or better than the state-of-art in each instance. An information theoretic examination of the trained network is also performed, utilizing various tools, to demonstrate how to glean interpretive insight into how the hidden layers of the network parse the underlying biological signals.Significance.The resulting DSNN is a subject-independent classifier that works for any collection of 1D functional neuroimaging signals, with the option of integrating domain specific information in the design.},
}
@article {pmid33171117,
year = {2021},
author = {Huang, X and Huang, P and Huang, L and Hu, Z and Liu, X and Shen, J and Xi, Y and Yang, Y and Fu, Y and Tao, Q and Lin, S and Xu, A and Xu, F and Xue, T and So, KF and Li, H and Ren, C},
title = {A Visual Circuit Related to the Nucleus Reuniens for the Spatial-Memory-Promoting Effects of Light Treatment.},
journal = {Neuron},
volume = {109},
number = {2},
pages = {347-362.e7},
doi = {10.1016/j.neuron.2020.10.023},
pmid = {33171117},
issn = {1097-4199},
mesh = {Animals ; Lighting/*methods ; Male ; Mice ; Mice, Inbred C57BL ; Midline Thalamic Nuclei/chemistry/*metabolism ; Nerve Net/chemistry/*metabolism ; Organ Culture Techniques ; *Photoperiod ; Spatial Memory/*physiology ; Visual Pathways/chemistry/*metabolism ; },
abstract = {Light exerts profound effects on cognitive functions across species, including humans. However, the neuronal mechanisms underlying the effects of light on cognitive functions are poorly understood. In this study, we show that long-term exposure to bright-light treatment promotes spatial memory through a di-synaptic visual circuit related to the nucleus reuniens (Re). Specifically, a subset of SMI-32-expressing ON-type retinal ganglion cells (RGCs) innervate CaMKIIα neurons in the thalamic ventral lateral geniculate nucleus and intergeniculate leaflet (vLGN/IGL), which in turn activate CaMKIIα neurons in the Re. Specific activation of vLGN/IGL-projecting RGCs, activation of Re-projecting vLGN/IGL neurons, or activation of postsynaptic Re neurons is sufficient to promote spatial memory. Furthermore, we demonstrate that the spatial-memory-promoting effects of light treatment are dependent on the activation of vLGN/IGL-projecting RGCs, Re-projecting vLGN/IGL neurons, and Re neurons. Our results reveal a dedicated subcortical visual circuit that mediates the spatial-memory-promoting effects of light treatment.},
}
@article {pmid33167561,
year = {2020},
author = {Zhang, K and Xu, G and Zheng, X and Li, H and Zhang, S and Yu, Y and Liang, R},
title = {Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {21},
pages = {},
pmid = {33167561},
issn = {1424-8220},
support = {2017YFC1308500//National Key Research &amp; Development Plan of China/ ; 2019GDASYL-0502002//GDAS' Project of Science and Technology Development/ ; No. 2018ZDCXL-GY-06-01//Key Research &amp; Development Plan of Shaanxi Province/ ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Humans ; *Machine Learning ; },
abstract = {The algorithms of electroencephalography (EEG) decoding are mainly based on machine learning in current research. One of the main assumptions of machine learning is that training and test data belong to the same feature space and are subject to the same probability distribution. However, this may be violated in EEG processing. Variations across sessions/subjects result in a deviation of the feature distribution of EEG signals in the same task, which reduces the accuracy of the decoding model for mental tasks. Recently, transfer learning (TL) has shown great potential in processing EEG signals across sessions/subjects. In this work, we reviewed 80 related published studies from 2010 to 2020 about TL application for EEG decoding. Herein, we report what kind of TL methods have been used (e.g., instance knowledge, feature representation knowledge, and model parameter knowledge), describe which types of EEG paradigms have been analyzed, and summarize the datasets that have been used to evaluate performance. Moreover, we discuss the state-of-the-art and future development of TL for EEG decoding. The results show that TL can significantly improve the performance of decoding models across subjects/sessions and can reduce the calibration time of brain-computer interface (BCI) systems. This review summarizes the current practical suggestions and performance outcomes in the hope that it will provide guidance and help for EEG research in the future.},
}
@article {pmid33166945,
year = {2021},
author = {Li, B and Lin, Y and Gao, X and Liu, Z},
title = {Enhancing the EEG classification in RSVP task by combining interval model of ERPs with spatial and temporal regions of interest.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abc8d5},
pmid = {33166945},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Evoked Potentials ; Humans ; Temporal Lobe ; },
abstract = {Objective.Brain-computer interface (BCI) systemsdirectly translate human intentions to instructions for machines by decoding the neural signals. The rapid serial visual presentation (RSVP) task is a typical paradigm of BCIs, in which subjects can detect the targets in the high-speed serial images. There are still two main challenges in electroencephalography (EEG) classification for RSVP tasks: inter-trial variability of event-related potentials (ERPs) and limited trial number of EEG training data.Approach.This study proposed an algorithm of discriminant analysis and classification for interval ERPs (DACIE) in RSVP tasks. Firstly, an interval model of ERPs was exploited to solve the inter-trial variability problem. Secondly, a spatial structured sparsity regularization was utilized to reinforce the important channels, which provided a spatial region of interest (sROI). Meanwhile, a temporal auto-weighting technique was conducted to emphasize the important discriminant components, which obtained a temporal regions of interest (tROIs). Thirdly, classification features were obtained by the discriminant eigenvalue analysis to avoid the ill-conditioned estimation of covariance matrix caused by fewer training trials.Main results.EEG datasets of 12 subjects in RSVP tasks were analyzed to evaluate the classification performance of proposed algorithm. The average accuracy rate, true positive rate, false positive rate and AUC value are 96.9%, 81.6%, 2.8% and 0.938, respectively. Compared with several state-of-the-art algorithms, the proposed algorithm can provide significantly better classification performance.Significance.The interval model of ERPs was exploited in a spatial linear discriminant framework to overcome the inter-trial variability. The sROIs and tROIs were explored to reinforce the pivotal channels and temporal components. And the proposed algorithm can provide good performance with fewer training trials.},
}
@article {pmid33166494,
year = {2020},
author = {Mofers, A and Selvaraju, K and Gubat, J and D'Arcy, P and Linder, S},
title = {Identification of proteasome inhibitors using analysis of gene expression profiles.},
journal = {European journal of pharmacology},
volume = {889},
number = {},
pages = {173709},
doi = {10.1016/j.ejphar.2020.173709},
pmid = {33166494},
issn = {1879-0712},
mesh = {Cell Line, Tumor ; Databases, Genetic ; *Drug Discovery ; *Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Humans ; Neoplasms/*drug therapy/enzymology/genetics/pathology ; Proteasome Endopeptidase Complex/*drug effects/genetics/metabolism ; Proteasome Inhibitors/*pharmacology ; Protein Interaction Maps ; Signal Transduction ; *Transcriptome ; },
abstract = {Inhibitors of the 20S proteasome such as bortezomib (Velcade®) and carfilzomib (Kypriolis®) are in clinical use for the treatment of patients with multiple myeloma and mantle cell lymphoma. In an attempt to identify novel inhibitors of the ubiquitin-proteasome system (UPS) we used the connectivity map (CMap) resource, based on alterations of gene expression profiles by perturbagens, and performed COMPARE analyses of drug sensitivity patterns in the NCI60 panel. Cmap analysis identified a large number of small molecules with strong connectivity to proteasome inhibition, including both well characterized inhibitors of the 20S proteasome and molecules previously not described to inhibit the UPS. A number of these compounds have been reported to be cytotoxic to tumor cells and were tested for their ability to decrease processing of proteasome substrates. The antibiotic thiostrepton and the natural products celastrol and curcumin induced strong accumulation of polyubiquitinated proteasome substrates in exposed cells. Other compounds elicited modest increases of proteasome substrates, including the protein phosphatase inhibitor BCI-Cl and the farnesyltransferase inhibitor manumycin A, suggesting that these compounds inhibit proteasome function. Induction of chaperone expression in the absence of proteasome inhibition was observed by a number of compounds, suggesting other effects on the UPS. We conclude that the combination of bioinformatic analyses and cellular assays resulted in the identification of compounds with potential to inhibit the UPS.},
}
@article {pmid33162885,
year = {2020},
author = {Dunlap, CF and Colachis, SC and Meyers, EC and Bockbrader, MA and Friedenberg, DA},
title = {Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: A Review.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {558987},
pmid = {33162885},
issn = {1662-5218},
abstract = {Brain-machine interfaces (BMIs) record and translate neural activity into a control signal for assistive or other devices. Intracortical microelectrode arrays (MEAs) enable high degree-of-freedom BMI control for complex tasks by providing fine-resolution neural recording. However, chronically implanted MEAs are subject to a dynamic in vivo environment where transient or systematic disruptions can interfere with neural recording and degrade BMI performance. Typically, neural implant failure modes have been categorized as biological, material, or mechanical. While this categorization provides insight into a disruption's causal etiology, it is less helpful for understanding degree of impact on BMI function or possible strategies for compensation. Therefore, we propose a complementary classification framework for intracortical recording disruptions that is based on duration of impact on BMI performance and requirement for and responsiveness to interventions: (1) Transient disruptions interfere with recordings on the time scale of minutes to hours and can resolve spontaneously; (2) Reversible disruptions cause persistent interference in recordings but the root cause can be remedied by an appropriate intervention; (3) Irreversible compensable disruptions cause persistent or progressive decline in signal quality, but their effects on BMI performance can be mitigated algorithmically; and (4) Irreversible non-compensable disruptions cause permanent signal loss that is not amenable to remediation or compensation. This conceptualization of intracortical BMI disruption types is useful for highlighting specific areas for potential hardware improvements and also identifying opportunities for algorithmic interventions. We review recording disruptions that have been reported for MEAs and demonstrate how biological, material, and mechanical mechanisms of disruption can be further categorized according to their impact on signal characteristics. Then we discuss potential compensatory protocols for each of the proposed disruption classes. Specifically, transient disruptions may be minimized by using robust neural decoder features, data augmentation methods, adaptive machine learning models, and specialized signal referencing techniques. Statistical Process Control methods can identify reparable disruptions for rapid intervention. In-vivo diagnostics such as impedance spectroscopy can inform neural feature selection and decoding models to compensate for irreversible disruptions. Additional compensatory strategies for irreversible disruptions include information salvage techniques, data augmentation during decoder training, and adaptive decoding methods to down-weight damaged channels.},
}
@article {pmid33157470,
year = {2021},
author = {Miladinović, A and Ajčević, M and Jarmolowska, J and Marusic, U and Colussi, M and Silveri, G and Battaglini, PP and Accardo, A},
title = {Effect of power feature covariance shift on BCI spatial-filtering techniques: A comparative study.},
journal = {Computer methods and programs in biomedicine},
volume = {198},
number = {},
pages = {105808},
doi = {10.1016/j.cmpb.2020.105808},
pmid = {33157470},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND AND OBJECTIVE: The input data distributions of EEG-based BCI systems can change during intra-session transitions due to nonstationarity caused by features covariate shifts, thus compromising BCI performance. We aimed to identify the most robust spatial filtering approach, among most used methods, testing them on calibration dataset, and test dataset recorded 30 min afterwards. In addition, we also investigated if their performance improved after application of Stationary Subspace Analysis (SSA).<br><br>METHODS: We have recorded, in 17 healthy subjects, the calibration set at the beginning of the upper limb motor imagery BCI experiment and testing set separately 30&#xa0;min afterwards. Both the calibration and test data were pre-processed and the BCI models were produced by using several spatial filtering approaches on the calibration set. Those models were subsequently evaluated on a test set. The differences between the accuracy estimated by cross-validation on the calibration dataset and the accuracy on the test dataset were investigated. The same procedure was performed with, and without SSA pre-processing step.<br><br>RESULTS: A significant reduction in accuracy on the test dataset was observed for CSP, SPoC and SpecRCSP approaches. For SLap and SpecCSP only a slight decreasing trend was observed, while FBCSP and FBCSPT largely maintained moderately high median accuracy >70%. In the case of application of SSA pre-processing, the differences between accuracy observed on calibration and test dataset were reduced. In addition, accuracy values both on calibration and test set were slightly higher in case of SSA pre-processing and also in this case FBCSP and FBCSPT presented slightly better performance compared to other methods.<br><br>CONCLUSION: The intrinsic signal nonstationarity characteristics, caused by covariance shifts of power features, reduced the accuracy of BCI model, therefore, suggesting that this evaluation framework should be considered for testing and simulating real life performance. FBCSP and FBSCPT approaches showed to be more robust to feature covariance shift. SSA can improve the models performance and reduce accuracy decline from calibration to test set.},
}
@article {pmid33157145,
year = {2021},
author = {Daly, I},
title = {Neural component analysis: A spatial filter for electroencephalogram analysis.},
journal = {Journal of neuroscience methods},
volume = {348},
number = {},
pages = {108987},
doi = {10.1016/j.jneumeth.2020.108987},
pmid = {33157145},
issn = {1872-678X},
mesh = {Algorithms ; Brain ; *Electroencephalography ; Event-Related Potentials, P300 ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Spatial filtering and source separation are valuable tools in the analysis of EEG data. However, despite the well-known spatial localisation of individual cognitive processes within the brain, the available methods for source separation, such as the widely used blind source separation technique, do not take into account the spatial distributions and locations of sources. This can result in sub-optimal source identification.<br><br>NEW METHOD: We present a new method for deriving a spatial filter for EEG data that attempts to identify sources that are maximally spatially distinct from one another in terms of the spatial distributions of their projections.<br><br>RESULTS: We first evaluate our method with simulated EEG and show that it is able to separate EEG signals into components with distinct spatial distributions that closely resemble the original simulated sources. We also evaluate our method with real EEG and show it is able to identify a spatial filter that can be used to significantly improve classification accuracy of the P300 event-related potential (ERP).<br><br>We compare our method to a state of the art blind source separation methods, fast independent component analysis (ICA) and common spatial patterns (CSP). We evaluate the methods suitability for a common source separation application, analysis of ERPs.<br><br>CONCLUSIONS: Our results show that our method is well suited to identifying spatial filters for EEG analysis. This has potential applications in a wide range of EEG signal processing applications.},
}
@article {pmid33148270,
year = {2020},
author = {Milosevic, M and Marquez-Chin, C and Masani, K and Hirata, M and Nomura, T and Popovic, MR and Nakazawa, K},
title = {Why brain-controlled neuroprosthetics matter: mechanisms underlying electrical stimulation of muscles and nerves in rehabilitation.},
journal = {Biomedical engineering online},
volume = {19},
number = {1},
pages = {81},
pmid = {33148270},
issn = {1475-925X},
support = {#19K23606//Japan Society for the Promotion of Science/ ; #20K19412//Japan Society for the Promotion of Science/ ; #18H04082//Japan Society for the Promotion of Science/ ; #18KK0272//Japan Society for the Promotion of Science/ ; MEI Grant B//Global Center for Medical Engineering and Informatics at Osaka University/ ; },
mesh = {*Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; Humans ; *Muscles ; *Nervous System ; *Prostheses and Implants ; Rehabilitation/*methods ; },
abstract = {Delivering short trains of electric pulses to the muscles and nerves can elicit action potentials resulting in muscle contractions. When the stimulations are sequenced to generate functional movements, such as grasping or walking, the application is referred to as functional electrical stimulation (FES). Implications of the motor and sensory recruitment of muscles using FES go beyond simple contraction of muscles. Evidence suggests that FES can induce short- and long-term neurophysiological changes in the central nervous system by varying the stimulation parameters and delivery methods. By taking advantage of this, FES has been used to restore voluntary movement in individuals with neurological injuries with a technique called FES therapy (FEST). However, long-lasting cortical re-organization (neuroplasticity) depends on the ability to synchronize the descending (voluntary) commands and the successful execution of the intended task using a FES. Brain-computer interface (BCI) technologies offer a way to synchronize cortical commands and movements generated by FES, which can be advantageous for inducing neuroplasticity. Therefore, the aim of this review paper is to discuss the neurophysiological mechanisms of electrical stimulation of muscles and nerves and how BCI-controlled FES can be used in rehabilitation to improve motor function.},
}
@article {pmid33147577,
year = {2021},
author = {Gonzalez-Astudillo, J and Cattai, T and Bassignana, G and Corsi, MC and De Vico Fallani, F},
title = {Network-based brain-computer interfaces: principles and applications.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
doi = {10.1088/1741-2552/abc760},
pmid = {33147577},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Neurofeedback ; *Neurosciences ; },
abstract = {Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback rehabilitation. In general, BCI usability depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modeling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from brain networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.},
}
@article {pmid33146148,
year = {2020},
author = {Kobler, RJ and Sburlea, AI and Mondini, V and Hirata, M and Müller-Putz, GR},
title = {Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056027},
doi = {10.1088/1741-2552/abb3b3},
pmid = {33146148},
issn = {1741-2552},
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Movement ; Upper Extremity ; },
abstract = {OBJECTIVE: One of the main goals in brain-computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy.<br><br>APPROACH: In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics.<br><br>MAIN RESULTS: At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories.<br><br>SIGNIFICANCE: We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.},
}
@article {pmid33142282,
year = {2020},
author = {Sun, P and Anumanchipalli, GK and Chang, EF},
title = {Brain2Char: a deep architecture for decoding text from brain recordings.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {},
pmid = {33142282},
issn = {1741-2552},
support = {U01 NS098971/NS/NINDS NIH HHS/United States ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Electrocorticography ; Humans ; Language ; *Speech ; },
abstract = {Objective.Decoding language representations directly from the brain can enable new brain-computer interfaces (BCIs) for high bandwidth human-human and human-machine communication. Clinically, such technologies can restore communication in people with neurological conditions affecting their ability to speak.Approach. In this study, we propose a novel deep network architecture Brain2Char, for directly decoding text (specifically character sequences) from direct brain recordings (called electrocorticography, ECoG). Brain2Char framework combines state-of-the-art deep learning modules-3D Inception layers for multiband spatiotemporal feature extraction from neural data and bidirectional recurrent layers, dilated convolution layers followed by language model weighted beam search to decode character sequences, and optimizing a connectionist temporal classification loss. Additionally, given the highly non-linear transformations that underlie the conversion of cortical function to character sequences, we perform regularizations on the network's latent representations motivated by insights into cortical encoding of speech production and artifactual aspects specific to ECoG data acquisition. To do this, we impose auxiliary losses on latent representations for articulatory movements, speech acoustics and session specific non-linearities.Main results.In three (out of four) participants reported here, Brain2Char achieves 10.6%, 8.5%, and 7.0% word error rates respectively on vocabulary sizes ranging from 1200 to 1900 words.Significance.These results establish a newend-to-end approachon decoding text frombrain signalsand demonstrate the potential of Brain2Char as a high-performance communication BCI.},
}
@article {pmid33137325,
year = {2021},
author = {Li, B and Liu, S and Hu, D and Li, G and Tang, R and Song, D and Lang, Y and He, J},
title = {Electrocortical activity in freely walking rats varies with environmental conditions.},
journal = {Brain research},
volume = {1751},
number = {},
pages = {147188},
doi = {10.1016/j.brainres.2020.147188},
pmid = {33137325},
issn = {1872-6240},
mesh = {Animals ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Cerebral Cortex/*metabolism ; China ; Electroencephalography/instrumentation/methods ; Gait/physiology ; Hindlimb/physiology ; Locomotion/physiology ; Male ; Rats ; Rats, Sprague-Dawley ; Walking/*physiology ; },
abstract = {Longstanding theories in the field of neurophysiology have held that walking in rats is an unconscious, rhythmic locomotion that does not require cortical involvement. However, recent studies have suggested that the extent of cortical involvement during walking actually varies depending on the environmental conditions. To determine the impact of environmental conditions on cortical engagement in freely walking rats, we recorded limb kinematics and signals from implanted electroencephalography arrays in rats performing a series of natural behaviors. We found that rat gaits were significantly different across various locomotion terrains (e.g. walking on an upslope vs. downslope). Further, rat forelimbs and hindlimbs showed similar patterns of motion. The results also suggested that rat cortical engagement during walking varied across environmental conditions. Specifically, α band power significantly increased during 30° downslope walking in the posterior parietal, left secondary motor, and left somatosensory clusters. Additionally, during 30° upslope walking, the β band power was greater in the left primary motor and left and right secondary motor sources. Further, rats walking on up- or downslopes of varying steepness were found to have different cortical activities. Compared with 10° downslope walking, α band power was greater during 30° downslope locomotion in the left primary motor and somatosensory sources. These findings support the hypothesis that cortical contribution during walking in rats is influenced by environmental conditions, underlining the importance of goal-directed behaviors for motor function rehabilitation and neuro-prosthetic control in brain-machine interfaces.},
}
@article {pmid33136338,
year = {2020},
author = {Chen, CX and Li, JQ and Dong, HL and Liu, GL and Bai, G and Wu, ZY},
title = {Identification and functional characterization of novel GDAP1 variants in Chinese patients with Charcot-Marie-Tooth disease.},
journal = {Annals of clinical and translational neurology},
volume = {7},
number = {12},
pages = {2381-2392},
pmid = {33136338},
issn = {2328-9503},
mesh = {Adult ; Aged ; Charcot-Marie-Tooth Disease/*genetics ; Child, Preschool ; China ; Female ; Humans ; Male ; Nerve Tissue Proteins/*genetics ; Pedigree ; *Sequence Analysis, DNA ; Exome Sequencing ; },
abstract = {OBJECTIVE: To identify and characterize the pathogenicity of novel variants in Chinese patients with Charcot-Marie-Tooth disease.<br><br>METHODS: Multiplex ligation-dependent probe amplification (MLPA) and whole-exome sequencing (WES) were performed in 30 unrelated CMT patients. Minigene assay was used to verify the effect of a novel splicing variant (c.694+1G>A) on pre-mRNA. Primary fibroblast cell lines were established from skin biopsies to characterize the biological effects of the novel variants p.L26R and p.S169fs. The mitochondrial structure was observed by an electron microscope. The expression level of protein was analyzed by Western Blotting. Mitochondrial dynamics and mitochondrial membrane potential (MMP, &#x394;&#x3c8;m) were analyzed via immunofluorescence study. Mitochondrial ATP levels were analyzed via bioluminescence assay. The rate of oxygen consumption was measured with a Seahorse Bioscience XF-96 extracellular flux analyzer.<br><br>RESULTS: We identified 10 pathogenic variants in three known CMT related genes, including three novel variants (p.L26R, p.S169fs, c.694+1G>A) and one known pathogenic variant (p.R120W) in GDAP1. Further, we described the clinical features of patients carrying pathogenic variants in GDAP1 and found that almost all Chinese CMT patients with GDAP1 variants present axonal type. The effect of c.694+1G>A on pre-mRNA was verified via minigene splice assay. Cellular biological effects showed ultrastructure damage of mitochondrial, reduced protein levels, different patterns of mitochondrial dynamics, decreased mitochondrial membrane potential (&#x394;&#x3c8;m), ATP content, and defects in respiratory capacity in the patient carrying p.L26R and p.S169fs in GDAP1.<br><br>INTERPRETATION: Our results broaden the genetic spectrum of GDAP1 and provided functional evidence for mitochondrial pathways in the pathogenesis of GDAP1 variants.},
}
@article {pmid33128787,
year = {2021},
author = {Wang, L and Zhang, Z and Han, D and Zhang, Z and Liu, Z and Liu, W},
title = {Single stimulus location for two inputs: A combined brain-computer interface based on Steady-State Visual Evoked Potential (SSVEP).},
journal = {The European journal of neuroscience},
volume = {53},
number = {3},
pages = {861-875},
doi = {10.1111/ejn.15030},
pmid = {33128787},
issn = {1460-9568},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Eye Movements ; Humans ; Photic Stimulation ; },
abstract = {Brain-computer interfaces (BCI) help severely paralyzed people communicate with the outside world. One type of BCI depends on eye movements and has high information transfer (ITR) but is tiring for users and not applicable to people with eye dyskinesia. Conversely, independent BCIs enable attention shifts across visual stimuli without eye movement, but at the cost of a lower ITR. Steady-state visual evoked potential (SSVEP) is an oscillatory brain response and typically used as BCI signal sources because of high signal-to-noise ratio (SNR). Considering the effect of attentional modulation on the SSVEP, we proposed the novel concept of one-to-two BCI to optimize existing problems, wherein the target and other stimuli shared the same location. Specifically, two spatially overlapping stimuli were displayed in the center-of-view field, as in the independent BCI, and participants were required to divide their attention between the right and left visual fields, as in the dependent BCI. Using three different design schemes in two experiments, we aimed to provide a new framework for BCI design by exploring the feasibility of a combined BCI that can realize a single stimulus location for two inputs. The results strongly demonstrated that, even when the targets and distractors overlapped spatially, the former evoked stronger SSVEP responses. Notably, the BCI scheme based on the object-based attention could achieve a recognition rate as high as 83.2% and an ITR of 12.5 bits per minute. The feasibility of a one-to-two BCI design, which simplified the keyboard layout, reduced the attention shift, and relieved user fatigue, was established.},
}
@article {pmid33126130,
year = {2020},
author = {Choi, JW and Huh, S and Jo, S},
title = {Improving performance in motor imagery BCI-based control applications via virtually embodied feedback.},
journal = {Computers in biology and medicine},
volume = {127},
number = {},
pages = {104079},
doi = {10.1016/j.compbiomed.2020.104079},
pmid = {33126130},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Humans ; Imagery, Psychotherapy ; Imagination ; Movement ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on motor imagery (MI) are commonly used for control applications. However, these applications require strong and discriminant neural patterns for which extensive experience in MI may be necessary. Inspired by the field of rehabilitation where embodiment is a key element for improving cortical activity, our study proposes a novel control scheme in which virtually embodiable feedback is provided during control to enhance performance.<br><br>METHODS: Subjects underwent two immersive virtual reality control scenarios in which they controlled the two-dimensional movement of a device using electroencephalography (EEG). The two scenarios only differ on whether embodiable feedback, which mirrors the movement of the classified intention, is provided. After undergoing each scenario, subjects also answered a questionnaire in which they rated how immersive the scenario and embodiable the feedback were.<br><br>RESULTS: Subjects exhibited higher control performance, greater discriminability in brain activity patterns, and enhanced cortical activation when using our control scheme compared to the standard control scheme in which embodiable feedback is absent. Moreover, the self-rated embodiment and presence scores showed significantly positive linear relationships with performance.<br><br>SIGNIFICANCE: The findings in our study provide evidence that providing embodiable feedback as guidance on how intention is classified may be effective for control applications by inducing enhanced neural activity and patterns with greater discriminability. By applying embodiable feedback to immersive virtual reality, our study also serves as another instance in which virtual reality is shown to be a promising tool for improving MI.},
}
@article {pmid33122990,
year = {2020},
author = {Zhang, S and Wang, Y and Zhang, L and Gao, X},
title = {A Benchmark Dataset for RSVP-Based Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {568000},
pmid = {33122990},
issn = {1662-4548},
abstract = {This paper reports on a benchmark dataset acquired with a brain-computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel electroencephalogram (EEG) data from 64 healthy subjects (sub1,…, sub64) while they performed a target image detection task. For each subject, the data contained two groups ("A" and "B"). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus images were street-view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1∼4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html.},
}
@article {pmid33120869,
year = {2020},
author = {Chowdhury, MSN and Dutta, A and Robison, MK and Blais, C and Brewer, GA and Bliss, DW},
title = {Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {21},
pages = {},
pmid = {33120869},
issn = {1424-8220},
mesh = {*Algorithms ; *Electroencephalography ; Humans ; *Neural Networks, Computer ; *Reaction Time ; },
abstract = {Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.},
}
@article {pmid33120025,
year = {2020},
author = {Luan, L and Robinson, JT and Aazhang, B and Chi, T and Yang, K and Li, X and Rathore, H and Singer, A and Yellapantula, S and Fan, Y and Yu, Z and Xie, C},
title = {Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability.},
journal = {Neuron},
volume = {108},
number = {2},
pages = {302-321},
pmid = {33120025},
issn = {1097-4199},
support = {R01 NS102917/NS/NINDS NIH HHS/United States ; U18 EB029353/EB/NIBIB NIH HHS/United States ; K25 HL140153/HL/NHLBI NIH HHS/United States ; U01 NS115588/NS/NINDS NIH HHS/United States ; R01 NS109361/NS/NINDS NIH HHS/United States ; U01 NS108680/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation/*instrumentation/methods ; Electrodes, Implanted ; Humans ; Neurons/*physiology ; Neurosciences/*instrumentation/methods ; Telemetry ; },
abstract = {Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.},
}
@article {pmid33120022,
year = {2020},
author = {Nurmikko, A},
title = {Challenges for Large-Scale Cortical Interfaces.},
journal = {Neuron},
volume = {108},
number = {2},
pages = {259-269},
doi = {10.1016/j.neuron.2020.10.015},
pmid = {33120022},
issn = {1097-4199},
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Neurosciences/*instrumentation/*methods ; Prostheses and Implants ; Telemetry ; },
abstract = {This Perspective examines the status of large-scale cortical interfaces through the lens of potential applications to active implants for brain-machine interfaces. Examples of research and development in a still embryonic field are discussed from a neuroengineer's perspective, touching on the design of scalable electrophysiological sensors with the ambition to access thousands of cortical points at near-cellular-level resolution. Important issues include microscale geometry of neural probes, design of implantable ultra-low-power electronics, implementation of high-data-rate wireless telemetry, and compatible device packaging-all requiring advanced solutions along a translational path for chronic human use.},
}
@article {pmid33120021,
year = {2020},
author = {Schiavone, G and Kang, X and Fallegger, F and Gandar, J and Courtine, G and Lacour, SP},
title = {Guidelines to Study and Develop Soft Electrode Systems for Neural Stimulation.},
journal = {Neuron},
volume = {108},
number = {2},
pages = {238-258},
doi = {10.1016/j.neuron.2020.10.010},
pmid = {33120021},
issn = {1097-4199},
mesh = {Brain/physiology ; Brain-Computer Interfaces ; Electric Stimulation/*instrumentation/methods ; *Electrodes, Implanted ; Humans ; Neurosciences/*instrumentation/methods ; Spinal Cord/physiology ; },
abstract = {Electrical stimulation of nervous structures is a widely used experimental and clinical method to probe neural circuits, perform diagnostics, or treat neurological disorders. The recent introduction of soft materials to design electrodes that conform to and mimic neural tissue led to neural interfaces with improved functionality and biointegration. The shift from stiff to soft electrode materials requires adaptation of the models and characterization methods to understand and predict electrode performance. This guideline aims at providing (1) an overview of the most common techniques to test soft electrodes in vitro and in vivo; (2) a step-by-step design of a complete study protocol, from the lab bench to in vivo experiments; (3) a case study illustrating the characterization of soft spinal electrodes in rodents; and (4) examples of how interpreting characterization data can inform experimental decisions. Comprehensive characterization is paramount to advancing soft neurotechnology that meets the requisites for long-term functionality in vivo.},
}
@article {pmid33117775,
year = {2020},
author = {Guggenberger, R and Raco, V and Gharabaghi, A},
title = {State-Dependent Gain Modulation of Spinal Motor Output.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {8},
number = {},
pages = {523866},
pmid = {33117775},
issn = {2296-4185},
abstract = {Afferent somatosensory information plays a crucial role in modulating efferent motor output. A better understanding of this sensorimotor interplay may inform the design of neurorehabilitation interfaces. Current neurotechnological approaches that address motor restoration after trauma or stroke combine motor imagery (MI) and contingent somatosensory feedback, e.g., via peripheral stimulation, to induce corticospinal reorganization. These interventions may, however, change the motor output already at the spinal level dependent on alterations of the afferent input. Neuromuscular electrical stimulation (NMES) was combined with measurements of wrist deflection using a kinematic glove during either MI or rest. We investigated 360 NMES bursts to the right forearm of 12 healthy subjects at two frequencies (30 and 100 Hz) in random order. For each frequency, stimulation was assessed at nine intensities. Measuring the induced wrist deflection across different intensities allowed us to estimate the input-output curve (IOC) of the spinal motor output. MI decreased the slope of the IOC independent of the stimulation frequency. NMES with 100 Hz vs. 30 Hz decreased the threshold of the IOC. Human-machine interfaces for neurorehabilitation that combine MI and NMES need to consider bidirectional communication and may utilize the gain modulation of spinal circuitries by applying low-intensity, high-frequency stimulation.},
}
@article {pmid33117215,
year = {2020},
author = {Zhang, L and Zhang, R and Yao, D and Shi, L and Gao, J and Hu, Y},
title = {Differences in Intersubject Early Readiness Potentials Between Voluntary and Instructed Actions.},
journal = {Frontiers in psychology},
volume = {11},
number = {},
pages = {529821},
pmid = {33117215},
issn = {1664-1078},
abstract = {Readiness potential (RP) is a slow negative electroencephalogram (EEG) potential prior to voluntary action and was first described by Kornhuber and Deecke (1965). Recent studies have demonstrated that a few subjects do not exhibit standard RP before voluntary action. In our previous study, we also found that some subjects did not show an early RP preceding instructed action. Although this phenomenon may be meaningful, no studies have yet investigated its origins. In the present study, we designed and implemented an experimental paradigm involving voluntary and instructed actions in the form of hand movements from 29 subjects with concurrent acquisition of EEGs. According to whether the subjects showed a standard RP waveform during instructed action, they were divided into the SHOW and NOSHOW group. Then, the RPs and voltage topographies were plotted for each group. Finally, the slope of each epoch at the early RP phase was estimated. We showed that early RPs were absent in 14 of 29 subjects during instructed actions. Besides, based on the slow cortical potential (SCP) sampling hypothesis, we also showed a decreased proportion in the negative potential for the NOSHOW group. Our results suggested that early RP is absent among approximately half of subjects during instructed action and that the decreased proportion of negative potential shifts may account for the absence of early RP in the NOSHOW group.},
}
@article {pmid33115813,
year = {2021},
author = {Oxley, TJ and Yoo, PE and Rind, GS and Ronayne, SM and Lee, CMS and Bird, C and Hampshire, V and Sharma, RP and Morokoff, A and Williams, DL and MacIsaac, C and Howard, ME and Irving, L and Vrljic, I and Williams, C and John, SE and Weissenborn, F and Dazenko, M and Balabanski, AH and Friedenberg, D and Burkitt, AN and Wong, YT and Drummond, KJ and Desmond, P and Weber, D and Denison, T and Hochberg, LR and Mathers, S and O'Brien, TJ and May, CN and Mocco, J and Grayden, DB and Campbell, BCV and Mitchell, P and Opie, NL},
title = {Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience.},
journal = {Journal of neurointerventional surgery},
volume = {13},
number = {2},
pages = {102-108},
pmid = {33115813},
issn = {1759-8486},
support = {MC_UU_00003/3/MRC_/Medical Research Council/United Kingdom ; },
mesh = {*Activities of Daily Living/psychology ; Aged ; *Brain-Computer Interfaces/psychology ; Feasibility Studies ; Female ; Humans ; Imaging, Three-Dimensional/methods ; *Implantable Neurostimulators ; Male ; Middle Aged ; Motor Cortex/diagnostic imaging/*physiology ; Paralysis/diagnostic imaging/physiopathology/*therapy ; Prospective Studies ; *Severity of Illness Index ; },
abstract = {BACKGROUND: Implantable brain-computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.<br><br>METHODS: Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.<br><br>RESULTS: Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%-100.00%) (trial mean (median, Q1-Q3)) at a rate of 13.81 (13.44, 10.96-16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%-100.00%) at 20.10 (17.73, 12.27-26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.<br><br>CONCLUSION: We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.},
}
@article {pmid33114646,
year = {2020},
author = {Al-Nafjan, A and Alharthi, K and Kurdi, H},
title = {Lightweight Building of an Electroencephalogram-Based Emotion Detection System.},
journal = {Brain sciences},
volume = {10},
number = {11},
pages = {},
pmid = {33114646},
issn = {2076-3425},
abstract = {Brain-computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies' practical applications in human-machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies.},
}
@article {pmid33110406,
year = {2020},
author = {Liang, CP and She, HC and Huang, LY and Chou, WC and Chen, SC and Jung, TP},
title = {Human Brain Dynamics Reflect the Correctness and Presentation Modality of Physics Concept Memory Retrieval.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {331},
pmid = {33110406},
issn = {1662-5161},
abstract = {Human memory retrieval is the core cognitive process of the human brain whenever it is processing the information. Less study has focused on exploring the neural correlates of the memory retrieval of scientific concepts when presented in word and picture modalities. Fewer studies have investigated the differences in the involved brain regions and how the brain dynamics in these regions would associate with the accuracy of the memory retrieval process. Therefore, this study specifically focused on investigating the human brain dynamics of participants when they retrieve physics concepts in word vs. pictorial modalities, and whether electroencephalogram (EEG) activities can predict the correctness of the retrieval of physics concepts. The results indicated that word modality induced a significant stronger right frontal theta augmentation than pictorial modality during the physics concepts retrieval process, whereas the picture modality induced a significantly greater right parietal alpha suppression than the word modality throughout the retrieval process spurred by the physics concept presentations. In addition, greater frontal midline theta augmentation was observed for incorrect responses than the correct responses during retrieve physics concepts. Moreover, the frontal midline theta power has greater negative predictive power for predicting the accuracy of physics concepts retrieval. In summary, the participants were more likely to retrieve physics concepts correctly if a lower amount of theta were allocated during the maintaining period from 2,000 ms through 3,500 ms before making responses. It provides insight for our future application of brain computer interface (BCI) in real-time science learning. This study implies that the lower frontal midline theta power is associated with a lower degree of cognitive control and active maintenance of representations as participants approach a correct answer.},
}
@article {pmid33109739,
year = {2021},
author = {Noordhoek, I and Treuner, K and Putter, H and Zhang, Y and Wong, J and Meershoek-Klein Kranenbarg, E and Duijm-de Carpentier, M and van de Velde, CJH and Schnabel, CA and Liefers, GJ},
title = {Breast Cancer Index Predicts Extended Endocrine Benefit to Individualize Selection of Patients with HR[+] Early-stage Breast Cancer for 10 Years of Endocrine Therapy.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {27},
number = {1},
pages = {311-319},
doi = {10.1158/1078-0432.CCR-20-2737},
pmid = {33109739},
issn = {1557-3265},
mesh = {Antineoplastic Agents, Hormonal/pharmacology/*therapeutic use ; Aromatase Inhibitors/pharmacology/*therapeutic use ; Biomarkers, Tumor/*analysis ; Breast Neoplasms/diagnosis/mortality/pathology/*therapy ; Chemotherapy, Adjuvant/methods/statistics &amp; numerical data ; Disease-Free Survival ; Drug Resistance, Neoplasm ; Female ; Homeodomain Proteins/analysis ; Humans ; Letrozole/pharmacology/therapeutic use ; Middle Aged ; Neoplasm Recurrence, Local/*epidemiology/prevention &amp; control ; Neoplasm Staging ; Patient Selection ; Prognosis ; Prospective Studies ; Receptors, Estrogen/analysis/metabolism ; Receptors, Interleukin-17/analysis ; Receptors, Progesterone/analysis/metabolism ; Retrospective Studies ; Time Factors ; },
abstract = {PURPOSE: Individualized selection of patients with early-stage hormone receptor-positive (HR[+]) breast cancer for extended endocrine therapy (EET) is required to balance modest gains in outcome with toxicities of prolonged use. This study examined the Breast Cancer Index [BCI; HOXB13/IL17BR ratio (H/I)] as a predictive biomarker of EET benefit in patients from the Investigation on the Duration of Extended Adjuvant Letrozole trial.<br><br>EXPERIMENTAL DESIGN: BCI was tested in primary tumor specimens from 908 patients randomized to receive 2.5 versus 5 years of extended letrozole. The primary endpoint was recurrence-free interval. Cox models and likelihood ratios tested the interaction between EET and BCI (H/I).<br><br>RESULTS: BCI (H/I)-high significantly predicted benefit from extended letrozole in the overall cohort [HR 0.42; 95% confidence interval (CI), 0.21-0.84; P = 0.011] and any aromatase inhibitor subset [HR 0.34; 95% CI, 0.16-0.73; P = 0.004), whereas BCI (H/I)-low patients did not derive significant benefit (HR 0.95; 95% CI, 0.58-1.56; P = 0.84 and HR 0.90; 95% CI, 0.53-1.55; P = 0.71, respectively) treatment to biomarker interaction was significant (P = 0.045, P = 0.025, respectively). BCI identified approximately 50% of patients with clinically high-risk disease that did not benefit, and with clinically low-risk disease that derived significant benefit, from an additional 2.5 years of EET.<br><br>CONCLUSIONS: BCI (H/I) predicted preferential benefit from 5 versus 2.5 years of EET and identified patients with improved outcomes from completing 10 years of adjuvant endocrine therapy. Findings expand the clinical utility of BCI (H/I) to a broader range of patients and beyond prognostic risk factors as a predictive endocrine response biomarker for early-stage HR[+] breast cancer.},
}
@article {pmid33108778,
year = {2021},
author = {Yao, L and Baker, JL and Schiff, ND and Purpura, KP and Shoaran, M},
title = {Predicting task performance from biomarkers of mental fatigue in global brain activity.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
pmid = {33108778},
issn = {1741-2552},
support = {R01 MH123634/MH/NIMH NIH HHS/United States ; R01 NS067249/NS/NINDS NIH HHS/United States ; R01 NS111019/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomarkers ; Brain ; Electrocorticography ; Humans ; *Mental Fatigue/diagnosis ; *Task Performance and Analysis ; },
abstract = {Objective.Detection and early prediction of mental fatigue (i.e. shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue.Approach.In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two healthy non-human primates (NHP) as they performed a sustained attention task over extended periods of time. We employed a set of spectrotemporal and connectivity biomarkers of the ECoG signals to identify periods of mental fatigue and a gradient boosting classifier to predict performance, up to several seconds prior to the behavioral response.Main results.Wavelet entropy and the instantaneous amplitude and frequency were among the best single features across sessions in both NHPs. The classification performance using higher order spectral-temporal (HOST) features was significantly higher than that of conventional spectral power features in both NHPs. Across the 99 sessions analyzed, average F1 scores of 77.5% ± 8.2% and 91.2% ± 3.6%, and accuracy of 79.5% ± 8.9% and 87.6% ± 3.9% for the classifier were obtained for each animal, respectively.Significance.Our results here demonstrate the feasibility of predicting performance and detecting periods of mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies.},
}
@article {pmid33108775,
year = {2020},
author = {Li, W and Ji, S and Chen, X and Kuai, B and He, J and Zhang, P and Li, Q},
title = {Multi-source domain adaptation for decoder calibration of intracortical brain-machine interface.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {},
doi = {10.1088/1741-2552/abc528},
pmid = {33108775},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Calibration ; Hand Strength ; Macaca mulatta ; Movement ; },
abstract = {Objective.For nonstationarity of neural recordings, daily retraining is required in the decoder calibration of intracortical brain-machine interfaces (iBMIs). Domain adaptation (DA) has started to be applied in iBMIs to solve the problem of daily retraining by taking advantage of historical data. However, previous DA studies used only a single source domain, which might lead to performance instability. In this study, we proposed a multi-source DA algorithm, by fully utilizing the historical data, to achieve a better and more robust decoding performance while reducing the decoder calibration time.Approach.The neural signals were recorded from two rhesus macaques using intracortical electrodes to decode the reaching and grasping movements. A principal component analysis (PCA)-based multi-source domain adaptation (PMDA) algorithm was proposed to apply the feature transfer to diminish the disparities between the target domain and each source domain. Moreover, the multiple weighted sub-classifiers based on multi-source domain data and small current sample set were constructed to accomplish the decoding.Main results.Our algorithm was able to make use of the multi-source domain data and achieve better and more robust decoding performance compared with other methods. Only a small current sample set was needed by our algorithm in order for the decoder calibration time to be effectively reduced.Significance.(1) The idea of the multi-source DA was introduced into the iBMIs to solve the problem of time consumption in the daily decoder retraining. (2) Instead of using only single-source domain data in the previous study, our algorithm made use of multi-day historical data, resulting in better and more robust decoding performance. (3) Our algorithm could be accomplished with only a small current sample set, and it can effectively reduce the decoder calibration time, which is important for further clinical applications.},
}
@article {pmid33108289,
year = {2021},
author = {Zhang, P and Li, W and Ma, X and He, J and Huang, J and Li, Q},
title = {Feature-Selection-Based Transfer Learning for Intracortical Brain-Machine Interface Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {29},
number = {},
pages = {60-73},
doi = {10.1109/TNSRE.2020.3034234},
pmid = {33108289},
issn = {1558-0210},
mesh = {Animals ; *Brain-Computer Interfaces ; Hand Strength ; Macaca mulatta ; Machine Learning ; Neurons ; },
abstract = {The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). Decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work; thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.},
}
@article {pmid33108279,
year = {2021},
author = {Wang, J and Bi, L and Fei, W and Guan, C},
title = {Decoding Single-Hand and Both-Hand Movement Directions From Noninvasive Neural Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {6},
pages = {1932-1940},
doi = {10.1109/TBME.2020.3034112},
pmid = {33108279},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Movement ; Support Vector Machine ; },
abstract = {Decoding human movement parameters from electroencephalograms (EEG) signals is of great value for human-machine collaboration. However, existing studies on hand movement direction decoding concentrate on the decoding of a single-hand movement direction from EEG signals given the opposite hand is maintained still. In practice, the cooperative movement of both hands is common. In this paper, we investigated the neural signatures and decoding of single-hand and both-hand movement directions from EEG signals. The potentials of EEG signals and power sums in the low frequency band of EEG signals from 24 channels were used as decoding features. The linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used for decoding. Experimental results showed a significant difference in the negative offset maximums of movement-related cortical potentials (MRCPs) at electrode Cz between single-hand and both-hand movements. The recognition accuracies for six-class classification, including two single-hand and four both-hand movement directions, reached 70.29%± 10.85% by using EEG potentials as features with the SVM classifier. These findings showed the feasibility of decoding single-hand and both-hand movement directions. This work can lay a foundation for the future development of an active human-machine collaboration system based on EEG signals and open a new research direction in the field of decoding hand movement parameters from EEG signals.},
}
@article {pmid33108242,
year = {2021},
author = {Buus, R and Sestak, I and Kronenwett, R and Ferree, S and Schnabel, CA and Baehner, FL and Mallon, EA and Cuzick, J and Dowsett, M},
title = {Molecular Drivers of Oncotype DX, Prosigna, EndoPredict, and the Breast Cancer Index: A TransATAC Study.},
journal = {Journal of clinical oncology : official journal of the American Society of Clinical Oncology},
volume = {39},
number = {2},
pages = {126-135},
pmid = {33108242},
issn = {1527-7755},
support = {16891/CRUK_/Cancer Research UK/United Kingdom ; 5032/CRUK_/Cancer Research UK/United Kingdom ; /DH_/Department of Health/United Kingdom ; },
mesh = {Aged ; Aged, 80 and over ; Anastrozole/therapeutic use ; Breast Neoplasms/drug therapy/*genetics/*pathology ; Female ; Humans ; Middle Aged ; Neoplasm Recurrence, Local/*genetics/*pathology ; Predictive Value of Tests ; RNA, Neoplasm/genetics ; Randomized Controlled Trials as Topic ; Risk Assessment/methods ; Tamoxifen/therapeutic use ; },
abstract = {PURPOSE: The Oncotype DX Recurrence Score (RS), Prosigna Prediction Analysis of Microarray 50 (PAM50) Risk of Recurrence (ROR), EndoPredict (EP), and Breast Cancer Index (BCI) are used clinically for estimating risk of distant recurrence for patients receiving endocrine therapy. Discordances in estimates occur between them. We aimed to identify the molecular features that drive the tests and lead to these differences.<br><br>PATIENTS AND METHODS: Analyses for RS, ROR, EP, and BCI were conducted by the manufacturers in the TransATAC sample collection that consisted of the tamoxifen or anastrozole arms of the ATAC trial. Estrogen receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative cases without chemotherapy treatment were included in which all four tests were available (n = 785). Clinicopathologic features included in some tests were excluded from the comparisons. Estrogen, proliferation, invasion, and HER2 module scores from RS were used to characterize the respective molecular features. Spearman correlation and analysis of variance tests were applied.<br><br>RESULTS: There were moderate to strong correlations among the four molecular scores (&#x3c1; = 0.63-0.74) except for RS versus ROR (&#x3c1; = 0.32) and RS versus BCI (&#x3c1; = 0.35). RS had strong negative correlation with its estrogen module (&#x3c1; = -0.79) and moderate positive correlation with its proliferation module (&#x3c1; = 0.36). RS's proliferation module explained 72.5% of ROR's variance, while the estrogen module explained only 0.6%. Most of EP's and BCI's variation was accounted for by the proliferation module (50.0% and 54.3%, respectively) and much less by the estrogen module (20.2% and 2.7%, respectively).<br><br>CONCLUSION: In contrast to common understanding, RSs are determined more strongly by estrogen-related features and only weakly by proliferation markers. However, the EP, BCI, and particularly ROR scores are determined largely by proliferative features. These relationships help to explain the differences in the prognostic performance of the tests.},
}
@article {pmid33104190,
year = {2021},
author = {Wu, Y and Lv, X and Wang, H and Qian, K and Ding, J and Wang, J and Hua, S and Sun, T and Zhou, Y and Yu, L and Qiu, S},
title = {Adaptor protein APPL1 links neuronal activity to chromatin remodeling in cultured hippocampal neurons.},
journal = {Journal of molecular cell biology},
volume = {13},
number = {5},
pages = {335-346},
pmid = {33104190},
issn = {1759-4685},
mesh = {Adaptor Proteins, Signal Transducing/*metabolism ; Animals ; Cell Line, Tumor ; Cell Nucleus/metabolism ; Chromatin Assembly and Disassembly/*physiology ; Endosomes/metabolism ; Hippocampus/*metabolism ; Male ; Mice ; Mice, Inbred C57BL ; Neuronal Plasticity/physiology ; Neurons/*metabolism ; PC12 Cells ; Rats ; Signal Transduction/physiology ; Synapses/metabolism ; },
abstract = {Local signaling events at synapses or axon terminals are communicated to the nucleus to elicit transcriptional responses, and thereby translate information about the external environment into internal neuronal representations. This retrograde signaling is critical to dendritic growth, synapse development, and neuronal plasticity. Here, we demonstrate that neuronal activity induces retrograde translocation and nuclear accumulation of endosomal adaptor APPL1. Disrupting the interaction of APPL1 with Importin α1 abolishes nuclear accumulation of APPL1, which in turn decreases the levels of histone acetylation. We further demonstrate that retrograde translocation of APPL1 is required for the regulation of gene transcription and then maintenance of hippocampal late-phase long-term potentiation. Thus, these results illustrate an APPL1-mediated pathway that contributes to the modulation of synaptic plasticity via coupling neuronal activity with chromatin remodeling.},
}
@article {pmid33103115,
year = {2020},
author = {Woods, GA and Rommelfanger, NJ and Hong, G},
title = {Bioinspired Materials for In Vivo Bioelectronic Neural Interfaces.},
journal = {Matter},
volume = {3},
number = {4},
pages = {1087-1113},
pmid = {33103115},
issn = {2590-2385},
support = {R00 AG056636/AG/NIA NIH HHS/United States ; },
abstract = {The success of in vivo neural interfaces relies on their long-term stability and large scale in interrogating and manipulating neural activity after implantation. Conventional neural probes, owing to their limited spatiotemporal resolution and scale, face challenges for studying the massive, interconnected neural network in its native state. In this review, we argue that taking inspiration from biology will unlock the next generation of in vivo bioelectronic neural interfaces. Reducing the feature sizes of bioelectronic neural interfaces to mimic those of neurons enables high spatial resolution and multiplexity. Additionally, chronic stability at the device-tissue interface is realized by matching the mechanical properties of bioelectronic neural interfaces to those of the endogenous tissue. Further, modeling the design of neural interfaces after the endogenous topology of the neural circuitry enables new insights into the connectivity and dynamics of the brain. Lastly, functionalization of neural probe surfaces with coatings inspired by biology leads to enhanced tissue acceptance over extended timescales. Bioinspired neural interfaces will facilitate future developments in neuroscience studies and neurological treatments by leveraging bidirectional information transfer and integrating neuromorphic computing elements.},
}
@article {pmid33101533,
year = {2020},
author = {Shen, F and Dai, G and Lin, G and Zhang, J and Kong, W and Zeng, H},
title = {EEG-based emotion recognition using 4D convolutional recurrent neural network.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {6},
pages = {815-828},
pmid = {33101533},
issn = {1871-4080},
abstract = {In this paper, we present a novel method, called four-dimensional convolutional recurrent neural network, which integrating frequency, spatial and temporal information of multichannel EEG signals explicitly to improve EEG-based emotion recognition accuracy. First, to maintain these three kinds of information of EEG, we transform the differential entropy features from different channels into 4D structures to train the deep model. Then, we introduce CRNN model, which is combined by convolutional neural network (CNN) and recurrent neural network with long short term memory (LSTM) cell. CNN is used to learn frequency and spatial information from each temporal slice of 4D inputs, and LSTM is used to extract temporal dependence from CNN outputs. The output of the last node of LSTM performs classification. Our model achieves state-of-the-art performance both on SEED and DEAP datasets under intra-subject splitting. The experimental results demonstrate the effectiveness of integrating frequency, spatial and temporal information of EEG for emotion recognition.},
}
@article {pmid33101403,
year = {2020},
author = {Ma, Y and Li, X and Duan, X and Peng, Y and Zhang, Y},
title = {Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {8822407},
pmid = {33101403},
issn = {1687-5273},
mesh = {Algorithms ; *Goals ; Image Processing, Computer-Assisted ; *Retinal Vessels/diagnostic imaging ; },
abstract = {PURPOSE: Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges.<br><br>METHODS: This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets.<br><br>RESULTS: The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%&#x223c;2% compared with the performance of the corresponding intradataset.<br><br>CONCLUSION: All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.},
}
@article {pmid33101229,
year = {2020},
author = {Ambroa, A and Blasco, L and López-Causapé, C and Trastoy, R and Fernandez-García, L and Bleriot, I and Ponce-Alonso, M and Pacios, O and López, M and Cantón, R and Kidd, TJ and Bou, G and Oliver, A and Tomás, M},
title = {Temperate Bacteriophages (Prophages) in Pseudomonas aeruginosa Isolates Belonging to the International Cystic Fibrosis Clone (CC274).},
journal = {Frontiers in microbiology},
volume = {11},
number = {},
pages = {556706},
pmid = {33101229},
issn = {1664-302X},
abstract = {Bacteriophages are important in bacterial ecology and evolution. Pseudomonas aeruginosa is the most prevalent bacterial pathogen in chronic bronchopulmonary infection in cystic fibrosis (CF). In this study, we used bioinformatics, microbiological and microscopy techniques to analyze the bacteriophages present in 24 P. aeruginosa isolates belonging to the international CF clone (ST274-CC274). Interestingly, we detected the presence of five members of the Inoviridae family of prophages (Pf1, Pf4, Pf5, Pf6, Pf7), which have previously been observed in P. aeruginosa. In addition, we identified a new filamentous prophage, designated Pf8, in the P. aeruginosa AUS411.500 isolate belonging to the international CF clone. We detected only one prophage, never previously described, from the family Siphoviridiae (with 66 proteins and displaying homology with PHAGE_Pseudo_phi297_NC_016762). This prophage was isolated from the P. aeruginosa AUS531 isolate carrying a new gene which is implicated in the phage infection ability, named Bacteriophage Control Infection (bci). We characterized the role of the Bci protein in bacteriophage infection and in regulating the host Quorum Sensing (QS) system, motility and biofilm and pyocyanin production in the P. aeruginosa isogenic mutant AUS531Δbci isolate. The findings may be relevant for the identification of targets in the development of new strategies to control P. aeruginosa infections, particularly in CF patients.},
}
@article {pmid33100996,
year = {2020},
author = {Zhang, R and Zhang, L and Guo, Y and Shi, L and Gao, J and Wang, X and Hu, Y},
title = {Effects of High-Definition Transcranial Direct-Current Stimulation on Resting-State Functional Connectivity in Patients With Disorders of Consciousness.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {560586},
pmid = {33100996},
issn = {1662-5161},
abstract = {Recently a positive treatment effect on disorders of consciousness (DOCs) with high-definition transcranial direct-current stimulation (HD-tDCS) has been reported; however, the neural modulation mechanisms of this treatment's efficacy need further investigation. To better understand the processing of HD-tDCS interventions, a long-lasting HD-tDCS protocol was applied to 15 unresponsive wakefulness syndrome (UWS) patients and 20 minimally conscious states (MCS) patients in this study. Ten minutes of resting-state electroencephalograms (EEGs) were recorded from the patients, and the coma recovery scale-revised scores (CRS-Rs) were assessed for each patient from four time-points (T0, T1, T2, and T3). Brain networks were constructed by calculating the EEG spectral connectivity using the debiased weighted phase lag index (dwPLI) and then quantified the network information transmission efficiency by graph theory. We found that there was an increasing trend in local and global information processing of beta and gamma bands in resting-state functional brain networks during the 14 days of HD-tDCS modulation for MCS patients. Furthermore, the increased functional connectivity not only occurred in the local brain area surrounding the stimulation position but was also present across more global brain areas. Our results suggest that long-lasting HD-tDCS on the precuneus may facilitate information processing among neural populations in MCS patients.},
}
@article {pmid33100985,
year = {2020},
author = {Lun, X and Yu, Z and Chen, T and Wang, F and Hou, Y},
title = {A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {338},
pmid = {33100985},
issn = {1662-5161},
abstract = {A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.},
}
@article {pmid33100959,
year = {2020},
author = {Simões, M and Borra, D and Santamaría-Vázquez, E and , and Bittencourt-Villalpando, M and Krzemiński, D and Miladinović, A and , and Schmid, T and Zhao, H and Amaral, C and Direito, B and Henriques, J and Carvalho, P and Castelo-Branco, M},
title = {BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {568104},
pmid = {33100959},
issn = {1662-4548},
abstract = {There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.},
}
@article {pmid33100953,
year = {2020},
author = {Roy, S and Chowdhury, A and McCreadie, K and Prasad, G},
title = {Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {918},
pmid = {33100953},
issn = {1662-4548},
abstract = {Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to determine whether it is feasible for the inter-subject continuous decoding of MI signals to provide contingent neurofeedback which is important for neurorehabilitative BCI designs. In this paper, we have shown how a convolutional neural network (CNN) based deep learning framework can be used for inter-subject continuous decoding of MI related electroencephalographic (EEG) signals using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities. These Mega Blocks have the capacity to repeat a specific architectural block several times such as one or more convolutional layers in a single Mega Block. The parameters of such Mega Blocks can be optimized using Bayesian hyperparameter optimization. The results, obtained on the publicly available BCI competition IV-2b dataset, yields an average inter-subject continuous decoding accuracy of 71.49% (κ = 0.42) and 70.84% (κ = 0.42) for two different training methods such as adaptive moment estimation (Adam) and stochastic gradient descent (SGDM), respectively, in 7 out of 9 subjects. Our results show for the first time that it is feasible to use CNN based architectures for inter-subject continuous decoding with a sufficient level of accuracy for developing calibration-free MI-BCIs for practical purposes.},
}
@article {pmid33100387,
year = {2020},
author = {Reilly, CM},
title = {Brain-Machine Interfaces as Commodities: Exchanging Mind for Matter.},
journal = {The Linacre quarterly},
volume = {87},
number = {4},
pages = {387-398},
pmid = {33100387},
issn = {0024-3639},
abstract = {UNLABELLED: Brain-machine interfaces (BMIs), which enable a two-way flow of signals, information, and directions between human neurons and computerized machines, offer spectacular opportunities for therapeutic and consumer applications, but they also present unique dangers to the safety, privacy, psychological health, and spiritual well-being of their users. The sale of these devices as commodities for profit exacerbates such issues and may subject the user to an unequal exchange with corporations. Catholic healthcare professionals and bioethicists should be especially concerned about the implications for the essential dignity of the persons using the new BMIs.<br><br>SUMMARY: The commercial sale of brain-machine interfaces (BMIs) generates and exacerbates problems for end-users' safety, psychological health, and spiritual well-being.},
}
@article {pmid33097634,
year = {2020},
author = {Errington, SP and Woodman, GF and Schall, JD},
title = {Dissociation of Medial Frontal β-Bursts and Executive Control.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {40},
number = {48},
pages = {9272-9282},
pmid = {33097634},
issn = {1529-2401},
support = {U54 HD083211/HD/NICHD NIH HHS/United States ; R01 MH055806/MH/NIMH NIH HHS/United States ; P30 EY008126/EY/NEI NIH HHS/United States ; R01 EY019882/EY/NEI NIH HHS/United States ; P50 HD103537/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Beta Rhythm/*physiology ; Electroencephalography ; Executive Function/*physiology ; Female ; Frontal Lobe/*physiology ; Macaca mulatta ; Macaca radiata ; Male ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Saccades/physiology ; Sensorimotor Cortex/physiology ; },
abstract = {The neural mechanisms of executive and motor control concern both basic researchers and clinicians. In human studies, preparation and cancellation of movements are accompanied by changes in the β-frequency band (15-29 Hz) of electroencephalogram (EEG). Previous studies with human participants performing stop signal (countermanding) tasks have described reduced frequency of transient β-bursts over sensorimotor cortical areas before movement initiation and increased β-bursting over medial frontal areas with movement cancellation. This modulation has been interpreted as contributing to the trial-by-trial control of behavior. We performed identical analyses of EEG recorded over the frontal lobe of macaque monkeys (one male, one female) performing a saccade countermanding task. While we replicate the occurrence and modulation of β-bursts associated with initiation and cancellation of saccades, we found that β-bursts occur too infrequently to account for the observed stopping behavior. We also found β-bursts were more common after errors, but their incidence was unrelated to response time (RT) adaptation. These results demonstrate the homology of this EEG signature between humans and macaques but raise questions about the current interpretation of β band functional significance.SIGNIFICANCE STATEMENT The finding of increased β-bursting over medial frontal cortex with movement cancellation in humans is difficult to reconcile with the finding of modulation too late to contribute to movement cancellation in medial frontal cortex of macaque monkeys. To obtain comparable measurement scales, we recorded electroencephalogram (EEG) over medial frontal cortex of macaques performing a stop signal (countermanding) task. We replicated the occurrence and modulation of β-bursts associated with the cancellation of movements, but we found that β-bursts occur too infrequently to account for observed stopping behavior. Unfortunately, this finding raises doubts whether β-bursts can be a causal mechanism of response inhibition, which impacts future applications in devices such as brain-machine interfaces.},
}
@article {pmid33097536,
year = {2020},
author = {Aflalo, T and Zhang, CY and Rosario, ER and Pouratian, N and Orban, GA and Andersen, RA},
title = {A shared neural substrate for action verbs and observed actions in human posterior parietal cortex.},
journal = {Science advances},
volume = {6},
number = {43},
pages = {},
pmid = {33097536},
issn = {2375-2548},
support = {323606/ERC_/European Research Council/International ; P50 MH094258/MH/NIMH NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {*Brain Mapping/methods ; Humans ; Language ; Magnetic Resonance Imaging ; *Motor Cortex/physiology ; Parietal Lobe/physiology ; },
abstract = {High-level sensory and motor cortical areas are activated when processing the meaning of language, but it is unknown whether, and how, words share a neural substrate with corresponding sensorimotor representations. We recorded from single neurons in human posterior parietal cortex (PPC) while participants viewed action verbs and corresponding action videos from multiple views. We find that PPC neurons exhibit a common neural substrate for action verbs and observed actions. Further, videos were encoded with mixtures of invariant and idiosyncratic responses across views. Action verbs elicited selective responses from a fraction of these invariant and idiosyncratic neurons, without preference, thus associating with a statistical sampling of the diverse sensory representations related to the corresponding action concept. Controls indicated that the results are not the product of visual imagery or arbitrary learned associations. Our results suggest that language may activate the consolidated visual experience of the reader.},
}
@article {pmid33094074,
year = {2020},
author = {Biondi, NL and Bhandari, M and Bhyan, P},
title = {Transient Right Bundle Branch Block Resulting From a Blunt Cardiac Injury During a Motor Vehicle Accident.},
journal = {Cureus},
volume = {12},
number = {9},
pages = {e10534},
pmid = {33094074},
issn = {2168-8184},
abstract = {Blunt chest trauma (BCT) has become increasingly more prevalent in recent years. As a result, the incidence of blunt cardiac injury (BCI), or cardiac or myocardial contusion, has also increased. The sequelae of BCI often are undiagnosed due to variability in the clinical presentation. This case highlights a transient right bundle branch block (RBBB) following a motor vehicle accident (MVA), resulting in BCI. Right-sided cardiac injuries predominate BCI owing to the anterior location of the right ventricle within the thoracic cage; however, the pathophysiologic mechanisms underlying the electrocardiographic manifestations are vaguely understood. In this case, a 66-year-old female sustained a BCI resulting in a transient RBBB. The patient fully recovered following a three-day hospitalization with complete recovery of normal cardiac conduction.},
}
@article {pmid33093669,
year = {2020},
author = {},
title = {The painstaking pace of bioelectronic interfaces.},
journal = {Nature biomedical engineering},
volume = {4},
number = {10},
pages = {933-934},
doi = {10.1038/s41551-020-00639-z},
pmid = {33093669},
issn = {2157-846X},
mesh = {Animals ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Humans ; *Prostheses and Implants ; Robotics ; Wearable Electronic Devices ; },
}
@article {pmid33093668,
year = {2020},
author = {Slutzky, MW},
title = {Increasing power efficiency.},
journal = {Nature biomedical engineering},
volume = {4},
number = {10},
pages = {937-938},
pmid = {33093668},
issn = {2157-846X},
mesh = {*Brain-Computer Interfaces ; },
}
@article {pmid33092554,
year = {2020},
author = {Kruse, A and Suica, Z and Taeymans, J and Schuster-Amft, C},
title = {Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis.},
journal = {BMC neurology},
volume = {20},
number = {1},
pages = {385},
pmid = {33092554},
issn = {1471-2377},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagination ; Middle Aged ; *Recovery of Function ; Stroke/physiopathology ; Stroke Rehabilitation/*methods ; },
abstract = {BACKGROUND: Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke.<br><br>METHODS: A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size.<br><br>RESULTS: In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/-&#x2009;5.8; mean time since stroke onset 15.7+/-&#x2009;18.2&#x2009;months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: -&#x2009;0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate.<br><br>CONCLUSION: This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects.<br><br>TRIAL REGISTRATION: PROSPERO registration: CRD42018105832 .},
}
@article {pmid33085573,
year = {2022},
author = {Hashimoto, Y and Kakui, T and Ushiba, J and Liu, M and Kamada, K and Ota, T},
title = {Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: A feasibility study.},
journal = {Assistive technology : the official journal of RESNA},
volume = {34},
number = {4},
pages = {402-410},
doi = {10.1080/10400435.2020.1836067},
pmid = {33085573},
issn = {1949-3614},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Feasibility Studies ; Humans ; Inpatients ; *Stroke/therapy ; *Stroke Rehabilitation/methods ; },
abstract = {The feasibility and safety of brain-computer interface (BCI) systems for patients with acute/subacute stroke have not been established. The aim of this study was to firstly demonstrate the feasibility and safety of a bedside BCI system for inpatients with acute/subacute stroke in a small cohort of inpatients. Four inpatients with early-phase hemiplegic stroke (7-24 days from stroke onset) participated in this study. The portable BCI system showed real-time feedback of sensorimotor rhythms extracted from scalp electroencephalograms (EEGs). Patients attempted to extend the wrist on their affected side, and neuromuscular electrical stimulation was applied only when the system detected significant movement intention-related changes in EEG. Between 120 and 200 training trials per patient were successfully and safely conducted at the bedside over 2-4 days. Our results clearly indicate that the proposed bedside BCI system is feasible and safe. Larger clinical studies are needed to determine the clinical efficacy of the system and its effect size in the population of patients with acute/subacute post-stroke hemiplegia.},
}
@article {pmid33080842,
year = {2020},
author = {Annen, J and Mertel, I and Xu, R and Chatelle, C and Lesenfants, D and Ortner, R and Bonin, EAC and Guger, C and Laureys, S and Müller, F},
title = {Auditory and Somatosensory P3 Are Complementary for the Assessment of Patients with Disorders of Consciousness.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {33080842},
issn = {2076-3425},
support = {-//FNRS/ ; 945539//HBP/ ; PRODEX//BELSPO/ ; EU-H2020-MSCA-RISE-778234//DOCMA/ ; H2020-MSCA-IF-2016-MoveAGAIN-752620/MCCC_/Marie Curie/United Kingdom ; H2020-MSCA-IF-2016-ADOC-752686/MCCC_/Marie Curie/United Kingdom ; },
abstract = {The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awareness. Some patients, although with remaining cognitive abilities, are not able to exhibit overt voluntary responses at the bedside and may be misdiagnosed as UWS. Several studies investigated functional neuroimaging and neurophysiology as an additional tool to evaluate the level of consciousness and to detect covert command following in DOC. Most of these studies are based on auditory stimulation, neglecting patients suffering from decreased or absent hearing abilities. In the present study, we aim to assess the response to a P3-based paradigm in 40 patients with DOC and 12 healthy participants using auditory (AEP) and vibrotactile (VTP) stimulation. To this end, an EEG-based brain-computer interface was used at DOC patient's bedside. We compared the significance of the P3 performance (i.e., the interpretation of significance of the evoked P3 response) as obtained by 'direct processing' (i.e., theoretical-based significance threshold) and 'offline processing' (i.e., permutation-based single subject level threshold). We evaluated whether the P3 performances were dependent on clinical variables such as diagnosis (UWS and MCS), aetiology and time since injury. Last we tested the dependency of AEP and VTP performances at the single subject level. Direct processing tends to overestimate P3 performance. We did not find any difference in the presence of a P3 performance according to the level of consciousness (UWS vs. MCS) or the aetiology (traumatic vs. non-traumatic brain injury). The performance achieved at the AEP paradigm was independent from what was achieved at the VTP paradigm, indicating that some patients performed better on the AEP task while others performed better on the VTP task. Our results support the importance of using multimodal approaches in the assessment of DOC patients in order to optimise the evaluation of patient's abilities.},
}
@article {pmid33076556,
year = {2020},
author = {Pavlov, AN and Pitsik, EN and Frolov, NS and Badarin, A and Pavlova, ON and Hramov, AE},
title = {Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {20},
pages = {},
pmid = {33076556},
issn = {1424-8220},
support = {17-72-30003//Russian Science Foundation/ ; },
mesh = {Aged ; *Electroencephalography ; Humans ; },
abstract = {The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain-computer interfaces.},
}
@article {pmid33075762,
year = {2021},
author = {Eles, JR and Stieger, KC and Kozai, TDY},
title = {The temporal pattern of intracortical microstimulation pulses elicits distinct temporal and spatial recruitment of cortical neuropil and neurons.},
journal = {Journal of neural engineering},
volume = {18},
number = {1},
pages = {},
pmid = {33075762},
issn = {1741-2552},
support = {R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R01 NS115707/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/methods ; Glutamic Acid ; Mice ; Microelectrodes ; *Neurons/physiology ; *Neuropil ; },
abstract = {Objective.The temporal spacing or distribution of stimulation pulses in therapeutic neurostimulation waveforms-referred to here as the Temporal Pattern (TP)-has emerged as an important parameter for tuning the response to deep-brain stimulation and intracortical microstimulation (ICMS). While it has long been assumed that modulating the TP of ICMS may be effective by altering the rate coding of the neural response, it is unclear how it alters the neural response at the network level. The present study is designed to elucidate the neural response to TP at the network level.Approach. We usein vivotwo-photon imaging of mice expressing the calcium sensorThy1-GCaMP or the glutamate sensorhSyn-iGluSnFr to examine the layer II/III neural response to ICMS with different TPs. We study the neuronal calcium and glutamate response to TPs with the same average frequency (10 Hz) and same total charge injection, but varying degrees of bursting. We also investigate one control pattern with an average frequency of 100 Hz and 10X the charge injection.Main Results. Stimulation trains with the same average frequency and same total charge injection but distinct TPs recruit distinct sets of neurons. More than half (60% of 309 cells) of neurons prefer one TP over the other. Despite their distinct spatial recruitment patterns, cells exhibit similar ability to follow 30 s trains of both TPs without failing, and they exhibit similar levels of glutamate release during stimulation. Both neuronal calcium and glutamate release entrain to the bursting TP pattern, with a ∼21-fold increase in relative power at the frequency of bursting. Bursting also results in a statistically significant elevation in the correlation between somatic calcium activity and neuropil activity, which we explore as a metric for inhibitory-excitatory tone. Interestingly, soma-neuropil correlation during the bursting pattern is a statistically significant predictor of cell preference for TP, which exposes a key link between TP and inhibitory-excitatory tone. Finally, using mesoscale imaging, we show that both TPs result in distal inhibition during stimulation, which reveals complex spatial and temporal interactions between TP and inhibitory-excitatory tone in ICMS.Significance. Our results may ultimately suggest that TP is a valuable parameter space to modulate inhibitory-excitatory tone and to recruit distinct network activity in ICMS. This presents a broader mechanism of action than rate coding, as previously thought. By implicating these additional mechanisms, TP may have broader utility in the clinic and should be pursued to expand the efficacy of ICMS therapies.},
}
@article {pmid33075211,
year = {2020},
author = {López-Pérez, JE and Meylan, PA and Goessling, JM},
title = {Sex-based trade-offs in the innate and acquired immune systems of Sternotherus minor.},
journal = {Journal of experimental zoology. Part A, Ecological and integrative physiology},
volume = {333},
number = {10},
pages = {820-828},
doi = {10.1002/jez.2424},
pmid = {33075211},
issn = {2471-5646},
mesh = {Adaptive Immunity/*immunology ; Animals ; Blood Bactericidal Activity ; Female ; Hemagglutination Tests ; Immunity, Innate/*immunology ; Leukocyte Count ; Longevity/immunology ; Male ; Sex Factors ; Testosterone/blood ; Turtles/*immunology ; },
abstract = {Longevity patterns in most vertebrates suggest that females benefit most from maintenance investment. A reversed longevity pattern in loggerhead musk turtles (Sternotherus minor) allowed us to test trade-offs between maintenance and survivorship. We tested the hypothesis that the sex with greater longevity has greater maintenance than the sex with shorter longevity. We also compared the following parameters between sexes: Bactericidal ability (BA) and heterophil:lymphocyte ratios (HLR). Baseline blood samples were collected from turtles in the field; a subset of turtles was returned to a laboratory for experiments of acquired immune responses to sheep red blood cells (SRBC). We found no support for the original hypothesis of reversal in sex-dependent immune trade-offs (difference between sex SRBC titers: p = .102; interaction between treatment and sex: p = .177; difference between treatments: p < .001; effect of sex on BA: p = .830; effect of sex on HLR: p = .717). However, we did find support for sex-dependent differences in immunity in the relationship between HLR and body condition (BCI) (effect of BCI on HLR: p = .015). In field conditions, we found that males with higher body condition indices express stressed phenotypes more than males with lower body condition indices (p = .002). However, females expressed similar stress loads across all body conditions (p = .900). Testosterone concentrations were assayed in free-living turtles and were not related to any of the immune parameters. Our results suggest that the immune systems play an important role in balancing sex-specific responses to different selective pressures in S. minor.},
}
@article {pmid33074461,
year = {2021},
author = {Abdelhakim, MA and Rammah, A and Abozamel, AH and El-Sheikh, MG and Abdelazeem, MS and Abdallah, SM and Abdelaziz, AY},
title = {Does detrusor underactivity affect the results of transurethral resection of prostate?.},
journal = {International urology and nephrology},
volume = {53},
number = {2},
pages = {199-204},
pmid = {33074461},
issn = {1573-2584},
mesh = {Aged ; Humans ; Male ; Middle Aged ; Postoperative Complications/epidemiology ; Prospective Studies ; Prostatic Hyperplasia/*complications/*surgery ; *Transurethral Resection of Prostate ; Treatment Outcome ; Urinary Bladder, Underactive/*complications ; Urinary Retention/epidemiology ; },
abstract = {PURPOSE: We aimed to evaluate the outcome of transurethral resection of the prostate (TURP) in patients with benign prostatic hyperplasia (BPH) and diagnosed to have weak detrusor contractility by urodynamic study.<br><br>METHODS: A prospective study of 32 male patients had BPH candidate for TURP diagnosed to have impaired detrusor contractility by preoperative urodynamic study. We studied the postoperative outcome after TURP regarding international prostate symptoms score (IPSS), maximum flow rate (Qmax), post-voiding residual urine (PVR), the patients need for catheter, and urodynamic pressure flow study (PFS) parameters (maximum detrusor contractility, bladder contractility index (BCI), maximum bladder capacity and compliance) after 6&#xa0;month follow-up.<br><br>RESULTS: Twenty-one cases presented with urethral catheter because of chronic or refractory retention. Twenty patients voided preoperatively during PFS with mean detrusor pressure (Pdet) at Qmax 23.97&#x2009;&#xb1;&#x2009;25.54 cmH2O and the mean BCI was 51.04&#x2009;&#xb1;&#x2009;23.86, while twelve patients did not void with mean maximum Pdet 21.75&#x2009;&#xb1;&#x2009;7.34. After 6&#xa0;month follow-up, there was significant improvement in IPSS, Qmax, and detrusor contractility (Pdet at Qmax and BCI) postoperatively in all patients, and there was no significant postoperative improvement of post-voiding residual urine (p value 0.92). Finally, 11 patients voided normally without RU, 7 patients needed timed triple voiding with crede maneuver and small RU, and 14 patients needed CIC.<br><br>CONCLUSIONS: There were significant improvements in IPSS, detrusor contractility, and urine flow after TURP in patients with BPH and weak bladder contractility, although the risk of postoperative urine retention in approximately 43% of cases and needed CIC.},
}
@article {pmid33074201,
year = {2021},
author = {Zhang, Y and Gao, Q and Song, Y and Wang, Z},
title = {Implementation of an SSVEP-based intelligent home service robot system.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {29},
number = {3},
pages = {541-556},
doi = {10.3233/THC-202442},
pmid = {33074201},
issn = {1878-7401},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; *Robotics ; },
abstract = {BACKGROUND: People with severe neuromuscular disorders caused by an accident or congenital disease cannot normally interact with the physical environment. The intelligent robot technology offers the possibility to solve this problem. However, the robot can hardly carry out the task without understanding the subject's intention as it relays on speech or gestures. Brain-computer interface (BCI), a communication system that operates external devices by directly converting brain activity into digital signals, provides a solution for this.<br><br>OBJECTIVE: In this study, a noninvasive BCI-based humanoid robotic system was designed and implemented for home service.<br><br>METHODS: A humanoid robot that is equipped with multi-sensors navigates to the object placement area under the guidance of a specific symbol "Naomark", which has a unique ID, and then sends the information of the scanned object back to the user interface. Based on this information, the subject gives commands to the robot to grab the wanted object and give it to the subject. To identify the subject's intention, the channel projection-based canonical correlation analysis (CP-CCA) method was utilized for the steady state visual evoked potential-based BCI system.<br><br>RESULTS: The offline results showed that the average classification accuracy of all subjects reached 90%, and the online task completion rate was over 95%.<br><br>CONCLUSION: Users can complete the grab task with minimum commands, avoiding the control burden caused by complex commands. This would provide a useful assistance means for people with severe motor impairment in their daily life.},
}
@article {pmid33070523,
year = {2020},
author = {Wang, H and Wu, J and Fang, K and Cai, L and Wang, LS and Dai, ZD},
title = {Application of robo-pigeon in ethological studies of bird flocks.},
journal = {Journal of integrative neuroscience},
volume = {19},
number = {3},
pages = {443-448},
doi = {10.31083/j.jin.2020.03.159},
pmid = {33070523},
issn = {0219-6352},
support = {61973159//National Natural Science Foundation of China/ ; 61375096//National Natural Science Foundation of China/ ; 31500858//National Natural Science Foundation of China/ ; NJ2019015//Jiangsu Provincial Key Laboratory of Bionic Functional Materials/ ; },
mesh = {Animals ; *Behavior, Animal ; Brain/*physiology ; Columbidae ; Electric Stimulation ; Ethology ; *Flight, Animal ; Hierarchy, Social ; },
abstract = {Birds flying collectively is a fascinating phenomenon in nature, which is central in ethological studies. Owing to the difficulty of introducing controlled variables into a natural bird flock, current animal behavior paradigms limit our understanding of collective behavior and mechanism. The recently developed technology of robo-pigeon, which allows behavior regulation over organisms through brain microstimulation, can potentially serve to design the controlled variables. However, it still poses challenges for unrestrained animals outdoors. Here we report the first application of robo-pigeon to the study of collective behavior, illustrating how intact pigeons in a flock interact with a program-controlled robo-pigeon. The controlled variable of direction manipulation introduced by the robo-pigeon may balance their preferred directional choice in the flock. Its effectivity depends on the hierarchical level to which the robo-pigeon belongs. This study suggests that direct manipulation of flight trajectories by a robo-pigeon might be a useful causal tool to study the collective behavior of bird flocks.},
}
@article {pmid33068331,
year = {2021},
author = {Amin, N and Soulby, AJ and Borsetto, D and Pai, I},
title = {Longitudinal economic analysis of Bonebridge 601 versus percutaneous bone-anchored hearing devices over a 5-year follow-up period.},
journal = {Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology &amp; Cervico-Facial Surgery},
volume = {46},
number = {1},
pages = {263-272},
doi = {10.1111/coa.13659},
pmid = {33068331},
issn = {1749-4486},
mesh = {Adult ; Aged ; *Bone Conduction ; Female ; Follow-Up Studies ; Hearing Aids/*economics ; Hearing Loss, Conductive/economics/*therapy ; Hearing Loss, Mixed Conductive-Sensorineural/economics/*therapy ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; Patient Reported Outcome Measures ; Prosthesis Design ; Time Factors ; },
abstract = {OBJECTIVES: Percutaneous bone-anchored hearing devices (pBAHDs) are the most commonly used bone conduction implants (BCI). Concerns surround the long-term complications, notably skin-related, in patients with percutaneous abutments. The active transcutaneous BCI Bonebridge system can help avoid some of these pitfalls but is often considered a second-line option due to various factors including perceived increased overall costs.<br><br>DESIGN: Longitudinal economic analysis of Bonebridge BCI 601 versus pBAHD over a 5-year follow-up period.<br><br>SETTING: A specialist hearing implant centre.<br><br>PARTICIPANTS: Adult patients (&#x2265;16&#xa0;years) with conductive hearing loss, mixed hearing loss or single-sided deafness, who received a Bonebridge or pBAHD implant between 1/7/2013 and 1/12/2018 with a minimum 12-month follow-up.<br><br>MAIN OUTCOME MEASURES: We compared the mean costs per implanted patient for both implants at 1, 3 and 5&#xa0;years postoperative time points. Clinical effectiveness was evaluated using objective and patient-reported outcome measures.<br><br>RESULTS: The mean total cost per patient of Bonebridge was significantly higher than pBAHD at 1-year post-implantation (&#xa3;8512 standard deviation [SD] &#xa3;715 vs &#xa3;5590 SD &#xa3;1394, P&#xa0;<&#xa0;.001); however, by 5-years post-implantation this difference was no longer statistically significant (&#xa3;12&#xa0;453 SD &#xa3;2159 vs &#xa3;12&#xa0;575 SD &#xa3;3854, P&#xa0;>&#xa0;.05). The overall cost convergence was mainly accounted for by the increased long-term complications, revision surgery rates and higher cost of the pBAHD external processor compared to Bonebridge.<br><br>CONCLUSIONS: Long-term costs of Bonebridge to healthcare providers are comparable to pBAHDs, whilst offering lower complication rates, comparable audiological benefit and patient satisfaction. Bonebridge should be considered as a first-line BCI option in appropriate cases.},
}
@article {pmid33068286,
year = {2021},
author = {Jia, X and Gao, Z and Hu, H},
title = {Microglia in depression: current perspectives.},
journal = {Science China. Life sciences},
volume = {64},
number = {6},
pages = {911-925},
pmid = {33068286},
issn = {1869-1889},
mesh = {Animals ; Antidepressive Agents/*therapeutic use ; Depressive Disorder, Major/*drug therapy/*pathology ; Humans ; Microglia/*drug effects/*pathology ; },
abstract = {Major depressive disorder (MDD) is a prevalent psychiatric disease that involves malfunctions of different cell types in the brain. Accumulating studies started to reveal that microglia, the primary resident immune cells, play an important role in the development and progression of depression. Microglia respond to stress-triggered neuroinflammation, and through the release of proinflammatory cytokines and their metabolic products, microglia may modulate the function of neurons and astrocytes to regulate depression. In this review, we focused on the role of microglia in the etiology of depression. We discussed the dynamic states of microglia; the correlative and causal evidence of microglial abnormalities in depression; possible mechanisms of how microglia sense depression-related stress and modulate depression state; and how antidepressive therapies affect microglia. Understanding the role of microglia in depression may shed light on developing new treatment strategies to fight against this devastating mental illness.},
}
@article {pmid33066374,
year = {2020},
author = {Mowla, MR and Gonzalez-Morales, JD and Rico-Martinez, J and Ulichnie, DA and Thompson, DE},
title = {A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {33066374},
issn = {2076-3425},
support = {1910526//National Science Foundation/ ; },
abstract = {P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.},
}
@article {pmid33066109,
year = {2020},
author = {Paszkiel, S and Dobrakowski, P and Łysiak, A},
title = {The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {33066109},
issn = {2076-3425},
abstract = {Everyone experiences stress at certain times in their lives. This feeling can motivate, however, if it persists for a prolonged period, it leads to negative changes in the human body. Stress is characterized, among other things, by increased blood pressure, increased pulse and decreased alpha-frequency brainwave activity. An overview of the literature indicates that music therapy can be an effective and inexpensive method of improving these factors. The objective of this study was to analyze the impact of various types of music on stress level in subjects. The conducted experiment involved nine females, aged 22. All participants were healthy and did not have any neurological or psychiatric disorders. The test included four types of audio stimuli: silence (control sample), rap, relaxing music and music triggering an autonomous sensory meridian response (ASMR) phenomenon. The impact of individual sound types was assessed using data obtained from four sources: a fourteen-channel electroencephalograph, a blood pressure monitor, a pulsometer and participant's subjective stress perception. The conclusions from the conducted study indicate that rap music negatively affects the reduction of stress level compared to the control group (p < 0.05), whereas relaxing music and ASMR calms subjects much faster than silence (p < 0.05).},
}
@article {pmid33062613,
year = {2020},
author = {Aghdam, NS and Moradi, MH and Shamsollahi, MB and Nasrabadi, AM and Setarehdan, SK and Shalchyan, V and Faradji, F and Makkiabadi, B},
title = {The 2017 and 2018 Iranian Brain-Computer Interface Competitions.},
journal = {Journal of medical signals and sensors},
volume = {10},
number = {3},
pages = {208-216},
pmid = {33062613},
issn = {2228-7477},
abstract = {This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.},
}
@article {pmid33062042,
year = {2020},
author = {Yao, Y and Ding, Y and Zhong, S and Cui, Z},
title = {EEG-Based Epilepsy Recognition via Multiple Kernel Learning.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {7980249},
pmid = {33062042},
issn = {1748-6718},
mesh = {*Algorithms ; Computational Biology ; Diagnosis, Computer-Assisted/methods/statistics &amp; numerical data ; Electroencephalography/*methods/statistics &amp; numerical data ; Epilepsy/*diagnosis ; Humans ; Least-Squares Analysis ; *Machine Learning ; Mathematical Concepts ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least squares support vector machine based on multikernel learning is proposed and applied to the recognition of epilepsy abnormal signals. The algorithm uses the style conversion matrix to represent the style information contained in the sample, regularizes it in the objective function, optimizes the objective function through the commonly used alternative optimization method, and simultaneously updates the style conversion matrix and classifier during the iteration process parameter. In order to use the learned style information in the prediction process, two new rules are added to the traditional prediction method, and the style conversion matrix is used to standardize the sample style before classification.},
}
@article {pmid33061899,
year = {2020},
author = {Bronte-Stewart, HM and Petrucci, MN and O'Day, JJ and Afzal, MF and Parker, JE and Kehnemouyi, YM and Wilkins, KB and Orthlieb, GC and Hoffman, SL},
title = {Perspective: Evolution of Control Variables and Policies for Closed-Loop Deep Brain Stimulation for Parkinson's Disease Using Bidirectional Deep-Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {353},
pmid = {33061899},
issn = {1662-5161},
support = {R21 NS096398/NS/NINDS NIH HHS/United States ; UH3 NS107709/NS/NINDS NIH HHS/United States ; },
abstract = {A deep brain stimulation system capable of closed-loop neuromodulation is a type of bidirectional deep brain-computer interface (dBCI), in which neural signals are recorded, decoded, and then used as the input commands for neuromodulation at the same site in the brain. The challenge in assuring successful implementation of bidirectional dBCIs in Parkinson's disease (PD) is to discover and decode stable, robust and reliable neural inputs that can be tracked during stimulation, and to optimize neurostimulation patterns and parameters (control policies) for motor behaviors at the brain interface, which are customized to the individual. In this perspective, we will outline the work done in our lab regarding the evolution of the discovery of neural and behavioral control variables relevant to PD, the development of a novel personalized dual-threshold control policy relevant to the individual's therapeutic window and the application of these to investigations of closed-loop STN DBS driven by neural or kinematic inputs, using the first generation of bidirectional dBCIs.},
}
@article {pmid33060178,
year = {2020},
author = {Liu, Z and Schieber, MH},
title = {Neuronal Activity Distributed in Multiple Cortical Areas during Voluntary Control of the Native Arm or a Brain-Computer Interface.},
journal = {eNeuro},
volume = {7},
number = {5},
pages = {},
pmid = {33060178},
issn = {2373-2822},
support = {R01 NS092626/NS/NINDS NIH HHS/United States ; },
mesh = {Arm ; Brain Mapping ; *Brain-Computer Interfaces ; Humans ; *Motor Cortex ; Neurons ; Parietal Lobe ; },
abstract = {Voluntary control of visually-guided upper extremity movements involves neuronal activity in multiple areas of the cerebral cortex. Studies of brain-computer interfaces (BCIs) that use spike recordings for input, however, have focused largely on activity in the region from which those neurons that directly control the BCI, which we call BCI units, are recorded. We hypothesized that just as voluntary control of the arm and hand involves activity in multiple cortical areas, so does voluntary control of a BCI. In two subjects (Macaca mulatta) performing a center-out task both with a hand-held joystick and with a BCI directly controlled by four primary motor cortex (M1) BCI units, we recorded the activity of other, non-BCI units in M1, dorsal premotor cortex (PMd) and ventral premotor cortex (PMv), primary somatosensory cortex (S1), dorsal posterior parietal cortex (dPPC), and the anterior intraparietal area (AIP). In most of these areas, non-BCI units were active in similar percentages and at similar modulation depths during both joystick and BCI trials. Both BCI and non-BCI units showed changes in preferred direction (PD). Additionally, the prevalence of effective connectivity between BCI and non-BCI units was similar during both tasks. The subject with better BCI performance showed increased percentages of modulated non-BCI units with increased modulation depth and increased effective connectivity during BCI as compared with joystick trials; such increases were not found in the subject with poorer BCI performance. During voluntary, closed-loop control, non-BCI units in a given cortical area may function similarly whether the effector is the native upper extremity or a BCI-controlled device.},
}
@article {pmid33059338,
year = {2020},
author = {Wu, X and Zhang, W and Fu, Z and Cheung, RTH and Chan, RHM},
title = {An investigation of in-ear sensing for motor task classification.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {},
doi = {10.1088/1741-2552/abc1b6},
pmid = {33059338},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Data Collection ; *Electroencephalography/methods ; Humans ; Movement ; },
abstract = {Objective.Our study aims to investigate the feasibility of in-ear sensing for human-computer interface.Approach.We first measured the agreement between in-ear biopotential and scalp-electroencephalogram (EEG) signals by channel correlation and power spectral density analysis. Then we applied EEG compact network (EEGNet) for the classification of a two-class motor task using in-ear electrophysiological signals.Main results.The best performance using in-ear biopotential with global reference reached an average accuracy of 70.22% (cf 92.61% accuracy using scalp-EEG signals), but the performance in-ear biopotential with near-ear reference was poor.Significance.Our results suggest in-ear sensing would be a viable human-computer interface for movement prediction, but careful consideration should be given to the position of the reference electrode.},
}
@article {pmid33059331,
year = {2020},
author = {Chen, KH and Gogia, AS and Tang, AM and Del Campo-Vera, RM and Sebastian, R and Nune, G and Wong, J and Liu, CY and Kellis, S and Lee, B},
title = {Beta-band modulation in the human hippocampus during a conflict response task.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {},
doi = {10.1088/1741-2552/abc1b8},
pmid = {33059331},
issn = {1741-2552},
mesh = {*Drug Resistant Epilepsy ; *Electroencephalography/methods ; Hippocampus/physiology ; Humans ; Stroop Test ; },
abstract = {Objective. Identify the role of beta-band (13-30 Hz) power modulation in the human hippocampus during conflict processing.Approach. We investigated changes in the spectral power of the beta band (13-30 Hz) as measured by depth electrode leads in the hippocampus during a modified Stroop task in six patients with medically refractory epilepsy. Previous work done with direct electrophysiological recordings in humans has shown hippocampal theta-band (3-8 Hz) modulation during conflict processing. Local field potentials sampled at 2 k Hz were used for analysis and a non-parametric cluster-permutationt-test was used to identify the time period and frequency ranges of significant power change during cue processing (i.e. post-stimulus, pre-response).Main results. In five of the six patients, we observe a statistically significant increase in hippocampal beta-band power during successful conflict processing in the incongruent trial condition (cluster-based correction for multiple comparisons,p< 0.05). There was no significant beta-band power change observed during the cue-processing period of the congruent condition in the hippocampus of these patients.Significance. The beta-power changes during conflict processing represented here are consistent with previous studies suggesting that the hippocampus plays a role in conflict processing, but it is the first time that the beta band has been shown to be involved in humans with direct electrophysiological evidence. We propose that beta-band modulation plays a role in successful conflict detection and automatic response inhibition in the human hippocampus as studied during a conflict response task.},
}
@article {pmid33058767,
year = {2020},
author = {Moreaux, LC and Yatsenko, D and Sacher, WD and Choi, J and Lee, C and Kubat, NJ and Cotton, RJ and Boyden, ES and Lin, MZ and Tian, L and Tolias, AS and Poon, JKS and Shepard, KL and Roukes, ML},
title = {Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time.},
journal = {Neuron},
volume = {108},
number = {1},
pages = {66-92},
pmid = {33058767},
issn = {1097-4199},
support = {U01 NS099717/NS/NINDS NIH HHS/United States ; U01 NS090596/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; DP1 EY023176/EY/NEI NIH HHS/United States ; U19 MH114830/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Brain/*diagnostic imaging/pathology/physiology ; Computer Simulation ; Computer Systems ; Functional Neuroimaging/instrumentation/*methods ; Microchip Analytical Procedures ; Neural Pathways/diagnostic imaging/pathology/physiology ; Neurons/*pathology/physiology ; Optical Imaging/instrumentation/*methods ; Optics and Photonics ; Optogenetics ; },
abstract = {We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm[3] volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.},
}
@article {pmid33058761,
year = {2020},
author = {Babayan, BM and Sarnaik, R and Zirlinger, M},
title = {Spotlight on Neurotechnology: Reading Networks.},
journal = {Neuron},
volume = {108},
number = {1},
pages = {1},
doi = {10.1016/j.neuron.2020.09.040},
pmid = {33058761},
issn = {1097-4199},
mesh = {Brain-Computer Interfaces ; Deep Learning ; Functional Neuroimaging ; Humans ; Nerve Net ; *Neurosciences ; Optical Imaging ; Synaptic Transmission ; *Technology ; Ultrasonography ; Wearable Electronic Devices ; },
}
@article {pmid33055383,
year = {2020},
author = {Roussel, P and Godais, GL and Bocquelet, F and Palma, M and Hongjie, J and Zhang, S and Giraud, AL and Mégevand, P and Miller, K and Gehrig, J and Kell, C and Kahane, P and Chabardés, S and Yvert, B},
title = {Observation and assessment of acoustic contamination of electrophysiological brain signals during speech production and sound perception.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056028},
doi = {10.1088/1741-2552/abb25e},
pmid = {33055383},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Acoustics ; Animals ; Brain ; Humans ; Noise ; Rats ; *Speech ; *Speech Perception ; },
abstract = {OBJECTIVE: A current challenge of neurotechnologies is to develop speech brain-computer interfaces aiming at restoring communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band, which happens to overlap the frequency range of speech acoustic signals, especially the fundamental frequency of the voice. Here, we analyzed human electrocorticographic and intracortical recordings during speech production and perception as well as a rat microelectrocorticographic recording during sound perception. We observed that several datasets, recorded with different recording setups, contained spectrotemporal features highly correlated with those of the sound produced by or delivered to the participants, especially within the high-gamma band and above, strongly suggesting a contamination of electrophysiological recordings by the sound signal. This study investigated the presence of acoustic contamination and its possible source.<br><br>APPROACH: We developed analysis methods and a statistical criterion to objectively assess the presence or absence of contamination-specific correlations, which we used to screen several datasets from five centers worldwide.<br><br>MAIN RESULTS: Not all but several datasets, recorded in a variety of conditions, showed significant evidence of acoustic contamination. Three out of five centers were concerned by the phenomenon. In a recording showing high contamination, the use of high-gamma band features dramatically facilitated the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing no significant contamination. Further analysis and in vitro replication suggest that the contamination is caused by the mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise affecting the biopotential measurements.<br><br>SIGNIFICANCE: Although this study does not per se question the presence of speech-relevant physiological information in the high-gamma range and above (multiunit activity), it alerts on the fact that acoustic contamination of neural signals should be proofed and eliminated before investigating the cortical dynamics of these processes. To this end, we make available a toolbox implementing the proposed statistical approach to quickly assess the extent of contamination in an electrophysiological recording (https://doi.org/10.5281/zenodo.3929296).},
}
@article {pmid33055382,
year = {2020},
author = {Nazeer, H and Naseer, N and Khan, RA and Noori, FM and Qureshi, NK and Khan, US and Khan, MJ},
title = {Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056025},
doi = {10.1088/1741-2552/abb417},
pmid = {33055382},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Humans ; Imagination ; *Motor Cortex ; Spectroscopy, Near-Infrared ; },
abstract = {OBJECTIVE: In this paper, a novel methodology for feature extraction to enhance classification accuracy of functional near-infrared spectroscopy (fNIRS)-based two-class and three-class brain-computer interface (BCI) is presented.<br><br>APPROACH: Novel features are extracted using vector-based phase analysis method. Changes in oxygenated [Formula: see text] and de-oxygenated [Formula: see text]) haemoglobin are used to calculate four novel features: change in cerebral blood volume ([Formula: see text]), change in cerebral oxygen exchange ([Formula: see text]), vector magnitude (|L|) and angle (k). [Formula: see text] is the sum and [Formula: see text] is difference of [Formula: see text] and [Formula: see text], whereas |L| is magnitude and k is angle of vector. fNIRS signals of seven healthy subjects, corresponding to left-hand index finger tapping (LFT), right-hand index finger tapping (RFT) and rest are acquired from motor cortex using multi-channel continuous-wave imaging system. After removing physiological and instrumental noises from the acquired signals, the four novel features are calculated. For validation, conventional temporal, spatial and spatiotemporal features; mean, peak, slope, variance, kurtosis and skewness are also calculated using [Formula: see text] and[Formula: see text]. All possible two-feature and three-feature combinations of the novel and conventional features are then used to classify two-class (LFT vs RFT) and three-class (LFT vs RFT vs rest) fNIRS-BCI using linear discriminant analysis.<br><br>MAIN RESULTS: Results demonstrate that combination of four novel features yields significantly higher average classification accuracies of 98.7 &#xb1; 1.0% and 85.4 &#xb1; 1.4% as compared to 68.7 &#xb1; 6.9% and 53.6 &#xb1; 10.6% using conventional features for two-class and three-class problem, respectively. Validation of proposed method on an open access database containing RFT, LFT and dominant side foot tapping tasks for 30 subjects also shows improvement in average classification accuracies for two-class and three-class fNIRS-BCIs.<br><br>SIGNIFICANCE: This study provides a step forward in improving the classification accuracies of state-of-the-art fNIRS-BCIs by showing significant improvement in classification accuracies of two-class and three-class fNIRS-BCIs using novel features extracted by vector-based phase analysis.},
}
@article {pmid33055363,
year = {2020},
author = {Vermaas, M and Piastra, MC and Oostendorp, TF and Ramsey, NF and Tiesinga, PHE},
title = {When to include ECoG electrode properties in volume conduction models.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056031},
doi = {10.1088/1741-2552/abb11d},
pmid = {33055363},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Electrodes ; Electrodes, Implanted ; Software ; },
abstract = {OBJECTIVE: Implantable electrodes, such as electrocorticography (ECoG) grids, are used to record brain activity in applications like brain computer interfaces. To improve the spatial sensitivity of ECoG grid recordings, electrode properties need to be better understood. Therefore, the goal of this study is to analyze the importance of including electrodes explicitly in volume conduction calculations.<br><br>APPROACH: We investigated the influence of ECoG electrode properties on potentials in three geometries with three different electrode models. We performed our simulations with FEMfuns, a volume conduction modeling software toolbox based on the finite element method.<br><br>MAIN RESULTS: The presence of the electrode alters the potential distribution by an amount that depends on its surface impedance, its distance from the source and the strength of the source. Our modeling results show that when ECoG electrodes are near the sources the potentials in the underlying tissue are more uniform than without electrodes. We show that the recorded potential can change up to a factor of 3, if no extended electrode model is used. In conclusion, when the distance between an electrode and the source is equal to or smaller than the size of the electrode, electrode effects cannot be disregarded. Furthermore, the potential distribution of the tissue under the electrode is affected up to depths equal to the radius of the electrode.<br><br>SIGNIFICANCE: This paper shows the importance of explicitly including electrode properties in volume conduction models for accurately interpreting ECoG measurements.},
}
@article {pmid33052887,
year = {2020},
author = {Hehenberger, L and Sburlea, AI and Müller-Putz, GR},
title = {Assessing the impact of vibrotactile kinaesthetic feedback on electroencephalographic signals in a center-out task.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056032},
doi = {10.1088/1741-2552/abb069},
pmid = {33052887},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Feedback ; Hand ; Humans ; Kinesthesis ; Movement ; },
abstract = {OBJECTIVE: An important part of restoring motor control via a brain-computer interface is to close the sensorimotor feedback loop. As part of our investigations into vibrotactile kinaesthetic feedback of arm movements, we studied electroencephalographic signals in the δ, µ and β bands obtained during a center-out movement task with four conditions: movement with real-time kinaesthetic feedback, movement with static vibrations, movement without vibrotactile input, and no movement with sham feedback.<br><br>APPROACH: Participants performed center-out movements with their palm on a flat table surface. One of three movement directions was cued visually before the movement. The palm position was tracked in order to provide real-time vibrotactile feedback. All analyses were performed offline.<br><br>MAIN RESULTS: Movement-related cortical potentials exhibit minor discrepancies between movement conditions as well as between movement directions, in peak amplitude and shape. Classification of each movement condition and each direction against rest yields peak accuracies of 60%-65% using low-frequency amplitude features, and 90% using &#xb5; and &#x3b2; power features. Within-class accuracies of four-way classification between conditions based on low-frequency amplitude features are around chance level for the movement conditions with vibrotactile stimulation, slightly above chance level for the movement condition without stimulation, and considerably higher for the non-movement condition. Four-way classification between conditions based on &#xb5; and &#x3b2; power features yields within-class accuracies slightly above chance level for all movement conditions, and considerably higher for the non-movement condition. Within-class accuracies of three-way classification between directions are slightly above chance level for low-frequency amplitude features, and at chance level for power features.<br><br>SIGNIFICANCE: We found that the vibrotactile stimulation does not interfere with movement-related features in the &#x3b4;, &#xb5; and &#x3b2; frequency ranges. Our feedback system may therefore feasibly be deployed in conjunction with a BCI based on movement-related cortical potentials or sensorimotor rhythms, without adversely affecting control.},
}
@article {pmid33052886,
year = {2020},
author = {Graudejus, O and Barton, C and Ponce Wong, RD and Rowan, CC and Oswalt, D and Greger, B},
title = {A soft and stretchable bilayer electrode array with independent functional layers for the next generation of brain machine interfaces.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056023},
pmid = {33052886},
issn = {1741-2552},
support = {R43 NS093714/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Cats ; Electrocorticography ; Electrodes, Implanted ; },
abstract = {OBJECTIVE: Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable).<br><br>APPROACH: Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system.<br><br>MAIN RESULTS: To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol.<br><br>SIGNIFICANCE: The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array (lahr&#x3bc;ECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.},
}
@article {pmid33052885,
year = {2020},
author = {Huang, Y and He, F and Xu, M and Qi, H},
title = {Operate P300 speller when performing other task.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056022},
doi = {10.1088/1741-2552/abb4a6},
pmid = {33052885},
issn = {1741-2552},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {OBJECTIVE: The P300 speller is a classic brain-computer interface (BCI) paradigm that has the potential to restore impaired motor control function. However, previous studies have confirmed that the letter recognition accuracy (LRA) of the P300 speller is a challenge when performing other tasks.<br><br>APPROACH: To address this, we implemented a dynamic stopping strategy (DSS) to maintain the P300 speller LRA when performing multiple tasks simultaneously. Multiple tasks with dynamic workload levels were adopted to simulate the brain's other thinking activities while operating P300 speller. A Bayes-based DSS offline model was built in single-task (only P300 speller task) and an online P300 speller system was established to test the DSS algorithm feasibility in dual-task.<br><br>MAIN RESULTS: Online experimental results showed that the P300 speller with DSS could achieve a high LRA (96.9%) under dual-task, which was similar to single-task (98.7%, p = 0.126). Under dual-task, DSS dynamically adjusted the discriminant confidence according to the workload levels of the distraction tasks (correlation coefficient r = -0.68). Therefore, DSS can increase the repeated sequences to compensate for the reduction of P300 speller signal-to-noise ratio caused by parallel thinking activities. The average of repeated sequences increased significantly from 4.98 times under single-task to 6.22 times under dual-task (p < 0.005). These results indicated that the P300 speller feature is robust and the DSS model built in single-task maintained the applicability in various dual-tasks.<br><br>SIGNIFICANCE: Overall, this study provides a basis for the implementation of laboratory-developed BCI in real-world environments.},
}
@article {pmid33052868,
year = {2021},
author = {Sun, J and Wang, S and Zhang, J and Zong, C},
title = {Neural Encoding and Decoding With Distributed Sentence Representations.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {2},
pages = {589-603},
doi = {10.1109/TNNLS.2020.3027595},
pmid = {33052868},
issn = {2162-2388},
mesh = {Algorithms ; Brain/diagnostic imaging ; Brain-Computer Interfaces ; Cerebral Cortex/anatomy &amp; histology/*physiology ; Computer Simulation ; Deep Learning ; Humans ; Image Processing, Computer-Assisted ; Language ; Linguistics ; Magnetic Resonance Imaging ; *Natural Language Processing ; *Neural Networks, Computer ; Occipital Lobe/diagnostic imaging ; Reading ; Reproducibility of Results ; Semantics ; Temporal Lobe/diagnostic imaging ; },
abstract = {Building computational models to account for the cortical representation of language plays an important role in understanding the human linguistic system. Recent progress in distributed semantic models (DSMs), especially transformer-based methods, has driven advances in many language understanding tasks, making DSM a promising methodology to probe brain language processing. DSMs have been shown to reliably explain cortical responses to word stimuli. However, characterizing the brain activities for sentence processing is much less exhaustively explored with DSMs, especially the deep neural network-based methods. What is the relationship between cortical sentence representations against DSMs? What linguistic features that a DSM catches better explain its correlation with the brain activities aroused by sentence stimuli? Could distributed sentence representations help to reveal the semantic selectivity of different brain areas? We address these questions through the lens of neural encoding and decoding, fueled by the latest developments in natural language representation learning. We begin by evaluating the ability of a wide range of 12 DSMs to predict and decipher the functional magnetic resonance imaging (fMRI) images from humans reading sentences. Most models deliver high accuracy in the left middle temporal gyrus (LMTG) and left occipital complex (LOC). Notably, encoders trained with transformer-based DSMs consistently outperform other unsupervised structured models and all the unstructured baselines. With probing and ablation tasks, we further find that differences in the performance of the DSMs in modeling brain activities can be at least partially explained by the granularity of their semantic representations. We also illustrate the DSM's selectivity for concept categories and show that the topics are represented by spatially overlapping and distributed cortical patterns. Our results corroborate and extend previous findings in understanding the relation between DSMs and neural activation patterns and contribute to building solid brain-machine interfaces with deep neural network representations.},
}
@article {pmid33051727,
year = {2022},
author = {Horst, K and Lichte, P and Bläsius, F and Weber, CD and Tonglet, M and Kobbe, P and Heussen, N and Hildebrand, F},
title = {mTICCS and its inter-rater reliability to predict the need for massive transfusion in severely injured patients.},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {48},
number = {1},
pages = {367-372},
pmid = {33051727},
issn = {1863-9941},
mesh = {*Blood Coagulation Disorders/diagnosis/therapy ; Blood Transfusion ; Humans ; *Multiple Trauma ; Reproducibility of Results ; Trauma Centers ; },
abstract = {PURPOSE: The modified Trauma-Induced Coagulopathy Clinical Score (mTICCS) presents a new scoring system for the early detection of the need for a massive transfusion (MT). This easily applicable score was validated in a large trauma cohort and proven comparable to more established complex scoring systems. However, the inter-rater reliability of the mTICCS has not yet been investigated.<br><br>METHODS: Therefore, a dataset of 15 randomly selected and severely injured patients (ISS&#x2009;&#x2265;&#x2009;16) derived from the database of a level I trauma centre (2010-2015) was used. Moreover, 15 severely injured subjects that received MT were chosen from the same databank. A web-based survey was sent to medical professionals working in the field of trauma care asking them to evaluate each patient using the mTICCS.<br><br>RESULTS: In total, 16 raters (9 residents and 7 specialists) completed the survey. Ratings from 15 medical professionals could be evaluated and led to an ICC of 0.7587 (95% Bootstrap confidence interval (BCI) 0.7149-0.8283). A comparison of working experience specific ICC (n&#x2009;=&#x2009;7 specialists, ICC: 0.7558, BCI: 0.7076-0.8270; n&#x2009;=&#x2009;8 residents, ICC: 0.7634, BCI: 0.7183-0.8335) showed no significant difference between the two groups (p&#x2009;=&#x2009;0.67).<br><br>CONCLUSION: In summary, reliability values need to be considered when making clinical decisions based on scoring systems. Due to its easy applicability and its almost perfect inter-rater reliability, even with non-specialists, the mTICCS might therefore be a useful tool to predict the early need for MT in multiple trauma.},
}
@article {pmid33051500,
year = {2020},
author = {Gembler, FW and Benda, M and Rezeika, A and Stawicki, PR and Volosyak, I},
title = {Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {17064},
pmid = {33051500},
issn = {2045-2322},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Language ; Male ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Keyboards and smartphones allow users to express their thoughts freely via manual control. Hands-free communication can be realized with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). Various variations of such spellers have been developed: Low-target systems, multi-target systems and systems with dictionary support. In general, it is not clear which kinds of systems are optimal in terms of reliability, speed, cognitive load, and visual load. The presented study investigates the feasibility of different speller variations. 58 users tested a 4-target speller and a 32-target speller with and without dictionary functionality. For classification, multiple individualized spatial filters were generated via canonical correlation analysis (CCA). We used an asynchronous implementation allowing non-control state, thus aiming for high accuracy rather than speed. All users were able to control the tested spellers. Interestingly, no significant differences in accuracy were found: 94.4%, 95.5% and 94.0% for 4-target spelling, 32-target spelling, and dictionary-assisted 32-target spelling. The mean ITRs were highest for the 32-target interface: 45.2, 96.9 and 88.9 bit/min. The output speed in characters per minute, was highest in dictionary-assisted spelling: 8.2, 19.5 and 31.6 characters/min. According to questionnaire results, 86% of the participants preferred the 32-target speller over the 4-target speller.},
}
@article {pmid33049729,
year = {2020},
author = {Suma, D and Meng, J and Edelman, BJ and He, B},
title = {Spatial-temporal aspects of continuous EEG-based neurorobotic control.},
journal = {Journal of neural engineering},
volume = {17},
number = {6},
pages = {},
pmid = {33049729},
issn = {1741-2552},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; RF1 MH114233/MH/NIMH NIH HHS/United States ; },
mesh = {*Brain ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Hand ; Humans ; Imagination ; },
abstract = {Objective.The goal of this work is to identify the spatio-temporal facets of state-of-the-art electroencephalography (EEG)-based continuous neurorobotics that need to be addressed, prior to deployment in practical applications at home and in the clinic.Approach.Nine healthy human subjects participated in five sessions of one-dimensional (1D) horizontal (LR), 1D vertical (UD) and two-dimensional (2D) neural tracking from EEG. Users controlled a robotic arm and virtual cursor to continuously track a Gaussian random motion target using EEG sensorimotor rhythm modulation via motor imagery (MI) commands. Continuous control quality was analyzed in the temporal and spatial domains separately.Main results.Axis-specific errors during 2D tasks were significantly larger than during 1D counterparts. Fatigue rates were larger for control tasks with higher cognitive demand (LR, left- and right-hand MI) compared to those with lower cognitive demand (UD, both hands MI and rest). Additionally robotic arm and virtual cursor control exhibited equal tracking error during all tasks. However, further spatial error analysis of 2D control revealed a significant reduction in tracking quality that was dependent on the visual interference of the physical device. In fact, robotic arm performance was significantly greater than that of virtual cursor control when the users' sightlines were not obstructed.Significance.This work emphasizes the need for practical interfaces to be designed around real-world tasks of increased complexity. Here, the dependence of control quality on cognitive task demand emphasizes the need for decoders that facilitate the translation of 1D task mastery to 2D control. When device footprint was accounted for, the introduction of a physical robotic arm improved control quality, likely due to increased user engagement. In general, this work demonstrates the need to consider both the physical footprint of devices, the complexity of training tasks, and the synergy of control strategies during the development of neurorobotic control.},
}
@article {pmid33048667,
year = {2020},
author = {Kwon, J and Im, CH},
title = {Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interfaces Using Transcranial Near-Infrared Photobiomodulation With the Same Device.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {12},
pages = {2608-2614},
doi = {10.1109/TNSRE.2020.3030639},
pmid = {33048667},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Humans ; Mathematics ; Prefrontal Cortex ; Psychomotor Performance ; Spectroscopy, Near-Infrared ; },
abstract = {Transcranial near-infrared photobiomodulation (tNIR-PBM) can modulate physiological characteristics of the human brain, such as the cerebral blood flow and oxidative metabolism. Here, we investigated whether the performance of near-infrared spectroscopy (NIRS)-based brain-computer interfaces (BCIs) can be improved by tNIR-PBM applied to the prefrontal cortex with the same NIRS device. A total of 14 healthy individuals participated in the NIRS-based BCI study where the aim was to distinguish the mental arithmetic task from the idle state (IS) task either after tNIR-PBM or after sham stimulation, with the two experiments being conducted at least two days apart. The tNIR-PBM was applied by simply turning on the NIRS recording equipment for 20 min. To evaluate the degree of performance improvement obtained after tNIR-PBM, the average BCI classification accuracy obtained under the tNIR-PBM condition was compared with that obtained under the sham stimulation condition. The classification accuracy of NIRS-based BCI was significantly improved upon conduction of tNIR-PBM (82.74%) as compared to that in the sham stimulation condition (76.07%, p < 0.005). Thus, our results suggest that simply turning on the NIRS recording equipment before the BCI experiment can improve the performance of the NIRS-based BCI system.},
}
@article {pmid33045685,
year = {2020},
author = {Feng, N and Hu, F and Wang, H and Gouda, MA},
title = {Decoding of voluntary and involuntary upper-limb motor imagery based on graph fourier transform and cross-frequency coupling coefficients.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056043},
doi = {10.1088/1741-2552/abc024},
pmid = {33045685},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Fourier Analysis ; Hand ; Humans ; Imagination ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology based on motor imagery (MI) control has become a research hotspot but continues to encounter numerous challenges. BCI can assist in the recovery of stroke patients and serve as a key technology in robot control. Current research on MI almost exclusively focuses on the hands, feet, and tongue. Therefore, the purpose of this paper is to establish a four-class MI BCI system, in which the four types are the four articulations within the right upper limbs, involving the shoulder, elbow, wrist, and hand.<br><br>APPROACH: Ten subjects were chosen to perform nine upper-limb analytic movements, after which the differences were compared in P300, movement-related potentials(MRPS), and event-related desynchronization/event-related synchronization under voluntary MI (V-MI) and involuntary MI (INV-MI). Next, the cross-frequency coupling (CFC) coefficient based on mutual information was extracted from the electrodes and frequency bands with interest. Combined with the image Fourier transform and twin bounded support vector machine classifier, four kinds of electroencephalography data were classified, and the classifier's parameters were optimized using a genetic algorithm.<br><br>MAIN RESULTS: The results were shown to be encouraging, with an average accuracy of 93.2% and 92.2% for V-MI and INV-MI, respectively, and over 95% for any three classes and any two classes. In most cases, the accuracy of feature extraction using the proximal articulations as the basis was found to be relatively high and had better performance.<br><br>SIGNIFICANCE: This paper discussed four types of MI according to three aspects under two modes and classed them by combining graph Fourier transform and CFC. Accordingly, the theoretical discussion and classification methods may provide a fundamental theoretical basis for BCI interface applications.},
}
@article {pmid33039972,
year = {2020},
author = {Pillette, L and Lotte, F and N'Kaoua, B and Joseph, PA and Jeunet, C and Glize, B},
title = {Why we should systematically assess, control and report somatosensory impairments in BCI-based motor rehabilitation after stroke studies.},
journal = {NeuroImage. Clinical},
volume = {28},
number = {},
pages = {102417},
pmid = {33039972},
issn = {2213-1582},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurofeedback ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {The neuronal loss resulting from stroke forces 80% of the patients to undergo motor rehabilitation, for which Brain-Computer Interfaces (BCIs) and NeuroFeedback (NF) can be used. During the rehabilitation, when patients attempt or imagine performing a movement, BCIs/NF provide them with a synchronized sensory (e.g., tactile) feedback based on their sensorimotor-related brain activity that aims at fostering brain plasticity and motor recovery. The co-activation of ascending (i.e., somatosensory) and descending (i.e., motor) networks indeed enables significant functional motor improvement, together with significant sensorimotor-related neurophysiological changes. Somatosensory abilities are essential for patients to perceive the feedback provided by the BCI system. Thus, somatosensory impairments may significantly alter the efficiency of BCI-based motor rehabilitation. In order to precisely understand and assess the impact of somatosensory impairments, we first review the literature on post-stroke BCI-based motor rehabilitation (14 randomized clinical trials). We show that despite the central role that somatosensory abilities play on BCI-based motor rehabilitation post-stroke, the latter are rarely reported and used as inclusion/exclusion criteria in the literature on the matter. We then argue that somatosensory abilities have repeatedly been shown to influence the motor rehabilitation outcome, in general. This stresses the importance of also considering them and reporting them in the literature in BCI-based rehabilitation after stroke, especially since half of post-stroke patients suffer from somatosensory impairments. We argue that somatosensory abilities should systematically be assessed, controlled and reported if we want to precisely assess the influence they have on BCI efficiency. Not doing so could result in the misinterpretation of reported results, while doing so could improve (1) our understanding of the mechanisms underlying motor recovery (2) our ability to adapt the therapy to the patients' impairments and (3) our comprehension of the between-subject and between-study variability of therapeutic outcomes mentioned in the literature.},
}
@article {pmid33039619,
year = {2021},
author = {Dikker, S and Michalareas, G and Oostrik, M and Serafimaki, A and Kahraman, HM and Struiksma, ME and Poeppel, D},
title = {Crowdsourcing neuroscience: Inter-brain coupling during face-to-face interactions outside the laboratory.},
journal = {NeuroImage},
volume = {227},
number = {},
pages = {117436},
doi = {10.1016/j.neuroimage.2020.117436},
pmid = {33039619},
issn = {1095-9572},
mesh = {Brain/*physiology ; Crowdsourcing ; Electroencephalography ; Empathy/*physiology ; Humans ; Interpersonal Relations ; Neurofeedback ; *Social Behavior ; },
abstract = {When we feel connected or engaged during social behavior, are our brains in fact "in sync" in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowdsource neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-min face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine-an artistic neurofeedback installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain coupling using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs' trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha (~7-10 Hz) and beta (~20-22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).},
}
@article {pmid33035721,
year = {2020},
author = {Neudorfer, C and Bhatia, K and Boutet, A and Germann, J and Elias, GJ and Loh, A and Paff, M and Krings, T and Lozano, AM},
title = {Endovascular deep brain stimulation: Investigating the relationship between vascular structures and deep brain stimulation targets.},
journal = {Brain stimulation},
volume = {13},
number = {6},
pages = {1668-1677},
doi = {10.1016/j.brs.2020.09.016},
pmid = {33035721},
issn = {1876-4754},
mesh = {Adult ; Brain/*blood supply/*diagnostic imaging/physiology ; Cerebral Arteries/diagnostic imaging/physiology ; Cerebral Veins/diagnostic imaging/physiology ; Cerebrovascular Circulation/physiology ; Deep Brain Stimulation/*methods ; *Electrodes, Implanted ; Endovascular Procedures/instrumentation/*methods ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; *Stents ; },
abstract = {BACKGROUND: Endovascular delivery of current using 'stentrodes' - electrode bearing stents - constitutes a potential alternative to conventional deep brain stimulation (DBS). The precise neuroanatomical relationships between DBS targets and the vascular system, however, are poorly characterized to date.<br><br>OBJECTIVE: To establish the relationships between cerebrovascular system and DBS targets and investigate the feasibility of endovascular stimulation as an alternative to DBS.<br><br>METHODS: Neuroanatomical targets as employed during deep brain stimulation (anterior limb of the internal capsule, dentatorubrothalamic tract, fornix, globus pallidus pars interna, medial forebrain bundle, nucleus accumbens, pedunculopontine nucleus, subcallosal cingulate cortex, subthalamic nucleus, and ventral intermediate nucleus) were superimposed onto probabilistic vascular atlases obtained from 42 healthy individuals. Euclidian distances between targets and associated vessels were measured. To determine the electrical currents necessary to encapsulate the predefined neurosurgical targets and identify potentially side-effect inducing substrates, a preliminary volume of tissue activated (VTA) analysis was performed.<br><br>RESULTS: Six out of ten DBS targets were deemed suitable for endovascular stimulation: medial forebrain bundle (vascular site: P1 segment of posterior cerebral artery), nucleus accumbens (vascular site: A1 segment of anterior cerebral artery), dentatorubrothalamic tract (vascular site: s2 segment of superior cerebellar artery), fornix (vascular site: internal cerebral vein), pedunculopontine nucleus (vascular site: lateral mesencephalic vein), and subcallosal cingulate cortex (vascular site: A2 segment of anterior cerebral artery). While VTAs effectively encapsulated mfb and NA at current thresholds of 3.5&#xa0;V and 4.5&#xa0;V respectively, incremental amplitude increases were required to effectively cover fornix, PPN and SCC target (mean voltage: 8.2&#xa0;&#xb1;&#xa0;4.8&#xa0;V, range: 3.0-17.0&#xa0;V). The side-effect profile associated with endovascular stimulation seems to be comparable to conventional lead implantation. Tailoring of targets towards vascular sites, however, may allow to reduce adverse effects, while maintaining the efficacy of neural entrainment within the target tissue.<br><br>CONCLUSIONS: While several challenges remain at present, endovascular stimulation of select DBS targets seems feasible offering novel and exciting opportunities in the neuromodulation armamentarium.},
}
@article {pmid33034634,
year = {2020},
author = {Jeong, JH and Cho, JH and Shim, KH and Kwon, BH and Lee, BH and Lee, DY and Lee, DH and Lee, SW},
title = {Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions.},
journal = {GigaScience},
volume = {9},
number = {10},
pages = {},
pmid = {33034634},
issn = {2047-217X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; Upper Extremity ; },
abstract = {BACKGROUND: Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants.<br><br>FINDINGS: We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method.<br><br>CONCLUSIONS: The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.},
}
@article {pmid33032212,
year = {2021},
author = {Wahid, F and Baig, S and Bhatti, MF and Manzoor, M and Ahmed, I and Arshad, M},
title = {Growth responses and rubisco activity influenced by antibiotics and organic amendments used for stress alleviation in Lactuca sativa.},
journal = {Chemosphere},
volume = {264},
number = {Pt 1},
pages = {128433},
doi = {10.1016/j.chemosphere.2020.128433},
pmid = {33032212},
issn = {1879-1298},
mesh = {Anti-Bacterial Agents/pharmacology ; Ecosystem ; Lactuca ; *Oryza ; Ribulose-Bisphosphate Carboxylase ; Soil ; *Soil Pollutants/analysis/toxicity ; },
abstract = {The global increase in the consumption of antibiotics has resulted in contamination of different ecosystems with severe implications on crop productivity. This study investigated the effects of ampicillin and ofloxacin on Lactuca sativa germination upon solution exposure and growth when cultivated in soils treated with three organic amendments (compost, rice husk and vermicompost). Two levels of both antibiotics 5 and 10 mg L[-1] (for solution) or mg kg[-1] (for soil) were tested in addition to the control. Results indicated that addition of compost significantly (p < 0.05) increased (50%) the root lengths of plant exposed to ampicillin (5 mg L[-1]). Similarly, vermicompost-amended treatments displayed a 64% increase (p < 0.05) in the shoot length of seedlings under the effect of 5 mg L[-1] ofloxacin, depicting a positive synergistic effect between the antibiotics and amendments in the germination test. Nevertheless, the germination percentage remained unaffected in all the treatments. In greenhouse experiment, enhanced plant biomass was observed with the use of rice husk across all the treatment groups. Comparable to the germination test, plants treated with rice husk and compost signaled a higher content of rubisco large subunit (157% and 85%, respectively) and soluble protein (248% and 108%, respectively) post antibiotics application. On the contrary, an antagonistic effect of the rice husk and ofloxacin 5 mg kg[-1] was observed on the chlorophyll content, evident by a 37% decrease. Overall, it was observed that the effect of antibiotics on different plant traits vary depending on the antibiotic concentration as well as type of amendment used.},
}
@article {pmid33029548,
year = {2020},
author = {Wyser, D and Mattille, M and Wolf, M and Lambercy, O and Scholkmann, F and Gassert, R},
title = {Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics.},
journal = {Neurophotonics},
volume = {7},
number = {3},
pages = {035011},
pmid = {33029548},
issn = {2329-423X},
abstract = {Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic "noise" by subtracting the signal measured at a short source-detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.},
}
@article {pmid33028005,
year = {2020},
author = {Lee, AH and Lee, J and Laiwalla, F and Leung, V and Huang, J and Nurmikko, A and Song, YK},
title = {A Scalable and Low Stress Post-CMOS Processing Technique for Implantable Microsensors.},
journal = {Micromachines},
volume = {11},
number = {10},
pages = {},
pmid = {33028005},
issn = {2072-666X},
support = {N666001-17-C-4013//Defense Advanced Research Projects Agency/ ; NRF-2016M3C7A1904987//National Research Foundation of Korea/ ; },
abstract = {Implantable active electronic microchips are being developed as multinode in-body sensors and actuators. There is a need to develop high throughput microfabrication techniques applicable to complementary metal-oxide-semiconductor (CMOS)-based silicon electronics in order to process bare dies from a foundry to physiologically compatible implant ensembles. Post-processing of a miniature CMOS chip by usual methods is challenging as the typically sub-mm size small dies are hard to handle and not readily compatible with the standard microfabrication, e.g., photolithography. Here, we present a soft material-based, low chemical and mechanical stress, scalable microchip post-CMOS processing method that enables photolithography and electron-beam deposition on hundreds of micrometers scale dies. The technique builds on the use of a polydimethylsiloxane (PDMS) carrier substrate, in which the CMOS chips were embedded and precisely aligned, thereby enabling batch post-processing without complication from additional micromachining or chip treatments. We have demonstrated our technique with 650 μm × 650 μm and 280 μm × 280 μm chips, designed for electrophysiological neural recording and microstimulation implants by monolithic integration of patterned gold and PEDOT:PSS electrodes on the chips and assessed their electrical properties. The functionality of the post-processed chips was verified in saline, and ex vivo experiments using wireless power and data link, to demonstrate the recording and stimulation performance of the microscale electrode interfaces.},
}
@article {pmid33026895,
year = {2022},
author = {Lee, SH and Kim, SS and Lee, BH},
title = {Action observation training and brain-computer interface controlled functional electrical stimulation enhance upper extremity performance and cortical activation in patients with stroke: a randomized controlled trial.},
journal = {Physiotherapy theory and practice},
volume = {38},
number = {9},
pages = {1126-1134},
doi = {10.1080/09593985.2020.1831114},
pmid = {33026895},
issn = {1532-5040},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Humans ; Recovery of Function/physiology ; *Stroke/therapy ; *Stroke Rehabilitation/methods ; Upper Extremity ; },
abstract = {PURPOSE: Brain-computer interface (BCI)-functional electronic stimulation (FES) systems are increasingly being explored as potential neuro-rehabilitation tools. Here, we investigate the effect of action observation training (AOT) plus electroencephalogram (EEG)-based BCI-controlled FES system on motor recovery of upper extremity and cortical activation in patients with stroke.<br><br>METHOD: There were a total of 26 patients: an AOT plus BCI-FES group (n&#xa0;=&#xa0;13) and a control group (n&#xa0;=&#xa0;13). The control group performed FES treatment and the conventional physical therapy, while the AOT plus BCI-FES group performed AOT plus BCI-FES and the conventional physical therapy. Upper extremity performance was measured using the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL) and Modified Barthel Index (MBI). Cortical activation was measured using electro-encephalographic recordings from alpha and beta power, concentration, and activation.<br><br>RESULTS: After intervention, there were significant differences between two groups in FMA-UE, WMFT, MAL and MBI and the results of EEG including alpha power, beta power, concentration and activation.<br><br>CONCLUSIONS: This study demonstrated that AOT plus BCI-FES can enhance motor function of upper extremity and cortical activation in patients with stroke. This training method may be feasible and suitable for individuals with stroke.},
}
@article {pmid33026825,
year = {2021},
author = {Cabrita, I and Benedetto, R and Wanitchakool, P and Lerias, J and Centeio, R and Ousingsawat, J and Schreiber, R and Kunzelmann, K},
title = {TMEM16A Mediates Mucus Production in Human Airway Epithelial Cells.},
journal = {American journal of respiratory cell and molecular biology},
volume = {64},
number = {1},
pages = {50-58},
doi = {10.1165/rcmb.2019-0442OC},
pmid = {33026825},
issn = {1535-4989},
mesh = {Anoctamin-1/*metabolism ; Calcium/metabolism ; Cell Line ; Cell Line, Tumor ; Chloride Channels/metabolism ; Cystic Fibrosis/metabolism ; Cystic Fibrosis Transmembrane Conductance Regulator/metabolism ; Epithelial Cells/*metabolism ; HEK293 Cells ; HT29 Cells ; Humans ; Interleukin-13/metabolism ; Mucus/*metabolism ; Neoplasm Proteins/*metabolism ; RNA, Small Interfering/metabolism ; Respiratory Mucosa/*metabolism ; Up-Regulation/physiology ; },
abstract = {TMEM16A is a Ca[2+]-activated chloride channel that was shown to enhance production and secretion of mucus in inflamed airways. It is, however, not clear whether TMEM16A directly supports mucus production, or whether mucin and TMEM16A are upregulated independently during inflammatory airway diseases such as asthma and cystic fibrosis (CF). We examined this question using BCi-NS1 cells, a human airway basal cell line that maintains multipotent differentiation capacity, and the two human airway epithelial cell lines, Calu-3 and CFBE. The data demonstrate that exposure of airway epithelial cells to IL-8 and IL-13, two cytokines known to be enhanced in CF and asthma, respectively, leads to an increase in mucus production. Expression of MUC5AC was fully dependent on expression of TMEM16A, as shown by siRNA knockdown of TMEM16A. In addition, different inhibitors of TMEM16A attenuated IL-13-induced mucus production. Interestingly, in CFBE cells expressing F508 delCFTR, IL-13 was unable to upregulate membrane expression of TMEM16A or Ca[2+]-activated whole cell currents. The regulator of TMEM16A, CLCA1, strongly augmented both Ca[2+]- and cAMP-activated Cl[-] currents in cells expressing wtCFTR but failed to augment membrane expression of TMEM16A in F508 delCFTR-expressing CFBE cells. The data confirm the functional relationship between CFTR and TMEM16A and suggest an impaired upregulation of TMEM16A by IL-13 or CLCA1 in cells expressing the most frequent CF-causing mutation F508 delCFTR.},
}
@article {pmid33025313,
year = {2020},
author = {Stramondo, JA},
title = {The right to assistive technology.},
journal = {Theoretical medicine and bioethics},
volume = {41},
number = {5-6},
pages = {247-271},
pmid = {33025313},
issn = {1573-0980},
support = {#EEC-1028725//National Science Foundation/ ; },
mesh = {Persons with Disabilities ; Human Rights/*trends ; Humans ; Self-Help Devices/*ethics ; Social Justice/ethics/*trends ; },
abstract = {In this paper, I argue that disabled people have a right to assistive technology (AT), but this right cannot be grounded simply in a broader right to health care or in a more comprehensive view like the capabilities approach to justice. Both of these options are plagued by issues that I refer to as the problem of constriction, where the theory does not justify enough of the AT that disabled people should have access to, and the problem of overextension, where the theory cannot adequately identify an upper limit on the AT that people have a right to. As an alternative to these justificatory frameworks, I argue that disabled people are owed access to AT at the expense of nondisabled people as a matter of compensatory justice. That is, I defend the position that disabled people are owed AT as part of due compensation for the harms they experience from being disadvantaged by society's dominant cooperative scheme and the violation of their right to equality of opportunity that such disadvantage entails. I also propose a method for identifying an upper limit to what this right to AT requires. In this way, I argue that compensatory justice avoids both the problem of constriction and the problem of overextension.},
}
@article {pmid33020435,
year = {2020},
author = {Velasquez-Martinez, L and Caicedo-Acosta, J and Acosta-Medina, C and Alvarez-Meza, A and Castellanos-Dominguez, G},
title = {Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {33020435},
issn = {2076-3425},
support = {COD-SIGP 57579//Programa de Investigaci&#xf3;n Reconstrucci&#xf3;n del Tejido Social en Zonas de Posconflicto en Colombia/ ; Contrato No. FP44842-213-2018//Fortalecimiento docente desde la alfabetizaci&#xf3;n medi&#xe1;tica Informacional y la CTel, como estrategia did&#xe1;ctico-pedag&#xf3;gica y soporte para la recuperaci&#xf3;n de la confianza del tejido social afectado por el conflicto" COD-SIGP 58950, funded by Convocatoria Colo/ ; 727//Convocatoria Doctorados Nacionales COLCIENCIAS/ ; },
abstract = {Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain-Computer Interface inefficiency of subjects.},
}
@article {pmid33020038,
year = {2021},
author = {Balkhoyor, AM and Mir, R and Mirghani, I and Pike, TW and Sheppard, WEA and Biyani, CS and Lodge, JPA and Mon-Williams, MA and Mushtaq, F and Manogue, M},
title = {Exploring the Presence of Core Skills for Surgical Practice Through Simulation.},
journal = {Journal of surgical education},
volume = {78},
number = {3},
pages = {980-986},
doi = {10.1016/j.jsurg.2020.08.036},
pmid = {33020038},
issn = {1878-7452},
mesh = {Clinical Competence ; Computer Simulation ; *Laparoscopy ; *Simulation Training ; User-Computer Interface ; *Virtual Reality ; },
abstract = {OBJECTIVE: The ability to simulate procedures in silico has transformed surgical training and practice. Today's simulators, designed for the training of a highly specialized set of procedures, also present a powerful scientific tool for understanding the neural control processes that underpin the learning and application of surgical skills. Here, we examined whether 2 simulators designed for training in 2 different surgical domains could be used to examine the extent to which fundamental sensorimotor skills transcend surgical specialty.<br><br>We used a high-fidelity virtual reality dental simulator and a laparoscopic box simulator to record the performance of 3 different groups. The groups comprised dentists, laparoscopic surgeons, and psychologists (each group n&#x202f;=&#x202f;19).<br><br>RESULTS: The results revealed a specialization of performance, with laparoscopic surgeons showing the highest performance on the laparoscopic box simulator, while dentists demonstrated the highest skill levels on the virtual reality dental simulator. Importantly, we also found that a transfer learning effect, with laparoscopic surgeons and dentists showing superior performance to the psychologists on both tasks.<br><br>CONCLUSIONS: There are core sensorimotor skills that cut across surgical specialty. We propose that the identification of such fundamental skills could lead to improved training provision prior to specialization.},
}
@article {pmid33019375,
year = {2020},
author = {Etienne, A and Laroia, T and Weigle, H and Afelin, A and Kelly, SK and Krishnan, A and Grover, P},
title = {Novel Electrodes for Reliable EEG Recordings on Coarse and Curly Hair.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {6151-6154},
doi = {10.1109/EMBC44109.2020.9176067},
pmid = {33019375},
issn = {2694-0604},
mesh = {Electric Impedance ; Electrodes ; *Electroencephalography ; Equipment Design ; Humans ; *Scalp ; },
abstract = {EEG is a powerful and affordable brain sensing and imaging tool used extensively for the diagnosis of neurological disorders (e.g. epilepsy), brain computer interfacing, and basic neuroscience. Unfortunately, most EEG electrodes and systems are not designed to accommodate coarse and curly hair common in individuals of African descent. In neuroscience studies, this can lead to poor quality data that might be discarded in scientific studies after recording from a broader population set. In clinical diagnoses, it may lead to an uncomfortable and/or emotionally taxing experience, and, in the worst cases, misdiagnosis. Our prior work demonstrated that braiding hair in cornrows to expose the scalp at target locations leads to reduced electrode-skin impedance for existing electrodes. In this work, we design and implement novel electrodes that harness braided hair, and demonstrate that, across time, our electrodes, in conjunction with braiding, lower the impedance further, attaining 10x lower impedance than existing systems.},
}
@article {pmid33019362,
year = {2020},
author = {Kelly, D and Jadavji, Z and Zewdie, E and Mitchell, E and Summerfield, K and Kirton, A and Kinney-Lang, E},
title = {A Child's Right to Play: Results from the Brain-Computer Interface Game Jam 2019 (Calgary Competition).},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {6099-6102},
doi = {10.1109/EMBC44109.2020.9176272},
pmid = {33019362},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Child ; Electroencephalography ; Humans ; North America ; User-Computer Interface ; },
abstract = {Children with severe neurological disabilities may be unable to communicate or interact with their environments, depriving them of their right to play. Brain-computer interfaces (BCI) offer a means for such children to control external devices using only their brain signals, thereby introducing new opportunities for interaction. We organized the first North American BCI Game Jam to incite the development of BCI-compatible games for children. Nine games were submitted by 30 participants across North America. Games were judged by researchers and disabled children currently using BCI. Preliminary results demonstrate variety in game criteria preferences amongst the children who judged the games. The BCI Game Jam demonstrated promising potential for the creation of enjoyable games to suit the individual needs and preferences of children with severe neurological disabilities.},
}
@article {pmid33019357,
year = {2020},
author = {Kinney-Lang, E and Murji, S and Kelly, D and Paffrath, B and Zewdie, E and Kirton, A},
title = {Designing a flexible tool for rapid implementation of brain-computer interfaces (BCI) in game development.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {6078-6081},
doi = {10.1109/EMBC44109.2020.9175801},
pmid = {33019357},
issn = {2694-0604},
mesh = {Adult ; Brain ; *Brain-Computer Interfaces ; Cognition ; Humans ; },
abstract = {Early neurological injury or disease can lead to severe life-long physical impairments, despite normal cognitive function. For such individuals, brain-computer interfaces (BCI) may provide a means to regain access to the world by offering control of systems through directly processing brain patterns. However, current BCI applications are often research driven and consequently seen as uninteresting, particularly for prolonged use and younger BCI-users. To help mitigate this concern, this paper establishes a tool for researchers and game developers alike to rapidly incorporate a BCI control scheme (the P300 oddball response) into a gaming environment. Preliminary results indicate the proposed P300 Dynamic Cube (PDC) asset works in online BCI environments (n=20, healthy adult participants), resulting in median classification accuracy of 75 ± 3.28%. Additionally, the PDC tool can be rapidly adapted for a variety of game designs, evidenced by its incorporation into submissions to the Brain-Computer Interface (BCI) Game Jam 2019 competition. These findings support the PDC as a useful asset in the design and development of BCI-based games.},
}
@article {pmid33019051,
year = {2020},
author = {Mei, J and Xu, M and Wang, L and Ke, Y and Wang, Y and Jung, TP and Ming, D},
title = {Using SSVEP-BCI to Continuous Control a Quadcopter with 4-DOF Motions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4745-4748},
doi = {10.1109/EMBC44109.2020.9176131},
pmid = {33019051},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Motion ; },
abstract = {Brain-computer interfaces (BCIs) allow for translating electroencephalogram (EEG) into control commands, e.g., to control a quadcopter. This study, we developed a practical BCI based on steady-state visually evoked potential (SSVEP) for continuous control of a quadcopter from the first-person perspective. Users watched with the video stream from a camera on the quadcopter. An innovative user interface was developed by embedding 12 SSVEP flickers into the video stream, which corresponded to the flight commands of 'take-off,' 'land,' 'hover,' 'keep-going,' 'clockwise,' 'counter-clockwise' and rectilinear motions in six directions, respectively. The command was updated every 400ms by decoding the collected EEG data using a combined classification algorithm based on task-related component analysis (TRCA) and linear discriminant analysis (LDA). The quadcopter flew in the 3-D space according to the control vector that was determined by the latest four commands. Three novices participated in this study. They were asked to control the quadcopter by either the brain or hands to fly through a circle and land on the target zone. As a result, the time consumption ratio of brain-control to hand-control was as low as 1.34, which means the BCI performance was close to hands. The information transfer rate reached a peak of 401.79 bits/min in the simulated online experiment. These results demonstrate the proposed SSVEP-BCI system is efficient for controlling the quadcopter.},
}
@article {pmid33019050,
year = {2020},
author = {Han, J and Xu, M and Wang, Y and Tang, J and Liu, M and An, X and Jung, TP and Ming, D},
title = {'Write' but not 'spell' Chinese characters with a BCI-controlled robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4741-4744},
doi = {10.1109/EMBC44109.2020.9175275},
pmid = {33019050},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Robotics ; Writing ; },
abstract = {Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are often written in a two-dimensional structure. Previous studies had never investigated how to use BCI to 'write' but not 'spell' Chinese characters. This study developed an innovative BCI-controlled robot for writing Chinese characters. The BCI system contained 108 commands displayed in a 9*12 array. A pixel-based writing method was proposed to map the starting point and ending point of each stroke of Chinese characters to the array. Connecting the starting and ending points for each stroke can make up any Chinese character. The large command set was encoded by the hybrid P300 and SSVEP features efficiently, in which each output needed only 1s of EEG data. The task-related component analysis was used to decode the combined features. Five subjects participated in this study and achieved an average accuracy of 87.23% and a maximal accuracy of 100%. The corresponding information transfer rate was 56.85 bits/min and 71.10 bits/min, respectively. The BCI-controlled robotic arm could write a Chinese character '' with 16 strokes within 5.7 seconds for the best subject. The demo video can be found at https://www.youtube.com/watch?v=A1w-e2dBGl0. The study results demonstrated that the proposed BCI-controlled robot is efficient for writing ideogram (e.g. Chinese characters) and phonogram (e.g. English letter), leading to broad prospects for real-world applications of BCIs.},
}
@article {pmid33019049,
year = {2020},
author = {Quiles, V and Ferrero, L and Ianez, E and Ortiz, M and Megia, A and Comino, N and Gil-Agudo, AM and Azorin, JM},
title = {Usability and acceptance of using a lower-limb exoskeleton controlled by a BMI in incomplete spinal cord injury patients: a case study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4737-4740},
doi = {10.1109/EMBC44109.2020.9175738},
pmid = {33019049},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Exoskeleton Device ; Gait ; Humans ; Lower Extremity ; *Spinal Cord Injuries ; },
abstract = {Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs), as they can be used to foster patients' neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.},
}
@article {pmid33018951,
year = {2020},
author = {Savolainen, OW and Constandinou, TG},
title = {Lossless Compression of Intracortical Extracellular Neural Recordings using Non-Adaptive Huffman Encoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4318-4321},
doi = {10.1109/EMBC44109.2020.9176352},
pmid = {33018951},
issn = {2694-0604},
mesh = {*Data Compression ; Neural Networks, Computer ; Physical Phenomena ; },
abstract = {This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data compression schemes for Wireless Intracortical Brain-Machine Interfaces (WI-BMI). The considered schemes include pre-trained Lone 1[st] and 2[nd] order encoding [1], pre-trained Delta encoding, and pre-trained Linear Neural Network Time (LNNT) encoding [2]. Maximum codeword-length limited versions are also considered to protect against overfit to training data. The considered signals are the Extracellular Action Potential signal, the Entire Spiking Activity signal, and the Local Field Potential signal. Sample resolutions of 5 to 13 bits are considered. The result show that overfit-protection dramatically improves compression, especially at higher sample resolutions. Across signals, 2[nd] order encoding generally performed best at lower sample resolutions, and 1[st] order, Delta and LNNT encoding performed best at higher sample resolutions. The proposed methods should generalise to other remote sensing applications where the distribution of the sensed data can be estimated a priori.},
}
@article {pmid33018889,
year = {2020},
author = {Yang, SY and Lin, YP},
title = {Validating a LEGO-Like EEG Headset for a Simultaneous Recording of Wet- and Dry-Electrode Systems During Treadmill Walking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4055-4058},
doi = {10.1109/EMBC44109.2020.9176190},
pmid = {33018889},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; *Walking ; },
abstract = {Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.},
}
@article {pmid33018888,
year = {2020},
author = {Moslehi, AH and Bagheri, M and Ludwig, AM and Davies, TC},
title = {Discrimination of Two-Class Motor Imagery in a fNIRS Based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4051-4054},
doi = {10.1109/EMBC44109.2020.9175808},
pmid = {33018888},
issn = {2694-0604},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Humans ; Imagery, Psychotherapy ; Spectroscopy, Near-Infrared ; },
abstract = {The purpose of this study was to discriminate between left- and right-hand motor imagery tasks. We recorded the brain signals from two participants using a fNIRS system and compared different feature extraction (mean, peak, minimum, skewness and kurtosis) and classification techniques (linear (LDA) and quadratic discriminant analysis (QDA), support vector machine (SVM), logistic regression, K-nearest-neighbor (KNN), and neural networks with Levenberg-Marquardt (LMA), Bayesian Regularization (BRANN) and Scaled Conjugate Gradient (SCGA) training algorithms). The results showed poor classification accuracies (<; 58%) when skewness and kurtosis were used. When mean, peak, and minimum were used as features, QDA, SVM and KNN produced higher classification accuracies relative to LDA and logistic regression. Overall, BRANN led to the highest accuracies (>98%) when mean, peak and minimum were used as features.},
}
@article {pmid33018887,
year = {2020},
author = {Wahalla, MN and Vaya, GP and Blume, H},
title = {CereBridge: An Efficient, FPGA-based Real-Time Processing Platform for True Mobile Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {4046-4050},
doi = {10.1109/EMBC44109.2020.9175623},
pmid = {33018887},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; Electric Power Supplies ; Signal Processing, Computer-Assisted ; },
abstract = {In general, the signal chain in modern mobile Brain-Computer Interfaces (BCIs) is subdivided into at least two blocks. These are usually wirelessly connected with digital signal processing part implemented separately and often stationary. This causes a limited mobility and results in an additional, although avoidable, latency due to the wireless transmission channel. Therefore, a novel, entirely mobile FPGA-based platform for BCIs has been designed and implemented. While featuring highly efficient adaptability to targeted algorithms due to the ultra low power Flash-based FPGA, the stackable system design and the configurable hardware ensure flexibility for the use in different application scenarios. Powered through a single Li-ion battery, the miniaturized system area of half the size of a credit card leads to high mobility and thus allow for real-world scenario applicability. A Bluetooth Low Energy extension can be connected without any significant area cost, if a wireless data or control signal transmission channel is required. The resulting system is capable of acquiring and fully processing of up to 32 EEG channels with 24 bit precision each and a sampling rate of 250-16k samples per second with a total weight less than 60 g.},
}
@article {pmid33018850,
year = {2020},
author = {Zhang, X and Li, H and Chen, F},
title = {EEG-based Classification of Imaginary Mandarin Tones.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3889-3892},
doi = {10.1109/EMBC44109.2020.9176608},
pmid = {33018850},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Speech ; },
abstract = {Speech imagery based brain-computer interface (BCI) has the potential to assist patients with communication disorders to recover their speech communication abilities. Mandarin is a tonal language, and its tones play an important role in language perception and semantic understanding. This work studied the electroencephalogram (EEG) based classification of Mandarin tones based on speech imagery, and also compared the classification performance of speech imagery based BCIs at two test conditions with visual-only and combined audio-visual stimuli, respectively. Participants imagined 4 Mandarin tones at each condition. Common spatial patterns were applied to extract feature vectors, and support vector machine was used to classify different Mandarin tones from EEG data. Experimental results showed that the tonal articulation imagination task achieved a higher classification accuracy at the combined audio-visual condition (i.e., 80.1%) than at the visual-only condition (i.e., 67.7%). The results in this work supported that Mandarin tone information could be decoded from EEG data recorded in a speech imagery task, particularly under the combined audio-visual condition.},
}
@article {pmid33018840,
year = {2020},
author = {Prieur-Coloma, Y and Delisle-Rodriguez, D and Mayeta-Revilla, L and Gurve, D and Reinoso-Leblanch, RA and Lopez-Delis, A and Bastos, T and Krishnan, S and da Rocha, AF},
title = {Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3848-3851},
doi = {10.1109/EMBC44109.2020.9175263},
pmid = {33018840},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; *Exoskeleton Device ; Shoulder ; Upper Extremity ; },
abstract = {This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.},
}
@article {pmid33018837,
year = {2020},
author = {Soriano-Segura, P and Ianez, E and Quiles, V and Ferrero, L and Ortiz, M and Azorin, JM},
title = {Selection of Spatial, Temporal and Frequency Features to Detect Direction Changes During Gait.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3835-3838},
doi = {10.1109/EMBC44109.2020.9176164},
pmid = {33018837},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; *Gait ; Humans ; Movement ; Walking ; },
abstract = {This paper studies the direction changes during the gait by means of two different distributions of electrodes located in the motor, premotor and occipital areas. The objective is analyzing which areas are involved in the detection of the intention of turning while the person is walking. The signals in both options are characterized with frequency and temporal features and classified following a cross-validation process. A 95% of success rate is achieved when the electrodes are disposed along the motor, premotor and occipital areas.Clinical Relevance- The objective of this study is applying the acknowledgements obtained in the designing of a brain-machine interface (BMI) based in the detection of the intention of the direction change during the gait. This BMI has clinical relevance in the rehabilitation of the gait in patients with motor injuries, assisting the patient to perform the movements as realistic as it is possible.},
}
@article {pmid33018759,
year = {2020},
author = {Katthi, JR and Ganapathy, S and Kothinti, S and Slaney, M},
title = {Deep Canonical Correlation Analysis For Decoding The Auditory Brain.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3505-3508},
doi = {10.1109/EMBC44109.2020.9176208},
pmid = {33018759},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Attention ; *Brain ; *Electroencephalography ; Humans ; Noise ; },
abstract = {The process of decoding the auditory brain for an acoustic stimulus involves finding the relationship between the audio input and the brain activity measured in terms of Electroencephalography (EEG) recordings. Prior methods focus on linear analysis methods like Canonical Correlation Analysis (CCA) to establish a relationship. In this paper, we present a deep learning framework that is learned to maximize correlation. For dealing with high levels of noise in EEG data, we employ regularization techniques and experiment with various model architectures. With a paired dataset of audio envelope and EEG, we perform several experiments with deep correlation analysis using forward and backward correlation models. In these experiments, we show that regularized deep CCA is consistently able to outperform the linear models in terms of providing improved correlation (up to 9% absolute improvement in Pearson correlation which is statistically significant). We present an analysis that highlights the benefits of using dropouts for neural network regularization in the deep CCA model.Clinical relevance - The proposed method helps to decode human auditory attention. In the case of overlapping speech from two speakers, decoding the auditory attention provides information about how well the sources are separated in the brain and which of the sources is attended. This can impact cochlear implants that use EEG for decoding attention as well as in development of BCI applications. The correlation method proposed in this work can also be extended to other modalities like visual stimuli.},
}
@article {pmid33018758,
year = {2020},
author = {Leahy, LP and Bohannon, A and Rangavajhala, S and Tweedell, AJ and Hogan, N and Bradford, JC},
title = {Torque Estimation Using Neural Drive for a Concentric Contraction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3501-3504},
doi = {10.1109/EMBC44109.2020.9175710},
pmid = {33018758},
issn = {2694-0604},
mesh = {Algorithms ; Electromyography ; *Movement ; *Muscle Contraction ; Torque ; },
abstract = {The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. A challenge across these research areas is improving user-interface control. One established approach is using neural control interfaces derived from surface electromyography (sEMG). Although there has been some success with sEMG controlled prosthetics, the coarse nature of traditional sEMG processing has limited the development of fully functional prosthetics and wearable robotics. To solve this problem, blind source separation (BSS) techniques have been implemented to extract the user's movement intent from high-density sEMG (HDsEMG) measurements; however, current methods have only been well validated during static, low-level muscle contractions, and it is unclear how they will perform during movement. In this paper we present a neural drive based method for predicting output torque during a constant force, concentric contraction. This was achieved by modifying an existing HDsEMG decomposition algorithm to decompose 1 sec. overlapping windows. The neural drive profile was computed using both rate coding and kernel smoothing. Neither rate coding nor kernel smoothing performed as well as HDsEMG amplitude estimation, indicating that there are still significant limitations in adapting current methods to decompose dynamic contractions, and that sEMG amplitude estimation methods still remain highly reliable estimators.},
}
@article {pmid33018756,
year = {2020},
author = {Lim, J and Wang, PT and Shaw, SJ and Armacost, M and Gong, H and Liu, CY and Do, AH and Heydari, P and Nenadic, Z},
title = {Pre-whitening and Null Projection as an Artifact Suppression Method for Electrocorticography Stimulation in Bi-Directional Brain Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3493-3496},
doi = {10.1109/EMBC44109.2020.9175760},
pmid = {33018756},
issn = {2694-0604},
mesh = {Artifacts ; *Brain-Computer Interfaces ; *Electrocorticography ; Projection ; Signal Processing, Computer-Assisted ; },
abstract = {Electrocorticography (ECoG)-based bi-directional (BD) brain-computer interfaces (BCIs) are a forthcoming technology promising to help restore function to those with motor and sensory deficits. A major problem with this paradigm is that the cortical stimulation necessary to elicit artificial sensation creates strong electrical artifacts that can disrupt BCI operation by saturating recording amplifiers or obscuring useful neural signal. Even with state-of-the-art hardware artifact suppression methods, robust signal processing techniques are still required to suppress residual artifacts that are present at the digital back-end. Herein we demonstrate the effectiveness of a pre-whitening and null projection artifact suppression method using ECoG data recorded during a clinical neurostimulation procedure. Our method achieved a maximum artifact suppression of 21.49 dB and significantly increased the number of artifact-free frequencies in the frequency domain. This performance surpasses that of a more traditional independent component analysis methodology, while retaining a reduced complexity and increased computational efficiency.},
}
@article {pmid33018755,
year = {2020},
author = {Farsiani, S and Sodagar, AM},
title = {Hardware and Power-Efficient Compression Technique Based on Discrete Tchebichef Transform for Neural Recording Microsystems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3489-3492},
doi = {10.1109/EMBC44109.2020.9175430},
pmid = {33018755},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computers ; *Data Compression ; Records ; },
abstract = {In this paper a new compression technique based on the discrete Tchebichef transform is presented. To comply with strict on-implant hardware implementation requirements, such as low power dissipation and small silicon area consumption, the discrete Tchebichef transform is modified and truncated. An algorithm is proposed to generate approximate transform matrices capable of truncation without suffering from destructive energy leakage among the coefficients. This is achieved by preserving orthogonality of the basis functions that convey majority portion of the signal energy. Based on the presented algorithm, a new truncated transformation matrix is proposed, which reduces the hardware complexity by up to 74% compared to that of the original transform. Hardware implementation of the proposed neural signal compression technique is prototyped using standard digital hardware. With pre-recorded neural signals as the input, compression rate of 26.15 is achieved while the root-mean-square of error is kept as low as 1.1%.Clinical Relevance- This paper proposes a technique for data compression in high-density neural recording brain implants, along with a power- and area-efficient hardware implementation. From among clinical applications of such implants one can point to neuro-prostheses, and brain-machine interfaces for therapeutic purposes.},
}
@article {pmid33018747,
year = {2020},
author = {An, WW and Pei, A and Noyce, AL and Shinn-Cunningham, B},
title = {Decoding auditory attention from single-trial EEG for a high-efficiency brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3456-3459},
doi = {10.1109/EMBC44109.2020.9175753},
pmid = {33018747},
issn = {2694-0604},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; },
abstract = {Brain-computer interface (BCI) systems enable humans to communicate with a machine in a non-verbal and covert way. Many past BCI designs used visual stimuli, due to the robustness of neural signatures evoked by visual input. However, these BCI systems can only be used when visual attention is available. This study proposes a new BCI design using auditory stimuli, decoding spatial attention from electroencephalography (EEG). Results show that this new approach can decode attention with a high accuracy (>75%) and has a high information transfer rate (>10 bits/min) compared to other auditory BCI systems. It also has the potential to allow decoding that does not depend on subject-specific training.},
}
@article {pmid33018738,
year = {2020},
author = {Paek, AY and Kilicarslan, A and Korenko, B and Gerginov, V and Knappe, S and Contreras-Vidal, JL},
title = {Towards a Portable Magnetoencephalography Based Brain Computer Interface with Optically-Pumped Magnetometers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3420-3423},
doi = {10.1109/EMBC44109.2020.9176159},
pmid = {33018738},
issn = {2694-0604},
support = {R01 EB027004/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Magnetoencephalography ; Movement ; },
abstract = {Brain Computer Interfaces (BCIs) allow individuals to control devices, machines and prostheses with their thoughts. Most feasibility studies with BCIs have utilized scalp electroencephalography (EEG), due to it being accessible, noninvasive, and portable. While BCIs have been studied with magnetoencephalography (MEG), the modality has limited applications due to the large immobile hardware. Here we propose that room-temperature, optically-pumped magnetometers (OPMs) can potentially serve a portable modality that can be used for BCIs. OPMs have the added advantage that low-frequency neuromagnetic fields are not affected by volume conduction, which is known to distort EEG signals. In this feasibility study, we tested an OPM system with a real-time BCI where able bodied participants controlled a cursor to reach two targets. This BCI system used alpha and beta-band power modulations associated with hand movements. Our preliminary results show significant alpha and beta-band desynchronization due to movement, as found in previous literature.},
}
@article {pmid33018725,
year = {2020},
author = {Kalafatovich, J and Lee, M and Lee, SW},
title = {Prediction of Memory Retrieval Performance Using Ear-EEG Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3363-3366},
doi = {10.1109/EMBC44109.2020.9175990},
pmid = {33018725},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Cognition ; *Electroencephalography ; Humans ; Memory ; },
abstract = {Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus: 74.06%, on-going stimulus: 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.},
}
@article {pmid33018724,
year = {2020},
author = {Wang, L and Xu, M and Mei, J and Han, J and Wang, Y and Jung, TP and Ming, D},
title = {Enhancing performance of SSVEP-based BCI by unsupervised learning information from test trials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3359-3362},
doi = {10.1109/EMBC44109.2020.9176851},
pmid = {33018724},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Unsupervised Machine Learning ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEPs) have become one of the most used neural signals for brain- computer interfaces (BCIs) due to their stability and high signal- to-noise rate. However, the performance of SSVEP-based BCIs would degrade with a few training samples. This study was proposed to enhance the detection of SSVEP by combining the supervised learning information from training samples and the unsupervised learning information from the trial to be tested. A new method, i.e. cyclic shift trials (CST), was proposed to generate new calibration samples from the test data, which were furtherly used to create the templates and spatial filters of task- related component analysis (TRCA). The test-trial templates and spatial filters were combined with training-sample templates and spatial filters to recognize SSVEP. The proposed algorithm was tested on a benchmark dataset. As a result, it reached significantly higher classification accuracy than traditional TRCA when only two training samples were used. Speciflcally, the accuracy was improved by 9.5% for 0.7s data. Therefore, this study demonstrates CST is effective to improve the performance of SSVEP-BCI.},
}
@article {pmid33018723,
year = {2020},
author = {Quick, KM and Weiss, JM and Clemente, F and Gaunt, RA and Collinger, JL},
title = {Intracortical Microstimulation Feedback Improves Grasp Force Accuracy in a Human Using a Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3355-3358},
pmid = {33018723},
issn = {2694-0604},
support = {UH3 NS107714/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Feedback ; Hand Strength ; Humans ; Somatosensory Cortex ; Touch ; },
abstract = {After a spinal cord injury, a person may grasp objects using a brain-computer interface (BCI) to control a robot arm. However, most BCIs do not restore somatosensory percepts that would enable someone to sense grasp force. Intracortical microstimulation (ICMS) in the somatosensory cortex can evoke tactile sensations and may therefore offer a viable solution to provide grasp force feedback. We investigated whether a bidirectional BCI could improve grasp force control over a BCI using only visual feedback. When evaluating the error of the applied force during a force matching task, we found that ICMS feedback improved overall applied grasp force accuracy.},
}
@article {pmid33018722,
year = {2020},
author = {Shen, X and Zhang, X and Huang, Y and Chen, S and Wang, Y},
title = {Reinforcement Learning based Decoding Using Internal Reward for Time Delayed Task in Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3351-3354},
doi = {10.1109/EMBC44109.2020.9175964},
pmid = {33018722},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; Prefrontal Cortex ; Rats ; Reinforcement, Psychology ; Reward ; },
abstract = {Reinforcement learning (RL) algorithm interprets neural signals into movement intentions with the guidance of the reward in Brain-machine interfaces (BMIs). Current RL algorithms generally work for the tasks with immediate rewards delivery, and lack of efficiency in delayed reward task. Prefrontal cortex, including medial prefrontal cortex(mPFC), has been demonstrated to assign credit to intermediate steps, which reinforces preceding action more efficiently. In this paper, we propose to simulate the functionality of mPFC activities as intermediate rewards to train a RL based decoder in a two-step movement task. A support vector machine (SVM) is adopted to verify if the subject expects a reward due to the accomplishment of a subtask from mPFC activity. Then this discrimination result will be utilized to guide the training of the RL decoder for each step respectively. Here, we apply the Sarsa-style attention-gated reinforcement learning (SAGREL) as the decoder to interpret motor cortex(M1) activity to action states. We test on in vivo primary motor cortex (M1) and mPFC data collected from rats, where the rats need to first trigger the start and then press lever for rewards using M1 signals. SAGREL using intermediate rewards from mPFC activities achieves a prediction accuracy of 66.8% ± 2.0.% (mean ± std) %, which is significantly better than the one using the reward by the end of trial (45.9.% ± 1.2%). This reveals the potentials of modelling mPFC activities as intermediate rewards for the delayed reward tasks.},
}
@article {pmid33018682,
year = {2020},
author = {Aliakbaryhosseinabadi, S and Mrachacz-Kersting, N},
title = {Adaptive Brain-Computer Interface with Attention Alterations in Patients with Amyotrophic Lateral Sclerosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3188-3191},
doi = {10.1109/EMBC44109.2020.9175997},
pmid = {33018682},
issn = {2694-0604},
mesh = {*Amyotrophic Lateral Sclerosis ; Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurofeedback ; },
abstract = {The users' mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users' attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users' attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.},
}
@article {pmid33018658,
year = {2020},
author = {Yue, L and Xiao, X and Xu, M and Chen, L and Wang, Y and Jung, TP and Ming, D},
title = {A brain-computer interface based on high-frequency steady-state asymmetric visual evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3090-3093},
doi = {10.1109/EMBC44109.2020.9176855},
pmid = {33018658},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Physical Phenomena ; Signal-To-Noise Ratio ; },
abstract = {Steady State Visual Evoked Potentials (SSVEPs) have been widely used in Brain-Computer Interfaces (BCIs). SSVEP-BCIs have advantages of high classification accuracy, high information transfer rate, and strong anti-interference ability. Traditional studies mostly used low/medium frequency SSVEPs as system control signals. However, visual flickers with low/medium frequencies are uncomfortable, and even cause visual fatigue and epilepsy seizure. High-frequency SSVEP is a promising approach to solve these problems, but its miniature amplitude and low signal-to-noise ratio (SNR) would pose great challenges for target recognition. This study developed an innovative BCI paradigm to enhance the SNR of high-frequency SSVEP, which is named Steady-State asymmetrically Visual Evoked Potential (SSaVEP). Ten characters were encoded by ten couples of asymmetric flickers whose durations only lasted one second and frequencies ranged from 31 to 40 Hz with a step of 1 Hz. Discriminative canonical pattern matching (DCPM) was used to decode the high-frequency SSaVEP signals. Four subjects participated in the offline experiment. As a result, the accuracy achieved an average of 87.5% with a peak of 97.1%. The simulated online information transfer rate reached 87.2 bits/min on average and 111.2 bits/min for maximum. The results of this study demonstrate the high-frequency SSaVEP paradigm is a promising approach to alleviate the discomfort caused by visual stimuli and thereby can broaden the applications of BCIs.},
}
@article {pmid33018657,
year = {2020},
author = {Zhang, X and Wang, Y},
title = {Covariant Cluster Transfer for Kernel Reinforcement Learning in Brain-Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3086-3089},
doi = {10.1109/EMBC44109.2020.9175985},
pmid = {33018657},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Learning ; Reinforcement, Psychology ; Reward ; },
abstract = {Brain-Machine Interface (BMI) provides a promising way to help disabled people restore their motor functions. The patients are able to control the external devices directly from their neural signals by the decoder. Due to various reasons such as mental fatigue and distraction, the distribution of the neural signals might change, which might lead to poor performance for the decoder. In this case, we need to calibrate the parameters before each session, which needs the professionals to label the data and is not convenient for the patient's usage at home. In this paper, we propose a covariant cluster transfer mechanism for the kernel reinforcement learning (RL) algorithm to speed up the adaptation across sessions. The parameters of the decoder will adaptively change according to a reward signal, which could be easily set by the patient. More importantly, we cluster the neural patterns in previous sessions. The cluster represents the conditional distribution from neural patterns to actions. When a distinct neural pattern appears in the new session, the nearest cluster will be transferred. In this way, the knowledge from the old session could be utilized to accelerate the learning in the new session. Our proposed algorithm is tested on the simulated neural data where the neural signal's distribution differs across sessions. Compared with the training from random initialization and a weight transfer policy, our proposed cluster transfer mechanism maintains a significantly higher success rate and a faster adaptation when the conditional distribution from neural signals to actions remains similar.},
}
@article {pmid33018656,
year = {2020},
author = {Sohn, WJ and Wang, PT and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {A Prototype of a Fully-Implantable Charge-Balanced Artificial Sensory Stimulator for Bi-directional Brain-Computer-Interface (BD-BCI).},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3083-3085},
doi = {10.1109/EMBC44109.2020.9176718},
pmid = {33018656},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Movement ; Sensation ; },
abstract = {Bi-directional brain-computer interfaces (BD-BCI) to restore movement and sensation must achieve concurrent operation of recording and decoding of motor commands from the brain and stimulating the brain with somatosensory feedback. Previously we developed and validated a benchtop prototype of a fully implantable BCI system for motor decoding. Here, a prototype artificial sensory stimulator was integrated into the benchtop system to develop a prototype of a fully-implantable BD-BCI. The artificial sensory stimulator incorporates an active charge balancing mechanism based on pulse-width modulation to ensure safe stimulation for chronically interfaced electrodes to prevent damage to brain tissue and electrodes. The feasibility of the BD-BCI system's active charge balancing was tested in phantom brain tissue. With the charge-balancing, the removal of the residual charges on an electrode was evident. This is a critical milestone toward fully-implantable BD-BCI systems.},
}
@article {pmid33018655,
year = {2020},
author = {Chen, S and Zhang, X and Shen, X and Huang, Y and Wang, Y},
title = {Estimating Neural Modulation via Adaptive Point Process Method in Brain-machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3078-3081},
doi = {10.1109/EMBC44109.2020.9175240},
pmid = {33018655},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; Movement ; Neurons ; },
abstract = {Brain-machine interfaces (BMIs) translate neural signals into digital commands to control external devices. During the use of BMI, neurons may change their activity corresponding to the same stimuli or movement. The changes are represented by the neural tuning parameters which may change gradually and abruptly. Adaptive algorithms were proposed to estimate the time-varying parameters in order to keep decoding performance stable. The existing methods only searched new parameters locally which failed to detect the abrupt changes. Global search helps but requires the known boundary of estimated parameter which is hard to be defined in many cases. We propose to estimate the neural modulation parameter by the global search using adaptive point process estimation. This neural modulation parameter represents the similarity between the kinematics and the neural preferred hyper tuning direction with finite range [0,1]. The preferred hyper tuning direction is then decoupled from the neural modulation parameter by gradient descent method. We apply the proposed method on real data to detect the abrupt change of the neural tuning parameter when the subject switched from manual control to brain control mode. The proposed method demonstrates better tracking on the neural hyper tuning parameters than local searching method and validated by KS statistical test.},
}
@article {pmid33018654,
year = {2020},
author = {Yu, H and Xu, M and Meng, J and Ma, Z and Ming, D},
title = {Classification of auditory attention focuses during speech perception.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3074-3077},
doi = {10.1109/EMBC44109.2020.9176300},
pmid = {33018654},
issn = {2694-0604},
mesh = {Attention ; Auditory Perception ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Speech Perception ; },
abstract = {Passive brain-computer interfaces (BCIs) covertly decode the cognitive and emotional states of users by using neurophysiological signals. An important issue for passive BCIs is to monitor the attentional state of the brain. Previous studies mainly focus on the classification of attention levels, i.e. high vs. low levels, but few has investigated the classification of attention focuses during speech perception. In this paper, we tried to use electroencephalography (EEG) to recognize the subject's attention focuses on either call sign or number when listening to a short sentence. Fifteen subjects participated in this study, and they were required to focus on either call sign or number for each listening task. A new algorithm was proposed to classify the EEG patterns of different attention focuses, which combined common spatial pattern (CSP), short-time Fourier transformation (STFT) and discriminative canonical pattern matching (DCPM). As a result, the accuracy reached an average of 78.38% with a peak of 93.93% for single trial classification. The results of this study demonstrate the proposed algorithm is effective to classify the auditory attention focuses during speech perception.},
}
@article {pmid33018653,
year = {2020},
author = {Chiang, KJ and Nakanishi, M and Jung, TP},
title = {Statistically Optimized Spatial Filtering in Decoding Steady-State Visual Evoked Potentials Based on Task-Related Component Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3070-3073},
doi = {10.1109/EMBC44109.2020.9176205},
pmid = {33018653},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination ; },
abstract = {Task-related component analysis (TRCA) has been the most effective spatial filtering method in implementing high-speed brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). TRCA is a data-driven method, in which spatial filters are optimized to maximize inter-trial covariance of time-locked electroencephalographic (EEG) data, formulated as a generalized eigenvalue problem. Although multiple eigenvectors can be obtained by TRCA, the traditional TRCA-based SSVEP detection considered only one that corresponds to the largest eigenvalue to reduce its computational cost. This study proposes using multiple eigen-vectors to classify SSVEPs. Specifically, this study integrates a task consistency test, which statistically identifies whether the component reconstructed by each eigenvector is task-related or not, with the TRCA-based SSVEP detection method. The proposed method was evaluated by using a 12-class SSVEP dataset recorded from 10 subjects. The study results indicated that the task consistency test usually identified and suggested more than one eigenvectors (i.e., spatial filters). Further, the use of additional spatial filters significantly improved the classification accuracy of the TRCA-based SSVEP detection.},
}
@article {pmid33018652,
year = {2020},
author = {Serrano-Amenos, C and Hu, F and Wang, PT and Kellis, S and Andersen, RA and Liu, CY and Heydari, P and Do, AH and Nenadic, Z},
title = {Thermal Analysis of a Skull Implant in Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3066-3069},
doi = {10.1109/EMBC44109.2020.9175483},
pmid = {33018652},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Hot Temperature ; Humans ; Prostheses and Implants ; Skull ; },
abstract = {The goal of this study is to estimate the thermal impact of a titanium skull unit (SU) implanted on the exterior aspect of the human skull. We envision this unit to house the front-end of a fully implantable electrocorticogram (ECoG)-based bi-directional (BD) brain-computer interface (BCI). Starting from the bio-heat transfer equation with physiologically and anatomically constrained tissue parameters, we used the finite element method (FEM) implemented in COMSOL to build a computational model of the SU's thermal impact. Based on our simulations, we predicted that the SU could consume up to 75 mW of power without raising the temperature of surrounding tissues above the safe limits (increase in temperature of 1°C). This power budget by far exceeds the power consumption of our front-end prototypes, suggesting that this design can sustain the SU's ability to record ECoG signals and deliver cortical stimulation. These predictions will be used to further refine the existing SU design and inform the design of future SU prototypes.},
}
@article {pmid33018651,
year = {2020},
author = {Torkamani-Azar, M and Jafarifarmand, A and Cetin, M},
title = {Prediction of Motor Imagery Performance based on Pre-Trial Spatio-Spectral Alertness Features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3062-3065},
doi = {10.1109/EMBC44109.2020.9175929},
pmid = {33018651},
issn = {2694-0604},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Imagination ; },
abstract = {Electroencephalogram (EEG) based brain-computer interfaces (BCIs) enable communication by interpreting the user intent based on measured brain electrical activity. Such interpretation is usually performed by supervised classifiers constructed in training sessions. However, changes in cognitive states of the user, such as alertness and vigilance, during test sessions lead to variations in EEG patterns, causing classification performance decline in BCI systems. This research focuses on effects of alertness on the performance of motor imagery (MI) BCI as a common mental control paradigm. It proposes a new protocol to predict MI performance decline by alertness-related pre-trial spatio-spectral EEG features. The proposed protocol can be used for adapting the classifier or restoring alertness based on the cognitive state of the user during BCI applications.},
}
@article {pmid33018650,
year = {2020},
author = {Miladinovic, A and Ajcevic, M and Busan, P and Jarmolowska, J and Silveri, G and Deodato, M and Mezzarobba, S and Battaglini, PP and Accardo, A},
title = {Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3058-3061},
doi = {10.1109/EMBC44109.2020.9176651},
pmid = {33018650},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Neurological Rehabilitation ; *Parkinson Disease ; },
abstract = {The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.},
}
@article {pmid33018649,
year = {2020},
author = {Jiang, L and Li, X and Wang, Y and Pei, W and Gao, X and Chen, H},
title = {Comparison of Pupil Size and Visual Evoked Potentials under 1-6Hz Visual Stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3054-3057},
doi = {10.1109/EMBC44109.2020.9175893},
pmid = {33018649},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Pupil ; },
abstract = {In order to explore the effect of low frequency stimulation on pupil size and electroencephalogram (EEG), we presented subjects with 1-6Hz black-and-white-alternating flickering stimulus, and compared the differences of signal-to-noise ratio (SNR) and classification performance between pupil size and visual evoked potentials (VEPs). The results showed that the SNR of the pupillary response reached the highest at 1Hz (17.19± 0.10dB) and 100% accuracy was obtained at 1s data length, while the performance was poor at the stimulation frequency above 3Hz. In contrast, the SNR of VEPs reached the highest at 6Hz (18.57± 0.37dB), and the accuracy of all stimulus frequencies could reach 100%, with the minimum data length of 1.5s. This study lays a theoretical foundation for further implementation of a hybrid brain-computer interface (BCI) that integrates pupillometry and EEG.},
}
@article {pmid33018648,
year = {2020},
author = {Wirth, C and Toth, J and Arvaneh, M},
title = {Four-Way Classification of EEG Responses To Virtual Robot Navigation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3050-3053},
doi = {10.1109/EMBC44109.2020.9176230},
pmid = {33018648},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; *Robotics ; },
abstract = {Studies have shown the possibility of using brain signals that are automatically generated while observing a navigation task as feedback for semi-autonomous control of a robot. This allows the robot to learn quasi-optimal routes to intended targets. We have combined the subclassification of two different types of navigational errors, with the subclassification of two different types of correct navigational actions, to create a 4-way classification strategy, providing detailed information about the type of action the robot performed. We used a 2-stage stepwise linear discriminant analysis approach, and tested this using brain signals from 8 and 14 participants observing two robot navigation tasks. Classification results were significantly above the chance level, with mean overall accuracy of 44.3% and 36.0% for the two datasets. As a proof of concept, we have shown that it is possible to perform fine-grained, 4-way classification of robot navigational actions, based on the electroencephalogram responses of participants who only had to observe the task. This study provides the next step towards comprehensive implicit brain-machine communication, and towards an efficient semi-autonomous brain-computer interface.},
}
@article {pmid33018647,
year = {2020},
author = {Zheng, Q and Zhang, Y and Wan, Z and Malik, WQ and Chen, W and Zhang, S},
title = {Orthogonalizing the Activity of Two Neural Units for 2D Cursor Movement Control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3046-3049},
doi = {10.1109/EMBC44109.2020.9175931},
pmid = {33018647},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; *Motor Cortex ; Movement ; Neurons ; },
abstract = {In the design of brain-machine interface (BMI), as the number of electrodes used to collect neural spike signals declines slowly, it is important to be able to decode with fewer units. We tried to train a monkey to control a cursor to perform a two-dimensional (2D) center-out task smoothly with spiking activities only from two units (direct units). At the same time, we studied how the direct units did change their tuning to the preferred direction during BMI training and tried to explore the underlying mechanism of how the monkey learned to control the cursor with their neural signals. In this study, we observed that both direct units slowly changed their preferred directions during BMI learning. Although the initial angles between the preferred directions of 3 pairs units are different, the angle between their preferred directions approached 90 degrees at the end of the training. Our results imply that BMI learning made the two units independent of each other. To our knowledge, it is the first time to demonstrate that only two units could be used to control a 2D cursor movements. Meanwhile, orthogonalizing the activities of two units driven by BMI learning in this study implies that the plasticity of the motor cortex is capable of providing an efficient strategy for motor control.},
}
@article {pmid33018646,
year = {2020},
author = {Ju, C and Gao, D and Mane, R and Tan, B and Liu, Y and Guan, C},
title = {Federated Transfer Learning for EEG Signal Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3040-3045},
doi = {10.1109/EMBC44109.2020.9175344},
pmid = {33018646},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Machine Learning ; Privacy ; },
abstract = {The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.},
}
@article {pmid33018644,
year = {2020},
author = {Ming, G and Wang, Y and Pei, W and Chen, H},
title = {Characteristics of High-Frequency SSVEPs Evoked by Visual Stimuli at Different Polar Angles.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3031-3034},
doi = {10.1109/EMBC44109.2020.9175498},
pmid = {33018644},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Visual Cortex ; },
abstract = {The mapping of visual space onto human striate cortex allows the location of stimuli to affect the scalp distributions of electroencephalogram (EEG). To clarify the relationship between the characteristics of elicited high-frequency steady-state visual evoked potentials (SSVEPs) and the polar angle of stimulus, this study divided the annulus into eight symmetrical annular sectors (i.e., octants) as separate visual stimuli. For both 30 Hz and 60 Hz, the response intensity and classification accuracy indicated that the annular sectors in the lower visual field evoked stronger responses than those in the upper visual field. This paper also evaluated the phase differences between SSVEPs at specific polar angles and found clear individual differences across subjects. These findings may lead to inspirations for the design of new space coding methods for the SSVEP-based brain-computer interfaces (BCIs).},
}
@article {pmid33018643,
year = {2020},
author = {de Melo, GC and Martes Sternlicht, V and Forner-Cordero, A},
title = {EEG Analysis in Coincident Timing Task Towards Motor Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3027-3030},
doi = {10.1109/EMBC44109.2020.9175851},
pmid = {33018643},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Movement ; Rehabilitation ; Upper Extremity ; },
abstract = {The identification of specific components in EEG signals is often key when designing EEG-based brain-computer interfaces (BCIs), and a good understanding of the factors that elicit such components can be helpful when it comes to precise, energy-efficient and time-accurate actuation of exoskeletons. CNVs (Contingent Negative Variations), ERDs or ERSs (Event-Related Desynchronizations/Synchronizations) as well as ErrPs (Error-Related Potentials) are particularly important components can be identified during motor tasks and related to specific events in a Coincident Timing (CT) task. This work investigates offline EEG signals acquired during an upper limb CT task and analyzes the task protocol with the purpose of correlating the aforementioned EEG features to movement onset. CNVs and ERD/ERS were successfully identified after averaging multiple trials, and it was further concluded that complementary information about muscle activity (via EMG) as well as video tracking of arm movement play a critical role in the synchronization of EEG components with movement onset. The framework for EEG analysis presented in this paper allows for future development of a BCI on top of this CT task capable of assessing motor learning and actuating an exoskeleton to enable faster motor rehabilitation.},
}
@article {pmid33018642,
year = {2020},
author = {Martineau, T and He, S and Vaidyanathan, R and Brown, P and Tan, H},
title = {Optimizing Time-Frequency Feature Extraction and Channel Selection through Gradient Backpropagation to Improve Action Decoding based on Subthalamic Local Field Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3023-3026},
pmid = {33018642},
issn = {2694-0604},
support = {MC_UU_00003/2/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Hand Strength ; Humans ; *Movement Disorders ; },
abstract = {Neural oscillating patterns, or time-frequency features, predicting voluntary motor intention, can be extracted from the local field potentials (LFPs) recorded from the sub-thalamic nucleus (STN) or thalamus of human patients implanted with deep brain stimulation (DBS) electrodes for the treatment of movement disorders. This paper investigates the optimization of signal conditioning processes using deep learning to augment time-frequency feature extraction from LFP signals, with the aim of improving the performance of real-time decoding of voluntary motor states. A brain-computer interface (BCI) pipeline capable of continuously classifying discrete pinch grip states from LFPs was designed in Pytorch, a deep learning framework. The pipeline was implemented offline on LFPs recorded from 5 different patients bilaterally implanted with DBS electrodes. Optimizing channel combination in different frequency bands and frequency domain feature extraction demonstrated improved classification accuracy of pinch grip detection and laterality of the pinch (either pinch of the left hand or pinch of the right hand). Overall, the optimized BCI pipeline achieved a maximal average classification accuracy of 79.67±10.02% when detecting all pinches and 67.06±10.14% when considering the laterality of the pinch.},
}
@article {pmid33018641,
year = {2020},
author = {Phyo Wai, AA and Lee, JC and Yang, T and So, R and Guan, C},
title = {Effects of Stimulus Spatial Resolution on SSVEP Responses under Overt and Covert Attention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3019-3022},
doi = {10.1109/EMBC44109.2020.9176678},
pmid = {33018641},
issn = {2694-0604},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Visual Fields ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) relies on overt spatial attention to exhibit reliable steady-state responses. There is a promising potential to employ the SSVEP paradigm in with vision research and clinical use, for instance, for visual field assessment. In this study, we investigate the SSVEP characteristics with different spatial attention, the different number of stimuli, and different viewing/visual angles. We collected data from eleven subjects in three experiment sessions, lasting about forty minutes, including the setup and calibration. Our evaluation results show similar SSVEP responses between overt and covert attention in multiple stimuli scenarios in most of the visual angles. We do not find any significant differences in SSVEP responses in visual angles between single and multi stimuli in covert attention. From this study, we found that reliable SSVEP responses can be achieved with covert spatial attention regardless of visual angles and stimulus spatial resolution.},
}
@article {pmid33018640,
year = {2020},
author = {Cho, JH and Jeong, JH and Lee, SW},
title = {Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3015-3018},
doi = {10.1109/EMBC44109.2020.9175784},
pmid = {33018640},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand Strength ; Motion ; Movement ; },
abstract = {Electroencephalogram (EEG) based braincomputer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49(±8.74)% for motor execution (ME) and 40.36(±3.39)% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.},
}
@article {pmid33018639,
year = {2020},
author = {Nur Chowdhury, MS and Dutta, A and Robison, MK and Blais, C and Brewer, G and Bliss, DW},
title = {A Generalized Model to Estimate Reaction Time Corresponding to Visual Stimulus Using Single-Trial EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3011-3014},
doi = {10.1109/EMBC44109.2020.9175239},
pmid = {33018639},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Reaction Time ; Regression Analysis ; },
abstract = {The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.},
}
@article {pmid33018638,
year = {2020},
author = {Premchand, B and Toe, KK and Wang, C and Shaikh, S and Libedinsky, C and Ang, KK and So, RQ},
title = {Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {3007-3010},
doi = {10.1109/EMBC44109.2020.9175593},
pmid = {33018638},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Microelectrodes ; Movement ; *Neural Networks, Computer ; },
abstract = {Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a nonhuman primate (NHP), implanted with microelectrode array (MEA) while performing a directional joystick task. Next, we compared the LSTM model in decoding the joystick trajectories from the neural signals against the prevailing KF model. The results showed that the LSTM model yielded significantly improved decoding accuracy measured by mean correlation coefficient (0.84, p < 10[-7]) than the KF model (0.72). In addition, using a principal component analysis (PCA)-based dimensionality reduction technique yielded slightly deteriorated accuracies for both the LSTM (0.80) and KF (0.70) models, but greatly reduced the computational complexity. The results showed that the LSTM decoding model holds promise to improve decoding in BMIs for paralyzed humans.},
}
@article {pmid33018635,
year = {2020},
author = {Lopes-Dias, C and Sburlea, AI and Muller-Putz, GR},
title = {A Generic Error-related Potential Classifier Offers a Comparable Performance to a Personalized Classifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2995-2998},
doi = {10.1109/EMBC44109.2020.9176640},
pmid = {33018635},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Feedback ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) provide more independence to people with severe motor disabilities but current BCIs' performance is still not optimal and often the user's intentions are misinterpreted. Error-related potentials (ErrPs) are the neurophysiological signature of error processing and their detection can help improving a BCI's performance.A major inconvenience of BCIs is that they commonly require a long calibration period, before the user can receive feedback of their own brain signals. Here, we use the data of 15 participants and compare the performance of a personalized ErrP classifier with a generic ErrP classifier. We concluded that there was no significant difference in classification performance between the generic and the personalized classifiers (Wilcoxon signed rank tests, two-sided and one-sided left and right). This results indicate that the use of a generic ErrP classifier is a good strategy to remove the calibration period of a ErrP classifier, allowing participants to receive immediate feedback of the ErrP detections.},
}
@article {pmid33018634,
year = {2020},
author = {Kato, M and Kanoga, S and Hoshino, T and Fukami, T},
title = {Motor Imagery Classification of Finger Motions Using Multiclass CSP.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2991-2994},
doi = {10.1109/EMBC44109.2020.9176612},
pmid = {33018634},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Fingers ; Imagery, Psychotherapy ; },
abstract = {Electroencephalogram (EEG) data during motor imagery tasks regarding small-scale physical dynamics such as finger motions have low discriminability because capturing the spatial difference of the motions is difficult. We assumed that more discriminative features can be captured if spatial filters maximize the independence of each class data. This study constructed spatial filters named multiclass common spatial pattern (CSP), which maximize an approximation of mutual in-formation of extracted components and class labels, and applied them to a five-class motor-imagery dataset containing finger motion tasks. By applying multiclass CSP, the classification accuracies were improved (Mean SD: 40.6 ± 10.1%) compared with classical CSP (21.8 ± 2.5%) and no spatial filtering case (38.7±10.0%). In addition, we visualized learned spatial filters to assess the trend of discriminative features of finger motions. For these results, it was clear that multiclass CSP captured task-specific spatial maps for each finger motion and outperformed multiclass motor-imagery classification performance about 2% even when the tasks are small-scale physical dynamics.},
}
@article {pmid33018633,
year = {2020},
author = {Wu, C and Qiu, S and Xing, J and He, H},
title = {A CNN-based compare network for classification of SSVEPs in human walking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2986-2990},
doi = {10.1109/EMBC44109.2020.9176649},
pmid = {33018633},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; *Walking ; },
abstract = {Brain-computer interface (BCI) can provide a way for the disabled to interact with the outside world. Steady-state visual evoked potential (SSVEP), which evokes potential through visual stimulation is one of important BCI paradigms. In laboratory environment, the classification accuracy of SSVEPs is excellent. However, in motion state, the accuracy will be greatly affected and reduce quite a lot. In this paper, in order to improve the classification accuracy of the SSVEP signals in the motion state, we collected SSVEP data of five targets at three speeds of 0km/h, 2.5km/h and 5km/h. A compare network based on convolutional neural network (CNN) was proposed to learn the relationship between EEG signal and the template corresponding to each stimulus frequency and classify. Compared with traditional methods (i.e., CCA, FBCCA and SVM) and state-of-the-art method (CNN) on the collected SSVEP datasets of 20 subjects, the method we proposed always performed best at different speeds. Therefore, these results validated the effectiveness of the method. In addition, compared with the speed of 0 km / h, the accuracy of the compare network at a high walking rate (5km/h) did not decrease much, and it could still maintain a good performance.},
}
@article {pmid33018631,
year = {2020},
author = {Giles, J and Ang, KK and Mihaylova, L and Arvaneh, M},
title = {Weighted Transfer Learning of Dynamic Time Warped Data for Motor Imagery based Brain Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2977-2980},
doi = {10.1109/EMBC44109.2020.9176635},
pmid = {33018631},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Machine Learning ; },
abstract = {A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.},
}
@article {pmid33018630,
year = {2020},
author = {Shin, GH and Lee, M and Kim, HJ and Lee, SW},
title = {Prediction of Event Related Potential Speller Performance Using Resting-State EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2973-2976},
doi = {10.1109/EMBC44109.2020.9175914},
pmid = {33018630},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Frontal Lobe ; Humans ; },
abstract = {Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.},
}
@article {pmid33018629,
year = {2020},
author = {An, X and Zhou, X and Zhong, W and Liu, S and Li, X and Ming, D},
title = {Weighted Subject-Semi-Independent ERP-based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2969-2972},
doi = {10.1109/EMBC44109.2020.9176683},
pmid = {33018629},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; Data Collection ; Humans ; Research Design ; },
abstract = {Subject-independent brain-computer interfaces (SI-BCIs) which require no calibration process, are increasingly affect researchers in BCI field. The efficiencies (accuracies), however, were not satisfying till now. In this paper, we proposed a weighted subject-semi-independent classification method (WSSICM) for ERP based BCI system in which a few blocks data of target subject were used. 47 participants were attended in this study. We compared the accuracies of proposed method with traditional subject-specific classification method（SSCM） which used 15 blocks data of target subject. The averaged accuracies were 95.2% for the WSSICM at 5 blocks and 95.7% for the SSCM at 15 blocks. The accuracies of two method did not show significant difference (p-value=0.652). The method we proposed in this paper which could reduce the calibration time can be used for future BCI systems.},
}
@article {pmid33018628,
year = {2020},
author = {Wei, W and Qiu, S and Ma, X and Li, D and Zhang, C and He, H},
title = {A Transfer Learning Framework for RSVP-based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2963-2968},
doi = {10.1109/EMBC44109.2020.9175581},
pmid = {33018628},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Learning ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.},
}
@article {pmid33018626,
year = {2020},
author = {Zhang, C and Qiu, S and Wang, S and Wei, W and He, H},
title = {Temporal Dynamics on Decoding Target Stimuli in Rapid Serial Visual Presentation using Magnetoencephalography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2954-2958},
doi = {10.1109/EMBC44109.2020.9176174},
pmid = {33018626},
issn = {2694-0604},
mesh = {Cerebral Cortex ; Frontal Lobe ; *Magnetic Resonance Imaging ; *Magnetoencephalography ; Multivariate Analysis ; },
abstract = {Rapid serial visual presentation (RSVP) is a high efficient paradigm in brain-computer interface (BCI). Target detection accuracy is the first consideration of RSVP-BCI. But the influence of different frequency bands and time ranges on decoding accuracy are still an open questions. Moreover, the underlying neural dynamic of the rapid target detecting process is still unclear. Methods: This work focused the temporal dynamic of the responses triggered by target stimuli in a static RSVP paradigm using paired structural Magnetic Resonance Imaging (MRI) and magnetoencephalography (MEG) signals with different frequency bands. Multivariate pattern analysis (MVPA) was applied on the MEG signal with different frequency bands and time points after stimuli onset. Cortical neuronal activation estimation technology was also applied to present the temporal-spatial dynamic on cortex surface. Results: The MVPA results showed that the low frequency signals (0.1 - 7 Hz) yield highest decoding accuracy, and the decoding power reached its peak at 0.4 second after target stimuli onset. The cortical neuronal activation method identified the target stimuli triggered regions, like bilateral parahippocampal cortex, precentral gyrus and insula cortex, and the averaged time series were presented.},
}
@article {pmid33018625,
year = {2020},
author = {Mane, R and Robinson, N and Vinod, AP and Lee, SW and Guan, C},
title = {A Multi-view CNN with Novel Variance Layer for Motor Imagery Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2950-2953},
doi = {10.1109/EMBC44109.2020.9175874},
pmid = {33018625},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Reproducibility of Results ; Republic of Korea ; },
abstract = {Accurate and robust classification of Motor Imagery (MI) from Electroencephalography (EEG) signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To address this challenge, this paper proposes a novel, neuro-physiologically inspired convolutional neural network (CNN) named Filter-Bank Convolutional Network (FBCNet) for MI classification. Capturing neurophysiological signatures of MI, FBCNet first creates a multi-view representation of the data by bandpass-filtering the EEG into multiple frequency bands. Next, spatially discriminative patterns for each view are learned using a CNN layer. Finally, the temporal information is aggregated using a new variance layer and a fully connected layer classifies the resultant features into MI classes. We evaluate the performance of FBCNet on a publicly available dataset from Korea University for classification of left vs right hand MI in a subject-specific 10-fold cross-validation setting. Results show that FBCNet achieves more than 6.7% higher accuracy compared to other state-of-the-art deep learning architectures while requiring less than 1% of the learning parameters. We explain the higher classification accuracy achieved by FBCNet using feature visualization where we show the superiority of FBCNet in learning interpretable and highly generalizable discriminative features. We provide the source code of FBCNet for reproducibility of results.},
}
@article {pmid33018624,
year = {2020},
author = {Mu, J and Grayden, DB and Tan, Y and Oetomo, D},
title = {Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2946-2949},
doi = {10.1109/EMBC44109.2020.9175838},
pmid = {33018624},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neurologic Examination ; Photic Stimulation ; },
abstract = {The steady-state visual evoked potential (SSVEP) is a robust brain activity that has been used in brain-computer interface (BCI) applications. However, previous studies of SSVEP-based BCIs give contradictory results on which stimulation medium provides the best performance. This paper describes a comparison of electroencephalography (EEG) decoding accuracy between using an LCD screen, clear LEDs, and frosted LEDs to deliver flashing light stimulation. The LCD screen and frosted LEDs achieved similar mean accuracies, and both of them were significantly better than clear LEDs. Background contrast with the LEDs did not significantly influence SSVEP decoding accuracy. A strong correlation was found between SSVEP accuracy and frequency domain magnitudes of EEG measurements.},
}
@article {pmid33018607,
year = {2020},
author = {Xu, R and Wang, Y and Wang, N and Shi, X and Meng, L and Ming, D},
title = {The effect of static and dynamic visual stimulations on error-evoked brain responses.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2877-2880},
doi = {10.1109/EMBC44109.2020.9175983},
pmid = {33018607},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Humans ; Photic Stimulation ; },
abstract = {Error-related potentials (ErrPs) can reflect the brain's response to errors. Recently, it has been used in the studies on neural mechanisms of human cognition, such as error detection and conflict monitoring. Moreover, ErrPs have provided technical support for the development of brain-computer interface (BCI). However, the different effects of visual stimulation modes (dynamic or static) on ErrPs have not been revealed. This may seriously affect the recognition accuracy of the ErrPs in practical applications. Therefore, the aim of this study was to investigate how people respond to different types of visual stimulations. Nineteen participants were recruited in the ErrPs-based tasks with two visual stimulation modes (dynamic and static). The ErrPs were analyzed and the feature values (N1, P2, P3, N6 and P8, named by the occurrence time) were statistically compared. The results showed that the difference between correctness and error was reflected in P3, N6, P8 in dynamic stimulation; and N1, P3, N6 and P8 in static stimulation. In the event-related potential based on error, the differences between dynamic and static tasks were reflected in N1 and P2. In conclusion, this study found that the features with later occurrence were significantly affected by correctness and error in both cases, while the error-related change in N1 only existed under the static stimulation. We also found that the recognition of stimulation modes came earlier within about 300 ms after the start of visual stimulation.},
}
@article {pmid33018605,
year = {2020},
author = {Shamsi, F and Haddad, A and Zadeh, LN},
title = {Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {2869-2872},
doi = {10.1109/EMBC44109.2020.9175627},
pmid = {33018605},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; *Imagination ; Pain/diagnosis ; },
abstract = {The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86-80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating pain/no-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.},
}
@article {pmid33018129,
year = {2020},
author = {Khazaei, Y and Shahkooh, AA and Sodagar, AM},
title = {Spatial Redundancy Reduction in Multi-Channel Implantable Neural Recording Microsystems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {898-901},
doi = {10.1109/EMBC44109.2020.9175732},
pmid = {33018129},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Prostheses and Implants ; *Plastic Surgery Procedures ; Records ; },
abstract = {This paper introduces a lossless approach for data reduction in multi-channel neural recording microsystems. The proposed approach benefits from eliminating the redundancy that exists in the signals recorded from the same space in the brain, e.g., local field potentials in intra-cortical recording from neighboring recording sites. In this approach, a single baseline component is extracted from the original neural signals, which is treated as the component all the channels share in common. What remains is a set of channel-specific difference components, which are much smaller in word length compared to the sample size of the original neural signals. To make the proposed approach more efficient in data reduction, length of the difference component words is adaptively determined according to their instantaneous amplitudes. This approach is low in both computational and hardware complexity, which introduces it as an attractive suggestion for high-density neural recording brain implants. Applied on multi-channel neural signals intra-cortically recorded using 16 multi-electrode array, the data is reduced by around 48%. Designed in TSMC 130-nm standard CMOS technology, hardware implementation of this technique for 16 parallel channels occupies a silicon area of 0.06 mm[2], and dissipates 6.4 μW of power per channel when operates at VDD=1.2V and 400 kHz.Clinical Relevance- This paper presents a lossless data reduction technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.},
}
@article {pmid33018128,
year = {2020},
author = {Mirzaei, S and Hosseini-Nejad, H and Sodagar, AM},
title = {Spike Detection Technique Based on Spike Augmentation with Low Computational and Hardware Complexity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {894-897},
doi = {10.1109/EMBC44109.2020.9176515},
pmid = {33018128},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {In this paper, a method for the detection and subsequently extraction of neural spikes in an intra-cortically recorded neural signal is proposed. This method distinguishes spikes from the background noise based on the natural difference between their time-domain amplitude variation patterns. According to this difference, a spike mask is generated, which takes on large values over the course of spikes, and much smaller values for the background noise. The "high" part of this mask is designed to be wide enough to contain a complete spike. By multiplying the input neural signal with the spike mask, spikes are amplified with a large factor while the background noise is not. The result is a spike-augmented signal with significantly larger signal-to-noise ratio, on which spike detection is performed much more easily and accurately. According to this detection mechanism, spikes of the original neural signal are extracted.Clinical Relevance-This paper presents an automatic spike detection technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.},
}
@article {pmid33018126,
year = {2020},
author = {Savolainen, OW and Constandinou, TG},
title = {Predicting Single-Unit Activity from Local Field Potentials with LSTMs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {884-887},
doi = {10.1109/EMBC44109.2020.9175265},
pmid = {33018126},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca ; *Motor Cortex ; },
abstract = {This paper investigates to what extent Long Short-Term Memory (LSTM) decoders can use Local Field Potentials (LFPs) to predict Single-Unit Activity (SUA) in Macaque Primary Motor cortex. The motivation is to determine to what degree the LFP signal can be used as a proxy for SUA, for both neuroscience and Brain-Computer Interface (BCI) applications. Firstly, the results suggest that the prediction quality varies significantly by implant location or animal. However, within each implant location / animal, the prediction quality seems to be correlated with the amount of power in certain LFP frequency bands (0-10, 10-20 and 40-50Hz, standardised LFPs). Secondly, the results suggest that bipolar LFPs are more informative as to SUA than unipolar LFPs. This suggests common mode rejection aids in the elimination of non-local neural information. Thirdly, the best individual bipolar LFPs generally perform better than when using all available unipolar LFPs. This suggests that LFP channel selection may be a simple but effective means of lossy data compression in Wireless Intracortical LFP-based BCIs. Overall, LFPs were moderately predictive of SUA, and improvements can likely be made.},
}
@article {pmid33018054,
year = {2020},
author = {Laureanti, R and Bilucaglia, M and Zito, M and Circi, R and Fici, A and Rivetti, F and Valesi, R and Oldrini, C and Mainardi, LT and Russo, V},
title = {Emotion assessment using Machine Learning and low-cost wearable devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {576-579},
doi = {10.1109/EMBC44109.2020.9175221},
pmid = {33018054},
issn = {2694-0604},
mesh = {Arousal ; *Brain-Computer Interfaces ; Emotions ; Humans ; Machine Learning ; *Wearable Electronic Devices ; },
abstract = {The advancement in bioelectrical measurement technologies and the push towards a higher impact of the Brain Computer Interfaces and Affective Computing in the daily life have made non-invasive and low-priced devices available to the large population to record physiological states. The aim of this study is the assessment of the abilities of the MUSE headband, together with the Shimmer GSR+ device, to assess the emotional state of people during stimuli exposure. Twenty-four pictures from the IAPS database were showed to 54 subjects and were evaluated in their emotional values by means of the Self-Assessment Manikin (SAM). Using a Machine Learning approach, fifty-two scalar features were extracted from the signals and used to train 6 binary classifiers to predict the valence and arousal elicited by each stimulus. In all classifiers we obtained accuracies ranging from 53.6% to 69.9%, confirming that these devices are able to give information about the emotional state.},
}
@article {pmid33018041,
year = {2020},
author = {Idowu, OP and Fang, P and Li, G},
title = {Bio-Inspired Algorithms for Optimal Feature Selection in Motor Imagery-Based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {519-522},
doi = {10.1109/EMBC44109.2020.9176244},
pmid = {33018041},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.},
}
@article {pmid33018040,
year = {2020},
author = {Zhang, C and Eskandarian, A},
title = {A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {514-518},
doi = {10.1109/EMBC44109.2020.9176705},
pmid = {33018040},
issn = {2694-0604},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a timefrequency analysis are performed for each experiment trial. Then, the optimal EEG signals for every experiment trials are selected based on the signal energy for CSP feature extraction. In the end, the extracted features are classified by three classifiers, linear discriminant analysis (LDA), naïve Bayes (NVB), and support vector machine (SVM), in parallel for classification accuracy comparison.The experiment results show the proposed algorithm average computation time is 37.22% less than the FBCSP (1[st] winner in the BCI Competition IV) and 4.98% longer than the conventional CSP method. For the classification rate, the proposed algorithm kappa value achieved 2nd highest compared with the top 3 winners in BCI Competition IV.},
}
@article {pmid33018039,
year = {2020},
author = {Yang, Q and Zhang, X and Chen, B},
title = {MI3DNet: A Compact CNN for Motor Imagery EEG Classification with Visualizable Dense Layer Parameters.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {510-513},
doi = {10.1109/EMBC44109.2020.9176738},
pmid = {33018039},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Imagination ; Neural Networks, Computer ; },
abstract = {Electroencephalography (EEG) based Brain Computer Interface (BCI) attracts more and more attention. Motor Imagery (MI) is a popular one among all the EEG paradigms. Building a subject-independent MI EEG classification procedure is a main challenge in practical applications. Recently, Convolutional Neural Network (CNN) has been introduced and achieved state-of-the-art performance in related areas. To extract subject-independent features in MI EEG classification, we propose the MI3DNet, using a remapped signal cubic as the input. Experiments show that MI3DNet has a higher performance with fewer parameters and layers. We also give a method to plot the parameters of the dense layer, and explain its effect.},
}
@article {pmid33018036,
year = {2020},
author = {Daly, I and Rybar, M},
title = {Neural component analysis: source localisation for motor imagery classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {498-501},
doi = {10.1109/EMBC44109.2020.9176690},
pmid = {33018036},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; *Imagination ; },
abstract = {The electroencephalogram (EEG) records a summed mixture of multiple sources of neural activity distributed throughout the brain. Source separation methods aim to un-mix the EEG in order to recover activity generated by the original sources. However, most current state-of-the-art source separation methods do not take into account the physical locations of sources of EEG activity.We present a new source separation method which uses an accurate model of the head to un-mix the EEG into individual sources based on their physical locations.We apply our method to an EEG dataset recorded during motor imagery and show that it is able to identify sources that are located in distinct physical regions of the brain. We compare our method to independent component analysis and show that our sources have higher spatial specificity and, furthermore, allow higher classification accuracies (a mean improvement in accuracy of 8.6% was achieved p =0.039).},
}
@article {pmid33018024,
year = {2020},
author = {Anam, K and Bukhori, S and Hanggara, FS and Pratama, M},
title = {Subject-independent Classification on Brain-Computer Interface using Autonomous Deep Learning for finger movement recognition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {447-450},
doi = {10.1109/EMBC44109.2020.9175718},
pmid = {33018024},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Deep Learning ; Electroencephalography ; Humans ; Movement ; Neural Networks, Computer ; },
abstract = {The degradation of the subject-independent classification on a brain-computer interface is a challenging issue. One method mostly taken to overcome this problem is by collecting as many subjects as possible and then training the system across all subjects. This article introduces streaming online learning called autonomous deep learning (ADL) to classify five individual fingers based on electroencephalography (EEG) signals to overcome the issue above. ADL is a deep learning architecture that can construct its structure by itself through streaming learning and adapt its structure to the changes occurring in the input. In this article, the input of ADL is a common spatial pattern (CSP) extracted from the EEG signal of healthy subjects. The experimental results on the subject-dependence classification across four subjects using 5fold cross-validation show that that ADL achieved the classification accuracy of around 77%. This performance was excellent compared to a random forest (RF) and a convolutional neural network (CNN). They achieved accuracies of about 53% and 72%, respectively. On the subject-independent classification, ADL outperforms CNN by resulting stable accuracies for both training and testing, different from CNN that experience accuracy degradation to approximately 50%. These results imply that ADL is a promising machine learning in dealing with the issue in the subject-independent classification.},
}
@article {pmid33018023,
year = {2020},
author = {Shahtalebi, S and Asif, A and Mohammadi, A},
title = {Siamese Neural Networks for EEG-based Brain-computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {442-446},
doi = {10.1109/EMBC44109.2020.9176001},
pmid = {33018023},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; Neural Networks, Computer ; },
abstract = {Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV2a and the results suggest a promising performance compared to its counterparts.},
}
@article {pmid33018022,
year = {2020},
author = {Yamamoto, MS and Sadatnejad, K and Tanaka, T and Islam, R and Tanaka, Y and Lotte, F},
title = {Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {438-441},
doi = {10.1109/EMBC44109.2020.9175456},
pmid = {33018022},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography ; },
abstract = {Automatically detecting and removing Electroencephalogram (EEG) outliers is essential to design robust brain-computer interfaces (BCIs). In this paper, we propose a novel outlier detection method that works on the Riemannian manifold of sample covariance matrices (SCMs). Existing outlier detection methods run the risk of erroneously rejecting some samples as outliers, even if there is no outlier, due to the detection being based on a reference matrix and a threshold. To address this limitation, our method, Riemannian Spectral Clustering (RiSC), detects outliers by clustering SCMs into non-outliers and outliers, based on a proposed similarity measure. This considers the Riemannian geometry of the space and magnifies the similarity within the non-outlier cluster and weakens it between non-outlier and outlier clusters, instead of setting a threshold. To assess RiSC performance, we generated artificial EEG datasets contaminated by different outlier strengths and numbers. Comparing Hit-False (HF) difference between RiSC and existing outlier detection methods confirmed that RiSC could detect outliers significantly better (p < 0.001). In particular, RiSC improved HF difference the most for datasets with the most severe outlier contamination.},
}
@article {pmid33018021,
year = {2020},
author = {Anwar, AM and Eldeib, AM},
title = {EEG Signal Classification Using Convolutional Neural Networks on Combined Spatial and Temporal Dimensions for BCI Systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {434-437},
doi = {10.1109/EMBC44109.2020.9175894},
pmid = {33018021},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Machine Learning ; Neural Networks, Computer ; },
abstract = {EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals. Besides, many studies have involved the time and frequency domain features to classify EEG signals. On the other hand, a very limited number of studies combine the spatial and temporal dimensions of the EEG signal. Brain dynamics are very complex across different mental tasks, thus it is difficult to design efficient algorithms with features based on prior knowledge. Therefore, in this study, we utilized the 2D AlexNet Convolutional Neural Network (CNN) to learn EEG features across different mental tasks without prior knowledge. First, this study adds spatial and temporal dimensions of EEG signals to a 2D EEG topographic map. Second, topographic maps at different time indices were cascaded to populate a 2D image for a given time window. Finally, the topographic maps enabled the AlexNet to learn features from the spatial and temporal dimensions of the brain signals. The classification performance was obtained by the proposed method on a multiclass dataset from BCI Competition IV dataset 2a. The proposed system obtained an average classification accuracy of 81.09%, outperforming the previous state-of-the-art methods by a margin of 4% for the same dataset. The results showed that converting the EEG classification problem from a (1D) time series to a (2D) image classification problem improves the classification accuracy for BCI systems. Also, our EEG topographic maps enabled CNN to learn subtle features from spatial and temporal dimensions, which better represent mental tasks than individual time or frequency domain features.},
}
@article {pmid33018020,
year = {2020},
author = {Aarabi, P and Aarabi, P},
title = {The Impact of Electrode Density and Precision on Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {430-433},
doi = {10.1109/EMBC44109.2020.9176553},
pmid = {33018020},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electric Conductivity ; Electric Impedance ; Electrodes ; Electronics ; },
abstract = {In this paper, we review several advances in different fields that provide new potential for brain-computer interfaces enabled by directly interfacing biological neural networks with electrodes, including recent successes with liquid injected conductive channels and mesh electronics supported by 3D scaffolds. Based on this review, it is clear that the success of biological neural connectivity is dependent on the precision and density of the inserted electrodes. In order to better understand the dynamics of this relationship, we propose a simple impedance-based electrode connectivity model, based on which we perform a simulation of the impact of both electrode density and electrode precision on the amount of information lost as part of the connection. Although the examples illustrated are more informative rather than conclusive, the fundamental takeaway from this work is that electrode density is a substantially important parameter while electrode precision is necessarily helpful.},
}
@article {pmid33018010,
year = {2020},
author = {Afzal Khan, MN and Raheel Bhutta, M and Hong, KS},
title = {Effect of stimulation duration to the existence of initial dip.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {390-393},
doi = {10.1109/EMBC44109.2020.9175930},
pmid = {33018010},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Fingers ; Hemodynamics ; Somatosensory Cortex ; *Spectroscopy, Near-Infrared ; },
abstract = {In this paper, we investigate the effect of stimulation durations on the hemodynamic responses (HRs) in the somatosensory cortex. In doing so, the relationship between stimulation duration and the initial dip is also investigated. The HRs are measured using functional near-infrared spectroscopy (fNIRS). The HR signals related to finger poking are acquired from the left somatosensory cortex. Two different stimulation durations (i.e., 1 and 5 sec) were tested in this study. From the results of the study, it is concluded that the stimulation duration of 1 sec (short stimulus) evokes initial dip in the somatosensory cortex, but it disappears as the stimulation duration gets longer. Therefore, the 1-sec stimulation duration can serve the purpose of the fNIRS-based brain-computer interface.},
}
@article {pmid33017981,
year = {2020},
author = {Ashley, AL and Arvaneh, M},
title = {Improving EEG-based error detection using relative peak features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {272-275},
doi = {10.1109/EMBC44109.2020.9176376},
pmid = {33017981},
issn = {2694-0604},
mesh = {Adult ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Young Adult ; },
abstract = {A brain-computer interface (BCI) potentially enables a severely disabled person to communicate using brain signals. Automatic detection of error-related potentials (ErrPs) in electroencephalograph (EEG) could improve BCI performance by allowing to correct the erroneous action made by the machine. However, the current low accuracy in detecting ErrPs, particularly in some users, can reduce its potential benefits. The paper addresses this problem by proposing a novel relative peak feature (RPF) selection method to improve performance and accuracy for recognising an ErrP in the EEG. Using data collected from 29 participants with a mean age of 24.14 years the relative peak features yielded an average across all classifiers of 81.63% accuracy in detecting the erroneous events and an average 78.87 % accuracy in detecting the correct events, using KNN, SVM and LDA classifiers. In comparison to the temporal feature selection, there was a gain in performance in all classifiers of 17.85% for error accuracy and a reduction of -6.16% for correct accuracy Specifically; our proposed RPF used significantly reduced the number of features by 91.7% when compared with the state of the art temporal features.In the future, this work will improve the human-robot interaction by improving the accuracy of detecting errors that enable the BCI to correct any mistakes.},
}
@article {pmid33017979,
year = {2020},
author = {Kocanaogullari, D and Mak, J and Kersey, J and Khalaf, A and Ostadabbas, S and Wittenberg, G and Skidmore, E and Akcakaya, M},
title = {EEG-based Neglect Detection for Stroke Patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {264-267},
doi = {10.1109/EMBC44109.2020.9176378},
pmid = {33017979},
issn = {2694-0604},
mesh = {*Brain Injuries ; Electroencephalography ; Humans ; Neural Networks, Computer ; *Perceptual Disorders/diagnosis ; *Stroke/complications ; },
abstract = {Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of penand-paper tests. These tests can be unreliable due to high variablility in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this paper, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our overall goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect.Clinical relevance-The proposed EEG-based BCI can be used to detect neglect in stroke patients with high accuracy, specificity and sensitivity. Further research will additionally allow for an estimation of a patient's field of view (FOV) for more detailed assessment of neglect.},
}
@article {pmid33017967,
year = {2020},
author = {Chiu, HYS and James, CJ},
title = {Introducing a Deflationary Approach to Space-Time ICA that uses temporal methods in Brain Signals Processing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {213-216},
doi = {10.1109/EMBC44109.2020.9176389},
pmid = {33017967},
issn = {2694-0604},
mesh = {*Algorithms ; Brain ; *Electroencephalography ; Principal Component Analysis ; Spatio-Temporal Analysis ; },
abstract = {For the extraction of underlying sources of brain activity, time structure-based techniques for applying Independent Component Analysis (ICA) have been demonstrably more robust than state-of-the-art statistical-based methods, such as FastICA. Since the early application of conventional ICA on electroencephalogram (EEG) recordings, Space-Time ICA (ST-ICA) has emerged as more capable approach for extracting complex underlying activity, but not without the 'curse of dimensionality'. The challenges in the future development of ST-ICA will require a focus on the optimisation of the mixing matrix, and on component clustering techniques. This paper proposes a new optimisation approach for the mixing matrix, which makes ST-ICA more tractable, when using a time structure-based ICA technique, LSDIAG. Such techniques rely on constructing a multi-layer covariance matrix, Cx[k] of the original dataset to generate the inverse of the mixing matrix; Cs[k] = WCx[k]W[T]. This means a simple truncation of the mixing matrix is not appropriate. To overcome this, we propose a deflationary approach to optimise a much smaller mixing matrix - based on the absolute values of the diagonals of the co-variance matrix, Cs[k], to represent the underlying sources. The preliminary results of the new technique applied to different channels of EEG recorded using the standard 10-20 system - including the full selection of all channels - are very promising.Clinical Relevance-The potential of this deflationary approach for Space-Time ICA, seeks to allow clinicians to identify underlying sources in the brain - that both spatially and spectrally overlap - to be identified, whilst making the 'dimensionality' challenges more tractable. In the long run, applications of this technique could enhance certain brain-computer interface paradigms.},
}
@article {pmid33017963,
year = {2020},
author = {Rybar, M and Daly, I and Poli, R},
title = {Potential pitfalls of widely used implementations of common spatial patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {196-199},
doi = {10.1109/EMBC44109.2020.9176314},
pmid = {33017963},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; },
abstract = {We have uncovered serious flaws in handling EEG signals with a decreased rank in implementations of the common spatial patterns (CSP). The CSP algorithm assumes covariance matrices of the signal to have full rank. However, preprocessing techniques, such as artifact removal using independent component analysis, may decrease the rank of the signal, leading to potential errors in the CSP decomposition. We inspect what could go wrong when CSP implementations do not take this into consideration on a binary motor imagery classification task. We review CSP implementations in open-source toolboxes for EEG signal analysis (FieldTrip, BBCI Toolbox, BioSig, EEGLAB, BCILAB, and MNE). We show that unprotected implementations decreased mean classification accuracy by up to 32%, with spatial filters resulting in complex numbers, for which corresponding spatial patterns do not have a clear interpretation. We encourage researchers to check their implementations and analysis pipelines.},
}
@article {pmid33017962,
year = {2020},
author = {Huang, W and Wang, L and Yan, Z and Liu, Y},
title = {Classify Motor Imagery by a Novel CNN with Data Augmentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {192-195},
doi = {10.1109/EMBC44109.2020.9176361},
pmid = {33017962},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {The brain-computer interface (BCI) based on electroencephalography (EEG) converts the subject's intentions into control signals. For the BCI, the study of motor imagery has been widely used. In recent years, a classification method based on a convolutional neural network (CNNs) has been proposed. However, most of the existing methods use a single convolution scale on CNN, and another problem that affects the results is limited training data. To solve these problems, we propose a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery. After classifying the BCI competition IV dataset 2b, the average classification accuracy is 81.52%. Compared with the existing methods, our method has a better classification result. This method effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.},
}
@article {pmid33017946,
year = {2020},
author = {Ghonchi, H and Fateh, M and Abolghasemi, V and Ferdowsi, S and Rezvani, M},
title = {Spatio-temporal deep learning for EEG-fNIRS brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {124-127},
doi = {10.1109/EMBC44109.2020.9176183},
pmid = {33017946},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Deep Learning ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode. In order to incorporate this knowledge, a deep neural network is employed in this work. Both motor-imagery EEG and bi-modal EEG-fNIRS datasets were used for this purpose. The results are compared for different scenarios and using different methods. The achieved results are promising and imply that combining both temporal and spatial information of the brain signals could be really effective and increases the performance.},
}
@article {pmid33017939,
year = {2020},
author = {Kaur, R and Korolkov, M and Hernandez, ME and Sowers, R},
title = {Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2020},
number = {},
pages = {95-98},
doi = {10.1109/EMBC44109.2020.9175741},
pmid = {33017939},
issn = {2694-0604},
mesh = {*Algorithms ; Brain ; *Deep Learning ; Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substantial training and time for removal of distinct unwanted independent components (ICs), generated via independent component analysis, corresponding to artifacts. The considerable subject-wise variations across these components motivates defining a procedural way to identify and eliminate these artifacts. We propose DeepIC-virtual, a convolutional neural network (CNN) deep learning classifier to automatically identify brain components in the ICs extracted from the subject's EEG data gathered while they are being immersed in a virtual reality (VR) environment. This work examined the feasibility of DL techniques to provide automated ICs classification on noisy and visually engaging upright stance EEG data. We collected the EEG data for six subjects while they were standing upright in a VR testing setup simulating pseudo-randomized variations in height and depth conditions and induced perturbations. An extensive 1432 IC representation images data set was generated and manually labelled via an expert as brain components or one of the six distinct removable artifacts. The supervised CNN architecture was utilized to categorize good brain ICs and bad artifactual ICs via generated images of topographical maps. Our model categorizing good versus bad IC topographical maps resulted in a binary classification accuracy and area under curve of 89.20% and 0.93 respectively. Despite significant imbalance, only 1 out of the 57 present brain ICs in the withheld testing set was miss-classified as an artifact. These results will hopefully encourage clinicians to integrate BCI methods and neurofeedback to control anxiety and provide a treatment of acrophobia, given the viability of automatic classification of artifactual ICs.},
}
@article {pmid33014987,
year = {2020},
author = {Ortiz, M and Ferrero, L and Iáñez, E and Azorín, JM and Contreras-Vidal, JL},
title = {Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {8},
number = {},
pages = {735},
pmid = {33014987},
issn = {2296-4185},
abstract = {Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.},
}
@article {pmid33014182,
year = {2020},
author = {Yan, W and Xu, G},
title = {Brain-computer interface method based on light-flashing and motion hybrid coding.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {5},
pages = {697-708},
pmid = {33014182},
issn = {1871-4080},
abstract = {The human best response frequency band for steady-state visual evoked potential stimulus is limited. This results in a reduced number of encoded targets. To circumvent this, we proposed a brain-computer interface (BCI) method based on light-flashing and motion hybrid coding. The hybrid paradigm pattern consisted of a circular light-flashing pattern and a motion pattern located in the inner ring of light-flashing pattern. The motion and light-flashing patterns had different frequencies. This study used five frequencies to encode nine targets. The motion frequency and the light-flashing frequency of the hybrid paradigm consisted of two frequencies in five frequencies. The experimental results showed that the hybrid paradigm could induce stable motion frequency, light-flashing frequency and its harmonic components. Moreover, the modulation between motion and light-flashing was weak. The average accuracy was 92.96% and the information transfer rate was 26.10 bits/min. The experimental results showed that the proposed method could be considered for practical BCI systems.},
}
@article {pmid33014181,
year = {2020},
author = {Shao, X and Lin, M},
title = {Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {5},
pages = {689-696},
pmid = {33014181},
issn = {1871-4080},
abstract = {Canonical correlation analysis (CCA) method and its extended methods have been widely and successfully applied to the frequency recognition in SSVEP-based BCI systems. As a state-of-the-art extended method, filter bank canonical correlation analysis has higher accuracy and information transmission rate (ITR) than CCA. However, in the CCA method, the temporally local structure of samples has not been well considered. In this correspondence, we proposed termed temporally local canonical correlation analysis (TCCA). In this new method, the original covariance matrix was replaced by the temporally local covariance matrix. Furthermore, we proposed an improved frequency identification method of filter bank based on TCCA, named filter bank temporally local canonical correlation analysis (FBTCCA). In the offline environment, we used a leave-one-subject-out validation strategy on datasets of ten testees to optimize the parameters of TCCA and FBTCCA and evaluate the two algorithms. The experimental results affirm that TCCA markedly outperformed CCA, and FBTCCA obtained the highest accuracy among the four methods. This study corroborates that TCCA-based approaches have great potential for implementing short time window SSVEP-based BCI systems.},
}
@article {pmid33014176,
year = {2020},
author = {Ergün, E and Aydemir, O},
title = {A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {5},
pages = {609-617},
pmid = {33014176},
issn = {1871-4080},
abstract = {Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.},
}
@article {pmid33013279,
year = {2020},
author = {Wang, H and Sun, Y and Li, Y and Chen, S and Zhou, W},
title = {Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {717},
pmid = {33013279},
issn = {1662-4548},
abstract = {The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter- and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.},
}
@article {pmid33010472,
year = {2021},
author = {Diab, RG and Tolba, MM and Ghazala, RA and Abu-Sheasha, GA and Webster, BL and Mady, RF},
title = {Intestinal schistosomiasis: Can a urine sample decide the infection?.},
journal = {Parasitology international},
volume = {80},
number = {},
pages = {102201},
doi = {10.1016/j.parint.2020.102201},
pmid = {33010472},
issn = {1873-0329},
mesh = {Adult ; Animals ; Bayes Theorem ; Diagnostic Tests, Routine/*methods ; Egypt/epidemiology ; Female ; Humans ; Male ; Prevalence ; Schistosoma mansoni/*isolation &amp; purification ; Schistosomiasis mansoni/*diagnosis/epidemiology/parasitology/urine ; Sensitivity and Specificity ; Young Adult ; },
abstract = {Intestinal schistosomiasis, one of the neglected tropical diseases whose control depends on accurate diagnosis of the disease prevalence. The use of low sensitive Kato Katz (KK) fecal egg detection method as a reference gold standard is not an accurate indication especially in low transmission areas. Latent class analysis frameworks especially the Bayesian could be used instead to compare between different diagnostic tests without the use of a gold standard method as a reference. Thus, this study compared two urine-based tests for the detection of circulating antigen and cell free DNA of Schistosoma mansoni versus KK method using the Bayesian latent class analytical framework and in two models where the trace results of point of contact - assay of circulating cathodic antigen (POC-CCA) were once estimated as positive, and as negative in the other model. The Bayesian framework in the trace CCA positive model showed an estimate of disease prevalence of 26% (95% BCI:0 to 60%). POC-CCA showed the highest sensitivity (74% with BCI: 9 to 91%) and lowest specificity for (20% with BCI: 0% to 37%) and the reverse for KK. For POC-CCA with traces considered negative, it was found that results between the three tests were moderated where the positivity for infection by Schistosoma antigen detection and PCR for cell free DNA approached that estimated by the Bayesian framework (44%), and the specificity for point of contact assay(81%; 95%BCI: 59% to 100%) rose in hand with its sensitivity(77%, 95% BCI:53% to 100%) and with results for PCR test (sensitivity = 80%; 95% BCI: 61% to 100%, specificity = 69%; 95% BIC: 47% to 100%). KK remains with the highest specificity while its sensitivity in the two models never exceeded 22%. Thus, we conclude that the use of a single urine sample could be very sensitive and highly specific in the diagnosis of intestinal schistosomiasis using either the trace negative model of point of contact assay, or conventional PCR, when compared to the fecal egg detection using duplicate KK. However, the use of a single tool restricts the management of the disease in areas of low endemicity.},
}
@article {pmid33009129,
year = {2020},
author = {Inker, LA and Chaudhari, J},
title = {GFR slope as a surrogate endpoint for CKD progression in clinical trials.},
journal = {Current opinion in nephrology and hypertension},
volume = {29},
number = {6},
pages = {581-590},
doi = {10.1097/MNH.0000000000000647},
pmid = {33009129},
issn = {1473-6543},
mesh = {Bayes Theorem ; Biomarkers ; *Disease Progression ; *Glomerular Filtration Rate ; Humans ; Male ; *Renal Insufficiency, Chronic/therapy ; },
abstract = {PURPOSE OF REVIEW: There is a paucity of therapies for chronic kidney disease (CKD), in part because of the slow nature of the disease which poses challenges in selection of endpoints in randomized controlled trials (RCT). There is increasing evidence for the use of glomerular filtration rate (GFR)-based endpoints either as percentage decline using time-to-event analyses, or as difference in slope between treatment arms. We reviewed the rationale for using surrogate endpoints and optimal methods for their evaluation prior to their use and evidence for GFR-based endpoints and particularly GFR slope as validated surrogate endpoints and considerations for their use in RCTs.<br><br>RECENT FINDINGS: In an individual patient meta-analysis of 47 studies (60 620 participants), treatment effects on the clinical endpoint were accurately predicted from treatment effects on 3-year total slope [median R = 0.97 (95% Bayesian confidence interval (BCI), 0.78-1.00] and on the chronic slope [R = 0.96 (95% BCI, 0.63-1.00)]. In a simulation study, GFR slope substantially reduced the required sample size and duration of follow-up compared to the clinical endpoint given high baseline GFR and absence of acute treatment effect. In the presence of acute effect, results were more complicated.<br><br>SUMMARY: GFR decline is accepted, and GFR slope is being considered, by regulatory authorities as a validated surrogate endpoint for CKD RCTs.},
}
@article {pmid33008536,
year = {2020},
author = {Swaab, DF and Bao, AM},
title = {Sex differences in stress-related disorders: Major depressive disorder, bipolar disorder, and posttraumatic stress disorder.},
journal = {Handbook of clinical neurology},
volume = {175},
number = {},
pages = {335-358},
doi = {10.1016/B978-0-444-64123-6.00023-0},
pmid = {33008536},
issn = {0072-9752},
mesh = {Adult ; *Bipolar Disorder ; *Depressive Disorder, Major ; Female ; Humans ; Male ; Pituitary-Adrenal System ; Sex Characteristics ; *Stress Disorders, Post-Traumatic/epidemiology ; },
abstract = {Stress-related disorders, such as mood disorders and posttraumatic stress disorder (PTSD), are more common in women than in men. This sex difference is at least partly due to the organizing effect of sex steroids during intrauterine development, while activating or inhibiting effects of circulating sex hormones in the postnatal period and adulthood also play a role. Such effects result in structural and functional changes in neuronal networks, neurotransmitters, and neuropeptides, which make the arousal- and stress-related brain systems more vulnerable to environmental stressful events in women. Certain brainstem nuclei, the amygdala, habenula, prefrontal cortex, and hypothalamus are important hubs in the stress-related neuronal network. Various hypothalamic nuclei play a central role in this sexually dimorphic network. This concerns not only the hypothalamus-pituitary-adrenal axis (HPA-axis), which integrates the neuro-endocrine-immune responses to stress, but also other hypothalamic nuclei and systems that play a key role in the symptoms of mood disorders, such as disordered day-night rhythm, lack of reward feelings, disturbed eating and sex, and disturbed cognitive functions. The present chapter focuses on the structural and functional sex differences that are present in the stress-related brain systems in mood disorders and PTSD, placing the HPA-axis in the center. The individual differences in the vulnerability of the discussed systems, caused by genetic and epigenetic developmental factors warrant further research to develop tailor-made therapeutic strategies.},
}
@article {pmid33007621,
year = {2020},
author = {Mowla, MR and Cano, RI and Dhuyvetter, KJ and Thompson, DE},
title = {Affective brain-computer interfaces: Choosing a meaningful performance measuring metric.},
journal = {Computers in biology and medicine},
volume = {126},
number = {},
pages = {104001},
doi = {10.1016/j.compbiomed.2020.104001},
pmid = {33007621},
issn = {1879-0534},
mesh = {Arousal ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; },
abstract = {Affective brain-computer interfaces are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. To assess the effectiveness of EEG recordings for recognizing affective states, we used data collected in our lab as well as the publicly available DEAP database. We also reviewed the articles that used the DEAP database and found that a significant number of articles did not consider the presence of the class imbalance in the DEAP. Failing to consider class imbalance creates misleading results. Further, ignoring class imbalance makes the comparison of the results between studies using different datasets impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level, hence it is vital to consider class bias while determining if the results are above chance. To properly account for the effect of class imbalance, we suggest the use of balanced accuracy as a performance metric, and its posterior distribution for computing credible intervals. For classification, we used features from the literature as well as theta beta-1 ratio. Results from DEAP and our data suggest that the beta band power, theta band power, and theta beta-1 ratio are better feature sets for classifying valence, arousal, and dominance, respectively.},
}
@article {pmid33004950,
year = {2020},
author = {Hazubski, S and Hoppe, H and Otte, A},
title = {Electrode-free visual prosthesis/exoskeleton control using augmented reality glasses in a first proof-of-technical-concept study.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {16279},
pmid = {33004950},
issn = {2045-2322},
mesh = {Algorithms ; Artificial Limbs ; *Augmented Reality ; Brain-Computer Interfaces ; Electromyography ; Electrooculography ; *Exoskeleton Device ; Eyeglasses ; Feedback, Sensory ; Hand Strength ; Humans ; Proof of Concept Study ; *Visual Prosthesis ; },
abstract = {In the field of neuroprosthetics, the current state-of-the-art method involves controlling the prosthesis with electromyography (EMG) or electrooculography/electroencephalography (EOG/EEG). However, these systems are both expensive and time consuming to calibrate, susceptible to interference, and require a lengthy learning phase by the patient. Therefore, it is an open challenge to design more robust systems that are suitable for everyday use and meet the needs of patients. In this paper, we present a new concept of complete visual control for a prosthesis, an exoskeleton or another end effector using augmented reality (AR) glasses presented for the first time in a proof-of-concept study. By using AR glasses equipped with a monocular camera, a marker attached to the prosthesis is tracked. Minimal relative movements of the head with respect to the prosthesis are registered by tracking and used for control. Two possible control mechanisms including visual feedback are presented and implemented for both a motorized hand orthosis and a motorized hand prosthesis. Since the grasping process is mainly controlled by vision, the proposed approach appears to be natural and intuitive.},
}
@article {pmid33003397,
year = {2020},
author = {He, Z and Li, Z and Yang, F and Wang, L and Li, J and Zhou, C and Pan, J},
title = {Advances in Multimodal Emotion Recognition Based on Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {33003397},
issn = {2076-3425},
support = {2019A1515011375//Natural Science Foundation of Guangdong Province/ ; 61876067//National Natural Science Foundation of China/ ; 202007030005//Research and Development Plan in Key Areas of Guangzhou Science and Technology Plan Project/ ; },
abstract = {With the continuous development of portable noninvasive human sensor technologies such as brain-computer interfaces (BCI), multimodal emotion recognition has attracted increasing attention in the area of affective computing. This paper primarily discusses the progress of research into multimodal emotion recognition based on BCI and reviews three types of multimodal affective BCI (aBCI): aBCI based on a combination of behavior and brain signals, aBCI based on various hybrid neurophysiology modalities and aBCI based on heterogeneous sensory stimuli. For each type of aBCI, we further review several representative multimodal aBCI systems, including their design principles, paradigms, algorithms, experimental results and corresponding advantages. Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI.},
}
@article {pmid33003367,
year = {2020},
author = {Lee, T and Kim, M and Kim, SP},
title = {Improvement of P300-Based Brain-Computer Interfaces for Home Appliances Control by Data Balancing Techniques.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {19},
pages = {},
pmid = {33003367},
issn = {1424-8220},
support = {2017-0-00432//Korean Government (MSIT)/ ; 1.200040.01//Ulsan National Institute of Science &amp; Technology (UNIST)/ ; 1.190042.01//Ulsan National Institute of Science &amp; Technology (UNIST)/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Support Vector Machine ; },
abstract = {The oddball paradigm used in P300-based brain-computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek's links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem.},
}
@article {pmid32999770,
year = {2020},
author = {Shirane, Y and Mori, F and Yamanaka, M and Nakanishi, M and Ishinazaka, T and Mano, T and Jimbo, M and Sashika, M and Tsubota, T and Shimozuru, M},
title = {Development of a noninvasive photograph-based method for the evaluation of body condition in free-ranging brown bears.},
journal = {PeerJ},
volume = {8},
number = {},
pages = {e9982},
pmid = {32999770},
issn = {2167-8359},
abstract = {Body condition is an important determinant of health, and its evaluation has practical applications for the conservation and management of mammals. We developed a noninvasive method that uses photographs to assess the body condition of free-ranging brown bears (Ursus arctos) in the Shiretoko Peninsula, Hokkaido, Japan. First, we weighed and measured 476 bears captured during 1998-2017 and calculated their body condition index (BCI) based on residuals from the regression of body mass against body length. BCI showed seasonal changes and was lower in spring and summer than in autumn. The torso height:body length ratio was strongly correlated with BCI, which suggests that it can be used as an indicator of body condition. Second, we examined the precision of photograph-based measurements using an identifiable bear in the Rusha area, a special wildlife protection area on the peninsula. A total of 220 lateral photographs of this bear were taken September 24-26, 2017, and classified according to bear posture. The torso height:body/torso length ratio was calculated with four measurement methods and compared among bear postures in the photographs. The results showed torso height:horizontal torso length (TH:HTL) to be the indicator that could be applied to photographs of the most diverse postures, and its coefficient of variation for measurements was <5%. In addition, when analyzing photographs of this bear taken from June to October during 2016-2018, TH:HTL was significantly higher in autumn than in spring/summer, which indicates that this ratio reflects seasonal changes in body condition in wild bears. Third, we calculated BCI from actual measurements of seven females captured in the Rusha area and TH:HTL from photographs of the same individuals. We found a significant positive relationship between TH:HTL and BCI, which suggests that the body condition of brown bears can be estimated with high accuracy based on photographs. Our simple and accurate method is useful for monitoring bear body condition repeatedly over the years and contributes to further investigation of the relationships among body condition, food habits, and reproductive success.},
}
@article {pmid32998379,
year = {2020},
author = {Stawicki, P and Volosyak, I},
title = {Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {32998379},
issn = {2076-3425},
abstract = {Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain-computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials-SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.},
}
@article {pmid32998113,
year = {2020},
author = {Roy, S and Rathee, D and Chowdhury, A and McCreadie, K and Prasad, G},
title = {Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056037},
doi = {10.1088/1741-2552/abbd21},
pmid = {32998113},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; *Magnetoencephalography ; },
abstract = {OBJECTIVE: Magnetoencephalography (MEG) based brain-computer interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels (NoC) means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy (CA) and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI.<br><br>APPROACH: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation, ReliefF, Random Forest, and Infinite Latent Feature Selection were applied across six binary tasks in three different frequency bands) were evaluated in this study on two state-of-the-art features, i.e. bandpower and common spatial pattern (CSP).<br><br>MAIN RESULTS: All four methods provided a statistically significant increase in CA compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12 Hz), beta (13-30 Hz), or broadband (&#x3b1; + &#x3b2;) (8-30 Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the NoC significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15 to 105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the NoC will help to decrease the computational cost and maintain numerical stability in cases of low trial numbers.<br><br>SIGNIFICANCE: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications.},
}
@article {pmid32997632,
year = {2020},
author = {Yuan, K and Wang, X and Chen, C and Lau, CC and Chu, WC and Tong, RK},
title = {Interhemispheric Functional Reorganization and its Structural Base After BCI-Guided Upper-Limb Training in Chronic Stroke.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {11},
pages = {2525-2536},
doi = {10.1109/TNSRE.2020.3027955},
pmid = {32997632},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; Recovery of Function ; *Stroke ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Brain-computer interface (BCI)-guided robot-assisted upper-limb training has been increasingly applied to stroke rehabilitation. However, the induced long-term neuroplasticity modulation still needs to be further characterized. This study investigated the functional reorganization and its structural base after BCI-guided robot-assisted training using resting-state fMRI, task-based fMRI, and diffusion tensor imaging (DTI) data. The clinical improvement and the neurological changes before, immediately after, and six months after 20-session BCI-guided robot hand training were explored in 14 chronic stroke subjects. The structural base of the induced functional reorganization and motor improvement were also investigated using DTI. Repeated measure ANOVA indicated long-term motor improvement was found (F[2, 26] = 6.367, p = 0.006). Significantly modulated functional connectivity (FC) was observed between ipsilesional motor regions (M1 and SMA) and some contralesional areas (SMA, PMd, SPL) in the seed-based analysis. Modulated FC with ipsilesional M1 was significantly correlated with motor function improvement (r = 0.6455, p = 0.0276). Besides, increased interhemispheric FC among the sensorimotor area from resting-state data and increased laterality index from task-based data together indicated the re-balance of the two hemispheres during the recovery. Multiple linear regression models suggested that both motor function improvement and the functional change between ipsilesional M1 and contralesional premotor area were significantly associated with the ipsilesional corticospinal tract integrity. The results in the current study provided solid support for stroke recovery mechanism in terms of interhemispheric interaction and its structural substrates, which could further enhance the understanding of BCI training in stroke rehabilitation. This study was registered at https://clinicaltrials.gov (NCT02323061).},
}
@article {pmid32997130,
year = {2021},
author = {McGrew, KM and Garwe, T and Jafarzadeh, SR and Drevets, DA and Zhao, YD and Williams, MB and Carabin, H},
title = {Misclassification Error-Adjusted Prevalence of Injection Drug Use Among Infective Endocarditis Hospitalizations in the United States: A Serial Cross-Sectional Analysis of the 2007-2016 National Inpatient Sample.},
journal = {American journal of epidemiology},
volume = {190},
number = {4},
pages = {588-599},
pmid = {32997130},
issn = {1476-6256},
support = {P30 AR072571/AR/NIAMS NIH HHS/United States ; R03 AG060272/AG/NIA NIH HHS/United States ; R21 AR074578/AR/NIAMS NIH HHS/United States ; U54 GM104938/GM/NIGMS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Algorithms ; Cross-Sectional Studies ; Endocarditis/*epidemiology/etiology/therapy ; Female ; Follow-Up Studies ; Hospitalization/*statistics &amp; numerical data ; Humans ; *Inpatients ; Male ; Middle Aged ; Prevalence ; *Registries ; Retrospective Studies ; Substance Abuse, Intravenous/*complications/epidemiology ; United States/epidemiology ; Young Adult ; },
abstract = {Administrative health databases have been used to monitor trends in infective endocarditis hospitalization related to nonprescription injection drug use (IDU) using International Classification of Diseases (ICD) code algorithms. Because no ICD code for IDU exists, drug dependence and hepatitis C virus (HCV) have been used as surrogate measures for IDU, making misclassification error (ME) a threat to the accuracy of existing estimates. In a serial cross-sectional analysis, we compared the unadjusted and ME-adjusted prevalences of IDU among 70,899 unweighted endocarditis hospitalizations in the 2007-2016 National Inpatient Sample. The unadjusted prevalence of IDU was estimated with a drug algorithm, an HCV algorithm, and a combination algorithm (drug and HCV). Bayesian latent class models were used to estimate the median IDU prevalence and 95% Bayesian credible intervals and ICD algorithm sensitivity and specificity. Sex- and age group-stratified IDU prevalences were also estimated. Compared with the misclassification-adjusted prevalence, unadjusted estimates were lower using the drug algorithm and higher using the combination algorithm. The median ME-adjusted IDU prevalence increased from 9.7% (95% Bayesian credible interval (BCI): 6.3, 14.8) in 2008 to 32.5% (95% BCI: 26.5, 38.2) in 2016. Among persons aged 18-34 years, IDU prevalence was higher in females than in males. ME adjustment in ICD-based studies of injection-related endocarditis is recommended.},
}
@article {pmid32994463,
year = {2020},
author = {Subileau, M and Acar, N and Carret, A and Bretillon, L and Vilgrain, I and Bailly, S and Vittet, D},
title = {Eye lymphatic defects induced by bone morphogenetic protein 9 deficiency have no functional consequences on intraocular pressure.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {16040},
pmid = {32994463},
issn = {2045-2322},
mesh = {Animals ; Anterior Chamber/physiology ; Aqueous Humor/metabolism ; Glaucoma/metabolism ; Growth Differentiation Factor 2/*metabolism ; Intraocular Pressure/*physiology ; Lymphangiogenesis/physiology ; Lymphatic Vessels/*metabolism/physiology ; Male ; Membrane Transport Proteins/metabolism ; Mice ; Mice, Inbred C57BL ; Sclera/physiology ; Tonometry, Ocular/methods ; Trabecular Meshwork/physiology ; },
abstract = {Aqueous humor drainage is essential for the regulation of intraocular pressure (IOP), a major risk factor for glaucoma. The Schlemm's canal and the non-conventional uveoscleral pathway are known to drain aqueous humor from the eye anterior chamber. It has recently been reported that lymphatic vessels are involved in this process, and that the Schlemm's canal responds to some lymphatic regulators. We have previously shown a critical role for bone morphogenetic protein 9 (BMP9) in lymphatic vessel maturation and valve formation, with repercussions in drainage efficiency. Here, we imaged eye lymphatic vessels and analyzed the consequences of Bmp9 (Gdf2) gene invalidation. A network of lymphatic vessel hyaluronan receptor 1 (LYVE-1)-positive lymphatic vessels was observed in the corneolimbus and the conjunctiva. In contrast, LYVE-1-positive cells present in the ciliary bodies were belonging to the macrophage lineage. Although enlarged conjunctival lymphatic trunks and a reduced valve number were observed in Bmp9-KO mice, there were no morphological differences in the Schlemm's canal compared to wild type animals. Moreover, there were no functional consequences on IOP in both basal control conditions and after laser-induced ocular hypertonia. Thus, the BMP9-activated signaling pathway does not constitute a wise target for new glaucoma therapeutic strategies.},
}
@article {pmid32994059,
year = {2020},
author = {Taschereau-Dumouchel, V and Roy, M},
title = {Could Brain Decoding Machines Change Our Minds?.},
journal = {Trends in cognitive sciences},
volume = {24},
number = {11},
pages = {856-858},
doi = {10.1016/j.tics.2020.09.006},
pmid = {32994059},
issn = {1879-307X},
support = {MR/T010614/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Brain ; Brain Mapping ; *Brain-Computer Interfaces ; Humans ; Learning ; Pain ; },
abstract = {In a recent experiment, Zhang and colleagues designed a closed-loop brain-machine interface that learned to reduce participants' pain by decoding pain-related brain activity. In doing so, they also highlighted some of the challenges associated with coadaptive processes in brain-machine communication.},
}
@article {pmid32992779,
year = {2020},
author = {Maruyama, Y and Ogata, Y and Martínez-Tejada, LA and Koike, Y and Yoshimura, N},
title = {Independent Components of EEG Activity Correlating with Emotional State.},
journal = {Brain sciences},
volume = {10},
number = {10},
pages = {},
pmid = {32992779},
issn = {2076-3425},
support = {JPMJPR17JA//Precursory Research for Embryonic Science and Technology/ ; 15K16080//Japan Society for the Promotion of Science/ ; 18K11499//Japan Society for the Promotion of Science/ ; 18K11499//Japan Society for the Promotion of Science/ ; },
abstract = {Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell's valence-arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.},
}
@article {pmid32992539,
year = {2020},
author = {Zhang, P and Chao, L and Chen, Y and Ma, X and Wang, W and He, J and Huang, J and Li, Q},
title = {Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain-Machine Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {19},
pages = {},
pmid = {32992539},
issn = {1424-8220},
support = {U1913207, 61233015, 61473131//National Natural Science Foundation of China/ ; 2013CB329506//National Program on Key Basic Research Project of China/ ; 2019M652652//Postdoctoral Science Foundation of China/ ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Hand/physiology ; Haplorhini ; *Machine Learning ; },
abstract = {BACKGROUND: For the nonstationarity of neural recordings in intracortical brain-machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders.<br><br>METHODS: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures.<br><br>RESULTS: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times.<br><br>CONCLUSIONS: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.},
}
@article {pmid32992242,
year = {2020},
author = {Berman, J and Masseau, I and Fecteau, G and Buczinski, S and Francoz, D},
title = {Comparison between thoracic ultrasonography and thoracic radiography for the detection of thoracic lesions in dairy calves using a two-stage Bayesian method.},
journal = {Preventive veterinary medicine},
volume = {184},
number = {},
pages = {105153},
doi = {10.1016/j.prevetmed.2020.105153},
pmid = {32992242},
issn = {1873-1716},
mesh = {Animals ; Bayes Theorem ; Cattle ; Cattle Diseases/*diagnosis ; Cross-Sectional Studies ; Female ; Male ; Prospective Studies ; Radiography, Thoracic/*veterinary ; Sensitivity and Specificity ; Ultrasonography/*veterinary ; },
abstract = {Infectious bronchopneumonia is a lower respiratory tract disease with major economic consequences in dairy calves. Thoracic radiography (TR) and thoracic ultrasonography (TUS) are two imaging diagnostic procedures available in bovine medicine for identifying thoracic lesions. However, no study has investigated whether one of these tests is superior to the other or if they provide comparable results for the detection of thoracic lesions in calves. The objective of this study was therefore to estimate and to compare the performances of TUS and TR for the detection of thoracic lesions in dairy calves. A prospective cross-sectional study was performed in a hospital setting. A total of 50 calves (≥7 days old; ≤100 kg; standing; pCO2 ≥ 53 mmHg; any reason of presentation) were enrolled. Every calf underwent TUS and TR. Only calves with thoracic lesions on TUS and/or TR were controlled by thoracic computed tomography (CT) (the gold standard). Calves without lesions were not controlled by CT. A two-stage Bayesian framework was used. The sensitivities (Se) and specificities (Sp) of both tests individually and used in series or parallel were estimated. The Se and Sp of TUS were 0.81 (95 % BCI (Bayesian Credible Interval): 0.65; 0.92) and 0.90 (95 % BCI: 0.81; 0.96), respectively. The Se and Sp of TR were 0.86 (95 % BCI: 0.62; 0.99) and 0.89 (95 % BCI: 0.67; 0.99), respectively. This study did not reveal any differences between both tests. Using TUS and TR in series was more specific than using both tests in parallel. The performances of TUS alone were not different from the performances of both tests in series or in parallel. In conclusion, TUS and TR were equivalent in detecting thoracic lesions in this study. Using TUS alone allowed an accurate detection of thoracic lesions in dairy calves. Further studies enrolling a larger sample (> 400 calves) and allowing adequate power to be achieved would be necessary to confirm these results.},
}
@article {pmid32990249,
year = {2020},
author = {Hesam-Shariati, N and Newton-John, T and Singh, AK and Tirado Cortes, CA and Do, TN and Craig, A and Middleton, JW and Jensen, MP and Trost, Z and Lin, CT and Gustin, SM},
title = {Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines.},
journal = {JMIR research protocols},
volume = {9},
number = {9},
pages = {e20979},
pmid = {32990249},
issn = {1929-0748},
abstract = {BACKGROUND: Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention.<br><br>OBJECTIVE: The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following spinal cord injury.<br><br>METHODS: We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic &#x3b3;-aminobutyric acid concentration.<br><br>RESULTS: This study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021.<br><br>CONCLUSIONS: This clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large samples is not feasible.<br><br>TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943; https://bit.ly/2RY1jRx.<br><br>PRR1-10.2196/20979.},
}
@article {pmid32989620,
year = {2020},
author = {Rahman, MA and Siddik, AB and Ghosh, TK and Khanam, F and Ahmad, M},
title = {A Narrative Review on Clinical Applications of fNIRS.},
journal = {Journal of digital imaging},
volume = {33},
number = {5},
pages = {1167-1184},
pmid = {32989620},
issn = {1618-727X},
mesh = {Brain/diagnostic imaging ; Functional Neuroimaging ; Humans ; Schizophrenia ; *Spectroscopy, Near-Infrared ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.},
}
@article {pmid32987871,
year = {2020},
author = {Yang, D and Nguyen, TH and Chung, WY},
title = {A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {19},
pages = {},
pmid = {32987871},
issn = {1424-8220},
support = {2019R1A2C1089139//Ministry of Science and ICT, South Korea/ ; },
mesh = {Automation ; *Bipolar Disorder ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study-a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time-might also offer a reference for the other BCI controlled applications.},
}
@article {pmid32987366,
year = {2020},
author = {Bagheri, M and Power, SD},
title = {EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056015},
doi = {10.1088/1741-2552/abbc27},
pmid = {32987366},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Spectroscopy, Near-Infrared ; Workload ; },
abstract = {OBJECTIVE: A passive brain-computer interface (pBCI) is a system that continuously adapts human-computer interaction to the user's state. Key to the efficacy of such a system is the reliable estimation of the user's state via neural signals, acquired through non-invasive methods like electroencephalography (EEG) or near-infrared spectroscopy (fNIRS). Many studies to date have explored the detection of mental workload in particular, usually for the purpose of improving safety in high risk work environments. In these studies, mental workload is generally modulated through the manipulation of task difficulty, and no other aspect of the user's state is taken into account. In real-life scenarios, however, different aspects of the user's state are likely to be changing simultaneously-for example, their cognitive state (e.g. level of mental workload) and affective state (e.g. level of stress/anxiety). This inevitable confounding of different states needs to be accounted for in the development of state detection algorithms in order for them to remain effective when taken outside the lab.<br><br>APPROACH: In this study we focussed on two different states that are of particular importance in high risk work environments, specifically mental workload and stress, and explored the effect of each on the ability to detect the other using EEG signals. We developed an experimental protocol in which participants performed a cognitive task under two different levels of workload (low workload and high workload) and at two levels of stress (relaxed and stressed) and then used a linear discriminant classifier to perform classification of workload level and stress level independently.<br><br>MAIN RESULTS: We found that the detection of both mental workload level (e.g. low workload vs. high workload) and stress level (e.g. stressed vs. relaxed) were significantly diminished if the training and test data came from different as opposed to the same level of the other state (e.g. for mental workload classification, training on data from a relaxed condition and testing on data from a stressed condition, rather than both training and testing on the relaxed condition). The reduction in classification accuracy observed was as much as 15%.<br><br>SIGNIFICANCE: The results of this study indicate the importance of considering multiple aspects of a user's state when developing detection algorithms for pBCI technologies.},
}
@article {pmid32986635,
year = {2021},
author = {Zhao, D and Li, X and Hou, X and Feng, M and Jiang, R},
title = {Synchrosqueezing with short-time fourier transform method for trinary frequency shift keying encoded SSVEP.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {29},
number = {3},
pages = {505-519},
doi = {10.3233/THC-202427},
pmid = {32986635},
issn = {1878-7401},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Fourier Analysis ; Humans ; Photic Stimulation ; },
abstract = {BACKGROUND: The frequencies that can evoke strong steady state visual evoked potentials (SSVEP) are limited, which leads to brain-computer interface (BCI) instruction limitation in the current SSVEP-BCI. To solve this problem, the visual stimulus signal modulated by trinary frequency shift keying was introduced.<br><br>OBJECTIVE: The main purpose of this paper is to find a more reliable recognition algorithm for SSVEP-BCI based on trinary frequency shift keying modulated stimuli.<br><br>METHODS: First, the signal modulated by trinary frequency shift keying is simulated by MATLAB. At different noise levels, the empirical mode decomposition, singular value decomposition, and synchrosqueezing with the short-time Fourier transform are used to extract the characteristic frequency and reconstruct the signal. Then, the coherent method is used to demodulate the reconstructed signal. Second, in the paradigm of BCI using trinary frequency shift keying modulated stimuli, the three methods mentioned above are used to reconstruct EEG signals, and canonical correlation analysis and coherent demodulation are used to recognize the BCI instructions.<br><br>RESULTS: For simulated signals, it is found that synchrosqueezing with short-time Fourier transform has a better effect on extracting the characteristic frequencies. For the EEG signal, it is found that the method combining synchrosqueezing with short-time Fourier transform and coherent demodulation has a higher accuracy and information translate rate than other methods.<br><br>CONCLUSION: The method combining synchrosqueezing with short-time Fourier transform and coherent demodulation proposed in this paper can be applied in the SSVEP system based on trinary frequency shift keying modulated stimuli.},
}
@article {pmid32986556,
year = {2020},
author = {Duan, L and Li, J and Ji, H and Pang, Z and Zheng, X and Lu, R and Li, M and Zhuang, J},
title = {Zero-Shot Learning for EEG Classification in Motor Imagery-Based BCI System.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {11},
pages = {2411-2419},
doi = {10.1109/TNSRE.2020.3027004},
pmid = {32986556},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Learning ; },
abstract = {A brain-computer interface (BCI) based on motor imagery (MI) translates human intentions into computer commands by recognizing the electroencephalogram (EEG) patterns of different imagination tasks. However, due to the scarcity of MI commands and the long calibration time, using the MI-based BCI system in practice is still challenging. Zero-shot learning (ZSL), which can recognize objects whose instances may not have been seen during training, has the potential to substantially reduce the calibration time. Thus, in this context, we first try to use a new type of motor imagery task, which is a combination of traditional tasks and propose a novel zero-shot learning model that can recognize both known and unknown categories of EEG signals. This is achieved by first learning a non-linear projection from EEG features to the target space and then applying a novelty detection method to differentiate unknown classes from known classes. Applications to a dataset collected from nine subjects confirm the possibility of identifying a new type of motor imagery only using already obtained motor imagery data. Results indicate that the classification accuracy of our zero-shot based method accounts for 91.81% of the traditional method which uses all categories of data.},
}
@article {pmid32986555,
year = {2020},
author = {Lim, LG and Ung, WC and Chan, YL and Lu, CK and Sutoko, S and Funane, T and Kiguchi, M and Tang, TB},
title = {A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {11},
pages = {2367-2376},
doi = {10.1109/TNSRE.2020.3026991},
pmid = {32986555},
issn = {1558-0210},
mesh = {Hemodynamics ; Humans ; *Prefrontal Cortex ; *Spectroscopy, Near-Infrared ; Support Vector Machine ; Workload ; },
abstract = {Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.},
}
@article {pmid32986142,
year = {2020},
author = {Gao, Y and Zhu, T and Xu, X},
title = {Bone age assessment based on deep convolution neural network incorporated with segmentation.},
journal = {International journal of computer assisted radiology and surgery},
volume = {15},
number = {12},
pages = {1951-1962},
doi = {10.1007/s11548-020-02266-0},
pmid = {32986142},
issn = {1861-6429},
support = {61971168//Yunyuan Gao/ ; },
mesh = {Adolescent ; Bone and Bones/*diagnostic imaging ; Child ; Child, Preschool ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Infant ; Male ; *Neural Networks, Computer ; X-Rays ; Young Adult ; },
abstract = {PURPOSE: Bone age assessment is not only an important means of assessing maturity of adolescents, but also plays an indispensable role in the fields of orthodontics, kinematics, pediatrics, forensic science, etc. Most studies, however, do not take into account the impact of background noise on the results of the assessment. In order to obtain accurate bone age, this paper presents an automatic assessment method, for bone age based on deep convolutional neural networks.<br><br>METHOD: Our method was divided into two phases. In the image segmentation stage, the segmentation network U-Net was used to acquire the mask image which was then compared with the original image to obtain the hand bone portion after removing the background interference. For the classification phase, in order to further improve the evaluation performance, an attention mechanism was added on the basis of Visual Geometry Group Network (VGGNet). Attention mechanisms can help the model invest more resources in important areas of the hand bone.<br><br>RESULT: The assessment model was tested on the RSNA2017 Pediatric Bone Age dataset. The results show that our adjusted model outperforms the VGGNet. The mean absolute error can reach 9.997 months, which outperforms other common methods for bone age assessment.<br><br>CONCLUSION: We explored the establishment of an automated bone age assessment method based on deep learning. This method can efficiently eliminate the influence of background interference on bone age evaluation, improve the accuracy of bone age evaluation, provide important reference value for bone age determination, and can aid in the prevention of adolescent growth and development diseases.},
}
@article {pmid32983573,
year = {2019},
author = {van den Boom, MA and Vansteensel, MJ and Koppeschaar, MI and Raemaekers, MAH and Ramsey, NF},
title = {Towards an intuitive communication-BCI: decoding visually imagined characters from the early visual cortex using high-field fMRI.},
journal = {Biomedical physics &amp; engineering express},
volume = {5},
number = {5},
pages = {},
pmid = {32983573},
issn = {2057-1976},
support = {320708/ERC_/European Research Council/International ; },
abstract = {Brain-computer interfaces aim to provide people with paralysis with the possibility to use their neural signals to control devices. For communication, most BCIs are based on the selection of letters from a (digital) letter board to spell words and sentences. Visual mental imagery of letters could offer a new, fast and intuitive way to spell in a BCI-communication solution. Here we provide a proof of concept for the decoding of visually imagined characters from the early visual cortex using 7 Tesla functional MRI. Sixteen healthy participants visually imagined three different characters for 3, 5 and 7 s in a slow event-related design. Using single-trial classification, we were able to decode the characters with an average accuracy of 54%, which is significantly above chance level (33%). Furthermore, the imagined characters were classifiable shortly after cue onset and remained classifiable with prolonged imagery. These properties, combined with the cortical location of the early visual cortex and its decodable activity, encourage further research on intracranial interfacing using surface electrodes to bring us closer to such a visual imagery based BCI communication solution.},
}
@article {pmid32982708,
year = {2020},
author = {Zhang, L and Wang, P and Zhang, R and Chen, M and Shi, L and Gao, J and Hu, Y},
title = {The Influence of Different EEG References on Scalp EEG Functional Network Analysis During Hand Movement Tasks.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {367},
pmid = {32982708},
issn = {1662-5161},
abstract = {Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched. To investigate the effects of the original reference (REF-CZ), the common average reference (CAR), and the reference electrode standardization technique (REST) on scalp EEG functional network analysis during hand movement tasks, EEGs of 17 right-handed subjects performing self-paced hand movements were collected, and scalp functional networks [coherence (COH), phase-locking value (PLV), phase lag index (PLI)] with different references were constructed. Compared with the REF-CZ reference, the networks with CAR and REST references exhibited more significant increases in connectivity during the left-/right-hand movement preparation (MP) and movement execution (ME) stages. The node degree of the channel near the reference electrode was significantly reduced by the REF-CZ reference. CAR and REST both decreased this reference effect, REST more so than CAR. We confirmed that the choice of reference would affect the analysis of the functional network during hand movement tasks, and the REST reference can greatly reduce the effects of the online recording reference on the analysis of EEG connectivity.},
}
@article {pmid32982703,
year = {2020},
author = {Liu, J and Wu, G and Luo, Y and Qiu, S and Yang, S and Li, W and Bi, Y},
title = {EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder.},
journal = {Frontiers in systems neuroscience},
volume = {14},
number = {},
pages = {43},
pmid = {32982703},
issn = {1662-5137},
abstract = {Emotion classification based on brain-computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.},
}
@article {pmid32973672,
year = {2020},
author = {Chew, E and Teo, WP and Tang, N and Ang, KK and Ng, YS and Zhou, JH and Teh, I and Phua, KS and Zhao, L and Guan, C},
title = {Using Transcranial Direct Current Stimulation to Augment the Effect of Motor Imagery-Assisted Brain-Computer Interface Training in Chronic Stroke Patients-Cortical Reorganization Considerations.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {948},
pmid = {32973672},
issn = {1664-2295},
abstract = {Introduction: Transcranial direct current stimulation (tDCS) has been shown to modulate cortical plasticity, enhance motor learning and post-stroke upper extremity motor recovery. It has also been demonstrated to facilitate activation of brain-computer interface (BCI) in stroke patients. We had previously demonstrated that BCI-assisted motor imagery (MI-BCI) can improve upper extremity impairment in chronic stroke participants. This study was carried out to investigate the effects of priming with tDCS prior to MI-BCI training in chronic stroke patients with moderate to severe upper extremity paresis and to investigate the cortical activity changes associated with training. Methods: This is a double-blinded randomized clinical trial. Participants were randomized to receive 10 sessions of 20-min 1 mA tDCS or sham-tDCS before MI-BCI, with the anode applied to the ipsilesional, and the cathode to the contralesional primary motor cortex (M1). Upper extremity sub-scale of the Fugl-Meyer Assessment (UE-FM) and corticospinal excitability measured by transcranial magnetic stimulation (TMS) were assessed before, after and 4 weeks after intervention. Results: Ten participants received real tDCS and nine received sham tDCS. UE-FM improved significantly in both groups after intervention. Of those with unrecordable motor evoked potential (MEP-) to the ipsilesional M1, significant improvement in UE-FM was found in the real-tDCS group, but not in the sham group. Resting motor threshold (RMT) of ipsilesional M1 decreased significantly after intervention in the real-tDCS group. Short intra-cortical inhibition (SICI) in the contralesional M1 was reduced significantly following intervention in the sham group. Correlation was found between baseline UE-FM score and changes in the contralesional SICI for all, as well as between changes in UE-FM and changes in contralesional RMT in the MEP- group. Conclusion: MI-BCI improved the motor function of the stroke-affected arm in chronic stroke patients with moderate to severe impairment. tDCS did not confer overall additional benefit although there was a trend toward greater benefit. Cortical activity changes in the contralesional M1 associated with functional improvement suggests a possible role for the contralesional M1 in stroke recovery in more severely affected patients. This has important implications in designing neuromodulatory interventions for future studies and tailoring treatment. Clinical Trial Registration: The study was registered at https://clinicaltrials.gov (NCT01897025).},
}
@article {pmid32973481,
year = {2020},
author = {Ortiz, M and Iáñez, E and Contreras-Vidal, JL and Azorín, JM},
title = {Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {48},
pmid = {32973481},
issn = {1662-5218},
abstract = {The use of brain-machine interfaces in combination with robotic exoskeletons is usually based on the analysis of the changes in power that some brain rhythms experience during a motion event. However, this variation in power is frequently obtained through frequency filtering and power estimation using the Fourier analysis. This paper explores the decomposition of the brain rhythms based on the Empirical Mode Decomposition, as an alternative for the analysis of electroencephalographic (EEG) signals, due to its adaptive capability to the local oscillations of the data, showcasing it as a viable tool for future BMI algorithms based on motor related events.},
}
@article {pmid32973435,
year = {2020},
author = {de Castro-Cros, M and Sebastian-Romagosa, M and Rodríguez-Serrano, J and Opisso, E and Ochoa, M and Ortner, R and Guger, C and Tost, D},
title = {Effects of Gamification in BCI Functional Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {882},
pmid = {32973435},
issn = {1662-4548},
abstract = {OBJECTIVE: To evaluate whether introducing gamification in BCI rehabilitation of the upper limbs of post-stroke patients has a positive impact on their experience without altering their efficacy in creating motor mental images (MI).<br><br>DESIGN: A game was designed purposely adapted to the pace and goals of an established BCI-rehabilitation protocol. Rehabilitation was based on a double feedback: functional electrostimulation and animation of a virtual avatar of the patient's limbs. The game introduced a narrative on top of this visual feedback with an external goal to achieve (protecting bits of cheese from a rat character). A pilot study was performed with 10 patients and a control group of six volunteers. Two rehabilitation sessions were done, each made up of one stage of calibration and two training stages, some stages with the game and others without. The accuracy of the classification computed was taken as a measure to compare the efficacy of MI. Users' opinions were gathered through a questionnaire. No potentially identifiable human images or data are presented in this study.<br><br>RESULTS: The gamified rehabilitation presented in the pilot study does not impact on the efficacy of MI, but it improves users experience making it more fun.<br><br>CONCLUSION: These preliminary results are encouraging to continue investigating how game narratives can be introduced in BCI rehabilitation to make it more gratifying and engaging.},
}
@article {pmid32973428,
year = {2020},
author = {Li, H and Huang, G and Lin, Q and Zhao, J and Fu, Q and Li, L and Mao, Y and Wei, X and Yang, W and Wang, B and Zhang, Z and Huang, D},
title = {EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {827},
pmid = {32973428},
issn = {1662-4548},
abstract = {This study investigated electroencephalogram (EEG) changes during movement preparation and execution in stroke patients. EEG-based event-related potential (ERP) technology was used to measure brain activity changes. Seventeen stroke patients participated in this study and completed ERP tests that were designed to measure EEG changes during unilateral upper limb movements in preparation and execution stages, with Instruction Response Movement (IRM) and Cued Instruction Response Movement (CIRM) paradigms. EEG data were analyzed using motor potential (MP) in the time domain and the mu-rhythm and beta frequency band response mean value (R-means) in the time-frequency domain. In IRM, the MP amplitude at Cz was higher during hemiplegic arm movement than during unaffected arm movement. MP latency was shorter at Cz and the contralesional motor cortex during hemiplegic arm movement in CIRM compared to IRM. No significant differences were found in R-means among locations, between movement sides in both ERP tests. This study presents the brain activity changes in the time and time-frequency domains in stroke patients during movement preparation and execution and supports the contralesional compensation and adjacent-region compensation mechanism of post-stroke brain reconstruction. These findings may contribute to future rehabilitation research about neuroplasticity and technology development such as the brain-computer interface.},
}
@article {pmid32970991,
year = {2020},
author = {Duan, D and Zhang, H and Yue, X and Fan, Y and Xue, Y and Shao, J and Ding, G and Chen, D and Li, S and Cheng, H and Zhang, X and Zou, W and Liu, J and Zhao, J and Wang, L and Zhao, B and Wang, Z and Xu, S and Wen, Q and Liu, J and Duan, S and Kang, L},
title = {Sensory Glia Detect Repulsive Odorants and Drive Olfactory Adaptation.},
journal = {Neuron},
volume = {108},
number = {4},
pages = {707-721.e8},
doi = {10.1016/j.neuron.2020.08.026},
pmid = {32970991},
issn = {1097-4199},
mesh = {Animals ; Animals, Genetically Modified ; GABAergic Neurons/physiology ; Mutation ; Neural Inhibition/physiology ; Neuroglia/*physiology ; Odorants/*analysis ; Olfactory Receptor Neurons/*physiology ; Receptors, Odorant/*physiology ; Signal Transduction ; Smell/*physiology ; },
abstract = {Glia are typically considered as supporting cells for neural development and synaptic transmission. Here, we report an active role of a glia in olfactory transduction. As a polymodal sensory neuron in C. elegans, the ASH neuron is previously known to detect multiple aversive odorants. We reveal that the AMsh glia, a sheath for multiple sensory neurons including ASH, cell-autonomously respond to aversive odorants via G-protein-coupled receptors (GPCRs) distinct from those in ASH. Upon activation, the AMsh glia suppress aversive odorant-triggered avoidance and promote olfactory adaptation by inhibiting the ASH neuron via GABA signaling. Thus, we propose a novel two-receptor model where the glia and sensory neuron jointly mediate adaptive olfaction. Our study reveals a non-canonical function of glial cells in olfactory transduction, which may provide new insights into the glia-like supporting cells in mammalian sensory procession.},
}
@article {pmid32965471,
year = {2021},
author = {Stieger, JR and Engel, S and Jiang, H and Cline, CC and Kreitzer, MJ and He, B},
title = {Mindfulness Improves Brain-Computer Interface Performance by Increasing Control Over Neural Activity in the Alpha Band.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {31},
number = {1},
pages = {426-438},
pmid = {32965471},
issn = {1460-2199},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Learning/*physiology ; Male ; Mindfulness/methods ; Psychomotor Performance/*physiology ; Rest/*physiology ; Task Performance and Analysis ; },
abstract = {Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here, we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in electroencephalogram signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals' mindfulness practice as well as performance on a breath counting task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.},
}
@article {pmid32965150,
year = {2020},
author = {Egert, D and Pettibone, JR and Lemke, S and Patel, PR and Caldwell, CM and Cai, D and Ganguly, K and Chestek, CA and Berke, JD},
title = {Cellular-scale silicon probes for high-density, precisely localized neurophysiology.},
journal = {Journal of neurophysiology},
volume = {124},
number = {6},
pages = {1578-1587},
pmid = {32965150},
issn = {1522-1598},
support = {U01 NS094375/NS/NINDS NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; UF1NS107659//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/International ; U01NS094375//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/International ; },
mesh = {Animals ; Brain/*physiology ; *Electrodes, Implanted ; Male ; Neurons/*physiology ; Neurophysiology/*instrumentation/*methods ; Rats, Long-Evans ; Silicon ; },
abstract = {Neural implants with large numbers of electrodes have become an important tool for examining brain functions. However, these devices typically displace a large intracranial volume compared with the neurons they record. This large size limits the density of implants, provokes tissue reactions that degrade chronic performance, and impedes the ability to accurately visualize recording sites within intact circuits. Here we report next-generation silicon-based neural probes at a cellular scale (5 × 10 µm cross section), with ultra-high-density packing (as little as 66 µm between shanks) and 64 or 256 closely spaced recording sites per probe. We show that these probes can be inserted into superficial or deep brain structures and record large spikes in freely behaving rats for many weeks. Finally, we demonstrate a slice-in-place approach for the precise registration of recording sites relative to nearby neurons and anatomical features, including striatal µ-opioid receptor patches. This scalable technology provides a valuable tool for examining information processing within neural circuits and potentially for human brain-machine interfaces.NEW &amp; NOTEWORTHY Devices with many electrodes penetrating into the brain are an important tool for investigating neural information processing, but they are typically large compared with neurons. This results in substantial damage and makes it harder to reconstruct recording locations within brain circuits. This paper presents high-channel-count silicon probes with much smaller features and a method for slicing through probe, brain, and skull all together. This allows probe tips to be directly observed relative to immunohistochemical markers.},
}
@article {pmid32963279,
year = {2020},
author = {Leinders, S and Vansteensel, MJ and Branco, MP and Freudenburg, ZV and Pels, EGM and Van der Vijgh, B and Van Zandvoort, MJE and Ramsey, NF and Aarnoutse, EJ},
title = {Dorsolateral prefrontal cortex-based control with an implanted brain-computer interface.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {15448},
pmid = {32963279},
issn = {2045-2322},
support = {ADV 320708/ERC_/European Research Council/International ; },
mesh = {*Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography ; Eye Movements/*physiology ; Female ; Humans ; Locked-In Syndrome/*physiopathology/*rehabilitation ; Magnetic Resonance Imaging ; Middle Aged ; Neuropsychological Tests ; Prefrontal Cortex/*physiology ; Psychomotor Performance ; User-Computer Interface ; },
abstract = {The objective of this study was to test the feasibility of using the dorsolateral prefrontal cortex as a signal source for brain-computer interface control in people with severe motor impairment. We implanted two individuals with locked-in syndrome with a chronic brain-computer interface designed to restore independent communication. The implanted system (Utrecht NeuroProsthesis) included electrode strips placed subdurally over the dorsolateral prefrontal cortex. In both participants, counting backwards activated the dorsolateral prefrontal cortex consistently over the course of 47 and 22 months, respectively. Moreover, both participants were able to use this signal to control a cursor in one dimension, with average accuracy scores of 78 ± 9% (standard deviation) and 71 ± 11% (chance level: 50%), respectively. Brain-computer interface control based on dorsolateral prefrontal cortex activity is feasible in people with locked-in syndrome and may become of relevance for those unable to use sensorimotor signals for control.},
}
@article {pmid32960770,
year = {2021},
author = {Jeng, PY and Wei, CS and Jung, TP and Wang, LC},
title = {Low-Dimensional Subject Representation-Based Transfer Learning in EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {25},
number = {6},
pages = {1915-1925},
doi = {10.1109/JBHI.2020.3025865},
pmid = {32960770},
issn = {2168-2208},
mesh = {*Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Machine Learning ; },
abstract = {Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach requires task-relevant data, which is impractical in real-life scenarios such as drowsiness during driving. This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG. Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial, and spectral domains. Then, the proposed framework assessed the characteristics to obtain the low-dimensional subject representations such that the subjects with similar brain dynamics can be identified. This method can leverage the existing data from other users, and a small number of data from a rapid, non-task, unsupervised calibration from a new user to build an accurate BCI. Our results demonstrated that, in terms of prediction accuracy, the proposed low-dimensional subject representation-based transfer learning (LDSR-TL) framework outperformed the random selection, and the Riemannian manifold approach in cognitive-state tracking, while requiring fewer training data. The results can greatly improve the practicability, and usability of EEG-based BCI in the real world.},
}
@article {pmid32956061,
year = {2020},
author = {Chen, J and Yu, Z and Gu, Z and Li, Y},
title = {Deep Temporal-Spatial Feature Learning for Motor Imagery-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {11},
pages = {2356-2366},
doi = {10.1109/TNSRE.2020.3023417},
pmid = {32956061},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery (MI) decoding is an important part of brain-computer interface (BCI) research, which translates the subject's intentions into commands that external devices can execute. The traditional methods for discriminative feature extraction, such as common spatial pattern (CSP) and filter bank common spatial pattern (FBCSP), have only focused on the energy features of the electroencephalography (EEG) and thus ignored the further exploration of temporal information. However, the temporal information of spatially filtered EEG may be critical to the performance improvement of MI decoding. In this paper, we proposed a deep learning approach termed filter-bank spatial filtering and temporal-spatial convolutional neural network (FBSF-TSCNN) for MI decoding, where the FBSF block transforms the raw EEG signals into an appropriate intermediate EEG presentation, and then the TSCNN block decodes the intermediate EEG signals. Moreover, a novel stage-wise training strategy is proposed to mitigate the difficult optimization problem of the TSCNN block in the case of insufficient training samples. Firstly, the feature extraction layers are trained by optimization of the triplet loss. Then, the classification layers are trained by optimization of the cross-entropy loss. Finally, the entire network (TSCNN) is fine-tuned by the back-propagation (BP) algorithm. Experimental evaluations on the BCI IV 2a and SMR-BCI datasets reveal that the proposed stage-wise training strategy yields significant performance improvement compared with the conventional end-to-end training strategy, and the proposed approach is comparable with the state-of-the-art method.},
}
@article {pmid32955675,
year = {2020},
author = {Rawnaque, FS and Rahman, KM and Anwar, SF and Vaidyanathan, R and Chau, T and Sarker, F and Mamun, KAA},
title = {Technological advancements and opportunities in Neuromarketing: a systematic review.},
journal = {Brain informatics},
volume = {7},
number = {1},
pages = {10},
pmid = {32955675},
issn = {2198-4018},
support = {IAR/01/19/SE/10//Institute of Advanced Research, United International University/ ; },
abstract = {Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.},
}
@article {pmid32954260,
year = {2019},
author = {Khan, S and Aziz, T},
title = {Transcending the brain: is there a cost to hacking the nervous system?.},
journal = {Brain communications},
volume = {1},
number = {1},
pages = {fcz015},
pmid = {32954260},
issn = {2632-1297},
abstract = {Great advancements have recently been made to understand the brain and the potential that we can extract out of it. Much of this has been centred on modifying electrical activity of the nervous system for improved physical and cognitive performance in those with clinical impairment. However, there is a risk of going beyond purely physiological performance improvements and striving for human enhancement beyond traditional human limits. Simple ethical guidelines and legal doctrine must be examined to keep ahead of technological advancement in light of the impending mergence between biology and machine. By understanding the role of modern ethics, this review aims to appreciate the fine boundary between what is considered ethically justified for current neurotechnology.},
}
@article {pmid32954024,
year = {2020},
author = {Papanastasiou, G and Drigas, A and Skianis, C and Lytras, M},
title = {Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review.},
journal = {Heliyon},
volume = {6},
number = {9},
pages = {e04250},
pmid = {32954024},
issn = {2405-8440},
abstract = {The aim of this article is to explore a paradigm shift on Brain Computer Interface (BCI) research, as well as on intervention best practices for training and rehabilitation of students with neurodevelopmental disorders. Recent studies indicate that BCI devices have positive impact on students' attention skills and working memory as well as on other skills, such as visuospatial, social, imaginative and emotional abilities. BCI applications aim to emulate humans' brain and address the appropriate understanding for each student's neurodevelopmental disorders. Studies conducted to provide knowledge about BCI-based intervention applications regarding memory, attention, visuospatial, learning, collaboration, and communication, social, creative and emotional skills are highlighted. Only non-invasive BCI type of applications are being investigated based upon representative, non-exhaustive and state-of-the-art studies within the field. This article examines the progress of BCI research so far, while different BCI paradigms are investigated. BCI-based applications could successfully regulate students' cognitive abilities when used for their training and rehabilitation. Future directions to investigate BCI-based applications for training and rehabilitation of students with neurodevelopmental disorders concerning the different populations involved are discussed.},
}
@article {pmid32951791,
year = {2020},
author = {Dunbar, J and Gilbert, JE and Lewis, B},
title = {Exploring differences between self-report and electrophysiological indices of drowsy driving: A usability examination of a personal brain-computer interface device.},
journal = {Journal of safety research},
volume = {74},
number = {},
pages = {27-34},
doi = {10.1016/j.jsr.2020.04.006},
pmid = {32951791},
issn = {1879-1247},
mesh = {Adult ; Awareness ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Distracted Driving/*statistics &amp; numerical data ; Electrophysiology/methods/*statistics &amp; numerical data ; Female ; Florida ; Humans ; Male ; Self Report/*statistics &amp; numerical data ; *Wakefulness ; Young Adult ; },
abstract = {INTRODUCTION: Impaired driving has resulted in numerous accidents, fatalities, and costly damage. One particularly concerning type of impairment is driver drowsiness. Despite advancements, modern vehicle safety systems remain ineffective at keeping drowsy drivers alert and aware of their state, even temporarily. Until recently the use of user-centric brain-computer interface (BCI) devices to capture electrophysiological data relating to driver drowsiness has been limited.<br><br>METHOD: In this study, 25 participants drove on a simulated roadway under drowsy conditions.<br><br>RESULTS: Neither subjective nor electrophysiological measures differed between individuals who showed overt signs of drowsiness (prolonged eye closure) during the drive. However, the directionality and effect size estimates provided by the BCI device suggested the practicality and feasibility of its future implementation in vehicle safety systems. Practical applications: This research highlights opportunities for future BCI device research for use to assess the state of drowsy drivers in a real-world context.},
}
@article {pmid32947766,
year = {2020},
author = {Sadiq, MT and Yu, X and Yuan, Z and Aziz, MZ},
title = {Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {18},
pages = {},
pmid = {32947766},
issn = {1424-8220},
support = {2018JQ6014//Natural Science Basic Research Plan in Shaanxi Province of China/ ; G2018KY0308//Fundamental Research Funds for the Central Universities/ ; 2018M641013//Chinese Postdoctoral Science Foundation/ ; 2018BSHYDZZ05//Postdoctoral Science Foundation of Shaanxi Province/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {The development of fast and robust brain-computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.},
}
@article {pmid32942303,
year = {2020},
author = {Little, MP and Pawel, D and Misumi, M and Hamada, N and Cullings, HM and Wakeford, R and Ozasa, K},
title = {Lifetime Mortality Risk from Cancer and Circulatory Disease Predicted from the Japanese Atomic Bomb Survivor Life Span Study Data Taking Account of Dose Measurement Error.},
journal = {Radiation research},
volume = {194},
number = {3},
pages = {259-276},
pmid = {32942303},
issn = {1938-5404},
support = {ZIA CP010135/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Atomic Bomb Survivors/*statistics &amp; numerical data ; Bayes Theorem ; Cohort Studies ; Humans ; Japan ; Models, Statistical ; Neoplasms, Radiation-Induced/*mortality ; Radiation Dosage ; Research Design ; Risk Assessment ; },
abstract = {Dosimetric measurement error is known to potentially bias the magnitude of the dose response, and can also affect the shape of dose response. In this report, generalized relative and absolute rate models are fitted to the latest Japanese atomic bomb survivor solid cancer, leukemia and circulatory disease mortality data (followed from 1950 through 2003), with the latest (DS02R1) dosimetry, using Bayesian techniques to adjust for errors in dose estimates and assessing other model uncertainties. Linear-quadratic models are fitted and used to assess lifetime mortality risks for contemporary UK, USA, French, Russian, Japanese and Chinese populations. For a test dose of 0.1 Gy absorbed dose weighted by neutron relative biological effectiveness, solid cancer, leukemia and circulatory disease mortality risks for a UK population using a generalized linear-quadratic relative rate model were estimated to be 3.88% Gy-1 [95% Bayesian credible interval (BCI): 1.17, 6.97], 0.35% Gy-1 (95% BCI: -0.03, 0.78) and 2.24% Gy-1 (95% BCI: -0.17, 13.76), respectively. Using a generalized absolute rate linear-quadratic model at 0.1 Gy, the lifetime risks for these three end points were estimated to be 3.56% Gy-1 (95% BCI: 0.54, 6.78), 0.41% Gy-1 (95% BCI: 0.01, 0.86) and 1.56% Gy-1 (95% BCI: -1.10, 7.21), respectively. There was substantial evidence of curvature for solid cancer (in particular, the group of solid cancers excluding lung, breast and stomach cancers) and leukemia, so that for solid cancer and leukemia, estimates of excess risk per unit dose were nearly doubled by increasing the dose from 0.01 to 1.0 Gy, with most of the increase occurring in the interval from 0.1 to 1.0 Gy. For circulatory disease, the dose-response curvature was inverse, so that risk per unit dose was nearly halved by going from 0.01 t o 1.0 Gy weighted absorbed dose, although there were substantial uncertainties. In general, there were higher radiation risks for females compared to males. This was true for solid cancer and circulatory disease overall, as well as for lung, breast, stomach and the group of other solid cancers, and was the case whether relative or absolute rate projection models were employed; however, for leukemia this pattern was reversed. Risk estimates varied somewhat between populations, with lower cancer risks in aggregate for China and Russia, but higher circulatory disease risks for Russia, particularly using the relative rate model. There was more pronounced variation for certain cancer sites and certain types of projection models, so that breast cancer risk was markedly lower in China and Japan using a relative rate model, but the opposite was the case for stomach cancer. There was less variation between countries using the absolute rate models for stomach cancer and breast cancer, but this was not the case for lung cancer and the group of other solid cancers, or for circulatory disease.},
}
@article {pmid32940119,
year = {2022},
author = {Versalovic, E and Diamond, M and Klein, E},
title = {"Re-identifying yourself": a qualitative study of veteran views on implantable BCI for mobility and communication in ALS.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {17},
number = {7},
pages = {807-814},
doi = {10.1080/17483107.2020.1817991},
pmid = {32940119},
issn = {1748-3115},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; *Veterans ; },
abstract = {OBJECTIVES: Brain-computer interface (BCI) technology to assist with mobility and communication is an active area of research in amyotrophic lateral sclerosis (ALS). Implantable BCI offers promise for individuals with severe disease, such as locked-in syndrome, but also raises important ethical issues. We undertook in-depth qualitative interviews with ALS patients from a Veterans Administration hospital ALS multi-disciplinary clinic and explored their perspectives on issues of identity, privacy, enhancement, informed consent, and responsibility related to implantable BCI.<br><br>METHODS: Semi-structured interviews were conducted with sixteen (n&#x2009;=&#x2009;16) individuals, and transcripts were analysed using a modified grounded theory approach.<br><br>RESULTS: Emergent themes included: (1) attitudes towards BCI were characterised by fear, hope, and hesitation about adoption of BCI technology; (2) analogies to other technologies were a useful tool in understanding and communicating opinions about ethical issues in BCI; (3) concerns about potentially socially stigmatising effects of BCI and the burden of adjustment to new therapeutic devices were important considerations to be weighed against the potential functional benefit of BCI use; (4) therapeutic decision-making in ALS often intimately involves loved ones; and (5) prospective decision-making about BCI was significantly affected by weighing the timing of the intervention with the progression of illness.<br><br>CONCLUSION: The interest in BCI and views on ethical issues raised by BCI is moderated by the experience of living with ALS. The findings from this study can help guide the development of implantable BCI technology for persons with ALS.Implications for rehabilitationLoved ones will play crucial roles in helping patients think through the possible benefits and burdens of getting a BCI device.Providers should consider how the ideal timing for getting an implantable BCI device will vary based on the priorities of persons with ALS and their disease stage.Concerns about social stigma, burden of adjustment, and the desire to maximise time left with loved ones may outweigh the potential functional benefits of BCI devices for some persons with ALS.},
}
@article {pmid32940002,
year = {2021},
author = {Dong, R and Liu, X and Cheng, S and Tang, L and Chen, M and Zhong, L and Chen, Z and Liu, S and Jiang, X},
title = {Highly Stretchable Metal-Polymer Conductor Electrode Array for Electrophysiology.},
journal = {Advanced healthcare materials},
volume = {10},
number = {4},
pages = {e2000641},
doi = {10.1002/adhm.202000641},
pmid = {32940002},
issn = {2192-2659},
mesh = {Electrodes ; Electronics ; *Electrophysiological Phenomena ; Electrophysiology ; *Polymers ; },
abstract = {Narrowing the mechanical mismatch between biological tissues (typically soft) and neural interfaces (hard) is essential for maintaining signal quality for the electrical recording of neural activity. However, only a few materials can satisfy all requirements for such electronics, which need to be both biocompatible and sufficiently soft. Here, a highly stretchable electrode array (SEA) is introduced, based on the liquid metal-polymer conductor (MPC), achieving high mechanical flexibility and good cytocompatability for neural interfaces. By utilizing the MPC, the SEA exhibits high stretchability (≈100%) and excellent cycling stability (>400 cycles). The cytocompatability of the SEA can allow for long-term culturing of primary neurons and enable signal recording of primary hippocampal neurons. In the future, the SEA could serve as a reliable and robust platform for diagnostics in neuronal tissues and greatly advance brain-machine interfaces.},
}
@article {pmid32938263,
year = {2021},
author = {Hashimoto, H and Kameda, S and Maezawa, H and Oshino, S and Tani, N and Khoo, HM and Yanagisawa, T and Yoshimine, T and Kishima, H and Hirata, M},
title = {A Swallowing Decoder Based on Deep Transfer Learning: AlexNet Classification of the Intracranial Electrocorticogram.},
journal = {International journal of neural systems},
volume = {31},
number = {11},
pages = {2050056},
doi = {10.1142/S0129065720500562},
pmid = {32938263},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; *Deglutition ; Electrocorticography ; Electrodes ; Humans ; Machine Learning ; },
abstract = {To realize a brain-machine interface to assist swallowing, neural signal decoding is indispensable. Eight participants with temporal-lobe intracranial electrode implants for epilepsy were asked to swallow during electrocorticogram (ECoG) recording. Raw ECoG signals or certain frequency bands of the ECoG power were converted into images whose vertical axis was electrode number and whose horizontal axis was time in milliseconds, which were used as training data. These data were classified with four labels (Rest, Mouth open, Water injection, and Swallowing). Deep transfer learning was carried out using AlexNet, and power in the high-[Formula: see text] band (75-150[Formula: see text]Hz) was the training set. Accuracy reached 74.01%, sensitivity reached 82.51%, and specificity reached 95.38%. However, using the raw ECoG signals, the accuracy obtained was 76.95%, comparable to that of the high-[Formula: see text] power. We demonstrated that a version of AlexNet pre-trained with visually meaningful images can be used for transfer learning of visually meaningless images made up of ECoG signals. Moreover, we could achieve high decoding accuracy using the raw ECoG signals, allowing us to dispense with the conventional extraction of high-[Formula: see text] power. Thus, the images derived from the raw ECoG signals were equivalent to those derived from the high-[Formula: see text] band for transfer deep learning.},
}
@article {pmid32931434,
year = {2021},
author = {Lin, B and Deng, S and Gao, H and Yin, J},
title = {A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {18},
number = {5},
pages = {1699-1709},
doi = {10.1109/TCBB.2020.3024228},
pmid = {32931434},
issn = {1557-9964},
mesh = {Algorithms ; Brain/physiology ; *Electroencephalography ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures.},
}
@article {pmid32924941,
year = {2020},
author = {Wei, W and Qiu, S and Ma, X and Li, D and Wang, B and He, H},
title = {Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {11},
pages = {2344-2355},
doi = {10.1109/TNSRE.2020.3023761},
pmid = {32924941},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; Learning ; },
abstract = {Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this article, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source framework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts.},
}
@article {pmid32924795,
year = {2023},
author = {Knudson, D},
title = {A tale of two instructional experiences: student engagement in active learning and emergency remote learning of biomechanics.},
journal = {Sports biomechanics},
volume = {22},
number = {11},
pages = {1485-1495},
doi = {10.1080/14763141.2020.1810306},
pmid = {32924795},
issn = {1752-6116},
mesh = {Humans ; *Problem-Based Learning ; Biomechanical Phenomena ; Pandemics ; *COVID-19 ; Students ; },
abstract = {This study documents student engagement in face-to-face low-tech active learning and student perceptions of emergency remote instruction due to the COVID-19 pandemic in introductory biomechanics. Students in two classes received 8 weeks of face-to-face instruction with five low-tech active learning techniques and then received 6 weeks of emergency remote, online instruction. Learning was measured using pre-test and post-test administrations of the biomechanics concept inventory (BCI). A survey of engagement in active learning with additional questions on active learning and online instruction were collected with the post-test. No student perceptions of engagement in active learning or online instruction were correlated with learning measured by normalised gain. Student's perception of the 'value of group activity' factor from survey was significantly correlated (r[2] = 12%) with the number of students typically in active learning groups. There was a significant correlation (r[2] = 46%) between student perception of reading the textbook before online video lessons and perception of value of the video lessons in the online portion of the course. Most students (59%) preferred face-to-face instruction in biomechanics. While up to 28% of students may have reported resistance to group-based active learning, low-tech active learning significantly improved mastery of biomechanics concepts above levels previously reported for lecture alone.},
}
@article {pmid32923476,
year = {2020},
author = {Hasan, MAH and Khan, MU and Mishra, D},
title = {A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation.},
journal = {BioMed research international},
volume = {2020},
number = {},
pages = {1838140},
pmid = {32923476},
issn = {2314-6141},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Motor Cortex/physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.},
}
@article {pmid32922254,
year = {2020},
author = {Chen, S and Cao, L and Shu, X and Wang, H and Ding, L and Wang, SH and Jia, J},
title = {Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain-Computer Interface With Exoskeleton Feedback.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {809},
pmid = {32922254},
issn = {1662-4548},
abstract = {BACKGROUND: Brain-computer interface (BCI) has been regarded as a newly developing intervention in promoting motor recovery in stroke survivors. Several studies have been performed in chronic stroke to explore its clinical and subclinical efficacy. However, evidence in subacute stroke was poor, and the longitudinal sensorimotor rhythm changes in subacute stroke after BCI with exoskeleton feedback were still unclear.<br><br>MATERIALS AND METHODS: Fourteen stroke patients in subacute stage were recruited and randomly allocated to BCI group (n = 7) and the control group (n = 7). Brain-computer interface training with exoskeleton feedback was applied in the BCI group three times a week for 4 weeks. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) scale was used to assess motor function improvement. Brain-computer interface performance was calculated across the 12-time interventions. Sensorimotor rhythm changes were explored by event-related desynchronization (ERD) changes and topographies.<br><br>RESULTS: After 1 month BCI intervention, both the BCI group (p = 0.032) and the control group (p = 0.048) improved in FMA-UE scores. The BCI group (12.77%) showed larger percentage of improvement than the control group (7.14%), and more patients obtained good motor recovery in the BCI group (57.1%) than did the control group (28.6%). Patients with good recovery showed relatively higher online BCI performance, which were greater than 70%. And they showed a continuous improvement in offline BCI performance and obtained a highest value in the last six sessions of interventions during BCI training. However, patients with poor recovery reached a platform in the first six sessions of interventions and did not improve any more or even showed a decrease. In sensorimotor rhythm, patients with good recovery showed an enhanced ERD along with time change. Topographies showed that the ipsilesional hemisphere presented stronger activations after BCI intervention.<br><br>CONCLUSION: Brain-computer interface training with exoskeleton feedback was feasible in subacute stroke patients. Brain-computer interface performance can be an index to evaluate the efficacy of BCI intervention. Patients who presented increasingly stronger or continuously strong activations (ERD) may obtain better motor recovery.},
}
@article {pmid32916675,
year = {2020},
author = {Kostas, D and Rudzicz, F},
title = {Thinker invariance: enabling deep neural networks for BCI across more people.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056008},
doi = {10.1088/1741-2552/abb7a7},
pmid = {32916675},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Machine Learning ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: Most deep neural networks (DNNs) used as brain computer interfaces (BCI) classifiers are rarely viable for more than one person and are relatively shallow compared to the state-of-the-art in the wider machine learning literature. The goal of this work is to frame these as a unified challenge and reconsider how transfer learning is used to overcome these difficulties.<br><br>APPROACH: We present two variations of a holistic approach to transfer learning with DNNs for BCI that rely on a deeper network called TIDNet. Our approaches use multiple subjects for training in the interest of creating a more universal classifier that is applicable for new (unseen) subjects. The first approach is purely subject-invariant and the second targets specific subjects, without loss of generality. We use five publicly accessible datasets covering a range of tasks and compare our approaches to state-of-the-art alternatives in detail.<br><br>MAIN RESULTS: We observe that TIDNet in conjunction with our training augmentations is more consistent when compared to shallower baselines, and in some cases exhibits large and significant improvements, for instance motor imagery classification improvements of over 8%. Furthermore, we show that our suggested multi-domain learning (MDL) strategy strongly outperforms simply fine-tuned general models when targeting specific subjects, while remaining more generalizable to still unseen subjects.<br><br>SIGNIFICANCE: TIDNet in combination with a data alignment-based training augmentation proves to be a consistent classification approach of single raw trials and can be trained even with the inclusion of corrupted trials. Our MDL strategy calls into question the intuition to fine-tune trained classifiers to new subjects, as it proves simpler and more accurate while remaining general. Furthermore, we show evidence that augmented TIDNet training makes better use of additional subjects, showing continued and greater performance improvement over shallower alternatives, indicating promise for a new subject-invariant paradigm rather than a subject-specific one.},
}
@article {pmid32915578,
year = {2020},
author = {Xiao, M and Ulloa Severino, FP and Iseppon, F and Cheng, G and Torre, V and Tang, M},
title = {3D Free-Standing Ordered Graphene Network Geometrically Regulates Neuronal Growth and Network Formation.},
journal = {Nano letters},
volume = {20},
number = {10},
pages = {7043-7051},
doi = {10.1021/acs.nanolett.0c02107},
pmid = {32915578},
issn = {1530-6992},
mesh = {Axons ; *Graphite ; Neurogenesis ; Neurons ; Tissue Engineering ; Tissue Scaffolds ; },
abstract = {The control of cell-microenvironment interactions plays a pivotal role in constructing specific scaffolds for tissue engineering. Here, we fabricated a 3D free-standing ordered graphene (3D-OG) network with a precisely defined pattern. When primary cortical cells are cultured on 3D-OG scaffolds, they form well-defined 3D connections. Astrocytes have a more ramified shape similar to that seen in vivo because of the nanosized ripples and wrinkles on the surface of graphene skeleton. Neurons have axons and dendrites aligned along the graphene skeleton, allowing the formation of neuronal networks with highly controlled connections. Neuronal networks have higher electrical activity with functional signaling over a long distance along the graphene skeleton. Our study, for the first time, investigated the geometrical cues on ordered neuronal growth and network formation with the support of graphene in 3D, which therefore advanced the development of customized scaffolds for brain-machine interfaces or neuroprosthetic devices.},
}
@article {pmid32908576,
year = {2020},
author = {Zhang, K and Xu, G and Chen, L and Tian, P and Han, C and Zhang, S and Duan, N},
title = {Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {1683013},
pmid = {32908576},
issn = {1748-6718},
mesh = {Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Deep Learning ; Electroencephalography/classification/statistics &amp; numerical data ; Humans ; Imagination ; Mathematical Concepts ; *Neural Networks, Computer ; Photic Stimulation ; Visual Perception/physiology ; },
abstract = {In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brain. This issue may lead to variations in feature distribution of electroencephalogram (EEG) across subjects, which greatly reduces the generalization ability of a classifier. Although subject-dependent (SD) strategy provides a promising way to solve the problem of personalized classification, it cannot achieve expected performance due to the limitation of the amount of data especially for a deep neural network (DNN) classification model. Herein, we propose an instance transfer subject-independent (ITSD) framework combined with a convolutional neural network (CNN) to improve the classification accuracy of the model during motor imagery (MI) task. The proposed framework consists of the following steps. Firstly, an instance transfer learning based on the perceptive Hash algorithm is proposed to measure similarity of spectrogram EEG signals between different subjects. Then, we develop a CNN to decode these signals after instance transfer learning. Next, the performance of classifications by different training strategies (subject-independent- (SI-) CNN, SD-CNN, and ITSD-CNN) are compared. To verify the effectiveness of the algorithm, we evaluate it on the dataset of BCI competition IV-2b. Experiments show that the instance transfer learning can achieve positive instance transfer using a CNN classification model. Among the three different training strategies, the average classification accuracy of ITSD-CNN can achieve 94.7 ± 2.6 and obtain obvious improvement compared with a contrast model (p < 0.01). Compared with other methods proposed in previous research, the framework of ITSD-CNN outperforms the state-of-the-art classification methods with a mean kappa value of 0.664.},
}
@article {pmid32908470,
year = {2020},
author = {Sun, KT and Hsieh, KL and Syu, SR},
title = {Towards an Accessible Use of a Brain-Computer Interfaces-Based Home Care System through a Smartphone.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {1843269},
pmid = {32908470},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; *Home Care Services ; Humans ; Smartphone ; User-Computer Interface ; },
abstract = {This study proposes a home care system (HCS) based on a brain-computer interface (BCI) with a smartphone. The HCS provides daily help to motor-disabled people when a caregiver is not present. The aim of the study is two-fold: (1) to develop a BCI-based home care system to help end-users control their household appliances, and (2) to assess whether the architecture of the HCS is easy for motor-disabled people to use. A motion-strip is used to evoke event-related potentials (ERPs) in the brain of the user, and the system immediately processes these potentials to decode the user's intentions. The system, then, translates these intentions into application commands and sends them via Bluetooth to the user's smartphone to make an emergency call or to execute the corresponding app to emit an infrared (IR) signal to control a household appliance. Fifteen healthy and seven motor-disabled subjects (including the one with ALS) participated in the experiment. The average online accuracy was 81.8% and 78.1%, respectively. Using component N2P3 to discriminate targets from nontargets can increase the efficiency of the system. Results showed that the system allows end-users to use smartphone apps as long as they are using their brain waves. More important, only one electrode O1 is required to measure EEG signals, giving the system good practical usability. The HCS can, thus, improve the autonomy and self-reliance of its end-users.},
}
@article {pmid32906731,
year = {2020},
author = {Torres P, EP and Torres, EA and Hernández-Álvarez, M and Yoo, SG},
title = {EEG-Based BCI Emotion Recognition: A Survey.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {18},
pages = {},
pmid = {32906731},
issn = {1424-8220},
mesh = {Algorithms ; Artificial Intelligence ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; *Emotions ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user's emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.},
}
@article {pmid32906650,
year = {2020},
author = {Li, M and Shang, Z and Zhao, K and Cheng, S and Wan, H},
title = {The Role of Hp-NCL Network in Goal-Directed Routing Information Encoding of Bird: A Review.},
journal = {Brain sciences},
volume = {10},
number = {9},
pages = {},
pmid = {32906650},
issn = {2076-3425},
support = {61673353//National Natural Science Foundation of China/ ; },
abstract = {Goal-directed navigation is a crucial behavior for the survival of animals, especially for the birds having extraordinary spatial navigation ability. In the studies of the neural mechanism of the goal-directed behavior, especially involving the information encoding mechanism of the route, the hippocampus (Hp) and nidopallium caudalle (NCL) of the avian brain are the famous regions that play important roles. Therefore, they have been widely concerned and a series of studies surrounding them have increased our understandings of the navigation mechanism of birds in recent years. In this paper, we focus on the studies of the information encoding mechanism of the route in the avian goal-directed behavior. We first summarize and introduce the related studies on the role of the Hp and NCL for goal-directed behavior comprehensively. Furthermore, we review the related cooperative interaction studies about the Hp-NCL local network and other relevant brain regions supporting the goal-directed routing information encoding. Finally, we summarize the current situation and prospect the existing important questions in this field. We hope this paper can spark fresh thinking for the following research on routing information encoding mechanism of birds.},
}
@article {pmid32906625,
year = {2020},
author = {Wang, L and Han, D and Qian, B and Zhang, Z and Zhang, Z and Liu, Z},
title = {The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli.},
journal = {Brain sciences},
volume = {10},
number = {9},
pages = {},
pmid = {32906625},
issn = {2076-3425},
support = {No. 31371039//National Natural Science Foundation of China/ ; },
abstract = {Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain-computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue-target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.},
}
@article {pmid32906103,
year = {2020},
author = {Reichert, C and Dürschmid, S and Bartsch, MV and Hopf, JM and Heinze, HJ and Hinrichs, H},
title = {Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056012},
doi = {10.1088/1741-2552/abb692},
pmid = {32906103},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Eye Movements ; Humans ; Photic Stimulation ; },
abstract = {OBJECTIVE: One of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts.<br><br>APPROACH: We present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component-a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure.<br><br>MAIN RESULTS: Despite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks.<br><br>SIGNIFICANCE: Our method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.},
}
@article {pmid32904287,
year = {2020},
author = {Centurelli, F and Fava, A and Monsurrò, P and Scotti, G and Tommasino, P and Trifiletti, A},
title = {Low power switched-resistor band-pass filter for neural recording channels in 130nm CMOS.},
journal = {Heliyon},
volume = {6},
number = {8},
pages = {e04723},
pmid = {32904287},
issn = {2405-8440},
abstract = {In this work, we present a low-power 2[nd] order band-pass filter for neural recording applications. The central frequency of the passband is set to 375Hz and the quality factor to 5 to properly process the neural signals related to the onset of epileptic seizure, and to strongly attenuate all the out of band biological signals and electrical disturbances. The biquad filter is based on a fully differential Tow Thomas architecture in which high-valued resistors are implemented through switched high-resistivity polysilicon resistors. A supply voltage as low as 0.8V and MOS transistors operating in the sub-threshold region are exploited to achieve a power consumption as low as 170nW, when driving a 1pF load capacitance. The filter exhibits a tuning range of the resonance frequency from 200Hz to 400Hz, and an area footprint of only 0.021 mm[2]. Very low power consumption and area occupation are key specifications for integrated, multiple-sensors, neural recording systems.},
}
@article {pmid32903775,
year = {2020},
author = {Schwarz, A and Escolano, C and Montesano, L and Müller-Putz, GR},
title = {Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {849},
pmid = {32903775},
issn = {1662-4548},
support = {681231/ERC_/European Research Council/International ; },
abstract = {Reaching and grasping is an essential part of everybody's life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-Versatile [TM] system and the dry-electrodes EEG-Hero [TM] headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets).},
}
@article {pmid32903663,
year = {2020},
author = {Lee, M and Yoon, JG and Lee, SW},
title = {Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {321},
pmid = {32903663},
issn = {1662-5161},
abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines-but does not perform-a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as "BCI-inefficiency," in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: r = 0.54; Session 2: r = 0.42). We also predicted MI performance using linear regression based on this connection (r-squared = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.},
}
@article {pmid32903354,
year = {2019},
author = {Tam, WK and Wu, T and Zhao, Q and Keefer, E and Yang, Z},
title = {Human motor decoding from neural signals: a review.},
journal = {BMC biomedical engineering},
volume = {1},
number = {},
pages = {22},
pmid = {32903354},
issn = {2524-4426},
abstract = {Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.},
}
@article {pmid32903340,
year = {2019},
author = {Ramshur, JT and Morshed, BI and de Jongh Curry, AL and Waters, RS},
title = {Telemetry-controlled simultaneous stimulation-and-recording device (SRD) to study interhemispheric cortical circuits in rat primary somatosensory (SI) cortex.},
journal = {BMC biomedical engineering},
volume = {1},
number = {},
pages = {19},
pmid = {32903340},
issn = {2524-4426},
support = {R01 NS055236/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: A growing need exists for neuroscience platforms that can perform simultaneous chronic recording and stimulation of neural tissue in animal models in a telemetry-controlled fashion with signal processing for analysis of the chronic recording data and external triggering capability. We describe the system design, testing, evaluation, and implementation of a wireless simultaneous stimulation-and-recording device (SRD) for modulating cortical circuits in physiologically identified sites in primary somatosensory (SI) cortex in awake-behaving and freely-moving rats. The SRD was developed using low-cost electronic components and open-source software. The function of the SRD was assessed by bench and in-vivo testing.<br><br>RESULTS: The SRD recorded spontaneous spiking and bursting neuronal activity, evoked responses to programmed intracortical microstimulation (ICMS) delivered internally by the SRD, and evoked responses to external peripheral forelimb stimulation.<br><br>CONCLUSIONS: The SRD is capable of wireless stimulation and recording on a predetermined schedule or can be wirelessly synchronized with external input as would be required in behavioral testing prior to, during, and following ICMS.},
}
@article {pmid32898573,
year = {2020},
author = {Gao, Y and Wang, X and Potter, T and Zhang, J and Zhang, Y},
title = {Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis.},
journal = {Journal of neuroscience methods},
volume = {346},
number = {},
pages = {108904},
doi = {10.1016/j.jneumeth.2020.108904},
pmid = {32898573},
issn = {1872-678X},
mesh = {Algorithms ; *Electroencephalography ; Emotions ; Entropy ; Image Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {BACKGROUND: Emotion recognition has been studied for decades, but the classification accuracy needs to be improved.<br><br>NEW METHOD: In this study, a novel emotional classification approach is proposed by combining the Histogram of Oriented Gradient (HOG) method with the Granger Causality (GC) or Transfer Entropy (TE) methods. HOG extracts local valid information from the GC/TE relationship matrices and then the Support Vector Machine (SVM) is employed to classify the emotional states of stress and calm.<br><br>RESULTS: Compared with previous studies, the classification accuracy has been greatly improved. The results of this study show that the classification based on GC or TE with HOG offers an average accuracy 88.93 % and 95.21 %, respectively. The achieved accuracy is about 12 % higher than that achieved without using HOG feature extraction.<br><br>Numerous efforts have been devoted to classify emotional states by extracting EEG characteristics on a single channel basis, the method developed in this study utilizes information interaction between brain channels as a feature to classify emotional states. Furthermore, this study combines HOG and relation matrices for the first time and uses image processing to extract EEG features.<br><br>CONCLUSION: Our results demonstrate the feasibility of combining TE with HOG for emotion recognition with improved classification accuracy by taking advantage of both network and gradient features. More specific features can be selected to improve classification accuracy by taking advantage of information exchanges between EEG channels directly or the extracted property of the relationship matrix based on information interactions.},
}
@article {pmid32895549,
year = {2021},
author = {Silversmith, DB and Abiri, R and Hardy, NF and Natraj, N and Tu-Chan, A and Chang, EF and Ganguly, K},
title = {Plug-and-play control of a brain-computer interface through neural map stabilization.},
journal = {Nature biotechnology},
volume = {39},
number = {3},
pages = {326-335},
pmid = {32895549},
issn = {1546-1696},
support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; },
mesh = {Adaptation, Physiological ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Motor Cortex/physiology/physiopathology ; Neuronal Plasticity ; Paralysis/physiopathology ; Psychomotor Performance ; Self-Help Devices ; },
abstract = {Brain-computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and 'plug-and-play' control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.},
}
@article {pmid32895430,
year = {2020},
author = {Kangassalo, L and Spapé, M and Ruotsalo, T},
title = {Neuroadaptive modelling for generating images matching perceptual categories.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {14719},
pmid = {32895430},
issn = {2045-2322},
abstract = {Brain-computer interfaces enable active communication and execution of a pre-defined set of commands, such as typing a letter or moving a cursor. However, they have thus far not been able to infer more complex intentions or adapt more complex output based on brain signals. Here, we present neuroadaptive generative modelling, which uses a participant's brain signals as feedback to adapt a boundless generative model and generate new information matching the participant's intentions. We report an experiment validating the paradigm in generating images of human faces. In the experiment, participants were asked to specifically focus on perceptual categories, such as old or young people, while being presented with computer-generated, photorealistic faces with varying visual features. Their EEG signals associated with the images were then used as a feedback signal to update a model of the user's intentions, from which new images were generated using a generative adversarial network. A double-blind follow-up with the participant evaluating the output shows that neuroadaptive modelling can be utilised to produce images matching the perceptual category features. The approach demonstrates brain-based creative augmentation between computers and humans for producing new information matching the human operator's perceptual categories.},
}
@article {pmid32891506,
year = {2021},
author = {Breton, J and Bernardeau, S and Vallée, M and Pillot, P and Lebacle, C and Delpech, PO and Charles, T and Biscans, C and Vallat, A and Pfister, C and Irani, J},
title = {[Single, immediate postoperative intra-vesical instillation (SI) compared to a single preoperative intra-vesical instillation of mitomycin C in non-muscle invasive bladder cancer (NMIBC). Phase II randomized trial].},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {31},
number = {2},
pages = {63-70},
doi = {10.1016/j.purol.2020.07.245},
pmid = {32891506},
issn = {1166-7087},
mesh = {Administration, Intravesical ; Antibiotics, Antineoplastic/*therapeutic use ; Female ; Humans ; Male ; Mitomycin/*administration &amp; dosage ; Neoplasm Invasiveness ; Pilot Projects ; Postoperative Care/*methods ; Preoperative Care/*methods ; Time Factors ; Urinary Bladder Neoplasms/*drug therapy/pathology/surgery ; },
abstract = {OBJECTIVE: A single immediate instillation of mitomycin C is recommended after a complete transurethral resection of the bladder (TURB) in low- and intermediate-risk patients with NMIBC. Actually, post-TURB instillation is seldom used due to logistical difficulties and surgical contraindications. Our aim was to compare patients with single pre-TURB intra-vesical instillation and patients with a single, immediate post-TURB intra-vesical instillation of mitomycin C.<br><br>METHODS: We performed a multicenter randomized trial between February 17, 2014 and November 24, 2016 (registration number 2012-004341-32). Sixty patients with two or less, primary or recurrent papillary bladder tumors and a negative urinary cytology were planned. Cystoscopy was performed at 3, 6 and 12 months after TURB. Our primary endpoint was disease-free interval. Secondary endpoints were recurrence rate at 3 and 12 months, rate of patients in whom instillation could not be performed and tolerance 1 month after TURB using BCI-Fr score.<br><br>RESULTS: Among 35 eligible participants, 20 were randomly assigned in the pre-TURB instillation group and 15 in the post-TURB instillation group. Follow-up was comparable: 12,3&#xb1;1,6 months in the SI group and 10,2&#xb1;4,5 months in the pre-TURB instillation group. In the post-TURB instillation group, 2 patients didn't have any instillation. We did not identify significant differences in disease-free interval. Tolerance at 1 month after TURB was similar in both groups.<br><br>CONCLUSION: Tolerance and efficacy were not significantly different. As expected, logisitics were easier for the health providers in the pre-TURB group where all patients had their instillation conversely to the post-TURB group. These results suggest that the advantages of a single immediate pre-TURB instillation warrant further evaluation of this strategy in a phase III randomized trial.},
}
@article {pmid32889400,
year = {2020},
author = {Castaño-Candamil, S and Piroth, T and Reinacher, P and Sajonz, B and Coenen, VA and Tangermann, M},
title = {Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease.},
journal = {NeuroImage. Clinical},
volume = {28},
number = {},
pages = {102376},
pmid = {32889400},
issn = {2213-1582},
mesh = {*Deep Brain Stimulation ; Hand ; Humans ; *Machine Learning ; *Parkinson Disease/therapy ; *Subthalamic Nucleus ; },
abstract = {The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.},
}
@article {pmid32887528,
year = {2020},
author = {Draaisma, LR and Wessel, MJ and Hummel, FC},
title = {Neurotechnologies as tools for cognitive rehabilitation in stroke patients.},
journal = {Expert review of neurotherapeutics},
volume = {20},
number = {12},
pages = {1249-1261},
doi = {10.1080/14737175.2020.1820324},
pmid = {32887528},
issn = {1744-8360},
mesh = {Cognitive Dysfunction/etiology/*rehabilitation ; Cognitive Remediation/*methods ; Humans ; Stroke/complications/*therapy ; Stroke Rehabilitation/*methods ; },
abstract = {INTRODUCTION: Cognitive impairments are one of the most common remaining symptoms after a stroke. The use of neurotechnologies to enhance cognitive performance is a rapidly emerging field with encouraging results.<br><br>AREAS COVERED: Here, the authors empirically review the respective literature and critically discuss the technologies that are currently most often used for cognitive enhancement in stroke patients, which are computerized cognitive training, virtual reality, noninvasive brain stimulation and brain-computer interfaces. The authors describe their advantages/disadvantages and the challenges and limitations to overcome.<br><br>EXPERT OPINION: Although the current results are promising, more research is needed to be able to make conclusive statements and translate these approaches successfully in daily clinical life. Multidiscipline collaborations could aid to improve current neurotechnologies and provide guidelines for future implementations.},
}
@article {pmid32886810,
year = {2020},
author = {Sinha, S and Matai, L},
title = {Is isolated bladder outlet obstruction associated with hydronephrosis? A database analysis.},
journal = {Neurourology and urodynamics},
volume = {39},
number = {8},
pages = {2361-2367},
doi = {10.1002/nau.24495},
pmid = {32886810},
issn = {1520-6777},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Databases, Factual ; Humans ; Hydronephrosis/*etiology/physiopathology ; Male ; Middle Aged ; Urinary Bladder Neck Obstruction/*complications/physiopathology ; Urodynamics/physiology ; Young Adult ; },
abstract = {AIMS: To examine whether isolated bladder outlet obstruction in the absence of associated lower urinary tract abnormality results in hydronephrosis. Isolated obstruction causes a brief rise in bladder pressure that might not trigger hydronephrosis.<br><br>METHODS: Data included adult men who underwent urodynamics for refractory non-neurogenic lower tract symptoms between 2011 and 2020. International Continence Society indices for obstruction (bladder outlet obstruction index [BOOI]&#x2009;&#x2265;&#x2009;40) and underactivity (bladder contractility index [BCI]&#x2009;<&#x2009;100) were calculated. Storage abnormality was defined as detrusor overactivity (DO) or poor compliance (<20&#x2009;ml/cm&#x2009;H2 0). Isolated obstruction was defined as BOOI&#x2009;&#x2265;&#x2009;40, BCI&#x2009;&#x2265;&#x2009;100 and no storage abnormality. Nonparametric tests using R program (3.5.0) applied (p&#x2009;<&#x2009;.05 significant). Logistic regression analyses were performed to study the relationships of hydronephrosis with lower urinary tract function.<br><br>RESULTS: A total 1596 men (range, 18-91 years; median, 51.0 years; Q3, 64.0 years; Q1, 34.0 years) were eligible. Hydronephrosis was noted in 274 (17.2%). A total of 45.4% were obstructed, 52.3% were underactive and 41.7% had storage abnormality. Storage abnormality (odds ratios [OR], 2.05; 95% confidence interval [CI]: 1.56, 2.69; p&#x2009;<&#x2009;.001) and bladder contractility (OR, 1.68; 95% CI, 1.25-2.26; p&#x2009;<&#x2009;.001) but not obstruction (OR, 1.07; 95% CI, 0.80-1.44; p&#x2009;=&#x2009;.634) was associated with hydronephrosis. Of eight possible combinations, men with BOO&#x2009;&#x2265;&#x2009;40, BCI&#x2009;&#x2265;&#x2009;100 and storage abnormality had highest probability of hydronephrosis (OR, 0.29; 95% CI, 0.24-0.33). Subanalysis showed that poor compliance (OR, 3.39; 95% CI, 2.49-4.60; p&#x2009;<&#x2009;.001) but not DO was associated with hydronephrosis. Younger age and higher postvoid residual urine were also associated with hydronephrosis.<br><br>CONCLUSIONS: In adult men with refractory non-neurogenic lower urinary tract symptoms, isolated bladder outlet obstruction is not associated with hydronephrosis.},
}
@article {pmid32882437,
year = {2020},
author = {Pinto, S and Quintarelli, S and Silani, V},
title = {New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic?.},
journal = {Journal of the neurological sciences},
volume = {418},
number = {},
pages = {117081},
pmid = {32882437},
issn = {1878-5883},
mesh = {Amyotrophic Lateral Sclerosis/*therapy ; *COVID-19/epidemiology ; Health Services Accessibility ; Humans ; Inventions/legislation &amp; jurisprudence ; Pandemics ; Telemedicine/*methods ; },
abstract = {Amyotrophic Lateral Sclerosis (ALS) is a fast-progressive neurodegenerative disease leading to progressive physical immobility with usually normal or mild cognitive and/or behavioural involvement. Many patients are relatively young, instructed, sensitive to new technologies, and professionally active when developing the first symptoms. Older patients usually require more time, encouragement, reinforcement and a closer support but, nevertheless, selecting user-friendly devices, provided earlier in the course of the disease, and engaging motivated carers may overcome many technological barriers. ALS may be considered a model for neurodegenerative diseases to further develop and test new technologies. From multidisciplinary teleconsults to telemonitoring of the respiratory function, telemedicine has the potentiality to embrace other fields, including nutrition, physical mobility, and the interaction with the environment. Brain-computer interfaces and eye tracking expanded the field of augmentative and alternative communication in ALS but their potentialities go beyond communication, to cognition and robotics. Virtual reality and different forms of artificial intelligence present further interesting possibilities that deserve to be investigated. COVID-19 pandemic is an unprecedented opportunity to speed up the development and implementation of new technologies in clinical practice, improving the daily living of both ALS patients and carers. The present work reviews the current technologies for ALS patients already in place or being under evaluation with published publications, prompted by the COVID-19 pandemic.},
}
@article {pmid32879507,
year = {2020},
author = {Bassett, DS and Cullen, KE and Eickhoff, SB and Farah, MJ and Goda, Y and Haggard, P and Hu, H and Hurd, YL and Josselyn, SA and Khakh, BS and Knoblich, JA and Poirazi, P and Poldrack, RA and Prinz, M and Roelfsema, PR and Spires-Jones, TL and Sur, M and Ueda, HR},
title = {Reflections on the past two decades of neuroscience.},
journal = {Nature reviews. Neuroscience},
volume = {21},
number = {10},
pages = {524-534},
doi = {10.1038/s41583-020-0363-6},
pmid = {32879507},
issn = {1471-0048},
support = {P01 DA008227/DA/NIDA NIH HHS/United States ; R01 MH119421/MH/NIMH NIH HHS/United States ; /MRC_/Medical Research Council/United Kingdom ; UF1 NS111695/NS/NINDS NIH HHS/United States ; },
mesh = {History, 21st Century ; Humans ; Neurosciences/*history ; },
abstract = {The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.},
}
@article {pmid32878038,
year = {2020},
author = {De Robles, MS and Young, CJ},
title = {Outcomes of Primary Repair and Anastomosis for Traumatic Colonic Injuries in a Tertiary Trauma Center.},
journal = {Medicina (Kaunas, Lithuania)},
volume = {56},
number = {9},
pages = {},
pmid = {32878038},
issn = {1648-9144},
mesh = {Anastomosis, Surgical ; *Colon/surgery ; Humans ; Retrospective Studies ; *Trauma Centers ; Treatment Outcome ; },
abstract = {Background: Surgical management for traumatic colonic injuries has undergone major changes in the past decades. Despite the increasing confidence in primary repair for both penetrating colonic injury (PCI) and blunt colonic injury (BCI), there are authors still advocating for a colostomy particularly for BCI. This study aims to describe the surgical management of colonic injuries in a level 1 metropolitan trauma center and compare patient outcomes between PCI and BCI. Methods: Twenty-one patients who underwent trauma laparotomy for traumatic colonic injuries between January 2011 and December 2018 were retrospectively reviewed. Results: BCI accounted for 67% and PCI for 33% of traumatic colonic injuries. The transverse colon was the most commonly injured part of the colon (43%), followed by the sigmoid colon (33%). Primary repair (52%) followed by resection-anastomosis (38%) remain the most common procedures performed regardless of the injury mechanism. Only two (10%) patients required a colostomy. There was no significant difference comparing patients who underwent primary repair, resection-anastomosis and colostomy formation in terms of complication rates (55% vs. 50% vs. 50%, p = 0.979) and length of hospital stay (21 vs. 21 vs. 19 days, p = 0.991). Conclusions: Regardless of the injury mechanism, either primary repair or resection and anastomosis is a safe method in the management of the majority of traumatic colonic injuries.},
}
@article {pmid32872379,
year = {2020},
author = {Abend, A and Steele, C and Schmidt, S and Frank, R and Jahnke, HG and Zink, M},
title = {Proliferation and Cluster Analysis of Neurons and Glial Cell Organization on Nanocolumnar TiN Sub-Strates.},
journal = {International journal of molecular sciences},
volume = {21},
number = {17},
pages = {},
pmid = {32872379},
issn = {1422-0067},
support = {100331685//S&#xe4;chsisches Staatsministerium f&#xfc;r Wissenschaft und Kunst/ ; },
mesh = {Algorithms ; Cell Culture Techniques/*methods ; Cell Line ; Cell Proliferation ; Gold/chemistry ; Humans ; Nanostructures ; Neuroglia/*cytology ; Neurons/*cytology ; Tin Compounds/chemistry ; Titanium/*chemistry ; },
abstract = {Biomaterials employed for neural stimulation, as well as brain/machine interfaces, offer great perspectives to combat neurodegenerative diseases, while application of lab-on-a-chip devices such as multielectrode arrays is a promising alternative to assess neural function in vitro. For bioelectronic monitoring, nanostructured microelectrodes are required, which exhibit an increased surface area where the detection sensitivity is not reduced by the self-impedance of the electrode. In our study, we investigated the interaction of neurons (SH-SY5Y) and glial cells (U-87 MG) with nanocolumnar titanium nitride (TiN) electrode materials in comparison to TiN with larger surface grains, gold, and indium tin oxide (ITO) substrates. Glial cells showed an enhanced proliferation on TiN materials; however, these cells spread evenly distributed over all the substrate surfaces. By contrast, neurons proliferated fastest on nanocolumnar TiN and formed large cell agglomerations. We implemented a radial autocorrelation function of cellular positions combined with various clustering algorithms. These combined analyses allowed us to quantify the largest cluster on nanocolumnar TiN; however, on ITO and gold, neurons spread more homogeneously across the substrates. As SH-SY5Y cells tend to grow in clusters under physiologic conditions, our study proves nanocolumnar TiN as a potential bioactive material candidate for the application of microelectrodes in contact with neurons. To this end, the employed K-means clustering algorithm together with radial autocorrelation analysis is a valuable tool to quantify cell-surface interaction and cell organization to evaluate biomaterials' performance in vitro.},
}
@article {pmid32870796,
year = {2020},
author = {Jin, J and Liu, C and Daly, I and Miao, Y and Li, S and Wang, X and Cichocki, A},
title = {Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {10},
pages = {2153-2163},
doi = {10.1109/TNSRE.2020.3020975},
pmid = {32870796},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).},
}
@article {pmid32869747,
year = {2020},
author = {Li, Y and Li, L and Wu, J and Zhu, Z and Feng, X and Qin, L and Zhu, Y and Sun, L and Liu, Y and Qiu, Z and Duan, S and Yu, YQ},
title = {Activation of astrocytes in hippocampus decreases fear memory through adenosine A1 receptors.},
journal = {eLife},
volume = {9},
number = {},
pages = {},
pmid = {32869747},
issn = {2050-084X},
support = {2016YFC1306700//The National Key Research and Development Program/International ; 2016YFA0501000//The National Key Research and Development Program/International ; 2018PT31041//The Non-profit Central Research Institute Fund of the Chinese Academy of Medical Sciences/International ; 2018B030331001//Science and technology Planning Project of Guangdong Province/International ; 2019FZA7009//Fundamental Research Funds for the Central Universitues/International ; 2016YFC1306700//National Key Research and Development Program/International ; 2016YFA0501000//National Key Research and Development Program/International ; 31970939//National Natural Science Foundation of China/International ; 81527901//National Natural Science Foundation of China/International ; 2018B030331001//Science and Technology Planning Project of Guangdong Province/International ; 31771167//National Natural Science Foundation of China/International ; 31571090//National Natural Science Foundation of China/International ; 81821091//National Natural Science Foundation of China/International ; 2019-I2M-5-057//CAMS Innovation Fund for Medical Science/International ; },
mesh = {Animals ; Anxiety ; Astrocytes/*physiology ; Behavior, Animal ; Fear/*physiology ; Female ; Hippocampus/cytology/*physiology ; Male ; Memory/*physiology ; Optogenetics ; Rats ; Rats, Sprague-Dawley ; Rats, Transgenic ; *Receptor, Adenosine A1/genetics/metabolism ; },
abstract = {Astrocytes respond to and regulate neuronal activity, yet their role in mammalian behavior remains incompletely understood. Especially unclear is whether, and if so how, astrocyte activity regulates contextual fear memory, the dysregulation of which leads to pathological fear-related disorders. We generated GFAP-ChR2-EYFP rats to allow the specific activation of astrocytes in vivo by optogenetics. We found that after memory acquisition within a temporal window, astrocyte activation disrupted memory consolidation and persistently decreased contextual but not cued fear memory accompanied by reduced fear-related anxiety behavior. In vivo microdialysis experiments showed astrocyte photoactivation increased extracellular ATP and adenosine concentrations. Intracerebral blockade of adenosine A1 receptors (A1Rs) reversed the attenuation of fear memory. Furthermore, intracerebral or intraperitoneal injection of A1R agonist mimicked the effects of astrocyte activation. Therefore, our findings provide a deeper understanding of the astrocyte-mediated regulation of fear memory and suggest a new and important therapeutic strategy against pathological fear-related disorders.},
}
@article {pmid32862336,
year = {2020},
author = {Usama, N and Kunz Leerskov, K and Niazi, IK and Dremstrup, K and Jochumsen, M},
title = {Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: a feature and classifier investigation.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {58},
number = {11},
pages = {2699-2710},
doi = {10.1007/s11517-020-02253-2},
pmid = {32862336},
issn = {1741-0444},
mesh = {Adult ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Activity ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {Error-related potentials (ErrPs) have been proposed for designing adaptive brain-computer interfaces (BCIs). Therefore, ErrPs must be decoded. The aim of this study was to evaluate ErrP decoding using combinations of different feature types and classifiers in BCI paradigms involving motor execution (ME) and imagination (MI). Fifteen healthy subjects performed 510 (ME) and 390 (MI) trials of right/left wrist extensions and foot dorsiflexions. Sham BCI feedback was delivered with an accuracy of 80% (ME) and 70% (MI). Continuous EEG was recorded and divided into ErrP and NonErrP epochs. Temporal, spectral, and discrete wavelet transform (DWT) marginals and template matching features were extracted, and all combinations of feature types were classified using linear discriminant analysis, support vector machine, and random forest classifiers. ErrPs were elicited for both ME and MI paradigms, and the average classification accuracies were significantly higher than the chance level. The highest average classification accuracy was obtained using temporal features and a combination of temporal + DWT features classified with random forest; 89 ± 9% and 83 ± 9% for ME and MI, respectively. These results generally indicate that temporal features should be used when detecting ErrPs, but there is great inter-subject variability, which means that user-specific features should be derived to maximize the performance. Graphical abstract.},
}
@article {pmid32862028,
year = {2020},
author = {Khare, SK and Bajaj, V},
title = {A facile and flexible motor imagery classification using electroencephalogram signals.},
journal = {Computer methods and programs in biomedicine},
volume = {197},
number = {},
pages = {105722},
doi = {10.1016/j.cmpb.2020.105722},
pmid = {32862028},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Reproducibility of Results ; },
abstract = {BACKGROUND: Mind machine interface (MMI) enables communication with milieu by measuring brain activities. The reliability of MMI systems is highly dependent on the identification of various motor imagery (MI) tasks. Perfect discrimination of brain activities is required to avoid miscommunication. Electroencephalogram (EEG) signals provide a scrupulous solution for the development of MMI. Analysis of multi-channel EEG signals increases the burden of computation drastically. The extraction of hidden information from raw EEG signals is difficult due to its complex nature. A signal is needed to be decomposed and classified for the extraction of hidden information from it. But selecting the uniform decomposition and hyperparameters for decomposition and classification of the signal can lead to information loss and misclassification.<br><br>METHOD: This paper presents a novel method for identifying right-hand and right-foot MI tasks. The method employs a single-channel adaptive decomposition and EEG signal classification. The multi-cluster unsupervised learning method is employed for the selection of significant channel. Further, flexible variational mode decomposition (F-VMD) is used for the adaptive decomposition of signals. The values of decomposition parameters are selected adaptively following the nature of EEG signals. The value of decomposition parameters is used to decompose the signals into narrow-band modes. Hjorth, entropy and quartile based features are elicited from the modes of F-VMD. These features are classified by using a flexible extreme learning machine (F-ELM). F-ELM selects the hyperparameters and kernel adaptively by reducing the classification error.<br><br>RESULTS: The performance of the proposed method is evaluated by measuring five performance parameters namely accuracy (ACC), sensitivity (SEN), specificity (SPE), Mathew's correlation coefficient (MCC), and F-1 score. An ACC, SEN, SPE, MCC and F-1 score is obtained as 100%, 100%, 100%, 100%, and 1. The performance parameters obtained by the proposed method prove the superiority over other methodologies using the same data-set.<br><br>CONCLUSION: The proposed method proved to be promising and efficient with a single channel and two features. This framework can be utilized for the development of a real-time mind-machine interface like robotic arm, wheel chairs, etc.},
}
@article {pmid32861918,
year = {2020},
author = {Moon, SE and Chen, CJ and Hsieh, CJ and Wang, JL and Lee, JS},
title = {Emotional EEG classification using connectivity features and convolutional neural networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {132},
number = {},
pages = {96-107},
doi = {10.1016/j.neunet.2020.08.009},
pmid = {32861918},
issn = {1879-2782},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Emotions/*physiology ; Humans ; *Neural Networks, Computer ; },
abstract = {Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However, this approach makes it difficult to exploit the brain connectivity information that can be effective in describing the functional brain network and estimating the perceptual state of the user. We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification by using three different types of connectivity measures. Furthermore, two data-driven methods to construct the connectivity matrix are proposed to maximize classification performance. Further analysis reveals that the level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.},
}
@article {pmid32860692,
year = {2021},
author = {Sciaraffa, N and Liu, J and Aricò, P and Flumeri, GD and Inguscio, BMS and Borghini, G and Babiloni, F},
title = {Multivariate model for cooperation: bridging social physiological compliance and hyperscanning.},
journal = {Social cognitive and affective neuroscience},
volume = {16},
number = {1-2},
pages = {193-209},
pmid = {32860692},
issn = {1749-5024},
mesh = {Adult ; Arousal/physiology ; Autonomic Nervous System/*physiology ; Brain/*physiology ; Brain Mapping/methods ; *Cooperative Behavior ; Electroencephalography ; Emotions/physiology ; Female ; Humans ; Male ; Young Adult ; },
abstract = {The neurophysiological analysis of cooperation has evolved over the past 20 years, moving towards the research of common patterns in neurophysiological signals of people interacting. Social physiological compliance (SPC) and hyperscanning represent two frameworks for the joint analysis of autonomic and brain signals, respectively. Each of the two approaches allows to know about a single layer of cooperation according to the nature of these signals: SPC provides information mainly related to emotions, and hyperscanning that related to cognitive aspects. In this work, after the analysis of the state of the art of SPC and hyperscanning, we explored the possibility to unify the two approaches creating a complete neurophysiological model for cooperation considering both affective and cognitive mechanisms We synchronously recorded electrodermal activity, cardiac and brain signals of 14 cooperative dyads. Time series from these signals were extracted, and multivariate Granger causality was computed. The results showed that only when subjects in a dyad cooperate there is a statistically significant causality between the multivariate variables representing each subject. Moreover, the entity of this statistical relationship correlates with the dyad's performance. Finally, given the novelty of this approach and its exploratory nature, we provided its strengths and limitations.},
}
@article {pmid32859773,
year = {2021},
author = {Huo, CC and Zheng, Y and Lu, WW and Zhang, TY and Wang, DF and Xu, DS and Li, ZY},
title = {Prospects for intelligent rehabilitation techniques to treat motor dysfunction.},
journal = {Neural regeneration research},
volume = {16},
number = {2},
pages = {264-269},
pmid = {32859773},
issn = {1673-5374},
abstract = {More than half of stroke patients live with different levels of motor dysfunction after receiving routine rehabilitation treatments. Therefore, new rehabilitation technologies are urgently needed as auxiliary treatments for motor rehabilitation. Based on routine rehabilitation treatments, a new intelligent rehabilitation platform has been developed for accurate evaluation of function and rehabilitation training. The emerging intelligent rehabilitation techniques can promote the development of motor function rehabilitation in terms of informatization, standardization, and intelligence. Traditional assessment methods are mostly subjective, depending on the experience and expertise of clinicians, and lack standardization and precision. It is therefore difficult to track functional changes during the rehabilitation process. Emerging intelligent rehabilitation techniques provide objective and accurate functional assessment for stroke patients that can promote improvement of clinical guidance for treatment. Artificial intelligence and neural networks play a critical role in intelligent rehabilitation. Multiple novel techniques, such as brain-computer interfaces, virtual reality, neural circuit-magnetic stimulation, and robot-assisted therapy, have been widely used in the clinic. This review summarizes the emerging intelligent rehabilitation techniques for the evaluation and treatment of motor dysfunction caused by nervous system diseases.},
}
@article {pmid32858596,
year = {2020},
author = {Guan Lim, C and Lim-Ashworth, NSJ and Fung, DSS},
title = {Updates in technology-based interventions for attention deficit hyperactivity disorder.},
journal = {Current opinion in psychiatry},
volume = {33},
number = {6},
pages = {577-585},
pmid = {32858596},
issn = {1473-6578},
mesh = {*Attention Deficit Disorder with Hyperactivity/psychology/therapy ; Behavior Rating Scale ; *Behavior Therapy/instrumentation/methods/trends ; Child ; *Cognitive Behavioral Therapy/instrumentation/methods ; *Computing Methodologies ; Humans ; *Technology Assessment, Biomedical ; },
abstract = {PURPOSE OF REVIEW: Technological advancement has led to the development of novel treatment approaches for attention deficit hyperactivity disorder (ADHD). This review aims to review recent studies which employ the use of technology to treat ADHD, with particular focus on studies published during a 1-year period from February 2019 to February 2020.<br><br>RECENT FINDINGS: Most recent studies involved children aged 12 years and below. Interventions included cognitive training through games, neurofeedback and a combination of several approaches. More novel approaches included trigeminal nerve stimulation and brain-computer interface, and studies had utilized technology such as X-box Kinect and eye tracker. There was a shift towards delivering intervention at home and in school, enabled by technology. The study outcomes were variable and mainly included executive functioning measures and clinical ratings. These interventions were generally safe with few reported adverse events.<br><br>SUMMARY: Technology has enabled interventions to be delivered outside of the clinic setting and presented an opportunity for increased access to care and early intervention. Better quality studies are needed to inform on the efficacy of these interventions.},
}
@article {pmid32853592,
year = {2020},
author = {Yadav, D and Yadav, S and Veer, K},
title = {A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges.},
journal = {Journal of neuroscience methods},
volume = {346},
number = {},
pages = {108918},
doi = {10.1016/j.jneumeth.2020.108918},
pmid = {32853592},
issn = {1872-678X},
mesh = {Brain ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {BACKGROUND: An uninterrupted channel of communication and control between the human brain and electronic processing units has led to an increased use of Brain Computer Interfaces (BCIs). This article attempts to present an all-encompassing review on BCI and the scientific advancements associated with it. The ultimate goal of this review is to provide a general overview of the BCI technology and to shed light on different aspects of BCIs. This review also underscores the applications, practical challenges and opportunities associated with BCI technology, which can be used to accelerate future developments in this field.<br><br>METHODS: This review is based on a systematic literature search for tracking down the relevant research annals and proceedings. Using a methodical search strategy, the search was carried out across major technical databases. The retrieved records were screened for their relevance and a total of 369 research chronicles were engulfed in this review based on the inclusion criteria.<br><br>RESULTS: This review describes the present scenario and recent advancements in BCI technology. It also identifies several application areas of BCI technology. This comprehensive review provides evidence that, while we are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system.<br><br>CONCLUSION: The findings of this review confirm the importance of BCI technology in various applications. It is concluded that BCI technology, still in its sprouting phase, requires significant explorations for further development.},
}
@article {pmid32852737,
year = {2020},
author = {Hao, S and Yang, Y and Helmy, M and Wang, H},
title = {Neural Regulation of Feeding Behavior.},
journal = {Advances in experimental medicine and biology},
volume = {1284},
number = {},
pages = {23-33},
doi = {10.1007/978-981-15-7086-5_3},
pmid = {32852737},
issn = {0065-2598},
mesh = {Animals ; Brain/*physiology ; Eating ; Energy Metabolism ; Feeding Behavior/*physiology ; Homeostasis ; Humans ; },
abstract = {Food intake and energy homeostasis determine survival of the organism and species. Information on total energy levels and metabolic state are sensed in the periphery and transmitted to the brain, where it is integrated and triggers the animal to forage, prey, and consume food. Investigating circuitry and cellular mechanisms coordinating energy balance and feeding behaviors has drawn on many state-of-the-art techniques, including gene manipulation, optogenetics, virus tracing, and single-cell sequencing. These new findings provide novel insights into how the central nervous system regulates food intake, and shed the light on potential therapeutic interventions for eating-related disorders such as obesity and anorexia.},
}
@article {pmid32852736,
year = {2020},
author = {Helmy, M and Zhang, J and Wang, H},
title = {Neurobiology and Neural Circuits of Aggression.},
journal = {Advances in experimental medicine and biology},
volume = {1284},
number = {},
pages = {9-22},
doi = {10.1007/978-981-15-7086-5_2},
pmid = {32852736},
issn = {0065-2598},
mesh = {*Aggression ; Animals ; Brain/*physiology ; Models, Animal ; *Neurobiology ; Neurotransmitter Agents ; },
abstract = {Aggression takes several forms and can be offensive or defensive. Aggression between animals of the same species or society aims to inflict harm upon another for the purpose of protecting a resource such as food, reproductive partners, territory, or status. This chapter explores the neurobiology of aggression. We summarize the behavior of aggression, rodent models of aggression, and the correlates of aggressive behavior in the context of neuroendocrinology, neurotransmitter systems, and neurocircuitry. Translational implications of rodent studies are briefly discussed, applying basic research to brain imaging data and therapeutic approaches to conditions where aggression is problematic.},
}
@article {pmid32849124,
year = {2020},
author = {de la Fuente-Anuncibay, R and González-Barbadillo, &#xc1; and Ortega-Sánchez, D and Pizarro-Ruiz, JP},
title = {Mindfulness and Empathy: Mediating Factors and Gender Differences in a Spanish Sample.},
journal = {Frontiers in psychology},
volume = {11},
number = {},
pages = {1915},
pmid = {32849124},
issn = {1664-1078},
abstract = {Numerous research studies link mindfulness training to improved empathy. However, few studies focus on the mediating factors of empathy. This work has three objectives: (a) to analyze the possible mediation of mindfulness as a feature in this relation, (b) to analyze the mindfulness factors that mediate in the increase of empathy and (c) to analyze the moderating role of gender. The sample was composed of 246 Spanish-speaking university students (M = 24.08 years, SD = 8.43). The instruments used were the Five Facet Mindfulness Questionnaire (FFMQ) and the Toronto Empathy Questionnaire (TEQ). For data analysis, the indirect effect was calculated using 10000 bootstrap samples for the bias-corrected bootstrap confidence intervals (BCI). The improvement of empathy is mediated by the changes in mindfulness trait (B = 0.233, p < 0.001), disappearing in the presence of this mediator, the direct effect of mindfulness practice on empathy (B = 0.161, p = 0.394). We did not find a differential functioning of this mediation according to gender. Observing and describing are the FFMQ factors that mediate significantly between mindfulness practice and empathy.},
}
@article {pmid32848688,
year = {2020},
author = {Chen, DW and Miao, R and Deng, ZY and Lu, YY and Liang, Y and Huang, L},
title = {Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification.},
journal = {Frontiers in neuroinformatics},
volume = {14},
number = {},
pages = {29},
pmid = {32848688},
issn = {1662-5196},
abstract = {Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q -type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1, Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.},
}
@article {pmid32848671,
year = {2020},
author = {Liu, S and Wang, W and Sheng, Y and Zhang, L and Xu, M and Ming, D},
title = {Improving the Cross-Subject Performance of the ERP-Based Brain-Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {296},
pmid = {32848671},
issn = {1662-5161},
abstract = {The brain-computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for cross-subject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value of 0.8, while the traditional random selection only achieved 0.65. These encouraging results suggest that with proper evoked paradigm and classification methods, it is feasible to get stable performance across subjects for ERP-based BCI. Thus, our findings provide a new approach to improve BCI performances.},
}
@article {pmid32848640,
year = {2020},
author = {Loutit, AJ and Potas, JR},
title = {Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli.},
journal = {Frontiers in systems neuroscience},
volume = {14},
number = {},
pages = {46},
pmid = {32848640},
issn = {1662-5137},
abstract = {Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals.},
}
@article {pmid32848581,
year = {2020},
author = {Jiang, K and Tang, J and Wang, Y and Qiu, C and Zhang, Y and Lin, C},
title = {EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {829},
pmid = {32848581},
issn = {1662-4548},
abstract = {In the field of brain-computer interface (BCI), selecting efficient and robust features is very seductive for artificial intelligence (AI)-assisted clinical diagnosis. In this study, based on an embedded feature selection model, we construct a stacked deep structure for feature selection in a layer-by-layer manner. Its promising performance is guaranteed by the stacked generalized principle that random projections added into the original features can help us to continuously open the manifold structure existing in the original feature space in a stacked way. With such benefits, the original input feature space becomes more linearly separable. We use the epilepsy EEG data provided by the University of Bonn to evaluate our model. Based on the EEG data, we construct three classification tasks. On each task, we use different feature selection models to select features and then use two classifiers to perform classification based on the selected features. Our experimental results show that features selected by our new structure are more meaningful and helpful to the classifier hence generates better performance than benchmarking models.},
}
@article {pmid32848566,
year = {2020},
author = {Klaproth, OW and Vernaleken, C and Krol, LR and Halbruegge, M and Zander, TO and Russwinkel, N},
title = {Tracing Pilots' Situation Assessment by Neuroadaptive Cognitive Modeling.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {795},
pmid = {32848566},
issn = {1662-4548},
abstract = {This study presents the integration of a passive brain-computer interface (pBCI) and cognitive modeling as a method to trace pilots' perception and processing of auditory alerts and messages during operations. Missing alerts on the flight deck can result in out-of-the-loop problems that can lead to accidents. By tracing pilots' perception and responses to alerts, cognitive assistance can be provided based on individual needs to ensure they maintain adequate situation awareness. Data from 24 participating aircrew in a simulated flight study that included multiple alerts and air traffic control messages in single pilot setup are presented. A classifier was trained to identify pilots' neurophysiological reactions to alerts and messages from participants' electroencephalogram (EEG). A neuroadaptive ACT-R model using EEG data was compared to a conventional normative model regarding accuracy in representing individual pilots. Results show that passive BCI can distinguish between alerts that are processed by the pilot as task-relevant or irrelevant in the cockpit based on the recorded EEG. The neuroadaptive model's integration of this data resulted in significantly higher performance of 87% overall accuracy in representing individual pilots' responses to alerts and messages compared to 72% accuracy of a normative model that did not consider EEG data. We conclude that neuroadaptive technology allows for implicit measurement and tracing of pilots' perception and processing of alerts on the flight deck. Careful handling of uncertainties inherent to passive BCI and cognitive modeling shows how the representation of pilot cognitive states can be improved iteratively for providing assistance.},
}
@article {pmid32848528,
year = {2020},
author = {Kohl, SH and Mehler, DMA and Lührs, M and Thibault, RT and Konrad, K and Sorger, B},
title = {The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback-A Systematic Review and Recommendations for Best Practice.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {594},
pmid = {32848528},
issn = {1662-4548},
abstract = {Background: The effects of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI)-neurofeedback on brain activation and behaviors have been studied extensively in the past. More recently, researchers have begun to investigate the effects of functional near-infrared spectroscopy-based neurofeedback (fNIRS-neurofeedback). FNIRS is a functional neuroimaging technique based on brain hemodynamics, which is easy to use, portable, inexpensive, and has reduced sensitivity to movement artifacts. Method: We provide the first systematic review and database of fNIRS-neurofeedback studies, synthesizing findings from 22 peer-reviewed studies (including a total of N = 441 participants; 337 healthy, 104 patients). We (1) give a comprehensive overview of how fNIRS-neurofeedback training protocols were implemented, (2) review the online signal-processing methods used, (3) evaluate the quality of studies using pre-set methodological and reporting quality criteria and also present statistical sensitivity/power analyses, (4) investigate the effectiveness of fNIRS-neurofeedback in modulating brain activation, and (5) review its effectiveness in changing behavior in healthy and pathological populations. Results and discussion: (1-2) Published studies are heterogeneous (e.g., neurofeedback targets, investigated populations, applied training protocols, and methods). (3) Large randomized controlled trials are still lacking. In view of the novelty of the field, the quality of the published studies is moderate. We identified room for improvement in reporting important information and statistical power to detect realistic effects. (4) Several studies show that people can regulate hemodynamic signals from cortical brain regions with fNIRS-neurofeedback and (5) these studies indicate the feasibility of modulating motor control and prefrontal brain functioning in healthy participants and ameliorating symptoms in clinical populations (stroke, ADHD, autism, and social anxiety). However, valid conclusions about specificity or potential clinical utility are premature. Conclusion: Due to the advantages of practicability and relatively low cost, fNIRS-neurofeedback might provide a suitable and powerful alternative to EEG and fMRI neurofeedback and has great potential for clinical translation of neurofeedback. Together with more rigorous research and reporting practices, further methodological improvements may lead to a more solid understanding of fNIRS-neurofeedback. Future research will benefit from exploiting the advantages of fNIRS, which offers unique opportunities for neurofeedback research.},
}
@article {pmid32843643,
year = {2020},
author = {Liu, Z and Tang, J and Gao, B and Yao, P and Li, X and Liu, D and Zhou, Y and Qian, H and Hong, B and Wu, H},
title = {Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces.},
journal = {Nature communications},
volume = {11},
number = {1},
pages = {4234},
pmid = {32843643},
issn = {2041-1723},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Computers, Analog ; Electrical Synapses/physiology ; Epilepsy/physiopathology ; Humans ; Models, Neurological ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted/*instrumentation ; Transistors, Electronic ; },
abstract = {Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.},
}
@article {pmid32842822,
year = {2022},
author = {Drissennek, L and Baron, C and Brouillet, S and Entezami, F and Hamamah, S and Haouzi, D},
title = {Endometrial miRNome profile according to the receptivity status and implantation failure.},
journal = {Human fertility (Cambridge, England)},
volume = {25},
number = {2},
pages = {356-368},
doi = {10.1080/14647273.2020.1807065},
pmid = {32842822},
issn = {1742-8149},
mesh = {*Abortion, Spontaneous ; Embryo Implantation/genetics ; Endometrium ; Female ; Humans ; *MicroRNAs/genetics ; Pregnancy ; Retrospective Studies ; },
abstract = {This is a retrospective study to evaluate if the miRNome profile of endometrium samples collected during the implantation window predicts Assisted Reproduction Technology (ART) outcomes. We first investigated the endometrial miRNome profile according to the receptivity status in 20 patients with repeated implantation failures (RIF) (discovery cohort). After customized embryo transfer, the miRNome profiles of receptive patients with a positive or negative β-hCG, and with early miscarriage or live birth were analysed. Some differentially expressed miRNAs were selected for validation by RT-qPCR in endometrial samples from 103 RIF patients (validation cohort). Analysis of the different miRNome profiles identified endometrial receptivity, implantation failure, and early miscarriage-associated miRNA signatures that included 11, 261, and 76 miRNAs, respectively. However, only four miRNAs associated with the endometrial receptivity status (miR-455-3p and miR-4423-3p) and implantation failure (miR-152-3p and miR-155-5p) were significantly validated in endometrial samples. The miRNome profile of endometrial tissues during the implantation window can predict the pregnancy outcome. These data are crucial for opening new perspectives to predict implantation failure and consequently, to increase ART success.},
}
@article {pmid32842635,
year = {2020},
author = {Zhang, S and Zhu, Z and Zhang, B and Feng, B and Yu, T and Li, Z},
title = {The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {17},
pages = {},
pmid = {32842635},
issn = {1424-8220},
support = {61967004, 11901137, and 81960324//National Natural Science Foundation of China/ ; 2018GXNSFBA281023//Natural Science Foundation of Guangxi Province/ ; YQ20113, YQ19209 and YQ18107//Guangxi Key Laboratory of Automatic Testing Technology and Instruments/ ; GCIS201927//Guangxi Key Laboratory of Cryptography and Information Security/ ; 2019YCXB03//Innovation Project of Guet Graduate Education/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1-4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.},
}
@article {pmid32841127,
year = {2021},
author = {Fahimi, F and Dosen, S and Ang, KK and Mrachacz-Kersting, N and Guan, C},
title = {Generative Adversarial Networks-Based Data Augmentation for Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {9},
pages = {4039-4051},
doi = {10.1109/TNNLS.2020.3016666},
pmid = {32841127},
issn = {2162-2388},
mesh = {Adult ; Algorithms ; Attention ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/statistics &amp; numerical data ; Female ; Healthy Volunteers ; Humans ; Imagination ; Machine Learning ; Male ; *Neural Networks, Computer ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention () and 5.45% for focused attention (). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% (). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.},
}
@article {pmid32841119,
year = {2020},
author = {Wong, CM and Wang, Z and Wang, B and Lao, KF and Rosa, A and Xu, P and Jung, TP and Chen, CLP and Wan, F},
title = {Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {10},
pages = {2123-2135},
doi = {10.1109/TNSRE.2020.3019276},
pmid = {32841119},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject's calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort.<br><br>METHODS: Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject.<br><br>RESULTS: The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18&#xb1;59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04&#xb1;57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively.<br><br>CONCLUSION: Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort.<br><br>SIGNIFICANCE: The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.},
}
@article {pmid32840083,
year = {2020},
author = {Yu, R and Yu, H and Wan, H},
title = {[Research on brain network for schizophrenia classification based on resting-state functional magnetic resonance imaging].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {4},
pages = {661-669},
pmid = {32840083},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; Brain Mapping ; Humans ; Magnetic Resonance Imaging ; *Schizophrenia/diagnostic imaging ; },
abstract = {How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson's correlation and sparse representation) and the commonly used feature selection methods (two-sample t-test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.},
}
@article {pmid32833646,
year = {2021},
author = {Jin, J and Xiao, R and Daly, I and Miao, Y and Wang, X and Cichocki, A},
title = {Internal Feature Selection Method of CSP Based on L1-Norm and Dempster-Shafer Theory.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {11},
pages = {4814-4825},
doi = {10.1109/TNNLS.2020.3015505},
pmid = {32833646},
issn = {2162-2388},
mesh = {*Algorithms ; Brain-Computer Interfaces/*trends ; *Databases, Factual ; Electroencephalography/*trends ; Humans ; },
abstract = {The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.},
}
@article {pmid32833640,
year = {2021},
author = {Liu, S and Wang, X and Zhao, L and Zhao, J and Xin, Q and Wang, SH},
title = {Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {18},
number = {5},
pages = {1710-1721},
doi = {10.1109/TCBB.2020.3018137},
pmid = {32833640},
issn = {1557-9964},
mesh = {Algorithms ; Brain/physiology ; *Electroencephalography ; Emotions/*classification ; Entropy ; Female ; Humans ; Male ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.},
}
@article {pmid32833102,
year = {2020},
author = {Osawa, T and Wei, JT and Abe, T and Honda, M and Yamada, S and Furumido, J and Kikuchi, H and Matsumoto, R and Hirakawa, K and Sato, Y and Sasaki, Y and Harabayashi, T and Takada, N and Minami, K and Tanaka, H and Morita, K and Kashiwagi, A and Miyajima, N and Akino, T and Murai, S and Ito, YM and Fukuhara, S and Ogasawara, K and Shinohara, N},
title = {Health-related quality of life in Japanese patients with bladder cancer measured by a newly developed Japanese version of the Bladder Cancer Index.},
journal = {International journal of clinical oncology},
volume = {25},
number = {12},
pages = {2090-2098},
pmid = {32833102},
issn = {1437-7772},
mesh = {Aged ; Cross-Sectional Studies ; Cystectomy ; Female ; Humans ; Ileum/surgery ; Japan ; Male ; Middle Aged ; *Quality of Life ; Reproducibility of Results ; Surveys and Questionnaires ; Ureterostomy ; Urinary Bladder Neoplasms/*surgery ; Urinary Diversion ; },
abstract = {INTRODUCTION: We validated a Japanese version of the Bladder Cancer Index (BCI) as a tool for measuring health-related quality of life (HRQOL) in bladder cancer patients treated with various surgical procedures.<br><br>METHODS: The reliability and validity of the Japanese BCI were examined in 397 Japanese patients with bladder cancer via cross-sectional analysis. The patients simultaneously completed the Short Form (SF)-12, EQ-5D, and the Functional Assessment of Cancer Therapy-General and Bladder (FACT-G and FACT-BL). The differences in BCI subscales among various treatment groups were analyzed.<br><br>RESULTS: This study involved 397 patients (301 males and 96 females), with a mean age of 70&#xa0;years and a median disease duration of 29&#xa0;months (IQR: 12-66&#xa0;months). Of these patients, 221 underwent transurethral resection of a bladder tumor, and 176 patients underwent radical cystectomy (ileal conduit: 101 patients, ileal neobladder: 49, and ureterostomy: 26). Cronbach's alpha coefficient was&#x2009;&#x2265;&#x2009;0.78 for all subscales, except the bowel bother subscale. Despite moderate correlations being detected between the function and bother score in urinary and bowel domains, the sexual function score was inversely correlated with the sexual bother score (r&#x2009;=&#x2009;-&#x2009;0.19). A missing value percentage of&#x2009;>&#x2009;15% was associated with old age (p&#x2009;<&#x2009;0.05). The mean domain scores differed significantly among distinct clinically relevant treatment groups.<br><br>CONCLUSIONS: Although revisions are needed to make it easier for elderly patients to comprehend, we confirmed the reliability and validity of the Japanese BCI. The Japanese BCI could be used for cross-cultural assessments of HRQOL in bladder cancer patients.},
}
@article {pmid32828924,
year = {2020},
author = {Hayashi, M and Mizuguchi, N and Tsuchimoto, S and Ushiba, J},
title = {Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere.},
journal = {NeuroImage},
volume = {223},
number = {},
pages = {117298},
doi = {10.1016/j.neuroimage.2020.117298},
pmid = {32828924},
issn = {1095-9572},
mesh = {Adult ; *Brain Waves ; Brain-Computer Interfaces ; Cross-Over Studies ; Double-Blind Method ; Feedback, Sensory ; Hand ; Humans ; Male ; Neurofeedback/*methods ; Scalp/physiology ; Sensorimotor Cortex/*physiology ; Shoulder ; Young Adult ; },
abstract = {Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.},
}
@article {pmid32827565,
year = {2021},
author = {Kim, H},
title = {Cerebral hemodynamics predicts the cortical area and coding scheme in the human brain for force generation by wrist muscles.},
journal = {Behavioural brain research},
volume = {396},
number = {},
pages = {112865},
doi = {10.1016/j.bbr.2020.112865},
pmid = {32827565},
issn = {1872-7549},
mesh = {Adult ; *Brain Mapping ; Humans ; Isometric Contraction/*physiology ; Male ; Motor Activity/*physiology ; Muscle, Skeletal/diagnostic imaging/*physiology ; Sensorimotor Cortex/diagnostic imaging/*physiology ; *Spectroscopy, Near-Infrared ; Wrist/*physiology ; Young Adult ; },
abstract = {The goal of this study is to identify the cortical area maximally active over the primary sensorimotor cortex (SM1) and characterize the cortical encoding for force production by wrist muscles in the human brain. The technique of functional near-infrared spectroscopy (fNIRS) was used to continuously monitor the changes in hemoglobin concentrations from the left hemisphere during isometric contractions of wrist flexion muscles over a broad range of load forces (0 ∼ 8 kgf) on the right hand. As previously shown in primate studies, this action produced hemodynamic activity predominantly in the wrist area localized dorsally to the finger region over SM1 and the hemodynamic response was systematically related to the level of load intensity. The coding scheme for force production in terms of hemodynamic signals was characterized defining eight trajectory parameters (four for amplitude coding and four for temporal coding) and analyzed for the area maximally activated over SM1. The trajectory parameter representing the oxygenated hemoglobin concentration change at the end of motor task (amplitude coding) and the timing of maximum change in oxygenated hemoglobin concentration (temporal coding) was most strongly correlated with the load variation in a superliner manner. All these results indicate the applicability of fNIRS to monitor and decode cortical activity that is correlated with low-level motor control such as isometric muscle contractions. This study may provide not only insights into cortical neural control of muscle force but also predictors of muscle force in clinical diagnostics and neural interfaces for the human brain.},
}
@article {pmid32827547,
year = {2020},
author = {Niu, X and Huang, S and Yang, S and Wang, Z and Li, Z and Shi, L},
title = {Comparison of pop-out responses to luminance and motion contrasting stimuli of tectal neurons in pigeons.},
journal = {Brain research},
volume = {1747},
number = {},
pages = {147068},
doi = {10.1016/j.brainres.2020.147068},
pmid = {32827547},
issn = {1872-6240},
mesh = {Animals ; Columbidae ; Motion Perception/*physiology ; Neurons/*physiology ; Photic Stimulation ; Tectum Mesencephali/*physiology ; Vision, Ocular/physiology ; Visual Pathways/*physiology ; Visual Perception/*physiology ; },
abstract = {The emergence of visual saliency has been widely studied in the primary visual cortex and the superior colliculus (SC) in mammals. There are fewer studies on the pop-out response to motion direction contrasting stimuli taken in the optic tectum (OT, homologous to mammalian SC), and these are mainly of owls and fish. To our knowledge the influence of spatial luminance has not been reported. In this study, we have recorded multi-units in pigeon OT and analyzed the tectal response to spatial luminance contrasting, motion direction contrasting, and contrasting stimuli from both feature dimensions. The comparison results showed that 1) the tectal response would pop-out in either motion direction or spatial luminance contrasting conditions. 2) The modulation from motion direction contrasting was independent of the temporal luminance variation of the visual stimuli. 3) When both spatial luminance and motion direction were salient, the response of tectal neurons was modulated more intensely by motion direction than by spatial luminance. The phenomenon was consistent with the innate instinct of avians in their natural environment. This study will help to deepen the understanding of mechanisms involved in bottom-up visual information processing and selective attention in the avian.},
}
@article {pmid32824559,
year = {2020},
author = {Cooney, C and Korik, A and Folli, R and Coyle, D},
title = {Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {16},
pages = {},
pmid = {32824559},
issn = {1424-8220},
support = {N/A//Northern Ireland Department for the Economy/ ; },
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Machine Learning ; Neural Networks, Computer ; *Speech ; },
abstract = {Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain-computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10[-7], chance: 16.67%; vowels: 30.00%, p < 1 × 10[-7], chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.},
}
@article {pmid32824011,
year = {2020},
author = {Park, S and Han, CH and Im, CH},
title = {Design of Wearable EEG Devices Specialized for Passive Brain-Computer Interface Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {16},
pages = {},
pmid = {32824011},
issn = {1424-8220},
support = {2017-0-00432//Institute for Information &amp; Communications Technology Promotion/ ; NRF-2019R1A2C2086593//National Research Foundation of Korea/ ; },
mesh = {Attention ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; *Emotions ; Humans ; *Wearable Electronic Devices ; },
abstract = {Owing to the increased public interest in passive brain-computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate emotional or attentional states. Emotion-specialized EEG headsets were designed to maximize the accuracy of classification of different emotional states using the emotion-associated EEG dataset, and attention-specialized EEG headsets were designed to maximize the temporal correlation between the EEG index and the behavioral attention index. General purpose electrode configurations were designed to maximize the overall performance in both applications for different numbers of electrodes (2, 4, 6, and 8). The performance was then compared with that of existing wearable EEG devices. Simulations indicated that the proposed electrode configurations allowed for more accurate estimation of the users' emotional and attentional states than the conventional electrode configurations, suggesting that wearable EEG devices should be designed according to the well-established EEG datasets associated with the target pBCI applications.},
}
@article {pmid32823303,
year = {2020},
author = {Senathirajah, Y and Pelayo, S and , },
title = {Human Factors and Organizational Issues.},
journal = {Yearbook of medical informatics},
volume = {29},
number = {1},
pages = {99-103},
pmid = {32823303},
issn = {2364-0502},
mesh = {Ambulatory Care/organization &amp; administration ; Decision Making, Computer-Assisted ; *Ergonomics ; Health Information Systems ; Humans ; Medical Errors/*prevention &amp; control ; Medical Informatics/*organization &amp; administration ; Patient Safety ; User-Computer Interface ; },
abstract = {OBJECTIVE: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2019.<br><br>METHODS: A rigorous extraction process based on queries from Web of Science&#xae; and PubMed/Medline was conducted to identify the scientific contributions published in 2019 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two editors led to a total of 30 papers. These papers were discussed for a selection of 15 finalist papers, which were then reviewed by the two editors and by three external reviewers from internationally renowned research teams.<br><br>RESULTS: The query process resulted in 626 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors.<br><br>CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.},
}
@article {pmid32816652,
year = {2021},
author = {Staiger, JF and Petersen, CCH},
title = {Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception.},
journal = {Physiological reviews},
volume = {101},
number = {1},
pages = {353-415},
doi = {10.1152/physrev.00019.2019},
pmid = {32816652},
issn = {1522-1210},
support = {310030B_166595/SNSF_/Swiss National Science Foundation/Switzerland ; ERC-2011-ADG 293660/ERC_/European Research Council/International ; },
mesh = {Animals ; Brain Diseases/physiopathology ; Brain-Computer Interfaces ; Humans ; Mice ; Neural Pathways/*physiology ; Sensation/*physiology ; Signal Transduction/physiology ; Somatosensory Cortex/*physiology ; Vibrissae/innervation/*physiology ; },
abstract = {The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.},
}
@article {pmid32814867,
year = {2020},
author = {Camus, L and Briaud, P and Bastien, S and Elsen, S and Doléans-Jordheim, A and Vandenesch, F and Moreau, K},
title = {Trophic cooperation promotes bacterial survival of Staphylococcus aureus and Pseudomonas aeruginosa.},
journal = {The ISME journal},
volume = {14},
number = {12},
pages = {3093-3105},
pmid = {32814867},
issn = {1751-7370},
mesh = {Biofilms ; Humans ; Microbial Interactions ; *Pseudomonas Infections ; Pseudomonas aeruginosa/genetics ; *Staphylococcal Infections ; Staphylococcus aureus/genetics ; },
abstract = {In the context of infection, Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated, particularly in cystic fibrosis (CF) patients. Within lungs, the two pathogens exhibit a range of competitive and coexisting interactions. In the present study, we explored the impact of S. aureus on the physiology of P. aeruginosa in the context of coexistence. Transcriptomic analyses showed that S. aureus significantly and specifically affects the expression of numerous genes involved in P. aeruginosa carbon and amino acid metabolism. In particular, 65% of the strains presented considerable overexpression of the genes involved in the acetoin catabolic (aco) pathway. We demonstrated that acetoin is (i) produced by clinical S. aureus strains, (ii) detected in sputa from CF patients and (iii) involved in P. aeruginosa's aco system induction. Furthermore, acetoin is catabolized by P. aeruginosa, a metabolic process that improves the survival of both pathogens by providing a new carbon source for P. aeruginosa and avoiding the toxic accumulation of acetoin on S. aureus. Due to its beneficial effects on both bacteria, acetoin catabolism could testify to the establishment of trophic cooperation between S. aureus and P. aeruginosa in the CF lung environment, thus promoting their persistence.},
}
@article {pmid32810473,
year = {2020},
author = {Jia, T and Liu, K and Qian, C and Li, C and Ji, L},
title = {Denoising Algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction.},
journal = {Journal of neuroscience methods},
volume = {346},
number = {},
pages = {108909},
doi = {10.1016/j.jneumeth.2020.108909},
pmid = {32810473},
issn = {1872-678X},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Intention ; *Robotics ; *Stroke ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal.<br><br>NEW METHOD: To address this problem, this paper proposed a self-adaptively denoised Event-Related Desynchronization (ERD)-based motor intention recognition algorithm (DeERD) in order to enable BMI training with a small sample of calibration data. This study recruited 8 stroke patients. Each patient was required to execute paralyzed upper-limb motor attempt for 20 trials and remain in resting state for 20 trials randomly. ERD-based motor intention recognition algorithm, Common spatial filter algorithm (CSP) and Directed Transfer Function analysis (DTF) were used to extract features for classification respectively and compared with the proposed DeERD analysis.<br><br>RESULTS: DeERD can filter the noise and extract the average lines as the principal trends. With denoising processing, Accuracy (ACC) was up to 70% for all 8 patients and they could be included in this BMI system effectively.<br><br>The proposed DeERD model generated statistically significant increase in True Positive Rate (TPR) and in ACC than the DTF model. TPR and ACC standard deviation of DeERD was smaller than that of CSP.<br><br>CONCLUSIONS: The proposed DeERD model can eliminate the principal noise and extract the principal trend of the time-frequency analysis. It provides a practical method to recruit more stroke patients into BMI training with fewer calibration trainings.},
}
@article {pmid32804653,
year = {2020},
author = {Han, CH and Muller, KR and Hwang, HJ},
title = {Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {10},
pages = {2102-2112},
doi = {10.1109/TNSRE.2020.3017167},
pmid = {32804653},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Online Systems ; Spectroscopy, Near-Infrared ; },
abstract = {Previous studies have shown the superior performance of hybrid electroencephalography (EEG)/ near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs). However, it has been veiled whether the use of a hybrid EEG/NIRS modality can provide better performance for a brain switch that can detect the onset of the intention to turn on a BCI. In this study, we developed such a hybrid EEG/NIRS brain switch and compared its performance with single modality EEG- and NIRS-based brain switch respectively, in terms of true positive rate (TPR), false positive rate (FPR), onset detection time (ODT), and information transfer rate (ITR). In an offline analysis, the performance of a hybrid EEG/NIRS brain switch was significantly improved over that of EEG- and NIRS-based brain switches in general, and in particular a significantly lower FPR was observed for the hybrid EEG/NIRS brain switch. A pseudo-online analysis was additionally performed to confirm the feasibility of implementing an online BCI system with our hybrid EEG/NIRS brain switch. The overall trend of pseudo-online analysis results generally coincided with that of the offline analysis results. No significant difference in all performance measures was also found between offline and pseudo online analysis schemes when the amount of training data was same, with one exception for the ITRs of an EEG brain switch. These offline and pseudo-online results demonstrate that a hybrid EEG/NIRS brain switch can be used to provide a better onset detection performance than that of a single neuroimaging modality.},
}
@article {pmid32802760,
year = {2020},
author = {Nasr, M and Pourmirzaei, M and Esmaeil Motlagh, M and Heshmat, R and Qorbani, M and Kelishadi, R},
title = {Mapping the relative risk of weight disorders in children and adolescents across provinces of Iran: the CASPIAN-V study.},
journal = {Health promotion perspectives},
volume = {10},
number = {3},
pages = {238-243},
pmid = {32802760},
issn = {2228-6497},
abstract = {Background: This study aimed to find possible spatial variation in children's weight disorders and in predicting the spatial distribution. Methods: The study population of this ecological study consisted of 7-18-year-old students living in 30 provinces of Iran. We used Besag, York and Mollie (BYM) model, a Bayesian model, to study the relative risk (RR) of underweight and excess weight (overweight and obese). The model was fitted to data using OpenBUGS (3.2.1) software. Results: The highest RR of underweight was found in southeastern provinces. Whereas, the highest RR of excess weight was documented in northern, northwestern and capital provinces.Sistan-Balouchestan (RR=1.973; Bayesian confidence interval [BCI]: 1.682, 2.289), Hormozgan(RR=1.482; BCI: 1.239, 1.749), South Khorasan (RR=1.422; BCI: 1.18, 1.687) and Kerman(RR=1.413; BCI: 1.18, 1.669) had the highest RR of underweight. Mazandaran (RR=1.366; BCI:1.172,1.581), Gilan (RR=1.346; BCI: 1.15,1.562), Tehran (RR=1.271; BCI: 1.086,1.472) and Alborz (RR=1.268; BCI: 1.079,1.475) provinces are high risk regions for excess weight. Conclusion: The significant variations in geographical distribution of weight disorders are because of various sociodemographic and ethnic differences. The current findings should be considered in health policy making in different regions of the country.},
}
@article {pmid32802720,
year = {2020},
author = {Saima, S and Fiaz, M and Manzoor, M and Zafar, R and Ahmed, I and Nawaz, U and Arshad, M},
title = {Molecular investigation of antibiotic resistant bacterial strains isolated from wastewater streams in Pakistan.},
journal = {3 Biotech},
volume = {10},
number = {9},
pages = {378},
pmid = {32802720},
issn = {2190-572X},
abstract = {Antibiotic resistance is a global public health issue and it is even more daunting in developing countries. The main objective of present study was to investigate molecular responses of antibiotic-resistant bacteria. The 48 bacterial strains, which were previously isolated and identified were subjected to disc diffusion and MIC (minimum inhibitory concentration) determination, followed by investigating the production of the three beta-lactamases (ESBLs (Extended-spectrum Beta-lactamases), MBLs (Metallo Beta-lactamases), AmpCs) and exploring prevalence of the two antibiotic-resistant genes (ARGs); blaTEM and qnrS. Higher MIC values were observed for penicillin(s) than that for fluoroquinolones (ampicillin > amoxicillin > ofloxacin > ciprofloxacin > levofloxacin). Resistance rates were high (58-89%) for all of the tested beta-lactams. Among the tested strains, 5 were ESBL producers (4 Aeromonas spp. and 1 Escherichia sp.), 2 were MBL producers (1 Stenotrophomonas sp. and 1 Citrobacter sp.) and 3 were AmpC producers (2 Pseudomonas spp. and 1 Morganella sp.). The ARGs qnrS2 and blaTEM were detected in Aeromonas spp. and Escherichia sp. The results highlighted the role of Aeromonas as a vector. The study reports bacteria of multidrug resistance nature in the wastewater environment of Pakistan, which harbor ARGs of clinical relevance and could present a public health concern.},
}
@article {pmid32802151,
year = {2020},
author = {Xu, S and Li, R and Wang, Y and Liu, Y and Hu, W and Wu, Y and Zhang, C and Liu, C and Ma, C},
title = {Research and Verification of Convolutional Neural Network Lightweight in BCI.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {5916818},
pmid = {32802151},
issn = {1748-6718},
mesh = {Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Data Compression ; Databases, Factual ; Deep Learning ; Electroencephalography/statistics &amp; numerical data ; Facial Expression ; Humans ; Models, Neurological ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Stochastic Processes ; },
abstract = {With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convolution and uses channel shuffle between blocks to alleviate the information jam. The method is aimed at reducing the dimensionality of parameters of in an original network structure and improving the efficiency of network operation. The verification performance of the ORL dataset shows that the classification accuracy and convergence efficiency are not reduced or even slightly improved when the network parameters are reduced, which supports the validity of block convolution in structure lightweight. Moreover, using a classic CIFAR-10 dataset, this network decreases parameter dimensionality while accelerating computational processing, with excellent convergence stability and efficiency when the network accuracy is only reduced by 1.3%.},
}
@article {pmid32802031,
year = {2020},
author = {Qi, Y and Ding, F and Xu, F and Yang, J},
title = {Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {8890477},
pmid = {32802031},
issn = {1687-5273},
mesh = {*Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Datasets as Topic ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. The BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme. Compared to a method without optimization (89% accuracy), the best classification accuracy of the PSO-based scheme was 99% when less than 10.5% of the original features were used, the test time was reduced by more than 90%, and it achieved Kappa values and F-score of 0.98 and 98.99%, respectively, and better signal-to-noise ratio, thereby outperforming existing algorithms. The results show that the channel and feature selection scheme can accelerate the speed of convergence to the global optimum and reduce the training time. As the proposed framework can significantly improve classification performance, effectively reduce the number of features, and greatly shorten the test time, it can serve as a reference for related real-time BCI application system research.},
}
@article {pmid32802027,
year = {2020},
author = {Gui, R and Chen, T and Nie, H},
title = {Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {7691294},
pmid = {32802027},
issn = {1687-5273},
mesh = {Deep Learning ; Humans ; Logistic Models ; *Machine Learning ; *Magnetic Resonance Imaging ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert-Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.},
}
@article {pmid32799771,
year = {2021},
author = {Khan, A and Chen, C and Yuan, K and Wang, X and Mehra, P and Liu, Y and Tong, KY},
title = {Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke.},
journal = {Topics in stroke rehabilitation},
volume = {28},
number = {4},
pages = {276-288},
doi = {10.1080/10749357.2020.1803584},
pmid = {32799771},
issn = {1945-5119},
mesh = {Electroencephalography ; Humans ; Magnetic Resonance Imaging ; *Robotic Surgical Procedures ; *Robotics ; *Stroke/diagnostic imaging ; *Stroke Rehabilitation ; },
abstract = {Introduction: In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated with brain injury and rehabilitation. These changes mainly involve an imbalance between the two hemispheres. We aimed to study the effect of brain computer interface (BCI)-based robotic hand training on stroke subjects using clinical assessment, electroencephalographic (EEG) complexity analysis, and functional magnetic resonance imaging (fMRI) connectivity analysis. Method: Resting-state simultaneous EEG-fMRI was conducted on 14 stroke subjects before and after training who underwent 20 sessions robot hand training. Fractal dimension (FD) analysis was used to assess neuronal impairment and functional recovery using the EEG data, and fMRI connectivity analysis was performed to assess changes in the connectivity of brain networks. Results: FD results indicated a significant asymmetric difference between the ipsilesional and contralesional hemispheres before training, which was reduced after robotic hand training. Moreover, a positive correlation between interhemispheric asymmetry change for central brain region and change in Fugl Meyer Assessment (FMA) scores for upper limb was observed. Connectivity results showed a significant difference between pre-training interhemispheric connectivity and post-training interhemispheric connectivity. Moreover, the change in connectivity correlated with the change in FMA scores. Results also indicated a correlation between the increase in connectivity for motor regions and decrease in FD interhemispheric asymmetry for central brain region covering the motor area. Conclusion: In conclusion, robotic hand training significantly facilitated stroke motor recovery, and FD, along with connectivity analysis can detect neuroplasticity changes.},
}
@article {pmid32798684,
year = {2020},
author = {Iwama, S and Tsuchimoto, S and Hayashi, M and Mizuguchi, N and Ushiba, J},
title = {Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles.},
journal = {NeuroImage},
volume = {222},
number = {},
pages = {117249},
doi = {10.1016/j.neuroimage.2020.117249},
pmid = {32798684},
issn = {1095-9572},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Fingers/*physiology ; Humans ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Muscles/*physiology ; Scalp/*physiology ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.},
}
@article {pmid32796607,
year = {2020},
author = {Zhang, K and Xu, G and Han, Z and Ma, K and Zheng, X and Chen, L and Duan, N and Zhang, S},
title = {Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {16},
pages = {},
pmid = {32796607},
issn = {1424-8220},
support = {51775415//National Natural Science Foundation of China/ ; 2017YFC1308500//National Key Research &amp; Development Plan of China/ ; 2018ZDCXL-GY-06-01//Key Research &amp; Development Plan of Shaanxi Province/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; *Neural Networks, Computer ; },
abstract = {As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fréchet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired t-tests were used to assess the significance of the results. The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (p < 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. Improvements in the classification accuracies of 17% and 21% (p < 0.01) were observed after DA for the two datasets. In addition, the hybrid network CNN-DCGAN outperformed the other classification methods, with average kappa values of 0.564 and 0.677 for the two datasets.},
}
@article {pmid32795441,
year = {2020},
author = {Zhang, S and Yoshida, W and Mano, H and Yanagisawa, T and Mancini, F and Shibata, K and Kawato, M and Seymour, B},
title = {Pain Control by Co-adaptive Learning in a Brain-Machine Interface.},
journal = {Current biology : CB},
volume = {30},
number = {20},
pages = {3935-3944.e7},
pmid = {32795441},
issn = {1879-0445},
support = {21537/VAC_/Versus Arthritis/United Kingdom ; 097490/WT_/Wellcome Trust/United Kingdom ; 21192/VAC_/Versus Arthritis/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; MR/T010614/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Electroencephalography/methods ; Gyrus Cinguli/*physiology ; Learning/physiology ; Magnetic Resonance Imaging ; Neural Pathways/physiology ; Neurofeedback/*methods ; Pain/pathology ; Pain Management/*methods ; Periaqueductal Gray/*physiology ; },
abstract = {Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.},
}
@article {pmid32785187,
year = {2020},
author = {Xu, R and Wang, Y and Shi, X and Wang, N and Ming, D},
title = {The Effect of Static and Dynamic Visual Stimulations on Error Detection Based on Error-Evoked Brain Responses.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {16},
pages = {},
pmid = {32785187},
issn = {1424-8220},
support = {2017YFB1300302//National Key Research and Development Program of China/ ; 81901860//National Natural Science Foundation of China/ ; 81630051//National Natural Science Foundation of China/ ; 61877042//National Natural Science Foundation of China/ ; 2019M651043//China Postdoctoral Science Foundation/ ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Photic Stimulation ; },
abstract = {Error-related potentials (ErrPs) have provided technical support for the brain-computer interface. However, different visual stimulations may affect the ErrPs, and furthermore, affect the error recognition based on ErrPs. Therefore, the study aimed to investigate how people respond to different visual stimulations (static and dynamic) and find the best time window for different stimulation. Nineteen participants were recruited in the ErrPs-based tasks with static and dynamic visual stimulations. Five ErrPs were statistically compared, and the classification accuracies were obtained through linear discriminant analysis (LDA) with nine different time windows. The results showed that the P3, N6, and P8 with correctness were significantly different from those with error in both stimulations, while N1 only existed in static. The differences between dynamic and static errors existed in N1 and P2. The highest accuracy was obtained in the time window related to N1, P3, N6, and P8 for the static condition, and in the time window related to P3, N6, and P8 for the dynamic. In conclusion, the early components of ErrPs may be affected by stimulation modes, and the late components are more sensitive to errors. The error recognition with static stimulation requires information from the entire epoch, while the late windows should be focused more within the dynamic case.},
}
@article {pmid32785025,
year = {2020},
author = {Riquelme-Ros, JV and Rodríguez-Bermúdez, G and Rodríguez-Rodríguez, I and Rodríguez, JV and Molina-García-Pardo, JM},
title = {On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {16},
pages = {},
pmid = {32785025},
issn = {1424-8220},
support = {TEC2016-78028-C3-2-P//Ministerio de Econom&#xed;a y Competitividad (MINECO), Spain/ ; PGC2018-0971-B-100//Ministerio de Ciencia, Innovacion y Universidades, Spain/ ; 20783/PI/18//Fundacion Seneca de la Region de Murcia, Spain/ ; },
mesh = {Adult ; Algorithms ; Brain ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Female ; Humans ; *Imagination ; Machine Learning ; Male ; *Motor Skills ; Movement ; *Music ; Young Adult ; },
abstract = {Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals' brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users' previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.},
}
@article {pmid32781712,
year = {2020},
author = {Zhang, B and Zhou, Z and Jiang, J},
title = {A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli.},
journal = {Brain sciences},
volume = {10},
number = {8},
pages = {},
pmid = {32781712},
issn = {2076-3425},
support = {2017YFB1002505//National Key Research and Development Program/ ; },
abstract = {To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.},
}
@article {pmid32781497,
year = {2021},
author = {Gatti, R and Atum, Y and Schiaffino, L and Jochumsen, M and Biurrun Manresa, J},
title = {Decoding kinetic features of hand motor preparation from single-trial EEG using convolutional neural networks.},
journal = {The European journal of neuroscience},
volume = {53},
number = {2},
pages = {556-570},
doi = {10.1111/ejn.14936},
pmid = {32781497},
issn = {1460-9568},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; Movement ; Neural Networks, Computer ; },
abstract = {Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single-trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state-of-the-art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four-class classification) from pre-movement single-trial EEG (100 ms and up to 1,600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single-trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation.},
}
@article {pmid32780720,
year = {2020},
author = {Chu, Y and Zhao, X and Zou, Y and Xu, W and Song, G and Han, J and Zhao, Y},
title = {Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046029},
doi = {10.1088/1741-2552/aba7cd},
pmid = {32780720},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Imagination ; Least-Squares Analysis ; Signal Processing, Computer-Assisted ; Upper Extremity ; },
abstract = {OBJECTIVE: Due to low spatial resolution and poor signal-to-noise ratio of electroencephalogram (EEG), high accuracy classifications still suffer from lots of obstacles in the context of motor imagery (MI)-based brain-machine interface (BMI) systems. Particularly, it is extremely challenging to decode multiclass MI EEG from the same upper limb. This research proposes a novel feature learning approach to address the classification problem of 6-class MI tasks, including imaginary elbow flexion/extension, wrist supination/pronation, and hand close/open within the unilateral upper limb.<br><br>APPROACH: Instead of the traditional common spatial pattern (CSP) or filter-bank CSP (FBCSP) manner, the Riemannian geometry (RG) framework involving Riemannian distance and Riemannian mean was directly adopted to extract tangent space (TS) features from spatial covariance matrices of the MI EEG trials. Subsequently, to reduce the dimensionality of the TS features, the algorithm of partial least squares regression was applied to obtain more separable and compact feature representations.<br><br>MAIN RESULTS: The performance of the learned RG feature representations was validated by a linear discriminative analysis and support vector machine classifier, with an average accuracy of 80.50% and 79.70% on EEG dataset collected from 12 participants, respectively.<br><br>SIGNIFICANCE: These results demonstrate that compared with CSP and FBCSP features, the proposed approach can significantly increase the decoding accuracy for multiclass MI tasks from the same upper limb. This approach is promising and could potentially be applied in the context of MI-based BMI control of a robotic arm or a neural prosthesis for motor disabled patients with highly impaired upper limb.},
}
@article {pmid32775173,
year = {2020},
author = {Du, M and Huang, L and Zheng, J and Xi, Y and Dai, Y and Zhang, W and Yan, W and Tao, G and Qiu, J and So, KF and Ren, C and Zhou, S},
title = {Flexible Fiber Probe for Efficient Neural Stimulation and Detection.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {7},
number = {15},
pages = {2001410},
pmid = {32775173},
issn = {2198-3844},
abstract = {Functional probes are a leading contender for the recognition and manipulation of nervous behavior and are characterized by substantial scientific and technological potential. Despite the recent development of functional neural probes, a flexible biocompatible probe unit that allows for long-term simultaneous stimulation and signaling is still an important task. Here, a category of flexible tiny multimaterial fiber probes (<0.3 g) is described in which the metal electrodes are regularly embedded inside a biocompatible polymer fiber with a double-clad optical waveguide by thermal drawing. Significantly, this arrangement enables great improvement in mechanical properties, achieves high optical transmission (>90%), and effectively minimizes the impedance (by up to one order of magnitude) of the probe. This ability allows to realize long-term (at least 10 weeks) simultaneous optical stimulation and neural recording at the single-cell level in behaving mice with signal-to-noise ratio (SNR = 30 dB) that is more than 6 times that of the benchmark probe such as an all-polymer fiber.},
}
@article {pmid32774445,
year = {2020},
author = {Xiong, Q and Zhang, X and Wang, WF and Gu, Y},
title = {A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {9812019},
pmid = {32774445},
issn = {1748-6718},
mesh = {*Algorithms ; Big Data ; Brain/physiology ; Brain-Computer Interfaces/statistics &amp; numerical data ; Databases, Factual/statistics &amp; numerical data ; Electroencephalography/instrumentation/*statistics &amp; numerical data ; Fourier Analysis ; Humans ; Pattern Recognition, Automated/statistics &amp; numerical data ; Programming Languages ; Signal Processing, Computer-Assisted ; },
abstract = {In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.},
}
@article {pmid32769159,
year = {2020},
author = {Flint, RD and Tate, MC and Li, K and Templer, JW and Rosenow, JM and Pandarinath, C and Slutzky, MW},
title = {The Representation of Finger Movement and Force in Human Motor and Premotor Cortices.},
journal = {eNeuro},
volume = {7},
number = {4},
pages = {},
pmid = {32769159},
issn = {2373-2822},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Hand Strength ; Humans ; *Motor Cortex ; Movement ; },
abstract = {The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force in rapid succession. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we asked the question of how movement and force were represented cortically, when executed sequentially with the same finger. We recorded high-density electrocorticography (ECoG) from the motor and premotor cortices of seven human subjects performing a movement-force motor task. We decoded finger movement [0.7 ± 0.3 fractional variance accounted for (FVAF)] and force (0.7 ± 0.2 FVAF) with high accuracy, yet found different spatial representations. In addition, we used a state-of-the-art deep learning method to uncover smooth, repeatable trajectories through ECoG state space during the movement-force task. We also summarized ECoG across trials and participants by developing a new metric, the neural vector angle (NVA). Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90 ± 6%). Thus, finger movement and force appear to have distinct representations in motor/premotor cortices. These results inform our understanding of the neural control of movement, as well as the design of grasp brain-machine interfaces (BMIs).},
}
@article {pmid32768038,
year = {2020},
author = {Khan, MA and Das, R and Iversen, HK and Puthusserypady, S},
title = {Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application.},
journal = {Computers in biology and medicine},
volume = {123},
number = {},
pages = {103843},
doi = {10.1016/j.compbiomed.2020.103843},
pmid = {32768038},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurological Rehabilitation ; *Stroke ; *Stroke Rehabilitation ; Upper Extremity ; },
abstract = {Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including "Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models," have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.},
}
@article {pmid32765639,
year = {2020},
author = {Miao, M and Hu, W and Yin, H and Zhang, K},
title = {Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {1981728},
pmid = {32765639},
issn = {1748-6718},
mesh = {Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Databases, Factual ; Deep Learning ; Electroencephalography/*statistics &amp; numerical data ; Humans ; *Imagination/physiology ; Mathematical Concepts ; Models, Neurological ; Motor Cortex/physiology ; *Neural Networks, Computer ; Pattern Recognition, Automated/statistics &amp; numerical data ; },
abstract = {EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feature extraction, common spatial pattern (CSP) is one of the most frequently used algorithms. However, in order to extract the optimal CSP features, prior knowledge and complex parameter adjustment are often required. Convolutional neural network (CNN) is one of the most popular deep learning models at present. Within CNN, feature learning and pattern classification are carried out simultaneously during the procedure of iterative updating of network parameters; thus, it can remove the complicated manual feature engineering. In this paper, we propose a novel deep learning methodology which can be used for spatial-frequency feature learning and classification of motor imagery EEG. Specifically, a multilayer CNN model is designed according to the spatial-frequency characteristics of MI EEG signals. An experimental study is carried out on two MI EEG datasets (BCI competition III dataset IVa and a self-collected right index finger MI dataset) to validate the effectiveness of our algorithm in comparison with several closely related competing methods. Superior classification performance indicates that our proposed method is a promising pattern recognition algorithm for MI-based BCI system.},
}
@article {pmid32765235,
year = {2020},
author = {Shin, J},
title = {Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {236},
pmid = {32765235},
issn = {1662-5161},
abstract = {The feasibility of the random subspace ensemble learning method was explored to improve the performance of functional near-infrared spectroscopy-based brain-computer interfaces (fNIRS-BCIs). Feature vectors have been constructed using the temporal characteristics of concentration changes in fNIRS chromophores such as mean, slope, and variance to implement fNIRS-BCIs systems. The mean and slope, which are the most popular features in fNIRS-BCIs, were adopted. Linear support vector machine and linear discriminant analysis were employed, respectively, as a single strong learner and multiple weak learners. All features in every channel and available time window were employed to train the strong learner, and the feature subsets were selected at random to train multiple weak learners. It was determined that random subspace ensemble learning is beneficial to enhance the performance of fNIRS-BCIs.},
}
@article {pmid32765212,
year = {2020},
author = {Sadeghi Najafabadi, M and Chen, L and Dutta, K and Norris, A and Feng, B and Schnupp, JWH and Rosskothen-Kuhl, N and Read, HL and Escabí, MA},
title = {Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {709},
pmid = {32765212},
issn = {1662-4548},
support = {R01 DK120824/DK/NIDDK NIH HHS/United States ; R03 DK114546/DK/NIDDK NIH HHS/United States ; },
abstract = {Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.},
}
@article {pmid32763652,
year = {2020},
author = {Tomović, L and Arsovski, D and Golubović, A and Bonnet, X},
title = {Inside the shell: body composition of free-ranging tortoises (Testudo hermanni).},
journal = {Zoology (Jena, Germany)},
volume = {142},
number = {},
pages = {125821},
doi = {10.1016/j.zool.2020.125821},
pmid = {32763652},
issn = {1873-2720},
mesh = {*Adipose Tissue ; Animals ; *Body Composition ; Female ; Male ; Sex Characteristics ; Turtles/*anatomy &amp; histology/*physiology ; },
abstract = {Body condition indices (BCI - mass scaled by size) are widely used in ecological studies. They presumably reflect variations of endogenous fat reserves in free-ranging animals. In the field, however, accurately quantifying internal body reserves is a difficult task. This is especially true in armoured animals where convenient clues that may guide BCI assessment (e.g. visible subcutaneous fat deposits) remain inaccessible. Alternatively, inclusive dissections may provide anatomical abacuses to estimate body reserves in living individuals. Sacrificing animals for this purpose is not acceptable. We opportunistically tested the ability of BCI to estimate body reserves in 13 free-ranging Hermann's tortoises (Gmelin, 1789) dissected soon after they died from natural causes. On average, BCI values were lower in dissected tortoises relative to living individuals (N > 10,000 measurements), but they remained within the range of variation of the studied populations. Shell mass relative to body mass was high and showed considerable inter-individual variation (33.5% to 52.3%). Stomach and digestive tract content represented another important and variable part of total body mass (4.4% to 14.5%). The contribution of fat bodies was negligible (0.0% to 0.5%). Overall, in the studied tortoises, variations of body condition are weakly determined by variations of fat stores. Other endogenous (e.g. muscles, visceral tissues, liver) and "exogenous" (e.g. digestive tract content, clutch) elements should be considered to better understand age and sex specific life-history trade-offs faced by chelonians.},
}
@article {pmid32759476,
year = {2020},
author = {Apollo, NV and Murphy, B and Prezelski, K and Driscoll, N and Richardson, AG and Lucas, TH and Vitale, F},
title = {Gels, jets, mosquitoes, and magnets: a review of implantation strategies for soft neural probes.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {041002},
pmid = {32759476},
issn = {1741-2552},
support = {R01 NS099348/NS/NINDS NIH HHS/United States ; R01 NS107550/NS/NINDS NIH HHS/United States ; R21 NS106434/NS/NINDS NIH HHS/United States ; T32 NS091006/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Culicidae ; Electrodes, Implanted ; Gels ; Humans ; *Magnets ; Microelectrodes ; },
abstract = {Implantable neuroelectronic interfaces have enabled breakthrough advances in the clinical diagnosis and treatment of neurological disorders, as well as in fundamental studies of brain function, behavior, and disease. Intracranial electroencephalography (EEG) mapping with stereo-EEG (sEEG) depth electrodes is routinely adopted for precise epilepsy diagnostics and surgical treatment, while deep brain stimulation has become the standard of care for managing movement disorders. Intracortical microelectrode arrays for high-fidelity recordings of neural spiking activity have led to impressive demonstrations of the power of brain-machine interfaces for motor and sensory functional recovery. Yet, despite the rapid pace of technology development, the issue of establishing a safe, long-term, stable, and functional interface between neuroelectronic devices and the host brain tissue still remains largely unresolved. A body of work spanning at least the last 15 years suggests that safe, chronic integration between invasive electrodes and the brain requires a close match between the mechanical properties of man-made components and the neural tissue. In other words, the next generation of invasive electrodes should be soft and compliant, without sacrificing biological and chemical stability. Soft neuroelectronic interfaces, however, pose a new and significant surgical challenge: bending and buckling during implantation that can preclude accurate and safe device placement. In this topical review, we describe the next generation of soft electrodes and the surgical implantation methods for safe and precise insertion into brain structures. We provide an overview of the most recent innovations in the field of insertion strategies for flexible neural electrodes such as dissolvable or biodegradable carriers, microactuators, biologically-inspired support structures, and electromagnetic drives. In our analysis, we also highlight approaches developed in different fields, such as robotic surgery, which could be potentially adapted and translated to the insertion of flexible neural probes.},
}
@article {pmid32758446,
year = {2020},
author = {Al-Sheikh, U and Kang, L},
title = {Molecular Crux of Hair Cell Mechanotransduction Machinery.},
journal = {Neuron},
volume = {107},
number = {3},
pages = {404-406},
doi = {10.1016/j.neuron.2020.07.007},
pmid = {32758446},
issn = {1097-4199},
mesh = {Animals ; *Hair Cells, Auditory ; *Mechanotransduction, Cellular ; Mice ; },
abstract = {Although 62 years have elapsed since the first report of hereditary deafness in a mouse strain, the molecular mechanism of hair cell mechanotransduction remains elusive. Three recent studies present crucial insights into the molecular crux of hair cell mechanotransduction machinery.},
}
@article {pmid32754019,
year = {2020},
author = {Eidel, M and Kübler, A},
title = {Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {265},
pmid = {32754019},
issn = {1662-5161},
abstract = {Tactile stimulation is less frequently used than visual for brain-computer interface (BCI) control, partly because of limitations in speed and accuracy. Non-visual BCI paradigms, however, may be required for patients who struggle with vision dependent BCIs because of a loss of gaze control. With the present study, we attempted to replicate earlier results by Herweg et al. (2016), with several minor adjustments and a focus on training effects and usability. We invited 16 healthy participants and trained them with a 4-class tactile P300-based BCI in five sessions. Their main task was to navigate a virtual wheelchair through a 3D apartment using the BCI. We found significant training effects on information transfer rate (ITR), which increased from a mean of 3.10-9.50 bits/min. Further, both online and offline accuracies significantly increased with training from 65% to 86% and 70% to 95%, respectively. We found only a descriptive increase of P300 amplitudes at Fz and Cz with training. Furthermore, we report subjective data from questionnaires, which indicated a relatively high workload and moderate to high satisfaction. Although our participants have not achieved the same high performance as in the Herweg et al. (2016) study, we provide evidence for training effects on performance with a tactile BCI and confirm the feasibility of the paradigm.},
}
@article {pmid32750892,
year = {2021},
author = {Lv, Z and Qiao, L and Wang, Q and Piccialli, F},
title = {Advanced Machine-Learning Methods for Brain-Computer Interfacing.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {18},
number = {5},
pages = {1688-1698},
doi = {10.1109/TCBB.2020.3010014},
pmid = {32750892},
issn = {1557-9964},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Imagination/classification ; *Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier.},
}
@article {pmid32748888,
year = {2020},
author = {Choi, I and Kwon, GH and Lee, S and Nam, CS},
title = {Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation.},
journal = {Brain sciences},
volume = {10},
number = {8},
pages = {},
pmid = {32748888},
issn = {2076-3425},
abstract = {Sensorimotor rhythm (SMR)-based brain-computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.},
}
@article {pmid32747834,
year = {2020},
author = {Even-Chen, N and Muratore, DG and Stavisky, SD and Hochberg, LR and Henderson, JM and Murmann, B and Shenoy, KV},
title = {Power-saving design opportunities for wireless intracortical brain-computer interfaces.},
journal = {Nature biomedical engineering},
volume = {4},
number = {10},
pages = {984-996},
pmid = {32747834},
issn = {2157-846X},
support = {I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Power Supplies ; Electrodes, Implanted ; Equipment Design ; Hand ; Humans ; Macaca mulatta ; Male ; Middle Aged ; Wireless Technology/*instrumentation ; },
abstract = {The efficacy of wireless intracortical brain-computer interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.},
}
@article {pmid32747740,
year = {2020},
author = {Yan, W and Richard, I and Kurtuldu, G and James, ND and Schiavone, G and Squair, JW and Nguyen-Dang, T and Das Gupta, T and Qu, Y and Cao, JD and Ignatans, R and Lacour, SP and Tileli, V and Courtine, G and Löffler, JF and Sorin, F},
title = {Structured nanoscale metallic glass fibres with extreme aspect ratios.},
journal = {Nature nanotechnology},
volume = {15},
number = {10},
pages = {875-882},
doi = {10.1038/s41565-020-0747-9},
pmid = {32747740},
issn = {1748-3395},
abstract = {Micro- and nanoscale metallic glasses offer exciting opportunities for both fundamental research and applications in healthcare, micro-engineering, optics and electronics. The scientific and technological challenges associated with the fabrication and utilization of nanoscale metallic glasses, however, remain unresolved. Here, we present a simple and scalable approach for the fabrication of metallic glass fibres with nanoscale architectures based on their thermal co-drawing within a polymer matrix with matched rheological properties. Our method yields well-ordered and uniform metallic glasses with controllable feature sizes down to a few tens of nanometres, and aspect ratios greater than 10[10]. We combine fluid dynamics and advanced in situ transmission electron microscopy analysis to elucidate the interplay between fluid instability and crystallization kinetics that determines the achievable feature sizes. Our approach yields complex fibre architectures that, combined with other functional materials, enable new advanced all-in-fibre devices. We demonstrate in particular an implantable metallic glass-based fibre probe tested in vivo for a stable brain-machine interface that paves the way towards innovative high-performance and multifunctional neuro-probes.},
}
@article {pmid32746342,
year = {2020},
author = {Kaveh, R and Doong, J and Zhou, A and Schwendeman, C and Gopalan, K and Burghardt, FL and Arias, AC and Maharbiz, MM and Muller, R},
title = {Wireless User-Generic Ear EEG.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {14},
number = {4},
pages = {727-737},
doi = {10.1109/TBCAS.2020.3001265},
pmid = {32746342},
issn = {1940-9990},
mesh = {Blinking/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Ear/*physiology ; Electrodes ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; Scalp/physiology ; *Wearable Electronic Devices ; Wireless Technology/*instrumentation ; },
abstract = {In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm[2] wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.},
}
@article {pmid32746323,
year = {2020},
author = {Li, M and Yang, G and Xu, G},
title = {The Effect of the Graphic Structures of Humanoid Robot on N200 and P300 Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {9},
pages = {1944-1954},
doi = {10.1109/TNSRE.2020.3010250},
pmid = {32746323},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Photic Stimulation ; *Robotics ; },
abstract = {Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, the element that increases the ERPs amplitude is unclear, and how to test the effect of it on the brain is a problem. This study used different graphic structures of an NAO humanoid robot to design three types of robot stimuli: a global robot, its local information, and its topological action. Ten subjects first conducted an odd-ball-based BCI (OD-BCI) by applying these stimuli. Then, they accomplished a delayed matching-to-sample task (DMST) that was used to specialize the encoding and retrieval phases of the OD-BCI task. In the retrieval phase of the DMST, the global stimulus induces the largest N200 and P300 potentials with the shortest latencies in the frontal, central, and occipital areas. This finding is in accordance with the P300 and classification performance of the OD-BCI task. When induced by the local stimulus, the subjects responded faster and more accurately in the retrieval phase of the DMST than in the other two conditions, indicating that the local stimulus improved the subject's responses. These results indicate that the OD-BCI task causes subject's retrieval work when the subject recognizes and outputs the stimulus. The global stimulus that contains topological and local elements could make brain react faster and induce larger ERPs, this finding could be used during the development of visual stimuli to improve BCI performance.},
}
@article {pmid32746322,
year = {2020},
author = {Song, X and Yan, B and Tong, L and Shu, J and Zeng, Y},
title = {Asynchronous Video Target Detection Based on Single-Trial EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {9},
pages = {1931-1943},
doi = {10.1109/TNSRE.2020.3009978},
pmid = {32746322},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems to detect sensitive targets. However, asynchronous BCI systems based on video-target-evoked ERPs can pose a challenge in real-world applications due to the absence of an explicit target onset time and the time jitter of the detection latency. To address this challenge, we developed an asynchronous detection framework for video target detection. In this framework, an ERP alignment method based on the principle of iterative minimum distance square error (MDSE) was proposed for constructing an ERP template and aligning signals on the same base to compensate for possible time jitter. Using this method, ERP response characteristics induced by video targets were estimated. Online video target detection results indicated that alignment methods reduced the false alarm more effectively than non-alignment methods. The false alarm of the proposed Aligned-MDSE method was one-third lower than that of existing alignment methods under the same right hit level using limited individual samples. Furthermore, cross-subject results indicated that untrained subjects could directly perform online detection tasks and achieve excellent performance by a general model trained from more than 10 subjects. The proposed asynchronous video target detection framework can thus have a significant impact on real-world BCI applications.},
}
@article {pmid32746321,
year = {2020},
author = {Li, H and Bi, L and Shi, H},
title = {Modeling of Human Operator Behavior for Brain-Actuated Mobile Robots Steering.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {9},
pages = {2063-2072},
doi = {10.1109/TNSRE.2020.3009376},
pmid = {32746321},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Robotics ; },
abstract = {Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated robot. To address this problem, in this paper, we propose an operator brain-controlled steering model consisting of an operator decision model based on the queuing network (QN) cognitive architecture and a brain-machine interface (BMI) performance model. The QN-based operator decision model can mimic the human decision process with the individual operator differences considered. The new BMI performance model is built to represent the varied accuracy of BMI during brain-controlled direction operations. Furthermore, the model is simulated and validated against the results of human operator-in-the-loop experiments. The results show that the proposed model can reproduce the behavior of human operators thanks to its similar direction control performance.},
}
@article {pmid32746310,
year = {2020},
author = {Maheshwari, J and Joshi, SD and Gandhi, TK},
title = {Tracking the Transitions of Brain States: An Analytical Approach Using EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {8},
pages = {1742-1749},
doi = {10.1109/TNSRE.2020.3005950},
pmid = {32746310},
issn = {1558-0210},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain.<br><br>METHODS: Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest in terms of changes in the invariant spaces spanned by eigen vectors as well as amount of variance along them. Based on this, our first approach is to track the brain state transitions by analysing invariant space variations over time. Whereas, our second approach analyses sub-band characteristic response vector formed using eigen values along with the eigen vectors to capture the dynamics.<br><br>RESULT: We have taken two real time EEG datasets to demonstrate the efficacy of proposed approaches. It has been observed that in case of unimodal experiment, invariant spaces explicitly show the transitions of brain states. Whereas sub-band characteristic response vector approach gives better performance in the case of cross-modal conditions.<br><br>CONCLUSIONS: Evolution of invariant spaces along with the eigen values may help in understanding and tracking the brain state transitions.<br><br>SIGNIFICANCE: The proposed approaches can track the activity transitions in real time. They do not require any training dataset.},
}
@article {pmid32746309,
year = {2020},
author = {Zhang, HY and Stevenson, CE and Jung, TP and Ko, LW},
title = {Stress-Induced Effects in Resting EEG Spectra Predict the Performance of SSVEP-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {8},
pages = {1771-1780},
doi = {10.1109/TNSRE.2020.3005771},
pmid = {32746309},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming the impact of such user variability on BCI performance is an impending and inevitable challenge for routine applications of BCIs in the real world. To systematically explore the factors affecting BCI performance, this study embeds a Steady-State Visually Evoked Potential (SSVEP) based BCI into a "game with a purpose" (GWAP) to obtain data over significant lengths of time, under both high- and low-stress conditions. Ten healthy volunteers played a GWAP that resembles popular match-three games, such as Jewel Quest, Zoo Boom, or Candy Crush. We recorded the target search time, target search accuracy, and EEG signals during gameplay to investigate the impacts of stress on EEG signals and BCI performance. We used Canonical Correlation Analysis (CCA) to determine whether the subject had found and attended to the correct target. The experimental results show that SSVEP target-classification accuracy is reduced by stress. We also found a negative correlation between EEG spectra and the SNR of EEG in the frontal and occipital regions during gameplay, with a larger negative correlation for the high-stress conditions. Furthermore, CCA also showed that when the EEG alpha and theta power increased, the search accuracy decreased, and the spectral amplitude drop was more evident under the high-stress situation. These results provide new, valuable insights into research on how to improve the robustness of BCIs in real-world applications.},
}
@article {pmid32746304,
year = {2020},
author = {Kristensen, AB and Subhi, Y and Puthusserypady, S},
title = {Vocal Imagery vs Intention: Viability of Vocal-Based EEG-BCI Paradigms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {8},
pages = {1750-1759},
doi = {10.1109/TNSRE.2020.3004924},
pmid = {32746304},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Intention ; Support Vector Machine ; },
abstract = {The viability of electroencephalogram (EEG) based vocal imagery (VIm) and vocal intention (VInt) Brain-Computer Interface (BCI) systems has been investigated in this study. Four different types of experimental tasks related to humming has been designed and exploited here. They are: (i) non-task specific (NTS), (ii) motor task (MT), (iii) VIm task, and (iv) VInt task. EEG signals from seventeen participants for each of these tasks were recorded from 16 electrode locations on the scalp and its features were extracted and analysed using common spatial pattern (CSP) filter. These features were subsequently fed into a support vector machine (SVM) classifier for classification. This analysis aimed to perform a binary classification, predicting whether the subject was performing one task or the other. Results from an extensive analysis showed a mean classification accuracy of 88.9% for VIm task and 91.1% for VInt task. This study clearly shows that VIm can be classified with ease and is a viable paradigm to integrate in BCIs. Such systems are not only useful for people with speech problems, but in general for people who use BCI systems to help them out in their everyday life, giving them another dimension of system control.},
}
@article {pmid32746291,
year = {2020},
author = {Ren, S and Wang, W and Hou, ZG and Liang, X and Wang, J and Shi, W},
title = {Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {8},
pages = {1846-1855},
doi = {10.1109/TNSRE.2020.3001990},
pmid = {32746291},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination ; Lower Extremity ; *Virtual Reality ; },
abstract = {Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients' motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the FES is used to stimulate the subjects' lower limbs before their imagination to make them experience the muscles' contraction and improve their attention on the lower limbs, by which it is supposed that the subjects' motor imagery (MI) abilities can be enhanced. On the other hand, a ball-kicking movement scenario from the first-person perspective is designed to provide visual guidance for performing MI tasks. The combination of FES and VR can be used to reduce the difficulties in performing MI tasks and improve classification accuracy. Finally, the comparison experiments were conducted on twelve healthy subjects to validate the performance of the enhanced MI-BCI. The results show that the classification performance can be improved significantly by using the proposed MI-BCI in terms of the classification accuracy (ACC), the area under the curve (AUC) and the F1 score (paired t-test,).},
}
@article {pmid32746067,
year = {2021},
author = {Rodrigues, PLC and Congedo, M and Jutten, C},
title = {Dimensionality Transcending: A Method for Merging BCI Datasets With Different Dimensionalities.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {2},
pages = {673-684},
doi = {10.1109/TBME.2020.3010854},
pmid = {32746067},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; },
abstract = {OBJECTIVE: We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (BCI).<br><br>METHOD: Our proposal uses a two-step procedure that transforms the data points so that they become matched in terms of dimensionality and statistical distribution. In the dimensionality matching step, we use isometric transformations to map each dataset into a common space without changing their geometric structures. The statistical matching is done using a domain adaptation technique adapted for the intrinsic geometry of the space where the datasets are defined.<br><br>RESULTS: We illustrate our proposal on time series obtained from BCI systems with different experimental setups (e.g., different number of electrodes, different placement of electrodes). The results show that the proposed method can be used to transfer discriminative information between BCI recordings that, in principle, would be incompatible.<br><br>CONCLUSION AND SIGNIFICANCE: Such findings pave the way to a new generation of BCI systems capable of reusing information and learning from several sources of data despite differences in their electrodes positioning.},
}
@article {pmid32746042,
year = {2021},
author = {Liu, D and Liu, C and Chen, J and Zhang, D and Hong, B},
title = {Doubling the Speed of N200 Speller via Dual-Directional Motion Encoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {1},
pages = {204-213},
doi = {10.1109/TBME.2020.3005518},
pmid = {32746042},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; *Evoked Potentials, Visual ; Humans ; Motion ; Photic Stimulation ; },
abstract = {OBJECTIVE: Motion-onset visual evoked potentials (mVEPs)-based spellers, also known as N200 spellers, have been successfully implemented, avoiding flashing stimuli that are common in visual brain-computer interface (BCI). However, their information transfer rates (ITRs), typically below 50 bits/min, are lower than other visual BCI spellers. In this study, we sought to improve the speed of N200 speller to a level above the well-known P300 spellers.<br><br>APPROACH: Based on our finding of the spatio-temporal asymmetry of N200 response elicited by leftward and rightward visual motion, a novel dual-directional N200 speller was implemented. By presenting visual stimuli moving in two different directions simultaneously, the new paradigm reduced the stimuli presentation time by half, while ensuring separable N200 features between two visual motion directions. Furthermore, a probability-based dynamic stopping algorithm was also proposed to shorten the decision time for each output further. Both offline and online tests were conducted to evaluate the performance in ten participants.<br><br>MAIN RESULTS: Offline results revealed contralateral dominant temporal and spatial patterns in N200 responses when subjects attended to stimuli moving leftward or rightward. In online experiments, the dual-directional paradigm achieved an average ITR of 79.8 bits/min, with the highest ITR of 124.8 bits/min. Compared with the traditional uni-directional N200 speller, the median gain on the ITR was 202%.<br><br>SIGNIFICANCE: The proposed dual-directional paradigm managed to double the speed of the N200 speller. Together with its non-flashing characteristics, this dual-directional N200 speller is promising to be a competent candidate for fast and reliable BCI applications.},
}
@article {pmid32746025,
year = {2021},
author = {Orset, B and Lee, K and Chavarriaga, R and Millan, JDR},
title = {User Adaptation to Closed-Loop Decoding of Motor Imagery Termination.},
journal = {IEEE transactions on bio-medical engineering},
volume = {68},
number = {1},
pages = {3-10},
doi = {10.1109/TBME.2020.3001981},
pmid = {32746025},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Movement ; },
abstract = {UNLABELLED: One of the most popular methods in non-invasive brain machine interfaces (BMI) relies on the decoding of sensorimotor rhythms associated to sustained motor imagery. Although motor imagery has been intensively studied, its termination is mostly neglected.<br><br>OBJECTIVE: Here, we provide insights in the decoding of motor imagery termination and investigate the use of such decoder in closed-loop BMI.<br><br>METHODS: Participants (N = 9) were asked to perform kinesthetic motor imagery of both hands simultaneously cued with a clock indicating the initiation and termination of the action. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between event-related desynchronization and event-related synchronization. Features for this decoder were correlates of motor termination in the upper &#x3bc; and &#x3b2; bands.<br><br>RESULTS: The decoder reached an accuracy of 76.2% (N = 9), revealing the high robustness of our approach. More importantly, this paper shows that the decoding of motor termination has an intrinsic latency mainly due to the delayed appearance of its correlates. Because the latency was consistent and thus predictable, users were able to compensate it after training.<br><br>CONCLUSION: Using our decoding system, BMI users were able to adapt their behavior and modulate their sensorimotor rhythm to stop the device (clock) accurately on time.<br><br>SIGNIFICANCE: These results show the importance of closed-loop evaluations of BMI decoders and open new possibilities for BMI control using decoding of movement termination.},
}
@article {pmid32745492,
year = {2020},
author = {Lashgari, E and Liang, D and Maoz, U},
title = {Data augmentation for deep-learning-based electroencephalography.},
journal = {Journal of neuroscience methods},
volume = {346},
number = {},
pages = {108885},
doi = {10.1016/j.jneumeth.2020.108885},
pmid = {32745492},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Humans ; Machine Learning ; Seizures ; },
abstract = {BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. NEW METHOD: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?<br><br>RESULTS: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.<br><br>Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8 % for recombination of segmentation and 36 % for noise addition and from 14 % for motor imagery to 56 % for mental workload-29 % on average.<br><br>CONCLUSIONS: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.},
}
@article {pmid32745332,
year = {2022},
author = {Vigué-Guix, I and Morís Fernández, L and Torralba Cuello, M and Ruzzoli, M and Soto-Faraco, S},
title = {Can the occipital alpha-phase speed up visual detection through a real-time EEG-based brain-computer interface (BCI)?.},
journal = {The European journal of neuroscience},
volume = {55},
number = {11-12},
pages = {3224-3240},
doi = {10.1111/ejn.14931},
pmid = {32745332},
issn = {1460-9568},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Parietal Lobe/physiology ; Photic Stimulation/methods ; Visual Perception/physiology ; },
abstract = {Electrical brain oscillations reflect fluctuations in neural excitability. Fluctuations in the alpha band (α, 8-12 Hz) in the occipito-parietal cortex are thought to regulate sensory responses, leading to cyclic variations in visual perception. Inspired by this theory, some past and recent studies have addressed the relationship between α-phase from extra-cranial EEG and behavioural responses to visual stimuli in humans. The latest studies have used offline approaches to confirm α-gated cyclic patterns. However, a particularly relevant implication is the possibility to use this principle online, whereby stimuli are time-locked to specific α-phases leading to predictable outcomes in performance. Here, we aimed at providing a proof of concept for such real-time neurotechnology. Participants performed a speeded response task to visual targets that were presented upon a real-time estimation of the α-phase via an EEG closed-loop brain-computer interface (BCI). According to the theory, we predicted a modulation of reaction times (RTs) along the α-cycle. Our BCI system achieved reliable trial-to-trial phase locking of stimuli to the phase of individual occipito-parietal α-oscillations. Yet, the behavioural results did not support a consistent relation between RTs and the phase of the α-cycle neither at group nor at single participant levels. We must conclude that although the α-phase might play a role in perceptual decisions from a theoretical perspective, its impact on EEG-based BCI application appears negligible.},
}
@article {pmid32745312,
year = {2020},
author = {Couderc, AL and Puchades, E and Villani, P and Arcani, R and Farnault, L and Daumas, A and Courcier, A and Greillier, L and Barlesi, F and Duffaud, F and Salas, S and Costello, R and Gentile, G and Pradel, V and Suchon, P and Venton, G},
title = {High Serum Vitamin B12 Levels Associated with C-Reactive Protein in Older Patients with Cancer.},
journal = {The oncologist},
volume = {25},
number = {12},
pages = {e1980-e1989},
pmid = {32745312},
issn = {1549-490X},
mesh = {Activities of Daily Living ; Aged ; Aged, 80 and over ; *C-Reactive Protein ; Geriatric Assessment ; Hospitalization ; Humans ; *Neoplasms ; Vitamin B 12 ; },
abstract = {BACKGROUND: A Comprehensive Geriatric Assessment (CGA) has been proposed to assess prognosis and to adapt oncological care in older patients with cancer. However, few biological markers are incorporated in the CGA.<br><br>METHODS: This comparative study on older patients with cancer was realized before final therapeutic decision and during a CGA that included biological markers. Our objective study was to know if the serum vitamin B12-C-reactive protein index (BCI) can help to estimate early death and unplanned hospitalization. Associations between BCI and unplanned hospitalization or mortality were analyzed using ordered multivariate logistic regression.<br><br>FINDINGS: We included 621 older cancer adults in outpatient care with a median age of 81&#x2009;years (range, 70-98&#x2009;years) from September 2015 to May 2018. In this study, 5.6% of patients died within 3 months, 8.8% had unplanned hospitalization within 1 month, and 11.4% had unplanned hospitalization within 3 months. Hypercobalaminemia was present in 83 patients (13.4%), and 34 patients (5.5%) had BCI >40,000. According to the multivariate analysis, BCI was a prognostic factor of mortality within 3 months and unplanned hospitalizations at 1 and 3 months. Impaired activities of daily living (ADL) and palliative care were also risk factors for mortality within 3 months. Impaired instrumental ADL, low albumin level, and palliative care were risk factors for unplanned hospitalization at 1 month.<br><br>INTERPRETATION: BCI could be routinely added to the CGA process, as part of a pretreatment workup, in order to assess more precisely the frailties and to adapt oncological care in older patients treated for cancer.<br><br>IMPLICATIONS FOR PRACTICE: Aging comes with an increase of frailties and comorbidities. To identify frailties in older patients with cancer, this study used a Comprehensive Geriatric Assessment, which allowed for the adaptation of each treatment plan in accordance with the individual needs of the patients. However, biological characteristics were not included in this assessment. This study showed that hypercobalaminemia and vitamin B12 -C-reactive protein index may be potential markers for cancer with poor prognosis, particularly in the older population. These biological markers can be used in geriatric oncology and general medicine.},
}
@article {pmid32745012,
year = {2021},
author = {Zhao, H and Zheng, Q and Ma, K and Li, H and Zheng, Y},
title = {Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {2},
pages = {535-545},
doi = {10.1109/TNNLS.2020.3010780},
pmid = {32745012},
issn = {2162-2388},
mesh = {Algorithms ; Brain Mapping ; Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/*classification ; Humans ; Image Processing, Computer-Assisted ; Movement ; Neural Networks, Computer ; Neuroimaging ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on data of multiple subjects from a specific domain typically degrades when applied to a different subject. While collecting enough samples from each subject would address this issue, it is often too time-consuming and impractical. To tackle this problem, we propose a novel end-to-end deep domain adaptation method to improve the classification performance on a single subject (target domain) by taking the useful information from multiple subjects (source domain) into consideration. Especially, the proposed method jointly optimizes three modules, including a feature extractor, a classifier, and a domain discriminator. The feature extractor learns the discriminative latent features by mapping the raw EEG signals into a deep representation space. A center loss is further employed to constrain an invariant feature space and reduce the intrasubject nonstationarity. Furthermore, the domain discriminator matches the feature distribution shift between source and target domains by an adversarial learning strategy. Finally, based on the consistent deep features from both domains, the classifier is able to leverage the information from the source domain and accurately predict the label in the target domain at the test time. To evaluate our method, we have conducted extensive experiments on two real public EEG data sets, data set IIa, and data set IIb of brain-computer interface (BCI) Competition IV. The experimental results validate the efficacy of our method. Therefore, our method is promising to reduce the calibration time for the use of BCI and promote the development of BCI.},
}
@article {pmid32740551,
year = {2020},
author = {Wenzel, C and Schilde, S and Plontke, SK and Rahne, T},
title = {Changes in Bone Conduction Implant Geometry Improve the Bone Fit in Mastoids of Children and Young Adults.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {41},
number = {10},
pages = {1406-1412},
doi = {10.1097/MAO.0000000000002798},
pmid = {32740551},
issn = {1537-4505},
mesh = {Adolescent ; Bone Conduction ; Child ; Child, Preschool ; *Hearing Aids ; Humans ; *Mastoid/diagnostic imaging/surgery ; Prostheses and Implants ; Retrospective Studies ; Young Adult ; },
abstract = {OBJECTIVES: In 2012 the first active bone conduction implant was introduced, but did not fit into the mastoids of some adults and many children. Thus, a geometry change of the transducer was proposed (BCI 602). In this study, we aimed to determine whether these changes improved the mastoid cavity fit of the implant in children and young adults.<br><br>DESIGN: We retrospectively analyzed computed tomography scans of 151 mastoids from 81 children and adolescents (age range, 5 mo to 20 yr) and 52 control mastoids from 33 adults. After three-dimensional reconstruction of the temporal bone from computed tomography, we virtually implanted the BCI 602 into the mastoids, and compared the bone fit with that of the BCI 601.<br><br>RESULTS: The BCI 602 could be virtually implanted in 100% of patients &#x2265;12 years old, while the BCI 601 transducer could be completely embedded in the bone of only 70% of these mastoids. Moreover, virtual implantation of the BCI 602 was possible in 75% of children 3 to 5 years of age, while the BCI 601 did not fit in the mastoids of any patients under 5 years old without the use of lifts.<br><br>CONCLUSIONS: Compared to the BCI 601, placement of the BCI 602 allegedly requires less bone removal. The newer BCI 602 transducer is more likely than its predecessor to be completely accommodated in the mastoid bone among all age groups and indications. Preoperative planning is still recommended to avoid exposure of delicate structures.},
}
@article {pmid32737181,
year = {2020},
author = {Glaser, JI and Benjamin, AS and Chowdhury, RH and Perich, MG and Miller, LE and Kording, KP},
title = {Machine Learning for Neural Decoding.},
journal = {eNeuro},
volume = {7},
number = {4},
pages = {},
pmid = {32737181},
issn = {2373-2822},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Machine Learning ; *Motor Cortex ; Neural Networks, Computer ; },
abstract = {Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help to advance engineering applications such as brain-machine interfaces. Our code package is available at github.com/kordinglab/neural_decoding.},
}
@article {pmid32735536,
year = {2021},
author = {Khare, SK and Bajaj, V},
title = {Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {7},
pages = {2901-2909},
doi = {10.1109/TNNLS.2020.3008938},
pmid = {32735536},
issn = {2162-2388},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Emotions ; False Positive Reactions ; Female ; Humans ; Machine Learning ; Male ; *Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Young Adult ; },
abstract = {Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral changes. Electroencephalogram (EEG) signals provide a noninvasive and nonradioactive solution for emotion identification. Accurate and automatic classification of emotions can boost the development of human-computer interface. This article proposes automatic extraction and classification of features through the use of different convolutional neural networks (CNNs). At first, the proposed method converts the filtered EEG signals into an image using a time-frequency representation. Smoothed pseudo-Wigner-Ville distribution is used to transform time-domain EEG signals into images. These images are fed to pretrained AlexNet, ResNet50, and VGG16 along with configurable CNN. The performance of four CNNs is evaluated by measuring the accuracy, precision, Mathew's correlation coefficient, F1-score, and false-positive rate. The results obtained by evaluating four CNNs show that configurable CNN requires very less learning parameters with better accuracy. Accuracy scores of 90.98%, 91.91%, 92.71%, and 93.01% obtained by AlexNet, ResNet50, VGG16, and configurable CNN show that the proposed method is best among other existing methods.},
}
@article {pmid32733860,
year = {2020},
author = {Guggenberger, R and Heringhaus, M and Gharabaghi, A},
title = {Brain-Machine Neurofeedback: Robotics or Electrical Stimulation?.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {8},
number = {},
pages = {639},
pmid = {32733860},
issn = {2296-4185},
abstract = {Neurotechnology such as brain-machine interfaces (BMI) are currently being investigated as training devices for neurorehabilitation, when active movements are no longer possible. When the hand is paralyzed following a stroke for example, a robotic orthosis, functional electrical stimulation (FES) or their combination may provide movement assistance; i.e., the corresponding sensory and proprioceptive neurofeedback is given contingent to the movement intention or imagination, thereby closing the sensorimotor loop. Controlling these devices may be challenging or even frustrating. Direct comparisons between these two feedback modalities (robotics vs. FES) with regard to the workload they pose for the user are, however, missing. Twenty healthy subjects controlled a BMI by kinesthetic motor imagery of finger extension. Motor imagery-related sensorimotor desynchronization in the EEG beta frequency-band (17-21 Hz) was turned into passive opening of the contralateral hand by a robotic orthosis or FES in a randomized, cross-over block design. Mental demand, physical demand, temporal demand, performance, effort, and frustration level were captured with the NASA Task Load Index (NASA-TLX) questionnaire by comparing these workload components to each other (weights), evaluating them individually (ratings), and estimating the respective combinations (adjusted workload ratings). The findings were compared to the task-related aspects of active hand movement with EMG feedback. Furthermore, both feedback modalities were compared with regard to their BMI performance. Robotic and FES feedback had similar workloads when weighting and rating the different components. For both robotics and FES, mental demand was the most relevant component, and higher than during active movement with EMG feedback. The FES task led to significantly more physical (p = 0.0368) and less temporal demand (p = 0.0403) than the robotic task in the adjusted workload ratings. Notably, the FES task showed a physical demand 2.67 times closer to the EMG task, but a mental demand 6.79 times closer to the robotic task. On average, significantly more onsets were reached during the robotic as compared to the FES task (17.22 onsets, SD = 3.02 vs. 16.46, SD = 2.94 out of 20 opportunities; p = 0.016), even though there were no significant differences between the BMI classification accuracies of the conditions (p = 0.806; CI = -0.027 to -0.034). These findings may inform the design of neurorehabilitation interfaces toward human-centered hardware for a more natural bidirectional interaction and acceptance by the user.},
}
@article {pmid32733182,
year = {2020},
author = {Sebastián-Romagosa, M and Udina, E and Ortner, R and Dinarès-Ferran, J and Cho, W and Murovec, N and Matencio-Peralba, C and Sieghartsleitner, S and Allison, BZ and Guger, C},
title = {EEG Biomarkers Related With the Functional State of Stroke Patients.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {582},
pmid = {32733182},
issn = {1662-4548},
abstract = {INTRODUCTION: Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment.<br><br>METHODS: Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps.<br><br>RESULTS: The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, &#x3c1; = -0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (&#x3c1; = -0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (&#x3c1; = -0.623 and P < 0.001) and FMA of lower extremity (&#x3c1; = -0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (&#x394;FMA = 1 median [IQR: 0-8], P = 0.002).<br><br>CONCLUSION: The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.},
}
@article {pmid32731432,
year = {2020},
author = {Tang, J and Xu, M and Han, J and Liu, M and Dai, T and Chen, S and Ming, D},
title = {Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {15},
pages = {},
pmid = {32731432},
issn = {1424-8220},
support = {2017YFB1300300//National Key Research and Development Program of China/ ; 81925020, 61976152, 81671861//National Natural Science Foundation of China/ ; 2018QNRC001//Young Elite Scientist Sponsorship Program by CAST/ ; },
abstract = {The brain-computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.},
}
@article {pmid32730917,
year = {2020},
author = {Kant, P and Laskar, SH and Hazarika, J and Mahamune, R},
title = {CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces.},
journal = {Journal of neuroscience methods},
volume = {345},
number = {},
pages = {108886},
doi = {10.1016/j.jneumeth.2020.108886},
pmid = {32730917},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {BACKGROUND: The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities.<br><br>NEW METHODS: Deep learning has been a success in many fields. However, for Electroencephalogram (EEG) signals, relatively minimal work has been carried out using deep learning. This paper proposes a combination of Continuous Wavelet Transform (CWT) along with deep learning-based transfer learning to solve the problem. CWT transforms one dimensional EEG signals into two-dimensional time-frequency-amplitude representation enabling us to exploit available deep networks through transfer learning.<br><br>RESULTS: The effectiveness of the proposed approach is evaluated in this study using an openly available BCI competition data-set. The results of the approach have been compared to earlier works on the same dataset, and a promising validation accuracy of 95.71% is achieved in our investigation.<br><br>Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.},
}
@article {pmid32726763,
year = {2020},
author = {Liang, L and Lin, J and Yang, C and Wang, Y and Chen, X and Gao, S and Gao, X},
title = {Optimizing a dual-frequency and phase modulation method for SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046026},
doi = {10.1088/1741-2552/abaa9b},
pmid = {32726763},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {OBJECTIVE: The design of the stimulation paradigm plays an important role in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) studies. Among various stimulation designs, the dual-frequency paradigm in which two frequencies are used to encode one target is of importance and interest. However, because the number of possible frequency combinations is huge, the existing dual-frequency modulation paradigms failed to optimize the encoding towards the best combinations. Thus, this work aiming at designing a new dual-frequency and phase modulation paradigm with the best combinations stimuli.<br><br>APPROACH: This study proposed a dual-frequency and phase modulation method, which can achieve a large number of targets by making different combinations of two frequencies and an initial phase. This study also designed a set of methods for quickly optimizing the stimulation codes for the dual-frequency and phase modulation method.<br><br>MAIN RESULTS: An online 40-class BCI experiment with 12 subjects obtained an accuracy of 96.06[Formula: see text]4.00% and an averaged information transfer rate (ITR) of 196.09[Formula: see text]15.25 bits min[-1], which were much higher than the existing dual-frequency modulation paradigms. Moreover, an offline simulation with a public dataset showed that the optimization method was also effective for optimizing the single-frequency and phase modulation paradigm.<br><br>SIGNIFICANCE: These results demonstrate the high performance of the proposed dual-frequency and phase modulation method and the high efficiency of the optimization method for designing SSVEP stimulation paradigms. In addition, the coding efficiency of the optimized dual-frequency and phase modulation paradigm is higher than that of the single-frequency and phase modulation paradigm, and it is expected to further realize the BCI paradigm with a large amount of targets.},
}
@article {pmid32726757,
year = {2020},
author = {Mousavi, M and Krol, LR and de Sa, VR},
title = {Hybrid brain-computer interface with motor imagery and error-related brain activity.},
journal = {Journal of neural engineering},
volume = {17},
number = {5},
pages = {056041},
doi = {10.1088/1741-2552/abaa9d},
pmid = {32726757},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems read and interpret brain activity directly from the brain. They can provide a means of communication or locomotion for patients suffering from neurodegenerative diseases or stroke. However, non-stationarity of brain activity limits the reliable transfer of the algorithms that were trained during a calibration session to real-time BCI control. One source of non-stationarity is the user's brain response to the BCI output (feedback), for instance, whether the BCI feedback is perceived as an error by the user or not. By taking such sources of non-stationarity into account, the reliability of the BCI can be improved.<br><br>APPROACH: In this work, we demonstrate a real-time implementation of a hybrid motor imagery BCI combining the information from the motor imagery signal and the error-related brain activity simultaneously so as to gain benefit from both sources.<br><br>MAIN RESULTS: We show significantly improved performance in real-time BCI control across 12 participants, compared to a conventional motor imagery BCI. The significant improvement is in terms of classification accuracy, target hit rate, subjective perception of control and information-transfer rate. Moreover, our offline analyses of the recorded EEG data show that the error-related brain activity provides a more reliable source of information than the motor imagery signal.<br><br>SIGNIFICANCE: This work shows, for the first time, that the error-related brain activity classifier compared to the motor imagery classifier is more consistent when trained on calibration data and tested during online control. This likely explains why the proposed hybrid BCI allows for a more reliable means of communication or rehabilitation for patients in need.},
}
@article {pmid32726599,
year = {2020},
author = {Zhang, L and Zhou, Y and Liu, C and Zheng, W and Yao, Z and Wang, Q and Jin, Y and Zhang, S and Chen, W and Chen, JF},
title = {Adenosine A2A receptor blockade improves neuroprosthetic learning by volitional control of population calcium signal in M1 cortical neurons.},
journal = {Neuropharmacology},
volume = {178},
number = {},
pages = {108250},
doi = {10.1016/j.neuropharm.2020.108250},
pmid = {32726599},
issn = {1873-7064},
mesh = {Adenosine A2 Receptor Antagonists/*pharmacology ; Animals ; Brain-Computer Interfaces ; Calcium Signaling/drug effects/*physiology ; *Implantable Neurostimulators ; Learning/drug effects/*physiology ; Mice ; Mice, Inbred C57BL ; Motor Cortex/drug effects/*metabolism ; Neurons/drug effects/metabolism ; Photometry/instrumentation/methods ; Purines/pharmacology ; Receptor, Adenosine A2A/*metabolism ; Volition/drug effects/*physiology ; },
abstract = {Volitional control is at the core of brain-machine interfaces (BMI) adaptation and neuroprosthetic-driven learning to restore motor function for disabled patients, but neuroplasticity changes and neuromodulation underlying volitional control of neuroprosthetic learning are largely unexplored. To better study volitional control at annotated neural population, we have developed an operant neuroprosthetic task with closed-loop feedback system by volitional conditioning of population calcium signal in the M1 cortex using fiber photometry recording. Importantly, volitional conditioning of the population calcium signal in M1 neurons did not improve within-session adaptation, but specifically enhanced across-session neuroprosthetic skill learning with reduced time-to-target and the time to complete 50 successful trials. With brain-behavior causality of the neuroprosthetic paradigm, we revealed that proficiency of neuroprosthetic learning by volitional conditioning of calcium signal was associated with the stable representational (plasticity) mapping in M1 neurons with the reduced calcium peak. Furthermore, pharmacological blockade of adenosine A2A receptors facilitated volitional conditioning of neuroprosthetic learning and converted an ineffective volitional conditioning protocol to be the effective for neuroprosthetic learning. These findings may help to harness neuroplasticity for better volitional control of neuroprosthetic training and suggest a novel pharmacological strategy to improve neuroprosthetic learning in BMI adaptation by targeting striatal A2A receptors.},
}
@article {pmid32725589,
year = {2021},
author = {Haouzi, D and Entezami, F and Torre, A and Innocenti, C and Antoine, Y and Mauries, C and Vincens, C and Bringer-Deutsch, S and Gala, A and Ferrieres-Hoa, A and Ohl, J and Gonzalez Marti, B and Brouillet, S and Hamamah, S},
title = {Customized Frozen Embryo Transfer after Identification of the Receptivity Window with a Transcriptomic Approach Improves the Implantation and Live Birth Rates in Patients with Repeated Implantation Failure.},
journal = {Reproductive sciences (Thousand Oaks, Calif.)},
volume = {28},
number = {1},
pages = {69-78},
pmid = {32725589},
issn = {1933-7205},
mesh = {Adult ; *Cryopreservation ; Embryo Implantation/*genetics ; Embryo Transfer/*adverse effects ; Female ; Fertilization in Vitro/*adverse effects ; France ; *Gene Expression Profiling ; Humans ; Infertility/diagnosis/physiopathology/*therapy ; Live Birth ; Pregnancy ; Pregnancy Rate ; Prospective Studies ; Time Factors ; *Transcriptome ; Treatment Failure ; },
abstract = {The aim of this prospective study was to evaluate outcome benefits expected in repeated implantation failure (RIF) patients (n = 217) after customized embryo transfer based upon identification of the receptivity window by transcriptomic approach using the Win-Test. In this test, the expression of 11 endometrial genes known to be predictive of endometrial receptivity is assessed by RT-PCR in biopsies collected during the implantation window (6-9 days after the spontaneous luteinizing hormone surge during natural cycles, 5-9 days after progesterone administration during hormone replacement therapy cycles). Then, patients underwent either customized embryo transfer (cET, n = 157 patients) according to the Win-Test results or embryo transfer according to the classical procedure (control group, n = 60). Pregnancy and live birth rates were compared in the two groups. The Win-Test showed that in 78.5% of women, the receptivity window lasted less than 48 h, although it could be shorter (< 24 h, 9.5%) or longer (> 48 h, 12%). This highlighted that only in 20% of patients with RIF the endometrium would have been receptive if the classical embryo transfer protocol was followed. In the other 80% of patients, the receptivity window was delayed by 1-3 days relative to the classical timing. This suggests that implantation failure could be linked to inadequate timing of embryo transfer. In agreement, both implantation (22.7% vs. 7.2%) and live birth rates per patient (31.8% vs. 8.3%) were significantly higher in the cET group than in the control group. cET on the basis of the Win-Test results could be proposed to improve pregnancy and live birth rates.ClinicalTrials.gov ID: NCT04192396; December 5, 2019, retrospectively registered.},
}
@article {pmid32725304,
year = {2021},
author = {Chan, G and Qu, LG and Gani, J},
title = {Evaluation of pre-operative bladder contractility as a predictor of improved response rate to a staged trial of sacral neuromodulation in patients with detrusor underactivity.},
journal = {World journal of urology},
volume = {39},
number = {6},
pages = {2113-2119},
pmid = {32725304},
issn = {1433-8726},
mesh = {Aged ; Electric Stimulation Therapy/*methods ; Female ; Humans ; Male ; Middle Aged ; *Muscle Contraction ; Preoperative Period ; Prognosis ; Retrospective Studies ; Urinary Bladder, Underactive/*physiopathology/*therapy ; },
abstract = {PURPOSE: Sacral neuromodulation (SNM) is one of the few management options shown to improve outcomes in patients with detrusor underactivity (DU). This original research will investigate if preserved bladder contractility can predict a successful treatment with SNM.<br><br>METHODS: This is a retrospective study of a prospectively collected database of consecutive patients with DU, who had a staged SNM trial from January 2013 to December 2018, with a minimum of 12&#xa0;months follow-up. The primary outcome was the success of stage 1 SNM trial.<br><br>RESULTS: In total, 69 patients with DU were followed. The median age was 67 [interquartile range (IQR) 74-55], median baseline bladder contractility index (BCI) 18 (IQR 67-0), and median post-void residual 200&#xa0;mL (IQR 300-130). There were 35 patients (51%) that responded to a SNM trial. At a median follow-up of 23&#xa0;months (IQR 39-12), three were removed for poor efficacy. In patients with detrusor acontractility (DAC), six responded (33%), compared to 29 patients (57%) with BCI&#x2009;>&#x2009;0. This was statistically significant, p value 0.03. Younger age was also a predictive factor for SNM response, p value 0.02. There were no differences noted in those with gender, neurogenic history, previous pelvic surgery, diabetes, or pre-operative voiding history.<br><br>CONCLUSION: Our study showed that patients with preserved bladder contractility are more likely to respond to a trial of SNM compared with those that have DAC. Younger age was also predictive of SNM response. UDS is the only method to accurately identify DAC patients. This information will help in patient selection and pre-operative counselling.},
}
@article {pmid32721539,
year = {2021},
author = {Baron, C and Haouzi, D and Gala, A and Ferrieres-Hoa, A and Vintejoux, E and Brouillet, S and Hamamah, S},
title = {[Endometrial receptivity in assisted reproductive techniques: An aspect to investigate in embryo implantation failure].},
journal = {Gynecologie, obstetrique, fertilite &amp; senologie},
volume = {49},
number = {2},
pages = {128-136},
doi = {10.1016/j.gofs.2020.07.003},
pmid = {32721539},
issn = {2468-7189},
mesh = {*Embryo Implantation ; Endometrium ; Female ; Humans ; *Infertility ; Reproductive Techniques, Assisted ; },
abstract = {Infertility affects between 8 and 12% of reproductive-age couples worldwide. Despite improvements in assisted reproductive techniques (ART), live birth rates are still limited. In clinical practice, imaging and microscopy are currently widely used, but their diagnostic effectiveness remains limited. In research, the emergence of innovative techniques named OMICS would improve the identification of the implantation window, while progressing in the understanding of the pathophysiological mechanisms involved in embryo implantation failures. To date, transcriptomic analysis seems to be the most promising approach in clinical research. The objective of this review is to present the results obtained with the different approaches available in clinical practice and in research to assess endometrial receptivity in patients undergoing ART.},
}
@article {pmid32721028,
year = {2020},
author = {Homco, J and Carabin, H and Nagykaldi, Z and Garwe, T and Duffy, FD and Kendrick, D and Martinez, S and Zhao, YD and Stoner, J},
title = {Validity of Medical Record Abstraction and Electronic Health Record-Generated Reports to Assess Performance on Cardiovascular Quality Measures in Primary Care.},
journal = {JAMA network open},
volume = {3},
number = {7},
pages = {e209411},
pmid = {32721028},
issn = {2574-3805},
support = {U54 GM104938/GM/NIGMS NIH HHS/United States ; },
mesh = {Aspirin/*therapeutic use ; Blood Pressure Determination/*statistics &amp; numerical data ; *Cardiovascular Diseases/diagnosis/epidemiology ; Cross-Sectional Studies ; Electronic Health Records/*standards ; Female ; Humans ; Male ; Middle Aged ; Platelet Aggregation Inhibitors/therapeutic use ; Primary Health Care/*methods ; Quality Improvement ; Quality Indicators, Health Care ; Reference Standards ; Reproducibility of Results ; *Risk Assessment/methods/standards/statistics &amp; numerical data ; Smoking/*epidemiology ; United States/epidemiology ; },
abstract = {IMPORTANCE: Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure).<br><br>OBJECTIVE: To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis.<br><br>This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019.<br><br>MAIN OUTCOMES AND MEASURES: The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models.<br><br>RESULTS: A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score.<br><br>CONCLUSIONS AND RELEVANCE: Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.},
}
@article {pmid32720738,
year = {2020},
author = {Rubilotta, E and Balzarro, M and Gubbiotti, M and Antonelli, A},
title = {Outcomes of transurethral resection of the prostate in unobstructed patients with concomitant detrusor underactivity.},
journal = {Neurourology and urodynamics},
volume = {39},
number = {8},
pages = {2179-2185},
doi = {10.1002/nau.24470},
pmid = {32720738},
issn = {1520-6777},
mesh = {Aged ; Humans ; Lower Urinary Tract Symptoms/complications/physiopathology/*surgery ; Male ; Middle Aged ; Prospective Studies ; Prostatic Hyperplasia/complications/physiopathology/*surgery ; Quality of Life ; *Transurethral Resection of Prostate ; Urinary Bladder, Underactive/complications/physiopathology/*surgery ; Urination/physiology ; Urodynamics/physiology ; },
abstract = {AIMS: The aim of the study was to evaluate the transurethral resection of the prostate (TURP) outcomes of unobstructed patients with detrusor underactivity (DUA), comparing the surgical results between obstructed and unobstructed males with concomitant DUA, at midterm follow-up.<br><br>METHODS: This was an observational, prospective, comparative, nonrandomized study. Candidates to TURP underwent preoperative urodynamics (UD), with a diagnosis of DUA, were divided in two cohorts: Group A unobstructed men, group B males with bladder outlet obstruction (BOO). Males were evaluated yearly with uroflowmetry (UF), post-void residual (PVR), and bladder voiding efficiency (BVE), International Prostate Symptom Score (IPSS) questionnaire, visual analogic scale (VAS) for subjective assessment of the quality of life. The degree of the variation of maximum flow rate (Qmax), PVR, BVE, IPSS, VAS between baseline and follow-up (&#x394;) was evaluated.<br><br>RESULTS: Patients in group A were 28 and in group B 23. Overall patient's mean&#x2009;&#xb1;&#x2009;SD age was 63.37&#x2009;&#xb1;&#x2009;12.41 years. Preoperative urodynamics characteristics: mean bladder contractility index (BCI) of 61.15 and 76.25 in group A and B, respectively; mean bladder outlet obstruction index (BOOI) of 17.25 and 50.15 in group A and group B, respectively. After surgery, overall patient group, group A, and group B showed a statistical improvement in IPSS score (P&#x2009;<&#x2009;.0001), Qmax (P&#x2009;<&#x2009;.0001), PVR (P&#x2009;<&#x2009;.0008), BVE (P&#x2009;<&#x2009;.03) and VAS (P&#x2009;<&#x2009;.0001).<br><br>CONCLUSIONS: BOO had an important impact on the degree of improvement of Qmax and PVR/BVE, while had a poor influence on lower urinary tract symptoms amelioration. The most relevant outcomes were found when BOO was associated with DUA, which was not a contraindication to surgery.},
}
@article {pmid32720212,
year = {2021},
author = {Guarnieri, R and Zhao, M and Taberna, GA and Ganzetti, M and Swinnen, SP and Mantini, D},
title = {RT-NET: real-time reconstruction of neural activity using high-density electroencephalography.},
journal = {Neuroinformatics},
volume = {19},
number = {2},
pages = {251-266},
pmid = {32720212},
issn = {1559-0089},
mesh = {Artifacts ; Brain/diagnostic imaging/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; *Computer Systems ; Electroencephalography/*methods ; Humans ; Magnetic Resonance Imaging/methods ; },
abstract = {High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain-computer interface applications such as source-based neurofeedback.},
}
@article {pmid32719512,
year = {2020},
author = {Nason, SR and Vaskov, AK and Willsey, MS and Welle, EJ and An, H and Vu, PP and Bullard, AJ and Nu, CS and Kao, JC and Shenoy, KV and Jang, T and Kim, HS and Blaauw, D and Patil, PG and Chestek, CA},
title = {A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.},
journal = {Nature biomedical engineering},
volume = {4},
number = {10},
pages = {973-983},
pmid = {32719512},
issn = {2157-846X},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; U01 NS094375/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; T32 NS007222/NS/NINDS NIH HHS/United States ; R01 GM111293/GM/NIGMS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; R21 EY029452/EY/NEI NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; OT2 OD024907/OD/NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Fingers ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/*physiology ; Neurons/*physiology ; Prostheses and Implants ; Rats, Long-Evans ; Signal-To-Noise Ratio ; },
abstract = {The large power requirement of current brain-machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300-1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.},
}
@article {pmid32716753,
year = {2020},
author = {Goering, S and Klein, E},
title = {Fostering Neuroethics Integration with Neuroscience in the BRAIN Initiative: Comments on the NIH Neuroethics Roadmap.},
journal = {AJOB neuroscience},
volume = {11},
number = {3},
pages = {184-188},
pmid = {32716753},
issn = {2150-7759},
support = {RF1 MH117800/MH/NIMH NIH HHS/United States ; },
mesh = {Brain ; Humans ; *Neurosciences ; },
abstract = {The BRAIN 2.0 roadmap lauds the neuroscientific advances made in the first decade of the BRAIN Initiative, but also calls attention to the need to carefully consider how these advances will inform and perhaps alter our understanding of "those deepest behaviors that, as humans we hold dear" (Roadmap, Executive Summary). In this short statement, we briefly consider several features of the BRAIN Neuroethics subgroup's roadmap that lie within our area of expertise, including the recommendations to (1) enhance integration of neuroscience and neuroethics, and (2) provide additional tools and resources for neuroscientists to recognize neuroethics issues and opportunities for neuroethics research.},
}
@article {pmid32716747,
year = {2020},
author = {Koroshetz, WJ and Ward, J and Grady, C},
title = {NeuroEthics and the BRAIN Initiative: Where Are We? Where Are We Going?.},
journal = {AJOB neuroscience},
volume = {11},
number = {3},
pages = {140-147},
doi = {10.1080/21507740.2020.1778119},
pmid = {32716747},
issn = {2150-7759},
mesh = {Animals ; Brain ; Brain Mapping ; Central Nervous System ; Humans ; Models, Animal ; *Neurosciences ; },
abstract = {From its inception, the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, an ambitious project focused on understanding the human brain, has made a concerted effort to integrate neuroethics into its science. In the past five years, the BRAIN Initiative has given rise to powerful tools and neurotechnologies capable of probing deeply into the brain circuits in animal models. As these tools mature and move to human applications they will raise a host of important neuroethical considerations not just for the medical community but for society as a whole. Now marks a pivotal moment to assess the status and consider the future of the BRAIN Initiative's neuroethics efforts. Here we describe core issues of neuroscience advances, the state of neurotechnologies in human neuroscience, and how ethics will be incorporated into the BRAIN Initiative as this ten-year project enters its second phase. BRAIN Initiative neurotechnologies have immense potential to transform the way we diagnose and treat neurological disease; therefore, they may become more commonplace in research, medicine, and society. We also discuss future global efforts to ensure continued guidance and open dialogue surrounding neuroethics.},
}
@article {pmid32714232,
year = {2020},
author = {Alanis-Espinosa, M and Gutiérrez, D},
title = {On the Assessment of Functional Connectivity in an Immersive Brain-Computer Interface During Motor Imagery.},
journal = {Frontiers in psychology},
volume = {11},
number = {},
pages = {1301},
pmid = {32714232},
issn = {1664-1078},
abstract = {New trends on brain-computer interface (BCI) design are aiming to combine this technology with immersive virtual reality in order to provide a sense of realism to its users. In this study, we propose an experimental BCI to control an immersive telepresence system using motor imagery (MI). The system is immersive in the sense that the users can control the movement of a NAO humanoid robot in a first person perspective (1PP), i.e., as if the movement of the robot was his/her own. We analyze functional brain connectivity between 1PP and 3PP during the control of our BCI using graph theory properties such as degree, betweenness centrality, and efficiency. Changes in these metrics are obtained for the case of the 1PP, as well as for the traditional third person perspective (3PP) in which the user can see the movement of the robot as feedback. As proof-of-concept, electroencephalography (EEG) signals were recorded from two subjects while they performed MI to control the movement of the robot. The graph theoretical analysis was applied to the binary directed networks obtained through the partial directed coherence (PDC). In our preliminary assessment we found that the efficiency in the α brain rhythm is greater in 1PP condition in comparison to the 3PP at the prefrontal cortex. Also, a stronger influence of signals measured at EEG channel C3 (primary motor cortex) to other regions was found in 1PP condition. Furthermore, our preliminary results seem to indicate that α and β brain rhythms have a high indegree at prefrontal cortex in 1PP condition, and this could be possibly related to the experience of sense of agency. Therefore, using the PDC combined with graph theory while controlling a telepresence robot in an immersive system may contribute to understand the organization and behavior of brain networks in these environments.},
}
@article {pmid32714167,
year = {2020},
author = {Jiang, J and Wang, C and Wu, J and Qin, W and Xu, M and Yin, E},
title = {Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {231},
pmid = {32714167},
issn = {1662-5161},
abstract = {Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.},
}
@article {pmid32714127,
year = {2020},
author = {Lennon, O and Tonellato, M and Del Felice, A and Di Marco, R and Fingleton, C and Korik, A and Guanziroli, E and Molteni, F and Guger, C and Otner, R and Coyle, D},
title = {A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {578},
pmid = {32714127},
issn = {1662-4548},
abstract = {Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics.},
}
@article {pmid32711441,
year = {2020},
author = {Mizukoshi, MM and Hossian, SZ and Poulos, A},
title = {Comparative Analysis of Breast Cancer Incidence Rates between Australia and Japan: Screening Target Implications.},
journal = {Asian Pacific journal of cancer prevention : APJCP},
volume = {21},
number = {7},
pages = {2123-2129},
pmid = {32711441},
issn = {2476-762X},
mesh = {Adolescent ; Adult ; Age Factors ; Aged ; Aged, 80 and over ; Australia/epidemiology ; Breast Neoplasms/diagnosis/*epidemiology ; Child ; Child, Preschool ; Early Detection of Cancer/*methods ; Female ; Follow-Up Studies ; Humans ; Incidence ; Infant ; Infant, Newborn ; Japan/epidemiology ; Mammography/*methods ; Middle Aged ; Prognosis ; Young Adult ; },
abstract = {BACKGROUND: The purpose of this analysis was to compare the age-specific incidence rates (ASIRs) of breast cancer in Australia and Japan to determine the appropriateness of national screening target age groups.<br><br>METHODS: The paper is based on secondary sources of data. The ASIRs in 2006-2015 were collected from the Australian Institute of Health and Welfare (AIHW) and the National Cancer Center Japan. Descriptive analysis was performed for a comparison of ASIRs between Australia and Japan by age and over time. Percentage change, rolling average and risk ratio were calculated for further analysis.<br><br>RESULTS: In Australia, ASIRs rose sharply from age 40 years and peaked at 65-69 years. Japanese data demonstrated a considerable increase each year and two peaks were recorded, at ages 45-49 and 60-64. The ASIRs after age 65 decreased with age in Japan but increased with age in Australia. The ASIRs of women aged 40-49 was lowest among Australian women and the highest among Japanese women, while they had similar ASIRs in the direct comparative analysis.<br><br>CONCLUSIONS: The screening age range of Australian and Japanese national breast cancer screening guidelines covers incidence peak ages in each country and therefore provides benefit for cancer screening. Our findings also indicated that further evidence is required to investigate the inclusion of Japanese migrant women in Australia aged 40-49 years into the screening target and the BCI rates of post-migrant women in Australia as different migrant groups have different ASIRs. This is to ensure that the groups of women with the highest cancer incidence are appropriately covered in screening programs.<br />.},
}
@article {pmid32710932,
year = {2020},
author = {Campos-Arteaga, G and Forcato, C and Wainstein, G and Lagos, R and Palacios-García, I and Artigas, C and Morales, R and Pedreira, ME and Rodríguez, E},
title = {Differential neurophysiological correlates of retrieval of consolidated and reconsolidated memories in humans: An ERP and pupillometry study.},
journal = {Neurobiology of learning and memory},
volume = {174},
number = {},
pages = {107279},
doi = {10.1016/j.nlm.2020.107279},
pmid = {32710932},
issn = {1095-9564},
mesh = {Adult ; Association Learning/*physiology ; Brain/*physiology ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Memory Consolidation/*physiology ; Mental Recall/*physiology ; Pupil ; Young Adult ; },
abstract = {Consolidated memories can return to a labile state if they are reactivated by unpredictable reminders. To persist, active memories must be re-stabilized through a process known as reconsolidation. Although there is consistent behavioral evidence about this process in humans, the retrieval process of reconsolidated memories remains poorly understood. In this context, one fundamental question is whether the same or different neurophysiological mechanisms are involved in retrieval of consolidated and reconsolidated memories. Because it has been demonstrated that the exposure to the reconsolidation process may restructure and strengthen memories, we hypothesized distinct neurophysiological patterns during retrieval of reconsolidated memories. In addition, we hypothesized that interfering with the reconsolidation process using a new learning can prevent these neurophysiological changes. To test it, consolidated, reconsolidated and declarative memories whose reconsolidation process was interfered (i.e., picture-word pairs) were evaluated in humans in an old/new associative recall task while the brain activity and the pupillary response were recorded using electroencephalography and eyetracking. Our results showed that retrieval of reconsolidated memories elicits specific patterns of brain activation, characterized by an earlier peak latency and a smaller magnitude of the left parietal ERP old/new effect compared to memories that were only consolidated or whose reconsolidation process was interfered by a new learning. Moreover, our results demonstrated that only retrieval of reconsolidated memories is associated with a late reversed mid-frontal effect in a 600-690 time window. Complementarily, memories that were reactivated showed an earlier peak latency of the pupil old/new effect compared to non-reactivated memories. These findings support the idea that reconsolidation has an important impact in how memories are retrieved in the future, showing that retrieval of reconsolidated memories is partially supported by specific brain mechanisms.},
}
@article {pmid32707766,
year = {2020},
author = {Fang, C and Zhang, Y and Zhang, M and Fang, Q},
title = {P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis.},
journal = {International journal of environmental research and public health},
volume = {17},
number = {15},
pages = {},
pmid = {32707766},
issn = {1660-4601},
mesh = {*Accidents, Traffic/prevention &amp; control ; Attention ; *Automobile Driving ; Cognition ; Reaction Time ; Risk Factors ; },
abstract = {Detecting signs for an increased level of risk during driving are critical for the effective prevention of road traffic accidents. The current study searched for literature through major databases such as PubMed, EBSCO, IEEE, and ScienceDirect. A total of 14 articles that measured P300 components in relation to driving tasks were included for a systematic review and meta-analysis. The risk factors investigated in the reviewed articles were summarized in five categories, including reduced attention, distraction, alcohol, challenging situations on the road, and negative emotion. A meta-analysis was conducted at both behavioral and neural levels. Behavioral performance was measured by the reaction time and driving performance, while the neural response was measured by P300 amplitude and latency. A significant increase in reaction time was identified when drivers were exposed to the risk factors. In addition, the significant effects of a reduced P300 amplitude and prolonged P300 latency indicated a reduced capacity for cognitive information processing. There was a tendency of driving performance decrement in relation to the risk factors, however, the effect was non-significant due to considerable variations and heterogeneity across the included studies. The results led to the conclusion that the P300 amplitude and latency are reliable indicators and predictors of the increased risk in driving. Future applications of the P300-based brain-computer interface (BCI) system may make considerable contributions toward preventing road traffic accidents.},
}
@article {pmid32706190,
year = {2020},
author = {Xu, FZ and Zheng, WF and Shan, DR and Yuan, Q and Zhou, WD},
title = {Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces.},
journal = {Journal of integrative neuroscience},
volume = {19},
number = {2},
pages = {259-272},
doi = {10.31083/j.jin.2020.02.1269},
pmid = {32706190},
issn = {0219-6352},
support = {61701270//National Natural Science Foundation of China/ ; 61701279//National Natural Science Foundation of China/ ; 81472159//National Natural Science Foundation of China/ ; 81871508//National Natural Science Foundation of China/ ; 61773246//National Natural Science Foundation of China/ ; 2019KJN010//Program for Youth Innovative Research Team in University of Shandong Province, China/ ; 2019TSLH0315//Key Program for Research and Development of Shandong Province, China (Key Project for Science and Technology Innovation, Department and City Cooperation)/ ; //Jinan Program for Development of Science and Technology/ ; //Jinan Program for Leaders of Science and Technology/ ; TSHW201502038//Taishan Scholar Program of Shandong Province of China/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Datasets as Topic ; Electrocorticography/*methods/standards ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Pattern Recognition, Automated/*methods/standards ; *Signal Processing, Computer-Assisted ; *Support Vector Machine/standards ; },
abstract = {One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.},
}
@article {pmid32706188,
year = {2020},
author = {Chalmers, T and Maharaj, S and Lees, T and Lin, CT and Newton, P and Clifton-Bligh, R and McLachlan, CS and Gustin, SM and Lal, S},
title = {Impact of acute stress on cortical electrical activity and cardiac autonomic coupling.},
journal = {Journal of integrative neuroscience},
volume = {19},
number = {2},
pages = {239-248},
doi = {10.31083/j.jin.2020.02.74},
pmid = {32706188},
issn = {0219-6352},
support = {/US/United States/United States ; },
mesh = {Adult ; Delta Rhythm/*physiology ; Electrocardiography ; Female ; Gamma Rhythm/*physiology ; Heart Rate/*physiology ; Humans ; Male ; Parasympathetic Nervous System/*physiology ; Prefrontal Cortex/*physiology ; Stress, Psychological/*physiopathology ; Young Adult ; },
abstract = {Assessment of heart rate variability (reflective of the cardiac autonomic nervous system) has shown some predictive power for stress. Further, the predictive power of the distinct patterns of cortical brain activity and - cardiac autonomic interactions are yet to be explored in the context of acute stress, as assessed by an electrocardiogram and electroencephalogram. The present study identified distinct patterns of neural-cardiac autonomic coupling during both resting and acute stress states. In particular, during the stress task, frontal delta waves activity was positively associated with low-frequency heart rate variability and negatively associated with high-frequency heart rate variability. Low high-frequency power is associated with stress and anxiety and reduced vagal control. A positive association between resting high-frequency heart rate variability and frontocentral gamma activity was found, with a direct inverse relationship of low-frequency heart rate variability and gamma wave coupling at rest. During the stress task, low-frequency heart rate variability was positively associated with frontal delta activity. That is, the parasympathetic nervous system is reduced during a stress task, whereas frontal delta wave activity is increased. Our findings suggest an association between cardiac parasympathetic nervous system activity and frontocentral gamma and delta activity at rest and during acute stress. This suggests that parasympathetic activity is decreased during acute stress, and this is coupled with neuronal cortical prefrontal activity. The distinct patterns of neural-cardiac coupling identified in this study provide a unique insight into the dynamic associations between brain and heart function during both resting and acute stress states.},
}
@article {pmid32702377,
year = {2020},
author = {Flügel, K and Hennig, R and Thommes, M},
title = {Impact of structural relaxation on mechanical properties of amorphous polymers.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {154},
number = {},
pages = {214-221},
doi = {10.1016/j.ejpb.2020.07.016},
pmid = {32702377},
issn = {1873-3441},
mesh = {Chemistry, Pharmaceutical/*methods ; *Compressive Strength ; Hardness ; Polymers/*chemistry ; Pyrrolidines/*chemistry ; *Stress, Mechanical ; Vinyl Compounds/*chemistry ; },
abstract = {Fusion based methods, such as hot-melt extrusion, are a common way of preparing amorphous solid dispersions. Since the amorphous glass, however, is not in a configurational equilibrium, the molecular arrangement of the obtained material can differ in dependence of the preparation conditions. Although the changes in the configuration of an amorphous material, which are commonly referred to as structural relaxation or physical aging, are well investigated, the impact on mechanical properties of amorphous solid dispersions have widely been neglected so far. The presented study investigated copovidone as a model polymer commonly used in amorphous solid dispersions and revealed that structural relaxation was already introduced into the polymer during hot-melt extrusion while its degree was cooling rate dependent. The degree of structural relaxation significantly affected the mechanical properties of copovidone as assessed by diametral compression tests, macroindentation and nanoindentation. An increase in Young's modulus and indentation hardness was observable with a higher degree of structural relaxation, which, during tablet compression, translated into tablets with significantly lower tensile strength. Furthermore, evaluation of the force-displacement curves during tablet compression revealed a decreased proportion of irreversible deformation with higher degree of structural relaxation correlating well with the increased indentation hardness during macroindentation. Thus, understanding structural relaxation and its impact on material properties is of utmost importance to assess the processability and compaction performance of amorphous solid dispersions in dependence of their preparation conditions and thermal history.},
}
@article {pmid32701140,
year = {2020},
author = {, and Yee, D and DeMichele, AM and Yau, C and Isaacs, C and Symmans, WF and Albain, KS and Chen, YY and Krings, G and Wei, S and Harada, S and Datnow, B and Fadare, O and Klein, M and Pambuccian, S and Chen, B and Adamson, K and Sams, S and Mhawech-Fauceglia, P and Magliocco, A and Feldman, M and Rendi, M and Sattar, H and Zeck, J and Ocal, IT and Tawfik, O and LeBeau, LG and Sahoo, S and Vinh, T and Chien, AJ and Forero-Torres, A and Stringer-Reasor, E and Wallace, AM and Pusztai, L and Boughey, JC and Ellis, ED and Elias, AD and Lu, J and Lang, JE and Han, HS and Clark, AS and Nanda, R and Northfelt, DW and Khan, QJ and Viscusi, RK and Euhus, DM and Edmiston, KK and Chui, SY and Kemmer, K and Park, JW and Liu, MC and Olopade, O and Leyland-Jones, B and Tripathy, D and Moulder, SL and Rugo, HS and Schwab, R and Lo, S and Helsten, T and Beckwith, H and Haugen, P and Hylton, NM and Van't Veer, LJ and Perlmutter, J and Melisko, ME and Wilson, A and Peterson, G and Asare, AL and Buxton, MB and Paoloni, M and Clennell, JL and Hirst, GL and Singhrao, R and Steeg, K and Matthews, JB and Asare, SM and Sanil, A and Berry, SM and Esserman, LJ and Berry, DA},
title = {Association of Event-Free and Distant Recurrence-Free Survival With Individual-Level Pathologic Complete Response in Neoadjuvant Treatment of Stages 2 and 3 Breast Cancer: Three-Year Follow-up Analysis for the I-SPY2 Adaptively Randomized Clinical Trial.},
journal = {JAMA oncology},
volume = {6},
number = {9},
pages = {1355-1362},
pmid = {32701140},
issn = {2374-2445},
support = {P30 CA013148/CA/NCI NIH HHS/United States ; P30 CA016672/CA/NCI NIH HHS/United States ; UL1 TR001863/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Antineoplastic Combined Chemotherapy Protocols/*administration &amp; dosage/adverse effects ; Breast Neoplasms/*drug therapy/genetics/pathology ; Bridged-Ring Compounds/administration &amp; dosage/adverse effects ; Cyclophosphamide/administration &amp; dosage/adverse effects ; Disease-Free Survival ; Doxorubicin/administration &amp; dosage/adverse effects ; Female ; Humans ; Middle Aged ; Neoadjuvant Therapy/*adverse effects ; Neoplasm Recurrence, Local/*drug therapy/genetics/pathology ; Progression-Free Survival ; Proportional Hazards Models ; Receptor, ErbB-2/genetics ; Taxoids/administration &amp; dosage/adverse effects ; Trastuzumab/administration &amp; dosage/adverse effects ; Treatment Outcome ; },
abstract = {IMPORTANCE: Pathologic complete response (pCR) is a known prognostic biomarker for long-term outcomes. The I-SPY2 trial evaluated if the strength of this clinical association persists in the context of a phase 2 neoadjuvant platform trial.<br><br>OBJECTIVE: To evaluate the association of pCR with event-free survival (EFS) and pCR with distant recurrence-free survival (DRFS) in subpopulations of women with high-risk operable breast cancer treated with standard therapy or one of several novel agents.<br><br>Multicenter platform trial of women with operable clinical stage 2 or 3 breast cancer with no prior surgery or systemic therapy for breast cancer; primary tumors were 2.5 cm or larger. Women with tumors that were ERBB2 negative/hormone receptor (HR) positive with low 70-gene assay score were excluded. Participants were adaptively randomized to one of several different investigational regimens or control therapy within molecular subtypes from March 2010 through 2016. The analysis included participants with follow-up data available as of February 26, 2019.<br><br>INTERVENTIONS: Standard-of-care neoadjuvant therapy consisting of taxane treatment with or without (as control) one of several investigational agents or combinations followed by doxorubicin and cyclophosphamide.<br><br>MAIN OUTCOMES AND MEASURES: Pathologic complete response and 3-year EFS and DRFS.<br><br>RESULTS: Of the 950 participants (median [range] age, 49 [23-77] years), 330 (34.7%) achieved pCR. Three-year EFS and DRFS for patients who achieved pCR were both 95%. Hazard ratios for pCR vs non-pCR were 0.19 for EFS (95% CI, 0.12-0.31) and 0.21 for DRFS (95% CI, 0.13-0.34) and were similar across molecular subtypes, varying from 0.14 to 0.18 for EFS and 0.10 to 0.20 for DRFS.<br><br>CONCLUSIONS AND RELEVANCE: The 3-year outcomes from the I-SPY2 trial show that, regardless of subtype and/or treatment regimen, including 9 novel therapeutic combinations, achieving pCR after neoadjuvant therapy implies approximately an 80% reduction in recurrence rate. The goal of the I-SPY2 trial is to rapidly identify investigational therapies that may improve pCR when validated in a phase 3 confirmatory trial. Whether pCR is a validated surrogate in the sense that a therapy that improves pCR rate can be assumed to also improve long-term outcome requires further study.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01042379.},
}
@article {pmid32698187,
year = {2020},
author = {Yang, F and Mao, C and Guo, L and Lin, J and Ming, Q and Xiao, P and Wu, X and Shen, Q and Guo, S and Shen, DD and Lu, R and Zhang, L and Huang, S and Ping, Y and Zhang, C and Ma, C and Zhang, K and Liang, X and Shen, Y and Nan, F and Yi, F and Luca, VC and Zhou, J and Jiang, C and Sun, JP and Xie, X and Yu, X and Zhang, Y},
title = {Structural basis of GPBAR activation and bile acid recognition.},
journal = {Nature},
volume = {587},
number = {7834},
pages = {499-504},
pmid = {32698187},
issn = {1476-4687},
mesh = {Allosteric Regulation/drug effects ; Bile Acids and Salts/chemistry/*metabolism ; Binding Sites/drug effects ; Cholic Acids/chemistry/pharmacology ; *Cryoelectron Microscopy ; GTP-Binding Protein alpha Subunits, Gs/chemistry/metabolism/ultrastructure ; Humans ; Ligands ; Models, Molecular ; Protein Binding ; Receptors, G-Protein-Coupled/agonists/chemistry/*metabolism/*ultrastructure ; Substrate Specificity ; },
abstract = {The G-protein-coupled bile acid receptor (GPBAR) conveys the cross-membrane signalling of a vast variety of bile acids and is a signalling hub in the liver-bile acid-microbiota-metabolism axis[1-3]. Here we report the cryo-electron microscopy structures of GPBAR-Gs complexes stabilized by either the high-affinity P395[4] or the semisynthesized bile acid derivative INT-777[1,3] at 3 Å resolution. These structures revealed a large oval pocket that contains several polar groups positioned to accommodate the amphipathic cholic core of bile acids, a fingerprint of key residues to recognize diverse bile acids in the orthosteric site, a putative second bile acid-binding site with allosteric properties and structural features that contribute to bias properties. Moreover, GPBAR undertakes an atypical mode of activation and G protein coupling that features a different set of key residues connecting the ligand-binding pocket to the Gs-coupling site, and a specific interaction motif that is localized in intracellular loop 3. Overall, our study not only reveals unique structural features of GPBAR that are involved in bile acid recognition and allosteric effects, but also suggests the presence of distinct connecting mechanisms between the ligand-binding pocket and the G-protein-binding site in the G-protein-coupled receptor superfamily.},
}
@article {pmid32698164,
year = {2020},
author = {Filippini, M and Morris, AP and Breveglieri, R and Hadjidimitrakis, K and Fattori, P},
title = {Decoding of standard and non-standard visuomotor associations from parietal cortex.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046027},
doi = {10.1088/1741-2552/aba87e},
pmid = {32698164},
issn = {1741-2552},
mesh = {Action Potentials ; Animals ; Bayes Theorem ; Humans ; Macaca fascicularis ; Movement ; *Parietal Lobe ; *Psychomotor Performance ; },
abstract = {OBJECTIVE: Neural signals can be decoded and used to move neural prostheses with the purpose of restoring motor function in patients with mobility impairments. Such patients typically have intact eye movement control and visual function, suggesting that cortical visuospatial signals could be used to guide external devices. Neurons in parietal cortex mediate sensory-motor transformations, encode the spatial coordinates for reaching goals, hand position and movements, and other spatial variables. We studied how spatial information is represented at the population level, and the possibility to decode not only the position of visual targets and the plans to reach them, but also conditional, non-spatial motor responses.<br><br>APPROACH: The animals first fixated one of nine targets in 3D space and then, after the target changed color, either reached toward it, or performed a non-spatial motor response (lift hand from a button). Spiking activity of parietal neurons was recorded in monkeys during two tasks. We then decoded different task related parameters.<br><br>MAIN RESULTS: We first show that a maximum-likelihood estimation (MLE) algorithm trained separately in each task transformed neural activity into accurate metric predictions of target location. Furthermore, by combining MLE with a Na&#xef;ve Bayes classifier, we decoded the monkey's motor intention (reach or hand lift) and the different phases of the tasks. These results show that, although V6A encodes the spatial location of a target during a delay period, the signals they carry are updated around the movement execution in an intention/motor specific way.<br><br>SIGNIFICANCE: These findings show the presence of multiple levels of information in parietal cortex that could be decoded and used in brain machine interfaces to control both goal-directed movements and more cognitive visuomotor associations.},
}
@article {pmid32695151,
year = {2020},
author = {Bastos, NS and Marques, BP and Adamatti, DF and Billa, CZ},
title = {Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {3598416},
pmid = {32695151},
issn = {1687-5273},
mesh = {Decision Trees ; Electroencephalography ; Humans ; Touch ; *Touch Perception ; *Persons with Visual Disabilities ; },
abstract = {An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.},
}
@article {pmid32694979,
year = {2020},
author = {Belkacem, AN and Jamil, N and Palmer, JA and Ouhbi, S and Chen, C},
title = {Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {692},
pmid = {32694979},
issn = {1662-4548},
abstract = {All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.},
}
@article {pmid32694794,
year = {2020},
author = {Kapara, A and Vannini, A and Peck, B},
title = {A micronutrient with major effects on cancer cell viability.},
journal = {Nature metabolism},
volume = {2},
number = {7},
pages = {564-565},
pmid = {32694794},
issn = {2522-5812},
}
@article {pmid32692687,
year = {2021},
author = {Huang, ZA and Zhu, Z and Yau, CH and Tan, KC},
title = {Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {7},
pages = {2847-2861},
doi = {10.1109/TNNLS.2020.3007943},
pmid = {32692687},
issn = {2162-2388},
mesh = {Algorithms ; Autism Spectrum Disorder/classification/*diagnostic imaging ; Brain Mapping ; *Brain-Computer Interfaces ; Computer Simulation ; Databases, Factual ; Deep Learning ; Humans ; Magnetic Resonance Imaging/*methods ; Neural Networks, Computer ; Neuroimaging ; },
abstract = {With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K -nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.},
}
@article {pmid32687861,
year = {2020},
author = {Nie, Q and Li, X and Chen, W and Liu, D and Chen, Y and Li, H and Li, D and Tian, M and Tan, W and Zai, J},
title = {Phylogenetic and phylodynamic analyses of SARS-CoV-2.},
journal = {Virus research},
volume = {287},
number = {},
pages = {198098},
pmid = {32687861},
issn = {1872-7492},
mesh = {Betacoronavirus/*classification/*genetics ; COVID-19 ; China/epidemiology ; Coronavirus Infections/*epidemiology/*virology ; Disease Outbreaks ; Evolution, Molecular ; *Genome, Viral ; *Genomics/methods ; Humans ; Pandemics ; *Phylogeny ; Pneumonia, Viral/*epidemiology/*virology ; SARS-CoV-2 ; },
abstract = {To investigate the evolutionary and epidemiological dynamics of the current COVID-19 outbreak, a total of 112 genomes of SARS-CoV-2 strains sampled from China and 12 other countries with sampling dates between 24 December 2019 and 9 February 2020 were analyzed. We performed phylogenetic, split network, likelihood-mapping, model comparison, and phylodynamic analyses of the genomes. Based on Bayesian time-scaled phylogenetic analysis with the best-fitting combination models, we estimated the time to the most recent common ancestor (TMRCA) and evolutionary rate of SARS-CoV-2 to be 12 November 2019 (95 % BCI: 11 October 2019 and 09 December 2019) and 9.90 × 10[-4] substitutions per site per year (95 % BCI: 6.29 × 10[-4]-1.35 × 10[-3]), respectively. Notably, the very low Re estimates of SARS-CoV-2 during the recent sampling period may be the result of the successful control of the pandemic in China due to extreme societal lockdown efforts. Our results emphasize the importance of using phylodynamic analyses to provide insights into the roles of various interventions to limit the spread of SARS-CoV-2 in China and beyond.},
}
@article {pmid32684621,
year = {2020},
author = {Corponi, F and Anmella, G and Pacchiarotti, I and Samalin, L and Verdolini, N and Popovic, D and Azorin, JM and Angst, J and Bowden, CL and Mosolov, S and Young, AH and Perugi, G and Vieta, E and Murru, A},
title = {Deconstructing major depressive episodes across unipolar and bipolar depression by severity and duration: a cross-diagnostic cluster analysis on a large, international, observational study.},
journal = {Translational psychiatry},
volume = {10},
number = {1},
pages = {241},
pmid = {32684621},
issn = {2158-3188},
mesh = {*Bipolar Disorder/diagnosis ; Cluster Analysis ; *Depressive Disorder, Major/diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Prospective Studies ; },
abstract = {A cross-diagnostic, post-hoc analysis of the BRIDGE-II-MIX study was performed to investigate how unipolar and bipolar patients suffering from an acute major depressive episode (MDE) cluster according to severity and duration. Duration of index episode, Clinical Global Impression-Bipolar Version-Depression (CGI-BP-D) and Global Assessment of Functioning (GAF) were used as clustering variables. MANOVA and post-hoc ANOVAs examined between-group differences in clustering variables. A stepwise backward regression model explored the relationship with the 56 clinical-demographic variables available. Agglomerative hierarchical clustering with two clusters was shown as the best fit and separated the study population (n = 2314) into 65.73% (Cluster 1 (C1)) and 34.26% (Cluster 2 (C2)). MANOVA showed a significant main effect for cluster group (p < 0.001) but ANOVA revealed that significant between-group differences were restricted to CGI-BP-D (p < 0.001) and GAF (p < 0.001), showing greater severity in C2. Psychotic features and a minimum of three DSM-5 criteria for mixed features (DSM-5-3C) had the strongest association with C2, that with greater disease burden, while non-mixed depression in bipolar disorder (BD) type II had negative association. Mixed affect defined as DSM-5-3C associates with greater acute severity and overall impairment, independently of the diagnosis of bipolar or unipolar depression. In this study a pure, non-mixed depression in BD type II significantly associates with lesser burden of clinical and functional severity. The lack of association for less restrictive, researched-based definitions of mixed features underlines DSM-5-3C specificity. If confirmed in further prospective studies, these findings would warrant major revisions of treatment algorithms for both unipolar and bipolar depression.},
}
@article {pmid32683906,
year = {2020},
author = {Carnevale, D},
title = {Neural Control of Immunity in Hypertension: Council on Hypertension Mid Career Award for Research Excellence, 2019.},
journal = {Hypertension (Dallas, Tex. : 1979)},
volume = {76},
number = {3},
pages = {622-628},
doi = {10.1161/HYPERTENSIONAHA.120.14637},
pmid = {32683906},
issn = {1524-4563},
mesh = {Autonomic Nervous System/*immunology ; Blood Pressure/*physiology ; Humans ; *Hypertension/immunology/physiopathology ; Immunity ; *Neuroimmunomodulation ; },
abstract = {The nervous system and the immune system share the common ability to exert gatekeeper roles at the interfaces between internal and external environment. Although interaction between these 2 evolutionarily highly conserved systems has been recognized for long time, the investigation into the pathophysiological mechanisms underlying their crosstalk has been tackled only in recent decades. Recent work of the past years elucidated how the autonomic nervous system controls the splenic immunity recruited by hypertensive challenges. This review will focus on the neural mechanisms regulating the immune response and the role of this neuroimmune crosstalk in hypertension. In this context, the review highlights the components of the brain-spleen axis with a focus on the neuroimmune interface established in the spleen, where neural signals shape the immune response recruited to target organs of high blood pressure.},
}
@article {pmid32682223,
year = {2020},
author = {Fatima, N and Shuaib, A and Saqqur, M},
title = {Intra-cortical brain-machine interfaces for controlling upper-limb powered muscle and robotic systems in spinal cord injury.},
journal = {Clinical neurology and neurosurgery},
volume = {196},
number = {},
pages = {106069},
doi = {10.1016/j.clineuro.2020.106069},
pmid = {32682223},
issn = {1872-6968},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/instrumentation/methods ; Humans ; Robotics/*instrumentation ; *Spinal Cord Injuries ; Upper Extremity ; },
abstract = {OBJECTIVE: Intracortical brain-machine interface (iBMI) is an assistive strategy to restore lost sensorimotor function by bridging the disrupted neural pathways to reanimate paralyzed limbs. However, to date, none of the studies explored the trade-offs between the performance criteria of different iBMI systems that decode discrete upper limb movements from intracortical neural recordings.<br><br>METHODS: A systematic review of electronic databases using different MeSH terms from January 1990 to December 2019 was conducted. IBM&#xae; SPSS statistics version 25 (Released 2017, Armonk, NY: IBM) was used to evaluate for differences between groups using independent sample t-tests.<br><br>RESULTS: A total of 18 patients from 15 studies were included in our analysis. The included studies involved iBMI controlled 5-robotic and 10-neuromuscular stimulated orthotics to perform skillful and coordinated movements that resulted in a clinically significant gain in tests of upper-limb functions. Pooled analysis revealed that the mean response time to execute 3-D reach and grasp task by the robotic-assisted limb was relatively longer (46.8 +/-101.5 s) compared to the neuro-muscular stimulated orthotics (15.8 +/-15.2 s); however, statistically insignificant [Mean difference (MD): 30.9, 95 % Confidence Interval (CI): -40.4-102.3, p = 0.35]. Furthermore, the accuracy in performing 3-D reach and grasp tasks after repetitive trials were better among patients with neuro-muscular stimulated orthotics (83.5 +/-12.7 %) compared to those with robotic-assisted prosthetic limb (69.1 +/- 23.6 %) with statistically significant difference (MD: 15.9, 95 % CI: 1.65-32.5, p = 0.05).<br><br>CONCLUSION: Our study demonstrates that iBMI-assisted prosthetic limbs showed better accuracy and shorter response time among patients with neuro-muscular stimulated orthotics compared to robotic neuro-prosthetics.},
}
@article {pmid32682092,
year = {2020},
author = {Chai, X and Zhang, Z and Guan, K and Zhang, T and Xu, J and Niu, H},
title = {Effects of fatigue on steady state motion visual evoked potentials: Optimised stimulus parameters for a zoom motion-based brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {196},
number = {},
pages = {105650},
doi = {10.1016/j.cmpb.2020.105650},
pmid = {32682092},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Motion ; Photic Stimulation ; },
abstract = {BACKGROUND AND OBJECTIVE: In flicker-based steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI), the system performance decreases due to prolonged repeated visual stimulation. To reduce the performance decrease due to visual fatigue, the zoom motion based steady-state motion visual evoked potentials (SSMVEPs) paradigm had been proposed. In this study, the stimulation parameters of the paradigm are optimised to mitigate the decrease in detection accuracy for SSMVEP due to visual fatigue.<br><br>METHODS: Eight zoom motion-based SSMVEP paradigms with different stimulation parameters were compared. The graph size, luminance, colour, and shape, as well as the frequency range and interval of the stimulation and refresh rate of the screen was changed to determine the optimal paradigm with high recognition accuracy and reduced fatigue effects. The signal-to-noise ratio (SNR) of SSMVEP was also calculated for four fatigue levels. Moreover, the power spectral density of electroencephalograph (EEG) alpha and theta bands during ongoing activity was calculated for the stimulation experiment to evaluate fatigue at the start and end of the stimulation task.<br><br>RESULTS: All stimulation SSMVEP paradigms exhibited high accuracies. Changes in luminance, colour, and shape did not impact the recognition accuracy, nor did a higher stimulation frequency or lower frequency interval of each stimulation block. However, the paradigm with smaller stimulus achieved the highest average target selection accuracy of 97.2%, compared to 94.9% for the standard paradigm. Furthermore, it exhibited almost zero reduction in recognition accuracy due to fatigue. From fatigue level 1 to level 4, the smaller zoom motion-based SSMVEP exhibited a lower decrease in the SNR of SSMVEP and a lower alpha/theta ratio decrease during ongoing stimulation activity compared to the standard paradigm.<br><br>CONCLUSIONS: For a zoom motion-based SSMVEP paradigm, changing multiple stimulation parameters can lead to the same high performance as the standard paradigm. Moreover, using a smaller stimulus can reduce the accuracy decrease caused by fatigue because the SNR decrease in the evoked SSMVEP state was negligible and the alpha/theta index decrease during ongoing activity was lower than that for the standard paradigm.},
}
@article {pmid32681134,
year = {2020},
author = {Ziebell, P and Stümpfig, J and Eidel, M and Kleih, SC and Kübler, A and Latoschik, ME and Halder, S},
title = {Stimulus modality influences session-to-session transfer of training effects in auditory and tactile streaming-based P300 brain-computer interfaces.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {11873},
pmid = {32681134},
issn = {2045-2322},
mesh = {*Acoustic Stimulation ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Data Analysis ; Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Germany ; Humans ; Male ; Models, Psychological ; Touch ; *Transfer, Psychology ; Young Adult ; },
abstract = {Despite recent successes, patients suffering from locked-in syndrome (LIS) still struggle to communicate using vision-independent brain-computer interfaces (BCIs). In this study, we compared auditory and tactile BCIs, regarding training effects and cross-stimulus-modality transfer effects, when switching between stimulus modalities. We utilized a streaming-based P300 BCI, which was developed as a low workload approach to prevent potential BCI-inefficiency. We randomly assigned 20 healthy participants to two groups. The participants received three sessions of training either using an auditory BCI or using a tactile BCI. In an additional fourth session, BCI versions were switched to explore possible cross-stimulus-modality transfer effects. Both BCI versions could be operated successfully in the first session by the majority of the participants, with the tactile BCI being experienced as more intuitive. Significant training effects were found mostly in the auditory BCI group and strong evidence for a cross-stimulus-modality transfer occurred for the auditory training group that switched to the tactile version but not vice versa. All participants were able to control at least one BCI version, suggesting that the investigated paradigms are generally feasible and merit further research into their applicability with LIS end-users. Individual preferences regarding stimulus modality should be considered.},
}
@article {pmid32676841,
year = {2020},
author = {She, Q and Zou, J and Luo, Z and Nguyen, T and Li, R and Zhang, Y},
title = {Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {58},
number = {9},
pages = {2119-2130},
doi = {10.1007/s11517-020-02227-4},
pmid = {32676841},
issn = {1741-0444},
support = {61871427//National Natural Science Foundation of China/ ; },
mesh = {Algorithms ; Benchmarking ; Biomedical Engineering ; Brain-Computer Interfaces/psychology/*statistics &amp; numerical data ; Databases, Factual ; Electroencephalography/*classification/*statistics &amp; numerical data ; Humans ; Imagination/physiology ; Least-Squares Analysis ; Neural Networks, Computer ; *Supervised Machine Learning ; Support Vector Machine ; },
abstract = {Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification performance as against supervised algorithms, it has been reported that unlabeled data occasionally undermine the performance of SSL in some cases. To overcome this challenge, we propose a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. Specifically, the ELM model is firstly used to predict unlabeled samples and then the collaborative representation (CR) approach is employed to reconstruct the unlabeled samples according to the obtained prediction results, from which the risk degree of unlabeled sample is defined. A risk-based regularization term is then constructed accordingly and embedded into the objective function of the SS-ELM. Experiments conducted on benchmark and EEG datasets demonstrate that the proposed method outperforms the ELM and SS-ELM algorithm. Moreover, the proposed CR-SSELM even offers the best performance while SS-ELM yields worse performance compared with its supervised counterpart (ELM). Graphical abstract This paper proposes a collaborative representation-based semi-supervised extreme learning machine (CR-SSELM) algorithm to evaluate the risk of unlabeled samples by a new safety-control mechanism. It is aim to solve the safety problem of SS-ELM method that SS-ELM yields worse performance than ELM. With the help of safety mechanism, the performance of our method is still better than supervised ELM method.},
}
@article {pmid32675074,
year = {2020},
author = {Yanagisawa, T and Fukuma, R and Seymour, B and Tanaka, M and Hosomi, K and Yamashita, O and Kishima, H and Kamitani, Y and Saitoh, Y},
title = {BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.},
journal = {Neurology},
volume = {95},
number = {4},
pages = {e417-e426},
pmid = {32675074},
issn = {1526-632X},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Cross-Over Studies ; Hand ; Humans ; Imagination/*physiology ; Magnetoencephalography ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Movement ; Phantom Limb/physiopathology/*rehabilitation ; *Robotics ; },
abstract = {OBJECTIVE: To determine whether training with a brain-computer interface (BCI) to control an image of a phantom hand, which moves based on cortical currents estimated from magnetoencephalographic signals, reduces phantom limb pain.<br><br>METHODS: Twelve patients with chronic phantom limb pain of the upper limb due to amputation or brachial plexus root avulsion participated in a randomized single-blinded crossover trial. Patients were trained to move the virtual hand image controlled by the BCI with a real decoder, which was constructed to classify intact hand movements from motor cortical currents, by moving their phantom hands for 3 days ("real training"). Pain was evaluated using a visual analogue scale (VAS) before and after training, and at follow-up for an additional 16 days. As a control, patients engaged in the training with the same hand image controlled by randomly changing values ("random training"). The 2 trainings were randomly assigned to the patients. This trial is registered at UMIN-CTR (UMIN000013608).<br><br>RESULTS: VAS at day 4 was significantly reduced from the baseline after real training (mean [SD], 45.3 [24.2]-30.9 [20.6], 1/100 mm; p = 0.009 < 0.025), but not after random training (p = 0.047 > 0.025). Compared to VAS at day 1, VAS at days 4 and 8 was significantly reduced by 32% and 36%, respectively, after real training and was significantly lower than VAS after random training (p < 0.01).<br><br>CONCLUSION: Three-day training to move the hand images controlled by BCI significantly reduced pain for 1 week.<br><br>CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that BCI reduces phantom limb pain.},
}
@article {pmid32674497,
year = {2020},
author = {Areiza-Laverde, HJ and Castro-Ospina, AE and Hernández, ML and Díaz, GM},
title = {A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {14},
pages = {},
pmid = {32674497},
issn = {1424-8220},
support = {RC740-2017//MinCiencias/ ; },
abstract = {Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain-Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.},
}
@article {pmid32673326,
year = {2020},
author = {Bartneck, C and Moltchanova, E},
title = {Expressing uncertainty in Human-Robot interaction.},
journal = {PloS one},
volume = {15},
number = {7},
pages = {e0235361},
pmid = {32673326},
issn = {1932-6203},
mesh = {Brain-Computer Interfaces/ethics/*trends ; Humans ; Probability ; Risk Factors ; Robotics/ethics/*trends ; },
abstract = {Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed that even the use of numerical values to express probabilities does not guarantee an accurate understanding by laypeople. We therefore investigate if words can be used to communicate probability, such as "likely" and "almost certainly not". We embedded these phrases in the context of the usage of autonomous vehicles. The results show that the association of phrases to percentages is not random and there is a preferred order of phrases. The association is, however, not as consistent as hoped for. Hence, it would be advisable to complement the use of words with numerical expression of uncertainty. This study provides an empirically verified list of probabilities phrases that HRI researchers can use to complement the numerical values.},
}
@article {pmid32670755,
year = {2020},
author = {Zhang, S and Zhou, Z and Zhong, J and Shi, Z and Mao, Y and Tao, TH},
title = {Body-Integrated, Enzyme-Triggered Degradable, Silk-Based Mechanical Sensors for Customized Health/Fitness Monitoring and In Situ Treatment.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {7},
number = {13},
pages = {1903802},
pmid = {32670755},
issn = {2198-3844},
abstract = {Mechanical signals such as pressure and strain reflect important psychological and physiological states of the human body. Body-integrated sensors, including skin-mounted and surgically implanted ones, allow personalized health monitoring for the general population as well as patients. However, the development of such measuring devices has been hindered by the strict requirements for human-biocompatible materials and the need for high performance sensors; most existing devices or sensors do not meet all the desired specifications. Here, a set of flexible, stretchable, wearable, implantable, and degradable mechanical sensors is reported with excellent mechanical robustness and compliance, outstanding biocompatibility, remotely-triggered degradation, and excellent sensing performance, using a conductive silk fibroin hydrogel (CSFH). They can detect multiple mechanical signals such as pressure, strain, and bending angles. Moreover, combined with a drug-loaded silk-based microneedle array, sensor-equipped devices are shown to be effective for real-time monitoring and in situ treatment of epilepsy in a rodent model. These sensors offer potential applications in custom health monitoring wearables, and in situ treatment of chronic clinical disorders.},
}
@article {pmid32664599,
year = {2020},
author = {Wang, Y and Zhang, M and Wu, R and Gao, H and Yang, M and Luo, Z and Li, G},
title = {Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities.},
journal = {Brain sciences},
volume = {10},
number = {7},
pages = {},
pmid = {32664599},
issn = {2076-3425},
support = {61773342//National Natural Science Foundation of China/ ; None//Fundamental Research Funds for the Central Universities/ ; },
abstract = {Silent speech decoding is a novel application of the Brain-Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles' movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.},
}
@article {pmid32662039,
year = {2020},
author = {Llorella, FR and Patow, G and Azorín, JM},
title = {Convolutional neural networks and genetic algorithm for visual imagery classification.},
journal = {Physical and engineering sciences in medicine},
volume = {43},
number = {3},
pages = {973-983},
doi = {10.1007/s13246-020-00894-z},
pmid = {32662039},
issn = {2662-4737},
mesh = {Adult ; *Algorithms ; Electrodes ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; *Neural Networks, Computer ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the present work we present a technique that employs visual imagery. Our technique uses neural networks to classify the signals produced in visual imagery. To this end, we have used densely connected neural and convolutional networks, together with a genetic algorithm to find the best parameters for these networks. The results we obtained are a 60% success rate in the classification of four imagined objects (a tree, a dog, an airplane and a house) plus a state of relaxation, thus outperforming the state of the art in visual imagery classification.},
}
@article {pmid32661304,
year = {2020},
author = {Wimalasena, LN and Miller, LE and Pandarinath, C},
title = {From unstable input to robust output.},
journal = {Nature biomedical engineering},
volume = {4},
number = {7},
pages = {665-667},
pmid = {32661304},
issn = {2157-846X},
mesh = {*Brain-Computer Interfaces ; Models, Biological ; },
}
@article {pmid32658503,
year = {2020},
author = {Rogel, A and Loomis, AM and Hamlin, E and Hodgdon, H and Spinazzola, J and van der Kolk, B},
title = {The impact of neurofeedback training on children with developmental trauma: A randomized controlled study.},
journal = {Psychological trauma : theory, research, practice and policy},
volume = {12},
number = {8},
pages = {918-929},
doi = {10.1037/tra0000648},
pmid = {32658503},
issn = {1942-969X},
mesh = {Adolescent ; Child ; Child Abuse/*psychology/*therapy ; Female ; Humans ; Male ; Neurofeedback/*methods ; Pilot Projects ; Stress Disorders, Post-Traumatic/*psychology/*therapy ; },
abstract = {OBJECTIVE: Developmental trauma or chronic early childhood exposure to abuse and neglect by caregivers has been shown to have a long-lasting pervasive impact on mental and neural development, including problems with attention, impulse control, self-regulation, and executive functioning. Its long-term effects are arguably the costliest public health challenge in the United States. Children with developmental trauma rarely have a satisfactory response to currently available evidence-based psychotherapeutic and pharmacological treatments. Neurofeedback training (NFT) is a clinical application of brain computer interface technology, aiming to alter electrical brain activity associated with various mental dysfunctions. NFT has shown promise to improve posttraumatic stress disorder (PTSD) symptoms.<br><br>METHOD: This randomized controlled study examined the effects of NFT on 37 children, aged 6-13 years with developmental trauma. Participants were randomly divided into active NFT (n = 20) or treatment-as-usual control (n = 17). Both groups underwent 4 assessments during equivalent timelines. The active group received 24 NFT sessions twice a week.<br><br>RESULTS: This pilot study demonstrated that 24 sessions of NFT significantly decreased PTSD symptoms, internalizing, externalizing, other behavioral and emotional symptoms, and significantly improved the executive functioning of children aged 6-13 years with severe histories of abuse and neglect who had not significantly benefited from any previous therapy.<br><br>CONCLUSIONS: NFT offers the possibility to improve learning, enhance self-efficacy, and develop better social relationships in this hitherto largely treatment-resistant population. (PsycInfo Database Record (c) 2020 APA, all rights reserved).},
}
@article {pmid32655708,
year = {2020},
author = {Yao, D and Zhang, Y and Liu, T and Xu, P and Gong, D and Lu, J and Xia, Y and Luo, C and Guo, D and Dong, L and Lai, Y and Chen, K and Li, J},
title = {Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {4},
pages = {425-442},
pmid = {32655708},
issn = {1871-4080},
abstract = {The brain is the most important organ of the human body, and the conversations between the brain and an apparatus can not only reveal a normally functioning or a dysfunctional brain but also can modulate the brain. Here, the apparatus may be a nonbiological instrument, such as a computer, and the consequent brain-computer interface is now a very popular research area with various applications. The apparatus may also be a biological organ or system, such as the gut and muscle, and their efficient conversations with the brain are vital for a healthy life. Are there any common bases that bind these different scenarios? Here, we propose a new comprehensive cross area: Bacomics, which comes from brain-apparatus conversations (BAC) + omics. We take Bacomics to cover at least three situations: (1) The brain is normal, but the conversation channel is disabled, as in amyotrophic lateral sclerosis. The task is to reconstruct or open up new channels to reactivate the brain function. (2) The brain is in disorder, such as in Parkinson's disease, and the work is to utilize existing or open up new channels to intervene, repair and modulate the brain by medications or stimulation. (3) Both the brain and channels are in order, and the goal is to enhance coordinated development between the brain and apparatus. In this paper, we elaborate the connotation of BAC into three aspects according to the information flow: the issue of output to the outside (BAC-1), the issue of input to the brain (BAC-2) and the issue of unity of brain and apparatus (BAC-3). More importantly, there are no less than five principles that may be taken as the cornerstones of Bacomics, such as feedforward and feedback control, brain plasticity, harmony, the unity of opposites and systems principles. Clearly, Bacomics integrates these seemingly disparate domains, but more importantly, opens a much wider door for the research and development of the brain, and the principles further provide the general framework in which to realize or optimize these various conversations.},
}
@article {pmid32655682,
year = {2020},
author = {Wu, Y and Xie, N},
title = {Attention Optimization Method for EEG via the TGAM.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {6427305},
pmid = {32655682},
issn = {1748-6718},
mesh = {*Algorithms ; *Attention ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Electroencephalography/*methods/statistics &amp; numerical data ; Humans ; Signal Processing, Computer-Assisted ; Virtual Reality ; },
abstract = {Since the 21st century, noninvasive brain-computer interface (BCI) has developed rapidly, and brain-computer devices have gradually moved from the laboratory to the mass market. Among them, the TGAM (ThinkGear Asic Module) and its encapsulate algorithm have been adopted by many research teams and faculty members around the world. However, due to the limited development cost, the effectiveness of the algorithm to calculate data is not satisfactory. This paper proposes an attention optimization algorithm based on the TGAM for EEG data feedback. Considering that the data output of the TGAM encapsulate algorithm fluctuates greatly, the delay is high and the accuracy is low. The experimental results demonstrated that our algorithm can optimize EEG data, so that with the same or even lower delay and without changing the encapsulate algorithm of the module itself, it can significantly improve the performance of attention data, greatly improve the stability and accuracy of data, and achieve better results in practical applications.},
}
@article {pmid32655503,
year = {2020},
author = {Meng, L and Liu, H and Lan, T and Dong, L and Hu, H and Zhao, S and Zhang, Y and Zheng, N and Wang, J},
title = {Antibiotic Resistance Patterns of Pseudomonas spp. Isolated From Raw Milk Revealed by Whole Genome Sequencing.},
journal = {Frontiers in microbiology},
volume = {11},
number = {},
pages = {1005},
pmid = {32655503},
issn = {1664-302X},
abstract = {Psychrotrophic bacteria in raw milk are most well known for their spoilage potential and the economic losses they cause to the dairy industry. Food-related psychrotrophic bacteria are increasingly reported to have antibiotic resistance features. The aim of this study was to evaluate the resistance patterns of Pseudomonas spp. isolated from bulk-tank milk. In total, we investigated the antibiotic susceptibility profiles of 86 Pseudomonas spp. isolates from raw milk. All strains were tested against 15 antimicrobial agents. Pseudomonas isolates were most highly resistant to imipenem (95.3%), followed by trimethoprim-sulfamethoxazole (69.8%), aztreonam (60.5%), chloramphenicol (45.3%), and meropenem (27.9%). Their multiple antibiotic resistance (MAR) index values ranged from 0.0 to 0.8. Whole-genome sequencing revealed the presence of intrinsic resistance determinants, such as BcI, ampC-09, blaCTX-M, oprD, sul1, dfrE, catA1, catB3, catI, floR, and cmlV. Moreover, resistance-nodulation-cell division (RND) and ATP-binding cassette (ABC) antibiotic efflux pumps were also found. This study provides further knowledge of the antibiotic resistance patterns of Pseudomonas spp. in milk, which may advance our understanding of resistance in Pseudomonas and suggests that antibiotic resistance of Pseudomonas spp. in raw milk should be a concern.},
}
@article {pmid32655358,
year = {2020},
author = {Liu, B and Huang, X and Wang, Y and Chen, X and Gao, X},
title = {BETA: A Large Benchmark Database Toward SSVEP-BCI Application.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {627},
pmid = {32655358},
issn = {1662-4548},
abstract = {The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades. Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing. However, the number of pubic databases is still limited in this field. Therefore, we present a BEnchmark database Towards BCI Application (BETA) in the study. The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task. The design and the acquisition of the BETA are in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios. We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA. We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively. The BETA database can be downloaded from the following link http://bci.med.tsinghua.edu.cn/download.html.},
}
@article {pmid32655353,
year = {2020},
author = {Asgher, U and Khalil, K and Khan, MJ and Ahmad, R and Butt, SI and Ayaz, Y and Naseer, N and Nazir, S},
title = {Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {584},
pmid = {32655353},
issn = {1662-4548},
abstract = {Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.},
}
@article {pmid32655347,
year = {2020},
author = {Fleury, M and Lioi, G and Barillot, C and Lécuyer, A},
title = {A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {528},
pmid = {32655347},
issn = {1662-4548},
abstract = {Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control of external devices. These approaches have historically employed visual stimuli. However, in some cases vision is unsuitable or inadequately engaging. Other sensory modalities, such as auditory or haptic feedback have been explored, and multisensory stimulation is expected to improve the quality of the interaction loop. Moreover, for motor imagery tasks, closing the sensorimotor loop through haptic feedback may be relevant for motor rehabilitation applications, as it can promote plasticity mechanisms. This survey reviews the various haptic technologies and describes their application to BCIs and NF. We identify major trends in the use of haptic interfaces for BCI and NF systems and discuss crucial aspects that could motivate further studies.},
}
@article {pmid32651645,
year = {2021},
author = {Sanz, JL and López-García, S and Lozano, A and Pecci-Lloret, MP and Llena, C and Guerrero-Gironés, J and Rodríguez-Lozano, FJ and Forner, L},
title = {Microstructural composition, ion release, and bioactive potential of new premixed calcium silicate-based endodontic sealers indicated for warm vertical compaction technique.},
journal = {Clinical oral investigations},
volume = {25},
number = {3},
pages = {1451-1462},
pmid = {32651645},
issn = {1436-3771},
support = {RD16/0011/0001//Instituto de Salud Carlos III/ ; },
mesh = {Calcium Compounds/pharmacology ; Epoxy Resins ; Humans ; Materials Testing ; Proteins ; *Root Canal Filling Materials/pharmacology ; Silicates/pharmacology ; },
abstract = {OBJECTIVE: The aim of this study was to evaluate the microstructural composition, ion release, cytocompatibility, and mineralization potential of Bio-C Sealer ION+ (BCI) and EndoSequence BC Sealer HiFlow (BCHiF), compared with AH Plus (AHP), in contact with human periodontal ligament cells (hPDLCs).<br><br>MATERIALS AND METHODS: The sealers' ionic composition and release were assessed using energy-dispersive spectroscopy (EDS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. For the biological assays, hPDLCs were isolated from third molars, and sealer extracts were prepared (undiluted, 1:2, and 1:4 ratios). An MTT assay, wound-healing assay, and cell morphology and adhesion analysis were performed. Activity-related gene expression was determined using RT-qPCR, and mineralization potential was assessed using Alizarin Red staining (ARS). Statistical analyses were performed using one-way ANOVA and Tukey's post hoc test (&#x3b1;&#x2009;<&#x2009;0.05).<br><br>RESULTS: The three sealers exhibited variable levels of silicon, calcium, zirconium, and tungsten release and in their composition. Both BCI and BCHiF groups showed positive results in cytocompatibility assays, unlike AHP. The BCHiF group showed an upregulation of CAP (p&#x2009;<&#x2009;0.01), CEMP1, ALP, and RUNX2 (p&#x2009;<&#x2009;0.001) compared with the negative control, while the BCI group showed an upregulation of CEMP1 (p&#x2009;<&#x2009;0.01), CAP, and RUNX2 (p&#x2009;<&#x2009;0.001). Both groups also exhibited a greater mineralization potential than the negative and positive controls (p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: The calcium silicate-based sealers considered in the present in vitro study exhibited a high calcium ion release, adequate cytocompatibility, upregulated osteo/cementogenic gene expression, and increased mineralized nodule formation in contact with hPDLCs.<br><br>CLINICAL RELEVANCE: From a biological perspective, BCI and BCHiF could be clinically suitable for root canal filling.},
}
@article {pmid32645409,
year = {2020},
author = {Huang, Z and Zheng, W and Wu, Y and Wang, Y},
title = {Ensemble or pool: A comprehensive study on transfer learning for c-VEP BCI during interpersonal interaction.},
journal = {Journal of neuroscience methods},
volume = {343},
number = {},
pages = {108855},
doi = {10.1016/j.jneumeth.2020.108855},
pmid = {32645409},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Learning ; Machine Learning ; },
abstract = {BACKGROUND: To reduce calibration time of brain-computer interface (BCI) or even implement zero-training BCI, researchers have been studying how to effectively apply transfer learning in the field. In order to thoroughly investigate the performance of transfer learning in BCI and the key factors affecting transfer performance in the field, we carried out a comprehensive study.<br><br>NEW METHOD: In general, transferring knowledge in BCI is implemented in two ways: ensemble or pool. In this work, we propose two different transfer approaches. One is to transfer the information of all channels as a whole from the source subjects to a target subject. The second approach is to transfer the information of corresponding channels between the subjects. A subject transfer framework is built by combining the two approaches with ensemble or pool.<br><br>RESULTS: We investigated the performances of eight implementations of this framework on a data set acquired by an interpersonal interaction (Chicken Game) experiment based on code-modulated visual evoked potential (c-VEP) BCI. The results show that transfer learning generally provides acceptable classification performance. Additionally, an in-depth analysis reveals that a target subject usually shares different brain signal distribution with different source subjects. In fact, this is a hypothesis usually implied by this kind of research.<br><br>CONCLUSIONS: Transfer learning for c-VEP BCI can be qualified for reducing calibration time or starting the recognition of BCI without sufficient subjects' own data. In addition, our finding suggests a solid validity of the hypothesis underlying transferring knowledge in BCI.},
}
@article {pmid32639738,
year = {2020},
author = {Wang, P and Zhang, E and Toledo, D and Smith, IT and Navarrete, B and Furman, N and Hernandez, AF and Telusma, M and McDaniel, D and Liang, P and Khizroev, S},
title = {Colossal Magnetoelectric Effect in Core-Shell Magnetoelectric Nanoparticles.},
journal = {Nano letters},
volume = {20},
number = {8},
pages = {5765-5772},
doi = {10.1021/acs.nanolett.0c01588},
pmid = {32639738},
issn = {1530-6992},
abstract = {Magnetoelectric coefficient values of above 5 and 2 V cm[-1] Oe[-1] in 20 nm CoFe2O4-BaTiO3 and NiFe2O4-BaTiO3 core-shell magnetoelectric nanoparticles were demonstrated. These colossal values, compared to 0.1 V cm[-1] Oe[-1] commonly reported for the 0-3 system, are attributed to (i) the heterostructural lattice-matched interface between the magnetostrictive core and the piezoelectric shell, confirmed through transmission electron microscopy, and (ii) in situ scanning tunneling microscopy nanoprobe-based ME characterization. The nanoprobe technique allows measurements of the ME effect at a single-nanoparticle level which avoids the charge leakage problem of traditional powder form measurements. The difference in the frequency dependence of the ME value between the two material systems is owed to the Ni-ferrite cores becoming superparamagnetic in the near-dc frequency range. The availability of novel nanostructures with colossal ME values promises to unlock many new applications ranging from energy-efficient information processing to nanomedicine and brain-machine interfaces.},
}
@article {pmid32638575,
year = {2021},
author = {Swavely, NR and Speich, JE and Klausner, AP},
title = {Artifacts and abnormal findings may limit the use of asymptomatic volunteers as controls for studies of multichannel urodynamics.},
journal = {Minerva urology and nephrology},
volume = {73},
number = {5},
pages = {655-661},
pmid = {32638575},
issn = {2724-6442},
support = {R01 DK101719/DK/NIDDK NIH HHS/United States ; },
mesh = {Artifacts ; Female ; Humans ; *Lower Urinary Tract Symptoms/diagnosis ; Male ; *Urinary Bladder Neck Obstruction ; Urodynamics ; Volunteers ; },
abstract = {BACKGROUND: Multichannel urodynamics is the gold standard for the evaluation of lower urinary tract symptoms (LUTS). When performing studies to validate new adjuncts to urodynamic testing with control patients undergoing urodynamic investigation, there is difficulty in the interpretation of urodynamic results in the asymptomatic patient due to artifacts and the invasive nature of the procedure. The purpose of this investigation was to examine urodynamics in asymptomatic volunteers in order to better understand the role of control participants in urodynamic research studies.<br><br>METHODS: Asymptomatic volunteers with no LUTS were recruited to undergo standard urodynamic testing as a comparison group in a study evaluating novel urodynamic techniques. To be eligible, participants had to report no LUTS, score &#x2264;1 on all symptom questions of the International Consultation on Incontinence Questionnaire Overactive Bladder Module (ICIq-OAB) survey, have no medical conditions or to undergo any medications that affect bladder function. The urodynamics was done according to ICS standards. All tracings were evaluated by an expert neuro-urologist. Data were analyzed categorically for the presence or absence of low compliance (<30 mL/cmH<inf>2</inf>0), detrusor overactivity, bladder outlet obstruction (Bladder Outlet Obstruction Index [BOOI]>40), weak contractility (bladder contractility index [BCI]<100), straining to void, poorly sustained detrusor contraction, uncoordinated EMG activity, and intermittent flow.<br><br>RESULTS: A total of 24 participants completed the study including 10 men and 14 women. All participants had at least 1 urodynamic abnormality/artifact with an average of 4.43&#xb1;1.28 abnormalities/participant. The most common abnormalities included uncoordinated electromyography (EMG) activity (87.50%), straining to void (79.17%), and intermittent flow (70.83%). There were no significant differences for sex, age, Body Mass Index.<br><br>CONCLUSIONS: This study demonstrated that healthy, asymptomatic volunteers have high rates of abnormal urodynamic findings, suggesting that asymptomatic participants are not the ideal controls in research studies that involve urodynamic testing.},
}
@article {pmid32635609,
year = {2020},
author = {Asghar, MA and Khan, MJ and Rizwan, M and Mehmood, RM and Kim, SH},
title = {An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {13},
pages = {},
pmid = {32635609},
issn = {1424-8220},
abstract = {Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user's emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.},
}
@article {pmid32635550,
year = {2020},
author = {Marin-Pardo, O and Laine, CM and Rennie, M and Ito, KL and Finley, J and Liew, SL},
title = {A Virtual Reality Muscle-Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {13},
pages = {},
pmid = {32635550},
issn = {1424-8220},
support = {16IRG26960017//American Heart Association/ ; W911NNF-14-D-0005//Army Research Office/ ; K01HD091283/NH/NIH HHS/United States ; },
mesh = {Adult ; Aged ; Computers ; Female ; Humans ; Male ; Middle Aged ; *Neurological Rehabilitation ; Pilot Projects ; Recovery of Function ; Stroke/*therapy ; *Stroke Rehabilitation ; Treatment Outcome ; *User-Computer Interface ; *Virtual Reality ; },
abstract = {Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain-computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain-computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12-30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle-computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.},
}
@article {pmid32630685,
year = {2020},
author = {Gurve, D and Delisle-Rodriguez, D and Bastos-Filho, T and Krishnan, S},
title = {Trends in Compressive Sensing for EEG Signal Processing Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {13},
pages = {},
pmid = {32630685},
issn = {1424-8220},
support = {NSERC RGPIN-2015-03990//Natural Sciences and Engineering Research Council of Canada/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Data Compression ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient's brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain-computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.},
}
@article {pmid32630512,
year = {2020},
author = {Matamoros, OM and Escobar, JJM and Tejeida Padilla, R and Lina Reyes, I},
title = {Neurodynamics of Patients during a Dolphin-Assisted Therapy by Means of a Fractal Intraneural Analysis.},
journal = {Brain sciences},
volume = {10},
number = {6},
pages = {},
pmid = {32630512},
issn = {2076-3425},
support = {20200638//Comisi&#xf3;n de Operaci&#xf3;n y Fomento de Actividades Acad&#xe9;micas, Instituto Polit&#xe9;cnico Nacional/ ; },
abstract = {The recent proliferation of sensor technology applications in therapies for children's disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public and research interest for these disorders' intervention and treatment. This work exposes the development of a system that controls brain-computer interaction when a patient with different abilities undergoes a DAT. To develop the proposed system, TGAM1, i.e., ThinkGear-AM1 series of NeuroSky company, was used, connecting it to an isolated Bluetooth 4.0 communication protocol from a brackish and humid environment, and a Notch Filter was applied to reduce the input noise. In this way, at Definiti Ixtapa-Mexico facilities, we explored the behavior of three children with Infantile Spastic Cerebral Palsy (Experiment 1), as well as the behavior of Obsessive Compulsive Disorder and neurotypic children (Experiment 2). This was done applying the Power Spectrum Density (PSD) and the Self-Affine Analysis (SSA) from Electroencephalogram (EEG) biosignals. The EEG Raw data were time series showing the cerebral brain activity (voltage versus time) before and during DAT for the Experiment 1, and before, during DAT and after for the Experiment 2. Likewise, the EEW RAW data were recorded by the first frontopolar electrode (FP1) by means of an EEG biosensor TGAM1 Module. From the PSD we found that in all child patients a huge increment of brain activity during DAT regarding the before and after therapy periods around 376.28%. Moreover, from the SSA we found that the structure function of the all five child patients displayed an antipersistent behavior, characterized by σ ∝ δ t H , for before, during DAT and after. Nonetheless, we propose that one way to assess whether a DAT is being efficient to the child patients is to increase the during DAT time when the samples are collected, supposing the data fitting by a power law will raise the time, displaying a persistent behavior or positive correlations, until a crossover appears and the curve tends to be horizontal, pointing out that our system has reached a stationary state.},
}
@article {pmid32630378,
year = {2020},
author = {Meng, J and Xu, M and Wang, K and Meng, Q and Han, J and Xiao, X and Liu, S and Ming, D},
title = {Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {12},
pages = {},
pmid = {32630378},
issn = {1424-8220},
support = {2017YFB1300300//National Key Research and Development Program of China/ ; 81925020//National Outstanding Youth Science Fund Project of National Natural Science Foundation of China/ ; 61976152//National Natural Science Foundation of China/ ; 2018QNRC001//Young Elite Scientist Sponsorship Program by CAST/ ; },
mesh = {Algorithms ; *Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Brain-computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.},
}
@article {pmid32628965,
year = {2020},
author = {Mohammed, M and Thelin, J and Gällentoft, L and Thorbergsson, PT and Kumosa, LS and Schouenborg, J and Pettersson, LME},
title = {Ice coating -A new method of brain device insertion to mitigate acute injuries.},
journal = {Journal of neuroscience methods},
volume = {343},
number = {},
pages = {108842},
doi = {10.1016/j.jneumeth.2020.108842},
pmid = {32628965},
issn = {1872-678X},
mesh = {Animals ; Astrocytes ; *Brain ; Electrodes, Implanted ; *Ice ; Neurons ; Rats ; Rats, Sprague-Dawley ; },
abstract = {BACKGROUND: Reduction of insertion injury is likely important to approach physiological conditions in the vicinity of implanted devices intended to interface with the surrounding brain.<br><br>NEW METHODS: We have developed a novel, low-friction coating around frozen, gelatin embedded needles. By introducing a layer of thawing ice onto the gelatin, decreasing surface friction, we mitigate damage caused by the implantation.<br><br>The acute effects of a transient stab on neuronal density and glial reactions were assessed 1 and 7 days post stab in rat cortex and striatum both within and outside the insertion track using immunohistochemical staining. The addition of a coat of melting ice to the frozen gelatin embedded needles reduced the insertion force with around 50 %, substantially reduced the loss neurons (i.e. reduced neuronal void), and yielded near normal levels of astrocytes within the insertion track 1 day after insertion, as compared to gelatin coated probes of the same temperature without ice coating. There were negligible effects on glial reactions and neuronal density immediately outside the insertion track of both ice coated and cold gelatin embedded needles. This new method of implantation presents a considerable improvement compared to existing modes of device insertion.<br><br>CONCLUSIONS: Acute brain injuries following insertion of e.g. ultra-flexible electrodes, can be reduced by providing an outer coat of ultra-slippery thawing ice. No adverse effect of lowered implant temperature was found, opening the possibility of locking fragile electrode construct configurations in frozen gelatin, prior to implantation into the brain.},
}
@article {pmid32619873,
year = {2020},
author = {Wang, S and Li, K and Zhao, S and Zhang, X and Yang, Z and Zhang, J and Zhang, T},
title = {Early-stage dysfunction of hippocampal theta and gamma oscillations and its modulation of neural network in a transgenic 5xFAD mouse model.},
journal = {Neurobiology of aging},
volume = {94},
number = {},
pages = {121-129},
doi = {10.1016/j.neurobiolaging.2020.05.002},
pmid = {32619873},
issn = {1558-1497},
mesh = {Alzheimer Disease/*metabolism/*pathology/*physiopathology/psychology ; Amyloid beta-Peptides/metabolism ; Animals ; Cognition ; Disease Models, Animal ; Female ; *Gamma Rhythm ; Hippocampus/metabolism/*physiopathology ; Male ; Mice, Transgenic ; Nerve Net/*pathology/*physiopathology ; *Theta Rhythm ; },
abstract = {Alzheimer's disease (AD) is pathologically characterized by amyloid-β (Aβ) accumulation, which induces Aβ-dependent neuronal dysfunctions. We focused on the early-stage disease progression and examined the neuronal network functioning in the 5xFAD mice. The simultaneous intracranial recordings were obtained from the hippocampal perforant path (PP) and the dentate gyrus (DG). Concomitant to Aβ accumulation, theta power was strongly attenuated in the PP and DG regions of 5xFAD mice compared to those in nontransgenic littermates. For either theta rhythm or gamma oscillation, the phase synchronization on the PP-DG pathway was impaired, evidenced by decreased phase locking value and diminished coherency index. To alleviate the neural oscillatory deficits in early-stage AD, a neural modulation approach (rTMS) was used to activate gamma oscillations and strengthen the synchronicity of neuronal activity on the PP-DG pathway. In brief, there was a significant neuronal network dysfunction at an early-stage AD-like pathology, which preceded the onset of cognitive deficits and was likely driven by Aβ accumulation, suggesting that the neural oscillation analysis played an important role in early AD diagnosis.},
}
@article {pmid32619588,
year = {2020},
author = {Fu, R and Han, M and Tian, Y and Shi, P},
title = {Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis.},
journal = {Journal of neuroscience methods},
volume = {343},
number = {},
pages = {108833},
doi = {10.1016/j.jneumeth.2020.108833},
pmid = {32619588},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: The classification of psychological tasks such as motor imagery based on electroencephalography (EEG) signals is an essential issue in the brain computer interface (BCI) system. The feature extraction is an important issue for improving classification accuracy of BCI system.<br><br>NEW METHOD: For extracting discriminative features, common spatial pattern (CSP) is an effective feature extraction method. However, features extracted by CSP are dense, and even feature patterns are repeatedly selected in the feature space. A sparse CSP algorithm is proposed, which embeds the sparse techniques and iterative search into the CSP. To improve the classification performance, two regularization parameters are added to the traditional linear discriminant analysis (LDA).<br><br>RESULTS: The sparse CSP algorithm can select several channels of EEG signals with the most obvious features. The improved regularized discriminant analysis is used to solve the singularity problem and improve the feature classification accuracy. Comparison with Existing Method(s): The proposed algorithm was evaluated by the data set I of the IVth BCI competition and our dataset. The experimental results of the BCI competition dataset show that accuracy of the improved algorithm is 10.75 % higher than that of the traditional algorithm. Comparing with the currently existing methods for the same data, it also shows excellent classification performance. The effectiveness of the improved algorithm is also shown in experiments on our dataset.<br><br>CONCLUSIONS: It sufficiently proves that the improved algorithm proposed in this paper improves the classification performance of motor intent recognition.},
}
@article {pmid32617332,
year = {2020},
author = {Zhang, X and Ma, Z and Zheng, H and Li, T and Chen, K and Wang, X and Liu, C and Xu, L and Wu, X and Lin, D and Lin, H},
title = {The combination of brain-computer interfaces and artificial intelligence: applications and challenges.},
journal = {Annals of translational medicine},
volume = {8},
number = {11},
pages = {712},
pmid = {32617332},
issn = {2305-5839},
abstract = {Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCIs. Over the past decade, a wide range of BCI applications with AI assistance have emerged. These "smart" BCIs including motor and sensory BCIs have shown notable clinical success, improved the quality of paralyzed patients' lives, expanded the athletic ability of common people and accelerated the evolution of robots and neurophysiological discoveries. However, despite technological improvements, challenges remain with regard to the long training periods, real-time feedback, and monitoring of BCIs. In this article, the authors review the current state of AI as applied to BCIs and describe advances in BCI applications, their challenges and where they could be headed in the future.},
}
@article {pmid32617117,
year = {2020},
author = {Qiu, S and Li, J and Cong, M and Wu, C and Qin, Y and Liang, T},
title = {Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {4930972},
pmid = {32617117},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Deep Learning ; Electroencephalography/statistics &amp; numerical data ; Female ; Humans ; Imaging, Three-Dimensional/statistics &amp; numerical data ; Male ; Radiographic Image Interpretation, Computer-Assisted/statistics &amp; numerical data ; Solitary Pulmonary Nodule/*diagnosis/*diagnostic imaging ; Tomography, X-Ray Computed/statistics &amp; numerical data ; },
abstract = {Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.},
}
@article {pmid32613947,
year = {2020},
author = {Mane, R and Chouhan, T and Guan, C},
title = {BCI for stroke rehabilitation: motor and beyond.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {041001},
doi = {10.1088/1741-2552/aba162},
pmid = {32613947},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Humans ; Recovery of Function ; *Stroke/complications ; *Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain-computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation. In spite of the success received by BCI-based interventions in the motor domain, non-motor impairments are yet to receive similar attention in research and clinical settings. Some preliminary encouraging results in post-stroke cognitive rehabilitation using BCI seem to suggest that it may also hold potential for treating non-motor deficits such as cognitive and emotion impairments. Moreover, past studies have shown an intricate relationship between motor, cognitive and emotion functions which might influence the overall post-stroke rehabilitation outcome. A number of studies highlight the inability of current treatment protocols to account for the implicit interplay between motor, cognitive and emotion functions. This indicates the necessity to explore an all-inclusive treatment plan targeting the synergistic influence of these standalone interventions. This approach may lead to better overall recovery than treating the individual deficits in isolation. In this paper, we review the recent advances in BCI-based post-stroke motor rehabilitation and highlight the potential for the use of BCI systems beyond the motor domain, in particular, in improving cognition and emotion of stroke patients. Building on the current results and findings of studies in individual domains, we next discuss the possibility of a holistic BCI system for motor, cognitive and affect rehabilitation which may synergistically promote restorative neuroplasticity. Such a system would provide an all-encompassing rehabilitation platform, leading to overarching clinical outcomes and transfer of these outcomes to a better quality of living. This is one of the first works to analyse the possibility of targeting cross-domain influence of post-stroke functional recovery enabled by BCI-based rehabilitation.},
}
@article {pmid32611201,
year = {2021},
author = {Gao, L and Wu, H and Cheng, W and Lan, B and Ren, H and Zhang, L and Wang, L},
title = {Enhanced Descending Corticomuscular Coupling During Hand Grip With Static Force Compared With Enhancing Force.},
journal = {Clinical EEG and neuroscience},
volume = {52},
number = {6},
pages = {436-443},
doi = {10.1177/1550059420933149},
pmid = {32611201},
issn = {2169-5202},
mesh = {Electroencephalography ; Electromyography ; *Hand Strength ; Humans ; *Motor Cortex ; Muscle, Skeletal ; },
abstract = {The interaction between cortex and muscles under hand motor with different force states has not been quantitatively investigated yet, which to some extent places the optimized movement tasks design for brain-computer interface (BCI) applications in hand motor rehabilitation under uncertainty. Converging evidence has suggested that both the descending corticospinal pathway and ascending sensory feedback pathway are involved in the generation of corticomuscular coupling. The present study aimed to explore the corticomuscular coupling during hand motor task with enhancing force and steady-state force. Twenty healthy subjects performed precision grip with enhancing and static force using the right hand with visual feedback of exerted force. Mutual information and Granger causal connectivity were assessed between electroencephalography (EEG) over primary motor cortex and electromyography (EMG) recordings, and statistically analyzed. The results showed that the mutual information value was significantly larger for static force in the beta and alpha frequency band than enhancing force state. Furthermore, compared with enhancing force, the Granger causal connectivity of descending pathways from cortex to muscle was significantly larger for static force in the beta and high alpha frequency band (10-20 Hz), indicating the connection between the primary motor cortex and muscle was strengthened for static force. In summary, the hand grip with static force resulted in an increasing corticomuscular coupling from EEG over the primary motor cortex to EMG compared with enhancing force, implying more attention was required in the static force state. These results have important implications toward motor rehabilitation therapy design for the recovery of impaired hand motor functions.},
}
@article {pmid32610296,
year = {2020},
author = {Wicks, RT and Witcher, MR and Couture, DE and Laxton, AW and Popli, G and Whitlow, CT and Fetterhoff, D and Dakos, AS and Roeder, BM and Deadwyler, SA and Hampson, RE},
title = {Hippocampal CA1 and CA3 neural recording in the human brain: validation of depth electrode placement through high-resolution imaging and electrophysiology.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E5},
doi = {10.3171/2020.4.FOCUS20164},
pmid = {32610296},
issn = {1092-0684},
mesh = {Deep Brain Stimulation/methods ; Electric Stimulation/methods ; Electrodes ; *Electrophysiology/methods ; Hippocampus/*physiology ; Humans ; Neural Pathways/*physiology ; Neurons/*physiology ; },
abstract = {OBJECTIVE: Intracranial human brain recordings typically utilize recording systems that do not distinguish individual neuron action potentials. In such cases, individual neurons are not identified by location within functional circuits. In this paper, verified localization of singly recorded hippocampal neurons within the CA3 and CA1 cell fields is demonstrated.<br><br>METHODS: Macro-micro depth electrodes were implanted in 23 human patients undergoing invasive monitoring for identification of epileptic seizure foci. Individual neurons were isolated and identified via extracellular action potential waveforms recorded via macro-micro depth electrodes localized within the hippocampus. A morphometric survey was performed using 3T MRI scans of hippocampi from the 23 implanted patients, as well as 46 normal (i.e., nonepileptic) patients and 26 patients with a history of epilepsy but no history of depth electrode placement, which provided average dimensions of the hippocampus along typical implantation tracks. Localization within CA3 and CA1 cell fields was tentatively assigned on the basis of recording electrode site, stereotactic positioning of the depth electrode in comparison with the morphometric survey, and postsurgical MRI. Cells were selected as candidate CA3 and CA1 principal neurons on the basis of waveform and firing rate characteristics and confirmed within the CA3-to-CA1 neural projection pathways via measures of functional connectivity.<br><br>RESULTS: Cross-correlation analysis confirmed that nearly 80% of putative CA3-to-CA1 cell pairs exhibited positive correlations compatible with feed-forward connection between the cells, while only 2.6% exhibited feedback (inverse) connectivity. Even though synchronous and long-latency correlations were excluded, feed-forward correlation between CA3-CA1 pairs was identified in 1071 (26%) of 4070 total pairs, which favorably compares to reports of 20%-25% feed-forward CA3-CA1 correlation noted in published animal studies.<br><br>CONCLUSIONS: This study demonstrates the ability to record neurons in vivo from specified regions and subfields of the human brain. As brain-machine interface and neural prosthetic research continues to expand, it is necessary to be able to identify recording and stimulation sites within neural circuits of interest.},
}
@article {pmid32610294,
year = {2020},
author = {Keogh, C},
title = {Optimizing the neuron-electrode interface for chronic bioelectronic interfacing.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E7},
doi = {10.3171/2020.4.FOCUS20178},
pmid = {32610294},
issn = {1092-0684},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Humans ; Neurons/*physiology ; Time ; },
abstract = {Engineering approaches have vast potential to improve the treatment of disease. Brain-machine interfaces have become a well-established means of treating some otherwise medically refractory neurological diseases, and they have shown promise in many more areas. More widespread use of implanted stimulating and recording electrodes for long-term intervention is, however, limited by the difficulty in maintaining a stable interface between implanted electrodes and the local tissue for reliable recording and stimulation.This loss of performance at the neuron-electrode interface is due to a combination of inflammation and glial scar formation in response to the implanted material, as well as electrical factors contributing to a reduction in function over time. An increasing understanding of the factors at play at the neural interface has led to greater focus on the optimization of this neuron-electrode interface in order to maintain long-term implant viability.A wide variety of approaches to improving device interfacing have emerged, targeting the mechanical, electrical, and biological interactions between implanted electrodes and the neural tissue. These approaches are aimed at reducing the initial trauma and long-term tissue reaction through device coatings, optimization of mechanical characteristics for maximal biocompatibility, and implantation techniques. Improved electrode features, optimized stimulation parameters, and novel electrode materials further aim to stabilize the electrical interface, while the integration of biological interventions to reduce inflammation and improve tissue integration has also shown promise.Optimization of the neuron-electrode interface allows the use of long-term, high-resolution stimulation and recording, opening the door to responsive closed-loop systems with highly selective modulation. These new approaches and technologies offer a broad range of options for neural interfacing, representing the possibility of developing specific implant technologies tailor-made to a given task, allowing truly personalized, optimized implant technology for chronic neural interfacing.},
}
@article {pmid32610292,
year = {2020},
author = {Miller, KJ and Pouratian, N and Chang, JW and Lee, KH},
title = {Introduction. Exploring neurosurgical innovations at the brain-machine interface.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E1},
doi = {10.3171/2020.4.FOCUS20352},
pmid = {32610292},
issn = {1092-0684},
mesh = {Brain/*surgery ; *Brain-Computer Interfaces ; Humans ; *Neurosurgical Procedures ; *User-Computer Interface ; },
}
@article {pmid32610291,
year = {2020},
author = {Soldozy, S and Young, S and Kumar, JS and Capek, S and Felbaum, DR and Jean, WC and Park, MS and Syed, HR},
title = {A systematic review of endovascular stent-electrode arrays, a minimally invasive approach to brain-machine interfaces.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E3},
doi = {10.3171/2020.4.FOCUS20186},
pmid = {32610291},
issn = {1092-0684},
mesh = {Brain/*surgery ; *Brain-Computer Interfaces ; Deep Brain Stimulation/methods ; *Electrodes, Implanted ; *Endovascular Procedures/methods ; Humans ; Stents/adverse effects ; },
abstract = {OBJECTIVE: The goal of this study was to systematically review the feasibility and safety of minimally invasive neurovascular approaches to brain-machine interfaces (BMIs).<br><br>METHODS: A systematic literature review was performed using the PubMed database for studies published between 1986 and 2019. All studies assessing endovascular neural interfaces were included. Additional studies were selected based on review of references of selected articles and review articles.<br><br>RESULTS: Of the 53 total articles identified in the original literature search, 12 studies were ultimately selected. An additional 10 articles were included from other sources, resulting in a total of 22 studies included in this systematic review. This includes primarily preclinical studies comparing endovascular electrode recordings with subdural and epidural electrodes, as well as studies evaluating stent-electrode gauge and material type. In addition, several clinical studies are also included.<br><br>CONCLUSIONS: Endovascular stent-electrode arrays provide a minimally invasive approach to BMIs. Stent-electrode placement has been shown to be both efficacious and safe, although further data are necessary to draw comparisons between subdural and epidural electrode measurements given the heterogeneity of the studies included. Greater access to deep-seated brain regions is now more feasible with stent-electrode arrays; however, further validation is needed in large clinical trials to optimize this neural interface. This includes the determination of ideal electrode material type, venous versus arterial approaches, the feasibility of deep brain stimulation, and more streamlined computational decoding techniques.},
}
@article {pmid32610290,
year = {2020},
author = {Miller, KJ and Hermes, D and Staff, NP},
title = {The current state of electrocorticography-based brain-computer interfaces.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E2},
doi = {10.3171/2020.4.FOCUS20185},
pmid = {32610290},
issn = {1092-0684},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation ; Brain/*physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Exoskeleton Device ; Humans ; Quadriplegia/*physiopathology ; Speech/physiology ; Stroke/physiopathology ; Stroke Rehabilitation ; },
abstract = {Brain-computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.},
}
@article {pmid32610288,
year = {2020},
author = {Gogia, AS and Martin Del Campo-Vera, R and Chen, KH and Sebastian, R and Nune, G and Kramer, DR and Lee, MB and Tafreshi, AR and Barbaro, MF and Liu, CY and Kellis, S and Lee, B},
title = {Gamma-band modulation in the human amygdala during reaching movements.},
journal = {Neurosurgical focus},
volume = {49},
number = {1},
pages = {E4},
pmid = {32610288},
issn = {1092-0684},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS065741/NS/NINDS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {Amygdala/*physiology ; Brain/physiology ; Electroencephalography/methods ; Epilepsy/*physiopathology ; Humans ; Motor Cortex/physiology ; Movement/*physiology ; Nerve Net/*physiology ; Parietal Lobe/physiology ; },
abstract = {OBJECTIVE: Motor brain-computer interface (BCI) represents a new frontier in neurological surgery that could provide significant benefits for patients living with motor deficits. Both the primary motor cortex and posterior parietal cortex have successfully been used as a neural source for human motor BCI, leading to interest in exploring other brain areas involved in motor control. The amygdala is one area that has been shown to have functional connectivity to the motor system; however, its role in movement execution is not well studied. Gamma oscillations (30-200 Hz) are known to be prokinetic in the human cortex, but their role is poorly understood in subcortical structures. Here, the authors use direct electrophysiological recordings and the classic "center-out" direct-reach experiment to study amygdaloid gamma-band modulation in 8 patients with medically refractory epilepsy.<br><br>METHODS: The study population consisted of 8 epilepsy patients (2 men; age range 21-62 years) who underwent implantation of micro-macro depth electrodes for seizure localization and EEG monitoring. Data from the macro contacts sampled at 2000 Hz were used for analysis. The classic center-out direct-reach experiment was used, which consists of an intertrial interval phase, a fixation phase, and a response phase. The authors assessed the statistical significance of neural modulation by inspecting for nonoverlapping areas in the 95% confidence intervals of spectral power for the response and fixation phases.<br><br>RESULTS: In 5 of the 8 patients, power spectral analysis showed a statistically significant increase in power within regions of the gamma band during the response phase compared with the fixation phase. In these 5 patients, the 95% bootstrapped confidence intervals of trial-averaged power in contiguous frequencies of the gamma band during the response phase were above, and did not overlap with, the confidence intervals of trial-averaged power during the fixation phase.<br><br>CONCLUSIONS: To the authors' knowledge, this is the first time that direct neural recordings have been used to show gamma-band modulation in the human amygdala during the execution of voluntary movement. This work indicates that gamma-band modulation in the amygdala could be a contributing source of neural signals for use in a motor BCI system.},
}
@article {pmid32606699,
year = {2020},
author = {Yalin, N and Young, AH},
title = {Pharmacological Treatment of Bipolar Depression: What are the Current and Emerging Options?.},
journal = {Neuropsychiatric disease and treatment},
volume = {16},
number = {},
pages = {1459-1472},
pmid = {32606699},
issn = {1176-6328},
abstract = {Depression accounts for the predominant burden associated with bipolar disorder. The identification and management of bipolar depression are challenging, since bipolar depression differs from unipolar depression, responding poorly to traditional antidepressants, which may also induce a switch to hypomania/mania, mixed states and/or cause rapid cycling. Current treatment options for bipolar depression are limited and guidelines vary greatly in their recommendations, reflecting gaps and inconsistencies in the current evidence base. Moreover, some treatment options, such as quetiapine and olanzapine-fluoxetine, although clearly efficacious, may be associated with adverse cardiometabolic side effects, which can be detrimental to the long-term physical health and well-being of patients, increasing the likelihood of treatment non-adherence and relapse. Evidence for some more recent therapeutic options, including lurasidone and cariprazine, suggests that patients' symptoms can be effectively managed without compromising their physical health. In addition, novel agents targeting alternative neurotransmitter pathways and inflammatory processes (such as ketamine and N-acetyl cysteine) are emerging as promising potential options for the treatment of bipolar depression in the future.},
}
@article {pmid32605077,
year = {2020},
author = {Chamola, V and Vineet, A and Nayyar, A and Hossain, E},
title = {Brain-Computer Interface-Based Humanoid Control: A Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {13},
pages = {},
pmid = {32605077},
issn = {1424-8220},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Machine Learning ; Robotics ; },
abstract = {A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.},
}
@article {pmid32604020,
year = {2020},
author = {Weber, LA and Ethofer, T and Ehlis, AC},
title = {Predictors of neurofeedback training outcome: A systematic review.},
journal = {NeuroImage. Clinical},
volume = {27},
number = {},
pages = {102301},
pmid = {32604020},
issn = {2213-1582},
mesh = {Brain/*pathology/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Learning/physiology ; Mental Disorders/*physiopathology ; *Neurofeedback/methods ; },
abstract = {Neurofeedback (NF), a training tool aimed at enhancing neural self-regulation, has been suggested as a complementary treatment option for neuropsychiatric disorders. Despite its potential as a neurobiological intervention directly targeting neural alterations underlying clinical symptoms, the efficacy of NF for the treatment of mental disorders has been questioned recently by negative findings obtained in randomized controlled trials (e.g., Cortese et al., 2016). A possible reason for insufficient group effects of NF trainings vs. placebo could be related to the high rate of participants who fail to self-regulate brain activity by NF ("non-learners"). Another reason could be the application of standardized NF protocols not adjusted to individual differences in pathophysiology. Against this background, we have summarized information on factors determining training and treatment success to provide a basis for the development of individualized training protocols and/or clinical indications. The present systematic review included 25 reports investigating predictors for the outcome of NF trainings in healthy individuals as well as patients affected by mental disorders or epilepsy. We selected these studies based on searches in EBSCOhost using combinations of the keywords "neurofeedback" and "predictor/predictors". As "NF training" we defined all NF applications with at least two sessions. The best available evidence exists for neurophysiological baseline parameters. Among them, the target parameters of the respective training seem to be of particular importance. However, particularities of the different experimental designs and outcome criteria restrict the interpretability of some of the information we extracted. Therefore, further research is needed to gain more profound knowledge about predictors of NF outcome.},
}
@article {pmid32597838,
year = {2020},
author = {Rana, M and Ruiz, S and Corzo, AS and Muehleck, A and Eck, S and Salinas, C and Zamorano, F and Silva, C and Rea, M and Batra, A and Birbaumer, N and Sitaram, R},
title = {Use of Real-Time Functional Magnetic Resonance Imaging-Based Neurofeedback to Downregulate Insular Cortex in Nicotine-Addicted Smokers.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {160},
pages = {},
doi = {10.3791/59441},
pmid = {32597838},
issn = {1940-087X},
mesh = {Cerebral Cortex/*diagnostic imaging/physiopathology ; Craving/physiology ; *Down-Regulation ; Follow-Up Studies ; Humans ; *Magnetic Resonance Imaging ; *Neurofeedback ; Nicotine/*adverse effects ; Oxygen/blood ; *Smokers ; Surveys and Questionnaires ; Tobacco Use Disorder/*diagnostic imaging/*physiopathology ; },
abstract = {It has been more than a decade since the first functional magnetic resonance imaging (fMRI)-based neurofeedback approach was successfully implemented. Since then, various studies have demonstrated that participants can learn to voluntarily control a circumscribed brain region. Consequently, real-time fMRI (rtfMRI) provided a novel opportunity to study modifications of behavior due to manipulation of brain activity. Hence, reports of rtfMRI applications to train self-regulation of brain activity and the concomitant modifications in behavioral and clinical conditions such as neurological and psychiatric disorders [e.g., schizophrenia, obsessive compulsive Disorder (OCD), stroke] have rapidly increased. Neuroimaging studies in addiction research have shown that the anterior cingulate cortex, orbitofrontal cortex, and insular cortex are activated during the presentation of drug-associated cues. Also, activity in both left and right insular cortices have been shown to be highly correlated with drug urges when participants are exposed to craving-eliciting cues. Hence, the bilateral insula is of particular importance in researching drug urges and addiction due to its role in the representation of bodily (interoceptive) states. This study explores the use of rtfMRI neurofeedback for the reduction in blood oxygen-level dependent (BOLD) activity in bilateral insular cortices of nicotine-addicted participants. The study also tests if there are neurofeedback training-associated modifications in the implicit attitudes of participants towards nicotine-craving cues and explicit-craving behavior.},
}
@article {pmid32597098,
year = {2020},
author = {Qin, G and Li, S and Xu, G},
title = {[Research progress on multiscale entropy algorithm and its application in neural signal analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {3},
pages = {541-548},
pmid = {32597098},
issn = {1001-5515},
mesh = {*Algorithms ; Electroencephalography ; Entropy ; *Signal Processing, Computer-Assisted ; },
abstract = {Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.},
}
@article {pmid32597093,
year = {2020},
author = {Wang, J and Wang, K and Chen, X and Wang, H and Xu, S and Liu, M},
title = {[Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {3},
pages = {502-511},
pmid = {32597093},
issn = {1001-5515},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Simulation Training ; *Wheelchairs ; },
abstract = {Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively (P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% (P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.},
}
@article {pmid32597084,
year = {2020},
author = {Li, M and Yang, G},
title = {[Influence of the concrete and abstract graphs on N200 and P300 potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {3},
pages = {427-433},
pmid = {32597084},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Support Vector Machine ; },
abstract = {Increasing the amplitude of event-related potential is one of the key methods to improve the accuracy of the potential-based brain-computer interface, e.g., P300-based brain-computer interface. The brain-computer interface systems often use symbols or controlled objects as vision stimuli, but what visual stimuli can induce more obvious event-related potential is still unknown. This paper designed three kinds of visual stimuli, i.e., a square, an arrow, and a robot attached with an arrow, to analyze the influence of concreteness degree of the graph on the N200 and P300 potentials, and applied a support vector machine to compare the performance of the brain-computer interface under different stimuli. The results showed that, compared with the square, the robot attached with arrow and the arrow both induced larger N200 potential (P = 1.6 × 10 [-3], P = 4.2 × 10 [-2]) and longer P300 potential (P = 2.2 × 10 [-3], P = 1.9 × 10 [-2]) in the frontal area, but the amplitude under the arrow condition is smaller than the one under the robot attached with arrow condition. The robot attached with arrow increased the N200 potential amplitude of the square and arrow from 3.12 μV and 5.19 μV to 7.21 μV (P = 1.6 × 10 [-3], P = 8.9 × 10 [-2]), and improved the accuracy rate from 59.95%, 61.67% to 74.45% (P = 2.1 × 10 [-2], P = 1.6 × 10 [-2]), and the information transfer rate from 35.00 bits/min, 35.98 bits/min to 56.71 bits/min (P = 2.6 × 10 [-2], P = 1.6 × 10 [-2]). This study shows that the concreteness of graphics could affect the N200 potential and the P300 potential. The abstract symbol could represent the meaning and evoke potentials, but the information contained in the concrete robot attached with an arrow is more correlated with the human experience, which is helpful to improve the amplitude. The results may provide new sight in modifying the stimulus interface of the brain-computer interface.},
}
@article {pmid32595868,
year = {2020},
author = {Kinnear, B and Applegate, L and Kelleher, M and Schumacher, DJ and Warm, EJ},
title = {Taking the Lid Off Learner Cognition in 2030: Measuring Competence in Nonprocedural Specialties Using Brain-Computer Interfaces.},
journal = {Journal of graduate medical education},
volume = {12},
number = {3},
pages = {361-362},
doi = {10.4300/JGME-D-19-00738.1},
pmid = {32595868},
issn = {1949-8357},
mesh = {*Brain-Computer Interfaces ; *Clinical Competence ; *Cognition ; Data Collection ; Electroencephalography ; Forecasting ; Humans ; Internal Medicine/*education ; *Internship and Residency ; Pilot Projects ; },
}
@article {pmid32595441,
year = {2020},
author = {Zhang, Y and Zhou, Z and Bai, H and Liu, W and Wang, L},
title = {Seizure Classification From EEG Signals Using an Online Selective Transfer TSK Fuzzy Classifier With Joint Distribution Adaption and Manifold Regularization.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {496},
pmid = {32595441},
issn = {1662-4548},
abstract = {To recognize abnormal electroencephalogram (EEG) signals for epileptics, in this study, we proposed an online selective transfer TSK fuzzy classifier underlying joint distribution adaption and manifold regularization. Compared with most of the existing transfer classifiers, our classifier has its own characteristics: (1) the labeled EEG epochs from the source domain cannot accurately represent the primary EEG epochs in the target domain. Our classifier can make use of very few calibration data in the target domain to induce the target predictive function. (2) A joint distribution adaption is used to minimize the marginal distribution distance and the conditional distribution distance between the source domain and the target domain. (3) Clustering techniques are used to select source domains so that the computational complexity of our classifier is reduced. We construct six transfer scenarios based on the original EEG signals provided by the Bonn University to verify the performance of our classifier and introduce four baselines and a transfer support vector machine (SVM) for benchmarking studies. Experimental results indicate that our classifier wins the best performance and is not very sensitive to its parameters.},
}
@article {pmid32593293,
year = {2020},
author = {Mattia, D and Pichiorri, F and Colamarino, E and Masciullo, M and Morone, G and Toppi, J and Pisotta, I and Tamburella, F and Lorusso, M and Paolucci, S and Puopolo, M and Cincotti, F and Molinari, M},
title = {The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response.},
journal = {BMC neurology},
volume = {20},
number = {1},
pages = {254},
pmid = {32593293},
issn = {1471-2377},
support = {RF-2018-12365210//Ministero della Salute/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Imagination/physiology ; Longitudinal Studies ; Male ; Middle Aged ; Motor Activity/physiology ; *Randomized Controlled Trials as Topic ; Recovery of Function/physiology ; Stroke Rehabilitation/instrumentation/*methods ; Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: Stroke is a leading cause of long-term disability. Cost-effective post-stroke rehabilitation programs for upper limb are critically needed. Brain-Computer Interfaces (BCIs) which enable the modulation of Electroencephalography (EEG) sensorimotor rhythms are promising tools to promote post-stroke recovery of upper limb motor function. The "Promotoer" study intends to boost the application of the EEG-based BCIs in clinical practice providing evidence for a short/long-term efficacy in enhancing post-stroke hand functional motor recovery and quantifiable indices of the participants response to a BCI-based intervention. To these aims, a longitudinal study will be performed in which subacute stroke participants will undergo a hand motor imagery (MI) training assisted by the Promotoer system, an EEG-based BCI system fully compliant with rehabilitation requirements.<br><br>METHODS: This longitudinal 2-arm randomized controlled superiority trial will include 48 first ever, unilateral, subacute stroke participants, randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and a hand MI training not supported by BCI. Both interventions are delivered (3 weekly session; 6&#x2009;weeks) as add-on regimen to standard intensive rehabilitation. A multidimensional assessment will be performed at: randomization/pre-intervention, 48&#x2009;h post-intervention, and at 1, 3 and 6&#x2009;month/s after end of intervention. Primary outcome measure is the Fugl-Meyer Assessment (FMA, upper extremity) at 48&#x2009;h post-intervention. Secondary outcome measures include: the upper extremity FMA at follow-up, the Modified Ashworth Scale, the Numeric Rating Scale for pain, the Action Research Arm Test, the National Institute of Health Stroke Scale, the Manual Muscle Test, all collected at the different timepoints as well as neurophysiological and neuroimaging measures.<br><br>DISCUSSION: We expect the BCI-based rewarding of hand MI practice to promote long-lasting retention of the early induced improvement in hand motor outcome and also, this clinical improvement to be sustained by a long-lasting neuroplasticity changes harnessed by the BCI-based intervention. Furthermore, the longitudinal multidimensional assessment will address the selection of those stroke participants who best benefit of a BCI-assisted therapy, consistently advancing the transfer of BCIs to a best clinical practice.<br><br>TRIAL REGISTRATION: Name of registry: BCI-assisted MI Intervention in Subacute Stroke (Promotoer).<br><br>TRIAL REGISTRATION NUMBER: NCT04353297 ; registration date on the ClinicalTrial.gov platform: April, 15/2020.},
}
@article {pmid32590363,
year = {2020},
author = {Lu, Z and Li, Q and Gao, N and Yang, J},
title = {Time-varying networks of ERPs in P300-speller paradigms based on spatially and semantically congruent audiovisual bimodality.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046015},
doi = {10.1088/1741-2552/aba07f},
pmid = {32590363},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Cognition ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials ; Photic Stimulation ; Semantics ; },
abstract = {OBJECTIVE: In the P300-speller paradigm using spatially and semantically congruent audiovisual (AV) stimuli, AV stimuli elicited significantly higher event-related potential (ERP) amplitudes than those for a single visual (V) stimulus. Nevertheless, it remains unclear whether ERPs in AV and V spelling paradigms are generated with identical brain network architecture.<br><br>APPROACH: In this study, we constructed time-varying networks for ERPs in AV and V spelling paradigms based on adaptive directed transfer function to investigate the dynamic processes underpinning the processing of stimuli in the two spelling paradigms.<br><br>MAIN RESULTS: Our analysis revealed that early AV integration, AV spatial and semantic information, and late AV integration enhanced activation in attention-related areas between 40&#x2013;160 ms, areas associated with attention to target stimuli between 200&#x2013;280 ms, and areas associated with decision-making between 320&#x2013;520 ms, respectively. Left temporal areas were associated with AV spatial-semantic information-processing and late AV integration, the activation of which impacted the activation of brain areas associated with attention (P3a) and decision-making (P3b). In addition, the ability and efficiency of information transmission from brain networks in the AV spelling paradigm were significantly stronger than those in the V spelling paradigm between 40 and 160 ms, 200 and 280 ms, and 320 and 520 ms.<br><br>SIGNIFICANCE: This work provides a theoretical basis for deepening our understanding of the neural mechanisms underscoring ERPs production in the AV P300-speller paradigm as well as a theoretical reference for further optimization of the induced paradigm based on AV stimuli and improves the performance of BCI system.},
}
@article {pmid32590360,
year = {2020},
author = {Su, J and Yang, Z and Yan, W and Sun, W},
title = {Electroencephalogram classification in motor-imagery brain-computer interface applications based on double-constraint nonnegative matrix factorization.},
journal = {Physiological measurement},
volume = {41},
number = {7},
pages = {075007},
doi = {10.1088/1361-6579/aba07b},
pmid = {32590360},
issn = {1361-6579},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Supervised Machine Learning ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are aimed at providing a new way of communication between the human brain and external devices. One of the major tasks associated with the BCI system is to improve classification performance of the motor imagery (MI) signal. Electroencephalogram (EEG) signals are widely used for the MI BCI system. The raw EEG signals are usually non-stationary time series with weak class properties, degrading the classification performance.<br><br>APPROACH: Nonnegative matrix factorization (NMF) has been successfully applied to pattern extraction which provides meaningful data presentation. However, NMF is unsupervised and cannot make use of the label information. Based on the label information of MI EEG data, we propose a novel method, called double-constrained nonnegative matrix factorization (DCNMF), to improve the classification performance of NMF on MI BCI. The proposed method constructs a couple of label matrices as the constraints on the NMF procedure to make the EEGs with the same class labels have the similar representation in the low-dimensional space, while the EEGs with different class labels have dissimilar representations as much as possible. Accordingly, the extracted features obtain obvious class properties, which are optimal to the classification of MI EEG.<br><br>MAIN RESULTS: This study is conducted on the BCI competition III datasets (I and IVa). The proposed method helps to achieve a higher average accuracy across two datasets (79.00% for dataset I, 77.78% for dataset IVa); its performance is about 10% better than the existing studies in the literature.<br><br>SIGNIFICANCE: Our study provides a novel solution for MI BCI analysis from the perspective of label constraint; it provides convenience for semi-supervised learning of features and significantly improves the classification performance.},
}
@article {pmid32588685,
year = {2020},
author = {Ortiz, M and Iáñez, E and Gaxiola-Tirado, JA and Gutiérrez, D and Azorín, JM},
title = {Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation.},
journal = {International journal of neural systems},
volume = {30},
number = {8},
pages = {2050038},
doi = {10.1142/S0129065720500380},
pmid = {32588685},
issn = {1793-6462},
mesh = {Adult ; *Brain-Computer Interfaces ; *Connectome ; *Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Psychomotor Performance/*physiology ; Transcranial Direct Current Stimulation ; },
abstract = {The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain-computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphere.},
}
@article {pmid32587629,
year = {2020},
author = {Ni, D and Wang, S and Liu, G},
title = {The EEG-Based Attention Analysis in Multimedia m-Learning.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {4837291},
pmid = {32587629},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; *Attention ; Brain-Computer Interfaces/statistics &amp; numerical data ; Computational Biology ; Computer-Assisted Instruction/methods/statistics &amp; numerical data ; *Computers, Handheld ; *Electroencephalography/statistics &amp; numerical data ; Female ; Humans ; *Learning ; Male ; Multimedia ; Young Adult ; },
abstract = {In recent years, research on brain-computer interfaces has been increasing in the field of education, and mobile learning has become a very important way of learning. In this study, EEG experiment of a group of iPad-based mobile learners was conducted through algorithm optimization on the TGAM chip. Under the three learning media (text, text + graphic, and video), the researchers analyzed the difference in learners' attention. The study found no significant difference in attention in different media, but learners using text media had the highest attention value. Later, the researchers studied the attention of learners with different learning styles and found that active and reflective learners' attention exhibited significant differences when using video media to learn.},
}
@article {pmid32585348,
year = {2020},
author = {Kajal, DS and Fioravanti, C and Elshahabi, A and Ruiz, S and Sitaram, R and Braun, C},
title = {Involvement of top-down networks in the perception of facial emotions: A magnetoencephalographic investigation.},
journal = {NeuroImage},
volume = {222},
number = {},
pages = {117075},
doi = {10.1016/j.neuroimage.2020.117075},
pmid = {32585348},
issn = {1095-9572},
mesh = {Adult ; Beta Rhythm/*physiology ; Cortical Synchronization/physiology ; Facial Recognition/*physiology ; Female ; Frontal Lobe/*physiology ; Functional Laterality/physiology ; *Functional Neuroimaging/methods ; Gamma Rhythm/*physiology ; Humans ; *Magnetoencephalography/methods ; Male ; Nerve Net/diagnostic imaging/*physiology ; Parietal Lobe/*physiology ; Perceptual Masking/physiology ; Young Adult ; },
abstract = {Conscious perception of the emotional valence of faces has been proposed to involve top-down and bottom-up information processing. Yet, the underlying neuronal mechanisms of these two processes and the implementation of their cooperation is still unclear. According to the global workspace model, higher level cognitive processing of visual emotional stimuli relies on both bottom-up and top-down processing. Using masking stimuli in a visual backward masking paradigm with delays at the perceptual threshold, at which stimuli can only partly be detected, suggests that only top-down processing differs between correctly and incorrectly perceived stimuli, while bottom-up visual processing is not compromised and comparable for both conditions. Providing visual stimulation near the perceptual threshold in the backward masking paradigm thus enabled us to compare differences in top-down modulation of the visual information of correctly and incorrectly recognized facial emotions in 12 healthy individuals using magnetoencephalography (MEG). For correctly recognized facial emotions, we found a right-hemispheric fronto-parietal network oscillating in the high-beta and low-gamma band and exerting top-down control as determined by the causality measure of phase slope index (PSI). In contrast, incorrect recognition was associated with enhanced coupling in the gamma band between left frontal and right parietal regions. Our results indicate that the perception of emotional face stimuli relies on the right-hemispheric dominance of synchronized fronto-parietal gamma-band activity.},
}
@article {pmid32581758,
year = {2020},
author = {Rashid, M and Sulaiman, N and P P Abdul Majeed, A and Musa, RM and Ab Nasir, AF and Bari, BS and Khatun, S},
title = {Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {25},
pmid = {32581758},
issn = {1662-5218},
abstract = {Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.},
}
@article {pmid32581739,
year = {2020},
author = {Kanoga, S and Hoshino, T and Asoh, H},
title = {Independent Low-Rank Matrix Analysis-Based Automatic Artifact Reduction Technique Applied to Three BCI Paradigms.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {173},
pmid = {32581739},
issn = {1662-5161},
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) can potentially enable people to non-invasively and directly communicate with others using brain activities. Artifacts generated from body activities (e.g., eyeblinks and teeth clenches) often contaminate EEGs and make EEG-based classification/identification hard. Although independent component analysis (ICA) is the gold-standard technique for attenuating the effects of such contamination, the estimated independent components are still mixed with artifactual and neuronal information because ICA relies only on the independence assumption. The same problem occurs when using independent vector analysis (IVA), an extended ICA method. To solve this problem, we designed an independent low-rank matrix analysis (ILRMA)-based automatic artifact reduction technique that clearly models sources from observations under the independence assumption and a low-rank nature in the frequency domain. For automatic artifact reduction, we combined the signal separation technique with an independent component classifier for EEGs named ICLabel. To assess the comparative efficiency of the proposed method, the discriminabilities of artifact-reduced EEGs using ICA, IVA, and ILRMA were determined using an open-access EEG dataset named OpenBMI, which contains EEG data obtained through three BCI paradigms [motor-imagery (MI), event-related potential (ERP), and steady-state visual evoked potential (SSVEP)]. BCI performances were obtained using these three paradigms after applying artifact reduction techniques, and the results suggested that our proposed method has the potential to achieve higher discriminability than ICA and IVA for BCIs. In addition, artifact reduction using the ILRMA approach clearly improved (by over 70%) the averaged BCI performances using artifact-reduced data sufficiently for most needs of the BCI community. The extension of ICA families to supervised separation that leaves the discriminative ability would further improve the usability of BCIs for real-life environments in which artifacts frequently contaminate EEGs.},
}
@article {pmid32581667,
year = {2020},
author = {Hofmann, UG and Capadona, JR},
title = {Editorial: Bridging the Gap in Neuroelectronic Interfaces.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {457},
pmid = {32581667},
issn = {1662-4548},
}
@article {pmid32579337,
year = {2020},
author = {Chen, N and Luo, B and Patil, AC and Wang, J and Gammad, GGL and Yi, Z and Liu, X and Yen, SC and Ramakrishna, S and Thakor, NV},
title = {Nanotunnels within Poly(3,4-ethylenedioxythiophene)-Carbon Nanotube Composite for Highly Sensitive Neural Interfacing.},
journal = {ACS nano},
volume = {14},
number = {7},
pages = {8059-8073},
doi = {10.1021/acsnano.0c00672},
pmid = {32579337},
issn = {1936-086X},
mesh = {Bridged Bicyclo Compounds, Heterocyclic ; Microelectrodes ; *Nanotubes, Carbon ; *Neural Prostheses ; Polymers ; },
abstract = {Neural electrodes are developed for direct communication with neural tissues for theranostics. Although various strategies have been employed to improve performance, creating an intimate electrode-tissue interface with high electrical fidelity remains a great challenge. Here, we report the rational design of a tunnel-like electrode coating comprising poly(3,4-ethylenedioxythiophene) (PEDOT) and carbon nanotubes (CNTs) for highly sensitive neural recording. The coated electrode shows a 50-fold reduction in electrochemical impedance at the biologically relevant frequency of 1 kHz, compared to the bare gold electrode. The incorporation of CNT significantly reinforces the nanotunnel structure and improves coating adhesion by ∼1.5 fold. In vitro primary neuron culture confirms an intimate contact between neurons and the PEDOT-CNT nanotunnel. During acute in vivo nerve recording, the coated electrode enables the capture of high-fidelity neural signals with low susceptibility to electrical noise and reveals the potential for precisely decoding sensory information through mechanical and thermal stimulation. These findings indicate that the PEDOT-CNT nanotunnel composite serves as an active interfacing material for neural electrodes, contributing to neural prosthesis and brain-machine interface.},
}
@article {pmid32575798,
year = {2020},
author = {Xu, J and Zheng, H and Wang, J and Li, D and Fang, X},
title = {Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {12},
pages = {},
pmid = {32575798},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; *Intention ; },
abstract = {Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.},
}
@article {pmid32574803,
year = {2020},
author = {Zich, C and Johnstone, N and Lührs, M and Lisk, S and Haller, SP and Lipp, A and Lau, JY and Kadosh, KC},
title = {Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females.},
journal = {NeuroImage},
volume = {220},
number = {},
pages = {117053},
pmid = {32574803},
issn = {1095-9572},
support = {/WT_/Wellcome Trust/United Kingdom ; 203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Adolescent ; Brain/*diagnostic imaging ; Brain Mapping/methods ; Cognition/*physiology ; Emotional Regulation/*physiology ; Female ; Humans ; Magnetic Resonance Imaging/*methods ; Neurofeedback/*methods ; },
abstract = {Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three experiments (Experiment 1: N = 18; Experiment 2: N = 30; Experiment 3: N = 20). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc-NF-effects.},
}
@article {pmid32563483,
year = {2020},
author = {Du, H and Zhong, Z and Zhang, B and Shi, K and Li, Z},
title = {Comparative study on pyrolysis of bamboo in microwave pyrolysis-reforming reaction by binary compound impregnation and chemical liquid deposition modified HZSM-5.},
journal = {Journal of environmental sciences (China)},
volume = {94},
number = {},
pages = {186-196},
doi = {10.1016/j.jes.2020.03.014},
pmid = {32563483},
issn = {1001-0742},
mesh = {Biofuels ; Biomass ; Catalysis ; Hot Temperature ; Microwaves ; *Pyrolysis ; *Zeolites ; },
abstract = {The deactivation of catalyst is a significant reason for its limited application during the catalytic fast pyrolysis (CFP) process. To reduce the coke formation, binary compound impregnation (BCI) and chemical liquid deposition (CLD) were used to modify HZSM-5 catalysts. At the same time, the self-designed microwave reactor separated the pyrolysis of bamboo and catalytic upgrading of primary vapor, which made the catalytic effect more thorough. Experimental results indicated that CLD used TiO2 deposition to cover external acid sites, while BCI by phosphorus-nickel could cover and partly destroy superficial acid sites through two different ways. Within the scope of the loaded amount studied, the yield of aromatic hydrocarbons in the oil phase increased at first and then decreased, while the coke formation reduced continuously. BTX (benzene, toluene and xylene), the most valuable product in bio-oil, drastically increased by 39.1% and 22.6% respectively over the CLD and BCI modified catalysts. Considering the catalytic performance as well as cost, CLD over HZSM-5 has more advantages in the CFP process to upgrade bio-oil.},
}
@article {pmid32562187,
year = {2021},
author = {Chevallier, S and Kalunga, EK and Barthélemy, Q and Monacelli, E},
title = {Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI.},
journal = {Neuroinformatics},
volume = {19},
number = {1},
pages = {93-106},
pmid = {32562187},
issn = {1559-0089},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; },
abstract = {The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters.The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian distance or divergence, whose definitions and reference implementations are spread across a wide mathematical literature. This paper reviews all the Riemannian distances and divergences to process covariance matrices, with an implementation compatible with BCI constraints. The impact of using different metrics is assessed on a steady-state visually evoked potentials (SSVEP) dataset, evaluating centers of classes and classification accuracy. Riemannian approaches embed crucial properties to process EEG data. The Riemannian centers of classes outperform Euclidean ones both in offline and online setups. Some Riemannian distances and divergences have better performances in terms of classification accuracy, while others have appealing computational efficiency.},
}
@article {pmid32561769,
year = {2020},
author = {Ma, X and Qiu, S and He, H},
title = {Multi-channel EEG recording during motor imagery of different joints from the same limb.},
journal = {Scientific data},
volume = {7},
number = {1},
pages = {191},
pmid = {32561769},
issn = {2052-4463},
mesh = {Brain/physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Hand/*physiology ; Humans ; *Imagination ; *Movement ; *Psychomotor Performance ; },
abstract = {Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI.},
}
@article {pmid32561504,
year = {2021},
author = {Wen, D and Fan, Y and Hsu, SH and Xu, J and Zhou, Y and Tao, J and Lan, X and Li, F},
title = {Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review.},
journal = {Annals of physical and rehabilitation medicine},
volume = {64},
number = {1},
pages = {101404},
doi = {10.1016/j.rehab.2020.03.015},
pmid = {32561504},
issn = {1877-0665},
mesh = {*Brain-Computer Interfaces ; Humans ; *Nervous System Diseases/rehabilitation ; *Neurological Rehabilitation ; User-Computer Interface ; *Virtual Reality ; },
abstract = {BACKGROUND: The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain-computer interface (BCI) with virtual reality (VR) has developed rapidly in recent years and has been used in rehabilitation in neurological diseases.<br><br>OBJECTIVES: This narrative review analyzed and characterized the development and application of the new training system (BCI-VR) in rehabilitation of neurological diseases from the perspective of the BCI paradigm, to provide a pathway for future research in this field.<br><br>METHODS: The review involved a search of the Web of Science-Science Citation Index/Social Sciences Citation Index and the China National Knowledge Infrastructure databases; 39 papers were selected. Advantages and challenges of BCI-VR&#xa0;-&#xa0;based neurological rehabilitation were analyzed in detail.<br><br>RESULTS: Most BCI-VR studies included could be classified by 3 major BCI paradigms: motor imagery, P300, and steady-state visual-evoked potential. Integrating VR scenes into BCI systems could effectively promote the recovery process from nervous system injuries as compared with traditional methods.<br><br>CONCLUSION: As compared with rehabilitation based on traditional BCI, rehabilitation based on BCI-VR can provide better feedback information for patients and promote the recovery of brain function. By solving the challenges and continual development, the BCI-VR system can be broadly applied to the clinical treatment of various neurological diseases.},
}
@article {pmid32557966,
year = {2021},
author = {Yokoyama, H and Kaneko, N and Masugi, Y and Ogawa, T and Watanabe, K and Nakazawa, K},
title = {Gait-phase-dependent and gait-phase-independent cortical activity across multiple regions involved in voluntary gait modifications in humans.},
journal = {The European journal of neuroscience},
volume = {54},
number = {12},
pages = {8092-8105},
doi = {10.1111/ejn.14867},
pmid = {32557966},
issn = {1460-9568},
mesh = {Animals ; Cats ; Electroencephalography ; *Gait ; Humans ; Movement ; *Walking ; },
abstract = {Modification of ongoing walking movement to fit changes in external environments requires accurate voluntary control. In cats, the motor and posterior parietal cortices have crucial roles for precisely adjusting limb trajectory during walking. In human walking, however, it remains unclear which cortical information contributes to voluntary gait modification. In this study, we investigated cortical activity changes associated with visually guided precision stepping using electroencephalography source analysis. Our results demonstrated frequency- and gait-event-dependent changes in the cortical power spectrum elicited by voluntary gait modification. The main differences between normal walking and precision stepping were as follows: (a) the alpha, beta or gamma power decrease during the swing phases in the sensorimotor, anterior cingulate and parieto-occipital cortices, and (b) a power decrease in the theta, alpha and beta bands and increase in the gamma band throughout the gait cycle in the parieto-occipital cortex. Based on the previous knowledge of brain functions, the former change was considered to be related to execution and planning of leg movement, while the latter change was considered to be related to multisensory integration and motor awareness. Therefore, our results suggest that the gait modification is achieved by higher cortical involvements associated with different sensorimotor-related functions across multiple cortical regions including the sensorimotor, anterior cingulate and parieto-occipital cortices. The results imply the critical importance of the cortical contribution to voluntary modification in human locomotion. Further, the observed cortical information related to voluntary gait modification would contribute to developing volitional control systems of brain-machine interfaces for walking rehabilitation.},
}
@article {pmid32548859,
year = {2020},
author = {Salari, E and Freudenburg, ZV and Vansteensel, MJ and Ramsey, NF},
title = {Classification of Facial Expressions for Intended Display of Emotions Using Brain-Computer Interfaces.},
journal = {Annals of neurology},
volume = {88},
number = {3},
pages = {631-636},
pmid = {32548859},
issn = {1531-8249},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrocorticography ; *Emotions ; *Facial Expression ; Female ; Humans ; Male ; },
abstract = {Facial expressions are important for intentional display of emotions in social interaction. For people with severe paralysis, the ability to display emotions intentionally can be impaired. Current brain-computer interfaces (BCIs) allow for linguistic communication but are cumbersome for expressing emotions. Here, we investigated the feasibility of a BCI to display emotions by decoding facial expressions. We used electrocorticographic recordings from the sensorimotor cortex of people with refractory epilepsy and classified five facial expressions, based on neural activity. The mean classification accuracy was 72%. This approach could be a promising avenue for development of BCI-based solutions for fast communication of emotions. ANN NEUROL 2020;88:631-636.},
}
@article {pmid32548772,
year = {2020},
author = {Rahman, MA and Khanam, F and Ahmad, M and Uddin, MS},
title = {Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation.},
journal = {Brain informatics},
volume = {7},
number = {1},
pages = {7},
pmid = {32548772},
issn = {2198-4018},
abstract = {This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain-computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.},
}
@article {pmid32541935,
year = {2020},
author = {Keene, ST and Lubrano, C and Kazemzadeh, S and Melianas, A and Tuchman, Y and Polino, G and Scognamiglio, P and Cinà, L and Salleo, A and van de Burgt, Y and Santoro, F},
title = {A biohybrid synapse with neurotransmitter-mediated plasticity.},
journal = {Nature materials},
volume = {19},
number = {9},
pages = {969-973},
pmid = {32541935},
issn = {1476-4660},
mesh = {Algorithms ; Animals ; Neural Networks, Computer ; *Neuronal Plasticity ; Neurotransmitter Agents/*physiology ; PC12 Cells ; Rats ; },
abstract = {Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems[1]. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics[2] and brain-machine interfaces[3]. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs[4,5] that can both directly interface with living tissue and adapt based on biofeedback[6,7]. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.},
}
@article {pmid32541806,
year = {2020},
author = {Daly, I and Nicolaou, N and Williams, D and Hwang, F and Kirke, A and Miranda, E and Nasuto, SJ},
title = {Neural and physiological data from participants listening to affective music.},
journal = {Scientific data},
volume = {7},
number = {1},
pages = {177},
pmid = {32541806},
issn = {2052-4463},
support = {EP/J003077/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J003077/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J003077/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J003077/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; EP/J002135/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; },
mesh = {*Affect ; Humans ; Music/*psychology ; *Nervous System ; *Physiological Phenomena ; },
abstract = {Music provides a means of communicating affective meaning. However, the neurological mechanisms by which music induces affect are not fully understood. Our project sought to investigate this through a series of experiments into how humans react to affective musical stimuli and how physiological and neurological signals recorded from those participants change in accordance with self-reported changes in affect. In this paper, the datasets recorded over the course of this project are presented, including details of the musical stimuli, participant reports of their felt changes in affective states as they listened to the music, and concomitant recordings of physiological and neurological activity. We also include non-identifying meta data on our participant populations for purposes of further exploratory analysis. This data provides a large and valuable novel resource for researchers investigating emotion, music, and how they affect our neural and physiological activity.},
}
@article {pmid32540786,
year = {2020},
author = {Singh, G},
title = {Zen Mind, Machine Mind.},
journal = {IEEE computer graphics and applications},
volume = {40},
number = {4},
pages = {5-7},
doi = {10.1109/MCG.2020.2995535},
pmid = {32540786},
issn = {1558-1756},
abstract = {Artificial intelligence and machine learning are often discussed in grandiose contexts. Global technologies, trillion-dollar companies or sweeping societal implications tend to dominate the arguments. Terms like big data and petabyte storage evoke vastness. As an artist, David Young wants nothing to do with any of this. He would rather concern himself with beauty, mystery, and the little things in life-machine life, that is. There is nothing "big" about his work.},
}
@article {pmid32534382,
year = {2020},
author = {Aydin, EA},
title = {Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces.},
journal = {Computer methods and programs in biomedicine},
volume = {195},
number = {},
pages = {105535},
doi = {10.1016/j.cmpb.2020.105535},
pmid = {32534382},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Imagination ; Spectroscopy, Near-Infrared ; Support Vector Machine ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined.<br><br>METHODS: In order to determine the subject-specific optimal feature subsets, stepwise regression analysis based on sequential feature selection (SWR-SFS) and ReliefF methods were employed. Feature selection is applied on time-domain features of fNIRS signals such as mean, slope, peak, skewness and kurtosis values of signals. Linear discriminant analysis, k nearest neighborhood and support vector machines are employed to evaluate the performance of the selected feature subsets. The proposed techniques are validated on benchmark motor imagery (MI) and mental arithmetic (MA) based fNIRS datasets collected from 29 healthy subjects.<br><br>RESULTS: Both SWR-SFS and reliefF feature selection methods have significantly improved the classification accuracy. However, the best results (88.67% (HbR) and 86.43% (HbO) for MA dataset and 77.01% (HbR) and 71.32% (HbO) for MI dataset) were achieved using SWR-SFS while feature selection provided extremely high feature reduction rates (89.50% (HbR) and 93.99% (HbO) for MA dataset and 94.04% (HbR) and 97.73% (HbO) for MI dataset).<br><br>CONCLUSIONS: The results of the study indicate that employing feature selection improves both MA and MI-based fNIRS signals classification performance significantly.},
}
@article {pmid32534126,
year = {2020},
author = {Gemein, LAW and Schirrmeister, RT and Chrabąszcz, P and Wilson, D and Boedecker, J and Schulze-Bonhage, A and Hutter, F and Ball, T},
title = {Machine-learning-based diagnostics of EEG pathology.},
journal = {NeuroImage},
volume = {220},
number = {},
pages = {117021},
doi = {10.1016/j.neuroimage.2020.117021},
pmid = {32534126},
issn = {1095-9572},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Brain/*physiopathology ; Brain Diseases/*diagnosis/physiopathology ; Brain-Computer Interfaces ; Child ; Child, Preschool ; Databases, Factual ; Electroencephalography/*methods ; Female ; Humans ; Infant ; Infant, Newborn ; *Machine Learning ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81 to 86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.},
}
@article {pmid32533446,
year = {2020},
author = {Naufel, S and Klein, E},
title = {Citizen Neuroscience: Brain-Computer Interface Researcher Perspectives on Do-It-Yourself Brain Research.},
journal = {Science and engineering ethics},
volume = {26},
number = {5},
pages = {2769-2790},
pmid = {32533446},
issn = {1471-5546},
support = {EEC#1028725.//National Science Foundation (US)/International ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; *Neurosciences ; Reproducibility of Results ; Surveys and Questionnaires ; },
abstract = {Devices that record from and stimulate the brain are currently available for consumer use. The increasing sophistication and resolution of these devices provide consumers with the opportunity to engage in do-it-yourself brain research and contribute to neuroscience knowledge. The rise of do-it-yourself (DIY) neuroscience may provide an enriched fund of neural data for researchers, but also raises difficult questions about data quality, standards, and the boundaries of scientific practice. We administered an online survey to brain-computer interface (BCI) researchers to gather their perspectives on DIY brain research. While BCI researcher concerns about data quality and reproducibility were high, the possibility of expert validation of data generated by citizen neuroscientists mitigated concerns. We discuss survey results in the context of an established ethical framework for citizen science, and describe the potential of constructive collaboration between citizens and researchers to both increase data collection and advance understanding of how the brain operates outside the confines of the lab.},
}
@article {pmid32529816,
year = {2020},
author = {He, Y and Shi, J and Maslov, KI and Cao, R and Wang, LV},
title = {Wave of single-impulse-stimulated fast initial dip in single vessels of mouse brains imaged by high-speed functional photoacoustic microscopy.},
journal = {Journal of biomedical optics},
volume = {25},
number = {6},
pages = {1-11},
pmid = {32529816},
issn = {1560-2281},
support = {R01 CA186567/CA/NCI NIH HHS/United States ; R01 NS102213/NS/NINDS NIH HHS/United States ; U01 NS090579/NS/NINDS NIH HHS/United States ; U01 NS099717/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Hemodynamics ; Hemoglobins ; Mice ; Microscopy ; *Photoacoustic Techniques ; },
abstract = {SIGNIFICANCE: The initial dip in hemoglobin-oxygenation response to stimulations is a spatially confined endogenous indicator that is faster than the blood flow response, making it a desired label-free contrast to map the neural activity. A fundamental question is whether a single-impulse stimulus, much shorter than the response delay, could produce an observable initial dip without repeated stimulation.<br><br>AIM: To answer this question, we report high-speed functional photoacoustic (PA) microscopy to investigate the initial dip in mouse brains.<br><br>APPROACH: We developed a Raman-laser-based dual-wavelength functional PA microscope that can image capillary-level blood oxygenation at a 1-MHz one-dimensional imaging rate. This technology was applied to monitor the hemodynamics of mouse cerebral vasculature after applying an impulse stimulus to the forepaw.<br><br>RESULTS: We observed a transient initial dip in cerebral microvessels starting as early as 0.13&#xa0;s after the onset of the stimulus. The initial dip and the subsequent overshoot manifested a wave pattern propagating across different microvascular compartments.<br><br>CONCLUSIONS: We quantified both spatially and temporally the single-impulse-stimulated microvascular hemodynamics in mouse brains at single-vessel resolution. Fast label-free imaging of single-impulse response holds promise for real-time brain-computer interfaces.},
}
@article {pmid32529035,
year = {2019},
author = {Pitt, KM and Brumberg, JS and Burnison, JD and Mehta, J and Kidwai, J},
title = {Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter.},
journal = {Perspectives of the ASHA special interest groups},
volume = {4},
number = {6},
pages = {1622-1636},
pmid = {32529035},
issn = {2381-4764},
support = {R01 DC016343/DC/NIDCD NIH HHS/United States ; },
abstract = {PURPOSE: Brain-computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand how electroencephalography (EEG) records the BCI related brain signals, what brain signals are recorded by EEG, and why these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive and motor processes, which are of interest to a range of related disciplines including speech science.<br><br>METHOD: This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC related signals of interest to speech-language-hearing.<br><br>RESULTS: An overview of BCI-AAC related signals are provided discussing 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) why these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background.<br><br>CONCLUSION: Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.},
}
@article {pmid32528398,
year = {2020},
author = {Gao, Y and Gao, B and Chen, Q and Liu, J and Zhang, Y},
title = {Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {375},
pmid = {32528398},
issn = {1664-2295},
abstract = {Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.},
}
@article {pmid32525885,
year = {2020},
author = {León, J and Escobar, JJ and Ortiz, A and Ortega, J and González, J and Martín-Smith, P and Gan, JQ and Damas, M},
title = {Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.},
journal = {PloS one},
volume = {15},
number = {6},
pages = {e0234178},
pmid = {32525885},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Middle Aged ; *Motor Activity ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the curse of dimensionality. Artificial Neural Networks (ANNs) have achieved promising performance in EEG-based Brain-Computer Interface (BCI) applications, but they involve computationally intensive training algorithms and hyperparameter optimization methods. Thus, an awareness of the quality-cost trade-off, although usually overlooked, is highly beneficial. In this paper, we apply a hyperparameter optimization procedure based on Genetic Algorithms to Convolutional Neural Networks (CNNs), Feed-Forward Neural Networks (FFNNs), and Recurrent Neural Networks (RNNs), all of them purposely shallow. We compare their relative quality and energy-time cost, but we also analyze the variability in the structural complexity of networks of the same type with similar accuracies. The experimental results show that the optimization procedure improves accuracy in all models, and that CNN models with only one hidden convolutional layer can equal or slightly outperform a 6-layer Deep Belief Network. FFNN and RNN were not able to reach the same quality, although the cost was significantly lower. The results also highlight the fact that size within the same type of network is not necessarily correlated with accuracy, as smaller models can and do match, or even surpass, bigger ones in performance. In this regard, overfitting is likely a contributing factor since deep learning approaches struggle with limited training examples.},
}
@article {pmid32519025,
year = {2020},
author = {Frey, B and Mika, J and Jelonek, K and Cruz-Garcia, L and Roelants, C and Testard, I and Cherradi, N and Lumniczky, K and Polozov, S and Napieralska, A and Widlak, P and Gaipl, US and Badie, C and Polanska, J and Candéias, SM},
title = {Systemic modulation of stress and immune parameters in patients treated for prostate adenocarcinoma by intensity-modulated radiation therapy or stereotactic ablative body radiotherapy.},
journal = {Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]},
volume = {196},
number = {11},
pages = {1018-1033},
pmid = {32519025},
issn = {1439-099X},
mesh = {Adenocarcinoma/immunology/physiopathology/*radiotherapy/*surgery ; Aged ; Aged, 80 and over ; Biomarkers ; Cytokines/blood ; Gene Expression Regulation, Neoplastic/radiation effects ; HLA Antigens/blood ; Humans ; Immune System/*radiation effects ; Inflammation Mediators/blood ; Lysophosphatidylcholines/blood ; Male ; Metabolome/*radiation effects ; Middle Aged ; Monocytes/immunology ; Neoplasm Proteins/*blood ; Phosphatidylcholines/blood ; Prostatic Neoplasms/immunology/physiopathology/*radiotherapy/*surgery ; Proteome/*radiation effects ; Radiosurgery/*methods ; Radiotherapy, Intensity-Modulated/*methods ; Stress, Physiological/*radiation effects ; },
abstract = {BACKGROUND: In this exploratory study, the impact of local irradiation on systemic changes in stress and immune parameters was investigated in eight patients treated with intensity-modulated radiation therapy (IMRT) or stereotactic ablative body radiotherapy (SABR) for prostate adenocarcinoma to gain deeper insights into how radiotherapy (RT) modulates the immune system.<br><br>PATIENTS AND METHODS: RT-qPCR, flow cytometry, metabolomics, and antibody arrays were used to monitor a&#xa0;panel of stress- and immune-related parameters before RT, after the first fraction (SABR) or the first week of treatment (IMRT), after the last fraction, and 3&#xa0;weeks later in the blood of IMRT (N&#x202f;=&#x2009;4) or SABR (N&#x202f;=&#x2009;4) patients. Effect size analysis was used for comparison of results at different timepoints.<br><br>RESULTS: Several parameters were found to be differentially modulated in IMRT and SABR patients: the expression of TGFB1, IL1B, and CCL3 genes; the expression of HLA-DR on circulating monocytes; the abundance and ratio of phosphatidylcholine and lysophosphatidylcholine metabolites in plasma. More immune modulators in plasma were modulated during IMRT than SABR, with only two common proteins, namely GDF-15 and Tim&#x2011;3.<br><br>CONCLUSION: Locally delivered RT induces systemic modulation of the immune system in prostate adenocarcinoma patients. IMRT and SABR appear to specifically affect distinct immune components.},
}
@article {pmid32518963,
year = {2020},
author = {Naghibi, SS and Fallah, A and Maleki, A and Ghassemi, F},
title = {Elbow angle generation during activities of daily living using a submovement prediction model.},
journal = {Biological cybernetics},
volume = {114},
number = {3},
pages = {389-402},
doi = {10.1007/s00422-020-00834-w},
pmid = {32518963},
issn = {1432-0770},
mesh = {*Activities of Daily Living ; Elbow Joint/*physiology ; Forecasting ; Humans ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.},
}
@article {pmid32513969,
year = {2020},
author = {Yang, B and Zhang, F and Cheng, F and Ying, L and Wang, C and Shi, K and Wang, J and Xia, K and Gong, Z and Huang, X and Yu, C and Li, F and Liang, C and Chen, Q},
title = {Strategies and prospects of effective neural circuits reconstruction after spinal cord injury.},
journal = {Cell death &amp; disease},
volume = {11},
number = {6},
pages = {439},
pmid = {32513969},
issn = {2041-4889},
mesh = {Humans ; Nerve Regeneration/*physiology ; Spinal Cord Injuries/physiopathology/*surgery ; Stem Cell Transplantation/*methods ; },
abstract = {Due to the disconnection of surviving neural elements after spinal cord injury (SCI), such patients had to suffer irreversible loss of motor or sensory function, and thereafter enormous economic and emotional burdens were brought to society and family. Despite many strategies being dealing with SCI, there is still no effective regenerative therapy. To date, significant progress has been made in studies of SCI repair strategies, including gene regulation of neural regeneration, cell or cell-derived exosomes and growth factors transplantation, repair of biomaterials, and neural signal stimulation. The pathophysiology of SCI is complex and multifaceted, and its mechanisms and processes are incompletely understood. Thus, combinatorial therapies have been demonstrated to be more effective, and lead to better neural circuits reconstruction and functional recovery. Combinations of biomaterials, stem cells, growth factors, drugs, and exosomes have been widely developed. However, simply achieving axon regeneration will not spontaneously lead to meaningful functional recovery. Therefore, the formation and remodeling of functional neural circuits also depend on rehabilitation exercises, such as exercise training, electrical stimulation (ES) and Brain-Computer Interfaces (BCIs). In this review, we summarize the recent progress in biological and engineering strategies for reconstructing neural circuits and promoting functional recovery after SCI, and emphasize current challenges and future directions.},
}
@article {pmid32512541,
year = {2020},
author = {Jochumsen, M and Niazi, IK},
title = {Detection and classification of single-trial movement-related cortical potentials associated with functional lower limb movements.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {035009},
doi = {10.1088/1741-2552/ab9a99},
pmid = {32512541},
issn = {1741-2552},
mesh = {*Activities of Daily Living ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Lower Extremity ; Movement ; },
abstract = {OBJECTIVE: Brain-computer interfaces that activate exoskeletons based on decoded movement-related activity have been shown to be useful for stroke rehabilitation. With the advances in the development of exoskeletons it is possible to replicate a number of different functional movements that are relevant to rehabilitate after stroke. In this study, the aim is to detect and classify six different movement tasks of the lower extremities that are used in the activities of daily living.<br><br>APPROACH: Thirteen healthy subjects performed six movement tasks (1) Stand-to-sit, (2) Sit-to-stand, (3) Walking, (4) Step up, (5) Side step, and (6) Back step. Each movement task was performed 50 times while continuous EEG was recorded. The continuous EEG was divided into epochs containing the movement intention associated with the movements, and idle activity was obtained from recordings during rest. Temporal, spectral and template matching features were extracted from the EEG channels covering the motor cortex and classified using Random Forest in two ways: (1) movement intention vs. idle activity (estimate of movement intention detection), and (2) classification of movement types.<br><br>MAIN RESULTS: The classification accuracies associated with movement intention detection were in the range of 80%-90%, while 54 &#xb1; 3% of all movement types were classified correctly. The stand-to-sit and sit-to-stand tasks were easiest to classify, while step up often was classified as walking.<br><br>SIGNIFICANCE: The results indicate that it is possible to detect and classify functional movements of the lower extremities from single-trial EEG. This may be implemented in a brain-computer interface that can control an exoskeleton and be used for neurorehabilitation.},
}
@article {pmid32508581,
year = {2020},
author = {Vickery, RM and Ng, KKW and Potas, JR and Shivdasani, MN and McIntyre, S and Nagi, SS and Birznieks, I},
title = {Tapping Into the Language of Touch: Using Non-invasive Stimulation to Specify Tactile Afferent Firing Patterns.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {500},
pmid = {32508581},
issn = {1662-4548},
abstract = {The temporal pattern of action potentials can convey rich information in a variety of sensory systems. We describe a new non-invasive technique that enables precise, reliable generation of action potential patterns in tactile peripheral afferent neurons by brief taps on the skin. Using this technique, we demonstrate sophisticated coding of temporal information in the somatosensory system, that shows that perceived vibration frequency is not encoded in peripheral afferents as was expected by either their firing rate or the underlying periodicity of the stimulus. Instead, a burst gap or silent gap between trains of action potentials conveys frequency information. This opens the possibility of new encoding strategies that could be deployed to convey sensory information using mechanical or electrical stimulation in neural prostheses and brain-machine interfaces, and may extend to senses beyond artificial encoding of aspects of touch. We argue that a focus on appropriate use of effective temporal coding offers more prospects for rapid improvement in the function of these interfaces than attempts to scale-up existing devices.},
}
@article {pmid32504624,
year = {2020},
author = {Bertrand, Q and Job, V and Maillard, AP and Imbert, L and Teulon, JM and Favier, A and Pellequer, JL and Huber, P and Attrée, I and Dessen, A},
title = {Exolysin (ExlA) from Pseudomonas aeruginosa Punctures Holes into Target Membranes Using a Molten Globule Domain.},
journal = {Journal of molecular biology},
volume = {432},
number = {16},
pages = {4466-4480},
doi = {10.1016/j.jmb.2020.05.025},
pmid = {32504624},
issn = {1089-8638},
mesh = {A549 Cells ; Bacterial Toxins/*chemistry/genetics/*metabolism ; Bacterial Translocation ; Humans ; Magnetic Resonance Spectroscopy ; Membrane Microdomains/*metabolism ; Microscopy, Atomic Force ; Mutation ; Protein Domains ; Pseudomonas aeruginosa/metabolism/*pathogenicity ; Scattering, Small Angle ; Virulence ; X-Ray Diffraction ; },
abstract = {Bacteria employ several mechanisms, and most notably secretion systems, to translocate effectors from the cytoplasm to the extracellular environment or the cell surface. Pseudomonas aeruginosa widely employs secretion machineries such as the Type III Secretion System to support virulence and cytotoxicity. However, recently identified P. aeruginosa strains that do not express the Type III Secretion System have been shown to express ExlA, an exolysin translocated through a two-partner secretion system, and are the causative agents of severe lung hemorrhage. Sequence predictions of ExlA indicate filamentous hemagglutinin (FHA-2) domains as the prevalent features, followed by a C-terminal domain with no known homologs. In this work, we have addressed the mechanism employed by ExlA to target membrane bilayers by using NMR, small-angle X-ray scattering, atomic force microscopy, and cellular infection techniques. We show that the C-terminal domain of ExlA displays a "molten globule-like" fold that punctures small holes into membranes composed of negatively charged lipids, while other domains could play a lesser role in target recognition. In addition, epithelial cells infected with P. aeruginosa strains expressing different ExlA variants allow localization of the toxin to lipid rafts. ExlA homologs have been identified in numerous bacterial strains, indicating that lipid bilayer destruction is an effective strategy employed by bacteria to establish interactions with multiple hosts.},
}
@article {pmid32504225,
year = {2020},
author = {Chen, B and Zhang, B and Chen, C and Hu, J and Qi, J and He, T and Tian, P and Zhang, X and Ni, G and Cheng, MM},
title = {Penetrating glassy carbon neural electrode arrays for brain-machine interfaces.},
journal = {Biomedical microdevices},
volume = {22},
number = {3},
pages = {43},
doi = {10.1007/s10544-020-00498-0},
pmid = {32504225},
issn = {1572-8781},
support = {51975360, 51775332, 51675329//National Natural Science Foundation of China/International ; },
mesh = {*Brain-Computer Interfaces ; Carbon/*chemistry ; Dimethylpolysiloxanes ; Electric Conductivity ; *Electrodes, Implanted ; Equipment Design ; Glass/*chemistry ; Neurons/cytology ; Nylons ; Printing, Three-Dimensional ; Signal-To-Noise Ratio ; Silicon/chemistry ; },
abstract = {This paper presents a fabrication method for glassy carbon neural electrode arrays that combines 3D printing and chemical pyrolysis technology. The carbon electrodes have excellent biological compatibility and can be used in neural signal recording. A pretreated Si wafer is used as the substrate for 3D printing, and then, stereolithography 3D printing technology is employed to print photosensitive resin into a cone shape. Next, chemical pyrolysis is applied to convert the 3D prints into glassy carbon electrodes and modify the electrochemical performance of the carbon electrodes. Finally, the glassy carbon electrodes are packed with conductive wires and PDMS. The proposed fabrication method simplifies the manufacturing process of carbon materials, and electrodes can be fabricated without the need of deep reactive ion etching (DRIE). The height of the carbon electrodes is 1.5 mm, and the exposure area of the tips is 0.78 mm[2], which is convenient for the implantation procedure. The specific capacitance of the glassy carbon arrays is higher than that of a platinum electrode (9.18 mF/cm[2] vs 3.32 mF/cm[2], respectively), and the impedance at 1 kHz is lower (7.1 kΩ vs 8.8 kΩ). The carbon electrodes were tested in vivo, and they showed excellent performance in neural signal recording. The signal-to-noise ratio of the carbon electrodes is 50.73 ± 6.11, which is higher than that of the Pt electrode (20.15 ± 5.32) under the same testing conditions. The proposed fabrication method of glassy carbon electrodes provides a novel approach to manufacture penetrating electrodes for nerve interfaces in biomedical engineering and microelectromechanical systems.},
}
@article {pmid32503162,
year = {2020},
author = {Ko, LW and Chikara, RK and Lee, YC and Lin, WC},
title = {Exploration of User's Mental State Changes during Performing Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {11},
pages = {},
pmid = {32503162},
issn = {1424-8220},
mesh = {Adolescent ; Adult ; Bayes Theorem ; *Brain-Computer Interfaces ; *Cognition ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Memory, Short-Term ; Photic Stimulation ; Young Adult ; },
abstract = {Substantial developments have been established in the past few years for enhancing the performance of brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). The past SSVEP-BCI studies utilized different target frequencies with flashing stimuli in many different applications. However, it is not easy to recognize user's mental state changes when performing the SSVEP-BCI task. What we could observe was the increasing EEG power of the target frequency from the user's visual area. BCI user's cognitive state changes, especially in mental focus state or lost-in-thought state, will affect the BCI performance in sustained usage of SSVEP. Therefore, how to differentiate BCI users' physiological state through exploring their neural activities changes while performing SSVEP is a key technology for enhancing the BCI performance. In this study, we designed a new BCI experiment which combined working memory task into the flashing targets of SSVEP task using 12 Hz or 30 Hz frequencies. Through exploring the EEG activity changes corresponding to the working memory and SSVEP task performance, we can recognize if the user's cognitive state is in mental focus or lost-in-thought. Experiment results show that the delta (1-4 Hz), theta (4-7 Hz), and beta (13-30 Hz) EEG activities increased more in mental focus than in lost-in-thought state at the frontal lobe. In addition, the powers of the delta (1-4 Hz), alpha (8-12 Hz), and beta (13-30 Hz) bands increased more in mental focus in comparison with the lost-in-thought state at the occipital lobe. In addition, the average classification performance across subjects for the KNN and the Bayesian network classifiers were observed as 77% to 80%. These results show how mental state changes affect the performance of BCI users. In this work, we developed a new scenario to recognize the user's cognitive state during performing BCI tasks. These findings can be used as the novel neural markers in future BCI developments.},
}
@article {pmid32502798,
year = {2020},
author = {Borra, D and Fantozzi, S and Magosso, E},
title = {Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {129},
number = {},
pages = {55-74},
doi = {10.1016/j.neunet.2020.05.032},
pmid = {32502798},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Humans ; Imagination ; *Machine Learning ; *Movement ; },
abstract = {Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. Nevertheless, the learned features are difficult to interpret and most of the existing CNNs introduce many trainable parameters. Here, we propose a lightweight and interpretable shallow CNN (Sinc-ShallowNet), by stacking a temporal sinc-convolutional layer (designed to learn band-pass filters, each having only the two cut-off frequencies as trainable parameters), a spatial depthwise convolutional layer (reducing channel connectivity and learning spatial filters tied to each band-pass filter), and a fully-connected layer finalizing the classification. This convolutional module limits the number of trainable parameters and allows direct interpretation of the learned spectral-spatial features via simple kernel visualizations. Furthermore, we designed a post-hoc gradient-based technique to enhance interpretation by identifying the more relevant and more class-specific features. Sinc-ShallowNet was evaluated on benchmark motor-execution and motor-imagery datasets and against different design choices and training strategies. Results show that (i) Sinc-ShallowNet outperformed a traditional machine learning algorithm and other CNNs for EEG decoding; (ii) The learned spectral-spatial features matched well-known EEG motor-related activity; (iii) The proposed architecture performed better with a larger number of temporal kernels still maintaining a good compromise between accuracy and parsimony, and with a trialwise rather than a cropped training strategy. In perspective, the proposed approach, with its interpretative capacity, can be exploited to investigate cognitive/motor aspects whose EEG correlates are yet scarcely known, potentially characterizing their relevant features.},
}
@article {pmid32498996,
year = {2020},
author = {Hosseini, MP and Tran, TX and Pompili, D and Elisevich, K and Soltanian-Zadeh, H},
title = {Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.},
journal = {Artificial intelligence in medicine},
volume = {104},
number = {},
pages = {101813},
doi = {10.1016/j.artmed.2020.101813},
pmid = {32498996},
issn = {1873-2860},
mesh = {Brain/diagnostic imaging ; Data Analysis ; *Deep Learning ; Electroencephalography ; *Epilepsy/diagnostic imaging ; Humans ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities.<br><br>METHODS: Functional connectivity analysis using electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI) has provided such meaningful input in cases of epilepsy. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for monitoring, evaluation, and regulation of the epileptic brain. First, an autonomic edge computing framework is proposed for the processing of big data as part of a decision support system for surgical candidacy. Second, a multimodal data analysis using independently acquired EEG and rs-fMRI is presented for estimation and prediction of the epileptogenic network. Third, an unsupervised feature extraction model is developed for EEG analysis and seizure prediction based on a Convolutional deep learning (CNN) structure for distinguishing preictal (pre-seizure) state from non-preictal periods by support vector machine (SVM) classifier.<br><br>RESULTS: Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.<br><br>CONCLUSIONS: The combination of rs-fMRI and EEG/iEEG can reveal more information about dynamic functional connectivity. However, simultaneous fMRI and EEG data acquisition present challenges. We have proposed system models for leveraging and processing independently acquired fMRI and EEG data.},
}
@article {pmid32498642,
year = {2020},
author = {Stojic, F and Chau, T},
title = {Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control.},
journal = {International journal of neural systems},
volume = {30},
number = {6},
pages = {2050026},
doi = {10.1142/S0129065720500264},
pmid = {32498642},
issn = {1793-6462},
mesh = {Adult ; Alpha Rhythm/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Feedback, Sensory/*physiology ; Humans ; Imagination/*physiology ; *Intention ; Pattern Recognition, Visual/*physiology ; Space Perception/*physiology ; Visual Cortex/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.},
}
@article {pmid32498049,
year = {2020},
author = {Wang, M and Li, G and Jiang, S and Wei, Z and Hu, J and Chen, L and Zhang, D},
title = {Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046043},
doi = {10.1088/1741-2552/ab9987},
pmid = {32498049},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; *Gestures ; Humans ; Movement ; Parietal Lobe ; },
abstract = {OBJECTIVE: Hand movement is a crucial function for humans' daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI.<br><br>APPROACH: Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r [2]. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding.<br><br>MAIN RESULTS: We find that a majority(L: [Formula: see text] 60%, R: [Formula: see text] 40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used.<br><br>SIGNIFICANCE: This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.},
}
@article {pmid32497788,
year = {2020},
author = {Kobler, RJ and Sburlea, AI and Lopes-Dias, C and Schwarz, A and Hirata, M and Müller-Putz, GR},
title = {Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals.},
journal = {NeuroImage},
volume = {218},
number = {},
pages = {117000},
doi = {10.1016/j.neuroimage.2020.117000},
pmid = {32497788},
issn = {1095-9572},
mesh = {*Algorithms ; *Artifacts ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Eye Movements ; Humans ; Magnetoencephalography/*methods ; Signal Processing, Computer-Assisted ; },
abstract = {Eye movements and blinks contaminate electroencephalographic (EEG) and magnetoencephalographic (MEG) activity. As the eye moves, the corneo-retinal dipole (CRD) and eyelid introduce potential/field changes in the M/EEG activity. These eye artifacts can affect a brain-computer interface and thereby impinge on neurofeedback quality. Here, we introduce the sparse generalized eye artifact subspace subtraction (SGEYESUB) algorithm that can correct these eye artifacts offline and in real time. We provide an open source reference implementation of the algorithm and the paradigm to obtain calibration data. Once the algorithm is fitted to calibration data (approx. 5 min), the eye artifact correction reduces to a matrix multiplication. We compared SGEYESUB with 4 state-of-the-art algorithms using M/EEG activity of 69 participants. SGEYESUB achieved the best trade-off between correcting the eye artifacts and preserving brain activity. Residual correlations between the corrected M/EEG channels and the eye artifacts were below 0.1. Error-related and movement-related cortical potentials were attenuated by less than 0.5 μV. Our results furthermore demonstrate that CRD and eyelid-related artifacts can be assumed to be stationary for at least 1-1.5 h, validating the feasibility of our approach in offline and online eye artifact correction.},
}
@article {pmid32496351,
year = {2020},
author = {Simpson, KR and Lyndon, A and Spetz, J and Gay, CL and Landstrom, GL},
title = {Missed Nursing Care During Labor and Birth and Exclusive Breast Milk Feeding During Hospitalization for Childbirth.},
journal = {MCN. The American journal of maternal child nursing},
volume = {45},
number = {5},
pages = {280-288},
pmid = {32496351},
issn = {1539-0683},
support = {R01 HS025715/HS/AHRQ HHS/United States ; },
mesh = {Adult ; Breast Feeding/*statistics &amp; numerical data ; California ; Female ; Hospitalization/statistics &amp; numerical data ; Humans ; Infant, Newborn ; *Labor, Obstetric ; Michigan ; Milk, Human ; New Jersey ; Nursing Care/*standards/statistics &amp; numerical data ; Pregnancy ; Surveys and Questionnaires ; },
abstract = {PURPOSE: The purpose of this study was to determine associations between missed nursing care and nurse staffing during labor and birth, and exclusive breast milk feeding at hospital discharge.<br><br>STUDY DESIGN AND METHODS: Labor and birth nurses in three states were surveyed about missed nursing care and their maternity units' adherence to the AWHONN (2010) nurse staffing guidelines for care during labor and birth, using the Perinatal Misscare Survey. Nursing responses were aggregated to the hospital level and estimated associations between missed nursing care, nurse staffing, and hospitals' exclusive breast milk feeding rates were measured using The Joint Commission's Perinatal Care Measure (PC-05).<br><br>RESULTS: Surveys from 512 labor nurses in 36 hospitals were included in the analysis. The mean exclusive breast milk feeding rate was 53% (range 13%-76%). Skin-to-skin care, breastfeeding within 1 hour of birth, and appropriate recovery care were on average occasionally missed (2.33 to 2.46 out of 4; 1 = rarely, 2 = occasionally, 3 = frequently, or 4 = always) and were associated with PC-05 [B(CI) -17.1(-29, -6.3), -17.9(-30.5, -6.2), and -15.4(-28.7, -2.1), respectively]. Adherence with overall staffing guidelines was associated with PC-05 [12.9(3.4, 24.3)]. Missed nursing care was an independent predictor of PC-05 [-14.6(-26.4, -2.7)] in a multilevel model adjusting for staffing guideline adherence, perceived quality, mean age of respondents, and nurse burnout.<br><br>CLINICAL IMPLICATIONS: Exclusive breast milk feeding is a national quality indicator of inpatient maternity care. Nurses have substantial responsibility for direct support of infant feeding during the childbirth hospitalization. These results support exclusive breast milk feeding (PC-05) as a nurse-sensitive quality indicator.},
}
@article {pmid32494819,
year = {2020},
author = {Downey, JE and Quick, KM and Schwed, N and Weiss, JM and Wittenberg, GF and Boninger, ML and Collinger, JL},
title = {The Motor Cortex Has Independent Representations for Ipsilateral and Contralateral Arm Movements But Correlated Representations for Grasping.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {30},
number = {10},
pages = {5400-5409},
doi = {10.1093/cercor/bhaa120},
pmid = {32494819},
issn = {1460-2199},
mesh = {Adult ; Arm/physiology ; Brain-Computer Interfaces ; Female ; Functional Laterality ; Hand/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; *Movement ; Neurons/*physiology ; },
abstract = {Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms.},
}
@article {pmid32494023,
year = {2020},
author = {Mao, C and Shen, C and Li, C and Shen, DD and Xu, C and Zhang, S and Zhou, R and Shen, Q and Chen, LN and Jiang, Z and Liu, J and Zhang, Y},
title = {Cryo-EM structures of inactive and active GABAB receptor.},
journal = {Cell research},
volume = {30},
number = {7},
pages = {564-573},
pmid = {32494023},
issn = {1748-7838},
mesh = {*Cryoelectron Microscopy ; GTP-Binding Protein alpha Subunits, Gi-Go/chemistry/metabolism ; Humans ; Models, Molecular ; Protein Domains ; Protein Multimerization ; Receptors, GABA-B/chemistry/*ultrastructure ; Structural Homology, Protein ; },
abstract = {Metabotropic GABAB G protein-coupled receptor functions as a mandatory heterodimer of GB1 and GB2 subunits and mediates inhibitory neurotransmission in the central nervous system. Each subunit is composed of the extracellular Venus flytrap (VFT) domain and transmembrane (TM) domain. Here we present cryo-EM structures of full-length human heterodimeric GABAB receptor in the antagonist-bound inactive state and in the active state complexed with an agonist and a positive allosteric modulator in the presence of Gi1 protein at a resolution range of 2.8-3.0 Å. Our structures reveal that agonist binding stabilizes the closure of GB1 VFT, which in turn triggers a rearrangement of TM interfaces between the two subunits from TM3-TM5/TM3-TM5 in the inactive state to TM6/TM6 in the active state and finally induces the opening of intracellular loop 3 and synergistic shifting of TM3, 4 and 5 helices in GB2 TM domain to accommodate the α5-helix of Gi1. We also observed that the positive allosteric modulator anchors at the dimeric interface of TM domains. These results provide a structural framework for understanding class C GPCR activation and a rational template for allosteric modulator design targeting the dimeric interface of GABAB receptor.},
}
@article {pmid32491928,
year = {2021},
author = {Uyulan, C and Ergüzel, TT and Unubol, H and Cebi, M and Sayar, GH and Nezhad Asad, M and Tarhan, N},
title = {Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.},
journal = {Clinical EEG and neuroscience},
volume = {52},
number = {1},
pages = {38-51},
doi = {10.1177/1550059420916634},
pmid = {32491928},
issn = {2169-5202},
mesh = {Adult ; Brain/*physiopathology ; Brain-Computer Interfaces/psychology ; *Deep Learning ; Depressive Disorder, Major/*physiopathology ; Diagnosis, Computer-Assisted/methods ; *Electroencephalography/methods ; Female ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; },
abstract = {The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.},
}
@article {pmid32488296,
year = {2020},
author = {Kageyama, Y and He, X and Shimokawa, T and Sawada, J and Yanagisawa, T and Shayne, M and Sakura, O and Kishima, H and Mochizuki, H and Yoshimine, T and Hirata, M},
title = {Nationwide survey of 780 Japanese patients with amyotrophic lateral sclerosis: their status and expectations from brain-machine interfaces.},
journal = {Journal of neurology},
volume = {267},
number = {10},
pages = {2932-2940},
doi = {10.1007/s00415-020-09903-3},
pmid = {32488296},
issn = {1432-1459},
support = {26282165//Ministry of Education, Culture, Sports, Science and Technology/ ; 23100101//Japan Agency for Medical Research and Development/ ; },
mesh = {Aged ; *Amyotrophic Lateral Sclerosis/therapy ; *Brain-Computer Interfaces ; Humans ; Japan ; Motivation ; *Neurodegenerative Diseases ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes eventual death through respiratory failure unless mechanical ventilation is provided. Brain-machine interfaces (BMIs) may provide brain control supports for communication and motor function. We investigated the interests and expectations of patients with ALS concerning BMIs based on a large-scale anonymous questionnaire survey supported by the Japan Amyotrophic Lateral Sclerosis Association.<br><br>METHODS: We surveyed 1918 patients with ALS regarding their present status, tracheostomy use, interest in BMIs, and their level of expectation for communication (conversation, emergency alarm, internet, and writing letters) and movement support (postural change, controlling the bed, controlling household appliances, robotic arms, and wheel chairs).<br><br>FINDINGS: Seven hundred and eighty participants responded. Fifty-eight percent of the participants underwent tracheostomy. Approximately, 80% of the patients experienced stress or trouble during communication. For all nine supports,&#x2009;>&#x2009;60% participants expressed expectations regarding BMIs. More than 98% of participants who underwent tracheostomy expected support with conversation and emergency alarms. Participants who did not undergo tracheostomy exhibited significantly greater expectations than participants with tracheostomy did regarding all five movement supports. Seventy-seven percent of participants were interested in BMIs. Participants aged&#x2009;<&#x2009;60&#xa0;years had greater interest in both BMIs.<br><br>INTERPRETATION: This is the first large-scale survey to reveal the present status of patients with ALS and probe their interests and expectations regarding BMIs. Communication and emergency alarms should be supported by BMIs initially. BMIs should provide wide-ranging and high-performance support that can easily be used by severely disabled elderly patients with ALS.},
}
@article {pmid32485702,
year = {2020},
author = {Pavone, L and Moyanova, S and Mastroiacovo, F and Fazi, L and Busceti, C and Gaglione, A and Martinello, K and Fucile, S and Bucci, D and Prioriello, A and Nicoletti, F and Fornai, F and Morales, P and Senesi, R},
title = {Chronic neural interfacing with cerebral cortex using single-walled carbon nanotube-polymer grids.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036032},
doi = {10.1088/1741-2552/ab98db},
pmid = {32485702},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex ; Electrodes ; *Nanotubes, Carbon ; Polymers ; Rats ; },
abstract = {OBJECTIVE: The development of electrode arrays able to reliably record brain electrical activity is a critical issue in brain machine interface (BMI) technology. In the present study we undertook a comprehensive physico-chemical, physiological, histological and immunohistochemical characterization of new single-walled carbon nanotubes (SWCNT)-based electrode arrays grafted onto medium-density polyethylene (MD-PE) films.<br><br>APPROACH: The long-term electrical stability, flexibility, and biocompatibility of the SWCNT arrays were investigated in vivo in laboratory rats by two-months recording and analysis of subdural electrocorticogram (ECoG). Ex-vivo characterization of a thin flexible and single probe SWCNT/polymer electrode is also provided.<br><br>MAIN RESULTS: The SWCNT arrays were able to capture high quality and very stable ECoG signals across 8 weeks. The histological and immunohistochemical analyses demonstrated that SWCNT arrays show promising biocompatibility properties and may be used in chronic conditions. The SWCNT-based arrays are flexible and stretchable, providing low electrode-tissue impedance, and, therefore, high compliance with the irregular topography of the cortical surface. Finally, reliable evoked synaptic local field potentials in rat brain slices were recorded using a special SWCNT-polymer-based flexible electrode.<br><br>SIGNIFICANCE: The results demonstrate that the SWCNT arrays grafted in MD-PE are suitable for manufacturing flexible devices for subdural ECoG recording and might represent promising candidates for long-term neural implants for epilepsy monitoring or neuroprosthetic BMI.},
}
@article {pmid32483588,
year = {2020},
author = {Hu, JM and Qian, MZ and Tanigawa, H and Song, XM and Roe, AW},
title = {Focal Electrical Stimulation of Cortical Functional Networks.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {30},
number = {10},
pages = {5532-5543},
doi = {10.1093/cercor/bhaa136},
pmid = {32483588},
issn = {1460-2199},
mesh = {Animals ; Cats ; Cerebral Cortex/*physiology ; Electric Stimulation/*methods ; Male ; Neural Pathways/physiology ; Optical Imaging ; Photic Stimulation ; Visual Cortex/*physiology ; },
abstract = {Traditional electrical stimulation of brain tissue typically affects relatively large volumes of tissue spanning multiple millimeters. This low spatial resolution stimulation results in nonspecific functional effects. In addition, a primary shortcoming of these designs was the failure to take advantage of inherent functional organization in the cerebral cortex. Here, we describe a new method to electrically stimulate the brain which achieves selective targeting of single feature-specific domains in visual cortex. We provide evidence that this paradigm achieves mesoscale, functional network-specificity, and intensity dependence in a way that mimics visual stimulation. Application of this approach to known feature domains (such as color, orientation, motion, and depth) in visual cortex may lead to important functional improvements in the specificity and sophistication of brain stimulation methods and has implications for visual cortical prosthetic design.},
}
@article {pmid32483482,
year = {2020},
author = {Saberi Moghadam, S and Behroozi, M},
title = {A Simulation Model of Neural Activity During Hand Reaching Movement.},
journal = {Basic and clinical neuroscience},
volume = {11},
number = {1},
pages = {121-128},
pmid = {32483482},
issn = {2008-126X},
abstract = {INTRODUCTION: The neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of spike times in the spike train. In previous studies, decreased neuronal response variability was observed in the cortex's various areas during motor preparatory in reaching tasks. The reasons for the reduction in Neural Variability (NV) are unclear. It could be influenced by an increased firing rate, or it could result from the intrinsic characteristic of cells during the Reaction Time (RT).<br><br>METHODS: A neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator was simulated in this research. Neural stimulation could help us understand the relationships between the complex data structures of cortical activities and their stability in detail during motor intention in arm-reaching tasks.<br><br>RESULTS: Our measurements indicated a similar pattern of results to the cortex, a sharp reduction of the normalized variance of simulated spike trains across all trials. We also observed a reverse relationship between activity and normalized variance.<br><br>CONCLUSION: The present study findings could be applied to neural engineering and brain-machine interfaces for controlling external devices, like the movement of a robot arm.},
}
@article {pmid32480381,
year = {2020},
author = {Tortora, S and Ghidoni, S and Chisari, C and Micera, S and Artoni, F},
title = {Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046011},
doi = {10.1088/1741-2552/ab9842},
pmid = {32480381},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Gait ; Humans ; Neural Networks, Computer ; },
abstract = {OBJECTIVE: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. The aim of this work is to propose and validate a deep learning model to decode gait phases from Electroenchephalography (EEG).<br><br>APPROACH: A Long-Short Term Memory (LSTM) deep neural network has been trained to deal with time-dependent information within brain signals during locomotion. The EEG signals have been preprocessed by means of Artifacts Subspace Reconstruction (ASR) and Reliable Independent Component Analysis (RELICA) to ensure that classification performance was not affected by movement-related artifacts.<br><br>MAIN RESULTS: The network was evaluated on the dataset of 11 healthy subjects walking on a treadmill. The proposed decoding approach shows a robust reconstruction (AUC&#x2009;>&#x2009;90%) of gait patterns (i.e. swing and stance states) of both legs together, or of each leg independently.<br><br>SIGNIFICANCE: Our results support for the first time the use of a memory-based deep learning classifier to decode walking activity from non-invasive brain recordings. We suggest that this classifier, exploited in real time, can be a more effective input for devices restoring locomotion in impaired people.},
}
@article {pmid32479971,
year = {2020},
author = {Zhang, W and Zhou, T and Zhao, J and Ji, B and Wu, Z},
title = {Recognition of the idle state based on a novel IFB-OCN method for an asynchronous brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {341},
number = {},
pages = {108776},
doi = {10.1016/j.jneumeth.2020.108776},
pmid = {32479971},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; Recognition, Psychology ; },
abstract = {BACKGROUND: A major difficulty for the asynchronous brain-computer interface (BCI) lies in the accurate recognition of the control and idle states. Although subject's attention level was found to be different in these states, the validity of recognizing them using attention features has not been studied.<br><br>NEW METHODS: This paper proposed a novel Individualized Frequency Band based Optimized Complex Network (IFB-OCN) method to enhance the performance of discriminating the control and idle states. The IFB-OCN method extracted the attention features from a single FPz channel, selected the first three individualized frequency bands with the highest accuracies, and integrated the features of these bands for classification.<br><br>RESULTS: The performance was evaluated using a steady-state visual evoked potential (SSVEP)-based BCI task. In the offline evaluation, the IFB-OCN method achieved the highest average accuracy of 93.5 % with the data length of 4&#x202f;s, and achieved the highest information transfer rate (ITR) of 47.3 bits/min with the data length of 0.5&#x202f;s. In the simulated online evaluation, the IFB-OCN method obtained a true positive rate (TPR) of 89.8 % and a true negative rate (TNR) of 86.2 %.<br><br>The proposed IFB-OCN method recognized the control and idle states using a single FPz channel rather than the occipital channels, and outperformed the existing algorithms in the accuracy of detecting the attention level.<br><br>CONCLUSIONS: These results demonstrate that the proposed IFB-OCN method is efficient in recognizing the idle state and has a great potential for enhancing the asynchronous BCIs.},
}
@article {pmid32479507,
year = {2020},
author = {Williamson, JH and Quek, M and Popescu, I and Ramsay, A and Murray-Smith, R},
title = {Efficient human-machine control with asymmetric marginal reliability input devices.},
journal = {PloS one},
volume = {15},
number = {6},
pages = {e0233603},
pmid = {32479507},
issn = {1932-6203},
mesh = {Brain-Computer Interfaces/*standards ; Calibration ; Computer Simulation ; Feedback ; Humans ; Movement ; Signal-To-Noise Ratio ; },
abstract = {Input devices such as motor-imagery brain-computer interfaces (BCIs) are often unreliable. In theory, channel coding can be used in the human-machine loop to robustly encapsulate intention through noisy input devices but standard feedforward error correction codes cannot be practically applied. We present a practical and general probabilistic user interface for binary input devices with very high noise levels. Our approach allows any level of robustness to be achieved, regardless of noise level, where reliable feedback such as a visual display is available. In particular, we show efficient zooming interfaces based on feedback channel codes for two-class binary problems with noise levels characteristic of modalities such as motor-imagery based BCI, with accuracy <75%. We outline general principles based on separating channel, line and source coding in human-machine loop design. We develop a novel selection mechanism which can achieve arbitrarily reliable selection with a noisy two-state button. We show automatic online adaptation to changing channel statistics, and operation without precise calibration of error rates. A range of visualisations are used to construct user interfaces which implicitly code for these channels in a way that it is transparent to users. We validate our approach with a set of Monte Carlo simulations, and empirical results from a human-in-the-loop experiment showing the approach operates effectively at 50-70% of the theoretical optimum across a range of channel conditions.},
}
@article {pmid32479499,
year = {2020},
author = {Capllonch-Juan, M and Sepulveda, F},
title = {Modelling the effects of ephaptic coupling on selectivity and response patterns during artificial stimulation of peripheral nerves.},
journal = {PLoS computational biology},
volume = {16},
number = {6},
pages = {e1007826},
pmid = {32479499},
issn = {1553-7358},
mesh = {*Action Potentials ; Algorithms ; Animals ; Axons/metabolism/physiology ; Computer Simulation ; Electric Stimulation/*methods ; Electrodes, Implanted ; Humans ; *Models, Neurological ; Neural Conduction ; Peripheral Nerves/*physiology ; Peripheral Nervous System/*physiology ; Ranvier's Nodes/physiology ; },
abstract = {Artificial electrical stimulation of peripheral nerves for sensory feedback restoration can greatly benefit from computational models for simulation-based neural implant design in order to reduce the trial-and-error approach usually taken, thus potentially significantly reducing research and development costs and time. To this end, we built a computational model of a peripheral nerve trunk in which the interstitial space between the fibers and the tissues was modelled using a resistor network, thus enabling distance-dependent ephaptic coupling between myelinated axons and between fascicles as well. We used the model to simulate a) the stimulation of a nerve trunk model with a cuff electrode, and b) the propagation of action potentials along the axons. Results were used to investigate the effect of ephaptic interactions on recruitment and selectivity stemming from artificial (i.e., neural implant) stimulation and on the relative timing between action potentials during propagation. Ephaptic coupling was found to increase the number of fibers that are activated by artificial stimulation, thus reducing the artificial currents required for axonal recruitment, and it was found to reduce and shift the range of optimal stimulation amplitudes for maximum inter-fascicular selectivity. During propagation, while fibers of similar diameters tended to lock their action potentials and reduce their conduction velocities, as expected from previous knowledge on bundles of identical axons, the presence of many other fibers of different diameters was found to make their interactions weaker and unstable.},
}
@article {pmid32477080,
year = {2020},
author = {Putze, F and Vourvopoulos, A and Lécuyer, A and Krusienski, D and Bermúdez I Badia, S and Mullen, T and Herff, C},
title = {Editorial: Brain-Computer Interfaces and Augmented/Virtual Reality.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {144},
pmid = {32477080},
issn = {1662-5161},
}
@article {pmid32476516,
year = {2020},
author = {Fried-Oken, M and Kinsella, M and Peters, B and Eddy, B and Wojciechowski, B},
title = {Human visual skills for brain-computer interface use: a tutorial.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {15},
number = {7},
pages = {799-809},
pmid = {32476516},
issn = {1748-3115},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Humans ; *User-Computer Interface ; Vision Disorders/*rehabilitation ; },
abstract = {Background and objectives: Many brain-computer interfaces (BCIs) for people with severe disabilities present stimuli in the visual modality with little consideration of the visual skills required for successful use. The primary objective of this tutorial is to present researchers and clinical professionals with basic information about the visual skills needed for functional use of visual BCIs, and to offer modifications that would render BCI technology more accessible for persons with vision impairments.Methods: First, we provide a background on BCIs that rely on a visual interface. We then describe the visual skills required for BCI technologies that are used for augmentative and alternative communication (AAC), as well as common eye conditions or impairments that can impact the user's performance. We summarize screening tools that can be administered by the non-eye care professional in a research or clinical setting, as well as the role of the eye care professional. Finally, we explore potential BCI design modifications to compensate for identified functional impairments. Information was generated from literature review and the clinical experience of vision experts.Results and conclusions: This in-depth description culminates in foundational information about visual skills and functional visual impairments that affect the design and use of visual interfaces for BCI technologies. The visual interface is a critical component of successful BCI systems. We can determine a BCI system for potential users with visual impairments and design BCI visual interfaces based on sound anatomical and physiological visual clinical science.Implications for RehabilitationAs brain-computer interfaces (BCIs) become possible access methods for people with severe motor impairments, it is critical that clinicians have a basic knowledge of the visual skills necessary for use of visual BCI interfaces.Rehabilitation providers must have a knowledge of objectively gathering information regarding a potential BCI user's functional visual skills.Rehabilitation providers must understand how to modify BCI visual interfaces for the potential user with visual impairments.Rehabilitation scientists should understand the visual demands of BCIs as they develop and evaluate these new access methods.},
}
@article {pmid32474459,
year = {2020},
author = {Shams, M and Sagheer, A},
title = {A natural evolution optimization based deep learning algorithm for neurological disorder classification.},
journal = {Bio-medical materials and engineering},
volume = {31},
number = {2},
pages = {73-94},
doi = {10.3233/BME-201081},
pmid = {32474459},
issn = {1878-3619},
mesh = {*Algorithms ; Brain/diagnostic imaging/physiopathology ; Brain Diseases/classification/diagnosis ; Brain-Computer Interfaces ; Calibration ; *Deep Learning/standards ; Electroencephalography/methods/standards ; Humans ; Nervous System Diseases/*classification/diagnosis/physiopathology ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration.<br><br>OBJECTIVE: Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification.<br><br>METHODS: The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand.<br><br>RESULTS: The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches.<br><br>CONCLUSION: The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.},
}
@article {pmid32471047,
year = {2020},
author = {Li, Z and Qiu, L and Li, R and He, Z and Xiao, J and Liang, Y and Wang, F and Pan, J},
title = {Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {11},
pages = {},
pmid = {32471047},
issn = {1424-8220},
support = {2019A1515011375//Natural Science Foundation of Guangdong Province/ ; 201710010038//Pearl River S and T Nova Program of Guangzhou/ ; 61876067//National Natural Science Foundation of China/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Emotions/*classification ; Humans ; Support Vector Machine ; },
abstract = {：Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects' emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.},
}
@article {pmid32469070,
year = {2020},
author = {Brouillet, S and Boursier, G and Anav, M and Du Boulet De La Boissière, B and Gala, A and Ferrieres-Hoa, A and Touitou, I and Hamamah, S},
title = {C-reactive protein and ART outcomes: a systematic review.},
journal = {Human reproduction update},
volume = {26},
number = {5},
pages = {753-773},
doi = {10.1093/humupd/dmaa012},
pmid = {32469070},
issn = {1460-2369},
mesh = {Anovulation/blood/etiology ; C-Reactive Protein/*physiology ; Embryo Transfer/methods ; Female ; Fertilization in Vitro ; Humans ; Infertility, Female/therapy ; Maternal Age ; Ovulation Induction/methods ; Pregnancy ; Pregnancy Outcome/epidemiology ; Pregnancy Rate ; *Reproductive Techniques, Assisted ; Treatment Outcome ; },
abstract = {BACKGROUND: A dynamic balance between pro- and anti-inflammatory factors contributes to regulating human female reproduction. Chronic low-grade inflammation has been detected in several female reproductive conditions, from anovulation to embryo implantation failure. C-reactive protein (CRP) is a reliable marker of inflammation that is extensively used in clinical practice. Recent studies quantified CRP in the serum of infertile women undergoing ART and suggested its potential for the prediction of ART reproductive outcomes.<br><br>OBJECTIVE AND RATIONALE: The first objective of this systematic review of the available literature was to evaluate the association between pre-implantation circulating CRP concentration and pregnancy rates in women undergoing ART. The second objective was to describe serum CRP concentration changes after early embryo implantation. The changes in circulating CRP throughout the ART cycle, clinical implications of CRP quantification for the management of women undergoing ART, and future therapeutic options will also be discussed.<br><br>SEARCH METHODS: The MEDLINE database was systematically searched from inception to March 2019 using the following key words: (C-reactive protein) AND (assisted reproductive techniques OR ovulation induction OR insemination OR in vitro fertilization). Only articles in English were considered. Studies were selected based on title and abstract. The full text of potentially relevant articles was retrieved and assessed for inclusion by two reviewers (S.B. and S.H.). The protocol was registered in the International prospective register of systematic reviews (PROSPERO; registration number: CRD148687).<br><br>OUTCOMES: In total, 10 studies were included in this systematic review. Most of these studies reported lower circulating CRP values before the window of implantation and higher circulating CRP values during the peri-implantation period in women with successful ART outcome (biochemical or clinical pregnancy) compared to women without a successful outcome. Several lifestyle factors and/or drugs that reduce the concentration of circulating CRP significantly improve ART outcomes. Subgroup analyses according to female BMI and baseline circulating CRP concentration are highly recommended in future analyses.<br><br>WIDER IMPLICATIONS: These findings highlight a possible detrimental impact of preconception high circulating CRP concentration on ART outcomes. However, the biochemical or clinical pregnancy rate endpoints used in the studies examined here are insufficient (there were no data on live birth outcome), and the impact of major variables that can influence CRP and/or ART, for example maternal age, BMI, number of transferred embryos, and use of anti-inflammatory drugs, were not considered in the analyses. CRP quantification may be a potential marker of ART outcome, but its predictive value still needs to be investigated in large prospective studies. In future, the quantification of circulating CRP before starting ART could help to identify patients with a poor ART prognosis, leading to ART cycle cancellation or to preconception treatment to minimize the medical risks and costs.},
}
@article {pmid32468668,
year = {2020},
author = {Shang, Y and Yan, Y and Chen, B and Zhang, J and Zhang, T},
title = {Over-expressed MST1 impaired spatial memory via disturbing neural oscillation patterns in mice.},
journal = {Genes, brain, and behavior},
volume = {19},
number = {6},
pages = {e12678},
doi = {10.1111/gbb.12678},
pmid = {32468668},
issn = {1601-183X},
mesh = {Adaptor Proteins, Signal Transducing/metabolism ; Animals ; Brain/metabolism/physiology ; Cognition ; Forkhead Box Protein O3/metabolism ; *Gamma Rhythm ; Glutamate Decarboxylase/genetics/metabolism ; Male ; Mice ; Mice, Inbred C57BL ; Parvalbumins/genetics/metabolism ; Protein Serine-Threonine Kinases/*genetics/metabolism ; Receptors, GABA-A/genetics/metabolism ; *Spatial Memory ; *Theta Rhythm ; Up-Regulation ; YAP-Signaling Proteins ; },
abstract = {The activated mammalian Ste20-like serine/threonine kinases 1 (MST1) was found in the central nervous system diseases, such as cerebral ischemia, stroke and ALS, which were related with cognitions. The aim of this study was to examine the effect of elevated MST1 on memory functions in C57BL/6J mice. We also explored the underlying mechanism about the pattern alteration of neural oscillations, closely associated with cognitive dysfunctions, at different physiological rhythms, which were related to a wide range of basic and higher-level cognitive activities. A mouse model of the adeno-associated virus (AAV)-mediated overexpression of MST1 was established. The behavioral experiments showed that spatial memory was significantly damaged in MST1 mice. The distribution of either theta or gamma power was clearly disturbed in MST1 animals. Moreover, the synchronization in both theta and gamma rhythms, and theta-gamma cross-frequency coupling were significantly weakened in MST1 mice. In addition, the expressions of GABAA receptor, GAD67 and parvalbumin (PV) were obviously increased in MST1 mice. Meanwhile, blocking MST1 activity could inhibit the activation of FOXO3a and YAP. The above data suggest that MST1-overexpression may induce memory impairments via disturbing the patterns of neural activities, which is possibly associated with the abnormal GABAergic expression level.},
}
@article {pmid32468640,
year = {2020},
author = {Villasco, A and D'Alonzo, M},
title = {Extended endocrine therapy in premenopausal breast cancer patients: Where are we now?.},
journal = {The breast journal},
volume = {26},
number = {10},
pages = {2018-2020},
doi = {10.1111/tbj.13895},
pmid = {32468640},
issn = {1524-4741},
mesh = {Antineoplastic Agents, Hormonal/therapeutic use ; Aromatase Inhibitors/therapeutic use ; *Breast Neoplasms/drug therapy ; Chemotherapy, Adjuvant ; Female ; Humans ; Neoplasm Recurrence, Local/drug therapy ; Premenopause ; Tamoxifen/therapeutic use ; },
abstract = {Approximately 25% of new breast cancers are diagnosed in premenopausal patients, 50%-70% presenting as estrogen receptor-positive (ER+) breast tumors. Five-year adjuvant endocrine therapy (ET) with Tamoxifen is the cornerstone treatment for those patients but the evidence that up to 50% of ER + breast cancer distant recurrences develop after this time has now raised some questions. ATLAS and aTTom trials are the only two studies addressing the extension of Tamoxifen beyond 5 years in premenopausal patients. They showed significant DFS and OS benefits at a cost of increased rates of endometrial cancer and pulmonary embolus. Therefore, the selection of the patients at higher recurrence risk and hence deemed to get the most benefit from an extended endocrine therapy has become a major concern. Many clinical and genomic prognostic tools have shown validity in identifying patients at high late recurrence risk, but only the BCI prognostic score was shown to also be predictive of response to extended endocrine therapy. Nevertheless, all the evidence available on extended endocrine therapy in premenopausal patients was derived from trials in which patients were treated with tamoxifen alone and are hardly applicable to the current clinical scenario. In fact, the results of the SOFT and TEXT trials demonstrated the superiority of the addition of ovarian function suppression (OFS) and its association with the aromatase inhibitor (AI) Exemestane to Tamoxifen alone. However, the introduction in the clinical practice of AI + OFS-based endocrine therapy for the premenopausal patients will very soon lead to an impasse since neither data exist on extended therapy after this treatment schedule nor is there an ongoing trial intended to obtain new evidence.},
}
@article {pmid32468543,
year = {2020},
author = {Kaimara, P and Plerou, A and Deliyannis, I},
title = {Cognitive Enhancement and Brain-Computer Interfaces: Potential Boundaries and Risks.},
journal = {Advances in experimental medicine and biology},
volume = {1194},
number = {},
pages = {275-283},
pmid = {32468543},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces/adverse effects/trends ; *Cognition/physiology ; Electroencephalography ; Humans ; Neurology/instrumentation/trends ; Nootropic Agents/adverse effects ; },
abstract = {Electroencephalography (EEG) systems and brain-computer interfaces (BCIs) are terms frequently involved in the field of neurological research. Under a technological point of view, BCI is considered to be a significant achievement within the frame of learning disabilities rehabilitation. Nevertheless, the specifications for efficient use for cognitive enhancement and its potential boundaries are under concern. Author's main objective is to discuss BCI concrete components and potential advances as well as depict potential limitations while using technological devices within the frame of the learning procedure. Within this context, requirements, advantages, possible addiction risks, and boundaries regarding the specifications for brain-computer interfaces and technology in order to serve long-term research and developmental learning goals are discussed.},
}
@article {pmid32468537,
year = {2020},
author = {Sagiadinou, M and Plerou, A},
title = {Brain-Computer Interface Design and Neurofeedback Training in the Case of ADHD Rehabilitation.},
journal = {Advances in experimental medicine and biology},
volume = {1194},
number = {},
pages = {217-224},
pmid = {32468537},
issn = {0065-2598},
mesh = {*Attention Deficit Disorder with Hyperactivity/rehabilitation ; *Brain-Computer Interfaces ; Child ; Humans ; *Neurofeedback ; Treatment Outcome ; *Video Games/standards/trends ; },
abstract = {Neurofeedback video games respond to electrical brain signals instead to a mouse, joystick, or game controller input. These games embody the concept of improving physiological functioning by rewarding specific healthy body signals with success at playing a video game. In this paper, a threefold framework in reference to attention deficit disorder (ADD) and attention deficit hyperactivity disorder (ADHD) treatment blending with neurofeedback techniques and video game implementation is presented. In particular, the specifications of a neurofeedback-based video game for children dealing with ADHD, in order to enhance attention and concentration skills, are analyzed. Potential boundaries of this cognitive enhancement approach and authors future directions are also discussed.},
}
@article {pmid32463934,
year = {2020},
author = {Martínez Cano, I and Shevliakova, E and Malyshev, S and Wright, SJ and Detto, M and Pacala, SW and Muller-Landau, HC},
title = {Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA-TV).},
journal = {Global change biology},
volume = {26},
number = {8},
pages = {4478-4494},
doi = {10.1111/gcb.15188},
pmid = {32463934},
issn = {1365-2486},
support = {//National Science Foundation/International ; //Princeton University/International ; },
mesh = {Biomass ; Carbon/analysis ; Carbon Cycle ; *Forests ; Panama ; Trees ; *Tropical Climate ; },
abstract = {Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.},
}
@article {pmid32463381,
year = {2020},
author = {Yan, W and Xu, G},
title = {A novel motion coupling coding method for brain-computer interfaces.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {65},
number = {5},
pages = {531-541},
doi = {10.1515/bmt-2019-0257},
pmid = {32463381},
issn = {1862-278X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Motion ; Photic Stimulation/methods ; },
abstract = {Objectives The best frequency response band for the steady-state visual evoked potential (SSVEP) stimulus for humans is limited. This results in a reduced number of encoded targets. Methods To circumvent these limitations, we propose a motion-coupled, steady-state motion visual evoked potential (SSMVEP) method. We designed a stimulus paradigm that couples both sinusoidal and square wave motions. The paradigm performs a spiral motion with a higher frequency in the form of sinusoidal wave, and alters the size of the lower frequency via the square wave form. Results The motion-coupled SSMVEP method could simultaneously induce stable motion frequency and coupling frequency, and there was no loss of frequency component. Conclusions The proposed method has been evaluated to have substantial potential for increasing the number of coding targets, which is an effective supplement to the existing studies.},
}
@article {pmid32463379,
year = {2020},
author = {Ayodele, KP and Akinboboye, EA and Komolafe, MA},
title = {The performance of a low-cost bio-amplifier on 3D human arm movement reconstruction.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {65},
number = {5},
pages = {577-585},
doi = {10.1515/bmt-2019-0085},
pmid = {32463379},
issn = {1862-278X},
mesh = {*Amplifiers, Electronic ; Arm ; Biomechanical Phenomena ; Costs and Cost Analysis ; Electrodes ; Electroencephalography/methods ; Hand ; Humans ; Motion ; Movement ; Scalp ; },
abstract = {Objectives In this study, the performance of OpenBCI, a low-cost bio-amplifier, is assessed when used for 3D motion reconstruction. Methods Eleven scalp electrode locations from three subjects were used, with sampling rate of 125 Hz, subsequently band-pass filtered from 0.5 to 40 Hz. After segmentation into epochs, information-rich frequency ranges were determined using filter bank common spatial filter. Simultaneously, the actual hand motions of subjects were captured using a Microsoft Kinect sensor. Multimodal data streams were synchronized using the lab streaming layer (LSL) application. A modified version of an existing multiple linear regression models was employed to learn the relationship between the electroencephalography (EEG) feature input and the recorded kinematic data. To assess system performance with limited data, 10-fold cross validation was used. Results The most information-rich frequency bands for subjects were found to be in the ranges of 5 - 9 Hz and 33 - 37 Hz. Hand lateralization accuracy for the three subjects were 97.4, 78.7 and 96.9% respectively. 3D position reconstructed with an average correlation coefficient of 0.21, 0.47 and 0.38 respectively along three pre-defined axes, with the corresponding average correlation coefficients for velocity being 0.21, 0.36 and 0.25 respectively. The results compare favourably with a cross-section of existing results, while cost-per-electrode costs were 76% lower than the average per-electrode cost for similar systems and 44% lower than the cheapest previously-reported system. Conclusions This study has shown that low-cost bio-amplifiers such as the OpenBCI can be used for 3D motion reconstruction tasks.},
}
@article {pmid32455706,
year = {2020},
author = {Chen, J and Yu, Q and Fu, W and Chen, X and Zhang, Q and Dong, S and Chen, H and Zhang, S},
title = {A Highly Sensitive Amperometric Glutamate Oxidase Microbiosensor Based on a Reduced Graphene Oxide/Prussian Blue Nanocube/Gold Nanoparticle Composite Film-Modified Pt Electrode.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {10},
pages = {},
pmid = {32455706},
issn = {1424-8220},
support = {2017YFE019550//National Key Research and Development Program of China/ ; 2017YFC1308501//National Key Research and Development Program of China/ ; 2019QNA5027//Fundamental Research Funds for the Central Universities/ ; K20200185//Fundamental Research Funds for the Central Universities/ ; 31627802//the National Natural Science Foundation of China/ ; 81571769//National Natural Science Foundation of China/ ; 2018EB0ZX01//Zhejiang Laboratory/ ; 2018C01037//Zhejiang University Education Foundation Global Partnership Fund and Zhejiang Province Key R &amp; D Programs/ ; 2020C03039//Zhejiang University Education Foundation Global Partnership Fund and Zhejiang Province Key R &amp; D Programs/ ; },
mesh = {*Biosensing Techniques ; Electrodes ; Ferrocyanides ; Glutamates ; Gold ; Graphite ; *Metal Nanoparticles ; Reproducibility of Results ; },
abstract = {A simple method that relies only on an electrochemical workstation has been investigated to fabricate a highly sensitive glutamate microbiosensor for potential neuroscience applications. In this study, in order to develop the highly sensitive glutamate electrode, a 100 µm platinum wire was modified by the electrochemical deposition of gold nanoparticles, Prussian blue nanocubes, and reduced graphene oxide sheets, which increased the electroactive surface area; and the chitosan layer, which provided a suitable environment to bond the glutamate oxidase. The optimization of the fabrication procedure and analytical conditions is described. The modified electrode was characterized using field emission scanning electron microscopy, impedance spectroscopy, and cyclic voltammetry. The results exhibited its excellent sensitivity for glutamate detection (LOD = 41.33 nM), adequate linearity (50 nM-40 µM), ascendant reproducibility (RSD = 4.44%), and prolonged stability (more than 30 repetitive potential sweeps, two-week lifespan). Because of the important role of glutamate in neurotransmission and brain function, this small-dimension, high-sensitivity glutamate electrode is a promising tool in neuroscience research.},
}
@article {pmid32451424,
year = {2020},
author = {Borghini, G and Di Flumeri, G and Aricò, P and Sciaraffa, N and Bonelli, S and Ragosta, M and Tomasello, P and Drogoul, F and Turhan, U and Acikel, B and Ozan, A and Imbert, JP and Granger, G and Benhacene, R and Babiloni, F},
title = {A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {8600},
pmid = {32451424},
issn = {2045-2322},
abstract = {Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health. In this regard, sixteen controllers were asked to perform a realistic air traffic management (ATM) simulation during which subjective data (i.e. stress perception) and neurophysiological data (i.e. brain activity, heart rate, and galvanic skin response) were collected with the aim of accurately characterising the controller's stress level experienced in the various experimental conditions. In addition, external supervisors regularly evaluated the controllers in terms of manifested stress, safety, and efficiency throughout the ATM scenario. The results demonstrated 1) how the stressful events caused both supervisors and controllers to underestimate the experienced stress level, 2) the advantage of taking into account both cognitive and hormonal processes in order to define a reliable stress index, and 3) the importance of the points in time at which stress is measured owing to the potential transient effect once the stressful events have ceased.},
}
@article {pmid32448143,
year = {2020},
author = {Marquez-Chin, C and Popovic, MR},
title = {Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: a review.},
journal = {Biomedical engineering online},
volume = {19},
number = {1},
pages = {34},
pmid = {32448143},
issn = {1475-925X},
mesh = {*Electric Stimulation Therapy ; Humans ; *Motor Activity ; *Recovery of Function ; Spinal Cord Injuries/*physiopathology/*therapy ; Stroke/*physiopathology/*therapy ; },
abstract = {Functional electrical stimulation is a technique to produce functional movements after paralysis. Electrical discharges are applied to a person's muscles making them contract in a sequence that allows performing tasks such as grasping a key, holding a toothbrush, standing, and walking. The technology was developed in the sixties, during which initial clinical use started, emphasizing its potential as an assistive device. Since then, functional electrical stimulation has evolved into an important therapeutic intervention that clinicians can use to help individuals who have had a stroke or a spinal cord injury regain their ability to stand, walk, reach, and grasp. With an expected growth in the aging population, it is likely that this technology will undergo important changes to increase its efficacy as well as its widespread adoption. We present here a series of functional electrical stimulation systems to illustrate the fundamentals of the technology and its applications. Most of the concepts continue to be in use today by modern day devices. A brief description of the potential future of the technology is presented, including its integration with brain-computer interfaces and wearable (garment) technology.},
}
@article {pmid32446984,
year = {2020},
author = {Sharif, S and Ali, SM},
title = {"I Felt the Ball"-The Future of Spine Injury Recovery.},
journal = {World neurosurgery},
volume = {140},
number = {},
pages = {602-613},
doi = {10.1016/j.wneu.2020.05.131},
pmid = {32446984},
issn = {1878-8769},
mesh = {*Brain-Computer Interfaces/history ; *Exoskeleton Device ; History, 20th Century ; History, 21st Century ; Humans ; Neurological Rehabilitation ; Paraplegia/etiology/*rehabilitation ; Quadriplegia/etiology/*rehabilitation ; Recovery of Function ; Spinal Cord Injuries/complications/physiopathology/*rehabilitation ; *Spinal Cord Stimulation ; },
abstract = {Spinal cord injury (SCI) has no cure and individuals with SCI become dependent on others for life. After injury, the signals below the lesion are disrupted, but the brain still produces motor commands. Researchers have bypassed this obstacle, which has given rise to the brain-machine interface (BMI). BMI devices are implanted in the brain or spinal cord, where they decode and send signals beyond the injured segment. Experiments were initially conducted on animals, with favorable results. BMIs are classified according to their type, function, signal generation, and so on. Because of invasiveness, their long-term use is questionable, because of infections and complications. The use of an implantable epidural array in patients with chronic SCI showed that participants were able to walk with the help of a stimulator, and after months of training, they were able to walk with the stimulator turned off. Another innovation is a robotic suit for paraplegics and tetraplegics that supports the movement of limbs. The research on stem cells has not shown favorable results. In future, one of these cutting-edge technologies will prevail over the other, but BMI seems to have the upper hand. The future of BMI with fusion of robotics and artificial intelligence is promising for patients with chronic SCI. These modern devices need to be less invasive, biocompatible, easily programmable, portable, and cost-effective. After these hurdles are overcome, the devices may become the mainstay of potential rehabilitation therapy for partial recovery. The time may come when all patients with severe SCI are told "You will walk again."},
}
@article {pmid32443512,
year = {2020},
author = {Schmidt, D and Villalba Diez, J and Ordieres-Meré, J and Gevers, R and Schwiep, J and Molina, M},
title = {Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {10},
pages = {},
pmid = {32443512},
issn = {1424-8220},
mesh = {Brain ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Humans ; *Manufacturing Industry ; },
abstract = {Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the "HOSHIN KANRI TREE" (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain's activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.},
}
@article {pmid32442987,
year = {2020},
author = {Okorokova, EV and Goodman, JM and Hatsopoulos, NG and Bensmaia, SJ},
title = {Decoding hand kinematics from population responses in sensorimotor cortex during grasping.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046035},
doi = {10.1088/1741-2552/ab95ea},
pmid = {32442987},
issn = {1741-2552},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R35 NS122333/NS/NINDS NIH HHS/United States ; R01 NS082865/NS/NINDS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; Hand ; Hand Strength ; *Motor Cortex ; Movement ; *Sensorimotor Cortex ; },
abstract = {OBJECTIVE: The hand-a complex effector comprising dozens of degrees of freedom of movement-endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those associated with proximal upper limb control.<br><br>APPROACH: To fill this gap, we trained two monkeys to grasp objects varying in size and shape while tracking their hand postures and recording single-unit activity from M1 and SC. We then decoded their hand kinematics across tens of joints from population activity in these areas.<br><br>MAIN RESULTS: We found that we could accurately decode kinematics with a small number of neural signals and that different cortical fields carry different amounts of information about hand kinematics. In particular, neural signals in rostral M1 led to better performance than did signals in caudal M1, whereas Brodmann's area 3a outperformed areas 1 and 2 in SC. Moreover, decoding performance was higher for joint angles than joint angular velocities, in contrast to what has been found with proximal limb decoders.<br><br>SIGNIFICANCE: We conclude that cortical signals can be used for dexterous hand control in brain machine interface applications and that postural representations in SC may be exploited via intracortical stimulation to close the sensorimotor loop.},
}
@article {pmid32442981,
year = {2020},
author = {Aydarkhanov, R and Ušćumlić, M and Chavarriaga, R and Gheorghe, L and Del R Millán, J},
title = {Spatial covariance improves BCI performance for late ERPs components with high temporal variability.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036030},
doi = {10.1088/1741-2552/ab95eb},
pmid = {32442981},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; },
abstract = {OBJECTIVE: Event Related Potentials (ERPs) reflecting cognitive response to external stimuli, are widely used in brain computer interfaces. ERP waveforms are characterized by a series of components of particular latency and amplitude. The classical ERP decoding methods exploit this waveform characteristic and thus achieve a high performance only if there is sufficient time- and phase-locking across trials. The required condition is not fulfilled if the experimental tasks are challenging or if it is needed to generalize across various experimental conditions. Features based on spatial covariances across channels can potentially overcome the latency jitter and delays since they aggregate the information across time.<br><br>APPROACH: We compared the performance stability of waveform and covariance-based features as well as their combination in two simulated scenarios: 1) generalization across experiments on Error-related Potentials and 2) dealing with larger latency jitter across trials.<br><br>MAIN RESULTS: The features based on spatial covariances provide a stable performance with a minor decline under jitter levels of up to &#xb1; 300 ms, whereas the decoding performance with waveform features quickly drops from 0.85 to 0.55 AUC. The generalization across ErrP experiments also resulted in a significantly more stable performance with covariance-based features.<br><br>SIGNIFICANCE: The results confirmed our hypothesis that covariance-based features can be used to: 1) classify more reliably ERPs with higher intrinsic variability in more challenging real-life applications and 2) generalize across related experimental protocols.},
}
@article {pmid32440689,
year = {2020},
author = {de Manzano, &#xd6; and Kuckelkorn, KL and Ström, K and Ullén, F},
title = {Action-Perception Coupling and Near Transfer: Listening to Melodies after Piano Practice Triggers Sequence-Specific Representations in the Auditory-Motor Network.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {30},
number = {10},
pages = {5193-5203},
pmid = {32440689},
issn = {1460-2199},
mesh = {Acoustic Stimulation/methods ; Adult ; Auditory Perception/*physiology ; Brain/*physiology ; Female ; Humans ; Learning/*physiology ; Male ; Movement/physiology ; Music ; Psychomotor Performance/*physiology ; Recognition, Psychology/physiology ; },
abstract = {Understanding how perception and action are coupled in the brain has important implications for training, rehabilitation, and brain-machine interfaces. Ideomotor theory postulates that willed actions are represented through previously experienced effects and initiated by the anticipation of those effects. Previous research has accordingly found that sensory events, if previously associated with action outcomes, can induce activity in motor regions. However, it remains unclear whether the motor-related activity induced during perception of more naturalistic sequences of actions actually represents "sequence-specific" information. In the present study, nonmusicians were firstly trained to play two melodies on the piano; secondly, they performed an fMRI experiment while listening to these melodies as well as novel, untrained melodies; thirdly, multivariate pattern analysis was used to test if voxel-wise patterns of brain activity could identify trained, but not novel melodies. The results importantly show that after associative learning, a series of sensory events can trigger sequence-specific representations in both sensory and motor networks. Interestingly, also novel melodies could be classified in multiple regions, including default mode regions. A control experiment confirmed these outcomes to be training-dependent. We discuss how action-perception coupling may enable spontaneous near transfer and action simulation during action observation.},
}
@article {pmid32440363,
year = {2019},
author = {Chen, K and Lam, S and Kozai, TD},
title = {What directions of improvements in electrode designs should we expect in the next 5-10 years?.},
journal = {Bioelectronics in medicine},
volume = {2},
number = {3},
pages = {119-122},
pmid = {32440363},
issn = {2059-1519},
support = {R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; },
}
@article {pmid32439535,
year = {2020},
author = {Sabbagh, D and Ablin, P and Varoquaux, G and Gramfort, A and Engemann, DA},
title = {Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.},
journal = {NeuroImage},
volume = {222},
number = {},
pages = {116893},
doi = {10.1016/j.neuroimage.2020.116893},
pmid = {32439535},
issn = {1095-9572},
mesh = {Adult ; *Brain Waves/physiology ; *Cerebral Cortex/physiology ; Computer Simulation ; Electroencephalography/*methods ; Electromyography ; Humans ; *Machine Learning ; Magnetoencephalography/*methods ; *Models, Theoretical ; Regression Analysis ; Signal Processing, Computer-Assisted ; Supervised Machine Learning ; },
abstract = {Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subject-level, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to prediction of outcomes at the subject-level, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing well even without preprocessing. Our results suggest each method has its niche: supervised spatial filtering is practical for event-level prediction while the Riemannian model may enable simple end-to-end learning.},
}
@article {pmid32439425,
year = {2020},
author = {Rieke, JD and Matarasso, AK and Yusufali, MM and Ravindran, A and Alcantara, J and White, KD and Daly, JJ},
title = {Development of a combined, sequential real-time fMRI and fNIRS neurofeedback system to enhance motor learning after stroke.},
journal = {Journal of neuroscience methods},
volume = {341},
number = {},
pages = {108719},
doi = {10.1016/j.jneumeth.2020.108719},
pmid = {32439425},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; *Neurofeedback ; *Stroke/diagnostic imaging ; },
abstract = {BACKGROUND: After stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors.<br><br>NEW METHOD: Combination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system user.<br><br>RESULTS: The user achieved a maximum of 71 % brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension.<br><br>Neurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal.<br><br>CONCLUSION: The system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significantly improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and some patient-use issues make it less feasible for clinical practice.},
}
@article {pmid32435182,
year = {2020},
author = {RaviPrakash, H and Korostenskaja, M and Castillo, EM and Lee, KH and Salinas, CM and Baumgartner, J and Anwar, SM and Spampinato, C and Bagci, U},
title = {Deep Learning Provides Exceptional Accuracy to ECoG-Based Functional Language Mapping for Epilepsy Surgery.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {409},
pmid = {32435182},
issn = {1662-4548},
abstract = {The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.},
}
@article {pmid32434491,
year = {2020},
author = {Ripping, TM and Kiemeney, LA and van Hoogstraten, LMC and Witjes, JA and Aben, KKH and , },
title = {Insight into bladder cancer care: study protocol of a large nationwide prospective cohort study (BlaZIB).},
journal = {BMC cancer},
volume = {20},
number = {1},
pages = {455},
pmid = {32434491},
issn = {1471-2407},
support = {IKNL 2015-7914//KWF Kankerbestrijding/ ; },
mesh = {Follow-Up Studies ; Humans ; Needs Assessment/*standards ; Netherlands ; Practice Guidelines as Topic/*standards ; Prognosis ; Prospective Studies ; Quality of Health Care/*standards ; *Quality of Life ; Urinary Bladder Neoplasms/*therapy ; },
abstract = {BACKGROUND: Despite the embedding of bladder cancer management in European guidelines, large variation in clinical practice exists for applied diagnostics and treatments. This variation may affect patients' outcomes including complications, disease recurrence, progression, survival, and health-related quality of life (HRQL). Lack of detailed clinical data and HRQL data hampers a comprehensive evaluation of bladder cancer care. Through prospective data registration, this study aims to provide insight in bladder cancer care in the Netherlands and to identify barriers and modulators of optimal bladder cancer care.<br><br>METHODS: This study is a nationwide prospective cohort study including all patients who were newly diagnosed with high-risk non-muscle invasive bladder cancer (HR-NMIBC; Tis and/or T1, N0, M0/x) or non-metastatic muscle invasive bladder cancer (MIBC; &#x2265;T2, N0/x-3, M0/x) in the Netherlands between November 1st 2017 and October 31st 2019. Extensive data on patient- and tumor characteristics, diagnostics, treatment and follow-up up to 2&#x2009;years after diagnosis will be collected prospectively from electronic health records in the participating hospitals by data managers of the Netherlands Cancer Registry (NCR). Additionally, patients will be requested to participate in a HRQL survey shortly after diagnosis and subsequently at 6, 12 and 24&#x2009;months. The HRQL survey includes six standardized questionnaires, e.g. SCQ Comorbidity score, EQ-5D-5&#x2009;L, EORTC-QLQ-C30, EORTC-QLQ-BLM30, EORTC-QLQ-NMIBC24 and BCI. Variation in care and deviation from the European guidelines will be assessed through descriptive analyses and multivariable multilevel analyses. Survival analyses will be used to assess the association between variation in care and relevant outcomes such as survival.<br><br>DISCUSSION: The results of this observational study will guide modifications of clinical practice and/or adaptation of guidelines and may set the agenda for new specific research questions in the management of bladder cancer.<br><br>TRIAL REGISTRATION: Retrospectively registered in the Netherlands Trial Register. Trial identification number: NL8106. Registered on October 22nd 2019.},
}
@article {pmid32431953,
year = {2020},
author = {Pawar, D and Dhage, S},
title = {Multiclass covert speech classification using extreme learning machine.},
journal = {Biomedical engineering letters},
volume = {10},
number = {2},
pages = {217-226},
pmid = {32431953},
issn = {2093-985X},
abstract = {The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e 'left', 'right', 'up' and 'down'. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain-Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research.},
}
@article {pmid32428706,
year = {2020},
author = {Makin, TR and Flor, H},
title = {Brain (re)organisation following amputation: Implications for phantom limb pain.},
journal = {NeuroImage},
volume = {218},
number = {},
pages = {116943},
pmid = {32428706},
issn = {1095-9572},
support = {/WT_/Wellcome Trust/United Kingdom ; 230249/ERC_/European Research Council/International ; 215575/Z/19/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Adult ; *Amputation, Surgical ; Amputees ; Brain/*diagnostic imaging/*physiopathology ; Brain Mapping ; Female ; Humans ; Male ; Middle Aged ; Pain/*diagnostic imaging/etiology/*physiopathology ; Phantom Limb/complications/*diagnostic imaging/*physiopathology ; Somatosensory Cortex/diagnostic imaging/physiopathology ; },
abstract = {Following arm amputation the region that represented the missing hand in primary somatosensory cortex (S1) becomes deprived of its primary input, resulting in changed boundaries of the S1 body map. This remapping process has been termed 'reorganisation' and has been attributed to multiple mechanisms, including increased expression of previously masked inputs. In a maladaptive plasticity model, such reorganisation has been associated with phantom limb pain (PLP). Brain activity associated with phantom hand movements is also correlated with PLP, suggesting that preserved limb functional representation may serve as a complementary process. Here we review some of the most recent evidence for the potential drivers and consequences of brain (re)organisation following amputation, based on human neuroimaging. We emphasise other perceptual and behavioural factors consequential to arm amputation, such as non-painful phantom sensations, perceived limb ownership, intact hand compensatory behaviour or prosthesis use, which have also been related to both cortical changes and PLP. We also discuss new findings based on interventions designed to alter the brain representation of the phantom limb, including augmented/virtual reality applications and brain computer interfaces. These studies point to a close interaction of sensory changes and alterations in brain regions involved in body representation, pain processing and motor control. Finally, we review recent evidence based on methodological advances such as high field neuroimaging and multivariate techniques that provide new opportunities to interrogate somatosensory representations in the missing hand cortical territory. Collectively, this research highlights the need to consider potential contributions of additional brain mechanisms, beyond S1 remapping, and the dynamic interplay of contextual factors with brain changes for understanding and alleviating PLP.},
}
@article {pmid32425763,
year = {2020},
author = {Luo, TJ and Fan, Y and Chen, L and Guo, G and Zhou, C},
title = {EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.},
journal = {Frontiers in neuroinformatics},
volume = {14},
number = {},
pages = {15},
pmid = {32425763},
issn = {1662-5196},
abstract = {Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.},
}
@article {pmid32423133,
year = {2020},
author = {Jochumsen, M and Knoche, H and Kjaer, TW and Dinesen, B and Kidmose, P},
title = {EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {10},
pages = {},
pmid = {32423133},
issn = {1424-8220},
support = {22357//Velux Fonden/ ; },
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/instrumentation ; Humans ; *Intention ; *Movement ; Reproducibility of Results ; },
abstract = {Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%-77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.},
}
@article {pmid32419492,
year = {2021},
author = {Yu, X and da Silva-Sauer, L and Donchin, E},
title = {Habituation of P300 in the Use of P300-based Brain-Computer Interface Spellers: Individuals With Amyotrophic Lateral Sclerosis Versus Age-Matched Controls.},
journal = {Clinical EEG and neuroscience},
volume = {52},
number = {3},
pages = {221-230},
doi = {10.1177/1550059420918755},
pmid = {32419492},
issn = {2169-5202},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Habituation, Psychophysiologic ; Humans ; User-Computer Interface ; },
abstract = {The P300-based brain-computer interface speller can provide motor independent communication to individuals with amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disorder that affects the motor system. P300 amplitude stability is critical for operation of the P300 speller. The P300 has good long-term stability, but to our knowledge, short-term habituation in the P300 speller has not been studied. In the current study, 15 participants: 8 ALS patients and 7 age-matched healthy volunteers (HVs), used 2 versions of P300 spellers, Face speller and Flash speller, each for 30 minutes. The ALS group performed as well as the HVs in both spellers and HVs did better with the Face speller than Flash speller while the ALS group performed equally well in both spellers. Neither intra-run P300 habituation nor inter-run P300 habituation was found. The P300 speller could be a reliable communication device for individuals with ALS.},
}
@article {pmid32417199,
year = {2020},
author = {Brouillet, S and Martinez, G and Coutton, C and Hamamah, S},
title = {Is cell-free DNA in spent embryo culture medium an alternative to embryo biopsy for preimplantation genetic testing? A systematic review.},
journal = {Reproductive biomedicine online},
volume = {40},
number = {6},
pages = {779-796},
doi = {10.1016/j.rbmo.2020.02.002},
pmid = {32417199},
issn = {1472-6491},
mesh = {*Cell-Free Nucleic Acids ; *Culture Media ; Embryo Culture Techniques ; Female ; Genetic Testing/*methods ; Humans ; Pregnancy ; Preimplantation Diagnosis/*methods ; },
abstract = {Preimplantation genetic testing (PGT) is increasingly used worldwide. It currently entails the use of invasive techniques, i.e. polar body, blastomere, trophectoderm biopsy or blastocentesis, to obtain embryonic DNA, with major technical limitations and ethical issues. Evidence suggests that invasive PGT can lead to genetic misdiagnosis in the case of embryo mosaicism, and, consequently, to the selection of affected embryos for implantation or to the destruction of healthy embryos. Recently, spent culture medium (SCM) has been proposed as an alternative source of embryonic DNA. An increasing number of studies have reported the detection of cell-free DNA in SCM and highlighted the diagnostic potential of non-invasive SCM-based PGT for assessing the genetic status of preimplantation human embryos obtained by IVF. The reliability of this approach for clinical applications, however, needs to be determined. In this systematic review, published evidence on non-invasive SCM-based PGT is presented, and its current benefits and limitations compared with invasive PGT. Then, ways of optimizing and standardizing procedures for non-invasive SCM-based PGT to prevent technical biases and to improve performance in future studies are discussed. Finally, clinical perspectives of non-invasive PGT are presented and its future applications in reproductive medicine highlighted.},
}
@article {pmid32416601,
year = {2020},
author = {Machizawa, MG and Lisi, G and Kanayama, N and Mizuochi, R and Makita, K and Sasaoka, T and Yamawaki, S},
title = {Quantification of anticipation of excitement with a three-axial model of emotion with EEG.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036011},
doi = {10.1088/1741-2552/ab93b4},
pmid = {32416601},
issn = {1741-2552},
mesh = {Arousal ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Emotions ; Humans ; Young Adult ; },
abstract = {OBJECTIVE: Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one's internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, 'Waku-Waku' (in Japanese), in which people predictively code upcoming emotional events.<br><br>APPROACH: Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.<br><br>MAIN RESULTS: A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations.<br><br>SIGNIFICANCE: The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.},
}
@article {pmid32415223,
year = {2020},
author = {Dai, H and Zhu, H and Zhang, D and Zhang, L and Liu, C and Zan, Y and Cai, P},
title = {The correlation between diffusion tensor imaging of the sacral cord and bladder contractility in people with tetraplegia.},
journal = {Spinal cord},
volume = {58},
number = {12},
pages = {1255-1262},
pmid = {32415223},
issn = {1476-5624},
support = {81971573//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Anisotropy ; Cross-Sectional Studies ; *Diffusion Tensor Imaging ; Humans ; Quadriplegia ; Spinal Cord ; *Spinal Cord Injuries/complications/diagnostic imaging ; Urinary Bladder/diagnostic imaging ; },
abstract = {STUDY DESIGN: Cross-sectional descriptive study.<br><br>OBJECTIVES: To compare the diffusion tensor imaging (DTI) changes of the sacral cord in people with complete cervical spinal cord injury (SCI) and neurogenic bladder versus people without SCI, and to explore the relationship between sacral cord DTI changes and bladder contractility.<br><br>SETTING: First Affiliated Hospital of Soochow University, Jiangsu Province, China.<br><br>METHODS: Forty participants were included: 25 participants with complete cervical SCI and 15 without SCI. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were calculated by DTI for ventral horn and intermediate column of sacral cord at S2-S4 level. All participants underwent urodynamic examination. The urodynamic parameters (voiding efficiency (VE), and bladder contractility index (BCI)) and DTI parameters were compared between people with and without SCI. The correlations between DTI values (FA and ADC) and urodynamic parameters were analyzed.<br><br>RESULTS: The FA values were significantly lower and the ADC values were significantly higher in the intermediate column and ventral horn at S2-S4 level of the participants with SCI compared with their able-bodied counterparts (p&#x2009;<&#x2009;0.05). VE and BCI were significantly different between the two groups (p&#x2009;<&#x2009;0.05). The FA values of intermediate column positively correlated with BCI (r&#x2009;=&#x2009;0.749, p&#x2009;<&#x2009;0.05) and the ADC values negatively correlated with BCI (r&#x2009;=&#x2009;-0.471, p&#x2009;<&#x2009;0.05) in participants with SCI. The DTI values of sacral cord were not correlated with each urodynamic parameter in participants without SCI (p&#x2009;>&#x2009;0.05).<br><br>CONCLUSIONS: Complete cervical SCI might lead to microstructural changes of the sacral cord, which might further affect bladder contraction.},
}
@article {pmid32413885,
year = {2020},
author = {Deng, X and Liang Yu, Z and Lin, C and Gu, Z and Li, Y},
title = {Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {045005},
doi = {10.1088/1741-2552/ab937e},
pmid = {32413885},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Reinforcement, Psychology ; *Wheelchairs ; },
abstract = {OBJECTIVE: For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability.<br><br>APPROACH: In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects' brain control ability online based on quantized attention-gated kernel reinforcement learning. With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator.<br><br>MAIN RESULTS: The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control.<br><br>SIGNIFICANCE: We discuss how our proposed method shows promise for BCI applications that can evaluate subjects' brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject's instruction and robot autonomy.},
}
@article {pmid32413533,
year = {2020},
author = {Ahmadi, A and Davoudi, S and Behroozi, M and Daliri, MR},
title = {Decoding covert visual attention based on phase transfer entropy.},
journal = {Physiology &amp; behavior},
volume = {222},
number = {},
pages = {112932},
doi = {10.1016/j.physbeh.2020.112932},
pmid = {32413533},
issn = {1873-507X},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Entropy ; Humans ; },
abstract = {Covert attention to spatial and color features in the visual field is a relatively new control signal for brain-computer interfaces (BCI). To guide the processing resources to the related visual scene aspects, covert attention should be decoded from human brain. Here, a novel expert system is designed to decode covert visual attention based on the EEG signal provided from 15 subjects during a new task based on a change in lumination to two blue and orange color on the right and the left side of the screen, which is evaluated in two cases of binary and multi-class systems. For the first time, Phase transfer entropy (PTE) has been used in these systems, and after selecting the optimal decoding feature, the frequency band (8-13 Hz) Alpha and Beta1 (13-20 Hz) have the best performance compared to other frequency bands. Two-class classification accuracies of the designed system in two frequency bands (Alpha and Beta1) are 91.87% and 89.53%, respectively. Also, the accuracies are 65.11% and 63.38% for multi-class classification in specified frequency bands. In these frequency bands, the parietal and frontal lobes showed the most significant difference in comparison to the other lobes. Also, the obtained results declared that the expert system's performance in the Alpha band by the extracted features from the Posterior region is better than all frequency bands in other different brain regions. The performance of the proposed expert system by PTE is significantly better than the previous phase synchronization based features. Results have shown that the PTE feature performs better than the common methods for decoding covert visual attention.},
}
@article {pmid32413247,
year = {2020},
author = {Vu, PP and Chestek, CA and Nason, SR and Kung, TA and Kemp, SWP and Cederna, PS},
title = {The future of upper extremity rehabilitation robotics: research and practice.},
journal = {Muscle &amp; nerve},
volume = {61},
number = {6},
pages = {708-718},
pmid = {32413247},
issn = {1097-4598},
support = {F31 HD098804/HD/NICHD NIH HHS/United States ; R01 NS105132/NS/NINDS NIH HHS/United States ; },
mesh = {Amputees/*rehabilitation ; Biomedical Research/methods/*trends ; Brain-Computer Interfaces/trends ; Forecasting ; Humans ; Robotics/methods/*trends ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Upper Extremity/*physiology ; },
abstract = {The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain-machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain-machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.},
}
@article {pmid32410938,
year = {2020},
author = {Benitez-Andonegui, A and Burden, R and Benning, R and Möckel, R and Lührs, M and Sorger, B},
title = {An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {346},
pmid = {32410938},
issn = {1662-4548},
abstract = {Augmented reality (AR) enhances the user's environment by projecting virtual objects into the real world in real-time. Brain-computer interfaces (BCIs) are systems that enable users to control external devices with their brain signals. BCIs can exploit AR technology to interact with the physical and virtual world and to explore new ways of displaying feedback. This is important for users to perceive and regulate their brain activity or shape their communication intentions while operating in the physical world. In this study, twelve healthy participants were introduced to and asked to choose between two motor-imagery tasks: mental drawing and interacting with a virtual cube. Participants first performed a functional localizer run, which was used to select a single fNIRS channel for decoding their intentions in eight subsequent choice-encoding runs. In each run participants were asked to select one choice of a six-item list. A rotating AR cube was displayed on a computer screen as the main stimulus, where each face of the cube was presented for 6 s and represented one choice of the six-item list. For five consecutive trials, participants were instructed to perform the motor-imagery task when the face of the cube that represented their choice was facing them (therewith temporally encoding the selected choice). In the end of each run, participants were provided with the decoded choice based on a joint analysis of all five trials. If the decoded choice was incorrect, an active error-correction procedure was applied by the participant. The choice list provided in each run was based on the decoded choice of the previous run. The experimental design allowed participants to navigate twice through a virtual menu that consisted of four levels if all choices were correctly decoded. Here we demonstrate for the first time that by using AR feedback and flexible choice encoding in form of search trees, we can increase the degrees of freedom of a BCI system. We also show that participants can successfully navigate through a nested menu and achieve a mean accuracy of 74% using a single motor-imagery task and a single fNIRS channel.},
}
@article {pmid32409507,
year = {2020},
author = {Forys, BJ and Xiao, D and Gupta, P and Murphy, TH},
title = {Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks.},
journal = {eNeuro},
volume = {7},
number = {3},
pages = {},
pmid = {32409507},
issn = {2373-2822},
support = {FDN-143209/CAPMC/CIHR/Canada ; },
mesh = {Animals ; Behavior, Animal ; Mice ; Movement ; *Neural Networks, Computer ; *Software ; },
abstract = {Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut-a robust movement-tracking deep neural network framework-which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered "closed loop" brain-machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.},
}
@article {pmid32408272,
year = {2020},
author = {Peng, Y and Wang, Z and Wong, CM and Nan, W and Rosa, A and Xu, P and Wan, F and Hu, Y},
title = {Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {045006},
doi = {10.1088/1741-2552/ab933e},
pmid = {32408272},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization ; Electrooculography ; *Evoked Potentials, Visual ; Photic Stimulation ; Reproducibility of Results ; },
abstract = {OBJECTIVE: The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance.<br><br>APPROACH: This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals.<br><br>MAIN RESULTS: Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend.<br><br>SIGNIFICANCE: This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.},
}
@article {pmid32406875,
year = {2020},
author = {Pawlaczyk-Łuszczyńska, M and Dudarewicz, A},
title = {Impact of very high-frequency sound and low-frequency ultrasound on people - the current state of the art.},
journal = {International journal of occupational medicine and environmental health},
volume = {33},
number = {4},
pages = {389-408},
doi = {10.13075/ijomeh.1896.01586},
pmid = {32406875},
issn = {1896-494X},
mesh = {Hearing Loss, Noise-Induced ; Humans ; Occupational Exposure/adverse effects ; Sound/*adverse effects ; Ultrasonic Waves/*adverse effects ; },
abstract = {For several decades, low-frequency ultrasound (<100 kHz) has been widely used in industry, medicine, commerce, military service and the home. The objective of the study was to present the current state of the art on the harmful effects of low-frequency airborne ultrasound on people, especially in occupational settings. The scientific literature search was performed using accessible medical and other databases (WOS, BCI, CCC, DRCI, DIIDW, KJD, MEDLINE, RSCI, SCIELO and ZOOREC), and the obtained results were then hand-searched to eliminate non-relevant papers. This review includes papers published in 1948-2018. The potential effects of the low-frequency airborne ultrasound have been classified as auditory and non-auditory effects, including subjective, physiological, and thermal effects. In particular, already in the 1960-1970s, it was demonstrated that ultrasonic exposure, when sufficiently intense, appeared to result in a syndrome involving nausea, headache, vomiting, disturbance of coordination, dizziness, and fatigue, and might cause a temporary or permanent hearing impairment. However, since that time, not too much work has been done. Further studies are needed before any firm conclusions can be drawn about the auditory and non-auditory effects of low-frequency airborne ultrasound. Int J Occup Med Environ Health. 2020;33(4):389-408.},
}
@article {pmid32405972,
year = {2020},
author = {Perera, Y and Ramos, Y and Padrón, G and Caballero, E and Guirola, O and Caligiuri, LG and Lorenzo, N and Gottardo, F and Farina, HG and Filhol, O and Cochet, C and Perea, SE},
title = {CIGB-300 anticancer peptide regulates the protein kinase CK2-dependent phosphoproteome.},
journal = {Molecular and cellular biochemistry},
volume = {470},
number = {1-2},
pages = {63-75},
doi = {10.1007/s11010-020-03747-1},
pmid = {32405972},
issn = {1573-4919},
support = {CIGB-300//This work was conducted with the financial support of the CIGB-300 Grant, Biomedical Research Division, CIGB, Cuba./ ; },
mesh = {Antineoplastic Agents/*pharmacology ; Carcinoma, Non-Small-Cell Lung/*metabolism ; Casein Kinase II/*metabolism ; Catalytic Domain ; Cell Line, Tumor ; Cell-Free System ; Gene Expression Regulation, Neoplastic ; Humans ; Lung Neoplasms/*metabolism ; Microscopy, Fluorescence ; Peptides, Cyclic/*pharmacology ; Phosphorylation ; Protein Binding ; Proteome ; Recombinant Proteins/metabolism ; },
abstract = {Casein-kinase CK2 is a Ser/Thr protein kinase that fosters cell survival and proliferation of malignant cells. The CK2 holoenzyme, formed by the association of two catalytic alpha/alpha' (CK2α/CK2α') and two regulatory beta subunits (CK2β), phosphorylates diverse intracellular proteins partaking in key cellular processes. A handful of such CK2 substrates have been identified as targets for the substrate-binding anticancer peptide CIGB-300. However, since CK2β also contains a CK2 phosphorylation consensus motif, this peptide may also directly impinge on CK2 enzymatic activity, thus globally modifying the CK2-dependent phosphoproteome. To address such a possibility, firstly, we evaluated the potential interaction of CIGB-300 with CK2 subunits, both in cell-free assays and cellular lysates, as well as its effect on CK2 enzymatic activity. Then, we performed a phosphoproteomic survey focusing on early inhibitory events triggered by CIGB-300 and identified those CK2 substrates significantly inhibited along with disturbed cellular processes. Altogether, we provided here the first evidence for a direct impairment of CK2 enzymatic activity by CIGB-300. Of note, both CK2-mediated inhibitory mechanisms of this anticancer peptide (i.e., substrate- and enzyme-binding mechanism) may run in parallel in tumor cells and help to explain the different anti-neoplastic effects exerted by CIGB-300 in preclinical cancer models.},
}
@article {pmid32403093,
year = {2020},
author = {Tian, Y and Ma, L},
title = {Auditory attention tracking states in a cocktail party environment can be decoded by deep convolutional neural networks.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036013},
doi = {10.1088/1741-2552/ab92b2},
pmid = {32403093},
issn = {1741-2552},
mesh = {Attention ; *Electroencephalography ; Entropy ; Humans ; *Neural Networks, Computer ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features and is widely used in classification tasks with scalp electroencephalogram (EEG) signals. However, the small number of samples and low signal-to-noise ratio involved in scalp EEG with low spatial resolution constitute a limitation that might restrict potential brain-computer interface (BCI) applications that are based on the CNN model. In the present study, a novel CNN model with source-spatial feature images (SSFIs) as the input is proposed to decode auditory attention tracking states in a cocktail party environment.<br><br>APPROACH: We first extract SSFIs using rhythm entropy and weighted minimum norm estimation. Next, we develop a CNN model with three convolutional layers. Furthermore, we estimate the performance of the proposed model via generalized performance, alternative models that deleted or replaced a model's component, and loss curves. Finally, we use a deep transfer model with fine-tuning for a low (poor) behavioral performance group (L-group).<br><br>MAIN RESULTS: Based on cortical activity reconstructions from the scalp EEGs, the classification accuracy (CA) of the proposed model is 80.4% (chance level: 52.5%), which is superior to that achieved by scalp EEG. Additionally, the performance of the proposed model is more stable when compared to alternative models that delete or replace specific model components. The proposed model identifies the difference between two auditory attention tracking states (successful versus unsuccessful) at an early stage with a short time window (250 ms after target offset). Furthermore, we propose a deep transfer learning model to improve the classification for the L-group. With this model, the CA of the L-group significantly increase by 5.3%.<br><br>SIGNIFICANCE: Our proposed model improves the performance of a decoder for auditory attention tracking, which could be suitable for relieving the difficulty with the attentional modulation of individual's neural responses. It provides a novel communication channel with auditory cognitive BCI for patients with attention and hearing impairment.},
}
@article {pmid32399166,
year = {2020},
author = {Shao, L and Zhang, L and Belkacem, AN and Zhang, Y and Chen, X and Li, J and Liu, H},
title = {EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.},
journal = {Journal of healthcare engineering},
volume = {2020},
number = {},
pages = {6968713},
pmid = {32399166},
issn = {2040-2309},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual ; Female ; Household Work ; Humans ; Male ; Robotics/*instrumentation ; Young Adult ; },
abstract = {The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people's lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.},
}
@article {pmid32399073,
year = {2020},
author = {Mora-Sánchez, A and Pulini, AA and Gaume, A and Dreyfus, G and Vialatte, FB},
title = {A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {3},
pages = {301-321},
pmid = {32399073},
issn = {1871-4080},
abstract = {We developed a brain-computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.},
}
@article {pmid32399071,
year = {2020},
author = {Pan, H and Mi, W and Wen, F and Zhong, W},
title = {An adaptive decoder design based on the receding horizon optimization in BMI system.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {3},
pages = {281-290},
pmid = {32399071},
issn = {1871-4080},
abstract = {In a motor brain-machine interface system, since the electroencephalogram signal is changing through out the process of the arm movement, the offline trained decoder with fixed weights is often unable to convert the electroencephalogram signal accurately, resulting in poor recovery of joint motor function. In this paper, a receding horizon optimization strategy is chosen to online update the decoder weights and design an adaptive Wiener-filter-based decoder. Firstly, a classical Wiener-filter-based decoder with fixed weights is brief reviewed. Secondly, the weights in Wiener-filter-based decoder are updated by minimizing the cost function, which is composed by the sum of squared position errors in the given horizon at each sampling time. The simulation shows that the recovery effect of joint motor function and neuron activity in the BMI system with the adaptive decoder are both better than that in the BMI system with the fixed decoder.},
}
@article {pmid32394192,
year = {2020},
author = {Zheng, M and Yang, B and Xie, Y},
title = {EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {58},
number = {7},
pages = {1515-1528},
doi = {10.1007/s11517-020-02176-y},
pmid = {32394192},
issn = {1741-0444},
support = {61976133//National Natural Science Foundation of China/ ; 2018YFC1312900//National Key R&amp;D Program of China/ ; D18003//111 Project/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Female ; Hand ; Healthy Volunteers ; Humans ; Imagery, Psychotherapy/methods ; *Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.},
}
@article {pmid32391760,
year = {2020},
author = {Gao, JM and Li, H and Wei, GB and Liu, CP and Du, DY and Kong, LW and Li, CH and Yang, J and Yang, Q},
title = {Blunt Cardiac Injury: A Single-Center 15-Year Experience.},
journal = {The American surgeon},
volume = {86},
number = {4},
pages = {354-361},
pmid = {32391760},
issn = {1555-9823},
mesh = {Accidents, Traffic ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Arrhythmias, Cardiac/drug therapy/etiology ; Cardiac Surgical Procedures/*methods ; Echocardiography ; Electrocardiography ; Female ; Heart/diagnostic imaging ; *Heart Injuries/diagnosis/mortality/therapy ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Retrospective Studies ; Thoracic Injuries/complications ; Tomography, X-Ray Computed ; *Wounds, Nonpenetrating/diagnosis/mortality/therapy ; Young Adult ; },
abstract = {In recent years, the incidence of blunt cardiac injury (BCI) has increased rapidly and is an important cause of death in trauma patients. This study aimed to explore early diagnosis and therapy to increase survival. All patients with BCI during the past 15 years were analyzed retrospectively regarding the mechanism of injury, diagnostic and therapeutic methods, and outcome. The patients were divided into two groups according to the needs of their condition-nonoperative (Group A) and operative (Group B). Comparisons of the groups were performed. A total of 348 patients with BCI accounted for 18.3 per cent of 1903 patients with blunt thoracic injury. The main cause of injury was traffic accidents, with an incidence of 48.3 per cent. In Group A (n = 305), most patients sustained myocardial contusion, and the mortality was 6.9 per cent. In Group B (n = 43), including those with cardiac rupture and pericardial hernia, the mortality was 32.6 per cent. Comparisons of the groups regarding the shock rate and mortality were significant (P < 0.01). Deaths directly resulting from BCI in Group B were greater than those in Group A (P < 0.05). In all 348 patients, the mortality rate was 10.1 per cent. When facing a patient with blunt thoracic injury, a high index of suspicion for BCI must be maintained. To manage myocardial contusion, it is necessary to protect the heart, alleviate edema of the myocardium, and control arrhythmia with drugs. To deal with those requiring operation, early recognition and expeditious thoracotomy are essential.},
}
@article {pmid32390937,
year = {2020},
author = {Raza, SA and Opie, NL and Morokoff, A and Sharma, RP and Mitchell, PJ and Oxley, TJ},
title = {Endovascular Neuromodulation: Safety Profile and Future Directions.},
journal = {Frontiers in neurology},
volume = {11},
number = {},
pages = {351},
pmid = {32390937},
issn = {1664-2295},
abstract = {Endovascular neuromodulation is an emerging technology that represents a synthesis between interventional neurology and neural engineering. The prototypical endovascular neural interface is the Stentrode[TM], a stent-electrode array which can be implanted into the superior sagittal sinus via percutaneous catheter venography, and transmits signals through a transvenous lead to a receiver located subcutaneously in the chest. Whilst the Stentrode[TM] has been conceptually validated in ovine models, questions remain about the long term viability and safety of this device in human recipients. Although technical precedence for venous sinus stenting already exists in the setting of idiopathic intracranial hypertension, long term implantation of a lead within the intracranial veins has never been previously achieved. Contrastingly, transvenous leads have been successfully employed for decades in the setting of implantable cardiac pacemakers and defibrillators. In the current absence of human data on the Stentrode[TM], the literature on these structurally comparable devices provides valuable lessons that can be translated to the setting of endovascular neuromodulation. This review will explore this literature in order to understand the potential risks of the Stentrode[TM] and define avenues where further research and development are necessary in order to optimize this device for human application.},
}
@article {pmid32383375,
year = {2020},
author = {Sun, Y and Nguyen, TNH and Anderson, A and Cheng, X and Gage, TE and Lim, J and Zhang, Z and Zhou, H and Rodolakis, F and Zhang, Z and Arslan, I and Ramanathan, S and Lee, H and Chubykin, AA},
title = {In Vivo Glutamate Sensing inside the Mouse Brain with Perovskite Nickelate-Nafion Heterostructures.},
journal = {ACS applied materials &amp; interfaces},
volume = {12},
number = {22},
pages = {24564-24574},
doi = {10.1021/acsami.0c02826},
pmid = {32383375},
issn = {1944-8252},
mesh = {Amino Acid Oxidoreductases/chemistry ; Animals ; Biosensing Techniques/instrumentation/methods ; Brain/*metabolism ; Electrochemical Techniques ; Electrodes ; Enzymes, Immobilized/chemistry ; Female ; Fluorocarbon Polymers/chemistry ; Glutamic Acid/*analysis/chemistry ; Hydrogen Peroxide/chemistry ; Limit of Detection ; Mice, Inbred C57BL ; Neodymium/chemistry ; Neurotransmitter Agents/*analysis/chemistry ; Nickel/chemistry ; },
abstract = {Glutamate, one of the main neurotransmitters in the brain, plays a critical role in communication between neurons, neuronal development, and various neurological disorders. Extracellular measurement of neurotransmitters such as glutamate in the brain is important for understanding these processes and developing a new generation of brain-machine interfaces. Here, we demonstrate the use of a perovskite nickelate-Nafion heterostructure as a promising glutamate sensor with a low detection limit of 16 nM and a response time of 1.2 s via amperometric sensing. We have designed and successfully tested novel perovskite nickelate-Nafion electrodes for recording of glutamate release ex vivo in electrically stimulated brain slices and in vivo from the primary visual cortex (V1) of awake mice exposed to visual stimuli. These results demonstrate the potential of perovskite nickelates as sensing media for brain-machine interfaces.},
}
@article {pmid32380925,
year = {2020},
author = {Wei, Q and Zhu, S and Wang, Y and Gao, X and Guo, H and Wu, X},
title = {A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs.},
journal = {International journal of neural systems},
volume = {30},
number = {5},
pages = {2050020},
doi = {10.1142/S0129065720500203},
pmid = {32380925},
issn = {1793-6462},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; *Pattern Recognition, Automated ; },
abstract = {Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA). In this study, we proposed a novel CCA method in which spatial filters are estimated using training data only. This is achieved by using observed EEG training data and their SSVEP components as the two inputs of CCA and the objective function is optimized by averaging multiple training trials. In this case, we proved in theory that the two spatial filters estimated by the CCA are equivalent, and that the CCA and TRCA are also equivalent under certain hypotheses. A benchmark SSVEP data set from 35 subjects was used to compare the performance of the two algorithms according to different lengths of data, numbers of channels and numbers of training trials. In addition, the CCA was also compared with power spectral density analysis (PSDA). The experimental results suggest that the CCA is equivalent to TRCA if the signal-to-noise ratio of training data is high enough; otherwise, the CCA outperforms TRCA in terms of classification accuracy. The CCA is much faster than PSDA in detecting time of targets. The robustness of the training data-driven CCA to noise gives it greater potential in practical applications.},
}
@article {pmid32380494,
year = {2020},
author = {Liu, T and Huang, G and Jiang, N and Yao, L and Zhang, Z},
title = {Reduce brain computer interface inefficiency by combining sensory motor rhythm and movement-related cortical potential features.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {035003},
doi = {10.1088/1741-2552/ab914d},
pmid = {32380494},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagery, Psychotherapy ; Imagination ; Movement ; },
abstract = {OBJECTIVE: Brain Computer Interface (BCI) inefficiency indicates that there would be 10% to 50% of users are unable to operate Motor-Imagery-based BCI systems. Importantly, the almost all previous studieds on BCI inefficiency were based on tests of Sensory Motor Rhythm (SMR) feature. In this work, we assessed the occurrence of BCI inefficiency with SMR and Movement-Related Cortical Potential (MRCP) features.<br><br>APPROACH: A pool of datasets of resting state and movements related EEG signals was recorded with 93 subjects during 2 sessions in separated days. Two methods, Common Spatial Pattern (CSP) and template matching, were used for SMR and MRCP feature extraction, and a winner-take-all strategy was applied to assess pattern recognition with posterior probabilities from Linear Discriminant Analysis to combine SMR and MRCP features.<br><br>MAIN RESULTS: The results showed that the two types of features showed high complementarity, in line with their weak intercorrelation. In the subject group with poor accuracies (< 70%) by SMR feature in the two-class problem (right foot vs. right hand), the combination of SMR and MRCP features improved the averaged accuracy from 62% to 79%. Importantly, accuracies obtained by feature combination exceeded the inefficiency threshold.<br><br>SIGNIFICANCE: The feature combination of SMR and MRCP is not new in BCI decoding, but the large scale and repeatable study on BCI inefficiency assessment by using SMR and MRCP features is novel. MRCP feature provides the similar classification accuracies on the two subject groups with poor (< 70%) and good (> 90%) accuracies by SMR feature. These results suggest that the combination of SMR and MRCP features may be a practical approach to reduce BCI inefficiency. While, 'BCI inefficiency' might be more aptly called 'SMR inefficiency' after this study.},
}
@article {pmid32380486,
year = {2020},
author = {Blum, S and Emkes, R and Minow, F and Anlauff, J and Finke, A and Debener, S},
title = {Flex-printed forehead EEG sensors (fEEGrid) for long-term EEG acquisition.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {034003},
doi = {10.1088/1741-2552/ab914c},
pmid = {32380486},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; *Forehead ; Humans ; },
abstract = {OBJECTIVE: In this report we present the fEEGrid, an electrode array applied to the forehead that allows convenient long-term recordings of electroencephalography (EEG) signals over many hours.<br><br>APPROACH: Twenty young, healthy participants wore the fEEGrid and completed traditional EEG paradigms in two sessions on the same day. The sessions were eight hours apart, participants performed the same tasks in an early and a late session. For the late session fEEGrid data were concurrently recorded with traditional cap EEG data.<br><br>MAIN RESULTS: Our analyses show that typical event-related potentials responses were captured reliably by the fEEGrid. Single-trial analyses revealed that classification was possible above chance level for auditory and tactile oddball paradigms. We also found that the signal quality remained high and impedances did not deteriorate, but instead improved over the course of the day. Regarding wearing comfort, all participants indicated that the fEEGrid was comfortable to wear and did not cause any pain even after 8 h of wearing it.<br><br>SIGNIFICANCE: We show in this report, that high quality EEG signals can be captured with the fEEGrid reliably, even in long-term recording scenarios and with a signal quality that may be considered suitable for online brain-computer Interface applications.},
}
@article {pmid32380480,
year = {2021},
author = {Chen, Y and Yang, C and Chen, X and Wang, Y and Gao, X},
title = {A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ab914e},
pmid = {32380480},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Objective. Filter bank canonical correlation analysis (FBCCA) is a widely-used classification approach implemented in steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). However, conventional detection algorithms for SSVEP recognition problems, including the FBCCA, were usually based on 'fixed window' strategy. That's to say, these algorithms always analyze data with fixed length. This study devoted to enhance the performance of SSVEP-based BCIs by designing a new dynamic window strategy which automatically finds an optimal data length to achieve higher information transfer rate (ITR).Approach. The main purpose of 'dynamic window' is to minimize the required data length while maintaining high accuracy. This study projected the correlation coefficients of FBCCA into probability space by softmax function and built a hypothesis testing model, which took risk function as evaluation of classification result's 'credibility'. In order to evaluate the superiority of this approach, FBCCA with fixed data length (FBCCA-FW) and spatial temporal equalization dynamic window (STE-DW) were implemented for comparison.Main results. Fourteen healthy subjects' results were concluded by a 40-target online SSVEP-based BCI speller system. The results suggest that this proposed approach significantly outperforms STE-DW and FBCCA-FW in terms of accuracy and ITR.Significance. By incorporating the fundamental ideas of FBCCA and dynamic window strategy, this study proposed a new training-free dynamical optimization algorithm, which significantly improved the performance of online SSVEP-based BCI systems.},
}
@article {pmid32377544,
year = {2020},
author = {Zhu, L and Haghani, S and Najafizadeh, L},
title = {On fractality of functional near-infrared spectroscopy signals: analysis and applications.},
journal = {Neurophotonics},
volume = {7},
number = {2},
pages = {025001},
pmid = {32377544},
issn = {2329-423X},
abstract = {Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.},
}
@article {pmid32376990,
year = {2020},
author = {Huber, ME and Chiovetto, E and Giese, M and Sternad, D},
title = {Rigid soles improve balance in beam walking, but improvements do not persist with bare feet.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {7629},
pmid = {32376990},
issn = {2045-2322},
support = {R01 HD045639/HD/NICHD NIH HHS/United States ; R01 HD087089/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Female ; Humans ; Male ; *Mechanical Phenomena ; *Postural Balance ; *Shoes ; Surface Properties ; Torque ; Walking/*physiology ; },
abstract = {Maintaining balance while walking on a narrow beam is a challenging motor task. One important factor is that the foot's ability to exert torque on the support surface is limited by the beam width. Still, the feet serve as a critical interface between the body and the external environment, and it is unclear how the mechanical properties of the feet affect balance. This study examined how constraining the motion of the foot joints with rigid soles influenced balance performance when walking on a beam. We recorded whole-body kinematics of subjects with varying skill levels as they walked on a narrow beam with and without wearing flat, rigid soles on their feet. We computed changes in whole-body motion and angular momentum across the two conditions. Results showed that walking with rigid soles improved balance performance in both expert and novice subjects, but that improvements in balance performance with rigid soles did not affect or transfer to subsequent task performance with bare feet. The absence of any aftereffects suggested that the improved balance performance resulting from constraining the foot joints by a rigid sole was the result of a mechanical effect rather than a change in neural control. Although wearing rigid soles can be used to assist balance, there appears to be limited benefit for training or rehabilitation of balance ability.},
}
@article {pmid32375139,
year = {2020},
author = {Ko, LW and Komarov, O and Lai, WK and Liang, WG and Jung, TP},
title = {Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention task.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036015},
doi = {10.1088/1741-2552/ab909f},
pmid = {32375139},
issn = {1741-2552},
mesh = {Adult ; Attention ; *Brain-Computer Interfaces ; Electroencephalography ; Fatigue ; *Forehead ; Humans ; Young Adult ; },
abstract = {OBJECTIVE: A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor.<br><br>APPROACH: Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth[Formula: see text] module.<br><br>MAIN RESULTS: The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 &#xb1; 0.019 s, and the r[2] is 0.885 &#xb1; 0.057 according to a within-session leave-one-trial-out cross-validation.<br><br>SIGNIFICANCE: The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.},
}
@article {pmid32375135,
year = {2020},
author = {Aliakbaryhosseinabadi, S and Farina, D and Mrachacz-Kersting, N},
title = {Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036017},
doi = {10.1088/1741-2552/ab909c},
pmid = {32375135},
issn = {1741-2552},
mesh = {*Amyotrophic Lateral Sclerosis/therapy ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Movement ; *Neurofeedback ; },
abstract = {OBJECTIVE: The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution.<br><br>APPROACH: Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status.<br><br>MAIN RESULTS: For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion.<br><br>SIGNIFICANCE: The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.},
}
@article {pmid32375134,
year = {2020},
author = {Lian, Q and Qi, Y and Pan, G and Wang, Y},
title = {Learning graph in graph convolutional neural networks for robust seizure prediction.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {035004},
doi = {10.1088/1741-2552/ab909d},
pmid = {32375134},
issn = {1741-2552},
mesh = {Algorithms ; Electroencephalography ; *Epilepsy ; Humans ; Neural Networks, Computer ; *Seizures/diagnosis ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) has demonstrated its effectiveness in epilepsy treatment and control. In a BCI-aided epilepsy treatment system, therapic electrical stimulus is delivered in response to the prediction of upcoming seizure onsets, therefore timely and accurate seizure prediction algorithm plays an important role. However, unlike typical signatures such as slow or sharp waves in ictal periods, the signal patterns in preictal periods are usually subtle, and highly individual-dependent. How to extract effective and robust preictal features is still a challenging problem.<br><br>APPROACH: Most recently, graph convolutional neural network (GCNN) has demonstrated the strength in the electroencephalogram (EEG) and intracranial electroencephalogram (iEEG) signal modeling, due to its advantages in describing complex relationships among different EEG/iEEG regions. However, current GCNN models are not suitable for seizure prediction. The effectiveness of GCNNs highly relies on prior graphs that describe the underlying relationships in EEG regions. However, due to the complex mechanism of seizure evolution, the underlying relationship in the preictal period can be diverse in different patients, making it almost impossible to build a proper prior graph in general. To deal with this problem, we propose a novel approach to automatically learn a patient-specific graph in a data-driven way, which is called the joint graph structure and representation learning network (JGRN). JGRN constructs a global-local graph convolutional neural network which jointly learns the graph structures and connection weights in a task-related learning process in iEEG signals, thus the learned graph and feature representations can be optimized toward the objective of seizure prediction.<br><br>MAIN RESULTS: Experimental results show that our JGRN outperforms CNN and GCNN models remarkably, and the improvement is more obvious when preictal features are subtle.<br><br>SIGNIFICANCE: The proposed approach promises to achieve robust seizure prediction performance and to have the potential to be extended to general problems in brain-computer interfaces.},
}
@article {pmid32375031,
year = {2020},
author = {Eichenlaub, JB and Jarosiewicz, B and Saab, J and Franco, B and Kelemen, J and Halgren, E and Hochberg, LR and Cash, SS},
title = {Replay of Learned Neural Firing Sequences during Rest in Human Motor Cortex.},
journal = {Cell reports},
volume = {31},
number = {5},
pages = {107581},
pmid = {32375031},
issn = {2211-1247},
support = {I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; I01 RX002295/RX/RRD VA/United States ; UH2 NS095548/NS/NINDS NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; },
mesh = {Electroencephalography/methods ; Humans ; Learning/*physiology ; Memory Consolidation/*physiology ; Motor Cortex/*physiology ; Neurons/physiology ; Rest/*physiology ; Sleep/physiology ; Wakefulness/physiology ; },
abstract = {The offline "replay" of neural firing patterns underlying waking experience, previously observed in non-human animals, is thought to be a mechanism for memory consolidation. Here, we test for replay in the human brain by recording spiking activity from the motor cortex of two participants who had intracortical microelectrode arrays placed chronically as part of a brain-computer interface pilot clinical trial. Participants took a nap before and after playing a neurally controlled sequence-copying game that consists of many repetitions of one "repeated" sequence sparsely interleaved with varying "control" sequences. Both participants performed repeated sequences more accurately than control sequences, consistent with learning. We compare the firing rate patterns that caused the cursor movements when performing each sequence to firing rate patterns throughout both rest periods. Correlations with repeated sequences increase more from pre- to post-task rest than do correlations with control sequences, providing direct evidence of learning-related replay in the human brain.},
}
@article {pmid32373416,
year = {2020},
author = {Amin, A and Ahmed, I and Khalid, N and Khan, IU and Ali, A and Dahlawi, SM and Li, WJ},
title = {Insights on comparative bacterial diversity between different arid zones of Cholistan Desert, Pakistan.},
journal = {3 Biotech},
volume = {10},
number = {5},
pages = {224},
pmid = {32373416},
issn = {2190-572X},
abstract = {The present study was conducted to analyze bacterial diversity profile of Cholistan desert located in Pakistan. The study investigates the influence of physicochemical parameters of soil on distribution of different bacteria at all taxonomic levels and also study the distribution pattern between different desert environments, particularly rhizospheric and bulk desert sands. Species richness showed phyla Proteobacteria and Chloroflexi as the dominant OTUs in all the samples. Besides the two phyla, the rhizospheric soils with root remnants were dominated by Firmicutes, Deinococcus-Thermus, Actinobacteria and Acidobacteri, while phylum Thermotogae was present in significant quantity in rhizosheaths devoid of roots. In non-rhizospheric desert soils, a considerable number of OTUs belonged to phyla Proteobacteria, Chloroflexi, Bacteroidetes and Acidobacteria. An important finding from this study is that a bulk portion of the OTUs were assigned to unclassified taxa, indicating a large repertoire of unexplored taxa in the desert ecology of Pakistan. Distribution of taxonomic groups among various regions of the desert was collaborating well with the physicochemical parameters of the sites. The findings of this study establish the fundamental relationships between desert ecosystem, specific native plant and the total bacterial flora. This is the first study of microbial community analysis of any desert in Pakistan and thus, will serve as a future platform to explore further on desert ecosystem functioning by employing the ever-changing biotechnological tools.},
}
@article {pmid32372929,
year = {2020},
author = {Xu, L and Xu, M and Ke, Y and An, X and Liu, S and Ming, D},
title = {Cross-Dataset Variability Problem in EEG Decoding With Deep Learning.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {103},
pmid = {32372929},
issn = {1662-5161},
abstract = {Cross-subject variability problems hinder practical usages of Brain-Computer Interfaces. Recently, deep learning has been introduced into the BCI community due to its better generalization and feature representation abilities. However, most studies currently only have validated deep learning models for single datasets, and the generalization ability for other datasets still needs to be further verified. In this paper, we validated deep learning models for eight MI datasets and demonstrated that the cross-dataset variability problem weakened the generalization ability of models. To alleviate the impact of cross-dataset variability, we proposed an online pre-alignment strategy for aligning the EEG distributions of different subjects before training and inference processes. The results of this study show that deep learning models with online pre-alignment strategies could significantly improve the generalization ability across datasets without any additional calibration data.},
}
@article {pmid32369802,
year = {2020},
author = {Stiso, J and Corsi, MC and Vettel, JM and Garcia, J and Pasqualetti, F and De Vico Fallani, F and Lucas, TH and Bassett, DS},
title = {Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046018},
pmid = {32369802},
issn = {1741-2552},
support = {R01 MH112847/MH/NIMH NIH HHS/United States ; R01 NS099348/NS/NINDS NIH HHS/United States ; R01 MH107235/MH/NIMH NIH HHS/United States ; R01 DC009209/DC/NIDCD NIH HHS/United States ; R21 MH106799/MH/NIMH NIH HHS/United States ; R01 HD086888/HD/NICHD NIH HHS/United States ; },
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Learning ; *Neurosciences ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning.<br><br>APPROACH: Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression.<br><br>MAIN RESULTS: We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention.<br><br>SIGNIFICANCE: The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.},
}
@article {pmid32365334,
year = {2020},
author = {Guo, L},
title = {Principles of functional neural mapping using an intracortical ultra-density microelectrode array (ultra-density MEA).},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036018},
doi = {10.1088/1741-2552/ab8fc5},
pmid = {32365334},
issn = {1741-2552},
mesh = {Action Potentials ; Microelectrodes ; *Neurons ; },
abstract = {OBJECTIVE: Intracortical electrical neural recording using solid-state electrodes is a prevalent approach in addressing neurophysiological queries and implementing brain-computer interfacing systems. As a variety of ultra-density microelectrode arrays (ultra-density MEAs) are being created more recently, this paper answers to the rising demand for a more rigorous theory concerning this new type of neural electrode technology, both to guide the proper design and to inform the proper usage.<br><br>APPROACH: This design and use problem of ultra-density MEAs for functional intracortical neuronal circuit mapping is approached from a signal analysis perspective. Starting with quantitative derivations of key basic concepts, the concept of ultra-density MEA is defined in the context for fully resolving the voltage sources within its view volume. Then, the principle of using such an ultra-density MEA for functional neural mapping is elaborated, and a recursive approach to completely resolve all voltage sources from the ultra-density MEA's recordings is proposed. This approach is further validated using a simulated experiment. Last, the limitations and implications of this work are discussed.<br><br>MAIN RESULTS: MEAs can only be used to map the extracellular somatic action potential (esAP) sources in a neural microcircuit, and AP propagation along individual axons cannot be detected. The key for the ultra-density MEA design is to make sure that each spatial unit of analysis (SUA) contains no more than one active esAP source. The unique neural resolving capability of ultra-density MEAs comparing to conventional MEAs is to be able to spatiotemporally resolve each esAP source within its view volume.<br><br>SIGNIFICANCE: The ultimate capability and limitation of neural electrode array technology at such an unprecedented fabrication resolution is unraveled. This work strives to further the discussions on this topic into a more quantitative and rational direction, while providing a theoretical guideline for the rational development and neuroscientific application of an ultra-density MEA for intracortical functional mapping.},
}
@article {pmid32364183,
year = {2020},
author = {Son, JE and Choi, H and Lim, H and Ku, J},
title = {Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {28},
number = {S1},
pages = {509-519},
pmid = {32364183},
issn = {1878-7401},
mesh = {Adult ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Rehabilitation/*instrumentation ; *Video Games ; Young Adult ; },
abstract = {BACKGROUND: This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed.<br><br>OBJECTIVE: In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES.<br><br>METHODS: The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22-27 years were recruited. The differences in brain activation in response to the two conditions were examined.<br><br>RESULTS: The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8-13&#xa0;Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21-30&#xa0;Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14-20&#xa0;Hz) for the action video in contrast to the noise video.<br><br>CONCLUSION: The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.},
}
@article {pmid32364149,
year = {2020},
author = {Cao, L and Fan, C and Wang, Z and Hou, L and Wang, H and Li, G},
title = {Alertness-based subject-dependent and subject-independent filter optimization for improving classification efficiency of SSVEP detection.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {28},
number = {S1},
pages = {173-180},
pmid = {32364149},
issn = {1878-7401},
mesh = {Adult ; Attention ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Machine Learning ; Male ; Mental Fatigue/*physiopathology ; Sleepiness ; Workload/psychology ; Young Adult ; },
abstract = {BACKGROUND: Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states.<br><br>OBJECTIVE: We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI.<br><br>METHODS: We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems.<br><br>RESULTS: The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system.<br><br>CONCLUSION: In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.},
}
@article {pmid32363792,
year = {2020},
author = {Yang, Q and Wu, B and Eles, JR and Vazquez, AL and Kozai, TDY and Cui, XT},
title = {Zwitterionic Polymer Coating Suppresses Microglial Encapsulation to Neural Implants In Vitro and In Vivo.},
journal = {Advanced biosystems},
volume = {4},
number = {6},
pages = {e1900287},
pmid = {32363792},
issn = {2366-7478},
support = {R21 DA043817/DA/NIDA NIH HHS/United States ; R21DA043817/GF/NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01NS062019/GF/NIH HHS/United States ; R01 NS110564/NS/NINDS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; R01NS089688/GF/NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; },
mesh = {3T3 Cells ; Animals ; *Coated Materials, Biocompatible/chemistry/pharmacology ; *Methacrylates/chemistry/pharmacology ; Mice ; Mice, Transgenic ; Microelectrodes ; Microglia/*metabolism ; *Neural Prostheses ; },
abstract = {For brain computer interfaces (BCI), the immune response to implanted electrodes is a major biological cause of device failure. Bioactive coatings such as neural adhesion molecule L1 have been shown to improve the biocompatibility, but are difficult to handle or produce in batches. Here, a synthetic zwitterionic polymer coating, poly(sulfobetaine methacrylate) (PSBMA) is developed for neural implants with the goal of reducing the inflammatory host response. In tests in vitro, the zwitterionic coating inhibits protein adsorption and the attachment of fibroblasts and microglia, and remains stable for at least 4 weeks. In vivo two-photon microscopy on CX3CR1-GFP mice shows that the zwitterionic coating significantly suppresses the microglial encapsulation of neural microelectrodes over a 6 h observation period. Furthermore, the lower microglial encapsulation on zwitterionic polymer-coated microelectrodes is revealed to originate from a reduction in the size but not the number of microglial end feet. This work provides a facile method for coating neural implants with zwitterionic polymers and illustrates the initial interaction between microglia and coated surface at high temporal and spatial resolution.},
}
@article {pmid32357223,
year = {2020},
author = {},
title = {Corrigendum to: Prognosis for patients with cognitive motor dissociation identified by brain-computer interface.},
journal = {Brain : a journal of neurology},
volume = {143},
number = {8},
pages = {e70},
doi = {10.1093/brain/awaa113},
pmid = {32357223},
issn = {1460-2156},
}
@article {pmid32351371,
year = {2020},
author = {Nagels-Coune, L and Benitez-Andonegui, A and Reuter, N and Lührs, M and Goebel, R and De Weerd, P and Riecke, L and Sorger, B},
title = {Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {113},
pmid = {32351371},
issn = {1662-5161},
abstract = {"Locked-in" patients lose their ability to communicate naturally due to motor system dysfunction. Brain-computer interfacing offers a solution for their inability to communicate by enabling motor-independent communication. Straightforward and convenient in-session communication is essential in clinical environments. The present study introduces a functional near-infrared spectroscopy (fNIRS)-based binary communication paradigm that requires limited preparation time and merely nine optodes. Eighteen healthy participants performed two mental imagery tasks, mental drawing and spatial navigation, to answer yes/no questions during one of two auditorily cued time windows. Each of the six questions was answered five times, resulting in five trials per answer. This communication paradigm thus combines both spatial (two different mental imagery tasks, here mental drawing for "yes" and spatial navigation for "no") and temporal (distinct time windows for encoding a "yes" and "no" answer) fNIRS signal features for information encoding. Participants' answers were decoded in simulated real-time using general linear model analysis. Joint analysis of all five encoding trials resulted in an average accuracy of 66.67 and 58.33% using the oxygenated (HbO) and deoxygenated (HbR) hemoglobin signal respectively. For half of the participants, an accuracy of 83.33% or higher was reached using either the HbO signal or the HbR signal. For four participants, effective communication with 100% accuracy was achieved using either the HbO or HbR signal. An explorative analysis investigated the differentiability of the two mental tasks based solely on spatial fNIRS signal features. Using multivariate pattern analysis (MVPA) group single-trial accuracies of 58.33% (using 20 training trials per task) and 60.56% (using 40 training trials per task) could be obtained. Combining the five trials per run using a majority voting approach heightened these MVPA accuracies to 62.04 and 75%. Additionally, an fNIRS suitability questionnaire capturing participants' physical features was administered to explore its predictive value for evaluating general data quality. Obtained questionnaire scores correlated significantly (r = -0.499) with the signal-to-noise of the raw light intensities. While more work is needed to further increase decoding accuracy, this study shows the potential of answer encoding using spatiotemporal fNIRS signal features or spatial fNIRS signal features only.},
}
@article {pmid32348980,
year = {2021},
author = {de'Sperati, C and Roatta, S and Zovetti, N and Baroni, T},
title = {Decoding overt shifts of attention in depth through pupillary and cortical frequency tagging.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ab8e8f},
pmid = {32348980},
issn = {1741-2552},
mesh = {Attention/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Pupil ; User-Computer Interface ; *Visual Cortex/physiology ; },
abstract = {Objective. We have recently developed a prototype of a novel human-computer interface for assistive communication based on voluntary shifts of attention (gaze) from a far target to a near target associated with a decrease of pupil size (Pupillary Accommodative Response, PAR), an automatic vegetative response that can be easily recorded. We report here an extension of that approach based on pupillary and cortical frequency tagging.Approach. In 18 healthy volunteers, we investigated the possibility of decoding attention shifts in depth by exploiting the evoked oscillatory responses of the pupil (Pupillary Oscillatory Response, POR, recorded through a low-cost device) and visual cortex (Steady-State Visual Evoked Potentials, SSVEP, recorded from 4 scalp electrodes). With a simple binary communication protocol (focusing on a far target meaning 'No', focusing on the near target meaning 'Yes'), we aimed at discriminating when observer's overt attention (gaze) shifted from the far to the near target, which were flickering at different frequencies.Main results. By applying a binary linear classifier (Support Vector Machine, SVM, with leave-one-out cross validation) to POR and SSVEP signals, we found that, with only twenty trials and no subjects' behavioural training, the offline median decoding accuracy was 75% and 80% with POR and SSVEP signals, respectively. When the two signals were combined together, accuracy reached 83%. The number of observers for whom accuracy was higher than 70% was 11/18, 12/18 and 14/18 with POR, SVVEP and combined features, respectively. A signal detection analysis confirmed these results.Significance. The present findings suggest that exploiting frequency tagging with pupillary or cortical responses during an attention shift in the depth plane, either separately or combined together, is a promising approach to realize a device for communicating with Complete Locked-In Syndrome (CLIS) patients when oculomotor control is unreliable and traditional assistive communication, even based on PAR, is unsuccessful.},
}
@article {pmid32346626,
year = {2020},
author = {Peterson, V and Galván, CM and Hernández, HS and Spies, R},
title = {Erratum to "A feasibility study of a complete low-cost consumer-grade brain-computer interface system" [Heliyon 6 (3) (March 2020) e03425].},
journal = {Heliyon},
volume = {6},
number = {4},
pages = {e03709},
doi = {10.1016/j.heliyon.2020.e03709},
pmid = {32346626},
issn = {2405-8440},
abstract = {[This corrects the article DOI: 10.1016/j.heliyon.2020.e03425.].},
}
@article {pmid32344820,
year = {2020},
author = {Martínez-Cerveró, J and Ardali, MK and Jaramillo-Gonzalez, A and Wu, S and Tonin, A and Birbaumer, N and Chaudhary, U},
title = {Open Software/Hardware Platform for Human-Computer Interface Based on Electrooculography (EOG) Signal Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {9},
pages = {},
pmid = {32344820},
issn = {1424-8220},
support = {(DFG) DFG BI 195/77-1//Deutsche Forschungsgemeinschaft/ ; 16SV7701 CoMiCon//Bundesministerium f&#xfc;r Bildung und Forschung/ ; LUMINOUS-H2020-FETOPEN-2014-2015-RIA (686764)//Horizon 2020 Framework Programme/ ; },
mesh = {Brain-Computer Interfaces ; Electrooculography/*methods ; Humans ; *Software ; Support Vector Machine ; User-Computer Interface ; },
abstract = {Electrooculography (EOG) signals have been widely used in Human-Computer Interfaces (HCI). The HCI systems proposed in the literature make use of self-designed or closed environments, which restrict the number of potential users and applications. Here, we present a system for classifying four directions of eye movements employing EOG signals. The system is based on open source ecosystems, the Raspberry Pi single-board computer, the OpenBCI biosignal acquisition device, and an open-source python library. The designed system provides a cheap, compact, and easy to carry system that can be replicated or modified. We used Maximum, Minimum, and Median trial values as features to create a Support Vector Machine (SVM) classifier. A mean of 90% accuracy was obtained from 7 out of 10 subjects for online classification of Up, Down, Left, and Right movements. This classification system can be used as an input for an HCI, i.e., for assisted communication in paralyzed people.},
}
@article {pmid32344755,
year = {2020},
author = {Rodríguez-Moreno, I and Martínez-Otzeta, JM and Goienetxea, I and Rodriguez-Rodriguez, I and Sierra, B},
title = {Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {8},
pages = {},
pmid = {32344755},
issn = {1424-8220},
support = {RTI2018-093337-B-I00//European Regional Development Fund/ ; IT900-16//Ministerio de Econom&#xed;a, Industria y Competitividad, Gobierno de Espa&#xf1;a/ ; GPU Grant program//Nvidia/ ; },
mesh = {Algorithms ; Brain-Computer Interfaces ; Discriminant Analysis ; Humans ; *Neural Networks, Computer ; Recognition, Psychology ; *Robotics ; Signal Processing, Computer-Assisted ; },
abstract = {Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social interaction. The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot. A sequence of skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm. Then, characteristics extracted from these filtered data are used as features for a classifier. A database with 46 individuals performing six different actions has been created to test the proposed method. The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler.},
}
@article {pmid32344712,
year = {2020},
author = {Stefaniak, A and Partyka, R and Duda, S and Ostręga, W and Niedziela, J and Nowak, J and Malinowska-Borowska, J and Rywik, T and Leszek, P and Hudzik, B and Zubelewicz-Szkodzińska, B and Rozentryt, P},
title = {The Association between Serum Levels of 25[OH]D, Body Weight Changes and Body Composition Indices in Patients with Heart Failure.},
journal = {Journal of clinical medicine},
volume = {9},
number = {4},
pages = {},
pmid = {32344712},
issn = {2077-0383},
abstract = {We try to determine the association between weight changes (WC), both loss or gain, body composition indices (BCI) and serum levels of 25[OH]D during heart failure (HF). WC was determined in 412 patients (14.3% female, aged: 53.6 ± 10.0 years, NYHA class: 2.5 ± 0.8). Body fat, fat percentage and fat-free mass determined by dual energy X-rays absorptiometry (DEXA) and serum levels of 25[OH]D were analyzed. Logistic regression was used to calculate odds ratios for 25[OH]D insufficiency (<30 ng/mL) or deficiency (<20 ng/mL) by quintiles of WC, in comparison to weight-stable subgroup. The serum 25[OH]D was lower in weight loosing than weight stable subgroup. In fully adjusted models the risk of either insufficient or deficient 25[OH]D levels was independent of BCI and HF severity markers. The risk was elevated in higher weight loss subgroups but also in weight gain subgroup. In full adjustment, the odds for 25[OH]D deficiency in the top weight loss and weight gain subgroups were 3.30; 95%CI: 1.37-7.93, p = 0.008 and 2.41; 95%CI: 0.91-6.38, p = 0.08, respectively. The risk of 25[OH]D deficiency/insufficiency was also independently associated with potential UVB exposure, but not with nutritional status and BCI. Metabolic instability in HF was reflected by edema-free WC, but not nutritional status. BCI is independently associated with deficiency/insufficiency of serum 25[OH]D.},
}
@article {pmid32344127,
year = {2020},
author = {Yang, L and Li, M and Yang, L and Wang, H and Wan, H and Shang, Z},
title = {Functional connectivity changes in the intra- and inter-brain during the construction of the multi-brain network of pigeons.},
journal = {Brain research bulletin},
volume = {161},
number = {},
pages = {147-157},
doi = {10.1016/j.brainresbull.2020.04.015},
pmid = {32344127},
issn = {1873-2747},
mesh = {Animals ; Brain/*physiology ; Columbidae ; Nerve Net/*physiology ; Psychomotor Performance/*physiology ; *Reward ; },
abstract = {Multi-brain network, also known as social cooperative network, is formed by multiple animal or human brains, whose changes of functional connectivity in the intra- and inter-brain during construction are unclear at present. To investigate the intra- and inter-brain functional connectivity of pigeons while performing a social cooperation task, we designed an inter-brain synchronization task to train three pigeons to synchronize their neural activities using cross-brain neurofeedback. Then the neural signals of three pigeons were simultaneously recorded by using a hyperscanning approach, and inter-brain synchronization was calculated using the phase-locked value (PLV) online. Finally, the intra- and inter-brain functional connectivity of three pigeons were analyzed. We found that during long-term neurofeedback training, with the increase of the inter-brain synchronization of three pigeons, the intra- and inter-brain functional connectivity also enhance significantly. Moreover, we also found that the above phenomenon relies on the external visual cue. These results suggest that the promotion of social cooperation is the result of the modulation between the intra- and inter-brain, which may be an underlying neural mechanism of the communication and cooperation among individuals in social networks.},
}
@article {pmid32341965,
year = {2019},
author = {Magosso, E and Ricci, G and Ursino, M},
title = {Modulation of brain alpha rhythm and heart rate variability by attention-related mechanisms.},
journal = {AIMS neuroscience},
volume = {6},
number = {1},
pages = {1-24},
pmid = {32341965},
issn = {2373-7972},
abstract = {According to recent evidence, oscillations in the alpha-band (8-14 Hz) play an active role in attention via allocation of cortical resources: decrease in alpha activity enhances neural processes in task-relevant regions, while increase in alpha activity reduces processing in task-irrelevant regions. Here, we analyzed changes in alpha-band power of 13-channel electroencephalogram (EEG) acquired from 30 subjects while performing four tasks that differently engaged visual, computational and motor attentional components. The complete (visual + computational + motor) task required to read and solve an arithmetical operation and provide a motor response; three simplified tasks involved a subset of these components (visual + computational task, visual task, motor task). Task-related changes in alpha power were quantified by aggregating electrodes into two main regions (fronto-central and parieto-occipital), to test regional specificity of alpha modulation depending on the involved attentional aspects. Independent Component Analysis (ICA) was applied to discover the main independent processes accounting for alpha power over the two scalp regions. Furthermore, we performed analysis of Heart Rate Variability (HRV) from one electrocardiogram signal acquired simultaneously with EEG, to test autonomic reaction to attentional loads. Results showed that alpha power modulation over the two scalp regions not only reflected the number of involved attentional components (the larger their number the larger the alpha power suppression) but was also fine-tuned by the nature of the recruited mechanisms (visual, computational, motor) relative to the functional specification of the regions. ICA revealed topologically dissimilar and differently attention-regulated processes of alpha power over the two regions. HRV indexes were less sensitive to different attentional aspects compared to alpha power, with vagal activity index presenting larger changes. This study contributes to improve our understanding of the electroencephalographic and autonomic correlates of attention and may have practical implications in neurofeedback, brain-computer interfaces, neuroergonomics as well as in clinical practice and neuroscience research exploring attention-deficit disorders.},
}
@article {pmid32340961,
year = {2021},
author = {Cui, F and Cui, Q and Song, Y},
title = {A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {4},
pages = {1418-1432},
doi = {10.1109/TNNLS.2020.2985588},
pmid = {32340961},
issn = {2162-2388},
mesh = {Artificial Intelligence ; Brain-Computer Interfaces ; Deep Learning ; Humans ; *Machine Learning ; Models, Theoretical ; *Natural Language Processing ; Neural Networks, Computer ; User-Computer Interface ; },
abstract = {With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction problems. The purpose of this article is to provide a comprehensive survey on learning-based human-machine dialog systems with a focus on the various dialog models. More specifically, we first introduce the fundamental process of establishing a dialog model. Second, we examine the features and classifications of the system dialog model, expound some representative models, and also compare the advantages and disadvantages of different dialog models. Third, we comb the commonly used database and evaluation metrics of the dialog model. Furthermore, the evaluation metrics of these dialog models are analyzed in detail. Finally, we briefly analyze the existing issues and point out the potential future direction on the human-machine dialog systems.},
}
@article {pmid32340276,
year = {2020},
author = {Browarczyk, J and Kurowski, A and Kostek, B},
title = {Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {8},
pages = {},
pmid = {32340276},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Confidence Intervals ; Electroencephalography ; Humans ; *Machine Learning ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch's method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.},
}
@article {pmid32337919,
year = {2019},
author = {Papadopoulou, SL and Dionyssiotis, Y and Krikonis, K and Lаgopati, N and Kamenov, I and Markoula, S},
title = {Therapeutic Approaches in Locked-in Syndrome.},
journal = {Folia medica},
volume = {61},
number = {3},
pages = {343-351},
doi = {10.3897/folmed.61.e39425},
pmid = {32337919},
issn = {1314-2143},
mesh = {Combined Modality Therapy ; Diagnosis, Differential ; Early Diagnosis ; Humans ; Locked-In Syndrome/diagnosis/etiology/*therapy ; Patient Care Team ; },
}
@article {pmid32336636,
year = {2020},
author = {Li, M and Wang, H and Shang, Z and Yang, Z and Zhang, Y and Wan, H},
title = {Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {78},
number = {},
pages = {175-180},
doi = {10.1016/j.jocn.2020.04.080},
pmid = {32336636},
issn = {1532-2653},
mesh = {Adolescent ; Astrocytoma/*diagnostic imaging ; Child ; Child, Preschool ; Diagnosis, Differential ; Ependymoma/*diagnostic imaging ; Female ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Infant ; Infant, Newborn ; Infratentorial Neoplasms/*diagnostic imaging ; *Machine Learning ; Magnetic Resonance Imaging/methods ; Male ; },
abstract = {Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal-Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significantdifferencescomparedwiththe overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.},
}
@article {pmid32334608,
year = {2020},
author = {Bai, Z and Fong, KNK and Zhang, JJ and Chan, J and Ting, KH},
title = {Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {17},
number = {1},
pages = {57},
pmid = {32334608},
issn = {1743-0003},
mesh = {*Brain-Computer Interfaces ; Feedback ; Humans ; *Recovery of Function/physiology ; Stroke/physiopathology ; Stroke Rehabilitation/*instrumentation/*methods ; Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery.<br><br>METHODS: The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke.<br><br>RESULTS: A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD&#x2009;=&#x2009;0.42; 95% CI&#x2009;=&#x2009;0.18-0.66; I[2]&#x2009;=&#x2009;48%; P&#x2009;<&#x2009;0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD&#x2009;=&#x2009;0.12; 95% CI&#x2009;=&#x2009;-&#x2009;0.28 - 0.52; I[2]&#x2009;=&#x2009;0%; P&#x2009;=&#x2009;0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P&#x2009;=&#x2009;0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P&#x2009;=&#x2009;0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD&#x2009;=&#x2009;-&#x2009;0.30; 95% CI&#x2009;=&#x2009;-&#x2009;0.96 - 0.36; I[2]&#x2009;=&#x2009;0%; P&#x2009;=&#x2009;0.370; fixed-effects model).<br><br>CONCLUSION: The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.},
}
@article {pmid32330415,
year = {2020},
author = {Ganzer, PD and Colachis, SC and Schwemmer, MA and Friedenberg, DA and Dunlap, CF and Swiftney, CE and Jacobowitz, AF and Weber, DJ and Bockbrader, MA and Sharma, G},
title = {Restoring the Sense of Touch Using a Sensorimotor Demultiplexing Neural Interface.},
journal = {Cell},
volume = {181},
number = {4},
pages = {763-773.e12},
doi = {10.1016/j.cell.2020.03.054},
pmid = {32330415},
issn = {1097-4172},
mesh = {Adult ; Brain-Computer Interfaces/psychology ; Feedback, Sensory/*physiology ; Hand/physiopathology ; Hand Strength/physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/physiology ; Spinal Cord Injuries/physiopathology ; Touch/*physiology ; Touch Perception/*physiology ; },
abstract = {Paralyzed muscles can be reanimated following spinal cord injury (SCI) using a brain-computer interface (BCI) to enhance motor function alone. Importantly, the sense of touch is a key component of motor function. Here, we demonstrate that a human participant with a clinically complete SCI can use a BCI to simultaneously reanimate both motor function and the sense of touch, leveraging residual touch signaling from his own hand. In the primary motor cortex (M1), residual subperceptual hand touch signals are simultaneously demultiplexed from ongoing efferent motor intention, enabling intracortically controlled closed-loop sensory feedback. Using the closed-loop demultiplexing BCI almost fully restored the ability to detect object touch and significantly improved several sensorimotor functions. Afferent grip-intensity levels are also decoded from M1, enabling grip reanimation regulated by touch signaling. These results demonstrate that subperceptual neural signals can be decoded from the cortex and transformed into conscious perception, significantly augmenting function.},
}
@article {pmid32329667,
year = {2020},
author = {Monini, S and Battilocchi, L and Salerno, G and Filippi, C and Barbara, M},
title = {Bone conductive implantation in asymmetric hearing loss (AHL).},
journal = {Acta oto-laryngologica},
volume = {140},
number = {8},
pages = {651-658},
doi = {10.1080/00016489.2020.1752396},
pmid = {32329667},
issn = {1651-2251},
mesh = {Audiometry, Pure-Tone ; Audiometry, Speech ; Auditory Threshold ; Bone Conduction ; Female ; *Hearing Aids ; Hearing Loss, Unilateral/*rehabilitation/surgery ; Humans ; Male ; Middle Aged ; Noise ; Retrospective Studies ; },
abstract = {Background: Bone conductive implants (BCI) represent one possible solution for rehabilitation of single-sided deafness (SSD).Aims: The aim of the present study was to verify the efficacy of bone conduction implantation in subjects with unilateral severe-to-profound hearing loss and contralaterally impaired hearing, that is, asymmetric hearing loss (AHL), and to compare it with known BCI indications for SSD.Material and methods: Twenty-one subjects received BCI for either SSD or AHL. All of the subjects underwent a battery of audiological and subjective tests, Data were collected and statistically evaluated within and between the SSD group and the AHL group.Results: A PTA threshold gain was observed in AHL patients along with improved values in speech audiometry in quiet and noise. The two visual analogue scale evaluations (QoL and QoS) and the GBI showed significantly better scores in AHL patients compared to SSD patients.Conclusions: BCI provided improvement for auditory or speech recognition in AHL subjects, as compare to SSD. From these findings, it is possible to predict a positive role of BCI for some audiological aspects of AHL subjects that are generally not present or not detectable in SSD cases.},
}
@article {pmid32329285,
year = {2020},
author = {Liu, X and Wang, D and Zhao, E and Ma, M and Shi, L},
title = {[Design and verification of microelectrode twisting machine].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {2},
pages = {317-323},
pmid = {32329285},
issn = {1001-5515},
mesh = {*Equipment Design ; *Microelectrodes ; *Printing, Three-Dimensional ; },
abstract = {As an interface between external electronic devices and internal neural nuclei, microelectrodes play an important role in many fields, such as animal robots, deep brain stimulation and neural prostheses. Aiming at the problem of high price and complicated fabrication process of microelectrode, a microelectrode twisting machine based on open source electronic prototyping platform (Arduino) and three-dimensional printing technology was proposed, and its microelectrode fabrication performance and neural stimulation performance were verified. The results show that during the fabrication of microelectrodes, the number of positive twisting turns of the electrode wire should generally be set to about 1.8 times of its length, and the number of reverse twisting rings is independent of the length, generally about 5. Moreover, compared with the traditional instrument, the device is not only inexpensive and simple to manufacture, but also has good expandability. It has a positive significance for both the personalization and popularization of microelectrode fabrication and the reduction of experimental cost.},
}
@article {pmid32329279,
year = {2020},
author = {Zhou, Y and Hu, Y and Li, M and Yang, L and Shang, Z},
title = {[A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {2},
pages = {271-279},
pmid = {32329279},
issn = {1001-5515},
mesh = {*Algorithms ; Microelectrodes ; Principal Component Analysis ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; *Wavelet Analysis ; },
abstract = {Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.},
}
@article {pmid32329278,
year = {2020},
author = {Li, Y and Xiong, X and Li, Z and Fu, Y},
title = {[Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {2},
pages = {262-270},
pmid = {32329278},
issn = {1001-5515},
mesh = {Brain/*diagnostic imaging ; Brain-Computer Interfaces ; *Foot ; Humans ; *Imagination ; Movement ; *Spectroscopy, Near-Infrared ; },
abstract = {Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.},
}
@article {pmid32327970,
year = {2020},
author = {Murovec, N and Heilinger, A and Xu, R and Ortner, R and Spataro, R and La Bella, V and Miao, Y and Jin, J and Chatelle, C and Laureys, S and Allison, BZ and Guger, C},
title = {Effects of a Vibro-Tactile P300 Based Brain-Computer Interface on the Coma Recovery Scale-Revised in Patients With Disorders of Consciousness.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {294},
pmid = {32327970},
issn = {1662-4548},
abstract = {Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients' cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8-12 runs each day. Vibrotactile tactors were placed on the each patient's left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (p = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.},
}
@article {pmid32327966,
year = {2020},
author = {Naskovska, K and Lau, S and Korobkov, AA and Haueisen, J and Haardt, M},
title = {Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {261},
pmid = {32327966},
issn = {1662-4548},
abstract = {Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications.},
}
@article {pmid32325633,
year = {2020},
author = {Benda, M and Volosyak, I},
title = {Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs.},
journal = {Brain sciences},
volume = {10},
number = {4},
pages = {},
pmid = {32325633},
issn = {2076-3425},
support = {IT-1-2-001//European Regional Development Fund/ ; },
abstract = {In this paper we compared different visual feedback methods, informing users about classification progress in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller application. According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers. In this experiment we further investigated these effects, and tested a 4-target SSVEP speller interface with a much higher number of subjects. Five different scenarios were used with variations in stimulus size and contrast, "no feedback", "size increasing", "size decreasing", "contrast increasing", and "contrast decreasing". With each of the five scenarios, 24 participants had to spell six letter words (at least 18 selections with this three-steps speller). The fastest feedback modalities were different for the users, there was no visual feedback which was generally better than the others. With the used interface, six users achieved significantly better Information Transfer Rates (ITRs) compared to the "no feedback" condition. Their average improvement by using the individually fastest feedback method was 46.52%. This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy.},
}
@article {pmid32324587,
year = {2021},
author = {Wang, B and Wong, CM and Kang, Z and Liu, F and Shui, C and Wan, F and Chen, CLP},
title = {Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces.},
journal = {IEEE transactions on cybernetics},
volume = {51},
number = {10},
pages = {5008-5020},
doi = {10.1109/TCYB.2020.2982901},
pmid = {32324587},
issn = {2168-2275},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Common spatial pattern (CSP) is one of the most successful feature extraction algorithms for brain-computer interfaces (BCIs). It aims to find spatial filters that maximize the projected variance ratio between the covariance matrices of the multichannel electroencephalography (EEG) signals corresponding to two mental tasks, which can be formulated as a generalized eigenvalue problem (GEP). However, it is challenging in principle to impose additional regularization onto the CSP to obtain structural solutions (e.g., sparse CSP) due to the intrinsic nonconvexity and invariance property of GEPs. This article reformulates the CSP as a constrained minimization problem and establishes the equivalence of the reformulated and the original CSPs. An efficient algorithm is proposed to solve this optimization problem by alternately performing singular value decomposition (SVD) and least squares. Under this new formulation, various regularization techniques for linear regression can then be easily implemented to regularize the CSPs for different learning paradigms, such as the sparse CSP, the transfer CSP, and the multisubject CSP. Evaluations on three BCI competition datasets show that the regularized CSP algorithms outperform other baselines, especially for the high-dimensional small training set. The extensive results validate the efficiency and effectiveness of the proposed CSP formulation in different learning contexts.},
}
@article {pmid32319751,
year = {2020},
author = {Lee, JH and Kim, H and Hwang, JY and Chung, J and Jang, TM and Seo, DG and Gao, Y and Lee, J and Park, H and Lee, S and Moon, HC and Cheng, H and Lee, SH and Hwang, SW},
title = {3D Printed, Customizable, and Multifunctional Smart Electronic Eyeglasses for Wearable Healthcare Systems and Human-Machine Interfaces.},
journal = {ACS applied materials &amp; interfaces},
volume = {12},
number = {19},
pages = {21424-21432},
doi = {10.1021/acsami.0c03110},
pmid = {32319751},
issn = {1944-8252},
mesh = {*Brain-Computer Interfaces ; *Eyeglasses ; Humans ; Monitoring, Physiologic/*instrumentation/methods ; Printing, Three-Dimensional ; Video Games ; *Wearable Electronic Devices ; },
abstract = {Personal accessories such as glasses and watches that we usually carry in our daily life can yield useful information from the human body, yet most of them are limited to exercise-related parameters or simple heart rates. Since these restricted characteristics might arise from interfaces between the body and items as one of the main reasons, an interface design considering such a factor can provide us with biologically meaningful data. Here, we describe three-dimensional-printed, personalized, multifunctional electronic eyeglasses (E-glasses), not only to monitor various biological phenomena but also to propose a strategy to coordinate the recorded data for active commands and game operations for human-machine interaction (HMI) applications. Soft, highly conductive composite electrodes embedded in the E-glasses enable us to achieve reliable, continuous recordings of physiological activities. UV-responsive, color-tunable lenses using an electrochromic ionic gel offer the functionality of both eyeglass and sunglass modes, and accelerometers provide the capability of tracking precise human postures and behaviors. Detailed studies of electrophysiological signals including electroencephalogram and electrooculogram demonstrate the feasibility of smart electronic glasses for practical use as a platform for future HMI systems.},
}
@article {pmid32319523,
year = {2020},
author = {Giroux, P and Bhajun, R and Segard, S and Picquenot, C and Charavay, C and Desquilles, L and Pinna, G and Ginestier, C and Denis, J and Cherradi, N and Guyon, L},
title = {miRViz: a novel webserver application to visualize and interpret microRNA datasets.},
journal = {Nucleic acids research},
volume = {48},
number = {W1},
pages = {W252-W261},
pmid = {32319523},
issn = {1362-4962},
mesh = {Adrenal Cortex Neoplasms/genetics/metabolism/mortality ; Adrenocortical Carcinoma/genetics/metabolism/mortality ; Animals ; Breast Neoplasms/genetics/metabolism ; Colonic Neoplasms/genetics/metabolism ; Datasets as Topic ; Exosomes/metabolism ; Female ; Humans ; Internet ; Mice ; MicroRNAs/genetics/*metabolism ; Neoplastic Stem Cells/metabolism ; *Software ; },
abstract = {MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the regulation of major pathways in eukaryotic cells through their binding to and repression of multiple mRNAs. With high-throughput methodologies, various outcomes can be measured that produce long lists of miRNAs that are often difficult to interpret. A common question is: after differential expression or phenotypic screening of miRNA mimics, which miRNA should be chosen for further investigation? Here, we present miRViz (http://mirviz.prabi.fr/), a webserver application designed to visualize and interpret large miRNA datasets, with no need for programming skills. MiRViz has two main goals: (i) to help biologists to raise data-driven hypotheses and (ii) to share miRNA datasets in a straightforward way through publishable quality data representation, with emphasis on relevant groups of miRNAs. MiRViz can currently handle datasets from 11 eukaryotic species. We present real-case applications of miRViz, and provide both datasets and procedures to reproduce the corresponding figures. MiRViz offers rapid identification of miRNA families, as demonstrated here for the miRNA-320 family, which is significantly exported in exosomes of colon cancer cells. We also visually highlight a group of miRNAs associated with pluripotency that is particularly active in control of a breast cancer stem-cell population in culture.},
}
@article {pmid32318732,
year = {2020},
author = {Owen, AM},
title = {Improving diagnosis and prognosis in disorders of consciousness.},
journal = {Brain : a journal of neurology},
volume = {143},
number = {4},
pages = {1050-1053},
pmid = {32318732},
issn = {1460-2156},
support = {408004//CIHR/Canada ; },
mesh = {*Brain-Computer Interfaces ; *Consciousness ; Consciousness Disorders ; Humans ; Prognosis ; },
abstract = {This scientific commentary refers to ‘Prognosis for patients with cognitive motor dissociation identified by brain-computer interface’, by Pan etal. (doi: 10.1093/brain/awaa026).},
}
@article {pmid32317917,
year = {2020},
author = {Dash, D and Ferrari, P and Wang, J},
title = {Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {290},
pmid = {32317917},
issn = {1662-4548},
support = {R01 DC016621/DC/NIDCD NIH HHS/United States ; R03 DC013990/DC/NIDCD NIH HHS/United States ; },
abstract = {Speech production is a hierarchical mechanism involving the synchronization of the brain and the oral articulators, where the intention of linguistic concepts is transformed into meaningful sounds. Individuals with locked-in syndrome (fully paralyzed but aware) lose their motor ability completely including articulation and even eyeball movement. The neural pathway may be the only option to resume a certain level of communication for these patients. Current brain-computer interfaces (BCIs) use patients' visual and attentional correlates to build communication, resulting in a slow communication rate (a few words per minute). Direct decoding of imagined speech from the neural signals (and then driving a speech synthesizer) has the potential for a higher communication rate. In this study, we investigated the decoding of five imagined and spoken phrases from single-trial, non-invasive magnetoencephalography (MEG) signals collected from eight adult subjects. Two machine learning algorithms were used. One was an artificial neural network (ANN) with statistical features as the baseline approach. The other was convolutional neural networks (CNNs) applied on the spatial, spectral and temporal features extracted from the MEG signals. Experimental results indicated the possibility to decode imagined and spoken phrases directly from neuromagnetic signals. CNNs were found to be highly effective with an average decoding accuracy of up to 93% for the imagined and 96% for the spoken phrases.},
}
@article {pmid32316162,
year = {2020},
author = {Dash, D and Ferrari, P and Dutta, S and Wang, J},
title = {NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {8},
pages = {},
pmid = {32316162},
issn = {1424-8220},
support = {362221//University of Texas System Brain Research Grant/ ; R03DC013990; R01DC016621/NH/NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Electrocardiography ; Electrooculography ; Female ; Humans ; Magnetoencephalography/*methods ; Male ; Middle Aged ; Neural Networks, Computer ; Nontherapeutic Human Experimentation ; *Signal Processing, Computer-Assisted ; Speech/*physiology ; Support Vector Machine ; Voice ; },
abstract = {Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a higher communication rate than the current BCIs. Although recent progress has demonstrated the potential of speech-BCIs from either invasive or non-invasive neural signals, the majority of the systems developed so far still assume knowing the onset and offset of the speech utterances within the continuous neural recordings. This lack of real-time voice/speech activity detection (VAD) is a current obstacle for future applications of neural speech decoding wherein BCI users can have a continuous conversation with other speakers. To address this issue, in this study, we attempted to automatically detect the voice/speech activity directly from the neural signals recorded using magnetoencephalography (MEG). First, we classified the whole segments of pre-speech, speech, and post-speech in the neural signals using a support vector machine (SVM). Second, for continuous prediction, we used a long short-term memory-recurrent neural network (LSTM-RNN) to efficiently decode the voice activity at each time point via its sequential pattern-learning mechanism. Experimental results demonstrated the possibility of real-time VAD directly from the non-invasive neural signals with about 88% accuracy.},
}
@article {pmid32313100,
year = {2020},
author = {Degenhart, AD and Bishop, WE and Oby, ER and Tyler-Kabara, EC and Chase, SM and Batista, AP and Yu, BM},
title = {Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.},
journal = {Nature biomedical engineering},
volume = {4},
number = {7},
pages = {672-685},
pmid = {32313100},
issn = {2157-846X},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; Behavior, Animal ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Electrophysiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Neurons/*physiology ; User-Computer Interface ; },
abstract = {The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs.},
}
@article {pmid32311375,
year = {2020},
author = {Zuo, C and Miao, Y and Wang, X and Wu, L and Jin, J},
title = {Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {340},
number = {},
pages = {108725},
doi = {10.1016/j.jneumeth.2020.108725},
pmid = {32311375},
issn = {1872-678X},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Motor imagery (MI) related features are typically extracted from a fixed frequency band and time window of EEG signal. Meanwhile, the time when the brain activity associated with the occurring task varies from person to person and trial to trial. Thus, some of the discarded EEG data with time may contain MI-related information.<br><br>NEW METHOD: This study proposes a temporal frequency joint sparse optimization and fuzzy fusion (TFSOFF) method for joint frequency band optimization and classification fusion on multiple time windows to effectively utilize the signals of all time period within the MI task. Raw EEG data are first segmented into multiple subtime windows using a sliding window approach. Then, a set of overlapping bandpass filters is performed on each time window to generate a set of overlapping subbands, and common spatial pattern is used for feature extraction at each subband. Joint frequency band optimization is conducted on multiple time windows using a joint sparse optimization model. Fuzzy integral is used to fuse each time window after joint optimization.<br><br>RESULTS: The proposed TFSOFF is validated on two public EEG datasets and compared with several other competing methods. Experimental results show that the proposed TFSOFF can effectively extract MI related features of all time period EEG signals within the MI task and helps improving the classification performance of MI.<br><br>The proposed TFSOFF exhibits superior performance in comparison with several competing methods.<br><br>CONCLUSIONS: The proposed method is a suitable method for improving the performance of MI-based BCIs.},
}
@article {pmid32309055,
year = {2019},
author = {Razzak, I and A Hameed, I and Xu, G},
title = {Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {7},
number = {},
pages = {2000508},
pmid = {32309055},
issn = {2168-2372},
abstract = {BACKGROUND: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix.<br><br>AIM: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals.<br><br>METHOD: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization.<br><br>RESULTS: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers.<br><br>CONCLUSION: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals.},
}
@article {pmid32307262,
year = {2020},
author = {Fernández-Sotos, P and García-Martínez, B and Ricarte, JJ and Latorre, JM and Sánchez-Morla, EM and Fernández-Caballero, A and Rodriguez-Jimenez, R},
title = {Electroencephalographic spectral analysis from a wireless low-cost brain-computer interface for symptom capture of auditory verbal hallucinations in schizophrenia.},
journal = {Schizophrenia research},
volume = {220},
number = {},
pages = {297-299},
doi = {10.1016/j.schres.2020.04.011},
pmid = {32307262},
issn = {1573-2509},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hallucinations/etiology ; Humans ; *Schizophrenia/complications ; },
}
@article {pmid32306231,
year = {2020},
author = {Vermaas, M and Piastra, MC and Oostendorp, TF and Ramsey, NF and Tiesinga, PHE},
title = {FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and Electrodes.},
journal = {Neuroinformatics},
volume = {18},
number = {4},
pages = {569-580},
pmid = {32306231},
issn = {1559-0089},
mesh = {Cerebral Cortex ; Electrocorticography/*methods ; Electrodes ; *Finite Element Analysis ; Humans ; *Models, Theoretical ; },
abstract = {Applications such as brain computer interfaces require recordings of relevant neuronal population activity with high precision, for example, with electrocorticography (ECoG) grids. In order to achieve this, both the placement of the electrode grid on the cortex and the electrode properties, such as the electrode size and material, need to be optimized. For this purpose, it is essential to have a reliable tool that is able to simulate the extracellular potential, i.e., to solve the so-called ECoG forward problem, and to incorporate the properties of the electrodes explicitly in the model. In this study, this need is addressed by introducing the first open-source pipeline, FEMfuns (finite element method for useful neuroscience simulations), that allows neuroscientists to solve the forward problem in a variety of different geometrical domains, including different types of source models and electrode properties, such as resistive and capacitive materials. FEMfuns is based on the finite element method (FEM) implemented in FEniCS and includes the geometry tessellation, several electrode-electrolyte implementations and adaptive refinement options. The Python code of the pipeline is available under the GNU General Public License version 3 at https://github.com/meronvermaas/FEMfuns . We tested our pipeline with several geometries and source configurations such as a dipolar source in a multi-layer sphere model and a five-compartment realistically-shaped head model. Furthermore, we describe the main scripts in the pipeline, illustrating its flexible and versatile use. Provided with a sufficiently fine tessellation, the numerical solution of the forward problem approximates the analytical solution. Furthermore, we show dispersive material and interface effects in line with previous literature. Our results indicate substantial capacitive and dispersive effects due to the electrode-electrolyte interface when using stimulating electrodes. The results demonstrate that the pipeline presented in this paper is an accurate and flexible tool to simulate signals generated on electrode grids by the spatiotemporal electrical activity patterns produced by sources and thereby allows the user to optimize grids for brain computer interfaces including exploration of alternative electrode materials/properties.},
}
@article {pmid32305929,
year = {2020},
author = {Abtahi, M and Bahram Borgheai, S and Jafari, R and Constant, N and Diouf, R and Shahriari, Y and Mankodiya, K},
title = {Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {6},
pages = {1246-1253},
doi = {10.1109/TNSRE.2020.2987888},
pmid = {32305929},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Parkinson Disease ; Spectroscopy, Near-Infrared ; },
abstract = {Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinsons Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data.},
}
@article {pmid32305926,
year = {2020},
author = {Zhang, X and Guo, Y and Gao, B and Long, J},
title = {Alpha Frequency Intervention by Electrical Stimulation to Improve Performance in Mu-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {6},
pages = {1262-1270},
doi = {10.1109/TNSRE.2020.2987529},
pmid = {32305926},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination ; Movement ; },
abstract = {The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the most widely used systems, of which the related methods to improve users' accuracy are still poorly studied, especially for the BCI illiteracy. Here, we examined a way to enhance mu-based BCI performance by electrically stimulating the ulnar nerve of the contralateral wrist at the alpha frequency (10 Hz) during left- and right-hand motor imagination in two BCI groups (literate and illiterate). We demonstrate that this alpha frequency intervention enhances the classification accuracy between left- and right-hand motor imagery from 66.41% to 81.57% immediately after intervention and to 75.28% two days after intervention in the BCI illiteracy group, while classification accuracy improves from 82.12% to 91.84% immediately after intervention and to 89.03% two days after intervention in the BCI literacy group. However, the classification accuracy did not change before and after the sham intervention (no electrical stimulation). Furthermore, the ERD on the primary sensorimotor cortex during left- or right-hand motor imagery tasks was more visible at the mu-rhythm (8-13 Hz) after alpha frequency intervention. Alpha frequency intervention increases the mu-rhythm power difference between left- and right-hand motor imagery tasks. These results provide evidence that alpha frequency intervention is an effective way to improve BCI performance by regulating the mu-rhythm which might provide a way to reduce BCI illiteracy.},
}
@article {pmid32302221,
year = {2020},
author = {Stokes, PRA and Jokinen, T and Amawi, S and Qureshi, M and Husain, MI and Yatham, LN and Strang, J and Young, AH},
title = {Pharmacological Treatment of Mood Disorders and Comorbid Addictions: A Systematic Review and Meta-Analysis: Traitement Pharmacologique des Troubles de L'humeur et des Dépendances Comorbides: Une Revue Systématique et une Méta-Analyse.},
journal = {Canadian journal of psychiatry. Revue canadienne de psychiatrie},
volume = {65},
number = {11},
pages = {749-769},
pmid = {32302221},
issn = {1497-0015},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Bipolar Disorder/drug therapy/epidemiology ; Comorbidity ; *Depressive Disorder, Major/drug therapy/epidemiology ; Humans ; Mood Disorders ; Selective Serotonin Reuptake Inhibitors ; },
abstract = {OBJECTIVE: Addiction comorbidity is an important clinical challenge in mood disorders, but the best way of pharmacologically treating people with mood disorders and addictions remains unclear. The aim of this study was to assess the efficacy of pharmacological treatments for mood and addiction symptoms in people with mood disorders and addiction comorbidity.<br><br>METHODS: A systematic search of placebo-controlled randomized controlled trials investigating the effects of pharmacological treatments in people with bipolar disorder (BD) or major depressive disorder (MDD), and comorbid addictions was performed. Treatment-related effects on mood and addiction measures were assessed in a meta-analysis, which also estimated risks of participant dropout and adverse effects.<br><br>RESULTS: A total of 32 studies met systematic review inclusion criteria. Pharmacological therapy was more effective than placebo for improving manic symptoms (standardized mean difference [SMD] = -0.15; 95% confidence interval [95% CI], -0.29 to -0.02; P = 0.03) but not BD depressive symptoms (SMD = -0.09; 95% CI, -0.22 to 0.03; P = 0.15). Quetiapine significantly improved manic symptoms (SMD = -0.23; 95% CI, -0.39 to -0.06; P = 0.008) but not BD depressive symptoms (SMD = -0.07; 95% CI, -0.23 to 0.10; P = 0.42). Pharmacological therapy was more effective than placebo for improving depressive symptoms in MDD (SMD = -0.16; 95% CI, -0.30 to -0.03; P = 0.02). Imipramine improved MDD depressive symptoms (SMD = -0.58; 95% CI, -1.03 to -0.13; P = 0.01) but Selective serotonin reuptake Inhibitors (SSRI)-based treatments had no effect (SMD = -0.06; 95% CI, -0.30 to 0.17; P = 0.60). Pharmacological treatment improved the odds of alcohol abstinence in MDD but had no effects on opiate abstinence.<br><br>CONCLUSIONS: Pharmacological treatments were significantly better than placebo in improving manic symptoms, MDD depressive symptoms, and alcohol abstinence but were not better for bipolar depression symptoms. Importantly, quetiapine was not more effective than placebo in improving bipolar depression symptoms nor were SSRI's for the treatment of MDD depression. Our findings highlight the need for further high-quality clinical trials of treatments for mood disorders and comorbid addictions.},
}
@article {pmid32301143,
year = {2020},
author = {Allison, BZ and Kübler, A and Jin, J},
title = {30+ years of P300 brain-computer interfaces.},
journal = {Psychophysiology},
volume = {57},
number = {7},
pages = {e13569},
doi = {10.1111/psyp.13569},
pmid = {32301143},
issn = {1469-8986},
mesh = {Auditory Perception/*physiology ; *Brain-Computer Interfaces/history ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; History, 20th Century ; History, 21st Century ; Humans ; Touch Perception/*physiology ; Visual Perception/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event-related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting each flash that corresponds to a specific target selection while ignoring other flashes will elicit P300s to only the target selection. P300 BCIs also have been implemented using auditory or tactile stimuli. P300 BCIs have been used with a variety of applications for severely disabled end users in their homes without frequent expert support. P300 BCI research and development has made substantial progress, but challenges remain before these tools can become practical devices for impaired patients and perhaps healthy people.},
}
@article {pmid32300372,
year = {2020},
author = {Su, X and Gong, Q and Zheng, Y and Liu, X and Li, KC},
title = {An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {3658795},
pmid = {32300372},
issn = {1748-6718},
mesh = {Algorithms ; Behavior ; *Brain-Computer Interfaces/statistics &amp; numerical data ; Computational Biology ; *Computer Security/statistics &amp; numerical data ; Humans ; Machine Learning ; *Mobile Applications/statistics &amp; numerical data ; },
abstract = {Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.},
}
@article {pmid32299460,
year = {2020},
author = {Shafiul Hasan, SM and Siddiquee, MR and Atri, R and Ramon, R and Marquez, JS and Bai, O},
title = {Prediction of gait intention from pre-movement EEG signals: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {17},
number = {1},
pages = {50},
pmid = {32299460},
issn = {1743-0003},
mesh = {Adult ; Amputees/rehabilitation ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Feasibility Studies ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: Prediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence.<br><br>METHODS: An eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between 'walk vs. stop' and 'rest vs. start' classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme.<br><br>RESULTS: Using a fully predictive intention detection system, 76.41&#xb1;4.47% accuracy, 72.85&#xb1;7.48% sensitivity, and 79.93&#xb1;5.50% specificity were achieved for 'rest vs. start' classification. While for 'walk vs. stop' classification, the obtained mean accuracy, sensitivity, and specificity were 74.12&#xb1;4.12%, 70.24&#xb1;6.45%, and 77.78&#xb1;7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06&#xb1;8.27% with 1.45 False Positives/min.<br><br>CONCLUSION: Extensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.},
}
@article {pmid32299441,
year = {2020},
author = {Khalaf, A and Akcakaya, M},
title = {A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.},
journal = {Biomedical engineering online},
volume = {19},
number = {1},
pages = {23},
pmid = {32299441},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electrodes ; *Electroencephalography ; Humans ; *Machine Learning ; Probability ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG-fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users.<br><br>RESULTS: Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm.<br><br>CONCLUSIONS: Data collected using the MI paradigm show better generalization across subjects.},
}
@article {pmid32297395,
year = {2020},
author = {Jeong, YC and Lee, HE and Shin, A and Kim, DG and Lee, KJ and Kim, D},
title = {Progress in Brain-Compatible Interfaces with Soft Nanomaterials.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {32},
number = {35},
pages = {e1907522},
doi = {10.1002/adma.201907522},
pmid = {32297395},
issn = {1521-4095},
support = {NRF-2016M3D1A1900035//Creative Materials Discovery Program/ ; NRF-2017M3C7A1029612//Brain Research Program/ ; NRF-2019M3E5D2A01066259//Bio &amp; Medical Technology Development Program/ ; //National Research Foundation of Korea/ ; //Ministry of Science, ICT and Future Planning/ ; SSTF-BA1301-07//Samsung Science and Technology Foundation/ ; },
mesh = {Animals ; Biocompatible Materials/*chemistry/pharmacology ; *Brain/drug effects ; Hardness ; Humans ; *Mechanical Phenomena ; *Nanostructures ; Nanotechnology/instrumentation/*methods ; },
abstract = {Neural interfaces facilitating communication between the brain and machines must be compatible with the soft, curvilinear, and elastic tissues of the brain and yet yield enough power to read and write information across a wide range of brain areas through high-throughput recordings or optogenetics. Biocompatible-material engineering has facilitated the development of brain-compatible neural interfaces to support built-in modulation of neural circuits and neurological disorders. Recent developments in brain-compatible neural interfaces that use soft nanomaterials more suitable for complex neural circuit analysis and modulation are reviewed. Preclinical tests of the compatibility and specificity of these interfaces in animal models are also discussed.},
}
@article {pmid32296323,
year = {2020},
author = {Yang, SH and Wang, HL and Lo, YC and Lai, HY and Chen, KY and Lan, YH and Kao, CC and Chou, C and Lin, SH and Huang, JW and Wang, CF and Kuo, CH and Chen, YY},
title = {Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.},
journal = {Frontiers in computational neuroscience},
volume = {14},
number = {},
pages = {22},
pmid = {32296323},
issn = {1662-5188},
abstract = {Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions. Approach: We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder. Main Results: The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology. Significance: These results suggested that the ECPNN-EF with partially connected topology could cope with both within- and across-day changes in neural recording conditions. The error feedback in the ECPNN-EF compensated for decreases in decoding performance when neural recording conditions changed. This mechanism made the ECPNN-EF robust against changes in functional mappings and thus improved the long-term decoding stability when only a few days of training data were available.},
}
@article {pmid32289760,
year = {2020},
author = {Smetanin, N and Belinskaya, A and Lebedev, M and Ossadtchi, A},
title = {Digital filters for low-latency quantification of brain rhythms in real time.},
journal = {Journal of neural engineering},
volume = {17},
number = {4},
pages = {046022},
doi = {10.1088/1741-2552/ab890f},
pmid = {32289760},
issn = {1741-2552},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurofeedback ; },
abstract = {OBJECTIVE: The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off.<br><br>APPROACH: Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples.<br><br>MAIN RESULTS: When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold.<br><br>SIGNIFICANCE: The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.},
}
@article {pmid32289756,
year = {2020},
author = {Shiraishi, Y and Kawahara, Y and Yamashita, O and Fukuma, R and Yamamoto, S and Saitoh, Y and Kishima, H and Yanagisawa, T},
title = {Neural decoding of electrocorticographic signals using dynamic mode decomposition.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036009},
doi = {10.1088/1741-2552/ab8910},
pmid = {32289756},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrocorticography ; Electroencephalography ; Hand ; Humans ; *Motor Cortex ; Movement ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals.<br><br>APPROACH: Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG signals during three types of hand movements, which were recorded from 11 patients implanted with subdural electrodes. From the signals at the time of the movements, the modes and powers were evaluated by DMD and FFT and were decoded using support vector machine. We used the Grassmann kernel to evaluate the distance between modes estimated by DMD (DMD mode). In addition, we decoded the DMD modes, in which the phase components were shuffled, to compare the classification accuracy.<br><br>MAIN RESULTS: The decoding accuracy using DMD modes was significantly better than that using FFT powers. The accuracy significantly decreased when the phases of the DMD mode were shuffled. Among the frequency bands, the DMD mode at approximately 100 Hz demonstrated the highest classification accuracy.<br><br>SIGNIFICANCE: DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients communicate.},
}
@article {pmid32287132,
year = {2020},
author = {Jabaley, CS and Lynde, GC and Caridi-Scheible, ME and O'Reilly-Shah, VN},
title = {The Human-Machine Interface in Anesthesiology: Corollaries and Lessons Learned From Aviation and Crewed Spaceflight.},
journal = {Anesthesia and analgesia},
volume = {130},
number = {5},
pages = {1255-1260},
doi = {10.1213/ANE.0000000000004628},
pmid = {32287132},
issn = {1526-7598},
mesh = {Anesthesiology/methods/*trends ; Aviation/methods/*trends ; Brain-Computer Interfaces/*trends ; Forecasting ; Humans ; Space Flight/methods/*trends ; },
}
@article {pmid32286993,
year = {2020},
author = {Zhang, W and Wu, D},
title = {Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {5},
pages = {1117-1127},
doi = {10.1109/TNSRE.2020.2985996},
pmid = {32286993},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Learning ; Machine Learning ; },
abstract = {Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject electroencephalogram (EEG) classification, i.e., we have labeled EEG trials from one or more source subjects, but only unlabeled EEG trials from the target subject. We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between the source and the target domains, while preserving their geometric structures. MEKT can cope with one or multiple source domains, and can be computed efficiently. We also propose a domain transferability estimation (DTE) approach to identify the most beneficial source domains, in case there are a large number of source domains. Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.},
}
@article {pmid32283388,
year = {2020},
author = {Hang, W and Feng, W and Liang, S and Wang, Q and Liu, X and Choi, KS},
title = {Deep stacked support matrix machine based representation learning for motor imagery EEG classification.},
journal = {Computer methods and programs in biomedicine},
volume = {193},
number = {},
pages = {105466},
doi = {10.1016/j.cmpb.2020.105466},
pmid = {32283388},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Machine Learning ; },
abstract = {BACKGROUND AND OBJECTIVE: Electroencephalograph (EEG) classification is an important technology that can establish a mapping relationship between EEG features and cognitive tasks. Emerging matrix classifiers have been successfully applied to motor imagery (MI) EEG classification, but they belong to shallow classifiers, making powerful stacked generalization principle not exploited for automatically learning deep EEG features. To learn the high-level representation and abstraction, we proposed a novel deep stacked support matrix machine (DSSMM) to improve the performance of existing shallow matrix classifiers in EEG classification.<br><br>METHODS: The main idea of our framework is founded on the stacked generalization principle, where support matrix machine (SMM) is introduced as the basic building block of deep stacked network. The weak predictions of all previous layers obtained via SMM are randomly projected to help move apart the manifold of the original input EEG feature, and then the newly generated features are fed into the next layer of DSSMM. The framework only involves an efficient feed-forward rather than parameter&#xa0;fine-tuning with backpropagation, each layer of which is a convex optimization problem, thus simplifying the objective function solving process.<br><br>RESULTS: Extensive experiments on three public EEG datasets and a self-collected EEG dataset are conducted. Experimental results demonstrate that our DSSMM outperforms the available state-of-the-art methods.<br><br>CONCLUSION: The proposed DSSMM inherits the characteristic of matrix classifiers that can learn the structural information of data as well as the powerful capability of deep representation learning, which makes it adapted to classify complex matrix-form EEG data.},
}
@article {pmid32283387,
year = {2020},
author = {Luo, J and Gao, X and Zhu, X and Wang, B and Lu, N and Wang, J},
title = {Motor imagery EEG classification based on ensemble support vector learning.},
journal = {Computer methods and programs in biomedicine},
volume = {193},
number = {},
pages = {105464},
doi = {10.1016/j.cmpb.2020.105464},
pmid = {32283387},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-computer interfaces build a communication pathway from the human brain to a computer. Motor imagery-based electroencephalogram (EEG) classification is a widely applied paradigm in brain-computer interfaces. The common spatial pattern, based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon, is one of the most popular algorithms for motor imagery-based EEG classification. Moreover, the spatiotemporal discrepancy feature based on the event-related potential phenomenon has been demonstrated to provide complementary information to ERD/ERS-based features. In this paper, aiming to improve the performance of motor imagery-based EEG classification in a few-channel situation, an ensemble support vector learning (ESVL)-based approach is proposed to combine the advantages of the ERD/ERS-based features and the event-related potential-based features in motor imagery-based EEG classification.<br><br>METHODS: ESVL is an ensemble learning algorithm based on support vector machine classifier. Specifically, the decision boundary with the largest interclass margin is obtained using the support vector machine algorithm, and the distances between sample points and the decision boundary are mapped to posterior probabilities. The probabilities obtained from different support vector machine classifiers are combined to make prediction. Thus, ESVL leverages the advantages of multiple trained support vector machine classifiers and makes a better prediction based on the posterior probabilities. The class discrepancy-guided sub-band-based common spatial pattern and the spatiotemporal discrepancy feature are applied to extract discriminative features, and then, the extracted features are used to train the ESVL classifier and make predictions.<br><br>RESULTS: The BCI Competition IV datasets 2a and 2b are employed to evaluate the performance of the proposed ESVL algorithm. Experimental comparisons with the state-of-the-art methods are performed, and the proposed ESVL-based approach achieves an average max kappa value of 0.60 and 0.71 on BCI Competition IV datasets 2a and 2b respectively. The results show that the proposed ESVL-based approach improves the performance of motor imagery-based brain-computer interfaces.<br><br>CONCLUSION: The proposed ESVL classifier could use the posterior probabilities to realize ensemble learning and the ESVL-based motor imagery classification approach takes advantage of the merits of ERD/ERS based feature and event-related potential based feature to improve the experimental performance.},
}
@article {pmid32283182,
year = {2020},
author = {Gu, L and Yu, Z and Ma, T and Wang, H and Li, Z and Fan, H},
title = {EEG-based Classification of Lower Limb Motor Imagery with Brain Network Analysis.},
journal = {Neuroscience},
volume = {436},
number = {},
pages = {93-109},
doi = {10.1016/j.neuroscience.2020.04.006},
pmid = {32283182},
issn = {1873-7544},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; *Imagination ; Movement ; },
abstract = {This study aims to investigate the difference in cortical signal characteristics between the left and right foot imaginary movements and to improve the classification accuracy of the experimental tasks. Raw signals were gathered from 64-channel scalp electroencephalograms of 11 healthy participants. Firstly, the cortical source model was defined with 62 regions of interest over the sensorimotor cortex (nine Brodmann areas). Secondly, functional connectivity was calculated by phase lock value for α and β rhythm networks. Thirdly, network-based statistics were applied to identify whether there existed stable and significant subnetworks that formed between the two types of motor imagery tasks. Meanwhile, ten graph theory indices were investigated for each network by t-test to determine statistical significance between tasks. Finally, sparse multinomial logistic regression (SMLR)-support vector machine (SVM), as a feature selection and classification model, was used to analyze the graph theory features. The specific time-frequency (α event-related desynchronization and β event-related synchronization) difference network between the two tasks was congregated at the midline and demonstrated significant connections in the premotor areas and primary somatosensory cortex. A few of statistically significant differences in the network properties were observed between tasks in the α and β rhythm. The SMLR-SVM classification model achieved fair discrimination accuracy between imaginary movements of the two feet (maximum 75% accuracy rate in single-trial analyses). This study reveals the network mechanism of the discrimination of the left and right foot motor imagery, which can provide a novel avenue for the BCI system by unilateral lower limb motor imagery.},
}
@article {pmid32278892,
year = {2020},
author = {Khairullah, E and Arican, M and Polat, K},
title = {Brain-computer interface speller system design from electroencephalogram signals with channel selection algorithms.},
journal = {Medical hypotheses},
volume = {141},
number = {},
pages = {109690},
doi = {10.1016/j.mehy.2020.109690},
pmid = {32278892},
issn = {1532-2777},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) have started to be used with the development of computer technology in order to enable individuals who are in this situation to communicate with their environment or move. This study focused on the spelling system that transforms the brain activities obtained with EEG signals into writing. In BCI systems working with P300 obtained from 64 electrodes, data recording and processing cause high cost and high processing load. By reducing the number of electrodes used, the physical dimensions, costs, and processing loads of the systems can be reduced. The main problem at this stage is to determine which electrodes are more effective. Randomness-based optimization methods perform their experiments within the framework of a specific fitness function, resulting in near-best results rather than the best result. The electrodes chosen as a result of the study are expected to contribute positively to the classifier performance. At the same time, an unbalanced data set is balanced, and an increase in system performance is expected.<br><br>METHOD: Electrode selection was performed in both the original dataset and ADASYN dataset using the Genetic Algorithm and Binary Particle Swarm Optimization methods. As a dataset, Wadsworth BCI Dataset (P300 Evoked Potentials) was used in the study. The channels chosen most frequently by optimization methods were determined and compared with the 64-channel classification results using LS-SVM and LDA.<br><br>RESULT: As a result of the optimization processes, the eight channels selected most frequently, the channels selected more than the average of all the selected channels and 64 channel results were compared. The highest accuracy was achieved with the LDA classifier for user A with 29 channels selected with BPSO with 97.250%.<br><br>CONCLUSIONS: The results obtained in the study showed that the number of channels decreased by optimization methods increases the classification performance. In addition, classifier training and test times have been greatly reduced. The application of the ADASYN method did not result in any significant difference.},
}
@article {pmid32276318,
year = {2020},
author = {Sciaraffa, N and Klados, MA and Borghini, G and Di Flumeri, G and Babiloni, F and Aricò, P},
title = {Double-Step Machine Learning Based Procedure for HFOs Detection and Classification.},
journal = {Brain sciences},
volume = {10},
number = {4},
pages = {},
pmid = {32276318},
issn = {2076-3425},
abstract = {The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.},
}
@article {pmid32273902,
year = {2020},
author = {Rodpongpun, S and Janyalikit, T and Ratanamahatana, CA},
title = {Influential Factors of an Asynchronous BCI for Movement Intention Detection.},
journal = {Computational and mathematical methods in medicine},
volume = {2020},
number = {},
pages = {8573754},
pmid = {32273902},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Electroencephalography/statistics &amp; numerical data ; Evoked Potentials ; Female ; Humans ; *Intention ; Linear Models ; Male ; *Movement/physiology ; Pattern Recognition, Automated/statistics &amp; numerical data ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation/methods/statistics &amp; numerical data ; Support Vector Machine ; Young Adult ; },
abstract = {In recent years, asynchronous brain computer interface (BCI) systems have been utilized in many domains such as robot controlling, assistive technology, and rehabilitation. In such BCI systems, movement intention detection algorithms are used to detect movement desires. In recent years, movement-related cortical potential (MRCP), an electroencephalogram (EEG) pattern representing voluntary movement intention, attracts wide attention in movement intention detection. Unfortunately, low MRCP detection accuracy makes the asynchronous BCI system impractical for real usage. In order to develop an effective MRCP detection algorithm, EEG data have to be properly preprocessed. In this work, we investigate the relationship and effects of three factors including frequency bands, spatial filters, and classifiers on MRCP classification performance to determine best settings. In particular, we performed a systematic performance investigation on combinations of five frequency bands, five spatial filters, and six classifiers. The EEG data were acquired from subjects performing series of self-paced ankle dorsiflexions. Analysis of variance (ANOVA) statistical test was performed on F1 scores to investigate effects of these three factors. The results show that frequency bands and spatial filters depend on each other. The combinations directly affect the F1 scores, so they have to be chosen carefully. The results can be used as guidelines for BCI researchers to effectively design a preprocessing method for an advanced asynchronous BCI system, which can assist the stroke rehabilitation.},
}
@article {pmid32272464,
year = {2020},
author = {Schwarz, A and Höller, MK and Pereira, J and Ofner, P and Müller-Putz, GR},
title = {Decoding hand movements from human EEG to control a robotic arm in a simulation environment.},
journal = {Journal of neural engineering},
volume = {17},
number = {3},
pages = {036010},
doi = {10.1088/1741-2552/ab882e},
pmid = {32272464},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Movement ; *Robotic Surgical Procedures ; },
abstract = {OBJECTIVE: Daily life tasks can become a significant challenge for motor impaired persons. Depending on the severity of their impairment, they require more complex solutions to retain an independent life. Brain-computer interfaces (BCIs) are targeted to provide an intuitive form of control for advanced assistive devices such as robotic arms or neuroprostheses. In our current study we aim to decode three different executed hand movements in an online BCI scenario from electroencephalographic (EEG) data.<br><br>APPROACH: Immersed in a desktop-based simulation environment, 15 non-disabled participants interacted with virtual objects from daily life by an avatar's robotic arm. In a short calibration phase, participants performed executed palmar and lateral grasps and wrist supinations. Using this data, we trained a classification model on features extracted from the low frequency time domain. In the subsequent evaluation phase, participants controlled the avatar's robotic arm and interacted with the virtual objects in case of a correct classification.<br><br>MAIN RESULTS: On average, participants scored online 48% of all movement trials correctly (3-condition scenario, adjusted chance level 40%, alpha = 0.05). The underlying movement-related cortical potentials (MRCPs) of the acquired calibration data show significant differences between conditions over contralateral central sensorimotor areas, which are retained in the data acquired from the online BCI use.<br><br>SIGNIFICANCE: We could show the successful online decoding of two grasps and one wrist supination movement using low frequency time domain features of the human EEG. These findings can potentially contribute to the development of a more natural and intuitive BCI-based control modality for upper limb motor neuroprostheses or robotic arms for people with motor impairments.},
}
@article {pmid32269316,
year = {2020},
author = {Hu, H and Cui, Y and Yang, Y},
title = {Circuits and functions of the lateral habenula in health and in disease.},
journal = {Nature reviews. Neuroscience},
volume = {21},
number = {5},
pages = {277-295},
pmid = {32269316},
issn = {1471-0048},
mesh = {Animals ; Basal Ganglia/*physiology/*physiopathology ; Habenula/*physiology/*physiopathology ; Health ; Humans ; Limbic System/*physiology/*physiopathology ; Mental Disorders/physiopathology ; Mesencephalon/*physiology/*physiopathology ; Neural Pathways/physiology/physiopathology ; },
abstract = {The past decade has witnessed exponentially growing interest in the lateral habenula (LHb) owing to new discoveries relating to its critical role in regulating negatively motivated behaviour and its implication in major depression. The LHb, sometimes referred to as the brain's 'antireward centre', receives inputs from diverse limbic forebrain and basal ganglia structures, and targets essentially all midbrain neuromodulatory systems, including the noradrenergic, serotonergic and dopaminergic systems. Its unique anatomical position enables the LHb to act as a hub that integrates value-based, sensory and experience-dependent information to regulate various motivational, cognitive and motor processes. Dysfunction of the LHb may contribute to the pathophysiology of several psychiatric disorders, especially major depression. Recently, exciting progress has been made in identifying the molecular and cellular mechanisms in the LHb that underlie negative emotional state in animal models of drug withdrawal and major depression. A future challenge is to translate these advances into effective clinical treatments.},
}
@article {pmid32268816,
year = {2020},
author = {Sorinas, J and Fernandez-Troyano, JC and Ferrandez, JM and Fernandez, E},
title = {Cortical Asymmetries and Connectivity Patterns in the Valence Dimension of the Emotional Brain.},
journal = {International journal of neural systems},
volume = {30},
number = {5},
pages = {2050021},
doi = {10.1142/S0129065720500215},
pmid = {32268816},
issn = {1793-6462},
mesh = {Adult ; Cerebral Cortex/*physiology ; *Connectome ; Electroencephalography ; Emotions/*physiology ; Functional Laterality/*physiology ; Humans ; Nerve Net/*physiology ; Visual Perception/physiology ; },
abstract = {Understanding the neurophysiology of emotions, the neuronal structures involved in processing emotional information and the circuits by which they act, is key to designing applications in the field of affective neuroscience, to advance both new treatments and applications of brain-computer interactions. However, efforts have focused on developing computational models capable of emotion classification instead of on studying the neural substrates involved in the emotional process. In this context, we have carried out a study of cortical asymmetries and functional cortical connectivity based on the electroencephalographic signal of 24 subjects stimulated with videos of positive and negative emotional content to bring some light to the neurobiology behind emotional processes. Our results show opposite interhemispheric asymmetry patterns throughout the cortex for both emotional categories and specific connectivity patterns regarding each of the studied emotional categories. However, in general, the same key areas, such as the right hemisphere and more anterior cortical regions, presented higher levels of activity during the processing of both valence emotional categories. These results suggest a common neural pathway for processing positive and negative emotions, but with different activation patterns. These preliminary results are encouraging for elucidating the neuronal circuits of the emotional valence dimension.},
}
@article {pmid32268333,
year = {2020},
author = {Persson, AC and Reinfeldt, S and Håkansson, B and Rigato, C and Fredén Jansson, KJ and Eeg-Olofsson, M},
title = {Three-Year Follow-Up with the Bone Conduction Implant.},
journal = {Audiology &amp; neuro-otology},
volume = {25},
number = {5},
pages = {263-275},
pmid = {32268333},
issn = {1421-9700},
mesh = {Adolescent ; Adult ; Aged ; Audiometry ; *Bone Conduction ; Female ; Follow-Up Studies ; Hearing/*physiology ; *Hearing Aids ; Hearing Loss, Conductive/physiopathology/*rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/physiopathology/*rehabilitation ; Hearing Tests ; Humans ; Male ; Middle Aged ; *Quality of Life ; Speech Perception/*physiology ; Surveys and Questionnaires ; Young Adult ; },
abstract = {BACKGROUND: The bone conduction implant (BCI) is an active transcutaneous bone conduction device where the transducer has direct contact to the bone, and the skin is intact. Sixteen patients have been implanted with the BCI with a planned follow-up of 5 years. This study reports on hearing, quality of life, and objective measures up to 36 months of follow-up in 10 patients.<br><br>METHOD: Repeated measures were performed at fitting and after 1, 3, 6, 12, and 36 months including sound field warble tone thresholds, speech recognition thresholds in quiet, speech recognition score in noise, and speech-to-noise thresholds for 50% correct words with adaptive noise. Three quality of life questionnaires were used to capture the benefit from the intervention, appreciation from different listening situations, and the ability to interact with other people when using the BCI. The results were compared to the unaided situation and a Ponto Pro Power on a soft band. The implant functionality was measured by nasal sound pressure, and the retention force from the audio processor against the skin was measured using a specially designed audio processor and a force gauge.<br><br>RESULTS: Audiometry and quality of life questionnaires using the BCI or the Ponto Pro Power on a soft band were significantly improved compared to the unaided situation and the results were statistically supported. There was generally no significant difference between the two devices. The nasal sound pressure remained stable over the study period and the force on the skin from the audio processor was 0.71 &#xb1; 0.22 N (mean &#xb1; 1 SD).<br><br>CONCLUSION: The BCI improves the hearing ability for tones and speech perception in quiet and in noise for the indicated patients. The results are stable over a 3-year period, and the patients subjectively report a beneficial experience from using the BCI. The transducer performance and contact to the bone is unchanged over time, and the skin area under the audio processor remains without complications during the 3-year follow-up.},
}
@article {pmid32266609,
year = {2020},
author = {Lu, L and Wang, R and Luo, M},
title = {An optical brain-to-brain interface supports rapid information transmission for precise locomotion control.},
journal = {Science China. Life sciences},
volume = {63},
number = {6},
pages = {875-885},
pmid = {32266609},
issn = {1869-1889},
mesh = {Animals ; Behavior Control ; Behavior, Animal/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Calcium/metabolism ; Calcium Signaling/physiology ; Computer Simulation ; Dependovirus/metabolism ; HEK293 Cells ; Humans ; Kinetics ; Locomotion/*physiology ; Mice ; Models, Biological ; Neurokinin B/analogs &amp; derivatives/physiology ; Neurons/physiology ; Optical Imaging/*methods ; Raphe Nuclei/physiology ; Support Vector Machine ; Transfection ; },
abstract = {Brain-to-brain interfaces (BtBIs) hold exciting potentials for direct communication between individual brains. However, technical challenges often limit their performance in rapid information transfer. Here, we demonstrate an optical brain-to-brain interface that transmits information regarding locomotor speed from one mouse to another and allows precise, real-time control of locomotion across animals with high information transfer rate. We found that the activity of the genetically identified neuromedin B (NMB) neurons within the nucleus incertus (NI) precisely predicts and critically controls locomotor speed. By optically recording Ca[2+] signals from the NI of a "Master" mouse and converting them to patterned optogenetic stimulations of the NI of an "Avatar" mouse, the BtBI directed the Avatar mice to closely mimic the locomotion of their Masters with information transfer rate about two orders of magnitude higher than previous BtBIs. These results thus provide proof-of-concept that optical BtBIs can rapidly transmit neural information and control dynamic behaviors across individuals.},
}
@article {pmid32265673,
year = {2020},
author = {Wang, X and Zhu, M and Samuel, OW and Wang, X and Zhang, H and Yao, J and Lu, Y and Wang, M and Mukhopadhyay, SC and Wu, W and Chen, S and Li, G},
title = {The Effects of Random Stimulation Rate on Measurements of Auditory Brainstem Response.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {78},
pmid = {32265673},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) signal is an electrophysiological recording from electrodes placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem response (ABR) is one type of EEG signals in response to an auditory stimulus, and it has been widely used to evaluate the potential disorders of the auditory function within the brain. Currently, the ABR measurements in the clinic usually adopt a fixed stimulation rate (FSR) technique in which the late evoked response could contaminate the ABR signals and deteriorate the waveform differentiation after averaging, thus compromising the overall auditory function assessment task. To resolve this issue, this study proposed a random stimulation rate (RSR) method by integrating a random interval between two adjacent stimuli. The results showed that the proposed RSR method was consistently repeatable and reliable in multiple trials of repeated measurements, and there was a large amplitude of successive late evoked response that would contaminate the ABR signals for conventional FSR methods. The ABR waveforms of the RSR method showed better wave I-V morphology across different stimulation rates and stimulus levels, and the improved ABR morphology played an important role in early diagnoses of auditory pathway abnormities. The correlation coefficients as functions of averaging time showed that the ABR waveform of the RSR method stabilizes significantly faster, and therefore, it could be used to speed up current ABR measurements with more reliable testing results. The study suggests that the proposed method would potentially aid the adequate reconstruction of ABR signals towards a more effective means of hearing loss screening, brain function diagnoses, and potential brain-computer interface.},
}
@article {pmid32259927,
year = {2020},
author = {Wang, F and Xu, Z and Zhang, W and Wu, S and Zhang, Y and Ping, J and Wu, C},
title = {Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.},
journal = {The Review of scientific instruments},
volume = {91},
number = {3},
pages = {034106},
doi = {10.1063/1.5142343},
pmid = {32259927},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, nonlinear, and susceptible to interference. As a feature extraction method for motor imagery, Common Spatial Pattern (CSP) has been proven to be very effective. However, its effectiveness depends heavily on the choice of frequency bands, and Euclidean space cannot effectively describe the inner relationship. To solve these problems, a classification approach for motor imagery using the Geodesic Filtering Common Spatial Pattern (GFCSP) and filter-bank Feature Weighted Support Vector Machine (FWSVM) is presented. First, GFCSP based on the Riemannian manifold is proposed, in which the extracted covariance features are spatially filtered in Riemannian tangent space, and the average covariance matrix is replaced by Riemannian mean in CSP. Second, filter-bank FWSVM with a feature weighted matrix is proposed. EEG signals are filtered into 8-12 Hz, 12-16 Hz, 18-22 Hz, 22-26 Hz, and a wide band of 8-24 Hz, and GFCSP features of these filtered signals are extracted. A feature weighted matrix is calculated using mutual information and the Pearson correlation coefficient from these features and class information. Then, the Support Vector Machine (SVM) is used for classification with the feature weighted matrix. Finally, the proposed method is validated on the dataset IVa in BCI competition III. Classification accuracies of the five subjects are 92.31%, 99.03%, 80.36%, 96.30%, and 97.67%, which demonstrate the effectiveness of our proposed method.},
}
@article {pmid32257126,
year = {2020},
author = {Rahman, MKM and Joadder, MAM},
title = {A space-frequency localized approach of spatial filtering for motor imagery classification.},
journal = {Health information science and systems},
volume = {8},
number = {1},
pages = {15},
pmid = {32257126},
issn = {2047-2501},
abstract = {Classification of Motor Imagery (MI) signals is the heart of Brain-Computer Interface (BCI) based applications. Spatial filtering is an important step in this process that produce new set of signals for better discrimination of two classes of EEG signals. In this work, a new approach of spatial filtering called Space-Frequency Localized Spatial Filtering (SFLSF) is proposed to enhance the performances of MI classification. The SFLSF method initially divides the scalp-EEG channels into local overlapping spatial windows. Then a filter bank is used to divide the signals into local frequency bands. The group of channels, localized in space and frequency, are then processed with spatial filter, and features are subsequently extracted for classification task. Experimental results corroborate that the proposed space localization helps to increase the classification accuracy when compared to the existing methods using spatial filters. The classification performance is further improved when frequency localization is incorporated. Thus, the proposed space-frequency localized approach of spatial filtering helps to deliver better classification result which is consistently 3-5% higher than traditional methods.},
}
@article {pmid32256553,
year = {2020},
author = {Gembler, FW and Rezeika, A and Benda, M and Volosyak, I},
title = {Five Shades of Grey: Exploring Quintary m-Sequences for More User-Friendly c-VEP-Based BCIs.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {7985010},
pmid = {32256553},
issn = {1687-5273},
mesh = {Adult ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation/*methods ; },
abstract = {Responsive EEG-based communication systems have been implemented with brain-computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs). The BCI targets are typically encoded with binary m-sequences because of their autocorrelation property; the digits one and zero correspond to different target colours (usually black and white), which are updated every frame according to the code. While binary flickering patterns enable high communication speeds, they are perceived as annoying by many users. Quintary (base 5) m-sequences, where the five digits correspond to different shades of grey, may yield a more subtle visual stimulation. This study explores two approaches to reduce the flickering sensation: (1) adjusting the flickering speed via refresh rates and (2) applying quintary codes. In this respect, six flickering modalities are tested using an eight-target spelling application: binary patterns and quintary patterns generated with 60, 120, and 240 Hz refresh rates. This study was conducted with 18 nondisabled participants. For all six flickering modalities, a copy-spelling task was conducted. According to questionnaire results, most users favoured the proposed quintary over the binary pattern while achieving similar performance to it (no statistical differences between the patterns were found). Mean accuracies across participants were above 95%, and information transfer rates were above 55 bits/min for all patterns and flickering speeds.},
}
@article {pmid32256550,
year = {2020},
author = {She, Q and Chen, K and Luo, Z and Nguyen, T and Potter, T and Zhang, Y},
title = {Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {3287589},
pmid = {32256550},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Machine Learning ; },
abstract = {Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface (BCI) systems, however. In this paper, a novel active learning method is proposed to minimize the amount of labeled, subject-specific EEG data required for effective classifier training, by combining measures of uncertainty and representativeness within an extreme learning machine (ELM). Following this approach, an ELM classifier was first used to select a relatively large batch of unlabeled examples, whose uncertainty was measured through the best-versus-second-best (BvSB) strategy. The diversity of each sample was then measured between the limited labeled training data and previously selected unlabeled samples, and similarity is measured among the previously selected samples. Finally, a tradeoff parameter is introduced to control the balance between informative and representative samples, and these samples are then used to construct a powerful ELM classifier. Extensive experiments were conducted using benchmark and multiclass motor imagery EEG datasets to evaluate the efficacy of the proposed method. Experimental results show that the performance of the new algorithm exceeds or matches those of several state-of-the-art active learning algorithms. It is thereby shown that the proposed method improves classifier performance and reduces the need for training samples in BCI applications.},
}
@article {pmid32256422,
year = {2020},
author = {Tortora, L and Meynen, G and Bijlsma, J and Tronci, E and Ferracuti, S},
title = {Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective.},
journal = {Frontiers in psychology},
volume = {11},
number = {},
pages = {220},
pmid = {32256422},
issn = {1664-1078},
abstract = {Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as 'A.I. neuroprediction,' and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.},
}
@article {pmid32250858,
year = {2020},
author = {Chen, X and Hu, N and Wang, Y and Gao, X},
title = {Validation of a brain-computer interface version of the digit symbol substitution test in healthy subjects.},
journal = {Computers in biology and medicine},
volume = {120},
number = {},
pages = {103729},
doi = {10.1016/j.compbiomed.2020.103729},
pmid = {32250858},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Healthy Volunteers ; Humans ; Photic Stimulation ; },
abstract = {Digit symbol substitution test (DSST), which is a valid and sensitive tool to assess human cognitive dysfunction, has been widely used in clinical neuropsychology. Although several versions of DSST are currently available, most of the existing DSST versions rely on examinees' intact motor function. This limits their utility in severely motor-impaired individuals. A brain-computer interface (BCI) version of DSST was implemented in this study. Steady-state visual evoked potential (SSVEP) was adopted to build the BCI. Nine symbols in the proposed SSVEP BCI-based DSST were designed with clearly different shapes for decreasing measurement errors due to misidentified symbols. To reduce practice effect, furthermore, the digit-symbol pairs of each trial were different. A two-target SSVEP BCI was designed to judge whether the digit-symbol probe in the center of the user interface matched one of the nine digit-symbol pairs above the user interface. All 12 examinees were able to perform the tasks using the proposed SSVEP BCI-based DSST with 96.17 ± 4.18% averaged accuracy, which was comparable with that of computerized DSST. Furthermore, for examinees participating in both offline and online experiment, the accuracies of the online and offline experiments were comparable, supporting that the proposed BCI-DSST was reliable for repeatedly evaluating examinees' cognitive function over time. These results verified that the proposed SSVEP BCI-based DSST was feasible and effective for healthy subjects.},
}
@article {pmid32248089,
year = {2020},
author = {Cheng, N and Phua, KS and Lai, HS and Tam, PK and Tang, KY and Cheng, KK and Yeow, RC and Ang, KK and Guan, C and Lim, JH},
title = {Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {12},
pages = {3339-3351},
doi = {10.1109/TBME.2020.2984003},
pmid = {32248089},
issn = {1558-2531},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Robotics ; *Stroke ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity ; },
abstract = {OBJECTIVE: This randomized controlled feasibility study investigates the ability for clinical application of the Brain-Computer Interface-based Soft Robotic Glove (BCI-SRG) incorporating activities of daily living (ADL)-oriented tasks for stroke rehabilitation.<br><br>METHODS: Eleven recruited chronic stroke patients were randomized into BCI-SRG or Soft Robotic Glove (SRG) group. Each group underwent 120-minute intervention per session comprising 30-minute standard arm therapy and 90-minute experimental therapy (BCI-SRG or SRG). To perform ADL tasks, BCI-SRG group used motor imagery-BCI and SRG, while SRG group used SRG without motor imagery-BCI. Both groups received 18 sessions of intervention over 6 weeks. Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores were measured at baseline (week 0), post- intervention (week 6), and follow-ups (week 12 and 24). In total, 10/11 patients completed the study with 5 in each group and 1 dropped out.<br><br>RESULTS: Though there were no significant intergroup differences for FMA and ARAT during 6-week intervention, the improvement of FMA and ARAT seemed to sustain beyond 6-week intervention for BCI-SRG group, as compared with SRG control. Incidentally, all BCI-SRG subjects reported a sense of vivid movement of the stroke-impaired upper limb and 3/5 had this phenomenon persisting beyond intervention while none of SRG did.<br><br>CONCLUSION: BCI-SRG suggested probable trends of sustained functional improvements with peculiar kinesthetic experience outlasting active intervention in chronic stroke despite the dire need for large-scale investigations to verify statistical significance.<br><br>SIGNIFICANCE: Addition of BCI to soft robotic training for ADL-oriented stroke rehabilitation holds promise for sustained improvements as well as elicited perception of motor movements.},
}
@article {pmid32243900,
year = {2020},
author = {Ito, H and Fujiki, S and Mori, Y and Kansaku, K},
title = {Self-reorganization of neuronal activation patterns in the cortex under brain-machine interface and neural operant conditioning.},
journal = {Neuroscience research},
volume = {156},
number = {},
pages = {279-292},
doi = {10.1016/j.neures.2020.03.008},
pmid = {32243900},
issn = {1872-8111},
mesh = {*Brain-Computer Interfaces ; Conditioning, Operant ; Learning ; *Motor Cortex ; Neurons ; },
abstract = {In this review, we describe recent experimental observations and model simulations in the research subject of brain-machine interface (BMI). Studies of BMIs have applied decoding models to extract functional characteristics of the recorded neurons, and some of these have more focused on adaptation based on neural operant conditioning. Under a closed loop feedback with the environment through BMIs, neuronal activities are forced to interact directly with the environment. These studies have shown that the neuron ensembles self-reorganized their activity patterns and completed a transition to adaptive state within a short time scale. Based on these observations, we discuss how the brain could identify the target neurons directly interacting with the environment and determine in which direction the activities of those neurons should be changed for adaptation. For adaptation over a short time scale, the changes of neuron ensemble activities seem to be restricted by the intrinsic correlation structure of the neuronal network (intrinsic manifold). On the other hand, for adaptation over a long time scale, modifications to the synaptic connections enable the neuronal network to generate a novel activation pattern required by BMI (extension of the intrinsic manifold). Understanding of the intrinsic constraints in adaptive changes of neuronal activities will provide the basic principles of learning mechanisms in the brain and methodological clues for better performance in engineering and clinical applications of BMI.},
}
@article {pmid32243792,
year = {2020},
author = {},
title = {Engineering Tissues and Organs: The Road to the Clinic.},
journal = {Cell},
volume = {181},
number = {1},
pages = {22-23},
doi = {10.1016/j.cell.2020.03.026},
pmid = {32243792},
issn = {1097-4172},
mesh = {Aging ; *Brain-Computer Interfaces ; Genetic Engineering ; Humans ; Regeneration ; Stem Cells ; *Tissue Engineering ; },
abstract = {With recent advances in both gene editing and stem cell biology, the promise of cellular therapies is now closer than ever. Clinical trials for the application of chimeric antigen receptor T cells has driven an enormous investment into the development of such cellular products and learnings from these emboldening investors and engaging regulators across the globe.},
}
@article {pmid32240985,
year = {2020},
author = {Dugan, EA and Bennett, C and Tamames, I and Dietrich, WD and King, CS and Prasad, A and Rajguru, SM},
title = {Therapeutic hypothermia reduces cortical inflammation associated with utah array implants.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026035},
pmid = {32240985},
issn = {1741-2552},
support = {DP2 EB022357/EB/NIBIB NIH HHS/United States ; R01 DC013798/DC/NIDCD NIH HHS/United States ; UL1 TR000460/TR/NCATS NIH HHS/United States ; UL1 TR002736/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; Electrodes, Implanted ; *Hypothermia, Induced ; *Inflammation/prevention &amp; control ; Male ; Microelectrodes ; Rats ; Rats, Sprague-Dawley ; Utah ; },
abstract = {OBJECTIVE: Neuroprosthetics hold tremendous promise to restore function through brain-computer interfaced devices. However, clinical applications of implantable microelectrodes remain limited given the challenges of maintaining neuronal signals for extended periods of time and with multiple biological mechanisms negatively affecting electrode performance. Acute and chronic inflammation, oxidative stress, and blood brain barrier disruption contribute to inconsistent electrode performance. We hypothesized that therapeutic hypothermia (TH) applied at the microelectrode insertion site will positively modulate both inflammatory and apoptotic pathways, promoting neuroprotection and improved performance in the long-term.<br><br>APPROACH: A custom device and thermoelectric system were designed to deliver controlled TH locally to the cortical implant site at the time of microelectrode array insertion and immediately following surgery. The TH paradigm was derived from in vivo cortical temperature measurements and finite element modeling of temperature distribution profiles in the cortex. Male Sprague-Dawley rats were implanted with non-functional Utah microelectrodes arrays (UMEA) consisting of 4 &#xd7; 4 grid of 1.5 mm long parylene-coated silicon shanks. In one group, TH was applied to the implant site for two hours following the UMEA implantation, while the other group was implanted under normothermic conditions without treatment. At 48 h, 72 h, 7 d and 14 d post-implantation, mRNA expression levels for genes associated with inflammation and apoptosis were compared between normothermic and hypothermia-treated groups.<br><br>MAIN RESULTS: The custom system delivered controlled TH to the cortical implant site and the numerical models confirmed that the temperature decrease was confined locally. Furthermore, a one-time application of TH post UMEA insertion significantly reduced the acute inflammatory response with a reduction in the expression of inflammatory regulating cytokines and chemokines.<br><br>SIGNIFICANCE: This work provides evidence that acutely applied hypothermia is effective in significantly reducing acute inflammation post intracortical electrode implantation.},
}
@article {pmid32238617,
year = {2021},
author = {Zhang, X and Xu, G and Ravi, A and Pearce, S and Jiang, N},
title = {Can a highly accurate multi-class SSMVEP BCI induce sensory-motor rhythm in the sensorimotor area?.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ab85b2},
pmid = {32238617},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Humans ; Photic Stimulation/methods ; *Sensorimotor Cortex ; },
abstract = {Objective. Different visual stimuli might have different effects on the brain, e.g. the change of brightness, non-biological movement and biological movement.Approach. In this study, flicker, checkerboard and gaiting stimuli were chosen as visual stimuli to investigate whether steady-state motion visual evoked potential (SSMVEP) effect on the sensorimotor area for rehabilitation. The gaiting stimulus was designed as the gaiting sequence of a human. The hypothesis is that only observing the designed gaiting stimulus would simultaneously induce: (1) SSMVEP in the occipital area, similarly to an SSVEP stimulus; and (2) sensorimotor rhythm (SMR) in the primary sensorimotor area, because such action observation could activate the mirror neuron system. Canonical correlation analysis was used to detect SSMVEP from occipital electroencephalograms (EEG), and event-related spectral perturbation was used to identify SMR in the EEG from the sensorimotor area.Main results. The results showed that the designed gaiting stimulus-induced SSMVEP, with classification accuracies of 88.9 ± 12.0% in a four-class scenario. More importantly, it induced clear and sustained event-related desynchronization/synchronization (ERD/ERS), while no ERD/ERS could be observed when the other two SSVEP stimuli were used. Further, for participants with a sufficiently clear SSMVEP pattern (classification accuracy >85%), the ERD index values in the mu-beta band induced by the proposed gaiting stimulus were statistically different from those of the other two types of stimulus.Significance. Therefore, a novel brain-computer interface (BCI) based on the designed stimulus has potential in neurorehabilitation applications because it simultaneously has the high accuracy of an SSMVEP (sim90% accuracy in a four-class setup) and the ability to activate the sensorimotor area.},
}
@article {pmid32238600,
year = {2020},
author = {Momennezhad, A},
title = {Matching pursuit algorithm for enhancing EEG signal quality and increasing the accuracy and efficiency of emotion recognition.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {65},
number = {4},
pages = {393-404},
doi = {10.1515/bmt-2019-0327},
pmid = {32238600},
issn = {1862-278X},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Emotions/physiology ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {In this paper, we suggest an efficient, accurate and user-friendly brain-computer interface (BCI) system for recognizing and distinguishing different emotion states. For this, we used a multimodal dataset entitled "MAHOB-HCI" which can be freely reached through an email request. This research is based on electroencephalogram (EEG) signals carrying emotions and excludes other physiological features, as it finds EEG signals more reliable to extract deep and true emotions compared to other physiological features. EEG signals comprise low information and signal-to-noise ratios (SNRs); so it is a huge challenge for proposing a robust and dependable emotion recognition algorithm. For this, we utilized a new method, based on the matching pursuit (MP) algorithm, to resolve this imperfection. We applied the MP algorithm for increasing the quality and SNRs of the original signals. In order to have a signal of high quality, we created a new dictionary including 5-scale Gabor atoms with 5000 atoms. For feature extraction, we used a 9-scale wavelet algorithm. A 32-electrode configuration was used for signal collection, but we used just eight electrodes out of that; therefore, our method is highly user-friendly and convenient for users. In order to evaluate the results, we compared our algorithm with other similar works. In average accuracy, the suggested algorithm is superior to the same algorithm without applying MP by 2.8% and in terms of f-score by 0.03. In comparison with corresponding works, the accuracy and f-score of the proposed algorithm are better by 10.15% and 0.1, respectively. So as it is seen, our method has improved past works in terms of accuracy, f-score and user-friendliness despite using just eight electrodes.},
}
@article {pmid32238204,
year = {2020},
author = {Jackson, J and Nugawela, MD and De Vocht, F and Moran, P and Hollingworth, W and Knipe, D and Munien, N and Gunnell, D and Redaniel, MT},
title = {Long-term impact of the expansion of a hospital liaison psychiatry service on patient care and costs following emergency department attendances for self-harm.},
journal = {BJPsych open},
volume = {6},
number = {3},
pages = {e34},
pmid = {32238204},
issn = {2056-4724},
abstract = {BACKGROUND: In September 2014, as part of a national initiative to increase access to liaison psychiatry services, the liaison psychiatry services at Bristol Royal Infirmary received new investment of £250 000 per annum, expanding its availability from 40 to 98 h per week. The long-term impact on patient outcomes and costs, of patients presenting to the emergency department with self-harm, is unknown.<br><br>AIMS: To assess the long-term impact of the investment on patient care outcomes and costs, of patients presenting to the emergency department with self-harm.<br><br>METHOD: Monthly data for all self-harm emergency department attendances between 1 September 2011 and 30 September 2017 was modelled using Bayesian structural time series to estimate expected outcomes in the absence of expanded operating hours (the counterfactual). The difference between the observed and expected trends for each outcome were interpreted as the effects of the investment.<br><br>RESULTS: Over the 3 years after service expansion, the mean number of self-harm attendances increased 13%. Median waiting time from arrival to psychosocial assessment was 2 h shorter (18.6% decrease, 95% Bayesian credible interval (BCI) -30.2% to -2.8%), there were 45 more referrals to other agencies (86.1% increase, 95% BCI 60.6% to 110.9%) and a small increase in the number of psychosocial assessments (11.7% increase, 95% BCI -3.4% to 28.5%) per month. Monthly mean net hospital costs were &#xa3;34 more per episode (5.3% increase, 95% BCI -11.6% to 25.5%).<br><br>CONCLUSIONS: Despite annual increases in emergency department attendances, investment was associated with reduced waiting times for psychosocial assessment and more referrals to other agencies, with only a small increase in cost per episode.},
}
@article {pmid32235406,
year = {2020},
author = {Wu, W and Zhang, W and Tian, L and Brown, BR and Walters, MS and Metcalf, JP},
title = {IRF7 Is Required for the Second Phase Interferon Induction during Influenza Virus Infection in Human Lung Epithelia.},
journal = {Viruses},
volume = {12},
number = {4},
pages = {},
pmid = {32235406},
issn = {1999-4915},
support = {P20 GM103636/GM/NIGMS NIH HHS/United States ; 5P20GM103648/GM/NIGMS NIH HHS/United States ; U54GM104938/NH/NIH HHS/United States ; },
mesh = {Alveolar Epithelial Cells/metabolism/pathology/virology ; Animals ; Biomarkers ; Cell Line ; Gene Knockdown Techniques ; Humans ; Influenza A virus/*physiology ; Influenza, Human/immunology/*metabolism/*virology ; Interferon Regulatory Factor-7/genetics/*metabolism ; Interferons/*biosynthesis ; Lung/immunology/*metabolism/pathology/*virology ; Respiratory Mucosa/immunology/metabolism/pathology/virology ; },
abstract = {Influenza A virus (IAV) infection is a major cause of morbidity and mortality. Retinoic acid-inducible protein I (RIG-I) plays an important role in the recognition of IAV in most cell types, and leads to the activation of interferon (IFN). We investigated mechanisms of RIG-I and IFN induction by IAV in the BCi-NS1.1 immortalized human airway basal cell line and in the A549 human alveolar epithelial cell line. We found that the basal expression levels of RIG-I and regulatory transcription factor (IRF) 7 were very low in BCi-NS1.1 cells. IAV infection induced robust RIG-I and IRF7, not IRF3, expression. siRNA against IRF7 and mitochondrial antiviral-signaling protein (MAVS), but not IRF3, significantly inhibited RIG-I mRNA expression and IFN induction by IAV infection. Most importantly, even without virus infection, IFN-β alone induced RIG-I, and siRNA against IRF7 did not inhibit RIG-I induction by IFN-β. Similar results were found in the alveolar basal epithelial A549 cell line. RIG-I and IRF7 expression in humans is highly inducible and greatly amplified by IFN produced from virus infected cells. IFN induction can be separated into two phases, that initially induced by the virus with basal RIG-I (the first phase), and that induced by the subsequent virus with amplified RIG-I from the first phase IFN (the second phase). The de novo synthesis of IRF7 is required for the second phase IFN induction during influenza virus infection in human lung bronchial and alveolar epithelial cells.},
}
@article {pmid33438596,
year = {2019},
author = {Tariq, M and Trivailo, PM and Simic, M},
title = {Classification of left and right foot kinaesthetic motor imagery using common spatial pattern.},
journal = {Biomedical physics &amp; engineering express},
volume = {6},
number = {1},
pages = {015008},
doi = {10.1088/2057-1976/ab54ad},
pmid = {33438596},
issn = {2057-1976},
mesh = {Adult ; Beta Rhythm ; *Brain-Computer Interfaces ; Cognition ; Discriminant Analysis ; Electroencephalography ; Female ; Foot/*anatomy &amp; histology/*physiology ; Foot Orthoses ; Humans ; Imagery, Psychotherapy/*methods ; Kinesthesis/*physiology ; Machine Learning ; Male ; Pattern Recognition, Physiological ; Psychomotor Performance ; ROC Curve ; Robotics ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems typically deploy common spatial pattern (CSP) for feature extraction of mu and beta rhythms based on upper-limbs kinaesthetic motor imageries (KMI). However, it was not used to classify the left versus right foot KMI, due to its location inside the mesial wall of sensorimotor cortex, which makes it difficult to be detected. We report novel classification of mu and beta EEG features, during left and right foot KMI cognitive task, using CSP, and filter bank common spatial pattern (FBCSP) method, to optimize the subject-specific band selection. We initially proposed CSP method, followed by the implementation of FBCSP for optimization of individual spatial patterns, wherein a set of CSP filters was learned, for each of the time/frequency filters in a supervised way. This was followed by the log-variance feature extraction and concatenation of all features (over all chosen spectral-filters). Subsequently, supervised machine learning was implemented, i.e. logistic regression (Logreg) and linear discriminant analysis (LDA), in order to compare the respective foot KMI classification rates. Training and testing data, used in the model, was validated using 10-fold cross validation. Four methodology paradigms are reported, i.e. CSP LDA, CSP Logreg, and FBCSP LDA, FBCSP Logreg. All paradigms resulted in an average classification accuracy rate above the statistical chance level of 60.0% (P < 0.01). On average, FBCSP LDA outperformed remaining paradigms with kappa score of 0.41 and classification accuracy of 70.28% ± 4.23. Similarly, this paradigm enabled discrimination between right and left foot KMI cognitive task at highest accuracy rate i.e. maximum 77.5% with kappa = 0.55 and the area under ROC curve as 0.70 (in single-trial analysis). The proposed novel paradigms, using CSP and FBCSP, established a potential to exploit the left versus right foot imagery classification, in synchronous 2-class BCI for controlling robotic foot, or foot neuroprosthesis.},
}
@article {pmid33543054,
year = {2019},
author = {Powell, ES and Westgate, PM and Goldstein, LB and Sawaki, L},
title = {Absence of Motor-Evoked Potentials Does Not Predict Poor Recovery in Patients With Severe-Moderate Stroke: An Exploratory Analysis.},
journal = {Archives of rehabilitation research and clinical translation},
volume = {1},
number = {3-4},
pages = {100023},
pmid = {33543054},
issn = {2590-1095},
abstract = {OBJECTIVE: To better understand the role of the presence or absence of motor-evoked potentials (MEPs) in predicting functional outcomes following a severe-moderate stroke.<br><br>DESIGN: Retrospective exploratory analysis. We compared the effects of the stimulation condition (active or sham), MEP status (+ or -), and a combination of stimulation condition and MEP status on outcome. Within-group and between-group changes were assessed with longitudinal repeated measures analysis of variance and longitudinal repeated measures analysis of covariance, respectively. The proportions of participants who achieved minimal clinically important differences (MCIDs) for the main outcome measures were calculated.<br><br>SETTING: University research laboratory within a rehabilitation hospital.<br><br>PARTICIPANTS: A total of 129 subjects with severe-moderate stroke-related motor impairments who participated in previous studies combining neuromodulation and motor training.<br><br>INTERVENTIONS: Neuromodulation (active or sham) and motor training.<br><br>MAIN OUTCOME MEASURES: Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT).<br><br>RESULTS: When participants were grouped by stimulation condition or MEP status, all groups improved from baseline to immediate postintervention and follow-up evaluations (all P<.05). Analysis by stimulation condition and MEP status found that the MEP-/active group improved by 4.2 points on FMA (P<.0001) and 1.8 on ARAT (P=.003) post intervention. The MEP+/active group improved by 5.7 points on FMA (P<.0001) and 3.9 points on ARAT (P<.0001) post intervention. There were no between-group differences (P>.05). Regarding MCIDs, in the MEP-/active group, 14.5% of individuals reached MCID on FMA and 8.3% on ARAT post intervention. In the MEP+/active group, 33.3% of individuals reached MCID on FMA and 27.3% on ARAT post intervention.<br><br>CONCLUSION: As expected, the MEP+ group had the greatest improvement in motor function. However, it was shown that individuals without MEPs can also achieve meaningful changes, as reflected by MCID, when neuromodulation is paired with motor training. To our knowledge, this is the first study to differentiate the effects of neuromodulation by MEP status.},
}
@article {pmid33517628,
year = {2019},
author = {Newman, EM and Allender, MC and Thompson, D and Glowacki, GA and Ivančić, M and Adkesson, MJ and Lindemann, DM},
title = {MEASURING FAT CONTENT USING COMPUTED TOMOGRAPHY TO ESTABLISH A BODY CONDITION INDEX IN FREE-RANGING BLANDING'S TURTLES (EMYDOIDEA BLANDINGII) IN ILLINOIS.},
journal = {Journal of zoo and wildlife medicine : official publication of the American Association of Zoo Veterinarians},
volume = {50},
number = {3},
pages = {594-603},
doi = {10.1638/2018-0154},
pmid = {33517628},
issn = {1042-7260},
mesh = {Adipose Tissue/*anatomy &amp; histology/diagnostic imaging ; Aging ; Animals ; Animals, Wild ; Body Composition/*physiology ; Female ; Male ; Tomography, X-Ray Computed ; Turtles/*anatomy &amp; histology ; },
abstract = {Health assessment of free-ranging populations requires an integrated approach, often incorporating a method to measure mass as a representation of the animals' ability to utilize environmental resources. In chelonians, direct measurements of mass have historically served as a corollary for body condition. However, this method may not accurately represent the true fat volume (FV) and may be skewed by the presence of eggs, shell size, or muscle mass. The objective of this study was to use computed tomography (CT) to develop a model for determining body condition index (BCI) in free-ranging Blanding's turtles (Emydoidea blandingii). Mass, shell measurements, and FV were measured by CT in 65 free-ranging Blanding's turtles from Lake and DuPage counties in Illinois. Twenty-one different models were built for BCI using both FV and fat percentage (FP) as dependent variables. The best fit model for FP included the relationship between mass and carapace length with nearly 60% model support. The model for FV demonstrated a similar relationship but had only 18% support. Linear models with BCI as the dependent variable showed that juveniles had a higher FP than adults and females with more eggs had a lower FP. FP can be calculated in the field with nearly 60% accuracy compared to CT-assessed FP as a component of a physical exam and population health survey to assess the effectiveness of conservation efforts for the endangered Blanding's turtle.},
}
@article {pmid32627414,
year = {2019},
author = {Rago, I and Rauti, R and Bevilacqua, M and Calaresu, I and Pozzato, A and Cibinel, M and Dalmiglio, M and Tavagnacco, C and Goldoni, A and Scaini, D},
title = {Carbon Nanotubes, Directly Grown on Supporting Surfaces, Improve Neuronal Activity in Hippocampal Neuronal Networks.},
journal = {Advanced biosystems},
volume = {3},
number = {5},
pages = {e1800286},
doi = {10.1002/adbi.201800286},
pmid = {32627414},
issn = {2366-7478},
mesh = {Animals ; *Brain-Computer Interfaces ; Hippocampus/cytology/*metabolism ; *Nanotubes, Carbon ; Nerve Net/cytology/*metabolism ; Neurons/cytology/*metabolism ; Rats ; },
abstract = {Carbon nanotube (CNT)-modified surfaces unequivocally demonstrate their biocompatibility and ability to boost the electrical activity of neuronal cells cultured on them. Reasons for this effect are still under debate. However, the intimate contact at the membrane level between these thready nanostructures and cells, in combination with their unique electrical properties, seems to play an important role. The entire existing literature exploiting the effect of CNTs on modulating cellular behavior deals with cell cultures grown on purified multiwalled carbon nanotubes (MWNTs) deposited on a supporting surface via drop-casting or mechanical entrapment. Here, for the first time, it is demonstrated that CNTs directly grown on a supporting silicon surface by a chemical vapor deposition (CVD)-assisted technique have the same effect. It is shown that primary neuronal cells developed above a carpet of CVD CNTs form a healthy and functional network. The resulting neuronal network shows increased electrical activity when compared to a similar network developed on a control glass surface. The low cost and high versatility of the here presented CVD-based synthesis process, together with the possibility to create on supporting substrate patterns of any arbitrary shape of CNTs, open up new opportunities for brain-machine interfaces or neuroprosthetic devices.},
}
@article {pmid33266945,
year = {2019},
author = {Martínez-Cagigal, V and Santamaría-Vázquez, E and Hornero, R},
title = {Asynchronous Control of P300-Based Brain-Computer Interfaces Using Sample Entropy.},
journal = {Entropy (Basel, Switzerland)},
volume = {21},
number = {3},
pages = {},
pmid = {33266945},
issn = {1099-4300},
support = {DPI2017-84280-R//Ministerio de Ciencia e Innovaci&#xf3;n/ ; DPI2017-84280-R//European Regional Development Fund/ ; An&#xe1;lisis y correlaci&#xf3;n entre el genoma completo y la actividad cerebral para la ayuda en el diagn&#xf3;stico de la enfermedad de Alzheimer//Interreg/ ; },
abstract = {Brain-computer interfaces (BCI) have traditionally worked using synchronous paradigms. In recent years, much effort has been put into reaching asynchronous management, providing users with the ability to decide when a command should be selected. However, to the best of our knowledge, entropy metrics have not yet been explored. The present study has a twofold purpose: (i) to characterize both control and non-control states by examining the regularity of electroencephalography (EEG) signals; and (ii) to assess the efficacy of a scaled version of the sample entropy algorithm to provide asynchronous control for BCI systems. Ten healthy subjects participated in the study, who were asked to spell words through a visual oddball-based paradigm, attending (i.e., control) and ignoring (i.e., non-control) the stimuli. An optimization stage was performed for determining a common combination of hyperparameters for all subjects. Afterwards, these values were used to discern between both states using a linear classifier. Results show that control signals are more complex and irregular than non-control ones, reaching an average accuracy of 94 . 40 % in classification. In conclusion, the present study demonstrates that the proposed framework is useful in monitoring the attention of a user, and granting the asynchrony of the BCI system.},
}
@article {pmid33094110,
year = {2019},
author = {Mousavi, M and de Sa, VR},
title = {Spatio-temporal analysis of error-related brain activity in active and passive brain-computer interfaces.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {6},
number = {4},
pages = {118-127},
pmid = {33094110},
issn = {2326-263X},
support = {T32 MH020002/MH/NIMH NIH HHS/United States ; },
abstract = {Electroencephalography (EEG)-based brain-computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods.},
}
@article {pmid33033729,
year = {2019},
author = {Huggins, JE and Guger, C and Aarnoutse, E and Allison, B and Anderson, CW and Bedrick, S and Besio, W and Chavarriaga, R and Collinger, JL and Do, AH and Herff, C and Hohmann, M and Kinsella, M and Lee, K and Lotte, F and Müller-Putz, G and Nijholt, A and Pels, E and Peters, B and Putze, F and Rupp, R and Schalk, G and Scott, S and Tangermann, M and Tubig, P and Zander, T},
title = {Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {6},
number = {3},
pages = {71-101},
pmid = {33033729},
issn = {2326-263X},
support = {90RE5017/ACL/ACL HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; },
abstract = {The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics in BCI (also called brain-machine interface) research. Workshops covered foundational topics such as hardware development and signal analysis algorithms, new and imaginative topics such as BCI for virtual reality and multi-brain BCIs, and translational topics such as clinical applications and ethical assumptions of BCI development. BCI research is expanding in the diversity of applications and populations for whom those applications are being developed. BCI applications are moving toward clinical readiness as researchers struggle with the practical considerations to make sure that BCI translational efforts will be successful. This paper summarizes each workshop, providing an overview of the topic of discussion, references for additional information, and identifying future issues for research and development that resulted from the interactions and discussion at the workshop.},
}
@article {pmid33033728,
year = {2019},
author = {Eddy, BS and Garrett, SC and Rajen, S and Peters, B and Wiedrick, J and O'Connor, A and Renda, A and Huggins, JE and Fried-Oken, M},
title = {Trends in research participant categories and descriptions in abstracts from the International BCI Meeting series, 1999 to 2016.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {6},
number = {1-2},
pages = {13-24},
pmid = {33033728},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; UL1 TR002240/TR/NCATS NIH HHS/United States ; UL1 TR002369/TR/NCATS NIH HHS/United States ; },
abstract = {Much brain-computer interface (BCI) research is intended to benefit people with disabilities (PWD), but inclusion of these individuals as study participants remains relatively rare. When participants with disabilities are included, they are described with a range of clinical and non-clinical terms with varying degrees of specificity, often leading to difficulty in interpreting or replicating results. This study examined trends in inclusion and description of study participants with disabilities across six International BCI Meetings from 1999 to 2016. Abstracts from each Meeting were analyzed by two trained independent reviewers. Results suggested a decline in participation by PWD across Meetings until the 2016 Meeting. Increased diagnostic specificity was noted at the 2013 and 2016 Meetings. Fifty-eight percent of the abstracts identified PWD as being the target beneficiaries of BCI research, though only twenty-two percent included participants with disabilities, suggesting evidence of a persistent translational gap. Participants with disabilities were most commonly described as having physical and/or communication impairments compared to impairments in other areas. Implementing participatory action research principles and user-centered design strategies continues to be necessary within BCI research to bridge the translational gap and facilitate use of BCI systems within functional environments for PWD.},
}
@article {pmid33501008,
year = {2018},
author = {Irimia, DC and Ortner, R and Poboroniuc, MS and Ignat, BE and Guger, C},
title = {High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training.},
journal = {Frontiers in robotics and AI},
volume = {5},
number = {},
pages = {130},
pmid = {33501008},
issn = {2296-9144},
abstract = {Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.},
}
@article {pmid33141729,
year = {2018},
author = {Penaloza, CI and Nishio, S},
title = {BMI control of a third arm for multitasking.},
journal = {Science robotics},
volume = {3},
number = {20},
pages = {},
doi = {10.1126/scirobotics.aat1228},
pmid = {33141729},
issn = {2470-9476},
abstract = {Brain-machine interface (BMI) systems have been widely studied to allow people with motor paralysis conditions to control assistive robotic devices that replace or recover lost function but not to extend the capabilities of healthy users. We report an experiment in which healthy participants were able to extend their capabilities by using a noninvasive BMI to control a human-like robotic arm and achieve multitasking. Experimental results demonstrate that participants were able to reliably control the robotic arm with the BMI to perform a goal-oriented task while simultaneously using their own arms to do a different task. This outcome opens possibilities to explore future human body augmentation applications for healthy people that not only enhance their capability to perform a particular task but also extend their physical capabilities to perform multiple tasks simultaneously.},
}
@article {pmid33265109,
year = {2018},
author = {Martín-Clemente, R and Olias, J and Thiyam, DB and Cichocki, A and Cruces, S},
title = {Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison.},
journal = {Entropy (Basel, Switzerland)},
volume = {20},
number = {1},
pages = {},
pmid = {33265109},
issn = {1099-4300},
support = {TEC2014-53103-P//Ministerio de Ciencia y Tecnolog&#xed;a/ ; 14.756.31.0001//MES Russian Federation/ ; },
abstract = {Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback-Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.},
}
@article {pmid33746239,
year = {2017},
author = {Nuyujukian, P and Kao, JC and Ryu, SI and Shenoy, KV},
title = {A Non-Human Primate Brain-Computer Typing Interface.},
journal = {Proceedings of the IEEE. Institute of Electrical and Electronics Engineers},
volume = {105},
number = {1},
pages = {66-72},
pmid = {33746239},
issn = {0018-9219},
support = {R01 NS064318/NS/NINDS NIH HHS/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.},
}
@article {pmid33157855,
year = {2016},
author = {Soekadar, SR and Witkowski, M and Gómez, C and Opisso, E and Medina, J and Cortese, M and Cempini, M and Carrozza, MC and Cohen, LG and Birbaumer, N and Vitiello, N},
title = {Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia.},
journal = {Science robotics},
volume = {1},
number = {1},
pages = {},
doi = {10.1126/scirobotics.aag3296},
pmid = {33157855},
issn = {2470-9476},
mesh = {Activities of Daily Living ; Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/statistics &amp; numerical data ; Electrooculography/statistics &amp; numerical data ; *Exoskeleton Device/statistics &amp; numerical data ; Female ; Hand ; Hand Strength/physiology ; Humans ; Male ; Motor Skills/physiology ; Quadriplegia/physiopathology/*rehabilitation ; Spinal Cord Injuries/physiopathology/rehabilitation ; Young Adult ; },
abstract = {Direct brain control of advanced robotic systems promises substantial improvements in health care, for example, to restore intuitive control of hand movements required for activities of daily living in quadriplegics, like holding a cup and drinking, eating with cutlery, or manipulating different objects. However, such integrated, brain- or neural-controlled robotic systems have yet to enter broader clinical use or daily life environments. We demonstrate full restoration of independent daily living activities, such as eating and drinking, in an everyday life scenario across six paraplegic individuals (five males, 30 ± 14 years) who used a noninvasive, hybrid brain/neural hand exoskeleton (B/NHE) to open and close their paralyzed hand. The results broadly suggest that brain/neural-assistive technology can restore autonomy and independence in quadriplegic individuals' everyday life.},
}
@article {pmid32235295,
year = {2020},
author = {Saeed, SMU and Anwar, SM and Khalid, H and Majid, M and Bagci, AU},
title = {EEG based Classification of Long-term Stress Using Psychological Labeling.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {7},
pages = {},
pmid = {32235295},
issn = {1424-8220},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Machine Learning ; Male ; Signal Processing, Computer-Assisted ; Stress, Psychological/*diagnosis/diagnostic imaging/physiopathology ; *Support Vector Machine ; Young Adult ; },
abstract = {Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.},
}
@article {pmid32232108,
year = {2019},
author = {Ciancibello, J and King, K and Meghrazi, MA and Padmanaban, S and Levy, T and Ramdeo, R and Straka, M and Bouton, C},
title = {Closed-loop neuromuscular electrical stimulation using feedforward-feedback control and textile electrodes to regulate grasp force in quadriplegia.},
journal = {Bioelectronic medicine},
volume = {5},
number = {},
pages = {19},
pmid = {32232108},
issn = {2332-8886},
abstract = {BACKGROUND: Transcutaneous neuromuscular electrical stimulation is routinely used in physical rehabilitation and more recently in brain-computer interface applications for restoring movement in paralyzed limbs. Due to variable muscle responses to repeated or sustained stimulation, grasp force levels can change significantly over time. Here we develop and assess closed-loop methods to regulate individual finger forces to facilitate functional movement. We combined this approach with custom textile-based electrodes to form a light-weight, wearable device and evaluated in paralyzed study participants.<br><br>METHODS: A textile-based electrode sleeve was developed by the study team and Myant, Corp. (Toronto, ON, Canada) and evaluated in a study involving three able-body participants and two participants with quadriplegia. A feedforward-feedback control structure was designed and implemented to accurately maintain finger force levels in a quadriplegic study participant.<br><br>RESULTS: Individual finger flexion and extension movements, along with functional grasping, were evoked during neuromuscular electrical stimulation. Closed-loop control methods allowed accurate steady state performance (<&#x2009;15% error) with a settling time of 0.67&#x2009;s (SD&#x2009;=&#x2009;0.42&#x2009;s) for individual finger contact force in a participant with quadriplegia.<br><br>CONCLUSIONS: Textile-based electrodes were identified to be a feasible alternative to conventional electrodes and facilitated individual finger movement and functional grasping. Furthermore, closed-loop methods demonstrated accurate control of individual finger flexion force. This approach may be a viable solution for enabling grasp force regulation in quadriplegia.<br><br>TRIAL REGISTRATION: NCT, NCT03385005. Registered Dec. 28, 2017.},
}
@article {pmid32232100,
year = {2019},
author = {Cho, N and Squair, JW and Bloch, J and Courtine, G},
title = {Neurorestorative interventions involving bioelectronic implants after spinal cord injury.},
journal = {Bioelectronic medicine},
volume = {5},
number = {},
pages = {10},
pmid = {32232100},
issn = {2332-8886},
abstract = {In the absence of approved treatments to repair damage to the central nervous system, the role of neurosurgeons after spinal cord injury (SCI) often remains confined to spinal cord decompression and vertebral fracture stabilization. However, recent advances in bioelectronic medicine are changing this landscape. Multiple neuromodulation therapies that target circuits located in the brain, midbrain, or spinal cord have been able to improve motor and autonomic functions. The spectrum of implantable brain-computer interface technologies is also expanding at a fast pace, and all these neurotechnologies are being progressively embedded within rehabilitation programs in order to augment plasticity of spared circuits and residual projections with training. Here, we summarize the impending arrival of bioelectronic medicine in the field of SCI. We also discuss the new role of functional neurosurgeons in neurorestorative interventional medicine, a new discipline at the intersection of neurosurgery, neuro-engineering, and neurorehabilitation.},
}
@article {pmid32231340,
year = {2020},
author = {Makin, JG and Moses, DA and Chang, EF},
title = {Machine translation of cortical activity to text with an encoder-decoder framework.},
journal = {Nature neuroscience},
volume = {23},
number = {4},
pages = {575-582},
pmid = {32231340},
issn = {1546-1726},
support = {U01 NS098971/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrocorticography ; Female ; Humans ; Middle Aged ; *Neural Networks, Computer ; *Speech ; *Speech Perception ; },
abstract = {A decade after speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech. Here we show how to decode the electrocorticogram with high accuracy and at natural-speech rates. Taking a cue from recent advances in machine translation, we train a recurrent neural network to encode each sentence-length sequence of neural activity into an abstract representation, and then to decode this representation, word by word, into an English sentence. For each participant, data consist of several spoken repeats of a set of 30-50 sentences, along with the contemporaneous signals from ~250 electrodes distributed over peri-Sylvian cortices. Average word error rates across a held-out repeat set are as low as 3%. Finally, we show how decoding with limited data can be improved with transfer learning, by training certain layers of the network under multiple participants' data.},
}
@article {pmid32226566,
year = {2020},
author = {Zuo, C and Jin, J and Yin, E and Saab, R and Miao, Y and Wang, X and Hu, D and Cichocki, A},
title = {Novel hybrid brain-computer interface system based on motor imagery and P300.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {2},
pages = {253-265},
pmid = {32226566},
issn = {1871-4080},
abstract = {Motor imagery (MI) is a mental representation of motor behavior and has been widely used in electroencephalogram based brain-computer interfaces (BCIs). Several studies have demonstrated the efficacy of MI-based BCI-feedback training in post-stroke rehabilitation. However, in the earliest stage of the training, calibration data typically contain insufficient discriminability, resulting in unreliable feedback, which may decrease subjects' motivation and even hinder their training. To improve the performance in the early stages of MI training, a novel hybrid BCI paradigm based on MI and P300 is proposed in this study. In this paradigm, subjects are instructed to imagine writing the Chinese character following the flash order of the desired Chinese character displayed on the screen. The event-related desynchronization/synchronization (ERD/ERS) phenomenon is produced with writing based on one's imagination. Simultaneously, the P300 potential is evoked by the flash of each stroke. Moreover, a fusion method of P300 and MI classification is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications. Twelve healthy naïve MI subjects participated in this study. Results demonstrated that the proposed hybrid BCI paradigm yielded significantly better performance than the single-modality BCI paradigm. The recognition accuracy of the fusion method is significantly higher than that of P300 (p < 0.05) and MI (p < 0.01). Moreover, the training data size can be reduced through fusion of these two modalities.},
}
@article {pmid32226230,
year = {2019},
author = {Raza, H and Rathee, D and Zhou, SM and Cecotti, H and Prasad, G},
title = {Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.},
journal = {Neurocomputing},
volume = {343},
number = {},
pages = {154-166},
pmid = {32226230},
issn = {0925-2312},
abstract = {The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.},
}
@article {pmid32224511,
year = {2020},
author = {Farrokhi, B and Erfanian, A},
title = {A state-based probabilistic method for decoding hand position during movement from ECoG signals in non-human primate.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026042},
doi = {10.1088/1741-2552/ab848b},
pmid = {32224511},
issn = {1741-2552},
mesh = {Animals ; Electrocorticography ; *Electroencephalography ; *Hand ; Movement ; Primates ; },
abstract = {OBJECTIVE: In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey.<br><br>APPROACH: A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced.<br><br>MAIN RESULTS: The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods [Formula: see text]. The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods [Formula: see text]. Investigating the kinematic information of each frequency band shows that more informative frequency bands were [Formula: see text] (15-30 Hz) and [Formula: see text](50-100 Hz) for ipsilateral and [Formula: see text] and [Formula: see text] (100-200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for [Formula: see text] (5-15 Hz) and [Formula: see text] bands, while contralateral movements was decoded better for [Formula: see text] (30-200 Hz) and hfECoG (200-400 Hz) bands.<br><br>SIGNIFICANCE: Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.},
}
@article {pmid32224508,
year = {2020},
author = {Xu, J and Grosse-Wentrup, M and Jayaram, V},
title = {Tangent space spatial filters for interpretable and efficient Riemannian classification.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026043},
doi = {10.1088/1741-2552/ab839e},
pmid = {32224508},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; },
abstract = {OBJECTIVE: Methods based on Riemannian geometry have proven themselves to be good models for decoding in brain-computer interfacing (BCI). However, these methods suffer from the curse of dimensionality and are not possible to deploy in high-density online BCI systems. In addition, the lack of interpretability of Riemannian methods leaves open the possibility that artifacts drive classification performance, which is problematic in the areas where artifactual control is crucial, e.g. neurofeedback and BCIs in patient populations.<br><br>APPROACH: We rigorously proved the exact equivalence between any linear function on the tangent space and corresponding derived spatial filters. Upon which, we further proposed a set of dimension reduction solutions for Riemannian methods without intensive optimization steps. The proposed pipelines are validated against classic common spatial patterns and tangent space classification using an open-access BCI analysis framework, which contains over seven datasets and 200 subjects in total. At last, the robustness of our framework is verified via visualizing the corresponding spatial patterns.<br><br>MAIN RESULTS: Proposed spatial filtering methods possess competitive, sometimes even slightly better, performances comparing to classic tangent space classification while reducing the time cost up to 97% in the testing stage. Importantly, the performances of proposed spatial filtering methods converge with using only four to six filter components regardless of the number of channels which is also cross validated by the visualized spatial patterns. These results reveal the possibility of underlying neuronal sources within each recording session.<br><br>SIGNIFICANCE: Our work promotes the theoretical understanding about Riemannian geometry based BCI classification and allows for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods.},
}
@article {pmid32224466,
year = {2021},
author = {Zheng, S and Shi, P and Wang, S and Shi, Y},
title = {Adaptive Neural Control for a Class of Nonlinear Multiagent Systems.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {2},
pages = {763-776},
doi = {10.1109/TNNLS.2020.2979266},
pmid = {32224466},
issn = {2162-2388},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Feedback ; *Neural Networks, Computer ; *Nonlinear Dynamics ; Uncertainty ; },
abstract = {This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.},
}
@article {pmid32224109,
year = {2020},
author = {Belbézier, A and Deroux, A and Sarrot-Reynauld, F and Colombe, B and Bosseray, A and Wintenberger, C and Dumanoir, P and Lugosi, M and Boccon-Gibod, I and Leroy, V and Maignan, M and Collomb-Muret, R and Viglino, D and Vaillant, M and Minotti, L and Lagrange, E and Epaulard, O and Dumestre-Perard, C and Lhomme, S and Lupo, J and Larrat, S and Morand, P and Schwebel, C and Vilotitch, A and Bosson, JL and Bouillet, L},
title = {Screening of hepatitis E in patients presenting for acute neurological disorders.},
journal = {Journal of infection and public health},
volume = {13},
number = {7},
pages = {1047-1050},
doi = {10.1016/j.jiph.2019.12.012},
pmid = {32224109},
issn = {1876-035X},
mesh = {Acute Disease/epidemiology ; Adult ; Aged ; Aged, 80 and over ; Brachial Plexus Neuritis/epidemiology ; Female ; France/epidemiology ; Guillain-Barre Syndrome/epidemiology ; Hepatitis Antibodies/*blood ; Hepatitis E/blood/diagnosis/*epidemiology/immunology ; Hepatitis E virus/*immunology ; Humans ; Immunoglobulin M/blood ; Male ; Middle Aged ; Nervous System Diseases/*epidemiology/immunology ; Prospective Studies ; RNA, Viral/blood ; Reverse Transcriptase Polymerase Chain Reaction ; Seroepidemiologic Studies ; Transaminases/blood ; Young Adult ; },
abstract = {INTRODUCTION: Hepatitis E virus (HEV) infection has been reported to be associated with neurological disorders. However, the real prevalence of acute hepatitis E in those diseases is still unknown. We determined the prevalence of anti-HEV IgM antibody in a population with acute non-traumatic, non-metabolic, non-vascular neurological injury.<br><br>METHOD: A registry was created in Grenoble Hospital University from 2014 to 2018 to collect data on patients with acute (<3 months) non-traumatic, non-metabolic, non-vascular neurological injuries. Acute hepatitis E was defined as anti-HEV IgM-positive serum in immunocompetent patient, and as anti-HEV IgM-positive serum or HEV RNA-positive serum in immunocompromised patients.<br><br>RESULTS: One hundred fifty-nine patients were included. Anti-HEV IgM seroprevalence in our cohort of non-traumatic, non-metabolic, non-vascular neurological injuries was 6.9% (eleven patients, including 4 Parsonage-Turner syndrome (PTS) and 2 Guillain-Barr&#xe9; syndrome (GBS)). Elevated transaminases were observed in only 64% of hepatitis E patients and cholestasis in 64%.<br><br>CONCLUSION: In this study, 6&#xb7;9% of patients with acute non-traumatic, non-metabolic, non-vascular neurological injuries had a probable recent HEV infection. HEV serology should be systematically performed in this population, even in patients with normal transaminase level.},
}
@article {pmid32223249,
year = {2020},
author = {Garcia-Cortadella, R and Schäfer, N and Cisneros-Fernandez, J and Ré, L and Illa, X and Schwesig, G and Moya, A and Santiago, S and Guirado, G and Villa, R and Sirota, A and Serra-Graells, F and Garrido, JA and Guimerà-Brunet, A},
title = {Switchless Multiplexing of Graphene Active Sensor Arrays for Brain Mapping.},
journal = {Nano letters},
volume = {20},
number = {5},
pages = {3528-3537},
doi = {10.1021/acs.nanolett.0c00467},
pmid = {32223249},
issn = {1530-6992},
mesh = {Animals ; Brain/*diagnostic imaging ; *Brain Mapping ; *Brain-Computer Interfaces ; *Graphite ; Rats ; },
abstract = {Sensor arrays used to detect electrophysiological signals from the brain are paramount in neuroscience. However, the number of sensors that can be interfaced with macroscopic data acquisition systems currently limits their bandwidth. This bottleneck originates in the fact that, typically, sensors are addressed individually, requiring a connection for each of them. Herein, we present the concept of frequency-division multiplexing (FDM) of neural signals by graphene sensors. We demonstrate the high performance of graphene transistors as mixers to perform amplitude modulation (AM) of neural signals in situ, which is used to transmit multiple signals through a shared metal line. This technology eliminates the need for switches, remarkably simplifying the technical complexity of state-of-the-art multiplexed neural probes. Besides, the scalability of FDM graphene neural probes has been thoroughly evaluated and their sensitivity demonstrated in vivo. Using this technology, we envision a new generation of high-count conformal neural probes for high bandwidth brain machine interfaces.},
}
@article {pmid32220309,
year = {2020},
author = {Won, SM and Song, E and Reeder, JT and Rogers, JA},
title = {Emerging Modalities and Implantable Technologies for Neuromodulation.},
journal = {Cell},
volume = {181},
number = {1},
pages = {115-135},
doi = {10.1016/j.cell.2020.02.054},
pmid = {32220309},
issn = {1097-4172},
mesh = {Animals ; Biocompatible Materials/*therapeutic use ; Humans ; *Nervous System ; *Prostheses and Implants ; Transcutaneous Electric Nerve Stimulation/methods ; },
abstract = {Techniques for neuromodulation serve as effective routes to care of patients with many types of challenging conditions. Continued progress in this field of medicine will require (1) improvements in our understanding of the mechanisms of neural control over organ function and (2) advances in technologies for precisely modulating these functions in a programmable manner. This review presents recent research on devices that are relevant to both of these goals, with an emphasis on multimodal operation, miniaturized dimensions, biocompatible designs, advanced neural interface schemes, and battery-free, wireless capabilities. A future that involves recording and modulating neural activity with such systems, including those that exploit closed-loop strategies and/or bioresorbable designs, seems increasingly within reach.},
}
@article {pmid32220308,
year = {2020},
author = {Willett, FR and Deo, DR and Avansino, DT and Rezaii, P and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Hand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way.},
journal = {Cell},
volume = {181},
number = {2},
pages = {396-409.e26},
pmid = {32220308},
issn = {1097-4172},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; I01 RX002295/RX/RRD VA/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; },
mesh = {Adult ; Brain Mapping ; Frontal Lobe/anatomy &amp; histology/*physiology ; Human Body ; Humans ; Motor Cortex/*anatomy &amp; histology/metabolism/*physiology ; Movement/physiology ; },
abstract = {Decades after the motor homunculus was first proposed, it is still unknown how different body parts are intermixed and interrelated in human motor cortical areas at single-neuron resolution. Using multi-unit recordings, we studied how face, head, arm, and leg movements are represented in the hand knob area of premotor cortex (precentral gyrus) in people with tetraplegia. Contrary to traditional expectations, we found strong representation of all movements and a partially "compositional" neural code that linked together all four limbs. The code consisted of (1) a limb-coding component representing the limb to be moved and (2) a movement-coding component where analogous movements from each limb (e.g., hand grasp and toe curl) were represented similarly. Compositional coding might facilitate skill transfer across limbs, and it provides a useful framework for thinking about how the motor system constructs movement. Finally, we leveraged these results to create a whole-body intracortical brain-computer interface that spreads targets across all limbs.},
}
@article {pmid32217479,
year = {2020},
author = {Zhao, J and Zhang, W and Wang, JH and Li, W and Lei, C and Chen, G and Liang, Z and Li, X},
title = {Decision-Making Selector (DMS) for Integrating CCA-Based Methods to Improve Performance of SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {5},
pages = {1128-1137},
doi = {10.1109/TNSRE.2020.2983275},
pmid = {32217479},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Multivariate Analysis ; Photic Stimulation ; },
abstract = {OBJECTIVE: Recent research has demonstrated improved performance of a brain-computer interface (BCI) using fusion based approaches. This paper proposes a novel decision-making selector (DMS) to integrate classification decisions of different frequency recognition methods based on canonical correlation analysis (CCA) which were used in decoding steady state visual evoked potentials (SSVEPs).<br><br>METHODS: The DMS method selects a decision more likely to be correct from two methods namely as M1 and M2 by separating the M1-false and M2-false trials. To measure the uncertainty of each decision, feature vectors were extracted using the largest and second largest correlation coefficients corresponding to all the stimulus frequencies. The proposed method was evaluated by integrating all pairs of 7 CCA-based algorithms, including CCA, individual template-based CCA (ITCCA), multi-set CCA (MsetCCA), L1-regularized multi-way CCA (L1-MCCA), filter bank CCA (FBCCA), extended CCA (ECCA), and task-related component analysis (TRCA).<br><br>MAIN RESULTS: The experimental results obtained from a 40-target dataset of thirty-five subjects showed that the proposed DMS method was validated to obtain an enhanced performance by integrating the algorithms with close accuracies.<br><br>CONCLUSION: The results suggest that the proposed DMS method is effective in integrating decisions of different methods to improve the performance of SSVEP-based BCIs.},
}
@article {pmid32210171,
year = {2020},
author = {Li, R and Lai, Y and Feng, C and Dev, R and Wang, Y and Hao, Y},
title = {Diarrhea in under Five Year-Old Children in Nepal: A Spatiotemporal Analysis Based on Demographic and Health Survey Data.},
journal = {International journal of environmental research and public health},
volume = {17},
number = {6},
pages = {},
pmid = {32210171},
issn = {1660-4601},
mesh = {Bayes Theorem ; Child, Preschool ; Demography ; *Diarrhea/epidemiology ; Female ; Health Surveys ; Humans ; Infant ; Male ; Nepal/epidemiology ; Spatio-Temporal Analysis ; },
abstract = {BACKGROUND: Diarrhea in children under five years of age remains a challenge in reducing child mortality in Nepal. Understanding the spatiotemporal patterns and influencing factors of the disease is important for control and intervention.<br><br>METHODS: Data regarding diarrhea prevalence and its potential influencing factors were extracted from the Demographic and Health Surveys in Nepal and other open-access databases. A Bayesian logistic regression model with district-specific spatio-temporal random effects was applied to explore the space and time patterns of diarrhea risk, as well as the relationships between the risk and the potential influencing factors.<br><br>RESULTS: Both the observed prevalence and the estimated spatiotemporal effects show a decreasing diarrhea risk trend from 2006 to 2016 in most districts of Nepal, with a few exceptions, such as Achham and Rasuwa. The disease risk decreased with mothers' years of education (OR 0.93, 95% Bayesian Credible Interval (BCI) 0.87, 0.997). Compared to spring, autumn and winter had lower risks of diarrhea. The risk firstly increased and then decreased with age and children under 12-24 months old were the highest risk group (OR 1.20, 95% BCI 1.04, 1.38). Boys had higher risk than girls (OR 1.24, 95% BCI 1.13, 1.39). Even though improved sanitation wasn't found significant within a 95% BCI, there was 93.2% of chance of it being a protective factor. There were no obvious spatiotemporal clusters among districts and each district tended to have its own spatiotemporal diarrhea prevalence pattern.<br><br>CONCLUSIONS: The important risk factors identified by our Bayesian spatial-temporal modeling provide insights for control and intervention on children diarrhea in Nepal. Special attention should be paid to high risk groups of children and high risk seasons, as well as districts with high risk or increased trend of risk. Effective actions should be implemented to improve sanitation and women's education level. District-specific control planning is recommended for local governments for effective control of children diarrhea in Nepal.},
}
@article {pmid32209743,
year = {2020},
author = {Welle, EJ and Patel, PR and Woods, JE and Petrossians, A and Della Valle, E and Vega-Medina, A and Richie, JM and Cai, D and Weiland, JD and Chestek, CA},
title = {Ultra-small carbon fiber electrode recording site optimization and improved in vivo chronic recording yield.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026037},
pmid = {32209743},
issn = {1741-2552},
support = {OT2 OD024907/OD/NIH HHS/United States ; U01 NS094375/NS/NINDS NIH HHS/United States ; UF1 NS107659/NS/NINDS NIH HHS/United States ; },
mesh = {Carbon Fiber ; Electrodes, Implanted ; Microelectrodes ; *Neurons ; *Silicon ; },
abstract = {OBJECTIVE: Carbon fiber electrodes may enable better long-term brain implants, minimizing the tissue response commonly seen with silicon-based electrodes. The small diameter fiber may enable high-channel count brain-machine interfaces capable of reproducing dexterous movements. Past carbon fiber electrodes exhibited both high fidelity single unit recordings and a healthy neuronal population immediately adjacent to the recording site. However, the recording yield of our carbon fiber arrays chronically implanted in the brain typically hovered around 30%, for previously unknown reasons. In this paper we investigated fabrication process modifications aimed at increasing recording yield and longevity.<br><br>APPROACH: We tested a new cutting method using a 532nm laser against traditional scissor methods for the creation of the electrode recording site. We verified the efficacy of improved recording sites with impedance measurements and in vivo array recording yield. Additionally, we tested potentially longer-lasting coating alternatives to PEDOT:pTS, including PtIr and oxygen plasma etching. New coatings were evaluated with accelerated soak testing and acute recording.<br><br>MAIN RESULTS: We found that the laser created a consistent, sustainable 257 &#xb1; 13.8 &#xb5;m[2] electrode with low 1 kHz impedance (19 &#xb1; 4 k&#x3a9; with PEDOT:pTS) and low fiber-to-fiber variability. The PEDOT:pTS coated laser cut fibers were found to have high recording yield in acute (97% >&#x2009;100 &#xb5;V pp , N = 34 fibers) and chronic (84% >&#x2009;100 &#xb5;V pp , day 7; 71% >&#x2009;100 &#xb5;V pp , day 63, N = 45 fibers) settings. The laser cut recording sites were good platforms for the PtIr coating and oxygen plasma etching, slowing the increase in 1 kHz impedance compared to PEDOT:pTS in an accelerated soak test.<br><br>SIGNIFICANCE: We have found that laser cut carbon fibers have a high recording yield that can be maintained for over two months in vivo and that alternative coatings perform better than PEDOT:pTS in accelerated aging tests. This work provides evidence to support carbon fiber arrays as a viable approach to high-density, clinically-feasible brain-machine interfaces.},
}
@article {pmid32208835,
year = {2020},
author = {Schultze-Kraft, M and Parés-Pujolràs, E and Matić, K and Haggard, P and Haynes, JD},
title = {Preparation and execution of voluntary action both contribute to awareness of intention.},
journal = {Proceedings. Biological sciences},
volume = {287},
number = {1923},
pages = {20192928},
pmid = {32208835},
issn = {1471-2954},
mesh = {*Awareness ; Brain ; Cues ; Humans ; *Intention ; Movement ; Volition ; },
abstract = {How and when motor intentions form has long been controversial. In particular, the extent to which motor preparation and action-related processes produce a conscious experience of intention remains unknown. Here, we used a brain-computer interface (BCI) while participants performed a self-paced movement task to trigger cues upon the detection of a readiness potential (a well-characterized brain signal that precedes movement) or in its absence. The BCI-triggered cues instructed participants either to move or not to move. Following this instruction, participants reported whether they felt they were about to move at the time the cue was presented. Participants were more likely to report an intention (i) when the cue was triggered by the presence of a readiness potential than when the same cue was triggered by its absence, and (ii) when they had just made an action than when they had not. We further describe a time-dependent integration of these two factors: the probability of reporting an intention was maximal when cues were triggered in the presence of a readiness potential, and when participants also executed an action shortly afterwards. Our results provide a first systematic investigation of how prospective and retrospective components are integrated in forming a conscious intention to move.},
}
@article {pmid32208379,
year = {2020},
author = {Pu, H and Lim, J and Kellis, S and Liu, CY and Andersen, RA and Do, AH and Heydari, P and Nenadic, Z},
title = {Optimal artifact suppression in simultaneous electrocorticography stimulation and recording for bi-directional brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026038},
doi = {10.1088/1741-2552/ab82ac},
pmid = {32208379},
issn = {1741-2552},
mesh = {Artifacts ; Brain ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes ; },
abstract = {OBJECTIVE: Electrocorticogram (ECoG)-based brain-computer interfaces (BCIs) are a promising platform for the restoration of motor and sensory functions to those with neurological deficits. Such bi-directional BCI operation necessitates simultaneous ECoG recording and stimulation, which is challenging given the presence of strong stimulation artifacts. This problem is exacerbated if the BCI's analog front-end operates in an ultra-low power regime, which is a basic requirement for fully implantable medical devices. In this study, we developed a novel method for the suppression of stimulation artifacts before they reach the analog front-end.<br><br>APPROACH: Using elementary biophysical considerations, we devised an artifact suppression method that employs a weak auxiliary stimulation delivered between the primary stimulator and the recording grid. The exact location and amplitude of this auxiliary stimulating dipole were then found through a constrained optimization procedure. The performance of our method was tested in both simulations and phantom brain tissue experiments.<br><br>MAIN RESULTS: The solution found through the optimization procedure matched the optimal canceling dipole in both simulations and experiments. Artifact suppression as large as 28.7 dB and 22.9 dB were achieved in simulations and brain phantom experiments, respectively.<br><br>SIGNIFICANCE: We developed a simple constrained optimization-based method for finding the parameters of an auxiliary stimulating dipole that yields optimal artifact suppression. Our method suppresses stimulation artifacts before they reach the analog front-end and may prevent the front-end amplifiers from saturation. Additionally, it can be used along with other artifact mitigation techniques to further reduce stimulation artifacts.},
}
@article {pmid32207635,
year = {2022},
author = {MacIntosh, A and Vignais, N and Vigneron, V and Fay, L and Musielak, A and Desailly, E and Biddiss, E},
title = {The design and evaluation of electromyography and inertial biofeedback in hand motor therapy gaming.},
journal = {Assistive technology : the official journal of RESNA},
volume = {34},
number = {2},
pages = {213-221},
doi = {10.1080/10400435.2020.1744770},
pmid = {32207635},
issn = {1949-3614},
support = {RN304779-379428//CIHR/Canada ; },
mesh = {Adolescent ; Biofeedback, Psychology ; *Cerebral Palsy/therapy ; Child ; Electromyography ; Humans ; Upper Extremity ; *Video Games ; },
abstract = {This article details the design of a co-created, evidence-based biofeedback therapy game addressing the research question: is the biofeedback implementation efficient, effective, and engaging for promoting quality movement during a therapy game focused on hand gestures? First, we engaged nine young people with Cerebral Palsy (CP) as design partners to co-create the biofeedback implementation. A commercially available, tap-controlled game was converted into a gesture-controlled game with added biofeedback. The game is controlled by forearm electromyography and inertial sensors. Changes required to integrate biofeedback are described in detail and highlight the importance of closely linking movement quality to short- and long-term game rewards. After development, 19 participants (8-17 years old) with CP played the game at home for 4 weeks. Participants played 17 ± 9 min/day, 4 ± 1 day/week. The biofeedback implementation proved efficient (i.e. participants reduced compensatory arm movements by 10.2 ± 4.0%), effective (i.e. participants made higher quality gestures over time), and engaging (i.e. participants consistently chose to review biofeedback). Participants found the game usable and enjoyable. Biofeedback design in therapy games should consider principles of motor learning, best practices in video game design, and user perspectives. Design recommendations for integrating biofeedback into therapy games are compiled in an infographic to support interdisciplinary knowledge sharing.},
}
@article {pmid32207440,
year = {2020},
author = {Osama, M and Aslam, MH},
title = {Emotiv EPOC+ fed electrical muscle stimulation system; an inexpensive brain-computer interface for rehabilitation of neuro muscular disorders.},
journal = {JPMA. The Journal of the Pakistan Medical Association},
volume = {70},
number = {3},
pages = {526-530},
doi = {10.5455/JPMA.16735},
pmid = {32207440},
issn = {0030-9982},
mesh = {*Brain-Computer Interfaces/economics/supply &amp; distribution ; Electrical Equipment and Supplies ; *Electroencephalography/instrumentation/methods ; Equipment Design ; Humans ; *Neurological Rehabilitation/instrumentation/methods ; Neuromuscular Diseases/diagnosis/physiopathology/*rehabilitation ; },
abstract = {Advancements in the Neuro-rehabilitation across Pakistan is warranted to effectively and efficiently deal with the disease burden of neurological conditions. Being a developing country, an in-expensive treatment approach is required to culminate the rise in the disease occurrence in Pakistan. Brain-Computer Interfaces (BCIs) have come up as a new channel for communication and control, eliminating the need of physical input, opening doors to a wide array of applications in terms of assistive and rehabilitative devices for paralyzed patients and those with neuromuscular disorders. Even with a promising prospect, BCIs and electroencephalograms (EEG) can be very expensive and therefore, they are not practically applicable. For this reason, the purpose of the current study was to come up with a possibility of an inexpensive BCI for rehabilitation of patients with neuro-muscular disorders in Pakistan by using a low-cost and readily available equipment like Emotiv EPOC+ EEG headset and electrical muscle stimulator.},
}
@article {pmid32206166,
year = {2019},
author = {Moxon, K and Saez, I and Ditterich, J},
title = {Mind Over Matter: Cognitive Neuroengineering.},
journal = {Cerebrum : the Dana forum on brain science},
volume = {2019},
number = {},
pages = {},
pmid = {32206166},
issn = {1524-6205},
support = {R01 NS096971/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-machine interface-once the stuff of science fiction novels-is coming to a computer near you. The only question is: How soon? While the technology is in its infancy, it is already helping people with spinal cord injuries. Our authors examine its potential to be the ultimate game changer for any number of neurodegenerative diseases, as well as behavior, learning, and memory. They take the temperature of where the technology is, where it is going, and the inevitable ethical and regulatory implications.},
}
@article {pmid32204785,
year = {2020},
author = {Chatthong, W and Khemthong, S and Wongsawat, Y},
title = {Brain Mapping Performance as an Occupational Therapy Assessment Aid in Attention Deficit Hyperactivity Disorder.},
journal = {The American journal of occupational therapy : official publication of the American Occupational Therapy Association},
volume = {74},
number = {2},
pages = {7402205070p1-7402205070p7},
doi = {10.5014/ajot.2020.035477},
pmid = {32204785},
issn = {0272-9490},
mesh = {*Attention Deficit Disorder with Hyperactivity/diagnosis/physiopathology ; Brain Mapping/*methods ; Child ; Cross-Sectional Studies ; Humans ; *Occupational Therapy ; Thailand ; },
abstract = {IMPORTANCE: Brain mapping performance (BMP) may provide strong predictors to analyze primary functional outcomes and support occupational therapy with clients with attention deficit hyperactivity disorder (ADHD).<br><br>OBJECTIVE: To clarify the value of quantitative electroencephalography to indicate BMP in children with ADHD.<br><br>DESIGN: One-year cross-sectional study.<br><br>SETTING: Brain Computer Interface Laboratory, Mahidol University, Salaya, Nakhon Pathom, Thailand.<br><br>PARTICIPANTS: Thai school-age children with and without ADHD (N = 305).<br><br>OUTCOMES AND MEASURES: We used &#x3b8; relative power in concordance with stepwise multiple regression analysis. Outcomes included measures of 12 brain locations that were compared between children with and without ADHD.<br><br>RESULTS: Significant differences were found between the groups, especially for Cz, T3, Fp1, Fz, F4, and F7. According to BMP, the group with ADHD had higher emotional awareness and language comprehension than the group without ADHD.<br><br>CONCLUSIONS AND RELEVANCE: Occupational therapy practitioners can use BMP as a valuable tool for setting occupational goals to help children with ADHD improve their social-emotional learning performance in school and in the community. BMP may provide an evaluation to support occupational therapy services for clients with ADHD. The result can be applied in clinical settings by quantitative electroencephalography training.<br><br>WHAT THIS ARTICLE ADDS: BMP can be used as a neuropsychological and behavioral assessment tool for setting SMART (specific, measurable, attainable, relevant, and time-oriented) goals for occupational therapy services for clients with ADHD.},
}
@article {pmid32203032,
year = {2021},
author = {Liu, P and Wang, J and Guo, Z},
title = {Multiple and Complete Stability of Recurrent Neural Networks With Sinusoidal Activation Function.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {32},
number = {1},
pages = {229-240},
doi = {10.1109/TNNLS.2020.2978267},
pmid = {32203032},
issn = {2162-2388},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Models, Neurological ; Neurons ; },
abstract = {This article presents new theoretical results on multistability and complete stability of recurrent neural networks with a sinusoidal activation function. Sufficient criteria are provided for ascertaining the stability of recurrent neural networks with various numbers of equilibria, such as a unique equilibrium, finite, and countably infinite numbers of equilibria. Multiple exponential stability criteria of equilibria are derived, and the attraction basins of equilibria are estimated. Furthermore, criteria for complete stability and instability of equilibria are derived for recurrent neural networks without time delay. In contrast to the existing stability results with a finite number of equilibria, the new criteria, herein, are applicable for both finite and countably infinite numbers of equilibria. Two illustrative examples with finite and countably infinite numbers of equilibria are elaborated to substantiate the results.},
}
@article {pmid32197536,
year = {2020},
author = {Magdič, J and Cmor, N and Kaube, M and Hojs Fabjan, T and Hauer, L and Sellner, J and Pikija, S},
title = {Intracranial Vertebrobasilar Calcification in Patients with Ischemic Stroke is a Predictor of Recurrent Stroke, Vascular Disease, and Death: A Case-Control Study.},
journal = {International journal of environmental research and public health},
volume = {17},
number = {6},
pages = {},
pmid = {32197536},
issn = {1660-4601},
mesh = {Aged ; *Brain Ischemia ; Calcinosis ; Case-Control Studies ; *Cerebral Infarction ; Humans ; Male ; Recurrence ; Risk Factors ; Slovenia ; *Stroke/diagnosis ; },
abstract = {Intracranial artery calcification can be detected on nonenhanced brain computer tomography (NECT) and is a predictor of early vascular events. Here, we assessed the impact of vertebrobasilar artery calcification (VBC) on the long-term risk for recurrent stroke and vascular events. We performed a case-control trial of all consecutive stroke patients admitted to the University Hospital of Maribor, Slovenia over a period of 14 months. VBC was defined as presence of a hyperdense area within vertebrobasilar arteries that exceeds > 90 Hounsfield units as seen on NECT. Clinical follow-up information was obtained from the hospital documentation system and mortality registry of the district and included recurrent stroke, subsequent vascular events (myocardial infarction, heart failure, peripheral arterial occlusive disease), and death. We followed a total of 448 patients for a median of 1505 days (interquartile range, IQR 188-2479). Evidence for VBC was present in 243 (54.2%) patients. Median age was 76 years, recurrent stroke occurred in 33 (7.4%), any vascular events in 71 (15.8%), and death in 276 (61.6%). VBC was associated with a higher risk of recurrent stroke (hazard ratio, HR 3.13, 95% confidence interval (CI 1.35-7.20)) and vascular events (HR 2.05, 95% CI 1.21-3.47). Advanced age, male gender, and ischemic stroke involving the entire anterior circulation raised the likelihood for death. We conclude that the presence of VBC in patients with ischemic stroke is a short- and long-term prognostic factor for stroke recurrence and subsequent manifestation of acute vascular disease. Further understanding of the pathophysiology of VBC is warranted.},
}
@article {pmid32194373,
year = {2020},
author = {Shin, J and Im, CH},
title = {Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {168},
pmid = {32194373},
issn = {1662-4548},
abstract = {Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets.},
}
@article {pmid32194131,
year = {2020},
author = {Shen, L and Dong, X and Li, Y},
title = {Analysis and classification of hybrid EEG features based on the depth DRDS videos.},
journal = {Journal of neuroscience methods},
volume = {338},
number = {},
pages = {108690},
doi = {10.1016/j.jneumeth.2020.108690},
pmid = {32194131},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Stereo vision cognition is a crucial advanced function of human beings, and stereoscopic acuity is an important index to detect stereo vision. Electroencephalograph (EEG) is an effective method of detection. Therefore, it has great significance to research the relationship between stereoscopic acuity and EEG signals for the development of 3D technology.<br><br>NEW METHOD: This paper proposes a multi-channel selection sparse time window common spatial group (MCS-STWCSG) multi-classification method. Firstly, a channel selection method based on improved common spatial pattern- (CSP-) rank is applied to select optimal channels to reduce redundant signal. Secondly, based on the one vs. one (OVO) computational model, we extend traditional CSP to the common spatial group (CSG) to implement three-classification. Finally, this paper optimizes time-frequency characteristics and hybrid signal features by sparse regression and utilizes a support vector machine (SVM) with radial basis function (RBF) kernel to identify depth dynamic random dot stereogram (DRDS) video tasks.<br><br>RESULTS: The selected channels are all located in and near the occipital region and time-frequency characteristics can acquire better classification results compared with frequency characteristics. The highest classification result can reach 94.67%.<br><br>The MCS-STWCSG multi-classification method optimizes features from multiple aspects and its performance is obviously better than other methods for hybrid EEG signals of depth DRDS.<br><br>CONCLUSIONS: Channel selection and time-frequency segmentation for feature extraction and classification algorithm of EEG signals can increase the classification accuracy. It proves the feasibility and accuracy of the proposed method.},
}
@article {pmid32191894,
year = {2020},
author = {Jeong, JH and Shim, KH and Kim, DJ and Lee, SW},
title = {Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {5},
pages = {1226-1238},
doi = {10.1109/TNSRE.2020.2981659},
pmid = {32191894},
issn = {1558-0210},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Movement ; *Robotics ; },
abstract = {Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.},
}
@article {pmid32187208,
year = {2020},
author = {Kwon, J and Shin, J and Im, CH},
title = {Toward a compact hybrid brain-computer interface (BCI): Performance evaluation of multi-class hybrid EEG-fNIRS BCIs with limited number of channels.},
journal = {PloS one},
volume = {15},
number = {3},
pages = {e0230491},
pmid = {32187208},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Models, Theoretical ; Motor Cortex/*diagnostic imaging/*physiology ; Psychomotor Performance/physiology ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {It has been demonstrated that the performance of typical unimodal brain-computer interfaces (BCIs) can be noticeably improved by combining two different BCI modalities. This so-called "hybrid BCI" technology has been studied for decades; however, hybrid BCIs that particularly combine electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) (hereafter referred to as hBCIs) have not been widely used in practical settings. One of the main reasons why hBCI systems are so unpopular is that their hardware is generally too bulky and complex. Therefore, to make hBCIs more appealing, it is necessary to implement a lightweight and compact hBCI system with minimal performance degradation. In this study, we investigated the feasibility of implementing a compact hBCI system with significantly less EEG channels and fNIRS source-detector (SD) pairs, but that can achieve a classification accuracy high enough to be used in practical BCI applications. EEG and fNIRS data were acquired while participants performed three different mental tasks consisting of mental arithmetic, right-hand motor imagery, and an idle state. Our analysis results showed that the three mental states could be classified with a fairly high classification accuracy of 77.6 ± 12.1% using an hBCI system with only two EEG channels and two fNIRS SD pairs.},
}
@article {pmid32187000,
year = {2020},
author = {Burkhart, MC and Brandman, DM and Franco, B and Hochberg, LR and Harrison, MT},
title = {The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models.},
journal = {Neural computation},
volume = {32},
number = {5},
pages = {969-1017},
pmid = {32187000},
issn = {1530-888X},
support = {P20 GM103645/GM/NIGMS NIH HHS/United States ; I50 RX002864/RX/RRD VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; 336092//CIHR/Canada ; R01 MH102840/MH/NIMH NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; },
mesh = {*Algorithms ; *Bayes Theorem ; *Brain-Computer Interfaces ; Humans ; Learning/physiology ; Models, Biological ; *Nonlinear Dynamics ; },
abstract = {The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation|state) is nonlinear. We argue that in many cases, a model for p(state|observation) proves both easier to learn and more accurate for latent state estimation. Approximating p(state|observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation|state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein-von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p(observation|state) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.},
}
@article {pmid32185216,
year = {2020},
author = {Zhao, X and Zhao, J and Liu, C and Cai, W},
title = {Deep Neural Network with Joint Distribution Matching for Cross-Subject Motor Imagery Brain-Computer Interfaces.},
journal = {BioMed research international},
volume = {2020},
number = {},
pages = {7285057},
pmid = {32185216},
issn = {2314-6141},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Motor Neurons/*physiology ; Movement/physiology ; Neural Networks, Computer ; Young Adult ; },
abstract = {Motor imagery brain-computer interfaces (BCIs) have demonstrated great potential and attract world-spread attentions. Due to the nonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This prevents them from going into our realistic life. In this paper, the source subject's data are explored to perform calibration for target subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to handle is the distribution shift. It is found that the performance of classification would be bad when only the marginal distributions of source and target are made closer, since the discriminative directions of the source and target domains may still be much different. In order to solve the problem, our idea comes that joint distribution adaptation is indispensable. It makes the classifier trained in the source domain perform well in the target domain. Specifically, a measure for joint distribution discrepancy (JDD) between the source and target is proposed. Experiments demonstrate that it can align source and target data according to the class they belong to. It has a direct relationship with classification accuracy and works well for transferring. Secondly, a deep neural network with joint distribution matching for zero-training motor imagery BCI is proposed. It explores both marginal and joint distribution adaptation to alleviate distribution discrepancy across subjects and obtain effective and generalized features in an aligned common space. Visualizations of intermediate layers illustrate how and why the network works well. Experiments on the two datasets prove the effectiveness and strength compared to outstanding counterparts.},
}
@article {pmid32184706,
year = {2020},
author = {Loutit, AJ and Potas, JR},
title = {Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {156},
pmid = {32184706},
issn = {1662-4548},
abstract = {Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.},
}
@article {pmid32183285,
year = {2020},
author = {Daeglau, M and Wallhoff, F and Debener, S and Condro, IS and Kranczioch, C and Zich, C},
title = {Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {6},
pages = {},
pmid = {32183285},
issn = {1424-8220},
support = {KR3433/3-1//Deutsche Forschungsgemeinschaft/ ; Signals and Cognition//Nieders&#xe4;chsisches Ministerium f&#xfc;r Wissenschaft und Kultur/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Neurofeedback/methods ; Robotics/*trends ; Sensorimotor Cortex/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.},
}
@article {pmid32182818,
year = {2020},
author = {Aricò, P and Sciaraffa, N and Babiloni, F},
title = {Brain-Computer Interfaces: Toward a Daily Life Employment.},
journal = {Brain sciences},
volume = {10},
number = {3},
pages = {},
pmid = {32182818},
issn = {2076-3425},
abstract = {Recent publications in the Electroencephalogram (EEG)-based brain-computer interface field suggest that this technology could be ready to go outside the research labs and enter the market as a new consumer product. This assumption is supported by the recent advantages obtained in terms of front-end graphical user interfaces, back-end classification algorithms, and technology improvement in terms of wearable devices and dry EEG sensors. This editorial paper aims at mentioning these aspects, starting from the review paper "Brain-Computer Interface Spellers: A Review" (Rezeika et al., 2018), published within the Brain Sciences journal, and citing other relevant review papers that discussed these points.},
}
@article {pmid32182270,
year = {2020},
author = {Tariq, M and Trivailo, PM and Simic, M},
title = {Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI.},
journal = {PloS one},
volume = {15},
number = {3},
pages = {e0230184},
pmid = {32182270},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electroencephalography/methods ; Female ; Foot/*physiology ; Functional Laterality/physiology ; Humans ; Imagery, Psychotherapy/methods ; Imagination/physiology ; Kinesthesis/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {The left and right foot representation area is located within the interhemispheric fissure of the sensorimotor cortex and share spatial proximity. This makes it difficult to visualize the cortical lateralization of event-related (de)synchronization (ERD/ERS) during left and right foot motor imageries. The aim of this study is to investigate the possibility of using ERD/ERS in the mu, low beta, and high beta bandwidth, during left and right foot dorsiflexion kinaesthetic motor imageries (KMI), as unilateral control commands for a brain-computer interface (BCI). EEG was recorded from nine healthy participants during cue-based left-right foot dorsiflexion KMI tasks. The features were analysed for common average and bipolar references. With each reference, mu and beta band-power features were analysed using time-frequency (TF) maps, scalp topographies, and average time course for ERD/ERS. The cortical lateralization of ERD/ERS, during left and right foot KMI, was confirmed. Statistically significant features were classified using LDA, SVM, and KNN model, and evaluated using the area under ROC curves. An increase in high beta power following the end of KMI for both tasks was recorded, from right and left hemispheres, respectively, at the vertex. The single trial analysis and classification models resulted in high discrimination accuracies, i.e. maximum 83.4% for beta ERS, 79.1% for beta ERD, and 74.0% for mu ERD. With each model the features performed above the statistical chance level of 2-class discrimination for a BCI. Our findings indicate these features can evoke left-right differences in single EEG trials. This suggests that any BCI employing unilateral foot KMI can attain classification accuracy suitable for practical implementation. Given results stipulate the novel utilization of mu and beta as independent control features for discrimination of bilateral foot KMI in a BCI.},
}
@article {pmid32181180,
year = {2020},
author = {Dhok, A and Gupta, P and Shaikh, ST},
title = {Evaluation of the Evan's and Bicaudate Index for Rural Population in Central India using Computed Tomography.},
journal = {Asian journal of neurosurgery},
volume = {15},
number = {1},
pages = {94-97},
pmid = {32181180},
issn = {1793-5482},
abstract = {INTRODUCTION: Evans index (EI) and Bicaudate index (BCI) are practical markers of ventricular volume and are helpful radiological markers in the diagnosis of normal pressure hydrocephalus. Worldwide, variation exists in normative studies for both these indices. Most of the studies conducted for EI and BCI are based on the Western population data. No study has been performed on the rural population of Central India. The purpose of this study is to develop normative data on EI and BCI that can be extrapolated for future reference.<br><br>MATERIALS AND METHODS: This was a retrospective study conducted from December 2018 to May 2019 in MGIMS Hospital, Sevagram, Maharashtra, India, which is a rural hospital in Central India. All patients with either a head injury or neurological complaints although with normal computed tomography (CT) brain were included in the study. Patients with diagnosed neurological disorder, clinical features suggesting hydrocephalus, or intracranial pathology on CT brain were excluded from the study. Five hundred and eleven patients were selected for this study, and EI and BCI was calculated for them.<br><br>RESULTS: The mean value of EI and BCI in our study was 0.2707 and 0.1121, respectively. Both indices showed a statistically significant difference between males and females. The value of both indices increased with age.<br><br>CONCLUSION: Although our study is in agreement with the cutoff value of EI to diagnose dilated lateral ventricles as 0.3 for age <70 years, cutoff value of EI for the older population should be reconsidered to 0.34.},
}
@article {pmid32180695,
year = {2020},
author = {Hermiz, J and Hossain, L and Arneodo, EM and Ganji, M and Rogers, N and Vahidi, N and Halgren, E and Gentner, TQ and Dayeh, SA and Gilja, V},
title = {Stimulus Driven Single Unit Activity From Micro-Electrocorticography.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {55},
pmid = {32180695},
issn = {1662-4548},
support = {R01 DC018446/DC/NIDCD NIH HHS/United States ; },
abstract = {High-fidelity measurements of neural activity can enable advancements in our understanding of the neural basis of complex behaviors such as speech, audition, and language, and are critical for developing neural prostheses that address impairments to these abilities due to disease or injury. We develop a novel high resolution, thin-film micro-electrocorticography (micro-ECoG) array that enables high-fidelity surface measurements of neural activity from songbirds, a well-established animal model for studying speech behavior. With this device, we provide the first demonstration of sensory-evoked modulation of surface-recorded single unit responses. We establish that single unit activity is consistently sensed from micro-ECoG electrodes over the surface of sensorimotor nucleus HVC (used as a proper name) in anesthetized European starlings, and validate responses with correlated firing in single units recorded simultaneously at surface and depth. The results establish a platform for high-fidelity recording from the surface of subcortical structures that will accelerate neurophysiological studies, and development of novel electrode arrays and neural prostheses.},
}
@article {pmid32175867,
year = {2020},
author = {Borgheai, SB and McLinden, J and Zisk, AH and Hosni, SI and Deligani, RJ and Abtahi, M and Mankodiya, K and Shahriari, Y},
title = {Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {5},
pages = {1198-1207},
pmid = {32175867},
issn = {1558-0210},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; *Communication ; Electroencephalography ; Humans ; Spectroscopy, Near-Infrared ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) based communication remains a challenge for people with later-stage amyotrophic lateral sclerosis (ALS) who lose all voluntary muscle control. Although recent studies have demonstrated the feasibility of functional near-infrared spectroscopy (fNIRS) to successfully control BCIs primarily for healthy cohorts, these systems are yet inefficient for people with severe motor disabilities like ALS. In this study, we developed a new fNIRS-based BCI system in concert with a single-trial Visuo-Mental (VM) paradigm to investigate the feasibility of enhanced communication for ALS patients, particularly those in the later stages of the disease.<br><br>METHODS: In the first part of the study, we recorded data from six ALS patients using our proposed protocol (fNIRS-VM) and compared the results with the conventional electroencephalography (EEG)-based multi-trial P3Speller (P3S). In the second part, we recorded longitudinal data from one patient in the late locked-in state (LIS) who had fully lost eye-gaze control. Using statistical parametric mapping (SPM) and correlation analysis, the optimal channels and hemodynamic features were selected and used in linear discriminant analysis (LDA).<br><br>RESULTS: Over all the subjects, we obtained an average accuracy of 81.3%&#xb1;5.7% within comparatively short times (< 4 sec) in the fNIRS-VM protocol relative to an average accuracy of 74.0%&#xb1;8.9% in the P3S, though not competitive in patients with no substantial visual problems. Our longitudinal analysis showed substantially superior accuracy using the proposed fNIRS-VM protocol (73.2%&#xb1;2.0%) over the P3S (61.8%&#xb1;1.5%).<br><br>SIGNIFICANCE: Our findings indicate the potential efficacy of our proposed system for communication and control for late-stage ALS patients.},
}
@article {pmid32175133,
year = {2020},
author = {Wolf, EJ and Cruz, TH and Emondi, AA and Langhals, NB and Naufel, S and Peng, GCY and Schulz, BW and Wolfson, M},
title = {Advanced technologies for intuitive control and sensation of prosthetics.},
journal = {Biomedical engineering letters},
volume = {10},
number = {1},
pages = {119-128},
pmid = {32175133},
issn = {2093-985X},
abstract = {The Department of Defense, Department of Veterans Affairs and National Institutes of Health have invested significantly in advancing prosthetic technologies over the past 25 years, with the overall intent to improve the function, participation and quality of life of Service Members, Veterans, and all United States Citizens living with limb loss. These investments have contributed to substantial advancements in the control and sensory perception of prosthetic devices over the past decade. While control of motorized prosthetic devices through the use of electromyography has been widely available since the 1980s, this technology is not intuitive. Additionally, these systems do not provide stimulation for sensory perception. Recent research has made significant advancement not only in the intuitive use of electromyography for control but also in the ability to provide relevant meaningful perceptions through various stimulation approaches. While much of this previous work has traditionally focused on those with upper extremity amputation, new developments include advanced bidirectional neuroprostheses that are applicable to both the upper and lower limb amputation. The goal of this review is to examine the state-of-the-science in the areas of intuitive control and sensation of prosthetic devices and to discuss areas of exploration for the future. Current research and development efforts in external systems, implanted systems, surgical approaches, and regenerative approaches will be explored.},
}
@article {pmid32174810,
year = {2020},
author = {Herff, C and Krusienski, DJ and Kubben, P},
title = {The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {123},
pmid = {32174810},
issn = {1662-4548},
abstract = {Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.},
}
@article {pmid32174331,
year = {2020},
author = {Liu, Y and Liu, Y and Tang, J and Yin, E and Hu, D and Zhou, Z},
title = {A self-paced BCI prototype system based on the incorporation of an intelligent environment-understanding approach for rehabilitation hospital environmental control.},
journal = {Computers in biology and medicine},
volume = {118},
number = {},
pages = {103618},
doi = {10.1016/j.compbiomed.2020.103618},
pmid = {32174331},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hospitals ; Humans ; Imagery, Psychotherapy ; Reproducibility of Results ; },
abstract = {This paper presents a self-paced brain-computer interface (BCI) based on the incorporation of an intelligent environment-understanding approach into a motor imagery (MI) BCI system for rehabilitation hospital environmental control. The interface integrates four types of daily assistance tasks: medical calls, service calls, appliance control and catering services. The system introduces intelligent environment understanding technology to establish preliminary predictions concerning a user's control intention by extracting potential operational objects in the current environment through an object detection neural network. According to the characteristics of the four types of control and services, we establish different response mechanisms and use an intelligent decision-making method to design and dynamically optimize the relevant control instruction set. The control feedback is communicated to the user via voice prompts; it avoids the use of visual channels throughout the interaction. The asynchronous and synchronous modes of the MI-BCI are designed to launch the control process and to select specific operations, respectively. In particular, the reliability of the MI-BCI is enhanced by the optimized identification algorithm. An online experiment demonstrated that the system can respond quickly and it generates an activation command in an average of 3.38s while effectively preventing false activations; the average accuracy of the BCI synchronization commands was 89.2%, which represents sufficiently effective control. The proposed system is efficient, applicable and can be used to both improve system information throughput and to reduce mental loads. The proposed system can be used to assist with the daily lives of patients with severe motor impairments.},
}
@article {pmid32173401,
year = {2020},
author = {Ziafati, A and Maleki, A},
title = {Fuzzy ensemble system for SSVEP stimulation frequency detection using the MLR and MsetCCA.},
journal = {Journal of neuroscience methods},
volume = {338},
number = {},
pages = {108686},
doi = {10.1016/j.jneumeth.2020.108686},
pmid = {32173401},
issn = {1872-678X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Recognition, Psychology ; },
abstract = {BACKGROUND: BCI systems based on steady-state visual evoked potentials (SSVEP) have formed an immense contribution to practical applications, due to their high recognition accuracy and ease of use. The MLR method has a better frequency recognition accuracy for short-term windows, and the MsetCCA method works more accurately in long-term windows.<br><br>NEW METHOD: The proposed fuzzy ensemble system can analyze the relevant SSVEP signals of each subject from 0.5 to 4&#x202f;s windows with 0.5&#x202f;s incremental steps. It is capable of taking decisions to improve the accuracy of SSVEP stimulation frequency recognition using the MLR and MsetCCA methods.<br><br>RESULTS: Our fuzzy system provides high-accuracy results for the stimulation frequency recognition in signals with the length of 1&#x202f;s and more. Specifically, the average accuracy of 2&#x202f;s windows has improved to 100 percent.<br><br>The recognition accuracy of the presented system is always better than both MLR and MsetCCA methods.<br><br>CONCLUSION: One of the capabilities of fuzzy systems is that they can use human information and knowledge to build engineering systems. The fuzzy ensemble system can utilize various methods or classifiers simultaneously. The new system has proposed to combine multiple methods using the fuzzy ensemble, which encompasses the benefits of all the subsystems.},
}
@article {pmid32168747,
year = {2020},
author = {Amo Usanos, C and Boquete, L and de Santiago, L and Barea Navarro, R and Cavaliere, C},
title = {Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {6},
pages = {},
pmid = {32168747},
issn = {1424-8220},
support = {DPI2017-88438-R//Secretariat of State for Research, Development and Innovation/ ; },
mesh = {Adult ; Brain/physiology ; Electroencephalography ; Electroencephalography Phase Synchronization/*physiology ; Female ; Gamma Rhythm/*physiology ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30-60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain-computer interfaces, and diagnosis of cognitive or motor pathologies.},
}
@article {pmid32167917,
year = {2020},
author = {Torkamani-Azar, M and Kanik, SD and Aydin, S and Cetin, M},
title = {Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task.},
journal = {IEEE journal of biomedical and health informatics},
volume = {24},
number = {9},
pages = {2550-2558},
doi = {10.1109/JBHI.2020.2980056},
pmid = {32167917},
issn = {2168-2208},
mesh = {Brain/diagnostic imaging ; Cognition ; *Electroencephalography ; Humans ; Reaction Time ; *Wakefulness ; },
abstract = {Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectro-spatial features of the resting-state electroencephalograms (EEG). In this study, ten healthy volunteers have participated in fixed-sequence, varying-duration sessions of sustained attention to response task (SART) for over 100 minutes. A novel and adaptive cumulative vigilance scoring (CVS) scheme is proposed based on tonic performance and response time. Multiple linear regression (MLR) using feature relevance analysis has shown that average CVS, average response time, and variabilities of these scores can be predicted (p < 0.05) from the resting-state band-power ratios of EEG signals. Cross-validated neural networks also captured different associations for narrow-band beta and wide-band gamma and differences between the high- and low-attention networks in temporal regions. The proposed framework and these first findings on stable and significant attention predictors from the power ratios of resting-state EEG can be useful in brain-computer interfacing and vigilance monitoring applications.},
}
@article {pmid32167903,
year = {2020},
author = {He, H and Wu, D},
title = {Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {5},
pages = {1091-1108},
doi = {10.1109/TNSRE.2020.2980299},
pmid = {32167903},
issn = {1558-0210},
mesh = {Adaptation, Physiological ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; PR-SET Domains ; },
abstract = {A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.},
}
@article {pmid32167707,
year = {2020},
author = {Kalil, J and D Ancona, CAL},
title = {Detrusor underactivity versus bladder outlet obstruction clinical and urodynamic factors.},
journal = {International braz j urol : official journal of the Brazilian Society of Urology},
volume = {46},
number = {3},
pages = {419-424},
pmid = {32167707},
issn = {1677-6119},
mesh = {Aged ; Humans ; Lower Urinary Tract Symptoms ; Male ; Retrospective Studies ; *Urinary Bladder Neck Obstruction ; Urinary Bladder, Underactive ; Urodynamics ; },
abstract = {OBJECTIVES: To evaluate the lower urinary tract symptoms, classified by the International Prostate Symptom Score (IPSS), urodynamic results (Watts Factor (WF), Bladder Contractility Index (BCI), and post void residual (PVR), in order to differentiate Detrusor Underactivity (DU) from Bladder Outlet Obstruction (BOO).<br><br>METHODS: Retrospective observational study performed from 2011 to 2018 at the Hospital das Cl&#xed;nicas of Unicamp. Two phases were done: first, to estimate sample size, and second, to evaluate the predicted parameters. Male patients with range age from 40 to 80 years were included. Patients were divided into two groups: Group 1, without BOO and with DU; Group 2, with BOO. Variables analyzed: age, comorbidities, symptoms, urodynamic data (BCI and WF) and PVR.<br><br>RESULTS: Twenty-two patients were included in each group, with medians of 68 (Group 1) and 67.5 years old (Group 2) (p = 0.8416). There was no difference for comorbidities. In relation to IPSS, medians were: 16.5 and 20.5, respectively (p = 0.858). As for symptoms, there was predominance of combination of storage and voiding symptoms in the two groups (p = 0.1810). Regarding PVR, 15 patients in Group 1 and 16 in Group 2 presented PVR> 30mL (p = 0.7411). BCI presented median values of 75 and 755.50 for Group 1 and Group 2, respectively (p < 0.0001), while WF had medians of 22.42 and 73.85 (p < 0.0001).<br><br>CONCLUSION: Isolated symptoms, classified by IPSS and PVR, could not differentiate patients with DU from those with BOO, but it was possible using urodynamic data.},
}
@article {pmid32166505,
year = {2020},
author = {Sharma, G and Chowdhury, SR},
title = {Statistical Analysis to Find out the Optimal Locations for Non Invasive Brain Stimulation.},
journal = {Journal of medical systems},
volume = {44},
number = {4},
pages = {85},
pmid = {32166505},
issn = {1573-689X},
mesh = {Algorithms ; Brain/*physiology ; Data Analysis ; Datasets as Topic ; *Electric Stimulation ; Electroencephalography/*standards ; Humans ; Pilot Projects ; Spectroscopy, Near-Infrared ; },
abstract = {Non-invasive brain electrical stimulation (NIBES) techniques are progressively used for modulation of neuronal membrane potentials, which alters cortical excitability. The neuronal activity depends on position of channel locations for electrodes and the amount and direction of injected weak current through the target neurons area. In the present paper hybrid near infrared spectroscopy and electroencephalogram (NIRS-EEG) open access dataset for brain computer interface (BCI) has been used to find the best locations for NIBES. The percentage oxygen saturation has been calculated with the help of provided NIRS experimental dataset of changes in concentration of oxy-hemoglobin (HbO2) and deoxy-hemoglobin (Hb) in thirty-six scalp site locations of twenty-eight healthy subjects. The variation in standard deviation have been calculated for given pre-processed EEG signals of thirty locations for same twenty-eight healthy subjects. The statistical one-way ANOVA method has been used to find out the best channels and locations which are having less variation in all motion artifacts. In this method, F value is calculated for these locations and those locations are selected which are significant at 99% confidence interval (P < 0.01). In this study, out of sixty-six locations sixteen best locations have been selected for non-invasive brain electrical stimulation. This pilot study has been used to find out the appropriate locations on the scalp sites to place the electrodes to provide weak direct current stimulation which are less affected by motion artifacts.},
}
@article {pmid32166422,
year = {2020},
author = {Xing, T and Ma, J and Jia, C and Ou, T},
title = {Neurogenic lower urinary tract dysfunction predicts prognosis in patients with multiple system atrophy.},
journal = {Clinical autonomic research : official journal of the Clinical Autonomic Research Society},
volume = {30},
number = {3},
pages = {247-254},
doi = {10.1007/s10286-020-00678-1},
pmid = {32166422},
issn = {1619-1560},
mesh = {Humans ; Male ; *Multiple System Atrophy/complications/diagnosis ; Prognosis ; Retrospective Studies ; Urinary Bladder ; Urodynamics ; },
abstract = {PURPOSE: To evaluate whether neurogenic lower urinary tract dysfunction and urodynamic parameters predict the outcomes of patients with multiple system atrophy (MSA).<br><br>METHODS: A retrospective study was performed in patients who were diagnosed with MSA and underwent urodynamic studies simultaneously from September 2014 to July 2018. The urodynamic traces were reviewed by urologists. Detrusor contractility was evaluated by the bladder contractility index (BCI) and Sch&#xe4;fer nomogram. Telephone follow-up was conducted in July 2019 to acquire survival data. Clinical and urodynamic parameters were analyzed for survival using Cox regression analysis.<br><br>RESULTS: Overall, 70 MSA patients were eligible for analysis, and 61 of them underwent urodynamic study within 3&#xa0;years of initial symptom onset. The parkinsonian subtype of MSA (MSA-P) had a smaller proportion of men as well as longer motor and lower urinary tract symptom durations than the cerebellar subtype (MSA-C). MSA-P also had a lower mean BCI than MSA-C (32.0&#x2009;&#xb1;&#x2009;27.0 versus 53.6&#x2009;&#xb1;&#x2009;33.4, p&#x2009;=&#x2009;0.025). The mean MSA survival time was 5.4 [95% confidence interval (CI) 4.8-6.3] years. Cox regression analysis showed that survival from baseline was correlated only with BCI [hazard ratio (HR) 0.983, 95% CI 0.969-0.997, p&#x2009;=&#x2009;0.020]. Overall survival was correlated with BCI (HR 0.982, 95% CI 0.966-0.999, p&#x2009;=&#x2009;0.039) and the presence of urinary incontinence (HR 3.007, 95% CI 0.993-9.220, p&#x2009;=&#x2009;0.052).<br><br>CONCLUSION: Detrusor contractility can be a prognostic marker in MSA patients. A high BCI value is a protective factor for survival from baseline and overall survival. The presence of urinary incontinence predicts shortened overall survival.},
}
@article {pmid32164872,
year = {2020},
author = {Aminoff, MJ and Boller, F and Swaab, DF},
title = {Foreword.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {vii-viii},
doi = {10.1016/B978-0-444-63934-9.09985-6},
pmid = {32164872},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; *Communication ; Electroencephalography/methods ; Humans ; *Research ; },
}
@article {pmid32164871,
year = {2020},
author = {Ramsey, NF and Millán, JDR},
title = {Preface.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {ix-x},
doi = {10.1016/B978-0-444-63934-9.09984-4},
pmid = {32164871},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; Humans ; *Research ; },
}
@article {pmid32164870,
year = {2020},
author = {Vilela, M and Hochberg, LR},
title = {Applications of brain-computer interfaces to the control of robotic and prosthetic arms.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {87-99},
doi = {10.1016/B978-0-444-63934-9.00008-1},
pmid = {32164870},
issn = {0072-9752},
mesh = {Brain/*physiopathology/*surgery ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Quality of Life ; *Robotic Surgical Procedures/methods ; Robotics ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to improve the quality of life of individuals with severe motor disabilities. BCIs capture the user's brain activity and translate it into commands for the control of an effector, such as a computer cursor, robotic limb, or functional electrical stimulation device. Full dexterous manipulation of robotic and prosthetic arms via a BCI system has been a challenge because of the inherent need to decode high dimensional and preferably real-time control commands from the user's neural activity. Nevertheless, such functionality is fundamental if BCI-controlled robotic or prosthetic limbs are to be used for daily activities. In this chapter, we review how this challenge has been addressed by BCI researchers and how new solutions may improve the BCI user experience with robotic effectors.},
}
@article {pmid32164869,
year = {2020},
author = {Vansteensel, MJ and Jarosiewicz, B},
title = {Brain-computer interfaces for communication.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {67-85},
doi = {10.1016/B978-0-444-63934-9.00007-X},
pmid = {32164869},
issn = {0072-9752},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Communication ; Communication Devices for People with Disabilities/psychology ; *Electroencephalography ; Humans ; Magnetic Resonance Imaging/methods ; },
abstract = {Locked-in syndrome (LIS) is characterized by an inability to move or speak in the presence of intact cognition and can be caused by brainstem trauma or neuromuscular disease. Quality of life (QoL) in LIS is strongly impaired by the inability to communicate, which cannot always be remedied by traditional augmentative and alternative communication (AAC) solutions if residual muscle activity is insufficient to control the AAC device. Brain-computer interfaces (BCIs) may offer a solution by employing the person's neural signals instead of relying on muscle activity. Here, we review the latest communication BCI research using noninvasive signal acquisition approaches (electroencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy) and subdural and intracortical implanted electrodes, and we discuss current efforts to translate research knowledge into usable BCI-enabled communication solutions that aim to improve the QoL of individuals with LIS.},
}
@article {pmid32164868,
year = {2020},
author = {Rupp, R},
title = {Spinal cord lesions.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {51-65},
doi = {10.1016/B978-0-444-63934-9.00006-8},
pmid = {32164868},
issn = {0072-9752},
mesh = {Humans ; Neuralgia/*physiopathology ; Paraplegia/*physiopathology ; Recovery of Function/physiology ; Spinal Cord/physiopathology ; Spinal Cord Diseases/complications/*physiopathology ; Spinal Cord Injuries/etiology/*physiopathology ; },
abstract = {A spinal cord injury (SCI) may result in impairments of motor, sensory, and autonomous functions below the injury level. Worldwide, the prevalence of SCI is 1:1000 and the incidence is between 4 and 9 new cases per 100,000 people per year. Most common causes for traumatic SCI are traffic accidents, falls, and violence. Nowadays, the proportion of patients with tetraplegia and paraplegia is equal. In industrialized countries, the percentage of nontraumatic injuries increases together with age. Most patients with initially preserved motor functions below the injury level show a substantial functional recovery, while three quarters of patients with initially complete SCI remain that way. In SCI, brain-computer interfaces (BCIs) may be used in the subacute phase as part of a restorative therapy program and, later, for control of assistive devices most needed by individuals with high cervical lesions. Research on structural and functional reorganization of the deefferented and deafferented brain after SCI is inconclusive mainly because of varying methods of analysis and the heterogeneity of the investigated populations. A better characterization of study participants with SCI together with documentation of confounding factors such as antispasticity medication or neuropathic pain is indicated.},
}
@article {pmid32164867,
year = {2020},
author = {Conde, V and Siebner, HR},
title = {Brain damage by trauma.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {39-49},
doi = {10.1016/B978-0-444-63934-9.00005-6},
pmid = {32164867},
issn = {0072-9752},
mesh = {Animals ; Brain/*physiopathology ; Brain Injuries, Traumatic/*physiopathology/*therapy ; Cognitive Dysfunction/physiopathology ; Deep Brain Stimulation/methods ; Humans ; Neuronal Plasticity/*physiology ; },
abstract = {Traumatic brain injury (TBI) represents a major clinical and economic challenge for health systems worldwide, and it is considered one of the leading causes of disability in young adults. The recent development of brain-computer interface (BCI) tools to target cognitive and motor impairments has led to the exploration of these techniques as potential therapeutic tools in patients with TBI. However, little evidence has been gathered so far to support applicability and efficacy of BCIs for TBI in a clinical setting. In the present chapter, results from studies using BCI approaches in conscious patients with TBI or in animal models of TBI as well as an overview of future directions in the use of BCIs to treat cognitive symptoms in this patient population will be presented.},
}
@article {pmid32164866,
year = {2020},
author = {Kübler, A and Nijboer, F and Kleih, S},
title = {Hearing the needs of clinical users.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {353-368},
doi = {10.1016/B978-0-444-63934-9.00026-3},
pmid = {32164866},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Hearing/*physiology ; Hearing Tests ; Humans ; Neurodegenerative Diseases/*physiopathology ; Reproducibility of Results ; },
abstract = {In the past 10 years, brain-computer interfaces (BCIs) for controlling assistive devices have seen tremendous progress with respect to reliability and learnability, and numerous exemplary applications were demonstrated to be controllable by a BCI. Yet, BCI-controlled applications are hardly used for patients with neurologic or neurodegenerative disease. Such patient groups are considered potential end-users of BCI, specifically for replacing or improving lost function. We argue that BCI research and development still faces a translational gap, i.e., the knowledge of how to bring BCIs from the laboratory to the field is insufficient. BCI-controlled applications lack usability and accessibility; both constitute two sides of one coin, which is the key to use in daily life and to prevent nonuse. To increase usability, we suggest rigorously adopting the user-centered design in applied BCI research and development. To provide accessibility, assistive technology (AT) experts, providers, and other stakeholders have to be included in the user-centered process. BCI experts have to ensure the transfer of knowledge to AT professionals, and listen to the needs of primary, secondary, and tertiary end-users of BCI technology. Addressing both, usability and accessibility, in applied BCI research and development will bridge the translational gap and ensure that the needs of clinical end-users are heard, understood, addressed, and fulfilled.},
}
@article {pmid32164865,
year = {2020},
author = {Pulliam, CL and Stanslaski, SR and Denison, TJ},
title = {Industrial perspectives on brain-computer interface technology.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {341-352},
doi = {10.1016/B978-0-444-63934-9.00025-1},
pmid = {32164865},
issn = {0072-9752},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Deep Brain Stimulation/methods ; Electroencephalography/methods ; Humans ; Parkinson Disease/*therapy ; Stroke/*therapy ; },
abstract = {Neuromodulation therapies offer a unique opportunity for translating brain-computer interface (BCI) technologies into a clinical setting. Several diseases such as Parkinson's disease are effectively treated by invasive device stimulation therapies, and the addition of sensing and algorithm technology is an obvious evolutionary expansion of capabilities. In addition, this infrastructure might enable a roadmap of novel BCI technologies. While the initial applications are focused on epilepsy and movement disorders, the technology is potentially transferable to a broader base of disorders, including stroke and rehabilitation. The ultimate potential of BCI technology will be determined by forthcoming chronic evaluation in multiple neurologic disorders.},
}
@article {pmid32164864,
year = {2020},
author = {Vaughan, TM},
title = {Brain-computer interfaces for people with amyotrophic lateral sclerosis.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {33-38},
doi = {10.1016/B978-0-444-63934-9.00004-4},
pmid = {32164864},
issn = {0072-9752},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/*physiopathology ; *Brain-Computer Interfaces ; Communication ; *Electroencephalography/methods ; Humans ; Nerve Net/*physiopathology ; },
abstract = {A brain-computer interface (BCI) records and extracts features from brain signals, and translates these features into commands that can replace, restore, enhance, supplement, or improve natural CNS outputs. As demonstrated in the other chapters of this book, the focus of the work of the last three decades of BCI research has been the replacement, restoration, or improvement of diminished or lost function in people with CNS disease or injury including those with amyotrophic lateral sclerosis (ALS). Due in part to the desire to conduct controlled studies, and, in part, to the complexity of BCI technology, most of this work has been carried out in laboratories with healthy controls or with limited numbers of potential consumers with a variety of diagnoses under supervised conditions. The intention of this chapter is to describe the growing body of BCI research that has included people with amyotrophic lateral sclerosis (ALS). People in the late stages of ALS can lose all voluntary control, including the ability to communicate; and while recent research has provided new insights into underlying mechanisms, ALS remains a disease with no cure. As a result, people with ALS and their families, caregivers, and advocates have an active interest in both the current and potential capabilities of BCI technology. The focus of BCI research for people with ALS is on communication, and this topic is well covered elsewhere in this volume. This chapter focuses on the efforts dedicated to make BCI technology useful to people with ALS in their daily lives with a discussion of how researchers, clinicians, and patients must become partners in that process.},
}
@article {pmid32164863,
year = {2020},
author = {Klein, E},
title = {Ethics and the emergence of brain-computer interface medicine.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {329-339},
doi = {10.1016/B978-0-444-63934-9.00024-X},
pmid = {32164863},
issn = {0072-9752},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Ethics ; *Goals ; Humans ; *Privacy ; },
abstract = {Brain-computer interface (BCI) technology will usher in profound changes to the practice of medicine. BCI devices, broadly defined as those capable of reading brain activity and translating this into operation of a device, will offer patients and clinicians new ways to address impairments of communication, movement, sensation, and mental health. These new capabilities will bring new responsibilities and raise a diverse set of ethical challenges. One way to understand and begin to address these challenges is to view them in terms of the goals of medicine. In this chapter, different ways in which BCI technology may subserve the goals of medicine is explored. This is followed by articulation of additional goals particularly relevant to BCI technology: neural diversity, neural privacy, agency, and authenticity. The goals of medicine provide a useful ethical framework for the introduction of BCI devices into medicine.},
}
@article {pmid32164862,
year = {2020},
author = {Iturrate, I and Chavarriaga, R and Millán, JDR},
title = {General principles of machine learning for brain-computer interfacing.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {311-328},
doi = {10.1016/B978-0-444-63934-9.00023-8},
pmid = {32164862},
issn = {0072-9752},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.},
}
@article {pmid32164861,
year = {2020},
author = {Bouton, CE},
title = {Merging brain-computer interface and functional electrical stimulation technologies for movement restoration.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {303-309},
doi = {10.1016/B978-0-444-63934-9.00022-6},
pmid = {32164861},
issn = {0072-9752},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; Electroencephalography/methods ; Humans ; Movement/*physiology ; Spinal Cord Injuries/*physiopathology ; },
abstract = {BCI (brain-computer interface) and functional electrical stimulation (FES) technologies have advanced significantly over the last several decades. Recent efforts have involved the integration of these technologies with the goal of restoring functional movement in paralyzed patients. Implantable BCIs have provided neural recordings with increased spatial resolution and have been combined with sophisticated neural decoding algorithms and increasingly capable FES systems to advance efforts toward this goal. This chapter reviews historical developments that have occurred as the exciting fields of BCI and FES have evolved and now overlapped to allow new breakthroughs in medicine, targeting restoration of movement and lost function in users with disabilities.},
}
@article {pmid32164860,
year = {2020},
author = {Sorger, B and Goebel, R},
title = {Real-time fMRI for brain-computer interfacing.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {289-302},
doi = {10.1016/B978-0-444-63934-9.00021-4},
pmid = {32164860},
issn = {0072-9752},
mesh = {Brain/*physiopathology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Computer Systems ; Humans ; *Magnetic Resonance Imaging/instrumentation/methods ; Speech/physiology ; },
abstract = {Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.},
}
@article {pmid32164859,
year = {2020},
author = {Heldman, DA and Moran, DW},
title = {Local field potentials for BCI control.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {279-288},
doi = {10.1016/B978-0-444-63934-9.00020-2},
pmid = {32164859},
issn = {0072-9752},
mesh = {Action Potentials/*physiology ; Animals ; Behavior/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Humans ; Neurons/physiology ; Psychomotor Performance/*physiology ; },
abstract = {The gold standard in brain-computer interface (BCI) modalities is multi single-unit recordings in the primary motor cortex. It yields the fastest and most elegant control (i.e., most degrees of freedom and bitrate). Unfortunately, single-unit electrodes are prone to encapsulation, which limit their single-unit recording life. However, encapsulation does not significantly affect intracortical local field potentials (LFPs). LFPs and single-unit activity were recorded from the motor cortices of three monkeys (Macaca fascicularis) while they performed a standard 3D center-out reaching task and a 3D circle-drawing task. The high frequency (HF) (60-200 Hz) spectral amplitudes of a subset of the LFPs were found to be directionally tuned much like single units. In fact, stable isolation of single units on the same electrode increased the likelihood that the HF-LFP would be significantly cosine tuned to hand direction. The presence of significantly tuned single units further increased the likelihood of a tuned HF-LFP, suggesting that this band of HF-LFP activity is at least partially generated by local neuronal action potential currents (i.e., single-unit activity). Given that encapsulation makes recording single units over a long period of time difficult, these results suggest that HF-LFPs may be a more stable and efficient method of monitoring neural activity for BCI applications.},
}
@article {pmid32164858,
year = {2020},
author = {Hermes, D and Miller, KJ},
title = {iEEG: Dura-lining electrodes.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {263-277},
doi = {10.1016/B978-0-444-63934-9.00019-6},
pmid = {32164858},
issn = {0072-9752},
mesh = {Brain/*physiopathology ; Brain Mapping/methods ; Dura Mater/physiopathology ; *Electrocorticography ; Epilepsy/*physiopathology ; Evoked Potentials/*physiology ; Humans ; },
abstract = {Intracranial electroencephalography (iEEG) is measured from electrodes placed in or on the brain. These measurements have an excellent signal-to-noise ratio and iEEG signals have often been used to decode brain activity or drive brain-computer interfaces (BCIs). iEEG recordings are typically done for seizure monitoring in epilepsy patients who have these electrodes placed for a clinical purpose: to localize both brain regions that are essential for function and others where seizures start. Brain regions not involved in epilepsy are thought to function normally and provide a unique opportunity to learn about human neurophysiology. Intracranial electrodes measure the aggregate activity of large neuronal populations and recorded signals contain many features. Different features are extracted by analyzing these signals in the time and frequency domain. The time domain may reveal an evoked potential at a particular time after the onset of an event. Decomposition into the frequency domain may show narrowband peaks in the spectrum at specific frequencies or broadband signal changes that span a wide range of frequencies. Broadband power increases are generally observed when a brain region is active while most other features are highly specific to brain regions, inputs, and tasks. Here we describe the spatiotemporal dynamics of several iEEG signals that have often been used to decode brain activity and drive BCIs.},
}
@article {pmid32164857,
year = {2020},
author = {Molinari, M and Masciullo, M},
title = {Stroke and potential benefits of brain-computer interface.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {25-32},
doi = {10.1016/B978-0-444-63934-9.00003-2},
pmid = {32164857},
issn = {0072-9752},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Recovery of Function/physiology ; Stroke/physiopathology/*therapy ; *Stroke Rehabilitation ; },
abstract = {To treat stroke and, in particular, to alleviate the personal and social burden of stroke survivors is a main challenge for neuroscience research. Advancements in the knowledge of neurobiologic mechanisms subserving stroke-related damage and recovery provide key data to guide clinicians to tailor interventions to specific patient's needs. How does the brain-computer interface (BCI) fit into this scenario? A technique created to allow completely paralyzed individuals to control the environment recently introduced a new line of development: to provide a means to possibly control formation and changes in the brain network organization. In a sort of revolution, similar to the change from geocentric to heliocentric planet organization envisioned by Copernicus, we are facing a critical change in BCI research, moving from a brain to computer direction to a computer to brain one. This direction change will profoundly open up new avenues for BCI research and clinical applications. In this chapter, we address this change and discuss present and future applications of this new line idea of BCI use in stroke.},
}
@article {pmid32164856,
year = {2020},
author = {Müller-Putz, GR},
title = {Electroencephalography.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {249-262},
doi = {10.1016/B978-0-444-63934-9.00018-4},
pmid = {32164856},
issn = {0072-9752},
mesh = {Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Electroencephalography/history/methods ; Evoked Potentials/*physiology ; History, 20th Century ; History, 21st Century ; Humans ; },
abstract = {The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Then an overview on EEG phenomena, namely, spontaneous EEG and event-related potentials build the middle part of this chapter. The last part discusses brain signals, which are used in current BCI research, including short descriptions and examples of applications.},
}
@article {pmid32164855,
year = {2020},
author = {Batista, A},
title = {Brain-computer interfaces for basic neuroscience.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {233-247},
doi = {10.1016/B978-0-444-63934-9.00017-2},
pmid = {32164855},
issn = {0072-9752},
mesh = {Brain/*physiology/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Learning/physiology ; Motor Activity/*physiology ; *Neurosciences ; },
abstract = {Brain-computer interfaces (BCIs) provide a powerful new tool for basic neuroscience investigators. This is because the BCI approach forges a tight link between the observation of neural activity and well-controlled manipulations of neural activity, driven by the BCI user's own volition. As in all branches of science, progress in neuroscience rests on observation and manipulation. In neuroscience, our observations are typically measurements of neural activity and behavior, and our manipulations are lesions and the addition of neural activity through direct neural stimulation, which cause changes in behavior and in the activity of other neurons. A BCI links observation and manipulation directly because the participant in the experiment observes a mapping of his or her own neural activity, and through volitional control, manipulates that activity. Researchers employing the BCI approach in a basic neuroscience context have made new progress toward understanding the neural basis of motor control, learning, and cognition. To date, most of the basic research using the BCI approach has been applied to understanding the motor system, but future basic science research objectives using the BCI approach include the neural basis of cognitive and emotional function, and explorations of the computational limits of neural circuitry.},
}
@article {pmid32164854,
year = {2020},
author = {Cannard, C and Brandmeyer, T and Wahbeh, H and Delorme, A},
title = {Self-health monitoring and wearable neurotechnologies.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {207-232},
doi = {10.1016/B978-0-444-63934-9.00016-0},
pmid = {32164854},
issn = {0072-9752},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Neurofeedback/*physiology ; *Self-Assessment ; Wearable Electronic Devices ; },
abstract = {Brain-computer interfaces and wearable neurotechnologies are now used to measure real-time neural and physiologic signals from the human body and hold immense potential for advancements in medical diagnostics, prevention, and intervention. Given the future role that wearable neurotechnologies will likely serve in the health sector, a critical state-of-the-art assessment is necessary to gain a better understanding of their current strengths and limitations. In this chapter we present wearable electroencephalography systems that reflect groundbreaking innovations and improvements in real-time data collection and health monitoring. We focus on specifications reflecting technical advantages and disadvantages, discuss their use in fundamental and clinical research, their current applications, limitations, and future directions. While many methodological and ethical challenges remain, these systems host the potential to facilitate large-scale data collection far beyond the reach of traditional research laboratory settings.},
}
@article {pmid32164853,
year = {2020},
author = {Borghini, G and Ronca, V and Vozzi, A and Aricò, P and Di Flumeri, G and Babiloni, F},
title = {Monitoring performance of professional and occupational operators.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {199-205},
doi = {10.1016/B978-0-444-63934-9.00015-9},
pmid = {32164853},
issn = {0072-9752},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Exercise/*physiology ; Humans ; Occupations ; *Workload ; },
abstract = {The human capacity to simultaneously perform several tasks depends on the quantity and the mode of mentally processing the information imposed by the tasks. Since operational environments are highly dynamic, priorities across tasks will be expected to change as the mission evolves, thus the capability to reallocate the mental resources dynamically depending on such changes is very important. The resources required in very complex situations, such as air traffic management (ATM), can exceed the user's available resources leading to increased workload and performance impairments. In this regard, the availability of information concerning the workload experienced by the operators while dealing with tasks will be fundamental for both warning them when overload conditions are approaching and improving interactions with the system. The idea of our work was to use neurophysiologic data collected from professional air traffic controllers (ATCOs) to provide additional information to standard measures with which to assess the ATCOs' expertise and a machine learning electroencephalography-based index to evaluate their mental workload during the execution of ATC tasks. The results showed that the proposed method was able to track the workload alongside the execution of the realistic ATM scenario, and provide added values to objectively assess the expertise of the ATCOs.},
}
@article {pmid32164852,
year = {2020},
author = {Leeb, R and Pérez-Marcos, D},
title = {Brain-computer interfaces and virtual reality for neurorehabilitation.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {183-197},
doi = {10.1016/B978-0-444-63934-9.00014-7},
pmid = {32164852},
issn = {0072-9752},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; Humans ; *Neurological Rehabilitation ; Video Games ; *Virtual Reality ; },
abstract = {Brain-computer interfaces (BCIs) and virtual reality (VR) are two technologic advances that are changing our way of interacting with the world. BCIs can be used to influence and can serve as a control mechanism in navigation tasks, communication, or other assistive functions. VR can create ad hoc interactive scenarios that involve all our senses, stimulate the brain in a multisensory fashion, and increase the motivation and fun with game-like environments. VR and motion tracking enable natural human-computer interaction at cognitive and physical levels. This includes both brain and body in the design of meaningful VR experiences; these cases in which participants feel naturally present could help augment the benefits of BCIs for assistive and neurorehabilitation applications for the relearning of motor and cognitive skills. VR technology is now available at the consumer level thanks to the proliferation of affordable head-mounted displays (HMDs). Merging both technologies into simplified, practical devices may help democratize these technologies.},
}
@article {pmid32164851,
year = {2020},
author = {Hughes, C and Herrera, A and Gaunt, R and Collinger, J},
title = {Bidirectional brain-computer interfaces.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {163-181},
doi = {10.1016/B978-0-444-63934-9.00013-5},
pmid = {32164851},
issn = {0072-9752},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Feedback, Sensory/*physiology ; Humans ; Motor Cortex/*physiology ; Quality of Life ; Somatosensory Cortex/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) are devices that interface with the brain to enable interaction with the environment. BCIs have the potential to improve the quality of life for many individuals affected by debilitating disorders of the brain, spine, limbs, and sensory organs through direct interface with the nervous system. While much progress has been made in terms of BCI motor control, significantly less attention has been given to the restoration of tactile, or cutaneous sensations, which can be very important during grasping or manipulation of objects. BCIs will need to integrate both the motor and sensory modalities to truly restore arm and hand function. Here we describe a bidirectional BCI, a system which translates neural signals recorded from the motor cortex into signals to control a device and provides somatosensory feedback by translating external sensor information to electric stimulation patterns delivered to the cortex. In this chapter, we review the neuroscience of somatosensation, the history of sensory feedback in BCI applications, specifically for restoration of hand function and cutaneous sensations, and describe additional work that needs to be completed to make bidirectional BCI a clinical reality.},
}
@article {pmid32164849,
year = {2020},
author = {Wolpaw, JR and Millán, JDR and Ramsey, NF},
title = {Brain-computer interfaces: Definitions and principles.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {15-23},
doi = {10.1016/B978-0-444-63934-9.00002-0},
pmid = {32164849},
issn = {0072-9752},
support = {I01 CX001812/CX/CSRD VA/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Movement/*physiology ; Reproducibility of Results ; },
abstract = {Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development. The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development. BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments. BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations. In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation. The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies. Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.},
}
@article {pmid32164848,
year = {2020},
author = {Annen, J and Laureys, S and Gosseries, O},
title = {Brain-computer interfaces for consciousness assessment and communication in severely brain-injured patients.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {137-152},
doi = {10.1016/B978-0-444-63934-9.00011-1},
pmid = {32164848},
issn = {0072-9752},
mesh = {Awareness/physiology ; Brain Injuries/*physiopathology ; *Brain-Computer Interfaces ; *Communication ; Consciousness/physiology ; Consciousness Disorders/diagnosis/*physiopathology ; Humans ; },
abstract = {Patients with disorders of consciousness (DOC) suffer from awareness deficits. Comorbidities such as motor disabilities or visual problems hamper clinical assessments, which can lead to misdiagnosis of the level of consciousness and render the patient unable to communicate. Objective measures of consciousness can reduce the risk of misdiagnosis and could enable patients to communicate by voluntarily modulating their brain activity. This chapter gives an overview of the literature regarding brain-computer interface (BCI) research in DOC patients. Different auditory, visual, and motor imagery paradigms are discussed, alongside their corresponding advantages and disadvantages. At this point, the use of BCIs for DOC patients in clinical applications is still preliminary. However, perspectives on the improvements in BCIs for DOC patients seem positive, and implementation during rehabilitation shows promise.},
}
@article {pmid32164846,
year = {2020},
author = {Pichiorri, F and Mattia, D},
title = {Brain-computer interfaces in neurologic rehabilitation practice.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {101-116},
doi = {10.1016/B978-0-444-63934-9.00009-3},
pmid = {32164846},
issn = {0072-9752},
mesh = {Brain/physiopathology ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Humans ; *Neurological Rehabilitation ; Recovery of Function/*physiology ; Stroke/*physiopathology ; },
abstract = {The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.},
}
@article {pmid32164845,
year = {2020},
author = {Ramsey, NF},
title = {Human brain function and brain-computer interfaces.},
journal = {Handbook of clinical neurology},
volume = {168},
number = {},
pages = {1-13},
doi = {10.1016/B978-0-444-63934-9.00001-9},
pmid = {32164845},
issn = {0072-9752},
mesh = {Brain/*physiology/physiopathology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Movement/*physiology ; Speech/*physiology ; },
abstract = {Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.},
}
@article {pmid32154404,
year = {2020},
author = {Peterson, V and Galván, C and Hernández, H and Spies, R},
title = {A feasibility study of a complete low-cost consumer-grade brain-computer interface system.},
journal = {Heliyon},
volume = {6},
number = {3},
pages = {e03425},
pmid = {32154404},
issn = {2405-8440},
abstract = {Brain-computer interfaces (BCIs) are technologies that provide the user with an alternative way of communication. A BCI measures brain activity (e.g. EEG) and converts it into output commands. Motor imagery (MI), the mental simulation of movements, can be used as a BCI paradigm, where the movement intention of the user can be translated into a real movement, helping patients in motor recovery rehabilitation. One of the main limitations for the broad use of such devices is the high cost associated with the high-quality equipment used for capturing the biomedical signals. Different low-cost consumer-grade alternatives have emerged with the objective of bringing these systems closer to the final users. The quality of the signals obtained with such equipments has already been evaluated and found to be competitive with those obtained with well-known clinical-grade devices. However, how these consumer-grade technologies can be integrated and used for practical MI-BCIs has not yet been explored. In this work, we provide a detailed description of the advantages and disadvantages of using OpenBCI boards, low-cost sensors and open-source software for constructing an entirely consumer-grade MI-BCI system. An analysis of the quality of the signals acquired and the MI detection ability is performed. Even though communication between the computer and the OpenBCI board is not always stable and the signal quality is sometimes affected by ambient noise, we find that by means of a filter-bank based method, similar classification performances can be achieved with an MI-BCI built under low-cost consumer-grade devices as compared to when clinical-grade systems are used. By means of this work we share with the BCI community our experience on working with emerging low-cost technologies, providing evidence that an entirely low-cost MI-BCI can be built. We believe that if communication stability and artifact rejection are improved, these technologies will become a valuable alternative to clinical-grade devices.},
}
@article {pmid32152333,
year = {2020},
author = {Nakagome, S and Luu, TP and He, Y and Ravindran, AS and Contreras-Vidal, JL},
title = {An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {4372},
pmid = {32152333},
issn = {2045-2322},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; *Gait ; Humans ; *Machine Learning ; *Neural Networks, Computer ; },
abstract = {Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs.},
}
@article {pmid32151908,
year = {2020},
author = {Ferracuti, F and Casadei, V and Marcantoni, I and Iarlori, S and Burattini, L and Monteriù, A and Porcaro, C},
title = {A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {191},
number = {},
pages = {105419},
doi = {10.1016/j.cmpb.2020.105419},
pmid = {32151908},
issn = {1872-7565},
mesh = {*Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND AND OBJECTIVES: An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials.<br><br>METHODS: EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) RESULTS: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification.<br><br>CONCLUSIONS: The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.},
}
@article {pmid32151472,
year = {2020},
author = {Méndez-Rubio, S and López-Pérez, E and Laso-Martín, S and Vírseda-Chamorro, M and Salinas-Casado, J and Esteban-Fuertes, M and Moreno-Sierra, J},
title = {The role of clean intermittent catheterization in the treatment for detrusor underactivity.},
journal = {Actas urologicas espanolas},
volume = {44},
number = {4},
pages = {233-238},
doi = {10.1016/j.acuro.2019.11.002},
pmid = {32151472},
issn = {2173-5786},
mesh = {Adult ; Aged ; Female ; Humans ; *Intermittent Urethral Catheterization ; Longitudinal Studies ; Male ; Middle Aged ; Retrospective Studies ; Urethra/*physiopathology ; Urinary Bladder/*physiopathology ; Urinary Bladder, Underactive/*complications ; Urinary Retention/*etiology/*therapy ; },
abstract = {OBJECTIVE: To study the influence of clean intermittent catheterization (CIC) on the lower urinary tract function in patients with urinary retention (UR) due to detrusor underactivity (DU).<br><br>MATERIAL AND METHODS: A longitudinal study was carried out on 49 patients (28 men, 21 women) of mean age 55years, who underwent CIC for UR secondary to DU. The mean CIC frequency was 3.15 times/day. Patients' clinical data were collected, and they underwent urodynamic study before and after CIC, with a mean interval of 4years. Fisher's exact test was used for the analysis of categorical variables and Student's t test for parametric variables. The level of significance was set at 0.05 for a two-tailed test.<br><br>RESULTS: The second urodynamic study showed a significantly increased bladder compliance, the Bladder Outlet Obstruction Index (BOOI) and the Bladder Contractility Index (BCI) also increased but without reaching statistical significance. There was a significantly higher percentage of benign prostatic hyperplasia (BPH) and acontractile detrusor cases among the group of patients whose BCI improved after CIC, with significantly lower CIC time.<br><br>CONCLUSIONS: CIC improved bladder compliance in the patients of our series. The BCI improved in BPH patients and in patients with acontractile detrusor.},
}
@article {pmid32149677,
year = {2022},
author = {Dai, C and Wu, J and Pi, D and Becker, SI and Cui, L and Zhang, Q and Johnson, B},
title = {Brain EEG Time-Series Clustering Using Maximum-Weight Clique.},
journal = {IEEE transactions on cybernetics},
volume = {52},
number = {1},
pages = {357-371},
doi = {10.1109/TCYB.2020.2974776},
pmid = {32149677},
issn = {2168-2275},
mesh = {Algorithms ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Cluster Analysis ; *Electroencephalography ; Time Factors ; },
abstract = {Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and nonstationary time series, has been recently widely applied in neurocognitive disorder diagnoses and brain-machine interface developments. With its specific features, unlabeled EEG is not well addressed by conventional unsupervised time-series learning methods. In this article, we handle the problem of unlabeled EEG time-series clustering and propose a novel EEG clustering algorithm, that we call mwcEEGc. The idea is to map the EEG clustering to the maximum-weight clique (MWC) searching in an improved Fréchet similarity-weighted EEG graph. The mwcEEGc considers the weights of both vertices and edges in the constructed EEG graph and clusters EEG based on their similarity weights instead of calculating the cluster centroids. To the best of our knowledge, it is the first attempt to cluster unlabeled EEG trials using MWC searching. The mwcEEGc achieves high-quality clusters with respect to intracluster compactness as well as intercluster scatter. We demonstrate the superiority of mwcEEGc over ten state-of-the-art unsupervised learning/clustering approaches by conducting detailed experimentations with the standard clustering validity criteria on 14 real-world brain EEG datasets. We also present that mwcEEGc satisfies the theoretical properties of clustering, such as richness, consistency, and order independence.},
}
@article {pmid32149649,
year = {2020},
author = {Carmona, L and Diez, PF and Laciar, E and Mut, V},
title = {Multisensory Stimulation and EEG Recording Below the Hair-Line: A New Paradigm on Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {4},
pages = {825-831},
doi = {10.1109/TNSRE.2020.2979684},
pmid = {32149649},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Auditory ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {To test the feasibility of implementing multisensory (auditory and visual) stimulation in combination with electrodes placed on non-hair positions to design more efficient and comfortable Brain-computer interfaces (BCI). Fifteen volunteers participated in the experiments. They were stimulated by visual, auditory and multisensory stimuli set at 37, 38, 39 and 40Hz and at different phases (0°, 90°, 180° and 270°). The electroencephalogram (EEG) was measured from Oz, T7, T8, Tp9 and Tp10 positions. To evaluate the amplitude of the visual and auditory evoked potentials, the signal-to-noise ratio (SNR) was used and the accuracy of detection was calculated using canonical correlation analysis. Additionally, the volunteers were asked about the discomfort of each kind of stimulus. The multisensory stimulation allows for attaining higher SNR on every electrode. Non-hair (Tp9 and Tp10) positions attained SNR and accuracy similar to the ones obtained from occipital positions on visual stimulation. No significant difference was found on the discomfort produced by each kind of stimulation. The results demonstrated that multisensory stimulation can help in obtaining high amplitude steady-state evoked responses with a similar discomfort level. Then, it is possible to design a more efficient and comfortable hybrid-BCI based on multisensory stimulation and electrodes on non-hair positions. The current article proposes a new paradigm for hybrid-BCI based on steady-state evoked potentials measured from the area behind-the-ears and elicited by multisensory stimulation, thus, allowing subjects to achieve similar performance to the one achieved by visual-occipital BCI, but measuring the EEG on a more comfortable electrode location.},
}
@article {pmid32149648,
year = {2020},
author = {Jiang, A and Shang, J and Liu, X and Tang, Y and Kwan, HK and Zhu, Y},
title = {Efficient CSP Algorithm With Spatio-Temporal Filtering for Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {4},
pages = {1006-1016},
doi = {10.1109/TNSRE.2020.2979464},
pmid = {32149648},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Sample Size ; Signal Processing, Computer-Assisted ; },
abstract = {Common spatial pattern (CSP) is an efficient algorithm widely used in feature extraction of EEG-based motor imagery classification. Traditional CSP depends only on spatial filtering, that aims to maximize or minimize the ratio of variances of filtered EEG signals in different classes. Recent advances of CSP approaches show that temporal filtering is also preferable to extract discriminative features. In view of this perspective, a novel spatio-temporal filtering strategy is proposed in this paper. To improve computational efficiency and alleviate the overfitting issue frequently encountered in the case of small sample size, the same temporal filter is designed by EEG signals of the same class and shared by all the spatial channels. Spatial and temporal filters can be updated alternatively in practice. Furthermore, each of the resulting designs can still be cast as a CSP problem and tackled efficiently by the eigenvalue decomposition. To alleviate the adverse effects of outliers or noisy EEG channels, sparse spatial or temporal filters can also be achieved by incorporating an l1 -norm-based regularization term in our CSP problem. The regularized spatial or temporal filter design is iteratively reformulated as a CSP problem via the reweighting technique. Two sets of motor imagery EEG data of BCI competitions are used in our experiments to verify the effectiveness of the proposed algorithm.},
}
@article {pmid32149645,
year = {2020},
author = {Bigirimana, AD and Siddique, N and Coyle, D},
title = {Emotion-Inducing Imagery Versus Motor Imagery for a Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {4},
pages = {850-859},
doi = {10.1109/TNSRE.2020.2978951},
pmid = {32149645},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Emotions ; Humans ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Neural correlates of intentionally induced human emotions may offer alternative imagery strategies to control brain-computer interface (BCI) applications. In this paper, a novel BCI control strategy i.e., imagining fictional or recalling mnemonic sad and happy events, emotion-inducing imagery (EII), is compared to motor imagery (MI) in a study involving multiple sessions using a two-class electroencephalogram (EEG)-based BCI paradigm with 12 participants. The BCI setup enabled online continuous visual feedback presentation in a game involving one-dimensional control of a game character. MI and EII are compared across different signal-processing frameworks which are based on neural-time-series-prediction-preprocessing (NTSPP), filter bank common spatial patterns (FBCSP) and hemispheric asymmetry (ASYM). Online single-trial classification accuracies (CA) results indicate that MI performance across all participants is 77.54% compared to EII performance of 68.78% (). The results show that an ensemble of the NTSPP, FBCSP and ASYM frameworks maximizes performance for EII with average CA of 71.64% across all participants. Furthermore, the participants' subjective responses indicate that they preferred MI over emotion-inducing imagery (EII) in controlling the game character, and MI was perceived to offer most control over the game character. The results suggest that EII is not a viable alternative to MI for the majority of participants in this study but may be an alternative imagery for a subset of BCI users based on acceptable EII performance (CA >70%) observed for some participants.},
}
@article {pmid32149621,
year = {2020},
author = {Xu, M and Han, J and Wang, Y and Jung, TP and Ming, D},
title = {Implementing Over 100 Command Codes for a High-Speed Hybrid Brain-Computer Interface Using Concurrent P300 and SSVEP Features.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {11},
pages = {3073-3082},
doi = {10.1109/TBME.2020.2975614},
pmid = {32149621},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Seizures ; },
abstract = {OBJECTIVE: Recently, electroencephalography (EEG)- based brain-computer interfaces (BCIs) have made tremendous progress in increasing communication speed. However, current BCI systems could only implement a small number of command codes, which hampers their applicability.<br><br>METHODS: This study developed a high-speed hybrid BCI system containing as many as 108 instructions, which were encoded by concurrent P300 and steady-state visual evoked potential (SSVEP) features and decoded by an ensemble task-related component analysis method. Notably, besides the frequency-phase-modulated SSVEP and time-modulated P300 features as contained in the traditional hybrid P300 and SSVEP features, this study found two new distinct EEG features for the concurrent P300 and SSVEP features, i.e., time-modulated SSVEP and frequency-phase- modulated P300. Ten subjects spelled in both offline and online cued-guided spelling experiments. Other ten subjects took part in online copy-spelling experiments.<br><br>RESULTS: Offline analyses demonstrate that the concurrent P300 and SSVEP features can provide adequate classification information to correctly select the target from 108 characters in 1.7 seconds. Online cued-guided spelling and copy-spelling tests further show that the proposed BCI system can reach an average information transfer rate (ITR) of 172.46&#xa0;&#xb1; 32.91 bits/min and 164.69&#xa0;&#xb1; 33.32 bits/min respectively, with a peak value of 238.41 bits/min (The demo video of online copy-spelling can be found at https://www.youtube.com/watch?v=EW2Q08oHSBo).<br><br>CONCLUSION: We expand a BCI instruction set to over 100 command codes with high-speed in an efficient manner, which significantly improves the degree of freedom of BCIs.<br><br>SIGNIFICANCE: This study hold promise for broadening the applications of BCI systems.},
}
@article {pmid32148471,
year = {2020},
author = {Chen, Z and Jin, J and Daly, I and Zuo, C and Wang, X and Cichocki, A},
title = {Effects of Visual Attention on Tactile P300 BCI.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {6549189},
pmid = {32148471},
issn = {1687-5273},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation ; Physical Stimulation ; Touch/physiology ; Vibration ; Visual Perception/physiology ; Young Adult ; },
abstract = {Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant's left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p < 0.05). Furthermore, participants reported that visually attending to the stimulus made it easier to identify the target stimulus in random sequences of vibration stimuli. Significance. These findings suggest that visual attention has positive effects on both tactile P300 BCI performance and user-evaluation.},
}
@article {pmid32143794,
year = {2020},
author = {K, V and A, D and J, M and M, S and A, A and Iraj, SA},
title = {A novel method of motor imagery classification using eeg signal.},
journal = {Artificial intelligence in medicine},
volume = {103},
number = {},
pages = {101787},
doi = {10.1016/j.artmed.2019.101787},
pmid = {32143794},
issn = {1873-2860},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination ; *Machine Learning ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {A subject of extensive research interest in the Brain Computer Interfaces (BCIs) niche is motor imagery (MI), where users imagine limb movements to control the system. This interest is owed to the immense potential for its applicability in gaming, neuro-prosthetics and neuro-rehabilitation, where the user's thoughts of imagined movements need to be decoded. Electroencephalography (EEG) equipment is commonly used for keeping track of cerebrum movement in BCI systems. The EEG signals are recognized by feature extraction and classification. The current research proposes a Hybrid-KELM (Kernel Extreme Learning Machine) method based on PCA (Principal Component Analysis) and FLD (Fisher's Linear Discriminant) for MI BCI classification of EEG data. The performance and results of the method are demonstrated using BCI competition dataset III, and compared with those of contemporary methods. The proposed method generated an accuracy of 96.54%.},
}
@article {pmid32142909,
year = {2020},
author = {Gong, M and Xu, G and Li, M and Lin, F},
title = {An idle state-detecting method based on transient visual evoked potentials for an asynchronous ERP-based BCI.},
journal = {Journal of neuroscience methods},
volume = {337},
number = {},
pages = {108670},
doi = {10.1016/j.jneumeth.2020.108670},
pmid = {32142909},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials ; *Evoked Potentials, Visual ; Humans ; },
abstract = {BACKGROUND: An asynchronous brain-computer interface (BCI) allows subject to freely switch between the working state and the idle state, improving the subject's comfort. However, using only the event-related potential (ERP) to detect these two states is difficult because of the small amplitude of the ERP.<br><br>METHOD: Our previous study finds that an odd-ball paradigm could evoke transient visual evoked potentials (TSVEPs) simultaneously with ERPs. This study adopts the TSVEP and the ERP to detect the idle state in the design of an asynchronous TSVEP-ERP-based BCI (T-E BCI). The T-E BCI extracts time and frequency features from brain signals and uses a novel probability-based fisher linear discriminant analysis (P-FLDA) to combine the classification results of the ERP and the TSVEP.<br><br>RESULT: Ten subjects perform visual speller and video watching experiments, and their brain signals are measured under the working and idle states. The main results show that the T-E BCI achieves a higher accuracy than the ERP-based BCI when judging the subject's intentions and the two states. The P-FLDA performs better than the FLDA in combining the classification results.<br><br>CONCLUSIONS: The study demonstrates that adding the TSVEP can substantially reduce the number of wrongly detected trials. The T-E BCI provides a new way of designing an asynchronous BCI without adding any additional visual stimuli, which makes the BCI more practical.},
}
@article {pmid32140722,
year = {2020},
author = {Jorge, A and Royston, DA and Tyler-Kabara, EC and Boninger, ML and Collinger, JL},
title = {Classification of Individual Finger Movements Using Intracortical Recordings in Human Motor Cortex.},
journal = {Neurosurgery},
volume = {87},
number = {4},
pages = {630-638},
doi = {10.1093/neuros/nyaa026},
pmid = {32140722},
issn = {1524-4040},
mesh = {Adult ; Brain-Computer Interfaces/*classification ; Cervical Vertebrae/diagnostic imaging/injuries ; Electrodes, Implanted ; Fingers/*physiology ; Humans ; *Intention ; Male ; Microelectrodes ; Motor Cortex/diagnostic imaging/*physiology ; Movement/*physiology ; Range of Motion, Articular/physiology ; Spinal Cord Injuries/diagnostic imaging/*physiopathology/psychology ; },
abstract = {BACKGROUND: Intracortical microelectrode arrays have enabled people with tetraplegia to use a brain-computer interface for reaching and grasping. In order to restore dexterous movements, it will be necessary to control individual fingers.<br><br>OBJECTIVE: To predict which finger a participant with hand paralysis was attempting to move using intracortical data recorded from the motor cortex.<br><br>METHODS: A 31-yr-old man with a C5/6 ASIA B spinal cord injury was implanted with 2 88-channel microelectrode arrays in left motor cortex. Across 3 d, the participant observed a virtual hand flex in each finger while neural firing rates were recorded. A 6-class linear discriminant analysis (LDA) classifier, with 10&#xa0;&#xd7;&#xa0;10-fold cross-validation, was used to predict which finger movement was being performed (flexion/extension of all 5 digits and adduction/abduction of the thumb).<br><br>RESULTS: The mean overall classification accuracy was 67% (range: 65%-76%, chance: 17%), which occurred at an average of 560 ms (range: 420-780 ms) after movement onset. Individually, thumb flexion and thumb adduction were classified with the highest accuracies at 92% and 93%, respectively. The index, middle, ring, and little achieved an accuracy of 65%, 59%, 43%, and 56%, respectively, and, when incorrectly classified, were typically marked as an adjacent finger. The classification accuracies were reflected in a low-dimensional projection of the neural data into LDA space, where the thumb-related movements were most separable from the finger movements.<br><br>CONCLUSION: Classification of intention to move individual fingers was accurately predicted by intracortical recordings from a human participant with the thumb being particularly independent.},
}
@article {pmid32139789,
year = {2020},
author = {Sivan, J and Hadad, S and Tesler, I and Rosenstrauch, A and Allan Degen, A and Kam, M},
title = {Relative tail length correlates with body condition in male but not in female crowned leafnose snakes (Lytorhynchus diadema).},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {4130},
pmid = {32139789},
issn = {2045-2322},
mesh = {Animals ; Body Size/physiology ; Colubridae/*anatomy &amp; histology/*physiology ; Female ; Male ; Reproduction/*physiology ; Sex Characteristics ; Tail/anatomy &amp; histology/physiology ; },
abstract = {Reproductive success is the ultimate measure of individual quality; however, it is difficult to determine in free-living animals. Therefore, indirect measures that are related to reproduction are generally employed. In snakes, males typically possess longer tails than females and this sexual size dimorphism in tail length (TL) has generally been attributed to the importance of the tail in mating and reproduction. Thus, intra-sexual differences in tail length, specifically within males, were hypothesized to reflect individual quality. We used a body condition index (BCI) as a measure of quality in snakes and predicted that tail length would be correlated with BCI in males. We tested our prediction by determining BCI in the free-ranging adult male and female crowned leafnose snake (Lytorhynchus diadema), a colubrid species that inhabits mainly desert sand dunes. The relative TL was correlated positively and significantly to BCI in males (F1,131 = 11.05; r[2]adj = 0.07; P < 0.01) but not in females, thus supporting our prediction. This is the first time that the relationship between TL and body condition was tested in a free-ranging species. In addition, sexual size dimorphism of TL increased intra-specifically with body size, which was also found in interspecific analyses following Rensch's rule.},
}
@article {pmid32132918,
year = {2020},
author = {Zafar, A and Hong, KS},
title = {Reduction of Onset Delay in Functional Near-Infrared Spectroscopy: Prediction of HbO/HbR Signals.},
journal = {Frontiers in neurorobotics},
volume = {14},
number = {},
pages = {10},
pmid = {32132918},
issn = {1662-5218},
abstract = {An intrinsic problem when using hemodynamic responses for the brain-machine interface is the slow nature of the physiological process. In this paper, a novel method that estimates the oxyhemoglobin changes caused by neuronal activations is proposed and validated. In monitoring the time responses of blood-oxygen-level-dependent signals with functional near-infrared spectroscopy (fNIRS), the early trajectories of both oxy- and deoxy-hemoglobins in their phase space are scrutinized. Furthermore, to reduce the detection time, a prediction method based upon a kernel-based recursive least squares (KRLS) algorithm is implemented. In validating the proposed approach, the fNIRS signals of finger tapping tasks measured from the left motor cortex are examined. The results show that the KRLS algorithm using the Gaussian kernel yields the best fitting for both ΔHbO (i.e., 87.5%) and ΔHbR (i.e., 85.2%) at q = 15 steps ahead (i.e., 1.63 s ahead at a sampling frequency of 9.19 Hz). This concludes that a neuronal activation can be concluded in about 0.1 s with fNIRS using prediction, which enables an almost real-time practice if combined with EEG.},
}
@article {pmid32132910,
year = {2020},
author = {Lioi, G and Butet, S and Fleury, M and Bannier, E and Lécuyer, A and Bonan, I and Barillot, C},
title = {A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {37},
pmid = {32132910},
issn = {1662-5161},
abstract = {Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients' population and enabled to identify inclusion criteria for the design of future clinical studies.},
}
@article {pmid32132909,
year = {2020},
author = {von Lühmann, A and Ortega-Martinez, A and Boas, DA and Yücel, MA},
title = {Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {30},
pmid = {32132909},
issn = {1662-5161},
support = {R24 NS104096/NS/NINDS NIH HHS/United States ; U01 EB029856/EB/NIBIB NIH HHS/United States ; },
abstract = {Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.},
}
@article {pmid32132894,
year = {2020},
author = {Abdalmalak, A and Milej, D and Yip, LCM and Khan, AR and Diop, M and Owen, AM and St Lawrence, K},
title = {Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {105},
pmid = {32132894},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.},
}
@article {pmid32131064,
year = {2021},
author = {Kramer, DR and Lee, MB and Barbaro, MF and Gogia, AS and Peng, T and Liu, CY and Kellis, S and Lee, B},
title = {Mapping of primary somatosensory cortex of the hand area using a high-density electrocorticography grid for closed-loop brain computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
pmid = {32131064},
issn = {1741-2552},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; Electrocorticography/methods ; Electrodes, Implanted ; Hand ; Humans ; Somatosensory Cortex/physiology ; },
abstract = {Objective.The ideal modality for generating sensation in sensorimotor brain computer interfaces (BCI) has not been determined. Here we report the feasibility of using a high-density 'mini'-electrocorticography (mECoG) grid in a somatosensory BCI system.Approach.Thirteen subjects with intractable epilepsy underwent standard clinical implantation of subdural electrodes for the purpose of seizure localization. An additional high-density mECoG grid was placed (Adtech, 8 by 8, 1.2 mm exposed, 3 mm center-to-center spacing) over the hand area of primary somatosensory cortex. Following implantation, cortical mapping was performed with stimulation parameters of frequency: 50 Hz, pulse-width: 250µs, pulse duration: 4 s, polarity: alternating, and current that ranged from 0.5 mA to 12 mA at the discretion of the epileptologist. Location of the evoked sensory percepts was recorded along with a description of the sensation. The hand was partitioned into 48 distinct boxes. A box was included if sensation was felt anywhere within the box.Main results.The percentage of the hand covered was 63.9% (± 34.4%) (mean ± s.d.). Mean redundancy, measured as electrode pairs stimulating the same box, was 1.9 (± 2.2) electrodes per box; and mean resolution, measured as boxes included per electrode pair stimulation, was 11.4 (± 13.7) boxes with 8.1 (± 10.7) boxes in the digits and 3.4 (± 6.0) boxes in the palm. Functional utility of the system was assessed by quantifying usable percepts. Under the strictest classification, 'dermatomally exclusive' percepts, the mean was 2.8 usable percepts per grid. Allowing 'perceptually unique' percepts at the same anatomical location, the mean was 5.5 usable percepts per grid.Significance.Compared to the small area of coverage and redundancy of a microelectrode system, or the poor resolution of a standard ECoG grid, a mECoG is likely the best modality for a somatosensory BCI system with good coverage of the hand and minimal redundancy.},
}
@article {pmid32131059,
year = {2020},
author = {Geravanchizadeh, M and Bakhshalipour Gavgani, S},
title = {Selective auditory attention detection based on effective connectivity by single-trial EEG.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026021},
doi = {10.1088/1741-2552/ab7c8d},
pmid = {32131059},
issn = {1741-2552},
mesh = {Attention ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Speech ; },
abstract = {OBJECTIVE: Focusing attention on one speaker in an environment with lots of speakers is one of the important abilities of the human auditory system. The temporal dynamics of the attention process and how the brain precisely performs this task are yet unknown. This paper proposes a new method for the selective auditory attention detection (SAAD) from single-trial EEG signals using the brain effective connectivity and complex network analysis for two groups of listeners attending to the left or right ear.<br><br>APPROACH: Here, the connectivity matrices of all subjects obtained from the Granger causality method are used to extract different features. Then, by employing the processes of feature selection and optimization, an optimized feature set is determined for the train of a classifier.<br><br>MAIN RESULTS: Among different measures of brain connectivity (i.e. segregation, integration, and centrality), the evaluation results show that the optimized feature set obtained by the combination of the centrality measures contain the most discriminative features for the classification process. The proposed SAAD method as compared with state-of-the-art attention detection approaches from the literature yields the best performance in terms of various measures.<br><br>SIGNIFICANCE: The new SAAD approach is advantageous, in the sense that the detection of attention is performed from single-trial EEG signals of each subject, without reconstructing the speech stimuli. This means that the proposed method could be employed for real-time applications such as smart hearing aid devices or brain-computer interface (BCI) systems.},
}
@article {pmid32129278,
year = {2020},
author = {Lozupone, E and Distefano, M and Calandrelli, R and Marca, GD and Pedicelli, A and Pilato, F},
title = {Reversible Cerebral Vasoconstriction Syndrome: A Severe Neurological Complication in Postpartum Period.},
journal = {Neurology India},
volume = {68},
number = {1},
pages = {192-198},
doi = {10.4103/0028-3886.279674},
pmid = {32129278},
issn = {1998-4022},
mesh = {Adult ; Brain/blood supply ; Cerebral Angiography/methods ; Cerebrovascular Disorders/complications/diagnosis ; Female ; Headache/complications ; Humans ; Magnetic Resonance Angiography ; Posterior Leukoencephalopathy Syndrome/*complications/diagnosis ; *Postpartum Period ; Subarachnoid Hemorrhage/*complications/diagnosis/etiology ; Vasospasm, Intracranial/*complications/diagnosis ; },
abstract = {A 38-year-old woman 12 days after delivery of her second pregnancy was admitted to emergency room for a severe occipital headache started 3 days before, associated with confusion, nausea, vomiting and walking impairment. Neurological examination showed left hemiparesis, hypoesthesia in left arm and leg. Brain computer tomography images showed a large intraparenchymal hematoma in the right frontoparietal lobes with mass effect on adjacent subarachnoid spaces and on lateral ventricle. The third day during hospitalization, the patient experienced a sudden worsening of the neurological symptoms and a severe headache peaking within 1 minute (min); a new brain computed tomography and brain magnetic revealed another small intraparenchymal hematoma in the left parietal lobe with increase of the amount of subarachnoid hemorrhage. Digital subtraction angiography discloses multifocal narrowing of the middle and small arteries in both anterior and posterior circulation with a relative spare of large vessels. Postpartum reversible cerebral vasoconstriction syndrome with intracranial hemorrhage is a rare clinical condition that can be misdiagnosed with other dramatic neurological diseases such as eclamptic encephalopathy, cortical venous thrombosis, primary angiitis of the central nervous system or posterior reversible encephalopathy syndrome with whom may share predisposing factors and neurological presentation but clinical course, treatment and prognosis is quite different and emergency physicians and neurologists should consider this diagnosis in postpartum patients with hemorrhage.},
}
@article {pmid32126540,
year = {2021},
author = {Bigelow, J and Malone, BJ},
title = {Extracellular voltage thresholds for maximizing information extraction in primate auditory cortex: implications for a brain computer interface.},
journal = {Journal of neural engineering},
volume = {18},
number = {3},
pages = {},
doi = {10.1088/1741-2552/ab7c19},
pmid = {32126540},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Action Potentials/physiology ; Animals ; *Auditory Cortex/physiology ; *Brain-Computer Interfaces ; Information Storage and Retrieval ; Primates ; },
abstract = {Objective. Research by Oby (2016J. Neural. Eng.13036009) demonstrated that the optimal threshold for extracting information from visual and motor cortices may differ from the optimal threshold for identifying single neurons via spike sorting methods. The optimal threshold for extracting information from auditory cortex has yet to be identified, nor has the optimal temporal scale for representing auditory cortical activity. Here, we describe a procedure to jointly optimize the extracellular threshold and bin size with respect to the decoding accuracy achieved by a linear classifier for a diverse set of auditory stimuli.Approach. We used linear multichannel arrays to record extracellular neural activity from the auditory cortex of awake squirrel monkeys passively listening to both simple and complex sounds. We executed a grid search of the coordinate space defined by the voltage threshold (in units of standard deviation) and the bin size (in units of milliseconds), and computed decoding accuracy at each point.Main results. The optimal threshold for information extraction was consistently near two standard deviations below the voltage trace mean, which falls significantly below the range of three to five standard deviations typically used as inputs to spike sorting algorithms in basic research and in brain-computer interface (BCI) applications. The optimal binwidth was minimized at the optimal voltage threshold, particularly for acoustic stimuli dominated by temporally dynamic features, indicating that permissive thresholding permits readout of cortical responses with temporal precision on the order of a few milliseconds.Significance. The improvements in decoding accuracy we observed for optimal readout parameters suggest that standard thresholding methods substantially underestimate the information present in auditory cortical spiking patterns. The fact that optimal thresholds were relatively low indicates that local populations of cortical neurons exhibit high temporal coherence that could be leveraged in service of future auditory BCI applications.},
}
@article {pmid32126528,
year = {2020},
author = {Nan, W and Yang, L and Wan, F and Zhu, F and Hu, Y},
title = {Alpha down-regulation neurofeedback training effects on implicit motor learning and consolidation.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026014},
doi = {10.1088/1741-2552/ab7c1b},
pmid = {32126528},
issn = {1741-2552},
mesh = {Brain ; Down-Regulation ; Female ; Hand ; Humans ; Male ; *Neurofeedback ; Single-Blind Method ; },
abstract = {OBJECTIVE: Implicit motor learning, which is a non-conscious form of learning characterized by motor performance improvement with practice, plays an essential role in various daily activities. Earlier study using neurofeedback training (NFT), a type of brain-computer interaction that enables the user to learn self-regulating his/her own brain activity, demonstrated that down-regulating alpha over primary motor cortex by NFT could immediately facilitate the implicit motor learning in a relatively simple motor task. However, detailed effects on EEG and implicit motor learning due to NFT especially in a more complex motor task are still unclear.<br><br>APPROACH: We designed a single-blind sham-controlled between-subject study to examine whether alpha down-regulation NFT could facilitate implicit motor learning and also its consolidation in a more difficult and motor predominant task. At left primary motor cortex (C3) in two days, the alpha NFT group received alpha down-regulation training through auditory feedback while the sham-control group received random beta NFT. At the end of NFT, all participants performed the continuous tracking task with their dominant (right) hand to evaluate the implicit motor learning immediately. Finally, the continuous tracking task was performed again on the next day to assess consolidation effects.<br><br>MAIN RESULTS: The alpha NFT group successfully decreased alpha amplitude during NFT, whereas the sham-control group maintained alpha at a relatively stable level. There was unfortunately no statistical evidence proving that the alpha NFT group significantly enhanced the implicit motor learning at the end of NFT and the consolidation on the next day compared to the sham-control group. Nevertheless, a significant correlation was found between the alpha change trend during NFT and the implicit motor learning for all participants, suggesting that faster alpha down-regulation was associated with better implicit motor learning.<br><br>SIGNIFICANCE: The findings suggested a close link between implicit motor learning and alpha change induced by NFT.},
}
@article {pmid32121648,
year = {2020},
author = {Dutta, A},
title = {Brain-Computer Interface Spellers forCommunication: Why We Need to Address Their Security and Authenticity.},
journal = {Brain sciences},
volume = {10},
number = {3},
pages = {},
pmid = {32121648},
issn = {2076-3425},
abstract = {Brain-Computer Interfaces (BCI) have witnessed significant research and development in the last 20 years where the main aim was to improve their accuracy and increase their information transfer rates (ITRs), while still making them portable and easy to use by a broad range of users [...].},
}
@article {pmid32116625,
year = {2020},
author = {LaRocco, J and Paeng, DG},
title = {Optimizing Computer-Brain Interface Parameters for Non-invasive Brain-to-Brain Interface.},
journal = {Frontiers in neuroinformatics},
volume = {14},
number = {},
pages = {1},
pmid = {32116625},
issn = {1662-5196},
abstract = {A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain-computer interface (BCI) and the computer-brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI's system latency and timeout threshold should be reflected in the BCI's update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.},
}
@article {pmid32116602,
year = {2020},
author = {Jochumsen, M and Knoche, H and Kidmose, P and Kjær, TW and Dinesen, BI},
title = {Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives.},
journal = {Frontiers in human neuroscience},
volume = {14},
number = {},
pages = {13},
pmid = {32116602},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) have successfully been used for motor recovery training in stroke patients. However, the setup of BCI systems is complex and may be divided into (1) mounting the headset and (2) calibration of the BCI. One of the major problems is mounting the headset for recording brain activity in a stroke rehabilitation context, and usability testing of this is limited. In this study, the aim was to compare the translational aspects of mounting five different commercially available headsets from a user perspective and investigate the design considerations associated with technology transfer to rehabilitation clinics and home use. No EEG signals were recorded, so the effectiveness of the systems have not been evaluated. Three out of five headsets covered the motor cortex which is needed to pick up movement intentions of attempted movements. The other two were as control and reference for potential design considerations. As primary stakeholders, nine stroke patients, eight therapists and two relatives participated; the stroke patients mounted the headsets themselves. The setup time was recorded, and participants filled in questionnaires related to comfort, aesthetics, setup complexity, overall satisfaction, and general design considerations. The patients had difficulties in mounting all headsets except for a headband with a dry electrode located on the forehead (control). The therapists and relatives were able to mount all headsets. The fastest headset to mount was the headband, and the most preferred headsets were the headband and a behind-ear headset (control). The most preferred headset that covered the motor cortex used water-based electrodes. The patients reported that it was important that they could mount the headset themselves for them to use it every day at home. These results have implications for design considerations for the development of BCI systems to be used in rehabilitation clinics and in the patient's home.},
}
@article {pmid32116533,
year = {2020},
author = {Jiang, T and Pellizzer, G and Asman, P and Bastos, D and Bhavsar, S and Tummala, S and Prabhu, S and Ince, NF},
title = {Power Modulations of ECoG Alpha/Beta and Gamma Bands Correlate With Time-Derivative of Force During Hand Grasp.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {100},
pmid = {32116533},
issn = {1662-4548},
abstract = {It is well-known that motor cortical oscillatory components are modulated in their amplitude during voluntary and imagined movements. These patterns have been used to develop brain-machine interfaces (BMI) which focused mostly on movement kinematics. In contrast, there have been only a few studies on the relation between brain oscillatory activity and the control of force, in particular, grasping force, which is of primary importance for common daily activities. In this study, we recorded intraoperative high-density electrocorticography (ECoG) from the sensorimotor cortex of four patients while they executed a voluntary isometric hand grasp following verbal instruction. The grasp was held for 2 to 3 s before being instructed to relax. We studied the power modulations of neural oscillations during the whole time-course of the grasp (onset, hold, and offset phases). Phasic event-related desynchronization (ERD) in the low-frequency band (LFB) from 8 to 32 Hz and event-related synchronization (ERS) in the high-frequency band (HFB) from 60 to 200 Hz were observed at grasp onset and offset. However, during the grasp holding period, the magnitude of LFB-ERD and HFB-ERS decreased near or at the baseline level. Overall, LFB-ERD and HFB-ERS show phasic characteristics related to the changes of grasp force (onset/offset) in all four patients. More precisely, the fluctuations of HFB-ERS primarily, and of LFB-ERD to a lesser extent, correlated with the time-course of the first time-derivative of force (yank), rather than with force itself. To the best of our knowledge, this is the first study that establishes such a correlation. These results have fundamental implications for the decoding of grasp in brain oscillatory activity-based neuroprosthetics.},
}
@article {pmid32116513,
year = {2020},
author = {Wirth, C and Toth, J and Arvaneh, M},
title = {"You Have Reached Your Destination": A Single Trial EEG Classification Study.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {66},
pmid = {32116513},
issn = {1662-4548},
abstract = {Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.},
}
@article {pmid32116497,
year = {2020},
author = {Unterweger, J and Seeber, M and Zanos, S and Ojemann, JG and Scherer, R},
title = {ECoG Beta Suppression and Modulation During Finger Extension and Flexion.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {35},
pmid = {32116497},
issn = {1662-4548},
abstract = {Neural oscillations originate predominantly from interacting cortical neurons and consequently reflect aspects of cortical information processing. However, their functional role is not yet fully understood and their interpretation is debatable. Amplitude modulations (AMs) in alpha (8-12 Hz), beta (13-30 Hz), and high gamma (70-150 Hz) band in invasive electrocorticogram (ECoG) and non-invasive electroencephalogram (EEG) signals change with behavior. Alpha and beta band AMs are typically suppressed (desynchronized) during motor behavior, while high gamma AMs highly correlate with the behavior. These two phenomena are successfully used for functional brain mapping and brain-computer interface (BCI) applications. Recent research found movement-phase related AMs (MPA) also in high beta/low gamma (24-40 Hz) EEG rhythms. These MPAs were found by separating the suppressed AMs into sustained and dynamic components. Sustained AM components are those with frequencies that are lower than the motor behavior. Dynamic components those with frequencies higher than the behavior. In this paper, we study ECoG beta/low gamma band (12-30 Hz/30-42 Hz) AM during repetitive finger movements addressing the question whether or not MPAs can be found in ECoG beta band. Indeed, MPA in the 12-18 Hz and 18-24 Hz band were found. This additional information may lead to further improvements in ECoG-based prediction and reconstruction of motor behavior by combining high gamma AM and beta band MPA.},
}
@article {pmid32116495,
year = {2020},
author = {Opris, I and Lebedev, MA and Pulgar, VM and Vidu, R and Enachescu, M and Casanova, MF},
title = {Editorial: Nanotechnologies in Neuroscience and Neuroengineering.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {33},
doi = {10.3389/fnins.2020.00033},
pmid = {32116495},
issn = {1662-4548},
}
@article {pmid32116484,
year = {2019},
author = {Zhou, Y and Wen, D and Lu, H and Yao, W and Liu, Y and Qian, W and Yuan, J},
title = {The Current Research of Spatial Cognitive Evaluation and Training With Brain-Computer Interface and Virtual Reality.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1439},
pmid = {32116484},
issn = {1662-4548},
}
@article {pmid32116092,
year = {2020},
author = {Hou, H and Zhang, X and Meng, Q},
title = {Olfactory EEG Signal Classification Using a Trapezoid Difference-Based Electrode Sequence Hashing Approach.},
journal = {International journal of neural systems},
volume = {30},
number = {3},
pages = {2050011},
doi = {10.1142/S0129065720500112},
pmid = {32116092},
issn = {1793-6462},
mesh = {Adult ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; Olfactory Perception/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain-computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an N-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on N optimized power-spectral-density features extracted from N real electrodes and N nonreal electrode's features. Subsequently, the N real electrodes' sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen's kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.},
}
@article {pmid32116091,
year = {2020},
author = {Li, M and Lin, F and Xu, G},
title = {A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.},
journal = {International journal of neural systems},
volume = {30},
number = {3},
pages = {2050009},
doi = {10.1142/S0129065720500094},
pmid = {32116091},
issn = {1793-6462},
mesh = {Adult ; *Brain-Computer Interfaces/standards ; Cerebral Cortex/*physiology ; Electroencephalography/*methods/standards ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Humans ; Neurofeedback/*physiology ; *Support Vector Machine ; },
abstract = {Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.},
}
@article {pmid32113225,
year = {2020},
author = {Pitsik, E and Frolov, N and Hauke Kraemer, K and Grubov, V and Maksimenko, V and Kurths, J and Hramov, A},
title = {Motor execution reduces EEG signals complexity: Recurrence quantification analysis study.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {30},
number = {2},
pages = {023111},
doi = {10.1063/1.5136246},
pmid = {32113225},
issn = {1089-7682},
mesh = {Adolescent ; Adult ; *Electroencephalography ; Female ; Humans ; Male ; Motor Activity/*physiology ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain-computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of μ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of μ-band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.},
}
@article {pmid32112156,
year = {2020},
author = {Handa, N and Mochizuki, S and Fujiwara, Y and Shimokawa, M and Wakao, R and Arai, H},
title = {Future development of artificial organs related with cutting edge emerging technology and their regulatory assessment: PMDA's perspective.},
journal = {Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs},
volume = {23},
number = {3},
pages = {203-206},
doi = {10.1007/s10047-020-01161-4},
pmid = {32112156},
issn = {1619-0904},
mesh = {*Artificial Intelligence ; *Artificial Organs ; *Biotechnology ; Device Approval/*legislation &amp; jurisprudence ; Drug Approval/*legislation &amp; jurisprudence ; Humans ; Japan ; },
abstract = {Future development of innovative artificial organs is closely related with cutting edge emerging technology. These technologies include brain machine or computer interface, organs made by three dimensional bioprinting, organs designed from induced-pluripotent stem cell for personalized tissue or organ, and xenotransplantation. To bridge the gap between scientific innovation and regulatory product review, Pharmaceuticals and Medical Devices Agency of Japan (PMDA) started the science board to discuss about the new scientific topics regarding medical products including medical device and regenerative products with external experts since 2012. Topics which PMDA raised for science board included cellular and tissue-based products from iPS cells, artificial intelligence and genome editing technology. In addition, PMDA started the horizon scanning to identify a new cutting edge technology which could potentially lead to innovative health technology or product, which has a strong impact on clinical medicine. Although the effectiveness and safety of the medical products must be reasonably assured before clinical use, PMDA introduced Sakigake review assignment (a review partner of device development) and conditional approval system to balance between pre-market and post-market evaluation.},
}
@article {pmid32107734,
year = {2020},
author = {Foodeh, R and Ebadollahi, S and Daliri, MR},
title = {Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces.},
journal = {Neuroinformatics},
volume = {18},
number = {3},
pages = {465-477},
doi = {10.1007/s12021-020-09455-x},
pmid = {32107734},
issn = {1559-0089},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Electrocorticography ; Haplorhini ; Least-Squares Analysis ; Rats ; *Signal Processing, Computer-Assisted ; },
abstract = {Continuous decoding is a crucial step in many types of brain-computer interfaces (BCIs). Linear regression techniques have been widely used to determine a linear relation between the input and desired output. A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular method which tries to overcome this problem. PLS calculates a set of latent variables which are maximally correlated to the output and determines a linear relation between a low-rank estimation of the input and output data. However, this method has shown its potential to overfit the training data in many cases. In this paper, a regularized version of PLS (RPLS) is proposed which tries to determine a linear relation between the latent vector of the input and desired output using the regularized least square instead of the ordinary one. This approach is able to control the effect of non-efficient and non-generalized latent vectors in prediction. We have shown that the proposed method outperforms Ridge regression (RR), PLS, and PLS with regularized weights (PLSRW) in estimating the output in two different real BCI datasets, Neurotycho public electrocorticogram (ECoG) dataset for decoding trajectory of hand movements in monkeys and our own local field potential (LFP) dataset for decoding applied force performed by rats. Furthermore, the results indicate that RPLS is more robust against the increase in the number of latent vectors compared to PLS and PLSRW. Next, we evaluated the resistance of our proposed method against the presence of different noise levels in a BCI application and compared it to other techniques using a semi-simulated dataset. This approach revealed that RPLS offered a higher performance compared with other techniques in all levels of noise. Finally, to illustrate the usability of RPLS in other type of data, we presented the application of this method in predicting relative active substance content of pharmaceutical tablets using near-infrared (NIR) transmittance spectroscopy data. This application showed a superior performance of the proposed method compared to other decoding methods.},
}
@article {pmid32103424,
year = {2020},
author = {Yang, J and Liu, X and Shu, J and Hou, Y and Chen, M and Yu, H and Ma, T and Du, H and Zhang, J and Qiao, Y and He, J and Niu, L and Yang, F and Li, Z},
title = {Abnormal Galactosylated-Glycans recognized by Bandeiraea Simplicifolia Lectin I in saliva of patients with breast Cancer.},
journal = {Glycoconjugate journal},
volume = {37},
number = {3},
pages = {373-394},
doi = {10.1007/s10719-020-09910-6},
pmid = {32103424},
issn = {1573-4986},
support = {81372365//National Natural Science Foundation of China/ ; },
mesh = {Biomarkers, Tumor/metabolism ; *Breast Neoplasms/metabolism ; Female ; Humans ; *Plant Lectins ; *Polysaccharides/analysis ; *Saliva/chemistry ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ; },
abstract = {Currently, the definitive diagnosis in breast cancer requires biopsy and histopathology, such the most effective markers are tissue-based. However, the advantages of saliva in collection and storage make it possible for assessing human pathology and contributing to the development of cancer-related biomarkers for clinical application. The present study validated alteration of salivary protein glycopatterns recognized by Bandeiraea simplicifolia lectin I (BS-I) in the saliva of patients with breast diseases using saliva microarrays, and the N/O-glycan profiles of their salivary glycoproteins isolated by the BS-I-magnetic particle conjugates from 259 female subjects (66 healthy volunteers (HV), 65 benign breast cyst or tumor patients (BB), 66 patients with breast cancer in stage I (BC-I) and 62 patients with breast cancer in stage II (BC-II)) were analyzed by MALDI-TOF/TOF-MS. The results showed that the expression level of galactosylated glycans recognized by BS-I was significantly increased in patients with breast cancer compared with HV (p < 0.05). Totally, there were 11/10, 10/19, 7/24 and 7/9 galactosylated N-/O-linked glycans were identified and annotated from the pooled salivary samples of HV, BB, BC-I and BC-II, respectively. One galactosylated N-glycan peak (m/z 2773.977), and 4 galactosylated O-glycan peaks (m/z 868.295, 882.243, 884.270 and 1030.348) were found only in BC-I. These findings could provide pivotal information on galactosylated N/O-linked glycans related to breast cancer, and promote the study of biomarkers for early-stage breast cancer based on precise alterations of galactosylated N/O-glycans in saliva.},
}
@article {pmid32101735,
year = {2020},
author = {Peles, O and Werner-Reiss, U and Bergman, H and Israel, Z and Vaadia, E},
title = {Phase-Specific Microstimulation Differentially Modulates Beta Oscillations and Affects Behavior.},
journal = {Cell reports},
volume = {30},
number = {8},
pages = {2555-2566.e3},
doi = {10.1016/j.celrep.2020.02.005},
pmid = {32101735},
issn = {2211-1247},
mesh = {Action Potentials/physiology ; Animals ; Behavior, Animal/*physiology ; Beta Rhythm/*physiology ; Conditioning, Operant ; Electric Stimulation ; Female ; Macaca mulatta ; },
abstract = {It is widely accepted that Beta-band oscillations play a role in sensorimotor behavior. To further explore this role, we developed a hybrid platform to combine neural operant conditioning and phase-specific intracortical microstimulation (ICMS). We trained monkeys, implanted with 96 electrode arrays in the motor cortex, to volitionally enhance local field potential (LFP) Beta-band (20-30 Hz) activity at selected sites using a brain-machine interface. We find that Beta oscillations of LFP and single-unit spiking activity increase dramatically with brain-machine interface training and that pre-movement Beta power is anti-correlated with task performance. We also find that phase-specific ICMS modulates the power and phase of oscillations, shifting local networks between oscillatory and non-oscillatory states. Furthermore, ICMS induces phase-dependent effects in animal reaction times and success rates. These findings contribute to unraveling the functional role of cortical oscillations and to the future development of clinical tools for ameliorating abnormal neuronal activities in brain disease.},
}
@article {pmid32101603,
year = {2020},
author = {Pan, J and Xie, Q and Qin, P and Chen, Y and He, Y and Huang, H and Wang, F and Ni, X and Cichocki, A and Yu, R and Li, Y},
title = {Prognosis for patients with cognitive motor dissociation identified by brain-computer interface.},
journal = {Brain : a journal of neurology},
volume = {143},
number = {4},
pages = {1177-1189},
pmid = {32101603},
issn = {1460-2156},
mesh = {Adult ; *Brain-Computer Interfaces ; Consciousness Disorders/*diagnosis ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; },
abstract = {Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients' behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cognitive motor dissociation have a better outcome than other patients. Our findings extend current knowledge of the prognosis for patients with cognitive motor dissociation and have important implications for brain-computer interface-based clinical diagnosis and prognosis for patients with disorders of consciousness.},
}
@article {pmid32101491,
year = {2020},
author = {Issar, D and Williamson, RC and Khanna, SB and Smith, MA},
title = {A neural network for online spike classification that improves decoding accuracy.},
journal = {Journal of neurophysiology},
volume = {123},
number = {4},
pages = {1472-1485},
pmid = {32101491},
issn = {1522-1598},
support = {R01 EY022928/EY/NEI NIH HHS/United States ; T32 GM008208/GM/NIGMS NIH HHS/United States ; R01 MH118929/MH/NIMH NIH HHS/United States ; T32 EY017271/EY/NEI NIH HHS/United States ; P30 EY008098/EY/NEI NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Brain-Computer Interfaces ; *Electrocorticography/methods ; Macaca mulatta ; Male ; *Neural Networks, Computer ; *Pattern Recognition, Automated ; Prefrontal Cortex/*physiology ; Saccades ; Spatial Memory ; },
abstract = {Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the network's stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the network's labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared with decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our network's classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both off-line neural data analyses and online decoding.NEW &amp; NOTEWORTHY Although there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both off-line data processing and online decoding.},
}
@article {pmid32098971,
year = {2020},
author = {Serb, A and Corna, A and George, R and Khiat, A and Rocchi, F and Reato, M and Maschietto, M and Mayr, C and Indiveri, G and Vassanelli, S and Prodromakis, T},
title = {Memristive synapses connect brain and silicon spiking neurons.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {2590},
pmid = {32098971},
issn = {2045-2322},
mesh = {Action Potentials/*physiology ; Animals ; Electronics/methods ; Embryo, Mammalian ; Excitatory Postsynaptic Potentials/*physiology ; Hippocampus/cytology/physiology ; Long-Term Potentiation/*physiology ; Microelectrodes ; *Models, Neurological ; Nerve Net/cytology/physiology ; Neural Networks, Computer ; Neurons/cytology/*physiology ; Primary Cell Culture ; Rats ; Silicon/chemistry ; Synapses/*physiology ; Titanium/chemistry ; },
abstract = {Brain function relies on circuits of spiking neurons with synapses playing the key role of merging transmission with memory storage and processing. Electronics has made important advances to emulate neurons and synapses and brain-computer interfacing concepts that interlink brain and brain-inspired devices are beginning to materialise. We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses. A memristor paired with a metal-thin film titanium oxide microelectrode connects a silicon neuron to a neuron of the rat hippocampus. Memristive plasticity accounts for modulation of connection strength, while transmission is mediated by weighted stimuli through the thin film oxide leading to responses that resemble excitatory postsynaptic potentials. The reverse brain-to-silicon link is established through a microelectrode-memristor pair. On these bases, we demonstrate a three-neuron brain-silicon network where memristive synapses undergo long-term potentiation or depression driven by neuronal firing rates.},
}
@article {pmid32098317,
year = {2020},
author = {Juliano, JM and Spicer, RP and Vourvopoulos, A and Lefebvre, S and Jann, K and Ard, T and Santarnecchi, E and Krum, DM and Liew, SL},
title = {Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {4},
pages = {},
pmid = {32098317},
issn = {1424-8220},
support = {T32 MH111360/MH/NIMH NIH HHS/United States ; K01HD091283/NH/NIH HHS/United States ; 16IRG26960017//American Heart Association/ ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Virtual Reality ; },
abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs) for motor rehabilitation aim to "close the loop" between attempted motor commands and sensory feedback by providing supplemental information when individuals successfully achieve specific brain patterns. Existing EEG-based BCIs use various displays to provide feedback, ranging from displays considered more immersive (e.g., head-mounted display virtual reality (HMD-VR)) to displays considered less immersive (e.g., computer screens). However, it is not clear whether more immersive displays improve neurofeedback performance and whether there are individual performance differences in HMD-VR versus screen-based neurofeedback. In this pilot study, we compared neurofeedback performance in HMD-VR versus a computer screen in 12 healthy individuals and examined whether individual differences on two measures (i.e., presence, embodiment) were related to neurofeedback performance in either environment. We found that, while participants' performance on the BCI was similar between display conditions, the participants' reported levels of embodiment were significantly different. Specifically, participants experienced higher levels of embodiment in HMD-VR compared to a computer screen. We further found that reported levels of embodiment positively correlated with neurofeedback performance only in HMD-VR. Overall, these preliminary results suggest that embodiment may relate to better performance on EEG-based BCIs and that HMD-VR may increase embodiment compared to computer screens.},
}
@article {pmid32098285,
year = {2020},
author = {Xu, G and Wu, Y and Li, M},
title = {The Study of Influence of Sound on Visual ERP-Based Brain Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {4},
pages = {},
pmid = {32098285},
issn = {1424-8220},
support = {51737003//Guizhi Xu/ ; 61806070//Mengfan Li/ ; F2018202088//Mengfan Li/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Sound ; Young Adult ; },
abstract = {The performance of the event-related potential (ERP)-based brain-computer interface (BCI) declines when applying it into the real environment, which limits the generality of the BCI. The sound is a common noise in daily life, and whether it has influence on this decline is unknown. This study designs a visual-auditory BCI task that requires the subject to focus on the visual interface to output commands and simultaneously count number according to an auditory story. The story is played at three speeds to cause different workloads. Data collected under the same or different workloads are used to train and test classifiers. The results show that when the speed of playing the story increases, the amplitudes of P300 and N200 potentials decrease by 0.86 μV (p = 0.0239) and 0.69 μV (p = 0.0158) in occipital-parietal area, leading to a 5.95% decline (p = 0.0101) of accuracy and 9.53 bits/min decline (p = 0.0416) of information transfer rate. The classifier that is trained by the high workload data achieves higher accuracy than the one trained by the low workload if using the high workload data to test the performance. The result indicates that the sound could affect the visual ERP-BCI by increasing the workload. The large similarity of the training data and testing data is as important as the amplitudes of the ERP on obtaining high performance, which gives us an insight on how make to the ERP-BCI generalized.},
}
@article {pmid32096375,
year = {2020},
author = {Li, Y and Xie, S and Yu, Z and Xie, X and Duan, X and Liu, C},
title = {[Analysis of imagery motor effective networks based on dynamic partial directed coherence].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {37},
number = {1},
pages = {38-44},
pmid = {32096375},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping ; *Electroencephalography ; Humans ; *Imagination ; },
abstract = {The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time-frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 (P = 0.007) and ROI3 (P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.},
}
@article {pmid32092027,
year = {2021},
author = {Chakraborty, B and Ghosh, L and Konar, A},
title = {Optimal Selection of EEG Electrodes Using Interval Type-2 Fuzzy-Logic-Based Semiseparating Signaling Game.},
journal = {IEEE transactions on cybernetics},
volume = {51},
number = {12},
pages = {6200-6212},
doi = {10.1109/TCYB.2020.2968625},
pmid = {32092027},
issn = {2168-2275},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Fuzzy Logic ; Signal Processing, Computer-Assisted ; },
abstract = {This article addresses the noise contamination in spatial filtering of brain responses using a novel signaling game-based approach to the optimal selection of EEG electrodes. The proposed method takes the standard common spatial pattern (CSP) filter as an input and produces an optimal electrode set as output for effective classification of different cognitive tasks. The standard CSP algorithms are highly prone to the inclusion of noise in the EEG data and may select noisy electrodes/signal sources that are redundant for a specific cognitive task which, in turn, may lead to a lower classification accuracy. A lot of literature exists in this area of research, most of which deals with adding the regularization term in the standard CSP algorithm. However, all of these methods lack capturing the uncertainty present in the EEG responses due to intrasession and intersession variations of subjective brain response. The novelty of this article lies in designing the fuzzy signaling game-based approach for optimal electrode selection using an interval type-2 fuzzy set, which can capture both the intrasession and intersession variability of EEG responses acquired from a subject's scalp. Experiments are undertaken over a wide variety of possible cognitive task classification problems which reveal that the proposed method yields superior results in electrode selection with respect to classification accuracy. Statistical tests undertaken using the Friedman test also confirm the superiority of the proposed method over its competitors.},
}
@article {pmid32091986,
year = {2020},
author = {Wong, CM and Wang, B and Wang, Z and Lao, KF and Rosa, A and Wan, F},
title = {Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {11},
pages = {3057-3072},
doi = {10.1109/TBME.2020.2975552},
pmid = {32091986},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Principal Component Analysis ; },
abstract = {OBJECTIVE: In the steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs), spatial filtering, which combines the multi-channel electroencephalography (EEG) signals in order to reduce the non-SSVEP-related component and thus enhance the signal-to-noise ratio (SNR), plays an important role in target recognition. Recently, various spatial filtering algorithms have been developed employing different prior knowledge and characteristics of SSVEPs, however how these algorithms interconnect and differ is not yet fully explored, leading to difficulties in further understanding, utilizing and improving them.<br><br>METHODS: We propose a unified framework under which the spatial filtering algorithms can be formulated as generalized eigenvalue problems (GEPs) with four different elements: data, temporal filter, orthogonal projection and spatial filter. Based on the framework, we design new spatial filtering algorithms for improvements through the choice of different elements.<br><br>RESULTS: The similarities, differences and relationships among nineteen mainstream spatial filtering algorithms are revealed under the proposed framework. Particularly, it is found that they originate from the canonical correlation analysis (CCA), principal component analysis (PCA), and multi-set CCA, respectively. Furthermore, three new spatial filtering algorithms are developed with enhanced performance validated on two public SSVEP datasets with 45 subjects.<br><br>CONCLUSION: The proposed framework provides insights into the underlying relationships among different spatial filtering algorithms and helps the design of new spatial filtering algorithms.<br><br>SIGNIFICANCE: This is a systematic study to explore, compare and improve the existing spatial filtering algorithms, which would be significant for further understanding and future development of high performance SSVEP-based BCIs.},
}
@article {pmid32091984,
year = {2020},
author = {Luo, J and Firflionis, D and Turnbull, M and Xu, W and Walsh, D and Escobedo-Cousin, E and Soltan, A and Ramezani, R and Liu, Y and Bailey, R and ONeill, A and Idil, AS and Donaldson, N and Constandinou, T and Jackson, A and Degenaar, P},
title = {The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {11},
pages = {3004-3015},
pmid = {32091984},
issn = {1558-2531},
support = {/WT_/Wellcome Trust/United Kingdom ; 102037/WT_/Wellcome Trust/United Kingdom ; 102037/Z/13/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Algorithms ; Animals ; Brain/surgery ; *Brain-Computer Interfaces ; *Epilepsy ; Optogenetics ; },
abstract = {Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.},
}
@article {pmid32090868,
year = {2020},
author = {Golabchi, A and Woeppel, KM and Li, X and Lagenaur, CF and Cui, XT},
title = {Neuroadhesive protein coating improves the chronic performance of neuroelectronics in mouse brain.},
journal = {Biosensors &amp; bioelectronics},
volume = {155},
number = {},
pages = {112096},
pmid = {32090868},
issn = {1873-4235},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; U01 NS113279/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Axons ; Blood-Brain Barrier/metabolism ; Brain/*physiology ; *Cell Adhesion Molecules/chemistry ; Cell Survival ; *Coated Materials, Biocompatible ; Dielectric Spectroscopy ; Electrodes, Implanted ; Electronics/*methods/standards ; Electrophysiological Phenomena ; Immunohistochemistry ; Mice ; Microelectrodes ; Neurons/*physiology ; Permeability ; *Proteins/chemistry ; },
abstract = {Intracortical microelectrodes are being developed to both record and stimulate neurons to understand brain circuitry or restore lost functions. However, the success of these probes is hampered partly due to the inflammatory host tissue responses to implants. To minimize the foreign body reactions, L1, a brain derived neuronal specific cell adhesion molecule, has been covalently bound to the neural electrode array surface. Here we evaluated the chronic recording performance of L1-coated silicon based laminar neural electrode arrays implanted into V1m cortex of mice. The L1 coating enhanced the overall visually evoked single-unit (SU) yield and SU amplitude, as well as signal-to-noise-ratio (SNR) in the mouse brain compared to the uncoated arrays across the 0-1500 μm depth. The improvement in recording is most dramatic in the hippocampus region, where the control group showed severe recording yield decrease after one week, while the L1 implants maintained a high SU yield throughout the 16 weeks. Immunohistological analysis revealed significant increases of axonal and neuronal density along with significantly lowered microglia activation around the L1 probe after 16 weeks. These results collectively confirm the effectiveness of L1 based biomimetic coating on minimizing inflammatory tissue response and improving neural recording quality and longevity. Improving chronic recording will benefit the brain-computer interface technologies and neuroscience studies involving chronic tracking of neural activities.},
}
@article {pmid32089811,
year = {2020},
author = {Kim, H and Yoshimura, N and Koike, Y},
title = {Investigation of Delayed Response during Real-Time Cursor Control Using Electroencephalography.},
journal = {Journal of healthcare engineering},
volume = {2020},
number = {},
pages = {1418437},
pmid = {32089811},
issn = {2040-2309},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Male ; Task Performance and Analysis ; Time Factors ; Young Adult ; },
abstract = {Error-related brain activation has been investigated for advanced brain-machine interfaces (BMI). However, how a delayed response of cursor control in BMI systems should be handled is not clear. Therefore, the purpose of this study was to investigate how participants responded to delayed cursor control. Six subjects participated in the experiment and performed a wrist-bending task. For three distinct delay intervals (an interval where participants could not perceive the delay, an interval where participants could not be sure whether there was a delay or not, and an interval where participants could perceive the delay), we assessed two types of binary classifications ("Yes + No" vs. "I don't know" and "Yes" vs. "No") based on participants' responses and applied delay times (thus, four types of classification, overall). For most participants, the "Yes vs. No" classification had higher accuracy than "Yes + No" vs. "I don't know" classification. For the "Yes + No" vs. "I don't know" classification, most participants displayed higher accuracy based on response classification than delay classification. Our results demonstrate that a class only for "I don't know" largely contributed to these differences. Many independent components (ICs) that exhibited high accuracy in "Yes + No" vs. "I don't know" response classification were associated with activation of areas from the frontal to parietal lobes, while many ICs that showed high accuracy in the "Yes vs. No" classification were associated with activation of an area ranging from the parietal to the occipital lobes and were more broadly localized in cortical regions than was seen for the "Yes + No" vs. "I don't know" classification. Our results suggest that small and large delays in real-time cursor control differ not only in the magnitude of the delay but should be handled as distinct information in different ways and might involve differential processing in the brain.},
}
@article {pmid32089668,
year = {2020},
author = {Baek, HJ and Kim, HS and Ahn, M and Cho, H and Ahn, S},
title = {Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction.},
journal = {Computational intelligence and neuroscience},
volume = {2020},
number = {},
pages = {4876397},
doi = {10.1155/2020/4876397},
pmid = {32089668},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces/trends ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; },
}
@article {pmid32086764,
year = {2020},
author = {Li, C and Xu, J and Zhu, Y and Kuang, S and Qu, W and Sun, L},
title = {Detecting self-paced walking intention based on fNIRS technology for the development of BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {58},
number = {5},
pages = {933-941},
pmid = {32086764},
issn = {1741-0444},
support = {61673286//National Natural Science Foundation of China/ ; U1713218//National Natural Science Foundation of China/ ; 2017T100397//Postdoctoral Science Foundation of Jiangsu Province/ ; },
mesh = {*Brain-Computer Interfaces ; Decision Trees ; Female ; Frontal Lobe/blood supply/diagnostic imaging ; Hemoglobins/analysis ; Humans ; Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Walking/*physiology ; },
abstract = {Since more and more elderly people suffer from lower extremity movement problems, it is of great social significance to assist persons with motor dysfunction to walk independently again and reduce the burden on caregivers. The self-paced walking intention, which could increase the ability of self-control on the start and stop of motion, was studied by applying brain-computer interface (BCI) technology, a novel research field. The cerebral hemoglobin signal, which was obtained from 30 subjects by applying functional near-infrared spectroscopy (fNIRS) technology, was processed to detect self-paced walking intention in this paper. Teager-Kaiser energy was extracted at each sampling point for five sub-bands (0.0095~0.021 Hz, 0.021~0.052 Hz, 0.052~0.145 Hz, 0.145~0.6 Hz, and 0.6~2.0 Hz). Gradient boosting decision tree (GBDT) was then utilized to establish the detecting model in real-time. The proposed method had a good performance to detect the walking intention and passed the pseudo-online test with a true positive rate of 100% (80/80), a false positive rate of 2.91% (4822/165171), and a detection latency of 0.39 ± 1.06 s. GBDT method had an area under the curve value of 0.944 and was 0.125 (p < 0.001) higher than linear discriminant analysis (LDA). The results reflected that it is feasible to decode self-paced walking intention by applying fNIRS technology. This study lays a foundation for applying fNIRS-based BCI technology to control walking assistive devices practically. Graphical abstract Graphical representation of the detecting process for pseudo-online test. The lower figure is a partial enlargement of the upper figure. In the lower figure, the blue line represents the probability of walking predicted by GBDT without smoothing and the orange-red line represents the smoothed probability. The dark-red ellipse shows the effect of the smoothing-threshold method.},
}
@article {pmid32082238,
year = {2019},
author = {Wu, Q and Yue, Z and Ge, Y and Ma, D and Yin, H and Zhao, H and Liu, G and Wang, J and Dou, W and Pan, Y},
title = {Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training.},
journal = {Frontiers in neurology},
volume = {10},
number = {},
pages = {1419},
pmid = {32082238},
issn = {1664-2295},
abstract = {Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.},
}
@article {pmid32082118,
year = {2020},
author = {Li, S and Jin, J and Daly, I and Zuo, C and Wang, X and Cichocki, A},
title = {Comparison of the ERP-Based BCI Performance Among Chromatic (RGB) Semitransparent Face Patterns.},
journal = {Frontiers in neuroscience},
volume = {14},
number = {},
pages = {54},
pmid = {32082118},
issn = {1662-4548},
abstract = {OBJECTIVE: Previous studies have shown that combing with color properties may be used as part of the display presented to BCI users in order to improve performance. Build on this, we explored the effects of combinations of face stimuli with three primary colors (RGB) on BCI performance which is assessed by classification accuracy and information transfer rate (ITR). Furthermore, we analyzed the waveforms of three patterns.<br><br>METHODS: We compared three patterns in which semitransparent face is overlaid three primary colors as stimuli: red semitransparent face (RSF), green semitransparent face (GSF), and blue semitransparent face (BSF). Bayesian linear discriminant analysis (BLDA) was used to construct the individual classifier model. In addition, a Repeated-measures ANOVA (RM-ANOVA) and Bonferroni correction were chosen for statistical analysis.<br><br>RESULTS: The results indicated that the RSF pattern achieved the highest online averaged accuracy with 93.89%, followed by the GSF pattern with 87.78%, while the lowest performance was caused by the BSF pattern with an accuracy of 81.39%. Furthermore, significant differences in classification accuracy and ITR were found between RSF and GSF (p < 0.05) and between RSF and BSF patterns (p < 0.05).<br><br>CONCLUSION: The semitransparent faces colored red (RSF) pattern yielded the best performance of the three patterns. The proposed patterns based on ERP-BCI system have a clinically significant impact by increasing communication speed and accuracy of the P300-speller for patients with severe motor impairment.},
}
@article {pmid32081721,
year = {2020},
author = {Fernández-Rodríguez, &#xc1; and Medina-Juliá, MT and Velasco-Álvarez, F and Ron-Angevin, R},
title = {Effects of Spatial Stimulus Overlap in a Visual P300-based Brain-computer Interface.},
journal = {Neuroscience},
volume = {431},
number = {},
pages = {134-142},
doi = {10.1016/j.neuroscience.2020.02.011},
pmid = {32081721},
issn = {1873-7544},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Eye Movements ; Humans ; Photic Stimulation ; User-Computer Interface ; },
abstract = {The rapid serial visual presentation (RSVP) paradigm seems to be one of the most appropriate for patients using P300-based brain-computer interface (BCI) applications, since non-ocular movements are required. However, according to previous works, the use of different locations for each stimulus may improve performance. Thus, the aim of the present work is to explore how spatial overlap between stimuli influences performance in controlling a visual P300-based BCI. Nineteen participants were tested using four levels of overlap between two stimuli: 100%, 66.7%, 33.3% and 0%. Significant differences in accuracy were found between the 0% overlapped condition and all the other conditions, and between 33.3% and higher overlap (66.7% and 100%). These results can be explained due to a modulation in the non-target stimulus amplitude signal caused by the overlapping factor. In short, the stimulus overlap provokes a modulation in performance using a P300-based BCI; this should be considered in future BCI proposals in which an optimal surface exploitation is convenient and potential users have only residual ocular movement.},
}
@article {pmid32078554,
year = {2020},
author = {Nakanishi, M and Xu, M and Wang, Y and Chiang, KJ and Han, J and Jung, TP},
title = {Questionable Classification Accuracy Reported in "Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs".},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {4},
pages = {1042-1043},
doi = {10.1109/TNSRE.2020.2974272},
pmid = {32078554},
issn = {1558-0210},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neurologic Examination ; },
abstract = {This commentary presents a replication study to verify the effectiveness of a sum of squared correlations (SSCOR)-based steady-state visual evoked potentials (SSVEPs) decoding method proposed by Kumar et al.. We implemented the SSCOR-based method in accordance with their descriptions and estimated its classification accuracy using a benchmark SSVEP dataset with cross validation. Our results showed significantly lower classification accuracy compared with the ones reported in Kumar et al.'s study. We further investigated the sources of performance discrepancy by simulating data leakage between training and test datasets. The classification performance of the simulation was remarkably similar to those reported by Kumar et al.. We, therefore, question the validity of evaluation and conclusions drawn in Kumar et al.'s study.},
}
@article {pmid32078552,
year = {2020},
author = {Romero-Laiseca, MA and Delisle-Rodriguez, D and Cardoso, V and Gurve, D and Loterio, F and Posses Nascimento, JH and Krishnan, S and Frizera-Neto, A and Bastos-Filho, T},
title = {A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {4},
pages = {988-996},
doi = {10.1109/TNSRE.2020.2974056},
pmid = {32078552},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Lower Extremity ; *Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {A low-cost Brain-Machine Interface (BMI) based on electroencephalography for lower-limb motor recovery of post-stroke patients is proposed here, which provides passive pedaling as feedback, when patients trigger a Mini-Motorized Exercise Bike (MMEB) by executing pedaling motor imagery (MI). This system was validated in an On-line phase by eight healthy subjects and two post-stroke patients, which felt a closed-loop commanding the MMEB due to the fast response of our BMI. It was developed using methods of low-computational cost, such as Riemannian geometry for feature extraction, Pair-Wise Feature Proximity (PWFP) for feature selection, and Linear Discriminant Analysis (LDA) for pedaling imagery recognition. The On-line phase was composed of two sessions, where each participant completed a total of 12 trials per session executing pedaling MI for triggering the MMEB. As a result, the MMEB was successfully triggered by healthy subjects for almost all trials (ACC up to 100%), while the two post-stroke patients, PS1 and PS2, achieved their best performance (ACC of 41.67% and 91.67%, respectively) in Session #2. These patients improved their latency (2.03 ± 0.42 s and 1.99 ± 0.35 s, respectively) when triggering the MMEB, and their performance suggests the hypothesis that our system may be used with chronic stroke patients for lower-limb recovery, providing neural relearning and enhancing neuroplasticity.},
}
@article {pmid32072963,
year = {2020},
author = {Ratcliffe, L and Puthusserypady, S},
title = {Importance of Graphical User Interface in the design of P300 based Brain-Computer Interface systems.},
journal = {Computers in biology and medicine},
volume = {117},
number = {},
pages = {103599},
doi = {10.1016/j.compbiomed.2019.103599},
pmid = {32072963},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; User-Computer Interface ; },
abstract = {OBJECTIVES: Develop an effective and intuitive Graphical User Interface (GUI) for a Brain-Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands.<br><br>METHODS: A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a 'drone prototype' capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right.<br><br>RESULTS: From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3&#xa0;&#xb1;&#xa0;4.8% and an ITR of 2.2&#xa0;&#xb1;&#xa0;1.1 commands/minute (12.2&#xa0;&#xb1;&#xa0;6.0 bits/minute) was achieved.<br><br>CONCLUSION: The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI.<br><br>SIGNIFICANCE: This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.},
}
@article {pmid32070938,
year = {2020},
author = {Zhou, Y and He, S and Huang, Q and Li, Y},
title = {A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {10},
pages = {2881-2892},
doi = {10.1109/TBME.2020.2972747},
pmid = {32070938},
issn = {1558-2531},
mesh = {Algorithms ; Blinking ; *Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; },
abstract = {OBJECTIVE: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals.<br><br>METHODS: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results.<br><br>RESULTS: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34&#xa0;s and an average false-positive rate (FPR) of 0.8%.<br><br>CONCLUSION: The proposed BCI generates multiple commands with a high ITR and low FPR.<br><br>SIGNIFICANCE: The hybrid asynchronous BCI has great potential for practical applications in communication and control.},
}
@article {pmid32070853,
year = {2020},
author = {Zhang, Y and Jia, S and Zheng, Y and Yu, Z and Tian, Y and Ma, S and Huang, T and Liu, JK},
title = {Reconstruction of natural visual scenes from neural spikes with deep neural networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {125},
number = {},
pages = {19-30},
doi = {10.1016/j.neunet.2020.01.033},
pmid = {32070853},
issn = {1879-2782},
mesh = {Animals ; Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Magnetic Resonance Imaging ; *Models, Neurological ; *Neural Networks, Computer ; Retinal Ganglion Cells/physiology ; Visual Cortex/diagnostic imaging/*physiology ; Visual Perception ; },
abstract = {Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.},
}
@article {pmid32070475,
year = {2020},
author = {Gridchyn, I and Schoenenberger, P and O'Neill, J and Csicsvari, J},
title = {Assembly-Specific Disruption of Hippocampal Replay Leads to Selective Memory Deficit.},
journal = {Neuron},
volume = {106},
number = {2},
pages = {291-300.e6},
doi = {10.1016/j.neuron.2020.01.021},
pmid = {32070475},
issn = {1097-4199},
mesh = {Animals ; Brain Mapping ; Conditioning, Operant ; Electrophysiological Phenomena ; Goals ; Hippocampus/*physiopathology ; Learning ; Memory Consolidation ; Memory Disorders/*physiopathology ; Mental Recall ; Rats ; Sleep ; },
abstract = {Memory consolidation is thought to depend on the reactivation of waking hippocampal firing patterns during sleep. Following goal learning, the reactivation of place cell firing can represent goals and predicts subsequent memory recall. However, it is unclear whether reactivation promotes the recall of the reactivated memories only or triggers wider reorganization. We trained animals to locate goals at fixed locations in two different environments. Following learning, by performing online assembly content decoding, the reactivation of only one environment was disrupted, leading to recall deficit in that environment. The place map of the disrupted environment was destabilized but re-emerged once the goal was relearned. These data demonstrate that sleep reactivation facilitates goal-memory retrieval by strengthening memories that enable the selection of context-specific hippocampal maps. However, sleep reactivation may not be needed for the stabilization of place maps considering that the map of the disrupted environment re-emerged after the retraining of goals.},
}
@article {pmid32066581,
year = {2020},
author = {Kanth, ST and Ray, S},
title = {Electrocorticogram (ECoG) Is Highly Informative in Primate Visual Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {40},
number = {12},
pages = {2430-2444},
pmid = {32066581},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; 500145-Z-09-Z/WTDBT_/DBT-Wellcome Trust India Alliance/India ; },
mesh = {Animals ; Behavior, Animal ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Electrophysiological Phenomena ; Evoked Potentials, Visual/physiology ; Female ; Macaca radiata ; Microelectrodes ; Photic Stimulation ; Visual Cortex/*physiology ; },
abstract = {Neural signals recorded at different scales contain information about environment and behavior and have been used to control Brain Machine Interfaces with varying degrees of success. However, a direct comparison of their efficacy has not been possible due to different recording setups, tasks, species, etc. To address this, we implanted customized arrays having both microelectrodes and electrocorticogram (ECoG) electrodes in the primary visual cortex of 2 female macaque monkeys, and also recorded electroencephalogram (EEG), while they viewed a variety of naturalistic images and parametric gratings. Surprisingly, ECoG had higher information and decodability than all other signals. Combining a few ECoG electrodes allowed more accurate decoding than combining a much larger number of microelectrodes. Control analyses showed that higher decoding accuracy of ECoG compared with local field potential was not because of differences in low-level visual features captured by them but instead because of larger spatial summation of the ECoG. Information was high in the 30-80 Hz range and at lower frequencies. Information in different frequencies and scales was nonredundant. These results have strong implications for Brain Machine Interface applications and for study of population representation of visual stimuli.SIGNIFICANCE STATEMENT Electrophysiological signals captured across scales by different recording electrodes are regularly used for Brain Machine Interfaces, but the information content varies due to electrode size and location. A systematic comparison of their efficiency for Brain Machine Interfaces is important but technically challenging. Here, we recorded simultaneous signals across four scales: spikes, local field potential, electrocorticogram (ECoG), and EEG, and compared their information and decoding accuracy for a large variety of naturalistic stimuli. We found that ECoGs were highly informative and outperformed other signals in information content and decoding accuracy.},
}
@article {pmid32059543,
year = {2020},
author = {Cha, HS and Han, CH and Im, CH},
title = {Prediction of Individual User's Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain-Computer Interface Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {4},
pages = {},
pmid = {32059543},
issn = {1424-8220},
support = {NRF-2019R1A2C2086593//National Research Foundation of Korea/ ; },
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Machine Learning ; Male ; },
abstract = {With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain-computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user's dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.},
}
@article {pmid32056315,
year = {2021},
author = {Gimmel, A and Öfner, S and Liesegang, A},
title = {Body condition scoring (BCS) in corn snakes (Pantherophis guttatus) and comparison to pre-existing body condition index (BCI) for snakes.},
journal = {Journal of animal physiology and animal nutrition},
volume = {105 Suppl 2},
number = {},
pages = {24-28},
doi = {10.1111/jpn.13291},
pmid = {32056315},
issn = {1439-0396},
mesh = {Animals ; *Snakes ; *Zea mays ; },
abstract = {In the veterinary profession, the body condition score (BCS) plays an important role in the assessment of patients. It is a subjective, tactile method of evaluating body fat and muscle mass and is used in numerous species. Recognizing obesity (or the contrary, emaciation) is important for veterinarians treating reptiles and could be facilitated by a BCS. An existing form of body condition assessment already used is the body condition index (BCI), where the residuals from a regression of body mass on body length are calculated. Therefore, the goal of this study was to provide practitioners with a BCS system for corn snakes (Pantherophis guttatus) and to test it against the BCI. A total of 22 corn snakes (Pantherophis guttatus), stationed at the "Auffangstation für Reptilien" in Munich (reptile rescue centre, RRC), were subject of this study. Each had the following measurements taken: body weight (BW), snout-tail tip length (STL), snout-vent length (SVL) and circumference in the middle (C). Manual palpation of spine, area between vertebral spinous and transverse process, ribs and neck of each snake was performed by three veterinarians and assigned to specific scores by each examiner. A BCS (mean of examiners' scores) was given to each snake according to manual palpation. The BCS system was chosen to be out of 5 in 0.5-point steps with 2.5 considered as ideal BCS. In the studied snakes, the BCS ranged from 1.5 to 3.5, with a median of 2.5. The median BW was 309 g (75-967 g), the median STL was 123 cm (79-153 cm), the median SVL was 104 cm (73-133 cm) and the median C was 7.5 cm (4.3-11 cm). BCS and BCI were positively correlated. A BCS includes a manual palpation of the animal and thus gives the examiner additional information to the objectively measured/calculated index.},
}
@article {pmid32054584,
year = {2020},
author = {De Marcellis, A and Stanchieri, GDP and Faccio, M and Palange, E and Constandinou, TG},
title = {A 300 Mbps 37 pJ/bit Pulsed Optical Biotelemetry.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {14},
number = {3},
pages = {441-451},
doi = {10.1109/TBCAS.2020.2972733},
pmid = {32054584},
issn = {1940-9990},
support = {UKDRI-7004/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Animals ; Brain-Computer Interfaces ; Equipment Design ; Humans ; Optics and Photonics/*instrumentation ; Prostheses and Implants ; Swine ; Telemetry/*instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {This article reports an implantable transcutaneous telemetry for a brain machine interface that uses a novel optical communication system to achieve a highly energy-efficient link. Based on an pulse-based coding scheme, the system uses sub-nanosecond laser pulses to achieve data rates up to 300 Mbps with relatively low power levels when compared to other methods of wireless communication. This has been implemented using a combination of discrete components (semiconductor laser and driver, fast-response Si photodiode and interface) integrated at board level together with reconfigurable logic (encoder, decoder and processing circuits implemented using Xilinx KCU105 board with Kintex UltraScale FPGA). Experimental validation has been performed using a tissue sample that achieves representative level of attenuation/scattering (porcine skin) in the optical path. Results reveal that the system can operate at data rates up to 300 Mbps with a bit error rate (BER) of less than 10 [-10], and an energy efficiency of 37 pJ/bit. This can communicate, for example, 1,024 channels of broadband neural data sampled at 18 kHz, 16-bit with only 11 mW power consumption.},
}
@article {pmid32050471,
year = {2020},
author = {Delval, A and Bayot, M and Defebvre, L and Dujardin, K},
title = {Cortical Oscillations during Gait: Wouldn't Walking be so Automatic?.},
journal = {Brain sciences},
volume = {10},
number = {2},
pages = {},
pmid = {32050471},
issn = {2076-3425},
abstract = {Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks…). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain-computer interfaces able to generate gait artificially.},
}
@article {pmid32046233,
year = {2020},
author = {Hashemi Noshahr, F and Nabavi, M and Sawan, M},
title = {Multi-Channel Neural Recording Implants: A Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {3},
pages = {},
pmid = {32046233},
issn = {1424-8220},
abstract = {The recently growing progress in neuroscience research and relevant achievements, as well as advancements in the fabrication process, have increased the demand for neural interfacing systems. Brain-machine interfaces (BMIs) have been revealed to be a promising method for the diagnosis and treatment of neurological disorders and the restoration of sensory and motor function. Neural recording implants, as a part of BMI, are capable of capturing brain signals, and amplifying, digitizing, and transferring them outside of the body with a transmitter. The main challenges of designing such implants are minimizing power consumption and the silicon area. In this paper, multi-channel neural recording implants are surveyed. After presenting various neural-signal features, we investigate main available neural recording circuit and system architectures. The fundamental blocks of available architectures, such as neural amplifiers, analog to digital converters (ADCs) and compression blocks, are explored. We cover the various topologies of neural amplifiers, provide a comparison, and probe their design challenges. To achieve a relatively high SNR at the output of the neural amplifier, noise reduction techniques are discussed. Also, to transfer neural signals outside of the body, they are digitized using data converters, then in most cases, the data compression is applied to mitigate power consumption. We present the various dedicated ADC structures, as well as an overview of main data compression methods.},
}
@article {pmid32046131,
year = {2020},
author = {Mannan, MMN and Kamran, MA and Kang, S and Choi, HS and Jeong, MY},
title = {A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {3},
pages = {},
pmid = {32046131},
issn = {1424-8220},
support = {2017R1A2B2006999//National Research Foundation of Korea/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Data Analysis ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Eye Movements/*physiology ; Female ; Humans ; *Language ; Male ; Middle Aged ; Online Systems ; Young Adult ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain-computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.},
}
@article {pmid32045838,
year = {2020},
author = {Mammone, N and Ieracitano, C and Morabito, FC},
title = {A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {124},
number = {},
pages = {357-372},
doi = {10.1016/j.neunet.2020.01.027},
pmid = {32045838},
issn = {1879-2782},
mesh = {Adult ; *Brain Waves ; Brain-Computer Interfaces ; *Deep Learning ; Humans ; *Models, Neurological ; Motor Cortex/*physiology ; Movement ; Reaction Time ; Upper Extremity/innervation/*physiology ; },
abstract = {A system that can detect the intention to move and decode the planned movement could help all those subjects that can plan motion but are unable to implement it. In this paper, motor planning activity is investigated by using electroencephalographic (EEG) signals with the aim to decode motor preparation phases. A publicly available database of 61-channels EEG signals recorded from 15 healthy subjects during the execution of different movements (elbow flexion/extension, forearm pronation/supination, hand open/close) of the right upper limb was employed to generate a dataset of EEG epochs preceding resting and movement's onset. A novel system is introduced for the classification of premovement vs resting and of premovement vs premovement epochs. For every epoch, the proposed system generates a time-frequency (TF) map of every source signal in the motor cortex, through beamforming and Continuous Wavelet Transform (CWT), then all the maps are embedded in a volume and used as input to a deep CNN. The proposed system succeeded in discriminating premovement from resting with an average accuracy of 90.3% (min 74.6%, max 100%), outperforming comparable methods in the literature, and in discriminating premovement vs premovement with an average accuracy of 62.47%. The achieved results encourage to investigate motor planning at source level in the time-frequency domain through deep learning approaches.},
}
@article {pmid32045783,
year = {2020},
author = {Welle, CG and Gao, YR and Ye, M and Lozzi, A and Boretsky, A and Abliz, E and Hammer, DX},
title = {Longitudinal neural and vascular structural dynamics produced by chronic microelectrode implantation.},
journal = {Biomaterials},
volume = {238},
number = {},
pages = {119831},
doi = {10.1016/j.biomaterials.2020.119831},
pmid = {32045783},
issn = {1878-5905},
mesh = {Animals ; Electrodes, Implanted ; Humans ; Microelectrodes ; *Neurons ; },
abstract = {Implanted microelectrode arrays sense local neuronal activity, signals which are used as control commands for brain computer interface (BCI) technology. Patients with tetraplegia have used BCI technology to achieve an extraordinary degree of interaction with their local environment. However, current microelectrode arrays for BCIs lose the ability to record high-quality neural signals in the months-to-years following implantation. Very little is known regarding the dynamic response of neurons and vasculature in the months following electrode array implantation, but loss of structural integrity near the electrode may contribute to the degradation of recording signals. Here, we use in-vivo dual-modality imaging to characterize neuronal and vasculature structures in the same animal for 3 months following electrode insertion. We find ongoing neuronal atrophy, but relative vascular stability, in close proximity to the electrode, along with evidence suggesting links between rare, abrupt hypoxic events and neuronal process atrophy.},
}
@article {pmid32045572,
year = {2020},
author = {Cicalese, PA and Li, R and Ahmadi, MB and Wang, C and Francis, JT and Selvaraj, S and Schulz, PE and Zhang, Y},
title = {An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease.},
journal = {Journal of neuroscience methods},
volume = {336},
number = {},
pages = {108618},
pmid = {32045572},
issn = {1872-678X},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {*Alzheimer Disease/diagnosis ; *Brain-Computer Interfaces ; *Cognitive Dysfunction/diagnosis ; Electroencephalography ; Humans ; Spectroscopy, Near-Infrared ; },
abstract = {BACKGROUND: Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.<br><br>NEW METHODS: Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.<br><br>RESULTS: The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.<br><br>Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.<br><br>CONCLUSIONS: These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.},
}
@article {pmid32045022,
year = {2021},
author = {Chaudhary, U and Mrachacz-Kersting, N and Birbaumer, N},
title = {Neuropsychological and neurophysiological aspects of brain-computer-interface (BCI) control in paralysis.},
journal = {The Journal of physiology},
volume = {599},
number = {9},
pages = {2351-2359},
doi = {10.1113/JP278775},
pmid = {32045022},
issn = {1469-7793},
mesh = {*Amyotrophic Lateral Sclerosis ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Humans ; Paralysis ; Quality of Life ; },
abstract = {Brain-computer interfaces (BCIs) aim to help paralysed patients to interact with their environment by controlling external devices using brain activity, thereby bypassing the dysfunctional motor system. Some neuronal disorders, such as amyotrophic lateral sclerosis (ALS), severely impair the communication capacity of patients. Several invasive and non-invasive brain-computer interfaces (BCIs), most notably using electroencephalography (EEG), have been developed to provide a means of communication to paralysed patients. However, except for a few reports, all available BCI literature for the paralysed (mostly ALS patients) describes patients with intact eye movement control, i.e. patients in a locked-in state (LIS) but not a completely locked-in state (CLIS). In this article we will discuss: (1) the fundamental neuropsychological learning factors and neurophysiological factors determining BCI performance in clinical applications; (2) the difference between LIS and CLIS; (3) recent development in BCIs for communication with patients in the completely locked-in state; (4) the effect of BCI-based communication on emotional well-being and quality of life; and (5) the outlook and the methodology needed to provide a means of communication for patients who have none. Thus, we present an overview of available studies and recent results and try to anticipate future developments which may open new doors for BCI communication with the completely paralysed.},
}
@article {pmid32043937,
year = {2020},
author = {Vukov, JM and Rempala, K},
title = {BCI-Mediated Action, Blame, and Responsibility.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {65-67},
doi = {10.1080/21507740.2019.1704929},
pmid = {32043937},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Social Behavior ; Social Responsibility ; },
}
@article {pmid32043936,
year = {2020},
author = {Lim, D and Duman, E},
title = {The Continuity of BCI-Mediated and Conventional Action.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {59-61},
doi = {10.1080/21507740.2019.1704928},
pmid = {32043936},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; User-Computer Interface ; },
}
@article {pmid32043934,
year = {2020},
author = {Buller, T},
title = {How to Do Things with BCIs.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {70-72},
doi = {10.1080/21507740.2019.1704930},
pmid = {32043934},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Social Behavior ; },
}
@article {pmid32043933,
year = {2020},
author = {Dasgupta, I and Versalovic, E and Schönau, A and Klein, E and Goering, S},
title = {BCI Mediated Action and Responsibility: Questioning the Distinction Between Recreation and Necessity.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {63-65},
doi = {10.1080/21507740.2019.1704932},
pmid = {32043933},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Recreation ; Sexual Behavior ; Social Behavior ; },
}
@article {pmid32043931,
year = {2020},
author = {Kuersten, A},
title = {Legal Ramifications of Brain-Computer-Interface Technology.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {61-63},
doi = {10.1080/21507740.2019.1704931},
pmid = {32043931},
issn = {2150-7759},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Technology ; User-Computer Interface ; },
}
@article {pmid32043928,
year = {2020},
author = {Miller, DJ},
title = {BCI-Mediated Behavior, Moral Luck, and Punishment.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {72-74},
doi = {10.1080/21507740.2019.1705428},
pmid = {32043928},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Morals ; *Punishment ; Social Behavior ; },
}
@article {pmid32043926,
year = {2020},
author = {Ivanković, V and Savić, L},
title = {Does Mental Discipline Partially Restore the Responsibility of BCI Users?.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {67-70},
doi = {10.1080/21507740.2019.1704922},
pmid = {32043926},
issn = {2150-7759},
support = {212764/Z/18/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Social Behavior ; },
}
@article {pmid32041316,
year = {2020},
author = {Sun, Y and Ayaz, H and Akansu, AN},
title = {Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression.},
journal = {Brain sciences},
volume = {10},
number = {2},
pages = {},
pmid = {32041316},
issn = {2076-3425},
abstract = {Human facial expressions are regarded as a vital indicator of one's emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain-computer interface applications.},
}
@article {pmid32040563,
year = {2020},
author = {Khawaldeh, S and Tinkhauser, G and Shah, SA and Peterman, K and Debove, I and Nguyen, TAK and Nowacki, A and Lachenmayer, ML and Schuepbach, M and Pollo, C and Krack, P and Woolrich, M and Brown, P},
title = {Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson's disease.},
journal = {Brain : a journal of neurology},
volume = {143},
number = {2},
pages = {582-596},
pmid = {32040563},
issn = {1460-2156},
support = {MR/K005464/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_00003/2/MRC_/Medical Research Council/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; MR/L023784/2/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Action Potentials/physiology ; Basal Ganglia/physiopathology ; Beta Rhythm/physiology ; Deep Brain Stimulation/methods ; Extremities/*physiopathology ; Female ; Humans ; Male ; Motor Cortex/physiopathology ; Movement/*physiology ; Parkinson Disease/*therapy ; Subthalamic Nucleus/physiology/*physiopathology ; },
abstract = {Whilst exaggerated bursts of beta frequency band oscillatory synchronization in the subthalamic nucleus have been associated with motor impairment in Parkinson's disease, a plausible mechanism linking the two phenomena has been lacking. Here we test the hypothesis that increased synchronization denoted by beta bursting might compromise information coding capacity in basal ganglia networks. To this end we recorded local field potential activity in the subthalamic nucleus of 18 patients with Parkinson's disease as they executed cued upper and lower limb movements. We used the accuracy of local field potential-based classification of the limb to be moved on each trial as an index of the information held by the system with respect to intended action. Machine learning using the naïve Bayes conditional probability model was used for classification. Local field potential dynamics allowed accurate prediction of intended movements well ahead of their execution, with an area under the receiver operator characteristic curve of 0.80 ± 0.04 before imperative cues when the demanded action was known ahead of time. The presence of bursts of local field potential activity in the alpha, and even more so, in the beta frequency band significantly compromised the prediction of the limb to be moved. We conclude that low frequency bursts, particularly those in the beta band, restrict the capacity of the basal ganglia system to encode physiologically relevant information about intended actions. The current findings are also important as they suggest that local subthalamic activity may potentially be decoded to enable effector selection, in addition to force control in restorative brain-machine interface applications.},
}
@article {pmid32039856,
year = {2020},
author = {Li, X and Liu, C and Wang, R},
title = {Light Modulation of Brain and Development of Relevant Equipment.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {74},
number = {1},
pages = {29-41},
doi = {10.3233/JAD-191240},
pmid = {32039856},
issn = {1875-8908},
mesh = {Aged ; Aged, 80 and over ; Alzheimer Disease/therapy ; Animals ; Brain/*growth &amp; development/*radiation effects ; Humans ; Laser Therapy ; Light ; Optogenetics ; Phototherapy/*instrumentation ; },
abstract = {Light modulation plays an important role in understanding the pathology of brain disorders and improving brain function. Optogenetic techniques can activate or silence targeted neurons with high temporal and spatial accuracy and provide precise control, and have recently become a method for quick manipulation of genetically identified types of neurons. Photobiomodulation (PBM) is light therapy that utilizes non-ionizing light sources, including lasers, light emitting diodes, or broadband light. It provides a safe means of modulating brain activity without any irreversible damage and has established optimal treatment parameters in clinical practice. This manuscript reviews 1) how optogenetic approaches have been used to dissect neural circuits in animal models of Alzheimer's disease, Parkinson's disease, and depression, and 2) how low level transcranial lasers and LED stimulation in humans improves brain activity patterns in these diseases. State-of-the-art brain machine interfaces that can record neural activity and stimulate neurons with light have good prospects in the future.},
}
@article {pmid32038219,
year = {2019},
author = {Zeng, H and Shen, Y and Hu, X and Song, A and Xu, B and Li, H and Wang, Y and Wen, P},
title = {Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {111},
pmid = {32038219},
issn = {1662-5218},
abstract = {Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.},
}
@article {pmid32038208,
year = {2019},
author = {Saha, S and Baumert, M},
title = {Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review.},
journal = {Frontiers in computational neuroscience},
volume = {13},
number = {},
pages = {87},
pmid = {32038208},
issn = {1662-5188},
abstract = {Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.},
}
@article {pmid32038198,
year = {2019},
author = {Spychala, N and Debener, S and Bongartz, E and Müller, HHO and Thorne, JD and Philipsen, A and Braun, N},
title = {Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {461},
pmid = {32038198},
issn = {1662-5161},
abstract = {Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.},
}
@article {pmid32034787,
year = {2020},
author = {McFarland, DJ},
title = {Brain-computer interfaces for amyotrophic lateral sclerosis.},
journal = {Muscle &amp; nerve},
volume = {61},
number = {6},
pages = {702-707},
pmid = {32034787},
issn = {1097-4598},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; Brain/*physiology ; Brain-Computer Interfaces/*trends ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Humans ; },
abstract = {A brain-computer interface (BCI) is a device that detects signals from the brain and transforms them into useful commands. Researchers have developed BCIs that utilize different kinds of brain signals. These different BCI systems have differing characteristics, such as the amount of training required and the degree to which they are or are not invasive. Much of the research on BCIs to date has involved healthy individuals and evaluation of classification algorithms. Some BCIs have been shown to have potential benefit for users with minimal muscular function as a result of amyotrophic lateral sclerosis. However, there are still several challenges that need to be successfully addressed before BCIs can be clinically useful.},
}
@article {pmid32034277,
year = {2020},
author = {Zapała, D and Zabielska-Mendyk, E and Augustynowicz, P and Cudo, A and Jaśkiewicz, M and Szewczyk, M and Kopiś, N and Francuz, P},
title = {The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {2087},
pmid = {32034277},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Brain/physiology ; Brain-Computer Interfaces/*psychology ; Electroencephalography ; *Feedback, Sensory/physiology ; Female ; *Functional Laterality/physiology ; Humans ; Male ; *Psychomotor Performance/physiology ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements' neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8-13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user's training should take into account individual differences in hand dominance.},
}
@article {pmid32033755,
year = {2020},
author = {Moser, T and Dieter, A},
title = {Towards optogenetic approaches for hearing restoration.},
journal = {Biochemical and biophysical research communications},
volume = {527},
number = {2},
pages = {337-342},
doi = {10.1016/j.bbrc.2019.12.126},
pmid = {32033755},
issn = {1090-2104},
mesh = {Animals ; Cochlear Implantation ; Cochlear Implants ; Hearing ; Hearing Loss/genetics/physiopathology/*therapy ; Humans ; Optogenetics/*methods ; },
abstract = {Hearing impairment (HI) is the most frequent sensory deficit in humans. As yet there is no causal therapy for sensorineural HI - the most common form - that results from cochlear dysfunction. Hearing aids and electrical cochlear implants (eCIs) remain the key options for hearing rehabilitation. The eCI, used by more than 0.7 Mio people with profound HI or deafness, is considered the most successful neuroprosthesis as it typically enables open speech comprehension in quiet. By electrically stimulating the auditory nerve, eCIs constitute a brain-machine interface re-connecting the patient with the auditory scene. Nonetheless, there are short-comings resulting from the wide spread of electric current inside the cochlea which limit the quality of artificial hearing. Since light can be better confined in space than electric current, optogenetic stimulation of the auditory nerve has been suggested as an alternative approach for hearing restoration, enabling higher resolution of artificial sound encoding. Future optical CIs (oCIS) promise increased spectral selectivity of artificial sound encoding, and hence might improve speech recognition in background noise as well as processing of music.},
}
@article {pmid32028964,
year = {2020},
author = {Song, Y and Sepulveda, F},
title = {Comparison between covert sound-production task (sound-imagery) vs. motor-imagery for onset detection in real-life online self-paced BCIs.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {17},
number = {1},
pages = {14},
pmid = {32028964},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; *Signal Processing, Computer-Assisted ; Software ; Young Adult ; },
abstract = {BACKGROUND: Even though the BCI field has quickly grown in the last few years, it is still mainly investigated as a research area. Increased practicality and usability are required to move BCIs to the real-world. Self-paced (SP) systems would reduce the problem but there is still the big challenge of what is known as the 'onset detection problem'.<br><br>METHODS: Our previous studies showed how a new sound-imagery (SI) task, high-tone covert sound production, is very effective for onset detection scenarios and we expect there are several advantages over most common asynchronous approaches used thus far, i.e., motor-imagery (MI): 1) Intuitiveness; 2) benefits to people with motor disabilities and, especially, those with lesions on cortical motor areas; and 3) no significant overlap with other common, spontaneous cognitive states, making it easier to use in daily-life situations. The approach was compared with MI tasks in online real-life scenarios, i.e., during activities such as watching videos and reading text. In our scenario, when a new message prompt from a messenger program appeared on the screen, participants watching a video (or reading text, browsing images) were asked to open the message by executing the SI or MI tasks, respectively, for each experimental condition.<br><br>RESULTS: The results showed the SI task performed statistically significantly better than the MI approach: 84.04% (SI) vs 66.79 (MI) True-False positive rate for the sliding image scenario, 80.84% vs 61.07% for watching video. The classification performance difference between SI and MI was found not to be significant in the text-reading scenario. Furthermore, the onset response speed showed SI (4.08&#x2009;s) being significantly faster than MI (5.46&#x2009;s). In terms of basic usability, 75% of subjects found SI easier to use.<br><br>CONCLUSIONS: Our novel SI task outperforms typical MI for SP onset detection BCIs, therefore it would be more easily used in daily-life situations. This could be a significant step forward for the BCI field which has so far been mainly restricted to research-oriented indoor laboratory settings.},
}
@article {pmid32027889,
year = {2020},
author = {Golshan, HM and Hebb, AO and Mahoor, MH},
title = {LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.},
journal = {Journal of neuroscience methods},
volume = {335},
number = {},
pages = {108621},
doi = {10.1016/j.jneumeth.2020.108621},
pmid = {32027889},
issn = {1872-678X},
mesh = {*Deep Brain Stimulation ; *Deep Learning ; Humans ; *Parkinson Disease/therapy ; Speech ; *Subthalamic Nucleus ; },
abstract = {BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is often used for patients with Parkinson's disease (PD) when medication cannot effectively tackle patients' motor symptoms. A DBS system capable of adaptively adjusting its parameters based on patients' activities may optimize therapy while reducing the stimulation side effects and improving the battery life.<br><br>METHOD: STN-LFP reveals motor and language behavior, making it a reliable source for behavior classification. This paper presents LFP-Net, an automated machine learning framework based on deep convolutional neural networks (CNN) for classification of human behavior using the time-frequency representation of STN-LFPs within the beta frequency range. CNNs learn different features based on the beta power patterns associated with different behaviors. The features extracted by the CNNs are passed through fully connected layers and then to the softmax layer for classification.<br><br>RESULTS: Our experiments on ten PD patients performing three behavioral tasks including "button press", "target reaching", and "speech" show that the proposed approach obtains an average classification accuracy of &#x223c;88 %. Comparison with existing methods: The proposed method outperforms other state-of-the-art classification methods based on STN-LFP signals. Compared to well-known deep neural networks such as AlexNet, our approach gives a higher accuracy using significantly fewer parameters.<br><br>CONCLUSIONS: CNNs show a high performance in decoding the brain neural response, which is crucial in designing the automatic brain-computer interfaces and closed-loop systems.},
}
@article {pmid32023225,
year = {2020},
author = {Stavisky, SD and Willett, FR and Avansino, DT and Hochberg, LR and Shenoy, KV and Henderson, JM},
title = {Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016049},
pmid = {32023225},
issn = {1741-2552},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; },
mesh = {Aged ; *Brain-Computer Interfaces/trends ; Cervical Vertebrae/injuries ; Humans ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Pilot Projects ; Psychomotor Performance/*physiology ; Speech/*physiology ; Spinal Cord Injuries/physiopathology/*rehabilitation ; },
abstract = {OBJECTIVE: Speech-related neural modulation was recently reported in 'arm/hand' area of human dorsal motor cortex that is used as a signal source for intracortical brain-computer interfaces (iBCIs). This raises the concern that speech-related modulation might deleteriously affect the decoding of arm movement intentions, for instance by affecting velocity command outputs. This study sought to clarify whether or not speaking would interfere with ongoing iBCI use.<br><br>APPROACH: A participant in the BrainGate2 iBCI clinical trial used an iBCI to control a computer cursor; spoke short words in a stand-alone speech task; and spoke short words during ongoing iBCI use. We examined neural activity in all three behaviors and compared iBCI performance with and without concurrent speech.<br><br>MAIN RESULTS: Dorsal motor cortex firing rates modulated strongly during stand-alone speech, but this activity was largely attenuated when speaking occurred during iBCI cursor control using attempted arm movements. 'Decoder-potent' projections of the attenuated speech-related neural activity were small, explaining why cursor task performance was similar between iBCI use with and without concurrent speaking.<br><br>SIGNIFICANCE: These findings indicate that speaking does not directly interfere with iBCIs that decode attempted arm movements. This suggests that patients who are able to speak will be able to use motor cortical-driven computer interfaces or prostheses without needing to forgo speaking while using these devices.},
}
@article {pmid32019957,
year = {2020},
author = {Ghoncheh, M and Lenarz, T and Maier, H},
title = {A Precision Driver Device for Intraoperative Stimulation of a Bone Conduction Implant.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {1797},
pmid = {32019957},
issn = {2045-2322},
mesh = {Bone Conduction/*physiology ; Hearing Loss, Conductive/*surgery ; Hearing Loss, Sensorineural/*surgery ; Humans ; Monitoring, Intraoperative ; *Ossicular Prosthesis ; Prosthesis Implantation/*methods ; },
abstract = {Semi-implantable bone conduction implants (BCI) and active middle ear implants (AMEI) for patients with sensorineural, conductive or mixed hearing loss commonly use an amplitude modulation technology to transmit power and sound signals from an external audio processor to the implant. In patients, the distance dependence of the signal amplitude is of minor importance as the skin thickness is constant and only varies between 3-7 mm. In this range, critical coupling transmission technique sufficiently reduces the variability in amplitude, but fails to provide well-defined amplitudes in many research and clinical applications such as intraoperative integrity tests where the distance range is exceeded by using sterile covers. Here we used the BCI Bonebridge (BB, Med-El, Austria) as an example to develop and demonstrate a system that synthesizes the transmission signal, determines the distance between the transmitter and the receiver implant coil and compensates transmission losses. When compared to an external audio processor (AP304) on an artificial mastoid, our system mainly decreased amplitude variability from over 11 dB to less than 3 dB for audio frequencies (0.1-10 kHz) at distances up to 15 mm, making it adequate for intraoperative and audiometric tests.},
}
@article {pmid32015765,
year = {2020},
author = {Miao, Y and Yin, E and Allison, BZ and Zhang, Y and Chen, Y and Dong, Y and Wang, X and Hu, D and Chchocki, A and Jin, J},
title = {An ERP-based BCI with peripheral stimuli: validation with ALS patients.},
journal = {Cognitive neurodynamics},
volume = {14},
number = {1},
pages = {21-33},
pmid = {32015765},
issn = {1871-4080},
abstract = {Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue and errors resulting from flashing adjacent stimuli, and is impractical for users who might want to use the BCI in tandem with other software or feedback in the center of the monitor. In this paper, we introduce and validate an alternate ERP-based BCI display approach. By presenting stimuli near the periphery of the display, we reduce the adjacency problem and leave the center of the display available for feedback or other applications. Two ERP-based display approaches were tested on 18 ALS patients to: (1) compare performance between a conventional matrix speller paradigm (Matrix-P, mean visual angle 6°) and a new speller paradigm with peripherally distributed stimuli (Peripheral-P, mean visual angle 8.8°); and (2) assess performance while spelling 42 characters online continuously, without a break. In the Peripheral-P condition, 12 subjects attained higher than 80% feedback accuracy during online performance, and 7 of these subjects obtained higher than 90% accuracy. The experimental results showed that the Peripheral-P condition yielded performance comparable to the conventional Matrix-P condition (p > 0.05) in accuracy and information transfer rate. This paper introduces a new display approach that leaves the center of the monitor open for feedback and/or other display elements, such as movies, games, art, or displays from other AAC software or conventional software tools.},
}
@article {pmid32015227,
year = {2020},
author = {Zhang, L and Wen, D and Li, C and Zhu, R},
title = {Ensemble classifier based on optimized extreme learning machine for motor imagery classification.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026004},
doi = {10.1088/1741-2552/ab7264},
pmid = {32015227},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Machine Learning ; },
abstract = {OBJECTIVE: Designing an effective classifier with high classification accuracy and strong generalization capability is essential for brain-computer interface (BCI) research. In this study, an extreme learning machine (ELM) based method is proposed to improve the classification accuracy of motor imagery electroencephalogram (EEG).<br><br>APPROACH: The proposed method constructs an ensemble classifier based on optimized ELMs. Particle swarm optimization is used to simultaneously optimize the input weights and hidden biases of ELM to avoid the randomness and instability of classification result when ELM uses randomly generated parameters, and majority voting strategy is used to fuse the classification results of multiple base classifiers to avoid the negative impact of ELM with local optimal parameters on classification result. The proposed method was compared with four competing methods in experiments based on two public EEG datasets and some existing methods reported in the literature using the same datasets as well.<br><br>MAIN RESULTS: The results indicate that the proposed method achieved significant higher classification accuracies than those of the competing methods on both two-class and four-class motor imagery data. Moreover, compared to the existing methods, it still obtained superior average accuracies of two-class classification and performed better for the subjects with relatively poor accuracies on both two-class and four-class classifications.<br><br>SIGNIFICANCE: The significant accuracy improvement demonstrates the superiority of the proposed method. It can be a promising candidate for accurate classification of motor imagery EEG in BCI systems.},
}
@article {pmid32014573,
year = {2021},
author = {Maruyama, Y and Yoshimura, N and Rana, A and Malekshahi, A and Tonin, A and Jaramillo-Gonzalez, A and Birbaumer, N and Chaudhary, U},
title = {Electroencephalography of completely locked-in state patients with amyotrophic lateral sclerosis.},
journal = {Neuroscience research},
volume = {162},
number = {},
pages = {45-51},
doi = {10.1016/j.neures.2020.01.013},
pmid = {32014573},
issn = {1872-8111},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; },
abstract = {Patients in completely locked-in state (CLIS) due to amyotrophic lateral sclerosis (ALS) lose the control of each and every muscle of their body rendering them motionless and without any means of communication. Though some studies have attempted to develop brain-computer interface (BCI)-based communication methods with CLIS patients, little information is available of the neuroelectric brain activity of CLIS patients. However, because of the difficulties with and often loss of communication, the neuroelectric signature may provide some indications of the state of consciousness in these patients. We recorded electroencephalography (EEG) signals from 10 CLIS patients during resting state and compared their power spectral densities with those of healthy participants in fronto-central, central, and centro-parietal channels. The results showed significant power reduction in the high alpha, beta, and gamma bands in CLIS patients, indicating the dominance of slower EEG frequencies in their oscillatory activity. This is the first study showing group-level EEG change of CLIS patients, though the reason for the observed EEG change cannot be concluded without any reliable communication methods with this population.},
}
@article {pmid32009923,
year = {2019},
author = {Lu, Z and Li, Q and Gao, N and Yang, J},
title = {The Self-Face Paradigm Improves the Performance of the P300-Speller System.},
journal = {Frontiers in computational neuroscience},
volume = {13},
number = {},
pages = {93},
pmid = {32009923},
issn = {1662-5188},
abstract = {Objective: Previous studies have shown that the performance of the famous face P300-speller was better than that of the classical row/column flashing P300-speller. Furthermore, in some studies, the brain was more active when responding to one's own face than to a famous face, and a self-face stimulus elicited larger amplitude event-related potentials (ERPs) than did a famous face. Thus, we aimed to study the role of the self-face paradigm on further improving the performance of the P300-speller system with the famous face P300-speller paradigm as the control paradigm. Methods: We designed two facial P300-speller paradigms based on the self-face and a famous face (Ming Yao, a sports star; the famous face spelling paradigm) with a neutral expression. Results: ERP amplitudes were significantly greater in the self-face than in the famous face spelling paradigm at the parietal area from 340 to 480 ms (P300), from 480 to 600 ms (P600f), and at the fronto-central area from 700 to 800 ms. Offline and online classification results showed that the self-face spelling paradigm accuracies were significantly higher than those of the famous face spelling paradigm at superposing first two times (P < 0.05). Similar results were found for information transfer rates (P < 0.05). Conclusions: The self-face spelling paradigm significantly improved the performance of the P300-speller system. This has significant practical applications for brain-computer interfaces (BCIs) and could avoid infringement issues caused by using images of other people's faces.},
}
@article {pmid32009595,
year = {2020},
author = {Young, MJ},
title = {Brain-Computer Interfaces and the Philosophy of Action.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {4-6},
doi = {10.1080/21507740.2019.1704309},
pmid = {32009595},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Humans ; Models, Neurological ; *Philosophy ; *Psychomotor Performance ; },
}
@article {pmid32009590,
year = {2020},
author = {Rainey, S and Maslen, H and Savulescu, J},
title = {When Thinking is Doing: Responsibility for BCI-Mediated Action.},
journal = {AJOB neuroscience},
volume = {11},
number = {1},
pages = {46-58},
pmid = {32009590},
issn = {2150-7759},
support = {/WT_/Wellcome Trust/United Kingdom ; 104848/WT_/Wellcome Trust/United Kingdom ; 104848/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; WT104848/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces/ethics ; Humans ; *Morals ; Psychomotor Performance ; Social Behavior ; *Social Responsibility ; Thinking ; },
abstract = {Technologies controlled directly by the brain are being developed, evolving based on insights gained from neuroscience, and rehabilitative medicine. Besides neuro-controlled prosthetics aimed at restoring function lost somehow, technologies controlled via brain-computer interfaces (BCIs) may also extend a user's horizon of action, freed from the need for bodily movement. Whilst BCI-mediated action ought to be, on the whole, treated as conventional action, law and policy ought to be amended to accommodate BCI action by broadening the definition of action as "willed bodily movement". Moreover, there are some dimensions of BCI mediated action that are significantly different to conventional cases. These relate to control. Specifically, to limits in both controllability of BCIs via neural states, and in foreseeability of outcomes from such actions. In some specific type of case, BCI-mediated action may be due to different ethical evaluation from conventional action.},
}
@article {pmid32008542,
year = {2019},
author = {Sahu, M and Vishwal, S and Usha Srivalli, S and Nagwani, NK and Verma, S and Shukla, S},
title = {Applying Auto-Regressive Model's Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients' Data.},
journal = {Current medical imaging reviews},
volume = {15},
number = {8},
pages = {749-760},
doi = {10.2174/1573405614666180322143503},
pmid = {32008542},
mesh = {Amyotrophic Lateral Sclerosis/*physiopathology ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Models, Statistical ; },
abstract = {OBJECTIVE: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI).<br><br>METHODS: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate &amp; interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction.<br><br>RESULTS: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51&#xb1;0.64).<br><br>CONCLUSION: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.},
}
@article {pmid32006952,
year = {2020},
author = {Kramer, DR and Lamorie-Foote, K and Barbaro, M and Lee, MB and Peng, T and Gogia, A and Nune, G and Liu, CY and Kellis, SS and Lee, B},
title = {Utility and lower limits of frequency detection in surface electrode stimulation for somatosensory brain-computer interface in humans.},
journal = {Neurosurgical focus},
volume = {48},
number = {2},
pages = {E2},
pmid = {32006952},
issn = {1092-0684},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*standards ; Drug Resistant Epilepsy/diagnostic imaging/*physiopathology ; Electric Stimulation/methods ; Electrocorticography/instrumentation/*methods ; Electrodes, Implanted/*standards ; Humans ; Magnetic Resonance Imaging/methods ; Psychomotor Performance/*physiology ; Random Allocation ; Somatosensory Cortex/*physiology ; Tomography, X-Ray Computed/methods ; },
abstract = {OBJECTIVE: Stimulation of the primary somatosensory cortex (S1) has been successful in evoking artificial somatosensation in both humans and animals, but much is unknown about the optimal stimulation parameters needed to generate robust percepts of somatosensation. In this study, the authors investigated frequency as an adjustable stimulation parameter for artificial somatosensation in a closed-loop brain-computer interface (BCI) system.<br><br>METHODS: Three epilepsy patients with subdural mini-electrocorticography grids over the hand area of S1 were asked to compare the percepts elicited with different stimulation frequencies. Amplitude, pulse width, and duration were held constant across all trials. In each trial, subjects experienced 2 stimuli and reported which they thought was given at a higher stimulation frequency. Two paradigms were used: first, 50 versus 100 Hz to establish the utility of comparing frequencies, and then 2, 5, 10, 20, 50, or 100 Hz were pseudorandomly compared.<br><br>RESULTS: As the magnitude of the stimulation frequency was increased, subjects described percepts that were "more intense" or "faster." Cumulatively, the participants achieved 98.0% accuracy when comparing stimulation at 50 and 100 Hz. In the second paradigm, the corresponding overall accuracy was 73.3%. If both tested frequencies were less than or equal to 10 Hz, accuracy was 41.7% and increased to 79.4% when one frequency was greater than 10 Hz (p = 0.01). When both stimulation frequencies were 20 Hz or less, accuracy was 40.7% compared with 91.7% when one frequency was greater than 20 Hz (p < 0.001). Accuracy was 85% in trials in which 50 Hz was the higher stimulation frequency. Therefore, the lower limit of detection occurred at 20 Hz, and accuracy decreased significantly when lower frequencies were tested. In trials testing 10 Hz versus 20 Hz, accuracy was 16.7% compared with 85.7% in trials testing 20 Hz versus 50 Hz (p < 0.05). Accuracy was greater than chance at frequency differences greater than or equal to 30 Hz.<br><br>CONCLUSIONS: Frequencies greater than 20 Hz may be used as an adjustable parameter to elicit distinguishable percepts. These findings may be useful in informing the settings and the degrees of freedom achievable in future BCI systems.},
}
@article {pmid32004862,
year = {2020},
author = {Kamel, MB and Sayed, T and Bigazzi, A},
title = {A composite zonal index for biking attractiveness and safety.},
journal = {Accident; analysis and prevention},
volume = {137},
number = {},
pages = {105439},
doi = {10.1016/j.aap.2020.105439},
pmid = {32004862},
issn = {1879-2057},
mesh = {Accidents, Traffic/*prevention &amp; control ; *Bicycling ; British Columbia ; Built Environment/*standards ; Cities ; Humans ; Risk Assessment ; Safety ; Travel ; },
abstract = {Zonal characteristics (e.g. built environment, network configuration, socio-demographics, and land use) have been shown to affect biking attractiveness and safety. However, previously developed bikeability indices do not account for cyclist-vehicle crash risk. This study aims to develop a comprehensive zone-based index to represent both biking attractiveness and cyclist crash risk. The developed Bike Composite Index (BCI) consists of two sub-indices representing bike attractiveness and bike safety, which are estimated using Bike Kilometers Travelled (BKT) and cyclist-vehicle crash data from 134 traffic analysis zones (TAZ) in the City of Vancouver, Canada. The Bike Attractiveness Index is calculated from five factors: bike network density, centrality, and weighted slope as well as land use mix and recreational density. The Bike Safety Index is calculated from bike network coverage, continuity, and complexity as well as signal density and recreational density. The correlation between the Bike Attractiveness Index and the Bike Safety Index in Vancouver is low (r = 0.11), supporting the need to account for both biking attractiveness and safety in the composite index.},
}
@article {pmid32001275,
year = {2020},
author = {Silveira Serra, D and Matias de Sousa, A and Costa da Silva Andrade, L and de Lima Gondim, F and Evangelista de Ávila Dos Santos, J and Moura de Oliveira, ML and Torres Ávila Pimenta, A},
title = {Effects of fixed oil of Caryocar coriaceum Wittm. Seeds on the respiratory system of rats in a short-term secondhand-smoke exposure model.},
journal = {Journal of ethnopharmacology},
volume = {252},
number = {},
pages = {112633},
doi = {10.1016/j.jep.2020.112633},
pmid = {32001275},
issn = {1872-7573},
mesh = {Acute Lung Injury/pathology/physiopathology/*prevention &amp; control ; Animals ; Disease Models, Animal ; *Ericales ; Lung/drug effects/pathology/physiology ; Male ; Plant Oils/*therapeutic use ; Rats, Wistar ; Respiratory Mechanics/*drug effects ; Seeds ; Tobacco Smoke Pollution/*adverse effects ; },
abstract = {Pequi fruit are obtained from the pequi tree (Caryocar coriaceum), from which the pulp and nut are used in order to extract an oil that is commonly used in popular medicine as an antiinflammatory agent, particularly for the treatment of colds, bronchitis and bronchopulmonary infections. Making use of the fixed oil of Caryocar coriaceum (FOCC), an attractive alternative for the treatment of diseases caused by exposure to environmental tobacco smoke.<br><br>AIM OF THE STUDY: To evaluate whether oral intake FOCC provides beneficial effects in the respiratory system of rats submitted to a short-term secondhand smoke (SHS) exposure model.<br><br>MATERIALS AND METHODS: The experiments were performed on Wistar rats divided into 4 groups; in the SHS&#xa0;+&#xa0;O and SHS&#xa0;+&#xa0;T groups, the animals were pretreated orally with 0.5&#xa0;mL of FOCC (SHS&#xa0;+&#xa0;O) or vehicle (Tween-80 [1%] solution) (SHS&#xa0;+&#xa0;T). Immediately after pretreatment, the animals were submitted to the SHS exposure protocol, for a total period of 14 days. Exposures were performed 6 times per day, with a duration of 40&#xa0;min per exposure (5 cigarettes per exposure), followed by a 1-h interval between subsequent exposures. In the AA&#xa0;+&#xa0;O and AA&#xa0;+&#xa0;T groups, animals were submitted to daily oral pretreatment with 0.5&#xa0;mL of FOCC (AA&#xa0;+&#xa0;O) or vehicle (AA&#xa0;+&#xa0;T). These animals were then subjected to the aforementioned exposure protocol, but using ambient air. After the exposure period, we investigated the effects of FOCC in respiratory mechanics in vivo (Newtonian resistance -RN, tissue elastance -H, tissue resistance -G, static compliance -CST, inspiratory capacity -IC, PV loop area) histopathology and lung parenchymal morphometry in vitro (polymorphonuclear cells -PMN, mean alveolar diameter -Lm, bronchoconstriction index -BCI), temporal evolution of subjects' masses, and percent composition of the FOCC.<br><br>RESULTS: Regarding the body mass of the animals, the results demonstrated an average body mass gain of 10.5&#xa0;g for the animals in the AA&#xa0;+&#xa0;T group, and 15.5&#xa0;g for those in the AA&#xa0;+&#xa0;O group. On the other hand, the body mass of animals in the SHS&#xa0;+&#xa0;T and SHS&#xa0;+&#xa0;O suffered an average loss of 14.4 and 4.75&#xa0;g, respectively. Regarding respiratory system analyzes, our results demonstrated significant changes in all respiratory mechanics variables and lung parenchyma morphometry analyzed for the SHS&#xa0;+&#xa0;T group when compared to the AA&#xa0;+&#xa0;T group (p&#xa0;<&#xa0;0,05), confirming the establishment of pulmonary injury induced by SHS exposure. We also observed that rats pretreated orally with FOCC (SHS&#xa0;+&#xa0;O) showed improvement in all variables when compared to the SHS&#xa0;+&#xa0;T group (p&#xa0;<&#xa0;0,05), thus demonstrating the effectiveness of FOCC in preventing lung damage induced by short-term SHS exposure.<br><br>CONCLUSION: In conclusion, our results demonstrate that FOCC was able to prevent lung injury in rats submitted to short-term SHS exposure.},
}
@article {pmid32001011,
year = {2020},
author = {Li, M and Liang, Y and Yang, L and Wang, H and Yang, Z and Zhao, K and Shang, Z and Wan, H},
title = {Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals.},
journal = {Computers in biology and medicine},
volume = {116},
number = {},
pages = {103572},
doi = {10.1016/j.compbiomed.2019.103572},
pmid = {32001011},
issn = {1879-0534},
mesh = {Action Potentials ; *Brain-Computer Interfaces ; Machine Learning ; },
abstract = {"Bad channels" in implantable multi-channel recordings bring troubles into the precise quantitative description and analysis of neural signals, especially in the current "big data" era. In this paper, we combine multimodal features based on local field potentials (LFPs) and spike signals to detect bad channels automatically using machine learning. On the basis of 2632 pairs of LFPs and spike recordings acquired from five pigeons, 12 multimodal features are used to quantify each channel's temporal, frequency, phase and firing-rate properties. We implement seven classifiers in the detection tasks, in which the synthetic minority oversampling technique (SMOTE) system and Fisher weighted Euclidean distance sorting (FWEDS) are used to cope with the class imbalance problem. The results of the two-dimensional scatterplots and classifications demonstrate that correlation coefficient, phase locking value, and coherence have good discriminability. For the multimodal features, almost all the classifiers can obtain high accuracy and bad channel detection rate after the SMOTE operation, in which the Random Forests classifier shows relatively better comprehensive performance (accuracy: 0.9092 ± 0.0081, precision: 0.9123 ± 0.0100, and recall: 0.9057 ± 0.0121). The proposed approach can automatically detect bad channels based on multimodal features, and the results provide valuable references for larger datasets.},
}
@article {pmid32000145,
year = {2020},
author = {Li, G and Wu, J and Xia, Y and Wu, Y and Tian, Y and Liu, J and Chen, D and He, Q},
title = {Towards emerging EEG applications: a novel printable flexible Ag/AgCl dry electrode array for robust recording of EEG signals at forehead sites.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026001},
doi = {10.1088/1741-2552/ab71ea},
pmid = {32000145},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electric Impedance ; Electrodes ; Electroencephalography ; Forehead ; *Silver ; },
abstract = {OBJECTIVES: With the rapid development of EEG-based wearable healthcare devices and brain-computer interfaces, reliable and user-friendly EEG sensors for EEG recording, especially at forehead sites, are highly desirable and challenging. However, existing EEG sensors cannot meet the requirements, since wet electrodes require tedious setup and conductive pastes or gels, and most dry electrodes show unacceptable high contact impedance. In addition, the existing electrodes cannot absorb sweat effectively; sweat would cause cross-interferences, and even short circuits, between adjacent electrodes, especially in the moving scenarios, or a hot and humid environment. To resolve these problems, a novel printable flexible Ag/AgCl dry electrode array was developed for EEG acquisition at forehead sites, mainly consisting of screen printing the Ag/AgCl coating, conductive sweat-absorbable sponges and flexible tines.<br><br>APPROACH: A systematic method was also established to evaluate the flexible dry electrode array.<br><br>MAIN RESULTS: The experimental results show the flexible dry electrode array has reproducible electrode potential, relatively low electrode-skin impedance, and good stability. Moreover, the EEG signals can be effectively captured with a high quality that is comparable to that of wet electrodes.<br><br>SIGNIFICANCE: All the results confirmed the feasibility of forehead EEG recording in real-world scenarios using the proposed flexible dry electrode array, with a rapid and facile operation as well as the advantages of self-application, user-friendliness and wearer comfort.},
}
@article {pmid31998057,
year = {2019},
author = {Pflüger, P and Pinnell, RC and Martini, N and Hofmann, UG},
title = {Chronically Implanted Microelectrodes Cause c-fos Expression Along Their Trajectory.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1367},
pmid = {31998057},
issn = {1662-4548},
abstract = {When designing electrodes and probes for brain-machine interfaces, one of the challenges faced involves minimizing the brain-tissue response, which would otherwise create an environment that is detrimental for the accurate functioning of such probes. Following the implantation process, the brain reacts with a sterile inflammation response and resulting astrocytic glial scar formation, potentially resulting in neuronal cell loss around the implantation site. Such alterations in the naïve brain tissue can hinder both the quality of neuronal recordings, and the efficacy of deep-brain stimulation. In this study, we chronically implanted a glass-supported polyimide microelectrode in the dorsolateral striatum of Sprague-Dawley rats. The effect of high-frequency stimulation (HFS) was investigated using c-fos immunoreactivity techniques. GFAP and ED1 immunohistochemistry were used to analyze the brain-tissue response. No changes in c-fos expression were found for either the acute or chronic stimulus groups; although a c-fos expression was found along the length of the implantation trajectory, following chronic implantation of our stiffened polyimide microelectrode. Furthermore, we also observed the formation of a glial scar around the microelectrode, with an accompanying low number of inflammation cells. Histological and statistical analysis of NeuN-positive cells did not demonstrate a pronounced "kill zone" with accompanying neuronal cell death around the implantation site, neither on the polymer side, nor on the glass side of the PI-glass probe.},
}
@article {pmid31997803,
year = {2020},
author = {Zheng, Y and Mao, YR and Yuan, TF and Xu, DS and Cheng, LM},
title = {Multimodal treatment for spinal cord injury: a sword of neuroregeneration upon neuromodulation.},
journal = {Neural regeneration research},
volume = {15},
number = {8},
pages = {1437-1450},
pmid = {31997803},
issn = {1673-5374},
abstract = {Spinal cord injury is linked to the interruption of neural pathways, which results in irreversible neural dysfunction. Neural repair and neuroregeneration are critical goals and issues for rehabilitation in spinal cord injury, which require neural stem cell repair and multimodal neuromodulation techniques involving personalized rehabilitation strategies. Besides the involvement of endogenous stem cells in neurogenesis and neural repair, exogenous neural stem cell transplantation is an emerging effective method for repairing and replacing damaged tissues in central nervous system diseases. However, to ensure that endogenous or exogenous neural stem cells truly participate in neural repair following spinal cord injury, appropriate interventional measures (e.g., neuromodulation) should be adopted. Neuromodulation techniques, such as noninvasive magnetic stimulation and electrical stimulation, have been safely applied in many neuropsychiatric diseases. There is increasing evidence to suggest that neuromagnetic/electrical modulation promotes neuroregeneration and neural repair by affecting signaling in the nervous system; namely, by exciting, inhibiting, or regulating neuronal and neural network activities to improve motor function and motor learning following spinal cord injury. Several studies have indicated that fine motor skill rehabilitation training makes use of residual nerve fibers for collateral growth, encourages the formation of new synaptic connections to promote neural plasticity, and improves motor function recovery in patients with spinal cord injury. With the development of biomaterial technology and biomechanical engineering, several emerging treatments have been developed, such as robots, brain-computer interfaces, and nanomaterials. These treatments have the potential to help millions of patients suffering from motor dysfunction caused by spinal cord injury. However, large-scale clinical trials need to be conducted to validate their efficacy. This review evaluated the efficacy of neural stem cells and magnetic or electrical stimulation combined with rehabilitation training and intelligent therapies for spinal cord injury according to existing evidence, to build up a multimodal treatment strategy of spinal cord injury to enhance nerve repair and regeneration.},
}
@article {pmid31996696,
year = {2020},
author = {Rastogi, A and Vargas-Irwin, CE and Willett, FR and Abreu, J and Crowder, DC and Murphy, BA and Memberg, WD and Miller, JP and Sweet, JA and Walter, BL and Cash, SS and Rezaii, PG and Franco, B and Saab, J and Stavisky, SD and Shenoy, KV and Henderson, JM and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {1429},
pmid = {31996696},
issn = {2045-2322},
support = {I01 RX002654/RX/RRD VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; T32 GM007250/GM/NIGMS NIH HHS/United States ; TL1 TR002549/TR/NCATS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; Biomedical Engineering ; Brain-Computer Interfaces ; Chronic Disease ; Hand Strength ; Humans ; Imagination ; Male ; Microelectrodes ; Middle Aged ; Motor Cortex/*physiology/surgery ; *Neural Prostheses ; Quadriplegia/*therapy ; Recovery of Function ; Synaptic Transmission ; Volition/*physiology ; },
abstract = {Hybrid kinetic and kinematic intracortical brain-computer interfaces (iBCIs) have the potential to restore functional grasping and object interaction capabilities in individuals with tetraplegia. This requires an understanding of how kinetic information is represented in neural activity, and how this representation is affected by non-motor parameters such as volitional state (VoS), namely, whether one observes, imagines, or attempts an action. To this end, this work investigates how motor cortical neural activity changes when three human participants with tetraplegia observe, imagine, and attempt to produce three discrete hand grasping forces with the dominant hand. We show that force representation follows the same VoS-related trends as previously shown for directional arm movements; namely, that attempted force production recruits more neural activity compared to observed or imagined force production. Additionally, VoS-modulated neural activity to a greater extent than grasping force. Neural representation of forces was lower than expected, possibly due to compromised somatosensory pathways in individuals with tetraplegia, which have been shown to influence motor cortical activity. Nevertheless, attempted forces (but not always observed or imagined forces) could be decoded significantly above chance, thereby potentially providing relevant information towards the development of a hybrid kinetic and kinematic iBCI.},
}
@article {pmid31992251,
year = {2020},
author = {Fossi Djembi, L and Vaiva, G and Debien, C and Duhem, S and Demarty, AL and Koudou, YA and Messiah, A},
title = {Changes in the number of suicide re-attempts in a French region since the inception of VigilanS, a regionwide program combining brief contact interventions (BCI).},
journal = {BMC psychiatry},
volume = {20},
number = {1},
pages = {26},
pmid = {31992251},
issn = {1471-244X},
mesh = {Adult ; Aftercare/*methods/trends ; Algorithms ; Early Medical Intervention/*methods/trends ; Female ; France/epidemiology ; *Health Resources/trends ; Humans ; Male ; Psychotherapy, Brief/*methods/trends ; Suicide, Attempted/*prevention &amp; control/*psychology/trends ; },
abstract = {BACKGROUND: Brief Contact Interventions (BCIs) after a suicide attempt (SA) are an important element of prevention against SA and suicide. They are easier to generalize to an entire population than other forms of intervention. VigilanS generalizes to a whole French region a BCI combining resource cards, telephone calls and mailings, according to a predefined algorithm. It was implemented gradually in the Nord-Pas-de-Calais (NPC), France, between 2015 and 2018. Here, we evaluate the effectiveness of VigilanS, in terms of SA reduction, using annual data collected by participating centers. Hypothesis tested: the higher the VigilanS implementation in a center (measured by penetrance), the greater the decrease in the number of SA observed in this center.<br><br>METHODS: The study period was from 2014 to 2018, across all of NPC centers. We performed a series of linear regressions, each center representing a statistical unit. The outcome was the change in the number of SA, relative to the initial number, and the predictive variable was VigilanS' penetrance: number of patients included in VigilanS over the total number of SA. Search for influential points (points beyond threshold values of 3 influence criteria) and weighted least squares estimations were performed.<br><br>RESULTS: Twenty-one centers were running VigilanS in 2018, with an average penetrance of 32%. A significant relationship was identified, showing a sharp decrease in SA as a function of penetrance (slope&#x2009;=&#x2009;-&#x2009;1.13; p&#x2009;=&#x2009;3*10[-&#x2009;5]). The model suggested that a 25% of penetrance would yield a SA decrease of 41%.<br><br>CONCLUSION: VigilanS has the potential to reduce SA. Subgroup analyzes are needed to further evaluate its effectiveness. Subgroup analyses remain to be done, in order to evaluate the specific variations of SA by group.},
}
@article {pmid31989946,
year = {2020},
author = {Chluba, C and Siemsen, K and Bechtold, C and Zamponi, C and Selhuber-Unkel, C and Quandt, E and Lima de Miranda, R},
title = {Microfabricated bioelectrodes on self-expandable NiTi thin film devices for implants and diagnostic instruments.},
journal = {Biosensors &amp; bioelectronics},
volume = {153},
number = {},
pages = {112034},
doi = {10.1016/j.bios.2020.112034},
pmid = {31989946},
issn = {1873-4235},
mesh = {Alloys/chemistry ; Animals ; Biocompatible Materials/chemistry ; Biosensing Techniques/*instrumentation ; Body Fluids/metabolism ; Electrochemical Techniques/*methods ; *Electrodes, Implanted ; Equipment and Supplies ; Humans ; Mechanical Phenomena ; *Microelectrodes ; Microtechnology ; Nickel/*chemistry ; Oxides/chemistry ; Polymers/chemistry ; Prostheses and Implants ; Surface Properties ; Titanium/*chemistry ; },
abstract = {State of the art minimally invasive treatments and diagnostics of neurological and cardiovascular diseases demand for flexible instruments and implants that enable sensing and stimulation of bioelectric signals. Besides medical applications, implantable bioelectronic brain-computer interfaces are envisioned as the next step in communication and data transfer. Conventional microelectrode arrays used for these types of applications are based on polymer substrates that are not suitable for biostable, rigid and self-expanding devices. Here, we present fully integrated bioelectrodes on superelastic NiTi carriers fabricated by microsystem technology processes. The insulation between the metallic NiTi structure and the Pt electrode layer is realized by different oxide layers (SiOx, TaOx and Yttrium stabilized Zirconia YSZ). Key properties of bioelectronic implants such as dissolution in body fluids, biocompatibility, mechanical properties and bioelectrical sensing/stimulation capabilities have been investigated by in vitro methods. Particular devices with YSZ are biostable and biocompatible, enabling sensing and stimulation. The major advantage of this system is the combination of medically approved materials and novel fabrication technology that enables miniaturization and integration beyond the state-of-the-art processes. The results demonstrate that this functionalization of superelastic NiTi is an enabling technology for the development of new kinds of bioelectronic devices.},
}
@article {pmid31988102,
year = {2020},
author = {Diacon, AH and De Jager, VR and Dawson, R and Narunsky, K and Vanker, N and Burger, DA and Everitt, D and Pappas, F and Nedelman, J and Mendel, CM},
title = {Fourteen-Day Bactericidal Activity, Safety, and Pharmacokinetics of Linezolid in Adults with Drug-Sensitive Pulmonary Tuberculosis.},
journal = {Antimicrobial agents and chemotherapy},
volume = {64},
number = {4},
pages = {},
pmid = {31988102},
issn = {1098-6596},
mesh = {Adult ; Antitubercular Agents/*therapeutic use ; Drug Therapy, Combination ; Ethambutol/therapeutic use ; Female ; Humans ; Isoniazid/therapeutic use ; Linezolid/*therapeutic use ; Male ; Microbial Sensitivity Tests ; Mycobacterium tuberculosis/*drug effects ; Pyrazinamide/therapeutic use ; Rifampin/therapeutic use ; South Africa ; Sputum/microbiology ; Tuberculosis, Pulmonary/*drug therapy ; },
abstract = {Linezolid is increasingly used for the treatment of tuberculosis resistant to first-line agents, but the most effective dosing strategy is yet unknown. From November 2014 to November 2016, we randomized 114 drug-sensitive treatment-naive pulmonary tuberculosis patients from Cape Town, South Africa, to one of six 14-day treatment arms containing linezolid at 300 mg once daily (QD), 300 mg twice daily (BD), 600 mg QD, 600 mg BD, 1,200 mg QD, 1,200 mg three times per week (TIW), or a combination of isoniazid, rifampin, pyrazinamide, and ethambutol. Sixteen-hour sputum samples were collected overnight, and bactericidal activity was characterized by the daily percentage change in time to positivity (TTP) and the daily rate of change in log10(CFU). We also assessed the safety and pharmacokinetics of the study treatments. We found that bactericidal activity increased with increasing doses of linezolid. Based on the daily percentage change in TTP, activity was highest for 1,200 mg QD (4.5%; 95% Bayesian confidence interval [BCI], 3.3 to 5.6), followed by 600 mg BD (4.1%; BCI, 2.5 to 5.7), 600 mg QD (4.1%; BCI, 2.9 to 5.3), 300 mg BD (3.3%; BCI, 1.9 to 4.7), 300 mg QD (2.3%; BCI, 1.1 to 3.5), and 1,200 mg TIW (2.2%; BCI, 1.1 to 3.3). Similar results were seen with bactericidal activity characterized by the daily rate of change in CFU count. Antimycobacterial activity correlated positively with plasma drug exposure and percentage time over MIC. There were no unexpected adverse events. All linezolid doses showed bactericidal activity. For the same total daily dose, once-daily dosing proved to be at least as effective as a divided twice-daily dose. An intermittent dosing regimen, with 1,200 mg given three times weekly, showed the least activity. (This study has been registered at ClinicalTrials.gov under identifier NCT02279875.).},
}
@article {pmid31986492,
year = {2020},
author = {Dijkstra, KV and Farquhar, JDR and Desain, PWM},
title = {The N400 for brain computer interfacing: complexities and opportunities.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {022001},
doi = {10.1088/1741-2552/ab702e},
pmid = {31986492},
issn = {1741-2552},
mesh = {Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Reaction Time ; Semantics ; },
abstract = {The N400 is an event related potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. A complicating aspect is the number of factors that can affect the N400 amplitude. In this paper, we provide an accessible overview of this range of N400 effects, and survey the three main BCI application areas that currently exploit the N400: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.},
}
@article {pmid31985451,
year = {2021},
author = {Qi, F and Wu, W and Yu, ZL and Gu, Z and Wen, Z and Yu, T and Li, Y},
title = {Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification.},
journal = {IEEE transactions on cybernetics},
volume = {51},
number = {2},
pages = {558-567},
doi = {10.1109/TCYB.2019.2963709},
pmid = {31985451},
issn = {2168-2275},
abstract = {Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility.},
}
@article {pmid31985429,
year = {2020},
author = {Mao, X and Li, W and Hu, H and Jin, J and Chen, G},
title = {Improve the Classification Efficiency of High-Frequency Phase-Tagged SSVEP by a Recursive Bayesian-Based Approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {3},
pages = {561-572},
doi = {10.1109/TNSRE.2020.2968579},
pmid = {31985429},
issn = {1558-0210},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Among the Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), the phase-tagged SSVEP (p-SSVEP) has been proved a reliable paradigm to extend the number of available targets, especially for high-frequency SSVEP-based BCIs. However, the recognition efficiency of the high-frequency p-SSVEP still remains relatively low. A longer data segment may achieve a higher classification accuracy, but the time consumption of computation leads to the decrease of information transfer rate. This paper presents a recursive Bayesian-based approach to improve the high-frequency p-SSVEP classification efficiency. In each signal processing period, the classification result is generated by the current scores, the condition probability and a recursive prior probability (dynamic prior probability). The experiment displays the SSVEP stimuli with 20 Hz and 30 Hz respectively, and each frequency contains six phases. This paper compared three classification approaches and the recursive Bayesian-based approach could obtain the highest classification accuracy and practical bit rate under the same data length. The mean accuracy and practical bit rate were 89.7% and 37.8 bits/min with 20Hz, and 89.0% and 36.5 bits/min with 30Hz, respectively Furthermore, the recursive Bayesian-based approach could reduce the individual differences among different subjects. Therefore, the recursive Bayesian-based approach can lead to high classification efficiency in high-frequency p-SSVEP.},
}
@article {pmid31985428,
year = {2020},
author = {Cecotti, H},
title = {Adaptive Time Segment Analysis for Steady-State Visual Evoked Potential Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {3},
pages = {552-560},
doi = {10.1109/TNSRE.2020.2968307},
pmid = {31985428},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {The research in non-invasive Brain-Computer Interface (BCI) has led to significant improvements in the recent years for potential end users. However, the user experience and the BCI illiteracy problem remains challenging areas to address for obtaining robust and resilient clinical applications. In this study, we address the choice of the time segment for the detection of steady state visual evoked potential (SSVEP) detection. This problem has been widely addressed for the detection of event-related potentials compared to SSVEP based BCIs. The choice of this parameter is typically fixed and has a direct influence on both the detection accuracy and the information transfer rate. We propose to shift the problem of the time segment to the choice of the threshold for determining if a response has been properly detected. We consider two open-datasets for benchmarking the rationale of the approach. The results support the conclusion that an adaptive time segment for each trial, based on the selection of a threshold, can lead to a substantial higher ITR (86.92 bits/min), compared to the time segment chosen at the user (79.56 bits/min) or group level (73.78 bits/min). Finally, the results suggest that the threshold could be determined automatically in relation to the number of classes. Such an approach can leverage the literacy of SSVEP based BCI.},
}
@article {pmid31983904,
year = {2019},
author = {Watson, TD},
title = {'Without A Key': A Classroom Case Study.},
journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience},
volume = {18},
number = {1},
pages = {C5-C7},
pmid = {31983904},
issn = {1544-2896},
abstract = {This case study uses a narrative focused on locked-in syndrome to engage upper-level undergraduate students with functional neuroanatomy, clinical neuroscience, and brain computer interface technology. Students 'diagnose' the etiology of a composite patient's symptoms using behavioral, neurological, neuroimaging, and electrophysiological test results. Students work both in small groups and as a class to develop analytical and communication skills by exploring the underpinnings, symptoms, and outcomes of locked-in syndrome and how behavioral and brain computer interface techniques could be used to improve quality of life in patients. A complete, detailed description of classroom implementation and the case narratives are available from the corresponding author or from cases.at.june@gmail.com.},
}
@article {pmid31983375,
year = {2020},
author = {Ali, JI and Viczko, J and Smart, CM},
title = {Efficacy of Neurofeedback Interventions for Cognitive Rehabilitation Following Brain Injury: Systematic Review and Recommendations for Future Research.},
journal = {Journal of the International Neuropsychological Society : JINS},
volume = {26},
number = {1},
pages = {31-46},
doi = {10.1017/S1355617719001061},
pmid = {31983375},
issn = {1469-7661},
mesh = {Brain Injuries/complications/*rehabilitation ; Cognitive Dysfunction/etiology/*rehabilitation ; Humans ; *Neurofeedback/methods ; *Neurological Rehabilitation/methods ; *Outcome Assessment, Health Care ; },
abstract = {OBJECTIVES: Interest in neurofeedback therapies (NFTs) has grown exponentially in recent years, encouraged both by escalating public interest and the financial support of health care funding agencies. Given NFTs' growing prevalence and anecdotally reported success in treating common effects of acquired brain injury (ABI), a systematic review of the efficacy of NFTs for the rehabilitation of ABI-related cognitive impairment is warranted.<br><br>METHODS: Eligible studies included adult samples (18+ years) with ABI, the use of neurofeedback technology for therapeutic purposes (as opposed to assessment), the inclusion of a meaningful control group/condition, and clear cognitive-neuropsychological outcomes. Initial automated search identified n = 86 candidate articles, however, only n = 4 studies met the stated eligibility criteria.<br><br>RESULTS: Results were inconsistent across studies and cognitive domains. Methodological and theoretical limitations precluded robust and coherent conclusions with respect to the cognitive rehabilitative properties of NFTs. We take the results of these systematic analyses as a reflection of the state of the literature at this time. These results offer a constructive platform to further discuss a number of methodological, theoretical, and ethical considerations relating to current and future NFT-ABI research and clinical intervention.<br><br>CONCLUSIONS: Given the limited quantity and quality of the available research, there appears to be insufficient evidence to comment on the efficacy of NFTs within an ABI rehabilitation context at this time. It is imperative that future work increase the level of theoretical and methodological rigour if meaningful advancements are to be made understanding and evaluating NFT-ABI applications.},
}
@article {pmid31980276,
year = {2020},
author = {Jalilpour, S and Hajipour Sardouie, S and Mijani, A},
title = {A novel hybrid BCI speller based on RSVP and SSVEP paradigm.},
journal = {Computer methods and programs in biomedicine},
volume = {187},
number = {},
pages = {105326},
doi = {10.1016/j.cmpb.2020.105326},
pmid = {31980276},
issn = {1872-7565},
mesh = {Adult ; Algorithms ; *Brain Mapping ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; Electroencephalography ; Event-Related Potentials, P300 ; Healthy Volunteers ; Humans ; Linear Models ; Male ; Pattern Recognition, Automated ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVE: Steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two approaches can enhance the proficiency of the P300 spellers.<br><br>METHODS: In this study, a new hybrid RSVP/SSVEP BCI is proposed to increase the classification accuracy and information transfer rate (ITR) as compared with the other RSVP speller paradigms. In this paradigm, RSVP (eliciting a P300 response) and SSVEP stimulations are presented in such a way that the target group of characters is identified by RSVP stimuli, and the target character is recognized by SSVEP stimuli.<br><br>RESULTS: The proposed paradigm achieved accuracy of 93.06%, and ITR of 23.41 bit/min averaged across six subjects.<br><br>CONCLUSIONS: The new hybrid system demonstrates that by using SSVEP stimulation in Triple RSVP speller paradigm, we could enhance the performance of the system as compared with the traditional Triple RSVP paradigm. Our work is the first hybrid paradigm in RSVP spellers that could obtain the higher classification accuracy and information transfer rate in comparison with the previous RSVP spellers.},
}
@article {pmid31980103,
year = {2020},
author = {Xiaoxiao, X and Bin, L and Ramkumar, S and Saravanan, S and Balaji, MSP and Dhanasekaran, S and Thimmiaraja, J},
title = {Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.},
journal = {Artificial intelligence in medicine},
volume = {102},
number = {},
pages = {101766},
doi = {10.1016/j.artmed.2019.101766},
pmid = {31980103},
issn = {1873-2860},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Simulation ; Electrodes ; Electroencephalography/*methods ; Female ; Healthy Volunteers ; Humans ; Locked-In Syndrome/*rehabilitation ; Male ; Middle Aged ; *Neural Networks, Computer ; Reproducibility of Results ; Sex Characteristics ; Spinal Cord Injuries/rehabilitation ; Wavelet Analysis ; Young Adult ; },
abstract = {Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.},
}
@article {pmid31980094,
year = {2020},
author = {Tang, W and Wang, A and Ramkumar, S and Nair, RKR},
title = {Signal identification system for developing rehabilitative device using deep learning algorithms.},
journal = {Artificial intelligence in medicine},
volume = {102},
number = {},
pages = {101755},
doi = {10.1016/j.artmed.2019.101755},
pmid = {31980094},
issn = {1873-2860},
mesh = {Adult ; Aging ; *Algorithms ; Brain-Computer Interfaces ; *Deep Learning ; Electrodes ; Electrooculography ; Equipment Design/*methods ; Eye Movements ; Female ; Healthy Volunteers ; Humans ; Male ; Rehabilitation/*instrumentation ; Reproducibility of Results ; Self-Help Devices ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent days to overcome such problem. In this paper we clearly check the possibilities of creating nine states HCI by our proposed method. Signals were captured through five electrodes placed on the subjects face around the eyes. These signals were amplified with ADT26 bio amplifier, filtered with notch filter, and processed with reference power and band power techniques to extract features to detect the eye movements and mapped with Time Delay Neural Network to classify the eye movements to generate control signal to control external hardware devices. Our experimental study reports that maximum average classification of 91.09% for reference power feature and 91.55%-for band power feature respectively. The obtained result confirms that band power features with TDNN network models shows better performance than reference features for all subjects. From this outcome we conclude that band power features with TDNN network models was more suitable for classifying the eleven difference eye movements for individual subjects. To validate the result obtained from this method we categorize the subjects in age wise to check the accuracy of the system. Single trail analysis was conducted in offline to identify the recognizing accuracy of the proposed system. The result summarize that band power features with TDNN network models exceed the reference power with TDNN network model used in this study. Through the outcome we conclude that that band power features with TDNN network was more suitable for designing EOG based HCI in offline mode.},
}
@article {pmid31980093,
year = {2020},
author = {Li, K and Ramkumar, S and Thimmiaraja, J and Diwakaran, S},
title = {Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients.},
journal = {Artificial intelligence in medicine},
volume = {102},
number = {},
pages = {101754},
doi = {10.1016/j.artmed.2019.101754},
pmid = {31980093},
issn = {1873-2860},
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Healthy Volunteers ; Humans ; Locked-In Syndrome ; Male ; Middle Aged ; *Neural Networks, Computer ; Patients ; Robotics ; *Wheelchairs ; Young Adult ; },
abstract = {Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable condition the person need rehabilitation devices through a Brain Computer Interfaces (BCI) to satisfy their needs. BMI using Electroencephalogram (EEG) receives the mental thoughts from brain and converts into control signals to activate the exterior communication appliances in the absence of biological channels. To design the BCI, we conduct our study with three normal male subjects, three normal female subjects and three ALS affected individuals from the age of 20-60 with three electrode systems for four tasks. One Dimensional Local Binary Patterns (LBP) technique was applied to reduce the digitally sampled features collected from nine subjects was treated with Grey wolf optimization Neural Network (GWONN) to classify the mentally composed words. Using these techniques, we compared the three types of subjects to identify the performances. The study proves that subjects from normal male categories performance was maximum compared with the other subjects. To assess the individual performance of the subject, we conducted the recognition accuracy test in offline mode. From the accuracy test also, we obtained the best performance from the normal male subjects compared with female and ALS subjects with an accuracy of 98.33 %, 95.00 % and 88.33 %. Finally our study concludes that patients with ALS attack need more training than that of the other subjects.},
}
@article {pmid31976916,
year = {2020},
author = {Zhang, D and Chen, K and Jian, D and Yao, L},
title = {Motor Imagery Classification via Temporal Attention Cues of Graph Embedded EEG Signals.},
journal = {IEEE journal of biomedical and health informatics},
volume = {24},
number = {9},
pages = {2570-2579},
doi = {10.1109/JBHI.2020.2967128},
pmid = {31976916},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Cues ; Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; },
abstract = {Motor imagery classification from EEG signals is essential for motor rehabilitation with a Brain-Computer Interface (BCI). Most current works on this issue require a subject-specific adaptation step before applied to a new user. Thus the research of directly extending a pre-trained model to new users is particularly desired and indispensable. As brain dynamics fluctuate considerably across different subjects, it is challenging to design practical hand-crafted features based on prior knowledge. Regarding this gap, this paper proposes a Graph-based Convolutional Recurrent Attention Model (G-CRAM) to explore EEG features across different subjects for motor imagery classification. A graph structure is first developed to represent the positioning information of EEG nodes. Then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and emphasizes on the most distinguishable temporal periods. We evaluate the proposed approach on two benchmark EEG datasets of motor imagery classification on the subject-independent testing. The results show that the G-CRAM achieves superior performance to state-of-the-art methods regarding recognition accuracy and ROC-AUC. Furthermore, model interpretation studies reveal the learning process of different neural network components and demonstrate that the proposed model can extract detailed features efficiently.},
}
@article {pmid31976634,
year = {2020},
author = {Jia, M and Rolandi, M},
title = {Soft and Ion-Conducting Materials in Bioelectronics: From Conducting Polymers to Hydrogels.},
journal = {Advanced healthcare materials},
volume = {9},
number = {5},
pages = {e1901372},
doi = {10.1002/adhm.201901372},
pmid = {31976634},
issn = {2192-2659},
mesh = {*Brain-Computer Interfaces ; Hydrogels ; Ions ; *Polymers ; },
abstract = {Bioelectronics devices that directly interface with cells and tissue have applications in neural and cardiac stimulation and recording, electroceuticals, and brain machine interfaces for prostheses. The interface between bioelectronic devices and biological tissue is inherently challenging due to the mismatch in both mechanical properties (hard vs soft) and charge carriers (electrons vs ions). In addition to conventional metals and silicon, new materials have bridged this interface, including conducting polymers, carbon-based nanomaterials, as well as ion-conducting polymers and hydrogels. This review provides an update on advances in soft bioelectronic materials for current and future therapeutic applications. Specifically, this review focuses on soft materials that can conduct both electrons and ions, and also deliver drugs and small molecules. The future opportunities and emerging challenges in the field are also highlighted.},
}
@article {pmid31973155,
year = {2020},
author = {Quiles, E and Suay, F and Candela, G and Chio, N and Jiménez, M and Álvarez-Kurogi, L},
title = {Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation.},
journal = {International journal of environmental research and public health},
volume = {17},
number = {3},
pages = {},
pmid = {31973155},
issn = {1660-4601},
mesh = {*Arm ; *Brain-Computer Interfaces ; Humans ; *Imagination ; Rehabilitation/*instrumentation ; *Robotics ; },
abstract = {Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user's motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain-computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action-action MI strategy versus an action-relaxation one. No statistically significant difference was found between the two action-action MI strategies.},
}
@article {pmid31972552,
year = {2020},
author = {Ma, X and Wang, D and Liu, D and Yang, J},
title = {DWT and CNN based multi-class motor imagery electroencephalographic signal recognition.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016073},
doi = {10.1088/1741-2552/ab6f15},
pmid = {31972552},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*classification/methods ; Humans ; Imagination/*physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; *Wavelet Analysis ; },
abstract = {OBJECTIVE: Brain computer interface (BCI) system allows humans to control external devices through motor imagery (MI) signals. However, many existing feature extraction algorithms cannot eliminate the influence of individual differences. This research proposed a new processing algorithm that can reduce the impact of individual differences on classification and improve the universality of the algorithm.<br><br>APPROACH: To select the optimal frequency band, the energy in each sub-band was calculated by the discrete wavelet transform. Power spectral density and visual geometric group network based convolutional neural network were used for feature extraction and classification respectively.<br><br>MAIN RESULTS: The test of the BCI Competition IV dataset IIa proved the superiority of the algorithm. In comparison with some commonly used methods, the proposed algorithm reduced classification calculation time while improving classification accuracy; the average classification accuracy rate reaches 96.21%, which is far exceeding the results obtained by the latest literature.<br><br>SIGNIFICANCE: The good classification performance of this research was rooted in the reduced number of parameters, the reduced consumption of computing resources, and the eliminated influence of individual differences. Therefore, the proposed algorithm can be applied to a real-time multi-class BCI system.},
}
@article {pmid31969321,
year = {2020},
author = {Zhang, CY and Aflalo, T and Revechkis, B and Rosario, E and Ouellette, D and Pouratian, N and Andersen, RA},
title = {Preservation of Partially Mixed Selectivity in Human Posterior Parietal Cortex across Changes in Task Context.},
journal = {eNeuro},
volume = {7},
number = {2},
pages = {},
pmid = {31969321},
issn = {2373-2822},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; P50 MH094258/MH/NIMH NIH HHS/United States ; },
mesh = {Hand ; Humans ; *Motor Cortex ; Movement ; *Parietal Lobe ; Psychomotor Performance ; },
abstract = {Recent studies in posterior parietal cortex (PPC) have found multiple effectors and cognitive strategies represented within a shared neural substrate in a structure termed "partially mixed selectivity" (Zhang et al., 2017). In this study, we examine whether the structure of these representations is preserved across changes in task context and is thus a robust and generalizable property of the neural population. Specifically, we test whether the structure is conserved from an open-loop motor imagery task (training) to a closed-loop cortical control task (online), a change that has led to substantial changes in neural behavior in prior studies in motor cortex. Recording from a 4 × 4 mm electrode array implanted in PPC of a human tetraplegic patient participating in a brain-machine interface (BMI) clinical trial, we studied the representations of imagined/attempted movements of the left/right hand and compare their individual BMI control performance using a one-dimensional cursor control task. We found that the structure of the representations is largely maintained between training and online control. Our results demonstrate for the first time that the structure observed in the context of an open-loop motor imagery task is maintained and accessible in the context of closed-loop BMI control. These results indicate that it is possible to decode the mixed variables found from a small patch of cortex in PPC and use them individually for BMI control. Furthermore, they show that the structure of the mixed representations is maintained and robust across changes in task context.},
}
@article {pmid31964948,
year = {2020},
author = {Yadav, AP and Li, D and Nicolelis, MAL},
title = {A Brain to Spine Interface for Transferring Artificial Sensory Information.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {900},
pmid = {31964948},
issn = {2045-2322},
support = {DP1 OD006798/OD/NIH HHS/United States ; R01 NS073125/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Brain-Computer Interfaces ; Choice Behavior ; Evoked Potentials ; Feedback, Sensory/*physiology ; Logistic Models ; Models, Neurological ; Motor Cortex/physiology ; Rats, Long-Evans ; Reinforcement, Psychology ; Spinal Cord/*physiology ; Spinal Cord Stimulation/*methods ; },
abstract = {Lack of sensory feedback is a major obstacle in the rapid absorption of prosthetic devices by the brain. While electrical stimulation of cortical and subcortical structures provides unique means to deliver sensory information to higher brain structures, these approaches require highly invasive surgery and are dependent on accurate targeting of brain structures. Here, we propose a semi-invasive method, Dorsal Column Stimulation (DCS) as a tool for transferring sensory information to the brain. Using this new approach, we show that rats can learn to discriminate artificial sensations generated by DCS and that DCS-induced learning results in corticostriatal plasticity. We also demonstrate a proof of concept brain-to-spine interface (BTSI), whereby tactile and artificial sensory information are decoded from the brain of an "encoder" rat, transformed into DCS pulses, and delivered to the spinal cord of a second "decoder" rat while the latter performs an analog-to-digital conversion during a sensory discrimination task. These results suggest that DCS can be used as an effective sensory channel to transmit prosthetic information to the brain or between brains, and could be developed as a novel platform for delivering tactile and proprioceptive feedback in clinical applications of brain-machine interfaces.},
}
@article {pmid31964514,
year = {2020},
author = {Chaudhary, S and Taran, S and Bajaj, V and Siuly, S},
title = {A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications.},
journal = {Computer methods and programs in biomedicine},
volume = {187},
number = {},
pages = {105325},
doi = {10.1016/j.cmpb.2020.105325},
pmid = {31964514},
issn = {1872-7565},
mesh = {Algorithms ; Artifacts ; Brain/*diagnostic imaging ; *Brain-Computer Interfaces ; Decision Trees ; Discriminant Analysis ; *Electroencephalography ; Foot/*physiology ; Hand/*physiology ; Humans ; Probability ; Reproducibility of Results ; Robotics ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; *Wavelet Analysis ; },
abstract = {BACKGROUND AND OBJECTIVE: Motor Imagery (MI) based Brain-Computer-Interface (BCI) is a rising support system that can assist disabled people to communicate with the real world, without any external help. It serves as an alternative communication channel between the user and computer. Electroencephalogram (EEG) recordings prove to be an appropriate choice for imaging MI tasks in a BCI system as it provides a non-invasive way for completing the task. The reliability of a BCI system confides on the efficiency of the assessment of different MI tasks.<br><br>METHODS: The present work proposes a new approach for the classification of distinct MI tasks based on EEG signals using the flexible analytic wavelet transform (FAWT) technique. The FAWT decomposes the EEG signal into sub-bands and temporal moment-based features are extracted from the sub-bands. Feature normalization is applied to minimize the bias nature of classifier. The FAWT-based features are utilized as inputs to multiple classifiers. Ensemble learning method based Subspace k-Nearest Neighbour (kNN) classifier is established as the best and robust classifier for the distinction of the right hand (RH) and right foot (RF) MI tasks.<br><br>RESULTS: The sub-band (SB) wise features are tested on multiple classifiers and best performance parameters are obtained using the ensemble method based subspace kNN classifier. The best results of parameters are obtained for fourth SB as accuracy 99.33%, sensitivity 99%, specificity 99.6%, F1-Score 0.9925, and kappa value 0.9865. The other sub-bands are also attained significant results using subspace KNN classifier.<br><br>CONCLUSIONS: The proposed work explores the utility of FAWT based features for the classification of RH and RF MI tasks EEG signals. The suggested work highlights the effectiveness of multiple classifiers for classification MI-tasks. The proposed method shows better performance in comparison to state-of-arts methods. Thus, the potential to implement a BCI system for controlling wheelchairs, robotic arms, etc.},
}
@article {pmid31962295,
year = {2020},
author = {Angjelichinoski, M and Choi, J and Banerjee, T and Pesaran, B and Tarokh, V},
title = {Cross-subject decoding of eye movement goals from local field potentials.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016067},
doi = {10.1088/1741-2552/ab6df3},
pmid = {31962295},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Eye Movements/*physiology ; *Goals ; Macaca mulatta ; Male ; Memory/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; },
abstract = {OBJECTIVE: We consider the cross-subject decoding problem from local field potential (LFP) signals, where training data collected from the prefrontal cortex (PFC) of a source subject is used to decode intended motor actions in a destination subject.<br><br>APPROACH: We propose a novel supervised transfer learning technique, referred to as data centering, which is used to adapt the feature space of the source to the feature space of the destination. The key ingredients of data centering are the transfer functions used to model the deterministic component of the relationship between the source and destination feature spaces. We propose an efficient data-driven estimation approach for linear transfer functions that uses the first and second order moments of the class-conditional distributions.<br><br>MAIN RESULTS: We apply our data centering technique with linear transfer functions for cross-subject decoding of eye movement intentions in an experiment where two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The results show peak cross-subject decoding performance of [Formula: see text], which marks a substantial improvement over random choice decoder. In addition to this, data centering also outperforms standard sampling-based methods in setups with imbalanced training data.<br><br>SIGNIFICANCE: The analyses presented herein demonstrate that the proposed data centering is a viable novel technique for reliable LFP-based cross-subject brain-computer interfacing and neural prostheses.},
}
@article {pmid31955863,
year = {2020},
author = {Keskpaik, T and Starkopf, J and Kirsimägi, &#xdc; and Mihnovitš, V and Lomp, A and Raamat, EM and Saar, S and Talving, P},
title = {The role of elevated high-sensitivity cardiac troponin on outcomes following severe blunt chest trauma.},
journal = {Injury},
volume = {51},
number = {5},
pages = {1177-1182},
doi = {10.1016/j.injury.2019.12.037},
pmid = {31955863},
issn = {1879-0267},
mesh = {Adult ; Aged ; Biomarkers/blood ; Estonia ; Female ; Hospital Mortality ; Humans ; Male ; Middle Aged ; Prognosis ; Retrospective Studies ; Survival Analysis ; Thoracic Injuries/*blood/mortality ; Trauma Severity Indices ; Troponin T/*blood ; Wounds, Nonpenetrating/*blood/mortality ; },
abstract = {BACKGROUND: Blunt cardiac injuries (BCI) result in poor outcomes following chest trauma. Admission ECG and troponin levels are frequently obtained in patients with suspected BCI, nevertheless, the prognostic value of cardiac troponins remains controversial. The purpose of the current study was to review the prognostic value of elevated high-sensitivity cardiac troponin T (hs-cTnT) in patients with severe blunt chest injuries. We hypothesized that elevated hs-cTnT result in poor outcomes in this subgroup of severe trauma patients.<br><br>METHODS: After IRB approval, all consecutive patients with Injury Severity Score (ISS)&#xa0;>&#xa0;15 and chest Abbreviated Injury Scale (AIS) score &#x2265;3 admitted to the major trauma centers between 1/2015 and 6/2017 were retrospectively reviewed. Primary outcomes were in-hospital and one-year mortality. Secondary outcomes included ventilator days and Glasgow Outcome Scale (GOS) score at hospital discharge.<br><br>RESULTS: Overall, 147 patients were included. Mean age was 49.0 (19.1) years and 75% were male. Serum troponin levels on admission were accrued in 82 (56%) patients with elevated and normal hs-cTnT levels found in 54 (66%) and in 28 (34%) patients, respectively. Elevated hs-cTnT group had significantly higher ISS and lactate level, and lower systolic blood pressure on admission. In-hospital mortality was significantly higher in patients with elevated hs-cTnT levels compared to patients with normal hs-cTnT levels (26% vs. 4%, p&#x202f;=&#x202f;0.02). Hs-cTnT level&#xa0;>&#xa0;14&#x202f;ng/L was significantly associated with extended ventilator days and lower GOS score at hospital discharge.<br><br>CONCLUSION: Blunt chest trauma victims with elevated hs-cTnT levels experience significantly poorer adjusted outcomes compared to patients with normal levels. Compliance with EAST practice management guidelines following severe blunt chest trauma was not fully complied in our study cohort that warrants prospective performance improvement measures.},
}
@article {pmid31954232,
year = {2020},
author = {Eles, JR and Kozai, TDY},
title = {In vivo imaging of calcium and glutamate responses to intracortical microstimulation reveals distinct temporal responses of the neuropil and somatic compartments in layer II/III neurons.},
journal = {Biomaterials},
volume = {234},
number = {},
pages = {119767},
pmid = {31954232},
issn = {1878-5905},
support = {R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS105691/NS/NINDS NIH HHS/United States ; R21 NS108098/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Calcium ; Electric Stimulation ; *Glutamic Acid ; Mice ; Microelectrodes ; Neurons ; Neuropil ; },
abstract = {OBJECTIVE: Intracortical microelectrode implants can generate a tissue response hallmarked by glial scarring and neuron cell death within 100-150 μm of the biomaterial device. Many have proposed that any performance decline in intracortical microstimulation (ICMS) due to this foreign body tissue response could be offset by increasing the stimulation amplitude. The mechanisms of this approach are unclear, however, as there has not been consensus on how increasing amplitude affects the spatial and temporal recruitment patterns of ICMS.<br><br>APPROACH: We clarify these unknowns using in vivo two-photon imaging of mice transgenically expressing the calcium sensor GCaMP6s in Thy1 neurons or virally expressing the glutamate sensor iGluSnFr in neurons. Calcium and neurotransmitter activity are tracked in the neuronal somas and neuropil during long-train stimulation in Layer II/III of somatosensory cortex.<br><br>MAIN RESULTS: Neural calcium activity and glutamate release are dense and strongest within 20-40&#xa0;&#x3bc;m around the electrode, falling off with distance from the electrode. Neuronal calcium increases with higher amplitude stimulations. During prolonged stimulation trains, a sub-population of somas fail to maintain calcium activity. Interestingly, neuropil calcium activity is 3-fold less correlated to somatic calcium activity for cells that drop-out during the long stimulation train compared to cells that sustain activity throughout the train. Glutamate release is apparent only within 20&#xa0;&#x3bc;m of the electrode and is sustained for at least 10s after cessation of the 15 and 20&#xa0;&#x3bc;A stimulation train, but not lower amplitudes.<br><br>SIGNIFICANCE: These results demonstrate that increasing amplitude can increase the radius and intensity of neural recruitment, but it also alters the temporal response of some neurons. Further, dense glutamate release is highest within the first 20&#xa0;&#x3bc;m of the electrode site even at high amplitudes, suggesting that there may be spatial limitations to the amplitude parameter space. The glutamate elevation outlasts stimulation, suggesting that high-amplitude stimulation may affect neurotransmitter re-uptake. This ultimately suggests that increasing the amplitude of ICMS device stimulation may fundamentally alter the temporal neural response, which could have implications for using amplitude to improve the ICMS effect or "offset" the effects of glial scarring.},
}
@article {pmid31953515,
year = {2020},
author = {Yeom, HG and Kim, JS and Chung, CK},
title = {Brain mechanisms in motor control during reaching movements: Transition of functional connectivity according to movement states.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {567},
pmid = {31953515},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; Basal Ganglia/*physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebellum/*physiology ; Female ; Humans ; Magnetoencephalography ; Male ; Motor Cortex/*physiology ; *Movement ; Psychomotor Performance ; Young Adult ; },
abstract = {Understanding how the brain controls movements is a critical issue in neuroscience. The role of brain changes rapidly according to movement states. To elucidate the motor control mechanism of brain, it is essential to investigate the changes in brain network in motor-related regions according to movement states. Therefore, the objective of this study was to investigate the brain network transitions according to movement states. We measured whole brain magnetoencephalography (MEG) signals and extracted source signals in 24 motor-related areas. Functional connectivity and centralities were calculated according to time flow. Our results showed that brain networks differed between states of motor planning and movement. Connectivities between most motor-related areas were increased in the motor-planning state. In contrast, only connectivities with cerebellum and basal ganglia were increased while those of other motor-related areas were decreased during movement. Our results indicate that most processes involved in motor control are completed before movement. Further, brain developed network related to feedback rather than motor decision during movements. Our findings also suggest that neural signals during motor planning might be more predictive than neural signals during movement. They facilitate accurate prediction of movement for brain-machine interfaces and provide insight into brain mechanisms in motor control.},
}
@article {pmid31952612,
year = {2020},
author = {Shi, Z and Lan, G and Hu, E and Lu, F and Qian, P and Liu, J and Dai, F and Xie, R},
title = {Puff pastry-like chitosan/konjac glucomannan matrix with thrombin-occupied microporous starch particles as a composite for hemostasis.},
journal = {Carbohydrate polymers},
volume = {232},
number = {},
pages = {115814},
doi = {10.1016/j.carbpol.2019.115814},
pmid = {31952612},
issn = {1879-1344},
mesh = {Chitosan/*chemistry ; Hemostatics/*chemistry ; Mannans/*chemistry ; Particle Size ; Porosity ; Starch/*chemistry ; Surface Properties ; Thrombin/*chemistry ; },
abstract = {Hemorrhage control is key for reducing mortality following severe trauma. In this study, we produced a puff pastry-like chitosan/konjac glucomannan matrix loaded with thrombin-occupied microporous starch particles to initiate hemostasis. The composite showed a hierarchical porous structure system consisting of porous and rough structures with evenly distributed microporous starch particles. Thrombin was evenly and independently distributed on the microporous starch particles within the hierarchical system and served to accelerate hemostasis. Meanwhile, the composite displayed excellent water absorption capacity, high zeta potential, roughness, and porosity. The composite was found to elicit desirable pro-thrombin time (PT), activated partial thrombin time (APTT), and whole blood clotting indices (BCI). Experiments using animal models demonstrated that the composite could effectively control wound hemorrhage. Furthermore, the composite showed good biocompatibility and effective degradability. Together, our results show that the puff pastry-like hemostat with hierarchical porous structure offers significant potential for use in hemostasis control.},
}
@article {pmid31952181,
year = {2020},
author = {Sebastiani, M and Di Flumeri, G and Aricò, P and Sciaraffa, N and Babiloni, F and Borghini, G},
title = {Neurophysiological Vigilance Characterisation and Assessment: Laboratory and Realistic Validations Involving Professional Air Traffic Controllers.},
journal = {Brain sciences},
volume = {10},
number = {1},
pages = {},
pmid = {31952181},
issn = {2076-3425},
abstract = {Vigilance degradation usually causes significant performance decrement. It is also considered the major factor causing the out-of-the-loop phenomenon (OOTL) occurrence. OOTL is strongly related to a high level of automation in operative contexts such as the Air Traffic Management (ATM), and it could lead to a negative impact on the Air Traffic Controllers' (ATCOs) engagement. As a consequence, being able to monitor the ATCOs' vigilance would be very important to prevent risky situations. In this context, the present study aimed to characterise and assess the vigilance level by using electroencephalographic (EEG) measures. The first study, involving 13 participants in laboratory settings allowed to find out the neurophysiological features mostly related to vigilance decrements. Those results were also confirmed under realistic ATM settings recruiting 10 professional ATCOs. The results demonstrated that (i) there was a significant performance decrement related to vigilance reduction; (ii) there were no substantial differences between the identified neurophysiological features in controlled and ecological settings, and the EEG-channel configuration defined in laboratory was able to discriminate and classify vigilance changes in ATCOs' vigilance with high accuracy (up to 84%); (iii) the derived two EEG-channel configuration was able to assess vigilance variations reporting only slight accuracy reduction.},
}
@article {pmid31952156,
year = {2020},
author = {Siddharth, S and Trivedi, MM},
title = {On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights.},
journal = {Brain sciences},
volume = {10},
number = {1},
pages = {},
pmid = {31952156},
issn = {2076-3425},
abstract = {Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver's awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.},
}
@article {pmid31952067,
year = {2020},
author = {Kuzovkin, I and Tretyakov, K and Uusberg, A and Vicente, R},
title = {Mental state space visualization for interactive modeling of personalized BCI control strategies.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016059},
doi = {10.1088/1741-2552/ab6d0b},
pmid = {31952067},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/psychology ; Electroencephalography/*methods ; *Facial Expression ; Humans ; *Machine Learning ; Mental Processes/*physiology ; },
abstract = {OBJECTIVE: Numerous studies in the area of BCI are focused on the search for a better experimental paradigm-a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are motor imagery, mental computations, etc. We propose a technique that instead allows the user to try different mental actions in the search for the ones that will work best.<br><br>APPROACH: The system is based on a modification of the self-organizing map (SOM) algorithm and enables interactive communication between the user and the learning system through a visualization of user's mental state space. During the interaction with the system the user converges on the paradigm that is most efficient and intuitive for that particular user.<br><br>MAIN RESULTS: Results of the two experiments, one allowing muscular activity, another permitting mental activity only, demonstrate soundness of the proposed method and offer preliminary validation of the performance improvement over the traditional closed-loop feedback approach.<br><br>SIGNIFICANCE: The proposed method allows a user to visually explore their mental state space in real time, opening new opportunities for scientific inquiry. The application of this method to the area of brain-computer interfaces enables more efficient search for the mental states that will allow a user to reliably control a BCI system.},
}
@article {pmid31947486,
year = {2019},
author = {Shahtalebi, S and Mohammadi, A},
title = {Feature Space Reduction for Single Trial EEG Classification based on Wavelet Decomposition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {7161-7164},
doi = {10.1109/EMBC.2019.8856340},
pmid = {31947486},
issn = {2694-0604},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {In contrary to recent signal/information processing advancements, human brain remains the most intriguing signal processing unit in existence with inconceivable capabilities to fuse various multi-modal signals, adaptively and in real-time fashion. To connect brain with the outer world, brain computer interfacing (BCI) via Electroencephalography (EEG) signals has received extensive attention. Extracting informative and discriminating features from EEG signals and decomposing the recorded signals into their underlying components is believed to yield compromising results. Different algorithms, therefore, are recently proposed combining signal decomposition techniques (e.g., spectral filterbanks, and Wavelet decomposition) with feature extracting methodologies (e.g., common spatial patterns (CSP), and Riemannian manifold learning). Although coupling filterbanks and Wavelet with the CSP has been investigated, to best of our knowledge, the potentials of coupling Wavelet with Riemannian manifold learning are not yet studied. The paper addresses this gap. In particular, we propose a level-based classification approach that couples the Wavelet decomposition with Riemannian manifold spatial learning (WvRiem). In the proposed WvRiem framework, the EEG signals are decomposed into several components (levels) and then spatial filtering via Riemannian manifold learning is performed on the best level which yields the most discriminating features. The proposed WvRiem is evaluated on the BCI Competition IV2a dataset and noticeably outperforms its counterparts.},
}
@article {pmid31947471,
year = {2019},
author = {Zheng, Y and Qin, X and Xi, Z and Chen, B},
title = {Mixed-Norm Based Broad Learning System for EEG Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {7092-7095},
doi = {10.1109/EMBC.2019.8856666},
pmid = {31947471},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; Brain-Computer Interfaces ; *Electroencephalography ; *Signal Processing, Computer-Assisted ; },
abstract = {How to design a powerful classifier with strong generalization capability is still an active topic in the brain computer interface (BCI) researches. In this paper, we propose a new classifier, which has the same structure of the recently proposed broad learning system (BLS), but the l2 norm based optimization model in BLS is replaced by a mixed-nrom based one. To optimize the proposed model efficiently, the augmented Lagrange multiplier (ALM) method is utilized. The most attractive feature of the proposed classifier is that it has the potential to maintain good performance in various noise environments, by flexibly setting the value of mixed parameter. Thus, compared with the standard BLS, as well as many existing classifiers, the proposed one is expected to be a more reasonable choice for classifying electroencephalography (EEG) signals, which are usually polluted by various artifacts. The experiments on two publicly available data sets are presented to confirm the desirable performance of the new method.},
}
@article {pmid31947470,
year = {2019},
author = {Guo, K and Yu, H and Chai, R and Nguyen, H and Su, SW},
title = {A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {7088-7091},
doi = {10.1109/EMBC.2019.8857698},
pmid = {31947470},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; Wavelet Analysis ; },
abstract = {Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.},
}
@article {pmid31947420,
year = {2019},
author = {Laiwalla, F and Lee, J and Lee, AH and Mok, E and Leung, V and Shellhammer, S and Song, YK and Larson, L and Nurmikko, A},
title = {A Distributed Wireless Network of Implantable Sub-mm Cortical Microstimulators for Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6876-6879},
doi = {10.1109/EMBC.2019.8857217},
pmid = {31947420},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Prostheses and Implants ; Radio Waves ; Telemetry ; *Wireless Technology ; },
abstract = {Scalability of implantable neural interface devices is a critical bottleneck in enhancing the performance of cortical Brain-Computer Interfaces (BCIs) through access to high density and multi-areal cortical signals. This is challenging to achieve through current monolithic constructs with 100-200 channels, often with bulky tethering and packaging, and a spatially distributed sensor approach has recently been explored by a few groups, including our laboratories [1]. In this paper, we describe a microscale (500 μm) programmable neural stimulator in the context of an epicortical wireless networked system of sub-mm "Neurograins" with wireless energy harvesting (near 1 GHz) and bidirectional telemetry. Stimulation neurograins are post-processed to integrate poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) planar electrodes or intracortical penetrating microwires, and ensembles of microdevices are hermetically encapsulated using liquid-crystal polymer (LCP) thermocompression for chronic implantability. Radio-frequency power and telecommunications management are handled by a wearable external "Epidermal Skinpatch" unit to cater to chronic clinical implant considerations. We describe the stimulation neurograin performance specifications and proof-of-concept in bench top and ex vivo rodent platforms.},
}
@article {pmid31947394,
year = {2019},
author = {Onishi, A and Nakagawa, S},
title = {Comparison of Classifiers for the Transfer Learning of Affective Auditory P300-Based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6766-6769},
doi = {10.1109/EMBC.2019.8856320},
pmid = {31947394},
issn = {2694-0604},
mesh = {Discriminant Analysis ; *Electroencephalography ; Female ; Humans ; Learning ; Male ; *Support Vector Machine ; },
abstract = {The auditory P300-based BCI was improved by changing stimuli. However, the current method needed time for recording training data. The time can be saved by the subject-to-subject transfer learning. However, the suitable classifier for the learning remains unknown. As a first step, this study compared the classifiers for the transfer learning of the BCI. They were evaluated on the dataset of a five-class affective auditory P300-based BCI. EEG data from sixteen subjects were assigned for the training, then data from the other six subjects were used for the testing. Classifiers such as the linear support-vector machine (SVM lin.), the kernel SVM (SVM RBF), the quadratic discriminant analysis were applied and compared. As a result, the SVM lin. and the SVM RBF were suitable for this problem. The best mean classification accuracy was achieved by the SVM lin. (68.7%), and a subject showed 86% accuracy at best. These results suggest that some subjects can operate the BCI without recording his/her training data.},
}
@article {pmid31947393,
year = {2019},
author = {Wang, M and Chen, L and Wang, Z and Zhang, L and Gu, X and Ming, D},
title = {Cortical Activations and BCI Performances at Different Speeds of Visual and Proprioceptive Stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6762-6765},
doi = {10.1109/EMBC.2019.8857510},
pmid = {31947393},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; *Neurofeedback ; },
abstract = {Motor imagery based brain-computer interface (MI-BCI) is one of the most common paradigms utilized in neurofeedback training (NFT) for rehabilitation engineering. Specifically, finding an appropriate feedback protocol is significantly important to improve the effectiveness of the motor training system. To this end, we investigated the electroencephalography(EEG) oscillatory patterns measured by event-related desynchronization (ERD) when sixteen participants accepted the visual and proprioceptive stimulation achieving the kinematic hand grasping movements at three different speeds (i.e. 1/3 Hz, 2/3 Hz and 1 Hz). The EEG results indicated that the ERD patterns showed no significant difference in sensorimotor cortex (i.e. C3 and FC3 channels) by comparing the three conditions. Nevertheless, the 2/3 Hz stimulation speed could achieve a significantly better classification performance than the other two conditions across all participants. Therefore, the visual and proprioceptive electrical stimulation achieving the kinematic hand grasping at 2/3 Hz speed might provide an available approach for the online MI-BCI system based NFT system in the future.},
}
@article {pmid31947392,
year = {2019},
author = {Kolkhorst, H and Karkkainen, S and Raheim, AF and Burgard, W and Tangermann, M},
title = {Influence of User Tasks on EEG-based Classification Performance in a Hazard Detection Paradigm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6758-6761},
doi = {10.1109/EMBC.2019.8857812},
pmid = {31947392},
issn = {2694-0604},
mesh = {Attention ; Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; User-Computer Interface ; },
abstract = {Attention-based brain-computer interface (BCI) paradigms offer a way to exert control, but also to provide insight into a user's perception and judgment of the environment. For a sufficient classification performance, user engagement and motivation are critical aspects. Consequently, many paradigms require the user to perform an auxiliary task, such as mentally counting subsets of stimuli or pressing a button when encountering them. In this work, we compare two user tasks, mental counting and button-presses, in a hazard detection paradigm in driving videos. We find that binary classification performance of events based on the electroencephalogram as well as user preference are higher for button presses. Amplitudes of evoked responses are higher for the counting task-an observation which holds even after projecting out motor-related potentials during the data preprocessing. Our results indicate that the choice of button-presses can be a preferable choice in such BCIs based on prediction performance as well as user preference.},
}
@article {pmid31947391,
year = {2019},
author = {Jagadish, B and Rajalakshmi, P},
title = {A Novel Feature Extraction Framework for Four Class Motor Imagery Classification using Log Determinant Regularized Riemannian Manifold.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6754-6757},
doi = {10.1109/EMBC.2019.8857393},
pmid = {31947391},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) systems allow the person in communicating with the external world using Electroencephalography (EEG). Motor Imagery (MI) based BCI systems play a vital role in interacting with the external environment. In this paper, we propose a novel robust feature extraction and classification framework for four class MI classification to improve the classification accuracy. The proposed architecture is developed using log-determinant (log-det) based Regularized Riemannian mean (LDRRM) and linear SVM. The robustness of features extracted from the four class MI data is improved to the outliers and noise by using the proposed LDRRM framework. We evaluated the performance of the proposed LDRRM classification framework on publicly available four class MI dataset 2a of BCI competition IV. The performance results show that the proposed LDRRM classification architecture obtained a mean classification accuracy of 69.12%, also achieved 1.54% higher classification accuracy when compared with the existing studies.},
}
@article {pmid31947390,
year = {2019},
author = {Schiatti, L and Barresi, G and Tessadori, J and King, LC and Mattos, LS},
title = {The Effect of Vibrotactile Feedback on ErrP-based Adaptive Classification of Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6750-6753},
doi = {10.1109/EMBC.2019.8857192},
pmid = {31947390},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Feedback ; Imagination ; Touch ; Vibration ; },
abstract = {This work presents an implementation of Error-related Potential (ErrP) detection to produce progressive adaptation of a motor imagery task classifier. The main contribution is in the evaluation of the effect of vibrotactile feedback on both ErrP and motor imagery detection. Results confirm the potential of self-adaptive techniques to improve motor imagery classification, and support the design of vibratory and in general tactile feedback into Brain-Computer Interfaces to improve both static and adaptive performance.},
}
@article {pmid31947292,
year = {2019},
author = {Jeong, H and Song, M and Oh, S and Kim, J and Kim, J},
title = {Toward Comparison of Cortical Activation with Different Motor Learning Methods Using Event-Related Design: EEG-fNIRS Study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6339-6342},
doi = {10.1109/EMBC.2019.8857693},
pmid = {31947292},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Motor Cortex/*physiology ; *Motor Skills ; *Spectroscopy, Near-Infrared ; },
abstract = {Recently, motor imagery brain-computer interface (MI-BCI) has been studied as a motor learning method and evaluated by comparing with conventional passive and active training. Most functional near-infrared spectroscopy (fNIRS) studies adopted block design for comparing those motor learning methods, including MI-BCI. Compared to the block design, event-related design would be more appropriate for estimating cortical activation in MI-BCI which provides feedback for each trial. This paper is a preliminary study to check the feasibility whether event-related design can be applicable for MI-BCI. To this end, three different motor learning methods involving MI-BCI were compared. In hemodynamic response, MI-BCI showed significantly stronger cortical activation than passive training (PT), and weaker than active training (AT), which conforms most existing studies. The results demonstrate that event-related design could be applied to investigate cortical effects of MI-BCI and comparing hemodynamic responses of different motor learning methods.},
}
@article {pmid31947291,
year = {2019},
author = {Wang, Z and Zhou, Y and Chen, L and Gu, B and Liu, S and Xu, M and Qi, H and He, F and Ming, D},
title = {A visual-haptic neurofeedback training improves sensorimotor cortical activations and BCI performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6335-6338},
doi = {10.1109/EMBC.2019.8856389},
pmid = {31947291},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Feedback, Sensory ; Humans ; *Neurofeedback ; Sensorimotor Cortex/*physiology ; },
abstract = {Neurofeedback training (NFT) could provide a novel way to investigate or restore the impaired brain function and neuroplasticity. However, it remains unclear how much the different feedback modes can contribute to NFT training. Specifically, whether they can enhance the cortical activations for motor training. To this end, our study proposed a brain-computer interface (BCI) based visual-haptic NFT incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10Hz, alpha_2: 11-13Hz, beta_1: 15-20Hz and beta_2: 22-28Hz) lateralized relative event-related desynchronization (lrERD) patterns were significantly enhanced after NFT. And the classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively low MI-BCI performance. These findings validate the feasibility of our proposed visual- haptic NFT approach to improve sensorimotor cortical activations and BCI performance during motor training.},
}
@article {pmid31947290,
year = {2019},
author = {Jiang, L and Wang, Y and Pei, W and Chen, H},
title = {A Four-Class Phase-Coded SSVEP BCI at 60Hz Using Refresh Rate.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6331-6334},
doi = {10.1109/EMBC.2019.8857326},
pmid = {31947290},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {A four-class brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEPs) was developed by presenting phase-coded 60Hz stimulations on a 240Hz LCD monitor. The task-related component analysis (TRCA) algorithm was used to detect SSVEPs with individual training data. In the BCI experiment with 10 subjects, the system achieved high classification accuracy of 94.50±6.70% and 92.71±7.56% in offline and online BCI experiments, resulting in information transfer rates (ITR) of 19.95±4.36 and 18.81±4.74 bpm, respectively. The behavioral tests on visual comfortableness and perception of flickering reveal that the proposed BCI system is very comfortable to use without any perception of flicker.},
}
@article {pmid31947288,
year = {2019},
author = {Ravi, A and Manuel, J and Heydari, N and Jiang, N},
title = {A Convolutional Neural Network for Enhancing the Detection of SSVEP in the Presence of Competing Stimuli.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6323-6326},
doi = {10.1109/EMBC.2019.8857822},
pmid = {31947288},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Neural Networks, Computer ; *Photic Stimulation ; },
abstract = {Stimulus proximity has been shown to have an influence on the classification performance of a steady-state visual evoked potential based brain-computer interface (SSVEP-BCI). Multiple visual stimuli placed close to each other compete for neural representations leading to the effect of competing stimuli. In this study, we propose a convolutional neural network (CNN) based classification method to enhance the detection accuracy of SSVEP in the presence of competing stimuli. A seven-class SSVEP dataset from ten healthy participants was used for evaluating the performance of the proposed method. The results were compared with the classic canonical correlation analysis (CCA) detection algorithm. We investigated whether the CNN parameters learned on one inter-stimulus distance (ISD) can generalize across to other ISDs and sessions. The proposed CNN obtained a significantly higher classification accuracy than CCA in both the offline (75.3% vs. 67.9%, (p <; 10[-3])) and the simulated online (71.3% vs. 60.7%, (p <; 10[-3])) conditions for the closest ISD. The results suggest the following: the CNN is robust in decoding SSVEP across different ISDs, and can be trained independent of the ISD resulting in a model that generalizes to other ISDs.},
}
@article {pmid31947287,
year = {2019},
author = {Lin, X and Chen, Z and Xu, K and Zhang, S},
title = {Development of a High-speed Mental Spelling System Combining Eye Tracking and SSVEP-based BCI with High Scalability.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6318-6322},
doi = {10.1109/EMBC.2019.8857408},
pmid = {31947287},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; *Eye Movement Measurements ; *Fixation, Ocular ; Humans ; Language ; Photic Stimulation ; },
abstract = {Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control. In this study, a hybrid BCI speller system combining steady-state visual evoked potentials (SSVEPs) and eye tracking has been proposed. In this system, the eye tracker was used to detect eye gaze position for a 3×3 block selection, after that classification of the command was achieved through filter bank canonical correlation analysis (FBCCA) method. Results showed that the 40-classes hybrid speller system outperformed the SSVEP-only method, achieved a mean accuracy of 92.1% and a mean information transfer rate (ITR) of 180.8 bits/min during online experiments, and the scalability of the proposed system also has been tested with larger number of commands.},
}
@article {pmid31947255,
year = {2019},
author = {Liang, L and Yang, C and Wang, Y and Gao, X},
title = {High-Frequency SSVEP Stimulation Paradigm Based On Dual Frequency Modulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6184-6187},
doi = {10.1109/EMBC.2019.8856903},
pmid = {31947255},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation/*methods ; },
abstract = {This study designed a brain-computer interface (BCI) with high frequency steady-state visual evoked potentials (SSVEPs) paradigm based on dual frequency modulation. The information transfer rates (ITR) of traditional low-frequency SSVEP-BCIs are high, but are very irritating to the human eye for long-term use. High-frequency stimulation can greatly improve the comfort of the system, but the communication rate of high-frequency systems is poor for the EEG response of high-frequency stimulation is weak. This study introduces a dual-frequency modulation method to improve the recognition accuracy of high-frequency BCI. Each target in the paradigm is composed of sinusoidal brightness modulated flicker light of the same initial phase with different stimulation frequencies in a space composition of the checkerboard. Using the above method, a relatively high-frequency SSVEP-BCI paradigm with a relatively complex code is proposed. Due to the complexity of the coding, only the training-based identification algorithm is used. With a data length of 0.5s, the average recognition accuracy is 91.02±7.77%, and ITR is 267.85±39.36bits/min. The performance is higher than the existing high frequency SSVEP-based BCI paradigms.},
}
@article {pmid31947254,
year = {2019},
author = {Sreeja, SR and Himanshu, and Samanta, D and Sarma, M},
title = {Weighted sparse representation for classification of motor imagery EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6180-6183},
doi = {10.1109/EMBC.2019.8857496},
pmid = {31947254},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Imagination ; Movement ; },
abstract = {Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l0-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.},
}
@article {pmid31947252,
year = {2019},
author = {Sharon, RA and Aggarwal, S and Goel, P and Joshi, R and Sur, M and Murthy, HA and Ganapathy, S},
title = {Level-wise Subject adaptation to improve classification of motor and mental EEG tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6172-6175},
doi = {10.1109/EMBC.2019.8857584},
pmid = {31947252},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Task Performance and Analysis ; },
abstract = {Classification of various cognitive and motor tasks using electroencephalogram (EEG) signals is necessary for building Brain Computer Interfaces (BCI) that are noninvasive. However, achieving high classification accuracy in a multi-subject multitask scenario is a challenge. A noticeable reduction in accuracy is observed when the subjects between train and test are mismatched. Drawing a similarity from speaker adaptation approaches in speech, we propose a method to perform subject-wise adaptation of EEG in order to improve the task classification performance. A Common Spatial Pattern (CSP) approach is employed for feature extraction. Gaussian Mixture Model (GMM) based subject-specific models are built for each of the tasks. Maximum a-posterior (MAP) adaptation is performed, and an absolute improvement of 1.22-7.26% is observed in the average accuracy.},
}
@article {pmid31947251,
year = {2019},
author = {Georgiadis, K and Laskaris, N and Nikolopoulos, S and Adamos, DA and Kompatsiaris, I},
title = {Using Discriminative Lasso to Detect a Graph Fourier Transform (GFT) Subspace for robust decoding in Motor Imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {6167-6171},
doi = {10.1109/EMBC.2019.8856973},
pmid = {31947251},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Fourier Analysis ; Imagery, Psychotherapy ; Imagination ; },
abstract = {A novel decoding scheme for motor imagery (MI) brain computer interfaces (BCI's) is introduced based on the GFT concept. It considers the recorded EEG activity as a signal defined over (the graph of) the sensor array. A graph encapsulating the functional covariations emerging during the execution of a specific imagined movement is first defined, from a small training set of relevant trials. The ensemble of graphs signals corresponding to a multi-trial training dataset is then analyzed using a graph-guided decomposition and, based on discriminative Lasso (dLasso), an information-rich GFT subspace is defined. After training, only simple matrix operations are required for transforming the multichannel signal into features to be fed into a classifier that decides whether brain activity conforms with the graph structure associated with the targeted movement. The proposed decoding scheme is evaluated based on two different datasets and found to compare favorably against popular alternatives in the field.},
}
@article {pmid31947208,
year = {2019},
author = {Tang, J and Xu, M and Liu, Z and Meng, J and Chen, S and Ming, D},
title = {A Multifocal SSVEPs-based Brain-Computer Interface with Less Calibration Time.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5975-5978},
doi = {10.1109/EMBC.2019.8857450},
pmid = {31947208},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {For the past few years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have gotten tremendous progress and attracted increasing attention. To broaden the application of BCIs, researchers have focused on the increasement of the BCI instruction number in recent years. However, with a large number of instructions, the BCI calibration time will be too long to be accepted in practical usage. This study proposed a new coding method based on multifocal steady-state visual evoked potentials (mfSSVEPs), in which 16 targets were binary coded by 4 frequencies. Notably, the training data needed for calibration corresponded to only five out of the sixteen targets. Five volunteers were recruited to test this paradigm. Task-related component analysis combined with a probabilistic model were employed for target recognition. As a result, the accuracy could reach as high as 93.1% with 1s-length data. The highest information transfer rate (ITR) was 101.1 bits/min with an average of 73.9 bits/min. The results indicate that this new paradigm is promising to encode a large BCI instruction set with less trainings.},
}
@article {pmid31947207,
year = {2019},
author = {Huang, S and Peng, H and Chen, Y and Sun, K and Shen, F and Wang, T and Ma, T},
title = {Tensor Discriminant Analysis for MI-EEG Signal Classification Using Convolutional Neural Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5971-5974},
doi = {10.1109/EMBC.2019.8857422},
pmid = {31947207},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Humans ; *Imagination ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Motor Imagery (MI) is a typical paradigm for Brain-Computer Interface (BCI) system. In this paper, we propose a new framework by introducing a tensor-based feature representation of the data and also utilizing a convolutional neural network (CNN) architecture for performing classification of MI-EEG signal. The tensor-based representation that includes the structural information in multi-channel time-varying EEG spectrum is generated from tensor discriminant analysis (TDA), and CNN is designed and optimized accordingly for this representation. Compared with CSP+SVM (the conventional framework which is the most successful in MI-based BCI) in the applications to the BCI competition III-IVa dataset, the proposed framework has the following advantages: (1) the most discriminant patterns can be obtained by applying optimum selection of spatial-spectral-temporal subspace for each subject; (2) the corresponding CNN can take full advantage of tensor-based representation and identify discriminative characteristics robustly. The results demonstrate that our framework can further improve classification performance and has great potential for the practical application of BCI.},
}
@article {pmid31947204,
year = {2019},
author = {Xu, M and Zhou, X and Xiao, X and Wang, Y and Jung, TP and Ming, D},
title = {Effects of stimulus position on the classification of miniature asymmetric VEPs for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5956-5959},
doi = {10.1109/EMBC.2019.8857789},
pmid = {31947204},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Photic Stimulation ; },
abstract = {The speed of visual brain-computer interfaces (BCIs) has been greatly improved in recent years. However, traditional visual BCI paradigm requires users to directly gaze at the intensive flickering items, which would cause severe problems in practical applications, such as visual fatigue and excessive visual resources consumption. A promising solution is to use small visual stimuli outside the central visual area to encode instructions, which had been demonstrated to be effective in our previous study. This study aims to further investigate the effects of stimulus position on the classification of miniature asymmetric visual evoked potentials (aVEPs). Small peripheral visual stimuli were designed with different eccentricities (1° and 2°) and directions (0°, 45°, 90°, 135°, 180°, -135°, -90°, and -45°) to induce different kinds of miniature aVEPs. Five subjects participated in this experiment. Discriminative canonical pattern matching (DCPM) was used to classify all possible pairs of miniature aVEPs. Study results showed that visual stimuli with less eccentricity could induce more distinct miniature aVEPs. The highest single-trial accuracy achieved was about 83% for the binary classifications of miniature aVEPs pairs corresponding to (1°, -135°) Vs (1°, 0°), (1°, -45°) Vs (1°, -135°) and (1°, -45°) Vs (1°, 180°). This finding is very important for the design and development of the miniature aVEPs-based BCIs.},
}
@article {pmid31947203,
year = {2019},
author = {Muller-Putz, GR and Rupp, R and Ofner, P and Pereira, J and Pinegger, A and Schwarz, A and Zube, M and Eck, U and Hessing, B and Schneiders, M},
title = {Applying intuitive EEG-controlled grasp neuroprostheses in individuals with spinal cord injury: Preliminary results from the MoreGrasp clinical feasibility study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5949-5955},
doi = {10.1109/EMBC.2019.8856491},
pmid = {31947203},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Feasibility Studies ; Hand ; Humans ; *Neural Prostheses ; *Spinal Cord Injuries ; },
abstract = {The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface (BCI) for control of a grasp neuroprostheses in individuals with high spinal cord injury (SCI). The first results of the ongoing MoreGrasp clinical feasibility study involving end users with SCI are presented. This includes BCI screening sessions, in which we investigate the electroencephalography (EEG) patterns associated with single, natural movements of the upper limb. These patterns will later be used to control the neuroprosthesis. Additionally, the MoreGrasp grasp neuroprosthesis consisting of electrode arrays embedded in an individualized textile forearm sleeve is presented. The general feasibility of this electrode array in terms of corrections of misalignments during donning is shown together with the functional results in end users of the electrode forearm sleeve.},
}
@article {pmid31947189,
year = {2019},
author = {Vujic, A and Krause, C and Tso, G and Lin, J and Han, B and Maes, P},
title = {Gut-Brain Computer Interfacing (GBCI) : Wearable Monitoring of Gastric Myoelectric Activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5886-5889},
doi = {10.1109/EMBC.2019.8856568},
pmid = {31947189},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; Stomach/*physiology ; *Wearable Electronic Devices ; },
abstract = {We propose a new area for wearable technology and interaction by acquiring gastrointestinal signals non-invasively from the abdomen. The mind-gut connection has flourished as a research area in the past two decades, elucidating the guts key role in stress, affect, and memory. Meanwhile, engineering advancements have shown potential in accuracy of non-invasive gastric recordings. Here, we investigate the design and specification of a wearable system for the recording of gut-brain activity non-invasively. We also present results from a preliminary pilot test of a wearable gut-brain computer interface (GBCI).},
}
@article {pmid31947127,
year = {2019},
author = {Yasemin, M and Sarikaya, MA and Ince, G},
title = {Emotional State Estimation using Sensor Fusion of EEG and EDA.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5609-5612},
doi = {10.1109/EMBC.2019.8856895},
pmid = {31947127},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Emotions ; Humans ; Neural Networks, Computer ; },
abstract = {Emotions potentially have a significant impact on human actions and recognizing affective states is an effective way of implementing Brain-Computer Interface (BCI) systems which process brain signals to allow direct communication and interaction with the environment. In this paper, a real-time emotion recognition model was developed on the basis of physiological signals. A sensor fusion method is developed to detect human emotion by using data acquired from ElectroEncephaloGraphy (EEG) and ElectroDermal Activity (EDA) sensors. The proposed physiology-based emotion recognition system using a neural network was implemented and tested on human subjects, and a classification accuracy of 94% on three different emotions was achieved.},
}
@article {pmid31947112,
year = {2019},
author = {Yi, W and Qiu, S and Fan, X and Zhang, L},
title = {Estimation of mental workload induced by different presentation rates in rapid serial visual presentation tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5552-5555},
doi = {10.1109/EMBC.2019.8857274},
pmid = {31947112},
issn = {2694-0604},
mesh = {Analysis of Variance ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Photic Stimulation ; *Workload ; },
abstract = {Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) is an efficient information detection technology by detecting event related brain response evoked by target stimuli. In the protocol design of the RSVP-BCI a range of parameters could influence the task difficulty, which may result in the changes of mental workload for subjects. This paper focused on the presentation rate in the RSVP paradigm aiming to investigate its influence on mental workload, and the separability of brain states during RSVP tasks with different setup of presentation rate. 64-channel Electroencephalographic (EEG) data were recorded during RSVP tasks with three levels of presentation rate in ten healthy subjects. The results show that different presentation rates indeed contribute to significant differences on mental workload revealed by one-way repeated measures analysis of variance (ANOVA) on z-scored RSME. Higher presentation rate results in the significant decrease on both behavioral and single-trial recognition performance of target images. Classification results on three levels of mental workload show that the mean accuracy reaches 65.5% and the highest accuracy reaches 88.3%. This work implies that mental workload induced by different presentation rates during RSVP tasks could be accurately recognized, and provides a possible method to monitor the mental workload in the application areas of RSVP-BCI.},
}
@article {pmid31947111,
year = {2019},
author = {Liu, P and Ke, Y and Du, J and Liu, W and Kong, L and Wang, N and An, X and Ming, D},
title = {An SSVEP-BCI in Augmented Reality.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5548-5551},
doi = {10.1109/EMBC.2019.8857859},
pmid = {31947111},
issn = {2694-0604},
mesh = {Algorithms ; *Augmented Reality ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Steady-State Visual Evoked Potentials (SSVEP) based Brain-Computer Interface (BCI) has achieved very high information transmission rate (ITR), but its portability and fundamental interactions with the surrounding environment were limited. The combination of Augmented Reality (AR) and BCI is expected to solve these problems. In this paper, we combined AR with the SSVEP-BCI to build a more portable and natural BCI system in Microsoft HoloLens. We designed the AR-BCI system and studied the influence of different algorithms on the system performance. The analysis of SSVEP signals collected in AR environment shows that the extended filter bank canonical correlation analysis was better than task-related component analysis. The average recognition accuracy and ITR obtained by using Electroencephalography (EEG) data of 1s, 1.5s, and 2s length were 87.7%,95.4%, 97.6% and 64.6 bit/min, 62.9 bit/min, 55.6 bit/min, respectively. Compared with the existing AR-BCI studies, the ITR has been greatly improved in this study.},
}
@article {pmid31947110,
year = {2019},
author = {Jeong, JH and Shim, KH and Kim, DJ and Lee, SW},
title = {Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5544-5547},
doi = {10.1109/EMBC.2019.8856312},
pmid = {31947110},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Movement ; *Robotics ; },
abstract = {Development of noninvasive brain-machine interface (BMI) systems based on electroencephalography (EEG), driven by spontaneous movement intentions, is a useful tool for controlling external devices or supporting a neuro- rehabilitation. In this study, we present the possibility of brain-controlled robot arm system using arm trajectory decoding. To do that, we first constructed the experimental system that can acquire the EEG data for not only movement execution (ME) task but also movement imagery (MI) tasks. Five subjects participated in our experiments and performed four directional reaching tasks (Left, right, forward, and backward) in the 3D plane. For robust arm trajectory decoding, we propose a subject-dependent deep neural network (DNN) architecture. The decoding model applies the principle of bi-directional long short-term memory (LSTM) network. As a result, we confirmed the decoding performance (r-value: >0.8) for all X-, Y-, and Z-axis across all subjects in the MI as well as ME tasks. These results show the feasibility of the EEG-based intuitive robot arm control system for high-level tasks (e.g., drink water or moving some objects). Also, we confirm that the proposed method has no much decoding performance variations between ME and MI tasks for the offline analysis. Hence, we will demonstrate that the decoding model is capable of robust trajectory decoding even in a real-time environment.},
}
@article {pmid31947109,
year = {2019},
author = {Zhang, X and Guo, Y and Gao, B and Long, J},
title = {Enhancing Mu-based BCI Performance with Rhythmic Electrical Stimulation at Alpha Frequency.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5540-5543},
doi = {10.1109/EMBC.2019.8857321},
pmid = {31947109},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electric Stimulation ; *Electroencephalography ; Humans ; Imagination ; Movement ; },
abstract = {The accuracy of brain-computer interfaces (BCIs) is important for effective communication and control. The mu-based BCI is one of the widely used systems, of which the related methods to improve users' accuracy is still poorly studied. Here, we examined the way to enhance the mu-based BCI performance by rhythmic electrical stimulation on the ulnar nerve at the contralateral wrist at the alpha frequency (10 Hz) during the left-and right-hand motor imagery. Time-frequency analysis, spectral analysis, and discriminant analysis were performed on the electroencephalograph (EEG) data before and after the intervention of electrical stimulation in 9 healthy subjects. We found that the ERD/S on the somatosensory and motor cortex during left-or right-hand imagination was more obvious at the mu rhythm after intervention. Furthermore, average classification accuracy between left-and right-hand imagery significantly increased from 78.43% to 88.17% after intervention, suggesting that the electrical stimulation at alpha frequency effectively regulates the brain's mu rhythm and enhances the discriminability of the left-hand and right-hand imagination tasks. These results provide evidence that the electrical stimulation at the alpha frequency is an effective way to improve the mu-based BCI performance.},
}
@article {pmid31947107,
year = {2019},
author = {Dash, D and Ferrari, P and Heitzman, D and Wang, J},
title = {Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5531-5535},
doi = {10.1109/EMBC.2019.8857874},
pmid = {31947107},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Machine Learning ; *Magnetoencephalography/methods ; *Neural Networks, Computer ; Speech ; },
abstract = {Decoding speech directly from the brain has the potential for the development of the next generation, more efficient brain computer interfaces (BCIs) to assist in the communication of patients with locked-in syndrome (fully paralyzed but aware). In this study, we have explored the spectral and temporal features of the magnetoencephalography (MEG) signals and trained those features with convolutional neural networks (CNN) for the classification of neural signals corresponding to phrases. Experimental results demonstrated the effectiveness of CNNs in decoding speech during perception, imagination, and production tasks. Furthermore, to overcome the long training time issue of CNNs, we leveraged principal component analysis (PCA) for spatial dimension reduction of MEG data and transfer learning for model initialization. Both PCA and transfer learning were found to be highly beneficial for faster model training. The best configuration (50 principal coefficients + transfer learning) led to more than 10 times faster training than the original setting while the speech decoding accuracy remained at a similarly high level.},
}
@article {pmid31947022,
year = {2019},
author = {Yano, H and Takiguchi, T and Nakagawa, S},
title = {Cortical Patterns for Prediction of Subjective Preference Induced by Chords.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5168-5171},
doi = {10.1109/EMBC.2019.8857941},
pmid = {31947022},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Male ; Models, Neurological ; *Music ; Neurophysiology ; Young Adult ; },
abstract = {To extract an effective feature in prediction of subjective impressions from single-trial neurophysiological recordings, the spatial filter that extracts brain activities related to impressions were constructed using the common spatial pattern (CSP). We focus on subjective preference induced by chords composed of 3 notes with different frequency ratio. Magnetic cortical activities while hearing chords and comparative judgment on pair of them were measured. The predictive model that predicts the scale value of preference was trained using the CSP-based feature for each participant. The result of the evaluation experiment shows that the CSP-based feature improved the mean prediction accuracy in all participants, compared with the other features without spatially filtering. Furthermore, the capability of construction of a spatial filter that extracts cortical activities varying with degree of preference using the comparative judgments was indicated.},
}
@article {pmid31947018,
year = {2019},
author = {Kobler, RJ and Sburlea, AI and Mondini, V and Muller-Putz, GR},
title = {HEAR to remove pops and drifts: the high-variance electrode artifact removal (HEAR) algorithm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {5150-5155},
doi = {10.1109/EMBC.2019.8857742},
pmid = {31947018},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; *Electrodes ; *Electroencephalography ; Humans ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {A high fraction of artifact-free signals is highly desirable in functional neuroimaging and brain-computer interfacing (BCI). We present the high-variance electrode artifact removal (HEAR) algorithm to remove transient electrode pop and drift (PD) artifacts from electroencephalographic (EEG) signals. Transient PD artifacts reflect impedance variations at the electrode scalp interface that are caused by ion concentration changes. HEAR and its online version (oHEAR) are open-source and publicly available. Both outperformed state of the art offline and online transient, high-variance artifact correction algorithms for simulated EEG signals. (o)HEAR attenuated PD artifacts by approx. 25 dB, and at the same time maintained a high SNR during PD artifact-free periods. For real-world EEG data, (o)HEAR reduced the fraction of outlier trials by half and maintained the waveform of a movement related cortical potential during a center-out reaching task. In the case of BCI training, using oHEAR can improve the reliability of the feedback a user receives through reducing a potential negative impact of PD artifacts.},
}
@article {pmid31946927,
year = {2019},
author = {Song, X and Chen, X and Wang, Z and An, X and Ming, D},
title = {MBLL with weighted partial path length for multi-distance probe configuration of fNIRS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4766-4769},
doi = {10.1109/EMBC.2019.8857684},
pmid = {31946927},
issn = {2694-0604},
mesh = {*Brain Mapping ; *Brain-Computer Interfaces ; Hand Strength ; Humans ; Movement ; *Spectroscopy, Near-Infrared ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) has broad prospects in both clinical application and brain-computer interface. To improve the spatial resolution, modified Beer-Lambert law with weighted partial optical path length (wMBLL) is proposed for multi-distance probe configuration. Taking both surface tissue layers and deep tissue layers into consideration, the partial optical path length is estimated as a function of the distance between source and detector. Besides, a multi-distance, 15mm and 30mm, probe configuration is designed, which approximates a rectangle. Constructed with 9 sources and 14 detectors, 40 channels are produced, including 20 short short-separation channel and 20 long-separation channel. Also, experiment is implemented with left hand grip-stretch movement and involves five healthy subjects. The concentration of HbO is used to image the brain activation map. Results demonstrate that, compared with the conventional method, the proposed wMBLL method is effective to detect brain activity with higher spatial resolution.},
}
@article {pmid31946926,
year = {2019},
author = {Ho, YL and Huang, YD and Wang, KY and Fang, WC},
title = {A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4762-4765},
doi = {10.1109/EMBC.2019.8856299},
pmid = {31946926},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Systems ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Independent component analysis (ICA) has been wildly used to improve EEG based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEG real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm[2] using 28nm CMOS technology. The EEG demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.},
}
@article {pmid31946901,
year = {2019},
author = {Banerjee, T and Khasnobish, A and Chowdhury, A and Chatterjee, D},
title = {Reckoning respiratory signals to affectively decipher mental state.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4654-4659},
doi = {10.1109/EMBC.2019.8857498},
pmid = {31946901},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; *Mental Status and Dementia Tests ; *Neural Networks, Computer ; *Respiration ; },
abstract = {Recognizing mental states from physiological signal is a concern not only for medical diagnostics, but also for cognitive science, behavioral studies as well as brain machine interfaces. This study employs an unique approach of solely utilizing the respiration signals in order to decipher mental states. A public dataset, Affective Pacman, is considered for this study, where the various physiological signals are acquired during normal and frustrated mental states. An efficient way to remove the non-linear baseline drifts in the signal is implemented to extract the respiratory features in most effective way. Another major adversity is the presence of class imbalance, which is effectively rectified using Synthetic Minority Oversampling TEchnique (SMOTE). Application of SMOTE algorithm to resolve class imbalance problem, not only increased the classification accuracy, but also reduced the classifier bias towards the majority class, which in turn exceedingly enhanced the classifier sensitivity. The multilayer perceptron classifier performed best with SMOTE generated feature set, with classification accuracy (CA), true positive rate (TPR) and true negative rate (TNR) of 97.9%, 92.6% and 99.3% respectively. The current approach is found to perform better compared to relevant literature.},
}
@article {pmid31946881,
year = {2019},
author = {Schembri, P and Pelc, M and Ma, J},
title = {Comparison between a Passive and Active response task and their effect on the Amplitude and Latency of the P300 component for Visual Stimuli while using Low Fidelity Equipment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4566-4571},
doi = {10.1109/EMBC.2019.8857093},
pmid = {31946881},
issn = {2694-0604},
mesh = {Algorithms ; *Biological Phenomena ; *Brain-Computer Interfaces ; *Electroencephalography ; Event-Related Potentials, P300 ; Humans ; },
abstract = {In this paper, we investigate the effect, in terms of amplitude and latency, of the P300 component in a separate active and passive task response condition. This work is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with five healthy subjects; while using a noninvasive Brain-Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. Our results suggest that an active task yielded a larger P300 peak amplitude while there was no discriminable difference in the peak latency. The signal was also morphological consistent in both scenarios, even though they did not yield identical P300 components. This groundwork yields imperative data for future work where we plan to introduce several distractions, including communication with the user while performing the P300 speller paradigm.},
}
@article {pmid31946875,
year = {2019},
author = {Zhang, S and Wang, K and Xu, M and Wang, Z and Chen, L and Wang, F and Zhang, L and Ming, D},
title = {Analysis and Classification for Single-Trial EEG Induced by Sequential Finger Movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4541-4544},
doi = {10.1109/EMBC.2019.8857117},
pmid = {31946875},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Fingers ; Hand ; Humans ; Imagination ; *Movement ; },
abstract = {In recent years, motor imagery-based BCIs (MI-BCIs) controlled various external devices successfully, which have great potential in neurological rehabilitation. In this paper, we designed a paradigm of sequential finger movements and utilized spatial filters for feature extraction to classify single-trial electroencephalography (EEG) induced by finger movements of left and right hand. Ten healthy subjects participated the experiment. The analysis of EEG patterns showed significant contralateral dominance. We investigated how data length affected the classification accuracy. The classification accuracy was improved with the increase of the keystrokes in one trial, and the results were 87.42%, 91.21%, 93.08% and 93.59% corresponding to single keystroke, two keystrokes, three keystrokes and four keystrokes. This study would be helpful to improve the decoding efficiency and optimize the encoding method of motor-related EEG information.},
}
@article {pmid31946874,
year = {2019},
author = {Maruthachalam, S and Kumar, MG and Murthy, HA},
title = {Time Warping Solutions for Classifying Artifacts in EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4537-4540},
doi = {10.1109/EMBC.2019.8856669},
pmid = {31946874},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; Blinking ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {The most common brain-computer interface (BCI) devices use electroencephalography (EEG). EEG signals are noisy owing to the presence of many artifacts, namely head movement, and facial movements like eye blinks or jaw movements. Removal of these artifacts from EEG signals is essential for the success of any downstream BCI application. These artifacts influence different sensors of the EEG. In this paper, we devise algorithms for detection and classification of artifacts. Classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warping, and dynamic time warping. The average accuracy of 85% and 90% is obtained using the former, and the later, respectively.},
}
@article {pmid31946873,
year = {2019},
author = {Mousavi, M and de Sa, VR},
title = {Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4533-4536},
doi = {10.1109/EMBC.2019.8857423},
pmid = {31946873},
issn = {2694-0604},
support = {T32 MH020002/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems are proposed as a means of communication for locked-in patients. One common BCI paradigm is motor imagery in which the user controls a BCI by imagining movements of different body parts. It is known that imagining different body parts results in event-related desynchronization (ERD) in various frequency bands. Existing methods such as common spatial patterns (CSP) and its refinement filterbank common spatial patterns (FB-CSP) aim at finding features that are informative for classification of the motor imagery class. Our proposed method is a temporally adaptive common spatial patterns implementation of the commonly used filter-bank common spatial patterns method using convolutional neural networks; hence it is called TA-CSPNN. With this method we aim to: (1) make the feature extraction and classification end-to-end, (2) base it on the way CSP/FBCSP extracts relevant features, and finally, (3) reduce the number of trainable parameters compared to existing deep learning methods to improve generalizability in noisy data such as EEG. More importantly, we show that this reduction in parameters does not affect performance and in fact the trained network generalizes better for data from some participants. We show our results on two datasets, one publicly available from BCI Competition IV, dataset 2a and another in-house motor imagery dataset.},
}
@article {pmid31946786,
year = {2019},
author = {Cecotti, H and Jha, G},
title = {3D Convolutional Neural Networks for Event-Related Potential detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {4160-4163},
doi = {10.1109/EMBC.2019.8856485},
pmid = {31946786},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Deep Learning ; *Electroencephalography ; Evoked Potentials ; Humans ; *Neural Networks, Computer ; },
abstract = {Deep learning techniques have recently been successful in the classification of brain evoked responses for multiple applications, including brain-machine interface. Single-trial detection in the electroencephalogram (EEG) of brain evoked responses, like event-related potentials (ERPs), requires multiple processing stages, in the spatial and temporal domains, to extract high level features. Convolutional neural networks, as a type of deep learning method, have been used for EEG signal detection as the underlying structure of the EEG signal can be included in such system, facilitating the learning step. The EEG signal is typically decomposed into 2 main dimensions: space and time. However, the spatial dimension can be decomposed into 2 dimensions that better represent the relationships between the sensors that are involved in the classification. We propose to analyze the performance of 2D and 3D convolutional neural networks for the classification of ERPs with a dataset based on 64 EEG channels. We propose and compare 6 conv net architectures: 4 using 3D convolutions, that vary in relation to the number of layers and feature maps, and 2 using 2D convolutions. The results support the conclusion that 3D convolutions provide better performance than 2D convolutions for the binary classification of ERPs.},
}
@article {pmid31946644,
year = {2019},
author = {Zhang, X and Wang, Y},
title = {A Weight Transfer Mechanism for Kernel Reinforcement Learning Decoding in Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3547-3550},
doi = {10.1109/EMBC.2019.8856555},
pmid = {31946644},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Humans ; Movement ; *Reinforcement, Psychology ; },
abstract = {Brain-Machine Interfaces (BMIs) aim to help disabled people brain control the external devices to finish a variety of movement tasks. The neural signals are decoded into the execution commands of the apparatus. However, most of the existing decoding algorithms in BMI are only trained for a single task. When facing a new task, even if it is similar to the previous one, the decoder needs to be re-trained from scratch, which is not efficient. Among the different types of decoders, reinforcement learning (RL) based algorithm has the advantage of adaptive training through trial-and-error over the recalibration used in supervised learning. But most of the RL algorithms in BMI do not actively leverage the acquired knowledge in the old task. In this paper, we propose a kernel RL algorithm with a weight transfer mechanism for new task learning. The existing neural patterns are clustered according to their similarities. A new pattern will be assigned with the weights that are transferred from the closest cluster. In this way, the most similar experiences from the previous task could be re-utilized in the new task to fasten the learning speed. The proposed algorithm is tested on synthetic neural data. Compared with the policy of re-training from scratch, the proposed weight transfer mechanism could maintain a significantly higher performance and achieve a faster learning speed on the new task.},
}
@article {pmid31946643,
year = {2019},
author = {Klosterman, SL and Eepp, JR},
title = {Investigating Ensemble Learning and Classifier Generalization in a Hybrid, Passive Brain-Computer Interface for Assessing Cognitive Workload.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3543-3546},
doi = {10.1109/EMBC.2019.8857882},
pmid = {31946643},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Cognition ; Humans ; Machine Learning ; Neural Networks, Computer ; },
abstract = {Hybrid, passive brain-computer (h/pBCI) interfaces have received much attention in regards to measuring various mental states. A high classification rate of operator workload state is necessary in order to be able to enhance operator performance. Physiological measures have been used with machine learning algorithms to classify workload state, however, these measures are hypothesized to suffer from inherent nonstationarity. To attain a more generalizable classifier, a prior solution has been to use a multi-day learning paradigm to train classifier models. In earlier work, we have shown that increasing the number of unique data sessions used to form a learning set can improve the accuracy of classifying mental workload where improved generalizability is partly attributable to the multi-day paradigm. To further investigate methods that produce more generalizable classifiers, we look to ensemble learning. Here we implement ensemble learning to increase accuracies, reduce variance, and leverage theoretical performance of the ensemble as compared to observed to make inference about generalization. An adaptive boosting method (AdaBoost) is used to train a "base learning algorithm" multiple times, adaptively adjusting to errors and forming a vote out of the resulting hypotheses using three different base learning algorithms: an artificial neural network (ANN), a support vector machine (SVM), and linear discriminant analysis (LDA). We observed that the ensemble converged on theoretical performance with respect to error and variance only when the training sets were formed using the multi-day paradigm. These results indicate that ensemble learning approaches can be used in examples of pBCI such as those designed here, but they are also affected by theorized nonstationarity in physiological response. The observation of ensemble convergence on theoretical performance may be used to make inference about generalizability when simple accuracy of detection can be misleading.},
}
@article {pmid31946553,
year = {2019},
author = {Wu, M and Qi, H},
title = {Using passive BCI to online control the air conditioner for obtaining the individual specific thermal comfort.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3139-3142},
doi = {10.1109/EMBC.2019.8856497},
pmid = {31946553},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Temperature ; *Thermosensing ; },
abstract = {Thermal comfort has an important impact on human health and work efficiency, which has attracted more attention in recent years. Although electroencephalogram (EEG) has been used to evaluate thermal comfort, it has not been reported to be used in controlling the air conditioner. This paper attempted to construct a passive EEG based brain-computer interface (BCI) system to regulate the room temperature. During the experiment, EEG signals in two conditions, thermal comfort and hot discomfort, were collected to build a discriminant model. And then, an online experiment was conducted to verify the thermal comfort effect of the BCI temperature control. Results showed that all the five subjects could obtain a better thermal sensation under the BCI control in an overheated environment. This study indicated the feasibility of indoor temperature control technology based on physiological signals. It can provide a new way to obtain personalized thermal comfort.},
}
@article {pmid31946551,
year = {2019},
author = {Pierguidi, L and Guazzini, A and Imbimbo, E and Righi, S and Sorelli, M and Bocchi, L},
title = {Validation of a low-cost EEG device in detecting neural correlates of social conformity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3131-3134},
doi = {10.1109/EMBC.2019.8856716},
pmid = {31946551},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/instrumentation ; Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; Social Conformity ; },
abstract = {The study of conformity from a neurobiological point of view has interested many authors: among them, Shestakova and colleagues (2013) have showed how conformity can be assessed through the analysis of event related potentials (ERPs). More specifically, the P300 component of the ERP was shown to be sensitive to the behavioral adjustment that an individual makes when not agreeing with the majority of a group. Starting from these contributions, in the present study, the famous experiment of Solomon Asch [1] was replicated online. The experiment was conducted on a sample of university students, using an innovative and low-cost tool capable of recording the brain signal (a wireless headset equipped with fourteen electrodes, called Emotiv EPOC). The present research aims to demonstrate how cheap and little sensitive tools enable the detection of ERP components that characterize social conformity in an ecological context.},
}
@article {pmid31946545,
year = {2019},
author = {Fukuda, N and Nambu, I and Wada, Y},
title = {Classification of Movement Direction From Electroencephalogram During Working Memory Time.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3107-3110},
doi = {10.1109/EMBC.2019.8857943},
pmid = {31946545},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Fourier Analysis ; Humans ; *Memory, Short-Term ; *Movement ; *Neural Networks, Computer ; },
abstract = {When humans perform cognitive tasks, it is necessary to hold information temporarily. This is done by a brain function called working memory (WM). Since WM is active during the whole time range from stimulus presentation to task execution, onset detection is unnecessary, in contrast to readiness potentials for movement. Therefore, it is possible to realize application in a brain-computer interface (BCI) in various tasks without onset detection by performing single-trial classification of electroencephalogram (EEG) signals during WM. The purpose of this research was to examine the possibility of WM application to BCI. We classified the EEG signals during WM-time that required the retention of movement direction information when performing a right arm movement in order of two instructed sequential target directions using a 3-layer neural network (3-NN). In classification based on the signal immediately after presentation of 1st target (WM1), the classification accuracy was significantly higher (62%) than chance level (50%). In addition, the accuracy was higher when providing the phase of the fast Fourier transform to the classifier as information rather than the spectrum. However, it could not be classified by WM requiring the retention of information regarding two tasks (WM2). In summary, these results suggest a possibility that single trial classification of EEG during the first WM (WM1) is possible, and that the WM information is included mainly in the phase. Future studies should aim at improving the classification accuracy by using other feature quantities and classifiers, and to examine classification of EEG in tasks other than arm movement. Furthermore, the relationship between WM and EEG distribution also needs to be investigated.},
}
@article {pmid31946543,
year = {2019},
author = {Bhattacharyya, S and Valeriani, D and Cinel, C and Citi, L and Poli, R},
title = {Collaborative Brain-Computer Interfaces to Enhance Group Decisions in an Outpost Surveillance Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3099-3102},
doi = {10.1109/EMBC.2019.8856309},
pmid = {31946543},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Decision Making ; Humans ; Reaction Time ; *Social Behavior ; },
abstract = {We present a two-layered collaborative Brain-Computer Interface (cBCI) to aid groups making decisions under time constraints in a realistic video surveillance setting - the very first cBCI application of this type. The cBCI first uses response times (RTs) to estimate the decision confidence the user would report after each decision. Such an estimate is then used with neural features extracted from EEG to refine the decision confidence so that it better correlates with the correctness of the decision. The refined confidence is then used to weigh individual responses and obtain group decisions. Results obtained with 10 participants indicate that cBCI-assisted groups are significantly more accurate than groups using standard majority or weighing decisions using reported confidence values. This two-layer architecture allows the cBCI to not only further enhance group performance but also speed up the decision process, as the cBCI does not have to wait for all users to report their confidence after each decision.},
}
@article {pmid31946542,
year = {2019},
author = {Du, J and Ke, Y and Liu, P and Liu, W and Kong, L and Wang, N and Xu, M and An, X and Ming, D},
title = {A two-step idle-state detection method for SSVEP BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3095-3098},
doi = {10.1109/EMBC.2019.8857024},
pmid = {31946542},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; },
abstract = {The role of detecting work/idle state in asynchronous Steady-state visual evoked potential (SSVEP) Brain-computer interface (BCI) or a self-paced SSVEP BCI has received increased attention in recent years. This study proposed a tree structure method which identifies the work/idle state based on the frequency recognition to detect work/idle state. Firstly, a frequency recognition estimated with task-related component analysis (TRCA). Then, the work/idle state is classified with step-wise linear discriminant analysis (SWLDA) using the data fusion of TRCA scores and power spectral density (PSD) as features. This method was evaluated by Electroencephalography (EEG) data from fourteen healthy participants with eight frequencies as work states and three idle state conditions. The averaged AUC of this method achieved 0.89 with data lengths of one second, which was significantly higher than that of the conventional power spectrum-based algorithm. The proposed method could identify the work/idle state fast and accurately, making the SSVEP BCI better suited for practical application.},
}
@article {pmid31946541,
year = {2019},
author = {Chen, J and Hong, B and Wang, Y and Gao, X and Zhang, D},
title = {Towards a fully spatially coded brain-computer interface: simultaneous decoding of visual eccentricity and direction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3091-3094},
doi = {10.1109/EMBC.2019.8856586},
pmid = {31946541},
issn = {2694-0604},
mesh = {Attention ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Motion Perception ; },
abstract = {By encoding visual targets with different locations relative to a stimulus, spatially coded brain-computer interface (BCI) has regained interest nowadays. Recent spatially coded BCI studies have demonstrated the feasibility of single-stimulus, multi-target BCIs, suggesting their potentials for simple and efficient applications. However, these studies have only decoded the visual direction information from the neural responses. To fully utilize the visual spatial information, it is necessary to include the visual eccentricity information as well. In the present study, the decodability of visual eccentricity information for BCI application was investigated for the first time. Sixteen targets were encoded simultaneously with eight directions and two eccentricities relative to a visual motion stimulus. Distinct neural spatial patterns and response strengths of motion-onset visual evoked potentials were elicited in the 16 attention conditions. The offline analysis reached an average classification accuracy of 63.1±11.5%, and the best-performing participant achieved an accuracy of 81.9%, well above the chance level (i.e., 6.25%) for 16-target classification. The results suggested the feasibility of simultaneous decoding of visual eccentricity and direction information towards a fully spatially coded BCI.},
}
@article {pmid31946540,
year = {2019},
author = {Zhang, R and Wang, Y and Li, X and Liu, B and Zhang, L and Chen, M and Hu, Y},
title = {Deep Learning of Motor Imagery EEG Classification for Brain-Computer Interface Illiterate Subject.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3087-3090},
doi = {10.1109/EMBC.2019.8857923},
pmid = {31946540},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; },
abstract = {BCI illiterate subject is defined as the subject who cannot achieve accuracy higher than 70%. BCI illiterate subject cannot produce stronger contralateral ERD/ERS activity, thus most of the frequency band-based algorithms cannot obtain higher accuracy. Deep learning with convolutional neural networks (CNN) has revolutionized in many recent studies to learn features and classify different types of data through end-to-end learning. We designed a CNN to extract motor imagery EEG features and then do classification for BCI illiterate subjects in this work. Results showed that the average classification accuracy increased by 18.4% compared with the CSP+LDA algorithm, and the accuracies obtained by CNN exceed 70% for 9 of 11 subjects particularly. CNN requires only a little prior knowledge, thus the features it extracted are not limited in frequency band, but because the poor interpretability of deep learning, we do not know which kind of feature CNN extracted until now. Our future study will focus on visualizing the extracted features to support our conclusions.},
}
@article {pmid31946539,
year = {2019},
author = {Gurve, D and Delisle-Rodriguez, D and Bastos, T and Krishnan, S},
title = {Motor Imagery Classification with Covariance Matrices and Non-Negative Matrix Factorization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3083-3086},
doi = {10.1109/EMBC.2019.8856677},
pmid = {31946539},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; },
abstract = {In this paper, we aim at finding the smallest set of EEG channels that can ensure highly accurate classification of motor imagery (MI) dataset and maintain the optimum Kappa score. Non-negative matrix factorization (NMF) is used for important and discriminant EEG channel selection. Further, the theory of Riemannian geometry in the manifold of covariance matrices is used for feature extraction. At last, the neighborhood component feature selection (NCFS) algorithm is used to select the small subset of important features from the given set of features. The significance of the proposed work is two-fold: 1) it greatly reduces the time complexity and the amount of overfitting by reducing the unnecessary EEG channels and redundant features. 2) it increases the classification accuracy of the model by selecting only subject-specific EEG channels. The proposed algorithm is tested on BCI Competition IV,2a dataset to validate the performance. The proposed approach has achieved 77.91% average classification accuracy and 0.626 mean Kappa score.},
}
@article {pmid31946538,
year = {2019},
author = {Colamarino, E and Muceli, S and Ibanez, J and Mrachacz-Kersting, N and Mattia, D and Cincotti, F and Farina, D},
title = {Adaptive learning in the detection of Movement Related Cortical Potentials improves usability of associative Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3079-3082},
doi = {10.1109/EMBC.2019.8856580},
pmid = {31946538},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; *Evoked Potentials, Motor ; Humans ; *Movement ; },
abstract = {Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.},
}
@article {pmid31946537,
year = {2019},
author = {Bagh, N and Reddy, MR},
title = {Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3075-3078},
doi = {10.1109/EMBC.2019.8857066},
pmid = {31946537},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; *Support Vector Machine ; },
abstract = {In recent decades, motor imagery (MI) based brain-computer interface (BCI) is served as a control system or rehabilitation tool for motor disabled people. But it has limited applications because of its lower classification performance (classification accuracy, Cohen's kappa coefficient and etc.). The performance depends on the feature extraction techniques and extraction of relevant features from the brain is challenging task. The existing techniques have low classification performance and are computationally inefficient. This paper introduces phase space reconstruction (PSR) to detect various MI activities and improve the performance of the system. First, raw signals were decomposed into multiple frequency sub-bands using filter bank technique. Second, PSR was applied to each sub-band and dynamical behavior of the brain activities has been analyzed. The optimal parameters (time delay and embedding dimension) of PSR were calculated by average mutual information (AMI) and false nearest neighbors (FNN) methods. The time delay and embedding dimension extracted significant features related to MI activities. The significant features were fed into multi-class support vector machine (SVM) and performance of the classifier was evaluated. The performance of the system is based on classification accuracy (%CA) and Cohen's kappa coefficient (K). The proposed algorithm and classifier were tested on BCI competition-2005, MI dataset-III-a. The results show that the proposed technique increases the classification accuracy by 3.7% and achieved higher performance (%CA = 89.20% and K= 0.85).},
}
@article {pmid31946535,
year = {2019},
author = {Han, C and Xu, G and Jiang, Y and Wang, H and Chen, X and Zhang, K and Xie, J and Liu, F},
title = {Stereoscopic Motion Perception Research Based on Steady-state Visual Motion Evoked Potential.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3067-3070},
doi = {10.1109/EMBC.2019.8857487},
pmid = {31946535},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Motion Perception ; },
abstract = {The combination of BCI technology and stereoscopic three-dimensional (3D) display has gradually become a trend, while stereo-based BCI has been used in rehabilitation training and medical testing. However, present stereo-based BCI research mainly stays in the static stereo environment, and the main method of visual stimulation is flickering, which does not effectively utilize the characteristic of the stereoscopic display technology. Therefore, we proposed a novel stimulation method based on stereoscopic motion. It utilized the stereo reciprocating motion of the plane of intensive line to elicit steady-state visual motion evoked potential (SSMVEP). The results shown that the correlation canonical analysis (CCA) coefficients of the EEG signal of stereoscopic motion (4.3 Hz-6.3 Hz) was significant higher than the non-stereoscopic motion, and more brain areas were activated. This stimulation method can induce significant visual response and has a great potential in the application of virtual reality stereo-based BCI system.},
}
@article {pmid31946534,
year = {2019},
author = {Huang, W and Yu, T and Xiao, J and Guo, Q and Li, Y},
title = {A P300-based Brain Computer Interface Using Stereo-electroencephalography Signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3062-3066},
doi = {10.1109/EMBC.2019.8857724},
pmid = {31946534},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electrodes, Implanted ; *Electroencephalography ; Epilepsy/*diagnosis ; *Event-Related Potentials, P300 ; Humans ; User-Computer Interface ; },
abstract = {Stereo-electroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes, which are currently used for epileptic diagnosis. In this study, we implemented a P300-based Brain Computer Interface (BCI) using SEEG signals. 40 buttons corresponding to 40 numbers displayed in a graphical user interface (GUI) were intensified in a random order. To select a number, the user could focus on the corresponding button when it was flashing. Five epileptic patients implanted with SEEG electrodes attended the experiment and achieved an average online accuracy of 97.33%. Moreover, through single contact decoding and simulated online analysis, we found that these subjects achieved an average accuracy of 82.00% using a single channel of signal. These results indicated that our SEEG-based BCI had a high performance, which was mainly because of the high quality of SEEG signals.},
}
@article {pmid31946533,
year = {2019},
author = {Zheng, L and Wang, Y and Pei, W and Chen, H},
title = {A Fast Brain Switch Based on Multi-Class Code-Modulated VEPs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3058-3061},
doi = {10.1109/EMBC.2019.8857617},
pmid = {31946533},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Reaction Time ; },
abstract = {To realize asynchronous control of a brain-computer interface (BCI) system, a fast brain switch with low false positive rate (FPR) is required. This paper proposed a brain switch based on code-modulated visual-evoked potential (c-VEP), in which seven 8-bit pseudorandom codes were used to modulate the electroencephalogram (EEG) signal. This study optimized and demonstrated the control strategy through an offline and an online experiments. By decoding the brain state continuously with the task-related component analysis (TRCA) algorithm, the brain switch achieved an average reaction time (RT) of 1.72 seconds and an average idle time of 183.53 seconds without false positive events in the online experiment.},
}
@article {pmid31946532,
year = {2019},
author = {Wang, R and Han, J and Chen, J and Li, M and Feng, L and Zhang, S},
title = {Decoding with Calcium Signals from Layer 2/3 Motor Cortex during A Pressing Movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3054-3057},
doi = {10.1109/EMBC.2019.8856331},
pmid = {31946532},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; *Calcium Signaling ; Mice ; Motor Cortex/*physiology ; *Movement ; Neurons/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) have been promising for not only neuroprosthesis research but also brain function investigation. Electrophysiological recording commonly used in traditional BMIs is spatially sparse and lack of information about neuron types and spatial organization. However, optical imaging methods might avoid these limitations by providing dense, spatially organized and annotated with genetic information over a large field of view. Here, we tried to demonstrate the potential of calcium imaging signals obtained through the one-photon microscope in neural decoding. When mice were trained to perform a lever press task to obtain water as rewards, the calcium signals of neurons in their layer 2/3 motor cortex were recorded by microscope. With the calcium signals, we analyzed the neural activity at both single individual neuron and neuronal population level. We found two typical classes of pressing-related neurons and distinct ensemble activity patterns between a pressing movement and baseline. The decoding results further demonstrated that the movement-related information could be more completely specified by population response structure. Our results suggested that neural signals from more types and a larger amount of neurons, are crucial for accurate decoding in BMI applications.},
}
@article {pmid31946531,
year = {2019},
author = {Jahangiri, A and Achanccaray, D and Sepulveda, F},
title = {A Novel EEG-Based Four-Class Linguistic BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3050-3053},
doi = {10.1109/EMBC.2019.8856644},
pmid = {31946531},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Linguistics ; *Speech ; Young Adult ; },
abstract = {In this work, we present a novel EEG-based Linguistic BCI, which uses the four phonemic structures "BA", "FO", "LE", and "RY" as covert speech task classes. Six neurologically healthy volunteers with the age range of 19-37 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. The duration of each trial is 312ms starting with the cue. The BCI was trained using a mixed randomized recording run containing 15 trials per class. The BCI is tested by playing a simple game of "Wack a mole" containing 5 trials per class presented in random order. The average classification accuracy for the 6 users is 82.5%. The most valuable features emerge after Auditory cue recognition (~100ms post onset), and within the 70-128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke's area (linked to Phonological code retrieval), the right IFG, and Broca's area (linked to syllabification). In this work, we have only scratched the surface of using Linguistic tasks for BCIs and the potential for creating much more capable systems in the future using this approach exists.},
}
@article {pmid31946530,
year = {2019},
author = {Craik, A and Kilicarslan, A and Contreras-Vidal, JL},
title = {Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3046-3049},
doi = {10.1109/EMBC.2019.8857575},
pmid = {31946530},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; *Machine Learning ; *Movement ; *Neural Networks, Computer ; },
abstract = {The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an optimized architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from subjects who lack full motor functionality. The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.},
}
@article {pmid31946529,
year = {2019},
author = {Zhang, L and Chen, L and Wang, Z and Liu, S and Wang, M and Chen, S and Ming, D},
title = {Study on brain computer interface combined tactile enhancement and time-varying features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3042-3045},
doi = {10.1109/EMBC.2019.8856609},
pmid = {31946529},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination ; Sensorimotor Cortex/*physiology ; *Touch ; },
abstract = {Neuroplasticity plays an important role in the recovery of injured nervous system. Both motor imagery (MI) and functional electrical stimulation (FES) can promote plasticity by activating the sensorimotor cortex. Specifically, MI as control strategy to activate FES in a brain computer interface (BCI) is a promising approach for motor functions recovery. This study demonstrated the efficiency of somatosensory input provided by electrical stimulation (ES) on cortical activation during MI. And the performance of classifiers with time-varying electroencephalography (EEG) features also be probed. We inspected the cortical activation by EEG for three experiment conditions, i.e. ES during MI, MI and ES. And the classification accuracy of three conditions were discussed respectively. Results showed that the ES during MI could induce stronger cortical activation than the other two conditions, and the classifier with time-varying EEG features had a higher classification accuracy. The results demonstrated that MI-based BCI combined MI and ES which fulfills two properties of somatosensory input and time-varying features is an available approach for motor neural rehabilitation.},
}
@article {pmid31946528,
year = {2019},
author = {Schwarz, A and Pereira, J and Lindner, L and Muller-Putz, GR},
title = {Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3036-3041},
doi = {10.1109/EMBC.2019.8857138},
pmid = {31946528},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand/*physiology ; Humans ; Motor Disorders ; *Movement ; },
abstract = {Brain-computer interfaces (BCIs) might provide an intuitive way for severely motor impaired persons to operate assistive devices to perform daily life activities. Recent studies have shown that complex hand movements, such as reach-and-grasp tasks, can be decoded from the low frequency of the electroencephalogram (EEG). In this work we investigated whether additional features extracted from the frequency-domain of alpha and beta bands could improve classification performance of rest vs. palmar vs. lateral grasp. We analysed two multi-class classification approaches, the first using features from the low frequency time-domain, and the second in which we combined the time-domain with frequency-domain features from alpha and beta bands. We measured EEG of ten participants without motor disability which performed self-paced reach-and-grasp actions on objects of daily life. For the time-domain classification approach, participants reached an average peak accuracy of 65%. For the combined approach, an average peak accuracy of 75% was reached. In both approaches and for all subjects, performance was significantly higher than chance level (38.1%, 3-class scenario). By computing the confusion matrices as well as feature rankings through the Fisher score, we show that movement vs. rest classification performance increased considerably in the combined approach and was the main responsible for the multi-class higher performance. These findings could help the development of BCIs in real-life scenarios, where decreasing false movement detections could drastically increase the end-user acceptance and usability of BCIs.},
}
@article {pmid31946527,
year = {2019},
author = {Xiao, X and Xu, M and Wang, Y and Jung, TP and Ming, D},
title = {A comparison of classification methods for recognizing single-trial P300 in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3032-3035},
doi = {10.1109/EMBC.2019.8857521},
pmid = {31946527},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; },
abstract = {P300s are one of the most popular and robust control signals for brain-computer interfaces (BCIs). Fast classifying P300s is vital for the good performance of P300-based BCIs. However, due to noisy background electroencephalography (EEG) environments, current P300-based BCI systems need to collect multiple trials for a reliable output, which is inefficient. This study compared a recently developed algorithm, i.e. discriminative canonical pattern matching (DCPM), with five traditional classification methods,i.e. linear discriminant analysis (LDA), stepwise LDA, Bayesian LDA, shrinkage LDA and spatial-temporal discriminant analysis (STDA), for the detection of single-trial P300s. Eight subjects participated in the classical P300-speller experiments. Study results showed that the DCPM significantly outperformed the other traditional methods in single-trial P300 classification even with small training samples, suggesting the DCPM is a promising classification algorithm for the P300-based BCI.},
}
@article {pmid31946526,
year = {2019},
author = {Shan, H and Stefanov, T},
title = {SLES: A Novel CNN-based Method for Sensor Reduction in P300 Speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3026-3031},
doi = {10.1109/EMBC.2019.8857087},
pmid = {31946526},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; *Fixation, Ocular ; Humans ; Language ; *Neural Networks, Computer ; Self-Help Devices ; },
abstract = {A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Current popular BCI character speller systems employ a large number of sensors, which prevents the utilization of such systems in human's daily life. Using sensor selection methods to select appropriate sensor subsets from an initial large sensor set can reduce the number of sensors needed to acquire brain signals without losing the character spelling accuracy, thereby promoting the BCI character spellers into people's daily life. However, current sensor selection methods cannot select an appropriate sensor subset such that they can further reduce the number of sensors needed to acquire brain signals without losing the spelling accuracy. To address this issue, we propose a novel sensor selection method based on a specific Convolutional Neural Network (CNN) we have devised. Our method uses a parametric backward elimination algorithm which uses our devised CNN as a ranking function to evaluate sensors and eliminate less important sensors. We perform experiments on three benchmark datasets and compare the minimal number of sensors selected by our proposed method and other selection methods to acquire brain signals while keeping the spelling accuracy the same as the accuracy achieved when the initial large sensor set is used. The results show that the minimal number of sensors selected by our method is lower than the minimal number of sensors selected by other methods in most cases. Compared with the minimal number of sensors selected by other methods, our method can reduce this number with up to 44 sensors.},
}
@article {pmid31946525,
year = {2019},
author = {Liu, W and Ke, Y and Liu, P and Du, J and Kong, L and Liu, S and An, X and Ming, D},
title = {A Cross-Subject SSVEP-BCI Based on Task Related Component Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3022-3025},
doi = {10.1109/EMBC.2019.8857064},
pmid = {31946525},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Reproducibility of Results ; Young Adult ; },
abstract = {SSVEP-BCIs have attracted extensive attention because of high information transfer rate. High-speed BCIs need to collect sufficient user's own data to train optimal subject-specific parameters. However, one of the challenges which limits the real-life application of BCIs is the time-consuming and tiring calibration process. This study developed two cross-subject frameworks. One of them uses data from all training subjects to train task-related component analysis based spatial filters (all-to-one, A2O), and the other uses data from each training subject to train task-related component analysis based spatial filters (one-to-one, O2O). Both of them do not need calibration process for a new user. The study further proposed O2O with threshold (O2O-Thr) to increase the reliability of recognition process. The proposed strategies can exploit information from existing subjects' SSVEP data and transfer it to new users. The performance of these methods was compared using an 8-class SSVEP dataset recorded from 10 subjects. O2O-Thr achieves average accuracy of 94.6% with data length of 1.5 seconds. The proposed methods have great potential for building subject-independent BCI that do not require any calibration data from new users, which make BCI more practical and user-friendly.},
}
@article {pmid31946524,
year = {2019},
author = {Hubner, D and Schall, A and Tangermann, M},
title = {Two Player Online Brain-Controlled Chess.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3018-3021},
doi = {10.1109/EMBC.2019.8856965},
pmid = {31946524},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials ; Games, Recreational ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) allow for translating brain signals into control commands, e.g. to control games. One of the biggest quests of the BCI community is to realize new exciting applications. In this paper, we present a two player online chess application where both players control their pieces solely with their brain activity. Control is realized in a two-step process where players first select the desired chess piece and then the field they want to move it to. A selection is accomplished with visual event-related potentials that are elicited by highlighting each possible piece or field one by one. Six healthy volunteers participated in our study by playing a game against each other in pairs over a free chess server. They successfully completed at least one match per pair. Our results show that even BCI-naive players obtain almost perfect control over the application. On average, 96% of the moves were correct. Most of the errors came from technical problems that can be corrected in future versions of the application. Given the high popularity of chess, this intuitive BCI game is an appealing new application for patients and for healthy users that want to explore BCIs.},
}
@article {pmid31946523,
year = {2019},
author = {Yun, YD and Jeong, JH and Cho, JH and Kim, DJ and Lee, SW},
title = {Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {3014-3017},
doi = {10.1109/EMBC.2019.8857674},
pmid = {31946523},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Forearm/*physiology ; Hand ; Humans ; Imagination ; *Movement ; *Robotics ; Rotation ; },
abstract = {Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive BCI. However, decoding of forearm rotation from imagined movements using electroencephalography (EEG) is difficult to decode degree of rotation accurately. In this paper, we reconstructed imagined forearm rotation from low- frequency (0.3-3 Hz) of EEG signals. We selected 20 EEG channel on motor cortex for analysis. Ten healthy subjects participated in our experiment. The subjects performed actual and imagined forearm rotation to reach different targets. We trained a reconstruction decoder which used the EEG signals measured from actual movements and the kinematic information only. Additionally, we applied a long short-term memory (LSTM) network to enhance decoding performances. As a result, we achieved the high correlation performance (Average: 0.67) to decode imagined forearm rotation angle. This result has demonstrated that the reconstruction decoder which is trained by the EEG data from actual movement has effective to decode robustly for the imagined forearm rotation angle.},
}
@article {pmid31946349,
year = {2019},
author = {Sosulski, J and Tangermann, M},
title = {Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {2256-2260},
doi = {10.1109/EMBC.2019.8857460},
pmid = {31946349},
issn = {2694-0604},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Decision Making ; *Electroencephalography ; *Evoked Potentials ; Humans ; *Machine Learning ; Photic Stimulation ; },
abstract = {The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.},
}
@article {pmid31946326,
year = {2019},
author = {Breault, MS and Gonzalez-Martinez, JA and Gale, JT and Sarma, SV},
title = {Neural Activity from Attention Networks Predicts Movement Errors.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {2149-2152},
doi = {10.1109/EMBC.2019.8856958},
pmid = {31946326},
issn = {2694-0604},
support = {R01 NS110423/NS/NINDS NIH HHS/United States ; },
mesh = {*Attention ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Humans ; *Movement ; },
abstract = {Traditionally, movement-related behavior is estimated using activity from motor regions in the brain. This predictive capability of interpreting neural signals into tangible outputs has led to the emergence of Brain-Computer Interface (BCI) systems. However, nonmotor regions can play a significant role in shaping how movements are executed. Our goal was to explore the contribution of nonmotor brain regions to movement using a unique experimental paradigm in which local field potential recordings of several cortical and subcortical regions were obtained from eight epilepsy patients implanted with depth electrodes as they performed goal-directed reaching movements. The instruction of the task was to move a cursor with a robotic arm to the indicated target with a specific speed, where correct trials were ones in which the subject achieved the instructed speed. We constructed subject-specific models that predict the speed error of each trial from neural activity in nonmotor regions. Neural features were found by averaging spectral power of activity in multiple frequency bands produced during the planning or execution of movement. Features with high predictive power were selected using a forward selection greedy search. Using our modeling framework, we were able to identify networks of regions related to attention that significantly contributed to predicting trial errors. Our results suggest that nonmotor brain regions contain relevant information about upcoming movements and should be further studied.},
}
@article {pmid31946213,
year = {2019},
author = {Cruz, A and Pires, G and Lopes, AC and Nunes, UJ},
title = {Detection of Stressful Situations Using GSR While Driving a BCI-controlled Wheelchair.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {1651-1656},
doi = {10.1109/EMBC.2019.8857748},
pmid = {31946213},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Galvanic Skin Response ; Humans ; *Robotics ; User-Computer Interface ; *Wheelchairs ; },
abstract = {This paper analyzes the galvanic skin response (GSR) recorded from healthy and motor disabled people while steering a robotic wheelchair (RobChair ISR-UC prototype), to infer whether GSR can help in the recognition of stressful situations. Seven healthy individuals and six individuals with motor disabilities were asked to drive the RobChair by means of a brain-computer interface in indoor office environments, including complex scenarios such as passing narrow doors, avoiding obstacles, and with situations of unexpected trajectories of the wheelchair (controlled by an operator without users knowledge). All these driving situations can trigger emotional arousals such as anxiety and stress. A method called feature-based peak detection (FBPD) was proposed for automatic detection of skin conductance response (SCR) which proved to be very effective compared to the state-of-the-art methods. We found that SCR was elicited in 100% of the occurrences of collisions (lateral scrapings) and 94% of unexpected trajectories.},
}
@article {pmid31946010,
year = {2019},
author = {Yang, B and Fan, C and Guan, C and Gu, X and Zheng, M},
title = {A Framework on Optimization Strategy for EEG Motor Imagery Recognition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {774-777},
doi = {10.1109/EMBC.2019.8857672},
pmid = {31946010},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {At present, in the process of encephalogram motor imagery decoding, facing the background of big data analysis, it has the necessity to design an effective system which is subject-independent. Pre-training is common to carry out before each experiment, which affects the practicability of the EEG system. In order to solve this problem, the most feasible method is to design a unified framework for deep learning optimization, which could capture the spatial and spectral dependence of original motor imagery EEG signals according to the features extracted by CNN and the temporal dependence extracted by RNN-LSTM. The framework is superimposed from both end-to-end and time-frequency domains so as to retain and learn interpretable motor imagery features. In addition, artificial EEG signals can be automatically generated by training the generated adversarial network, which can generate the feature distribution similar to the original EEG signals, increase the capacity of EEG samples, and ultimately improve the classification performance and robustness of EEG motor imagery recognition. This deep learning framework can improve the classification accuracy of motor imagery for different subjects. In addition, the network can learn from the original data with the least amount of preprocessing, thus eliminating the time-consuming data preparation process.},
}
@article {pmid31946008,
year = {2019},
author = {Oikonomou, VP and Nikolopoulos, S and Kompatsiaris, I},
title = {Discrimination of SSVEP responses using a kernel based approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {762-766},
doi = {10.1109/EMBC.2019.8857685},
pmid = {31946008},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Brain Computer Interfaces based on Steady State Visual Evoked Potentials have gained increased attention due to their low training requirements and higher information transfer rates. In this work, a method based on sparse kernel machines is proposed for the discrimination of Steady State Visual Evoked Potentials responses. More specifically, a new kernel based on Partial Least Squares is introduced to describe the similarities between EEG trials, while the estimation of regression weights is performed using the Sparse Bayesian Learning framework. The experimental results obtained on two benchmarking datasets, have shown that the proposed method provides significantly better performance compared to state of the art approaches of the related literature.},
}
@article {pmid31946007,
year = {2019},
author = {Ling, SH and Makgawinata, H and Monsivais, FH and Dos Santos Goncalves Lourenco, A and Lyu, J and Chai, R},
title = {Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {758-761},
doi = {10.1109/EMBC.2019.8857933},
pmid = {31946007},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.},
}
@article {pmid31945985,
year = {2019},
author = {Ho, YL and Huang, YD and Wang, KY and Fang, WC},
title = {A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {664-667},
doi = {10.1109/EMBC.2019.8856322},
pmid = {31945985},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Computer Systems ; *Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {Independent component analysis (ICA) has been wildly used to improve EEG based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEG real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm[2] using 28nm CMOS technology. The EEG demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.},
}
@article {pmid31945930,
year = {2019},
author = {Gaxiola-Tirado, JA and Ianez, E and Ortiz, M and Gutierrez, D and Azorin, JM},
title = {Effects of an Exoskeleton-Assisted Gait Motor Imagery Training in Functional Brain Connectivity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {429-432},
doi = {10.1109/EMBC.2019.8857232},
pmid = {31945930},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Gait ; Humans ; Imagery, Psychotherapy ; },
abstract = {Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed during exoskeleton-assisted gait motor imagery (MI) training. Partial Directed Coherence (PDC) analysis was employed to assess the exchange of information flow between EEG signals during gait MI in four healthy subjects, two using an exoskeleton and two without using it. Besides, in order to explore the functional connectivity, an outflow index based on the number of significant directed connectivities revealed by the PDC analysis is proposed. We found that the outflow index increases in the central zone (C2, C3, C4) while decreases in the central-parietal (CP1, CP2) and fronto-central (FC1) zones when the training was assisted by an exoskeleton. The results obtained can be useful to obtain informative features for BCI applications as well as in motor rehabilitation.},
}
@article {pmid31945882,
year = {2019},
author = {Sikdar, D and Roy, R and Mahadevappa, M},
title = {Regression between EEG and Speech Signals for Spoken Vowels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2019},
number = {},
pages = {221-224},
doi = {10.1109/EMBC.2019.8856839},
pmid = {31945882},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Humans ; Phonetics ; *Speech ; },
abstract = {One of the primary difference of mankind from other species is his ability to communicate verbally. Our brain upon framing a sentence, coordinates with the oro-pharyngeal-laryngeal muscle groups to produce the speech with the help of vocal cord and mouth aperture. However, some individuals due to congenital or illness, may loose their ability to speak in spite of their brain framing speech. Research on speech restoration through brain computer interface (BCI) is still at an early stage. Through this study, we have explored the regression between the chaos parameters of acoustic signal and electroencephalography (EEG) signal for different vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. We have selected the EEG channels based on their higher contribution towards the first principal component. Goodness of fit parameters were evaluated for the regression analysis to explore the most suitable chaos parameter.},
}
@article {pmid31945755,
year = {2020},
author = {Nagasawa, T and Sato, T and Nambu, I and Wada, Y},
title = {fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016068},
doi = {10.1088/1741-2552/ab6cb9},
pmid = {31945755},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; Psychomotor Performance/*physiology ; Spectroscopy, Near-Infrared/methods ; Support Vector Machine ; Young Adult ; },
abstract = {OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train classification models; hence, the fNIRS-BCI accuracy decreases.<br><br>APPROACH: In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data augmentation method using Wasserstein generative adversarial networks (WGANs). Using fNIRS data during hand-grasping tasks, we evaluated whether the proposed data augmentation method could generate artificial fNIRS data and improve the classification performance using support vector machines and simple neural networks.<br><br>MAIN RESULTS: Trial-averaged temporal profiles of WGAN-generated fNIRS data were similar to those of the measured data except that they contained an extra noise component. By augmenting the generated data to training data, the accuracies for classifying four different task types were improved irrespective of the classifiers.<br><br>SIGNIFICANCE: This result suggests that the artificial fNIRS data generated by the proposed data augmentation method is useful for improving BCI performance.},
}
@article {pmid31945751,
year = {2020},
author = {Bulhões da Silva Costa, T and Fernanda Suarez Uribe, L and Negreiros de Carvalho, S and Coutinho Soriano, D and Castellano, G and Suyama, R and Attux, R and Panazio, C},
title = {Channel capacity in brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016060},
doi = {10.1088/1741-2552/ab6cb7},
pmid = {31945751},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/psychology ; Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation/methods ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain-computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression-also based on that channel model, but with less restrictive assumptions-and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system.<br><br>APPROACH: The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs.<br><br>MAIN RESULTS: Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 [Formula: see text] 1.68 to 4.79 [Formula: see text] 0.70 bits per symbol, with p&#x200a;-value&#x2009;&#x2009;<0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol.<br><br>SIGNIFICANCE: Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.},
}
@article {pmid31944982,
year = {2020},
author = {Jeong, JH and Kwak, NS and Guan, C and Lee, SW},
title = {Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {3},
pages = {687-698},
doi = {10.1109/TNSRE.2020.2966826},
pmid = {31944982},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Humans ; Intention ; Movement ; },
abstract = {An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement () compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.},
}
@article {pmid31940543,
year = {2020},
author = {Li, C and Su, M and Xu, J and Jin, H and Sun, L},
title = {A Between-Subject fNIRS-BCI Study on Detecting Self-Regulated Intention During Walking.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {2},
pages = {531-540},
doi = {10.1109/TNSRE.2020.2965628},
pmid = {31940543},
issn = {1558-0210},
mesh = {Algorithms ; Area Under Curve ; *Brain-Computer Interfaces ; Decision Trees ; Entropy ; False Positive Reactions ; Female ; Gait ; Healthy Volunteers ; Humans ; *Intention ; Male ; Reproducibility of Results ; Spectroscopy, Near-Infrared/*methods ; Support Vector Machine ; Walking/*psychology ; Young Adult ; },
abstract = {OBJECTIVE: Most BCI (brain-computer interface) studies have focused on detecting motion intention from a resting state. However, the dynamic regulation of two motion states, which usually happens in real life, is rarely studied. Besides, popular within-subject methods also require an extensive and time-consuming learning stage when testing on a new subject. This paper proposed a method to discriminate dynamic gait- adjustment intention with strong adaptability for different subjects.<br><br>METHODS: Cerebral hemoglobin signals obtained from 30 subjects were studied to decode gait-adjustment intention. Cerebral hemoglobin information was recorded by using fNIRS (functional near infrared spectroscopy) technology. Mathematical morphology filtering was applied to remove zero drift and EWM (Entropy Weight Method) was used to calculate the average hemoglobin values over Regions of Interest (ROIs). The gradient boosting decision tree (GBDT) was utilized to detect the onset of self-regulated intention. A 2-layer-GA-SVM (Genetic Algorithm-Support Vector Machine) model based on stacking algorithm was further proposed to identify the four types of self-regulated intention (speed increase, speed reduction, step increase, and step reduction).<br><br>RESULTS: It was found that GBDT had a good performance to detect the onset intention with an average AUC (Area Under Curve) of 0.894. The 2-layer-GA-SVM model boosted the average ACC (accuracy) of four types of intention from 70.6% to 84.4% (p = 0.005) from the single GA-SVM model. Furthermore, the proposed method passed pseudo-online test with the average results as following: AUC = 0.883, TPR (True Positive Rate) = 97.5%, FPR (False Positive Rate) = 0.11%, and LAY (Detection Latency) = -0.52 &#xb1; 2.57 seconds for the recognition of gait-adjustment intention; ACC = 80% for the recognition of adjusted gait.<br><br>CONCLUSION: The results indicate that it is feasible to decode dynamic gait-adjustment intentions from a motion state for different subjects based on fNIRS technology. It has a potential to realize the practical application of fNIRS-based brain-computer interface technology in controlling walking-assistive devices.},
}
@article {pmid31940515,
year = {2020},
author = {Jin, J and Chen, Z and Xu, R and Miao, Y and Wang, X and Jung, TP},
title = {Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {9},
pages = {2585-2593},
doi = {10.1109/TBME.2020.2965178},
pmid = {31940515},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Cheek ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Touch ; },
abstract = {OBJECTIVE: Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential. Until now, tactile P300 BCIs have been successfully constructed by situating tactile stimuli at various parts of the human body. This article proposed a novel tactile P300 BCI paradigm for further expanding the tactile stimulation methods.<br><br>METHODS: In our proposed paradigm, the spatial target vibrotactile stimuli were delivered to subject's left and right cheeks. To validate the feasibility of our proposed paradigm, a traditional tactile P300 BCI paradigm employing spatial target vibrotactile stimuli to subject's left and right wrists was used for comparison.<br><br>RESULTS: The experimental results of nine healthy subjects demonstrated that the proposed paradigm could obtain significantly higher classification accuracy and information transfer rate than the traditional paradigm (both for p < 0.05). Furthermore, the subjective feedback showed that our proposed paradigm was more favored by the subjects compared to the traditional paradigm, and most subjects reported that the new paradigm helped them easily distinguish between targets and non-targets.<br><br>CONCLUSION: The proposed tactile P300 BCI paradigm is feasible, and can bring about superior performance and use-evaluation.<br><br>SIGNIFICANCE: The new paradigm might lead to many promising applications of such BCIs.},
}
@article {pmid31936250,
year = {2020},
author = {Han, CH and Kim, E and Im, CH},
title = {Development of a Brain-Computer Interface Toggle Switch with Low False-Positive Rate Using Respiration-Modulated Photoplethysmography.},
journal = {Sensors (Basel, Switzerland)},
volume = {20},
number = {2},
pages = {},
pmid = {31936250},
issn = {1424-8220},
support = {2017-0-00432//Institute of Information &amp; Communications Technology Planning &amp; Evaluation/ ; NRF-2019R1A2C2086593//National Research Foundation of Korea/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Breath Holding ; Female ; Humans ; Male ; *Photoplethysmography ; *Respiration ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {Asynchronous brain-computer interfaces (BCIs) based on electroencephalography (EEG) generally suffer from poor performance in terms of classification accuracy and false-positive rate (FPR). Thus, BCI toggle switches based on electrooculogram (EOG) signals were developed to toggle on/off synchronous BCI systems. The conventional BCI toggle switches exhibit fast responses with high accuracy; however, they have a high FPR or cannot be applied to patients with oculomotor impairments. To circumvent these issues, we developed a novel BCI toggle switch that users can employ to toggle on or off synchronous BCIs by holding their breath for a few seconds. Two states-normal breath and breath holding-were classified using a linear discriminant analysis with features extracted from the respiration-modulated photoplethysmography (PPG) signals. A real-time BCI toggle switch was implemented with calibration data trained with only 1-min PPG data. We evaluated the performance of our PPG switch by combining it with a steady-state visual evoked potential-based BCI system that was designed to control four external devices, with regard to the true-positive rate and FPR. The parameters of the PPG switch were optimized through an offline experiment with five subjects, and the performance of the switch system was evaluated in an online experiment with seven subjects. All the participants successfully turned on the BCI by holding their breath for approximately 10 s (100% accuracy), and the switch system exhibited a very low FPR of 0.02 false operations per minute, which is the lowest FPR reported thus far. All participants could successfully control external devices in the synchronous BCI mode. Our results demonstrated that the proposed PPG-based BCI toggle switch can be used to implement practical BCIs.},
}
@article {pmid31935220,
year = {2020},
author = {Mehdizavareh, MH and Hemati, S and Soltanian-Zadeh, H},
title = {Enhancing performance of subject-specific models via subject-independent information for SSVEP-based BCIs.},
journal = {PloS one},
volume = {15},
number = {1},
pages = {e0226048},
pmid = {31935220},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; *Patient-Specific Modeling ; Young Adult ; },
abstract = {Recently, brain-computer interface (BCI) systems developed based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high information transfer rate (ITR) and increasing number of targets. However, SSVEP-based methods can be improved in terms of their accuracy and target detection time. We propose a new method based on canonical correlation analysis (CCA) to integrate subject-specific models and subject-independent information and enhance BCI performance. We propose to use training data of other subjects to optimize hyperparameters for CCA-based model of a specific subject. An ensemble version of the proposed method is also developed for a fair comparison with ensemble task-related component analysis (TRCA). The proposed method is compared with TRCA and extended CCA methods. A publicly available, 35-subject SSVEP benchmark dataset is used for comparison studies and performance is quantified by classification accuracy and ITR. The ITR of the proposed method is higher than those of TRCA and extended CCA. The proposed method outperforms extended CCA in all conditions and TRCA for time windows greater than 0.3 s. The proposed method also outperforms TRCA when there are limited training blocks and electrodes. This study illustrates that adding subject-independent information to subject-specific models can improve performance of SSVEP-based BCIs.},
}
@article {pmid31933620,
year = {2019},
author = {Gao, Q and Zhang, Y and Wang, Z and Dong, E and Song, X and Song, Y},
title = {Channel Projection-Based CCA Target Identification Method for an SSVEP-Based BCI System of Quadrotor Helicopter Control.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {2361282},
pmid = {31933620},
issn = {1687-5273},
mesh = {Adult ; Aviation ; Brain/physiology ; *Brain-Computer Interfaces ; *Electrical Equipment and Supplies ; *Electroencephalography/methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Spatial Navigation ; Young Adult ; },
abstract = {The brain-computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection-based canonical correlation analysis (CP-CCA) target identification method for steady-state visual evoked potential- (SSVEP-) based BCI system was proposed. The single-channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single-channel EEG signals of multiple trials and sine-cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5-class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D-helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR). Furthermore, the online experiments of 3-DOF helicopter control achieved an average accuracy of 87.94 ± 5.93% with an ITR of 21.07 ± 4.42 bit/min.},
}
@article {pmid31931266,
year = {2020},
author = {Rosenblum, D and Unick, J and Ciccarone, D},
title = {The Rapidly Changing US Illicit Drug Market and the Potential for an Improved Early Warning System: Evidence from Ohio Drug Crime Labs.},
journal = {Drug and alcohol dependence},
volume = {208},
number = {},
pages = {107779},
pmid = {31931266},
issn = {1879-0046},
support = {R01 DA037820/DA/NIDA NIH HHS/United States ; },
mesh = {Analgesics, Opioid/adverse effects ; Crime/*legislation &amp; jurisprudence/*trends ; Drug Overdose/mortality ; Female ; Fentanyl/adverse effects/analogs &amp; derivatives ; Forensic Medicine/methods/trends ; Heroin/adverse effects ; Humans ; Illicit Drugs/adverse effects/*legislation &amp; jurisprudence ; Law Enforcement/*methods ; Male ; Ohio/epidemiology ; Opiate Overdose/*epidemiology/prevention &amp; control ; },
abstract = {BACKGROUND: The US has seen a rapid increase in synthetic opioid-related overdose deaths. We investigate Ohio, a state with one of the highest overdose death rates in 2017 and substantial numbers of deaths related to fentanyl, carfentanil, and other fentanyl analogs, to provide detailed evidence about the relationship between changes in the illicit drug market and overdose deaths.<br><br>METHODS: We investigate the illicit drug market using Ohio's Bureau of Criminal Investigation's (BCI) crime lab data from 2009 to 2018 that shows the content of drugs seized by law enforcement. We use Poisson regression analysis to estimate the relationship between monthly crime lab data and monthly unintentional drug overdose death data at the county level.<br><br>RESULTS: During this time period there has been a rapid change in the composition of drugs analyzed by the BCI labs, with a rapid fall in heroin observations, simultaneous rise in synthetic opioids, and an increase in the number of different fentanyl analogs. We find that the increased presence of fentanyl, carfentanil, and other fentanyl analogs have a strong correlation with an increase in overdose deaths. The types of opioids most associated with deaths varies by the population size of the county.<br><br>CONCLUSIONS: Crime lab data has the potential to be used as an early warning system to alert persons who inject drugs, harm reduction services, first responders, and law enforcement about changes in the illicit opioid risk environment.},
}
@article {pmid31930482,
year = {2020},
author = {David, L and Schwan, P and Lobedann, M and Borchert, SO and Budde, B and Temming, M and Kuerschner, M and Alberti Aguilo, FM and Baumarth, K and Thüte, T and Maiser, B and Blank, A and Kistler, V and Weber, N and Brandt, H and Poggel, M and Kaiser, K and Geisen, K and Oehme, F and Schembecker, G},
title = {Side-by-side comparability of batch and continuous downstream for the production of monoclonal antibodies.},
journal = {Biotechnology and bioengineering},
volume = {117},
number = {4},
pages = {1024-1036},
doi = {10.1002/bit.27267},
pmid = {31930482},
issn = {1097-0290},
support = {005-1010-0009//German Federal State North Rhine-Westphalia/International ; 005-1010-0009//European Regional Development Fund/International ; 031A616M//Bundesministerium f&#xfc;r Bildung und Forschung/International ; },
mesh = {Animals ; *Antibodies, Monoclonal/analysis/isolation &amp; purification/metabolism ; Batch Cell Culture Techniques/*methods ; *Bioreactors ; CHO Cells ; Chromatography, Liquid ; Cricetinae ; Cricetulus ; Drug Contamination/prevention &amp; control ; Pilot Projects ; },
abstract = {Continuous processing is the future production method for monoclonal antibodies (mAbs). A fully continuous, fully automated downstream process based on disposable equipment was developed and implemented inside the MoBiDiK pilot plant. However, a study evaluating the comparability between batch and continuous processing based on product quality attributes was not conducted before. The work presented fills this gap comparing both process modes experimentally by purifying the same harvest material (side-by-side comparability). Samples were drawn at different time points and positions in the process for batch and continuous mode. Product quality attributes, product-related impurities, as well as process-related impurities were determined. The resulting polished material was processed to drug substance and further evaluated regarding storage stability and degradation behavior. The in-process control data from the continuous process showed the high degree of accuracy in providing relevant process parameters such as pH, conductivity, and protein concentration during the entire process duration. Minor differences between batch and continuous samples are expected as different processing conditions are unavoidable due to the different nature of batch and continuous processing. All tests revealed no significant differences in the intermediates and comparability in the drug substance between the samples of both process modes. The stability study of the final product also showed no differences in the stability profile during storage and forced degradation. Finally, online data analysis is presented as a powerful tool for online-monitoring of chromatography columns during continuous processing.},
}
@article {pmid31927130,
year = {2020},
author = {Corsi, MC and Chavez, M and Schwartz, D and George, N and Hugueville, L and Kahn, AE and Dupont, S and Bassett, DS and De Vico Fallani, F},
title = {Functional disconnection of associative cortical areas predicts performance during BCI training.},
journal = {NeuroImage},
volume = {209},
number = {},
pages = {116500},
pmid = {31927130},
issn = {1095-9572},
support = {R01 HD086888/HD/NICHD NIH HHS/United States ; R01 NS099348/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; *Connectome ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Learning/*physiology ; Longitudinal Studies ; Magnetoencephalography ; Male ; Motor Activity/*physiology ; Nerve Net/*physiology ; *Reinforcement, Psychology ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.},
}
@article {pmid31927056,
year = {2020},
author = {Li, M and Yang, G and Li, H},
title = {Effect of the concreteness of robot motion visual stimulus on an event-related potential-based brain-computer interface.},
journal = {Neuroscience letters},
volume = {720},
number = {},
pages = {134752},
doi = {10.1016/j.neulet.2020.134752},
pmid = {31927056},
issn = {1872-7972},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Evoked Potentials ; Female ; Humans ; Male ; Motion Perception/*physiology ; Photic Stimulation ; Robotics ; Young Adult ; },
abstract = {Event-related potential (ERP)-based brain-computer interface (BCI) has been widely used in robot control. Increasing the amplitude of the ERPs is key for improving the performance of ERP-based BCI. However, using images of robot motion as visual stimuli has not been studied widely. The aim of this study is to explore the concreteness of robot motion images on ERPs. Fifteen subjects used five kinds of visual spellers employing different images as visual stimuli: squares, arrows, a single kind of robot motion, multiple kinds of robot motions, and multiple kinds of robot motions with arrows. The three robot motion stimuli induced larger N200 and P300 potentials than non-robot motion stimuli. The topography shows that robot motion stimuli also evoke stronger negativities in the anterior and occipital areas. Concrete images provide more information to the subject about the robot motion, which might help the brain extract the meaning of the image more automatically. We use a support vector machine to detect the subject's intentions. There is substantial improvement in the classification performance when using robot motion images as visual stimuli, which implies that concrete visual stimuli improve the performance of the ERP-based BCI.},
}
@article {pmid31926635,
year = {2020},
author = {Coleman, JRI and Gaspar, HA and Bryois, J and , and , and Breen, G},
title = {The Genetics of the Mood Disorder Spectrum: Genome-wide Association Analyses of More Than 185,000 Cases and 439,000 Controls.},
journal = {Biological psychiatry},
volume = {88},
number = {2},
pages = {169-184},
pmid = {31926635},
issn = {1873-2402},
support = {G0901245/MRC_/Medical Research Council/United Kingdom ; R01 MH077139/MH/NIMH NIH HHS/United States ; EP-C-15-001/EPA/EPA/United States ; U01 MH105653/MH/NIMH NIH HHS/United States ; G0701420/MRC_/Medical Research Council/United Kingdom ; U01 MH109536/MH/NIMH NIH HHS/United States ; R01 MH085548/MH/NIMH NIH HHS/United States ; R01 MH086026/MH/NIMH NIH HHS/United States ; R01 MH063480/MH/NIMH NIH HHS/United States ; R37 AA007728/AA/NIAAA NIH HHS/United States ; R01 MH085542/MH/NIMH NIH HHS/United States ; R01 MH090553/MH/NIMH NIH HHS/United States ; R01 MH078151/MH/NIMH NIH HHS/United States ; 04036/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; R01 AA007535/AA/NIAAA NIH HHS/United States ; RC2 AG036607/AG/NIA NIH HHS/United States ; R01 MH072802/MH/NIMH NIH HHS/United States ; R01 MH103368/MH/NIMH NIH HHS/United States ; R01 MH100549/MH/NIMH NIH HHS/United States ; G0801418/MRC_/Medical Research Council/United Kingdom ; R01 DA017932/DA/NIDA NIH HHS/United States ; R01 AA007728/AA/NIAAA NIH HHS/United States ; R01 MH059567/MH/NIMH NIH HHS/United States ; WT083573/Z/07/Z/WT_/Wellcome Trust/United Kingdom ; G0200243/MRC_/Medical Research Council/United Kingdom ; MR/N01104X/2/MRC_/Medical Research Council/United Kingdom ; G1001799/MRC_/Medical Research Council/United Kingdom ; G1000708/MRC_/Medical Research Council/United Kingdom ; ZIA MH002843/ImNIH/Intramural NIH HHS/United States ; R01 DA034076/DA/NIDA NIH HHS/United States ; MR/N01104X/1/MRC_/Medical Research Council/United Kingdom ; MR/N015746/1/MRC_/Medical Research Council/United Kingdom ; WT090532/Z/09/Z/WT_/Wellcome Trust/United Kingdom ; R01 MH081804/MH/NIMH NIH HHS/United States ; WT089269/Z/09/Z/WT_/Wellcome Trust/United Kingdom ; U01 MH109514/MH/NIMH NIH HHS/United States ; R01 MH061613/MH/NIMH NIH HHS/United States ; R01 AA010249/AA/NIAAA NIH HHS/United States ; R01 MH106531/MH/NIMH NIH HHS/United States ; U01 MH109532/MH/NIMH NIH HHS/United States ; 104036/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; U01 MH105578/MH/NIMH NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; G0800509/MRC_/Medical Research Council/United Kingdom ; G19/2/MRC_/Medical Research Council/United Kingdom ; U01 MH109528/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Bipolar Disorder/genetics ; *Depressive Disorder, Major/genetics ; Genome-Wide Association Study ; Mice ; Mood Disorders/genetics ; Risk Factors ; },
abstract = {BACKGROUND: Mood disorders (including major depressive disorder and bipolar disorder) affect 10% to 20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Multiple approaches have shown considerable sharing of risk factors across mood disorders despite their diagnostic distinction.<br><br>METHODS: To clarify the shared molecular genetic basis of major depressive disorder and bipolar disorder and to highlight disorder-specific associations, we meta-analyzed data from the latest Psychiatric Genomics Consortium genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; nonoverlapping N&#xa0;= 609,424).<br><br>RESULTS: Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More loci from the Psychiatric Genomics Consortium analysis of major depression than from that for bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single-episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment-the relationship is positive in bipolar disorder but negative in major depressive disorder.<br><br>CONCLUSIONS: The mood disorders share several genetic associations, and genetic studies of major depressive disorder and bipolar disorder can be combined effectively to enable the discovery of variants not identified by studying either disorder alone. However, we demonstrate several differences between these disorders. Analyzing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum.},
}
@article {pmid31925438,
year = {2020},
author = {Trouillon, J and Sentausa, E and Ragno, M and Robert-Genthon, M and Lory, S and Attrée, I and Elsen, S},
title = {Species-specific recruitment of transcription factors dictates toxin expression.},
journal = {Nucleic acids research},
volume = {48},
number = {5},
pages = {2388-2400},
pmid = {31925438},
issn = {1362-4962},
mesh = {A549 Cells ; Bacterial Proteins/metabolism ; Bacterial Toxins/*metabolism ; Base Sequence ; Gene Expression Regulation, Bacterial ; Humans ; Operon/genetics ; Promoter Regions, Genetic ; Protein Binding ; Pseudomonas/genetics/pathogenicity ; Repressor Proteins/metabolism ; Species Specificity ; Transcription Factors/*metabolism ; Virulence ; },
abstract = {Tight and coordinate regulation of virulence determinants is essential for bacterial biology and involves dynamic shaping of transcriptional regulatory networks during evolution. The horizontally transferred two-partner secretion system ExlB-ExlA is instrumental in the virulence of different Pseudomonas species, ranging from soil- and plant-dwelling biocontrol agents to the major human pathogen Pseudomonas aeruginosa. Here, we identify a Cro/CI-like repressor, named ErfA, which together with Vfr, a CRP-like activator, controls exlBA expression in P. aeruginosa. The characterization of ErfA regulon across P. aeruginosa subfamilies revealed a second conserved target, the ergAB operon, with functions unrelated to virulence. To gain insights into this functional dichotomy, we defined the pan-regulon of ErfA in several Pseudomonas species and found ergAB as the sole conserved target of ErfA. The analysis of 446 exlBA promoter sequences from all exlBA+ genomes revealed a wide variety of regulatory sequences, as ErfA- and Vfr-binding sites were found to have evolved specifically in P. aeruginosa and nearly each species carries different regulatory sequences for this operon. We propose that the emergence of different regulatory cis-elements in the promoters of horizontally transferred genes is an example of plasticity of regulatory networks evolving to provide an adapted response in each individual niche.},
}
@article {pmid31923910,
year = {2020},
author = {Ravi, A and Beni, NH and Manuel, J and Jiang, N},
title = {Comparing user-dependent and user-independent training of CNN for SSVEP BCI.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026028},
doi = {10.1088/1741-2552/ab6a67},
pmid = {31923910},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Neural Networks, Computer ; Photic Stimulation ; },
abstract = {OBJECTIVE: We presented a comparative study on the training methodologies of a convolutional neural network (CNN) for the detection of steady-state visually evoked potentials (SSVEP). Two training scenarios were also compared: user-independent (UI) training and user-dependent (UD) training.<br><br>APPROACH: The CNN was trained in both UD and UI scenarios on two types of features for SSVEP classification: magnitude spectrum features (M-CNN) and complex spectrum features (C-CNN). The canonical correlation analysis (CCA), widely used in SSVEP processing, was used as the baseline. Additional comparisons were performed with task-related components analysis (TRCA) and filter-bank canonical correlation analysis (FBCCA). The performance of the proposed CNN pipelines, CCA, FBCCA and TRCA were evaluated with two datasets: a seven-class SSVEP dataset collected on 21 healthy participants and a twelve-class publicly available SSVEP dataset collected on ten healthy participants.<br><br>MAIN RESULTS: The UD based training methods consistently outperformed the UI methods when all other conditions were the same, as one would expect. However, the proposed UI-C-CNN approach performed similarly to the UD-M-CNN across all cases investigated on both datasets. On Dataset 1, the average accuracies of the different methods for 1&#x2009;s window length were: CCA: 69.1%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;10.8%, TRCA: 13.4%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;1.5%, FBCCA: 64.8%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;15.6%, UI-M-CNN: 73.5%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;16.1%, UI-C-CNN: 81.6%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;12.3%, UD-M-CNN: 87.8%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;7.6% and UD-C-CNN: 92.5%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;5%. On Dataset 2, the average accuracies of the different methods for data length of 1&#x2009;s were: UD-C-CNN: 92.33%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;11.1%, UD-M-CNN: 82.77%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;16.7%, UI-C-CNN: 81.6%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;18%, UI-M-CNN: 70.5%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;22%, FBCCA: 67.1%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;21%, CCA: 62.7%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;21.5%, TRCA: 40.4%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;14%. Using t-SNE, visualizing the features extracted by the CNN pipelines further revealed that the C-CNN method likely learned both the amplitude and phase related information from the SSVEP data for classification, resulting in superior performance than the M-CNN methods. The results suggested that UI-C-CNN method proposed in this study offers a good balance between performance and cost of training data.<br><br>SIGNIFICANCE: The proposed C-CNN based method is a suitable candidate for SSVEP-based BCIs and provides an improved performance in both UD and UI training scenarios.},
}
@article {pmid31923521,
year = {2020},
author = {Li, X and Zhuo, C and Guo, H and Du, J and Wang, H and Wang, J and Li, J and Zhao, W and Li, Y and Sun, C and Zhang, J and Yang, Q and Xu, Y},
title = {Mechanism differences between typical yin and typical yang personality individuals assessed by Five-Pattern Personality Inventory (FPPI): Evidence from resting-state brain functional networks.},
journal = {Neuroscience letters},
volume = {718},
number = {},
pages = {134745},
doi = {10.1016/j.neulet.2020.134745},
pmid = {31923521},
issn = {1872-7972},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; China ; Emotions/physiology ; Female ; Frontal Lobe/physiology ; Gyrus Cinguli/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Neural Pathways ; Parietal Lobe/physiology ; Personality/*physiology ; Personality Inventory ; },
abstract = {BACKGROUND: Most studies assessing brain-personality mechanisms have used Western personality questionnaires. However, Western personality questionnaires may not objectively reflect the personality characteristics of individuals in Eastern cultures such as China. Hence, we adopted the functional magnetic resonance imaging (fMRI) and the Chinese localized scale, FPPI, to explore the brain mechanisms differences of typical yin and typical yang personalities of individuals in China.<br><br>METHODS: 30 typical yin personality participants (TYI) and 34 typical yang personality participants (TYA) were enrolled according to the FPPI. The group differences of the functional brain networks among 90 specific brain regions were mapped using fMRI data and then analyzed by the conventional network metrics (CNM) and frequency subgraph mining (FSM).<br><br>RESULTS: The CNM and FSM differences between two typical personality groups were traced to the frontal, temporal, and parietal cortices. The yin group, reflecting the rich emotions and feelings of individuals, showed higher betweenness centrality (BCi) and nodal efficiency (Ei) values in putamen and middle frontal gyrus. The yang group, reflecting active behaviors and tendency to adapting to the changing surroundings, showed higher BCi and Ei values in precuneus, posterior cingulate gyrus, and inferior parietal lobule, brain areas in the default mode network (DMN).<br><br>CONCLUSION: These results supplied evidence for the neurobiological differences between typical yin and typical yang personality participants based on Chinese culture. These results also provide a new perspective to help researchers understand brain mechanism differences between yin and yang personality groups in the Chinese culture.},
}
@article {pmid31920616,
year = {2019},
author = {Kumar, A and Fang, Q and Fu, J and Pirogova, E and Gu, X},
title = {Error-Related Neural Responses Recorded by Electroencephalography During Post-stroke Rehabilitation Movements.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {107},
pmid = {31920616},
issn = {1662-5218},
abstract = {Error-related potential (ErrP) based assist-as-needed robot-therapy can be an effective rehabilitation method. To date, several studies have shown the presence of ErrP under various task situations. However, in the context of assist-as-needed methods, the existence of ErrP is unexplored. Therefore, the principal objective of this study is to determine if an ErrP can be evoked when a subject is unable to complete a physical exercise in a given time. Fifteen stroke patients participated in an experiment that involved performing a physical rehabilitation exercise. Results showed that the electroencephalographic (EEG) response of the trials, where patients failed to complete the exercise, against the trials, where patients successfully completed the exercise, significantly differ from each other, and the resulting difference of event-related potentials resembles the previously reported ErrP signals as well as has some unique features. Along with the highly statistically significant difference, the trials differ in time-frequency patterns and scalp distribution maps. In summary, the results of the study provide a novel basis for the detection of the failure against the success events while executing rehabilitation exercises that can be used to improve the state-of-the-art robot-assisted rehabilitation methods.},
}
@article {pmid31920602,
year = {2019},
author = {Pereira, JA and Sepulveda, P and Rana, M and Montalba, C and Tejos, C and Torres, R and Sitaram, R and Ruiz, S},
title = {Self-Regulation of the Fusiform Face Area in Autism Spectrum: A Feasibility Study With Real-Time fMRI Neurofeedback.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {446},
pmid = {31920602},
issn = {1662-5161},
abstract = {One of the most important and early impairments in autism spectrum disorder (ASD) is the abnormal visual processing of human faces. This deficit has been associated with hypoactivation of the fusiform face area (FFA), one of the main hubs of the face-processing network. Neurofeedback based on real-time fMRI (rtfMRI-NF) is a technique that allows the self-regulation of circumscribed brain regions, leading to specific neural modulation and behavioral changes. The aim of the present study was to train participants with ASD to achieve up-regulation of the FFA using rtfMRI-NF, to investigate the neural effects of FFA up-regulation in ASD. For this purpose, three groups of volunteers with normal I.Q. and fluent language were recruited to participate in a rtfMRI-NF protocol of eight training runs in 2 days. Five subjects with ASD participated as part of the experimental group and received contingent feedback to up-regulate bilateral FFA. Two control groups, each one with three participants with typical development (TD), underwent the same protocol: one group with contingent feedback and the other with sham feedback. Whole-brain and functional connectivity analysis using each fusiform gyrus as independent seeds were carried out. The results show that individuals with TD and ASD can achieve FFA up-regulation with contingent feedback. RtfMRI-NF in ASD produced more numerous and stronger short-range connections among brain areas of the ventral visual stream and an absence of the long-range connections to insula and inferior frontal gyrus, as observed in TD subjects. Recruitment of inferior frontal gyrus was observed in both groups during FAA up-regulation. However, insula and caudate nucleus were only recruited in subjects with TD. These results could be explained from a neurodevelopment perspective as a lack of the normal specialization of visual processing areas, and a compensatory mechanism to process visual information of faces. RtfMRI-NF emerges as a potential tool to study visual processing network in ASD, and to explore its clinical potential.},
}
@article {pmid31920594,
year = {2019},
author = {Sasaki, M and Iversen, J and Callan, DE},
title = {Music Improvisation Is Characterized by Increase EEG Spectral Power in Prefrontal and Perceptual Motor Cortical Sources and Can be Reliably Classified From Non-improvisatory Performance.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {435},
pmid = {31920594},
issn = {1662-5161},
abstract = {This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.},
}
@article {pmid31920588,
year = {2019},
author = {Kaas, A and Goebel, R and Valente, G and Sorger, B},
title = {Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {427},
pmid = {31920588},
issn = {1662-5161},
abstract = {Real-time functional magnetic resonance imaging (fMRI) is a promising non-invasive method for brain-computer interfaces (BCIs). BCIs translate brain activity into signals that allow communication with the outside world. Visual and motor imagery are often used as information-encoding strategies, but can be challenging if not grounded in recent experience in these modalities, e.g., in patients with locked-in-syndrome (LIS). In contrast, somatosensory imagery might constitute a more suitable information-encoding strategy as the somatosensory function is often very robust. Somatosensory imagery has been shown to activate the somatotopic cortex, but it has been unclear so far whether it can be reliably detected on a single-trial level and successfully classified according to specific somatosensory imagery content. Using ultra-high field 7-T fMRI, we show reliable and high-accuracy single-trial decoding of left-foot (LF) vs. right-hand (RH) somatosensory imagery. Correspondingly, higher decoding accuracies were associated with greater spatial separation of hand and foot decoding-weight patterns in the primary somatosensory cortex (S1). Exploiting these novel neuroscientific insights, we developed-and provide a proof of concept for-basic BCI communication by showing that binary (yes/no) answers encoded by somatosensory imagery can be decoded with high accuracy in simulated real-time (in 7 subjects) as well as in real-time (1 subject). This study demonstrates that body part-specific somatosensory imagery differentially activates somatosensory cortex in a topographically specific manner; evidence which was surprisingly still lacking in the literature. It also offers proof of concept for a novel somatosensory imagery-based fMRI-BCI control strategy, with particularly high potential for visually and motor-impaired patients. The strategy could also be transferred to lower MRI field strengths and to mobile functional near-infrared spectroscopy. Finally, given that communication BCIs provide the BCI user with a form of feedback based on their brain signals and can thus be considered as a specific form of neurofeedback, and that repeated use of a BCI has been shown to enhance underlying representations, we expect that the current BCI could also offer an interesting new approach for somatosensory rehabilitation training in the context of stroke and phantom limb pain.},
}
@article {pmid31920510,
year = {2019},
author = {Gilmour, A and Poole-Warren, L and Green, RA},
title = {An Improved in vitro Model of Cortical Tissue.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1349},
pmid = {31920510},
issn = {1662-4548},
abstract = {Intracortical electrodes for brain-machine interfaces rely on intimate contact with tissues for recording signals and stimulating neurons. However, the long-term viability of intracortical electrodes in vivo is poor, with a major contributing factor being the development of a glial scar. In vivo approaches for evaluating responses to intracortical devices are resource intensive and complex, making statistically significant, high throughput data difficult to obtain. In vitro models provide an alternative to in vivo studies; however, existing approaches have limitations which restrict the translation of the cellular reactions to the implant scenario. Notably, there is no current robust model that includes astrocytes, microglia, oligodendrocytes and neurons, the four principle cell types, critical to the health, function and wound responses of the central nervous system (CNS). In previous research a co-culture of primary mouse mature mixed glial cells and immature neural precursor cells were shown to mimic several key properties of the CNS response to implanted electrode materials. However, the method was not robust and took up to 63 days, significantly affecting reproducibility and widespread use for assessing brain-material interactions. In the current research a new co-culture approach has been developed and evaluated using immunocytochemistry and quantitative polymerase chain reaction (qPCR). The resulting method reduced the time in culture significantly and the culture model was shown to have a genetic signature similar to that of healthy adult mouse brain. This new robust CNS culture model has the potential to significantly improve the capacity to translate in vitro data to the in vivo responses.},
}
@article {pmid31920506,
year = {2019},
author = {Bringas Vega, ML and Nunez, P and Riera, J and Zhang, R and Valdes-Sosa, PA},
title = {Editorial: Through a Glass, Darkly: The Influence of the EEG Reference on Inference About Brain Function and Disorders.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1341},
pmid = {31920506},
issn = {1662-4548},
}
@article {pmid31919820,
year = {2020},
author = {Serra, DS and de Souza, KCL and Naidu, ST and de Lima, JR and de Lima Gondim, F and Gomes, MDM and Araújo, RDS and de Oliveira, MLM and Cavalcante, FS&#xc1;},
title = {Lung injury caused by exposure to the gaseous fraction of exhaust from biomass combustion (cashew nut shells): a mice model.},
journal = {Environmental science and pollution research international},
volume = {27},
number = {9},
pages = {9568-9581},
pmid = {31919820},
issn = {1614-7499},
mesh = {Air Pollutants/*analysis ; *Anacardium ; Animals ; Biomass ; Gases/analysis ; *Lung Injury ; Mice ; Nuts/chemistry ; Particulate Matter/analysis ; Vehicle Emissions/analysis ; },
abstract = {Currently, to reduce the use of nonrenewable energy sources in energy matrices, some industries have already incorporated biomass as a source of energy for their processes. Additionally, filters are used in an attempt to retain the particulate matter present in exhaust gases. In this work, the emission gases of a cashew nut shell (CNS) combustion reactor and the deleterious effects on the respiratory system of mice exposed to gaseous fraction present in CNS emissions (GF-CNS) are analyzed. The system for CNS combustion is composed of a cylindrical stainless steel burner, and exhaust gases generated by CNS combustion were directed through a chimney to a system containing two glass fiber filters to retain all the PM present in the CNS exhaust and, posteriorly, were directed to a mice exposure chamber. The results show changes in the variables of respiratory system mechanics (G, H, CST, IC, and PV loop area) in oxidative stress (SOD, CAT, and NO2[-]), as well as in the histopathological analysis and lung morphometry (alveolar collapse, PMN cells, mean alveolar diameter, and BCI). Through our results, it has been demonstrated that even with the use of filters by industries for particulate material retention, special attention should still be given to the gaseous fraction that is released into the environment.},
}
@article {pmid31919460,
year = {2020},
author = {Tanaka, H},
title = {Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis.},
journal = {Scientific reports},
volume = {10},
number = {1},
pages = {84},
pmid = {31919460},
issn = {2045-2322},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; *Group Processes ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; },
abstract = {EEG is known to contain considerable inter-trial and inter-subject variability, which poses a challenge in any group-level EEG analyses. A true experimental effect must be reproducible even with variabilities in trials, sessions, and subjects. Extracting components that are reproducible across trials and subjects benefits both understanding common mechanisms in neural processing of cognitive functions and building robust brain-computer interfaces. This study extends our previous method (task-related component analysis, TRCA) by maximizing not only trial-by-trial reproducibility within single subjects but also similarity across a group of subjects, hence referred to as group TRCA (gTRCA). The problem of maximizing reproducibility of time series across trials and subjects is formulated as a generalized eigenvalue problem. We applied gTRCA to EEG data recorded from 35 subjects during a steady-state visual-evoked potential (SSVEP) experiment. The results revealed: (1) The group-representative data computed by gTRCA showed higher and consistent spectral peaks than other conventional methods; (2) Scalp maps obtained by gTRCA showed estimated source locations consistently within the occipital lobe; And (3) the high-dimensional features extracted by gTRCA are consistently mapped to a low-dimensional space. We conclude that gTRCA offers a framework for group-level EEG data analysis and brain-computer interfaces alternative in complement to grand averaging.},
}
@article {pmid31918621,
year = {2020},
author = {Carino-Escobar, RI and Galicia-Alvarado, M and Marrufo, OR and Carrillo-Mora, P and Cantillo-Negrete, J},
title = {Brain-computer interface performance analysis of monozygotic twins with discordant hand dominance: A case study.},
journal = {Laterality},
volume = {25},
number = {5},
pages = {513-536},
doi = {10.1080/1357650X.2019.1710525},
pmid = {31918621},
issn = {1464-0678},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Functional Laterality ; Humans ; Imagination ; Psychomotor Performance/physiology ; Twins, Monozygotic ; },
abstract = {Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.},
}
@article {pmid31915978,
year = {2020},
author = {Gao, JM and Du, DY and Kong, LW and Yang, J and Li, H and Wei, GB and Li, CH and Liu, CP},
title = {Emergency Surgery for Blunt Cardiac Injury: Experience in 43 Cases.},
journal = {World journal of surgery},
volume = {44},
number = {5},
pages = {1666-1672},
pmid = {31915978},
issn = {1432-2323},
mesh = {Accidents, Traffic ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Echocardiography ; Emergencies ; Female ; Heart Injuries/*diagnosis/etiology/*surgery ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Survival Rate ; Thoracotomy ; Time Factors ; Tomography, X-Ray Computed ; Wounds, Nonpenetrating/*diagnosis/etiology/*surgery ; Young Adult ; },
abstract = {BACKGROUND: Blunt cardiac injury (BCI) increases with traffic accidents and is an important cause of death in trauma patients. In particular, for patients who need surgical treatment, the mortality rate is extremely high unless the patient is promptly operated on. This study aimed to explore early recognition and expeditious surgical intervention to increase survival.<br><br>METHODS: All patients with BCIs during the past 15&#xa0;years were reviewed, and those who underwent operative treatment were analyzed retrospectively regarding the mechanism of injury, diagnostic and therapeutic methods, and outcome.<br><br>RESULTS: A total of 348 patients with BCIs accounted for 18.3% of 1903 patients with blunt thoracic injury (BTI). Of 348 patients, 43 underwent operative treatment. The main cause of injury was traffic accidents, with an incidence of 48.8%. Of them, steering wheel injuries occurred in 15 patients. In 26 patients, a preoperative diagnosis was obtained by echocardiography, CT scanning, etc. In the remaining 17, who had to undergo urgent thoracotomy without any preoperative imaging, a definitive diagnosis of BCI was proven during the operation. The volume of preoperative infusion or crystalloid was <1000&#xa0;ml in 31 cases. Preoperative pericardiocentesis was not used in anyone. In 12 patients, the operation commenced within 1&#xa0;h. Overall mortality was 32.6%. The death was caused by BCI in 9.<br><br>CONCLUSIONS: Facing a patient with BTI, a high index of suspicion for BCI must be maintained. To manage those requiring operations, early recognition and expeditious thoracotomy are essential. Preoperatively, limited fluid resuscitation is emphasized. We do not advocate preoperative pericardiocentesis.},
}
@article {pmid31907010,
year = {2020},
author = {Letourneau, S and Zewdie, ET and Jadavji, Z and Andersen, J and Burkholder, LM and Kirton, A},
title = {Clinician awareness of brain computer interfaces: a Canadian national survey.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {17},
number = {1},
pages = {2},
pmid = {31907010},
issn = {1743-0003},
support = {10075523//CIHR/Canada ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Canada ; Child ; Cross-Sectional Studies ; Persons with Disabilities/*rehabilitation ; Female ; *Health Knowledge, Attitudes, Practice ; Humans ; Male ; *Neurologists ; *Physiatrists ; Quality of Life ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Individuals with severe neurological disabilities but preserved cognition, including children, are often precluded from connecting with their environments. Brain computer interfaces (BCI) are a potential solution where advancing technologies create new clinical opportunities. We evaluated clinician awareness as a modifiable barrier to progress and identified eligible populations.<br><br>METHODS: We executed a national, population-based, cross-sectional survey of physician specialists caring for persons with severe disability. An evidence- and experience-based survey had three themes: clinician BCI knowledge, eligible populations, and potential impact. A BCI knowledge index was created and scored. Canadian adult and pediatric neurologists, physiatrists and a subset of developmental pediatricians were contacted. Secure, web-based software administered the survey via email with online data collection.<br><br>RESULTS: Of 922 valid emails (664 neurologists, 253 physiatrists), 137 (15%) responded. One third estimated that &#x2265;10% of their patients had severe neurological disability with cognitive capacity. BCI knowledge scores were low with >&#x2009;40% identifying as less than "vaguely aware" and only 15% as "somewhat familiar" or better. Knowledge did not differ across specialties. Only 6 physicians&#xa0;(4%) had patients using BCI. Communication and wheelchair control rated highest for potentially improving quality of life. Most (81%) felt BCI had high potential to improve quality of life. Estimates suggested that >&#x2009;13,000 Canadians (36&#x2009;M population) might benefit from BCI technologies.<br><br>CONCLUSIONS: Despite high potential and thousands of patients who might benefit, BCI awareness among clinicians caring for disabled persons is poor. Further, functional priorities for BCI applications may differ between medical professionals and potential BCI users, perhaps reflecting that clinicians possess a less accurate understanding of the desires and needs of potential end-users. Improving knowledge and engaging both clinicians and patients could facilitate BCI program development to improve patient outcomes.},
}
@article {pmid31906947,
year = {2020},
author = {Kögel, J and Jox, RJ and Friedrich, O},
title = {What is it like to use a BCI? - insights from an interview study with brain-computer interface users.},
journal = {BMC medical ethics},
volume = {21},
number = {1},
pages = {2},
pmid = {31906947},
issn = {1472-6939},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Female ; Grounded Theory ; Humans ; Interviews as Topic ; Male ; Middle Aged ; Patients/*psychology ; *Self Report ; Social Participation ; },
abstract = {BACKGROUND: The neurotechnology behind brain-computer interfaces (BCIs) raises various ethical questions. The ethical literature has pinpointed several issues concerning safety, autonomy, responsibility and accountability, psychosocial identity, consent, privacy and data security. This study aims to assess BCI users' experiences, self-observations and attitudes in their own right and looks for social and ethical implications.<br><br>METHODS: We conducted nine semi-structured interviews with BCI users, who used the technology for medical reasons. The transcribed interviews were analyzed according to the Grounded Theory coding method.<br><br>RESULTS: BCI users perceive themselves as active operators of a technology that offers them social participation and impacts their self-definition. Each of these aspects bears its own opportunities and risks. BCIs can contribute to retaining or regaining human capabilities. At the same time, BCI use contains elements that challenge common experiences, for example when the technology is in conflict with the affective side of BCI users. The potential benefits of BCIs are regarded as outweighing the risks in that BCI use is considered to promote valuable qualities and capabilities. BCI users appreciate the opportunity to regain lost capabilities as well as to gain new ones.<br><br>CONCLUSIONS: BCI users appreciate the technology for various reasons. The technology is highly appreciated in cases where it is beneficial in terms of agency, participation and self-definitions. Rather than questioning human nature, the technology can retain and restore characteristics and abilities which enrich our lives.},
}
@article {pmid31902769,
year = {2020},
author = {Malekzadeh-Arasteh, O and Pu, H and Lim, J and Liu, CY and Do, AH and Nenadic, Z and Heydari, P},
title = {An Energy-Efficient CMOS Dual-Mode Array Architecture for High-Density ECoG-Based Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {14},
number = {2},
pages = {332-342},
doi = {10.1109/TBCAS.2019.2963302},
pmid = {31902769},
issn = {1940-9990},
mesh = {Amplifiers, Electronic ; Brain/diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Electrocorticography/*instrumentation ; Equipment Design ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {This article presents an energy-efficient electrocorticography (ECoG) array architecture for fully-implantable brain machine interface systems. A novel dual-mode analog signal processing method is introduced that extracts neural features from high- γ band (80-160 Hz) at the early stages of signal acquisition. Initially, brain activity across the full-spectrum is momentarily observed to compute the feature weights in the digital back-end during full-band mode operation. Subsequently, these weights are fed back to the front-end and the system reverts to base-band mode to perform feature extraction. This approach utilizes a distinct optimized signal pathway based on power envelope extraction, resulting in 1.72× power reduction in the analog blocks and up to 50× potential power savings for digitization and processing (implemented off-chip in this article). A prototype incorporating a 32-channel ultra-low power signal acquisition front-end is fabricated in 180 nm CMOS process with 0.8 V supply. This chip consumes 1.05 μW (0.205 μW for feature extraction only) power and occupies 0.245 [Formula: see text] die area per channel. The chip measurement shows better than 76.5-dB common-mode rejection ratio (CMRR), 4.09 noise efficiency factor (NEF), and 10.04 power efficiency factor (PEF). In-vivo human tests have been carried out with electroencephalography and ECoG signals to validate the performance and dual-mode operation in comparison to commercial acquisition systems.},
}
@article {pmid31902767,
year = {2020},
author = {Uehlin, JP and Smith, WA and Pamula, VR and Perlmutter, SI and Rudell, JC and Sathe, VS},
title = {A 0.0023 mm [2]/ch. Delta-Encoded, Time-Division Multiplexed Mixed-Signal ECoG Recording Architecture With Stimulus Artifact Suppression.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {14},
number = {2},
pages = {319-331},
pmid = {31902767},
issn = {1940-9990},
support = {P51 OD010425/OD/NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; R01 NS099872/NS/NINDS NIH HHS/United States ; },
mesh = {Artifacts ; Brain-Computer Interfaces ; Electrocorticography/*instrumentation ; Equipment Design ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {This article demonstrates a scalable, time-division multiplexed biopotential recording front-end capable of real-time differential- and common-mode artifact suppression. A delta-encoded recording architecture exploits the power spectral density (PSD) characteristics of Electrocorticography (ECoG) recordings, combining an 8-bit ADC, and an 8-bit DAC to achieve 14 bits of dynamic range. The flexibility of the digital feedback architecture is leveraged to time-division multiplex 64 differential input channels onto a shared mixed-signal front-end, reducing channel area by 2x compared to the state-of-the-art. The feedback DAC used for delta-encoding also serves to cancel differential artifacts with an off-chip adaptive loop. Analysis of this architecture and measured silicon performance of a 65 nm CMOS test-chip implementation, both on the bench and in-vivo, are included with this paper.},
}
@article {pmid31899430,
year = {2020},
author = {Shaikh, S and So, R and Sibindi, T and Libedinsky, C and Basu, A},
title = {Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {2},
pages = {380-389},
doi = {10.1109/TNSRE.2019.2962708},
pmid = {31899430},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Discriminant Analysis ; Macaca fascicularis ; *Machine Learning ; Male ; Neural Networks, Computer ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈21 %, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. Furthermore, improvements of up to ≈7(14)%, 8(15)%, 9(19)%, 7(15)% in NHP A and 8(15)%, 12(20)%, 15(23)%, 12(19)% in NHP B over Random Forest (Long-short Term Memory) have been obtained by sparse ensemble LDA, SVM, ELM, MLP respectively.},
}
@article {pmid31898242,
year = {2020},
author = {Heydari Beni, N and Foodeh, R and Shalchyan, V and Daliri, MR},
title = {Force decoding using local field potentials in primary motor cortex: PLS or Kalman filter regression?.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1007/s13246-019-00833-7},
pmid = {31898242},
issn = {1879-5447},
support = {119//Cognitive Sciences and Technologies Council of Iran (CSTC)/ ; },
abstract = {The development of brain-computer interface (BCI) systems is an important approach in brain studies. Control of communication devices and prostheses in real-world scenarios requires complex movement parameters. Decoding a variety of neural signals captured by micro-wire arrays is a potential applicant for extracting movement-related information. The present work was conducted to compare the functionality of partial least square (PLS) regression and Kalman filter to predict the force parameter from local field potential (LFP) signals of the primary motor cortex (M1). The signals were recorded using a 16-channel micro-wire array from the forelimb-related area of the M1 of three rats performing a behavioral task in which the force signal of the rat's forelimb paw was generated. Our results show that PLS regression and Kalman filters with the mean performance of 0.75 and 0.72 in terms of the correlation coefficient (CC) and 0.37 and 0.48 in terms of normalized mean square error (NMSE), respectively, are effective methods for decoding the force parameter from LFPs. Kalman filter underperforms PLS both in performance and speed. Although adding nonlinearity to the Kalman filter results in equally accurate CC performance as PLS, it has even more computational cost. Therefore, it is inferred that nonlinear methods do not necessarily have better functionality than linear ones and PLS, as a simple fast linear method could be an effectively applicable regression technique for BCIs.},
}
@article {pmid31895542,
year = {2020},
author = {Nam, J and Lim, HK and Kim, NH and Park, JK and Kang, ES and Kim, YT and Heo, C and Lee, OS and Kim, SG and Yun, WS and Suh, M and Kim, YH},
title = {Supramolecular Peptide Hydrogel-Based Soft Neural Interface Augments Brain Signals through a Three-Dimensional Electrical Network.},
journal = {ACS nano},
volume = {14},
number = {1},
pages = {664-675},
doi = {10.1021/acsnano.9b07396},
pmid = {31895542},
issn = {1936-086X},
mesh = {Animals ; Brain/*metabolism ; Electricity ; Electrochemical Techniques ; Electrodes ; Hydrogels/*chemistry ; Macromolecular Substances/chemistry ; Male ; Mice ; Mice, Inbred C57BL ; Molecular Dynamics Simulation ; Nerve Tissue/chemistry/*metabolism ; Particle Size ; Peptides/*chemistry ; Surface Properties ; },
abstract = {Recording neural activity from the living brain is of great interest in neuroscience for interpreting cognitive processing or neurological disorders. Despite recent advances in neural technologies, development of a soft neural interface that integrates with neural tissues, increases recording sensitivity, and prevents signal dissipation still remains a major challenge. Here, we introduce a biocompatible, conductive, and biostable neural interface, a supramolecular β-peptide-based hydrogel that allows signal amplification via tight neural/hydrogel contact without neuroinflammation. The non-biodegradable β-peptide forms a multihierarchical structure with conductive nanomaterial, creating a three-dimensional electrical network, which can augment brain signal efficiently. By achieving seamless integration in brain tissue with increased contact area and tight neural tissue coupling, the epidural and intracortical neural signals recorded with the hydrogel were augmented, especially in the high frequency range. Overall, our tissuelike chronic neural interface will facilitate a deeper understanding of brain oscillation in broad brain states and further lead to more efficient brain-computer interfaces.},
}
@article {pmid31888176,
year = {2019},
author = {Jiang, S and Qi, H and Zhang, J and Zhang, S and Xu, R and Liu, Y and Meng, L and Ming, D},
title = {A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {24},
pages = {},
pmid = {31888176},
issn = {1424-8220},
support = {2017YFB1300302//National Key Research and Development Program of China/ ; 81630051//National Natural Science Foundation of China/ ; 91648122//National Natural Science Foundation of China/ ; 81601565//National Natural Science Foundation of China/ ; 17ZXRGGX00020//Tianjin Key Technology R&amp;D Program/ ; 16ZXHLSY00270//Tianjin Key Technology R&amp;D Program/ ; },
abstract = {In the human-robot hybrid system, due to the error recognition of the pattern recognition system, the robot may perform erroneous motor execution, which may lead to falling-risk. While, the human can clearly detect the existence of errors, which is manifested in the central nervous activity characteristics. To date, the majority of studies on falling-risk detection have focused primarily on computer vision and physical signals. There are no reports of falling-risk detection methods based on neural activity. In this study, we propose a novel method to monitor multi erroneous motion events using electroencephalogram (EEG) features. There were 15 subjects who participated in this study, who kept standing with an upper limb supported posture and received an unpredictable postural perturbation. EEG signal analysis revealed a high negative peak with a maximum averaged amplitude of -14.75 ± 5.99 μV, occurring at 62 ms after postural perturbation. The xDAWN algorithm was used to reduce the high-dimension of EEG signal features. And, Bayesian linear discriminant analysis (BLDA) was used to train a classifier. The detection rate of the falling-risk onset is 98.67%. And the detection latency is 334ms, when we set detection rate beyond 90% as the standard of dangerous event onset. Further analysis showed that the falling-risk detection method based on postural perturbation evoked potential features has a good generalization ability. The model based on typical event data achieved 94.2% detection rate for unlearned atypical perturbation events. This study demonstrated the feasibility of using neural response to detect dangerous fall events.},
}
@article {pmid31887332,
year = {2020},
author = {Lu, RR and Zheng, MX and Li, J and Gao, TH and Hua, XY and Liu, G and Huang, SH and Xu, JG and Wu, Y},
title = {Motor imagery based brain-computer interface control of continuous passive motion for wrist extension recovery in chronic stroke patients.},
journal = {Neuroscience letters},
volume = {718},
number = {},
pages = {134727},
doi = {10.1016/j.neulet.2019.134727},
pmid = {31887332},
issn = {1872-7972},
mesh = {Adult ; Aged ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Middle Aged ; Range of Motion, Articular ; Recovery of Function ; Stroke/*complications ; *Stroke Rehabilitation ; Wrist ; *Wrist Joint ; },
abstract = {Motor recovery of wrist and fingers is still a great challenge for chronic stroke survivors. The present study aimed to verify the efficiency of motor imagery based brain-computer interface (BCI) control of continuous passive motion (CPM) in the recovery of wrist extension due to stroke. An observational study was conducted in 26 chronic stroke patients, aged 49.0 ± 15.4 years, with upper extremity motor impairment. All patients showed no wrist extension recovery. A 24-channel highresolution electroencephalogram (EEG) system was used to acquire cortical signal while they were imagining extension of the affected wrist. Then, 20 sessions of BCI-driven CPM training were carried out for 6 weeks. Primary outcome was the increase of active range of motion (ROM) of the affected wrist from the baseline to final evaluation. Improvement of modified Barthel Index, EEG classification and motor imagery pattern of wrist extension were recorded as secondary outcomes. Twenty-one patients finally passed the EEG screening and completed all the BCI-driven CPM trainings. From baseline to the final evaluation, the increase of active ROM of the affected wrists was (24.05 ± 14.46)˚. The increase of modified Barthel Index was 3.10 ± 4.02 points. But no statistical difference was detected between the baseline and final evaluations (P > 0.05). Both EEG classification and motor imagery pattern improved. The present study demonstrated beneficial outcomes of MI-based BCI control of CPM training in motor recovery of wrist extension using motor imagery signal of brain in chronic stroke patients.},
}
@article {pmid31885534,
year = {2019},
author = {Ramírez-Moreno, MA and Gutiérrez, D},
title = {Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {9374802},
pmid = {31885534},
issn = {1687-5273},
mesh = {*Artificial Intelligence ; Biomechanical Phenomena ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Female ; Goals ; Humans ; Imagination/physiology ; Male ; Mental Fatigue/etiology ; Models, Theoretical ; Motor Activity/physiology ; Proof of Concept Study ; Robotics/*methods ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.},
}
@article {pmid31885530,
year = {2019},
author = {Xu, Y and Hua, J and Zhang, H and Hu, R and Huang, X and Liu, J and Guo, F},
title = {Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {2087132},
pmid = {31885530},
issn = {1687-5273},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; *Electroencephalography/methods ; Foot ; Hand ; Humans ; *Imagination/physiology ; Models, Theoretical ; *Motor Activity/physiology ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; Time Factors ; Tongue ; },
abstract = {Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training based on the transductive support vector machine (TSVM) framework. We first introduce an improved TSVM (ITSVM) method, in which a comprehensive feature of each sample consists of its common spatial patterns (CSP) feature and its geometric feature. Moreover, we use the concave-convex procedure (CCCP) to solve the optimization problem of TSVM under a new balancing constraint that can address the unknown distribution of the unlabelled set by considering various possible distributions. In addition, we propose an improved self-training TSVM (IST-TSVM) method that can iteratively perform CSP feature extraction and ITSVM classification using an expanded labelled set. Extensive experimental results on dataset IV-a from BCI competition III and dataset II-a from BCI competition IV show that our algorithms outperform the other competing algorithms, where the sizes and distributions of the labelled sets are variable. In particular, IST-TSVM provides average accuracies of 63.25% and 69.43% with the abovementioned two datasets, respectively, where only four positive labelled samples and sixteen negative labelled samples are used. Therefore, our algorithms can provide an alternative way to reduce the calibration time.},
}
@article {pmid31885398,
year = {2019},
author = {Chabuda, A and Dovgialo, M and Duszyk, A and Stróż, A and Pawlisz, M and Durka, P},
title = {Successful BCI communication via high‑frequency SSVEP or visual, audio or tactile P300 in 30 tested volunteers.},
journal = {Acta neurobiologiae experimentalis},
volume = {79},
number = {4},
pages = {421-431},
pmid = {31885398},
issn = {1689-0035},
mesh = {Adolescent ; Adult ; *Brain Mapping ; Calibration ; Computer Literacy ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Evoked Potentials, Auditory/physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Persistent Vegetative State/physiopathology ; Touch/physiology ; *User-Computer Interface ; Vibration ; Young Adult ; },
abstract = {In the pursuit to clarify the concept of "BCI illiteracy", we investigated the possibilities of attaining basic binary (yes/no) communication via brain‑computer interface (BCI). We tested four BCI paradigms: steady‑state visual evoked potentials (SSVEP), tactile, visual, and auditory evoked potentials (P300). The proposed criterion for assessing for the possibility of communication are based on the number of correct choices obtained in a given BCI paradigm after a short calibration session, without prior training. In this study users answered 20 simple "yes/no" questions. Fourteen or more correct answers rejected the null hypothesis of random choices at P=0.05. All of the 30 healthy volunteers were able to attain above‑chance choices in at least one of the four paradigms. Additionally, we tested the system in clinical settings on a patient recovering from disorders of consciousness, achieving successful communication in 2 out of 3 paradigms. In light of these facts, after a review of the sparse literature, and in the interest of motivating further research, we propose a paraphrase of de Finetti's provocative statement: "BCI illiteracy does not exist".},
}
@article {pmid31884180,
year = {2020},
author = {Ieracitano, C and Mammone, N and Hussain, A and Morabito, FC},
title = {A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {123},
number = {},
pages = {176-190},
doi = {10.1016/j.neunet.2019.12.006},
pmid = {31884180},
issn = {1879-2782},
mesh = {Brain-Computer Interfaces ; Dementia/*physiopathology ; Electroencephalography/classification/*methods ; Humans ; *Machine Learning ; Wavelet Analysis ; },
abstract = {Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things (IoT) and Brain-Computer Interface (BCI) applications. From a signal processing perspective, EEGs yield a nonlinear and nonstationary, multivariate representation of the underlying neural circuitry interactions. In this paper, a novel multi-modal Machine Learning (ML) based approach is proposed to integrate EEG engineered features for automatic classification of brain states. EEGs are acquired from neurological patients with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD) and the aim is to discriminate Healthy Control (HC) subjects from patients. Specifically, in order to effectively cope with nonstationarities, 19-channels EEG signals are projected into the time-frequency (TF) domain by means of the Continuous Wavelet Transform (CWT) and a set of appropriate features (denoted as CWT features) are extracted from δ, θ, α1, α2, β EEG sub-bands. Furthermore, to exploit nonlinear phase-coupling information of EEG signals, higher order statistics (HOS) are extracted from the bispectrum (BiS) representation. BiS generates a second set of features (denoted as BiS features) which are also evaluated in the five EEG sub-bands. The CWT and BiS features are fed into a number of ML classifiers to perform both 2-way (AD vs. HC, AD vs. MCI, MCI vs. HC) and 3-way (AD vs. MCI vs. HC) classifications. As an experimental benchmark, a balanced EEG dataset that includes 63 AD, 63 MCI and 63 HC is analyzed. Comparative results show that when the concatenation of CWT and BiS features (denoted as multi-modal (CWT+BiS) features) is used as input, the Multi-Layer Perceptron (MLP) classifier outperforms all other models, specifically, the Autoencoder (AE), Logistic Regression (LR) and Support Vector Machine (SVM). Consequently, our proposed multi-modal ML scheme can be considered a viable alternative to state-of-the-art computationally intensive deep learning approaches.},
}
@article {pmid31882708,
year = {2019},
author = {Peternel, L and Babič, J},
title = {Target of initial sub-movement in multi-component arm-reaching strategy.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {20101},
pmid = {31882708},
issn = {2045-2322},
mesh = {Arm/*physiology ; Brain-Computer Interfaces ; Humans ; *Models, Biological ; *Movement ; *Psychomotor Performance ; Time Factors ; },
abstract = {Goal-directed human reaching often involves multi-component strategy with sub-movements. In general, the initial sub-movement is fast and less precise to bring the limb's endpoint in the vicinity of the target as soon as possible. The final sub-movement then corrects the error accumulated during the previous sub-movement in order to reach the target. We investigate properties of a temporary target of the initial sub-movement. We hypothesise that the peak spatial dispersion of movement trajectories in the axis perpendicular to the movement is in front of the final reaching target, and that it indicates the temporary target of the initial sub-movement. The reasoning is that the dispersion accumulates, due to signal-dependent noise during the initial sub-movement, until the final corrective sub-movement is initiated, which then reduces the dispersion to successfully reach the actual target. We also hypothesise that the reaching movement distance and size of the actual target affect the properties of the temporary target of the initial sub-movement. The increased reaching movement distance increases the magnitude of peak dispersion and moves its location away from the actual target. On the other hand, the increased target size increases the magnitude of peak dispersion and moves its location closer to the actual target.},
}
@article {pmid31881401,
year = {2020},
author = {Floriano, A and Delisle-Rodriguez, D and Diez, PF and Bastos-Filho, TF},
title = {Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field.},
journal = {Computer methods and programs in biomedicine},
volume = {184},
number = {},
pages = {105271},
doi = {10.1016/j.cmpb.2019.105271},
pmid = {31881401},
issn = {1872-7565},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Multivariate Analysis ; Photic Stimulation ; *Vision, Ocular ; },
abstract = {BACKGROUND AND OBJECTIVE: Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears).<br><br>METHODS: Two high-frequency stimuli (31&#xa0;Hz and 32&#xa0;Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method.<br><br>RESULTS: The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min.<br><br>CONCLUSION: These findings contribute to the development of more safe and practical BCI.},
}
@article {pmid31877493,
year = {2020},
author = {Athalye, VR and Carmena, JM and Costa, RM},
title = {Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes.},
journal = {Current opinion in neurobiology},
volume = {60},
number = {},
pages = {145-154},
doi = {10.1016/j.conb.2019.11.023},
pmid = {31877493},
issn = {1873-6882},
support = {F32 MH118714/MH/NIMH NIH HHS/United States ; U19 NS104649/NS/NINDS NIH HHS/United States ; },
mesh = {*Basal Ganglia ; Corpus Striatum ; Dopamine ; Neural Pathways ; *Reinforcement, Psychology ; },
abstract = {How do organisms learn to do again, on-demand, a behavior that led to a desirable outcome? Dopamine-dependent cortico-striatal plasticity provides a framework for learning behavior's value, but it is less clear how it enables the brain to re-enter desired behaviors and refine them over time. Reinforcing behavior is achieved by re-entering and refining the neural patterns that produce it. We review studies using brain-machine interfaces which reveal that reinforcing cortical population activity requires cortico-basal ganglia circuits. Then, we propose a formal framework for how reinforcement in cortico-basal ganglia circuits acts on the neural dynamics of cortical populations. We propose two parallel mechanisms: i) fast reinforcement which selects the inputs that permit the re-entrance of the particular cortical population dynamics which naturally produced the desired behavior, and ii) slower reinforcement which leads to refinement of cortical population dynamics and more reliable production of neural trajectories driving skillful behavior on-demand.},
}
@article {pmid31877161,
year = {2019},
author = {Lee, MH and Williamson, J and Kee, YJ and Fazli, S and Lee, SW},
title = {Robust detection of event-related potentials in a user-voluntary short-term imagery task.},
journal = {PloS one},
volume = {14},
number = {12},
pages = {e0226236},
pmid = {31877161},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials ; Female ; Humans ; Male ; Photic Stimulation ; User-Computer Interface ; },
abstract = {Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual or auditory stimulation and are widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-target stimuli are repeatedly flashed, and the ERP trials are averaged over time in order to improve their decoding accuracy. To reduce this time-consuming process, previous studies have attempted to evoke stronger ERP responses by changing certain experimental parameters like color, size, or the use of a face image as a target symbol. Since these exogenous potentials can be naturally evoked by merely looking at a target symbol, the BCI system could generate unintended commands while subjects are gazing at one of the symbols in a non-intentional mental state. We approached this problem of unintended command generation by assuming that a greater effort by the user in a short-term imagery task would evoke a discriminative ERP response. Three tasks were defined: passive attention, counting, and pitch-imagery. Users were instructed to passively attend to a target symbol, or to perform a mental tally of the number of target presentations, or to perform the novel task of imagining a high-pitch tone when the target symbol was highlighted. The decoding accuracy were 71.4%, 83.5%, and 89.2% for passive attention, counting, and pitch-imagery, respectively, after the fourth averaging procedure. We found stronger deflections in the N500 component corresponding to the levels of mental effort (passive attention: -1.094 ±0.88 μV, counting: -2.226 ±0.97 μV, and pitch-imagery: -2.883 ±0.74 μV), which highly influenced the decoding accuracy. In addition, the rate of binary classification between passive attention and pitch-imagery tasks was 73.5%, which is an adequate classification rate that motivated us to propose a two-stage classification strategy wherein the target symbols are estimated in the first stage and the passive or active mental state is decoded in the second stage. In this study, we found that the ERP response and the decoding accuracy are highly influenced by the user's voluntary mental tasks. This could lead to a useful approach in practical ERP systems in two respects. Firstly, the user-voluntary tasks can be easily utilized in many different types of BCI systems, and performance enhancement is less dependent on the manipulation of the system's external, visual stimulus parameters. Secondly, we propose an ERP system that classifies the brain state as intended or unintended by considering the measurable differences between passively gazing and actively performing the pitch-imagery tasks in the EEG signal thus minimizing unintended commands to the BCI system.},
}
@article {pmid31875363,
year = {2019},
author = {Fu, R and Tian, Y and Bao, T},
title = {[Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {36},
number = {6},
pages = {911-915},
pmid = {31875363},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {This paper aims to realize the decoding of single trial motor imagery electroencephalogram (EEG) signal by extracting and classifying the optimized features of EEG signal. In the classification and recognition of multi-channel EEG signals, there is often a lack of effective feature selection strategies in the selection of the data of each channel and the dimension of spatial filters. In view of this problem, a method combining sparse idea and greedy search (GS) was proposed to improve the feature extraction of common spatial pattern (CSP). The improved common spatial pattern could effectively overcome the problem of repeated selection of feature patterns in the feature vector space extracted by the traditional method, and make the extracted features have more obvious characteristic differences. Then the extracted features were classified by Fisher linear discriminant analysis (FLDA). The experimental results showed that the classification accuracy obtained by proposed method was 19% higher on average than that of traditional common spatial pattern. And high classification accuracy could be obtained by selecting feature set with small size. The research results obtained in the feature extraction of EEG signals lay the foundation for the realization of motor imagery EEG decoding.},
}
@article {pmid31873054,
year = {2020},
author = {Rigato, C and Reinfeldt, S and Asp, F},
title = {The effect of an active transcutaneous bone conduction device on spatial release from masking.},
journal = {International journal of audiology},
volume = {59},
number = {5},
pages = {348-359},
doi = {10.1080/14992027.2019.1705406},
pmid = {31873054},
issn = {1708-8186},
mesh = {Adult ; Aged ; Auditory Threshold ; Bone Conduction/*physiology ; Female ; *Hearing Aids ; Hearing Loss, Bilateral/physiopathology/rehabilitation ; Hearing Loss, Conductive/physiopathology/*rehabilitation ; Hearing Loss, Unilateral/physiopathology/rehabilitation ; Humans ; Male ; Middle Aged ; *Neural Prostheses ; Perceptual Masking/*physiology ; Speech Reception Threshold Test ; Treatment Outcome ; Young Adult ; },
abstract = {Objective: The aim was to quantify the effect of the experimental active transcutaneous Bone Conduction Implant (BCI) on spatial release from masking (SRM) in subjects with bilateral or unilateral conductive and mixed hearing loss.Design: Measurements were performed in a sound booth with five loudspeakers at 0°, +/-30° and +/-150° azimuth. Target speech was presented frontally, and interfering speech from either the front (co-located) or surrounding (separated) loudspeakers. SRM was calculated as the difference between the separated and the co-located speech recognition threshold (SRT).Study Sample: Twelve patients (aged 22-76 years) unilaterally implanted with the BCI were included.Results: A positive SRM, reflecting a benefit of spatially separating interferers from target speech, existed for all subjects in unaided condition, and for nine subjects (75%) in aided condition. Aided SRM was lower compared to unaided in nine of the subjects. There was no difference in SRM between patients with bilateral and unilateral hearing loss. In aided situation, SRT improved only for patients with bilateral hearing loss.Conclusions: The BCI fitted unilaterally in patients with bilateral or unilateral conductive/mixed hearing loss seems to reduce SRM. However, data indicates that SRT is improved or maintained for patients with bilateral and unilateral hearing loss, respectively.},
}
@article {pmid31872171,
year = {2019},
author = {Kadıoğlu, B and Yıldız, &#x130; and Closas, P and Fried-Oken, MB and Erdoğmuş, D},
title = {Robust Fusion of c-VEP and Gaze.},
journal = {IEEE sensors letters},
volume = {3},
number = {1},
pages = {},
pmid = {31872171},
issn = {2475-1472},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCIs) are one of the developing technologies, serving as a communication interface for people with neuromuscular disorders. Electroencephalography (EEG) and gaze signals are among the commonly used inputs for the user intent classification problem arising in BCIs. Fusing different types of input modalities, i.e. EEG and gaze, is an obvious but effective solution for achieving high performance on this problem. Even though there are some simplistic approaches for fusing these two evidences, a more effective method is required for classification performances and speeds suitable for real-life scenarios. One of the main problems that is left unrecognized is highly noisy real-life data. In the context of the BCI framework utilized in this work, noisy data stem from user error in the form of tracking a nontarget stimuli, which in turn results in misleading EEG and gaze signals. We propose a method for fusing aforementioned evidences in a probabilistic manner that is highly robust against noisy data. We show the performance of the proposed method on real EEG and gaze data for different configurations of noise control variables. Compared to the regular fusion method, robust method achieves up to 15% higher classification accuracy.},
}
@article {pmid31871144,
year = {2019},
author = {Andersen, RA and Aflalo, T and Kellis, S},
title = {From thought to action: The brain-machine interface in posterior parietal cortex.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {116},
number = {52},
pages = {26274-26279},
pmid = {31871144},
issn = {1091-6490},
support = {R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
abstract = {A dramatic example of translational monkey research is the development of neural prosthetics for assisting paralyzed patients. A neuroprosthesis consists of implanted electrodes that can record the intended movement of a paralyzed part of the body, a computer algorithm that decodes the intended movement, and an assistive device such as a robot limb or computer that is controlled by these intended movement signals. This type of neuroprosthetic system is also referred to as a brain-machine interface (BMI) since it interfaces the brain with an external machine. In this review, we will concentrate on BMIs in which microelectrode recording arrays are implanted in the posterior parietal cortex (PPC), a high-level cortical area in both humans and monkeys that represents intentions to move. This review will first discuss the basic science research performed in healthy monkeys that established PPC as a good source of intention signals. Next, it will describe the first PPC implants in human patients with tetraplegia from spinal cord injury. From these patients the goals of movements could be quickly decoded, and the rich number of action variables found in PPC indicates that it is an appropriate BMI site for a very wide range of neuroprosthetic applications. We will discuss research on learning to use BMIs in monkeys and humans and the advances that are still needed, requiring both monkey and human research to enable BMIs to be readily available in the clinic.},
}
@article {pmid31870987,
year = {2020},
author = {He, S and Zhou, Y and Yu, T and Zhang, R and Huang, Q and Chuai, L and Mustafa, MU and Gu, Z and Yu, ZL and Tan, H and Li, Y},
title = {EEG- and EOG-Based Asynchronous Hybrid BCI: A System Integrating a Speller, a Web Browser, an E-Mail Client, and a File Explorer.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {2},
pages = {519-530},
doi = {10.1109/TNSRE.2019.2961309},
pmid = {31870987},
issn = {1558-0210},
support = {MC_UU_00003/2/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Algorithms ; Blinking ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; *Electronic Mail ; Electrooculography/*methods ; Healthy Volunteers ; Humans ; Imagination ; Male ; *Web Browser ; Young Adult ; },
abstract = {This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)-related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target. These two methods are further combined to develop the integrated hybrid BCI system. With the hybrid BCI, users can input text, access the internet, communicate with others via e-mail, and manage files in their computer using only EEG and EOG without any body movements. Ten healthy subjects participated in a comprehensive online experiment, and superior performance was achieved compared with our previously developed P300- and MI-based BCI and some other asynchronous BCIs, therefore demonstrating the system's effectiveness.},
}
@article {pmid31870944,
year = {2020},
author = {von Lühmann, A and Li, X and Müller, KR and Boas, DA and Yücel, MA},
title = {Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis.},
journal = {NeuroImage},
volume = {208},
number = {},
pages = {116472},
pmid = {31870944},
issn = {1095-9572},
support = {R24 NS104096/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Artifacts ; Female ; Functional Neuroimaging/methods/*standards ; Humans ; Linear Models ; Male ; *Models, Statistical ; Spectroscopy, Near-Infrared/methods/*standards ; Young Adult ; },
abstract = {For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.},
}
@article {pmid31868222,
year = {2020},
author = {David, L and Bayer, MP and Lobedann, M and Schembecker, G},
title = {Simulation of continuous low pH viral inactivation inside a coiled flow inverter.},
journal = {Biotechnology and bioengineering},
volume = {117},
number = {4},
pages = {1048-1062},
doi = {10.1002/bit.27255},
pmid = {31868222},
issn = {1097-0290},
mesh = {Antibodies, Monoclonal/isolation &amp; purification ; *Bioreactors ; Biotechnology/*instrumentation ; *Computer Simulation ; Equipment Design ; Hydrodynamics ; Hydrogen-Ion Concentration ; *Virus Inactivation ; Viruses/metabolism ; },
abstract = {Continuous production of monoclonal antibodies is gaining more and more importance. To ensure continuous flow through the entire process as well as viral safety, continuous viral clearance needs to be investigated as well. This study focuses on low pH viral inactivation inside a coiled flow inverter (CFI). Computational fluid dynamics (CFD) simulation is used to gain further insight into the inactivation process inside the apparatus. The influence of viruses in comparison to different tracer elements on the residence time distribution (RTD) behavior is investigated. Finally, the viral inactivation kinetics are implemented into the CFD simulation and real process conditions are simulated. These are compared to experimental results. To the authors' knowledge, this study represents the first successful simulation of continuous viral inactivation inside a CFI. It allows the detailed analysis of processes inside the apparatus and the prediction of experimental virus study results and will therefore contribute to the effective planning of future validation studies.},
}
@article {pmid31866143,
year = {2020},
author = {Valentini, FA and Marti, BG and Robain, G and Zimern, PE and Nelson, PP},
title = {Comparison of indices allowing an evaluation of detrusor contractility in women.},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {30},
number = {7},
pages = {396-401},
doi = {10.1016/j.purol.2019.11.004},
pmid = {31866143},
issn = {1166-7087},
mesh = {Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Lower Urinary Tract Symptoms/*physiopathology ; Middle Aged ; *Muscle Contraction ; Retrospective Studies ; Urinary Bladder/*physiopathology ; Urinary Incontinence/*physiopathology ; *Urodynamics ; Young Adult ; },
abstract = {AIMS: To compare 3 detrusor contractility indices, projected isovolumetric pressure (PIP-BCI), PIP1, and k from the VBN mathematical model, for women referred for evaluation of various lower urinary tract symptoms (LUTS) in relationship to age, presenting complaint and urodynamic diagnosis.<br><br>METHODS: Urodynamic tracings of non-neurologic women were analyzed. Three indices of detrusor contractility were measured from the pressure-flow study. Exclusion criteria were voided volume<100mL, stage>2 prolapse, interrupted flow, abdominal straining. Age sub-groups were pre-, peri- and post-menopause. Urodynamic diagnosis included incontinence findings and detrusor activity during voiding.<br><br>RESULTS: Main complaint was incontinence (354 women); 95 women (Other) had non-incontinence LUTS. PIP-BCI, PIP1 and k decreased significantly with ageing in each sub-group. PIP-BCI was significantly different between MUI and Other (P=.0259) while PIP1 was significantly higher in UUI vs. Other (P=.0161) and k in UUI vs. SUI (P=.0107), MUI (P=.0010) and Other (P=.0224). Low value of PIP-BCI for bladder outlet obstruction vs. detrusor overactivity while PIP1 and k values were high and similar for these two diagnoses and a high value of PIP-BCI for detrusor overactivity with impaired contractility close to the value for bladder outlet obstruction while PIP1 and k were low.<br><br>CONCLUSION: Evaluation of detrusor contractility in women is easily obtained using indices PIP-BCI and PIP1 or using the VBN nomogram giving indice-parameter k. PIP1 and parameter k produced comparable and consistent results with the urodynamic diagnosis while PIP-BCI leads to inconsistencies.<br><br>LEVEL OF EVIDENCE: 4.},
}
@article {pmid31863913,
year = {2020},
author = {Hampson, M and Ruiz, S and Ushiba, J},
title = {Neurofeedback.},
journal = {NeuroImage},
volume = {218},
number = {},
pages = {116473},
doi = {10.1016/j.neuroimage.2019.116473},
pmid = {31863913},
issn = {1095-9572},
support = {R01 MH100068/MH/NIMH NIH HHS/United States ; },
mesh = {Brain/*physiology ; Humans ; Neurofeedback/*methods/*physiology ; },
}
@article {pmid31863249,
year = {2020},
author = {Yilmaz, BH and Yilmaz, CM and Kose, C},
title = {Diversity in a signal-to-image transformation approach for EEG-based motor imagery task classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {58},
number = {2},
pages = {443-459},
pmid = {31863249},
issn = {1741-0444},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Nowadays, motor imagery-based brain-computer interfaces (BCIs) have been developed rapidly. In these systems, electroencephalogram (EEG) signals are recorded when a subject is involved in the imagination of doing any motor imagery movement like the imagination of the right/left hands, etc. In this paper, we sought to validate and enhance our previously proposed angle-amplitude transformation (AAT) technique, which is a simple signal-to-image transformation approach for the classification of EEG and MEG signals. For this purpose, we diversified our previous method and proposed four new angle-amplitude graph (AAG) representation methods for AAT transformation. These modifications were made on some points such as using different left/right side changing points at a different distance. To confirm the validity of the proposed methods, we performed experiments on the BCI Competition III Dataset IIIa, which is a benchmark dataset widely used for EEG-based multi-class motor imagery tasks. The procedure of proposed methods can be summarized in a concise manner as follows: (i) convert EEG signals to AAG images by using the proposed AAT transformation approaches; (ii) extract image features by employing Scale Invariant Feature Transform (SIFT)-based Bag of Visual Word (BoW); and (iii) classify features with k-Nearest Neighbor (k NN) algorithm. Experimental results showed that the changes in the baseline AAT approaches enhanced the classification performance on Dataset IIIa with an accuracy of 96.50% for two-class problem (left/right hand movement imaginations) and 97.99% for four-class problem (left/right hand, foot and tongue movement imaginations). These achievements are mainly due to the help of effective enhancements on AAG image representations. Graphical Abstract The flow diagram of the proposed methodology.},
}
@article {pmid31860902,
year = {2020},
author = {Azab, AM and Ahmadi, H and Mihaylova, L and Arvaneh, M},
title = {Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016061},
doi = {10.1088/1741-2552/ab64a0},
pmid = {31860902},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/psychology ; Databases, Factual ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; *Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning.<br><br>APPROACH: The proposed framework combines the subject-specific covariance matrix ([Formula: see text]) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix ([Formula: see text]) estimated using previous subjects' trials. In the proposed [Formula: see text], the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials.<br><br>MAIN RESULTS: The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training.<br><br>SIGNIFICANCE: Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class.},
}
@article {pmid31860655,
year = {2019},
author = {Pierella, C and Casadio, M and Mussa-Ivaldi, FA and Solla, SA},
title = {The dynamics of motor learning through the formation of internal models.},
journal = {PLoS computational biology},
volume = {15},
number = {12},
pages = {e1007118},
pmid = {31860655},
issn = {1553-7358},
support = {R01 EB024058/EB/NIBIB NIH HHS/United States ; R01 HD072080/HD/NICHD NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/psychology/statistics &amp; numerical data ; Computational Biology ; Humans ; Learning/*physiology ; Models, Biological ; Models, Neurological ; Motor Skills/*physiology ; Movement ; Robotics ; },
abstract = {A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions, while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of the proposed model of learning dynamics and the learning performance observed in a group of subjects demonstrate a first-order exponential convergence of the learning process toward a particular state that depends only on the initial state of the inverse and forward models and on the sequence of targets supplied to the users. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.},
}
@article {pmid31854466,
year = {2020},
author = {Zhu, C and Zhao, Y and Wu, X and Qiang, C and Liu, J and Shi, J and Gou, J and Pei, D and Li, A},
title = {The therapeutic role of baicalein in combating experimental periodontitis with diabetes via Nrf2 antioxidant signaling pathway.},
journal = {Journal of periodontal research},
volume = {55},
number = {3},
pages = {381-391},
doi = {10.1111/jre.12722},
pmid = {31854466},
issn = {1600-0765},
support = {xjj2017025//Fundamental Research Funds for the Central Universities/ ; xjj2017097//Fundamental Research Funds for the Central Universities/ ; 81901019//National Natural Science Foundation of China/ ; 81870798//National Natural Science Foundation of China/ ; },
mesh = {Animals ; Antioxidants/metabolism ; *Diabetes Mellitus ; Flavanones/*therapeutic use ; Humans ; NF-E2-Related Factor 2/*metabolism ; Oxidative Stress ; Periodontitis/complications/*drug therapy ; Rats ; Rats, Sprague-Dawley ; Reactive Oxygen Species/metabolism ; *Signal Transduction ; X-Ray Microtomography ; },
abstract = {BACKGROUND AND OBJECTIVE: Oxidative stress has been suggested as an important pathogenic factor contributing to chronic periodontitis with diabetes mellitus (CPDM). Previous studies have revealed the potential therapeutic properties of baicalein (BCI) in oxidative stress-related diseases; however, the antioxidant effects of BCI on therapy for individual with CPDM remain largely unexplored. Nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in cellular defence against oxidative stress. In this study, we aim to determine whether BCI prevents diabetes-related periodontal tissue destruction by regulating Nrf2 signaling pathway.<br><br>MATERIAL AND METHODS: Human gingival epithelial cells (hGECs) were challenged with high glucose (HG, 25&#xa0;mmol/L) and/or lipopolysaccharide (LPS, 20&#xa0;&#xb5;g/mL). Reactive oxygen species (ROS) were detected by fluorescence-activated cell sorting. The changes of antioxidant-related genes, including Nrf2, catalase (Cat), glutamate-cysteine ligase catalytic subunit (Gclc), superoxide dismutase 1 (Sod1), and superoxide dismutase 2 (Sod2), were quantified by real-time PCR. The localization of phospho-Nrf2 (pNrf2, S40) in the nucleus was detected by immunofluorescence staining and laser scanning confocal microscope (LSCM). PNrf2 and total form of Nrf2 were determined using&#xa0;western blot. The above indicators together with mitochondrial membrane potential (MMP) were further investigated in hGECs pre-treated with different concentrations of BCI (0.01, 0.1, or 0.5&#xa0;&#xb5;g/mL) before stimulated with HG plus LPS (GP). Finally, the role of BCI in activating Nrf2 signaling pathway and relieving the alveolar bone absorption was examined in the CPDM model of Sprague Dawley rats. CPDM rats were oral gavaged with BCI (50, 100, or 200&#xa0;mg/kg daily). The pNrf2 was detected by immunohistochemistry, and the alveolar bone absorption was examined by microcomputed tomography.<br><br>RESULTS: Our results showed that ROS were significantly increased in both groups of HG and LPS, with the strongest generation in the GP group. In terms of ROS-related gene expression, we found that the mRNA levels of Nrf2, Cat, Gclc, Sod1, and Sod2 were significantly decreased in HG and LPS groups. In consistent with the strongest induction of ROS in GP group, the gene expression in GP group was further decreased as compared to those of HG and LPS groups. Also, the expression of pNrf2 exhibited the same trend with the expression of those antioxidant genes. However, the generation of ROS and the loss of mitochondrial membrane potential induced by GP were abolished by pre-treatment with different concentrations of BCI (0.01, 0.1, or 0.5&#xa0;&#xb5;g/mL). Interestingly, we observed that BCI promoted the nucleus translocation of pNrf2, as well as the gene expression levels of pNrf2 and its target genes (Cat, Gclc, Sod1, and Sod2). Finally, in the CPDM animal model, we found that BCI (at concentrations: 50, 100, and 200&#xa0;mg/kg) markedly increased the number of pNrf2-positive cells in periodontal tissue and mitigated the alveolar bone loss.<br><br>CONCLUSIONS: Our data revealed a potential role for clinic application of BCI under CPDM conditions, suggesting a new therapeutic drug for CPDM patients.},
}
@article {pmid31852007,
year = {2020},
author = {Zhong, P and Zhao, YR and Qiao, BM and Yang, FJ and Zhu, Y and Yang, ZQ and Niu, YJ},
title = {Comparison of Two Numerical Parameters to Assess Detrusor Contractility in Prognosing Short-Term Outcome after Transurethral Resection of the Prostate.},
journal = {Urologia internationalis},
volume = {104},
number = {5-6},
pages = {361-366},
doi = {10.1159/000503331},
pmid = {31852007},
issn = {1423-0399},
mesh = {Aged ; Humans ; Male ; Mathematical Concepts ; Middle Aged ; Muscle Contraction/*physiology ; Muscle, Smooth/*physiopathology ; Prognosis ; Prostatic Hyperplasia/*surgery ; Retrospective Studies ; Time Factors ; *Transurethral Resection of Prostate ; Treatment Outcome ; Urinary Bladder/*physiopathology ; },
abstract = {OBJECTIVE: To investigate and compare the influence of two numerical detrusor contractility parameters, the bladder contractility index (BCI) and the maximum Watts factor (WFmax), on transurethral resection of the prostate (TURP) outcome.<br><br>METHODS: A retrospective study was conducted on 236 patients who had undergone urodynamic assessment preoperatively and TURP for benign prostatic obstruction. They were evaluated by International Prostate Symptom Score (IPSS) and uroflowmetry preoperatively and 3 months postoperatively. Related criteria were established to determine the overall efficacy of TURP. Logistic regression analysis and receiver operating characteristic curves were made to investigate the influence of the BCI and WFmax on TURP efficacy.<br><br>RESULTS: Among the 236 patients, 195 treatments were effective and 41 ineffective. Multivariate analysis showed that both the BCI (OR 1.038) and the WFmax (OR 1.291) could influence TURP efficacy. For predicting TURP efficacy, the optimal cut-off values of the BCI and WFmax were 98.7 and 10.27 W/m2, respectively. The AUC, sensitivity and specificity of the BCI were 0.722, 78.5% and 61.0%; those of the WFmax were 0.761, 73.9% and 73.2%, with no significant difference (p > 0.05).<br><br>CONCLUSIONS: To some extent, the BCI and the WFmax can predict TURP efficacy equally well. A discrimination level of 10.27 W/m2 may be a threshold value for detrusor underactivity (DU); as regards the BCI, the current threshold value is appropriate to diagnose DU.},
}
@article {pmid31849634,
year = {2019},
author = {Pizzolato, C and Saxby, DJ and Palipana, D and Diamond, LE and Barrett, RS and Teng, YD and Lloyd, DG},
title = {Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {97},
pmid = {31849634},
issn = {1662-5218},
abstract = {Concurrent stimulation and reinforcement of motor and sensory pathways has been proposed as an effective approach to restoring function after developmental or acquired neurotrauma. This can be achieved by applying multimodal rehabilitation regimens, such as thought-controlled exoskeletons or epidural electrical stimulation to recover motor pattern generation in individuals with spinal cord injury (SCI). However, the human neuromusculoskeletal (NMS) system has often been oversimplified in designing rehabilitative and assistive devices. As a result, the neuromechanics of the muscles is seldom considered when modeling the relationship between electrical stimulation, mechanical assistance from exoskeletons, and final joint movement. A powerful way to enhance current neurorehabilitation is to develop the next generation prostheses incorporating personalized NMS models of patients. This strategy will enable an individual voluntary interfacing with multiple electromechanical rehabilitation devices targeting key afferent and efferent systems for functional improvement. This narrative review discusses how real-time NMS models can be integrated with finite element (FE) of musculoskeletal tissues and interface multiple assistive and robotic devices with individuals with SCI to promote neural restoration. In particular, the utility of NMS models for optimizing muscle stimulation patterns, tracking functional improvement, monitoring safety, and providing augmented feedback during exercise-based rehabilitation are discussed.},
}
@article {pmid31849632,
year = {2019},
author = {Volkova, K and Lebedev, MA and Kaplan, A and Ossadtchi, A},
title = {Decoding Movement From Electrocorticographic Activity: A Review.},
journal = {Frontiers in neuroinformatics},
volume = {13},
number = {},
pages = {74},
pmid = {31849632},
issn = {1662-5196},
abstract = {Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.},
}
@article {pmid31849588,
year = {2019},
author = {De La Pava Panche, I and Alvarez-Meza, AM and Orozco-Gutierrez, A},
title = {A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1277},
pmid = {31849588},
issn = {1662-4548},
abstract = {Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory brain effective connectivity analyses. Like all information theoretic quantities, TE is defined regarding the probability distributions of the system under study, which in practice are unknown and must be estimated from data. Commonly used methods for TE estimation rely on a local approximation of the probability distributions from nearest neighbor distances, or on symbolization schemes that then allow the probabilities to be estimated from the symbols' relative frequencies. However, probability estimation is a challenging problem, and avoiding this intermediate step in TE computation is desirable. In this work, we propose a novel TE estimator using functionals defined on positive definite and infinitely divisible kernels matrices that approximate Renyi's entropy measures of order α. Our data-driven approach estimates TE directly from data, sidestepping the need for probability distribution estimation. Also, the proposed estimator encompasses the well-known definition of TE as a sum of Shannon entropies in the limiting case when α → 1. We tested our proposal on a simulation framework consisting of two linear models, based on autoregressive approaches and a linear coupling function, respectively, and on the public electroencephalogram (EEG) database BCI Competition IV, obtained under a motor imagery paradigm. For the synthetic data, the proposed kernel-based TE estimation method satisfactorily identifies the causal interactions present in the data. Also, it displays robustness to varying noise levels and data sizes, and to the presence of multiple interaction delays in the same connected network. Obtained results for the motor imagery task show that our approach codes discriminant spatiotemporal patterns for the left and right-hand motor imagination tasks, with classification performances that compare favorably to the state-of-the-art.},
}
@article {pmid31849587,
year = {2019},
author = {Wu, H and Niu, Y and Li, F and Li, Y and Fu, B and Shi, G and Dong, M},
title = {A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1275},
pmid = {31849587},
issn = {1662-4548},
abstract = {OBJECTIVE: Electroencephalogram (EEG) based brain-computer interfaces (BCI) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio (SNR) and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods. However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI.<br><br>APPROACH: In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for inter-subject classification on small datasets. We compare our MSFBCNN with the state-of-the-art approaches on open datasets.<br><br>RESULTS: The proposed method has a higher accuracy than the baselines in intra-subject classification. In addition, the transfer learning experiments indicate that our network can build an individual model and obtain acceptable results in inter-subject classification. The results suggest that the proposed network has superior performance, robustness, and transfer learning ability.},
}
@article {pmid31847114,
year = {2019},
author = {Yazici, M and Ulutas, M and Okuyan, M},
title = {A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery.},
journal = {Brain sciences},
volume = {9},
number = {12},
pages = {},
pmid = {31847114},
issn = {2076-3425},
abstract = {Brain-computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1-30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.},
}
@article {pmid31841514,
year = {2019},
author = {, },
title = {Retraction: Brain-Computer Interface-Based Communication in the Completely Locked-In State.},
journal = {PLoS biology},
volume = {17},
number = {12},
pages = {e3000607},
pmid = {31841514},
issn = {1545-7885},
}
@article {pmid31841500,
year = {2019},
author = {, },
title = {Retraction: Response to: "Questioning the evidence for BCI-based communication in the complete locked-in state".},
journal = {PLoS biology},
volume = {17},
number = {12},
pages = {e3000608},
pmid = {31841500},
issn = {1545-7885},
}
@article {pmid31840111,
year = {2019},
author = {Heelan, C and Lee, J and O'Shea, R and Lynch, L and Brandman, DM and Truccolo, W and Nurmikko, AV},
title = {Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates.},
journal = {Communications biology},
volume = {2},
number = {},
pages = {466},
pmid = {31840111},
issn = {2399-3642},
mesh = {Acoustic Stimulation ; Algorithms ; Animals ; Auditory Cortex/*physiology ; Brain Mapping ; Electrophysiological Phenomena ; Models, Theoretical ; Neurons/*physiology ; Primates ; *Speech ; },
abstract = {Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.},
}
@article {pmid31835546,
year = {2019},
author = {Elvira, M and Iáñez, E and Quiles, V and Ortiz, M and Azorín, JM},
title = {Pseudo-Online BMI Based on EEG to Detect the Appearance of Sudden Obstacles during Walking.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {24},
pages = {},
pmid = {31835546},
issn = {1424-8220},
support = {RTI2018-096677-B-I00//Spanish Ministry of Science, Innovation and Universities, the Spanish State Agency of Research, and the European Union through the European Regional Development Fund/ ; },
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Exoskeleton Device ; Female ; Gait/*physiology ; Humans ; Male ; Middle Aged ; *Monitoring, Physiologic ; Signal Processing, Computer-Assisted ; Walking/*physiology ; },
abstract = {The aim of this paper is to describe new methods for detecting the appearance of unexpected obstacles during normal gait from EEG signals, improving the accuracy and reducing the false positive rate obtained in previous studies. This way, an exoskeleton for rehabilitation or assistance of people with motor limitations commanded by a Brain-Machine Interface (BMI) could be stopped in case that an obstacle suddenly appears during walking. The EEG data of nine healthy subjects were collected during their normal gait while an obstacle appearance was simulated by the projection of a laser line in a random pattern. Different approaches were considered for selecting the parameters of the BMI: subsets of electrodes, time windows and classifier probabilities, which were based on a linear discriminant analysis (LDA). The pseudo-online results of the BMI for detecting the appearance of obstacles, with an average percentage of 63.9% of accuracy and 2.6 false positives per minute, showed a significant improvement over previous studies.},
}
@article {pmid31831401,
year = {2020},
author = {Xiao, X and Xu, M and Jin, J and Wang, Y and Jung, TP and Ming, D},
title = {Discriminative Canonical Pattern Matching for Single-Trial Classification of ERP Components.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {8},
pages = {2266-2275},
doi = {10.1109/TBME.2019.2958641},
pmid = {31831401},
issn = {1558-2531},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; *Evoked Potentials, Visual ; Humans ; },
abstract = {Event-related potentials (ERPs) are one of the most popular control signals for brain-computer interfaces (BCIs). However, they are very weak and sensitive to the experimental settings including paradigms, stimulation parameters and even surrounding environments, resulting in a diversity of ERP patterns across different BCI experiments. It's still a challenge to develop a general decoding algorithm that can adapt to the ERP diversities of different BCI datasets with small training sets. This study compared a recently developed algorithm, i.e., discriminative canonical pattern matching (DCPM), with seven ERP-BCI classification methods, i.e., linear discriminant analysis (LDA), stepwise LDA, bayesian LDA, shrinkage LDA, spatial-temporal discriminant analysis (STDA), xDAWN and EEGNet for the single-trial classification of two private EEG datasets and three public EEG datasets with small training sets. The feature ERPs of the five datasets included P300, motion visual evoked potential (mVEP), and miniature asymmetric visual evoked potential (aVEP). Study results showed that the DCPM outperformed other classifiers for all of the tested datasets, suggesting the DCPM is a robust classification algorithm for assessing a wide range of ERP components.},
}
@article {pmid31827418,
year = {2019},
author = {Hildt, E},
title = {Multi-Person Brain-To-Brain Interfaces: Ethical Issues.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1177},
pmid = {31827418},
issn = {1662-4548},
}
@article {pmid31827203,
year = {2019},
author = {Chen, SF and Lee, CL and Kuo, HC},
title = {Change of Detrusor Contractility in Patients with and without Bladder Outlet Obstruction at Ten or More Years of follow-up.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {18887},
pmid = {31827203},
issn = {2045-2322},
mesh = {Adult ; Aged ; Female ; Follow-Up Studies ; Humans ; Lower Urinary Tract Symptoms/*physiopathology ; Male ; Middle Aged ; Muscle Contraction/*physiology ; Urinary Bladder/*physiopathology ; Urinary Bladder Neck Obstruction/*physiopathology ; Urodynamics ; },
abstract = {To analyze the change of detrusor contractility by investigating urodynamic characteristics with long term follow-up. This study retrospectively reviewed 166 lower urinary tract symptoms patients without bladder outlet obstruction (BOO) and 63 patients with BOO who underwent repeated urodynamic studies at the first time and more than 10 years later. The urodynamic parameters, bladder contractility index (BCI), and BOO index (BOOI) were compared before and after. As time goes by, detrusor pressure at maximum flow rate (PdetQmax) significantly decreased and post-void residual (PVR) volume significantly increased in both men and women. Full sensation, urge sensation, voided volume, and BCI significantly decreased. We also compared men with and without BOO, PdetQmax, maximum flow rate (Qmax), voided volume, and BCI all significantly decreased in both groups without difference. PVR increased greater in men with BOO after >10 years significantly (p = 0.036). Women with detrusor overactivity (DO) under antimuscarinic showed no significant BCI change compared to patients without DO (p = 0.228). Detrusor contractility decreases in men and women after >10 years of follow-up. However, this finding suggests that patients with BOO or DO under adequate medical treatment, detrusor contractility is not aggravated over 10 or more years of follow-up.},
}
@article {pmid31827158,
year = {2019},
author = {Kupers, SJ and Wirth, C and Engelbrecht, BMJ and Hernández, A and Condit, R and Wright, SJ and Rüger, N},
title = {Performance of tropical forest seedlings under shade and drought: an interspecific trade-off in demographic responses.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {18784},
pmid = {31827158},
issn = {2045-2322},
mesh = {Acclimatization ; Climate Change ; Droughts ; Light ; *Rainforest ; Seedlings/*growth &amp; development ; },
abstract = {Seedlings in moist tropical forests must cope with deep shade and seasonal drought. However, the interspecific relationship between seedling performance in shade and drought remains unsettled. We quantified spatiotemporal variation in shade and drought in the seasonal moist tropical forest on Barro Colorado Island (BCI), Panama, and estimated responses of naturally regenerating seedlings as the slope of the relationship between performance and shade or drought intensity. Our performance metrics were relative height growth and first-year survival. We investigated the relationship between shade and drought responses for up to 63 species. There was an interspecific trade-off in species responses to shade versus species responses to dry season intensity; species that performed worse in the shade did not suffer during severe dry seasons and vice versa. This trade-off emerged in part from the absence of species that performed particularly well or poorly in both drought and shade. If drought stress in tropical forests increases with climate change and as solar radiation is higher during droughts, the trade-off may reinforce a shift towards species that resist drought but perform poorly in the shade by releasing them from deep shade.},
}
@article {pmid31825869,
year = {2020},
author = {Deng, X and Yu, ZL and Lin, C and Gu, Z and Li, Y},
title = {A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {1},
pages = {328-338},
doi = {10.1109/TNSRE.2019.2958076},
pmid = {31825869},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Bayes Theorem ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory/physiology ; Evoked Potentials, Visual/physiology ; Humans ; Male ; Psychomotor Performance ; *Robotics ; *Wheelchairs ; Young Adult ; },
abstract = {To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain machine interface (BMI) is presented for all-time continuous wheelchair navigation task. Moreover, to obtain more accurate brain control commands for shared controller and adapt the proposed system to the uncertainty of electroencephalogram (EEG), a hierarchical brain control mechanism with feedback rule is designed. Experiments have been conducted to verify the proposed system in several scenarios. Eleven subjects participated in our experiments and the results illustrate the effectiveness of the proposed method.},
}
@article {pmid31824257,
year = {2019},
author = {Herff, C and Diener, L and Angrick, M and Mugler, E and Tate, MC and Goldrick, MA and Krusienski, DJ and Slutzky, MW and Schultz, T},
title = {Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1267},
pmid = {31824257},
issn = {1662-4548},
support = {F32 DC015708/DC/NIDCD NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; UL1 TR000150/TR/NCATS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
abstract = {Neural interfaces that directly produce intelligible speech from brain activity would allow people with severe impairment from neurological disorders to communicate more naturally. Here, we record neural population activity in motor, premotor and inferior frontal cortices during speech production using electrocorticography (ECoG) and show that ECoG signals alone can be used to generate intelligible speech output that can preserve conversational cues. To produce speech directly from neural data, we adapted a method from the field of speech synthesis called unit selection, in which units of speech are concatenated to form audible output. In our approach, which we call Brain-To-Speech, we chose subsequent units of speech based on the measured ECoG activity to generate audio waveforms directly from the neural recordings. Brain-To-Speech employed the user's own voice to generate speech that sounded very natural and included features such as prosody and accentuation. By investigating the brain areas involved in speech production separately, we found that speech motor cortex provided more information for the reconstruction process than the other cortical areas.},
}
@article {pmid31824249,
year = {2019},
author = {Loza, CA and Reddy, CG and Akella, S and Príncipe, JC},
title = {Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1248},
pmid = {31824249},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCI) aim to bypass the peripheral nervous system to link the brain to external devices via successful modeling of decoding mechanisms. BCI based on electrocorticogram or ECoG represent a viable compromise between clinical practicality, spatial resolution, and signal quality when it comes to extracellular electrical potentials from local neuronal assemblies. Classic analysis of ECoG traces usually falls under the umbrella of Time-Frequency decompositions with adaptations from Fourier analysis and wavelets as its most prominent variants. However, analyzing such high-dimensional, multivariate time series demands for specialized signal processing and neurophysiological principles. We propose a generative model for single-channel ECoGs that is able to fully characterize reoccurring rhythm-specific neuromodulations as weighted activations of prototypical templates over time. The set of timings, weights and indexes comprise a temporal marked point process (TMPP) that accesses a set of bases from vector spaces of different dimensions-a dictionary. The shallow nature of the model admits the equivalence between latent variables and representations. In this way, learning the model parameters is a case of unsupervised representation learning. We exploit principles of Minimum Description Length (MDL) encoding to effectively yield a data-driven framework where prototypical neuromodulations (not restricted to a particular duration) can be estimated alongside the timings and features of the TMPP. We validate the proposed methodology on discrimination of movement-related tasks utilizing 32-electrode grids implanted in the frontal cortex of six epileptic subjects. We show that the learned representations from the high-gamma band (85-145 Hz) are not only interpretable, but also discriminant in a lower dimensional space. The results also underscore the practicality of our algorithm, i.e., 2 main hyperparameters that can be readily set via neurophysiology, and emphasize the need of principled and interpretable representation learning in order to model encoding mechanisms in the brain.},
}
@article {pmid31824245,
year = {2019},
author = {Huang, Q and Zhang, Z and Yu, T and He, S and Li, Y},
title = {An EEG-/EOG-Based Hybrid Brain-Computer Interface: Application on Controlling an Integrated Wheelchair Robotic Arm System.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1243},
pmid = {31824245},
issn = {1662-4548},
abstract = {Most existing brain-computer Interfaces (BCIs) are designed to control a single assistive device, such as a wheelchair, a robotic arm or a prosthetic limb. However, many daily tasks require combined functions which can only be realized by integrating multiple robotic devices. Such integration raises the requirement of the control accuracy and is more challenging to achieve a reliable control compared with the single device case. In this study, we propose a novel hybrid BCI with high accuracy based on electroencephalogram (EEG) and electrooculogram (EOG) to control an integrated wheelchair robotic arm system. The user turns the wheelchair left/right by performing left/right hand motor imagery (MI), and generates other commands for the wheelchair and the robotic arm by performing eye blinks and eyebrow raising movements. Twenty-two subjects participated in a MI training session and five of them completed a mobile self-drinking experiment, which was designed purposely with high accuracy requirements. The results demonstrated that the proposed hBCI could provide satisfied control accuracy for a system that consists of multiple robotic devices, and showed the potential of BCI-controlled systems to be applied in complex daily tasks.},
}
@article {pmid31822715,
year = {2019},
author = {Renton, AI and Mattingley, JB and Painter, DR},
title = {Optimising non-invasive brain-computer interface systems for free communication between naïve human participants.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {18705},
pmid = {31822715},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*psychology/*trends ; Communication ; Cues ; Electroencephalography/*instrumentation/methods ; Female ; Humans ; Male ; User-Computer Interface ; },
abstract = {Free communication is one of the cornerstones of modern civilisation. While manual keyboards currently allow us to interface with computers and manifest our thoughts, a next frontier is communication without manual input. Brain-computer interface (BCI) spellers often achieve this by decoding patterns of neural activity as users attend to flickering keyboard displays. To date, the highest performing spellers report typing rates of ~10.00 words/minute. While impressive, these rates are typically calculated for experienced users repetitively typing single phrases. It is therefore not clear whether naïve users are able to achieve such high rates with the added cognitive load of genuine free communication, which involves continuously generating and spelling novel words and phrases. In two experiments, we developed an open-source, high-performance, non-invasive BCI speller and examined its feasibility for free communication. The BCI speller required users to focus their visual attention on a flickering keyboard display, thereby producing unique cortical activity patterns for each key, which were decoded using filter-bank canonical correlation analysis. In Experiment 1, we tested whether seventeen naïve users could maintain rapid typing during prompted free word association. We found that information transfer rates were indeed slower during this free communication task than during typing of a cued character sequence. In Experiment 2, we further evaluated the speller's efficacy for free communication by developing a messaging interface, allowing users to engage in free conversation. The results showed that free communication was possible, but that information transfer was reduced by voluntary textual corrections and turn-taking during conversation. We evaluated a number of factors affecting the suitability of BCI spellers for free communication, and make specific recommendations for improving classification accuracy and usability. Overall, we found that developing a BCI speller for free communication requires a focus on usability over reduced character selection time, and as such, future performance appraisals should be based on genuine free communication scenarios.},
}
@article {pmid31821091,
year = {2020},
author = {Li, H and Wu, J and Shang, X and Geng, M and Gao, J and Zhao, S and Yu, X and Liu, D and Kang, Z and Wang, X and Wang, X},
title = {WRKY Transcription Factors Shared by BTH-Induced Resistance and NPR1-Mediated Acquired Resistance Improve Broad-Spectrum Disease Resistance in Wheat.},
journal = {Molecular plant-microbe interactions : MPMI},
volume = {33},
number = {3},
pages = {433-443},
doi = {10.1094/MPMI-09-19-0257-R},
pmid = {31821091},
issn = {0894-0282},
mesh = {Disease Resistance/*genetics ; Gene Expression Regulation, Plant ; Hordeum/genetics ; Plant Diseases/genetics/microbiology ; Plant Proteins/*genetics ; Plants, Genetically Modified ; Thiadiazoles/*pharmacology ; Transcription Factors/*genetics ; Transcriptome ; Triticum/*genetics ; },
abstract = {In Arabidopsis, both pathogen invasion and benzothiadiazole (BTH) treatment activate the nonexpresser of pathogenesis-related genes 1 (NPR1)-mediated systemic acquired resistance, which provides broad-spectrum disease resistance to secondary pathogen infection. However, the BTH-induced resistance in Triticeae crops of wheat and barley seems to be accomplished through an NPR1-independent pathway. In the current investigation, we applied transcriptome analysis on barley transgenic lines overexpressing wheat wNPR1 (wNPR1-OE) and knocking down barley HvNPR1 (HvNPR1-Kd) to reveal the role of NPR1 during the BTH-induced resistance. Most of the previously designated barley chemical-induced (BCI) genes were upregulated in an NPR1-independent manner, whereas the expression levels of several pathogenesis-related (PR) genes were elevated upon BTH treatment only in wNPR1-OE. Two barley WRKY transcription factors, HvWRKY6 and HvWRKY70, were predicted and further validated as key regulators shared by the BTH-induced resistance and the NPR1-mediated acquired resistance. Wheat transgenic lines overexpressing HvWRKY6 and HvWRKY70 showed different degrees of enhanced resistance to Puccinia striiformis f. sp. tritici pathotype CYR32 and Blumeria graminis f. sp. tritici pathotype E20. In conclusion, the transcriptional changes of BTH-induced resistance in barley were initially profiled, and the identified key regulators would be valuable resources for the genetic improvement of broad-spectrum disease resistance in wheat.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.},
}
@article {pmid31820736,
year = {2019},
author = {Stavisky, SD and Willett, FR and Wilson, GH and Murphy, BA and Rezaii, P and Avansino, DT and Memberg, WD and Miller, JP and Kirsch, RF and Hochberg, LR and Ajiboye, AB and Druckmann, S and Shenoy, KV and Henderson, JM},
title = {Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis.},
journal = {eLife},
volume = {8},
number = {},
pages = {},
pmid = {31820736},
issn = {2050-084X},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; R01DC014034/DC/NIDCD NIH HHS/United States ; T32 MH020016/MH/NIMH NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 NS098968/NS/NINDS NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; 5U01NS098968-02/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Arm/physiopathology ; Brain-Computer Interfaces ; Electrocorticography ; Hand/physiopathology ; Humans ; Lip/physiopathology ; Models, Neurological ; Motor Cortex/*physiopathology ; Movement/physiology ; Nerve Net/*physiopathology ; Quadriplegia/*physiopathology ; Sensorimotor Cortex/physiopathology ; Speech/*physiology ; Tongue/physiopathology ; },
abstract = {Speaking is a sensorimotor behavior whose neural basis is difficult to study with single neuron resolution due to the scarcity of human intracortical measurements. We used electrode arrays to record from the motor cortex 'hand knob' in two people with tetraplegia, an area not previously implicated in speech. Neurons modulated during speaking and during non-speaking movements of the tongue, lips, and jaw. This challenges whether the conventional model of a 'motor homunculus' division by major body regions extends to the single-neuron scale. Spoken words and syllables could be decoded from single trials, demonstrating the potential of intracortical recordings for brain-computer interfaces to restore speech. Two neural population dynamics features previously reported for arm movements were also present during speaking: a component that was mostly invariant across initiating different words, followed by rotatory dynamics during speaking. This suggests that common neural dynamical motifs may underlie movement of arm and speech articulators.},
}
@article {pmid31817941,
year = {2019},
author = {Gakopoulos, S and Nica, IG and Bekteshi, S and Aerts, JM and Monbaliu, E and Hallez, H},
title = {Development of a Data Logger for Capturing Human-Machine Interaction in Wheelchair Head-Foot Steering Sensor System in Dyskinetic Cerebral Palsy.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {24},
pages = {},
pmid = {31817941},
issn = {1424-8220},
support = {C32/17/056//Onderzoeksraad, KU Leuven/ ; },
mesh = {*Brain-Computer Interfaces ; Cerebral Palsy/*physiopathology/psychology ; Persons with Disabilities ; Equipment Design ; Humans ; Movement ; Robotics/instrumentation/*methods ; Signal Processing, Computer-Assisted ; Wheelchairs ; Wireless Technology ; },
abstract = {The use of data logging systems for capturing wheelchair and user behavior has increased rapidly over the past few years. Wheelchairs ensure more independent mobility and better quality of life for people with motor disabilities. Especially, for people with complex movement disorders, such as dyskinetic cerebral palsy (DCP) who lack the ability to walk or to handle objects, wheelchairs offer a means of integration into daily life. The mobility of DCP patients is based on a head-foot wheelchair steering system. In this work, a data logging system is proposed to capture data from human-wheelchair interaction for the head-foot steering system. Additionally, the data logger provides an interface to multiple Inertial Measurement Units (IMUs) placed on the body of the wheelchair user. The system provides accurate and real-time information from head-foot navigation system pressure sensors on the wheelchair during driving. This system was used as a tool to obtain further insights into wheelchair control and steering behavior of people diagnosed with DCP in comparison with a healthy subject.},
}
@article {pmid31816868,
year = {2019},
author = {Kwon, M and Han, S and Kim, K and Jun, SC},
title = {Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network-Feasibility Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {23},
pages = {},
pmid = {31816868},
issn = {1424-8220},
support = {2018R1A2B2005687//National Research Foundation of Korea/ ; 2017-0-00451//Institute of Information &amp; communications Technology Planning &amp; Evaluation/ ; 2019-0-01842//Institute of Information &amp; communications Technology Planning &amp; Evaluation/ ; 2019 - GP2019-0018//Korea Research Institute of Standards and Science (KRISS)/ ; },
mesh = {Adult ; Algorithms ; Brain/*pathology ; Brain-Computer Interfaces ; Computer Simulation ; *Electroencephalography ; Feasibility Studies ; Humans ; Machine Learning ; *Neural Networks, Computer ; Normal Distribution ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach's feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.},
}
@article {pmid31815564,
year = {2020},
author = {Kwon, CS and Jetté, N and Ghatan, S},
title = {Perspectives on the current developments with neuromodulation for the treatment of epilepsy.},
journal = {Expert review of neurotherapeutics},
volume = {20},
number = {2},
pages = {189-194},
doi = {10.1080/14737175.2020.1700795},
pmid = {31815564},
issn = {1744-8360},
mesh = {*Artificial Intelligence ; Electric Stimulation Therapy/*trends ; Epilepsy/*therapy ; Humans ; },
abstract = {Introduction: As deep brain stimulation revolutionized the treatment of movement disorders in the late 80s, neuromodulation in the treatment of epilepsy will undoubtedly undergo transformative changes in the years to come with the exponential growth of technological development moving into mainstream practice; the appearance of companies such as Facebook, Google, Neuralink within the realm of brain-computer interfaces points to this trend.Areas covered: This perspective piece will talk about the history of brain stimulation in epilepsy, current-approved treatments, technical developments and the future of neurostimulation.Expert opinion: Further understanding of the brain alongside machine learning and innovative technology will be the future of neuromodulation for the treatment of epilepsy. All of these innovations and advances should pave the way toward overcoming the vexing underutilization of surgery in the therapeutic armamentarium against medically refractory seizures, given the implicit advantage of a neuromodulatory rather than neurodestructive approach.},
}
@article {pmid31813489,
year = {2019},
author = {Tang, X and Wang, T and Du, Y and Dai, Y},
title = {Motor imagery EEG recognition with KNN-based smooth auto-encoder.},
journal = {Artificial intelligence in medicine},
volume = {101},
number = {},
pages = {101747},
doi = {10.1016/j.artmed.2019.101747},
pmid = {31813489},
issn = {1873-2860},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; },
abstract = {As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals cannot only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE). K-SAE looks for the nearest neighbor values of the samples to construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE). The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature. Besides, the data information and spatial position of the feature map are recorded by max-pooling and unpooling, that help to prevent loss of important information. The method is applied to two data sets for feature extraction and classification experiments of motor imaging EEG signals. The experimental results show that k-SAE achieves good recognition accuracy and outperforms other state-of-the-art recognition algorithms.},
}
@article {pmid31812835,
year = {2020},
author = {Singh, J and Aballay, A},
title = {Neural control of behavioral and molecular defenses in C. elegans.},
journal = {Current opinion in neurobiology},
volume = {62},
number = {},
pages = {34-40},
pmid = {31812835},
issn = {1873-6882},
support = {R01 AI117911/AI/NIAID NIH HHS/United States ; R01 GM070977/GM/NIGMS NIH HHS/United States ; R37 GM070977/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; *Caenorhabditis elegans ; Caenorhabditis elegans Proteins ; Immunity, Innate ; Nervous System ; Signal Transduction ; },
abstract = {The nervous and immune systems use bi-directional communication to control host responses against microbial pathogens. Recent studies at the interface of the two systems have highlighted important roles of the nervous system in the regulation of both microbicidal pathways and pathogen avoidance behaviors. Studies on the neural circuits in the simple model host Caenorhabditis elegans have significantly improved our understanding of the roles of conserved neural mechanisms in controlling innate immunity. Moreover, behavioral studies have advanced our understanding of how the nervous system may sense potential pathogens and consequently elicit pathogen avoidance, reducing the risk of infection. In this review, we discuss the neural circuits that regulate both behavioral immunity and molecular immunity in C. elegans.},
}
@article {pmid31808396,
year = {2019},
author = {Pazeto, CL and Baccaglini, W and Tourinho-Barbosa, RR and Glina, S and Cathelineau, X and Sanchez-Salas, R},
title = {HRQOL related to urinary diversion in Radical Cystectomy: a systematic review of recent literature.},
journal = {International braz j urol : official journal of the Brazilian Society of Urology},
volume = {45},
number = {6},
pages = {1094-1104},
pmid = {31808396},
issn = {1677-6119},
mesh = {Cystectomy/methods/psychology/*rehabilitation ; Female ; Humans ; Male ; *Quality of Life/psychology ; Surveys and Questionnaires/standards ; Time Factors ; Treatment Outcome ; Urinary Diversion/methods/psychology/*rehabilitation ; },
abstract = {INTRODUCTION: The health-related QoL is a patient-centered evaluation covering several aspects. This evaluation seems to be particularly important in patients submitted to radical cystectomy (RC) and urinary diversion with ileal conduit (IC) or a neobladder (NB).<br><br>OBJECTIVE: Review all recent data comparing QoL outcomes after radical cystectomy with NB and IC diversions.<br><br>EVIDENCE ACQUISITION: A systematic search in PubMed/Medline, Embase, and Cochrane databases was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement in December 2018. All articles published from January 01, 2012 to December 31, 2018, were included. A study was considered relevant if it compared QoL outcomes using validated questionnaires (EORTC QLQ C30, FACT-G, FACT-BL, FACT-VCI, and BCI).<br><br>EVIDENCE SYNTHESIS: In 11 included studies, a total of 1389 participants were accounted (730 NB and 659 IC cases). The studies were conducted in 8 different countries, two were prospective, and none was randomized. There were two studies favoring results with a neobladder, 3 with incontinent diversion and 6 with no differences. The EORTC-QLQ-C30 was the most used instrument (5 studies) followed by FACT VCI and BCI (3 studies each). Given the heterogeneity of data and lack of prospective studies, a meta-analysis was not performed.<br><br>CONCLUSION: No superiority of one urinary diversion was characterized. It seems that the choice must be individualized with an extensive preoperative orientation of the patient and their relatives. That will probably infl uence how the patient accepts the new condition.},
}
@article {pmid31803039,
year = {2019},
author = {Band, GPH and Borghini, G and Brookhuis, K and Mehler, B},
title = {Editorial: Psychophysiological Contributions to Traffic Safety.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {410},
pmid = {31803039},
issn = {1662-5161},
}
@article {pmid31802435,
year = {2020},
author = {Caria, A and da Rocha, JLD and Gallitto, G and Birbaumer, N and Sitaram, R and Murguialday, AR},
title = {Brain-Machine Interface Induced Morpho-Functional Remodeling of the Neural Motor System in Severe Chronic Stroke.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {17},
number = {2},
pages = {635-650},
pmid = {31802435},
issn = {1878-7479},
mesh = {Adult ; Brain/*physiopathology ; Brain Mapping ; *Brain-Computer Interfaces ; Diffusion Tensor Imaging ; Electroencephalography/instrumentation/methods ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Male ; Middle Aged ; Neural Pathways/*physiopathology ; Neuronal Plasticity/*physiology ; Orthotic Devices ; Recovery of Function/physiology ; Stroke/physiopathology ; *Stroke Rehabilitation/instrumentation/methods ; Upper Extremity ; },
abstract = {Brain-machine interfaces (BMI) permit bypass motor system disruption by coupling contingent neuroelectric signals related to motor activity with prosthetic devices that enhance afferent and proprioceptive feedback to the somatosensory cortex. In this study, we investigated neural plasticity in the motor network of severely impaired chronic stroke patients after an EEG-BMI-based treatment reinforcing sensorimotor contingency of ipsilesional motor commands. Our structural connectivity analysis revealed decreased fractional anisotropy in the splenium and body of the corpus callosum, and in the contralesional hemisphere in the posterior limb of the internal capsule, the posterior thalamic radiation, and the superior corona radiata. Functional connectivity analysis showed decreased negative interhemispheric coupling between contralesional and ipsilesional sensorimotor regions, and decreased positive intrahemispheric coupling among contralesional sensorimotor regions. These findings indicate that BMI reinforcing ipsilesional brain activity and enhancing proprioceptive function of the affected hand elicits reorganization of contralesional and ipsilesional somatosensory and motor-assemblies as well as afferent and efferent connection-related motor circuits that support the partial re-establishment of the original neurophysiology of the motor system even in severe chronic stroke.},
}
@article {pmid31798438,
year = {2019},
author = {Korik, A and Sosnik, R and Siddique, N and Coyle, D},
title = {Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {94},
pmid = {31798438},
issn = {1662-5218},
abstract = {Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.},
}
@article {pmid31798403,
year = {2019},
author = {Ghosh, A and Dal Maso, F and Roig, M and Mitsis, GD and Boudrias, MH},
title = {Unfolding the Effects of Acute Cardiovascular Exercise on Neural Correlates of Motor Learning Using Convolutional Neural Networks.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1215},
pmid = {31798403},
issn = {1662-4548},
abstract = {Cardiovascular exercise is known to promote the consolidation of newly acquired motor skills. Previous studies seeking to understand the neural correlates underlying motor memory consolidation that is modulated by exercise, have relied so far on using traditional statistical approaches for a priori selected features from neuroimaging data, including EEG. With recent advances in machine learning, data-driven techniques such as deep learning have shown great potential for EEG data decoding for brain-computer interfaces, but have not been explored in the context of exercise. Here, we present a novel Convolutional Neural Network (CNN)-based pipeline for analysis of EEG data to study the brain areas and spectral EEG measures modulated by exercise. To the best of our knowledge, this work is the first one to demonstrate the ability of CNNs to be trained in a limited sample size setting. Our approach revealed discriminative spectral features within a refined frequency band (27-29 Hz) as compared to the wider beta bandwidth (15-30 Hz), which is commonly used in data analyses, as well as corresponding brain regions that were modulated by exercise. These results indicate the presence of finer EEG spectral features that could have been overlooked using conventional hypothesis-driven statistical approaches. Our study thus demonstrates the feasibility of using deep network architectures for neuroimaging analysis, even in small-scale studies, to identify robust brain biomarkers and investigate neuroscience-based questions.},
}
@article {pmid31795445,
year = {2019},
author = {Jeong, JH and Yu, BW and Lee, DH and Lee, SW},
title = {Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals.},
journal = {Brain sciences},
volume = {9},
number = {12},
pages = {},
pmid = {31795445},
issn = {2076-3425},
support = {06-201-305-001//Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD) of Korea/ ; },
abstract = {Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot's mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.},
}
@article {pmid31795398,
year = {2019},
author = {Benda, M and Volosyak, I},
title = {Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain-Computer Interface (BCI) Purposes.},
journal = {Brain sciences},
volume = {9},
number = {12},
pages = {},
pmid = {31795398},
issn = {2076-3425},
support = {IT-1-2-001//European Regional Development Fund/ ; },
abstract = {Brain-computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively.},
}
@article {pmid31795095,
year = {2019},
author = {Asghar, MA and Khan, MJ and Fawad, and Amin, Y and Rizwan, M and Rahman, M and Badnava, S and Mirjavadi, SS},
title = {EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {23},
pages = {},
pmid = {31795095},
issn = {1424-8220},
support = {Tdf/67/2017//Higher Education Commission (HEC)/ ; },
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Emotions/*physiology ; Female ; Humans ; Male ; Models, Theoretical ; Young Adult ; },
abstract = {Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.},
}
@article {pmid31794401,
year = {2020},
author = {Jin, J and Li, S and Daly, I and Miao, Y and Liu, C and Wang, X and Cichocki, A},
title = {The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {1},
pages = {3-12},
doi = {10.1109/TNSRE.2019.2956488},
pmid = {31794401},
issn = {1558-0210},
mesh = {Adolescent ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Discriminant Analysis ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Healthy Volunteers ; Humans ; Machine Learning ; Male ; Models, Statistical ; Young Adult ; },
abstract = {P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.},
}
@article {pmid31788791,
year = {2020},
author = {Sepúlveda, P and Cortinez, LI and Irani, M and Egaña, JI and Contreras, V and Sánchez Corzo, A and Acosta, I and Sitaram, R},
title = {Differential frontal alpha oscillations and mechanisms underlying loss of consciousness: a comparison between slow and fast propofol infusion rates.},
journal = {Anaesthesia},
volume = {75},
number = {2},
pages = {196-201},
doi = {10.1111/anae.14885},
pmid = {31788791},
issn = {1365-2044},
mesh = {Adult ; Anesthetics, Intravenous/*administration &amp; dosage ; Consciousness/*drug effects ; Electroencephalography/*methods ; Female ; Frontal Lobe/*drug effects ; Humans ; Infusions, Intravenous ; Male ; Middle Aged ; Propofol/*administration &amp; dosage ; Single-Blind Method ; Time Factors ; Young Adult ; },
abstract = {Mechanisms underlying loss of consciousness following propofol administration remain incompletely understood. The objective of this study was to compare frontal lobe electroencephalography activity and brainstem reflexes during intravenous induction of general anaesthesia, in patients receiving a typical bolus dose (fast infusion) of propofol compared with a slower infusion rate. We sought to determine whether brainstem suppression ('bottom-up') predominates over loss of cortical function ('top-down'). Sixteen ASA physical status-1 patients were randomly assigned to either a fast or slow propofol infusion group. Loss of consciousness and brainstem reflexes were assessed every 30 s by a neurologist blinded to treatment allocation. We performed a multitaper spectral analysis of all electroencephalography data obtained from each participant. Brainstem reflexes were present in all eight patients in the slow infusion group, while being absent in all patients in the fast infusion group, at the moment of loss of consciousness (p = 0.010). An increase in alpha band power was observed before loss of consciousness only in participants allocated to the slow infusion group. Alpha band power emerged several minutes after the loss of consciousness in participants allocated to the fast infusion group. Our results show a predominance of 'bottom-up' mechanisms during fast infusion rates and 'top-down' mechanisms during slow infusion rates. The underlying mechanisms by which propofol induces loss of consciousness are potentially influenced by the speed of infusion.},
}
@article {pmid31787885,
year = {2019},
author = {Wairagkar, M and Hayashi, Y and Nasuto, SJ},
title = {Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement.},
journal = {Frontiers in systems neuroscience},
volume = {13},
number = {},
pages = {66},
pmid = {31787885},
issn = {1662-5137},
abstract = {Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement.},
}
@article {pmid31787871,
year = {2019},
author = {Xie, J and Du, G and Xu, G and Zhao, X and Fang, P and Li, M and Cao, G and Li, G and Xue, T and Zhang, Y},
title = {Performance Evaluation of Visual Noise Imposed Stochastic Resonance Effect on Brain-Computer Interface Application: A Comparison Between Motion-Reversing Simple Ring and Complex Checkerboard Patterns.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1192},
pmid = {31787871},
issn = {1662-4548},
abstract = {Adding noise to a weak input signal can enhance the response of a non-linear system, a phenomenon known as stochastic resonance (SR). SR has been demonstrated in a variety of diverse sensory systems including the visual system, where visual noise enhances human motion perception and detection performance. The SR effect has not been extensively studied in brain-computer interface (BCI) applications. This study compares the performance of BCIs based on SR-influenced steady-state motion visual evoked potentials. Stimulation paradigms were used between a periodically monochromatic motion-reversing simple ring and complex alternating checkerboard stimuli. To induce the SR effect, dynamic visual noise was masked on both the periodic simple and complex stimuli. Offline results showed that the recognition accuracy of different stimulation targets followed an inverted U-shaped function of noise level, which is a hallmark of SR. With the optimal visual noise level, the proposed visual noise masked checkerboard BCI paradigm achieved faster and more stable detection performance due to the noise-enhanced brain responses. This work demonstrates that the SR effect can be employed in BCI applications and can achieve considerable performance improvements.},
}
@article {pmid31784577,
year = {2019},
author = {Keshmiri, S and Sumioka, H and Yamazaki, R and Shiomi, M and Ishiguro, H},
title = {Information Content of Prefrontal Cortex Activity Quantifies the Difficulty of Narrated Stories.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {17959},
pmid = {31784577},
issn = {2045-2322},
mesh = {Cognition ; Female ; Humans ; Interpersonal Relations ; Male ; *Narration ; Prefrontal Cortex/*physiology ; *Robotics/methods ; },
abstract = {The ability to realize the individuals' impressions during the verbal communication allows social robots to significantly facilitate their social interactions in such areas as child education and elderly care. However, such impressions are highly subjective and internalized and therefore cannot be easily comprehended through behavioural observations. Although brain-machine interface suggests the utility of the brain information in human-robot interaction, previous studies did not consider its potential for estimating the internal impressions during verbal communication. In this article, we introduce a novel approach to estimation of the individuals' perceived difficulty of stories using the quantified information content of their prefrontal cortex activity. We demonstrate the robustness of our approach by showing its comparable performance in face-to-face, humanoid, speaker, and video-chat settings. Our results contribute to the field of socially assistive robotics by taking a step toward enabling robots determine their human companions' perceived difficulty of conversations, thereby enabling these media to sustain their communication with humans by adapting to individuals' pace and interest in response to conversational nuances and complexity.},
}
@article {pmid31783646,
year = {2019},
author = {Kosmyna, N and Maes, P},
title = {AttentivU: An EEG-Based Closed-Loop Biofeedback System for Real-Time Monitoring and Improvement of Engagement for Personalized Learning.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {23},
pages = {},
pmid = {31783646},
issn = {1424-8220},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Learning/*physiology ; Male ; Neurofeedback/physiology ; Vibration ; *Wearable Electronic Devices ; },
abstract = {Information about a person's engagement and attention might be a valuable asset in many settings including work situations, driving, and learning environments. To this end, we propose the first prototype of a device called AttentivU-a system that uses a wearable system which consists of two main components. Component 1 is represented by an EEG headband used to measure the engagement of a person in real-time. Component 2 is a scarf, which provides subtle, haptic feedback (vibrations) in real-time when the drop in engagement is detected. We tested AttentivU in two separate studies with 48 adults. The participants were engaged in a learning scenario of either watching three video lectures on different subjects or participating in a set of three face-to-face lectures with a professor. There were three conditions administrated during both studies: (1) biofeedback, meaning the scarf (component 2 of the system) was vibrating each time the EEG headband detected a drop in engagement; (2) random feedback, where the vibrations did not correlate or depend on the engagement level detected by the system, and (3) no feedback, when no vibrations were administered. The results show that the biofeedback condition redirected the engagement of the participants to the task at hand and improved their performance on comprehension tests.},
}
@article {pmid31783392,
year = {2020},
author = {Mladenovic, J and Frey, J and Joffily, M and Maby, E and Lotte, F and Mattout, J},
title = {Active inference as a unifying, generic and adaptive framework for a P300-based BCI.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016054},
doi = {10.1088/1741-2552/ab5d5c},
pmid = {31783392},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/instrumentation/methods ; *Signal Processing, Computer-Assisted/instrumentation ; Young Adult ; },
abstract = {OBJECTIVE: Going adaptive is a major challenge for the field of brain-computer interface (BCI). This entails a machine that optimally articulates inference about the user's intentions and its own actions. Adaptation can operate over several dimensions which calls for a generic and flexible framework.<br><br>APPROACH: We appeal to one of the most comprehensive computational approach to brain (adaptive) functions: the active inference (AI) framework. It entails an explicit (probabilistic) model of the user that the machine interacts with, here involved in a P300-spelling task. This takes the form of a discrete input-output state-space model establishing the link between the machine's (i) observations-a P300 or error potential for instance, (ii) representations-of the user intentions to spell or pause, and (iii) actions-to flash, spell or switch-off the application.<br><br>MAIN RESULTS: Using simulations with real EEG data from 18 subjects, results demonstrate the ability of AI to yield a significant increase in bit rate (17%) over state-of-the-art approaches, such as dynamic stopping.<br><br>SIGNIFICANCE: Thanks to its flexibility, this one model enables to implement optimal (dynamic) stopping but also optimal flashing (i.e. active sampling), automated error correction, and switching off when the user does not look at the screen anymore. Importantly, this approach enables the machine to flexibly arbitrate between all these possible actions. We demonstrate AI as a unifying and generic framework to implement a flexible interaction behaviour in a given BCI context.},
}
@article {pmid31783374,
year = {2020},
author = {Fatemi, M and Daliri, MR},
title = {Nonlinear sparse partial least squares: an investigation of the effect of nonlinearity and sparsity on the decoding of intracranial data.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016055},
doi = {10.1088/1741-2552/ab5d47},
pmid = {31783374},
issn = {1741-2552},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; *Data Analysis ; Databases, Factual/statistics &amp; numerical data ; Least-Squares Analysis ; Male ; *Nonlinear Dynamics ; Rats ; Rats, Wistar ; },
abstract = {OBJECTIVE: Partial Least Squares (PLS) regression is a suitable linear decoder model for correlated and high dimensional neural data. This algorithm has been widely used in the application of brain-computer interface (BCI) for the decoding of motor parameters. PLS does not consider nonlinear relations between brain signal features. The nonlinear version of PLS that considers a nonlinear relationship between the latent variables has not been proposed for the decoding of intracranial data. This nonlinear model may cause overfitting in some cases due to a larger number of free parameters. In this paper, we develop a new version of nonlinear PLS, namely nonlinear sparse PLS (NLS PLS) and test it in BCI applications.<br><br>APPROACH: In motor related BCI systems, improving the decoding accuracy of both kinetic and kinematic parameters of movement is crucial. To do this, two BCI datasets were chosen to decode the force amplitude and position of hand trajectory using the nonlinear and sparse versions of PLS algorithm. In our new NLS PLS method, we considered a polynomial relationship between the latent variables and used the lasso penalization in the latent space to avoid overfitting and to improve the decoding accuracy.<br><br>MAIN RESULTS: Some linear and nonlinear based PLS models and our new proposed method, NLS PLS, were applied to the two datasets. According to our results, significant improvement from the NLS PLS method is confirmed over other methods. Our results show that nonlinear PLS outperforms generic PLS in the force decoding but it has lower accuracy in the hand trajectory decoding because of high dimensional feature space. By using lasso penalization, we presented a sparse nonlinear PLS-based model that outperforms generic PLS in both datasets and improves the coefficient of determination, 34% in the force decoding and 10% in the hand trajectory decoding.<br><br>SIGNIFICANCE: We constructed a simple PLS-based model that considers a nonlinear relationship between features and it is also robust to overfitting because of using the lasso penalty in the latent space. This model is suitable for a high dimensional and correlated datasets, like intracranial data and can improve the accuracy of estimation.},
}
@article {pmid31782576,
year = {2020},
author = {Araki, T and Uemura, T and Yoshimoto, S and Takemoto, A and Noda, Y and Izumi, S and Sekitani, T},
title = {Wireless Monitoring Using a Stretchable and Transparent Sensor Sheet Containing Metal Nanowires.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {32},
number = {15},
pages = {e1902684},
doi = {10.1002/adma.201902684},
pmid = {31782576},
issn = {1521-4095},
support = {//Integrated Neurotechnologies for Disease Studies/ ; //Japan Agency for Medical Research and Development/ ; //National Institute of Information and Communications Technology/ ; //COI/ ; //Japan Science and Technology Agency/ ; //Japan Society for the Promotion of Science/ ; //New Energy and Industrial Technology Development Organization/ ; },
mesh = {Animals ; Brain Diseases/diagnosis ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Metals/*chemistry ; Monitoring, Physiologic/instrumentation/*methods ; Nanowires/*chemistry ; Wearable Electronic Devices ; *Wireless Technology ; },
abstract = {Mechanically and visually imperceptible sensor sheets integrated with lightweight wireless loggers are employed in ultimate flexible hybrid electronics (FHE) to reduce vital stress/nervousness and monitor natural biosignal responses. The key technologies and applications for conceptual sensor system fabrication are reported, as exemplified by the use of a stretchable sensor sheet completely conforming to an individual's body surface to realize a low-noise wireless monitoring system (<1 µV) that can be attached to the human forehead for recording electroencephalograms. The above system can discriminate between Alzheimer's disease and the healthy state, thus offering a rapid in-home brain diagnosis possibility. Moreover, the introduction of metal nanowires to improve the transparency of the biocompatible sensor sheet allows one to wirelessly acquire electrocorticograms of nonhuman primates and simultaneously offers optogenetic stimulation such as toward-the-brain-machine interface under free movement. Also discussed are effective methods of improving electrical reliability, biocompatibility, miniaturization, etc., for metal nanowire based tracks and exploring the use of an organic amplifier as an important component to realize a flexible active probe with a high signal-to-noise ratio. Overall, ultimate FHE technologies are demonstrated to achieve efficient closed-loop systems for healthcare management, medical diagnostics, and preclinical studies in neuroscience and neuroengineering.},
}
@article {pmid31780914,
year = {2019},
author = {Karran, AJ and Demazure, T and Leger, PM and Labonte-LeMoyne, E and Senecal, S and Fredette, M and Babin, G},
title = {Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {393},
pmid = {31780914},
issn = {1662-5161},
abstract = {We report results of a study that utilizes a BCI to drive an interactive interface countermeasure that allows users to self-regulate sustained attention while performing an ecologically valid, long-duration business logistics task. An engagement index derived from EEG signals was used to drive the BCI while fNIRS measured hemodynamic activity for the duration of the task. Participants (n = 30) were split into three groups (1) no countermeasures (NOCM), (2) continuous countermeasures (CCM), and (3) event synchronized, level-dependent countermeasures (ECM). We hypothesized that the ability to self-regulate sustained attention through a neurofeedback mechanism would result in greater task engagement, decreased error rate and improved task performance. Data were analyzed by wavelet coherence analysis, statistical analysis, performance metrics and self-assessed cognitive workload via RAW-TLX. We found that when the BCI was used to deliver continuous interface countermeasures (CCM), task performance was moderately enhanced in terms of total 14,785 (σ = 423) and estimated missed sales 7.46% (σ = 1.76) when compared to the NOCM 14,529 (σ = 510), 9.79% (σ = 2.75), and the ECM 14,180 (σ = 875), 9.62% (σ = 4.91) groups. An "actions per minute" (APM) metric was used to determine interface interaction activity which showed that overall the CCM and ECM groups had a higher APM of 3.460 (SE = 0.140) and 3.317 (SE = 0.139) respectively when compared with the NOCM group 2.65 (SE = 0.097). Statistical analysis showed a significant difference between ECM - NOCM and CCM - NOCM (p < 0.001) groups, but no significant difference between the ECM - CCM groups. Analysis of the RAW-TLX scores showed that the CCM group had lowest total score 7.27 (σ = 3.1) when compared with the ECM 9.7 (σ = 3.3) and NOCM 9.2 (σ = 3.4) groups. No statistical difference was found between the RAW-TLX or the subscales, except for self-perceived performance (p < 0.028) comparing the CCM and ECM groups. The results suggest that providing a means to self-regulate sustained attention has the potential to keep operators engaged over long periods, and moderately increase on-task performance while decreasing on-task error.},
}
@article {pmid31779744,
year = {2019},
author = {Ferguson, CS},
title = {Assessing the KING IV Corporate Governance Report in relation to business continuity and resilience.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {13},
number = {2},
pages = {174-185},
pmid = {31779744},
issn = {1749-9216},
mesh = {*Commerce ; *Disaster Planning ; Organizations ; Private Sector ; Risk Management ; },
abstract = {Within South Africa and on the African continent, the various reports of the KING Committees on Corporate Governance have become guiding principles for organisations in both the public and private sector. This paper focuses on the KING IV report and discusses its relevance to the different but interrelated fields of business continuity, organisational resilience and risk management. The paper suggests that organisations seeking to comply with KING IV will need to familiarise themselves with ISO 22301 and the BCI Good Practice Guidelines, as well as ISO 31000.},
}
@article {pmid31778982,
year = {2019},
author = {Sombeck, JT and Miller, LE},
title = {Short reaction times in response to multi-electrode intracortical microstimulation may provide a basis for rapid movement-related feedback.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016013},
pmid = {31778982},
issn = {1741-2552},
support = {R01 NS095251/NS/NINDS NIH HHS/United States ; T32 HD007418/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; *Electrodes, Implanted ; Feedback, Sensory/*physiology ; Macaca mulatta ; Male ; Microelectrodes ; Photic Stimulation/methods ; Reaction Time/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Tetraplegic patients using brain-machine interfaces can make visually guided reaches with robotic arms. However, restoring proprioceptive feedback to these patients will be critical, as evidenced by the movement deficit in patients with proprioceptive loss. Proprioception is critical in large part because it provides faster feedback than vision. Intracortical microstimulation (ICMS) is a promising approach, but the ICMS-evoked reaction time (RT) is typically slower than that to natural proprioceptive and often even visual cues, implying that ICMS feedback may not be fast enough to guide movement.<br><br>APPROACH: For most sensory modalities, RT decreases with increased stimulus intensity. Thus, it may be that stimulation intensities beyond what has previously been used will result in faster RTs. To test this, we compared the RT to ICMS applied through multi-electrode arrays in area 2 of somatosensory cortex to that of mechanical and visual cues.<br><br>MAIN RESULTS: We found that the RT to single-electrode ICMS decreased with increased current, frequency, and train length. For 100 &#xb5;A, 330 Hz stimulation, the highest single-electrode intensity we tested routinely, most electrodes resulted in RTs slower than the mechanical cue but slightly faster than the visual cue. While increasing the current beyond 100 &#xb5;A resulted in faster RTs, sustained stimulation at this level may damage tissue. Alternatively, by stimulating through multiple electrodes (mICMS), a large amount of current can be injected while keeping that through each electrode at a safe level. We found that stimulation with at least 480 &#xb5;A equally distributed over 16 electrodes could produce RTs as much as 20&#x2009;ms faster than the mechanical cue, roughly the conduction delay to cortex from the periphery.<br><br>SIGNIFICANCE: These results suggest that mICMS may provide a means to supply rapid, movement-related feedback. Future neuroprosthetics may need spatiotemporally patterned mICMS to convey useful somatosensory information. Novelty &amp; Significance Intracortical microstimulation (ICMS) is a promising approach for providing artificial somatosensation to patients with spinal cord injury or limb amputation, but in prior experiments, subjects have been unable to respond as quickly to it as to natural cues. We have investigated the use of multi-electrode stimulation (mICMS) and discovered that it can produce reaction times as fast or faster even than natural mechanical cues. Although our stimulus trains were not modulated in time, this result opens the door to more complex spatiotemporal patterns of mICMS that might be used to rapidly write in complex somatosensory information to the CNS.},
}
@article {pmid31778265,
year = {2020},
author = {Ray, AM and Figueiredo, TDC and López-Larraz, E and Birbaumer, N and Ramos-Murguialday, A},
title = {Brain oscillatory activity as a biomarker of motor recovery in chronic stroke.},
journal = {Human brain mapping},
volume = {41},
number = {5},
pages = {1296-1308},
pmid = {31778265},
issn = {1097-0193},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Alpha Rhythm ; Biomarkers ; Brain/*physiopathology ; Brain-Computer Interfaces ; Double-Blind Method ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Female ; Functional Laterality ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/physiopathology ; Paralysis/physiopathology ; Physical Therapy Modalities ; Predictive Value of Tests ; *Recovery of Function ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; Young Adult ; },
abstract = {In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper-limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain-machine interfaces and physiotherapy of several weeks recorded in a double-blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.},
}
@article {pmid31778228,
year = {2020},
author = {Savić, AM and Lontis, ER and Mrachacz-Kersting, N and Popović, MB},
title = {Dynamics of movement-related cortical potentials and sensorimotor oscillations during palmar grasp movements.},
journal = {The European journal of neuroscience},
volume = {51},
number = {9},
pages = {1962-1970},
doi = {10.1111/ejn.14629},
pmid = {31778228},
issn = {1460-9568},
support = {Project no. 175016//Ministry of Education, Science and Technological Development, Belgrade, Serbia/International ; },
mesh = {Cortical Synchronization ; Electroencephalography ; Evoked Potentials ; Hand Strength ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Movement-related cortical potentials (MRCP) and sensorimotor oscillatory electroencephalographic (EEG) activity (event-related desynchronization/synchronization-ERD/ERS) provide complementary information of the associated motor activity. The aim of this study was to provide comparative spatio-temporal analysis of both EEG phenomena associated with palmar grasping motions including hand opening and closing phases. Nine healthy participants were instructed to perform self-paced, right hand grasping movements. EEG was recorded from 28 sites synchronous with electromyography (EMG) of wrist/fingers extensors and flexors. Statistical analysis of the EEG data revealed significant differences (p < .05) between the idle state (baseline) and motor preparation/execution periods in majority of recorded channels. The earliest statistical significance in MRCPs was observed for channel FC3 at -460.9 ms, while the earliest significant ERD was observed at 164.1 ms for channel C3. MRCP and ERD/ERS topographies in our study are in line with the results of previous studies comparing MRCP and ERD/ERS spatio-temporal patterns during upper limb movements, however, results of our study show that MRCP significant differences compared to the baseline appear in most channels earlier than ERD (on average 613.6 ± 191.5 ms earlier). This implies an advantage of MRCP signals for grasping movements' prediction, which is in contrast to previous reports. Moreover, combined spatio-temporal information on MRCP and ERD/ERS presented in this paper may serve for future optimization of grasp movement prediction/detection hybrid algorithms in the context of restorative brain-computer interface technology.},
}
@article {pmid31772232,
year = {2019},
author = {Lopes-Dias, C and Sburlea, AI and Müller-Putz, GR},
title = {Online asynchronous decoding of error-related potentials during the continuous control of a robot.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {17596},
pmid = {31772232},
issn = {2045-2322},
abstract = {Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.},
}
@article {pmid31770724,
year = {2020},
author = {Krol, LR and Haselager, P and Zander, TO},
title = {Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {012001},
doi = {10.1088/1741-2552/ab5bb5},
pmid = {31770724},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; Biomedical Technology/*methods/trends ; *Brain-Computer Interfaces/trends ; Cognition/*physiology ; Humans ; },
abstract = {OBJECTIVE: The interpretation of neurophysiological measurements has a decades-long history, culminating in current real-time brain-computer interfacing (BCI) applications for both patient and healthy populations. Over the course of this history, one focus has been on the investigation of cortical responses to specific stimuli. Such responses can be informative with respect to the human user's mental state at the time of presentation. An ability to decode neurophysiological responses to stimuli in real time becomes particularly powerful when combined with a simultaneous ability to autonomously produce such stimuli. This allows a computer to gather stimulus-response samples and iteratively produce new stimuli based on the information gathered from previous samples, thus acquiring more, and more specific, information. This information can even be obtained without the explicit, voluntary involvement of the user.<br><br>APPROACH: We define cognitive and affective probing, referring to an application of active learning where repeated sampling is done by eliciting implicit brain responses. In this tutorial, we provide a definition of this method that unifies different past and current implementations based on common aspects. We then discuss a number of aspects that differentiate various possible implementations of cognitive probing.<br><br>MAIN RESULTS: We argue that a key element is the user model, which serves as both information storage and basis for subsequent probes. Cognitive probing can be used to continuously and autonomously update this model, refining the probes, and obtaining increasingly detailed or accurate information from the resulting brain activity. In contrast to a number of potential advantages of the method, cognitive probing may also pose a threat to informed consent, our privacy of thought, and our ability to assign responsibility to actions mediated by the system.<br><br>SIGNIFICANCE: This tutorial provides guidelines to both implement, and critically discuss potential ethical implications of, novel cognitive probing applications and research endeavours.},
}
@article {pmid31768504,
year = {2019},
author = {Dash, D and Ferrari, P and Malik, S and Montillo, A and Maldjian, JA and Wang, J},
title = {Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective.},
journal = {Brain informatics : international conference, BI 2018, Arlington, TX, USA, December 7-9, 2018, proceedings. International Conference on Brain Informatics (2018 : Arlington, Tex.)},
volume = {11309},
number = {},
pages = {163-172},
pmid = {31768504},
support = {R01 NS082453/NS/NINDS NIH HHS/United States ; R03 DC013990/DC/NIDCD NIH HHS/United States ; },
abstract = {Advancing the knowledge about neural speech mechanisms is critical for developing next-generation, faster brain computer interface to assist in speech communication for the patients with severe neurological conditions (e.g., locked-in syndrome). Among current neuroimaging techniques, Magnetoencephalography (MEG) provides direct representation for the large-scale neural dynamics of underlying cognitive processes based on its optimal spatiotemporal resolution. However, the MEG measured neural signals are smaller in magnitude compared to the background noise and hence, MEG usually suffers from a low signal-to-noise ratio (SNR) at the single-trial level. To overcome this limitation, it is common to record many trials of the same event-task and use the time-locked average signal for analysis, which can be very time consuming. In this study, we investigated the effect of the number of MEG recording trials required for speech decoding using a machine learning algorithm. We used a wavelet filter for generating the denoised neural features to train an Artificial Neural Network (ANN) for speech decoding. We found that wavelet based denoising increased the SNR of the neural signal prior to analysis and facilitated accurate speech decoding performance using as few as 40 single-trials. This study may open up the possibility of limiting MEG trials for other task evoked studies as well.},
}
@article {pmid31768227,
year = {2019},
author = {He, S and Syed, E and Torrecillos, F and Tinkhauser, G and Fischer, P and Pogosyan, A and Pereira, E and Ashkan, K and Hasegawa, H and Brown, P and Tan, H},
title = {Beta Oscillation-Targeted Neurofeedback Training Based on Subthalamic LFPs in Parkinsonian Patients.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2019},
number = {},
pages = {81-84},
pmid = {31768227},
issn = {1948-3546},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Increased oscillatory activities in the beta frequency band (13-30 Hz) in the subthalamic nucleus (STN), and in particular prolonged episodes of increased synchrony in this frequency band, have been associated with motor symptoms such as bradykinesia and rigidity in Parkinson's disease (PD). Numerous studies have investigated sensorimotor cortical beta oscillations either as a control signal for Brain Computer Interfaces (BCI) or as target signal for neurofeedback training (NFB). However, it still remains unknown whether patients with PD can gain control of the pathological oscillations recorded from a subcortical site - the STN - with neurofeedback training. We tried to address this question in the current study. Specifically, we designed a simple basketball game, in which the position of a basketball changes based on the occurrence of events of temporally increased beta power quantified in real-time. Participants practised in the game to control the position of the basketball, which requires modulation of the beta oscillations recorded from STN local field potentials (LFPs). Our results suggest that it is possible to use neurofeedback training for PD patients to downregulate pathological beta oscillations in STN LFPs, and that this can lead to a reduction of beta oscillations in the cortical-STN motor network.},
}
@article {pmid31767813,
year = {2019},
author = {Turovsky, YA and Gureev, AP and Vitkalova, IY and Popov, VN},
title = {Connection between polymorphisms in HTR2A, TPH2, BDNF, TOMM40 genes and the successful mastering of human-computer interfaces.},
journal = {Journal of genetics},
volume = {98},
number = {},
pages = {},
pmid = {31767813},
issn = {0973-7731},
mesh = {Adolescent ; Alleles ; Brain-Derived Neurotrophic Factor/*genetics ; Electromyography ; *Genetic Association Studies ; Genotype ; Genotyping Techniques ; Humans ; Male ; Membrane Transport Proteins/*genetics ; Mitochondrial Precursor Protein Import Complex Proteins ; Polymorphism, Single Nucleotide/*genetics ; Receptor, Serotonin, 5-HT2A/*genetics ; Serotonin/genetics/metabolism ; Software ; Tryptophan Hydroxylase/*genetics ; Young Adult ; },
abstract = {The development of human-computer interfaces in different individuals occur with different efficiencies, this is due to the individual characteristics of the genotype determined by the single-nucleotide polymorphism (SNP) of a person. Here, we checked the connection between the success of the acquisition of the brain-computer, eye-tracking, electromyographic, and respiratory interfaces and SNP of the TOMM40, BDNF, HTR2A and TPH2 genes. Here, we show that the T-allele in rs6313 of the HTR2A gene is associated with an increase in the number of correctly submitted commands of the electromyographic and eye-tracking interfaces. This is probably due to the fact that, the T-allele carriers decrease expression of this serotonin receptor. The decreased expression of HTR2A may be a reason for an increase in the number of accurately submitted commands. It was shown that the TT genotype of rs4290270 polymorphism was associated with an increase in the accuracy of work with the myographic interface. In addition, the association of subjective interfaces work time with polymorphisms rs429358 and rs2030324 was noted. Thus, the genotypic characteristics of individuals can be a predictive sign for the degree of success of mastering human-computer interfaces, which can allow to expand the understanding of training the neural mechanisms when working with this class of devices.},
}
@article {pmid31766026,
year = {2020},
author = {Naufel, S and Klein, E},
title = {Brain-computer interface (BCI) researcher perspectives on neural data ownership and privacy.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016039},
doi = {10.1088/1741-2552/ab5b7f},
pmid = {31766026},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/psychology/*standards ; Female ; Humans ; *Information Dissemination/methods ; Male ; Middle Aged ; Ownership/*standards ; *Privacy/psychology ; Research Personnel/psychology/*standards ; *Surveys and Questionnaires ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) research and commercially available neural devices generate large amounts of neural data. These data have significant potential value to researchers and industry. Individuals from whose brains neural data derive may want to exert control over what happens to their neural data at study conclusion or as a result of using a consumer device. It is unclear how BCI researchers understand the relationship between neural data and BCI users and what control individuals should have over their neural data.<br><br>APPROACH: An online survey of BCI researchers (n&#x2009;&#x2009;=&#x2009;&#x2009;122) gathered perspectives on control of neural data generated in research and non-research contexts. The survey outcomes are discussed and other relevant concerns are highlighted.<br><br>MAIN RESULTS: The study found that 58% of BCI researchers endorsed giving research participants access to their raw neural data at the conclusion of a study. However, researchers felt that individuals should be limited in their freedom to either donate or sell these data. A majority of researchers viewed raw neural data as a kind of medical data. Survey respondents felt that current laws and regulations were inadequate to protect consumer neural data privacy, though many respondents were also unfamiliar with the details of existing guidelines.<br><br>SIGNIFICANCE: The majority of BCI researchers believe that individuals should have some but not unlimited control over neural data produced in research and non-research contexts.},
}
@article {pmid31765472,
year = {2019},
author = {Choi, GY and Han, CH and Jung, YJ and Hwang, HJ},
title = {A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface.},
journal = {GigaScience},
volume = {8},
number = {11},
pages = {},
pmid = {31765472},
issn = {2047-217X},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Databases, Factual ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; },
abstract = {BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator).<br><br>FINDINGS: To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results.<br><br>CONCLUSIONS: Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.},
}
@article {pmid31765302,
year = {2020},
author = {Liang, KF and Kao, JC},
title = {Deep Learning Neural Encoders for Motor Cortex.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {8},
pages = {2145-2158},
doi = {10.1109/TBME.2019.2955722},
pmid = {31765302},
issn = {1558-2531},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; *Deep Learning ; Humans ; Macaca mulatta ; *Motor Cortex ; },
abstract = {Intracortical brain-machine interfaces (BMIs) transform neural activity into control signals to drive a prosthesis or communication device, such as a robotic arm or computer cursor. To be clinically viable, BMI decoders must achieve high accuracy and robustness. Optimizing these decoders is expensive, traditionally requiring animal or human experiments spanning months to years. This is because BMIs are closed-loop systems, where the user updates his or her motor commands in response to an imperfectly decoded output. Decoder optimization using previously collected "offline" data will therefore not capture this closed-loop response. An alternative approach to significantly accelerate decoder optimization is to use a closed-loop experimental simulator. A key component of this simulator is the neural encoder, which synthetically generates neural population activity from kinematics. Prior neural encoders do not model important features of neural population activity. To overcome these limitations, we use deep learning neural encoders. We find these models significantly outperform prior neural encoders in reproducing peri-stimulus time histograms (PSTHs) and neural population dynamics. We also find that deep learning neural encoders better match neural decoding results in offline data and closed-loop experimental data. We anticipate these deep-learning neural encoders will substantially improve simulators for BMIs, enabling faster evaluation, optimization, and characterization of BMI decoder algorithms.},
}
@article {pmid31763091,
year = {2019},
author = {Romanelli, P and Valiante, TA and Seri, S and Puttilli, C and Picciafuoco, M and Jakobs, M and Lozano, A},
title = {A Wireless Neuroprosthesis for Patients with Drug-refractory Epilepsy: A Proof-of-Concept Study.},
journal = {Cureus},
volume = {11},
number = {10},
pages = {e5868},
pmid = {31763091},
issn = {2168-8184},
abstract = {Objective Acute or protracted cortical recording may be necessary for patients with drug-refractory epilepsy to identify the ictogenic regions before undergoing resection. Currently, these invasive recording techniques present certain limitations, one of which is the need for cables connecting the recording electrodes placed in the intracranial space with external devices displaying the recorded electrocorticographic signals. This equates to a direct connection between the sterile intracranial space with the non-sterile environment. Due to the increasing likelihood of infections with time, subdural grids are typically removed a few days after implantation, a limiting factor in localizing the epileptogenic zone if seizures are not frequent enough to be captured within this time-frame. Furthermore, patients are bound to stay in the hospital, connected by the wires to the recording device, thus increasing substantially the treatment costs. To address some of the current shortcomings of invasive monitoring, we developed a neuroprosthesis made of a subdural silicone grid connected to a wireless transmitter allowing prolonged electrocorticografic recording and direct cortical stimulation. This device consists of a silicone grid with 128-platinum/iridium contacts, connected to an implantable case providing wireless recording and stimulation. The case also houses a wirelessly rechargeable battery for chronic long-term implants. We report the results of the first human proof-of-concept trial for wireless transmission of electrocorticographic recordings using a device suited for long-term implantation in three patients with drug-refractory epilepsy. Methods Three patients with medically refractory epilepsy underwent the temporary intraoperative placement of the subdural grid connected to the wireless device for recording and transmission of electrocorticographic signals for a duration of five minutes before the conventional recording electrodes were placed or the ictal foci were resected. Results Wireless transmission of brain signals was successfully achieved. The wireless electrocorticographic signal was judged of excellent quality by a blinded neurophysiologist. Conclusions This preliminary experience reports the first successful placement of a wireless electrocorticographic recording device in humans. Long-term placement for prolonged wireless electrocorticographic recording in epilepsy patients will be the next step.},
}
@article {pmid31752106,
year = {2019},
author = {Kunori, N and Takashima, I},
title = {An Implantable Cranial Window Using a Collagen Membrane for Chronic Voltage-Sensitive Dye Imaging.},
journal = {Micromachines},
volume = {10},
number = {11},
pages = {},
pmid = {31752106},
issn = {2072-666X},
support = {16H06532, 17H01810, 19K19937, 19K12190, 19K22990//Japan Society for the Promotion of Science/ ; NEDO//New Energy and Industrial Technology Development Organization/ ; },
abstract = {Incorporating optical methods into implantable neural sensing devices is a challenging approach for brain-machine interfacing. Specifically, voltage-sensitive dye (VSD) imaging is a powerful tool enabling visualization of the network activity of thousands of neurons at high spatiotemporal resolution. However, VSD imaging usually requires removal of the dura mater for dye staining, and thereafter the exposed cortex needs to be protected using an optically transparent artificial dura. This is a major disadvantage that limits repeated VSD imaging over the long term. To address this issue, we propose to use an atelocollagen membrane as the dura substitute. We fabricated a small cranial chamber device, which is a tubular structure equipped with a collagen membrane at one end of the tube. We implanted the device into rats and monitored neural activity in the frontal cortex 1 week following surgery. The results indicate that the collagen membrane was chemically transparent, allowing VSD staining across the membrane material. The membrane was also optically transparent enough to pass light; forelimb-evoked neural activity was successfully visualized through the artificial dura. Because of its ideal chemical and optical manipulation capability, this collagen membrane may be widely applicable in various implantable neural sensors.},
}
@article {pmid31751279,
year = {2020},
author = {Lees, S and McCullagh, P and Payne, P and Maguire, L and Lotte, F and Coyle, D},
title = {Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {1},
pages = {113-122},
doi = {10.1109/TNSRE.2019.2953975},
pmid = {31751279},
issn = {1558-0210},
mesh = {Adult ; Area Under Curve ; Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation/*methods ; Reproducibility of Results ; Visual Perception/physiology ; Young Adult ; },
abstract = {Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer's event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identification of a single type of image, namely pictures, here we study the capability of RSVP-BCI to detect three different target image types: pictures, numbers and words. The impact of presentation duration (speed) i.e., 100-200ms (5-10Hz), 200-300ms (3.3-5Hz) or 300-400ms (2.5-3.3Hz), is also investigated. 2-way repeated measure ANOVA on accuracies of detecting targets from non-target stimuli (ratio 1:9) measured via area under the receiver operator characteristics curve (AUC) for N=15 subjects revealed a significant effect of factor Stimulus-Type (pictures, numbers, words) (F (2,28) = 7.243, p = 0.003) and for Stimulus-Duration (F (2,28) = 5.591, p = 0.011). Furthermore, there is an interaction between stimulus type and duration: F (4,56) = 4.419, p = 0.004). The results indicate that when designing RSVP-BCI paradigms, the content of the images and the rate at which images are presented impact on the accuracy of detection and hence these parameters are key experimental variables in protocol design and applications, which apply RSVP for multimodal image datasets.},
}
@article {pmid31751218,
year = {2020},
author = {Chakraborty, B and Ghosh, L and Konar, A},
title = {Designing Phase-Sensitive Common Spatial Pattern Filter to Improve Brain-Computer Interfacing.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {7},
pages = {2064-2072},
doi = {10.1109/TBME.2019.2954470},
pmid = {31751218},
issn = {1558-2531},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Computers ; Electroencephalography ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper addresses an interesting problem to model common spatial pattern (CSP) using an objective function employed to segregate EEG signals for a given cognitive task into two classes. The novelty of the present research is to include phase information of the EEG signal along with the amplitude for differentiating class boundaries. Two modified CSP algorithms are proposed in this paper. The first one introduces the composite effect of amplitude and phase angle of the EEG signal in CSP formulation and is solved using Lagrange's multiplier method taking phase information of EEG into account. In the second approach, a novel CSP algorithm is proposed in this paper which has the efficacy of handling the non-linearities hidden in the brain signal, here EEG. Experiments undertaken confirm that the proposed phase-sensitive CSP yields the best performance than their non-phase sensitive counterparts by a large margin with respect to classification accuracy.},
}
@article {pmid31747655,
year = {2020},
author = {Sosnik, R and Ben Zur, O},
title = {Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016065},
doi = {10.1088/1741-2552/ab59a7},
pmid = {31747655},
issn = {1741-2552},
mesh = {Adult ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Elbow/*physiology ; Electroencephalography/*methods ; Hand/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Shoulder/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: The ability to decode kinematics of imagined movement from neural activity is essential for the development of prosthetic devices that can aid motor-disabled persons. To date, non-invasive recording methods, including electroencephalogram (EEG) were used to decode actual and imagined hand trajectory to control neuromotor prostheses, commonly by applying multi-dimensional linear regression (mLR) models to adjust the two temporal signals-neural signal and limb kinematics. It is still debated, however, whether the EEG signal, in general, and slow cortical potentials (SCPs), in specific, hold motor neural correlates. Moreover, it has not yet been tested whether the trajectory of proximal arm joints, i.e. shoulder, can also be reconstructed and if decoding performance is dependent on movement speed and/or position variance.<br><br>APPROACH: We predicted hand, elbow and shoulder trajectories in 3D space in time series of both movement types (actual and imagined) of seven subjects using an mLR model, commonly applied for motion trajectory prediction (MTP) and used source localization to detect and compare between brain areas activated during actual and imagined movements for each arm joint.<br><br>MAIN RESULTS: For all arm joints and movement types, SCPs contributed the most to trajectory reconstruction, and decoding accuracy peaked using neural signals preceding kinematics by 120-210&#x2009;ms. The average (across subjects) Pearson's correlation coefficient between predicted and actual trajectories ranged 0.24-0.49, 0.41-0.48 and 0.18-0.40 for the hand, elbow and shoulder, respectively, and was significantly higher than chance level (p&#x200a;&#x2009;&#x2009;<&#x2009;&#x2009;0.01) for all subjects. For the imagined movements, reconstruction accuracy ranged between 0.09-0.23, 0.20-0.27 and 0.11-0.18 for the hand, elbow and shoulder, respectively, and was significantly higher than chance level (p&#x200a;&#x2009;&#x2009;<&#x2009;&#x2009;0.05) for all or some of the arm joints. The model performance was positively correlated with movement speed and negatively correlated with position variance. Source localization suggested that the neural circuits engaged in motor imagery are more diffuse and bilateral; motor imagery was, when compared to movement execution, more associated with recruiting premotor regions and a large area of the left parietal cortex.<br><br>SIGNIFICANCE: Our results demonstrate the feasibility of predicting 3D imagined trajectories of all arm joints from scalp EEG and imply the existence of movement related neural correlates in slow cortical potentials.},
}
@article {pmid31747642,
year = {2020},
author = {Wang, K and Xu, M and Wang, Y and Zhang, S and Chen, L and Ming, D},
title = {Enhance decoding of pre-movement EEG patterns for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016033},
doi = {10.1088/1741-2552/ab598f},
pmid = {31747642},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Fingers/physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: In recent years, brain-computer interface (BCI) systems based on electroencephalography (EEG) have developed rapidly. However, the decoding of voluntary finger pre-movements from EEG is still a challenge for BCIs. This study aimed to analyze the pre-movement EEG features in time and frequency domains and design an efficient method to decode the movement-related patterns.<br><br>APPROACH: In this study, we first investigated the EEG features induced by the intention of left and right finger movements. Specifically, the movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted using discriminative canonical pattern matching (DCPM) and common spatial patterns (CSP), respectively. Then, the two types of features were classified by two fisher discriminant analysis (FDA) classifiers, respectively. Their decision values were further assembled to facilitate the classification. To verify the validity of the proposed method, a private dataset containing 12 subjects and a public dataset from BCI competition II were used for estimating the classification accuracy.<br><br>MAIN RESULTS: As a result, for the private dataset, the combination of DCPM and CSP achieved an average accuracy of 80.96%, which was 5.08% higher than the single DCPM method (p&#x200a;&#x2009;&#x2009;<&#x2009;&#x2009;0.01) and 10.23% higher than the single CSP method (p&#x200a;&#x2009;&#x2009;<&#x2009;&#x2009;0.01). Notably, the highest accuracy could achieve 91.5% for the combination method. The test accuracy of dataset IV of BCI competition II was 90%, which was equal to the best result in the existing literature.<br><br>SIGNIFICANCE: The results demonstrate the MRCP and ERD features of pre-movements contain significantly discriminative information, which are complementary to each other, and thereby could be well recognized by the proposed combination method of DCPM and CSP. Therefore, this study provides a promising approach for the decoding of pre-movement EEG patterns, which is significant for the development of BCIs.},
}
@article {pmid31747103,
year = {2020},
author = {Bullard, AJ and Hutchison, BC and Lee, J and Chestek, CA and Patil, PG},
title = {Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {23},
number = {4},
pages = {411-426},
doi = {10.1111/ner.13069},
pmid = {31747103},
issn = {1525-1403},
mesh = {Deep Brain Stimulation/*adverse effects/*instrumentation ; Electrodes, Implanted/*adverse effects ; Equipment Failure ; Humans ; Movement Disorders/therapy ; },
abstract = {OBJECTIVE: A new age of neuromodulation is emerging: one of restorative neuroengineering and neuroprosthetics. As novel device systems move toward regulatory evaluation and clinical trials, a critical need arises for evidence-based identification of potential sources of hardware-related complications to assist in clinical trial design and mitigation of potential risk.<br><br>MATERIALS AND METHODS: The objective of this systematic review is to provide a detailed safety analysis for future intracranial, fully implanted, modular neuroprosthetic systems. To achieve this aim, we conducted an evidence-based analysis of hardware complications for the most established clinical intracranial modular system, deep brain stimulation (DBS), as well as the most widely used intracranial human experimental system, the silicon-based (Utah) array.<br><br>RESULTS: Of 2328 publications identified, 240 articles met the inclusion criteria and were reviewed for DBS hardware complications. The most reported adverse events were infection (4.57%), internal pulse generator malfunction (3.25%), hemorrhage (2.86%), lead migration (2.58%), lead fracture (2.56%), skin erosion (2.22%), and extension cable malfunction (1.63%). Of 433 publications identified, 76 articles met the inclusion criteria and were reviewed for Utah array complications. Of 48 human subjects implanted with the Utah array, 18 have chronic implants. Few specific complications are described in the literature; hence, implant duration served as a lower bound for complication-free operation. The longest reported duration of a person with a Utah array implant is 1975&#x2009;days (~5.4&#x2009;years).<br><br>CONCLUSIONS: Through systematic review of the clinical and human-trial literature, our study provides the most comprehensive safety review to date of DBS hardware and human neuroprosthetic research using the Utah array. The evidence-based analysis serves as an important reference for investigators seeking to identify hardware-related safety data, a necessity to meet regulatory requirements and to design clinical trials for future intracranial, fully implanted, modular neuroprosthetic systems.},
}
@article {pmid31743684,
year = {2020},
author = {Larson, CE and Meng, E},
title = {A review for the peripheral nerve interface designer.},
journal = {Journal of neuroscience methods},
volume = {332},
number = {},
pages = {108523},
doi = {10.1016/j.jneumeth.2019.108523},
pmid = {31743684},
issn = {1872-678X},
mesh = {Electrodes ; Electrophysiological Phenomena ; *Peripheral Nerves ; *Peripheral Nervous System ; },
abstract = {Informational density and relative accessibility of the peripheral nervous system make it an attractive site for therapeutic intervention. Electrode-based electrophysiological interfaces with peripheral nerves have been under development since the 1960s and, for several applications, have seen widespread clinical implementation. However, many applications require a combination of neural target resolution and stability which has thus far eluded existing peripheral nerve interfaces (PNIs). With the goal of aiding PNI designers in development of devices that meet the demands of next-generation applications, this review seeks to collect and present practical considerations and best practices which emerge from the literature, including both lessons learned during early PNI development and recent ideas. Fundamental and practical principles guiding PNI design are reviewed, followed by an updated and critical account of existing PNI designs and strategies. Finally, a brief survey of in vitro and in vivo PNI characterization methods is presented.},
}
@article {pmid31741692,
year = {2019},
author = {Tafreshi, TF and Daliri, MR and Ghodousi, M},
title = {Functional and effective connectivity based features of EEG signals for object recognition.},
journal = {Cognitive neurodynamics},
volume = {13},
number = {6},
pages = {555-566},
pmid = {31741692},
issn = {1871-4080},
abstract = {Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.},
}
@article {pmid31738941,
year = {2020},
author = {Lopez-Rincon, A and Cantu, C and Etcheverry, G and Soto, R and Shimoda, S},
title = {Function Based Brain Modeling and Simulation of an Ischemic Region in Post-Stroke Patients using the Bidomain.},
journal = {Journal of neuroscience methods},
volume = {331},
number = {},
pages = {108464},
doi = {10.1016/j.jneumeth.2019.108464},
pmid = {31738941},
issn = {1872-678X},
mesh = {Computer Simulation ; Humans ; *Sensorimotor Cortex ; Spectroscopy, Near-Infrared ; *Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Several studies have shown that post-stroke patients develop divergent activity in the sensorimotor areas of the affected hemisphere of the brain compared to healthy people during motor tasks. Proper mathematical models will help us understand this activity and clarify the associated underlying mechanisms. New Method. This research describes an anatomically based brain computer model in post-stroke patients. We simulate an ischemic region for arm motion using the bidomain approach. Two scenarios are considered: a healthy subject and a post-stroke patient with motion impairment. Next, we limit the volume of propagation considering only the sensorimotor area of the brain. Comparison with existing methods. In comparison to existing methods, we combine the use of the bidomain for modeling the propagation of the electrical activity across the brain volume with functional information to limit the volume of propagation and the position of the expected stimuli, given a specific task. Whereas just using the bidomain without limiting the functional volume, propagates the electrical activity into non-expected areas.<br><br>RESULTS: To validate the simulation, we compare the activity with patient measurements using functional near-infrared spectroscopy during arm motion (n=5) against controls (n=3). The results are consistent with empirical measurements and previous research and show that there is a disparity between position and number of spikes in post-stroke patients in contrast to healthy subjects.<br><br>CONCLUSIONS: These results hold promise in improving the understanding of brain deterioration in stroke patients and the re-arrangement of brain networks. Furthermore, shows the use of functionality based brain modeling.},
}
@article {pmid31736727,
year = {2019},
author = {Shen, YW and Lin, YP},
title = {Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {366},
pmid = {31736727},
issn = {1662-5161},
abstract = {Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.},
}
@article {pmid31736690,
year = {2019},
author = {Kim, H and Yoshimura, N and Koike, Y},
title = {Characteristics of Kinematic Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG).},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1148},
pmid = {31736690},
issn = {1662-4548},
abstract = {The utility of premovement electroencephalography (EEG) for decoding movement intention during a reaching task has been demonstrated. However, the kind of information the brain represents regarding the intended target during movement preparation remains unknown. In the present study, we investigated which movement parameters (i.e., direction, distance, and positions for reaching) can be decoded in premovement EEG decoding. Eight participants performed 30 types of reaching movements that consisted of 1 of 24 movement directions, 7 movement distances, 5 horizontal target positions, and 5 vertical target positions. Event-related spectral perturbations were extracted using independent components, some of which were selected via an analysis of variance for further binary classification analysis using a support vector machine. When each parameter was used for class labeling, all possible binary classifications were performed. Classification accuracies for direction and distance were significantly higher than chance level, although no significant differences were observed for position. For the classification in which each movement was considered as a different class, the parameters comprising two vectors representing each movement were analyzed. In this case, classification accuracies were high when differences in distance were high, the sum of distances was high, angular differences were large, and differences in the target positions were high. The findings further revealed that direction and distance may provide the largest contributions to movement. In addition, regardless of the parameter, useful features for classification are easily found over the parietal and occipital areas.},
}
@article {pmid31734404,
year = {2020},
author = {Dartevel, A and Toussaint, B and Trocme, C and Arnaud, M and Simon, N and Faure, P and Bouillet, L},
title = {Serum amyloid A as a marker of disease activity in Giant cell arteritis.},
journal = {Autoimmunity reviews},
volume = {19},
number = {1},
pages = {102428},
doi = {10.1016/j.autrev.2019.102428},
pmid = {31734404},
issn = {1873-0183},
mesh = {Aged ; Aged, 80 and over ; Biomarkers/blood ; Diagnosis, Differential ; Female ; Giant Cell Arteritis/*blood ; Humans ; Male ; Serum Amyloid A Protein/*analysis ; },
}
@article {pmid31733330,
year = {2020},
author = {Bedell, HW and Schaub, NJ and Capadona, JR and Ereifej, ES},
title = {Differential expression of genes involved in the acute innate immune response to intracortical microelectrodes.},
journal = {Acta biomaterialia},
volume = {102},
number = {},
pages = {205-219},
pmid = {31733330},
issn = {1878-7568},
support = {IK1 RX001664/RX/RRD VA/United States ; UL1 TR002548/TR/NCATS NIH HHS/United States ; IK2 RX002628/RX/RRD VA/United States ; I01 RX001495/RX/RRD VA/United States ; I01 RX000334/RX/RRD VA/United States ; R01 NS082404/NS/NINDS NIH HHS/United States ; UL1 TR000439/TR/NCATS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; IK6 RX003077/RX/RRD VA/United States ; T32 DE007057/DE/NIDCR NIH HHS/United States ; Z99 TR999999/ImNIH/Intramural NIH HHS/United States ; I01 RX002611/RX/RRD VA/United States ; },
mesh = {Animals ; Brain Injuries/genetics/*physiopathology ; Cerebral Cortex/*physiopathology/surgery ; Electrodes, Implanted/*adverse effects ; Immunity, Innate/*genetics ; Inflammation/genetics/*physiopathology ; Male ; Mice, Inbred C57BL ; Microelectrodes/adverse effects ; Transcriptome ; },
abstract = {Higher order tasks in development for brain-computer interfacing applications require the invasiveness of intracortical microelectrodes. Unfortunately, the resulting inflammatory response contributes to the decline of detectable neural signal. The major components of the neuroinflammatory response to microelectrodes have been well-documented with histological imaging, leading to the identification of broad pathways of interest for its inhibition such as oxidative stress and innate immunity. To understand how to mitigate the neuroinflammatory response, a more precise understanding is required. Advancements in genotyping have led the development of new tools for developing temporal gene expression profiles. Therefore, we have meticulously characterized the gene expression profiles of the neuroinflammatory response to mice implanted with non-functional intracortical probes. A time course of differential acute expression of genes of the innate immune response were compared to naïve sham mice, identifying significant changes following implantation. Differential gene expression analysis revealed 22 genes that could inform future therapeutic targets. Particular emphasis is placed on the largest changes in gene expression occurring 24 h post-implantation, and in genes that are involved in multiple innate immune sets including Itgam, Cd14, and Irak4. STATEMENT OF SIGNIFICANCE: Current understanding of the cellular response contributing to the failure of intracortical microelectrodes has been limited to the evaluation of cellular presence around the electrode. Minimal research investigating gene expression profiles of these cells has left a knowledge gap identifying their phenotype. This manuscript represents the first robust investigation of the changes in gene expression levels specific to the innate immune response following intracortical microelectrode implantation. To understand the role of the complement system in response to implanted probes, we performed gene expression profiling over acute time points from implanted subjects and compared them to no-surgery controls. This manuscript provides valuable insights into inflammatory mechanisms at the tissue-probe interface, thus having a high impact on those using intracortical microelectrodes to study and treat neurological diseases and injuries.},
}
@article {pmid31731489,
year = {2019},
author = {Lin, CT and Liu, CH and Wang, PS and King, JT and Liao, LD},
title = {Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems.},
journal = {Micromachines},
volume = {10},
number = {11},
pages = {},
pmid = {31731489},
issn = {2072-666X},
abstract = {A brain-computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.},
}
@article {pmid31731377,
year = {2019},
author = {Ma, Z and Xie, ZX and Qiu, TS and Cheng, J},
title = {Driving event-related potential-based speller by localized posterior activities: An offline study.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {17},
number = {1},
pages = {789-801},
doi = {10.3934/mbe.2020041},
pmid = {31731377},
issn = {1551-0018},
mesh = {Algorithms ; Brain/*diagnostic imaging ; Brain-Computer Interfaces ; Computer Graphics ; Computer Simulation ; Electrodes ; Electroencephalography ; *Evoked Potentials ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; *Psychomotor Performance ; Reproducibility of Results ; Visual Perception ; },
abstract = {Multi-sensor recordings are normally used in event-related potential (ERP)-based brain computer interfaces (BCIs), for capturing brain activities widely distributed over the cortical surface. However, this may lead to an increased number of sensors for boosting classification performance, as well as a complicated computational effort for optimizing/reducing sensors, limiting the popularization of mobile/wearable BCIs for the end use. The localization of brain activities may help fix this issue by making useful information concentrated on relatively local brain areas, thus greatly reducing the number of sensors required and computational burden arising from the sensor selection. In the present study, we examined localization of brain activities for an ERP speller, by using novel visual graphic stimuli to induce specific brain responses. Participants were instructed to perform a spelling task under both the graphic stimuli-based and traditional character-flashing-based ERP speller paradigms. Experimental results showed that, compared to character-flashing stimuli, localized brain activities, concentrated over the posterior region, were observed for the graphic stimuli. Classification accuracies and information transfer rates were further evaluated and compared among full- (FS), normal- (NS), and localized- (LS) sensor settings. Effects of PARADIGM, SENSORSETTING, and TRIAL LENGTH were examined by a three-way repeated measure analysis of variance (ANOVA). ANOVA results showed that, the graphic paradigm achieved significantly better performance under LS than those achieved by the traditional paradigm at any of the three sensor settings, indicating that with visual graphic stimuli, localized posterior activities were enough to drive an ERP-based speller to achieve comparable or even better performance, compared to the traditional paradigm using global activities.},
}
@article {pmid31731283,
year = {2020},
author = {de Freitas, AM and Sanchez, G and Lecaignard, F and Maby, E and Soares, AB and Mattout, J},
title = {EEG artifact correction strategies for online trial-by-trial analysis.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016035},
doi = {10.1088/1741-2552/ab581d},
pmid = {31731283},
issn = {1741-2552},
mesh = {Adult ; *Artifacts ; Brain-Computer Interfaces/standards ; Electroencephalography/*methods/standards ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {UNLABELLED: Brain-machine interfaces (BMIs) use brain signals to control closed-loop systems in real-time. This comes with substantial challenges, such as having to remove artifacts in order to extract reliable features, especially when using electroencephalography (EEG). Some approaches have been described in the literature to address online artifact correction. However, none are being used as a 'gold-standard' method, and no research has been conducted to analyze and compare their respective effects on statistical data analysis (inference-based decision).<br><br>OBJECTIVE: In this paper, we evaluate methods for artifact correction and describe the necessary adjustments to implement them for online EEG data analysis.<br><br>APPROACH: We investigate the following methods: artifact subspace reconstruction (ASR), fully online and automated artifact removal for brain-computer interfacing (FORCe), online empirical model decomposition (EMD), and online independent component analysis. For assessment, we simulated online data processing using real data from an auditory oddball task. We compared the above methods with classical offline data processing, in their ability (i) to reveal a significant mismatch negativity (MMN) response to auditory stimuli; (ii) to reveal the more subtle modulation of the MMN by contextual changes (namely, the predictability of the sound sequence), and (iii) to identify the most likely learning process that explains the MMN response.<br><br>MAIN RESULTS: Our results show that ASR and EMD are both able to reveal a significant MMN and its modulation by predictability, and even appear more sensitive than the offline analysis when comparing alternative models of perception underlying auditory evoked responses.<br><br>SIGNIFICANCE: ASR and EMD show many advantages when compared to other online artifact correction methods. Besides, subtle modulation analysis of the MMN, embedded in perception computational models is a novel method for assessing the quality of artifact correction methods.},
}
@article {pmid31731073,
year = {2019},
author = {Tarafdar, KK and Pradhan, BK and Nayak, SK and Khasnobish, A and Chakravarty, S and Ray, SS and Pal, K},
title = {Data mining based approach to study the effect of consumption of caffeinated coffee on the generation of the steady-state visual evoked potential signals.},
journal = {Computers in biology and medicine},
volume = {115},
number = {},
pages = {103526},
doi = {10.1016/j.compbiomed.2019.103526},
pmid = {31731073},
issn = {1879-0534},
mesh = {Adult ; Brain-Computer Interfaces ; *Coffee ; *Data Mining ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {The steady-state visual evoked potentials (SSVEP), are elicited at the parieto-occipital region of the cortex when a light source (3.5-75 Hz), flickering at a constant frequency, stimulates the retinal cells. In the last few decades, researchers have reported that caffeine enhances the vigilance and the executive control of visual attention. However, no study has investigated the effect of caffeinated coffee on the SSVEP response, which is used for controlling the brain-computer interface (BCI) devices for rehabilitative applications. The current work proposes a data mining-based approach to gain insight into the alterations in the SSVEP signals after the consumption of caffeinated coffee. Recurrence quantification analysis (RQA) of the electroencephalogram (EEG) signals was employed for this purpose. The EEG signals were acquired at seven frequencies of photic stimuli. The stimuli frequencies were chosen such that they were distributed throughout the EEG frequency bands. The prominent SSVEP signals were identified using the Canonical Correlation Analysis (CCA) method. Several statistical features were extracted from the recurrence plot of the SSVEP signals. Statistical analyses using the t-test and decision tree-based methods helped to select the most relevant features, which were then classified using Automated Neural Network (ANN). The relevant features could be classified with a maximum accuracy of 97%. This supports our hypothesis that the consumption of caffeinated coffee can alter the SSVEP response. In conclusion, utmost care should be taken in selecting the features for designing BCI devices.},
}
@article {pmid31730042,
year = {2019},
author = {Zhu, M and Cai, W and Li, L and Guo, Y and Monroe-Wise, A and Li, Y and Zeng, C and Qiao, J and Xu, Z and Zhang, H and Zeng, Y and Liu, C},
title = {Mediators of Intervention Effects on Depressive Symptoms Among People Living With HIV: Secondary Analysis of a Mobile Health Randomized Controlled Trial Using Latent Growth Curve Modeling.},
journal = {JMIR mHealth and uHealth},
volume = {7},
number = {11},
pages = {e15489},
pmid = {31730042},
issn = {2291-5222},
mesh = {Adult ; China ; Depression/*complications/psychology ; Female ; HIV Infections/complications/*psychology ; Humans ; Male ; Middle Aged ; Psychometrics/instrumentation/methods ; Quality of Health Care/*standards/statistics &amp; numerical data ; Surveys and Questionnaires ; Telemedicine/instrumentation/methods/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Although several studies have investigated the effects of mobile health (mHealth) interventions on depression among people living with HIV, few studies have explored mediators of mHealth-based interventions to improve mental health in people living with HIV. Identifying influential mediators may enhance and refine effective components of mHealth interventions to improve mental health of people living with HIV.<br><br>OBJECTIVE: This study aimed to examine mediating factors of the effects of a mHealth intervention, Run4Love, designed to reduce depression among people living with HIV using 4 time-point measurement data.<br><br>METHODS: This study used data from a randomized controlled trial of a mHealth intervention among people living with HIV with elevated depressive symptoms in Guangzhou, China. A total of 300 patients were assigned to receive either the mHealth intervention (n=150) or a waitlist control group (n=150) through computer-generated block randomization. Depressive symptoms, coping, and HIV-related stigma were measured at baseline, 3-, 6-, and 9-month follow-ups. The latent growth curve model was used to examine the effects of the intervention on depressive symptoms via potential mediators. Mediating effects were estimated using bias-corrected 95% bootstrapped CIs (BCIs) with resampling of 5000.<br><br>RESULTS: Enhanced positive coping and reduced HIV-related stigma served as effective treatment mediators in the mHealth intervention. Specially, there was a significant indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of positive coping (beta=-2.86; 95% BCI -4.78 to -0.94). The indirect effect of the mHealth intervention on the slope of depressive symptoms via the slope of HIV-related stigma was also statistically significant (beta=-1.71; 95% BCI -3.03 to -0.40). These findings indicated that enhancement of positive coping and reduction of HIV-related stigma were important mediating factors of the mHealth intervention in reducing depression among people living with HIV.<br><br>CONCLUSIONS: This study revealed the underlying mediators of a mHealth intervention to reduce depression among people living with HIV using latent growth curve model and 4 time-point longitudinal measurement data. The study results underscored the importance of improving positive coping skills and mitigating HIV-related stigma in mHealth interventions to reduce depression among people living with HIV.},
}
@article {pmid31729840,
year = {2019},
author = {Lu, Z and Li, Q and Gao, N and Yang, J and Bai, O},
title = {Happy emotion cognition of bimodal audiovisual stimuli optimizes the performance of the P300 speller.},
journal = {Brain and behavior},
volume = {9},
number = {12},
pages = {e01479},
pmid = {31729840},
issn = {2162-3279},
mesh = {Acoustic Stimulation/*methods/psychology ; Adult ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography ; Emotions/*physiology ; Event-Related Potentials, P300/*physiology ; *Happiness ; Humans ; Male ; Mental Processes/physiology ; Photic Stimulation/*methods ; },
abstract = {OBJECTIVE: Prior studies of emotional cognition have found that emotion-based bimodal face and voice stimuli can elicit larger event-related potential (ERP) amplitudes and enhance neural responses compared with visual-only emotional face stimuli. Recent studies on brain-computer interface have shown that emotional face stimuli have significantly improved the performance of the traditional P300 speller system, but its performance needs to be further improved for practical applications. Therefore, we herein propose a novel audiovisual P300 speller based on bimodal emotional cognition to further improve the performance of the P300 system.<br><br>METHODS: The audiovisual P300 speller we proposed is based on happy emotions, with visual and auditory stimuli that consist of several pairs of smiling faces and audible chuckles (E-AV spelling paradigm) of different ages and sexes. The control paradigm was the visual-only emotional face P300 speller (E-V spelling paradigm).<br><br>RESULTS: We compared the ERP amplitudes, accuracy, and raw bit rate between the E-AV and E-V spelling paradigms. The target stimuli elicited significantly increased P300 amplitudes (p&#xa0;<&#xa0;.05) and P600 amplitudes (p&#xa0;<&#xa0;.05) in the E-AV spelling paradigm compared with those in the E-V paradigm. The E-AV spelling paradigm also significantly improved the spelling accuracy and the raw bit rate compared with those in the E-V paradigm at one superposition (p&#xa0;<&#xa0;.05) and at two superpositions (p&#xa0;<&#xa0;.05).<br><br>SIGNIFICANCE: The proposed emotion-based audiovisual spelling paradigm not only significantly improves the performance of the P300 speller, but also provides a basis for the development of various bimodal P300 speller systems, which is a step forward in the clinical application of brain-computer interfaces.},
}
@article {pmid31729678,
year = {2019},
author = {Laiwalla, F and Nurmikko, A},
title = {Future of Neural Interfaces.},
journal = {Advances in experimental medicine and biology},
volume = {1101},
number = {},
pages = {225-241},
doi = {10.1007/978-981-13-2050-7_9},
pmid = {31729678},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces/trends ; Equipment Design/trends ; Humans ; Neurons ; Neurosciences ; *Prostheses and Implants/trends ; },
abstract = {The technological ability to capture electrophysiological activity of populations of cortical neurons through chronic implantable devices has led to significant advancements in the field of brain-computer interfaces. Recent progress in the field has been driven by developments in integrated microelectronics, wireless communications, materials science, and computational neuroscience. Here, we review major device development landmarks in the arena of neural interfaces from FDA-approved clinical systems to prototype head-mounted and fully implantable wireless systems for multi-channel neural recording. Additionally, we provide an outlook toward next-generation, highly miniaturized technologies for minimally invasive, vastly parallel neural interfaces for naturalistic, closed-loop neuroprostheses.},
}
@article {pmid31729677,
year = {2019},
author = {Wang, J and Chen, Z},
title = {Neuromodulation for Pain Management.},
journal = {Advances in experimental medicine and biology},
volume = {1101},
number = {},
pages = {207-223},
doi = {10.1007/978-981-13-2050-7_8},
pmid = {31729677},
issn = {0065-2598},
mesh = {Animals ; Humans ; *Neurotransmitter Agents/therapeutic use ; *Pain Management/methods/trends ; },
abstract = {Pain is a salient and complex sensory experience with important affective and cognitive dimensions. The current definition of pain relies on subjective reports in both humans and experimental animals. Such definition lacks basic mechanistic insights and can lead to a high degree of variability. Research on biomarkers for pain has previously focused on genetic analysis. However, recent advances in human neuroimaging and research in animal models have begun to show the promise of a circuit-based neural signature for pain. At the treatment level, pharmacological therapy for pain remains limited. Neuromodulation has emerged as a specific form of treatment without the systemic side effects of pharmacotherapies. In this review, we will discuss some of the current neuromodulatory modalities for pain, research on newer targets, as well as emerging possibility for an integrated brain-computer interface approach for pain management.},
}
@article {pmid31729674,
year = {2019},
author = {Zhang, J and Xu, K and Zhang, S and Wang, Y and Zheng, N and Pan, G and Chen, W and Wu, Z and Zheng, X},
title = {Brain-Machine Interface-Based Rat-Robot Behavior Control.},
journal = {Advances in experimental medicine and biology},
volume = {1101},
number = {},
pages = {123-147},
doi = {10.1007/978-981-13-2050-7_5},
pmid = {31729674},
issn = {0065-2598},
mesh = {Animals ; *Behavior Control ; Brain/physiology ; *Brain-Computer Interfaces ; Locomotion ; Rats ; *Robotics ; },
abstract = {Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system. With the technology of BMI, animals' locomotion behavior can be precisely controlled as robots, which made the animal turning into bio-robot. In this chapter, we reviewed our lab works focused on rat-robot navigation. The principles of rat-robot system have been briefly described first, including the target brain sites chosen for locomotion control and the design of remote control system. Some methodological advances made by optogenetic technologies for better modulation control have then been introduced. Besides, we also introduced our implementation of "mind-controlled" rat navigation system. Moreover, we have presented our efforts made on combining biological intelligence with artificial intelligence, with developments of automatic control and training system assisted with images or voices inputs. We concluded this chapter by discussing further developments to acquire environmental information as well as promising applications with write-in BMIs.},
}
@article {pmid31729672,
year = {2019},
author = {Jin, Y and Chen, J and Zhang, S and Chen, W and Zheng, X},
title = {Invasive Brain Machine Interface System.},
journal = {Advances in experimental medicine and biology},
volume = {1101},
number = {},
pages = {67-89},
doi = {10.1007/978-981-13-2050-7_3},
pmid = {31729672},
issn = {0065-2598},
mesh = {Animals ; *Brain-Computer Interfaces/standards/trends ; Clinical Trials as Topic ; Humans ; },
abstract = {Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.},
}
@article {pmid31729671,
year = {2019},
author = {Wang, Y and Nakanishi, M and Zhang, D},
title = {EEG-Based Brain-Computer Interfaces.},
journal = {Advances in experimental medicine and biology},
volume = {1101},
number = {},
pages = {41-65},
doi = {10.1007/978-981-13-2050-7_2},
pmid = {31729671},
issn = {0065-2598},
mesh = {*Brain-Computer Interfaces ; Communication ; *Electroencephalography ; Evoked Potentials ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) provide a direct communication channel between human brain and output devices. Due to advantages such as non-invasiveness, ease of use, and low cost, electroencephalography (EEG) is the most popular method for current BCIs. This chapter gives an overview of the current EEG-based BCIs for the main purpose of communication and control. This chapter first provides a taxonomy of the EEG-based BCI systems by categorizing them into three major groups: (1) BCIs based on event-related potentials (ERPs), (2) BCIs based on sensorimotor rhythms, and (3) hybrid BCIs. Next, this chapter describes challenges and potential solutions in developing practical BCI systems toward high communication speed, convenient system use, and low user variation. Then this chapter briefly reviews both medical and non-medical applications of current BCIs. Finally, this chapter concludes with a summary of current stage and future perspectives of the EEG-based BCI technology.},
}
@article {pmid31728934,
year = {2019},
author = {Atum, Y and Pacheco, M and Acevedo, R and Tabernig, C and Biurrun Manresa, J},
title = {A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {12},
pages = {2705-2715},
doi = {10.1007/s11517-019-02065-z},
pmid = {31728934},
issn = {1741-0444},
support = {PID N&#xb0; 6163//Universidad Nacional de Entre R&#xed;os/ ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Young Adult ; },
abstract = {Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencephalogram (EEG). In order to transfer laboratory-tested BCIs into systems that can be used by at homes, it is relevant to investigate if it is possible to select a limited set of EEG channels that work for most subjects and across different sessions without a significant decrease in performance. In this work, two strategies for channel selection for a single-trial P300 brain computer interface were evaluated and compared. The first strategy was tailored specifically for each subject, whereas the second strategy aimed at finding a subject-independent set of channels. In both strategies, genetic algorithms (GAs) and recursive feature elimination algorithms were used. The classification stage was performed using a linear discriminant. A dataset of EEG recordings from 18 healthy subjects was used test the proposed configurations. Performance indexes were calculated to evaluate the system. Results showed that a fixed subset of four subject-independent EEG channels selected using GA provided the best compromise between BCI setup and single-trial system performance.},
}
@article {pmid31726745,
year = {2019},
author = {Badesa, FJ and Diez, JA and Catalan, JM and Trigili, E and Cordella, F and Nann, M and Crea, S and Soekadar, SR and Zollo, L and Vitiello, N and Garcia-Aracil, N},
title = {Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {22},
pages = {},
pmid = {31726745},
issn = {1424-8220},
support = {64532//Horizon 2020 Framework Programme/ ; DPI2015-70415-C2-R//Ministerio de Ciencia y Tecnolog&#xed;a/ ; },
abstract = {When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user's physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject's workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.},
}
@article {pmid31726440,
year = {2020},
author = {Freer, D and Yang, GZ},
title = {Data augmentation for self-paced motor imagery classification with C-LSTM.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016041},
doi = {10.1088/1741-2552/ab57c0},
pmid = {31726440},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*classification ; Data Science/*classification/methods ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events.<br><br>APPROACH: In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored.<br><br>MAIN RESULTS: The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian MDM classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively.<br><br>SIGNIFICANCE: This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.},
}
@article {pmid31725394,
year = {2020},
author = {Kwon, OY and Lee, MH and Guan, C and Lee, SW},
title = {Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {31},
number = {10},
pages = {3839-3852},
doi = {10.1109/TNNLS.2019.2946869},
pmid = {31725394},
issn = {2162-2388},
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain/physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Deep Learning ; Electroencephalography ; Female ; Humans ; Imagination ; Information Theory ; Male ; Movement ; *Neural Networks, Computer ; Reproducibility of Results ; Young Adult ; },
abstract = {For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left- and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral-spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral-spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral-spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral-spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatio-spectral filter optimization (BSSFO)].},
}
@article {pmid31725383,
year = {2020},
author = {Ma, X and Qiu, S and Wei, W and Wang, S and He, H},
title = {Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {1},
pages = {297-306},
doi = {10.1109/TNSRE.2019.2953121},
pmid = {31725383},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Elbow ; Electrodes ; Electroencephalography ; Electroencephalography Phase Synchronization ; Female ; Hand ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Machine Learning ; Male ; Movement/physiology ; *Nerve Net ; Reproducibility of Results ; Young Adult ; },
abstract = {Motor imagery (MI) is an important brain-computer interface (BCI) paradigm, which can be applied without external stimulus. Imagining different joint movements from the same limb allows intuitive control of the outer devices. However, few researches focused on this field, and the decoding accuracy limited the applications for practical use. In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in the 3-class scenario. The results demonstrated the effectiveness of deep learning method for decoding MI of different joints from the same limb and the potential of this fine paradigm to be applied in practice.},
}
@article {pmid31722321,
year = {2019},
author = {Kanoga, S and Nakanishi, M and Murai, A and Tada, M and Kanemura, A},
title = {Robustness analysis of decoding SSVEPs in humans with head movements using a moving visual flicker.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016009},
doi = {10.1088/1741-2552/ab5760},
pmid = {31722321},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Eye Movements/physiology ; Female ; Head Movements/*physiology ; Humans ; Male ; Motion Perception/*physiology ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: The emergence of mobile electroencephalogram (EEG) platforms have expanded the use cases of brain-computer interfaces (BCIs) from laboratory-oriented experiments to our daily life. In challenging situations where humans' natural behaviors such as head movements are unrestrained, various artifacts could deteriorate the performance of BCI applications. This paper explored the effect of muscular artifacts generated by participants' head movements on the signal characteristics and classification performance of steady-state visual evoked potentials (SSVEPs).<br><br>APPROACH: A moving visual flicker was employed to induce not only SSVEPs but also horizontal and vertical head movements at controlled speeds, leading to acquiring EEG signals with intensity-manipulated muscular artifacts. To properly induce neck muscular activities, a laser light was attached to participants' heads to give visual feedback; the laser light indicates the direction of the head independently from eye movements. The visual stimulus was also modulated by four distinct frequencies (10, 11, 12, and 13 Hz). The amplitude and signal-to-noise ratio (SNR) were estimated to quantify the effects of head movements on the signal characteristics of the elicited SSVEPs. The frequency identification accuracy was also estimated by using well-established decoding algorithms including calibration-free and fully-calibrated approaches.<br><br>MAIN RESULTS: The amplitude and SNR of SSVEPs tended to deteriorate when the participants moved their heads, and this tendency was significantly stronger in the vertical head movements than in the horizontal movements. The frequency identification accuracy also deteriorated in proportion to the speed of head movements. Importantly, the accuracy was significantly higher than its chance-level regardless of the level of artifact contamination and algorithms.<br><br>SIGNIFICANCE: The results suggested the feasibility of decoding SSVEPs in humans freely moving their head directions, facilitating the real-world applications of mobile BCIs.},
}
@article {pmid31717510,
year = {2019},
author = {Straßer, T and Kramer, S and Kempf, M and Peters, T and Kurtenbach, A and Zrenner, E},
title = {Visual Evoked Potentials Used to Evaluate a Commercially Available Superabsorbent Polymer as a Cheap and Efficient Material for Preparation-Free Electrodes for Recording Electrical Potentials of the Human Visual Cortex.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {22},
pages = {},
pmid = {31717510},
issn = {1424-8220},
support = {0//Tistou and Charlotte Kerstan Foundation/ ; EXC307//Deutsche Forschungsgemeinschaft/ ; },
mesh = {Electrodes, Implanted ; Electrophysiology ; Evoked Potentials, Visual/*physiology ; Humans ; Polymers/*chemistry ; Visual Cortex/physiology ; },
abstract = {The aim of this study was to investigate the use of inexpensive and easy-to-use hydrogel "marble" electrodes for the recording of electrical potentials of the human visual cortex using visual evoked potentials (VEPs) as example. Top hat-shaped holders for the marble electrodes were developed with an electrode cap to acquire the signals. In 12 healthy volunteers, we compared the VEPs obtained with conventional gold-cup electrodes to those obtained with marble electrodes. Checkerboards of two check sizes-0.8° and 0.25°-were presented. Despite the higher impedance of the marble electrodes, the line noise could be completely removed by averaging 64 single traces, and VEPs could be recorded. Linear mixed-effect models using electrode type, stimulus, and recording duration revealed a statistically significant effect of the electrode type on only VEP N75 peak latency (mean ± SEM: 1.0 ± 1.2 ms) and amplitude (mean ± SEM: 0.8 ± 0.9 µV) The mean amplitudes of the delta, theta, alpha, beta, and gamma frequency bands of marble electrodes were statistically significantly different and, on average, 25% higher than those of gold-cup electrodes. However, the mean amplitudes showed a statistically significant strong correlation (Pearson's r = 0.8). We therefore demonstrate the potential of the inexpensive and efficient hydrogel electrode to replace conventional gold-cup electrodes for the recording of VEPs and possibly other recordings from the human cortex.},
}
@article {pmid31717412,
year = {2019},
author = {Yahya, N and Musa, H and Ong, ZY and Elamvazuthi, I},
title = {Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {22},
pages = {},
pmid = {31717412},
issn = {1424-8220},
mesh = {Algorithms ; Electroencephalography/*methods ; Signal Processing, Computer-Assisted ; Support Vector Machine ; *Wavelet Analysis ; },
abstract = {In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.},
}
@article {pmid31711336,
year = {2021},
author = {Casey, A and Azhar, H and Grzes, M and Sakel, M},
title = {BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {16},
number = {5},
pages = {525-537},
doi = {10.1080/17483107.2019.1683239},
pmid = {31711336},
issn = {1748-3115},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Quality of Life ; Reproducibility of Results ; *Robotic Surgical Procedures ; },
abstract = {PURPOSE: Brain-computer interface (BCI)-controlled assistive robotic systems have been developed with increasing success with the aim to rehabilitation of patients after brain injury to increase independence and quality of life. While such systems may use surgically implanted invasive sensors, non-invasive alternatives can be better suited due to the ease of use, reduced cost, improvements in accuracy and reliability with the advancement of the technology and practicality of use. The consumer-grade BCI devices are often capable of integrating multiple types of signals, including Electroencephalogram (EEG) and Electromyogram (EMG) signals.<br><br>MATERIALS AND METHODS: This paper summarizes the development of a portable and cost-efficient BCI-controlled assistive technology using a non-invasive BCI headset "OpenBCI" and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm. To avoid risks of injury while the device is being used in clinical settings, appropriate measures were incorporated into the software control of the arm. A short survey was used following the system usability scale (SUS), to measure the usability of the technology to be trialed in clinical settings.<br><br>RESULTS: From the experimental results, it was found that EMG is a very reliable method for assistive technology control, provided that the user specific EMG calibration is done. With the EEG, even though the results were promising, due to insufficient detection of the signal, the controller was not adequate to be used within a neurorehabilitation environment. The survey indicated that the usability of the system is not a barrier for moving the system into clinical trials.Implication on rehabilitationFor the rehabilitation of patients suffering from neurological disabilities (particularly those suffering from varying degrees of paralysis), it is necessary to develop technology that bypasses the limitations of their condition. For example, if a patient is unable to walk due to the unresponsiveness in their motor neurons, technology can be developed that used an alternate input to move an exoskeleton, which enables the patient to walk again with the assistance of the exoskeleton.This research focuses on neuro-rehabilitation within the framework of the NHS at the Kent and Canterbury Hospital in UK. The hospital currently does not have any system in place for self-driven rehabilitation and instead relies on traditional rehabilitation methods through assistance from physicians and exercise regimens to maintain muscle movement.This paper summarises the development of a portable and cost-efficient BCI controlled assistive technology using a non-invasive BCI headset "OpenBCI" and an open source robotic arm, U-Arm, to accomplish tasks related to rehabilitation, such as access to resources, adaptability or home use. The resulting system used a combination of EEG and EMG sensor readings to control the arm, which could perform a number of different tasks such as picking/placing objects or assist users in eating.},
}
@article {pmid31711330,
year = {2019},
author = {Gaur, P and McCreadie, K and Pachori, RB and Wang, H and Prasad, G},
title = {Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {29},
number = {10},
pages = {1950025},
doi = {10.1142/S0129065719500254},
pmid = {31711330},
issn = {1793-6462},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; Machine Learning ; *Models, Neurological ; },
abstract = {The performance of a brain-computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier's generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific "multivariate empirical-mode decomposition" preprocessing technique by taking a fixed band of 8-30Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.},
}
@article {pmid31710259,
year = {2019},
author = {Håkansson, B and Reinfeldt, S and Persson, AC and Jansson, KF and Rigato, C and Hultcrantz, M and Eeg-Olofsson, M},
title = {The bone conduction implant - a review and 1-year follow-up.},
journal = {International journal of audiology},
volume = {58},
number = {12},
pages = {945-955},
doi = {10.1080/14992027.2019.1657243},
pmid = {31710259},
issn = {1708-8186},
mesh = {Adolescent ; Adult ; Aged ; *Bone Conduction ; Female ; Follow-Up Studies ; *Hearing Aids ; Hearing Loss, Conductive/rehabilitation/*surgery ; Humans ; Male ; Middle Aged ; Prospective Studies ; Prosthesis Implantation/*methods ; Young Adult ; },
abstract = {Objective: The objective of this study is to evaluate its safety and effectiveness of the bone conduction implant (BCI) having an implanted transducer and to review similar bone conduction devices.Design: This is a consecutive prospective case series study where the patients were evaluated after 1, 3, 6 and 12 months. Outcome measures were focussed on intraoperative and postoperative safety, the effectiveness of the device in terms of audiological performance and patient's experience.Study sample: Sixteen patients with average age of 40.2 (range 18-74) years have been included. Thirteen patients were operated in Gothenburg and three in Stockholm.Results: It was found that the procedure for installing the BCI is safe and the transmission condition was stable over the follow-up time. No serious adverse events or severe adverse device effects occurred. The hearing sensitivity, speech in noise and the self-assessment as compared with the unaided condition improved significantly with the BCI. These patients also performed similar or better than with a conventional bone conduction reference device on a softband.Conclusions: In summary, it was found that the BCI can provide a safe and effective hearing rehabilitation alternative for patients with mild-to-moderate conductive or mixed hearing impairments.},
}
@article {pmid31705031,
year = {2019},
author = {Siddharth, S and Jung, TP and Sejnowski, TJ},
title = {Impact of Affective Multimedia Content on the Electroencephalogram and Facial Expressions.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {16295},
pmid = {31705031},
issn = {2045-2322},
mesh = {Acoustic Stimulation ; Brain/*physiology ; Cues ; *Electroencephalography ; Emotions ; *Facial Expression ; Female ; Humans ; Male ; *Multimedia ; Photic Stimulation ; Recognition, Psychology ; },
abstract = {Most of the research in the field of affective computing has focused on detecting and classifying human emotions through electroencephalogram (EEG) or facial expressions. Designing multimedia content to evoke certain emotions has been largely motivated by manual rating provided by users. Here we present insights from the correlation of affective features between three modalities namely, affective multimedia content, EEG, and facial expressions. Interestingly, low-level Audio-visual features such as contrast and homogeneity of the video and tone of the audio in the movie clips are most correlated with changes in facial expressions and EEG. We also detect the regions associated with the human face and the brain (in addition to the EEG frequency bands) that are most representative of affective responses. The computational modeling between the three modalities showed a high correlation between features from these regions and user-reported affective labels. Finally, the correlation between different layers of convolutional neural networks with EEG and Face images as input provides insights into human affection. Together, these findings will assist in (1) designing more effective multimedia contents to engage or influence the viewers, (2) understanding the brain/body bio-markers of affection, and (3) developing newer brain-computer interfaces as well as facial-expression-based algorithms to read emotional responses of the viewers.},
}
@article {pmid31702559,
year = {2019},
author = {Coiera, E},
title = {The Last Mile: Where Artificial Intelligence Meets Reality.},
journal = {Journal of medical Internet research},
volume = {21},
number = {11},
pages = {e16323},
pmid = {31702559},
issn = {1438-8871},
mesh = {Algorithms ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Technology ; },
abstract = {Although much effort is focused on improving the technical performance of artificial intelligence, there are compelling reasons to focus more on the implementation of this technology class to solve real-world applications. In this "last mile" of implementation lie many complex challenges that may make technically high-performing systems perform poorly. Instead of viewing artificial intelligence development as a linear one of algorithm development through to eventual deployment, there are strong reasons to take a more agile approach, iteratively developing and testing artificial intelligence within the context in which it finally will be used.},
}
@article {pmid31701703,
year = {2019},
author = {Kim, DW and Kim, E and Lee, C and Im, CH},
title = {Can Anodal Transcranial Direct Current Stimulation Increase Steady-State Visual Evoked Potential Responses?.},
journal = {Journal of Korean medical science},
volume = {34},
number = {43},
pages = {e285},
pmid = {31701703},
issn = {1598-6357},
support = {2019M3C7A1031278/NRF/National Research Foundation of Korea/Korea ; 2015M3C7A1031969/NRF/National Research Foundation of Korea/Korea ; 2019R1C1C1006561/NRF/National Research Foundation of Korea/Korea ; },
mesh = {Adult ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Signal-To-Noise Ratio ; *Transcranial Direct Current Stimulation ; Visual Cortex/physiology ; Young Adult ; },
abstract = {BACKGROUND: It has been frequently reported that non-negligible numbers of individuals have steady-state visual evoked potential (SSVEP) responses of low signal-to-noise-ratio (SNR) to specific stimulation frequencies, which makes detection of the SSVEP difficult especially in brain-computer interface applications. We investigated whether SSVEP can be modulated by anodal transcranial direct-current stimulation (tDCS) of the visual cortex.<br><br>METHODS: Each participant participated in two 20-min experiments-an actual tDCS experiment and a sham tDCS experiment-that were conducted on different days. Two representative electroencephalogram (EEG) features used for the SSVEP detection, SNR and amplitude, were tested for pre- and post-tDCS conditions to observe the effect of the anodal tDCS.<br><br>RESULTS: The EEG features were significantly enhanced by the anodal tDCS for the electrodes with low pre-tDCS SNR values, whereas the effect was not significant for electrodes with relatively higher SNR values.<br><br>CONCLUSION: Anodal tDCS of the visual cortex may be effective in enhancing the SNR and amplitude of the SSVEP response especially for individuals with low-SNR SSVEP.},
}
@article {pmid31701702,
year = {2019},
author = {Chung, BS},
title = {Illiteracy of Brain-Computer Interface.},
journal = {Journal of Korean medical science},
volume = {34},
number = {43},
pages = {e281},
doi = {10.3346/jkms.2019.34.e281},
pmid = {31701702},
issn = {1598-6357},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Literacy ; *Transcranial Direct Current Stimulation ; User-Computer Interface ; },
}
@article {pmid31697038,
year = {2019},
author = {Morikawa, Y and Yamagiwa, S and Sawahata, H and Numano, R and Koida, K and Kawano, T},
title = {Donut-Shaped Stretchable Kirigami: Enabling Electronics to Integrate with the Deformable Muscle.},
journal = {Advanced healthcare materials},
volume = {8},
number = {23},
pages = {e1900939},
doi = {10.1002/adhm.201900939},
pmid = {31697038},
issn = {2192-2659},
support = {17H03250//Grants-in-Aid for Scientific Research (B)/International ; 16H05434//Grants-in-Aid for Scientific Research (B)/International ; 26709024//Young Scientists/International ; 15H05917//Innovative Areas/International ; //Strategic Advancement of Multi-Purpose Ultra-Human Robot and Artificial Intelligence Technologies program/International ; //Takeda Science Foundation/International ; },
mesh = {Elastic Modulus ; *Electrodes ; Electromyography ; *Electronics ; Microelectrodes ; *Wearable Electronic Devices ; },
abstract = {Electronic devices used to record biological signals are important in neuroscience, brain-machine interfaces, and medical applications. Placing electronic devices below the skin surface and recording the muscle offers accurate and robust electromyography (EMG) recordings. The device stretchability and flexibility must be similar to the tissues to achieve an intimate integration of the electronic device with the biological tissues. However, conventional elastomer-based EMG electrodes have a Young's modulus that is ≈20 times higher than that of muscle. In addition, these stretchable devices also have an issue of displacement on the tissue surface, thereby causing some challenges during accurate and robust EMG signal recordings. In general, devices with kirigami design solve the issue of the high Young's modulus of conventional EMG devices. In this study, donut-shaped kirigami bioprobes are proposed to reduce the device displacement on the muscle surface. The fabricated devices are tested on an expanding balloon and they show no significant device (microelectrode) displacement. As the package, the fabricated device is embedded in a dissolvable material-based scaffold for easy-to-use stretchable kirigami device in an animal experiment. Finally, the EMG signal recording capability and stability using the fabricated kirigami device is confirmed in in vivo experiments without significant device displacements.},
}
@article {pmid31696938,
year = {2021},
author = {Nierhaus, T and Vidaurre, C and Sannelli, C and Mueller, KR and Villringer, A},
title = {Immediate brain plasticity after one hour of brain-computer interface (BCI).},
journal = {The Journal of physiology},
volume = {599},
number = {9},
pages = {2435-2451},
doi = {10.1113/JP278118},
pmid = {31696938},
issn = {1469-7793},
mesh = {Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; *Neurofeedback ; Neuronal Plasticity ; },
abstract = {KEY POINTS: Two groups of inexperienced brain-computer interface users underwent a purely mental EEG-BCI session that rapidly impacted on their brain. Modulations in structural and functional MRI were found after only 1 h of BCI training. Two different types of BCI (based on motor imagery or visually evoked potentials) were employed and analyses showed that the brain plastic changes are spatially specific for the respective neurofeedback. This spatial specificity promises tailored therapeutic interventions (e.g. for stroke patients).<br><br>ABSTRACT: A brain-computer-interface (BCI) allows humans to control computational devices using only neural signals. However, it is still an open question, whether performing BCI also impacts on the brain itself, i.e. whether brain plasticity is induced. Here, we show rapid and spatially specific signs of brain plasticity measured with functional and structural MRI after only 1&#xa0;h of purely mental BCI training in BCI-naive subjects. We employed two BCI approaches with neurofeedback based on (i) modulations of EEG rhythms by motor imagery (MI-BCI) or (ii) event-related potentials elicited by visually targeting flashing letters (ERP-BCI). Before and after the BCI session we performed structural and functional MRI. For both BCI approaches we found increased T1-weighted MR signal in the grey matter of the respective target brain regions, such as occipital/parietal areas after ERP-BCI and precuneus and sensorimotor regions after MI-BCI. The latter also showed increased functional connectivity and higher task-evoked BOLD activity in the same areas. Our results demonstrate for the first time that BCI by means of targeted neurofeedback rapidly impacts on MRI measures of brain structure and function. The spatial specificity of BCI-induced brain plasticity promises therapeutic interventions tailored to individual functional deficits, for example in patients after stroke.},
}
@article {pmid31695893,
year = {2019},
author = {Levine, BA and Douglas, MR and Yackel Adams, AA and Lardner, B and Reed, RN and Savidge, JA and Douglas, ME},
title = {Genomic pedigree reconstruction identifies predictors of mating and reproductive success in an invasive vertebrate.},
journal = {Ecology and evolution},
volume = {9},
number = {20},
pages = {11863-11877},
pmid = {31695893},
issn = {2045-7758},
abstract = {The persistence of an invasive species is influenced by its reproductive ecology, and a successful control program must operate on this premise. However, the reproductive ecology of invasive species may be enigmatic due to factors that also limit their management, such as cryptic coloration and behavior. We explored the mating and reproductive ecology of the invasive Brown Treesnake (BTS: Boiga irregularis) by reconstructing a multigenerational genomic pedigree based on 654 single nucleotide polymorphisms for a geographically closed population established in 2004 on Guam (N = 426). The pedigree allowed annual estimates of individual mating and reproductive success to be inferred for snakes in the study population over a 14-year period. We then employed generalized linear mixed models to gauge how well phenotypic and genomic data could predict sex-specific annual mating and reproductive success. Average snout-vent length (SVL), average body condition index (BCI), and trappability were significantly related to annual mating success for males, with average SVL also related to annual mating success for females. Male and female annual reproductive success was positively affected by SVL, BCI, and trappability. Surprisingly, the degree to which individuals were inbred had no effect on annual mating or reproductive success. When juxtaposed with current control methods, these results indicate that baited traps, a common interdiction tool, may target fecund BTS in some regards but not others. Our study emphasizes the importance of reproductive ecology as a focus for improving BTS control and promotes genomic pedigree reconstruction for such an endeavor in this invasive species and others.},
}
@article {pmid31693928,
year = {2020},
author = {Abdalmalak, A and Milej, D and Cohen, DJ and Anazodo, U and Ssali, T and Diop, M and Owen, AM and St Lawrence, K},
title = {Using fMRI to investigate the potential cause of inverse oxygenation reported in fNIRS studies of motor imagery.},
journal = {Neuroscience letters},
volume = {714},
number = {},
pages = {134607},
doi = {10.1016/j.neulet.2019.134607},
pmid = {31693928},
issn = {1872-7972},
mesh = {Adult ; Brain-Computer Interfaces ; Female ; *Functional Neuroimaging ; Healthy Volunteers ; Hemoglobins/*metabolism ; Humans ; Imagination/*physiology ; *Magnetic Resonance Imaging ; Male ; *Motor Activity ; Motor Cortex/*diagnostic imaging/metabolism ; Oxyhemoglobins/*metabolism ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Motor imagery (MI) is a commonly used cognitive task in brain-computer interface (BCI) applications because it produces reliable activity in motor-planning regions. However, a number of functional near-infrared spectroscopy (fNIRS) studies have reported the unexpected finding of inverse oxygenation: increased deoxyhemoglobin and decreased oxyhemoglobin during task periods. This finding questions the reliability of fNIRS for BCI applications given that MI activation should result in a focal increase in blood oxygenation. In an attempt to elucidate this phenomenon, fMRI and fNIRS data were acquired on 15 healthy participants performing a MI task. The fMRI data provided global coverage of brain activity, thus allowing visualization of all potential brain regions activated and deactivated during task periods. Indeed, fMRI results from seven subjects included activation in the primary motor cortex and/or the pre-supplementary motor area during the rest periods in addition to the expected activation in the supplementary motor and premotor areas. Of these seven subjects, two showed inverse oxygenation with fNIRS. The proximity of the regions showing inverse oxygenation to the motor planning regions suggests that inverse activation detected by fNIRS may likely be a consequence of partial volume errors due to the sensitivity of the optodes to both primary motor and motor planning regions.},
}
@article {pmid31692449,
year = {2019},
author = {Valle, G},
title = {The Connection Between the Nervous System and Machines: Commentary.},
journal = {Journal of medical Internet research},
volume = {21},
number = {11},
pages = {e16344},
pmid = {31692449},
issn = {1438-8871},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Humans ; Quality of Life ; *Robotics ; User-Computer Interface ; },
abstract = {Decades of technological developments have populated the field of brain-machine interfaces and neuroprosthetics with several replacement strategies, neural modulation treatments, and rehabilitation techniques to improve the quality of life for patients affected by sensory and motor disabilities. This field is now quickly expanding thanks to advances in neural interfaces, machine learning techniques, and robotics. Despite many clinical successes, and multiple innovations in animal models, brain-machine interfaces remain mainly confined to sophisticated laboratory environments indicating a necessary step forward in the used technology. Interestingly, Elon Musk and Neuralink have recently presented a new brain-machine interface platform with thousands of channels, fast implantation, and advanced signal processing. Here, how their work takes part in the context of the restoration of sensory-motor functions through neuroprostheses is commented.},
}
@article {pmid31687006,
year = {2019},
author = {Li, Z and Zhang, S and Pan, J},
title = {Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {3807670},
pmid = {31687006},
issn = {1687-5273},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Humans ; },
abstract = {Conventional brain-computer interface (BCI) systems have been facing two fundamental challenges: the lack of high detection performance and the control command problem. To this end, the researchers have proposed a hybrid brain-computer interface (hBCI) to address these challenges. This paper mainly discusses the research progress of hBCI and reviews three types of hBCI, namely, hBCI based on multiple brain models, multisensory hBCI, and hBCI based on multimodal signals. By analyzing the general principles, paradigm designs, experimental results, advantages, and applications of the latest hBCI system, we found that using hBCI technology can improve the detection performance of BCI and achieve multidegree/multifunctional control, which is significantly superior to single-mode BCIs.},
}
@article {pmid31687005,
year = {2019},
author = {Alchalabi, B and Faubert, J},
title = {A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {2503431},
pmid = {31687005},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/methods ; Female ; Humans ; Male ; Neurofeedback/*physiology ; *Virtual Reality ; },
abstract = {A brain-computer interface (BCI) decodes the brain signals representing a desire to do something and transforms those signals into a control command. However, only a limited number of mental tasks have been previously investigated and classified. This study aimed to investigate two motor imagery (MI) commands, moving forward and moving backward, using a small number of EEG channels, to be used in a neurofeedback context. This study also aimed to simulate a BCI and investigate the offline classification between MI movements in forward and backward directions, using different features and classification methods. Ten healthy people participated in a two-session (48 min each) experiment. This experiment investigated neurofeedback of navigation in a virtual tunnel. Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen. Three electrodes were mounted bilaterally over the motor cortex. Trials were conducted with feedback. Data from session 1 were analyzed offline to train classifiers and to calculate thresholds for both tasks. These thresholds were used to form control signals that were later used online in session 2 in neurofeedback training to trigger the virtual tunnel to move in the direction requested by the user's brain signals. After 96 min of training, the online band-power neurofeedback training achieved an average classification of 76%, while the offline BCI simulation using power spectral density asymmetrical ratio and AR-modeled band power as features, and using LDA and SVM as classifiers, achieved an average classification of 80%.},
}
@article {pmid31685885,
year = {2019},
author = {Kufareva, I and Bestgen, B and Brear, P and Prudent, R and Laudet, B and Moucadel, V and Ettaoussi, M and Sautel, CF and Krimm, I and Engel, M and Filhol, O and Borgne, ML and Lomberget, T and Cochet, C and Abagyan, R},
title = {Discovery of holoenzyme-disrupting chemicals as substrate-selective CK2 inhibitors.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {15893},
pmid = {31685885},
issn = {2045-2322},
support = {R35 HL135737/HL/NHLBI NIH HHS/United States ; R35 GM131881/GM/NIGMS NIH HHS/United States ; R01 AI118985/AI/NIAID NIH HHS/United States ; R01 NS102432/NS/NINDS NIH HHS/United States ; R01 GM117424/GM/NIGMS NIH HHS/United States ; R01 GM071872/GM/NIGMS NIH HHS/United States ; R01 GM074832/GM/NIGMS NIH HHS/United States ; },
mesh = {Adenosine Triphosphate/metabolism ; Binding Sites ; Casein Kinase II/*antagonists &amp; inhibitors/metabolism ; Catalytic Domain ; Cell Line, Tumor ; Cell Movement/drug effects ; Cell Proliferation/drug effects ; Crystallography, X-Ray ; Holoenzymes/chemistry/*metabolism ; Humans ; Kinetics ; Molecular Docking Simulation ; Peptides/chemistry/metabolism ; Phosphorylation ; Protein Kinase Inhibitors/*chemistry/metabolism/pharmacology ; Protein Subunits/antagonists &amp; inhibitors/metabolism ; Substrate Specificity ; Surface Plasmon Resonance ; },
abstract = {CK2 is a constitutively active protein kinase overexpressed in numerous malignancies. Interaction between CK2α and CK2β subunits is essential for substrate selectivity. The CK2α/CK2β interface has been previously targeted by peptides to achieve functional effects; however, no small molecules modulators were identified due to pocket flexibility and open shape. Here we generated numerous plausible conformations of the interface using the fumigation modeling protocol, and virtually screened a compound library to discover compound 1 that suppressed CK2α/CK2β interaction in vitro and inhibited CK2 in a substrate-selective manner. Orthogonal SPR, crystallography, and NMR experiments demonstrated that 4 and 6, improved analogs of 1, bind to CK2α as predicted. Both inhibitors alter CK2 activity in cells through inhibition of CK2 holoenzyme formation. Treatment with 6 suppressed MDA-MB231 triple negative breast cancer cell growth and induced apoptosis. Altogether, our findings exemplify an innovative computational-experimental approach and identify novel non-peptidic inhibitors of CK2 subunit interface disclosing substrate-selective functional effects.},
}
@article {pmid31683586,
year = {2019},
author = {Shih, DH and Lu, KC and Shih, PY},
title = {Exploring Shopper's Browsing Behavior and Attention Level with an EEG Biosensor Cap.},
journal = {Brain sciences},
volume = {9},
number = {11},
pages = {},
pmid = {31683586},
issn = {2076-3425},
abstract = {The online shopping market is developing rapidly, meaning that it is important for retailers and manufacturers to understand how consumers behave online compared to when in brick-and-mortar stores. Retailers want consumers to spend time shopping, browsing, and searching for products in the hope a purchase is made. On the other hand, consumers may want to restrict their duration of stay on websites due to perceived risk of loss of time or convenience. This phenomenon underlies the need to reduce the duration of consumer stay (namely, time pressure) on websites. In this paper, the browsing behavior and attention span of shoppers engaging in online shopping under time pressure were investigated. The attention and meditation level are measured by an electroencephalogram (EEG) biosensor cap. The results indicated that when under time pressure shoppers engaging in online shopping are less attentive. Thus, marketers may need to find strategies to increase a shopper's attention. Shoppers unfamiliar with product catalogs on shopping websites are less attentive, therefore marketers should adopt an interesting style for product catalogs to hold a shopper's attention. We discuss our findings and outline their business implications.},
}
@article {pmid31683268,
year = {2020},
author = {Talukdar, U and Hazarika, SM and Gan, JQ},
title = {Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016020},
doi = {10.1088/1741-2552/ab53f1},
pmid = {31683268},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Mental Fatigue/diagnosis/*physiopathology/psychology ; Movement/*physiology ; Photic Stimulation/methods ; *Unsupervised Machine Learning ; },
abstract = {OBJECTIVE: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user.<br><br>APPROACH: Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only six completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin index (DBI), Fisher score (FS) and Dunn's index (DI).<br><br>MAIN RESULTS: Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP.<br><br>SIGNIFICANCE: Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features.},
}
@article {pmid31683267,
year = {2019},
author = {Wirth, C and Dockree, PM and Harty, S and Lacey, E and Arvaneh, M},
title = {Towards error categorisation in BCI: single-trial EEG classification between different errors.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016008},
doi = {10.1088/1741-2552/ab53fe},
pmid = {31683267},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Brain-Computer Interfaces/*classification ; Electroencephalography/*classification/methods ; Female ; Hand Strength/*physiology ; Humans ; Male ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; *Research Design ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: Error-related potentials (ErrP) are generated in the brain when humans perceive errors. These ErrP signals can be used to classify actions as erroneous or non-erroneous, using single-trial electroencephalography (EEG). A small number of studies have demonstrated the feasibility of using ErrP detection as feedback for reinforcement-learning-based brain-computer interfaces (BCI), confirming the possibility of developing more autonomous BCI. These systems could be made more efficient with specific information about the type of error that occurred. A few studies differentiated the ErrP of different errors from each other, based on direction or severity. However, errors cannot always be categorised in these ways. We aimed to investigate the feasibility of differentiating very similar error conditions from each other, in the absence of previously explored metrics.<br><br>APPROACH: In this study, we used two data sets with 25 and 14 participants to investigate the differences between errors. The two error conditions in each task were similar in terms of severity, direction and visual processing. The only notable differences between them were the varying cognitive processes involved in perceiving the errors, and differing contexts in which the errors occurred. We used a linear classifier with a small feature set to differentiate the errors on a single-trial basis.<br><br>MAIN RESULTS: For both data sets, we observed neurophysiological distinctions between the ErrPs related to each error type. We found further distinctions between age groups. Furthermore, we achieved statistically significant single-trial classification rates for most participants included in the classification phase, with mean overall accuracy of 65.2% and 65.6% for the two tasks.<br><br>SIGNIFICANCE: As a proof of concept our results showed that it is feasible, using single-trial EEG, to classify these similar error types against each other. This study paves the way for more detailed and efficient learning in BCI, and thus for a more autonomous human-machine interaction.},
}
@article {pmid31680914,
year = {2019},
author = {Abu-Rmileh, A and Zakkay, E and Shmuelof, L and Shriki, O},
title = {Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {362},
pmid = {31680914},
issn = {1662-5161},
abstract = {Motor imagery (MI) based brain computer interfaces (BCI) detect changes in brain activity associated with imaginary limb movements, and translate them into device commands. MI based BCIs require training, during which the user gradually learns how to control his or her brain activity with the help of feedback. Additionally, machine learning techniques are frequently used to boost BCI performance and to adapt the decoding algorithm to the user's brain. Thus, both the brain and the machine need to adapt in order to improve performance. To study the utility of co-adaptive training in the BCI paradigm and the time scales involved, we investigated the performance of two groups of subjects, in a 4-day MI experiment using EEG recordings. One group (control, n = 9 subjects) performed the BCI task using a fixed classifier based on MI data from day 1. In the second group (experimental, n = 9 subjects), the classifier was regularly adapted based on brain activity patterns during the experiment days. We found that the experimental group showed a significantly larger change in performance following training compared to the control group. Specifically, although the experimental group exhibited a decrease in performance between days, it showed an increase in performance within each day, which compensated for the decrease. The control group showed decreases both within and between days. A correlation analysis in subjects who had a notable improvement in performance following training showed that performance was mainly associated with modulation of power in the α frequency band. To conclude, continuous updating of the classification algorithm improves the performance of subjects in longitudinal BCI training.},
}
@article {pmid31680806,
year = {2019},
author = {Freudenburg, ZV and Branco, MP and Leinders, S and van der Vijgh, BH and Pels, EGM and Denison, T and van den Berg, LH and Miller, KJ and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Sensorimotor ECoG Signal Features for BCI Control: A Comparison Between People With Locked-In Syndrome and Able-Bodied Controls.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1058},
pmid = {31680806},
issn = {1662-4548},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
abstract = {The sensorimotor cortex is a frequently targeted brain area for the development of Brain-Computer Interfaces (BCIs) for communication in people with severe paralysis and communication problems (locked-in syndrome; LIS). It is widely acknowledged that this area displays an increase in high-frequency band (HFB) power and a decrease in the power of the low frequency band (LFB) during movement of, for example, the hand. Upon termination of hand movement, activity in the LFB band typically shows a short increase (rebound). The ability to modulate the neural signal in the sensorimotor cortex by imagining or attempting to move is crucial for the implementation of sensorimotor BCI in people who are unable to execute movements. This may not always be self-evident, since the most common causes of LIS, amyotrophic lateral sclerosis (ALS) and brain stem stroke, are associated with significant damage to the brain, potentially affecting the generation of baseline neural activity in the sensorimotor cortex and the modulation thereof by imagined or attempted hand movement. In the Utrecht NeuroProsthesis (UNP) study, a participant with LIS caused by ALS and a participant with LIS due to brain stem stroke were implanted with a fully implantable BCI, including subdural electrocorticography (ECoG) electrodes over the sensorimotor area, with the purpose of achieving ECoG-BCI-based communication. We noted differences between these participants in the spectral power changes generated by attempted movement of the hand. To better understand the nature and origin of these differences, we compared the baseline spectral features and task-induced modulation of the neural signal of the LIS participants, with those of a group of able-bodied people with epilepsy who received a subchronic implant with ECoG electrodes for diagnostic purposes. Our data show that baseline LFB oscillatory components and changes generated in the LFB power of the sensorimotor cortex by (attempted) hand movement differ between participants, despite consistent HFB responses in this area. We conclude that the etiology of LIS may have significant effects on the LFB spectral components in the sensorimotor cortex, which is relevant for the development of communication-BCIs for this population.},
}
@article {pmid31677441,
year = {2020},
author = {Virgilio G, CD and Sossa A, JH and Antelis, JM and Falcón, LE},
title = {Spiking Neural Networks applied to the classification of motor tasks in EEG signals.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {122},
number = {},
pages = {130-143},
doi = {10.1016/j.neunet.2019.09.037},
pmid = {31677441},
issn = {1879-2782},
mesh = {Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Imagination ; *Neural Networks, Computer ; *Psychomotor Performance ; },
abstract = {Motivated by the recent progress of Spiking Neural Network (SNN) models in pattern recognition, we report on the development and evaluation of brain signal classifiers based on SNNs. The work shows the capabilities of this type of Spiking Neurons in the recognition of motor imagery tasks from EEG signals and compares their performance with other traditional classifiers commonly used in this application. This work includes two stages: the first stage consists of comparing the performance of the SNN models against some traditional neural network models. The second stage, compares the SNN models performance in two input conditions: input features with constant values and input features with temporal information. The EEG signals employed in this work represent five motor imagery tasks: i.e. rest, left hand, right hand, foot and tongue movements. These EEG signals were obtained from a public database provided by the Technological University of Graz (Brunner et al., 2008). The feature extraction stage was performed by applying two algorithms: power spectral density and wavelet decomposition. Likewise, this work uses raw EEG signals for the second stage of the problem solution. All of the models were evaluated in the classification between two motor imagery tasks. This work demonstrates that with a smaller number of Spiking neurons, simple problems can be solved. Better results are obtained by using patterns with temporal information, thereby exploiting the capabilities of the SNNs.},
}
@article {pmid31676874,
year = {2019},
author = {Lemprière, S},
title = {Brain-machine interaction improves mobility.},
journal = {Nature reviews. Neurology},
volume = {15},
number = {12},
pages = {685},
pmid = {31676874},
issn = {1759-4766},
mesh = {Algorithms ; *Artificial Limbs ; Brain ; Cognition ; },
}
@article {pmid31676475,
year = {2019},
author = {Caldera, EJ and Chevrette, MG and McDonald, BR and Currie, CR},
title = {Local Adaptation of Bacterial Symbionts within a Geographic Mosaic of Antibiotic Coevolution.},
journal = {Applied and environmental microbiology},
volume = {85},
number = {24},
pages = {},
pmid = {31676475},
issn = {1098-5336},
support = {T32 GM008505/GM/NIGMS NIH HHS/United States ; U19 TW009872/TW/FIC NIH HHS/United States ; U19 AI109673/AI/NIAID NIH HHS/United States ; },
mesh = {Acclimatization/*physiology ; Actinobacteria/genetics/*metabolism ; Animals ; Anti-Bacterial Agents/*metabolism/pharmacology ; Ants/microbiology ; *Biological Coevolution ; Biosynthetic Pathways/genetics ; Costa Rica ; Host Microbial Interactions/physiology ; Hypocreales/drug effects/pathogenicity ; Secondary Metabolism/genetics ; Symbiosis/genetics/*physiology ; },
abstract = {The geographic mosaic theory of coevolution (GMC) posits that coevolutionary dynamics go beyond local coevolution and are comprised of the following three components: geographic selection mosaics, coevolutionary hot spots, and trait remixing. It is unclear whether the GMC applies to bacteria, as horizontal gene transfer and cosmopolitan dispersal may violate theoretical assumptions. Here, we test key GMC predictions in an antibiotic-producing bacterial symbiont (genus Pseudonocardia) that protects the crops of neotropical fungus-farming ants (Apterostigma dentigerum) from a specialized pathogen (genus Escovopsis). We found that Pseudonocardia antibiotic inhibition of common Escovopsis pathogens was elevated in A. dentigerum colonies from Panama compared to those from Costa Rica. Furthermore, a Panama Canal Zone population of Pseudonocardia on Barro Colorado Island (BCI) was locally adapted, whereas two neighboring populations were not, consistent with a GMC-predicted selection mosaic and a hot spot of adaptation surrounded by areas of maladaptation. Maladaptation was shaped by incongruent Pseudonocardia-Escovopsis population genetic structure, whereas local adaptation was facilitated by geographic isolation on BCI after the flooding of the Panama Canal. Genomic assessments of antibiotic potential of 29 Pseudonocardia strains identified diverse and unique biosynthetic gene clusters in BCI strains despite low genetic diversity in the core genome. The strength of antibiotic inhibition was not correlated with the presence/absence of individual biosynthetic gene clusters or with parasite location. Rather, biosynthetic gene clusters have undergone selective sweeps, suggesting that the trait remixing dynamics conferring the long-term maintenance of antibiotic potency rely on evolutionary genetic changes within already-present biosynthetic gene clusters and not simply on the horizontal acquisition of novel genetic elements or pathways.IMPORTANCE Recently, coevolutionary theory in macroorganisms has been advanced by the geographic mosaic theory of coevolution (GMC), which considers how geography and local adaptation shape coevolutionary dynamics. Here, we test GMC in an ancient symbiosis in which the ant Apterostigma dentigerum cultivates fungi in an agricultural system analogous to human farming. The cultivars are parasitized by the fungus Escovopsis The ants maintain symbiotic actinobacteria with antibiotic properties that help combat Escovopsis infection. This antibiotic symbiosis has persisted for tens of millions of years, raising the question of how antibiotic potency is maintained over these time scales. Our study tests the GMC in a bacterial defensive symbiosis and in a multipartite symbiosis framework. Our results show that this multipartite symbiotic system conforms to the GMC and demonstrate that this theory is applicable in both microbes and indirect symbiont-symbiont interactions.},
}
@article {pmid31674923,
year = {2019},
author = {Pisarchik, AN and Maksimenko, VA and Hramov, AE},
title = {From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink.},
journal = {Journal of medical Internet research},
volume = {21},
number = {10},
pages = {e16356},
pmid = {31674923},
issn = {1438-8871},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Technology ; User-Computer Interface ; },
}
@article {pmid31674921,
year = {2019},
author = {Kirsch, RF and Ajiboye, AB and Miller, JP},
title = {The Reconnecting the Hand and Arm with Brain (ReHAB) Commentary on "An Integrated Brain-Machine Interface Platform With Thousands of Channels".},
journal = {Journal of medical Internet research},
volume = {21},
number = {10},
pages = {e16339},
pmid = {31674921},
issn = {1438-8871},
mesh = {Arm ; Brain ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Microelectrodes ; },
abstract = {Intracortical brain-machine interfaces are a promising technology for allowing people with chronic and severe neurological disorders that resulted in loss of function to potentially regain those functions through neuroprosthetic devices. The penetrating microelectrode arrays used in almost all previous studies of intracortical brain-machine interfaces in people had a limited recording life (potentially due to issues with long-term biocompatibility), as well as a limited number of recording electrodes with limited distribution in the brain. Significant advances are required in this array interface to deal with the issues of long-term biocompatibility and lack of distributed recordings. The Musk and Neuralink manuscript proposes a novel and potentially disruptive approach to advancing the brain-electrode interface technology, with the potential of addressing many of these hurdles. Our commentary addresses the potential advantages of the proposed approach, as well as the remaining challenges to be addressed.},
}
@article {pmid31674917,
year = {2019},
author = {Maynard, AD and Scragg, M},
title = {The Ethical and Responsible Development and Application of Advanced Brain Machine Interfaces.},
journal = {Journal of medical Internet research},
volume = {21},
number = {10},
pages = {e16321},
pmid = {31674917},
issn = {1438-8871},
mesh = {*Brain-Computer Interfaces ; Humans ; Privacy ; Research Personnel ; Technology ; },
abstract = {Advanced brain machine interfaces provide potentially transformative approaches to treating neurological conditions and enhancing the performance of users. Yet, as technological capabilities continue to progress in leaps and bounds, there is a possibility that these capabilities outstrip our collective understanding of how to ensure brain machine interfaces are developed and used ethically and responsibly. In this case, there is an overt danger of rapid technological developments leading to unanticipated harm through a lack of foresight including threats to privacy, autonomy, self-identity, and other areas of personal and social value which, while hard to quantify, represent substantial risks. There is also a very real likelihood of such risks undermining value creation around the technologies and the associated enterprises, as key stakeholders push back against perceived and actual threats to what they, in turn, hold to be of value. In order to successfully traverse the resulting risk landscape, researchers and developers will need to become increasingly adept at integrating a sophisticated understanding of ethical and socially responsible innovation into their enterprises. Here, we illustrate how a "risk innovation" approach may provide novel insights into mapping out this landscape and revealing potentially blindsiding risks. We show how this approach can be used to illuminate challenges and opportunities to the successful, ethical, and responsible development of advanced brain machine interfaces. In addition, we emphasize how success will ultimately depend on the willingness of innovators and others to take ethical and responsible innovation seriously and to draw on the interdisciplinary and transdisciplinary expertise that is necessary to translate good intentions into positive outcomes.},
}
@article {pmid31673936,
year = {2020},
author = {Dobberfuhl, AD and Zhang, X and Comiter, CV},
title = {The mechanical stop test and isovolumetric detrusor contractile reserve are associated with immediate spontaneous voiding after transurethral resection of prostate.},
journal = {International urology and nephrology},
volume = {52},
number = {2},
pages = {239-246},
pmid = {31673936},
issn = {1573-2584},
support = {1L30DK115056-01/NH/NIH HHS/United States ; 5KL2TR001083-05/NH/NIH HHS/United States ; 1L30DK115056-01/NH/NIH HHS/United States ; 5KL2TR001083-05/NH/NIH HHS/United States ; },
mesh = {Aged ; Diagnostic Techniques, Urological ; Humans ; Male ; Muscle Contraction ; Postoperative Care/methods ; *Postoperative Complications/diagnosis/physiopathology ; Prostatic Hyperplasia/*surgery ; Transurethral Resection of Prostate/*adverse effects/methods ; *Urinary Bladder Neck Obstruction/diagnosis/etiology/physiopathology ; *Urinary Incontinence/diagnosis/etiology/physiopathology ; Urodynamics/physiology ; },
abstract = {PURPOSE: To identify urodynamic factors associated with the mechanical stop test and immediate spontaneous voiding following transurethral resection of prostate (TURP).<br><br>METHODS: We identified 90 men who underwent TURP over a 12-month period. Forty-three (mean age 68&#xa0;years) underwent urodynamic evaluation prior to TURP. Isovolumetric detrusor contractile pressure (Piso) was obtained using the mechanical stop test during the voiding phase, and used to calculate detrusor contractile reserve (Pres&#x2009;=&#x2009;Piso - Pdet@Qmax). Primary outcome was spontaneous voiding after TURP.<br><br>RESULTS: Preoperative catheter-free spontaneous voiding was present in 63% of men (27/43) with a urodynamic (mean&#x2009;&#xb1;&#x2009;SD): Qmax 6.2&#x2009;&#xb1;&#x2009;2.7&#xa0;mL/s, Pdet@Qmax 102&#x2009;&#xb1;&#x2009;47 cmH2O, Piso 124&#x2009;&#xb1;&#x2009;49 cmH2O, Pres 22&#x2009;&#xb1;&#x2009;16 cmH2O, bladder outlet obstruction index (BOOI) 90&#x2009;&#xb1;&#x2009;49, and bladder contractility index (BCI) 132&#x2009;&#xb1;&#x2009;44. The remaining 16 catheter-dependent men demonstrated a urodynamic (mean&#x2009;&#xb1;&#x2009;SD): Qmax 3.6&#x2009;&#xb1;&#x2009;3.3&#xa0;mL/s, Pdet@Qmax 87&#x2009;&#xb1;&#x2009;38 cmH2O, Piso 99&#x2009;&#xb1;&#x2009;51 cmH2O, Pres 10&#x2009;&#xb1;&#x2009;18 cmH2O, BOOI 82&#x2009;&#xb1;&#x2009;36, and BCI 106&#x2009;&#xb1;&#x2009;48. Following TURP, 67% of men voided spontaneously with their first void trial, and in receiver operator analysis of urodynamic measures (Pdet@Qmax, Piso, Pres, BOOI and BCI), only Pres was significantly associated with immediate spontaneous voiding after TURP (threshold Pres&#x2009;&#x2265;&#x2009;9 cmH2O, AUC&#x2009;=&#x2009;0.681, p&#x2009;=&#x2009;0.035).<br><br>CONCLUSIONS: In men who underwent TURP, a Pres&#x2009;&#x2265;&#x2009;9 cmH2O was associated with immediate spontaneous voiding and may be easily incorporated into the postoperative pathway.},
}
@article {pmid31670508,
year = {2019},
author = {Liu, S and Moncion, C and Zhang, J and Balachandar, L and Kwaku, D and Riera, JJ and Volakis, JL and Chae, J},
title = {Fully Passive Flexible Wireless Neural Recorder for the Acquisition of Neuropotentials from a Rat Model.},
journal = {ACS sensors},
volume = {4},
number = {12},
pages = {3175-3185},
doi = {10.1021/acssensors.9b01491},
pmid = {31670508},
issn = {2379-3694},
mesh = {Algorithms ; Animals ; Biocompatible Materials/chemistry ; Brain/metabolism ; Electrodes, Implanted ; Electroencephalography/instrumentation/*methods ; *Evoked Potentials, Somatosensory ; Machine Learning ; Rats, Wistar ; Wireless Technology/instrumentation ; },
abstract = {Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.},
}
@article {pmid31666096,
year = {2019},
author = {Al-Taleb, MKH and Purcell, M and Fraser, M and Petric-Gray, N and Vuckovic, A},
title = {Home used, patient self-managed, brain-computer interface for the management of central neuropathic pain post spinal cord injury: usability study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {128},
pmid = {31666096},
issn = {1743-0003},
mesh = {Adolescent ; Adult ; Aged ; Alpha Rhythm ; Beta Rhythm ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Neuralgia/*etiology/*therapy ; Neurofeedback/methods ; Pain Measurement ; Patient Satisfaction ; Self Care/*methods ; Spinal Cord Injuries/*complications ; Theta Rhythm ; Young Adult ; },
abstract = {BACKGROUND: Central Neuropathic Pain (CNP) is a frequent chronic condition in people with spinal cord injury (SCI). Previously, we showed that using laboratory brain-computer interface (BCI) technology for neurofeedback (NFB) training, it was possible to reduce CNP in people with SCI. In this study, we show results of patient self-managed treatment in their homes with a BCI-NFB using a consumer EEG device.<br><br>METHODS: Users: People with chronic SCI (17&#x2009;M, 3&#x2009;F, 50.6&#x2009;&#xb1;&#x2009;14.1&#x2009;years old), and CNP &#x2265;4 on a Visual Numerical Scale.<br><br>LOCATION: Laboratory training (up to 4 sessions) followed by home self-managed NFB. User Activity: Upregulating the EEG alpha band power by 10% above a threshold and at the same time downregulating the theta and upper beta (20-30&#x2009;Hz) band power by 10% at electrode location C4. Technology: A consumer grade multichannel EEG headset (Epoch, Emotiv, USA), a tablet computer and custom made NFB software.<br><br>EVALUATION: EEG analysis, before and after NFB assessment, interviews and questionnaires.<br><br>RESULTS: Effectiveness: Out of 20 initially assessed participants, 15 took part in the study. Participants used the system for 6.9&#x2009;&#xb1;&#x2009;5.5 (median 4) weeks. Twelve participants regulated their brainwaves in a frequency specific manner and were most successful upregulating the alpha band power. However they typically upregulated power around their individual alpha peak (7.6&#x2009;&#xb1;&#x2009;0.8&#x2009;Hz) that was lower than in people without CNP. The reduction in pain experienced was statistically significant in 12 and clinically significant (greater than 30%) in 8 participants. Efficiency: The donning was between 5 and 15&#x2009;min, and approximately 10-20% of EEG data recorded in the home environment was noise. Participants were mildly stressed when self-administering NFB at home (2.4 on a scale 1-10). User satisfaction: Nine participants who completed the final assessment reported a high level of satisfaction (QUESQ, 4.5&#x2009;&#xb1;&#x2009;0.8), naming effectiveness, ease of use and comfort as main priorities. The main factors influencing frequency of NFB training were: health related issues, free time and pain intensity.<br><br>CONCLUSION: Portable NFB is a feasible solution for home-based self-managed treatment of CNP. Compared to pharmacological treatments, NFB has less side effects and provides users with active control over pain.<br><br>TRIAL REGISTRATION: GN15NE124 , Registered 9th June 2016.},
}
@article {pmid31662834,
year = {2019},
author = {Chen, C and Zhang, J and Belkacem, AN and Zhang, S and Xu, R and Hao, B and Gao, Q and Shin, D and Wang, C and Ming, D},
title = {G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery.},
journal = {Journal of healthcare engineering},
volume = {2019},
number = {},
pages = {5068283},
pmid = {31662834},
issn = {2040-2309},
mesh = {Adult ; Brain/physiology ; Electroencephalography ; Female ; Fingers/*physiology ; Humans ; Imagination/*physiology ; Male ; Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Stroke Rehabilitation ; Young Adult ; },
abstract = {Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.},
}
@article {pmid31658616,
year = {2019},
author = {Lin, BS and Lin, BS and Yen, TH and Hsu, CC and Wang, YC},
title = {Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface.},
journal = {Micromachines},
volume = {10},
number = {10},
pages = {},
pmid = {31658616},
issn = {2072-666X},
abstract = {Brain-computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).},
}
@article {pmid31656937,
year = {2019},
author = {Edelman, BJ and Meng, J and Suma, D and Zurn, C and Nagarajan, E and Baxter, BS and Cline, CC and He, B},
title = {Noninvasive neuroimaging enhances continuous neural tracking for robotic device control.},
journal = {Science robotics},
volume = {4},
number = {31},
pages = {},
pmid = {31656937},
issn = {2470-9476},
support = {RF1 MH114233/MH/NIMH NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; F31 NS096964/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) utilizing signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems significantly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly impact the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework utilizing electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by respectively increasing user engagement through a continuous pursuit task and associated training paradigm, and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by over 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Finally, this framework was deployed in a physical task, demonstrating a near seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications on the eventual development and implementation of neurorobotics by means of noninvasive BCI.},
}
@article {pmid31656595,
year = {2019},
author = {Rahman, MA and Uddin, MS and Ahmad, M},
title = {Modeling and classification of voluntary and imagery movements for brain-computer interface from fNIR and EEG signals through convolutional neural network.},
journal = {Health information science and systems},
volume = {7},
number = {1},
pages = {22},
pmid = {31656595},
issn = {2047-2501},
abstract = {Practical brain-computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR-EEG data. The results reveal that the combined fNIR-EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.},
}
@article {pmid31652579,
year = {2019},
author = {Lee, S and Cho, H and Kim, K and Jun, SC},
title = {Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {20},
pages = {},
pmid = {31652579},
issn = {1424-8220},
support = {2017-0-00451//Institute of Information &amp; Communications Technology Planning &amp; Evaluation/ ; },
mesh = {Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Reproducibility of Results ; },
abstract = {Social interaction is one of humans' most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system's development-such as synchronization techniques, system loads, electrodes, and applications-are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain-computer interface, among others.},
}
@article {pmid31649523,
year = {2019},
author = {Fiederer, LDJ and Völker, M and Schirrmeister, RT and Burgard, W and Boedecker, J and Ball, T},
title = {Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep Regression.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {76},
pmid = {31649523},
issn = {1662-5218},
abstract = {Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the question of how to continuously evaluate the quality of robotic behavior in a hybrid brain-computer interfacing (BCI) task, combining brain and non-brain signals, and how to use the collected information to adapt the robot's behavior accordingly. To this aim, we developed a rating system compatible with EEG recordings, requiring the users to execute only small movements with their thumb on a wireless controller to rate the robot's behavior on a continuous scale. The ratings were recorded together with dry EEG, respiration, ECG, and robotic joint angles in ROS. Pilot experiments were conducted with three users that had different levels of previous experience with robots. The results demonstrate the feasibility to obtain continuous rating data that give insight into the subjective user perception during direct human-robot interaction. The rating data suggests differences in subjective perception for users with no, moderate, or substantial previous robot experience. Furthermore, a variety of regression techniques, including deep CNNs, allowed us to predict the subjective ratings. Performance was better when using the position of the robotic hand than when using EEG, ECG, or respiration. A consistent advantage of features expected to be related to a motor bias could not be found. Across-user predictions showed that the models most likely learned a combination of general and individual features across-users. A transfer of pre-trained regressor to a new user was especially accurate in users with more experience. For future research, studies with more participants will be needed to evaluate the methodology for its use in practice. Data and code to reproduce this study are available at https://github.com/TNTLFreiburg/NiceBot.},
}
@article {pmid31649517,
year = {2019},
author = {Vortmann, LM and Kroll, F and Putze, F},
title = {EEG-Based Classification of Internally- and Externally-Directed Attention in an Augmented Reality Paradigm.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {348},
pmid = {31649517},
issn = {1662-5161},
abstract = {One problem faced in the design of Augmented Reality (AR) applications is the interference of virtually displayed objects in the user's visual field, with the current attentional focus of the user. Newly generated content can disrupt internal thought processes. If we can detect such internally-directed attention periods, the interruption could either be avoided or even used intentionally. In this work, we designed a special alignment task in AR with two conditions: one with externally-directed attention and one with internally-directed attention. Apart from the direction of attention, the two tasks were identical. During the experiment, we performed a 16-channel EEG recording, which was then used for a binary classification task. Based on selected band power features, we trained a Linear Discriminant Analysis classifier to predict the label for a 13-s window of each trial. Parameter selection, as well as the training of the classifier, were done in a person-dependent manner in a 5-fold cross-validation on the training data. We achieved an average score of approximately 85.37% accuracy on the test data (± 11.27%, range = [66.7%, 100%], 6 participants > 90%, 3 participants = 100%). Our results show that it is possible to discriminate the two states with simple machine learning mechanisms. The analysis of additionally collected data dispels doubts that we classified the difference in movement speed or task load. We conclude that a real-time assessment of internal and external attention in an AR setting in general will be possible.},
}
@article {pmid31649289,
year = {2019},
author = {Zeng, FG and Tran, P and Richardson, M and Sun, S and Xu, Y},
title = {Human Sensation of Transcranial Electric Stimulation.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {15247},
pmid = {31649289},
issn = {2045-2322},
support = {R01 DC015587/DC/NIDCD NIH HHS/United States ; T32 DC010775/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Auditory Perception ; Cochlear Nerve/physiology ; Electrodes, Implanted/standards ; *Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; *Sensation ; Transcranial Direct Current Stimulation/adverse effects/instrumentation/*methods ; Visual Perception ; },
abstract = {Noninvasive transcranial electric stimulation is increasingly being used as an advantageous therapy alternative that may activate deep tissues while avoiding drug side-effects. However, not only is there limited evidence for activation of deep tissues by transcranial electric stimulation, its evoked human sensation is understudied and often dismissed as a placebo or secondary effect. By systematically characterizing the human sensation evoked by transcranial alternating-current stimulation, we observed not only stimulus frequency and electrode position dependencies specific for auditory and visual sensation but also a broader presence of somatic sensation ranging from touch and vibration to pain and pressure. We found generally monotonic input-output functions at suprathreshold levels, and often multiple types of sensation occurring simultaneously in response to the same electric stimulation. We further used a recording circuit embedded in a cochlear implant to directly and objectively measure the amount of transcranial electric stimulation reaching the auditory nerve, a deep intercranial target located in the densest bone of the skull. We found an optimal configuration using an ear canal electrode and low-frequency (<300 Hz) sinusoids that delivered maximally ~1% of the transcranial current to the auditory nerve, which was sufficient to produce sound sensation even in deafened ears. Our results suggest that frequency resonance due to neuronal intrinsic electric properties need to be explored for targeted deep brain stimulation and novel brain-computer interfaces.},
}
@article {pmid31647886,
year = {2020},
author = {Brown, AF and Welsh, T and Panton, LB and Moffatt, RJ and Ormsbee, MJ},
title = {Higher-protein intake improves body composition index in female collegiate dancers.},
journal = {Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme},
volume = {45},
number = {5},
pages = {547-554},
doi = {10.1139/apnm-2019-0517},
pmid = {31647886},
issn = {1715-5320},
mesh = {Adolescent ; Athletic Performance ; *Body Composition ; Dancing/*physiology ; *Diet ; Dietary Proteins/*administration &amp; dosage ; Female ; Food Analysis ; Humans ; Young Adult ; },
abstract = {Aesthetic athletes strive to attain an ideal body image and the physical demands placed on dancers make their body composition and fitness equally as important as their technique. Body composition has shown positive changes in response to increased protein intake and may improve aesthetics of dance performance. The purpose of this study was to determine the extent to which supplemental whey protein (PRO) would improve body composition in female collegiate dancers compared with an isocaloric placebo (PLA). Twenty-one (age, 19.6 ± 1.4 years) female collegiate dancers were randomly assigned to consume PRO or PLA (25 g, 3×/day) for 12 weeks. Laboratory testing at weeks 0, 6, and 12 included 24-h urine collection, body composition (dual-energy X-ray absorptiometry), resting metabolic rate, and performance. Data were reported as means ± SD. Significance was accepted at p < 0.05. Body weight, fat mass, and lean soft tissue did not change between groups or over time. Body composition index (BCI = [(LSTpost - LSTpre) + (FMpre - FMpost)]; where LST is lean soft tissue, FM is fat mass, pre is pre-intervention, and post is post-intervention) significantly improved over time in PRO (+0.6 ± 1.9) but not PLA (-1.8 ± 3.1; p = 0.048); however, neither group demonstrated changes in laboratory performance tests. Protein supplementation for 12 weeks significantly improved BCI and provided a simple way to improve the diet in female collegiate dancers. Novelty Twelve weeks of protein supplementation does not change body weight in female collegiate dancers. BCI improves following protein supplementation in female collegiate dancers.},
}
@article {pmid31647453,
year = {2021},
author = {Abadia, I and Naveros, F and Garrido, JA and Ros, E and Luque, NR},
title = {On Robot Compliance: A Cerebellar Control Approach.},
journal = {IEEE transactions on cybernetics},
volume = {51},
number = {5},
pages = {2476-2489},
doi = {10.1109/TCYB.2019.2945498},
pmid = {31647453},
issn = {2168-2275},
mesh = {Action Potentials/physiology ; *Brain-Computer Interfaces ; Cerebellum/*physiology ; Humans ; *Models, Neurological ; Movement ; Neuronal Plasticity/*physiology ; *Robotics ; Upper Extremity/physiology ; },
abstract = {The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.},
}
@article {pmid31647122,
year = {2021},
author = {Nierula, B and Spanlang, B and Martini, M and Borrell, M and Nikulin, VV and Sanchez-Vives, MV},
title = {Agency and responsibility over virtual movements controlled through different paradigms of brain-computer interface.},
journal = {The Journal of physiology},
volume = {599},
number = {9},
pages = {2419-2434},
doi = {10.1113/JP278167},
pmid = {31647122},
issn = {1469-7793},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Movement ; *Sensorimotor Cortex ; },
abstract = {KEY POINTS: Embodiment of a virtual body was induced and its movements were controlled by two different brain-computer interface (BCI) paradigms - one based on signals from sensorimotor versus one from visual cortical areas. BCI-control of movements engenders agency, but not equally for all paradigms. Cortical sensorimotor activation correlates with agency and responsibility. This has significant implications for neurological rehabilitation and neuroethics.<br><br>ABSTRACT: Agency is the attribution of an action to the self and is a prerequisite for experiencing responsibility over its consequences. Here we investigated agency and responsibility by studying the control of movements of an embodied avatar, via brain-computer interface (BCI) technology, in immersive virtual reality. After induction of virtual body ownership by visuomotor correlations, healthy participants performed a motor task with their virtual body. We compared the passive observation of the subject's 'own' virtual arm performing the task with (1) the control of the movement through activation of sensorimotor areas (motor imagery) and (2) the control of the movement through activation of visual areas (steady-state visually evoked potentials). The latter two conditions were carried out using a BCI and both shared the intention and the resulting action. We found that BCI-control of movements engenders the sense of agency, which is strongest for sensorimotor area activation. Furthermore, increased activity of sensorimotor areas, as measured using EEG, correlates with levels of agency and responsibility. We discuss the implications of these results for the neural basis of agency.},
}
@article {pmid31645318,
year = {2020},
author = {Titgemeyer, Y and Surges, R and Altenmüller, DM and Fauser, S and Kunze, A and Lanz, M and Malter, MP and Nass, RD and von Podewils, F and Remi, J and von Spiczak, S and Strzelczyk, A and Ramos, RM and Kutafina, E and Jonas, SM},
title = {Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {103},
number = {Pt A},
pages = {106507},
doi = {10.1016/j.yebeh.2019.106507},
pmid = {31645318},
issn = {1525-5069},
mesh = {Adult ; *Electroencephalography/instrumentation/standards ; Epileptic Syndromes/*diagnosis ; Female ; Humans ; Male ; Middle Aged ; *Monitoring, Ambulatory/instrumentation/standards ; Pilot Projects ; Reproducibility of Results ; Seizures/*diagnosis ; Sensitivity and Specificity ; *Wearable Electronic Devices/standards ; },
abstract = {Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.},
}
@article {pmid31642810,
year = {2019},
author = {Musk, E and , },
title = {An Integrated Brain-Machine Interface Platform With Thousands of Channels.},
journal = {Journal of medical Internet research},
volume = {21},
number = {10},
pages = {e16194},
pmid = {31642810},
issn = {1438-8871},
support = {R01 NS089679/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*standards ; Humans ; Sensation/*physiology ; },
abstract = {Brain-machine interfaces hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical brain-machine interfaces have not yet been widely adopted, in part, because modest channel counts have limited their potential. In this white paper, we describe Neuralink's first steps toward a scalable high-bandwidth brain-machine interface system. We have built arrays of small and flexible electrode "threads," with as many as 3072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitization: The package for 3072 channels occupies less than 23×18.5×2 mm[3]. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 70% in chronically implanted electrodes. Neuralink's approach to brain-machine interface has unprecedented packaging density and scalability in a clinically relevant package.},
}
@article {pmid31642755,
year = {2019},
author = {Kreitmair, KV},
title = {Dimensions of Ethical Direct-to-Consumer Neurotechnologies.},
journal = {AJOB neuroscience},
volume = {10},
number = {4},
pages = {152-166},
doi = {10.1080/21507740.2019.1665120},
pmid = {31642755},
issn = {2150-7759},
mesh = {Biomedical Technology ; Brain-Computer Interfaces/*ethics ; Codes of Ethics ; Humans ; Morals ; Privacy ; Technology/*ethics ; },
abstract = {The direct-to-consumer (DTC) neurotechnology market, which includes some brain-computer interfaces, neurostimulation devices, virtual reality systems, wearables, and smartphone apps is rapidly growing. Given this technology's intimate relationship with the brain, a number of ethical dimensions must be addressed so that the technology can achieve the goal of contributing to human flourishing. This paper identifies safety, transparency, privacy, epistemic appropriateness, existential authenticity, just distribution, and oversight as such dimensions. After an initial exploration of the relevant ethical foundations for DTC neurotechnologies, this paper lays out each dimension and uses examples to justify its inclusion.},
}
@article {pmid31640635,
year = {2019},
author = {Nambuusi, BB and Ssempiira, J and Makumbi, FE and Utzinger, J and Kasasa, S and Vounatsou, P},
title = {Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda.},
journal = {BMC public health},
volume = {19},
number = {1},
pages = {1330},
pmid = {31640635},
issn = {1471-2458},
support = {grant number: IZ01Z0-147286//Swiss Programme for Research on Global Issues for Development project (R4D)/ ; grant number: 323180//European Research Council advanced grant project/ ; },
mesh = {Antimalarials/therapeutic use ; Child Health Services/*organization &amp; administration ; Child Mortality/*trends ; Child Welfare/*statistics &amp; numerical data ; Child, Preschool ; Female ; Health Surveys ; Humans ; Infant ; Infant, Newborn ; Insecticides/therapeutic use ; Proportional Hazards Models ; Risk Factors ; Social Determinants of Health/*statistics &amp; numerical data ; Uganda ; },
abstract = {BACKGROUND: To reduce the under-five mortality (U5M), fine-gained spatial assessment of the effects of health interventions is critical because national averages can obscure important sub-national disparities. In turn, sub-national estimates can guide control programmes for spatial targeting. The purpose of our study is to quantify associations of interventions with U5M rate at national and sub-national scales in Uganda and to identify interventions associated with the largest reductions in U5M rate at the sub-national scale.<br><br>METHODS: Spatially explicit data on U5M, interventions and sociodemographic indicators were obtained from the 2011 Uganda Demographic and Health Survey (DHS). Climatic data were extracted from remote sensing sources. Bayesian geostatistical Weibull proportional hazards models with spatially varying effects at sub-national scales were utilized to quantify associations between all-cause U5M and interventions at national and regional levels. Bayesian variable selection was employed to select the most important determinants of U5M.<br><br>RESULTS: At the national level, interventions associated with the highest reduction in U5M were artemisinin-based combination therapy (hazard rate ratio (HRR)&#x2009;=&#x2009;0.60; 95% Bayesian credible interval (BCI): 0.11, 0.79), initiation of breastfeeding within 1&#xa0;h of birth (HR&#x2009;=&#x2009;0.70; 95% BCI: 0.51, 0.86), intermittent preventive treatment (IPTp) (HRR&#x2009;=&#x2009;0.74; 95% BCI: 0.67, 0.97) and access to insecticide-treated nets (ITN) (HRR&#x2009;=&#x2009;0.75; 95% BCI: 0.63, 0.84). In Central 2, Mid-Western and South-West, largest reduction in U5M was associated with access to ITNs. In Mid-North and West-Nile, improved source of drinking water explained most of the U5M reduction. In North-East, improved sanitation facilities were associated with the highest decline in U5M. In Kampala and Mid-Eastern, IPTp had the largest associated with U5M. In Central1 and East-Central, oral rehydration solution and postnatal care were associated with highest decreases in U5M respectively.<br><br>CONCLUSION: Sub-national estimates of the associations between U5M and interventions can guide control programmes for spatial targeting and accelerate progress towards mortality-related Sustainable Development Goals.},
}
@article {pmid31640081,
year = {2020},
author = {Shim, S and Yun, S and Kim, S and Choi, GJ and Baek, C and Jang, J and Jung, Y and Sung, J and Park, JH and Seo, K and Seo, JM and Song, YK and Kim, SJ},
title = {A handheld neural stimulation controller for avian navigation guided by remote control.},
journal = {Bio-medical materials and engineering},
volume = {30},
number = {5-6},
pages = {497-507},
doi = {10.3233/BME-191070},
pmid = {31640081},
issn = {1878-3619},
mesh = {Animals ; Brain-Computer Interfaces ; Columbidae/physiology ; *Computers, Handheld ; Electric Power Supplies ; Electric Stimulation ; Electrodes ; Electrodes, Implanted/*veterinary ; Equipment Design ; Feasibility Studies ; Flight, Animal/*physiology ; Geographic Information Systems/instrumentation ; Orientation, Spatial/*physiology ; Remote Sensing Technology/instrumentation/veterinary ; Robotics/instrumentation/methods ; Spatial Navigation/physiology ; Wireless Technology/*instrumentation ; },
abstract = {BACKGROUND: Animal learning based on brain stimulation is an application in a brain-computer interface. Especially for birds, such a stimulation system should be sufficiently light without interfering with movements of wings.<br><br>OBJECTIVE: We proposed a fully-implantable system for wirelessly navigating a pigeon. In this paper, we report a handheld neural stimulation controller for this avian navigation guided by remote control.<br><br>METHODS: The handheld controller employs ZigBee to control pigeon's behaviors through brain stimulation. ZigBee can manipulate brain stimulation remotely while powered by batteries. Additionally, simple switches enable users to customize parameters of stimuli like a gamepad. These handheld and user-friendly interfaces make it easy to use the controller while a pigeon flies in open areas.<br><br>RESULTS: An electrode was inserted into a nucleus (formatio reticularis medialis mesencephalic) of a pigeon and connected to a stimulator fully-implanted in the pigeon's back. Receiving signals sent from the controller, the stimulator supplied biphasic pulses with a duration of 0.080&#xa0;ms and an amplitude of 0.400&#xa0;mA to the nucleus. When the nucleus was stimulated, a 180-degree turning-left behavior of the pigeon was consistently observed.<br><br>CONCLUSIONS: The feasibility of remote avian navigation using the controller was successfully verified.},
}
@article {pmid31635424,
year = {2019},
author = {Ortiz-Echeverri, CJ and Salazar-Colores, S and Rodríguez-Reséndiz, J and Gómez-Loenzo, RA},
title = {A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {20},
pages = {},
pmid = {31635424},
issn = {1424-8220},
abstract = {Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.},
}
@article {pmid31631636,
year = {2019},
author = {Zhou, X and Xu, M and Xiao, X and Chen, L and Gu, X and Ming, D},
title = {[A review of researches on electroencephalogram decoding algorithms in brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {36},
number = {5},
pages = {856-861},
pmid = {31631636},
issn = {1001-5515},
mesh = {*Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Pattern Recognition, Automated ; },
abstract = {Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.},
}
@article {pmid31631326,
year = {2020},
author = {David, L and Waldschmidt, LM and Lobedann, M and Schembecker, G},
title = {Simulation of pH level distribution inside a coiled flow inverter for continuous low pH viral inactivation.},
journal = {Biotechnology and bioengineering},
volume = {117},
number = {2},
pages = {429-437},
doi = {10.1002/bit.27201},
pmid = {31631326},
issn = {1097-0290},
support = {005-1010-0009//European Union (European Regional Development Fund)/International ; 031A616M//German Federal Ministry of Education and Research/International ; 005-1010-0009//German Federal State North Rhine-Westphalia/International ; },
mesh = {Biotechnology/*instrumentation ; Chromatography, Affinity/*instrumentation ; Computer Simulation ; Equipment Design ; Hydrodynamics ; *Hydrogen-Ion Concentration ; Staphylococcal Protein A ; *Virus Inactivation ; },
abstract = {The continuous production of monoclonal antibodies (mAbs) with the help of disposable equipment poses one of the future major changes in the pharmaceutical industry. Consequently, continuous viral clearance needs to be developed as well. The coiled flow inverter (CFI) was successfully implemented in the continuous downstream as a residence time module for low pH viral inactivation. As the elution profile of the upstream continuously operated protein A chromatography results in fluctuating pH values, the pH level distribution inside the CFI is highly relevant. This study presents a detailed investigation of pH level distribution inside the CFI at varying inlet conditions with the help of computational fluid dynamics simulation. The simulation model was validated first with the help of experimental data. Afterwards, the model was used for further investigations. It was determined that with a pH sine curve as input, the duration until steady state at the outlet requires two times the minimum residence time of the apparatus. Moreover, it could be observed that the CFI itself offers a progressive dampening effect on the pH level distribution. Afterwards, different forms of the sine curve representing different operation modes of the continuous protein A chromatograph were tested to evaluate this dampening capability. It became clear that the switch time has the highest influence on the resulting pH of the outlet stream and should be considered for process development. Finally, the radial pH profiles at different positions inside the CFI were determined. This once again revealed the high radial mixing capability of the CFI and its influence on the resulting product stream.},
}
@article {pmid31630226,
year = {2019},
author = {Shibata, E and Kaneko, F},
title = {Event-related desynchronization possibly discriminates the kinesthetic illusion induced by visual stimulation from movement observation.},
journal = {Experimental brain research},
volume = {237},
number = {12},
pages = {3233-3240},
pmid = {31630226},
issn = {1432-1106},
support = {the Development of Medical Devices//Japan Agency for Medical Research and Development/ ; Systems Advanced Medical Services//Japan Agency for Medical Research and Development/ ; },
mesh = {Adult ; Brain Waves/*physiology ; Cortical Synchronization/*physiology ; Evoked Potentials/*physiology ; Humans ; Illusions/*physiology ; Kinesthesis/*physiology ; Motion Perception/*physiology ; Photic Stimulation ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {Visual stimulation of a repetitive self-movement image can evoke kinesthetic illusion when a virtual body part is set over the actual body part (kinesthetic illusion induced by visual stimulation, KINVIS). KINVIS induces activity in cerebral network, similar to that produced during motor execution, and triggers motor imagery passively. This study sought to identify a biomarker of KINVIS using event-related desynchronization (ERD) to improve the application of KINVIS to brain-machine interface (BMI) therapy of patients with stroke with hemiparesis. We included healthy adults in whom KINVIS could be induced. Scalp electroencephalograms were recorded during the KINVIS condition, where KINVIS was induced using a self-movement image. The findings were compared to signals recorded during an observation (OB) condition where only the self-movement image was viewed. For the signal intensity of the α- and low β-frequency bands, we calculated ERD during a movie period. The ERD of the α-frequency band in P3 and CP3 during KINVIS was significantly higher than that during OB. Furthermore, using the ERD of the α-frequency band recorded from FC3 and CP3, we could discriminate illusory perception with a 70% success rate. In this study, KINVIS could be detected using the ERD of the α-frequency band recorded from the posterior portion of the sensorimotor cortex. Furthermore, adding ERD recorded from FC3 to that recorded from CP3 may enable the objective discrimination of KINVIS from OB. When applying KINVIS in BMI therapy, the combination ERD of FC3 and CP3 will become a parameter for objectively judging the degree of kinesthetic perception achieved.},
}
@article {pmid31629829,
year = {2020},
author = {Li, F and Tao, Q and Peng, W and Zhang, T and Si, Y and Zhang, Y and Yi, C and Biswal, B and Yao, D and Xu, P},
title = {Inter-subject P300 variability relates to the efficiency of brain networks reconfigured from resting- to task-state: Evidence from a simultaneous event-related EEG-fMRI study.},
journal = {NeuroImage},
volume = {205},
number = {},
pages = {116285},
doi = {10.1016/j.neuroimage.2019.116285},
pmid = {31629829},
issn = {1095-9572},
mesh = {Adult ; Cerebral Cortex/diagnostic imaging/*physiology ; *Connectome ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Nerve Net/diagnostic imaging/*physiology ; Psychomotor Performance/*physiology ; Rest/*physiology ; Young Adult ; },
abstract = {The P300 event-related potential (ERP) varies across individuals, and exploring this variability deepens our knowledge of the event, and scope for its potential applications. Previous studies exploring the P300 have relied on either electroencephalography (EEG) or functional magnetic resonance imaging (fMRI). We applied simultaneous event-related EEG-fMRI to investigate how the network structure is updated from rest to the P300 task so as to guarantee information processing in the oddball task. We first identified 14 widely distributed regions of interest (ROIs) that were task-associated, including the inferior frontal gyrus and the middle frontal gyrus, etc. The task-activated network was found to closely relate to the concurrent P300 amplitude, and moreover, the individuals with optimized resting-state brain architectures experienced the pruning of network architecture, i.e. decreasing connectivity, when the brain switched from rest to P300 task. Our present simultaneous EEG-fMRI study explored the brain reconfigurations governing the variability in P300 across individuals, which provided the possibility to uncover new biomarkers to predict the potential for personalized control of brain-computer interfaces.},
}
@article {pmid31624252,
year = {2019},
author = {Wagner, J and Martinez-Cancino, R and Delorme, A and Makeig, S and Solis-Escalante, T and Neuper, C and Mueller-Putz, G},
title = {High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.},
journal = {Scientific data},
volume = {6},
number = {1},
pages = {211},
pmid = {31624252},
issn = {2052-4463},
support = {R01 NS047293/NS/NINDS NIH HHS/United States ; 5R01-NS047293-13//U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH)/International ; },
mesh = {Adult ; Auditory Perception ; Brain/*physiology ; Cues ; *Electroencephalography ; Electromyography ; Female ; *Gait Analysis ; Humans ; Male ; Muscle, Skeletal/physiology ; Neuroimaging ; Walking ; Young Adult ; },
abstract = {In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.},
}
@article {pmid31620798,
year = {2020},
author = {Ravina, K and Lin, L and Liu, CY and Thomas, D and Hasson, D and Wang, LV and Russin, JJ},
title = {Prospects of Photo- and Thermoacoustic Imaging in Neurosurgery.},
journal = {Neurosurgery},
volume = {87},
number = {1},
pages = {11-24},
doi = {10.1093/neuros/nyz420},
pmid = {31620798},
issn = {1524-4040},
mesh = {Brain/*diagnostic imaging ; Humans ; Neuroimaging/*methods ; Neurosurgery/*methods ; Photoacoustic Techniques/*methods ; },
abstract = {The evolution of neurosurgery has been, and continues to be, closely associated with innovations in technology. Modern neurosurgery is wed to imaging technology and the future promises even more dependence on anatomic and, perhaps more importantly, functional imaging. The photoacoustic phenomenon was described nearly 140 yr ago; however, biomedical applications for this technology have only recently received significant attention. Light-based photoacoustic and microwave-based thermoacoustic technologies represent novel biomedical imaging modalities with broad application potential within and beyond neurosurgery. These technologies offer excellent imaging resolution while generally considered safer, more portable, versatile, and convenient than current imaging technologies. In this review, we summarize the current state of knowledge regarding photoacoustic and thermoacoustic imaging and their potential impact on the field of neurosurgery.},
}
@article {pmid31620349,
year = {2019},
author = {Fadel, R and El-Menyar, A and ElKafrawy, S and Gad, MG},
title = {Traumatic blunt cardiac injuries: An updated narrative review.},
journal = {International journal of critical illness and injury science},
volume = {9},
number = {3},
pages = {113-119},
pmid = {31620349},
issn = {2229-5151},
abstract = {Blunt cardiac injury (BCI) is defined as injuries sustained due to blunt trauma to the heart, and it remains unchanged for long time. The spectrum of BCI ranges from a minor "bruise" to specific postcontusion cardiac conditions such as free-wall rupture. This is a narrative review provides a continued and updates details regarding BCIs from 2008 to 2017. For this purpose, a narrative review of literature was conducted using appropriate database for retrieval of articles through systematic search methodology. Autopsy-based studies are very limited. It can be concluded that regardless of the variability in the spectrum of modalities and medical/surgical resources, BCIs diagnosis and management remain a puzzle and needs further prospective studies.},
}
@article {pmid31619680,
year = {2019},
author = {Kato, K and Sawada, M and Nishimura, Y},
title = {Bypassing stroke-damaged neural pathways via a neural interface induces targeted cortical adaptation.},
journal = {Nature communications},
volume = {10},
number = {1},
pages = {4699},
pmid = {31619680},
issn = {2041-1723},
mesh = {Adaptation, Physiological/*physiology ; Animals ; Arm ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology/physiopathology ; *Electric Stimulation ; Electrocorticography ; Hand ; Macaca fuscata ; Macaca mulatta ; Motor Cortex/physiology/*physiopathology ; Muscle, Skeletal/*physiology ; Neural Pathways/physiopathology ; Paralysis ; Somatosensory Cortex/physiology/*physiopathology ; Stroke/*physiopathology ; Stroke Rehabilitation ; Wrist ; },
abstract = {Regaining the function of an impaired limb is highly desirable in paralyzed individuals. One possible avenue to achieve this goal is to bridge the interrupted pathway between preserved neural structures and muscles using a brain-computer interface. Here, we demonstrate that monkeys with subcortical stroke were able to learn to use an artificial cortico-muscular connection (ACMC), which transforms cortical activity into electrical stimulation to the hand muscles, to regain volitional control of a paralysed hand. The ACMC induced an adaptive change of cortical activities throughout an extensive cortical area. In a targeted manner, modulating high-gamma activity became localized around an arbitrarily-selected cortical site controlling stimulation to the muscles. This adaptive change could be reset and localized rapidly to a new cortical site. Thus, the ACMC imparts new function for muscle control to connected cortical sites and triggers cortical adaptation to regain impaired motor function after stroke.},
}
@article {pmid31618215,
year = {2019},
author = {, },
title = {Expression of Concern: Brain-Computer Interface-Based Communication in the Completely Locked-In State.},
journal = {PLoS biology},
volume = {17},
number = {10},
pages = {e3000527},
pmid = {31618215},
issn = {1545-7885},
}
@article {pmid31616269,
year = {2019},
author = {Škola, F and Tinková, S and Liarokapis, F},
title = {Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {329},
pmid = {31616269},
issn = {1662-5161},
abstract = {This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. The aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, with average peak accuracy in the session = 75.84%. This confirms the proposed training method succeeded in improvement of the MI-BCI skills; moreover, participants were leaving the session in high positive affect. Although the performance was not directly correlated to the degree of embodiment, subjective magnitude of the body ownership transfer illusion correlated with the ability to modulate the sensorimotor rhythm.},
}
@article {pmid31616237,
year = {2019},
author = {Gruenwald, J and Znobishchev, A and Kapeller, C and Kamada, K and Scharinger, J and Guger, C},
title = {Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {901},
pmid = {31616237},
issn = {1662-4548},
abstract = {Invasive brain-computer interfaces yield remarkable performance in a multitude of applications. For classification experiments, high-gamma bandpower features and linear discriminant analysis (LDA) are commonly used due to simplicity and robustness. However, LDA is inherently static and not suited to account for transient information that is typically present in high-gamma features. To resolve this issue, we here present an extension of LDA to the time-variant feature space. We call this method time-variant linear discriminant analysis (TVLDA). It intrinsically provides a feature reduction stage, which makes external approaches thereto obsolete, such as feature selection techniques or common spatial patterns (CSPs). As well, we propose a time-domain whitening stage which equalizes the pronounced 1/f-shape of the typical brain-wave spectrum. We evaluated our proposed architecture based on recordings from 15 epilepsy patients with temporarily implanted subdural grids, who participated in additional research experiments besides clinical treatment. The experiments featured two different motor tasks involving three high-level gestures and individual finger movement. We used log-transformed bandpower features from the high-gamma band (50-300 Hz, excluding power-line harmonics) for classification. On average, whitening improved the classification performance by about 11%. On whitened data, TVLDA outperformed LDA with feature selection by 11.8%, LDA with CSPs by 13.9%, and regularized LDA with vectorized features by 16.4%. At the same time, TVLDA only required one or two internal features to achieve this. TVLDA provides stable results even if very few trials are available. It is easy to implement, fully automatic and deterministic. Due to its low complexity, TVLDA is suited for real-time brain-computer interfaces. Training is done in less than a second. TVLDA performed particularly well in experiments with data from high-density electrode arrays. For example, the three high-level gestures were correctly identified at a rate of 99% over all subjects. Similarly, the decoding accuracy of individual fingers was 96% on average over all subjects. To our knowledge, these mean accuracies are the highest ever reported for three-class and five-class motor-control BCIs.},
}
@article {pmid31614343,
year = {2020},
author = {Gurve, D and Delisle-Rodriguez, D and Romero-Laiseca, M and Cardoso, V and Loterio, F and Bastos, T and Krishnan, S},
title = {Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition.},
journal = {Journal of neural engineering},
volume = {17},
number = {2},
pages = {026029},
doi = {10.1088/1741-2552/ab4dba},
pmid = {31614343},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Lower Extremity ; },
abstract = {OBJECTIVE: This study aims to propose and validate a subject-specific approach to recognize two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the relevant electroencephalogram (EEG) channels. The main aims of the proposed work are: (i) to reduce the computational complexity of the BCI systems during MI detection by selecting the relevant EEG channels, (ii) to reduce the amount of data overfitting that may arise due to unnecessary channels and redundant features, and (iii) to reduce the classification time for real-time BCI applications.<br><br>APPROACH: The proposed method selects subject-specific EEG channels and features based on their MI. In this work, we make use of non-negative matrix factorization to extract the weight of the EEG channels based on their contribution to MI detection. Further, the neighborhood component analysis is used for subject-specific feature selection.<br><br>MAIN RESULTS: We executed the experiments using EEG signals recorded for MI where ten healthy subjects performed MI movement of the lower limb to generate motor commands. An average accuracy of 96.66%, average true positive rate (TPR) of 97.77%, average false positives rate of 4.44%, and average Kappa of 93.33% were obtained. The proposed subject-specific EEG channel selection based MI recognition system provides 13.20% improvement in detection accuracy, and 27% improvement in Kappa value with less number of EEG channels compared to the results obtained using all EEG channels.<br><br>SIGNIFICANCE: The proposed subject-specific BCI system has been found significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduce computational complexity and processing time (two times faster) but also improve the MI detection performance. The proposed method selects EEG locations related to the foot movement, which may be relevant for neuro-rehabilitation using lower-limb movements that may provide a real-time and more natural interface between patient and robotic device.},
}
@article {pmid31614342,
year = {2020},
author = {Ke, Y and Liu, P and An, X and Song, X and Ming, D},
title = {An online SSVEP-BCI system in an optical see-through augmented reality environment.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016066},
doi = {10.1088/1741-2552/ab4dc6},
pmid = {31614342},
issn = {1741-2552},
mesh = {Adult ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Robotics/instrumentation/methods ; Young Adult ; },
abstract = {OBJECTIVE: This study aimed to design and evaluate a high-speed online steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI) in an optical see-through (OST) augmented reality (AR) environment.<br><br>APPROACH: An eight-class BCI was designed in an OST-AR headset which is wearable and allows users to see the user interface of the BCI and the device to be controlled in the same view field via the OST head-mounted display. The accuracies, information transfer rates (ITRs), and SSVEP signal characteristics of the AR-BCI were evaluated and compared with a computer screen-based BCI implemented with a laptop in offline and online cue-guided tasks. Then, the performance of the AR-BCI was evaluated in an online robotic arm control task.<br><br>MAIN RESULTS: The offline results obtained during the cue-guided task performed with the AR-BCI showed maximum averaged ITRs of 65.50&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;9.86 bits min[-1] according to the extended canonical correlation analysis-based target identification method. The online cue-guided task achieved averaged ITRs of 65.03&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;11.40 bits min[-1]. The online robotic arm control task achieved averaged ITRs of 45.57&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;7.40 bits min[-1]. Compared with the screen-based BCI, some limitations of the AR environment impaired BCI performance and the quality of SSVEP signals.<br><br>SIGNIFICANCE: The results showed the potential for providing a high-performance brain-control interaction method by combining AR and BCI. This study could provide methodological guidelines for developing more wearable BCIs in OST-AR environments and will also encourage more interesting applications involving BCIs and AR techniques.},
}
@article {pmid31614183,
year = {2020},
author = {Emami, Z and Chau, T},
title = {The effects of visual distractors on cognitive load in a motor imagery brain-computer interface.},
journal = {Behavioural brain research},
volume = {378},
number = {},
pages = {112240},
doi = {10.1016/j.bbr.2019.112240},
pmid = {31614183},
issn = {1872-7549},
mesh = {Adult ; Alpha Rhythm/*physiology ; Attention/*physiology ; *Brain-Computer Interfaces ; Female ; Fingers/physiology ; Humans ; Imagination/*physiology ; Male ; Memory, Short-Term/*physiology ; Motor Activity/*physiology ; Parietal Lobe/*physiology ; Pattern Recognition, Visual/*physiology ; Theta Rhythm/*physiology ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a system that translates neural activity into a practical output. Its functionality, therefore, depends not only on the computer itself, but also on the cognitive system of the user. Distractors have the potential to capture attention, increase cognitive load, and may therefore impact BCI use. The purpose of the current study is to determine the effects of small visual distractors on the cognitive load of users of a motor imagery-BCI, and to examine whether these distractor-mediated effects can be improved by modifying the task interface. Sixteen typically-developed participants completed two sessions of online motor imagery to control an EEG-BCI, under conditions of no distractors, visual distractors, and cognitive strategies (intended to mitigate cognitive load) amid distractors. Cognitive load for each session was assessed through both a ratio of theta to alpha power and the NASA-Task Load Index (NASA-TLX). Task-irrelevant visual stimuli were found to significantly increase the objective measure of cognitive load, particularly for parietal channels. Subjective cognitive load as indexed by the NASA-TLX was predictive of a decrease in BCI performance for participants with below 0.75 classification accuracy (R[2] = 0.32, p < 0.001), which may indicate a differential susceptibility to changes in workload for "low"-performing participants. Quantifying and addressing the increased cognitive load imparted by distractors on BCI users can aid in the future applicability of the technology in real-world settings.},
}
@article {pmid31613780,
year = {2019},
author = {Valencia, D and Thies, J and Alimohammad, A},
title = {Frameworks for Efficient Brain-Computer Interfacing.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {6},
pages = {1714-1722},
doi = {10.1109/TBCAS.2019.2947130},
pmid = {31613780},
issn = {1940-9990},
mesh = {Action Potentials ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Data Compression/*methods ; Humans ; Lab-On-A-Chip Devices ; Models, Neurological ; Signal Processing, Computer-Assisted/*instrumentation ; Wireless Technology ; },
abstract = {One challenge present in brain-computer interface (BCI) circuits is finding a balance between real-time on-chip processing in-vivo and wireless transmission of neural signals for off-chip in-silico processing. This article presents three potential frameworks for investigating an area- and energy-efficient realization of BCI circuits. The first framework performs spike detection on the filtered neural signal on a brain-implantable chip and only transmits detected spikes wirelessly for offline classification and decoding. The second framework performs in-vivo compression of the on-chip detected spikes prior to wireless transmission for substantially reducing wireless transmission overhead. The third framework performs spike sorting in-vivo on the brain-implantable chip to classify detected spikes on-chip and hence, even further reducing wireless data transmission rate at the expense of more signal processing. To alleviate the on-chip computation of spike sorting and also utilizing a more area- and energy-effective design, this work employs, for the first time, to the best of our knowledge, an artificial neural network (ANN) instead of using relatively computationally-intensive conventional spike sorting algorithms. The ASIC implementation results of the designed frameworks are presented and their feasibility for efficient in-vivo processing of neural signals is discussed. Compared to the previously-published BCI systems, the presented frameworks reduce the area and power consumption of implantable circuits.},
}
@article {pmid31611912,
year = {2019},
author = {Zeng, H and Yang, C and Zhang, H and Wu, Z and Zhang, J and Dai, G and Babiloni, F and Kong, W},
title = {A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {3761203},
pmid = {31611912},
issn = {1687-5273},
mesh = {Algorithms ; Automobile Driving/psychology ; Brain/*physiology ; *Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. The comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI).},
}
@article {pmid31611770,
year = {2019},
author = {Lu, Z and Li, Q and Gao, N and Yang, J and Bai, O},
title = {A Novel Audiovisual P300-Speller Paradigm Based on Cross-Modal Spatial and Semantic Congruence.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1040},
pmid = {31611770},
issn = {1662-4548},
abstract = {OBJECTIVE: Although many studies have attempted to improve the performance of the visual-based P300-speller system, its performance is still not satisfactory. The current system has limitations for patients with neurodegenerative diseases, in which muscular control of the eyes may be impaired or deteriorate over time. Some studies have shown that the audiovisual stimuli with spatial and semantic congruence elicited larger event-related potential (ERP) amplitudes than do unimodal visual stimuli. Therefore, this study proposed a novel multisensory P300-speller based on audiovisual spatial and semantic congruence.<br><br>METHODS: We designed a novel audiovisual P300-speller paradigm (AV spelling paradigm) in which the pronunciation and visual presentation of characters were matched in spatial position and semantics. We analyzed the ERP waveforms elicited in the AV spelling paradigm and visual-based spelling paradigm (V spelling paradigm) and compared the classification accuracies between these two paradigms.<br><br>RESULTS: ERP analysis revealed significant differences in ERP amplitudes between the two paradigms in the following areas (AV > V): the frontal area at 60-140 ms, frontal-central-parietal area at 360-460 ms, frontal area at 700-800 ms, right temporal area at 380-480 and 700-780 ms, and left temporal area at 500-780 ms. Offline classification results showed that the accuracies were significantly higher in the AV spelling paradigm than in the V spelling paradigm after superposing 1, 2, 5, 6, 9, and 10 times (P < 0.05), and there were trends toward improvement in the accuracies at superposing 3, 4, 7, and 8 times (P = 0.06). Similar results were found for information transfer rate between V and AV spelling paradigms at 1, 2, 5, 6, and 10 superposition times (P < 0.05).<br><br>SIGNIFICANCE: The proposed audiovisual P300-speller paradigm significantly improved the classification accuracies compared with the visual-based P300-speller paradigm. Our novel paradigm combines spatial and semantic features of two sensory modalities, and the present findings provide valuable insights into the development of multimodal ERP-based BCI paradigms.},
}
@article {pmid31607880,
year = {2019},
author = {Klein, F and Kranczioch, C},
title = {Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {331},
pmid = {31607880},
issn = {1662-5161},
abstract = {Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with the task frequency. This is a problem when signals are averaged for analysis, even more so when single trial data are used as in online neurofeedback or BCI applications where insufficiently preprocessed data means feeding back noise instead of brain activity or when looking for brain-behavior relationships on a trial-by-trial basis. For removing this task-related noise statistical approaches have been proposed. Yet as evidence is lacking on how these approaches perform on independent data, choosing one approach over another can be difficult. Here signal quality at the single trial level was considered together with statistical effects to inform this choice. Compared were conventional band-pass filtering and wavelet minimum description length detrending and the combination of both with a more elaborate, published preprocessing approach for a motor execution-motor imagery data set. Temporal consistency between Δ[HbO] and Δ[HbR] and two measures of the spatial specificity of signals that are proposed here served as measures of data quality. Both improved strongly for the combinationed preprocessing approaches. Statistical effects showed a strong tendency toward getting smaller for the combined approaches. This underlines the importance to adequately deal with noise in fNIRS recordings and demonstrates how the quality of statistical correction approaches can be estimated.},
}
@article {pmid31607854,
year = {2019},
author = {Kirin, SC and Yanagisawa, T and Oshino, S and Edakawa, K and Tanaka, M and Kishima, H and Nishimura, Y},
title = {Somatosensation Evoked by Cortical Surface Stimulation of the Human Primary Somatosensory Cortex.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {1019},
pmid = {31607854},
issn = {1662-4548},
abstract = {Electrical stimulation of the primary somatosensory cortex using intracranial electrodes is crucial for the evocation of artificial somatosensations, typically tactile sensations associated with specific regions of the body, in brain-machine interface (BMI) applications. The qualitative characteristics of these artificially evoked somatosensations has been well documented. As of yet, however, the quantitative aspects of these evoked somatosensations, that is to say the quantitative relationship between intensity of electrical stimulation and perceived intensity of the resultant somatosensation remains obscure. This study aimed to explore this quantitative relationship by surface electrical stimulation of the primary somatosensory cortex in two human participants undergoing electrocorticographic monitoring prior to surgical treatment of intractable epilepsy. Electrocorticogram electrodes on the primary somatosensory cortical surface were stimulated with varying current intensities, and a visual analogue scale was employed to provide a quantitative measure of intensity of the evoked sensations. Evoked sensations included those of the thumb, tongue, and hand. A clear linear relationship between current intensity and perceived intensity of sensation was observed. These findings provide novel insight into the quantitative nature of primary somatosensory cortex electrical stimulation-evoked sensation for development of somatosensory neuroprosthetics for clinical use.},
}
@article {pmid31603790,
year = {2019},
author = {Cui, Y and Xu, Y and Wu, D},
title = {EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {11},
pages = {2263-2273},
doi = {10.1109/TNSRE.2019.2945794},
pmid = {31603790},
issn = {1558-0210},
mesh = {Accidents, Traffic ; Adult ; Algorithms ; Automobile Driving/*psychology ; Calibration ; Electroencephalography/*methods ; Female ; Humans ; Individuality ; Male ; Reaction Time ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Wakefulness/*physiology ; Young Adult ; },
abstract = {Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may improve driving safety. However, individual differences among different drivers make this task very challenging. A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly. Many approaches have been proposed to reduce the calibration effort, but few can completely eliminate it. This paper proposes a novel approach, feature weighted episodic training (FWET), to completely eliminate the calibration requirement. It integrates two techniques: feature weighting to learn the importance of different features, and episodic training for domain generalization. Experiments on EEG-based driver drowsiness estimation demonstrated that both feature weighting and episodic training are effective, and their integration can further improve the generalization performance. FWET does not need any labelled or unlabelled calibration data from the new subject, and hence could be very useful in plug-and-play brain-computer interfaces.},
}
@article {pmid31601871,
year = {2019},
author = {Dijkstra, K and Farquhar, J and Desain, P},
title = {Electrophysiological responses of relatedness to consecutive word stimuli in relation to an actively recollected target word.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {14514},
pmid = {31601871},
issn = {2045-2322},
abstract = {In this paper, we investigate the robustness of electrophysiological responses of relatedness to multiple consecutive word stimuli (probes), in relation to an actively recollected target word. Such relatedness information could be used by a Brain Computer Interface to infer the active semantic concept on a user's mind, by integrating the knowledge of the relationship between the multiple probe words and the 'unknown' target. Such a BCI can take advantage of the N400: an event related potential that is sensitive to semantic content of a stimulus in relation to an established semantic context. However, it is unknown whether the N400 is suited for the multiple probing paradigm we propose, as other intervening words might distract from the established context (i.e., the target word). We perform an experiment in which we present up to ten words after an initial target word, and find no attenuation of the strength of the N400 in grand average ERPs and no decrease in classification accuracy for probes occurring later in the sequences. These results are groundwork for developing a BCI that infers the concept on a user's mind through repeated probing, however, low single trial decoding accuracy, and high subject variability may limit practical applicability.},
}
@article {pmid31597125,
year = {2019},
author = {Hosni, SM and Deligani, RJ and Zisk, A and McLinden, J and Borgheai, SB and Shahriari, Y},
title = {An exploration of neural dynamics of motor imagery for people with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016005},
pmid = {31597125},
issn = {1741-2552},
support = {P20 GM103430/GM/NIGMS NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*physiopathology/psychology ; *Brain-Computer Interfaces/psychology ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Studies of the neuropathological effects of amyotrophic lateral sclerosis (ALS) on the underlying motor system have investigated abnormalities in the magnitude and timing of the event-related desynchronization (ERD) and synchronization (ERS) during motor execution (ME). However, the spatio-spectral-temporal dynamics of these sensorimotor oscillations during motor imagery (MI) have not been fully explored for these patients. This study explores the neural dynamics of sensorimotor oscillations for ALS patients during MI by quantifying ERD/ERS features in frequency, time, and space.<br><br>APPROACH: Electroencephalogram (EEG) data were recorded from six patients with ALS and 11 age-matched healthy controls (HC) while performing a MI task. ERD/ERS features were extracted using wavelet-based time-frequency analysis and compared between the two groups to quantify the abnormal neural dynamics of ALS in terms of both time and frequency. Topographic correlation analysis was conducted to compare the localization of MI activity between groups and to identify subject-specific frequencies in the &#xb5; and &#x3b2; frequency bands.<br><br>MAIN RESULTS: Overall, reduced and delayed ERD was observed for ALS patients, particularly during right-hand MI. ERD features were also correlated with ALS clinical scores, specifically disease duration, bulbar, and cognitive functions.<br><br>SIGNIFICANCE: The analyses in this study quantify abnormalities in the magnitude and timing of sensorimotor oscillations for ALS patients during MI tasks. Our findings reveal notable differences between MI and existing results on ME in ALS. The observed alterations are speculated to reflect disruptions in the underlying cortical networks involved in MI functions. Quantifying the neural dynamics of MI plays an important role in the study of EEG-based cortical markers for ALS.},
}
@article {pmid31597123,
year = {2019},
author = {Barra, B and Badi, M and Perich, MG and Conti, S and Mirrazavi Salehian, SS and Moreillon, F and Bogaard, A and Wurth, S and Kaeser, M and Passeraub, P and Milekovic, T and Billard, A and Micera, S and Capogrosso, M},
title = {A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016004},
doi = {10.1088/1741-2552/ab4c77},
pmid = {31597123},
issn = {1741-2552},
support = {F30 NS100253/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Equipment Design/instrumentation/*methods ; Female ; Hand Strength/*physiology ; Haplorhini ; Macaca fascicularis ; Microelectrodes ; Movement/*physiology ; Psychomotor Performance/*physiology ; Robotics/instrumentation/*methods ; Sensorimotor Cortex/physiology ; Upper Extremity/*physiology ; },
abstract = {OBJECTIVE: Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-defined upper limb tasks for monkeys.<br><br>APPROACH: We configured the platform to position instrumented objects in a three-dimensional workspace and provide an interactive dynamic force-field.<br><br>MAIN RESULTS: We show the relevance of our platform for fundamental and translational studies with three example applications. First, we study the kinematics of natural grasp in response to variable interaction forces. We then show simultaneous and independent encoding of kinematic and forces in single unit intra-cortical recordings from sensorimotor cortical areas. Lastly, we demonstrate the relevance of our platform to develop clinically relevant brain computer interfaces in a kinematically unconstrained motor task.<br><br>SIGNIFICANCE: Our versatile control structure does not depend on the specific robotic arm used and allows for the design and implementation of a variety of tasks that can support both fundamental and translational studies of motor control.},
}
@article {pmid31594269,
year = {2020},
author = {Hussain, L and Aziz, W and Alshdadi, AA and Abbasi, AA and Majid, A and Marchal, AR},
title = {Multiscale entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {28},
number = {3},
pages = {259-273},
doi = {10.3233/THC-191803},
pmid = {31594269},
issn = {1878-7401},
mesh = {Brain Mapping/*methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Foot/physiology ; Hand/physiology ; Humans ; Movement/*physiology ; Wavelet Analysis ; },
abstract = {BACKGROUND: Brain neural activity is measured using electroencephalography (EEG) recording from the scalp. The EEG motor/imagery tasks help disabled people to communicate with the external environment.<br><br>OBJECTIVE: In this paper, robust multiscale sample entropy (MSE) and wavelet entropy measures are employed using topographic maps' analysis and tabulated form to quantify the dynamics of EEG motor movements tasks with actual and imagery opening and closing of fist or feet movements.<br><br>METHODS: To distinguish these conditions, we used the topographic maps which visually show the significance level of the brain regions and probes for dominant activities. The paired t-test and Posthoc Tukey test are used to find the significance levels.<br><br>RESULTS: The topographic maps results obtained using MSE reveal that maximum electrodes show the significance in frontpolar, frontal, and few frontal and parietal brain regions at temporal scales 3, 4, 6 and 7. Moreover, it was also observed that the distribution of significance is from frontoparietal brain regions. Using wavelet entropy, the significant results are obtained at frontpolar, frontal, and few electrodes in right hemisphere. The highest significance is obtained at frontpolar electrodes followed by frontal and few central and parietal electrodes.},
}
@article {pmid31591860,
year = {2020},
author = {Ku, CH and Kim, SW and Kim, JY and Paik, SW and Yang, HJ and Lee, JH and Seo, YJ},
title = {Measurement of Skull Size on Computed Tomography Images for Developing a Bone Conduction Headset Suitable for the Korean Standard Head Size.},
journal = {Journal of audiology &amp; otology},
volume = {24},
number = {1},
pages = {17-23},
pmid = {31591860},
issn = {2384-1621},
support = {//Ministry of Trade, Industry and Energy/ ; 2019K1A3A1A47000527//National Research Foundation of Korea/ ; },
abstract = {BACKGROUND AND OBJECTIVES: We aimed to measure the head dimensions on computed tomography (CT) images, to compare them to directly measured head dimensions, and to predict a new parameter of bone thickness for aiding bone conduction implant (BCI) placement.<br><br>SUBJECTS AND METHODS: We reviewed the facial and mandibular bone CT images of 406 patients. Their head sizes were analyzed using five parameters included in the 6th Size Korea project, and they were divided into age groups (ranging from the 10s to the 80s). We compared the head length, head width, sagittal arc, bitragion arc, and head circumference in the CT and Size Korea groups. We also added the parameter bone thickness for aiding BCI placement.<br><br>RESULTS: All the head size parameters measured using CT were significantly smaller than those measured directly, with head length showing the smallest difference at 7.85 mm. The differences in the other four parameters between the two groups according to patient age were not statistically significantly different. Bone thickness had the highest value of 4.89&#xb1;0.93 mm in the 70s and the lowest value of 4.10&#xb1;0.99 mm in the 10s. Bone thickness also significantly correlated with head width (p=0.038).<br><br>CONCLUSIONS: Our findings suggested that the CT and direct measurements yielded consistent data. Moreover, CT enabled the measurement of bone sizes, including bone thickness, that are impossible to measure directly. CT measurements may complement direct measurements in the Size Korea data when used for developing bone conduction hearing devices (BCIs and headsets) for the Korean population.},
}
@article {pmid31591224,
year = {2019},
author = {O'Doherty, JE and Shokur, S and Medina, LE and Lebedev, MA and Nicolelis, MAL},
title = {Creating a neuroprosthesis for active tactile exploration of textures.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {116},
number = {43},
pages = {21821-21827},
pmid = {31591224},
issn = {1091-6490},
support = {DP1 OD006798/OD/NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation ; Feedback, Sensory/*physiology ; Macaca mulatta ; Pattern Recognition, Physiological/*physiology ; Prostheses and Implants ; Somatosensory Cortex/physiology ; Touch/*physiology ; },
abstract = {Intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1) can produce percepts that mimic somatic sensation and, thus, has potential as an approach to sensorize prosthetic limbs. However, it is not known whether ICMS could recreate active texture exploration-the ability to infer information about object texture by using one's fingertips to scan a surface. Here, we show that ICMS of S1 can convey information about the spatial frequencies of invisible virtual gratings through a process of active tactile exploration. Two rhesus monkeys scanned pairs of visually identical screen objects with the fingertip of a hand avatar-controlled first via a joystick and later via a brain-machine interface-to find the object with denser virtual gratings. The gratings consisted of evenly spaced ridges that were signaled through individual ICMS pulses generated whenever the avatar's fingertip crossed a ridge. The monkeys learned to interpret these ICMS patterns, evoked by the interplay of their voluntary movements and the virtual textures of each object, to perform a sensory discrimination task. Discrimination accuracy followed Weber's law of just-noticeable differences (JND) across a range of grating densities; a finding that matches normal cutaneous sensation. Moreover, 1 monkey developed an active scanning strategy where avatar velocity was integrated with the ICMS pulses to interpret the texture information. We propose that this approach could equip upper-limb neuroprostheses with direct access to texture features acquired during active exploration of natural objects.},
}
@article {pmid31590628,
year = {2020},
author = {Suneel, D and Davidson, LS and Lieu, J},
title = {Self-reported hearing quality of life measures in pediatric cochlear implant recipients with bilateral input.},
journal = {Cochlear implants international},
volume = {21},
number = {2},
pages = {83-91},
pmid = {31590628},
issn = {1754-7628},
support = {R01 DC012778/DC/NIDCD NIH HHS/United States ; T35 DC008765/DC/NIDCD NIH HHS/United States ; },
mesh = {Child ; Cochlear Implantation/*psychology ; Cochlear Implants/psychology ; Correction of Hearing Impairment/instrumentation/methods/*psychology ; Female ; Hearing Aids/*psychology ; Hearing Loss, Bilateral/*psychology/rehabilitation ; Humans ; Male ; Patient Reported Outcome Measures ; Quality of Life/*psychology ; Self Report ; Speech Perception ; Speech Reception Threshold Test ; Treatment Outcome ; },
abstract = {Objective: Self-reported hearing quality of life (QoL) for pediatric cochlear implant (CI) recipients was examined, asking whether 1) children with CIs have similar QoL as those with less severe hearing loss (HL); 2) children with different bilateral CI (BCI) device configurations report different QoL; and 3) do audiological, demographic and spoken language factors affect hearing QoL?Design: One hundred four children (ages 7-11 years) using bimodal devices or BCIs participated. The Hearing Environments and Reflection of Quality of Life (HEAR-QL) questionnaire, receptive language and speech perception tests were administered. HEAR-QL scores of CI recipients were compared to scores of age-mates with normal hearing and mild to profound HL.Results: HEAR-QL scores for CI participants were similar to those of children with less severe HL and did not differ with device configuration. Emotion identification and word recognition in noise correlated significantly with HEAR-QL scores.Discussion: CI recipients reported that HL hinders social participation. Better understanding of speech in noise and emotional content was associated with fewer hearing-related difficulties on the HEAR-QL.Conclusions: Noisy situations encountered in educational settings should be addressed for children with HL. The link between perception of emotion and hearing-related QoL for CI recipients should be further examined.},
}
@article {pmid31588169,
year = {2018},
author = {Koçanaoğulları, A and Marghi, YM and Akçakaya, M and Erdoğmuş, D},
title = {Optimal Query Selection Using Multi-Armed Bandits.},
journal = {IEEE signal processing letters},
volume = {25},
number = {12},
pages = {1870-1874},
pmid = {31588169},
issn = {1070-9908},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Query selection for latent variable estimation is conventionally performed by opting for observations with low noise or optimizing information theoretic objectives related to reducing the level of estimated uncertainty based on the current best estimate. In these approaches, typically the system makes a decision by leveraging the current available information about the state. However, trusting the current best estimate results in poor query selection when truth is far from the current estimate, and this negatively impacts the speed and accuracy of the latent variable estimation procedure. We introduce a novel sequential adaptive action value function for query selection using the multi-armed bandit (MAB) framework which allows us to find a tractable solution. For this adaptive-sequential query selection method, we analytically show: (i) performance improvement in the query selection for a dynamical system, (ii) the conditions where the model outperforms competitors. We also present favorable empirical assessments of the performance for this method, compared to alternative methods, both using Monte Carlo simulations and human-in-the-loop experiments with a brain computer interface (BCI) typing system where the language model provides the prior information.},
}
@article {pmid31587955,
year = {2019},
author = {Benabid, AL and Costecalde, T and Eliseyev, A and Charvet, G and Verney, A and Karakas, S and Foerster, M and Lambert, A and Morinière, B and Abroug, N and Schaeffer, MC and Moly, A and Sauter-Starace, F and Ratel, D and Moro, C and Torres-Martinez, N and Langar, L and Oddoux, M and Polosan, M and Pezzani, S and Auboiroux, V and Aksenova, T and Mestais, C and Chabardes, S},
title = {An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration.},
journal = {The Lancet. Neurology},
volume = {18},
number = {12},
pages = {1112-1122},
doi = {10.1016/S1474-4422(19)30321-7},
pmid = {31587955},
issn = {1474-4465},
mesh = {Adult ; *Brain-Computer Interfaces ; Cervical Vertebrae/diagnostic imaging/injuries/surgery ; Epidural Space/diagnostic imaging/surgery ; *Exoskeleton Device ; Humans ; *Implantable Neurostimulators ; Magnetic Resonance Imaging/methods ; Magnetoencephalography/methods ; Male ; *Proof of Concept Study ; Quadriplegia/diagnostic imaging/*rehabilitation/surgery ; Sensorimotor Cortex/diagnostic imaging/surgery ; Spinal Cord Injuries/diagnostic imaging/rehabilitation/surgery ; *Wireless Technology/instrumentation ; },
abstract = {BACKGROUND: Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton.<br><br>METHODS: We recruited two participants at Clinatec research centre, associated with Grenoble University Hospital, Grenoble, France, into our ongoing clinical trial. Inclusion criteria were age 18-45 years, stability of neurological deficits, a need for additional mobility expressed by the patient, ambulatory or hospitalised monitoring, registration in the French social security system, and signed informed consent. The exclusion criteria were previous brain surgery, anticoagulant treatments, neuropsychological sequelae, depression, substance dependence or misuse, and contraindications to magnetoencephalography (MEG), EEG, or MRI. One participant was excluded because of a technical problem with the implants. The remaining participant was a 28-year-old man, who had tetraplegia following a C4-C5 spinal cord injury. Two bilateral wireless epidural recorders, each with 64 electrodes, were implanted over the upper limb sensorimotor areas of the brain. Epidural electrocorticographic (ECoG) signals were processed online by an adaptive decoding algorithm to send commands to effectors (virtual avatar or exoskeleton). Throughout the 24 months of the study, the patient did various mental tasks to progressively increase the number of degrees of freedom.<br><br>FINDINGS: Between June 12, 2017, and July 21, 2019, the patient cortically controlled a programme that simulated walking and made bimanual, multi-joint, upper-limb movements with eight degrees of freedom during various reach-and-touch tasks and wrist rotations, using a virtual avatar at home (64&#xb7;0% [SD 5&#xb7;1] success) or an exoskeleton in the laboratory (70&#xb7;9% [11&#xb7;6] success). Compared with microelectrodes, epidural ECoG is semi-invasive and has similar efficiency. The decoding models were reusable for up to approximately 7 weeks without recalibration.<br><br>INTERPRETATION: These results showed long-term (24-month) activation of a four-limb neuroprosthetic exoskeleton by a complete brain-machine interface system using continuous, online epidural ECoG to decode brain activity in a tetraplegic patient. Up to eight degrees of freedom could be simultaneously controlled using a unique model, which was reusable without recalibration for up to about 7 weeks.<br><br>FUNDING: French Atomic Energy Commission, French Ministry of Health, Edmond J Safra Philanthropic Foundation, Fondation Motrice, Fondation Nanosciences, Institut Carnot, Fonds de Dotation Clinatec.},
}
@article {pmid31587954,
year = {2019},
author = {Shakespeare, T and Watson, N},
title = {Is a four-limb exoskeleton a step in the wrong direction?.},
journal = {The Lancet. Neurology},
volume = {18},
number = {12},
pages = {1071-1072},
doi = {10.1016/S1474-4422(19)30352-7},
pmid = {31587954},
issn = {1474-4465},
mesh = {*Brain-Computer Interfaces ; *Exoskeleton Device ; Gait ; },
}
@article {pmid31585454,
year = {2020},
author = {Hou, Y and Zhou, L and Jia, S and Lun, X},
title = {A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016048},
doi = {10.1088/1741-2552/ab4af6},
pmid = {31585454},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Networks, Computer ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: To develop and implement a novel approach which combines the technique of scout EEG source imaging (ESI) with convolutional neural network (CNN) for the classification of motor imagery (MI) tasks.<br><br>APPROACH: The technique of ESI uses a boundary element method (BEM) and weighted minimum norm estimation (WMNE) to solve the EEG forward and inverse problems, respectively. Ten scouts are then created within the motor cortex to select the region of interest (ROI). We extract features from the time series of scouts using a Morlet wavelet approach. Lastly, CNN is employed for classifying MI tasks.<br><br>MAIN RESULTS: The overall mean accuracy on the Physionet database reaches 94.5% and the individual accuracy of each task reaches 95.3%, 93.3%, 93.6%, 96% for the left fist, right fist, both fists and both feet, correspondingly, validated using ten-fold cross validation. We report an increase of up to 14.4% for overall classification compared with the competitive results from the state-of-the-art MI classification methods. Then, we add four new subjects to verify the validity of the method and the overall mean accuracy is 92.5%. Furthermore, the global classifier was adapted to single subjects improving the overall mean accuracy to 94.54%.<br><br>SIGNIFICANCE: The combination of scout ESI and CNN enhances BCI performance of decoding EEG four-class MI tasks.},
}
@article {pmid31585451,
year = {2019},
author = {Wang, PT and Camacho, E and Wang, M and Li, Y and Shaw, SJ and Armacost, M and Gong, H and Kramer, D and Lee, B and Andersen, RA and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {A benchtop system to assess the feasibility of a fully independent and implantable brain-machine interface.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066043},
pmid = {31585451},
issn = {1741-2552},
support = {R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods ; *Electrodes, Implanted ; Equipment Design/instrumentation/*methods ; Feasibility Studies ; Humans ; },
abstract = {OBJECTIVE: State-of-the-art invasive brain-machine interfaces (BMIs) have shown significant promise, but rely on external electronics and wired connections between the brain and these external components. This configuration presents health risks and limits practical use. These limitations can be addressed by designing a fully implantable BMI similar to existing FDA-approved implantable devices. Here, a prototype BMI system whose size and power consumption are comparable to those of fully implantable medical devices was designed and implemented, and its performance was tested at the benchtop and bedside.<br><br>APPROACH: A prototype of a fully implantable BMI system was designed and implemented as a miniaturized embedded system. This benchtop analogue was tested in its ability to acquire signals, train a decoder, perform online decoding, wirelessly control external devices, and operate independently on battery. Furthermore, performance metrics such as power consumption were benchmarked.<br><br>MAIN RESULTS: An analogue of a fully implantable BMI was fabricated with a miniaturized form factor. A patient undergoing epilepsy surgery evaluation with an electrocorticogram (ECoG) grid implanted over the primary motor cortex was recruited to operate the system. Seven online runs were performed with an average binary state decoding accuracy of 87.0% (lag optimized, or 85.0% at fixed latency). The system was powered by a wirelessly rechargeable battery, consumed&#x2009;&#x2009;&#x223c;150 mW, and operated for&#x2009;&#x2009;>60&#x2009;h on a single battery cycle.<br><br>SIGNIFICANCE: The BMI analogue achieved immediate and accurate decoding of ECoG signals underlying hand movements. A wirelessly rechargeable battery and other supporting functions allowed the system to function independently. In addition to the small footprint and acceptable power and heat dissipation, these results suggest that fully implantable BMI systems are feasible.},
}
@article {pmid31585085,
year = {2019},
author = {Kim, J and Gulati, T and Ganguly, K},
title = {Competing Roles of Slow Oscillations and Delta Waves in Memory Consolidation versus Forgetting.},
journal = {Cell},
volume = {179},
number = {2},
pages = {514-526.e13},
pmid = {31585085},
issn = {1097-4172},
support = {I01 RX001640/RX/RRD VA/United States ; K02 NS093014/NS/NINDS NIH HHS/United States ; R00 NS097620/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Delta Rhythm ; Male ; Memory Consolidation/*physiology ; Rats ; Rats, Long-Evans ; Sleep/*physiology ; },
abstract = {Sleep has been implicated in both memory consolidation and forgetting of experiences. However, it is unclear what governs the balance between consolidation and forgetting. Here, we tested how activity-dependent processing during sleep might differentially regulate these two processes. We specifically examined how neural reactivations during non-rapid eye movement (NREM) sleep were causally linked to consolidation versus weakening of the neural correlates of neuroprosthetic skill. Strikingly, we found that slow oscillations (SOs) and delta (δ) waves have dissociable and competing roles in consolidation versus forgetting. By modulating cortical spiking linked to SOs or δ waves using closed-loop optogenetic methods, we could, respectively, weaken or strengthen consolidation and thereby bidirectionally modulate sleep-dependent performance gains. We further found that changes in the temporal coupling of spindles to SOs relative to δ waves could account for such effects. Thus, our results indicate that neural activity driven by SOs and δ waves have competing roles in sleep-dependent memory consolidation.},
}
@article {pmid31584354,
year = {2019},
author = {Qian, Y and Wang, Q and Jiao, J and Wang, G and Gu, Z and Huang, D and Wang, Z},
title = {Intrathecal injection of dexmedetomidine ameliorates chronic neuropathic pain via the modulation of MPK3/ERK1/2 in a mouse model of chronic neuropathic pain.},
journal = {Neurological research},
volume = {41},
number = {12},
pages = {1059-1068},
doi = {10.1080/01616412.2019.1672391},
pmid = {31584354},
issn = {1743-1328},
mesh = {Analgesics, Non-Narcotic/*administration &amp; dosage ; Animals ; Chronic Pain/*drug therapy/*metabolism ; Dexmedetomidine/*administration &amp; dosage ; Disease Models, Animal ; Injections, Spinal ; MAP Kinase Signaling System/*drug effects ; Male ; Mice ; Mitogen-Activated Protein Kinase 1/metabolism ; Mitogen-Activated Protein Kinase 3/metabolism ; Neuralgia/*drug therapy/*metabolism ; },
abstract = {Objective: Despite the application of dexmedetomidine (DEX) as a perioperative adjuvant in local analgesia, the exact analgesic mechanism underpinning chronic neuropathic pain (CNP) awaits our elucidation. Methods: We investigated the molecular mechanisms of the anti-nociceptive effect of DEX on neuropathic pain in a mouse model induced by chronic constriction injury (CCI). Results: DEX administration significantly increased the paw withdrawal latency (PWL) values 0.5 to 2 h post-injection in CCI-induced CNP mice at day 5 to 21 versus dimethyl sulfoxide (DMSO)-treated mice, confirming its analgesic effect. The c-Fos expression was significantly elevated in CCI mice versus the sham-operated group, whereas the elevation was mitigated by DEX injection. Subsequently, the involvement of MKP1 and MKP3 in the pathogenesis of chronic neuropathic pain was evaluated. Western blotting analyses revealed significant decrease in both MKP1 and MKP3 in the spinal cord in CCI group versus the sham group. DEX markedly elevated the MKP3 expression and modestly reduced the MKP1 expression, with insignificant difference in the latter. Co-injection of BCI (an MKP3 inhibitor) and DEX evidently reduced the PWL values in CCI mice. Furthermore, DEX significantly downregulated the phosphorylation of extracellular-signal-regulated kinase (ERK) 1/2, down-stream effector of MKP3 in CCI mice, whereas the downregulation was reversed by BCI. Conclusion: We confirmed that DEX exerts the analgesic effect on chronic neuropathic pain via the regulation of MKP3/ERK1/2 signaling pathway, which may contribute to clarification of the molecular mechanism and novel therapy for chronic neuropathic pain.},
}
@article {pmid31581619,
year = {2019},
author = {Lin, YP and Chen, TY and Chen, WJ},
title = {Cost-efficient and Custom Electrode-holder Assembly Infrastructure for EEG Recordings.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {19},
pages = {},
pmid = {31581619},
issn = {1424-8220},
support = {CMRPG8I0371//National Sun Yat-sen University, Taiwan/ ; },
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Scalp/physiology ; Young Adult ; },
abstract = {Mobile electroencephalogram (EEG)-sensing technologies have rapidly progressed and made the access of neuroelectrical brain activity outside the laboratory in everyday life more realistic. However, most existing EEG headsets exhibit a fixed design, whereby its immobile montage in terms of electrode density and coverage inevitably poses a great challenge with applicability and generalizability to the fundamental study and application of the brain-computer interface (BCI). In this study, a cost-efficient, custom EEG-electrode holder infrastructure was designed through the assembly of primary components, including the sensor-positioning ring, inter-ring bridge, and bridge shield. It allows a user to (re)assemble a compact holder grid to accommodate a desired number of electrodes only to the regions of interest of the brain and iteratively adapt it to a given head size for optimal electrode-scalp contact and signal quality. This study empirically demonstrated its easy-to-fabricate nature by a low-end fused deposition modeling (FDM) 3D printer and proved its practicability of capturing event-related potential (ERP) and steady-state visual-evoked potential (SSVEP) signatures over 15 subjects. This paper highlights the possibilities for a cost-efficient electrode-holder assembly infrastructure with replaceable montage, flexibly retrofitted in an unlimited fashion, for an individual for distinctive fundamental EEG studies and BCI applications.},
}
@article {pmid31581098,
year = {2019},
author = {Shaikh, S and So, R and Sibindi, T and Libedinsky, C and Basu, A},
title = {Towards Intelligent Intracortical BMI (i [2]BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {6},
pages = {1615-1624},
doi = {10.1109/TBCAS.2019.2944486},
pmid = {31581098},
issn = {1940-9990},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Humans ; Intelligence/*physiology ; Machine Learning ; Primates/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; Wireless Technology ; },
abstract = {Fully-implantable wireless intracortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10× is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data-rate is reduced drastically. Earlier work on neuromorphic decoder chips only showed classification of discrete states. We present results for continuous state decoding using a low-power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA) that implements a nonlinear decoder without memory and its memory-based version with time-delayed bins, SELMA-bins. We have compared SELMA, SELMA-bins against state-of-the-art Steady-State Kalman Filter (SSKF), a linear decoder with memory, across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% (20%) or more increase in the fraction of variance accounted for (FVAF) by SELMA (SELMA-bins) over SSKF across the datasets. Estimated energy consumption comparison shows SELMA (SELMA-bins) consuming ≈ 9 nJ/update (23 nJ/update) against SSKF's ≈ 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF while consuming energy in the same range as SSKF whereas SELMA-bins performs the best with moderately increased energy consumption, albeit far less than energy required for raw data transmission. This paves the way for reducing transmission data rates in future scaled iBMI systems.},
}
@article {pmid31581095,
year = {2019},
author = {Kim, C and Park, J and Ha, S and Akinin, A and Kubendran, R and Mercier, PP and Cauwenberghs, G},
title = {A 3 mm × 3 mm Fully Integrated Wireless Power Receiver and Neural Interface System-on-Chip.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {6},
pages = {1736-1746},
doi = {10.1109/TBCAS.2019.2943506},
pmid = {31581095},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; Electric Power Supplies ; Electrocorticography/*instrumentation/methods ; Electrodes ; Equipment Design ; Miniaturization ; Wireless Technology ; },
abstract = {A miniaturized, fully integrated wireless power receiver system-on-chip with embedded 16-channel electrode array and data transceiver for electrocortical neural recording and stimulation is presented. An H-tree power and signal distribution network throughout the SoC maintains high quality factor up to 11 in the on-chip receiver coil at 144 MHz resonant frequency while rejecting RF interference in sensitive neural interface circuits owing to its perpendicular and equidistant geometry. A multi-mode buck-boost resonant regulating rectifier (B [2]R [3]) offers greater than 11-dB input dynamic range in RF reception and less than 1 mV overshoot in transient load regulation. At 10 mm link distance, the 9 mm [2] neural interface SoC fabricated in a 180 nm silicon-on-insulator (SOI) process attains an overall wireless power transmission system efficiency (WSE) of 3.4% in driving a 160 μW load yielding a WSE figure-of-merit of 131, while maintaining signal integrity in analog recording and wireless data transmission that comprise the on-chip load.},
}
@article {pmid31579345,
year = {2019},
author = {Yu, R and Qiao, L and Chen, M and Lee, SW and Fei, X and Shen, D},
title = {Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.},
journal = {Pattern recognition},
volume = {90},
number = {},
pages = {220-231},
pmid = {31579345},
issn = {0031-3203},
support = {R01 EB022880/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.},
}
@article {pmid31578420,
year = {2019},
author = {Salari, E and Freudenburg, ZV and Branco, MP and Aarnoutse, EJ and Vansteensel, MJ and Ramsey, NF},
title = {Classification of Articulator Movements and Movement Direction from Sensorimotor Cortex Activity.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {14165},
pmid = {31578420},
issn = {2045-2322},
mesh = {Adult ; Articulation Disorders/complications/*physiopathology ; *Brain-Computer Interfaces ; Electrocorticography ; Epilepsy/complications ; Female ; Humans ; Male ; *Movement ; Sensorimotor Cortex/*physiopathology ; Tongue/innervation/*physiopathology ; Voice ; },
abstract = {For people suffering from severe paralysis, communication can be difficult or nearly impossible. Technology systems called brain-computer interfaces (BCIs) are being developed to assist these people with communication by using their brain activity to control a computer without any muscle activity. To benefit the development of BCIs that employ neural activity related to speech, we investigated if neural activity patterns related to different articulator movements can be distinguished from each other. We recorded with electrocorticography (ECoG), the neural activity related to different articulator movements in 4 epilepsy patients and classified which articulator participants moved based on the sensorimotor cortex activity patterns. The same was done for different movement directions of a single articulator, the tongue. In both experiments highly accurate classification was obtained, on average 92% for different articulators and 85% for different tongue directions. Furthermore, the data show that only a small part of the sensorimotor cortex is needed for classification (ca. 1 cm[2]). We show that recordings from small parts of the sensorimotor cortex contain information about different articulator movements which might be used for BCI control. Our results are of interest for BCI systems that aim to decode neural activity related to (actual or attempted) movements from a contained cortical area.},
}
@article {pmid31577342,
year = {2019},
author = {Dragioti, E and Solmi, M and Favaro, A and Fusar-Poli, P and Dazzan, P and Thompson, T and Stubbs, B and Firth, J and Fornaro, M and Tsartsalis, D and Carvalho, AF and Vieta, E and McGuire, P and Young, AH and Shin, JI and Correll, CU and Evangelou, E},
title = {Association of Antidepressant Use With Adverse Health Outcomes: A Systematic Umbrella Review.},
journal = {JAMA psychiatry},
volume = {76},
number = {12},
pages = {1241-1255},
pmid = {31577342},
issn = {2168-6238},
support = {ICA-CL-2017-03-001/DH_/Department of Health/United Kingdom ; },
mesh = {Adolescent ; Antidepressive Agents/*adverse effects ; *Apgar Score ; Autism Spectrum Disorder/*chemically induced ; Child ; Female ; Humans ; Mental Disorders/*drug therapy ; Meta-Analysis as Topic ; Observational Studies as Topic ; Pregnancy ; Premature Birth/*chemically induced ; Prenatal Exposure Delayed Effects/*chemically induced ; *Suicide, Attempted/statistics &amp; numerical data ; *Suicide, Completed/statistics &amp; numerical data ; },
abstract = {IMPORTANCE: Antidepressant use is increasing worldwide. Yet, contrasting evidence on the safety of antidepressants is available from meta-analyses, and the credibility of these findings has not been quantified.<br><br>OBJECTIVE: To grade the evidence from published meta-analyses of observational studies that assessed the association between antidepressant use or exposure and adverse health outcomes.<br><br>DATA SOURCES: PubMed, Scopus, and PsycINFO were searched from database inception to April 5, 2019.<br><br>EVIDENCE REVIEW: Only meta-analyses of observational studies with a cohort or case-control study design were eligible. Two independent reviewers recorded the data and assessed the methodological quality of the included meta-analyses. Evidence of association was ranked according to established criteria as follows: convincing, highly suggestive, suggestive, weak, or not significant.<br><br>RESULTS: Forty-five meta-analyses (17.9%) from 4471 studies identified and 252 full-text articles scrutinized were selected that described 120 associations, including data from 1012 individual effect size estimates. Seventy-four (61.7%) of the 120 associations were nominally statistically significant at P&#x2009;&#x2264;&#x2009;.05 using random-effects models. Fifty-two associations (43.4%) had large heterogeneity (I2&#x2009;>&#x2009;50%), whereas small-study effects were found for 17 associations (14.2%) and excess significance bias was found for 9 associations (7.5%). Convincing evidence emerged from both main and sensitivity analyses for the association between antidepressant use and risk of suicide attempt or completion among children and adolescents, autism spectrum disorders with antidepressant exposure before and during pregnancy, preterm birth, and low Apgar scores. None of these associations remained supported by convincing evidence after sensitivity analysis, which adjusted for confounding by indication.<br><br>CONCLUSIONS AND RELEVANCE: This study's findings suggest that most putative adverse health outcomes associated with antidepressant use may not be supported by convincing evidence, and confounding by indication may alter the few associations with convincing evidence. Antidepressant use appears to be safe for the treatment of psychiatric disorders, but more studies matching for underlying disease are needed to clarify the degree of confounding by indication and other biases. No absolute contraindication to antidepressants emerged from this umbrella review.},
}
@article {pmid31572146,
year = {2019},
author = {McKendrick, R and Feest, B and Harwood, A and Falcone, B},
title = {Theories and Methods for Labeling Cognitive Workload: Classification and Transfer Learning.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {295},
pmid = {31572146},
issn = {1662-5161},
abstract = {There are a number of key data-centric questions that must be answered when developing classifiers for operator functional states. "Should a supervised or unsupervised learning approach be used? What degree of labeling and transformation must be performed on the data? What are the trade-offs between algorithm flexibility and model interpretability, as generally these features are at odds?" Here, we focus exclusively on the labeling of cognitive load data for supervised learning. We explored three methods of labeling cognitive states for three-state classification. The first method labels states derived from a tertiary split of trial difficulty during a spatial memory task. The second method was more adaptive; it employed a mixed-effects stress-strain curve and estimated an individual's performance asymptotes with respect to the same spatial memory task. The final method was similar to the second approach; however, it employed a mixed-effects Rasch model to estimate individual capacity limits within the context of item response theory for the spatial memory task. To assess the strength of each of these labeling approaches, we compared the area under the curve (AUC) for receiver operating curves (ROCs) from elastic net and random forest classifiers. We chose these classifiers based on a combination of interpretability, flexibility, and past modeling success. We tested these techniques across two groups of individuals and two tasks to test the effects of different labeling techniques on cross-person and cross-task transfer. Overall, we observed that the Rasch model labeling paired with a random forest classifier led to the best model fits and showed evidence of both cross-person and cross-task transfer.},
}
@article {pmid31571608,
year = {2019},
author = {Yousefi, R and Rezazadeh Sereshkeh, A and Chau, T},
title = {Development of a robust asynchronous brain-switch using ErrP-based error correction.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066042},
doi = {10.1088/1741-2552/ab4943},
pmid = {31571608},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Mathematical Concepts ; Mental Processes/*physiology ; Spatial Processing/*physiology ; },
abstract = {OBJECTIVE: The ultimate goal of many brain-computer interface (BCI) research efforts is to provide individuals with severe motor impairments with a communication channel that they can control at will. To achieve this goal, an important system requirement is asynchronous control, whereby users can initiate intentional brain activation in a self-paced rather than system-cued manner. However, to date, asynchronous BCIs have been explored in a minority of BCI studies and their performance is generally below that of system-paced alternatives. In this paper, we present an asynchronous electroencephalography (EEG) BCI that detects a non-motor imagery cognitive task and investigated the possibility of improving its performance using error-related potentials (ErrP).<br><br>APPROACH: Ten able-bodied adults attended two sessions of data collection each, one for training and one for testing the BCI. The visual interface consisted of a centrally located cartoon icon. For each participant, an asynchronous BCI differentiated among the idle state and a personally selected cognitive task (mental arithmetic, word generation or figure rotation). The BCI continuously analyzed the EEG data stream and displayed real-time feedback (i.e. icon fell over) upon detection of brain activity indicative of a cognitive task. The BCI also monitored the EEG signals for the presence of error-related potentials following the presentation of feedback. An ErrP classifier was invoked to automatically alter the task classifier outcome when an error-related potential was detected.<br><br>MAIN RESULTS: The average post-error correction trial success rate across participants, 85% [Formula: see text] 12%, was significantly higher (p&#x200a;&#x2009;&#x2009;<&#x2009;&#x2009;0.05) than that pre-error correction (78% [Formula: see text] 11%).<br><br>SIGNIFICANCE: Our findings support the addition of ErrP-correction to maximize the performance of asynchronous BCIs..},
}
@article {pmid31570723,
year = {2019},
author = {Matran-Fernandez, A and Rodríguez Martínez, IJ and Poli, R and Cipriani, C and Citi, L},
title = {SEEDS, simultaneous recordings of high-density EMG and finger joint angles during multiple hand movements.},
journal = {Scientific data},
volume = {6},
number = {1},
pages = {186},
pmid = {31570723},
issn = {2052-4463},
support = {687905//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; 687905//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; 687905//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; 687905//EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)/International ; EP/N031806/1//RCUK | Engineering and Physical Sciences Research Council (EPSRC)/International ; },
mesh = {Adolescent ; Adult ; Biomechanical Phenomena ; Databases, Factual ; Electrodes ; *Electromyography ; Female ; Finger Joint/*physiology ; Fingers/physiology ; Forearm/physiology ; Hand/*physiology ; Humans ; Male ; Middle Aged ; *Movement ; Muscle, Skeletal/physiology ; Young Adult ; },
abstract = {We present the SurfacE Electromyographic with hanD kinematicS (SEEDS) database. It contains electromyographic (EMG) signals and hand kinematics recorded from the forearm muscles of 25 non-disabled subjects while performing 13 different movements at normal and slow-paced speeds. EMG signals were recorded with a high-density 126-channel array centered on the extrinsic flexors of the fingers and 8 further electrodes placed on the extrinsic extensor muscles. A data-glove was used to record 18 angles from the joints of the wrist and fingers. The correct synchronisation of the data-glove and the EMG was ascertained and the resulting data were further validated by implementing a simple classification of the movements. These data can be used to test experimental hypotheses regarding EMG and hand kinematics. Our database allows for the extraction of the neural drive as well as performing electrode selection from the high-density EMG signals. Moreover, the hand kinematic signals allow the development of proportional methods of control of the hand in addition to the more traditional movement classification approaches.},
}
@article {pmid31569036,
year = {2019},
author = {Meziani, A and Djouani, K and Medkour, T and Chibani, A},
title = {A Lasso quantile periodogram based feature extraction for EEG-based motor imagery.},
journal = {Journal of neuroscience methods},
volume = {328},
number = {},
pages = {108434},
doi = {10.1016/j.jneumeth.2019.108434},
pmid = {31569036},
issn = {1872-678X},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Imagination ; *Motor Activity ; Neurosciences/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: The extraction of relevant and distinct features from the electroencephalogram (EEG) signals is one of the most challenging task when implementing Brain Computer Interface (BCI) based systems. Frequency analysis techniques are recognised as one of the most suitable methods to have distinct information from EEG signals. However, existing studies use mostly classical approaches assuming that the signal is Gaussian, stationary and linear. These properties are not verified in the EEG case considering the complexity of the brain electrical activity.<br><br>NEW METHOD: This paper proposes two new spectral estimators that are robust against non-Gaussian, non-linear and non-stationary signals. These two approaches use quantile regression and L1-norm regularisation to estimate the spectrum of the motor imagery (MI) related EEG.<br><br>RESULTS: A dataset collected during a study of BCI motor imagery project conducted at Tshwane University of Technology (TUT), Pretoria, South Africa, is used to validate the proposed estimators. Experimental results demonstrate that the newly proposed approaches help improve the classification performance of MI.<br><br>In order to show the effectiveness of the proposed estimators, a comparative study is conducted, considering classical commonly used techniques such as FFT and Welch periodogram through 5 classification algorithms.<br><br>CONCLUSIONS: The proposed Quantile-based spectral estimators are potential methods to improve the classification performance of the EEG-Based motor imagery systems.},
}
@article {pmid31568896,
year = {2020},
author = {Boloukian, B and Safi-Esfahani, F},
title = {Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {121},
number = {},
pages = {186-207},
doi = {10.1016/j.neunet.2019.07.012},
pmid = {31568896},
issn = {1879-2782},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; Speech Disorders/*physiopathology ; *Speech Recognition Software ; },
abstract = {There is an essential requirement to support people with speech and communication disabilities. A brain-computer interface using electroencephalography (EEG) is applied to satisfy this requirement. A number of research studies to recognize brain signals using machine learning and deep neural networks (DNNs) have been performed to increase the brain signal detection rate, yet there are several defects and limitations in the techniques. Among them is the use in specific circumstances of machine learning. On the one hand, DNNs extract the features well and automatically. On the other hand, their use results in overfitting and vanishing problems. Consequently, in this research, a deep network is designed on the basis of an autoencoder neural Turing machine (DN-AE-NTM) to resolve the problems by the use of NTM external memory. In addition, the DN-AE-NTM copes with all kinds of signals with high detection rates. The data were collected by P300 EEG devices from several individuals under the same conditions. During the test, each individual was requested to skim images with one to six labels and focus on only one of the images. Not to focus on some images is analogous to producing unimportant information in the individual's brain, which provides unfamiliar signals. Besides the main P300 EEG dataset, EEG recordings of individuals with alcoholism and control individuals and the EEGMMIDB, MNIST, and ORL datasets were implemented and tested. The proposed DN-AE-NTM method classifies data with an average detection rate of 97.5%, 95%, 98%, 99.4%, and 99.1%, respectively, in situations where the signals are noisy so that only 20% of the data are reliable and include useful information.},
}
@article {pmid31568895,
year = {2020},
author = {Kumarasinghe, K and Kasabov, N and Taylor, D},
title = {Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {121},
number = {},
pages = {169-185},
doi = {10.1016/j.neunet.2019.08.029},
pmid = {31568895},
issn = {1879-2782},
mesh = {Action Potentials/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces/trends ; Cognition/physiology ; *Deep Learning/trends ; Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, in a partial case in the form of deep spatio-temporal rules. This is a promising direction for building new types of Brain-Computer Interfaces called Brain-Inspired Brain-Computer Interfaces (BI-BCI). A theoretical framework and its experimental validation on deep knowledge extraction and representation using SNN are presented.<br><br>RESULTS: The proposed methodology was applied in a case study to extract deep knowledge of the functional and structural organisation of the brain's neural network during the execution of a Grasp and Lift task. The BI-BCI successfully extracted the neural trajectories that represent the dorsal and ventral visual information processing streams as well as its connection to the motor cortex in the brain. Deep spatiotemporal rules on functional and structural interaction of distinct brain areas were then used for event prediction in BI-BCI.<br><br>SIGNIFICANCE: The computational framework can be used for unveiling the topological patterns of the brain and such knowledge can be effectively used to enhance the state-of-the-art in BCI.},
}
@article {pmid31568731,
year = {2019},
author = {John, SE and Grayden, DB and Yanagisawa, T},
title = {The future potential of the Stentrode.},
journal = {Expert review of medical devices},
volume = {16},
number = {10},
pages = {841-843},
doi = {10.1080/17434440.2019.1674139},
pmid = {31568731},
issn = {1745-2422},
}
@article {pmid31567096,
year = {2020},
author = {Lansdell, B and Milovanovic, I and Mellema, C and Fetz, EE and Fairhall, AL and Moritz, CT},
title = {Reconfiguring Motor Circuits for a Joint Manual and BCI Task.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {28},
number = {1},
pages = {248-257},
pmid = {31567096},
issn = {1558-0210},
support = {P51 RR000166/RR/NCRR NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Efferent Pathways/*physiology ; Electric Stimulation ; Entropy ; Macaca nemestrina ; Male ; Motor Cortex/physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Torque ; Wrist/physiology ; },
abstract = {Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI. In this study we investigated how primary motor cortex accomplishes simultaneous BCI control and motor control in a task that explicitly required both activities to be driven from the same brain region (i.e. a dual-control task). Single-unit activity was recorded from intracortical, multi-electrode arrays while a non-human primate performed this dual-control task. Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque. Using a measure of effective connectivity, we observed that control units decrease their connectivity. Through an analysis of variance we found that the intrinsic variability of the control units has a significant effect on task proficiency. When this variance is accounted for, motor cortical activity is flexible enough to perform novel BCI tasks that require active decoupling of natural associations to wrist motion. This study provides insight into the neural activity that enables a dual-control brain-computer interface.},
}
@article {pmid31564974,
year = {2019},
author = {Mubarik, S and Malik, SS and Wang, Z and Li, C and Fawad, M and Yu, C},
title = {Recent insights into breast cancer incidence trends among four Asian countries using age-period-cohort model.},
journal = {Cancer management and research},
volume = {11},
number = {},
pages = {8145-8155},
pmid = {31564974},
issn = {1179-1322},
abstract = {PURPOSE: Breast cancer is one of the rapidly increasing cancers among women and a significant cause of cancer-related morbidity and mortality worldwide. Therefore, the current study was designed to examine and compare trends of breast cancer incidence (BCI) during the observed period (1990-2015) in specific age groups and investigate age-specific, time period, and birth cohort-related effects on BCI in China, India, Pakistan, and Thailand.<br><br>PATIENTS AND METHOD: Data related to BCI were retrieved from the Institute for Health Metrics and Evaluation. Age-period-cohort model joint with intrinsic estimator algorithm was used to estimate the effect of age, period, and birth cohort on BCI. BCI rates were analyzed among different age groups ranging from 20 to 84 years in specified periods.<br><br>RESULT: Overall, results showed an increasing trend of BCI among four Asian countries during the study period especially in age groups 50 to 84 years. Higher incidence rates were observed in 2015 in the age group 70-74, 65-69, 50-54, and 60-64 in Pakistan, China, India, and Thailand, respectively. Age period cohort analysis revealed significantly raised effect of age and period and declined effect of the cohort on incidence rates.<br><br>CONCLUSION: The current study reported increased BCI with time in selected four Asian countries. Overall, BCI remained high in Pakistan as compared to China, India, and Thailand. Although proper registries are not available in most of the developing Asian countries, the current study highlighted the increased incidence and may play an essential role in registries development or spreading awareness against this disease. Therefore, maintaining proper records to build registries at the national level along with advancements in breast cancer screening and treatment are highly recommended to deal with the increasing burden of this disease.},
}
@article {pmid31562095,
year = {2019},
author = {Zhang, D and Yao, L and Chen, K and Wang, S and Haghighi, PD and Sullivan, C},
title = {A Graph-Based Hierarchical Attention Model for Movement Intention Detection from EEG Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {11},
pages = {2247-2253},
doi = {10.1109/TNSRE.2019.2943362},
pmid = {31562095},
issn = {1558-0210},
mesh = {Algorithms ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods/statistics &amp; numerical data ; Humans ; *Intention ; *Models, Psychological ; Neural Networks, Computer ; Psychomotor Performance ; },
abstract = {An EEG-based Brain-Computer Interface (BCI) is a system that enables a user to communicate with and intuitively control external devices solely using the user's intentions. Current EEG-based BCI research usually involves a subject-specific adaptation step before a BCI system is ready to be employed by a new user. However, the subject-independent scenario, in which a well-trained model can be directly applied to new users without pre-calibration, is particularly desirable yet rarely explored. Considering this critical gap, our focus in this paper is the subject-independent scenario of EEG-based human intention recognition. We present a G raph-based H ierarchical A ttention M odel (G-HAM) that utilizes the graph structure to represent the spatial information of EEG sensors and the hierarchical attention mechanism to focus on both the most discriminative temporal periods and EEG nodes. Extensive experiments on a large EEG dataset containing 105 subjects indicate that our model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches.},
}
@article {pmid31561235,
year = {2020},
author = {Rácz, M and Liber, C and Németh, E and Fiáth, R and Rokai, J and Harmati, I and Ulbert, I and Márton, G},
title = {Spike detection and sorting with deep learning.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016038},
doi = {10.1088/1741-2552/ab4896},
pmid = {31561235},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; *Deep Learning ; *Neural Networks, Computer ; Rats ; Rats, Wistar ; Somatosensory Cortex/cytology/*physiology ; },
abstract = {OBJECTIVE: The extraction and identification of single-unit activities in intracortically recorded electric signals have a key role in basic neuroscience, but also in applied fields, like in the development of high-accuracy brain-computer interfaces. The purpose of this paper is to present our current results on the detection, classification and prediction of neural activities based on multichannel action potential recordings.<br><br>APPROACH: Throughout our investigations, a deep learning approach utilizing convolutional neural networks and a combination of recurrent and convolutional neural networks was applied, with the latter used in case of spike detection and the former used for cases of sorting and predicting spiking activities.<br><br>MAIN RESULTS: In our experience, the algorithms applied prove to be useful in accomplishing the tasks mentioned above: our detector could reach an average recall of 69%, while we achieved an average accuracy of 89% in classifying activities produced by more than 20 distinct neurons.<br><br>SIGNIFICANCE: Our findings support the concept of creating real-time, high-accuracy action potential based BCIs in the future, providing a flexible and robust algorithmic background for further development.},
}
@article {pmid31561099,
year = {2019},
author = {Olcay, BO and Karaçalı, B},
title = {Evaluation of synchronization measures for capturing the lagged synchronization between EEG channels: A cognitive task recognition approach.},
journal = {Computers in biology and medicine},
volume = {114},
number = {},
pages = {103441},
doi = {10.1016/j.compbiomed.2019.103441},
pmid = {31561099},
issn = {1879-0534},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; Databases, Factual ; Discriminant Analysis ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization/*physiology ; Humans ; Imagination/physiology ; },
abstract = {During cognitive, perceptual and sensory tasks, connectivity profile changes across different regions of the brain. Variations of such connectivity patterns between different cognitive tasks can be evaluated using pairwise synchronization measures applied to electrophysiological signals, such as electroencephalography (EEG). However, connectivity-based task recognition approaches achieving viable recognition performance have been lacking from the literature. By using several synchronization measures, we identify time lags between channel pairs during different cognitive tasks. We employed mutual information, cross correntropy, cross correlation, phase locking value, cosine similarity and nonlinear interdependence measures. In the training phase, for each type of cognitive task, we identify the time lags that maximize the average synchronization between channel pairs. These lags are used to calculate pairwise synchronization values with which we construct the train and test feature vectors for recognition of the cognitive task carried out using Fisher's linear discriminant (FLD) analysis. We tested our framework in a motor imagery activity recognition scenario on PhysioNet Motor Movement/Imagery and BCI Competition-III Ⅳa datasets. For PhysioNet dataset, average performance results ranging between % 51 and % 61 across 20 subjects. For BCI Competition-Ⅲ dataset, we achieve an average recognition performance of % 76 which is above the minimum reliable communication rate (% 70). We achieved an average accuracy over the minimum reliable communication rate on the BCI Competition-Ⅲ dataset. Performance levels were lower on the PhysioNet dataset. These results indicate that a viable task recognition system is achievable using pairwise synchronization measures evaluated at the proper task specific lags.},
}
@article {pmid31559004,
year = {2019},
author = {Li, C and Jia, T and Xu, Q and Ji, L and Pan, Y},
title = {Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients.},
journal = {Journal of healthcare engineering},
volume = {2019},
number = {},
pages = {3817124},
pmid = {31559004},
issn = {2040-2309},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex ; Motor Skills ; Movement ; Reproducibility of Results ; Sensorimotor Cortex/physiopathology ; Stroke/*physiopathology ; *Stroke Rehabilitation ; Support Vector Machine ; Tomography, X-Ray Computed ; },
abstract = {In the last decade, technology-assisted stroke rehabilitation has been the focus of research. Electroencephalogram- (EEG-) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients since the closed loop between motor intention and the actual movement established by BCI can stimulate the neural pathways of motor control. Due to the deficits in the brain, motor intention expression may shift to other brain regions during and even after neural reorganization. The objective of this paper was to study the event-related desynchronization (ERD) topography during motor attempt tasks of the paretic hand in stroke patients and compare the classification performance using different channel-selection strategies in EEG-based BCI. Fifteen stroke patients were recruited in this study. A cue-based experimental paradigm was applied in the experiment, in which each patient was required to open the palm of the paretic or the unaffected hand. EEG was recorded and analyzed to measure the motor intention and indicate the activated brain regions. Support vector machine (SVM) combined with common spatial pattern (CSP) algorithm was used to calculate the offline classification accuracy between the motor attempt of the paretic hand and the resting state applying different channel-selection strategies. Results showed individualized ERD topography during the motor attempt of the paretic hand due to the deficits caused by stroke. Statistical analysis showed a significant increase in the classification accuracy by analyzing the channels showing ERD than analyzing the channels from the contralateral sensorimotor cortex (SM1). The results indicated that for stroke patients whose affected motor cortex is extensively damaged, the compensated brain regions should be considered for implementing EEG-based BCI for motor rehabilitation as the closed loop between the altered activated brain regions and the paretic hand can be stimulated more accurately using the individualized channel-selection strategy.},
}
@article {pmid31555113,
year = {2019},
author = {Di Flumeri, G and De Crescenzio, F and Berberian, B and Ohneiser, O and Kramer, J and Aricò, P and Borghini, G and Babiloni, F and Bagassi, S and Piastra, S},
title = {Brain-Computer Interface-Based Adaptive Automation to Prevent Out-Of-The-Loop Phenomenon in Air Traffic Controllers Dealing With Highly Automated Systems.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {296},
pmid = {31555113},
issn = {1662-5161},
abstract = {Increasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo's mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called "Vigilance and Attention Controller", a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human-machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA.},
}
@article {pmid31555079,
year = {2019},
author = {Choi, SI and Hwang, HJ},
title = {Corrigendum: Effects of Different Re-referencing Methods on Spontaneously Generated Ear-EEG.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {908},
doi = {10.3389/fnins.2019.00908},
pmid = {31555079},
issn = {1662-4548},
abstract = {[This corrects the article DOI: 10.3389/fnins.2019.00822.].},
}
@article {pmid31552554,
year = {2019},
author = {Scott, J and Hidalgo-Mazzei, D and Strawbridge, R and Young, A and Resche-Rigon, M and Etain, B and Andreassen, OA and Bauer, M and Bennabi, D and Blamire, AM and Boumezbeur, F and Brambilla, P and Cattane, N and Cattaneo, A and Chupin, M and Coello, K and Cointepas, Y and Colom, F and Cousins, DA and Dubertret, C and Duchesnay, E and Ferro, A and Garcia-Estela, A and Goikolea, J and Grigis, A and Haffen, E and Høegh, MC and Jakobsen, P and Kalman, JL and Kessing, LV and Klohn-Saghatolislam, F and Lagerberg, TV and Landén, M and Lewitzka, U and Lutticke, A and Mazer, N and Mazzelli, M and Mora, C and Muller, T and Mur-Mila, E and Oedegaard, KJ and Oltedal, L and Pålsson, E and Papadopoulos Orfanos, D and Papiol, S and Perez-Sola, V and Reif, A and Ritter, P and Rossi, R and Schulze, T and Senner, F and Smith, FE and Squarcina, L and Steen, NE and Thelwall, PE and Varo, C and Vieta, E and Vinberg, M and Wessa, M and Westlye, LT and Bellivier, F},
title = {Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative.},
journal = {International journal of bipolar disorders},
volume = {7},
number = {1},
pages = {20},
pmid = {31552554},
issn = {2194-7511},
support = {MR/L006642/1/MRC_/Medical Research Council/United Kingdom ; 754907//H2020 Research and Innovation Program (EU.3.1.1. Understanding health, wellbeing and disease: Grant No 754907)/ ; },
abstract = {BACKGROUND: Lithium is recommended as a first line treatment for bipolar disorders. However, only 30% of patients show an optimal outcome and variability in lithium response and tolerability is poorly understood. It remains difficult for clinicians to reliably predict which patients will benefit without recourse to a lengthy treatment trial. Greater precision in the early identification of individuals who are likely to respond to lithium is a significant unmet clinical need.<br><br>STRUCTURE: The H2020-funded Response to Lithium Network (R-LiNK; http://www.r-link.eu.com/) will undertake a prospective cohort study of over 300 individuals with bipolar-I-disorder who have agreed to commence a trial of lithium treatment following a recommendation by their treating clinician. The study aims to examine the early prediction of lithium response, non-response and tolerability by combining systematic clinical syndrome subtyping with examination of multi-modal biomarkers (or biosignatures), including omics, neuroimaging, and actigraphy, etc. Individuals will be followed up for 24&#xa0;months and an independent panel will assess and classify each participants' response to lithium according to predefined criteria that consider evidence of relapse, recurrence, remission, changes in illness activity or treatment failure (e.g. stopping lithium; new prescriptions of other mood stabilizers) and exposure to lithium. Novel elements of this study include the recruitment of a large, multinational, clinically representative sample specifically for the purpose of studying candidate biomarkers and biosignatures; the application of lithium-7 magnetic resonance imaging to explore the distribution of lithium in the brain; development of a digital phenotype (using actigraphy and ecological momentary assessment) to monitor daily variability in symptoms; and economic modelling of the cost-effectiveness of introducing biomarker tests for the customisation of lithium treatment into clinical practice. Also, study participants with sub-optimal medication adherence will be offered brief interventions (which can be delivered via a clinician or smartphone app) to enhance treatment engagement and to minimize confounding of lithium non-response with non-adherence.<br><br>CONCLUSIONS: The paper outlines the rationale, design and methodology of the first study being undertaken by the newly established R-LiNK collaboration and describes how the project may help to refine the clinical response phenotype and could translate into the personalization of lithium treatment.},
}
@article {pmid31551735,
year = {2019},
author = {Aricò, P and Reynal, M and Di Flumeri, G and Borghini, G and Sciaraffa, N and Imbert, JP and Hurter, C and Terenzi, M and Ferreira, A and Pozzi, S and Betti, V and Marucci, M and Telea, AC and Babiloni, F},
title = {How Neurophysiological Measures Can be Used to Enhance the Evaluation of Remote Tower Solutions.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {303},
pmid = {31551735},
issn = {1662-5161},
abstract = {New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an electroencephalography (EEG)-based neurophysiological index (WEEG index) in comparing two different solutions (i.e., Normal and Augmented) in terms of experienced workload. In this regard, 16 professional air traffic controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e., operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioral effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioral and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g., design process) could allow a more holistic and accurate evaluation of new solutions.},
}
@article {pmid31551595,
year = {2019},
author = {Shanechi, MM},
title = {Brain-machine interfaces from motor to mood.},
journal = {Nature neuroscience},
volume = {22},
number = {10},
pages = {1554-1564},
pmid = {31551595},
issn = {1546-1726},
mesh = {Affect/*physiology ; Animals ; *Brain-Computer Interfaces ; Humans ; Learning/physiology ; Mental Disorders/physiopathology/psychology/therapy ; Movement/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) create closed-loop control systems that interact with the brain by recording and modulating neural activity and aim to restore lost function, most commonly motor function in paralyzed patients. Moreover, by precisely manipulating the elements within the control loop, motor BMIs have emerged as new scientific tools for investigating the neural mechanisms underlying control and learning. Beyond motor BMIs, recent work highlights the opportunity to develop closed-loop mood BMIs for restoring lost emotional function in neuropsychiatric disorders and for probing the neural mechanisms of emotion regulation. Here we review significant advances toward functional restoration and scientific discovery in motor BMIs that have been guided by a closed-loop control view. By focusing on this unifying view of BMIs and reviewing recent work, we then provide a perspective on how BMIs could extend to the neuropsychiatric domain.},
}
@article {pmid31550554,
year = {2019},
author = {Guttmann-Flury, E and Sheng, X and Zhang, D and Zhu, X and , },
title = {A new algorithm for blink correction adaptive to inter- and intra-subject variability.},
journal = {Computers in biology and medicine},
volume = {114},
number = {},
pages = {103442},
doi = {10.1016/j.compbiomed.2019.103442},
pmid = {31550554},
issn = {1879-0534},
mesh = {Adult ; *Algorithms ; Artifacts ; Blinking/*physiology ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Electroencephalographic (EEG) signals are constantly superimposed with biological artifacts. In particular, spontaneous blinks represent a recurrent event that cannot be easily avoided. The main goal of this paper is to present a new algorithm for blink correction (ABC) that is adaptive to inter- and intra-subject variability. The whole process of designing a Brain-Computer Interface (BCI)-based EEG experiment is highlighted. From sample size determination to classification, a mixture of the standardized low-resolution electromagnetic tomography (sLORETA) for source localization and time restriction, followed by Riemannian geometry classifiers is featured. Comparison between ABC and the commonly-used Independent Component Analysis (ICA) for blinks removal shows a net amelioration with ABC. With the same pipeline using uncorrected data as a reference, ABC improves classification by 5.38% in average, whereas ICA deteriorates by -2.67%. Furthermore, while ABC accurately reconstructs blink-free data from simulated data, ICA yields a potential difference up to 200% from the original blink-free signal and an increased variance of 30.42%. Finally, ABC's major advantages are ease of visualization and understanding, low computation load favoring simple real-time implementation, and lack of spatial filtering, which allows for more flexibility during the classification step.},
}
@article {pmid31549331,
year = {2019},
author = {Yan, J and Chen, S and Deng, S},
title = {A EEG-based emotion recognition model with rhythm and time characteristics.},
journal = {Brain informatics},
volume = {6},
number = {1},
pages = {7},
pmid = {31549331},
issn = {2198-4018},
abstract = {As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long-Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.},
}
@article {pmid31546195,
year = {2020},
author = {Mohseni, M and Shalchyan, V and Jochumsen, M and Niazi, IK},
title = {Upper limb complex movements decoding from pre-movement EEG signals using wavelet common spatial patterns.},
journal = {Computer methods and programs in biomedicine},
volume = {183},
number = {},
pages = {105076},
doi = {10.1016/j.cmpb.2019.105076},
pmid = {31546195},
issn = {1872-7565},
mesh = {Adult ; Algorithms ; Brain/*diagnostic imaging ; Brain Mapping ; Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Female ; Humans ; Linear Models ; Male ; *Movement ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Upper Extremity/*physiopathology ; *Wavelet Analysis ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVE: Decoding functional movements from electroencephalographic (EEG) activity for motor disability rehabilitation is essential to develop home-use brain-computer interface systems. In this paper, the classification of five complex functional upper limb movements is studied by using only the pre-movement planning and preparation recordings of EEG data.<br><br>METHODS: Nine healthy volunteers performed five different upper limb movements. Different frequency bands of the EEG signal are extracted by the stationary wavelet transform. Common spatial patterns are used as spatial filters to enhance separation of the five movements in each frequency band. In order to increase the efficiency of the system, a mutual information-based feature selection algorithm is applied. The selected features are classified using the k-nearest neighbor, support vector machine, and linear discriminant analysis methods.<br><br>RESULTS: K-nearest neighbor method outperformed the other classifiers and resulted in an average classification accuracy of 94.0&#xa0;&#xb1;&#xa0;2.7% for five classes of movements across subjects. Further analysis of each frequency band's contribution in the optimal feature set, showed that the gamma and beta frequency bands had the most contribution in the classification. To reduce the complexity of the EEG recording system setup, we selected a subset of the 10 most effective EEG channels from 64 channels, by which we could reach an accuracy of 70%. Those EEG channels were mostly distributed over the prefrontal and frontal areas.<br><br>CONCLUSIONS: Overall, the results indicate that it is possible to classify complex movements before the movement onset by using spatially selected EEG data.},
}
@article {pmid31546180,
year = {2019},
author = {Kober, SE and Pinter, D and Enzinger, C and Damulina, A and Duckstein, H and Fuchs, S and Neuper, C and Wood, G},
title = {Self-regulation of brain activity and its effect on cognitive function in patients with multiple sclerosis - First insights from an interventional study using neurofeedback.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {130},
number = {11},
pages = {2124-2131},
doi = {10.1016/j.clinph.2019.08.025},
pmid = {31546180},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiopathology ; Cognition/*physiology ; Electroencephalography ; Executive Function/physiology ; Female ; Humans ; Male ; Memory, Long-Term/physiology ; Multiple Sclerosis/physiopathology/*psychology ; Neurofeedback/*methods ; Neuropsychological Tests ; },
abstract = {OBJECTIVE: To investigate the effects of EEG-based neurofeedback training, in which one can learn to self-regulate one's own brain activity, on cognitive function in patients with multiple sclerosis (pwMS).<br><br>METHODS: Fourteen pwMS performed ten neurofeedback training sessions within 3-4&#x202f;weeks at home using a tele-rehabilitation system. The aim of the neurofeedback training was to increase voluntarily the sensorimotor rhythm (SMR, 12-15&#x202f;Hz) in the EEG over central brain areas by receiving visual real-time feedback thereof. Cognitive function was assessed before and after all neurofeedback training sessions using a comprehensive standardized neuropsychological test battery.<br><br>RESULTS: Half of the pwMS (N&#x202f;=&#x202f;7) showed cognitive improvements in long-term memory and executive functions after neurofeedback training. These patients successfully learned to self-regulate their own brain activity by means of neurofeedback training. The other half of pwMS (N&#x202f;=&#x202f;7) did neither show any cognitive changes when comparing the pre- and post-assessment nor were they able to modulate their own brain activity in the desired direction during neurofeedback training.<br><br>CONCLUSIONS: Data from this interventional study provide first preliminary evidence that successful self-regulation of one's own brain activity may be associated with cognitive improvements in pwMS.<br><br>SIGNIFICANCE: These promising results should stimulate further studies. Neurofeedback might be a promising and alternative tool for future cognitive rehabilitation.},
}
@article {pmid31545732,
year = {2019},
author = {Martin-Clemente, R and Olias, J and Cruces, S and Zarzoso, V},
title = {Unsupervised Common Spatial Patterns.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2135-2144},
doi = {10.1109/TNSRE.2019.2936411},
pmid = {31545732},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Electrooculography ; Healthy Volunteers ; Humans ; Imagination ; Normal Distribution ; },
abstract = {The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto directions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurtosis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.},
}
@article {pmid31543766,
year = {2019},
author = {Xu, K and Huang, YY and Duann, JR},
title = {The Sensitivity of Single-Trial Mu-Suppression Detection for Motor Imagery Performance as Compared to Motor Execution and Motor Observation Performance.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {302},
pmid = {31543766},
issn = {1662-5161},
abstract = {Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.},
}
@article {pmid31541677,
year = {2019},
author = {Zhao, K and Nie, J and Yang, L and Liu, X and Shang, Z and Wan, H},
title = {Hippocampus-nidopallium caudolaterale interactions exist in the goal-directed behavior of pigeon.},
journal = {Brain research bulletin},
volume = {153},
number = {},
pages = {257-265},
doi = {10.1016/j.brainresbull.2019.09.005},
pmid = {31541677},
issn = {1873-2747},
mesh = {Animals ; Behavior, Animal/physiology ; Brain/metabolism ; Caudate Nucleus/*metabolism ; Choice Behavior/*physiology ; Cognition/physiology ; Columbidae/physiology ; Decision Making/physiology ; Electroencephalography/methods ; Female ; Hippocampus/*metabolism ; Male ; Neurons/metabolism ; Photic Stimulation ; Reward ; },
abstract = {Avian hippocampus (Hp) and nidopallium caudolaterale (NCL) are believed to play key roles in goal-directed behavior. However, it is still unclear whether there are interactions between the two brain regions in the goal-directed behavior of pigeons. To investigate the interactions between the Hp and the NCL in the goal-directed behavior, we recorded local field potential (LFP) signals from the two regions simultaneously when the pigeons performed a goal-directed decision-making task. Amplitude-amplitude coupling analysis revealed that the coupling value between the LFP recorded from the Hp and that from the NCL increased significantly (P < 0.05) in slow gamma-band (40-60 Hz) during the turning area. In addition, the LFP functional network analysis demonstrated the LFP functional connections between the Hp and the NCL increased significantly (P < 0.05) in the turning area. The result of partial directed coherence (PDC) analysis showed that the predominant direction of information flow is thought to be from the Hp to the NCL. These findings suggest that there are causal functional interactions between the Hp and the NCL by which information is transmitted between the two regions relevant to goal-directed behavior.},
}
@article {pmid31539292,
year = {2019},
author = {Carrera Arias, FJ and Boucher, L and Tartar, JL},
title = {The Effects of Videogaming with a Brain-Computer Interface on Mood and Physiological Arousal.},
journal = {Games for health journal},
volume = {8},
number = {5},
pages = {366-369},
doi = {10.1089/g4h.2018.0133},
pmid = {31539292},
issn = {2161-7856},
mesh = {Adolescent ; Affect/*physiology ; Arousal/*physiology ; Brain-Computer Interfaces/*psychology/trends ; Female ; Humans ; Male ; Video Games/*psychology/trends ; Young Adult ; },
abstract = {Objective: In recent years, immersive videogame technologies such as virtual reality have been shown to affect psychological welfare in such way that they can be applied to clinical psychology treatments. However, the effects of videogaming with other immersive gaming apparatuses such as commercial electroencephalography (EEG)-based brain-computer interfaces (BCIs) on psychological welfare have not been extensively researched. Thus, we aimed at providing early insights into some of these effects by looking at how videogaming with a commercial EEG-based BCI would impact mood and physiological arousal. Materials and Methods: A total of 26 participants were sampled. Participants were randomly assigned to either a BCI condition or a traditional condition wherein they played an action videogame with a commercial EEG-based BCI or a standard keyboard and mouse interface for 20 minutes. In both conditions, participants filled out the profile of mood states to assess mood and the perceived stress scale to control for stress. We also measured heart rate, heart rate variability as measured by the root mean square of successive differences, and galvanic skin response (GSR) amplitude differences. Results: Participants in the BCI condition overall reported a significantly higher total mood disturbance (P < 0.05), tension (P < 0.05), confusion (P < 0.05), and significantly less vigor (P < 0.05). We also found that participants in the BCI condition had significantly lower GSR amplitude differences between gaming and baseline (P < 0.05). Conclusion: The results suggest that the use of commercial EEG-based BCIs for playing with videogames can induce greater frustration and negative moods than playing with a traditional keyboard and mouse interface, possibly limiting their use in clinical psychology settings.},
}
@article {pmid31536009,
year = {2019},
author = {Kiran Kumar, GR and Ramasubba Reddy, M},
title = {Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2044-2050},
doi = {10.1109/TNSRE.2019.2941349},
pmid = {31536009},
issn = {1558-0210},
mesh = {Algorithms ; Benchmarking ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Neurologic Examination ; Photic Stimulation ; Principal Component Analysis ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {This study illustrates and evaluates a novel subject-specific target detection framework, sum of squared correlations (SSCOR), for improving the performance of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). The SSCOR spatial filter learns a common SSVEP representation space through the optimization of the individual SSVEP templates. The projection onto this SSVEP response subspace improves the signal to noise ratio (SNR) of the SSVEP components embedded in the recorded electroencephalographic (EEG) data. To demonstrate the effectiveness of the proposed framework, the target detection performance of the SSCOR method is compared with the state of the art task-related component analysis (TRCA). The evaluation is conducted on a 40 target SSVEP benchmark data collected from 35 subjects. The results of the extensive comparisons of the performance metrics show that the proposed SSCOR method outperforms the TRCA method. The ensemble version of the SSCOR framework provides an offline simulated information transfer rate (ITR) of 387 ± 9 bits/min which is much higher than that of the ensemble TRCA approach (max. ITR 216 ± 27 bits/min). The significant improvement in the detection accuracy and simulated ITR demonstrates the efficacy of the proposed framework for target detection in SSVEP based BCI applications.},
}
@article {pmid31536006,
year = {2019},
author = {Yang, Z and Guo, D and Zhang, Y and Wu, S and Yao, D},
title = {Visual Evoked Response Modulation Occurs in a Complementary Manner Under Dynamic Circuit Framework.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2005-2014},
doi = {10.1109/TNSRE.2019.2940712},
pmid = {31536006},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Alpha Rhythm ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Neural Networks, Computer ; Photic Stimulation ; Visual Cortex/physiology ; },
abstract = {The steady-state visual-evoked potential (SSVEP) induced by the periodic visual stimulus plays an important role in vision research. An increasing number of studies use the SSVEP to manipulate intrinsic oscillation and further regulate test performance. However, how the internal state modulates fundamental properties of the SSVEP remains poorly understood. Here, we identified a multiscale computational model to investigate the neural mechanism underlying low-frequency SSVEP modulation. We found that intrinsic alpha oscillation mirroring the circuit coupling state modulates the SSVEP in a complementary manner at different spatial levels, which unifies prior seemingly contradictory observations. Specifically, our model demonstrates that the laminar-specific organization induced by intercortical communication possibly underlies the commonly observed inverse SSVEP-alpha relation and that the individual peak alpha frequency (iPAF) transformation characterizing local coupling contributes to the individual-specific resonance frequency responses. Our dynamic circuit framework builds a link between SSVEP gain modulation and intrinsic alpha oscillation and paves the way for the further reconstruction of SSVEP global dynamics.},
}
@article {pmid31534448,
year = {2019},
author = {Saavedra, C and Salas, R and Bougrain, L},
title = {Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {8432953},
pmid = {31534448},
issn = {1687-5273},
mesh = {*Algorithms ; Artifacts ; Brain/*physiology ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Based on similarity measures in the wavelet domain under a multichannel EEG setting, two new methods are developed for single-trial event-related potential (ERP) detection. The first method, named "multichannel EEG thresholding by similarity" (METS), simultaneously denoises all of the information recorded by the channels. The second approach, named "semblance-based ERP window selection" (SEWS), presents two versions to automatically localize the ERP in time for each subject to reduce the time window to be analysed by removing useless features. We empirically show that when these methods are used independently, they are suitable for ERP denoising and feature extraction. Meanwhile, the combination of both methods obtains better results compared to using them independently. The denoising algorithm was compared with classic thresholding methods based on wavelets and was found to obtain better results, which shows its suitability for ERP processing. The combination of the two algorithms for denoising the signals and selecting the time window has been compared to xDAWN, which is an efficient algorithm to enhance ERPs. We conclude that our wavelet-based semblance method performs better than xDAWN for single-trial detection in the presence of artifacts or noise.},
}
@article {pmid31531400,
year = {2019},
author = {Kern, M and Bert, S and Glanz, O and Schulze-Bonhage, A and Ball, T},
title = {Erratum: Author Correction: Human motor cortex relies on sparse and action-specific activation during laughing, smiling and speech production.},
journal = {Communications biology},
volume = {2},
number = {},
pages = {339},
doi = {10.1038/s42003-019-0593-1},
pmid = {31531400},
issn = {2399-3642},
abstract = {[This corrects the article DOI: 10.1038/s42003-019-0360-3.].},
}
@article {pmid31527334,
year = {2019},
author = {Chang, L and Luo, Q and Chai, Y and Shu, H},
title = {Accidental awareness while under general anaesthesia.},
journal = {Bioscience trends},
volume = {13},
number = {4},
pages = {364-366},
doi = {10.5582/bst.2019.01237},
pmid = {31527334},
issn = {1881-7823},
mesh = {Anesthesia, General/*methods ; Anesthetics, General/*administration &amp; dosage ; Consciousness Monitors ; Dose-Response Relationship, Drug ; Female ; Humans ; Incidence ; Intraoperative Awareness/diagnosis/*epidemiology/etiology/prevention &amp; control ; Monitoring, Intraoperative/instrumentation/methods ; },
abstract = {Accidental awareness during general anaesthesia may cause many intraoperative discomforts and bring further moderate to severe long-term symptoms including flashbacks, nightmares, hyperarousal or post-traumatic stress disorder. The incidence of awareness varied from 0.017% to 4% among studies. The relatively reliable incidence of intraoperative awareness with postoperative recall is 0.02%. The reason causing awareness was unclear. Insufficient anaesthetic dosing was thought as the principal cause. Even awareness was not comprehensively understood, some endeavors have been raised to prevent or reduce it, including i) Reducing the insufficient anaesthetic dosing induced by negligence; ii) Providing close clinical observation and clinical parameters from the monitor such as bispectral index or electroencephalogram, as well as isolated forearm technique and passive brain-computer interface may bring some effects sometimes. Because current studies still have some flaws, further trials with new detecting approach, superior methodology and underlying aetiology are needed to unfasten the possible factors causing awareness.},
}
@article {pmid31525201,
year = {2019},
author = {McCartney, B and Martinez-Del-Rincon, J and Devereux, B and Murphy, B},
title = {A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.},
journal = {PloS one},
volume = {14},
number = {9},
pages = {e0214342},
pmid = {31525201},
issn = {1932-6203},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Machine Learning ; *Visual Perception ; },
abstract = {Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature selection methods, and which maps EEG activity to biologically inspired computer vision and linguistic models. We apply this approach to solve the problem of identifying viewed images from recorded brain activity in a reliable and scalable way. We demonstrate competitive decoding accuracies across two EEG datasets, using a zero-shot learning framework more applicable to real-world image retrieval than traditional classification techniques.},
}
@article {pmid31524919,
year = {2019},
author = {Wu, P and Xiao, A and Zhao, Y and Chen, F and Ke, M and Zhang, Q and Zhang, J and Shi, X and He, X and Chen, Y},
title = {An implantable and versatile piezoresistive sensor for the monitoring of human-machine interface interactions and the dynamical process of nerve repair.},
journal = {Nanoscale},
volume = {11},
number = {44},
pages = {21103-21118},
doi = {10.1039/c9nr03925b},
pmid = {31524919},
issn = {2040-3372},
mesh = {Animals ; *Brain-Computer Interfaces ; Female ; Humans ; Mice ; *Nerve Regeneration ; Rats ; Rats, Sprague-Dawley ; *Wearable Electronic Devices ; },
abstract = {Flexible wearable and implantable piezoresistive sensors have attracted lots of attention in the applications of healthcare monitoring, disease diagnostics, and human-machine interactions. However, the restricted sensing range, low sensing sensitivity at small strains, limited mechanical stability at high strains, and sophisticated fabrication processes restrict the far-reaching applications of these sensors for ultrasensitive full-range healthcare monitoring. In this work, an implantable and versatile piezoresistive sensor was developed from a series of conductive composites. The conductive composites, hydroxyethyl cellulose (HEC)/soy protein isolate (SPI)/polyaniline (PANI) sponges (HSPSs), were prepared by lyophilization of HEC/SPI solution and then in situ polymerization of aniline. The sensitivity, response time, and mechanical robustness of the HSPS sensors were characterized, and they can achieve a gauge factor of -0.29, a response time of 0.14 s, and sensing stability for at least 100 cycles. The HSPS sensors could efficiently work in vivo for 4 weeks for the measurement of stimuli, without severe inflammatory reaction. When the versatile HSPS sensors were attached to different parts of the human body, they could detect a variety of human motions including coughing, bending of fingers and elbow, abdominal breathing and walking. Notably, the HSPS sensors could be used to monitor the nerve repair in rats and the results are highly consistent with the electrophysiological data. At the same time a new score system was developed to evaluate rat nerve repair. These results indicate that the HSPS sensors exhibit good biocompatibility, sensitivity, sensing stability and fast response time. The HSPS sensors can be used not only as implantable sensors in vivo but also for analyzing human body motions. Furthermore, they provide an effective sensor device and a real-time, dynamic method for evaluating nerve repair without damage and death of animals. Hence, HSPSs might have great potential in in vivo detection, monitoring of human-machine interfacing interactions and the nerve tissue engineering field.},
}
@article {pmid31524193,
year = {2019},
author = {Djebrouni, M and Phelan, S and Aldersey, H and Wolbring, G},
title = {Utility of science, technology and innovation governance for occupational discourses from the perspective of occupational therapy students.},
journal = {Work (Reading, Mass.)},
volume = {64},
number = {2},
pages = {249-270},
doi = {10.3233/WOR-192990},
pmid = {31524193},
issn = {1875-9270},
mesh = {Education, Professional/*methods/trends ; Humans ; Inventions/*trends ; Occupational Therapists/education/*psychology ; Occupational Therapy/education ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Science, technology and innovation (STI) governance concerns itself with the societal impact of STI. Occupation, whether used with the meaning of paid, unpaid work or any activity that is considered meaningful to the individual on an everyday basis, is one area of societal impact of STI. Fields such as occupational therapy, occupational science and occupational health and safety concern themselves with the relationship between occupation and the health and well-being of human beings albeit all with different foci.<br><br>OBJECTIVE: To ascertain the knowledge of students from two Occupational Therapy programs on STI governance, specific STI products and their views on the impact of STI governance and STI products on occupational therapy and its clients.<br><br>METHODS: Online survey employing Yes/No' questions with comment boxes and open-ended textbox questions. Descriptive quantitative and thematic qualitative data was generated.<br><br>RESULTS: Students were unfamiliar with STI governance discourses but felt that they should be aware of them. Students stated that how one governs STI impacts occupational therapy on all levels and that the occupational therapy community has expertise that would enrich STI governance discourses around occupation.<br><br>CONCLUSION: Education actions seem to be warranted on the level of students and practitioners by the occupational therapy and STI governance communities.},
}
@article {pmid31522355,
year = {2019},
author = {Schwarz, A and Brandstetter, J and Pereira, J and Müller-Putz, GR},
title = {Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {11},
pages = {2347-2357},
pmid = {31522355},
issn = {1741-0444},
support = {643955//Horizon 2020 Framework Programme/ ; 681231/ERC_/European Research Council/International ; },
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; Image Processing, Computer-Assisted ; *Imagination ; },
abstract = {For Brain-Computer interfaces (BCIs), system calibration is a lengthy but necessary process for successful operation. Co-adaptive BCIs aim to shorten training and imply positive motivation to users by presenting feedback already at early stages: After just 5 min of gathering calibration data, the systems are able to provide feedback and engage users in a mutual learning process. In this work, we investigate whether the retraining stage of co-adaptive BCIs can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. The aim of the current work was to evaluate whether a semi-supervised co-adaptive BCI could successfully compete with a supervised co-adaptive BCI model. In a supporting two-class (190 trials per condition) BCI study based on motor imagery tasks, we evaluated both approaches in two separate groups of 10 participants online, while we simulated the other approach in each group offline. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart. We believe that these findings contribute to developing BCIs for long-term use, where continuous adaptation becomes imperative for maintaining meaningful BCI performance. Graphical abstract In this work, we investigate whether the retraining stage of a co-adaptive BCI can be adapted to a semi-supervised concept, where only a small amount of labeled data is available and all additional data needs to be labeled by the BCI itself. In two groups of 10 persons, we evaluate a supervised as well as a semi-supervised approach. Our results indicate that despite the lack of true labeled data, the semi-supervised driven BCI did not perform significantly worse (p > 0.05) than the supervised counterpart.},
}
@article {pmid31522328,
year = {2019},
author = {Michels, CTJ and Wijburg, CJ and Abma, IL and Witjes, JA and Grutters, JPC and Rovers, MM},
title = {Translation and validation of two disease-specific patient-reported outcome measures (Bladder Cancer Index and FACT-Bl-Cys) in Dutch bladder cancer patients.},
journal = {Journal of patient-reported outcomes},
volume = {3},
number = {1},
pages = {62},
pmid = {31522328},
issn = {2509-8020},
support = {843002602//ZonMw/ ; },
abstract = {BACKGROUND: The Bladder Cancer Index (BCI) and Functional Assessment of Cancer Therapy-Bladder-Cystectomy (FACT-Bl-Cys) were developed to measure disease-specific health-related quality of life (HRQOL) in bladder cancer patients and patients treated with radical cystectomy, respectively. Both patient-reported outcome measures (PROMs) are frequently used in clinical practice, but are not yet validated according to the COSMIN criteria and not yet available in Dutch. Therefore, the aim of this study was to translate the BCI and FACT-Bl-Cys into Dutch and to evaluate their measurement properties according to the COSMIN criteria.<br><br>METHODS: The BCI and FACT-Bl-Cys were translated into Dutch using a forward-backward method, and subsequently administered at baseline (pre-operatively) and 3&#x2009;months post-operatively in bladder cancer patients who received a radical cystectomy. Validity (content and construct), reliability (internal consistency, test-retest reliability, and measurement error), floor and ceiling effects, and responsiveness were assessed according to the COSMIN criteria.<br><br>RESULTS: Forward-backward translation encountered no particular linguistic problems. In total 260 patients completed the baseline measurement, while 182 patients completed the three-month measurement. Only a ceiling effect was identified for the BCI. Hypotheses testing for construct validity was satisfying, as 67% and 92% of the hypothesized correlations were confirmed. Structural validity was moderate for both measures, as confirmatory factor analyses showed limited fit. Reliability of both PROMs was good. The intraclass correlation coefficient (ICC) of the BCI domains ranged from 0.47 to 0.93, minimal value of Cronbach's &#x3b1; was 0.70, smallest detectable change on group level (SDC group) ranged from 1.9 to 8.6. The ICC of the FACT-Bl-Cys domains ranged from 0.43 to 0.83, minimal value of Cronbach's &#x3b1; was 0.77, SDC group was around 1. Only the FACT-Bl-Cys total score was found to be responsive to changes in generic quality of life.<br><br>CONCLUSIONS: The Dutch versions of the BCI and FACT-Bl-Cys were shown to be reliable and have good content validity. Structural validity was limited for both measures. Only the FACT-Bl-Cys total score was responsive to changes in generic HRQOL. Despite some limitations, both PROMs seem suitable for use in clinical practice and research.},
}
@article {pmid31518515,
year = {2020},
author = {Qian, Y and Wang, Z and Zhou, S and Zhao, W and Yin, C and Cao, J and Wang, Z and Li, Y},
title = {MKP1 in the medial prefrontal cortex modulates chronic neuropathic pain via regulation of p38 and JNK1/2.},
journal = {The International journal of neuroscience},
volume = {130},
number = {7},
pages = {643-652},
doi = {10.1080/00207454.2019.1667785},
pmid = {31518515},
issn = {1563-5279},
mesh = {Animals ; Dual Specificity Phosphatase 1/*metabolism ; Male ; Mice ; Mitogen-Activated Protein Kinase 8/metabolism ; Mitogen-Activated Protein Kinase 9/metabolism ; Neuralgia/*metabolism ; Pain Measurement ; Prefrontal Cortex/*metabolism ; *Signal Transduction ; p38 Mitogen-Activated Protein Kinases/metabolism ; },
abstract = {Aim: The medial prefrontal context (mPFC) plays pivotal roles in initiation, development, and maintenance of chronic pain, whereas the underlying molecular mechanisms remain elusive, which invited investigation of potential involvement of MKP1 in mPFC in mice in neuropathic pain, and its cellular and molecular mechanisms.Materials and methods: Neuropathic pain model was established in adult male Kunming mice via chronic constrictive injury (CCI) of the sciatic nerve. Paw withdrawal latency (PWL) was measured at the plantar area by radiant heat test. Stereotaxic microinjection was applied in mice as per the atlas of Mouse Brain in Stereotaxic Coordinates. mRNA levels of MKP1 in mPFC in CCI mice were assessed by RT-PCR; protein expressions of MKP1, p-p38, p-JNK and p-ERK in mPFC in CCI mice were analyzed by Western blotting, and expressions of the c-Fos in mPFC in CCI mice evaluated by immunohistochemistry. Moreover, Lenti-MKP1 particles or BCI treatment was employed to inhibit MKP1 in mPFC contralateral to the injury.Results: MKP1 was activated and persistently upregulated in mPFC neurons in CCI mice. Inhibition of MKP1 in the mPFC contralateral to the injury could reverse CCI-induced pain behavior and neuronal activity either via employment of Lenti-MKP1 particles or BCI treatment. MKP1 in the mPFC modulated neuropathic pain via dephosphorization of p38 and JNK1/2.Conclusion: The findings demonstrated that MKP1 in mPFC could play a paramount role in the modulation of neuropathic pain, which might be associated to the increased neuronal excitability in the mPFC and downregulated p-p38 and p-JNK expression.},
}
@article {pmid31515175,
year = {2020},
author = {Blitzer, DN and Ottochian, M and O'Connor, JV and Feliciano, DV and Morrison, JJ and DuBose, JJ and Scalea, TM},
title = {Timing of intervention may influence outcomes in blunt injury to the carotid artery.},
journal = {Journal of vascular surgery},
volume = {71},
number = {4},
pages = {1323-1332.e5},
doi = {10.1016/j.jvs.2019.05.059},
pmid = {31515175},
issn = {1097-6809},
mesh = {Adult ; Carotid Artery Injuries/*therapy ; Databases, Factual ; Female ; Humans ; Male ; Patient Selection ; Propensity Score ; Retrospective Studies ; *Time-to-Treatment ; Trauma Centers ; United States ; Wounds, Nonpenetrating/*therapy ; },
abstract = {OBJECTIVE: Blunt carotid artery injury (BCI) is present in approximately 1.0% to 2.7% of all blunt trauma admissions and can result in significant morbidity and mortality. Management ranges from antithrombotic therapy alone to surgery, where potential indications include pseudoaneurysm, failed or contraindication to medical therapy, and progression of neurologic symptoms. Still, optimal management, including approach and timing, continues to be an active area for debate. The goal of this study was to assess the epidemiologic characteristics of BCI, and, after controlling for presenting features intrinsic to the data, compare outcomes based on management, operative approach, and timing of intervention.<br><br>METHODS: A retrospective review was conducted of adult BCI patients identified within the National Trauma Data Bank from 2002 to 2016. The National Trauma Data Bank is the largest trauma database in the United States, collating data from each trauma admission for more than 900 trauma centers. Independent variables of interest included nonoperative versus operative management (OM); endovascular versus open intervention, and early (within 24&#xa0;hours) versus delayed (after 24&#xa0;hours) intervention. For each independent variable, groups were compared after propensity score matching to control for presenting factors and patterns of injury.<br><br>RESULTS: There were 9190 patients who met the inclusion criteria, 812 of whom underwent operative intervention (open, n&#xa0;= 288; endovascular, n&#xa0;= 481, both: n&#xa0;= 43). During the review, there was no difference in proportion of OM over time, although there was a statistically significant decrease in the proportion of open intervention (0.48% per year; P&#xa0;< .05). For outcomes, operative versus nonoperative management (nOM) resulted in no difference in mortality, but the operative group demonstrated an increased risk of stroke (11.8% vs 6.5%), longer hospital and intensive care length of stay, and more days on mechanical ventilation (P&#xa0;< .001 for each). With regard to timing: mortality was increased for early intervention (early, 16% vs delayed, 6.3%; P&#xa0;< .001), which was predominantly driven by the endovascular cohort (early, 19.2% vs delayed, 2.5%; P&#xa0;< .001).<br><br>CONCLUSIONS: In this study, there was no significant trend in the overall volume of operative or nOM; however, when considering approach to OM, there was a significant decrease in open procedures. Consistent with previous literature, injury to the neck, head, and chest was significant associated with BCI. Also outcomes demonstrated an increased prevalence of stroke after operative relative to nOM. Importantly, after critically assessing the timing to intervention, results strongly suggested that, if possible, intervention should be delayed for at least 24&#xa0;hours.},
}
@article {pmid31505367,
year = {2019},
author = {Zahn, R and Weingartner, JH and Basilio, R and Bado, P and Mattos, P and Sato, JR and de Oliveira-Souza, R and Fontenelle, LF and Young, AH and Moll, J},
title = {Blame-rebalance fMRI neurofeedback in major depressive disorder: A randomised proof-of-concept trial.},
journal = {NeuroImage. Clinical},
volume = {24},
number = {},
pages = {101992},
pmid = {31505367},
issn = {2213-1582},
support = {G0902304/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Depressive Disorder, Major/diagnostic imaging/*physiopathology ; Double-Blind Method ; Female ; *Functional Neuroimaging ; *Guilt ; Gyrus Cinguli/diagnostic imaging/*physiopathology ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neurofeedback/*physiology ; Proof of Concept Study ; *Self Concept ; Temporal Lobe/diagnostic imaging/*physiopathology ; },
abstract = {Previously, using fMRI, we demonstrated lower connectivity between right anterior superior temporal (ATL) and anterior subgenual cingulate (SCC) regions while patients with major depressive disorder (MDD) experience guilt. This neural signature was detected despite symptomatic remission which suggested a putative role in vulnerability. This randomised controlled double-blind parallel group clinical trial investigated whether patients with MDD are able to voluntarily modulate this neural signature. To this end, we developed a fMRI neurofeedback software (FRIEND), which measures ATL-SCC coupling and displays its levels in real time. Twenty-eight patients with remitted MDD were randomised to two groups, each receiving one session of fMRI neurofeedback whilst retrieving guilt and indignation/anger-related autobiographical memories. They were instructed to feel the emotion whilst trying to increase the level of a thermometer-like display on a screen. Active intervention group: The thermometer levels increased with increasing levels of ATL-SCC correlations in the guilt condition. Control intervention group: The thermometer levels decreased when correlation levels deviated from the previous baseline level in the guilt condition, thus reinforcing stable correlations. Both groups also received feedback during the indignation condition reinforcing stable correlations. We confirmed our predictions that patients in the active intervention group were indeed able to increase levels of ATL-SCC correlations for guilt vs. indignation and their self-esteem after training compared to before training and that this differed significantly from the control intervention group. These data provide proof-of-concept for a novel treatment target for MDD patients and are in keeping with the hypothesis that ATL-SCC connectivity plays a key role in self-worth. https://clinicaltrials.gov/ct2/show/results/NCT01920490.},
}
@article {pmid31504126,
year = {2019},
author = {Bartlett, JMS and Sgroi, DC and Treuner, K and Zhang, Y and Ahmed, I and Piper, T and Salunga, R and Brachtel, EF and Pirrie, SJ and Schnabel, CA and Rea, DW},
title = {Breast Cancer Index and prediction of benefit from extended endocrine therapy in breast cancer patients treated in the Adjuvant Tamoxifen-To Offer More? (aTTom) trial.},
journal = {Annals of oncology : official journal of the European Society for Medical Oncology},
volume = {30},
number = {11},
pages = {1776-1783},
pmid = {31504126},
issn = {1569-8041},
support = {12125/CRUK_/Cancer Research UK/United Kingdom ; 25354/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Aged ; Antineoplastic Agents, Hormonal/*therapeutic use ; Biomarkers, Tumor/*metabolism ; Breast/pathology/surgery ; Breast Neoplasms/mortality/pathology/*therapy ; Chemotherapy, Adjuvant/methods ; Disease-Free Survival ; Female ; Homeodomain Proteins/metabolism ; Humans ; Kaplan-Meier Estimate ; Mastectomy ; Middle Aged ; Neoplasm Recurrence, Local/*epidemiology/prevention &amp; control ; Prognosis ; Prospective Studies ; Receptors, Estrogen/metabolism ; Receptors, Interleukin-17/metabolism ; Receptors, Progesterone/metabolism ; Retrospective Studies ; Tamoxifen/*therapeutic use ; },
abstract = {BACKGROUND: Extending the duration of adjuvant endocrine therapy reduces the risk of recurrence in a subset of women with early-stage hormone receptor-positive (HR+) breast cancer. Validated predictive biomarkers of endocrine response could significantly improve patient selection for extended therapy. Breast cancer index (BCI) [HOXB13/IL17BR ratio (H/I)] was evaluated for its ability to predict benefit from extended endocrine therapy in patients previously randomized in the Adjuvant Tamoxifen-To Offer More? (aTTom) trial.<br><br>PATIENTS AND METHODS: Trans-aTTom is a multi-institutional, prospective-retrospective study in patients with available formalin-fixed paraffin-embedded primary tumor blocks. BCI testing and central determination of estrogen receptor (ER) and progesterone receptor (PR) status by immunohistochemistry were carried out blinded to clinical outcome. Survival endpoints were evaluated using Kaplan-Meier analysis and Cox regression with recurrence-free interval (RFI) as the primary endpoint. Interaction between extended endocrine therapy and BCI (H/I) was assessed using the likelihood ratio test.<br><br>RESULTS: Of 583 HR+, N+ patients analyzed, 49% classified as BCI (H/I)-High derived a significant benefit from 10 versus 5&#x2009;years of tamoxifen treatment [hazard ratio (HR): 0.35; 95% confidence interval (CI) 0.15-0.86; 10.2% absolute risk reduction based on RFI, P&#x2009;=&#x2009;0.027]. BCI (H/I)-low patients showed no significant benefit from extended endocrine therapy (HR: 1.07; 95% CI 0.69-1.65; -0.2% absolute risk reduction; P&#x2009;=&#x2009;0.768). Continuous BCI (H/I) levels predicted the magnitude of benefit from extended tamoxifen, whereas centralized ER and PR did not. Interaction between extended tamoxifen treatment and BCI (H/I) was statistically significant (P&#x2009;=&#x2009;0.012), adjusting for clinicopathological factors.<br><br>CONCLUSION: BCI by high H/I expression was predictive of endocrine response and identified a subset of HR+, N+ patients with significant benefit from 10 versus 5&#x2009;years of tamoxifen therapy. These data provide further validation, consistent with previous MA.17 data, establishing level 1B evidence for BCI as a predictive biomarker of benefit from extended endocrine therapy.<br><br>TRIAL REGISTRATION: ISRCTN17222211; NCT00003678.},
}
@article {pmid31503345,
year = {2020},
author = {Lyu, T and Jiang, Y and Jia, N and Che, X and Li, Q and Yu, Y and Hua, K and Bast, RC and Feng, W},
title = {SMYD3 promotes implant metastasis of ovarian cancer via H3K4 trimethylation of integrin promoters.},
journal = {International journal of cancer},
volume = {146},
number = {6},
pages = {1553-1567},
doi = {10.1002/ijc.32673},
pmid = {31503345},
issn = {1097-0215},
mesh = {Adult ; Aged ; Ascites/etiology/*pathology ; Carcinoma, Ovarian Epithelial/genetics/*secondary ; Cell Adhesion/genetics ; Cell Culture Techniques ; Cell Line, Tumor ; DNA Methylation ; Down-Regulation ; Female ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Knockdown Techniques ; Histone-Lysine N-Methyltransferase/genetics/*metabolism ; Histones/metabolism ; Humans ; Integrins/*genetics ; Middle Aged ; Ovarian Neoplasms/genetics/*pathology ; Peritoneal Neoplasms/genetics/*secondary ; Promoter Regions, Genetic/genetics ; Spheroids, Cellular ; Xenograft Model Antitumor Assays ; },
abstract = {Detachment of cancer cells from the primary tumor and formation of spheroids in ascites is required for implantation metastasis in epithelial ovarian cancer (EOC), but the underlying mechanism of this process has not been thoroughly elucidated. To mimic this process, ovarian cancer cells were grown in 3D and 2D culture. Hey and OVCA433 spheroids exhibited decreased cell proliferation and enhanced adhesion and invasion. SMYD3 expression was elevated in ovarian carcinoma spheroids in association with increased H3K4 methylation. Depletion of SMYD3 by transient siRNA, stable shRNA knockdown and the SMYD3 inhibitor BCI-121 all decreased spheroid invasion and adhesion. Gene expression arrays revealed downregulation of integrin family members. Inhibition assays confirmed that invasion and adhesion of spheroids are mediated by ITGB6 and ITGAM. SMYD3-deficient cells regained the ability to invade and adhere after forced overexpression of SMYD3, ITGB6 and ITGAM. However, this biological ability was not restored by forced overexpression of SMYD3 in ITGB6- and/or ITGAM-deficient cancer cells. SMYD3 and H3K4me3 binding at the ITGB6 and ITGAM promoters was increased in spheroids compared to that in monolayer cells, and the binding was decreased when SMYD3 expression was inhibited, consistent with the expression changes in integrins. SMYD3 expression and integrin-mediated adhesion were also activated in an intraperitoneal xenograft model and in EOC patient spheroids. In vivo, SMYD3 knockdown inhibited tumor metastasis and reduced ascites volume in both the intraperitoneal xenograft model and a PDX model. Overall, our results suggest that the SMYD3-H3K4me3-integrin pathway plays a crucial role in ovarian cancer metastasis to the peritoneal surface.},
}
@article {pmid31502984,
year = {2019},
author = {Bi, L and Zhang, J and Lian, J},
title = {EEG-Based Adaptive Driver-Vehicle Interface Using Variational Autoencoder and PI-TSVM.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2025-2033},
doi = {10.1109/TNSRE.2019.2940046},
pmid = {31502984},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Automobile Driving/*psychology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Healthy Volunteers ; Humans ; Male ; Pattern Recognition, Automated/methods ; Principal Component Analysis ; Psychomotor Performance ; Signal-To-Noise Ratio ; Support Vector Machine ; Young Adult ; },
abstract = {Event-related potential (ERP)-based driver-vehicle interfaces (DVIs) have been developed to provide a communication channel for people with disabilities to drive a vehicle. However, they require a tedious and time-consuming training procedure to build the decoding model, which can translate EEG signals into commands. In this paper, to address this problem, we propose an adaptive DVI by using a new semi-supervised algorithm. The decoding model of the proposed DVI is first built with a small labeled training set, and then gradually improved by updating the proposed semi-supervised decoding model with new collected unlabeled EEG signals. In our semi-supervised algorithm, independent component analysis (ICA) and Kalman smoother are first used to improve the signal-to-noise ratio (SNR). After that, variational autoencoder is applied to provide a robust feature representation of EEG signals. Finally, a prior information-based transductive support vector machine (PI-TSVM) classifier is developed to translate these features into commands. Experimental results show that the proposed DVI can significantly reduce the training effort. After a short updating, its performance can be close to that of the supervised DVI requiring a lengthy training procedure. This work is vital for advancing the application of these DVIs.},
}
@article {pmid31501005,
year = {2019},
author = {Morse, LR and Biering-Soerensen, F and Carbone, LD and Cervinka, T and Cirnigliaro, CM and Johnston, TE and Liu, N and Troy, KL and Weaver, FM and Shuhart, C and Craven, BC},
title = {Bone Mineral Density Testing in Spinal Cord Injury: 2019 ISCD Official Position.},
journal = {Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry},
volume = {22},
number = {4},
pages = {554-566},
doi = {10.1016/j.jocd.2019.07.012},
pmid = {31501005},
issn = {1094-6950},
mesh = {Absorptiometry, Photon/*standards ; *Bone Density ; *Consensus Development Conferences as Topic ; Humans ; Osteoporosis/complications/*diagnosis ; Societies, Medical ; Spinal Cord Injuries/*diagnosis/etiology ; },
abstract = {Spinal cord injury (SCI) causes rapid osteoporosis that is most severe below the level of injury. More than half of those with motor complete SCI will experience an osteoporotic fracture at some point following their injury, with most fractures occurring at the distal femur and proximal tibia. These fractures have devastating consequences, including delayed union or nonunion, cellulitis, skin breakdown, lower extremity amputation, and premature death. Maintaining skeletal integrity and preventing fractures is imperative following SCI to fully benefit from future advances in paralysis cure research and robotic-exoskeletons, brain computer interfaces and other evolving technologies. Clinical care has been previously limited by the lack of consensus derived guidelines or standards regarding dual-energy X-ray absorptiometry-based diagnosis of osteoporosis, fracture risk prediction, or monitoring response to therapies. The International Society of Clinical Densitometry convened a task force to establish Official Positions for bone density assessment by dual-energy X-ray absorptiometry in individuals with SCI of traumatic or nontraumatic etiology. This task force conducted a series of systematic reviews to guide the development of evidence-based position statements that were reviewed by an expert panel at the 2019 Position Development Conference in Kuala Lumpur, Malaysia. The resulting the International Society of Clinical Densitometry Official Positions are intended to inform clinical care and guide the diagnosis of osteoporosis as well as fracture risk management of osteoporosis following SCI.},
}
@article {pmid31496929,
year = {2019},
author = {Sauter-Starace, F and Ratel, D and Cretallaz, C and Foerster, M and Lambert, A and Gaude, C and Costecalde, T and Bonnet, S and Charvet, G and Aksenova, T and Mestais, C and Benabid, AL and Torres-Martinez, N},
title = {Long-Term Sheep Implantation of WIMAGINE[®], a Wireless 64-Channel Electrocorticogram Recorder.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {847},
pmid = {31496929},
issn = {1662-4548},
abstract = {This article deals with the long-term preclinical validation of WIMAGINE[®] (Wireless Implantable Multi-channel Acquisition system for Generic Interface with Neurons), a 64-channel wireless implantable recorder that measures the electrical activity at the cortical surface (electrocorticography, ECoG). The WIMAGINE[®] implant was designed for chronic wireless neuronal signal acquisition, to be used e.g., as an intracranial Brain-Computer Interface (BCI) for severely motor-impaired patients. Due to the size and shape of WIMAGINE[®], sheep appeared to be the best animal model on which to carry out long-term in vivo validation. The devices were implanted in two sheep for a follow-up period of 10 months, including idle state cortical recordings and Somato-Sensory Evoked Potential (SSEP) sessions. ECoG and SSEP demonstrated relatively stable behavior during the 10-month observation period. Information recorded from the SensoriMotor Cortex (SMC) showed an SSEP phase reversal, indicating the cortical site of the sensorimotor activity was retained after 10 months of contact. Based on weekly recordings of raw ECoG signals, the effective bandwidth was in the range of 230 Hz for both animals and remarkably stable over time, meaning preservation of the high frequency bands valuable for decoding of the brain activity using BCIs. The power spectral density (in dB/Hz), on a log scale, was of the order of 2.2, -4.5 and -18 for the frequency bands (10-40), (40-100), and (100-200) Hz, respectively. The outcome of this preclinical work is the first long-term in vivo validation of the WIMAGINE[®] implant, highlighting its ability to record the brain electrical activity through the dura mater and to send wireless digitized data to the external base station. Apart from local adhesion of the dura to the skull, the neurosurgeon did not face any difficulty in the implantation of the WIMAGINE[®] device and post-mortem analysis of the brain revealed no side effect related to the implantation. We also report on the reliability of the system; including the implantable device, the antennas module and the external base station.},
}
@article {pmid31495453,
year = {2019},
author = {Kavehei, O and Hamilton, TJ and Truong, ND and Nikpour, A},
title = {Opportunities for Electroceuticals in Epilepsy.},
journal = {Trends in pharmacological sciences},
volume = {40},
number = {10},
pages = {735-746},
doi = {10.1016/j.tips.2019.08.001},
pmid = {31495453},
issn = {1873-3735},
mesh = {Animals ; Brain-Computer Interfaces ; Deep Brain Stimulation/instrumentation/*methods ; Electroencephalography/methods ; Epilepsy/diagnosis/physiopathology/*therapy ; Humans ; Microelectrodes ; Monitoring, Physiologic/instrumentation/methods ; Seizures/prevention &amp; control ; Transcranial Direct Current Stimulation/instrumentation/*methods ; },
abstract = {Epilepsy is a neurological disorder that affects ∼1% of the world population. Nearly 30% of epilepsy patients suffer from pharmacoresistant epilepsy that cannot be treated with antiepileptic drugs. Depending on seizure type, a diverse range of therapies are available, including surgery, vagus nerve stimulation, and deep brain stimulation. We review the sensing and stimulation technologies most used in neurological disorders, and provide a vision of minimally invasive electroceuticals to enable accurate forecasting of epileptic seizures and therapy. The use of such systems could potentially help patients to prevent injuries and, in combination with an intervention mechanism, could provide a method of suppressing seizures in epileptic patients.},
}
@article {pmid31490999,
year = {2019},
author = {Nagel, S and Spüler, M},
title = {World's fastest brain-computer interface: Combining EEG2Code with deep learning.},
journal = {PloS one},
volume = {14},
number = {9},
pages = {e0221909},
pmid = {31490999},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Signal Processing, Computer-Assisted ; Software ; Time Factors ; },
abstract = {We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can either be used in a passive Brain-Computer Interface (BCI) to predict properties of a visual stimulus the person is viewing, or it can be used to actively control a BCI application. Both scenarios were tested, whereby an average information transfer rate (ITR) of 701 bit/min was achieved for the passive BCI approach with the best subject achieving an online ITR of 1237 bit/min. Further, it allowed the discrimination of 500,000 different visual stimuli based on only 2 seconds of EEG data with an accuracy of up to 100%. When using the method for an asynchronous self-paced BCI for spelling, an average utility rate of 175 bit/min was achieved, which corresponds to an average of 35 error-free letters per minute. As the presented method extracts more than three times more information than the previously fastest approach, we suggest that EEG signals carry more information than generally assumed. Finally, we observed a ceiling effect such that information content in the EEG exceeds that required for BCI control, and therefore we discuss if BCI research has reached a point where the performance of non-invasive visual BCI control cannot be substantially improved anymore.},
}
@article {pmid31489795,
year = {2019},
author = {Carli, S and Bianchi, M and Zucchini, E and Di Lauro, M and Prato, M and Murgia, M and Fadiga, L and Biscarini, F},
title = {Electrodeposited PEDOT:Nafion Composite for Neural Recording and Stimulation.},
journal = {Advanced healthcare materials},
volume = {8},
number = {19},
pages = {e1900765},
doi = {10.1002/adhm.201900765},
pmid = {31489795},
issn = {2192-2659},
mesh = {Animals ; *Brain-Computer Interfaces ; Bridged Bicyclo Compounds, Heterocyclic/*chemistry ; Coated Materials, Biocompatible ; Electric Conductivity ; Electric Stimulation ; Electrodes, Implanted ; *Electroplating ; Fluorocarbon Polymers/chemistry ; Male ; Micelles ; Microelectrodes ; Microscopy, Atomic Force ; Nanocomposites/*chemistry ; Neurons/*physiology ; Oxygen/chemistry ; Polymers/*chemistry ; Polystyrenes/chemistry ; Rats ; Rats, Wistar ; },
abstract = {Microelectrode arrays are used for recording and stimulation in neurosciences both in vitro and in vivo. The electrodeposition of conductive polymers, such as poly(3,4-ethylene dioxythiophene) (PEDOT), is widely adopted to improve both the in vivo recording and the charge injection limit of metallic microelectrodes. The workhorse of conductive polymers in the neurosciences is PEDOT:PSS, where PSS represents polystyrene-sulfonate. In this paper, the counterion is the fluorinated polymer Nafion, so the composite PEDOT:Nafion is deposited onto a flexible neural microelectrode array. PEDOT:Nafion coated electrodes exhibit comparable in vivo recording capability to the reference PEDOT:PSS, providing a large signal-to-noise ratio in a murine animal model. Importantly, PEDOT:Nafion exhibits a minimized polarization during electrical stimulation, thereby resulting in an improved charge injection limit equal to 4.4 mC cm[-2] , almost 80% larger than the 2.5 mC cm[-2] that is observed for PEDOT:PSS.},
}
@article {pmid31489233,
year = {2019},
author = {Rangarajan, K and Somani, BK},
title = {Trends in quality of life reporting for radical cystectomy and urinary diversion over the last four decades: A systematic review of the literature.},
journal = {Arab journal of urology},
volume = {17},
number = {3},
pages = {181-194},
pmid = {31489233},
issn = {2090-598X},
abstract = {Objective: To report the trends in quality of life (QoL) reporting for radical cystectomy (RC) and urinary diversion (UD) over the last four decades, as RC for bladder cancer is associated with significant morbidity and QoL issues. Material and methods: We searched PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Cochrane library for published studies from January 1980 to January 2017 in the English language. We divided the published articles into three time periods: period-1 (1980-1997), period-2 (1998-2007) and period-3 (2008-2017). Results: A total of 85 QoL studies (8417 patients) were identified, of which 3347 (39.8%) patients had an ileal conduit (IC), 1078 (12.8%) had a continent UD (CD), 3264 (38.8%) had a neobladder (NB), and in the remaining 728 (8.6%) the type of UD was not specified. Whilst there were 15, 24 and 41 studies in period-1, period-2 and period-3 respectively, two (13%), 20 (83%) and 37 (90%) used a validated QoL tool; and none, six (25%) and 23 (56%) used a urology specific QoL tool during these three time periods. Similarly, the number of prospective studies increased from one (7%) to four (17%) and 14 (34%) in these three time periods. The proportion of reported IC patients reduced from 65% (784 patients) to 36% (899) and 35% (1664) from period-1 to period-3, whereas the proportion of NB patients increased from 4.5% (54) to 44% (1105) and 44% (2105). Over the last few years there have been QoL studies on laparoscopic and robotic IC and NB UDs. Conclusion: Our review suggests an increasing use of validated, bladder cancer-specific questionnaires with UD-specific constructs. Abbreviations: BCI: Bladder Cancer Index; BDI: Beck Depression Inventory; BIS: Body Image Scale; CD: continent urinary diversion; EORTC QLQ-30C: European Organisation for the Research and Treatment of Cancer Quality of Life 30-item core questionnaire; ERAS: enhanced recovery after surgery; FACT(-BL)(-G)(-VCI): Functional Assessment of Cancer Therapy(-Bladder Cancer)(-General)(-Vanderbilt Cystectomy Index); IC: ileal conduit; NB: neobladder; (HR)QoL: (health-related) quality of life; (RA)RC: (robot-assisted) radical cystectomy; SF-36: 36-item short-form health survey; SIP: Sickness Impact Profile; UD: urinary diversion.},
}
@article {pmid31485883,
year = {2019},
author = {Mebarkia, K and Reffad, A},
title = {Multi optimized SVM classifiers for motor imagery left and right hand movement identification.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {42},
number = {4},
pages = {949-958},
doi = {10.1007/s13246-019-00793-y},
pmid = {31485883},
issn = {1879-5447},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Hand/*physiology ; Humans ; *Imagery, Psychotherapy ; Motor Activity/*physiology ; *Movement ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; Task Performance and Analysis ; },
abstract = {EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an attractive technology which permits control and interaction with a computer or a machine using EEG signals. Brain task identification based on EEG signals is very difficult task and is still challenging researchers. In this paper, the motor imagery of left and right hand actions are identified using new features which are fed to a set of optimized SVM classifiers. Multi classifiers based classification showed having high faculty to improve the classification accuracy when using different kind or diversified features. Features selection was performed by genetic algorithm optimization. In single optimized SVM classifier, a mean classification accuracy of 89.8% was reached. To further improve the rate of classification, three SVMs classifiers have been suggested and optimized in order to find suitable features for each classifier. The three SVMs classifiers were optimized and achieved a performance mean of 94.11%. The achieved performance is a significant improvement comparing to the existing methods which does not exceed 81% while using the same database. Here, combining multi classifiers with selecting suitable features by optimization can be a good alternative for BCI applications.},
}
@article {pmid31485045,
year = {2019},
author = {},
title = {Brain-machine interfaces.},
journal = {Nature biotechnology},
volume = {37},
number = {9},
pages = {1001},
doi = {10.1038/s41587-019-0251-7},
pmid = {31485045},
issn = {1546-1696},
}
@article {pmid31484971,
year = {2019},
author = {Lee, WH and Kim, E and Seo, HG and Oh, BM and Nam, HS and Kim, YJ and Lee, HH and Kang, MG and Kim, S and Bang, MS},
title = {Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {12770},
pmid = {31484971},
issn = {2045-2322},
mesh = {Adult ; *Brain/diagnostic imaging/physiology ; Brain Mapping ; Hand Strength/*physiology ; Humans ; *Magnetic Resonance Imaging ; Male ; Psychomotor Performance/*physiology ; },
abstract = {Motor imagery (MI) for target-oriented movements, which is a basis for functional activities of daily living, can be more appropriate than non-target-oriented MI as tasks to promote motor recovery or brain-computer interface (BCI) applications. This study aimed to explore different characteristics of brain activation among target-oriented kinesthetic imagery (KI) and visual imagery (VI) in the first-person (VI-1) and third-person (VI-3) perspectives. Eighteen healthy volunteers were evaluated for MI ability, trained for the three types of target-oriented MIs, and scanned using 3 T functional magnetic resonance imaging (fMRI) under MI and perceptual control conditions, presented in a block design. Post-experimental questionnaires were administered after fMRI. Common brain regions activated during the three types of MI were the left premotor area and inferior parietal lobule, irrespective of the MI modalities or perspectives. Contrast analyses showed significantly increased brain activation only in the contrast of KI versus VI-1 and KI versus VI-3 for considerably extensive brain regions, including the supplementary motor area and insula. Neural activity in the orbitofrontal cortex and cerebellum during VI-1 and KI was significantly correlated with MI ability measured by mental chronometry and a self-reported questionnaire, respectively. These results can provide a basis in developing MI-based protocols for neurorehabilitation to improve motor recovery and BCI training in severely paralyzed individuals.},
}
@article {pmid31482852,
year = {2019},
author = {Radüntz, T and Meffert, B},
title = {User Experience of 7 Mobile Electroencephalography Devices: Comparative Study.},
journal = {JMIR mHealth and uHealth},
volume = {7},
number = {9},
pages = {e14474},
pmid = {31482852},
issn = {2291-5222},
mesh = {Adult ; Aged ; Electroencephalography/instrumentation/*standards/statistics &amp; numerical data ; Female ; Humans ; Male ; Middle Aged ; Patients/*psychology/statistics &amp; numerical data ; Technology Assessment, Biomedical/methods ; Telemedicine/instrumentation/*standards/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Registration of brain activity has become increasingly popular and offers a way to identify the mental state of the user, prevent inappropriate workload, and control other devices by means of brain-computer interfaces. However, electroencephalography (EEG) is often related to user acceptance issues regarding the measuring technique. Meanwhile, emerging mobile EEG technology offers the possibility of gel-free signal acquisition and wireless signal transmission. Nonetheless, user experience research about the new devices is lacking.<br><br>OBJECTIVE: This study aimed to evaluate user experience aspects of emerging mobile EEG devices and, in particular, to investigate wearing comfort and issues related to emotional design.<br><br>METHODS: We considered 7 mobile EEG devices and compared them for their wearing comfort, type of electrodes, visual appearance, and subjects' preference for daily use. A total of 24 subjects participated in our study and tested every device independently of the others. The devices were selected in a randomized order and worn on consecutive day sessions of 60-min duration. At the end of each session, subjects rated the devices by means of questionnaires.<br><br>RESULTS: Results indicated a highly significant change in maximal possible wearing duration among the EEG devices (&#x3c7;[2]6=40.2, n=24; P<.001). Regarding the visual perception of devices' headset design, results indicated a significant change in the subjects' ratings (&#x3c7;[2]6=78.7, n=24; P<.001). Results of the subjects' ratings regarding the practicability of the devices indicated highly significant differences among the EEG devices (&#x3c7;[2]6=83.2, n=24; P<.001). Ranking order and posthoc tests offered more insight and indicated that pin electrodes had the lowest wearing comfort, in particular, when coupled with a rigid, heavy headset. Finally, multiple linear regression for each device separately revealed that users were not willing to accept less comfort for a more attractive headset design.<br><br>CONCLUSIONS: The study offers a differentiated look at emerging mobile and gel-free EEG technology and the relation between user experience aspects and device preference. Our research could be seen as a precondition for the development of usable applications with wearables and contributes to consumer health informatics and health-enabling technologies. Furthermore, our results provided guidance for the technological development direction of new EEG devices related to the aspects of emotional design.},
}
@article {pmid31480734,
year = {2019},
author = {Kaya, I and Bohórquez, J and Özdamar, &#xd6;},
title = {A BCI Gaze Sensing Method Using Low Jitter Code Modulated VEP.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {17},
pages = {},
pmid = {31480734},
issn = {1424-8220},
abstract = {Visual evoked potentials (VEPs) are used in clinical applications in ophthalmology, neurology, and extensively in brain-computer interface (BCI) research. Many BCI implementations utilize steady-state VEP (SSVEP) and/or code modulated VEP (c-VEP) as inputs, in tandem with sophisticated methods to improve information transfer rates (ITR). There is a gap in knowledge regarding the adaptation dynamics and physiological generation mechanisms of the VEP response, and the relation of these factors with BCI performance. A simple, dual pattern display setup was used to evoke VEPs and to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50, and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits quasi-steady-state VEPs (QSS-VEPs) that are utilized for the simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs, and it is assumed that it shares similar generators of the SSVEPs. Eight subjects were recorded, and the performance of the overall system was analyzed using receiver operating characteristic (ROC) curves, accuracy plots, and ITRs. In summary, QSS-VEPs performed better than transient VEPs (TR-VEP). It was found that in general, 32 rps stimulation had the highest ROC area, accuracy, and ITRs. Moreover, QSS-VEPs were found to lead to higher accuracy by template matching compared to SSVEPs at 32 rps. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated.},
}
@article {pmid31480570,
year = {2019},
author = {Chikara, RK and Ko, LW},
title = {Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {17},
pages = {},
pmid = {31480570},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials/physiology ; Humans ; },
abstract = {Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.},
}
@article {pmid31480390,
year = {2019},
author = {Park, Y and Chung, W},
title = {Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {17},
pages = {},
pmid = {31480390},
issn = {1424-8220},
support = {2017-0-00451//Information &amp; Communications Technology Planning &amp; Evaluation (IITP)/ ; },
abstract = {This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance. The performance of the proposed method was evaluated using BCI Competition III Dataset IVa (18 channels) and BCI Competition IV Dataset I (59 channels).},
}
@article {pmid31480029,
year = {2020},
author = {Prins, NW and Mylavarapu, R and Shoup, AM and Debnath, S and Prasad, A},
title = {Spinal cord neural interfacing in common marmosets (Callithrix jacchus).},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016031},
pmid = {31480029},
issn = {1741-2552},
support = {DP2 EB022357/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Callithrix ; *Electrodes, Implanted ; Male ; Psychomotor Performance/*physiology ; Spinal Cord/*physiology ; Upper Extremity/physiology ; },
abstract = {OBJECTIVE: Spinal cord injury remains an ailment with no comprehensive cure, and affected patients suffer from a greatly diminished quality of life. This large population could significantly benefit from prosthetic technologies to replace missing limbs, reanimate nonfunctional limbs, and enable new modes of technologies to restore muscle control and function. While cortically driven brain machine interfaces have achieved great success in interfacing with an external device to restore lost functions, interfacing with the spinal cord can provide an additional site to record motor control signals, which can have its own advantages, despite challenges from using a smaller non-human primate (NHP) model. The goal of this study is to develop such a spinal cord neural interface to record motor signals from the high cervical levels of the spinal cord in a common marmoset (Callithrix jacchus) model. Approach and main results. Detailed methods are discussed for this smaller NHP model that includes behavioral training, surgical methods for electrode placement, connector placement and wire handling, electrode specifications and modifications for accessing high cervical level interneurons and motorneurons. The study also discusses the methods and challenges involved in behavioral multi-channel extracellular recording from the marmoset spinal cord, including the major recording failure mechanisms encountered during the study.<br><br>SIGNIFICANCE: Marmosets provide a good step between rodent and larger NHP models due to their small size, ease of handling, cognitive abilities, and similarities to other primate motor systems. The study shows the feasibility of recording spinal cord signals and using marmosets as a smaller NHP model in behavioral neuroscience studies. Interfacing with the spinal cord in chronically implanted animals can provide useful information about how motor control signals within the spinal cord are transformed to cause limb movements.},
}
@article {pmid31479645,
year = {2019},
author = {Mijani, AM and Shamsollahi, MB and Sheikh Hassani, M},
title = {A novel dual and triple shifted RSVP paradigm for P300 speller.},
journal = {Journal of neuroscience methods},
volume = {328},
number = {},
pages = {108420},
doi = {10.1016/j.jneumeth.2019.108420},
pmid = {31479645},
issn = {1872-678X},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Communication Devices for People with Disabilities/*standards ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Eye Movements/*physiology ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; *User-Computer Interface ; },
abstract = {BACKGROUND: A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen.<br><br>NEW METHOD: Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character twice and three times respectively, compared to the one-time appearance in the single paradigm. To compare the named paradigms, three subjects participated in experiments using all three paradigms.<br><br>RESULTS: Offline results demonstrate an average character detection accuracy of 97% for the single and double protocols, and 80% for the Triple paradigm. In addition, average ITR is calculated to be 5.45, 7.62 and 7.90 bit/min for the single, Dual and Triple paradigms respectively. Results identify the Dual RSVP paradigm as the most suitable approach that provides the best balance between ITR and character detection accuracy.<br><br>The novel speller system (the Dual paradigm) suggested in this paper demonstrates improved performance compared to existing methods, and overcomes the gaze dependency issue.<br><br>CONCLUSIONS: Overall, our novel method is a reliable alternative that both removes limitations for users suffering from impaired oculomotor control and improves performance.},
}
@article {pmid31478872,
year = {2019},
author = {Rosenthal, J and Sharma, A and Kampianakis, E and Reynolds, MS},
title = {A 25 Mbps, 12.4 pJ/b DQPSK Backscatter Data Uplink for the NeuroDisc Brain-Computer Interface.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {5},
pages = {858-867},
doi = {10.1109/TBCAS.2019.2938511},
pmid = {31478872},
issn = {1940-9990},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca nemestrina ; *Signal Processing, Computer-Assisted ; *Wireless Technology ; },
abstract = {Wireless brain-computer interfaces (BCIs) are used to study neural activity in freely moving non-human primates (NHPs). However, the high energy consumption of conventional active radios is proving to be an obstacle as research drives for wireless BCIs that can provide continuous high-rate data uplinks for longer durations (i.e. multiple days). We present a differential quadrature phase shift keying (DQPSK) backscatter uplink for the NeuroDisc BCI as an alternative to active radios. The uplink achieves a 25 Mbps throughput while operating in the 915 MHz industrial, scientific, and medical (ISM) band. The DQPSK backscatter modulator was measured to have an error-vector magnitude (EVM) of 9.7% and a measured power consumption of 309 μW during continuous, full-rate transmissions, yielding an analog communication efficiency of 12.4 pJ/bit. The NeuroDisc is capable of recording 16 channels of neural data with 16-bit resolution at up to 20 kSps per channel with a measured input-referred noise of 2.35 μV. In previous work, we demonstrated the DQPSK backscatter uplink, but bandwidth constraints in the signal chain limited the uplink rate to 6.25 Mbps and the neural sampling rate to 5 kSps. This work provides new innovations to increase the bandwidth of the system, including an ultra-high frequency (UHF) antenna design with a -10 dB return loss bandwidth of 12.5 MHz and a full-duplex receiver with an average self-jammer cancellation of 89 dB. We present end-to-end characterization of the NeuroDisc and validate the backscatter uplink using pre-recorded neural data as well as in vivo recordings from a pigtail macaque.},
}
@article {pmid31478864,
year = {2019},
author = {Zhao, X and Zhang, H and Zhu, G and You, F and Kuang, S and Sun, L},
title = {A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2164-2177},
doi = {10.1109/TNSRE.2019.2938295},
pmid = {31478864},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; Imagination/*physiology ; Machine Learning ; Movement/*physiology ; *Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes. The multi-branch 3D CNN and classification strategy are designed accordingly for the 3D representation. Experimental evaluation reveals that the proposed framework reaches state-of-the-art classification kappa value level and significantly outperforms other algorithms by 50% decrease in standard deviation of different subjects, which shows good performance and excellent robustness on different subjects. The framework also shows great performance with only nine sampling electrodes, which can significantly enhance its practicality. Moreover, the multi-branch structure exhibits its low latency and a strong ability in mitigating overfitting issues which often occur in MI classification because of the small training dataset.},
}
@article {pmid31476751,
year = {2019},
author = {Paek, AY and Gailey, A and Parikh, PJ and Santello, M and Contreras-Vidal, JL},
title = {Regression-based reconstruction of human grip force trajectories with noninvasive scalp electroencephalography.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066030},
doi = {10.1088/1741-2552/ab4063},
pmid = {31476751},
issn = {1741-2552},
mesh = {Activities of Daily Living ; Electroencephalography/*methods ; Female ; Hand Strength/*physiology ; Humans ; Isometric Contraction/*physiology ; Male ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Scalp/*physiology ; },
abstract = {OBJECTIVE: Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual's grip forces produced by the hand.<br><br>APPROACH: Brain rhythms and grip forces were recorded from able-bodied human subjects while they performed an isometric force production task and a grasp-and-lift task. Grip force trajectories were reconstructed with a linear model that incorporated delta band (0.1-1 Hz) voltage potentials and spectral power in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma (30-50 Hz), mid gamma (70-110 Hz), and high gamma (130-200 Hz) bands. Trajectory reconstruction models were trained and tested through 10-fold cross validation.<br><br>MAIN RESULTS: Modest accuracies were attained in reconstructing grip forces during isometric force production (median r&#x2009;&#x2009;=&#x2009;&#x2009;0.42), and the grasp-and-lift task (median r&#x2009;&#x2009;=&#x2009;&#x2009;0.51). Predicted trajectories were also analyzed further to assess the linear models' performance based on task requirements. For the isometric force production task, we found that predicted grip trajectories did not yield static grip forces that were distinguishable in magnitude across three task conditions. For the grasp-and-lift task, we estimate there would be an approximate 25% error in distinguishing when a user wants to hold or release an object.<br><br>SIGNIFICANCE: These findings indicate that EEG, a noninvasive neuroimaging modality, has predictive information in neural features associated with finger force control and can potentially contribute to the development of brain machine interfaces (BMI) for performing activities of daily living.},
}
@article {pmid31476744,
year = {2019},
author = {Ahmadi, S and Borhanazad, M and Tump, D and Farquhar, J and Desain, P},
title = {Low channel count montages using sensor tying for VEP-based BCI.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066038},
doi = {10.1088/1741-2552/ab4057},
pmid = {31476744},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; Printing, Three-Dimensional/*instrumentation ; },
abstract = {OBJECTIVE: Brain computer interfaces (BCIs) are slowly making their appearance on the consumer market, accompanied by a higher popularity among the general public. This new group of users requires easy-to-use headsets with robustness to non-precise placement. In this paper, an optimized fixed montage EEG headset for VEP BCIs is proposed.<br><br>APPROACH: The proposed layout covers only the most relevant area with large sensors to account for slight misplacement. To obtain large sensors, without having them physically available, we tie multiple sensors together and simulate the effect by averaging the signal of multiple sensors.<br><br>MAIN RESULTS: In simulations based on recorded 256-channel EEG data, it is shown that a circular center-surround configuration with sensor tying, leading to only eight channels covering a large part of the occipital lobe, can provide high performance and good robustness to misplacement. Automatically optimized layouts were unable to achieve better performance, demonstrating the utility of this manual design. Finally, the performance and benefits of sensor tying in the manual design are then validated in a physical experiment.<br><br>SIGNIFICANCE: The resulting proposed layout fulfills most requirements of an easy to use consumer EEG headset.},
}
@article {pmid31476743,
year = {2020},
author = {Dai, G and Zhou, J and Huang, J and Wang, N},
title = {HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016025},
doi = {10.1088/1741-2552/ab405f},
pmid = {31476743},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: Electroencephalography (EEG) motor imagery classification has been widely used in healthcare applications such as mobile assistive robots and post-stroke rehabilitation. Recently, EEG motor imagery classification methods based on convolutional neural networks (CNNs) have been proposed and have achieved relatively high classification accuracy. However, these methods use single convolution scale in the CNN, while the best convolution scale differs from subject to subject. This limits the classification accuracy. Another issue is that the classification accuracy degrades when training data is limited.<br><br>APPROACH: To address these issues, we have proposed a hybrid-scale CNN architecture with a data augmentation method for EEG motor imagery classification.<br><br>MAIN RESULTS: Compared with several state-of-the-art methods, the proposed method achieves an average classification accuracy of 91.57% and 87.6% on two commonly used datasets, which outperforms several state-of-the-art EEG motor imagery classification methods.<br><br>SIGNIFICANCE: The proposed method effectively addresses the issues of existing CNN-based EEG motor imagery classification methods and improves the classification accuracy.},
}
@article {pmid31475659,
year = {2019},
author = {Rainey, S and Maslen, H and Mégevand, P and Arnal, LH and Fourneret, E and Yvert, B},
title = {Neuroprosthetic Speech: The Ethical Significance of Accuracy, Control and Pragmatics.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {28},
number = {4},
pages = {657-670},
doi = {10.1017/S0963180119000604},
pmid = {31475659},
issn = {1469-2147},
mesh = {Brain-Computer Interfaces/ethics/standards ; Communication Devices for People with Disabilities/*ethics/*standards ; Electroencephalography ; Humans ; *Neural Prostheses/ethics ; Semantics ; *Speech, Alaryngeal ; },
abstract = {Neuroprosthetic speech devices are an emerging technology that can offer the possibility of communication to those who are unable to speak. Patients with 'locked in syndrome,' aphasia, or other such pathologies can use covert speech-vividly imagining saying something without actual vocalization-to trigger neural controlled systems capable of synthesizing the speech they would have spoken, but for their impairment.We provide an analysis of the mechanisms and outputs involved in speech mediated by neuroprosthetic devices. This analysis provides a framework for accounting for the ethical significance of accuracy, control, and pragmatic dimensions of prosthesis-mediated speech. We first examine what it means for the output of the device to be accurate, drawing a distinction between technical accuracy on the one hand and semantic accuracy on the other. These are conceptual notions of accuracy.Both technical and semantic accuracy of the device will be necessary (but not yet sufficient) for the user to have sufficient control over the device. Sufficient control is an ethical consideration: we place high value on being able to express ourselves when we want and how we want. Sufficient control of a neural speech prosthesis requires that a speaker can reliably use their speech apparatus as they want to, and can expect their speech to authentically represent them. We draw a distinction between two relevant features which bear on the question of whether the user has sufficient control: voluntariness of the speech and the authenticity of the speech. These can come apart: the user might involuntarily produce an authentic output (perhaps revealing private thoughts) or might voluntarily produce an inauthentic output (e.g., when the output is not semantically accurate). Finally, we consider the role of the interlocutor in interpreting the content and purpose of the communication.These three ethical dimensions raise philosophical questions about the nature of speech, the level of control required for communicative accuracy, and the nature of 'accuracy' with respect to both natural and prosthesis-mediated speech.},
}
@article {pmid31475162,
year = {2019},
author = {de Bronsvoort, BMC and Bagninbom, JM and Ndip, L and Kelly, RF and Handel, I and Tanya, VN and Morgan, KL and Ngu Ngwa, V and Mazeri, S and Nfon, C},
title = {Comparison of Two Rift Valley Fever Serological Tests in Cameroonian Cattle Populations Using a Bayesian Latent Class Approach.},
journal = {Frontiers in veterinary science},
volume = {6},
number = {},
pages = {258},
pmid = {31475162},
issn = {2297-1769},
abstract = {Rift Valley Fever is an important zoonotic viral disease of livestock occurring across much of Africa causing acute febrile illness, abortion, and neonatal death in livestock particularly sheep and cattle and a range of disease in humans from mild flu-like symptoms to more severe haemorrhagic fever and death. Understanding the epidemiology requires well-evaluated tools including antibody detection ELISAs. It is well-recognized that tests developed in one population do not necessarily perform as well when used in different populations and it is therefore important to assess tests in the populations in which they are to be used. Here we describe the performance of a commercial RVF ELISA (ID.Vet) and an in-house plaque reduction neutralization test (PRNT80). A Bayesian no gold standard latent class model for two tests and ≥2 populations based on the Hui-Walter model was used to estimate the test parameters using a range of populations based on geographical separation and age to assess consistency of performance across different sub-populations. The ID.Vet ELISA had an estimated diagnostic sensitivity (Se) of 0.854 (0.655-0.991 95%BCI) and specificity (Sp) of 0.986 (0.971-0.998 95%BCI) using all the data and splitting the population by geographical region compared to 0.844 (0.660-0.973 95%BCI) and 0.981 (0.965-0.996 95%BCI) for the PRNT80. There was slight variation in the mean Se and Sp in different sub-populations mainly in Se estimates due to small numbers of positives in the sub-populations but the 95% BCI generally overlapped suggesting a very consistent performance across the different geographical areas and ages of animals. This is one of few reports of serological evidence of RVF in Central Africa and strongly suggests the virus is actively circulating in this cattle population. This has important public health implications and RVF should be considered as a differential in both livestock disease cases as well as human febrile cases in West and Central Africa not just East Africa. We also demonstrate that the performance of the commercial ELISA is comparable to the PRNT80 but has the advantages of speed, lower cost and no containment needs making it a much more useful test for low and middle income settings (LMICs).},
}
@article {pmid31473020,
year = {2020},
author = {Levey, AS and Gansevoort, RT and Coresh, J and Inker, LA and Heerspink, HL and Grams, ME and Greene, T and Tighiouart, H and Matsushita, K and Ballew, SH and Sang, Y and Vonesh, E and Ying, J and Manley, T and de Zeeuw, D and Eckardt, KU and Levin, A and Perkovic, V and Zhang, L and Willis, K},
title = {Change in Albuminuria and GFR as End Points for Clinical Trials in Early Stages of CKD: A Scientific Workshop Sponsored by the National Kidney Foundation in Collaboration With the US Food and Drug Administration and European Medicines Agency.},
journal = {American journal of kidney diseases : the official journal of the National Kidney Foundation},
volume = {75},
number = {1},
pages = {84-104},
doi = {10.1053/j.ajkd.2019.06.009},
pmid = {31473020},
issn = {1523-6838},
mesh = {Albuminuria/*metabolism ; Bayes Theorem ; Biomarkers ; Creatinine/urine ; Disease Progression ; Drug Approval ; Europe ; *Glomerular Filtration Rate ; Humans ; Kidney Failure, Chronic/*metabolism ; Renal Insufficiency, Chronic/*metabolism ; United States ; United States Food and Drug Administration ; },
abstract = {The US Food and Drug Administration (FDA) and European Medicines Agency (EMA) are currently willing to consider a 30% to 40% glomerular filtration rate (GFR) decline as a surrogate end point for kidney failure for clinical trials of kidney disease progression under appropriate conditions. However, these end points may not be practical for early stages of kidney disease. In March 2018, the National Kidney Foundation sponsored a scientific workshop in collaboration with the FDA and EMA to evaluate changes in albuminuria or GFR as candidate surrogate end points. Three parallel efforts were presented: meta-analyses of observational studies (cohorts), meta-analyses of clinical trials, and simulations of trial design. In cohorts, after accounting for measurement error, relationships between change in urinary albumin-creatinine ratio (UACR) or estimated GFR (eGFR) slope and the clinical outcome of kidney disease progression were strong and consistent. In trials, the posterior median R[2] of treatment effects on the candidate surrogates with the clinical outcome was 0.47 (95% Bayesian credible interval [BCI], 0.02-0.96) for early change in UACR and 0.72 (95% BCI, 0.05-0.99) when restricted to baseline UACR>30mg/g, and 0.97 (95% BCI, 0.78-1.00) for total eGFR slope at 3 years and 0.96 (95% BCI, 0.63-1.00) for chronic eGFR slope (ie, the slope excluding the first 3 months from baseline, when there might be acute changes in eGFR). The magnitude of the relationships of changes in the candidate surrogates with risk for clinical outcome was consistent across cohorts and trials: a UACR reduction of 30% or eGFR slope reduction by 0.5 to 1.0mL/min/1.73m[2] per year were associated with an HR of ∼0.7 for the clinical outcome in cohorts and trials. In simulations, using GFR slope as an end point substantially reduced the required sample size and duration of follow-up compared with the clinical end point when baseline eGFR was high, treatment effects were uniform, and there was no acute effect of the treatment. We conclude that both early change in albuminuria and GFR slope fulfill criteria for surrogacy for use as end points in clinical trials for chronic kidney disease progression under certain conditions, with stronger support for change in GFR than albuminuria. Implementation requires understanding conditions under which each surrogate is likely to perform well and restricting its use to those settings.},
}
@article {pmid31469717,
year = {2019},
author = {Shang, Z and Liang, Y and Li, M and Zhao, K and Yang, L and Wan, H},
title = {Sequential neural information processing in nidopallium caudolaterale of pigeons during the acquisition process of operant conditioning.},
journal = {Neuroreport},
volume = {30},
number = {14},
pages = {966-973},
doi = {10.1097/WNR.0000000000001312},
pmid = {31469717},
issn = {1473-558X},
mesh = {Animals ; Behavior, Animal/*physiology ; Brain/*physiology ; Columbidae/physiology ; Conditioning, Operant/*physiology ; Extinction, Psychological/physiology ; Photic Stimulation ; },
abstract = {The avian nidopallium caudolaterale, a key region of information integration and processing, is considered to be playing an important role in operant conditioning acquisition and extinction. To reveal sequential neural information processing in the process, neural signals of different experimental periods (induction, acquisition, and extinction) from the nidopallium caudolaterale of pigeons were acquired and the energy of the specific frequency band was analyzed from the light stimulation input to the pecking action output. We found that during the induction period, the pigeons establish a relationship between the visual cue and decision behavior. The neural coding activities of pecking intention are earlier than that of light stimulation. Moreover, the neural coding activities of pecking intention move forward through strengthening and consolidation of the acquisition period. During the extinction period, the relationship of the visual cue and decision behavior is broken. The coding of light stimulation and pecking intention disappears gradually, and the disappearance of intention coding activities is earlier. The results show that there may be present an elaborate time-course contingency between the light stimulation and the pecking intention in the nidopallium caudolaterale. This study provides the electrophysiological experimental evidence for the dynamic coding mechanism of nidopallium caudolaterale.},
}
@article {pmid31464094,
year = {2019},
author = {Ferguson, M and Sharma, D and Ross, D and Zhao, F},
title = {A Critical Review of Microelectrode Arrays and Strategies for Improving Neural Interfaces.},
journal = {Advanced healthcare materials},
volume = {8},
number = {19},
pages = {e1900558},
pmid = {31464094},
issn = {2192-2659},
support = {1703570//National Science Foundation/International ; R15 HL115521/HL/NHLBI NIH HHS/United States ; R15 HL145654/HL/NHLBI NIH HHS/United States ; R15 CA202656/CA/NCI NIH HHS/United States ; 1R15CA202656/NH/NIH HHS/United States ; },
mesh = {Animals ; Biocompatible Materials/chemistry ; Blood-Brain Barrier ; Brain/physiology ; Brain-Computer Interfaces/*trends ; Drug Delivery Systems ; *Electrodes, Implanted ; Extracellular Matrix/metabolism ; Foreign-Body Reaction/prevention &amp; control ; Humans ; *Microelectrodes ; Motion ; Neuroglia/physiology ; Neurons/*physiology ; Polymers/chemistry ; Rats ; Surface Properties ; },
abstract = {Though neural interface systems (NISs) can provide a potential solution for mitigating the effects of limb loss and central nervous system damage, the microelectrode array (MEA) component of NISs remains a significant limiting factor to their widespread clinical applications. Several strategies can be applied to MEA designs to increase their biocompatibility. Herein, an overview of NISs and their applications is provided, along with a detailed discussion of strategies for alleviating the foreign body response (FBR) and abnormalities seen at the interface of MEAs and the brain tissue following MEA implantation. Various surface modifications, including natural/synthetic surface coatings, hydrogels, and topography alterations, have shown to be highly successful in improving neural cell adhesion, reducing gliosis, and increasing MEA longevity. Different MEA surface geometries, such as those seen in the Utah and Michigan arrays, can help alleviate the resultant FBR by reducing insertion damage, while providing new avenues for improving MEA recording performance and resolution. Increasing overall flexibility of MEAs as well as reducing their stiffness is also shown to reduce MEA induced micromotion along with FBR severity. By combining multiple different properties into a single MEA, the severity and duration of an FBR postimplantation can be reduced substantially.},
}
@article {pmid31462979,
year = {2019},
author = {Neghabi, M and Marateb, HR and Mahnam, A},
title = {Comparing Steady-State Visually Evoked Potentials Frequency Estimation Methods in Brain-Computer Interface With the Minimum Number of EEG Channels.},
journal = {Basic and clinical neuroscience},
volume = {10},
number = {3},
pages = {245-256},
pmid = {31462979},
issn = {2008-126X},
abstract = {INTRODUCTION: Brain-Computer Interface (BCI) systems provide a communication pathway between users and systems. BCI systems based on Steady-State Visually Evoked Potentials (SSVEP) are widely used in recent decades. Different feature extraction methods have been introduced in the literature to estimate SSVEP responses to BCI applications.<br><br>METHODS: In this study, the new algorithms, including Canonical Correlation Analysis (CCA), Least Absolute Shrinkage and Selection Operator (LASSO), L1-regularized Multi-way CCA (L1-MCCA), Multi-set CCA (MsetCCA), Common Feature Analysis (CFA), and Multiple Logistic Regression (MLR) are compared using proper statistical methods to determine which one has better performance with the least number of EEG electrodes.<br><br>RESULTS: It was found that MLR, MsetCCA, and CFA algorithms provided the highest performances and significantly outperformed CCA, LASSO, and L1-MCCA algorithms when using 8 EEG channels. However, when using only 1 or 2 EEG channels d, CFA method provided the highest F-scores. This algorithm not only outperformed MLR and MsetCCA when applied on different electrode montages but also provided the fastest computation time on the test set.<br><br>CONCLUSION: Although MLR method has already demonstrated to have higher performance in comparison with other frequency recognition algorithms, this study showed that in a practical SSVEP-based BCI system with 1 or 2 EEG channels and short-time windows, CFA method outperforms other algorithms. Therefore, it is proposed that CFA algorithm is a promising choice for the expansion of practical SSVEP-based BCI systems.},
}
@article {pmid31455386,
year = {2019},
author = {Rimbert, S and Schmartz, D and Bougrain, L and Meistelman, C and Baumann, C and Guerci, P},
title = {MOTANA: study protocol to investigate motor cerebral activity during a propofol sedation.},
journal = {Trials},
volume = {20},
number = {1},
pages = {534},
pmid = {31455386},
issn = {1745-6215},
mesh = {Adolescent ; Adult ; Anesthetics, Intravenous/*administration &amp; dosage/adverse effects ; Cortical Synchronization ; *Electroencephalography ; France ; Healthy Volunteers ; Humans ; Intraoperative Awareness/diagnosis/physiopathology/*prevention &amp; control ; Intraoperative Neurophysiological Monitoring/*methods ; Male ; *Motor Activity ; Motor Cortex/*drug effects/physiopathology ; Predictive Value of Tests ; Propofol/*administration &amp; dosage/adverse effects ; Prospective Studies ; Randomized Controlled Trials as Topic ; Time Factors ; Treatment Outcome ; Young Adult ; },
abstract = {BACKGROUND: Accidental Accidental awareness during general anesthesia (AAGA) occurs in 1-2% of high-risk practice patients and is a cause of severe psychological trauma, termed post-traumatic stress disorder (PTSD). However, no monitoring techniques can accurately predict or detect AAGA. Since the first reflex for a patient during AAGA is to move, a passive brain-computer interface (BCI) based on the detection of an intention of movement would be conceivable to alert the anesthetist. However, the way in which propofol (i.e., an anesthetic commonly used for the general anesthesia induction) affects motor brain activity within the electroencephalographic (EEG) signal has been poorly investigated and is not clearly understood. For this reason, a detailed study of the motor activity behavior with a step-wise increasing dose of propofol is required and would provide a proof of concept for such an innovative BCI. The main goal of this study is to highlight the occurrence of movement attempt patterns, mainly changes in oscillations called event-related desynchronization (ERD) and event-related synchronization (ERS), in the EEG signal over the motor cortex, in healthy subjects, without and under propofol sedation, during four different motor tasks.<br><br>METHODS: MOTANA is an interventional, prospective, exploratory, physiological, monocentric, and randomized study conducted in healthy volunteers under light anesthesia, involving EEG measurements before and after target-controlled infusion of propofol at three different effect-site concentrations (0 &#x3bc;g.ml [-1], 0.5 &#x3bc;g.ml [-1], and 1.0 &#x3bc;g.ml [-1]). In this exploratory study, 30 healthy volunteers will perform 50 trials for the four motor tasks (real movement, motor imagery, motor imagery with median nerve stimulation, and median nerve stimulation alone) in a randomized sequence. In each conditions and for each trial, we will observe changes in terms of ERD and ERS according to the three propofol concentrations. Pre- and post-injection comparisons of propofol will be performed by paired series tests.<br><br>DISCUSSION: MOTANA is an exploratory study aimed at designing an innovative BCI based on EEG-motor brain activity that would detect an attempt to move by a patient under anesthesia. This would be of interest in the prevention of AAGA.<br><br>TRIAL REGISTRATION: Agence Nationale de S&#xe9;curit&#xe9; du M&#xe9;dicament (EUDRACT 2017-004198-1), NCT03362775. Registered on 29 August 2018. https://clinicaltrials.gov/ct2/show/NCT03362775?term=03362775&amp;rank=1.},
}
@article {pmid31454250,
year = {2019},
author = {Lin, S and Liu, J and Li, W and Wang, D and Huang, Y and Jia, C and Li, Z and Murtaza, M and Wang, H and Song, J and Liu, Z and Huang, K and Zu, D and Lei, M and Hong, B and Wu, H},
title = {A Flexible, Robust, and Gel-Free Electroencephalogram Electrode for Noninvasive Brain-Computer Interfaces.},
journal = {Nano letters},
volume = {19},
number = {10},
pages = {6853-6861},
doi = {10.1021/acs.nanolett.9b02019},
pmid = {31454250},
issn = {1530-6992},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; Female ; Humans ; Male ; Nanowires/chemistry/ultrastructure ; Silver/chemistry ; },
abstract = {Brain-computer interfaces (BCIs) enable direct and near-instant communication between the brain and electronic devices. One of the biggest remaining challenges is to develop an effective noninvasive BCI that allows the recording electrodes to avoid hair on human skin without the inconveniences and complications of using a conductive gel. In this study, we developed a cost-effective, easily manufacturable, flexible, robust, and gel-free silver nanowire/polyvinyl butyral (PVB)/melamine sponge (AgPMS) electroencephalogram (EEG) electrode that circumvents problems with hair. Because of surface metallization by the silver nanowires (AgNWs), the sponge has a high conductivity of 917 S/m while its weight remains the same. The flexible sponge framework and self-locking AgNWs combine to give the new electrode remarkable mechanical stability (the conductivity remains unchanged after 10 000 cycles at 10% compression) and the ability to bypass hair. A BCI application based on steady-state visual evoked potential (SSVEP) measurements on hairless skin shows that the BCI accuracy of the new electrode (86%) is approximately the same as that of conventional electrodes supported by a conductive gel (88%). Most importantly, the performance of the AgPMS on hairy skin is not significantly reduced, which indicates that the new electrode can replace conventional electrodes for both hairless and hairy skin BCIs and other EEG applications.},
}
@article {pmid31452007,
year = {2020},
author = {Kobayashi, H and Nakayama, R and Hizukuri, A and Ishida, M and Kitagawa, K and Sakuma, H},
title = {Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network.},
journal = {Journal of digital imaging},
volume = {33},
number = {2},
pages = {497-503},
pmid = {31452007},
issn = {1618-727X},
mesh = {Humans ; Image Processing, Computer-Assisted ; *Magnetic Resonance Angiography ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Signal-To-Noise Ratio ; },
abstract = {Whole-heart coronary magnetic resonance angiography (WHCMRA) permits the noninvasive assessment of coronary artery disease without radiation exposure. However, the image resolution of WHCMRA is limited. Recently, convolutional neural networks (CNNs) have obtained increased interest as a method for improving the resolution of medical images. The purpose of this study is to improve the resolution of WHCMRA images using a CNN. Free-breathing WHCMRA images with 512 × 512 pixels (pixel size = 0.65 mm) were acquired in 80 patients with known or suspected coronary artery disease using a 1.5 T magnetic resonance (MR) system with 32 channel coils. A CNN model was optimized by evaluating CNNs with different structures. The proposed CNN model was trained based on the relationship of signal patterns between low-resolution patches (small regions) and the corresponding high-resolution patches using a training dataset collected from 40 patients. Images with 512 × 512 pixels were restored from 256 × 256 down-sampled WHCMRA images (pixel size = 1.3 mm) with three different approaches: the proposed CNN, bicubic interpolation (BCI), and the previously reported super-resolution CNN (SRCNN). High-resolution WHCMRA images obtained using the proposed CNN model were significantly better than those of BCI and SRCNN in terms of root mean squared error, peak signal to noise ratio, and structure similarity index measure with respect to the original WHCMRA images. The proposed CNN approach can provide high-resolution WHCMRA images with better accuracy than BCI and SRCNN. The high-resolution WHCMRA obtained using the proposed CNN model will be useful for identifying coronary artery disease.},
}
@article {pmid31449242,
year = {2019},
author = {Xu, K and Chen, J and Sun, G and Hao, Y and Zhang, S and Ran, X and Chen, W and Zheng, X},
title = {Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {150},
pages = {},
doi = {10.3791/59932},
pmid = {31449242},
issn = {1940-087X},
mesh = {Biomechanical Phenomena/*physiology ; Female ; Humans ; Imaging, Three-Dimensional/*methods ; Male ; Psychomotor Performance/*physiology ; },
abstract = {Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish reaching and grasping encodings, it is essential to present different object identities independent of their positions. Presented here is the design of an automatic apparatus that is assembled with a turning table and three-dimensional (3D) translational device to achieve this goal. The turning table switches different objects corresponding to different grip types while the 3D translational device transports the turning table in 3D space. Both are driven independently by motors so that the target position and object are combined arbitrarily. Meanwhile, wrist trajectory and grip types are recorded via the motion capture system and touch sensors, respectively. Furthermore, representative results that demonstrate successfully trained monkey using this system are described. It is expected that this apparatus will facilitate researchers to study kinematics, neural principles, and brain-machine interfaces related to upper limb function.},
}
@article {pmid31446048,
year = {2019},
author = {Zheng, XS and Snyder, NR and Woeppel, K and Barengo, JH and Li, X and Eles, J and Kolarcik, CL and Cui, XT},
title = {A superoxide scavenging coating for improving tissue response to neural implants.},
journal = {Acta biomaterialia},
volume = {99},
number = {},
pages = {72-83},
pmid = {31446048},
issn = {1878-7568},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Apoptosis ; Coated Materials, Biocompatible/*chemistry ; Electric Stimulation Therapy/*instrumentation ; Inflammation ; Male ; Microelectrodes ; Microglia/metabolism ; *Neural Prostheses ; Neurons/metabolism ; Nitric Oxide/chemistry ; Oxidative Stress ; Oxygen/chemistry ; Porphyrins/chemistry ; Rats ; Rats, Sprague-Dawley ; Reactive Nitrogen Species/metabolism ; Reactive Oxygen Species/metabolism ; Superoxide Dismutase/chemistry ; Superoxides/*chemistry ; },
abstract = {The advancement of neural prostheses requires implantable neural electrodes capable of electrically stimulating or recording signals from neurons chronically. Unfortunately, the implantation injury and presence of foreign bodies lead to chronic inflammation, resulting in neuronal death in the vicinity of electrodes. A key mediator of inflammation and neuronal loss are reactive oxygen and nitrogen species (RONS). To mitigate the effect of RONS, a superoxide dismutase mimic compound, manganese(III) meso-tetrakis-(N-(2-aminoethyl)pyridinium-2-yl) porphyrin (iSODm), was synthesized to covalently attach to the neural probe surfaces. This new compound showed high catalytic superoxide scavenging activity. In microglia cell line cultures, the iSODm coating effectively reduced superoxide production and altered expression of iNOS, NADPH oxidase, and arginase. After 1 week of implantation, iSODm coated electrodes showed significantly lower expression of markers for oxidative stress immediately adjacent to the electrode surface, as well as significantly less neurons undergoing apoptosis. STATEMENT OF SIGNIFICANCE: One critical challenge in the translation of neural electrode technology to clinically viable devices for brain computer interface or deep brain stimulation applications is the chronic degradation of the device performance due to neuronal degeneration around the implants. One of the key mediators of inflammation and neuronal degeneration is reactive oxygen and nitrogen species released by injured neurons and inflammatory microglia. This research takes a biomimetic approach to synthesize a compound having similar reactivity as superoxide dismutase, which can catalytically scavenge reactive oxygen and nitrogen species, thereby reducing oxidative stress and decreasing neuronal degeneration. By immobilizing the compound covalently on the surface of neural implants, we show that the neuronal degeneration and oxidative stress around the implants is significantly reduced.},
}
@article {pmid31443868,
year = {2019},
author = {Tariq, T and Satti, MH and Kamboh, HM and Saeed, M and Kamboh, AM},
title = {Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits.},
journal = {Computer methods and programs in biomedicine},
volume = {179},
number = {},
pages = {104986},
doi = {10.1016/j.cmpb.2019.104986},
pmid = {31443868},
issn = {1872-7565},
mesh = {*Action Potentials ; Algorithms ; Brain-Computer Interfaces/statistics &amp; numerical data ; Computer Simulation ; Electrodes, Implanted/statistics &amp; numerical data ; Humans ; Implantable Neurostimulators/*statistics &amp; numerical data ; Models, Neurological ; Neurons/physiology ; Online Systems ; Pattern Recognition, Automated/statistics &amp; numerical data ; Signal Processing, Computer-Assisted ; Unsupervised Machine Learning ; },
abstract = {BACKGROUND: Spike sorting is a basic step for implantable neural interfaces. With the growing number of channels, the process should be computationally efficient, automatic,robust and applicable on implantable circuits.<br><br>NEW METHOD: The proposed method is a combination of fully-automatic offline and online processes. It introduces a novel method for automatically determining a data-aware spike detection threshold, computationally efficient spike feature extraction, automatic optimal cluster number evaluation and verification coupled with Self-Organizing Maps to accurately determine cluster centroids. The system has the ability of unsupervised online operation after initial fully-automatic offline training. The prime focus of this paper is to fully-automate the complete spike detection and sorting pipeline, while keeping the accuracy high.<br><br>RESULTS: The proposed system is simulated on two well-known datasets. The automatic threshold improves detection accuracies significantly(&#x202f;>&#x202f;15%) as compared to the most common detector. The system is able to effectively handle background multi-unit activity with improved performance.<br><br>COMPARISON: Most of the existing methods are not fully-automatic; they require supervision and expert intervention at various stages of the pipeline. Secondly, existing works focus on foreground neural activity. Recent research has highlighted importance of background multi-unit activity, and this work is amongst the first efforts that proposes and verifies an automatic methodology to effectively handle them as well.<br><br>CONCLUSION: This paper proposes a fully-automatic, computationally efficient system for spike sorting for both single-unit and multi-unit spikes. Although the scope of this work is design and verification through computer simulations, the system has been designed to be easily transferable into an integrated hardware form.},
}
@article {pmid31443657,
year = {2019},
author = {Wang, G and Lou, HH and Salit, J and Leopold, PL and Driscoll, S and Schymeinsky, J and Quast, K and Visvanathan, S and Fine, JS and Thomas, MJ and Crystal, RG},
title = {Characterization of an immortalized human small airway basal stem/progenitor cell line with airway region-specific differentiation capacity.},
journal = {Respiratory research},
volume = {20},
number = {1},
pages = {196},
pmid = {31443657},
issn = {1465-993X},
support = {HL118541/HL/NHLBI NIH HHS/United States ; R01 HL118541/HL/NHLBI NIH HHS/United States ; HL107882-S/HL/NHLBI NIH HHS/United States ; N/A//Boehringer Ingelheim/ ; UL1 TR002384/TR/NCATS NIH HHS/United States ; HL113443/HL/NHLBI NIH HHS/United States ; HL107882/HL/NHLBI NIH HHS/United States ; },
mesh = {Cell Differentiation ; Cell Line ; Cell Proliferation ; Cytological Techniques ; Electric Impedance ; Female ; Gene Expression ; Humans ; Male ; Middle Aged ; Polymerase Chain Reaction ; Respiratory System/*cytology ; *Stem Cells/metabolism ; Telomerase/metabolism ; Tight Junctions ; Transcriptome ; },
abstract = {BACKGROUND: The pathology of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF) and most lung cancers involves the small airway epithelium (SAE), the single continuous layer of cells lining the airways ≥ 6th generations. The basal cells (BC) are the stem/progenitor cells of the SAE, responsible for the differentiation into intermediate cells and ciliated, club and mucous cells. To facilitate the study of the biology of the human SAE in health and disease, we immortalized and characterized a normal human SAE basal cell line.<br><br>METHODS: Small airway basal cells were purified from brushed SAE of a healthy nonsmoker donor with a characteristic normal SAE transcriptome. The BC were immortalized by retrovirus-mediated telomerase reverse transcriptase (TERT) transduction and single cell drug selection. The resulting cell line (hSABCi-NS1.1) was characterized by RNAseq, TaqMan PCR, protein immunofluorescence, differentiation capacity on an&#xa0;air-liquid interface (ALI) culture, transepithelial electrical resistance (TEER), airway region-associated features and response to genetic modification with SPDEF.<br><br>RESULTS: The hSABCi-NS1.1 single-clone-derived cell line continued to proliferate for >&#x2009;200 doubling levels and&#x2009;>&#x2009;70 passages, continuing to maintain basal cell features (TP63[+], KRT5[+]). When cultured on ALI, hSABCi-NS1.1 cells&#xa0;consistently formed tight junctions and differentiated into ciliated, club (SCGB1A1[+]), mucous (MUC5AC[+], MUC5B[+]), neuroendocrine (CHGA[+]), ionocyte (FOXI1[+]) and surfactant protein positive cells (SFTPA[+], SFTPB[+], SFTPD[+]), observations confirmed by RNAseq and TaqMan PCR. Annotation enrichment analysis showed that "cilium" and "immunity" were enriched in functions of the top-1500 up-regulated genes. RNAseq reads alignment corroborated expression of CD4, CD74 and MHC-II. Compared to the large airway cell line BCi-NS1.1, differentiated of hSABCi-NS1.1 cells&#xa0;on ALI were enriched with small airway epithelial genes, including surfactant protein genes, LTF and small airway development relevant transcription factors NKX2-1, GATA6, SOX9, HOPX, ID2 and ETV5. Lentivirus-mediated expression of SPDEF in hSABCi-NS1.1 cells&#xa0;induced secretory cell metaplasia, accompanied with characteristic COPD-associated SAE secretory cell changes, including up-regulation of MSMB, CEACAM5 and down-regulation of LTF.<br><br>CONCLUSIONS: The immortalized hSABCi-NS1.1 cell line has diverse differentiation capacities and retains SAE features, which will be useful for understanding the biology of SAE, the pathogenesis of SAE-related diseases, and testing new pharmacologic agents.},
}
@article {pmid31443037,
year = {2019},
author = {Tian, Y and Zhang, H and Jiang, Y and Li, P and Li, Y},
title = {A Fusion Feature for Enhancing the Performance of Classification in Working Memory Load With Single-Trial Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {1985-1993},
doi = {10.1109/TNSRE.2019.2936997},
pmid = {31443037},
issn = {1558-0210},
mesh = {Algorithms ; Biomarkers ; *Brain-Computer Interfaces ; Cognition/physiology ; Electroencephalography/*classification/methods ; Entropy ; Feasibility Studies ; Healthy Volunteers ; Humans ; Male ; Memory, Short-Term/*physiology ; Photic Stimulation ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Support Vector Machine ; Workload ; Young Adult ; },
abstract = {In traditional brain-computer interfaces (BCIs), using only a certain type of feature or a simple mixture of different features cannot meet the requirements for high performance in classification. Moreover, a simple mixture of various features might lead to information redundancies and thus increase the computational complexity. In this paper, we studied the feasibility of integrating two kinds of features, which showed opposite variation trends as the memory load levels increase, into a single fusion feature. We also proposed a feature fusion framework based on non-invasive electroencephalography to classify the memory load levels and estimate the workload for a series of challenging working memory (WM) tasks (involving delayed match-to-sample tasks) on a single-trial basis. A novel fusion feature called spectral entropy/Lempel-Ziv complexity (SEn/LZC) was proposed to classify three memory load levels. The results showed that the generalization of the support vector machine (SVM) with SEn/LZC was significantly higher than the generalization of an SVM with four other types of feature, namely SEn, LZC, SEn&amp;LZC and LZC/SEn. The findings suggested that the proposed fusion feature could act as a biomarker to successfully distinguish different load levels and that the constructed framework could achieve consistency between optimal cognitive performance and fusion features. In addition, the proposed fusion framework could provide a new method of successfully promoting the classification generalization of BCI and implicitly evaluating the mental workload.},
}
@article {pmid31443036,
year = {2019},
author = {Mashat, MEM and Lin, CT and Zhang, D},
title = {Effects of Task Complexity on Motor Imagery-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2178-2185},
doi = {10.1109/TNSRE.2019.2936987},
pmid = {31443036},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Alpha Rhythm ; Beta Rhythm ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Electroencephalography Phase Synchronization ; Female ; Gamma Rhythm ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {The performance of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) still needs improvements for real world applications. An improvement on BCIs could be achieved by enhancing brain signals from the source via subject intention-based modulation. In this work, we aim to investigate the effects of task complexity on performance of motor imagery (MI) based BCIs. In specific, we studied the effects of motor imagery of a complex task versus a simple task on discriminability of brain activation patterns using EEG. The results show an increase of up to 7.25% in BCI classification accuracy for motor imagery of the complex task in comparison to the simple task. Furthermore, spectral power analysis in low frequency bands, alpha and beta, shows a significant decrease in power value for the complex task. However, high frequency gamma band analysis unveils a significant increase for the complex task. These findings may lead to designing better BCIs with high performance.},
}
@article {pmid31443035,
year = {2019},
author = {Bjanes, DA and Moritz, CT},
title = {A Robust Encoding Scheme for Delivering Artificial Sensory Information via Direct Brain Stimulation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {1994-2004},
doi = {10.1109/TNSRE.2019.2936739},
pmid = {31443035},
issn = {1558-0210},
mesh = {Animals ; Artificial Limbs ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electrodes, Implanted ; Evoked Potentials ; Female ; Neural Prostheses ; Proprioception/physiology ; Prosthesis Design ; Psychomotor Performance ; Rats ; Rats, Long-Evans ; Sensation/*physiology ; Touch/physiology ; Touch Perception ; },
abstract = {Innovations for creating somatosensation via direct electrical stimulation of the brain will be required for the next generation of bi-directional cortical neuroprostheses. The current lack of tactile perception and proprioceptive input likely imposes a fundamental limit on speed and accuracy of brain-controlled prostheses or re-animated limbs. This study addresses the unique challenge of identifying a robust, high bandwidth sensory encoding scheme in a high-dimensional parameter space. Previous studies demonstrated single dimensional encoding schemes delivering low bandwidth sensory information, but no comparison has been performed across parameters, nor with update rates suitable for real-time operation of a neuroprosthesis. Here, we report the first comprehensive measurement of the resolution of key stimulation parameters such as pulse amplitude, pulse width, frequency, train interval and number of pulses. Surprisingly, modulation of stimulation frequency was largely undetectable. While we initially expected high frequency content to be an ideal candidate for passing high throughput sensory signals to the brain, we found only modulation of very low frequencies were detectable. Instead, the charge-per-phase of each pulse yields the highest resolution sensory signal, and is the key parameter modulating perceived intensity. The stimulation encoding patterns were designed for high-bandwidth information transfer that will be required for bi-directional brain interfaces. Our discovery of the stimulation features which best encode perceived intensity have significant implications for design of any neural interface seeking to convey information directly to the brain via electrical stimulation.},
}
@article {pmid31443031,
year = {2019},
author = {Kristoffersen, MB and Franzke, AW and van der Sluis, CK and Murgia, A and Bongers, RM},
title = {The Effect of Feedback During Training Sessions on Learning Pattern-Recognition-Based Prosthesis Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {10},
pages = {2087-2096},
doi = {10.1109/TNSRE.2019.2929917},
pmid = {31443031},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cues ; Electromyography ; *Feedback, Sensory ; Female ; Hand ; Hand Strength ; Healthy Volunteers ; Humans ; Male ; Motion Perception ; Pattern Recognition, Automated/*methods ; Photic Stimulation ; Principal Component Analysis ; Prostheses and Implants ; Psychomotor Performance ; Young Adult ; },
abstract = {Human-machine interfaces have not yet advanced to enable intuitive control of multiple degrees of freedom as offered by modern myoelectric prosthetic hands. Pattern Recognition (PR) control has been proposed to make human-machine interfaces in myoelectric prosthetic hands more intuitive, but it requires the user to generate high-quality, i.e., consistent and separable, electromyogram (EMG) patterns. To generate such patterns, user training is required and has shown promising results. However, how different levels of feedback affect effectivity in training differently, has not been established yet. Furthermore, a correlation between qualities of the EMG patterns (the focus of training) and user performance has not been shown yet. In this study, 37 able-bodied participants (mean age 21 years, 19 males) were recruited and trained PR control over five days. Three levels of feedback were tested for their effectiveness: no external feedback, visual feedback and visual feedback with coaching. Training resulted in improved performance from pre- to post-test with no interaction effect of feedback. Feedback did however affect the quality of the EMG patterns where people who did not receive external feedback generated higher amplitude patterns. A weak correlation was found between a principal component, composed of EMG amplitude and pattern variability, and performance. Our results show that training is highly effective in improving PR control regardless of feedback and that none of the quality metrics correlate with performance. We discuss how different levels of feedback can be leveraged to improve PR control training.},
}
@article {pmid31441252,
year = {2019},
author = {Xu, G and Lin, F and Gong, M and Li, M and Yu, H},
title = {[A TrAdaBoost-based method for detecting multiple subjects' P300 potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {36},
number = {4},
pages = {531-540},
pmid = {31441252},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; *Support Vector Machine ; },
abstract = {Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects' data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.},
}
@article {pmid31440133,
year = {2019},
author = {Kramer, DR and Lamorie-Foote, K and Barbaro, M and Lee, M and Peng, T and Gogia, A and Liu, CY and Kellis, SS and Lee, B},
title = {Functional Frequency Discrimination From Cortical Somatosensory Stimulation in Humans.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {832},
pmid = {31440133},
issn = {1662-4548},
support = {R25 NS099008/NS/NINDS NIH HHS/United States ; },
abstract = {Recently, efforts to produce artificial sensation through cortical stimulation of primary somatosensory cortex (PSC) in humans have proven safe and reliable. Changes in stimulation parameters like frequency and amplitude have been shown to elicit different percepts, but without clearly defined psychometric profiles. This study investigates the functionally useful limits of frequency changes on the percepts felt by three epilepsy patients with subdural electrocorticography (ECoG) grids. Subjects performing a hidden target task were stimulated with parameters of constant amplitude, pulse-width, and pulse-duration, and a randomly selected set of two frequencies (20, 30, 40, 50, 60, and 100 Hz). They were asked to decide which target had the "higher" frequency. Objectively, an increase in frequency differences was associated with an increase in perceived intensity. Reliable detection of stimulation occurred at and above 40 Hz with a lower limit of detection around 20 Hz and a just-noticeable difference estimated at less than 10 Hz. These findings suggest that frequency can be used as a reliable, adjustable parameter and may be useful in establishing settings and thresholds of functionality in future BCI systems.},
}
@article {pmid31440129,
year = {2019},
author = {Choi, SI and Hwang, HJ},
title = {Effects of Different Re-referencing Methods on Spontaneously Generated Ear-EEG.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {822},
pmid = {31440129},
issn = {1662-4548},
abstract = {In recent years, electroencephalography (EEG) measured around the ears, called ear-EEG, has been introduced to develop unobtrusive and ambulatory EEG-based applications. When measuring ear-EEGs, the availability of a reference site is restricted due to the miniaturized device structure, and therefore a reference electrode is generally placed near the recording electrodes. As the electrical brain activity recorded at a reference electrode closely placed to recording electrodes may significantly cancel or influence the brain activity recorded by the recording electrodes, an appropriate re-referencing method is often required to mitigate the impact of the reference brain activity. In this study, therefore, we systematically investigated the impact of different re-referencing methods on ear-EEGs spontaneously generated from endogenous paradigms. To this end, we used two ear-EEG datasets recorded behind both ears while subjects performed an alpha modulation task [eyes-closed (EC) and eyes-open (EO)] and two mental tasks [mental arithmetic (MA) and mental singing (MS)]. The measured ear-EEGs were independently re-referenced using five different methods: (i) all-mean, (ii) contralateral-mean, (iii) ipsilateral-mean, (iv) contralateral-bipolar, and (v) ipsilateral-bipolar. We investigated the changes in alpha power during EO and EC tasks, as well as event-related (de) synchronization (ERD/ERS) during MA and MS. To evaluate the effects of re-referencing methods on ear-EEGs, we estimated the signal-to-noise ratios (SNRs) of the two ear-EEG datasets, and assessed the classification performance of the two mental tasks (MA vs. MS). Overall patterns of changes in alpha power and ERD/ERS were similar among the five re-referencing methods, but the contralateral-mean method showed statistically higher SNRs than did the other methods for both ear-EEG datasets, except in the contralateral-bipolar method for the two mental tasks. In concordance with the SNR results, classification performance was also statistically higher for the contralateral-mean method than it was for the other re-referencing methods. The results suggest that employing contralateral mean information can be an efficient way to re-reference spontaneously generated ear-EEGs, thereby maximizing the reliability of ear-EEG-based applications in endogenous paradigms.},
}
@article {pmid31440127,
year = {2019},
author = {Caldwell, DJ and Ojemann, JG and Rao, RPN},
title = {Direct Electrical Stimulation in Electrocorticographic Brain-Computer Interfaces: Enabling Technologies for Input to Cortex.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {804},
pmid = {31440127},
issn = {1662-4548},
support = {T32 GM007266/GM/NIGMS NIH HHS/United States ; },
abstract = {Electrocorticographic brain computer interfaces (ECoG-BCIs) offer tremendous opportunities for restoring function in individuals suffering from neurological damage and for advancing basic neuroscience knowledge. ECoG electrodes are already commonly used clinically for monitoring epilepsy and have greater spatial specificity in recording neuronal activity than techniques such as electroencephalography (EEG). Much work to date in the field has focused on using ECoG signals recorded from cortex as control outputs for driving end effectors. An equally important but less explored application of an ECoG-BCI is directing input into cortex using ECoG electrodes for direct electrical stimulation (DES). Combining DES with ECoG recording enables a truly bidirectional BCI, where information is both read from and written to the brain. We discuss the advantages and opportunities, as well as the barriers and challenges presented by using DES in an ECoG-BCI. In this article, we review ECoG electrodes, the physics and physiology of DES, and the use of electrical stimulation of the brain for the clinical treatment of disorders such as epilepsy and Parkinson's disease. We briefly discuss some of the translational, regulatory, financial, and ethical concerns regarding ECoG-BCIs. Next, we describe the use of ECoG-based DES for providing sensory feedback and for probing and modifying cortical connectivity. We explore future directions, which may draw on invasive animal studies with penetrating and surface electrodes as well as non-invasive stimulation methods such as transcranial magnetic stimulation (TMS). We conclude by describing enabling technologies, such as smaller ECoG electrodes for more precise targeting of cortical areas, signal processing strategies for simultaneous stimulation and recording, and computational modeling and algorithms for tailoring stimulation to each individual brain.},
}
@article {pmid31437551,
year = {2019},
author = {Paret, C and Goldway, N and Zich, C and Keynan, JN and Hendler, T and Linden, D and Cohen Kadosh, K},
title = {Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements.},
journal = {NeuroImage},
volume = {202},
number = {},
pages = {116107},
doi = {10.1016/j.neuroimage.2019.116107},
pmid = {31437551},
issn = {1095-9572},
mesh = {Brain/*physiology ; Brain Mapping/*methods ; Humans ; Magnetic Resonance Imaging/*methods ; Neurofeedback/*methods/physiology ; },
abstract = {Neurofeedback (NF) is a research and clinical technique, characterized by live demonstration of brain activation to the subject. The technique has become increasingly popular as a tool for the training of brain self-regulation, fueled by the superiority in spatial resolution and fidelity brought along with real-time analysis of fMRI (functional magnetic resonance imaging) data, compared to the more traditional EEG (electroencephalography) approach. NF learning is a complex phenomenon and a controversial discussion on its feasibility and mechanisms has arisen in the literature. Critical aspects of the design of fMRI-NF studies include the localization of neural targets, cognitive and operant aspects of the training procedure, personalization of training, and the definition of training success, both through neural effects and (for studies with therapeutic aims) through clinical effects. In this paper, we argue that a developmental perspective should inform neural target selection particularly for pediatric populations, and different success metrics may allow in-depth analysis of NF learning. The relevance of the functional neuroanatomy of NF learning for brain target selection is discussed. Furthermore, we address controversial topics such as the role of strategy instructions, sometimes given to subjects in order to facilitate learning, and the timing of feedback. Discussion of these topics opens sight on problems that require further conceptual and empirical work, in order to improve the impact that fMRI-NF could have on basic and applied research in future.},
}
@article {pmid31432550,
year = {2019},
author = {Dobberfuhl, AD and Chen, A and Alkaram, AF and De, EJB},
title = {Spontaneous voiding is surprisingly recoverable via outlet procedure in men with underactive bladder and documented detrusor underactivity on urodynamics.},
journal = {Neurourology and urodynamics},
volume = {38},
number = {8},
pages = {2224-2232},
doi = {10.1002/nau.24122},
pmid = {31432550},
issn = {1520-6777},
mesh = {Aged ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Recovery of Function ; Treatment Outcome ; Urinary Bladder/*surgery ; Urinary Bladder Neck Obstruction/complications/surgery ; Urinary Bladder, Underactive/complications/*surgery ; *Urination ; Urodynamics ; Urologic Surgical Procedures/*methods ; },
abstract = {AIMS: To identify clinical and urodynamic factors leading to spontaneous voiding in men with detrusor underactivity (DU) and suspected bladder outlet obstruction who underwent an outlet de-obstruction procedure.<br><br>METHODS: We identified 614 men who underwent an outlet procedure at our institution from 2005 to 2014. Men were stratified by bladder contractility index (BCI). The primary outcome was spontaneous voiding after surgery. Data were analyzed in Statistical analysis system software.<br><br>RESULTS: Of the 131 men who underwent preoperative urodynamics, 122 (mean age 68 years) had tracings available for review. DU (BCI&#x2009;<&#x2009;100) was identified in 54% (66 of 122), of whom only 68% (45 of 66) voided spontaneously before surgery, compared with 82% (46 of 56) of men with BCI&#x2009;&#x2265;&#x2009;100. At a mean follow-up of 6.4 months postoperatively, 79% (52 of 66) of men with DU were able to void spontaneously, compared with 96% (54 of 56) of men with BCI&#x2009;&#x2265;&#x2009;100. In men with a BCI&#x2009;<&#x2009;100 unable to void before surgery, 57% (12 of 21) recovered spontaneous voiding after surgery. On logistic regression for the outcome postoperative spontaneous voiding, significant preoperative characteristics, and urodynamic factors included preoperative spontaneous voiding (odds ratio [OR]&#x2009;=&#x2009;9.460; 95% confidence interval [CI]&#x2009;=&#x2009;2.955-30.289), increased maximum flow rate (Qmax; OR&#x2009;=&#x2009;1.184; 95% CI&#x2009;=&#x2009;1.014-1.382), increased detrusor pressure at maximum flow (Pdet@Qmax; OR&#x2009;=&#x2009;1.032; 95% CI&#x2009;=&#x2009;1.012-1.052), DU with BCI&#x2009;<&#x2009;100 (OR&#x2009;=&#x2009;0.138; 95% CI&#x2009;=&#x2009;0.030-0.635), and obstruction with bladder outlet obstruction index&#x2009;>&#x2009;40 (OR&#x2009;=&#x2009;5.595; 95% CI&#x2009;=&#x2009;1.685-18.575).<br><br>CONCLUSION: Outlet de-obstruction improves spontaneous voiding in men with DU and may benefit men who do not meet the urodynamic threshold for obstruction.},
}
@article {pmid31428313,
year = {2019},
author = {Joadder, MAM and Myszewski, JJ and Rahman, MH and Wang, I},
title = {A performance based feature selection technique for subject independent MI based BCI.},
journal = {Health information science and systems},
volume = {7},
number = {1},
pages = {15},
pmid = {31428313},
issn = {2047-2501},
abstract = {PURPOSE: Significant research has been conducted in the field of brain computer interface (BCI) algorithm development, however, many of the resulting algorithms are both complex, and specific to a particular user as the most successful methodology can vary between individuals and sessions. The objective of this study was to develop a simple yet effective method of feature selection to improve the accuracy of a subject independent BCI algorithm and streamline the process of BCI algorithm development. Over the past several years, several high precision features have been suggested by researchers to classify different motor imagery tasks. This research applies fourteen of these features as a feature pool that can be used as a reference for future researchers. Additionally, we look for the most efficient feature or feature set with four different classifiers that best differentiates several motor imagery tasks. In this work we have successfully employed a feature fusion method to obtain the best sub-set of features. We have proposed a novel computer aided feature selection method to determine the best set of features for distinguishing between motor imagery tasks in lieu of the manual feature selection that has been performed in past studies. The features selected by this method were then fed into a Linear Discriminant Analysis, K-nearest neighbor, decision tree, or support vector machine classifier for classification to determine the overall performance.<br><br>METHODS: The methods used were a novel performance based additive feature fusion algorithm working in conjunction with machine learning in order to classify the motor imagery signals into particular states. The data used for this study was collected from BCI competition III dataset IVa.<br><br>RESULT: The result of this algorithm was a classification accuracy of 99% for a subject independent algorithm with less computation cost compared to traditional methods, in addition to multiple feature/classifier combinations that outperform current subject independent methods.<br><br>CONCLUSION: The conclusion of this study and its significance is that it developed a viable methodology for simple, efficient feature selection and BCI algorithm development, which leads to an overall increase in algorithm classification accuracy.},
}
@article {pmid31427941,
year = {2019},
author = {Castaño-Candamil, S and Meinel, A and Tangermann, M},
title = {Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods.},
journal = {Frontiers in neuroinformatics},
volume = {13},
number = {},
pages = {55},
pmid = {31427941},
issn = {1662-5196},
abstract = {Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge: the amount of available M/EEG data typically is limited, labels can be unreliable, and raw signals often are contaminated with artifacts. To overcome some of these problems, simulation frameworks have been introduced which support the development of data-driven decoding algorithms and their benchmarking. For generating artificial brain signals, however, most of the existing frameworks make strong and partially unrealistic assumptions about brain activity. This limits the generalization of results observed in the simulation to real-world scenarios. In the present contribution, we show how to overcome several shortcomings of existing simulation frameworks. We propose a versatile alternative, which allows for an objective evaluation and benchmarking of novel decoding algorithms using real neural signals. It allows to generate comparatively large datasets with labels being deterministically recoverable from the arbitrary M/EEG recordings. A novel idea to generate these labels is central to this framework: we determine a subspace of the true M/EEG recordings and utilize it to derive novel labels. These labels contain realistic information about the oscillatory activity of some underlying neural sources. For two categories of subspace-defining methods, we showcase how such labels can be obtained-either by an exclusively data-driven approach (independent component analysis-ICA), or by a method exploiting additional anatomical constraints (minimum norm estimates-MNE). We term our framework post-hoc labeling of M/EEG recordings. To support the adoption of the framework by practitioners, we have exemplified its use by benchmarking three standard decoding methods-i.e., common spatial patterns (CSP), source power-comodulation (SPoC), and convolutional neural networks (ConvNets)-wrt. Varied dataset sizes, label noise, and label variability. Source code and data are made available to the reader for facilitating the application of our post-hoc labeling framework.},
}
@article {pmid31425051,
year = {2019},
author = {Muratore, DG and Tandon, P and Wootters, M and Chichilnisky, EJ and Mitra, S and Murmann, B},
title = {A Data-Compressive Wired-OR Readout for Massively Parallel Neural Recording.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {6},
pages = {1128-1140},
doi = {10.1109/TBCAS.2019.2935468},
pmid = {31425051},
issn = {1940-9990},
mesh = {Action Potentials ; Algorithms ; Animals ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation/methods ; Neurons/physiology ; Primates ; Principal Component Analysis ; Retina/*physiology ; Semiconductors ; Signal Processing, Computer-Assisted ; },
abstract = {Neural interfaces of the future will be used to help restore lost sensory, motor, and other capabilities. However, realizing this futuristic promise requires a major leap forward in how electronic devices interface with the nervous system. Next generation neural interfaces must support parallel recording from tens of thousands of electrodes within the form factor and power budget of a fully implanted device, posing a number of significant engineering challenges. In this paper, we exploit sparsity and diversity of neural signals to achieve simultaneous data compression and channel multiplexing for neural recordings. The architecture uses wired-OR interactions within an array of single-slope A/D converters to obtain massively parallel digitization of neural action potentials. The achieved compression is lossy but effective at retaining the critical samples belonging to action potentials, enabling efficient spike sorting and cell type identification. Simulation results of the architecture using data obtained from primate retina ex-vivo with a 512-channel electrode array show average compression rates up to ∼ 40× while missing less than 5% of cells. In principle, the techniques presented here could be used to design interfaces to other parts of the nervous system.},
}
@article {pmid31421898,
year = {2019},
author = {},
title = {Human-robotic interfaces to shape the future of prosthetics.},
journal = {EBioMedicine},
volume = {46},
number = {},
pages = {1},
doi = {10.1016/j.ebiom.2019.08.018},
pmid = {31421898},
issn = {2352-3964},
mesh = {Brain-Computer Interfaces ; Humans ; *Prostheses and Implants/trends ; Robotic Surgical Procedures/instrumentation/methods ; *Robotics/instrumentation/methods ; *User-Computer Interface ; },
}
@article {pmid31421595,
year = {2019},
author = {Buccelli, S and Bornat, Y and Colombi, I and Ambroise, M and Martines, L and Pasquale, V and Bisio, M and Tessadori, J and Nowak, P and Grassia, F and Averna, A and Tedesco, M and Bonifazi, P and Difato, F and Massobrio, P and Levi, T and Chiappalone, M},
title = {A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks.},
journal = {iScience},
volume = {19},
number = {},
pages = {402-414},
pmid = {31421595},
issn = {2589-0042},
abstract = {Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.},
}
@article {pmid31421162,
year = {2019},
author = {Belwafi, K and Gannouni, S and Aboalsamh, H and Mathkour, H and Belghith, A},
title = {A dynamic and self-adaptive classification algorithm for motor imagery EEG signals.},
journal = {Journal of neuroscience methods},
volume = {327},
number = {},
pages = {108346},
doi = {10.1016/j.jneumeth.2019.108346},
pmid = {31421162},
issn = {1872-678X},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Datasets as Topic ; Electroencephalography/methods ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; Movement ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a communication pathway applied for pathological analysis or functional substitution. BCI based on functional substitution enables the recognition of a subject's intention to control devices such as prosthesis and wheelchairs. Discrimination of electroencephalography (EEG) trials related to left- and right-hand movements requires complex EEG signal processing to achieve good system performance.<br><br>NEW METHOD: In this study, a novel dynamic and self-adaptive algorithm (DSAA) based on the least-squares method is proposed to select the most appropriate feature extraction and classification algorithms couple for each subject. Specifically, the best couple identified during the training of the system is updated during online testing in order to check the stability of the selected couple and maintain high system accuracy.<br><br>RESULTS: Extensive and systematic experiments were conducted on public datasets of 17 subjects in the BCI-competition and the results show an improved performance for DSAA over other selected state-of-the-art methods.<br><br>The results show that the proposed system enhanced the classification accuracy for the three chosen public datasets by 8% compared to other approaches. Moreover, the proposed system was successful in selecting the best path despite the unavailability of reference labels.<br><br>CONCLUSIONS: Performing dynamic and self-adaptive selection for the best feature extraction and classification algorithm couple increases the recognition rate of trials despite the unavailability of reference trial labels. This approach allows the development of a complete BCI system with excellent accuracy.},
}
@article {pmid31417652,
year = {2019},
author = {Jiang, Y and Lyu, T and Che, X and Jia, N and Li, Q and Feng, W},
title = {Overexpression of SMYD3 in Ovarian Cancer is Associated with Ovarian Cancer Proliferation and Apoptosis via Methylating H3K4 and H4K20.},
journal = {Journal of Cancer},
volume = {10},
number = {17},
pages = {4072-4084},
pmid = {31417652},
issn = {1837-9664},
abstract = {Background: Epigenetic regulation has been verified as a key mechanism in tumorigenesis. SET and MYND domain-containing protein 3 (SMYD3), a histone methyltransferase, is a promising epigenetic therapeutic target and is overexpressed in numerous human tumors. SMYD3 can promote oncogenic progression by methylating lysines to integrate cytoplasmic kinase signaling cascades or by methylating histone lysines to regulate specific gene transcription. However, the exact role of SMYD3 in the progression of ovarian cancer is still unknown. Methods: Immunohistochemistry was employed to test SMYD3 expression in ovarian cancer tissues from clinical patients. CCK-8 assay, Real-time cell analysis (RTCA), colony formation assay, cell cycle and apoptosis tested by Flow cytometer were employed to test the effects of SMYD3 on cell proliferation and apoptosis in ovarian cancer cell lines. A PCR array was used to identify the downstream targets of SMYD3. And, PCR and Western blot were used to verify their expression. The binding of SMYD3 on the promoter of target genes were tested by ChIP assays. We also use nude mice subcutaneous tumor model and patient-derived xenograft (PDX) model to investigate the tumor promotive function of SMYD3 in vivo. Results: SMYD3 expression was higher in ovarian cancer tissues and cell lines than in normal ovarian epithelial tissue and human ovarian surface epithelial cells (HOSEpiC). After silencing SMYD3, the proliferation of ovarian cancer cells was significantly inhibited in vitro. In addition, the SMYD3-specific small-molecule inhibitor BCI-121 suppressed ovarian cancer cell proliferation. Downregulation of SMYD3 led to S phase arrest and increased the cell apoptosis rate. Furthermore, a PCR array revealed that SMYD3 knockdown caused the upregulation of the cyclin-dependent kinase (CDK) inhibitors CDKN2A (p16[INK4]), CDKN2B (p15[INK4B]), CDKN3 and CDC25A, which may be responsible for the S phase arrest. In addition, the upregulation of CD40LG and downregulation of BIRC3 may explain the increased cell apoptosis rate after silencing SMYD3. We also discovered that SMYD3 bound on the promoter of CDKN2A and down-regulated its expression by triple-methylating H4K20. In addition, SMYD3 bound on the promoter of BIRC3 and up-regulated its expression by triple-methylating H3K4. Finally, knocking down SMYD3 could inhibit ovarian cancer growth in nude mice subcutaneous tumor model and PDX model. Conclusion: Our results demonstrated that SMYD3 was overexpressed in ovarian cancer and contributes to the regulation of tumor proliferation and apoptosis via SMYD3-H4K20me3-CDKN2A pathway and SMYD3-H3K4me3-BIRC3 pathway. Thus, SMYD3 is a promising epigenetic therapeutic target for ovarian cancer.},
}
@article {pmid31417382,
year = {2019},
author = {Won, K and Kwon, M and Jang, S and Ahn, M and Jun, SC},
title = {P300 Speller Performance Predictor Based on RSVP Multi-feature.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {261},
pmid = {31417382},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller's performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller's performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300's speller performance. We found that several of the RSVP's event-related potential (ERP) and behavioral features were correlated with the P300 speller's offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.},
}
@article {pmid31416561,
year = {2019},
author = {Lin, BS and Huang, YK and Lin, BS},
title = {Design of smart EEG cap.},
journal = {Computer methods and programs in biomedicine},
volume = {178},
number = {},
pages = {41-46},
doi = {10.1016/j.cmpb.2019.06.009},
pmid = {31416561},
issn = {1872-7565},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electric Conductivity ; *Electrodes ; Electroencephalography/*instrumentation ; *Equipment Design ; Head ; Humans ; Motion ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Skin/pathology ; Wireless Technology ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain machine interface (BMI) is a system which communicates the brain with the external machines. In general, an electroencephalograph (EEG) machine has to be used to monitor multi-channel brain responses to improve the BMI performance. However, the bulky size of the EEG machine and applying conductive gels in EEG electrodes also cause the inconvenience of daily life applications. How to select the relevant EEG channel and remove irrelevant channels is important and useful for the development of BMIs.<br><br>METHODS: In this research, a smart EEG cap was proposed to improve the above issues. Different from the conventional EEG machine, the proposed smart EEG cap contain a spatial filtering circuit to enhance EEG features in local area, and it could also select the relevant EEG channel automatically. Moreover, the novel dry active electrodes were also designed to acquire EEG without conductive gels in the hairy skin of the head, to improve the convenience in use.<br><br>RESULTS: Finally, the proposed smart EEG cap was applied in motion imagery-based BMI and several experiments were tested to valid the system performance. The proposed smart EEG cap could effectively enhance EEG features and select relevant EEG channel, and the information transfer rate of BMI was about 6.06 bits/min.<br><br>CONCLUSIONS: The proposed smart EEG cap has advantages of measuring EEG without conductive gels and wireless transmission to effectively improve the convenience of use, and reduce the limitation of activity in daily life. In the future, it might be widely applied in other BMI applications.},
}
@article {pmid31416258,
year = {2019},
author = {Ji, N and Ma, L and Dong, H and Zhang, X},
title = {EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy.},
journal = {Brain sciences},
volume = {9},
number = {8},
pages = {},
pmid = {31416258},
issn = {2076-3425},
support = {No.61271334//National Natural Science Foundation of China/ ; },
abstract = {The classification recognition rate of motor imagery is a key factor to improve the performance of brain-computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support&amp;nbsp;vector&amp;nbsp;machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.},
}
@article {pmid31416059,
year = {2019},
author = {Farahat, A and Reichert, C and Sweeney-Reed, CM and Hinrichs, H},
title = {Convolutional neural networks for decoding of covert attention focus and saliency maps for EEG feature visualization.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066010},
doi = {10.1088/1741-2552/ab3bb4},
pmid = {31416059},
issn = {1741-2552},
mesh = {Attention/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: Convolutional neural networks (CNNs) have proven successful as function approximators and have therefore been used for classification problems including electroencephalography (EEG) signal decoding for brain-computer interfaces (BCI). Artificial neural networks, however, are considered black boxes, because they usually have thousands of parameters, making interpretation of their internal processes challenging. Here we systematically evaluate the use of CNNs for EEG signal decoding and investigate a method for visualizing the CNN model decision process.<br><br>APPROACH: We developed a CNN model to decode the covert focus of attention from EEG event-related potentials during object selection. We compared the CNN and the commonly used linear discriminant analysis (LDA) classifier performance, applied to datasets with different dimensionality, and analyzed transfer learning capacity. Moreover, we validated the impact of single model components by systematically altering the model. Furthermore, we investigated the use of saliency maps as a tool for visualizing the spatial and temporal features driving the model output.<br><br>MAIN RESULTS: The CNN model and the LDA classifier achieved comparable accuracy on the lower-dimensional dataset, but CNN exceeded LDA performance significantly on the higher-dimensional dataset (without hypothesis-driven preprocessing), achieving an average decoding accuracy of 90.7% (chance level&#x2009;&#x2009;=&#x2009;&#x2009;8.3%). Parallel convolutions, tanh or ELU activation functions, and dropout regularization proved valuable for model performance, whereas the sequential convolutions, ReLU activation function, and batch normalization components reduced accuracy or yielded no significant difference. Saliency maps revealed meaningful features, displaying the typical spatial distribution and latency of the P300 component expected during this task.<br><br>SIGNIFICANCE: Following systematic evaluation, we provide recommendations for when and how to use CNN models in EEG decoding. Moreover, we propose a new approach for investigating the neural correlates of a cognitive task by training CNN models on raw high-dimensional EEG data and utilizing saliency maps for relevant feature extraction.},
}
@article {pmid31415824,
year = {2019},
author = {Duprès, A and Cabestaing, F and Rouillard, J and Tiffreau, V and Pradeau, C},
title = {Toward a hybrid brain-machine interface for palliating motor handicap with Duchenne muscular dystrophy: A case report.},
journal = {Annals of physical and rehabilitation medicine},
volume = {62},
number = {5},
pages = {379-381},
doi = {10.1016/j.rehab.2019.07.005},
pmid = {31415824},
issn = {1877-0665},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/*methods ; Humans ; Male ; Muscular Dystrophy, Duchenne/*rehabilitation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
}
@article {pmid31414731,
year = {2019},
author = {Skok, T and Tabakow, P and Chmielak, K},
title = {Methods of integrating the human nervous system with electronic circuits.},
journal = {Advances in clinical and experimental medicine : official organ Wroclaw Medical University},
volume = {28},
number = {8},
pages = {1125-1135},
doi = {10.17219/acem/103414},
pmid = {31414731},
issn = {1899-5276},
mesh = {*Artificial Limbs ; *Electronics ; Humans ; *Nervous System/anatomy &amp; histology ; Nervous System Physiological Phenomena ; Neurons ; },
abstract = {In recent years, many attempts have been made to connect electrical circuits with the human nervous system. The objective of type of research was diverse - from the desire to understand the physiology of the nervous system, through attempting to substitute nervous tissue defects with synthetic systems, to creating an interface that allows computers to be controlled directly with one's thought. Regardless of the original purpose, the creation of any form of such a combination would entail a series of subsequent discoveries, allowing for a real revolution in both theoretical and clinical neuroscience. Computers based on neurons, neurochips or mind prostheses are just some examples of technologies that could soon become part of everyday life. Despite numerous attempts, there is still no interface that meets all the expectations of the scholars. However, many scientific groups seem to be on the right track and their achievements raise extraordinary expectations. This paper evaluates historical theories and contemporary ideas about such interfaces to smoothly describe the major medical and scientific utility of the subject. Thus it presents the main issues surrounding the concept of integrating the human nervous system with electronic circuits.},
}
@article {pmid31410041,
year = {2019},
author = {Hadi Alijanvand, M and Aminorroaya, A and Kazemi, I and Aminorroaya Yamini, S and Janghorbani, M and Amini, M and Mansourian, M},
title = {Cross-sectional and longitudinal assessments of risk factors associated with hypertension and moderately increased albuminuria comorbidity in patients with type 2 diabetes: a 9-year open cohort study.},
journal = {Diabetes, metabolic syndrome and obesity : targets and therapy},
volume = {12},
number = {},
pages = {1123-1139},
pmid = {31410041},
issn = {1178-7007},
abstract = {BACKGROUND: Moderately increased albuminuria (MIA) is strongly associated with hypertension (HTN) in patients with type 2 diabetic mellitus (T2DM). However, the association between risk factors and coexisting HTN and MIA remains unassessed.<br><br>OBJECTIVES: This study aimed to determine both cross-sectional and longitudinal associations of risk factors with HTN and MIA comorbidity in patients with T2DM.<br><br>METHODS: A total of 1,600 patients with T2DM were examined at baseline and longitudinal data were obtained from 1,337 T2DM patients with at least 2 follow-up visits to assess the presence of HTN alone (yes/no), MIA alone (yes/no) and the coexistence of both (yes/no) in a 9-year open cohort study between 2004 and 2013. Bivariate mixed-effects logistic regression with a Bayesian approach was employed to evaluate associations of risk factors with HTN and MIA&#x200e; comorbidity in the longitudinal assessment.<br><br>RESULTS: After adjustment for age and BMI, patients with uncontrolled plasma glucose, as a combined index of the glucose profile, were more likely to have HTN [odds ratio (OR): 1.73 with 95% Bayesian credible intervals (BCI) 1.29-2.20] and MIA [OR: 1.34 (&#x200e;95% BCI 1.13-1.62)]. The risks of having HTN and MIA were increased by a one-year raise in diabetes duration [with 0.89 (95% BCI 0.84-0.96) and 0.81 (95% BCI 0.73-0.92) ORs, respectively] and a one-unit increase in non-high-density lipoprotein-cholesterol (Non-HDL-C) [with 1.30 (95% BCI 1.23-1.34) and 1.24 (95% BCI 1.14-1.33) ORs, respectively].<br><br>CONCLUSIONS: T2DM patients with HTN,&#x200e; MIA, and the coexistence of both had uncontrolled plasma glucose, significantly higher Non-HDL-C, and shorter diabetes duration than the other T2DM patients. Duration of diabetes and uncontrolled plasma glucose index showed the stronger effects on HTN and MIA comorbidity than on each condition separately.},
}
@article {pmid31409713,
year = {2019},
author = {Richardson, AG and Ghenbot, Y and Liu, X and Hao, H and Rinehart, C and DeLuccia, S and Torres Maldonado, S and Boyek, G and Zhang, M and Aflatouni, F and Van der Spiegel, J and Lucas, TH},
title = {Learning active sensing strategies using a sensory brain-machine interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {116},
number = {35},
pages = {17509-17514},
pmid = {31409713},
issn = {1091-6490},
support = {K12 NS080223/NS/NINDS NIH HHS/United States ; R01 NS107550/NS/NINDS NIH HHS/United States ; TL1 TR001880/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation ; *Feedback, Sensory ; *Learning ; Male ; Maze Learning ; Models, Biological ; Rats ; },
abstract = {Diverse organisms, from insects to humans, actively seek out sensory information that best informs goal-directed actions. Efficient active sensing requires congruity between sensor properties and motor strategies, as typically honed through evolution. However, it has been difficult to study whether active sensing strategies are also modified with experience. Here, we used a sensory brain-machine interface paradigm, permitting both free behavior and experimental manipulation of sensory feedback, to study learning of active sensing strategies. Rats performed a searching task in a water maze in which the only task-relevant sensory feedback was provided by intracortical microstimulation (ICMS) encoding egocentric bearing to the hidden goal location. The rats learned to use the artificial goal direction sense to find the platform with the same proficiency as natural vision. Manipulation of the acuity of the ICMS feedback revealed distinct search strategy adaptations. Using an optimization model, the different strategies were found to minimize the effort required to extract the most salient task-relevant information. The results demonstrate that animals can adjust motor strategies to match novel sensor properties for efficient goal-directed behavior.},
}
@article {pmid31406328,
year = {2019},
author = {Smalley, E},
title = {The business of brain-computer interfaces.},
journal = {Nature biotechnology},
volume = {37},
number = {9},
pages = {978-982},
pmid = {31406328},
issn = {1546-1696},
mesh = {Biocompatible Materials ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Humans ; Machine Learning ; Neuronal Plasticity ; *User-Computer Interface ; },
}
@article {pmid31404915,
year = {2019},
author = {Ghanbari, A and Lee, CM and Read, HL and Stevenson, IH},
title = {Modeling stimulus-dependent variability improves decoding of population neural responses.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066018},
doi = {10.1088/1741-2552/ab3a68},
pmid = {31404915},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; Cerebral Cortex/*physiology ; Databases, Factual ; Macaca ; Male ; *Models, Neurological ; Neurons/*physiology ; Poisson Distribution ; Rats ; },
abstract = {OBJECTIVE: Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding.<br><br>APPROACH: In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor&#x2009;&#x2009;=&#x2009;&#x2009;1). Here we use two alternative models-the Conway-Maxwell-Poisson (CMP) model and negative binomial (NB) model-to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor&#x2009;&#x2009;>&#x2009;&#x2009;1) or, for the CMP model, less than the mean (Fano factor&#x2009;&#x2009;<&#x2009;&#x2009;1).<br><br>MAIN RESULTS: We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%-12% compared to the Poisson model.<br><br>SIGNIFICANCE: Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.},
}
@article {pmid31404255,
year = {2019},
author = {Ogino, M and Kanoga, S and Muto, M and Mitsukura, Y},
title = {Analysis of Prefrontal Single-Channel EEG Data for Portable Auditory ERP-Based Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {250},
pmid = {31404255},
issn = {1662-5161},
abstract = {An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.},
}
@article {pmid31403437,
year = {2019},
author = {Santamaria-Vazquez, E and Martinez-Cagigal, V and Gomez-Pilar, J and Hornero, R},
title = {Asynchronous Control of ERP-Based BCI Spellers Using Steady-State Visual Evoked Potentials Elicited by Peripheral Stimuli.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1883-1892},
doi = {10.1109/TNSRE.2019.2934645},
pmid = {31403437},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; *Communication Devices for People with Disabilities ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Signal-To-Noise Ratio ; },
abstract = {Brain-computer interface (BCI) spellers based on event related potentials (ERPs) are intrinsically synchronous systems. Therefore, selections are constantly made, even when users are not paying attention to the stimuli. This poses a major limitation in real-life applications, in which an asynchronous control is required. The aim of this study is to design, develop and test a novel method to discriminate whether the user is controlling the system (i.e., control state) or is engaged in other task (i.e., non-control state). To achieve such an asynchronous control, our method detects the steady-state visual evoked potentials (SSVEPs) elicited by peripheral stimuli of ERP-based spellers. A characterization experiment was conducted with 5 subjects to investigate general aspects of this phenomenon. Then, the proposed method was validated with 15 subjects in offline and online sessions. Results show that the proposed method provides a reliable asynchronous control, achieving an average accuracy of 95.5% for control state detection during the online sessions. Furthermore, our approach is independent of the ERP classification stage, and to the best of our knowledge, is the first procedure that does not need to extend the duration of the calibration sessions to acquire non-control observations.},
}
@article {pmid31403435,
year = {2019},
author = {Ge, S and Jiang, Y and Wang, P and Wang, H and Zheng, W},
title = {Training -Free Steady-State Visual Evoked Potential Brain-Computer Interface Based on Filter Bank Canonical Correlation Analysis and Spatiotemporal Beamforming Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1714-1723},
doi = {10.1109/TNSRE.2019.2934496},
pmid = {31403435},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {A brain-computer interface (BCI) provides a novel non-muscular communication pathway for individuals with severe neuromuscular diseases. BCI systems based on steady-state visual evoked potentials (SSVEPs) have high classification accuracy, information transfer rate, and signal-to-noise ratio, giving them high research and application value. However, SSVEP-based BCI has several limitations in real-world applications. The main challenge is how to reduce or eliminate the need for a dedicated training process while maintaining high classification accuracy. Filter bank canonical correlation analysis (FBCCA) is a powerful and widely used feature extraction method for SSVEP-based BCI systems. However, the reference signals of FBCCA are fixed-frequency sine-cosine waves, which makes it difficult to accurately describe the complex, mutative, and individually different physiological SSVEPs. Therefore, there is huge room for improvement in classification performance based on the FBCCA method. In contrast, although spatiotemporal beamforming (BF) detects SSVEPs with high accuracy, it needs an additional training process, which limits its application. In this study, we propose a bimodal decoding algorithm (FBCCA+BF), which combines the advantages of the training-free classification of FBCCA and the data-driven and adaptive features of BF. Six-channel SSVEP data corresponding to eight targets measured from 15 subjects were used to test the effectiveness of three different CCA-based methods, BF, and our proposed FBCCA+BF methods. It was found that the classification accuracies for BF and FBCCA+BF are 95.6% and 92.2%, respectively, which are significantly higher than the other CCA-based methods. Notably, both BF and FBCCA+BF obtain state-of-the-art performance, but FBCCA+BF does this without the need for a dedicated training process. Therefore, we conclude that our proposed FBCCA+BF method provides a training-free and high-accuracy approach for SSVEP-based BCIs.},
}
@article {pmid31403433,
year = {2019},
author = {Zhang, X and Libedinsky, C and So, R and Principe, JC and Wang, Y},
title = {Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1684-1694},
doi = {10.1109/TNSRE.2019.2934176},
pmid = {31403433},
issn = {1558-0210},
mesh = {*Algorithms ; Animals ; Attention ; *Brain-Computer Interfaces ; Cluster Analysis ; Computer Simulation ; Electrodes, Implanted ; Haplorhini ; *Machine Learning ; Motor Cortex/physiology ; Movement/physiology ; *Neural Prostheses ; *Reinforcement, Psychology ; },
abstract = {Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correction may over-dominate the user's intention. Reinforcement learning decoder allows users to actively adjust their brain patterns through trial and error, which better represents the subject's motive. The computational challenge is to quickly establish new state-action mapping before the subject becomes frustrated. Recently proposed quantized attention-gated kernel reinforcement learning (QAGKRL) explores the optimal nonlinear neural-action mapping in the Reproducing Kernel Hilbert Space (RKHS). However, considering all past data in RKHS is less efficient and sensitive to detect the new neural patterns emerging in brain control. In this paper, we propose a clustering-based kernel RL algorithm. New neural patterns emerge and are clustered to represent the novel knowledge in brain control. The current neural data only activate the nearest subspace in RKHS for more efficient decoding. The dynamic clustering makes our algorithm more sensitive to new brain patterns. We test our algorithm on both the synthetic and real-world spike data. Compared with QAGKRL, our algorithm can achieve a quicker knowledge adaptation in brain control with less computational complexity.},
}
@article {pmid31402858,
year = {2019},
author = {Qiu, JM and Casey, MA and Diamond, SG},
title = {Assessing Feedback Response With a Wearable Electroencephalography System.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {258},
pmid = {31402858},
issn = {1662-5161},
support = {R24 HD065703/HD/NICHD NIH HHS/United States ; },
abstract = {Background: Event related potential (ERP) components, such as P3, N2, and FRN, are potential metrics for assessing feedback response as a form of performance monitoring. Most research studies investigate these ERP components using clinical or research-grade electroencephalography (EEG) systems. Wearable EEGs, which are an affordable alternative, have the potential to assess feedback response using ERPs but have not been sufficiently evaluated. Feedback-related ERPs also have not been scientifically evaluated in interactive settings that are similar to daily computer use. In this study, a consumer-grade wearable EEG system was assessed for its feasibility to collect feedback-related ERPs through an interactive software module that provided an environment in which users were permitted to navigate freely within the program to make decisions. Methods: The recording hardware, which costs < $1,500 in total, incorporated the OpenBCI Cyton Board with Daisy chain, a consumer-grade EEG system that costs $949 USD. Seventeen participants interacted with an oddball paradigm and an interactive module designed to elicit feedback-related ERPs. The features of interests for the oddball paradigm were the P3 and N2 components. The features of interests for the interactive module were the P3, N2, and FRN components elicited in response to positive, neutral, and two types of negative feedback. The FRN was calculated by subtracting the positive feedback response from the negative feedback responses. Results: The P3 and N2 components of the oddball paradigm indicated statistically significant differences between infrequent targets and frequent targets which is in line with current literature. The P3 and N2 components elicited in the interactive module indicated statistically significant differences between positive, neutral, and negative feedback responses. There were no significant differences between the FRN types and significant interactions with channel group and FRN type. Conclusion: The OpenBCI Cyton, after some modifications, shows potential for eliciting and assessing P3, N2, and FRN components, which are important indicators for performance monitoring, in an interactive setting.},
}
@article {pmid31401488,
year = {2019},
author = {Pels, EGM and Aarnoutse, EJ and Leinders, S and Freudenburg, ZV and Branco, MP and van der Vijgh, BH and Snijders, TJ and Denison, T and Vansteensel, MJ and Ramsey, NF},
title = {Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {130},
number = {10},
pages = {1798-1803},
pmid = {31401488},
issn = {1872-8952},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/*diagnosis/physiopathology/*therapy ; Brain-Computer Interfaces/*trends ; Electrocorticography/methods/*trends ; Female ; Humans ; Implantable Neurostimulators/*trends ; Longitudinal Studies ; Middle Aged ; Motor Cortex/*physiology ; },
abstract = {OBJECTIVE: We investigated the long-term functional stability and home use of a fully implanted electrocorticography (ECoG)-based brain-computer interface (BCI) for communication by an individual with late-stage Amyotrophic Lateral Sclerosis (ALS).<br><br>METHODS: Data recorded from the cortical surface of the motor and prefrontal cortex with an implanted brain-computer interface device was evaluated for 36&#x202f;months after implantation of the system in an individual with late-stage ALS. In addition, electrode impedance and BCI control accuracy were assessed. Key measures included frequency of use of the system for communication, user and system performance, and electrical signal characteristics.<br><br>RESULTS: User performance was high consistently over the three years. Power in the high frequency band, used for the control signal, declined slowly in the motor cortex, but control over the signal remained unaffected by time. Impedance increased until month 5, and then remained constant. Frequency of home use increased steadily, indicating adoption of the system by the user.<br><br>CONCLUSIONS: The implanted brain-computer interface proves to be robust in an individual with late-stage ALS, given stable performance and control signal for over 36&#x202f;months.<br><br>SIGNIFICANCE: These findings are relevant for the future of implantable brain-computer interfaces along with other brain-sensing technologies, such as responsive neurostimulation.},
}
@article {pmid31398200,
year = {2019},
author = {Lai, YS and Biedermann, P and Shrestha, A and Chammartin, F and À Porta, N and Montresor, A and Mistry, NF and Utzinger, J and Vounatsou, P},
title = {Risk profiling of soil-transmitted helminth infection and estimated number of infected people in South Asia: A systematic review and Bayesian geostatistical Analysis.},
journal = {PLoS neglected tropical diseases},
volume = {13},
number = {8},
pages = {e0007580},
pmid = {31398200},
issn = {1935-2735},
support = {001/WHO_/World Health Organization/International ; },
mesh = {Ancylostomatoidea/isolation &amp; purification ; Animals ; Ascariasis/parasitology ; Ascaris lumbricoides/isolation &amp; purification ; Asia/epidemiology ; Bangladesh/epidemiology ; *Bayes Theorem ; Databases, Factual ; Helminthiasis/*epidemiology ; Helminths/isolation &amp; purification ; Hookworm Infections/epidemiology ; Humans ; India/epidemiology ; Nepal/epidemiology ; Pakistan/epidemiology ; Prevalence ; Risk Factors ; Socioeconomic Factors ; Soil/*parasitology ; Trichuriasis/epidemiology ; Trichuris/isolation &amp; purification ; },
abstract = {BACKGROUND: In South Asia, hundreds of millions of people are infected with soil-transmitted helminths (Ascaris lumbricoides, hookworm, and Trichuris trichiura). However, high-resolution risk profiles and the estimated number of people infected have yet to be determined. In turn, such information will assist control programs to identify priority areas for allocation of scarce resource for the control of soil-transmitted helminth infection.<br><br>METHODOLOGY: We pursued a systematic review to identify prevalence surveys pertaining to soil-transmitted helminth infections in four mainland countries (i.e., Bangladesh, India, Nepal, and Pakistan) of South Asia. PubMed and ISI Web of Science were searched from inception to April 25, 2019, without restriction of language, study design, and survey date. We utilized Bayesian geostatistical models to identify environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region.<br><br>PRINCIPAL FINDINGS: A total of 536, 490, and 410 georeferenced surveys were identified for A. lumbricoides, hookworm, and T. trichiura, respectively. We estimate that 361 million people (95% Bayesian credible interval (BCI) 331-395 million), approximately one-quarter of the South Asia population, was infected with at least one soil-transmitted helminth species in 2015. A. lumbricoides was the predominant species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region, as well as the southern coastal areas of India. The annual treatment needs for the school-age population requiring preventive chemotherapy was estimated at 165 million doses (95% BCI: 146-185 million).<br><br>CONCLUSIONS/SIGNIFICANCE: Our risk maps provide an overview of the geographic distribution of soil-transmitted helminth infection in four mainland countries of South Asia and highlight the need for up-to-date surveys to accurately evaluate the disease burden in the region.},
}
@article {pmid31396068,
year = {2019},
author = {Saha, S and Hossain, MS and Ahmed, K and Mostafa, R and Hadjileontiadis, L and Khandoker, A and Baumert, M},
title = {Wavelet Entropy-Based Inter-subject Associative Cortical Source Localization for Sensorimotor BCI.},
journal = {Frontiers in neuroinformatics},
volume = {13},
number = {},
pages = {47},
pmid = {31396068},
issn = {1662-5196},
abstract = {We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern (RCSP). EEG from five healthy individuals (Dataset IVa, BCI Competition III) were evaluated by a cross-subject paradigm. Prediction performance was evaluated via a two-layer feed-forward neural network, where the classifier was trained and tested by data from two subjects independently. On average, the overall mean prediction accuracies obtained using all 118 channels are (55.98±6.53) and (71.20±5.32) in cases of CSP and RCSP, respectively, which are slightly lower than the accuracies obtained using only the selected channels, i.e., (58.95±6.90) and (71.41±6.65), respectively. The highest mean prediction accuracy achieved for a specific subject pair by using selected EEG channels was on average (90.36±5.59) and outperformed that achieved by using all available channels (86.07 ± 10.71). Spatially projected cortical sources approximated using wMEM may be useful for capturing inter-subject associative sensorimotor brain dynamics and pave the way toward an enhanced subject-independent BCI.},
}
@article {pmid31395905,
year = {2019},
author = {Ciccarelli, G and Nolan, M and Perricone, J and Calamia, PT and Haro, S and O'Sullivan, J and Mesgarani, N and Quatieri, TF and Smalt, CJ},
title = {Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {11538},
pmid = {31395905},
issn = {2045-2322},
support = {T32 DC000038/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Algorithms ; Attention/*physiology ; Auditory Cortex/diagnostic imaging/*physiology ; *Brain-Computer Interfaces ; Electrocorticography ; *Electroencephalography ; Hearing Aids/trends ; Humans ; Linear Models ; Neural Networks, Computer ; Noise ; Nonlinear Dynamics ; Speech Perception/physiology ; },
abstract = {Auditory attention decoding (AAD) through a brain-computer interface has had a flowering of developments since it was first introduced by Mesgarani and Chang (2012) using electrocorticograph recordings. AAD has been pursued for its potential application to hearing-aid design in which an attention-guided algorithm selects, from multiple competing acoustic sources, which should be enhanced for the listener and which should be suppressed. Traditionally, researchers have separated the AAD problem into two stages: reconstruction of a representation of the attended audio from neural signals, followed by determining the similarity between the candidate audio streams and the reconstruction. Here, we compare the traditional two-stage approach with a novel neural-network architecture that subsumes the explicit similarity step. We compare this new architecture against linear and non-linear (neural-network) baselines using both wet and dry electroencephalogram (EEG) systems. Our results indicate that the new architecture outperforms the baseline linear stimulus-reconstruction method, improving decoding accuracy from 66% to 81% using wet EEG and from 59% to 87% for dry EEG. Also of note was the finding that the dry EEG system can deliver comparable or even better results than the wet, despite the latter having one third as many EEG channels as the former. The 11-subject, wet-electrode AAD dataset for two competing, co-located talkers, the 11-subject, dry-electrode AAD dataset, and our software are available for further validation, experimentation, and modification.},
}
@article {pmid31394509,
year = {2019},
author = {Sample, M and Aunos, M and Blain-Moraes, S and Bublitz, C and Chandler, JA and Falk, TH and Friedrich, O and Groetzinger, D and Jox, RJ and Koegel, J and McFarland, D and Neufield, V and Rodriguez-Arias, D and Sattler, S and Vidal, F and Wolbring, G and Wolkenstein, A and Racine, E},
title = {Brain-computer interfaces and personhood: interdisciplinary deliberations on neural technology.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {063001},
doi = {10.1088/1741-2552/ab39cd},
pmid = {31394509},
issn = {1741-2552},
mesh = {Biomedical Technology/methods/*trends ; Brain-Computer Interfaces/psychology/*trends ; Communication ; Communication Devices for People with Disabilities/psychology/*trends ; Education/methods/trends ; Humans ; *Personhood ; },
abstract = {OBJECTIVE: Scientists, engineers, and healthcare professionals are currently developing a variety of new devices under the category of brain-computer interfaces (BCIs). Current and future applications are both medical/assistive (e.g. for communication) and non-medical (e.g. for gaming). This array of possibilities has been met with both enthusiasm and ethical concern in various media, with no clear resolution of these conflicting sentiments.<br><br>APPROACH: To better understand how BCIs may either harm or help the user, and to investigate whether ethical guidance is required, a meeting entitled 'BCIs and Personhood: A Deliberative Workshop' was held in May 2018.<br><br>MAIN RESULTS: We argue that the hopes and fears associated with BCIs can be productively understood in terms of personhood, specifically the impact of BCIs on what it means to be a person and to be recognized as such by others.<br><br>SIGNIFICANCE: Our findings suggest that the development of neural technologies raises important questions about the concept of personhood and its role in society. Accordingly, we propose recommendations for BCI development and governance.},
}
@article {pmid31393827,
year = {2019},
author = {Jagadish, B and Mishra, PK and Kiran, MPRS and Rajalakshmi, P},
title = {A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments.},
journal = {Neural computation},
volume = {31},
number = {10},
pages = {1915-1944},
doi = {10.1162/neco_a_01223},
pmid = {31393827},
issn = {1530-888X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Imagination ; *Internet of Things ; *Machine Learning ; Male ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research.},
}
@article {pmid31389846,
year = {2020},
author = {Mattingly, JK and Banakis Hartl, RM and Jenkins, HA and Tollin, DJ and Cass, SP and Greene, NT},
title = {A Comparison of Intracochlear Pressures During Ipsilateral and Contralateral Stimulation With a Bone Conduction Implant.},
journal = {Ear and hearing},
volume = {41},
number = {2},
pages = {312-322},
pmid = {31389846},
issn = {1538-4667},
support = {R21 DC017809/DC/NIDCD NIH HHS/United States ; T32 DC012280/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; *Bone Conduction ; Cochlea ; Hearing ; Humans ; *Scala Vestibuli ; Sound ; },
abstract = {OBJECTIVES: To compare contralateral to ipsilateral stimulation with percutaneous and transcutaneous bone conduction implants.<br><br>BACKGROUND: Bone conduction implants (BCIs) effectively treat conductive and mixed hearing losses. In some cases, such as in single-sided deafness, the BCI is implanted contralateral to the remaining healthy ear in an attempt to restore some of the benefits provided by binaural hearing. While the benefit of contralateral stimulation has been shown in at least some patients, it is not clear what cues or mechanisms contribute to this function. Previous studies have investigated the motion of the ossicular chain, skull, and round window in response to bone vibration. Here, we extend those reports by reporting simultaneous measurements of cochlear promontory velocity and intracochlear pressures during bone conduction stimulation with two common BCI attachments, and directly compare ipsilateral to contralateral stimulation.<br><br>METHODS: Fresh-frozen whole human heads were prepared bilaterally with mastoidectomies. Intracochlear pressure (PIC) in the scala vestibuli (PSV) and tympani (PST) was measured with fiber optic pressure probes concurrently with cochlear promontory velocity (VProm) via laser Doppler vibrometry during stimulation provided with a closed-field loudspeaker or a BCI. Stimuli were pure tones between 120 and 10,240 Hz, and response magnitudes and phases for PIC and VProm were measured for air and bone conducted sound presentation.<br><br>RESULTS: Contralateral stimulation produced lower response magnitudes and longer delays than ipsilateral in all measures, particularly for high-frequency stimulation. Contralateral response magnitudes were lower than ipsilateral response magnitudes by up to 10 to 15 dB above ~2 kHz for a skin-penetrating abutment, which increased to 25 to 30 dB and extended to lower frequencies when applied with a transcutaneous (skin drive) attachment.<br><br>CONCLUSIONS: Transcranial attenuation and delay suggest that ipsilateral stimulation will be dominant for frequencies over ~1 kHz, and that complex phase interactions will occur during bilateral or bimodal stimulation. These effects indicate a mechanism by which bilateral users could gain some bilateral advantage.},
}
@article {pmid31382248,
year = {2019},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Medina-Juliá, MT and Ron-Angevin, R},
title = {Evaluation of emotional and neutral pictures as flashing stimuli using a P300 brain-computer interface speller.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056024},
doi = {10.1088/1741-2552/ab386d},
pmid = {31382248},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Emotions/*physiology ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Signal Processing, Computer-Assisted ; Surveys and Questionnaires ; Young Adult ; },
abstract = {OBJECTIVE: Previous works have reported that complex emotional and visual stimuli can increase the amplitude of the P300 brain potential. Thus, the aim of the present work is to assess these kinds of images in a P300 brain-computer interface (BCI) speller as flashing stimuli.<br><br>APPROACH: Twenty-three volunteers controlled four spellers with different sets of flashing stimuli: flashing letters, neutral pictures (NP), emotional pleasant pictures (EPP) and emotional unpleasant pictures (EUP).<br><br>MAIN RESULTS: The sets of pictures showed a higher performance than the letters in accuracy and information transfer rate. These results were supported by the analysis of the P300 signal, where the picture sets offered the greatest amplitudes. The NP and EPP sets were the best evaluated in the subjective questionnaire.<br><br>SIGNIFICANCE: In short, despite the fact that the effect of emotional stimuli could not be observed in the performance metrics, picture sets have offered a high performance and should be considered in future proposals for visual P300-based BCI applications.},
}
@article {pmid31380764,
year = {2019},
author = {Lobo-Prat, J and Dong, Y and Moreso, G and Lew, C and Sharifrazi, N and Radom-Aizik, S and Reinkensmeyer, DJ},
title = {Development and Evaluation of MOVit: An Exercise-Enabling Interface for Driving a Powered Wheelchair.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1770-1779},
doi = {10.1109/TNSRE.2019.2932121},
pmid = {31380764},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Arm/physiology ; *Brain-Computer Interfaces ; Exercise/*physiology ; Female ; Healthy Volunteers ; Heart Rate/physiology ; Humans ; Learning ; Male ; Movement/physiology ; Oxygen Consumption/physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; *Wheelchairs ; Young Adult ; },
abstract = {Powered wheelchair users can experience negative health effects from reduced physical activity. If a user could exercise by driving the chair, it might improve fitness. This paper presents the development of MOVit, an exercise-enabling, wheelchair driving interface. The design goal of MOVit was that users cyclically move their arms to drive the chair, thereby providing a light level of exercise while driving. MOVit supports this arm movement with custom mobile arm supports that also serve as the sensors that provide controller inputs. Here, we first quantified how increasing the frequency and amplitude of arm movement increase oxygen consumption and heart rate. Then, we evaluated two novel control methods for driving by moving the arm supports. Participants without impairment (N = 24) were randomized to one of the two methods, or conventional joystick control, and performed driving tests over two days on a simulator and test course. Our results indicate that driving speed and accuracy were significantly lowered with the exercise-enabling methods compared to joystick control (ANOVA,), but the decreases were small (speed was ~0.1 m/s less and course tracking error ~1 cm greater). These results show, for the first time, the feasibility of exercising while driving a powered wheelchair.},
}
@article {pmid31380763,
year = {2019},
author = {Jochumsen, M and Navid, MS and Rashid, U and Haavik, H and Niazi, IK},
title = {EMG- Versus EEG-Triggered Electrical Stimulation for Inducing Corticospinal Plasticity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1901-1908},
doi = {10.1109/TNSRE.2019.2932104},
pmid = {31380763},
issn = {1558-0210},
mesh = {Adult ; Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electroencephalography/*methods ; Electromyography/*methods ; Evoked Potentials, Motor ; Feedback, Sensory ; Female ; Humans ; Male ; Neuronal Plasticity/*physiology ; Pyramidal Tracts/*physiology ; Stroke Rehabilitation ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {Brain-computer interfaces have been proposed for stroke rehabilitation. Motor cortical activity derived from the electroencephalography (EEG) can trigger external devices that provide congruent sensory feedback. However, many stroke patients regain residual muscle (EMG: electromyography) control due to spontaneous recovery and rehabilitation; therefore, EEG may not be necessary as a control signal. In this paper, a direct comparison was made between the induction of corticospinal plasticity using either EEG- or EMG-controlled electrical nerve stimulation. Twenty healthy participants participated in two intervention sessions consisting of EEG- and EMG-controlled electrical stimulation. The sessions consisted of 50 pairings between foot dorsiflexion movements (decoded through either EEG or EMG) and electrical stimulation of the common peroneal nerve. Before, immediately after and 30 minutes after the intervention, 15 motor evoked potentials (MEPs) were elicited in tibialis anterior through transcranial magnetic stimulation. Increased MEPs were observed immediately after (62 ± 26%, 73 ± 27% for EEG- and EMG-triggered electrical stimulation, respectively) and 30 minutes after each of the two interventions (79 ± 26% and 72 ± 27%) compared to the pre-intervention measurement. There was no difference between the interventions. Both EEG- and EMG-controlled electrical stimulation can induce corticospinal plasticity which suggests that stroke patients with residual EMG can use that modality instead of EEG to trigger stimulation.},
}
@article {pmid31379934,
year = {2019},
author = {Choi, JW and Kim, KH},
title = {Covert Intention to Answer "Yes" or "No" Can Be Decoded from Single-Trial Electroencephalograms (EEGs).},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {4259369},
pmid = {31379934},
issn = {1687-5273},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Comprehension/physiology ; *Electroencephalography/methods ; Female ; Humans ; *Intention ; Male ; Support Vector Machine ; Young Adult ; },
abstract = {Interpersonal communication is based on questions and answers, and the most useful and simplest case is the binary "yes or no" question and answer. The purpose of this study is to show that it is possible to decode intentions on "yes" or "no" answers from multichannel single-trial electroencephalograms, which were recorded while covertly answering to self-referential questions with either "yes" or "no." The intention decoding algorithm consists of a common spatial pattern and support vector machine, which are employed for the feature extraction and pattern classification, respectively, after dividing the overall time-frequency range into subwindows of 200 ms × 2 Hz. The decoding accuracy using the information within each subwindow was investigated to find useful temporal and spectral ranges and found to be the highest for 800-1200 ms in the alpha band or 200-400 ms in the theta band. When the features from multiple subwindows were utilized together, the accuracy was significantly increased up to ∼86%. The most useful features for the "yes/no" discrimination was found to be focused in the right frontal region in the theta band and right centroparietal region in the alpha band, which may reflect the violation of autobiographic facts and higher cognitive load for "no" compared to "yes." Our task requires the subjects to answer self-referential questions just as in interpersonal conversation without any self-regulation of the brain signals or high cognitive efforts, and the "yes" and "no" answers are decoded directly from the brain activities. This implies that the "mind reading" in a true sense is feasible. Beyond its contribution in fundamental understanding of the neural mechanism of human intention, the decoding of "yes" or "no" from brain activities may eventually lead to a natural brain-computer interface.},
}
@article {pmid31376634,
year = {2019},
author = {Zhang, Y and Yin, E and Li, F and Zhang, Y and Guo, D and Yao, D and Xu, P},
title = {Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {119},
number = {},
pages = {1-9},
doi = {10.1016/j.neunet.2019.07.007},
pmid = {31376634},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Random Allocation ; Recognition, Psychology/*physiology ; Young Adult ; },
abstract = {Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary techniques for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) method to investigate the efficiency and effectiveness of the proposed framework. Experimental results were obtained from a benchmark dataset of thirty-five subjects and indicate that the extended CORRCA method used within the framework significantly outperforms the original CORCCA method. Accordingly, the proposed framework holds promise to enhance the performance of frequency recognition methods in SSVEP-based BCIs.},
}
@article {pmid31375306,
year = {2019},
author = {Lee, MB and Kramer, DR and Peng, T and Barbaro, MF and Liu, CY and Kellis, S and Lee, B},
title = {Clinical neuroprosthetics: Today and tomorrow.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {68},
number = {},
pages = {13-19},
pmid = {31375306},
issn = {1532-2653},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*trends ; Deep Brain Stimulation/instrumentation/methods/*trends ; Humans ; Nervous System Diseases/*therapy ; Vagus Nerve Stimulation/instrumentation/methods/*trends ; },
abstract = {Implantable neurostimulation devices provide a direct therapeutic link to the nervous system and can be considered brain-computer interfaces (BCI). Under this definition, BCI are not simply science fiction, they are part of existing neurosurgical practice. Clinical BCI are standard of care for historically difficult to treat neurological disorders. These systems target the central and peripheral nervous system and include Vagus Nerve Stimulation, Responsive Neurostimulation, and Deep Brain Stimulation. Recent advances in clinical BCI have focused on creating "closed-loop" systems. These systems rely on biomarker feedback and promise individualized therapy with optimal stimulation delivery and minimal side effects. Success of clinical BCI has paralleled research efforts to create BCI that restore upper extremity motor and sensory function to patients. Efforts to develop closed loop motor/sensory BCI is linked to the successes of today's clinical BCI.},
}
@article {pmid31374781,
year = {2019},
author = {Missiroli, F and Barsotti, M and Leonardis, D and Gabardi, M and Rosati, G and Frisoli, A},
title = {Haptic Stimulation for Improving Training of a Motor Imagery BCI Developed for a Hand-Exoskeleton in Rehabilitation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2019},
number = {},
pages = {1127-1132},
doi = {10.1109/ICORR.2019.8779370},
pmid = {31374781},
issn = {1945-7901},
mesh = {Brain-Computer Interfaces ; *Exoskeleton Device ; Feedback, Sensory/physiology ; Hand/*physiology ; Hand Strength/physiology ; Humans ; Wrist/physiology ; },
abstract = {The use of robotic devices to provide active motor support and sensory feedback of ongoing motor intention, by means of a Brain Computer Interface (BCI), has received growing support by recent literature, with particular focus on neurorehabilitation therapies. At the same time, performance in the use of the BCI has become a more critical factor, since it directly influences congruency and consistency of the provided sensory feedback. As motor imagery is the mental simulation of a given movement without depending on residual function, training of patients in the use of motor imagery BCI can be extended beyond each rehabilitation session, and practiced by using simpler devices than rehabilitation robots available in the hospital. In this work, we investigated the use of haptic stimulation provided by vibrating electromagnetic motors to enhance BCI system training. The BCI is based on motor imagery of hand grasping and designed to operate a hand exoskeleton. We investigated whether haptic stimulation at fingerpads proves to be more effective than stimulation at wrist, already experimented in literature, due to the higher density of mechano-receptors. Our results did not show significant differences between the two body locations in BCI performance, yet a wider and more stable event-relateddesynchronization appeared for the finger-located stimulation. Future investigations will put in relation training with haptic feedback at fingerpads with BCI performance using the handexoskeleton, in grasping tasks that naturally involve haptic feedback at fingerpads.},
}
@article {pmid31374771,
year = {2019},
author = {Kaseler, RL and Leerskov, K and Andreasen Struijk, LNS and Dremstrup, K and Jochumsen, M},
title = {Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2019},
number = {},
pages = {1067-1072},
doi = {10.1109/ICORR.2019.8779376},
pmid = {31374771},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Quality of Life ; *Robotics ; },
abstract = {An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steady-state visual evoked potentials (SSVEP) based BCI are among the best performing. Thus, this study investigates the design of a system for a full workspace control of a 7 degrees of freedom ARM. A SSVEP signal is elicited by observing a visual stimulus flickering at a specific frequency and phase. This study investigates the best combination of unique frequencies and phases to provide a 16-target BCI by testing three different systems off line. Furthermore, a fourth system is developed to investigate the impact of the stimulating monitor refresh rate. Experiments conducted on two subjects suggest that a 16-target BCI created by four unique frequencies and 16-unique phases provide the best performance. Subject 1 reaches a maximum estimated ITR of 235 bits/min while subject 2 reaches 140 bits/min. The findings suggest that the optimal SSVEP stimuli to generate 16 targets are a low number of frequencies and a high number of unique phases. Moreover, the findings do not suggest any need for considering the monitor refresh rate if stimuli are modulated using a sinusoidal signal sampled at the refresh rate.},
}
@article {pmid31374711,
year = {2019},
author = {Tottrup, L and Leerskov, K and Hadsund, JT and Kamavuako, EN and Kaseler, RL and Jochumsen, M},
title = {Decoding covert speech for intuitive control of brain-computer interfaces based on single-trial EEG: a feasibility study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2019},
number = {},
pages = {689-693},
doi = {10.1109/ICORR.2019.8779499},
pmid = {31374711},
issn = {1945-7901},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Feasibility Studies ; Female ; Humans ; Male ; Motor Activity/physiology ; Movement ; Speech/*physiology ; Young Adult ; },
abstract = {For individuals with severe motor deficiencies, controlling external devices such as robotic arms or wheelchairs can be challenging, as many devices require some degree of motor control to be operated, e.g. when controlled using a joystick. A brain-computer interface (BCI) relies only on signals from the brain and may be used as a controller instead of muscles. Motor imagery (MI) has been used in many studies as a control signal for BCIs. However, MI may not be suitable for all control purposes, and several people cannot obtain BCI control with MI. In this study, the aim was to investigate the feasibility of decoding covert speech from single-trial EEG and compare and combine it with MI. In seven healthy subjects, EEG was recorded with twenty-five channels during six different actions: Speaking three words (both covert and overt speech), two arm movements (both motor imagery and execution), and one idle class. Temporal and spectral features were derived from the epochs and classified with a random forest classifier. The average classification accuracy was $67 \pm 9$ % and $75\pm 7$ % for covert and overt speech, respectively; this was 5-10 % lower than the movement classification. The performance of the combined movement-speech decoder was $61 \pm 9$ % and $67\pm 7$ % (covert and overt), but it is possible to have more classes available for control. The possibility of using covert speech for controlling a BCI was outlined; this is a step towards a multimodal BCI system for improved usability.},
}
@article {pmid31374255,
year = {2019},
author = {Zurzolo, C and Enninga, J},
title = {The best of both worlds- bringing together cell biology and infection at the Institut Pasteur.},
journal = {Microbes and infection},
volume = {21},
number = {5-6},
pages = {254-262},
doi = {10.1016/j.micinf.2019.06.014},
pmid = {31374255},
issn = {1769-714X},
abstract = {Only a profound understanding of the structure and function of cells - either as single units or in the context of tissues and whole organisms - will allow a comprehension of what happens in pathological conditions and provides the means to fight disease. The Cell Biology and Infection (BCI for Biologie Cellulaire et Infection) department was created in 2002 at the Institut Pasteur in Paris to develop a research program under the umbrella of cell biology, infection biology and microbiology. Its visionary ambition was to shape a common framework for cellular microbiology, and to interface the latter with hard sciences like physics and mathematics and cutting-edge technology. This concept, ahead of time, has given high visibility to the field of cellular microbiology and quantitative cell biology, and it has allowed the successful execution of highly interdisciplinary research programs linking a molecular understanding of cellular events with disease. Now, the BCI department embraces additional pathologies, namely cancer and neurodegenerative diseases. Here, we will portray how the integrative research approach of BCI has led to major scientific breakthroughs during the last ten years, and where we see scientific opportunities for the near future.},
}
@article {pmid31370406,
year = {2019},
author = {Gao, ZK and Guo, W and Cai, Q and Ma, C and Zhang, YB and Kurths, J},
title = {Characterization of SSMVEP-based EEG signals using multiplex limited penetrable horizontal visibility graph.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {29},
number = {7},
pages = {073119},
doi = {10.1063/1.5108606},
pmid = {31370406},
issn = {1089-7682},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Models, Neurological ; *Photic Stimulation ; *Support Vector Machine ; },
abstract = {The steady state motion visual evoked potential (SSMVEP)-based brain computer interface (BCI), which incorporates the motion perception capabilities of the human visual system to alleviate the negative effects caused by strong visual stimulation from steady-state VEP, has attracted a great deal of attention. In this paper, we design a SSMVEP-based experiment by Newton's ring paradigm. Then, we use the canonical correlation analysis and Support Vector Machines to classify SSMVEP signals for the SSMVEP-based electroencephalography (EEG) signal detection. We find that the classification accuracy of different subjects under fatigue state is much lower than that in the normal state. To probe into this, we develop a multiplex limited penetrable horizontal visibility graph method, which enables to infer a brain network from 62-channel EEG signals. Subsequently, we analyze the variation of the average weighted clustering coefficient and the weighted global efficiency corresponding to these two brain states and find that both network measures are lower under fatigue state. The results suggest that the associations and information transfer efficiency among different brain regions become weaker when the brain state changes from normal to fatigue, which provide new insights into the explanations for the reduced classification accuracy. The promising classification results and the findings render the proposed methods particularly useful for analyzing EEG recordings from SSMVEP-based BCI system.},
}
@article {pmid31369381,
year = {2019},
author = {Lu, Y and Bi, L},
title = {Combined Lateral and Longitudinal Control of EEG Signals-Based Brain-Controlled Vehicles.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {9},
pages = {1732-1742},
doi = {10.1109/TNSRE.2019.2931360},
pmid = {31369381},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Automobile Driving ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Neurological ; Psychomotor Performance ; Young Adult ; },
abstract = {Using brain signals rather than limbs to control a vehicle may not only help persons with disabilities to acquire driving ability, but also provide healthy persons with a new alternative way to drive. In this paper, we propose a combined lateral and longitudinal control system for electroencephalogram (EEG) signals-based brain-controlled vehicles (BCVs). The proposed system is designed by integrating a user interface, a brain-computer interface (BCI), a control interface model, a lateral controller, and a longitudinal controller. We conduct driver-and-hardware-in-the-loop experiments under two control conditions (i.e., the brain- and manual-control conditions) with different subjects and three driving tests (i.e., the lane-changing, path-selection, and car-following tests). Experimental results show the feasibility of using brain signals to continuously perform both the lateral and longitudinal control of a vehicle. This study not only promotes the development of BCVs, but also provides some insights on how to apply BCIs in conjunction with assistant controllers to control other dynamic systems.},
}
@article {pmid31369283,
year = {2019},
author = {Ganji, M and Paulk, AC and Yang, JC and Vahidi, NW and Lee, SH and Liu, R and Hossain, L and Arneodo, EM and Thunemann, M and Shigyo, M and Tanaka, A and Ryu, SB and Lee, SW and Tchoe, Y and Marsala, M and Devor, A and Cleary, DR and Martin, JR and Oh, H and Gilja, V and Gentner, TQ and Fried, SI and Halgren, E and Cash, SS and Dayeh, SA},
title = {Selective Formation of Porous Pt Nanorods for Highly Electrochemically Efficient Neural Electrode Interfaces.},
journal = {Nano letters},
volume = {19},
number = {9},
pages = {6244-6254},
pmid = {31369283},
issn = {1530-6992},
support = {DP2 EB029757/EB/NIBIB NIH HHS/United States ; R01 EY029022/EY/NEI NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; U01 NS099700/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Animals ; *Biocompatible Materials ; *Brain-Computer Interfaces ; Electric Stimulation ; Electrodes ; Macaca mulatta ; Male ; Mice ; *Nanotubes ; Neurons/*metabolism ; *Platinum ; Songbirds ; Visual Cortex/*physiology ; },
abstract = {The enhanced electrochemical activity of nanostructured materials is readily exploited in energy devices, but their utility in scalable and human-compatible implantable neural interfaces can significantly advance the performance of clinical and research electrodes. We utilize low-temperature selective dealloying to develop scalable and biocompatible one-dimensional platinum nanorod (PtNR) arrays that exhibit superb electrochemical properties at various length scales, stability, and biocompatibility for high performance neurotechnologies. PtNR arrays record brain activity with cellular resolution from the cortical surfaces in birds and nonhuman primates. Significantly, strong modulation of surface recorded single unit activity by auditory stimuli is demonstrated in European Starling birds as well as the modulation of local field potentials in the visual cortex by light stimuli in a nonhuman primate and responses to electrical stimulation in mice. PtNRs record behaviorally and physiologically relevant neuronal dynamics from the surface of the brain with high spatiotemporal resolution, which paves the way for less invasive brain-machine interfaces.},
}
@article {pmid31365911,
year = {2019},
author = {Wang, Z and Zhou, Y and Chen, L and Gu, B and Liu, S and Xu, M and Qi, H and He, F and Ming, D},
title = {A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066012},
doi = {10.1088/1741-2552/ab377d},
pmid = {31365911},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*classification ; Electroencephalography/classification/methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Neurofeedback/*methods/*physiology ; Photic Stimulation/methods ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: We proposed a brain-computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively.<br><br>APPROACH: 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD).<br><br>MAIN RESULTS: By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8-10 Hz, alpha_2: 11-13 Hz, beta_1: 15-20 Hz and beta_2: 22-28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT.<br><br>SIGNIFICANCE: These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.},
}
@article {pmid31365298,
year = {2019},
author = {Criner, GJ and Delage, A and Voelker, K and Hogarth, DK and Majid, A and Zgoda, M and Lazarus, DR and Casal, R and Benzaquen, SB and Holladay, RC and Wellikoff, A and Calero, K and Rumbak, MJ and Branca, PR and Abu-Hijleh, M and Mallea, JM and Kalhan, R and Sachdeva, A and Kinsey, CM and Lamb, CR and Reed, MF and Abouzgheib, WB and Kaplan, PV and Marrujo, GX and Johnstone, DW and Gasparri, MG and Meade, AA and Hergott, CA and Reddy, C and Mularski, RA and Case, AH and Makani, SS and Shepherd, RW and Chen, B and Holt, GE and Martel, S},
title = {Improving Lung Function in Severe Heterogenous Emphysema with the Spiration Valve System (EMPROVE). A Multicenter, Open-Label Randomized Controlled Clinical Trial.},
journal = {American journal of respiratory and critical care medicine},
volume = {200},
number = {11},
pages = {1354-1362},
pmid = {31365298},
issn = {1535-4970},
support = {P20 GM125498/GM/NIGMS NIH HHS/United States ; UL1 TR001102/TR/NCATS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {Aged ; Bronchi/physiopathology ; Female ; Forced Expiratory Volume ; Humans ; Inhalation ; Lung/*physiopathology ; Male ; *Prostheses and Implants/adverse effects ; Pulmonary Emphysema/physiopathology/*therapy ; Treatment Outcome ; },
abstract = {Rationale: Less invasive, nonsurgical approaches are needed to treat severe emphysema.Objectives: To evaluate the effectiveness and safety of the Spiration Valve System (SVS) versus optimal medical management.Methods: In this multicenter, open-label, randomized, controlled trial, subjects aged 40 years or older with severe, heterogeneous emphysema were randomized 2:1 to SVS with medical management (treatment) or medical management alone (control).Measurements and Main Results: The primary efficacy outcome was the difference in mean FEV1 from baseline to 6 months. Secondary effectiveness outcomes included: difference in FEV1 responder rates, target lobe volume reduction, hyperinflation, health status, dyspnea, and exercise capacity. The primary safety outcome was the incidence of composite thoracic serious adverse events. All analyses were conducted by determining the 95% Bayesian credible intervals (BCIs) for the difference between treatment and control arms. Between October 2013 and May 2017, 172 participants (53.5% male; mean age, 67.4 yr) were randomized to treatment (n = 113) or control (n = 59). Mean FEV1 showed statistically significant improvements between the treatment and control groups-between-group difference at 6 and 12 months, respectively, of 0.101 L (95% BCI, 0.060-0.141) and 0.099 L (95% BCI, 0.048-0.151). At 6 months, the treatment group had statistically significant improvements in all secondary endpoints except 6-minute-walk distance. Composite thoracic serious adverse event incidence through 6 months was greater in the treatment group (31.0% vs. 11.9%), primarily due to a 12.4% incidence of serious pneumothorax.Conclusions: In patients with severe heterogeneous emphysema, the SVS shows significant improvement in multiple efficacy outcomes, with an acceptable safety profile.Clinical trial registered with www.clinicaltrials.gov (NCT01812447).},
}
@article {pmid31363096,
year = {2019},
author = {Moses, DA and Leonard, MK and Makin, JG and Chang, EF},
title = {Real-time decoding of question-and-answer speech dialogue using human cortical activity.},
journal = {Nature communications},
volume = {10},
number = {1},
pages = {3096},
pmid = {31363096},
issn = {2041-1723},
mesh = {Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electrocorticography/instrumentation/methods ; Electrodes, Implanted ; Epilepsy/diagnosis/physiopathology ; Female ; Humans ; Speech/*physiology ; Time Factors ; },
abstract = {Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate.},
}
@article {pmid31362282,
year = {2019},
author = {Rejer, I and Górski, P},
title = {MAICA: an ICA-based method for source separation in a low-channel EEG recording.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056025},
doi = {10.1088/1741-2552/ab36db},
pmid = {31362282},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: The paper aims to present a method that enables the application of independent component analysis (ICA) to a low-channel EEG recording. The idea behind the method (called moving average ICA or MAICA) is to extend the original low-sensor matrix of signals by applying a set of zero-phase moving average filters to each of the recorded signals.<br><br>APPROACH: The paper discusses the theoretical background of the MAICA algorithm and verifies its usefulness under three exemplary settings: (i) a pure mathematic system composed of ten source sinusoids; (ii) real EEG data recorded from 64 channels; (iii) real EEG data recorded from five subjects during 200 trials with motor imagery brain-computer interface.<br><br>MAIN RESULTS: The first system shows that MAICA is able to decompose two mixed signals (composed of ten source sinusoids) into ten components with an extremely high correlation between the source patterns and identified components (99%-100%). The second system shows that when used over five channels, MAICA is able to recognize more artefact components than those recognized by classic ICA used over 64 channels. Finally, the third system demonstrates that MAICA is capable of working in an online mode without significant delays; the additional time needed to run MAICA for one trial was less than 6ms in the survey reported in the paper.<br><br>SIGNIFICANCE: The method presented in the paper should have a significant impact on all areas of medical signal processing where a large number of known and/or unknown patterns have to be retrieved in real time from complex signals recorded from a small number of external/internal body sensors.},
}
@article {pmid31361011,
year = {2020},
author = {Martini, ML and Oermann, EK and Opie, NL and Panov, F and Oxley, T and Yaeger, K},
title = {Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review.},
journal = {Neurosurgery},
volume = {86},
number = {2},
pages = {E108-E117},
doi = {10.1093/neuros/nyz286},
pmid = {31361011},
issn = {1524-4040},
mesh = {*Algorithms ; Artificial Intelligence/trends ; Brain/diagnostic imaging/*physiology ; Brain-Computer Interfaces/*trends ; Electrocorticography/methods/trends ; Electrodes, Implanted ; Electroencephalography/methods/trends ; Humans ; Neuroimaging/methods/trends ; },
abstract = {Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.},
}
@article {pmid31358521,
year = {2020},
author = {Eberhardson, M and Tarnawski, L and Centa, M and Olofsson, PS},
title = {Neural Control of Inflammation: Bioelectronic Medicine in Treatment of Chronic Inflammatory Disease.},
journal = {Cold Spring Harbor perspectives in medicine},
volume = {10},
number = {3},
pages = {},
pmid = {31358521},
issn = {2157-1422},
mesh = {Adaptive Immunity ; Animals ; Arthritis, Rheumatoid/*immunology ; Humans ; *Immune System ; Immunity, Innate ; Inflammation ; Inflammatory Bowel Diseases/*immunology ; *Neuroimmunomodulation ; Reflex ; },
abstract = {Inflammation is important for antimicrobial defense and for tissue repair after trauma. The inflammatory response and its resolution are both active processes that must be tightly regulated to maintain homeostasis. Excessive inflammation and nonresolving inflammation cause tissue damage and chronic disease, including autoinflammatory and cardiovascular diseases. An improved understanding of the cellular and molecular mechanisms that regulate inflammation has supported development of novel therapies for several inflammatory diseases, including rheumatoid arthritis and inflammatory bowel disease. Many of the specific anticytokine therapies carry a risk for excessive immunosuppression and serious side effects. The discovery of the inflammatory reflex and the increasingly detailed understanding of the molecular interactions between homeostatic neural reflexes and the immune system have laid the foundation for bioelectronic medicine in the field of inflammatory diseases. Neural interfaces and nerve stimulators are now being tested in human clinical trials and may, as the technology develops further, have advantages over conventional drugs in terms of better compliance, continuously adaptable control of dosing, better monitoring, and reduced risks for unwanted side effects. Here, we review the current mechanistic understanding of common autoinflammatory conditions, consider available therapies, and discuss the potential use of increasingly capable devices in the treatment of inflammatory disease.},
}
@article {pmid31358057,
year = {2019},
author = {You, P and Siegel, LH and Kassam, Z and Hebb, M and Parnes, L and Ladak, HM and Agrawal, SK},
title = {The middle fossa approach with self-drilling screws: a novel technique for BONEBRIDGE implantation.},
journal = {Journal of otolaryngology - head &amp; neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale},
volume = {48},
number = {1},
pages = {35},
pmid = {31358057},
issn = {1916-0216},
mesh = {Audiometry, Pure-Tone ; *Bone Screws ; Hearing Loss, Mixed Conductive-Sensorineural/*surgery ; Humans ; Prosthesis Design ; Prosthesis Implantation/*methods ; Retrospective Studies ; Surgery, Computer-Assisted ; },
abstract = {BACKGROUND: Bone conduction implants can be used in the treatment of conductive or mixed hearing loss. The BONEBRIDGE bone conduction implant (BB-BCI) is an active, transcutaneous device. BB-BCI implantation can be performed through either the transmastoid or retrosigmoid approach with their respective limitations. Here, we present a third, novel approach for BB-BCI implantation.<br><br>OBJECTIVE: Describe the detailed surgical technique of BB-BCI implantation through a middle fossa approach with self-drilling screws and present preliminary audiometric outcome data following this approach.<br><br>METHODS: A single institution, retrospective chart review was completed for patients implanted with the BB-BCI via the middle fossa approach. Preoperative planning and modelling were performed using 3D Slicer. Audiological testing was performed pre- and post-operatively following standard audiometric techniques.<br><br>RESULTS: Forty patients underwent BB-BCI implantation using the middle fossa approach. Modelling techniques allowed for implantation through the use of external landmarks, obviating the need for intraoperative image guidance. The surgical technique was refined over time through experience and adaptation. Mean follow-up was 29&#x2009;months (range 3-71&#x2009;months) with no surgical complications, favourable cosmesis, and expected audiometric outcomes. An average functional gain of 39.6&#x2009;dB (&#xb1; 14.7 SD) was found.<br><br>CONCLUSION: The middle fossa technique with self-drilling screws is a safe and effective option for BONEBRIDGE implantation. As a reference for other groups considering this approach, an annotated video has been included as a supplement to the study.},
}
@article {pmid31357147,
year = {2019},
author = {Noel, JP and Chatelle, C and Perdikis, S and Jöhr, J and Lopes Da Silva, M and Ryvlin, P and De Lucia, M and Millán, JDR and Diserens, K and Serino, A},
title = {Peri-personal space encoding in patients with disorders of consciousness and cognitive-motor dissociation.},
journal = {NeuroImage. Clinical},
volume = {24},
number = {},
pages = {101940},
pmid = {31357147},
issn = {2213-1582},
mesh = {Acoustic Stimulation/*methods ; Adult ; Aged ; Cognition/*physiology ; Consciousness Disorders/diagnostic imaging/*physiopathology ; Electroencephalography/methods ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; *Personal Space ; Psychomotor Performance/*physiology ; Touch/*physiology ; Young Adult ; },
abstract = {Behavioral assessments of consciousness based on overt command following cannot differentiate patients with disorders of consciousness (DOC) from those who demonstrate a dissociation between intent/awareness and motor capacity: cognitive motor dissociation (CMD). We argue that delineation of peri-personal space (PPS) - the multisensory-motor space immediately surrounding the body - may differentiate these patients due to its central role in mediating human-environment interactions, and putatively in scaffolding a minimal form of selfhood. In Experiment 1, we determined a normative physiological index of PPS by recording electrophysiological (EEG) responses to tactile, auditory, or audio-tactile stimulation at different distances (5 vs. 75 cm) in healthy volunteers (N = 19). Contrasts between paired (AT) and summed (A + T) responses demonstrated multisensory supra-additivity when AT stimuli were presented near, i.e., within the PPS, and highlighted somatosensory-motor sensors as electrodes of interest. In Experiment 2, we recorded EEG in patients behaviorally diagnosed as DOC or putative CMD (N = 17, 30 sessions). The PPS-measure developed in Experiment 1 was analyzed in relation with both standard clinical diagnosis (i.e., Coma Recovery Scale; CRS-R) and a measure of neural complexity associated with consciousness. Results demonstrated a significant correlation between the PPS measure and neural complexity, but not with the CRS-R, highlighting the added value of the physiological recordings. Further, multisensory processing in PPS was preserved in putative CMD but not in DOC patients. Together, the findings suggest that indexing PPS allows differentiating between groups of patients whom both show overt motor impairments (DOC and CMD) but putatively distinct levels of awareness or motor intent.},
}
@article {pmid31356896,
year = {2019},
author = {Tsuchimoto, S and Shindo, K and Hotta, F and Hanakawa, T and Liu, M and Ushiba, J},
title = {Sensorimotor Connectivity after Motor Exercise with Neurofeedback in Post-Stroke Patients with Hemiplegia.},
journal = {Neuroscience},
volume = {416},
number = {},
pages = {109-125},
doi = {10.1016/j.neuroscience.2019.07.037},
pmid = {31356896},
issn = {1873-7544},
mesh = {Adult ; Double-Blind Method ; Electroencephalography/methods ; Exercise/*physiology ; Female ; Hemiplegia/*physiopathology ; Humans ; Imagination/physiology ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Motor Cortex/physiology/physiopathology ; Movement/*physiology ; Neurofeedback/methods ; Sensorimotor Cortex/physiology/physiopathology ; Stroke/*physiopathology ; },
abstract = {Impaired finger motor function in post-stroke hemiplegia is a debilitating condition with no evidence-based or accessible treatments. Here, we evaluated the neurophysiological effectiveness of direct brain control of robotic exoskeleton that provides movement support contingent with brain activity. To elucidate the mechanisms underlying the neurofeedback intervention, we assessed resting-state functional connectivity with functional magnetic resonance imaging (rsfcMRI) between the ipsilesional sensory and motor cortices before and after a single 1-h intervention. Eighteen stroke patients were randomly assigned to crossover interventions in a double-blind and sham-controlled design. One patient dropped out midway through the study, and 17 patients were included in this analysis. Interventions involved motor imagery, robotic assistance, and neuromuscular electrical stimulation administered to a paretic finger. The neurofeedback intervention delivered stimulations contingent on desynchronized ipsilesional electroencephalographic (EEG) oscillations during imagined movement, and the control intervention delivered sensorimotor stimulations that were independent of EEG oscillations. There was a significant time × intervention interaction in rsfcMRI in the ipsilesional sensorimotor cortex. Post-hoc analysis showed a larger gain in increased functional connectivity during the neurofeedback intervention. Although the neurofeedback intervention delivered fewer total sensorimotor stimulations compared to the sham-control, rsfcMRI in the ipsilesional sensorimotor cortices was increased during the neurofeedback intervention compared to the sham-control. Higher coactivation of the sensory and motor cortices during neurofeedback intervention enhanced rsfcMRI in the ipsilesional sensorimotor cortices. This study showed neurophysiological evidence that EEG-contingent neurofeedback is a promising strategy to induce intrinsic ipsilesional sensorimotor reorganization, supporting the importance of integrating closed-loop sensorimotor processing at a neurophysiological level.},
}
@article {pmid31354804,
year = {2019},
author = {Choi, KM and Park, S and Im, CH},
title = {Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual Comfort.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {9680697},
pmid = {31354804},
issn = {1687-5273},
mesh = {Asthenopia/etiology/prevention &amp; control ; Brain/physiology ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; *Photic Stimulation/methods ; *Virtual Reality ; Visual Perception/physiology ; Young Adult ; },
abstract = {Recent studies on brain-computer interfaces (BCIs) based on the steady-state visual evoked potential (SSVEP) have demonstrated their use to control objects or generate commands in virtual reality (VR) environments. However, most SSVEP-based BCI studies performed in VR environments have adopted visual stimuli that are typically used in conventional LCD environments without considering the differences in the rendering devices (head-mounted displays (HMDs) used in the VR environments). The proximity between the visual stimuli and the eyes in HMDs can readily cause eyestrain, degrading the overall performance of SSVEP-based BCIs. Therefore, in the present study, we have tested two different types of visual stimuli-pattern-reversal checkerboard stimulus (PRCS) and grow/shrink stimulus (GSS)-on young healthy participants wearing HMDs. Preliminary experiments were conducted to investigate the visual comfort of each participant during the presentation of the visual stimuli. In subsequent online avatar control experiments, we observed considerable differences in the classification accuracy of individual participants based on the type of visual stimuli used to elicit SSVEP. Interestingly, there was a close relationship between the subjective visual comfort score and the online performance of the SSVEP-based BCI: most participants showed better classification accuracy under visual stimulus they were more comfortable with. Our experimental results suggest the importance of an appropriate visual stimulus to enhance the overall performance of the SSVEP-based BCIs in VR environments. In addition, it is expected that the appropriate visual stimulus for a certain user might be readily selected by surveying the user's visual comfort for different visual stimuli, without the need for the actual BCI experiments.},
}
@article {pmid31354802,
year = {2019},
author = {Ron-Angevin, R and Garcia, L and Fernández-Rodríguez, &#xc1; and Saracco, J and André, JM and Lespinet-Najib, V},
title = {Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {7876248},
pmid = {31354802},
issn = {1687-5273},
mesh = {Adult ; Attention/physiology ; Brain/physiology ; *Brain-Computer Interfaces/psychology ; *Communication Devices for People with Disabilities/psychology ; *Event-Related Potentials, P300 ; *Eye Movements/physiology ; Fatigue/etiology ; Female ; Humans ; Male ; Writing ; Young Adult ; },
abstract = {The vast majority of P300-based brain-computer interface (BCI) systems are based on the well-known P300 speller presented by Farwell and Donchin for communication purposes and an alternative to people with neuromuscular disabilities, such as impaired eye movement. The purpose of the present work is to study the effect of speller size on P300-based BCI usability, measured in terms of effectiveness, efficiency, and satisfaction under overt and covert attention conditions. To this end, twelve participants used three speller sizes under both attentional conditions to spell 12 symbols. The results indicated that the speller size had, in both attentional conditions, a significant influence on performance. In both conditions (covert and overt), the best performances were obtained with the small and medium speller sizes, both being the most effective. The speller size did not significantly affect workload on the three speller sizes. In contrast, covert attention condition produced very high workload due to the increased resources expended to complete the task. Regarding users' preferences, significant differences were obtained between speller sizes. The small speller size was considered as the most complex, the most stressful, the less comfortable, and the most tiring. The medium speller size was always considered in the medium rank, which is the speller size that was evaluated less frequently and, for each dimension, the worst one. In this sense, the medium and the large speller sizes were considered as the most satisfactory. Finally, the medium speller size was the one to which the three standard dimensions were collected: high effectiveness, high efficiency, and high satisfaction. This work demonstrates that the speller size is an important parameter to consider in improving the usability of P300 BCI for communication purposes. The obtained results showed that using the proposed medium speller size, performance and satisfaction could be improved.},
}
@article {pmid31354460,
year = {2019},
author = {Vourvopoulos, A and Jorge, C and Abreu, R and Figueiredo, P and Fernandes, JC and Bermúdez I Badia, S},
title = {Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {244},
pmid = {31354460},
issn = {1662-5161},
abstract = {To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.},
}
@article {pmid31353935,
year = {2019},
author = {Sreedharan, S and Arun, KM and Sylaja, PN and Kesavadas, C and Sitaram, R},
title = {Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback.},
journal = {Brain connectivity},
volume = {9},
number = {8},
pages = {613-626},
pmid = {31353935},
issn = {2158-0022},
mesh = {Aphasia, Broca/diagnostic imaging/etiology/physiopathology/*therapy ; Brain/diagnostic imaging/*physiopathology ; Brain Mapping ; Humans ; *Language ; *Magnetic Resonance Imaging ; Neural Pathways/diagnostic imaging/physiopathology ; *Neurofeedback ; Stroke/complications/diagnostic imaging/physiopathology/*therapy ; Treatment Outcome ; },
abstract = {Stroke lesions in the language centers of the brain impair the language areas and their connectivity. This article describes the dynamics of functional connectivity (FC) of language areas (FCL) during real-time functional magnetic resonance imaging (RT-fMRI)-based neurofeedback training for poststroke patients with expressive aphasia. The hypothesis is that FCL increases during the upregulation of language areas during neurofeedback training and that the training improves FCL with an increasing number of sessions and restores it toward normalcy. Four test and four control patients with expressive aphasia were recruited for the study along with four healthy volunteers termed as the normal group. The test and normal groups were administered four neurofeedback training sessions in between two test sessions, whereas the control group underwent only the two test sessions. The training session requires the subject to exercise language activity covertly so that it upregulates the feedback signal obtained from the Broca's area (in left inferior frontal gyrus) and amplifies the feedback when it is correlated with the Wernicke's area (in left superior temporal gyrus) using RT-fMRI. FC was measured by Pearson's correlation coefficient. The results indicate that the FC of the test group was weaker in the left hemisphere than that of the normal group, and post-training the connections have strengthened (correlation coefficient increases) in the left hemisphere when compared with the control group. The connections of language areas strengthened in both hemispheres during neurofeedback-based upregulation, and multiple training sessions strengthened new pathways and restored left hemispheric connections toward normalcy.},
}
@article {pmid31348345,
year = {2019},
author = {Clites, TR and Carty, MJ and Srinivasan, SS and Talbot, SG and Brånemark, R and Herr, HM},
title = {Caprine Models of the Agonist-Antagonist Myoneural Interface Implemented at the Above- and Below-Knee Amputation Levels.},
journal = {Plastic and reconstructive surgery},
volume = {144},
number = {2},
pages = {218e-229e},
doi = {10.1097/PRS.0000000000005864},
pmid = {31348345},
issn = {1529-4242},
mesh = {Amputation, Surgical/*methods ; Animals ; Artificial Limbs ; Disease Models, Animal ; Electrodes, Implanted ; Electromyography/*methods ; Female ; Femur/surgery ; Goats ; Male ; Muscle, Skeletal/innervation ; *Proprioception ; *Prosthesis Design ; Prosthesis Implantation/*methods ; Tibia/surgery ; },
abstract = {BACKGROUND: Traditional approaches to amputation are not capable of reproducing the dynamic muscle relationships that are essential for proprioceptive sensation and joint control. In this study, the authors present two caprine models of the agonist-antagonist myoneural interface (AMI), a surgical approach designed to improve bidirectional neural control of a bionic limb. The key advancement of the AMI is the surgical coaptation of natively innervated agonist-antagonist muscle pairs within the residual limb.<br><br>METHODS: One AMI was surgically created in the hindlimb of each of two African Pygmy goats at the time of primary transtibial amputation. Each animal was also implanted with muscle electrodes and sonomicrometer crystals to enable measurement of muscle activation and muscle state, respectively. Coupled agonist-antagonist excursion in the agonist-antagonist myoneural interface muscles was measured longitudinally for each animal. Fibrosis in the residual limb was evaluated grossly in each animal as part of a planned terminal procedure.<br><br>RESULTS: Electromyographic and muscle state measurements showed coupled agonist-antagonist motion within the AMI in the presence of both neural activation and artificial muscle stimulation. Gross observation of the residual limb during a planned terminal procedure revealed a thin fibrotic encapsulation of the AMI constructs, which was not sufficient to preclude coupled muscle excursion.<br><br>CONCLUSIONS: These findings highlight the AMI's potential to provide coupled motion of distal agonist-antagonist muscle pairs preserved during below- or above-knee amputation at nearly human scale. Guided by these findings, it is the authors' expectation that further development of the AMI architecture will improve neural control of advanced limb prostheses through incorporation of physiologically relevant muscle-tendon proprioception.},
}
@article {pmid31342926,
year = {2019},
author = {Gabriel, PG and Chen, KJ and Alasfour, A and Pailla, T and Doyle, WK and Devinsky, O and Friedman, D and Dugan, P and Melloni, L and Thesen, T and Gonda, D and Sattar, S and Wang, SG and Gilja, V},
title = {Neural correlates of unstructured motor behaviors.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066026},
doi = {10.1088/1741-2552/ab355c},
pmid = {31342926},
issn = {1741-2552},
mesh = {Adolescent ; Brain/*physiology ; Electrocorticography/*methods ; Epilepsy/diagnosis/*physiopathology ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Video Recording/*methods ; },
abstract = {OBJECTIVE: We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings.<br><br>APPROACH: We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow.<br><br>MAIN RESULTS: We found that neural signal features (8-32 Hz and 76-100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. 'Idle' and 'Move', 'Right' and 'Left'); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects.<br><br>SIGNIFICANCE: We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain-computer interfaces.},
}
@article {pmid31342924,
year = {2019},
author = {El-Atab, N and Shaikh, SF and Hussain, MM},
title = {Nano-scale transistors for interfacing with brain: design criteria, progress and prospect.},
journal = {Nanotechnology},
volume = {30},
number = {44},
pages = {442001},
doi = {10.1088/1361-6528/ab3534},
pmid = {31342924},
issn = {1361-6528},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Graphite/chemistry ; Humans ; Nanostructures/*chemistry ; Prostheses and Implants ; Silicon/chemistry ; Spatio-Temporal Analysis ; *Transistors, Electronic ; },
abstract = {According to the World Health Organization, one quarter of the world's population suffers from various neurological disorders ranging from depression to Alzheimer's disease. Thus, understanding the operation mechanism of the brain enables us to help those who are suffering from these diseases. In addition, recent clinical medicine employs electronic brain implants, despite the fact of being invasive, to treat disorders ranging from severe coronary conditions to traumatic injuries. As a result, the deaf could hear, the blind could see, and the paralyzed could control robotic arms and legs. Due to the requirement of high data management capability with a power consumption as low as possible, designing nanoscale transistors as essential I/O electronics is a complex task. Herein, we review the essential design criteria for such nanoscale transistors, progress and prospect for implantable brain-machine-interface electronics. This article also discusses their technological challenges for practical implementation.},
}
@article {pmid31341310,
year = {2019},
author = {Drew, L},
title = {The ethics of brain-computer interfaces.},
journal = {Nature},
volume = {571},
number = {7766},
pages = {S19-S21},
pmid = {31341310},
issn = {1476-4687},
mesh = {*Brain-Computer Interfaces ; Humans ; Indenes ; Intelligence ; *Neurosciences ; },
}
@article {pmid31341093,
year = {2019},
author = {Zhang, R and Zong, Q and Dou, L and Zhao, X},
title = {A novel hybrid deep learning scheme for four-class motor imagery classification.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066004},
doi = {10.1088/1741-2552/ab3471},
pmid = {31341093},
issn = {1741-2552},
mesh = {Brain/*physiology ; Deep Learning/*classification/trends ; Electroencephalography/*classification/trends ; Humans ; Imagination/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated.<br><br>APPROACH: An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a. A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract the features of the four-class MI signal. b. A hybrid deep network based on the convolutional neural network and long-term short-term memory network is proposed to extract and learn the spatial and temporal features of the MI signal simultaneously.<br><br>MAIN RESULTS: The main contribution of this paper is to propose a hybrid deep network framework to improve the classification accuracy of the four-class MI-EEG signal. The hybrid deep network is a subject-independent shared neural network, which means it can be trained by using the training data from all subjects to form one model.<br><br>SIGNIFICANCE: The classification performance obtained by the proposed algorithm on brain-computer interface (BCI) competition IV dataset 2a in terms of accuracy is 83% and Cohen's kappa value is 0.80. Finally, the shared hybrid deep network is evaluated by every subject respectively, and the experimental results illustrate that the shared neural network has satisfactory accuracy. Thus, the proposed algorithm could be of great interest for real-life BCIs.},
}
@article {pmid31338780,
year = {2019},
author = {Murdoch, R},
title = {An Experiential Learning-Based Approach to Neurofeedback Visualisation in Serious Games.},
journal = {Advances in experimental medicine and biology},
volume = {1156},
number = {},
pages = {97-109},
doi = {10.1007/978-3-030-19385-0_7},
pmid = {31338780},
issn = {0065-2598},
mesh = {Brain-Computer Interfaces/economics/standards/trends ; Humans ; *Mental Health/education ; *Neurofeedback ; *Problem-Based Learning ; *Video Games/psychology/trends ; },
abstract = {This study explores brain-computer interfacing, its possible use in serious or educational games and frameworks. Providing real-time feedback regarding cognitive states and behaviours can be a powerful tool for mental health education and games can offer unique and engaging environments for these neurofeedback experiences. We explore how EEG neurofeedback systems can be affordably created for further research and experimentation and suggest design choices that may assist in developing effective experiences of this nature.},
}
@article {pmid31337400,
year = {2019},
author = {Tamburella, F and Moreno, JC and Herrera Valenzuela, DS and Pisotta, I and Iosa, M and Cincotti, F and Mattia, D and Pons, JL and Molinari, M},
title = {Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {95},
pmid = {31337400},
issn = {1743-0003},
mesh = {Aged ; Biofeedback, Psychology/*instrumentation ; Biomechanical Phenomena ; Cross-Over Studies ; Electromyography/instrumentation ; Female ; Gait Disorders, Neurologic/etiology/*rehabilitation ; Humans ; Male ; Middle Aged ; Robotics/*instrumentation/*methods ; Self-Help Devices ; Stroke/complications ; Stroke Rehabilitation/*instrumentation/methods ; Torque ; },
abstract = {BACKGROUND: Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.<br><br>METHODS: This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients' performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients. 12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40&#x2009;min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40&#x2009;min per day, 5&#x2009;days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.<br><br>RESULTS: Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.<br><br>CONCLUSIONS: Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.},
}
@article {pmid31334590,
year = {2020},
author = {Laventure, S and Benchenane, K},
title = {Validating the theoretical bases of sleep reactivation during sharp-wave ripples and their association with emotional valence.},
journal = {Hippocampus},
volume = {30},
number = {1},
pages = {19-27},
doi = {10.1002/hipo.23143},
pmid = {31334590},
issn = {1098-1063},
mesh = {Animals ; Brain Waves/*physiology ; Emotions/*physiology ; Hippocampus/*physiology ; Humans ; Learning/*physiology ; Memory/*physiology ; Memory Consolidation/physiology ; Sleep/*physiology ; },
abstract = {Sleep is important for memory consolidation, and an abundant literature suggests that reactivation in the hippocampus during sleep is instrumental to this process. Yet, the current interpretation of activity during sharp-waves ripples (SWRs), as replay of wake experiences, relies on hypotheses that, while widely accepted, have only recently begun to be tested directly. Moreover, this theory has been mainly studied in the context of pure spatial learning, and it is still not clear how emotional valence can fit into this conceptual framework when considering reward- or punishment-based learning. In this review, we will present recent experimental arguments validating the interpretation of sleep replay as reactivation of awake experiences and examine the evidence showing that the emotional valence is also replayed during sleep in a coordinated fashion with hippocampal SWRs. Finally, we will detail recent experiments showing that brain-computer interfaces can be used to modify the emotional valence associated with sleep replay.},
}
@article {pmid31333439,
year = {2019},
author = {Ramele, R and Villar, AJ and Santos, JM},
title = {Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection.},
journal = {Frontiers in computational neuroscience},
volume = {13},
number = {},
pages = {43},
pmid = {31333439},
issn = {1662-5188},
abstract = {The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects.},
}
@article {pmid31332000,
year = {2019},
author = {Mohan, H and de Haan, R and Broersen, R and Pieneman, AW and Helmchen, F and Staiger, JF and Mansvelder, HD and de Kock, CPJ},
title = {Functional Architecture and Encoding of Tactile Sensorimotor Behavior in Rat Posterior Parietal Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {39},
number = {37},
pages = {7332-7343},
pmid = {31332000},
issn = {1529-2401},
mesh = {Animals ; Male ; Movement/*physiology ; Parietal Lobe/cytology/*physiology ; Rats ; Rats, Wistar ; Somatosensory Cortex/cytology/*physiology ; Touch Perception/*physiology ; Vibrissae/cytology/innervation/*physiology ; },
abstract = {The posterior parietal cortex (PPC) in rodents is reciprocally connected to primary somatosensory and vibrissal motor cortices. The PPC neuronal circuitry could thus encode and potentially integrate incoming somatosensory information and whisker motor output. However, the information encoded across PPC layers during refined sensorimotor behavior remains largely unknown. To uncover the sensorimotor features represented in PPC during voluntary whisking and object touch, we performed loose-patch single-unit recordings and extracellular recordings of ensemble activity, covering all layers of PPC in anesthetized and awake, behaving male rats. First, using single-cell receptive field mapping, we revealed the presence of coarse somatotopy along the mediolateral axis in PPC. Second, we found that spiking activity was modulated during exploratory whisking in layers 2-4 and layer 6, but not in layer 5 of awake, behaving rats. Population spiking activity preceded actual movement, and whisker trajectory endpoints could be decoded by population spiking, suggesting that PPC is involved in movement planning. Finally, population spiking activity further increased in response to active whisker touch but only in PPC layers 2-4. Thus, we find layer-specific processing, which emphasizes the computational role of PPC during whisker sensorimotor behavior.SIGNIFICANCE STATEMENT The posterior parietal cortex (PPC) is thought to merge information on motor output and sensory input to orchestrate interaction with the environment, but the function of different PPC microcircuit components is poorly understood. We recorded neuronal activity in rat PPC during sensorimotor behavior involving motor and sensory pathways. We uncovered that PPC layers have dedicated function: motor and sensory information is merged in layers 2-4; layer 6 predominantly represents motor information. Collectively, PPC activity predicts future motor output, thus entailing a motor plan. Our results are important for understanding how PPC computationally processes motor output and sensory input. This understanding may facilitate decoding of brain activity when using brain-machine interfaces to overcome loss of function after, for instance, spinal cord injury.},
}
@article {pmid31331895,
year = {2019},
author = {Thies, J and Alimohammad, A},
title = {Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {8},
pages = {1529-1538},
doi = {10.1109/TNSRE.2019.2929081},
pmid = {31331895},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Data Compression ; Electrodes, Implanted ; Epilepsy/physiopathology ; Humans ; Neural Networks, Computer ; Principal Component Analysis ; Prostheses and Implants ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue. This paper presents a data compression system designed for brain-computer interfaces (BCIs) based on undercomplete autoencoders. To the best of our knowledge, the proposed system is the first to achieve an average spike reconstruction quality of 14-dB signal-to-noise-and-distortion ratio (SNDR) at a 32× compression ratio (CR), 18-dB SNDR at a 16× CR, 22-dB SNDR at an 8× CR, and 35-dB SNDR at a 4× CR of neural spikes. The spike detection and autoencoder-based compression modules are designed and implemented in a standard 45-nm CMOS process. The post-synthesis simulation results report that the compression module consumes between 1.4 and 222.5 [Formula: see text] of power per channel and takes between 0.018 and 0.082mm[2] of silicon area, depending on the desired CR and number of channels.},
}
@article {pmid31330318,
year = {2019},
author = {Ghouchani, A and Rouhi, G and Ebrahimzadeh, MH},
title = {Investigation on distal femoral strength and reconstruction failure following curettage and cementation: In-vitro tests with finite element analyses.},
journal = {Computers in biology and medicine},
volume = {112},
number = {},
pages = {103360},
doi = {10.1016/j.compbiomed.2019.103360},
pmid = {31330318},
issn = {1879-0534},
mesh = {Bone Cements/*chemistry ; *Cementation ; Female ; *Femur/chemistry/injuries ; Finite Element Analysis ; Humans ; Male ; Middle Aged ; Weight-Bearing ; },
abstract = {Cement augmentation following benign bone tumor surgery, i.e. curettage and cementation, is recommended in patients at high risk of fracture. Nonetheless, identifying appropriate cases and devices for augmentation remains debatable. Our goal was to develop a validated biomechanical tool to: predict the post-surgery strength of a femoral bone, assess the precision and accuracy of the predicted strength, and discover the mechanisms of reconstruction failure, with the aim of finding a safe biomechanical fixation. Tumor surgery was mimicked in quantitative-CT (QCT) scanned cadaveric human distal femora, and subsequently tested in compression to measure bone strength (FExp). Finite element (FE) models considering bone material non-homogeneity and non-linearity were constructed to predict bone strength (FFE). Analyses of contact, damage, and crack initiation at the bone-cement interface (BCI) were completed to investigate critical failure locations. Results of paired t-tests did not show a significant difference between FExp and FFE (P > 0.05); linear regression analysis resulted in good correlation between FExp and FFE (R[2] = 0.94). Evaluation of the models precision using linear regression analysis yielded R[2] = 0.89, with the slope = 1.08 and intercept = -324.16 N. FE analyses showed the initiation of damage and crack and a larger cement debonding area at the proximal end and most interior part of BCI, respectively. Therefore, we speculated that devices that reinforce critical failure locations offer the most biomechanical advantage. The QCT-based FE method proved to be a reliable tool to predict distal femoral strength, identify some causes of reconstruction failure, and assist in a safer selection of fixation devices to reduce post-operative fracture risk.},
}
@article {pmid31329105,
year = {2020},
author = {Chang, CY and Hsu, SH and Pion-Tonachini, L and Jung, TP},
title = {Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {4},
pages = {1114-1121},
doi = {10.1109/TBME.2019.2930186},
pmid = {31329105},
issn = {1558-2531},
mesh = {Algorithms ; *Artifacts ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Artifact subspace reconstruction (ASR) is an automatic, online-capable, component-based method that can effectively remove transient or large-amplitude artifacts contaminating electroencephalographic (EEG) data. However, the effectiveness of ASR and the optimal choice of its parameter have not been systematically evaluated and reported, especially on actual EEG data.<br><br>METHODS: This paper systematically evaluates ASR on 20 EEG recordings taken during simulated driving experiments. Independent component analysis (ICA) and an independent component classifier are applied to separate artifacts from brain signals to quantitatively assess the effectiveness of the ASR.<br><br>RESULTS: ASR removes more eye and muscle components than brain components. Even though some eye and muscle components retain after ASR cleaning, the power of their temporal activities is reduced. Study results also showed that ASR cleaning improved the quality of a subsequent ICA decomposition.<br><br>CONCLUSIONS: Empirical results show that the optimal ASR parameter is between 20 and 30, balancing between removing non-brain signals and retaining brain activities.<br><br>SIGNIFICANCE: With an appropriate choice of parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.},
}
@article {pmid31329104,
year = {2020},
author = {Nakanishi, M and Wang, YT and Wei, CS and Chiang, KJ and Jung, TP},
title = {Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {4},
pages = {1105-1113},
doi = {10.1109/TBME.2019.2929745},
pmid = {31329104},
issn = {1558-2531},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {OBJECTIVE: This paper proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems.<br><br>METHODS: The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques, including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA), were employed to extract the shared responses from different devices. The transferred data were integrated into a template-matching-based algorithm to detect SSVEPs. To evaluate its transferability, this paper conducted two sessions of simulated online BCI experiments with ten subjects using 40 visual stimuli modulated by joint frequency-phase coding method. In each session, two different EEG devices were used: first, the Quick-30 system (Cognionics, Inc.) with dry electrodes, and second, the ActiveTwo system (BioSemi, Inc.) with wet electrodes.<br><br>RESULTS: The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method.<br><br>CONCLUSION: This paper validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs.<br><br>SIGNIFICANCE: The proposed method has great potentials to enhance practicability and usability of real-world SSVEP-based BCI applications by leveraging user-specific data recorded in previous sessions even with different EEG systems and montages.},
}
@article {pmid31326660,
year = {2019},
author = {Jin, J and Miao, Y and Daly, I and Zuo, C and Hu, D and Cichocki, A},
title = {Correlation-based channel selection and regularized feature optimization for MI-based BCI.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {118},
number = {},
pages = {262-270},
doi = {10.1016/j.neunet.2019.07.008},
pmid = {31326660},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods/standards ; Humans ; *Support Vector Machine ; },
abstract = {Multi-channel EEG data are usually necessary for spatial pattern identification in motor imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some channels containing redundant information and noise may degrade BCI performance. We assume that the channels related to MI should contain common information when participants are executing the MI tasks. Based on this hypothesis, a correlation-based channel selection (CCS) method is proposed to select the channels that contained more correlated information in this study. The aim is to improve the classification performance of MI-based BCIs. Furthermore, a novel regularized common spatial pattern (RCSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with the Radial Basis Function (RBF) kernel is trained to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) to validate the effectiveness of the proposed methods. The results show that the CCS algorithm obtained superior classification accuracy (78% versus 56.4% for dataset1, 86.6% versus 76.5% for dataset 2 and 91.3% versus 85.1% for dataset 3) compared to the algorithm using all channels (AC), when CSP is used to extract the features. Furthermore, RCSP could further improve the classification accuracy (81.6% for dataset1, 87.4% for dataset2 and 91.9% for dataset 3), when CCS is used to select the channels.},
}
@article {pmid31325584,
year = {2019},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Medina-Juliá, MT and Ron-Angevin, R},
title = {Evaluation of flashing stimuli shape and colour heterogeneity using a P300 brain-computer interface speller.},
journal = {Neuroscience letters},
volume = {709},
number = {},
pages = {134385},
doi = {10.1016/j.neulet.2019.134385},
pmid = {31325584},
issn = {1872-7972},
mesh = {Brain-Computer Interfaces/*standards ; Color Perception/*physiology ; Electroencephalography/methods/standards ; Event-Related Potentials, P300/*physiology ; Female ; Form Perception/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {Previous works using a visual P300-based speller have reported an improvement modifying the shape or colour of the presented stimulus. However, the effects of both blended factors have not been yet studied. Thus, the aim of the present work was to study both factors and assess the interaction between them. Fifteen naïve participants tested four different spellers in a calibration and online task. All spellers were similar except the employed illumination of the target stimulus: white letters, white blocks, coloured letters, and coloured blocks. Regarding the results, the block-shaped conditions offered an improvement versus the letter-shaped conditions in the calibration (accuracy) and online (accuracy and correct commands per minute) tasks. The analysis of the event-related potential waveforms showed a larger difference between target and no target stimuli waveforms for the block-shaped conditions versus the letter-shaped. The hypothesis regarding the colour heterogeneity of the stimuli was not found at any level of the analysis. Therefore, this first study combining block-shaped and colour factors, and offering an exhaustive evaluation of both, demonstrated the superiority of block-shaped illumination versus the standard letter-shaped flashing stimuli in classification performance.},
}
@article {pmid31323653,
year = {2019},
author = {Xiao, F and Yang, D and Guo, X and Wang, Y},
title = {VMD-based denoising methods for surface electromyography signals.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056017},
doi = {10.1088/1741-2552/ab33e4},
pmid = {31323653},
issn = {1741-2552},
mesh = {Adult ; Aged ; *Artifacts ; *Brain-Computer Interfaces ; Electromyography/*methods/standards ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Stroke/physiopathology/therapy ; Young Adult ; },
abstract = {OBJECTIVE: Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising.<br><br>APPROACH: The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method.<br><br>MAIN RESULTS: The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance.<br><br>SIGNIFICANCE: This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.},
}
@article {pmid31322998,
year = {2019},
author = {Balart-Sánchez, SA and Vélez-Pérez, H and Rivera-Tello, S and Gómez Velázquez, FR and González-Garrido, AA and Romo-Vázquez, R},
title = {A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {64},
number = {6},
pages = {655-667},
doi = {10.1515/bmt-2017-0189},
pmid = {31322998},
issn = {1862-278X},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; Humans ; },
abstract = {The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson's correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.},
}
@article {pmid31321955,
year = {2019},
author = {Zhang, L and Wei, Q},
title = {Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm.},
journal = {Journal of integrative neuroscience},
volume = {18},
number = {2},
pages = {141-152},
doi = {10.31083/j.jin.2019.02.17},
pmid = {31321955},
issn = {0219-6352},
support = {61663025//National Natural Science Foundation of China/International ; CX2018160//Postgraduate Innovation Fund of Nanchang University/International ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; Psychomotor Performance/*physiology ; Quantum Theory ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {The number of electrode channels in a brain-computer interface affects not only its classification performance, but also its convenience in practical applications. However, an effective method for determining the number of channels has not yet been established for motor imagery-based brain-computer interfaces. This paper proposes a novel evolutionary search algorithm, binary quantum-behaved particle swarm optimization, for channel selection, which is implemented in a wrapping manner, coupling common spatial pattern for feature extraction, and support vector machine for classification. The fitness function of binary quantum-behaved particle swarm optimization is defined as the weighted sum of classification error rate and relative number of channels. The classification performance of the binary quantum-behaved particle swarm optimization-based common spatial pattern was evaluated on an electroencephalograph data set and an electrocorticography data set. It was subsequently compared with that of other three common spatial pattern methods: using the channels selected by binary particle swarm optimization, all channels in raw data sets, and channels selected manually. Experimental results showed that the proposed binary quantum-behaved particle swarm optimization-based common spatial pattern method outperformed the other three common spatial pattern methods, significantly decreasing the classification error rate and number of channels, as compared to the common spatial pattern method using whole channels in raw data sets. The proposed method can significantly improve the practicability and convenience of a motor imagery-based brain-computer interface system.},
}
@article {pmid31318702,
year = {2019},
author = {Richner, TJ and Brodnick, SK and Thongpang, S and Sandberg, AA and Krugner-Higby, LA and Williams, JC},
title = {Phase relationship between micro-electrocorticography and cortical neurons.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066028},
doi = {10.1088/1741-2552/ab335b},
pmid = {31318702},
issn = {1741-2552},
support = {T90 DK070079/DK/NIDDK NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Cerebral Cortex/*physiology ; Electrocorticography/instrumentation/*methods ; *Electrodes, Implanted ; Male ; Microelectrodes ; Neurons/*physiology ; Rats ; Rats, Sprague-Dawley ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG) is commonly used to map epileptic foci and to implement brain-computer interfaces. Understanding the spatiotemporal correspondence between potentials recorded from the brain's surface and the firing patterns of neurons within the cortex would inform the interpretation of ECoG signals and the design of (microfabricated) micro-ECoG electrode arrays. Based on the theory that synaptic potentials generated by neurons firing in synchrony superimpose to generate local field potentials (LFPs), we hypothesized that neurons in the cortex would fire at preferential phases of the micro-ECoG signal in a spatially dependent way.<br><br>APPROACH: We custom fabricated micro-ECoG electrode arrays with a small opening for silicon arrays (NeuroNexus) to be inserted into the cortex.<br><br>MAIN RESULTS: We found that the spectral coherence between micro-ECoG signals and intracortical LFPs decreased with distance and frequency, but the coherence with spiking units did not simply decrease over distance, likely due to the structure of the cortex. The majority of sorted units spiked during a preferred phase (usually downward) and frequency (usually below 20 Hz) of the micro-ECoG signal. Their preferred frequency decreased with administration of dexmeditomidine, a sedative commonly used for cortical mapping in patients with epilepsy prior to surgical resection. Dexmedetomidine concomitantly shifted the micro-ECoG spectral density towards lower frequencies. Therefore, the phase relationship between micro-ECoG signals and cortical spiking depends on the state of the brain, and spectrum shifts towards lower frequencies in the electrocorticography signal are a signature of increased spike-phase coupling. However, spike-phase coupling is not a static property since visual stimuli were found to modulate the magnitude of phase coupling at gamma frequency ranges (30-80 Hz), providing empirical evidence that neurons transiently phase-lock.<br><br>SIGNIFICANCE: The phase relationship between intracortical spikes and micro-ECoG signals depends on brain state, site separation, cortical structure, and external stimuli.},
}
@article {pmid31317285,
year = {2020},
author = {Schneider, C and Pereira, M and Tonin, L and Millán, JDR},
title = {Real-time EEG Feedback on Alpha Power Lateralization Leads to Behavioral Improvements in a Covert Attention Task.},
journal = {Brain topography},
volume = {33},
number = {1},
pages = {48-59},
doi = {10.1007/s10548-019-00725-9},
pmid = {31317285},
issn = {1573-6792},
mesh = {Adult ; Attention/*physiology ; Cognition ; Cues ; Electroencephalography/*methods ; Female ; Functional Laterality/*physiology ; Humans ; Magnetoencephalography/methods ; Male ; Neurofeedback ; Reaction Time/physiology ; Young Adult ; },
abstract = {Visual attention can be spatially oriented, even in the absence of saccadic eye-movements, to facilitate the processing of incoming visual information. One behavioral proxy for this so-called covert visuospatial attention (CVSA) is the validity effect (VE): the reduction in reaction time (RT) to visual stimuli at attended locations and the increase in RT to stimuli at unattended locations. At the electrophysiological level, one correlate of CVSA is the lateralization in the occipital [Formula: see text]-band oscillations, resulting from [Formula: see text]-power increases ipsilateral and decreases contralateral to the attended hemifield. While this [Formula: see text]-band lateralization has been considerably studied using electroencephalography (EEG) or magnetoencephalography (MEG), little is known about whether it can be trained to improve CVSA behaviorally. In this cross-over sham-controlled study we used continuous real-time feedback of the occipital [Formula: see text]-lateralization to modulate behavioral and electrophysiological markers of covert attention. Fourteen subjects performed a cued CVSA task, involving fast responses to covertly attended stimuli. During real-time feedback runs, trials extended in time if subjects reached states of high [Formula: see text]-lateralization. Crucially, the ongoing [Formula: see text]-lateralization was fed back to the subject by changing the color of the attended stimulus. We hypothesized that this ability to self-monitor lapses in CVSA and thus being able to refocus attention accordingly would lead to improved CVSA performance during subsequent testing. We probed the effect of the intervention by evaluating the pre-post changes in the VE and the [Formula: see text]-lateralization. Behaviorally, results showed a significant interaction between feedback (experimental-sham) and time (pre-post) for the validity effect, with an increase in performance only for the experimental condition. We did not find corresponding pre-post changes in the [Formula: see text]-lateralization. Our findings suggest that EEG-based real-time feedback is a promising tool to enhance the level of covert visuospatial attention, especially with respect to behavioral changes. This opens up the exploration of applications of the proposed training method for the cognitive rehabilitation of attentional disorders.},
}
@article {pmid31316556,
year = {2019},
author = {Baek, HJ and Chang, MH and Heo, J and Park, KS},
title = {Enhancing the Usability of Brain-Computer Interface Systems.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {5427154},
pmid = {31316556},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation/methods ; Ergonomics/instrumentation/methods ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) aim to enable people to interact with the external world through an alternative, nonmuscular communication channel that uses brain signal responses to complete specific cognitive tasks. BCIs have been growing rapidly during the past few years, with most of the BCI research focusing on system performance, such as improving accuracy or information transfer rate. Despite these advances, BCI research and development is still in its infancy and requires further consideration to significantly affect human experience in most real-world environments. This paper reviews the most recent studies and findings about ergonomic issues in BCIs. We review dry electrodes that can be used to detect brain signals with high enough quality to apply in BCIs and discuss their advantages, disadvantages, and performance. Also, an overview is provided of the wide range of recent efforts to create new interface designs that do not induce fatigue or discomfort during everyday, long-term use. The basic principles of each technique are described, along with examples of current applications in BCI research. Finally, we demonstrate a user-friendly interface paradigm that uses dry capacitive electrodes that do not require any preparation procedure for EEG signal acquisition. We explore the capacitively measured steady-state visual evoked potential (SSVEP) response to an amplitude-modulated visual stimulus and the auditory steady-state response (ASSR) to an auditory stimulus modulated by familiar natural sounds to verify their availability for BCI. We report the first results of an online demonstration that adopted this ergonomic approach to evaluating BCI applications. We expect BCI to become a routine clinical, assistive, and commercial tool through advanced EEG monitoring techniques and innovative interface designs.},
}
@article {pmid31316343,
year = {2019},
author = {Yang, T and Kim, SP},
title = {Group-Level Neural Responses to Service-to-Service Brand Extension.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {676},
pmid = {31316343},
issn = {1662-4548},
abstract = {Brand extension is a marketing strategy leveraging well-established brand to promote new offerings provided as goods or service. The previous neurophysiological studies on goods-to-goods brand extension have proposed that categorization and semantic memory processes are involved in brand extension evaluation. However, it is unknown whether these same processes also underlie service-to-service brand extension. The present study, therefore, aims to investigate neural processes in consumers underlying their judgment of service-to-service brand extension. Specifically, we investigated human electroencephalographic responses to extended services that were commonly considered to fit well or badly with parent brand among consumers. For this purpose, we proposed a new stimulus grouping method to find commonly acceptable or unacceptable service extensions. In the experiment, participants reported the acceptability of 56 brand extension pairs, consisting of parent brand name (S1) and extended service name (S2). From individual acceptability responses, we assigned each pair to one of the three fit levels: high- (i.e., highly acceptable), low-, and mid-fit. Next, we selected stimuli that received high/low-fit evaluations from a majority of participants (i.e., >85%) and assigned them to a high/low population-fit group. A comparison of event-related potentials (ERPs) between population-fit groups through a paired t-test showed significant differences in the fronto-central N2 and fronto-parietal P300 amplitudes. We further evaluated inter-subject variability of these ERP components by a decoding analysis that classified N2 and/or P300 amplitudes into a high, or low population-fit class using a support vector machine. Leave-one-subject-out validation revealed classification accuracy of 60.35% with N2 amplitudes, 78.95% with P300, and 73.68% with both, indicating a relatively high inter-subject variability of N2 but low for P300. This validation showed that fronto-parietal P300 reflected neural processes more consistent across subjects in service-to-service brand extension. We further observed that the left frontal P300 amplitude was increased as fit-level increased across stimuli, indicating a semantic retrieval process to evaluate a semantic link between S1 and S2. Parietal P300 showed a higher amplitude in the high population-fit group, reflecting a similarity-based categorization process. In sum, our results suggest that service-to-service brand extension evaluation may share similar neural processes with goods-to-goods brand extension.},
}
@article {pmid31312575,
year = {2019},
author = {Saleem, M and Ahmed, F and Patel, K and Munir, MB and Warden, M},
title = {Story of an Unfortunate Fall: Cardiac Contusion Presenting with an Atrioventricular Block.},
journal = {Cureus},
volume = {11},
number = {5},
pages = {e4650},
pmid = {31312575},
issn = {2168-8184},
abstract = {Blunt cardiac injury (BCI), also referred to in the literature as a cardiac contusion, is a known cause of myocardial injury. It is often challenging to diagnose this condition in the absence of clear diagnostic criteria. Furthermore, its clinical presentation is highly variable depending on the severity, type, and duration of the trauma, as well as the timing from the initial insult. The clinical manifestation of BCI ranges from none to fatal arrhythmias to cardiac wall rupture seen on post-mortem examination. Cardiac biomarkers and electrocardiograms (EKG) are usually helpful in identifying cardiac trauma but are not necessarily abnormal in all cases. Falls by slipping on ice are common in the winter, but rarely do people present with a myocardial injury with these mechanical events. We describe the case of a cardiac contusion with an unusual presentation and an unusual cause, whereby both the initial EKG and troponin level were normal, and the patient presented with an atrioventricular (AV) block two weeks after "slipping on ice".},
}
@article {pmid31306609,
year = {2019},
author = {Brumberg, JS and Pitt, KM},
title = {Motor-Induced Suppression of the N100 Event-Related Potential During Motor Imagery Control of a Speech Synthesizer Brain-Computer Interface.},
journal = {Journal of speech, language, and hearing research : JSLHR},
volume = {62},
number = {7},
pages = {2133-2140},
pmid = {31306609},
issn = {1558-9102},
support = {R01 DC016343/DC/NIDCD NIH HHS/United States ; R03 DC011304/DC/NIDCD NIH HHS/United States ; U54 HD090216/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Auditory Perception/physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography ; Evoked Potentials, Auditory/*physiology ; Evoked Potentials, Motor/physiology ; Feedback, Sensory/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Psychomotor Performance/*physiology ; Speech/*physiology ; Young Adult ; },
abstract = {Purpose Speech motor control relies on neural processes for generating sensory expectations using an efference copy mechanism to maintain accurate productions. The N100 auditory event-related potential (ERP) has been identified as a possible neural marker of the efference copy with a reduced amplitude during active listening while speaking when compared to passive listening. This study investigates N100 suppression while controlling a motor imagery speech synthesizer brain-computer interface (BCI) with instantaneous auditory feedback to determine whether similar mechanisms are used for monitoring BCI-based speech output that may both support BCI learning through existing speech motor networks and be used as a clinical marker for the speech network integrity in individuals without severe speech and physical impairments. Method The motor-induced N100 suppression is examined based on data from 10 participants who controlled a BCI speech synthesizer using limb motor imagery. We considered listening to auditory target stimuli (without motor imagery) in the BCI study as passive listening and listening to BCI-controlled speech output (with motor imagery) as active listening since audio output depends on imagined movements. The resulting ERP was assessed for statistical significance using a mixed-effects general linear model. Results Statistically significant N100 ERP amplitude differences were observed between active and passive listening during the BCI task. Post hoc analyses confirm the N100 amplitude was suppressed during active listening. Conclusion Observation of the N100 suppression suggests motor planning brain networks are active as participants control the BCI synthesizer, which may aid speech BCI mastery.},
}
@article {pmid31298507,
year = {2022},
author = {Kapgate, D and Kalbande, D and Shrawankar, U},
title = {An optimized facial stimuli paradigm for hybrid SSVEP+P300 brain computer interface.},
journal = {Journal of neurosurgical sciences},
volume = {66},
number = {5},
pages = {456-464},
doi = {10.23736/S0390-5616.19.04755-6},
pmid = {31298507},
issn = {1827-1855},
mesh = {Adult ; Female ; Humans ; Male ; Young Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Event-Related Potentials, P300/physiology ; *Evoked Potentials, Visual/physiology ; *Facial Recognition/physiology ; },
abstract = {BACKGROUND: In this paper, hybrid brain-computer interface (BCI) that merges event related potentials (P300) and steady state visual evoked potentials (SSVEP) simultaneously is proposed to enhance the performance of four-choice command system. Here we integrate human facial structure in external visual stimulus to evoke stronger cortical responses in hybrid SSVEP+P300 BCI and compare it with non-face stimuli. We also addressed question of feasibility of eliciting one potential by face and other by non-face stimuli. To evoke SSVEP and P300 responses, paradigms with non-face stimuli, neutral face stimuli, and facial expression changes stimuli are proposed. We also projected additional paradigm where SSVEP is elicited by non-face and P300 by flashing different facial expressions. Results proved the last paradigm evoke stronger cortical potentials and thereby improve system accuracy and ITR than other paradigms. We discuss external stimulus parameters that might affect simultaneous evocation of multiple brain potentials and their deterioration effect on individual potentials.<br><br>METHODS: Ten healthy volunteers (seven male, aged 22-28 years, mean 25.6 years) contribute in our experiment. LCD screen with refresh rate of 60 Hz is used for external stimulus and all trials are performed in darkened room.<br><br>RESULTS: The ideal BCI system should have maximum ITR and comfortable rating. We have proven among our paradigms, face emotional and flickering paradigm has maximum comfortableness with highest ITR.<br><br>CONCLUSIONS: In this paper, we have proposed four paradigms as non-face paradigm, face neutral paradigm, face emotional paradigm, and face-emotional and flicker paradigm. We have tested all paradigms and compared them with each other about potential response strength, accuracy, ITR and comfortableness. Result show that face-emotional and flicker paradigm performed better in all aspects than all four paradigms.},
}
@article {pmid31296796,
year = {2019},
author = {Branco, MP and Geukes, SH and Aarnoutse, EJ and Vansteensel, MJ and Freudenburg, ZV and Ramsey, NF},
title = {High-frequency band temporal dynamics in response to a grasp force task.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056009},
pmid = {31296796},
issn = {1741-2552},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Adolescent ; Adult ; Child ; Electrocorticography/instrumentation/*methods ; Electrodes, Implanted ; Epilepsy/diagnosis/*physiopathology ; Female ; Hand Strength/*physiology ; Humans ; Male ; Psychomotor Performance/*physiology ; Reaction Time/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are being developed to restore reach and grasping movements of paralyzed individuals. Recent studies have shown that the kinetics of grasping movement, such as grasp force, can be successfully decoded from electrocorticography (ECoG) signals, and that the high-frequency band (HFB) power changes provide discriminative information that contribute to an accurate decoding of grasp force profiles. However, as the models used in these studies contained simultaneous information from multiple spectral features over multiple areas in the brain, it remains unclear what parameters of movement and force are encoded by the HFB signals and how these are represented temporally and spatially in the SMC.<br><br>APPROACH: To investigate this, and to gain insight in the temporal dynamics of the HFB during grasping, we continuously modelled the ECoG HFB response recorded from nine individuals with epilepsy temporarily implanted with ECoG grids, who performed three different grasp force tasks.<br><br>MAIN RESULTS: We show that a model based on the force onset and offset consistently provides a better fit to the HFB power responses when compared with a model based on the force magnitude, irrespective of electrode location.<br><br>SIGNIFICANCE: Our results suggest that HFB power, although potentially useful for continuous decoding, is more closely related to the changes in movement. This finding may potentially contribute to the more natural decoding of grasping movement in neural prosthetics.},
}
@article {pmid31295908,
year = {2019},
author = {Al-Hudhud, G and Alqahtani, L and Albaity, H and Alsaeed, D and Al-Turaiki, I},
title = {Analyzing Passive BCI Signals to Control Adaptive Automation Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {14},
pages = {},
pmid = {31295908},
issn = {1424-8220},
support = {1//Research Center of the Female Scientific and Medical Colleges, the Deanship of Scientific Research, King Saud University./ ; },
abstract = {Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment's temperature and lighting and responds to users' feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers' comfort levels; (b) an application that analyzes workers' feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers' attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices.},
}
@article {pmid31295624,
year = {2019},
author = {Šumak, B and Špindler, M and Debeljak, M and Heričko, M and Pušnik, M},
title = {An empirical evaluation of a hands-free computer interaction for users with motor disabilities.},
journal = {Journal of biomedical informatics},
volume = {96},
number = {},
pages = {103249},
doi = {10.1016/j.jbi.2019.103249},
pmid = {31295624},
issn = {1532-0480},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Commerce ; Computers ; Persons with Disabilities ; Female ; Healthy Volunteers ; Humans ; Internet ; Language ; Male ; Middle Aged ; Motor Disorders/*rehabilitation ; *Motor Skills ; *Self-Help Devices ; *User-Computer Interface ; Young Adult ; },
abstract = {Standard computer input devices such as a mouse or a keyboard are not well suited to the needs of users with severe motor disabilities in their interaction with standard computer interfaces. The emergence of contemporary human computer interfaces has allowed for the development of innovative solutions for hands-free Human-Computer Interaction (HCI), which can improve the quality and accessibility of Information and Communication Technology (ICT) for motor-impaired users. The objectives of this study were to design, develop and evaluate a solution for a hands-free HCI, based on the Emotiv EPOC+ device, which, among other capabilities, also enables controlling the computer with facial expressions and motion sensors. Ten non-disabled adults and eight adults with a severe motor disability participated in an experiment to evaluate the proposed HCI solution. Eighteen users completed six experimental tasks successfully using both their existing computer use approach as well as the proposed hands-free computer use approach. The times necessary to complete the tasks were measured and analyzed, along with users' subjective observations about the difficulty level of both computer use approaches. Users' perceptions about the new hands-free computer use approach were assessed as well. Although there were no significant differences in both user types regarding the difficulty level in completing the tasks, disabled users solved the tasks with less effort. Positive perceptions about perceived usefulness, ease of use, appropriateness of the technology, and satisfaction with the proposed solution for touchless interaction were assessed for both user types. Scores were significantly higher for disabled users in the case of measuring the perceived usefulness, perceived ease of use and satisfaction with the solution. This study showed that users with severe motor difficulties find new HCI less challenging compared to their existing computer use approach than the non-disabled who use standard HCI. When compared with non-disabled users, the disabled ones can be equally effective when confronted with a new HCI technology. Future work is needed to improve the proposed solution and to analyze the impact of different factors on users with motor disabilities and their adoption of an innovative technology for touchless interaction with a computer.},
}
@article {pmid31295141,
year = {2020},
author = {Kwak, NS and Lee, SW},
title = {Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain-Computer Interfaces.},
journal = {IEEE transactions on cybernetics},
volume = {50},
number = {8},
pages = {3654-3667},
doi = {10.1109/TCYB.2019.2924237},
pmid = {31295141},
issn = {2168-2275},
mesh = {Adult ; *Brain-Computer Interfaces ; Ear/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Nonlinear Dynamics ; *Signal Processing, Computer-Assisted ; },
abstract = {Ear-electroencephalography (EEG) is a promising tool for practical brain-computer interface (BCI) applications because it is more unobtrusive, comfortable, and mobile than a typical scalp-EEG system. However, an ear-EEG has a natural constraint of electrode location (e.g., limited in or around the ear) for acquiring informative brain signals sufficiently. Achieving reliable performance of ear-EEG in specific BCI paradigms that do not utilize brain signals on the temporal lobe around the ear is difficult. For example, steady-state visual evoked potentials (SSVEPs), which are mainly generated in the occipital area, have a significantly attenuated and distorted amplitude in ear-EEG. Therefore, preserving the high level of decoding accuracy is challenging and essential for SSVEP BCI based on ear-EEG. In this paper, we first investigate linear and nonlinear regression methods to increase the decoding accuracy of ear-EEG regarding SSVEP paradigm by utilizing the estimated target EEG signals on the occipital area. Then, we investigate an ensemble method to consider the prediction variability of the regression methods. Finally, we propose an error correction regression (ECR) framework to reduce the prediction errors by adding an additional nonlinear regression process (i.e., kernel ridge regression). We evaluate the ECR framework in terms of single session, session-to-session transfer, and subject-transfer decoding. We also validate the online decoding ability of the proposed framework with a short-time window size. The average accuracies are observed to be 91.11±9.14%, 90.52±8.67%, 86.96±12.13%, and 78.79±12.59%. This paper demonstrates that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed ECR framework.},
}
@article {pmid31295119,
year = {2019},
author = {Kartsch, V and Tagliavini, G and Guermandi, M and Benatti, S and Rossi, D and Benini, L},
title = {BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {5},
pages = {893-906},
doi = {10.1109/TBCAS.2019.2927551},
pmid = {31295119},
issn = {1940-9990},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Machine Learning ; Male ; Middle Aged ; *Signal Processing, Computer-Assisted ; *Wearable Electronic Devices ; },
abstract = {Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40 mm × 20 mm × 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31 mW, providing up to 38 h operation (65 mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI.},
}
@article {pmid31294964,
year = {2019},
author = {Chae, H and Kwon, HJ and Kim, YK and Won, Y and Kim, D and Park, HJ and Kim, S and Gandla, S},
title = {Laser-Processed Nature-Inspired Deformable Structures for Breathable and Reusable Electrophysiological Sensors toward Controllable Home Electronic Appliances and Psychophysiological Stress Monitoring.},
journal = {ACS applied materials &amp; interfaces},
volume = {11},
number = {31},
pages = {28387-28396},
doi = {10.1021/acsami.9b06363},
pmid = {31294964},
issn = {1944-8252},
mesh = {*Brain-Computer Interfaces ; *Electrophysiological Phenomena ; Humans ; Monitoring, Physiologic ; Pilot Projects ; Stress, Psychological/*physiopathology ; *Wearable Electronic Devices ; },
abstract = {Physiological monitoring through skin patch stretchable devices has received extensive attention because of their significant findings in many human-machine interaction applications. In this paper, we present novel nature-inspired, kiri-spider, serpentine structural designs to sustain mechanical deformations under complex stress environments. Strain-free mechanical structures involving stable high areal coverage (spiderweb), three-dimensional out-of-plane deformations (kirigami), and two-dimensional (2D) stretchable (2D spring) electrodes demonstrated high levels of mechanical loading under various strains, which were verified through theoretical and experimental studies. Alternative to conventional microfabrication procedures, sensors fabricated by a facile and rapid benchtop programmable laser machine enabled the realization of low-cost, high-throughput manufacture, followed by transferring procedures with a nearly 100% yield. For the first time, we demonstrated laser-processed thin (∼10 μm) flexible filamentary patterns embedded within the solution-processed polyimide to make it compatible with current flexible printed circuit board electronics. A patch-based sensor with thin, breathable, and sticky nature exhibited remarkable water permeability >20 g h[-1] m[-2] at a thickness of 250 μm. Moreover, the reusability of the sensor patch demonstrated the significance of our patch-based electrophysiological sensor. Furthermore, this wearable sensor was successfully implemented to control human-machine interfaces to operate home electronic appliances and monitor mental stress in a pilot study. These advances in novel mechanical architectures with good sensing performances provide new opportunities in wearable smart sensors.},
}
@article {pmid31292197,
year = {2019},
author = {Inker, LA and Heerspink, HJL and Tighiouart, H and Levey, AS and Coresh, J and Gansevoort, RT and Simon, AL and Ying, J and Beck, GJ and Wanner, C and Floege, J and Li, PK and Perkovic, V and Vonesh, EF and Greene, T},
title = {GFR Slope as a Surrogate End Point for Kidney Disease Progression in Clinical Trials: A Meta-Analysis of Treatment Effects of Randomized Controlled Trials.},
journal = {Journal of the American Society of Nephrology : JASN},
volume = {30},
number = {9},
pages = {1735-1745},
pmid = {31292197},
issn = {1533-3450},
support = {UL1 TR002544/TR/NCATS NIH HHS/United States ; },
mesh = {Bayes Theorem ; Biomarkers ; Creatinine/blood ; *Disease Progression ; *Glomerular Filtration Rate ; Humans ; Kidney Failure, Chronic/etiology/physiopathology ; Predictive Value of Tests ; Randomized Controlled Trials as Topic ; Renal Insufficiency, Chronic/complications/*physiopathology/therapy ; },
abstract = {BACKGROUND: Surrogate end points are needed to assess whether treatments are effective in the early stages of CKD. GFR decline leads to kidney failure, but regulators have not approved using differences in the change in GFR from the beginning to the end of a randomized, controlled trial as an end point in CKD because it is not clear whether small changes in the GFR slope will translate to clinical benefits.<br><br>METHODS: To assess the use of GFR slope as a surrogate end point for CKD progression, we performed a meta-analysis of 47 RCTs that tested 12 interventions in 60,620 subjects. We estimated treatment effects on GFR slope (mean difference in GFR slope between the randomized groups), for the total slope starting at baseline, chronic slope starting at 3 months after randomization, and on the clinical end point (doubling of serum creatinine, GFR<15 ml/min per 1.73 m[2], or ESKD) for each study. We used Bayesian mixed-effects analyses to describe the association of treatment effects on GFR slope with the clinical end point and to test how well the GFR slope predicts a treatment's effect on the clinical end point.<br><br>RESULTS: Across all studies, the treatment effect on 3-year total GFR slope (median R[2]=0.97; 95% Bayesian credible interval [BCI], 0.78 to 1.00) and on the chronic slope (R[2] 0.96; 95% BCI, 0.63 to 1.00) accurately predicted treatment effects on the clinical end point. With a sufficient sample size, a treatment effect of 0.75 ml/min per 1.73 m[2]/yr or greater on total slope over 3 years or chronic slope predicts a clinical benefit on CKD progress with at least 96% probability.<br><br>CONCLUSIONS: With large enough sample sizes, GFR slope may be a viable surrogate for clinical end points in CKD RCTs.},
}
@article {pmid31289253,
year = {2019},
author = {Ushiba, J},
title = {[Brain-Machine Interface and Neuro-Rehabilitation].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {71},
number = {7},
pages = {793-804},
doi = {10.11477/mf.1416201352},
pmid = {31289253},
issn = {1881-6096},
mesh = {Brain ; *Brain-Computer Interfaces ; Electric Stimulation ; Humans ; Movement ; Neuronal Plasticity ; Rehabilitation/*trends ; *Robotics ; },
abstract = {Brain-Machine Interface (BMI) is a technology that enables users to control computers/machines intuitively via their volitional brain activities. Controlling robotic arms and tablet PCs, assisting the movement of paretic limbs through robotic action/neuromuscular electrical stimulation, and other types of cybernetic device controls have been demonstrated. The continued use of BMI promotes the plasticity of brains, hence the functional reorganization of sensorimotor nervous systems can be induced in patients with motor disabilities. The application of BMI for the compensation and neurological recovery of physical movement might be clinically tolerated in the future.},
}
@article {pmid31285468,
year = {2019},
author = {Chholak, P and Niso, G and Maksimenko, VA and Kurkin, SA and Frolov, NS and Pitsik, EN and Hramov, AE and Pisarchik, AN},
title = {Visual and kinesthetic modes affect motor imagery classification in untrained subjects.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {9838},
pmid = {31285468},
issn = {2045-2322},
mesh = {Adult ; Artificial Intelligence ; Brain/*physiology ; Brain-Computer Interfaces ; Female ; Humans ; Imagery, Psychotherapy ; Kinesthesis/*physiology ; Magnetoencephalography/*methods ; Male ; Neural Networks, Computer ; Photic Stimulation ; Young Adult ; },
abstract = {The understanding of neurophysiological mechanisms responsible for motor imagery (MI) is essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery, kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas in motor-related α- and β-frequency regions. Although the brain activity corresponding to MI is usually observed in specially trained subjects or athletes, we show that it is also possible to identify particular features of MI in untrained subjects. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of α- and β-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. In particular, the analysis of evoked responses shows that in all KI subjects the activity in the frontal cortex is suppressed during MI, while in the VI subjects the frontal cortex is always active. The accuracy in classification of left-arm and right-arm MI using artificial intelligence is similar for KI and VI. Since untrained subjects usually demonstrate the VI imagery mode, the possibility to increase the accuracy for VI is in demand for BCIs. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.},
}
@article {pmid31283483,
year = {2019},
author = {Agarwal, A and Dowsley, R and McKinney, ND and Wu, D and Lin, CT and De Cock, M and Nascimento, ACA},
title = {Protecting Privacy of Users in Brain-Computer Interface Applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {8},
pages = {1546-1555},
doi = {10.1109/TNSRE.2019.2926965},
pmid = {31283483},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Automobile Driving ; *Brain-Computer Interfaces ; Computer Security ; *Confidentiality ; Databases, Factual ; Electroencephalography ; Female ; Humans ; Linear Models ; Male ; Young Adult ; },
abstract = {Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations.},
}
@article {pmid31281339,
year = {2019},
author = {Lai, CQ and Ibrahim, H and Abdullah, MZ and Abdullah, JM and Suandi, SA and Azman, A},
title = {Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {7895924},
pmid = {31281339},
issn = {1687-5273},
mesh = {*Algorithms ; *Biometric Identification/methods ; Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Machine Learning ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.},
}
@article {pmid31281335,
year = {2019},
author = {Dawud, AM and Yurtkan, K and Oztoprak, H},
title = {Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {4629859},
pmid = {31281335},
issn = {1687-5273},
mesh = {*Deep Learning ; *Hemorrhage ; Humans ; Intracranial Hemorrhages/diagnosis ; *Machine Learning ; *Neural Networks, Computer ; Radiologists ; Support Vector Machine ; },
abstract = {In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model "AlexNet-SVM" can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.},
}
@article {pmid31276578,
year = {2019},
author = {Alfaidy, N and Baron, C and Antoine, Y and Reynaud, D and Traboulsi, W and Gueniffey, A and Lamotte, A and Melloul, E and Dunand, C and Villaret, L and Bessonnat, J and Mauroy, C and Boueihl, T and Coutton, C and Martinez, G and Hamamah, S and Hoffmann, P and Hennebicq, S and Brouillet, S},
title = {Prokineticin 1 is a new biomarker of human oocyte competence: expression and hormonal regulation throughout late folliculogenesis.},
journal = {Biology of reproduction},
volume = {101},
number = {4},
pages = {832-841},
doi = {10.1093/biolre/ioz114},
pmid = {31276578},
issn = {1529-7268},
mesh = {Biomarkers/*analysis/metabolism ; Cells, Cultured ; Cohort Studies ; *Embryonic Development/drug effects/genetics/physiology ; Female ; Fertilization in Vitro ; Follicular Fluid/*chemistry/metabolism ; France ; Gastrointestinal Hormones/*analysis/genetics/metabolism ; Gene Expression/drug effects ; Hormones/pharmacology ; Humans ; Oocyte Retrieval/standards ; Oocytes/cytology/*physiology ; Oogenesis/drug effects/genetics/physiology ; Pregnancy ; Pregnancy Rate ; Prognosis ; Prospective Studies ; Quality Control ; Sperm Injections, Intracytoplasmic ; Treatment Outcome ; Vascular Endothelial Growth Factor, Endocrine-Gland-Derived/*analysis/genetics/metabolism ; },
abstract = {CONTEXT: Prokineticin 1 (PROK1) quantification in global follicular fluid (FF) has been recently reported as a predictive biomarker of in vitro fertilization (IVF) outcome. It is now necessary to evaluate its clinical usefulness in individual follicles.<br><br>OBJECTIVES: To evaluate the clinical value of PROK1 secretion in individual FF to predict oocyte competence. To determine the impact of follicular size, oocyte maturity, and gonadotropin treatments on PROK1 secretion.<br><br>DESIGN AND SETTING: Prospective cohort study from May 2015 to May 2017 at the University Hospital of Grenoble.<br><br>PATIENTS: A total of 69 infertile couples underwent IVF.<br><br>INTERVENTION(S): Collection of 298 individual FF from 44 women undergoing IVF; 52 individual cumulus cell (CC) samples and 15 CC primary cultures from 25 women undergoing IVF-intracytoplasmic sperm injection (ICSI).<br><br>MAIN OUTCOME MEASURE(S): Oocyte competence was defined as the ability to sustain embryo development to the blastocyst stage. Follicular size was measured by 2D-sonography. PROK1 concentration was quantified by ELISA assay.<br><br>RESULTS: PROK1 concentration was correlated to follicular size (r = 0.85, P = 2.2 &#xd7; 10-16). Normalized PROK1 concentration in FF was predictive of subsequent oocyte competence (AUROC curve = 0.76 [95% CI, 0.69-0.83]; P = 1.7 &#xd7; 10-9), irrespectively of day-2 embryo morphokinetic parameters. The expression and secretion of PROK1 were increased in FF and CC of mature oocytes (P < 0.01). Follicle Stimulating Hormone and hCG up-regulated PROK1 secretion in CC primary cultures (P < 0.01; P < 0.05), probably through the cAMP pathway (P < 0.01).<br><br>CONCLUSIONS: PROK1 quantification in individual FF could constitute a new predictive biomarker of oocyte competence in addition with embryo morphokinetic parameters.<br><br>TRIAL REGISTRATION NUMBER: none.},
}
@article {pmid31276505,
year = {2019},
author = {Ibáñez-Soria, D and Soria-Frisch, A and Garcia-Ojalvo, J and Ruffini, G},
title = {Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks.},
journal = {PloS one},
volume = {14},
number = {7},
pages = {e0218771},
pmid = {31276505},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Middle Aged ; Models, Neurological ; *Neural Networks, Computer ; Photic Stimulation/methods ; Visual Cortex/*physiology ; },
abstract = {State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.},
}
@article {pmid31275126,
year = {2019},
author = {Vourvopoulos, A and Pardo, OM and Lefebvre, S and Neureither, M and Saldana, D and Jahng, E and Liew, SL},
title = {Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {210},
pmid = {31275126},
issn = {1662-5161},
support = {K01 HD091283/HD/NICHD NIH HHS/United States ; },
abstract = {Rehabilitation for stroke patients with severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side) is burdensome and difficult because most current rehabilitation options require some volitional movement to retrain the affected side. However, although these patients participate in therapy requiring volitional movement, previous research has shown that they may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke, in the absence of volitional movement. The purpose of this study was to combine the principles of VR and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Here we tested four chronic stroke survivors and show that this EEG-based BCI can be safely used over repeated sessions by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments may benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. We note that although this work is promising, due to the small sample size, these results are preliminary. Future research is needed to confirm these findings in a larger and more diverse population.},
}
@article {pmid31275105,
year = {2019},
author = {Rimbert, S and Riff, P and Gayraud, N and Schmartz, D and Bougrain, L},
title = {Median Nerve Stimulation Based BCI: A New Approach to Detect Intraoperative Awareness During General Anesthesia.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {622},
pmid = {31275105},
issn = {1662-4548},
abstract = {Hundreds of millions of general anesthesia are performed each year on patients all over the world. Among these patients, 0.1-0.2% are victims of Accidental Awareness during General Anesthesia (AAGA), i.e., an unexpected awakening during a surgical procedure under general anesthesia. Although anesthesiologists try to closely monitor patients using various techniques to prevent this terrifying phenomenon, there is currently no efficient solution to accurately detect its occurrence. We propose the conception of an innovative passive brain-computer interface (BCI) based on an intention of movement to prevent AAGA. Indeed, patients typically try to move to alert the medical staff during an AAGA, only to discover that they are unable to. First, we examine the challenges of such a BCI, i.e., the lack of a trigger to facilitate when to look for an intention to move, as well as the necessity for a high classification accuracy. Then, we present a solution that incorporates Median Nerve Stimulation (MNS). We investigate the specific modulations that MNS causes in the motor cortex and confirm that they can be altered by an intention of movement. Finally, we perform experiments on 16 healthy participants to assess whether an MI-based BCI using MNS is able to generate high classification accuracies. Our results show that MNS may provide a foundation for an innovative BCI that would allow the detection of AAGA.},
}
@article {pmid31274121,
year = {2019},
author = {Vírseda-Chamorro, M and Salinas-Casado, J and Méndez-Rubio, S and Barroso Manso, &#xc1; and Esteban-Fuertes, M},
title = {[Functional changes during the voiding phase in males with non-neurogenic detrusor underactivity undergoing bladder catheterization.].},
journal = {Archivos espanoles de urologia},
volume = {72},
number = {6},
pages = {564-569},
pmid = {31274121},
issn = {0004-0614},
mesh = {Aged ; Follow-Up Studies ; Humans ; Male ; *Urinary Bladder Neck Obstruction ; *Urinary Bladder, Underactive ; Urinary Catheterization ; Urodynamics ; },
abstract = {OBJECTIVES: To investigate urodynamic changes during the voiding phase in males with detrusor underactivity (DU) undergoing bladder catheterization for urinary retention.<br><br>METHODS: From a total of 64 patients with urinary retention, a follow-up study was performed in 17 males with a mean age of 77 years. Patients received a urodynamic diagnosis of DU based on a Bladder Contractility Index (BCI) score of <&#xa0;100 and underwent permanent bladder catheterization (16 cases) or clean intermittent catheterization (1 case) for acute urinary retention (14 cases), or post void residual urine (3 cases). Patients underwent a second urodynamic study after a mean 13 months of follow-up. Fisher's exact test was used with categorical variables and Student's t test with parametric variables. The level of significance was set at p <&#xa0;0.05 for a two-sided test.<br><br>RESULTS: The second urodynamic study showed a significant increase in maximal detrusor pressure, pressure at maximum flow rate, BCI score, Bladder Outlet Obstruction Index (BOOI) score, and number of patients who urinated during the pressure-flow study.<br><br>CONCLUSIONS: Bladder catheterization in men with DU significantly improves bladder contractility and revealed obstructions of the lower urinary tract that were masked by insufficient detrusor pressure in relation to the DU of these patients. These findings could have diagnostic as well as prognostic and therapeutic applications.},
}
@article {pmid31263113,
year = {2019},
author = {Daly, I and Williams, D and Hwang, F and Kirke, A and Miranda, ER and Nasuto, SJ},
title = {Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {9415},
pmid = {31263113},
issn = {2045-2322},
mesh = {Acoustic Stimulation ; Adult ; Brain/diagnostic imaging/*physiology ; Brain Mapping ; Electroencephalography ; Female ; Humans ; Magnetic Resonance Imaging ; *Music ; Young Adult ; },
abstract = {The ability of music to evoke activity changes in the core brain structures that underlie the experience of emotion suggests that it has the potential to be used in therapies for emotion disorders. A large volume of research has identified a network of sub-cortical brain regions underlying music-induced emotions. Additionally, separate evidence from electroencephalography (EEG) studies suggests that prefrontal asymmetry in the EEG reflects the approach-withdrawal response to music-induced emotion. However, fMRI and EEG measure quite different brain processes and we do not have a detailed understanding of the functional relationships between them in relation to music-induced emotion. We employ a joint EEG - fMRI paradigm to explore how EEG-based neural correlates of the approach-withdrawal response to music reflect activity changes in the sub-cortical emotional response network. The neural correlates examined are asymmetry in the prefrontal EEG, and the degree of disorder in that asymmetry over time, as measured by entropy. Participants' EEG and fMRI were recorded simultaneously while the participants listened to music that had been specifically generated to target the elicitation of a wide range of affective states. While listening to this music, participants also continuously reported their felt affective states. Here we report on co-variations in the dynamics of these self-reports, the EEG, and the sub-cortical brain activity. We find that a set of sub-cortical brain regions in the emotional response network exhibits activity that significantly relates to prefrontal EEG asymmetry. Specifically, EEG in the pre-frontal cortex reflects not only cortical activity, but also changes in activity in the amygdala, posterior temporal cortex, and cerebellum. We also find that, while the magnitude of the asymmetry reflects activity in parts of the limbic and paralimbic systems, the entropy of that asymmetry reflects activity in parts of the autonomic response network such as the auditory cortex. This suggests that asymmetry magnitude reflects affective responses to music, while asymmetry entropy reflects autonomic responses to music. Thus, we demonstrate that it is possible to infer activity in the limbic and paralimbic systems from pre-frontal EEG asymmetry. These results show how EEG can be used to measure and monitor changes in the limbic and paralimbic systems. Specifically, they suggest that EEG asymmetry acts as an indicator of sub-cortical changes in activity induced by music. This shows that EEG may be used as a measure of the effectiveness of music therapy to evoke changes in activity in the sub-cortical emotion response network. This is also the first time that the activity of sub-cortical regions, normally considered "invisible" to EEG, has been shown to be characterisable directly from EEG dynamics measured during music listening.},
}
@article {pmid31260473,
year = {2019},
author = {Millogo, O and Doamba, JEO and Sié, A and Utzinger, J and Vounatsou, P},
title = {Geographical variation in the association of child, maternal and household health interventions with under-five mortality in Burkina Faso.},
journal = {PloS one},
volume = {14},
number = {7},
pages = {e0218163},
pmid = {31260473},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Bayes Theorem ; Burkina Faso/epidemiology ; Child ; Child Mortality/*trends ; Child, Preschool ; Communicable Disease Control/methods/*statistics &amp; numerical data ; Communicable Diseases/epidemiology/*mortality ; Delivery of Health Care/economics/*organization &amp; administration ; Delivery, Obstetric/statistics &amp; numerical data ; Drinking Water/analysis ; Family Characteristics ; Female ; Health Surveys ; Humans ; Infant ; Infant Mortality/*trends ; Male ; Mass Vaccination/statistics &amp; numerical data ; Middle Aged ; Pregnancy ; Prenatal Care/*organization &amp; administration/statistics &amp; numerical data ; Proportional Hazards Models ; Sanitation/methods/statistics &amp; numerical data ; Socioeconomic Factors ; Vitamin A/administration &amp; dosage ; },
abstract = {BACKGROUND: Over the past 15 years, scaling up of cost effective interventions resulted in a remarkable decline of under-five mortality rates (U5MR) in sub-Saharan Africa. However, the reduction shows considerable heterogeneity. We estimated the association of child, maternal, and household interventions with U5MR in Burkina Faso at national and subnational levels and identified the regions with least effective interventions.<br><br>METHODS: Data on health-related interventions and U5MR were extracted from the Burkina Faso Demographic and Health Survey (DHS) 2010. Bayesian geostatistical proportional hazards models with a Weibull baseline hazard were fitted on the mortality outcome. Spatially varying coefficients were considered to assess the geographical variation in the association of the health interventions with U5MR. The analyses were adjusted for child, maternal, and household characteristics, as well as climatic and environmental factors.<br><br>FINDINGS: The average U5MR was as high as 128 per 1000 ranging from 81 (region of Centre-Est) to 223 (region of Sahel). At national level, DPT3 immunization and baby post-natal check within 24 hours after birth had the most important association with U5MR (hazard rates ratio (HRR) = 0.89, 95% Bayesian credible interval (BCI): 0.86-0.98 and HRR = 0.89, 95% BCI: 0.86-0.92, respectively). At sub-national level, the most effective interventions are the skilled birth attendance, and improved drinking water, followed by baby post-natal check within 24 hours after birth, vitamin A supplementation, antenatal care visit and all-antigens immunization (including BCG, Polio3, DPT3, and measles immunization). Centre-Est, Sahel, and Sud-Ouest were the regions with the highest number of effective interventions. There was no intervention that had a statistically important association with child survival in the region of Hauts Bassins.<br><br>INTERPRETATION: The geographical variation in the magnitude and statistical importance of the association between health interventions and U5MR raises the need to deliver and reinforce health interventions at a more granular level. Priority interventions are DPT3 immunization, skilled birth attendance, baby post-natal visits in the regions of Sud-Ouest, Sahel, and Hauts Bassins, respectively. Our methodology could be applied to other national surveys, as it allows an incisive, data-driven and specific decision-making approach to optimize the allocation of health interventions at subnational level.},
}
@article {pmid31257411,
year = {2019},
author = {Coscia, M and Wessel, MJ and Chaudary, U and Millán, JDR and Micera, S and Guggisberg, A and Vuadens, P and Donoghue, J and Birbaumer, N and Hummel, FC},
title = {Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke.},
journal = {Brain : a journal of neurology},
volume = {142},
number = {8},
pages = {2182-2197},
pmid = {31257411},
issn = {1460-2156},
mesh = {Brain-Computer Interfaces ; Electric Stimulation Therapy/instrumentation/methods ; Exercise Therapy/instrumentation/methods ; Humans ; Robotics/instrumentation/methods ; Stroke Rehabilitation/*instrumentation/*methods ; },
abstract = {Upper limb motor deficits in severe stroke survivors often remain unresolved over extended time periods. Novel neurotechnologies have the potential to significantly support upper limb motor restoration in severely impaired stroke individuals. Here, we review recent controlled clinical studies and reviews focusing on the mechanisms of action and effectiveness of single and combined technology-aided interventions for upper limb motor rehabilitation after stroke, including robotics, muscular electrical stimulation, brain stimulation and brain computer/machine interfaces. We aim at identifying possible guidance for the optimal use of these new technologies to enhance upper limb motor recovery especially in severe chronic stroke patients. We found that the current literature does not provide enough evidence to support strict guidelines, because of the variability of the procedures for each intervention and of the heterogeneity of the stroke population. The present results confirm that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients, but the combination of interventions often lacks understanding of single intervention mechanisms of action, which may not reflect the summation of single intervention's effectiveness. Stroke rehabilitation is a long and complex process, and one single intervention administrated in a short time interval cannot have a large impact for motor recovery, especially in severely impaired patients. To design personalized interventions combining or proposing different interventions in sequence, it is necessary to have an excellent understanding of the mechanisms determining the effectiveness of a single treatment in this heterogeneous population of stroke patients. We encourage the identification of objective biomarkers for stroke recovery for patients' stratification and to tailor treatments. Furthermore, the advantage of longitudinal personalized trial designs compared to classical double-blind placebo-controlled clinical trials as the basis for precise personalized stroke rehabilitation medicine is discussed. Finally, we also promote the necessary conceptual change from 'one-suits-all' treatments within in-patient clinical rehabilitation set-ups towards personalized home-based treatment strategies, by adopting novel technologies merging rehabilitation and motor assistance, including implantable ones.},
}
@article {pmid31255942,
year = {2019},
author = {Can, M and Ayyala, RS and Sahiner, N},
title = {Crosslinked poly(Lactose) microgels and nanogels for biomedical applications.},
journal = {Journal of colloid and interface science},
volume = {553},
number = {},
pages = {805-812},
doi = {10.1016/j.jcis.2019.06.078},
pmid = {31255942},
issn = {1095-7103},
mesh = {Biomedical Research ; Cross-Linking Reagents/chemical synthesis/*chemistry ; Lactose/chemical synthesis/*chemistry ; Microgels/*chemistry ; Molecular Structure ; Particle Size ; Polymers/chemical synthesis/*chemistry ; Surface Properties ; },
abstract = {HYPOTHESIS: Lactose (LAC) is a primary carbohydrate and energy source of milk has received intensive attention due to its' unique functional and nutritional properties. Many biological beneficences of LAC make it an appealing molecule to seek for designing functional interfaces. Therefore, crosslinked poly(lactose) (p(LAC)) microgel from lactose disaccharides for potential biomedical applications was pursued as biocolloids for the first time. EXPERIMENT: p(LAC) microgels prepared by chemical crosslinking with DiVinyl Sulfone (DVS) were chemically modified with ethylenediamine (EDA) to obtain amine-modified p(LAC) (p(LAC)-EDA) microgels to induce new functionalities and properties. Blood compatibilities of bare p(LAC)-EDA microgels were tested through hemolysis and blood clotting tests. Rosmarinic acid (RA) used as a model drug was loaded into p(LAC) and p(LAC)-EDA microgels to demonstrate their applicability to be used in drug loading and release applications.<br><br>FINDINGS: A facile preparation of p(LAC) microgels with high yield, 90&#x202f;&#xb1;&#x202f;5% and 0.5-50 &#xb5;m size range was accomplished via water-in-oil (w/o) microemulsion crosslinking method. Upon chemical modification, the isoelectric point (IEP) from pH 1.8 for p(LAC) microgels changed to pH 7.7 for p(LAC)-EDA microgels, and the blood compatibility studies revealed that both microgels can be considered as blood compatible up to 2&#x202f;mg/mL concentration, and only slight decrease in blood clotting index (BCI) of p(LAC)-EDA microgels was observed. Rosmarinic Acid (RA) was demonstrated to be released up to 4&#x202f;days in phosphate buffer saline (PBS) with a linear release profile for p(LAC)-EDA microgels.},
}
@article {pmid31255596,
year = {2019},
author = {Zhang, C and Qiao, K and Wang, L and Tong, L and Hu, G and Zhang, RY and Yan, B},
title = {A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging.},
journal = {Journal of neuroscience methods},
volume = {325},
number = {},
pages = {108318},
doi = {10.1016/j.jneumeth.2019.108318},
pmid = {31255596},
issn = {1872-678X},
mesh = {*Deep Learning ; Functional Neuroimaging/*methods ; Humans ; Magnetic Resonance Imaging/*methods ; Models, Biological ; *Transfer, Psychology ; Visual Cortex/diagnostic imaging/*physiology ; Visual Perception/*physiology ; },
abstract = {BACKGROUND: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve high prediction accuracy on neural signals, visual encoding models should include precise visual features and appropriate prediction algorithms. Most existing visual encoding models employ hand-craft visual features (e.g., Gabor wavelets or semantic labels) or data-driven features (e.g., features extracted from deep neural networks (DNN)). They also assume a linear mapping between feature representations to brain activity. However, it remains unknown whether such linear mapping is sufficient for maximizing prediction accuracy.<br><br>NEW METHOD: We construct a new visual encoding framework to predict cortical responses in a benchmark functional magnetic resonance imaging (fMRI) dataset. In this framework, we employ the transfer learning technique to incorporate a pre-trained DNN (i.e., AlexNet) and train a nonlinear mapping from visual features to brain activity. This nonlinear mapping replaces the conventional linear mapping and is supposed to improve prediction accuracy on measured activity in the human visual cortex.<br><br>RESULTS: The proposed framework can significantly predict responses of over 20% voxels in early visual areas (i.e., V1-lateral occipital region, LO) and achieve unprecedented prediction accuracy.<br><br>Comparing to two conventional visual encoding models, we find that the proposed encoding model shows consistent higher prediction accuracy in all early visual areas, especially in relatively anterior visual areas (i.e., V4 and LO).<br><br>CONCLUSIONS: Our work proposes a new framework to utilize pre-trained visual features and train non-linear mappings from visual features to brain activity.},
}
@article {pmid31252666,
year = {2019},
author = {Nguyen, TH and Chung, WY},
title = {Detection of Driver Braking Intention Using EEG Signals During Simulated Driving.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {13},
pages = {},
pmid = {31252666},
issn = {1424-8220},
support = {NRF-2016R1A2B4015818//Mid-Career Researcher Program through an NRF grant funded by the Korean Government (MSIT)/ ; },
mesh = {Accidents, Traffic/*prevention &amp; control ; Algorithms ; *Automobile Driving ; Brain/*physiology ; Cognition ; *Electroencephalography ; Humans ; Intention ; Neural Networks, Computer ; User-Computer Interface ; },
abstract = {In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.},
}
@article {pmid31252557,
year = {2019},
author = {Ha, KW and Jeong, JW},
title = {Motor Imagery EEG Classification Using Capsule Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {13},
pages = {},
pmid = {31252557},
issn = {1424-8220},
support = {2016R1D1A1B03931672//National Research Foundation of Korea/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Fourier Analysis ; Hand/*diagnostic imaging/physiology ; Humans ; Imagination/physiology ; Machine Learning ; Movement/*physiology ; Neural Networks, Computer ; },
abstract = {Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.},
}
@article {pmid31247558,
year = {2019},
author = {Mane, R and Chew, E and Phua, KS and Ang, KK and Robinson, N and Vinod, AP and Guan, C},
title = {Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {8},
pages = {1654-1664},
doi = {10.1109/TNSRE.2019.2924742},
pmid = {31247558},
issn = {1558-0210},
mesh = {Adult ; Aged ; Biomarkers ; *Brain-Computer Interfaces ; Chronic Disease ; Double-Blind Method ; *Electroencephalography ; Female ; Humans ; Imagination ; Male ; Middle Aged ; Predictive Value of Tests ; Prognosis ; Stroke/physiopathology ; Stroke Rehabilitation/*methods ; Transcranial Direct Current Stimulation ; Treatment Outcome ; },
abstract = {With the availability of multiple rehabilitative interventions, identifying the one that elicits the best motor outcome based on the unique neuro-clinical profile of the stroke survivor is a challenging task. Predicting the potential of recovery using biomarkers specific to an intervention hence becomes important. To address this, we investigate intervention-specific prognostic and monitory biomarkers of motor function improvements using quantitative electroencephalography (QEEG) features in 19 chronic stroke patients following two different upper extremity rehabilitative interventions viz. Brain-computer interface (BCI) and transcranial direct current stimulation coupled BCI (tDCS-BCI). Brain symmetry index was found to be the best prognostic QEEG for clinical gains following BCI intervention (r = -0.80 , p = 0.02), whereas power ratio index (PRI) was observed to be the best predictor for tDCS-BCI (r = -0.96 , p = 0.004) intervention. Importantly, statistically significant between-intervention differences observed in the predictive capabilities of these features suggest that intervention-specific biomarkers can be identified. This approach can be further pursued to distinctly predict the expected response of a patient to available interventions. The intervention with the highest predicted gains may then be recommended to the patient, thereby enabling a personalized rehabilitation regime.},
}
@article {pmid31245509,
year = {2019},
author = {Fernandez-Fraga, SM and Aceves-Fernandez, MA and Pedraza-Ortega, JC},
title = {EEG data collection using visual evoked, steady state visual evoked and motor image task, designed to brain computer interfaces (BCI) development.},
journal = {Data in brief},
volume = {25},
number = {},
pages = {103871},
pmid = {31245509},
issn = {2352-3409},
abstract = {A set of electroencephalogram (EEG) data from 29 subjects obtained from a study, in which the subjects performed a set of tests based on visual stimuli and motor images of the hands is presented. Three types of data are provided in this article: (1) Signals based on visual events (VEP), (2) signals based on steady state visual events (SSVEP) and (3) signals based upon Motor Imagery (MI). Several research projects have used this data to test the detection of visual stimuli, classification and selection of characteristics of brain signals, EEG preprocessing and for optimization processes based on heuristic algorithms and algorithms based upon collective animal intelligence. The data was acquired using an Emotiv Epoc + portable EEG with 14 data channels and two reference channels.},
}
@article {pmid31244629,
year = {2019},
author = {Friedman, N and Fekete, T and Gal, K and Shriki, O},
title = {EEG-Based Prediction of Cognitive Load in Intelligence Tests.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {191},
pmid = {31244629},
issn = {1662-5161},
abstract = {Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental well-being of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal framework because it presents problems at increasing levels of difficulty and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a generic model and that such models are robust to decreasing the number of available channels as well.},
}
@article {pmid31239790,
year = {2019},
author = {Reinfeldt, S and Rigato, C and Håkansson, B and Fredén Jansson, KJ and Eeg-Olofsson, M},
title = {Nasal sound pressure as objective verification of implant in active transcutaneous bone conduction devices.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {12},
number = {},
pages = {193-202},
pmid = {31239790},
issn = {1179-1470},
abstract = {Objective: Active transcutaneous bone conduction devices consist of an external audio processor and an internal implant under intact skin. During the surgical procedure, it is important to verify the functionality of the implant before the surgical wound is closed. In a clinical study with the new bone conduction implant (BCI), the functionality of the implant was tested with an electric transmission test, where the output was the nasal sound pressure (NSP) recorded in the ipsilateral nostril. The same measurement was performed in all follow-up visits to monitor the implant's functionality and transmission to bone over time. The objective of this study was to investigate the validity of the NSP method as a tool to objectively verify the implant's performance intraoperatively, as well as to follow-up the implant's performance over time. Design: Thirteen patients with the BCI were included, and the NSP measurement was part of the clinical study protocol. The implant was electrically stimulated with an amplitude-modulated signal generator using a swept sine 0.1-10 kHz. The NSP was measured with a probe tube microphone in the ipsilateral nostril. Results: The NSP during surgery was above the noise floor for most patients within the frequency interval 0.4-5 kHz, showing NSP values for expected normal transmission of a functioning implant. Inter-subject comparison showed large variability, but follow-up results showed only minor variability within each subject. Further investigation showed that the NSP was stable over time. Conclusion: The NSP method is considered applicable to verify the implant's functionality during and after surgery. Such a method is important for implantable devices, but should be simplified and clinically adapted. Large variations between subjects were found, as well as smaller variability in intra-subject comparisons. As the NSP was found to not change significantly over time, stable transmission to bone, and implant functionality, were indicated.},
}
@article {pmid31239461,
year = {2019},
author = {Cortney Bradford, J and Lukos, JR and Passaro, A and Ries, A and Ferris, DP},
title = {Effect of locomotor demands on cognitive processing.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {9234},
pmid = {31239461},
issn = {2045-2322},
mesh = {Adult ; Behavior/physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; Evoked Potentials ; Female ; Healthy Volunteers ; Humans ; Locomotion/*physiology ; Male ; },
abstract = {Understanding how brain dynamics change with dual cognitive and motor tasks can improve our knowledge of human neurophysiology. The primary goals of this study were to: (1) assess the feasibility of extracting electrocortical signals from scalp EEG while performing sustained, physically demanding dual-task walking and (2) test hypotheses about how the P300 event-related potential is affected by walking physical exertion. Participants walked on a treadmill for an hour either carrying an empty rucksack or one filled with 40% of their body weight. During the walking conditions and during a seated control condition, subjects periodically performed a visual oddball task. We recorded scalp EEG and examined electrocortical dynamics time-locked to the target stimulus. Channel-level event-related potential analysis demonstrated that it is feasible to extract reliable signals during long duration loaded walking. P300 amplitude was reduced during loaded walking versus seated, but there was no effect of time on task. Source level activity and frequency analysis revealed that sensorimotor, parietal, and cingulate brain areas all contributed to the reduced P300 amplitude during dual-task walking. We interpret the results as supporting a prioritization of cortical resources for walking, leading to fewer resources being directed toward the oddball task during dual-task locomotion.},
}
@article {pmid31238429,
year = {2019},
author = {Singh, M and Sharma, M and Kaur, M and Grewal, AM and Yadav, D and Handa, S and Yangzes, S and Zadeng, Z and Gupta, P},
title = {Nasal endoscopic features and outcomes of nasal endoscopy guided bicanalicular intubation for complex persistent congenital nasolacrimal duct obstructions.},
journal = {Indian journal of ophthalmology},
volume = {67},
number = {7},
pages = {1137-1142},
pmid = {31238429},
issn = {1998-3689},
mesh = {Child, Preschool ; Endoscopy/*methods ; Female ; Follow-Up Studies ; Humans ; Intubation/*methods ; Lacrimal Duct Obstruction/congenital/diagnosis/*therapy ; Male ; Nasolacrimal Duct/diagnostic imaging/*surgery ; Nose ; Prospective Studies ; *Stents ; },
abstract = {PURPOSE: To study the clinical presentation, nasal endoscopic features, and outcomes of nasal endoscopy guided (NEG) bicanalicular intubation (BCI) in children with complex persistent congenital nasolacrimal duct obstruction (pCNLDO).<br><br>METHODS: A prospective, interventional study including eligible children (age &#x2264; 12 years) having complex pCNLDO. The demographics, number of previous probings, nasal endoscopy findings, and outcomes; were noted in all children who underwent NEG-BCI with Crawford's stents. Matting of eyelashes (MoE, upper, and lower eyelid), tear-film height (TFH), and fluorescein dye disappearance test (FDDT) was assessed pre and postoperatively. The minimum stent in-situ period was 12 weeks, and the minimum follow-up was 6 months (after stent removal).<br><br>RESULTS: Total 32 children (36 eyes) including 18 females (56.25%) were studied. At a mean age of 4.9 years, all children had epiphora and discharge with MoE (both upper and lower), raised TFH and positive FDDT. Previously, all children underwent conventional probing (s)- once in 12 (33.3%), twice in 18 (50%) and thrice in 6 (16.7%) eyes. The general ophthalmologists performed the majority (n = 21, 58.33%) of those. The BCI was performed under GA in all eyes, and at a mean follow-up of 8.5 months, the "complete" success was noted in 29 eyes (80.5%), 'partial' success in 4 (11.1%) and failure in 3 (8.3%). The stent prolapse was seen in three.<br><br>CONCLUSION: NEG-BCI may provide a satisfactory resolution to complex pCNLDO after single or multiple failed probings. NEG provides confident and efficient management of coexistent intranasal complexities related to the inferior turbinate and meatus.},
}
@article {pmid31236108,
year = {2019},
author = {Salazar-Varas, R and Vazquez, RA},
title = {Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {9174307},
pmid = {31236108},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; *Fractals ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.},
}
@article {pmid31235800,
year = {2019},
author = {Kumar, S and Sharma, A and Tsunoda, T},
title = {Brain wave classification using long short-term memory network based OPTICAL predictor.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {9153},
pmid = {31235800},
issn = {2045-2322},
mesh = {Benchmarking ; *Brain Waves ; Electroencephalography ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL .},
}
@article {pmid31233971,
year = {2019},
author = {Kim, HH and Jeong, J},
title = {Decoding electroencephalographic signals for direction in brain-computer interface using echo state network and Gaussian readouts.},
journal = {Computers in biology and medicine},
volume = {110},
number = {},
pages = {254-264},
doi = {10.1016/j.compbiomed.2019.05.024},
pmid = {31233971},
issn = {1879-0534},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Machine Learning ; Male ; *Models, Neurological ; },
abstract = {BACKGROUND: Noninvasive brain-computer interfaces (BCI) for movement control via an electroencephalogram (EEG) have been extensively investigated. However, most previous studies decoded user intention for movement directions based on sensorimotor rhythms during motor imagery. BCI systems based on mapping imagery movement of body parts (e.g., left or right hands) to movement directions (left or right directional movement of a machine or cursor) are less intuitive and less convenient due to the complex training procedures. Thus, direct decoding methods for detecting user intention about movement directions are urgently needed.<br><br>METHODS: Here, we describe a novel direct decoding method for user intention about the movement directions using the echo state network and Gaussian readouts. Importantly parameters in the network were optimized using the genetic algorithm method to achieve better decoding performance. We tested the decoding performance of this method with four healthy subjects and an inexpensive wireless EEG system containing 14 channels and then compared the performance outcome with that of a conventional machine learning method.<br><br>RESULTS: We showed that this decoding method successfully classified eight directions of intended movement (approximately 95% of an accuracy).<br><br>CONCLUSIONS: We suggest that the echo state network and Gaussian readouts can be a useful decoding method to directly read user intention of movement directions even using an inexpensive and portable EEG system.},
}
@article {pmid31231200,
year = {2019},
author = {Berger, A and Horst, F and Müller, S and Steinberg, F and Doppelmayr, M},
title = {Current State and Future Prospects of EEG and fNIRS in Robot-Assisted Gait Rehabilitation: A Brief Review.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {172},
pmid = {31231200},
issn = {1662-5161},
abstract = {Gait and balance impairments are frequently considered as the most significant concerns among individuals suffering from neurological diseases. Robot-assisted gait training (RAGT) has shown to be a promising neurorehabilitation intervention to improve gait recovery in patients following stroke or brain injury by potentially initiating neuroplastic changes. However, the neurophysiological processes underlying gait recovery through RAGT remain poorly understood. As non-invasive, portable neuroimaging techniques, electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide new insights regarding the neurophysiological processes occurring during RAGT by measuring different perspectives of brain activity. Due to spatial information about changes in cortical activation patterns and the rapid temporal resolution of bioelectrical changes, more features correlated with brain activation and connectivity can be identified when using fused EEG-fNIRS, thus leading to a detailed understanding of neurophysiological mechanisms underlying motor behavior and impairments due to neurological diseases. Therefore, multi-modal integrations of EEG-fNIRS appear promising for the characterization of neurovascular coupling in brain network dynamics induced by RAGT. In this brief review, we surveyed neuroimaging studies focusing specifically on robotic gait rehabilitation. While previous studies have examined either EEG or fNIRS with respect to RAGT, a multi-modal integration of both approaches is lacking. Based on comparable studies using fused EEG-fNIRS integrations either for guiding non-invasive brain stimulation or as part of brain-machine interface paradigms, the potential of this methodologically combined approach in RAGT is discussed. Future research directions and perspectives for targeted, individualized gait recovery that optimize the outcome and efficiency of RAGT in neurorehabilitation were further derived.},
}
@article {pmid31227159,
year = {2019},
author = {Wang, Y and Zhou, P and Xiao, D and Zhu, Y and Zhong, Y and Zhang, J and Sui, X and Feng, X and Xu, H and Mao, Z},
title = {Chitosan-bound carboxymethylated cotton fabric and its application as wound dressing.},
journal = {Carbohydrate polymers},
volume = {221},
number = {},
pages = {202-208},
doi = {10.1016/j.carbpol.2019.05.082},
pmid = {31227159},
issn = {1879-1344},
mesh = {Animals ; Blood Coagulation/drug effects ; Carboxymethylcellulose Sodium/chemistry/*pharmacology ; Chitosan/chemistry/*pharmacology ; *Cotton Fiber ; Femoral Artery/injuries ; Gossypium/*chemistry ; Hemostatics/chemistry/*pharmacology ; Liver/injuries ; *Occlusive Dressings ; Rabbits ; Rats, Sprague-Dawley ; },
abstract = {Cotton fabric (CF) is commonly used in wound treatment, however, its hemostatic efficiency is far from sufficient. In this study, modified cotton fabric (MCF-0.39) was obtained by a carboxymethylation process, which endowed MCF-0.39 with good swelling ability and water absorption capacity. Chitosan (CHI) was bound to MCF-0.39 by the binder sodium carboxymethyl cellulose (NaCMC) via flat-screen printing technique to prepare the hybrid hemostatic material (CHI-MCF-0.39). The blood clotting index (BCI) of CHI-MCF-0.39 was 3.15-fold lower than that of CF, demonstrating the good clotting ability of the material. In rat liver injury and femoral artery animal model, the groups using CHI-MCF-0.39 had less hemostasis time and blood loss compared with those groups using CF. All the above results indicate that the prepared CHI-MCF-0.39 has promising future applications as effective hemostatic material in trauma treatment.},
}
@article {pmid31226684,
year = {2019},
author = {Gornet, MF and Lanman, TH and Burkus, JK and Dryer, RF and McConnell, JR and Hodges, SD and Schranck, FW},
title = {Two-level cervical disc arthroplasty versus anterior cervical discectomy and fusion: 10-year outcomes of a prospective, randomized investigational device exemption clinical trial.},
journal = {Journal of neurosurgery. Spine},
volume = {31},
number = {4},
pages = {508-518},
doi = {10.3171/2019.4.SPINE19157},
pmid = {31226684},
issn = {1547-5646},
abstract = {OBJECTIVE: The authors assessed the 10-year clinical safety and effectiveness of cervical disc arthroplasty (CDA) to treat degenerative cervical spine disease at 2 adjacent levels compared to anterior cervical discectomy and fusion (ACDF).<br><br>METHODS: A prospective, randomized, controlled, multicenter FDA-approved clinical trial was conducted comparing the low-profile titanium ceramic composite-based Prestige LP Cervical Disc (n = 209) at two levels with ACDF (n = 188). Ten-year follow-up data from a postapproval study were available on 148 CDA and 118 ACDF patients and are reported here. Clinical and radiographic evaluations were completed preoperatively, intraoperatively, and at regular postoperative follow-up intervals for up to 10 years. The primary endpoint was overall success, a composite variable that included key safety and efficacy considerations. Ten-year follow-up rates were 86.0% for CDA and 84.9% for ACDF.<br><br>RESULTS: From 2 to 10 years, CDA demonstrated statistical superiority over ACDF for overall success, with rates at 10 years of 80.4% versus 62.2%, respectively (posterior probability of superiority [PPS] = 99.9%). Neck Disability Index (NDI) success was also superior, with rates at 10 years of 88.4% versus 76.5% (PPS = 99.5%), as was neurological success (92.6% vs 86.1%; PPS = 95.6%). Improvements from preoperative results in NDI and neck pain scores were consistently statistically superior for CDA compared to ACDF. All other study effectiveness measures were at least noninferior for CDA compared to ACDF through the 10-year follow-up period, including disc height. Mean angular ranges of motion at treated levels were maintained in the CDA group for up to 10 years. The rates of grade IV heterotopic ossification (HO) at the superior and inferior levels were 8.2% and 10.3%, respectively. The rate of severe HO (grade III or IV) did not increase significantly from 7 years (42.4%) to 10 years (39.0%). The CDA group had fewer serious (grade 3-4) implant-related or implant/surgical procedure-related adverse events (3.8% vs 8.1%; posterior mean 95% Bayesian credible interval [BCI] of the log hazard ratio [LHR] -0.92 [-1.88, -0.01]). The CDA group also had statistically fewer secondary surgical procedures at the index levels (4.7%) than the ACDF group (17.6%) (LHR [95% BCI] -1.39 [-2.15, -0.61]) as well as at adjacent levels (9.0% vs 17.9%).<br><br>CONCLUSIONS: The Prestige LP Cervical Disc, implanted at two adjacent levels, maintains improved clinical outcomes and segmental motion 10 years after surgery and is a safe and effective alternative to fusion.Clinical trial registration no.: NCT00637156 (clinicaltrials.gov).},
}
@article {pmid31226078,
year = {2019},
author = {Bianchi, L and Liti, C and Piccialli, V},
title = {A New Early Stopping Method for P300 Spellers.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {8},
pages = {1635-1643},
doi = {10.1109/TNSRE.2019.2924080},
pmid = {31226078},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Calibration ; *Communication Devices for People with Disabilities ; Databases, Factual ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Linear Models ; Machine Learning ; Signal-To-Noise Ratio ; },
abstract = {In event-related potentials based brain-computer interfaces, the responses evoked by a well defined stimuli sequence are usually averaged to overcome the limitations caused by the intrinsic poor EEG signal-to-noise ratio. This, however, implies that the time necessary to detect the brain signals increases and then that the communication rate can be dramatically reduced. A common approach is then at first to estimate an optimal fixed number of responses to be averaged on a calibration data set and then to use this number on the online/testing dataset. In contrast to this strategy, several early stopping methods have been successfully proposed, aiming at dynamically stopping the stimulation sequence when a certain condition is met. We propose an efficient and easy to implement early stopping method that outperforms the ones proposed in the literature, showing its effectiveness on several publicly available datasets recorded from either healthy subjects or amyotrophic lateral sclerosis patients.},
}
@article {pmid31222030,
year = {2019},
author = {Willett, FR and Young, DR and Murphy, BA and Memberg, WD and Blabe, CH and Pandarinath, C and Stavisky, SD and Rezaii, P and Saab, J and Walter, BL and Sweet, JA and Miller, JP and Henderson, JM and Shenoy, KV and Simeral, JD and Jarosiewicz, B and Hochberg, LR and Kirsch, RF and Bolu Ajiboye, A},
title = {Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {8881},
pmid = {31222030},
issn = {2045-2322},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; N01HD10018//U.S. Department of Health &amp; Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)/International ; R01DC014034//U.S. Department of Health &amp; Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/International ; U01 NS098968/NS/NINDS NIH HHS/United States ; R01NS066311//U.S. Department of Health &amp; Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/International ; },
mesh = {Algorithms ; *Biofeedback, Psychology ; *Brain-Computer Interfaces ; Calibration ; Humans ; *Models, Neurological ; Psychomotor Performance ; },
abstract = {Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.},
}
@article {pmid31220820,
year = {2019},
author = {Chen, X and Wang, Y and Zhang, S and Xu, S and Gao, X},
title = {Effects of stimulation frequency and stimulation waveform on steady-state visual evoked potentials using a computer monitor.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {066007},
doi = {10.1088/1741-2552/ab2b7d},
pmid = {31220820},
issn = {1741-2552},
mesh = {Adult ; *Computers ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/instrumentation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; Young Adult ; },
abstract = {OBJECTIVE: A visual stimulator plays a vital part in brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP). The properties of visual stimulation, such as frequency, color, and waveform, will influence SSVEP-based BCI performance to some extent. Recently, the computer monitor serves as a visual stimulator that is widespread in SSVEP-based BCIs because of its great flexibility in generating visual stimuli. However, stimulation properties based on a computer monitor have received very little attention. For a better comprehension of SSVEPs, this study explored the stimulation effects of waveforms and frequencies, when evoking SSVEPs through a computer monitor.<br><br>APPROACH: This study utilized the approximation methods to realize sine- and square-wave temporal modulations at 18 stimulation frequencies ranging from 6 to 40 Hz on a conventional 120 Hz LCD screen. We collected electroencephalogram (EEG) datasets from 12 healthy subjects and compared the signal-to-noise ratios (SNRs), amplitudes, and topographic mapping of SSVEPs evoked by these two temporal modulation flickers (sine- and square-wave). In addition, a BCI experiment with two nine-target BCIs (i.e. low-frequency BCI and high-frequency BCI) was implemented to compare the two stimulation waveforms in terms of BCI performance.<br><br>MAIN RESULTS: For both sine- and square-wave stimulation conditions, strong SSVEPs over the occipital area were observed for each stimulation frequency. SSVEP amplitudes at the stimulation frequency exhibited a global peak in the low-frequency band. The second harmonic SSVEP frequency-response functions showed the largest amplitude at 6 Hz and fell sharply for higher frequencies. In the BCI experiment, the classification performance of the square-wave stimuli was notably higher than that of the sine-wave stimuli when using shorter data lengths.<br><br>SIGNIFICANCE: These results suggested that the square-wave flicker was more efficient at implementing high-speed BCIs based on SSVEP when using a computer monitor as a visual stimulator.},
}
@article {pmid31220818,
year = {2019},
author = {Kilicarslan, A and Contreras Vidal, JL},
title = {Characterization and real-time removal of motion artifacts from EEG signals.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056027},
doi = {10.1088/1741-2552/ab2b61},
pmid = {31220818},
issn = {1741-2552},
mesh = {Adult ; *Artifacts ; Electroencephalography/*methods/standards ; Exercise Test/*methods/standards ; Gait/*physiology ; Humans ; *Motion ; Walking/*physiology ; },
abstract = {OBJECTIVE: Accurate implementation of real-time non-invasive brain-machine/computer interfaces (BMI/BCI) requires handling physiological and nonphysiological artifacts associated with the measurement modalities. For example, scalp electroencephalographic (EEG) measurements are often considered prone to excessive motion artifacts and other types of artifacts that contaminate the EEG recordings. Although the magnitude of such artifacts heavily depends on the task and the setup, complete minimization or isolation of such artifacts is generally not possible.<br><br>APPROACH: We present an adaptive de-noising framework with robustness properties, using a Volterra based non-linear mapping to characterize and handle the motion artifact contamination in EEG measurements. We asked healthy able-bodied subjects to walk on a treadmill at gait speeds of 1-to-4 mph, while we tracked the motion of select EEG electrodes with an infrared video-based motion tracking system. We also placed inertial measurement unit (IMU) sensors on the forehead and feet of the subjects for assessing the overall head movement and segmenting the gait.<br><br>MAIN RESULTS: We discuss in detail the characteristics of the motion artifacts and propose a real-time compatible solution to filter them. We report the effective handling of both the fundamental frequency of contamination (synchronized to the walking speed) and its harmonics. Event-related spectral perturbation (ERSP) analysis for walking shows that the gait dependency of artifact contamination is also eliminated on all target frequencies.<br><br>SIGNIFICANCE: The real-time compatibility and generalizability of our adaptive filtering framework allows for the effective use of non-invasive BMI/BCI systems and greatly expands the implementation type and application domains to other types of problems where signal denoising is desirable. Combined with our previous efforts of filtering ocular artifacts, the presented technique allows for a comprehensive adaptive filtering framework to increase the EEG signal to noise ratio (SNR). We believe the implementation will benefit all non-invasive neural measurement modalities, including studies discussing neural correlates of movement and other internal states, not necessarily of BMI focus.},
}
@article {pmid31217132,
year = {2020},
author = {Wu, X and Zhou, B and Lv, Z and Zhang, C},
title = {To Explore the Potentials of Independent Component Analysis in Brain-Computer Interface of Motor Imagery.},
journal = {IEEE journal of biomedical and health informatics},
volume = {24},
number = {3},
pages = {775-787},
doi = {10.1109/JBHI.2019.2922976},
pmid = {31217132},
issn = {2168-2208},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; Imagination/*classification/physiology ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.},
}
@article {pmid31217122,
year = {2019},
author = {Azab, AM and Mihaylova, L and Ang, KK and Arvaneh, M},
title = {Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {7},
pages = {1352-1359},
doi = {10.1109/TNSRE.2019.2923315},
pmid = {31217122},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Healthy Volunteers ; Humans ; Imagination/*physiology ; *Machine Learning ; Movement/*physiology ; Psychomotor Performance ; Space Perception/physiology ; },
abstract = {One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically, a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this paper, a new similarity measure based on the Kullback-Leibler divergence (KL) is used to measure the similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared with the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results, particularly when few subject-specific trials were available for training (p < 0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms.},
}
@article {pmid31215829,
year = {2019},
author = {Ramzan, M and Dawn, S},
title = {Learning-based classification of valence emotion from electroencephalography.},
journal = {The International journal of neuroscience},
volume = {129},
number = {11},
pages = {1085-1093},
doi = {10.1080/00207454.2019.1634070},
pmid = {31215829},
issn = {1563-5279},
mesh = {Adult ; Brain/*physiology ; Brain Waves/*physiology ; Datasets as Topic ; Electroencephalography/*methods ; Emotions/*physiology ; Humans ; *Machine Learning ; Models, Theoretical ; *Neural Networks, Computer ; Recognition, Psychology/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The neuroimaging research field has been revolutionized with the development of human cognitive functions without the use of brain pathways. To assist such systems, electroencephalography (EEG) based measures play an important role. In this study, the publicly available database of emotion analysis using physiological signals, has been used to identify the human emotions such as valence (positive/negative) from the given recorded EEG signals. With the identification of such emotion, the feeling of goodness or badness related individual experiences with the situation can be identified from his/her brain signals. The different machine learning classifiers such as random forest, decisions trees, K-nearest neighbor, support vector machines, naive Bayes and neural network have been used to identify and evaluate such emotions. The previous work done by the other authors on the same dataset using various quantitative approaches are compared with the approaches used in this study yields higher accuracy rates with the random forest and decision tree. The effectiveness of each classifier in terms of statistical measures such as accuracy, F-score, etc. has been evaluated. The random forest classifier was found to outperform with an accuracy of 98%, closely followed by the Decision tree at 94% are the most effective classifiers in classifying the valence emotions of the EEG data for 6 subjects.},
}
@article {pmid31214255,
year = {2019},
author = {Feng, JK and Jin, J and Daly, I and Zhou, J and Niu, Y and Wang, X and Cichocki, A},
title = {An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {8068357},
pmid = {31214255},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination/physiology ; Motor Activity/physiology ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features.<br><br>NEW METHOD: To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy.<br><br>RESULTS: The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.},
}
@article {pmid31214009,
year = {2019},
author = {Duan, X and Xie, S and Xie, X and Meng, Y and Xu, Z},
title = {Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {23},
pmid = {31214009},
issn = {1662-5218},
abstract = {Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world.},
}
@article {pmid31213599,
year = {2019},
author = {Molina-Lopez, F and Gao, TZ and Kraft, U and Zhu, C and Öhlund, T and Pfattner, R and Feig, VR and Kim, Y and Wang, S and Yun, Y and Bao, Z},
title = {Inkjet-printed stretchable and low voltage synaptic transistor array.},
journal = {Nature communications},
volume = {10},
number = {1},
pages = {2676},
pmid = {31213599},
issn = {2041-1723},
support = {P2ELP2_155355//Schweizerischer Nationalfonds zur F&#xf6;rderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)/International ; DE-SC0016523//U.S. Department of Energy (DOE)/International ; },
mesh = {Brain-Computer Interfaces ; Electronics, Medical/*methods ; Equipment Design ; Humans ; Nanotechnology/*methods ; Nanotubes, Carbon/chemistry ; Neurons/physiology ; Polymers/chemistry ; Printing/*methods ; Synaptic Transmission/physiology ; Transistors, Electronic ; *Wearable Electronic Devices ; },
abstract = {Wearable and skin electronics benefit from mechanically soft and stretchable materials to conform to curved and dynamic surfaces, thereby enabling seamless integration with the human body. However, such materials are challenging to process using traditional microelectronics techniques. Here, stretchable transistor arrays are patterned exclusively from solution by inkjet printing of polymers and carbon nanotubes. The additive, non-contact and maskless nature of inkjet printing provides a simple, inexpensive and scalable route for stacking and patterning these chemically-sensitive materials over large areas. The transistors, which are stable at ambient conditions, display mobilities as high as 30 cm[2] V[-1] s[-1] and currents per channel width of 0.2 mA cm[-1] at operation voltages as low as 1 V, owing to the ionic character of their printed gate dielectric. Furthermore, these transistors with double-layer capacitive dielectric can mimic the synaptic behavior of neurons, making them interesting for conformal brain-machine interfaces and other wearable bioelectronics.},
}
@article {pmid31212098,
year = {2019},
author = {Etard, O and Kegler, M and Braiman, C and Forte, AE and Reichenbach, T},
title = {Decoding of selective attention to continuous speech from the human auditory brainstem response.},
journal = {NeuroImage},
volume = {200},
number = {},
pages = {1-11},
doi = {10.1016/j.neuroimage.2019.06.029},
pmid = {31212098},
issn = {1095-9572},
mesh = {Adult ; Attention/*physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Auditory, Brain Stem/*physiology ; Female ; Humans ; Male ; Speech Perception/*physiology ; Young Adult ; },
abstract = {Humans are highly skilled at analysing complex acoustic scenes. The segregation of different acoustic streams and the formation of corresponding neural representations is mostly attributed to the auditory cortex. Decoding of selective attention from neuroimaging has therefore focussed on cortical responses to sound. However, the auditory brainstem response to speech is modulated by selective attention as well, as recently shown through measuring the brainstem's response to running speech. Although the response of the auditory brainstem has a smaller magnitude than that of the auditory cortex, it occurs at much higher frequencies and therefore has a higher information rate. Here we develop statistical models for extracting the brainstem response from multi-channel scalp recordings and for analysing the attentional modulation according to the focus of attention. We demonstrate that the attentional modulation of the brainstem response to speech can be employed to decode the attentional focus of a listener from short measurements of 10 s or less in duration. The decoding remains accurate when obtained from three EEG channels only. We further show how out-of-the-box decoding that employs subject-independent models, as well as decoding that is independent of the specific attended speaker is capable of achieving similar accuracy. These results open up new avenues for investigating the neural mechanisms for selective attention in the brainstem and for developing efficient auditory brain-computer interfaces.},
}
@article {pmid31211812,
year = {2019},
author = {Salazar-Ramirez, A and Martin, JI and Martinez, R and Arruti, A and Muguerza, J and Sierra, B},
title = {A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface.},
journal = {PloS one},
volume = {14},
number = {6},
pages = {e0218181},
pmid = {31211812},
issn = {1932-6203},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; Humans ; Imagination/*physiology ; Machine Learning ; Recognition, Psychology ; },
abstract = {A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.},
}
@article {pmid31209655,
year = {2019},
author = {Jahangiri, A and Sepulveda, F},
title = {Correction to: The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data.},
journal = {Journal of medical systems},
volume = {43},
number = {8},
pages = {237},
doi = {10.1007/s10916-019-1379-1},
pmid = {31209655},
issn = {1573-689X},
abstract = {The author regrets that the acknowledgment was left out from the original publication. The acknowledgement is written below.},
}
@article {pmid31207952,
year = {2019},
author = {Ngan, CGY and Kapsa, RMI and Choong, PFM},
title = {Strategies for neural control of prosthetic limbs: from electrode interfacing to 3D printing.},
journal = {Materials (Basel, Switzerland)},
volume = {12},
number = {12},
pages = {},
pmid = {31207952},
issn = {1996-1944},
abstract = {Limb amputation is a major cause of disability in our community, for which motorised prosthetic devices offer a return to function and independence. With the commercialisation and increasing availability of advanced motorised prosthetic technologies, there is a consumer need and clinical drive for intuitive user control. In this context, rapid additive fabrication/prototyping capacities and biofabrication protocols embrace a highly-personalised medicine doctrine that marries specific patient biology and anatomy to high-end prosthetic design, manufacture and functionality. Commercially-available prosthetic models utilise surface electrodes that are limited by their disconnect between mind and device. As such, alternative strategies of mind-prosthetic interfacing have been explored to purposefully drive the prosthetic limb. This review investigates mind to machine interfacing strategies, with a focus on the biological challenges of long-term harnessing of the user's cerebral commands to drive actuation/movement in electronic prostheses. It covers the limitations of skin, peripheral nerve and brain interfacing electrodes, and in particular the challenges of minimising the foreign-body response, as well as a new strategy of grafting muscle onto residual peripheral nerves. In conjunction, this review also investigates the applicability of additive tissue engineering at the nerve-electrode boundary, which has led to pioneering work in neural regeneration and bioelectrode development for applications at the neuroprosthetic interface.},
}
@article {pmid31206827,
year = {2020},
author = {Obidin, N and Tasnim, F and Dagdeviren, C},
title = {The Future of Neuroimplantable Devices: A Materials Science and Regulatory Perspective.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {32},
number = {15},
pages = {e1901482},
doi = {10.1002/adma.201901482},
pmid = {31206827},
issn = {1521-4095},
mesh = {Animals ; Brain/*physiology ; Electrocorticography ; Electrodes, Implanted ; *Government Regulation ; Humans ; Optogenetics ; *Prostheses and Implants ; Risk Factors ; Wireless Technology ; },
abstract = {The past two decades have seen unprecedented progress in the development of novel materials, form factors, and functionalities in neuroimplantable technologies, including electrocorticography (ECoG) systems, multielectrode arrays (MEAs), Stentrode, and deep brain probes. The key considerations for the development of such devices intended for acute implantation and chronic use, from the perspective of biocompatible hybrid materials incorporation, conformable device design, implantation procedures, and mechanical and biological risk factors, are highlighted. These topics are connected with the role that the U.S. Food and Drug Administration (FDA) plays in its regulation of neuroimplantable technologies based on the above parameters. Existing neuroimplantable devices and efforts to improve their materials and implantation protocols are first discussed in detail. The effects of device implantation with regards to biocompatibility and brain heterogeneity are then explored. Topics examined include brain-specific risk factors, such as bacterial infection, tissue scarring, inflammation, and vasculature damage, as well as efforts to manage these dangers through emerging hybrid, bioelectronic device architectures. The current challenges of gaining clinical approval by the FDA-in particular, with regards to biological, mechanical, and materials risk factors-are summarized. The available regulatory pathways to accelerate next-generation neuroimplantable devices to market are then discussed.},
}
@article {pmid31204431,
year = {2020},
author = {Naros, G and Lehnertz, T and Leão, MT and Ziemann, U and Gharabaghi, A},
title = {Brain State-dependent Gain Modulation of Corticospinal Output in the Active Motor System.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {30},
number = {1},
pages = {371-381},
doi = {10.1093/cercor/bhz093},
pmid = {31204431},
issn = {1460-2199},
mesh = {Adult ; Brain-Computer Interfaces ; *Cortical Excitability ; Electroencephalography ; Electromyography ; *Evoked Potentials, Motor ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Muscle, Skeletal/physiology ; Pyramidal Tracts/*physiology ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {The communication through coherence hypothesis suggests that only coherently oscillating neuronal groups can interact effectively and predicts an intrinsic response modulation along the oscillatory rhythm. For the motor cortex (MC) at rest, the oscillatory cycle has been shown to determine the brain's responsiveness to external stimuli. For the active MC, however, the demonstration of such a phase-specific modulation of corticospinal excitability (CSE) along the rhythm cycle is still missing. Motor evoked potentials in response to transcranial magnetic stimulation (TMS) over the MC were used to probe the effect of cortical oscillations on CSE during several motor conditions. A brain-machine interface (BMI) with a robotic hand orthosis allowed investigating effects of cortical activity on CSE without the confounding effects of voluntary muscle activation. Only this BMI approach (and not active or passive hand opening alone) revealed a frequency- and phase-specific cortical modulation of CSE by sensorimotor beta-band activity that peaked once per oscillatory cycle and was independent of muscle activity. The active MC follows an intrinsic response modulation in accordance with the communication through coherence hypothesis. Furthermore, the BMI approach may facilitate and strengthen effective corticospinal communication in a therapeutic context, for example, when voluntary hand opening is no longer possible after stroke.},
}
@article {pmid31201893,
year = {2019},
author = {Xie, L and Bao, X and Cai, T and Silva, SG and Ma, J and Zhang, Z and Guo, X and Marks, LB},
title = {Elevated Risk of Radiation Therapy-Associated Second Malignant Neoplasms in Young African-American Women Survivors of Stage I-IIIA Breast Cancer.},
journal = {International journal of radiation oncology, biology, physics},
volume = {105},
number = {2},
pages = {275-284},
doi = {10.1016/j.ijrobp.2019.06.010},
pmid = {31201893},
issn = {1879-355X},
mesh = {Adult ; *Black or African American/statistics &amp; numerical data ; Age Factors ; Breast Neoplasms/pathology/*radiotherapy ; Cancer Survivors ; Carcinoma, Ductal, Breast/pathology/*radiotherapy ; Female ; Follow-Up Studies ; Gene Expression ; Humans ; Incidence ; Lung Neoplasms/epidemiology/etiology ; *Neoplasms, Radiation-Induced/epidemiology ; Neoplasms, Second Primary/epidemiology/*etiology ; Proportional Hazards Models ; Radiation Tolerance/genetics ; SEER Program ; Statistics, Nonparametric ; United States/epidemiology ; White People/*statistics &amp; numerical data ; Young Adult ; },
abstract = {PURPOSE: To estimate the effect of radiation therapy (RT) on nonbreast second malignant neoplasms (SMNs) in young women survivors of stage I-IIIA breast cancer.<br><br>METHODS AND MATERIALS: Women aged 20 to 44 years who received a diagnosis of stage I-IIIA breast cancer (1988-2008) were identified in the Surveillance, Epidemiology, and End Results 9 registries. Bootstrapping approach and competing-risk proportional hazards models were used to evaluate the effect of RT on nonbreast SMN risk. The analysis was repeated in racial subgroups. Radiotolerance score analysis of normal airway epithelium was performed using Gene Expression Omnibus (GEO) data sets.<br><br>RESULTS: Within records of 30,003 women with primary breast cancer, 20,516 eligible patients were identified, including 2,183 African Americans (AAs) and 16,009 Caucasians. The 25-year cumulative incidences of SMN were 5.2% and 3.6% (RT vs no-RT) for AAs, with 12.8-year and 17.4-year (RT vs no-RT) median follow-up (hazard ratio [HR] = 1.81; 95% bootstrapping confidence interval [BCI], 1.02-2.50; P < .05), respectively, and 6.4% and 5.9% (RT vs no-RT) for Caucasians with 14.3-year and&#xa0;18.1-year (RT vs no-RT) median follow-up (HR = 1.10; 95% BCI, 0.61-1.40; P > .05), respectively. The largest portion of excess RT-related SMN risk was lung cancer (AA: HR = 2.08, 95% BCI, 1.02-5.39, P < .05; Caucasian: HR = 1.50, 95% BCI, 0.84-5.38, P > .05). Subpopulation Treatment Effect Pattern Plot (STEPP) analysis revealed higher&#xa0;post-RT nonbreast SMN risk in those 20 to 44 years of age, with larger HRs for RT in AAs. Radiotolerance score (RTS) of normal airway epithelium from young AA women was significantly lower than that from young Caucasian women (P = .038).<br><br>CONCLUSIONS: With a projected 25-year follow-up, RT is associated with elevated risk of nonbreast SMNs, particularly second lung cancer, in young women survivors of stage I-IIIA breast cancer. Nonbreast SMNs associated with RT are higher in AA women than Caucasian women.},
}
@article {pmid31200908,
year = {2019},
author = {Sarasa, G and Granados, A and Rodriguez, FB},
title = {Algorithmic clustering based on string compression to extract P300 structure in EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {176},
number = {},
pages = {225-235},
doi = {10.1016/j.cmpb.2019.03.009},
pmid = {31200908},
issn = {1872-7565},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Cluster Analysis ; Computer Simulation ; Data Compression/*methods ; Electrodes ; *Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; },
abstract = {BACKGROUND AND OBJECTIVES: P300 is an Event Related Potential control signal widely used in Brain Computer Interfaces. Using the oddball paradigm, a P300 speller allows a human to spell letters through P300 events produced by his/her brain. One of the most common issues in the detection of this event is that its structure may differ between different subjects and over time for a specific subject. The main purpose of this work is to deal with this inherent variability and identify the main structure of P300 using algorithmic clustering based on string compression.<br><br>METHODS: In this work, we make use of the Normalized Compression Distance (NCD) to extract the main structure of the signal regardless of its inherent variability. In order to apply compression distances, we carry out a novel signal-to-ASCII process that transforms and merges different events into suitable objects to be used by a compression algorithm. Once the ASCII objects are created, we use NCD-driven clustering as a tool to analyze if our object creation method suitably represents the information contained in the signals and to explore if compression distances are a valid tool for identifying P300 structure. With the purpose of increasing the level of generalization of our study, we apply two different clustering methods: a hierarchical clustering algorithm based on the minimum quartet tree method and a multidimensional projection method.<br><br>RESULTS: Our experimental results show good clustering performance over different experiments, showing the structure extraction capabilities of our procedure. Two datasets with recordings in different scenarios were used to analyze the problem and validate our results, respectively. It has to be pointed out that when the clustering performance over individual electrodes is analyzed, higher P300 activity is found in similar regions to other articles using the same datasets. This suggests that our approach might be used as an electrode-selection criteria.<br><br>CONCLUSIONS: The proposed NCD-driven clustering methodology can be used to discover the structural characteristics of EEG and thereby, it is suitable as a complementary methodology for the P300 analysis.},
}
@article {pmid31200902,
year = {2019},
author = {Arican, M and Polat, K},
title = {Pairwise and variance based signal compression algorithm (PVBSC) in the P300 based speller systems using EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {176},
number = {},
pages = {149-157},
doi = {10.1016/j.cmpb.2019.05.011},
pmid = {31200902},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Systems ; *Data Compression ; *Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; Internet ; Models, Theoretical ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-Computer Interfaces (BCI) are used to provide environmental interaction among individuals, especially people with disabilities. Spelling systems, one of the BCI applications, are based on the principle of detecting P300 waves from EEG signals. The aim of speller systems is to identify the P300 waves and determine the letter on a screen opposite the person. The purpose of the operating speller systems is to minimize the processing cost of the system with smaller data sizes to be obtained by compressing EEG data before the pre-processing step. In this study, a hybrid model was presented. With Pairwise and variance-based signal compression Algorithm, first of all, data is compressed and then preprocessing, and classification is performed. The proposed hybrid model is intended to be stored in offline systems and to increase the speed of operation in online systems.<br><br>METHODS: In this paper, we proposed a new hybrid model with the compression algorithm called Pairwise and Variance Based Signal Compression Algorithm (PVBSC) for P300-based speller systems. The proposed method wasevaluated on Wadsworth BCI speller dataset. As the focus is the compression algorithm, the channel selection has been applied to increase the working speed. Channel selection was made by detecting eight channels most commonly used in the literature.<br><br>RESULTS: As the first step in the compression algorithm was segmentation, the study was repeated with 16, 32 and 64 channel lengths to see the effect of the window length. Then, to find the target character from EEG signals, we have used two different classifiers including an ensemble of LS-SVMs and ensemble of LDAs. In this study, as the best classification accuracy, 1.437 compression ratio and 94.166% accuracy rate by Ensemble of LDAs was achieved with PVBSC with 32 window lengths.<br><br>CONCLUSIONS: The obtained results have shown that the proposed compression method could be confidently used in the compressing the P300 wave-containing EEG signals and reduce the data size significantly.},
}
@article {pmid31200460,
year = {2019},
author = {Dobrowolski, A and Pieloth, D and Wiggers, H and Thommes, M},
title = {Electrostatic Precipitation of Submicron Particles in a Molten Carrier.},
journal = {Pharmaceutics},
volume = {11},
number = {6},
pages = {},
pmid = {31200460},
issn = {1999-4923},
abstract = {Recently, submicron particles have been discussed as a means to increase the bioavailability of poorly water-soluble drugs. Separation of these small particles is done with both fibre and membrane filters, as well as electrostatic precipitators. A major disadvantage of an electrostatic precipitator (ESP) is the agglomerate formation on the precipitation electrode. These agglomerates frequently show low bioavailability, due to the decreased specific surface area and poor wettability. In this work, a new melt electrostatic precipitator was developed and tested to convert submicron particles into a solid dispersion in order to increase the bioavailability of active pharmaceutical ingredients. The submicron particles were generated by spray drying and transferred to the ESP, where the collection electrode is covered with a melt, which served as matrix after solidification. The newly developed melt electrostatic precipitator was able to collect isolated naproxen particles in a molten carrier. A solid naproxen xylitol dispersion was prepared, which showed a reduction of the dissolution time by 82%, and a release of 80% of the total drug, compared to the physical mixture.},
}
@article {pmid31200107,
year = {2019},
author = {Goel, R and Nakagome, S and Rao, N and Paloski, WH and Contreras-Vidal, JL and Parikh, PJ},
title = {Fronto-Parietal Brain Areas Contribute to the Online Control of Posture during a Continuous Balance Task.},
journal = {Neuroscience},
volume = {413},
number = {},
pages = {135-153},
doi = {10.1016/j.neuroscience.2019.05.063},
pmid = {31200107},
issn = {1873-7544},
mesh = {Adult ; Electroencephalography ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Parietal Lobe/*physiology ; Transcranial Magnetic Stimulation ; },
abstract = {Neuroimaging studies have provided evidence for the involvement of frontal and parietal cortices in postural control. However, the specific role of these brain areas for postural control remains to be known. Here, we investigated the effects of disruptive transcranial magnetic stimulation (TMS) over supplementary motor areas (SMA) during challenging continuous balance task in healthy young adults. We hypothesized that a virtual lesion of SMA will alter activation within the brain network identified using electroencephalography (EEG) and impair performance of the postural task. Twenty healthy young adults received either continuous theta burst stimulation (cTBS) or sham stimulation over SMA followed by the performance of a continuous balance task with or without somatosensory input distortion created by sway-referencing the support surface. cTBS over SMA compared to sham stimulation showed a smaller increase in root mean square of center of pressure as the difficulty of continuous balance task increased suggestive of altered postural control mechanisms to find a stable solution under challenging sensory conditions. Consistent with earlier studies, we found sources of EEG activation within anterior cingulate (AC), cingulate gyrus (CG), bilateral posterior parietal regions (PPC) during the balance task. Importantly, cTBS over SMA compared to sham stimulation altered EEG power within the identified fronto-parietal regions. These findings suggest that the changes in activation within distant fronto-parietal brain areas following cTBS over SMA contributed to the altered postural behavior. Our study confirms a critical role of AC, CG, and both PPC regions in calibrating online postural responses during a challenging continuous balance task.},
}
@article {pmid31199263,
year = {2019},
author = {Park, Y and Chung, W},
title = {Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {7},
pages = {1378-1388},
doi = {10.1109/TNSRE.2019.2922713},
pmid = {31199263},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; Movement/*physiology ; Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This paper presents a novel feature extraction approach for motor imagery classification overcoming the weakness of conventional common spatial pattern (CSP) methods, especially for small sample settings. We consider local CSPs generated from individual channels and their neighbors (termed "local regions") rather than a global CSP generated from all channels. The novelty is to select a few good local regions using interquartile range (IQR) or an "above the mean" rule based on variance ratio dispersion score (VRDS) and inter-class feature distance (ICFD); instead of computationally expensive cross-validation method. Furthermore, we develop frequency optimization using filter banks by extending the VRDS and ICFD to frequency-optimized local CSPs. The proposed methods are tested on three publicly available brain-computer interface (BCI) datasets: BCI competition III dataset IVa, BCI competition IV dataset I, and BCI competition IV dataset IIb. The proposed method exhibits substantially improved classification accuracy compared to recent related motor imagery (MI) classification methods.},
}
@article {pmid31194817,
year = {2019},
author = {Gembler, F and Stawicki, P and Saboor, A and Volosyak, I},
title = {Dynamic time window mechanism for time synchronous VEP-based BCIs-Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP.},
journal = {PloS one},
volume = {14},
number = {6},
pages = {e0218177},
pmid = {31194817},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) based on visual evoked potentials (VEPs) allow high communication speeds and accuracies. The fastest speeds can be achieved if targets are identified in a synchronous way (i.e., after a pre-set time period the system will produce a command output). The duration a target needs to be fixated on until the system classifies an output command affects the overall system performance. Hence, extracting a data window dedicated for the classification is of critical importance for VEP-based BCIs. Secondly, unintentional fixation on a target could easily lead to its selection. For the practical usability of BCI applications it is desirable to distinguish between intentional and unintentional fixations. This can be achieved by using threshold-based target identification methods. The study explores personalized dynamic classification time windows for threshold-based time synchronous VEP BCIs. The proposed techniques were tested employing the SSVEP and the c-VEP paradigm. Spelling performance was evaluated using an 8-target dictionary-supported BCI utilizing an n-gram word prediction model. The performance of twelve healthy participants was assessed with the information transfer rate (ITR) and accuracy. All participants completed sentence spelling tasks, reaching average accuracies of 94% and 96.3% for the c-VEP and the SSVEP paradigm, respectively. Average ITRs around 57 bpm were achieved for both paradigms.},
}
@article {pmid31191948,
year = {2018},
author = {Tabernig, CB and Lopez, CA and Carrere, LC and Spaich, EG and Ballario, CH},
title = {Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface.},
journal = {Journal of rehabilitation and assistive technologies engineering},
volume = {5},
number = {},
pages = {2055668318789280},
pmid = {31191948},
issn = {2055-6683},
abstract = {INTRODUCTION: Brain computer interface is an emerging technology to treat the sequelae of stroke. The purpose of this study was to explore the motor imagery related desynchronization of sensorimotor rhythms of stroke patients and to assess the efficacy of an upper limb neurorehabilitation therapy based on functional electrical stimulation controlled by a brain computer interface.<br><br>METHODS: Eight severe chronic stroke patients were recruited. The study consisted of two stages: screening and therapy. During screening, the ability of patients to desynchronize the contralateral oscillatory sensorimotor rhythms by motor imagery of the most affected hand was assessed. In the second stage, a therapeutic intervention was performed. It involved 20 sessions where an electrical stimulator was activated when the patient's cerebral activity related to motor imagery was detected. The upper limb was assessed, before and after the intervention, by the Fugl-Meyer score (primary outcome). Spasticity, motor activity, range of movement and quality of life were also evaluated (secondary outcomes).<br><br>RESULTS: Desynchronization was identified in all screened patients. Significant post-treatment improvement (p&#x2009;<&#x2009;0.05) was detected in the primary outcome measure and in the majority of secondary outcome scores.<br><br>CONCLUSIONS: The results suggest that the proposed therapy could be beneficial in the neurorehabilitation of stroke individuals.},
}
@article {pmid31191631,
year = {2019},
author = {Li, M and Xi, H and Zhu, X},
title = {An Incremental Version of L-MVU for the Feature Extraction of MI-EEG.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {4317078},
pmid = {31191631},
issn = {1687-5273},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagination ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy. As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG. However, L-MVU still requires considerable computation costs for out-of-sample data. An incremental version of L-MVU (denoted as IL-MVU) is proposed in this paper. The low-dimensional representation of the training data is generated by L-MVU. For each out-of-sample data, its nearest neighbors will be found in the high-dimensional training samples and the corresponding reconstruction weight matrix be calculated to generate its low-dimensional representation as well. IL-MVU is further combined with the dual-tree complex wavelet transform (DTCWT), which develops a hybrid feature extraction method (named as IL-MD). IL-MVU is applied to extract the nonlinear features of the specific subband signals, which are reconstructed by DTCWT and have the obvious event-related synchronization/event-related desynchronization phenomenon. The average energy features of α and β waves are calculated simultaneously. The two types of features are fused and are evaluated by a linear discriminant analysis classifier. Based on the two public datasets with 12 subjects, extensive experiments were conducted. The average recognition accuracies of 10-fold cross-validation are 92.50% on Dataset 3b and 88.13% on Dataset 2b, and they gain at least 1.43% and 3.45% improvement, respectively, compared to existing methods. The experimental results show that IL-MD can extract more accurate features with relatively lower consumption cost, and it also has better feature visualization and self-adaptive characteristics to subjects. The t-test results and Kappa values suggest the proposed feature extraction method reaches statistical significance and has high consistency in classification.},
}
@article {pmid31191218,
year = {2019},
author = {Wilson, NR and Sarma, D and Wander, JD and Weaver, KE and Ojemann, JG and Rao, RPN},
title = {Cortical Topography of Error-Related High-Frequency Potentials During Erroneous Control in a Continuous Control Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {502},
pmid = {31191218},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) benefit greatly from performance feedback, but current systems lack automatic, task-independent feedback. Cortical responses elicited from user error have the potential to serve as state-based feedback to BCI decoders. To gain a better understanding of local error potentials, we investigate responsive cortical power underlying error-related potentials (ErrPs) from the human cortex during a one-dimensional center-out BCI task, tracking the topography of high-gamma (70-100 Hz) band power (HBP) specific to BCI error. We measured electrocorticography (ECoG) in three human subjects during dynamic, continuous control over BCI cursor velocity. Subjects used motor imagery and rest to move the cursor toward and subsequently dwell within a target region. We then identified and labeled epochs where the BCI decoder incorrectly moved the cursor in the direction opposite of the subject's expectations (i.e., BCI error). We found increased HBP in various cortical areas 100-500 ms following BCI error with respect to epochs of correct, intended control. Significant responses were noted in primary somatosensory, motor, premotor, and parietal areas and generally regardless of whether the subject was using motor imagery or rest to move the cursor toward the target. Parts of somatosensory, temporal, and parietal areas exclusively had increased HBP when subjects were using motor imagery. In contrast, only part of the parietal cortex near the angular gyrus exclusively had an increase in HBP during rest. This investigation is, to our knowledge, the first to explore cortical fields changes in the context of continuous control in ECoG BCI. We present topographical changes in HBP characteristic specific to the generation of error. By focusing on continuous control, instead of on discrete control for simple selection, we investigate a more naturalistic setting and provide high ecological validity for characterizing error potentials. Such potentials could be considered as design elements for co-adaptive BCIs in the future as task-independent feedback to the decoder, allowing for more robust and individualized BCIs.},
}
@article {pmid31190527,
year = {2019},
author = {Yang, J and Bai, R and Li, J and Yang, C and Yao, X and Liu, Q and Vlassak, JJ and Mooney, DJ and Suo, Z},
title = {Design Molecular Topology for Wet-Dry Adhesion.},
journal = {ACS applied materials &amp; interfaces},
volume = {11},
number = {27},
pages = {24802-24811},
doi = {10.1021/acsami.9b07522},
pmid = {31190527},
issn = {1944-8252},
abstract = {Recent innovations highlight the integration of diverse materials with synthetic and biological hydrogels. Examples include brain-machine interfaces, tissue regeneration, and soft ionic devices. Existing methods of strong adhesion mostly focus on the chemistry of bonds and the mechanics of dissipation but largely overlook the molecular topology of connection. Here, we highlight the significance of molecular topology by designing a specific bond-stitch topology. The bond-stitch topology achieves strong adhesion between preformed hydrogels and various materials, where the hydrogels have no functional groups for chemical coupling, and the adhered materials have functional groups on the surface. The adhesion principle requires a species of polymer chains to form a bond with a material through complementary functional groups and form a network in situ that stitches with the polymer network of a hydrogel. We study the physics and chemistry of this topology and describe its potential applications in medicine and engineering.},
}
@article {pmid31188636,
year = {2019},
author = {Ashraf, S and Hegazy, YK and Harmancey, R},
title = {Nuclear receptor subfamily 4 group A member 2 inhibits activation of ERK signaling and cell growth in response to β-adrenergic stimulation in adult rat cardiomyocytes.},
journal = {American journal of physiology. Cell physiology},
volume = {317},
number = {3},
pages = {C513-C524},
pmid = {31188636},
issn = {1522-1563},
support = {R01 HL136438/HL/NHLBI NIH HHS/United States ; P20 GM103476/GM/NIGMS NIH HHS/United States ; R00 HL112952/HL/NHLBI NIH HHS/United States ; P01 HL051971/HL/NHLBI NIH HHS/United States ; P20 GM104357/GM/NIGMS NIH HHS/United States ; P30 GM103328/GM/NIGMS NIH HHS/United States ; P20 GM121334/GM/NIGMS NIH HHS/United States ; },
mesh = {Adrenergic beta-Agonists/*pharmacology ; Age Factors ; Animals ; Cell Proliferation/drug effects/*physiology ; Cells, Cultured ; MAP Kinase Signaling System/drug effects/*physiology ; Male ; Myocytes, Cardiac/drug effects/*metabolism ; Nuclear Receptor Subfamily 4, Group A, Member 2/*biosynthesis/genetics ; Rats ; Rats, Sprague-Dawley ; Receptors, Adrenergic, beta/*metabolism ; },
abstract = {Sustained elevation of sympathetic activity is an important contributor to pathological cardiac hypertrophy, ventricular arrhythmias, and left ventricular contractile dysfunction in chronic heart failure. The orphan nuclear receptor NR4A2 is an immediate early-response gene activated in the heart under β-adrenergic stimulation. The goal of this study was to identify the transcriptional remodeling events induced by increased NR4A2 expression in cardiomyocytes and their impact on the physiological response of those cells to sustained β-adrenergic stimulation. Treatment of adult rat ventricular myocytes with isoproterenol induced a rapid (<4 h) increase in NR4A2 levels that was accompanied by a transient (<24 h) increase in nuclear localization of the transcription factor. Adenovirus-mediated overexpression of NR4A2 to similar levels modulated the expression of genes linked to adrenoceptor signaling, calcium signaling, cell growth and proliferation and counteracted the increase in protein synthesis rate and cell surface area mediated by chronic isoproterenol stimulation. Consistent with those findings, NR4A2 overexpression also blocked the phosphorylative activation of growth-related kinases ERK1/2, Akt, and p70 S6 kinase. Prominent among the transcriptional changes induced by NR4A2 was the upregulation of the dual-specificity phosphatases DUSP2 and DUSP14, two known inhibitors of ERK1/2. Pretreatment of NR4A2-overexpressing cardiomyocytes with the DUSP inhibitor BCI [(E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one] prevented the inhibition of ERK1/2 following isoproterenol stimulation. In conclusion, our results suggest that NR4A2 acts as a novel negative feedback regulator of the β-adrenergic receptor-mediated growth response in cardiomyocytes and this at least partly through DUSP-mediated inhibition of ERK1/2 signaling.},
}
@article {pmid31185416,
year = {2019},
author = {Hou, H and Sun, B and Meng, Q},
title = {Slow cortical potential signal classification using concave-convex feature.},
journal = {Journal of neuroscience methods},
volume = {324},
number = {},
pages = {108303},
doi = {10.1016/j.jneumeth.2019.05.012},
pmid = {31185416},
issn = {1872-678X},
mesh = {Adult ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: The classification of the slow cortical potential (SCP) signals plays a key role in a variety of research areas, including disease diagnostics, human-machine interaction, and education. The widely used classification methods, which combine multiple kinds of features, can be unsuitable in practical applications due to their low robustness to scenario changes.<br><br>NEW METHOD: A flexible concave-convex (C-C) feature is reported. The C-C feature is extracted by two steps: (1) the low-frequency node coefficients of the SCP signals are first extracted using wavelet packet decomposition; (2) then the underlying trend of the low-frequency node coefficients is estimated using third-order polynomial fitting, and the feature is constructed using the minimum and maximum second derivative values of the trend curve as |ymin|&#x202f;-&#x202f;ymax where y is the second derivative value.<br><br>RESULTS: Experimental results on real datasets reveal that our method with the single C-C feature exhibits high average classification accuracies (92.5% and 84.9% on the BCI competition II dataset Ia and the TJU dataset). The accuracy can be further improved (94.5% and 85.9%) by adding the commonly used mean voltage feature and using the naive Bayesian classifier, indicating the flexibility and scalability of the proposed method.<br><br>The proposed C-C feature based method outperforms state-of-the-art (SOTA) multi-feature classification method from the perspective of classification accuracy.<br><br>CONCLUSIONS: The effectiveness of the C-C feature for SCP classification is validated. The proposed feature will represent a useful contribution to the SCP classification, balancing the strengths of traditional features and the proposed one.},
}
@article {pmid31184716,
year = {2019},
author = {Abbasi, J},
title = {Synthesizing Speech From Brain Activity.},
journal = {JAMA},
volume = {321},
number = {22},
pages = {2155},
doi = {10.1001/jama.2019.7064},
pmid = {31184716},
issn = {1538-3598},
mesh = {Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Humans ; Speech ; Speech Disorders/*rehabilitation ; },
}
@article {pmid31182595,
year = {2019},
author = {Oby, ER and Golub, MD and Hennig, JA and Degenhart, AD and Tyler-Kabara, EC and Yu, BM and Chase, SM and Batista, AP},
title = {New neural activity patterns emerge with long-term learning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {116},
number = {30},
pages = {15210-15215},
pmid = {31182595},
issn = {1091-6490},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; T32 NS007391/NS/NINDS NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; R01 HD090125/HD/NICHD NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; R01 NS129584/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Haplorhini ; Learning/*physiology ; Memory, Long-Term/*physiology ; Motor Cortex/anatomy &amp; histology/*physiology ; Motor Skills/*physiology ; Nerve Net/anatomy &amp; histology/*physiology ; Neurons/physiology ; },
abstract = {Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.},
}
@article {pmid31181846,
year = {2019},
author = {López-Hernández, JL and González-Carrasco, I and López-Cuadrado, JL and Ruiz-Mezcua, B},
title = {Towards the Recognition of the Emotions of People with Visual Disabilities through Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {11},
pages = {},
pmid = {31181846},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Emotions/*physiology ; Humans ; Signal Processing, Computer-Assisted ; Vision Disorders/physiopathology ; },
abstract = {A brain-computer interface is an alternative for communication between people and computers, through the acquisition and analysis of brain signals. Research related to this field has focused on serving people with different types of motor, visual or auditory disabilities. On the other hand, affective computing studies and extracts information about the emotional state of a person in certain situations, an important aspect for the interaction between people and the computer. In particular, this manuscript considers people with visual disabilities and their need for personalized systems that prioritize their disability and the degree that affects them. In this article, a review of the state of the techniques is presented, where the importance of the study of the emotions of people with visual disabilities, and the possibility of representing those emotions through a brain-computer interface and affective computing, are discussed. Finally, the authors propose a framework to study and evaluate the possibility of representing and interpreting the emotions of people with visual disabilities for improving their experience with the use of technology and their integration into today's society.},
}
@article {pmid31180893,
year = {2019},
author = {Ko, LW and Komarov, O and Lin, SC},
title = {Enhancing the Hybrid BCI Performance With the Common Frequency Pattern in Dual-Channel EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {7},
pages = {1360-1369},
doi = {10.1109/TNSRE.2019.2920748},
pmid = {31180893},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual ; Female ; Fourier Analysis ; Healthy Volunteers ; Humans ; Imagination/physiology ; Male ; Psychomotor Performance ; Recognition, Psychology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The brain-computer interface establishes a direct communication pathway between the human brain and an external device by recognizing specific patterns in cortical activities. The principle of hybridization stands for combining at least two different BCI modalities into a single interface with the aim of improving the information transfer rate by increasing the recognition accuracy and number of choices available for the user. This study proposes a simultaneous hybrid BCI system that recognizes the motor imagery (MI) and the steady-state visually evoked potentials (SSVEP) using the EEG signals from a dual-channel EEG setting with sensors placed over the central area (C3 and C4 channels). The data processing implements a supervised optimization algorithm for the feature extraction, named the common frequency pattern, which finds the optimal spectral filter that maximizes the separability of the data by classes. The experiment compares the classification accuracy in a two-class task using the MI, SSVEP and hybrid approaches on seventeen healthy 18-29 years old subjects with various dual-channel setups and complete set of thirty EEG electrodes. The designed system reaches a high accuracy of 97.4 ± 1.1% in the hybrid task using the C3-C4 channel configuration, which is marginally lower than the 98.8 ± 0.5% accuracy achieved with the complete set of channels while applying the support vector classifier; in the plain SSVEP task the accuracy drops from 91.3 ± 3.9% to 86.0 ± 2.5% while moving from the occipital to central area under the dual-channel condition. The results demonstrate that by combining the principles of hybridization and data-driven spectral filtering for the feature selection it is feasible to compensate a lack of spatial information and implement the proposed BCI using a portable few channel EEG device even under sub-optimal conditions for the sensors placement.},
}
@article {pmid31180829,
year = {2020},
author = {Foong, R and Ang, KK and Quek, C and Guan, C and Phua, KS and Kuah, CWK and Deshmukh, VA and Yam, LHL and Rajeswaran, DK and Tang, N and Chew, E and Chua, KSG},
title = {Assessment of the Efficacy of EEG-Based MI-BCI With Visual Feedback and EEG Correlates of Mental Fatigue for Upper-Limb Stroke Rehabilitation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {3},
pages = {786-795},
doi = {10.1109/TBME.2019.2921198},
pmid = {31180829},
issn = {1558-2531},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Feedback, Sensory/physiology ; Humans ; Imagination/physiology ; Mental Fatigue/*physiopathology ; Middle Aged ; Motor Skills/physiology ; Stroke Rehabilitation/*methods ; Upper Extremity/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: This single-arm multisite trial investigates the efficacy of the neurostyle brain exercise therapy towards enhanced recovery (nBETTER) system, an electroencephalogram (EEG)-based motor imagery brain-computer interface (MI-BCI) employing visual feedback for upper-limb stroke rehabilitation, and the presence of EEG correlates of mental fatigue during BCI usage.<br><br>METHODS: A total of 13 recruited stroke patients underwent thrice-weekly nBETTER therapy coupled with standard arm therapy over six weeks. Upper-extremity Fugl-Meyer motor assessment (FMA) scores were measured at baseline (week 0), post-intervention (week 6), and follow-ups (weeks 12 and 24). In total, 11/13 patients (mean age 55.2 years old, mean post-stroke duration 333.7 days, mean baseline FMA 35.5) completed the study.<br><br>RESULTS: Significant FMA gains relative to baseline were observed at weeks 6 and 24. Retrospectively comparing to the standard arm therapy (SAT) control group and BCI with haptic knob (BCI-HK) intervention group from a previous similar study, the SAT group had no significant gains, whereas the BCI-HK group had significant gains at weeks 6, 12, and 24. EEG analysis revealed significant positive correlations between relative beta power and BCI performance in the frontal and central brain regions, suggesting that mental fatigue may contribute to poorer BCI performance.<br><br>CONCLUSION: nBETTER, an EEG-based MI-BCI employing only visual feedback, helps stroke survivors sustain short-term FMA improvement. Analysis of EEG relative beta power indicates that mental fatigue may be present.<br><br>SIGNIFICANCE: This study adds nBETTER to the growing literature of safe and effective stroke rehabilitation MI-BCI, and suggests an additional fatigue-monitoring role in future such BCI.},
}
@article {pmid31178706,
year = {2019},
author = {Tseng, YL and Liu, HH and Liou, M and Tsai, AC and Chien, VSC and Shyu, ST and Yang, ZS},
title = {Lingering Sound: Event-Related Phase-Amplitude Coupling and Phase-Locking in Fronto-Temporo-Parietal Functional Networks During Memory Retrieval of Music Melodies.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {150},
pmid = {31178706},
issn = {1662-5161},
abstract = {Brain oscillations and connectivity have emerged as promising measures of evaluating memory processes, including encoding, maintenance, and retrieval, as well as the related executive function. Although many studies have addressed the neural mechanisms underlying working memory, most of these studies have focused on the visual modality. Neurodynamics and functional connectivity related to auditory working memory are yet to be established. In this study, we explored the dynamic of high density (128-channel) electroencephalography (EEG) in a musical delayed match-to-sample task (DMST), in which 36 participants were recruited and were instructed to recognize and distinguish the target melodies from similar distractors. Event-related spectral perturbations (ERSPs), event-related phase-amplitude couplings (ERPACs), and phase-locking values (PLVs) were used to determine the corresponding brain oscillations and connectivity. First, we observed that low-frequency oscillations in the frontal, temporal, and parietal regions were increased during the processing of both target and distracting melodies. Second, the cross-frequency coupling between low-frequency phases and high-frequency amplitudes was elevated in the frontal and parietal regions when the participants were distinguishing between the target from distractor, suggesting that the phase-amplitude coupling could be an indicator of neural mechanisms underlying memory retrieval. Finally, phase-locking, an index evaluating brain functional connectivity, revealed that there was fronto-temporal phase-locking in the theta band and fronto-parietal phase-locking in the alpha band during the recognition of the two stimuli. These findings suggest the existence of functional connectivity and the phase-amplitude coupling in the neocortex during musical memory retrieval, and provide a highly resolved timeline to evaluate brain dynamics. Furthermore, the inter-regional phase-locking and phase-amplitude coupling among the frontal, temporal and parietal regions occurred at the very beginning of musical memory retrieval, which might reflect the precise timing when cognitive resources were involved in the retrieval of targets and the rejection of similar distractors. To the best of our knowledge, this is the first EEG study employing a naturalistic task to study auditory memory processes and functional connectivity during memory retrieval, results of which can shed light on the use of natural stimuli in studies that are closer to the real-life applications of cognitive evaluations, mental treatments, and brain-computer interface.},
}
@article {pmid31176468,
year = {2019},
author = {Daly, I and Bourgaize, J and Vernitski, A},
title = {Mathematical mindsets increase student motivation: Evidence from the EEG.},
journal = {Trends in neuroscience and education},
volume = {15},
number = {},
pages = {18-28},
doi = {10.1016/j.tine.2019.02.005},
pmid = {31176468},
issn = {2211-9493},
mesh = {Brain/*physiology ; Electroencephalography ; Female ; Humans ; Learning/*physiology ; Male ; Mathematics/education ; *Models, Neurological ; Motivation/*physiology ; Young Adult ; },
abstract = {Mathematical mindset theory suggests learner motivation in mathematics may be increased by opening problems using a set of recommended ideas. However, very little evidence supports this theory. We explore motivation through self-reports while learners attempt problems formulated according to mindset theory and standard problems. We also explore neural correlates of motivation and felt-affect while participants attempt the problems. Notably, we do not tell participants what mindset theory is and instead simply investigate whether mindset problems affect reported motivation levels and neural correlates of motivation in learners. We find significant increases in motivation for mindset problems compared to standard problems. We also find significant differences in brain activity in prefrontal EEG asymmetry between problems. This provides some of the first evidence that mathematical mindset theory increases motivation (even when participants are not aware of mindset theory), and that this change is reflected in brain activity of learners attempting mathematical problems.},
}
@article {pmid31171607,
year = {2019},
author = {An, J and Yadav, T and Hessburg, JP and Francis, JT},
title = {Reward Expectation Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex.},
journal = {eNeuro},
volume = {6},
number = {3},
pages = {},
pmid = {31171607},
issn = {2373-2822},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Animals ; *Brain Waves ; Cues ; Female ; Hand Strength ; Macaca mulatta ; Macaca radiata ; Male ; Motor Cortex/*physiology ; Psychomotor Performance/*physiology ; *Reward ; Signal Processing, Computer-Assisted ; },
abstract = {Reward modulation (M1) could be exploited in developing an autonomously updating brain-computer interface (BCI) based on a reinforcement learning (RL) architecture. For an autonomously updating RL-based BCI system, we would need a reward prediction error, or a state-value representation from the user's neural activity, which the RL-BCI agent could use to update its BCI decoder. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and single/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while four non-human primates (NHPs) performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of α (8-14 Hz) power, α-γ comodulation, α spike-field coherence (SFC), and firing rates (FRs) in general in M1. Furthermore, we found that an increase in α-band power was correlated with a decrease in neural spiking activity, that FRs were highest at the trough of the α-band cycle and lowest at the peak of its cycle. These findings imply that α oscillations modulated by reward expectation have an influence on spike FR and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BCI decoding (An et al., 2018; Zhao et al., 2018).},
}
@article {pmid31171448,
year = {2019},
author = {Trautmann, EM and Stavisky, SD and Lahiri, S and Ames, KC and Kaufman, MT and O'Shea, DJ and Vyas, S and Sun, X and Ryu, SI and Ganguli, S and Shenoy, KV},
title = {Accurate Estimation of Neural Population Dynamics without Spike Sorting.},
journal = {Neuron},
volume = {103},
number = {2},
pages = {292-308.e4},
pmid = {31171448},
issn = {1097-4199},
support = {DP1 OD006409/OD/NIH HHS/United States ; F31 NS089376/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; //Wellcome Trust/United Kingdom ; F31 NS103409/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; /HHMI_/Howard Hughes Medical Institute/United States ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Computer Simulation ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/*cytology ; Neurons/*physiology ; *Nonlinear Dynamics ; },
abstract = {A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.},
}
@article {pmid31168331,
year = {2019},
author = {Lamti, HA and Ben Khelifa, MM and Hugel, V},
title = {Mental fatigue level detection based on event related and visual evoked potentials features fusion in virtual indoor environment.},
journal = {Cognitive neurodynamics},
volume = {13},
number = {3},
pages = {271-285},
pmid = {31168331},
issn = {1871-4080},
abstract = {The purpose of this work is to set up a model that can estimate the mental fatigue of users based on the fusion of relevant features extracted from Positive 300 (P300) and steady state visual evoked potentials (SSVEP) measured by electroencephalogram. To this end, an experimental protocol describes the induction of P300, SSVEP and mental workload (which leads to mental fatigue by varying time-on-task) in different scenarios where environmental artifacts are controlled (obstacles number, obstacles velocities, ambient luminosity). Ten subjects took part in the experiment (with two suffering from cerebral palsy). Their mission is to navigate along a corridor from a starting point A to a goal point B where specific flickering stimuli are introduced to perform the P300 task. On the other hand, SSVEP task is elicited thanks to 10 Hz flickering lights. Correlated features are considered as inputs to fusion block which estimates mental workload. In order to deal with uncertainties and heterogeneity of P300 and SSVEP features, Dempster-Shafer (D-S) evidential reasoning is introduced. As the goal is to assess the reliability for the estimation of mental fatigue levels, D-S is compared to multi layer perception and linear discriminant analysis. The results show that D-S globally outperforms the other classifiers (although its performance significantly decreases between healthy and palsied groups). Finally we discuss the feasibility of such a fusion proposal in real life situation.},
}
@article {pmid31168330,
year = {2019},
author = {Gaume, A and Dreyfus, G and Vialatte, FB},
title = {A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task.},
journal = {Cognitive neurodynamics},
volume = {13},
number = {3},
pages = {257-269},
pmid = {31168330},
issn = {1871-4080},
abstract = {We introduce a cognitive brain-computer interface based on a continuous performance task for the monitoring of variations of visual sustained attention, i.e. the self-directed maintenance of cognitive focus in non-arousing conditions while possibly ignoring distractors and avoiding mind wandering. We introduce a visual sustained attention continuous performance task with three levels of task difficulty. Pairwise discrimination of these task difficulties from electroencephalographic features was performed using a leave-one-subject-out cross validation approach. Features were selected using the orthogonal forward regression supervised feature selection method. Cognitive load was best predicted using a combination of prefrontal theta power, broad spatial range gamma power, fronto-central beta power, and fronto-central alpha power. Generalization performance estimates for pairwise classification of task difficulty using these features reached 75% for 5 s epochs, and 85% for 30 s epochs.},
}
@article {pmid31167106,
year = {2019},
author = {Ratner, BD},
title = {Biomaterials: Been There, Done That, and Evolving into the Future.},
journal = {Annual review of biomedical engineering},
volume = {21},
number = {},
pages = {171-191},
doi = {10.1146/annurev-bioeng-062117-120940},
pmid = {31167106},
issn = {1545-4274},
mesh = {Adaptive Immunity ; Animals ; Biocompatible Materials/*chemistry ; Biodegradation, Environmental ; Brain-Computer Interfaces ; Capsules ; Carbon/pharmacology ; Electrodes ; History, 20th Century ; History, 21st Century ; Humans ; Immunity, Innate ; In Vitro Techniques ; Materials Testing ; Nanotechnology/methods/trends ; Needles ; Peptides/chemistry ; Polymers/chemistry ; Regenerative Medicine ; Tissue Engineering/history/methods/*trends ; },
abstract = {Biomaterials as we know them today had their origins in the late 1940s with off-the-shelf commercial polymers and metals. The evolution of materials for medical applications from these simple origins has been rapid and impactful. This review relates some of the early history; addresses concerns after two decades of development in the twenty-first century; and discusses how advanced technologies in both materials science and biology will address concerns, advance materials used at the biointerface, and improve outcomes for patients.},
}
@article {pmid31164954,
year = {2019},
author = {James, NE and Beffa, L and Oliver, MT and Borgstadt, AD and Emerson, JB and Chichester, CO and Yano, N and Freiman, RN and DiSilvestro, PA and Ribeiro, JR},
title = {Inhibition of DUSP6 sensitizes ovarian cancer cells to chemotherapeutic agents via regulation of ERK signaling response genes.},
journal = {Oncotarget},
volume = {10},
number = {36},
pages = {3315-3327},
pmid = {31164954},
issn = {1949-2553},
support = {P30 GM114750/GM/NIGMS NIH HHS/United States ; },
abstract = {Dual specificity phosphatase 6 (DUSP6) is a protein phosphatase that deactivates extracellular-signal-regulated kinase (ERK). Since the ovarian cancer biomarker human epididymis protein 4 (HE4) interacts with the ERK pathway, we sought to determine the relationship between DUSP6 and HE4 and elucidate DUSP6's role in epithelial ovarian cancer (EOC). Viability assays revealed a significant decrease in cell viability with pharmacological inhibition of DUSP6 using (E/Z)-BCI hydrochloride in ovarian cancer cells treated with carboplatin or paclitaxel, compared to treatment with either agent alone. Quantitative PCR was used to evaluate levels of ERK pathway response genes to BCI in combination with recombinant HE4 (rHE4), carboplatin, and paclitaxel. Expression of EGR1, a promoter of apoptosis, was higher in cells co-treated with BCI and paclitaxel or carboplatin than in cells treated with chemotherapeutic agents alone, while expression of the proto-oncogene c-JUN was decreased with co-treatment. The effect of BCI on the expression of these two genes opposed that of rHE4. Pathway focused quantitative PCR also revealed suppression of ERBB3 in cells co-treated with BCI plus carboplatin or paclitaxel. Finally, expression levels of DUSP6 in EOC tissue were evaluated by immunohistochemistry, revealing significantly increased levels of DUSP6 in serous EOC tissue compared to adjacent normal tissue. A positive correlation between HE4 and DUSP6 levels was determined by Spearman Rank correlation. In conclusion, DUSP6 inhibition sensitizes ovarian cancer cells to chemotherapeutic agents and alters gene expression of ERK response genes, suggesting that DUSP6 could plausibly function as a novel therapeutic target to reduce chemoresistance in EOC.},
}
@article {pmid31164679,
year = {2019},
author = {Nagel, S and Spüler, M},
title = {Asynchronous non-invasive high-speed BCI speller with robust non-control state detection.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {8269},
pmid = {31164679},
issn = {2045-2322},
mesh = {Adult ; Brain/diagnostic imaging/*physiology ; *Brain-Computer Interfaces ; Communication ; *Computers ; Electroencephalography ; Evoked Potentials, Visual ; Female ; Humans ; Language ; Male ; Neurologic Examination ; Photic Stimulation ; },
abstract = {Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Recent BCIs based on visual evoked potentials (VEPs) have shown to be suitable for high-speed communication. However, all recent high-speed BCIs are synchronous, which means that the system works with fixed time slots so that the user is not able to select a command at his own convenience, which poses a problem in real-world applications. In this paper, we present the first asynchronous high-speed BCI with robust distinction between intentional control (IC) and non-control (NC), with a nearly perfect NC state detection of only 0.075 erroneous classifications per minute. The resulting asynchronous speller achieved an average information transfer rate (ITR) of 122.7 bit/min using a 32 target matrix-keyboard. Since the method is based on random stimulation patterns it allows to use an arbitrary number of targets for any application purpose, which was shown by using an 55 target German QWERTZ-keyboard layout which allowed the participants to write an average of 16.1 (up to 30.7) correct case-sensitive letters per minute. As the presented system is the first asynchronous high-speed BCI speller with a robust non-control state detection, it is an important step for moving BCI applications out of the lab and into real-life.},
}
@article {pmid31163412,
year = {2019},
author = {de Neeling, M and Van Hulle, MM},
title = {Single-paradigm and hybrid brain computing interfaces and their use by disabled patients.},
journal = {Journal of neural engineering},
volume = {16},
number = {6},
pages = {061001},
doi = {10.1088/1741-2552/ab2706},
pmid = {31163412},
issn = {1741-2552},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/*trends ; *Persons with Disabilities/psychology ; Electroencephalography/methods/trends ; Event-Related Potentials, P300/physiology ; Humans ; Paralysis/physiopathology/psychology/*rehabilitation ; Self-Help Devices/psychology/trends ; Virtual Reality Exposure Therapy/methods/trends ; },
abstract = {Brain computer interfacing (BCI) has enjoyed increasing interest not only from research communities such as engineering and neuroscience but also from visionaries that predict it will change the way we will interact with technology. Since BCIs establish an alternative communication channel between the brain and the outside world, they have been hailed to provide solutions for patients suffering from severe motor- and/or communication disabilities such as fully paralyzed locked-in syndrome patients. However, despite single-case successes, which sometimes reach a broad audience, BCIs are actually not routinely used to support patients in their daily life activities. This review focusses on non-invasive BCIs, introduces the main paradigms and applications, and shows how the technology has improved over recent years. We identify patient groups that potentially can benefit from BCIs by referring to disability levels and etiology. We list the requirements, indicate how BCIs can tap into their spared competences, and discuss performance issues also in view of other assistive communication technologies. We discuss hybrid BCIs, a more recent development that combines paradigms and signals, possibly also of non-brain origin, to increase performance in terms of accuracy and/or communication speed, also as a way to counter the low performance with a given paradigm by involving another, more suitable one (BCI illiteracy). Finally, we list a few hybrid BCI solutions for patients and note that demonstrations with the ones based entirely on brain activity are still scarce.},
}
@article {pmid31163204,
year = {2019},
author = {Botrel, L and Kübler, A},
title = {Week-long visuomotor coordination and relaxation trainings do not increase sensorimotor rhythms (SMR) based brain-computer interface performance.},
journal = {Behavioural brain research},
volume = {372},
number = {},
pages = {111993},
doi = {10.1016/j.bbr.2019.111993},
pmid = {31163204},
issn = {1872-7549},
mesh = {Adult ; Brain-Computer Interfaces/*psychology ; Electroencephalography/methods ; Feedback, Sensory/*physiology ; Female ; Hand/physiology ; Humans ; Imagery, Psychotherapy/methods ; Male ; Periodicity ; Psychomotor Performance/physiology ; Relaxation Therapy/methods ; Rest/psychology ; },
abstract = {Brain-computer interfaces (BCI) translate brain activity into control signals or commands for a device. Motor imagery of the limbs allows for modulating the sensorimotor rhythms (SMR), but there are up to 30% of the participants for whom electroencephalography (EEG) based SMR-BCI cannot detect any imagery-related changes. Individual variables, such as ability to concentrate on a task and error duration in a two-hand visuomotor coordination (VMC) task have been previously found to predict accuracy in an SMR-BCI. A first study attempted to substantiate those predictors by introducing a 30 min relaxation or VMC training period prior to an SMR-BCI session, but performance did not increase when compared to a control group. As the predictor training may have been too short, we applied 4 such training sessions on consecutive days in the current study. In a pre-post design, SMR-BCI accuracy of n = 39 participants increased from session 1 before to session 2 after the predictor training. While the manipulation of the predictor variables was successful, there was no effect on SMR-BCI performance. BCI accuracy correlated positively with the neurophysiological SMR predictor identified by Blankertz et al. [3], consolidating its predictive value, and with the state mindfulness scale. No other psychological predictor could be identified or replicated. Further studies should therefore focus more on delineating (partially) replicated or potential predictors such as VMC or mindfulness to help refining a sound model to predict SMR-BCI accuracy.},
}
@article {pmid31159454,
year = {2019},
author = {Jochumsen, M and Navid, MS and Nedergaard, RW and Signal, N and Rashid, U and Hassan, A and Haavik, H and Taylor, D and Niazi, IK},
title = {Self-Paced Online vs. Cue-Based Offline Brain-Computer Interfaces for Inducing Neural Plasticity.},
journal = {Brain sciences},
volume = {9},
number = {6},
pages = {},
pmid = {31159454},
issn = {2076-3425},
abstract = {: Brain-computer interfaces (BCIs), operated in a cue-based (offline) or self-paced (online) mode, can be used for inducing cortical plasticity for stroke rehabilitation by the pairing of movement-related brain activity with peripheral electrical stimulation. The aim of this study was to compare the difference in cortical plasticity induced by the two BCI modes. Fifteen healthy participants participated in two experimental sessions: cue-based BCI and self-paced BCI. In both sessions, imagined dorsiflexions were extracted from continuous electroencephalogram (EEG) and paired 50 times with the electrical stimulation of the common peroneal nerve. Before, immediately after, and 30 minutes after each intervention, the cortical excitability was measured through the motor-evoked potentials (MEPs) of tibialis anterior elicited through transcranial magnetic stimulation. Linear mixed regression models showed that the MEP amplitudes increased significantly (p < 0.05) from pre- to post- and 30-minutes post-intervention in terms of both the absolute and relative units, regardless of the intervention type. Compared to pre-interventions, the absolute MEP size increased by 79% in post- and 68% in 30-minutes post-intervention in the self-paced mode (with a true positive rate of ~75%), and by 37% in post- and 55% in 30-minutes post-intervention in the cue-based mode. The two modes were significantly different (p = 0.03) at post-intervention (relative units) but were similar at both post timepoints (absolute units). These findings suggest that immediate changes in cortical excitability may have implications for stroke rehabilitation, where it could be used as a priming protocol in conjunction with another intervention; however, the findings need to be validated in studies involving stroke patients.},
}
@article {pmid31158476,
year = {2019},
author = {Vidaurre, C and Ramos Murguialday, A and Haufe, S and Gómez, M and Müller, KR and Nikulin, VV},
title = {Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation.},
journal = {NeuroImage},
volume = {199},
number = {},
pages = {375-386},
doi = {10.1016/j.neuroimage.2019.05.074},
pmid = {31158476},
issn = {1095-9572},
mesh = {Adult ; Afferent Pathways/physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Calibration ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Neurofeedback/physiology ; Sensory Thresholds/*physiology ; },
abstract = {An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).},
}
@article {pmid31156367,
year = {2019},
author = {Zeng, H and Sun, Y and Xu, G and Wu, C and Song, A and Xu, B and Li, H and Hu, C},
title = {The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {480},
pmid = {31156367},
issn = {1662-4548},
abstract = {It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses.},
}
@article {pmid31151119,
year = {2019},
author = {Roy, Y and Banville, H and Albuquerque, I and Gramfort, A and Falk, TH and Faubert, J},
title = {Deep learning-based electroencephalography analysis: a systematic review.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {051001},
doi = {10.1088/1741-2552/ab260c},
pmid = {31151119},
issn = {1741-2552},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/*trends ; Databases, Factual/trends ; Deep Learning/*trends ; Electroencephalography/methods/*trends ; Humans ; },
abstract = {CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.<br><br>OBJECTIVE: In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.<br><br>METHODS: Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends.<br><br>RESULTS: Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code.<br><br>SIGNIFICANCE: To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.},
}
@article {pmid31151035,
year = {2019},
author = {Khalili Ardali, M and Rana, A and Purmohammad, M and Birbaumer, N and Chaudhary, U},
title = {Semantic and BCI-performance in completely paralyzed patients: Possibility of language attrition in completely locked in syndrome.},
journal = {Brain and language},
volume = {194},
number = {},
pages = {93-97},
doi = {10.1016/j.bandl.2019.05.004},
pmid = {31151035},
issn = {1090-2155},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Cognition ; Female ; Humans ; Male ; Quadriplegia/*physiopathology/psychology ; *Semantics ; },
abstract = {Patients with completely locked-in syndrome (CLIS) are incapable of any voluntary muscle movement and do not have any means of communication. Recently functional near infrared spectroscopy (fNIRS) based brain computer interface (BCI) has been successfully used to enable communication with these patients. The developed fNIRS-BCI system relies on the intactness of language comprehension in these patients in all dimensions of language. Interwoven language and motor cortex in brain, and lack of muscular activity in long run, can cause language attrition due to complete immobility in CLIS patients. In this study we have investigated effects of semantic content of sentences presented to a CLIS patient on the performance of the BCI system during a YES/NO paradigm. Comparison of communication success rate in BCI classification between different semantic categories indicate that semantic content of sentences presented to a CLIS patient can affect the BCI performance. Affected concepts are mostly associated with executive words. These findings can be beneficial towards development of more reliable communication device for patients in CLIS. In addition, these results may assist in elucidating the cognitive changes in completely paralyzed patients with the passage of time since the onset of total immovability.},
}
@article {pmid31150335,
year = {2020},
author = {Cao, J and Grover, P},
title = {STIMULUS: Noninvasive Dynamic Patterns of Neurostimulation Using Spatio-Temporal Interference.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {3},
pages = {726-737},
doi = {10.1109/TBME.2019.2919912},
pmid = {31150335},
issn = {1558-2531},
mesh = {Brain/physiology ; Deep Brain Stimulation/*methods ; Electroencephalography ; Head/physiology ; Humans ; *Models, Neurological ; Neurons/physiology ; Signal Processing, Computer-Assisted ; Transcranial Direct Current Stimulation/*methods ; },
abstract = {OBJECTIVE: This paper obtains strategies that can achieve spatially precise noninvasive deep brain stimulation using electrical currents.<br><br>METHODS: We provide the Spatio-Temporal Interference-based stiMULation focUsing Strategy (STIMULUS) that generates rich patterns of spatiotemporally interfering currents to stimulate precisely and deep inside the brain. To calibrate and compare the accuracy of stimulation using different techniques, we utilize computational Hodgkin-Huxley-type models for neurons and a model of current dispersion in the head.<br><br>RESULTS: In this computational model, STIMULUS dramatically outperforms the recently proposed Temporal Interference (TI) stimulation strategy in spatial precision. Our results also suggest that STIMULUS can attain steerable and multisite stimulation, which can be important in giving feedback in brain-machine interfaces. Finally, by examining more mammalian neuron types, we also observe that not every neuron exhibits temporal-interference stimulation.<br><br>CONCLUSIONS: Computer simulations suggest that the proposed STIMULUS strategy has potential to achieve noninvasive electrical deep brain stimulation with high spatial precision and, further, has the flexibility of generating rich stimulation patterns. The fact that some neuron types do not exhibit TI stimulation suggests that caution is needed in evaluating conclusions of application of TI stimulation on large mammalian brains.<br><br>SIGNIFICANCE: A technique to reliably, noninvasively, and precisely stimulate deep inside the human brain could revolutionize human neuroscience and clinical treatments. We obtain the first computational demonstration of the recently proposed TI stimulation. Advancing on that, we propose a novel strategy that can perform stimulation with high precision and flexibility.},
}
@article {pmid31144834,
year = {2019},
author = {Czekierda, K and Horodyska, K and Banik, A and Wilhelm, L and Knoll, N and Luszczynska, A},
title = {Meaning in life and physical quality of life: Cross-lagged associations during inpatient rehabilitation.},
journal = {Rehabilitation psychology},
volume = {64},
number = {4},
pages = {425-434},
doi = {10.1037/rep0000281},
pmid = {31144834},
issn = {1939-1544},
support = {//National Science Center/ ; //National Science Council of Taiwanenter/ ; //Foundation for Polish Science/ ; //Ministry of Science and Higher Education/ ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; Central Nervous System Diseases/*psychology/*rehabilitation ; Female ; Humans ; Inpatients/*psychology/statistics &amp; numerical data ; Longitudinal Studies ; Male ; Middle Aged ; Musculoskeletal Diseases/*psychology/*rehabilitation ; Quality of Life/*psychology ; Rehabilitation Centers ; Self Report ; Surveys and Questionnaires ; Young Adult ; },
abstract = {OBJECTIVES: This study investigated reciprocal associations between meaning in life and physical quality of life (QOL) in the rehabilitation context. It was hypothesized that a higher level of meaning in life at Time 1 (T1) would predict better physical QOL at Time 2 (T2), and that better physical QOL (T1) would predict a higher level of meaning in life (T2).<br><br>RESEARCH METHOD: This longitudinal study enrolled 339 participants (aged 19-84 years, 57.9% women) who provided self-report data (T1) at the beginning of the inpatient rehabilitation for central nervous system diseases (CNSD; e.g., stroke; n = 89) or musculoskeletal system diseases (MSD; e.g., dorsopathies; n = 250), and at the end of the inpatient rehabilitation (T2, 1-month follow-up). Data were collected in 6 inpatient rehabilitation centers. Manifest cross-lagged panel analyses were conducted for the total sample.<br><br>RESULTS: Path analyses indicated a significant cross-lagged-effect (.126, p < .002 [95% BCI: 0.020, 0.132]) from meaning in life (T1) to physical QOL at the follow-up (T2). Physical QOL (T1) did not precede meaning in life (T2).<br><br>CONCLUSIONS: Among patients participating in rehabilitation due to CNSD or MSD, a higher level of meaning in life may precede better physical QOL. Interventions aimed at physical QOL improvement among patients who participated in an inpatient rehabilitation may benefit from a focus on raising patients' meaning in life. (PsycINFO Database Record (c) 2019 APA, all rights reserved).},
}
@article {pmid31144639,
year = {2019},
author = {Nagai, H and Tanaka, T},
title = {Action Observation of Own Hand Movement Enhances Event-Related Desynchronization.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {7},
pages = {1407-1415},
doi = {10.1109/TNSRE.2019.2919194},
pmid = {31144639},
issn = {1558-0210},
mesh = {Algorithms ; Alpha Rhythm ; Brain-Computer Interfaces ; Cues ; Electroencephalography ; *Electroencephalography Phase Synchronization ; Evoked Potentials/*physiology ; Feedback, Sensory ; *Hand ; Healthy Volunteers ; Humans ; Imagination ; Male ; Movement/*physiology ; *Observation ; Psychomotor Performance/physiology ; Stroke Rehabilitation/methods ; Young Adult ; },
abstract = {A stroke occurs when blood flow to the brain is critically reduced or blocked, potentially resulting in motor paralysis. One of the most promising and effective neurorehabilitation methods for strokes is a closed-loop brain-computer interface (BCI) based on the motor imagery (MI). For the design of MI-based BCI, action observation (AO) during MI facilitates the detection of a user's motor intention. In this paper, we investigated whether or not the AO's targeted objects (the hand of a participant or another person) affects brain activity during MI. To investigate the differences in brain activity induced by the targeted objection, we recorded electroencephalography (EEG) data of 15 healthy right-handed males during three different conditions: 1) MI and AO of a participant's hand (MI + ownAO); 2) MI and AO of a non-participant's hand (MI + otherAO); and 3) MI only. The results showed that the event-related desynchronization (ERD) responses in the alpha frequency band (8-13 Hz) during MI + ownAO over the sensorimotor area (at the C3 and C4 channel locations) were stronger than those during the other two conditions. The results also showed that the difference between the participants' and non-participants' hands affected ERD responses during MI + ownAO and MI + otherAO.},
}
@article {pmid31143204,
year = {2019},
author = {Cartocci, G and Modica, E and Rossi, D and Inguscio, BMS and Aricò, P and Martinez Levy, AC and Mancini, M and Cherubino, P and Babiloni, F},
title = {Antismoking Campaigns' Perception and Gender Differences: A Comparison among EEG Indices.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {7348795},
pmid = {31143204},
issn = {1687-5273},
mesh = {Brain/physiology/physiopathology ; Brain Mapping/methods ; *Electroencephalography/methods ; Female ; Humans ; Male ; Sex Factors ; Smoking/*adverse effects ; *Smoking Prevention ; },
abstract = {Human factors' aim is to understand and evaluate the interactions between people and tasks, technologies, and environment. Among human factors, it is possible then to include the subjective reaction to external stimuli, due to individual's characteristics and states of mind. These processes are also involved in the perception of antismoking public service announcements (PSAs), the main tool for governments to contrast the first cause of preventable deaths in the world: tobacco addiction. In the light of that, in the present article, it has been investigated through the comparison of different electroencephalographic (EEG) indices a typical item known to be able of influencing PSA perception, that is gender. In order to investigate the neurophysiological underpinnings of such different perception, we tested two PSAs: one with a female character and one with a male character. Furthermore, the experimental sample was divided into men and women, as well as smokers and nonsmokers. The employed EEG indices were the mental engagement (ME: the ratio between beta activity and the sum of alpha and theta activity); the approach/withdrawal (AW: the frontal alpha asymmetry in the alpha band); and the frontal theta activity and the spectral asymmetry index (SASI: the ratio between beta minus theta and beta plus theta). Results suggested that the ME and the AW presented an opposite trend, with smokers showing higher ME and lower AW than nonsmokers. The ME and the frontal theta also evidenced a statistically significant interaction between the kind of the PSA and the gender of the observers; specifically, women showed higher ME and frontal theta activity for the male character PSA. This study then supports the usefulness of the ME and frontal theta for purposes of PSAs targeting on the basis of gender issues and of the ME and the AW and for purposes of PSAs targeting on the basis of smoking habits.},
}
@article {pmid31141703,
year = {2019},
author = {Luo, J and Wang, J and Xu, R and Xu, K},
title = {Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification.},
journal = {Journal of neuroscience methods},
volume = {323},
number = {},
pages = {98-107},
doi = {10.1016/j.jneumeth.2019.05.011},
pmid = {31141703},
issn = {1872-678X},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {BACKGROUND: Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control external auxiliary equipment. Therefore, EEG-based motor imagery classification has received increasing attention in the fields of neuroscience. The common spatial pattern (CSP) algorithm has recently achieved great success in motor imagery classification. However, varying discriminative frequency bands and few-channel EEG limit the performance of CSP.<br><br>NEW METHOD: A class discrepancy-guided sub-band filter-based CSP (CDFCSP) algorithm is proposed to automatically recognize and augment the discriminative frequency bands for CSP algorithms. Specifically, a priori knowledge and templates obtained from the training set were applied as the design guidelines of the class discrepancy-guided sub-band filter (CDF). Second, a filter bank CSP was used to extract features from EEG traces filtered by the CDF. Finally, the CSP features of multiple frequency bands were leveraged to train linear support vector machine classifier and generate prediction.<br><br>RESULTS: BCI competition IV datasets 2a and 2b, which include EEGs from 18 subjects, were used to validate the performance improvement provided by the CDF. Student's t-tests of the CDFCSP versus the filter bank CSP without the CDF showed that the performance improvement was significant (i.e., p-values of 0.040 and 0.032 for the ratio and normalization mode CDFCSP, respectively).<br><br>The experiments show that the proposed CDFCSP improves the CSP algorithm and outperforms the other state-of-the-art algorithms evaluated in this paper.<br><br>CONCLUSIONS: The increased performance of the proposed CDFCSP algorithm can promote the application of BCI systems.},
}
@article {pmid31139966,
year = {2019},
author = {Enriquez-Geppert, S and Smit, D and Pimenta, MG and Arns, M},
title = {Neurofeedback as a Treatment Intervention in ADHD: Current Evidence and Practice.},
journal = {Current psychiatry reports},
volume = {21},
number = {6},
pages = {46},
pmid = {31139966},
issn = {1535-1645},
mesh = {Attention Deficit Disorder with Hyperactivity/physiopathology/psychology/*therapy ; Humans ; Meta-Analysis as Topic ; *Neurofeedback ; Randomized Controlled Trials as Topic ; Treatment Outcome ; },
abstract = {PURPOSE OF REVIEW: Current traditional treatments for ADHD present serious limitations in terms of long-term maintenance of symptom remission and side effects. Here, we provide an overview of the rationale and scientific evidence of the efficacy of neurofeedback in regulating the brain functions in ADHD. We also review the institutional and professional regulation of clinical neurofeedback implementations.<br><br>RECENT FINDINGS: Based on meta-analyses and (large multicenter) randomized controlled trials, three standard neurofeedback training protocols, namely theta/beta (TBR), sensori-motor rhythm (SMR), and slow cortical potential (SCP), turn out to be efficacious and specific. However, the practical implementation of neurofeedback as a clinical treatment is currently not regulated. We conclude that neurofeedback based on standard protocols in ADHD should be considered as a viable treatment alternative and suggest that further research is needed to understand how specific neurofeedback protocols work. Eventually, we emphasize the need for standard neurofeedback training for practitioners and binding standards for use in clinical practice.},
}
@article {pmid31139853,
year = {2019},
author = {Liu, J and Rasheed, A and He, Z and Imtiaz, M and Arif, A and Mahmood, T and Ghafoor, A and Siddiqui, SU and Ilyas, MK and Wen, W and Gao, F and Xie, C and Xia, X},
title = {Genome-wide variation patterns between landraces and cultivars uncover divergent selection during modern wheat breeding.},
journal = {TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik},
volume = {132},
number = {9},
pages = {2509-2523},
pmid = {31139853},
issn = {1432-2242},
support = {31461143021//National Natural Science Foundation of China/ ; 31550110212//National Natural Science Foundation of China/ ; 2016YFD0101802//National Key Research and Development Program of China/ ; 2016YFE0108600//National Key Research and Development Program of China/ ; 2014BAD01B05//National Key Research and Development Program of China/ ; },
mesh = {Chromosome Mapping ; Chromosomes, Plant ; Crops, Agricultural/*genetics/growth &amp; development ; *Genome, Plant ; Genotype ; Linkage Disequilibrium ; Phenotype ; *Plant Breeding ; *Polymorphism, Single Nucleotide ; *Selection, Genetic ; Triticum/*genetics/growth &amp; development ; },
abstract = {Genetic diversity, population structure, LD decay, and selective sweeps in 687 wheat accessions were analyzed, providing relevant guidelines to facilitate the use of the germplasm in wheat breeding. Common wheat (Triticum aestivum L.) is one of the most widely grown crops in the world. Landraces were subjected to strong human-mediated selection in developing high-yielding, good quality, and widely adapted cultivars. To investigate the genome-wide patterns of allelic variation, population structure and patterns of selective sweeps during modern wheat breeding, we tested 687 wheat accessions, including landraces (148) and cultivars (539) mainly from China and Pakistan in a wheat 90 K single nucleotide polymorphism array. Population structure analysis revealed that cultivars and landraces from China and Pakistan comprised three relatively independent genetic clusters. Cultivars displayed lower nucleotide diversity and a wider average LD decay across whole genome, indicating allelic erosion and a diversity bottleneck due to the modern breeding. Analysis of genetic differentiation between landraces and cultivars from China and Pakistan identified allelic variants subjected to selection during modern breeding. In total, 477 unique genome regions showed signatures of selection, where 109 were identified in both China and Pakistan germplasm. The majority of genomic regions were located in the B genome (225), followed by the A genome (175), and only 77 regions were located in the D genome. EigenGWAS was further used to identify key selection loci in modern wheat cultivars from China and Pakistan by comparing with global winter wheat and spring wheat diversity panels, respectively. A few known functional genes or loci found within these genome regions corresponded to known phenotypes for disease resistance, vernalization, quality, adaptability and yield-related traits. This study uncovered molecular footprints of modern wheat breeding and explained the genetic basis of polygenic adaptation in wheat. The results will be useful for understanding targets of modern wheat breeding, and in devising future breeding strategies to target beneficial alleles currently not pursued.},
}
@article {pmid31135364,
year = {2019},
author = {Kang, YN and Chou, N and Jang, JW and Byun, D and Kang, H and Moon, DJ and Kim, J and Kim, S},
title = {An Intrafascicular Neural Interface With Enhanced Interconnection for Recording of Peripheral Nerve Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1312-1319},
doi = {10.1109/TNSRE.2019.2917916},
pmid = {31135364},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Dogs ; Electrochemical Techniques ; Electrodes, Implanted ; Equipment Design ; Mechanical Phenomena ; Microelectrodes ; Peripheral Nerves/*physiology ; *Polymers ; Sciatic Nerve/physiology ; Tensile Strength ; Wireless Technology ; *Xylenes ; },
abstract = {For implantable devices, Parylene C (hereafter referred to as Parylene) has shown promising properties such as flexibility, biocompatibility, biostability, and good barrier properties. Parylene-based flexible interconnection cable (FIC) was previously developed to connect a flexible penetrating microelectrode array (FPMA) with a recording system. However, Parylene-based FIC was difficult to handle and prone to damage during the implantation surgery because of its low mechanical strength. To improve the mechanical properties of the FIC, we suggest a mechanically enhanced flexible interconnection cable (enhanced FIC) obtained using a combination of Parylene and polyimide. To investigate the long-term stability of the enhanced FIC, Parylene-only FIC, and enhanced FIC were tested and their mechanical properties were compared under an accelerated aging condition. During the course of six months of soaking, the maximum strength of the enhanced FIC remained twice as high as that of the Parylene-only FIC throughout the experiment, although the mechanical strength of both FICs decreased over time. To show the capability of the enhanced FIC in the context of nerve signal recording as a part of a neural interfacing device, it was assembled together with the FPMA and custom-made wireless recording electronics. We demonstrated the feasibility of the enhanced FIC in an in vivo application by recording acute nerve signals from canine sciatic nerves.},
}
@article {pmid31135349,
year = {2019},
author = {Weber, C},
title = {Identifying Neurotechnology Challenges at NeuroCAS.},
journal = {IEEE pulse},
volume = {10},
number = {3},
pages = {26-30},
doi = {10.1109/MPULS.2019.2911809},
pmid = {31135349},
issn = {2154-2317},
mesh = {*Biomedical Engineering ; *Brain-Computer Interfaces ; Congresses as Topic ; *Electrocorticography ; Humans ; Ohio ; },
abstract = {The second NeuroCAS event held 20-21 October 2018 in Cleveland, OH, USA, was attended by researchers and entrepreneurs across the fields of biosignals and neurotechnology. On the heels of the IEEE BioCAS Conference, the intent of this collaborative workshop was to offer an opportunity for those within the larger biomedical circuits and systems community interested in brain activity and neurotechnology to interact, hear perspectives outside of their community, and most importantly, discuss current challenges as well as new opportunities across the fields of electrocorticography (ECoG) and brain-body axis (PNS/ANS) interfaces.},
}
@article {pmid31133779,
year = {2019},
author = {Keren, H and Partzsch, J and Marom, S and Mayr, CG},
title = {A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {432},
pmid = {31133779},
issn = {1662-4548},
abstract = {Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.},
}
@article {pmid31133777,
year = {2019},
author = {Sun, W and Suzuki, K and Toptunov, D and Stoyanov, S and Yuzaki, M and Khiroug, L and Dityatev, A},
title = {In vivo Two-Photon Imaging of Anesthesia-Specific Alterations in Microglial Surveillance and Photodamage-Directed Motility in Mouse Cortex.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {421},
pmid = {31133777},
issn = {1662-4548},
abstract = {Two-photon imaging of fluorescently labeled microglia in vivo provides a direct approach to measure motility of microglial processes as a readout of microglial function that is crucial in the context of neurodegenerative diseases, as well as to understand the neuroinflammatory response to implanted substrates and brain-computer interfaces. In this longitudinal study, we quantified surveilling and photodamage-directed microglial processes motility in both acute and chronic cranial window preparations and compared the motility under isoflurane and ketamine anesthesia to an awake condition in the same animal. The isoflurane anesthesia increased the length of surveilling microglial processes in both acute and chronic preparations, while ketamine increased the number of microglial branches in acute preparation only. In chronic (but not acute) preparation, the extension of microglial processes toward the laser-ablated microglial cell was faster under isoflurane (but not ketamine) anesthesia than in awake mice, indicating distinct effects of anesthetics and of preparation type. These data reveal potentiating effects of isoflurane on microglial response to damage, and provide a framework for comparison and optimal selection of experimental conditions for quantitative analysis of microglial function using two-photon microscopy in vivo.},
}
@article {pmid31133772,
year = {2019},
author = {Merrill, N and Curran, MT and Gandhi, S and Chuang, J},
title = {One-Step, Three-Factor Passthought Authentication With Custom-Fit, In-Ear EEG.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {354},
pmid = {31133772},
issn = {1662-4548},
abstract = {In-ear EEG offers a promising path toward usable, discreet brain-computer interfaces (BCIs) for both healthy individuals and persons with disabilities. To test the promise of this modality, we produced a brain-based authentication system using custom-fit EEG earpieces. In a sample of N = 7 participants, we demonstrated that our system has high accuracy, higher than prior work using non-custom earpieces. We demonstrated that both inherence and knowledge factors contribute to authentication accuracy, and performed a simulated attack to show our system's robustness against impersonation. From an authentication standpoint, our system provides three factors of authentication in a single step. From a usability standpoint, our system does not require a cumbersome, head-worn device.},
}
@article {pmid31133637,
year = {2019},
author = {Risso, G and Valle, G and Iberite, F and Strauss, I and Stieglitz, T and Controzzi, M and Clemente, F and Granata, G and Rossini, PM and Micera, S and Baud-Bovy, G},
title = {Optimal integration of intraneural somatosensory feedback with visual information: a single-case study.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {7916},
pmid = {31133637},
issn = {2045-2322},
mesh = {Amputees/*rehabilitation ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electric Stimulation/instrumentation/methods ; Electrodes, Implanted ; Electromyography ; Feedback, Sensory/*physiology ; Female ; Forearm/innervation/physiology ; Humans ; Middle Aged ; Single-Case Studies as Topic ; Treatment Outcome ; Ulnar Nerve/*physiology ; },
abstract = {Providing somatosensory feedback to amputees is a long-standing objective in prosthesis research. Recently, implantable neural interfaces have yielded promising results in this direction. There is now considerable evidence that the nervous system integrates redundant signals optimally, weighting each signal according to its reliability. One question of interest is whether artificial sensory feedback is combined with other sensory information in a natural manner. In this single-case study, we show that an amputee with a bidirectional prosthesis integrated artificial somatosensory feedback and blurred visual information in a statistically optimal fashion when estimating the size of a hand-held object. The patient controlled the opening and closing of the prosthetic hand through surface electromyography, and received intraneural stimulation proportional to the object's size in the ulnar nerve when closing the robotic hand on the object. The intraneural stimulation elicited a vibration sensation in the phantom hand that substituted the missing haptic feedback. This result indicates that sensory substitution based on intraneural feedback can be integrated with visual feedback and make way for a promising method to investigate multimodal integration processes.},
}
@article {pmid31127535,
year = {2019},
author = {Rodrigues, PG and Filho, CAS and Attux, R and Castellano, G and Soriano, DC},
title = {Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {8},
pages = {1709-1725},
pmid = {31127535},
issn = {1741-0444},
support = {2015/24260-7//FAPESP/ ; 2013/07559-3//FAPESP/ ; 449467/2014- 7//CNPq/ ; 305616/2016-1//CNPq/ ; 305621/2015-7//CNPq/ ; 01.16.0067.00//FINEP/ ; },
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Cortical Synchronization ; Databases, Factual ; Electrodes ; Electroencephalography/instrumentation/*methods ; Foot ; Hand ; Humans ; Imagination/*physiology ; Motor Activity/physiology ; Nontherapeutic Human Experimentation ; Signal Processing, Computer-Assisted ; Tongue ; },
abstract = {This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification.},
}
@article {pmid31112937,
year = {2020},
author = {Wong, CM and Wan, F and Wang, B and Wang, Z and Nan, W and Lao, KF and Mak, PU and Vai, MI and Rosa, A},
title = {Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {17},
number = {1},
pages = {016026},
doi = {10.1088/1741-2552/ab2373},
pmid = {31112937},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Humans ; Learning/*physiology ; Recognition, Psychology/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data.<br><br>APPROACH: A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits.<br><br>MAIN RESULTS: Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration.<br><br>SIGNIFICANCE: A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.},
}
@article {pmid31112554,
year = {2019},
author = {Lim, CG and Poh, XWW and Fung, SSD and Guan, C and Bautista, D and Cheung, YB and Zhang, H and Yeo, SN and Krishnan, R and Lee, TS},
title = {A randomized controlled trial of a brain-computer interface based attention training program for ADHD.},
journal = {PloS one},
volume = {14},
number = {5},
pages = {e0216225},
pmid = {31112554},
issn = {1932-6203},
mesh = {*Attention ; Attention Deficit Disorder with Hyperactivity/*therapy ; Behavior Therapy ; *Brain-Computer Interfaces ; Child ; Education/*methods ; Female ; Humans ; Male ; Treatment Outcome ; },
abstract = {OBJECTIVE: The use of brain-computer interface in neurofeedback therapy for attention deficit hyperactivity disorder (ADHD) is a relatively new approach. We conducted a randomized controlled trial (RCT) to determine whether an 8-week brain computer interface (BCI)-based attention training program improved inattentive symptoms in children with ADHD compared to a waitlist-control group, and the effects of a subsequent 12-week lower-intensity training.<br><br>STUDY DESIGN: We randomized 172 children aged 6-12 attending an outpatient child psychiatry clinic diagnosed with inattentive or combined subtypes of ADHD and not receiving concurrent pharmacotherapy or behavioral intervention to either the intervention or waitlist-control group. Intervention involved 3 sessions of BCI-based training for 8 weeks, followed by 3 training sessions per month over the subsequent 12 weeks. The waitlist-control group received similar 20-week intervention after a wait-time of 8 weeks.<br><br>RESULTS: The participants' mean age was 8.6 years (SD = 1.51), with 147 males (85.5%) and 25 females (14.5%). Modified intention to treat analyzes conducted on 163 participants with at least one follow-up rating showed that at 8 weeks, clinician-rated inattentive symptoms on the ADHD-Rating Scale (ADHD-RS) was reduced by 3.5 (SD 3.97) in the intervention group compared to 1.9 (SD 4.42) in the waitlist-control group (between-group difference of 1.6; 95% CI 0.3 to 2.9 p = 0.0177). At the end of the full 20-week treatment, the mean reduction (pre-post BCI) of the pooled group was 3.2 (95% CI 2.4 to 4.1).<br><br>CONCLUSION: The results suggest that the BCI-based attention training program can improve ADHD symptoms after a minimum of 24 sessions and maintenance training may sustain this improvement. This intervention may be an option for treating milder cases or as an adjunctive treatment.},
}
@article {pmid31111753,
year = {2019},
author = {Gok, S and Sahin, M},
title = {Prediction of Forelimb EMGs and Movement Phases from Corticospinal Signals in the Rat During the Reach-to-Pull Task.},
journal = {International journal of neural systems},
volume = {29},
number = {7},
pages = {1950009},
doi = {10.1142/S0129065719500096},
pmid = {31111753},
issn = {1793-6462},
mesh = {Animals ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electromyography/*methods ; Forecasting ; Forelimb/innervation/*physiology ; Movement/*physiology ; Pyramidal Tracts/*physiology ; Rats ; Rats, Long-Evans ; Spinal Cord Injuries/diagnosis/physiopathology/rehabilitation ; },
abstract = {Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were 80 ± 10% (min: 62% to max: 97%) and 55 ± 8% (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was 0.5 ± 0.13 (n = 124), whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.},
}
@article {pmid31110919,
year = {2019},
author = {Symes, LB and Wershoven, NL and Hoeger, LO and Ralston, JS and Martinson, SJ and Ter Hofstede, HM and Palmer, CM},
title = {Applying and refining DNA analysis to determine the identity of plant material extracted from the digestive tracts of katydids.},
journal = {PeerJ},
volume = {7},
number = {},
pages = {e6808},
pmid = {31110919},
issn = {2167-8359},
support = {P20 GM103449/GM/NIGMS NIH HHS/United States ; },
abstract = {BACKGROUND: Feeding habits are central to animal ecology, but it is often difficult to characterize the diet of organisms that are arboreal, nocturnal, rare, or highly mobile. Genetic analysis of gut contents is a promising approach for expanding our understanding of animal feeding habits. Here, we adapt a laboratory protocol for extracting and sequencing plant material from gut contents and apply it to Neotropical forest katydids (Orthoptera: Tettigoniidae) on Barro Colorado Island (BCI) in Panama.<br><br>METHODS: Our approach uses three chloroplast primer sets that were previously developed to identify vegetation on BCI. We describe the utility and success rate of each primer set. We then test whether there is a significant difference in the amplification and sequencing success of gut contents based on the size or sex of the katydid, the time of day that it was caught, and the color of the extracted gut contents.<br><br>RESULTS: We find that there is a significant difference in sequencing success as a function of gut color. When extracts were yellow, green, or colorless the likelihood of successfully amplifying DNA ranged from ~30-60%. When gut extracts were red, orange, or brown, amplification success was exceptionally low (0-8%). Amplification success was also higher for smaller katydids and tended to be more successful in katydids that were captured earlier in the night. Strength of the amplified product was indicative of the likelihood of sequencing success, with strong bands having a high likelihood of success. By anticipating which samples are most likely to succeed, we provide information useful for estimating the number of katydids that need to be collected and minimizing the costs of purifying, amplifying, and sequencing samples that are unlikely to succeed. This approach makes it possible to understand the herbivory patterns of these trophically important katydids and can be applied more broadly to understand the diet of other tropical herbivores.},
}
@article {pmid31110601,
year = {2018},
author = {McLean, J and Quivira, F and Erdoğmuş, D},
title = {IMPROVED CLASSIFICATION IN TACTILE BCIS USING A NOISY LABEL MODEL.},
journal = {Proceedings. IEEE International Symposium on Biomedical Imaging},
volume = {2018},
number = {},
pages = {757-761},
pmid = {31110601},
issn = {1945-7928},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Tactile BCIs have gained recent popularity in the BCI community due to the advantages of using a stimulation medium which does not inhibit the users visual or auditory senses, is naturally inconspicuous, and can still be used by a person who may be visually or auditorily impaired. While many systems have been proposed which utilize the P300 response elicited through an oddball task, these systems struggle to classify user responses with accuracies comparable to many visual stimulus based systems. In this study, we model the tactile ERP generation as label noise and develop a novel BCI paradigm for binary communication designed to minimize label confusion. The classification model is based on a modified Gaussian mixture and trained using expectation maximization (EM). Finally, we show after testing on multiple subjects that this approach yields cross-validated accuracies for all users which are significantly above chance and suggests that such an approach is robust and reliable for a variety of binary communication-based applications.},
}
@article {pmid31110600,
year = {2018},
author = {Koçanaoğulları, A and Quivira, F and Erdoğmuş, D},
title = {INCORPORATING TEMPORAL DEPENDENCY ON ERP BASED BCI.},
journal = {Proceedings. IEEE International Symposium on Biomedical Imaging},
volume = {2018},
number = {},
pages = {752-756},
pmid = {31110600},
issn = {1945-7928},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {In brain computer interface (BCI) systems based on event related potentials (ERPs), a windowed electroencephalography (EEG) signal is taken into consideration for the assumed duration of the ERP potential. In BCI applications inter stimuli interval is shorter than the ERP duration. This causes temporal dependencies over observation potentials thus disallows taking the data into consideration independently. However, conventionally the data is assumed to be independent for decreasing complexity. In this paper we propose a graphical model which covers the temporal dependency into consideration by labeling each time sample. We also propose a formulation to exploit the time series structure of the EEG.},
}
@article {pmid31110515,
year = {2019},
author = {Carino-Escobar, RI and Carrillo-Mora, P and Valdés-Cristerna, R and Rodriguez-Barragan, MA and Hernandez-Arenas, C and Quinzaños-Fresnedo, J and Galicia-Alvarado, MA and Cantillo-Negrete, J},
title = {Longitudinal Analysis of Stroke Patients' Brain Rhythms during an Intervention with a Brain-Computer Interface.},
journal = {Neural plasticity},
volume = {2019},
number = {},
pages = {7084618},
pmid = {31110515},
issn = {1687-5443},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Alpha Rhythm ; *Beta Rhythm ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Female ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; *Neuronal Plasticity ; Recovery of Function ; Robotics ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; },
abstract = {Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke's onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research. Electroencephalography (EEG) acquired brain rhythms in alpha and beta bands, during motor tasks, such as motor imagery/intention (MI), could provide insight of motor-related neural plasticity occurring during a BCI intervention. Hence, a longitudinal analysis of subacute stroke patients' brain rhythms during a BCI coupled to robotic device intervention was performed in this study. Data of 9 stroke patients were acquired across 12 sessions of the BCI intervention. Alpha and beta event-related desynchronization/synchronization (ERD/ERS) trends across sessions and their association with time since stroke onset and clinical upper extremity recovery were analyzed, using correlation and linear stepwise regression, respectively. More EEG channels presented significant ERD/ERS trends across sessions related with time since stroke onset, in beta, compared to alpha. Linear models implied a moderate relationship between alpha rhythms in frontal, temporal, and parietal areas with upper limb motor recovery and suggested a strong association between beta activity in frontal, central, and parietal regions with upper limb motor recovery. Higher association of beta with both time since stroke onset and upper limb motor recovery could be explained by beta relation with closed-loop communication between the sensorimotor cortex and the paralyzed upper limb, and alpha being probably more associated with motor learning mechanisms. The association between upper limb motor recovery and beta activations reinforces the hypothesis that broader regions of the cortex activate during movement tasks as a compensatory mechanism in stroke patients with severe motor impairment. Therefore, EEG across BCI interventions could provide valuable information for prognosis and BCI cortical activity targets.},
}
@article {pmid31109020,
year = {2019},
author = {Türk, &#xd6; and Özerdem, MS},
title = {Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals.},
journal = {Brain sciences},
volume = {9},
number = {5},
pages = {},
pmid = {31109020},
issn = {2076-3425},
abstract = {The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.},
}
@article {pmid31108970,
year = {2019},
author = {Cheng, MY and Damalerio, RB and Chen, W and Rajkumar, R and Dawe, GS},
title = {Ultracompact Multielectrode Array for Neurological Monitoring.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {10},
pages = {},
pmid = {31108970},
issn = {1424-8220},
support = {IAF311022//Agency for Science, Technology and Research/ ; },
mesh = {Action Potentials/physiology ; Animals ; *Biosensing Techniques ; Brain/*physiopathology ; Brain-Computer Interfaces ; Electric Impedance ; Electrodes, Implanted ; Electroencephalography ; Humans ; Micro-Electrical-Mechanical Systems/*methods ; Microelectrodes ; Neurons/*pathology/physiology ; Rats ; Spinal Cord Injuries/diagnosis/physiopathology/rehabilitation ; },
abstract = {Patients with paralysis, spinal cord injury, or amputated limbs could benefit from using brain-machine interface technology for communication and neurorehabilitation. In this study, a 32-channel three-dimensional (3D) multielectrode probe array was developed for the neural interface system of a brain-machine interface to monitor neural activity. A novel microassembly technique involving lead transfer was used to prevent misalignment in the bonding plane during the orthogonal assembly of the 3D multielectrode probe array. Standard microassembly and biopackaging processes were utilized to implement the proposed lead transfer technique. The maximum profile of the integrated 3D neural device was set to 0.50 mm above the pia mater to reduce trauma to brain cells. Benchtop tests characterized the electrical impedance of the neural device. A characterization test revealed that the impedance of the 3D multielectrode probe array was on average approximately 0.55 MΩ at a frequency of 1 KHz. Moreover, in vitro cytotoxicity tests verified the biocompatibility of the device. Subsequently, 3D multielectrode probe arrays were implanted in rats and exhibited the capability to record local field potentials and spike signals.},
}
@article {pmid31108931,
year = {2019},
author = {Muhammad, Y and Vaino, D},
title = {Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN).},
journal = {Bioengineering (Basel, Switzerland)},
volume = {6},
number = {2},
pages = {},
pmid = {31108931},
issn = {2306-5354},
support = {.//Eesti Teadusagentuur/ ; },
abstract = {The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg-Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg-Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.},
}
@article {pmid31108477,
year = {2019},
author = {Shahriari, Y and Vaughan, TM and McCane, LM and Allison, BZ and Wolpaw, JR and Krusienski, DJ},
title = {An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056031},
pmid = {31108477},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/diagnosis/*physiopathology ; Brain-Computer Interfaces/*trends ; *Data Analysis ; Electroencephalography/methods/*trends ; Humans ; Longitudinal Studies ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology enables people to use direct measures of brain activity for communication and control. The National Center for Adaptive Neurotechnologies and Helen Hayes Hospital are studying long-term independent home use of P300-based BCIs by people with amyotrophic lateral sclerosis (ALS). This BCI use takes place without technical oversight, and users can encounter substantial variation in their day-to-day BCI performance. The purpose of this study is to identify and evaluate features in the electroencephalogram (EEG) that correlate with successful BCI performance during home use with the goal of improving BCI for people with neuromuscular disorders.<br><br>APPROACH: Nine people with ALS used a P300-based BCI at home over several months for communication and computer control. Sessions from a routine calibration task were categorized as successful ([Formula: see text]70%) or unsuccessful (<70%) BCI performance. The correlation of temporal and spectral EEG features with BCI performance was then evaluated.<br><br>MAIN RESULTS: BCI performance was positively correlated with an increase in alpha-band (8-14 Hz) activity at locations PO8, P3, Pz, and P4; and beta-band (15-30 Hz) activity at occipital locations. In addition, performance was significantly positively correlated with a positive deflection in EEG amplitude around 220&#x2009;ms at frontal mid-line locations (i.e. Fz and Cz). BCI performance was negatively correlated with delta-band (1-3 Hz) activity recorded from occipital locations.<br><br>SIGNIFICANCE: These results highlight the variability found in the EEG and describe EEG features that correlate with successful BCI performance during day-to-day use of a P300-based BCI by people with ALS. These results should inform studies focused on improved BCI reliability for people with neuromuscular disorders.},
}
@article {pmid31107003,
year = {2019},
author = {Qian, DD and Kuang, X and Wang, XG and Lin, F and Yuan, ZQ and Ye, J and Hao, ZQ},
title = {[Spatio-temporal dynamics of woody plants seed rains in broad-leaved Korean pine mixed forest in Changbai Mountains form 2006 to 2017, China.].},
journal = {Ying yong sheng tai xue bao = The journal of applied ecology},
volume = {30},
number = {5},
pages = {1487-1493},
doi = {10.13287/j.1001-9332.201905.012},
pmid = {31107003},
issn = {1001-9332},
mesh = {China ; Ecosystem ; *Forests ; *Pinus ; Seeds ; Trees ; },
abstract = {Seeds are the basis for forest regeneration. To examine the composition and spatio-temporal dynamics of seed rains, a total of 150 seed traps of 0.5 m[2] were installed in a 25 hm[2] broad-leaved Korean pine (Pinus koraiensis) mixed forest plot in Changbai Mountains. With a total of 252 collections from May 2006 to September 2017, we collected 764299 mature and immature seeds which were belonged to 27 species, 17 genera, and 12 families. More than 90% of all collected seeds (704231 seeds) were from 13 canopy species. Seeds of four tree species, including Tilia amurensis, Fraxinus mandschurica, Acer mono, and Acer pseudo-sieboldianum could be collected every year from each trap. Mast-seeding was found in every canopy layer, but it happened one to two years earlier in the overstorey layer than midstorey and understorey layer. Almost all species produced seeds in autumn, with considerable spatiotemporal variation. Generally, the spatial variation of seeds was larger than temporal variation. Compared with annual variation coefficient of seeds in tropical forest of the Barro Colorado Island (BCI) and subtropical evergreen forest in the Gutianshan, annual variation coefficient of seeds in Changbai Mountains was higher, which supported the hypothesis that annual variation in seed rains would be lower in the tropics than that in higher latitudes.},
}
@article {pmid31106271,
year = {2019},
author = {Han, C and O'Sullivan, J and Luo, Y and Herrero, J and Mehta, AD and Mesgarani, N},
title = {Speaker-independent auditory attention decoding without access to clean speech sources.},
journal = {Science advances},
volume = {5},
number = {5},
pages = {eaav6134},
pmid = {31106271},
issn = {2375-2548},
support = {R01 DC014279/DC/NIDCD NIH HHS/United States ; R21 MH114166/MH/NIMH NIH HHS/United States ; },
mesh = {Acoustics ; Algorithms ; Attention ; Auditory Cortex/*physiology ; *Auditory Perception ; Behavior ; Brain-Computer Interfaces ; Electrodes ; Female ; Hearing ; Humans ; Language ; Male ; Neural Networks, Computer ; Pattern Recognition, Automated ; *Speech ; *Speech Perception ; },
abstract = {Speech perception in crowded environments is challenging for hearing-impaired listeners. Assistive hearing devices cannot lower interfering speakers without knowing which speaker the listener is focusing on. One possible solution is auditory attention decoding in which the brainwaves of listeners are compared with sound sources to determine the attended source, which can then be amplified to facilitate hearing. In realistic situations, however, only mixed audio is available. We utilize a novel speech separation algorithm to automatically separate speakers in mixed audio, with no need for the speakers to have prior training. Our results show that auditory attention decoding with automatically separated speakers is as accurate and fast as using clean speech sounds. The proposed method significantly improves the subjective and objective quality of the attended speaker. Our study addresses a major obstacle in actualization of auditory attention decoding that can assist hearing-impaired listeners and reduce listening effort for normal-hearing subjects.},
}
@article {pmid31105544,
year = {2019},
author = {Guo, M and Jin, J and Jiao, Y and Wang, X and Cichockia, A},
title = {Investigation of Visual Stimulus With Various Colors and the Layout for the Oddball Paradigm in Evoked Related Potential-Based Brain-Computer Interface.},
journal = {Frontiers in computational neuroscience},
volume = {13},
number = {},
pages = {24},
pmid = {31105544},
issn = {1662-5188},
abstract = {Objective: Stimulus visual patterns, such as size, content, color, luminosity, and interval, play key roles for brain-computer interface (BCI) performance. However, the three primary colors to be intercompared as a single variable or factor on the same platform are poorly studied. In this work, we configured the visual stimulus patterns with red, green, and blue operating on a newly designed layout of the flash pattern of BCI to study the waveforms and performance of the evoked related potential (ERP). Approach: Twelve subjects participated in our experiment, and each subject was required to finish three different color sub-experiments. Four blocks of the interface were presented along the edge of the screen, and the other four were assembled in the center, aiming to investigate the problem of adjacency distraction. Repeated-measures ANOVA and Bonferroni correction were applied for statistical analysis. Main results: The averaged online accuracy was 98.44% for the red paradigm, higher than 92.71% for the green paradigm, and 93.23% for the blue paradigm. Furthermore, significant differences in online accuracy (p < 0.05) and information transfer rate (p < 0.05) were found between the red and green paradigms. Significance: The red stimulus paradigm yielded the best performance. The proposed design of ERP-based BCI was practical and effective for many potential applications.},
}
@article {pmid31105543,
year = {2019},
author = {Blum, S and Jacobsen, NSJ and Bleichner, MG and Debener, S},
title = {A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {141},
pmid = {31105543},
issn = {1662-5161},
abstract = {Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions.},
}
@article {pmid31104704,
year = {2019},
author = {Mirzaee, MS and Moghimi, S},
title = {Detection of reaching intention using EEG signals and nonlinear dynamic system identification.},
journal = {Computer methods and programs in biomedicine},
volume = {175},
number = {},
pages = {151-161},
doi = {10.1016/j.cmpb.2019.04.023},
pmid = {31104704},
issn = {1872-7565},
mesh = {Adult ; Algorithms ; Arm/physiology ; Brain Mapping ; Brain-Computer Interfaces ; *Electroencephalography ; Electromyography ; Female ; Healthy Volunteers ; Humans ; Male ; Movement ; Nonlinear Dynamics ; Predictive Value of Tests ; ROC Curve ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVES: Low frequency electroencephalography (EEG) signals are associated with preparation of movement and thus provide valuable information for brain-machine interface applications. The purpose of this study was to detect movement intention from EEG signals before execution of self-paced arm reaching movements.<br><br>METHODS: Ten healthy individuals were recruited. Movement onset was determined from surface electromyography recordings time-locked with EEG signals. Unlike previous studies, which employed feature extraction and classification for decoding, a nonlinear dynamic multiple-input/single output (MISO) model was developed. The MISO model consisted of a cascade of Volterra structures and a threshold block, generating the binary output corresponding to intention/no-intention. The modeling process included input selection from a pool of different EEG channels. The predictive performance of the model was evaluated using the receiver operating characteristics curve, from which the optimum threshold was also selected. The Mann-Whitney statistics was employed to select the significant EEG channels for the output by examining the statistical significance of improvement in the predictive capability of the model when the respective channels were included.<br><br>RESULTS: With the proposed approach, movement intention was detected approximately 500&#x202f;ms before the movement onset and on average, with an accuracy of 96.37&#xa0;&#xb1;&#xa0;0.94%, a sensitivity of 77.93&#xa0;&#xb1;&#xa0;4.40% and a specificity of 98.52&#xa0;&#xb1;&#xa0;1.19%.<br><br>CONCLUSIONS: The model output can be converted to motion commands for neuroprosthetic devices and exoskeletons in future applications.},
}
@article {pmid31101819,
year = {2019},
author = {Kupers, SJ and Wirth, C and Engelbrecht, BMJ and Rüger, N},
title = {Dry season soil water potential maps of a 50 hectare tropical forest plot on Barro Colorado Island, Panama.},
journal = {Scientific data},
volume = {6},
number = {1},
pages = {63},
pmid = {31101819},
issn = {2052-4463},
support = {FZT 118//Deutsche Forschungsgemeinschaft (German Research Foundation)/International ; Short-Term Fellowship//Smithsonian | Smithsonian Tropical Research Institute (Smithsonian Tropical Research Institution)/International ; },
abstract = {Fine scale spatial variation in soil moisture influences plant performance, species distributions and diversity. However, detailed information on local soil moisture variation is scarce, particularly in species-rich tropical forests. We measured soil water potential and soil water content in the 50-ha Forest Dynamics Plot on Barro Colorado Island (BCI), Panama, one of the best-studied tropical forests in the world. We present maps of soil water potential for several dry season stages during a regular year and during an El Niño drought. Additionally, we provide code that allows users to create maps for specific dates. The maps can be combined with other freely available datasets such as long-term vegetation censuses (ranging from seeds to adult trees), data on other resources (e.g. light and nutrients) and remote sensing data (e.g. LiDAR and imaging spectroscopy). Users can study questions in various disciplines such as population and community ecology, plant physiology and hydrology under current and future climate conditions.},
}
@article {pmid31096192,
year = {2019},
author = {Georgiadis, K and Laskaris, N and Nikolopoulos, S and Kompatsiaris, I},
title = {Connectivity steered graph Fourier transform for motor imagery BCI decoding.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056021},
doi = {10.1088/1741-2552/ab21fd},
pmid = {31096192},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Discrimination Learning/*physiology ; Electroencephalography/methods ; Female ; *Fourier Analysis ; Humans ; Imagination/*physiology ; Male ; Nerve Net/*physiology ; },
abstract = {OBJECTIVE: Graph signal processing (GSP) concepts are exploited for brain activity decoding and particularly the detection and recognition of a motor imagery (MI) movement. A novel signal analytic technique that combines graph Fourier transform (GFT) with estimates of cross-frequency coupling (CFC) and discriminative learning is introduced as a means to recover the subject's intention from the multichannel signal.<br><br>APPROACH: Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis. A personalized decoding scheme supporting a binary decision, either 'left versus right' or 'rest versus MI', is crafted from a small set of training trials. Electroencephalographic (EEG) activity from 12 volunteers recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition is used to introduce and validate our methodology. In addition, the introduced methodology was further validated based on dataset IVa of BCI III competition.<br><br>MAIN RESULTS: Our GFT-domain decoding scheme achieves nearly optimal performance and proves superior to alternative techniques that are very popular in the field.<br><br>SIGNIFICANCE: At a conceptual level, our work suggests a fruitful way to introduce network neuroscience in BCI research. At a more practical level, it is characterized by efficiency. Training is realized using a small number of exemplar trials and decoding requires very simple operations that leaves room for real-time implementation.},
}
@article {pmid31096188,
year = {2019},
author = {Hayashi, M and Tsuchimoto, S and Mizuguchi, N and Miyatake, M and Kasuga, S and Ushiba, J},
title = {Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056020},
doi = {10.1088/1741-2552/ab221a},
pmid = {31096188},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/*methods ; Humans ; Male ; Muscle Contraction/*physiology ; Scalp/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: A critical feature for the maintenance of precise skeletal muscle force production by the human brain is its ability to configure motor function activity dynamically and adaptively in response to visual and somatosensory information. Existing studies have concluded that not only the sensorimotor area but also distributed cortical areas act cooperatively in the generation of motor commands for voluntary force production to the desired level. However, less attention has been paid to such physiological mechanisms in conventional brain-computer interface (BCI) design and implementation. We proposed a new, physiologically inspired two-stage decoding method to see its contribution on accuracy improvement of BCI.<br><br>APPROACH: We performed whole-head high-density scalp electroencephalographic (EEG) recording during a right finger force-matching task at three strength levels (20%, 40%, and 60% maximal voluntary contraction following a resting state). A two-stage regression approach was employed that decodes muscle contraction level from EEG signals in the multi-level force-matching task and translates them into: (1) presence/absence of muscle contraction as a first stage; and (2) muscle contraction level as a second stage. Dimensionality reduction of the EEG signals, using principal component analysis, avoided multicollinearity during multiple regression, and data-driven stepwise multiple regression identified EEG components that were involved in the multi-level force-matching task.<br><br>MAIN RESULTS: An alternatively tuned two-stage regressor accurately decoded muscle contraction level with online processing rather than the conventional decoders, and identified EEG components that were related to voluntary force production. Relaxation/contraction state-dependent EEG components were localized dominantly in the contralateral parieto-temporal regions, whereas multi-level force regulation-dependent EEG components came from the fronto-parietal regions.<br><br>SIGNIFICANCE: Our findings identify respective cortical signalings during relaxation/contraction and multi-level force regulation using a sensor-based approach with EEG. Simulation-based assessment of the current physiologically inspired decoding technique proved improved accuracy in online BCI control.},
}
@article {pmid31095507,
year = {2019},
author = {Onay, FK and Köse, C},
title = {Assessment of CSP-based two-stage channel selection approach and local transformation-based feature extraction for classification of motor imagery/movement EEG data.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {64},
number = {6},
pages = {643-653},
doi = {10.1515/bmt-2018-0201},
pmid = {31095507},
issn = {1862-278X},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; Humans ; Research Design ; Support Vector Machine ; },
abstract = {The main idea of brain-computer interfaces (BCIs) is to facilitate the lives of patients having difficulties to move their muscles due to a disorder of their motor nervous systems but healthy cognitive functions. BCIs are usually electroencephalography (EEG)-based, and the success of the BCIs relies on the precision of signal preprocessing, detection of distinctive features, usage of suitable classifiers and selection of effective channels. In this study, a two-stage channel selection and local transformation-based feature extraction are proposed for the classification of motor imagery/movement tasks. In the first stage of the channel selection, the channels were combined according to the neurophysiological information about brain functions acquired from the literature, then averaged and a single channel was formed. In the second stage, selective channels were specified with the common spatial pattern-linear discriminant analysis (CSP-LDA)-based sequential channel removal. After the channel selection phase, the feature extraction was carried out with local transformation-based methods (LTBM): local centroid pattern (LCP), one-dimensional-local gradient pattern (1D-LGP), local neighborhood descriptive pattern (LNDP) and one-dimensional-local ternary pattern (1D-LTP). The distinctions and deficiencies of these methods were compared with other methods in the literature and the classification performances of the k-nearest neighbor (k-NN) and the support vector machines (SVM) were evaluated. As a result, the proposed methods yielded the highest average classification accuracies as 99.34%, 95.95%, 98.66% and 99.90% with the LCP, 1D-LGP, LNDP and 1D-LTP when using k-NN, respectively. The two-stage channel selection and 1D-LTP method showed promising results for recognition of motor tasks. The LTBM will contribute to the development of EEG-based BCIs with the advantages of high classification accuracy, easy implementation and low computational complexity.},
}
@article {pmid31095487,
year = {2019},
author = {Jafarifarmand, A and Badamchizadeh, MA},
title = {EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1200-1208},
doi = {10.1109/TNSRE.2019.2915801},
pmid = {31095487},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Artifacts ; Automobile Driving/*psychology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Eye Movements/physiology ; Functional Laterality ; Fuzzy Logic ; Humans ; Imagination ; Male ; Muscle, Skeletal/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {One of the main issues restricting the practical efficiency of brain-computer interface (BCI) systems is the inevitable occurrence of physiological artifacts during electroencephalography (EEG) recordings. The effects of the artifacts are, however, mostly discarded in practical BCI systems, due to the time-consuming and complicated computational processes. This paper presents the influences of the artifacts and the efficiency of reducing these influences in a practical BCI. Ocular and muscular artifacts are considered due to the high-amplitude and frequent presence. The paradigm is designed based on the mental controlling of a radio-control (RC) car. Two motor imagery commands, containing the imagination of movement of left/right hand, are used to navigate the BCI-based RC car to turn left/right. The results indicate that the artifacts can highly affect the system performance; reducing their influence significantly improves the efficiency.},
}
@article {pmid31091845,
year = {2019},
author = {Zhao, K and Wan, H and Shang, Z and Liu, X and Liu, L},
title = {Intracortical microstimulation parameters modulate flight behavior in pigeon.},
journal = {Journal of integrative neuroscience},
volume = {18},
number = {1},
pages = {23-32},
doi = {10.31083/j.jin.2019.01.14},
pmid = {31091845},
issn = {0219-6352},
support = {61673353//National Natural Science Foundation of China/ ; HNBBL17005//Open Foundation of Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology/ ; },
mesh = {Animals ; Behavior, Animal/physiology ; Cerebral Cortex/*physiology ; Columbidae/*physiology ; Electric Stimulation/*methods ; Electrodes, Implanted ; Female ; Flight, Animal/*physiology ; Male ; *Microelectrodes ; Nonlinear Dynamics ; Video Recording ; Wireless Technology ; },
abstract = {Pigeons have a natural affinity for travel by flight. Researchers have recently achieved modulation of pigeon locomotor behaviour by intracortical microstimulation. However, there is a lack of research focused on the analysis of microstimulations parameters in the control of pigeon flight. Here, chronic microelectrode implantation technology is employed to establish a model for evaluation of the effects of pigeon flight modulation. Furthermore, three stimulation parameters are compared (amplitude, frequency, and duty ratio) and analyzed as to how they and their interactions affect the flight of pigeons. Results show that microstimulation of the pigeon formation reticularis medialis mesencephali area has significant effects on modulation of pigeon flight and there is a significant non-linear correlation between the stimulation parameters employed and modulation of the flight trajectory. Additionally, we found that the amplitude interacts with both frequency and duty ratio. These results indicate that the flight trajectory of a pigeon can be modulated by alterations made to microstimulation parameters.},
}
@article {pmid31089955,
year = {2020},
author = {Sreedharan, S and Chandran, A and Yanamala, VR and Sylaja, PN and Kesavadas, C and Sitaram, R},
title = {Self-regulation of language areas using real-time functional MRI in stroke patients with expressive aphasia.},
journal = {Brain imaging and behavior},
volume = {14},
number = {5},
pages = {1714-1730},
doi = {10.1007/s11682-019-00106-7},
pmid = {31089955},
issn = {1931-7565},
mesh = {Aphasia, Broca/diagnostic imaging/therapy ; Humans ; Language ; Magnetic Resonance Imaging ; *Self-Control ; *Stroke/complications/diagnostic imaging/therapy ; },
abstract = {The objectives of this study were to test (i) If stroke patients with expressive Aphasia could learn to up-regulate the Blood Oxygenation Level Dependent (BOLD) signal in language areas of the brain, namely Inferior Frontal Gyrus (Broca's area) and Superior Temporal Gyrus (Wernicke's area), with real-time fMRI based neurofeedback of the BOLD activation and functional connectivity between the language areas; and (ii) acquired up-regulation could lead to an improvement in expression of language. The study was performed on three groups: Group 1 (n = 4) of Test patients and group 2 (n = 4) of healthy volunteers underwent the neurofeedback training, whereas group 3 (n = 4) of Control patients underwent treatment as usual. Language performance and recovery were assessed using western aphasia battery and picture naming tasks, before and after the neurofeedback training. Results show that the Test group had significant increase in activation of the Broca's area and its right homologue, while the Normal group achieved the greatest activation during neurofeedback. For the Test group both perilesional and contralateral activations were observed. The improvement in language ability of the test patients was not significantly greater than that of the control patients. Neurofeedback training in Aphasia patients induced significant activation of the Broca's area, Wernicke's area and their right homologues, although healthy individuals achieved greater activations in these regions than the patient groups. Training also activated perilesional areas of Rolandic operculum, precentral gyrus and postcentral gyrus for the Test patients significantly. However, lack of behavioral and symptom modifications in the Test group calls for improvements in the efficacy of the approach.},
}
@article {pmid31088645,
year = {2019},
author = {Zangeneh Soroush, M and Maghooli, K and Setarehdan, SK and Nasrabadi, AM},
title = {Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory.},
journal = {Medical hypotheses},
volume = {127},
number = {},
pages = {34-45},
doi = {10.1016/j.mehy.2019.03.025},
pmid = {31088645},
issn = {1532-2777},
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain Mapping ; Brain-Computer Interfaces ; Computer Systems ; *Electroencephalography ; *Emotions ; Female ; Humans ; Male ; Nonlinear Dynamics ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Emotions play an important role in our life. Emotion recognition which is considered a subset of brain computer interface (BCI), has drawn a great deal of attention during recent years. Researchers from different fields have tried to classify emotions through physiological signals. Nonlinear analysis has been reported to be successful and effective in emotion classification due to the nonlinear and non-stationary behavior of biological signals. In this study, phase space reconstruction and Poincare planes are employed to describe the dynamics of electroencephalogram (EEG) in emotional states. EEG signals are taken from a reliable database and phase space is reconstructed. A new transformation is introduced in order to quantify the phase space. Dynamic characteristics of the new space are considered as features. Most significant features are selected and samples are classified into four groups including high arousal - high valence (HAHV), low arousal - high valence (LAHV), high arousal - low valence (HALV) and low arousal - low valence (LALV). Classification accuracy was about 90% on average. Results suggest that the proposed method is successful and classification performance is good in comparison with most studies in this field. Brain activity is also reported with respect to investigating brain function during emotion elicitation. We managed to introduce a new way to analyze EEG phase space. The proposed method is applied in a real world and challenging application (i.e. emotion classification). Not only does the proposed method describe EEG changes due to different emotional states but also it is able to represent new characteristics of complex systems. The suggested approach paves the way for researchers to analyze and understand more about chaotic signals and systems.},
}
@article {pmid31075785,
year = {2019},
author = {Xu, R and Dosen, S and Jiang, N and Yao, L and Farooq, A and Jochumsen, M and Mrachacz-Kersting, N and Dremstrup, K and Farina, D},
title = {Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056001},
doi = {10.1088/1741-2552/ab20e5},
pmid = {31075785},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; Electroencephalography/methods ; Electromyography/methods ; Female ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Perception/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI.<br><br>APPROACH: Eleven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented four (straight movements) or eight commands (straight and diagonal movements).<br><br>MAIN RESULTS: The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when four-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for four and eight commands.<br><br>SIGNIFICANCE: These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.},
}
@article {pmid31073142,
year = {2019},
author = {Ofner, P and Schwarz, A and Pereira, J and Wyss, D and Wildburger, R and Müller-Putz, GR},
title = {Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {7134},
pmid = {31073142},
issn = {2045-2322},
mesh = {Adult ; Aged ; Arm/*physiopathology ; Brain-Computer Interfaces ; Cervical Vertebrae ; Electroencephalography/*methods ; Female ; Hand/*physiopathology ; Hand Strength ; Humans ; Male ; Middle Aged ; Movement ; Proof of Concept Study ; Spinal Cord Injuries/*diagnostic imaging/physiopathology ; Wheelchairs ; },
abstract = {We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).},
}
@article {pmid31071699,
year = {2019},
author = {Song, B and Ma, N and Liu, G and Zhang, H and Yu, L and Liu, L and Zhang, J},
title = {Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056002},
doi = {10.1088/1741-2552/ab20bc},
pmid = {31071699},
issn = {1741-2552},
mesh = {Adolescent ; Brain/*physiology ; Brain Mapping/methods ; *Data Analysis ; Female ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Nerve Net/*physiology ; *Neural Networks, Computer ; },
abstract = {OBJECTIVE: The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations in diseased brains. However, little is known about the underlying dynamic mechanism with which such a dynamic FC is flexibly organized under the constraint of structural connections. In this work, we explore the relationship between critical dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer models.<br><br>APPROACH: First, we proposed the connectivity number entropy (CNE), which was an entropy measure for the flexibility of FC. Through an analysis of resting-state fMRI (rs-fMRI) data from 95 healthy participants, we explored the correlation between CNE and long-range temporal correlations (LRTCs), which can represent the critical dynamics. Then, we employed a whole-brain computer model based on diffusion tensor imaging (DTI) to further demonstrate this relationship.<br><br>MAIN RESULTS: We found that the most flexible FC is present when the brain is operating close to the critical point of a phase transition. Additionally, around this point, our model can yield the best prediction for the regional distribution of CNE because structural information is reflected the most by the CNE through critical dynamics.<br><br>SIGNIFICANCE: Our results not only reveal the underlying dynamic mechanism for the organization of time-dependent FC but also provide a possible pathway to model the flexible functional organization in the human brain and may have potential application in the analysis of altered dynamic FC in diseased brains.},
}
@article {pmid31071348,
year = {2019},
author = {Paret, C and Zaehringer, J and Ruf, M and Ende, G and Schmahl, C},
title = {The orbitofrontal cortex processes neurofeedback failure signals.},
journal = {Behavioural brain research},
volume = {369},
number = {},
pages = {111938},
doi = {10.1016/j.bbr.2019.111938},
pmid = {31071348},
issn = {1872-7549},
mesh = {Adult ; Amygdala/physiology ; Brain/physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Female ; Healthy Volunteers ; Humans ; Learning/physiology ; Magnetic Resonance Imaging/methods ; Neurofeedback/*methods ; Prefrontal Cortex/*physiology ; Reinforcement, Psychology ; Reward ; Young Adult ; },
abstract = {Receiving feedback from neural activity, dubbed neurofeedback, can reinforce brain self-regulation. In a real-time functional magnetic resonance imaging (fMRI) experiment, healthy participants received amygdala neurofeedback via a visual brain-computer interface. The brain response to signals of reward and failure was modeled. In contrast to previous analyses, we take into account feedback that immediately preceded these signals. That means we tested whether responses were modulated while participants observed sequent reward and failure signals. The orbitofrontal cortex (OFC) showed a negative Blood Oxygenation Level Dependent (BOLD) response to failure signals, when they were preceded by more failure signals. When failure signals were preceded by reward, in contrast, the response was less pronounced. The results suggest weighted processing of neurofeedback value in the OFC. Learning to self-regulate the brain with neurofeedback may involve similar neural networks as the learning of goal-directed action.},
}
@article {pmid31071287,
year = {2019},
author = {Owen, AM},
title = {The Search for Consciousness.},
journal = {Neuron},
volume = {102},
number = {3},
pages = {526-528},
doi = {10.1016/j.neuron.2019.03.024},
pmid = {31071287},
issn = {1097-4199},
mesh = {Awareness ; Brain/*diagnostic imaging ; Brain Injury, Chronic/*diagnostic imaging ; Brain-Computer Interfaces ; *Consciousness ; Diagnostic Errors ; Electroencephalography ; Ethics, Medical ; Functional Neuroimaging ; Humans ; *Life Support Care/ethics/legislation &amp; jurisprudence ; Magnetic Resonance Imaging ; Persistent Vegetative State/*diagnostic imaging ; Recovery of Function ; Spectroscopy, Near-Infrared ; },
abstract = {In recent years, rapid technological developments in the field of neuroimaging have provided new methods for assessing residual cognition, detecting consciousness, and even communicating with patients who clinically appear to be in a vegetative state. Here, I highlight some of the major implications of these developments, discuss their scientific, clinical, legal, and ethical relevance, and make my own recommendations for future directions in this field.},
}
@article {pmid31071052,
year = {2019},
author = {Jia, Y and Mirbozorgi, SA and Zhang, P and Inan, OT and Li, W and Ghovanloo, M},
title = {A Dual-Band Wireless Power Transmission System for Evaluating mm-Sized Implants.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {4},
pages = {595-607},
pmid = {31071052},
issn = {1940-9990},
support = {R21 EB018561/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Computer Simulation ; *Electric Power Supplies ; Equipment Design ; *Prostheses and Implants ; Sheep ; *Wireless Technology/instrumentation ; },
abstract = {Distributed neural interfaces made of many mm-sized implantable medical devices (IMDs) are poised to play a key role in future brain-computer interfaces because of less damage to the surrounding tissue. Evaluating them wirelessly at preclinical stage (e.g., in a rodent model), however, is a major challenge due to weak coupling and significant losses, resulting in limited power delivery to the IMD within a nominal experimental arena, like a homecage, without surpassing the specific absorption rate limit. To address this problem, we present a dual-band EnerCage system with two multi-coil inductive links, which first deliver power at 13.56 MHz from the EnerCage (46 × 24 × 20 cm[3]) to a headstage (18 × 18 × 15 mm[3], 4.8 g) that is carried by the animal via a 4-coil inductive link. Then, a 60 MHz 3-coil inductive link from the headstage powers up the small IMD (2.5 × 2.5 × 1.5 mm[3], 15 mg), which in this case is a free floating, wirelessly powered, implantable optical stimulator (FF-WIOS). The power transfer efficiency and power delivered to the load (PDL) from EnerCage to the headstage at 7 cm height were 14.9%-22.7% and 122 mW; and from headstage to FF-WIOS at 5 mm depth were 18% and 2.7 mW, respectively. Bidirectional data connectivity between EnerCage-headstage was established via bluetooth low energy. Between headstage and FF-WIOS, on-off keying and load-shift-keying were used for downlink and uplink data, respectively. Moreover, a closed-loop power controller stabilized PDL to both the headstage and the FF-WIOS against misalignments.},
}
@article {pmid31071048,
year = {2019},
author = {Li, Y and Zhang, XR and Zhang, B and Lei, MY and Cui, WG and Guo, YZ},
title = {A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1170-1180},
doi = {10.1109/TNSRE.2019.2915621},
pmid = {31071048},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Gamma Rhythm ; Humans ; Imagination/*physiology ; Machine Learning ; Movement/*physiology ; *Neural Networks, Computer ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.},
}
@article {pmid31071045,
year = {2019},
author = {Yu, Y and Liu, Y and Yin, E and Jiang, J and Zhou, Z and Hu, D},
title = {An Asynchronous Hybrid Spelling Approach Based on EEG-EOG Signals for Chinese Character Input.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1292-1302},
doi = {10.1109/TNSRE.2019.2914916},
pmid = {31071045},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Asian People ; Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Electrooculography/*methods ; Equipment Design ; Event-Related Potentials, P300 ; Female ; Healthy Volunteers ; Humans ; Male ; Pilot Projects ; *Reading ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this paper, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroencephalography (EOG) into the conventional electroencephalography (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling an asynchronous operation of the system. Then, the user could input sinograms by alternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG detector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across 18 subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be expanded to users whose languages are written in logographic scripts to serve as an assistive communication tool.},
}
@article {pmid31071044,
year = {2019},
author = {Zhang, X and Xu, G and Mou, X and Ravi, A and Li, M and Wang, Y and Jiang, N},
title = {A Convolutional Neural Network for the Detection of Asynchronous Steady State Motion Visual Evoked Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1303-1311},
doi = {10.1109/TNSRE.2019.2914904},
pmid = {31071044},
issn = {1558-0210},
mesh = {Adult ; Aged ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Healthy Volunteers ; Humans ; Intention ; Male ; Middle Aged ; *Neural Networks, Computer ; Psychomotor Performance ; Stroke/physiopathology ; Young Adult ; },
abstract = {A key issue in brain-computer interface (BCI) is the detection of intentional control (IC) states and non-intentional control (NC) states in an asynchronous manner. Furthermore, for steady-state visual evoked potential (SSVEP) BCI systems, multiple states (sub-states) exist within the IC state. Existing recognition methods rely on a threshold technique, which is difficult to realize high accuracy, i.e., simultaneously high true positive rate and low false positive rate. To address this issue, we proposed a novel convolutional neural network (CNN) to detect IC and NC states in a SSVEP-BCI system for the first time. Specifically, the steady-state motion visual evoked potentials (SSMVEP) paradigm, which has been shown to induce less visual discomfort, was chosen as the experimental paradigm. Two processing pipelines were proposed for the detection of IC and NC states. The first one was using CNN as a multi-class classifier to discriminate between all the states in IC and NC state (FFT-CNN). The second one was using CNN to discriminate between IC and NC states, and using canonical correlation analysis (CCA) to perform classification tasks within the IC (FFT-CNN-CCA). We demonstrated that both pipelines achieved a significant increase in accuracy for low-performance healthy participants when traditional algorithms such as CCA threshold were used. Furthermore, the FFT-CNN-CCA pipeline achieved better performance than the FFT-CNN pipeline based on the stroke patients' data. In summary, we showed that CNN can be used for robust detection in an asynchronous SSMVEP-BCI with great potential for out-of-lab BCI applications.},
}
@article {pmid31071043,
year = {2019},
author = {Rashid, U and Niazi, IK and Jochumsen, M and Krol, LR and Signal, N and Taylor, D},
title = {Automated Labeling of Movement- Related Cortical Potentials Using Segmented Regression.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1282-1291},
doi = {10.1109/TNSRE.2019.2913880},
pmid = {31071043},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Electromyography ; Evoked Potentials/physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Theoretical ; Movement/*physiology ; Regression Analysis ; Reproducibility of Results ; },
abstract = {The movement-related cortical potential (MRCP) is a brain signal related to planning and execution of motor tasks. From an MRCP, three notable features can be identified: the early Bereitschaftspotential (BP1), the late Bereitschaftspotential (BP2), and the negative peak (PN). These features have been used in past studies to quantify neurophysiological changes in response to motor training. Currently, either manual labeling or a priori specification of time points is used to extract these features. The limitation of these methods is the inability to fully model the features. This paper proposes the segmented regression along with a local peak method for automated labeling of the features. The proposed method derives the onsets, amplitudes at onsets, and slopes of BP1 and BP2 along with time and amplitude of the PN in a typical average MRCP. To choose the most suitable regression technique a bounded segmented regression method, a change point method and multivariate adaptive regression splines were evaluated using the root-mean-square error on a dataset of 6000 simulated MRCPs. The best-performing regression technique combined with the local peak method was then applied to a smaller set of 123 simulated MRCPs. Error in onsets of BP1 and BP2 and time of PN were compared with the errors in manual labeling by an expert. The performance of the proposed method was also evaluated on an experimental dataset of MRCPs derived from electroencephalography (EEG) recorded across two sessions from 22 healthy participants during a lower limb task. The Bland-Altman plots were used to evaluate the absolute reliability of the proposed method. On experimental data, the proposed method was also compared with manual labeling by an expert. Bounded segmented regression produced the smallest error on the simulation data. For the experimental data, our proposed method did not exhibit statistically significant bias in any of the modeled features. Furthermore, its performance was comparable to manual labeling by experts. We conclude that the proposed method can be used to automatically obtain robust estimates for the MRCP features with known measurement error.},
}
@article {pmid31070551,
year = {2019},
author = {Bublitz, JC},
title = {Privacy Concerns in Brain-Computer Interfaces.},
journal = {AJOB neuroscience},
volume = {10},
number = {1},
pages = {30-32},
doi = {10.1080/21507740.2019.1595783},
pmid = {31070551},
issn = {2150-7759},
mesh = {*Brain-Computer Interfaces ; Computer Security ; Confidentiality ; Privacy ; *Psychiatry ; },
}
@article {pmid31065516,
year = {2019},
author = {Shi, Z and Zheng, F and Zhou, Z and Li, M and Fan, Z and Ye, H and Zhang, S and Xiao, T and Chen, L and Tao, TH and Sun, YL and Mao, Y},
title = {Silk-Enabled Conformal Multifunctional Bioelectronics for Investigation of Spatiotemporal Epileptiform Activities and Multimodal Neural Encoding/Decoding.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {6},
number = {9},
pages = {1801617},
pmid = {31065516},
issn = {2198-3844},
abstract = {Flexible electronics can serve as powerful tools for biomedical diagnosis and therapies of neurological disorders, particularly for application cases with brain-machine interfaces (BMIs). Existing conformal soft bioelectrodes are applicable for basic electrocorticogram (ECoG) collecting/monitoring. Nevertheless, as an emerging and promising approach, further multidisciplinary efforts are still demanded for in-depth exploitations with these conformal soft electronics toward their practical neurophysiological applications in both scientific research and real-world clinical operation. Here, clinically-friendly silk-supported/delivered soft bioelectronics are developed, and multiple functions and features valuable for customizable intracranial applications (e.g., biocompatible and spontaneously conformal coupling with cortical surface, spatiotemporal ECoG detecting/monitoring, electro-neurophysiological neural stimulating/decoding, controllable loading/delivery of therapeutic molecules, and parallel optical readouts of operating states) are integrated.},
}
@article {pmid31062175,
year = {2019},
author = {Fu, R and Tian, Y and Bao, T and Meng, Z and Shi, P},
title = {Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis.},
journal = {Journal of medical systems},
volume = {43},
number = {6},
pages = {169},
pmid = {31062175},
issn = {1573-689X},
support = {51605419//National Natural Science Foundation of China/ ; 51475407//National Natural Science Foundation of China/ ; E2018203433//Natural Science Foundation of Hebei Province/ ; 2016M600193//China Postdoctoral Science Foundation/ ; CL201727//Hebei Province Funding Project for Returned Overseas Scholar/ ; },
mesh = {Algorithms ; Area Under Curve ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*classification/*methods ; Humans ; Movement ; },
abstract = {Mental tasks classification such as motor imagery, based on EEG signals is an important problem in brain computer interface systems (BCI). One of the major concerns in BCI is to have a high classification accuracy. The other concerning one is with the favorable result is guaranteed how to improve the computational efficiency. In this paper, Mu/Beta rhythm was obtained by bandpass filter from EEG signal. And the classical linear discriminant analysis (LDA) was used for deciding which rhythm can give the better classification performance. During this, the common spatial pattern (CSP) was used to project data subject to the ratio of projected energy of one class to that of the other class was maximized. The optimal projection dimension was determined corresponding to the maximum of area under the curve (AUC) for each participant. Eventually, regularized linear discriminant analysis (RLDA) is possible to decode the imagined motor sensed using electroencephalogram (EEG). Results show that higher classification accuracy can be provided by RLDA. And optimal projection dimensions determined by LDA and RLDA are of consistent solution, this improves computational efficiency of CSP-RLDA method without computation of projection dimension.},
}
@article {pmid31059799,
year = {2019},
author = {Zubarev, I and Zetter, R and Halme, HL and Parkkonen, L},
title = {Adaptive neural network classifier for decoding MEG signals.},
journal = {NeuroImage},
volume = {197},
number = {},
pages = {425-434},
pmid = {31059799},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Auditory Perception/physiology ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography ; *Evoked Potentials ; Female ; Humans ; *Magnetoencephalography ; Male ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Touch Perception/physiology ; Visual Perception/physiology ; },
abstract = {We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain-computer interfaces (BCI).},
}
@article {pmid31057918,
year = {2018},
author = {Shen, W and Das, S and Vitale, F and Richardson, A and Ananthakrishnan, A and Struzyna, LA and Brown, DP and Song, N and Ramkumar, M and Lucas, T and Cullen, DK and Litt, B and Allen, MG},
title = {Microfabricated intracortical extracellular matrix-microelectrodes for improving neural interfaces.},
journal = {Microsystems &amp; nanoengineering},
volume = {4},
number = {},
pages = {30},
pmid = {31057918},
issn = {2055-7434},
support = {R21 EB022209/EB/NIBIB NIH HHS/United States ; U01 NS094340/NS/NINDS NIH HHS/United States ; },
abstract = {Intracortical neural microelectrodes, which can directly interface with local neural microcircuits with high spatial and temporal resolution, are critical for neuroscience research, emerging clinical applications, and brain computer interfaces (BCI). However, clinical applications of these devices remain limited mostly by their inability to mitigate inflammatory reactions and support dense neuronal survival at their interfaces. Herein we report the development of microelectrodes primarily composed of extracellular matrix (ECM) proteins, which act as a bio-compatible and an electrochemical interface between the microelectrodes and physiological solution. These ECM-microelectrodes are batch fabricated using a novel combination of micro-transfer-molding and excimer laser micromachining to exhibit final dimensions comparable to those of commercial silicon-based microelectrodes. These are further integrated with a removable insertion stent which aids in intracortical implantation. Results from electrochemical models and in vivo recordings from the rat's cortex indicate that ECM encapsulations have no significant effect on the electrochemical impedance characteristics of ECM-microelectrodes at neurologically relevant frequencies. ECM-microelectrodes are found to support a dense layer of neuronal somata and neurites on the electrode surface with high neuronal viability and exhibited markedly diminished neuroinflammation and glial scarring in early chronic experiments in rats.},
}
@article {pmid31057381,
year = {2019},
author = {Li, Q and Lu, Z and Gao, N and Yang, J},
title = {Optimizing the Performance of the Visual P300-Speller Through Active Mental Tasks Based on Color Distinction and Modulation of Task Difficulty.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {130},
pmid = {31057381},
issn = {1662-5161},
abstract = {Objective: P300-speller is the most commonly used brain-computer interface (BCI) for providing a means of communication to patients with amyotrophic lateral sclerosis. However, the performance of the P300-speller BCI is still inadequate. We investigated whether the performance of P300-speller can be further improved by increasing the mental effort required of the user. Methods: We designed two active mental tasks for a P300-speller based on a differently colored smiling cartoon-face paradigm. The tasks were based on color distinction, and their difficulty was modulated. One of the active mental tasks (DC task) required participants to focus on and distinguish the color of a target, while the other task (CN + DC task) required participants to simultaneously count the number of times a target flashed and distinguish its color. Results: The amplitudes of the event-related potentials (ERPs) in both DC and CN + DC tasks were higher than that in the CN task. The significant difference in the amplitudes between the DC and CN tasks was observed around the parietal-central area from 440 to 800 ms (late positive component, LPC), and that between the CN + DC and CN tasks was observed around the left-frontal and right-frontal areas from 320 to 480 ms (P3a) and the parietal-central area from 480 to 800 ms (P3b and LPC). The latency of the P300 potential in the CN + DC task was significantly longer than that in the CN task at F3, Fz, F4, C4, Pz, and P4 (P < 0.05). Offline (P < 0.05 at superposing once, twice, and thrice) and online (P < 0.001) classification results showed that the average accuracies in the CN + DC task were significantly greater than that in the CN task. Similar results were found for online information transfer rates (ITRs; P < 0.001). In addition, we found that the average online accuracies in the DC task were greater than those in the CN task, although the difference was not statistically significant (P = 0.051). Significance: The active mental task based on task difficulty modulation can significantly improve the performance of the P300-speller, and that based on color distinction shows a trend of improved performance.},
}
@article {pmid31057380,
year = {2019},
author = {Meng, J and He, B},
title = {Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {128},
pmid = {31057380},
issn = {1662-5161},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; },
abstract = {Electroencephalography based brain-computer interfaces (BCIs) show promise of providing an alternative communication channel between the brain and an external device. It is well acknowledged that BCI control is a skill and could be improved through practice and training. In this study, we explore the change of BCI behavioral performance as well as the electrophysiological properties across three training sessions in a pool of 42 human subjects. Our results show that the group average of BCI accuracy and the information transfer rate improved significantly in the third session compared to the first session; especially the significance reached in a smaller subset of a low BCI performance group (average accuracy <70%) as well. There was a significant difference of event-related desynchronization (ERD) lateralization for BCI control between the left- and right-hand imagination task in the last two sessions, but this significant difference was not revealed in the first training sessions. No significant change of R [2] value or event-related desynchronization and synchronization (ERD/ERS) for either channel C3 or channel C4, which were used for online control, was found across the training sessions. The change of ERD lateralization was also not significant across the training sessions. The present results indicate that BCI training could induce a change of behavioral performance and electrophysiological properties quickly, within just a few hours of training, distributed into three sessions. Multiple training sessions might especially be beneficial for the low BCI performers.},
}
@article {pmid31054838,
year = {2019},
author = {Yokoyama, H and Kaneko, N and Ogawa, T and Kawashima, N and Watanabe, K and Nakazawa, K},
title = {Cortical Correlates of Locomotor Muscle Synergy Activation in Humans: An Electroencephalographic Decoding Study.},
journal = {iScience},
volume = {15},
number = {},
pages = {623-639},
pmid = {31054838},
issn = {2589-0042},
abstract = {Muscular control during walking is believed to be simplified by the coactivation of muscles called muscle synergies. Although significant corticomuscular connectivity during walking has been reported, the level at which the cortical activity is involved in muscle activity (muscle synergy or individual muscle level) remains unclear. Here we examined cortical correlates of muscle activation during walking by brain decoding of activation of muscle synergies and individual muscles from electroencephalographic signals. We demonstrated that the activation of locomotor muscle synergies was decoded from slow cortical waves. In addition, the decoding accuracy for muscle synergies was greater than that for individual muscles and the decoding of individual muscle activation was based on muscle-synergy-related cortical information. These results indicate the cortical correlates of locomotor muscle synergy activation. These findings expand our understanding of the relationships between brain and locomotor muscle synergies and could accelerate the development of effective brain-machine interfaces for walking rehabilitation.},
}
@article {pmid31049051,
year = {2019},
author = {Li, M and Wang, R and Yang, J and Duan, L},
title = {An Improved Refined Composite Multivariate Multiscale Fuzzy Entropy Method for MI-EEG Feature Extraction.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {7529572},
pmid = {31049051},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Entropy ; Humans ; Normal Distribution ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Feature extraction of motor imagery electroencephalogram (MI-EEG) has shown good application prospects in the field of medical health. Also, multivariate entropy-based feature extraction methods have been gradually applied to analyze complex multichannel biomedical signals, such as EEG and electromyography. Compared with traditional multivariate entropies, refined composite multivariate multiscale fuzzy entropy (RCmvMFE) overcomes the defect of unstable entropy values caused by the scale factor increase and is beneficial towards obtaining richer feature information. However, the coarse-grained process of RCmvMFE is mean filtered, which weakens Gaussian noise and is powerless against random impulse noise interference. This yields poor quality feature information and low accuracy classification. In this paper, RCmvMFE is improved (IRCmvMFE) by using composite filters in the coarse-grained procedure to enhance filter performance. Median filters are employed to remove the impulse noise interference from multichannel MI-EEG signals, and these filtered MI-EEGs are further smoothed by the mean filters. The multiscale IRCmvMFEs are calculated for all channels of composite filtered MI-EEGs, forming a feature vector, and a support vector machine is used for pattern classification. Based on two public datasets with different motor imagery tasks, the recognition results of 10 × 10-fold cross-validation achieved 99.43% and 99.86%, respectively, and the statistical analysis of experimental results was completed, showing the effectiveness of IRCmvMFE, as well. The proposed IRCmvMFE-based feature extraction method is superior compared to entropy-based and traditional methods.},
}
@article {pmid31046991,
year = {2019},
author = {Taran, S and Bajaj, V},
title = {Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method.},
journal = {Computer methods and programs in biomedicine},
volume = {173},
number = {},
pages = {157-165},
doi = {10.1016/j.cmpb.2019.03.015},
pmid = {31046991},
issn = {1872-7565},
mesh = {Algorithms ; Brain/*diagnostic imaging ; Brain-Computer Interfaces ; *Electroencephalography ; *Emotions ; *Facial Expression ; Humans ; Image Processing, Computer-Assisted ; Least-Squares Analysis ; Linear Models ; Neurophysiology ; Pattern Recognition, Automated ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Video Recording ; Wavelet Analysis ; },
abstract = {BACKGROUND AND OBJECTIVE: The recognition of emotional states is a crucial step in the development of a brain-computer interface (BCI) system. Emotion recognition system finds applications in medical science for the impaired and disabled people. Electroencephalography assesses the neurophysiology of the brain for recognition of different emotional states.<br><br>METHODS: The audio-video stimulus based experimental setup is arranged for the electroencephalogram (EEG) recordings of happy, fear, sad, and relax emotions and a two-stage filtering method is proposed for the recognition of emotion EEG signals. At the first stage, a correlation-criterion is suggested for removal of noisy intrinsic mode functions (IMFs) from the IMFs obtained by applying the empirical mode decomposition on the raw EEG signal. The noise-free IMFs are used to reconstruct the denoised EEG signal with improved stationarity characteristics. The denoised EEG signal is further decomposed into modes using the variational mode decomposition (VMD). At the second stage, the instantaneous-frequency based filtering of VMD modes is performed and filtered modes are retained for the reconstruction of denoised EEG signal with the desired frequency range. After two-stage filtering, the non-linear measures of filtered EEG signals are used as input features to multi-class least squares support vector machine (MC-LS-SVM) classifier for emotion recognition.<br><br>RESULTS: The different kernel functions are tested in MC-LS-SVM classifier for emotion recognition. The Morlet wavelet (MW) kernel function provides the best individual classification accuracies for happy, fear, sad, and relax emotions as 92.79%, 87.62%, 88.98%, and 93.13%, respectively. The MW-kernel function also obtained the best overall accuracy of 90.63%, F1-score 0.9064, and kappa value 0.8751.<br><br>CONCLUSIONS: The Audio-video stimulus based emotion EEG-dataset is recorded. A new filtering method is proposed for EEG signals. The proposed method provides better emotion recognition performance as compared to the state-of-the-art methods and classifies emotions using single-bipolar EEG channel, which can greatly reduce the complexity of emotion-recognition based BCI systems.},
}
@article {pmid31043756,
year = {2019},
author = {Stahl, EA and Breen, G and Forstner, AJ and McQuillin, A and Ripke, S and Trubetskoy, V and Mattheisen, M and Wang, Y and Coleman, JRI and Gaspar, HA and de Leeuw, CA and Steinberg, S and Pavlides, JMW and Trzaskowski, M and Byrne, EM and Pers, TH and Holmans, PA and Richards, AL and Abbott, L and Agerbo, E and Akil, H and Albani, D and Alliey-Rodriguez, N and Als, TD and Anjorin, A and Antilla, V and Awasthi, S and Badner, JA and Bækvad-Hansen, M and Barchas, JD and Bass, N and Bauer, M and Belliveau, R and Bergen, SE and Pedersen, CB and Bøen, E and Boks, MP and Boocock, J and Budde, M and Bunney, W and Burmeister, M and Bybjerg-Grauholm, J and Byerley, W and Casas, M and Cerrato, F and Cervantes, P and Chambert, K and Charney, AW and Chen, D and Churchhouse, C and Clarke, TK and Coryell, W and Craig, DW and Cruceanu, C and Curtis, D and Czerski, PM and Dale, AM and de Jong, S and Degenhardt, F and Del-Favero, J and DePaulo, JR and Djurovic, S and Dobbyn, AL and Dumont, A and Elvsåshagen, T and Escott-Price, V and Fan, CC and Fischer, SB and Flickinger, M and Foroud, TM and Forty, L and Frank, J and Fraser, C and Freimer, NB and Frisén, L and Gade, K and Gage, D and Garnham, J and Giambartolomei, C and Pedersen, MG and Goldstein, J and Gordon, SD and Gordon-Smith, K and Green, EK and Green, MJ and Greenwood, TA and Grove, J and Guan, W and Guzman-Parra, J and Hamshere, ML and Hautzinger, M and Heilbronner, U and Herms, S and Hipolito, M and Hoffmann, P and Holland, D and Huckins, L and Jamain, S and Johnson, JS and Juréus, A and Kandaswamy, R and Karlsson, R and Kennedy, JL and Kittel-Schneider, S and Knowles, JA and Kogevinas, M and Koller, AC and Kupka, R and Lavebratt, C and Lawrence, J and Lawson, WB and Leber, M and Lee, PH and Levy, SE and Li, JZ and Liu, C and Lucae, S and Maaser, A and MacIntyre, DJ and Mahon, PB and Maier, W and Martinsson, L and McCarroll, S and McGuffin, P and McInnis, MG and McKay, JD and Medeiros, H and Medland, SE and Meng, F and Milani, L and Montgomery, GW and Morris, DW and Mühleisen, TW and Mullins, N and Nguyen, H and Nievergelt, CM and Adolfsson, AN and Nwulia, EA and O'Donovan, C and Loohuis, LMO and Ori, APS and Oruc, L and Ösby, U and Perlis, RH and Perry, A and Pfennig, A and Potash, JB and Purcell, SM and Regeer, EJ and Reif, A and Reinbold, CS and Rice, JP and Rivas, F and Rivera, M and Roussos, P and Ruderfer, DM and Ryu, E and Sánchez-Mora, C and Schatzberg, AF and Scheftner, WA and Schork, NJ and Shannon Weickert, C and Shehktman, T and Shilling, PD and Sigurdsson, E and Slaney, C and Smeland, OB and Sobell, JL and Søholm Hansen, C and Spijker, AT and St Clair, D and Steffens, M and Strauss, JS and Streit, F and Strohmaier, J and Szelinger, S and Thompson, RC and Thorgeirsson, TE and Treutlein, J and Vedder, H and Wang, W and Watson, SJ and Weickert, TW and Witt, SH and Xi, S and Xu, W and Young, AH and Zandi, P and Zhang, P and Zöllner, S and , and , and Adolfsson, R and Agartz, I and Alda, M and Backlund, L and Baune, BT and Bellivier, F and Berrettini, WH and Biernacka, JM and Blackwood, DHR and Boehnke, M and Børglum, AD and Corvin, A and Craddock, N and Daly, MJ and Dannlowski, U and Esko, T and Etain, B and Frye, M and Fullerton, JM and Gershon, ES and Gill, M and Goes, F and Grigoroiu-Serbanescu, M and Hauser, J and Hougaard, DM and Hultman, CM and Jones, I and Jones, LA and Kahn, RS and Kirov, G and Landén, M and Leboyer, M and Lewis, CM and Li, QS and Lissowska, J and Martin, NG and Mayoral, F and McElroy, SL and McIntosh, AM and McMahon, FJ and Melle, I and Metspalu, A and Mitchell, PB and Morken, G and Mors, O and Mortensen, PB and Müller-Myhsok, B and Myers, RM and Neale, BM and Nimgaonkar, V and Nordentoft, M and Nöthen, MM and O'Donovan, MC and Oedegaard, KJ and Owen, MJ and Paciga, SA and Pato, C and Pato, MT and Posthuma, D and Ramos-Quiroga, JA and Ribasés, M and Rietschel, M and Rouleau, GA and Schalling, M and Schofield, PR and Schulze, TG and Serretti, A and Smoller, JW and Stefansson, H and Stefansson, K and Stordal, E and Sullivan, PF and Turecki, G and Vaaler, AE and Vieta, E and Vincent, JB and Werge, T and Nurnberger, JI and Wray, NR and Di Florio, A and Edenberg, HJ and Cichon, S and Ophoff, RA and Scott, LJ and Andreassen, OA and Kelsoe, J and Sklar, P and , },
title = {Genome-wide association study identifies 30 loci associated with bipolar disorder.},
journal = {Nature genetics},
volume = {51},
number = {5},
pages = {793-803},
pmid = {31043756},
issn = {1546-1718},
support = {R01 MH104964/MH/NIMH NIH HHS/United States ; MR/L010305/1/MRC_/Medical Research Council/United Kingdom ; G1000708/MRC_/Medical Research Council/United Kingdom ; U01 MH109536/MH/NIMH NIH HHS/United States ; U01 MH109514/MH/NIMH NIH HHS/United States ; R01 MH085548/MH/NIMH NIH HHS/United States ; R00 MH101367/MH/NIMH NIH HHS/United States ; R01 MH123451/MH/NIMH NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; MR/L023784/2/MRC_/Medical Research Council/United Kingdom ; 001/WHO_/World Health Organization/International ; R01 MH119243/MH/NIMH NIH HHS/United States ; },
mesh = {Bipolar Disorder/classification/*genetics ; Case-Control Studies ; Depressive Disorder, Major/genetics ; Female ; *Genetic Loci ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Male ; Polymorphism, Single Nucleotide ; Psychotic Disorders/genetics ; Schizophrenia/genetics ; Systems Biology ; },
abstract = {Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10[-4] in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10[-8]) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.},
}
@article {pmid31043637,
year = {2019},
author = {Selfslagh, A and Shokur, S and Campos, DSF and Donati, ARC and Almeida, S and Yamauti, SY and Coelho, DB and Bouri, M and Nicolelis, MAL},
title = {Non-invasive, Brain-controlled Functional Electrical Stimulation for Locomotion Rehabilitation in Individuals with Paraplegia.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {6782},
pmid = {31043637},
issn = {2045-2322},
mesh = {Adult ; Brain/*physiopathology ; Electric Stimulation Therapy/*methods ; *Exercise Therapy ; Gait ; Humans ; *Locomotion ; Neurological Rehabilitation/*methods ; Paraplegia/physiopathology/*rehabilitation ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Walking ; },
abstract = {Spinal cord injury (SCI) impairs the flow of sensory and motor signals between the brain and the areas of the body located below the lesion level. Here, we describe a neurorehabilitation setup combining several approaches that were shown to have a positive effect in patients with SCI: gait training by means of non-invasive, surface functional electrical stimulation (sFES) of the lower-limbs, proprioceptive and tactile feedback, balance control through overground walking and cue-based decoding of cortical motor commands using a brain-machine interface (BMI). The central component of this new approach was the development of a novel muscle stimulation paradigm for step generation using 16 sFES channels taking all sub-phases of physiological gait into account. We also developed a new BMI protocol to identify left and right leg motor imagery that was used to trigger an sFES-generated step movement. Our system was tested and validated with two patients with chronic paraplegia. These patients were able to walk safely with 65-70% body weight support, accumulating a total of 4,580 steps with this setup. We observed cardiovascular improvements and less dependency on walking assistance, but also partial neurological recovery in both patients, with substantial rates of motor improvement for one of them.},
}
@article {pmid31042684,
year = {2019},
author = {Saif-Ur-Rehman, M and Lienkämper, R and Parpaley, Y and Wellmer, J and Liu, C and Lee, B and Kellis, S and Andersen, R and Iossifidis, I and Glasmachers, T and Klaes, C},
title = {SpikeDeeptector: a deep-learning based method for detection of neural spiking activity.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056003},
doi = {10.1088/1741-2552/ab1e63},
pmid = {31042684},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Adult ; Brain/*physiology ; *Deep Learning ; Female ; Humans ; Male ; Middle Aged ; *Neural Networks, Computer ; Neurons/*physiology ; Quadriplegia/diagnosis/physiopathology ; Young Adult ; },
abstract = {OBJECTIVE: In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain-computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas.<br><br>APPROACH: We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise.<br><br>MAIN RESULTS: We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs.<br><br>SIGNIFICANCE: The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training.<br><br>The clinical trial registration number for patients implanted with the Utah array is NCT01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation.},
}
@article {pmid31042607,
year = {2019},
author = {Cartocci, G and Scorpecci, A and Borghini, G and Maglione, AG and Inguscio, BMS and Giannantonio, S and Giorgi, A and Malerba, P and Rossi, D and Modica, E and Aricò, P and Di Flumeri, G and Marsella, P and Babiloni, F},
title = {EEG rhythms lateralization patterns in children with unilateral hearing loss are different from the patterns of normal hearing controls during speech-in-noise listening.},
journal = {Hearing research},
volume = {379},
number = {},
pages = {31-42},
doi = {10.1016/j.heares.2019.04.011},
pmid = {31042607},
issn = {1878-5891},
mesh = {Adolescent ; Auditory Perception/*physiology ; Brain Waves/physiology ; Case-Control Studies ; Child ; Electroencephalography ; Female ; Functional Laterality/*physiology ; Healthy Volunteers ; Hearing Loss, Unilateral/*physiopathology/psychology ; Humans ; Male ; Noise ; Speech Perception/*physiology ; },
abstract = {Unilateral hearing loss constitutes a field of growing interest in the scientific community. In fact, this kind of patients represent a unique and physiological way to investigate how neuroplasticity overcame unilateral deafferentation by implementing particular strategies that produce apparently next- to- normal hearing behavioural performances. This explains why such patients have been underinvestigated for a long time. Thanks to the availability of techniques able to study the cerebral activity underlying the mentioned behavioural outcomes, the aim of the present research was to elucidate whether different electroencephalographic (EEG) patterns occurred in unilateral hearing loss (UHL) children in comparison to normal hearing (NH) controls during speech-in-noise listening. Given the intrinsic lateralized nature of such patients, due to the unilateral side of hearing impairment, the experimental question was to assess whether this would reflect a different EEG pattern while performing a word in noise recognition task varying the direction of the noise source. Results showed a correlation between the period of deafness and the cortical activity asymmetry toward the hearing ear side in the frontal, parietal and occipital areas in all the experimental conditions. Concerning alpha and beta activity in the frontal and central areas highlighted that in the NH group, the lateralization was always left-sided during the Quiet condition, while it was right-sided in noise conditions; this evidence was not, however, detected also in the UHL group. In addition, focusing on the theta and alpha activity in the frontal areas (Broca area) during noise conditions, while the activity was always left-lateralized in the NH group, it was ipsilateral to the direction of the background noise in the UHL group, and of a weaker extent than in NH controls. Furthermore, in noise conditions, only the UHL group showed a higher theta activity in the temporal areas ipsilateral to the side where the background noise was directed to. Finally, in the case of bilateral noise (background noise and word signal both coming from the same two sources), the theta and alpha activity in the frontal areas (Broca area) was left-lateralized in the case of the NH group and lateralized towards the side of the better hearing ear in the case of the UHL group. Taken together, this evidence supports the establishment of a particular EEG pattern occurrence in UHL children taking place in the frontal (Broca area), temporal and parietal lobes, probably physiologically established in order to deal with different sound and noise source directions.},
}
@article {pmid31040915,
year = {2019},
author = {Caroline, R and Sofia, G and Catherine, P and Remi, B and Pierre, C and Olivier, B and Laurent, G and Valentin, A and Claude, C and Odile, F},
title = {Correction: Combined inhibition of PI3K and Src kinases demonstrates synergistic therapeutic efficacy in clear-cell renal carcinoma.},
journal = {Oncotarget},
volume = {10},
number = {22},
pages = {2236},
doi = {10.18632/oncotarget.26807},
pmid = {31040915},
issn = {1949-2553},
abstract = {[This corrects the article DOI: 10.18632/oncotarget.25700.].},
}
@article {pmid31040775,
year = {2019},
author = {Chai, X and Zhang, Z and Guan, K and Liu, G and Niu, H},
title = {A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {127},
pmid = {31040775},
issn = {1662-5161},
abstract = {Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed. Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated. Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion. Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue.},
}
@article {pmid31040764,
year = {2019},
author = {Khorasani, A and Shalchyan, V and Daliri, MR},
title = {Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {350},
pmid = {31040764},
issn = {1662-4548},
abstract = {Intracortical data recorded with multi-electrode arrays provide rich information about kinematic and kinetic states of movement in the brain-machine interface (BMI) systems. Direct estimation of kinetic information such as the force from cortical data has the same importance as kinematic information to make a functional BMI system. Various types of the information including single unit activity (SUA), multiunit activity (MUA) and local field potential (LFP) can be used as an input information to extract motor commands for control of the external devices in BMI. Here we combine LFP and MUA information to improve decoding accuracy of the force signal from the multi-channel intracortical data of freely moving rats. We suggest a weighted common average referencing (CAR) algorithm in order to valid interpretation of the force decoding from different data types. The proposed spatial filter adaptively identifies contribution of the common noise on the channels employing Kalman filter method. We evaluated the efficacy of the proposed artifact algorithm on both simulation and real data. In the simulation study, the average R [2] between the original and reconstructed signal of all channels after applying the proposed artifact removal method was computed for input SNRs in the range of -45 to 0 dB. Weighted CAR method can effectively reconstruct the original signal with average R [2] higher than 0.5 for input SNRs higher than -s10 dB in case of adding simulated outlier and motion artifacts. We also show that the proposed artifact removal algorithm 33% improves the accuracy of force decoding in terms of R [2] value compared to standard CAR filters.},
}
@article {pmid31034968,
year = {2019},
author = {Tanaka, H and Miyakoshi, M},
title = {Cross-correlation task-related component analysis (xTRCA) for enhancing evoked and induced responses of event-related potentials.},
journal = {NeuroImage},
volume = {197},
number = {},
pages = {177-190},
doi = {10.1016/j.neuroimage.2019.04.049},
pmid = {31034968},
issn = {1095-9572},
mesh = {Algorithms ; Auditory Perception/physiology ; Brain/*physiology ; *Electroencephalography ; *Evoked Potentials ; Humans ; Models, Neurological ; Multivariate Analysis ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {We propose an analysis method that extracts trial-reproducible (i.e., recurring) event-related spatiotemporal EEG patterns by optimizing a spatial filter as well as trial timings of task-related components in the time domain simultaneously in a unified manner. Event-related responses are broadly categorized into evoked and induced responses, but those are analyzed commonly in the time and the time-frequency domain, respectively. To facilitate a comparison of evoked and induced responses, a unified method for analyzing both evoked and induced responses is desired. Here we propose a method of cross-correlation task-related component analysis (xTRCA) as an extension of our previous method. xTRCA constructs a linear spatial filter and then optimizes trial timings of single trials based on trial reproducibility as an objective function. The spatial filter enhances event-related responses, and the temporal optimization compensates trial-by-trial latencies that are inherent to ERPs. We first applied xTRCA to synthetic data of induced responses whose phases varied from trial to trial, and found that xTRCA could realign the induced responses by compensating the phase differences. We then demonstrated with mismatch negativity data that xTRCA enhanced the event-related-potential waveform observed at a single channel. Finally, a classification accuracy was improved when trial timings were optimized by xTRCA, suggesting a practical application of the method for a brain computer interface. We conclude that xTRCA provides a unified framework to analyze and enhance event-related evoked and induced responses in the time domain by objectively maximizing trial reproducibility.},
}
@article {pmid31034407,
year = {2020},
author = {He, H and Wu, D},
title = {Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {2},
pages = {399-410},
doi = {10.1109/TBME.2019.2913914},
pmid = {31034407},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Databases, Factual ; Electroencephalography/*classification ; Evoked Potentials/physiology ; Humans ; Imagination/physiology ; *Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain-computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data?<br><br>METHODS: We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject.<br><br>RESULTS: Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment.<br><br>CONCLUSION: The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs.<br><br>SIGNIFICANCE: Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.},
}
@article {pmid31027220,
year = {2019},
author = {De la Torre, GG and Gonzalez-Torre, S and Muñoz, C and Garcia, MA},
title = {Wireless Computer-Supported Cooperative Work: A Pilot Experiment on Art and Brain[-]Computer Interfaces.},
journal = {Brain sciences},
volume = {9},
number = {4},
pages = {},
pmid = {31027220},
issn = {2076-3425},
abstract = {The present case study looked into the feasibility of using brain-computer interface (BCI) technology combined with computer-supported cooperative work (CSCW) in a wireless network. We had two objectives; first, to test the wireless BCI-based configuration and the practical use of this idea we assessed workload perception in participants located several kilometers apart taking part in the same drawing task. Second, we studied the cortical activation patterns of participants performing the drawing task with and without the BCI technology. Results showed higher mental workload perception and broader cortical activation (frontal-temporal-occipital) under BCI experimental conditions. This idea shows a possible application of BCI research in the social field, where two or more users could engage in a computer networking task using BCI technology over the internet. New research avenues for CSCW are discussed and possibilities for future research are given.},
}
@article {pmid31026831,
year = {2020},
author = {Kuo, CH and Blakely, TM and Wander, JD and Sarma, D and Wu, J and Casimo, K and Weaver, KE and Ojemann, JG},
title = {Context-dependent relationship in high-resolution micro-ECoG studies during finger movements.},
journal = {Journal of neurosurgery},
volume = {132},
number = {5},
pages = {1358-1366},
doi = {10.3171/2019.1.JNS181840},
pmid = {31026831},
issn = {1933-0693},
abstract = {OBJECTIVE: The activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.<br><br>METHODS: Three neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 &#xd7; 8 &#xb5;ECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70-230 Hz) filtered &#xb5;ECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with &#xb5;ECoG recording.<br><br>RESULTS: In all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3-6-mm apart by the HG-filtered &#xb5;ECoG signal. For HG power of cortical activation measured with &#xb5;ECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).<br><br>CONCLUSIONS: HG-filtered &#xb5;ECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the &#xb5;ECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered &#xb5;ECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.},
}
@article {pmid31025562,
year = {2019},
author = {Liu, Q and Liu, Y and Li, J and Lau, C and Wu, F and Zhang, A and Li, Z and Chen, M and Fu, H and Draper, J and Cao, X and Zhou, C},
title = {Fully Printed All-Solid-State Organic Flexible Artificial Synapse for Neuromorphic Computing.},
journal = {ACS applied materials &amp; interfaces},
volume = {11},
number = {18},
pages = {16749-16757},
doi = {10.1021/acsami.9b00226},
pmid = {31025562},
issn = {1944-8252},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electronics ; Humans ; Neuronal Plasticity/drug effects/*genetics ; Oxygen/chemistry ; Plasma Gases/chemistry ; Polymers/*chemistry ; Printing, Three-Dimensional ; Robotics ; Synapses/*chemistry ; },
abstract = {Nonvolatile, flexible artificial synapses that can be used for brain-inspired computing are highly desirable for emerging applications such as human-machine interfaces, soft robotics, medical implants, and biological studies. Printed devices based on organic materials are very promising for these applications due to their sensitivity to ion injection, intrinsic printability, biocompatibility, and great potential for flexible/stretchable electronics. Herein, we report the experimental realization of a nonvolatile artificial synapse using organic polymers in a scalable fabrication process. The three-terminal electrochemical neuromorphic device successfully emulates the key features of biological synapses: long-term potentiation/depression, spike timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow energy consumption. The artificial synapse network exhibits an excellent endurance against bending tests and enables a direct emulation of logic gates, which shows the feasibility of using them in futuristic hierarchical neural networks. Based on our demonstration of 100 distinct, nonvolatile conductance states, we achieved a high accuracy in pattern recognition and face classification neural network simulations.},
}
@article {pmid31025545,
year = {2019},
author = {Tondera, C and Akbar, TF and Thomas, AK and Lin, W and Werner, C and Busskamp, V and Zhang, Y and Minev, IR},
title = {Highly Conductive, Stretchable, and Cell-Adhesive Hydrogel by Nanoclay Doping.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {15},
number = {27},
pages = {e1901406},
doi = {10.1002/smll.201901406},
pmid = {31025545},
issn = {1613-6829},
support = {678071/ERC_/European Research Council/International ; },
mesh = {Acrylic Resins/chemistry ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Cell Adhesion ; Clay/*chemistry ; *Electric Conductivity ; Humans ; Hydrogels/*chemistry ; Induced Pluripotent Stem Cells/*cytology ; Nanoparticles/*chemistry ; Polymerization ; Polymers/chemistry ; Silicates/chemistry ; },
abstract = {Electrically conductive materials that mimic physical and biological properties of tissues are urgently required for seamless brain-machine interfaces. Here, a multinetwork hydrogel combining electrical conductivity of 26 S m[-1] , stretchability of 800%, and tissue-like elastic modulus of 15 kPa with mimicry of the extracellular matrix is reported. Engineering this unique set of properties is enabled by a novel in-scaffold polymerization approach. Colloidal hydrogels of the nanoclay Laponite are employed as supports for the assembly of secondary polymer networks. Laponite dramatically increases the conductivity of in-scaffold polymerized poly(ethylene-3,4-diethoxy thiophene) in the absence of other dopants, while preserving excellent stretchability. The scaffold is coated with a layer containing adhesive peptide and polysaccharide dextran sulfate supporting the attachment, proliferation, and neuronal differentiation of human induced pluripotent stem cells directly on the surface of conductive hydrogels. Due to its compatibility with simple extrusion printing, this material promises to enable tissue-mimetic neurostimulating electrodes.},
}
@article {pmid31024239,
year = {2019},
author = {Kireev, D and Rincón Montes, V and Stevanovic, J and Srikantharajah, K and Offenhäusser, A},
title = {N[3]-MEA Probes: Scooping Neuronal Networks.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {320},
pmid = {31024239},
issn = {1662-4548},
abstract = {In the current work, we introduce a brand new line of versatile, flexible, and multifunctional MEA probes, the so-called Nano Neuro Net, or N[3]-MEAs. Material choice, dimensions, and room for further upgrade, were carefully considered when designing such probes in order to cover the widest application range possible. Proof of the operation principle of these novel probes is shown in the manuscript via the recording of extracellular signals, such as action potentials and local field potentials from cardiac cells and retinal ganglion cells of the heart tissue and eye respectively. Reasonably large signal to noise ratio (SNR) combined with effortless operation of the devices, mechanical and chemical stability, multifunctionality provide, in our opinion, an unprecedented blend. We show successful recordings of (1) action potentials from heart tissue with a SNR up to 13.2; (2) spontaneous activity of retinal ganglion cells with a SNR up to 12.8; and (3) local field potentials with an ERG-like waveform, as well as spiking responses of the retina to light stimulation. The results reveal not only the multi-functionality of these N[3]-MEAs, but high quality recordings of electrogenic tissues.},
}
@article {pmid31023574,
year = {2019},
author = {Barbaro, MF and Kramer, DR and Nune, G and Lee, MB and Peng, T and Liu, CY and Kellis, S and Lee, B},
title = {Directional tuning during reach planning in the supramarginal gyrus using local field potentials.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {64},
number = {},
pages = {214-219},
pmid = {31023574},
issn = {1532-2653},
support = {R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adult ; Humans ; Male ; Motor Cortex/physiology ; Neurons/physiology ; Parietal Lobe/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Previous work in directional tuning for brain machine interfaces has primarily relied on algorithm sorted neuronal action potentials in primary motor cortex. However, local field potential has been utilized to show directional tuning in macaque studies, and inferior parietal cortex has shown increased neuronal activity in reaching tasks that relied on MRI imaging. In this study we utilized local field potential recordings from a human subject performing a delayed reach task and show that high frequency band (76-100 Hz) spectral power is directionally tuned to different reaching target locations during an active reach. We also show that during the delay phase of the task, directional tuning is present in areas of the inferior parietal cortex, in particular, the supramarginal gyrus.},
}
@article {pmid31023299,
year = {2019},
author = {Liang, H and Fung, IC and Tse, ZTH and Yin, J and Chan, CH and Pechta, LE and Smith, BJ and Marquez-Lameda, RD and Meltzer, MI and Lubell, KM and Fu, KW},
title = {How did Ebola information spread on twitter: broadcasting or viral spreading?.},
journal = {BMC public health},
volume = {19},
number = {1},
pages = {438},
pmid = {31023299},
issn = {1471-2458},
support = {15IPA1509134; 16IPA1609578//Centers for Disease Control and Prevention/ ; 16IPA1619505//Centers for Disease Control and Prevention/ ; },
mesh = {*Hemorrhagic Fever, Ebola ; Humans ; Information Dissemination/*methods ; *Online Social Networking ; Social Media/*statistics &amp; numerical data ; },
abstract = {BACKGROUND: Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages.<br><br>METHODS: Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns.<br><br>RESULTS: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users.<br><br>CONCLUSIONS: Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.},
}
@article {pmid31022416,
year = {2019},
author = {Kadam, ST and Dhaimodker, VMN and Patil, MM and Reddy Edla, D and Kuppili, V},
title = {EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine.},
journal = {Journal of neuroscience methods},
volume = {322},
number = {},
pages = {71-82},
doi = {10.1016/j.jneumeth.2019.04.008},
pmid = {31022416},
issn = {1872-678X},
mesh = {Brain/*physiology ; Brain Waves ; Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/*methods ; Humans ; Intelligence/physiology ; *Intelligence Tests ; Neuropsychological Tests ; *Wavelet Analysis ; },
abstract = {BACKGROUND: The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and we have attempted to quantify using EEG signals.<br><br>NEW METHOD: This paper aims at devising a novel non-invasive method of measuring human intelligence. A newly devised scoring scheme is used to ultimately generate a score for the subjects. Wavelet packet transform approach for feature extraction is applied to 5 channel EEG data. This approach uses db-8 as the mother wavelet. Hierarchical extreme learning machine is used for classification of the EEG signals.<br><br>RESULT: 80.00% training accuracy and 73.33% testing accuracy was measured for the classifier. The average sensitivity and specificity across all three classes was measured to be 0.8133 and 0.8923 respectively. An aggregate score was determined from the classification of EEG data. The power spectral analysis of the EEG data was conducted and regions of the brain responsible for various activities was confirmed. In the memory test, theta and beta bands exhibit high power, for arithmetic test, alpha and beta bands are strong, whereas in linguistic test, theta, alpha and beta bands are equally strong.<br><br>COMPARISON: The traditional IQ test determines intelligence indirectly, based on the score obtained from Wechsler test. In this paper an attempt is made to measure intelligence based on various brain activities - memory, arithmetic, linguistic.<br><br>CONCLUSION: A new method to measure intelligence using direct approach by classifying the EEG signals is proposed.},
}
@article {pmid31021810,
year = {2020},
author = {Zhang, D and Yao, L and Chen, K and Wang, S and Chang, X and Liu, Y},
title = {Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.},
journal = {IEEE transactions on cybernetics},
volume = {50},
number = {7},
pages = {3033-3044},
doi = {10.1109/TCYB.2019.2905157},
pmid = {31021810},
issn = {2168-2275},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/*methods ; Female ; Humans ; *Intention ; Male ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; },
abstract = {Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.},
}
@article {pmid31021802,
year = {2019},
author = {Abbaspourazad, H and Hsieh, HL and Shanechi, MM},
title = {A Multiscale Dynamical Modeling and Identification Framework for Spike-Field Activity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1128-1138},
doi = {10.1109/TNSRE.2019.2913218},
pmid = {31021802},
issn = {1558-0210},
mesh = {Algorithms ; Behavior ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Computer Simulation ; Evoked Potentials/physiology ; Humans ; *Models, Neurological ; Monte Carlo Method ; Movement ; Neurons/physiology ; Psychomotor Performance ; },
abstract = {Dynamical encoding models characterize neural activity with low-dimensional hidden states that dynamically evolve in time and gienerate behavior. Current methods have identified these models from single-scale activity, either spikes or fields. However, behavior is simultaneously encoded across multiple spatiotemporal scales of activity, from spikes of individual neurons to neural population activity measured through fields. Identifying a multiscale dynamical model to extract hidden states that simultaneously describe spike-field activities is challenging because of their fundamental differences. Spikes are binary-valued with fast millisecond time-scales while fields are continuous-valued with slower time-scales. Here, we develop a novel multiscale dynamical modeling and identification algorithm to simultaneously characterize multiscale spike-field dynamics and extract multiscale hidden states. We also devise a modal approach to dissociate task-relevant and task-irrelevant dynamics. Using extensive simulations, we show that the algorithm accurately identifies a multiscale dynamical model to simultaneously describe spike-field dynamics. Furthermore, the algorithm extracts hidden states that are multiscale, i.e., contain information from both spikes and fields and accurately predict behavior. Finally, the algorithm detects which of the identified dynamics are task-relevant and to what extent. This multiscale dynamical modeling and identification framework can help study neural dynamics across spatiotemporal scales and may facilitate future neurotechnologies.},
}
@article {pmid31021801,
year = {2019},
author = {Razzak, I and Blumenstein, M and Xu, G},
title = {Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {6},
pages = {1117-1127},
doi = {10.1109/TNSRE.2019.2913142},
pmid = {31021801},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; Machine Learning ; Mental Processes/physiology ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.},
}
@article {pmid31021800,
year = {2019},
author = {Vaidya, M and Flint, RD and Wang, PT and Barry, A and Li, Y and Ghassemi, M and Tomic, G and Yao, J and Carmona, C and Mugler, EM and Gallick, S and Driver, S and Brkic, N and Ripley, D and Liu, C and Kamper, D and Do, AH and Slutzky, MW},
title = {Hemicraniectomy in Traumatic Brain Injury: A Noninvasive Platform to Investigate High Gamma Activity for Brain Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {7},
pages = {1467-1472},
pmid = {31021800},
issn = {1558-0210},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Artifacts ; Brain Injuries, Traumatic/*surgery ; *Brain-Computer Interfaces ; Decompressive Craniectomy/*methods ; Electroencephalography ; Female ; Fingers/innervation ; *Gamma Rhythm ; Humans ; Magnetoencephalography ; Male ; Muscle Contraction ; Prosthesis Design ; Psychomotor Performance ; },
abstract = {Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth and more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, such electroencephalography (EEG) or magnetoencephalography (MEG), which have limited bandwidth and information content. Invasive recordings provide more information and spatiotemporal resolution, but do incur risk, and thus are not usually investigated in people with stroke or traumatic brain injury (TBI). Here, in this paper, we describe a new BMI paradigm to investigate the use of higher frequency signals in brain-injured subjects without incurring significant risk. We recorded EEG in TBI subjects who required hemicraniectomies (removal of a part of the skull). EEG over the hemicraniectomy (hEEG) contained substantial information in the high gamma frequency range (65-115 Hz). Using this information, we decoded continuous finger flexion force with moderate to high accuracy (variance accounted for 0.06 to 0.52), which at best approaches that using epidural signals. These results indicate that people with hemicraniectomies can provide a useful resource for developing BMI therapies for the treatment of brain injury.},
}
@article {pmid31019256,
year = {2019},
author = {Zurzolo, C and Enninga, J},
title = {The best of both worlds-bringing together cell biology and infection at the Institut Pasteur.},
journal = {Genes and immunity},
volume = {20},
number = {5},
pages = {426-435},
doi = {10.1038/s41435-019-0068-x},
pmid = {31019256},
issn = {1476-5470},
mesh = {Cytological Techniques/*methods ; France ; Infectious Disease Medicine/*methods ; Interdisciplinary Research/*methods ; },
abstract = {Only a profound understanding of the structure and function of cells-either as single units or in the context of tissues and whole organisms-will allow a comprehension of what happens in pathological conditions and provides the means to fight disease. The Cell Biology and Infection (BCI for Biologie Cellulaire et Infection) department was created in 2002 at the Institut Pasteur in Paris to develop a research program under the umbrella of cell biology, infection biology, and microbiology. Its visionary ambition was to shape a common framework for cellular microbiology, and to interface the latter with hard sciences like physics and mathematics and cutting-edge technology. This concept, ahead of time, has given high visibility to the field of cellular microbiology and quantitative cell biology, and it has allowed the successful execution of highly interdisciplinary research programs linking a molecular understanding of cellular events with disease. Now, the BCI department embraces additional pathologies, namely cancer and neurodegenerative diseases. Here, we will portray how the integrative research approach of BCI has led to major scientific breakthroughs during the last 10 years, and where we see scientific opportunities for the near future.},
}
@article {pmid31015029,
year = {2019},
author = {Umeda, T and Koizumi, M and Katakai, Y and Saito, R and Seki, K},
title = {Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys.},
journal = {NeuroImage},
volume = {197},
number = {},
pages = {512-526},
doi = {10.1016/j.neuroimage.2019.04.045},
pmid = {31015029},
issn = {1095-9572},
mesh = {Animals ; Brain-Computer Interfaces ; Callithrix ; Electrocorticography/*methods ; Electromyography/*methods ; Female ; Movement ; Muscle, Skeletal/*physiology ; Sensorimotor Cortex/*physiology ; },
abstract = {Remarkable advances have recently been made in the development of Brain-Machine Interface (BMI) technologies for restoring or enhancing motor function. However, the application of these technologies may be limited to patients in static conditions, as these developments have been largely based on studies of animals (e.g., non-human primates) in constrained movement conditions. The ultimate goal of BMI technology is to enable individuals to move their bodies naturally or control external devices without physical constraints. Here, we demonstrate accurate decoding of muscle activity from electrocorticogram (ECoG) signals in unrestrained, freely behaving monkeys. We recorded ECoG signals from the sensorimotor cortex as well as electromyogram signals from multiple muscles in the upper arm while monkeys performed two types of movements with no physical restraints, as follows: forced forelimb movement (lever-pull task) and natural whole-body movement (free movement within the cage). As in previous reports using restrained monkeys, we confirmed that muscle activity during forced forelimb movement was accurately predicted from simultaneously recorded ECoG data. More importantly, we demonstrated that accurate prediction of muscle activity from ECoG data was possible in monkeys performing natural whole-body movement. We found that high-gamma activity in the primary motor cortex primarily contributed to the prediction of muscle activity during natural whole-body movement as well as forced forelimb movement. In contrast, the contribution of high-gamma activity in the premotor and primary somatosensory cortices was significantly larger during natural whole-body movement. Thus, activity in a larger area of the sensorimotor cortex was needed to predict muscle activity during natural whole-body movement. Furthermore, decoding models obtained from forced forelimb movement could not be generalized to natural whole-body movement, which suggests that decoders should be built individually and according to different behavior types. These results contribute to the future application of BMI systems in unrestrained individuals.},
}
@article {pmid31013673,
year = {2019},
author = {Elsahar, Y and Hu, S and Bouazza-Marouf, K and Kerr, D and Mansor, A},
title = {Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {8},
pages = {},
pmid = {31013673},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Communication Devices for People with Disabilities/*trends ; Humans ; Self-Help Devices ; Speech/*physiology ; Speech Disorders/physiopathology/*rehabilitation ; *Telemedicine ; },
abstract = {High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain-computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user's environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.},
}
@article {pmid31010105,
year = {2019},
author = {Ko, LW and Chang, Y and Wu, PL and Tzou, HA and Chen, SF and Tang, SC and Yeh, CL and Chen, YJ},
title = {Development of a Smart Helmet for Strategical BCI Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {8},
pages = {},
pmid = {31010105},
issn = {1424-8220},
support = {NCSIST - AGV - V101 (107)//Chung-Shan Institute of Science and Technology/ ; MOST 107-2221-E-009-150//Ministry of Science and Technology (MOST), Taiwan/ ; },
abstract = {Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain-computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.},
}
@article {pmid31002837,
year = {2020},
author = {Song, K and Takahashi, S and Sakurai, Y},
title = {Reinforcement schedules differentially affect learning in neuronal operant conditioning in rats.},
journal = {Neuroscience research},
volume = {153},
number = {},
pages = {62-67},
doi = {10.1016/j.neures.2019.04.003},
pmid = {31002837},
issn = {1872-8111},
mesh = {Animals ; Conditioning, Operant/*physiology ; Male ; Motor Cortex/physiology ; Neurons/*physiology ; Rats ; Rats, Wistar ; *Reinforcement Schedule ; },
abstract = {Operant conditioning of neuronal activity is a core process for better operation of brain-machine interfaces. However, few studies have investigated the role of reinforcement schedules in neuronal operant conditioning, although they are very effective in behavioral operant conditioning. To test the effect of different reinforcement schedules, the authors trained single-neuron activity in the motor cortex using fixed ratio (FR) and variable ratio (VR) schedules in rats. Neuronal firing rates were enhanced in the FR but not in the VR schedule during conditioning, suggesting that the principles of operant conditioning of neuronal activity are different from those of behavioral responses.},
}
@article {pmid31002760,
year = {2019},
author = {Lee, M and Shim, HJ and Choi, C and Kim, DH},
title = {Soft High-Resolution Neural Interfacing Probes: Materials and Design Approaches.},
journal = {Nano letters},
volume = {19},
number = {5},
pages = {2741-2749},
doi = {10.1021/acs.nanolett.8b04895},
pmid = {31002760},
issn = {1530-6992},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electronics ; Humans ; Immunity, Cellular/physiology ; Neural Conduction/physiology ; Neurons/immunology/*physiology ; },
abstract = {Neural interfacing probes are located between the nervous system and the implanted electronic device in order to acquire information on the complex neuronal activity and to reconstruct impaired neural connectivity. Despite remarkable advancement in recent years, conventional neural interfacing is still unable to completely accomplish these goals, especially in long-term brain interfacing. The major limitation arises from physical and mechanical differences between neural interfacing probes and neural tissues that cause local immune responses and production of scar cells near the interface. Therefore, neural interfaces should ideally be extremely soft and have the physical scale of cells to mitigate the boundary between biotic and abiotic systems. Soft materials for neural interfaces have been intensively investigated to improve both interfacing and long-term signal transmission. The design and fabrication of micro and nanoscale devices have drastically decreased the stiffness of probes and enabled single-neuron measurement. In this Mini Review, we discuss materials and design approaches for developing soft high-resolution neural probes intended for long-term brain interfacing and outline existent challenges for achieving next-generation neural interfacing probes.},
}
@article {pmid31000969,
year = {2018},
author = {Sillay, KA and Ondoma, S and Wingeier, B and Schomberg, D and Sharma, P and Kumar, R and Miranpuri, GS and Williams, J},
title = {Long-Term Surface Electrode Impedance Recordings Associated with Gliosis for a Closed-Loop Neurostimulation Device.},
journal = {Annals of neurosciences},
volume = {25},
number = {4},
pages = {289-298},
pmid = {31000969},
issn = {0972-7531},
abstract = {BACKGROUND: Closed-loop neurostimulation is a novel alternative therapy for medically intractable focal epilepsy for patients who are not candidates for surgical resection of a seizure focus. Electrodes for this system can be implanted either within the brain parenchyma or in the subdural space. The electrodes then serve the dual role of detecting seizures and delivering an electrical signal aimed at aborting seizure activity. The Responsive Neurostimulation (RNS®) system (Neuropace, Mountain View, CA, USA) is an FDA-approved implantable device designed for this purpose.<br><br>OBJECTIVE: One of the challenges of the brain machine interface devices is the potential for implanted neurostimulator devices to induce progressive gliosis, apart from that associated with the minimal trauma at implantation. Gliosis has the potential to alter impedances over time, thereby affecting the clinical efficacy of these devices, and also poses a challenge to the prospects of in vivo repositioning of depth electrodes. We present a clinical case with 3-year follow-up and pathology.<br><br>METHODS: Single-case, retrospective review within a randomized trial with specific minimum follow-up and impedance measurements.<br><br>RESULTS: Impedance changes in the surface electrode over time were observed. Surgical pathological findings revealed significant gliosis in the leptomeninges of the cortices.<br><br>CONCLUSION: We report, for the first time, long-term impedance recordings from a surface electrode associated with pathologic findings of gliosis at the Neuropace device-tissue interface in a patient who was enrolled in the multicenter RNS System Pivotal Clinical Investigation. Further study is required to elucidate the temporal relationship of pathological findings over time. Impedance changes were more complex than can be explained by a progressive or transient pathological mechanism. Further effort is required to elucidate the relationship between impedance change and seizure event capture.},
}
@article {pmid30998456,
year = {2020},
author = {Kalaganis, FP and Laskaris, NA and Chatzilari, E and Nikolopoulos, S and Kompatsiaris, I},
title = {A Riemannian Geometry Approach to Reduced and Discriminative Covariance Estimation in Brain Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {1},
pages = {245-255},
doi = {10.1109/TBME.2019.2912066},
pmid = {30998456},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Automobile Driving ; Brain/physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Spatial covariance matrices are extensively employed as brain activity descriptors in brain computer interface (BCI) research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading.<br><br>METHODS: Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. The introduced optimization task, namely discriminative covariance reduction (DCR), lacks an analytical solution and is tackled via the cross-entropy optimization technique.<br><br>RESULTS: Based on two different EEG datasets and three distinct classification schemes, we demonstrate that the DCR approach provides a noteworthy gain in terms of accuracy (in some cases exceeding 20%) and a remarkable reduction in classification time (on average 82%). Additionally, results include the intriguing empirical finding that the pattern of selected sensors in the case of disabled persons depends on the type of disability.<br><br>CONCLUSION: The proposed DCR framework can speed up the classification time in BCI-systems operating on the SPD manifolds by simultaneously enhancing their reliability. This is achieved without sacrificing the neuroscientific interpretability endowed in the topographical arrangement of the selected sensors.<br><br>SIGNIFICANCE: Riemannian geometry is exploited for DCR in BCI systems, in a dimensionality-agnostic manner, guaranteeing improved performance.},
}
@article {pmid30998455,
year = {2020},
author = {Narayanan, AM and Bertrand, A},
title = {Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {1},
pages = {234-244},
doi = {10.1109/TBME.2019.2911728},
pmid = {30998455},
issn = {1558-2531},
mesh = {Attention/physiology ; Auditory Perception/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Humans ; Miniaturization/*methods ; Monitoring, Ambulatory/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm.<br><br>METHODS: Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes that locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings.<br><br>RESULTS: The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if, in both cases, the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes.<br><br>CONCLUSION: When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD.<br><br>SIGNIFICANCE: WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.},
}
@article {pmid30997842,
year = {2020},
author = {Philip, JT and George, ST},
title = {Visual P300 Mind-Speller Brain-Computer Interfaces: A Walk Through the Recent Developments With Special Focus on Classification Algorithms.},
journal = {Clinical EEG and neuroscience},
volume = {51},
number = {1},
pages = {19-33},
doi = {10.1177/1550059419842753},
pmid = {30997842},
issn = {2169-5202},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities/*psychology ; Event-Related Potentials, P300/*physiology ; Humans ; *Neural Networks, Computer ; User-Computer Interface ; },
abstract = {Brain-computer interfaces are sophisticated signal processing systems, which directly operate on neuronal signals to identify specific human intents. These systems can be applied to overcome certain disabilities or to enhance the natural capabilities of human beings. The visual P300 mind-speller is a prominent one among them, which has opened up tremendous possibilities in movement and communication applications. Today, there exist many state-of-the-art visual P300 mind-speller implementations in the literature as a result of numerous researches in this domain over the past 2 decades. Each of these systems can be evaluated in terms of performance metrics like classification accuracy, information transfer rate, and processing time. Various classification techniques associated with these systems, which include but are not limited to discriminant analysis, support vector machine, neural network, distance-based and ensemble of classifiers, have major roles in determining the overall system performances. The significance of a proper review on the recent developments in visual P300 mind-spellers with proper emphasis on their classification algorithms is the key insight for this work. This article is organized with a brief introduction to P300, concepts of visual P300 mind-spellers, the survey of literature with special focus on classification algorithms, followed by the discussion of various challenges and future directions.},
}
@article {pmid30995019,
year = {2019},
author = {Tian, B and Lieber, CM},
title = {Nanowired Bioelectric Interfaces.},
journal = {Chemical reviews},
volume = {119},
number = {15},
pages = {9136-9152},
pmid = {30995019},
issn = {1520-6890},
support = {DP1 EB025835/EB/NIBIB NIH HHS/United States ; DP2 NS101488/NS/NINDS NIH HHS/United States ; R21 DA043985/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Bacteria ; Brain-Computer Interfaces ; *Electrical Equipment and Supplies ; Humans ; Nanowires/*chemistry ; *Prostheses and Implants ; Semiconductors ; Transistors, Electronic ; },
abstract = {Biological systems have evolved biochemical, electrical, mechanical, and genetic networks to perform essential functions across various length and time scales. High-aspect-ratio biological nanowires, such as bacterial pili and neurites, mediate many of the interactions and homeostasis in and between these networks. Synthetic materials designed to mimic the structure of biological nanowires could also incorporate similar functional properties, and exploiting this structure-function relationship has already proved fruitful in designing biointerfaces. Semiconductor nanowires are a particularly promising class of synthetic nanowires for biointerfaces, given (1) their unique optical and electronic properties and (2) their high degree of synthetic control and versatility. These characteristics enable fabrication of a variety of electronic and photonic nanowire devices, allowing for the formation of well-defined, functional bioelectric interfaces at the biomolecular level to the whole-organ level. In this Focus Review, we first discuss the history of bioelectric interfaces with semiconductor nanowires. We next highlight several important, endogenous biological nanowires and use these as a framework to categorize semiconductor nanowire-based biointerfaces. Within this framework we then review the fundamentals of bioelectric interfaces with semiconductor nanowires and comment on both material choice and device design to form biointerfaces spanning multiple length scales. We conclude with a discussion of areas with the potential for greatest impact using semiconductor nanowire-enabled biointerfaces in the future.},
}
@article {pmid30992474,
year = {2019},
author = {Jiang, L and Stocco, A and Losey, DM and Abernethy, JA and Prat, CS and Rao, RPN},
title = {BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {6115},
pmid = {30992474},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Communication ; *Cooperative Behavior ; *Decision Making, Computer-Assisted ; *Decision Making, Shared ; Electroencephalography ; Female ; Healthy Volunteers ; Humans ; Male ; Reproducibility of Results ; Social Networking ; Transcranial Magnetic Stimulation ; Trust ; Young Adult ; },
abstract = {We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender's decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders' decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders' decisions are delivered to the Receiver's brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver's decision and send feedback to the Receiver's brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects' decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender's signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a "social network" of connected brains.},
}
@article {pmid30991369,
year = {2019},
author = {Angjelichinoski, M and Banerjee, T and Choi, J and Pesaran, B and Tarokh, V},
title = {Minimax-optimal decoding of movement goals from local field potentials using complex spectral features.},
journal = {Journal of neural engineering},
volume = {16},
number = {4},
pages = {046001},
doi = {10.1088/1741-2552/ab1a1f},
pmid = {30991369},
issn = {1741-2552},
support = {P30 EY013079/EY/NEI NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Eye Movements/*physiology ; *Goals ; Macaca mulatta ; Male ; Movement/physiology ; Photic Stimulation/methods ; Prefrontal Cortex/*physiology ; },
abstract = {OBJECTIVE: We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder.<br><br>APPROACH: Previous reports have mainly relied on the spectral amplitude of the LFPs as decoding feature, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker's theorem to generate minimax-optimal estimates of the LFP signals which are used as decoding features. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space.<br><br>MAIN RESULTS: We show that the proposed complex spectrum-based decoder achieves prediction accuracy of up to [Formula: see text] at superficial cortical depths near the surface of the prefrontal cortex; this marks a significant performance improvement over conventional power spectrum-based decoders.<br><br>SIGNIFICANCE: The presented analyses showcase the promising potential of low-pass filtered LFP signals for highly reliable neural decoding of intended motor actions.},
}
@article {pmid30990183,
year = {2019},
author = {Olias, J and Martin-Clemente, R and Sarmiento-Vega, MA and Cruces, S},
title = {EEG Signal Processing in MI-BCI Applications With Improved Covariance Matrix Estimators.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {5},
pages = {895-904},
doi = {10.1109/TNSRE.2019.2905894},
pmid = {30990183},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Humans ; *Imagination ; Models, Theoretical ; Movement ; Normal Distribution ; *Signal Processing, Computer-Assisted ; },
abstract = {In brain-computer interfaces (BCIs), the typical models of the EEG observations usually lead to a poor estimation of the trial covariance matrices, given the high non-stationarity of the EEG sources. We propose the application of two techniques that significantly improve the accuracy of these estimations and can be combined with a wide range of motor imagery BCI (MI-BCI) methods. The first one scales the observations in such a way that implicitly normalizes the common temporal strength of the source activities. When the scaling applies independently to the trials of the observations, the procedure justifies and improves the classical preprocessing for the EEG data. In addition, when the scaling is instantaneous and independent for each sample, the procedure particularizes to Tyler's method in statistics for obtaining a distribution-free estimate of scattering. In this case, the proposal provides an original interpretation of this existing method as a technique that pursuits an implicit instantaneous power-normalization of the underlying source processes. The second technique applies to the classifier and improves its performance through a convenient regularization of the features covariance matrix. Experimental tests reveal that a combination of the proposed techniques with the state-of-the-art algorithms for motor-imagery classification provides a significant improvement in the classification results.},
}
@article {pmid30989706,
year = {2019},
author = {Majima, T and Funahashi, Y and Matsukawa, Y and Inoue, S and Sassa, N and Kato, M and Yamamoto, T and Gotoh, M},
title = {Investigation of the relationship between bladder function and sarcopenia using pressure flow studies in elderly male patients.},
journal = {Neurourology and urodynamics},
volume = {38},
number = {5},
pages = {1417-1422},
doi = {10.1002/nau.24001},
pmid = {30989706},
issn = {1520-6777},
mesh = {Aged ; Aged, 80 and over ; Diagnostic Techniques, Urological ; Humans ; Male ; Muscle Contraction/physiology ; Retrospective Studies ; Sarcopenia/complications/*physiopathology ; Urinary Bladder/*physiopathology ; Urinary Bladder, Underactive/etiology/*physiopathology ; Urodynamics/*physiology ; },
abstract = {AIMS: Although detrusor underactivity is often encountered in elderly patients, the etiology remains unclear. We have hypothesized that sarcopenia was associated with impaired bladder contractility. Therefore, we have evaluated the relationship between bladder contractility and clinical parameters including sarcopenia markers in elderly male patients.<br><br>METHODS: This retrospective, single-centre study included male patients over 65 years of age who underwent a pressure flow study (PFS). We excluded patients with any previous medical histories that could affect bladder function, currently on urinary medication, and with no available data of abdominal CT scan. The psoas muscle area (PMA) (cm[2] /m[2]) was measured as a surrogate for psoas muscle mass on computed tomography. PMA, serum CRP, and albumin are known as sarcopenia markers. Correlation and multiple regression analyses were performed to evaluate the association of bladder contractility index (BCI) with the following parameters: age, body mass index (BMI), prostate volume, bladder outlet obstruction index (BOOI), serum C-reactive protein (CRP), serum albumin, and PMA.<br><br>RESULTS: Out of 558 male patients identified in our PFS database, 119 patients were enrolled. In the correlation analysis, age, prostate volume, serum albumin, BOOI, and PMA significantly correlated with BCI. However, no significant correlation of BCI with CRP or BMI was observed. Multiple linear regression analysis showed that serum albumin, BOOI, and PMA were significantly associated with BCI.<br><br>CONCLUSIONS: We have demonstrated that serum albumin and PMA were significantly positively associated with detrusor contractility. It is possible that sarcopenia is associated with impaired detrusor contractility.},
}
@article {pmid30988434,
year = {2018},
author = {Reutskaja, E and Lindner, A and Nagel, R and Andersen, RA and Camerer, CF},
title = {Choice overload reduces neural signatures of choice set value in dorsal striatum and anterior cingulate cortex.},
journal = {Nature human behaviour},
volume = {2},
number = {12},
pages = {925-935},
pmid = {30988434},
issn = {2397-3374},
mesh = {Adult ; Choice Behavior/*physiology ; Corpus Striatum/diagnostic imaging/*physiology ; Decision Making/physiology ; Female ; Functional Neuroimaging ; Gyrus Cinguli/diagnostic imaging/*physiology ; Humans ; Magnetic Resonance Imaging ; Male ; },
abstract = {Modern societies offer a large variety of choices[1,2], which is generally thought to be valuable[3-7]. But having too much choice can be detrimental[1-3,8-11] if the costs of choice outweigh its benefits due to 'choice overload'[12-14]. Current explanatory models of choice overload mainly derive from behavioural studies[13,14]. A neuroscientific investigation could further inform these models by revealing the covert mental processes during decision-making. We explored choice overload using functional magnetic resonance imaging while subjects were either choosing from varying-sized choice sets or were browsing them. When choosing from sets of 6, 12 or 24 items, functional magnetic resonance imaging activity in the striatum and anterior cingulate cortex resembled an inverted U-shaped function of choice set size. Activity was highest for 12-item sets, which were perceived as having 'the right amount' of options and was lower for 6-item and 24-item sets, which were perceived as 'too small' and 'too large', respectively. Enhancing choice set value by adding a dominant option led to an overall increase of activity. When subjects were browsing, the decision costs were diminished and the inverted U-shaped activity patterns vanished. Activity in the striatum and anterior cingulate reflects choice set value and can serve as neural indicator of choice overload.},
}
@article {pmid30985902,
year = {2019},
author = {Kokkinos, V and Sisterson, ND and Wozny, TA and Richardson, RM},
title = {Association of Closed-Loop Brain Stimulation Neurophysiological Features With Seizure Control Among Patients With Focal Epilepsy.},
journal = {JAMA neurology},
volume = {76},
number = {7},
pages = {800-808},
pmid = {30985902},
issn = {2168-6157},
support = {R01 NS110424/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Cohort Studies ; Deep Brain Stimulation/*methods ; Drug Resistant Epilepsy/physiopathology/*therapy ; Electrocorticography ; Epilepsies, Partial/physiopathology/*therapy ; Female ; Humans ; *Implantable Neurostimulators ; Male ; Middle Aged ; Prognosis ; Retrospective Studies ; Young Adult ; },
abstract = {IMPORTANCE: A bidirectional brain-computer interface that performs neurostimulation has been shown to improve seizure control in patients with refractory epilepsy, but the therapeutic mechanism is unknown.<br><br>OBJECTIVE: To investigate whether electrographic effects of responsive neurostimulation (RNS), identified in electrocorticographic (ECOG) recordings from the device, are associated with patient outcomes.<br><br>Retrospective review of ECOG recordings and accompanying clinical meta-data from 11 consecutive patients with focal epilepsy who were implanted with a neurostimulation system between January 28, 2015, and June 6, 2017, with 22 to 112 weeks of follow-up. Recorded ECOG data were obtained from the manufacturer; additional system-generated meta-data, including recording and detection settings, were collected directly from the manufacturer's management system using an in-house, custom-built platform. Electrographic seizure patterns were identified in RNS recordings and evaluated in the time-frequency domain, which was locked to the onset of the seizure pattern.<br><br>MAIN OUTCOMES AND MEASURES: Patterns of electrophysiological modulation were identified and then classified according to their latency of onset in relation to triggered stimulation events. Seizure control after RNS implantation was assessed by 3 main variables: mean frequency of seizure occurrence, estimated mean severity of seizures, and mean duration of seizures. Overall seizure outcomes were evaluated by the extended Personal Impact of Epilepsy Scale questionnaires, a patient-reported outcome measure of 3 domains (seizure characteristics, medication adverse effects, and quality of life), with a range of possible scores from 0 to 300 in which lower scores indicate worse status, and the Engel scale, which comprises 4 classes (I-IV) in which lower numbers indicate greater improvement.<br><br>RESULTS: Electrocorticographic data from 11 patients (8 female; mean [range] age, 35 [19-65] years; mean [range] duration of epilepsy, 19 [5-37] years) were analyzed. Two main categories of electrophysiological signatures of stimulation-induced modulation of the seizure network were discovered: direct and indirect effects. Direct effects included ictal inhibition and early frequency modulation but were not associated with improved clinical outcomes (odds ratio [OR], 0.67; 95% CI, 0.06-7.35; P&#x2009;>&#x2009;.99). Only indirect effects-those occurring remote from triggered stimulation-were associated with improved clinical outcomes (OR, infinity; 95% CI, -infinity to infinity; P&#x2009;=&#x2009;.02). These indirect effects included spontaneous ictal inhibition, frequency modulation, fragmentation, and ictal duration modulation.<br><br>CONCLUSIONS AND RELEVANCE: These findings suggest that RNS effectiveness may be explained by long-term, stimulation-induced modulation of seizure network activity rather than by direct effects on each detected seizure.},
}
@article {pmid30985403,
year = {2019},
author = {Bian, Y and Qi, H and Zhao, L and Ming, D and Guo, T and Fu, X},
title = {Dynamic visual guidance with complex task improves intracortical source activities during motor imagery.},
journal = {Neuroreport},
volume = {30},
number = {9},
pages = {645-652},
doi = {10.1097/WNR.0000000000001251},
pmid = {30985403},
issn = {1473-558X},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Young Adult ; },
abstract = {Motor imagery (MI) based brain-computer interfaces could be used clinically to trigger neurological recovery and improve motor function for patients with neural injuries. However, the factors that impact on MI performance and rehabilitative effect of MI-based brain-computer interfaces have not been characterized. According to our previous study, complex imagery tasks with dynamic visual paradigm could induce stronger MI features and obtain significantly higher average classification accuracy than nondynamic guidance. This study attempted to further investigate intracortical activities under different instructive paradigms and explore their potential effects on motor recovery. Eleven participants performed four types of different paradigms, including a nondynamic visual paradigm with simple MI task and three other dynamic visual/audiovisual paradigms with simple/complex MI tasks. A 64-channel electroencephalography was acquired and a voxel by voxel grand average of cortical source activities with statistical nonparametric mapping based on standardized low-resolution brain electromagnetic tomography were performed for comparisons among these paradigms in both alpha and beta bands. Moreover, seven regions of interest were selected to further analyze mean current source density variations for each paradigm with statistical analysis between dynamic and nondynamic paradigms. The outcomes uncovered that the dynamic visual aided paradigm with complex imagery tasks stimulated stronger cortical activities in core motor-related regions and triggered more extensive activation in the classical frontoparietal mirror regions than nondynamic paradigm. Involvement of these areas had a positive impact on the recovery of motor deficits in patients with neural injuries.},
}
@article {pmid30983986,
year = {2019},
author = {Zhang, J and Wang, B and Zhang, C and Xiao, Y and Wang, MY},
title = {An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand.},
journal = {Frontiers in neurorobotics},
volume = {13},
number = {},
pages = {7},
pmid = {30983986},
issn = {1662-5218},
abstract = {Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.},
}
@article {pmid30983978,
year = {2019},
author = {Lucas, A and Tomlinson, T and Rohani, N and Chowdhury, R and Solla, SA and Katsaggelos, AK and Miller, LE},
title = {Neural Networks for Modeling Neural Spiking in S1 Cortex.},
journal = {Frontiers in systems neuroscience},
volume = {13},
number = {},
pages = {13},
pmid = {30983978},
issn = {1662-5137},
support = {R01 NS095251/NS/NINDS NIH HHS/United States ; },
abstract = {Somatosensation is composed of two distinct modalities: touch, arising from sensors in the skin, and proprioception, resulting primarily from sensors in the muscles, combined with these same cutaneous sensors. In contrast to the wealth of information about touch, we know quite less about the nature of the signals giving rise to proprioception at the cortical level. Likewise, while there is considerable interest in developing encoding models of touch-related neurons for application to brain machine interfaces, much less emphasis has been placed on an analogous proprioceptive interface. Here we investigate the use of Artificial Neural Networks (ANNs) to model the relationship between the firing rates of single neurons in area 2, a largely proprioceptive region of somatosensory cortex (S1) and several types of kinematic variables related to arm movement. To gain a better understanding of how these kinematic variables interact to create the proprioceptive responses recorded in our datasets, we train ANNs under different conditions, each involving a different set of input and output variables. We explore the kinematic variables that provide the best network performance, and find that the addition of information about joint angles and/or muscle lengths significantly improves the prediction of neural firing rates. Our results thus provide new insight regarding the complex representations of the limb motion in S1: that the firing rates of neurons in area 2 may be more closely related to the activity of peripheral sensors than it is to extrinsic hand position. In addition, we conduct numerical experiments to determine the sensitivity of ANN models to various choices of training design and hyper-parameters. Our results provide a baseline and new tools for future research that utilizes machine learning to better describe and understand the activity of neurons in S1.},
}
@article {pmid30983957,
year = {2019},
author = {Adewole, DO and Serruya, MD and Wolf, JA and Cullen, DK},
title = {Bioactive Neuroelectronic Interfaces.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {269},
pmid = {30983957},
issn = {1662-4548},
support = {I01 BX003748/BX/BLRD VA/United States ; T32 NS091006/NS/NINDS NIH HHS/United States ; U01 NS094340/NS/NINDS NIH HHS/United States ; },
abstract = {Within the neural engineering field, next-generation implantable neuroelectronic interfaces are being developed using biologically-inspired and/or biologically-derived materials to improve upon the stability and functional lifetime of current interfaces. These technologies use biomaterials, bioactive molecules, living cells, or some combination of these, to promote host neuronal survival, reduce the foreign body response, and improve chronic device-tissue integration. This article provides a general overview of the different strategies, milestones, and evolution of bioactive neural interfaces including electrode material properties, biological coatings, and "decoration" with living cells. Another such biohybrid approach developed in our lab uses preformed implantable micro-tissue featuring long-projecting axonal tracts encased within carrier biomaterial micro-columns. These so-called "living electrodes" have been engineered with carefully tailored material, mechanical, and biological properties to enable natural, synaptic based modulation of specific host circuitry while ultimately being under computer control. This article provides an overview of these living electrodes, including design and fabrication, performance attributes, as well as findings to date characterizing in vitro and in vivo functionality.},
}
@article {pmid30983948,
year = {2019},
author = {Martins, NRB and Angelica, A and Chakravarthy, K and Svidinenko, Y and Boehm, FJ and Opris, I and Lebedev, MA and Swan, M and Garan, SA and Rosenfeld, JV and Hogg, T and Freitas, RA},
title = {Human Brain/Cloud Interface.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {112},
pmid = {30983948},
issn = {1662-4548},
abstract = {The Internet comprises a decentralized global system that serves humanity's collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a "human brain/cloud interface" ("B/CI"), would be based on technologies referred to here as "neuralnanorobotics." Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain's ∼86 × 10[9] neurons and ∼2 × 10[14] synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood-brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 10[16] bits per second of synaptically processed and encoded human-brain electrical information via auxiliary nanorobotic fiber optics (30 cm[3]) with the capacity to handle up to 10[18] bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as "transparent shadowing" (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.},
}
@article {pmid30979355,
year = {2019},
author = {Tseng, PH and Urpi, NA and Lebedev, M and Nicolelis, M},
title = {Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network.},
journal = {Neural computation},
volume = {31},
number = {6},
pages = {1085-1113},
doi = {10.1162/neco_a_01189},
pmid = {30979355},
issn = {1530-888X},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; Somatosensory Cortex/*physiology ; },
abstract = {Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large (N = 134-402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.},
}
@article {pmid30979081,
year = {2019},
author = {Xi, Y and Ji, B and Guo, Z and Li, W and Liu, J},
title = {Fabrication and Characterization of Micro-Nano Electrodes for Implantable BCI.},
journal = {Micromachines},
volume = {10},
number = {4},
pages = {},
pmid = {30979081},
issn = {2072-666X},
abstract = {Signal recording and stimulation with high spatial and temporal resolution are of increasing interest with the development of implantable brain-computer interfaces (BCIs). However, implantable BCI technology still faces challenges in the biocompatibility and long-term stability of devices after implantation. Due to the cone structure, needle electrodes have advantages in the biocompatibility and stability as nerve recording electrodes. This paper develops the fabrication of Ag needle micro/nano electrodes with a laser-assisted pulling method and modifies the electrode surface by electrochemical oxidation. A significant impedance reduction of the modified Ag/AgCl electrodes compared to the Ag electrodes is demonstrated by the electrochemical impedance spectrum (EIS). Furthermore, the stability of modified Ag/AgCl electrodes is confirmed by cyclic voltammogram (CV) scanning. These findings suggest that these micro/nano electrodes have a great application prospect in neural interfaces.},
}
@article {pmid30978978,
year = {2019},
author = {Majidov, I and Whangbo, T},
title = {Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {7},
pages = {},
pmid = {30978978},
issn = {1424-8220},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Decision Trees ; Deep Learning ; Electroencephalography/*methods ; Humans ; Models, Theoretical ; Movement/physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Software ; },
abstract = {Single-trial motor imagery classification is a crucial aspect of brain-computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain-computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain-computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.},
}
@article {pmid30978530,
year = {2020},
author = {Lugo, ZR and Pokorny, C and Pellas, F and Noirhomme, Q and Laureys, S and Müller-Putz, G and Kübler, A},
title = {Mental imagery for brain-computer interface control and communication in non-responsive individuals.},
journal = {Annals of physical and rehabilitation medicine},
volume = {63},
number = {1},
pages = {21-27},
doi = {10.1016/j.rehab.2019.02.005},
pmid = {30978530},
issn = {1877-0665},
mesh = {Adult ; Brain/*physiopathology ; *Communication ; Consciousness ; Electroencephalography ; Female ; Humans ; Locked-In Syndrome/*physiopathology ; Male ; Middle Aged ; Patient Satisfaction ; Software ; *User-Computer Interface ; Workload ; Young Adult ; },
abstract = {BACKGROUND: People who survive severe brain damage may eventually develop a prolonged consciousness disorder. Others can regain full consciousness but remain unable to speak or move because of the severity of the lesions, as for those with locked-in syndrome (LIS). Brain-computer interface techniques can be useful to disentangle these states by detecting neurophysiological correlates of conscious processing of information to enable communication with these individuals after the diagnosis.<br><br>OBJECTIVE: The goal of our study was to evaluate with a user-centered design approach the usability of a mental imagery task to detect signs of voluntary information processing and enabling communication in a group of severely disabled individuals.<br><br>METHODS: Five individuals with LIS participated in the study. Participants were instructed to imagine hand, arm or feet movements during electroencephalography (EEG) to detect patterns of event-related synchronization/desynchronization associated with each task. After the user-centered design, usability was evaluated (i.e., efficiency, effectiveness and satisfaction).<br><br>RESULTS: Two participants achieved significant levels of accuracy in 2 different tasks. The associated workload and levels of satisfaction perceived by the users were moderate and were mainly related to the time demand of the task.<br><br>CONCLUSION: Results showed lack of effectiveness of the task to detect voluntary brain activity and thus detect consciousness or communicate with non-responsive individuals. The application must be modified to be sufficiently satisfying for the intended end-users and suggestions are made in this regard.},
}
@article {pmid30977967,
year = {2019},
author = {Du, M and Guan, S and Gao, L and Lv, S and Yang, S and Shi, J and Wang, J and Li, H and Fang, Y},
title = {Flexible Micropillar Electrode Arrays for In Vivo Neural Activity Recordings.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {15},
number = {20},
pages = {e1900582},
doi = {10.1002/smll.201900582},
pmid = {30977967},
issn = {1613-6829},
mesh = {Animals ; Brain/*physiology ; Dura Mater/physiology ; Electrochemistry ; Microelectrodes ; Pliability ; Rats, Sprague-Dawley ; },
abstract = {Flexible electronics that can form tight interfaces with neural tissues hold great promise for improving the diagnosis and treatment of neurological disorders and advancing brain/machine interfaces. Here, the facile fabrication of a novel flexible micropillar electrode array (µPEA) is described based on a biotemplate method. The flexible and compliant µPEA can readily integrate with the soft surface of a rat cerebral cortex. Moreover, the recording sites of the µPEA consist of protruding micropillars with nanoscale surface roughness that ensure tight interfacing and efficient electrical coupling with the nervous system. As a result, the flexible µPEA allows for in vivo multichannel recordings of epileptiform activity with a high signal-to-noise ratio of 252 ± 35. The ease of preparation, high flexibility, and biocompatibility make the µPEA an attractive tool for in vivo spatiotemporal mapping of neural activity.},
}
@article {pmid30972604,
year = {2019},
author = {Anzolin, A and Presti, P and Van De Steen, F and Astolfi, L and Haufe, S and Marinazzo, D},
title = {Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources.},
journal = {Brain topography},
volume = {32},
number = {4},
pages = {655-674},
doi = {10.1007/s10548-019-00705-z},
pmid = {30972604},
issn = {1573-6792},
mesh = {Algorithms ; Brain ; Brain Mapping/*methods ; Electroencephalography/*methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that address the so-called EEG "inverse problem". Once the activity of sources located inside of the brain has been reconstructed, it is often desirable to study the statistical dependencies among them, in particular to quantify directional dynamical interactions between brain areas. Unfortunately, even when performing source reconstruction, the superposition of signals that is due to the propagation of activity from sources to sensors cannot be completely undone, resulting in potentially biased estimates of directional functional connectivity. Here we perform a set of simulations involving interacting sources to quantify source connectivity estimation performance as a function of the location of the sources, their distance to each other, the noise level, the source reconstruction algorithm, and the connectivity estimator. The generated source activity was projected onto the scalp and projected back to the cortical level using two source reconstruction algorithms, linearly constrained minimum variance beamforming and 'Exact' low-resolution tomography (eLORETA). In source space, directed connectivity was estimated using multi-variate Granger causality and time-reversed Granger causality, and compared with the imposed ground truth. Our results demonstrate that all considered factors significantly affect the connectivity estimation performance.},
}
@article {pmid30971871,
year = {2019},
author = {Alkawadri, R},
title = {Brain-Computer Interface (BCI) Applications in Mapping of Epileptic Brain Networks Based on Intracranial-EEG: An Update.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {191},
pmid = {30971871},
issn = {1662-4548},
support = {KL2 TR000140/TR/NCATS NIH HHS/United States ; },
abstract = {The main applications of the Brain-Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of "surgical" patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain-Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.},
}
@article {pmid30971693,
year = {2019},
author = {Zhang, HT and Zuo, F and Li, F and Chan, H and Wu, Q and Zhang, Z and Narayanan, B and Ramadoss, K and Chakraborty, I and Saha, G and Kamath, G and Roy, K and Zhou, H and Chubykin, AA and Sankaranarayanan, SKRS and Choi, JH and Ramanathan, S},
title = {Perovskite nickelates as bio-electronic interfaces.},
journal = {Nature communications},
volume = {10},
number = {1},
pages = {1651},
pmid = {30971693},
issn = {2041-1723},
support = {R01 MH116500/MH/NIMH NIH HHS/United States ; R01 MH116500/NH/NIH HHS/United States ; },
mesh = {Animals ; Bioengineering/*instrumentation ; Biosensing Techniques/*instrumentation ; Brain-Computer Interfaces ; Calcium Compounds/*chemistry ; Corpus Striatum/metabolism ; Electric Stimulation/instrumentation ; Electrodes ; Electronics ; Electrons ; Glucose/chemistry/metabolism ; Glucose Oxidase/*metabolism ; Hydrogen/metabolism ; Mice ; Mice, Inbred C57BL ; Molecular Dynamics Simulation ; Neurotransmitter Agents/metabolism ; Oxidation-Reduction ; Oxides/*chemistry ; Titanium/*chemistry ; },
abstract = {Functional interfaces between electronics and biological matter are essential to diverse fields including health sciences and bio-engineering. Here, we report the discovery of spontaneous (no external energy input) hydrogen transfer from biological glucose reactions into SmNiO3, an archetypal perovskite quantum material. The enzymatic oxidation of glucose is monitored down to ~5 × 10[-16] M concentration via hydrogen transfer to the nickelate lattice. The hydrogen atoms donate electrons to the Ni d orbital and induce electron localization through strong electron correlations. By enzyme specific modification, spontaneous transfer of hydrogen from the neurotransmitter dopamine can be monitored in physiological media. We then directly interface an acute mouse brain slice onto the nickelate devices and demonstrate measurement of neurotransmitter release upon electrical stimulation of the striatum region. These results open up avenues for use of emergent physics present in quantum materials in trace detection and conveyance of bio-matter, bio-chemical sciences, and brain-machine interfaces.},
}
@article {pmid30964206,
year = {2019},
author = {Desroches-Castan, A and Tillet, E and Ricard, N and Ouarné, M and Mallet, C and Belmudes, L and Couté, Y and Boillot, O and Scoazec, JY and Bailly, S and Feige, JJ},
title = {Bone Morphogenetic Protein 9 Is a Paracrine Factor Controlling Liver Sinusoidal Endothelial Cell Fenestration and Protecting Against Hepatic Fibrosis.},
journal = {Hepatology (Baltimore, Md.)},
volume = {70},
number = {4},
pages = {1392-1408},
doi = {10.1002/hep.30655},
pmid = {30964206},
issn = {1527-3350},
support = {//Association pour la Recherche sur le Cancer/International ; ANR-10-LABX-49-01//Labex GRAL/International ; DRF/BIG//Commissariat &#xe0; l'&#xc9;nergie Atomique et aux &#xc9;nergies Alternatives/International ; BCI//Universit&#xe9; Grenoble Alpes/International ; ANR-17-CE14-0006//Agence Nationale de la Recherche/International ; ANR-10-INBS-08-01//PROFI/International ; //Fondation pour la Recherche M&#xe9;dicale/International ; //Association Maladie de Rendu-Osler/International ; //Ligue D&#xe9;partementale contre le Cancer de l'Is&#xe8;re/International ; //Ligue D&#xe9;partementale contre le Cancer de la Savoie/International ; U1036//Institut National de la Sant&#xe9; et de la Recherche M&#xe9;dicale/International ; },
mesh = {Activin Receptors, Type II/*genetics ; Animals ; Cells, Cultured ; Disease Models, Animal ; Endothelial Cells/cytology/*metabolism ; *Gene Expression Regulation ; Growth Differentiation Factor 2/*genetics/metabolism ; Hepatic Stellate Cells/pathology ; Humans ; Liver Cirrhosis/*genetics/*pathology ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Proteomics ; RNA, Messenger/genetics ; Random Allocation ; Statistics, Nonparametric ; Tissue Culture Techniques/methods ; },
abstract = {Bone morphogenetic protein 9 (BMP9) is a circulating factor produced by hepatic stellate cells that plays a critical role in vascular quiescence through its endothelial receptor activin receptor-like kinase 1 (ALK1). Mutations in the gene encoding ALK1 cause hereditary hemorrhagic telangiectasia type 2, a rare genetic disease presenting hepatic vessel malformations. Variations of both the circulating levels and the hepatic mRNA levels of BMP9 have been recently associated with various forms of hepatic fibrosis. However, the molecular mechanism that links BMP9 with liver diseases is still unknown. Here, we report that Bmp9 gene deletion in 129/Ola mice triggers hepatic perisinusoidal fibrosis that was detectable from 15 weeks of age. An inflammatory response appeared within the same time frame as fibrosis, whereas sinusoidal vessel dilation developed later on. Proteomic and mRNA analyses of primary liver sinusoidal endothelial cells (LSECs) both revealed that the expression of the LSEC-specifying transcription factor GATA-binding protein 4 was strongly reduced in Bmp9 gene knockout (Bmp9-KO) mice as compared with wild-type mice. LSECs from Bmp9-KO mice also lost the expression of several terminal differentiation markers (Lyve1, Stab1, Stab2, Ehd3, Cd209b, eNos, Maf, Plvap). They gained CD34 expression and deposited a basal lamina, indicating that they were capillarized. Another main characteristic of differentiated LSECs is the presence of permeable fenestrae. LSECs from Bmp9-KO mice had a significantly reduced number of fenestrae. This was already observable in 2-week-old pups. Moreover, we could show that addition of BMP9 to primary cultures of LSECs prevented the loss of their fenestrae and maintained the expression levels of Gata4 and Plvap. Conclusion: Taken together, our observations show that BMP9 is a key paracrine regulator of liver homeostasis, controlling LSEC fenestration and protecting against perivascular hepatic fibrosis.},
}
@article {pmid30959496,
year = {2019},
author = {Yao, P and Xu, G and Jia, L and Duan, J and Han, C and Tao, T and Wang, Y and Zhang, S},
title = {Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036032},
doi = {10.1088/1741-2552/ab16f9},
pmid = {30959496},
issn = {1741-2552},
mesh = {Adult ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; *Signal-To-Noise Ratio ; Stochastic Processes ; Young Adult ; },
abstract = {OBJECTIVE: As one of the commonly used control signals of brain-computer interface (BCI), steady-state visual evoked potential (SSVEP) exhibits advantages of stability, periodicity and minimal training requirements. However, SSVEP retains the non-linear, non-stationary and low signal-to-noise ratio (SNR) characteristics of EEG. The traditional SSVEP extraction methods regard noise as harmful information and highlight the useful signal by suppressing the noise. In the collected EEG, noise and SSVEP are usually coupled together, the useful signal is inevitably attenuated while the noise is suppressed. Also, an additional band-pass filter is needed to eliminate the multi-scale noise, which causes the edge effect.<br><br>APPROACH: To address this issue, a novel method based on underdamped second-order stochastic resonance (USSR) is proposed in this paper for SSVEP extraction.<br><br>MAIN RESULTS: A synergistic effect produced by noise, useful signal and the nonlinear system can force the energy of noise to be transferred into SSVEP, and hence amplifying the useful signal while suppressing multi-scale noise. The recognition performances of detection are compared with the widely-used canonical coefficient analysis (CCA) and multivariate synchronization index (MSI).<br><br>SIGNIFICANCE: The comparison results indicate that USSR exhibits increased accuracy and faster processing speed, which effectively improves the information transmission rate (ITR) of SSVEP-based BCI.},
}
@article {pmid30958815,
year = {2019},
author = {Chaudhary, U and Pathak, S and Birbaumer, N},
title = {Response to: "Questioning the evidence for BCI-based communication in the complete locked-in state".},
journal = {PLoS biology},
volume = {17},
number = {4},
pages = {e3000063},
pmid = {30958815},
issn = {1545-7885},
mesh = {*Brain-Computer Interfaces ; Communication ; Humans ; Quadriplegia ; },
}
@article {pmid30958814,
year = {2019},
author = {Spüler, M},
title = {Questioning the evidence for BCI-based communication in the complete locked-in state.},
journal = {PLoS biology},
volume = {17},
number = {4},
pages = {e2004750},
pmid = {30958814},
issn = {1545-7885},
mesh = {*Brain-Computer Interfaces ; Communication ; Humans ; Quadriplegia ; },
}
@article {pmid30958813,
year = {2019},
author = {Scherer, R},
title = {Thought-based interaction: Same data, same methods, different results?.},
journal = {PLoS biology},
volume = {17},
number = {4},
pages = {e3000190},
pmid = {30958813},
issn = {1545-7885},
mesh = {Brain-Computer Interfaces/*trends ; Communication ; Data Interpretation, Statistical ; Electroencephalography ; Models, Statistical ; Probability ; Quadriplegia/rehabilitation ; *Reproducibility of Results ; Research Design/*statistics &amp; numerical data ; Sample Size ; User-Computer Interface ; },
abstract = {Restoration of communication in people with complete motor paralysis-a condition called complete locked-in state (CLIS)-is one of the greatest challenges of brain-computer interface (BCI) research. New findings have recently been presented that bring us one step closer to this goal. However, the validity of the evidence has been questioned: independent reanalysis of the same data yielded significantly different results. Reasons for the failure to replicate the findings must be of a methodological nature. What is the best practice to ensure that results are stringent and conclusive and analyses replicable? Confirmation bias and the counterintuitive nature of probability may lead to an overly optimistic interpretation of new evidence. Lack of detail complicates replicability.},
}
@article {pmid30955363,
year = {2019},
author = {Kodama, T and Katayama, O and Nakano, H and Ueda, T and Murata, S},
title = {Treatment of Medial Medullary Infarction Using a Novel iNems Training: A Case Report and Literature Review.},
journal = {Clinical EEG and neuroscience},
volume = {50},
number = {6},
pages = {429-435},
doi = {10.1177/1550059419840246},
pmid = {30955363},
issn = {2169-5202},
mesh = {Aged ; Asian People ; Brain Stem Infarctions/complications/*rehabilitation ; Catastrophization/etiology/therapy ; Electroencephalography ; Humans ; Japan ; Male ; Medulla Oblongata/*physiology ; Neurofeedback/*methods ; Neurological Rehabilitation/*methods ; Paralysis/etiology/rehabilitation ; Treatment Outcome ; },
abstract = {Objective. We describe the case of a 66-year-old Japanese male patient who developed medial medullary infarction along with severe motor paralysis and intense numbness of the left arm, pain catastrophizing, and abnormal physical sensation. We further describe his recovery using a new imagery neurofeedback-based multisensory systems (iNems) training method. Clinical Course and Intervention. The patient underwent physical therapy for the rehabilitation of motor paralysis and numbness of the paralyzed upper limbs; in addition, we implemented iNems training using EEG activity, which aims to synchronize movement intent (motor imagery) with sensory information (feedback visual information). Results. Considerable improvement in motor function, pain catastrophizing, representation of the body in the brain, and abnormal physical sensations was accomplished with iNems training. Furthermore, iNems training improved the neural activity of the default mode network at rest and the sensorimotor region when the movement was intended. Conclusions. The newly developed iNems could prove a novel, useful tool for neurorehabilitation considering that both behavioral and neurophysiological changes were observed in our case.},
}
@article {pmid30954862,
year = {2019},
author = {Rao, RP},
title = {Towards neural co-processors for the brain: combining decoding and encoding in brain-computer interfaces.},
journal = {Current opinion in neurobiology},
volume = {55},
number = {},
pages = {142-151},
pmid = {30954862},
issn = {1873-6882},
support = {R01 MH112166/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Neural Networks, Computer ; },
abstract = {The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a 'co-processor' for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These 'neural co-processors' can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function.},
}
@article {pmid30953426,
year = {2019},
author = {Stévenin, V and Enninga, J},
title = {Cellular Imaging of Intracellular Bacterial Pathogens.},
journal = {Microbiology spectrum},
volume = {7},
number = {2},
pages = {},
pmid = {30953426},
issn = {2165-0497},
mesh = {Bacteria/*cytology/pathogenicity/ultrastructure ; Bacterial Infections/diagnostic imaging/*microbiology ; Bacteriological Techniques/*methods ; Fluorescent Dyes ; Host-Pathogen Interactions ; Humans ; Image Processing, Computer-Assisted/methods ; Microscopy, Electron/methods ; Microscopy, Fluorescence/methods ; Staining and Labeling/methods ; },
abstract = {The spatial dimensions of host cells and bacterial microbes are perfectly suited to being studied by microscopy techniques. Therefore, cellular imaging has been instrumental in uncovering many paradigms of the intracellular lifestyle of microbes. Initially, microscopy was used as a qualitative, descriptive tool. However, with the onset of specific markers and the power of computer-assisted image analysis, imaging can now be used to gather quantitative data on biological processes. This makes imaging a driving force for the study of cellular phenomena. One particular imaging modality stands out, which is based on the physical principles of fluorescence. Fluorescence is highly specific and therefore can be exploited to label biomolecules of choice. It is also very sensitive, making it possible to follow individual molecules with this approach. Also, microscopy hardware has played an important role in putting microscopy in the spotlight for host-pathogen investigations. For example, microscopes have been automated for microscopy-based screenings. A new generation of microscopes and molecular probes are being used to image events below the resolution limit of light. Finally, workflows are being developed to link light microscopy with electron microscopy methods via correlative light electron microscopy. We are witnessing a golden age of cellular imaging in cellular microbiology.},
}
@article {pmid30952963,
year = {2019},
author = {Cao, Z and Chuang, CH and King, JK and Lin, CT},
title = {Multi-channel EEG recordings during a sustained-attention driving task.},
journal = {Scientific data},
volume = {6},
number = {1},
pages = {19},
pmid = {30952963},
issn = {2052-4463},
mesh = {Adult ; *Attention ; *Automobile Driving/psychology ; Brain/physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Psychomotor Performance ; },
abstract = {We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5-10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities.},
}
@article {pmid30951847,
year = {2019},
author = {Vukelić, M and Belardinelli, P and Guggenberger, R and Royter, V and Gharabaghi, A},
title = {Different oscillatory entrainment of cortical networks during motor imagery and neurofeedback in right and left handers.},
journal = {NeuroImage},
volume = {195},
number = {},
pages = {190-202},
doi = {10.1016/j.neuroimage.2019.03.067},
pmid = {30951847},
issn = {1095-9572},
mesh = {Adult ; Brain-Computer Interfaces ; Female ; Functional Laterality/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Activity/physiology ; Neural Pathways/physiology ; Neurofeedback/*methods ; Sensorimotor Cortex/*physiology ; Stroke Rehabilitation/methods ; },
abstract = {Volitional modulation and neurofeedback of sensorimotor oscillatory activity is currently being evaluated as a strategy to facilitate motor restoration following stroke. Knowledge on the interplay between this regional brain self-regulation, distributed network entrainment and handedness is, however, limited. In a randomized cross-over design, twenty-one healthy subjects (twelve right-handers [RH], nine left-handers [LH]) performed kinesthetic motor imagery of left (48 trials) and right finger extension (48 trials). A brain-machine interface turned event-related desynchronization in the beta frequency-band (16-22 Hz) during motor imagery into passive hand opening by a robotic orthosis. Thereby, every participant subsequently activated either the dominant (DH) or non-dominant hemisphere (NDH) to control contralateral hand opening. The task-related cortical networks were studied with electroencephalography. The magnitude of the induced oscillatory modulation range in the sensorimotor cortex was independent of both handedness (RH, LH) and hemispheric specialization (DH, NDH). However, the regional beta-band modulation was associated with different alpha-band networks in RH and LH: RH presented a stronger inter-hemispheric connectivity, while LH revealed a stronger intra-hemispheric interaction. Notably, these distinct network entrainments were independent of hemispheric specialization. In healthy subjects, sensorimotor beta-band activity can be robustly modulated by motor imagery and proprioceptive feedback in both hemispheres independent of handedness. However, right and left handers show different oscillatory entrainment of cortical alpha-band networks during neurofeedback. This finding may inform neurofeedback interventions in future to align them more precisely with the underlying physiology.},
}
@article {pmid30951472,
year = {2019},
author = {Zhang, X and Wu, D},
title = {On the Vulnerability of CNN Classifiers in EEG-Based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {5},
pages = {814-825},
doi = {10.1109/TNSRE.2019.2908955},
pmid = {30951472},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Deep Learning ; *Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Imagination ; Machine Learning ; Models, Theoretical ; *Neural Networks, Computer ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To the best of our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.},
}
@article {pmid30951239,
year = {2019},
author = {Soh, D and Ten Brinke, TR and Lozano, AM and Fasano, A},
title = {Therapeutic Window of Deep Brain Stimulation Using Cathodic Monopolar, Bipolar, Semi-Bipolar, and Anodic Stimulation.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {22},
number = {4},
pages = {451-455},
doi = {10.1111/ner.12957},
pmid = {30951239},
issn = {1525-1403},
mesh = {Aged ; Deep Brain Stimulation/instrumentation/standards/*trends ; Electrodes/standards/trends ; Electrodes, Implanted/standards/*trends ; Female ; Humans ; Male ; Middle Aged ; Parkinson Disease/diagnosis/*therapy ; },
abstract = {OBJECTIVES: To compare the therapeutic window (TW) of cathodic monopolar, bipolar, anodic monopolar, and a novel "semi-bipolar" stimulation in ten Parkinson's disease patients who underwent deep brain stimulation of the subthalamic nucleus.<br><br>MATERIALS AND METHODS: Patients were assessed in the "OFF" L-dopa condition. Each upper limb was tested separately for therapeutic threshold, TW and side-effect threshold (SET). Battery consumption index (BCI) also was documented.<br><br>RESULTS: Compared to cathodic stimulation, therapeutic threshold was significantly higher for anodic, bipolar, and semi-bipolar stimulation (3.8&#x2009;&#xb1;&#x2009;1.6 vs. 4.9&#x2009;&#xb1;&#x2009;2.1, 5.0&#x2009;&#xb1;&#x2009;1.9, and 5.2&#x2009;&#xb1;&#x2009;1.9 mA, p&#x2009;=&#x2009;0.0006, 0.0002, and 0.008, respectively). SET was significantly higher for bipolar stimulation (10.9&#x2009;&#xb1;&#x2009;2.5 mA) vs. cathodic (6.8&#x2009;&#xb1;&#x2009;2.2 mA, p&#x2009;<&#x2009;0.0001) and anodic stimulation (9.2&#x2009;&#xb1;&#x2009;2.6 mA, p&#x2009;=&#x2009;0.005). The SET of anodic and semi-bipolar stimulation was significantly higher vs. cathodic stimulation (p&#x2009;<&#x2009;0.0001). TW of cathodic stimulation (2.5&#x2009;&#xb1;&#x2009;1.5 mA) was significantly narrower vs. bipolar (5.4&#x2009;&#xb1;&#x2009;2.0 mA, p&#x2009;<&#x2009;0.0001), semi-bipolar (4.6&#x2009;&#xb1;&#x2009;2.6 mA, p&#x2009;=&#x2009;0.001) and anodic stimulation (4.3&#x2009;&#xb1;&#x2009;2.3 mA, p&#x2009;<&#x2009;0.0001). Bipolar (p&#x2009;=&#x2009;0.005) and semi-bipolar (p&#x2009;=&#x2009;0.0005) stimulation had a significantly wider TW vs. anodic stimulation. BCI of cathodic stimulation (5.9&#x2009;&#xb1;&#x2009;1.3) was significantly lower compared to bipolar (13.7&#x2009;&#xb1;&#x2009;6.8, p&#x2009;<&#x2009;0.0001), semi-bipolar (11.0&#x2009;&#xb1;&#x2009;4.3, p&#x2009;=&#x2009;0.0005), and anodic stimulation (8.1&#x2009;&#xb1;&#x2009;3.0, p&#x2009;<&#x2009;0.0001). Anodic BCI was significantly lower than bipolar (p&#x2009;=&#x2009;0.005) and semi-bipolar (p&#x2009;=&#x2009;0.0002) stimulation while semi-bipolar BCI was lower than bipolar stimulation (p&#x2009;=&#x2009;0.0005).<br><br>CONCLUSIONS: While awaiting further studies, our findings suggest that cathodic stimulation should be preferred in light of its reduced battery consumption, possibly followed by semi-bipolar in case of stimulation-induced side-effects.},
}
@article {pmid30950673,
year = {2019},
author = {Barbara, M and Covelli, E and Filippi, C and Margani, V and De Luca, A and Monini, S},
title = {Transitions in auditory rehabilitation with bone conduction implants (BCI).},
journal = {Acta oto-laryngologica},
volume = {139},
number = {4},
pages = {379-382},
doi = {10.1080/00016489.2019.1592220},
pmid = {30950673},
issn = {1651-2251},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Bone Conduction ; Female ; *Hearing Aids ; Hearing Loss/*rehabilitation ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Young Adult ; },
abstract = {BACKGROUND: The bone conductive implants (BCI) are nowadays a reliable alternative for rehabilitation of specific forms of hearing loss, i.e. conductive, mixed or single sided deafness (SSD). Aims/Objective: To analyse the various factors in play when considering an auditory rehabilitation with a bone-conductive device (BCI).<br><br>MATERIALS AND METHODS: The clinical charts of subjects who underwent BCI application at the same Implanting Center from 2005 to 2018 were retrieved analysing also the reason for eventual explantation and the alternative option (transition) for hearing rehabilitation.<br><br>RESULTS: Nine BAHA Compact, 4 BAHA Intenso, 21 BAHA Divino, 3 BAHA BP100, 4 Ponto, 2 Sophono, 5 Bonebridge, 5 BAHA5 Attract; 11 BAHA5 Connect were used in 12 unilateral COM; 16 bilateral COM; 3 unilateral cholesteatoma; 6 bilateral cholesteatoma; 2 unilateral otosclerosis; 5 bilateral otosclerosis; 9 congenital malformations; 6 major otoneurosurgical procedures; 5 sudden deafness. Explantation was necessary for five subjects.<br><br>CONCLUSIONS: Middle ear pathology and sequels from surgery represent the most common reason for BCI implantation, both in unilateral and in bilateral cases. Transition from one implantable device to another one can be predictable, mostly when explantation is necessary.<br><br>SIGNIFICANCE: The role of BCI for rehabilitation in middle ear pathology may be extremely important.},
}
@article {pmid30947227,
year = {2019},
author = {Smith, MM},
title = {Innovations for Supporting Communication: Opportunities and Challenges for People with Complex Communication Needs.},
journal = {Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP)},
volume = {71},
number = {4},
pages = {156-167},
doi = {10.1159/000496729},
pmid = {30947227},
issn = {1421-9972},
mesh = {Audiovisual Aids ; Brain-Computer Interfaces ; *Communication Devices for People with Disabilities/economics ; Communication Barriers ; Communication Disorders ; Community Participation ; Computers ; Computers, Handheld ; Persons with Disabilities ; Fixation, Ocular ; Forecasting ; Health Services Accessibility ; Health Services Needs and Demand ; Humans ; Intersectoral Collaboration ; Inventions/economics ; Symbolism ; User-Computer Interface ; },
abstract = {Individuals with complex communication needs have benefited greatly from technological innovations over the past two decades, as well as from social movements that have shifted focus from disability to functioning and participation in society. Three strands of technological innovation are reviewed in this paper: (1) innovations in the tools that have become available, specifically tablet technologies; (2) innovations in access methods (eye gaze technologies and brain-computer interfaces); and (3) innovations in output, specifically speech technologies. The opportunities these innovations offer are explored, as are some of the challenges that they imply, not only for individuals with complex communication needs, but also for families, professionals, and researchers.},
}
@article {pmid30946880,
year = {2019},
author = {Khalaf, A and Sejdic, E and Akcakaya, M},
title = {Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {320},
number = {},
pages = {98-106},
doi = {10.1016/j.jneumeth.2019.03.018},
pmid = {30946880},
issn = {1872-678X},
mesh = {Adult ; Brain/*diagnostic imaging ; *Brain-Computer Interfaces ; Electroencephalography/*methods/standards ; Female ; Functional Neuroimaging/*methods/standards ; Humans ; Imagination/physiology ; Male ; Motor Activity/physiology ; Pattern Recognition, Automated/*methods/standards ; *Support Vector Machine ; Ultrasonography, Doppler, Transcranial/*methods/standards ; Visual Perception/physiology ; },
abstract = {BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR). Previously, we introduced a novel hybrid BCI in which EEG and fTCD modalities are used simultaneously to measure electrical brain activity and cerebral blood velocity during motor imagery (MI) tasks.<br><br>NEW METHOD: In this paper, we used multi-scale analysis and common spatial pattern algorithm to extract EEG and fTCD features. Moreover, we proposed probabilistic fusion of EEG and fTCD evidences instead of concatenating EEG and fTCD feature vectors corresponding to each trial. A Bayesian approach was proposed to fuse EEG and fTCD evidences under 3 different assumptions.<br><br>RESULTS: Experimental results showed that 93.85%, 93.71%, and 100% average accuracies and 19.89, 26.55, and 40.83 bits/min average ITRs were achieved for right MI vs baseline, left MI versus baseline, and right MI versus left MI respectively.<br><br>These performance measures outperformed the results we obtained before in our preliminary study in which average accuracies of 88.33%, 89.48%, and 82.38% and average ITRs of 4.17, 5.45, and 10.57 bits/min were achieved for right MI versus baseline, left MI versus baseline, and right MI versus left MI respectively. Moreover, in terms of both accuracy and speed, the EEG- fTCD BCI with the proposed analysis techniques outperformed all EEG- fNIRS studies in comparison.<br><br>CONCLUSIONS: The proposed system is a more accurate and faster alternative to EEG-fNIRS systems.},
}
@article {pmid30946671,
year = {2019},
author = {Rathee, D and Chowdhury, A and Meena, YK and Dutta, A and McDonough, S and Prasad, G},
title = {Brain-Machine Interface-Driven Post-Stroke Upper-Limb Functional Recovery Correlates With Beta-Band Mediated Cortical Networks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {5},
pages = {1020-1031},
doi = {10.1109/TNSRE.2019.2908125},
pmid = {30946671},
issn = {1558-0210},
mesh = {Aged ; Algorithms ; Arm ; *Beta Rhythm ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiopathology ; Exoskeleton Device ; Female ; Hand Strength ; Humans ; Magnetoencephalography ; Male ; Middle Aged ; Nerve Net/*physiopathology ; Paresis/rehabilitation ; Recovery of Function ; Robotics ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; },
abstract = {Brain-machine interface (BMI)-driven robot-assisted neurorehabilitation intervention has demonstrated improvement in upper-limb (UL) motor function, specifically, with post-stroke hemiparetic patients. However, neurophysiological patterns related to such interventions are not well understood. This paper examined the longitudinal changes in band-limited resting-state (RS) functional connectivity (FC) networks in association with post-stroke UL functional recovery achieved by a multimodal intervention involving motor attempt (MA)-based BMI and robotic hand-exoskeleton. Four adults were rehabilitated with the intervention for a period lasting up to six weeks. RS magnetoencephalography (MEG) signals, Action Research Arm Test (ARAT), and grip strength (GS) measures were recorded at five equispaced sessions over the intervention period. An average post-interventional increase of 100.0% (p=0.00028) and 88.0% was attained for ARAT and GS, respectively. A cluster-based statistical test involving correlation estimates between beta-band (15-26 Hz) RS-MEG FCs and UL functional recovery provided the positively correlated sub-networks in both the contralesional and ipsilesional motor cortices. The frontoparietal FC exhibited hemispheric lateralization wherein the majority of the positively and negatively correlated connections were found in contralesional and ipsilesional hemispheres, respectively. Our findings are consistent with the theory of bilateral motor cortical association with UL recovery and predict novel FC patterns that can be important for higher level cognitive functions.},
}
@article {pmid30941033,
year = {2019},
author = {James, NE and Oliver, MT and Ribeiro, JR and Cantillo, E and Rowswell-Turner, RB and Kim, KK and Chichester, CO and DiSilvestro, PA and Moore, RG and Singh, RK and Yano, N and Zhao, TC},
title = {Human Epididymis Secretory Protein 4 (HE4) Compromises Cytotoxic Mononuclear Cells via Inducing Dual Specificity Phosphatase 6.},
journal = {Frontiers in pharmacology},
volume = {10},
number = {},
pages = {216},
pmid = {30941033},
issn = {1663-9812},
support = {R01 HL089405/HL/NHLBI NIH HHS/United States ; R01 HL115265/HL/NHLBI NIH HHS/United States ; },
abstract = {While selective overexpression of serum clinical biomarker Human epididymis secretory protein 4 (HE4) is indicative of ovarian cancer tumorigenesis, much is still known about the mechanistic role of the HE4 gene or gene product. Here, we examine the role of the secretory glycoprotein HE4 in ovarian cancer immune evasion. Through modified subtractive hybridization analyses of human peripheral blood mononuclear cells (PBMCs), we have characterized gene targets of HE4 and established a preliminary mechanism of HE4-mediated immune failure in ovarian tumors. Dual specificity phosphatase 6 (DUSP6) emerged as the most upregulated gene in PBMCs upon in vitro exposure to HE4. DUSP6 was found to be upregulated in CD8[+] cells and CD56[+] cells. HE4 exposure reduced Erk1/2 phosphorylation specifically in these cell populations and the effect was erased by co-incubation with a DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI). In co-culture with PBMCs, HE4-silenced SKOV3 human ovarian cancer cells exhibited enhanced proliferation upon exposure to external HE4, while this effect was partially attenuated by adding BCI to the culture. Additionally, the reversal effects of BCI were erased in the co-culture with CD8[+] / CD56[+] cell deprived PBMCs. Taken together, these findings show that HE4 enhances tumorigenesis of ovarian cancer by compromising cytotoxic CD8[+] and CD56[+] cells through upregulation of self-produced DUSP6.},
}
@article {pmid30937400,
year = {2019},
author = {Kern, M and Bert, S and Glanz, O and Schulze-Bonhage, A and Ball, T},
title = {Human motor cortex relies on sparse and action-specific activation during laughing, smiling and speech production.},
journal = {Communications biology},
volume = {2},
number = {},
pages = {118},
pmid = {30937400},
issn = {2399-3642},
mesh = {Adult ; Brain Mapping ; Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography ; Epilepsy/*physiopathology ; Female ; Gamma Rhythm ; Humans ; *Laughter ; Lip ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Movement ; Preoperative Period ; *Smiling ; *Speech ; },
abstract = {Smiling, laughing, and overt speech production are fundamental to human everyday communication. However, little is known about how the human brain achieves the highly accurate and differentiated control of such orofacial movement during natural conditions. Here, we utilized the high spatiotemporal resolution of subdural recordings to elucidate how human motor cortex is functionally engaged during control of real-life orofacial motor behaviour. For each investigated movement class-lip licking, speech production, laughing and smiling-our findings reveal a characteristic brain activity pattern within the mouth motor cortex with both spatial segregation and overlap between classes. Our findings thus show that motor cortex relies on sparse and action-specific activation during real-life orofacial behaviour, apparently organized in distinct but overlapping subareas that control different types of natural orofacial movements.},
}
@article {pmid30936430,
year = {2019},
author = {Hunt, DL and Lai, C and Smith, RD and Lee, AK and Harris, TD and Barbic, M},
title = {Multimodal in vivo brain electrophysiology with integrated glass microelectrodes.},
journal = {Nature biomedical engineering},
volume = {3},
number = {9},
pages = {741-753},
pmid = {30936430},
issn = {2157-846X},
mesh = {Algorithms ; Animals ; Brain/*physiology ; CA1 Region, Hippocampal ; Electrochemistry ; Electrophysiology/instrumentation/*methods ; Glass ; Male ; Mice ; *Microelectrodes ; Neurons/physiology ; Patch-Clamp Techniques/instrumentation/*methods ; Rats ; },
abstract = {Electrophysiology is the most used approach for the collection of functional data in basic and translational neuroscience, but it is typically limited to either intracellular or extracellular recordings. The integration of multiple physiological modalities for the routine acquisition of multimodal data with microelectrodes could be useful for biomedical applications, yet this has been challenging owing to incompatibilities of fabrication methods. Here, we present a suite of glass pipettes with integrated microelectrodes for the simultaneous acquisition of multimodal intracellular and extracellular information in vivo, electrochemistry assessments, and optogenetic perturbations of neural activity. We used the integrated devices to acquire multimodal signals from the CA1 region of the hippocampus in mice and rats, and show that these data can serve as ground-truth validation for the performance of spike-sorting algorithms. The microdevices are applicable for basic and translational neurobiology, and for the development of next-generation brain-machine interfaces.},
}
@article {pmid30936125,
year = {2019},
author = {Ramkissoon, A and Chaney, KE and Milewski, D and Williams, KB and Williams, RL and Choi, K and Miller, A and Kalin, TV and Pressey, JG and Szabo, S and Azam, M and Largaespada, DA and Ratner, N},
title = {Targeted Inhibition of the Dual Specificity Phosphatases DUSP1 and DUSP6 Suppress MPNST Growth via JNK.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {25},
number = {13},
pages = {4117-4127},
pmid = {30936125},
issn = {1557-3265},
support = {R01 CA211594/CA/NCI NIH HHS/United States ; R01 NS086219/NS/NINDS NIH HHS/United States ; T35 DK060444/DK/NIDDK NIH HHS/United States ; },
mesh = {Animals ; Antineoplastic Agents/*pharmacology ; Cell Line, Tumor ; DNA Copy Number Variations ; Disease Models, Animal ; Dual Specificity Phosphatase 1/*antagonists &amp; inhibitors ; Dual Specificity Phosphatase 6/*antagonists &amp; inhibitors ; Gene Knockdown Techniques ; Humans ; JNK Mitogen-Activated Protein Kinases/*metabolism ; Mice ; Nerve Sheath Neoplasms/*metabolism/*pathology ; Neurofibromatosis 1/genetics ; Protein Kinase Inhibitors/*pharmacology ; Signal Transduction/drug effects ; Xenograft Model Antitumor Assays ; },
abstract = {PURPOSE: In neurofibromatosis type 1 (NF1) and in highly aggressive malignant peripheral nerve sheath tumors (MPNSTs), constitutively active RAS-GTP and increased MAPK signaling are important in tumorigenesis. Dual specificity phosphatases (DUSPs) are negative regulators of MAPK signaling that dephosphorylate p38, JNK, and ERK in different settings. Although often acting as tumor suppressors, DUSPs may also act as oncogenes, helping tumor cells adapt to high levels of MAPK signaling. We hypothesized that inhibiting DUSPs might be selectively toxic to cells from NF1-driven tumors.<br><br>EXPERIMENTAL DESIGN: We examined DUSP gene and protein expression in neurofibroma and MPNSTs. We used small hairpin RNA (shRNA) to knock down DUSP1 and DUSP6 to evaluate cell growth, downstream MAPK signaling, and mechanisms of action. We evaluated the DUSP inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), in MPNST cell lines and in cell-line and patient-derived MPNST xenografts.<br><br>RESULTS: DUSP1 and DUSP6 are expressed in NF1-deleted tumors. Knockdown of DUSP1 and DUSP6, alone or in combination, reduced MPNST cell growth and led to ERK and JNK hyperactivation increasing downstream TP53 and p-ATM. The DUSP inhibitor, BCI, diminished the survival of NF1-deleted Schwann cells and MPNST cell lines through activation of JNK. In vivo, treatment of an established cell-line xenograft or a novel patient-derived xenograft (PDX) of MPNSTs with BCI increased ERK and JNK activation, caused tumor necrosis and fibrosis, and reduced tumor volume in one model.<br><br>CONCLUSIONS: Targeting DUSP1 and DUSP6 genetically or with BCI effectively inhibits MPNST cell growth and promotes cell death, in vitro and in xenograft models. The data support further investigation of DUSP inhibition in MPNSTs.},
}
@article {pmid30934931,
year = {2019},
author = {Kim, J and Lee, J and Han, C and Park, K},
title = {An Instant Donning Multi-Channel EEG Headset (with Comb-Shaped Dry Electrodes) and BCI Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {7},
pages = {},
pmid = {30934931},
issn = {1424-8220},
support = {NRF-2017R1A5A101559//National Research Foundation of Korea/ ; },
abstract = {We developed a new type of electroencephalogram (EEG) headset system with comb-shaped electrodes that enables the wearer to quickly don and utilize it in daily life. Two models that can measure EEG signals using up to eight channels have been implemented. The electrodes implemented in the headsets are similar to a comb and are placed quickly by wiping the hair (as done with a comb) using the headset. To verify this headset system, donning time was measured and three brain computer interface (BCI) application experiments were conducted. Alpha rhythm-based, steady-state visual evoked potential (SSVEP)-based, and auditory steady state response (ASSR)-based BCI systems were adopted for the validation experiments. Four subjects participated and ten trials were repeated in the donning experiment. The results of the validation experiments show that reliable EEG signal measurement is possible immediately after donning the headsets without any preparation. It took approximately 10 s for healthy subjects to don the headsets, including an earclip with reference and ground electrodes. The results of alpha rhythm-based BCI showed 100% accuracy. Furthermore, the results of SSVEP-based and ASSR-based BCI experiments indicate that performance is sufficient for BCI applications; 95.7% and 76.0% accuracies were obtained, respectively. The results of BCI paradigm experiments indicate that the new headset type is feasible for various BCI applications.},
}
@article {pmid30934010,
year = {2019},
author = {Loi, F and Berchialla, P and Masu, G and Masala, G and Scaramozzino, P and Carvelli, A and Caligiuri, V and Santi, A and Bona, MC and Maresca, C and Zanoni, MG and Capelli, G and Iannetti, S and Coccollone, A and Cappai, S and Rolesu, S and Piseddu, T},
title = {Prevalence estimation of Italian ovine cystic echinococcosis in slaughterhouses: A retrospective Bayesian data analysis, 2010-2015.},
journal = {PloS one},
volume = {14},
number = {4},
pages = {e0214224},
pmid = {30934010},
issn = {1932-6203},
mesh = {*Abattoirs ; Animals ; Bayes Theorem ; *Data Analysis ; Echinococcosis/*epidemiology/*veterinary ; Echinococcus granulosus/growth &amp; development ; Geography ; Italy/epidemiology ; Life Cycle Stages ; Prevalence ; Regression Analysis ; Retrospective Studies ; Sheep/*parasitology ; Sheep Diseases/*epidemiology ; Software ; },
abstract = {Cystic echinococcosis (CE) is a complex zoonosis with domestic and sylvatic life-cycles, involving different intermediate and definitive host species. Many previous studies have highlighted the lack of a surveillance system for CE, its persistence in Italy, and endemicity in several Italian regions. Because of the absence of a uniform surveillance program for both humans and animals, disease occurrence is widely underestimated. This study aimed to estimate the prevalence of ovine CE in Italy. Survey data on the prevalence of Echinococcus granulosus complex infections in Italian sheep farms from 2010 to 2015 were obtained in collaboration with Regional Veterinary Epidemiology Observatories (OEVRs). Bayesian analysis was performed to estimate the true CE farm prevalence. The prior true CE prevalence was estimated using data from Sardinia. Second, Bayesian modelling of the observed prevalence in different regions and the true prevalence estimation from the first step were used to ultimately estimate the prevalence of ovine CE in Italy. We obtained survey data from 10 OEVRs, covering 14 Italian regions. We observed that the risk of CE infection decreased over the years, and it was strictly correlated with the density of susceptible species. Using Sardinia as prior distribution, where the disease farm prevalence was approximately 19% (95% CI, 18.82-20.02), we estimated that the highest endemic CE farm prevalence was in Basilicata with a value of 12% (95% BCI: 7.49-18.9%) and in Piemonte 7.64%(95% BCI: 4.12-13.04%). Our results provide spatially relevant data crucial for guiding CE control in Italy. Precise information on disease occurrence location would aid in the identification of priority areas for disease control implementation by the authorities. The current underestimation of CE occurrence should urge the Italian and European governments to become aware of the public health importance of CE and implement targeted interventions for high-risk areas.},
}
@article {pmid30933636,
year = {2019},
author = {Miller, MW},
title = {GABA as a Neurotransmitter in Gastropod Molluscs.},
journal = {The Biological bulletin},
volume = {236},
number = {2},
pages = {144-156},
pmid = {30933636},
issn = {1939-8697},
support = {G12 MD007600/MD/NIMHD NIH HHS/United States ; P20 GM103642/GM/NIGMS NIH HHS/United States ; SC3 GM087200/GM/NIGMS NIH HHS/United States ; U54 MD007600/MD/NIMHD NIH HHS/United States ; },
mesh = {Animals ; Gastropoda/chemistry/*physiology ; Motor Neurons/chemistry/physiology ; Neurons/chemistry/physiology ; Neurotransmitter Agents ; gamma-Aminobutyric Acid/*metabolism/physiology ; },
abstract = {The neurotransmitter gamma-aminobutyric acid (GABA) is widely distributed in the mammalian central nervous system, where it acts as a major mediator of synaptic inhibition. GABA also serves as a neurotransmitter in a range of invertebrate phyla, including arthropods, echinoderms, annelids, nematodes, and platyhelminthes. This article reviews evidence supporting the neurotransmitter role of GABA in gastropod molluscs, with an emphasis on its presence in identified neurons and well-characterized neural circuits. The collective findings indicate that GABAergic signaling participates in the selection and specification of motor programs, as well as the bilateral coordination of motor circuits. While relatively few in number, GABAergic neurons can influence neural circuits via inhibitory, excitatory, and modulatory synaptic actions. GABA's colocalization with peptidergic and classical neurotransmitters can broaden its integrative capacity. The functional properties of GABAergic neurons in simpler gastropod systems may provide insight into the role of this neurotransmitter phenotype in more complex brains.},
}
@article {pmid30932860,
year = {2020},
author = {Li, J and Qiu, S and Shen, YY and Liu, CL and He, H},
title = {Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition.},
journal = {IEEE transactions on cybernetics},
volume = {50},
number = {7},
pages = {3281-3293},
doi = {10.1109/TCYB.2019.2904052},
pmid = {30932860},
issn = {2168-2275},
mesh = {Adult ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*classification/*methods ; Emotions/*classification ; Humans ; *Machine Learning ; Pattern Recognition, Automated/methods ; Young Adult ; },
abstract = {Electroencephalogram (EEG) has been widely used in emotion recognition due to its high temporal resolution and reliability. Since the individual differences of EEG are large, the emotion recognition models could not be shared across persons, and we need to collect new labeled data to train personal models for new users. In some applications, we hope to acquire models for new persons as fast as possible, and reduce the demand for the labeled data amount. To achieve this goal, we propose a multisource transfer learning method, where existing persons are sources, and the new person is the target. The target data are divided into calibration sessions for training and subsequent sessions for test. The first stage of the method is source selection aimed at locating appropriate sources. The second is style transfer mapping, which reduces the EEG differences between the target and each source. We use few labeled data in the calibration sessions to conduct source selection and style transfer. Finally, we integrate the source models to recognize emotions in the subsequent sessions. The experimental results show that the three-category classification accuracy on benchmark SEED improves by 12.72% comparing with the nontransfer method. Our method facilitates the fast deployment of emotion recognition models by reducing the reliance on the labeled data amount, which has practical significance especially in fast-deployment scenarios.},
}
@article {pmid30932828,
year = {2020},
author = {Ozdenizci, O and Erdogmus, D},
title = {Information Theoretic Feature Transformation Learning for Brain Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {67},
number = {1},
pages = {69-78},
pmid = {30932828},
issn = {1558-2531},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/physiology ; *Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics. In the light of broad evidence demonstrating the potential sub-optimality of ranking-based feature selection by any criterion, we propose to extend this focus with an information theoretic learning-driven feature transformation concept.<br><br>METHODS: We present a maximum mutual information linear transformation and a nonlinear transformation framework derived by a general definition of the feature transformation learning problem. Empirical assessments are performed based on electroencephalographic data recorded during a four class motor imagery brain-computer interface (BCI) task. Exploiting the state-of-the-art methods for initial feature vector construction, we compare the proposed approaches with conventional feature selection-based dimensionality reduction techniques, which are widely used in brain interfaces. Furthermore, for the multi-class problem, we present and exploit a hierarchical graphical model-based BCI decoding system.<br><br>RESULTS: Both binary and multi-class decoding analyses demonstrate significantly better performances with the proposed methods.<br><br>CONCLUSION: Information theoretic feature transformations are capable of tackling potential confounders of conventional approaches in various settings.<br><br>SIGNIFICANCE: We argue that this concept provides significant insights to extend the focus on feature selection heuristics to a broader definition of feature transformation learning in brain interfaces.},
}
@article {pmid30931146,
year = {2018},
author = {Branco, MP and Freudenburg, ZV and Aarnoutse, EJ and Vansteensel, MJ and Ramsey, NF},
title = {Optimization of sampling rate and smoothing improves classification of high frequency power in electrocorticographic brain signals.},
journal = {Biomedical physics &amp; engineering express},
volume = {4},
number = {},
pages = {},
pmid = {30931146},
issn = {2057-1976},
support = {320708/ERC_/European Research Council/International ; },
abstract = {OBJECTIVE: High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings.<br><br>APPROACH: With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients.<br><br>MAIN RESULTS: The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1-0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4-0.6 s and 0.9-2.7 s).<br><br>SIGNIFICANCE: The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.},
}
@article {pmid30923753,
year = {2019},
author = {Nambuusi, BB and Ssempiira, J and Makumbi, FE and Kasasa, S and Vounatsou, P},
title = {The effects and contribution of childhood diseases on the geographical distribution of all-cause under-five mortality in Uganda.},
journal = {Parasite epidemiology and control},
volume = {5},
number = {},
pages = {e00089},
pmid = {30923753},
issn = {2405-6731},
abstract = {INTRODUCTION: Information on the causes of death among under-five children is key in designing and implementation of appropriate interventions. In Uganda, civil death registration is incomplete which limits the estimation of disease-related mortality burden especially at a local scale. In the absence of routine cause-specific data, we used household surveys to quantify the effects and contribution of main childhood diseases such as malaria, severe or moderate anaemia, severe or moderate malnutrition, diarrhoea and acute respiratory infections (ARIs) on all-cause under-five mortality (U5M) at national and sub-national levels. We related all-cause U5M with risks of childhood diseases after adjusting for geographical disparities in coverages of health interventions, socio-economic, environmental factors and disease co-endemicities.<br><br>METHODS: Data on U5M, disease prevalence, socio-economic and intervention coverage indicators were obtained from the 2011 Demographic and Health Survey, while data on malaria prevalence were extracted from the 2009 Malaria Indicator Survey. Bayesian geostatistical Weibull proportional hazards models with spatially varying disease effects at sub-national scales were fitted to quantify the associations between childhood diseases and the U5M. Spatial correlation between clusters was incorporated via locational random effects while region-specific random effects with conditional autoregressive prior distributions modeled the geographical variation in the effects of childhood diseases. The models addressed geographical misalignment in the locations of the two surveys. The contribution of childhood diseases to under-five mortality was estimated using population attributable fractions.<br><br>RESULTS: The overall U5M rate was 90 deaths per 1000 live births. Large regional variations in U5M rates were observed, lowest in Kampala at 56 and highest in the North-East at 152 per 1000 live births. National malaria parasitemia prevalence was 42%, with Kampala experiencing the lowest of 5% and the Mid-North the highest of 62%. About 27% of Ugandan children aged 6-59&#x202f;months were severely or moderately anaemic; lowest in South-West (8%) and highest in East-Central (46%). Overall, 17% of children were either severely or moderately malnourished. The percentage of moderately/severely malnourished children varied by region with Kampala having the lowest (8%) and North-East the highest (45%). Nearly a quarter of the children under-five years were reported to have diarrhoea at national level, and this proportion was highest in East-Central (32%) and Mid-Eastern (33%) and lowest in South-West (14%). Overall, ARIs in the two weeks before the survey was 15%; highest in Mid-North (22%) and lowest in Central 1 (9%). At national level, the U5M was associated with prevalence of malaria (hazard ratio (HR)&#x202f;=&#x202f;1.74; 95% BCI: 1.42, 2.16), severe or moderate anaemia (HR =1.37; 95% BCI: 1.20, 1.75), severe or moderate malnutrition (HR&#x202f;=&#x202f;1.49; 95% BCI: 1.25, 1.66) and diarrhoea (HR&#x202f;=&#x202f;1.61; 95% BCI: 1.31, 2.05). The relationship between malaria and U5M was important in the regions of Central 2, East-Central, Mid-North, North-East and West-Nile. Diarrhoea was associated with under-five deaths in Central 2, East-central, Mid-Eastern and Mid-Western. Moderate/severe malnutrition was associated with U5M in East-Central, Mid-Eastern and North-East. Moderate/severe anaemia was associated with deaths in Central 1, Kampala, Mid-North, Mid-Western, North-East, South-West and West-Nile.At the national level, 97% (PAF&#x202f;=&#x202f;96.9; 95%BCI: 94.4, 98.0), 91% (PAF&#x202f;=&#x202f;90.9; 95%BCI: 84.4, 95.3), 89% (PAF&#x202f;=&#x202f;89.3; 95%BCI: 76.0,93.8) and 93% (PAF&#x202f;=&#x202f;93.3 95%BCI: 87.7,96.0) of the deaths among children less than five years in Uganda were attributable to malaria, severe/moderate anaemia, severe/moderate malnutrition and diarrhoea respectively. The attribution of malaria was comparable in Central 2, East-Central, Mid-North, North-East and West-Nile while severe/moderate anaemia was more common in all regions except Central 2, East-Central and Mid-Eastern. The attribution of diarrhoea in Central 2, East-Central, Mid-Eastern and Mid-Western was similar. The attribution of severe/moderate malnutrition was common in East-Central, Mid-Eastern and North-East.<br><br>CONCLUSION: In Uganda, the contribution and effects of childhood diseases on U5M vary by region. Majority of the under-five deaths are due to malaria, followed by diarrhoea, severe/moderate anaemia and severe/moderate malnutrition. Thus, strengthening disease-specific interventions especially in the affected regions may be an important strategy to accelerate progress towards the reduction of the U5M as per the SDG target by 2030. In particular, Indoor Residual Spraying, iron supplementation, deworming, exclusive breastfeeding, investment in nutrition and education in nutrition practices, oral rehydration therapy or recommended home fluid, improved sanitation facilities should be improved.},
}
@article {pmid30918269,
year = {2019},
author = {Stavisky, SD and Kao, JC and Nuyujukian, P and Pandarinath, C and Blabe, C and Ryu, SI and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Publisher Correction: Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {5528},
doi = {10.1038/s41598-018-37930-8},
pmid = {30918269},
issn = {2045-2322},
abstract = {A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.},
}
@article {pmid30917990,
year = {2019},
author = {Mutz, J and Vipulananthan, V and Carter, B and Hurlemann, R and Fu, CHY and Young, AH},
title = {Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: systematic review and network meta-analysis.},
journal = {BMJ (Clinical research ed.)},
volume = {364},
number = {},
pages = {l1079},
pmid = {30917990},
issn = {1756-1833},
mesh = {Depressive Disorder, Major/*therapy ; Electroconvulsive Therapy/*methods ; Female ; Humans ; Male ; Middle Aged ; Randomized Controlled Trials as Topic ; Transcranial Direct Current Stimulation/*methods ; Transcranial Magnetic Stimulation/*methods ; Treatment Outcome ; },
abstract = {OBJECTIVE: To estimate the comparative clinical efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults.<br><br>DESIGN: Systematic review with pairwise and network meta-analysis.<br><br>DATA SOURCES: Electronic search of Embase, PubMed/Medline, and PsycINFO up to 8 May 2018, supplemented by manual searches of bibliographies of several reviews (published between 2009 and 2018) and included trials.<br><br>Clinical trials with random allocation to electroconvulsive therapy (ECT), transcranial magnetic stimulation (repetitive (rTMS), accelerated, priming, deep, and synchronised), theta burst stimulation, magnetic seizure therapy, transcranial direct current stimulation (tDCS), or sham therapy.<br><br>MAIN OUTCOME MEASURES: Primary outcomes were response (efficacy) and all cause discontinuation (discontinuation of treatment for any reason) (acceptability), presented as odds ratios with 95% confidence intervals. Remission and continuous depression severity scores after treatment were also examined.<br><br>RESULTS: 113 trials (262 treatment arms) that randomised 6750 patients (mean age 47.9 years; 59% women) with major depressive disorder or bipolar depression met the inclusion criteria. The most studied treatment comparisons were high frequency left rTMS and tDCS versus sham therapy, whereas recent treatments remain understudied. The quality of the evidence was typically of low or unclear risk of bias (94 out of 113 trials, 83%) and the precision of summary estimates for treatment effect varied considerably. In network meta-analysis, 10 out of 18 treatment strategies were associated with higher response compared with sham therapy: bitemporal ECT (summary odds ratio 8.91, 95% confidence interval 2.57 to 30.91), high dose right unilateral ECT (7.27, 1.90 to 27.78), priming transcranial magnetic stimulation (6.02, 2.21 to 16.38), magnetic seizure therapy (5.55, 1.06 to 28.99), bilateral rTMS (4.92, 2.93 to 8.25), bilateral theta burst stimulation (4.44, 1.47 to 13.41), low frequency right rTMS (3.65, 2.13 to 6.24), intermittent theta burst stimulation (3.20, 1.45 to 7.08), high frequency left rTMS (3.17, 2.29 to 4.37), and tDCS (2.65, 1.55 to 4.55). Network meta-analytic estimates of active interventions contrasted with another active treatment indicated that bitemporal ECT and high dose right unilateral ECT were associated with increased response. All treatment strategies were at least as acceptable as sham therapy.<br><br>CONCLUSIONS: These findings provide evidence for the consideration of non-surgical brain stimulation techniques as alternative or add-on treatments for adults with major depressive episodes. These findings also highlight important research priorities in the specialty of brain stimulation, such as the need for further well designed randomised controlled trials comparing novel treatments, and sham controlled trials investigating magnetic seizure therapy.},
}
@article {pmid30917349,
year = {2019},
author = {Kiran Kumar, GR and Ramasubba Reddy, M},
title = {Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {16},
number = {4},
pages = {046004},
doi = {10.1088/1741-2552/ab13d1},
pmid = {30917349},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Random Allocation ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: This study introduces and evaluates a novel target identification method, latent common source extraction (LCSE), that uses subject-specific training data for the enhancement of detection of steady-state visual evoked potential (SSVEP).<br><br>APPROACH: LCSE seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of electroencephalographic (EEG) data. The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data. In this study a comparison of SSVEP identification performance between the proposed method, extended canonical correlation analysis (ExtCCA) and multiset canonical correlation analysis (MsetCCA) was conducted using SSVEP benchmark data of 40 targets recorded from 35 subjects to validate the effectiveness of the LCSE framework.<br><br>MAIN RESULTS: The results indicate that the LCSE framework significantly outperforms the other two methods in terms of both classification accuracy and information transfer rates (ITRs).<br><br>SIGNIFICANCE: The significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain-computer interface (BCI) systems.},
}
@article {pmid30915592,
year = {2019},
author = {Jauhar, S and Young, AH},
title = {Controversies in bipolar disorder; role of second-generation antipsychotic for maintenance therapy.},
journal = {International journal of bipolar disorders},
volume = {7},
number = {1},
pages = {10},
pmid = {30915592},
issn = {2194-7511},
support = {-//NIHR, Biomedical Research Centre, South London and Maudsley NHS Foundtion Trust and King's College, London/ ; -//JMAS Sim Fellowship, Royal College of Physicians, Edinburgh/ ; },
abstract = {In this narrative review, we discuss use of second-generation antipsychotics (SGAs) in maintenance treatment of bipolar disorder. We compare their use to historically more established treatments (particularly lithium, the gold standard). To compare we review evidence on efficacy, effectiveness and tolerability across illness poles, possible mechanisms of treatment response, guidance given by guideline groups and pragmatic clinical considerations. We then illustrate the controversies in maintenance antipsychotic use, with the example of first episode mania and its treatment within first episode psychosis services. Finally, we make suggestions for future studies to unpick these differences.},
}
@article {pmid30914446,
year = {2019},
author = {Dubey, A and Ray, S},
title = {Cortical Electrocorticogram (ECoG) Is a Local Signal.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {39},
number = {22},
pages = {4299-4311},
pmid = {30914446},
issn = {1529-2401},
support = {//Wellcome Trust/United Kingdom ; 500145-Z-09-Z/WTDBT_/DBT-Wellcome Trust India Alliance/India ; },
mesh = {Animals ; Electrocorticography/*methods ; Female ; Macaca radiata ; *Models, Neurological ; Visual Cortex/*physiology ; },
abstract = {Electrocorticogram (ECoG), obtained by low-pass filtering the brain signal recorded from a macroelectrode placed on the cortex, is extensively used to find the seizure focus in drug-resistant epilepsy and is of growing importance in cognitive and brain-machine-interfacing studies. To accurately estimate the epileptogenic cortex or to make inferences about cognitive processes, it is important to determine the "spatial spread" of ECoG (i.e., the extent of cortical tissue that contributes to its activity). However, the ECoG spread is currently unknown; even the spread of local field potential (LFP) obtained from microelectrodes is debated, with estimates ranging from a few hundred micrometers to several millimeters. Spatial spread can be estimated by measuring the receptive field (RF) and multiplying by the cortical magnification factor, but this method overestimates the spread because RF size gets inflated due to several factors. This issue can be partially addressed using a model that compares the RFs of two measures, such as LFP and multi-unit activity (MUA). To use this approach for ECoG, we designed a customized array containing both microelectrodes and ECoG electrodes to simultaneously map MUA, LFP, and ECoG RFs from the primary visual cortex of awake monkeys (three female Macaca radiata). The spatial spread of ECoG was surprisingly local (diameter ∼3 mm), only 3 times that of the LFP. Similar results were obtained using a model to simulate ECoG as a sum of LFPs of varying electrode sizes. Our results further validate the use of ECoG in clinical and basic cognitive research.SIGNIFICANCE STATEMENT Brains signals capture different attributes of the neural network depending on the size and location of the recording electrode. Electrocorticogram (ECoG), obtained by placing macroelectrodes (typically 2-3 mm diameter) on the exposed surface of the cortex, is widely used by neurosurgeons to identify the source of seizures in drug-resistant epileptic patients. The brain area responsible for seizures is subsequently surgically removed. Accurate estimation of the epileptogenic cortex and its removal requires the estimation of spatial spread of ECoG. Here, we estimated the spatial spread of ECoG in five behaving monkeys using two different approaches. Our results suggest that ECoG is a local signal (diameter of ∼3 mm), which can provide a useful tool for clinical, cognitive neuroscience, and brain-machine-interfacing applications.},
}
@article {pmid30911159,
year = {2019},
author = {Sitaram, R and Ros, T and Stoeckel, L and Haller, S and Scharnowski, F and Lewis-Peacock, J and Weiskopf, N and Blefari, ML and Rana, M and Oblak, E and Birbaumer, N and Sulzer, J},
title = {Author Correction: Closed-loop brain training: the science of neurofeedback.},
journal = {Nature reviews. Neuroscience},
volume = {20},
number = {5},
pages = {314},
doi = {10.1038/s41583-019-0161-1},
pmid = {30911159},
issn = {1471-0048},
abstract = {In this article, the affiliation for Mohit Rana was incorrectly listed as the Institute for Biological and Medical Engineering, Department of Psychiatry, and Section of Neuroscience, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860 Hernán Briones, piso 2, Macul 782-0436, Santiago, Chile. The listed affiliation should have been the following: Departamento de Psiquiatría, Escuela de Medicina, Centro Interdisciplinario de Neurociencias, Pontificia Universidad Católica de Chile, Santiago, Chile; and the Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile. An acknowledgement to Mohit Rana's funding source was also missing. The following sentence should have been included in the acknowledgments section: M.R. is supported by a Fondecyt postdoctoral fellowship (project no. 3100648).},
}
@article {pmid30910728,
year = {2019},
author = {Lührs, M and Riemenschneider, B and Eck, J and Andonegui, AB and Poser, BA and Heinecke, A and Krause, F and Esposito, F and Sorger, B and Hennig, J and Goebel, R},
title = {The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution.},
journal = {NeuroImage},
volume = {194},
number = {},
pages = {228-243},
doi = {10.1016/j.neuroimage.2019.03.046},
pmid = {30910728},
issn = {1095-9572},
mesh = {Adult ; Artifacts ; Brain/physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Male ; Neurofeedback/*methods ; Young Adult ; },
abstract = {Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain-computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. MR-Encephalography (MREG) has been shown to provide the high temporal resolution required to unalias and correct for physiological fluctuations and leads to increased specificity and sensitivity for mapping task-based activation and functional connectivity as well as for detecting dynamic changes in connectivity over time. By comparing a simultaneous multislice echo planar imaging (SMS-EPI) sequence and an MREG sequence using the same nominal spatial resolution in an offline analysis for three different experimental fMRI paradigms (perception of house and face stimuli, motor imagery, Stroop task), the potential of this novel technique for future BCI and NF applications was investigated. First, adapted general linear model pre-whitening which accounts for the high temporal resolution in MREG was implemented to calculate proper statistical results and be able to compare these with the SMS-EPI sequence. Furthermore, the respiration- and cardiac pulsation-related signals were successfully separated from the MREG signal using independent component analysis which were then included as regressors for a GLM analysis. Only the MREG sequence allowed to clearly separate cardiac pulsation and respiration components from the signal time course. It could be shown that these components highly correlate with the recorded respiration and cardiac pulsation signals using a respiratory belt and fingertip pulse plethysmograph. Temporal signal-to-noise ratios of SMS-EPI and MREG were comparable. Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.},
}
@article {pmid30909489,
year = {2019},
author = {Padfield, N and Zabalza, J and Zhao, H and Masero, V and Ren, J},
title = {EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {6},
pages = {},
pmid = {30909489},
issn = {1424-8220},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.},
}
@article {pmid30902640,
year = {2019},
author = {Bréchet, L and Brunet, D and Birot, G and Gruetter, R and Michel, CM and Jorge, J},
title = {Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI.},
journal = {NeuroImage},
volume = {194},
number = {},
pages = {82-92},
doi = {10.1016/j.neuroimage.2019.03.029},
pmid = {30902640},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/methods ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Thinking/*physiology ; },
abstract = {The temporal structure of self-generated cognition is a key attribute to the formation of a meaningful stream of consciousness. When at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 22sec. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective enhanced activity in temporal, parietal and occipital areas during the memory compared to the mental arithmetic condition, evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts, previously associated to "scene-reconstruction" and "self-experience" subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in the memory and in the mental arithmetic condition, respectively. EEG source analysis revealed similar spatial distributions of the sources of these microstates and the regions identified with fMRI. These findings imply a functional link between BOLD activity changes in regions related to a certain mental activity and the temporal dynamics of mentation, and support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring brief periods of integrated coherent neuronal activity, lasting only fractions of seconds.},
}
@article {pmid30902630,
year = {2019},
author = {Bockbrader, M and Annetta, N and Friedenberg, D and Schwemmer, M and Skomrock, N and Colachis, S and Zhang, M and Bouton, C and Rezai, A and Sharma, G and Mysiw, WJ},
title = {Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation.},
journal = {Archives of physical medicine and rehabilitation},
volume = {100},
number = {7},
pages = {1201-1217},
doi = {10.1016/j.apmr.2018.07.445},
pmid = {30902630},
issn = {1532-821X},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Forearm/*physiopathology ; Hand Strength/*physiology ; Humans ; Male ; Quadriplegia/physiopathology/*rehabilitation ; },
abstract = {OBJECTIVE: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic.<br><br>DESIGN: Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to&#xa0;1478.<br><br>SETTING: Tertiary care outpatient rehabilitation center.<br><br>PARTICIPANT: A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury INTERVENTIONS: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements.<br><br>MAIN OUTCOME MEASURES: Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure-Self-Report [SCIM-SR]) with and without the BCI-FES.<br><br>RESULTS: With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted "Battleship" game and manipulate household objects.<br><br>CONCLUSIONS: Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics.},
}
@article {pmid30902124,
year = {2019},
author = {Velasco-Álvarez, F and Sancha-Ros, S and García-Garaluz, E and Fernández-Rodríguez, &#xc1; and Medina-Juliá, MT and Ron-Angevin, R},
title = {UMA-BCI Speller: An easily configurable P300 speller tool for end users.},
journal = {Computer methods and programs in biomedicine},
volume = {172},
number = {},
pages = {127-138},
doi = {10.1016/j.cmpb.2019.02.015},
pmid = {30902124},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Paralysis ; Software ; User-Computer Interface ; Word Processing ; },
abstract = {BACKGROUND AND OBJECTIVE: Some neurodegenerative conditions can severely limit patients' capability to communicate because of the loss of muscular control. Brain-computer interfaces may help in the restoration of communication with these patients, bypassing the muscular activity, so that brain signals can be directly interpreted by a computer. There are many studies regarding brain-controlled spellers; however, these systems do not usually leap out of the lab because of technical and economic requirements. As a consequence, the potential end users do not benefit from these scientific advances in their daily life. The objective of this paper is to present a novel brain-controlled speller designed to be used by patients due to its versatility and ease of use.<br><br>METHODS: The brain-computer interface research group of the University of M&#xe1;laga (UMA-BCI) has developed a speller application based on the well-known P300 potential which can be easily installed, configured and used. The application supports the common P300 paradigms: the Row-Column Paradigm and the Rapid Serial Visual Presentation Paradigm. The inner core of the application is implemented with a widely used and studied platform, BCI2000, which ensures its reliability and allows other researchers to apply modifications at will in order to test new features. Ten na&#xef;ve volunteers carried out exercises using the application and completed usability tests for evaluation purposes.<br><br>RESULTS: New subjects using the application managed to set up and use the proposed speller in less than an hour. The positive results of the evaluation through the usability tests support this application's ease of use.<br><br>CONCLUSIONS: A new brain-controlled spelling tool has been presented whose aim is to be used by severely paralyzed patients in their daily lives, as well as by researchers to test new spelling features.},
}
@article {pmid30899211,
year = {2019},
author = {Remsik, AB and Williams, L and Gjini, K and Dodd, K and Thoma, J and Jacobson, T and Walczak, M and McMillan, M and Rajan, S and Young, BM and Nigogosyan, Z and Advani, H and Mohanty, R and Tellapragada, N and Allen, J and Mazrooyisebdani, M and Walton, LM and van Kan, PLE and Kang, TJ and Sattin, JA and Nair, VA and Edwards, DF and Williams, JC and Prabhakaran, V},
title = {Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {53},
pmid = {30899211},
issn = {1662-4548},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01 NS105646/NS/NINDS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; UL1 TR002373/TR/NCATS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; },
abstract = {Loss of motor function is a common deficit following stroke insult and often manifests as persistent upper extremity (UE) disability which can affect a survivor's ability to participate in activities of daily living. Recent research suggests the use of brain-computer interface (BCI) devices might improve UE function in stroke survivors at various times since stroke. This randomized crossover-controlled trial examines whether intervention with this BCI device design attenuates the effects of hemiparesis, encourages reorganization of motor related brain signals (EEG measured sensorimotor rhythm desynchronization), and improves movement, as measured by the Action Research Arm Test (ARAT). A sample of 21 stroke survivors, presenting with varied times since stroke and levels of UE impairment, received a maximum of 18-30 h of intervention with a novel electroencephalogram-based BCI-driven functional electrical stimulator (EEG-BCI-FES) device. Driven by spectral power recordings from contralateral EEG electrodes during cued attempted grasping of the hand, the user's input to the EEG-BCI-FES device modulates horizontal movement of a virtual cursor and also facilitates concurrent stimulation of the impaired UE. Outcome measures of function and capacity were assessed at baseline, mid-therapy, and at completion of therapy while EEG was recorded only during intervention sessions. A significant increase in r-squared values [reflecting Mu rhythm (8-12 Hz) desynchronization as the result of attempted movements of the impaired hand] presented post-therapy compared to baseline. These findings suggest that intervention corresponds with greater desynchronization of Mu rhythm in the ipsilesional hemisphere during attempted movements of the impaired hand and this change is related to changes in behavior as a result of the intervention. BCI intervention may be an effective way of addressing the recovery of a stroke impaired UE and studying neuromechanical coupling with motor outputs. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.},
}
@article {pmid30898278,
year = {2019},
author = {Lee, MB and Kramer, DR and Peng, T and Barbaro, MF and Liu, CY and Kellis, S and Lee, B},
title = {Brain-Computer Interfaces in Quadriplegic Patients.},
journal = {Neurosurgery clinics of North America},
volume = {30},
number = {2},
pages = {275-281},
doi = {10.1016/j.nec.2018.12.009},
pmid = {30898278},
issn = {1558-1349},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Humans ; Quadriplegia/physiopathology/*rehabilitation ; },
abstract = {Brain-computer interfaces (BCI) are implantable devices that interface directly with the nervous system. BCI for quadriplegic patients restore function by reading motor intent from the brain and use the signal to control physical, virtual, and native prosthetic effectors. Future closed-loop motor BCI will incorporate sensory feedback to provide patients with an effective and intuitive experience. Development of widely available BCI for patients with neurologic injury will depend on the successes of today's clinical BCI. BCI are an exciting next step in the frontier of neuromodulation.},
}
@article {pmid30897556,
year = {2019},
author = {Saidi, P and Vosoughi, A and Atia, G},
title = {Detection of brain stimuli using Ramanujan periodicity transforms.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036021},
doi = {10.1088/1741-2552/ab123a},
pmid = {30897556},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Periodicity ; Photic Stimulation/methods ; },
abstract = {OBJECTIVE: The ability to efficiently match the frequency of the brain's response to repetitive visual stimuli in real time is the basis for reliable SSVEP-based brain-computer-interfacing (BCI).<br><br>APPROACH: The detection of different stimuli is posed as a composite hypothesis test, where SSVEPs are assumed to admit a sparse representation in a Ramanujan periodicity transform (RPT) dictionary. For the binary case, we develop and analyze the performance of an RPT detector based on a derived generalized likelihood ratio test. Our approach is extended to multi-hypothesis multi-electrode settings, where we capture the spatial correlation between the electrodes using pre-stimulus data. We also introduce a new metric for evaluating SSVEP detection schemes based on their achievable efficiency and discrimination rate tradeoff for given system resources.<br><br>MAIN RESULTS: We obtain exact distributions of the test statistic in terms of confluent hypergeometric functions. Results based on extensive simulations with both synthesized and real data indicate that the RPT detector substantially outperforms spectral-based methods. Its performance also surpasses the calibration-free state-of-the-art canonical correlation analysis (CCA) and filter bank CCA (FBCCA) methods with respect to accuracy and sample complexity in short data lengths regimes crucial for real-time applications. The proposed approach is asymptotically optimal as it closes the gap to a perfect measurement bound as the data length increases. In contrast to existing supervised methods which are highly data-dependent, the RPT detector only uses pre-stimulus data to estimate the per-subject spatial correlation, thereby dispensing with considerable overhead associated with data collection for a large number of subjects and stimuli.<br><br>SIGNIFICANCE: Our work advances the theory and practice of emerging real-time BCI and affords a new framework for comparing SSVEP detection schemes across a wider spectrum of operating regimes.},
}
@article {pmid30897519,
year = {2019},
author = {Zhao, D and Tang, F and Si, B and Feng, X},
title = {Learning joint space-time-frequency features for EEG decoding on small labeled data.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {114},
number = {},
pages = {67-77},
doi = {10.1016/j.neunet.2019.02.009},
pmid = {30897519},
issn = {1879-2782},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Machine Learning ; *Neural Networks, Computer ; },
abstract = {Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating brain activities measured by electroencephalography (EEG) into correct control commands is a critical problem in this field. Most existing EEG decoding methods separate feature extraction from classification and thus are not robust across different BCI users. In this paper, we propose to learn subject-specific features jointly with the classification rule. We develop a deep convolutional network (ConvNet) to decode EEG signals end-to-end by stacking time-frequency transformation, spatial filtering, and classification together. Our proposed ConvNet implements a joint space-time-frequency feature extraction scheme for EEG decoding. Morlet wavelet-like kernels used in our network significantly reduce the number of parameters compared with classical convolutional kernels and endow the features learned at the corresponding layer with a clear interpretation, i.e. spectral amplitude. We further utilize subject-to-subject weight transfer, which uses parameters of the networks trained for existing subjects to initialize the network for a new subject, to solve the dilemma between a large number of demanded data for training deep ConvNets and small labeled data collected in BCIs. The proposed approach is evaluated on three public data sets, obtaining superior classification performance compared with the state-of-the-art methods.},
}
@article {pmid30893791,
year = {2019},
author = {Di Flumeri, G and Aricò, P and Borghini, G and Sciaraffa, N and Di Florio, A and Babiloni, F},
title = {The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {6},
pages = {},
pmid = {30893791},
issn = {1424-8220},
support = {723386//European Commission/ ; BrainSafeDrive//Ministero dell'Istruzione, dell'Universit&#xe0; e della Ricerca/ ; },
mesh = {Adult ; Area Under Curve ; Artifacts ; Brain/*physiology ; Electrodes ; Electroencephalography/*methods ; Gold/chemistry ; Humans ; Machine Learning ; Male ; ROC Curve ; Silver/chemistry ; Wearable Electronic Devices ; },
abstract = {One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.},
}
@article {pmid30887790,
year = {2019},
author = {Wu, H and Li, L and Li, L and Liu, T and Wang, J},
title = {[Review of comprehensive intervention by hand rehabilitation robot after stroke].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {36},
number = {1},
pages = {151-156},
pmid = {30887790},
issn = {1001-5515},
abstract = {Using intelligent rehabilitation robot to intervene hand function after stroke is an important physical treatment. With the development of biomedical engineering and the improvement of clinical demand, the comprehensive intervention of hand-function rehabilitation robot combined with new technologies is gradually emerging. This article summarizes the hand rehabilitation robots based on electromyogram (EMG), the brain-computer interface (BCI) hand rehabilitation robots, the somatosensory hand rehabilitation robots and the hand rehabilitation robots with functional electrostimulation. The advantages and disadvantages of various intervention methods are discussed, and the research trend about comprehensive intervention of hand rehabilitation robot is analyzed.},
}
@article {pmid30885442,
year = {2019},
author = {Batail, JM and Bioulac, S and Cabestaing, F and Daudet, C and Drapier, D and Fouillen, M and Fovet, T and Hakoun, A and Jardri, R and Jeunet, C and Lotte, F and Maby, E and Mattout, J and Medani, T and Micoulaud-Franchi, JA and Mladenovic, J and Perronet, L and Pillette, L and Ros, T and Vialatte, F and , },
title = {EEG neurofeedback research: A fertile ground for psychiatry?.},
journal = {L'Encephale},
volume = {45},
number = {3},
pages = {245-255},
doi = {10.1016/j.encep.2019.02.001},
pmid = {30885442},
issn = {0013-7006},
mesh = {Cognitive Behavioral Therapy/methods ; *Electroencephalography ; Humans ; Mental Disorders/therapy ; Neurofeedback/*methods ; Psychiatry/*methods ; },
abstract = {The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human-computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human-computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.},
}
@article {pmid30883569,
year = {2019},
author = {Ehrlich, SK and Agres, KR and Guan, C and Cheng, G},
title = {A closed-loop, music-based brain-computer interface for emotion mediation.},
journal = {PloS one},
volume = {14},
number = {3},
pages = {e0213516},
pmid = {30883569},
issn = {1932-6203},
mesh = {Adult ; *Algorithms ; Auditory Perception/*physiology ; *Brain-Computer Interfaces ; Emotions/*physiology ; Female ; Humans ; Male ; Music ; Pilot Projects ; },
abstract = {Emotions play a critical role in rational and intelligent behavior; a better fundamental knowledge of them is indispensable for understanding higher order brain function. We propose a non-invasive brain-computer interface (BCI) system to feedback a person's affective state such that a closed-loop interaction between the participant's brain responses and the musical stimuli is established. We realized this concept technically in a functional prototype of an algorithm that generates continuous and controllable patterns of synthesized affective music in real-time, which is embedded within a BCI architecture. We evaluated our concept in two separate studies. In the first study, we tested the efficacy of our music algorithm by measuring subjective affective responses from 11 participants. In a second pilot study, the algorithm was embedded in a real-time BCI architecture to investigate affective closed-loop interactions in 5 participants. Preliminary results suggested that participants were able to intentionally modulate the musical feedback by self-inducing emotions (e.g., by recalling memories), suggesting that the system was able not only to capture the listener's current affective state in real-time, but also potentially provide a tool for listeners to mediate their own emotions by interacting with music. The proposed concept offers a tool to study emotions in the loop, promising to cast a complementary light on emotion-related brain research, particularly in terms of clarifying the interactive, spatio-temporal dynamics underlying affective processing in the brain.},
}
@article {pmid30883278,
year = {2019},
author = {Tang, XL and Ma, WC and Kong, DS and Li, W},
title = {Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.},
journal = {Neural computation},
volume = {31},
number = {5},
pages = {919-942},
doi = {10.1162/neco_a_01183},
pmid = {30883278},
issn = {1530-888X},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Humans ; Imagination/*physiology ; *Machine Learning ; Motor Activity/*physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an adaptive learning rate strategy (SADSN) is proposed to solve the sample loss caused by supervised learning of EEG data and the extraction of manual features. The SADSN adopts the idea of an adaptive learning rate into a contrastive divergence (CD) algorithm to accelerate its convergence. Prior knowledge is introduced into the intermediary layer of the deep stacking network, and a restricted Boltzmann machine is trained by a semisupervised method in which the adjusting scope of the coefficient in learning rate is determined by performance analysis. Several EEG data sets are carried out to evaluate the performance of the proposed method. The results show that the recognition accuracy of SADSN is advanced with a more significant convergence rate and successfully classifies motor imagery.},
}
@article {pmid30881798,
year = {2018},
author = {Clites, TR and Herr, HM and Srinivasan, SS and Zorzos, AN and Carty, MJ},
title = {The Ewing Amputation: The First Human Implementation of the Agonist-Antagonist Myoneural Interface.},
journal = {Plastic and reconstructive surgery. Global open},
volume = {6},
number = {11},
pages = {e1997},
pmid = {30881798},
issn = {2169-7574},
abstract = {BACKGROUND: The agonist-antagonist myoneural interface (AMI) comprises a surgical construct and neural control architecture designed to serve as a bidirectional interface, capable of reflecting proprioceptive sensation of prosthetic joint position, speed, and torque from and advanced limb prosthesis onto the central nervous system. The AMI surgical procedure has previously been vetted in animal models; we here present the surgical results of its translation to human subjects.<br><br>METHODS: Modified unilateral below knee amputations were performed in the elective setting in 3 human subjects between July 2016 and April 2017. AMIs were constructed in each subject to control and interpret proprioception from the bionic ankle and subtalar joints. Intraoperative, perioperative, and postoperative residual-limb outcome measures were recorded and analyzed, including electromyographic and radiographic imaging of AMI musculature.<br><br>RESULTS: Mean subject age was 38&#x2009;&#xb1;&#x2009;13 years, and mean body mass index was 29.5&#x2009;&#xb1;&#x2009;5.5&#x2009;kg/m[2]. Mean operative time was 346&#x2009;&#xb1;&#x2009;87 minutes, including 120 minutes of tourniquet time per subject. Complications were minor and included transient cellulitis and one instance of delayed wound healing. All subjects demonstrated mild limb hypertrophy postoperatively, and intact construct excursion with volitional muscle activation. All patients reported a high degree of phantom limb position perception with no reports of phantom pain.<br><br>CONCLUSIONS: The AMI offers the possibility of improved prosthetic control and restoration of muscle-tendon proprioception. Initial results in this first cohort of human patients are promising and provide evidence as to the potential role of AMIs in the care of patients requiring below knee amputation.},
}
@article {pmid30881150,
year = {2019},
author = {Fredén Jansson, KJ and Håkansson, B and Rigato, C and Eeg-Olofsson, M and Reinfeldt, S},
title = {Robustness and lifetime of the bone conduction implant - a pilot study.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {12},
number = {},
pages = {89-100},
pmid = {30881150},
issn = {1179-1470},
abstract = {OBJECTIVES: The objective of this study was to develop methods for evaluating the mechanical robustness and estimating the lifetime of the novel bone conduction implant (BCI) that is used in a clinical study. The methods are intended to be applicable to any similar device.<br><br>MATERIALS AND METHODS: The robustness was evaluated using tests originally developed for cochlear implants comprising a random vibration test, a shock test, a pendulum test, and an impact test. Furthermore, magnetically induced torque and demagnetization during magnetic resonance imaging at 1.5 T were investigated using a dipole electromagnet. To estimate the lifetime of the implant, a long-term age-accelerated test was performed.<br><br>RESULTS: Out of all the tests, the pendulum and the impact tests had the largest effect on the electro-acoustic performance of the BCI implant, even if the change in performance was within acceptable limits (<20%). In comparison with baseline data, the lower and higher resonance peaks shifted down in frequency by 13% and 18%, respectively, and with a loss in magnitude of 1.1 and 2.0 dB, respectively, in these tests.<br><br>CONCLUSION: A complete series of tests were developed, and the BCI passed all the tests; its lifetime was estimated to be at least 26 years for patients who are using the implant for 12 hours on a daily basis.},
}
@article {pmid30880525,
year = {2019},
author = {Wu, Q and Zhang, Y and Liu, J and Sun, J and Cichocki, A and Gao, F},
title = {Regularized Group Sparse Discriminant Analysis for P300-Based Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {29},
number = {6},
pages = {1950002},
doi = {10.1142/S0129065719500023},
pmid = {30880525},
issn = {1793-6462},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Discriminant Analysis ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; },
abstract = {Event-related potentials (ERPs) especially P300 are popular effective features for brain-computer interface (BCI) systems based on electroencephalography (EEG). Traditional ERP-based BCI systems may perform poorly for small training samples, i.e. the undersampling problem. In this study, the ERP classification problem was investigated, in particular, the ERP classification in the high-dimensional setting with the number of features larger than the number of samples was studied. A flexible group sparse discriminative analysis algorithm based on Moreau-Yosida regularization was proposed for alleviating the undersampling problem. An optimization problem with the group sparse criterion was presented, and the optimal solution was proposed by using the regularized optimal scoring method. During the alternating iteration procedure, the feature selection and classification were performed simultaneously. Two P300-based BCI datasets were used to evaluate our proposed new method and compare it with existing standard methods. The experimental results indicated that the features extracted via our proposed method are efficient and provide an overall better P300 classification accuracy compared with several state-of-the-art methods.},
}
@article {pmid30872993,
year = {2019},
author = {Chivukula, S and Jafari, M and Aflalo, T and Yong, NA and Pouratian, N},
title = {Cognition in Sensorimotor Control: Interfacing With the Posterior Parietal Cortex.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {140},
pmid = {30872993},
issn = {1662-4548},
support = {R25 NS079198/NS/NINDS NIH HHS/United States ; },
abstract = {Millions of people worldwide are afflicted with paralysis from a disruption of neural pathways between the brain and the muscles. Because their cortical architecture is often preserved, these patients are able to plan movements despite an inability to execute them. In such people, brain machine interfaces have great potential to restore lost function through neuroprosthetic devices, circumventing dysfunctional corticospinal circuitry. These devices have typically derived control signals from the motor cortex (M1) which provides information highly correlated with desired movement trajectories. However, sensorimotor control simultaneously engages multiple cognitive processes such as intent, state estimation, decision making, and the integration of multisensory feedback. As such, cortical association regions upstream of M1 such as the posterior parietal cortex (PPC) that are involved in higher order behaviors such as planning and learning, rather than in encoding movement itself, may enable enhanced, cognitive control of neuroprosthetics, termed cognitive neural prosthetics (CNPs). We illustrate in this review, through a small sampling, the cognitive functions encoded in the PPC and discuss their neural representation in the context of their relevance to motor neuroprosthetics. We aim to highlight through examples a role for cortical signals from the PPC in developing CNPs, and to inspire future avenues for exploration in their research and development.},
}
@article {pmid30872723,
year = {2019},
author = {Zhang, N and Liu, Y and Yin, E and Deng, B and Cao, L and Jiang, J and Zhou, Z and Hu, D},
title = {Retinotopic and topographic analyses with gaze restriction for steady-state visual evoked potentials.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {4472},
pmid = {30872723},
issn = {2045-2322},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; Retina/*physiology ; Young Adult ; },
abstract = {Although the mechanisms of steady-state visual evoked potentials (SSVEPs) have been well studied, none of them have been implemented with strictly experimental conditions. Our objective was to create an ideal observer condition to exploit the features of SSVEPs. We present here an electroencephalographic (EEG) eye tracking experimental paradigm that provides biofeedback for gaze restriction during the visual stimulation. Specifically, we designed an EEG eye tracking synchronous data recording system for successful trial selection. Forty-six periodic flickers within a visual field of 11.5° were successively presented to evoke SSVEP responses, and online biofeedback based on an eye tracker was provided for gaze restriction. For eight participants, SSVEP responses in the visual field and topographic maps from full-brain EEG were plotted and analyzed. The experimental results indicated that the optimal visual flicking arrangement to boost SSVEPs should include the features of circular stimuli within a 4-6° spatial distance and increased stimulus area below the fixation point. These findings provide a basis for determining stimulus parameters for neural engineering studies, e.g. SSVEP-based brain-computer interface (BCI) designs. The proposed experimental paradigm could also provide a precise framework for future SSVEP-related studies.},
}
@article {pmid30872236,
year = {2019},
author = {Podmore, JJ and Breckon, TP and Aznan, NKN and Connolly, JD},
title = {On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-Based Bio-Signal Decoding in BCI Speller Applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {4},
pages = {611-618},
doi = {10.1109/TNSRE.2019.2904791},
pmid = {30872236},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Cues ; Electrodes ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; *Neural Networks, Computer ; Young Adult ; },
abstract = {Brain-computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alphanumeric characters allowing users to select target numbers and letters. Advances in BCI spellers can, in part, be accredited to subject-specific optimization, including; 1) custom electrode arrangements; 2) filter sub-band assessments; and 3) stimulus parameter tuning. Here, we apply deep convolutional neural networks (DCNNs) demonstrating cross-subject functionality for the classification of frequency and phase encoded SSVEP. Electroencephalogram (EEG) data are collected and classified using the same parameters across subjects. Subjects fixate forty randomly cued flickering characters (5 ×8 keyboard array) during concurrent wet-EEG acquisition. These data are provided by an open source SSVEP dataset. Our proposed DCNN, PodNet, achieves 86% and 77% offline accuracy of classification across-subjects for two data capture periods, respectively, 6-seconds (information transfer rate = 40 bpm) and 2-seconds (information transfer rate = 101 bpm). Subjects demonstrating sub-optimal (<70%) performance are classified to similar levels after a short subject-specific training period. PodNet outperforms filter-bank canonical correlation analysis for a low volume (3-channel) clinically feasible occipital electrode configuration. The networks defined in this study achieve functional performance for the largest number of SSVEP classes decoded via DCNN to date. Our results demonstrate PodNet achieves cross-subject, calibrationless classification and adaptability to sub-optimal subject data, and low-volume EEG electrode arrangements.},
}
@article {pmid30872232,
year = {2019},
author = {Chen, ML and Fu, D and Boger, J and Jiang, N},
title = {Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {4},
pages = {603-610},
doi = {10.1109/TNSRE.2019.2890968},
pmid = {30872232},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Aging/*physiology ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Evoked Potentials/physiology ; Female ; Functional Laterality/physiology ; Humans ; Male ; Middle Aged ; Neuronal Plasticity/physiology ; Touch/*physiology ; Vibration ; Young Adult ; },
abstract = {The rapid increase in the number of older adults around the world is accelerating research in applications to support age-related conditions, such as brain-computer interface (BCI) applications for post-stroke neurorehabilitation. The signal processing algorithms for electroencephalogram (EEG) and other physiological signals that are currently used in BCI have been developed on data from much younger populations. It is unclear how age-related changes may affect the EEG signal and therefore the use of BCI by older adults. This research investigated the EEG response to vibro-tactile stimulation from 11 younger (21.7±2.76 years old) and 11 older (72.0±8.07 years old) subjects. The results showed that: 1) the spatial patterns of cortical activation in older subjects were significantly different from those of younger subjects, with markedly reduced lateralization; 2) there is a general power reduction of the EEG measured from older subjects. The average left vs. right BCI performance accuracy of older subjects was 66.4±5.70%, 15.9% lower than that of the younger subjects (82.3±12.4%) and statistically significantly different (t(10)= -3.57, p= 0.005). Future research should further investigate age-differences that may exist in electrophysiology and take these into consideration when developing applications that target the older population.},
}
@article {pmid30869627,
year = {2019},
author = {Beveridge, R and Wilson, S and Callaghan, M and Coyle, D},
title = {Neurogaming With Motion-Onset Visual Evoked Potentials (mVEPs): Adults Versus Teenagers.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {4},
pages = {572-581},
doi = {10.1109/TNSRE.2019.2904260},
pmid = {30869627},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Aging/*psychology ; Algorithms ; *Brain-Computer Interfaces ; Child ; Computer Graphics ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Motion Perception/physiology ; Photic Stimulation ; Psychomotor Performance ; Video Games ; Young Adult ; },
abstract = {Motion-onset visually evoked potentials (mVEPs) are neural potentials that are time-locked to the onset of motion of evoking stimuli. Due to their visually elegant properties, mVEP stimuli may be suited to video game control given gaming's inherent demand on the users' visual attention and the requirement to process rapidly changing visual information. Here, we investigate mVEPs associated with five different stimuli to control the position of a car in a visually rich 3D racing game in a group of 15 BCI naïve teenagers and compared with 19 BCI naive adults. Results from an additional 14 BCI experienced adults were compared with BCI naïve adults. Our results demonstrate that the game control accuracy is related to the number of trials used to make a decision on the users' chosen button/stimulus (76%, 62%, and 35% for 5, 3, and 1 trials, respectively) and information transfer rate (ITR) (13.4, 13.9, and 6.6 bits per minute (BPM)), although, even though accuracy decreases when using three compared to the commonly used five trial repetitions, ITR is maintained. A Kruskal-Wallis test suggests that BCI naïve adults do not outperform BCI naïve teenagers in the 3D racing game in the first and seconds laps (p > 0.05), but do outperform in the third lap (p < 0.05). A comparison between BCI naïve and BCI experienced adults indicates BCI experienced adults do not perform better than BCI naïve adults (p > 0.05).},
}
@article {pmid30869624,
year = {2019},
author = {Gonzalez-Navarro, P and Marghi, YM and Azari, B and Akcakaya, M and Erdogmus, D},
title = {An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {5},
pages = {798-804},
pmid = {30869624},
issn = {1558-0210},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Area Under Curve ; *Brain-Computer Interfaces ; Calibration ; Computer Simulation ; *Electroencephalography ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Theoretical ; Normal Distribution ; Psychomotor Performance ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Electroencephalography (EEG) is an effective non-invasive measurement method to infer user intent in brain-computer interface (BCI) systems for control and communication, however, these systems often lack sufficient accuracy and speed due to low separability of class-conditional EEG feature distributions. Many factors impact system performance, including inadequate training datasets and models' ignorance of the temporal dependency of brain responses to serial stimuli. Here, we propose a signal model for event-related responses in the EEG evoked with a rapid sequence of stimuli in BCI applications. The model describes the EEG as a superposition of impulse responses time-locked to stimuli corrupted with an autoregressive noise process. The performance of the signal model is assessed in the context of RSVP keyboard, a language-model-assisted EEG-based BCI for typing. EEG data obtained for model calibration from 10 healthy participants are used to fit and compare two models: the proposed sequence-based EEG model and the trial-based feature-class-conditional distribution model that ignores temporal dependencies, which has been used in the previous work. The simulation studies indicate that the earlier model that ignores temporal dependencies may be causing drastic reductions in achievable information transfer rate (ITR). Furthermore, the proposed model, with better regularization, may achieve improved accuracy with fewer calibration data samples, potentially helping to reduce calibration time. Specifically, results show an average 8.6% increase in (cross-validated) calibration AUC for a single channel of EEG, and 54% increase in the ITR in a typing task.},
}
@article {pmid30868377,
year = {2020},
author = {Steinert, S and Friedrich, O},
title = {Wired Emotions: Ethical Issues of Affective Brain-Computer Interfaces.},
journal = {Science and engineering ethics},
volume = {26},
number = {1},
pages = {351-367},
pmid = {30868377},
issn = {1471-5546},
mesh = {Affect/*ethics ; Bias ; Brain-Computer Interfaces/*ethics ; Decision Making ; Emotions/*ethics ; Humans ; Informed Consent ; Motivation ; Personal Autonomy ; Privacy ; },
abstract = {Ethical issues concerning brain-computer interfaces (BCIs) have already received a considerable amount of attention. However, one particular form of BCI has not received the attention that it deserves: Affective BCIs that allow for the detection and stimulation of affective states. This paper brings the ethical issues of affective BCIs in sharper focus. The paper briefly reviews recent applications of affective BCIs and considers ethical issues that arise from these applications. Ethical issues that affective BCIs share with other neurotechnologies are presented and ethical concerns that are specific to affective BCIs are identified and discussed.},
}
@article {pmid30862731,
year = {2019},
author = {Faller, J and Cummings, J and Saproo, S and Sajda, P},
title = {Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {116},
number = {13},
pages = {6482-6490},
pmid = {30862731},
issn = {1091-6490},
mesh = {Adult ; Arousal/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Heart Rate ; Humans ; Male ; Neurofeedback/*methods ; New York City ; Psychomotor Performance/*physiology ; Pupil Disorders ; Task Performance and Analysis ; Young Adult ; },
abstract = {Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use online neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance. Specifically, we use a brain-computer interface (BCI) that uses information in the EEG to generate a neurofeedback signal that dynamically adjusts an individual's arousal state when they are engaged in a boundary-avoidance task (BAT). The BAT is a demanding sensory-motor task paradigm that we implement as an aerial navigation task in virtual reality and which creates cognitive conditions that escalate arousal and quickly results in task failure (e.g., missing or crashing into the boundary). We demonstrate that task performance, measured as time and distance over which the subject can navigate before failure, is significantly increased when veridical neurofeedback is provided. Simultaneous measurements of pupil dilation and heart-rate variability show that the neurofeedback indeed reduces arousal. Our work demonstrates a BCI system that uses online neurofeedback to shift arousal state and increase task performance in accordance with the Yerkes-Dodson law.},
}
@article {pmid30856388,
year = {2019},
author = {Zhang, Y and Zhang, X and Sun, H and Fan, Z and Zhong, X},
title = {Portable brain-computer interface based on novel convolutional neural network.},
journal = {Computers in biology and medicine},
volume = {107},
number = {},
pages = {248-256},
doi = {10.1016/j.compbiomed.2019.02.023},
pmid = {30856388},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation ; Equipment Design ; Humans ; Imagination/physiology ; Male ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear uncomfortable EEG hats. These procedures usually create a challenge for subjects. In the present study, we propose a portable EEG signal acquisition system. This study consists of two main parts: 1) A novel, portable dry-electrode and wireless brain-computer interface is designed. The EEG signal acquisition board is based on 24 bit, analog-to-digital converters chip and wireless microprocessor unit. The wireless portable brain computer interface device acquires an EEG signal comfortably, and the EEG signals are transmitted to a personal computer via Bluetooth. 2) A convolutional neural network (CNN) classification algorithm is implemented to classify the motor imagery (MI) experiment using novel feature 3-dimension input. The time dimension was reshaped to represent the first and second dimension, and the frequency band was used as the third dimension. Specifically, frequency domain representations of EEG signals obtained via wavelet package decomposition (WPD) are obtained to train CNN. The classification performance in terms of the value of kappa is 0.564 for the proposed method. The t-test results show that the performance improvement of CNN over other selected state-of-the-art methods is statistically significant. Our results show that the proposed design is reliable in measuring EEG signals, and the 3D CNN provides better classification performance than other method for MI experiments.},
}
@article {pmid30853905,
year = {2019},
author = {Kim, H and Yoshimura, N and Koike, Y},
title = {Classification of Movement Intention Using Independent Components of Premovement EEG.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {63},
pmid = {30853905},
issn = {1662-5161},
abstract = {Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.},
}
@article {pmid30853300,
year = {2019},
author = {Sakellaridi, S and Christopoulos, VN and Aflalo, T and Pejsa, KW and Rosario, ER and Ouellette, D and Pouratian, N and Andersen, RA},
title = {Intrinsic Variable Learning for Brain-Machine Interface Control by Human Anterior Intraparietal Cortex.},
journal = {Neuron},
volume = {102},
number = {3},
pages = {694-705.e3},
pmid = {30853300},
issn = {1097-4199},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; UG1 EY032039/EY/NEI NIH HHS/United States ; },
mesh = {Adaptation, Physiological/physiology ; *Brain-Computer Interfaces ; Cervical Vertebrae ; Cognition/*physiology ; Female ; Humans ; Learning/*physiology ; Middle Aged ; Neurons/*physiology ; Parietal Lobe/*cytology/physiology ; Quadriplegia/*rehabilitation ; Spinal Cord Injuries/rehabilitation ; },
abstract = {Although animal studies provided significant insights in understanding the neural basis of learning and adaptation, they often cannot dissociate between different learning mechanisms due to the lack of verbal communication. To overcome this limitation, we examined the mechanisms of learning and its limits in a human intracortical brain-machine interface (BMI) paradigm. A tetraplegic participant controlled a 2D computer cursor by modulating single-neuron activity in the anterior intraparietal area (AIP). By perturbing the neuron-to-movement mapping, the participant learned to modulate the activity of the recorded neurons to solve the perturbations by adopting a target re-aiming strategy. However, when no cognitive strategies were adequate to produce correct responses, AIP failed to adapt to perturbations. These findings suggest that learning is constrained by the pre-existing neuronal structure, although it is possible that AIP needs more training time to learn to generate novel activity patterns when cognitive re-adaptation fails to solve the perturbations.},
}
@article {pmid30849284,
year = {2021},
author = {Knudson, D and Wallace, B},
title = {Student perceptions of low-tech active learning and mastery of introductory biomechanics concepts.},
journal = {Sports biomechanics},
volume = {20},
number = {4},
pages = {458-468},
doi = {10.1080/14763141.2019.1570322},
pmid = {30849284},
issn = {1752-6116},
mesh = {Biomechanical Phenomena ; Biophysics/*education ; Humans ; *Perception ; Problem-Based Learning/*methods ; Students/*psychology ; United States ; Universities ; },
abstract = {This study documented student perceptions of five low-tech active learning exercises, their epistemology of learning, and examined the association between these variables and mastery of biomechanics concepts. Students (N = 152) in four introductory biomechanics courses at two universities completed the Biomechanics Concept Inventory (BCI) at the beginning and the end of the course. An additional 10-question survey was used at the end of the course to determine student perceptions of the active learning exercises and their epistemology of learning. Student learning of biomechanical concepts improved over levels reported in previous studies of traditional lecture instruction, but not as much as seen in other studies of active learning pedagogy in biomechanics and physics. Student perceptions of active learning were positive, particularly in individual rather than group exercises. A minority (12-16%) of these students had negative perceptions of group-based active learning exercises. Student perception of epistemology of learning was primarily constructivist; however, there was no evidence of these perceptions had associations with learning biomechanical concepts. Biomechanics instructors planning to use low-tech active learning exercises should communicate their philosophy of learning, expectations for the course, and progressively implement individual-based and group-based active learning experiences early in the course.},
}
@article {pmid30849042,
year = {2019},
author = {Uyulan, C and Ergüzel, TT and Tarhan, N},
title = {Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {64},
number = {5},
pages = {529-542},
doi = {10.1515/bmt-2018-0105},
pmid = {30849042},
issn = {1862-278X},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Entropy ; Humans ; Neural Networks, Computer ; Wavelet Analysis ; },
abstract = {Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.},
}
@article {pmid30845952,
year = {2019},
author = {Kögel, J and Schmid, JR and Jox, RJ and Friedrich, O},
title = {Using brain-computer interfaces: a scoping review of studies employing social research methods.},
journal = {BMC medical ethics},
volume = {20},
number = {1},
pages = {18},
pmid = {30845952},
issn = {1472-6939},
mesh = {Biomedical Research/*ethics ; Brain-Computer Interfaces/*ethics/*psychology ; Caregivers/ethics/*psychology ; Communication Devices for People with Disabilities ; Electroencephalography ; Ethics, Research ; Humans ; Personhood ; Quality of Life/*psychology ; User-Computer Interface ; },
abstract = {BACKGROUND: The rapid expansion of research on Brain-Computer Interfaces (BCIs) is not only due to the promising solutions offered for persons with physical impairments. There is also a heightened need for understanding BCIs due to the challenges regarding ethics presented by new technology, especially in its impact on the relationship between man and machine. Here we endeavor to present a scoping review of current studies in the field to gain insight into the complexity of BCI use. By examining studies related to BCIs that employ social research methods, we seek to demonstrate the multitude of approaches and concerns from various angles in considering the social and human impact of BCI technology.<br><br>METHODS: For this scoping review of research on BCIs' social and ethical implications, we systematically analyzed six databases, encompassing the fields of medicine, psychology, and the social sciences, in order to identify empirical studies on BCIs. The search yielded 73 publications that employ quantitative, qualitative, or mixed methods.<br><br>RESULTS: Of the 73 publications, 71 studies address the user perspective. Some studies extend to consideration of other BCI stakeholders such as medical technology experts, caregivers, or health care professionals. The majority of the studies employ quantitative methods. Recurring themes across the studies examined were general user opinion towards BCI, central technical or social issues reported, requests/demands made by users of the technology, the potential/future of BCIs, and ethical aspects of BCIs.<br><br>CONCLUSIONS: Our findings indicate that while technical aspects of BCIs such as usability or feasibility are being studied extensively, comparatively little in-depth research has been done on the self-image and self-experience of the BCI user. In general there is also a lack of focus or examination of the caregiver's perspective.},
}
@article {pmid30844780,
year = {2019},
author = {Sadatnejad, K and Rahmati, M and Rostami, R and Kazemi, R and Ghidary, SS and Müller, A and Alimardani, F},
title = {EEG representation using multi-instance framework on the manifold of symmetric positive definite matrices.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036016},
doi = {10.1088/1741-2552/ab0dad},
pmid = {30844780},
issn = {1741-2552},
mesh = {Adolescent ; Brain/*physiology/*physiopathology ; *Brain-Computer Interfaces ; Child ; Electroencephalography/*methods ; Female ; Humans ; Iran/epidemiology ; Male ; Mental Disorders/epidemiology/*physiopathology ; Switzerland/epidemiology ; Young Adult ; },
abstract = {OBJECTIVE: The generalization and robustness of an electroencephalogram (EEG)-based system are crucial requirements in actual practices.<br><br>APPROACH: To reach these goals, we propose a new EEG representation that provides a more realistic view of brain functionality by applying multi-instance (MI) framework to consider the non-stationarity of the EEG signal. In this representation, the non-stationarity of EEG is considered by describing the signal as a bag of relevant and irrelevant concepts. The concepts are provided by a robust representation of homogeneous segments of EEG signal using spatial covariance matrices. Due to the nonlinear geometry of the space of covariance matrices, we determine the boundaries of the homogeneous segments based on adaptive segmentation of the signal in a Riemannian framework. Each subject is described as a bag of covariance matrices of homogeneous segments and the bag-level discriminative information is used for classification.<br><br>MAIN RESULTS: To evaluate the performance of the proposed approach, we examine it in a cultural neuroscience application for classification Iranian versus Swiss normal subjects to discover if strongly differing cultures can result in distinguishing patterns in brain electrical activity of the subjects. To confirm the effectiveness of the proposed representation, we also evaluate the proposed representation in EEG-based mental disorder diagnosis application for attention deficit hyperactivity disorder (ADHD)/bipolar mood disorder (BMD), Schizophrenia/ normal, and Major Depression Disorder/normal diagnosis applications.<br><br>SIGNIFICANCE: Experimental results confirm the superiority of the proposed approach, which is gained due to the robustness of covariance descriptor, the effectiveness of Riemannian geometry, the benefits of considering the inherent non-stationary nature of the brain by applying bag-level discriminative information, and automatic handling the artifacts.},
}
@article {pmid30843846,
year = {2019},
author = {Wang, Z and Zhou, Y and Chen, L and Gu, B and Yi, W and Liu, S and Xu, M and Qi, H and He, F and Ming, D},
title = {BCI Monitor Enhances Electroencephalographic and Cerebral Hemodynamic Activations During Motor Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {4},
pages = {780-787},
doi = {10.1109/TNSRE.2019.2903685},
pmid = {30843846},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Cerebrovascular Circulation/*physiology ; Electric Stimulation Therapy ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization ; Female ; Healthy Volunteers ; Humans ; Imagination ; Male ; Monitoring, Physiologic ; Neurofeedback ; Oxygen/blood ; Physical Education and Training/*methods ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Motor imagery-based brain-computer interface (MI-BCI) controlling functional electrical stimulation (FES) is promising for disabled patients to restore their motor functions. However, it remains unclear how much the BCI part can contribute to the functional coupling between the brain and muscle. Specifically, whether it can enhance the cerebral activation for motor training? Here, we investigate the electroencephalographic and cerebral hemodynamic responses for MI-BCI-FES training and MI-FES training, respectively. Twelve healthy subjects were recruited in the motor training study when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. Compared with the MI-FES training conditions, the MI-BCI-FES could induce significantly stronger event-related desynchronization (ERD) and blood oxygen response, which demonstrates that BCI indeed plays a functional role in the closed-loop motor training. Therefore, this paper verifies the feasibility of using BCI to train motor functions in a closed-loop manner.},
}
@article {pmid30842752,
year = {2019},
author = {Krauth, R and Schwertner, J and Vogt, S and Lindquist, S and Sailer, M and Sickert, A and Lamprecht, J and Perdikis, S and Corbet, T and Millán, JDR and Hinrichs, H and Heinze, HJ and Sweeney-Reed, CM},
title = {Cortico-Muscular Coherence Is Reduced Acutely Post-stroke and Increases Bilaterally During Motor Recovery: A Pilot Study.},
journal = {Frontiers in neurology},
volume = {10},
number = {},
pages = {126},
pmid = {30842752},
issn = {1664-2295},
abstract = {Motor recovery following stroke is believed to necessitate alteration in functional connectivity between cortex and muscle. Cortico-muscular coherence has been proposed as a potential biomarker for post-stroke motor deficits, enabling a quantification of recovery, as well as potentially indicating the regions of cortex involved in recovery of function. We recorded simultaneous EEG and EMG during wrist extension from healthy participants and patients following ischaemic stroke, evaluating function at three time points post-stroke. EEG-EMG coherence increased over time, as wrist mobility recovered clinically, and by the final evaluation, coherence was higher in the patient group than in the healthy controls. Moreover, the cortical distribution differed between the groups, with coherence involving larger and more bilaterally scattered areas of cortex in the patients than in the healthy participants. The findings suggest that EEG-EMG coherence has the potential to serve as a biomarker for motor recovery and to provide information about the cortical regions that should be targeted in rehabilitation therapies based on real-time EEG.},
}
@article {pmid30842588,
year = {2019},
author = {Wang, Y and Xie, BH and Lin, WH and Huang, YH and Ni, JY and Hu, J and Cui, W and Zhou, J and Shen, L and Xu, LF and Lian, F and Li, HP},
title = {Amplification of SMYD3 promotes tumorigenicity and intrahepatic metastasis of hepatocellular carcinoma via upregulation of CDK2 and MMP2.},
journal = {Oncogene},
volume = {38},
number = {25},
pages = {4948-4961},
pmid = {30842588},
issn = {1476-5594},
mesh = {Animals ; Bile Duct Neoplasms/genetics/secondary ; Bile Ducts, Intrahepatic/pathology ; Carcinogenesis/*genetics ; Carcinoma, Hepatocellular/diagnosis/genetics/*pathology ; Cyclin-Dependent Kinase 2/*genetics ; Disease Progression ; Female ; *Gene Amplification/physiology ; Gene Expression Regulation, Enzymologic ; Gene Expression Regulation, Neoplastic ; HEK293 Cells ; Hep G2 Cells ; Histone-Lysine N-Methyltransferase/*genetics ; Humans ; Liver Neoplasms/diagnosis/genetics/*pathology ; Matrix Metalloproteinase 2/*genetics ; Mice ; Mice, Inbred BALB C ; Mice, Nude ; Neoplasm Invasiveness ; Neoplasm Metastasis ; Neoplasm Recurrence, Local/genetics/pathology ; Transcriptional Activation ; Tumor Cells, Cultured ; Up-Regulation/genetics ; },
abstract = {SMYD3, a member that belongs to the SET and MYND-domain (SMYD) family, has also been proven to largely participate in gene transcription regulation and progression of several human cancers as a histone lysine methyltransferase. However, the role and significance of SMYD3 in both the clinic and progression of hepatocellular carcinoma (HCC) remain unclear. Herein, we find that SMYD3 is increased in cirrhotic livers, and strikingly upregulated in hepatocellular carcinoma (HCC) tissues and cell lines. Subsequent analyses suggest that high expression level of SMYD3 significantly correlates with the malignant characteristics of HCC, and predicts poor prognosis in patients. Our results show that overexpression of SMYD3 increases, while silencing of SMYD3 inhibits, cell proliferation, invasiveness and tumorigenicity both in vitro and in vivo. SMYD3 also promotes intrahepatic metastasis of HCC cells. For the mechanisms, we identify that SMYD3 bound to CDK2 and MMP2 promoter and increased H3K4me3 modification at the corresponding promoters to promote gene transcription. Importantly, pharmacological targeting of SMYD3 with BCI-121 inhibitor effectively repressed the tumorigenicity of HCC cells. Finally, our results show that gene locus amplification is a cause for SMYD3 overexpression in HCC. These findings not only uncover that SMYD3 overexpression promotes the tumorigenicity and intrahepatic metastasis of HCC cell via upregulation of CDK2 and MMP2, but also suggest SMYD3 could be a practical prognosis marker or therapeutic target against the disease.},
}
@article {pmid30840712,
year = {2019},
author = {Nguyen, CH and Karavas, GK and Artemiadis, P},
title = {Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.},
journal = {PloS one},
volume = {14},
number = {3},
pages = {e0212620},
pmid = {30840712},
issn = {1932-6203},
mesh = {*Adaptation, Physiological ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Learning/*physiology ; Male ; },
abstract = {This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications.},
}
@article {pmid30840671,
year = {2019},
author = {Shirzhiyan, Z and Keihani, A and Farahi, M and Shamsi, E and GolMohammadi, M and Mahnam, A and Haidari, MR and Jafari, AH},
title = {Introducing chaotic codes for the modulation of code modulated visual evoked potentials (c-VEP) in normal adults for visual fatigue reduction.},
journal = {PloS one},
volume = {14},
number = {3},
pages = {e0213197},
pmid = {30840671},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain/physiology ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Fatigue/*pathology ; Female ; Humans ; Male ; Photic Stimulation ; Young Adult ; },
abstract = {Code modulated Visual Evoked Potentials (c-VEP) based BCI studies usually employ m-sequences as a modulating codes for their broadband spectrum and correlation property. However, subjective fatigue of the presented codes has been a problem. In this study, we introduce chaotic codes containing broadband spectrum and similar correlation property. We examined whether the introduced chaotic codes could be decoded from EEG signals and also compared the subjective fatigue level with m-sequence codes in normal subjects. We generated chaotic code from one-dimensional logistic map and used it with conventional 31-bit m-sequence code. In a c-VEP based study in normal subjects (n = 44, 21 females) we presented these codes visually and recorded EEG signals from the corresponding codes for their four lagged versions. Canonical correlation analysis (CCA) and spatiotemporal beamforming (STB) methods were used for target identification and comparison of responses. Additionally, we compared the subjective self-declared fatigue using VAS caused by presented m-sequence and chaotic codes. The introduced chaotic code was decoded from EEG responses with CCA and STB methods. The maximum total accuracy values of 93.6 ± 11.9% and 94 ± 14.4% were achieved with STB method for chaotic and m-sequence codes for all subjects respectively. The achieved accuracies in all subjects were not significantly different in m-sequence and chaotic codes. There was significant reduction in subjective fatigue caused by chaotic codes compared to the m-sequence codes. Both m-sequence and chaotic codes were similar in their accuracies as evaluated by CCA and STB methods. The chaotic codes significantly reduced subjective fatigue compared to the m-sequence codes.},
}
@article {pmid30840663,
year = {2019},
author = {Valeriani, D and Poli, R},
title = {Cyborg groups enhance face recognition in crowded environments.},
journal = {PloS one},
volume = {14},
number = {3},
pages = {e0212935},
pmid = {30840663},
issn = {1932-6203},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; *Decision Making ; Electroencephalography ; Evoked Potentials, Visual/physiology ; Facial Recognition/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; *Neural Networks, Computer ; Reaction Time/physiology ; },
abstract = {Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.},
}
@article {pmid30837823,
year = {2019},
author = {Milsap, G and Collard, M and Coogan, C and Rabbani, Q and Wang, Y and Crone, NE},
title = {Keyword Spotting Using Human Electrocorticographic Recordings.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {60},
pmid = {30837823},
issn = {1662-4548},
support = {R01 NS088606/NS/NINDS NIH HHS/United States ; R01 NS091139/NS/NINDS NIH HHS/United States ; },
abstract = {Neural keyword spotting could form the basis of a speech brain-computer-interface for menu-navigation if it can be done with low latency and high specificity comparable to the "wake-word" functionality of modern voice-activated AI assistant technologies. This study investigated neural keyword spotting using motor representations of speech via invasively-recorded electrocorticographic signals as a proof-of-concept. Neural matched filters were created from monosyllabic consonant-vowel utterances: one keyword utterance, and 11 similar non-keyword utterances. These filters were used in an analog to the acoustic keyword spotting problem, applied for the first time to neural data. The filter templates were cross-correlated with the neural signal, capturing temporal dynamics of neural activation across cortical sites. Neural vocal activity detection (VAD) was used to identify utterance times and a discriminative classifier was used to determine if these utterances were the keyword or non-keyword speech. Model performance appeared to be highly related to electrode placement and spatial density. Vowel height (/a/ vs /i/) was poorly discriminated in recordings from sensorimotor cortex, but was highly discriminable using neural features from superior temporal gyrus during self-monitoring. The best performing neural keyword detection (5 keyword detections with two false-positives across 60 utterances) and neural VAD (100% sensitivity, ~1 false detection per 10 utterances) came from high-density (2 mm electrode diameter and 5 mm pitch) recordings from ventral sensorimotor cortex, suggesting the spatial fidelity and extent of high-density ECoG arrays may be sufficient for the purpose of speech brain-computer-interfaces.},
}
@article {pmid30837822,
year = {2019},
author = {Onishi, A and Nakagawa, S},
title = {How Does the Degree of Valence Influence Affective Auditory P300-Based BCIs?.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {45},
pmid = {30837822},
issn = {1662-4548},
abstract = {A brain-computer interface (BCI) translates brain signals into commands for the control of devices and for communication. BCIs enable persons with disabilities to communicate externally. Positive and negative affective sounds have been introduced to P300-based BCIs; however, how the degree of valence (e.g., very positive or positive) influences the BCI has not been investigated. To further examine the influence of affective sounds in P300-based BCIs, we applied sounds with five degrees of valence to the P300-based BCI. The sound valence ranged from very negative to very positive, as determined by Scheffe's method. The effect of sound valence on the BCI was evaluated by waveform analyses, followed by the evaluation of offline stimulus-wise classification accuracy. As a result, the late component of P300 showed significantly higher point-biserial correlation coefficients in response to very positive and very negative sounds than in response to the other sounds. The offline stimulus-wise classification accuracy was estimated from a region-of-interest. The analysis showed that the very negative sound achieved the highest accuracy and the very positive sound achieved the second highest accuracy, suggesting that the very positive sound and the very negative sound may be required to improve the accuracy.},
}
@article {pmid30835708,
year = {2019},
author = {Midha, R and Grochmal, J},
title = {Surgery for nerve injury: current and future perspectives.},
journal = {Journal of neurosurgery},
volume = {130},
number = {3},
pages = {675-685},
doi = {10.3171/2018.11.JNS181520},
pmid = {30835708},
issn = {1933-0693},
mesh = {Humans ; Neurosurgical Procedures ; Peripheral Nerve Injuries/*surgery ; Plastic Surgery Procedures ; },
abstract = {In this review article, the authors offer their perspective on nerve surgery for nerve injury, with a focus on recent evolution of management and the current surgical management. The authors provide a brief historical perspective to lay the foundations of the modern understanding of clinical nerve injury and its evolving management, especially over the last century. The shift from evaluation of the nerve injury using macroscopic techniques of exploration and external neurolysis to microscopic interrogation, interfascicular dissection, and internal neurolysis along with the use of intraoperative electrophysiology were important advances of the past 50 years. By the late 20th century, the advent and popularization of interfascicular nerve grafting techniques heralded a major advance in nerve reconstruction and allowed good outcomes to be achieved in a large percentage of nerve injury repair cases. In the past 2 decades, there has been a paradigm shift in surgical nerve repair, wherein surgeons are not only directing the repair at the injury zone, but also are deliberately performing distal-targeted nerve transfers as a preferred alternative in an attempt to restore function. The peripheral rewiring approach allows the surgeon to convert a very proximal injury with long regeneration distances and (often) uncertain outcomes to a distal injury and repair with a greater potential of regenerative success and functional recovery. Nerve transfers, originally performed as a salvage procedure for severe brachial plexus avulsion injuries, are now routinely done for various less severe brachial plexus injuries and many other proximal nerve injuries, with reliably good to even excellent results. The outcomes from nerve transfers for select clinical nerve injury are emphasized in this review. Extension of the rewiring paradigm with nerve transfers for CNS lesions such as spinal cord injury and stroke are showing great potential and promise. Cortical reeducation is required for success, and an emerging field of rehabilitation and restorative neurosciences is evident, which couples a nerve transfer procedure to robotically controlled limbs and mind-machine interfacing. The future for peripheral nerve repair has never been more exciting.},
}
@article {pmid30828715,
year = {2019},
author = {Ma, Y and Pan, C and Wang, Q},
title = {Crystal structure of bacterial cyclopropane-fatty-acyl-phospholipid synthase with phospholipid.},
journal = {Journal of biochemistry},
volume = {166},
number = {2},
pages = {139-147},
doi = {10.1093/jb/mvz018},
pmid = {30828715},
issn = {1756-2651},
mesh = {Amino Acid Sequence ; Binding Sites ; Crystallization ; Escherichia coli/cytology/metabolism ; Kinetics ; Lactobacillus acidophilus/*enzymology/metabolism ; Methyltransferases/chemistry/*metabolism ; Molecular Docking Simulation ; Molecular Structure ; Phospholipids/chemistry/*metabolism ; Sequence Alignment ; },
abstract = {The lipids containing cyclopropane-fatty-acid (CFA) protect bacteria from adverse conditions such as acidity, freeze-drying desiccation and exposure to pollutants. CFA is synthesized when cyclopropane-fatty-acyl-phospholipid synthase (CFA synthase, CFAS) transfers a methylene group from S-adenosylmethionine (SAM) across the cis double bonds of unsaturated fatty acyl chains. Here, we reported a 2.7-Å crystal structure of CFAS from Lactobacillus acidophilus. The enzyme is composed of N- and C-terminal domain, which belong to the sterol carrier protein and methyltransferase superfamily, respectively. A phospholipid in the substrate binding site and a bicarbonate ion (BCI) acting as a general base in the active site were discovered. To elucidate the mechanism, a docking experiment using CFAS from L. acidophilus and SAM was carried out. The analysis of this structure demonstrated that three groups, the carbons from the substrate, the BCI and the methyl of S(CHn)3 group, were close enough to form a cyclopropane ring with the help of amino acids in the active site. Therefore, the structure supports the hypothesis that CFAS from L. acidophilus catalyzes methyl transfer via a carbocation mechanism. These findings provide a structural basis to more deeply understand enzymatic cyclopropanation.},
}
@article {pmid30823716,
year = {2019},
author = {Minati, L and Yoshimura, N and Frasca, M and Drożdż, S and Koike, Y},
title = {Warped phase coherence: An empirical synchronization measure combining phase and amplitude information.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {29},
number = {2},
pages = {021102},
doi = {10.1063/1.5082749},
pmid = {30823716},
issn = {1089-7682},
abstract = {The entrainment between weakly coupled nonlinear oscillators, as well as between complex signals such as those representing physiological activity, is frequently assessed in terms of whether a stable relationship is detectable between the instantaneous phases extracted from the measured or simulated time-series via the analytic signal. Here, we demonstrate that adding a possibly complex constant value to this normally null-mean signal has a non-trivial warping effect. Among other consequences, this introduces a level of sensitivity to the amplitude fluctuations and average relative phase. By means of simulations of Rössler systems and experiments on single-transistor oscillator networks, it is shown that the resulting coherence measure may have an empirical value in improving the inference of the structural couplings from the dynamics. When tentatively applied to the electroencephalogram recorded while performing imaginary and real movements, this straightforward modification of the phase locking value substantially improved the classification accuracy. Hence, its possible practical relevance in brain-computer and brain-machine interfaces deserves consideration.},
}
@article {pmid30822756,
year = {2019},
author = {Dai, J and Zhang, P and Sun, H and Qiao, X and Zhao, Y and Ma, J and Li, S and Zhou, J and Wang, C},
title = {Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain-machine interface.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036011},
doi = {10.1088/1741-2552/ab0bfb},
pmid = {30822756},
issn = {1741-2552},
mesh = {Animals ; Brain-Computer Interfaces/*standards ; Electrodes, Implanted/*standards ; Macaca mulatta ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Reproducibility of Results ; Sensorimotor Cortex/*physiology ; },
abstract = {OBJECTIVE: For intracortical neurophysiological studies, spike sorting is an important procedure to isolate single units for analyzing specific functions. However, whether spike sorting is necessary or not for neural decoding applications is controversial. Several studies showed that using threshold crossings (TC) instead of spike sorting could also achieve a similar satisfactory performance. However, such studies were limited in similar behavioral tasks, and the neural signal source mainly focused on the motor-related cortical regions. It is not certain if this conclusion is applicable to other situations. Therefore, we compared the performance of TC and spike sorting in neural decoding with more comprehensive paradigms and parameters.<br><br>APPROACH: Two rhesus macaques implanted with Utah or floating microelectrode arrays (FMAs) in motor or sensory-related cortical regions were trained to perform a motor or a sensory task. Data from each monkey were preprocessed with three different schemes: TC, automatic sorting (AS), and manual sorting (MS). A support vector machine was used as the decoder, and the decoding accuracy was used for evaluating the performance of three preprocessing methods. Different neural signal sources, different decoders, and related parameters and decoding stability were further tested to systematically compare three preprocessing methods.<br><br>MAIN RESULTS: TC could achieve a similar (-4.5 RMS threshold) or better (-3.0 RMS threshold) decoding performance compared to the other two sorting methods in the motor or sensory tasks even if the neural signal sources or decoder-related parameters were changed. Moreover, TC was much more stable in neural decoding across sessions and robust to changes of threshold.<br><br>SIGNIFICANCE: Our results indicated that spike-firing patterns could be stably extracted through TC from multiple cortices in both motor and sensory neural decoding applications. Considering the stability of TC, it might be more suitable for neural decoding compared to sorting methods.},
}
@article {pmid30818297,
year = {2019},
author = {Khalaf, A and Sejdic, E and Akcakaya, M},
title = {EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036014},
doi = {10.1088/1741-2552/ab0b7f},
pmid = {30818297},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Decision Making/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Photic Stimulation/methods ; Ultrasonography, Doppler, Transcranial/*methods ; },
abstract = {OBJECTIVE: We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work.<br><br>APPROACH: To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences.<br><br>MAIN RESULTS: Average accuracy and average ITR of 98.11% and 21.29 bits min[-1] were achieved for WG versus MR classification while MR versus baseline yielded 86.27% average accuracy and 8.95 bit min[-1] average ITR. In addition, average accuracy of 85.29% and average ITR of 8.34 bits min[-1] were obtained for WG versus baseline.<br><br>SIGNIFICANCE: The proposed analysis techniques significantly improved the hybrid BCI performance. Specifically, for MR/WG versus baseline problems, we achieved twice of the ITRs obtained in our previous study. Moreover, the ITR of WG versus MR problem is 4-times the ITR we obtained before for the same problem. The current analysis methods boosted the performance of our EEG-fTCD BCI such that it outperformed the existing EEG-fNIRS BCIs in comparison.},
}
@article {pmid30818024,
year = {2019},
author = {Kopel, R and Sladky, R and Laub, P and Koush, Y and Robineau, F and Hutton, C and Weiskopf, N and Vuilleumier, P and Van De Ville, D and Scharnowski, F},
title = {No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI.},
journal = {NeuroImage},
volume = {191},
number = {},
pages = {421-429},
pmid = {30818024},
issn = {1095-9572},
support = {/WT_/Wellcome Trust/United Kingdom ; },
mesh = {*Algorithms ; *Artifacts ; Brain/*physiology ; Brain Mapping/methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; },
abstract = {As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLM[window]). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.},
}
@article {pmid30814923,
year = {2018},
author = {Milsap, G and Collard, M and Coogan, C and Crone, NE},
title = {BCI2000Web and WebFM: Browser-Based Tools for Brain Computer Interfaces and Functional Brain Mapping.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {1030},
pmid = {30814923},
issn = {1662-4548},
abstract = {BCI2000 has been a popular platform for development of real-time brain computer interfaces (BCIs). Since BCI2000's initial release, web browsers have evolved considerably, enabling rapid development of internet-enabled applications and interactive visualizations. Linking the amplifier abstraction and signal processing native to BCI2000 with the host of technologies and ease of development afforded by modern web browsers could enable a new generation of browser-based BCIs and visualizations. We developed a server and filter module called BCI2000Web providing an HTTP connection capable of escalation into an RFC6455 WebSocket, which enables direct communication between a browser and a BCI2000 distribution in real-time, facilitating a number of novel applications. We also present a JavaScript module, bci2k.js, that allows web developers to create paradigms and visualizations using this interface in an easy-to-use and intuitive manner. To illustrate the utility of BCI2000Web, we demonstrate a browser-based implementation of a real-time electrocorticographic (ECoG) functional mapping suite called WebFM. We also explore how the unique characteristics of our browser-based framework make BCI2000Web an attractive tool for future BCI applications. BCI2000Web leverages the advances of BCI2000 to provide real-time browser-based interactions with human neurophysiological recordings, allowing for web-based BCIs and other applications, including real-time functional brain mapping. Both BCI2000 and WebFM are provided under open source licenses. Enabling a powerful BCI suite to communicate with today's most technologically progressive software empowers a new cohort of developers to engage with BCI technology, and could serve as a platform for internet-enabled BCIs.},
}
@article {pmid30809117,
year = {2019},
author = {Drebitz, E and Schledde, B and Kreiter, AK and Wegener, D},
title = {Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity.},
journal = {Frontiers in neuroscience},
volume = {13},
number = {},
pages = {83},
pmid = {30809117},
issn = {1662-4548},
abstract = {Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of ESA-based RF-detection still hold true when using an iterative procedure for determining the optimal spike threshold for each MUA individually. Moreover, selectivity measures for ESA-based RFs were quite compatible with MUA-based RFs. Regarding RF size, ESA delivered larger RFs than thresholded MUA, but size difference was consistent over all SNR fractions. Regarding orientation selectivity, ESA delivered more sites with significant orientation-dependent responses but with somewhat lower orientation indexes than MUA. However, preferred orientations were similar for both signal types. The results suggest that ESA is a powerful signal for applications requiring automated, fast, and reliable response detection, as e.g., brain-computer interfaces and neuroprosthetics, due to its high sensitivity and its independence from user-dependent intervention. Because the full information of the spiking activity is preserved, ESA also constitutes a valuable alternative for offline analysis of data with limited SNR.},
}
@article {pmid30809003,
year = {2019},
author = {Souza, TML and Vieira, YR and Delatorre, E and Barbosa-Lima, G and Luiz, RLF and Vizzoni, A and Jain, K and Miranda, MM and Bhuva, N and Gogarten, JF and Ng, J and Thakkar, R and Calheiros, AS and Monteiro, APT and Bozza, PT and Bozza, FA and Tschoeke, DA and Leomil, L and Mendonça, MCL and Rodrigues, CDDS and Torres, MC and Filippis, AMB and Nogueira, RMR and Thompson, FL and Lemos, C and Durovni, B and Cerbino-Neto, J and Morel, CM and Lipkin, WI and Mishra, N},
title = {Emergence of the East-Central-South-African genotype of Chikungunya virus in Brazil and the city of Rio de Janeiro may have occurred years before surveillance detection.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {2760},
pmid = {30809003},
issn = {2045-2322},
support = {U19 AI109761/AI/NIAID NIH HHS/United States ; },
mesh = {Bayes Theorem ; Brazil/epidemiology ; Chikungunya Fever/*diagnosis/epidemiology/virology ; Chikungunya virus/classification/*genetics/isolation &amp; purification ; Genotype ; High-Throughput Nucleotide Sequencing ; Humans ; Phylogeny ; RNA, Viral/chemistry/metabolism ; Sequence Analysis, RNA ; },
abstract = {Brazil, which is hyperendemic for dengue virus (DENV), has had recent Zika (ZIKV) and (CHIKV) Chikungunya virus outbreaks. Since March 2016, CHIKV is the arbovirus infection most frequently diagnosed in Rio de Janeiro. In the analysis of 1835 syndromic patients, screened by real time RT-PCR, 56.4% of the cases were attributed to CHIKV, 29.6% to ZIKV, and 14.1% to DENV-4. Sequence analyses of CHIKV from sixteen samples revealed that the East-Central-South-African (ECSA) genotype of CHIKV has been circulating in Brazil since 2013 [95% bayesian credible interval (BCI): 03/2012-10/2013], almost a year before it was detected by arbovirus surveillance program. Brazilian cases are related to Central African Republic sequences from 1980's. To the best of our knowledge, given the available sequence published here and elsewhere, the ECSA genotype was likely introduced to Rio de Janeiro early on 2014 (02/2014; BCI: 07/2013-08/2014) through a single event, after primary circulation in the Bahia state at the Northestern Brazil in the previous year. The observation that the ECSA genotype of CHIKV was circulating undetected underscores the need for improvements in molecular methods for viral surveillance.},
}
@article {pmid30808014,
year = {2019},
author = {Craik, A and He, Y and Contreras-Vidal, JL},
title = {Deep learning for electroencephalogram (EEG) classification tasks: a review.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {031001},
doi = {10.1088/1741-2552/ab0ab5},
pmid = {30808014},
issn = {1741-2552},
mesh = {Animals ; Brain/*physiology ; Brain-Computer Interfaces/classification ; Deep Learning/*classification ; Electroencephalography/*classification ; Humans ; *Neural Networks, Computer ; Psychomotor Performance/physiology ; },
abstract = {OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks?<br><br>APPROACH: A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture.<br><br>MAIN RESULTS: For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review.<br><br>SIGNIFICANCE: This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.},
}
@article {pmid30806249,
year = {2018},
author = {Hermann, JK and Capadona, JR},
title = {Understanding the Role of Innate Immunity in the Response to Intracortical Microelectrodes.},
journal = {Critical reviews in biomedical engineering},
volume = {46},
number = {4},
pages = {341-367},
pmid = {30806249},
issn = {1943-619X},
support = {I01 RX001495/RX/RRD VA/United States ; R01 NS082404/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces/adverse effects ; Cytokines/immunology/physiology ; Drosophila ; Electrodes, Implanted/*adverse effects ; Encephalitis ; Foreign Bodies/*immunology ; Humans ; *Immunity, Innate/immunology/physiology ; Microelectrodes/*adverse effects ; *Neuroimmunomodulation/immunology/physiology ; },
abstract = {Intracortical microelectrodes exhibit enormous potential for researching the nervous system, steering assistive devices and functional electrode stimulation systems for severely paralyzed individuals, and augmenting the brain with computing power. Unfortunately, intracortical microelectrodes often fail to consistently record signals over clinically useful periods. Biological mechanisms, such as the foreign body response to intracortical microelectrodes and self-perpetuating neuroinflammatory cascades, contribute to the inconsistencies and decline in recording performance. Unfortunately, few studies have directly correlated microelectrode performance with the neuroinflammatory response to the implanted devices. However, of those select studies that have, the role of the innate immune system remains among the most likely links capable of corroborating the results of different studies, across laboratories. Therefore, the overall goal of this review is to highlight the role of innate immunity signaling in the foreign body response to intracortical microelectrodes and hypothesize as to appropriate strategies that may become the most relevant in enabling brain-dwelling electrodes of any geometry, or location, for a range of clinical applications.},
}
@article {pmid30805610,
year = {2019},
author = {Vasylyeva, TI and du Plessis, L and Pineda-Peña, AC and Kühnert, D and Lemey, P and Vandamme, AM and Gomes, P and Camacho, RJ and Pybus, OG and Abecasis, AB and Faria, NR},
title = {Tracing the Impact of Public Health Interventions on HIV-1 Transmission in Portugal Using Molecular Epidemiology.},
journal = {The Journal of infectious diseases},
volume = {220},
number = {2},
pages = {233-243},
pmid = {30805610},
issn = {1537-6613},
support = {/WT_/Wellcome Trust/United Kingdom ; 204311/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Bayes Theorem ; HIV Infections/*epidemiology/*transmission/virology ; HIV-1/*genetics ; Humans ; Molecular Epidemiology ; Phylogeny ; Portugal/epidemiology ; Public Health ; pol Gene Products, Human Immunodeficiency Virus/genetics ; },
abstract = {BACKGROUND: Estimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies.<br><br>METHODS: Portuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups.<br><br>RESULTS: We identified 5 subtype B Portuguese clades (26-79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998-2000) and 2000 (95% BCI, 1998-2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73-2.09) to 0.62 (95% BCI,.52-.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39-1.59) to 0.72 (95% BCI, .63-.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively.<br><br>CONCLUSIONS: The estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.},
}
@article {pmid30804988,
year = {2019},
author = {Guan, S and Zhao, K and Yang, S},
title = {Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry.},
journal = {Computational intelligence and neuroscience},
volume = {2019},
number = {},
pages = {5627156},
pmid = {30804988},
issn = {1687-5273},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Decision Trees ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.},
}
@article {pmid30804509,
year = {2019},
author = {Yang, X and Zhou, T and Zwang, TJ and Hong, G and Zhao, Y and Viveros, RD and Fu, TM and Gao, T and Lieber, CM},
title = {Bioinspired neuron-like electronics.},
journal = {Nature materials},
volume = {18},
number = {5},
pages = {510-517},
pmid = {30804509},
issn = {1476-4660},
support = {DP1 EB025835/EB/NIBIB NIH HHS/United States ; K99 AG056636/AG/NIA NIH HHS/United States ; R00 AG056636/AG/NIA NIH HHS/United States ; R21 DA043985/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Animals, Newborn ; Astrocytes/cytology ; Biocompatible Materials/*chemistry ; Biomimetics ; Brain/diagnostic imaging/growth &amp; development ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; *Electronics ; Electrophysiological Phenomena ; Green Fluorescent Proteins/metabolism ; Hippocampus/diagnostic imaging ; Humans ; Imaging, Three-Dimensional ; Inflammation ; Male ; Materials Testing ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Nanomedicine ; Neurites ; Neurons/*physiology ; Refractometry ; Research Design ; Stereotaxic Techniques ; Stress, Mechanical ; },
abstract = {As an important application of functional biomaterials, neural probes have contributed substantially to studying the brain. Bioinspired and biomimetic strategies have begun to be applied to the development of neural probes, although these and previous generations of probes have had structural and mechanical dissimilarities from their neuron targets that lead to neuronal loss, neuroinflammatory responses and measurement instabilities. Here, we present a bioinspired design for neural probes-neuron-like electronics (NeuE)-where the key building blocks mimic the subcellular structural features and mechanical properties of neurons. Full three-dimensional mapping of implanted NeuE-brain interfaces highlights the structural indistinguishability and intimate interpenetration of NeuE and neurons. Time-dependent histology and electrophysiology studies further reveal a structurally and functionally stable interface with the neuronal and glial networks shortly following implantation, thus opening opportunities for next-generation brain-machine interfaces. Finally, the NeuE subcellular structural features are shown to facilitate migration of endogenous neural progenitor cells, thus holding promise as an electrically active platform for transplantation-free regenerative medicine.},
}
@article {pmid30803072,
year = {2019},
author = {Wang, K and Frewin, CL and Esrafilzadeh, D and Yu, C and Wang, C and Pancrazio, JJ and Romero-Ortega, M and Jalili, R and Wallace, G},
title = {High-Performance Graphene-Fiber-Based Neural Recording Microelectrodes.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {31},
number = {15},
pages = {e1805867},
doi = {10.1002/adma.201805867},
pmid = {30803072},
issn = {1521-4095},
support = {//Australian National Fabrication Facility/ ; CE 140100012//Australian Research Council Centre of Excellence Scheme/ ; DE180100215//Australian Research Council Discovery Early Career Researcher Award/ ; },
abstract = {Fabrication of flexible and free-standing graphene-fiber- (GF-) based microelectrode arrays with a thin platinum coating, acting as a current collector, results in a structure with low impedance, high surface area, and excellent electrochemical properties. This modification results in a strong synergistic effect between these two constituents leading to a robust and superior hybrid material with better performance than either graphene electrodes or Pt electrodes. The low impedance and porous structure of the GF results in an unrivalled charge injection capacity of 10.34 mC cm[-2] with the ability to record and detect neuronal activity. Furthermore, the thin Pt layer transfers the collected signals along the microelectrode efficiently. In vivo studies show that microelectrodes implanted in the rat cerebral cortex can detect neuronal activity with remarkably high signal-to-noise ratio (SNR) of 9.2 dB in an area as small as an individual neuron.},
}
@article {pmid30802868,
year = {2019},
author = {Kassiri, H and Chen, F and Salam, MT and Chang, M and Vatankhahghadim, B and Carlen, P and Valiante, TA and Genov, R},
title = {Arbitrary-Waveform Electro-Optical Intracranial Neurostimulator With Load-Adaptive High-Voltage Compliance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {4},
pages = {582-593},
doi = {10.1109/TNSRE.2019.2900455},
pmid = {30802868},
issn = {1558-0210},
mesh = {Animals ; *Brain-Computer Interfaces ; Computer Systems ; Electric Impedance ; Electric Stimulation Therapy ; Electrocorticography ; Electrodes, Implanted ; Electronics ; Equipment Design ; *Implantable Neurostimulators ; Neurons/physiology ; Photic Stimulation ; Rats ; Rats, Wistar ; Wireless Technology ; },
abstract = {A hybrid 16-channel current-mode and the 8-channel optical implantable neurostimulating system is presented. The system generates arbitrary-waveform charge-balanced current-mode electrical pulses with an amplitude ranging from 50 [Formula: see text] to 10 mA. An impedance monitoring feedback loop is employed to automatically adjust the supply voltage, yielding a load-optimized power dissipation. The 8-channel optical stimulator drives an array of LEDs, each with a maximum of 25 mA current amplitude, and reuses the arbitrary-waveform generation function of the electrical stimulator. The LEDs are assembled within a custom-made 4×4 ECoG grid electrode array, enabling precise optical stimulation of neurons with a 300 [Formula: see text] pitch between the LEDs and simultaneous monitoring of the neural response by the ECoG electrode, at different distances of the stimulation site. The hybrid stimulation system is implemented on a mini-PCB, and receives power and stimulation commands inductively through a second board and a coil stacked on top of it. The entire system is sized at 3×2 . 5×1 cm[3] and weighs 7 grams. The system efficacy for electrical and optical stimulation is validated in-vivo using separate chronic and acute experiments.},
}
@article {pmid30802693,
year = {2019},
author = {Malan, NS and Sharma, S},
title = {Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.},
journal = {Computers in biology and medicine},
volume = {107},
number = {},
pages = {118-126},
doi = {10.1016/j.compbiomed.2019.02.009},
pmid = {30802693},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {In motor imagery (MI) based brain-computer interface (BCI) signal analysis, mu and beta rhythms of electroencephalograms (EEGs) are widely investigated due to their high temporal resolution and capability to define the different movement-related mental tasks separately. However, due to the high dimensions and subject-specific behaviour of EEG features, there is a need for a suitable feature selection algorithm that can select the optimal features to give the best classification performance along with increased computational efficiency. The present study proposes a feature selection algorithm based on neighbourhood component analysis (NCA) with modification of the regularization parameter. In the experiment, time, frequency, and phase features of the EEG are extracted using a dual-tree complex wavelet transform (DTCWT). Afterwards, the proposed algorithm selects the most significant EEG features, and using these selected features, a support vector machine (SVM) classifier performs the classification of MI signals. The proposed algorithm has been validated experimentally on two public BCI datasets (BCI Competition II Dataset III and BCI Competition IV Dataset 2b). The classification performance of the algorithm is quantified by the average accuracy and kappa coefficient, whose values are 80.7% and 0.615 respectively. The performance of the proposed algorithm is compared with standard feature selection methods based on Genetic Algorithm (GA), Principal Component Analysis (PCA), and ReliefF and performs better than these methods. Further, the proposed algorithm selects the lowest number of features and results in increased computational efficiency, which makes it a promising feature selection tool for an MI-based BCI system.},
}
@article {pmid30797109,
year = {2019},
author = {Wang, MH and Gu, XW and Ji, BW and Wang, LC and Guo, ZJ and Yang, B and Wang, XL and Li, CY and Liu, JQ},
title = {Three-dimensional drivable optrode array for high-resolution neural stimulations and recordings in multiple brain regions.},
journal = {Biosensors &amp; bioelectronics},
volume = {131},
number = {},
pages = {9-16},
doi = {10.1016/j.bios.2019.01.019},
pmid = {30797109},
issn = {1873-4235},
mesh = {Action Potentials/*physiology ; Animals ; *Biosensing Techniques ; Brain/pathology ; Brain-Computer Interfaces ; Humans ; Lasers, Semiconductor ; Mediodorsal Thalamic Nucleus/physiology ; Mice ; Motor Cortex/physiology ; Nervous System Diseases/*diagnosis ; Neurons/pathology/*physiology ; },
abstract = {The brain-computer interface (BCI) devices are of prime important for study of nervous system as well as diagnosis and treatment of neurological disorders. To meet the needs of the BCI devices in high-density integration and multi-functionalization, 3-dimensional (3D) drivable optrode array with laser diodes (LDs) coupled waveguides was developed. The unique device realizes the 3D integration of the optrodes and avoids fiber tangle and tissue heating by adopting LD coupled waveguide structure. Besides, the postoperative position adjustment of the optrode array was achieved by integrating with a 3D printed micro-drive. Most importantly, high-resolution neural stimulations and recordings were achieved for study of working memory related neural circuits in four brain regions of mice including prelimbic cortex (PrL), mediodorsal thalamic nucleus (MD), dorsal medial caudate nucleus (dmCP) and posterior motor cortex 2 (pM2). The results indicate that this novel device is promising for the research of complex neural networks.},
}
@article {pmid30796470,
year = {2019},
author = {Kramer, DR and Barbaro, MF and Lee, M and Peng, T and Nune, G and Liu, CY and Kellis, S and Lee, B},
title = {Electrocorticographic changes in field potentials following natural somatosensory percepts in humans.},
journal = {Experimental brain research},
volume = {237},
number = {5},
pages = {1155-1167},
pmid = {30796470},
issn = {1432-1106},
support = {N/A//Cal-Brain: a Neurotechnology Program for California/ ; R25 NS099008/NS/NINDS NIH HHS/United States ; NS099008-01/NS/NINDS NIH HHS/United States ; KL2TR001854/TR/NCATS NIH HHS/United States ; KL2 TR001854/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Brain Waves/*physiology ; Electric Stimulation ; Electrocorticography/*methods ; Electrodes, Implanted ; Epilepsy/physiopathology ; Female ; Hand/*physiology ; Humans ; Male ; Middle Aged ; Somatosensory Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Restoration of somatosensory deficits in humans requires a clear understanding of the neural representations of percepts. To characterize the cortical response to naturalistic somatosensation, we examined field potentials in the primary somatosensory cortex of humans.<br><br>METHODS: Four patients with intractable epilepsy were implanted with subdural electrocorticography (ECoG) electrodes over the hand area of S1. Three types of stimuli were applied, soft-repetitive touch, light touch, and deep touch. Power in the alpha (8-15&#xa0;Hz), beta (15-30&#xa0;Hz), low-gamma (30-50&#xa0;Hz), and high-gamma (50-125&#xa0;Hz) frequency bands were evaluated for significance.<br><br>RESULTS: Seventy-seven percent of electrodes over the hand area of somatosensory cortex exhibited changes in these bands. High-gamma band power increased for all stimuli, with concurrent alpha and beta band power decreases. Earlier activity was seen in these bands in deep touch and light touch compared to soft touch.<br><br>CONCLUSIONS: These findings are consistent with prior literature and suggest a widespread response to focal touch, and a different encoding of deeper pressure touch than soft touch.},
}
@article {pmid30794541,
year = {2019},
author = {Even-Chen, N and Sheffer, B and Vyas, S and Ryu, SI and Shenoy, KV},
title = {Structure and variability of delay activity in premotor cortex.},
journal = {PLoS computational biology},
volume = {15},
number = {2},
pages = {e1006808},
pmid = {30794541},
issn = {1553-7358},
support = {R01 NS076460/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; Electrodes, Implanted ; Electromyography ; Macaca mulatta/physiology ; Motor Cortex/metabolism/*physiology ; Movement ; Psychomotor Performance/*physiology ; Reaction Time/*physiology ; },
abstract = {Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an 'initial condition' which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs.},
}
@article {pmid30794504,
year = {2019},
author = {Zhang, N and Zhou, Z and Liu, Y and Yin, E and Jiang, J and Hu, D},
title = {A Novel Single-Character Visual BCI Paradigm With Multiple Active Cognitive Tasks.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {11},
pages = {3119-3128},
doi = {10.1109/TBME.2019.2900555},
pmid = {30794504},
issn = {1558-2531},
mesh = {Adult ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Models, Neurological ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: To introduce a novel event-related potential (ERP)-based brain-computer interface (BCI) paradigm with active mental tasks multiplying precise judgment and visual cognitive capacities and evaluate its performance.<br><br>METHODS: This study employed a paradigm with three types of targets (true-, pseudo-, and non-), double flash codes, colors and color terms, and four test conditions. The primary hypothesis was that active mental tasks combining multiple cognitive capacities and clear judgment for different categories of stimuli increase the BCI performance and evoke stronger or specific ERPs. Classification methods were proposed and evaluated, and two were used in online experiments.<br><br>RESULTS: The modes containing active mental tasks provided higher accuracy than the control mode (by up to 19.06%). The color-word matching mode had the highest judgment level and achieved the best performance. True-stimuli evoked strong P3b, while pseudotarget signals provided obvious N4, but the control mode seemed less sensitive to both of them. Different types of stimuli evoked distinctive N2 and P3a components.<br><br>CONCLUSION: An appropriate boost in the judgment level using multiple stimuli and cognitive approaches could be investigated to improve the BCI performance and evoke or enhance ERPs. Utilizing active mental tasks may be a promising way to promote BCIs.<br><br>SIGNIFICANCE: Active mental tasks combining multiple cognitive capacities and precise judgments were adopted in an ERP-based BCI. Color and color words were introduced as stimuli to construct an alternative paradigm, and the judgment levels of different conditions were calculated. High accuracies and the participants' preferences were obtained, which may promote the effective use of BCIs.},
}
@article {pmid30794165,
year = {2019},
author = {Aliakbaryhosseinabadi, S and Kamavuako, EN and Jiang, N and Farina, D and Mrachacz-Kersting, N},
title = {Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {11},
pages = {3060-3071},
doi = {10.1109/TBME.2019.2900206},
pmid = {30794165},
issn = {1558-2531},
mesh = {Adult ; Attention/*physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*classification/methods ; Female ; Humans ; *Intention ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors.<br><br>METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features.<br><br>RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 &#xb1; 2.7%) was greater than using temporal (69 &#xb1; 2.2%) and spectral (80.3 &#xb1; 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 &#xb1; 1.3%, central and parietal channels: 87.2 &#xb1; 1.5%).<br><br>CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors.<br><br>SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.},
}
@article {pmid30791814,
year = {2019},
author = {Classen, S and Jeghers, M and Morgan-Daniel, J and Winter, S and King, L and Struckmeyer, L},
title = {Smart In-Vehicle Technologies and Older Drivers: A Scoping Review.},
journal = {OTJR : occupation, participation and health},
volume = {39},
number = {2},
pages = {97-107},
doi = {10.1177/1539449219830376},
pmid = {30791814},
issn = {1938-2383},
mesh = {Accidents, Traffic/*prevention &amp; control ; Aged ; *Aging ; *Automobile Driving ; *Automobiles ; *Brain-Computer Interfaces ; Humans ; },
abstract = {In-vehicle technologies may decrease crash risk in drivers with age-related declines. Researchers determined the impact of in-vehicle information systems (IVIS) or advanced driving assistance systems (ADAS) on driving. Through a scoping review, the effect of IVIS or ADAS on older drivers' convenience (i.e., meets one's needs), comfort (i.e., physical or psychological ease), or safety (i.e., absence of errors or crashes) was examined. Researchers synopsized findings from 28 studies, including driving simulators and on-road environments. Findings indicated that IVIS or ADAS enhanced safety and mitigated age-related declines. Notably, IVIS may reduce cognitive workload, but may jeopardize safety if the systems are overly complicated. The ADAS enhanced safety and comfort by increasing speed control, lane maintenance, and braking responses. However, no studies addressed convenience. In-vehicle technologies may enhance safety and comfort while driving, if one's cognitive workload is not compromised. Naturalistic studies are needed to elucidate the risks and benefits of IVIS and ADAS for older drivers.},
}
@article {pmid30788215,
year = {2019},
author = {Collins, KL and Sarma, D and Hakimian, S and Tsai, JJ and Ojemann, JG},
title = {Preserved evoked conscious perception of phosphenes with direct stimulation of deafferented primary visual cortex.},
journal = {Epilepsy &amp; behavior case reports},
volume = {11},
number = {},
pages = {84-86},
pmid = {30788215},
issn = {2213-3232},
support = {R01 NS065186/NS/NINDS NIH HHS/United States ; R25 NS079200/NS/NINDS NIH HHS/United States ; },
abstract = {The premise of neuro-rehabilitation after injury is to access the residual capacity of the nervous system to improve function. We describe a patient who developed a quadrantopsia and drug-resistant focal epilepsy after an arteriovenous malformation hemorrhage. Thirty years later, he underwent placement of subdural electrodes for seizure mapping. Phosphenes were elicited in the blind right visual field with stimulation of occipital cortex. This case demonstrates that visual cortex may retain functional organization after a partial subcortical visual pathway injury. This persistent conscious mapping suggests that disconnected visual cortex could serve as a region for interfacing with neural prosthetic devices for acquired blindness.},
}
@article {pmid30786265,
year = {2019},
author = {Delisle-Rodriguez, D and Cardoso, V and Gurve, D and Loterio, F and Alejandra Romero-Laiseca, M and Krishnan, S and Bastos-Filho, T},
title = {System based on subject-specific bands to recognize pedaling motor imagery: towards a BCI for lower-limb rehabilitation.},
journal = {Journal of neural engineering},
volume = {16},
number = {5},
pages = {056005},
doi = {10.1088/1741-2552/ab08c8},
pmid = {30786265},
issn = {1741-2552},
mesh = {Adult ; Bicycling/*physiology ; *Brain-Computer Interfaces ; Female ; Fourier Analysis ; Humans ; Imagination/*physiology ; Lower Extremity/*physiology ; Male ; Stroke Rehabilitation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns.<br><br>APPROACH: After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability.<br><br>MAIN RESULTS: For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of [Formula: see text] and mean Kappa of [Formula: see text].<br><br>SIGNIFICANCE: Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors.},
}
@article {pmid30785928,
year = {2019},
author = {Ventura, M and Belleudi, V and Sciattella, P and Di Domenicantonio, R and Di Martino, M and Agabiti, N and Davoli, M and Fusco, D},
title = {High quality process of care increases one-year survival after acute myocardial infarction (AMI): A cohort study in Italy.},
journal = {PloS one},
volume = {14},
number = {2},
pages = {e0212398},
pmid = {30785928},
issn = {1932-6203},
mesh = {Adrenergic beta-Antagonists/*therapeutic use ; Aged ; Aged, 80 and over ; Angiotensin-Converting Enzyme Inhibitors/*therapeutic use ; Female ; Guideline Adherence/*statistics &amp; numerical data ; Hospitalization/statistics &amp; numerical data ; Humans ; Male ; Myocardial Infarction/*mortality/therapy ; Patient Discharge/statistics &amp; numerical data ; Percutaneous Coronary Intervention/*mortality ; Prognosis ; *Quality of Health Care ; Retrospective Studies ; Survival Rate ; Travel ; },
abstract = {BACKGROUND: The relationship between guideline adherence and outcomes in patients with acute myocardial infarction (AMI) has been widely investigated considering the emergency, acute, post-acute phases separately, but the effectiveness of the whole care process is not known.<br><br>AIM: The study aim was to evaluate the effect of the multicomponent continuum of care on 1-year survival after AMI.<br><br>METHODS: We conducted a cohort study selecting all incident cases of AMI from health information systems during 2011-2014 in the Lazio region. Patients' clinical history was defined by retrieving previous hospitalizations and drugs prescriptions. For each subject the probability to reach the hospital and the conditional probabilities to survive to 30 days from admission and to 31-365 days post discharge were estimated through multivariate logistic models. The 1-year survival probability was calculated as the product of the three probabilities. Quality of care indicators were identified in terms of emergency timeliness (time between residence and the nearest hospital), hospital performance in treatment of acute phase (number/timeliness of PCI on STEMI) and drug therapy in post-acute phase (number of drugs among antiplatelet, &#x3b2;-blockers, ACE inhibitors/ARBs, statins). The 1-year survival Probability Ratio (PR) and its Bootstrap Confidence Intervals (BCI) between who were exposed to the highest level of quality of care (timeliness<10', hospitalization in high performance hospital, complete drug therapy) and who exposed to the worst (timeliness&#x2265;10', hospitalization in low performance hospital, suboptimal drug therapy) were calculated for a mean-severity patient and varying gender and age. PRs for patients with diabetes and COPD were also evaluated.<br><br>RESULTS: We identified 38,517 incident cases of AMI. The out-of-hospital mortality was 27.6%. Among the people arrived in hospital, 42.9% had a hospitalization for STEMI with 11.1% of mortality in acute phase and 5.4% in post-acute phase. For a mean-severity patient the PR was 1.19 (BCI 1.14-1.24). The ratio did not change by gender, while it moved from 1.06 (BCI 1.05-1.08) for age<65 years to 1.62 (BCI 1.45-1.80) for age >85 years. For patients with diabetes and COPD a slight increase in PRs was also observed.<br><br>CONCLUSIONS: The 1-year survival probability post AMI depends strongly on the quality of the whole multicomponent continuum of care. Improving the performance in the different phases, taking into account the relationship among these, can lead to considerable saving of lives, in particular for the elderly and for subjects with chronic diseases.},
}
@article {pmid30785814,
year = {2019},
author = {Milekovic, T and Bacher, D and Sarma, AA and Simeral, JD and Saab, J and Pandarinath, C and Yvert, B and Sorice, BL and Blabe, C and Oakley, EM and Tringale, KR and Eskandar, E and Cash, SS and Shenoy, KV and Henderson, JM and Hochberg, LR and Donoghue, JP},
title = {Volitional control of single-electrode high gamma local field potentials by people with paralysis.},
journal = {Journal of neurophysiology},
volume = {121},
number = {4},
pages = {1428-1450},
pmid = {30785814},
issn = {1522-1598},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electrodes, Implanted/adverse effects/standards ; Feedback, Physiological ; *Gamma Rhythm ; Humans ; Motor Cortex/*physiopathology ; Movement ; Quadriplegia/*physiopathology/rehabilitation ; },
abstract = {Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW &amp; NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.},
}
@article {pmid30781869,
year = {2019},
author = {Gargiulo, GD and Bifulco, P and Cesarelli, M and McEwan, A and Nikpour, A and Jin, C and Gunawardana, U and Sreenivasan, N and Wabnitz, A and Hamilton, TJ},
title = {Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {4},
pages = {},
pmid = {30781869},
issn = {1424-8220},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Humans ; User-Computer Interface ; },
abstract = {The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.},
}
@article {pmid30778314,
year = {2019},
author = {Salillas, E and Korostenskaja, M and Kleineschay, T and Mehta, S and Vega, A and Castillo, EM},
title = {A MEG Study on the Processing of Time and Quantity: Parietal Overlap but Functional Divergence.},
journal = {Frontiers in psychology},
volume = {10},
number = {},
pages = {139},
pmid = {30778314},
issn = {1664-1078},
abstract = {A common magnitude system for the processing of time and numerosity, supported by areas in the posterior parietal cortex, has been proposed by some authors. The present study aims to investigate possible intersections between the neural processing of non-numerical (time) and numerical magnitudes in the posterior parietal lobe. Using Magnetoencephalography for the comparison of brain source activations during the processing of duration and numerosity contrasts, we demonstrate parietal overlap as well as dissociations between these two dimensions. Within the parietal cortex, the main areas of overlap were bilateral precuneus, bilateral intraparietal sulci, and right supramarginal gyrus. Interestingly, however, these regions did not equivalently correlated with the behavior for the two dimensions: left and right precuneus together with the right supramarginal gyrus accounted functionally for durational judgments, whereas numerosity judgments were accounted by the activation pattern in the right intraparietal sulcus. Present results, indeed, demonstrate an overlap between the neural substrates for processing duration and quantity. However, the functional relevance of parietal overlapping areas for each dimension is not the same. In fact, our data indicates that the same parietal sites rule differently non-numerical and numerical dimensions, as parts of broader networks.},
}
@article {pmid30778293,
year = {2019},
author = {Zhang, J and Jadavji, Z and Zewdie, E and Kirton, A},
title = {Evaluating If Children Can Use Simple Brain Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {24},
pmid = {30778293},
issn = {1662-5161},
abstract = {Background: The options for severely disabled children with intact cognition to interact with their environment are extremely limited. A brain computer interface (BCI) has the potential to allow such persons to gain meaningful function, communication, and independence. While the pediatric population might benefit most from BCI technology, research to date has been predominantly in adults. Methods: In this prospective, cross-over study, we quantified the ability of healthy school-aged children to perform simple tasks using a basic, commercially available, EEG-based BCI. Typically developing children aged 6-18 years were recruited from the community. BCI training consisted of a brief set-up and EEG recording while performing specific tasks using an inexpensive, commercially available BCI system (EMOTIV EPOC). Two tasks were trained (driving a remote-control car and moving a computer cursor) each using two strategies (sensorimotor and visual imagery). Primary outcome was the kappa coefficient between requested and achieved performance. Effects of task, strategy, age, and learning were also explored. Results: Twenty-six of thirty children completed the study (mean age 13.2 ± 3.6 years, 27% female). Tolerability was excellent with >90% reporting the experience as neutral or pleasant. Older children achieved performance comparable to adult studies, but younger age was associated with lesser though still good performance. The car task demonstrated higher performance compared to the cursor task (p = 0.027). Thought strategy was also associated with performance with visual imagery strategies outperforming sensorimotor approaches (p = 0.031). Conclusion: Children can quickly achieve control and execute multiple tasks using simple EEG-based BCI systems. Performance depends on strategy, task and age. Such success in the developing brain mandates exploration of such practical systems in severely disabled children.},
}
@article {pmid30768447,
year = {2020},
author = {Jovanovic, LI and Kapadia, N and Lo, L and Zivanovic, V and Popovic, MR and Marquez-Chin, C},
title = {Restoration of Upper Limb Function After Chronic Severe Hemiplegia: A Case Report on the Feasibility of a Brain-Computer Interface-Triggered Functional Electrical Stimulation Therapy.},
journal = {American journal of physical medicine &amp; rehabilitation},
volume = {99},
number = {3},
pages = {e35-e40},
doi = {10.1097/PHM.0000000000001163},
pmid = {30768447},
issn = {1537-7385},
mesh = {*Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Feasibility Studies ; Hemiplegia/*physiopathology/*rehabilitation ; Humans ; Male ; Middle Aged ; Recovery of Function ; Stroke Rehabilitation/*methods ; Upper Extremity/*physiopathology ; },
abstract = {Functional electrical stimulation therapy (FEST) is a state-of-the-art treatment for retraining motor function after neurological injuries. Recent literature suggests that FEST can be further improved with brain-computer interface (BCI) technology. In this case study, we assessed the feasibility of using BCI-triggered FEST (BCI-FEST) to restore upper limb function in a 57-yr-old man with severe left hemiplegia resulting from a stroke 6 yrs before enrollment in the study. The intervention consisted of two blocks of forty 1-hr BCI-FEST sessions, with three sessions delivered weekly. During therapy, a single-channel BCI was used to trigger the stimulation programmed to facilitate functional movements. The measure of the feasibility of the BCI-FEST included assessing the implementation and safety of the intervention. Clinical improvements were assessed using (a) Functional Independence Measure, (b) Action Research Arm Test, (c) Toronto Rehabilitation Institute - Hand Function Test, and (d) Fugl-Meyer Assessment Upper Extremity test. Upon completion of 80 therapy sessions, 14-, 17-, and 18-point changes were recorded on Action Research Arm Test, Fugl-Meyer Assessment Upper Extremity test, and Toronto Rehabilitation Institute - Hand Function Test, respectively. The participant also indicated improvement as demonstrated by his ability to perform various day-to-day tasks. The results suggest that BCI-FEST is safe and viable.},
}
@article {pmid30766510,
year = {2018},
author = {Keyl, P and Schneiders, M and Schuld, C and Franz, S and Hommelsen, M and Weidner, N and Rupp, R},
title = {Differences in Characteristics of Error-Related Potentials Between Individuals With Spinal Cord Injury and Age- and Sex-Matched Able-Bodied Controls.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {1192},
pmid = {30766510},
issn = {1664-2295},
abstract = {Background: Non-invasive brain-computer interfaces (BCI) represent an emerging technology for enabling persons with impaired or lost grasping and reaching functions due to high spinal cord injury (SCI) to control assistive devices. A major drawback of BCIs is a high rate of false classifications. The robustness and performance of BCIs might be improved using cerebral electrophysiological correlates of error recognition (error-related potentials, ErrPs). As ErrPs have never been systematically examined in subjects with SCI, this study compares the characteristics of ErrPs in individuals with SCI with those of able-bodied control subjects. Methods: ErrPs at FCz and Cz were analyzed in 11 subjects with SCI (9 male, median age 28 y) and in 11 sex- and age-matched controls. Moving a shoulder joystick according to a visual cue, subjects received feedback about the match/mismatch of the performed movement. ErrPs occurring after "error"-feedback were evaluated by comparing means of voltage values within three consecutive time windows after feedback (wP1, wN1, wP2 containing peak voltages P1, N1, P2) using repeated-measurement analysis of variance. Results: In the control group, mean voltage values for the "error" and "correct" feedback condition differed significantly around N1 (FCz: 254 ms, Cz: 252 ms) and P2 (FCz: 347 ms, Cz: 345 ms), but not around P1 (FCz: 181 ms, Cz: 179 ms). ErrPs of the control and the SCI group showed similar morphology, however mean amplitudes of ErrPs were significantly smaller in individuals with SCI compared to controls for wN1 (FCz: control = -1.55 μV, SCI = -0.27 μV, p = 0.02; Cz: control = -1.03 μV, SCI = 0.11 μV, p = 0.04) and wP2 (FCz: control = 2.79 μV, SCI = 1.29 μV, p = 0.011; Cz: control = 2.12 μV, SCI = 0.81 μV, p = 0.003). Mean voltage values in wP1, wN1, and wP2 did not correlate significantly with either chronicity after or level of injury. Conclusion: The morphology of ErrPs in subjects with and without SCI is comparable, however, with reduced mean amplitude in wN1 and wP2 in the SCI group. Further studies should evaluate whether ErrP-classification can be used for online correction of false BCI-commands in individuals with SCI.},
}
@article {pmid30766483,
year = {2019},
author = {Cinel, C and Valeriani, D and Poli, R},
title = {Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects.},
journal = {Frontiers in human neuroscience},
volume = {13},
number = {},
pages = {13},
pmid = {30766483},
issn = {1662-5161},
abstract = {Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.},
}
@article {pmid32232094,
year = {2019},
author = {Bullard, AJ and Nason, SR and Irwin, ZT and Nu, CS and Smith, B and Campean, A and Peckham, PH and Kilgore, KL and Willsey, MS and Patil, PG and Chestek, CA},
title = {Design and testing of a 96-channel neural interface module for the Networked Neuroprosthesis system.},
journal = {Bioelectronic medicine},
volume = {5},
number = {},
pages = {3},
pmid = {32232094},
issn = {2332-8886},
support = {T32 NS007222/NS/NINDS NIH HHS/United States ; },
abstract = {BACKGROUND: The loss of motor functions resulting from spinal cord injury can have devastating implications on the quality of one's life. Functional electrical stimulation has been used to help restore mobility, however, current functional electrical stimulation (FES) systems require residual movements to control stimulation patterns, which may be unintuitive and not useful for individuals with higher level cervical injuries. Brain machine interfaces (BMI) offer a promising approach for controlling such systems; however, they currently still require transcutaneous leads connecting indwelling electrodes to external recording devices. While several wireless BMI systems have been designed, high signal bandwidth requirements limit clinical translation. Case Western Reserve University has developed an implantable, modular FES system, the Networked Neuroprosthesis (NNP), to perform combinations of myoelectric recording and neural stimulation for controlling motor functions. However, currently the existing module capabilities are not sufficient for intracortical recordings.<br><br>METHODS: Here we designed and tested a 1&#x2009;&#xd7;&#x2009;4 cm, 96-channel neural recording module prototype to fit within the specifications to mate with the NNP. The neural recording module extracts power between 0.3-1&#x2009;kHz, instead of transmitting the raw, high bandwidth neural data to decrease power requirements.<br><br>RESULTS: The module consumed 33.6&#x2009;mW while sampling 96 channels at approximately 2 kSps. We also investigated the relationship between average spiking band power and neural spike rate, which produced a maximum correlation of R&#x2009;=&#x2009;0.8656 (Monkey N) and R&#x2009;=&#x2009;0.8023 (Monkey W).<br><br>CONCLUSION: Our experimental results show that we can record and transmit 96 channels at 2ksps within the power restrictions of the NNP system and successfully communicate over the NNP network. We believe this device can be used as an extension to the NNP to produce a clinically viable, fully implantable, intracortically-controlled FES system and advance the field of bioelectronic medicine.},
}
@article {pmid30762562,
year = {2019},
author = {Catrambone, V and Greco, A and Averta, G and Bianchi, M and Valenza, G and Scilingo, EP},
title = {Predicting Object-Mediated Gestures From Brain Activity: An EEG Study on Gender Differences.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {411-418},
doi = {10.1109/TNSRE.2019.2898469},
pmid = {30762562},
issn = {1558-0210},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Brain-Computer Interfaces ; *Electroencephalography ; Electromyography ; Female ; *Gestures ; Healthy Volunteers ; Humans ; Magnetic Resonance Imaging ; Male ; Movement ; Parietal Lobe/physiology ; Prefrontal Cortex/physiology ; Reproducibility of Results ; Sex Characteristics ; Upper Extremity ; Young Adult ; },
abstract = {Recent functional magnetic resonance imaging (fMRI) studies have identified specific neural patterns related to three different categories of movements: intransitive (i.e., meaningful gestures that do not include the use of objects), transitive (i.e., actions involving an object), and tool-mediated (i.e., actions involving a tool to interact with an object). However, fMRI intrinsically limits the exploitation of these results in a real scenario, such as a brain-machine interface. In this paper, we propose a new approach to automatically predict intransitive, transitive, or tool-mediated movements of the upper limb using electroencephalography (EEG) spectra estimated during a motor planning phase. To this end, high-resolution EEG data gathered from 33 healthy subjects were used as input of a three-class k-nearest neighbors classifier. Different combinations of EEG-derived spatial and frequency information were investigated to find the most accurate feature vector. In addition, we studied gender differences further splitting the dataset into only-male data, and only-female data. A remarkable difference was found between accuracies achieved with male and female data, the latter yielding the best performance (78.55% of accuracy for the prediction of intransitive, transitive, and tool-mediated actions). These results potentially suggest that different gender-based models should be employed for the future BMI applications.},
}
@article {pmid30761896,
year = {2019},
author = {Tsai, YJ and Wang, CM and Chang, TS and Sutradhar, S and Chang, CW and Chen, CY and Hsieh, CH and Liao, WS},
title = {Multilayered Ag NP-PEDOT-Paper Composite Device for Human-Machine Interfacing.},
journal = {ACS applied materials &amp; interfaces},
volume = {11},
number = {10},
pages = {10380-10388},
doi = {10.1021/acsami.8b21390},
pmid = {30761896},
issn = {1944-8252},
mesh = {*Biosensing Techniques ; Brain-Computer Interfaces ; Bridged Bicyclo Compounds, Heterocyclic/chemistry ; Electric Conductivity ; Humans ; Metal Nanoparticles/*chemistry ; Nanotubes, Carbon/chemistry ; Polymers/chemistry ; *Pressure ; Silver/chemistry ; Wearable Electronic Devices ; },
abstract = {Flexible pressure sensors have attracted increasing interest because of their potential applications on wearable sensing devices for human-machine interface connections, but challenges regarding material cost, fabrication robustness, signal transduction, sensitivity improvement, detection range, and operation convenience still need to be overcome. Herein, with a simple, low-cost, and scalable approach, a flexible and wearable pressure-sensing device fabricated by utilizing filter paper as the solid support, poly(3,4-ethylenedioxythiophene) to enhance conductivity, and silver nanoparticles to provide a rougher surface is introduced. Sandwiching and laminating composite material layers with two thermoplastic polypropylene films lead to robust integration of sensing devices, where assembling four layers of composite materials results in the best sensitivity toward applied pressure. This practical pressure-sensing device possessing properties such as high sensitivity of 0.119 kPa[-1], high durability of 2000 operation cycles, and an ultralow energy consumption level of 10[-5] W is a promising candidate for contriving point-of-care wearable electronic devices and applying it to human-machine interface connections.},
}
@article {pmid30760971,
year = {2018},
author = {Wen, Z and Yu, T and Yang, X and Li, Y},
title = {Goal-Directed Processing of Naturalistic Stimuli Modulates Large-Scale Functional Connectivity.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {1003},
pmid = {30760971},
issn = {1662-4548},
abstract = {Humans selectively process external information according to their internal goals. Previous studies have found that cortical activity and interactions between specific cortical areas such as frontal-parietal regions are modulated by behavioral goals. However, these results are largely based on simple stimuli and task rules in laboratory settings. Here, we investigated how top-down goals modulate whole-brain functional connectivity (FC) under naturalistic conditions. Analyses were conducted on a publicly available functional magnetic resonance imaging (fMRI) dataset (OpenfMRI database, accession number: ds000233) collected on twelve participants who made either behavioral or taxonomic judgments of behaving animals containing in naturalistic video clips. The task-evoked FC patterns of the participants were extracted using a novel inter-subject functional correlation (ISFC) method that increases the signal-to-noise ratio for detecting task-induced inter-regional correlation compared with standard FC analysis. Using multivariate pattern analysis (MVPA) methods, we successfully predicted the task goals of the participants with ISFC patterns but not with standard FC patterns, suggests that the ISFC method may be an efficient tool for exploring subtle network differences between brain states. We further examined the predictive power of several canonical brain networks and found that many within-network and across-network ISFC measures supported task goals classification. Our findings suggest that goal-directed processing of naturalistic stimuli systematically modulates large-scale brain networks but is not limited to the local neural activity or connectivity of specific regions.},
}
@article {pmid30755283,
year = {2019},
author = {Bogart, LM and Castro, G and Cohen, DA},
title = {A qualitative exploration of parents', youths' and food establishment managers' perceptions of beverage industry self-regulation for obesity prevention.},
journal = {Public health nutrition},
volume = {22},
number = {5},
pages = {805-813},
pmid = {30755283},
issn = {1475-2727},
mesh = {Adolescent ; Adolescent Behavior ; Adult ; *Beverages ; Child ; Child Behavior ; Cross-Sectional Studies ; Dietary Sucrose/*administration &amp; dosage/adverse effects ; Energy Intake ; Feeding Behavior ; Female ; *Food Industry ; *Health Knowledge, Attitudes, Practice ; Humans ; Los Angeles ; Male ; *Marketing ; Middle Aged ; Mississippi ; Obesity/etiology/*prevention &amp; control ; Parents ; *Stakeholder Participation ; Sweetening Agents/administration &amp; dosage/adverse effects ; Young Adult ; },
abstract = {OBJECTIVE: We aimed to explore the range of stakeholders' perceptions of the Balance Calories Initiative (BCI), under which the American Beverage Association pledged to decrease per capita US consumption of beverage energy by 20 % by 2025.<br><br>DESIGN: Semi-structured cross-sectional interviews were conducted in 2017.<br><br>SETTING: Participants were recruited from communities targeted by the BCI (Montgomery, AL; North Mississippi Delta, MS; Eastern Los Angeles, CA).ParticipantsA total of thirty-three parents and thirty-eight youths aged 10-17 years were recruited through youth-serving organizations, street intercept and snowball sampling; sixteen store/restaurant managers were recruited at businesses. Participants were asked about their awareness of the BCI. Parents and youths were asked to 'think aloud' as they viewed BCI messages (e.g. 'Balance What You Eat, Drink, and Do') and managers were asked about beverage marketing.<br><br>RESULTS: Twelve parents and twenty-four youths had seen BCI messages; only four managers were aware of the BCI. Many parents and youths showed some misunderstanding of BCI messages (e.g. that they should drink more sugar-sweetened beverages (SSB) or they needed to equalize healthy and unhealthy beverage intake). Only one manager had communicated with beverage companies about the BCI.<br><br>CONCLUSIONS: We found mixed comprehension and low awareness of BCI messages in communities targeted by the American Beverage Association for reduced SSB consumption. Industry self-regulation attempts to reduce SSB consumption may have limited effectiveness if stakeholder input is not addressed. Public health practitioners should be aware of the need to address youths' and parents' misunderstandings about SSB consumption, especially in BCI-targeted communities.},
}
@article {pmid30754031,
year = {2019},
author = {Barroso, FO and Yoder, B and Tentler, D and Wallner, JJ and Kinkhabwala, AA and Jantz, MK and Flint, RD and Tostado, PM and Pei, E and Satish, ADR and Brodnick, SK and Suminski, AJ and Williams, JC and Miller, LE and Tresch, MC},
title = {Decoding neural activity to predict rat locomotion using intracortical and epidural arrays.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036005},
doi = {10.1088/1741-2552/ab0698},
pmid = {30754031},
issn = {1741-2552},
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; Electromyography/methods ; Epidural Space/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Female ; Forecasting ; Locomotion/*physiology ; Neurons/*physiology ; Rats ; Rats, Sprague-Dawley ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Recovery of voluntary gait after spinal cord injury (SCI) requires the restoration of effective motor cortical commands, either by means of a mechanical connection to the limbs, or by restored functional connections to muscles. The latter approach might use functional electrical stimulation (FES), driven by cortical activity, to restore voluntary movements. Moreover, there is evidence that this peripheral stimulation, synchronized with patients' voluntary effort, can strengthen descending projections and recovery. As a step towards establishing such a cortically-controlled FES system for restoring function after SCI, we evaluate here the type and quantity of neural information needed to drive such a brain machine interface (BMI) in rats. We compared the accuracy of the predictions of hindlimb electromyograms (EMG) and kinematics using neural data from an intracortical array and a less-invasive epidural array.<br><br>APPROACH: Seven rats were trained to walk on a treadmill with a stable pattern. One group of rats (n&#x2009;&#x2009;=&#x2009;&#x2009;4) was implanted with intracortical arrays spanning the hindlimb sensorimotor cortex and EMG electrodes in the contralateral hindlimb. Another group (n&#x2009;&#x2009;=&#x2009;&#x2009;3) was implanted with epidural arrays implanted on the dura overlying hindlimb sensorimotor cortex. EMG, kinematics and neural data were simultaneously recorded during locomotion. EMGs and kinematics were decoded using linear and nonlinear methods from multiunit activity and field potentials.<br><br>MAIN RESULTS: Predictions of both kinematics and EMGs were effective when using either multiunit spiking or local field potentials (LFPs) recorded from intracortical arrays. Surprisingly, the signals from epidural arrays were essentially uninformative. Results from somatosensory evoked potentials (SSEPs) confirmed that these arrays recorded neural activity, corroborating our finding that this type of array is unlikely to provide useful information to guide an FES-BMI for rat walking.<br><br>SIGNIFICANCE: We believe that the accuracy of our decoders in predicting EMGs from multiunit spiking activity is sufficient to drive an FES-BMI. Our future goal is to use this rat model to evaluate the potential for cortically-controlled FES to be used to restore locomotion after SCI, as well as its further potential as a rehabilitative technology for improving general motor function.},
}
@article {pmid30747951,
year = {2019},
author = {Abbasi, J},
title = {Advanced Brain-Computer Interface for People With Paralysis.},
journal = {JAMA},
volume = {321},
number = {6},
pages = {537},
doi = {10.1001/jama.2019.0294},
pmid = {30747951},
issn = {1538-3598},
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electrodes, Implanted ; Humans ; Paralysis/*rehabilitation ; },
}
@article {pmid30745864,
year = {2019},
author = {Triplett, MA and Goodhill, GJ},
title = {Probabilistic Encoding Models for Multivariate Neural Data.},
journal = {Frontiers in neural circuits},
volume = {13},
number = {},
pages = {1},
pmid = {30745864},
issn = {1662-5110},
mesh = {Animals ; Brain-Computer Interfaces ; Calcium/metabolism ; Humans ; *Models, Neurological ; *Nerve Net ; Neural Networks, Computer ; Neurons/*physiology ; *Probability ; },
abstract = {A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.},
}
@article {pmid32207716,
year = {2019},
author = {Kotov, SV and Isakova, EV and Slyun'kova, EV},
title = {[Usage of brain - computer interface+exoskeleton technology as a part of complex multimodal stimulation in the rehabilitation of patients with stroke].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {119},
number = {12. Vyp. 2},
pages = {37-42},
doi = {10.17116/jnevro201911912237},
pmid = {32207716},
issn = {1997-7298},
mesh = {Brain/physiology/physiopathology ; *Brain-Computer Interfaces ; Cognition ; *Exoskeleton Device ; Humans ; Imagination ; Stroke/*physiopathology/*psychology ; Stroke Rehabilitation/*instrumentation/*methods ; },
abstract = {INTRODUCTION: A study of 'neural interface brain - computer + exoskeleton' (BCI) technology in training of the movement imagination seems to be an interesting task in the restoration of higher mental functions of the patient. At the same time, it is necessary to evaluate the effectiveness of multimodal stimulation with the inclusion of various information channels, which should contribute to the stimulation of neuroplasticity and improve interhemispheric relationships.<br><br>AIM: To study an impact of multimodal stimulation using BCI on the recovery of cognitive functions in post stroke patients.<br><br>MATERIAL AND METHODS: Forty-four patients were examined and treated in the period of 2 months to 2 years after a stroke. In accordance with the purpose and objectives of the study, all patients were divided into two comparable groups, the main (n=22) and the comparison group (n=22). Patients of the main group underwent a program of complex multimodal stimulation, which included the use of procedures using BCI technology, cognitive training and training on a stabilometric platform with biological feedback on the base reaction and vibrotherapy. Patients of the comparison group had only training with the use of BCI.<br><br>RESULTS: After the treatment, significantly better therapeutic results were observed in the form of improved memory, attention, visual and constructive skills in patients of the main group compared with patients of the comparison group.<br><br>CONCLUSION: The issues of cognitive training using BCI technology are currently a new direction of neurorehabilitation, and the obtained encouraging results indicate the prospects of this direction.},
}
@article {pmid31988964,
year = {2018},
author = {Schunkert, EM and Zhao, W and Zänker, K},
title = {Breast Cancer Recurrence Risk Assessment: Is Non-Invasive Monitoring an Option?.},
journal = {Biomedicine hub},
volume = {3},
number = {3},
pages = {1-17},
pmid = {31988964},
issn = {2296-6870},
abstract = {BACKGROUND: Metastatic breast cancer (MBC) represents a life-threatening disease with a median survival time of 18-24 months that often can only be treated palliatively. The majority of women suffering from MBC are those who had been previously diagnosed with locally advanced disease and subsequently experienced cancer recurrence in the form of metastasis. However, according to guidelines, no systemic follow-up for monitoring purposes is recommended for these women. The purpose of this article is to review current methods of recurrent risk assessment as well as non-invasive monitoring options for women at risk for distant disease relapse and metastasis formation.<br><br>METHODS: We used PubMed and national guidelines, such as the National Comprehensive Cancer Network (NCCN), to find recently published studies on breast cancer recurrence risk assessment and systemic monitoring of breast cancer patients through non-invasive means.<br><br>RESULTS: The options for recurrence risk assessment of locally invasive breast cancer has improved due to diverse genetic tests, such as Oncotype DX, MammaPrint, the PAM50 (now known as the "Prosigna Test") assay, EndoPredict (EP), and the Breast Cancer Index (BCI), which evaluate a women's risk of relapse according to certain cancer-gene expression patterns. Different promising non-invasive urinary protein-based biomarkers with metastasis surveillance potential that have been identified are MMP-2, MMP-9, NGAL, and ADAM12. In particular, &#x3b1;&#x3b1;CTX, &#x3b2;&#x3b2;CTX, and NTX could help to monitor bone metastasis.<br><br>CONCLUSION: In times of improved recurrence risk assessment of women with breast cancer, non-invasive biomarkers are urgently needed as potential monitoring options for women who have an increased risk of recurrence. Urine as a bioliquid of choice provides several advantages - it is non-invasive, can be obtained easily and frequently, and is economical. Promising biomarkers that could help to follow up women with increased recurrence risk have been identified. In order for them to be implemented in clinical usage and national guideline recommendations, further validation in larger independent cohorts will be needed.},
}
@article {pmid32232087,
year = {2018},
author = {Zhang, M and Schwemmer, MA and Ting, JE and Majstorovic, CE and Friedenberg, DA and Bockbrader, MA and Jerry Mysiw, W and Rezai, AR and Annetta, NV and Bouton, CE and Bresler, HS and Sharma, G},
title = {Extracting wavelet based neural features from human intracortical recordings for neuroprosthetics applications.},
journal = {Bioelectronic medicine},
volume = {4},
number = {},
pages = {11},
pmid = {32232087},
issn = {2332-8886},
abstract = {BACKGROUND: Understanding the long-term behavior of intracortically-recorded signals is essential for improving the performance of Brain Computer Interfaces. However, few studies have systematically investigated chronic neural recordings from an implanted microelectrode array in the human brain.<br><br>METHODS: In this study, we show the applicability of wavelet decomposition method to extract and demonstrate the utility of long-term stable features in neural signals obtained from a microelectrode array implanted in the motor cortex of a human with tetraplegia. Wavelet decomposition was applied to the raw voltage data to generate mean wavelet power (MWP) features, which were further divided into three sub-frequency bands, low-frequency MWP (lf-MWP, 0-234&#xa0;Hz), mid-frequency MWP (mf-MWP, 234&#xa0;Hz-3.75&#xa0;kHz) and high-frequency MWP (hf-MWP, >3.75&#xa0;kHz). We analyzed these features using data collected from two experiments that were repeated over the course of about 3&#xa0;years and compared their signal stability and decoding performance with the more standard threshold crossings, local field potentials (LFP), multi-unit activity (MUA) features obtained from the raw voltage recordings.<br><br>RESULTS: All neural features could stably track neural information for over 3&#xa0;years post-implantation and were less prone to signal degradation compared to threshold crossings. Furthermore, when used as an input to support vector machine based decoding algorithms, the mf-MWP and MUA demonstrated significantly better performance, respectively, in classifying imagined motor tasks than using the lf-MWP, hf-MWP, LFP, or threshold crossings.<br><br>CONCLUSIONS: Our results suggest that using MWP features in the appropriate frequency bands can provide an effective neural feature for brain computer interface intended for chronic applications.<br><br>TRIAL REGISTRATION: This study was approved by the U.S. Food and Drug Administration (Investigational Device Exemption) and the Ohio State University Medical Center Institutional Review Board (Columbus, Ohio). The study conformed to institutional requirements for the conduct of human subjects and was filed on ClinicalTrials.gov (Identifier NCT01997125).},
}
@article {pmid31871396,
year = {2018},
author = {Koçanaoğulları, A and Akçakay, M and Erdoğmuş, D},
title = {On Analysis of Active Querying for Recursive State Estimation.},
journal = {IEEE signal processing letters},
volume = {25},
number = {6},
pages = {743-747},
pmid = {31871396},
issn = {1070-9908},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {In stochastic linear/non-linear active dynamic systems, states are estimated with the evidence through recursive measurements in response to queries of the system about the state to be estimated. Therefore, query selection is essential for such systems to improve state estimation accuracy and time. Query selection is conventionally achieved by minimization of the evidence variance or optimization of various information theoretic objectives. It was shown that optimization of mutual information-based objectives and variance-based objectives arrive at the same solution. However, existing approaches optimize approximations to the intended objectives rather than solving the exact optimization problems. To overcome these shortcomings, we propose an active querying procedure using mutual information maximization in recursive state estimation. First we show that mutual information generalizes variance based query selection methods and show the equivalence between objectives if the evidence likelihoods have unimodal distributions. We then solve the exact optimization problem for query selection and propose a query (measurement) selection algorithm. We specifically formulate the mutual information maximization for query selection as a combinatorial optimization problem and show that the objective is sub-modular, therefore can be solved efficiently with guaranteed convergence bounds through a greedy approach. Additionally, we analyze the performance of the query selection algorithm by testing it through a brain computer interface typing system.},
}
@article {pmid31236425,
year = {2018},
author = {Oken, B and Memmott, T and Eddy, B and Wiedrick, J and Fried-Oken, M},
title = {Vigilance state fluctuations and performance using brain-computer interface for communication.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {5},
number = {4},
pages = {146-156},
pmid = {31236425},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {The effect of fatigue and drowsiness on brain-computer interface (BCI) performance was evaluated. 20 healthy participants performed a standardized 11-minute calibration of a Rapid Serial Visual Presentation BCI system five times over two hours. For each calibration, BCI performance was evaluated using area under the receiver operating characteristic curve (AUC). Self-rated measures were obtained following each calibration including the Karolinska Sleepiness Scale and a standardized boredom scale. Physiological measures were obtained during each calibration including P300 amplitude, theta power, alpha power, median power frequency and eye-blink rate. There was a significant decrease in AUC over the five sessions. This was paralleled by increases in self-rated sleepiness and boredom and decreases in P300 amplitude. Alpha power, median power frequency, and eye-blink rate also increased but more modestly. AUC changes were only partly explained by changes in P300 amplitude. There was a decrease in BCI performance over time that related to increases in sleepiness and boredom. This worsened performance was only partly explained by decreases in P300 amplitude. Thus, drowsiness and boredom have a negative impact on BCI performance. Increased BCI performance may be possible by developing physiological measures to provide feedback to the user or to adapt the classifier to state.},
}
@article {pmid31118975,
year = {2018},
author = {Haghighi, M and Moghadamfalahi, M and Akcakaya, M and Erdogmus, D},
title = {EEG-assisted Modulation of Sound Sources in the Auditory Scene.},
journal = {Biomedical signal processing and control},
volume = {39},
number = {},
pages = {263-270},
pmid = {31118975},
issn = {1746-8094},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a novel attempt to investigate the feasibility of online modulation of sound sources by probabilistic detection of auditory attention, using a noninvasive EEG-based brain computer interface. Proposed online system modulates the upcoming sound sources through gain adaptation which employs probabilistic decisions (soft decisions) from a classifier trained on offline calibration data. In this work, calibration EEG data were collected in sessions where the participants listened to two sound sources (one attended and one unattended). Cross-correlation coefficients between the EEG measurements and the attended and unattended sound source envelope (estimates) are used to show differences in sharpness and delays of neural responses for attended versus unattended sound source. Salient features to distinguish attended sources from the unattended ones in the correlation patterns have been identified, and later they have been used to train an auditory attention classifier. Using this classifier, we have shown high offline detection performance with single channel EEG measurements compared to the existing approaches in the literature which employ large number of channels. In addition, using the classifier trained offline in the calibration session, we have shown the performance of the online sound source modulation system. We observe that online sound source modulation system is able to keep the level of attended sound source higher than the unattended source.},
}
@article {pmid30895198,
year = {2018},
author = {Peters, B and Higger, M and Quivira, F and Bedrick, S and Dudy, S and Eddy, B and Kinsella, M and Memmott, T and Wiedrick, J and Fried-Oken, M and Erdogmus, D and Oken, B},
title = {Effects of simulated visual acuity and ocular motility impairments on SSVEP brain-computer interface performance: An experiment with Shuffle Speller.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {5},
number = {2-3},
pages = {58-72},
pmid = {30895198},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Individuals with severe speech and physical impairments may have concomitant visual acuity impairments (VAI) or ocular motility impairments (OMI) impacting visual BCI use. We report on the use of the Shuffle Speller typing interface for an SSVEP BCI copy-spelling task under three conditions: simulated VAI, simulated OMI, and unimpaired vision. To mitigate the effect of visual impairments, we introduce a method that adaptively selects a user-specific trial length to maximize expected information transfer rate (ITR); expected ITR is shown to closely approximate the rate of correct letter selections. All participants could type under the unimpaired and simulated VAI conditions, with no significant differences in typing accuracy or speed. Most participants (31 of 37) could not type under the simulated OMI condition; some achieved high accuracy but with slower typing speeds. Reported workload and discomfort were low, and satisfaction high, under the unimpaired and simulated VAI conditions. Implications and future directions to examine effect of visual impairment on BCI use is discussed.},
}
@article {pmid30854523,
year = {2018},
author = {Bedell, HW and Capadona, JR},
title = {Anti-inflammatory Approaches to Mitigate the Neuroinflammatory Response to Brain-Dwelling Intracortical Microelectrodes.},
journal = {Journal of immunological sciences},
volume = {2},
number = {4},
pages = {15-21},
pmid = {30854523},
issn = {2578-3009},
support = {I01 RX001495/RX/RRD VA/United States ; I01 RX002611/RX/RRD VA/United States ; R01 NS082404/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {Intracortical microelectrodes are used both in basic research to increase our understanding of the nervous system and for rehabilitation purposes through brain-computer interfaces. Yet, challenges exist preventing the widespread clinical use of this technology. A prime challenge is with the neuroinflammatory response to intracortical microelectrodes. This mini-review details immunomodulatory strategies employed to decrease the inflammatory response to these devices. Over time, broad-spectrum anti-inflammatory approaches, such as dexamethasone and minocycline, evolved into more targeted treatments since the underlying biology of the neuroinflammation was elucidated. This review also presents studies which examine novel prospective targets for future immunomodulatory targeting.},
}
@article {pmid31360834,
year = {2017},
author = {Nunes, AT and Collyar, DE and Harris, LN},
title = {Gene Expression Assays for Early-Stage Hormone Receptor-Positive Breast Cancer: Understanding the Differences.},
journal = {JNCI cancer spectrum},
volume = {1},
number = {1},
pages = {pkx008},
pmid = {31360834},
issn = {2515-5091},
abstract = {Biomarkers are frequently used to guide decisions for treatment of early-stage estrogen (ER) and progesterone (PR) receptor-positive (ER/PR+) invasive breast cancers and have been incorporated into guidelines. The American Society of Clinical Oncology (ASCO) 2016 guideline and a 2017 update were recently published to help clinicians use the tests available. ASCO currently recommends five tests that show evidence of clinical utility based on the parameters defined in the guideline. These include the 21-gene assay (Oncotype DX), Prediction of Analysis of Microarray-50 (PAM50), 12-gene risk score (Endopredict), Breast Cancer Index (BCI), and, most recently, the 70-gene assay (Mammaprint). However, discordance is often seen when the results of these gene assays are compared in a particular patient, for a number of reasons: the assays were initially developed to answer different questions, and the molecular makeup of each signature reflects this; the patient populations that were studied also differed and may not reflect the patient being tested; furthermore, the study design and statistical analysis varied between each test, leading to different scoring scales that may not be comparable. In this review, the background on the development and validation of these assays is discussed, and studies comparing them are reviewed. To provide guidance on which test to choose, the studies that support the level of evidence for clinical utility are presented. However, the choice of a particular test will also be influenced by socioeconomic factors, clinical factors, and patient preferences. We hope that a better understanding of the scientific and clinical rationale for each test will allow patients and providers to make optimal decisions for treatment of early-stage ER/PR+ breast cancer.},
}
@article {pmid31015712,
year = {2017},
author = {Dyer, EL and Gheshlaghi Azar, M and Perich, MG and Fernandes, HL and Naufel, S and Miller, LE and Körding, KP},
title = {A cryptography-based approach for movement decoding.},
journal = {Nature biomedical engineering},
volume = {1},
number = {12},
pages = {967-976},
pmid = {31015712},
issn = {2157-846X},
support = {R01 MH103910/MH/NIMH NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; U01 MH109100/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Macaca mulatta ; *Machine Learning ; Models, Neurological ; Motor Cortex/*physiology ; *Movement ; Neurons/*physiology ; },
abstract = {Brain decoders use neural recordings to infer the activity or intent of a user. To train a decoder, one generally needs to infer the measured variables of interest (covariates) from simultaneously measured neural activity. However, there are cases for which obtaining supervised data is difficult or impossible. Here, we describe an approach for movement decoding that does not require access to simultaneously measured neural activity and motor outputs. We use the statistics of movement-much like cryptographers use the statistics of language-to find a mapping between neural activity and motor variables, and then align the distribution of decoder outputs with the typical distribution of motor outputs by minimizing their Kullback-Leibler divergence. By using datasets collected from the motor cortex of three non-human primates performing either a reaching task or an isometric force-production task, we show that the performance of such a distribution-alignment decoding algorithm is comparable to the performance of supervised approaches. Distribution-alignment decoding promises to broaden the set of potential applications of brain decoding.},
}
@article {pmid31015709,
year = {2017},
author = {Gilja, V},
title = {Cryptographic decoding of movement.},
journal = {Nature biomedical engineering},
volume = {1},
number = {12},
pages = {929-930},
doi = {10.1038/s41551-017-0175-9},
pmid = {31015709},
issn = {2157-846X},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; *Machine Learning ; *Movement ; *Neural Prostheses ; },
}
@article {pmid31871392,
year = {2017},
author = {Moghadamfalahi, M and Akcakaya, M and Nezamfar, H and Sourati, J and Erdogmus, D},
title = {An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.},
journal = {IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society},
volume = {65},
number = {20},
pages = {5381-5392},
pmid = {31871392},
issn = {1053-587X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {A class of brain computer interfaces (BCIs) employs noninvasive recordings of electroencephalography (EEG) signals to enable users with severe speech and motor impairments to interact with their environment and social network. For example, EEG based BCIs for typing popularly utilize event related potentials (ERPs) for inference. Presentation paradigm design in current ERP-based letter by letter typing BCIs typically query the user with an arbitrary subset characters. However, the typing accuracy and also typing speed can potentially be enhanced with more informed subset selection and flash assignment. In this manuscript, we introduce the active recursive Bayesian state estimation (active-RBSE) framework for inference and sequence optimization. Prior to presentation in each iteration, rather than showing a subset of randomly selected characters, the developed framework optimally selects a subset based on a query function. Selected queries are made adaptively specialized for users during each intent detection. Through a simulation-based study, we assess the effect of active-RBSE on the performance of a language-model assisted typing BCI in terms of typing speed and accuracy. To provide a baseline for comparison, we also utilize standard presentation paradigms namely, row and column matrix presentation paradigm and also random rapid serial visual presentation paradigms. The results show that utilization of active-RBSE can enhance the online performance of the system, both in terms of typing accuracy and speed. Moreover, we conduct real time experiments with human participants to study the human-in-the-loop effect on the performance of the proposed active-RBSE framework and consistent with the simulation results, the results of these experiments show improvement both in typing speed and accuracy.},
}
@article {pmid31110907,
year = {2017},
author = {Özdenizci, O and Quivira, F and Erdoğmuş, D},
title = {INFORMATION THEORETIC FEATURE PROJECTION FOR SINGLE-TRIAL BRAIN-COMPUTER INTERFACES.},
journal = {IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing},
volume = {2017},
number = {},
pages = {},
pmid = {31110907},
issn = {2161-0363},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Current approaches on optimal spatio-spectral feature extraction for single-trial BCIs exploit mutual information based feature ranking and selection algorithms. In order to overcome potential confounders underlying feature selection by information theoretic criteria, we propose a non-parametric feature projection framework for dimensionality reduction that utilizes mutual information based stochastic gradient descent. We demonstrate the feasibility of the protocol based on analyses of EEG data collected during execution of open and close palm hand gestures. We further discuss the approach in terms of potential insights in the context of neurophysiologically driven prosthetic hand control.},
}
@article {pmid31057819,
year = {2016},
author = {Xiang, Z and Liu, J and Lee, C},
title = {A flexible three-dimensional electrode mesh: An enabling technology for wireless brain-computer interface prostheses.},
journal = {Microsystems &amp; nanoengineering},
volume = {2},
number = {},
pages = {16012},
pmid = {31057819},
issn = {2055-7434},
abstract = {The neural interface is a key component in wireless brain-computer prostheses. In this study, we demonstrate that a unique three-dimensional (3D) microneedle electrode on a flexible mesh substrate, which can be fabricated without complicated micromachining techniques, is conformal to the tissues with minimal invasiveness. Furthermore, we demonstrate that it can be applied to different functional layers in the nervous system without length limitation. The microneedle electrode is fabricated using drawing lithography technology from biocompatible materials. In this approach, the profile of a 3D microneedle electrode array is determined by the design of a two-dimensional (2D) pattern on the mask, which can be used to access different functional layers in different locations of the brain. Due to the sufficient stiffness of the electrode and the excellent flexibility of the mesh substrate, the electrode can penetrate into the tissue with its bottom layer fully conformal to the curved brain surface. Then, the exposed contact at the end of the microneedle electrode can successfully acquire neural signals from the brain.},
}
@article {pmid30744661,
year = {2019},
author = {Kim, YJ and Nam, HS and Lee, WH and Seo, HG and Leigh, JH and Oh, BM and Bang, MS and Kim, S},
title = {Vision-aided brain-machine interface training system for robotic arm control and clinical application on two patients with cervical spinal cord injury.},
journal = {Biomedical engineering online},
volume = {18},
number = {1},
pages = {14},
pmid = {30744661},
issn = {1475-925X},
support = {NRCTR-EX-16008//Korea National Rehabilitation Center/ ; 2016M3C7A1904984//National Research Foundation of Korea/ ; Grant 2017R1A2B2006163//National Research Foundation/ ; },
mesh = {*Arm ; *Brain-Computer Interfaces ; Cervical Vertebrae/*injuries ; Humans ; Magnetic Resonance Imaging ; Robotics/*instrumentation ; Software ; Spinal Cord Injuries/diagnostic imaging/physiopathology/*therapy ; *Visual Perception ; },
abstract = {BACKGROUND: While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain-machine interface training system for robotic arm control is proposed and developed.<br><br>METHODS: The proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects.<br><br>RESULTS: In a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p&#x2009;<&#x2009;0.0001). Functional magnetic resonance imaging after five sessions of observation-based training with the developed system showed brain activation patterns with tendency of focusing to ipsilateral primary motor and sensory cortex, posterior parietal cortex, and contralateral cerebellum. However, shared control with blending parameter &#x3b1; less than 1 was not successful and success rate for touching an instructed target was less than the chance level (=&#x2009;50%).<br><br>CONCLUSIONS: The pilot clinical study utilizing the training system suggested potential beneficial effects in characterizing the brain activation patterns.},
}
@article {pmid30744081,
year = {2019},
author = {Borghini, G and Aricò, P and Di Flumeri, G and Sciaraffa, N and Babiloni, F},
title = {Correlation and Similarity between Cerebral and Non-Cerebral Electrical Activity for User's States Assessment.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {3},
pages = {},
pmid = {30744081},
issn = {1424-8220},
support = {723386//European Commission/ ; },
mesh = {Adult ; Brain/*physiology ; Brain Waves/*physiology ; *Electric Conductivity ; Electroencephalography ; Hand/physiology ; Humans ; Machine Learning ; Neck/physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body. Since no works have been found in this direction, the proposed study aimed at overcoming this lack of evidence and seeking analogies between the brain activity and the electrical activity of non-cerebral locations, such as the neck and wrists, to determine if i) cerebral parameters can be estimated from non-cerebral sites, and if ii) non-cerebral sensors can replace cerebral sensors for the evaluation of the users under specific experimental conditions, such as eyes open or closed. In fact, the use of cerebral sensors requires high-qualified personnel, and reliable recording systems, which are still expensive. Therefore, the possibility to use cheaper and easy-to-use equipment to estimate cerebral parameters will allow making some brain-based applications less invasive and expensive, and easier to employ. The results demonstrated the occurrence of significant correlations and analogies between cerebral and non-cerebral electrical activity. Furthermore, the same discrimination and classification accuracy were found in using the cerebral or non-cerebral sites for the user's status assessment.},
}
@article {pmid30742605,
year = {2019},
author = {Rogers, N and Hermiz, J and Ganji, M and Kaestner, E and Kılıç, K and Hossain, L and Thunemann, M and Cleary, DR and Carter, BS and Barba, D and Devor, A and Halgren, E and Dayeh, SA and Gilja, V},
title = {Correlation Structure in Micro-ECoG Recordings is Described by Spatially Coherent Components.},
journal = {PLoS computational biology},
volume = {15},
number = {2},
pages = {e1006769},
pmid = {30742605},
issn = {1553-7358},
support = {R01 DA050159/DA/NIDA NIH HHS/United States ; R01 MH111359/MH/NIMH NIH HHS/United States ; R01 NS057198/NS/NINDS NIH HHS/United States ; RF1 MH117155/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Cerebral Cortex ; Electric Conductivity ; Electrocorticography/*methods ; Electrodes, Implanted ; Electroencephalography/methods ; Electrophysiological Phenomena ; Humans ; Mice ; Microelectrodes ; Polymers ; Records ; },
abstract = {Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.},
}
@article {pmid30737625,
year = {2019},
author = {Zheng, Y and Xu, G},
title = {Quantifying mode mixing and leakage in multivariate empirical mode decomposition and application in motor imagery-based brain-computer interface system.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {6},
pages = {1297-1311},
pmid = {30737625},
issn = {1741-0444},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Motor Activity/*physiology ; Multivariate Analysis ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Improper selection of the number and the amplitude of noise channels in noise-assisted multivariate empirical mode decomposition (NA-MEMD) would induce mode mixing and leakage in the obtained intrinsic mode functions (IMF), which would degrade the performance in applications like brain-computer interface (BCI) systems based on motor imagery. A measurement (ML-index) using no prior knowledge of the underlying components of the original signals was proposed to quantify the amount of mode mixing and leakage of IMFs. Both synthetic signals and electroencephalography (EEG) recordings from motor imagery experiments were used to test the validity. The BCI classification performance using NA-MEMD with the optimal parameters selected based on the ML-index was compared with the performance under the non-optimal parameter condition and the performance using the conventional filtering method. Test on synthetic signals demonstrated the ML-index can effectively quantify the amount of mode mixing and leakage, and help to improve the accuracy of extracting the underlying components. Test on EEG recordings showed the BCI classification performance can be significantly improved under the optimal parameter condition. This study provided a method to quantify the amount of mode mixing and leakage in IMFs and realized the optimization of the parameters associated with noise channels in NA-MEMD. Graphical abstract One of the synthetic multivariate signals comprised four components oscillating at different rates (middle column). Noise-assisted multivariate empirical mode decomposition (noise-assisted MEMD) was used to extract different components. Mode mixing issue occurred under the non-optimal parameter condition (left column). The issue was alleviated under the optimal parameter condition (right column) which can be obtained with the proposed method in this study.},
}
@article {pmid30733957,
year = {2019},
author = {Kaczmarczyk, K and Błażkiewicz, M and Wiszomirska, I and Pietrasik, K and Zdrodowska, A and Wit, A and Barton, G and Skarżyński, H},
title = {Assessing Gait Stability before and after Cochlear Implantation.},
journal = {BioMed research international},
volume = {2019},
number = {},
pages = {2474273},
pmid = {30733957},
issn = {2314-6141},
mesh = {Case-Control Studies ; *Cochlear Implantation ; Female ; Gait/*physiology ; Humans ; Male ; Middle Aged ; Postural Balance ; Time Factors ; },
abstract = {BACKGROUND: It is known that cochlear implantation may alter the inner ear and induce vestibular disorders.<br><br>RESEARCH QUESTION: How does cochlear implantation influence gait stability? Material and Methods. An experimental group of twenty-one subjects scheduled for cochlear implantation underwent gait testing twice, on the day before cochlear implantation (BCI) and three months after cochlear implantation (ACI), using a motion capture system. A control group of 30 age-matched healthy individuals were also tested.<br><br>RESULTS: In the experimental group, the gait stability ratio (GSR) was found to improve in 17 subjects after implantation, by an average of 6%. Certain other parameters also showed statistically significant improvement between the two experimental group tests: step time (p<0.001), single-support phase walking speed (p<0.05), and center of mass (CoM) (p<0.05). Using the CoM results of the control group, we devised a stability classification system and applied it to the pre- and postimplantation subjects. After implantation, increases were seen in the number of subjects classified in interval II (strong stability) and III (weak stability). The number of subjects in interval I (perfect stability) decreased by 1 and in interval IV (no stability) by 4.<br><br>SIGNIFICANCE: (1) Although cochlear implantation intervenes in the vestibular area, we found evidence that gait stability improves in most subjects after the surgery, reducing the risk of falls. (2) We found statistically significant improvements in individual parameters (such as single-support phase time), in GSR, and in CoM. (3) Based on CoM results, we proposed a new rule-of-thumb way of classifying patients into gait stability intervals, for use in rehabilitation planning and monitoring.},
}
@article {pmid30731512,
year = {2019},
author = {Esser, P and Metelmann, M and Hartung, T and Claßen, J and Mehnert, A and Koranyi, S},
title = {[Psychosocial Care For Patients With Amyotrophic Lateral Sclerosis: A Narrative Review].},
journal = {Psychotherapie, Psychosomatik, medizinische Psychologie},
volume = {69},
number = {9-10},
pages = {372-381},
doi = {10.1055/a-0806-7862},
pmid = {30731512},
issn = {1439-1058},
mesh = {Amyotrophic Lateral Sclerosis/psychology/*therapy ; Health Services ; Humans ; *Psychosocial Support Systems ; Quality of Life ; },
abstract = {This narrative review gives a broad summary of the psychosocial strain in patients with amyotrophic lateral sclerosis (ALS) and psychotherapeutic interventions addressing these issues. ALS is a fatal, rapidly progressing neurodegenerative disease, which leads to weakness and atrophy in almost all muscles of the body, resulting in impairment and finally inability in all domains of daily life including mobility, food intake, respiration or communication. In addition to these mainly motor impairments, most patients are also affected by severe cognitive-emotional and behavioral alterations and deficits which may lead to additional distress. Due to the severe symptomatology and poor diagnosis, ALS can lead to significant psychosocial strain including heightened levels of depressive and anxious symptomatology, hopelessness and even the wish for hastened death. A large body of research demonstrates the strong effect of psychosocial aspects on quality of life (QoL) in ALS patients. Nevertheless, research on psychotherapeutic interventions for patients with ALS is very sparse to date. Besides the general lack of interventions and the methodological limitations in testing their efficacy, few of these therapeutic concepts incorporate the palliative character and the specific symptomatology of the disease such as impaired communication or problems with emotion control. Further research on psychosocial interventions in this patient group is therefore urgently needed. Future research could aim to adapt therapy programs that already have been proven to be effective in other populations with advanced diseases. Such research should also test the applicability of the therapy models using alternative communication including computer with a voice synthesizer or brain-computer-interfaces.},
}
@article {pmid30728772,
year = {2018},
author = {Rimbert, S and Gayraud, N and Bougrain, L and Clerc, M and Fleck, S},
title = {Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {529},
pmid = {30728772},
issn = {1662-5161},
abstract = {Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.},
}
@article {pmid30726164,
year = {2019},
author = {Zhou, X and Tien, RN and Ravikumar, S and Chase, SM},
title = {Distinct types of neural reorganization during long-term learning.},
journal = {Journal of neurophysiology},
volume = {121},
number = {4},
pages = {1329-1341},
pmid = {30726164},
issn = {1522-1598},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Macaca mulatta ; Male ; *Memory, Long-Term ; Motor Cortex/cytology/*physiology ; Motor Skills ; Neurons/*physiology ; },
abstract = {What are the neural mechanisms of skill acquisition? Many studies find that long-term practice is associated with a functional reorganization of cortical neural activity. However, the link between these changes in neural activity and the behavioral improvements that occur is not well understood, especially for long-term learning that takes place over several weeks. To probe this link in detail, we leveraged a brain-computer interface (BCI) paradigm in which rhesus monkeys learned to master nonintuitive mappings between neural spiking in primary motor cortex and computer cursor movement. Critically, these BCI mappings were designed to disambiguate several different possible types of neural reorganization. We found that during the initial phase of learning, lasting minutes to hours, rapid changes in neural activity common to all neurons led to a fast suppression of motor error. In parallel, local changes to individual neurons gradually accrued over several weeks of training. This slower timescale cortical reorganization persisted long after the movement errors had decreased to asymptote and was associated with more efficient control of movement. We conclude that long-term practice evokes two distinct neural reorganization processes with vastly different timescales, leading to different aspects of improvement in motor behavior. NEW &amp; NOTEWORTHY We leveraged a brain-computer interface learning paradigm to track the neural reorganization occurring throughout the full time course of motor skill learning lasting several weeks. We report on two distinct types of neural reorganization that mirror distinct phases of behavioral improvement: a fast phase, in which global reorganization of neural recruitment leads to a quick suppression of motor error, and a slow phase, in which local changes in individual tuning lead to improvements in movement efficiency.},
}
@article {pmid30722727,
year = {2019},
author = {Ramos-Murguialday, A and Curado, MR and Broetz, D and Yilmaz, &#xd6; and Brasil, FL and Liberati, G and Garcia-Cossio, E and Cho, W and Caria, A and Cohen, LG and Birbaumer, N},
title = {Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up.},
journal = {Neurorehabilitation and neural repair},
volume = {33},
number = {3},
pages = {188-198},
pmid = {30722727},
issn = {1552-6844},
support = {Z01 NS003030-01//Intramural NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Chronic Disease/rehabilitation ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Recovery of Function ; Stroke/diagnosis/*physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients.<br><br>OBJECTIVE: Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention.<br><br>METHODS: A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2).<br><br>RESULTS: The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 &#xb1; 1.96) as compared with the Pre session (11.16 &#xb1; 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions.<br><br>CONCLUSIONS: BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.},
}
@article {pmid30719252,
year = {2018},
author = {Sadeghi, S and Maleki, A},
title = {Recent Advances in Hybrid Brain-Computer Interface Systems: A Technological and Quantitative Review.},
journal = {Basic and clinical neuroscience},
volume = {9},
number = {5},
pages = {373-388},
pmid = {30719252},
issn = {2008-126X},
abstract = {Brain-Computer Interface (BCI) is a system that enables users to transmit commands to the computer using their brain activity recorded by electroencephalography. In a Hybrid Brain-Computer Interface (HBCI), a BCI control signal combines with one or more BCI control signals or with Human-Machine Interface (HMI) biosignals to increase classification accuracy, boost system speed, and improve user's satisfaction. HBCI systems are categorized according to the type of combined signals and the combination technique (simultaneous or sequential). They have been used in several applications such as cursor control, target selection, and spellers. Increasing the number of articles published in this field indicates the significance of these systems. In this paper, different HBCI combinations, their important features, and potential applications are discussed. In most cases, the combination of a BCI control signal with a HMI biosignal yields higher information transfer rate than two BCI control signals.},
}
@article {pmid30718518,
year = {2019},
author = {Zhang, S and Yuan, S and Huang, L and Zheng, X and Wu, Z and Xu, K and Pan, G},
title = {Human Mind Control of Rat Cyborg's Continuous Locomotion with Wireless Brain-to-Brain Interface.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {1321},
pmid = {30718518},
issn = {2045-2322},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Locomotion/*physiology ; Longitudinal Studies ; Microelectrodes ; Rats ; },
abstract = {Brain-machine interfaces (BMIs) provide a promising information channel between the biological brain and external devices and are applied in building brain-to-device control. Prior studies have explored the feasibility of establishing a brain-brain interface (BBI) across various brains via the combination of BMIs. However, using BBI to realize the efficient multidegree control of a living creature, such as a rat, to complete a navigation task in a complex environment has yet to be shown. In this study, we developed a BBI from the human brain to a rat implanted with microelectrodes (i.e., rat cyborg), which integrated electroencephalogram-based motor imagery and brain stimulation to realize human mind control of the rat's continuous locomotion. Control instructions were transferred from continuous motor imagery decoding results with the proposed control models and were wirelessly sent to the rat cyborg through brain micro-electrical stimulation. The results showed that rat cyborgs could be smoothly and successfully navigated by the human mind to complete a navigation task in a complex maze. Our experiments indicated that the cooperation through transmitting multidimensional information between two brains by computer-assisted BBI is promising.},
}
@article {pmid30718075,
year = {2019},
author = {Chandra, R and Iqbal, HMN and Vishal, G and Lee, HS and Nagra, S},
title = {Algal biorefinery: A sustainable approach to valorize algal-based biomass towards multiple product recovery.},
journal = {Bioresource technology},
volume = {278},
number = {},
pages = {346-359},
doi = {10.1016/j.biortech.2019.01.104},
pmid = {30718075},
issn = {1873-2976},
mesh = {Biofuels ; *Biomass ; Plants/*metabolism ; },
abstract = {In recent years, ever-increasing socio-economic awareness, and negative impact of excessive petro consumption have redirected the research interests towards bio-resources such as algal-based biomass. In order to meet current bio-economy challenges to produce high-value multiple products at a time, new integrated processes in research and development are necessary. Though various strategies have been posited for conversion of algal-based biomass to fuel and fine chemicals, none of them has been proved as economically viable and energetically feasible. Therefore, a range of other bio-products needs to be pursued. In this context, the algal bio-refinery concept has appeared with notable solution to recover multiple products from a single operation process. Herein, an algal-based bio-refinery platform for fuel, food, and pharmaceuticals considering Bio-refinery Complexity Index (BCI) has been evaluated, as an indicator of techno-economic risks. This review presents recent developments on algal-biomass utilization for various value-added products as part of an integrated bio-refinery.},
}
@article {pmid30716043,
year = {2019},
author = {Mao, X and Li, W and Lei, C and Jin, J and Duan, F and Chen, S},
title = {A Brain-Robot Interaction System by Fusing Human and Machine Intelligence.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {533-542},
doi = {10.1109/TNSRE.2019.2897323},
pmid = {30716043},
issn = {1558-0210},
mesh = {Algorithms ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Evoked Potentials, Somatosensory/physiology ; Fuzzy Logic ; Humans ; Image Processing, Computer-Assisted ; *Intelligence ; Robotics/*methods ; Vision, Ocular/physiology ; },
abstract = {This paper presents a new brain-robot interaction system by fusing human and machine intelligence to improve the real-time control performance. This system consists of a hybrid P300 and steady-state visual evoked potential (SSVEP) mode conveying a human being's intention, and the machine intelligence combining a fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object of interest via P300, and the classification algorithm transfers the corresponding parameters to an improved fuzzy color extractor for object extraction. A central vision tracking strategy automatically guides the NAO humanoid robot to the destination selected by the subject intentions represented by brainwaves. During this process, human supervises the system at high level, while machine intelligence assists the robot in accomplishing tasks by analyzing image feeding back from the camera, distance monitoring using out-of-gauge alarms from sonars, and collision detecting from bumper sensors. In this scenario, the SSVEP takes over the situations in which the machine intelligence cannot make decisions. The experimental results show that the subjects can control the robot to a destination of interest, with fewer commands than only using a brain-robot interface. Therefore, the fusion of human and machine intelligence greatly alleviates the brain load and enhances the robot executive efficiency of a brain-robot interaction system.},
}
@article {pmid30714927,
year = {2019},
author = {Wang, F and He, Y and Qu, J and Cao, Y and Liu, Y and Li, F and Yu, Z and Yu, R and Li, Y},
title = {A Brain-Computer Interface Based on Three-Dimensional Stereo Stimuli for Assisting Clinical Object Recognition Assessment in Patients With Disorders of Consciousness.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {507-513},
doi = {10.1109/TNSRE.2019.2896092},
pmid = {30714927},
issn = {1558-0210},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Aged ; *Brain-Computer Interfaces ; Coma/diagnosis ; Computer Simulation ; Consciousness Disorders/*diagnosis/psychology ; Electroencephalography ; Feedback ; Female ; Healthy Volunteers ; Humans ; *Imaging, Three-Dimensional ; Male ; Middle Aged ; Photic Stimulation ; *Recognition, Psychology ; Recovery of Function ; Young Adult ; },
abstract = {The coma recovery scale-revised (CRS-R) behavioral scale is commonly used for the clinical evaluation of patients with disorders of consciousness (DOC). However, since DOC patients generally cannot supply stable and efficient behavioral responses to external stimulation, evaluation results based on behavioral scales are not sufficiently accurate. In this paper, we proposed a novel brain-computer interface (BCI) based on 3D stereo audiovisual stimuli to supplement object recognition evaluation in the CRS-R. During the experiment, subjects needed to follow the instructions and to focus on the target object on the screen, whereas EEG data were recorded and analyzed in real time to determine the object of focus, and the detection result was output as feedback. Thirteen DOC patients participated in the object recognition assessments using the 3D audiovisual BCI and CRS-R. None of the patients showed object recognition function in the CRS-R assessment before the BCI experiment. However, six of these DOC patients achieved accuracies that were significantly higher than the chance level in the BCI-based assessment, indicating the successful detection of object recognition function in these six patients using our 3D audiovisual BCI system. These results suggest that the BCI method may provide a more sensitive object recognition evaluation compared with CRS-R and may be used to assist clinical CRS-R for DOC patients.},
}
@article {pmid30711312,
year = {2019},
author = {Mina, S and Pardo Munevar, CA and Osorio, D and García-Perdomo, HA},
title = {Life quality evaluation in patients with bladder cancer: A systematic review.},
journal = {Actas urologicas espanolas},
volume = {43},
number = {4},
pages = {198-204},
doi = {10.1016/j.acuro.2018.07.006},
pmid = {30711312},
issn = {2173-5786},
mesh = {Humans ; Psychometrics ; *Quality of Life ; Surveys and Questionnaires ; *Urinary Bladder Neoplasms/therapy ; Practice Guidelines as Topic ; },
abstract = {OBJECTIVE: To identify scale validation studies for life quality evaluation in patients with bladder cancer.<br><br>METHODS: Bibliographic search was performed on MEDLINE&#xae; via ovid, EMBASE, CENTRAL and LILACS. Subsequently, each of the articles was evaluated, identifying eligibility criteria. This information was confirmed and verified by the researchers, and in cases of missing information, the authors were contacted to complete the data. Due to the nature of the study, no statistical analysis was performed.<br><br>RESULTS: From 1760 articles found, only 5were included in the qualitative analysis. Five validated questionnaires for quality of life in patients with bladder cancer (BCI, EORTC QLQ-NMIBC24, FACT-VCI, BUSS, FACT-BL). The BCI; most frequently used instrument in bladder cancer studies published to date. The FACT-VCI, instrument of application limited to unique therapeutic options within the spectrum of the disease. The EORTC QLQ-NMIBC24, widely acceptable questionnaire in the European community due to its psychometric characteristics. The BUSS evaluates the patient regardless the stage of the disease. The FACT-BL evaluates life quality in patients with non-muscle invasive bladder cancer.<br><br>CONCLUSION: The use of validated instruments such as: BCI, EORTC QLQ-NMIBC24, FACT-VI, BUSS and FACT-BL which allow evaluating the impact of disease and the established therapies, is recommended.},
}
@article {pmid30711286,
year = {2019},
author = {Kramer, DR and Kellis, S and Barbaro, M and Salas, MA and Nune, G and Liu, CY and Andersen, RA and Lee, B},
title = {Technical considerations for generating somatosensation via cortical stimulation in a closed-loop sensory/motor brain-computer interface system in humans.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {63},
number = {},
pages = {116-121},
pmid = {30711286},
issn = {1532-2653},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; },
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electrocorticography/methods ; Electrodes, Implanted ; Hand/innervation ; Humans ; Microelectrodes ; Sensation ; Somatosensory Cortex/*physiology ; },
abstract = {Somatosensory feedback is the next step in brain computer interface (BCI). Here, we compare three cortical stimulating array modalities for generating somatosensory percepts in BCI. We compared human subjects with either a 64-channel "mini"-electrocorticography grid (mECoG; 1.2-mm diameter exposed contacts with 3-mm spacing, N = 1) over the hand area of primary somatosensory cortex (S1), or a standard grid (sECoG; 1.5-mm diameter exposed contacts with 1-cm spacing, N = 1), to generate artificial somatosensation through direct electrical cortical stimulation. Finally, we reference data in the literature from a patient implanted with microelectrode arrays (MEA) placed in the S1 hand area. We compare stimulation results to assess coverage and specificity of the artificial percepts in the hand. Using the mECoG array, hand mapping revealed coverage of 41.7% of the hand area versus 100% for the sECoG array, and 18.8% for the MEA. On average, stimulation of a single electrode corresponded to sensation reported in 4.42 boxes (range 1-11 boxes) for the mECoG array, 19.11 boxes (range 4-48 boxes) for the sECoG grid, and 2.3 boxes (range 1-5 boxes) for the MEA. Sensation in any box, on average, corresponded to stimulation from 2.65 electrodes (range 1-5 electrodes) for the mECoG grid, 3.58 electrodes for the sECoG grid (range 2-4 electrodes), and 11.22 electrodes (range 2-17 electrodes) for the MEA. Based on these findings, we conclude that mECoG grids provide an excellent balance between spatial cortical coverage of the hand area of S1 and high-density resolution.},
}
@article {pmid30709004,
year = {2019},
author = {Grosselin, F and Navarro-Sune, X and Vozzi, A and Pandremmenou, K and De Vico Fallani, F and Attal, Y and Chavez, M},
title = {Quality Assessment of Single-Channel EEG for Wearable Devices.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {3},
pages = {},
pmid = {30709004},
issn = {1424-8220},
mesh = {Algorithms ; Artifacts ; Brain/physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Humans ; Wearable Electronic Devices ; },
abstract = {The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.},
}
@article {pmid30707710,
year = {2019},
author = {Messiah, A and Notredame, CE and Demarty, AL and Duhem, S and Vaiva, G and , },
title = {Combining green cards, telephone calls and postcards into an intervention algorithm to reduce suicide reattempt (AlgoS): P-hoc analyses of an inconclusive randomized controlled trial.},
journal = {PloS one},
volume = {14},
number = {2},
pages = {e0210778},
pmid = {30707710},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; *Algorithms ; Female ; Follow-Up Studies ; *Hotlines ; Humans ; Male ; Middle Aged ; Postcards as Topic ; Suicide, Attempted/*prevention &amp; control/psychology ; },
abstract = {BACKGROUND: Brief contact interventions (BCIs) might be reliable suicide prevention strategies. BCI efficacy trials, however, gave equivocal results. AlgoS trial is a composite BCI that yielded inconclusive results when analyzed with Intention-To-Treat strategy. In order to elicit intervention strengths and weaknesses, post-hoc analyses of AlgoS data were performed.<br><br>METHODS: AlgoS was a randomized controlled trial conducted in 23 French hospitals. Suicide attempters were randomly assigned to either the intervention group (AlgoS) or the control group (Treatment as usual TAU). In the AlgoS arm, first-time suicide attempters received crisis cards; non first-time suicide attempters received a phone call, and post-cards if the call could not be completed, or if the participant was in crisis and/or non-compliant with the post-discharge treatment. An As Treated strategy, accounting for the actual intervention received, was combined with subgroup analyses.<br><br>RESULTS: 1,040 patients were recruited and randomized into two groups of N = 520, from which 53 withdrew participation; 15 were excluded after inclusion/exclusion criteria reassessment. AlgoS first attempters were less likely to reiterate suicide attempt (SA) than their TAU counterparts at 6 and 13-14 months (RR [95% CI]: 0.46 [0.25-0.85] and 0.50 [0.31-0.81] respectively). AlgoS non-first attempters had similar SA rates as their TAU counterparts at 6 and 13-14 months (RR [95% CI]: 0.84 [0.57-1.25] and 1.00 [0.73-1.37] respectively). SA rates were dissimilar within the AlgoS non-first attempter group.<br><br>CONCLUSIONS: This new set of analysis suggests that crisis cards could be efficacious to prevent new SA attempts among first-time attempters, while phone calls were probably not significantly efficacious among multi-attempters. Importantly, phone calls were informative of new SA risk, thus a key component of future interventions.},
}
@article {pmid30705750,
year = {2018},
author = {Mitrasinovic, S and Brown, APY and Schaefer, AT and Chang, SD and Appelboom, G},
title = {Silicon Valley new focus on brain computer interface: hype or hope for new applications?.},
journal = {F1000Research},
volume = {7},
number = {},
pages = {1327},
pmid = {30705750},
issn = {2046-1402},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Neurons ; *Neurosciences ; },
abstract = {In the last year there has been increasing interest and investment into developing devices to interact with the central nervous system, in particular developing a robust brain-computer interface (BCI). In this article, we review the most recent research advances and the current host of engineering and neurological challenges that must be overcome for clinical application. In particular, space limitations, isolation of targeted structures, replacement of probes following failure, delivery of nanomaterials and processing and understanding recorded data. Neural engineering has developed greatly over the past half-century, which has allowed for the development of better neural recording techniques and clinical translation of neural interfaces. Implementation of general purpose BCIs face a number of constraints arising from engineering, computational, ethical and neuroscientific factors that still have to be addressed. Electronics have become orders of magnitude smaller and computationally faster than neurons, however there is much work to be done in decoding the neural circuits. New interest and funding from the non-medical community may be a welcome catalyst for focused research and development; playing an important role in future advancements in the neuroscience community.},
}
@article {pmid30704243,
year = {2019},
author = {Baranes, K and Hibsh, D and Cohen, S and Yamin, T and Efroni, S and Sharoni, A and Shefi, O},
title = {Comparing Transcriptome Profiles of Neurons Interfacing Adjacent Cells and Nanopatterned Substrates Reveals Fundamental Neuronal Interactions.},
journal = {Nano letters},
volume = {19},
number = {3},
pages = {1451-1459},
doi = {10.1021/acs.nanolett.8b03879},
pmid = {30704243},
issn = {1530-6992},
mesh = {Animals ; Axons/chemistry/metabolism ; Gene Expression Regulation ; Humans ; Neurogenesis/*genetics ; Neurons/*chemistry ; Regenerative Medicine ; Signal Transduction/genetics ; Synapses/chemistry/*genetics ; Transcriptome/*genetics ; },
abstract = {Developing neuronal axons are directed by chemical and physical signals toward a myriad of target cells. According to current dogma, the resulting network architecture is critically shaped by electrical interconnections, the synapses; however, key mechanisms translating neuronal interactions into neuronal growth behavior during network formation are still unresolved. To elucidate these mechanisms, we examined neurons interfacing nanopatterned substrates and compared them to natural interneuron interactions. We grew similar neuronal populations under three connectivity conditions, (1) the neurons are isolated, (2) the neurons are interconnected, and (3) the neurons are connected only to artificial substrates, then quantitatively compared both the cell morphologies and the transcriptome-expression profiles. Our analysis shows that whereas axon-guidance signaling pathways in isolated neurons are predominant, in isolated neurons interfacing nanotopography, these pathways are downregulated, similar to the interconnected neurons. Moreover, in nanotopography, interfacing neuron genes related to synaptogenesis and synaptic regulation are highly expressed, that is, again resembling the behavior of interconnected neurons. These molecular findings demonstrate that interactions with nanotopographies, although not leading to electrical coupling, play a comparable functional role in two major routes, neuronal guidance and network formation, with high relevance to the design of regenerative interfaces.},
}
@article {pmid30703032,
year = {2019},
author = {Lei, B and Liu, X and Liang, S and Hang, W and Wang, Q and Choi, KS and Qin, J},
title = {Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {497-506},
doi = {10.1109/TNSRE.2019.2895064},
pmid = {30703032},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/statistics &amp; numerical data ; Female ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; *Neural Networks, Computer ; Paralysis/rehabilitation ; Support Vector Machine ; Virtual Reality ; Walking/*physiology ; Young Adult ; },
abstract = {Brain-computer interfaces based on motor imagery (MI) have been widely used to support the rehabilitation of motor functions of the upper limbs rather than lower limbs. This is probably because it is more difficult to detect the brain activities of lower limb MI. In order to reliably detect the brain activities of lower limbs to restore or improve the walking ability of the disabled, we propose a new paradigm of walking imagery (WI) in a virtual environment (VE), in order to elicit the reliable brain activities and achieve a significant training effect. First, we extract and fuse both the spatial and time-frequency features as a multi-view feature to represent the patterns in the brain activity. Second, we design a multi-view multi-level deep polynomial network (MMDPN) to explore the complementarity among the features so as to improve the detection of walking from an idle state. Our extensive experimental results show that the VE-based paradigm significantly performs better than the traditional text-based paradigm. In addition, the VE-based paradigm can effectively help users to modulate the brain activities and improve the quality of electroencephalography signals. We also observe that the MMDPN outperforms other deep learning methods in terms of classification performance.},
}
@article {pmid30703031,
year = {2019},
author = {Song, M and Kim, J},
title = {A Paradigm to Enhance Motor Imagery Using Rubber Hand Illusion Induced by Visuo-Tactile Stimulus.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {477-486},
doi = {10.1109/TNSRE.2019.2895029},
pmid = {30703031},
issn = {1558-0210},
mesh = {Adult ; Brain-Computer Interfaces ; Cortical Synchronization ; Electroencephalography ; Female ; Functional Laterality/physiology ; *Hand ; Humans ; *Illusions ; *Imagination ; Male ; *Photic Stimulation ; *Physical Stimulation ; Psychomotor Performance/physiology ; Rehabilitation/methods ; Touch ; Virtual Reality ; },
abstract = {Enhancing motor imagery (MI) results in amplified event-related desynchronization (ERD) and is important for MI-based rehabilitation and brain-computer interface (BCI) applications. Many attempts to enhance the MI by providing a visual guidance have been reported. We believe that the rubber hand illusion (RHI), which induces body ownership over an external object, can provide better guidance to enhance MI; thus, an RHI-based paradigm with motorized moving rubber hand was proposed. To validate the proposed MI enhancing paradigm, we conducted an experimental comparison among paradigms with 20 healthy subjects. The peak amplitude and arrival times of ERD were compared at contralateral and ipsilateral electroencephalogram channels. We found significantly amplified ERD caused by the proposed paradigm, which is similar to the ERD caused by motor execution. In addition, the arrival time suggests that the proposed paradigm is applicable for BCI. In conclusion, the proposed paradigm can significantly enhance the MI with better characteristics for use with BCI.},
}
@article {pmid30703030,
year = {2019},
author = {Meinel, A and Kolkhorst, H and Tangermann, M},
title = {Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {3},
pages = {378-388},
doi = {10.1109/TNSRE.2019.2894914},
pmid = {30703030},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Cluster Analysis ; Data Mining/*methods ; Electroencephalography/*statistics &amp; numerical data ; Healthy Volunteers ; Humans ; Machine Learning ; Male ; Psychomotor Performance/physiology ; },
abstract = {Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface protocols in stroke rehabilitation.},
}
@article {pmid30700310,
year = {2019},
author = {Han, CH and Kim, YW and Kim, DY and Kim, SH and Nenadic, Z and Im, CH},
title = {Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {18},
pmid = {30700310},
issn = {1743-0003},
support = {2017-0-00432//Institute of Information &amp; Communications Technology Planning &amp; Evaluation/International ; },
mesh = {Amyotrophic Lateral Sclerosis/complications ; Brain/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Locked-In Syndrome/*physiopathology ; Middle Aged ; *Nonverbal Communication ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).<br><br>METHODS: In this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.<br><br>RESULTS: An online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5&#x2009;s.<br><br>CONCLUSIONS: Our results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.},
}
@article {pmid30699946,
year = {2019},
author = {Dai, M and Zheng, D and Na, R and Wang, S and Zhang, S},
title = {EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {3},
pages = {},
pmid = {30699946},
issn = {1424-8220},
support = {61873021//National Natural Science Foundation of China/ ; 2014YQ350461//National Key Scientific Instrument and Equipment Development Project/ ; },
abstract = {Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.},
}
@article {pmid30699389,
year = {2019},
author = {Ai, Q and Chen, A and Chen, K and Liu, Q and Zhou, T and Xin, S and Ji, Z},
title = {Feature extraction of four-class motor imagery EEG signals based on functional brain network.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026032},
doi = {10.1088/1741-2552/ab0328},
pmid = {30699389},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; },
abstract = {OBJECTIVE: A motor-imagery-based brain-computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects.<br><br>APPROACH: This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method.<br><br>MAIN RESULTS: As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance.<br><br>SIGNIFICANCE: The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.},
}
@article {pmid30698704,
year = {2019},
author = {Lee, MH and Kwon, OY and Kim, YJ and Kim, HK and Lee, YE and Williamson, J and Fazli, S and Lee, SW},
title = {EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy.},
journal = {GigaScience},
volume = {8},
number = {5},
pages = {},
pmid = {30698704},
issn = {2047-217X},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Movement/physiology ; },
abstract = {BACKGROUND: Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature.<br><br>RESULTS: Average decoding accuracies across all subjects and sessions were 71.1% (&#xb1; 0.15), 96.7% (&#xb1; 0.05), and 95.1% (&#xb1; 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system.<br><br>CONCLUSIONS: Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.},
}
@article {pmid30696881,
year = {2019},
author = {Akbari, H and Khalighinejad, B and Herrero, JL and Mehta, AD and Mesgarani, N},
title = {Towards reconstructing intelligible speech from the human auditory cortex.},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {874},
pmid = {30696881},
issn = {2045-2322},
support = {R01 DC014279/DC/NIDCD NIH HHS/United States ; R21 MH114166/MH/NIMH NIH HHS/United States ; },
mesh = {Acoustic Stimulation/methods ; Algorithms ; Auditory Cortex/physiology ; Brain Mapping ; Deep Learning ; Evoked Potentials, Auditory/physiology ; Humans ; Neural Networks, Computer ; Neural Prostheses ; Speech/*physiology ; Speech Intelligibility/*physiology ; Speech Perception/*physiology ; },
abstract = {Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.},
}
@article {pmid30693612,
year = {2019},
author = {Halder, S and Leinfelder, T and Schulz, SM and Kübler, A},
title = {Neural mechanisms of training an auditory event-related potential task in a brain-computer interface context.},
journal = {Human brain mapping},
volume = {40},
number = {8},
pages = {2399-2412},
pmid = {30693612},
issn = {1097-0193},
support = {//Alexander von Humboldt-Stiftung/International ; HBP-SP3-SGA1 Conscious Brain 72027//Human Brain Project/International ; Neurophysiological assessment of consciousness 262950/F20//Norges Forskningsr&#xe5;d/International ; //Alexander von Humboldt Foundation/International ; },
mesh = {Adult ; Attention/*physiology ; Auditory Perception/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/diagnostic imaging/*physiopathology ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Auditory/*physiology ; Female ; *Functional Neuroimaging ; Humans ; Magnetic Resonance Imaging ; Male ; Motor Cortex/diagnostic imaging/physiology ; Parietal Lobe/*diagnostic imaging/physiology ; *Practice, Psychological ; Prefrontal Cortex/diagnostic imaging/*physiology ; Putamen/diagnostic imaging/*physiology ; Temporal Lobe/diagnostic imaging/physiology ; Young Adult ; },
abstract = {Effective use of brain-computer interfaces (BCIs) typically requires training. Improved understanding of the neural mechanisms underlying BCI training will facilitate optimisation of BCIs. The current study examined the neural mechanisms related to training for electroencephalography (EEG)-based communication with an auditory event-related potential (ERP) BCI. Neural mechanisms of training in 10 healthy volunteers were assessed with functional magnetic resonance imaging (fMRI) during an auditory ERP-based BCI task before (t1) and after (t5) three ERP-BCI training sessions outside the fMRI scanner (t2, t3, and t4). Attended stimuli were contrasted with ignored stimuli in the first-level fMRI data analysis (t1 and t5); the training effect was verified using the EEG data (t2-t4); and brain activation was contrasted before and after training in the second-level fMRI data analysis (t1 vs. t5). Training increased the communication speed from 2.9 bits/min (t2) to 4 bits/min (t4). Strong activation was found in the putamen, supplementary motor area (SMA), and superior temporal gyrus (STG) associated with attention to the stimuli. Training led to decreased activation in the superior frontal gyrus and stronger haemodynamic rebound in the STG and supramarginal gyrus. The neural mechanisms of ERP-BCI training indicate improved stimulus perception and reduced mental workload. The ERP task used in the current study showed overlapping activations with a motor imagery based BCI task from a previous study on the neural mechanisms of BCI training in the SMA and putamen. This suggests commonalities between the neural mechanisms of training for both BCI paradigms.},
}
@article {pmid30691813,
year = {2019},
author = {Heidelberg, L and Uhlich, R and Bosarge, P and Kerby, J and Hu, P},
title = {The Depth of Sternal Fracture Displacement Is Not Associated With Blunt Cardiac Injury.},
journal = {The Journal of surgical research},
volume = {235},
number = {},
pages = {322-328},
doi = {10.1016/j.jss.2018.08.051},
pmid = {30691813},
issn = {1095-8673},
mesh = {Adult ; Aged ; Female ; Fractures, Bone/*complications ; Heart Injuries/*etiology ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Sternum/*injuries ; Thoracic Injuries/*complications ; },
abstract = {BACKGROUND: Little evidence exist associating displaced sternal fractures with blunt cardiac injury (BCI), especially regarding the depth and severity of sternal fracture displacement and risk of BCI. The purpose of this study was to quantify sternal fracture severity by the degree of displacement and to evaluate the association of fracture severity with BCI.<br><br>MATERIALS AND METHODS: A single institution retrospective review was performed from 2011 to 2014. All adult patients with sternal fracture were identified from the trauma registry, and sternal fracture displacement was quantified as mild (>0 mm, <5 mm), moderate (&#x2265;5 mm, <10 mm), or severe (&#x2265;10 mm). BCI was diagnosed according to standard AAST grading. Analysis was performed to assess the association of sternal fracture displacement with BCI, which was the primary outcome of interest.<br><br>RESULTS: Two hundred thirty-five patients with sternal fractures were included in the study. Forty-five percentage of patients suffered a displaced fracture, and 42.6% were diagnosed with BCI. There was no difference in mean fracture displacement when compared to patients without BCI (2.4 versus 1.6 mm, P = 0.07). There was no significant increase in BCI with sternal fracture displacement when compared to patients with nondisplaced fractures (44.3% versus 41.1%, P = 0.62). Neither fracture displacement (OR 1.10, CI 95% 0.65-1.88) nor severe displacement (OR 2.34, CI 95% 0.64-8.54) was associated with significantly increased risk of BCI.<br><br>CONCLUSIONS: There is no significant association between the depth of sternal fracture displacement and BCI. Further evaluation and management for BCI should be reserved in the absence of additional symptoms or findings.},
}
@article {pmid30691180,
year = {2019},
author = {Masood, N and Farooq, H},
title = {Investigating EEG Patterns for Dual-Stimuli Induced Human Fear Emotional State.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {3},
pages = {},
pmid = {30691180},
issn = {1424-8220},
support = {NOT APPLICABLE//Higher Education Commision, Pakistan/ ; },
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Emotions/*physiology ; Fear/*physiology ; Humans ; Support Vector Machine ; },
abstract = {Most electroencephalography (EEG) based emotion recognition systems make use of videos and images as stimuli. Few used sounds, and even fewer studies were found involving self-induced emotions. Furthermore, most of the studies rely on single stimuli to evoke emotions. The question of "whether different stimuli for same emotion elicitation generate any subject-independent correlations" remains unanswered. This paper introduces a dual modality based emotion elicitation paradigm to investigate if emotions can be classified induced with different stimuli. A method has been proposed based on common spatial pattern (CSP) and linear discriminant analysis (LDA) to analyze human brain signals for fear emotions evoked with two different stimuli. Self-induced emotional imagery is one of the considered stimuli, while audio/video clips are used as the other stimuli. The method extracts features from the CSP algorithm and LDA performs classification. To investigate associated EEG correlations, a spectral analysis was performed. To further improve the performance, CSP was compared with other regularized techniques. Critical EEG channels are identified based on spatial filter weights. To the best of our knowledge, our work provides the first contribution for the assessment of EEG correlations in the case of self versus video induced emotions captured with a commercial grade EEG device.},
}
@article {pmid30691041,
year = {2019},
author = {Blanco, JA and Vanleer, AC and Calibo, TK and Firebaugh, SL},
title = {Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {3},
pages = {},
pmid = {30691041},
issn = {1424-8220},
support = {N001613WX20992//Office of Naval Research/ ; },
mesh = {Adult ; Algorithms ; Cognition/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; },
abstract = {This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color[-]word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4[-]8 Hz), alpha (8[-]13 Hz), and beta (13[-]30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode[-]feature combinations worked broadly well across subjects to distinguish stress states.},
}
@article {pmid30687057,
year = {2018},
author = {Tramonte, S and Sorbello, R and Guger, C and Chella, A},
title = {Acceptability Study of A3-K3 Robotic Architecture for a Neurorobotics Painting.},
journal = {Frontiers in neurorobotics},
volume = {12},
number = {},
pages = {81},
pmid = {30687057},
issn = {1662-5218},
abstract = {In this paper, authors present a novel architecture for controlling an industrial robot via Brain Computer Interface. The robot used is a Series 2000 KR 210-2. The robotic arm was fitted with DI drawing devices that clamp, hold and manipulate various artistic media like brushes, pencils, pens. User selected a high-level task, for instance a shape or movement, using a human machine interface and the translation in robot movement was entirely demanded to the Robot Control Architecture defining a plan to accomplish user's task. The architecture was composed by a Human Machine Interface based on P300 Brain Computer Interface and a robotic architecture composed by a deliberative layer and a reactive layer to translate user's high-level command in a stream of movement for robots joints. To create a real-case scenario, the architecture was presented at Ars Electronica Festival, where the A3-K3 architecture has been used for painting. Visitors completed a survey to address 4 self-assessed different dimensions related to human-robot interaction: the technology knowledge, the personal attitude, the innovativeness and the satisfaction. The obtained results have led to further exploring the border of human-robot interaction, highlighting the possibilities of human expression in the interaction process with a machine to create art.},
}
@article {pmid30682766,
year = {2019},
author = {Valeriani, D and Cinel, C and Poli, R},
title = {Brain[-]Computer Interfaces for Human Augmentation.},
journal = {Brain sciences},
volume = {9},
number = {2},
pages = {},
pmid = {30682766},
issn = {2076-3425},
abstract = {The field of brain[-]computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...].},
}
@article {pmid30682025,
year = {2019},
author = {Sannelli, C and Vidaurre, C and Müller, KR and Blankertz, B},
title = {A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity.},
journal = {PloS one},
volume = {14},
number = {1},
pages = {e0207351},
pmid = {30682025},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Aged ; Biofeedback, Psychology ; *Brain-Computer Interfaces ; Calibration ; Female ; Humans ; Male ; Middle Aged ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.},
}
@article {pmid30681569,
year = {2019},
author = {Li, X and Wang, Z and Yang, Y and Meng, F and He, Y and Yang, P},
title = {Myocardial infarction following a blunt chest trauma: A case report.},
journal = {Medicine},
volume = {98},
number = {4},
pages = {e14103},
pmid = {30681569},
issn = {1536-5964},
mesh = {Adult ; Diagnostic Errors ; Heart Failure/etiology ; Humans ; Male ; Myocardial Infarction/complications/*etiology ; Thoracic Injuries/*complications ; Time-to-Treatment ; Wounds, Nonpenetrating/*complications ; },
abstract = {RATIONALE: Blunt cardiac injury (BCI) is a common complication after blunt chest trauma, which can lead to mild arrhythmia, severe chamber or valvular rupture, or even death. Myocardial infarction following blunt chest trauma is a rare but fatal condition.<br><br>PATIENT CONCERNS: A 38-year-old, previously healthy, man was admitted to our hospital with a complaint of dyspnea. He had a history of being hit in the chest by a high-speed screw while working in a factory 3 months before he was admitted to the hospital.<br><br>DIAGNOSIS: After performing coronary angiography and echocardiography, he was finally diagnosed with myocardial infarction.<br><br>INTERVENTIONS: He received optimized medications, including diuretics, &#x3b2;-blockers, and cardiac stimulants.<br><br>OUTCOMES: At the 4-year follow-up, the patient was diagnosed as having chronic heart failure with a reduced ejection fraction.<br><br>LESSONS: Owing to the first doctor's lack of experience and knowledge with this case, the patient was misdiagnosed and treatment was delayed, which subsequently led to heart failure.BCI can lead to myocardial infarction if patients are misdiagnosed and treatment is delayed. Thus, surgeons and physicians should consider cardiac complications in patients with chest trauma to reduce the incidence of its misdiagnosis.},
}
@article {pmid30678038,
year = {2019},
author = {Budoff, SA and Yano, KM and de Mesquita, FC and Doerl, JG and de Santana, MB and Nascimento, MSL and Kunicki, ACB and de Araújo, MFP},
title = {Astrocytic Response to Acutely- and Chronically-Implanted Microelectrode Arrays in the Marmoset (Callithrix jacchus) Brain.},
journal = {Brain sciences},
volume = {9},
number = {2},
pages = {},
pmid = {30678038},
issn = {2076-3425},
abstract = {Microelectrode implants are an important tool in neuroscience research and in developing brain[-]machine interfaces. Data from rodents have consistently shown that astrocytes are recruited to the area surrounding implants, forming a glial scar that increases electrode impedance and reduces chronic utility. However, studies in non-human primates are scarce, with none to date in marmosets. We used glial fibrillary acidic protein (GFAP) immunostaining to characterize the acute and chronic response of the marmoset brain to microelectrodes. By using densitometry, we showed that marmoset astrocytes surround brain implants and that a glial scar is formed over time, with significant increase in the chronic condition relative to the acute condition animal.},
}
@article {pmid30677307,
year = {2019},
author = {Morone, G and Spitoni, GF and De Bartolo, D and Ghanbari Ghooshchy, S and Di Iulio, F and Paolucci, S and Zoccolotti, P and Iosa, M},
title = {Rehabilitative devices for a top-down approach.},
journal = {Expert review of medical devices},
volume = {16},
number = {3},
pages = {187-195},
doi = {10.1080/17434440.2019.1574567},
pmid = {30677307},
issn = {1745-2422},
mesh = {Acoustics ; Brain-Computer Interfaces ; Humans ; Music Therapy ; Neurological Rehabilitation/*instrumentation ; Robotics ; Virtual Reality ; },
abstract = {INTRODUCTION: In recent years, neurorehabilitation has moved from a 'bottom-up' to a 'top down' approach. This change has also involved the technological devices developed for motor and cognitive rehabilitation. It implies that during a task or during therapeutic exercises, new 'top-down' approaches are being used to stimulate the brain in a more direct way to elicit plasticity-mediated motor re-learning. This is opposed to 'Bottom up' approaches, which act at the physical level and attempt to bring about changes at the level of the central neural system.<br><br>AREAS COVERED: In the present unsystematic review, we present the most promising innovative technological devices that can effectively support rehabilitation based on a top-down approach, according to the most recent neuroscientific and neurocognitive findings. In particular, we explore if and how the use of new technological devices comprising serious exergames, virtual reality, robots, brain computer interfaces, rhythmic music and biofeedback devices might provide a top-down based approach.<br><br>EXPERT COMMENTARY: Motor and cognitive systems are strongly harnessed in humans and thus cannot be separated in neurorehabilitation. Recently developed technologies in motor-cognitive rehabilitation might have a greater positive effect than conventional therapies.},
}
@article {pmid30677013,
year = {2019},
author = {Mabud, TS and de Lourdes Delgado Alves, M and Ko, AI and Basu, S and Walter, KS and Cohen, T and Mathema, B and Colijn, C and Lemos, E and Croda, J and Andrews, JR},
title = {Evaluating strategies for control of tuberculosis in prisons and prevention of spillover into communities: An observational and modeling study from Brazil.},
journal = {PLoS medicine},
volume = {16},
number = {1},
pages = {e1002737},
pmid = {30677013},
issn = {1549-1676},
support = {DP2 MD010478/MD/NIMHD NIH HHS/United States ; D43 TW010540/TW/FIC NIH HHS/United States ; N01AI30058/AI/NIAID NIH HHS/United States ; U54 MD010724/MD/NIMHD NIH HHS/United States ; R25 TW009338/TW/FIC NIH HHS/United States ; R01 AI130058/AI/NIAID NIH HHS/United States ; },
mesh = {Brazil/epidemiology ; Community-Acquired Infections/epidemiology/prevention &amp; control/transmission ; Female ; Humans ; Incidence ; Latent Tuberculosis/epidemiology/prevention &amp; control/transmission ; Male ; Models, Statistical ; *Prisons/organization &amp; administration/statistics &amp; numerical data ; Proportional Hazards Models ; Residence Characteristics ; Time Factors ; Tuberculosis, Pulmonary/epidemiology/*prevention &amp; control/transmission ; },
abstract = {BACKGROUND: It has been hypothesized that prisons serve as amplifiers of general tuberculosis (TB) epidemics, but there is a paucity of data on this phenomenon and the potential population-level effects of prison-focused interventions. This study (1) quantifies the TB risk for prisoners as they traverse incarceration and release, (2) mathematically models the impact of prison-based interventions on TB burden in the general population, and (3) generalizes this model to a wide range of epidemiological contexts.<br><br>METHODS AND FINDINGS: We obtained individual-level incarceration data for all inmates (n = 42,925) and all reported TB cases (n = 5,643) in the Brazilian state of Mato Grosso do Sul from 2007 through 2013. We matched individuals between prisoner and TB databases and estimated the incidence of TB from the time of incarceration and the time of prison release using Cox proportional hazards models. We identified 130 new TB cases diagnosed during incarceration and 170 among individuals released from prison. During imprisonment, TB rates increased from 111 cases per 100,000 person-years at entry to a maximum of 1,303 per 100,000 person-years at 5.2 years. At release, TB incidence was 229 per 100,000 person-years, which declined to 42 per 100,000 person-years (the average TB incidence in Brazil) after 7 years. We used these data to populate a compartmental model of TB transmission and incarceration to evaluate the effects of various prison-based interventions on the incidence of TB among prisoners and the general population. Annual mass TB screening within Brazilian prisons would reduce TB incidence in prisons by 47.4% (95% Bayesian credible interval [BCI], 44.4%-52.5%) and in the general population by 19.4% (95% BCI 17.9%-24.2%). A generalized model demonstrates that prison-based interventions would have maximum effectiveness in reducing community incidence in populations with a high concentration of TB in prisons and greater degrees of mixing between ex-prisoners and community members. Study limitations include our focus on a single Brazilian state and our retrospective use of administrative databases.<br><br>CONCLUSIONS: Our findings suggest that the prison environment, more so than the prison population itself, drives TB incidence, and targeted interventions within prisons could have a substantial effect on the broader TB epidemic.},
}
@article {pmid30671723,
year = {2019},
author = {Athif, M and Ren, H},
title = {WaveCSP: a robust motor imagery classifier for consumer EEG devices.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {42},
number = {1},
pages = {159-168},
doi = {10.1007/s13246-019-00721-0},
pmid = {30671723},
issn = {1879-5447},
support = {R-397-000-227-112//Singapore Academic Research Fund/ ; },
mesh = {Algorithms ; Electroencephalography/*instrumentation ; Humans ; *Imagery, Psychotherapy ; Machine Learning ; *Motor Activity ; *Wavelet Analysis ; },
abstract = {There is an increasing demand for reliable motor imagery (MI) classification algorithms for applications in consumer level brain-computer interfacing (BCI). For the practical use, such algorithms must be robust to both device limitations and subject variability, which make MI classification a challenging task. This study proposes methods to study the effect of limitations including a limited number of electrodes, limited spatial distribution of electrodes, lower signal quality, subject variabilities and BCI literacy, on the performance of MI classification. To mitigate these limitations, we propose a machine learning approach, WaveCSP that uses 24 features extracted from EEG signals using wavelet transform and common spatial pattern (CSP) filtering techniques. The algorithm shows better performance in terms of subject variability compared to existing work. The application of WaveCSP to Physionet MI database shows more than 50% of the 109 subjects achieving accuracy higher than 64%. The data obtained from a commercial EEG headset using the same experimental protocol result in up to four out of five subjects who had prior BCI experience (out of a total of 25 subjects) performing with accuracy higher than 64%.},
}
@article {pmid30671443,
year = {2018},
author = {Umar Saeed, SM and Anwar, SM and Majid, M and Awais, M and Alnowami, M},
title = {Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset.},
journal = {BioMed research international},
volume = {2018},
number = {},
pages = {1049257},
pmid = {30671443},
issn = {2314-6141},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Stress, Psychological/*physiopathology ; Support Vector Machine ; Surveys and Questionnaires ; Young Adult ; },
abstract = {A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.},
}
@article {pmid30668507,
year = {2019},
author = {Abibullaev, B and Zollanvari, A},
title = {Learning Discriminative Spatiospectral Features of ERPs for Accurate Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {5},
pages = {2009-2020},
doi = {10.1109/JBHI.2018.2883458},
pmid = {30668507},
issn = {2168-2208},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Machine Learning ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Constructing accurate predictive models is at the heart of brain-computer interfaces (BCIs) because these models can ultimately translate brain activities into communication and control commands. The majority of the previous work in BCI use spatial, temporal, or spatiotemporal features of event-related potentials (ERPs). In this study, we examined the discriminatory effect of their spatiospectral features to capture the most relevant set of neural activities from electroencephalographic recordings that represent users' mental intent. In this regard, we model ERP waveforms using a sum of sinusoids with unknown amplitudes, frequencies, and phases. The effect of this signal modeling step is to represent high-dimensional ERP waveforms in a substantially lower dimensionality space, which includes their dominant power spectral contents. We found that the most discriminative frequencies for accurate decoding of visual attention modulated ERPs lie in a spectral range less than 6.4 Hz. This was empirically verified by treating dominant frequency contents of ERP waveforms as feature vectors in the state-of-the-art machine learning techniques used herein. The constructed predictive models achieved remarkable performance, which for some subjects was as high as 94% as measured by the area under curve. Using these spectral contents, we further studied the discriminatory effect of each channel and proposed an efficient strategy to choose subject-specific subsets of channels that generally led to classifiers with comparable performance.},
}
@article {pmid30668500,
year = {2019},
author = {Gu, Z and Chen, Z and Zhang, J and Zhang, X and Yu, ZL},
title = {An Online Interactive Paradigm for P300 Brain-Computer Interface Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {2},
pages = {152-161},
doi = {10.1109/TNSRE.2019.2892967},
pmid = {30668500},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Online Systems ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {For each brain-computer interface system, efficiency is a key issue that considers both accuracy and speed. The P300 spellers built upon oddball paradigm are usually less efficient due to the repetitive stimulation of multiple characters for reliable detection. In this paper, based on the online EEG signal, we propose an interactive paradigm for P300 speller to improve its efficiency, primarily focusing within the single characterP300 paradigm. Specifically, after each stimulation, we first evaluate the posterior probability of each character in the stimuli set to be the target. The lowprobability characters are then removed fromthe stimuli set in the subsequent round(s), as character flash continues until the probability of any character surpasses a predefined threshold. Then, the character is selected as the target and data collection for the trial terminates. By reducing stimulus sequence characters, the system efficiency can be substantially improved. The spelling accuracy is insignificantly affected as the characters being removed have low probability to be the target. The online experimental results from a total of eight subjects show that an average practical information transfer rate of 50.26 bits/min (i.e. 9.07 characters/min) has been achieved, with 91% average spelling accuracy rate.},
}
@article {pmid30663899,
year = {2019},
author = {Koch Fager, S and Fried-Oken, M and Jakobs, T and Beukelman, DR},
title = {New and emerging access technologies for adults with complex communication needs and severe motor impairments: State of the science.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {35},
number = {1},
pages = {13-25},
pmid = {30663899},
issn = {1477-3848},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; R43 DC014294/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Cerebral Palsy ; Communication Devices for People with Disabilities ; Communication Disorders/complications/*rehabilitation ; Dysarthria/rehabilitation ; *Eye Movement Measurements ; Eye Movements ; Humans ; *Inventions ; Locked-In Syndrome ; Motor Disorders/*complications ; Severity of Illness Index ; Speech Recognition Software ; Stroke ; Wearable Electronic Devices ; },
abstract = {Individuals with complex communication needs often use alternative access technologies to control their augmentative and alternative communication (AAC) devices, their computers, and mobile technologies. While a range of access devices is available, many challenges continue to exist, particularly for those with severe motor-control limitations. For some, access options may not be readily available or access itself may be inaccurate and frustrating. For others, access may be available but only under optimal conditions and support. There is an urgent need to develop new options for individuals with severe motor impairments and to leverage existing technology to improve efficiency, increase accuracy, and decrease fatigue of access. This paper describes person-centred research and development activities related to new and emerging access technologies, with a particular focus on adults with acquired neurological conditions.},
}
@article {pmid30662400,
year = {2018},
author = {Arvaneh, M and Robertson, IH and Ward, TE},
title = {A P300-Based Brain-Computer Interface for Improving Attention.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {524},
pmid = {30662400},
issn = {1662-5161},
abstract = {A Brain-computer Interface (BCI) can be used as a neurofeedback training tool to improve cognitive performance. BCIs aim to improve the effectiveness and efficiency of the conventional neurofeedback methods by focusing on the self-regulation of individualized neuromarkers rather than generic ones in a graphically appealing training environment. In this work, for the first time, we have modified a widely used P300-based speller BCI and used it as an engaging neurofeedack training game to enhance P300. According to the user's performance the game becomes more difficult in an adaptive manner, requiring the generation of a larger and stronger P300 (i.e., in terms of total energy) in response to target stimuli. Since the P300 is generated naturally without conscious effort in response to a target trial, unlike many rhythm-based neurofeedback tools, the ability to control the proposed P300-based neurofeedback training is obtained after a short calibration without undergoing tedious trial and error sessions. The performance of the proposed neurofeedback training was evaluated over a short time scale (approximately 30 min training) using 28 young adult participants who were randomly assigned to either the experimental group or the control group. In summary, our results show that the proposed P300-based BCI neurofeedback training yielded a significant enhancement in the ERP components of the target trials (i.e., 150-550 ms after the onset of stimuli which includes P300) as well as attenuation in the corresponding ERP components of the non-target trials. In addition, more centro-parietal alpha suppression was observed in the experimental group during the neurofeedback training as well as a post-training spatial attention task. Interestingly, a significant improvement in the response time of a spatial attention task performed immediately after the neurofeedback training was observed in the experimental group. This paper, as a proof-of-concept study, suggests that the proposed neurofeedback training tool is a promising tool for improving attention particularly for those who are at risk of attention deficiency.},
}
@article {pmid30660765,
year = {2020},
author = {Gui, Y and Duan, S and Xiao, L and Tang, J and Li, A},
title = {Bexarotent Attenuated Chronic Constriction Injury-Induced Spinal Neuroinflammation and Neuropathic Pain by Targeting Mitogen-Activated Protein Kinase Phosphatase-1.},
journal = {The journal of pain},
volume = {21},
number = {11-12},
pages = {1149-1159},
doi = {10.1016/j.jpain.2019.01.007},
pmid = {30660765},
issn = {1528-8447},
mesh = {Animals ; Bexarotene/*administration &amp; dosage ; Constriction ; Dose-Response Relationship, Drug ; Drug Delivery Systems/*methods ; Dual Specificity Phosphatase 1/antagonists &amp; inhibitors/*metabolism ; Inflammation Mediators/antagonists &amp; inhibitors/*metabolism ; Male ; Neuralgia/drug therapy/*metabolism ; Rats ; Spinal Cord/drug effects/*metabolism ; },
abstract = {It is widely accepted that neuroinflammation in the spinal cord contributes to the development of central sensitization in neuropathic pain. Mitogen-activated protein kinase (MAPK) activation plays a vital role in the development of neuroinflammation in the spinal cord. In this study, we investigated the effect of bexarotene (bex), a retinoid X receptor agonist, on MAPKs activation in chronic constriction injury (CCI)-induced neuropathic pain. The data showed that daily treatment with bex 50 mg/kg significantly alleviated CCI-induced nociceptive hypersensitivity in rats. Bex 50 mg/kg/day inhibited CCI-induced MAPKs (p38MAPK, ERK1/2, and JNK) activation and upregulation of proinflammatory factors (IL-1β, tumor necrosis factor-α and IL-6). Bex also reversed CCI-induced microglia activation in the ipsilateral spinal cord. Furthermore, bex treatment significantly upregulated MKP-1 in the spinal cord. These effects were completely abrogated by MKP-1 inhibitor BCI. These results indicated that bex relieved CCI-induced neuroinflammation and neuropathic pain by targeting MKP-1. Therefore, bex might be a potential agent for the treatment of neuropathic pain. PERSPECTIVE: Bex could relieve neuropathic pain behaviors in animals by reversing MKP-1 downregulation and MAPKs activation in the spinal cord. Therapeutic applications of bex may be extended beyond cutaneous T-cell lymphoma.},
}
@article {pmid30660481,
year = {2019},
author = {Dodia, S and Edla, DR and Bablani, A and Ramesh, D and Kuppili, V},
title = {An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis.},
journal = {Journal of neuroscience methods},
volume = {314},
number = {},
pages = {31-40},
doi = {10.1016/j.jneumeth.2019.01.007},
pmid = {30660481},
issn = {1872-678X},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Deception ; Discriminant Analysis ; Electroencephalography/*methods ; Event-Related Potentials, P300 ; Female ; Humans ; *Lie Detection ; Linear Models ; Male ; Pattern Recognition, Automated/*methods ; Visual Perception/physiology ; *Wavelet Analysis ; Young Adult ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. METHODOLOGY: In this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts.<br><br>Wavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed.<br><br>RESULTS: A novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity.<br><br>CONCLUSION: The method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI.},
}
@article {pmid30659844,
year = {2019},
author = {Lim, H and Ku, J},
title = {Multiple-command single-frequency SSVEP-based BCI system using flickering action video.},
journal = {Journal of neuroscience methods},
volume = {314},
number = {},
pages = {21-27},
doi = {10.1016/j.jneumeth.2019.01.005},
pmid = {30659844},
issn = {1872-678X},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Elbow ; *Electroencephalography/methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Mirror Neurons/physiology ; *Motion Perception/physiology ; Motor Activity ; Photic Stimulation/*methods ; Rest ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: The number of commands in a brain-computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy.<br><br>NEW METHOD: We expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19-21&#x2009;Hz), Mu power (8-13&#x2009;Hz), and simultaneous SSVEP and Mu power.<br><br>RESULTS: The SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right.<br><br>The video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together.<br><br>CONCLUSIONS: This paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.},
}
@article {pmid30658656,
year = {2019},
author = {Günter, C and Delbeke, J and Ortiz-Catalan, M},
title = {Safety of long-term electrical peripheral nerve stimulation: review of the state of the art.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {13},
pmid = {30658656},
issn = {1743-0003},
mesh = {Animals ; Electric Stimulation/*adverse effects/*methods ; Humans ; *Peripheral Nerves ; },
abstract = {BACKGROUND: Electrical stimulation of peripheral nerves is used in a variety of applications such as restoring motor function in paralyzed limbs, and more recently, as means to provide intuitive sensory feedback in limb prostheses. However, literature on the safety requirements for stimulation is scarce, particularly for chronic applications. Some aspects of nerve interfacing such as the effect of stimulation parameters on electrochemical processes and charge limitations have been reviewed, but often only for applications in the central nervous system. This review focuses on the safety of electrical stimulation of peripheral nerve in humans.<br><br>METHODS: We analyzed early animal studies evaluating damage thresholds, as well as more recent investigations in humans. Safety requirements were divided into two main categories: passive and active safety. We made the distinction between short-term (<&#x2009;30&#x2009;days) and chronic (>&#x2009;30&#x2009;days) applications, as well as between electrode preservation (biostability) and body tissue healthy survival (harmlessness). In addition, transferability of experimental results between different tissues and species was considered.<br><br>RESULTS: At present, extraneural electrodes have shown superior long-term stability in comparison to intraneural electrodes. Safety limitations on pulse amplitude (and consequently, charge injection) are dependent on geometrical factors such as electrode placement, size, and proximity to the stimulated fiber. In contrast, other parameters such as stimulation frequency and percentage of effective stimulation time are more generally applicable. Currently, chronic stimulation at frequencies below 30&#x2009;Hz and percentages of effective stimulation time below 50% is considered safe, but more precise data drawn from large databases are necessary. Unfortunately, stimulation protocols are not systematically documented in the literature, which limits the feasibility of meta-analysis and impedes the generalization of conclusions. We therefore propose a standardized list of parameters necessary to define electrical stimulation and allow future studies to contribute to meta-analyses.<br><br>CONCLUSION: The safety of chronic continuous peripheral nerve stimulation at frequencies higher than 30&#x2009;Hz has yet to be documented. Precise parameter values leading to stimulation-induced depression of neuronal excitability (SIDNE) and neuronal damage, as well as the transition between the two, are still lacking. At present, neural damage mechanisms through electrical stimulation remain obscure.},
}
@article {pmid30658523,
year = {2019},
author = {Singh, A and Lal, S and Guesgen, HW},
title = {Reduce Calibration Time in Motor Imagery Using Spatially Regularized Symmetric Positives-Definite Matrices Based Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {2},
pages = {},
pmid = {30658523},
issn = {1424-8220},
support = {RM20730//Massey University/ ; },
abstract = {Electroencephalogram (EEG) based motor imagery brain[-]computer interface (BCI) requires large number of subject specific training trials to calibrate the system for a new subject. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of a brain[-]computer interface is to reduce calibration time or completely eliminate it. To address this problem, existing approaches use covariance matrices of electroencephalography (EEG) trials as descriptors for decoding BCI but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing SPD based classification approach. However, SPD-based classification has limited applicability in small training sets because the dimensionality of covariance matrices is large in proportion to the number of trials. To overcome this drawback, our paper proposes a new framework that transforms SPD matrices in lower dimension through spatial filter regularized by prior information of EEG channels. The efficacy of the proposed approach was validated on the small sample scenario through Dataset IVa from BCI Competition III. The proposed approach achieved mean accuracy of 86.13 % and mean kappa of 0.72 on Dataset IVa. The proposed method outperformed other approaches in existing studies on Dataset IVa. Finally, to ensure the robustness of the proposed method, we evaluated it on Dataset IIIa from BCI Competition III and Dataset IIa from BCI Competition IV. The proposed method achieved mean accuracy 92.22 % and 81.21 % on Dataset IIIa and Dataset IIa, respectively.},
}
@article {pmid30658503,
year = {2019},
author = {Shokoueinejad, M and Park, DW and Jung, YH and Brodnick, SK and Novello, J and Dingle, A and Swanson, KI and Baek, DH and Suminski, AJ and Lake, WB and Ma, Z and Williams, J},
title = {Progress in the Field of Micro-Electrocorticography.},
journal = {Micromachines},
volume = {10},
number = {1},
pages = {},
pmid = {30658503},
issn = {2072-666X},
support = {N66001-12-C-4025//Defense Advanced Research Projects Agency/ ; W911NF-14-1-0652//Army Research Office/ ; 2018R1C1B6001529//National Research Foundation of Korea/ ; },
abstract = {Since the 1940s electrocorticography (ECoG) devices and, more recently, in the last decade, micro-electrocorticography (µECoG) cortical electrode arrays were used for a wide set of experimental and clinical applications, such as epilepsy localization and brain[-]computer interface (BCI) technologies. Miniaturized implantable µECoG devices have the advantage of providing greater-density neural signal acquisition and stimulation capabilities in a minimally invasive fashion. An increased spatial resolution of the µECoG array will be useful for greater specificity diagnosis and treatment of neuronal diseases and the advancement of basic neuroscience and BCI research. In this review, recent achievements of ECoG and µECoG are discussed. The electrode configurations and varying material choices used to design µECoG arrays are discussed, including advantages and disadvantages of µECoG technology compared to electroencephalography (EEG), ECoG, and intracortical electrode arrays. Electrode materials that are the primary focus include platinum, iridium oxide, poly(3,4-ethylenedioxythiophene) (PEDOT), indium tin oxide (ITO), and graphene. We discuss the biological immune response to µECoG devices compared to other electrode array types, the role of µECoG in clinical pathology, and brain[-]computer interface technology. The information presented in this review will be helpful to understand the current status, organize available knowledge, and guide future clinical and research applications of µECoG technologies.},
}
@article {pmid30655191,
year = {2019},
author = {Michalovic, E and Rocchi, M and Sweet, SN},
title = {Motivation and participation in daily and social activities among adults with spinal cord injury: Applying self-determination theory.},
journal = {Disability and health journal},
volume = {12},
number = {3},
pages = {489-494},
doi = {10.1016/j.dhjo.2018.11.015},
pmid = {30655191},
issn = {1876-7583},
mesh = {Activities of Daily Living/*psychology ; Adult ; Aged ; Aged, 80 and over ; Cross-Sectional Studies ; Persons with Disabilities/*psychology ; Female ; Humans ; *Interpersonal Relations ; Male ; Middle Aged ; *Motivation ; *Personal Autonomy ; *Personal Satisfaction ; Spinal Cord Injuries/*psychology ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: Individuals with a spinal cord injury (SCI) report decreased participation in daily and social activities. Self-determination theory (SDT) posits that individuals' need satisfaction and frustration predicts participation in health-related behaviours and this relationship is mediated by their motivation.<br><br>OBJECTIVES: This study explored the role of psychological needs and motivation in relation to participation in daily and social activities among adults with SCI. It was hypothesized that: a) need satisfaction and need frustration were positively associated with autonomous and controlled motivation, respectively, which, positively and negatively predicted participation in daily and social activities; b) autonomous and controlled motivation mediated the need satisfaction/frustration and participation relationship, respectively; and c) need frustration was positively related to amotivation, with no relationship between amotivation and activity participation.<br><br>METHODS: In this cross-sectional study, adults with SCI (N&#x202f;=&#x202f;131) completed a questionnaire regarding their need satisfaction/frustration, autonomous and controlled motivation, amotivation, and participation in daily and social activities.<br><br>RESULTS: Need satisfaction was positively related to autonomous motivation (&#x3b2;&#x202f;=&#x202f;0.29, 95%bias-corrected confidence interval (bCI): [0.04, 0.67]) and need frustration to controlled motivation (&#x3b2;&#x202f;=&#x202f;0.28, 95%bCI: [0.09, 0.55]). Autonomous motivation was positively associated with six participation categories: autonomous indoor, autonomous outdoor, family role, health, social life, and work/education. Autonomous motivation also mediated the relationship between need satisfaction and all six participation categories; whereas, neither controlled motivation nor amotivation mediated the relationship between need frustration and participation.<br><br>CONCLUSIONS: This study found that SDT provides a meaningful framework for understanding participation among adults with SCI.},
}
@article {pmid30653519,
year = {2019},
author = {Sahlu, I and Bauer, C and Ganaba, R and Preux, PM and Cowan, LD and Dorny, P and Millogo, A and Carabin, H},
title = {The impact of imperfect screening tools on measuring the prevalence of epilepsy and headaches in Burkina Faso.},
journal = {PLoS neglected tropical diseases},
volume = {13},
number = {1},
pages = {e0007109},
pmid = {30653519},
issn = {1935-2735},
support = {F31 NS093983/NS/NINDS NIH HHS/United States ; R01 NS064901/NS/NINDS NIH HHS/United States ; T32 HD007338/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Burkina Faso/epidemiology ; Child ; Child, Preschool ; Cross-Sectional Studies ; Epilepsy/*diagnosis/*epidemiology ; Female ; Headache Disorders/*diagnosis/*epidemiology ; Humans ; Male ; Mass Screening/*methods ; Middle Aged ; Neurocysticercosis/*complications ; Prevalence ; Randomized Controlled Trials as Topic ; Sensitivity and Specificity ; Surveys and Questionnaires/*standards ; Young Adult ; },
abstract = {BACKGROUND: Epilepsy and progressively worsening severe chronic headaches (WSCH) are the two most common clinical manifestations of neurocysticercosis, a form of cysticercosis. Most community-based studies in sub-Saharan Africa (SSA) use a two-step approach (questionnaire and confirmation) to estimate the prevalence of these neurological disorders and neurocysticercosis. Few validate the questionnaire in the field or account for the imperfect nature of the screening questionnaire and the fact that only those who screen positive have the opportunity to be confirmed. This study aims to obtain community-based validity estimates of a screening questionnaire, and to assess the impact of verification bias and misclassification error on prevalence estimates of epilepsy and WSCH.<br><br>Baseline screening questionnaire followed by neurological examination data from a cluster randomized controlled trial collected between February 2011 and January 2012 were used. Bayesian latent-class models were applied to obtain verification bias adjusted validity estimates for the screening questionnaire. These models were also used to compare the adjusted prevalence estimates of epilepsy and WSCH to those directly obtained from the data (i.e. unadjusted prevalence estimates). Different priors were used and their corresponding posterior inference was compared for both WSCH and epilepsy. Screening data were available for 4768 individuals. For epilepsy, posterior estimates for the sensitivity varied with the priors used but remained robust for the specificity, with the highest estimates at 66.1% (95%BCI: 56.4%;75.3%) for sensitivity and 88.9% (88.0%;89.8%) for specificity. For WSCH, the sensitivity and specificity estimates remained robust, with the highest at 59.6% (49.7%;69.1%) and 88.6% (87.6%;89.6%), respectively. The unadjusted prevalence estimates were consistently lower than the adjusted prevalence estimates for both epilepsy and WSCH.<br><br>CONCLUSIONS/SIGNIFICANCE: This study demonstrates that in some settings, the prevalence of epilepsy and WSCH can be considerably underestimated when using the two-step approach. We provide an analytic solution to obtain more valid prevalence estimates of these neurological disorders, although more community-based validity studies are needed to reduce the uncertainty of the estimates. Valid estimates of these two neurological disorders are essential to obtain accurate burden values for neglected tropical diseases such as neurocysticercosis that manifest as epilepsy or WSCH.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT03095339.},
}
@article {pmid30653259,
year = {2019},
author = {Varga, Z and Sinn, P and Seidman, AD},
title = {Summary of head-to-head comparisons of patient risk classifications by the 21-gene Recurrence Score® (RS) assay and other genomic assays for early breast cancer.},
journal = {International journal of cancer},
volume = {145},
number = {4},
pages = {882-893},
doi = {10.1002/ijc.32139},
pmid = {30653259},
issn = {1097-0215},
mesh = {Breast Neoplasms/*genetics/pathology ; Female ; Genomics/methods ; Humans ; Neoplasm Recurrence, Local/*genetics/pathology ; Prognosis ; Prospective Studies ; },
abstract = {Many genomic assays that assess recurrence risk in early breast cancer (EBC) are prognostic, but they differ in risk group stratification, which can affect clinical utility. Prospective outcomes of >60 K patients treated based on the 21-gene assay results have shown that chemotherapy may be safely omitted in EBC patents with low Recurrence Score (RS) results (RS < 18). Because of its extensive validation and wide clinical use, the RS assay is a common comparator in head-to-head studies with other assays. Published/presented studies of the RS assay performed on the same tumor samples with Breast Cancer Index (BCI), EndoPredict (EP) or EP+ clinical features (EPclin), MammaPrint (MMP) and/or Prosigna (ROR) assays were reviewed. Study findings were summarized descriptively.},
}
@article {pmid30650392,
year = {2019},
author = {Jones, MR and Sellers, EW},
title = {Faces, locations, and tools: a proposed two-stimulus P300 brain computer interface.},
journal = {Journal of neural engineering},
volume = {16},
number = {3},
pages = {036026},
doi = {10.1088/1741-2552/aaff22},
pmid = {30650392},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Facial Recognition/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Brain computer interface (BCI) technology can be important for those unable to communicate due to loss of muscle control. Given that the P300 Speller provides a relatively slow rate of communication, highly accurate classification is of great importance. Previous studies have shown that alternative stimuli (e.g. faces) can improve BCI speed and accuracy. The present study uses two new alternative stimuli, locations and graspable tools. Functional MRI studies have shown that images of familiar locations produce brain responses in the parahippocampal place area and graspable tools produce brain responses in premotor cortex.<br><br>APPROACH: The current studies show that location and tool stimuli produce unique and discriminable brain responses that can be used to improve offline classification accuracy. Experiment 1 presented face stimuli and location stimuli and Experiment 2 presented location and tool stimuli.<br><br>MAIN RESULTS: In both experiments, offline results showed that a stimulus specific classifier provided higher accuracy, speed, and bit rate.<br><br>SIGNIFICANCE: This study was used to provide preliminary offline support for using unique stimuli to improve speed and accuracy of the P300 Speller. Additional experiments should be conducted to examine the online efficacy of this novel paradigm.},
}
@article {pmid30646915,
year = {2019},
author = {Bertomeu-Motos, A and Ezquerro, S and Barios, JA and Lledó, LD and Domingo, S and Nann, M and Martin, S and Soekadar, SR and Garcia-Aracil, N},
title = {User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {10},
pmid = {30646915},
issn = {1743-0003},
support = {645322//H2020 LEIT Information and Communication Technologies/International ; DPI2015-70415-C2-2-R//Ministerio de Econom&#xed;a, Industria y Competitividad, Gobierno de Espa&#xf1;a/International ; },
mesh = {Activities of Daily Living ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Electrooculography ; Exoskeleton Device ; Female ; Humans ; Male ; Pilot Projects ; Quality of Life ; *Software ; *Spinal Cord Injuries ; User-Computer Interface ; },
abstract = {BACKGROUND: Assistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. Several studies have demonstrated the effectiveness in the use of assistive technology for environment control and communication systems. The progress of brain-computer interfaces (BCI) research together with exoskeleton enable a person with motor impairment to interact with new elements in the environment. This paper aims to evaluate the environment control interface (ECI) developed under the AIDE project conditions, a multimodal interface able to analyze and extract relevant information from the environments as well as from the identification of residual abilities, behaviors, and intentions of the user.<br><br>METHODS: This study evaluated the ECI in a simulated scenario using a two screen layout: one with the ECI and the other with a simulated home environment, developed for this purpose. The sensorimotor rhythms and the horizontal oculoversion, acquired through BCI2000, a multipurpose standard BCI platform, were used to online control the ECI after the user training and system calibration. Eight subjects with different neurological diseases and spinal cord injury participated in this study. The subjects performed simulated activities of daily living (ADLs), i.e. actions in the simulated environment as drink, switch on a lamp or raise the bed head, during ten minutes in two different modes, AIDE mode, using a prediction model, to recognize the user intention facilitating the scan, and Manual mode, without a prediction model.<br><br>RESULTS: The results show that the mean task time spent in the AIDE mode was less than in the Manual, i.e the users were able to perform more tasks in the AIDE mode during the same time. The results showed a statistically significant differences with p<0.001. Regarding the steps, i.e the number of abstraction levels crossed in the ECI to perform an ADL, the users performed one step in the 90% of the tasks using the AIDE mode and three steps, at least, were necessary in the Manual mode. The user's intention prediction was performed through conditional random fields (CRF), with a global accuracy about 87%.<br><br>CONCLUSIONS: The environment analysis and the identification of the user's behaviors can be used to predict the user intention opening a new paradigm in the design of the ECIs. Although the developed ECI was tested only in a simulated home environment, it can be easily adapted to a real environment increasing the user independence at home.},
}
@article {pmid30644660,
year = {2019},
author = {Yasui, T and Yamagiwa, S and Sawahata, H and Idogawa, S and Kubota, Y and Kita, Y and Yamashita, K and Numano, R and Koida, K and Kawano, T},
title = {A Magnetically Assembled High-Aspect-Ratio Needle Electrode for Recording Neuronal Activity.},
journal = {Advanced healthcare materials},
volume = {8},
number = {5},
pages = {e1801081},
doi = {10.1002/adhm.201801081},
pmid = {30644660},
issn = {2192-2659},
support = {17H03250//Grants-in-Aid for Scientific Research/International ; 26709024//Grants-in-Aid for Scientific Research/International ; 15H05917//Grants-in-Aid for Scientific Research/International ; //NEDO/International ; //Takeda Science Foundation/International ; //Toyota Physical &amp; Chemical Research Institute Scholars/International ; },
mesh = {Action Potentials/physiology ; Animals ; Brain/physiology ; Electric Impedance ; Electrodes, Implanted ; Electroencephalography/methods ; Magnetics/methods ; Mice ; Microelectrodes ; Needles ; Neurons/*physiology ; },
abstract = {Microelectrode devices, which enable the detection of neuronal signals in brain tissues, have made significant contributions in the field of neuroscience and the brain-machine interfaces. To further develop such microelectrode devices, the following requirements must be met: i) a fine needle's diameter (<30 µm) to reduce damage to tissues; ii) a long needle (e.g., ≈1 mm for rodents and ≈2 mm for macaques); and iii) multiple electrodes to achieve high spatial recording (<100 µm in pitch). In order to meet these requirements, this study herein reports an assembly technique for high-aspect-ratio microneedles, which employs a magnet. The assembly is demonstrated, in which nickel wires of length 750 µm and diameter 25 µm are produced on a silicon substrate. The impedance magnitude of the assembled needle-like electrode measured at 1 kHz is 5.6 kΩ, exhibiting output and input signal amplitudes of 96.7% at 1 kHz. To confirm the recording capability of the fabricated device, neuronal signal recordings are performed using mouse cerebra in vivo. The packaged single microneedle electrode penetrates the barrel field in the primary somatosensory cortex of the mouse and enables the detection of evoked neuronal activity of both local field potentials and action potentials.},
}
@article {pmid30643354,
year = {2018},
author = {Jones, DL and Rodriguez, VJ and De La Rosa, A and Dietch, J and Kumar, M},
title = {The role of sleep dysfunction in the relationship between trauma, neglect and depression in methamphetamine using men.},
journal = {Neurology, psychiatry, and brain research},
volume = {30},
number = {},
pages = {30-34},
pmid = {30643354},
issn = {0941-9500},
support = {P30 AI073961/AI/NIAID NIH HHS/United States ; R01 DA031201/DA/NIDA NIH HHS/United States ; },
abstract = {BACKGROUND: Childhood abuse and neglect, or childhood trauma (CT), has been associated with methamphetamine use, HIV, and depression. This study explored the potential for sleep dysfunction to influence the relationship between CT and depression in methamphetamine using men.<br><br>METHODS: A total of N = 347 men were enrolled: 1) HIV-uninfected, non-methamphetamine (MA) using heterosexual and homosexual men (HIV- MA-; n = 148), 2) MA-using MSM living with HIV (HIV + MA +; n = 147) and 3) HIV-uninfected, MA using MSM (HIV- MA +; n = 52). Participants completed measures of demographic characteristics, sleep dysfunction, childhood trauma, and depression.<br><br>RESULTS: Participants were on average 37 years old (SD = 9.65). Half of participants were Hispanic, and 48.1% had a monthly personal income of less than USD$500. Controlling for sleep dysfunction and control variables, the impact of CT on depression decreased significantly, b = 0.203, p < 0.001, and the indirect effect of CT on depression was significant according to a 95% bCI, b = 0.091, bCI (95% CI 0.057, 0.130). That is, sleep dysfunction partially explained the relationship between CT on depression.<br><br>LIMITATIONS: Important limitations included the cross-sectional design of the study, and the self-reported measure of sleep.<br><br>CONCLUSIONS: Results highlight the use of sleep interventions to prevent and treat depression, and the utility of assessing sleep disturbances in clinical care.},
}
@article {pmid30642781,
year = {2019},
author = {De Vico Fallani, F and Bassett, DS},
title = {Network neuroscience for optimizing brain-computer interfaces.},
journal = {Physics of life reviews},
volume = {31},
number = {},
pages = {304-309},
doi = {10.1016/j.plrev.2018.10.001},
pmid = {30642781},
issn = {1873-1457},
mesh = {*Brain-Computer Interfaces ; Humans ; Models, Neurological ; *Neurosciences ; },
abstract = {Human-machine interactions are being increasingly explored to create alternative ways of communication and to improve our daily life. Based on a classification of the user's intention from the user's underlying neural activity, brain-computer interfaces (BCIs) allow direct interactions with the external environment while bypassing the traditional effector of the musculoskeletal system. Despite the enormous potential of BCIs, there are still a number of challenges that limit their societal impact, ranging from the correct decoding of a human's thoughts, to the application of effective learning strategies. Despite several important engineering advances, the basic neuroscience behind these challenges remains poorly explored. Indeed, BCIs involve complex dynamic changes related to neural plasticity at a diverse range of spatiotemporal scales. One promising antidote to this complexity lies in network science, which provides a natural language in which to model the organizational principles of brain architecture and function as manifest in its interconnectivity. Here, we briefly review the main limitations currently affecting BCIs, and we offer our perspective on how they can be addressed by means of network theoretic approaches. We posit that the emerging field of network neuroscience will prove to be an effective tool to unlock human-machine interactions.},
}
@article {pmid30640636,
year = {2019},
author = {Borhani, S and Kilmarx, J and Saffo, D and Ng, L and Abiri, R and Zhao, X},
title = {Optimizing Prediction Model for a Noninvasive Brain-Computer Interface Platform Using Channel Selection, Classification, and Regression.},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {6},
pages = {2475-2482},
doi = {10.1109/JBHI.2019.2892379},
pmid = {30640636},
issn = {2168-2208},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/physiology ; Male ; Models, Statistical ; Regression Analysis ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {A brain-computer interface (BCI) platform can be utilized by a user to control an external device without making any overt movements. An EEG-based computer cursor control task is commonly used as a testbed for BCI applications. While traditional computer cursor control schemes are based on sensorimotor rhythm, a new scheme has recently been developed using imagined body kinematics (IBK) to achieve natural cursor movement in a shorter time of training. This article attempts to explore optimal decoding algorithms for an IBK paradigm using EEG signals with application to neural cursor control. The study is based on an offline analysis of 32 healthy subjects' training data. Various machine learning techniques were implemented to predict the kinematics of the computer cursor using EEG signals during the training tasks. Our results showed that a linear regression least squares model yielded the highest goodness-of-fit scores in the cursor kinematics model (70% in horizontal prediction and 40% in vertical prediction using a Theil-Sen regressor). Additionally, the contribution of each EEG channel on the predictability of cursor kinematics was examined for horizontal and vertical directions, separately. A directional classifier was also proposed to classify horizontal versus vertical cursor kinematics using EEG signals. By incorporating features extracted from specific frequency bands, we achieved 80% classification accuracy in differentiating horizontal and vertical cursor movements. The findings of the current study could facilitate a pathway to designing an optimized online neural cursor control.},
}
@article {pmid30640634,
year = {2019},
author = {Gao, Z and Wang, X and Yang, Y and Mu, C and Cai, Q and Dang, W and Zuo, S},
title = {EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {30},
number = {9},
pages = {2755-2763},
doi = {10.1109/TNNLS.2018.2886414},
pmid = {30640634},
issn = {2162-2388},
mesh = {Adult ; *Automobile Driving/psychology ; Electroencephalography/*methods ; Fatigue/diagnosis/*physiopathology/psychology ; Female ; Humans ; Male ; *Neural Networks, Computer ; Photic Stimulation/methods ; Time Factors ; *Virtual Reality ; Young Adult ; },
abstract = {Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel EEG-based spatial-temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal dependencies from EEG signals. Then, we employ dense layers to fuse spatial features and realize classification. The developed network could automatically learn valid features from EEG signals, which outperforms the classical two-step machine learning algorithms. Importantly, we carry out fatigue driving experiments to collect EEG signals from eight subjects being alert and fatigue states. Using 2800 samples under within-subject splitting, we compare the effectiveness of ESTCNN with eight competitive methods. The results indicate that ESTCNN fulfills a better classification accuracy of 97.37% than these compared methods. Furthermore, the spatial-temporal structure of this framework advantages in computational efficiency and reference time, which allows further implementations in the brain-computer interface online systems.},
}
@article {pmid30640620,
year = {2019},
author = {Li, J and Yu, ZL and Gu, Z and Tan, M and Wang, Y and Li, Y},
title = {Spatial-Temporal Discriminative Restricted Boltzmann Machine for Event-Related Potential Detection and Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {2},
pages = {139-151},
doi = {10.1109/TNSRE.2019.2892960},
pmid = {30640620},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; Brain Mapping ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Models, Neurological ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Space Perception/physiology ; Time Perception/physiology ; Young Adult ; },
abstract = {Detecting event-related potential (ERP) is a challenging problem because of its low signal-to-noise ratio and complex spatial-temporal features. Conventional detection methods usually rely on the ensemble averaging technique, which may eliminate subtle but important information in ERP signals and lead to poor detection performance. Inspired by the good performance of discriminative restricted Boltzmann machine (DRBM) in feature extraction and classification, we propose a spatial-temporal DRBM (ST-DRBM) to extract spatial and temporal features for ERP detection. The experimental results and statistical analyses demonstrate that the proposed method is able to achieve state-of-the-art ERP detection performance. The ST-DRBM is not only an effective ERP detector, but also a practical tool for ERP analysis. Based on the proposed model, similar scalp distribution and temporal variations were found in the ERP signals of different sessions, which indicated the feasibility of cross-session ERP detection. Given its state-of-the-art performance and effective analytical technique, ST-DRBM is promising for ERP-based brain-computer interfaces and neuroscience research.},
}
@article {pmid30639548,
year = {2019},
author = {Shin, H and Na, K},
title = {In situ vaccination with biocompatibility controllable immuno-sensitizer inducing antitumor immunity.},
journal = {Biomaterials},
volume = {197},
number = {},
pages = {32-40},
doi = {10.1016/j.biomaterials.2019.01.015},
pmid = {30639548},
issn = {1878-5905},
mesh = {Adjuvants, Immunologic/chemistry/*therapeutic use ; Animals ; Antigens, Neoplasm/immunology ; Biocompatible Materials/chemistry/*therapeutic use ; Cell Line, Tumor ; Dendritic Cells/immunology ; Humans ; Immunity, Cellular ; Immunotherapy/*methods ; Male ; Mice ; Mice, Inbred BALB C ; Mice, Nude ; Neoplasms/immunology/*therapy ; Polyethylene/chemistry/therapeutic use ; T-Lymphocytes, Cytotoxic/immunology ; Tumor Microenvironment ; },
abstract = {Anticancer immunotherapy is emerging as a promising tumor treatment that can replace the conventional tumor treatment such as surgery, radiation and chemo drug, but its therapeutic effect against solid tumor is limited due to the tumor microenvironment (TME). Herein, to overcome this limitation, the biocompatibility controllable immuno-sensitizer (BCI) based on polyethylene imine that can be applied to solid tumors is developed. BCI accumulates in the tumors by EPR effect and induces in situ tumor destruction that convert tumors into antigen source by biocompatibility change through surface charge switching in response to the acidic TME. Generated tumor antigens promote the maturation of dendritic cells and recruitment of cytotoxic T cells in tumors. Results from in vitro and in vivo experiments reveal that the BCI effectively induces tumor destruction and antitumor immune response. In consequence, the synergic effect of in situ tumor destruction and antitumor immune response induced by BCI's biocompatibility conversion remarkably enhances immunotherapeutic effect. This study may provide a way to improve immunotherapeutic effect on solid tumors by demonstrating the therapeutic effect of BCI against solid tumor and suggest a platform to control the toxicity of cationic polymer for the its extended biomedical application.},
}
@article {pmid30637491,
year = {2019},
author = {Senden, M and Emmerling, TC and van Hoof, R and Frost, MA and Goebel, R},
title = {Reconstructing imagined letters from early visual cortex reveals tight topographic correspondence between visual mental imagery and perception.},
journal = {Brain structure &amp; function},
volume = {224},
number = {3},
pages = {1167-1183},
pmid = {30637491},
issn = {1863-2661},
support = {269853//European Research Council/International ; 7202070//Horizon 2020/ ; 779860//European Research Council ()/ ; },
mesh = {Acoustic Stimulation ; Adult ; *Brain Mapping ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Photic Stimulation ; Surveys and Questionnaires ; Visual Cortex/diagnostic imaging/*physiology ; Visual Pathways/diagnostic imaging/*physiology ; Visual Perception/*physiology ; *Vocabulary ; Young Adult ; },
abstract = {Visual mental imagery is the quasi-perceptual experience of "seeing in the mind's eye". While a tight correspondence between imagery and perception in terms of subjective experience is well established, their correspondence in terms of neural representations remains insufficiently understood. In the present study, we exploit the high spatial resolution of functional magnetic resonance imaging (fMRI) at 7T, the retinotopic organization of early visual cortex, and machine-learning techniques to investigate whether visual imagery of letter shapes preserves the topographic organization of perceived shapes. Sub-millimeter resolution fMRI images were obtained from early visual cortex in six subjects performing visual imagery of four different letter shapes. Predictions of imagery voxel activation patterns based on a population receptive field-encoding model and physical letter stimuli provided first evidence in favor of detailed topographic organization. Subsequent visual field reconstructions of imagery data based on the inversion of the encoding model further showed that visual imagery preserves the geometric profile of letter shapes. These results open new avenues for decoding, as we show that a denoising autoencoder can be used to pretrain a classifier purely based on perceptual data before fine-tuning it on imagery data. Finally, we show that the autoencoder can project imagery-related voxel activations onto their perceptual counterpart allowing for visually recognizable reconstructions even at the single-trial level. The latter may eventually be utilized for the development of content-based BCI letter-speller systems.},
}
@article {pmid30636899,
year = {2018},
author = {Choi, JR and Kim, SM and Ryu, RH and Kim, SP and Sohn, JW},
title = {Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects.},
journal = {Experimental neurobiology},
volume = {27},
number = {6},
pages = {453-471},
pmid = {30636899},
issn = {1226-2560},
abstract = {A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes.},
}
@article {pmid30635226,
year = {2019},
author = {Heerspink, HJL and Greene, T and Tighiouart, H and Gansevoort, RT and Coresh, J and Simon, AL and Chan, TM and Hou, FF and Lewis, JB and Locatelli, F and Praga, M and Schena, FP and Levey, AS and Inker, LA and , },
title = {Change in albuminuria as a surrogate endpoint for progression of kidney disease: a meta-analysis of treatment effects in randomised clinical trials.},
journal = {The lancet. Diabetes &amp; endocrinology},
volume = {7},
number = {2},
pages = {128-139},
doi = {10.1016/S2213-8587(18)30314-0},
pmid = {30635226},
issn = {2213-8595},
mesh = {Albuminuria/complications/*physiopathology ; Disease Progression ; Humans ; Kidney Diseases/*diagnosis/etiology/pathology ; Prognosis ; Randomized Controlled Trials as Topic ; Renal Insufficiency, Chronic/pathology/*therapy ; Risk Factors ; },
abstract = {BACKGROUND: Change in albuminuria has strong biological plausibility as a surrogate endpoint for progression of chronic kidney disease, but empirical evidence to support its validity is lacking. We aimed to determine the association between treatment effects on early changes in albuminuria and treatment effects on clinical endpoints and surrograte endpoints, to inform the use of albuminuria as a surrogate endpoint in future randomised controlled trials.<br><br>METHODS: In this meta-analysis, we searched PubMed for publications in English from Jan 1, 1946, to Dec 15, 2016, using search terms including "chronic kidney disease", "chronic renal insufficiency", "albuminuria", "proteinuria", and "randomized controlled trial"; key inclusion criteria were quantifiable measurements of albuminuria or proteinuria at baseline and within 12 months of follow-up and information on the incidence of end-stage kidney disease. We requested use of individual patient data from the authors of eligible studies. For all studies that the authors agreed to participate and that had sufficient data, we estimated treatment effects on 6-month change in albuminuria and the composite clinical endpoint of treated end-stage kidney disease, estimated glomerular filtration rate of less than 15 mL/min per 1&#xb7;73 m[2], or doubling of serum creatinine. We used a Bayesian mixed-effects meta-regression analysis to relate the treatment effects on albuminuria to those on the clinical endpoint across studies and developed a prediction model for the treatment effect on the clinical endpoint on the basis of the treatment effect on albuminuria.<br><br>FINDINGS: We identified 41 eligible treatment comparisons from randomised trials (referred to as studies) that provided sufficient patient-level data on 29&#x2008;979 participants (21&#x2008;206 [71%] with diabetes). Over a median follow-up of 3&#xb7;4 years (IQR 2&#xb7;3-4&#xb7;2), 3935 (13%) participants reached the composite clinical endpoint. Across all studies, with a meta-regression slope of 0&#xb7;89 (95% Bayesian credible interval [BCI] 0&#xb7;13-1&#xb7;70), each 30% decrease in geometric mean albuminuria by the treatment relative to the control was associated with an average 27% lower hazard for the clinical endpoint (95% BCI 5-45%; median R[2] 0&#xb7;47, 95% BCI 0&#xb7;02-0&#xb7;96). The association strengthened after restricting analyses to patients with baseline albuminuria of more than 30 mg/g (ie, 3&#xb7;4 mg/mmol; R[2] 0&#xb7;72, 0&#xb7;05-0&#xb7;99]). For future trials, the model predicts that treatments that decrease the geometric mean albuminuria to 0&#xb7;7 (ie, 30% decrease in albuminuria) relative to the control will provide an average hazard ratio (HR) for the clinical endpoint of 0&#xb7;68, and 95% of sufficiently large studies would have HRs between 0&#xb7;47 and 0&#xb7;95.<br><br>INTERPRETATION: Our results support a role for change in albuminuria as a surrogate endpoint for the progression of chronic kidney disease, particularly in patients with high baseline albuminuria; for patients with low baseline levels of albuminuria this association is less certain.<br><br>FUNDING: US National Kidney Foundation.},
}
@article {pmid30634177,
year = {2019},
author = {Erdoĝan, SB and Özsarfati, E and Dilek, B and Kadak, KS and Hanoĝlu, L and Akın, A},
title = {Classification of motor imagery and execution signals with population-level feature sets: implications for probe design in fNIRS based BCI.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026029},
doi = {10.1088/1741-2552/aafdca},
pmid = {30634177},
issn = {1741-2552},
mesh = {Adolescent ; Algorithms ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Networks, Computer ; Photic Stimulation/methods ; Somatosensory Cortex/*physiology ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {OBJECTIVE: The aim of this study was to introduce a novel methodology for classification of brain hemodynamic responses collected via functional near infrared spectroscopy (fNIRS) during rest, motor imagery (MI) and motor execution (ME) tasks which involves generating population-level training sets.<br><br>APPROACH: A 48-channel fNIRS system was utilized to obtain hemodynamic signals from the frontal (FC), primary motor (PMC) and somatosensory cortex (SMC) of ten subjects during an experimental paradigm consisting of MI and ME of various right hand movements. Classification accuracies of random forest (RF), support vector machines (SVM), and artificial neural networks (ANN) were computed at the single subject level by training each classifier with subject specific features, and at the group level by training with features from all subjects for ME versus Rest, MI versus Rest and MI versus ME conditions. The performances were also computed for channel data restricted to FC, PMC and SMC regions separately to determine optimal probe location.<br><br>MAIN RESULTS: RF, SVM and ANN had comparably high classification accuracies for ME versus Rest (%94, %96 and %98 respectively) and for MI versus Rest (%95, %95 and %98 respectively) when fed with group level feature sets. The accuracy performance of each algorithm in localized brain regions were comparable (>%93) to the accuracy performance obtained with whole brain channels (>%94) for both ME versus Rest and MI versus Rest conditions.<br><br>SIGNIFICANCE: By demonstrating the feasibility of generating a population level training set with a high classification performance for three different classification algorithms, the findings pave the path for removing the necessity to acquire subject specific training data and hold promise for a novel, real-time fNIRS based BCI system design which will be most effective for application to disease populations for whom obtaining data to train a classification algorithm is not possible.},
}
@article {pmid30634095,
year = {2019},
author = {Eles, JR and Vazquez, AL and Kozai, TDY and Cui, XT},
title = {Meningeal inflammatory response and fibrous tissue remodeling around intracortical implants: An in vivo two-photon imaging study.},
journal = {Biomaterials},
volume = {195},
number = {},
pages = {111-123},
pmid = {30634095},
issn = {1878-5905},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS094404/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; CX3C Chemokine Receptor 1/metabolism ; Electrodes, Implanted ; Hydrogels/*chemistry ; Inflammation/metabolism ; Intravital Microscopy/methods ; Male ; Meninges/metabolism ; Mice ; *Microelectrodes ; },
abstract = {Meningeal inflammation and encapsulation of neural electrode arrays is a leading cause of device failure, yet little is known about how it develops over time or what triggers it. This work characterizes the dynamic changes of meningeal inflammatory cells and collagen-I in order to understand the meningeal tissue response to neural electrode implantation. We use in vivo two-photon microscopy of CX3CR1-GFP mice over the first month after electrode implantation to quantify changes in inflammatory cell behavior as well as meningeal collagen-I remodeling. We define a migratory window during the first day after electrode implantation hallmarked by robust inflammatory cell migration along electrodes in the meninges as well as cell trafficking through meningeal venules. This migratory window attenuates by 2 days post-implant, but over the next month, the meningeal collagen-I remodels to conform to the surface of the electrode and thickens. This work shows that there are distinct time courses for initial meningeal inflammatory cell infiltration and meningeal collagen-I remodeling. This may indicate a therapeutic window early after implantation for modulation and mitigation of meningeal inflammation.},
}
@article {pmid30633413,
year = {2019},
author = {Branco, MP and de Boer, LM and Ramsey, NF and Vansteensel, MJ},
title = {Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective.},
journal = {The European journal of neuroscience},
volume = {50},
number = {5},
pages = {2755-2772},
pmid = {30633413},
issn = {1460-9568},
support = {U01 DC016686/DC/NIDCD NIH HHS/United States ; },
mesh = {Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Fingers/physiology ; Humans ; Magnetic Resonance Imaging ; Movement/*physiology ; Neurons/*physiology ; Sensorimotor Cortex/diagnostic imaging/*physiology ; },
abstract = {For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.},
}
@article {pmid30631262,
year = {2018},
author = {Maksimenko, VA and Hramov, AE and Frolov, NS and Lüttjohann, A and Nedaivozov, VO and Grubov, VV and Runnova, AE and Makarov, VV and Kurths, J and Pisarchik, AN},
title = {Increasing Human Performance by Sharing Cognitive Load Using Brain-to-Brain Interface.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {949},
pmid = {30631262},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions.},
}
@article {pmid30630057,
year = {2019},
author = {Alazrai, R and Alwanni, H and Daoud, MI},
title = {EEG-based BCI system for decoding finger movements within the same hand.},
journal = {Neuroscience letters},
volume = {698},
number = {},
pages = {113-120},
doi = {10.1016/j.neulet.2018.12.045},
pmid = {30630057},
issn = {1872-7972},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; *Electroencephalography/methods ; Female ; Fingers/*physiology ; Hand/*physiology ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Young Adult ; },
abstract = {Decoding the movements of different fingers within the same hand can increase the control's dimensions of the electroencephalography (EEG)-based brain-computer interface (BCI) systems. This in turn enables the subjects who are using assistive devices to better perform various dexterous tasks. However, decoding the movements performed by different fingers within the same hand by analyzing the EEG signals is considered a challenging task. In this paper, we present a new EEG-based BCI system for decoding the movements of each finger within the same hand based on analyzing the EEG signals using a quadratic time-frequency distribution (QTFD), namely the Choi-William distribution (CWD). In particular, the CWD is employed to characterize the time-varying spectral components of the EEG signals and extract features that can capture movement-related information encapsulated within the EEG signals. The extracted CWD-based features are used to build a two-layer classification framework that decodes finger movements within the same hand. The performance of the proposed system is evaluated by recording the EEG signals for eighteen healthy subjects while performing twelve finger movements using their right hands. The results demonstrate the efficacy of the proposed system to decode finger movements within the same hand of each subject.},
}
@article {pmid30627143,
year = {2018},
author = {de Albuquerque, VHC and Pinheiro, PR and Martis, RJ and Tavares, JMRS},
title = {Recent Advances in Brain Signal Analysis: Methods and Applications 2018.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {5971086},
doi = {10.1155/2018/5971086},
pmid = {30627143},
issn = {1687-5273},
mesh = {*Artificial Intelligence ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Humans ; },
}
@article {pmid30626450,
year = {2019},
author = {D'Andola, M and Giulioni, M and Dante, V and Del Giudice, P and Sanchez-Vives, MV},
title = {Control of cortical oscillatory frequency by a closed-loop system.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {16},
number = {1},
pages = {7},
pmid = {30626450},
issn = {1743-0003},
support = {BFU2017-85048-R//Ministerio de Econom&#xed;a y Competitividad/International ; 600806//FP7 Information and Communication Technologies/International ; FLAG-ERA (PCIN-2015-162-C02-01)//Ministerio de Econom&#xed;a, Industria y Competitividad, Gobierno de Espa&#xf1;a/International ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Female ; Ferrets ; Male ; Organ Culture Techniques ; },
abstract = {BACKGROUND: We present a closed-loop system able to control the frequency of slow oscillations (SO) spontaneously generated by the cortical network in vitro. The frequency of SO can be controlled by direct current (DC) electric fields within a certain range. Here we set out to design a system that would be able to autonomously bring the emergent oscillatory activity to a target frequency determined by the experimenter.<br><br>METHODS: The cortical activity was recorded through an electrode and was analyzed online. Once a target frequency was set, the frequency of the slow oscillation was steered through the injection of DC of variable intensity that generated electric fields of proportional amplitudes in the brain slice. To achieve such closed-loop control, we designed a custom programmable stimulator ensuring low noise and accurate tuning over low current levels. For data recording and analysis, we relied on commercial acquisition and software tools.<br><br>RESULTS: The result is a flexible and reliable system that ensures control over SO frequency in vitro. The system guarantees artifact removal, minimal gaps in data acquisition and robustness in spite of slice heterogeneity.<br><br>CONCLUSIONS: Our tool opens new possibilities for the investigation of dynamics of cortical slow oscillations-an activity pattern that is associated with cognitive processes such as memory consolidation, and that is altered in several neurological conditions-and also for potential applications of this technology.},
}
@article {pmid30626132,
year = {2019},
author = {Tayeb, Z and Fedjaev, J and Ghaboosi, N and Richter, C and Everding, L and Qu, X and Wu, Y and Cheng, G and Conradt, J},
title = {Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {19},
number = {1},
pages = {},
pmid = {30626132},
issn = {1424-8220},
support = {91645515//Deutscher Akademischer Austauschdienst/ ; NO grant number//Deutsche Forschungsgemeinschaft/ ; funding programme Open Access Publishing//Technische Universit&#xe4;t M&#xfc;nchen/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Hand/physiology ; Humans ; Machine Learning ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.},
}
@article {pmid30624221,
year = {2019},
author = {Thomas, TM and Candrea, DN and Fifer, MS and McMullen, DP and Anderson, WS and Thakor, NV and Crone, NE},
title = {Decoding Native Cortical Representations for Flexion and Extension at Upper Limb Joints Using Electrocorticography.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {2},
pages = {293-303},
pmid = {30624221},
issn = {1558-0210},
support = {R01 NS088606/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain-Computer Interfaces ; Elbow Joint/physiology ; Electrocorticography/*methods ; Feasibility Studies ; Female ; Fingers/physiology ; Humans ; Joints/*physiology ; Linear Models ; Machine Learning ; Male ; Photic Stimulation ; Sensorimotor Cortex/*physiology ; Upper Extremity/*physiology ; Wrist Joint/physiology ; Young Adult ; },
abstract = {Brain-machine interface (BMI) researchers have traditionally focused on modeling endpoint reaching tasks to provide the control of neurally driven prosthetic arms. Most previous research has focused on achieving an endpoint control through a Cartesian-coordinate-centered approach. However, a joint-centered approach could potentially be used to intuitively control a wide range of limb movements. We systematically investigated the feasibility of discriminating between flexion and extension of different upper limb joints using electrocorticography(ECoG) recordings from sensorimotor cortex. Four subjects implanted with macro-ECoG (10-mm spacing), high-density ECoG (5-mm spacing), and/or micro-ECoG arrays (0.9-mm spacing and 4 mm × 4 mm coverage), performed randomly cued flexions or extensions of the fingers, wrist, or elbow contralateral to the implanted hemisphere. We trained a linear model to classify six movements using averaged high-gamma power (70-110 Hz) modulations at different latencies with respect to movement onset, and within a time interval restricted to flexion or extension at each joint. Offline decoding models for each subject classified these movements with accuracies of 62%-83%. Our results suggest that the widespread ECoG coverage of sensorimotor cortex could allow a whole limb BMI to sample native cortical representations in order to control flexion and extension at multiple joints.},
}
@article {pmid30624220,
year = {2019},
author = {Wirdatmadja, S and Johari, P and Desai, A and Bae, Y and Stachowiak, EK and Stachowiak, MK and Jornet, JM and Balasubramaniam, S},
title = {Analysis of Light Propagation on Physiological Properties of Neurons for Nanoscale Optogenetics.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {2},
pages = {108-117},
doi = {10.1109/TNSRE.2019.2891271},
pmid = {30624220},
issn = {1558-0210},
mesh = {Algorithms ; Axons/radiation effects ; *Brain-Computer Interfaces ; Cell Shape/radiation effects ; Cerebral Cortex/cytology/radiation effects ; Humans ; Light ; *Nanotechnology ; Neural Stem Cells/radiation effects/ultrastructure ; Neurons/*physiology/*radiation effects/ultrastructure ; Optogenetics/*methods ; *Photic Stimulation ; Scattering, Radiation ; },
abstract = {Miniaturization of implantable devices is an important challenge for future brain-computer interface applications, and in particular for achieving precise neuron stimulation. For stimulation that utilizes light, i.e., optogenetics, the light propagation behavior and interaction at the nanoscale with elements within the neuron is an important factor that needs to be considered when designing the device. This paper analyzes the effect of light behavior for a single neuron stimulation and focuses on the impact from different cell shapes. Based on the Mie scattering theory, the paper analyzes how the shape of the soma and the nucleus contributes to the focusing effect resulting in an intensity increase, which ensures that neurons can assist in transferring light through the tissue toward the target cells. At the same time, this intensity increase can in turn also stimulate neighboring cells leading to interference within the neural circuits. This paper also analyzes the ideal placements of the device with respect to the angle and position within the cortex that can enable axonal biophoton communications, which can contain light within the cell to avoid the interference.},
}
@article {pmid30623892,
year = {2019},
author = {Peterson, V and Wyser, D and Lambercy, O and Spies, R and Gassert, R},
title = {A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016019},
doi = {10.1088/1741-2552/aaf046},
pmid = {30623892},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; Data Analysis ; Electroencephalography/*methods ; Female ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; *Intention ; Male ; Motor Skills/*physiology ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: Motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal.<br><br>APPROACH: In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific [Formula: see text] temporal and [Formula: see text] frequency bands. Features are extracted at each [Formula: see text]-[Formula: see text] band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window.<br><br>MAIN RESULTS: The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to [Formula: see text] (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations.<br><br>SIGNIFICANCE: This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.},
}
@article {pmid30622873,
year = {2019},
author = {Iqbal, A and Arshad, M and Karthikeyan, R and Gentry, TJ and Rashid, J and Ahmed, I and Schwab, AP},
title = {Diesel degrading bacterial endophytes with plant growth promoting potential isolated from a petroleum storage facility.},
journal = {3 Biotech},
volume = {9},
number = {1},
pages = {35},
pmid = {30622873},
issn = {2190-572X},
abstract = {Thirteen (13) endophytic bacterial strains were isolated from Echinochloa crus-galli (Cockspur grass) and Cynodon dactylon (Bermuda grass) growing in an oil-contaminated site at a petroleum storage and transportation facility. Of the 13 strains assessed for their potential to degrade monoaromatic compounds (phenol, toluene, and xylene) and diesel and for their plant growth promoting (PGP) ability (phosphate solubilization, siderophores and 1-aminocyclopropane-1-carboxylate (ACC) deaminase production), isolate J10 (identified as Pseudomonas sp. by 16S rRNA gene sequencing) was found to the best diesel biodegrader with the best PGP traits. The Monod model used for Pseudomonas sp. J10 growth kinetics on diesel fuel as the sole carbon source showed that the maximum specific bacterial growth rate was 0.0644 h[- 1] and the half velocity constant (K s) was estimated as 4570 mg L[- 1]. The overall growth yield coefficient and apparent growth yield were determined to be 0.271 g h[- 1] and 0.127 g cells/g substrate, respectively. Pseudomonas sp. J10 removed 69% diesel in four days as determined by gas chromatographic (GC) analysis. These findings could assist in developing an endophyte assisted efficient diesel biodegradation system using Pseudomonas sp. J10 isolated from Echinochloa crus-galli.},
}
@article {pmid30621897,
year = {2019},
author = {Berman, J and Francoz, D and Dufour, S and Buczinski, S},
title = {Bayesian estimation of sensitivity and specificity of systematic thoracic ultrasound exam for diagnosis of bovine respiratory disease in pre-weaned calves.},
journal = {Preventive veterinary medicine},
volume = {162},
number = {},
pages = {38-45},
doi = {10.1016/j.prevetmed.2018.10.025},
pmid = {30621897},
issn = {1873-1716},
mesh = {Animals ; Animals, Newborn ; Bovine Respiratory Disease Complex/*diagnostic imaging ; Cattle ; Sensitivity and Specificity ; Thorax/diagnostic imaging ; Ultrasonography/*veterinary ; },
abstract = {Among the different clinical presentations of bovine respiratory disease, active pneumonia, defined as an infection of the lower airway with signs of inflammation, is the most important to diagnose correctly so appropriate treatment can be initiated. Diagnostic tests that accurately identify cases of active pneumonia are lacking; however, thoracic ultrasonography (TUS) seems promising. The primary objective of this study was to estimate the accuracy of TUS compared to reference tests for the diagnosis of active pneumonia in pre-weaned calves, using a latent-class model method (LCM). The tests used for comparison were the Wisconsin Clinical Respiratory Scoring Chart (CRSC, positive if ≥5) and serum haptoglobin concentration (Hap, positive if ≥15 mg/dL). Secondary objectives were to assess the incremental value on TUS accuracy of combining TUS of the right cranial part and caudal parts, and to determine the accuracy of various thresholds for depth of consolidation (≥0 cm, ≥1 cm, or ≥3 cm) for diagnosis of active pneumonia. One population of veal calves (n = 209) and one of dairy calves (n = 301) were enrolled. TUS, CRSC and Hap were all performed on each calf on the same day. TUS was performed by screening the mid to ventral portion of the lung caudal of the heart (the caudal sites), as well as the right parenchyma cranial to the heart (the cranial site). The maximal depth of consolidation (DEPTH) on TUS was recorded and noted separately for caudal and cranial sites. Different TUS cases were defined according to site and DEPTH. The accuracy of TUS was estimated by LCM for three tests conducted in two populations. Prevalence of active pneumonia was low (0.05) in both populations. In general, higher minimal consolidation depth thresholds led to increased TUS specificity (Sp) estimates, with minimal effects on TUS sensitivity (Se). With a TUS DEPTH threshold of ≥3 cm, adding TUS of the cranial site had little effect on accuracy. Using the ≥3 cm threshold with caudal sites only, posterior Se and Sp median estimates of 0.89 (95%BCI: 0.55, 1.0) and 0.95 (95%BCI: 0.92, 0.98), respectively, were obtained. In conclusion, in populations with low active pneumonia prevalence, adding TUS of the cranial site did not enhance the performance of the test. We suggest using a DEPTH threshold of ≥3 cm solely on caudal sites to detect active pneumonia.},
}
@article {pmid30621886,
year = {2019},
author = {Arango-Sabogal, JC and Dubuc, J and Krug, C and Denis-Robichaud, J and Dufour, S},
title = {Accuracy of leukocyte esterase test, endometrial cytology and vaginal discharge score for diagnosing postpartum reproductive tract health status in dairy cows at the moment of sampling, using a latent class model fit within a Bayesian framework.},
journal = {Preventive veterinary medicine},
volume = {162},
number = {},
pages = {1-10},
doi = {10.1016/j.prevetmed.2018.11.003},
pmid = {30621886},
issn = {1873-1716},
mesh = {Animals ; Bayes Theorem ; Carboxylic Ester Hydrolases/*metabolism ; Cattle/*anatomy &amp; histology ; Cattle Diseases/diagnosis/pathology ; Endometrium/anatomy &amp; histology/*cytology ; Female ; Health Status ; Latent Class Analysis ; Postpartum Period ; Retrospective Studies ; Sensitivity and Specificity ; Vagina/*anatomy &amp; histology ; },
abstract = {The objectives of this retrospective study were: 1) to determine the sensitivity (Se) and specificity (Sp) of leukocyte esterase test (LE), endometrial cytology (CYTO) and vaginal discharge score (VDS) for diagnosis of reproductive tract diseases in dairy cows at the moment of sampling; 2) to assess the impact of different thresholds on test accuracy and misclassification costs; and 3) to quantify herd prevalence of reproductive tract diseases in dairy farms from Québec, Canada. Data from 2092 cows (39 herds) enrolled in two randomized control trials were included. Cows were examined at 35 (± 7) days in milk using LE, CYTO, and VDS to determine their reproductive tract health status. A latent class model assuming conditional dependence of CYTO and LE was fit within a Bayesian framework. Non-informative priors were used for the Se and Sp of LE, CYTO, and VDS, while prior information for disease prevalence was obtained from expert opinions (mode = 20%, 5th percentile = 10%). Posterior inferences (median and 95% Bayesian credible intervals; BCI) were obtained using the WinBUGS statistical freeware. An initial model was built using thresholds of ≥ 1 (small amount of leukocytes), of ≥ 6%, and of ≥ 3 (mucopurulent discharge) for the LE, CYTO, and VDS, respectively. Then, the impact on tests accuracy and misclassification costs of using different thresholds was explored. Optimal thresholds balancing the need for good antimicrobial stewardship and animal health considerations were proposed. The optimal thresholds obtained in the final model were: LE ≥ 2 (moderate amount of leukocytes), CYTO ≥ 6%, and VDS ≥ 2 (mucus with flecks of pus). In the final model, median (95% BCI) Se for LE, CYTO and VDS were 42.6% (38.8-47.0), 45.9% (41.9-50.7), and 64.2% (59.1-70.3), respectively. Median Sp was 90.9% (88.0-93.6) for LE, 92.2% (89.2-94.9) for CYTO and 96.6% (91.3-99.8) for VDS. Median within-herd prevalence of reproductive tract disease was estimated at 47.9% (39.0-56.7). At a threshold ≥ 2 (mucus with flecks of pus), VDS had the highest Se and Sp among the tests evaluated. The LE is an interesting diagnostic alternative for detecting reproductive tract disease in dairy cows. At a threshold ≥ 2 (moderate amount of leukocytes), LE Se and Sp were comparable to those of CYTO. This is the first study reporting the accuracy of LE, CYTO, and VDS for diagnosing reproductive tract diseases in dairy cows at the moment of sampling.},
}
@article {pmid30620927,
year = {2019},
author = {Huang, Q and Chen, Y and Zhang, Z and He, S and Zhang, R and Liu, J and Zhang, Y and Shao, M and Li, Y},
title = {An EOG-based wheelchair robotic arm system for assisting patients with severe spinal cord injuries.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026021},
doi = {10.1088/1741-2552/aafc88},
pmid = {30620927},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Blinking/*physiology ; Electrooculography/instrumentation/*methods ; Eye Movements/physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Psychomotor Performance/physiology ; Robotics/instrumentation/*methods ; Spinal Cord Injuries/physiopathology/*rehabilitation ; User-Computer Interface ; *Wheelchairs ; Young Adult ; },
abstract = {OBJECTIVE: In this study, we combine a wheelchair and an intelligent robotic arm based on an electrooculogram (EOG) signal to help patients with spinal cord injuries (SCIs) accomplish a self-drinking task. The main challenge is to accurately control the wheelchair to ensure that the randomly located object is within a limited reachable space of the robotic arm (length: 0.8 m; width: 0.4 m; height: 0.6 m), which requires decimeter-level precision, and is still undemonstrated for EOG-based systems as well as EEG-based systems.<br><br>APPROACH: A novel high-precision EOG-based human machine interface (HMI) is proposed which can effectively translate two kinds of eye movements (i.e. blinking and eyebrow raising) into various commands. For the wheelchair, positional precision can reach decimeter-level and the minimal steering angle is [Formula: see text]. For the intelligent robotic arm, shared control is implemented based on an EOG-based HMI, two cameras and the arm's own intelligence.<br><br>MAIN RESULTS: After brief training, five healthy subjects and five paralyzed patients with severe SCIs successfully completed three experiments. For the healthy subjects/patients with SCIs, the system achieved an average accuracy of 99.3%/97.3%, an average response time of 1.91 s/2.02 s per command and an average stop-response time of 1.30 s/1.36 s with a 0 false operation rate.<br><br>SIGNIFICANCE: The EOG-based HMI can provide sufficient precision control to integrate a wheelchair and a robotic arm into a system which can help patients with SCIs to accomplish a self-drinking task. (ChiCTR1800019764).},
}
@article {pmid30619079,
year = {2018},
author = {Frolov, AA and Bobrov, PD and Biryukova, EV and Silchenko, AV and Kondur, AA and Dzhalagoniya, IZ and Massion, J},
title = {Electrical, Hemodynamic, and Motor Activity in BCI Post-stroke Rehabilitation: Clinical Case Study.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {1135},
pmid = {30619079},
issn = {1664-2295},
abstract = {The goal of the paper is to present an example of integrated analysis of electrical, hemodynamic, and motor activity accompanying the motor function recovery in a post-stroke patient having an extensive cortical lesion. The patient underwent a course of neurorehabilitation assisted with the hand exoskeleton controlled by brain-computer interface based on kinesthetic motor imagery. The BCI classifier was based on discriminating covariance matrices of EEG corresponding to motor imagery. The clinical data from three successive 2 weeks hospitalizations with 4 and 8 month intervals, respectively were under analysis. The rehabilitation outcome was measured by Fugl-Meyer scale and biomechanical analysis. Both measures indicate prominent improvement of the motor function of the paretic arm after each hospitalization. The analysis of brain activity resulted in three main findings. First, the sources of EEG activity in the intact brain areas, most specific to motor imagery, were similar to the patterns we observed earlier in both healthy subjects and post-stroke patients with mild subcortical lesions. Second, two sources of task-specific activity were localized in primary somatosensory areas near the lesion edge. The sources exhibit independent mu-rhythm activity with the peak frequency significantly lower than that of mu-rhythm in healthy subjects. The peculiarities of the detected source activity underlie changes in EEG covariance matrices during motor imagery, thus serving as the BCI biomarkers. Third, the fMRI data processing showed significant reduction in size of areas activated during the paretic hand movement imagery and increase for those activated during the intact hand movement imagery, shifting the activations to the same level. This might be regarded as the general index of the motor recovery. We conclude that the integrated analysis of EEG, fMRI, and motor activity allows to account for the reorganization of different levels of the motor system and to provide a comprehensive basis for adequate assessment of the BCI+ exoskeleton rehabilitation efficiency.},
}
@article {pmid30618704,
year = {2018},
author = {Smetanin, N and Volkova, K and Zabodaev, S and Lebedev, MA and Ossadtchi, A},
title = {NFBLab-A Versatile Software for Neurofeedback and Brain-Computer Interface Research.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {100},
pmid = {30618704},
issn = {1662-5196},
abstract = {Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented. Partly, the existing controversy is due to suboptimal conditions under which the NFB training is undertaken. Therefore, new experimental tools attempting to provide optimal or close to optimal training conditions are needed to further exploration of NFB paradigms and comparison of their effects across subjects and training days. To this end, we have developed open-source NFBLab, a versatile, Python-based software for conducting NFB experiments with completely reproducible paradigms and low-latency feedback presentation. Complex experimental protocols can be configured using the GUI and saved in NFBLab's internal XML-based language that describes signal processing tracts, experimental blocks and sequences including randomization of experimental blocks. NFBLab implements interactive modules that enable individualized EEG/MEG signal processing tracts specification using spatial and temporal filters for feature selection and artifacts removal. NFBLab supports direct interfacing to MNE-Python software to facilitate source-space NFB based on individual head models and properly tailored individual inverse solvers. In addition to the standard algorithms for extraction of brain rhythms dynamics from EEG and MEG data, NFBLab implements several novel in-house signal processing algorithms that afford significant reduction in latency of feedback presentation and may potentially improve training effects. The software also supports several standard BCI paradigms. To interface with external data acquisition devices NFBLab employs Lab Streaming Layer protocol supported by the majority of EEG vendors. MEG devices are interfaced through the Fieldtrip buffer.},
}
@article {pmid30618686,
year = {2018},
author = {Di Flumeri, G and Borghini, G and Aricò, P and Sciaraffa, N and Lanzi, P and Pozzi, S and Vignali, V and Lantieri, C and Bichicchi, A and Simone, A and Babiloni, F},
title = {EEG-Based Mental Workload Neurometric to Evaluate the Impact of Different Traffic and Road Conditions in Real Driving Settings.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {509},
pmid = {30618686},
issn = {1662-5161},
abstract = {Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver's behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver's workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver's perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers' behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers' behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.},
}
@article {pmid30618572,
year = {2018},
author = {Li, R and Zhang, X and Lu, Z and Liu, C and Li, H and Sheng, W and Odekhe, R},
title = {An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {943},
pmid = {30618572},
issn = {1662-4548},
abstract = {One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.},
}
@article {pmid30618401,
year = {2018},
author = {Likhodiievsky, V and Korsak, A and Klimovskaya, A and Chaikovsky, Y},
title = {SILICON WIRES FOR NERVE GAP MANAGEMENT: ROLE OF SURFACE PROPERTIES IN NERVE REGENERATION.},
journal = {Georgian medical news},
volume = {},
number = {284},
pages = {115-120},
pmid = {30618401},
issn = {1512-0112},
mesh = {Animals ; Disease Models, Animal ; Male ; Nerve Fibers/*physiology ; Nerve Regeneration/*physiology ; Peripheral Nerve Injuries/physiopathology/*surgery ; *Prostheses and Implants ; Rats, Wistar ; Sciatic Nerve/injuries/*physiopathology ; Silicon/*chemistry ; Surface Properties ; },
abstract = {Up to 15% of combat trauma cases are accompanied by neuroinjuries with nerve gap formation that need to be bridged using various techniques and materials. Both with this prevalence of limb loss, especially traumatic amputations, tends to grow. Loosed limbs must be prosthetized by modern functional mind-controlled prosthesis based on nerve- or brain-computer interfaces. This study aimed at morphological evaluation of interaction between nerve fibers and silicon wires with different surface properties using peripheral nerve injury and grafting model. Experiment was performed on 50 male Wistar rats, weighing 180-250 g. Rats from experimental groups underwent sciatic nerve injury Sunderland 5 degree with a 10 mm gap formation that was subsequently filled with conduit consisting of decellularized aorta, carboxymethylcellulose gel and a set of longitudinally oriented p-type silicon wires 2-20 µm in diameter. We used silicon wires with native oxide in group Ia, with hydrogen-cleaned surface in group Ib and thermally grown oxide in group Ic. The gap in control groups was filled with decellularized aorta with gel alone (group II) or by autoneurograft (group III). 6 weeks postoperatively the conduit site was harvested and light microscopy performed. Implantation of conduit with native oxide on silicon wires surface resulted in more complete and equal neurotization of the conduit site with close adherence between the newly-formed nerve fibers and silicon wires, in comparison with groups where wires with other surface properties have been used. P-type silicon wires with native oxide are seems to be more suitable than other types of wires for further electrode preparation as a part of regenerative implants.},
}
@article {pmid30617930,
year = {2019},
author = {Abi Nahed, R and Reynaud, D and Borg, AJ and Traboulsi, W and Wetzel, A and Sapin, V and Brouillet, S and Dieudonné, MN and Dakouane-Giudicelli, M and Benharouga, M and Murthi, P and Alfaidy, N},
title = {NLRP7 is increased in human idiopathic fetal growth restriction and plays a critical role in trophoblast differentiation.},
journal = {Journal of molecular medicine (Berlin, Germany)},
volume = {97},
number = {3},
pages = {355-367},
pmid = {30617930},
issn = {1432-1440},
mesh = {Adaptor Proteins, Signal Transducing/*metabolism ; Adult ; Cell Differentiation ; Cell Line ; Female ; Fetal Growth Retardation/*metabolism ; Humans ; Hypoxia/metabolism ; Interleukin-18/blood ; Interleukin-1beta/blood ; Placenta/*metabolism ; Pregnancy ; Pregnancy Trimester, First/metabolism ; Trophoblasts/*physiology ; },
abstract = {Fetal growth restriction (FGR) the leading cause of perinatal mortality and morbidity is highly related to abnormal placental development, and placentas from FGR pregnancies are often characterized by increased inflammation. However, the mechanisms of FGR-associated inflammation are far from being understood. NLRP7, a member of a family of receptors involved in the innate immune responses, has been shown to be associated with gestational trophoblastic diseases. Here, we characterized the expression and the functional role of NLRP7 in the placenta and investigated its involvement in the pathogenesis of FGR. We used primary trophoblasts and placental explants that were collected during early pregnancy, and established trophoblast-derived cell lines, human placental villi, and serum samples from early pregnancy (n = 38) and from FGR (n = 40) and age-matched controls (n = 32). Our results show that NLRP7 (i) is predominantly expressed in the trophoblasts during the hypoxic period of placental development and its expression is upregulated by hypoxia and (ii) increases trophoblast proliferation ([[3]H]-thymidine) and controls the precocious differentiation of trophoblasts towards syncytium (syncytin 1 and 2 and β-hCG production and xCELLigence analysis) and towards invasive extravillous trophoblast (2D and 3D cultures). We have also demonstrated that NLRP7 inflammasome activation in trophoblast cells increases IL-1β, but not IL-18 secretion. In relation to the FGR, we demonstrated that major components of NLRP7 inflammasome machinery are increased and that IL-1β but not IL-18 circulating levels are increased in FGR. Altogether, our results identified NLRP7 as a critical placental factor and provided evidence for its deregulation in FGR. NLRP7 inflammasome is abundantly expressed by trophoblast cells. It is regulated by a key parameter of placental development, hypoxia. It controls trophoblast proliferation, migration, and invasion and exhibits anti-apoptotic role. NLRP7 machinery is deregulated in FGR pregnancies. KEY MESSAGES: NLRP7 inflammasome is abundantly expressed by trophoblast cells. It is regulated by a key parameter of placental development, hypoxia. It controls trophoblast proliferation, migration, and invasion and exhibits anti-apoptotic role. NLRP7 machinery is deregulated in FGR pregnancies.},
}
@article {pmid30617653,
year = {2019},
author = {Rabbani, Q and Milsap, G and Crone, NE},
title = {The Potential for a Speech Brain-Computer Interface Using Chronic Electrocorticography.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {16},
number = {1},
pages = {144-165},
pmid = {30617653},
issn = {1878-7479},
support = {R01 NS091139/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Humans ; *Speech ; },
abstract = {A brain-computer interface (BCI) is a technology that uses neural features to restore or augment the capabilities of its user. A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. Such a technology would potentially restore communication and improve quality of life for locked-in patients and other patients with severe communication disorders. There have been many recent developments in neural decoders, neural feature extraction, and brain recording modalities facilitating BCI for the control of prosthetics and in automatic speech recognition (ASR). Indeed, ASR and related fields have developed significantly over the past years, and many lend many insights into the requirements, goals, and strategies for speech BCI. Neural speech decoding is a comparatively new field but has shown much promise with recent studies demonstrating semantic, auditory, and articulatory decoding using electrocorticography (ECoG) and other neural recording modalities. Because the neural representations for speech and language are widely distributed over cortical regions spanning the frontal, parietal, and temporal lobes, the mesoscopic scale of population activity captured by ECoG surface electrode arrays may have distinct advantages for speech BCI, in contrast to the advantages of microelectrode arrays for upper-limb BCI. Nevertheless, there remain many challenges for the translation of speech BCIs to clinical populations. This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail.},
}
@article {pmid30615650,
year = {2019},
author = {Owora, AH and Carabin, H and Garwe, T and Anderson, MP},
title = {Are we validly assessing major depression disorder risk and associated factors among mothers of young children? A cross-sectional study involving home visitation programs.},
journal = {PloS one},
volume = {14},
number = {1},
pages = {e0209735},
pmid = {30615650},
issn = {1932-6203},
support = {U54 GM104938/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; Child, Preschool ; Cross-Sectional Studies ; Depressive Disorder, Major/*diagnosis/psychology ; Emigrants and Immigrants/psychology ; Female ; *House Calls ; Humans ; Infant ; Male ; Models, Theoretical ; Mothers/*psychology ; *Postnatal Care ; Risk Assessment ; Risk Factors ; },
abstract = {Failure to account for misclassification error accruing from imperfect case-finding instruments can produce biased estimates of suspected major depression disorder (MDD) risk factor associations. The objective of this study was to estimate the impact of misclassification error on the magnitude of measures of association between suspected risk factors and MDD assessed using the Center of Epidemiological Studies on Depression-Short Form during the prenatal and postnatal periods. Baseline data were collected from 520 mothers participating in two home visitation studies in Oklahoma City between 2010 and 2014. A Bayesian binomial latent class model was used to compare the prevalence proportion ratio (PPR) between suspected risk factors and MDD with and without adjustment for misclassification error and confounding by period of MDD symptom on-set. Adjustment for misclassification error and confounding by period of MDD on-set (prenatal vs postnatal) showed that the association between suspected risk factors and MDD is underestimated (-) and overestimated (+) differentially in different source populations of low-income mothers. The median bias in the magnitude of PPR estimates ranged between -.47 (95% Bayesian Credible Intervals [BCI]: -10.67, 1.90) for intimate partner violence to +.06 (95%BCI: -0.37, 0.47) for race/ethnicity among native-born US residents. Among recent Hispanic immigrants, bias ranged from -.77 (95%BCI: -15.31, 0.96) for history of childhood maltreatment to +.10 (95%BCI: -0.17, 0.39) for adequacy of family resources. Overall, the extent of bias on measures of association between maternal MDD and suspected risk factors is considerable without adjustment for misclassification error and is even higher for confounding by period of MDD assessment. Consideration of these biases in MDD prevention research is warranted.},
}
@article {pmid30609208,
year = {2019},
author = {Carvalho, R and Dias, N and Cerqueira, JJ},
title = {Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review.},
journal = {Physiotherapy research international : the journal for researchers and clinicians in physical therapy},
volume = {24},
number = {2},
pages = {e1764},
doi = {10.1002/pri.1764},
pmid = {30609208},
issn = {1471-2865},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Hemiplegia/rehabilitation ; Humans ; Imagery, Psychotherapy/*methods ; Randomized Controlled Trials as Topic ; Robotics ; Stroke/*diagnostic imaging/physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.<br><br>OBJECTIVE: The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery.<br><br>METHODS: The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.<br><br>RESULTS: From 309 titles, we included nine studies with high quality (PEDro&#xa0;&#x2265;&#xa0;6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.<br><br>CONCLUSIONS: Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.},
}
@article {pmid30607143,
year = {2018},
author = {Jestrzemski, D and Kuzyakova, I},
title = {Morphometric characteristics and seasonal proximity to water of the Cypriot blunt-nosed viper Macrovipera lebetina lebetina (Linnaeus, 1758).},
journal = {The journal of venomous animals and toxins including tropical diseases},
volume = {24},
number = {},
pages = {42},
pmid = {30607143},
issn = {1678-9199},
abstract = {BACKGROUND: The blunt-nosed viper Macrovipera lebetina (Linnaeus, 1758) is a medically important snake species in the Middle East. Its nominate subspecies Macrovipera l. lebetina is confined to Cyprus, where it is the only dangerously venomous snake species and heavily pursued. Despite the viper's large size, data on its body mass and sex-specific morphological differences are scarce. It is commonly believed that M. l. lebetina prefers freshwater proximity during summer. Hence, we aimed at investigating M. l. lebetina sex-specific morphological differences and its possible attraction to freshwater bodies in late summer.<br><br>METHODS: Morphometric characteristics, proximity to water and conservation status of M. l. lebetina were investigated in Paphos district (Cyprus) in 2014, 2015 and 2017. Vipers were caught in different habitats, examined morphologically for metric and meristic characters, and released back into their habitat. Additionally, local people were interviewed about the conservation situation of the species.<br><br>RESULTS: Of 38 recorded blunt-nosed vipers, morphological characteristics were collected from 34 (10 adult males, 16 adult females, eight unsexed juveniles). Rounded total length (ToL) ranged from 23.5&#x2009;cm to 133.0&#x2009;cm and weight between 10&#x2009;g and 1456&#x2009;g. Adult males significantly exceeded adult females in tail length (TaL), ToL and head length (HL). No significant sex-specific differences were found in snout-vent length (SVL), head width (HW), weight or body condition index (BCI), nor for the ratios TaL / SVL, TaL / ToL, HL / SVL or HL / HW. Adult females from late summer (2015) had a significantly lower mean BCI than those from spring (2014).Distances of blunt-nosed vipers to the nearest water bodies (natural and artificial, respectively) did not differ significantly between spring (2014) and late summer (2015). There was also no significant difference between the distances of vipers to natural and to artificial water bodies in spring (and late summer).<br><br>CONCLUSIONS: Adult male blunt-nosed vipers exceed adult females in TaL, ToL and HL. Adult females are likely in a more vulnerable body condition in late summer than in spring. Periodic drying out of freshwater bodies in summer probably does not affect the species' occurrence. Educational workshops and habitat conservation are recommended for reducing human-viper conflict.},
}
@article {pmid30606824,
year = {2019},
author = {Servick, K},
title = {Computers turn neural signals into speech.},
journal = {Science (New York, N.Y.)},
volume = {363},
number = {6422},
pages = {14},
doi = {10.1126/science.363.6422.14},
pmid = {30606824},
issn = {1095-9203},
mesh = {Brain Neoplasms ; *Brain-Computer Interfaces ; *Computers ; Epilepsy ; Humans ; *Neural Networks, Computer ; *Speech ; },
}
@article {pmid30606756,
year = {2019},
author = {Chandrasekaran, C and Bray, IE and Shenoy, KV},
title = {Frequency Shifts and Depth Dependence of Premotor Beta Band Activity during Perceptual Decision-Making.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {39},
number = {8},
pages = {1420-1435},
pmid = {30606756},
issn = {1529-2401},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; K99 NS092972/NS/NINDS NIH HHS/United States ; R00 NS092972/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Attention/physiology ; Beta Rhythm/*physiology ; Color Perception/*physiology ; Computer Simulation ; Cues ; Decision Making/*physiology ; Discrimination, Psychological/*physiology ; Gamma Rhythm/physiology ; Hand Strength ; Macaca mulatta ; Male ; Models, Neurological ; Models, Psychological ; Motor Cortex/*physiology ; Photic Stimulation ; Psychomotor Performance/*physiology ; Reaction Time/*physiology ; Wavelet Analysis ; },
abstract = {Neural activity in the premotor and motor cortices shows prominent structure in the beta frequency range (13-30 Hz). Currently, the behavioral relevance of this beta band activity (BBA) is debated. The underlying source of motor BBA and how it changes as a function of cortical depth are also not completely understood. Here, we addressed these unresolved questions by investigating BBA recorded using laminar electrodes in the dorsal premotor cortex of 2 male rhesus macaques performing a visual reaction time (RT) reach discrimination task. We observed robust BBA before and after the onset of the visual stimulus but not during the arm movement. While poststimulus BBA was positively correlated with RT throughout the beta frequency range, prestimulus correlation varied by frequency. Low beta frequencies (∼12-20 Hz) were positively correlated with RT, and high beta frequencies (∼22-30 Hz) were negatively correlated with RT. Analysis and simulations suggested that these frequency-dependent correlations could emerge due to a shift in the component frequencies of the prestimulus BBA as a function of RT, such that faster RTs are accompanied by greater power in high beta frequencies. We also observed a laminar dependence of BBA, with deeper electrodes demonstrating stronger power in low beta frequencies both prestimulus and poststimulus. The heterogeneous nature of BBA and the changing relationship between BBA and RT in different task epochs may be a sign of the differential network dynamics involved in cue expectation, decision-making, motor preparation, and movement execution.SIGNIFICANCE STATEMENT Beta band activity (BBA) has been implicated in motor tasks, in disease states, and as a potential signal for brain-machine interfaces. However, the behavioral relevance of BBA and its laminar organization in premotor cortex have not been completely elucidated. Here we addressed these unresolved issues using simultaneous recordings from multiple cortical layers of the premotor cortex of monkeys performing a decision-making task. Our key finding is that BBA is not a monolithic signal. Instead, BBA consists of at least two frequency bands. The relationship between BBA and eventual behavior, such as reaction time, also dynamically changes depending on task epoch. We also provide further evidence that BBA is laminarly organized, with greater power in deeper electrodes for low beta frequencies.},
}
@article {pmid30605482,
year = {2019},
author = {Kosmyna, N and Lécuyer, A},
title = {A conceptual space for EEG-based brain-computer interfaces.},
journal = {PloS one},
volume = {14},
number = {1},
pages = {e0210145},
pmid = {30605482},
issn = {1932-6203},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.},
}
@article {pmid30603614,
year = {2018},
author = {Sadeghi, S and Maleki, A},
title = {The Empirical Mode Decomposition-Decision Tree Method to Recognize the Steady-State Visual Evoked Potentials with Wide Frequency Range.},
journal = {Journal of medical signals and sensors},
volume = {8},
number = {4},
pages = {225-230},
pmid = {30603614},
issn = {2228-7477},
abstract = {BACKGROUND: The empirical mode decomposition (EMD) is a technique to analyze the steady-state visual evoked potential (SSVEP) which decomposes the signal into its intrinsic mode functions (IMFs). Although for the limited stimulation frequency range, choosing the effective IMF leads to good results, but extending this range will seriously challenge the method so that even the combination of IMFs is associated with error.<br><br>METHODS: Stimulation frequencies ranged from 6 to 16 Hz with an interval of 0.5 Hz were generated using Psychophysics toolbox of MATLAB. SSVEP signal was recorded from six subjects. The EMD was used to extract the effective IMFs. Two features, including the frequency related to the peak of spectrum and normalized local energy in this frequency, were extracted for each of six conditions (each IMF, the combination of two consecutive IMFs and the combination of all three IMFs).<br><br>RESULTS: The instantaneous frequency histogram and the recognition accuracy diagram indicate that for wide stimulation frequency range, not only one IMF, but also the combination of IMFs does not have desirable efficiency. Total recognition accuracy of the proposed method was 79.75%, while the highest results obtained from the EMD-fast Fourier transform (FFT) and the CCA were 72.05% and 77.31%, respectively.<br><br>CONCLUSION: The proposed method has improved the recognition rate more than 2.4% and 7.7% compared to the CCA and EMD-FFT, respectively, by providing the solution for situations with wide stimulation frequency range.},
}
@article {pmid30603613,
year = {2018},
author = {Ajami, S and Mahnam, A and Behtaj, S and Abootalebi, V},
title = {An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences.},
journal = {Journal of medical signals and sensors},
volume = {8},
number = {4},
pages = {215-224},
pmid = {30603613},
issn = {2228-7477},
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) provide high rates of accuracy and information transfer rate, but need user's attention to flickering visual stimuli. This quickly leads to eye-fatigue when the flickering frequency is in the low-frequency range. High-frequency flickering stimuli (>30 Hz) have been proposed with significantly lower eye-fatigue. However, SSVEP responses in this frequency range are remarkably weaker, leading to doubts about usability of high-frequency stimuli to develop efficient BCI systems. The purpose of this study was to evaluate if a practical SSVEP Speller can be developed with Repetitive Visual Stimuli in the high-frequency range.<br><br>METHODS: An asynchronous high-frequency (35-40 Hz) speller for typing in Persian language was developed using five flickering visual stimuli. Least absolute shrinkage and selection operator algorithm with two user-calibrated thresholds was used to detect the user's selections. A total of 14 volunteers evaluated the system in an ordinary office environment to type 9 sentences consist of 81 characters with a multistage virtual keyboard.<br><br>RESULTS: Despite very high performance of 6.9 chars/min overall typing speed, average accuracy of 98.3%, and information transfer rate of 64.9 bpm for eight of the participants, the other six participants had serious difficulty in spelling with the system and could not complete the typing experiment.<br><br>CONCLUSIONS: The results of this study in accordance with some previous studies suggest that high rate of illiteracy in high-frequency SSVEP-based BCI systems may be a major burden for their practical application.},
}
@article {pmid30603612,
year = {2018},
author = {Shojaedini, SV and Morabbi, S and Keyvanpour, M},
title = {A New Method for Detecting P300 Signals by Using Deep Learning: Hyperparameter Tuning in High-Dimensional Space by Minimizing Nonconvex Error Function.},
journal = {Journal of medical signals and sensors},
volume = {8},
number = {4},
pages = {205-214},
pmid = {30603612},
issn = {2228-7477},
abstract = {BACKGROUND: P300 signal detection is an essential problem in many fields of Brain-Computer Interface (BCI) systems. Although deep neural networks have almost ubiquitously used in P300 detection, in such networks, increasing the number of dimensions leads to growth ratio of saddle points to local minimums. This phenomenon results in slow convergence in deep neural network. Hyperparameter tuning is one of the approaches in deep learning, which leads to fast convergence because of its ability to find better local minimums. In this paper, a new adaptive hyperparameter tuning method is proposed to improve training of Convolutional Neural Networks (CNNs).<br><br>METHODS: The aim of this paper is to introduce a novel method to improve the performance of deep neural networks in P300 signal detection. To reach this purpose, the proposed method transferred the non-convex error function of CNN) into Lagranging paradigm, then, Newton and dual active set techniques are utilized for hyperparameter tuning in order to minimize error of objective function in high dimensional space of CNN.<br><br>RESULTS: The proposed method was implemented on MATLAB 2017 package and its performance was evaluated on dataset of Ecole Polytechnique F&#xe9;d&#xe9;rale de Lausanne (EPFL) BCI group. The obtained results depicted that the proposed method detected the P300 signals with 95.34% classification accuracy in parallel with high True Positive Rate (i.e., 92.9%) and low False Positive Rate (i.e., 0.77%).<br><br>CONCLUSIONS: To estimate the performance of the proposed method, the achieved results were compared with the results of Naive Hyperparameter (NHP) tuning method. The comparisons depicted the superiority of the proposed method against its alternative, in such way that the best accuracy by using the proposed method was 6.44%, better than the accuracy of the alternative method.},
}
@article {pmid30599076,
year = {2019},
author = {Xiong, X and Fu, Y and Chen, J and Liu, L and Zhang, X},
title = {Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network.},
journal = {Brain topography},
volume = {32},
number = {2},
pages = {240-254},
pmid = {30599076},
issn = {1573-6792},
mesh = {Adult ; Brain/anatomy &amp; histology/*physiology ; Brain Mapping/*methods ; Electroencephalography/methods ; Electromyography ; Female ; Hand/*physiology ; Humans ; Imagination ; Kinetics ; Machine Learning ; Male ; Muscle Contraction/physiology ; Nerve Net/*physiology ; Recognition, Psychology ; Support Vector Machine ; Young Adult ; },
abstract = {To provide optional force and speed control parameters for brain-computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.},
}
@article {pmid30596565,
year = {2019},
author = {Rodrigues, PLC and Jutten, C and Congedo, M},
title = {Riemannian Procrustes Analysis: Transfer Learning for Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {8},
pages = {2390-2401},
doi = {10.1109/TBME.2018.2889705},
pmid = {30596565},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain-computer interfaces (BCI) and poses a challenge for systems that try to reuse data from previous recordings to avoid a calibration phase for new users or new sessions for the same user.<br><br>METHOD: We propose a method based on Procrustes analysis for matching the statistical distributions of two datasets using simple geometrical transformations (translation, scaling, and rotation) over the data points. We use symmetric positive definite matrices (SPD) as statistical features for describing the EEG signals, so the geometrical operations on the data points respect the intrinsic geometry of the SPD manifold. Because of its geometry-aware nature, we call our method the Riemannian Procrustes analysis (RPA). We assess the improvement in transfer learning via RPA by performing classification tasks on simulated data and on eight publicly available BCI datasets covering three experimental paradigms (243 subjects in total).<br><br>RESULTS: Our results show that the classification accuracy with RPA is superior in comparison to other geometry-aware methods proposed in the literature. We also observe improvements in ensemble classification strategies when the statistics of the datasets are matched via RPA.<br><br>CONCLUSION AND SIGNIFICANCE: We present a simple yet powerful method for matching the statistical distributions of two datasets, thus paving the way to BCI systems capable of reusing data from previous sessions and avoid the need of a calibration procedure.},
}
@article {pmid30595661,
year = {2018},
author = {Pugh, J and Pycroft, L and Sandberg, A and Aziz, T and Savulescu, J},
title = {Brainjacking in deep brain stimulation and autonomy.},
journal = {Ethics and information technology},
volume = {20},
number = {3},
pages = {219-232},
pmid = {30595661},
issn = {1388-1957},
support = {/WT_/Wellcome Trust/United Kingdom ; 104848/WT_/Wellcome Trust/United Kingdom ; 203132/WT_/Wellcome Trust/United Kingdom ; 203195/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
abstract = {'Brainjacking' refers to the exercise of unauthorized control of another's electronic brain implant. Whilst the possibility of hacking a Brain-Computer Interface (BCI) has already been proven in both experimental and real-life settings, there is reason to believe that it will soon be possible to interfere with the software settings of the Implanted Pulse Generators (IPGs) that play a central role in Deep Brain Stimulation (DBS) systems. Whilst brainjacking raises ethical concerns pertaining to privacy and physical or psychological harm, we claim that the possibility of brainjacking DBS raises particularly profound concerns about individual autonomy, since the possibility of hacking such devices raises the prospect of third parties exerting influence over the neural circuits underpinning the subject's cognitive, emotional and motivational states. However, although it seems natural to assume that brainjacking represents a profound threat to individual autonomy, we suggest that the implications of brainjacking for individual autonomy are complicated by the fact that technologies targeted by brainjacking often serve to enhance certain aspects of the user's autonomy. The difficulty of ascertaining the implications of brainjacking DBS for individual autonomy is exacerbated by the varied understandings of autonomy in the neuroethical and philosophical literature. In this paper, we seek to bring some conceptual clarity to this area by mapping out some of the prominent views concerning the different dimension of autonomous agency, and the implications of brainjacking DBS for each dimension. Drawing on three hypothetical case studies, we show that there could plausibly be some circumstances in which brainjacking could potentially be carried out in ways that could serve to enhance certain dimensions of the target's autonomy. Our analysis raises further questions about the power, scope, and necessity of obtaining prior consent in seeking to protect patient autonomy when directly interfering with their neural states, in particular in the context of self-regulating closed-loop stimulation devices.},
}
@article {pmid30595547,
year = {2019},
author = {Subileau, M and Merdzhanova, G and Ciais, D and Collin-Faure, V and Feige, JJ and Bailly, S and Vittet, D},
title = {Bone Morphogenetic Protein 9 Regulates Early Lymphatic-Specified Endothelial Cell Expansion during Mouse Embryonic Stem Cell Differentiation.},
journal = {Stem cell reports},
volume = {12},
number = {1},
pages = {98-111},
pmid = {30595547},
issn = {2213-6711},
mesh = {Activin Receptors, Type I/genetics/metabolism ; Activin Receptors, Type II/genetics/metabolism ; Animals ; Calcineurin/metabolism ; *Cell Differentiation ; Cell Proliferation ; Cells, Cultured ; Endothelial Cells/*cytology/metabolism ; Growth Differentiation Factor 2/*pharmacology ; Humans ; *Lymphangiogenesis ; Lymphatic Vessels/cytology ; Mice ; Mouse Embryonic Stem Cells/*cytology/drug effects/metabolism ; NFATC Transcription Factors/genetics/metabolism ; Vascular Endothelial Growth Factor A/genetics/metabolism ; },
abstract = {Exogenous cues involved in the regulation of the initial steps of lymphatic endothelial development remain largely unknown. We have used an in vitro model based on the co-culture of vascular precursors derived from mouse embryonic stem cell (ESC) differentiation and OP9 stromal cells to examine the first steps of lymphatic specification and expansion. We found that bone morphogenetic protein 9 (BMP9) induced a dose-dependent biphasic effect on ESC-derived vascular precursors. At low concentrations, below 1 ng/mL, BMP9 expands the LYVE-1-positive lymphatic progeny and activates the calcineurin phosphatase/NFATc1 signaling pathway. In contrast, higher BMP9 concentrations preferentially enhance the formation of LYVE-1-negative endothelial cells. This effect results from an OP9 stromal cell-mediated VEGF-A secretion. RNA-silencing experiments indicate specific involvement of ALK1 and ALK2 receptors in these different BMP9 responses. BMP9 at low concentrations may be a useful tool to generate lymphatic endothelial cells from stem cells for cell-replacement strategies.},
}
@article {pmid30590086,
year = {2019},
author = {Khalaf, A and Sejdic, E and Akcakaya, M},
title = {A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound.},
journal = {Journal of neuroscience methods},
volume = {313},
number = {},
pages = {44-53},
doi = {10.1016/j.jneumeth.2018.11.017},
pmid = {30590086},
issn = {1872-678X},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Equipment Design ; Female ; Humans ; Imagination ; Male ; Models, Neurological ; Movement/*physiology ; Support Vector Machine ; Ultrasonography, Doppler, Transcranial/*methods ; Young Adult ; },
abstract = {BACKGROUND: Hybrid brain computer interfaces (BCIs) combining multiple brain imaging modalities have been proposed recently to boost the performance of single modality BCIs.<br><br>NEW METHOD: In this paper, we propose a novel motor imagery (MI) hybrid BCI that uses electrical brain activity recorded using Electroencephalography (EEG) as well as cerebral blood flow velocity measured using functional transcranial Doppler ultrasound (fTCD). Features derived from the power spectrum for both EEG and fTCD signals were calculated. Mutual information and linear support vector machines (SVM) were employed for feature selection and classification.<br><br>RESULTS: Using the EEG-fTCD combination, average accuracies of 88.33%, 89.48%, and 82.38% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Compared to performance measures obtained using EEG only, the hybrid system provided significant improvement in terms of accuracy by 4.48%, 5.36%, and 4.76% respectively. In addition, average transmission rates of 4.17, 5.45, and 10.57 bits/min were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively.<br><br>Compared to EEG-fNIRS hybrid BCIs in literature, we achieved similar or higher accuracies with shorter task duration.<br><br>CONCLUSIONS: The proposed hybrid system is a promising candidate for real-time BCI applications.},
}
@article {pmid30587046,
year = {2019},
author = {Barios, JA and Ezquerro, S and Bertomeu-Motos, A and Nann, M and Badesa, FJ and Fernandez, E and Soekadar, SR and Garcia-Aracil, N},
title = {Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain-Machine Interface Control.},
journal = {International journal of neural systems},
volume = {29},
number = {5},
pages = {1850045},
doi = {10.1142/S0129065718500454},
pmid = {30587046},
issn = {1793-6462},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Delta Rhythm/*physiology ; Electroencephalography ; Feedback, Sensory/physiology ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Theta Rhythm/*physiology ; },
abstract = {Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain-Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11-14 Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.},
}
@article {pmid30586920,
year = {2018},
author = {Liang, H and Zhu, C and Iwata, Y and Maedono, S and Mochita, M and Liu, C and Ueda, N and Li, P and Yu, H and Yan, Y and Duan, F},
title = {Feature Extraction of Shoulder Joint's Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {6},
number = {1},
pages = {},
pmid = {30586920},
issn = {2306-5354},
abstract = {Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals' daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals' features of the shoulder joint, which is the most complex joint in the human's body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300[-]400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.},
}
@article {pmid30585651,
year = {2019},
author = {Michelson, NJ and Eles, JR and Vazquez, AL and Ludwig, KA and Kozai, TDY},
title = {Calcium activation of cortical neurons by continuous electrical stimulation: Frequency dependence, temporal fidelity, and activation density.},
journal = {Journal of neuroscience research},
volume = {97},
number = {5},
pages = {620-638},
pmid = {30585651},
issn = {1097-4547},
support = {R21 NS108098/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS094404/NS/NINDS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Calcium/*metabolism ; Calcium-Binding Proteins/metabolism ; Electric Stimulation ; Green Fluorescent Proteins/metabolism ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Neurons/metabolism/*physiology ; Somatosensory Cortex/cytology/metabolism/*physiology ; },
abstract = {Electrical stimulation of the brain has become a mainstay of fundamental neuroscience research and an increasingly prevalent clinical therapy. Despite decades of use in basic neuroscience research and the growing prevalence of neuromodulation therapies, gaps in knowledge regarding activation or inactivation of neural elements over time have limited its ability to adequately interpret evoked downstream responses or fine-tune stimulation parameters to focus on desired responses. In this work, in vivo two-photon microscopy was used to image neuronal calcium activity in layer 2/3 neurons of somatosensory cortex (S1) in male C57BL/6J-Tg(Thy1-GCaMP6s)GP4.3Dkim/J mice during 30 s of continuous electrical stimulation at varying frequencies. We show frequency-dependent differences in spatial and temporal somatic responses during continuous stimulation. Our results elucidate conflicting results from prior studies reporting either dense spherical activation of somas biased toward those near the electrode, or sparse activation of somas at a distance via axons near the electrode. These findings indicate that the neural element specific temporal response local to the stimulating electrode changes as a function of applied charge density and frequency. These temporal responses need to be considered to properly interpret downstream circuit responses or determining mechanisms of action in basic science experiments or clinical therapeutic applications.},
}
@article {pmid30583321,
year = {2018},
author = {Zhang, C and Xiong, X and Ren, H and Fu, Y},
title = {[Direct brain-controlled multi-robot cooperation task].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {6},
pages = {943-952},
pmid = {30583321},
issn = {1001-5515},
abstract = {Brain control is a new control method. The traditional brain-controlled robot is mainly used to control a single robot to accomplish a specific task. However, the brain-controlled multi-robot cooperation (MRC) task is a new topic to be studied. This paper presents an experimental research which received the "Innovation Creative Award" in the brain-computer interface (BCI) brain-controlled robot contest at the World Robot Contest. Two effective brain switches were set: total control brain switch and transfer switch, and BCI based steady-state visual evoked potentials (SSVEP) was adopted to navigate a humanoid robot and a mechanical arm to complete the cooperation task. Control test of 10 subjects showed that the excellent SSVEP-BCI can be used to achieve the MRC task by appropriately setting up the brain switches. This study is expected to provide inspiration for the future practical brain-controlled MRC task system.},
}
@article {pmid30582661,
year = {2019},
author = {Cho, MC and Yoo, S and Park, J and Cho, SY and Son, H and Oh, SJ and Paick, JS},
title = {Effect of preoperative detrusor underactivity on long-term surgical outcomes of photovaporization and holmium laser enucleation in men with benign prostatic hyperplasia: a lesson from 5-year serial follow-up data.},
journal = {BJU international},
volume = {123},
number = {5A},
pages = {E34-E42},
doi = {10.1111/bju.14661},
pmid = {30582661},
issn = {1464-410X},
mesh = {Aged ; Follow-Up Studies ; Humans ; *Laser Therapy ; Lasers, Solid-State/*therapeutic use ; Lower Urinary Tract Symptoms/etiology/*prevention &amp; control ; Male ; Middle Aged ; Prostatic Hyperplasia/*complications/*surgery ; Quality of Life ; Time Factors ; Treatment Outcome ; Urinary Bladder, Underactive/*complications ; },
abstract = {OBJECTIVES: To investigate the impact of preoperative detrusor underactivity (DU) on serial treatment outcomes over the course of 5 years after photovaporization (PV) or holmium laser enucleation (HoLEP) in patients with benign prostatic hyperplasia (BPH), to compare its impact after PV vs HoLEP, and to identify predictors of long-term lower urinary tract symptoms (LUTS) improvement.<br><br>MATERIALS AND METHODS: This study involved 245 patients with BPH who had complete 5-year follow-up data (PV using 120W-HPS, n = 143, HoLEP, n = 102), grouped as follows: PV-HPS-DU(+), n = 114; PV-HPS-DU(-), n = 29; HoLEP-DU(+), n = 56; and HoLEP-DU(-), n = 46. Bladder contractility index (BCI) < 100 was regarded as DU. Serial treatment outcomes for the International Prostate Symptom Score (IPSS) questionnaire, uroflowmetry and serum PSA level at 6&#xa0;months, and at 1, 2, 3, 4 and 5&#xa0;years after surgery, were compared among the groups. LUTS improvement was defined as a reduction in total IPSS of &#x2265;50% relative to baseline.<br><br>RESULTS: Improvement in total IPSS, quality of life (QoL) index and post-void residual urine volume (PVR) in the PV-HPS-DU(+) and PV-HPS-DU(-) groups were maintained up to 5&#xa0;years after PV, except for maximum urinary flow rate (Qmax) and bladder voiding efficiency. In the HoLEP-DU(+) and HoLEP-DU(-) groups, improvements in all outcome variables were maintained up to 5&#xa0;years after HoLEP. Deteriorations in subtotal voiding symptom score, total IPSS and Qmax with time during the long-term period after surgery were more pronounced in the PV-HPS-DU(+) and HoLEP-DU(+) groups than in the PV-HPS-DU(-) and HoLEP-DU(-) groups. Reductions in subtotal voiding symptom score, total IPSS, QoL index, and serum PSA were greater in the HoLEP-DU(+) group than in the PV-HPS-DU(+) group throughout follow-up. The type of surgery (HoLEP vs PV) and higher baseline BCI were independent predictors of LUTS improvement at 5 years after surgery.<br><br>CONCLUSION: Generally, improvement of micturition symptoms, QoL and PVR in patients with DU appears to be maintained up to 5&#xa0;years after PV or HoLEP. Deterioration of voiding symptoms and urinary flow rate at long-term follow-up visits after PV or HoLEP was more pronounced in patients with LUTS/BPH with DU than in those without DU. Patients with BPH with DU may benefit from more complete removal of prostatic adenoma by HoLEP and greater baseline bladder contractility in terms of micturition symptoms and QoL.},
}
@article {pmid30582549,
year = {2019},
author = {Lu, Y and Bi, L},
title = {EEG Signals-Based Longitudinal Control System for a Brain-Controlled Vehicle.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {2},
pages = {323-332},
doi = {10.1109/TNSRE.2018.2889483},
pmid = {30582549},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Automobile Driving ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Feasibility Studies ; Female ; Humans ; Male ; *Motor Vehicles ; Young Adult ; },
abstract = {Directly using brain signals to drive a vehicle may not only help persons with disabilities to regain driving ability but also provide a new alternative way for healthy people to control a vehicle. In this paper, we propose a new longitudinal control system based on electroencephalogram signals for brain-controlled vehicles (BCVs) by combining a user interface, a brain-computer interface (BCI) system, and a longitudinal control module. Driver-in-the-loop experiments were conducted by using two driving tests (i.e., the destination-approaching and car-following tests) with different subjects under two control conditions, i.e., the brain and manual control conditions. Experimental results show the feasibility of alone using brain signals to continuously perform the longitudinal control of a vehicle at a relatively high speed, at least for some users. This paper not only promotes the development of BCVs but also provides some insights into the research on how to apply BCIs to control other high-speed dynamic systems.},
}
@article {pmid30577494,
year = {2018},
author = {Wu, CE and Koay, TS and Esfandiari, A and Ho, YH and Lovat, P and Lunec, J},
title = {ATM Dependent DUSP6 Modulation of p53 Involved in Synergistic Targeting of MAPK and p53 Pathways with Trametinib and MDM2 Inhibitors in Cutaneous Melanoma.},
journal = {Cancers},
volume = {11},
number = {1},
pages = {},
pmid = {30577494},
issn = {2072-6694},
abstract = {MAPK and p14[ARF][-]MDM2[-]p53 pathways are critical in cutaneous melanomas. Here, synergistic combination of the MEK inhibitor, trametinib, with MDM2 inhibitors, nutlin-3/RG7388/HDM201, and the mechanistic basis of responses, for BRAF[V600E] and p53[WT] melanoma cells, are reported. The combination treatments induced higher levels of p53 target gene transcripts and protein products, resulting in increased cell cycle arrest and apoptosis compared with MDM2 inhibitors alone, suggesting trametinib synergized with MDM2 inhibitors via upregulation of p53-dependent pathways. In addition, DUSP6 phosphatase involvement was indicated by downregulation of its mRNA and protein following pERK reduction by trametinib. Furthermore, suppression of DUSP6 by siRNA, or inhibition with the small molecule inhibitor, BCI, at a dose without cytotoxicity, potentiated the effect of MDM2 inhibitors through increased ATM-dependent p53 phosphorylation, as demonstrated by complete reversal with the ATM inhibitor, KU55933. Trametinib synergizes with MDM2 inhibitors through a novel DUSP6 mechanism in BRAF[V600E] and p53[WT] melanoma cells, in which DUSP6 regulation of p53 phosphorylation is mediated by ATM. This provides a new therapeutic rationale for combination treatments involving activation of the ATM/p53 pathway and MAPK pathway inhibition.},
}
@article {pmid30577030,
year = {2019},
author = {Behrenbeck, J and Tayeb, Z and Bhiri, C and Richter, C and Rhodes, O and Kasabov, N and Espinosa-Ramos, JI and Furber, S and Cheng, G and Conradt, J},
title = {Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026014},
doi = {10.1088/1741-2552/aafabc},
pmid = {30577030},
issn = {1741-2552},
mesh = {Algorithms ; Electroencephalography ; Electromyography ; Female ; Hand ; Hand Strength/physiology ; Humans ; Machine Learning ; Male ; Models, Neurological ; *Neural Networks, Computer ; Prostheses and Implants ; Prosthesis Design ; Young Adult ; },
abstract = {OBJECTIVE: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking model on low-power neuromorphic hardware.<br><br>APPROACH: The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing.<br><br>MAIN RESULTS: Experimental results reveal a better classification accuracy using the NeuCube model compared to traditional machine learning methods. For sEMG classification, we reached a training accuracy of 85% and a test accuracy of 84.8%, as well as less than 19% of relative root mean square error (rRMSE) when estimating finger forces from six subjects. For the EEG classification, a mean accuracy of 75% was obtained when tested on raw EEG data from nine subjects from the existing 2b dataset from 'BCI competition IV'.<br><br>SIGNIFICANCE: This work provides a proof of concept for a successful implementation of the NeuCube spiking model on the SpiNNaker neuromorphic platform for raw sEMG and EEG decoding, which could chart a route ahead for a new generation of portable closed-loop and low-power neuroprostheses.},
}
@article {pmid30571710,
year = {2018},
author = {Hickey, JR and Sollmann, R},
title = {A new mark-recapture approach for abundance estimation of social species.},
journal = {PloS one},
volume = {13},
number = {12},
pages = {e0208726},
pmid = {30571710},
issn = {1932-6203},
mesh = {Animals ; Behavior, Animal ; Computer Simulation ; Conservation of Natural Resources/*methods ; Gorilla gorilla ; *Models, Biological ; Population Density ; },
abstract = {Accurate estimates of population abundance are a critical component of species conservation efforts in order to monitor the potential recovery of populations. Capture-mark-recapture (CMR) is a widely used approach to estimate population abundance, yet social species moving in groups violate the assumption of CMR approaches that all individuals in the population are detected independently. We developed a closed CMR model that addresses an important characteristic of group-living species-that individual-detection probability typically is conditional on group detection. Henceforth termed the Two-Step model, this approach first estimates group-detection probability and then-conditional on group detection-estimates individual-detection probability for individuals within detected groups. Overall abundance is estimated assuming that undetected groups have the same average group size as detected groups. We compared the performance of this Two-Step CMR model to a conventional (One-Step) closed CMR model that ignored group structure. We assessed model sensitivity to variation in both group- and individual-detection probability. Both models returned overall unbiased estimates of abundance, but the One-Step model returned deceptively narrow Bayesian confidence intervals (BCI) that failed to encompass the correct population abundance an average 52% of the time. Contrary, under the Two-Step model, CI coverage was on average 96%. Both models had similar root mean squared errors (RMSE), except for scenarios with low group detection probability, where the Two-Step model had much lower RMSE. For illustration with a real data set, we applied the Two-Step and regular model to non-invasive genetic capture-recapture data of mountain gorillas (Gorilla beringei beringei). As with simulations, abundance estimates under both models were similar, but the Two-Step model estimate had a wider confidence interval. Results support using the Two-Step model for species living in constant groups, particularly when group detection probability is low, to reduce risk of bias and adequately portray uncertainty in abundance estimates. Important sources of variation in detection need to be incorporated into the Two-Step model when applying it to field data.},
}
@article {pmid30571435,
year = {2018},
author = {Lin, DJ and Finklestein, SP and Cramer, SC},
title = {New Directions in Treatments Targeting Stroke Recovery.},
journal = {Stroke},
volume = {49},
number = {12},
pages = {3107-3114},
pmid = {30571435},
issn = {1524-4628},
support = {K24 HD074722/HD/NICHD NIH HHS/United States ; R25 NS065743/NS/NINDS NIH HHS/United States ; R44 NS095381/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Carbidopa/therapeutic use ; Cell- and Tissue-Based Therapy ; Chorionic Gonadotropin/therapeutic use ; Dopamine Agonists/therapeutic use ; Drug Combinations ; Epoetin Alfa/therapeutic use ; Humans ; Levodopa/therapeutic use ; Occupational Therapy ; Physical Therapy Modalities ; *Recovery of Function ; Robotics ; Selective Serotonin Reuptake Inhibitors/therapeutic use ; Speech Therapy ; Stroke/*therapy ; Stroke Rehabilitation/*trends ; Telerehabilitation ; Transcranial Direct Current Stimulation ; Transcranial Magnetic Stimulation ; Vagus Nerve Stimulation ; Virtual Reality ; },
}
@article {pmid30569742,
year = {2019},
author = {Kouadri, A and El Khatib, M and Cormenier, J and Chauvet, S and Zeinyeh, W and El Khoury, M and Macari, L and Richaud, P and Coraux, C and Michaud-Soret, I and Alfaidy, N and Benharouga, M},
title = {Involvement of the Prion Protein in the Protection of the Human Bronchial Epithelial Barrier Against Oxidative Stress.},
journal = {Antioxidants &amp; redox signaling},
volume = {31},
number = {1},
pages = {59-74},
doi = {10.1089/ars.2018.7500},
pmid = {30569742},
issn = {1557-7716},
mesh = {A549 Cells ; Adherens Junctions/metabolism ; Bronchi/*cytology/metabolism ; Cell Line ; Cell Polarity ; Copper Sulfate/*adverse effects ; Epithelial Cells/cytology/metabolism ; Humans ; Oxidative Stress ; Prion Proteins/*genetics/*metabolism ; },
abstract = {Aim: Bronchial epithelium acts as a defensive barrier against inhaled pollutants and microorganisms. This barrier is often compromised in inflammatory airway diseases that are characterized by excessive oxidative stress responses, leading to bronchial epithelial shedding, barrier failure, and increased bronchial epithelium permeability. Among proteins expressed in the junctional barrier and participating to the regulation of the response to oxidative and to environmental stresses is the cellular prion protein (PrP[C]). However, the role of PrP[C] is still unknown in the bronchial epithelium. Herein, we investigated the cellular mechanisms by which PrP[C] protein participates into the junctional complexes formation, regulation, and oxidative protection in human bronchial epithelium. Results: Both PrP[C] messenger RNA and mature protein were expressed in human epithelial bronchial cells. PrP[C] was localized in the apical domain and became lateral, at high degree of cell polarization, where it colocalized and interacted with adherens (E-cadherin/γ-catenin) and desmosomal (desmoglein/desmoplakin) junctional proteins. No interaction was detected with tight junction proteins. Disruption of such interactions induced the loss of the epithelial barrier. Moreover, we demonstrated that PrP[C] protection against copper-associated oxidative stress was involved in multiple processes, including the stability of adherens and desmosomal junctional proteins. Innovation: PrP[C] is a pivotal protein in the protection against oxidative stress that is associated with the degradation of adherens and desmosomal junctional proteins. Conclusion: Altogether, these results demonstrate that the loss of the integrity of the epithelial barrier by oxidative stress is attenuated by the activation of PrP[C] expression, where deregulation might be associated with respiratory diseases.},
}
@article {pmid30566170,
year = {2019},
author = {Sun, D and Cao, F and Cong, L and Xu, W and Chen, Q and Shi, W and Xu, S},
title = {Cellular heterogeneity identified by single-cell alkaline phosphatase (ALP) via a SERRS-microfluidic droplet platform.},
journal = {Lab on a chip},
volume = {19},
number = {2},
pages = {335-342},
doi = {10.1039/c8lc01006d},
pmid = {30566170},
issn = {1473-0189},
mesh = {Alkaline Phosphatase/*analysis/metabolism ; Cell Line ; Cell Survival/physiology ; Equipment Design ; Hep G2 Cells ; Humans ; Indoles/analysis/metabolism ; Microfluidic Analytical Techniques/*instrumentation ; Single-Cell Analysis/*instrumentation/methods ; Spectrum Analysis, Raman/*instrumentation ; },
abstract = {Alkaline phosphatase (ALP) is a useful indicator for disease state diagnosis and clinical outcome. Investigation of ALP expression among cells is still challenging since ALP expression in a single cell is too low to be detectable. In our work, an ultrasensitive, high-throughput analytical method was applied for ALP determination in a single cell by using a surface-enhanced resonance Raman scattering (SERRS)-based microfluidic droplet technique. An ALP catalyzed substrate (5-bromo-4-chloro-3-indolyl phosphate, BCIP) was used to evaluate ALP activity in the cell within one droplet. When BCIP was incubated with cells, ALP can catalyze a hydrolysis reaction of colorless BCIP and oxidize the intermediate compound to form blue 5,5'-dibromo-4,4'-dichloro-1H,1H-[2,2']biindolylidene-3,3'-dione (BCI), which is a resonant Raman-active species. The encapsulation of BCI in droplets is favorable for detecting extremely low levels of molecules due to an accumulation effect along with reaction time. The ALP concentration as low as 1.0 × 10-15 M can be successfully detected in a uniform droplet. In addition, cellular heterogeneity profiled by ALP expression on single-cell resolution was monitored with this SERRS-based microfluidic droplet technique. Ultrasensitive determination of ALP secreted from individual cells can help us to understand cell-to-cell heterogeneity.},
}
@article {pmid30564961,
year = {2018},
author = {Jahangiri, A and Sepulveda, F},
title = {The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data.},
journal = {Journal of medical systems},
volume = {43},
number = {2},
pages = {20},
pmid = {30564961},
issn = {1573-689X},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Speech/*physiology ; Young Adult ; },
abstract = {Word production begins with high-Gamma automatic linguistic processing functions followed by speech motor planning and articulation. Phonetic properties are processed in both linguistic and motor stages of word production. Four phonetically dissimilar phonemic structures "BA", "FO", "LE", and "RY" were chosen as covert speech tasks. Ten neurologically healthy volunteers with the age range of 21-33 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. Initially, one-second trials were used, which contained linguistic and motor imagery activities. The four-class true positive rate was calculated. In the next stage, 312 ms trials were used to exclude covert articulation from analysis. By eliminating the covert articulation stage, the four-class grand average classification accuracy dropped from 96.4% to 94.5%. The most valuable features emerge after Auditory cue recognition (~100 ms post onset), and within the 70-128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke's area (linked to Phonological code retrieval), the right IFG, and Broca's area (linked to syllabification). Alpha and Beta band oscillations associated with motor imagery do not contain enough information to fully reflect the complexity of speech movements. Over 90% of the most class-dependent features were in the 30-128 Hz range, even during the covert articulation stage. As a result, compared to linguistic functions, the contribution of motor imagery of articulation in class separability of covert speech tasks from EEG data is negligible.},
}
@article {pmid30560884,
year = {2018},
author = {Ssempiira, J and Kasirye, I and Kissa, J and Nambuusi, B and Mukooyo, E and Opigo, J and Makumbi, F and Kasasa, S and Vounatsou, P},
title = {Measuring health facility readiness and its effects on severe malaria outcomes in Uganda.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {17928},
pmid = {30560884},
issn = {2045-2322},
mesh = {Capacity Building ; Delivery of Health Care/*organization &amp; administration ; Health Facilities ; Health Information Systems ; Humans ; Malaria/*mortality ; Prognosis ; Severity of Illness Index ; Uganda/epidemiology ; },
abstract = {There is paucity of evidence for the role of health service delivery to the malaria decline in Uganda We developed a methodology to quantify health facility readiness and assessed its role on severe malaria outcomes among lower-level facilities (HCIIIs and HCIIs) in the country. Malaria data was extracted from the Health Management Information System (HMIS). General service and malaria-specific readiness indicators were obtained from the 2013 Uganda service delivery indicator survey. Multiple correspondence analysis (MCA) was used to construct a composite facility readiness score based on multiple factorial axes. Geostatistical models assessed the effect of facility readiness on malaria deaths and severe cases. Malaria readiness was achieved in one-quarter of the facilities. The composite readiness score explained 48% and 46% of the variation in the original indicators compared to 23% and 27%, explained by the first axis alone for HCIIIs and HCIIs, respectively. Mortality rate was 64% (IRR = 0.36, 95% BCI: 0.14-0.61) and 68% (IRR = 0.32, 95% BCI: 0.12-0.54) lower in the medium and high compared to low readiness groups, respectively. A composite readiness index is more informative and consistent than the one based on the first MCA factorial axis. In Uganda, higher facility readiness is associated with a reduced risk of severe malaria outcomes.},
}
@article {pmid30560145,
year = {2018},
author = {Speier, W and Arnold, C and Chandravadia, N and Roberts, D and Pendekanti, S and Pouratian, N},
title = {Improving P300 Spelling Rate using Language Models and Predictive Spelling.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {5},
number = {1},
pages = {13-22},
pmid = {30560145},
issn = {2326-263X},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
abstract = {The P300 Speller Brain-Computer Interface (BCI) provides a means of communication for those suffering from advanced neuromuscular diseases such as amyotrophic lateral sclerosis (ALS). Recent literature has incorporated language-based modelling, which uses previously chosen characters and the structure of natural language to modify the interface and classifier. Two complementary methods of incorporating language models have previously been independently studied: predictive spelling uses language models to generate suggestions of complete words to allow for the selection of multiple characters simultaneously, and language model-based classifiers have used prior characters to create a prior probability distribution over the characters based on how likely they are to follow. In this study, we propose a combined method which extends a language-based classifier to generate prior probabilities for both individual characters and complete words. In order to gauge the efficiency of this new model, results across 12 healthy subjects were measured. Incorporating predictive spelling increased typing speed using the P300 speller, with an average increase of 15.5% in typing rate across subjects, demonstrating that language models can be effectively utilized to create full word suggestions for predictive spelling. When combining predictive spelling with language model classification, typing speed is significantly improved, resulting in better typing performance.},
}
@article {pmid30555316,
year = {2018},
author = {Xie, J and Xu, G and Zhao, X and Li, M and Wang, J and Han, C and Han, X},
title = {Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface.},
journal = {Frontiers in neurorobotics},
volume = {12},
number = {},
pages = {82},
pmid = {30555316},
issn = {1662-5218},
abstract = {Neuroplasticity, also known as brain plasticity, is an inclusive term that covers the permanent changes in the brain during the course of an individual's life, and neuroplasticity can be broadly defined as the changes in function or structure of the brain in response to the external and/or internal influences. Long-term potentiation (LTP), a well-characterized form of functional synaptic plasticity, could be influenced by rapid-frequency stimulation (or "tetanus") within in vivo human sensory pathways. Also, stochastic resonance (SR) has brought new insight into the field of visual processing for the study of neuroplasticity. In the present study, a brain-computer interface (BCI) intervention based on rapid and repetitive motion-reversal visual stimulation (i.e., a "tetanizing" stimulation) associated with spatiotemporal visual noise was implemented. The goal was to explore the possibility that the induction of LTP-like plasticity in the visual cortex may be enhanced by the SR formalism via changes in the amplitude of visual evoked potentials (VEPs) measured non-invasively from the scalp of healthy subjects. Changes in the absolute amplitude of P1 and N1 components of the transient VEPs during the initial presentation of the steady-state stimulation were used to evaluate the LTP-like plasticity between the non-noise and noise-tagged BCI interventions. We have shown that after adding a moderate visual noise to the rapid-frequency visual stimulation, the degree of the N1 negativity was potentiated following an ~40-min noise-tagged visual tetani. This finding demonstrated that the SR mechanism could enhance the plasticity-like changes in the human visual cortex.},
}
@article {pmid30555313,
year = {2018},
author = {Khan, MJ and Ghafoor, U and Hong, KS},
title = {Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {479},
pmid = {30555313},
issn = {1662-5161},
abstract = {Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For a successful BCI, early detection of brain commands in time is essential. In this paper, we propose a novel classifier using a modified vector phase diagram and the power of electroencephalography (EEG) signal for early prediction of hemodynamic responses. EEG and functional near-infrared spectroscopy (fNIRS) signals for a motor task (thumb tapping) were obtained concurrently. Upon the resting state threshold circle in the vector phase diagram that uses the maximum values of oxy- and deoxy-hemoglobin (ΔHbO and ΔHbR) during the resting state, we introduce a secondary (inner) threshold circle using the ΔHbO and ΔHbR magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of ΔHbO and ΔHbR touches the resting state threshold circle after passing through the inner circle, this indicates that ΔHbO was increasing and ΔHbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 s for an fNIRS signal to cross the resting state threshold circle after crossing the EEG-based circle. Thus, an fNIRS-based BCI command can be generated in 1.5 s. We achieved an improved accuracy of 86.0% using the proposed method in comparison with the 63.8% accuracy obtained using linear discriminant analysis in a window of 0~1.5 s. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 s of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 s using the proposed method.},
}
@article {pmid30552945,
year = {2019},
author = {Mendell, AL and MacLusky, NJ},
title = {The testosterone metabolite 3α-androstanediol inhibits oxidative stress-induced ERK phosphorylation and neurotoxicity in SH-SY5Y cells through an MKP3/DUSP6-dependent mechanism.},
journal = {Neuroscience letters},
volume = {696},
number = {},
pages = {60-66},
doi = {10.1016/j.neulet.2018.12.012},
pmid = {30552945},
issn = {1872-7972},
mesh = {Dual Specificity Phosphatase 6/*metabolism ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Humans ; Hydrogen Peroxide/metabolism/*pharmacology ; Neurons/drug effects/metabolism ; Neurotransmitter Agents/metabolism ; Oxidative Stress/drug effects ; Phosphorylation/*drug effects ; Receptors, Androgen/drug effects/metabolism ; Testosterone/*metabolism ; },
abstract = {Testosterone exerts neuroprotective effects on the brain, but the mechanisms by which these effects are exerted appear to be different in males and females. While in females they involve local conversion to estradiol, in males they may be androgen receptor-dependent, or mediated through metabolism to neurosteroids such as 5α-androstane-3α,17β-diol (3α-diol), which acts through different mechanisms than testosterone itself. Recently, we demonstrated that 3α-diol can protect neurons and neuronal-like cells against oxidative stress-induced neurotoxicity associated with prolonged phosphorylation of the extracellular signal-regulated kinase (ERK). The mechanism(s) responsible for these effects remain unknown. In the present study, we sought to determine whether the ERK-specific phosphatase, mitogen-activated protein kinase phosphatase 3/dual specificity phosphatase 6 (MKP3/DUSP6), is involved in the cytoprotective effects of 3α-diol in SH-SY5Y human female neuroblastoma cells. 3α-diol inhibited ERK phosphorylation and ameliorated cell death induced by the oxidative stressor hydrogen peroxide (H2O2). These protective effects were significantly reduced by pre-treatment with the MKP3/DUSP6 inhibitor BCI. In addition, H2O2 decreased expression of MKP3/DUSP6, and this was prevented by co-treatment with 3α-diol. These findings suggest that the protective effects of 3α-diol are mediated through regulation of ERK phosphorylation in neurotoxic conditions and indicate that these effects may be exerted through modulation of MKP3/DUSP6. Targeting the regulation of MKP3/DUSP6 may be beneficial in reducing toxicity under conditions of oxidative stress.},
}
@article {pmid30551792,
year = {2019},
author = {Demesmaeker, A and Benard, V and Leroy, A and Vaiva, G},
title = {[Impacts of a brief contact intervention in suicide prevention on medical care consumptions].},
journal = {L'Encephale},
volume = {45 Suppl 1},
number = {},
pages = {S27-S31},
doi = {10.1016/j.encep.2018.09.004},
pmid = {30551792},
issn = {0013-7006},
mesh = {Adult ; Female ; France/epidemiology ; *Health Care Costs/statistics &amp; numerical data ; Health Resources/*economics/*statistics &amp; numerical data ; Hospitalization/economics/statistics &amp; numerical data ; Humans ; Interviews as Topic/standards/statistics &amp; numerical data ; Male ; Middle Aged ; *Population Surveillance/methods ; Preventive Psychiatry/economics/methods/statistics &amp; numerical data ; *Psychotherapy, Brief/economics/methods/statistics &amp; numerical data ; Single-Blind Method ; Suicide/economics/psychology ; Suicide, Attempted/economics/prevention &amp; control/psychology/statistics &amp; numerical data ; Young Adult ; *Suicide Prevention ; },
abstract = {INTRODUCTION: Suicide prevention is a major objective in public health. The development of alternative approaches to the prevention of suicide, such as monitoring systems, is growing quickly. The results are encouraging, but the analysis of the effectiveness remains complex. The objective of this study is to evaluate the medico-economic impact of the ALGOS brief contact intervention (BCI) on the consumption of medical care.<br><br>METHOD: ALGOS is a prospective, comparative, multicentre, single-blind, randomized, controlled trial, which compared two groups after a suicide attempt (SA). The ALGOS algorithm assigned each BCI to the subgroup of participants. The medico-economic impact of each intervention was evaluated at 6 and 13 months after inclusion.<br><br>RESULTS: In all, 987 patients were included. There was no significant difference between the two groups at 6 months and at 13 months after SA in the total number of patients who had been hospitalized in psychiatry or other care services. However, the average number of rheumatology visits was significantly higher in the control group (P=0.01) at 13 months. The total number of rheumatologist and physiotherapist visits was significantly higher in the control group at 6 and 13 months.<br><br>CONCLUSION: Our results suggest that the use of a BCI after SA does not lead to increased consumption of medical care.},
}
@article {pmid30544612,
year = {2018},
author = {Huang, X and Xu, J and Wang, Z},
title = {A Novel Instantaneous Phase Detection Approach and Its Application in SSVEP-Based Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {12},
pages = {},
pmid = {30544612},
issn = {1424-8220},
support = {61671012//National Natural Science Foundation of China/ ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Fourier Analysis ; Humans ; Photic Stimulation ; },
abstract = {This paper proposes a novel phase estimator based on fully-traversed Discrete Fourier Transform (DFT) which takes all possible truncated DFT spectra into account such that it possesses two merits of `direct phase extraction' (namely accurate instantaneous phase information can be extracted without any correction) and suppressing spectral leakage. This paper also proves that the proposed phase estimator complies with the 2-parameter joint estimation model rather than the conventional 3-parameter joint model. Numerical results verify the above two merits and demonstrate that the proposed estimator can extract phase information from noisy multi-tone signals. Finally, real data analysis shows that fully-traversed DFT can achieve a better classification on the phase of steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) than the conventional DFT estimator does. Besides, the proposed phase estimator imposes no restrictions on the relationship between the sampling rates and the stimulus frequencies, thus it is capable of wider applications in phase-coded SSVEP BCIs, when compared with the existing estimators.},
}
@article {pmid30542258,
year = {2018},
author = {Mazrooyisebdani, M and Nair, VA and Loh, PL and Remsik, AB and Young, BM and Moreno, BS and Dodd, KC and Kang, TJ and William, JC and Prabhakaran, V},
title = {Evaluation of Changes in the Motor Network Following BCI Therapy Based on Graph Theory Analysis.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {861},
pmid = {30542258},
issn = {1662-4548},
support = {T32 GM008692/GM/NIGMS NIH HHS/United States ; },
abstract = {Despite the established effectiveness of the brain-computer interface (BCI) therapy during stroke rehabilitation (Song et al., 2014a, 2015; Young et al., 2014a,b,c, 2015; Remsik et al., 2016), little is understood about the connections between motor network reorganization and functional motor improvements. The aim of this study was to investigate changes in the network reorganization of the motor cortex during BCI therapy. Graph theoretical approaches are used on resting-state functional magnetic resonance imaging (fMRI) data acquired from stroke patients to evaluate these changes. Correlations between changes in graph measurements and behavioral measurements were also examined. Right hemisphere chronic stroke patients (average time from stroke onset = 38.23 months, standard deviation (SD) = 46.27 months, n = 13, 6 males, 10 right-handed) with upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device. Eyes-closed resting-state fMRI (rs-fMRI) scans, along with T-1 weighted anatomical scans on 3.0T MRI scanners were collected from these patients at four test points. Immediate therapeutic effects were investigated by comparing pre and post-therapy results. Results displayed that th average clustering coefficient of the motor network increased significantly from pre to post-therapy. Furthermore, increased regional centrality of ipsilesional primary motor area (p = 0.02) and decreases in regional centrality of contralesional thalamus (p = 0.05), basal ganglia (p = 0.05 in betweenness centrality analysis and p = 0.03 for degree centrality), and dentate nucleus (p = 0.03) were observed (uncorrected). These findings suggest an overall trend toward significance in terms of involvement of these regions. Increased centrality of primary motor area may indicate increased efficiency within its interactive network as an effect of BCI therapy. Notably, changes in centrality of the bilateral cerebellum regions have strong correlations with both clinical variables [the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT)].},
}
@article {pmid30541962,
year = {2018},
author = {Nayak, L and Dasgupta, A and Das, R and Ghosh, K and De, RK},
title = {Computational neuroscience and neuroinformatics: Recent progress and resources.},
journal = {Journal of biosciences},
volume = {43},
number = {5},
pages = {1037-1054},
pmid = {30541962},
issn = {0973-7138},
mesh = {Brain/anatomy &amp; histology/diagnostic imaging/*physiology ; Computational Biology/*methods ; Connectome/instrumentation/*methods ; Databases, Factual ; Humans ; Nerve Net/anatomy &amp; histology/diagnostic imaging/*physiology ; Neural Networks, Computer ; Neuroglia/cytology/physiology ; Neurons/cytology/physiology ; Neurosciences/*methods ; Software ; },
abstract = {The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 +/- 8.1 +/- billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. 'Computational neuroscience' which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by 'neuroinformatics' approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-ofthe- art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain- computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.},
}
@article {pmid30524257,
year = {2018},
author = {Rashid, N and Iqbal, J and Mahmood, F and Abid, A and Khan, US and Tiwana, MI},
title = {Artificial Immune System-Negative Selection Classification Algorithm (NSCA) for Four Class Electroencephalogram (EEG) Signals.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {439},
pmid = {30524257},
issn = {1662-5161},
abstract = {Artificial immune systems (AIS) are intelligent algorithms derived from the principles inspired by the human immune system. In this study, electroencephalography (EEG) signals for four distinct motor movements of human limbs are detected and classified using a negative selection classification algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV-Graz dataset 2a, comprising nine subjects) has been used. Mel frequency cepstral coefficients (MFCCs) are extracted as selected features from recorded EEG signals. Dimensionality reduction of data is carried out by applying two hidden layered stacked auto-encoder. Genetic algorithm (GA) optimized detectors (artificial lymphocytes) are trained using negative selection algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with the optimized radius of the detector as a nonself detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over nine subjects with a maximum individual subject classification accuracy of 97.5% for subject number eight.},
}
@article {pmid30524109,
year = {2019},
author = {Chu, VC and D'Zmura, M},
title = {Tracking feature-based attention.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016022},
doi = {10.1088/1741-2552/aaed17},
pmid = {30524109},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Visual Cortex/*physiology ; Visual Fields/*physiology ; },
abstract = {OBJECTIVE: Feature-based attention (FBA) helps one detect objects with a particular color, motion, or orientation. FBA works globally; the attended feature is enhanced at all positions in the visual field. This global property of FBA lets one use stimuli presented in the peripheral visual field to track attention in a task presented centrally. The present study explores the use of SSVEPs, generated by flicker presented peripherally, to track attention in a visual search task presented centrally. We evaluate whether this use of EEG to track FBA is robust enough to track attention when performing visual search within a dynamic 3D environment presented with a head-mounted display (HMD).<br><br>APPROACH: Observers first performed a visual search task presented in the central visual field within a stationary virtual environment. The purpose of this first experiment was to establish whether flicker presented peripherally can produce SSVEPs during HMD use. The second experiment placed observers in a dynamic virtual environment in which observers moved around a racetrack. Peripheral flicker was again used to track attention to the color of the target in the visual search task.<br><br>MAIN RESULTS: SSVEPs produced by flicker in the peripheral visual field are influenced strongly by attention in observers with stationary or moving viewpoints. Offline classification results show that one can track an observer's attended color, which suggests that these methods may provide a viable means for tracking FBA in a real-time task.<br><br>SIGNIFICANCE: Current FBA and brain-computer interface (BCI) studies primarily use foveal flicker to produce SSVEP responses. The present study's finding that one can use peripherally-presented flicker to track attention in dynamic virtual environments promises a more flexible and practical approach to BCIs based on FBA.},
}
@article {pmid30524105,
year = {2019},
author = {Twardowski, MD and Roy, SH and Li, Z and Contessa, P and De Luca, G and Kline, JC},
title = {Motor unit drive: a neural interface for real-time upper limb prosthetic control.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016012},
pmid = {30524105},
issn = {1741-2552},
support = {R43 NS093651/NS/NINDS NIH HHS/United States ; R44 HD094626/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electromyography/*methods ; Female ; Humans ; Male ; Middle Aged ; Prosthesis Design/instrumentation/*methods ; Recruitment, Neurophysiological/*physiology ; Upper Extremity/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Modern prosthetic limbs have made strident gains in recent years, incorporating terminal electromechanical devices that are capable of mimicking the human hand. However, access to these advanced control capabilities has been prevented by fundamental limitations of amplitude-based myoelectric neural interfaces, which have remained virtually unchanged for over four decades. Consequently, nearly 23% of adults and 32% of children with major traumatic or congenital upper-limb loss abandon regular use of their myoelectric prosthesis. To address this healthcare need, we have developed a noninvasive neural interface technology that maps natural motor unit increments of neural control and force into biomechanically informed signals for improved prosthetic control.<br><br>APPROACH: Our technology, referred to as motor unit drive (MU Drive), utilizes real-time machine learning algorithms for directly measuring motor unit firings from surface electromyographic signals recorded from residual muscles of an amputated or congenitally missing limb. The extracted firings are transformed into biomechanically informed signals based on the force generating properties of individual motor units to provide a control source that represents the intended movement.<br><br>MAIN RESULTS: We evaluated the characteristics of the MU Drive control signals and compared them to conventional amplitude-based myoelectric signals in healthy subjects as well as subjects with congenital or traumatic trans-radial limb-loss. Our analysis established a vital proof-of-concept: MU Drive provides a more responsive real-time signal with improved smoothness and more faithful replication of intended limb movement that overcomes the trade-off between performance and latency inherent to amplitude-based myoelectric methods.<br><br>SIGNIFICANCE: MU Drive is the first neural interface for prosthetic control that provides noninvasive real-time access to the natural motor control mechanisms of the human nervous system. This new neural interface holds promise for improving prosthetic function by achieving advanced control that better reflects the user intent. Beyond the immediate advantages in the field of prosthetics, MU Drive provides an innovative alternative for advancing the control of exoskeletons, assistive devices, and other robotic rehabilitation applications.},
}
@article {pmid30524070,
year = {2019},
author = {Pailla, T and Miller, KJ and Gilja, V},
title = {Autoencoders for learning template spectrograms in electrocorticographic signals.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016025},
doi = {10.1088/1741-2552/aaf13f},
pmid = {30524070},
issn = {1741-2552},
mesh = {Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebral Cortex/diagnostic imaging/*physiology/physiopathology ; Drug Resistant Epilepsy/diagnosis/physiopathology ; Electrocorticography/*methods ; *Electrodes, Implanted ; Electroencephalography/methods ; Humans ; Learning/*physiology ; Magnetic Resonance Imaging/methods ; Movement/*physiology ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG) based studies generally analyze features from specific frequency bands selected by manual evaluation of spectral power. However, the definition of these features can vary across subjects, cortical areas, tasks and across time for a given subject. We propose an autoencoder based approach for summarizing ECoG data with 'template spectrograms', i.e. informative time-frequency (t-f) patterns, and demonstrate their efficacy in two contexts: brain-computer interfaces (BCIs) and functional brain mapping.<br><br>APPROACH: We use a publicly available dataset wherein subjects perform a finger flexion task in response to a visual cue. We train autoencoders to learn t-f patterns and use them in a deep neural network to decode finger flexions. Additionally, we propose and evaluate an unsupervised method for clustering electrode channels based on their aggregated activity.<br><br>MAIN RESULTS: We show that the learnt t-f patterns can be used to classify individual finger movements with consisentently higher accuracy than with traditional spectral features. Furthermore, electrodes within automatically generated clusters tend to demonstrate functionally similar activity.<br><br>SIGNIFICANCE: With increasing interest in and active development towards higher spatial resolution ECoG, along with the availability of large scale datasets from epilepsy monitoring units, there is an opportunity to develop automated and scalable unsupervised methods to learn effective summaries of spatial, temporal and frequency patterns in these data. The proposed methods reduce the effort required by neural engineers to develop effective features for BCI decoders. The clustering approach has applications in functional mapping studies for identifying brain regions associated with behavioral changes.},
}
@article {pmid30524056,
year = {2019},
author = {Fahimi, F and Zhang, Z and Goh, WB and Lee, TS and Ang, KK and Guan, C},
title = {Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026007},
doi = {10.1088/1741-2552/aaf3f6},
pmid = {30524056},
issn = {1741-2552},
mesh = {Algorithms ; Attention/physiology ; *Brain-Computer Interfaces ; Color Perception ; Deep Learning ; Electroencephalography/classification/*methods ; Humans ; Machine Learning ; *Neural Networks, Computer ; Stroop Test ; *Transfer, Psychology ; },
abstract = {OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data.<br><br>APPROACH: We develop an end-to-end deep CNN to decode the attentional information from an EEG time series. We also explore the consequences of input representations on the performance of deep CNN by feeding three different EEG representations into the network. To ensure the practical application of the proposed framework and avoid time-consuming re-training, we perform inter-subject transfer learning techniques as a classification strategy. Eventually, to interpret the learned attentional patterns, we visualize and analyse the network perception of the attention and non-attention classes.<br><br>MAIN RESULTS: The average classification accuracy is 79.26%, with only 15.83% of 120 subjects having an accuracy below 70% (a generally accepted threshold for BCI). This is while with the inter-subject approach, it is literally difficult to output high classification accuracy. This end-to-end classification framework surpasses conventional classification methods for attention detection. The visualization results demonstrate that the learned patterns from the raw data are meaningful.<br><br>SIGNIFICANCE: This framework significantly improves attention detection accuracy with inter-subject classification. Moreover, this study sheds light on the research on end-to-end learning; the proposed network is capable of learning from raw data with the least amount of pre-processing, which in turn eliminates the extensive computational load of time-consuming data preparation and feature extraction.},
}
@article {pmid30523962,
year = {2019},
author = {Chen, X and Zhao, B and Wang, Y and Gao, X},
title = {Combination of high-frequency SSVEP-based BCI and computer vision for controlling a robotic arm.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026012},
doi = {10.1088/1741-2552/aaf594},
pmid = {30523962},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Arm/*physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation ; *Robotics ; *Visual Prosthesis ; Young Adult ; },
abstract = {OBJECTIVE: Recent attempts in developing brain-computer interface (BCI)-controlled robots have shown the potential of this area in the field of assistive robots. However, implementing the process of picking and placing objects using a BCI-controlled robotic arm still remains challenging. BCI performance, system portability, and user comfort need to be further improved.<br><br>APPROACH: In this study, a novel control approach, which combines high-frequency steady-state visual evoked potential (SSVEP)-based BCI and computer vision-based object recognition, is proposed to control a robotic arm for performing pick and place tasks that require control with multiple degrees of freedom. The computer vision can identify objects in the workspace and locate their positions, while the BCI allows the user to select one of these objects to be acted upon by the robotic arm. The robotic arm was programmed to be able to autonomously pick up and place the selected target object without moment-by-moment supervision by the user.<br><br>MAIN RESULTS: Online results obtained from ten healthy subjects indicated that a BCI command for the proposed system could be selected from four possible choices in 6.5&#x2009;s (i.e. 2.25&#x2009;s for visual stimulation and 4.25&#x2009;s for gaze shifting) with 97.75% accuracy. All subjects could successfully complete the pick and place tasks using the proposed system.<br><br>SIGNIFICANCE: These results demonstrated the feasibility and efficiency of combining high-frequency SSVEP-based BCI and computer vision-based object recognition to control robotic arms. The control strategy presented here could be extended to control robotic arms to perform other complicated tasks.},
}
@article {pmid30523959,
year = {2019},
author = {Ingel, A and Kuzovkin, I and Vicente, R},
title = {Direct information transfer rate optimisation for SSVEP-based BCI.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016016},
doi = {10.1088/1741-2552/aae8c7},
pmid = {30523959},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Information Technology ; Photic Stimulation/methods ; *Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {OBJECTIVE: In this work, a classification method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is proposed. The method is based on information transfer rate (ITR) maximisation.<br><br>APPROACH: The proposed classification method uses features extracted by traditional SSVEP-based BCI methods and finds optimal discrimination thresholds for each feature to classify the targets. Optimising the thresholds is formalised as a maximisation task of a performance measure of BCIs called information transfer rate. However, instead of the standard method of calculating ITR, which makes certain assumptions about the data, a more general formula is derived to avoid incorrect ITR calculation when the standard assumptions are not met.<br><br>MAIN RESULTS: The proposed method shows good performance in classifying targets of a BCI, outperforming previously reported results on the same dataset by a factor of 2 in terms of ITR. The highest achieved ITR on the used dataset was 62 bit min-1.<br><br>SIGNIFICANCE: This approach allows the optimal discrimination thresholds to be automatically calculated and thus eliminates the need for manual parameter selection or performing computationally expensive grid searches. The proposed method also provides a way to reduce false classifications, which is important in real-world applications.},
}
@article {pmid30523919,
year = {2019},
author = {Abiri, R and Borhani, S and Sellers, EW and Jiang, Y and Zhao, X},
title = {A comprehensive review of EEG-based brain-computer interface paradigms.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {011001},
doi = {10.1088/1741-2552/aaf12e},
pmid = {30523919},
issn = {1741-2552},
support = {R56 AG060608/AG/NIA NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces/trends ; *Communication Devices for People with Disabilities/trends ; Electroencephalography/*methods/trends ; Evoked Potentials, Visual/physiology ; Humans ; Neurological Rehabilitation/methods/trends ; },
abstract = {Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.},
}
@article {pmid30523860,
year = {2019},
author = {Alasfour, A and Gabriel, P and Jiang, X and Shamie, I and Melloni, L and Thesen, T and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Gonda, D and Sattar, S and Wang, S and Halgren, E and Gilja, V},
title = {Coarse behavioral context decoding.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016021},
doi = {10.1088/1741-2552/aaee9c},
pmid = {30523860},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Behavior/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electrocorticography/*methods ; Electrodes, Implanted ; Electroencephalography/*methods ; Female ; Humans ; Male ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Current brain-computer interface (BCI) studies demonstrate the potential to decode neural signals obtained from structured and trial-based tasks to drive actuators with high performance within the context of these tasks. Ideally, to maximize utility, such systems will be applied to a wide range of behavioral settings or contexts. Thus, we explore the potential to augment such systems with the ability to decode abstract behavioral contextual states from neural activity.<br><br>APPROACH: To demonstrate the feasibility of such context decoding, we used electrocorticography (ECoG) and stereo-electroencephalography (sEEG) data recorded from the cortical surface and deeper brain structures, respectively, continuously across multiple days from three subjects. During this time, the subjects were engaged in a range of naturalistic behaviors in a hospital environment. Behavioral contexts were labeled manually from video and audio recordings; four states were considered: engaging in dialogue, rest, using electronics, and watching television. We decode these behaviors using a factor analysis and support vector machine (SVM) approach.<br><br>MAIN RESULTS: We demonstrate that these general behaviors can be decoded with high accuracies of 73% for a four-class classifier for one subject and 71% and 62% for a three-class classifier for two subjects.<br><br>SIGNIFICANCE: To our knowledge, this is the first demonstration of the potential to disambiguate abstract naturalistic behavioral contexts from neural activity recorded throughout the day from implanted electrodes. This work motivates further study of context decoding for BCI applications using continuously recorded naturalistic activity in the clinical setting.},
}
@article {pmid30523839,
year = {2019},
author = {Young, D and Willett, F and Memberg, WD and Murphy, B and Rezaii, P and Walter, B and Sweet, J and Miller, J and Shenoy, KV and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {Closed-loop cortical control of virtual reach and posture using Cartesian and joint velocity commands.},
journal = {Journal of neural engineering},
volume = {16},
number = {2},
pages = {026011},
doi = {10.1088/1741-2552/aaf606},
pmid = {30523839},
issn = {1741-2552},
mesh = {Arm/physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Calibration ; Humans ; Joints/*physiology ; Learning ; Male ; Middle Aged ; Motor Cortex/*physiology ; Pilot Projects ; Posture/*physiology ; Psychomotor Performance/*physiology ; Quadriplegia/*rehabilitation ; Self-Help Devices ; Signal Processing, Computer-Assisted ; Virtual Reality ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are a promising technology for the restoration of function to people with paralysis, especially for controlling coordinated reaching. Typical BCI studies decode Cartesian endpoint velocities as commands, but human arm movements might be better controlled in a joint-based coordinate frame, which may match underlying movement encoding in the motor cortex. A better understanding of BCI controlled reaching by people with paralysis may lead to performance improvements in brain-controlled assistive devices.<br><br>APPROACH: Two intracortical BCI participants in the BrainGate2 pilot clinical trial performed a visual 3D endpoint virtual reality reaching task using two decoders: Cartesian and joint velocity. Task performance metrics (i.e. success rate and path efficiency) and single feature and population tuning were compared across the two decoder conditions. The participants also demonstrated the first BCI control of a fourth dimension of reaching, the arm's swivel angle, in a 4D posture matching task.<br><br>MAIN RESULTS: Both users achieved significantly higher success rates using Cartesian velocity control, and joint controlled trajectories were more variable and significantly more curved. Neural tuning analyses showed that most single feature activity was best described by a Cartesian kinematic encoding model, and population analyses revealed only slight differences in aggregate activity between the decoder conditions. Simulations of a BCI user reproduced trajectory features seen during closed-loop joint control when assuming only Cartesian-tuned features passed through a joint decoder. With minimal training, both participants controlled the virtual arm's swivel angle to complete a 4D posture matching task, and achieved significantly higher success using a Cartesian&#x2009;&#x2009;+&#x2009;&#x2009;swivel velocity decoder compared to a joint velocity decoder.<br><br>SIGNIFICANCE: These results suggest that Cartesian velocity command interfaces may provide better BCI control of arm movements than other kinematic variables, even in 4D posture tasks with swivel angle targets.},
}
@article {pmid30523833,
year = {2019},
author = {Hsieh, HL and Wong, YT and Pesaran, B and Shanechi, MM},
title = {Multiscale modeling and decoding algorithms for spike-field activity.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016018},
doi = {10.1088/1741-2552/aaeb1a},
pmid = {30523833},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; *Algorithms ; *Brain-Computer Interfaces ; Linear Models ; *Models, Neurological ; Normal Distribution ; },
abstract = {OBJECTIVE: Behavior is encoded across multiple spatiotemporal scales of brain activity. Modern technology can simultaneously record various scales, from spiking of individual neurons to large neural populations measured with field activity. This capability necessitates developing multiscale modeling and decoding algorithms for spike-field activity, which is challenging because of the fundamental differences in statistical characteristics and time-scales of these signals. Spikes are binary-valued with a millisecond time-scale while fields are continuous-valued with slower time-scales.<br><br>APPROACH: We develop a multiscale encoding model, adaptive learning algorithm, and decoder that explicitly incorporate the different statistical profiles and time-scales of spikes and fields. The multiscale model consists of combined point process and Gaussian process likelihood functions. The multiscale filter (MSF) for decoding runs at the millisecond time-scale of spikes while adding information from fields at their slower time-scales. The adaptive algorithm learns all spike-field multiscale model parameters simultaneously, in real time, and at their different time-scales.<br><br>MAIN RESULTS: We validated the multiscale framework within motor tasks using both closed-loop brain-machine interface (BMI) simulations and non-human primate (NHP) spike and local field potential (LFP) motor cortical activity during a naturalistic 3D reach task. Our closed-loop simulations show that the MSF can add information across scales and that the adaptive MSF can accurately learn all parameters in real time. We also decoded the seven joint angular trajectories of the NHP arm using spike-LFP activity. These data showed that the MSF outperformed single-scale decoding, this improvement was due to the addition of information across scales rather than the dominance of one scale and was largest in the low-information regime, and the improvement was similar regardless of the degree of overlap between spike and LFP channels.<br><br>SIGNIFICANCE: This multiscale framework provides a tool to study encoding across scales and may help enhance future neurotechnologies such as motor BMIs.},
}
@article {pmid30523823,
year = {2019},
author = {Li, J and Chen, X and Li, Z},
title = {Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016014},
doi = {10.1088/1741-2552/aaeaae},
pmid = {30523823},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; Electrodes, Implanted ; Macaca mulatta ; Male ; *Markov Chains ; Motor Cortex/*physiology ; },
abstract = {OBJECTIVE: Detection and sorting (classification) of action potentials from extracellular recordings are two important pre-processing steps for brain-computer interfaces (BCIs) and some neuroscientific studies. Traditional approaches perform these two steps serially, but using shapes of action potential waveforms during detection, i.e. combining the two steps, may lead to better performance, especially during high noise. We propose a hidden Markov model (HMM) based method for combined detecting and sorting of spikes, with the aim of improving the final decoding accuracy of BCIs.<br><br>APPROACH: The states of the HMM indicate whether there is a spike, what unit a spike belongs to, and the time course within a waveform. The HMM outputs probabilities of spike detection, and from this we can calculate expectations of spike counts in time bins, which can replace integer spike counts as input to BCI decoders. We evaluate the HMM method on simulated spiking data. We then examine the impact of using this method on decoding real neural data recorded from primary motor cortex of two Rhesus monkeys.<br><br>MAIN RESULTS: Our comparisons on simulated data to detection-then-sorting approaches and combined detection-and-sorting algorithms indicate that the HMM method performs more accurately at detection and sorting (0.93 versus 0.73 spike count correlation, 0.73 versus 0.49 adjusted mutual information). On real neural data, the HMM method led to higher adjusted mutual information between spike counts and kinematics (monkey K: 0.034 versus 0.027; monkey M: 0.033 versus 0.022) and better neuron encoding model predictions (K: 0.016 dB improvement; M: 0.056 dB improvement). Lastly, the HMM method facilitated higher offline decoding accuracy (Kalman filter, K: 8.5% mean squared error reduction, M: 18.6% reduction).<br><br>SIGNIFICANCE: The HMM spike detection and sorting method offers a new approach to spike pre-processing for BCIs and neuroscientific studies.},
}
@article {pmid30522993,
year = {2018},
author = {Mizuno, K and Abe, T and Ushiba, J and Kawakami, M and Ohwa, T and Hagimura, K and Ogura, M and Okuyama, K and Fujiwara, T and Liu, M},
title = {Evaluating the Effectiveness and Safety of the Electroencephalogram-Based Brain-Machine Interface Rehabilitation System for Patients With Severe Hemiparetic Stroke: Protocol for a Randomized Controlled Trial (BEST-BRAIN Trial).},
journal = {JMIR research protocols},
volume = {7},
number = {12},
pages = {e12339},
pmid = {30522993},
issn = {1929-0748},
abstract = {BACKGROUND: We developed a brain-machine interface (BMI) system for poststroke patients with severe hemiplegia to detect event-related desynchronization (ERD) on scalp electroencephalogram (EEG) and to operate a motor-driven hand orthosis combined with neuromuscular electrical stimulation. ERD arises when the excitability of the ipsi-lesional sensorimotor cortex increases.<br><br>OBJECTIVE: The aim of this study was to evaluate our hypothesis that motor training using this BMI system could improve severe hemiparesis that is resistant to improvement by conventional rehabilitation. We, therefore, planned and implemented a randomized controlled clinical trial (RCT) to evaluate the effectiveness and safety of intensive rehabilitation using the BMI system.<br><br>METHODS: We conducted a single blind, multicenter RCT and recruited chronic poststroke patients with severe hemiparesis more than 90 days after onset (N=40). Participants were randomly allocated to the BMI group (n=20) or the control group (n=20). Patients in the BMI group repeated 10-second motor attempts to operate EEG-BMI 40 min every day followed by 40 min of conventional occupational therapy. The interventions were repeated 10 times in 2 weeks. Control participants performed a simple motor imagery without servo-action of the orthosis, and electrostimulation was given for 10 seconds for 40 min, similar to the BMI intervention. Overall, 40 min of conventional occupational therapy was also given every day after the control intervention, which was also repeated 10 times in 2 weeks. Motor functions and electrophysiological phenotypes of the paretic hands were characterized before (baseline), immediately after (post), and 4 weeks after (follow-up) the intervention. Improvement in the upper extremity score of the Fugl-Meyer assessment between baseline and follow-up was the main outcome of this study.<br><br>RESULTS: Recruitment started in March 2017 and ended in July 2018. This trial is currently in the data correcting phase. This RCT is expected to be completed by October 31, 2018.<br><br>CONCLUSIONS: No widely accepted intervention has been established to improve finger function of chronic poststroke patients with severe hemiparesis. The results of this study will provide clinical data for regulatory approval and novel, important understanding of the role of sensory-motor feedback based on BMI to induce neural plasticity and motor recovery.<br><br>TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000026372; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi? recptno=R000030299 (Archived by WebCite at http://www.webcitation.org/743zBJj3D).<br><br>DERR1-10.2196/12339.},
}
@article {pmid30540741,
year = {2018},
author = {Chaudhary, U and Xia, B and Silvoni, S and Cohen, LG and Birbaumer, N},
title = {Correction: Brain-Computer Interface-Based Communication in the Completely Locked-In State.},
journal = {PLoS biology},
volume = {16},
number = {12},
pages = {e3000089},
pmid = {30540741},
issn = {1545-7885},
abstract = {[This corrects the article DOI: 10.1371/journal.pbio.1002593.].},
}
@article {pmid30533323,
year = {2018},
author = {Darvishi, S and Gharabaghi, A and Ridding, MC and Abbott, D and Baumert, M},
title = {Reaction Time Predicts Brain-Computer Interface Aptitude.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {6},
number = {},
pages = {2000311},
pmid = {30533323},
issn = {2168-2372},
abstract = {There is evidence that 15-30% of the general population cannot effectively operate brain-computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity. Thus, a simple and objective BCI performance predictor is desirable. Neurofeedback (NFB) training involves performing a cognitive task (motor imagery) instructed via sensory stimuli and re-adjusted through ongoing real-time feedback. A simple reaction time (SRT) test reflects the time required for a subject to respond to a defined stimulus. Thus, we postulated that individuals with shorter reaction times operate a BCI with rapidly updated feedback better than individuals with longer reaction times. Furthermore, we investigated how changing the feedback update interval (FUI), i.e., modification of the feedback provision frequency, affects the correlation between the SRT and BCI performance. Ten participants attended four NFB sessions with FUIs of 16, 24, 48, and 96 ms in a randomized order. We found that: 1) SRT is correlated with the BCI performance with FUIs of 16 and 96 ms; 2) good and poor performers elicit stronger ERDs and control BCIs more effectively (i.e., produced larger information transfer rates) with 16 and 96 ms FUIs, respectively. Our findings suggest that SRT may be used as a simple and objective surrogate for BCI aptitude with FUIs of 16 and 96 ms. It also implies that the FUI customization according to participants SRT measure may enhance the BCI performance.},
}
@article {pmid30532801,
year = {2018},
author = {Loeb, GE},
title = {Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies.},
journal = {Applied bionics and biomechanics},
volume = {2018},
number = {},
pages = {1435030},
pmid = {30532801},
issn = {1176-2322},
abstract = {Implantable electrical interfaces with the nervous system were first enabled by cardiac pacemaker technology over 50 years ago and have since diverged into almost all of the physiological functions controlled by the nervous system. There have been a few major clinical and commercial successes, many contentious claims, and some outright failures. These tend to be reviewed within each clinical subspecialty, obscuring the many commonalities of neural control, biophysics, interface materials, electronic technologies, and medical device regulation that they share. This review cites a selection of foundational and recent journal articles and reviews for all major applications of neural prosthetic interfaces in clinical use, trials, or development. The hard-won knowledge and experience across all of these fields can now be amalgamated and distilled into more systematic processes for development of clinical products instead of the often empirical (trial and error) approaches to date. These include a frank assessment of a specific clinical problem, the state of its underlying science, the identification of feasible targets, the availability of suitable technologies, and the path to regulatory and reimbursement approval. Increasing commercial interest and investment facilitates this systematic approach, but it also motivates projects and products whose claims are dubious.},
}
@article {pmid30531921,
year = {2018},
author = {Inoue, Y and Mao, H and Suway, SB and Orellana, J and Schwartz, AB},
title = {Decoding arm speed during reaching.},
journal = {Nature communications},
volume = {9},
number = {1},
pages = {5243},
pmid = {30531921},
issn = {2041-1723},
mesh = {Algorithms ; Animals ; Arm/*physiology ; Electroencephalography ; Intention ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/cytology/*physiology ; Movement/*physiology ; Neurons/physiology ; Psychomotor Performance/*physiology ; },
abstract = {Neural prostheses decode intention from cortical activity to restore upper extremity movement. Typical decoding algorithms extract velocity-a vector quantity with direction and magnitude (speed) -from neuronal firing rates. Standard decoding algorithms accurately recover arm direction, but the extraction of speed has proven more difficult. We show that this difficulty is due to the way speed is encoded by individual neurons and demonstrate how standard encoding-decoding procedures produce characteristic errors. These problems are addressed using alternative brain-computer interface (BCI) algorithms that accommodate nonlinear encoding of speed and direction. Our BCI approach leads to skillful control of both direction and speed as demonstrated by stereotypic bell-shaped speed profiles, straight trajectories, and steady cursor positions before and after the movement.},
}
@article {pmid30529410,
year = {2019},
author = {Aydemir, O and Ergün, E},
title = {A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {313},
number = {},
pages = {60-67},
doi = {10.1016/j.jneumeth.2018.12.004},
pmid = {30529410},
issn = {1872-678X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {BACKGROUND: The input signals of electroencephalography (EEG) based brain computer interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However, multi-channel signals might contain redundant information and increase computational complexity. Furthermore, using only effective channels, rather than all channels, may enhance the performance of the BCI in terms of classification accuracy (CA).<br><br>NEW METHOD: We proposed a robust and subject-specific sequential forward search method (RSS-SFSM) for effective channel selection (ECS). The ECS procedure executes a sequential search among each of the candidate channels in order to find the channels which maximize the CA performance of the validation set. It should be noted that in order to avoid the problems of random selections in the validation set, we applied the ECS procedure for 100 times. Then, the total numbers of the selection of each channel present the effective ones. To demonstrate its reliability and robustness, the proposed method was applied to two data sets.<br><br>RESULTS: The achieved results showed that the proposed method not only improved the average CA by 15.98%, but also decreased the considered number of channels and computational complexity by 71.53% on average.<br><br>Compared with the existing methods, we achieved better results in terms of both the classification accuracy improvement and channel reduction rates.<br><br>CONCLUSIONS: Features extracted by Hilbert transform and sum derivative methods were effectively classified by support vector machine. In conclusion, the results obtained proved that the RSS-SFSM shows great potential for determining effective channel(s).},
}
@article {pmid30521175,
year = {2019},
author = {Giammò, A and Ammirati, E and Tullio, A and Bodo, G and Manassero, A and Gontero, P and Carone, R},
title = {Implant of ATOMS® system for the treatment of postoperative male stress urinary incontinence: results of a single centre.},
journal = {International braz j urol : official journal of the Brazilian Society of Urology},
volume = {45},
number = {1},
pages = {127-136},
pmid = {30521175},
issn = {1677-6119},
mesh = {Aged ; Humans ; Male ; Postoperative Complications/*surgery ; Quality of Life ; Retrospective Studies ; Severity of Illness Index ; *Suburethral Slings ; Urinary Incontinence, Stress/etiology/*surgery ; },
abstract = {PURPOSE: The aim of our study is to evaluate the efficacy and safety of ATOMS® system for the treatment of postoperative male stress urinary incontinence (SUI).<br><br>MATERIALS AND METHODS: We retrospectively evaluated all patients treated at our institution for postoperative male SUI with ATOMS&#xae; implant. We excluded patients with low bladder compliance (< 20 mL / cmH2O), uncontrolled detrusor overactivity, detrusor underactivity (BCI < 100), urethral or bladder neck stricture and low cystometric capacity (< 200 mL).<br><br>RESULTS: From October 2014 to July 2017 we treated 52 patients, mean age 73.6 years. Most of them (92.3%) had undergone radical prostatectomy, 3.85% simple open prostatectomy, 3.85% TURP; 28.8% of patients had undergone urethral surgery, 11.5% adjuvant radiotherapy; 57.7% had already undergone surgical treatment for urinary incontinence. The average24 hours pad test was 411.6 g (180 - 1100). The mean follow-up was 20.1 months (8.1 - 41.5) 30.8% of patients were dry, 59.6% improved &#x2265; 50%, 7.7% improved < 50% and 1.9% unchanged. In total 73.1% reached social continence. There was a significant reduction of the 24 hours pad test and ICIQ - UI SF scores (p < 0.01). In the postoperative follow-up we detected complications in 8 patients (19%): 5 cases of displacement of the scrotal port, in 2 cases catheterization difficulties, one case of epididimitis and concomitant superficial wound infection; no prosthesis infection, nor explants. Radiotherapy, previous urethral surgery,previous incontinence surgery were not statistically related to social continence rates (p 0.65;p 0.11;p 0.11).<br><br>CONCLUSIONS: The ATOMS&#xae; system is an effective and safe surgical treatment of mild and moderate male postoperative SUI with durable results in the short term.},
}
@article {pmid30519999,
year = {2020},
author = {Dalboni da Rocha, JL and Coutinho, G and Bramati, I and Moll, FT and Sitaram, R},
title = {Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders.},
journal = {Brain imaging and behavior},
volume = {14},
number = {3},
pages = {641-652},
doi = {10.1007/s11682-018-0002-2},
pmid = {30519999},
issn = {1931-7565},
support = {1171313//Fondo Nacional de Desarrollo Cient&#xed;fico y Tecnol&#xf3;gico/ ; 1171320//Fondo Nacional de Desarrollo Cient&#xed;fico y Tecnol&#xf3;gico/ ; FKZ 01DQ13004//New INDIGO/ ; },
mesh = {*Alzheimer Disease/diagnostic imaging ; Anisotropy ; Brain/diagnostic imaging ; Diffusion Tensor Imaging ; Humans ; Magnetic Resonance Imaging ; *White Matter/diagnostic imaging ; },
abstract = {This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer's disease and healthy controls, 83% between Alzheimer's disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others.},
}
@article {pmid30507513,
year = {2019},
author = {Hossain, I and Khosravi, A and Hettiarachchi, I and Nahavandi, S},
title = {Batch Mode Query by Committee for Motor Imagery-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {27},
number = {1},
pages = {13-21},
doi = {10.1109/TNSRE.2018.2883594},
pmid = {30507513},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Computer Simulation ; Discriminant Analysis ; Electroencephalography ; Entropy ; Humans ; Imagination/*physiology ; Machine Learning ; Movement/*physiology ; },
abstract = {Although brain-computer interface (BCI) has potential application in the rehabilitation of neural disease and performance improvement of the human in the loop system, it is restricted in the laboratory environment. One of the hindrances behind this restriction is the requirement of a long training data collection session for the user prior to operation of the system at each time. Several approaches have been proposed including the reduction of training data maintaining the robust performance. One of them is active learning (AL) which asks for labeling the training samples and it has the potential to reach robust performance using reduced informative training set. In this paper, one of the AL methods, query by committee (QBC), is applied by forming the committee in heterogeneous and homogeneous feature space. In heterogeneous feature space, three state-of-the-art feature extraction methods are coupled with linear discriminant analysis classifier. For homogeneous feature space, random K -fold sampling is applied after extracting the features using a single method to form the committee of K -members. The joint accuracy by QBC-heterogeneous has obtained the baselines using maximum 35% of the whole training set. It also shows a significant difference at the 5% significance level from QBC-homogeneous selection as well as other contemporary AL methods and random selection method. Thus, QBC-heterogeneous has reduced the labeling effort and the training data collection effort significantly more than that of random labeling process. It infers that QBC is a potential candidate for abridging overall calibration time of BCI systems.},
}
@article {pmid30506366,
year = {2019},
author = {Yang, D and Gu, Y and Thakor, NV and Liu, H},
title = {Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration.},
journal = {Experimental brain research},
volume = {237},
number = {2},
pages = {291-311},
pmid = {30506366},
issn = {1432-1106},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; *Electromyography ; *Electrophysiological Phenomena ; Humans ; *Motor Activity/physiology ; *Muscle, Skeletal/physiology ; },
abstract = {The development of advanced and effective human-machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.},
}
@article {pmid30506106,
year = {2019},
author = {Zhang, F and Tang, B and Zhang, Z and Xu, D and Ma, G},
title = {DUSP6 Inhibitor (E/Z)-BCI Hydrochloride Attenuates Lipopolysaccharide-Induced Inflammatory Responses in Murine Macrophage Cells via Activating the Nrf2 Signaling Axis and Inhibiting the NF-κB Pathway.},
journal = {Inflammation},
volume = {42},
number = {2},
pages = {672-681},
pmid = {30506106},
issn = {1573-2576},
mesh = {Animals ; Anti-Inflammatory Agents/pharmacology ; Cytokines/metabolism ; Dual Specificity Phosphatase 6/*antagonists &amp; inhibitors ; Enzyme Inhibitors/*pharmacology/therapeutic use ; Humans ; Inflammation/chemically induced/*drug therapy ; Lipopolysaccharides ; Macrophages/drug effects/*pathology ; Mice ; NF-E2-Related Factor 2/metabolism ; NF-kappa B/antagonists &amp; inhibitors/metabolism ; Reactive Oxygen Species/metabolism ; Signal Transduction ; },
abstract = {Macrophages play a fundamental role in human chronic diseases such as rheumatoid arthritis, atherosclerosis, and cancer. In the present study, we demonstrated that dual-specificity phosphatase 6 (DUSP6) was upregulated by lipopolysaccharide (LPS) treatment of macrophages. (E/Z)-BCI hydrochloride (BCI) functions as a small molecule inhibitor of DUSP6, and BCI treatment inhibited DUSP6 expression in LPS-activated macrophages. BCI treatment inhibited LPS-triggered inflammatory cytokine production, including IL-1β and IL-6, but not TNF-α, and also affected macrophage polarization to an M1 phenotype. In addition, BCI treatment decreased reactive oxygen species (ROS) production and significantly elevated the levels of Nrf2. Interestingly, pharmacological inhibition of DUSP6 attenuated LPS-induced inflammatory responses was independent of extracellular signal-regulated kinase (ERK) signaling. Furthermore, BCI treatment inhibited phosphorylation of P65 and nuclear P65 expression in LPS-activated macrophages. These results demonstrated that pharmacological inhibition of DUSP6 attenuated LPS-induced inflammatory mediators and ROS production in macrophage cells via activating the Nrf2 signaling axis and inhibiting the NF-κB pathway. These anti-inflammatory effects indicated that BCI may be considered as a therapeutic agent for blocking inflammatory disorders.},
}
@article {pmid30505289,
year = {2018},
author = {Guigou, C and Toupet, M and Delemps, B and Heuschen, S and Aho, S and Bozorg Grayeli, A},
title = {Effect of Rotating Auditory Scene on Postural Control in Normal Subjects, Patients With Bilateral Vestibulopathy, Unilateral, or Bilateral Cochlear Implants.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {972},
pmid = {30505289},
issn = {1664-2295},
abstract = {Objective: The aim of this study was to investigate the impact of a rotating sound stimulation on the postural performances in normal subjects, patients with bilateral vestibulopathy (BVP), unilateral (UCI), and bilateral (BCI) cochlear implantees. Materials and Methods: Sixty-nine adults were included (32 women and 37 men) in a multicenter prospective study. The group included 37 healthy subjects, 10 BVP, 15 UCI, and 7 BCI patients. The average of age was 47 ± 2.0 (range: 23-82). In addition to a complete audiovestibular work up, a dynamic posturography (Multitest Framiral, Grasse) was conducted in silence and with a rotating cocktail party sound delivered by headphone. The center of pressure excursion surface (COPS), sensory preferences, as well as fractal, diffusion, and wavelet analysis of stabilometry were collected. Results: The rotating sound seemed to influenced balance in all subgroups except in controls. COPS increased with sound in the BVP and BCI groups in closed eyes and sway-referenced condition indicating a destabilizing effect while it decreased in UCI in the same condition suggesting stabilization (p < 0.05, linear mixed model corrected for age, n = 69). BVP had higher proprioceptive preferences, BCI had higher vestibular and visual preferences, and UCI had only higher vestibular preferences than controls. Sensory preferences were not altered by rotating sound. Conclusions: The rotating sound destabilized BVP and BCI patients with binaural hearing while it stabilized UCI patients with monaural hearing and no sound rotation effect. This difference suggests that binaural auditory cues are exploited in BCI patients for their balance.},
}
@article {pmid30505265,
year = {2018},
author = {Silva, GA},
title = {A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {843},
pmid = {30505265},
issn = {1662-4548},
abstract = {A confluence of technological capabilities is creating an opportunity for machine learning and artificial intelligence (AI) to enable "smart" nanoengineered brain machine interfaces (BMI). This new generation of technologies will be able to communicate with the brain in ways that support contextual learning and adaptation to changing functional requirements. This applies to both invasive technologies aimed at restoring neurological function, as in the case of neural prosthesis, as well as non-invasive technologies enabled by signals such as electroencephalograph (EEG). Advances in computation, hardware, and algorithms that learn and adapt in a contextually dependent way will be able to leverage the capabilities that nanoengineering offers the design and functionality of BMI. We explore the enabling capabilities that these devices may exhibit, why they matter, and the state of the technologies necessary to build them. We also discuss a number of open technical challenges and problems that will need to be solved in order to achieve this.},
}
@article {pmid30502371,
year = {2019},
author = {Chen, J and Hao, Y and Zhang, S and Sun, G and Xu, K and Chen, W and Zheng, X},
title = {An automated behavioral apparatus to combine parameterized reaching and grasping movements in 3D space.},
journal = {Journal of neuroscience methods},
volume = {312},
number = {},
pages = {139-147},
doi = {10.1016/j.jneumeth.2018.11.022},
pmid = {30502371},
issn = {1872-678X},
mesh = {Animals ; Behavior, Animal ; Behavioral Research/*instrumentation ; Biomechanical Phenomena ; Equipment Design ; *Hand Strength ; Macaca ; Male ; Motor Activity ; Motor Cortex/*physiology ; *Movement ; Neurons/*physiology ; Psychomotor Performance/physiology ; Upper Extremity/physiology ; Video Recording ; },
abstract = {BACKGROUND: The neural principles underlying reaching and grasping movements have been studied extensively in primates for decades. However, few experimental apparatuses have been developed to enable a flexible combination of reaching and grasping in one task in three-dimensional (3D) space.<br><br>NEW METHOD: By combining a custom turning table with a 3D translational device, we have developed a highly flexible apparatus that enables the subject to reach multiple positions in 3D space, and grasp differently shaped objects with multiple grip types in each position. Meanwhile, hand trajectory and grip types can be recorded via optical motion tracking cameras and touch sensors, respectively.<br><br>RESULTS: We have used the apparatus to successfully train a macaque monkey to accomplish a visually-guided reach-to-grasp task, in which, six objects, fixed on the turning table, were grasped appropriately when they were transported to multiple positions in 3D space. A preliminary analysis of neural signals recorded in primary motor cortex, shows that plenty of neurons exhibit significant tuning to both target position and grip type.<br><br>Our apparatus realizes an arbitrary combination of parameterized reaching and grasping movements in a single task, which were usually separated or fixed in other systems. Meanwhile, the apparatus has high expansibility in terms of dynamic range, object shapes and applicable subjects.<br><br>CONCLUSIONS: The apparatus provides a valuable platform to study upper limb functions in behavioral and neurophysiological studies, and may facilitate simultaneous reconstruction of reaching and grasping movements in brain-machine interfaces (BMIs).},
}
@article {pmid30502227,
year = {2019},
author = {Sitaram, R and Yu, T and Halsband, U and Vogel, D and Müller, F and Lang, S and Birbaumer, N and Kotchoubey, B},
title = {Spatial characteristics of spontaneous and stimulus-induced individual functional connectivity networks in severe disorders of consciousness.},
journal = {Brain and cognition},
volume = {131},
number = {},
pages = {10-21},
doi = {10.1016/j.bandc.2018.11.007},
pmid = {30502227},
issn = {1090-2147},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Aged ; Brain/*diagnostic imaging/physiopathology ; Consciousness/*physiology ; Consciousness Disorders/*diagnostic imaging/physiopathology ; Electroencephalography ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Nerve Net/*diagnostic imaging/physiopathology ; Rest ; Young Adult ; },
abstract = {BACKGROUND: Functional connectivity (fcMRI) analyses of resting state functional magnetic resonance imaging (fMRI) data revealed substantial differences between states of consciousness. The underlying cause-effect linkage, however, remains unknown to the present day. The aim of this study was to examine the relationship between fcMRI measures and Disorders of Consciousness (DOC) in resting state and under adequate stimulation.<br><br>METHODS AND FINDINGS: fMRI data from thirteen patients with unresponsive wakefulness syndrome, eight patients in minimally conscious state, and eleven healthy controls were acquired in rest and during the application of nociceptive and emotional acoustic stimuli. We compared spatial characteristics and anatomical topography of seed-based fcMRI networks on group and individual levels. The anatomical topography of fcMRI networks of patients was altered in all three conditions as compared with healthy controls. Spread and distribution of individual fcMRI networks, however, differed significantly between patients and healthy controls in stimulation conditions only. The exploration of individual metric values identified two patients whose spatial metrics did not deviate from metric distributions of healthy controls in a statistically meaningful manner.<br><br>CONCLUSIONS: These findings suggest that the disturbance of consciousness in DOC is related to deficits in global topographical network organization rather than a principal inability to establish long-distance connections. In addition, the results question the claim that task-free measurements are particularly valuable as a tool for individual diagnostics in severe neurological disorders. Further studies comparing connectivity indices with outcome of DOC patients are needed to determine the clinical relevance of spatial metrics and stimulation paradigms for individual diagnosis, prognosis and treatment in DOC.},
}
@article {pmid30510569,
year = {2018},
author = {She, Q and Chen, K and Ma, Y and Nguyen, T and Zhang, Y},
title = {Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {9593682},
pmid = {30510569},
issn = {1687-5273},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Datasets as Topic ; *Electroencephalography ; Evoked Potentials, Motor/*physiology ; Humans ; Imagery, Psychotherapy/*methods ; Learning/*physiology ; *Machine Learning ; Neural Networks, Computer ; },
abstract = {Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.},
}
@article {pmid30510537,
year = {2018},
author = {Höller, Y and Thomschewski, A and Uhl, A and Bathke, AC and Nardone, R and Leis, S and Trinka, E and Höller, P},
title = {HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {955},
pmid = {30510537},
issn = {1664-2295},
support = {W 1233/FWF_/Austrian Science Fund FWF/Austria ; },
abstract = {Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10-20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.},
}
@article {pmid30499562,
year = {2018},
author = {Liburkina, SP and Vasilyev, AN and Kaplan, AY and Ivanova, GE and Chukanova, AS},
title = {[Brain-computer interface-based motor imagery training for patients with neurological movement disorders].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {118},
number = {9. Vyp. 2},
pages = {63-68},
doi = {10.17116/jnevro201811809263},
pmid = {30499562},
issn = {1997-7298},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Movement ; *Movement Disorders/therapy ; },
abstract = {AIM: To study the electric brain activity during motor imagery task in the brain-computer interface (BCI) in motor-disabled patients to determine the optimal ways for using BCI-based ideomotor training in medical rehabilitation.<br><br>MATERIAL AND METHODS: The study included 26 patients with arm motor dysfunction caused by a stroke or a spinal cord injury. They were involved in motor imagery training in the BCI. The power and localization of electroencephalographic (EEG) event-related desynchronization during imagery of different arm movements were measured. The accuracy in the two-command BCI was assessed.<br><br>RESULTS AND CONCLUSION: The pattern of imagery-related EEG desynchronization showed the typical localization for such tasks. Despite the fact that the power of EEG reactions during motor imagery in motor-disabled patients was on average lower than in healthy subjects during a similar task, all the patients were able to achieve high accuracy in the two-command BCI system after several (at least three) training sessions. Our results demonstrate the great potential for using BCI-based motor imagery training for neurorehabilitation of patients with motor dysfunctions.},
}
@article {pmid30498878,
year = {2019},
author = {Zou, Y and Zhao, X and Chu, Y and Zhao, Y and Xu, W and Han, J},
title = {An inter-subject model to reduce the calibration time for motion imagination-based brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {4},
pages = {939-952},
pmid = {30498878},
issn = {1741-0444},
support = {2015AA042301//National High Technology Research and Development Program of China/ ; 61773369//the National Natural Science Foundation of China/ ; 61573340//the National Natural Science Foundation of China/ ; 61503374//the National Natural Science Foundation of China/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Humans ; *Imagination ; *Models, Neurological ; *Motion ; Time Factors ; },
abstract = {A major factor blocking the practical application of brain-computer interfaces (BCI) is the long calibration time. To obtain enough training trials, participants must spend a long time in the calibration stage. In this paper, we propose a new framework to reduce the calibration time through knowledge transferred from the electroencephalogram (EEG) of other subjects. We trained the motor recognition model for the target subject using both the target's EEG signal and the EEG signals of other subjects. To reduce the individual variation of different datasets, we proposed two data mapping methods. These two methods separately diminished the variation caused by dissimilarities in the brain activation region and the strength of the brain activation in different subjects. After these data mapping stages, we adopted an ensemble method to aggregate the EEG signals from all subjects into a final model. We compared our method with other methods that reduce the calibration time. The results showed that our method achieves a satisfactory recognition accuracy using very few training trials (32 samples). Compared with existing methods using few training trials, our method achieved much greater accuracy. Graphical abstract The framework of the proposed method. The workflow of the framework have three steps: 1, process each subjects EEG signals according to the target subject's EEG signal. 2, generate models from each subjects' processed signals. 3, ensemble these models to a final model, the final model is a model for the target subject.},
}
@article {pmid30496189,
year = {2018},
author = {Shokur, S and Donati, ARC and Campos, DSF and Gitti, C and Bao, G and Fischer, D and Almeida, S and Braga, VAS and Augusto, P and Petty, C and Alho, EJL and Lebedev, M and Song, AW and Nicolelis, MAL},
title = {Training with brain-machine interfaces, visuo-tactile feedback and assisted locomotion improves sensorimotor, visceral, and psychological signs in chronic paraplegic patients.},
journal = {PloS one},
volume = {13},
number = {11},
pages = {e0206464},
pmid = {30496189},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Chronic Disease/psychology/rehabilitation ; Feedback, Sensory/*physiology ; Female ; Humans ; *Locomotion ; Male ; Neurological Rehabilitation/*methods ; Paraplegia/physiopathology/*psychology/*rehabilitation ; Quality of Life ; Recovery of Function ; *Touch Perception ; },
abstract = {Spinal cord injury (SCI) induces severe deficiencies in sensory-motor and autonomic functions and has a significant negative impact on patients' quality of life. There is currently no systematic rehabilitation technique assuring recovery of the neurological impairments caused by a complete SCI. Here, we report significant clinical improvement in a group of seven chronic SCI patients (six AIS A, one AIS B) following a 28-month, multi-step protocol that combined training with non-invasive brain-machine interfaces, visuo-tactile feedback and assisted locomotion. All patients recovered significant levels of nociceptive sensation below their original SCI (up to 16 dermatomes, average 11 dermatomes), voluntary motor functions (lower-limbs muscle contractions plus multi-joint movements) and partial sensory function for several modalities (proprioception, tactile, pressure, vibration). Patients also recovered partial intestinal, urinary and sexual functions. By the end of the protocol, all patients had their AIS classification upgraded (six from AIS A to C, one from B to C). These improvements translated into significant changes in the patients' quality of life as measured by standardized psychological instruments. Reexamination of one patient that discontinued the protocol after 12 months of training showed that the 16-month break resulted in neurological stagnation and no reclassification. We suggest that our neurorehabilitation protocol, based uniquely on non-invasive technology (therefore necessitating no surgical operation), can become a promising therapy for patients diagnosed with severe paraplegia (AIS A, B), even at the chronic phase of their lesion.},
}
@article {pmid30488148,
year = {2019},
author = {Talukdar, U and Hazarika, SM and Gan, JQ},
title = {Motor imagery and mental fatigue: inter-relationship and EEG based estimation.},
journal = {Journal of computational neuroscience},
volume = {46},
number = {1},
pages = {55-76},
pmid = {30488148},
issn = {1573-6873},
mesh = {Adult ; Brain/*physiopathology ; *Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Mental Fatigue/*physiopathology ; *Models, Neurological ; Movement/*physiology ; Young Adult ; },
abstract = {Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue. The growth of fatigue was monitored using the Kernel Partial Least Square (KPLS) algorithm on the remaining 6 participants which shows that MI induces substantial mental fatigue. Statistical analysis of the effect of fatigue on motor imagery performance shows that high fatigue level significantly decreases MI EEG separability. Collectively, these results portray an MI-fatigue inter-connection, emphasizing the necessity of developing adaptive MI BCI by tracking mental fatigue.},
}
@article {pmid30483367,
year = {2018},
author = {Zeng, H and Yang, C and Dai, G and Qin, F and Zhang, J and Kong, W},
title = {EEG classification of driver mental states by deep learning.},
journal = {Cognitive neurodynamics},
volume = {12},
number = {6},
pages = {597-606},
pmid = {30483367},
issn = {1871-4080},
abstract = {Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .},
}
@article {pmid30482328,
year = {2018},
author = {Wirz, M and van Hedel, HJA},
title = {Balance, gait, and falls in spinal cord injury.},
journal = {Handbook of clinical neurology},
volume = {159},
number = {},
pages = {367-384},
doi = {10.1016/B978-0-444-63916-5.00024-0},
pmid = {30482328},
issn = {0072-9752},
mesh = {*Accidental Falls ; Gait Disorders, Neurologic/*etiology ; Humans ; Postural Balance/*physiology ; Sensation Disorders/*etiology ; Spinal Cord Injuries/*complications ; },
abstract = {This chapter covers balance, gait, and falls in individuals with spinal cord injury (SCI) from a clinical perspective. First, the consequences of an SCI on functioning are explained, including etiology, clinical presentation, classification, and epidemiologic data. Then, the specific aspects of balance disorders, gait disorders, and falls are discussed with respect to motor complete (cSCI) and incomplete (iSCI) SCI. Typically, these activities are affected by impaired afferent and efferent nerves, but not by central nervous processing. Performance of daily life activities in cSCI depends on the ability to control the interaction between the center of mass and the base of support or limits of stability. In iSCI, impaired proprioception and muscle strength are important factors for completing balancing tasks and for walking. Falls are common in patients with SCI. Subsequent sections describe therapy approaches aimed at modifying balance, gait, and the risk for falls by means of therapeutic exercises, assistive devices like robots or functional electric stimulation, and environmental adaptations. The last part covers recent developments and future directions. These encompass interventions for maximizing residual neural function and regeneration of axons, as well as technical solutions like epidural or intraspinal electric stimulation, powered exoskeletons, and brain computer interfaces.},
}
@article {pmid30475577,
year = {2018},
author = {Alegret, N and Dominguez-Alfaro, A and González-Domínguez, JM and Arnaiz, B and Cossío, U and Bosi, S and Vázquez, E and Ramos-Cabrer, P and Mecerreyes, D and Prato, M},
title = {Three-Dimensional Conductive Scaffolds as Neural Prostheses Based on Carbon Nanotubes and Polypyrrole.},
journal = {ACS applied materials &amp; interfaces},
volume = {10},
number = {50},
pages = {43904-43914},
doi = {10.1021/acsami.8b16462},
pmid = {30475577},
issn = {1944-8252},
mesh = {Animals ; Astrocytes/cytology/*metabolism ; Biocompatible Materials/*chemistry ; Cell Line ; Mice ; Nanotubes, Carbon/*chemistry ; *Neural Prostheses ; Porosity ; *Tissue Engineering ; Tissue Scaffolds/*chemistry ; },
abstract = {Three-dimensional scaffolds for cellular organization need to enjoy a series of specific properties. On the one hand, the morphology, shape and porosity are critical parameters and eventually related with the mechanical properties. On the other hand, electrical conductivity is an important asset when dealing with electroactive cells, so it is a desirable property even if the conductivity values are not particularly high. Here, we construct three-dimensional (3D) porous and conductive composites, where C8-D1A astrocytic cells were incubated to study their biocompatibility. The manufactured scaffolds are composed exclusively of carbon nanotubes (CNTs), a most promising material to interface with neuronal tissue, and polypyrrole (PPy), a conjugated polymer demonstrated to reduce gliosis, improve adaptability, and increase charge-transfer efficiency in brain-machine interfaces. We developed a new and easy strategy, based on the vapor phase polymerization (VPP) technique, where the monomer vapor is polymerized inside a sucrose sacrificial template containing CNT and an oxidizing agent. After removing the sucrose template, a 3D porous scaffold was obtained and its physical, chemical, and electrical properties were evaluated. The obtained scaffold showed very low density, high and homogeneous porosity, electrical conductivity, and Young's Modulus similar to the in vivo tissue. Its high biocompatibility was demonstrated even after 6 days of incubation, thus paving the way for the development of new conductive 3D scaffolds potentially useful in the field of electroactive tissues.},
}
@article {pmid30474793,
year = {2019},
author = {Li, F and Yi, C and Song, L and Jiang, Y and Peng, W and Si, Y and Zhang, T and Zhang, R and Yao, D and Zhang, Y and Xu, P},
title = {Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG.},
journal = {Brain topography},
volume = {32},
number = {2},
pages = {304-314},
doi = {10.1007/s10548-018-0688-x},
pmid = {30474793},
issn = {1573-6792},
mesh = {Algorithms ; Brain/*physiology ; Cerebral Cortex/physiology ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Nerve Net/*physiology ; Rest/physiology ; Scalp ; },
abstract = {Mentally imagining rather physically executing the motor behaviors is defined as motor imagery (MI). During MI, the mu rhythmical oscillation of cortical neurons is the event-related desynchronization (ERD) subserving the physiological basis of MI-based brain-computer interface. In our work, we investigated the specific brain network reconfiguration from rest idle to MI task states, and also probed the underlying relationship between the brain network reconfiguration and MI related ERD. Findings revealed that comparing to rest state, the MI showed the enhanced motor area related linkages and the deactivated activity of default mode network. In addition, the reconfigured network index was closely related to the ERDs, i.e., the higher the reconfigured network index was, the more obvious the ERDs were. These findings consistently implied that the reconfiguration from rest to task states underlaid the reallocation of related brain resources, and the efficient brain reconfiguration corresponded to a better MI performance, which provided the new insights into understanding the mechanism of MI as well as the potential biomarker to evaluate the rehabilitation quality for those patients with deficits of motor function.},
}
@article {pmid30471238,
year = {2019},
author = {Nicks, SE and Wray, RJ and Peavler, O and Jackson, S and McClure, S and Enard, K and Schwartz, T},
title = {Examining peer support and survivorship for African American women with breast cancer.},
journal = {Psycho-oncology},
volume = {28},
number = {2},
pages = {358-364},
doi = {10.1002/pon.4949},
pmid = {30471238},
issn = {1099-1611},
mesh = {Black or African American/*psychology ; Aged ; Breast Neoplasms/*psychology ; Cancer Survivors/*psychology ; Female ; Humans ; Middle Aged ; *Peer Group ; Qualitative Research ; Quality of Life/*psychology ; *Social Adjustment ; *Social Support ; *Survivorship ; United States ; },
abstract = {OBJECTIVE: More than 3.5 million female breast cancer (BrCa) survivors live in the United States, and the number continues to grow. Health status and quality of life among survivors are variable, and African American (AA) survivors suffer disproportionately from BrCa morbidity and mortality. Emerging evidence suggests that peer support is an effective strategy to promote positive survivorship outcomes for AA BrCa survivors. This study aimed to explore the role of peer support in the BrCa experiences of AA survivors.<br><br>METHODS: Working collaboratively with The Breakfast Club, Inc. (BCI), a community-based BrCa peer support organization, we conducted a quasiexperiment to compare the BrCa experiences of AA survivors. We conducted in-depth interviews with two survivor groups (N&#xa0;=&#xa0;12 per group), categorized according to receiving peer support during their BrCa experiences.<br><br>RESULTS: Survivors who received peer support reported greater access to and utilization of alternative support sources, more capacity to process BrCa-related stress, and improved quality of life and adjustment to life as BrCa survivors compared with those who did not receive peer support.<br><br>CONCLUSIONS: Peer relationships provide consistent, quality social support. Consistent peer support helps survivors cope with the continued stress of BrCa, with implications for psychosocial health and quality of life. Findings expand our current understanding of peer support and may enable public health and clinical practitioners to better recognize and intervene with those for whom additional support services are needed.},
}
@article {pmid30467461,
year = {2018},
author = {Remsik, AB and Dodd, K and Williams, L and Thoma, J and Jacobson, T and Allen, JD and Advani, H and Mohanty, R and McMillan, M and Rajan, S and Walczak, M and Young, BM and Nigogosyan, Z and Rivera, CA and Mazrooyisebdani, M and Tellapragada, N and Walton, LM and Gjini, K and van Kan, PLE and Kang, TJ and Sattin, JA and Nair, VA and Edwards, DF and Williams, JC and Prabhakaran, V},
title = {Behavioral Outcomes Following Brain-Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {752},
pmid = {30467461},
issn = {1662-4548},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain-computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as "responders" (<1 = "non-responders") and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors. Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.},
}
@article {pmid30465705,
year = {2019},
author = {Omejc, N and Rojc, B and Battaglini, PP and Marusic, U},
title = {Review of the therapeutic neurofeedback method using electroencephalography: EEG Neurofeedback.},
journal = {Bosnian journal of basic medical sciences},
volume = {19},
number = {3},
pages = {213-220},
pmid = {30465705},
issn = {1840-4812},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods/statistics &amp; numerical data ; Humans ; Nervous System Diseases/psychology/therapy ; Neurofeedback/*methods ; Signal Processing, Computer-Assisted ; Treatment Outcome ; },
abstract = {Electroencephalographic neurofeedback (EEG-NFB) represents a broadly used method that involves a real-time EEG signal measurement, immediate data processing with the extraction of the parameter(s) of interest, and feedback to the individual in a real-time. Using such a feedback loop, the individual may gain better control over the neurophysiological parameters, by inducing changes in brain functioning and, consequently, behavior. It is used as a complementary treatment for a variety of neuropsychological disorders and improvement of cognitive capabilities, creativity or relaxation in healthy subjects. In this review, various types of EEG-NFB training are described, including training of slow cortical potentials (SCPs) and frequency and coherence training, with their main results and potential limitations. Furthermore, some general concerns about EEG-NFB methodology are presented, which still need to be addressed by the NFB community. Due to the heterogeneity of research designs in EEG-NFB protocols, clear conclusions on the effectiveness of this method are difficult to draw. Despite that, there seems to be a well-defined path for the EEG-NFB research in the future, opening up possibilities for improvement.},
}
@article {pmid30462665,
year = {2018},
author = {Ahmadi, N and Constandinou, TG and Bouganis, CS},
title = {Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS).},
journal = {PloS one},
volume = {13},
number = {11},
pages = {e0206794},
pmid = {30462665},
issn = {1932-6203},
mesh = {*Action Potentials ; Algorithms ; Animals ; Bayes Theorem ; Computer Simulation ; Haplorhini ; *Models, Neurological ; Motor Cortex/physiology ; Neurons/*physiology ; Visual Cortex/physiology ; Visual Perception/physiology ; },
abstract = {Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on a kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under an empirical Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.},
}
@article {pmid30462658,
year = {2018},
author = {Nuyujukian, P and Albites Sanabria, J and Saab, J and Pandarinath, C and Jarosiewicz, B and Blabe, CH and Franco, B and Mernoff, ST and Eskandar, EN and Simeral, JD and Hochberg, LR and Shenoy, KV and Henderson, JM},
title = {Cortical control of a tablet computer by people with paralysis.},
journal = {PloS one},
volume = {13},
number = {11},
pages = {e0204566},
pmid = {30462658},
issn = {1932-6203},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Adult ; *Brain Waves ; *Brain-Computer Interfaces ; *Computers, Handheld ; Electrodes ; Female ; Humans ; Male ; Middle Aged ; *Quadriplegia ; *Software ; },
abstract = {General-purpose computers have become ubiquitous and important for everyday life, but they are difficult for people with paralysis to use. Specialized software and personalized input devices can improve access, but often provide only limited functionality. In this study, three research participants with tetraplegia who had multielectrode arrays implanted in motor cortex as part of the BrainGate2 clinical trial used an intracortical brain-computer interface (iBCI) to control an unmodified commercial tablet computer. Neural activity was decoded in real time as a point-and-click wireless Bluetooth mouse, allowing participants to use common and recreational applications (web browsing, email, chatting, playing music on a piano application, sending text messages, etc.). Two of the participants also used the iBCI to "chat" with each other in real time. This study demonstrates, for the first time, high-performance iBCI control of an unmodified, commercially available, general-purpose mobile computing device by people with tetraplegia.},
}
@article {pmid30459588,
year = {2018},
author = {Wierzgała, P and Zapała, D and Wojcik, GM and Masiak, J},
title = {Most Popular Signal Processing Methods in Motor-Imagery BCI: A Review and Meta-Analysis.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {78},
pmid = {30459588},
issn = {1662-5196},
abstract = {Brain-Computer Interfaces (BCI) constitute an alternative channel of communication between humans and environment. There are a number of different technologies which enable the recording of brain activity. One of these is electroencephalography (EEG). The most common EEG methods include interfaces whose operation is based on changes in the activity of Sensorimotor Rhythms (SMR) during imagery movement, so-called Motor Imagery BCI (MIBCI).The present article is a review of 131 articles published from 1997 to 2017 discussing various procedures of data processing in MIBCI. The experiments described in these publications have been compared in terms of the methods used for data registration and analysis. Some of the studies (76 reports) were subjected to meta-analysis which showed corrected average classification accuracy achieved in these studies at the level of 51.96%, a high degree of heterogeneity of results (Q = 1806577.61; df = 486; p < 0.001; I [2] = 99.97%), as well as significant effects of number of channels, number of mental images, and method of spatial filtering. On the other hand the meta-regression failed to provide evidence that there was an increase in the effectiveness of the solutions proposed in the articles published in recent years. The authors have proposed a newly developed standard for presenting results acquired during MIBCI experiments, which is designed to facilitate communication and comparison of essential information regarding the effects observed. Also, based on the findings of descriptive analysis and meta-analysis, the authors formulated recommendations regarding practices applied in research on signal processing in MIBCIs.},
}
@article {pmid30459580,
year = {2018},
author = {Brouwer, AM and van der Waa, J and Stokking, H},
title = {BCI to Potentially Enhance Streaming Images to a VR Headset by Predicting Head Rotation.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {420},
pmid = {30459580},
issn = {1662-5161},
abstract = {While numerous studies show that brain signals contain information about an individual's current state that are potentially valuable for smoothing man-machine interfaces, this has not yet lead to the use of brain computer interfaces (BCI) in daily life. One of the main challenges is the common requirement of personal data that is correctly labeled concerning the state of interest in order to train a model, where this trained model is not guaranteed to generalize across time and context. Another challenge is the requirement to wear electrodes on the head. We here propose a BCI that can tackle these issues and may be a promising case for BCI research and application in everyday life. The BCI uses EEG signals to predict head rotation in order to improve images presented in a virtual reality (VR) headset. When presenting a 360° video to a headset, field-of-view approaches only stream the content that is in the current field of view and leave out the rest. When the user rotates the head, other content parts need to be made available soon enough to go unnoticed by the user, which is problematic given the available bandwidth. By predicting head rotation, the content parts adjacent to the currently viewed part could be retrieved in time for display when the rotation actually takes place. We here studied whether head rotations can be predicted on the basis of EEG sensor data and if so, whether application of such predictions could be applied to improve display of streaming images. Eleven participants generated left- and rightward head rotations while head movements were recorded using the headsets motion sensing system and EEG. We trained neural network models to distinguish EEG epochs preceding rightward, leftward, and no rotation. Applying these models to streaming EEG data that was withheld from the training showed that 400 ms before rotation onset, the probability "no rotation" started to decrease and the probabilities of an upcoming right- or leftward rotation started to diverge in the correct direction. In the proposed BCI scenario, users already wear a device on their head allowing for integrated EEG sensors. Moreover, it is possible to acquire accurately labeled training data on the fly, and continuously monitor and improve the model's performance. The BCI can be harnessed if it will improve imagery and therewith enhance immersive experience.},
}
@article {pmid30459543,
year = {2018},
author = {Zhang, D and Dubey, VN and Yu, W and Low, KH},
title = {Editorial: Biomechatronics: Harmonizing Mechatronic Systems With Human Beings.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {768},
doi = {10.3389/fnins.2018.00768},
pmid = {30459543},
issn = {1662-4548},
}
@article {pmid30459542,
year = {2018},
author = {Skomrock, ND and Schwemmer, MA and Ting, JE and Trivedi, HR and Sharma, G and Bockbrader, MA and Friedenberg, DA},
title = {A Characterization of Brain-Computer Interface Performance Trade-Offs Using Support Vector Machines and Deep Neural Networks to Decode Movement Intent.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {763},
pmid = {30459542},
issn = {1662-4548},
abstract = {Laboratory demonstrations of brain-computer interface (BCI) systems show promise for reducing disability associated with paralysis by directly linking neural activity to the control of assistive devices. Surveys of potential users have revealed several key BCI performance criteria for clinical translation of such a system. Of these criteria, high accuracy, short response latencies, and multi-functionality are three key characteristics directly impacted by the neural decoding component of the BCI system, the algorithm that translates neural activity into control signals. Building a decoder that simultaneously addresses these three criteria is complicated because optimizing for one criterion may lead to undesirable changes in the other criteria. Unfortunately, there has been little work to date to quantify how decoder design simultaneously affects these performance characteristics. Here, we systematically explore the trade-off between accuracy, response latency, and multi-functionality for discrete movement classification using two different decoding strategies-a support vector machine (SVM) classifier which represents the current state-of-the-art for discrete movement classification in laboratory demonstrations and a proposed deep neural network (DNN) framework. We utilized historical intracortical recordings from a human tetraplegic study participant, who imagined performing several different hand and finger movements. For both decoders, we found that response time increases (i.e., slower reaction) and accuracy decreases as the number of functions increases. However, we also found that both the increase of response times and the decline in accuracy with additional functions is less for the DNN than the SVM. We also show that data preprocessing steps can affect the performance characteristics of the two decoders in drastically different ways. Finally, we evaluated the performance of our tetraplegic participant using the DNN decoder in real-time to control functional electrical stimulation (FES) of his paralyzed forearm. We compared his performance to that of able-bodied participants performing the same task, establishing a quantitative target for ideal BCI-FES performance on this task. Cumulatively, these results help quantify BCI decoder performance characteristics relevant to potential users and the complex interactions between them.},
}
@article {pmid30458838,
year = {2018},
author = {Spüler, M and López-Larraz, E and Ramos-Murguialday, A},
title = {On the design of EEG-based movement decoders for completely paralyzed stroke patients.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {15},
number = {1},
pages = {110},
pmid = {30458838},
issn = {1743-0003},
mesh = {Aged ; *Brain-Computer Interfaces ; Double-Blind Method ; Electroencephalography/*instrumentation/methods ; Female ; Humans ; Male ; Middle Aged ; Paralysis/rehabilitation ; *Signal Processing, Computer-Assisted ; Stroke/complications ; Stroke Rehabilitation/*instrumentation/methods ; },
abstract = {BACKGROUND: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question.<br><br>METHODS: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4&#xa0;weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions.<br><br>RESULTS: We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity.<br><br>CONCLUSIONS: This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.},
}
@article {pmid30457706,
year = {2018},
author = {Khalil-Mgharbel, A and Polena, H and Dembélé, PK and Hasan Sohag, MM and Alcaraz, JP and Martin, DK and Vilgrain, I},
title = {A Biomimetic Lipid Membrane Device Reveals the Interaction of Cancer Biomarkers with Human Serum Lipidic Moieties.},
journal = {Biotechnology journal},
volume = {13},
number = {12},
pages = {e1800463},
doi = {10.1002/biot.201800463},
pmid = {30457706},
issn = {1860-7314},
support = {//Grenoble University Hospital/ ; AGIR-PEPS program (2015-2016) (Alpes Grenoble Inno)//University Grenoble Alpes/ ; //Institut National de la Sant&#xe9; et de la Recherche M&#xe9;dicale/ ; //the French Atomic Energy and Alternative Energies Commission/ ; //ARC Fondation/ ; //GEFLUC Is&#xe8;re/ ; //Ligue Contre le Cancer/ ; INCA 07/3D1616/PL-96-031/NG-NC//Institut National du Cancer/ ; },
mesh = {Antigens, CD/blood ; Apolipoprotein A-I/blood ; Biomarkers, Tumor/*blood ; *Biomimetic Materials ; Biotechnology ; Cadherins/blood ; Cholesterol/*blood ; HEK293 Cells ; Humans ; Immunoprecipitation ; Kidney Neoplasms/blood ; Lipoproteins/blood ; Models, Theoretical ; Nanotechnology ; },
abstract = {A major problem for the detection of cancer biomarkers in plasma or serum is that common clinical practice does not require the patient to be in a fasting state. Considering that lipoproteins are the main population affected by food intake, the authors hypothesized that biomarkers could be embedded in lipid particles and thereby opens a new avenue for detection. Using the recently published biomarker, soluble VE-cadherin (sVE), the authors tested our hypothesis using techniques of biophysics, biochemistry and the tools of nanobiotechnology on serum samples from kidney cancer patients (n = 106). Optical density as well as contact angle measurements of serum revealed heterogeneity in the particle content of the serum samples. Isolation of the lipidic moieties by ultracentrifugation showed that sVE was detected in this compartment. Further, isolation of lipoprotein subclasses by precipitation with sodium phosphotungstate and MgCl2 , showed that HDL carried the majority of sVE. Immunoprecipitation of sVE confirmed that it was associated with Apolipoprotein A1, a major compound of HDL. Using a biomimetic lipid bilayer membrane coupled with impedance spectroscopy the authors quantified, in real-time, that the sVE adsorbed to the lipid bilayer membrane without altering its structure. Taken together, these results show for the first time a direct interaction of a cancer biomarker with lipids. The authors anticipate these results to prompt fasting for future blood tests for large-scale studies in the biomarkers research field.},
}
@article {pmid30455621,
year = {2018},
author = {Vaskov, AK and Irwin, ZT and Nason, SR and Vu, PP and Nu, CS and Bullard, AJ and Hill, M and North, N and Patil, PG and Chestek, CA},
title = {Cortical Decoding of Individual Finger Group Motions Using ReFIT Kalman Filter.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {751},
pmid = {30455621},
issn = {1662-4548},
support = {R01 GM111293/GM/NIGMS NIH HHS/United States ; },
abstract = {Objective: To date, many brain-machine interface (BMI) studies have developed decoding algorithms for neuroprostheses that provide users with precise control of upper arm reaches with some limited grasping capabilities. However, comparatively few have focused on quantifying the performance of precise finger control. Here we expand upon this work by investigating online control of individual finger groups. Approach: We have developed a novel training manipulandum for non-human primate (NHP) studies to isolate the movements of two specific finger groups: index and middle-ring-pinkie (MRP) fingers. We use this device in combination with the ReFIT (Recalibrated Feedback Intention-Trained) Kalman filter to decode the position of each finger group during a single degree of freedom task in two rhesus macaques with Utah arrays in motor cortex. The ReFIT Kalman filter uses a two-stage training approach that improves online control of upper arm tasks with substantial reductions in orbiting time, thus making it a logical first choice for precise finger control. Results: Both animals were able to reliably acquire fingertip targets with both index and MRP fingers, which they did in blocks of finger group specific trials. Decoding from motor signals online, the ReFIT Kalman filter reliably outperformed the standard Kalman filter, measured by bit rate, across all tested finger groups and movements by 31.0 and 35.2%. These decoders were robust when the manipulandum was removed during online control. While index finger movements and middle-ring-pinkie finger movements could be differentiated from each other with 81.7% accuracy across both subjects, the linear Kalman filter was not sufficient for decoding both finger groups together due to significant unwanted movement in the stationary finger, potentially due to co-contraction. Significance: To our knowledge, this is the first systematic and biomimetic separation of digits for continuous online decoding in a NHP as well as the first demonstration of the ReFIT Kalman filter improving the performance of precise finger decoding. These results suggest that novel nonlinear approaches, apparently not necessary for center out reaches or gross hand motions, may be necessary to achieve independent and precise control of individual fingers.},
}
@article {pmid30453482,
year = {2018},
author = {Ramele, R and Villar, AJ and Santos, JM},
title = {EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces.},
journal = {Brain sciences},
volume = {8},
number = {11},
pages = {},
pmid = {30453482},
issn = {2076-3425},
support = {ITBACyT-15//Instituto Tecnol&#xf3;gico de Buenos Aires/ ; },
abstract = {The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.},
}
@article {pmid30452976,
year = {2019},
author = {Chowdhury, A and Raza, H and Meena, YK and Dutta, A and Prasad, G},
title = {An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation.},
journal = {Journal of neuroscience methods},
volume = {312},
number = {},
pages = {1-11},
doi = {10.1016/j.jneumeth.2018.11.010},
pmid = {30452976},
issn = {1872-678X},
mesh = {Adult ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Electromyography/*methods ; Female ; Hand ; Hemiplegia/*rehabilitation ; Humans ; Male ; Middle Aged ; Neurological Rehabilitation/instrumentation/*methods ; *Orthotic Devices ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system.<br><br>NEW METHOD: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment.<br><br>RESULTS: The classification accuracy of the CBPT-based BCI system was found to be 92.81&#x202f;&#xb1;&#x202f;2.09% for the healthy experimental group and 84.53&#x202f;&#xb1;&#x202f;4.58% for the patients' group.<br><br>The CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy.<br><br>CONCLUSIONS: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.},
}
@article {pmid30452349,
year = {2018},
author = {Shu, X and Chen, S and Meng, J and Yao, L and Sheng, X and Jia, J and Farina, D and Zhu, X},
title = {Tactile Stimulation Improves Sensorimotor Rhythm-based BCI Performance in Stroke Patients.},
journal = {IEEE transactions on bio-medical engineering},
volume = {},
number = {},
pages = {},
doi = {10.1109/TBME.2018.2882075},
pmid = {30452349},
issn = {1558-2531},
abstract = {OBJECTIVE: BCI decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity in stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR)-based BCI performance by integrating motor tasks with tactile stimulation.<br><br>METHODS: Eleven stroke patients were recruited for three experimental conditions, i.e., motor attempt (MA) condition, tactile stimulation (TS) condition, and tactile stimulation-assisted motor attempt (TS-MA) condition. Tactile stimulation was delivered to the paretic hand wrist during both task and idle states using a DC vibrator.<br><br>RESULTS: We observed that the TS-MA condition achieved greater motor-related cortical activation (MRCA) in alpha-beta band when compared with both TS and MA conditions. Consequently, online BCI decoding accuracies between task and idle states were significantly improved from 74.5% in the MA condition to 85.1% in the TS-MA condition (p < 0.001), whereas the accuracy in the TS condition was 54.6% (approaching to the chance level of 50%).<br><br>CONCLUSION: This finding demonstrates that sensory afferent from peripheral nerves benefits the neural process of sensorimotor cortex in stroke patients. With appropriate sensory stimulation, MRCA is enhanced and corresponding brain patterns are more discriminative.<br><br>SIGNIFICANCE: This novel SMR-BCI paradigm shows great promise to facilitate the practical application of BCI-based stroke rehabilitation.},
}
@article {pmid30450694,
year = {2019},
author = {David, L and Maiser, B and Lobedann, M and Schwan, P and Lasse, M and Ruppach, H and Schembecker, G},
title = {Virus study for continuous low pH viral inactivation inside a coiled flow inverter.},
journal = {Biotechnology and bioengineering},
volume = {116},
number = {4},
pages = {857-869},
doi = {10.1002/bit.26872},
pmid = {30450694},
issn = {1097-0290},
mesh = {Animals ; Antibodies, Monoclonal/metabolism ; Biotechnology/*instrumentation ; Cell Line ; Equipment Design ; Hydrogen-Ion Concentration ; Leukemia Virus, Murine/isolation &amp; purification/physiology ; Mice ; *Virus Inactivation ; },
abstract = {Continuous processing for the production of monoclonal antibodies (mAb) gains more and more importance. Several solutions exist for all the necessary production steps, leading to the possibility to build fully continuous processes. Low pH viral inactivation is a part of the standard platform process for mAb production. Consequently, Klutz et al. introduced the coiled flow inverter (CFI) as a tool for continuous low pH viral inactivation. Besides theoretical calculations of viral reduction, no viral clearance study has been presented so far. In addition, the validation of continuous viral clearance is often neglected in the already existing studies for continuous processing. This study shows in detail the development and execution of a virus study for continuous low pH viral inactivation inside a CFI. The concept presented is also valid for adaptation to other continuous viral clearance steps. The development of this concept includes the technical rationale for an experimental setup, a valid spiking procedure, and finally a sampling method. The experimental results shown represent a viral study using xenotropic murine leukemia virus as a model virus. Two different protein A (ProtA) chromatography setups with varying pH levels were tested. In addition, one of these setups was tested against a batch experiment utilizing the same process material. The results show that sufficient low pH viral inactivation (decadic logarithm reduction value >4) was achieved in all experiments. Complete viral inactivation took place within the first 14.5 min for both continuous studies and the batch study, hence showing similar results. This study therefore represents a successful virus study concept and experiment for a continuous viral inactivation step. Moreover, it was shown that the transfer from batch results to the continuous process is possible. This is accomplished by the narrow residence time distribution of the CFI, showing how close the setup approaches the ideal plug flow and with that batch operation.},
}
@article {pmid30449603,
year = {2019},
author = {Bublitz, C and Wolkenstein, A and Jox, RJ and Friedrich, O},
title = {Legal liabilities of BCI-users: Responsibility gaps at the intersection of mind and machine?.},
journal = {International journal of law and psychiatry},
volume = {65},
number = {},
pages = {101399},
doi = {10.1016/j.ijlp.2018.10.002},
pmid = {30449603},
issn = {1873-6386},
mesh = {*Brain-Computer Interfaces/adverse effects/psychology ; Female ; Humans ; *Liability, Legal ; Machine Learning ; Male ; Risk ; User-Computer Interface ; Wheelchairs ; },
}
@article {pmid30446609,
year = {2018},
author = {Camac, JS and Condit, R and FitzJohn, RG and McCalman, L and Steinberg, D and Westoby, M and Wright, SJ and Falster, DS},
title = {Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {115},
number = {49},
pages = {12459-12464},
pmid = {30446609},
issn = {1091-6490},
mesh = {Islands ; Longevity ; Panama ; *Rainforest ; Species Specificity ; Trees/*growth &amp; development ; },
abstract = {Tree death drives population dynamics, nutrient cycling, and evolution within plant communities. Mortality variation across species is thought to be influenced by different factors relative to variation within species. The unified model provided here separates mortality rates into growth-dependent and growth-independent hazards. This model creates the opportunity to simultaneously estimate these hazards both across and within species. Moreover, it provides the ability to examine how species traits affect growth-dependent and growth-independent hazards. We derive this unified mortality model using cross-validated Bayesian methods coupled with mortality data collected over three census intervals for 203 tropical rainforest tree species at Barro Colorado Island (BCI), Panama. We found that growth-independent mortality tended to be higher in species with lower wood density, higher light requirements, and smaller maximum diameter at breast height (dbh). Mortality due to marginal carbon budget as measured by near-zero growth rate tended to be higher in species with lower wood density and higher light demand. The total mortality variation attributable to differences among species was large relative to variation explained by these traits, emphasizing that much remains to be understood. This additive hazards model strengthens our capacity to parse and understand individual-level mortality in highly diverse tropical forests and hence to predict its consequences.},
}
@article {pmid30445780,
year = {2018},
author = {Carapelli, A and Soltani, A and Leo, C and Vitale, M and Amri, M and Mediouni-Ben Jemâa, J},
title = {Cryptic Diversity Hidden within the Leafminer Genus Liriomyza (Diptera: Agromyzidae).},
journal = {Genes},
volume = {9},
number = {11},
pages = {},
pmid = {30445780},
issn = {2073-4425},
abstract = {Leafminer insects of the genus Liriomyza are small flies whose larvae feed on the internal tissue of some of the most important crop plants for the human diet. Several of these pest species are highly uniform from the morphological point of view, meaning molecular data represents the only reliable taxonomic tool useful to define cryptic boundaries. In this study, both mitochondrial and nuclear molecular markers have been applied to investigate the population genetics of some Tunisian populations of the polyphagous species Liriomyza cicerina, one of the most important pest of chickpea cultivars in the whole Mediterranean region. Molecular data have been collected on larvae isolated from chickpea, faba bean, and lentil leaves, and used for population genetics, phylogenetics, and species delimitation analyses. Results point toward high differentiation levels between specimens collected on the three different legume crops, which, according to the species delimitation methods, are also sufficient to define incipient species differentiation and cryptic species occurrence, apparently tied up with host choice. Genetic data have also been applied for a phylogenetic comparison among Liriomyza species, further confirming their decisive role in the systematic studies of the genus.},
}
@article {pmid30444217,
year = {2019},
author = {Weiss, JM and Flesher, SN and Franklin, R and Collinger, JL and Gaunt, RA},
title = {Artifact-free recordings in human bidirectional brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016002},
doi = {10.1088/1741-2552/aae748},
pmid = {30444217},
issn = {1741-2552},
mesh = {Action Potentials/physiology ; Adult ; *Artifacts ; *Brain-Computer Interfaces/standards ; Deep Brain Stimulation/instrumentation/methods ; Electrodes, Implanted/standards ; Humans ; Male ; *Microelectrodes/standards ; Motor Cortex/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Intracortical microstimulation has shown promise as a means of evoking somatosensory percepts as part of a bidirectional brain-computer interface (BCI). However, microstimulation generates large electrical artifacts that dominate the recordings necessary for BCI control. These artifacts must be eliminated from the signal in real-time to allow for uninterrupted BCI decoding.<br><br>APPROACH: We present a simple, robust modification to an existing clinical BCI system to allow for simultaneous recording and stimulation using a combination of signal blanking and digital filtering, without needing to explicitly account for varying parameters such as electrode locations or amplitudes. We validated our artifact rejection scheme by recording from microelectrodes in primary motor cortex (M1) while stimulating in somatosensory cortex of a person with a spinal cord injury.<br><br>MAIN RESULTS: M1 recordings were digitally blanked using a sample-and-hold circuit triggered just prior to stimulus onset and a first-order 750 Hz high-pass Butterworth filter was used to reduce distortion of the remaining artifact. This scheme enabled spike detection in M1 to resume as soon as 740 &#xb5;s after each stimulus pulse. We demonstrated the effectiveness of the complete bidirectional BCI system by comparing functional performance during a 5&#x2009;degree of freedom robotic arm control task, with and without stimulation. When stimulation was delivered without this artifact rejection scheme, the number of objects the subject was able to move across a table in 2&#x2009;min under BCI control declined significantly compared to trials without stimulation (p&#x2009;&#x2009;<&#x2009;&#x2009;0.01). When artifact rejection was implemented, performance was no different than in trials that did not include stimulation (p&#x2009;&#x2009;=&#x2009;&#x2009;0.621).<br><br>SIGNIFICANCE: The proposed technique uses simple changes in filtering and digital signal blanking with FDA-cleared hardware and enables artifact-free recordings during bidirectional BCI control.},
}
@article {pmid30443203,
year = {2018},
author = {Buch, VP and Richardson, AG and Brandon, C and Stiso, J and Khattak, MN and Bassett, DS and Lucas, TH},
title = {Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {790},
pmid = {30443203},
issn = {1662-4548},
support = {K12 NS080223/NS/NINDS NIH HHS/United States ; R01 NS107550/NS/NINDS NIH HHS/United States ; },
abstract = {Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics.},
}
@article {pmid30442610,
year = {2018},
author = {Yu, Y and Liu, Y and Jiang, J and Yin, E and Zhou, Z and Hu, D},
title = {An Asynchronous Control Paradigm Based on Sequential Motor Imagery and Its Application in Wheelchair Navigation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {12},
pages = {2367-2375},
doi = {10.1109/TNSRE.2018.2881215},
pmid = {30442610},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Functional Laterality ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Online Systems ; Psychomotor Performance ; *Wheelchairs ; Young Adult ; },
abstract = {In this paper, an asynchronous control paradigm based on sequential motor imagery (sMI) is proposed to enrich the control commands of a motor imagery -based brain-computer interface. We test the feasibility and report the performance of this paradigm in wheelchair navigation control. By sequentially imaging left- and right-hand movements, the subjects can complete four sMI tasks in an asynchronous mode that are then encoded to control six steering functions of a wheelchair, including moving forward, turning left, turning right, accelerating, decelerating, and stopping. Two experiments, a simulated experiment, and an online wheelchair navigation experiment, were conducted to evaluate the performance of the proposed approach in seven subjects. In summary, the subjects completed 99 of 105 experimental trials along a predefined route. The success rate was 94.2% indicating the practicality and the effectiveness of the proposed asynchronous control paradigm in wheelchair navigation control.},
}
@article {pmid30441732,
year = {2018},
author = {Zhang, X and Principe, JC and Wang, Y},
title = {Clustering Based Kernel Reinforcement Learning for Neural Adaptation in Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {6125-6128},
doi = {10.1109/EMBC.2018.8513597},
pmid = {30441732},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; Movement ; Reinforcement, Psychology ; },
abstract = {Reinforcement learning (RL) interprets subject's movement intention in Brain Machine Interfaces (BMIs) through trial-and-error with the advantage that it does not need the real limb movements. When the subjects try to control the external devices purely using brain signals without actual movements (brain control), they adjust the neural firing patterns to adapt to device control, which expands the state-action space for the RL decoder to explore. The challenge is to quickly explore the new knowledge in the sizeable state-action space and maintain good performance. Recently quantized attention-gated kernel reinforcement learning (QAGKRL) was proposed to quickly explore the global optimum in Reproducing Kernel Hilbert Space (RKHS). However, its network size will grow large when the new input comes, which makes it computationally inefficient. In addition, the output is generated using the whole input structure without being sensitive to the new knowledge. In this paper, we propose a new kernel based reinforcement learning algorithm that utilizes the clustering technique in the input domain. The similar neural inputs are grouped, and a new input only activates its nearest cluster, which either utilizes an existing sub-network or forms a new one. In this way, we can build the sub-feature space instead of the global mapping to calculate the output, which transfers the old knowledge effectively and also consequently reduces the computational complexity. To evaluate our algorithm, we test on the synthetic spike data, where the subject's task mode switches between manual control and brain control. Compared with QAGKRL, the simulation results show that our algorithm can achieve a faster learning curve, less computational time, and more accuracy. This indicates our algorithm to be a promising method for the online implementation of BMIs.},
}
@article {pmid30441707,
year = {2018},
author = {Li, Y and Wang, PT and Vaidya, MP and Charles Liu, Y and Slutzky, MW and Do, AH},
title = {A novel algorithm for removing artifacts from EEG data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {6014-6017},
doi = {10.1109/EMBC.2018.8513658},
pmid = {30441707},
issn = {2694-0604},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; *Artifacts ; Brain ; Brain Injuries, Traumatic ; *Electroencephalography ; Electromyography ; Hand ; Humans ; Movement ; *Signal Processing, Computer-Assisted ; },
abstract = {In recent years, many studies examined if EEG signals from traumatic brain injury (TBI) patients can be used for new rehabilitation technologies, such as BCI systems. However, extraction of the high-gamma band related to movement remains challenging due to the presence of surface electromyogram (sEMG) caused by unconscious facial and head movement of patients. In this paper, we proposed a modified independent component analysis (ICA) model for EMG artifact removal in the EEG data from TBI patients with a hemicraniectomy. Here, simulated EMG was generated and added to the raw EEG data as the extra channels for independent components calculation. After running ICA, the independent components (ICs) related to artifacts were identified and rejected automatically through several criteria. EEG data underlying hand movement from one healthy subject and one TBI patient with a hemicraniectomy were conducted to verify the efficacy of this algorithm. Results showed that the proposed algorithm removed sEMG artifacts from the EEG data by up to 86.72% while preserving the associated brain features. In particular, the high-gamma band (80 to 160 Hz) was found to arise principally from the hemicraniectomy area after this technique was applied. Meanwhile, we found that the magnitude of gamma power during movement improved after removal of sEMG artifacts.},
}
@article {pmid30441486,
year = {2018},
author = {Rathee, D and Cecotti, H and Prasad, G},
title = {Current source density estimates improve the discriminability of scalp-level brain connectivity features related to motor-imagery tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {5093-5096},
doi = {10.1109/EMBC.2018.8513417},
pmid = {30441486},
issn = {2694-0604},
mesh = {*Brain ; Brain-Computer Interfaces ; Electroencephalography ; Imagination ; *Scalp ; },
abstract = {Recent progress in the number of studies involving brain connectivity analysis of motor imagery (MI) tasks for brain-computer interface (BCI) systems has warranted the need for pre-processing methods. The objective of this study is to evaluate the impact of current source density (CSD) estimation from raw electroencephalogram (EEG) signals on the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial Granger causality (PGC) method was implemented on the raw EEG signals and CSD signals of a publicly available dataset for the estimation of brain connectivity features. Moreover, pairwise binary classifications of four different MI tasks were performed in inter-session and intra-session conditions using a support vector machine classifier. The results showed that CSD provided a statistically significant increase of the AUC: 20.28% in the inter-session condition; 12.54% and 13.92% with session 01 and session 02, respectively, in the intra-session condition. These results show that pre-processing of EEG signals is crucial for single-trial connectivity features based MI-BCI systems and CSD can enhance their overall performance.},
}
@article {pmid30441485,
year = {2018},
author = {Yohanandan, SAC and Kiral-Kornek, I and Tang, J and Mshford, BS and Asif, U and Harrer, S},
title = {A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {5089-5092},
doi = {10.1109/EMBC.2018.8513429},
pmid = {30441485},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Neurofeedback ; },
abstract = {Motor imagery (MI) based Brain-Computer Interfaces (BCIs) are a viable option for giving locked-in syndrome patients independence and communicability. BCIs comprising expensive medical-grade EEG systems evaluated in carefully-controlled, artificial environments are impractical for take-home use. Previous studies evaluated low-cost systems; however, performance was suboptimal or inconclusive. Here we evaluated a low-cost EEG system, OpenBCI, in a natural environment and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. $\mu-$rhythm data collected over the sensorimotor cortex from healthy participants performing relaxation and right-handed MI tasks were used to train a multi-layer perceptron binary classifier using deep learning. We showed that our method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device.},
}
@article {pmid30441484,
year = {2018},
author = {Kar, A and Bera, S and Karri, SPK and Ghosh, S and Mahadevappa, M and Sheet, D},
title = {A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {5085-5088},
doi = {10.1109/EMBC.2018.8513451},
pmid = {30441484},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer ; },
abstract = {Motor imagery (MI) based brain-computer interface (BCI) plays a crucial role in various scenarios ranging from post-traumatic rehabilitation to control prosthetics. Computer-aided interpretation of MI has augmented prior mentioned scenarios since decades but failed to address interpersonal variability. Such variability further escalates in case of multi-class MI, which is currently a common practice. The failures due to interpersonal variability can be attributed to handcrafted features as they failed to extract more generalized features. The proposed approach employs convolution neural network (CNN) based model with both filtering (through axis shuffling) and feature extraction to avail end-to-end training. Axis shuffling is performed adopted in initial blocks of the model for 1D preprocessing and reduce the parameters required. Such practice has avoided the overfitting which resulted in an improved generalized model. Publicly available BCI Competition-IV 2a dataset is considered to evaluate the proposed model. The proposed model has demonstrated the capability to identify subject-specific frequency band with an average and highest accuracy of 70.5% and S3.6% respectively. Proposed CNN model can classify in real time without relying on accelerated computing device like GPU.},
}
@article {pmid30441483,
year = {2018},
author = {Bera, S and Roy, R and Sikdar, D and Kar, A and Mukhopadhyay, R and Mahadevappal, M},
title = {A Randomised Ensemble Learning Approach for Multiclass Motor Imagery Classification Using Error Correcting Output Coding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {5081-5084},
doi = {10.1109/EMBC.2018.8513421},
pmid = {30441483},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Imagery, Psychotherapy ; Imagination ; },
abstract = {Common Spectral Pattern (CSP) algorithm remains predominant for feature extraction from multichannel EEG motor imagery data. However, multiclass classification of from this featureset has been a challenging job. Different approaches have been proposed to be applied on featureset of different EEG subbands to achieve significant classification accuracy. Ensemble learning is very effective in this context to achieve higher accuracy in Brain-Computer Interface (BCI) domain. In this study, we have proposed enhanced classification algorithms to achieve higher classification accuracies. The methods were evaluated against the motor imagery data from Dataset 2a of the publicly available BCI Competition IV (2008). This dataset consists of 22 channels EEG data of 9 subjects for four different movements. A tree based ensemble approach for supervised classification, Extra-Trees algorithm, has been proposed in this paper and also evaluated for its efficacy on this dataset to classify between left hand and right hand movement imaginations. Moreover, this classifier has its inherent capability to select optimum features. Furthermore, in this paper an extension of the binary classification into multiclass domain is also implemented with error correcting output codes (ECOC) approach using the same dataset. Subject-specific frequency bands $\alpha$ (8-12Hz) and $\beta$ (12-30Hz) along with $HG$ (70-100Hz) were considered to extract CSP features. We have achieved an individual peak accuracy of 98ȥ and 84ȥ in binary class and multiclass classification respectively. Furthermore, the results yielded a mean kappa value of 0.58 across all the subjects. This kappa value is higher than of the winner of competition and also from the most of the other approaches applied in this dataset.},
}
@article {pmid30441481,
year = {2018},
author = {Jiang, X and Gu, X and Mei, Z and Ren, H and Chen, W},
title = {A Modified Common Spatial Pattern Algorithm Customized for Feature Dimensionality Reduction in fNIRS-Based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {5073-5076},
doi = {10.1109/EMBC.2018.8513454},
pmid = {30441481},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Spectroscopy, Near-Infrared ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) technique. Most previous studies have focused on constructing high dimensional features from whole channels, adding to the complexity of their classifiers. Another multi-channel source for BCI is electroencephalograph (EEG), which possesses different spatial and temporal features from fNIRS. In EEG field, Common Spatial Pattern (CSP) algorithm is widely used aimed at dimensionality reduction. In our article, we modified it based on the characteristics of fNIRS and evaluated its effectiveness in discriminating Mental Arithmetic (MA) against resting status in an open-access dataset. The Modified Common Spatial Pattern algorithm significantly outperforms CSP algorithm in fNIRS-based BCI and shows its potential in further BCI related explorations.},
}
@article {pmid30441426,
year = {2018},
author = {Kanoga, S and Nakanishi, M and Murai, A and Tada, M and Kanemura, A},
title = {Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {4824-4827},
doi = {10.1109/EMBC.2018.8513180},
pmid = {30441426},
issn = {2694-0604},
mesh = {Artifacts ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Photic Stimulation ; *Superficial Back Muscles ; },
abstract = {Muscular artifacts often contaminate electroencephalograms (EEGs) and deteriorate the performance of brain-computer interfaces (BCIs). Although many artifact reduction techniques are available, most of the studies have focused on their reduction ability (i.e. reconstruction errors), and it has been missing to evaluate their effect on the performance of BCIs. This study aims at evaluating the performance of a state-of-the-art muscular artifact reduction technique on a scenario of a steady-state visual evoked potentials (SSVEPs)based BCI. The performance was evaluated based on a semisimulation setting using a benchmark dataset of SSVEPs artificially contaminated by muscular artifacts acquired from the trapezius. Our results showed that combining the artifact reduction method and the classification algorithm based on the task-related component analysis gained improved classification accuracy. Interestingly, the artifact reduction setting minimizing the reconstruction errors, i.e. elaborately recovering the true EEG waveforms, was inconsistent to the one maximizing the classification performance. The results suggest that artifact reduction methods should be tuned so as to tomaximize performance of BCIs.},
}
@article {pmid30441410,
year = {2018},
author = {Lim, J and Wang, PT and Bidhendi, AK and Arasteh, OM and Shaw, SJ and Armacost, M and Gong, H and Liu, CY and Heydari, P and Do, AH and Nenadic, Z},
title = {Characterization of Stimulation Artifact Behavior in Simultaneous Electrocorticography Grid Stimulation and Recording.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {4748-4751},
doi = {10.1109/EMBC.2018.8513216},
pmid = {30441410},
issn = {2694-0604},
mesh = {Artifacts ; Brain-Computer Interfaces ; Cerebral Cortex ; *Electrocorticography ; Electrodes ; },
abstract = {Bi-directional brain-computer interfaces (BCIs) require simultaneous stimulation and recording to achieve closed-loop operation. It is therefore important that the interface be able to distinguish between neural signals of interest and stimulation artifacts. Current bi-directional BCIs address this problem by temporally multiplexing stimulation and recording. This approach, however, is suboptimal in many BCI applications. Alternative artifact mitigation methods can be devised by investigating the mechanics of artifact propagation. To characterize stimulation artifact behaviors, we collected and analyzed electrocorticography (ECoG) data from eloquent cortex mapping. Ratcheting and phase-locking of stimulation artifacts were observed, as well as dipole-like properties. Artifacts as large as ±1,100 μV appeared as far as 15-37 mm away from the stimulating channel when stimulating at 10 mA. Analysis also showed that the majority of the artifact power was concentrated at the stimulation pulse train frequency (50 Hz) and its super-harmonics (100, 150, 200 Hz). Lower frequencies (0-32 Hz) experienced minimal artifact contamination. These findings could inform the design of future bi-directional ECoG-based BCIs.},
}
@article {pmid30441377,
year = {2018},
author = {Kalantar, G and Mohammadi, A},
title = {Graph-based Dimensionality Reduction of EEG Signals via Functional Clustering and Total Variation Measure for BCI Systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {4603-4606},
doi = {10.1109/EMBC.2018.8513190},
pmid = {30441377},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; *Electroencephalography ; Humans ; Imagination ; },
abstract = {In this paper, we propose a novel and intuitively pleasing graph-based spatio-temporal feature extraction framework for classification of motor imagery tasks from elec- troencephalography (EEG) signals for brain-computer interface systems (BCIs). In particular, to account for the observation that measurements obtained from the EEG channels form a non-uniformly distributed sensor field, a representation graph is constructed using geographical distances between sensors to form connectivity neighborhoods. By capitalizing on the fact that functionality of different connectivity neighborhoods varies based on the intensity of the performed activity and concentration level of the subject, we formed an initial func- tional clustering of EEG electrodes by designing a separate adjacency matrix for each identified functional cluster. Using a collapsing methodology based on total variation measures on graphs, the overall model will eventually be reduced (collapsed) into two functional clusters. The proposed framework offers two main superiorities over its state-of-the-art counterparts: (i) First, the resulting dimensionality reduction is subject-adaptive and respects the brain plasticity of subjects, and; (ii) Second, the proposed methodology identifies active regions of the brain during the motor imagery task, which can be used to re-align EEG electrodes to improve accuracy during consecutive data collection sessions. The experimental results based on Dataset IVa from BCI Competition III show that the proposed method can provide higher classification accuracy as compared to the other existing methods.},
}
@article {pmid30441189,
year = {2018},
author = {Abbaspourazad, H and Wong, Y and Pesaran, B and Shanechi, MM},
title = {Identifying multiscale hidden states to decode behavior.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3778-3781},
doi = {10.1109/EMBC.2018.8513242},
pmid = {30441189},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Brain-Computer Interfaces ; *Movement ; },
abstract = {A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior. This recent evidence motivates developing dynamical encoding models with hidden states that encode movement. Regardless of their type, encoding models have vastly characterized a single scale of activity, e.g., either spikes or local field potentials (LFP). In our recent work we developed a multiscale representational encoding model to simultaneously characterize and decode discrete spikes and continuous field activity. However, learning a multiscale dynamical model from simultaneous spike-field recordings in the presence of hidden states is challenging. Here we present an unsupervised learning algorithm for estimating a multiscale state-space model with hidden states and validate it using spike-LFP activity during a reaching movement. We use the learned multiscale statespace model and a corresponding decoder to identify hidden states from spike-LFP activity. We then decode the movement trajectories using these hidden states. We find that the identified states can accurately decode the trajectories. Moreover, we demonstrate that adding LFP to spikes improves the decoding accuracy, suggesting that our unsupervised learning algorithm incorporates information across scales. This learning algorithm could serve as a new tool to study encoding across scales and to enhance future BMI systems.},
}
@article {pmid30441158,
year = {2018},
author = {Mane, R and Chew, E and Phua, KS and Ang, KK and Vinod, AP and Guan, C},
title = {Quantitative EEG as Biomarkers for the Monitoring of Post-Stroke Motor Recovery in BCI and tDCS Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3610-3613},
doi = {10.1109/EMBC.2018.8512920},
pmid = {30441158},
issn = {2694-0604},
mesh = {Biomarkers ; Electroencephalography ; Humans ; Motor Activity ; *Stroke ; *Stroke Rehabilitation ; *Transcranial Direct Current Stimulation ; },
abstract = {This study investigates the neurological changes in the brain activity of chronic stroke patients undergoing different types of motor rehabilitative interventions and their relationship with the clinical recovery using the Quantitative Electroencephalography (QEEG) features. Over a period of two weeks, 19 hemiplegic chronic stroke patients underwent 10 sessions of upper extremity motor rehabilitation using a brain-computer interface paradigm (BCI group, n=9) and transcranial direct current stimulation coupled BCI paradigm (tDCS group, n=10). The pre- and post-treatment brain activations, as well as the intervention-induced changes in the neuronal activity, were quantified using 11 QEEG features and their relationship with clinical motor improvement was investigated. Significant treatment-induced change in the relative theta power was observed in the BCI group and the change was significantly correlated with the clinical improvements. Also, in the BCI group, the relative theta power and interactions between the theta, alpha, and beta power were identified as monitory biomarkers of motor recovery. On the contrary, the tDCS group was characterized by the significant change in brain asymmetry. Furthermore, we observed significant intergroup differences in the predictive capabilities of post-intervention QEEG features between the BCI and tDCS group. Based on the intergroup differences observed in this study and convergent results from the other neuroimaging analysis performed on the same cohort, we suggest that distinctly different mechanisms of neuronal recovery were facilitated by tDCS and BCI interventions and these treatment specific mechanisms can be encapsulated using QEEG.},
}
@article {pmid30441093,
year = {2018},
author = {Zhou, Y and Xu, T and Li, S and Li, S},
title = {Confusion State Induction and EEG-based Detection in Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3290-3293},
doi = {10.1109/EMBC.2018.8512943},
pmid = {30441093},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Emotions ; Machine Learning ; },
abstract = {Confusion, as an affective state, has been proved beneficial for learning, although this emotion is always mentioned as negative affect. Confusion causes the learner to solve the problem and overcome difficulties in order to restore the cognitive equilibrium. Once the confusion is successfully resolved, a deeper learning is generated. Therefore, quantifying and visualizing the confusion that occurs in learning as well as intervening has gained great interest by researchers. Among these researches, triggering confusion precisely and detecting it is the critical step and underlies other studies. In this paper, we explored the induction of confusion states and the feasibility of detecting confusion using EEG as a first step towards an EEG-based Brain Computer Interface for monitoring the confusion and intervening in the learning. 16 participants EEG data were recorded and used. Our experiment design to induce confusion was based on tests of Raven's Standard Progressive Matrices. Each confusing and not-confusing test item was presented during 15 seconds and the raw EEG data was collected via Emotiv headset. To detect the confusion emotion in learning, we propose an end-to-end EEG analysis method. End-to-end classification of Deep Learning in Machine Learning has revolutionized computer vision, which has gained interest to adopt this method to EEG analysis. The result of this preliminary study was promising, which showed a 71.36% accuracy in classifying users' confused and unconfused states when they are inferring the rules in the tests.},
}
@article {pmid30441053,
year = {2018},
author = {Loza, CA and Principe, JC},
title = {The Embedding Transform. A Novel Analysis of Non-Stationarity in the EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3112-3115},
doi = {10.1109/EMBC.2018.8512974},
pmid = {30441053},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Rest ; },
abstract = {We introduce a novel technique to analyze nonstationarity in single-channel Electroencephalogram (EEG) traces: the Embedding Transform. The approach is based on Walter J. Freeman's studies concerning active and rest stages and deviations from Gaussianity. Specifically, we generalize his idea in order to include cases where the neuromodulations are sparse in time. Specifically, the transform maps the temporal sequences to a set of $\ell ^{2}
$-norms where modulated patters are emphasized. In this way, the background, chaotic activity can be modeled as the main lobe of the distribution, while the relevant synchronizations (or desynchronizations) fall on the right (or left) tail of the density of such norms. We test the algorithm on two different datasets: alpha bursts on synthetic data simulated in the BESA software and low-gamma oscillations in the motor cortex from the Brain-Computer Interface (BCI) Competition 4 Dataset 4. The results are promising and place the Embedding Transform as a quick, single-parameter tool to effectively assess which channels (or regions) are actively engaged in particular behaviors and which are in a more silent type of stage.},
}
@article {pmid30441040,
year = {2018},
author = {Catrambone, V and Greco, A and Averta, G and Bianchi, M and Bicchi, A and Scilingo, EP and Valenza, G},
title = {EEG Complexity Maps to Characterise Brain Dynamics during Upper Limb Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3060-3063},
doi = {10.1109/EMBC.2018.8512912},
pmid = {30441040},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; *Upper Extremity ; },
abstract = {The Electroencephalogram (EEG) can be considered as the output of a nonlinear system whose dynamics is significantly affected by motor tasks. Nevertheless, computational approaches derived from the complex system theory has not been fully exploited for characterising motor imagery tasks. To this extent, in this study we investigated EEG complexity changes throughout the following categories of imaginary motor tasks of the upper limb: transitive (actions involving an object), intransitive (meaningful gestures that do not include the use of objects), and tool-mediated (actions using an object to interact with another one). EEG irregularity was quantified following the definition of Fuzzy Entropy, which has been demonstrated to be a reliable quantifier of system complexity with low dependence on data length. Experimental results from paired statistical analyses revealed minor topographical changes between EEG complexity associated with transitive and tool-mediated tasks, whereas major significant differences were shown between the intransitive actions vs. the others. Our results suggest that EEG complexity level during motor imagery tasks of the upper limb are strongly biased by the presence of an object.},
}
@article {pmid30441039,
year = {2018},
author = {Jiang, T and Jiang, T and Wang, T and Mei, S and Liu, Q and Li, Y and Wang, X and Prabhu, S and Sha, Z and Ince, NF},
title = {Investigation of the Influence of ECoG Grid Spatial Density on Decoding Hand Flexion and Extension.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {3052-3055},
doi = {10.1109/EMBC.2018.8513008},
pmid = {30441039},
issn = {2694-0604},
mesh = {Brain Mapping ; Electrocorticography ; Electrodes ; *Electroencephalography ; *Hand ; Humans ; *Motor Cortex ; Movement ; },
abstract = {Electrocorticogram (ECoG) has been used as a reliable modality to control a brain machine interface (BMI). Recently, promising results of high-density ECoG have shown that non redundant information can be recorded with finer spatial resolution from the cortical surface. In this study, highdensity ECoG was recorded intraoperatively from two patients during awake brain surgery while performing instructed hand flexion and extension. Event related desynchronization (ERD) were found in the low frequency band (LFB: 8-32 Hz) band while event related synchronization (ERS) were found in the high frequency band (HFB: 60-200 Hz). The classification between hand flexion and extension was performed by using common spatial pattern (CSP) as a feature extraction technique and linear discriminant analysis (LDA) as a classifier. In order to compare the high-density ECoG and normal ECoG in terms of classifying between hand flexion and extension, we simulated a typical clinical ECoG (8 mm spacing) by averaging the neural activity of nearest four channels. The same classification methods were applied on the averaged recordings. In HFB, the classification error rate using simulated ECoG greatly increased and lagged the movement onset compared to the original highdensity ECoG. In LFB, the differences between them were not prominent. These results indicated that high-density ECoG is able to capture non-redundant task-related information from the motor cortex and potentially serves as a better modality to drive a neural prosthetic compared to typical clinical electrodes.},
}
@article {pmid30440950,
year = {2018},
author = {Kanemura, A and Cheng, Y and Kaneko, T and Nozawa, K and Fukunaga, S},
title = {Imputing Missing Values in EEG with Multivariate Autoregressive Models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2639-2642},
doi = {10.1109/EMBC.2018.8512790},
pmid = {30440950},
issn = {2694-0604},
mesh = {*Artifacts ; *Electroencephalography ; },
abstract = {Wearable measurement for electroencephalogram (EEG) is expected to enable brain-computer interfaces, biomedical engineering, and neuroscience studies in real environments. When wearable devices are in practical use, only the user (subject) can take care of measurement, unlike laboratory- oriented experiments, where experimenters are always with the subject. As a result, measurement troubles such as artifact contamination or electrode impairment cannot be easily corrected, and EEG recordings will become incomplete, including many missing values. If the missing values are imputed (interpolated) and complete data without missing entries are available, we can employ existing signal analysis techniques that assume compete data. In this paper, we propose an EEG signal imputation method based on multivariate autoregressive (MAR) modeling and its iterative estimation and simulation, inspired by the multiple imputation procedure. We evaluated the proposed method with real data with artificial missing entries. Experimental results show that the proposed method outperforms popular baseline interpolation methods. Our iterative scheme is simple yet effective, and can be the foundation for many extensions.},
}
@article {pmid30440927,
year = {2018},
author = {Ahmadi, N and Constandinou, TG and Bouganis, CS},
title = {Spike Rate Estimation Using Bayesian Adaptive Kernel Smoother (BAKS) and Its Application to Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2547-2550},
doi = {10.1109/EMBC.2018.8512830},
pmid = {30440927},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Neurons ; },
abstract = {Brain Machine Interfaces (BMIs) mostly utilise spike rate as an input feature for decoding a desired motor output as it conveys a useful measure to the underlying neuronal activity. The spike rate is typically estimated by a using non-overlap binning method that yields a coarse estimate. There exist several methods that can produce a smooth estimate which could potentially improve the decoding performance. However, these methods are relatively computationally heavy for real-time BMIs. To address this issue, we propose a new method for estimating spike rate that is able to yield a smooth estimate and also amenable to real-time BMIs. The proposed method, referred to as Bayesian adaptive kernel smoother (BAKS), employs kernel smoothing technique that considers the bandwidth as a random variable with prior distribution which is adaptively updated through a Bayesian framework. With appropriate selection of prior distribution and kernel function, an analytical expression can be achieved for the kernel bandwidth. We apply BAKS and evaluate its impact on offline BMI decoding performance using Kalman filter. The results reveal that BAKS can improve the decoding performance compared to the binning method. This suggests the feasibility and the potential use of BAKS for real-time BMIs.},
}
@article {pmid30440926,
year = {2018},
author = {McDaniel, JR and Gordon, SM and Solon, AJ and Lawhern, VJ},
title = {Analyzing P300 Distractors for Target Reconstruction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2543-2546},
doi = {10.1109/EMBC.2018.8512854},
pmid = {30440926},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; },
abstract = {P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and nontarget stimulus categories during model training, which imparts bias into the model. Additionally, not all non-targets are created equal; some may contain visual features that resemble targets or may otherwise be visually salient. Current research has indicated that non-target distractors may elicit attenuated P300 responses based on the perceptual similarity of these distractors to the target category. To minimize this bias, and enable a more nuanced analysis, we use a generalized BCI approach that is fit to neither user nor task. We do not seek to improve the overall accuracy of the BCI with our generalized approach; we instead demonstrate the utility of our approach for identifying targetrelated image features. When combined with other intelligent agents, such as computer vision systems, the performance of the generalized model equals that of the user-specific models, without any user specific data.},
}
@article {pmid30440925,
year = {2018},
author = {Lin, YC and Chou, C and Yang, SH and Lai, HY and Lo, YC and Chen, YY},
title = {Neural Decoding Forelimb Trajectory Using Evolutionary Neural Networks with Feedback-Error-Learning Schemes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2539-2542},
doi = {10.1109/EMBC.2018.8512775},
pmid = {30440925},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Feedback ; Forelimb ; Macaca mulatta ; *Neural Networks, Computer ; Rats ; },
abstract = {Changes in the functional mapping between neural activities and kinematic parameters over time poses a challenge to current neural decoder of brain machine interfaces (BMIs). Traditional decoders robust to changes in functional mappings required many day's training data. The decoder may not be robust when it was trained by data from only few days. Therefore, a decoder should be trained to handle a variety of neural-to-kinematic mappings using limited training data. We proposed an evolutionary neural network with error feedback, ECPNN-EF, as a neural decoder, that considered the previous error as an input to the decoder in order to improve the robustness. The decoder was validated to reconstruct rat's forelimb movement in a water-reward lever-pressing task. Two days of data were only used to train the decoder while ten days of data were used to test the decoder. The results showed that the performance of ECPNN-EF was significantly higher than that of standard recurrent neural network without error feedback, which was commonly used in BMI. This suggested that ECPNN-EF trained with few days of training data can be robust to changes in functional mappings.},
}
@article {pmid30440923,
year = {2018},
author = {Chen, X and Wang, Y and Zhang, S and Gao, X},
title = {Enhancing Detection of SSVEPs with Intermodulation Frequencies Using Individual Calibration Data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2531-2534},
doi = {10.1109/EMBC.2018.8512783},
pmid = {30440923},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between human brain and external devices. Recently, we proposed an intermodulation frequency-based stimulation approach to increase the number of visual stimuli that can be presented on a computer monitor. Although our recent studies have demonstrated that this approach can encode more visual stimuli by only one flickering frequency, the performance of the intermodulation frequency-based SSVEP BCI remains poor and needs further improvement. This study aims to incorporate filter bank analysis and individual SSVEP calibration data into canonical correlation analysis (CCA) to improve the detection of SSVEPs with intermodulation frequencies. Results on classification accuracy and information transfer rate (ITR) suggest that the employment of individual calibration data can significantly improve the performance of the intermodulation frequency-based SSVEP BCI.},
}
@article {pmid30440922,
year = {2018},
author = {Wang, Z and Chen, L and Yi, W and Gu, B and Liu, S and An, X and Xu, M and Qi, H and He, F and Wan, B and Ming, D},
title = {Enhancement of cortical activation for motor imagery during BCI-FES training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2527-2530},
doi = {10.1109/EMBC.2018.8512749},
pmid = {30440922},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Imagination ; *Motor Cortex ; },
abstract = {Brain-computer Interfaces (BCIs) provide a direct pathway between the brain and the outward environment. Specifically, motor imagery (MI)-based BCI controlling functional electric stimulation (FES) is a promising approach for disabled patients with intact mind to restore or rehabilitate their motor functions. This study probed for the improvement of cortical activation for motor imagery during the closed-loop BCI-FES training. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to inspect the cortical activation for four different training strategies, i.e. MI-BCI-FES, MI-FES, MI and FES. Compared with the other three training conditions, the MI-BCI-FES could achieve stronger cortical activation viewing from the event-related desynchronization (ERD) and the blood oxygen response. The results demonstrate that the closed-loop MI training using BCI-FES can prospectively increase the cortical activation of motor cortical areas.},
}
@article {pmid30440897,
year = {2018},
author = {Wang, PT and McCrimmon, CM and Heydari, P and Do, AH and Nenadic, Z},
title = {Subspace-Based Suppression of Cortical Stimulation Artifacts.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2426-2429},
doi = {10.1109/EMBC.2018.8512809},
pmid = {30440897},
issn = {2694-0604},
mesh = {*Artifacts ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Humans ; Movement ; },
abstract = {Bi-directional brain-computer interfaces for the restoration of movement and sensation must simultaneously record neural signals and deliver cortical stimulation. This poses a challenge since stimulation artifacts can be orders of magnitude stronger than neural signals. In this article, we propose a novel subspace-based method for the removal of cortical electrical stimulation artifacts. We demonstrate the practical application of our approach on experimentally recorded electroencephalogram data, where artifacts were suppressed by as much as $30-40\mathrm {d}
\mathrm {B}
$. Our method is computationally simple, yet it achieves superior results to the state-of-the art methods.},
}
@article {pmid30440859,
year = {2018},
author = {Shu, X and Chen, S and Chai, G and Sheng, X and Jia, J and Zhu, X},
title = {Neural Modulation By Repetitive Transcranial Magnetic Stimulation (rTMS) for BCI Enhancement in Stroke Patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2272-2275},
doi = {10.1109/EMBC.2018.8512860},
pmid = {30440859},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Stroke Rehabilitation/instrumentation/methods ; *Transcranial Magnetic Stimulation ; },
abstract = {Brain-computer interface (BCI) is a novel method for stroke rehabilitation. However, lacking of sufficient motor-related cortical activity greatly decreases the BCI performance in stroke patients. Interestingly, high-frequency repetitive transcranial magnetic stimulation (rTMS) has been shown to increase the cortical excitability of lesioned hemisphere in stroke patients. This stimulation effect may have benefits on the enhancement of BCI decoding. This study recruited 16 stroke patients to evaluate the stimulation effect on BCI accuracy, with 8 patients were assigned to the TMS-group and the other 8 patients were assigned to the Control-group. Patients in the TMS-group underwent 12 sessions of 10-Hz TMS interventions in four consecutive weeks, whereas no stimulation was applied during this period in the Control-group. Meanwhile, three BCI evaluation sessions were carried out in one day before, one day after, and three days after the TMS intervention, separately. The results showed that the TMS intervention significantly improved the BCI accuracy from 63.5% to 74.3% in motor imagery (MI) tasks, and from 81.9% to 91.1% in motor execution (ME) tasks. This finding provides a novel method for the cure of BCI-inefficiency problem, and may facilitate the clinical application of BCI-based stroke rehabilitation.},
}
@article {pmid30440834,
year = {2018},
author = {Rodriguez-Ugarte, M and Ianez, E and Ortiz, M and Azorin, JM},
title = {Novel tDCS montage favors lower limb motor imagery detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2170-2173},
doi = {10.1109/EMBC.2018.8512656},
pmid = {30440834},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Lower Extremity ; *Motor Cortex ; *Transcranial Direct Current Stimulation ; Healthy Volunteers ; Humans ; },
abstract = {This work studies a novel transcranial direct current stimulation (tDCS) montage to improve a brain-machine interface (BMI) lower limb motor imagery detection. The tDCS montage is composed by two anodes and one cathode. One anode is located over the motor cortex and the other one over the cerebellum. Ten healthy subjects participated in this experiment. They were randomly separated into two groups: sham, which received a fake stimulation, and active tDCS, which received a real stimulation. Each subject was experimented on five consecutive days. Results pointed out that there was a significant difference $(p < 0 .05)$ in the classification accuracy between the sham and the active tDCS group. On each of the five days of the experiment the active tDCS group achieved better accuracy results than the sham group: 4%, 10%, 10%, 9% and 7% higher respectively.},
}
@article {pmid30440802,
year = {2018},
author = {Yang, L and Lu, Y},
title = {EEG Neural Correlates of Self-Paced Left- and Right-Hand Movement Intention during a Reaching Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2040-2043},
doi = {10.1109/EMBC.2018.8512725},
pmid = {30440802},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; *Intention ; Movement ; },
abstract = {The study of neural correlates of self-paced movement intention helps develop a more natural and practical brain computer interface. In this paper, we studied EEG neural correlates of self-paced left- and right-hand movement intention during a reaching task. A slow decreased movement related cortical potential (MRCP) related to movement intention was found before both the left- and right-hand movements. The temporal start point and decrement of MRCP before the left- and right-hand reaching movements showed differently. Moreover, alpha band power increase/decrease was observed before the onset of both left- and right-hand movements. Alpha band powers increase before both movement conditions in the frontal-central area. In the central area, alpha band powers decrease consistently at electrodes C3, Cz and C4 before the right-hand reaching movement. While alpha band powers increase at electrodes C3 and Cz before the left-hand reaching movement.},
}
@article {pmid30440799,
year = {2018},
author = {Nishifuji, S and Nakamura, H and Matsubara, A},
title = {Brain Computer Interface Using Modulation of Auditory Steady-State Response with Help of Stochastic Resonance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2028-2031},
doi = {10.1109/EMBC.2018.8512686},
pmid = {30440799},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; Attention ; *Auditory Perception ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Auditory ; Humans ; Male ; Sound ; Young Adult ; },
abstract = {This paper proposes an eye-movement independent brain computer interface based on the modulations of auditory steady-state response (ASSR-BCI) to amplitude-modulated (AM) tones elicited by paying selective attention to one of the two AM tones. Moreover, the proposed ASSR-BCI exploits a stochastic resonance effect to improve the signal separation and attained the mean classification accuracy of 77 % across nine normal subjects under a noise-added condition with sound pressures 60 dB for the two tones and 30 dB for the noise added to the two AM tones. Results from information transfer rate and its inter-individual difference suggest that it may be adequate to set an inter-trial interval at 2∽3 s for a trial time length. It is consequently feasible to develop a practical eye-movement-independent BCI available in eyes-closed state by optimizing the parameters such as the trial time length and electrode sites each user.},
}
@article {pmid30440796,
year = {2018},
author = {Soleymanpour, R and Patel, C and Kim, I},
title = {Non-contact Wearable EEG Sensors for SSVEP-based Brain Computer Interface Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2016-2019},
doi = {10.1109/EMBC.2018.8512712},
pmid = {30440796},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Wearable Electronic Devices ; },
abstract = {Electroencephalography (EEG) based brain computer interfaces (BCI) introduces promising communication pathway between the brain and external devices, not only for the motor-impaired but also the healthy users. However, the current EEG-based interface device is not convenient enough for daily uses. In this study, we developed an EEG acquisition system that records brain signals without contacting scalp. The proposed system consists of a small sized ($5.5\,\times 3\,\mathrm{cm}
^{2}
$ acquisition hardware and four stainless steel electrodes integrated in a regular sport hat. To demonstrate the concept, we used an in-house developed steady-state visual evoked potentials (SSVEP) paradigm and recorded EEG signals using the proposed system. The EEG signals were compared with three different brain states - Eye Closed, Eye Open, and Visual Stimulation. The results show that the BCI system can record SSVEP from the brain without any professional setups or expensive dry-electrodes.},
}
@article {pmid30440793,
year = {2018},
author = {Giles, J and Ang, KK and Mihaylova, L and Arvaneh, M},
title = {Data Space Adaptation for Multiclass Motor Imagery-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2004-2007},
doi = {10.1109/EMBC.2018.8512643},
pmid = {30440793},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; *Signal Processing, Computer-Assisted ; },
abstract = {Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. This paper proposes a supervised multiclass data space adaptation technique (MDSA) to transform the test data using a linear transformation such that the distribution difference between the multiclass train and test data is minimized. The results of using the proposed MDSA on BCI Competition IV dataset 2a improved the classification accuracy by an average of 4.3% when 20 trials per class were used from the test session to estimate adaptation transformation. The results also showed that the proposed MDSA algorithm outperformed the multi pooled mean linear discrimination (MPMLDA) technique with as few as 10 trials per class used for calculating the transformation matrix. Hence the results showed the effectiveness of the proposed MDSA algorithm in addressing non-stationarity issue for multiclass EEG-based BCI.},
}
@article {pmid30440792,
year = {2018},
author = {Loopez-Larraz, E and Birbaumer, N and Ramos-Murguialday, A},
title = {A hybrid EEG-EMG BMI improves the detection of movement intention in cortical stroke patients with complete hand paralysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {2000-2003},
doi = {10.1109/EMBC.2018.8512711},
pmid = {30440792},
issn = {2694-0604},
mesh = {Body Mass Index ; *Brain-Computer Interfaces ; *Electroencephalography ; *Electromyography ; Hand/physiopathology ; Humans ; *Intention ; Movement ; Stroke/*physiopathology ; Stroke Rehabilitation ; },
abstract = {Motor rehabilitation based on brain-machine interfaces (BMI) has been shown as a feasible option for stroke patients with complete paralysis. However, the pathologic EEG activity after a stroke makes the detection of movement intentions in these patients challenging, especially in those with damages involving the motor cortex. Residual electromyographic activity in those patients has been shown to be decodable, even in cases when the movement is not possible. Hybrid BMIs combining EEG and EMG activity have been recently proposed, although there is little evidence about how they work for completely paralyzed stroke patients. In this study we propose a neural interface, relying on EEG, EMG or EEG+EMG features, to detect movement attempts. Twenty patients with a chronic stroke affecting their motor cortex were recruited, and asked to open and close their paralyzed hand while their electrophysiological signals were recorded. We show how EEG and EMG activities provide complementary information for detecting the movement intentions, being the accuracy of the hybrid BMI significantly higher than the EEG-based system. The obtained results encourage the integration of hybrid BMI systems for motor rehabilitation of patients with paralysis due to stroke.},
}
@article {pmid30440791,
year = {2018},
author = {Yang, T and Ang, KK and Phua, KS and Yu, J and Toh, V and Ng, WH and So, RQ},
title = {EEG Channel Selection Based on Correlation Coefficient for Motor Imagery Classification: A Study on Healthy Subjects and ALS Patient.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1996-1999},
doi = {10.1109/EMBC.2018.8512701},
pmid = {30440791},
issn = {2694-0604},
mesh = {Algorithms ; *Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; *Electroencephalography ; Healthy Volunteers ; Humans ; *Imagination ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) provides an alternate channel of interaction for people with severe motor disabilities. The Common Spatial Pattern (CSP) algorithm is effective in extracting discriminative features from EEG data for motor imagery-based Brain-Computer Interface (BCI). CSP yields signal from various locations for better performance. In this study, we selected a subset of EEG channels using correlation coefficient of spectral entropy and compared the classification performance using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. We conducted experiments on 4 healthy subjects and one Amyotrophic Lateral Sclerosis (ALS) patient. The results showed that the proposed channel selection method increased classification accuracy of all subjects from 1.25% to 8.22%. Optimal performance was obtained using between 13 to 24 channels, and channels located over the motor cortex zone possess higher probabilities of being selected. Comparing with the channels manually selected to over the motor cortex area, the correlation coefficient method is able to identify the optimal channel combination and improve the motor imagery decoding accuracy of Healthy and ALS subjects.},
}
@article {pmid30440790,
year = {2018},
author = {Yang, H and Ang, KK and Libedinsky, C and So, RQ},
title = {Robust Local Field Potential-based Neural Decoding by Actively Selecting Discriminative Channels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1992-1995},
doi = {10.1109/EMBC.2018.8512628},
pmid = {30440790},
issn = {2694-0604},
mesh = {*Action Potentials ; Animals ; *Brain-Computer Interfaces ; Motor Cortex ; Primates ; Robotics ; },
abstract = {Local field potentials (LFPs) have been proposed as a neural decoding signal to compensate for spike signal deterioration in invasive brain-machine interface applications. However, the presence of redundancy among LFP signals at different frequency bands across multiple channels may affect the decoding performance. In order to remove redundant LFP channels, we proposed a novel Fisher-distance ratio-based method to actively batch select discriminative channels to maximize the separation between classes. Experimental evaluation was conducted on 5 non-consecutive days of data from a non-human primate. For data from each day, the first experimental session was used to generate the training model, which was then used to perform 4-class decoding of signals from other sessions. Decoding achieved an average accuracy of 79.55%, 79.02% and 79.40% using selected LFP channels for beta, low gamma and high gamma frequency bands, respectively. Compared with decoding using full LFP channels, decoding using selected LFP channels in high gamma band resulted in an increase of 8.67% in accuracy, even if this accuracy was still 7.26% lower than that of spike-based decoding. These results demonstrate the effectiveness of the proposed method in selecting discriminative LFP channels for neural decoding.},
}
@article {pmid30440789,
year = {2018},
author = {Zhang, Z and Foong, R and Phua, KS and Wang, C and Ang, KK},
title = {Modeling EEG-based Motor Imagery with Session to Session Online Adaptation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1988-1991},
doi = {10.1109/EMBC.2018.8512706},
pmid = {30440789},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Retrospective Studies ; Stroke Rehabilitation ; },
abstract = {Subject-specific calibration plays an important role in electroencephalography (EEG)-based Brain-Computer Interface (BCI) for Motor Imagery (MI) detection. A calibration session is often introduced to build a subject specific model, which then can be deployed into BCI system for MI detection in the following rehabilitation sessions. The model is termed as a fixed calibration model. Progressive adaptive models can also be built by using data not only from calibration session, but also from available rehabilitation sessions. It was reported that the progressive adaptive model yielded significant improved MI detection compared to the fixed model in a retrospective clinical study. In this work, we deploy the progressive adaptation model in a BCI-based stroke rehabilitation system and bring it online. We dub this system nBETTER (Neurostyle Brain Exercise Therapy Towards Enhanced Recovery). A clinical trial using the nBETTER system was conducted to evaluate the performance of 11 stroke patients who underwent a calibration session followed by 18 rehabilitation sessions over 6 weeks. We conduct retrospective analysis to compare the performance of various modeling strategies: the fixed calibration model, the online progressive adaptation model and a light-weight adaptation model, where the second one is generated online by nBETTER system and the other two models are obtained retrospectively. The mean accuracy of the three models across 11 subjects are 68.17%, 74.04% and 74.53% respectively. Statistical test conducted on the three groups using ANOVA yields a p-value of 9.83-e06. The test result shows that the two adaptation models both have significant different mean from fixed mode. Hence our study confirmed the effectiveness of using the progressive adaptive model for EEGbased BCI to detect MI in an online setting.},
}
@article {pmid30440788,
year = {2018},
author = {Chin, ZY and Zhang, X and Wang, C and Ang, KK},
title = {EEG-based discrimination of different cognitive workload levels from mental arithmetic.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1984-1987},
doi = {10.1109/EMBC.2018.8512675},
pmid = {30440788},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Cognition ; *Electroencephalography ; *Workload ; },
abstract = {Cognitive workload, which is the level of mental effort required for a cognitive task, can be assessed by monitoring the changes in neurophysiological measures such as electroencephalogram (EEG). This study investigates the performance of an EEG-based Brain-Computer Interface (BCI) to discriminate different difficulty levels in performing a mental arithmetic task. EEG data from 10 subjects were collected while performing mental addition with 3 difficulty levels (easy, medium and hard). EEG features were then extracted using band power and Common Spatial Pattern features and subsequently features were selected using Fisher Ratio to train a Linear Discriminant Classifier. The results from 10-fold cross-validation yielded averaged accuracy of 90% for 2 classes (easy versus hard tasks) and 66% for 3 classes (easy versus medium versus hard tasks). Hence the results showed the feasibility of using EEG-based BCI to measure cognitive workload in performing mental arithmetic.},
}
@article {pmid30440787,
year = {2018},
author = {Tian, S and Wang, Y and Dong, G and Pei, W and Chen, H},
title = {Mental Fatigue Estimation Using EEG in a Vigilance Task and Resting States.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1980-1983},
doi = {10.1109/EMBC.2018.8512666},
pmid = {30440787},
issn = {2694-0604},
mesh = {Electroencephalography ; Humans ; *Mental Fatigue ; Psychomotor Performance ; Reaction Time ; *Wakefulness ; },
abstract = {Mental fatigue induced by long time mental work can cause deterioration in task performance and increase the risk of accidents. Recently, electroencephalogram (EEG)-based monitoring of mental fatigue has received increasing attention in the field of brain-computer interfaces (BCI). This study aims to employ EEG signals to measure the mental fatigue level by estimating reaction time (RT) in a psychomotor vigilance task (PVT). In a 36-hour sleep deprivation experiment, EEG data from 18 subjects were recorded every four hours in nine blocks, each consisting of three tasks: a 6-minute PVT task and two 3-minute resting states (eyes closed and eyes open). The mean RT in the PVT task showed a generally increasing trend during the 36-hour awake period, reflecting the increase of fatigue over time. For each task, multiple EEG features were extracted and selected to better estimate RT using a multiple linear regression (MLR) method. The correlation between predicted RT and actual RT was evaluated using a leave-one-subject-out (LOSO) validation strategy. After parameter optimization, EEG data from the PVT task obtained a mean correlation coefficient of $0.81 \pm 0.16$ across all subjects. Resting-state EEG data showed lower correlations (eyes-closed: $0.65 \pm 0.20$, eyes-open: $0.50 \pm 0.30)$ partially due to the involvement of shorter data lengths. These results demonstrate the feasibility and robustness of the EEG-based fatigue monitoring method, which could be potential for applications in operational environments.},
}
@article {pmid30440786,
year = {2018},
author = {Mowla, MR and Huggins, JE and Natarajan, B and Thompson, DE},
title = {P300 Latency Estimation Using Least Mean Squares Filter.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1976-1979},
doi = {10.1109/EMBC.2018.8512644},
pmid = {30440786},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Least-Squares Analysis ; },
abstract = {Event-related potentials (ERPs) are the brain response directly related to specific events or stimuli. The P300 ERP is a positive deflection nominally 300ms post-stimulus that is related to mental decision making processes and also used in P300-based speller systems. Single-trial estimation of P300 responses will help to understand the underlying cognitive process more precisely and also to improve the speed of speller brain-computer interfaces (BCIs). This paper aims to develop a single-trial estimation of the P300 amplitudes and latencies by using the least mean squares (LMS) adaptive filtering method. Results for real data from people with amyotrophic lateral sclerosis (ALS) have shown that the LMS filter can be effectively used to estimate P300 latencies.},
}
@article {pmid30440785,
year = {2018},
author = {Chan, WH and Chiang, KJ and Nakanishi, M and Wang, YT and Jung, TP},
title = {Evaluating the Performance of Non-Hair SSVEP-Based BCIs Featuring Template-Based Decoding Methods.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1972-1975},
doi = {10.1109/EMBC.2018.8512662},
pmid = {30440785},
issn = {2694-0604},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Hair ; Humans ; Photic Stimulation ; },
abstract = {Our previous study has demonstrated the feasibility of employing non-hair-bearing electrodes to build a Steadystate Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, relaxing technical barriers in preparation time and offering an ease-of-use apparatus. The signal quality of the SSVEPs and the resultant performance of the non-hair BCI, however, did not close upon those reported in the state-of-the-art BCI studies based on the electroencephalogram (EEG) measured from the occipital regions. Recently, advanced decoding algorithms such as task-related component analysis have made a breakthrough in enhancing the signal quality of the occipital SSVEPs and the performance of SSVEP-based BCIs in a well-controlled laboratory environment. However, it remains unclear if the advanced decoding algorithms can extract highfidelity SSVEPs from the non-hair EEG and enhance the practicality of non-hair BCIs in real-world environments. This study aims to quantitatively evaluate whether, and if so, to what extent the non-hair BCIs can leverage the state-of-art decoding algorithms. Eleven healthy individuals participated in a 5-target SSVEP BCI experiment. A high-density EEG cap recorded SSVEPs from both hair-covered and non-hair-bearing regions. By evaluating and demonstrating the accessibility of nonhair-bearing behind-ear signals, our assessment characterized constraints on data length, trial numbers, channels, and their relationships with the decoding algorithms, providing practical guidelines to optimize SSVEP-based BCI systems in real-life applications.},
}
@article {pmid30440784,
year = {2018},
author = {Hori, J and Akagawa, R},
title = {Improvement in Classification of Tactile Event-Related Potentials using Random-Interval Tasks[∗].},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1968-1971},
doi = {10.1109/EMBC.2018.8512670},
pmid = {30440784},
issn = {2694-0604},
mesh = {Attention ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials ; *Touch ; },
abstract = {The classification accuracy of an event-related potential (ERP) induced by a tactile stimulus is investigated to create a brain-computer interface (BCI). Mechanical tactile stimuli were applied to the left and right index fingers using two piezo actuator probes that were used as a Braille display for the visually impaired. In an experiment, two-class classification was investigated using three kinds of tactile stimulus pattern. The subjects were instructed to pay attention to unusual target stimuli while avoiding other frequent nontarget stimuli. The extracted features were classified using stepwise linear discriminant analysis. As a result, high accuracy was obtained by the task of random intervals compared with the task of constant intervals. It was suggested that the accuracy of the BCI using tactile stimuli is influenced by the concentration on the task.},
}
@article {pmid30440783,
year = {2018},
author = {Ozdenizci, O and Gunay, SY and Quivira, F and Erdogmug, D},
title = {Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1964-1967},
pmid = {30440783},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {*Awareness ; *Brain-Computer Interfaces ; Electroencephalography ; Gestures ; Robotics ; },
abstract = {We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.},
}
@article {pmid30440782,
year = {2018},
author = {Petersen, J and Iversen, HK and Puthusserypady, S},
title = {Motor Imagery based Brain Computer Interface Paradigm for Upper Limb Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1960-1963},
doi = {10.1109/EMBC.2018.8512615},
pmid = {30440782},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Stroke ; *Stroke Rehabilitation ; },
abstract = {Motor Imagery (MI) based Brain Computer Interface (BCI) systems have shown potential to serve as a tool for neurorehabilitation for post stroke patients to complement the standard therapy. The aim of this study was to develop an MI based BCI system that could potentially be used in neurorehabilitation of hand motor function in stroke patients. Two co-adaptive, three-class MI based BCI systems for realtime processing were developed and compared using the publicly available data from the BCI Competition III Dataset V as well as our own data. The first algorithm utilizes the Filterbank Common Spatial Pattern (FBCSP) for feature extraction, and the other utilizes the Separable Common Spatio-Spectral Pattern (SCSSP) - both combined with a Multi-layer Perceptron (MLP) for classification. The proposed system proved successful when using the competition data showing an average accuracy of 64.71 % for the SCSSP compared to 60.48% for the FBCSP. This proved superior to a related study using the same feature extraction methods, but with other classification methods. The proposed system, however did show results around chance level for the 3-class MI experimental data that we have collected in our laboratory. Further studies needs to be conducted to improve the performance as well as to realize such a system to put in use.},
}
@article {pmid30440781,
year = {2018},
author = {Jao, PK and Chavarriaga, R and Millan, JDR},
title = {Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1956-1959},
doi = {10.1109/EMBC.2018.8512687},
pmid = {30440781},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Principal Component Analysis ; },
abstract = {Practical brain-computer interfaces need to overcome several challenges, including tolerance to signal variability within- and across sessions. We introduce Robust Principal Component Analysis (RPCA) as a potential approach to tackle intra-trial variability. Assuming that subjects undergo the same cognitive process or perform the same task in a short period (e.g., a few seconds), as a result, the signal of interest should be represented by only a few components. We verified this approach on a workload detection task, where subjects needed to pilot a simulated drone. We used RPCA as a processing step to decrease trial variability and assessed its impact on classification accuracy. Our results showed that RPCA significantly increased performance in both at group and subject level analysis. On average, class-balanced accuracy when simulating RPCA online increased from 63.9% up to 70.6% $(p~ 0 . 001)$.},
}
@article {pmid30440780,
year = {2018},
author = {AlSaleh, M and Moore, R and Christensen, H and Arvaneh, M},
title = {Discriminating Between Imagined Speech and Non-Speech Tasks Using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1952-1955},
doi = {10.1109/EMBC.2018.8512681},
pmid = {30440780},
issn = {2694-0604},
mesh = {Brain Mapping ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; *Speech ; },
abstract = {People who are severely disabled (e.g Locked-in patients) need a communication tool translating their thoughts using their brain signals. This technology should be intuitive and easy to use. To this line, this study investigates the possibility of discriminating between imagined speech and two types of non-speech tasks related to either a visual stimulus or relaxation. In comparison to previous studies, this work examines a variety of different words with only single imagination in each trial. Moreover, EEG data are recorded from a small number of electrodes using a low-cost portable EEG device. Thus, our experiment is closer to what we want to achieve in the future as communication tool for locked-in patients. However, this design makes the EEG classification more challenging due to a higher level of noise and variations in EEG signals. Spectral and temporal features, with and without common spatial filtering, were used for classifying every imagined word (and for a group of words) against the non-speech tasks. The results show the potential for discriminating between each imagined word and non-speech tasks. Importantly, the results are different between subjects using different features showing the need for having subject specific features.},
}
@article {pmid30440778,
year = {2018},
author = {Benda, M and Stawicki, P and Gembler, F and Grichnik, R and Rezeika, A and Saboor, A and Volosyak, I},
title = {Different Feedback Methods For An SSVEP-Based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1939-1943},
doi = {10.1109/EMBC.2018.8512622},
pmid = {30440778},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Healthy Volunteers ; Humans ; },
abstract = {In this paper we examined different ways to inform the user of the classification progress in our online SSVEPbased BCI speller. Different user feedback was given based on the distance from the classification threshold, separately calculated for each stimulus. We focused on the comparison of the accuracies and spelling times associated with each different feedback type. We tested eight different methods, one without feedback for comparison, and the two paradigms each (an increase and a decrease), for three varying parameters, during an online spelling task. The eighth method was a combination of the best performing feedback modalities. A 28 target speller was used for spelling the same word with different feedback methods. The level of comfort was assessed by the seven healthy participants, using a questionnaire. We found substantial decreases in spelling times; they were reduced to 12-77% of the no-feedback condition spelling times, for each of our subjects, with at least one of the parameters. However, this parameter, as expected, was different for each user. According to the personal fastest feedback methods, a combination of them was also used for spelling. These combined feedback methods usually resulted in a slower spelling than the individual best feedback, but still faster than without any feedback. Overall, the average spelling times with the different feedback methods were: no feedback, 95.09 s, increasing size, 62.94 s, decreasing size, 87.73 s, increasing contrast, 77.80 s, decreasing contrast, 124.37 s, increasing duty-cycle, 134.70 s, and decreasing dutycycle, 103.77 s.},
}
@article {pmid30440777,
year = {2018},
author = {Xue, Y and Tang, J and Zhou, P and Xu, M and Ming, D and Qi, H},
title = {Does A Subject Independent Dynamic Stopping Model for P300 Speller Work on Different Flash Durations and Inter Stimulus Intervals?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1935-1938},
doi = {10.1109/EMBC.2018.8512602},
pmid = {30440777},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography ; *Event-Related Potentials, P300 ; },
abstract = {Event-related potential (ERP)-based brain- computer interfacing (BCI) is an effective communication method. However, calibration itself can be unintuitive and tedious for users. The no-calibration Subject Independent Brain Computer Interface (SIBCI) is a popular solution to the lengthy calibration. Researches have proved the subject independent model is efficient in some P300 spellers, but it is still need to be explored whether the subject independent model works when the flash durations (FDs) and the inter stimulus intervals (ISIs) are changed in a P300 speller. This study introduces a subject independent dynamical stopping model (SIDSM), which based on a subject independent model to dynamically stop the data collection process. The performance of the SIDSM is studied by modifying the FDs and ISIs in online experiments for 8 subjects. Results showed the SIDSM has an average accuracy of 92.45% for different settings. This research proved that the SIDSM is very robust to different stimulus parameters as good performance is observed across all experimental sessions.},
}
@article {pmid30440773,
year = {2018},
author = {Wirth, C and Lacey, E and Dockree, P and Arvaneh, M},
title = {Single-Trial EEG Classification of Similar Errors.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1919-1922},
doi = {10.1109/EMBC.2018.8512700},
pmid = {30440773},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; },
abstract = {When humans recognise errors, either committed by themselves or observed, error-related potentials (ErrP) are produced in the brain. Recently, a few studies have shown that it is possible to differentiate between the ErrPs generated for errors of different direction, severity, or type (e.g., response errors, interaction errors). However, in real-world scenarios, errors cannot always be delineated by these metrics. As such, it is important to consider whether errors that are similar in all of the aforementioned aspects can be classified against each other on a single-trial basis. In this paper, for the first time, we consider two different response errors, which are of equal severity and have no associated direction. This study used electroencephalogram (EEG) data from a sustainedattention based time-critical reaction task, where time pressure caused subjects to commit two different errors. Using data from 16 subjects, we applied time domain EEG features and an ensemble of linear classifiers to separate these two error conditions on a single-trial basis. We achieved a mean balanced accuracy of 63.23% and, for most of these subjects, achieved statistically significant (p ¡ 0.05) separation of the two error conditions. The ability to classify similar error conditions, such as these, increases the scope of possible applications for EEG error detection, and has the potential to improve brain-machine interaction.},
}
@article {pmid30440769,
year = {2018},
author = {Ma, X and Qiu, S and Du, C and Xing, J and He, H},
title = {Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1903-1906},
doi = {10.1109/EMBC.2018.8512590},
pmid = {30440769},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Deep Learning ; *Electroencephalography ; Humans ; Models, Neurological ; *Movement ; *Neural Networks, Computer ; },
abstract = {Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. There are extensive studies about MI-based intention recognition, most of which heavily rely on staged handcrafted EEG feature extraction and classifier design. For end-to-end deep learning methods, researchers encode spatial information with convolution neural networks (CNNs) from raw EEG data. Compared with CNNs, recurrent neural networks (RNNs) allow for long-range lateral interactions between features. In this paper, we proposed a pure RNNs-based parallel method for encoding spatial and temporal sequential raw data with bidirectional Long Short- Term Memory (bi-LSTM) and standard LSTM, respectively. Firstly, we rearranged the index of EEG electrodes considering their spatial location relationship. Secondly, we applied sliding window method over raw EEG data to obtain more samples and split them into training and testing sets according to their original trial index. Thirdly, we utilized the samples and their transposed matrix as input to the proposed pure RNNs- based parallel method, which encodes spatial and temporal information simultaneously. Finally, the proposed method was evaluated in the public MI-based eegmmidb dataset and compared with the other three methods (CSP+LDA, FBCSP+LDA, and CNN-RNN method). The experiment results demonstrated the superior performance of our proposed pure RNNs-based parallel method. In the multi-class trial-wise movement intention classification scenario, our approach obtained an average accuracy of 68.20% and significantly outperformed other three methods with an 8.25% improvement of relative accuracy on average, which proves the feasibility of our approach for the real-world BCI system.},
}
@article {pmid30440725,
year = {2018},
author = {Hagengruber, A and Vogel, J},
title = {Functional Tasks Performed by People with Severe Muscular Atrophy Using an sEMG Controlled Robotic Manipulator.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1713-1718},
doi = {10.1109/EMBC.2018.8512703},
pmid = {30440725},
issn = {2694-0604},
mesh = {Activities of Daily Living ; *Electromyography ; Female ; Humans ; Middle Aged ; *Muscular Atrophy/rehabilitation ; *Robotics/instrumentation/standards ; },
abstract = {For paralyzed people activities of daily living like eating or drinking are impossible without external assistance. Robotic assistance systems can give these people a part of their independence back. Especially if the operation with a joystick is not possible anymore due to a missing hand function, people need innovative interfaces to control assistive robots in 3D. Besides brain computer interfaces an approach based on surface electromyography (sEMG) can present an opportunity for people with a strong muscular atrophy. In this work we show that two people with proceeded spinal muscular atrophy can perform functional tasks using an sEMG controlled robotic manipulator. The interface provides a continuous control of three degrees of freedom of the endeffector of the robot. The performance was assessed with two clinical measures of upper limb functionality: the Box and Blocks Test and the Action Research Arm Test. Additionally, the participant could show that they can drink by themselves with the provided system.},
}
@article {pmid30440716,
year = {2018},
author = {Kwon, Y and Dwivedi, A and McDaid, AJ and Liarokapis, M},
title = {On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1672-1675},
doi = {10.1109/EMBC.2018.8512624},
pmid = {30440716},
issn = {2694-0604},
mesh = {Artificial Limbs ; *Brain-Computer Interfaces ; *Electromyography ; Forearm/physiology ; Hand/*physiology ; Humans ; Motion ; Muscle, Skeletal/*physiology ; Pilot Projects ; },
abstract = {The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task. In this work, we investigate optimal muscle selections for sEMG based decoding of dexterous in-hand manipulation motions. To do that, we use EMG signals derived from 14 muscle sites of interest (7 on the hand and 7 on the forearm) and an optical motion capture system that records the object motion. The regression problem is formulated using the Random Forests methodology that is based on decision trees. Regarding features selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 5-fold cross validation procedure is used for model assessment purposes and the importance values are calculated for each feature. This pilot study shows that the muscles of the hand contribute more than the muscles of the forearm to the execution of inhand manipulation tasks and that the myoelectric activations of the hand muscles provide better estimation accuracies for the decoding of manipulation motions. These outcomes suggest that the loss of the hand muscles in certain amputations limits the amputees' ability to perform a dexterous, EMG based control of a prosthesis in manipulation tasks. The results discussed can also be used for improving the efficiency and intuitiveness of EMG based interfaces for healthy subjects.},
}
@article {pmid30440615,
year = {2018},
author = {Chang, CY and Hsu, SH and Pion-Tonachini, L and Jung, TP},
title = {Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1242-1245},
doi = {10.1109/EMBC.2018.8512547},
pmid = {30440615},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; Brain/*physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {One of the greatest challenges that hinder the decoding and application of electroencephalography (EEG) is that EEG recordings almost always contain artifacts - non-brain signals. Among existing automatic artifact-removal methods, artifact subspace reconstruction (ASR) is an online and realtime capable, component-based method that can effectively remove transient or large-amplitude artifacts. However, the effectiveness of ASR and the optimal choice of its parameter have not been evaluated and reported, especially on real EEG data. This study systematically validates ASR on ten EEG recordings in a simulated driving experiment. Independent component analysis (ICA) is applied to separate artifacts from brain signals to allow a quantitative assessment of ASR's effectiveness in removing various types of artifacts and preserving brain activities. Empirical results show that the optimal ASR parameter is between 10 and 100, which is small enough to remove activities from artifacts and eye-related components and large enough to retain signals from brain-related components. With the appropriate choice of the parameter, ASR can be a powerful and automatic artifact removal approach for offline data analysis or online real-time EEG applications such as clinical monitoring and brain-computer interfaces.},
}
@article {pmid30440578,
year = {2018},
author = {Rosa So, Q and Yang, T and Phua, KS and Yu, J and Toh, V and Ng, WH and Ang, KK},
title = {Increased Theta Oscillations During Motor Imagery in a Subject with Late-stage ALS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1078-1081},
doi = {10.1109/EMBC.2018.8512411},
pmid = {30440578},
issn = {2694-0604},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy ; Movement ; },
abstract = {Non-invasive brain computer interface (BCI) has been successfully used to control cursors, helicopters and robotic arms. However, this technology is not widely adopted by people with late-stage amyotrophic lateral sclerosis (ALS) due to poor effectiveness. In this study, we attempt to assess the cognitive state of a completely locked-in ALS subject, and her ability to use motor imagery-based BCI for control. The subject achieves above chance level accuracies for both open loop (62.2%) and closed-loop (68.7%) 2-class movement vs. idle decoding. We also observe a prominent theta oscillation with peak frequency at 4.5 Hz during the experiments. Quantification shows that the theta oscillatory power increases during motor imagery tasks compared to idle tasks for both open-loop as well as closed-loop BCI tasks. Furthermore, for closed-loop sessions, theta oscillation power correlates positively with feedback accuracy during movement tasks, and negatively with feedback accuracy during idle tasks. Our study demonstrates the feasibility of motor imagery-based BCI for late-stage ALS subjects, and highlights the importance of feedback during BCI implementation.},
}
@article {pmid30440576,
year = {2018},
author = {Heelan, C and Nurmikko, AV and Truccolo, W},
title = {FPGA implementation of deep-learning recurrent neural networks with sub-millisecond real-time latency for BCI-decoding of large-scale neural sensors (104 nodes).},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1070-1073},
doi = {10.1109/EMBC.2018.8512415},
pmid = {30440576},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Neurons ; },
abstract = {Advances in neurotechnology are expected to provide access to thousands of neural channel recordings including neuronal spiking, multiunit activity and local field potentials. In addition, recent studies have shown that deep learning, in particular recurrent neural networks (RNNs), provide promising approaches for decoding of large-scale neural data. These approaches involve computationally intensive algorithms with millions of parameters. In this context, an important challenge in the application of neural decoding to next generation brain-computer interfaces for complex human tasks is the development of low-latency real-time implementations. We demonstrate a Field-Programmable Gate Array (FPGA) implementation of Long Short-Term Memory (LSTM) RNNs for decoding 10,000 channels of neural data on a mobile lowpower embedded system platform called "NeuroCoder". We provide a proof of concept in the context of decoding 20dimensional spectrotemporal representation of spoken words from simulated 10,000 neural channels. In this particular case, the LSTM model included 4,042,420 parameters. In addition to providing multiple communication interfaces for the BCI system, the NeuroCoder platform can achieve sub-millisecond real-time latencies.},
}
@article {pmid30440575,
year = {2018},
author = {Saga, N and Yano, H and Takiguchi, T and Soeta, Y and Nakagawa, S},
title = {Spatiotemporal Characteristics of Cortical Activities Associated with Articulation of Speech Perception.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1066-1069},
doi = {10.1109/EMBC.2018.8512500},
pmid = {30440575},
issn = {2694-0604},
mesh = {*Auditory Cortex ; Brain ; Brain Mapping ; *Broca Area ; Humans ; Speech ; *Speech Perception ; },
abstract = {Recently, brain computer interface (BCI) technologies that control external devices with human brain signals have been developed. However, most of the BCI systems, such as P300-speller, can only discriminate among options that have been given in advance. Therefore, the ability to decode the state of a person's perception and recognition, as well as that person's fundamental intention and emotions, from cortical activity is needed to develop a more general-use BCI system. In this study, two experiments were conducted. First, articulations were measured for Japanese monosyllabic utterances masked by several levels of noise. Second, auditory brain magnetic fields evoked by the monosyllable stimuli used in the first experiment were recorded, and neuronal current sources were localized in regions associated with speech perception and recognition - the auditory cortex (BA41), the Wernicke's area (posterior part of BA22), Broca's area (BA22), motor (BA4), and premotor (BA6) areas. Although the source intensity did not systematically change with SNR, the peak latency changed along SNR in the posterior superior temporal gyrus in the right hemisphere. The results suggest that the information associated with articulation is processed in this area.},
}
@article {pmid30440574,
year = {2018},
author = {Koizumi, K and Ueda, K and Nakao, M},
title = {Development of a Cognitive Brain-Machine Interface Based on a Visual Imagery Method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1062-1065},
doi = {10.1109/EMBC.2018.8512520},
pmid = {30440574},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Prefrontal Cortex ; Speech ; },
abstract = {In the field of brain-machine interface (BMI) research, the development of cognitive BMI is a hot topic because it may lead to more intuitive and goal-directed findings than existing BMI technology. In this study, we devised a "visual-imagery method," which enables visual imaging of the operation of a target. We also investigated an "inner-speech method," which comprised internal pronunciation of words without emitting sounds, and an "inner-speech + visual-imagery method," which combined the two methods. When only the high $\gamma$ band (60-120 Hz) power in the prefrontal cortex was used, the average accuracy of the 15 participants, with 20-fold crossvalidation, was 81.3% in inner speech, 84.6% in visual imagery, and 83.2% in inner speech + visual imagery. This study also found that the frontal pole was the most useful region in the prefrontal cortex.},
}
@article {pmid30440573,
year = {2018},
author = {Tan, C and Sun, F and Zhang, W and Kong, T},
title = {Electroencephalography Classification in Brain-Computer Interface with Manifold Constraints Transfer.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1058-1061},
doi = {10.1109/EMBC.2018.8512507},
pmid = {30440573},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {Insufficient training data is a serious problem in all domains related to bioinformatics. Transfer learning is a promising tool to solve this problem, which relaxes the hypothesis that training data must be independent and identically distributed with the test data. We construct a sophisticated electroencephalography (EEG) signal representation and obtain an efficient EEG feature extractor through manifold constraints-based joint adversarial training with training data from other domains. EEG signal is more easily distinguished in the feature space mapped by the feature extractor. Negative transfer is one of the most challenging problems in transfer learning. In our approach, we apply manifold constraints to overcome this problem, which can avoid the geometric manifolds in the target domain being destroyed. The experiments demonstrate that our approach has many advantages when applied to EEG classification tasks.},
}
@article {pmid30440571,
year = {2018},
author = {Hu, M and Ji, F and Lu, Z and Huang, W and Khosrowabadi, R and Zhao, L and Ang, KK and Phua, KS and Nasrallah, FA and Chuang, KH and Stephenson, MC and Totman, J and Jiang, X and Chew, E and Guan, C and Zhou, J},
title = {Differential Amplitude of Low-Frequency Fluctuations in brain networks after BCI Training with and without tDCS in Stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1050-1053},
doi = {10.1109/EMBC.2018.8512395},
pmid = {30440571},
issn = {2694-0604},
mesh = {*Brain ; *Brain-Computer Interfaces ; Humans ; *Stroke ; *Stroke Rehabilitation ; *Transcranial Direct Current Stimulation ; },
abstract = {Mapping the brain alterations post stroke and post intervention is important for rehabilitation therapy development. Previous work has shown changes in functional connectivity based on resting-state fMRI, structural connectivity derived from diffusion MRI and perfusion as a result of brain-computer interface-assisted motor imagery (MI-BCI) and transcranial direct current stimulation (tDCS) in upper-limb stroke rehabilitation. Besides functional connectivity, regional amplitude of local low-frequency fluctuations (ALFF) may provide complementary information on the underlying neural mechanism in disease. Yet, findings on spontaneous brain activity during resting-state in stroke patients after intervention are limited and inconsistent. Here, we sought to investigate the different brain alteration patterns induced by tDCS compared to MI-BCI for upper-limb rehabilitation in chronic stroke patients using resting-state fMRI-based ALFF method. Our results suggested that stroke patients have lower ALFF in the ipsilesional somatomotor network compared to controls at baseline. Increased ALFF at contralesional somatomotor network and alterations in higher-level cognitive networks such as the default mode network (DMN) and salience networks accompany motor recovery after intervention; though the MI-BCI alone group and MI-BCI combined with tDCS group exhibit differential patterns.},
}
@article {pmid30440411,
year = {2018},
author = {Torres, JMM and Clarkson, T and Stepanov, EA and Luhmann, CC and Lerner, MD and Riccardi, G},
title = {Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {360-363},
doi = {10.1109/EMBC.2018.8512183},
pmid = {30440411},
issn = {2694-0604},
support = {R01 MH110585/MH/NIMH NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Decision Making ; Humans ; *Neural Networks, Computer ; },
abstract = {Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g., feature/channel selection), thus selecting fronto-central channels as discriminative features within-subject. In this paper, we propose the inclusion of error-related potential activity as a generalized two-dimensional feature set and a Convolutional Neural Network for classification of EEG-based human error detection. We evaluate this pipeline using the BNCI2020 - Monitoring Error-Related Potential dataset obtaining a maximum error detection accuracy of 79.8% in a within-session 10-fold cross-validation modality, and outperforming current state of the art.},
}
@article {pmid30440400,
year = {2018},
author = {Zhang, Z and Wang, C and Ang, KK and Wai, AAP and Nanyang, CG},
title = {Spectrum and Phase Adaptive CCA for SSVEP-based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {311-314},
doi = {10.1109/EMBC.2018.8512267},
pmid = {30440400},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography ; Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Among various brain activity patterns, Steady State Visual Evoked Potential (SSVEP) based Brain Computer Inter-face (BCI) requires the least training time while carrying the fastest information transfer rate, making it highly suitable for deploying efficient self-paced BCI systems. In this study, we propose a Spectrum and Phase Adaptive CCA (SPACCA) for subject-and device-specific SSVEP-based BCI. Cross subject heterogeneity of spectrum distribution is taken into consideration to improve the prediction accuracy. We design a library of phase shifting reference signals to accommodate subjective and device-related response time lag. With the flexible reference signal generating approach, the system can be optimized for any specific flickering source, include LED, computer screen and mobile devices. We evaluated the performance of SPACCA using three sets of data that use LED, computer screen and mobile device (tablet) as stimuli sources respectively. The first two data sets are publicly available whereas the third data set is collected in our BCI lab. Across different data sets, SPACCA consistently performs better than the baseline, i.e. standard CCA approach. Statistical test to compare the overall results across three data sets yield a p-value of 1.66e-6, implying the improvement is significant.},
}
@article {pmid30440381,
year = {2018},
author = {Arnin, J and Kahani, D and Lakany, H and Conway, BA},
title = {Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {235-238},
doi = {10.1109/EMBC.2018.8512193},
pmid = {30440381},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one's movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such real-time BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.},
}
@article {pmid30440378,
year = {2018},
author = {Carella, T and De Silvestri, M and Finedore, M and Haniff, I and Esmailbeigi, H},
title = {Emotion Recognition for Brain Machine Interface: Non-linear Spectral Analysis of EEG Signals Using Empirical Mode Decomposition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {223-226},
doi = {10.1109/EMBC.2018.8512228},
pmid = {30440378},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Emotions ; Humans ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Emotions are a fundamental part of the human experience but currently there are no methods that can objectively detect and categorize them. This study utilizes the empirical mode decomposition (EMD) method to categorize emotions from encephalography (EEG) recordings. In the past, EMD has proven to be a very useful signal analysis tool because of its ability to decompose nonstationary signals, like those from an EEG, into component signals with varying frequency content called intrinsic mode functions (IMFs). The method in this paper utilizes three features extracted from the IMFs-the first difference of time, the first difference of phase, and the normalized energy-for data categorization using support vector machine (SVM) classifiers. Two classifiers were trained for each subject, one for valence and another for arousal. The mean accuracies yielded for valence and arousal were 75.86% and 75.31%, respectively. The results of this study verify previous findings by other researchers that these three features are useful in emotion recognition when applied to previously recorded EEG data, though we add the caveat that subject-specific classifiers are needed instead of generalized, global classifiers.},
}
@article {pmid30440377,
year = {2018},
author = {Abbas, W and Khan, NA},
title = {DeepMI: Deep Learning for Multiclass Motor Imagery Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {219-222},
doi = {10.1109/EMBC.2018.8512271},
pmid = {30440377},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Neural Networks, Computer ; },
abstract = {In Brain-Computer Interface (BCI) Research,Electroencephalography (EEG) has obtained great attention for biomedical applications. In BCI system, feature representation and classification are important tasks as the accuracy of classification highly depends on these stages. In this paper, we propose a model in which Common Spatial Pattern (CSP) is used to discriminate inter-class data using co-variance maximization and Fast Fourier Transform Energy Map (FFTEM) is used for feature selection and mapping of 1D data into 2D data (energy maps). Convolutional Neural Network is used for classification of multi-class Motor Imagery (MI) signals. Further, this paper investigates near-optimal parameter selection for feature mapping, frequency bands selection, and temporal segmentation. It is shown that our proposed method outperformed the reported methods by achieving 0.61 mean kappa value.},
}
@article {pmid30440376,
year = {2018},
author = {Abbas, W and Khan, NA},
title = {FBCSP-based Multi-class Motor Imagery Classification using BP and TDP features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {215-218},
doi = {10.1109/EMBC.2018.8512238},
pmid = {30440376},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; *Signal Processing, Computer-Assisted ; },
abstract = {Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment. Filter Bank Common Spatial Pattern (FBCSP) algorithm is a promising feature extraction technique to deal with subject-specific behavior in Motor Imagery classification. Using FBCSP on EEG we have developed an accurate but less computationally expensive approach by making use of Time Domain Parameters (TDP) and Band Power (BP) features to form a combined feature set. The novelty of our approach is also the use of optimal time segmentation to overcome non-stationary state behavior of Event-Related Desynchronization (ERD) and Event-Related Synchronization (ERS) over time. We analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV, the highest reported for FBCSP approaches, along with the lowest inter-subject variation.},
}
@article {pmid30440375,
year = {2018},
author = {Wang, K and Xu, M and Zhang, S and Ke, Y and Ming, D},
title = {Analysis and Classification for EEG Patterns of Force Motor Imagery Using Movement Related Cortical Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {211-214},
doi = {10.1109/EMBC.2018.8512184},
pmid = {30440375},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Movement ; },
abstract = {Motor imagery-based BCIs are the most natural human-computer interaction paradigms. In recent years, researchers have tried to decode the kinetic information of motor imagery. In this paper, we analyzed and discriminated the EEG patterns of different force levels motor imagery using MRCPs. In the experiment, nine healthy subjects were required to perform the hand force motor imagery tasks (30% MVC and 10% MVC). From the view of MRCPs, the most significant discrimination between the two levels of mental tasks was the manifestation of motor planning. The average classification accuracy for features involving both MRCP and CSP was 78.3%, which was 8.5% higher than the CSP-based features (p¡0.001) and 2% higher than the MRCP-based features. The results demonstrated the feasibility of using MRCPs for hand force motor imagery classification.},
}
@article {pmid30440374,
year = {2018},
author = {Oikonomou, VP and Nikolopoulos, S and Petrantonakis, P and Kompatsiaris, I},
title = {Sparse Kernel Machines for motor imagery EEG classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {207-210},
doi = {10.1109/EMBC.2018.8512195},
pmid = {30440374},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Brain-computer interfaces (BCIs) make humancomputer interaction more natural, especially for people with neuro-muscular disabilities. Among various data acquisition modalities the electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work, a method based on sparse kernel machines is proposed for the classification of motor imagery (MI) EEG data. More specifically, a new sparse prior is proposed for the selection of the most important information and the estimation of model parameters is performed using the bayesian framework. The experimental results obtained on a benchmarking EEG dataset for MI, have shown that the proposed method compares favorably with state of the art approaches in BCI literature.},
}
@article {pmid30440373,
year = {2018},
author = {Ortiz, M and Rodriguez-Ugarte, M and Iaez, E and Azorin, JM},
title = {Comparison of different EEG signal analysis techniques for an offline lower limb motor imagery brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {203-206},
doi = {10.1109/EMBC.2018.8512256},
pmid = {30440373},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Imagination ; Lower Extremity ; Neuronal Plasticity ; *Signal Processing, Computer-Assisted ; },
abstract = {The use of motion assistance devices improves the rehabilitation process of patients that have motor disabilities. In the case these devices are controlled by brain-machine interfaces, the rehabilitation process can be improved due to neuroplasticity. However, in the case of lower limb rehabilitation, the limited accuracy of the control algorithms is a serious difficulty to overcome. In this research, different EEG signal's processing techniques, based on motor imagery, are tested for a brain-computer interface in an offline scenario, in order to detect the limitations of the models previous to its realtime implementation. The results reveal that motor imagery is very dependent on the subject and that Stockwell Transform provides the best accuracy among the models tested.},
}
@article {pmid30440371,
year = {2018},
author = {Zhang, Z and Chen, S and Yang, Z and Wang, Y},
title = {Tracking the Time Varying Neural Tuning via Adam on Point Process Observations.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {195-198},
doi = {10.1109/EMBC.2018.8512241},
pmid = {30440371},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; Algorithms ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; *Movement/physiology ; *Neurons/physiology ; },
abstract = {Brain machine interfaces(BMIs) translate the neural activity into the control of movement by understanding how the neural activity responds to the movement intension. However, the neural tuning property, where the modulation depth and preferred direction describe how a neuron responses to stimuli, is time varying gradually and abruptly during the interaction with environment. There has been some research to address such an issue considering either one of the cases, but never address them in a general framework. We propose a novel optimization algorithm based on the point process observations to capture these two changes at the same time. At each time index, the tuning parameter is updated stochastically according to the gradient based Adam searching method, which maximizes the likelihood of point process. Our algorithm is compared with the Adaptive Point Process Estimation (APPE), where the abrupt change is addressed by sampling all the possibilities globally, on synthetic neural data. The results show that our algorithm leads to a better prediction of tuning parameters as well as kinematics over 16.8% and 20% respectively.},
}
@article {pmid30440354,
year = {2018},
author = {Marghi, YM and Gonzalez-Navarro, P and Azari, B and Erdogmus, D},
title = {A Parametric EEG Signal Model for BCIs with Rapid-Trial Sequences.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {118-122},
doi = {10.1109/EMBC.2018.8512217},
pmid = {30440354},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/physiology ; *Brain Mapping/methods ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; },
abstract = {Electroencephalogram (EEG) signals have been shown very effective for inferring user intents in brain-computer interface (BCI) applications. However, existing EEG-based BCIs, in many cases, lack sufficient performance due to utilizing classifiers that operate on EEG signals induced by individual trials. While many factors influence the classification performance, an important aspect that is often ignored is the temporal dependency of these trial-EEG signals, in some cases impacted by interference of brain responses to consecutive target and non-target trials. In this study, the EEG signals are analyzed in a parametric sequence-based fashion, which considers all trials that induce brain responses in a rapid-sequence fashion, including a mixture of consecutive target and non-target trials. EEG signals are described as a linear combination of time-shifted cortical source activities plus measurement noise. Using a superposition of time invariant with an auto-regressive (AR) process, EEG signals are treated as a linear combination of a stationary Gaussian process and time-locked impulse responses to the stimulus (input events) onsets. The model performance is assessed in the framework of a rapid serial visualization presentation (RSVP) based typing task for three healthy subjects across two sessions. Signal modeling in this fashion yields promising performance outcomes considering a single EEG channel to estimate the user intent.},
}
@article {pmid30440348,
year = {2018},
author = {Nakanishi, M and Wang, YT and Jung, TP},
title = {Transferring Shared Responses Across Electrode Montages for Facilitating Calibration in High-Speed Brain Spellers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {89-92},
doi = {10.1109/EMBC.2018.8512269},
pmid = {30440348},
issn = {2694-0604},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; Electrodes ; *Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; *Language ; Photic Stimulation ; },
abstract = {Recent studies have shown that using the user's average steady-state visual evoked responses (SSVEPs) as the template to template-matching methods could significantly improve the accuracy and speed of the SSVEP-based brain- computer interface (BCI). However, collecting the pilot data for each individual can be time-consuming. To resolve this practical issue, this study aims to explore the feasibility of leveraging pre- recorded datasets from the same users by transferring common electroencephalogram (EEG) responses across different sessions with the same or different electrode montages. The proposed method employs spatial filtering techniques including response averaging, canonical correlation analysis (CCA), and task- related component analysis (TRCA) to project scalp EEG recordings onto a shared response domain. The transferability was evaluated by using 40-class SSVEPs recorded from eight subjects with nine electrodes on two different days. Three subsets of electrode montages were selected to simulate different scenarios such as identical, partly overlapped, and non-overlapped electrode placements across two sessions. The target identification accuracy of the proposed methods with transferred training data significantly outperformed a conventional training-free algorithm. The result suggests training data required in the BCI speller could be transferred from different EEG montages and/or headsets.},
}
@article {pmid30440347,
year = {2018},
author = {Petrantonakis, PC and Kompatsiaris, I},
title = {Detection of Mental Task Related Activity in NIRS-BCI systems Using Dirichlet Energy over Graphs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {85-88},
doi = {10.1109/EMBC.2018.8512180},
pmid = {30440347},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Hemodynamics/physiology ; Humans ; *Spectroscopy, Near-Infrared/methods ; },
abstract = {Near Infrared Spectroscopy (NIRS)-based Brain Computer Interfaces (NIRS-BCI) rely mainly on the mean concentration changes and slope of the hemodynamic responses in separate recording channels to detect the mental-task related brain activity. Nevertheless, spatial patterns across the measurement channels are also present and should be taken into account for reliable evaluation of the aforementioned detection. In this work the Dirichlet Energy of NIRS signals over a graph is considered for the definition of a measure that would take into account the spatial NIRS features and would integrate the activity of multiple NIRS channels for robust mental task related activity detection. The application of the proposed measure on a real NIRS dataset demonstrates the efficiency of the proposed measure.},
}
@article {pmid30440344,
year = {2018},
author = {An, J and Yadav, T and Ahmadi, MB and Tarigoppula, VSA and Francis, JT},
title = {Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {73-76},
doi = {10.1109/EMBC.2018.8512274},
pmid = {30440344},
issn = {2694-0604},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning ; *Motor Cortex ; *Reinforcement, Psychology ; Reward ; },
abstract = {We are developing an autonomously updating brain machine interface (BMI) utilizing reinforcement learning principles. One aspect of this system is a neural critic that determines reward expectations from neural activity. This critic is then used to update a BMI decoder toward an improved performance from the user's perspective. Here we demonstrate the ability of a neural critic to classify trial reward value given activity from the primary motor cortex (M1), using neural features from single/multi units (SU/MU), and local field potentials (LFPs) with prediction accuracies up to 97% correct. A nonhuman primate subject conducted a cued center out reaching task, either manually, or observationally. The cue indicated the reward value of a trial. Features such as power spectral density (PSD) of the LFPs and spike-field coherence (SFC) between SU/MU and corresponding LFPs were calculated and used as inputs to several classifiers. We conclude that hybrid features of PSD and SFC show higher classification performance than PSD or SFC alone (accuracy was 92% for manual tasks, and 97% for observational). In the future, we will employ these hybrid features toward our autonomously updating BMI.},
}
@article {pmid30440328,
year = {2018},
author = {Cecotti, H},
title = {Relationships Between Behavioral And Single-Trial Target Detection Performance With Magnetoencephalography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {9-12},
doi = {10.1109/EMBC.2018.8512253},
pmid = {30440328},
issn = {2694-0604},
mesh = {Adult ; *Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials/physiology ; *Facial Expression ; Fear ; Female ; Happiness ; Humans ; *Magnetoencephalography/methods ; Male ; Reaction Time ; },
abstract = {Target detection during serial visual presentation tasks is an active research topic in the brain-computer interface (BCI) community as this type of paradigm allows to take advantage of event-related potentials (ERPs) through electroencephalography (EEG) recordings to enhance the accuracy of target detection. The detection of brain evoked responses at the single-trial level remains a challenging task and can be exploited in various applications. Typical non-invasive BCIs based on event-related brain responses use EEG. In clinical settings, brain signals recorded with magnetoencephalography (MEG) can be advantageously used thanks to their high spatial and temporal resolution. In this study, we address the problem of the relationships between behavioral performance and single-trial detection by considering a task with different levels of difficulty. We consider images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). We consider MEG signals recorded on ten healthy participants in six sessions where targets were one of the six types of facial expressions in each session. The results support the conclusion that a high performance can be obtained at the single-trial level $({AUC }
= 0 . 903 \pm 0 .045)$, and that the performance is correlated with the behavioral performance (reaction time and hit rate).},
}
@article {pmid30440266,
year = {2018},
author = {Saidutta, YM and Zou, J and Fekri, F},
title = {Increasing the learning Capacity of BCI Systems via CNN-HMM models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1-4},
doi = {10.1109/EMBC.2018.8512714},
pmid = {30440266},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy ; *Machine Learning ; Neural Networks, Computer ; },
abstract = {Despite all the work in the Brain Computer Interface (BCI) community, one of the main issues that prevents it from becoming pervasive is the limitation on the number of commands with a satisfactory accuracy of detection. In this paper, we propose a solution to increase the number of commands while maintaining a satisfactory accuracy performance via a hybrid Convolutional Neural Network (CNN)- Hidden Markov Model (HMM). The setup makes use of a classifier (a CNN) that works over a small alphabet of established mental tasks like the motor imagery task and detects sequences comprised of these tasks using HMMs. To optimize the learning capacity, we select a subset of sequences by measuring the distance between HMM models. This system, based on the experiments we have conducted, shows a 14% gain in accuracy over the non-sequenced classifier. Alternatively, it can be used to increase the command set size by 4 times when using all the channels or by 1.5 times when using only 1/3 of the EEG channels and have the same performance as a non-sequenced classifier that uses all available channels. This shows that the CNN-HMM hybrid model is a viable approach to increase the capacity of learning in BCI.},
}
@article {pmid30440265,
year = {2018},
author = {Onishil, A and Nakagawal, S},
title = {Ensemble Convoluted Feature Extraction for Affective Auditory P300 Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1-4},
doi = {10.1109/EMBC.2018.8512688},
pmid = {30440265},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Research Design ; Young Adult ; },
abstract = {Since most of P300 brain-computer interface (BCI) methods have assumed visual events, they are not always suitable for the BCI with auditory events, and feature extraction methods appropriate for auditory P300 BCI are required. This study proposed ensemble convoluted feature extraction for affective auditory P300 BCI, which took advantage of auditory responses elicited by different affective sounds. The proposed method was compared to feature extraction method that uses the canonical correlation analysis in addition to the traditional method. Those methods were evaluated on the dataset recorded from the improved affective auditory P300 BCI system. The mean online classification accuracy was 84.1% when using the traditional feature. The offline analysis showed that the proposed ensemble convolution feature extraction achieved significantly higher accuracy (86.75%) than the traditional method. The propose feature extraction may effective for the multi-channel time-series BCI that is featured by different stimuli.},
}
@article {pmid30440262,
year = {2018},
author = {Sikdar, D and Roy, R and Bakshi, K and Mahadevappa, M},
title = {Multifractal Analysis of Speech Imagery of IPA Vowels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1-4},
doi = {10.1109/EMBC.2018.8512579},
pmid = {30440262},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Imagination ; Phonetics ; Speech/*physiology ; Speech Perception/physiology ; Young Adult ; },
abstract = {In Brain Computer Interfacing (BCI), speech imagery is still at nascent stage of development. There are few studies reported considering mostly vowels or monosyllabic words. However, language specific vowels or words made it harder to standardise the whole analysis of electroencephalography (EEG) while distinguishing between them. Through this study, we have explored significance of multifractal parameters for different imagined vowels chosen from International Phonetic Alphabets (IPA). The vowels were categorised into two categories, namely, soft vowels and diphthongs. Multifractal analysis at EEG subband levels were evaluated. We have also reported significant contrasts between spatiotemporal distributions with fractal analysis for activation of different brain regions in imagining vowels.},
}
@article {pmid30440256,
year = {2018},
author = {Yao, P and Xu, G and Han, C and Zhang, S and Luo, A and Zhang, Q},
title = {SSVEP Transient Feature Extraction and Rapid Recognition Method Based on Bistable Stochastic Resonance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2018},
number = {},
pages = {1-4},
doi = {10.1109/EMBC.2018.8512506},
pmid = {30440256},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination ; Stochastic Processes ; Time Factors ; Vibration ; },
abstract = {Steady-state Visual Evoked Potential, SSVEP), as the most commonly used communication paradigm for non-implantable Brain-Computer Interface (BCI), boasts the advantages of unnecessity of training, noise immunity and periodicity. The traditional SSVEP extraction methods can effectively identify the target frequency contained in original EEG, however, the required data length usually lasts a few seconds. In this paper, bistable stochastic resonance (BSR) is applied to SSVEP extraction. BSR is very sensitive to amplitude mutation and frequency fluctuation of the input signal, making the output difference can be used for the detection of the target frequency. The processing results illustrate that the proposed method not only has a high recognition accuracy, but also effectively shortens the recognition time, thus improving the calculating speed. Therefore, SSVEP extraction based on BSR has a higher information transmission rate (ITR), which is more suitable for the real-time BCI system.},
}
@article {pmid30429772,
year = {2018},
author = {Downey, JE and Weiss, JM and Flesher, SN and Thumser, ZC and Marasco, PD and Boninger, ML and Gaunt, RA and Collinger, JL},
title = {Implicit Grasp Force Representation in Human Motor Cortical Recordings.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {801},
pmid = {30429772},
issn = {1662-4548},
abstract = {In order for brain-computer interface (BCI) systems to maximize functionality, users will need to be able to accurately modulate grasp force to avoid dropping heavy objects while also being able to handle fragile items. We present a case-study consisting of two experiments designed to identify whether intracortical recordings from the motor cortex of a person with tetraplegia could predict intended grasp force. In the first task, we were able classify neural responses to attempted grasps of four objects, each of which required similar grasp kinematics but different implicit grasp force targets, with 69% accuracy. In the second task, the subject attempted to move a virtual robotic arm in space to grasp a simple virtual object. For each trial, the subject was asked to grasp the virtual object with the force appropriate for one of the four objects from the first experiment, with the goal of measuring an implicit representation of grasp force. While the subject knew the grasp force during all phases of the trial, accurate classification was only achieved during active grasping, not while the hand moved to, transported, or released the object. In both tasks, misclassifications were most often to the object with an adjacent force requirement. In addition to the implications for understanding the representation of grasp force in motor cortex, these results are a first step toward creating intelligent algorithms to help BCI users grasp and manipulate a variety of objects that will be encountered in daily life. Clinical Trial Identifier: NCT01894802 https://clinicaltrials.gov/ct2/show/NCT01894802.},
}
@article {pmid30429767,
year = {2018},
author = {Susi, G and Antón Toro, L and Canuet, L and López, ME and Maestú, F and Mirasso, CR and Pereda, E},
title = {A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {780},
pmid = {30429767},
issn = {1662-4548},
abstract = {Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multi-neuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, which allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multi-dimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor-inhibitory cognitive task. The results show that out method exhibits similar performance compared with other classification methods, indicating the effectiveness of our approach. In addition, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication, or early detection of diseases.},
}
@article {pmid30429511,
year = {2018},
author = {Okahara, Y and Takano, K and Nagao, M and Kondo, K and Iwadate, Y and Birbaumer, N and Kansaku, K},
title = {Long-term use of a neural prosthesis in progressive paralysis.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {16787},
pmid = {30429511},
issn = {2045-2322},
mesh = {Amyotrophic Lateral Sclerosis/*therapy ; Brain/physiology ; *Brain-Computer Interfaces ; Disease Progression ; Evoked Potentials, Visual/physiology ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; *Neural Prostheses ; Quadriplegia/therapy ; },
abstract = {Brain-computer interfaces (BCIs) enable communication with others and allow machines or computers to be controlled in the absence of motor activity. Clinical studies evaluating neural prostheses in amyotrophic lateral sclerosis (ALS) patients have been performed; however, to date, no study has reported that ALS patients who progressed from locked-in syndrome (LIS), which has very limited voluntary movement, to a completely locked-in state (CLIS), characterized by complete loss of voluntary movements, were able to continue controlling neural prostheses. To clarify this, we used a BCI system to evaluate three late-stage ALS patients over 27 months. We employed steady-state visual evoked brain potentials elicited by flickering green and blue light-emitting diodes to control the BCI system. All participants reliably controlled the system throughout the entire period (median accuracy: 83.3%). One patient who progressed to CLIS was able to continue operating the system with high accuracy. Furthermore, this patient successfully used the system to respond to yes/no questions. Thus, this CLIS patient was able to operate a neuroprosthetic device, suggesting that the BCI system confers advantages for patients with severe paralysis, including those exhibiting complete loss of muscle movement.},
}
@article {pmid30427765,
year = {2019},
author = {Castiello, U and Dadda, M},
title = {A review and consideration on the kinematics of reach-to-grasp movements in macaque monkeys.},
journal = {Journal of neurophysiology},
volume = {121},
number = {1},
pages = {188-204},
doi = {10.1152/jn.00598.2018},
pmid = {30427765},
issn = {1522-1598},
mesh = {Animals ; Biomechanical Phenomena ; Humans ; Macaca ; *Motor Activity/physiology ; *Movement/physiology ; *Upper Extremity/physiology ; },
abstract = {The bases for understanding the neuronal mechanisms that underlie the control of reach-to-grasp movements among nonhuman primates, particularly macaques, has been widely studied. However, only a few kinematic descriptions of their prehensile actions are available. A thorough understanding of macaques' prehensile movements is manifestly critical, in light of their role in biomedical research as valuable models for studying neuromotor disorders and brain mechanisms, as well as for developing brain-machine interfaces to facilitate arm control. This article aims to review the current state of knowledge on the kinematics of grasping movements that macaques perform in naturalistic, seminaturalistic, and laboratory settings, to answer the following questions: Are kinematic signatures affected by the context within which the movement is performed? In what ways are kinematics of humans' and macaques' prehensile actions similar/dissimilar? Our analysis reflects the challenges involved in making comparisons across settings and species due to the heterogeneous picture in terms of the number of subjects, stimuli, conditions, and hands used. The kinematics of free-ranging macaques are characterized by distinctive features that are exhibited neither by macaques in laboratory setting nor by human subjects. The temporal incidence of key kinematic landmarks diverges significantly between species, indicating disparities in the overall organization of movement. Given such complexities, we attempt a synthesis of the extant body of evidence, intending to generate some significant implications for directions that future research might take to recognize the remaining gaps and pursue the insights and resolutions to generate an interpretation of movement kinematics that accounts for all settings and subjects.},
}
@article {pmid30425630,
year = {2018},
author = {Putze, F and Mühl, C and Lotte, F and Fairclough, S and Herff, C},
title = {Editorial: Detection and Estimation of Working Memory States and Cognitive Functions Based on Neurophysiological Measures.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {440},
pmid = {30425630},
issn = {1662-5161},
}
@article {pmid30424427,
year = {2018},
author = {Nicolai, EN and Michelson, NJ and Settell, ML and Hara, SA and Trevathan, JK and Asp, AJ and Stocking, KC and Lujan, JL and Kozai, TDY and Ludwig, KA},
title = {Design Choices for Next-Generation Neurotechnology Can Impact Motion Artifact in Electrophysiological and Fast-Scan Cyclic Voltammetry Measurements.},
journal = {Micromachines},
volume = {9},
number = {10},
pages = {},
pmid = {30424427},
issn = {2072-666X},
support = {R21 NS108098/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; TL1 TR002380/TR/NCATS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; },
abstract = {Implantable devices to measure neurochemical or electrical activity from the brain are mainstays of neuroscience research and have become increasingly utilized as enabling components of clinical therapies. In order to increase the number of recording channels on these devices while minimizing the immune response, flexible electrodes under 10 µm in diameter have been proposed as ideal next-generation neural interfaces. However, the representation of motion artifact during neurochemical or electrophysiological recordings using ultra-small, flexible electrodes remains unexplored. In this short communication, we characterize motion artifact generated by the movement of 7 µm diameter carbon fiber electrodes during electrophysiological recordings and fast-scan cyclic voltammetry (FSCV) measurements of electroactive neurochemicals. Through in vitro and in vivo experiments, we demonstrate that artifact induced by motion can be problematic to distinguish from the characteristic signals associated with recorded action potentials or neurochemical measurements. These results underscore that new electrode materials and recording paradigms can alter the representation of common sources of artifact in vivo and therefore must be carefully characterized.},
}
@article {pmid30420874,
year = {2018},
author = {Shin, D and Kambara, H and Yoshimura, N and Koike, Y},
title = {Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {2580165},
pmid = {30420874},
issn = {1687-5273},
mesh = {Animals ; Arm/*physiology ; Biomechanical Phenomena ; Brain/physiology ; *Brain-Computer Interfaces ; *Electrocorticography/methods ; *Electromyography/methods ; Female ; Joints/*physiology ; Macaca ; Motor Activity/physiology ; Muscles/physiology ; Neural Prostheses ; Robotics/*methods ; },
abstract = {Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.},
}
@article {pmid30420724,
year = {2018},
author = {Sburlea, AI and Müller-Putz, GR},
title = {Exploring representations of human grasping in neural, muscle and kinematic signals.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {16669},
pmid = {30420724},
issn = {2045-2322},
support = {ERC - 681231//EC | European Research Council (ERC)/International ; },
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Movement covariates, such as electromyographic or kinematic activity, have been proposed as candidates for the neural representation of hand control. However, it remains unclear how these movement covariates are reflected in electroencephalographic (EEG) activity during different stages of grasping movements. In this exploratory study, we simultaneously acquired EEG, kinematic and electromyographic recordings of human subjects performing 33 types of grasps, yielding the largest such dataset to date. We observed that EEG activity reflected different movement covariates in different stages of grasping. During the pre-shaping stage, centro-parietal EEG in the lower beta frequency band reflected the object's shape and size, whereas during the finalization and holding stages, contralateral parietal EEG in the mu frequency band reflected muscle activity. These findings contribute to the understanding of the temporal organization of neural grasping patterns, and could inform the design of noninvasive neuroprosthetics and brain-computer interfaces with more natural control.},
}
@article {pmid30417084,
year = {2018},
author = {Martin, S and Iturrate, I and Chavarriaga, R and Leeb, R and Sobolewski, A and Li, AM and Zaldivar, J and Peciu-Florianu, I and Pralong, E and Castro-Jiménez, M and Benninger, D and Vingerhoets, F and Knight, RT and Bloch, J and Millán, JDR},
title = {Differential contributions of subthalamic beta rhythms and 1/f broadband activity to motor symptoms in Parkinson's disease.},
journal = {NPJ Parkinson's disease},
volume = {4},
number = {},
pages = {32},
pmid = {30417084},
issn = {2373-8057},
support = {R37 NS021135/NS/NINDS NIH HHS/United States ; },
abstract = {Excessive beta oscillatory activity in the subthalamic nucleus (STN) is linked to Parkinson's Disease (PD) motor symptoms. However, previous works have been inconsistent regarding the functional role of beta activity in untreated Parkinsonian states, questioning such role. We hypothesized that this inconsistency is due to the influence of electrophysiological broadband activity -a neurophysiological indicator of synaptic excitation/inhibition ratio- that could confound measurements of beta activity in STN recordings. Here we propose a data-driven, automatic and individualized mathematical model that disentangles beta activity and 1/f broadband activity in the STN power spectrum, and investigate the link between these individual components and motor symptoms in thirteen Parkinsonian patients. We show, using both modeled and actual data, how beta oscillatory activity significantly correlates with motor symptoms (bradykinesia and rigidity) only when broadband activity is not considered in the biomarker estimations, providing solid evidence that oscillatory beta activity does correlate with motor symptoms in untreated PD states as well as the significant impact of broadband activity. These findings emphasize the importance of data-driven models and the identification of better biomarkers for characterizing symptom severity and closed-loop applications.},
}
@article {pmid30416440,
year = {2018},
author = {Hong, KS and Zafar, A},
title = {Existence of Initial Dip for BCI: An Illusion or Reality.},
journal = {Frontiers in neurorobotics},
volume = {12},
number = {},
pages = {69},
pmid = {30416440},
issn = {1662-5218},
abstract = {A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.},
}
@article {pmid30415714,
year = {2018},
author = {Eilbeigi, E and Setarehdan, SK},
title = {Global optimal constrained ICA and its application in extraction of movement related cortical potentials from single-trial EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {166},
number = {},
pages = {155-169},
doi = {10.1016/j.cmpb.2018.07.013},
pmid = {30415714},
issn = {1872-7565},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials ; False Positive Reactions ; Humans ; Movement ; *Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {BACKGROUND AND OBJECTIVE: The constrained ICA (cICA) is a recent approach which can extract the desired source signal by using prior information. cICA employs gradient-based algorithms to optimize non convex objective functions and therefore global optimum solution is not guaranteed. In this study, we propose the Global optimal constrained ICA (GocICA) algorithm for solving the conventional cICA problems. Due to the importance of movement related cortical potentials (MRCPs) for neurorehabilitation and developing a suitable mechanism for detection of movement intention, single-trial MRCP extraction is presented as an application of GocICA.<br><br>METHODS: In order to evaluate the performance of the proposed technique, two kinds of datasets including simulated and real EEG data have been utilized in this paper. The GocICA method has been implemented based on the most popular meta-heuristic optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Charged System Search (CSS) where the results have been compared with those of conventional cICA and two ICA-based methods (JADE and Infomax).<br><br>RESULTS: It was found that GocICA enhanced the extracted MRCP from multi-channel EEG better than both conventional cICA and ICA-based methods and also outperformed them in single-trial MRCP detection with higher true positive rates (TPRs) and lower false positive rates (FPRs). Moreover, CSS-cICA resulted in the greatest TPR (91.2232&#x202f;&#xb1;&#x202f;3.4708) and the lowest FPR (8.7465&#x202f;&#xb1;&#x202f;3.7705) for single-trial MRCP detection from real EEG data and the greatest signal-to-noise ratio (SNR) (39.2818) and the lowest mean square error (MSE) and individual performance index (IPI) (41.8230 and 0.0012, respectively) for single-trial MRCP extraction from simulated EEG data.<br><br>CONCLUSIONS: These results confirm the superiority of GocICA with respect to conventional cICA that is due to the ability of meta-heuristic optimization algorithms to escape from local optimal point. As such, GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of event related cortical potentials (ERPs) such as P300 and also for EEG artifact removal.},
}
@article {pmid30415710,
year = {2018},
author = {de Souza, AP and Soares, QB and Felix, LB and Mendes, EMAM},
title = {Classification of auditory selective attention using spatial coherence and modular attention index.},
journal = {Computer methods and programs in biomedicine},
volume = {166},
number = {},
pages = {107-113},
doi = {10.1016/j.cmpb.2018.10.002},
pmid = {30415710},
issn = {1872-7565},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Attention/*physiology ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Calibration ; Electrodes ; *Electroencephalography ; Evoked Potentials, Auditory ; Female ; Humans ; Male ; *Pattern Recognition, Automated ; Psychomotor Performance ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVE: Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. The latter is a new method using modular attention index. The new classifier was developed to implement an auditory BCI where a volunteer makes binary choices using selective attention under the amplitude-modulated tones stimulation.<br><br>METHODS: Contralateral and modular classifiers were applied to the electroencephalogram (EEG) recorded from 144 subjects under the BCI protocol. The best set of parameters (carriers of the stimulus, channels and trials of signal) for this BCI was investigated taking into consideration the hit rate and the information transfer rate.<br><br>RESULTS: The best result obtained using the modular classifier was a hit rate of 91.67% and information transfer rate of 6.74&#x2009;bits/min using 0.5&#x2009;kHz/4.0&#x2009;kHz as stimuli and three windows (5.10&#x2009;sec of EEG signal). These results were obtained with five electrodes (C3, P3, F8, P4, O2) using exhaustive search to identify regions with greater coherence.<br><br>CONCLUSION: The modular classifier - using electroencephalogram channels from the central, frontal, occipital and parietal areas - improves the performance of auditory BCIs based on selective attention.},
}
@article {pmid30415631,
year = {2019},
author = {Sorinas, J and Grima, MD and Ferrandez, JM and Fernandez, E},
title = {Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion Recognition.},
journal = {International journal of neural systems},
volume = {29},
number = {2},
pages = {1850044},
doi = {10.1142/S0129065718500442},
pmid = {30415631},
issn = {1793-6462},
mesh = {Adult ; Auditory Perception/*physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Emotions/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; Time Factors ; Visual Perception/*physiology ; },
abstract = {The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1 s to 12 s segments, was 12 s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.},
}
@article {pmid30414824,
year = {2019},
author = {Jeunet, C and Glize, B and McGonigal, A and Batail, JM and Micoulaud-Franchi, JA},
title = {Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects.},
journal = {Neurophysiologie clinique = Clinical neurophysiology},
volume = {49},
number = {2},
pages = {125-136},
doi = {10.1016/j.neucli.2018.10.068},
pmid = {30414824},
issn = {1769-7131},
mesh = {Animals ; Brain Diseases/physiopathology/rehabilitation ; *Brain Waves ; *Brain-Computer Interfaces ; Humans ; Imagination ; Mental Disorders/physiopathology/rehabilitation ; Models, Neurological ; *Motor Skills ; Neurofeedback/instrumentation/*methods ; Neuronal Plasticity ; Sensorimotor Cortex/*physiology/physiopathology ; },
abstract = {Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedback studies and to summarize the background from neurophysiological and neuroplasticity studies that led to SMR being considered as reliable and valid EEG targets to improve motor skills through BCI/neurofeedback procedures. The second objective of this review is to introduce the main findings regarding SMR BCI/neurofeedback in healthy subjects. Third, the main findings regarding BCI/neurofeedback efficiency in patients with hypokinetic activities (in particular, motor deficit following stroke) as well as in patients with hyperkinetic activities (in particular, Attention Deficit Hyperactivity Disorder, ADHD) will be introduced. Due to a range of limitations, a clear association between SMR BCI/neurofeedback training and enhanced motor skills has yet to be established. However, SMR BCI/neurofeedback appears promising, and highlights many important challenges for clinical neurophysiology with regards to therapeutic approaches using BCI/neurofeedback.},
}
@article {pmid30410454,
year = {2018},
author = {Wriessnegger, SC and Brunner, C and Müller-Putz, GR},
title = {Frequency Specific Cortical Dynamics During Motor Imagery Are Influenced by Prior Physical Activity.},
journal = {Frontiers in psychology},
volume = {9},
number = {},
pages = {1976},
pmid = {30410454},
issn = {1664-1078},
abstract = {Motor imagery is often used inducing changes in electroencephalographic (EEG) signals for imagery-based brain-computer interfacing (BCI). A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the last years, there is increasing interest using BCIs also for healthy people in terms of enhancement or gaming. Most studies focusing on improving signal processing feature extraction and classification methods, but the performance of a BCI can also be improved by optimizing the user's control strategies, e.g., using more vivid and engaging mental tasks for control. We used multichannel EEG to investigate neural correlates of a sports imagery task (playing tennis) compared to a simple motor imagery task (squeezing a ball). To enhance the vividness of both tasks participants performed a short physical exercise between two imagery sessions. EEG was recorded from 60 closely spaced electrodes placed over frontal, central, and parietal areas of 30 healthy volunteers divided in two groups. Whereas Group 1 (EG) performed a physical exercise between the two imagery sessions, Group 2 (CG) watched a landscape movie without physical activity. Spatiotemporal event-related desynchronization (ERD) and event-related synchronization (ERS) patterns during motor imagery (MI) tasks were evaluated. The results of the EG showed significant stronger ERD patterns in the alpha frequency band (8-13 Hz) during MI of tennis after training. Our results are in evidence with previous findings that MI in combination with motor execution has beneficial effects. We conclude that sports MI combined with an interactive game environment could be a future promising task in motor learning and rehabilitation improving motor functions in late therapy processes or support neuroplasticity.},
}
@article {pmid30405340,
year = {2018},
author = {Rodríguez-Ugarte, M and Iáñez, E and Ortiz, M and Azorín, JM},
title = {Improving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {757},
pmid = {30405340},
issn = {1662-4548},
abstract = {The aim of this work was to test if a novel transcranial direct current stimulation (tDCS) montage boosts the accuracy of lower limb motor imagery (MI) detection by using a real-time brain-machine interface (BMI) based on electroencephalographic (EEG) signals. The tDCS montage designed was composed of two anodes and one cathode: one anode over the right cerebrocerebellum, the other over the motor cortex in Cz, and the cathode over FC2 (using the International 10-10 system). The BMI was designed to detect two MI states: relax and gait MI; and was based on finding the power at the frequency which attained the maximum power difference between the two mental states at each selected EEG electrode. Two different single-blind experiments were conducted, E1 and a pilot test E2. E1 was based on visual cues and feedback and E2 was based on auditory cues and a lower limb exoskeleton as feedback. Twelve subjects participated in E1, while four did so in E2. For both experiments, subjects were separated into two equally-sized groups: sham and active tDCS. The active tDCS group achieved 12.6 and 8.2% higher detection accuracy than the sham group in E1 and E2, respectively, reaching 65 and 81.6% mean detection accuracy in each experiment. The limited results suggest that the exoskeleton (E2) enhanced the detection of the MI tasks with respect to the visual feedback (E1), increasing the accuracy obtained in 16.7 and 21.2% for the active tDCS and sham groups, respectively. Thus, the small pilot study E2 indicates that using an exoskeleton in real-time has the potential of improving the rehabilitation process of cerebrovascular accident (CVA) patients, but larger studies are needed in order to further confirm this claim.},
}
@article {pmid30405326,
year = {2018},
author = {Munyon, CN},
title = {Neuroethics of Non-primary Brain Computer Interface: Focus on Potential Military Applications.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {696},
pmid = {30405326},
issn = {1662-4548},
abstract = {The field of neuroethics has had to adapt rapidly in the face of accelerating technological advancement; a particularly striking example is the realm of Brain-Computer Interface (BCI). A significant source of funding for the development of new BCI technologies has been the United States Department of Defense, and while the predominant focus has been restoration of lost function for those wounded in battle, there is also significant interest in augmentation of function to increase survivability, coordination, and lethality of US combat forces. While restoration of primary motor and sensory function (primary BCI) has been the main focus of research, there has been marked progress in interface with areas of the brain subserving memory and association. Non-Primary BCI has a different subset of potential applications, each of which also carries its own ethical considerations. Given the amount of BCI research funding coming from the Department of Defense, it is particularly important that potential military applications be examined from a neuroethical standpoint.},
}
@article {pmid30403970,
year = {2018},
author = {Krumpe, T and Scharinger, C and Rosenstiel, W and Gerjets, P and Spüler, M},
title = {Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning.},
journal = {Biological psychology},
volume = {139},
number = {},
pages = {163-172},
doi = {10.1016/j.biopsycho.2018.09.008},
pmid = {30403970},
issn = {1873-6246},
mesh = {Adult ; Cerebral Cortex/*physiology ; *Electroencephalography ; Executive Function/*physiology ; Female ; Humans ; *Inhibition, Psychological ; Male ; Memory, Short-Term/*physiology ; Psychomotor Performance/*physiology ; *Support Vector Machine ; Young Adult ; },
abstract = {OBJECTIVE: According to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches.<br><br>METHODS: An existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroencephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis.<br><br>RESULTS: The SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power.<br><br>SIGNIFICANCE: In this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.},
}
@article {pmid30402801,
year = {2018},
author = {Ruan, J and Wu, X and Zhou, B and Guo, X and Lv, Z},
title = {An Automatic Channel Selection Approach for ICA-Based Motor Imagery Brain Computer Interface.},
journal = {Journal of medical systems},
volume = {42},
number = {12},
pages = {253},
pmid = {30402801},
issn = {1573-689X},
support = {61271352//National Natural Science Foundation of China/ ; KJ2016A043//University Natural Science Research Project of Anhui Province/ ; ADXXBZ201505//Anhui University Center of Information Support &amp; Assurance Technology Open Foundation/ ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Independent component analysis (ICA) is a potential spatial filtering method for the implementation of motor imagery brain-computer interface (MIBCI). However, ICA-based MIBCI (ICA-MIBCI) is sensitive to electroencephalogram (EEG) channels and the quality of the training data, which are two crucial factors affecting the stability and classification performance of ICA-MIBCI. To address these problems, this paper is mainly focused on the investigation of EEG channel optimization. As a reference, we constructed a single-trial-based ICA-MIBCI system with commonly used channels and common spatial pattern-based MIBCI (CSP-MIBCI). To minimize the impact of artifacts on EEG channel optimization, a data-quality evaluation method, named "self-testing" in this paper, was used in a single-trial-based ICA-MIBCI system to evaluate the quality of single trials in each dataset; the resulting self-testing accuracies were used for the selection of high-quality trials. Given several candidate channel configurations, ICA filters were calculated using selected high-quality trials and applied to the corresponding ICA-MIBCI implementation. Optimal channels for each dataset were assessed and selected according to the self-testing results related to various candidate configurations. Forty-eight MI datasets of six subjects were employed in this study to validate the proposed methods. Experimental results revealed that the average classification accuracy of the optimal channels yielded a relative increment of 2.8% and 8.5% during self-testing, 14.4% and 9.5% during session-to-session transfer, and 36.2% and 26.7% during subject-to-subject transfer compared to CSP-MIBCI and ICA-MIBCI with fixed the channel configuration. This work indicates that the proposed methods can efficiently improve the practical feasibility of ICA-MIBCI.},
}
@article {pmid30400325,
year = {2018},
author = {Jochumsen, M and Cremoux, S and Robinault, L and Lauber, J and Arceo, JC and Navid, MS and Nedergaard, RW and Rashid, U and Haavik, H and Niazi, IK},
title = {Investigation of Optimal Afferent Feedback Modality for Inducing Neural Plasticity with A Self-Paced Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {11},
pages = {},
pmid = {30400325},
issn = {1424-8220},
mesh = {Adult ; *Brain-Computer Interfaces ; Evoked Potentials, Motor ; *Feedback ; Female ; Humans ; Male ; Models, Statistical ; *Neuronal Plasticity ; ROC Curve ; Time Factors ; },
abstract = {Brain-computer interfaces (BCIs) can be used to induce neural plasticity in the human nervous system by pairing motor cortical activity with relevant afferent feedback, which can be used in neurorehabilitation. The aim of this study was to identify the optimal type or combination of afferent feedback modalities to increase cortical excitability in a BCI training intervention. In three experimental sessions, 12 healthy participants imagined a dorsiflexion that was decoded by a BCI which activated relevant afferent feedback: (1) electrical nerve stimulation (ES) (peroneal nerve-innervating tibialis anterior), (2) passive movement (PM) of the ankle joint, or (3) combined electrical stimulation and passive movement (Comb). The cortical excitability was assessed with transcranial magnetic stimulation determining motor evoked potentials (MEPs) in tibialis anterior before, immediately after and 30 min after the BCI training. Linear mixed regression models were used to assess the changes in MEPs. The three interventions led to a significant (p < 0.05) increase in MEP amplitudes immediately and 30 min after the training. The effect sizes of Comb paradigm were larger than ES and PM, although, these differences were not statistically significant (p > 0.05). These results indicate that the timing of movement imagery and afferent feedback is the main determinant of induced cortical plasticity whereas the specific type of feedback has a moderate impact. These findings can be important for the translation of such a BCI protocol to the clinical practice where by combining the BCI with the already available equipment cortical plasticity can be effectively induced. The findings in the current study need to be validated in stroke populations.},
}
@article {pmid30397281,
year = {2018},
author = {Stavisky, SD and Kao, JC and Nuyujukian, P and Pandarinath, C and Blabe, C and Ryu, SI and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {16357},
pmid = {30397281},
issn = {2045-2322},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Arm/physiology ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Male ; Motor Cortex/*physiology/physiopathology ; Movement ; Paralysis/physiopathology ; Time Factors ; },
abstract = {Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.},
}
@article {pmid30391345,
year = {2019},
author = {Valente, G and Kaas, AL and Formisano, E and Goebel, R},
title = {Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs.},
journal = {NeuroImage},
volume = {186},
number = {},
pages = {369-381},
doi = {10.1016/j.neuroimage.2018.10.080},
pmid = {30391345},
issn = {1095-9572},
mesh = {Adult ; Bayes Theorem ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Female ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Psychomotor Performance ; *Research Design ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback. In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design. The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks. Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.},
}
@article {pmid30389490,
year = {2019},
author = {Na, Y and Choi, I and Jang, DP and Kang, JK and Woo, J},
title = {Semantic-hierarchical model improves classification of spoken-word evoked electrocorticography.},
journal = {Journal of neuroscience methods},
volume = {311},
number = {},
pages = {253-258},
doi = {10.1016/j.jneumeth.2018.10.034},
pmid = {30389490},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electrocorticography ; Evoked Potentials ; Humans ; Male ; *Models, Neurological ; Pattern Recognition, Automated/*methods ; *Semantics ; *Signal Processing, Computer-Assisted ; Speech ; Speech Perception/*physiology ; Young Adult ; },
abstract = {Classification of spoken word-evoked potentials is useful for both neuroscientific and clinical applications including brain-computer interfaces (BCIs). By evaluating whether adopting a biology-based structure improves a classifier's accuracy, we can investigate the importance of such structure in human brain circuitry, and advance BCI performance. In this study, we propose a semantic-hierarchical structure for classifying spoken word-evoked cortical responses. The proposed structure decodes the semantic grouping of the words first (e.g., a body part vs. a number) and then decodes which exact word was heard. The proposed classifier structure exhibited a consistent ∼10% improvement of classification accuracy when compared with a non-hierarchical structure. Our result provides a tool for investigating the neural representation of semantic hierarchy and the acoustic properties of spoken words in human brains. Our results suggest an improved algorithm for BCIs operated by decoding heard, and possibly imagined, words.},
}
@article {pmid30388995,
year = {2018},
author = {Arango-Sabogal, JC and Fecteau, G and Paré, J and Roy, JP and Labrecque, O and Côté, G and Wellemans, V and Schiller, I and Dendukuri, N and Buczinski, S},
title = {Estimating diagnostic accuracy of fecal culture in liquid media for the detection of Mycobacterium avium subsp. paratuberculosis infections in Québec dairy cows: A latent class model.},
journal = {Preventive veterinary medicine},
volume = {160},
number = {},
pages = {26-34},
doi = {10.1016/j.prevetmed.2018.09.025},
pmid = {30388995},
issn = {1873-1716},
mesh = {Animals ; Bacteriological Techniques/veterinary ; Cattle ; Cattle Diseases/*diagnosis/epidemiology/microbiology ; Enzyme-Linked Immunosorbent Assay/veterinary ; Feces/*microbiology ; Female ; *Mycobacterium avium subsp. paratuberculosis ; Paratuberculosis/*diagnosis/epidemiology ; Quebec/epidemiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {A latent class model fit within a Bayesian framework was used to estimate the sensitivity and specificity of individual fecal culture (IFC) in liquid medium (Para TB culture liquid medium and BACTEC MGIT 960 system) for the detection of Mycobacterium avium subsp. paratuberculosis (MAP) infections in Québec dairy cows. As a secondary objective, the within-herd paratuberculosis prevalence was estimated. A dataset including 21 commercial Québec dairy herds participating in previous research projects was retrospectively analyzed. In total, 1386 adult cows on which both IFC and serum-ELISA were available were included. The selected latent class model assumed conditional dependence between the tests. Non-informative priors for IFC accuracy and paratuberculosis prevalence were used while informative priors, obtained from the literature, were used for serum-ELISA accuracy. The WinBUGS statistical freeware was used to obtain posterior estimates (medians and 95% Bayesian credibility intervals (95% BCI)) for each parameter. The sensitivity and specificity estimates for IFC were 34.4% (95% BCI: 20.3-66.1) and 99.5% (95% BCI: 98.6-100), respectively. Sensitivity and specificity for serum-ELISA were 27.3% (95% BCI: 18.1-38.3) and 97.4% (95% BCI: 96.6-98.0). Median paratuberculosis within herd prevalence was estimated to be 0.3% (0-3.3). In conclusion, a higher sensitivity of IFC compared to serum-ELISA was observed both in the unconditional and conditional dependent models. Since the sensitivity of both IFC and serum-ELISA was relatively low, conditional dependence between the tests is more likely in the true disease positive animals. We hypothesize that conditional dependence arises because an unmeasured covariate influences the performance of both tests among disease positive animals causing both tests to incorrectly misclassify the animal as negative. One limitation of this study was the very low within herd prevalence of the participant herds.},
}
@article {pmid30388836,
year = {2018},
author = {Rashid, U and Niazi, IK and Signal, N and Taylor, D},
title = {An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {11},
pages = {},
pmid = {30388836},
issn = {1424-8220},
support = {None//Callaghan Innovation/ ; None//Medical Technologies Centre of Research Excellence (MedTech CoRE)/ ; },
abstract = {Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.},
}
@article {pmid30388182,
year = {2018},
author = {Hussain, A and Zaheer, S and Shafique, K},
title = {School-based behavioral intervention to reduce the habit of smokeless tobacco and betel quid use in high-risk youth in Karachi: A randomized controlled trial.},
journal = {PloS one},
volume = {13},
number = {11},
pages = {e0206919},
pmid = {30388182},
issn = {1932-6203},
mesh = {Adolescent ; Asia ; *Behavior ; Behavior Control/*psychology ; Female ; Habits ; Health Knowledge, Attitudes, Practice ; Humans ; Male ; Nicotine Chewing Gum/*adverse effects ; Oral Health ; Schools ; Tobacco, Smokeless/*adverse effects ; },
abstract = {There have been recent surges in the use of smokeless tobacco (SLT) and betel quid (BQ) chew among adolescents in South East Asian countries, with an increase, on average, of 7% to 15% between 2004 and 2013, necessitating interventional investigations to modify this behavior. The current intervention was aimed towards changing adolescents' perceptions regarding the harmful effects of SLT and BQ use and encouraging them to quit. This randomized control trial involved 2140 adolescents from 26 private and public-sector schools in Karachi, Pakistan. After randomization, 1185 individuals were placed in the intervention group and administered a behavior changing intervention (BCI), while 955 individuals constituted the control group. A generalized estimating equation was employed to measure differences in repeated measures for both groups. The beta coefficients were reported after adjusting the covariates with the 95% confidence interval, and the p-value was considered significant at <0.050. Cohen's d was employed to report the effect size of the intervention. The BCI resulted in a 0.176-unit (95% CI 0.078-0.274, p-value <0.001) increase in knowledge scores regarding the health hazards of SLT and BQ, a 0.141-unit (95% CI 0.090-0.192, p-value <0.001) increase in use perception scores, and a 0.067-unit (95% CI 0.006-0.129, p-value 0.031) increase in quit perception scores in the intervention group compared with those in the control group. A knowledge related module (p-value 0.024) and quit preparation module (p-value 0.005) were found to be helpful by adolescents in either changing their perceptions regarding SLT and/or BQ chew use or in quitting. The role of BCI is promising in improving adolescents' knowledge and changing their perceptions in a positive manner regarding their harmful SLT and BQ use. Convincing results may be achieved if interventions are tailored, with an emphasis on the identification of the products that are used by adolescents in addition to highlighting their ill effects and how students may manage to quit them. If included in the schools' curricula, this BCI method may help in developing schools that are free of SLT and BQ use. Trial registration: ClinicalTrials.gov NCT03488095.},
}
@article {pmid30386355,
year = {2018},
author = {Gao, J and Bi, W and Li, H and Wu, J and Yu, X and Liu, D and Wang, X},
title = {WRKY Transcription Factors Associated With NPR1-Mediated Acquired Resistance in Barley Are Potential Resources to Improve Wheat Resistance to Puccinia triticina.},
journal = {Frontiers in plant science},
volume = {9},
number = {},
pages = {1486},
pmid = {30386355},
issn = {1664-462X},
abstract = {Systemic acquired resistance (SAR) in Arabidopsis is established beyond the initial pathogenic infection or is directly induced by treatment with salicylic acid or its functional analogs (SA/INA/BTH). NPR1 protein and WRKY transcription factors are considered the master regulators of SAR. Our previous study showed that NPR1 homologs in wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) regulated the expression of genes encoding pathogenesis-related (PR) proteins during acquired resistance (AR) triggered by Pseudomonas syringae pv. tomato DC3000. In the present examination, AR induced by P. syringae DC3000 was also found to effectively improve wheat resistance to Puccinia triticina (Pt). However, with more complex genomes, genes associated with this SAR-like response in wheat and barley are largely unknown and no specific WRKYs has been reported to be involved in this biological process. In our subsequent analysis, barley transgenic line overexpressing wheat wNPR1 (wNPR1-OE) showed enhanced resistance to Magnaporthe oryzae isolate Guy11, whereas AR to Guy11 was suppressed in a barley transgenic line with knocked-down barley HvNPR1 (HvNPR1-Kd). We performed RNA-seq to reveal the genes that were differentially expressed among these transgenic lines and the wild-type barley plants during the AR. Several PR and BTH-induced (BCI) genes were designated as downstream genes of NPR1. The expression of few WRKYs was significantly associated with NPR1 expression during the AR events. The transient expression of three WRKY genes, including HvWRKY6, HvWRKY40, and HvWRKY70, in wheat leaves by Agrobacterium-mediated infiltration enhanced the resistance to Pt. In conclusion, a profile of genes associated with NPR1-mediated AR in barley was drafted and WRKYs discovered in the current study showed a substantial potential for improving wheat resistance to Pt.},
}
@article {pmid30384845,
year = {2018},
author = {Takahashi, K and Kato, K and Mizuguchi, N and Ushiba, J},
title = {Precise estimation of human corticospinal excitability associated with the levels of motor imagery-related EEG desynchronization extracted by a locked-in amplifier algorithm.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {15},
number = {1},
pages = {93},
pmid = {30384845},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; Pyramidal Tracts/physiology ; *Signal Processing, Computer-Assisted ; Transcranial Magnetic Stimulation ; },
abstract = {BACKGROUND: Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke. In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Recently, we demonstrated a novel online lock-in amplifier (LIA) algorithm to estimate the amplitude modulation of motor-related SM1 ERD. With this algorithm, the delay time, accuracy, and stability to estimate motor-related SM1 ERD were significantly improved compared with the conventional fast Fourier transformation (FFT) algorithm. These technical improvements to extract an ERD trace imply a potential advantage for a better trace of the excitatory status of the SM1 in a BCI context. Therefore, the aim of this study was to assess the precision of LIA-based ERD tracking for estimation of corticospinal excitability using a transcranial magnetic stimulation (TMS) paradigm.<br><br>METHODS: The motor evoked potentials (MEPs) induced by single-pulse TMS over the primary motor cortex depending on the magnitudes of SM1 ERD (i.e., 35% and 70%) extracted by the online LIA or FFT algorithm were monitored during a motor imagery task of wrist extension in 17 healthy participants. Then, the peak-to-peak amplitudes of MEPs and their variabilities were assessed to investigate the precision of the algorithms.<br><br>RESULTS: We found greater MEP amplitude evoked by single-pulse TMS triggered by motor imagery-related alpha SM1 ERD than at rest. This enhancement was associated with the magnitude of ERD in both FFT and LIA algorithms. Moreover, we found that the variabilities of peak-to-peak MEP amplitudes at 35% and 70% ERDs calculated by the novel online LIA algorithm were smaller than those extracted using the conventional FFT algorithm.<br><br>CONCLUSIONS: The present study demonstrated that the calculation of motor imagery-related SM1 ERDs using the novel online LIA algorithm led to a more precise estimation of corticospinal excitability than when the ordinary FFT-based algorithm was used.},
}
@article {pmid30383695,
year = {2018},
author = {Liu, D and Chen, M and Han, X and Li, Y},
title = {Comparative study of the maximum Watts factor and Schafer contractility grade, bladder contractility index in male patients with lower urinary tract symptoms.},
journal = {Medicine},
volume = {97},
number = {44},
pages = {e13101},
pmid = {30383695},
issn = {1536-5964},
mesh = {Adult ; Aged ; Humans ; Lower Urinary Tract Symptoms/*physiopathology ; Male ; Middle Aged ; Muscle Contraction ; Retrospective Studies ; Sensitivity and Specificity ; Urinary Bladder/*physiopathology ; Urodynamics ; },
abstract = {To investigate whether the maximum Watts factor (WF) is 1 parameter of describing detrusor contraction in male patients with lower urinary tract symptoms (LUTS).We retrospectively reviewed urodynamic data of male subjects with LUTS. Data on age, maximum flow rate (Qmax), post-void residual (PVR), detrusor pressure at maximum flow rate (PdetQmax), maximum Watts factor (WFmax), and Schafer contractility grades were collected. First, all patients were divided into 6 groups according to Schafer contractility grade. The urodynamic parameters include WFmax and bladder contractility index (BCI) were compared and analyzed among the 6 groups by using Kruskal-Wallis test statistically. The box plot of Schafer contractility grade with WFmax or BCI were plotted and analyzed. Second, the correlation scatter diagram between WFmax and BCI was plotted and analyzed. Spearman's correlation test was performed. Third, we drew the Receiver Operating Characteristic (ROC) curve and confirmed the area under the curve, the Optimal Operating Point (OOP) and corresponding sensitivity and specificity for WFmax by the reference standard of Schafer contractility grade and BCI respectively.A total of 455 men were included. The mean age of patients was 57 ± 17.9 years, ranging from 18 to 87 years. Median of WFmax increased from 5.8 W/m in very week (VW) group to 19.5 W/m in strong (ST) group, while BCI rose from 70 to 170. The box plot of Schafer contractility grade with WFmax or BCI showed that both WFmax and BCI were positively correlated with Schafer contractility grade. Kruskal-Wallis test among the 6 groups showed statistically significant difference (P <.001). The correlation scatter diagram showed that WFmax increased significantly with BCI (), the linear regression equation being Y = 3.33 + 0.07X, R2 = 0.298. Spearman's correlation test revealed that WFmax and BCI were positively correlated, with the correlation coefficient being 0.616 (P <.001). The WFmax area under ROC curve by Schafer contractility grade was 0.894 and WFmax OOP was interpreted at 11.1 W/m. In addition, the area under ROC curve by BCI was 0.802 and WFmax OOP was interpreted at 9.8 W/m.Our findings suggestted that WFmax was a good parameter of evaluating detrusor contraction as well as Schafer contractility grade and BCI, which should be widely used in clinical.},
}
@article {pmid30382882,
year = {2018},
author = {Zangeneh Soroush, M and Maghooli, K and Setarehdan, SK and Nasrabadi, AM},
title = {A novel approach to emotion recognition using local subset feature selection and modified Dempster-Shafer theory.},
journal = {Behavioral and brain functions : BBF},
volume = {14},
number = {1},
pages = {17},
pmid = {30382882},
issn = {1744-9081},
mesh = {Adult ; Brain/*physiology ; Databases, Factual ; Electroencephalography/*methods ; Emotions/*physiology ; Female ; Humans ; *Machine Learning ; Male ; Music/psychology ; Recognition, Psychology/*physiology ; Video Recording/methods ; Young Adult ; },
abstract = {BACKGROUND: Emotion recognition is an increasingly important field of research in brain computer interactions.<br><br>INTRODUCTION: With the advance of technology, automatic emotion recognition systems no longer seem far-fetched. Be that as it may, detecting neural correlates of emotion has remained a substantial bottleneck. Settling this issue will be a breakthrough of significance in the literature.<br><br>METHODS: The current study aims to identify the correlations between different emotions and brain regions with the help of suitable electrodes. Initially, independent component analysis algorithm is employed to remove artifacts and extract the independent components. The informative channels are then selected based on the thresholded average activity value for obtained components. Afterwards, effective features are extracted from selected channels common between all emotion classes. Features are reduced using the local subset feature selection method and then fed to a new classification model using modified Dempster-Shafer theory of evidence.<br><br>RESULTS: The presented method is employed to DEAP dataset and the results are compared to those of previous studies, which highlights the significant ability of this method to recognize emotions through electroencephalography, by the accuracy of about 91%. Finally, the obtained results are discussed and new aspects are introduced.<br><br>CONCLUSIONS: The present study addresses the long-standing challenge of finding neural correlates between human emotions and the activated brain regions. Also, we managed to solve uncertainty problem in emotion classification which is one of the most challenging issues in this field. The proposed method could be employed in other practical applications in future.},
}
@article {pmid30381431,
year = {2018},
author = {Pandarinath, C and Ames, KC and Russo, AA and Farshchian, A and Miller, LE and Dyer, EL and Kao, JC},
title = {Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {44},
pages = {9390-9401},
pmid = {30381431},
issn = {1529-2401},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces/*trends ; Humans ; Motor Cortex/cytology/*physiology ; Movement/*physiology ; Neurons/*physiology ; Time Factors ; },
abstract = {In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.},
}
@article {pmid30380372,
year = {2020},
author = {Pitt, K and Brumberg, J},
title = {A screening protocol incorporating brain-computer interface feature matching considerations for augmentative and alternative communication.},
journal = {Assistive technology : the official journal of RESNA},
volume = {32},
number = {3},
pages = {161-172},
doi = {10.1080/10400435.2018.1512175},
pmid = {30380372},
issn = {1949-3614},
mesh = {Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/*diagnosis ; *Brain-Computer Interfaces ; Communication ; Female ; Humans ; Male ; Middle Aged ; },
abstract = {Purpose: The use of standardized screening protocols may inform brain-computer interface (BCI) research procedures to help maximize BCI performance outcomes and provide foundational information for clinical translation. Therefore, in this study we developed and evaluated a new BCI screening protocol incorporating cognitive, sensory, motor and motor imagery tasks. Methods: Following development, BCI screener outcomes were compared to the Amyotrophic Lateral Sclerosis Cognitive Behavioral Screen (ALS-CBS), and ALS Functional Rating Scale (ALS-FRS) for twelve individuals with a neuromotor disorder. Results: Scores on the cognitive portion of the BCI screener demonstrated limited variability, indicating all participants possessed core BCI-related skills. When compared to the ALS-CBS, the BCI screener was able to modestly discriminate possible cognitive difficulties that are likely to influence BCI performance. In addition, correlations between the motor imagery section of the screener and ALS-CBS and ALS-FRS were non-significant, suggesting the BCI screener may provide information not captured on other assessment tools. Additional differences were found between motor imagery tasks, with greater self-ratings on first-person explicit imagery of familiar tasks compared to unfamiliar/generic BCI tasks. Conclusion: The BCI screener captures factors likely relevant for BCI, which has value for guiding person-centered BCI assessment across different devices to help inform BCI trials.},
}
@article {pmid30379871,
year = {2018},
author = {Shafiei, SB and Hussein, AA and Guru, KA},
title = {Dynamic changes of brain functional states during surgical skill acquisition.},
journal = {PloS one},
volume = {13},
number = {10},
pages = {e0204836},
pmid = {30379871},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Clinical Competence/*standards ; Education, Medical/*methods ; Female ; Humans ; Learning Curve ; Male ; Robotic Surgical Procedures/*education ; Students, Medical ; },
abstract = {There is lack of a standardized measure of technical proficiency and skill acquisition for robot-assisted surgery (RAS). Learning surgical skills, in addition to the interaction with the machine and the new surgical environment adds to the complexity of the learning process. Moreover, evaluation of surgeon performance in operating room is required to optimize patient safety. In this study, we investigated the dynamic changes of RAS trainee's brain functional states by practice. We also developed brain functional state measurements to find the relationship between RAS skill acquisition (especially human-machine interaction skills) and reconfiguration of brain functional states. This relationship may help in providing trainees with helpful, structured feedback regarding skills requiring improvement and will help in tailoring training activities.},
}
@article {pmid30379603,
year = {2019},
author = {Naufel, S and Glaser, JI and Kording, KP and Perreault, EJ and Miller, LE},
title = {A muscle-activity-dependent gain between motor cortex and EMG.},
journal = {Journal of neurophysiology},
volume = {121},
number = {1},
pages = {61-73},
pmid = {30379603},
issn = {1522-1598},
support = {F31 EY025532/EY/NEI NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; *Electromyography ; Isometric Contraction/*physiology ; Linear Models ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Neurons/physiology ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; Torque ; Wrist/physiology ; },
abstract = {Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions. However, a model with a gain parameter that increased with the target force remained accurate across forces and dynamical loads. Surprisingly, this model showed that a greater proportion of EMG changes were explained by the nonlinear gain than the linear mapping from M1. In addition to its theoretical implications, the strength of this nonlinearity has important implications for brain-computer interfaces (BCIs). If BCI decoders are to be used to control movement dynamics (including interaction forces) directly, they will need to be nonlinear and include training data from broad data sets to function effectively across tasks. Our study reinforces the need to investigate neural control of movement across a wide range of conditions to understand its basic characteristics as well as translational implications. NEW &amp; NOTEWORTHY We explored the motor cortex-to-electromyogram (EMG) mapping across a wide range of forces and loading conditions, which we found to be highly nonlinear. A greater proportion of EMG was explained by a nonlinear gain than a linear mapping. This nonlinearity allows motor cortex to control the wide range of forces encountered in the real world. These results unify earlier observations and inform the next-generation brain-computer interfaces that will control movement dynamics and interaction forces.},
}
@article {pmid30376160,
year = {2018},
author = {Zemunik, G and Davies, SJ and Turner, BL},
title = {Soil drivers of local-scale tree growth in a lowland tropical forest.},
journal = {Ecology},
volume = {99},
number = {12},
pages = {2844-2852},
doi = {10.1002/ecy.2532},
pmid = {30376160},
issn = {0012-9658},
mesh = {Colorado ; Forests ; Islands ; Panama ; *Soil ; *Trees ; Tropical Climate ; },
abstract = {Soil nutrients influence the distribution of tree species in lowland tropical forests, but their effect on productivity, especially at local scales, remains unclear. We used tree census, canopy occupancy, and soil data from the Barro Colorado Island (BCI; Panama) 50-ha forest dynamics plot to investigate the influence of soil nutrients and potential toxins on aboveground tree productivity. Growth was calculated as the increase in diameter of 150,000 individual stems ≥1 cm diameter at breast height, representing 207 species. The effects of soil variables and other strong predictors of growth (e.g., light) were estimated using hierarchical, linear, mixed-effects models. Growth was weakly positively associated with phosphorus (P), particularly for understory tree species that are typically considered to be limited by light. In contrast, growth was strongly negatively related to manganese (Mn) and aluminum (Al), although the latter effect was confounded by strong correlations between Al and other soil variables. The negative response to increasing Mn (and Al) suggests a toxicity effect due to solubilization and uptake of amorphous pools of metal oxides in the soil. These results show that P limits tropical tree growth at local scale on BCI, but that toxic metals represent an even greater constraint on productivity.},
}
@article {pmid30374295,
year = {2018},
author = {Zhang, X and Xu, G and Zhang, X and Wu, Q},
title = {A Light Spot Humanoid Motion Paradigm Modulated by the Change of Brightness to Recognize the Stride Motion Frequency.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {377},
pmid = {30374295},
issn = {1662-5161},
abstract = {The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) usually has the advantages of high information transfer rate (ITR) and no need for training. However, low frequencies, such as the human stride motion frequency, cannot easily induce SSVEP. To solve this problem, a light spot humanoid motion paradigm modulated by the change of brightness was designed in this study. The characteristics of the brain response to the motion paradigm modulated by the change of brightness were analyzed for the first time. The results showed that the designed paradigm could induce not only the high flicker frequency but also the modulation frequencies between the change of brightness and the motion in the primary visual cortex. Thus, the stride motion frequency can be recognized through the modulation frequencies by using the designed paradigm. Also, in an online experiment, this paradigm was employed to control a lower limb robot to achieve same frequency stimulation, which meant that the visual stimulation frequency was the same as the motion frequency of the robot. Also, canonical correlation analysis (CCA) was used to distinguish three different stride motion frequencies. The average accuracies of the classification in three walking speeds using the designed paradigm with the same and different high frequencies reached 87 and 95% respectively. Furthermore, the angles of the knee joint of the robot were obtained to demonstrate the feasibility of the electroencephalograph (EEG)-driven robot with same stimulation.},
}
@article {pmid30373716,
year = {2018},
author = {Ciliberti, D and Michon, F and Kloosterman, F},
title = {Real-time classification of experience-related ensemble spiking patterns for closed-loop applications.},
journal = {eLife},
volume = {7},
number = {},
pages = {},
pmid = {30373716},
issn = {2050-084X},
support = {G0D7516N//Fonds Wetenschappelijk Onderzoek/International ; },
mesh = {*Action Potentials ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Hippocampus/*physiology ; Nerve Net/*physiology ; Neurons/physiology ; Rats ; },
abstract = {Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 -200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain-computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.},
}
@article {pmid30373619,
year = {2018},
author = {Georgiadis, K and Laskaris, N and Nikolopoulos, S and Kompatsiaris, I},
title = {Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {15},
number = {1},
pages = {90},
pmid = {30373619},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Neuromuscular Diseases/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {BACKGROUND: Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject's intention from the streaming data.<br><br>METHODS: Electroencephalographic activity from six patients suffering from neuromuscular disease (NMD) and six healthy individuals was recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition. The metric of Phase locking value (PLV) was used to describe the functional coupling between all recording sites. The functional connectivity patterns and the associate network organization was first compared between the two cohorts. Next, working at the level of individual patients, we trained support vector machines (SVMs) to discriminate between "left" and "right" based on different instantiations of connectivity patterns (depending on the encountered brain rhythm and the temporal interval). Finally, we designed and realized a novel brain decoding scheme that could interpret the intention from streaming connectivity patterns, based on an ensemble of SVMs.<br><br>RESULTS: The group-level analysis revealed increased phase synchrony and richer network organization in patients. This trend was also seen in the performance of the employed classifiers. Time-resolved connectivity led to superior performance, with distinct SVMs acting as local experts, specialized in the patterning emerged within specific temporal windows (defined with respect to the external trigger). This empirical finding was further exploited in implementing a decoding scheme that can be activated without the need of the precise timing of a trigger.<br><br>CONCLUSION: The increased phase synchrony in NMD patients can turn to a valuable tool for MI decoding. Considering the fast implementation for the PLV pattern computation in multichannel signals, we can envision the development of efficient personalized BCI systems in assistance of these patients.},
}
@article {pmid30371380,
year = {2018},
author = {Lim, H and Ku, J},
title = {A Brain-Computer Interface-Based Action Observation Game That Enhances Mu Suppression.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {12},
pages = {2290-2296},
doi = {10.1109/TNSRE.2018.2878249},
pmid = {30371380},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods/statistics &amp; numerical data ; Electroencephalography Phase Synchronization ; Feedback, Sensory ; Female ; *Games, Experimental ; Healthy Volunteers ; Humans ; Male ; Mirror Neurons/*physiology ; Motor Cortex/physiology ; Observation ; Photic Stimulation ; Stroke Rehabilitation/methods ; Young Adult ; },
abstract = {Action observation training based on the theory of activation of the mirror-neuron system has been used for the rehabilitation of patients with stroke. In this paper, we sought to assess whether a brain-computer interface (BCI)-based action observation rehabilitation game, using a flickering action video, could preferentially activate the mirror-neuron system. Feedback of stimulus observation, evoked by the flickering action video, was provided using steady state visually evoked potential and event-related desynchronization. Fifteen healthy subjects have experienced the game with BCI interaction (game and interaction), without BCI interaction (game without interaction), observed non-flickering stimuli, and flickering stimuli without the game background (stimuli only) in a counter-balanced order. The game and interface condition was resulted in significantly stronger activation of the mirror-neuron system than did the other three conditions. In addition, the amount of mirror-neuron system activation is gradually decreased in the game without interface, non-flickering stimuli, and stimuli only conditions in a time-dependent manner; however, in the game and interface condition, the amount of mirror-neuron system activation was maintained until the end of the training. Taken together, these data suggest that the proposed game paradigm, which integrates the action observation paradigm with BCI technology, could provide interactive responses for whether watching video clips can engage patients and enhance rehabilitation.},
}
@article {pmid30371377,
year = {2018},
author = {Shahtalebi, S and Mohammadi, A},
title = {Bayesian Optimized Spectral Filters Coupled With Ternary ECOC for Single-Trial EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {12},
pages = {2249-2259},
doi = {10.1109/TNSRE.2018.2877987},
pmid = {30371377},
issn = {1558-0210},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/*classification/statistics &amp; numerical data ; Humans ; Imagination/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Motivated by the promising emergence of brain-computer interfaces (BCIs) within assistive/rehabilitative systems for therapeutic applications, this paper proposes a novel Bayesian framework that simultaneously optimizes a number of subject-specific filter banks and spatial filters. Optimized double-band spectro-spatial filters are derived based on common spatial patterns coupled with the error-correcting output coding (ECOC) classifiers. The proposed framework constructs optimized subject-specific spectral filters in an intuitive fashion resulting in creation of significantly discriminant features, which is a crucial requirement for any EEG-based BCI system. Through incorporation of the ECOC approach, the classification problem is then modeled as communication over a noisy channel where the misclassification error is corrected by error correction techniques borrowed from an information theory. This paper also proposes a modified version of the ECOC adopted to EEG classification problems by deploying ternary class codewords to increase the Hamming distance between the codewords and introduce more robustness to misclassification error. The proposed framework is evaluated over two different datasets from the BCI Competition (i.e., BCIC- and BCIC-). The results indicate that the proposed approach outperforms its counterparts and validate the essential role of optimized spectral filters on the overall classification accuracy.},
}
@article {pmid30371375,
year = {2018},
author = {Vuckovic, A and Pangaro, S and Finda, P},
title = {Unimanual Versus Bimanual Motor Imagery Classifiers for Assistive and Rehabilitative Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {12},
pages = {2407-2415},
doi = {10.1109/TNSRE.2018.2877620},
pmid = {30371375},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/classification ; Electroencephalography Phase Synchronization ; Evoked Potentials ; Female ; Functional Laterality/*physiology ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Rehabilitation/*instrumentation ; *Self-Help Devices ; Support Vector Machine ; Young Adult ; },
abstract = {Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet, electroencephalography (EEG)-based assistive and rehabilitative brain-computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this paper, we present a classifier which discriminates between uni-and bi-manual MI. Ten able-bodied participants took part in cue-based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32-channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R, and B-L hands were created, with features based on wide band common spatial patterns (CSP) 8-30 Hz, and band specifics common spatial patterns (CSPb). Event-related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for the L-R task (73% ± 9% and 75% ± 10%, respectively) and for the L-B task (73% ± 11% and 70% ± 9%, respectively). However, for the R-B task (67% ± 3% and 72% ± 6%, respectively), it was significantly higher for CSPb (). Six participants whose L-R classification accuracy exceeded 70% were included in an online task a week later, using the unmodified offline CSPb classifier, achieving 69% ± 3% and 66% ± 3% accuracy for L-R and R-B tasks, respectively. Combined uni- and bi-manual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits.},
}
@article {pmid30370722,
year = {2018},
author = {Zhou, H and Xu, J and Shi, C and Zuo, G},
title = {[Research progress about brain-computer interface technology based on cognitive brain areas and its applications in rehabilitation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {5},
pages = {799-804},
pmid = {30370722},
issn = {1001-5515},
abstract = {Brain-computer interface (BCI) technology enable humans to interact with external devices by decoding their brain signals. Despite it has made some significant breakthroughs in recent years, there are still many obstacles in its applications and extensions. The current used BCI control signals are generally derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of limb movement intention. Therefore, additional sources of brain signals for controlling BCI systems need to be explored. Brain signals derived from the cognitive brain areas are more intuitive and effective. These signals can be used for expand the brain signal sources as a new approach. This paper reviewed the research status of cognitive BCI based on the single brain area and multiple hybrid brain areas, and summarized its applications in the rehabilitation medicine. It's believed that cognitive BCI technologies would become a possible breakthrough for future BCI rehabilitation applications.},
}
@article {pmid30370720,
year = {2018},
author = {Liu, X and Ping, Y and Wang, D and Yao, R and Wan, H},
title = {[Comparison of decoding performance between spike and local field potential signals during goal-directed decision-making task of pigeons].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {5},
pages = {786-793},
pmid = {30370720},
issn = {1001-5515},
abstract = {Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO- kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO- kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.},
}
@article {pmid30370718,
year = {2018},
author = {Fu, R and Hou, P and Li, M},
title = {[Single trial classification of motor imagery electroencephalogram based on Fisher criterion].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {5},
pages = {774-778},
pmid = {30370718},
issn = {1001-5515},
abstract = {In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.},
}
@article {pmid30370454,
year = {2018},
author = {Lange, J and Massart, C and Mouraux, A and Standaert, FX},
title = {Side-channel attacks against the human brain: the PIN code case study (extended version).},
journal = {Brain informatics},
volume = {5},
number = {2},
pages = {12},
pmid = {30370454},
issn = {2198-4018},
abstract = {We revisit the side-channel attacks with brain-computer interfaces (BCIs) first put forward by Martinovic et al. at the USENIX 2012 Security Symposium. For this purpose, we propose a comprehensive investigation of concrete adversaries trying to extract a PIN code from electroencephalogram signals. Overall, our results confirm the possibility of partial PIN recovery with high probability of success in a more quantified manner and at the same time put forward the challenges of full/systematic PIN recovery. They also highlight that the attack complexities can significantly vary in function of the adversarial capabilities (e.g., supervised/profiled vs. unsupervised/non-profiled), hence leading to an interesting trade-off between their efficiency and practical relevance. We then show that similar attack techniques can be used to threat the privacy of BCI users. We finally use our experiments to discuss the impact of such attacks for the security and privacy of BCI applications at large, and the important emerging societal challenges they raise.},
}
@article {pmid30369960,
year = {2018},
author = {Huang, Z and Li, M and Ma, Y},
title = {Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI.},
journal = {Computational and mathematical methods in medicine},
volume = {2018},
number = {},
pages = {4089021},
pmid = {30369960},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Computer Systems ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Fourier Analysis ; Humans ; Internet ; Language ; Male ; Middle Aged ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; Young Adult ; },
abstract = {This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.},
}
@article {pmid30369458,
year = {2019},
author = {Oikonomou, VP and Nikolopoulos, S and Kompatsiaris, I},
title = {A Bayesian Multiple Kernel Learning Algorithm for SSVEP BCI Detection.},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {5},
pages = {1990-2001},
doi = {10.1109/JBHI.2018.2878048},
pmid = {30369458},
issn = {2168-2208},
mesh = {*Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper deals with the classification of steady-state visual evoked potentials (SSVEP), which is a multiclass classification problem addressed in SSVEP-based brain-computer interfaces. In particular, our method named MultiLRM_MKL uses multiple linear regression models under a Sparse Bayesian Learning (SBL) framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces. We verify the effectiveness of our method in handling different kernel spaces by evaluating its performance with a new kernel based on canonical correlation analysis. In particular, we prove the benefit of combining multiple kernels by outperforming several state-of-the-art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area (Oz, O1, and O2).},
}
@article {pmid30368637,
year = {2019},
author = {Hosni, SM and Shedeed, HA and Mabrouk, MS and Tolba, MF},
title = {EEG-EOG based Virtual Keyboard: Toward Hybrid Brain Computer Interface.},
journal = {Neuroinformatics},
volume = {17},
number = {3},
pages = {323-341},
pmid = {30368637},
issn = {1559-0089},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography/*methods ; Eye Movements ; Humans ; *User-Computer Interface ; },
abstract = {The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI). Working towards this purpose, existing research in EOG based Human Computer Interaction (HCI) applications, must be organized and surveyed in order to develop a vision on the potential benefits of combining both input modalities and give rise to new designs that maximize these benefits. Our aim is to support and inspire the design of new hBCI systems based on both EEG and EOG signals, in doing so; first the current EOG based HCI systems were surveyed with a particular focus on EOG based systems for communication using virtual keyboard. Then, a survey of the current EEG-EOG virtual keyboard was performed highlighting the design protocols employed. We concluded with a discussion of the potential advantages of combining both systems with recommendations to give deep insight for future design issues for all EEG-EOG hBCI systems. Finally, a general architecture was proposed for a new EEG-EOG hBCI system. The proposed hybrid system completely alters the traditional view of the eye movement features present in EEG signal as artifacts that should be removed; instead EOG traces are extracted from EEG in our proposed hybrid architecture and are considered as an additional input modality sharing control according to the chosen design protocol.},
}
@article {pmid30358339,
year = {2018},
author = {Liu, G and Zhang, Z and Chai, X and Lu, Y and Fan, Y and Niu, H},
title = {[Study of Steady State Motion Visual Evoked Potential-based Visual Stimulation of BCI System].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {42},
number = {5},
pages = {313-316},
doi = {10.3969/j.issn.1671-7104.2018.05.001},
pmid = {30358339},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation ; *Evoked Potentials, Visual ; Motion ; *Photic Stimulation ; },
abstract = {OBJECTIVE: To propose a square's ring motion stimulation based on steady-state motion visual evoked potential, and compare it with the commonly used visual stimulation modes (Newton's ring motion, square flicker and circular flicker).<br><br>METHODS: EEG signals were collected while 9 experimental subjects gazing at four stimulation and pattern analyzed by Canonical Correlation Analysis (CCA). Stimulation were evaluated by recognition accuracy and subjective scores.<br><br>RESULTS: The classification accuracies of SSVEP elicited by the square's ring motion(82.8%&#xb1;14.1%) and Newton's ring(83.3%&#xb1;11.5%) have no significant difference between them, which are lower than that of the square flicker(98.3%&#xb1;4.1%) and the circular flicker(99.2%&#xb1;1.8%). The shape of the figure has no significant influence on the classification accuracy either in motion mode or flicker mode. The comfort of the square's ring motion is higher than the other three stimulation according to subjective scores.<br><br>CONCLUSIONS: The square's ring motion can elicit EEG and reduce the discomfort caused by flicker modes. The square's ring motion can be used as a visual stimulation in SSMVEP-based BCI system.},
}
@article {pmid30356771,
year = {2018},
author = {Liu, Y and Ayaz, H},
title = {Speech Recognition via fNIRS Based Brain Signals.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {695},
pmid = {30356771},
issn = {1662-4548},
abstract = {In this paper, we present the first evidence that perceived speech can be identified from the listeners' brain signals measured via functional-near infrared spectroscopy (fNIRS)-a non-invasive, portable, and wearable neuroimaging technique suitable for ecologically valid settings. In this study, participants listened audio clips containing English stories while prefrontal and parietal cortices were monitored with fNIRS. Machine learning was applied to train predictive models using fNIRS data from a subject pool to predict which part of a story was listened by a new subject not in the pool based on the brain's hemodynamic response as measured by fNIRS. fNIRS signals can vary considerably from subject to subject due to the different head size, head shape, and spatial locations of brain functional regions. To overcome this difficulty, a generalized canonical correlation analysis (GCCA) was adopted to extract latent variables that are shared among the listeners before applying principal component analysis (PCA) for dimension reduction and applying logistic regression for classification. A 74.7% average accuracy has been achieved for differentiating between two 50 s. long story segments and a 43.6% average accuracy has been achieved for differentiating four 25 s. long story segments. These results suggest the potential of an fNIRS based-approach for building a speech decoding brain-computer-interface for developing a new type of neural prosthetic system.},
}
@article {pmid30355792,
year = {2018},
author = {Duhem, S and Berrouiguet, S and Debien, C and Ducrocq, F and Demarty, AL and Messiah, A and Courtet, P and Jehel, L and Thomas, P and Deplanque, D and Danel, T and Walter, M and Notredame, CE and Vaiva, G},
title = {Combining brief contact interventions (BCI) into a decision-making algorithm to reduce suicide reattempt: the VigilanS study protocol.},
journal = {BMJ open},
volume = {8},
number = {10},
pages = {e022762},
pmid = {30355792},
issn = {2044-6055},
mesh = {*Algorithms ; Case Management/*organization &amp; administration ; Continuity of Patient Care/*organization &amp; administration ; Cross-Sectional Studies ; Decision Making ; Health Services Accessibility/*organization &amp; administration ; Hotlines ; Humans ; *Mental Health Services/organization &amp; administration ; Patient Discharge ; Program Evaluation ; Suicidal Ideation ; Suicide, Attempted/*prevention &amp; control/psychology ; },
abstract = {INTRODUCTION: The early postattempt period is considered to be one of the most at-risk time windows for suicide reattempt or completion. Among the postcrisis prevention programmes developed to compensate for this risk, brief contact interventions (BCIs) have been proven to be efficient but not equally for each subpopulation of attempters. VigilanS is a region-wide programme that relies on an algorithmic system to tailor surveillance and BCI provisions to individuals discharged from the hospital after a suicide attempt.<br><br>AIM: VigilanS' main objective is to reduce suicide and suicide reattempt rates both at the individual level (patients included in VigilanS) and at the populational level (inhabitants of the Nord-Pas-de-Calais region).<br><br>METHODS AND ANALYSIS: At discharge, every attempter coming from a participating centre is given a crisis card with an emergency number to contact in case of distress. Patients are then systematically recontacted 6 months later. An additional 10-day call is also given if the index suicide attempt is not the first one. Depending on the clinical evaluation during the phone call, the call team may carry out proportionated crisis interventions. Personalised postcards are sent whenever patients are unreachable by phone or in distress. On the populational level, mean suicide and suicide attempt rates in Nord-Pas-de-Calais will be compared before and after the implementation of the programme. Here/there cross-sectional comparisons with a control region will test the spatial specificity of the observed fluctuations, while time-series analyses will be performed to corroborate the temporal plausibility of imputing these fluctuations to the implementation of the programme. On the individual level, patients entered in VigilanS will be prospectively compared with a matched control cohort by means of survival analyses (survival curve comparisons and Cox models).<br><br>DISCUSSION: VigilanS interventional components fall under the ordinary law care regime, and the individuals' general rights as patients apply with no addendums or restrictions for their participation in the programme. The research section received authorisation from the Ethical Committee of Lille Nord-Ouest under the caption 'Study aimed at evaluating routine care' and is registered in 'Clinical Trials'. The French Ministry of Health plans to extend the experimentation to other regions and probe the relevance of this type of 'bottom-up' territorial prevention policy at the national level.<br><br>TRIAL REGISTRATION NUMBER: NCT03134885.},
}
@article {pmid30355377,
year = {2018},
author = {da Silva-Sauer, L and Valero-Aguayo, L and Velasco-Álvarez, F and Fernández-Rodríguez, &#xc1; and Ron-Angevin, R},
title = {A Shaping Procedure to Modulate Two Cognitive Tasks to Improve a Sensorimotor Rhythm-Based Brain-Computer Interface System.},
journal = {The Spanish journal of psychology},
volume = {21},
number = {},
pages = {E44},
doi = {10.1017/sjp.2018.39},
pmid = {30355377},
issn = {1988-2904},
mesh = {Adult ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Female ; Humans ; Learning/*physiology ; Male ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {This study aimed to propose an adapted feedback using a psychological learning technique based on Skinner's shaping method to help the users to modulate two cognitive tasks (right-hand motor imagination and relaxed state) and improve better control in a Brain-Computer Interface. In the first experiment, a comparative study between performance in standard feedback (N = 9) and shaping method (N = 10) was conducted. The NASA Task Load Index questionnaire was applied to measure the user's workload. In the second experiment, a single case study was performed (N = 5) to verify the continuous learning by the shaping method. The first experiment showed significant interaction effect between sessions and group (F(1, 17) = 5.565; p = .031) which the shaping paradigm was applied. A second interaction effect demonstrates a higher performance increase in the relax state task with shaping procedure (F(1, 17) = 5. 038; p = .038). In NASA-TXL an interaction effect was obtained between the group and the cognitive task in Mental Demand (F(1, 17) = 6, 809; p = .018), Performance (F(1, 17) = 5, 725; p = .029), and Frustration (F(1, 17) = 9, 735; p = .006), no significance was found in Effort. In the second experiment, a trial-by-trial analysis shows an ascendant trend learning curve for the cognitive task with the lowest initial acquisition (relax state). The results suggest the effectiveness of the shaping procedure to modulate brain rhythms, improving mainly the cognitive task with greater initial difficulty and provide better interaction perception.},
}
@article {pmid30350836,
year = {2018},
author = {Zeng, H and He, H and Fu, Y and Zhao, T and Han, W and Xing, L and Zhang, Y and Zhan, Y and Xue, X},
title = {A self-powered brain-linked biosensing electronic-skin for actively tasting beverage and its potential application in artificial gustation.},
journal = {Nanoscale},
volume = {10},
number = {42},
pages = {19987-19994},
doi = {10.1039/c8nr06178e},
pmid = {30350836},
issn = {2040-3372},
mesh = {Alcohol Oxidoreductases/chemistry/metabolism ; Alcohols/*analysis ; Animals ; Beverages/*analysis ; Biosensing Techniques/instrumentation/*methods ; Brain/*physiology ; Electrodes, Implanted ; *Electronics ; Enzymes, Immobilized ; Hydrogen-Ion Concentration ; Mice ; Mice, Inbred C57BL ; Nanostructures/chemistry ; Polymers/chemistry ; Pyrroles/chemistry ; Taste/physiology ; },
abstract = {A new self-powered brain-linked biosensing electronic-skin (e-skin) for detecting pH value and alcoholicity of beverages has been realized based on polydimethysiloxane/polypyrrole (PDMS/Ppy) nanostructures. This e-skin (linking brain and transmitting signal to the specific encephalic region) can work as an artificial gustation system for gustatory perception substitution without an external electricity source. The sensing units on the e-skin can efficiently convert mechanical energy (human motion) into triboelectric impulse. The triboelectric output can be influenced by pH value and alcohol concentration in common beverages (acidic, alkaline or alcoholic drinks), which can be treated as the bio-chemical sensing signal. The bio-chemical sensing behavior arises from the triboelectrification/bio-chemical-sensing coupling effect. The biosensing e-skin is simply linked to the brain of a mouse at the primary motor cortex area, and the inputting signal can take part in the mouse perception, thus realizing behavior interventions, e.g., shaking of legs. This study provides a novel approach for developing artificial gustation e-skin and self-powered brain-machine interaction system with low cost.},
}
@article {pmid30350269,
year = {2019},
author = {Jerevall, PL and Brock, J and Palazzo, J and Wieczorek, T and Misialek, M and Guidi, AJ and Wu, Y and Erlander, MG and Zhang, Y and Schnabel, CA and Goss, PE and Horick, N and Sgroi, DC},
title = {Discrepancy in risk assessment of hormone receptor positive early-stage breast cancer patients using breast cancer index and recurrence score.},
journal = {Breast cancer research and treatment},
volume = {173},
number = {2},
pages = {375-383},
doi = {10.1007/s10549-018-5013-6},
pmid = {30350269},
issn = {1573-7217},
mesh = {Adult ; Age Factors ; Aged ; Breast/pathology ; Breast Neoplasms/epidemiology/*pathology ; Carcinoma, Ductal, Breast/epidemiology/*pathology ; Female ; Humans ; Middle Aged ; Neoplasm Grading ; Neoplasm Recurrence, Local/*diagnosis/epidemiology ; Prognosis ; Prospective Studies ; Risk Assessment/methods ; Risk Factors ; Tumor Burden ; },
abstract = {PURPOSE: A recent comparison of the prognostic accuracy of Breast Cancer Index (BCI) and the Recurrence Score (RS) showed that BCI was more precise than RS. BCI identified a subset of RS low and intermediate risk patients with clinically relevant elevated rates of distant recurrences (DR). The current study analyzed the correlation of BCI and RS risk classification to clinical and pathological parameters and further examined the re-categorization between the two risk group indices in a multi-institutional cohort of hormone receptor positive (HR+) breast cancer patients.<br><br>METHODS: 560 women with HR+, lymph node-negative breast cancer who underwent testing with RS as part of their routine clinical care were included in the final analysis. Individual risk was assessed using predefined categories of RS and BCI (Low, Intermediate and High, respectively). Correlations between BCI, RS, and standard clinical-pathological prognostic factors were examined, and re-categorization of risk groups between BCI and RS was analyzed.<br><br>RESULTS: An overall significant association between histological tumor grade and RS or BCI was observed with high-grade tumors more prevalent among RS and BCI high-risk patients. The invasive ductal carcinoma histologic subtype was associated with 98% and 93% of high-risk RS and BCI cases, respectively. The invasive lobular subtype accounted for 0% and 6% of high-risk RS and BCI cases, respectively. A poor agreement between the two biomarker risk group indices was demonstrated with more than 51% of the total cohort stratified differently between BCI and RS. As compared with RS, BCI stratified fewer patients into the intermediate-risk group (29% vs. 39%, BCI and RS, respectively) and more patients into the high-risk group (19% vs. 7%, BCI and RS, respectively). Subsets of both RS low- and intermediate-risk patients were identified by BCI as high risk.<br><br>CONCLUSIONS: In this clinical series, BCI and RS risk groups demonstrated a significant association with histological tumor grade. BCI showed a modest correlation with tumor size and no correlation with age, while RS showed no correlation with tumor size or age. Compared with RS, BCI classifies fewer intermediate risk patients, identifies subsets of low and intermediate RS risk patients as high-risk, and provides distinct individualized risk assessment for patients with early-stage breast cancer.},
}
@article {pmid30349004,
year = {2018},
author = {Yoo, PE and Oxley, TJ and John, SE and Opie, NL and Ordidge, RJ and O'Brien, TJ and Hagan, MA and Wong, YT and Moffat, BA},
title = {Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {15556},
pmid = {30349004},
issn = {2045-2322},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Feasibility Studies ; Female ; Humans ; *Imagination ; Magnetic Resonance Imaging/*methods/standards ; Male ; *Movement ; Psychomotor Performance ; },
abstract = {Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant's decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant's decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.},
}
@article {pmid30347262,
year = {2018},
author = {Bondar, A and Shubina, L},
title = {Nonlinear reactions of limbic structure electrical activity in response to rhythmical photostimulation in guinea pigs.},
journal = {Brain research bulletin},
volume = {143},
number = {},
pages = {73-82},
doi = {10.1016/j.brainresbull.2018.10.002},
pmid = {30347262},
issn = {1873-2747},
mesh = {Amygdala/physiology ; Animals ; Brain ; Electric Stimulation/*methods ; Electroencephalography/methods ; Entorhinal Cortex/physiology ; Female ; Guinea Pigs ; Hippocampus/physiology ; Limbic System/metabolism/*physiology ; Male ; Photic Stimulation/*methods ; Temporal Lobe ; },
abstract = {Photostimulation of the visual analyzer with a periodic signal is widely used in research and clinical practice, as well as in brain-computer interface technologies. In most studies of rhythmic photostimulation in structures of visual system at all its levels, the nonlinear nature of the response reactions is noted. However, the mechanism of formation of the induced electrophysiological reactions remains unclear. In addition, there is no literature data on the nature of response reactions of "non-visual" brain structures. The goal of the present study was to investigate the peculiarities and dynamics of the electrophysiological response of the limbic system to rhythmic photostimulation and analize the dynamics of harmonic components in the response spectra. We investigated the electrical activity of the guinea pig limbic system in response to photostimulation with a 10 Hz sinusoidal signal. Local field potentials were recorded simultaneously from the hippocampus, entorhinal cortex, medial septum and amygdala. Similar to the visual system structures, we have shown that response reactions in the limbic system had a pronounced nonlinear character, consisting in the presence of the stimulation frequency harmonics in the local field potential spectra. The correlation analysis of the dynamics of the harmonics' amplitudes did not reveal reliable relationships between them. The dynamics of the phase difference between the stimulus and individual harmonics varied in time, following different logic. Based on the results of the present work, we propose that the harmonics reveal independent processes having a different functional purpose and the nervous system operates with these harmonics independently.},
}
@article {pmid30346983,
year = {2018},
author = {Nagel, S and Spüler, M},
title = {Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface.},
journal = {PloS one},
volume = {13},
number = {10},
pages = {e0206107},
pmid = {30346983},
issn = {1932-6203},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Models, Neurological ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {Visual evoked potentials (VEPs) can be measured in the EEG as response to a visual stimulus. Commonly, VEPs are displayed by averaging multiple responses to a certain stimulus or a classifier is trained to identify the response to a certain stimulus. While the traditional approach is limited to a set of predefined stimulation patterns, we present a method that models the general process of VEP generation and thereby can be used to predict arbitrary visual stimulation patterns from EEG and predict how the brain responds to arbitrary stimulation patterns. We demonstrate how this method can be used to model single-flash VEPs, steady state VEPs (SSVEPs) or VEPs to complex stimulation patterns. It is further shown that this method can also be used for a high-speed BCI in an online scenario where it achieved an average information transfer rate (ITR) of 108.1 bit/min. Furthermore, in an offline analysis, we show the flexibility of the method allowing to modulate a virtually unlimited amount of targets with any desired trial duration resulting in a theoretically possible ITR of more than 470 bit/min.},
}
@article {pmid30346293,
year = {2019},
author = {Li, Z and Li, J and Zhao, S and Yuan, Y and Kang, Y and Chen, CLP},
title = {Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain-Machine Interfaces.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {30},
number = {12},
pages = {3558-3571},
doi = {10.1109/TNNLS.2018.2872595},
pmid = {30346293},
issn = {2162-2388},
mesh = {Adaptation, Physiological/*physiology ; Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces/trends ; Electroencephalography/methods ; *Exoskeleton Device/trends ; Humans ; *Neural Networks, Computer ; },
abstract = {In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram. By extracting the features from neural activity, the proposed intention decoding algorithm can generate the commands to control the exoskeleton robot. To achieve optimal motion, a redundancy resolution at the velocity level has been implemented through neural dynamics optimization. Considering human-robot interaction force as well as coupled dynamics during the exoskeleton operation, an adaptive controller with redundancy resolution has been designed to drive the exoskeleton tracking the planned trajectory in human brain and to offer a convenient method of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiments which employed a few subjects have been carried out. In the experiments, subjects successfully fulfilled the given manipulation tasks with convergence of tracking errors, which verified that the proposed brain-controlled exoskeleton robot system is effective.},
}
@article {pmid30345590,
year = {2018},
author = {Bockhorst, T and Pieper, F and Engler, G and Stieglitz, T and Galindo-Leon, E and Engel, AK},
title = {Synchrony surfacing: Epicortical recording of correlated action potentials.},
journal = {The European journal of neuroscience},
volume = {48},
number = {12},
pages = {3583-3596},
doi = {10.1111/ejn.14167},
pmid = {30345590},
issn = {1460-9568},
mesh = {Action Potentials/*physiology ; Animals ; Brain/*physiology ; Electric Stimulation ; Electrodes, Implanted ; Female ; Ferrets ; Microelectrodes ; Neurons/*physiology ; Visual Cortex/*physiology ; },
abstract = {Synchronous spiking of multiple neurons is a key phenomenon in normal brain function and pathologies. Recently, approaches to record spikes from the intact cortical surface using small high-density arrays of microelectrodes have been reported. It remained unaddressed how epicortical spiking relates to intracortical unit activity. We introduced a mesoscale approach using an array of 64 electrodes with intermediate diameter (250 μm) and combined large-coverage epicortical recordings in ferrets with intracortical recordings via laminar probes. Empirical data and modelling strongly suggest that our epicortical electrodes selectively captured synchronized spiking of neurons in the cortex beneath. As a result, responses to sensory stimulation were more robust and less noisy compared to intracortical activity, and receptive field properties were well preserved in epicortical recordings. This should promote insights into assembly-coding beyond the informative value of subdural EEG or single-unit spiking, and be advantageous to real-time applications in brain-machine interfacing.},
}
@article {pmid30344600,
year = {2018},
author = {Modica, E and Cartocci, G and Rossi, D and Martinez Levy, AC and Cherubino, P and Maglione, AG and Di Flumeri, G and Mancini, M and Montanari, M and Perrotta, D and Di Feo, P and Vozzi, A and Ronca, V and Aricò, P and Babiloni, F},
title = {Neurophysiological Responses to Different Product Experiences.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {9616301},
pmid = {30344600},
issn = {1687-5273},
mesh = {Adult ; Brain/*physiology ; *Consumer Behavior ; Electroencephalography ; Feeding Behavior/physiology/psychology ; Female ; Food ; Humans ; Male ; Philosophy ; Recognition, Psychology/physiology ; Touch Perception/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {It is well known that the evaluation of a product from the shelf considers the simultaneous cerebral and emotional evaluation of the different qualities of the product such as its colour, the eventual images shown, and the envelope's texture (hereafter all included in the term "product experience"). However, the measurement of cerebral and emotional reactions during the interaction with food products has not been investigated in depth in specialized literature. The aim of this paper was to investigate such reactions by the EEG and the autonomic activities, as elicited by the cross-sensory interaction (sight and touch) across several different products. In addition, we investigated whether (i) the brand (Major Brand or Private Label), (ii) the familiarity (Foreign or Local Brand), and (iii) the hedonic value of products (Comfort Food or Daily Food) influenced the reaction of a group of volunteers during their interaction with the products. Results showed statistically significantly higher tendency of cerebral approach (as indexed by EEG frontal alpha asymmetry) in response to comfort food during the visual exploration and the visual and tactile exploration phases. Furthermore, for the same index, a higher tendency of approach has been found toward foreign food products in comparison with local food products during the visual and tactile exploration phase. Finally, the same comparison performed on a different index (EEG frontal theta) showed higher mental effort during the interaction with foreign products during the visual exploration and the visual and tactile exploration phases. Results from the present study could deepen the knowledge on the neurophysiological response to food products characterized by different nature in terms of hedonic value familiarity; moreover, they could have implications for food marketers and finally lead to further study on how people make food choices through the interactions with their commercial envelope.},
}
@article {pmid30344504,
year = {2018},
author = {Li, H and Huang, G and Lin, Q and Zhao, JL and Lo, WA and Mao, YR and Chen, L and Zhang, ZG and Huang, DF and Li, L},
title = {Combining Movement-Related Cortical Potentials and Event-Related Desynchronization to Study Movement Preparation and Execution.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {822},
pmid = {30344504},
issn = {1664-2295},
abstract = {This study applied a comprehensive electroencephalography (EEG) analysis for movement-related cortical potentials (MRCPs) and event-related desynchronization (ERD) in order to understand movement-related brain activity changes during movement preparation and execution stage of unilateral wrist extension. Thirty-four healthy subjects completed two event-related potential tests in the same sequence. Unilateral wrist extension was involved in both tests as the movement task. Instruction Response Movement (IRM) was a brisk movement response task with visual "go" signal, while Cued Instruction Response Movement (CIRM) added a visual cue contenting the direction information to create a prolonged motor preparation stage. Recorded EEG data were segmented and averaged to show time domain changes and then transformed into time-frequency mapping to show the time-frequency changes. All components were calculated and compared among C3, Cz, and C4 locations. The motor potential appeared bilaterally in both tests' movement execution stages, and Cz had the largest peak value among the investigated locations (p < 0.01). In CIRM, a contingent negative variation (CNV) component presented bilaterally during the movement preparation stage with the largest amplitude at Cz. ERD of the mu rhythm (mu ERD) presented bilateral sensorimotor cortices during movement execution stages in both tests and was the smallest at Cz among the investigated locations. In the movement preparation stage of CIRM, mu ERD presented mainly in the contralateral sensory motor cortex area (C3 and C4 for right and left wrist movements, respectively) and showed significant differences between different locations. EEG changes in the time and time-frequency domains showed different topographical features. Movement execution was controlled bilaterally, while movement preparation was controlled mainly by contralateral sensorimotor cortices. Mu ERD was found to have stronger contra-lateralization features in the movement preparation stage and might be a better indicator for detecting movement intentions. This information could be helpful and might provide comprehensive information for studying movement disorders (such as those in post-stroke hemiplegic patients) or for facilitating the development of neuro-rehabilitation engineering technology such as brain computer interface.},
}
@article {pmid30342063,
year = {2019},
author = {Peyton, CC and Henriksen, C and Reich, RR and Azizi, M and Gilbert, SM},
title = {Estimating Minimally Important Differences for the Bladder Cancer Index Using Distribution and Anchor Based Approaches.},
journal = {The Journal of urology},
volume = {201},
number = {4},
pages = {709-714},
pmid = {30342063},
issn = {1527-3792},
support = {P30 CA076292/CA/NCI NIH HHS/United States ; },
mesh = {Aged ; Cystectomy ; *Diagnostic Self Evaluation ; Female ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; Patient Reported Outcome Measures ; Prospective Studies ; *Quality of Life ; *Urinary Bladder Neoplasms/complications/physiopathology/surgery ; },
abstract = {PURPOSE: The BCI (Bladder Cancer Index) is a validated, condition specific health questionnaire assessing urinary, bowel and sexual function and quality of life among patients with bladder cancer. We aimed to establish minimally important difference score thresholds that signal clinical importance.<br><br>MATERIALS AND METHODS: For 1 year after surgery we followed a prospective cohort of 150 patients who underwent radical cystectomy between 2013 and 2016. Usable data on 138 patients were analyzed. The BCI and the Medical Outcomes Study SF-36 (36-Item Short Form Health Survey) questionnaires were completed prior to cystectomy, and 3, 6 and 12 months postoperatively. Distribution based, minimally important differences were estimated at &#x2153; and &#xbd; SD for each index domain across time points. Changes in index domain scores anchored to changes in a SF-36 overall health assessment question were used to estimate anchor based, minimally important differences. Pooled averages are reported between time points and methods.<br><br>RESULTS: The distribution based, minimally important difference of &#x2153; SDs for urinary, bowel and sexual domains ranged between 5.3 and 7.3, 4.6 and 5.6, and 6.0 and 8.9 points, respectively. Ranges of &#xbd; SDs were 8.8 and 10.9, 6.8 and 8.4, and 8.9 and 13.5 points, respectively. The anchor based approach resulted in minimally important difference estimates of 6.2, 7.3 and 6.8 points, respectively. Aggregated results across the 2 approaches resulted in minimally important differences of 6 to 9, 5 to 8 and 7 to 11 points for urinary, bowel and sexual domains, respectively.<br><br>CONCLUSIONS: Using 2 independent approaches to our knowledge we established the first minimally important difference estimates for the BCI. Defining patient reported outcome thresholds is important to interpret changes or differences in BCI scores.},
}
@article {pmid30338495,
year = {2018},
author = {Mahmoudi, M and Shamsi, M},
title = {Multi-class EEG classification of motor imagery signal by finding optimal time segments and features using SNR-based mutual information.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {41},
number = {4},
pages = {957-972},
doi = {10.1007/s13246-018-0691-2},
pmid = {30338495},
issn = {1879-5447},
mesh = {Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {The electroencephalogram signals are used to distinguish different motor imagery tasks in brain-computer interfaces. In most studies, in order to classify the EEG signals recorded in a cue-guided BCI paradigm, time segments for feature extraction after the onset of the visual cue were selected manually. In addition, in these studies the authors have selected a single identical time segment for different subjects. The present study emphasized on the inter-individual variability and difference between different motor imagery tasks as the potential source of erroneous results and used mutual information and the subject specific time interval to overcome this problem. More specifically, a new method was proposed to automatically find the best subject specific time intervals for the classification of four-class motor imagery tasks by using MI between the BCI input and output. Moreover, the signal-to-noise ratio was used to calculate the MI values, while the MI values were used as feature selection criteria to select the discriminative features. The time segments and the best discriminative features were found by using training data and used to assess the evaluation data. Furthermore, the CSP algorithm was used to extract signal features. The dataset 2A of BCI competition IV used in this study consisted of four different motor imagery signals, which were obtained from nine different subjects. One Vs One decomposition scheme was used to deal with the multi-class nature of the problem. The MI values showed that the obtained time segments not only varied between different subjects but also varied between different classifiers of different pair of classes. Finally, the results suggested that the proposed method was efficient in classifying multi-class motor imagery signals as compared to other classification strategies proposed by the other studies.},
}
@article {pmid30336595,
year = {2018},
author = {Torres, F and Puente, ST and Úbeda, A},
title = {Assistance Robotics and Biosensors.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {10},
pages = {},
pmid = {30336595},
issn = {1424-8220},
mesh = {Biosensing Techniques/instrumentation/*methods ; Brain-Computer Interfaces ; Persons with Disabilities ; Electroencephalography/instrumentation ; Electromyography/instrumentation ; Exoskeleton Device ; Humans ; Robotics/*instrumentation ; },
abstract = {This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain[-]machine interfacing.},
}
@article {pmid30336362,
year = {2018},
author = {Costa, AP and Møller, JS and Iversen, HK and Puthusserypady, S},
title = {An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm.},
journal = {Computers in biology and medicine},
volume = {103},
number = {},
pages = {24-33},
doi = {10.1016/j.compbiomed.2018.09.021},
pmid = {30336362},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; *Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Young Adult ; },
abstract = {This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.},
}
@article {pmid30334800,
year = {2018},
author = {Wang, P and Jiang, A and Liu, X and Shang, J and Zhang, L},
title = {LSTM-Based EEG Classification in Motor Imagery Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {11},
pages = {2086-2095},
doi = {10.1109/TNSRE.2018.2876129},
pmid = {30334800},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; Imagination/*physiology ; Movement/physiology ; Neural Networks, Computer ; Reproducibility of Results ; },
abstract = {Classification of motor imagery electroencephalograph signals is a fundamental problem in brain-computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.},
}
@article {pmid30334770,
year = {2019},
author = {Valentin, O and Ducharme, M and Cretot-Richert, G and Monsarrat-Chanon, H and Viallet, G and Delnavaz, A and Voix, J},
title = {Validation and Benchmarking of a Wearable EEG Acquisition Platform for Real-World Applications.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {13},
number = {1},
pages = {103-111},
doi = {10.1109/TBCAS.2018.2876240},
pmid = {30334770},
issn = {1940-9990},
mesh = {Acoustic Stimulation ; Adult ; Amplifiers, Electronic ; *Benchmarking ; Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Mobile Applications ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; *Wearable Electronic Devices ; Young Adult ; },
abstract = {This paper presents the experimental validation of a readout circuit for the acquisition, amplification, and transmission of extremely weak biopotentials with a focus on electroencephalography (EEG) signals. The device, dubbed CochlEEG, benefits from a low-power design for long-term power autonomy and provides configurable gain and sampling rates to suit the needs of various EEG applications. CochlEEG features high sampling rates, up to 4 kHz, low-noise signal acquisitions, support for active electrodes, and a potential for Wi-Fi data transmission. Moreover, it is lightweight, pocket size, and affordable, which makes CochlEEG suitable for wearable and real-world applications. The efficiency of CochlEEG in EEG data acquisition is also investigated in this paper. Auditory steady-state responses acquisition results validate CochlEEG's capability in recording EEG with a signal quality comparable to commercial mobile or research EEG acquisition devices. Moreover, the results of an oddball paradigm experiment prove the capability of CochlEEG in recording event-related potentials and demonstrate its potential for brain-computer interface applications and electrophysiological research applications requiring higher temporal resolution.},
}
@article {pmid30332782,
year = {2018},
author = {Kim, GH and Kim, K and Lee, E and An, T and Choi, W and Lim, G and Shin, JH},
title = {Recent Progress on Microelectrodes in Neural Interfaces.},
journal = {Materials (Basel, Switzerland)},
volume = {11},
number = {10},
pages = {},
pmid = {30332782},
issn = {1996-1944},
support = {NRF-2016R1C1B1015521//National Research Foundation of Korea/ ; },
abstract = {Brain‒machine interface (BMI) is a promising technology that looks set to contribute to the development of artificial limbs and new input devices by integrating various recent technological advances, including neural electrodes, wireless communication, signal analysis, and robot control. Neural electrodes are a key technological component of BMI, as they can record the rapid and numerous signals emitted by neurons. To receive stable, consistent, and accurate signals, electrodes are designed in accordance with various templates using diverse materials. With the development of microelectromechanical systems (MEMS) technology, electrodes have become more integrated, and their performance has gradually evolved through surface modification and advances in biotechnology. In this paper, we review the development of the extracellular/intracellular type of in vitro microelectrode array (MEA) to investigate neural interface technology and the penetrating/surface (non-penetrating) type of in vivo electrodes. We briefly examine the history and study the recently developed shapes and various uses of the electrode. Also, electrode materials and surface modification techniques are reviewed to measure high-quality neural signals that can be used in BMI.},
}
@article {pmid30327667,
year = {2018},
author = {Modica, E and Rossi, D and Cartocci, G and Perrotta, D and Di Feo, P and Mancini, M and Aricò, P and Inguscio, BMS and Babiloni, F},
title = {Neurophysiological Profile of Antismoking Campaigns.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {9721561},
pmid = {30327667},
issn = {1687-5273},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Eye Movements ; Female ; *Health Promotion ; Humans ; Male ; Middle Aged ; *Persuasive Communication ; Sex Factors ; Smoking/physiopathology/psychology ; *Smoking Prevention ; Visual Perception/*physiology ; },
abstract = {Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). The eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. This study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA's message and then enhance the related impact.},
}
@article {pmid30327256,
year = {2018},
author = {Kao, TC and Hennequin, G},
title = {Null Ain't Dull: New Perspectives on Motor Cortex.},
journal = {Trends in cognitive sciences},
volume = {22},
number = {12},
pages = {1069-1071},
doi = {10.1016/j.tics.2018.09.005},
pmid = {30327256},
issn = {1879-307X},
support = {202111/Z/16/Z//Wellcome Trust/United Kingdom ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Motor Cortex/*physiology ; Nerve Net/*physiology ; },
abstract = {Classical work has viewed primary motor cortex (M1) as a controller of muscle and body dynamics. A recent brain-computer interface (BCI) experiment suggests a new, complementary perspective: M1 is itself a dynamical system under active control of other circuits.},
}
@article {pmid30325349,
year = {2018},
author = {Kaya, M and Binli, MK and Ozbay, E and Yanar, H and Mishchenko, Y},
title = {A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces.},
journal = {Scientific data},
volume = {5},
number = {},
pages = {180211},
pmid = {30325349},
issn = {2052-4463},
mesh = {Action Potentials ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; },
abstract = {Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.},
}
@article {pmid30323749,
year = {2018},
author = {Hübner, D and Schall, A and Prange, N and Tangermann, M},
title = {Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {391},
pmid = {30323749},
issn = {1662-5161},
abstract = {Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. In this work, we investigate the option to use a BCI with eyes-closed. Twelve healthy subjects participated in a single electroencephalography (EEG) session where they were listening to a rapid stream of bisyllabic words while alternatively having their eyes open or closed. In addition, we assessed usability aspects for the two conditions with a questionnaire. Our analysis shows that eyes-closed does not reduce the number of eye artifacts and that event-related potential (ERP) responses and classification accuracies are comparable between both conditions. Importantly, we found that subjects expressed a significant general preference toward the eyes-closed condition and were also less tensed in that condition. Furthermore, switching between eyes-closed and eyes-open and vice versa is possible without a severe drop in classification accuracy. These findings suggest that eyes-closed should be considered as a viable alternative in auditory BCIs that might be especially useful for subjects with limited control over their eye movements.},
}
@article {pmid30323737,
year = {2018},
author = {Chu, Y and Zhao, X and Zou, Y and Xu, W and Han, J and Zhao, Y},
title = {A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {680},
pmid = {30323737},
issn = {1662-4548},
abstract = {High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.},
}
@article {pmid30322205,
year = {2018},
author = {Opałka, S and Stasiak, B and Szajerman, D and Wojciechowski, A},
title = {Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {10},
pages = {},
pmid = {30322205},
issn = {1424-8220},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Neural Networks, Computer ; ROC Curve ; *Signal Processing, Computer-Assisted ; },
abstract = {Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti's multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.},
}
@article {pmid30321344,
year = {2018},
author = {Carriel, D and Simon Garcia, P and Castelli, F and Lamourette, P and Fenaille, F and Brochier-Armanet, C and Elsen, S and Gutsche, I},
title = {A Novel Subfamily of Bacterial AAT-Fold Basic Amino Acid Decarboxylases and Functional Characterization of Its First Representative: Pseudomonas aeruginosa LdcA.},
journal = {Genome biology and evolution},
volume = {10},
number = {11},
pages = {3058-3075},
pmid = {30321344},
issn = {1759-6653},
mesh = {Cadaverine/metabolism ; Carboxy-Lyases/*genetics ; Cyanobacteria/genetics ; Firmicutes/genetics ; Multigene Family ; Phylogeny ; Proteobacteria/genetics ; Pseudomonas aeruginosa/enzymology/*genetics ; },
abstract = {Polyamines are small amino-acid derived polycations capable of binding negatively charged macromolecules. Bacterial polyamines are structurally and functionally diverse, and are mainly produced biosynthetically by pyridoxal-5-phosphate-dependent amino acid decarboxylases referred to as Lysine-Arginine-Ornithine decarboxylases (LAOdcs). In a phylogenetically limited group of bacteria, LAOdcs are also induced in response to acid stress. Here, we performed an exhaustive phylogenetic analysis of the AAT-fold LAOdcs which showcased the ancient nature of their short forms in Cyanobacteria and Firmicutes, and emergence of distinct subfamilies of long LAOdcs in Proteobacteria. We identified a novel subfamily of lysine decarboxylases, LdcA, ancestral in Betaproteobacteria and Pseudomonadaceae. We analyzed the expression of LdcA from Pseudomonas aeruginosa, and uncovered its role, intimately linked to cadaverine (Cad) production, in promoting growth and reducing persistence of this multidrug resistant human pathogen during carbenicillin treatment. Finally, we documented a certain redundancy in the function of the three main polyamines-Cad, putrescine (Put), and spermidine (Spd)-in P. aeruginosa by demonstrating the link between their intracellular level, as well as the capacity of Put and Spd to complement the growth phenotype of the ldcA mutant.},
}
@article {pmid30319386,
year = {2018},
author = {Wittevrongel, B and Khachatryan, E and Fahimi Hnazaee, M and Camarrone, F and Carrette, E and De Taeye, L and Meurs, A and Boon, P and Van Roost, D and Van Hulle, MM},
title = {Decoding Steady-State Visual Evoked Potentials From Electrocorticography.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {65},
pmid = {30319386},
issn = {1662-5196},
abstract = {We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency- and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG- and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding benefits from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suffice. This study shows, for the first time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes.},
}
@article {pmid30319382,
year = {2018},
author = {Jirakittayakorn, N and Wongsawat, Y},
title = {A Novel Insight of Effects of a 3-Hz Binaural Beat on Sleep Stages During Sleep.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {387},
pmid = {30319382},
issn = {1662-5161},
abstract = {The dichotic presentation of two almost equivalent pure tones with slightly different frequencies leads to virtual beat perception by the brain. In this phenomenon, the so-called binaural beat has a frequency equaling the difference of the frequencies of the two pure tones. The binaural beat can entrain neural activities to synchronize with the beat frequency and induce behavioral states related to the neural activities. This study aimed to investigate the effect of a 3-Hz binaural beat on sleep stages, which is considered a behavioral state. Twenty-four participants were allocated to experimental and control groups. The experimental period was three consecutive nights consisting of an adaptation night, a baseline night, and an experimental night. Participants in both groups underwent the same procedures, but only the experimental group was exposed to the 3-Hz binaural beat on the experimental night. The stimulus was initiated when the first epoch of the N2 sleep stage was detected and stopped when the first epoch of the N3 sleep stage detected. For the control group, a silent sham stimulus was used. However, the participants were blinded to their stimulus group. The results showed that the N3 duration of the experimental group was longer than that of the control group, and the N2 duration of the experimental group was shorter than that of the control group. Moreover, the N3 latency of the experimental group was shorter.},
}
@article {pmid30315905,
year = {2019},
author = {Kadosh, KC and Staunton, G},
title = {A systematic review of the psychological factors that influence neurofeedback learning outcomes.},
journal = {NeuroImage},
volume = {185},
number = {},
pages = {545-555},
doi = {10.1016/j.neuroimage.2018.10.021},
pmid = {30315905},
issn = {1095-9572},
mesh = {Attention ; Humans ; *Learning ; Magnetic Resonance Imaging/methods ; *Neurofeedback/methods ; Treatment Outcome ; },
abstract = {Real-time functional magnetic resonance imaging (fMRI)-based neurofeedback represents the latest applied behavioural neuroscience methodology developed to train participants in the self-regulation of brain regions or networks. However, as with previous biofeedback approaches which rely on electroencephalography (EEG) or related approaches such as brain-machine interface technology (BCI), individual success rates vary significantly, and some participants never learn to control their brain responses at all. Given that these approaches are often being developed for eventual use in a clinical setting (albeit there is also significant interest in using NF for neuro-enhancement in typical populations), this represents a significant hurdle which requires more research. Here we present the findings of a systematic review which focused on how psychological variables contribute to learning outcomes in fMRI-based neurofeedback. However, as this is a relatively new methodology, we also considered findings from EEG-based neurofeedback and BCI. 271 papers were found and screened through PsycINFO, psycARTICLES, Psychological and Behavioural Sciences Collection, ISI Web of Science and Medline and 21 were found to contribute towards the aim of this survey. Several main categories emerged: Attentional variables appear to be of importance to both performance and learning, motivational factors and mood have been implicated as moderate predictors of success, while personality factors have mixed findings. We conclude that future research will need to systematically manipulate psychological variables such as motivation or mood, and to define clear thresholds for a successful neurofeedback effect. Non-responders need to be targeted for interventions and tested with different neurofeedback setups to understand whether their non-response is specific or general. Also, there is a need for qualitative evidence to understand how psychological variables influence participants throughout their training. This will help us to understand the subtleties of psychological effects over time. This research will allow interventions to be developed for non-responders and better selection procedures in future to improve the efficacy of neurofeedback.},
}
@article {pmid30314263,
year = {2018},
author = {Pei, G and Wu, J and Chen, D and Guo, G and Liu, S and Hong, M and Yan, T},
title = {Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {10},
pages = {},
pmid = {30314263},
issn = {1424-8220},
support = {61727807//National Natural Science Foundation of China/ ; 61473043//National Natural Science Foundation of China/ ; 81671776//National Natural Science Foundation of China/ ; Z161100002616020//Beijing Municipal Science &amp; Technology Commission/ ; Z171100001117057//Beijing Nova Program/ ; },
mesh = {Attention/physiology ; Brain Waves/physiology ; Cognition/*physiology ; Electrodes ; Electroencephalography/instrumentation/*methods ; Equipment Design ; Female ; Healthy Volunteers ; Humans ; Male ; Memory, Short-Term ; Neurofeedback/*instrumentation/methods ; Single-Blind Method ; Young Adult ; },
abstract = {Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system's operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use.},
}
@article {pmid30312940,
year = {2018},
author = {López-Larraz, E and Figueiredo, TC and Insausti-Delgado, A and Ziemann, U and Birbaumer, N and Ramos-Murguialday, A},
title = {Event-related desynchronization during movement attempt and execution in severely paralyzed stroke patients: An artifact removal relevance analysis.},
journal = {NeuroImage. Clinical},
volume = {20},
number = {},
pages = {972-986},
pmid = {30312940},
issn = {2213-1582},
mesh = {Adult ; Aged ; *Artifacts ; Brain/*physiopathology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Eye Movements/physiology ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Paralysis/*physiopathology ; Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; },
abstract = {The electroencephalogram (EEG) constitutes a relevant tool to study neural dynamics and to develop brain-machine interfaces (BMI) for rehabilitation of patients with paralysis due to stroke. However, the EEG is easily contaminated by artifacts of physiological origin, which can pollute the measured cortical activity and bias the interpretations of such data. This is especially relevant when recording EEG of stroke patients while they try to move their paretic limbs, since they generate more artifacts due to compensatory activity. In this paper, we study how physiological artifacts (i.e., eye movements, motion artifacts, muscle artifacts and compensatory movements with the other limb) can affect EEG activity of stroke patients. Data from 31 severely paralyzed stroke patients performing/attempting grasping movements with their healthy/paralyzed hand were analyzed offline. We estimated the cortical activation as the event-related desynchronization (ERD) of sensorimotor rhythms and used it to detect the movements with a pseudo-online simulated BMI. Automated state-of-the-art methods (linear regression to remove ocular contaminations and statistical thresholding to reject the other types of artifacts) were used to minimize the influence of artifacts. The effect of artifact reduction was quantified in terms of ERD and BMI performance. The results reveal a significant contamination affecting the EEG, being involuntary muscle activity the main source of artifacts. Artifact reduction helped extracting the oscillatory signatures of motor tasks, isolating relevant information from noise and revealing a more prominent ERD activity. Lower BMI performances were obtained when artifacts were eliminated from the training datasets. This suggests that artifacts produce an optimistic bias that improves theoretical accuracy but may result in a poor link between task-related oscillatory activity and BMI peripheral feedback. With a clinically relevant dataset of stroke patients, we evidence the need of appropriate methodologies to remove artifacts from EEG datasets to obtain accurate estimations of the motor brain activity.},
}
@article {pmid30308211,
year = {2019},
author = {Canna, A and Prinster, A and Fratello, M and Puglia, L and Magliulo, M and Cantone, E and Pirozzi, MA and Di Salle, F and Esposito, F},
title = {A low-cost open-architecture taste delivery system for gustatory fMRI and BCI experiments.},
journal = {Journal of neuroscience methods},
volume = {311},
number = {},
pages = {1-12},
doi = {10.1016/j.jneumeth.2018.10.003},
pmid = {30308211},
issn = {1872-678X},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Equipment Design ; Humans ; *Magnetic Resonance Imaging ; Male ; Neurophysiology/*instrumentation ; Software ; Taste Perception/*physiology ; },
abstract = {BACKGROUND: Tasting is a complex process involving chemosensory perception and cognitive evaluation. Different experimental designs and solution delivery approaches may in part explain the variability reported in literature. These technical aspects certainly limit the development of taste-related brain computer interface devices.<br><br>NEW METHOD: We propose a novel modular, scalable and low-cost device for rapid injection of small volumes of taste solutions during fMRI experiments that gathers the possibility to flexibly increase the number of channels, allowing complex multi-dimensional taste experiments. We provide the full description of the hardware and software architecture and illustrate the application of the working prototype in single-subject event-related fMRI experiments by showing the BOLD responses to basic taste qualities and to five intensities of tastes during the course of perception.<br><br>RESULTS: The device is shown to be effective in activating multiple clusters within the gustatory pathway and a precise time-resolved event-related analysis is shown to be possible by the impulsive nature of the induced perception.<br><br>This gustometer represents the first implementation of a low-cost, easily replicable and portable device that is suitable for all kinds of fMRI taste experiments. Its scalability will boost the experimental design of more complex multi-dimensional fMRI studies of the human taste pathway.<br><br>CONCLUSIONS: The gustometer represents a valid open-architecture alternative to other available devices and its spread and development may contribute to an increased standardization of experimental designs in human fMRI studies of taste perception and pave the way to the development of novel taste-related BCIs.},
}
@article {pmid30307871,
year = {2018},
author = {Ajami, S and Mahnam, A and Abootalebi, V},
title = {An Adaptive SSVEP-Based Brain-Computer Interface to Compensate Fatigue-Induced Decline of Performance in Practical Application.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {11},
pages = {2200-2209},
doi = {10.1109/TNSRE.2018.2874975},
pmid = {30307871},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Communication Devices for People with Disabilities ; *Evoked Potentials, Somatosensory ; Female ; Healthy Volunteers ; Humans ; Male ; *Muscle Fatigue ; Photic Stimulation ; *Psychomotor Performance ; Young Adult ; },
abstract = {Brain-computer interfaces based on steady-state visual evoked potentials are promising communication systems for people with speech and motor disabilities. However, reliable SSVEP response requires user's attention, which degrades over time due to significant eye-fatigue when low-frequency visual stimuli (5-15 Hz) are used. Previous studies have shown that eye-fatigue can be reduced using high-frequency flickering stimuli (>25 Hz). Here, it is quantitatively demonstrated that the performance of a high-frequency SSVEP BCI decreases over time, but this amount of decrease can be compensated effectively by using two proposed adaptive algorithms. This leaded to a robust alternative communication system for practical applications. The asynchronous spelling system implemented in this study uses a threshold-based version of LASSO algorithm for frequency recognition. In long online experiments, when participants typed a sentence with the BCI system for 16 times, accuracy of the system was close to its maximum along the experiment. However, regression analysis on typing speed of each sentence demonstrated a significant decrease in all 7 subjects () when thresholds obtained from a calibration test were kept fixed over the experiment. In comparison, no significant change in typing speed was observed when the proposed adaptive algorithms were used. The analysis of variances revealed that the average typing speed of the last four sentences when using adaptive relational algorithm (8.7 char/min) was significantly higher than the tolerance-based algorithm (8.1 char/min) and significantly above 6 char/min when the fixed thresholds were used. Therefore, the relational algorithm proposed in this paper could successfully compensate for the effect of fatigue on performance of the SSVEP BCI system.},
}
@article {pmid30307853,
year = {2019},
author = {Kshirsagar, GB and Londhe, ND},
title = {Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {11},
pages = {2992-3005},
doi = {10.1109/TBME.2018.2875024},
pmid = {30307853},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Deep Learning ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-column (RC) paradigm which exhibits issues like crowding effect, adjacency, fatigue, task difficulty, and required large number of trials for character recognition. For P300 detection, deep learning algorithms have shown the state of art performance compared to the conventional machine learning algorithms in the recent past. Therefore, authors have been motivated to develop a deep learning architecture for DS-based P300 speller which can detect the target characters more accurately and in less number of trials. For this, two proven deep learning algorithms, stacked autoencoder (SAE) and deep convolution neural network (DCNN) have been adopted. For further bettering their performances, batch normalization and innovative double batch training is included here to achieve accelerated training and alleviate the problem of overfitting. Additionally, a leaky ReLU activation function has also been used in DCNN to overcome dying ReLU problem. The experiments have been performed on self-generated dataset of 20 Devanagari words with 79 characters acquired from 10 subjects using 16 channel actiCAP Xpress EEG recorder. The experimental results illustrated that the proposed DCNN is able to detect 88.22% correct targets in just three trials. Moreover, it also provides ITR of 20.58 bits per minutes, which is significantly higher than existing techniques.},
}
@article {pmid30306635,
year = {2019},
author = {Burgar, JM and Burton, AC and Fisher, JT},
title = {The importance of considering multiple interacting species for conservation of species at risk.},
journal = {Conservation biology : the journal of the Society for Conservation Biology},
volume = {33},
number = {3},
pages = {709-715},
doi = {10.1111/cobi.13233},
pmid = {30306635},
issn = {1523-1739},
mesh = {Alberta ; Animals ; Bayes Theorem ; Conservation of Natural Resources ; *Deer ; Oil and Gas Fields ; Predatory Behavior ; *Reindeer ; *Wolves ; },
abstract = {Conservation of species at risk of extinction is complex and multifaceted. However, mitigation strategies are typically narrow in scope, an artifact of conservation research that is often limited to a single species or stressor. Knowledge of an entire community of strongly interacting species would greatly enhance the comprehensiveness and effectiveness of conservation decisions. We investigated how camera trapping and spatial count models, an extension of spatial-recapture models for unmarked populations, can accomplish this through a case study of threatened boreal woodland caribou (Rangifer tarandus caribou). Population declines in caribou are precipitous and well documented, but recovery strategies focus heavily on control of wolves (Canis lupus) and pay less attention to other known predators and apparent competitors. Obtaining necessary data on multispecies densities has been difficult. We used spatial count models to concurrently estimate densities of caribou, their predators (wolf, black bear [Ursus americanus], and coyote [Canis latrans]), and alternative prey (moose [Alces alces] and white-tailed deer [Odocoileus virginianus]) from a camera-trap array in a highly disturbed landscape within northern Alberta's Oil Sands Region. Median densities were 0.22 caribous (95% Bayesian credible interval [BCI] = 0.08-0.65), 0.77 wolves (95% BCI = 0.26-2.67), 2.39 moose (95% BCI = 0.56-7.00), 2.64 coyotes (95% BCI = 0.45-6.68), and 3.63 black bears (95% BCI = 1.25-8.52) per 100 km[2] . (The white-tailed deer model did not converge.) Although wolf densities were higher than densities recommended for caribou conservation, we suggest the markedly higher black bear and coyote densities may be of greater concern, especially if government wolf control further releases these species. Caribou conservation with a singular focus on wolf control may leave caribou vulnerable to other predators. We recommend a broader focus on the interacting species within a community when conserving species.},
}
@article {pmid30303130,
year = {2018},
author = {Bashford, L and Wu, J and Sarma, D and Collins, K and Rao, RPN and Ojemann, JG and Mehring, C},
title = {Concurrent control of a brain-computer interface and natural overt movements.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066021},
doi = {10.1088/1741-2552/aadf3d},
pmid = {30303130},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Cerebral Cortex/diagnostic imaging/physiology ; Efferent Pathways ; Electrocorticography ; Female ; Fingers/innervation/physiology ; Humans ; Learning ; Magnetic Resonance Imaging ; Male ; Motor Skills ; Movement/*physiology ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: A primary control signal in brain-computer interfaces (BCIs) have been cortical signals related to movement. However, in cases where natural motor function remains, BCI control signals may interfere with other possibly simultaneous activity for useful ongoing movement. We sought to determine if the brain could learn to control both a BCI and concurrent overt movement execution in such cases.<br><br>APPROACH: We designed experiments where BCI and overt movements must be used concurrently and in coordination to achieve a 2D centre out control. Power in the 70-90 Hz band of human electrocorticography (ECoG) signals, was used to generate BCI control commands for vertical movement of the cursor. These signals were deliberately recorded from the same human cortical site that produced the strongest movement related activity associated with the concurrent overt finger movements required for the horizontal movement of the cursor.<br><br>MAIN RESULTS: We demonstrate that three subjects were able to perform the concurrent BCI task, controlling BCI and natural movements simultaneously and to a large extent independently. We conclude that the brain is capable of dissociating the original control signal dependency on movement, producing specific BCI control signals in the presence of motor related responses from the ongoing overt behaviour with which the BCI signal was initially correlated.<br><br>SIGNIFICANCE: We demonstrate a novel human brain-computer interface (BCI) which can be used to control movement concurrently and in coordination with movements of the natural limbs. This demonstrates the dissociation of cortical activity from the behaviour with which it was originally associated despite the ongoing behaviour and shows the feasibility of achieving simultaneous BCI control of devices with natural movements.},
}
@article {pmid30301759,
year = {2018},
author = {Bundy, DT and Szrama, N and Pahwa, M and Leuthardt, EC},
title = {Unilateral, 3D Arm Movement Kinematics Are Encoded in Ipsilateral Human Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {47},
pages = {10042-10056},
pmid = {30301759},
issn = {1529-2401},
support = {TL1 TR000449/TR/NCATS NIH HHS/United States ; UL1 TR000448/TR/NCATS NIH HHS/United States ; UL1 TR002345/TR/NCATS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Arm/*physiology ; Biomechanical Phenomena/physiology ; Electrocorticography/methods ; Electrodes, Implanted ; Female ; Functional Laterality/*physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {There is increasing evidence that the hemisphere ipsilateral to a moving limb plays a role in planning and executing movements. However, the exact relationship between cortical activity and ipsilateral limb movements is uncertain. We sought to determine whether 3D arm movement kinematics (speed, velocity, and position) could be decoded from cortical signals recorded from the hemisphere ipsilateral to the moving limb. By having invasively monitored patients perform unilateral reaches with each arm, we also compared the encoding of contralateral and ipsilateral limb kinematics from a single cortical hemisphere. In four motor-intact human patients (three male, one female) implanted with electrocorticography electrodes for localization of their epileptic foci, we decoded 3D movement kinematics of both arms with accuracies above chance. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar, enabling cross-prediction of kinematics between arms. These results clarify our understanding that the ipsilateral hemisphere robustly contributes to motor execution and supports that the information of complex movements is more bihemispherically represented in humans than has been previously understood.SIGNIFICANCE STATEMENT Although limb movements are traditionally understood to be driven by the cortical hemisphere contralateral to a moving limb, movement-related neural activity has also been found in the ipsilateral hemisphere. This study provides the first demonstration that 3D arm movement kinematics can be decoded from human electrocorticographic signals ipsilateral to the moving limb. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar. The finding that specific kinematics are encoded in the ipsilateral hemisphere demonstrates that the ipsilateral hemisphere contributes to the execution of unilateral limb movements, improving our understanding of motor control. Additionally, the bihemisheric representation of voluntary movements has implications for the development of neuroprosthetic systems for reaching and for neurorehabilitation strategies following cortical injuries.},
}
@article {pmid30301238,
year = {2018},
author = {Al-Quraishi, MS and Elamvazuthi, I and Daud, SA and Parasuraman, S and Borboni, A},
title = {EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {10},
pages = {},
pmid = {30301238},
issn = {1424-8220},
mesh = {Animal Shells/diagnostic imaging ; Animals ; Electroencephalography/*methods ; Humans ; Lower Extremity/*diagnostic imaging ; Upper Extremity/*diagnostic imaging ; },
abstract = {Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.},
}
@article {pmid30300878,
year = {2018},
author = {Stieger, C and Siemens, X and Honegger, F and Roushan, K and Bodmer, D and Allum, J},
title = {Balance Control during Stance and Gait after Cochlear Implant Surgery.},
journal = {Audiology &amp; neuro-otology},
volume = {23},
number = {3},
pages = {165-172},
doi = {10.1159/000492524},
pmid = {30300878},
issn = {1421-9700},
mesh = {Adult ; Aged ; *Cochlear Implantation ; Cochlear Implants ; Deafness/*rehabilitation ; Dizziness ; Female ; *Gait ; Humans ; Male ; Middle Aged ; Postoperative Complications/*epidemiology/physiopathology ; *Postural Balance ; Reflex, Abnormal ; Reflex, Vestibulo-Ocular ; Sensation Disorders/*epidemiology/physiopathology ; Vertigo ; },
abstract = {BACKGROUND: After cochlear implant (CI) surgery, some patients experience vertigo, dizziness and/or deficits in vestibulo-ocular reflexes. However, little is known about the effect of CI surgery on balance control. Therefore, we examined differences in stance and gait balance control before versus after CI surgery.<br><br>METHODS: Balance control of 30 CI patients (mean age 59, SD 15.4 years), receiving a first unilateral CI surgery, was measured preoperatively and postoperatively 1 month after the initial implant stimulation (2 months after surgery). Trunk sway was measured during 14 stance and gait tests using an angular-velocity system mounted at lumbar vertebrae 1-3.<br><br>RESULTS: For pre- versus postoperative comparisons across all 30 patients, a nonsignificant worsening in balance control was observed. Significant changes were, however, found within subgroups. Patients younger than 60 years of age had a significant worsening of an overall balance control index (BCI) after CI surgery (p = 0.008), as did patients with a normal BCI preoperatively (p = 0.005). Gait task measures comprising the BCI followed a similar pattern, but stance control was unchanged. In contrast, patients over 60 years or with a pathological BCI preoperatively showed improved tandem walking postoperatively (p = 0.0235).<br><br>CONCLUSION: Across all CI patients, CI surgery has a minor effect on balance control 2 months postoperatively. However, for patients younger than 60 years and those with normal balance control preoperatively, balance control worsened for gait indicating the need for preoperative counseling.},
}
@article {pmid30297993,
year = {2018},
author = {Williams, NJ and Daly, I and Nasuto, SJ},
title = {Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data.},
journal = {Frontiers in computational neuroscience},
volume = {12},
number = {},
pages = {76},
pmid = {30297993},
issn = {1662-5188},
abstract = {The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional "states" are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate "trials" from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each 'state' were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.},
}
@article {pmid30296948,
year = {2018},
author = {Xie, Q and Pan, J and Chen, Y and He, Y and Ni, X and Zhang, J and Wang, F and Li, Y and Yu, R},
title = {A gaze-independent audiovisual brain-computer Interface for detecting awareness of patients with disorders of consciousness.},
journal = {BMC neurology},
volume = {18},
number = {1},
pages = {144},
pmid = {30296948},
issn = {1471-2377},
support = {2015CB351703//National Key Basic Research Program of China (973 Program)/ ; 91120305//National Natural Science Foundation of China/ ; 61503143//National Natural Science Foundation of China/ ; 2014A030312005//Natural Science Foundation of Guangdong Province (CN)/ ; 2014A030310244//Natural Science Foundation of Guangdong Province (CN)/ ; 2015A030313609//Guangdong Natural Science Foundation/ ; 201710010038//Pearl River S&amp;T Nova Program of Guangzhou/ ; },
mesh = {Adult ; Awareness/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Consciousness/*physiology ; Consciousness Disorders/*physiopathology ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Young Adult ; },
abstract = {BACKGROUND: Currently, it is challenging to detect the awareness of patients who suffer disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which do not depend on the behavioral response of patients, may serve for detecting the awareness in patients with DOC. However, we must develop effective BCIs for these patients because their ability to use BCIs does not as good as healthy users.<br><br>METHODS: Because patients with DOC generally do not exhibit eye movements, a gaze-independent audiovisual BCI is put forward in the study where semantically congruent and incongruent audiovisual number stimuli were sequentially presented to evoke event-related potentials (ERPs). Subjects were required to pay attention to congruent audiovisual stimuli (target) and ignore the incongruent audiovisual stimuli (non-target). The BCI system was evaluated by analyzing online and offline data from 10 healthy subjects followed by being applied to online awareness detection in 8 patients with DOC.<br><br>RESULTS: According to the results on healthy subjects, the audiovisual BCI system outperformed the corresponding auditory-only and visual-only systems. Multiple ERP components, including the P300, N400 and late positive complex (LPC), were observed using the audiovisual system, strengthening different brain responses to target stimuli and non-target stimuli. The results revealed the abilities of three of eight patients to follow commands and recognize numbers.<br><br>CONCLUSIONS: This gaze-independent audiovisual BCI system represents a useful auxiliary bedside tool to detect the awareness of patients with DOC.},
}
@article {pmid30296666,
year = {2018},
author = {Cooney, C and Folli, R and Coyle, D},
title = {Neurolinguistics Research Advancing Development of a Direct-Speech Brain-Computer Interface.},
journal = {iScience},
volume = {8},
number = {},
pages = {103-125},
pmid = {30296666},
issn = {2589-0042},
abstract = {A direct-speech brain-computer interface (DS-BCI) acquires neural signals corresponding to imagined speech, then processes and decodes these signals to produce a linguistic output in the form of phonemes, words, or sentences. Recent research has shown the potential of neurolinguistics to enhance decoding approaches to imagined speech with the inclusion of semantics and phonology in experimental procedures. As neurolinguistics research findings are beginning to be incorporated within the scope of DS-BCI research, it is our view that a thorough understanding of imagined speech, and its relationship with overt speech, must be considered an integral feature of research in this field. With a focus on imagined speech, we provide a review of the most important neurolinguistics research informing the field of DS-BCI and suggest how this research may be utilized to improve current experimental protocols and decoding techniques. Our review of the literature supports a cross-disciplinary approach to DS-BCI research, in which neurolinguistics concepts and methods are utilized to aid development of a naturalistic mode of communication.},
}
@article {pmid30295628,
year = {2019},
author = {Trigui, A and Hached, S and Ammari, AC and Savaria, Y and Sawan, M},
title = {Maximizing Data Transmission Rate for Implantable Devices Over a Single Inductive Link: Methodological Review.},
journal = {IEEE reviews in biomedical engineering},
volume = {12},
number = {},
pages = {72-87},
doi = {10.1109/RBME.2018.2873817},
pmid = {30295628},
issn = {1941-1189},
mesh = {Brain-Computer Interfaces/trends ; Capsule Endoscopes/trends ; Chronic Disease/*therapy ; Humans ; Implantable Neurostimulators/*trends ; Infusion Pumps, Implantable/*trends ; Visual Prosthesis/trends ; Wireless Technology/*trends ; },
abstract = {Due to the constantly growing geriatric population and the projected increase of the prevalence of chronic diseases that are refractory to drugs, implantable medical devices (IMDs) such as neurostimulators, endoscopic capsules, artificial retinal prostheses, and brain-machine interfaces are being developed. According to many business forecast firms, the IMD market is expected to grow and they are subject to much research aiming to overcome the numerous challenges of their development. One of these challenges consists of designing a wireless power and data transmission system that has high power efficiency, high data rates, low power consumption, and high robustness against noise. This is in addition to minimal design and implementation complexity. This manuscript concerns a comprehensive survey of the latest techniques used to power up and communicate between an external base station and an IMD. Patient safety considerations related to biological, physical, electromagnetic, and electromagnetic interference concerns for wireless IMDs are also explored. The simultaneous powering and data communication techniques using a single inductive link for both power transfer and bidirectional data communication, including the various data modulation/demodulation techniques, are also reviewed. This review will hopefully contribute to the persistent efforts to implement compact reliable IMDs while lowering their cost and upsurging their benefits.},
}
@article {pmid30295623,
year = {2020},
author = {Si-Mohammed, H and Petit, J and Jeunet, C and Argelaguet, F and Spindler, F and Evain, A and Roussel, N and Casiez, G and Lecuyer, A},
title = {Towards BCI-Based Interfaces for Augmented Reality: Feasibility, Design and Evaluation.},
journal = {IEEE transactions on visualization and computer graphics},
volume = {26},
number = {3},
pages = {1608-1621},
doi = {10.1109/TVCG.2018.2873737},
pmid = {30295623},
issn = {1941-0506},
mesh = {Adult ; *Augmented Reality ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/physiology ; Feasibility Studies ; Head/physiology ; Humans ; Photic Stimulation ; Task Performance and Analysis ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs). Experimental results showed that a BCI and an OST-HMD equipment (EEG headset and Hololens in our case) are well compatible and that small movements of the head can be tolerated when using the BCI. Second, we introduced a design space for command display strategies based on BCI in AR, when exploiting a famous brain pattern called Steady-State Visually Evoked Potential (SSVEP). Our design space relies on five dimensions concerning the visual layout of the BCI menu; namely: orientation, frame-of-reference, anchorage, size and explicitness. We implemented various BCI-based display strategies and tested them within the context of mobile robot control in AR. Our findings were finally integrated within an operational prototype based on a real mobile robot that is controlled in AR using a BCI and a HoloLens headset. Taken together our results (4 user studies) and our methodology could pave the way to future interaction schemes in Augmented Reality exploiting 3D User Interfaces based on brain activity and BCIs.},
}
@article {pmid30295567,
year = {2019},
author = {Congard, A and Christophe, V and Duprez, C and Baudry, AS and Antoine, P and Lesur, A and Loustalot, C and Guillemet, C and Leclercq, M and Segura, C and Carlier, D and Lefeuvre-Plesse, C and Simon, H and Frenel, JS and Vanlemmens, L},
title = {The self-reported perceptions of the repercussions of the disease and its treatments on daily life for young women with breast cancer and their partners.},
journal = {Journal of psychosocial oncology},
volume = {37},
number = {1},
pages = {50-68},
doi = {10.1080/07347332.2018.1479326},
pmid = {30295567},
issn = {1540-7586},
mesh = {Adult ; *Attitude to Health ; Breast Neoplasms/*psychology/therapy ; Cross-Sectional Studies ; Female ; Humans ; Male ; Middle Aged ; Self Report ; Sexual Partners/*psychology ; },
abstract = {PURPOSE: This study aimed to compare the self-reported perceptions of the repercussions of the disease and its treatments and emotional distress in young women with breast cancer and their partners.<br><br>DESIGN: Cross-sectional study using self-reported questionnaires.<br><br>SAMPLE: 491 couples in which women were aged <45&#x2009;years when diagnosed with non-metastatic breast cancer in four different groups of treatment: during chemotherapy with or without Trastuzumab; under Trastuzumab with or without hormone therapy; during hormone therapy; and during the follow-up period.<br><br>METHODS: Patients and partners completed a questionnaire assessing their self-reported perceptions of the disease and treatments (Patient YW-BCI and Partner YW-BCI for the partners) and their emotional distress (CESD; STAI).<br><br>FINDINGS: Patients reported more difficulties than partners in the management of child(ren) and everyday life, body image and sexuality, negative affectivity about the disease and apprehension about the future, career management, and finances. While the difficulties were generally more marked in the chemotherapy and Trastuzumab groups than in the hormone therapy and follow-up groups, the negative affectivity about the disease and apprehension about the future was high in all four groups, especially in patients. The partners reported more difficulties in sharing with close relatives, and even more in those groups reflecting the latest treatment phases. No difference appeared between patients and partners in couple cohesion and deterioration of relationships with relatives. Partners were less anxious than patients but as depressed as them.<br><br>CONCLUSIONS: Difficulties of patients and partners seem particularly severe in the early care pathway, maybe reflecting better adjustment in women under surveillance and their partners. A longitudinal study will substantiate this finding and enable a better identification of some explanatory processes of these differences and similarities in the daily self-reported repercussions of the disease throughout the cancer care pathway. Implications for psychosocial oncology: It seems important to support young women with breast cancer and their partners, as our results evidence distress in both and differences according to the type of treatment the woman is currently receiving. Healthcare providers need consistent methods to identify and respond to couples' distress and reduce significant disparities in support.},
}
@article {pmid30287015,
year = {2018},
author = {Jaworska, MM and Stępniak, I and Galiński, M and Kasprzak, D and Biniaś, D and Górak, A},
title = {Modification of chitin structure with tailored ionic liquids.},
journal = {Carbohydrate polymers},
volume = {202},
number = {},
pages = {397-403},
doi = {10.1016/j.carbpol.2018.09.012},
pmid = {30287015},
issn = {1879-1344},
abstract = {Chitin, poly N-acetylglucosamine, has a great potential for use on an industrial scale as an enzyme carrier but it has an unfavorable particle structure that can be modified using ionic liquids (ILs). Several ionic liquids were investigated that have the same substituents on the ring (methyl- and propyl-) but differed in the type of cationic ring (pyrrolidinium, piperidinium, and piperazinium). Organic acid ions (acetic and lactic) were used as counter ions. 1-ethyl-3-methyl-imidazolium acetate and 1-ethyl-3-methyl-imidazolium lactate were used as a reference. The results confirm that the chitin particle structure or size, or both, simultaneously changes if chitin is dissolved in an IL and then precipitated. Organic acid anions and short substituents on the cationic ring of ILs influenced particle modification substantially, whereas the type of ring played a minor role. Additionally, the ionic liquids [MPpyrr][OAc], [MPpip][OAc] and [DMPpz][OAc] could be reused up to at least 4 times without losing their ability to dissolve chitin.},
}
@article {pmid30286960,
year = {2018},
author = {Black, L and Gaebler-Spira, D},
title = {Nonsurgical Treatment Options for Upper Limb Spasticity.},
journal = {Hand clinics},
volume = {34},
number = {4},
pages = {455-464},
doi = {10.1016/j.hcl.2018.06.003},
pmid = {30286960},
issn = {1558-1969},
mesh = {Algorithms ; Brain-Computer Interfaces ; Humans ; Muscle Relaxants, Central/therapeutic use ; Muscle Spasticity/*physiopathology/*therapy ; Nerve Block ; Neuromuscular Agents/therapeutic use ; Orthotic Devices ; Physical Therapy Modalities ; Robotics ; Transcranial Magnetic Stimulation ; Upper Extremity/*physiopathology ; Virtual Reality ; },
abstract = {There are many nonsurgical treatment options for patients with upper limb spasticity. This article presents an algorithmic approach to management, encompassing evidence-based rehabilitation therapies, medications, and promising new orthotic and robotic innovations.},
}
@article {pmid30281468,
year = {2018},
author = {Lindgren, JT and Merlini, A and Lecuyer, A and Andriulli, FP},
title = {simBCI - A framework for studying BCI methods by simulated EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {},
number = {},
pages = {},
doi = {10.1109/TNSRE.2018.2873061},
pmid = {30281468},
issn = {1558-0210},
abstract = {Brain-Computer Interface (BCI) methods are commonly studied using Electroencephalogram (EEG) data recorded from human experiments. For understanding and developing BCI signal processing techniques real data is costly to obtain and its composition is apriori unknown. The brain mechanisms generating the EEG are not directly observable and their states cannot be uniquely identified from the EEG. Subsequently, we do not have generative ground truth for real data. In this paper we propose a novel convenience framework called simBCI to alleviate testing and studying BCI signal processing methods in simulated, controlled conditions. The framework can be used to generate artificial BCI data and to test classification pipelines with such data. Models and parameters on both data generation and the signal processing side can be iterated over to examine the interplay of different combinations. The framework provides the first time open source implementations of several models and methods. We invite researchers to insert more advanced models. The proposed system does not intend to replace human experiments. Instead, it can be used to discover hypotheses, study algorithms, to educate about BCI, and to debug signal processing pipelines of other BCI systems. The proposed framework is modular, extensible and freely available as open source1. It currently requires Matlab.},
}
@article {pmid30281467,
year = {2018},
author = {Lim, WL and Sourina, O and Wang, LP},
title = {STEW: Simultaneous Task EEG Workload Data Set.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {11},
pages = {2106-2114},
doi = {10.1109/TNSRE.2018.2872924},
pmid = {30281467},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*statistics &amp; numerical data ; Humans ; Male ; Models, Neurological ; Reproducibility of Results ; Support Vector Machine ; Workload/*psychology ; },
abstract = {This paper describes an open access electroencephalography (EEG) data set for multitasking mental workload activity induced by a single-session simultaneous capacity (SIMKAP) experiment with 48 subjects. To validate the database, EEG spectral activity was evaluated with EEGLAB and the significant channels and activities for the experiment are highlighted. Classification performance was evaluated by training a support vector regression model on selected features from neighborhood component analysis based on a nine-point workload rating scale. With a reduced feature dimension, 69% classification accuracy was obtained for 3 identified workload levels from the rating scale with Cohen's kappa of 0.46. Accurate discrimination of mental workload is a desirable outcome in the field of operator performance analysis and BCI development; thus, we hope that our provided database and analyses can contribute to future investigations in this research field.},
}
@article {pmid30281428,
year = {2018},
author = {Meng, J and Streitz, T and Gulachek, N and Suma, D and He, B},
title = {Three-Dimensional Brain-Computer Interface Control Through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {11},
pages = {2417-2427},
pmid = {30281428},
issn = {1558-2531},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Attention/*physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Imaging, Three-Dimensional/*methods ; Male ; Middle Aged ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: While noninvasive electroenceph-alography (EEG) based brain-computer interfacing (BCI) has been successfully demonstrated in two-dimensional (2-D) control tasks, little work has been published regarding its extension to practical three-dimensional (3-D) control.<br><br>METHODS: In this study, we developed a new BCI approach for 3-D control by combining a novel form of endogenous visuospatial attentional modulation, defined as overt spatial attention (OSA), and motor imagery (MI).<br><br>RESULTS: OSA modulation was shown to provide comparable control to conventional MI modulation in both 1-D and 2-D tasks. Furthermore, this paper provides evidence for the functional independence of traditional MI and OSA, as well as an investigation into the simultaneous use of both. Using this newly proposed BCI paradigm, 16 participants successfully completed a 3-D eight-target control task. Nine of these subjects further demonstrated robust 3-D control in a 12-target task, significantly outperforming the information transfer rate achieved in the 1-D and 2-D control tasks (29.7 &#xb1; 1.6 b/min).<br><br>CONCLUSION: These results strongly support the hypothesis that noninvasive EEG-based BCI can provide robust 3-D control through endogenous neural modulation in broader populations with limited training.<br><br>SIGNIFICANCE: Through the combination of the two strategies (MI and OSA), a substantial portion of the recruited subjects were capable of robustly controlling a virtual cursor in 3-D space. The proposed novel approach could broaden the dimensionality of BCI control and shorten the training time.},
}
@article {pmid30279982,
year = {2018},
author = {Bajaj, V and Taran, S and Sengur, A},
title = {Emotion classification using flexible analytic wavelet transform for electroencephalogram signals.},
journal = {Health information science and systems},
volume = {6},
number = {1},
pages = {12},
pmid = {30279982},
issn = {2047-2501},
abstract = {Emotion based brain computer system finds applications for impaired people to communicate with surroundings. In this paper, electroencephalogram (EEG) database of four emotions (happy, fear, sad, and relax) is recorded and flexible analytic wavelet transform (FAWT) is proposed for the emotion classification. FAWT analyzes the EEG signal into sub-bands and statistical measures are computed from the sub-bands for extraction of emotion specific information. The emotion classification performance of sub-band wise extracted features is examined over the variants of k-nearest-neighbor (KNN) classifier. The weighted-KNN provides the best emotion classification performance 86.1% as compared to other KNN variants. The proposed method shows better emotion classification performance as compared to other existing four emotions classification methods.},
}
@article {pmid30279463,
year = {2018},
author = {Xing, X and Wang, Y and Pei, W and Guo, X and Liu, Z and Wang, F and Ming, G and Zhao, H and Gui, Q and Chen, H},
title = {A High-Speed SSVEP-Based BCI Using Dry EEG Electrodes.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {14708},
pmid = {30279463},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Signal Processing, Computer-Assisted/*instrumentation ; Time Factors ; Wearable Electronic Devices ; },
abstract = {A high-speed steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system using dry EEG electrodes was demonstrated in this study. The dry electrode was fabricated in our laboratory. It was designed as claw-like structure with a diameter of 14 mm, featuring 8 small fingers of 6 mm length and 2 mm diameter. The structure and elasticity can help the fingers pass through the hair and contact the scalp when the electrode is placed on head. The electrode was capable of recording spontaneous EEG and evoked brain activities such as SSVEP with high signal-to-noise ratio. This study implemented a twelve-class SSVEP-based BCI system with eight electrodes embedded in a headband. Subjects also completed a comfort level questionnaire with the dry electrodes. Using a preprocessing algorithm of filter bank analysis (FBA) and a classification algorithm based on task-related component analysis (TRCA), the average classification accuracy of eleven participants was 93.2% using 1-second-long SSVEPs, leading to an average information transfer rate (ITR) of 92.35 bits/min. All subjects did not report obvious discomfort with the dry electrodes. This result represented the highest communication speed in the dry-electrode based BCI systems. The proposed system could provide a comfortable user experience and a stable control method for developing practical BCIs.},
}
@article {pmid30279309,
year = {2018},
author = {Waytowich, N and Lawhern, VJ and Garcia, JO and Cummings, J and Faller, J and Sajda, P and Vettel, JM},
title = {Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066031},
doi = {10.1088/1741-2552/aae5d8},
pmid = {30279309},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*classification ; Evoked Potentials, Visual/*physiology ; Healthy Volunteers ; Humans ; Machine Learning ; *Neural Networks, Computer ; Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Visual Cortex/physiology ; },
abstract = {OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail.<br><br>APPROACH: In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration.<br><br>MAIN RESULTS: The Compact-CNN demonstrates across subject mean accuracy of approximately 80%, out-performing current state-of-the-art, hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase- and amplitude-related features associated with the structure of the dataset.<br><br>SIGNIFICANCE: We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g. asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.},
}
@article {pmid30278823,
year = {2018},
author = {Mwale, M and Muula, AS},
title = {Effects of adolescent exposure to behaviour change interventions on their HIV risk reduction in Northern Malawi: a situation analysis.},
journal = {SAHARA J : journal of Social Aspects of HIV/AIDS Research Alliance},
volume = {15},
number = {1},
pages = {146-154},
pmid = {30278823},
issn = {1813-4424},
mesh = {Adolescent ; Adolescent Behavior/*psychology ; Child ; Female ; HIV Infections/*prevention &amp; control ; Health Knowledge, Attitudes, Practice ; Health Surveys ; Humans ; Malawi/epidemiology ; Male ; *Risk Reduction Behavior ; *School Health Services ; *Schools ; Sexual Behavior/*psychology ; Students/*psychology ; },
abstract = {Understanding adolescents' translation of HIV and AIDS-related behaviour change interventions (BCI) knowledge and skills into expected behavioural outcomes helps us appreciate behaviour change dynamics among young people and informs evidence-based programming. We explored the effects of adolescents' exposure to BCI on their HIV risk reduction in selected schools in Nkhatabay and Mzimba districts and Mzuzu city in Northern Malawi. The study used questionnaires as instruments. Data were collected between January and April 2017. Adolescent boys and girls [n = 552], ages 11-19 were randomly sampled to participate. Data analysis was through multiple regression and content analysis. Respondents included 324 female [58.7%] and 228 male [41.3%]. Multiple regression analysis indicated that exposure to BCI did not affect risk reduction in the study area. The best stepwise model isolated sexual experience ([Beta = .727, p = .0001, p < .05]) as having the strongest correlation with the dependent variable - risk reduction. BCI exposure was stepwise excluded ([Beta = -.082, p = .053, p > .05]). There was therefore no evidence against the null hypothesis of no relationship between adolescent exposure to BCI and their HIV risk reduction. Overall there was limited BCI knowledge and skills translation to behavioural risk reduction. The study points to the need to evaluate and redesign adolescent BCI in line with current behavioural dynamics among young people in Malawi. The findings have been used to inform the design and programming of a model to be tested for feasibility through a quasi-experiment in the second phase of our project.},
}
@article {pmid30278338,
year = {2018},
author = {Al-Nafjan, A and Al-Wabil, A and AlMudhi, A and Hosny, M},
title = {Measuring and monitoring emotional changes in children who stutter.},
journal = {Computers in biology and medicine},
volume = {102},
number = {},
pages = {138-150},
doi = {10.1016/j.compbiomed.2018.09.022},
pmid = {30278338},
issn = {1879-0534},
mesh = {Adolescent ; Algorithms ; Brain/physiopathology ; Brain-Computer Interfaces ; Child ; Cognition ; *Electroencephalography ; *Emotions ; Female ; Humans ; Male ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Speech Disorders/physiopathology/psychology ; Speech-Language Pathology ; Stuttering/physiopathology/*psychology ; },
abstract = {The assessment of clients with speech disorders presents challenges for speech-language pathologists. For example, having a reliable way of measuring the severity of the case, determining which remedial program is aligned with a patient's needs, and measuring of treatment processes. There is potential for brain-computer interface (BCI) applications to enhance speech therapy sessions by providing objective insights and real-time visualization of brain activity during the sessions. This paper presents a study on emotional state detection during speech pathology. The goal of this study is to investigate affective-motivational brain responses to stimuli in children who stutter. To this end, we conducted an experiment that involved recording frontal electroencephalography (EEG) activity from fifteen children with stuttering whilst they looked at visual stimuli. The contribution of our study is to provide a comprehensive background and a framework for emotional state detection experiments as assessment and monitoring tool in speech pathology. It mainly discusses the feasibility and potential benefits of applying EEG-based emotion detection in speech-language therapy contexts of use. The findings of our research indicate that emotional recognition using non-invasive EEG-based BCI system is sufficient to differentiate between affective states of individuals in treatment contexts.},
}
@article {pmid30277219,
year = {2018},
author = {Robinson, N and Thomas, KP and Vinod, AP},
title = {Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066032},
doi = {10.1088/1741-2552/aae597},
pmid = {30277219},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Beta Rhythm/physiology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography/classification/*methods ; Entropy ; Female ; Gamma Rhythm/physiology ; Humans ; Imagination/physiology ; Male ; Models, Neurological ; Movement/physiology ; Psychomotor Performance ; Reproducibility of Results ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {UNLABELLED: Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR)-brain-computer interface (BCI) to boost its potential real-world use.<br><br>OBJECTIVE: This paper investigates two vital factors in efficient and robust SMR-BCI design-algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region.<br><br>APPROACH: A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper.<br><br>MAIN RESULTS: The proposed technique results in a significant ([Formula: see text]) increase in average ([Formula: see text]) classification accuracy by [Formula: see text] accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest.<br><br>SIGNIFICANCE: Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance.},
}
@article {pmid30274210,
year = {2018},
author = {McClay, W},
title = {A Magnetoencephalographic/Encephalographic (MEG/EEG) Brain-Computer Interface Driver for Interactive iOS Mobile Videogame Applications Utilizing the Hadoop Ecosystem, MongoDB, and Cassandra NoSQL Databases.},
journal = {Diseases (Basel, Switzerland)},
volume = {6},
number = {4},
pages = {},
pmid = {30274210},
issn = {2079-9721},
abstract = {In Phase I, we collected data on five subjects yielding over 90% positive performance in Magnetoencephalographic (MEG) mid-and post-movement activity. In addition, a driver was developed that substituted the actions of the Brain Computer Interface (BCI) as mouse button presses for real-time use in visual simulations. The process was interfaced to a flight visualization demonstration utilizing left or right brainwave thought movement, the user experiences, the aircraft turning in the chosen direction, or on iOS Mobile Warfighter Videogame application. The BCI's data analytics of a subject's MEG brain waves and flight visualization performance videogame analytics were stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse. In Phase II portion of the project involves the Emotiv Encephalographic (EEG) Wireless Brain[-]Computer interfaces (BCIs) allow for people to establish a novel communication channel between the human brain and a machine, in this case, an iOS Mobile Application(s). The EEG BCI utilizes advanced and novel machine learning algorithms, as well as the Spark Directed Acyclic Graph (DAG), Cassandra NoSQL database environment, and also the competitor NoSQL MongoDB database for housing BCI analytics of subject's response and users' intent illustrated for both MEG/EEG brainwave signal acquisition. The wireless EEG signals that were acquired from the OpenVibe and the Emotiv EPOC headset can be connected via Bluetooth to an iPhone utilizing a thin Client architecture. The use of NoSQL databases were chosen because of its schema-less architecture and Map Reduce computational paradigm algorithm for housing a user's brain signals from each referencing sensor. Thus, in the near future, if multiple users are playing on an online network connection and an MEG/EEG sensor fails, or if the connection is lost from the smartphone and the webserver due to low battery power or failed data transmission, it will not nullify the NoSQL document-oriented (MongoDB) or column-oriented Cassandra databases. Additionally, NoSQL databases have fast querying and indexing methodologies, which are perfect for online game analytics and technology. In Phase II, we collected data on five MEG subjects, yielding over 90% positive performance on iOS Mobile Applications with Objective-C and C++, however on EEG signals utilized on three subjects with the Emotiv wireless headsets and (n < 10) subjects from the OpenVibe EEG database the Variational Bayesian Factor Analysis Algorithm (VBFA) yielded below 60% performance and we are currently pursuing extending the VBFA algorithm to work in the time-frequency domain referred to as VBFA-TF to enhance EEG performance in the near future. The novel usage of NoSQL databases, Cassandra and MongoDB, were the primary main enhancements of the BCI Phase II MEG/EEG brain signal data acquisition, queries, and rapid analytics, with MapReduce and Spark DAG demonstrating future implications for next generation biometric MEG/EEG NoSQL databases.},
}
@article {pmid30271700,
year = {2018},
author = {Coogan, CG and He, B},
title = {Brain-computer interface control in a virtual reality environment and applications for the internet of things.},
journal = {IEEE access : practical innovations, open solutions},
volume = {6},
number = {},
pages = {10840-10849},
pmid = {30271700},
issn = {2169-3536},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) have enabled individuals to control devices such as spellers, robotic arms, drones, and wheelchairs, but often these BCI applications are restricted to research laboratories. With the advent of virtual reality (VR) systems and the internet of things (IoT) we can couple these technologies to offer real-time control of a user's virtual and physical environment. Likewise, BCI applications are often single-use with user's having no control outside of the restrictions placed upon the applications at the time of creation. Therefore, there is a need to create a tool that allows users the flexibility to create and modularize aspects of BCI applications for control of IoT devices and VR environments. Using a popular video game engine, Unity, and coupling it with BCI2000, we can create diverse applications that give the end-user additional autonomy during the task at hand. We demonstrate the validity of controlling a Unity-based VR environment and several commercial IoT devices via direct neural interfacing processed through BCI2000.},
}
@article {pmid30271337,
year = {2018},
author = {Tian, Y and Zhang, H and Pang, Y and Lin, J},
title = {Classification for Single-Trial N170 During Responding to Facial Picture With Emotion.},
journal = {Frontiers in computational neuroscience},
volume = {12},
number = {},
pages = {68},
pmid = {30271337},
issn = {1662-5188},
abstract = {Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive and negative emotions of around 170 ms at the occipitotemporal electrodes (i.e., N170). Then, we further proposed the application of the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERPs were obtained by averaging most of the time while ignoring the trial-to-trial variation. In order to find an optimal classifier for emotional classification with single-trial N170 as a feature, three types of classifiers, namely, linear discriminant analysis (LDA), L1-regularized logistic regression (L1LR), and support vector machine with radial basis function (RBF-SVM), were comparatively investigated. The results showed that the single-trial N170 could be used as a classification feature to successfully distinguish positive emotion from negative emotion. L1-regularized logistic regression classifiers showed a good generalization, whereas LDA showed a relatively poor generalization. Moreover, when compared with L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle while applying it to the online operating system of brain-computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expression and that the single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain.},
}
@article {pmid30271333,
year = {2018},
author = {Parto Dezfouli, M and Khamechian, MB and Treue, S and Esghaei, M and Daliri, MR},
title = {Neural Activity Predicts Reaction in Primates Long Before a Behavioral Response.},
journal = {Frontiers in behavioral neuroscience},
volume = {12},
number = {},
pages = {207},
pmid = {30271333},
issn = {1662-5153},
abstract = {How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit's speed in information processing.},
}
@article {pmid30271318,
year = {2018},
author = {Mohanty, R and Sinha, AM and Remsik, AB and Dodd, KC and Young, BM and Jacobson, T and McMillan, M and Thoma, J and Advani, H and Nair, VA and Kang, TJ and Caldera, K and Edwards, DF and Williams, JC and Prabhakaran, V},
title = {Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {624},
pmid = {30271318},
issn = {1662-4548},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study cohort consisted of 20 chronic-stage stroke subjects exhibiting persistent upper-extremity motor deficits who received the intervention using a closed-loop neurofeedback BCI device. Over the course of this intervention, resting state functional MRI scans were collected at four distinct time points: namely, pre-intervention, mid-intervention, post-intervention and 1-month after completion of intervention. Behavioral assessments were administered outside the scanner at each time-point to collect objective measures such as the Action Research Arm Test, Nine-Hole Peg Test, and Barthel Index as well as subjective measures including the Stroke Impact Scale. The present analysis focused on neuroplasticity and behavioral outcomes measured across pre-intervention, post-intervention and 1-month post-intervention to study immediate and carry-over effects. Rs-FC, changes in rs-FC within the motor network and the behavioral measures at preceding stages were used as input features and behavioral measures and associated changes at succeeding stages were used as outcomes for machine-learning-based support vector regression (SVR) models. Potential clinical confounding factors such as age, gender, lesion hemisphere, and stroke severity were included as additional features in each of the regression models. Sequential forward feature selection procedure narrowed the search for important correlates. Behavioral outcomes at preceding time-points outperformed rs-FC-based correlates. Rs-FC and changes associated with bilateral primary motor areas were found to be important correlates of across several behavioral outcomes and were stable upon inclusion of clinical variables as well. NIH Stroke Scale and motor impairment severity were the most influential clinical variables. Comparatively, linear SVR models aided in evaluation of contribution of individual correlates and seed regions while non-linear SVR models achieved higher performance in prediction of behavioral outcomes.},
}
@article {pmid30269809,
year = {2018},
author = {Solinsky, R and Specker Sullivan, L},
title = {Ethical Issues Surrounding a New Generation of Neuroprostheses for Patients With Spinal Cord Injuries.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {10},
number = {9 Suppl 2},
pages = {S244-S248},
doi = {10.1016/j.pmrj.2018.05.020},
pmid = {30269809},
issn = {1934-1563},
mesh = {Brain-Computer Interfaces/*ethics ; Electric Stimulation Therapy/*ethics/instrumentation ; *Electrodes, Implanted ; Humans ; Neurological Rehabilitation/*ethics/instrumentation ; Spinal Cord Injuries/physiopathology/*rehabilitation ; },
}
@article {pmid30269808,
year = {2018},
author = {Bockbrader, MA and Francisco, G and Lee, R and Olson, J and Solinsky, R and Boninger, ML},
title = {Brain Computer Interfaces in Rehabilitation Medicine.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {10},
number = {9 Suppl 2},
pages = {S233-S243},
doi = {10.1016/j.pmrj.2018.05.028},
pmid = {30269808},
issn = {1934-1563},
mesh = {*Algorithms ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Humans ; *User-Computer Interface ; },
abstract = {One innovation currently influencing physical medicine and rehabilitation is brain-computer interface (BCI) technology. BCI systems used for motor control record neural activity associated with thoughts, perceptions, and motor intent; decode brain signals into commands for output devices; and perform the user's intended action through an output device. BCI systems used for sensory augmentation transduce environmental stimuli into neural signals interpretable by the central nervous system. Both types of systems have potential for reducing disability by facilitating a user's interaction with the environment. Investigational BCI systems are being used in the rehabilitation setting both as neuroprostheses to replace lost function and as potential plasticity-enhancing therapy tools aimed at accelerating neurorecovery. Populations benefitting from motor and somatosensory BCI systems include those with spinal cord injury, motor neuron disease, limb amputation, and stroke. This article discusses the basic components of BCI for rehabilitation, including recording systems and locations, signal processing and translation algorithms, and external devices controlled through BCI commands. An overview of applications in motor and sensory restoration is provided, along with ethical questions and user perspectives regarding BCI technology.},
}
@article {pmid30268089,
year = {2018},
author = {Luo, TJ and Zhou, CL and Chao, F},
title = {Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.},
journal = {BMC bioinformatics},
volume = {19},
number = {1},
pages = {344},
pmid = {30268089},
issn = {1471-2105},
support = {61673326//National Natural Science Foundation of China/ ; 61673322//National Natural Science Foundation of China/ ; 20720160126//Fundamental Research Funds for the Central Universities/ ; 2017J01128//Natural Science Foundation of Fujian Province of China/ ; 2017J01129//Natural Science Foundation of Fujian Province of China/ ; 663830//European Union's Horizon 2020 research and innovation programme/ ; 91746103//National Natural Science Foundation of China (CN)/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Databases, Genetic ; Datasets as Topic ; Electroencephalography/*classification/*methods ; Humans ; Imagination/*physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.<br><br>METHODS: Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals.<br><br>RESULTS: Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets.<br><br>CONCLUSION: By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.},
}
@article {pmid30267255,
year = {2019},
author = {Acevedo, R and Atum, Y and Gareis, I and Biurrun Manresa, J and Medina Bañuelos, V and Rufiner, L},
title = {A comparison of feature extraction strategies using wavelet dictionaries and feature selection methods for single trial P300-based BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {3},
pages = {589-600},
pmid = {30267255},
issn = {1741-0444},
support = {PID 6101//Universidad Nacional de Entre R&#xed;os/ ; },
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/methods ; *Event-Related Potentials, P300 ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {The P300 component of event-related potentials (ERPs) is widely used in the implementation of brain computer interfaces (BCI). In this context, one of the main issues to solve is the binary classification problem that entails differentiating between electroencephalographic (EEG) signals with and without P300. Given the particularly unfavorable signal-to-noise ratio (SNR) in the single-trial detection scenario, this is a challenging problem in the pattern recognition field. To the best of our knowledge, there are no previous experimental studies comparing feature extraction and selection methods for single trial P300-based BCIs using unified criteria and data. In order to improve the performance and robustness of single-trial classifiers, we analyzed and compared different alternatives for the feature generation and feature selection blocks. We evaluated different orthogonal decompositions based on the wavelet transform for feature extraction, as well as different filter, wrapper, and embedded alternatives for feature selection. Accuracies over 75% were obtained for most of the analyzed strategies with a relatively low computational cost, making them attractive for a practical BCI implementation using inexpensive hardware. Graphical Abstract Experiments performed for P300 detection.},
}
@article {pmid30262356,
year = {2018},
author = {Long, Y and Liu, H and Li, Y and Jin, X and Zhou, Y and Li, J and Zheng, Z and Liu, P and Zhao, Y and Zheng, J and Zhang, J and Chen, M and Hao, J and Yang, Y and Liu, W},
title = {Early auditory skills development in Mandarin speaking children after bilateral cochlear implantation.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {114},
number = {},
pages = {153-158},
doi = {10.1016/j.ijporl.2018.08.039},
pmid = {30262356},
issn = {1872-8464},
mesh = {Age Factors ; Audiometry/methods ; Child Development/physiology ; Child, Preschool ; China ; Cochlear Implantation/*methods ; Cochlear Implants/statistics &amp; numerical data ; Deafness/physiopathology/*surgery ; Female ; Hearing/*physiology ; Humans ; Infant ; Language ; Male ; Peer Group ; Surveys and Questionnaires ; },
abstract = {OBJECTIVES: The purpose of the present study was: (1) to investigate the early auditory preverbal behaviors of infants/toddlers with bilateral cochlear implants (BCI), and to compare their performance with that of unilateral cochlear implant (UCI) peers; (2) to investigate effects of age of implantation, education level of caregivers, living environment, and unaided behavioral threshold before operation on early auditory preverbal development.<br><br>METHODS: The evaluation material of the present study was the Mandarin version of the LittlEARS[&#xae;] Auditory Questionnaire (LEAQ). Assessments were administrated at 0, 1, 2, 3, 6, 9, 12 and 24 months after cochlear implants (CIs) were switched on. A one-way ANOVA was used to analyze the differences of early auditory preverbal performance between each two contiguous test intervals. A two-sample t-test was used to analyze the difference of behaviors between infants/toddlers with BCI and UCI. Non-parametric tests were used to analyze the effects of potential affecting factors on auditory preverbal skills.<br><br>RESULTS: Nineteen subjects aging from 9 to 54 months (Mean&#x202f;=&#x202f;22.7, SD&#x202f;=&#x202f;13.7) were recruited in the study. At each evaluation time, the average scores of LEAQ were 4.58, 9.00, 16.00, 18.56, 22.00, 31.50, 29.67, and 34.35 respectively. The total score and semantic auditory behavior score increased significantly during the second months after CIs activation (the total score: LSD-t&#x202f;=&#x202f;3.157, p&#x202f;=&#x202f;0.030; semantic auditory behavior score: LSD-t&#x202f;=&#x202f;1.972, p&#x202f;=&#x202f;0.034). The score of BCI group was significantly higher than UCI group after 1, 3 and 6 months of CI use (1 month: t&#x202f;=&#x202f;3.257, p&#x202f;=&#x202f;0.002; 3 months: t&#x202f;=&#x202f;5.042, p&#x202f;=&#x202f;0.000; 6 months: t&#x202f;=&#x202f;4.054, p&#x202f;=&#x202f;0.000). Education level of caregivers had a positive effect on receptive auditory behavior (H&#x202f;=&#x202f;6.538, p&#x202f;=&#x202f;0.035) after CIs switched on for 3 months. The LEAQ performance was not significantly correlated with pre-operative behavioral threshold although they showed a trend of negative correlation in the first 3 months after activation.<br><br>CONCLUSION: The study indicated that infants and toddlers who underwent BCI had better auditory preverbal skills than their UCI peers. Higher caregivers' education level positively correlated with the early development of auditory preverbal skills. Better pre-operative behavioral threshold might also benefit early auditory preverbal skills development for BCI children.},
}
@article {pmid30262338,
year = {2019},
author = {Schneck, N and Tu, T and Bonanno, GA and Shear, MK and Sajda, P and Mann, JJ},
title = {Self-generated Unconscious Processing of Loss Linked to Less Severe Grieving.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {4},
number = {3},
pages = {271-279},
pmid = {30262338},
issn = {2451-9030},
support = {K23 MH114021/MH/NIMH NIH HHS/United States ; T32 MH015144/MH/NIMH NIH HHS/United States ; T32 MH020004/MH/NIMH NIH HHS/United States ; },
mesh = {Adaptation, Psychological ; Adult ; Brain/*physiology ; Brain Mapping ; Female ; *Grief ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neural Pathways ; Stroop Test ; *Unconscious, Psychology ; },
abstract = {BACKGROUND: The intense loss processing that characterizes grieving may help people to adapt to the loss. However, empirical studies show that more conscious loss-related thinking and greater reactivity to reminders of the deceased correspond to poorer adaptation. These findings raise the possibility that loss processing that is unconscious rather than conscious and is self-generated rather than reactive may facilitate adaptation. Here, we used machine learning to detect a functional magnetic resonance imaging (fMRI) signature of self-generated unconscious loss processing that we hypothesized to correlate with lower grief severity.<br><br>METHODS: A total of 29 subjects bereaved within the past 14 months participated. Participants performed a modified Stroop fMRI task using deceased-related words. A machine-learning regression, trained on Stroop fMRI data, learned a neural pattern for deceased-related selective attention (d-SA), the allocation of attention to the deceased. Expression of this pattern was tracked during a subsequent sustained attention fMRI task interspersed with deceased-related thought probes (SART-PROBES). d-SA pattern expression during SART-PROBES blocks without reported thoughts of loss indicated self-generated unconscious loss processing. Grief severity was measured with the Inventory for Complicated Grief.<br><br>RESULTS: d-SA expression during SART-PROBES blocks without conscious deceased-related thinking correlated negatively with Inventory for Complicated Grief score (r25&#xa0;=&#xa0;-.711, p < .001, 95% confidence interval&#xa0;=&#xa0;-0.89 to&#xa0;-0.42), accounting for 50% of variance. This relationship remained significant independent of demographic correlates of Inventory for Complicated Grief (B25&#xa0;=&#xa0;-30, t&#xa0;=&#xa0;-2.64, p&#xa0;= .02, 95% confidence interval&#xa0;=&#xa0;-56.2 to&#xa0;-4.6). Unconscious d-SA pattern expression also correlated with activity in dorsolateral prefrontal cortex and temporal parietal junction during the SART-PROBES (voxel: p < .001, cluster: p < .05).<br><br>CONCLUSIONS: Self-generated unconscious loss processing correlated with reduced grief severity. This activity, supported by a cognitive social neural architecture, may advance adaptation to the loss.},
}
@article {pmid30260322,
year = {2018},
author = {Jochumsen, M and Shafique, M and Hassan, A and Niazi, IK},
title = {Movement intention detection in adolescents with cerebral palsy from single-trial EEG.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066030},
doi = {10.1088/1741-2552/aae4b8},
pmid = {30260322},
issn = {1741-2552},
mesh = {Adolescent ; Cerebral Palsy/*physiopathology/psychology/rehabilitation ; Child ; Electrodes ; Electroencephalography/classification/*methods ; Electromyography ; Female ; Humans ; *Intention ; Male ; *Movement ; Psychomotor Performance ; Reproducibility of Results ; },
abstract = {OBJECTIVE: As for stroke rehabilitation, brain-computer interfaces could potentially be used for inducing neural plasticity in patients with cerebral palsy by pairing movement intentions with relevant somatosensory feedback. Therefore, the aim of this study was to investigate if movement intentions from children with cerebral palsy can be detected from single-trial EEG. Moreover, different feature types and electrode setups were evaluated.<br><br>APPROACH: Eight adolescents with cerebral palsy performed self-paced dorsiflexions of the ankle while nine channels of EEG were recorded. The EEG was divided into movement intention epochs and idle epochs. The data were pre-processed and temporal, spectral and template matching features were extracted and classified using a random forest classifier. The classification accuracy of the 2-class problem was used as an estimation of the detection performance. This analysis was repeated using a single EEG channel, a large Laplacian filtered channel and nine channels.<br><br>MAIN RESULTS: A classification accuracy of ~70% was obtained using only a single channel. This increased to ~80% for the Laplacian filtered data, while ~75% of the data were correctly classified when using nine channels. In general, the highest accuracies were obtained using temporal features or using all of them combined.<br><br>SIGNIFICANCE: The results indicate that it is possible to detect movement intentions in patients with cerebral palsy; this may be used in the development of a brain-computer interface for motor rehabilitation of patients with cerebral palsy.},
}
@article {pmid30260320,
year = {2019},
author = {Rezazadeh Sereshkeh, A and Yousefi, R and Wong, AT and Chau, T},
title = {Online classification of imagined speech using functional near-infrared spectroscopy signals.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016005},
doi = {10.1088/1741-2552/aae4b9},
pmid = {30260320},
issn = {1741-2552},
mesh = {Adult ; Cerebral Cortex/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Photic Stimulation/methods ; Random Allocation ; Spectroscopy, Near-Infrared/classification/methods ; Speech/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Most brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or music imagery to convey a message or to answer simple yes or no questions. These cognitive tasks usually have no direct association with the communicative intent, which makes them difficult for users to perform.<br><br>APPROACH: In this paper, a 3-class intuitive BCI is presented which enables users to directly answer yes or no questions by covertly rehearsing the word 'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of unconstrained rest which constitutes the third discernable task. Twelve participants each completed one offline block and six online blocks over the course of two sessions. The mean value of the change in oxygenated hemoglobin concentration during a trial was calculated for each channel and used to train a regularized linear discriminant analysis (RLDA) classifier.<br><br>MAIN RESULTS: By the final online block, nine out of 12 participants were performing above chance (p&#x2009;&#x2009;<&#x2009;&#x2009;0.001 using the binomial cumulative distribution), with a 3-class accuracy of 83.8%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;9.4%. Even when considering all participants, the average online 3-class accuracy over the last three blocks was 64.1 %&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;20.6%, with only three participants scoring below chance (p&#x2009;&#x2009;<&#x2009;&#x2009;0.001). For most participants, channels in the left temporal and temporoparietal cortex provided the most discriminative information.<br><br>SIGNIFICANCE: To our knowledge, this is the first report of an online 3-class imagined speech BCI. Our findings suggest that imagined speech can be used as a reliable activation task for selected users for development of more intuitive BCIs for communication.},
}
@article {pmid30258458,
year = {2018},
author = {Alonso-Valerdi, LM and Mercado-García, VR},
title = {Corrigendum to "Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments".},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {7129735},
pmid = {30258458},
issn = {1687-5273},
abstract = {[This corrects the article DOI: 10.1155/2017/6076913.].},
}
@article {pmid30258355,
year = {2018},
author = {Lebedev, MA and Opris, I and Casanova, MF},
title = {Editorial: Augmentation of Brain Function: Facts, Fiction and Controversy.},
journal = {Frontiers in systems neuroscience},
volume = {12},
number = {},
pages = {45},
doi = {10.3389/fnsys.2018.00045},
pmid = {30258355},
issn = {1662-5137},
}
@article {pmid30257858,
year = {2018},
author = {Mugler, EM and Tate, MC and Livescu, K and Templer, JW and Goldrick, MA and Slutzky, MW},
title = {Differential Representation of Articulatory Gestures and Phonemes in Precentral and Inferior Frontal Gyri.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {46},
pages = {9803-9813},
pmid = {30257858},
issn = {1529-2401},
support = {UL1 TR000150/TR/NCATS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; KL2 TR001424/TR/NCATS NIH HHS/United States ; F32 DC015708/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/instrumentation/*methods ; Electrocorticography/*methods ; Female ; Frontal Lobe/*physiology ; *Gestures ; Humans ; Male ; Movement/physiology ; Photic Stimulation/methods ; Prefrontal Cortex/*physiology ; Speech/*physiology ; },
abstract = {Speech is a critical form of human communication and is central to our daily lives. Yet, despite decades of study, an understanding of the fundamental neural control of speech production remains incomplete. Current theories model speech production as a hierarchy from sentences and phrases down to words, syllables, speech sounds (phonemes), and the actions of vocal tract articulators used to produce speech sounds (articulatory gestures). Here, we investigate the cortical representation of articulatory gestures and phonemes in ventral precentral and inferior frontal gyri in men and women. Our results indicate that ventral precentral cortex represents gestures to a greater extent than phonemes, while inferior frontal cortex represents both gestures and phonemes. These findings suggest that speech production shares a common cortical representation with that of other types of movement, such as arm and hand movements. This has important implications both for our understanding of speech production and for the design of brain-machine interfaces to restore communication to people who cannot speak.SIGNIFICANCE STATEMENT Despite being studied for decades, the production of speech by the brain is not fully understood. In particular, the most elemental parts of speech, speech sounds (phonemes) and the movements of vocal tract articulators used to produce these sounds (articulatory gestures), have both been hypothesized to be encoded in motor cortex. Using direct cortical recordings, we found evidence that primary motor and premotor cortices represent gestures to a greater extent than phonemes. Inferior frontal cortex (part of Broca's area) appears to represent both gestures and phonemes. These findings suggest that speech production shares a similar cortical organizational structure with the movement of other body parts.},
}
@article {pmid30257425,
year = {2018},
author = {Bouillot, S and Reboud, E and Huber, P},
title = {Functional Consequences of Calcium Influx Promoted by Bacterial Pore-Forming Toxins.},
journal = {Toxins},
volume = {10},
number = {10},
pages = {},
pmid = {30257425},
issn = {2072-6651},
mesh = {Animals ; Bacterial Toxins/*toxicity ; Calcium/*metabolism ; Cell Death/drug effects ; Humans ; Intercellular Junctions/drug effects ; Pore Forming Cytotoxic Proteins/*toxicity ; },
abstract = {Bacterial pore-forming toxins induce a rapid and massive increase in cytosolic Ca[2+] concentration due to the formation of pores in the plasma membrane and/or activation of Ca[2+]-channels. As Ca[2+] is an essential messenger in cellular signaling, a sustained increase in Ca[2+] concentration has dramatic consequences on cellular behavior, eventually leading to cell death. However, host cells have adapted mechanisms to protect against Ca[2+] intoxication, such as Ca[2+] efflux and membrane repair. The final outcome depends upon the nature and concentration of the toxin and on the cell type. This review highlights the repercussions of Ca[2+] overload on the induction of cell death, repair mechanisms, cellular adhesive properties, and the inflammatory response.},
}
@article {pmid30256217,
year = {2018},
author = {Li, L and Negoita, S},
title = {Brain-to-speech decoding will require linguistic and pragmatic data.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {063001},
doi = {10.1088/1741-2552/aae466},
pmid = {30256217},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Communication ; Electrocorticography ; Humans ; *Linguistics ; *Speech ; },
abstract = {OBJECTIVE: Advances in electrophysiological methods such as electrocorticography (ECoG) have enabled researchers to decode phonemes, syllables, and words from brain activity. The ultimate aspiration underlying these efforts is the development of a brain-machine interface (BMI) that will enable speakers to produce real-time, naturalistic speech. In the effort to create such a device, researchers have typically followed a bottom-up approach whereby low-level units of language (e.g. phonemes, syllables, or letters) are decoded from articulation areas (e.g. premotor cortex) with the aim of assembling these low-level units into words and sentences.<br><br>APPROACH: In this paper, we recommend that researchers supplement the existing bottom-up approach with a novel top-down approach. According to the top-down proposal, initial decoding of top-down information may facilitate the subsequent decoding of downstream representations by constraining the hypothesis space from which low-level units are selected.<br><br>MAIN RESULTS: We identify types and sources of top-down information that may crucially inform BMI decoding ecosystems: communicative intentions (e.g. speech acts), situational pragmatics (e.g. recurrent communicative pressures), and formal linguistic data (e.g. syntactic rules and constructions, lexical collocations, speakers' individual speech histories).<br><br>SIGNIFICANCE: Given the inherently interactive nature of communication, we further propose that BMIs be entrained on neural responses associated with interactive dialogue tasks, as opposed to the typical practice of entraining BMIs with non-interactive presentations of language stimuli.},
}
@article {pmid30254967,
year = {2018},
author = {Gorgey, AS},
title = {Robotic exoskeletons: The current pros and cons.},
journal = {World journal of orthopedics},
volume = {9},
number = {9},
pages = {112-119},
pmid = {30254967},
issn = {2218-5836},
abstract = {Robotic exoskeletons have emerged as rehabilitation tool that may ameliorate several of the existing health-related consequences after spinal cord injury (SCI). However, evidence to support its clinical application is still lacking considering their prohibitive cost. The current mini-review is written to highlight the main limitations and potential benefits of using exoskeletons in the rehabilitation of persons with SCI. We have recognized two main areas relevant to the design of exoskeletons and to their applications on major health consequences after SCI. The design prospective refers to safety concerns, fitting time and speed of exoskeletons. The health prospective refers to factors similar to body weight, physical activity, pressure injuries and bone health. Clinical trials are currently underway to address some of these limitations and to maximize the benefits in rehabilitation settings. Future directions highlight the need to use exoskeletons in conjunction with other existing and emerging technologies similar to functional electrical stimulation and brain-computer interface to address major limitations. Exoskeletons have the potential to revolutionize rehabilitation following SCI; however, it is still premature to make solid recommendations about their clinical use after SCI.},
}
@article {pmid30251977,
year = {2018},
author = {Lyukmanov, RK and Aziatskaya, GA and Mokienko, OA and Varako, NA and Kovyazina, MS and Suponeva, NA and Chernikova, LA and Frolov, AA and Piradov, MA},
title = {[Post-stroke rehabilitation training with a brain-computer interface: a clinical and neuropsychological study].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {118},
number = {8},
pages = {43-51},
doi = {10.17116/jnevro201811808143},
pmid = {30251977},
issn = {1997-7298},
mesh = {*Brain-Computer Interfaces ; Humans ; Middle Aged ; Paresis ; Recovery of Function ; *Stroke/complications/physiopathology ; *Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {AIM: To evaluate the clinical efficacy of BCI-supported mental practice and to reveal specific cognitive impairment which determine mental practice ineffectiveness and inability to perform MI.<br><br>MATERIAL AND METHODS: Fifty-five hemiplegic patients after first-time stroke (median age 54. 0 [44.0; 61.0], time from onset 6.0 [3.0; 13.0] month) were randomized into two groups - BCI and sham-controlled. Severity of arm paresis was measured by Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA) and Action Research Arm Test (ARAT). Twelve patients from the BCI group were examined using neuropsychological testing. After assessment, patients were trained to imagine kinesthetically a movement under control of BCI with the feedback presented via an exoskeleton. Patients underwent 12 training sessions lasting up to 30 min. In the end of the study, the scores on movement scales, electroencephalographic results obtained during training sessions were analyzed and compared to the results of neuropsychological testing.<br><br>RESULTS: Evaluation of the UL clinical assessments indicated that both groups improved on ARAT and FMA (sections A-D, H, I) but only the BCI group showed an improvement in the ARAT's grasp score (p=0.012), pinch score (p=0.012), gross movement score (p=0,002). The significant correlation was revealed between particular neuropsychological tests (Taylor Figure test, choice reaction test, Head test) and online accuracy rate.<br><br>CONCLUSION: These results suggest that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Neuropsychological testing can be used for screening before mental practice admission and promote personalized rehabilitation.},
}
@article {pmid30250422,
year = {2018},
author = {Zhao, Y and Hessburg, JP and Asok Kumar, JN and Francis, JT},
title = {Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor Representations.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {579},
pmid = {30250422},
issn = {1662-4548},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
abstract = {Neural activity in the primary motor cortex (M1) is known to correlate with movement related variables including kinematics and dynamics. Our recent work, which we believe is part of a paradigm shift in sensorimotor research, has shown that in addition to these movement related variables, activity in M1 and the primary somatosensory cortex (S1) are also modulated by context, such as value, during both active movement and movement observation. Here we expand on the investigation of reward modulation in M1, showing that reward level changes the neural tuning function of M1 units to both kinematic as well as dynamic related variables. In addition, we show that this reward-modulated activity is present during brain machine interface (BMI) control. We suggest that by taking into account these context dependencies of M1 modulation, we can produce more robust BMIs. Toward this goal, we demonstrate that we can classify reward expectation from M1 on a movement-by-movement basis under BMI control and use this to gate multiple linear BMI decoders toward improved offline performance. These findings demonstrate that it is possible and meaningful to design a more accurate BMI decoder that takes reward and context into consideration. Our next step in this development will be to incorporate this gating system, or a continuous variant of it, into online BMI performance.},
}
@article {pmid30250141,
year = {2018},
author = {Schwemmer, MA and Skomrock, ND and Sederberg, PB and Ting, JE and Sharma, G and Bockbrader, MA and Friedenberg, DA},
title = {Meeting brain-computer interface user performance expectations using a deep neural network decoding framework.},
journal = {Nature medicine},
volume = {24},
number = {11},
pages = {1669-1676},
doi = {10.1038/s41591-018-0171-y},
pmid = {30250141},
issn = {1546-170X},
mesh = {Adult ; Algorithms ; Brain/*physiopathology ; Brain-Computer Interfaces/*standards/trends ; Electric Stimulation ; Hand Strength/physiology ; Humans ; Male ; Motivation/physiology ; Movement/physiology ; Nerve Net/physiopathology ; Quadriplegia/*physiopathology/rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices[1-9]. Surveys of potential end-users have identified key BCI system features[10-14], including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI's neural decoding algorithm[1,15], which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network[16] decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure[3,17-20], responds faster than competing methods[8], and can increase functionality with minimal retraining by using a technique known as transfer learning[21]. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)[22]. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.},
}
@article {pmid30248056,
year = {2018},
author = {Yeo, SN and Lee, TS and Sng, WT and Heo, MQ and Bautista, D and Cheung, YB and Zhang, HH and Wang, C and Chin, ZY and Feng, L and Zhou, J and Chong, MS and Ng, TP and Krishnan, KR and Guan, C},
title = {Effectiveness of a Personalized Brain-Computer Interface System for Cognitive Training in Healthy Elderly: A Randomized Controlled Trial.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {66},
number = {1},
pages = {127-138},
doi = {10.3233/JAD-180450},
pmid = {30248056},
issn = {1875-8908},
mesh = {Aged ; Aged, 80 and over ; Brain-Computer Interfaces/*psychology ; Cognitive Behavioral Therapy/methods ; Cognitive Dysfunction/*psychology/*therapy ; Female ; Health Status ; Humans ; Male ; Middle Aged ; Neurofeedback/*methods ; Precision Medicine/*methods/*psychology ; Treatment Outcome ; },
abstract = {BACKGROUND: Cognitive training has been demonstrated to improve cognitive performance in older adults. To date, no study has explored personalized training that targets the brain activity of each individual.<br><br>OBJECTIVE: This is the first large-scale trial that examines the usefulness of personalized neurofeedback cognitive training.<br><br>METHODS: We conducted a randomized-controlled trial with participants who were 60-80 years old, with Clinical Dementia Rating (CDR) score of 0-0.5, Mini-Mental State Examination (MMSE) score of 24 and above, and with no neuropsychiatric diagnosis. Participants were randomly assigned to the Intervention or Waitlist-Control group. The training system, BRAINMEM, has attention, working memory, and delayed recall game components. The intervention schedule comprised 24 sessions over eight weeks and three monthly booster sessions. The primary outcome was the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score after the 24-session training.<br><br>RESULTS: There were no significant between-subjects differences in overall cognitive performance post-intervention. However, a sex moderation effect (p = 0.014) was present. Men in the intervention group performed better than those in the waitlist group (mean difference, +4.03 (95% CI 0.1 to 8.0), p = 0.046. Among females, however, both waitlist-control and intervention participants improved from baseline, although the between-group difference in improvement did not reach significance. BRAINMEM also received positive appraisal and intervention adherence from the participants.<br><br>CONCLUSION: A personalized neurofeedback intervention is potentially feasible for use in cognitive training for older males. The sex moderation effect warrants further investigation and highlights the importance of taking sex into account during cognitive training.},
}
@article {pmid30246690,
year = {2018},
author = {Woods, V and Trumpis, M and Bent, B and Palopoli-Trojani, K and Chiang, CH and Wang, C and Yu, C and Insanally, MN and Froemke, RC and Viventi, J},
title = {Long-term recording reliability of liquid crystal polymer µECoG arrays.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066024},
pmid = {30246690},
issn = {1741-2552},
support = {K99 DC015543/DC/NIDCD NIH HHS/United States ; R00 DC015543/DC/NIDCD NIH HHS/United States ; U01 NS099697/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Acoustic Stimulation ; Algorithms ; Animals ; Auditory Cortex ; Brain-Computer Interfaces ; Electric Impedance ; Electrocorticography/*methods ; Electrodes, Implanted ; Epidural Space ; Female ; *Polymers ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: The clinical use of microsignals recorded over broad cortical regions is largely limited by the chronic reliability of the implanted interfaces.<br><br>APPROACH: We evaluated the chronic reliability of novel 61-channel micro-electrocorticographic (&#xb5;ECoG) arrays in rats chronically implanted for over one year and using accelerated aging. Devices were encapsulated with polyimide (PI) or liquid crystal polymer (LCP), and fabricated using commercial manufacturing processes. In vitro failure modes and predicted lifetimes were determined from accelerated soak testing. Successful designs were implanted epidurally over the rodent auditory cortex. Trends in baseline signal level, evoked responses and decoding performance were reported for over one year of implantation.<br><br>MAIN RESULTS: Devices fabricated with LCP consistently had longer in vitro lifetimes than PI encapsulation. Our accelerated aging results predicted device integrity beyond 3.4 years. Five implanted arrays showed stable performance over the entire implantation period (247-435 d). Our regression analysis showed that impedance predicted signal quality and information content only in the first 31 d of recordings and had little predictive value in the chronic phase (>31 d). In the chronic phase, site impedances slightly decreased yet decoding performance became statistically uncorrelated with impedance. We also employed an improved statistical model of spatial variation to measure sensitivity to locally varying fields, which is typically concealed in standard signal power calculations.<br><br>SIGNIFICANCE: These findings show that &#xb5;ECoG arrays can reliably perform in chronic applications in vivo for over one year, which facilitates the development of a high-density, clinically viable interface.},
}
@article {pmid30245616,
year = {2018},
author = {Starr, PA},
title = {Totally Implantable Bidirectional Neural Prostheses: A Flexible Platform for Innovation in Neuromodulation.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {619},
pmid = {30245616},
issn = {1662-4548},
support = {R01 NS090913/NS/NINDS NIH HHS/United States ; UH3 NS100544/NS/NINDS NIH HHS/United States ; },
abstract = {Implantable neural prostheses are in widespread use for treating a variety of brain disorders. Until recently, most implantable brain devices have been unidirectional, either delivering neurostimulation without brain sensing, or sensing brain activity to drive external effectors without a stimulation component. Further, many neural interfaces that incorporate a sensing function have relied on hardwired connections, such that subjects are tethered to external computers and cannot move freely. A new generation of neural prostheses has become available, that are both bidirectional (stimulate as well as record brain activity) and totally implantable (no externalized connections). These devices provide an opportunity for discovering the circuit basis for neuropsychiatric disorders, and to prototype personalized neuromodulation therapies that selectively interrupt neural activity underlying specific signs and symptoms.},
}
@article {pmid30242363,
year = {2018},
author = {Taylor, CP and Shepard, TG and Rucker, FJ and Eskew, RT},
title = {Sensitivity to S-Cone Stimuli and the Development of Myopia.},
journal = {Investigative ophthalmology &amp; visual science},
volume = {59},
number = {11},
pages = {4622-4630},
pmid = {30242363},
issn = {1552-5783},
support = {R01 EY023281/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; Color Perception/physiology ; Cone Opsins/*physiology ; Contrast Sensitivity/physiology ; Female ; Humans ; Male ; Myopia/*physiopathology ; Psychophysics ; Retinal Cone Photoreceptor Cells/*physiology ; Sensory Thresholds/physiology ; Young Adult ; },
abstract = {PURPOSE: Longitudinal chromatic aberration (LCA) is a color signal available to the emmetropization process that causes greater myopic defocus of short wavelengths than long wavelengths. We measured individual differences in chromatic sensitivity to explore the role LCA may play in the development of refractive error.<br><br>METHODS: Forty-four observers were tested psychophysically after passing color screening tests and a questionnaire for visual defects. Refraction was measured and only subjects with myopia or hyperopia without severe astigmatism participated. Psychophysical detection thresholds for 3 cyc/deg achromatic, L-, M-, and S-cone-isolating Gabor patches and low-frequency S-cone increment (S+) and decrement (S-) blobs were measured. Parametric Pearson correlations for refractive error versus threshold were calculated and nonparametric bootstrap 95% percentage confidence intervals (BCIs) for r were computed.<br><br>RESULTS: S-cone Gabor detection thresholds were higher than achromatic, L-, and M-cone Gabors. S-cone Gabor thresholds were higher than either S+ or S- blobs. These results are consistent with studies using smaller samples of practiced observers. None of the thresholds for the Gabor stimuli were correlated with refractive error (RE). A negative correlation with RE was observed for both S+ (r = -0.28; P = 0.06; BCI: r = -0.5, -0.04) and S- (r = -0.23; P = 0.13; BCI = -0.46, 0.01) blobs, although this relationship did not reach conventional statistical significance.<br><br>CONCLUSIONS: Thresholds for S+ and S- stimuli were negatively related to RE, indicating that myopes may have reduced sensitivity to low spatial frequency S-cone stimuli. This reduced S-cone sensitivity might have played a role in their failure to emmetropize normally.},
}
@article {pmid30240902,
year = {2019},
author = {Wen, Z and Yu, T and Yu, Z and Li, Y},
title = {Grouped sparse Bayesian learning for voxel selection in multivoxel pattern analysis of fMRI data.},
journal = {NeuroImage},
volume = {184},
number = {},
pages = {417-430},
doi = {10.1016/j.neuroimage.2018.09.031},
pmid = {30240902},
issn = {1095-9572},
mesh = {Bayes Theorem ; Brain/*physiology ; Brain Mapping/*methods ; Datasets as Topic ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Pattern Recognition, Automated/*methods ; },
abstract = {Multivoxel pattern analysis (MVPA) methods have been widely applied in recent years to classify human brain states in functional magnetic resonance imaging (fMRI) data analysis. Voxel selection plays an important role in MVPA studies not only because it can improve decoding accuracy but also because it is useful for understanding brain functions. There are many voxel selection methods that have been proposed in fMRI literature. However, most of these methods either overlook the structure information of fMRI data or require additional cross-validation procedures to determine the hyperparameters of the models. In the present work, we proposed a voxel selection method for binary brain decoding called group sparse Bayesian logistic regression (GSBLR). This method utilizes the group sparse property of fMRI data by using a grouped automatic relevance determination (GARD) as a prior for model parameters. All the parameters in the GSBLR can be estimated automatically, thereby avoiding additional cross-validation. Experimental results based on two publicly available fMRI datasets and simulated datasets demonstrate that GSBLR achieved better classification accuracies and yielded more stable solutions than several state-of-the-art methods.},
}
@article {pmid30238924,
year = {2018},
author = {Salari, E and Freudenburg, ZV and Vansteensel, MJ and Ramsey, NF},
title = {The influence of prior pronunciations on sensorimotor cortex activity patterns during vowel production.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066025},
pmid = {30238924},
issn = {1741-2552},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes, Implanted ; Epilepsy/physiopathology ; Female ; Humans ; Language ; Male ; Motor Cortex ; Movement ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensorimotor Cortex/anatomy &amp; histology/*physiology ; Speech/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: In recent years, brain-computer interface (BCI) systems have been investigated for their potential as a communication device to assist people with severe paralysis. Decoding speech sensorimotor cortex activity is a promising avenue for the generation of BCI control signals, but is complicated by variability in neural patterns, leading to suboptimal decoding. We investigated whether neural pattern variability associated with sound pronunciation can be explained by prior pronunciations and determined to what extent prior speech affects BCI decoding accuracy.<br><br>APPROACH: Neural patterns in speech motor areas were evaluated with electrocorticography in five epilepsy patients, who performed a simple speech task that involved pronunciation of the /i/ sound, preceded by either silence, the /a/ sound or the /u/ sound.<br><br>MAIN RESULTS: The neural pattern related to the /i/ sound depends on previous sounds and is therefore associated with multiple distinct sensorimotor patterns, which is likely to reflect differences in the movements towards this sound. We also show that these patterns still contain a commonality that is distinct from the other vowel sounds (/a/ and /u/). Classification accuracies for the decoding of different sounds do increase, however, when the multiple patterns for the /i/ sound are taken into account. Simply including multiple forms of the /i/ vowel in the training set for the creation of a single /i/ model performs as well as training individual models for each /i/ variation.<br><br>SIGNIFICANCE: Our results are of interest for the development of BCIs that aim to decode speech sounds from the sensorimotor cortex, since they argue that a multitude of cortical activity patterns associated with speech movements can be reduced to a basis set of models which reflect meaningful language units (vowels), yet it is important to account for the variety of neural patterns associated with a single sound in the training process.},
}
@article {pmid30238916,
year = {2018},
author = {Zurita, M and Montalba, C and Labbé, T and Cruz, JP and Dalboni da Rocha, J and Tejos, C and Ciampi, E and Cárcamo, C and Sitaram, R and Uribe, S},
title = {Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data.},
journal = {NeuroImage. Clinical},
volume = {20},
number = {},
pages = {724-730},
pmid = {30238916},
issn = {2213-1582},
mesh = {Adolescent ; Adult ; Brain/*diagnostic imaging/pathology/physiopathology ; Brain Mapping/*methods ; *Diffusion Magnetic Resonance Imaging ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; Middle Aged ; Multiple Sclerosis, Relapsing-Remitting/*diagnostic imaging/pathology/physiopathology ; Prospective Studies ; Support Vector Machine ; Young Adult ; },
abstract = {Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.},
}
@article {pmid30238309,
year = {2019},
author = {Salari, E and Freudenburg, ZV and Vansteensel, MJ and Ramsey, NF},
title = {Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex.},
journal = {Brain topography},
volume = {32},
number = {1},
pages = {97-110},
pmid = {30238309},
issn = {1573-6792},
mesh = {Adult ; Brain Mapping ; Brain-Computer Interfaces ; *Electrocorticography ; Female ; Humans ; Male ; Movement/*physiology ; Sensorimotor Cortex/*physiology ; Speech/*physiology ; Young Adult ; },
abstract = {The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain-computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75-135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.},
}
@article {pmid30233341,
year = {2018},
author = {Seeland, A and Krell, MM and Straube, S and Kirchner, EA},
title = {Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {340},
pmid = {30233341},
issn = {1662-5161},
abstract = {The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons.},
}
@article {pmid30227150,
year = {2019},
author = {Perez-Garcia, G and Gama Sosa, MA and De Gasperi, R and Tschiffely, AE and McCarron, RM and Hof, PR and Gandy, S and Ahlers, ST and Elder, GA},
title = {Blast-induced "PTSD": Evidence from an animal model.},
journal = {Neuropharmacology},
volume = {145},
number = {Pt B},
pages = {220-229},
doi = {10.1016/j.neuropharm.2018.09.023},
pmid = {30227150},
issn = {1873-7064},
support = {I01 RX000996/RX/RRD VA/United States ; I01 RX002333/RX/RRD VA/United States ; I01 RX002660/RX/RRD VA/United States ; },
mesh = {Animals ; Blast Injuries/*complications/drug therapy ; Brain Injuries, Traumatic/drug therapy/*etiology ; Humans ; Stress Disorders, Post-Traumatic/drug therapy/*etiology ; },
abstract = {A striking observation among veterans returning from the recent conflicts in Iraq and Afghanistan has been the co-occurrence of blast-related mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). PTSD and mTBI might coexist due to additive effects of independent psychological and physical traumas experienced in a war zone. Alternatively blast injury might induce PTSD-related traits or damage brain structures that mediate responses to psychological stressors, increasing the likelihood that PTSD will develop following a subsequent psychological stressor. Rats exposed to repetitive low-level blasts consisting of three 74.5 kPa exposures delivered once daily for three consecutive days develop a variety of anxiety and PTSD-related behavioral traits that are present for at least 9 months after blast exposure. A single predator scent challenge delivered 8 months after the last blast exposure induces additional anxiety-related changes that are still present 45 days later. Because the blast injuries occur under general anesthesia, it appears that blast exposure in the absence of a psychological stressor can induce chronic PTSD-related traits. The reaction to a predator scent challenge delivered many months after blast exposure suggests that blast exposure in addition sensitizes the brain to react abnormally to subsequent psychological stressors. The development of PTSD-related behavioral traits in the absence of a psychological stressor suggests the existence of blast-induced "PTSD". Findings that PTSD-related behavioral traits can be reversed by BCI-838, a group II metabotropic glutamate receptor antagonist offers insight into pathogenesis and possible treatment options for blast-related brain injury. This article is part of the Special Issue entitled "Novel Treatments for Traumatic Brain Injury".},
}
@article {pmid30221626,
year = {2019},
author = {Han, X and Lin, K and Gao, S and Gao, X},
title = {A novel system of SSVEP-based human-robot coordination.},
journal = {Journal of neural engineering},
volume = {16},
number = {1},
pages = {016006},
doi = {10.1088/1741-2552/aae1ba},
pmid = {30221626},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Robotics/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Human-robot coordination (HRC) aims to enable human and robot to form a tightly coupled system to accomplish a task. One of its important application prospects is to improve the physical function of the disabled. However, the low level of the coordination between human and robot and the limited potential users still hamper the efficiency of such systems.<br><br>APPROACH: To deal with such challenges, a novel steady-state visual evoked potential (SSVEP) based human-robot coordinated brain-computer interface (BCI) system was proposed to finish a target capturing task. In this system, the robot, by combining the information obtained during the human's natural interaction with itself to capture a target, could optimize the same object capturing task and yield a better performance automatically. The combination of human dealing with the uncertainty problem and the robot dealing with the complexity problem was the key to the system. Meanwhile, an asynchronous BCI based on SSVEP was used as the system interface, and a novel asynchronous recognition algorithm was used to discriminate the electroencephalogram (EEG) signal.<br><br>MAIN RESULTS: The results show that the proposed system can lower the fatigue level of the subject and simplify the operation of the system. Meanwhile, the signal recognition accuracy and the efficiency of the system were also improved.<br><br>SIGNIFICANCE: Under the help of the close and natural coordination relationship design between human and robot, and the asynchronous SSVEP based BCI design which requires no limb movement to control a robot, the users would be provided with an accurate and efficient control experience. Moreover, people with severe motor diseases might potentially benefit from such a system. Also, the proposed methods can be easily integrated into other BCI diagrams, which would ameliorate the predicament of the HRC.},
}
@article {pmid30216140,
year = {2018},
author = {Brandman, DM and Burkhart, MC and Kelemen, J and Franco, B and Harrison, MT and Hochberg, LR},
title = {Robust Closed-Loop Control of a Cursor in a Person with Tetraplegia using Gaussian Process Regression.},
journal = {Neural computation},
volume = {30},
number = {11},
pages = {2986-3008},
pmid = {30216140},
issn = {1530-888X},
support = {P20 GM103645/GM/NIGMS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 MH102840/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Humans ; Male ; *Neural Networks, Computer ; *Quadriplegia ; *User-Computer Interface ; },
abstract = {Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testing data sets. This includes changes to the neural tuning profiles and baseline shifts in firing rates of recorded neurons, as well as nonphysiological noise. While progress has been made toward providing long-term user control via decoder recalibration, relatively little work has been dedicated to making the decoding algorithm more resilient to signal nonstationarities. Here, we describe how principled kernel selection with gaussian process regression can be used within a Bayesian filtering framework to mitigate the effects of commonly encountered nonstationarities. Given a supervised training set of (neural features, intention to move in a direction)-pairs, we use gaussian process regression to predict the intention given the neural data. We apply kernel embedding for each neural feature with the standard radial basis function. The multiple kernels are then summed together across each neural dimension, which allows the kernel to effectively ignore large differences that occur only in a single feature. The summed kernel is used for real-time predictions of the posterior mean and variance under a gaussian process framework. The predictions are then filtered using the discriminative Kalman filter to produce an estimate of the neural intention given the history of neural data. We refer to the multiple kernel approach combined with the discriminative Kalman filter as the MK-DKF. We found that the MK-DKF decoder was more resilient to nonstationarities frequently encountered in-real world settings yet provided similar performance to the currently used Kalman decoder. These results demonstrate a method by which neural decoding can be made more resistant to nonstationarities.},
}
@article {pmid30215610,
year = {2018},
author = {Tayeb, Z and Waniek, N and Fedjaev, J and Ghaboosi, N and Rychly, L and Widderich, C and Richter, C and Braun, J and Saveriano, M and Cheng, G and Conradt, J},
title = {Gumpy: a Python toolbox suitable for hybrid brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {065003},
doi = {10.1088/1741-2552/aae186},
pmid = {30215610},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Hand ; Humans ; Imagination/physiology ; Machine Learning ; Movement/physiology ; Programming Languages ; Prostheses and Implants ; Psychomotor Performance/physiology ; Reproducibility of Results ; *Software ; },
abstract = {OBJECTIVE: The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI).<br><br>APPROACH: Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided. Gumpy can be used for both EEG and EMG biosignal analysis, visualization, real-time streaming and decoding.<br><br>RESULTS: The usage of the toolbox was demonstrated through two different offline example studies, namely movement prediction from EEG motor imagery, and the decoding of natural grasp movements with the applied finger forces from surface EMG (sEMG) signals. Additionally, gumpy was used for real-time control of a robot arm using steady-state visually evoked potentials (SSVEP) as well as for real-time prosthetic hand control using sEMG. Overall, obtained results with the gumpy toolbox are comparable or better than previously reported results on the same datasets.<br><br>SIGNIFICANCE: Gumpy is a free and open source software, which allows end-users to perform online hybrid BCIs and provides different techniques for processing and decoding of EEG and EMG signals. More importantly, the achieved results reveal that gumpy's deep learning toolbox can match or outperform the state-of-the-art in terms of accuracy. This can therefore enable BCI researchers to develop more robust decoding algorithms using novel techniques and hence chart a route ahead for new BCI improvements.},
}
@article {pmid30211695,
year = {2018},
author = {Arora, A and Lin, JJ and Gasperian, A and Maldjian, J and Stein, J and Kahana, M and Lega, B},
title = {Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066028},
pmid = {30211695},
issn = {1741-2552},
support = {R01 NS107357/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Computer Simulation ; *Deep Learning ; Electrodes, Implanted ; Electroencephalography/*classification/statistics &amp; numerical data ; Humans ; *Logistic Models ; *Memory, Episodic ; Mental Recall ; Predictive Value of Tests ; Stochastic Processes ; *Support Vector Machine ; Theta Rhythm ; },
abstract = {OBJECTIVE: We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep learning) for the problem of predicting successful episodic memory encoding using direct brain recordings obtained from human stereo EEG subjects. We also sought to test the impact of applying t-distributed stochastic neighbor embedding (tSNE) for unsupervised dimensionality reduction, as well as testing the effect of reducing input features to a core set of memory relevant brain areas. This work builds upon published efforts to develop a closed-loop stimulation device to improve memory performance.<br><br>APPROACH: We used a unique data set consisting of 30 stereo EEG patients with electrodes implanted into a core set of five common brain regions (along with other areas) who performed the free recall episodic memory task as brain activity was recorded. Using three different machine learning strategies, we trained classifiers to predict successful versus unsuccessful memory encoding and compared the difference in classifier performance (as measured by the AUC) at the subject level and in aggregate across modalities. We report the impact of feature reduction on the classifiers, including reducing the number of input brain regions, frequency bands, and the impact of tSNE.<br><br>RESULTS: Deep learning classifiers outperformed both support vector machines (SVM) and logistic regression (LR). A priori selection of core brain regions also improved classifier performance for LR and SVM models, especially when combined with tSNE.<br><br>SIGNIFICANCE: We report for the first time a direct comparison among traditional and deep learning methods of binary classification to the problem of predicting successful memory encoding using human brain electrophysiological data. Our findings will inform the design of brain machine interface devices to affect memory processing.},
}
@article {pmid30210272,
year = {2018},
author = {Pan, G and Li, JJ and Qi, Y and Yu, H and Zhu, JM and Zheng, XX and Wang, YM and Zhang, SM},
title = {Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {555},
pmid = {30210272},
issn = {1662-4548},
abstract = {Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures.},
}
@article {pmid30206733,
year = {2019},
author = {Xiao, Z and Hu, S and Zhang, Q and Tian, X and Chen, Y and Wang, J and Chen, Z},
title = {Ensembles of change-point detectors: implications for real-time BMI applications.},
journal = {Journal of computational neuroscience},
volume = {46},
number = {1},
pages = {107-124},
pmid = {30206733},
issn = {1573-6873},
support = {R01 NS100065/NS/NINDS NIH HHS/United States ; R01 MH118928/MH/NIMH NIH HHS/United States ; R01 GM115384/GM/NIGMS NIH HHS/United States ; R01 NS100016/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Acute Pain/*physiopathology ; Animals ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Male ; *Models, Neurological ; Neurons/physiology ; Rats ; Support Vector Machine ; },
abstract = {Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.},
}
@article {pmid30206721,
year = {2018},
author = {Devipriya, A and Nagarajan, N},
title = {A Novel Method of Segmentation and Classification for Meditation in Health Care Systems.},
journal = {Journal of medical systems},
volume = {42},
number = {10},
pages = {193},
pmid = {30206721},
issn = {1573-689X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cross-Sectional Studies ; *Electroencephalography ; Humans ; *Meditation ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequences of meditation. In the field of cognitive science, neuroscience and physiological research, Electroencephalogram (EEG) is extensively utilized. The primary tasks of EEG signal analysis is to identify the noisy signal as well as enormous data that create signal processing and subsequent analysis. Beforehand any analysis of the EEG signal, the obtained raw signal must be preprocessed for eliminating undesirable artifacts as well as horrible noise. With the aim of resolving this issue, in this research, raw signals are preprocessed with the help of Band-Pass Filter (BPF) for noise removal method. Instead, in adaptive Sliding Window with Fuzzy C Means Clustering (SW-FCM) segmentation is presented, which precisely as well as automatically segments the signals. So as to analyze the accuracy, five features such as electroencephalography alpha spectrum, frequency of the main peak, Amplitude of the main peak, Higher Order Crossing (HOC), and wavelet features are used as the evaluating variables. Lastly to assess the meditation experience with Fuzzy Kernel least square Support Vector Machine (FKLSSVM) classifier, the presented method with a cross-sectional analysis is utilized. These two classifiers are utilized for meditation experience classification by utilizing an individual feature vector values from equivalent EEG signals. The dataset samples are gathered from Open source Brain-Computer Interface (Open BCI) platform. Outcomes attained are matched up for diverse techniques for identifying as well as for classifying signal segments features using MATLAB. Presented classifiers of the meditation process validate quick interpretation methods that differentiate meditation experience and valuable performance related to artificial approaches for the big-data analysis.},
}
@article {pmid30205476,
year = {2018},
author = {Kamavuako, EN and Sheikh, UA and Gilani, SO and Jamil, M and Niazi, IK},
title = {Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {9},
pages = {},
pmid = {30205476},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Broca Area/physiology ; Healthy Volunteers ; Hemoglobins/metabolism ; Humans ; *Spectroscopy, Near-Infrared ; *Speech ; *Support Vector Machine ; },
abstract = {People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca's area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain[-]Computer Interfaces (BCIs) based on NIRS.},
}
@article {pmid30204127,
year = {2018},
author = {Ehrlich, SK and Cheng, G},
title = {Human-agent co-adaptation using error-related potentials.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066014},
doi = {10.1088/1741-2552/aae069},
pmid = {30204127},
issn = {1741-2552},
mesh = {Adaptation, Psychological ; Adult ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feedback, Psychological/*physiology ; Female ; Fixation, Ocular ; Humans ; Intention ; Interpersonal Relations ; Male ; Motor Skills ; Robotics/*methods ; },
abstract = {OBJECTIVE: Error-related potentials (ErrP) have been proposed as an intuitive feedback signal decoded from the ongoing electroencephalogram (EEG) of a human observer for improving human-robot interaction (HRI). While recent demonstrations of this approach have successfully studied the use of ErrPs as a teaching signal for robot skill learning, so far, no efforts have been made towards HRI scenarios where mutual adaptations between human and robot are expected or required. These are collaborative or social interactive scenarios without predefined dominancy of the human partner and robots being perceived as intentional agents. Here we explore the usability of ErrPs as a feedback signal from the human for mediating co-adaptation in human-robot interaction.<br><br>APPROACH: We experimentally demonstrate ErrPs-based mediation of co-adaptation in a human-robot interaction study where successful interaction depended on co-adaptive convergence to a consensus between them. While subjects adapted to the robot by reflecting upon its behavior, the robot adapted its behavior based on ErrPs decoded online from the human partner's ongoing EEG.<br><br>MAIN RESULTS: ErrPs were decoded online in single trial with an avg. accuracy of 81.8%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;8.0% across 13 subjects, which was sufficient for effective adaptation of robot behavior. Successful co-adaptation was demonstrated by significant improvements in human-robot interaction efficacy and efficiency, and by the robot behavior that emerged during co-adaptation. These results indicate the potential of ErrPs as a useful feedback signal for mediating co-adaptation in human-robot interaction as demonstrated in a practical example.<br><br>SIGNIFICANCE: As robots become more widely embedded in society, methods for aligning them to human expectations and conventions will become increasingly important in the future. In this quest, ErrPs may constitute a promising complementary feedback signal for guiding adaptations towards human preferences. In this paper we extended previous research to less constrained HRI scenarios where mutual adaptations between human and robot are expected or required.},
}
@article {pmid30200321,
year = {2018},
author = {Cavazza, M},
title = {A Motivational Model of BCI-Controlled Heuristic Search.},
journal = {Brain sciences},
volume = {8},
number = {9},
pages = {},
pmid = {30200321},
issn = {2076-3425},
abstract = {Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain[-]Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user's disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.},
}
@article {pmid30198467,
year = {2018},
author = {Klein, E and Peters, B and Higger, M},
title = {Ethical Considerations in Ending Exploratory Brain-Computer Interface Research Studies in Locked-in Syndrome.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {27},
number = {4},
pages = {660-674},
pmid = {30198467},
issn = {1469-2147},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; T32 MH016259/MH/NIMH NIH HHS/United States ; },
mesh = {Biomedical Research/*ethics ; Brain-Computer Interfaces/*ethics ; Communication Devices for People with Disabilities/ethics ; Humans ; *Informed Consent ; Male ; *Quadriplegia/rehabilitation ; Randomized Controlled Trials as Topic/ethics ; *Therapeutic Equipoise ; },
abstract = {Brain-computer interface (BCI) is a promising technology for restoring communication in individuals with locked-in syndrome (LIS). BCI technology offers a potential tool for individuals with impaired or absent means of effective communication to use brain activity to control an output device such as a computer keyboard. Exploratory studies of BCI devices for communication in people with LIS are underway. Research with individuals with LIS presents not only technological challenges, but ethical challenges as well. Whereas recent attention has been focused on ethical issues that arise at the initiation of studies, such as how to obtain valid consent, relatively little attention has been given to issues at the conclusion of studies. BCI research in LIS highlights one such challenge: How to decide when an exploratory BCI research study should end. In this article, we present the case of an individual with presumed LIS enrolled in an exploratory BCI study. We consider whether two common ethical frameworks for stopping randomized clinical trials-equipoise and nonexploitation-can be usefully applied to elucidating researcher obligations to end exploratory BCI research. We argue that neither framework is a good fit for exploratory BCI research. Instead, we apply recent work on clinician-researcher fiduciary obligations and in turn offer some preliminary recommendations for BCI researchers on how to end exploratory BCI studies.},
}
@article {pmid30198466,
year = {2018},
author = {Wolkenstein, A and Jox, RJ and Friedrich, O},
title = {Brain-Computer Interfaces: Lessons to Be Learned from the Ethics of Algorithms.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {27},
number = {4},
pages = {635-646},
doi = {10.1017/S0963180118000130},
pmid = {30198466},
issn = {1469-2147},
mesh = {*Algorithms ; *Bioethical Issues ; Brain-Computer Interfaces/*ethics ; Humans ; Neurosciences/ethics ; Social Responsibility ; },
abstract = {Brain-computer interfaces (BCIs) are driven essentially by algorithms; however, the ethical role of such algorithms has so far been neglected in the ethical assessment of BCIs. The goal of this article is therefore twofold: First, it aims to offer insights into whether (and how) the problems related to the ethics of BCIs (e.g., responsibility) can be better grasped with the help of already existing work on the ethics of algorithms. As a second goal, the article explores what kinds of solutions are available in that body of scholarship, and how these solutions relate to some of the ethical questions around BCIs. In short, the article asks what lessons can be learned about the ethics of BCIs from looking at the ethics of algorithms. To achieve these goals, the article proceeds as follows. First, a brief introduction into the algorithmic background of BCIs is given. Second, the debate about epistemic concerns and the ethics of algorithms is sketched. Finally, this debate is transferred to the ethics of BCIs.},
}
@article {pmid30197426,
year = {2018},
author = {Cartocci, G and Maglione, AG and Vecchiato, G and Modica, E and Rossi, D and Malerba, P and Marsella, P and Scorpecci, A and Giannantonio, S and Mosca, F and Leone, CA and Grassia, R and Babiloni, F},
title = {Frontal brain asymmetries as effective parameters to assess the quality of audiovisual stimuli perception in adult and young cochlear implant users.},
journal = {Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale},
volume = {38},
number = {4},
pages = {346-360},
pmid = {30197426},
issn = {1827-675X},
mesh = {Adult ; *Auditory Perception ; Child ; Child, Preschool ; *Cochlear Implants ; *Electroencephalography ; Female ; Frontal Lobe/*physiology ; Humans ; Infant ; Male ; Middle Aged ; *Music ; Physical Stimulation ; *Visual Perception ; },
abstract = {How is music perceived by cochlear implant (CI) users? This question arises as "the next step" given the impressive performance obtained by these patients in language perception. Furthermore, how can music perception be evaluated beyond self-report rating, in order to obtain measurable data? To address this question, estimation of the frontal electroencephalographic (EEG) alpha activity imbalance, acquired through a 19-channel EEG cap, appears to be a suitable instrument to measure the approach/withdrawal (AW index) reaction to external stimuli. Specifically, a greater value of AW indicates an increased propensity to stimulus approach, and vice versa a lower one a tendency to withdraw from the stimulus. Additionally, due to prelingually and postlingually deafened pathology acquisition, children and adults, respectively, would probably differ in music perception. The aim of the present study was to investigate children and adult CI users, in unilateral (UCI) and bilateral (BCI) implantation conditions, during three experimental situations of music exposure (normal, distorted and mute). Additionally, a study of functional connectivity patterns within cerebral networks was performed to investigate functioning patterns in different experimental populations. As a general result, congruency among patterns between BCI patients and control (CTRL) subjects was seen, characterised by lowest values for the distorted condition (vs. normal and mute conditions) in the AW index and in the connectivity analysis. Additionally, the normal and distorted conditions were significantly different in CI and CTRL adults, and in CTRL children, but not in CI children. These results suggest a higher capacity of discrimination and approach motivation towards normal music in CTRL and BCI subjects, but not for UCI patients. Therefore, for perception of music CTRL and BCI participants appear more similar than UCI subjects, as estimated by measurable and not self-reported parameters.},
}
@article {pmid30194614,
year = {2019},
author = {Niketeghad, S and Pouratian, N},
title = {Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {16},
number = {1},
pages = {134-143},
pmid = {30194614},
issn = {1878-7479},
support = {UH3 NS103442/NS/NINDS NIH HHS/United States ; },
mesh = {Blindness, Cortical/*therapy ; *Brain-Computer Interfaces ; Humans ; *Visual Prosthesis ; },
abstract = {Loss of vision alters the day to day life of blind individuals and may impose a significant burden on their family and the economy. Cortical visual prosthetics have been shown to have the potential of restoring a useful degree of vision via stimulation of primary visual cortex. Due to current advances in electrode design and wireless power and data transmission, development of these prosthetics has gained momentum in the past few years and multiple sites around the world are currently developing and testing their designs. In this review, we briefly outline the visual prosthetic approaches and describe the history of cortical visual prosthetics. Next, we focus on the state of the art of cortical visual prosthesis by briefly explaining the design of current devices that are either under development or in the clinical testing phase. Lastly, we shed light on the challenges of each design and provide some potential solutions.},
}
@article {pmid30190543,
year = {2018},
author = {Pereira, J and Sburlea, AI and Müller-Putz, GR},
title = {EEG patterns of self-paced movement imaginations towards externally-cued and internally-selected targets.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {13394},
pmid = {30190543},
issn = {2045-2322},
support = {ERC-681231//EC | European Research Council (ERC)/International ; },
mesh = {Adult ; Brain-Computer Interfaces ; Cognition/*physiology ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Parietal Lobe/*physiology ; },
abstract = {In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.},
}
@article {pmid30188835,
year = {2018},
author = {Bi, L and Wang, H and Teng, T and Guan, C},
title = {A Novel Method of Emergency Situation Detection for a Brain-Controlled Vehicle by Combining EEG Signals With Surrounding Information.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {10},
pages = {1926-1934},
doi = {10.1109/TNSRE.2018.2868486},
pmid = {30188835},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Automobile Driving ; *Brain-Computer Interfaces ; Computer Systems ; Decision Making ; Electroencephalography/*methods/statistics &amp; numerical data ; *Emergencies ; Female ; Humans ; Intention ; Male ; Reaction Time ; Reproducibility of Results ; Young Adult ; },
abstract = {In this paper, to address the safety of brain-controlled vehicles under emergency situations, we propose a novel method of emergency situation detection by fusing driver electroencephalography (EEG) signals with surrounding information. We first build a novel EEG-based detection model of driver emergency braking intention. We then recognize emergency situations by fusing the result of the proposed EEG-based intention detection model with that of the obstacle detection model based on surrounding information. The real-time detection system of driver emergency braking intention is implemented on an embedded system, and the driver-and-hardware-in-the-loop-experiment of the proposed detection method of emergency situations is performed. Experimental results show that the proposed method can detect emergency situations with the system accuracy of 94.89%, false alarm rate of 0.05%, and response time of 540 ms. This paper has important values in the future development of brain-controlled vehicles, human-centric advanced driver assistant systems, and self-driving vehicles and opens a new avenue on how cognitive neuroscience may be applied to human-machine integration.},
}
@article {pmid30188809,
year = {2019},
author = {Siddharth, and Patel, AN and Jung, TP and Sejnowski, TJ},
title = {A Wearable Multi-Modal Bio-Sensing System Towards Real-World Applications.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {4},
pages = {1137-1147},
doi = {10.1109/TBME.2018.2868759},
pmid = {30188809},
issn = {1558-2531},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrocardiography ; Electroencephalography ; Equipment Design ; Evoked Potentials, Visual/physiology ; Head Movements/physiology ; Humans ; *Machine Learning ; Monitoring, Physiologic/*instrumentation ; Photoplethysmography ; *Video Games ; *Wearable Electronic Devices ; },
abstract = {Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality among other areas. However, none of the existing bio-sensing systems support multi-modality in a wearable manner outside well-controlled laboratory environments with research-grade measurements. This paper attempts to bridge this gap by developing a wearable multi-modal bio-sensing system capable of collecting, synchronizing, recording, and transmitting data from multiple bio-sensors: PPG, EEG, eye-gaze headset, body motion capture, GSR, etc., while also providing task modulation features including visual-stimulus tagging. This study describes the development and integration of various components of our system. We evaluate the developed sensors by comparing their measurements to those obtained by a standard research-grade bio-sensors. We first evaluate different sensor modalities of our headset, namely, earlobe-based PPG module with motion-noise canceling for ECG during heart-beat calculation. We also compare the steady-state visually evoked potentials measured by our shielded dry EEG sensors with the potentials obtained by commercially available dry EEG sensors. We also investigate the effect of head movements on the accuracy and precision of our wearable eye-gaze system. Furthermore, we carry out two practical tasks to demonstrate the applications of using multiple sensor modalities for exploring previously unanswerable questions in bio-sensing. Specifically, utilizing bio-sensing, we show which strategy works best for playing "Where is Waldo?" visual-search game, changes in EEG corresponding to true vs. false target fixations in this game, and predicting the loss/draw/win states through bio-sensing modalities while learning their limitations in a "Rock-Paper-Scissors" game.},
}
@article {pmid30188521,
year = {2018},
author = {Ienca, M and Haselager, P and Emanuel, EJ},
title = {Brain leaks and consumer neurotechnology.},
journal = {Nature biotechnology},
volume = {36},
number = {9},
pages = {805-810},
pmid = {30188521},
issn = {1546-1696},
mesh = {Biotechnology/*trends ; Brain/*pathology ; Brain-Computer Interfaces ; Community Participation ; Electroencephalography ; Humans ; Internet ; Mobile Applications ; Neurosciences/*trends ; Privacy ; Software ; },
}
@article {pmid30185802,
year = {2018},
author = {Kosmyna, N and Lindgren, JT and Lécuyer, A},
title = {Attending to Visual Stimuli versus Performing Visual Imagery as a Control Strategy for EEG-based Brain-Computer Interfaces.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {13222},
pmid = {30185802},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination ; Male ; Middle Aged ; Photic Stimulation ; Visual Perception ; Young Adult ; },
abstract = {Currently the most common imagery task used in Brain-Computer Interfaces (BCIs) is motor imagery, asking a user to imagine moving a part of the body. This study investigates the possibility to build BCIs based on another kind of mental imagery, namely "visual imagery". We study to what extent can we distinguish alternative mental processes of observing visual stimuli and imagining it to obtain EEG-based BCIs. Per trial, we instructed each of 26 users who participated in the study to observe a visual cue of one of two predefined images (a flower or a hammer) and then imagine the same cue, followed by rest. We investigated if we can differentiate between the different subtrial types from the EEG alone, as well as detect which image was shown in the trial. We obtained the following classifier performances: (i) visual imagery vs. visual observation task (71% of classification accuracy), (ii) visual observation task towards different visual stimuli (classifying one observation cue versus another observation cue with an accuracy of 61%) and (iii) resting vs. observation/imagery (77% of accuracy between imagery task versus resting state, and the accuracy of 75% between observation task versus resting state). Our results show that the presence of visual imagery and specifically related alpha power changes are useful to broaden the range of BCI control strategies.},
}
@article {pmid30185104,
year = {2018},
author = {Li, W and Li, M and Zhou, H and Chen, G and Jin, J and Duan, F},
title = {A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {28},
number = {10},
pages = {1850034},
doi = {10.1142/S012906571850034X},
pmid = {30185104},
issn = {1793-6462},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Models, Neurological ; Neural Pathways/*physiology ; Perception/physiology ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.},
}
@article {pmid30184652,
year = {2018},
author = {Zhang, J and Wang, B and Li, T and Hong, J},
title = {Non-invasive decoding of hand movements from electroencephalography based on a hierarchical linear regression model.},
journal = {The Review of scientific instruments},
volume = {89},
number = {8},
pages = {084303},
doi = {10.1063/1.5049191},
pmid = {30184652},
issn = {1089-7623},
mesh = {Adult ; *Electroencephalography ; Female ; Hand/*physiology ; Humans ; Linear Models ; Male ; *Movement ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {A non-invasive brain-computer interface (BCI) is an assistive technology with basic communication and control capabilities that decodes continuous electroencephalography (EEG) signals generated by the human brain and converts them into commands to control external devices naturally. However, the decoding efficiency is limited at present because it is unclear which decoding parameters can be used to effectively improve the overall decoding performance. In this paper, five subjects performed experiments involving self-initiated upper-limb movements during three experimental phases. The decoding method based on a hierarchical linear regression (HLR) model was devised to investigate the influence of decoding efficiency according to the characteristic parameters of brain functional networks. Then the optimal set of channels and most sensitive frequency bands were selected using the p value from a Kruskal-Wallis test in the experimental phases. Eventually, the trajectories of free movement and conical helix movement could be decoded using HLR. The experimental result showed that the Pearson correlation coefficient (R) between the measured and decoded paths is 0.66 with HLR, which was higher than the value of 0.46 obtained with the multiple linear regression model. The HLR from a decoding efficiency perspective holds promise for the development of EEG-based BCI to aid in the restoration of hand movements in post-stroke rehabilitation.},
}
@article {pmid30181532,
year = {2018},
author = {Kalaganis, FP and Chatzilari, E and Nikolopoulos, S and Kompatsiaris, I and Laskaris, NA},
title = {An error-aware gaze-based keyboard by means of a hybrid BCI system.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {13176},
pmid = {30181532},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Eye Movements ; Female ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Gaze-based keyboards offer a flexible way for human-computer interaction in both disabled and able-bodied people. Besides their convenience, they still lead to error-prone human-computer interaction. Eye tracking devices may misinterpret user's gaze resulting in typesetting errors, especially when operated in fast mode. As a potential remedy, we present a novel error detection system that aggregates the decision from two distinct subsystems, each one dealing with disparate data streams. The first subsystem operates on gaze-related measurements and exploits the eye-transition pattern to flag a typo. The second, is a brain-computer interface that utilizes a neural response, known as Error-Related Potentials (ErrPs), which is inherently generated whenever the subject observes an erroneous action. Based on the experimental data gathered from 10 participants under a spontaneous typesetting scenario, we first demonstrate that ErrP-based Brain Computer Interfaces can be indeed useful in the context of gaze-based typesetting, despite the putative contamination of EEG activity from the eye-movement artefact. Then, we show that the performance of this subsystem can be further improved by considering also the error detection from the gaze-related subsystem. Finally, the proposed bimodal error detection system is shown to significantly reduce the typesetting time in a gaze-based keyboard.},
}
@article {pmid30181429,
year = {2018},
author = {Chouhan, T and Robinson, N and Vinod, AP and Ang, KK and Guan, C},
title = {Wavlet phase-locking based binary classification of hand movement directions from EEG.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066008},
doi = {10.1088/1741-2552/aadeed},
pmid = {30181429},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain Mapping ; Brain-Computer Interfaces ; Electroencephalography/*methods/*statistics &amp; numerical data ; Evoked Potentials/physiology ; Hand/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; *Wavelet Analysis ; },
abstract = {OBJECTIVE: Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions.<br><br>APPROACH: In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions. EEG data collected from seven subjects performing right hand movements in four orthogonal directions in the horizontal plane is used for this analysis.<br><br>MAIN RESULTS: Our proposed W-PLV based method achieves a mean binary classification accuracy of 76.85% over seven subjects using wavelet levels corresponding to &#x2a7d;12 Hz EEG. The results also show direction-dependent information in various wavelet levels and indicate the presence of relevant information in slow cortical potentials (<1 Hz) as well as higher wavelet levels (&#x2a7d;12 Hz).<br><br>SIGNIFICANCE: This study presents a thorough analysis of the phase-locking patterns extracted from EEG corresponding to hand movements in different directions using W-PLV across various wavelet levels and verifies their discriminative ability in the single trial binary classification of hand movement directions.},
}
@article {pmid30180616,
year = {2018},
author = {Gao, ZK and Liu, CY and Yang, YX and Cai, Q and Dang, WD and Du, XL and Jia, HX},
title = {Multivariate weighted recurrence network analysis of EEG signals from ERP-based smart home system.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {28},
number = {8},
pages = {085713},
doi = {10.1063/1.5018824},
pmid = {30180616},
issn = {1089-7682},
mesh = {Electroencephalography/instrumentation/*methods ; *Evoked Potentials ; Humans ; *Signal Processing, Computer-Assisted ; *Wireless Technology ; },
abstract = {Smart home has been widely used to improve the living quality of people. Recently, the brain-computer interface (BCI) contributes greatly to the smart home system. We design a BCI-based smart home system, in which the event-related potentials (ERP) are induced by the image interface based on the oddball paradigm. Then, we investigate the influence of mental fatigue on the ERP classification by the Fisher linear discriminant analysis. The results indicate that the classification accuracy of ERP decreases as the brain evolves from the normal stage to the mental fatigue stage. In order to probe into the difference of the brain, cognitive process between mental fatigue and normal states, we construct multivariate weighted recurrence networks and analyze the variation of the weighted clustering coefficient and weighted global efficiency corresponding to these two brain states. The findings suggest that these two network metrics allow distinguishing normal and mental fatigue states and yield novel insights into the brain fatigue behavior resulting from a long use of the ERP-based smart home system. These properties render the multivariate recurrence network, particularly useful for analyzing electroencephalographic recordings from the ERP-based smart home system.},
}
@article {pmid30177583,
year = {2018},
author = {Jayaram, V and Barachant, A},
title = {MOABB: trustworthy algorithm benchmarking for BCIs.},
journal = {Journal of neural engineering},
volume = {15},
number = {6},
pages = {066011},
doi = {10.1088/1741-2552/aadea0},
pmid = {30177583},
issn = {1741-2552},
mesh = {*Algorithms ; Benchmarking/*methods ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; Humans ; Machine Learning ; Reproducibility of Results ; Software ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) algorithm development has long been hampered by two major issues: small sample sets and a lack of reproducibility. We offer a solution to both of these problems via a software suite that streamlines both the issues of finding and preprocessing data in a reliable manner, as well as that of using a consistent interface for machine learning methods.<br><br>APPROACH: By building on recent advances in software for signal analysis implemented in the MNE toolkit, and the unified framework for machine learning offered by the scikit-learn project, we offer a system that can improve BCI algorithm development. This system is fully open-source under the BSD licence and available at https://github.com/NeuroTechX/moabb.<br><br>MAIN RESULTS: We analyze a set of state-of-the-art decoding algorithms across 12 open access datasets, including over 250 subjects. Our results show that even for the best methods, there are datasets which do not show significant improvements, and further that many previously validated methods do not generalize well outside the datasets they were tested on.<br><br>SIGNIFICANCE: Our analysis confirms that BCI algorithms validated on single datasets are not representative, highlighting the need for more robust validation in the machine learning for BCIs community.},
}
@article {pmid30176008,
year = {2018},
author = {Goss-Varley, M and Shoffstall, AJ and Dona, KR and McMahon, JA and Lindner, SC and Ereifej, ES and Capadona, JR},
title = {Rodent Behavioral Testing to Assess Functional Deficits Caused by Microelectrode Implantation in the Rat Motor Cortex.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {138},
pages = {},
pmid = {30176008},
issn = {1940-087X},
support = {IK1 RX002492/RX/RRD VA/United States ; },
mesh = {Animals ; Behavior, Animal/*physiology ; Electrodes, Implanted/*statistics &amp; numerical data ; Male ; Microelectrodes/*statistics &amp; numerical data ; Motor Cortex/*physiology ; Rats ; Rodentia ; },
abstract = {Medical devices implanted in the brain hold tremendous potential. As part of a Brain Machine Interface (BMI) system, intracortical microelectrodes demonstrate the ability to record action potentials from individual or small groups of neurons. Such recorded signals have successfully been used to allow patients to interface with or control computers, robotic limbs, and their own limbs. However, previous animal studies have shown that a microelectrode implantation in the brain not only damages the surrounding tissue but can also result in functional deficits. Here, we discuss a series of behavioral tests to quantify potential motor impairments following the implantation of intracortical microelectrodes into the motor cortex of a rat. The methods for open field grid, ladder crossing, and grip strength testing provide valuable information regarding the potential complications resulting from a microelectrode implantation. The results of the behavioral testing are correlated with endpoint histology, providing additional information on the pathological outcomes and impacts of this procedure on the adjacent tissue.},
}
@article {pmid30172725,
year = {2018},
author = {Guggenberger, R and Kraus, D and Naros, G and Leão, MT and Ziemann, U and Gharabaghi, A},
title = {Extended enhancement of corticospinal connectivity with concurrent cortical and peripheral stimulation controlled by sensorimotor desynchronization.},
journal = {Brain stimulation},
volume = {11},
number = {6},
pages = {1331-1335},
doi = {10.1016/j.brs.2018.08.012},
pmid = {30172725},
issn = {1876-4754},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Pyramidal Tracts/*physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {BACKGROUND: Pairing cortical and peripheral input during motor imagery (MI)-related sensorimotor desynchronization (ERD) modulates corticospinal excitability at the cortical representation (hotspot) of the imagined movement.<br><br>OBJECTIVE: To determine the effects of this associative stimulation protocol on the cortical motor map beyond the hotspot.<br><br>METHODS: In healthy subjects, peripheral stimulation through passive hand opening by a robotic orthosis and single-pulse transcranial magnetic stimulation to the respective cortical motor representation were applied in a brain-machine interface environment. State-dependency was investigated by concurrent, delayed or non-specific stimulation with respect to ERD in the beta-band (16-22&#x202f;Hz) during MI of finger extension.<br><br>RESULTS: Concurrent stimulation led to increased excitability of an extended motor map. Delayed and non-specific stimulation led to heterogeneous changes, i.e., opposite patterns of increased excitability in either the center or the periphery of the motor map.<br><br>CONCLUSION: These results could be instrumental in closed-loop, state-dependent stimulation in the context of neurorehabilitation.},
}
@article {pmid30172003,
year = {2018},
author = {Takemi, M and Maeda, T and Masakado, Y and Siebner, HR and Ushiba, J},
title = {Muscle-selective disinhibition of corticomotor representations using a motor imagery-based brain-computer interface.},
journal = {NeuroImage},
volume = {183},
number = {},
pages = {597-605},
doi = {10.1016/j.neuroimage.2018.08.070},
pmid = {30172003},
issn = {1095-9572},
mesh = {Adult ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization/physiology ; Evoked Potentials, Motor/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Muscle, Skeletal/*physiology ; Neural Inhibition/*physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {Bridging between brain activity and machine control, brain-computer interface (BCI) can be employed to activate distributed neural circuits implicated in a specific aspect of motor control. Using a motor imagery-based BCI paradigm, we previously found a disinhibition within the primary motor cortex contralateral to the imagined movement, as evidenced by event-related desynchronization (ERD) of oscillatory cortical activity. Yet it is unclear whether this BCI approach does selectively facilitate corticomotor representations targeted by the imagery. To address this question, we used brain state-dependent transcranial magnetic stimulation while participants performed kinesthetic motor imagery of wrist movements with their right hand and received online visual feedback of the ERD. Single and paired-pulse magnetic stimulation were given to the left primary motor cortex at a low or high level of ERD to assess intracortical excitability. While intracortical facilitation showed no modulation by ERD, short-latency intracortical inhibition was reduced the higher the ERD. Intracortical disinhibition was only found in the agonist muscle targeted by motor imagery at high ERD level, but not in the antagonist muscle. Single pulse motor-evoked potential was also increased the higher the ERD. However, at high ERD level, this facilitatory effect on overall corticospinal excitability was not selective to the agonist muscle. Analogous results were found in two independent experiments, in which participants either performed kinesthetic motor imagery of wrist extension or flexion. Our results showed that motor imagery-based BCI can selectively disinhibit the corticomotor output to the agonist muscle, enabling effector-specific training in patients with motor paralysis.},
}
@article {pmid30170529,
year = {2019},
author = {Rodriguez, VJ and Butts, SA and Mandell, LN and Weiss, SM and Kumar, M and Jones, DL},
title = {The role of social support in the association between childhood trauma and depression among HIV-infected and HIV-uninfected individuals.},
journal = {International journal of STD &amp; AIDS},
volume = {30},
number = {1},
pages = {29-36},
doi = {10.1177/0956462418793736},
pmid = {30170529},
issn = {1758-1052},
support = {R01 DA034589/DA/NIDA NIH HHS/United States ; P30 AI073961/AI/NIAID NIH HHS/United States ; },
mesh = {Adult ; Adult Survivors of Child Abuse/*psychology ; Adult Survivors of Child Adverse Events/*psychology ; Case-Control Studies ; Child ; Child Abuse, Sexual/*psychology ; Cross-Sectional Studies ; Depression/*complications/psychology ; Female ; HIV Infections/complications/*psychology ; HIV Seronegativity ; Humans ; Male ; Middle Aged ; *Social Support ; },
abstract = {Childhood trauma (CT) - emotional, physical or sexual abuse, or emotional or physical neglect - has been associated with HIV infection and can lead to poor health outcomes and depression in adulthood. Though the impact of CT on depression may be decreased by social support, this may not be true of individuals living with HIV, due to the additive traumatic effects of both CT and acquisition of HIV. This study examined social support, depression, and CT among HIV-infected (n = 134) and HIV-uninfected (n = 306) men and women. Participants (N = 440) were assessed regarding sociodemographic characteristics, CT, depression, and social support. Participants were racially and ethnically diverse, 36 ± 9 years of age on average, and 44% had an income of less than USD$500 a month. Among HIV-uninfected individuals, social support explained the association between depression in persons with CT (b = 0.082, bCI [0.044, 0.130]). Among HIV-infected individuals, after accounting for sociodemographic characteristics, social support did not explain the association between depression and CT due to lower levels of social support among HIV-infected individuals [95% CI: -0.006, 0.265]. The quality of social support may differ among HIV-infected persons due to decreased social support and smaller social networks among those living with HIV. Depressive symptoms among those living with HIV appear to be less influenced by social support, likely due to the additive effects of HIV infection combined with CT.},
}
@article {pmid30159311,
year = {2018},
author = {Hermann, JK and Lin, S and Soffer, A and Wong, C and Srivastava, V and Chang, J and Sunil, S and Sudhakar, S and Tomaszewski, WH and Protasiewicz, G and Selkirk, SM and Miller, RH and Capadona, JR},
title = {The Role of Toll-Like Receptor 2 and 4 Innate Immunity Pathways in Intracortical Microelectrode-Induced Neuroinflammation.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {6},
number = {},
pages = {113},
pmid = {30159311},
issn = {2296-4185},
support = {R01 NS082404/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
abstract = {We have recently demonstrated that partial inhibition of the cluster of differentiation 14 (CD14) innate immunity co-receptor pathway improves the long-term performance of intracortical microelectrodes better than complete inhibition. We hypothesized that partial activation of the CD14 pathway was critical to a neuroprotective response to the injury associated with initial and sustained device implantation. Therefore, here we investigated the role of two innate immunity receptors that closely interact with CD14 in inflammatory activation. We implanted silicon planar non-recording neural probes into knockout mice lacking Toll-like receptor 2 (Tlr2[-/-]), knockout mice lacking Toll-like receptor 4 (Tlr4[-/-]), and wildtype (WT) control mice, and evaluated endpoint histology at 2 and 16 weeks after implantation. Tlr4[-/-] mice exhibited significantly lower BBB permeability at acute and chronic time points, but also demonstrated significantly lower neuronal survival at the chronic time point. Inhibition of the Toll-like receptor 2 (TLR2) pathway had no significant effect compared to control animals. Additionally, when investigating the maturation of the neuroinflammatory response from 2 to 16 weeks, transgenic knockout mice exhibited similar histological trends to WT controls, except that knockout mice did not exhibit changes in microglia and macrophage activation over time. Together, our results indicate that complete genetic removal of Toll-like receptor 4 (TLR4) was detrimental to the integration of intracortical neural probes, while inhibition of TLR2 had no impact within the tests performed in this study. Therefore, approaches focusing on incomplete or acute inhibition of TLR4 may still improve intracortical microelectrode integration and long term recording performance.},
}
@article {pmid30158847,
year = {2018},
author = {Schaeffer, MC and Aksenova, T},
title = {Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {540},
pmid = {30158847},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.},
}
@article {pmid30158845,
year = {2018},
author = {Lulé, D and Hörner, K and Vazquez, C and Aho-Özhan, H and Keller, J and Gorges, M and Uttner, I and Ludolph, AC},
title = {Screening for Cognitive Function in Complete Immobility Using Brain-Machine Interfaces: A Proof of Principle Study.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {517},
pmid = {30158845},
issn = {1662-4548},
abstract = {Background: In many neurological conditions, there is a combination of decline in physical function and cognitive abilities. For far advanced stages of physical disability where speaking and hand motor abilities are severely impaired, there is a lack of standardized approach to screen for cognitive profile. Methods:N = 40 healthy subjects were included in the study. For proof of principle, N = 6 ALS patients were additionally measured. For cognitive screening, we used the Edinburgh cognitive and behavioral ALS screen (ECAS) in the standard paper-and-pencil version. Additionally, we adapted the ECAS to a brain-machine interface (BMI) control module to screen for cognition in severely advanced patients. Results: There was a high congruency between BMI version and the paper-and-pencil version of the ECAS. Sensitivity and specificity of the ECAS-BMI were mostly high whereas stress and weariness for the patient were low. Discussion/Conclusion: We hereby present evidence that adaptation of a standardized neuropsychological test for BMI control is feasible. BMI driven neuropsychological test provides congruent results compared to standardized tests with a good specificity and sensitivity but low patient load.},
}
@article {pmid30158505,
year = {2018},
author = {Choi, SI and Han, CH and Choi, GY and Shin, J and Song, KS and Im, CH and Hwang, HJ},
title = {On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {9},
pages = {},
pmid = {30158505},
issn = {1424-8220},
support = {2017-0-00451//Institute for Information and communications Technology Promotion/ ; },
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Ear ; Electroencephalography/*methods ; Feasibility Studies ; Female ; Humans ; Male ; Reproducibility of Results ; Scalp ; Young Adult ; },
abstract = {Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.},
}
@article {pmid30154834,
year = {2018},
author = {Xygonakis, I and Athanasiou, A and Pandria, N and Kugiumtzis, D and Bamidis, PD},
title = {Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {7957408},
pmid = {30154834},
issn = {1687-5273},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.},
}
@article {pmid30150363,
year = {2018},
author = {Amundsen Huffmaster, SL and Van Acker, GM and Luchies, CW and Cheney, PD},
title = {Muscle Synergies Obtained from Comprehensive Mapping of the Cortical Forelimb Representation Using Stimulus Triggered Averaging of EMG Activity.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {41},
pages = {8759-8771},
pmid = {30150363},
issn = {1529-2401},
support = {P50 NS098573/NS/NINDS NIH HHS/United States ; R01 NS051825/NS/NINDS NIH HHS/United States ; R01 NS085188/NS/NINDS NIH HHS/United States ; R01 NS064054/NS/NINDS NIH HHS/United States ; R01 NS088679/NS/NINDS NIH HHS/United States ; P30 HD002528/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation ; Electromyography ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; *Muscle Contraction ; Muscle, Skeletal/*physiology ; Upper Extremity/innervation/*physiology ; },
abstract = {Neuromuscular control of voluntary movement may be simplified using muscle synergies similar to those found using non-negative matrix factorization. We recently identified synergies in electromyography (EMG) recordings associated with both voluntary movement and movement evoked by high-frequency long-duration intracortical microstimulation applied to the forelimb representation of the primary motor cortex (M1). The goal of this study was to use stimulus-triggered averaging (StTA) of EMG activity to investigate the synergy profiles and weighting coefficients associated with poststimulus facilitation, as synergies may be hard-wired into elemental cortical output modules and revealed by StTA. We applied StTA at low (LOW, ∼15 μA) and high intensities (HIGH, ∼110 μA) to 247 cortical locations of the M1 forelimb region in two male rhesus macaques while recording the EMG of 24 forelimb muscles. Our results show that 10-11 synergies accounted for 90% of the variation in poststimulus EMG facilitation peaks from the LOW-intensity StTA dataset while only 4-5 synergies were needed for the HIGH-intensity dataset. Synergies were similar across monkeys and current intensities. Most synergy profiles strongly activated only one or two muscles; all joints were represented and most, but not all, joint directions of motion were represented. Cortical maps of the synergy weighting coefficients suggest only a weak organization. StTA of M1 resulted in highly diverse muscle activations, suggestive of the limiting condition of requiring a synergy for each muscle to account for the patterns observed.SIGNIFICANCE STATEMENT Coordination of muscle activity and the neural origin of potential muscle synergies remains a fundamental question of neuroscience. We previously demonstrated that high-frequency long-duration intracortical microstimulation-evoked synergies were unrelated to voluntary movement synergies and were not clearly organized in the cortex. Here we present stimulus-triggered averaging facilitation-related muscle synergies, suggesting that when fundamental cortical output modules are activated, synergies approach the limit of single-muscle control. Thus, we conclude that if the CNS controls movement via linear synergies, those synergies are unlikely to be called from M1. This information is critical for understanding neural control of movement and the development of brain-machine interfaces.},
}
@article {pmid30147716,
year = {2018},
author = {Liang, Y and Liu, X and Qiu, L and Zhang, S},
title = {An EEG Study of a Confusing State Induced by Information Insufficiency during Mathematical Problem-Solving and Reasoning.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {1943565},
pmid = {30147716},
issn = {1687-5273},
mesh = {Analysis of Variance ; Attention/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Comprehension/physiology ; Cortical Synchronization ; *Electroencephalography ; Fourier Analysis ; Humans ; Male ; *Mathematical Concepts ; Neuropsychological Tests ; Problem Solving/*physiology ; Recognition, Psychology/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Confusion is a complex cognitive state that is prevalent during learning and problem-solving. The aim of this study is to explore the brain activity reflected by electroencephalography (EEG) during a confusing state induced by two kinds of information insufficiencies during mathematical problem-solving, namely, an explicit situation that clearly lacked information and an implicit situation in which the missing information was hidden in the problem itself, and whether there is an EEG difference between these two situations. Two experimental tasks and three control tasks were created. Short time Fourier transformation (STFT) was used for time-frequency analysis; then the alpha task-related-power (TRP) changes and distributions, which are closely related to cognitive processing, were calculated, and repeated measures ANOVA were performed to find the significant difference between task conditions. The results showed that the alpha power decreased significantly in the regions related to calculation when the participants encountered both explicit and implicit information insufficiency tasks compared to the control tasks, suggesting that confusion can cause more brain activity in the cortical regions related to the tasks that induce confusion. In addition, the implicit information insufficiency task elicited more activity in the parietal and right temporal regions, whereas the explicit information insufficiency task elicited additional activity in the frontal lobe, which revealed that the frontal region is related to the processing of novel or unfamiliar information and the parietal-temporal regions are involved in sustained attention or reorientation during confusing states induced by information insufficiency. In conclusion, this study has preliminarily investigated the EEG characteristics of confusion states, suggests that EEG is a promising methodology to detect confusion, and provides a basis for future studies aiming to achieve automatic recognition of confusing states.},
}
@article {pmid30138866,
year = {2018},
author = {Elder, T and Tuma, F},
title = {Bilateral vertebral artery transection following blunt trauma.},
journal = {International journal of surgery case reports},
volume = {51},
number = {},
pages = {29-32},
pmid = {30138866},
issn = {2210-2612},
abstract = {INTRODUCTION: Blunt vertebral artery injury (BVI) is a potentially catastrophic event associated with a variety of trauma mechanisms, particularly in the setting of cervical spine injury. Early detection and treatment of BVI and blunt carotid artery injury (BCI) - collectively termed blunt cerebrovascular injuries (BCVI) - is a known determinant of favorable outcomes, except in the case of complete transection injuries. The limited existing reports of these injuries demonstrate a 100% mortality rate regardless of the management approach taken, and further investigation is essential in better understanding the nature of the injury and improving patient outcomes.<br><br>PRESENTATION OF CASE: A 55&#x2009;year old previously healthy patient was brought to the Emergency Department following a motor vehicle collision. The patient was alert upon arrival to the ED and complained of neck pain. Initial assessment was significant for open fracture of the left upper extremity, swelling of the anterior neck, and no purposeful movements noted of the lower extremities. Shortly thereafter, the patient showed a sudden decline in mental status and became hemodynamically unstable. Focused Assessment with Sonography for Trauma was positive, and after remaining unstable despite resuscitation efforts, the patient was brought emergently to the operating room.<br><br>DISCUSSION: Following exploratory laparotomy for bleeding control and washout of the open fracture, CT angiogram of the head and neck was obtained. This revealed significant C5-C6 dissociation as well as bilateral vertebral artery transection and large prevertebral hematoma. Prior to any further surgical intervention, the patient's neurologic function continued to decline, and brain CT demonstrated infarcts in the posterior cerebral artery distribution. Brain death was confirmed the next day, and all care was subsequently withdrawn.<br><br>CONCLUSIONS: Analysis of outcomes in patients with BCVI suggests that BVI should be distinguished from the better-studied CVI. Each injury type has been found to possess its own distinct risk factors, likelihoods of progression, and surgical accessibility, all of which affect management. Data on complete vessel transections remains limited for all BCVIs, with no documented cases of bilateral BVI to date. Our case study supports the 100% mortality rate seen in previously reported BCVI transections. Furthermore, our findings suggest that BVI transections occur in patients with coexisting injuries, which challenges the ability to attribute a single neurologic outcome to any one injury.},
}
@article {pmid30134219,
year = {2018},
author = {Ng, ZY and Wong, JY and Panneerselvam, J and Madheswaran, T and Kumar, P and Pillay, V and Hsu, A and Hansbro, N and Bebawy, M and Wark, P and Hansbro, P and Dua, K and Chellappan, DK},
title = {Assessing the potential of liposomes loaded with curcumin as a therapeutic intervention in asthma.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {172},
number = {},
pages = {51-59},
doi = {10.1016/j.colsurfb.2018.08.027},
pmid = {30134219},
issn = {1873-4367},
mesh = {Asthma/*drug therapy/pathology ; Biomarkers/metabolism ; Cell Line ; Computer Simulation ; Curcumin/chemistry/*therapeutic use ; Drug Liberation ; Humans ; Inflammation/pathology ; Liposomes ; Models, Molecular ; Particle Size ; Spectroscopy, Fourier Transform Infrared ; Static Electricity ; },
abstract = {Curcumin a component of turmeric, which is derived from Curcuma longa is used as a colouring agent and as a dietary spice for centuries. Extensive studies have been done on the anti-inflammatory activity of curcumin along with its molecular mechanism involving different signalling pathways. However, the physicochemical and biological properties such as poor solubility and rapid metabolism of curcumin have led to low bioavailability and hence limits its application. Current therapies for asthma such as bronchodilators and inhaled corticosteroids (ICS) are aimed at controlling disease symptoms and prevent asthma exacerbation. However, this approach requires lifetime therapy and is associated with a constellation of side effects. This creates a clear unmet medical need and there is an urgent demand for new and more-effective treatments. The present study is aimed to formulate liposomes containing curcumin and evaluate for its anti-inflammatory effects on lipopolysaccharide (LPS)-induced inflammation on BCi-NS1.1 cell line. Curcumin and salbutamol liposomes were formulated using lipid hydration method. The prepared liposomes were characterized in terms of particle size, zeta potential, encapsulation efficiency and in-vitro release profile. The liposomes were tested on BCI-NS1.1 cell line to evaluate its anti-inflammatory properties. The various pro-inflammatory markers studied were Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin-1β (IL-1β) and Tumour Necrosis Factor-a (TNF-a). Additionally, molecular mechanics simulations were used to elucidate the positioning, energy minimization, and aqueous dispersion of the liposomal architecture involving lecithin and curcumin. The prepared curcumin formulation showed an average size and zeta potential of 271.3 ± 3.06 nm and -61.0 mV, respectively. The drug encapsulation efficiency of liposomal curcumin is 81.1%. Both curcumin-loaded liposomes formulation (1 μg/mL, 5 μg/mL) resulted in significant (p < 0.05) reduction in the level of pro-inflammatory marker expression such as IL-6, IL-8, IL-1β and TNF-a compared to positive control group. Liposomal curcumin with the dose of 1 μg/mL reduced the inflammatory markers more effectively compared to that of 5 μg/mL. Liposomal curcumin could be a promising intervention for asthma therapy showing their efficacy in suppressing the important pro-inflammatory markers involved in the pathogenesis of asthma.},
}
@article {pmid30132452,
year = {2018},
author = {Shukin, IA and Lebedeva, AV and Soldatov, MA and Fidler, MC},
title = {[Post-stroke rehabilitation training with a brain-computer interface: a clinical and neuropsychological study].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {118},
number = {7},
pages = {25-29},
doi = {10.17116/jnevro20181187125},
pmid = {30132452},
issn = {1997-7298},
mesh = {Aged ; *Brain Ischemia ; *Brain-Computer Interfaces ; *Cognition Disorders ; *Cognitive Dysfunction ; Female ; Humans ; Male ; Middle Aged ; Stroke ; *Stroke Rehabilitation ; },
abstract = {AIM: To evaluate the efficacy of cytoflavin in the treatment of patients with chronic cerebral ischemia and mild cognitive impairment predominantly of vascular origin.<br><br>MATERIAL AND METHODS: Treatment results of 140 patients, aged 60-74, with chronic cerebral ischemia were analyzed. The main group included 77 patients (35 men and 42 women of average age 66.38&#xb1;4.64 years) who received cytoflavin throughout the observation period: 2 tablets twice a day 30 minutes before meals. The comparison group included 63 patients (26 men and 37 women of average age 67.48&#xb1;5.22 years) who during the whole period of observation received ethyl methyl hydroxypyridine succinate: 2 tablets (250 mg) twice a day, according to the same scheme as in the main group. Treatment efficacy was assessed by neuropsychological testing and P300 evoked potentials.<br><br>RESULTS AND CONCLUSION: During treatment, there was an improvement in neurophysiological parameters in both groups, which was more pronounced in patients treated with cytoflavin: their P300 amplitude increased by1.3 times in the left hemisphere (from 9.21 (8.36, 10.11) to 12.41 (10.23, 13.37 &#x3bc;V) and 1,7 times in the right hemisphere (from 6.48 (5.26, 7.35) to 11.04 (9.29, 12.18) &#x3bc;V). Our study confirms the advisability of using drugs that have complex cytoprotective and energy-correcting mechanism in patients with cognitive impairment. Cytoflavin has shown the high efficacy and safety and can be recommended as part of complex therapy for cognitive disorders. Using simple and inexpensive instrumental methods (assessment of cognitive P300 evoked potential) along with diagnostic scales in patients with cognitive impairment can significantly objectify the assessment of treatment dynamics.},
}
@article {pmid30131670,
year = {2018},
author = {Wong, DDE and Fuglsang, SA and Hjortkjær, J and Ceolini, E and Slaney, M and de Cheveigné, A},
title = {A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {531},
pmid = {30131670},
issn = {1662-4548},
abstract = {The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies.},
}
@article {pmid30131666,
year = {2018},
author = {Matsushita, K and Hirata, M and Suzuki, T and Ando, H and Yoshida, T and Ota, Y and Sato, F and Morris, S and Sugata, H and Goto, T and Yanagisawa, T and Yoshimine, T},
title = {A Fully Implantable Wireless ECoG 128-Channel Recording Device for Human Brain-Machine Interfaces: W-HERBS.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {511},
pmid = {30131666},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless systems for clinical ECoG BMIs is a major goal in the field. We developed a fully implantable ECoG signal recording device for human ECoG BMI, i.e., a wireless human ECoG-based real-time BMI system (W-HERBS). In this system, three-dimensional (3D) high-density subdural multiple electrodes are fitted to the brain surface and ECoG measurement units record 128-channel (ch) ECoG signals at a sampling rate of 1 kHz. The units transfer data to the data and power management unit implanted subcutaneously in the abdomen through a subcutaneous stretchable spiral cable. The data and power management unit then communicates with a workstation outside the body and wirelessly receives 400 mW of power from an external wireless transmitter. The workstation records and analyzes the received data in the frequency domain and controls external devices based on analyses. We investigated the performance of the proposed system. We were able to use W-HERBS to detect sine waves with a 4.8-μV amplitude and a 60-200-Hz bandwidth from the ECoG BMIs. W-HERBS is the first fully implantable ECoG-based BMI system with more than 100 ch. It is capable of recording 128-ch subdural ECoG signals with sufficient input-referred noise (3 μVrms) and with an acceptable time delay (250 ms). The system contributes to the clinical application of high-performance BMIs and to experimental brain research.},
}
@article {pmid30130642,
year = {2018},
author = {Alderson, TH and Bokde, ALW and Kelso, JAS and Maguire, L and Coyle, D and , },
title = {Metastable neural dynamics in Alzheimer's disease are disrupted by lesions to the structural connectome.},
journal = {NeuroImage},
volume = {183},
number = {},
pages = {438-455},
pmid = {30130642},
issn = {1095-9572},
support = {R01 MH080838/MH/NIMH NIH HHS/United States ; U01 AG024904/AG/NIA NIH HHS/United States ; },
mesh = {Aged ; Aged, 80 and over ; *Alzheimer Disease/diagnostic imaging/pathology/physiopathology ; Connectome/*methods ; Databases, Factual ; Diffusion Tensor Imaging/methods ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Male ; Middle Aged ; *Nerve Net/diagnostic imaging/pathology/physiopathology ; },
abstract = {Current theory suggests brain regions interact to reconcile the competing demands of integration and segregation by leveraging metastable dynamics. An emerging consensus recognises the importance of metastability in healthy neural dynamics where the transition between network states over time is dependent upon the structural connectivity between brain regions. In Alzheimer's disease (AD) - the most common form of dementia - these couplings are progressively weakened, metastability of neural dynamics are reduced and cognitive ability is impaired. Accordingly, we use a joint empirical and computational approach to reveal how behaviourally relevant changes in neural metastability are contingent on the structural integrity of the anatomical connectome. We estimate the metastability of fMRI BOLD signal in subjects from across the AD spectrum and in healthy controls and demonstrate the dissociable effects of structural disconnection on synchrony versus metastability. In addition, we reveal the critical role of metastability in general cognition by demonstrating the link between an individuals cognitive performance and their metastable neural dynamic. Finally, using whole-brain computer modelling, we demonstrate how a healthy neural dynamic is conditioned upon the topological integrity of the structural connectome. Overall, the results of our joint computational and empirical analysis suggest an important causal relationship between metastable neural dynamics, cognition, and the structural efficiency of the anatomical connectome.},
}
@article {pmid30130168,
year = {2019},
author = {Mishchenko, Y and Kaya, M and Ozbay, E and Yanar, H},
title = {Developing a Three- to Six-State EEG-Based Brain-Computer Interface for a Virtual Robotic Manipulator Control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {4},
pages = {977-987},
doi = {10.1109/TBME.2018.2865941},
pmid = {30130168},
issn = {1558-2531},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Female ; Humans ; Male ; Neural Prostheses ; Robotics/*instrumentation ; Self-Help Devices ; Signal Processing, Computer-Assisted/*instrumentation ; Young Adult ; },
abstract = {OBJECTIVE: We develop an electroencephalography (EEG)-based noninvasive brain-computer interface (BCI) system having short training time (15 min) that can be applied for high-performance control of robotic prosthetic systems.<br><br>METHODS: A signal processing system for detecting user's mental intent from EEG data based on up to six-state BCI paradigm is developed and used.<br><br>RESULTS: We examine the performance of the developed system on experimental data collected from 12 healthy participants and analyzed offline. Out of 12 participants 3 achieve an accuracy of six-state communication in 80%-90% range, while 2 participants do not achieve a satisfactory accuracy. We further implement an online BCI system for control of a virtual 3 degree-of-freedom (dof) prosthetic manipulator and test it with our three best participants. Two participants are able to successfully complete 100% of the test tasks, demonstrating on average the accuracy rate of 80% and requiring 5-10 s to execute a manipulator move. One participant failed to demonstrate a satisfactory performance in online trials.<br><br>CONCLUSION: We show that our offline EEG BCI system can correctly identify different motor imageries in EEG data with high accuracy and our online BCI system can be used for control of a virtual 3 dof prosthetic manipulator.<br><br>SIGNIFICANCE: Our results prepare foundation for further development of higher performance EEG BCI-based robotic assistive systems and demonstrate that EEG-based BCI may be feasible for robotic control by paralyzed and immobilized individuals.},
}
@article {pmid30128674,
year = {2019},
author = {Meinel, A and Castaño-Candamil, S and Blankertz, B and Lotte, F and Tangermann, M},
title = {Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems : Guidelines Derived from Simulation and Real-World Data.},
journal = {Neuroinformatics},
volume = {17},
number = {2},
pages = {235-251},
pmid = {30128674},
issn = {1559-0089},
support = {bwHPC//Ministerium f&#xfc;r Wissenschaft, Forschung und Kunst Baden-W&#xfc;rttemberg/International ; EXC 1086//Deutsche Forschungsgemeinschaft/International ; INST 39/963-1 FUGG//Deutsche Forschungsgemeinschaft/International ; ANR-15-CE23-0013-01//Agence Nationale de la Recherche/International ; ERC-2016-STG-714567//H2020 European Research Council/International ; },
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Humans ; Magnetoencephalography ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.},
}
@article {pmid30127730,
year = {2018},
author = {Tariq, M and Trivailo, PM and Simic, M},
title = {EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {312},
pmid = {30127730},
issn = {1662-5161},
abstract = {Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user's mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications' key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-based multifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field.},
}
@article {pmid30126416,
year = {2018},
author = {Tang, J and Liu, Y and Hu, D and Zhou, Z},
title = {Towards BCI-actuated smart wheelchair system.},
journal = {Biomedical engineering online},
volume = {17},
number = {1},
pages = {111},
pmid = {30126416},
issn = {1475-925X},
support = {2015CB351706//National Basic Research Program/ ; 61375117//National Natural Science Foundation of China/ ; },
mesh = {*Brain-Computer Interfaces ; Cerebral Infarction ; Electroencephalography ; Humans ; Spinal Cord Injuries ; Stroke ; *Wheelchairs ; },
abstract = {BACKGROUND: Electroencephalogram-based brain-computer interfaces (BCIs) represent novel human machine interactive technology that allows people to communicate and interact with the external world without relying on their peripheral muscles and nervous system. Among BCI systems, brain-actuated wheelchairs are promising systems for the rehabilitation of severely motor disabled individuals who are unable to control a wheelchair by conventional interfaces. Previous related studies realized the easy use of brain-actuated wheelchairs that enable people to navigate the wheelchair through simple commands; however, these systems rely on offline calibration of the environment. Other systems do not rely on any prior knowledge; however, the control of the system is time consuming. In this paper, we have proposed an improved mobile platform structure equipped with an omnidirectional wheelchair, a lightweight robotic arm, a target recognition module and an auto-control module. Based on the you only look once (YOLO) algorithm, our system can, in real time, recognize and locate the targets in the environment, and the users confirm one target through a P300-based BCI. An expert system plans a proper solution for a specific target; for example, the planned solution for a door is opening the door and then passing through it, and the auto-control system then jointly controls the wheelchair and robotic arm to complete the operation. During the task execution, the target is also tracked by using an image tracking technique. Thus, we have formed an easy-to-use system that can provide accurate services to satisfy user requirements, and this system can accommodate different environments.<br><br>RESULTS: To validate and evaluate our system, an experiment simulating the daily application was performed. The tasks included the user driving the system closer to a walking man and having a conversation with him; going to another room through a door; and picking up a bottle of water on the desk and drinking water. Three patients (cerebral infarction; spinal injury; and stroke) and four healthy subjects participated in the test and all completed the tasks.<br><br>CONCLUSION: This article presents a brain-actuated smart wheelchair system. The system is intelligent in that it provides efficient and considerate services for users. To test the system, three patients and four healthy subjects were recruited to participate in a test. The results demonstrate that the system works smartly and efficiently; with this system, users only need to issue small commands to get considerate services. This system is of significance for accelerating the application of BCIs in the practical environment, especially for patients who will use a BCI for rehabilitation applications.},
}
@article {pmid30124025,
year = {2018},
author = {Zhang, R and Lu, P and Niu, X and Liu, S and Hu, Y},
title = {[A research for single trial detection of error related negativity].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {4},
pages = {606-612},
pmid = {30124025},
issn = {1001-5515},
abstract = {Error related negativity (ERN) is generated in frontal and central cortical regions when individuals perceive errors. Because ERN has low signal-to-noise ratio and large individual difference, it is difficult for single trial ERN recognition. In current study, the optimized electroencephalograph (EEG) channels were selected based on the brain topography of ERN activity and ERN offline recognition rate, and the optimized EEG time segments were selected based on the ERN offline recognition rate, then the low frequency time domain and high frequency time-frequency domain features were analyzed based on wavelet transform, after which the ERN single detection algorithm was proposed based on the above procedures. Finally, we achieved average recognition rate of 72.0% ± 9.6% in 10 subjects by using the sample points feature in 0~3.9 Hz and the power and variance features in 3.9~15.6 Hz from the EEG segments of 200~600 ms on the selected 6 channels. Our work has the potential to help the error command real-time correction technique in the application of online brain-computer interface system.},
}
@article {pmid30121627,
year = {2019},
author = {Raj, D and Yang, MH and Rodgers, D and Hampton, EN and Begum, J and Mustafa, A and Lorizio, D and Garces, I and Propper, D and Kench, JG and Kocher, HM and Young, TS and Aicher, A and Heeschen, C},
title = {Switchable CAR-T cells mediate remission in metastatic pancreatic ductal adenocarcinoma.},
journal = {Gut},
volume = {68},
number = {6},
pages = {1052-1064},
pmid = {30121627},
issn = {1468-3288},
support = {C16420/A18066//Cancer Research UK/United Kingdom ; },
mesh = {Animals ; Antigens, Neoplasm/genetics ; Biopsy, Needle ; Carcinoma, Pancreatic Ductal/immunology/*pathology/*therapy ; Enzyme-Linked Immunosorbent Assay ; Flow Cytometry ; Gene Expression Regulation, Neoplastic ; Humans ; Immunohistochemistry ; Immunotherapy/methods ; Immunotherapy, Adoptive/*methods ; Neoplasm Invasiveness/pathology ; Neoplasm Metastasis ; Neoplasm Staging ; Pancreatic Neoplasms/immunology/*pathology/*therapy ; Receptor, ErbB-2/genetics ; Statistics, Nonparametric ; Treatment Outcome ; Tumor Cells, Cultured ; Xenograft Model Antitumor Assays/methods ; },
abstract = {OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is a disease of unmet medical need. While immunotherapy with chimeric antigen receptor T (CAR-T) cells has shown much promise in haematological malignancies, their efficacy for solid tumours is challenged by the lack of tumour-specific antigens required to avoid on-target, off-tumour effects. Switchable CAR-T cells whereby activity of the CAR-T cell is controlled by dosage of a tumour antigen-specific recombinant Fab-based 'switch' to afford a fully tunable response may overcome this translational barrier.<br><br>DESIGN: In this present study, we have used conventional and switchable CAR-T cells to target the antigen HER2, which is upregulated on tumour cells, but also present at low levels on normal human tissue. We used patient-derived xenograft models derived from patients with stage IV PDAC that mimic the most aggressive features of PDAC, including severe liver and lung metastases.<br><br>RESULTS: Switchable CAR-T cells followed by administration of the switch directed against human epidermal growth factor receptor 2 (HER2)-induced complete remission in difficult-to-treat, patient-derived advanced pancreatic tumour models. Switchable HER2 CAR-T cells were as effective as conventional HER2 CAR-T cells in vivo testing a range of different CAR-T cell doses.<br><br>CONCLUSION: These results suggest that a switchable CAR-T system is efficacious against aggressive and disseminated tumours derived from patients with advanced PDAC while affording the potential safety of a control switch.},
}
@article {pmid30120378,
year = {2018},
author = {Fuchigami, T and Shikauchi, Y and Nakae, K and Shikauchi, M and Ogawa, T and Ishii, S},
title = {Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {12342},
pmid = {30120378},
issn = {2045-2322},
mesh = {Adult ; *Brain Mapping/methods ; *Diffusion Tensor Imaging/methods ; Female ; Gray Matter/physiology ; Humans ; Image Processing, Computer-Assisted ; *Imaging, Three-Dimensional ; *Magnetic Resonance Imaging/methods ; Male ; White Matter/physiology ; Young Adult ; },
abstract = {Functional magnetic resonance imaging (fMRI) acquisitions include a great deal of individual variability. This individuality often generates obstacles to the efficient use of databanks from multiple subjects. Although recent studies have suggested that inter-regional connectivity reflects individuality, conventional three-dimensional (3D) registration methods that calibrate inter-subject variability are based on anatomical information about the gray matter shape (e.g., T1-weighted). Here, we present a new registration method focusing more on the white matter structure, which is directly related to the connectivity in the brain, and apply it to subject-transfer brain decoding. Our registration method based on diffusion tensor imaging (DTI) transferred functional maps of each individual to a common anatomical space, where a decoding analysis of multi-voxel patterns was performed. The decoder trained on functional maps from other individuals in the common space showed a transfer decoding accuracy comparable to that of an individual decoder trained on single-subject functional maps. The DTI-based registration allowed more precise transformation of gray matter boundaries than a well-established T1-based method. These results suggest that the DTI-based registration is a promising tool for standardization of the brain functions, and moreover, will allow us to perform 'zero-shot' learning of decoders which is profitable in brain machine interface scenes.},
}
@article {pmid30118504,
year = {2018},
author = {Liu, Y and Wei, Q and Lu, Z},
title = {A multi-target brain-computer interface based on code modulated visual evoked potentials.},
journal = {PloS one},
volume = {13},
number = {8},
pages = {e0202478},
pmid = {30118504},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Models, Neurological ; },
abstract = {The number of selectable targets is one of the main factors that affect the performance of a brain-computer interface (BCI). Most existing code modulated visual evoked potential (c-VEP) based BCIs use a single pseudorandom binary sequence and its circularly shifting sequences to modulate different stimulus targets, making the number of selectable targets limited by the length of modulation codes. This paper proposes a novel paradigm for c-VEP BCIs, which divides the stimulus targets into four target groups and each group of targets are modulated by a unique pseudorandom binary code and its circularly shifting codes. Based on the paradigm, a four-group c-VEP BCI with a total of 64 stimulus targets was developed and eight subjects were recruited to participate in the BCI experiment. Based on the experimental data, the characteristics of the c-VEP BCI were explored by the analyses of auto- and cross-correlation, frequency spectrum, signal to noise ratio and correlation coefficient. On the basis, single-trial data with the length of one stimulus cycle were classified and the attended target was recognized. The averaged classification accuracy across subjects was 88.36% and the corresponding information transfer rate was as high as 184.6 bit/min. These results suggested that the c-VEP BCI paradigm is both feasible and effective, and provides a new solution for BCI study to substantially increase the number of available targets.},
}
@article {pmid30117107,
year = {2019},
author = {Thompson, MC},
title = {Critiquing the Concept of BCI Illiteracy.},
journal = {Science and engineering ethics},
volume = {25},
number = {4},
pages = {1217-1233},
pmid = {30117107},
issn = {1471-5546},
mesh = {*Brain-Computer Interfaces ; Equipment Design ; Humans ; *Learning ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are a form of technology that read a user's neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as "BCI illiteracy," and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.},
}
@article {pmid30114547,
year = {2018},
author = {Safi, SMM and Pooyan, M and Motie Nasrabadi, A},
title = {SSVEP recognition by modeling brain activity using system identification based on Box-Jenkins model.},
journal = {Computers in biology and medicine},
volume = {101},
number = {},
pages = {82-89},
doi = {10.1016/j.compbiomed.2018.08.011},
pmid = {30114547},
issn = {1879-0534},
mesh = {Adult ; *Algorithms ; *Brain Mapping ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Models, Neurological ; *Photic Stimulation ; },
abstract = {The steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received increasing attention in recent years. The present study proposes a new method for recognition based on system identification. The method relies on modeling the electroencephalogram (EEG) signals using the Box-Jenkins model. In this approach, the recorded EEG signal is considered as a combination of an SSVEP signal evoked by periodic visual stimulation and a background EEG signal whose components are modeled by a moving average (MA) process and an auto-regressive moving average (ARMA) process, respectively. Then, the target frequency is determined by comparing the modeled SSVEP signals for all stimulation frequencies. The experimental results of the proposed method for recorded EEG signals from five subjects (each subject with four stimulation frequencies) demonstrated a significant improvement in the accuracy of the SSVEP recognition in contrast to canonical correlation analysis, least absolute shrinkage and selection operator, and multivariate linear regression methods. The proposed method exhibits enhanced accuracy especially for short data length and a small number of channels. This superiority suggests that the proposed method is an appropriate choice for the implementation of real-time SSVEP based BCI systems.},
}
@article {pmid30114381,
year = {2018},
author = {Krol, LR and Pawlitzki, J and Lotte, F and Gramann, K and Zander, TO},
title = {SEREEGA: Simulating event-related EEG activity.},
journal = {Journal of neuroscience methods},
volume = {309},
number = {},
pages = {13-24},
doi = {10.1016/j.jneumeth.2018.08.001},
pmid = {30114381},
issn = {1872-678X},
mesh = {Computer Simulation ; Electroencephalography/*instrumentation/*methods ; *Evoked Potentials ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted/instrumentation ; Software/standards ; Sound Spectrography/*instrumentation/*methods ; },
abstract = {BACKGROUND: Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods.<br><br>NEW METHOD: We present SEREEGA, Simulating Event-Related EEG Activity. SEREEGA is a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions. The toolbox is available at https://github.com/lrkrol/SEREEGA.<br><br>RESULTS: The simulated data allows established analysis pipelines and classification methods to be applied and is capable of producing realistic results.<br><br>Most simulated EEG is coded from scratch. The few open-source methods in existence focus on specific applications or signal types, such as connectivity. SEREEGA unifies the majority of past simulation methods reported in the literature into one toolbox.<br><br>CONCLUSION: SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.},
}
@article {pmid30112275,
year = {2018},
author = {Mottaz, A and Corbet, T and Doganci, N and Magnin, C and Nicolo, P and Schnider, A and Guggisberg, AG},
title = {Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study.},
journal = {NeuroImage. Clinical},
volume = {20},
number = {},
pages = {336-346},
pmid = {30112275},
issn = {2213-1582},
mesh = {Adult ; Aged ; Cross-Over Studies ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; *Motor Activity/physiology ; Motor Cortex/*diagnostic imaging/physiology ; Nerve Net/*diagnostic imaging/physiology ; Neurofeedback/*methods/physiology ; Photic Stimulation/methods ; Stroke/*diagnostic imaging/physiopathology ; Stroke Rehabilitation/*methods ; },
abstract = {Synchronization of neural activity as measured with functional connectivity (FC) is increasingly used to study the neural basis of brain disease and to develop new treatment targets. However, solid evidence for a causal role of FC in disease and therapy is lacking. Here, we manipulated FC of the ipsilesional primary motor cortex in ten chronic human stroke patients through brain-computer interface technology with visual neurofeedback. We conducted a double-blind controlled crossover study to test whether manipulation of FC through neurofeedback had a behavioral effect on motor performance. Patients succeeded in increasing FC in the motor cortex. This led to improvement in motor function that was significantly greater than during neurofeedback training of a control brain area and proportional to the degree of FC enhancement. This result provides evidence that FC has a causal role in neurological function and that it can be effectively targeted with therapy.},
}
@article {pmid30111993,
year = {2018},
author = {Kuo, CH and Chen, HH and Chou, HC and Chen, PN and Kuo, YC},
title = {Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {2301804},
pmid = {30111993},
issn = {1687-5273},
mesh = {Animals ; Bees ; Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*instrumentation ; Equipment Design ; *Event-Related Potentials, P300 ; Evoked Potentials, Visual ; Female ; Fuzzy Logic ; Humans ; Male ; Models, Biological ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Visual Perception/physiology ; *Wireless Technology ; Young Adult ; },
abstract = {Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).},
}
@article {pmid30109848,
year = {2018},
author = {Hennig, JA and Golub, MD and Lund, PJ and Sadtler, PT and Oby, ER and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Yu, BM and Chase, SM},
title = {Constraints on neural redundancy.},
journal = {eLife},
volume = {7},
number = {},
pages = {},
pmid = {30109848},
issn = {2050-084X},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; NCS BCS1533672//National Science Foundation/International ; R01 NS105318/NS/NINDS NIH HHS/United States ; 280028//Craig H. Neilsen Foundation/International ; CRCNS R01 NS105318/NH/NIH HHS/United States ; R01 HD071686/NH/NIH HHS/United States ; 364994//Simons Foundation/International ; Research Formula Grant SAP 4100077048//Pennsylvania Department of Health/International ; Career award IOS1553252//National Science Foundation/International ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Models, Neurological ; Motor Cortex/*physiology ; Movement/physiology ; Neural Pathways/physiology ; Neurons/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.},
}
@article {pmid30108476,
year = {2018},
author = {Heilinger, A and Ortner, R and La Bella, V and Lugo, ZR and Chatelle, C and Laureys, S and Spataro, R and Guger, C},
title = {Performance Differences Using a Vibro-Tactile P300 BCI in LIS-Patients Diagnosed With Stroke and ALS.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {514},
pmid = {30108476},
issn = {1662-4548},
abstract = {Patients with locked-in syndrome (LIS) are typically unable to move or communicate and can be misdiagnosed as patients with disorders of consciousness (DOC). Behavioral assessment scales are limited in their ability to detect signs of consciousness in this population. Recent research has shown that brain-computer interface (BCI) technology could supplement behavioral scales and allows to establish communication with these severely disabled patients. In this study, we compared the vibro-tactile P300 based BCI performance in two groups of patients with LIS of different etiologies: stroke (n = 6) and amyotrophic lateral sclerosis (ALS) (n = 9). Two vibro-tactile paradigms were administered to the patients to assess conscious function and command following. The first paradigm is called vibrotactile evoked potentials (EPs) with two tactors (VT2), where two stimulators were placed on the patient's left and right wrist, respectively. The patients were asked to count the rare stimuli presented to one wrist to elicit a P300 complex to target stimuli only. In the second paradigm, namely vibrotactile EPs with three tactors (VT3), two stimulators were placed on the wrists as done in VT2, and one additional stimulator was placed on his/her back. The task was to count the rare stimuli presented to one wrist, to elicit the event-related potentials (ERPs). The VT3 paradigm could also be used for communication. For this purpose, the patient had to count the stimuli presented to the left hand to answer "yes" and to count the stimuli presented to the right hand to answer "no." All patients except one performed above chance level in at least one run in the VT2 paradigm. In the VT3 paradigm, all 6 stroke patients and 8/9 ALS patients showed at least one run above chance. Overall, patients achieved higher accuracies in VT2 than VT3. LIS patients due to ALS exhibited higher accuracies that LIS patients due to stroke, in both the VT2 and VT3 paradigms. These initial data suggest that controlling this type of BCI requires specific cognitive abilities that may be impaired in certain sub-groups of severely motor-impaired patients. Future studies on a larger cohort of patients are needed to better identify and understand the underlying cortical mechanisms of these differences.},
}
@article {pmid30108472,
year = {2018},
author = {Gummadavelli, A and Zaveri, HP and Spencer, DD and Gerrard, JL},
title = {Expanding Brain-Computer Interfaces for Controlling Epilepsy Networks: Novel Thalamic Responsive Neurostimulation in Refractory Epilepsy.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {474},
pmid = {30108472},
issn = {1662-4548},
abstract = {Seizures have traditionally been considered hypersynchronous excitatory events and epilepsy has been separated into focal and generalized epilepsy based largely on the spatial distribution of brain regions involved at seizure onset. Epilepsy, however, is increasingly recognized as a complex network disorder that may be distributed and dynamic. Responsive neurostimulation (RNS) is a recent technology that utilizes intracranial electroencephalography (EEG) to detect seizures and delivers stimulation to cortical and subcortical brain structures for seizure control. RNS has particular significance in the clinical treatment of medically refractory epilepsy and brain-computer interfaces in epilepsy. Closed loop RNS represents an important step forward to understand and target nodes in the seizure network. The thalamus is a central network node within several functional networks and regulates input to the cortex; clinically, several thalamic nuclei are safe and feasible targets. We highlight the network theory of epilepsy, potential targets for neuromodulation in epilepsy and the first reported use of RNS as a first generation brain-computer interface to detect and stimulate the centromedian intralaminar thalamic nucleus in a patient with bilateral cortical onset of seizures. We propose that advances in network analysis and neuromodulatory techniques using brain-computer interfaces will significantly improve outcomes in patients with epilepsy. There are numerous avenues of future direction in brain-computer interface devices including multi-modal sensors, flexible electrode arrays, multi-site targeting, and wireless communication.},
}
@article {pmid30106679,
year = {2018},
author = {Goh, SK and Abbass, HA and Tan, KC and Al-Mamun, A and Thakor, N and Bezerianos, A and Li, J},
title = {Spatio-Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {9},
pages = {1858-1867},
doi = {10.1109/TNSRE.2018.2864119},
pmid = {30106679},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Biomechanical Phenomena/physiology ; Brain-Computer Interfaces ; Electroencephalography/*classification ; Exoskeleton Device ; Gait/*physiology ; Humans ; Learning/*physiology ; Lower Extremity/physiology ; Male ; Motor Cortex/physiology ; Neural Networks, Computer ; Walking/physiology ; Young Adult ; },
abstract = {The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans' gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.},
}
@article {pmid30105920,
year = {2018},
author = {Yang, C and Han, X and Wang, Y and Saab, R and Gao, S and Gao, X},
title = {A Dynamic Window Recognition Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Spatio-Temporal Equalizer.},
journal = {International journal of neural systems},
volume = {28},
number = {10},
pages = {1850028},
doi = {10.1142/S0129065718500284},
pmid = {30105920},
issn = {1793-6462},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Models, Neurological ; *Nonlinear Dynamics ; Online Systems ; *Pattern Recognition, Automated ; Photic Stimulation ; },
abstract = {The past decade has witnessed rapid development in the field of brain-computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious training process and the poor ease-of-use have become the most significant challenges. In this study, a spatio-temporal equalization dynamic window (STE-DW) recognition algorithm is proposed for steady-state visual evoked potential (SSVEP)-based BCIs. The algorithm can adaptively control the stimulus time while maintaining the recognition accuracy, which significantly improves the information transfer rate (ITR) and enhances the adaptability of the system to different subjects. Specifically, a spatio-temporal equalization algorithm is used to reduce the adverse effects of spatial and temporal correlation of background noise. Based on the theory of multiple hypotheses testing, a stimulus termination criterion is used to adaptively control the dynamic window. The offline analysis which used a benchmark dataset and an offline dataset collected from 16 subjects demonstrated that the STE-DW algorithm is superior to the filter bank canonical correlation analysis (FBCCA), canonical variates with autoregressive spectral analysis (CVARS), canonical correlation analysis (CCA) and CCA reducing variation (CCA-RV) algorithms in terms of accuracy and ITR. The results show that in the benchmark dataset, the STE-DW algorithm achieved an average ITR of 134 bits/min, which exceeds the FBCCA, CVARS, CCA and CCA-RV. In off-line experiments, the STE-DW algorithm also achieved an average ITR of 116 bits/min. In addition, the online experiment also showed that the STE-DW algorithm can effectively expand the number of applicable users of the SSVEP-based BCI system. We suggest that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.},
}
@article {pmid30103356,
year = {2018},
author = {Gao, Q and Zhao, X and Yu, X and Song, Y and Wang, Z},
title = {Controlling of smart home system based on brain-computer interface.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {26},
number = {5},
pages = {769-783},
doi = {10.3233/THC-181292},
pmid = {30103356},
issn = {1878-7401},
mesh = {*Brain-Computer Interfaces ; Cost-Benefit Analysis ; Persons with Disabilities/*rehabilitation ; Evoked Potentials, Visual ; Humans ; Quality of Life ; Remote Sensing Technology/*methods ; *Wireless Technology ; },
abstract = {BACKGROUND: Brain computer interface (BCI) technology is a communication and control approach. Up to now many studies have attempted to develop an EEG-based BCI system to improve the quality of life of people with severe disabilities, such as amyotrophic lateral sclerosis (ALS), paralysis, brain stroke and so on. The proposed BCIBSHS could help to provide a new way for supporting life of paralyzed people and elderly people.<br><br>OBJECTIVE: The goal of this paper is to explore how to set up a cost-effective and safe-to-use online BCIBSHS to recognize multi-commands and control smart devices based on SSVEP.<br><br>METHODS: The portable EEG acquisition device (Emotiv EPOC) was used to collect EEG signals. The raw signals were denoised by discrete wavelet transform (DWT) method, and then the canonical correlation analysis (CCA) method was used for feature extraction and classification. Another part is the control of smart home devices. The classification results of SSVEP can be translated into commands to control several devices for the smart home.<br><br>RESULTS: Here, the Power over Ethernet (PoE) technology was utilized to provide electrical energy and communication for those devices. During online experiments, four different control commands have been achieved to control four smart home devices (lamp, web camera, guardianship telephone and intelligent blinds). Experimental results showed that the online BCIBSHS obtained 86.88 &#xb1; 5.30% average classification accuracy rate.<br><br>CONCLUSION: The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal. In this paper, we proposed an online steady-state visual evoked potential (SSVEP) based BCI system on controlling several smart home devices.},
}
@article {pmid30102642,
year = {2018},
author = {Lee, MH and Williamson, J and Lee, YE and Lee, SW},
title = {Mental fatigue in central-field and peripheral-field steady-state visually evoked potential and its effects on event-related potential responses.},
journal = {Neuroreport},
volume = {29},
number = {15},
pages = {1301-1308},
pmid = {30102642},
issn = {1473-558X},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Mental Fatigue/*physiopathology ; Photic Stimulation ; Retina/physiology ; Signal Processing, Computer-Assisted ; Visual Perception/*physiology ; Young Adult ; },
abstract = {The steady-state visually evoked potential (SSVEP) is a natural response of the brain to visual stimulation at specific frequencies and is used widely for electroencephalography-based brain-computer interface (BCI) systems. Although the SSVEP is useful for its high level of decoding accuracy, visual fatigue from the repetitive visual flickering is an unavoidable problem. In addition, hybrid BCI systems that combine the SSVEP with the event-related potential (ERP) have been proposed recently. These hybrid BCI systems would improve the decoding accuracy; however, the competing effect by simultaneous presentation of the visual stimulus could possibly supervene the signal in the hybrid system. Nevertheless, previous studies have not sufficiently reported these problems of visual fatigue with SSVEP stimuli or the competing effect in the SSVEP+ERP system. In this study, two different experiments were designed to explore our claims. The first experiment evaluated the visual fatigue level and decoding accuracy for the different types of SSVEP stimuli, which were the peripheral-field SSVEP (pSSVEP) and the central-field SSVEP (cSSVEP). We report that the pSSVEP could reduce the visual fatigue level by avoiding direct exposure of the eye-retina to the flickering visual stimulus, while also delivering a decoding accuracy comparable to that of cSSVEP. The second experiment was designed to examine the competing effect of the SSVEP stimuli on ERP performance and vice versa. To do this, the visual stimuli of ERP and SSVEP were presented simultaneously as part of the BCI speller layout. We found a clear competing effect wherein the evoked brain potentials were influenced by the SSVEP stimulus and the band power at the target frequencies was also decreased significantly by the ERP stimuli. Nevertheless, these competing effects did not lead to a significant loss in decoding accuracy; their features preserved sufficient information for discriminating a target class. Our work is the first to evaluate the visual fatigue and competing effect together, which should be considered when designing BCI applications. Furthermore, our findings suggest that the pSSVEP is a viable substitution for the cSSVEP because of its ability to reduce the level of visual fatigue while maintaining a minimal loss of decoding accuracy.},
}
@article {pmid30102598,
year = {2018},
author = {Zhang, Y and Guo, D and Li, F and Yin, E and Zhang, Y and Li, P and Zhao, Q and Tanaka, T and Yao, D and Xu, P and Nakanishi, M},
title = {Correction to "Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface".},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {8},
pages = {1645-1646},
doi = {10.1109/TNSRE.2018.2851318},
pmid = {30102598},
issn = {1558-0210},
abstract = {In the above paper [1], a method has been proposed to use the correlated component analysis (CORCA) to learn spatial filters with multiple blocks of individual training data for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) scenario. In order to evaluate the performance of CORCA, the task-related component analysis (TRCA)-based method was used as a baseline method [2]. For a fair and convincing comparison, the MATLAB codes on the website (https://github.com/mnakanishi/TRCA-SSVEP) for implementing TRCA method provided by Dr. Masaki Nakanishi, the first author of [2], were used to take the role of the TRCA method. At that time, the proposed CORCA-based method outperforms the TRCA-based method [1].},
}
@article {pmid30101753,
year = {2018},
author = {Chang, H and Yang, J},
title = {Genetic-based feature selection for efficient motion imaging of a brain-computer interface framework.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056020},
doi = {10.1088/1741-2552/aad567},
pmid = {30101753},
issn = {1741-2552},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrocorticography ; Genetics/*statistics &amp; numerical data ; Humans ; Imagination/physiology ; *Motion ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {OBJECTIVE: A brain-computer interface (BCI) equips humans with the ability to control computers and technical devices mentally. However, the enormous data and the existing irrelevant features of the electrocorticogram signal limit the performance of the classifier. To address these problems, a novel signal processing framework for a binary motor imagery-based BCI system (MI-BCI) is proposed in this paper.<br><br>APPROACH: Stockwell transform and Bayesian linear discriminant analysis were applied to feature extraction and classification, respectively, and a genetic algorithm (GA) was used in the process of feature selection to extract the most relevant features for classification. The superiority of the algorithm is demonstrated through test results based on the BCI Competition III dataset I.<br><br>MAIN RESULTS: By comparing the processes with or without feature selection, the performance of the classification was proven to improve using the GA. By adjusting the parameters of the GA, the best feature set (selected 48.6% features) was selected to achieve classification sensitivity, specificity, precision, and accuracy of 94%, 98%, 97.9%, and 96%, respectively, exceeding the results of the existing state-of-the art algorithms.<br><br>SIGNIFICANCE: As the proposed method can reduce the number of features and select the best feature set, its classification performance was improved and the classification time was shortened; thus, it can be applied to various BCI systems.},
}
@article {pmid30101469,
year = {2018},
author = {Tee, BCK and Ouyang, J},
title = {Soft Electronically Functional Polymeric Composite Materials for a Flexible and Stretchable Digital Future.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {30},
number = {47},
pages = {e1802560},
doi = {10.1002/adma.201802560},
pmid = {30101469},
issn = {1521-4095},
support = {NRF2017-13//Singapore National Research Foundation/ ; //National University of Singapore/ ; R-284-000-156-112//Ministry of Education, Singapore/ ; },
abstract = {Flexible/stretchable electronic devices and systems are attracting great attention because they can have important applications in many areas, such as artificial intelligent (AI) robotics, brain-machine interfaces, medical devices, structural and environmental monitoring, and healthcare. In addition to the electronic performance, the electronic devices and systems should be mechanically flexible or even stretchable. Traditional electronic materials including metals and semiconductors usually have poor mechanical flexibility and very limited elasticity. Three main strategies are adopted for the development of flexible/stretchable electronic materials. One is to use organic or polymeric materials. These materials are flexible, and their elasticity can be improved through chemical modification or composition formation with plasticizers or elastomers. Another strategy is to exploit nanometer-scale materials. Many inorganic materials in nanometer sizes can have high flexibility. They can be stretchable through the composition formation with elastomers. Ionogels can be considered as the third type of materials because they can be stretchable and ionically conductive. This article provides the recent progress of soft functional materials development including intrinsically conductive polymers for flexible/stretchable electrodes, and thermoelectric conversion and polymer composites for large area, flexible stretchable electrodes, and tactile sensors.},
}
@article {pmid30101383,
year = {2019},
author = {Wang, H and Li, T and Bezerianos, A and Huang, H and He, Y and Chen, P},
title = {The control of a virtual automatic car based on multiple patterns of motor imagery BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {1},
pages = {299-309},
pmid = {30101383},
issn = {1741-0444},
support = {2016KTSCX141//Innovation Projects for Science supported by Department of Education of Guangdong Province/ ; 2018td01//Science Foundation for Young Teachers of Wuyi University/ ; 2017A0101034//Technology Development Project of Guangdong Province/ ; },
mesh = {Adult ; Algorithms ; *Automobiles ; *Brain-Computer Interfaces ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; *Pattern Recognition, Automated ; },
abstract = {Multiple degrees of freedom (DOF) commands are required for a brain-actuated virtual automatic car, which makes the brain-computer interface (BCI) control strategy a big challenge. In order to solve the challenging issue, a mixed model of BCI combining P300 potentials and motor imagery had been realized in our previous study. However, compared with single model BCI, more training procedures are needed for the mixed model and more mental workload for users to bear. In the present study, we propose a multiple patterns of motor imagery (MPMI) BCI method, which is based on the traditional two patterns of motor imagery. Our motor imagery BCI approach had been extended to multiple patterns: right-hand motor imagery, left-hand motor imagery, foot motor imagery, and both hands motor imagery resulting in turning right, turning left, acceleration, and deceleration for a virtual automatic car control. Ten healthy subjects participated in online experiments, the experimental results not only show the efficiency of our proposed MPMI-BCI strategy but also indicate that those users can control the virtual automatic car spontaneously and efficiently without any other visual attention. Furthermore, the metric of path length optimality ratio (1.23) is very encouraging and the time optimality ratio (1.28) is especially remarkable. Graphical Abstract The paradigm of multiple patterns of motor imagery detection and the relevant topographies of CSP weights for different MI patterns.},
}
@article {pmid30100896,
year = {2018},
author = {Linse, K and Aust, E and Joos, M and Hermann, A},
title = {Communication Matters-Pitfalls and Promise of Hightech Communication Devices in Palliative Care of Severely Physically Disabled Patients With Amyotrophic Lateral Sclerosis.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {603},
pmid = {30100896},
issn = {1664-2295},
abstract = {Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease, leading to progressive paralysis, dysarthria, dysphagia, and respiratory disabilities. Therapy is mostly focused on palliative interventions. During the course of the disease, verbal as well as nonverbal communicative abilities become more and more impaired. In this light, communication has been argued to be "the essence of human life" and crucial for patients' quality of life. High-tech augmentative and alternative communication (HT-AAC) technologies such as eyetracking based computer devices and brain-computer-interfaces provide the possibility to maintain caregiver-independent communication and environmental control even in the advanced disease state of ALS. Thus, they enable patients to preserve social participation and to independently communicate end-of-life-decisions. In accordance with these functions of HT-AAC, their use is reported to strengthen self-determination, increase patients' quality of life and reduce caregiver burden. Therefore, HT-AAC should be considered as standard of (palliative) care for people with ALS. On the other hand, the supply with individually tailored HT-AAC technologies is limited by external and patient-inherent variables. This review aims to provide an overview of the possibilities and limitations of HT-AAC technologies and discuss their role in the palliative care for patients with ALS.},
}
@article {pmid30097579,
year = {2018},
author = {Qian, X and Loo, BRY and Castellanos, FX and Liu, S and Koh, HL and Poh, XWW and Krishnan, R and Fung, D and Chee, MW and Guan, C and Lee, TS and Lim, CG and Zhou, J},
title = {Brain-computer-interface-based intervention re-normalizes brain functional network topology in children with attention deficit/hyperactivity disorder.},
journal = {Translational psychiatry},
volume = {8},
number = {1},
pages = {149},
pmid = {30097579},
issn = {2158-3188},
mesh = {Attention Deficit Disorder with Hyperactivity/*psychology/*rehabilitation ; Brain/diagnostic imaging/*physiopathology ; Brain Mapping ; *Brain-Computer Interfaces ; Child ; Cluster Analysis ; Defense Mechanisms ; Executive Function ; Humans ; *Magnetic Resonance Imaging ; Male ; Neural Pathways ; Psychomotor Agitation ; Singapore ; },
abstract = {A brain-computer-interface (BCI)-based attention training game system has shown promise for treating attention deficit/hyperactivity disorder (ADHD) children with inattentive symptoms. However, little is known about brain network organizational changes underlying behavior improvement following BCI-based training. To cover this gap, we aimed to examine the topological alterations of large-scale brain functional networks induced by the 8-week BCI-based attention intervention in ADHD boys using resting-state functional magnetic resonance imaging method. Compared to the non-intervention (ADHD-NI) group, the intervention group (ADHD-I) showed greater reduction of inattention symptoms accompanied with differential brain network reorganizations after training. Specifically, the ADHD-NI group had increased functional connectivity (FC) within the salience/ventral attention network (SVN) and increased FC between task-positive networks (including the SVN, dorsal attention (DAN), somatomotor, and executive control network) and subcortical regions; in contrast ADHD-I group did not have this pattern. In parallel, ADHD-I group had reduced degree centrality and clustering coefficient as well as increased closeness in task-positive and the default mode networks (prefrontal regions) after the training. More importantly, these reduced local functional processing mainly in the SVN were associated with less inattentive/internalizing problems after 8-week BCI-based intervention across ADHD patients. Our findings suggest that the BCI-based attention training facilitates behavioral improvement in ADHD children by reorganizing brain functional network from more regular to more random configurations, particularly renormalizing salience network processing. Future long-term longitudinal neuroimaging studies are needed to develop the BCI-based intervention approach to promote brain maturation in ADHD.},
}
@article {pmid30090061,
year = {2018},
author = {Braun, JM and Wörgötter, F and Manoonpong, P},
title = {Modular Neural Mechanisms for Gait Phase Tracking, Prediction, and Selection in Personalizable Knee-Ankle-Foot-Orthoses.},
journal = {Frontiers in neurorobotics},
volume = {12},
number = {},
pages = {37},
pmid = {30090061},
issn = {1662-5218},
abstract = {Orthoses for the lower limbs support patients to perform movements that they could not perform on their own. In traditional devices, generic gait models for a limited set of supported movements restrict the patients mobility and device acceptance. To overcome such limitations, we propose a modular neural control approach with user feedback for personalizable Knee-Ankle-Foot-Orthoses (KAFO). The modular controller consists of two main neural components: neural orthosis control for gait phase tracking and neural internal models for gait prediction and selection. A user interface providing online feedback allows the user to shape the control output that adjusts the knee damping parameter of a KAFO. The accuracy and robustness of the control approach were investigated in different conditions including walking on flat ground and descending stairs as well as stair climbing. We show that the controller accurately tracks and predicts the user's movements and generates corresponding gaits. Furthermore, based on the modular control architecture, the controller can be extended to support various distinguishable gaits depending on differences in sensory feedback.},
}
@article {pmid30090056,
year = {2018},
author = {Thompson, AK and Carruth, H and Haywood, R and Hill, NJ and Sarnacki, WA and McCane, LM and Wolpaw, JR and McFarland, DJ},
title = {Effects of Sensorimotor Rhythm Modulation on the Human Flexor Carpi Radialis H-Reflex.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {505},
pmid = {30090056},
issn = {1662-4548},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; U54 GM104941/GM/NIGMS NIH HHS/United States ; },
abstract = {People can learn over training sessions to increase or decrease sensorimotor rhythms (SMRs) in the electroencephalogram (EEG). Activity-dependent brain plasticity is thought to guide spinal plasticity during motor skill learning; thus, SMR training may affect spinal reflexes and thereby influence motor control. To test this hypothesis, we investigated the effects of learned mu (8-13 Hz) SMR modulation on the flexor carpi radialis (FCR) H-reflex in 6 subjects with no known neurological conditions and 2 subjects with chronic incomplete spinal cord injury (SCI). All subjects had learned and practiced over more than 10 < 30-min training sessions to increase (SMR-up trials) and decrease (SMR-down trials) mu-rhythm amplitude over the hand/arm area of left sensorimotor cortex with ≥80% accuracy. Right FCR H-reflexes were elicited at random times during SMR-up and SMR-down trials, and in between trials. SMR modulation affected H-reflex size. In all the neurologically normal subjects, the H-reflex was significantly larger [116% ± 6 (mean ± SE)] during SMR-up trials than between trials, and significantly smaller (92% ± 1) during SMR-down trials than between trials (p < 0.05 for both, paired t-test). One subject with SCI showed similar H-reflex size dependence (high for SMR-up trials, low for SMR-down trials): the other subject with SCI showed no dependence. These results support the hypothesis that SMR modulation has predictable effects on spinal reflex excitability in people who are neurologically normal; they also suggest that it might be used to enhance therapies that seek to improve functional recovery in some individuals with SCI or other CNS disorders.},
}
@article {pmid30087646,
year = {2018},
author = {Lorenzetti, V and Melo, B and Basílio, R and Suo, C and Yücel, M and Tierra-Criollo, CJ and Moll, J},
title = {Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {390},
pmid = {30087646},
issn = {1664-2295},
abstract = {Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.},
}
@article {pmid30087594,
year = {2018},
author = {Yu, Z and Li, L and Song, J and Lv, H},
title = {The Study of Visual-Auditory Interactions on Lower Limb Motor Imagery.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {509},
pmid = {30087594},
issn = {1662-4548},
abstract = {In order to improve the activation of the mirror neuron system and the ability of the visual-cued motor imagery further, the multi-stimuli-cued unilateral lower limb motor imagery is studied in this paper. The visual-auditory evoked pathway is proposed and the sensory process is studied. To analyze the visual-auditory interactions, the kinesthetic motor imagery with the visual-auditory stimulus, visual stimulus and no stimulus are involved. The motor-related rhythm suppression is applied on quantitative evaluation. To explore the statistical sensory process, the causal relationships among the functional areas and the event-related potentials are investigated. The results have demonstrated the outstanding performances of the visual-auditory evoked motor imagery on the improvement of the mirror neuron system activation and the motor imagery ability. Besides, the abundant information interactions among functional areas and the positive impacts of the auditory stimulus in the motor and the visual areas have been revealed. The possibility that the sensory processes evoked by the visual-auditory interactions differ from the one elicited by kinesthetic motor imagery, has also been indicated. This study will promisingly offer an efficient way to motor rehabilitation, thus favorable for hemiparesis and partial paralysis patients.},
}
@article {pmid30084708,
year = {2018},
author = {Yesufe, AA and Assefa, M and Bekele, A and Ergete, W and Aynalem, A and Wondemagegnehu, T and Tausjø, J and Assefa Tessema, G and Kantelhardt, EJ and Gansler, T and Jemal, A},
title = {Adequacy of Pathologic Reports of Invasive Breast Cancer From Mastectomy Specimens at Tikur Anbessa Specialized Hospital Oncology Center in Ethiopia.},
journal = {Journal of global oncology},
volume = {4},
number = {},
pages = {1-12},
pmid = {30084708},
issn = {2378-9506},
mesh = {Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/pathology/*surgery ; Ethiopia ; Female ; Humans ; Mastectomy/*methods ; Medical Oncology/*organization &amp; administration ; Middle Aged ; Young Adult ; },
abstract = {Purpose Although information from pathology reports is essential to the care of individuals with cancer and to population-level cancer control, no systematic evidence exists regarding the adequacy of breast pathology reporting in Ethiopia. This study audited pathology reports of mastectomy specimens from patients evaluated at the Tikur Anbessa Specialized Hospital Oncology Center in Addis Ababa, Ethiopia. Methods Mastectomy pathology reports from February 2014 through January 2016 were assessed for gross and microscopic information considered by the Breast Cancer Initiative 2.5 (BCI 2.5; formerly the Breast Health Global Initiative) guideline to be necessary for care of patients with breast cancer stratified according to basic, limited, and enhanced resource settings. Results Fewer than two thirds (61.6%) of the 417 reports we reviewed included all four of the BCI 2.5 basic pathology data elements we could evaluate with available data (tumor category, lymph node category, histologic type, and histologic grade). Only 1.0% of reports included all three pathology data elements recommended for limited resource settings (estrogen receptor status, margin status, and lymphovascular invasion). Several elements were significantly more likely to be noted in reports from nonpublic hospitals than from public hospitals. Although only three of 417 reports included checklists or templates, all three of these reports included all of the basic pathology information, and they all included at least two of the three limited pathology elements not already on the basic list. Conclusion More than one third (38.4%) of mastectomy pathology reports did not meet BCI 2.5 standards for basic resource settings. Quality measurement and improvement programs and capacity-building interventions by national pathology and oncology organizations, collaboration with medical and public health organizations in neighboring countries, adoption of synoptic reporting templates, use of electronic pathology reporting, and histotechnology and histopathology training collaborations with laboratories in high-resource regions are recommended.},
}
@article {pmid30084262,
year = {2018},
author = {López-Larraz, E and Escolano, C and Montesano, L and Minguez, J},
title = {Reactivating the Dormant Motor Cortex After Spinal Cord Injury With EEG Neurofeedback: A Case Study With a Chronic, Complete C4 Patient.},
journal = {Clinical EEG and neuroscience},
volume = {},
number = {},
pages = {1550059418792153},
doi = {10.1177/1550059418792153},
pmid = {30084262},
issn = {2169-5202},
abstract = {Chronic spinal cord injury (SCI) patients present poor motor cortex activation during movement attempts. The reactivation of this brain region can be beneficial for them, for instance, allowing them to use brain-machine interfaces for motor rehabilitation or restoration. These brain-machine interfacess generally use electroencephalography (EEG) to measure the cortical activation during the attempts of movement, quantifying it as the event-related desynchronization (ERD) of the alpha/mu rhythm. Based on previous evidence showing that higher tonic EEG alpha power is associated with higher ERD, we hypothesized that artificially increasing the alpha power over the motor cortex of these patients could enhance their ERD (ie, motor cortical activation) during movement attempts. We used EEG neurofeedback (NF) to enhance the tonic EEG alpha power, providing real-time visual feedback of the alpha oscillations measured over the motor cortex. This approach was evaluated in a C4, ASIA A, SCI patient (9 months after the injury) who did not present ERD during the movement attempts of his paralyzed hands. The patient performed 4 NF sessions (in 4 consecutive days) and screenings of his EEG activity before and after each session. After the intervention, the patient presented a significant increase in the alpha power over the motor cortex, and a significant enhancement of the mu ERD in the contralateral motor cortex when he attempted to close the assessed right hand. As a proof of concept investigation, this article shows how a short NF intervention might be used to enhance the motor cortical activation in patients with chronic tetraplegia.},
}
@article {pmid30080152,
year = {2018},
author = {Chowdhury, A and Meena, YK and Raza, H and Bhushan, B and Uttam, AK and Pandey, N and Hashmi, AA and Bajpai, A and Dutta, A and Prasad, G},
title = {Active Physical Practice Followed by Mental Practice Using BCI-Driven Hand Exoskeleton: A Pilot Trial for Clinical Effectiveness and Usability.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {6},
pages = {1786-1795},
doi = {10.1109/JBHI.2018.2863212},
pmid = {30080152},
issn = {2168-2208},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Exoskeleton Device ; Female ; Hand/*physiology ; Humans ; Male ; Middle Aged ; Neurofeedback ; Pilot Projects ; Signal Processing, Computer-Assisted/*instrumentation ; *Stroke Rehabilitation/instrumentation/methods ; Young Adult ; },
abstract = {Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP stage was mediated by a force sensor feedback-based assist-as-needed control strategy, whereas the MP stage provided BCI-based multimodal neurofeedback combining anthropomorphic visual feedback and proprioceptive feedback of the impaired hand extension attempt. A six week long clinical trial was conducted on four hemiparetic stroke patients (screened out of 16) with a left-hand disability. The primary outcome, motor functional recovery, was measured in terms of changes in grip-strength (GS) and action research arm test (ARAT) scores; whereas the secondary outcome, usability of the system was measured in terms of changes in mood, fatigue, and motivation on a visual-analog-scale. A positive rehabilitative outcome was found as the group mean changes from the baseline in the GS and ARAT were +6.38 kg and +5.66 accordingly. The VAS scale measurements also showed betterment in mood (1.38), increased motivation (+2.10) and reduced fatigue (0.98) as compared to the baseline. Thus, the proposed neurorehabilitation protocol is found to be promising both in terms of clinical effectiveness and usability.},
}
@article {pmid30080140,
year = {2019},
author = {Chiesi, M and Guermandi, M and Placati, S and Scarselli, EF and Guerrieri, R},
title = {Creamino: A Cost-Effective, Open-Source EEG-Based BCI System.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {4},
pages = {900-909},
doi = {10.1109/TBME.2018.2863198},
pmid = {30080140},
issn = {1558-2531},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography/economics/instrumentation/methods ; Equipment Design ; Humans ; Male ; *Signal Processing, Computer-Assisted ; *Software ; Young Adult ; },
abstract = {This paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the-shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux, and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos, and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications.},
}
@article {pmid30078146,
year = {2018},
author = {Bascil, MS},
title = {A New Approach on HCI Extracting Conscious Jaw Movements Based on EEG Signals Using Machine Learnings.},
journal = {Journal of medical systems},
volume = {42},
number = {9},
pages = {169},
pmid = {30078146},
issn = {1573-689X},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Jaw ; *Machine Learning ; Movement ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Machine computer interfaces (MCI) are assistive technologies enabling paralyzed peoples to control and communicate their environments. This study aims to discover and represents a new approach on MCI using left/right motions of voluntary jaw movements stored in electroencephalogram (EEG). It extracts brain electrical activities on EEG produced by voluntary jaw movements and converts these activities to machine control commands. Jaw-operated machine computer interface is a new way of MCI entitled as jaw machine interface (JMI) provides new functionality for paralyzed people to assist available environmental devices using their jaw motions. In this article, root mean square (RMS) and standard deviation (STD) features of signals are extracted and hemispherical pattern changes are computed and compared as offline analysis approach. A statistical algorithm, principle component analysis (PCA), is used to reduce high dimensional data and two types of machine learning algorithms which are linear discriminant analysis (LDA) and support vector machine (SVM) incorporating k-fold cross validation technique are employed to identify pattern changes by utilizing the features of horizontal jaw movements stored in EEG.},
}
@article {pmid30071853,
year = {2018},
author = {Suwannarat, A and Pan-Ngum, S and Israsena, P},
title = {Comparison of EEG measurement of upper limb movement in motor imagery training system.},
journal = {Biomedical engineering online},
volume = {17},
number = {1},
pages = {103},
pmid = {30071853},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand/*physiology ; Humans ; *Movement ; Wrist/*physiology ; },
abstract = {BACKGROUND: One of the most promising applications for electroencephalogram (EEG)-based brain computer interface is for stroke rehabilitation. Implemented as a standalone motor imagery (MI) training system or as part of a rehabilitation robotic system, many studies have shown benefits of using them to restore motor control in stroke patients. Hand movements have widely been chosen as MI tasks. Although potentially more challenging to analyze, wrist and forearm movement such as wrist flexion/extension and forearm pronation/supination should also be considered for MI tasks, because these movements are part of the main exercises given to patients in conventional stroke rehabilitation. This paper will evaluate the effectiveness of such movements for MI tasks.<br><br>METHODS: Three hand and wrist movement tasks which were hand opening/closing, wrist flexion/extension and forearm pronation/supination were chosen as motor imagery tasks for both hands. Eleven subjects participated in the experiment. All of them completed hand opening/closing task session. Ten subjects completed two MI task sessions which were hand opening/closing and wrist flexion/extension. Five subjects completed all three MI tasks sessions. Each MI task comprised 8 sessions spanning a 4&#xa0;weeks period. For classification, feature extraction based on common spatial pattern (CSP) algorithm was used. Two types were implemented, one with conventional CSP (termed WB) and one with an increase number of features achieved by filtering EEG data into five bands (termed FB). Classification was done by linear discriminant analysis (LDA) and support vector machine (SVM).<br><br>RESULTS: Eight-fold cross validation was applied on EEG data. LDA and SVM gave comparable classification accuracy. FB achieved significantly higher classification accuracy compared to WB. The accuracy of classifying wrist flexion/extension task were higher than that of classifying hand opening/closing task in all subjects. Classifying forearm pronation/supination task achieved higher accuracy than classifying hand opening/closing task in most subjects but achieved lower accuracy than classifying wrist flexion/extension task in all subjects. Significant improvements of classification accuracy were found in nine subjects when considering individual sessions of experiments of all MI tasks. The results of classifying hand opening/closing task and wrist flexion/extension task were comparable to the results of classifying hand opening/closing task and forearm pronation/supination task. Classification accuracy of wrist flexion/extension task and forearm pronation/supination task was lower than those of hand movement tasks and wrist movement tasks.<br><br>CONCLUSION: High classification accuracy of the three MI tasks support the possibility of using EEG-based stroke rehabilitation system with these movements. Either LDA or SVM can equally be chosen as a classifier since the difference of their accuracies is not statistically significant. Significantly higher classification accuracy made FB more suitable for classifying MI task compared to WB. More training sessions could potentially lead to better accuracy as evident in most subjects in this experiment.},
}
@article {pmid30068128,
year = {2018},
author = {Dai, Y and Zhang, X and Chen, Z and Xu, X},
title = {Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine.},
journal = {The Review of scientific instruments},
volume = {89},
number = {7},
pages = {074302},
doi = {10.1063/1.5006511},
pmid = {30068128},
issn = {1089-7623},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; *Electroencephalography/methods ; Humans ; Imagination/*physiology ; *Machine Learning ; Motor Activity/*physiology ; Pattern Recognition, Automated/methods ; Principal Component Analysis ; Time Factors ; *Wavelet Analysis ; },
abstract = {Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and non-stationary characteristics. This paper introduces an optimal and intelligent method for motor imagery BCIs. Because of the robustness to noise, wavelet packet decomposition and common spatial pattern (CSP) methods were implemented to reduce the dimensions of preprocessed signals. And a novel and efficient classifier projection extreme learning machine (PELM) was employed to recognize the labels of electroencephalogram signals. Experiments have been performed on the BCI Competition Dataset to demonstrate the superiority of wavelet-CSP in BCI and the outperformance of the PELM-based method. Results show that the average recognition rate of PELM approaches approximately 70%, while the optimal rate of other methods is 72%, whose training time and classification time are relatively longer as 11.00 ms and 11.66 ms, respectively, compared with 4.75 ms and 4.87 ms obtained by using the proposed BCI system.},
}
@article {pmid30065638,
year = {2018},
author = {Labonte-Lemoyne, E and Courtemanche, F and Louis, V and Fredette, M and Sénécal, S and Léger, PM},
title = {Dynamic Threshold Selection for a Biocybernetic Loop in an Adaptive Video Game Context.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {282},
pmid = {30065638},
issn = {1662-5161},
abstract = {Passive Brain-Computer interfaces (pBCIs) are a human-computer communication tool where the computer can detect from neurophysiological signals the current mental or emotional state of the user. The system can then adjust itself to guide the user toward a desired state. One challenge facing developers of pBCIs is that the system's parameters are generally set at the onset of the interaction and remain stable throughout, not adapting to potential changes over time such as fatigue. The goal of this paper is to investigate the improvement of pBCIs with settings adjusted according to the information provided by a second neurophysiological signal. With the use of a second signal, making the system a hybrid pBCI, those parameters can be continuously adjusted with dynamic thresholding to respond to variations such as fatigue or learning. In this experiment, we hypothesize that the adaptive system with dynamic thresholding will improve perceived game experience and objective game performance compared to two other conditions: an adaptive system with single primary signal biocybernetic loop and a control non-adaptive game. A within-subject experiment was conducted with 16 participants using three versions of the game Tetris. Each participant plays 15 min of Tetris under three experimental conditions. The control condition is the traditional game of Tetris with a progressive increase in speed. The second condition is a cognitive load only biocybernetic loop with the parameters presented in Ewing et al. (2016). The third condition is our proposed biocybernetic loop using dynamic threshold selection. Electroencephalography was used as the primary signal and automatic facial expression analysis as the secondary signal. Our results show that, contrary to our expectations, the adaptive systems did not improve the participants' experience as participants had more negative affect from the BCI conditions than in the control condition. We endeavored to develop a system that improved upon the authentic version of the Tetris game, however, our proposed adaptive system neither improved players' perceived experience, nor their objective performance. Nevertheless, this experience can inform developers of hybrid passive BCIs on a novel way to employ various neurophysiological features simultaneously.},
}
@article {pmid30063219,
year = {2018},
author = {Norman, SL and McFarland, DJ and Miner, A and Cramer, SC and Wolbrecht, ET and Wolpaw, JR and Reinkensmeyer, DJ},
title = {Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056026},
pmid = {30063219},
issn = {1741-2552},
support = {I01 CX001812/CX/CSRD VA/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; UL1 TR001414/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; *Brain-Computer Interfaces ; Cues ; Electroencephalography ; Exoskeleton Device ; Female ; Fingers/*physiopathology ; Humans ; Male ; Middle Aged ; Movement ; Reaction Time ; Recovery of Function ; Robotics ; Stroke Rehabilitation/*methods ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke.<br><br>APPROACH: Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance.<br><br>MAIN RESULTS: Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;7.5 blocks versus 3.5&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score.<br><br>SIGNIFICANCE: These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.},
}
@article {pmid30061811,
year = {2018},
author = {Amaral, C and Mouga, S and Simões, M and Pereira, HC and Bernardino, I and Quental, H and Playle, R and McNamara, R and Oliveira, G and Castelo-Branco, M},
title = {A Feasibility Clinical Trial to Improve Social Attention in Autistic Spectrum Disorder (ASD) Using a Brain Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {477},
pmid = {30061811},
issn = {1662-4548},
abstract = {Deficits in the interpretation of others' intentions from gaze-direction or other social attention cues are well-recognized in ASD. Here we investigated whether an EEG brain computer interface (BCI) can be used to train social cognition skills in ASD patients. We performed a single-arm feasibility clinical trial and enrolled 15 participants (mean age 22y 2m) with high-functioning ASD (mean full-scale IQ 103). Participants were submitted to a BCI training paradigm using a virtual reality interface over seven sessions spread over 4 months. The first four sessions occurred weekly, and the remainder monthly. In each session, the subject was asked to identify objects of interest based on the gaze direction of an avatar. Attentional responses were extracted from the EEG P300 component. A final follow-up assessment was performed 6-months after the last session. To analyze responses to joint attention cues participants were assessed pre and post intervention and in the follow-up, using an ecologic "Joint-attention task." We used eye-tracking to identify the number of social attention items that a patient could accurately identify from an avatar's action cues (e.g., looking, pointing at). As secondary outcome measures we used the Autism Treatment Evaluation Checklist (ATEC) and the Vineland Adaptive Behavior Scale (VABS). Neuropsychological measures related to mood and depression were also assessed. In sum, we observed a decrease in total ATEC and rated autism symptoms (Sociability; Sensory/Cognitive Awareness; Health/Physical/Behavior); an evident improvement in Adapted Behavior Composite and in the DLS subarea from VABS; a decrease in Depression (from POMS) and in mood disturbance/depression (BDI). BCI online performance and tolerance were stable along the intervention. Average P300 amplitude and alpha power were also preserved across sessions. We have demonstrated the feasibility of BCI in this kind of intervention in ASD. Participants engage successfully and consistently in the task. Although the primary outcome (rate of automatic responses to joint attention cues) did not show changes, most secondary neuropsychological outcome measures showed improvement, yielding promise for a future efficacy trial. (clinical-trial ID: NCT02445625-clinicaltrials.gov).},
}
@article {pmid30060913,
year = {2019},
author = {Grigorian, A and Milliken, J and Livingston, JK and Spencer, D and Gabriel, V and Schubl, SD and Kong, A and Barrios, C and Joe, V and Nahmias, J},
title = {National risk factors for blunt cardiac injury: Hemopneumothorax is the strongest predictor.},
journal = {American journal of surgery},
volume = {217},
number = {4},
pages = {639-642},
doi = {10.1016/j.amjsurg.2018.07.043},
pmid = {30060913},
issn = {1879-1883},
mesh = {Databases, Factual ; Esophagus/*injuries ; Female ; Fractures, Bone/*complications/epidemiology ; Hemopneumothorax/*complications/epidemiology ; Humans ; Incidence ; Male ; Middle Aged ; Myocardial Contusions/*complications/epidemiology ; Risk Factors ; Sternum/*injuries ; United States/epidemiology ; },
abstract = {BACKGROUND: Blunt cardiac injury (BCI) can occur after chest trauma and may be associated with sternal fracture (SF). We hypothesized that injuries demonstrating a higher transmission of force to the thorax, such as thoracic aortic injury (TAI), would have a higher association with BCI.<br><br>METHODS: We queried the National Trauma Data Bank (NTDB) from 2007-2015 to identify adult blunt trauma patients.<br><br>RESULTS: BCI occurred in 15,976 patients (0.3%). SF had a higher association with BCI (OR&#x202f;=&#x202f;5.52, CI&#x202f;=&#x202f;5.32-5.73, p&#x202f;<&#x202f;0.001) compared to TAI (OR&#x202f;=&#x202f;4.82, CI&#x202f;=&#x202f;4.50-5.17, p&#x202f;<&#x202f;0.001). However, the strongest independent predictor was hemopneumothorax (OR&#x202f;=&#x202f;9.53, CI&#x202f;=&#x202f;7.80-11.65, p&#x202f;<&#x202f;0.001) followed by SF and esophageal injury (OR&#x202f;=&#x202f;5.47, CI&#x202f;=&#x202f;4.05-7.40, p&#x202f;<&#x202f;0.001).<br><br>CONCLUSION: SF after blunt trauma is more strongly associated with BCI compared to TAI. However, hemopneumothorax is the strongest predictor of BCI. We propose all patients presenting after blunt chest trauma with high-risk features including hemopneumothorax, sternal fracture, esophagus injury, and TAI be screened for BCI.<br><br>SUMMARY: Using the National Trauma Data Bank, sternal fracture is more strongly associated with blunt cardiac injury than blunt thoracic aortic injury. However, hemopneumothorax was the strongest predictor.},
}
@article {pmid30059311,
year = {2018},
author = {Petrantonakis, PC and Kompatsiaris, I},
title = {Single-Trial NIRS Data Classification for Brain-Computer Interfaces Using Graph Signal Processing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {9},
pages = {1700-1709},
doi = {10.1109/TNSRE.2018.2860629},
pmid = {30059311},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Electroencephalography ; Female ; Humans ; Infrared Rays ; Male ; Mathematics ; Mental Processes/physiology ; Psychomotor Performance/physiology ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*statistics &amp; numerical data ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) systems use feature extraction methods relying mainly on the slope characteristics and mean changes of the hemodynamic responses in respect to certain mental tasks. Nevertheless, spatial patterns across the measurement channels have been detected and should be considered during the feature vector extraction stage of the BCI realization. In this paper, a graph signal processing (GSP) approach for feature extraction is adopted in order to capture the aforementioned spatial information of the NIRS signals. The proposed GSP-based methodology for feature extraction in NIRS-based BCI systems, namely graph NIRS (GNIRS), is applied on a publicly available dataset of NIRS recordings during a mental arithmetic task. GNIRS exhibits higher classification rates (CRs), up to 92.52%, as compared to the CRs of two state-of-the-art feature extraction methodologies related to slope and mean values of hemodynamic response, i.e., 90.35% and 82.60%, respectively. In addition, GNIRS leads to the formation of feature vectors with reduced dimensionality in comparison with the baseline approaches. Moreover, it is shown to facilitate high CRs even from the first second after the onset of the mental task, paving the way for faster NIRS-based BCI systems.},
}
@article {pmid30056435,
year = {2018},
author = {López-Larraz, E and Sarasola-Sanz, A and Irastorza-Landa, N and Birbaumer, N and Ramos-Murguialday, A},
title = {Brain-machine interfaces for rehabilitation in stroke: A review.},
journal = {NeuroRehabilitation},
volume = {43},
number = {1},
pages = {77-97},
doi = {10.3233/NRE-172394},
pmid = {30056435},
issn = {1878-6448},
mesh = {*Brain-Computer Interfaces ; Humans ; Stroke Rehabilitation/*methods ; },
abstract = {BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement.<br><br>OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke.<br><br>METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces.<br><br>RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG).<br><br>CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.},
}
@article {pmid30056196,
year = {2018},
author = {Iturrate, I and Chavarriaga, R and Pereira, M and Zhang, H and Corbet, T and Leeb, R and Millán, JDR},
title = {Human EEG reveals distinct neural correlates of power and precision grasping types.},
journal = {NeuroImage},
volume = {181},
number = {},
pages = {635-644},
doi = {10.1016/j.neuroimage.2018.07.055},
pmid = {30056196},
issn = {1095-9572},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/methods ; Electrooculography/methods ; Female ; Functional Neuroimaging/*methods ; Hand/*physiology ; Hand Strength/physiology ; Humans ; Male ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Parietal Lobe/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Hand grasping is a sophisticated motor task that has received much attention by the neuroscientific community, which demonstrated how grasping activates a network involving parietal, pre-motor and motor cortices using fMRI, ECoG, LFPs and spiking activity. Yet, there is a need for a more precise spatio-temporal analysis as it is still unclear how these brain activations over large cortical areas evolve at the sub-second level. In this study, we recorded ten human participants (1 female) performing visually-guided, self-paced reaching and grasping with precision or power grips. Following the results, we demonstrate the existence of neural correlates of grasping from broadband EEG in self-paced conditions and show how neural correlates of precision and power grasps differentially evolve as grasps unfold. 100 ms before the grasp is secured, bilateral parietal regions showed increasingly differential patterns. Afterwards, sustained differences between both grasps occurred over the bilateral motor and parietal regions, and medial pre-frontal cortex. Furthermore, these differences were sufficiently discriminable to allow single-trial decoding with 70% decoding performance. Functional connectivity revealed differences at the network level between grasps in fronto-parietal networks, in terms of upper-alpha cortical oscillatory power with a strong involvement of ipsilateral hemisphere. Our results supported the existence of fronto-parietal recurrent feedback loops, with stronger interactions for precision grips due to the finer motor control required for this grasping type.},
}
@article {pmid30054779,
year = {2019},
author = {She, Q and Hu, B and Luo, Z and Nguyen, T and Zhang, Y},
title = {A hierarchical semi-supervised extreme learning machine method for EEG recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {57},
number = {1},
pages = {147-157},
pmid = {30054779},
issn = {1741-0444},
support = {61201302//National Nature Science Foundation/ ; 61671197//National Nature Science Foundation/ ; LY15F010009//Natural Science Foundation of Zhejiang Province (CN)/ ; },
mesh = {Algorithms ; Benchmarking ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Machine Learning ; },
abstract = {Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals. Graphical abstract The schematic of the proposed HSS-ELM algorithm.},
}
@article {pmid30050566,
year = {2018},
author = {Guan, S and Zhao, K and Wang, F},
title = {Multiclass Motor Imagery Recognition of Single Joint in Upper Limb Based on NSGA- II OVO TWSVM.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {6265108},
pmid = {30050566},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; Linear Models ; Motor Activity/*physiology ; Nonlinear Dynamics ; Pattern Recognition, Automated/*methods ; Shoulder Joint/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Upper Extremity/*physiology ; },
abstract = {In the study of the brain computer interface (BCI) system, electroencephalogram (EEG) signals induced by different movements of the same joint are hard to distinguish. This paper proposes a novel scheme that combined amplitude-frequency (AF) information of intrinsic mode function (IMF) with common spatial pattern (CSP), namely, AF-CSP to extract motor imagery (MI) features, and to improve classification performance, the second generation nondominated sorting evolutionary algorithm (NSGA-II) is used to tune hyperparameters for linear and nonlinear kernel one versus one twin support vector machine (OVO TWSVM). This model is compared with least squares support vector machine (LS-SVM), back propagation (BP), extreme learning machine (ELM), particle swarm optimization support vector machine (PSO-SVM), and grid search OVO TWSVM (GS OVO TWSVM) on our dataset; the recognition accuracy increased by 5.92%, 22.44%, 22.65%, 8.69%, and 5.75%. The proposed method has helped to achieve higher accuracy in BCI systems.},
}
@article {pmid30050405,
year = {2018},
author = {Fukuma, R and Yanagisawa, T and Yokoi, H and Hirata, M and Yoshimine, T and Saitoh, Y and Kamitani, Y and Kishima, H},
title = {Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {478},
pmid = {30050405},
issn = {1662-4548},
abstract = {Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33). Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.},
}
@article {pmid30050400,
year = {2018},
author = {Mrachacz-Kersting, N and Aliakbaryhosseinabadi, S},
title = {Comparison of the Efficacy of a Real-Time and Offline Associative Brain-Computer-Interface.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {455},
pmid = {30050400},
issn = {1662-4548},
abstract = {An associative brain-computer-interface (BCI) that correlates in time a peripherally generated afferent volley with the peak negativity (PN) of the movement related cortical potential (MRCP) induces plastic changes in the human motor cortex. However, in this associative BCI the movement timed to a cue is not detected in real time. Thus, possible changes in reaction time caused by factors such as attention shifts or fatigue will lead to a decreased accuracy, less pairings, and likely reduced plasticity. The aim of the current study was to compare the effectiveness of this associative BCI intervention on plasticity induction when the MRCP PN time is pre-determined from a training data set (BCIoffline), or detected online (BCIonline). Ten healthy participants completed both interventions in randomized order. The average detection accuracy for the BCIonline intervention was 71 ± 3% with 2.8 ± 0.7 min[-1] false detections. For the BCIonline intervention the PN did not differ significantly between the training set and the actual intervention (t9 = 0.87, p = 0.41). The peak-to-peak motor evoked potentials (MEPs) were quantified prior to, immediately following, and 30 min after the cessation of each intervention. MEP results revealed a significant main effect of time, F(2,18) = 4.46, p = 0.027. The mean TA MEP amplitudes were significantly larger 30 min after (277 ± 72 μV) the BCI interventions compared to pre-intervention MEPs (233 ± 64 μV) regardless of intervention type and stimulation intensity (p = 0.029). These results provide further strong support for the associative nature of the associative BCI but also suggest that they likely differ to the associative long-term potentiation protocol they were modeled on in the exact sites of plasticity.},
}
@article {pmid30047893,
year = {2018},
author = {Crouch, DL and Pan, L and Filer, W and Stallings, JW and Huang, H},
title = {Comparing Surface and Intramuscular Electromyography for Simultaneous and Proportional Control Based on a Musculoskeletal Model: A Pilot Study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {9},
pages = {1735-1744},
doi = {10.1109/TNSRE.2018.2859833},
pmid = {30047893},
issn = {1558-0210},
mesh = {Adult ; Amputees ; Artificial Limbs ; Brain-Computer Interfaces ; Computer Systems ; Electrodes ; Electrodes, Implanted ; Electromyography/instrumentation/*methods ; Female ; Hand/physiology ; Healthy Volunteers ; Humans ; Male ; Models, Biological ; Muscle, Skeletal ; *Musculoskeletal Physiological Phenomena ; Pilot Projects ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom). However, differences in signal characteristics between sEMG and iEMG may influence SPC performance. We conducted a pilot study to determine the effect of electrode type (sEMG and iEMG) on real-time task performance with SPC based on a novel 2-degree-of-freedom EMG-driven musculoskeletal model of the wrist and hand. Four able-bodied subjects and one transradial amputee performed a virtual posture matching task with either sEMG or iEMG. There was a trend of better task performance with sEMG than iEMG for both able-bodied and amputee subjects, though the difference was not statistically significant. Thus, while iEMG may permit targeted recording of EMG, its signal characteristics may not be as ideal for SPC as those of sEMG. The tradeoff between recording specificity and signal characteristics is an important consideration for development and clinical implementation of SPC for neural-machine interfaces.},
}
@article {pmid30047890,
year = {2018},
author = {Ahani, A and Moghadamfalahi, M and Erdogmus, D},
title = {Language-Model Assisted And Icon-based Communication Through a Brain Computer Interface With Different Presentation Paradigms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {},
number = {},
pages = {},
doi = {10.1109/TNSRE.2018.2859432},
pmid = {30047890},
issn = {1558-0210},
abstract = {Augmentative and alternative communication (AAC) is typically used by people with severe speech and physical disabilities (SSPI) and is one of the main application areas for brain computer interface (BCI) technology. The target population includes people with cerebral palsy (CP), amyotrophic lateral sclerosis (ALS) and locked-in-syndrome (LIS). Word-based AAC systems are mainly faster than letter-based counterparts and are usually supplemented by icons to aid the users. Those iconbased AAC systems that use binary signaling methods such as single click can convert into a single input BCI systems such as ERP detection. Matrix speller paradigm are typically used to help users identify their target icon on the screen, however it ties screen space to vocabulary size and navigation complexity, which may require users to make repetitive head, neck, or eye movements to visually locate their intended targets on the screen. Rapid serial visual presentation (RSVP) is an alternative interface that minimizes required movement by displaying all icons at a fix location, one at a time. IconMessenger is an icon-based BCI-AAC system that combines ERP signal detection with a unified framework for different presentation paradigms including RSVP, matrix speller row&amp;column presentation (RCP) and matrix speller single character presentation (SCP). Icon- Messenger also take advantage of a unique sem-gram language model, incorporated tightly in the inference engine. In this study, we assess the ERP shape, classification accuracy and typing performance of different presentation paradigms on 10 healthy participants.},
}
@article {pmid30046388,
year = {2018},
author = {Roelants, C and Giacosa, S and Pillet, C and Bussat, R and Champelovier, P and Bastien, O and Guyon, L and Arnoux, V and Cochet, C and Filhol, O},
title = {Combined inhibition of PI3K and Src kinases demonstrates synergistic therapeutic efficacy in clear-cell renal carcinoma.},
journal = {Oncotarget},
volume = {9},
number = {53},
pages = {30066-30078},
pmid = {30046388},
issn = {1949-2553},
abstract = {Potent inhibitors of PI3K (GDC-0941) and Src (Saracatinib) exhibit as individual agents, excellent oral anticancer activity in preclinical models and have entered phase II clinical trials in various cancers. We found that PI3K and Src kinases are dysregulated in clear cell renal carcinomas (ccRCCs), an aggressive disease without effective targeted therapies. In this study we addressed this challenge by testing GDC-0941 and Saracatinib as either single agents or in combination in ccRCC cell lines, as well as in mouse and PDX models. Our findings demonstrate that combined inhibition of PI3K and Src impedes cell growth and invasion and induces cell death of renal carcinoma cells providing preclinical evidence for a pairwise combination of these anticancer drugs as a rational strategy to improve renal cancer treatment.},
}
@article {pmid30045647,
year = {2018},
author = {Zafar, A and Hong, KS},
title = {Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study.},
journal = {International journal of neural systems},
volume = {28},
number = {10},
pages = {1850031},
doi = {10.1142/S0129065718500314},
pmid = {30045647},
issn = {1793-6462},
mesh = {Adult ; *Brain Mapping ; Brain-Computer Interfaces ; Decision Making/physiology ; Discriminant Analysis ; Fingers/innervation ; Functional Laterality ; *Hemodynamics ; Hemoglobins/metabolism ; Humans ; Male ; Motor Cortex/*physiology ; Psychomotor Performance/*physiology ; *Spectroscopy, Near-Infrared ; },
abstract = {In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes (Δ HbO) of functional near-infrared spectroscopy (fNIRS) is developed. The vector phase diagram displays the trajectories of Δ HbO and deoxy-hemoglobin changes (Δ HbR), as orthogonal components, in the Δ HbO- Δ HbR polar coordinates. To determine the occurrence of an initial dip, dual threshold circles (an inner circle from the resting state, an outer circle from the peak values of the initial dip and the main HR) are incorporated into the phase diagram for making decisions. The proposed scheme is then applied to a brain-computer interface scheme, and its performance is evaluated in classifying two finger tapping tasks (right-hand thumb and little finger) from the left motor cortex. Three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. In classifying two tapping tasks, the signal mean and signal minimum values during 0-2.5 s, as features of initial dip, are used. The linear discriminant analysis was utilized as a classifier. The experimental results show that the active brain locations of the two tasks were quite distinctive (p < 0.05), and moreover, spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s. Also, the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.},
}
@article {pmid30044783,
year = {2018},
author = {Espinoza-Romo, BA and Sainz-Hernández, JC and Ley-Quiñónez, CP and Hart, CE and Leal-Moreno, R and Aguirre, AA and Zavala-Norzagaray, AA},
title = {Blood biochemistry of olive ridley (Lepidochelys olivacea) sea turtles foraging in northern Sinaloa, Mexico.},
journal = {PloS one},
volume = {13},
number = {7},
pages = {e0199825},
pmid = {30044783},
issn = {1932-6203},
mesh = {Animals ; Blood Proteins/analysis ; Female ; Hematocrit ; Male ; Mexico ; Turtles/*blood ; },
abstract = {Blood parameters provide an excellent tool to evaluate the health status of wildlife. However, there are few studies about health parameters of sea turtles in Mexico. For olive ridley turtles (Lepidochelys olivacea), no information was available to establish the health baseline for the species. The objective of this study was to establish reference blood biochemistry values for olive ridley turtles in the northern Sinaloa foraging area. Between 2013 and 2015, 82 olive ridley turtles were captured. Body condition index (BCI) presented a mean of 1.46 ± 0.14 (1.17-2.02) that categorized the population with excellent body condition; in addition, 99% of the turtles captured had a good physical appearance. Blood was collected for biochemistry analysis from 60 turtles. Significantly higher values of total protein, albumin, A/G ratio (albumin/globulin) and PCV (packed cell volume or hematocrit) were observed in adult when compared to subadult turtles. On the other hand, no significant differences were found when females and males were compared. Based on the BCI, physical assessment, and blood parameters, and compared to other sea turtle species, olive ridley turtles in northern Sinaloa were considered in excellent health. To the best of our knowledge, this is the first study to establish normal blood biochemistry values of foraging olive ridley turtles in northern Sinaloa.},
}
@article {pmid30042493,
year = {2019},
author = {Lionnard, L and Duc, P and Brennan, MS and Kueh, AJ and Pal, M and Guardia, F and Mojsa, B and Damiano, MA and Mora, S and Lassot, I and Ravichandran, R and Cochet, C and Aouacheria, A and Potts, PR and Herold, MJ and Desagher, S and Kucharczak, J},
title = {TRIM17 and TRIM28 antagonistically regulate the ubiquitination and anti-apoptotic activity of BCL2A1.},
journal = {Cell death and differentiation},
volume = {26},
number = {5},
pages = {902-917},
pmid = {30042493},
issn = {1476-5403},
mesh = {Apoptosis/*genetics ; Cell Death/genetics ; Cell Line, Tumor ; Doxycycline/pharmacology ; Gene Expression Regulation, Neoplastic/drug effects ; Glycogen Synthase Kinase 3/genetics ; Humans ; Minor Histocompatibility Antigens/*genetics ; Neoplasms/drug therapy/*genetics/pathology ; Phosphorylation/genetics ; Proteasome Endopeptidase Complex/genetics ; Protein Binding/genetics ; Protein Stability ; Proteolysis/drug effects ; Proto-Oncogene Proteins c-bcl-2/*genetics ; Tripartite Motif Proteins/*genetics ; Tripartite Motif-Containing Protein 28/*genetics ; Ubiquitin-Protein Ligases/*genetics ; Ubiquitination/genetics ; },
abstract = {BCL2A1 is an anti-apoptotic member of the BCL-2 family that contributes to chemoresistance in a subset of tumors. BCL2A1 has a short half-life due to its constitutive processing by the ubiquitin-proteasome system. This constitutes a major tumor-suppressor mechanism regulating BCL2A1 function. However, the enzymes involved in the regulation of BCL2A1 protein stability are currently unknown. Here, we provide the first insight into the regulation of BCL2A1 ubiquitination. We present evidence that TRIM28 is an E3 ubiquitin-ligase for BCL2A1. Indeed, endogenous TRIM28 and BCL2A1 bind to each other at the mitochondria and TRIM28 knock-down decreases BCL2A1 ubiquitination. We also show that TRIM17 stabilizes BCL2A1 by blocking TRIM28 from binding and ubiquitinating BCL2A1, and that GSK3 is involved in the phosphorylation-mediated inhibition of BCL2A1 degradation. BCL2A1 and its close relative MCL1 are thus regulated by common factors but with opposite outcome. Finally, overexpression of TRIM28 or knock-out of TRIM17 reduced BCLA1 protein levels and restored sensitivity of melanoma cells to BRAF-targeted therapy. Therefore, our data describe a molecular rheostat in which two proteins of the TRIM family antagonistically regulate BCL2A1 stability and modulate cell death.},
}
@article {pmid30042372,
year = {2018},
author = {Ivorra, E and Ortega, M and Catalán, JM and Ezquerro, S and Lledó, LD and Garcia-Aracil, N and Alcañiz, M},
title = {Intelligent Multimodal Framework for Human Assistive Robotics Based on Computer Vision Algorithms.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {8},
pages = {},
pmid = {30042372},
issn = {1424-8220},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Electrooculography ; Humans ; Robotics/*methods ; *Vision, Ocular ; },
abstract = {Assistive technologies help all persons with disabilities to improve their accessibility in all aspects of their life. The AIDE European project contributes to the improvement of current assistive technologies by developing and testing a modular and adaptive multimodal interface customizable to the individual needs of people with disabilities. This paper describes the computer vision algorithms part of the multimodal interface developed inside the AIDE European project. The main contribution of this computer vision part is the integration with the robotic system and with the other sensory systems (electrooculography (EOG) and electroencephalography (EEG)). The technical achievements solved herein are the algorithm for the selection of objects using the gaze, and especially the state-of-the-art algorithm for the efficient detection and pose estimation of textureless objects. These algorithms were tested in real conditions, and were thoroughly evaluated both qualitatively and quantitatively. The experimental results of the object selection algorithm were excellent (object selection over 90%) in less than 12 s. The detection and pose estimation algorithms evaluated using the LINEMOD database were similar to the state-of-the-art method, and were the most computationally efficient.},
}
@article {pmid30039806,
year = {2018},
author = {Aricò, P and Borghini, G and Di Flumeri, G and Sciaraffa, N and Babiloni, F},
title = {Passive BCI beyond the lab: current trends and future directions.},
journal = {Physiological measurement},
volume = {39},
number = {8},
pages = {08TR02},
doi = {10.1088/1361-6579/aad57e},
pmid = {30039806},
issn = {1361-6579},
mesh = {*Brain-Computer Interfaces ; Humans ; Laboratories ; },
abstract = {Over the last decade, passive brain-computer interface (BCI) algorithms and biosignal acquisition technologies have experienced a significant growth that has allowed the real-time analysis of biosignals, with the aim to quantify relevant insights, such as mental and emotional states, of the users. Several passive BCI-based applications have been tested in laboratory settings, and just a few of them in real or, at least, simulated but highly realistic settings. Nevertheless, works performed in laboratory settings are not able to take into account all those factors (artefacts, non-brain influences, other mental states) that could impair the usability of passive BCIs during real applications, naturally characterized by higher complexity. The present review takes into account the most recent trends in using advanced passive BCI technologies in real settings, especially for real-time mental state evaluations in operational environments, evaluation of team resources, training and expertise assessment, gaming and neuromarketing applications. The objective of the work is to draw a mark on where we are to date and the future challenges, in order to make passive BCIs closer to being integrated into daily life applications.},
}
@article {pmid30023604,
year = {2017},
author = {Arvin-Berod, M and Desroches-Castan, A and Bonte, S and Brugière, S and Couté, Y and Guyon, L and Feige, JJ and Baussanne, I and Demeunynck, M},
title = {Indolizine-Based Scaffolds as Efficient and Versatile Tools: Application to the Synthesis of Biotin-Tagged Antiangiogenic Drugs.},
journal = {ACS omega},
volume = {2},
number = {12},
pages = {9221-9230},
pmid = {30023604},
issn = {2470-1343},
abstract = {We describe the design and optimization of polyfunctional scaffolds based on a fluorescent indolizine core derivatized with various orthogonal groups (amines, esters, oximes, alkynes, etc.). To show one application as tools in biology, the scaffold was used to prepare drug-biotin conjugates that were then immobilized onto avidin-agarose for affinity chromatography. More specifically, the antiangiogenic drug COB223, whose mechanism of action remained unclear, was chosen as a proof-of-concept drug. The drug-selective discrimination of proteins observed after elution of the cell lysates through the affinity columns, functionalized either with the biologically active COB223 or a structurally related inactive analogue (COB236), is a clear indication that the presence of the indolizine core does not limit drug-protein interaction and confirms the usefulness of the indolizine scaffold. Furthermore, the separation of COB223-interacting proteins from human placental extracts unveiled unanticipated protein targets belonging to the family of regulatory RNA-binding proteins, which opens the way to new hypotheses on the mode of action of this antiangiogenic drug.},
}
@article {pmid30021931,
year = {2018},
author = {Khalaf, A and Sejdic, E and Akcakaya, M},
title = {Towards optimal visual presentation design for hybrid EEG-fTCD brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056019},
doi = {10.1088/1741-2552/aad46f},
pmid = {30021931},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Cognition/physiology ; Electroencephalography/classification/*instrumentation ; Female ; Fixation, Ocular/physiology ; Humans ; Imagination/physiology ; Male ; Photic Stimulation ; Psychomotor Performance/physiology ; Reproducibility of Results ; Rotation ; Support Vector Machine ; Ultrasonography, Doppler, Transcranial/classification/*instrumentation ; },
abstract = {OBJECTIVE: In this paper, we introduce a novel hybrid brain-computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy.<br><br>APPROACH: Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes.<br><br>MAIN RESULTS: EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3.92, and 5.60 bits min[-1] were obtained for MR versus baseline, WG versus baseline, and MR versus WG problems respectively.<br><br>SIGNIFICANCE: In terms of accuracy, the current visual presentation outperforms the motor imagery visual presentation we designed before for the EEG-fTCD system by 10% accuracy for task versus task problem. Moreover, the proposed system outperforms the state of the art hybrid EEG-fNIRS BCIs in terms of accuracy and/or information transfer rate. Even though there are still limitations of the proposed system, such promising results show that the proposed hybrid system is a feasible candidate for real-time BCIs.},
}
@article {pmid30020844,
year = {2018},
author = {Black, BJ and Kanneganti, A and Joshi-Imre, A and Rihani, R and Chakraborty, B and Abbott, J and Pancrazio, JJ and Cogan, SF},
title = {Chronic recording and electrochemical performance of Utah microelectrode arrays implanted in rat motor cortex.},
journal = {Journal of neurophysiology},
volume = {120},
number = {4},
pages = {2083-2090},
doi = {10.1152/jn.00181.2018},
pmid = {30020844},
issn = {1522-1598},
mesh = {Animals ; Electrochemical Techniques/instrumentation/methods ; Electrodes, Implanted/standards ; Electroencephalography/instrumentation/*methods ; Male ; Microelectrodes/standards ; Motor Cortex/*physiology ; Rats ; Signal-To-Noise Ratio ; },
abstract = {Multisite implantable electrode arrays serve as a tool to understand cortical network connectivity and plasticity. Furthermore, they enable electrical stimulation to drive plasticity, study motor/sensory mapping, or provide network input for controlling brain-computer interfaces. Neurobehavioral rodent models are prevalent in studies of motor cortex injury and recovery as well as restoration of auditory/visual cues due to their relatively low cost and ease of training. Therefore, it is important to understand the chronic performance of relevant electrode arrays in rodent models. In this report, we evaluate the chronic recording and electrochemical performance of 16-channel Utah electrode arrays, the current state-of-the-art in pre-/clinical cortical recording and stimulation, in rat motor cortex over a period of 6 mo. The single-unit active electrode yield decreased from 52.8 ± 10.0 (week 1) to 13.4 ± 5.1% (week 24). Similarly, the total number of single units recorded on all electrodes across all arrays decreased from 106 to 15 over the same time period. Parallel measurements of electrochemical impedance spectra and cathodic charge storage capacity exhibited significant changes in electrochemical characteristics consistent with development of electrolyte leakage pathways over time. Additionally, measurements of maximum cathodal potential excursion indicated that only a relatively small fraction of electrodes (10-35% at 1 and 24 wk postimplantation) were capable of delivering relevant currents (20 µA at 4 nC/ph) without exceeding negative or positive electrochemical potential limits. In total, our findings suggest mainly abiotic failure modes, including mechanical wire breakage as well as degradation of conducting and insulating substrates. NEW &amp; NOTEWORTHY Multisite implantable electrode arrays serve as a tool to record cortical network activity and enable electrical stimulation to drive plasticity or provide network feedback. The use of rodent models in these fields is prevalent. We evaluated chronic recording and electrochemical performance of 16-channel Utah electrode arrays in rat motor cortex over a period of 6 mo. We primarily observed abiotic failure modes suggestive of mechanical wire breakage and/or degradation of insulation.},
}
@article {pmid30018334,
year = {2018},
author = {Crea, S and Nann, M and Trigili, E and Cordella, F and Baldoni, A and Badesa, FJ and Catalán, JM and Zollo, L and Vitiello, N and Aracil, NG and Soekadar, SR},
title = {Feasibility and safety of shared EEG/EOG and vision-guided autonomous whole-arm exoskeleton control to perform activities of daily living.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {10823},
pmid = {30018334},
issn = {2045-2322},
support = {645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 645322//EC | Horizon 2020 (European Union Framework Programme for Research and Innovation)/International ; 759370//EC | European Research Council (ERC)/International ; },
mesh = {Activities of Daily Living ; Adult ; Brain-Computer Interfaces ; *Electroencephalography ; *Electrooculography ; Feasibility Studies ; Hand/*physiopathology ; Humans ; Male ; Paralysis/*physiopathology ; Young Adult ; },
abstract = {Arm and finger paralysis, e.g. due to brain stem stroke, often results in the inability to perform activities of daily living (ADLs) such as eating and drinking. Recently, it was shown that a hybrid electroencephalography/electrooculography (EEG/EOG) brain/neural hand exoskeleton can restore hand function to quadriplegics, but it was unknown whether such control paradigm can be also used for fluent, reliable and safe operation of a semi-autonomous whole-arm exoskeleton restoring ADLs. To test this, seven abled-bodied participants (seven right-handed males, mean age 30 ± 8 years) were instructed to use an EEG/EOG-controlled whole-arm exoskeleton attached to their right arm to perform a drinking task comprising multiple sub-tasks (reaching, grasping, drinking, moving back and releasing a cup). Fluent and reliable control was defined as average 'time to initialize' (TTI) execution of each sub-task below 3 s with successful initializations of at least 75% of sub-tasks within 5 s. During use of the system, no undesired side effects were reported. All participants were able to fluently and reliably control the vision-guided autonomous whole-arm exoskeleton (average TTI 2.12 ± 0.78 s across modalities with 75% successful initializations reached at 1.9 s for EOG and 4.1 s for EEG control) paving the way for restoring ADLs in severe arm and hand paralysis.},
}
@article {pmid30014347,
year = {2018},
author = {Xiao, J and Pan, J and He, Y and Xie, Q and Yu, T and Huang, H and Lv, W and Zhang, J and Yu, R and Li, Y},
title = {Visual Fixation Assessment in Patients with Disorders of Consciousness Based on Brain-Computer Interface.},
journal = {Neuroscience bulletin},
volume = {34},
number = {4},
pages = {679-690},
pmid = {30014347},
issn = {1995-8218},
mesh = {Adolescent ; Adult ; Aged ; Brain/physiopathology ; *Brain-Computer Interfaces ; Consciousness Disorders/*diagnosis/physiopathology ; *Diagnosis, Computer-Assisted/methods ; *Electroencephalography/methods ; Evoked Potentials ; Female ; *Fixation, Ocular/physiology ; Humans ; Male ; Middle Aged ; Neurologic Examination ; Pilot Projects ; Severity of Illness Index ; User-Computer Interface ; },
abstract = {Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised (CRS-R). Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness (DOCs). Brain-computer interface (BCI) can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expression. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.},
}
@article {pmid30010581,
year = {2018},
author = {Bittencourt-Villalpando, M and Maurits, NM},
title = {Stimuli and Feature Extraction Algorithms for Brain-Computer Interfaces: A Systematic Comparison.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {9},
pages = {1669-1679},
doi = {10.1109/TNSRE.2018.2855801},
pmid = {30010581},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Communication Devices for People with Disabilities ; Electroencephalography/statistics &amp; numerical data ; Event-Related Potentials, P300/physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Neurofeedback ; Psychomotor Performance ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a system that allows communication between the central nervous system and an external device. The BCIs developed by various research groups differ in their main features and the comparison across studies is therefore challenging. Here, in the same group of 19 healthy participants, we investigate three different tasks (SSVEP, P300, and hybrid) that allowed four choices to the user without previous neurofeedback training. We used the same 64-channel EEG equipment to acquire data, while participants performed each of the tasks. We systematically compared the participants' offline performance on the following parameters: 1) accuracy; 2) BCI Utility (in bits/min); and 3) inefficiency/illiteracy. In addition, we evaluated the accuracy as a function of the number of electrodes. In this paper, the SSVEP task outperformed the other tasks in bit rate, reaching an average and maximum BCI Utility of 63.4 and 91.3 bits/min, respectively. All participants achieved an accuracy level above70% on both SSVEP and P300 tasks. Furthermore, the average accuracy of all tasks was highest if a reduced subset with 4-12 electrodes was used. These results are relevant for the development of online BCIs intended for the real-life applications.},
}
@article {pmid30010579,
year = {2018},
author = {Lu, Y and Bi, L and Lian, J and Li, H},
title = {Mathematical Modeling of EEG Signals-Based Brain-Control Behavior.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {8},
pages = {1535-1543},
doi = {10.1109/TNSRE.2018.2855263},
pmid = {30010579},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Artifacts ; Behavior/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Decision Making/physiology ; Electroencephalography/*statistics &amp; numerical data ; Event-Related Potentials, P300 ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Neurological ; Models, Theoretical ; Self-Help Devices ; Young Adult ; },
abstract = {Brain-control behaviors (BCBs) are behaviors of humans that communicate with external devices by means of the human brain rather than peripheral nerves or muscles. In this paper, to understand and simulate such behaviors, we propose a mathematical model by combining a queuing network-based encoding model with a brain-computer interface model. Experimental results under the static tests show the effectiveness of the proposed model in simulating real BCBs. Furthermore, we verify the effectiveness and applicability of the proposed model through the dynamic experimental tests in a simulated vehicle. This paper not only promotes the understanding and prediction of BCBs, but also provides some insights into assistive technology on brain-controlled systems and extends the scope of research on human behavior modeling.},
}
@article {pmid30010089,
year = {2018},
author = {Luo, J and Sun, W and Wu, Y and Liu, H and Wang, X and Yan, T and Song, R},
title = {Characterization of the coordination of agonist and antagonist muscles among stroke patients, healthy late middle-aged and young controls using a myoelectric-controlled interface.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056015},
doi = {10.1088/1741-2552/aad387},
pmid = {30010089},
issn = {1741-2552},
mesh = {Adult ; Aged ; Aging/physiology ; *Brain-Computer Interfaces ; Elbow/physiopathology ; *Electromyography ; Female ; Humans ; Isometric Contraction/physiology ; Male ; Middle Aged ; Muscle, Skeletal/*physiopathology ; Psychomotor Performance ; Stroke/*physiopathology/psychology ; Torque ; Young Adult ; },
abstract = {OBJECTIVE: The coordination of agonist and antagonist muscles around a single joint plays an important role in daily activities. The aim of this study was to apply a myoelectric-controlled interface (MCI) with different dimensions to investigate stroke- and aging-induced deteriorations in the coordination of agonist and antagonist muscles.<br><br>APPROACH: Eight stroke patients (affected sides), ten healthy late middle-aged controls and eighteen healthy young controls were enrolled to perform tracking tasks during voluntary isometric elbow flexion and extension by modulating their biceps and triceps activities with 1D or 2D MCI. The root mean square error (RMSE) between actual and target agonist activations, normalized agonist and antagonist activations, and co-contraction index (CI) and normalized elbow torque were used to quantify the movement performance.<br><br>MAIN RESULTS: During elbow extension, significant increases in RMSE were identified in stroke patients with increasing MCI dimensionality, whereas significant decreases in normalized agonist and antagonist activations and normalized elbow torque were observed in all three groups. In addition, significant decreases in CI were observed in both control groups (P&#x2009;&#x2009;<&#x2009;&#x2009;0.05). During elbow flexion and extension, RMSE increased in the following order: young controls&#x2009;&#x2009;<&#x2009;&#x2009;late middle-aged controls&#x2009;&#x2009;<&#x2009;&#x2009;stroke patients. By contrast, CI was significantly higher in stroke patients and late middle-aged controls than in young controls, possibly due to stroke- and aging-induced loss of skill in modulating the coordination of agonist and antagonist muscles when meeting the demands of a changing environment.<br><br>SIGNIFICANCE: This study suggests that 2D MCI might be applied as a rehabilitation tool to achieve fine control of abnormal muscle coordination.},
}
@article {pmid30010005,
year = {2018},
author = {Kober, SE and Witte, M and Grinschgl, S and Neuper, C and Wood, G},
title = {Placebo hampers ability to self-regulate brain activity: A double-blind sham-controlled neurofeedback study.},
journal = {NeuroImage},
volume = {181},
number = {},
pages = {797-806},
doi = {10.1016/j.neuroimage.2018.07.025},
pmid = {30010005},
issn = {1095-9572},
mesh = {Adult ; Brain Waves/*physiology ; Cerebral Cortex/*physiology ; Connectome ; Double-Blind Method ; Electroencephalography/*methods ; Female ; Humans ; Male ; Neurofeedback/*physiology ; *Placebos ; *Self-Control ; Transcranial Direct Current Stimulation/*methods ; Young Adult ; },
abstract = {It is still poorly understood how unspecific effects peripheral to the supposed action mechanism of neurofeedback (NF) influence the ability to self-regulate one's own brain signals. Recently, skeptical researchers have even attributed the lion's part of therapeutic outcomes of NF to placebo and other psychosocial factors. Here, we investigated whether and by which mechanisms unspecific factors influence neural self-regulation during NF. To manipulate the impact of unspecific influences on NF performance, we used a sham transcranial direct current stimulation (tDCS) as active placebo intervention suggesting positive effects on NF performance. Our results show that the expectation of receiving brain stimulation, which should boost neural self-regulation, interferes with the ability to self-regulate the sensorimotor rhythm in the EEG. Hence, these results provide evidence that placebo reduces NF performance, and thereby challenge current theories on unspecific effects related to NF.},
}
@article {pmid30008659,
year = {2018},
author = {Guger, C and Spataro, R and Pellas, F and Allison, BZ and Heilinger, A and Ortner, R and Cho, W and Xu, R and La Bella, V and Edlinger, G and Annen, J and Mandalá, G and Chatelle, C and Laureys, S},
title = {Assessing Command-Following and Communication With Vibro-Tactile P300 Brain-Computer Interface Tools in Patients With Unresponsive Wakefulness Syndrome.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {423},
pmid = {30008659},
issn = {1662-4548},
abstract = {Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor disablities, and thus assessing their spared cognitive abilities can be difficult. Recent research from several groups has shown that non-invasive brain-computer interface (BCI) technology can provide assessments of these patients' cognitive function that can supplement information provided through conventional behavioral assessment methods. In rare cases, BCIs may provide a binary communication mechanism. Here, we present results from a vibrotactile BCI assessment aiming at detecting command-following and communication in 12 unresponsive wakefulness syndrome (UWS) patients. Two different paradigms were administered at least once for every patient: (i) VT2 with two vibro-tactile stimulators fixed on the patient's left and right wrists and (ii) VT3 with three vibro-tactile stimulators fixed on both wrists and on the back. The patients were instructed to mentally count either the stimuli on the left or right wrist, which may elicit a robust P300 for the target wrist only. The EEG data from -100 to +600 ms around each stimulus were extracted and sub-divided into 8 data segments. This data was classified with linear discriminant analysis (using a 10 × 10 cross validation) and used to calibrate a BCI to assess command following and YES/NO communication abilities. The grand average VT2 accuracy across all patients was 38.3%, and the VT3 accuracy was 26.3%. Two patients achieved VT3 accuracy ≥80% and went through communication testing. One of these patients answered 4 out of 5 questions correctly in session 1, whereas the other patient answered 6/10 and 7/10 questions correctly in sessions 2 and 4. In 6 other patients, the VT2 or VT3 accuracy was above the significance threshold of 23% for at least one run, while in 4 patients, the accuracy was always below this threshold. The study highlights the importance of repeating EEG assessments to increase the chance of detecting command-following in patients with severe brain injury. Furthermore, the study shows that BCI technology can test command following in chronic UWS patients and can allow some of these patients to answer YES/NO questions.},
}
@article {pmid30004882,
year = {2018},
author = {Liu, D and Chen, W and Lee, K and Chavarriaga, R and Iwane, F and Bouri, M and Pei, Z and Millan, JDR},
title = {EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {8},
pages = {1626-1635},
doi = {10.1109/TNSRE.2018.2855053},
pmid = {30004882},
issn = {1558-0210},
mesh = {Adult ; Artifacts ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Electroencephalography/*classification/*statistics &amp; numerical data ; Female ; Functional Laterality/physiology ; Gait Disorders, Neurologic/rehabilitation ; Healthy Volunteers ; Humans ; Lower Extremity/*physiology ; Male ; Movement/*physiology ; Young Adult ; },
abstract = {Brain-machine interfaces have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography. Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This paper presents the decoding of lower-limb movement-related cortical potentials with continuous classification and asynchronous detection. We executed experiments in a customized gait trainer, where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between the left and right legs in terms of neural signatures of movement and classification performance. We obtained a higher true positive rate, lower false positives, and comparable latencies with respect to the existing online detection methods. This paper demonstrates the feasibility of the proposed framework to build a closed-loop gait trainer. Potential applications include gait training neurorehabilitation in clinical trials.},
}
@article {pmid30004466,
year = {2018},
author = {Tonin, A and Birbaumer, N and Chaudhary, U},
title = {A 20-Questions-Based Binary Spelling Interface for Communication Systems.},
journal = {Brain sciences},
volume = {8},
number = {7},
pages = {},
pmid = {30004466},
issn = {2076-3425},
abstract = {Brain computer interfaces (BCIs) enables people with motor impairments to communicate using their brain signals by selecting letters and words from a screen. However, these spellers do not work for people in a complete locked-in state (CLIS). For these patients, a near infrared spectroscopy-based BCI has been developed, allowing them to reply to "yes"/"no" questions with a classification accuracy of 70%. Because of the non-optimal accuracy, a usual character-based speller for selecting letters or words cannot be used. In this paper, a novel spelling interface based on the popular 20-questions-game has been presented, which will allow patients to communicate using only "yes"/"no" answers, even in the presence of poor classification accuracy. The communication system is based on an artificial neural network (ANN) that estimates a statement thought by the patient asking less than 20 questions. The ANN has been tested in a web-based version with healthy participants and in offline simulations. Both results indicate that the proposed system can estimate a patient's imagined sentence with an accuracy that varies from 40%, in the case of a "yes"/"no" classification accuracy of 70%, and up to 100% in the best case. These results show that the proposed spelling interface could allow patients in CLIS to express their own thoughts, instead of only answer to "yes"/"no" questions.},
}
@article {pmid30002623,
year = {2018},
author = {Hong, KS and Khan, MJ and Hong, MJ},
title = {Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {246},
pmid = {30002623},
issn = {1662-5161},
abstract = {In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.},
}
@article {pmid30002615,
year = {2018},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Bonnet-Save, M and Ron-Angevin, R},
title = {Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {438},
pmid = {30002615},
issn = {1662-4548},
abstract = {A brain-computer interface (BCI) is a technology allowing patients with severe motor dysfunctions to use their electroencephalographic signals to create a communication channel to control devices. The objective of this paper is to study the feasibility of continuous and switch control modes for a brain-controlled wheelchair (BCW) using sensorimotor rhythms (SMR) modulated through a right-hand motor imagery task. Previous studies, which used a continuous navigation control with SMR, have reported the difficulty of maintaining the motor imagery task for a long time, especially for the forward command. The switch control has been presented as a proposal that may help to solve this issue since this task is only used temporary for either disabling or enabling the movement. Regarding the methodology, 10 of 15 able-bodied users, who had overcome the criterion of 30% error rate in the calibration phase, controlled the BCW using both paradigms. The navigation tasks consisted of a straight path divided in five sections: in three of them the users had to move forward, and in the other two the users had to maintain their position. To assess user performance in the device management, a usability approach was adopted, measuring the factors of effectiveness, efficiency, and satisfaction. Then, variables related to the time employed and commands selected by the user or parameters related to the confusion matrix were applied. In addition, the scores in NASA-TLX and two ad hoc questionnaires were considered to discuss the user experience controlling the wheelchair. Despite the results showed that the best system for a specific user relies on his/her abilities and preferences, the switch control mode obtained better accuracy (0.59 ± 0.17 for continuous and 0.72 ± 0.05 for switch). Furthermore, the switch paradigm can be recommended for the advance sections as with it users could complete the advance sections in less time (42.2 ± 28.7 s for continuous and 15.47 ± 3.43 s for switch), while the continuous mode seems to be better at keeping the wheelchair stopped (42.45 ± 16.01 s for continuous and 24.35 ± 10.94 s for switch).},
}
@article {pmid30002452,
year = {2018},
author = {Hu, K and Jamali, M and Moses, ZB and Ortega, CA and Friedman, GN and Xu, W and Williams, ZM},
title = {Decoding unconstrained arm movements in primates using high-density electrocorticography signals for brain-machine interface use.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {10583},
pmid = {30002452},
issn = {2045-2322},
mesh = {Animals ; Arm/innervation/*physiology ; Behavior, Animal/physiology ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/methods ; Electrocorticography/instrumentation ; Electrodes, Implanted ; Macaca mulatta ; Male ; Models, Animal ; Motor Cortex/*physiology ; Movement/*physiology ; Paralysis/rehabilitation ; },
abstract = {Motor deficit is among the most debilitating aspects of injury to the central nervous system. Despite ongoing progress in brain-machine interface (BMI) development and in the functional electrical stimulation of muscles and nerves, little is understood about how neural signals in the brain may be used to potentially control movement in one's own unconstrained paralyzed limb. We recorded from high-density electrocorticography (ECoG) electrode arrays in the ventral premotor cortex (PMv) of a rhesus macaque and used real-time motion tracking techniques to correlate spatial-temporal changes in neural activity with arm movements made towards objects in three-dimensional space at millisecond precision. We found that neural activity from a small number of electrodes within the PMv can be used to accurately predict reach-return movement onset and directionality. Also, whereas higher gamma frequency field activity was more predictive about movement direction during performance, mid-band (beta and low gamma) activity was more predictive of movement prior to onset. We speculate these dual spatiotemporal signals may be used to optimize both planning and execution of movement during natural reaching, with prospective relevance to the future development of neural prosthetics aimed at restoring motor control over one's own paralyzed limb.},
}
@article {pmid29998936,
year = {2018},
author = {Yanagisawa, T and Fukuma, R and Seymour, B and Hosomi, K and Kishima, H and Shimizu, T and Yokoi, H and Hirata, M and Yoshimine, T and Kamitani, Y and Saitoh, Y},
title = {MEG-BMI to Control Phantom Limb Pain.},
journal = {Neurologia medico-chirurgica},
volume = {58},
number = {8},
pages = {327-333},
pmid = {29998936},
issn = {1349-8029},
support = {//Wellcome Trust/United Kingdom ; 21537/VAC_/Versus Arthritis/United Kingdom ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Cross-Over Studies ; Female ; *Hand ; Humans ; Magnetoencephalography ; Male ; Middle Aged ; Phantom Limb/*therapy ; *Rhizotomy ; *Robotics ; Spinal Nerve Roots/*surgery ; },
abstract = {A brachial plexus root avulsion (BPRA) causes intractable pain in the insensible affected hands. Such pain is partly due to phantom limb pain, which is neuropathic pain occurring after the amputation of a limb and partial or complete deafferentation. Previous studies suggested that the pain was attributable to maladaptive plasticity of the sensorimotor cortex. However, there is little evidence to demonstrate the causal links between the pain and the cortical representation, and how much cortical factors affect the pain. Here, we applied lesioning of the dorsal root entry zone (DREZotomy) and training with a brain-machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. The DREZotomy successfully reduced the shooting pain after BPRA, but a part of the pain remained. The BMI training successfully induced some plastic changes in the sensorimotor representation of the phantom hand movements and helped control the remaining pain. When the patient tried to control the robotic hand by moving their phantom hand through association with the representation of the intact hand, this especially decreased the pain while decreasing the classification accuracy of the phantom hand movements. These results strongly suggested that pain after the BPRA was partly attributable to cortical representation of phantom hand movements and that the BMI training controlled the pain by inducing appropriate cortical reorganization. For the treatment of chronic pain, we need to know how to modulate the cortical representation by novel methods.},
}
@article {pmid29997429,
year = {2018},
author = {Likitlersuang, J and Koh, R and Gong, X and Jovanovic, L and Bolivar-Tellería, I and Myers, M and Zariffa, J and Márquez-Chin, C},
title = {EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof-of-Concept Study.},
journal = {Topics in spinal cord injury rehabilitation},
volume = {24},
number = {3},
pages = {265-274},
pmid = {29997429},
issn = {1945-5763},
mesh = {Adult ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; *Electroencephalography ; Female ; Hand Strength/*physiology ; Humans ; Male ; Middle Aged ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Treatment Outcome ; Upper Extremity/*physiopathology ; Young Adult ; },
abstract = {Background: Functional electrical stimulation therapy (FEST) is a promising intervention for the restoration of upper extremity function after cervical spinal cord injury (SCI). Objectives: This study describes and evaluates a novel FEST system designed to incorporate voluntary movement attempts and massed practice of functional grasp through the use of brain-computer interface (BCI) and computer vision (CV) modules. Methods: An EEG-based BCI relying on a single electrode was used to detect movement initiation attempts. A CV system identified the target object and selected the appropriate grasp type. The required grasp type and trigger command were sent to an FES stimulator, which produced one of four multichannel muscle stimulation patterns (precision, lateral, palmar, or lumbrical grasp). The system was evaluated with five neurologically intact participants and one participant with complete cervical SCI. Results: An integrated BCI-CV-FES system was demonstrated. The overall classification accuracy of the CV module was 90.8%, when selecting out of a set of eight objects. The average latency for the BCI module to trigger the movement across all participants was 5.9 ± 1.5 seconds. For the participant with SCI alone, the CV accuracy was 87.5% and the BCI latency was 5.3 ± 9.4 seconds. Conclusion: BCI and CV methods can be integrated into an FEST system without the need for costly resources or lengthy setup times. The result is a clinically relevant system designed to promote voluntary movement attempts and more repetitions of varied functional grasps during FEST.},
}
@article {pmid29997428,
year = {2018},
author = {Kilgore, KL and Bryden, A and Keith, MW and Hoyen, HA and Hart, RL and Nemunaitis, GA and Peckham, PH},
title = {Evolution of Neuroprosthetic Approaches to Restoration of Upper Extremity Function in Spinal Cord Injury.},
journal = {Topics in spinal cord injury rehabilitation},
volume = {24},
number = {3},
pages = {252-264},
pmid = {29997428},
issn = {1945-5763},
support = {R01 NS029549/NS/NINDS NIH HHS/United States ; },
mesh = {Activities of Daily Living ; Adult ; Brain-Computer Interfaces ; Electric Stimulation Therapy ; *Electrodes, Implanted ; Female ; Hand Strength/*physiology ; Humans ; Male ; Middle Aged ; *Prosthesis Design ; Recovery of Function/*physiology ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Upper Extremity/*physiopathology ; },
abstract = {Background: Spinal cord injury (SCI) occurring at the cervical levels can result in significantly impaired arm and hand function. People with cervical-level SCI desire improved use of their arms and hands, anticipating that regained function will result in improved independence and ultimately improved quality of life. Neuroprostheses provide the most promising method for significant gain in hand and arm function for persons with cervical-level SCI. Neuroprostheses utilize small electrical currents to activate peripheral motor nerves, resulting in controlled contraction of paralyzed muscles. Methods: A myoelectrically-controlled neuroprosthesis was evaluated in 15 arms in 13 individuals with cervical-level SCI. All individuals had motor level C5 or C6 tetraplegia. Results: This study demonstrates that an implanted neuroprosthesis utilizing myoelectric signal (MES)-controlled stimulation allows considerable flexibility in the control algorithms that can be utilized for a variety of arm and hand functions. Improved active range of motion, grip strength, and the ability to pick up and release objects were improved in all arms tested. Adverse events were few and were consistent with the experience with similar active implantable devices. Conclusion: For individuals with cervical SCI who are highly motivated, implanted neuroprostheses provide the opportunity to gain arm and hand function that cannot be gained through the use of orthotics or surgical intervention alone. Upper extremity neuroprostheses have been shown to provide increased function and independence for persons with cervical-level SCI.},
}
@article {pmid29994667,
year = {2019},
author = {Zhang, Y and Nam, CS and Zhou, G and Jin, J and Wang, X and Cichocki, A},
title = {Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI.},
journal = {IEEE transactions on cybernetics},
volume = {49},
number = {9},
pages = {3322-3332},
doi = {10.1109/TCYB.2018.2841847},
pmid = {29994667},
issn = {2168-2275},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; *Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain-computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way. This is likely to result in a suboptimal feature extraction since the time period when the brain responses to the mental tasks occurs may not be accurately detected. In this paper, we propose a novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG. Specifically, spectrum-specific signals are first derived by bandpass filtering from raw EEG data at a set of overlapping filter bands. Each of the spectrum-specific signals is further segmented into multiple subseries using sliding window approach. We then devise a joint sparse optimization of filter bands and time windows with temporal smoothness constraint to extract robust CSP features under a multitask learning framework. A linear support vector machine classifier is trained on the optimized EEG features to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI Competition III dataset IIIa, BCI Competition IV datasets IIa, and BCI Competition IV dataset IIb) to validate the effectiveness of TSGSP in comparison to several other competing methods. Superior classification performance (averaged accuracies are 88.5%, 83.3%, and 84.3% for the three datasets, respectively) based on the experimental results confirms that the proposed algorithm is a promising candidate for performance improvement of MI-based BCIs.},
}
@article {pmid29994123,
year = {2018},
author = {Yao, L and Mrachacz-Kersting, N and Sheng, X and Zhu, X and Farina, D and Jiang, N},
title = {A Multi-Class BCI Based on Somatosensory Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {8},
pages = {1508-1515},
doi = {10.1109/TNSRE.2018.2848883},
pmid = {29994123},
issn = {1558-0210},
mesh = {Adolescent ; Attention/physiology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Functional Laterality ; Humans ; Imagination/*physiology ; Male ; Orientation/physiology ; Reproducibility of Results ; Sensation/*physiology ; Somatosensory Cortex/physiology ; Touch/physiology ; Vibration ; Young Adult ; },
abstract = {In this paper, we investigated the performance of a multi-class brain-computer interface (BCI). The BCI system is based on the concept of somatosensory attentional orientation (SAO), in which the user shifts and maintains somatosensory attention by imagining the sensation of tactile stimulation of a body part. At the beginning of every trial, a vibration stimulus (200 ms) informed the subjects to prepare for the task. Four SAO tasks were performed following randomly presented cues: SAO of the left hand (SAO-LF), SAO of the right hand (SAO-RT), bilateral SAO (SAO-BI), and SAO suppressed or idle state (SAO-ID). Analysis of the event-related desynchronization and synchronization (ERD/ERS) in the EEG indicated that the four SAO tasks had different somatosensory cortical activation patterns. SAO-LF and SAO-RT exhibited stronger contralateral ERD, whereas bilateral ERD activation was indicative of SAO-BI, and bilateral ERS activation was associated with SAO-ID. By selecting the frequency bands and/or optimal classes, classification accuracy of the system reached 85.2%±11.2% for two classes, 69.5%±16.2% for three classes, and 55.9%±15.8% for four classes. The results validated a multi-class BCI system based on SAO, on a single trial basis. Somatosensory attention to different body parts induces diverse oscillatory dynamics within the somatosensory area of the brain, and the proposed SAO paradigm provided a new approach for a multiple-class BCI that is potentially stimulus independent.},
}
@article {pmid29994075,
year = {2018},
author = {Sakhavi, S and Guan, C and Yan, S},
title = {Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {29},
number = {11},
pages = {5619-5629},
doi = {10.1109/TNNLS.2018.2789927},
pmid = {29994075},
issn = {2162-2388},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagination ; Learning/*physiology ; *Neural Networks, Computer ; *Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; },
abstract = {Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (CNN) architecture for classification. The new representation is generated from modifying the filter-bank common spatial patterns method, and the CNN is designed and optimized accordingly for the representation. Our framework outperforms the best classification method in the literature on the BCI competition IV-2a 4-class MI data set by 7% increase in average subject accuracy. Furthermore, by studying the convolutional weights of the trained networks, we gain an insight into the temporal characteristics of EEG.},
}
@article {pmid29994055,
year = {2019},
author = {Jiao, Y and Zhang, Y and Chen, X and Yin, E and Jin, J and Wang, X and Cichocki, A},
title = {Sparse Group Representation Model for Motor Imagery EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {23},
number = {2},
pages = {631-641},
doi = {10.1109/JBHI.2018.2832538},
pmid = {29994055},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/classification/methods ; Humans ; Imagination/*physiology ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {A potential limitation of a motor imagery (MI) based brain-computer interface (BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust classifier training. The calibration burden during data acquisition phase will most probably cause a subject to be reluctant to use a BCI system. To alleviate this issue, we propose a novel sparse group representation model (SGRM) for improving the efficiency of MI-based BCI by exploiting the intersubject information. Specifically, preceded by feature extraction using common spatial pattern, a composite dictionary matrix is constructed with training samples from both the target subject and other subjects. By explicitly exploiting within-group sparse and group-wise sparse constraints, the most compact representation of a test sample of the target subject is then estimated as a linear combination of columns in the dictionary matrix. Classification is implemented by calculating the class-specific representation residual based on the significant training samples corresponding to the nonzero representation coefficients. Accordingly, the proposed SGRM method effectively reduces the required training samples from the target subject due to auxiliary data available from other subjects. With two public EEG data sets, extensive experimental comparisons are carried out between SGRM and other state-of-the-art approaches. Superior classification performance of our method using 40 trials of the target subject for model calibration (Averaged accuracy = 78.2%, Kappa = 0.57 and Averaged accuracy = 77.7%, Kappa = 0.55 for the two data sets, respectively) indicates its promising potential for improving the practicality of MI-based BCI.},
}
@article {pmid29993552,
year = {2018},
author = {Molla, MKI and Morikawa, N and Islam, MR and Tanaka, T},
title = {Data-Adaptive Spatiotemporal ERP Cleaning for Single-Trial BCI Implementation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1334-1344},
doi = {10.1109/TNSRE.2018.2844109},
pmid = {29993552},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Discriminant Analysis ; Electroencephalography/*statistics &amp; numerical data ; Electrooculography ; Evoked Potentials/*physiology ; Female ; Healthy Volunteers ; Humans ; Machine Learning ; Male ; Photic Stimulation ; Wavelet Analysis ; Young Adult ; },
abstract = {This paper presents a data-adaptive approach to enhance the discriminative information of event-related potential (ERP) for the implementation of a brain-computer interface (BCI). The use of single-trial ERP in a real-time BCI application is challenging, due to its inherent noise contamination. Usually, multiple-trial ERPs are averaged to derive discriminative features of different classes by reducing their noise effects. Time-domain filtering is implemented here using an array wavelet transform. Sometimes, several channels can carry the signals, which are irrelevant to actual EPR information against the respective stimuli. A spatial filtering method based on clustering is introduced, to suppress such channels if any. Hence, the single-trial ERP is filtered in both the spatial and temporal domains to improve its discriminative features. The spatial-temporal discriminate analysis is employed to derive the features leading to the performance of target and non-target classification by using linear discriminant analysis. The proposed method is validated using a data set recorded from our experiments. The experimental results show that the performance of the proposed method is superior to that of the recently developed algorithms for single-trial ERP classification.},
}
@article {pmid29993520,
year = {2018},
author = {Safavi, SM and Lopour, B and Chou, PH},
title = {Reducing the Computational Complexity of EEG Source Localization With Cortical Patch Decomposition and Optimal Electrode Selection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {10},
pages = {2298-2310},
doi = {10.1109/TBME.2018.2793882},
pmid = {29993520},
issn = {1558-2531},
mesh = {Algorithms ; Brain Mapping/*methods ; Cerebral Cortex/physiology ; Cluster Analysis ; Electrodes ; Electroencephalography/*methods ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Models, Biological ; Models, Statistical ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Real-time implementation of EEG source localization can be employed in a broad area of applications such as clinical diagnosis of neurologic diseases and brain-computer interface. However, a power-efficient, low-complexity, and real-time implementation of EEG source localization is still challenging due to extensive iterations in the solutions. In this study, two techniques are introduced to reduce the computational burden of the subspace-based MUltiple SIgnal Classification (MUSIC) algorithm.<br><br>METHODS: To shrink the exhaustive search inherent in MUSIC, the cortex is parsed into cortical regions. A novel nomination procedure involving a dictionary learning step will pick a number of regions to be searched for the active sources. In addition, a new electrode selection algorithm based on the Cramer-Rao bound of the errors is introduced to pick the best set of an arbitrary number of electrodes out of the total.<br><br>RESULTS: The performance of the proposed techniques were evaluated using simulated EEG signal under variation of different parameters such as the number of nominated regions, the signal to noise ratio, and the number of electrodes. The proposed techniques can reduce the computational complexity by up to $90\%$. Furthermore, the proposed techniques were tested on EEG data from an auditory oddball experiment.<br><br>CONCLUSION: A good concordance was observed in the comparison of the topographies and the localization errors derived from the proposed technique and regular MUSIC.<br><br>SIGNIFICANCE: Such reduction can be exploited in the real-time, long-run, and mobile monitoring of cortical activity for clinical diagnosis and research purposes.},
}
@article {pmid29993483,
year = {2019},
author = {Yao, L and Sheng, X and Mrachacz-Kersting, N and Zhu, X and Farina, D and Jiang, N},
title = {Sensory Stimulation Training for BCI System Based on Somatosensory Attentional Orientation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {3},
pages = {640-646},
doi = {10.1109/TBME.2018.2852755},
pmid = {29993483},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Evoked Potentials, Somatosensory/physiology ; Female ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Neurological Rehabilitation ; Somatosensory Cortex/*physiology ; Touch/physiology ; Wrist/physiology ; Young Adult ; },
abstract = {In this study, we propose a sensory stimulation training (SST) approach to improve the performance of a brain-computer interface (BCI) based on somatosensory attentional orientation (SAO). In this BCI, subjects imagine the tactile sensation and maintain the attention on the corresponding hand as if there was a tactile stimulus on the wrist skin. Twenty BCI naïve subjects were recruited and randomly divided into a Control-Group and an SST-Group. In the Control-Group, subjects performed left hand and right hand SAO tasks in six consecutive runs (with 40 trials in each run), divided into three blocks with each having two runs. For the SST-Group, two runs included real tactile stimulation to the left or right hand (SST training block), between the first two (Pre-SST block) and the last two SAO runs (Post-SST block). Results showed that the SST-Group had a significantly improved performance of 9.4% between the last block and the first block after SST training (F(2,18) = 11.11, p = 0.0007); in contrast, no significant difference was found in the Control-Group between the first, second, and the last block (F(2,18) = 2.07, p = 0.1546), indicating no learning effect. The tactile sensation-induced oscillatory dynamics were similar to those induced by SAO. In the SST-Group, R[2] discriminative information was enhanced around the somatosensory cortex due to the real sensory stimulation as compared with that in the Control-Group. Since the SAO mental task is inherently an internal process, the proposed SST method is meant as an adjuvant to SAO to facilitate subjects in achieving an initial SAO-based BCI control.},
}
@article {pmid29993456,
year = {2019},
author = {Chen, J and Li, Z and Hong, B and Maye, A and Engel, AK and Zhang, D},
title = {A Single-Stimulus, Multitarget BCI Based on Retinotopic Mapping of Motion-Onset VEPs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {2},
pages = {464-470},
doi = {10.1109/TBME.2018.2849102},
pmid = {29993456},
issn = {1558-2531},
mesh = {Adolescent ; Adult ; Attention/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/classification/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Movement/physiology ; Retina/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: We present a new type of brain-computer interface (BCI) that utilizes the retinotopic mapping of motion-onset visual evoked potentials (mVEP) to accomplish four control channels using a single motion stimulus.<br><br>METHODS: Participants selected a BCI command by fixating one of four target locations around a centrally presented visual motion stimulus. A template-matching method was employed to recognize the users' intention by decoding the position of the motion stimulus in the peripheral visual field, and classification performances were evaluated in an offline manner. The motion stimulus eccentricity between the targets and the visual motion stimulus varied among 5.1&#xb0;, 6.7&#xb0;, 9.8&#xb0;, and 13.0&#xb0;.<br><br>RESULTS: Distinct N200 spatial patterns were elicited when participants directed attention overtly to the target locations. A four-class classification accuracy of 72.2 &#xb1; 5.05% was achieved with a distance of 5.1&#xb0; visual angle between the targets and the visual motion stimulus. The classification accuracies decreased with increasing motion stimulus eccentricities but remained separable well above the chance level at 13.0&#xb0; (47.3 &#xb1; 8.54%).<br><br>CONCLUSION: Our results support the feasibility of a single-stimulus, multitarget mVEP BCI.<br><br>SIGNIFICANCE: The proposed system can simplify the visual stimulation of mVEP BCIs, improve user experience and pave the way for simple yet efficient BCI communication.},
}
@article {pmid29993452,
year = {2019},
author = {Camarrone, F and Van Hulle, MM},
title = {Fast Multiway Partial Least Squares Regression.},
journal = {IEEE transactions on bio-medical engineering},
volume = {66},
number = {2},
pages = {433-443},
doi = {10.1109/TBME.2018.2847404},
pmid = {29993452},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Brain/physiology ; Electrocorticography/methods ; Electroencephalography/*methods ; Humans ; Least-Squares Analysis ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Multiway array decomposition has been successful in providing a better understanding of the structure underlying data and in discovering potentially hidden feature dependences serving high-performance decoder applications. However, the computational cost of multiway algorithms can become prohibitive, especially when considering large datasets, rendering them unsuitable for time-critical applications.<br><br>METHODS: We propose a multiway regression model for large-scale tensors with optimized performance in terms of time complexity, called fast higher order partial least squares (fHOPLS).<br><br>RESULTS: We compare fHOPLS with its native version, higher order partial least squares (HOPLS), the state-of-the-art in multilinear regression, under different noise conditions and tensor dimensionalities using synthetic data. We also compare their performance when used for predicting scalp-recorded electroencephalography signals from invasively recorded electrocorticography signals in an oddball experiment. For the sake of exposition, we evaluated the performance of standard unfolded partial least squares (PLS) and linear regression.<br><br>CONCLUSION: Our results show that fHOPLS is significantly faster than HOPLS, in particular for big data. In addition, the regression performances of fHOPLS and HOPLS are comparable and outperform both unfolded PLS and linear regression. Another interesting result is that multiway array decoding yields more accurate results than epoch-based averaging procedures traditionally used in the brain computer interfacing community.},
}
@article {pmid29993449,
year = {2018},
author = {Balasubramanian, S and Garcia-Cossio, E and Birbaumer, N and Burdet, E and Ramos-Murguialday, A},
title = {Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {12},
pages = {2790-2797},
doi = {10.1109/TBME.2018.2817688},
pmid = {29993449},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Electromyography/*methods ; Female ; Fingers/physiology ; Humans ; *Intention ; Male ; Middle Aged ; Movement/physiology ; *Signal Processing, Computer-Assisted ; Stroke Rehabilitation/*methods ; },
abstract = {OBJECTIVE: In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements.<br><br>METHODS: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training.<br><br>RESULTS: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes.<br><br>CONCLUSION: A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size.<br><br>SIGNIFICANCE: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.},
}
@article {pmid29990133,
year = {2018},
author = {Sreeja, SR and Sahay, RR and Samanta, D and Mitra, P},
title = {Removal of Eye Blink Artifacts From EEG Signals Using Sparsity.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {5},
pages = {1362-1372},
doi = {10.1109/JBHI.2017.2771783},
pmid = {29990133},
issn = {2168-2208},
mesh = {Adult ; Algorithms ; Artifacts ; Blinking/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.},
}
@article {pmid29990114,
year = {2018},
author = {Ge, S and Shi, YH and Wang, RM and Lin, P and Gao, JF and Sun, GP and Iramina, K and Yang, YK and Leng, Y and Wang, HX and Zheng, WM},
title = {Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {5},
pages = {1373-1384},
doi = {10.1109/JBHI.2017.2775657},
pmid = {29990114},
issn = {2168-2208},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/physiology ; Multivariate Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that can be applied to nonlinear and nonstationary EEG signals for BCI data processing. However, because white Gaussian noise occupies a broad range of frequencies, some redundant components are introduced. To solve this leakage problem, in this study, we propose using a sinusoidal assisted signal that occupies the same frequency ranges as the original signals to improve MEMD performance. To verify the effectiveness of the proposed sinusoidal signal assisted MEMD (SA-MEMD) method, we compared the decomposition performances of MEMD, NA-MEMD, and the proposed SA-MEMD using synthetic signals and a real-world BCI dataset. The spectral decomposition results indicate that the proposed SA-MEMD can avoid the generation of redundant components and over decomposition, thus, substantially reduce the mode mixing and misalignment that occurs in MEMD and NA-MEMD. Moreover, using SA-MEMD as a signal preprocessing method instead of MEMD or NA-MEMD can significantly improve BCI classification accuracy and reduce calculation time, which indicates that SA-MEMD is a powerful spectral decomposition method for BCI.},
}
@article {pmid29990112,
year = {2018},
author = {Barua, S and Ahmed, MU and Ahlstrom, C and Begum, S and Funk, P},
title = {Automated EEG Artifact Handling With Application in Driver Monitoring.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {5},
pages = {1350-1361},
doi = {10.1109/JBHI.2017.2773999},
pmid = {29990112},
issn = {2168-2208},
mesh = {Algorithms ; Artifacts ; Brain/physiology ; Brain Waves/physiology ; Cluster Analysis ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.},
}
@article {pmid29989946,
year = {2018},
author = {Suefusa, K and Tanaka, T},
title = {Asynchronous Brain-Computer Interfacing Based on Mixed-Coded Visual Stimuli.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {9},
pages = {2119-2129},
doi = {10.1109/TBME.2017.2785412},
pmid = {29989946},
issn = {1558-2531},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: One of the challenges in the area of brain-computer interfacing (BCI) is to develop an asynchronous BCI or a self-paced BCI that detects whether a user intends to pass messages. This paper proposes a novel asynchronous BCI that uses mixed frequency and phase-coded visual stimuli, which can provide high-speed and accurate command entries.<br><br>METHODS: The mixed-coded visual stimuli were presented as flickers with a following blank interval to synchronize the recorder of electroencephalogram (EEG) with the stimuli, which was aimed to detect the phase in an asynchronous situation. For decoding from the measured EEG, multiset canonical correlation analysis (MCCA) was efficiently exploited for recognizing the intentional state and the intending command. The proposed asynchronous BCI was tested on 11 healthy subjects.<br><br>RESULTS: The proposed decoder was capable of discriminating between the intentional control/noncontrol state and determining the command faster and more accurately than the contrast methods, achieving area under the curve of 0.9191 $\pm$ 0.1206 and command recognition accuracy of 91.08 $\pm$ 13.97 $\%$ with data lengths of 3.0&#xa0;s.<br><br>CONCLUSION: The BCI based on mixed-coded visual stimuli was able to be implemented in an asynchronous manner, and the MCCA-based decoder outperformed the conventional ones in terms of discriminability of intentional states and command recognition accuracy.<br><br>SIGNIFICANCE: The present study showed that an asynchronous BCI can be implemented with mixed-coded visual stimuli for the first time, which enables a large increase in the number of choices/commands.},
}
@article {pmid29989931,
year = {2018},
author = {Even-Chen, N and Stavisky, SD and Pandarinath, C and Nuyujukian, P and Blabe, CH and Hochberg, LR and Henderson, JM and Shenoy, KV},
title = {Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {8},
pages = {1771-1784},
doi = {10.1109/TBME.2017.2776204},
pmid = {29989931},
issn = {1558-2531},
support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/rehabilitation ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography ; Female ; Hand/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/rehabilitation ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) aim to help people with impaired movement ability by directly translating their movement intentions into command signals for assistive technologies. Despite large performance improvements over the last two decades, BCI systems still make errors that need to be corrected manually by the user. This decreases system performance and is also frustrating for the user. The deleterious effects of errors could be mitigated if the system automatically detected when the user perceives that an error was made and automatically intervened with a corrective action; thus, sparing users from having to make the correction themselves. Our previous preclinical work with monkeys demonstrated that task-outcome correlates exist in motor cortical spiking activity and can be utilized to improve BCI performance. Here, we asked if these signals also exist in the human hand area of motor cortex, and whether they can be decoded with high accuracy.<br><br>METHODS: We analyzed posthoc the intracortical neural activity of two BrainGate2 clinical trial participants who were neurally controlling a computer cursor to perform a grid target selection task and a keyboard-typing task.<br><br>RESULTS: Our key findings are that: 1) there exists a putative outcome error signal reflected in both the action potentials and local field potentials of the human hand area of motor cortex, and 2) target selection outcomes can be classified with high accuracy (70-85%) of errors successfully detected with minimal (0-3%) misclassifications of success trials, based on neural activity alone.<br><br>SIGNIFICANCE: These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.},
}
@article {pmid29989927,
year = {2018},
author = {Willett, FR and Murphy, BA and Young, DR and Memberg, WD and Blabe, CH and Pandarinath, C and Franco, B and Saab, J and Walter, BL and Sweet, JA and Miller, JP and Henderson, JM and Shenoy, KV and Simeral, JD and Jarosiewicz, B and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {9},
pages = {2066-2078},
pmid = {29989927},
issn = {1558-2531},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Computer Simulation ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Models, Neurological ; Motor Cortex/*physiology ; Movement/physiology ; Quadriplegia/rehabilitation ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Recent reports indicate that making better assumptions about the user's intended movement can improve the accuracy of decoder calibration for intracortical brain-computer interfaces. Several methods now exist for estimating user intent, including an optimal feedback control model, a piecewise-linear feedback control model, ReFIT, and other heuristics. Which of these methods yields the best decoding performance?<br><br>METHODS: Using data from the BrainGate2 pilot clinical trial, we measured how a steady-state velocity Kalman filter decoder was affected by the choice of intention estimation method. We examined three separate components of the Kalman filter: dimensionality reduction, temporal smoothing, and output gain (speed scaling).<br><br>RESULTS: The decoder's dimensionality reduction properties were largely unaffected by the intention estimation method. Decoded velocity vectors differed by <5% in terms of angular error and speed vs. target distance curves across methods. In contrast, the smoothing and gain properties of the decoder were greatly affected (> 50% difference in average values). Since the optimal gain and smoothing properties are task-specific (e.g. lower gains are better for smaller targets but worse for larger targets), no one method was better for all tasks.<br><br>CONCLUSION: Our results show that, when gain and smoothing differences are accounted for, current intention estimation methods yield nearly equivalent decoders and that simple models of user intent, such as a position error vector (target position minus cursor position), perform comparably to more elaborate models. Our results also highlight that simple differences in gain and smoothing properties have a large effect on online performance and can confound decoder comparisons.},
}
@article {pmid29989591,
year = {2018},
author = {Brantley, JA and Luu, TP and Nakagome, S and Zhu, F and Contreras-Vidal, JL},
title = {Full body mobile brain-body imaging data during unconstrained locomotion on stairs, ramps, and level ground.},
journal = {Scientific data},
volume = {5},
number = {},
pages = {180133},
pmid = {29989591},
issn = {2052-4463},
support = {F99 NS105210/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/diagnostic imaging/*physiology ; *Electroencephalography ; *Electromyography ; Gait ; Humans ; *Locomotion ; Muscle, Skeletal/physiology ; Neuroimaging ; Walking ; },
abstract = {Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain's involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.},
}
@article {pmid29988806,
year = {2018},
author = {Lunghi, E and Ficetola, GF and Mulargia, M and Cogoni, R and Veith, M and Corti, C and Manenti, R},
title = {Batracobdella leeches, environmental features and Hydromantes salamanders.},
journal = {International journal for parasitology. Parasites and wildlife},
volume = {7},
number = {1},
pages = {48-53},
pmid = {29988806},
issn = {2213-2244},
abstract = {Leeches can parasitize many vertebrate taxa. In amphibians, leech parasitism often has potential detrimental effects including population decline. Most of studies on the host-parasite interactions involving leeches and amphibians focus on freshwater environments, while they are very scarce for terrestrial amphibians. In this work, we studied the relationship between the leech Batracobdella algira and the European terrestrial salamanders of the genus Hydromantes, identifying environmental features related to the presence of the leeches and their possible effects on the hosts. We performed observation throughout Sardinia (Italy), covering the distribution area of all Hydromantes species endemic to this island. From September 2015 to May 2017, we conducted >150 surveys in 26 underground environments, collecting data on 2629 salamanders and 131 leeches. Water hardness was the only environmental feature correlated with the presence of B. algira, linking this leech to active karstic systems. Leeches were more frequently parasitizing salamanders with large body size. Body Condition Index was not significantly different between parasitized and non-parasitized salamanders. Our study shows the importance of abiotic environmental features for host-parasite interactions, and poses new questions on complex interspecific interactions between this ectoparasite and amphibians.},
}
@article {pmid29986504,
year = {2018},
author = {Nezamfar, H and Mohseni Salehi, SS and Higger, M and Erdogmus, D},
title = {Code-VEP vs. Eye Tracking: A Comparison Study.},
journal = {Brain sciences},
volume = {8},
number = {7},
pages = {},
pmid = {29986504},
issn = {2076-3425},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Even with state-of-the-art techniques there are individuals whose paralysis prevents them from communicating with others. Brain[-]Computer-Interfaces (BCI) aim to utilize brain waves to construct a voice for those whose needs remain unmet. In this paper we compare the efficacy of a BCI input signal, code-VEP via Electroencephalography, against eye gaze tracking, among the most popular modalities used. These results, on healthy individuals without paralysis, suggest that while eye tracking works well for some, it does not work well or at all for others; the latter group includes individuals with corrected vision or those who squint their eyes unintentionally while focusing on a task. It is also evident that the performance of the interface is more sensitive to head/body movements when eye tracking is used as the input modality, compared to using c-VEP. Sensitivity to head/body movement could be better in eye tracking systems which are tracking the head or mounted on the face and are designed specifically as assistive devices. The sample interface developed for this assessment has the same reaction time when driven with c-VEP or with eye tracking; approximately 0.5[-]1 second is needed to make a selection among the four options simultaneously presented. Factors, such as system reaction time and robustness play a crucial role in participant preferences.},
}
@article {pmid29985155,
year = {2018},
author = {Shah, SA and Tan, H and Tinkhauser, G and Brown, P},
title = {Towards Real-Time, Continuous Decoding of Gripping Force From Deep Brain Local Field Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1460-1468},
pmid = {29985155},
issn = {1558-0210},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Aged ; Algorithms ; Basal Ganglia ; *Brain-Computer Interfaces ; Computer Systems ; *Deep Brain Stimulation ; Electrodes, Implanted ; Female ; Hand Strength/*physiology ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Parkinsonian Disorders/physiopathology/rehabilitation ; Subthalamic Nucleus ; },
abstract = {Lack of force information and longevity issues are impediments to the successful translation of brain-computer interface systems for prosthetic control from experimental settings to widespread clinical application. The ability to decode force using deep brain stimulation electrodes in the subthalamic nucleus (STN) of the basal ganglia provides an opportunity to address these limitations. This paper explores the use of various classes of algorithms (Wiener filter, Wiener-Cascade model, Kalman filter, and dynamic neural networks) and recommends the use of a Wiener-Cascade model for decoding force from STN. This recommendation is influenced by a combination of accuracy and practical considerations to enable real-time, continuous operation. This paper demonstrates an ability to decode a continuous signal (force) from the STN in real time, allowing the possibility of decoding more than two states from the brain at low latency.},
}
@article {pmid29985154,
year = {2018},
author = {Lee, MH and Williamson, J and Won, DO and Fazli, S and Lee, SW},
title = {A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1443-1459},
doi = {10.1109/TNSRE.2018.2839116},
pmid = {29985154},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Calibration ; *Communication Devices for People with Disabilities ; Decision Making/physiology ; Electroencephalography/*methods ; Electrooculography/*methods ; Event-Related Potentials, P300 ; Eye Movements ; *Feedback, Sensory ; Female ; Healthy Volunteers ; Humans ; Male ; Reproducibility of Results ; Young Adult ; },
abstract = {In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.},
}
@article {pmid29985153,
year = {2018},
author = {Pan, L and Crouch, DL and Huang, H},
title = {Myoelectric Control Based on a Generic Musculoskeletal Model: Toward a Multi-User Neural-Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1435-1442},
doi = {10.1109/TNSRE.2018.2838448},
pmid = {29985153},
issn = {1558-0210},
mesh = {Adult ; Amputees ; Artificial Limbs ; Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces ; Computer Simulation ; *Electromyography ; Female ; Healthy Volunteers ; Humans ; Male ; Models, Anatomic ; *Musculoskeletal System ; Posture/physiology ; Wrist/physiology ; Young Adult ; },
abstract = {This paper aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion betweenmuscle contractionmetacarpophalangeal (MCP) and wrist flexion/extension. The NMI requires a minimum calibration procedure that only involves capturing maximal voluntary muscle contraction for themonitoredmuscles for individual users. At the center of the NMI is a user-generic musculoskeletal model based on the experimental data collected from six able-bodied (AB) subjects and nine different upper limb postures. The generic model was evaluated on-line on both AB subjects and a transradial amputee. The subjectswere instructed to performa virtual hand/wrist posture matching task with different upper limb postures. The on-line performanceof the genericmodelwas also compared with that of the musculoskeletal model customized to each individual user (called "specific model"). All subjects accomplished the assigned virtual tasks while using the user-generic NMI, although the AB subjects produced better performance than the amputee subject. Interestingly, compared with the specific model, the generic model produced comparable completion time, a reduced number of overshoots, and improved path efficiency in the virtual hand/wrist posture matching task. The results suggested that it is possible to design an EMG-driven NMI based on a musculoskeletalmodelthat could fit multiple users, including upper limb amputees, for predicting coordinated MCP and wrist motion. The present new method might address the challenges of existing advanced EMG-based NMI that require frequent and lengthy customization and calibration. Our future research will focus on evaluating the developed NMI for powered prosthetic arms.},
}
@article {pmid29985144,
year = {2018},
author = {Song, Y and Sepulveda, F},
title = {A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1353-1362},
doi = {10.1109/TNSRE.2018.2847316},
pmid = {29985144},
issn = {1558-0210},
mesh = {Acoustic Stimulation ; Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Cognition ; Data Interpretation, Statistical ; Electroencephalography/methods/*statistics &amp; numerical data ; Electromyography/methods/*statistics &amp; numerical data ; Humans ; Principal Component Analysis ; Psychomotor Performance ; Reproducibility of Results ; },
abstract = {Electromyography artifacts are a well-known problem in electroencephalography studies [brain-computer interfaces (BCIs), brain mapping, and clinical areas]. Blind source separation (BSS) techniques are commonly used to handle artifacts. However, these may remove not only the EMG artifacts but also some useful electroencephalography (EEG) sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). The EMG-CCh is selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artifacts played a significant role in class separation. To ensure that the promising results are not due to the weak EMG removal, reliability tests were done In our data set, the comparison results between BSS artifact removal applied in two ways, to all channels and only to EMG-CCh showed that ICA, PCA, and BSS-CCA can yield significantly better () class separation with the proposed method (79% of the cases for ICA, 53% for PCA, and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. There are no existing methods for removing EMG artifacts based on the correlation between the EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artifact handling methods. For these reasons, we believe that this method can be useful for other EEG studies.},
}
@article {pmid29985141,
year = {2018},
author = {Zhang, Y and Yin, E and Li, F and Zhang, Y and Tanaka, T and Zhao, Q and Cui, Y and Xu, P and Yao, D and Guo, D},
title = {Two-Stage Frequency Recognition Method Based on Correlated Component Analysis for SSVEP-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {7},
pages = {1314-1323},
doi = {10.1109/TNSRE.2018.2848222},
pmid = {29985141},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*statistics &amp; numerical data ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation ; Psychomotor Performance ; Young Adult ; },
abstract = {A canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA has achieved the best performance. However, the CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for the EEG analysis. In this paper, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark data set of 35 subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This paper demonstrates that CORRCA-based methods have a great potential for implementing high-performance SSVEP-based BCI systems.},
}
@article {pmid29977518,
year = {2018},
author = {McIntyre, RS and Young, AH and Haddad, PM},
title = {Rethinking the spectrum of mood disorders: implications for diagnosis and management - Proceedings of a symposium presented at the 30th Annual European College of Neuropsychopharmacology Congress, 4 September 2017, Paris, France.},
journal = {Therapeutic advances in psychopharmacology},
volume = {8},
number = {1 Suppl},
pages = {1-16},
pmid = {29977518},
issn = {2045-1253},
support = {//Wellcome Trust/United Kingdom ; },
abstract = {The simultaneous occurrence of manic and depressive features has been recognized since classical times, but the term 'mixed state' was first used by Kraepelin at the end of the 19th century. From the 1980s, until the advent of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), psychiatric disorders were classified using a categorical approach. However, it was recognized that such an approach was too rigid to encompass the range of symptomatology encountered in clinical practice. Therefore, a dimensional approach was adopted in DSM-5, in which affective states are considered to be distributed across a continuum ranging from pure mania to pure depression. In addition, the copresence of symptoms of the opposite pole are captured using a 'with mixed features' specifier, applied when three or more nonoverlapping subthreshold symptoms of the opposite pole are present. Mixed features are common in patients with mood episodes, complicating the course of illness, reducing treatment response and worsening outcomes. However, research in this area is scarce and treatment options are limited. Current evidence indicates that antidepressants should be avoided for the treatment of bipolar mixed states. Evidence for bipolar mixed states supports the use of several second-generation antipsychotics, valproate and electroconvulsive therapy. One randomized controlled trial has demonstrated the efficacy of lurasidone, compared with placebo, in patients with major depressive disorder with mixed features, and there is limited evidence supporting the use of ziprasidone in such patients. Further research is required to determine whether other antipsychotic agents, or additional therapeutic approaches, might also be effective in this setting.},
}
@article {pmid29977189,
year = {2018},
author = {Martin, S and Iturrate, I and Millán, JDR and Knight, RT and Pasley, BN},
title = {Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {422},
pmid = {29977189},
issn = {1662-4548},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; },
abstract = {Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke, and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units. We focused on intracranial recordings, as this technique allows monitoring brain activity with high spatial, temporal, and spectral resolution, and therefore is a good candidate to investigate inner speech. Despite intense efforts, investigating how the human cortex encodes inner speech remains an elusive challenge, due to the lack of behavioral and observable measures. We emphasize various challenges commonly encountered when investigating inner speech decoding, and propose potential solutions in order to get closer to a natural speech assistive device.},
}
@article {pmid29975604,
year = {2018},
author = {Del Vecchio, A and Úbeda, A and Sartori, M and Azorín, JM and Felici, F and Farina, D},
title = {Central nervous system modulates the neuromechanical delay in a broad range for the control of muscle force.},
journal = {Journal of applied physiology (Bethesda, Md. : 1985)},
volume = {125},
number = {5},
pages = {1404-1410},
doi = {10.1152/japplphysiol.00135.2018},
pmid = {29975604},
issn = {1522-1601},
mesh = {Adult ; Central Nervous System/*physiology ; Electromyography ; Humans ; Male ; Motor Neurons/*physiology ; *Muscle Contraction ; },
abstract = {Force is generated by muscle units according to the neural activation sent by motor neurons. The motor unit is therefore the interface between the neural coding of movement and the musculotendinous system. Here we propose a method to accurately measure the latency between an estimate of the neural drive to muscle and force. Furthermore, we systematically investigate this latency, which we refer to as the neuromechanical delay (NMD), as a function of the rate of force generation. In two experimental sessions, eight men performed isometric finger abduction and ankle dorsiflexion sinusoidal contractions at three frequencies and peak-to-peak amplitudes {0.5, 1, and 1.5 Hz; 1, 5, and 10 of maximal force [%maximal voluntary contraction (MVC)]},
with a mean force of 10% MVC. The discharge timings of motor units of the first dorsal interosseous (FDI) and tibialis anterior (TA) muscle were identified by high-density surface EMG decomposition. The neural drive was estimated as the cumulative discharge timings of the identified motor units. The neural drive predicted 80 ± 0.4% of the force fluctuations and consistently anticipated force by 194.6 ± 55 ms (average across conditions and muscles). The NMD decreased nonlinearly with the rate of force generation (R[2] = 0.82 ± 0.07; exponential fitting) with a broad range of values (from 70 to 385 ms) and was 66 ± 0.01 ms shorter for the FDI than TA (P < 0.001). In conclusion, we provided a method to estimate the delay between the neural control and force generation, and we showed that this delay is muscle-dependent and is modulated within a wide range by the central nervous system. NEW &amp; NOTEWORTHY The motor unit is a neuromechanical interface that converts neural signals into mechanical force with a delay determined by neural and peripheral properties. Classically, this delay has been assessed from the muscle resting level or during electrically elicited contractions. In the present study, we introduce the neuromechanical delay as the latency between the neural drive to muscle and force during variable-force contractions, and we show that it is broadly modulated by the central nervous system.},
}
@article {pmid29974560,
year = {2018},
author = {Zubarev, I and Parkkonen, L},
title = {Evidence for a general performance-monitoring system in the human brain.},
journal = {Human brain mapping},
volume = {39},
number = {11},
pages = {4322-4333},
pmid = {29974560},
issn = {1097-0193},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Executive Function/physiology ; Feedback, Psychological/*physiology ; Female ; Humans ; Learning/*physiology ; Magnetic Resonance Imaging ; Male ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action-monitoring system has not been tested directly. Here, we use concurrent EEG-MEG measurements and a probabilistic learning task to demonstrate that event-related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high- versus low-surprise negative feedback, and (d) erroneous versus correct brain-computer-interface output. We further show that these patterns originate from highly-overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.},
}
@article {pmid29973875,
year = {2018},
author = {Kamran, MA and Mannann, MMN and Jeong, MY},
title = {Differential Path-Length Factor's Effect on the Characterization of Brain's Hemodynamic Response Function: A Functional Near-Infrared Study.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {37},
pmid = {29973875},
issn = {1662-5196},
abstract = {Functional near-infrared spectroscopy (fNIRS) has evolved as a neuro-imaging modality over the course of the past two decades. The removal of superfluous information accompanying the optical signal, however, remains a challenge. A comprehensive analysis of each step is necessary to ensure the extraction of actual information from measured fNIRS waveforms. A slight change in shape could alter the features required for fNIRS-BCI applications. In the present study, the effect of the differential path-length factor (DPF) values on the characteristics of the hemodynamic response function (HRF) was investigated. Results were compiled for both simulated data sets and healthy human subjects over a range of DPF values from three to eight. Different sets of activation durations and stimuli were used to generate the simulated signals for further analysis. These signals were split into optical densities under a constrained environment utilizing known values of DPF. Later, different values of DPF were used to analyze the variations of actual HRF. The results, as summarized into four categories, suggest that the DPF can change the main and post-stimuli responses in addition to other interferences. Six healthy subjects participated in this study. Their observed optical brain time-series were fed into an iterative optimization problem in order to estimate the best possible fit of HRF and physiological noises present in the measured signals with free parameters. A series of solutions was derived for different values of DPF in order to analyze the variations of HRF. It was observed that DPF change is responsible for HRF creep from actual values as well as changes in HRF characteristics.},
}
@article {pmid29973645,
year = {2018},
author = {Halme, HL and Parkkonen, L},
title = {Across-subject offline decoding of motor imagery from MEG and EEG.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {10087},
pmid = {29973645},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Humans ; Imagery, Psychotherapy/methods ; Magnetoencephalography/*methods ; Male ; Movement/physiology ; Neurofeedback/physiology ; },
abstract = {Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.},
}
@article {pmid29970846,
year = {2018},
author = {Uktveris, T and Jusas, V},
title = {Development of a Modular Board for EEG Signal Acquisition.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {7},
pages = {},
pmid = {29970846},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Electric Power Supplies ; Electroencephalography/*instrumentation ; *Equipment Design ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a &amp;minus;97 dB common mode rejection ratio in 0[-]70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.},
}
@article {pmid29965965,
year = {2018},
author = {McFarland, DJ and Wolpaw, JR},
title = {Brain-computer interface use is a skill that user and system acquire together.},
journal = {PLoS biology},
volume = {16},
number = {7},
pages = {e2006719},
pmid = {29965965},
issn = {1545-7885},
mesh = {*Brain-Computer Interfaces ; Humans ; Machine Learning ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a computer-based system that acquires, analyzes, and translates brain signals into output commands in real time. Perdikis and colleagues demonstrate superior performance in a Cybathlon BCI race using a system based on "three pillars": machine learning, user training, and application. These results highlight the fact that BCI use is a learned skill and not simply a matter of "mind reading."},
}
@article {pmid29958722,
year = {2018},
author = {Al-Qaysi, ZT and Zaidan, BB and Zaidan, AA and Suzani, MS},
title = {A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations.},
journal = {Computer methods and programs in biomedicine},
volume = {164},
number = {},
pages = {221-237},
doi = {10.1016/j.cmpb.2018.06.012},
pmid = {29958722},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Computer Simulation ; *Persons with Disabilities/rehabilitation ; Electric Power Supplies ; *Electroencephalography ; Equipment Design ; Event-Related Potentials, P300 ; Humans ; Motion ; Robotics ; Safety ; User-Computer Interface ; *Wheelchairs ; Wireless Technology ; },
abstract = {CONTEXT: Intelligent wheelchair technology has recently been utilised to address several mobility problems. Techniques based on brain-computer interface (BCI) are currently used to develop electric wheelchairs. Using human brain control in wheelchairs for people with disability has elicited widespread attention due to its flexibility.<br><br>OBJECTIVE: This study aims to determine the background of recent studies on wheelchair control based on BCI for disability and map the literature survey into a coherent taxonomy. The study intends to identify the most important aspects in this emerging field as an impetus for using BCI for disability in electric-powered wheelchair (EPW) control, which remains a challenge. The study also attempts to provide recommendations for solving other existing limitations and challenges.<br><br>METHODS: We systematically searched all articles about EPW control based on BCI for disability in three popular databases: ScienceDirect, IEEE and Web of Science. These databases contain numerous articles that considerably influenced this field and cover most of the relevant theoretical and technical issues.<br><br>RESULTS: We selected 100 articles on the basis of our inclusion and exclusion criteria. A large set of articles (55) discussed on developing real-time wheelchair control systems based on BCI for disability signals. Another set of articles (25) focused on analysing BCI for disability signals for wheelchair control. The third set of articles (14) considered the simulation of wheelchair control based on BCI for disability signals. Four articles designed a framework for wheelchair control based on BCI for disability signals. Finally, one article reviewed concerns regarding wheelchair control based on BCI for disability signals.<br><br>DISCUSSION: Since 2007, researchers have pursued the possibility of using BCI for disability in EPW control through different approaches. Regardless of type, articles have focused on addressing limitations that impede the full efficiency of BCI for disability and recommended solutions for these limitations.<br><br>CONCLUSIONS: Studies on wheelchair control based on BCI for disability considerably influence society due to the large number of people with disability. Therefore, we aim to provide researchers and developers with a clear understanding of this platform and highlight the challenges and gaps in the current and future studies.},
}
@article {pmid29958301,
year = {2018},
author = {Dong, E and Zhu, G and Chen, C and Tong, J and Jiao, Y and Du, S},
title = {Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification.},
journal = {PloS one},
volume = {13},
number = {6},
pages = {e0198786},
pmid = {29958301},
issn = {1932-6203},
mesh = {*Electroencephalography ; Female ; Humans ; Male ; *Models, Theoretical ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {This paper addresses a chaos kernel function for the relevance vector machine (RVM) in EEG signal classification, which is an important component of Brain-Computer Interface (BCI). The novel kernel function has evolved from a chaotic system, which is inspired by the fact that human brain signals depict some chaotic characteristics and behaviors. By introducing the chaotic dynamics to the kernel function, the RVM will be enabled for higher classification capacity. The proposed method is validated within the framework of one versus one common spatial pattern (OVO-CSP) classifier to classify motor imagination (MI) of four movements in a public accessible dataset. To illustrate the performance of the proposed kernel function, Gaussian and Polynomial kernel functions are considered for comparison. Experimental results show that the proposed kernel function achieved higher accuracy than Gaussian and Polynomial kernel functions, which shows that the chaotic behavior consideration is helpful in the EEG signal classification.},
}
@article {pmid29957379,
year = {2018},
author = {Noble, BT and Brennan, FH and Popovich, PG},
title = {The spleen as a neuroimmune interface after spinal cord injury.},
journal = {Journal of neuroimmunology},
volume = {321},
number = {},
pages = {1-11},
doi = {10.1016/j.jneuroim.2018.05.007},
pmid = {29957379},
issn = {1872-8421},
support = {R01 NS099532/NS/NINDS NIH HHS/United States ; R01 NS083942/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Autoimmunity/*physiology ; Humans ; Neuroimmunomodulation/*physiology ; Spinal Cord Injuries/*immunology/metabolism/therapy ; Spleen/*immunology/innervation/metabolism ; },
abstract = {Traumatic spinal cord injury (SCI) causes widespread damage to neurons, glia and endothelia located throughout the spinal parenchyma. In response to the injury, resident and blood-derived leukocytes orchestrate an intraspinal inflammatory response that propagates secondary neuropathology and also promotes tissue repair. SCI also negatively affects autonomic control over peripheral immune organs, notably the spleen. The spleen is the largest secondary lymphoid organ in mammals, with major roles in blood filtration and host defense. Splenic function is carefully regulated by neuroendocrine mechanisms that ensure that the immune responses to infection or injury are proportionate to the initiating stimulus, and can be terminated when the stimulus is cleared. After SCI, control over the viscera, including endocrine and lymphoid tissues is lost due to damage to spinal autonomic (sympathetic) circuitry. This review begins by examining the normal structure and function of the spleen including patterns of innervation and the role played by the nervous system in regulating spleen function. We then describe how after SCI, loss of proper neural control over splenic function leads to systems-wide neuropathology, immune suppression and autoimmunity. We conclude by discussing opportunities for targeting the spleen to restore immune homeostasis, reduce morbidity and mortality, and improve functional recovery after SCI.},
}
@article {pmid29956589,
year = {2018},
author = {Williges, B and Jürgens, T and Hu, H and Dietz, M},
title = {Coherent Coding of Enhanced Interaural Cues Improves Sound Localization in Noise With Bilateral Cochlear Implants.},
journal = {Trends in hearing},
volume = {22},
number = {},
pages = {2331216518781746},
pmid = {29956589},
issn = {2331-2165},
mesh = {Acoustic Stimulation ; Adolescent ; Aged ; *Algorithms ; *Cochlear Implants ; *Cues ; Hearing Loss/etiology/physiopathology/rehabilitation ; Humans ; Middle Aged ; *Noise ; Proof of Concept Study ; Signal Processing, Computer-Assisted ; *Sound Localization ; *Speech Intelligibility ; Speech Perception ; Young Adult ; },
abstract = {Bilateral cochlear implant (BCI) users only have very limited spatial hearing abilities. Speech coding strategies transmit interaural level differences (ILDs) but in a distorted manner. Interaural time difference (ITD) information transmission is even more limited. With these cues, most BCI users can coarsely localize a single source in quiet, but performance quickly declines in the presence of other sound. This proof-of-concept study presents a novel signal processing algorithm specific for BCIs, with the aim to improve sound localization in noise. The core part of the BCI algorithm duplicates a monophonic electrode pulse pattern and applies quasistationary natural or artificial ITDs or ILDs based on the estimated direction of the dominant source. Three experiments were conducted to evaluate different algorithm variants: Experiment 1 tested if ITD transmission alone enables BCI subjects to lateralize speech. Results showed that six out of nine BCI subjects were able to lateralize intelligible speech in quiet solely based on ITDs. Experiments 2 and 3 assessed azimuthal angle discrimination in noise with natural or modified ILDs and ITDs. Angle discrimination for frontal locations was possible with all variants, including the pure ITD case, but for lateral reference angles, it was only possible with a linearized ILD mapping. Speech intelligibility in noise, limitations, and challenges of this interaural cue transmission approach are discussed alongside suggestions for modifying and further improving the BCI algorithm.},
}
@article {pmid29952752,
year = {2018},
author = {Guarnieri, R and Marino, M and Barban, F and Ganzetti, M and Mantini, D},
title = {Online EEG artifact removal for BCI applications by adaptive spatial filtering.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056009},
doi = {10.1088/1741-2552/aacfdf},
pmid = {29952752},
issn = {1741-2552},
mesh = {Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Humans ; Linear Models ; Online Systems ; Principal Component Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: The performance of brain-computer interfaces (BCIs) based on electroencephalography (EEG) data strongly depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for BCI applications, which combines blind source separation (BSS) and regression (REG) analysis.<br><br>APPROACH: The BSS-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using BSS. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on a linear regression technique.<br><br>MAIN RESULTS: Our results showed that the BSS-REG method is capable of attenuating different kinds of artifacts, including ocular and muscular, while preserving true neural activity. Thanks to its low computational requirements, BSS-REG can be applied to low-density as well as high-density EEG data.<br><br>SIGNIFICANCE: We argue that BSS-REG may enable the development of novel BCI applications requiring high-density recordings, such as source-based neurofeedback and closed-loop neuromodulation.},
}
@article {pmid29952751,
year = {2018},
author = {Norton, JJS and Mullins, J and Alitz, BE and Bretl, T},
title = {The performance of 9-11-year-old children using an SSVEP-based BCI for target selection.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056012},
pmid = {29952751},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aging/psychology ; Brain-Computer Interfaces/*psychology ; Calibration ; Child ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: In this paper, we report the performance of 9-11-year-old children using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) and provide control data collected from adults for comparison. Children in our study achieved a much higher performance (79% accuracy; average age 9.64 years old) than the only previous investigation of children using an SSVEP-based BCI (∼50% accuracy; average age 9.86 years old).<br><br>APPROACH: Experiments were conducted in two phases, a short calibration phase and a longer experimental phase. An offline analysis of the data collected during the calibration phase was used to set two parameters for a classifier and to screen participants who did not achieve a minimum accuracy of 85%.<br><br>MAIN RESULTS: Eleven of the 14 children and all 11 of the adults who completed the calibration phase met the minimum accuracy requirement. During the experimental phase, children selected targets with a similar accuracy (79% for children versus 78% for adults), latency (2.1 s for children versus 1.9 s for adults), and bitrate (0.50 bits s[-1] for children and 0.56 bits s[-1] for adults) as adults.<br><br>SIGNIFICANCE: This study shows that children can use an SSVEP-based BCI with higher performance than previously believed and is the first to report the performance of children using an SSVEP-based BCI in terms of latency and bitrate. The results of this study imply that children with severe motor disabilities (such as locked-in syndrome) may use an SSVEP-based BCI to restore/replace the ability to communicate.},
}
@article {pmid29952144,
year = {2018},
author = {Park, SW and Kim, J and Kang, M and Lee, W and Park, BS and Kim, H and Choi, SY and Yang, S and Ahn, JH and Yang, S},
title = {Epidural Electrotherapy for Epilepsy.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {14},
number = {30},
pages = {e1801732},
doi = {10.1002/smll.201801732},
pmid = {29952144},
issn = {1613-6829},
mesh = {Animals ; *Electric Stimulation Therapy ; Electrodes ; Epidural Space ; Epilepsy/*therapy ; Graphite/chemistry ; Mice, Inbred C57BL ; Neurons/pathology ; },
abstract = {Penetrating electronics have been used for treating epilepsy, yet their therapeutic effects are debated largely due to the lack of a large-scale, real-time, and safe recording/stimulation. Here, the proposed technology integrates ultrathin epidural electronics into an electrocorticography array, therein simultaneously sampling brain signals in a large area for diagnostic purposes and delivering electrical pulses for treatment. The system is empirically tested to record the ictal-like activities of the thalamocortical network in vitro and in vivo using the epidural electronics. Also, it is newly demonstrated that the electronics selectively diminish epileptiform activities, but not normal signal transduction, in live animals. It is proposed that this technology heralds a new generation of diagnostic and therapeutic brain-machine interfaces. Such an electronic system can be applicable for several brain diseases such as tinnitus, Parkinson's disease, Huntington's disease, depression, and schizophrenia.},
}
@article {pmid29951089,
year = {2018},
author = {Damaševičius, R and Maskeliūnas, R and Kazanavičius, E and Woźniak, M},
title = {Combining Cryptography with EEG Biometrics.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {1867548},
pmid = {29951089},
issn = {1687-5273},
mesh = {Area Under Curve ; Biometric Identification/*methods ; Brain/physiology ; Brain-Computer Interfaces ; *Computer Security ; Electroencephalography/*methods ; Humans ; ROC Curve ; Rest ; },
abstract = {Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.},
}
@article {pmid29950438,
year = {2018},
author = {Birbaumer, N and Hochberg, LR},
title = {A useful communication in brain-computer interfaces.},
journal = {Neurology},
volume = {91},
number = {3},
pages = {109-110},
doi = {10.1212/WNL.0000000000005804},
pmid = {29950438},
issn = {1526-632X},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Communication ; Electroencephalography ; Humans ; },
}
@article {pmid29950436,
year = {2018},
author = {Wolpaw, JR and Bedlack, RS and Reda, DJ and Ringer, RJ and Banks, PG and Vaughan, TM and Heckman, SM and McCane, LM and Carmack, CS and Winden, S and McFarland, DJ and Sellers, EW and Shi, H and Paine, T and Higgins, DS and Lo, AC and Patwa, HS and Hill, KJ and Huang, GD and Ruff, RL},
title = {Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis.},
journal = {Neurology},
volume = {91},
number = {3},
pages = {e258-e267},
pmid = {29950436},
issn = {1526-632X},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/diagnosis/physiopathology/*therapy ; Brain-Computer Interfaces/*standards/trends ; Electroencephalography/standards/trends ; Home Care Services/*standards/trends ; Humans ; Male ; Middle Aged ; Self Care/*standards/trends ; Therapy, Computer-Assisted/*standards/trends ; United States/epidemiology ; United States Department of Veterans Affairs/*standards/trends ; },
abstract = {OBJECTIVE: To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.<br><br>METHODS: Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28-79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.<br><br>RESULTS: Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.<br><br>CONCLUSION: The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.},
}
@article {pmid29949504,
year = {2019},
author = {Harnarinesingh, RES and Syan, CS},
title = {Investigation of the mirrored-word reading paradigm for BCI implementation.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {64},
number = {3},
pages = {325-337},
doi = {10.1515/bmt-2017-0223},
pmid = {29949504},
issn = {1862-278X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Humans ; Reading ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {Brain-computer interface (BCI) applications such as keyboard control and vehicular navigation present significant assistive merit for disabled individuals. However, there are limitations associated with BCI paradigms which restrict a wider adoption of BCI technology. For example, rapid serial visual presentation (RSVP) paradigms can induce seizures in photosensitive epileptic subjects. This paper evaluates the novel mirrored-word reading paradigm (MWRP) for BCI implementation using an offline experimental study. The offline study obtained an average single-trial classification accuracy of 74.10%. The results also demonstrate that the use of multiple trials for classification can increase the accuracy as is common with BCIs. The developed MWRP-based BCI also utilized a low presentation frequency which averts the possibility of paradigm induced photosensitivity. However, there are multiple avenues for future work. The MWRP can be implemented in the online format for real-time device control. For example, a vehicular application platform can be used where the word orientation represents directions for travel. The MWRP can also be investigated across a wider range of stimulus presentation parameters such as timing, color and stimulus size. Such studies can be used to suggest further improvements to the paradigm which can enhance its applicability for online device control.},
}
@article {pmid29947593,
year = {2018},
author = {Quick, KM and Mischel, JL and Loughlin, PJ and Batista, AP},
title = {The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates.},
journal = {Journal of neurophysiology},
volume = {120},
number = {5},
pages = {2164-2181},
pmid = {29947593},
issn = {1522-1598},
support = {R01 HD090125/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; *Feedback, Physiological ; Haplorhini ; *Models, Neurological ; *Motor Activity ; Neurons, Afferent/*physiology ; Neurons, Efferent/*physiology ; Postural Balance ; *Psychomotor Performance ; },
abstract = {Everyday behaviors require that we interact with the environment, using sensory information in an ongoing manner to guide our actions. Yet, by design, many of the tasks used in primate neurophysiology laboratories can be performed with limited sensory guidance. As a consequence, our knowledge about the neural mechanisms of motor control is largely limited to the feedforward aspects of the motor command. To study the feedback aspects of volitional motor control, we adapted the critical stability task (CST) from the human performance literature (Jex H, McDonnell J, Phatak A. IEEE Trans Hum Factors Electron 7: 138-145, 1966). In the CST, our monkey subjects interact with an inherently unstable (i.e., divergent) virtual system and must generate sensory-guided actions to stabilize it about an equilibrium point. The difficulty of the CST is determined by a single parameter, which allows us to quantitatively establish the limits of performance in the task for different sensory feedback conditions. Two monkeys learned to perform the CST with visual or vibrotactile feedback. Performance was better under visual feedback, as expected, but both monkeys were able to utilize vibrotactile feedback alone to successfully perform the CST. We also observed changes in behavioral strategy as the task became more challenging. The CST will have value for basic science investigations of the neural basis of sensory-motor integration during ongoing actions, and it may also provide value for the design and testing of bidirectional brain computer interface systems. NEW &amp; NOTEWORTHY Currently, most behavioral tasks used in motor neurophysiology studies require primates to make short-duration, stereotyped movements that do not necessitate sensory feedback. To improve our understanding of sensorimotor integration, and to engineer meaningful artificial sensory feedback systems for brain-computer interfaces, it is crucial to have a task that requires sensory feedback for good control. The critical stability task demands that sensory information be used to guide long-duration movements.},
}
@article {pmid29939073,
year = {2018},
author = {Magliulo, G and Iannella, G and De Vincentiis, M and Turchetta, R and Portanova, G and Angeletti, D and Mancini, P},
title = {Transcutaneous bone conductive implants in patients with conductive/mixed hearing loss: audiological outcomes in noise condition.},
journal = {Acta oto-laryngologica},
volume = {138},
number = {9},
pages = {822-829},
doi = {10.1080/00016489.2018.1478128},
pmid = {29939073},
issn = {1651-2251},
mesh = {Adult ; Aged ; Bone Conduction ; Female ; *Hearing Aids ; Hearing Loss, Conductive/*rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/*rehabilitation ; Hearing Tests ; Humans ; Male ; Middle Aged ; Noise ; *Prostheses and Implants ; Prosthesis Design ; Retrospective Studies ; Speech Perception ; },
abstract = {BACKGROUND: Recently, the use of transcutaneous bone conduction implants (BCIs) has been increased. However, scarce data about BCI hearing recovery in noise conditions have been reported.<br><br>OBJECTIVES: To investigate the audiological benefits obtained with transcutaneous BCI-Sophono Alpha System in noise conditions. To evaluate post-implantation clinical outcomes and patient satisfaction levels.<br><br>MATERIALS AND METHODS: Fourteen patients suffering from conductive or mixed hearing loss implanted with the Sophono Alpha System were evaluated. Patients underwent physical examination, free-field pure-tone and speech audiometry both in unaided and aided conditions. The matrix sentence test was employed with fixed noise at 65&#x2009;dB, and with a fluctuating primary signal, in three different conditions of noise presentations (S0/N0, S0/Ncontra, S0/Nipsi).<br><br>RESULTS: Hearing gain, expressed as the difference between pre-implant AC and post-implant SAS free field, was on average 26.7&#x2009;dB. The unaided speech recognition score in quiet conditions had a mean value of 64.6%, and improved after SAS implantation, achieving mean values of 98.2%. SRT50 with the matrix sentence test improved in all three conditions of noise presentation.<br><br>CONCLUSIONS: Sophono Alpha System devices represent a valid treatment option for hearing rehabilitation of patients with conductive or mixed hearing loss. The audiological results regarding hearing gain in noise conditions were good.},
}
@article {pmid29938940,
year = {2018},
author = {Zhao, L and Li, X and Bian, Y and Wang, X and Yang, G},
title = {[Study on feature modulation of electroencephalogram induced by motor imagery under multi-modal stimulation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {3},
pages = {343-349},
pmid = {29938940},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Fingers ; Hand Strength ; Humans ; Recovery of Function ; },
abstract = {Event-related desynchronization (ERD) is the basic feature of electroencephalogram (EEG), and the brain-computer interface based on motor imagery (MI-BCI) with the foundation of the analysis of ERD is of great significance in motor function recovery. The valid ERD characteristics extracted from EEG are the key to the performance of the BCI, so the study of which kind of stimulation mode can prompt subjects to generate more obvious characteristics of ERD is crucial. Four different stimulation modes are designed in this paper, and the effects of motion imagery tasks under static text stimulation, grip video stimulation, serial motion video stimulation of fingers as well as serial motion video stimulation of fingers with sound on the characteristics of ERD are analyzed. Combining the analysis of time-frequency spectrum, the power spectral density curve, ERD value and brain topographic map, it is shown that the ERD under serial motion video stimulation of fingers and serial motion video stimulation of fingers with sound modes is much stronger and has wider range of activation, and the BCI based on the analysis of ERD will have a better effect on practical application. As a result, the recognition and acceptance of the users of BCI system are improved in some extent.},
}
@article {pmid29934518,
year = {2018},
author = {Kim, DY and Han, CH and Im, CH},
title = {Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {9505},
pmid = {29934518},
issn = {2045-2322},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Case-Control Studies ; *Communication ; *Electrooculography ; *Eye Movements ; Female ; Humans ; Male ; *Rotation ; *Sound ; Young Adult ; },
abstract = {Individuals who have lost normal pathways for communication need augmentative and alternative communication (AAC) devices. In this study, we propose a new electrooculogram (EOG)-based human-computer interface (HCI) paradigm for AAC that does not require a user's voluntary eye movement for binary yes/no communication by patients in locked-in state (LIS). The proposed HCI uses a horizontal EOG elicited by involuntary auditory oculogyric reflex, in response to a rotating sound source. In the proposed HCI paradigm, a user was asked to selectively attend to one of two sound sources rotating in directions opposite to each other, based on the user's intention. The user's intentions could then be recognised by quantifying EOGs. To validate its performance, a series of experiments was conducted with ten healthy subjects, and two patients with amyotrophic lateral sclerosis (ALS). The online experimental results exhibited high-classification accuracies of 94% in both healthy subjects and ALS patients in cases where decisions were made every six seconds. The ALS patients also participated in a practical yes/no communication experiment with 26 or 30 questions with known answers. The accuracy of the experiments with questionnaires was 94%, demonstrating that our paradigm could constitute an auxiliary AAC system for some LIS patients.},
}
@article {pmid29932424,
year = {2018},
author = {Lawhern, VJ and Solon, AJ and Waytowich, NR and Gordon, SM and Hung, CP and Lance, BJ},
title = {EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056013},
doi = {10.1088/1741-2552/aace8c},
pmid = {29932424},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/*methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Middle Aged ; Movement/physiology ; *Neural Networks, Computer ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.<br><br>APPROACH: In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).<br><br>MAIN RESULTS: We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.<br><br>SIGNIFICANCE: Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.},
}
@article {pmid29928196,
year = {2018},
author = {Halder, S and Takano, K and Kansaku, K},
title = {Comparison of Four Control Methods for a Five-Choice Assistive Technology.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {228},
pmid = {29928196},
issn = {1662-5161},
abstract = {Severe motor impairments can affect the ability to communicate. The ability to see has a decisive influence on the augmentative and alternative communication (AAC) systems available to the user. To better understand the initial impressions users have of AAC systems we asked naïve healthy participants to compare two visual (a visual P300 brain-computer interface (BCI) and an eye-tracker) and two non-visual systems (an auditory and a tactile P300 BCI). Eleven healthy participants performed 20 selections in a five choice task with each system. The visual P300 BCI used face stimuli, the auditory P300 BCI used Japanese Hiragana syllables and the tactile P300 BCI used a stimulator on the small left finger, middle left finger, right thumb, middle right finger and small right finger. The eye-tracker required a dwell time of 3 s on the target for selection. We calculated accuracies and information-transfer rates (ITRs) for each control method using the selection time that yielded the highest ITR and an accuracy above 70% for each system. Accuracies of 88% were achieved with the visual P300 BCI (4.8 s selection time, 20.9 bits/min), of 70% with the auditory BCI (19.9 s, 3.3 bits/min), of 71% with the tactile BCI (18 s, 3.4 bits/min) and of 100% with the eye-tracker (5.1 s, 28.2 bits/min). Performance between eye-tracker and visual BCI correlated strongly, correlation between tactile and auditory BCI performance was lower. Our data showed no advantage for either non-visual system in terms of ITR but a lower correlation of performance which suggests that choosing the system which suits a particular user is of higher importance for non-visual systems than visual systems.},
}
@article {pmid29925890,
year = {2018},
author = {Biasiucci, A and Leeb, R and Iturrate, I and Perdikis, S and Al-Khodairy, A and Corbet, T and Schnider, A and Schmidlin, T and Zhang, H and Bassolino, M and Viceic, D and Vuadens, P and Guggisberg, AG and Millán, JDR},
title = {Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke.},
journal = {Nature communications},
volume = {9},
number = {1},
pages = {2421},
pmid = {29925890},
issn = {2041-1723},
mesh = {Arm/innervation/physiopathology ; Brain/physiopathology ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Movement ; Neural Pathways/physiopathology ; Neuronal Plasticity/physiology ; Recovery of Function ; Stereotaxic Techniques ; Stroke/diagnosis/*physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; },
abstract = {Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6-12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI-FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways.},
}
@article {pmid29924714,
year = {2018},
author = {Mangalam, M},
title = {Emergent coordination with a brain-machine interface: implications for the neural basis of motor learning.},
journal = {Journal of neurophysiology},
volume = {120},
number = {3},
pages = {889-892},
doi = {10.1152/jn.00361.2018},
pmid = {29924714},
issn = {1522-1598},
mesh = {*Brain-Computer Interfaces ; Hand Strength ; Learning ; *Motor Cortex ; Movement ; },
abstract = {How patterns of covariance in motor output and neural activity emerge over the course of learning is a topic of ongoing investigation. Vaidya et al. (Vaidya M, Balasubramanian K, Southerland J, Badreldin I, Eleryan A, Shattuck K, Gururangan S, Slutzky M, Osborne L, Fagg A, Oweiss K, Hatsopoulos NG. J Neurophysiol 119: 1291-1304, 2018) investigate the emergence of patterns of covariance in the motor output and neural activity in chronically amputated macaques learning reach-to-grasp movements with a brain-machine interface. The authors' findings have implications for uncovering general principles of how neural coordination unfolds while learning a different motor behavior.},
}
@article {pmid29923502,
year = {2018},
author = {Hill, M and Rios, E and Sudhakar, SK and Roossien, DH and Caldwell, C and Cai, D and Ahmed, OJ and Lempka, SF and Chestek, CA},
title = {Quantitative simulation of extracellular single unit recording from the surface of cortex.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {056007},
pmid = {29923502},
issn = {1741-2552},
support = {R01 MH110932/MH/NIMH NIH HHS/United States ; R03 MH111316/MH/NIMH NIH HHS/United States ; U01 NS094375/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Computer Simulation ; Electrocorticography/*methods ; Extracellular Space/*physiology ; Microelectrodes ; Models, Neurological ; Neurons/*physiology ; Patch-Clamp Techniques ; Pyramidal Cells/physiology ; Rats ; },
abstract = {OBJECTIVE: Neural recording is important for a wide variety of clinical applications. Until recently, recording from the surface of the brain, even when using micro-electrocorticography (μECoG) arrays, was not thought to enable recording from individual neurons. Recent results suggest that when the surface electrode contact size is sufficiently small, it may be possible to record single neurons from the brain's surface. In this study, we use computational techniques to investigate the ability of surface electrodes to record the activity of single neurons.<br><br>APPROACH: The computational model included the rat head, &#x3bc;ECoG electrode, two existing multi-compartmental neuron models, and a novel multi-compartmental neuron model derived from patch clamp experiments in layer 1 of the cortex.<br><br>MAIN RESULTS: Using these models, we reproduced single neuron recordings from &#x3bc;ECoG arrays, and elucidated their possible source. The model resembles the experimental data when spikes originate from layer 1 neurons that are less than 60 &#x3bc;m from the cortical surface. We further used the model to explore the design space for surface electrodes. Although this model does not include biological or thermal noise, the results indicate the electrode contact area should be 100 &#x3bc;m[2] or smaller to maintain a detectable waveform amplitude. Furthermore, the model shows the width of lateral insulation could be reduced, which may reduce scar formation, while retaining 95% of signal amplitude.<br><br>SIGNIFICANCE: Overall, the model suggests single-unit surface recording is limited to neurons in layer 1 and further improvement in electrode design is needed.},
}
@article {pmid29923303,
year = {2018},
author = {Powell, TL and Koven, CD and Johnson, DJ and Faybishenko, B and Fisher, RA and Knox, RG and McDowell, NG and Condit, R and Hubbell, SP and Wright, SJ and Chambers, JQ and Kueppers, LM},
title = {Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity.},
journal = {The New phytologist},
volume = {219},
number = {3},
pages = {932-946},
doi = {10.1111/nph.15271},
pmid = {29923303},
issn = {1469-8137},
mesh = {*Biodiversity ; *Biomass ; Colorado ; Computer Simulation ; Droughts ; *Forests ; Models, Theoretical ; Rain ; *Tropical Climate ; *Water ; },
abstract = {The fate of tropical forests under climate change is unclear as a result, in part, of the uncertainty in projected changes in precipitation and in the ability of vegetation models to capture the effects of drought-induced mortality on aboveground biomass (AGB). We evaluated the ability of a terrestrial biosphere model with demography and hydrodynamics (Ecosystem Demography, ED2-hydro) to simulate AGB and mortality of four tropical tree plant functional types (PFTs) that operate along light- and water-use axes. Model predictions were compared with observations of canopy trees at Barro Colorado Island (BCI), Panama. We then assessed the implications of eight hypothetical precipitation scenarios, including increased annual precipitation, reduced inter-annual variation, El Niño-related droughts and drier wet or dry seasons, on AGB and functional diversity of the model forest. When forced with observed meteorology, ED2-hydro predictions capture multiple BCI benchmarks. ED2-hydro predicts that AGB will be sustained under lower rainfall via shifts in the functional composition of the forest, except under the drier dry-season scenario. These results support the hypothesis that inter-annual variation in mean and seasonal precipitation promotes the coexistence of functionally diverse PFTs because of the relative differences in mortality rates. If the hydroclimate becomes chronically drier or wetter, functional evenness related to drought tolerance may decline.},
}
@article {pmid29922477,
year = {2018},
author = {Santamaria, L and James, C},
title = {Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems.},
journal = {Healthcare technology letters},
volume = {5},
number = {3},
pages = {88-93},
pmid = {29922477},
issn = {2053-3713},
abstract = {Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain-computer interface (BCI) systems.},
}
@article {pmid29915532,
year = {2018},
author = {Lee, B and Kramer, D and Armenta Salas, M and Kellis, S and Brown, D and Dobreva, T and Klaes, C and Heck, C and Liu, C and Andersen, RA},
title = {Engineering Artificial Somatosensation Through Cortical Stimulation in Humans.},
journal = {Frontiers in systems neuroscience},
volume = {12},
number = {},
pages = {24},
pmid = {29915532},
issn = {1662-5137},
support = {KL2 TR001854/TR/NCATS NIH HHS/United States ; },
abstract = {Sensory feedback is a critical aspect of motor control rehabilitation following paralysis or amputation. Current human studies have demonstrated the ability to deliver some of this sensory information via brain-machine interfaces, although further testing is needed to understand the stimulation parameters effect on sensation. Here, we report a systematic evaluation of somatosensory restoration in humans, using cortical stimulation with subdural mini-electrocorticography (mini-ECoG) grids. Nine epilepsy patients undergoing implantation of cortical electrodes for seizure localization were also implanted with a subdural 64-channel mini-ECoG grid over the hand area of the primary somatosensory cortex (S1). We mapped the somatotopic location and size of receptive fields evoked by stimulation of individual channels of the mini-ECoG grid. We determined the effects on perception by varying stimulus parameters of pulse width, current amplitude, and frequency. Finally, a target localization task was used to demonstrate the use of artificial sensation in a behavioral task. We found a replicable somatotopic representation of the hand on the mini-ECoG grid across most subjects during electrical stimulation. The stimulus-evoked sensations were usually of artificial quality, but in some cases were more natural and of a cutaneous or proprioceptive nature. Increases in pulse width, current strength and frequency generally produced similar quality sensations at the same somatotopic location, but with a perception of increased intensity. The subjects produced near perfect performance when using the evoked sensory information in target acquisition tasks. These findings indicate that electrical stimulation of somatosensory cortex through mini-ECoG grids has considerable potential for restoring useful sensation to patients with paralysis and amputation.},
}
@article {pmid29914312,
year = {2018},
author = {Shin, J and Im, CH},
title = {Performance Prediction for a Near-Infrared Spectroscopy-Brain-Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex.},
journal = {International journal of neural systems},
volume = {28},
number = {10},
pages = {1850023},
doi = {10.1142/S0129065718500235},
pmid = {29914312},
issn = {1793-6462},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; *Models, Neurological ; Nonlinear Dynamics ; Online Systems ; Photic Stimulation ; Prefrontal Cortex/*physiology ; Psychomotor Performance ; *Rest ; *Spectroscopy, Near-Infrared ; Time Factors ; Young Adult ; },
abstract = {One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.},
}
@article {pmid29912838,
year = {2018},
author = {Shapiro, S and Ramadan, J and Cassis, A},
title = {BAHA Skin Complications in the Pediatric Population: Systematic Review With Meta-analysis.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {39},
number = {7},
pages = {865-873},
doi = {10.1097/MAO.0000000000001877},
pmid = {29912838},
issn = {1537-4505},
mesh = {Adolescent ; Child ; Child, Preschool ; Cochlear Implantation/*adverse effects ; Humans ; Infant ; Infant, Newborn ; Reoperation ; Skin Diseases/epidemiology/*etiology ; },
abstract = {OBJECTIVE: Compare the incidence of skin and surgical site complications for children undergoing percutaneous and transcutaneous bone conduction implant (pBCI and tBCI) surgery via systematic review and meta-analysis of the available data.<br><br>DATA SOURCES: 1) Search of PubMed, Web of Science, and EBSCOhost databases from January 2012 to April 2017. 2) References of studies meeting initial criteria.<br><br>STUDY SELECTION: Inclusion criteria were studies that involved patients less than 18 years old undergoing tBCI or pBCI surgery with a BI300 implant and reported skin complications, implant loss, and need for revision surgery. Exclusion criterion was use of a previous generation implant.<br><br>DATA EXTRACTION: Implants used, number of patients, age, surgical technique, Holgers score, incidence of skin complication, implant loss, and reoperation. Bias assessment performed with the Newcastle-Ottawa Scale.<br><br>DATA SYNTHESIS: Twenty-two studies (14 tBCI, 8 pBCI) met criteria. Meta-analysis was performed using a random effects model. Cochran's Q score and I inconsistency were used to assess for heterogeneity. Overall estimated skin complication rate for tBCIs was 6.3% versus 30% for pBCIs (p&#x200a;=&#x200a;4 &#xd7; 10). Implant loss was 0% for tBCIs and 5.3% for pBCIs (p&#x200a;=&#x200a;0.004). Reoperation rate was 3.0% and 6.2% for tBCIs and pBCIs respectively (p&#x200a;=&#x200a;0.00002).<br><br>CONCLUSION: There is strong evidence to suggest that in pediatric patients, the incidence of skin complications, implant loss, and rate of reoperation are higher for pBCIs compared with tBCIs. This information should be part of any discussion about BCI surgery on a pediatric patient.},
}
@article {pmid29910708,
year = {2018},
author = {Annen, J and Blandiaux, S and Lejeune, N and Bahri, MA and Thibaut, A and Cho, W and Guger, C and Chatelle, C and Laureys, S},
title = {BCI Performance and Brain Metabolism Profile in Severely Brain-Injured Patients Without Response to Command at Bedside.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {370},
pmid = {29910708},
issn = {1662-4548},
abstract = {Detection and interpretation of signs of "covert command following" in patients with disorders of consciousness (DOC) remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish "covert command following." These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed "covert command following" (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without "covert command-following" but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe "covert command following" in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with DOC.},
}
@article {pmid29909026,
year = {2018},
author = {Kurz, EM and Wood, G and Kober, SE and Schippinger, W and Pichler, G and Müller-Putz, G and Bauernfeind, G},
title = {Towards using fNIRS recordings of mental arithmetic for the detection of residual cognitive activity in patients with disorders of consciousness (DOC).},
journal = {Brain and cognition},
volume = {125},
number = {},
pages = {78-87},
doi = {10.1016/j.bandc.2018.06.002},
pmid = {29909026},
issn = {1090-2147},
mesh = {Adult ; Aged ; Awareness/physiology ; Brain/diagnostic imaging/physiopathology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Consciousness/*physiology ; Consciousness Disorders/*diagnostic imaging/physiopathology ; Female ; Functional Neuroimaging/*methods ; Humans ; Male ; Mathematics ; Problem Solving/*physiology ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {BACKGROUND: Recently, fNIRS has been proposed as a promising approach for awareness detection, and a possible method to establish basic communication in patients with disorders of consciousness (DOC).<br><br>AIM: Using fNIRS, the present study evaluated the applicability of auditory presented mental-arithmetic tasks in this respect.<br><br>METHODS: We investigated the applicability of active attention to serial subtractions for awareness detection in ten healthy controls (HC, 21-32 y/o), by comparing the measured patterns to patterns induced by self-performance of the same task. Furthermore, we examined the suitability of ignoring the given task as additional control signal to implement a two-class brain-computer interface (BCI) paradigm. Finally, we compared our findings in HC with recordings in one DOC patient (78 y/o).<br><br>RESULTS AND CONCLUSION: Results of the HC revealed no differences between the self-performance and the attention condition, making the attention task suitable for awareness detection. However, there was no general difference between the ignore and attend condition, making the tasks less suitable for BCI control. Despite inconsistent correlations between the patient data and the HC group, single runs of the patient recordings revealed task-synchronous patterns - however, we cannot conclude whether the measured activation derives from instruction based task performance and thus awareness.},
}
@article {pmid29907789,
year = {2018},
author = {Yin, A and Tseng, PH and Rajangam, S and Lebedev, MA and Nicolelis, MAL},
title = {Place Cell-Like Activity in the Primary Sensorimotor and Premotor Cortex During Monkey Whole-Body Navigation.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {9184},
pmid = {29907789},
issn = {2045-2322},
mesh = {Animals ; Brain-Computer Interfaces ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/physiology ; Proprioception/*physiology ; Somatosensory Cortex/*physiology ; Spatial Navigation/*physiology ; },
abstract = {Primary motor (M1), primary somatosensory (S1) and dorsal premotor (PMd) cortical areas of rhesus monkeys previously have been associated only with sensorimotor control of limb movements. Here we show that a significant number of neurons in these areas also represent body position and orientation in space. Two rhesus monkeys (K and M) used a wheelchair controlled by a brain-machine interface (BMI) to navigate in a room. During this whole-body navigation, the discharge rates of M1, S1, and PMd neurons correlated with the two-dimensional (2D) room position and the direction of the wheelchair and the monkey head. This place cell-like activity was observed in both monkeys, with 44.6% and 33.3% of neurons encoding room position in monkeys K and M, respectively, and the overlapping populations of 41.0% and 16.0% neurons encoding head direction. These observations suggest that primary sensorimotor and premotor cortical areas in primates are likely involved in allocentrically representing body position in space during whole-body navigation, which is an unexpected finding given the classical hierarchical model of cortical processing that attributes functional specialization for spatial processing to the hippocampal formation.},
}
@article {pmid29903485,
year = {2018},
author = {Murat Yilmaz, C and Kose, C and Hatipoglu, B},
title = {A Quasi-probabilistic distribution model for EEG Signal classification by using 2-D signal representation.},
journal = {Computer methods and programs in biomedicine},
volume = {162},
number = {},
pages = {187-196},
doi = {10.1016/j.cmpb.2018.05.026},
pmid = {29903485},
issn = {1872-7565},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Computer Systems ; *Electroencephalography ; Humans ; Models, Statistical ; Normal Distribution ; Probability ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) is a method that measures and records the electrical activity of the human brain. These biomedical signals are currently being actively used in many research fields and have a wide range of potential uses in brain-computer interfaces (BCIs). The main aim of the present work is to improve the classification of EEG patterns for EEG-based BCI systems.<br><br>METHODS: In this paper, we presented a classification approach for EEG-based BCIs. For this purpose, in the training stage, 2-D representations of signals were extracted and a quasi-probabilistic learning model was built for binary classification. In the testing stage, the estimation of class membership probability was performed with an untrained sub-data set. To confirm the validity of the proposed method, we conducted experiments on the BCI Competition 2003 Data Sets (Ia and Ib). The classification performances were evaluated for accuracy, sensitivity, specificity and F-measure measurements using the five-fold leave-one-out cross-validation technique ten times.<br><br>RESULTS: The proposed method yielded an average classification accuracy of 95.54% (with sensitivity and specificity of 100.00% and 91.80% respectively) for Data Set Ia and accuracy of 72.37% (with sensitivity and specificity of 75.76% and 69.77% respectively) for Data Set Ib, which are the highest rates ever reported for both data sets.<br><br>CONCLUSIONS: It is apparent from the results that the proposed method has potential and can assist in the development of effective EEG-based BCIs. The advantage of this method lies in its relatively simple algorithm and easy computational implementation. The experimental results also showed that the selection of relevant channels is an important step in developing efficient EEG-based BCI systems.},
}
@article {pmid29899704,
year = {2018},
author = {Hudson, AL and Niérat, MC and Raux, M and Similowski, T},
title = {The Relationship Between Respiratory-Related Premotor Potentials and Small Perturbations in Ventilation.},
journal = {Frontiers in physiology},
volume = {9},
number = {},
pages = {621},
pmid = {29899704},
issn = {1664-042X},
abstract = {Respiratory-related premotor potentials from averaged electroencephalography (EEG) over the motor areas indicate cortical activation in healthy participants to maintain ventilation in the face of moderate inspiratory or expiratory loads. These experimental conditions are associated with respiratory discomfort, i.e., dyspnea. Premotor potentials are also observed in resting breathing in patients with reduced automatic respiratory drive or respiratory muscle strength due to respiratory or neurological disease, presumably in an attempt to maintain ventilation. The aim of this study was to determine if small voluntary increases in ventilation or smaller load-capacity imbalances, that generate an awareness of breathing but aren't necessarily dyspneic, give rise to respiratory premotor potentials in healthy participants. In 15 healthy subjects, EEG was recorded during voluntary large breaths (∼3× tidal volume, that were interspersed with smaller non-voluntary breaths in the same trial; in 10 subjects) and breathing with a 'low' inspiratory threshold load (∼7 cmH2O; in 8 subjects). Averaged EEG signals at Cz and FCz were assessed for premotor potentials prior to inspiration. Premotor potential incidence in large breaths was 40%, similar to that in the smaller non-voluntary breaths in the same trial (20%; p > 0.05) and to that in a separate trial of resting breathing (0%; p > 0.05). The incidence of premotor potentials was 25% in the low load condition, similar to that in resting breathing (0%; p > 0.05). In contrast, voluntary sniffs were always associated with a higher incidence of premotor potentials (100%; p < 0.05). We have demonstrated that in contrast to respiratory and neurological disease, there is no significant cortical contribution to increase tidal volume or to maintain the load-capacity balance with a small inspiratory threshold load in healthy participants as detected using event-related potential methodology. A lack of cortical contribution during loading was associated with low ratings of respiratory discomfort and minimal changes in ventilation. These findings advance our understanding of the neural control of breathing in health and disease and how respiratory-related EEG may be used for medical technologies such as brain-computer interfaces.},
}
@article {pmid29896082,
year = {2018},
author = {Mohanty, R and Sinha, AM and Remsik, AB and Dodd, KC and Young, BM and Jacobson, T and McMillan, M and Thoma, J and Advani, H and Nair, VA and Kang, TJ and Caldera, K and Edwards, DF and Williams, JC and Prabhakaran, V},
title = {Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {353},
pmid = {29896082},
issn = {1662-4548},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.},
}
@article {pmid29892219,
year = {2018},
author = {Keihani, A and Shirzhiyan, Z and Farahi, M and Shamsi, E and Mahnam, A and Makkiabadi, B and Haidari, MR and Jafari, AH},
title = {Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {201},
pmid = {29892219},
issn = {1662-5161},
abstract = {Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects. Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23-30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated. Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35). Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies.},
}
@article {pmid29892218,
year = {2018},
author = {Riccio, A and Schettini, F and Simione, L and Pizzimenti, A and Inghilleri, M and Olivetti-Belardinelli, M and Mattia, D and Cincotti, F},
title = {On the Relationship Between Attention Processing and P300-Based Brain Computer Interface Control in Amyotrophic Lateral Sclerosis.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {165},
pmid = {29892218},
issn = {1662-5161},
abstract = {Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (p = 0.13). Differently, the performance in controlling the P3-speller expressed as ITR values (calculated offline) were compromised in ALS patients (p < 0.05), with a delay in the latency of P3 when processing BCI stimuli as compared with Control group (p < 0.01). Furthermore, the temporal aspect of attentional filtering which was related to BCI control (r = 0.51; p < 0.05) and to the P3 wave amplitude (r = 0.63; p < 0.05) was also altered in ALS patients (p = 0.01). These findings ground the knowledge required to develop sensible classes of BCI specifically designed by taking into account the influence of the cognitive characteristics of the possible candidates in need of a BCI system for communication.},
}
@article {pmid29891139,
year = {2018},
author = {Buczinski, S and Fecteau, G and Dubuc, J and Francoz, D},
title = {Validation of a clinical scoring system for bovine respiratory disease complex diagnosis in preweaned dairy calves using a Bayesian framework.},
journal = {Preventive veterinary medicine},
volume = {156},
number = {},
pages = {102-112},
pmid = {29891139},
issn = {1873-1716},
mesh = {Animals ; Animals, Newborn ; *Bayes Theorem ; Bovine Respiratory Disease Complex/*diagnosis ; Cattle ; Logistic Models ; Prevalence ; Ultrasonography ; },
abstract = {Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used. A model accounting for imperfect accuracy of thoracic ultrasound examination was used to obtain updated weights for the clinical signs included in the Californian scoring system. There were 20 points (95% Bayesian credible intervals: 11-29) for abnormal breathing pattern, 16 points (95% BCI: 4-29) for ear drop/head tilt, 16 points (95% BCI: 9-25) for cough, 10 points (95% BCI: 3-18) for the presence of nasal discharge, 7 points (95% BCI: -1 to 8) for rectal temperature ≥39.2 °C, and -1 points (95% BCI: -9 to 8) for the presence of ocular discharge. The optimal cut-offs were determined using the misclassification cost-term term (MCT) approach with different possible scenarios of expected prevalence and different plausible ratio of false negative costs/false positive costs. The predicted probabilities of active infection of the lower respiratory tract were also obtained using posterior densities of the main logistic regression model. Depending on the context, cut-off varying from 9 to 16 can minimized the MCT. The optimal cut-off decreased when expected prevalence of disease and false negative/false positive ratio increased.},
}
@article {pmid29890509,
year = {2018},
author = {Eilbeigi, E and Setarehdan, SK},
title = {Detecting intention to execute the next movement while performing current movement from EEG using global optimal constrained ICA.},
journal = {Computers in biology and medicine},
volume = {99},
number = {},
pages = {63-75},
doi = {10.1016/j.compbiomed.2018.05.024},
pmid = {29890509},
issn = {1879-0534},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; *Intention ; Male ; Movement/*physiology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are a promising tool in neurorehabilitation. The intention to perform a motor action can be detected from brain signals and used to control robotic devices. Most previous studies have focused on the starting of movements from a resting state, while in daily life activities, motions occur continuously and the neural activities correlated to the evolving movements are yet to be investigated.<br><br>METHOD: First we investigate the existence of neural correlates of intention to replace an object on the table during a holding phase. Next, we present a new method to extract the movement-related cortical potentials (MRCP) from a single-trial EEG. A novel method called Global optimal constrained ICA (GocICA) is proposed to overcome the limitations of cICA which is implemented based on Particle Swarm Optimization (PSO) and Charged System Search (CSS) techniques. GocICA is then utilized for decoding the intention to grasp and lift and intention to replace movements where the results were compared.<br><br>RESULTS: It was found that GocICA significantly improves the intention detection performance. Best results in offline detection were obtained with CSS-cICA for both kinds of intentions. Furthermore, pseudo-online decoding showed that GocICA was able to predict both intentions before the onset of related movements with the highest probability.<br><br>CONCLUSIONS: Decoding of the next movement intention during current movement is possible, which can be used to create more natural neuroprostheses. The results demonstrate that GocICA is a promising new algorithm for single-trial MRCP detection which can be used for detecting other types of ERPs such as P300.},
}
@article {pmid29890196,
year = {2018},
author = {Kiran Kumar, GR and Ramasubba Reddy, M},
title = {Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {307},
number = {},
pages = {164-174},
doi = {10.1016/j.jneumeth.2018.06.003},
pmid = {29890196},
issn = {1872-678X},
mesh = {Adult ; *Algorithms ; Brain Mapping ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; *Pattern Recognition, Automated ; Photic Stimulation ; *Principal Component Analysis ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Traditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost.<br><br>NEW METHOD: In this paper, Periodic component analysis (&#x3c0;CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The &#x3c0;CA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates.<br><br>RESULTS: Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results.<br><br>The experimental results show that &#x3c0;CA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions.<br><br>CONCLUSIONS: The results demonstrate that &#x3c0;CA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence &#x3c0;CA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI).},
}
@article {pmid29888252,
year = {2018},
author = {Rashid, N and Iqbal, J and Javed, A and Tiwana, MI and Khan, US},
title = {Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis.},
journal = {BioMed research international},
volume = {2018},
number = {},
pages = {2695106},
pmid = {29888252},
issn = {2314-6141},
mesh = {Adult ; Artificial Limbs ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Female ; Fingers ; Hand/physiology ; Humans ; Male ; Middle Aged ; Movement/*physiology ; *Prostheses and Implants ; Signal Processing, Computer-Assisted ; Thumb/*physiology ; Upper Extremity/*physiology ; },
abstract = {Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8-30 Hz) containing most of the movement data were retained through filtering using "Arduino Uno" microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.},
}
@article {pmid29887797,
year = {2018},
author = {Sakurai, Y and Osako, Y and Tanisumi, Y and Ishihara, E and Hirokawa, J and Manabe, H},
title = {Multiple Approaches to the Investigation of Cell Assembly in Memory Research-Present and Future.},
journal = {Frontiers in systems neuroscience},
volume = {12},
number = {},
pages = {21},
pmid = {29887797},
issn = {1662-5137},
abstract = {In this review article we focus on research methodologies for detecting the actual activity of cell assemblies, which are populations of functionally connected neurons that encode information in the brain. We introduce and discuss traditional and novel experimental methods and those currently in development and briefly discuss their advantages and disadvantages for the detection of cell-assembly activity. First, we introduce the electrophysiological method, i.e., multineuronal recording, and review former and recent examples of studies showing models of dynamic coding by cell assemblies in behaving rodents and monkeys. We also discuss how the firing correlation of two neurons reflects the firing synchrony among the numerous surrounding neurons that constitute cell assemblies. Second, we review the recent outstanding studies that used the novel method of optogenetics to show causal relationships between cell-assembly activity and behavioral change. Third, we review the most recently developed method of live-cell imaging, which facilitates the simultaneous observation of firings of a large number of neurons in behaving rodents. Currently, all these available methods have both advantages and disadvantages, and no single measurement method can directly and precisely detect the actual activity of cell assemblies. The best strategy is to combine the available methods and utilize each of their advantages with the technique of operant conditioning of multiple-task behaviors in animals and, if necessary, with brain-machine interface technology to verify the accuracy of neural information detected as cell-assembly activity.},
}
@article {pmid29887338,
year = {2018},
author = {Williams, AH and Kim, TH and Wang, F and Vyas, S and Ryu, SI and Shenoy, KV and Schnitzer, M and Kolda, TG and Ganguli, S},
title = {Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.},
journal = {Neuron},
volume = {98},
number = {6},
pages = {1099-1115.e8},
pmid = {29887338},
issn = {1097-4199},
support = {R21 NS104833/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; F31 NS103409/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; /HHMI_/Howard Hughes Medical Institute/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Mice ; Motor Cortex/*physiology ; *Neural Networks, Computer ; Prefrontal Cortex/*physiology ; Principal Component Analysis ; Spatial Navigation/*physiology ; Time Factors ; *Unsupervised Machine Learning ; },
abstract = {Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.},
}
@article {pmid29879201,
year = {2018},
author = {Waudby-Smith, IER and Tran, N and Dubin, JA and Lee, J},
title = {Sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients.},
journal = {PloS one},
volume = {13},
number = {6},
pages = {e0198687},
pmid = {29879201},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Critical Care Nursing ; Critical Illness/*mortality/therapy ; *Emotions ; Female ; Humans ; Intensive Care Units ; Kaplan-Meier Estimate ; Male ; Middle Aged ; *Natural Language Processing ; *Nursing Records ; Prognosis ; Young Adult ; },
abstract = {BACKGROUND: Nursing notes have not been widely used in prediction models for clinical outcomes, despite containing rich information. Advances in natural language processing have made it possible to extract information from large scale unstructured data like nursing notes. This study extracted the sentiment-impressions and attitudes-of nurses, and examined how sentiment relates to 30-day mortality and survival.<br><br>METHODS: This study applied a sentiment analysis algorithm to nursing notes extracted from MIMIC-III, a public intensive care unit (ICU) database. A multiple logistic regression model was fitted to the data to correlate measured sentiment with 30-day mortality while controlling for gender, type of ICU, and SAPS-II score. The association between measured sentiment and 30-day mortality was further examined in assessing the predictive performance of sentiment score as a feature in a classifier, and in a survival analysis for different levels of measured sentiment.<br><br>RESULTS: Nursing notes from 27,477 ICU patients, with an overall 30-day mortality of 11.02%, were extracted. In the presence of known predictors of 30-day mortality, mean sentiment polarity was a highly significant predictor in a multiple logistic regression model (Adjusted OR = 0.4626, p < 0.001, 95% CI: [0.4244, 0.5041]) and led to improved predictive accuracy (AUROC = 0.8189 versus 0.8092, 95% BCI of difference: [0.0070, 0.0126]). The Kaplan Meier survival curves showed that mean sentiment polarity quartiles are positively correlated with patient survival (log-rank test: p < 0.001).<br><br>CONCLUSIONS: This study showed that quantitative measures of unstructured clinical notes, such as sentiment of clinicians, correlate with 30-day mortality and survival, thus can also serve as a predictor of patient outcomes in the ICU. Therefore, further research is warranted to study and make use of the wealth of data that clinical notes have to offer.},
}
@article {pmid29877855,
year = {2018},
author = {Belkacem, AN and Nishio, S and Suzuki, T and Ishiguro, H and Hirata, M},
title = {Neuromagnetic Decoding of Simultaneous Bilateral Hand Movements for Multidimensional Brain-Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {6},
pages = {1301-1310},
doi = {10.1109/TNSRE.2018.2837003},
pmid = {29877855},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Female ; Functional Laterality/physiology ; *Hand ; Healthy Volunteers ; Humans ; Imagination/physiology ; Magnetic Resonance Imaging ; Magnetoencephalography/*methods ; Male ; Middle Aged ; *Movement ; Psychomotor Performance ; Robotics ; Sensorimotor Cortex/physiology ; Support Vector Machine ; Wavelet Analysis ; Young Adult ; },
abstract = {To provide multidimensional control, we describe the first reported decoding of bilateral hand movements by using single-trial magnetoencephalography signals as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-machine interface. Ten healthy participants performed or imagined four types of bilateral hand movements during neuromagnetic measurements. By applying a support vector machine (SVM) method to classify the four movements regarding the sensor data obtained from the sensorimotor area, we found the mean accuracy of a two-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real-time applications, with accuracies comparable to those obtained in previous studies involving unilateral movement. The sensor data from over the sensorimotor cortex showed discriminative time-series waveforms and time-frequency maps in the bilateral hemispheres according to the four tasks. Furthermore, we used four-class classification algorithms based on the SVM method to decode all types of bilateral movements. Our results provided further proof that the slow components of neuromagnetic fields carry sufficient neural information to classify even bilateral hand movements and demonstrated the potential utility of decoding bilateral movements for engineering purposes such as multidimensional motor control.},
}
@article {pmid29877842,
year = {2018},
author = {Wei, Q and Liu, Y and Gao, X and Wang, Y and Yang, C and Lu, Z and Gong, H},
title = {A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {6},
pages = {1178-1187},
doi = {10.1109/TNSRE.2018.2837501},
pmid = {29877842},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Healthy Volunteers ; Humans ; Male ; Neurologic Examination ; Photic Stimulation ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes. Canonical correlation analysis is applied to each group for yielding a spatial filter and templates for all targets in a group are constructed based on spatially filtered signals. Template matching is applied to each group and the attended target is recognized by finding the maximal correlation coefficients of all groups. Based on the paradigm, a BCI with a total of 48 targets divided into three groups was implemented; 12 and 10 subjects participated in an off-line and a simulated online experiments, respectively. Data analysis of the offline experiment showed that the paradigm can massively increase the number of targets from 16 to 48 at the cost of slight compromise in accuracy (95.49% vs. 92.85%). Results of the simulated online experiment suggested that although the averaged accuracy across subjects of all three groups of targets was lower than that of a single group of targets (91.67% vs. 94.9%), the average ITR of the former was substantially higher than that of the later (181 bits/min vs. 135.6 bit/min) due to the large increase of the number of targets. The proposed paradigm significantly improves the performance of the c-VEP BCI, and thereby facilitates its practical applications such as high-speed spelling.},
}
@article {pmid29877838,
year = {2018},
author = {Xiao, J and Xie, Q and Lin, Q and Yu, T and Yu, R and Li, Y},
title = {Assessment of Visual Pursuit in Patients With Disorders of Consciousness Based on a Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {6},
pages = {1141-1151},
doi = {10.1109/TNSRE.2018.2835813},
pmid = {29877838},
issn = {1558-0210},
mesh = {Adult ; Aged ; Algorithms ; Brain Injuries, Traumatic/physiopathology/psychology ; *Brain-Computer Interfaces ; Coma/psychology ; Consciousness Disorders/*psychology ; Electroencephalography ; Evoked Potentials, Visual ; Female ; Healthy Volunteers ; Humans ; Male ; Middle Aged ; Photic Stimulation ; Recovery of Function ; *Saccades ; Young Adult ; },
abstract = {Visual pursuit assessment is extensively applied in the behavioral scale-based clinical examination of patients with disorders of consciousness (DOC). However, this assessment is challenging because it relies on behavioral markers, and these patients severely lack behavioral responses. Brain-computer interfaces (BCIs) may provide a potential solution to detect brain responses to external stimuli without requiring behavioral expressions. A BCI system was designed to simulate visual pursuit detection in the coma recovery scale-revised (CRS-R). The graphical user interface included four buttons, one that moved on the screen and three that did not. These buttons flashed in a random order. The patients were prompted to follow the moving button. Based on the collected electroencephalography data, the algorithm determined whether the patient focused on the moving target. Among the 14 DOC patients who participated in the assessments based on the BCI system and the CRS-R, four patients exhibited visual pursuit, and three were nonresponsive in both assessments. More importantly, seven patients who did not exhibit visual pursuit in CRS-R were detected to be responsive to the moving target stimuli in the BCI assessment. Furthermore, five out of seven recovered consciousness to some degree and showed visual pursuit in the second CRS-R assessment. The proposed BCI system is better able to detect visual pursuit than the behavioral scale-based assessment and thus can assist in clinically evaluating the challenging population of DOC patients.},
}
@article {pmid29877820,
year = {2018},
author = {Fall, CL and Quevillon, F and Blouin, M and Latour, S and Campeau-Lecours, A and Gosselin, C and Gosselin, B},
title = {A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {12},
number = {3},
pages = {564-575},
doi = {10.1109/TBCAS.2018.2810256},
pmid = {29877820},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; *Persons with Disabilities ; *Electromyography ; Female ; Humans ; Male ; *Self-Help Devices ; Wireless Technology/*instrumentation ; },
abstract = {This paper describes a multimodal body-machine interface (BoMI) to help individuals with upper-limb disabilities using advanced assistive technologies, such as robotic arms. The proposed system uses a wearable and wireless body sensor network (WBSN) supporting up to six sensor nodes to measure the natural upper-body gesture of the users and translate it into control commands. Natural gesture of the head and upper-body parts, as well as muscular activity, are measured using inertial measurement units (IMUs) and surface electromyography (sEMG) using custom-designed multimodal wireless sensor nodes. An IMU sensing node is attached to a headset worn by the user. It has a size of 2.9 cm 2.9 cm, a maximum power consumption of 31 mW, and provides angular precision of 1. Multimodal patch sensor nodes, including both IMU and sEMG sensing modalities are placed over the user able-body parts to measure the motion and muscular activity. These nodes have a size of 2.5 cm 4.0 cm and a maximum power consumption of 11 mW. The proposed BoMI runs on a Raspberry Pi. It can adapt to several types of users through different control scenarios using the head and shoulder motion, as well as muscular activity, and provides a power autonomy of up to 24 h. JACO, a 6-DoF assistive robotic arm, is used as a testbed to evaluate the performance of the proposed BoMI. Ten able-bodied subjects performed ADLs while operating the AT device, using the Test d'Évaluation des Membres Supérieurs de Personnes Âgées to evaluate and compare the proposed BoMI with the conventional joystick controller. It is shown that the users can perform all tasks with the proposed BoMI, almost as fast as with the joystick controller, with only 30% time overhead on average, while being potentially more accessible to the upper-body disabled who cannot use the conventional joystick controller. Tests show that control performance with the proposed BoMI improved by up to 17% on average, after three trials.},
}
@article {pmid29877819,
year = {2018},
author = {Miao, Y and Koomson, VJ},
title = {A CMOS-Based Bidirectional Brain Machine Interface System With Integrated fdNIRS and tDCS for Closed-Loop Brain Stimulation.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {12},
number = {3},
pages = {554-563},
doi = {10.1109/TBCAS.2018.2798924},
pmid = {29877819},
issn = {1940-9990},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy/instrumentation/methods ; Humans ; Nervous System Diseases/*physiopathology/*therapy ; },
abstract = {A CMOS-based bidirectional brain machine interface system with on-chip frequency-domain near infrared spectroscopy (fdNIRS) and transcranial direct-current stimulation (tDCS) is designed to enable noninvasive closed-loop brain stimulation for neural disorders treatment and cognitive performance enhancement. The dual channel fdNIRS can continuously monitor absolute cerebral oxygenation during the entire tDCS process by measuring NIR light's attenuation and phase shift across brain tissue. Each fdNIRS channel provides 120 dBΩ transimpedance gain at 80 MHz with a power consumption of 30 mW while tolerating up to 8 pF input capacitance. A photocurrent between 10 and 450 nA can be detected with a phase resolution down to 0.2°. A lensless system with subnanowatt sensitivity is realized by using an avalanche photodiode. The on-chip programmable voltage-controlled resistor stimulator can support a stimulation current from 0.6 to 2.2 mA with less than 1% variation, which covers the required current range of tDCS. The chip is fabricated in a standard 130-nm CMOS process and occupies an area of 2.25 mm[2].},
}
@article {pmid29874804,
year = {2018},
author = {Shin, J and Kim, DW and Müller, KR and Hwang, HJ},
title = {Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {6},
pages = {},
pmid = {29874804},
issn = {1424-8220},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Female ; Humans ; Male ; Photic Stimulation ; Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.},
}
@article {pmid29874033,
year = {2018},
author = {Inal, S and Rivnay, J and Suiu, AO and Malliaras, GG and McCulloch, I},
title = {Conjugated Polymers in Bioelectronics.},
journal = {Accounts of chemical research},
volume = {51},
number = {6},
pages = {1368-1376},
doi = {10.1021/acs.accounts.7b00624},
pmid = {29874033},
issn = {1520-4898},
mesh = {Biology/instrumentation/*methods ; Electrodes ; Electronics/instrumentation/*methods ; Molecular Structure ; Polymers/*chemistry ; Tissue Engineering/instrumentation/methods ; Transistors, Electronic ; },
abstract = {The emerging field of organic bioelectronics bridges the electronic world of organic-semiconductor-based devices with the soft, predominantly ionic world of biology. This crosstalk can occur in both directions. For example, a biochemical reaction may change the doping state of an organic material, generating an electronic readout. Conversely, an electronic signal from a device may stimulate a biological event. Cutting-edge research in this field results in the development of a broad variety of meaningful applications, from biosensors and drug delivery systems to health monitoring devices and brain-machine interfaces. Conjugated polymers share similarities in chemical "nature" with biological molecules and can be engineered on various forms, including hydrogels that have Young's moduli similar to those of soft tissues and are ionically conducting. The structure of organic materials can be tuned through synthetic chemistry, and their biological properties can be controlled using a variety of functionalization strategies. Finally, organic electronic materials can be integrated with a variety of mechanical supports, giving rise to devices with form factors that enable integration with biological systems. While these developments are innovative and promising, it is important to note that the field is still in its infancy, with many unknowns and immense scope for exploration and highly collaborative research. The first part of this Account details the unique properties that render conjugated polymers excellent biointerfacing materials. We then offer an overview of the most common conjugated polymers that have been used as active layers in various organic bioelectronics devices, highlighting the importance of developing new materials. These materials are the most popular ethylenedioxythiophene derivatives as well as conjugated polyelectrolytes and ion-free organic semiconductors functionalized for the biological interface. We then discuss several applications and operation principles of state-of-the-art bioelectronics devices. These devices include electrodes applied to sense/trigger electrophysiological activity of cells as well as electrolyte-gated field-effect and electrochemical transistors used for sensing of biochemical markers. Another prime application example of conjugated polymers is cell actuators. External modulation of the redox state of the underlying conjugated polymer films controls the adhesion behavior and viability of cells. These smart surfaces can be also designed in the form of three-dimensional architectures because of the processability of conjugated polymers. As such, cell-loaded scaffolds based on electroactive polymers enable integrated sensing or stimulation within the engineered tissue itself. A last application example is organic neuromorphic devices, an alternative computing architecture that takes inspiration from biology and, in particular, from the way the brain works. Leveraging ion redistribution inside a conjugated polymer upon application of an electrical field and its coupling with electronic charges, conjugated polymers can be engineered to act as artificial neurons or synapses with complex, history-dependent behavior. We conclude this Account by highlighting main factors that need to be considered for the design of a conjugated polymer for applications in bioelectronics-although there can be various figures of merit given the broad range of applications, as emphasized in this Account.},
}
@article {pmid29872420,
year = {2018},
author = {Rupawala, M and Dehghani, H and Lucas, SJE and Tino, P and Cruse, D},
title = {Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {350},
pmid = {29872420},
issn = {1664-2295},
support = {BB/H012508/1/BB_/Biotechnology and Biological Sciences Research Council/United Kingdom ; },
abstract = {Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behavior from spontaneous behavior. As many such behaviors are minimal and inconsistent, behavioral assessments are susceptible to diagnostic errors. Advanced neuroimaging tools can bypass behavioral responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. The majority of reports to date have employed the neuroimaging methods of functional magnetic resonance imaging, positron emission tomography, and electroencephalography (EEG). However, each neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.). Here, we describe a burgeoning technique of non-invasive optical neuroimaging-functional near-infrared spectroscopy (fNIRS)-and review its potential to address the clinical challenges of prolonged disorders of consciousness. We also outline the potential for simultaneous EEG to complement the fNIRS signal and suggest the future directions of research that are required in order to realize its clinical potential.},
}
@article {pmid29872059,
year = {2018},
author = {Christopoulos, VN and Kagan, I and Andersen, RA},
title = {Lateral intraparietal area (LIP) is largely effector-specific in free-choice decisions.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {8611},
pmid = {29872059},
issn = {2045-2322},
support = {R01 EY007492/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Decision Making ; GABA-A Receptor Agonists/administration &amp; dosage ; Haplorhini ; Muscimol/administration &amp; dosage ; Parietal Lobe/*physiology ; *Saccades ; },
abstract = {Despite many years of intense research, there is no strong consensus about the role of the lateral intraparietal area (LIP) in decision making. One view of LIP function is that it guides spatial attention, providing a "saliency map" of the external world. If this were the case, it would contribute to target selection regardless of which action would be performed to implement the choice. On the other hand, LIP inactivation has been shown to influence spatial selection and oculomotor metrics in free-choice decisions, which are made using eye movements, arguing that it contributes to saccade decisions. To dissociate between a more general attention role and a more effector specific saccade role, we reversibly inactivated LIP while non-human primates freely selected between two targets, presented in the two hemifields, with either saccades or reaches. Unilateral LIP inactivation induced a strong choice bias to ipsilesional targets when decisions were made with saccades. Interestingly, the inactivation also caused a reduction of contralesional choices when decisions were made with reaches, albeit the effect was less pronounced. These findings suggest that LIP is part of a network for making oculomotor decisions and is largely effector-specific in free-choice decisions.},
}
@article {pmid29869996,
year = {2018},
author = {Zerafa, R and Camilleri, T and Falzon, O and Camilleri, KP},
title = {To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {15},
number = {5},
pages = {051001},
doi = {10.1088/1741-2552/aaca6e},
pmid = {29869996},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Somatosensory ; Humans ; },
abstract = {OBJECTIVE: Despite the vast research aimed at improving the performance of steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), several limitations exist that restrict the use of such applications for long-term users in the real-world. One of the main challenges has been to reduce training time while maintaining good BCI performance. In view of this challenge, this survey identifies and compares the different training requirements of feature extraction methods for SSVEP-based BCIs.<br><br>APPROACH: This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature.<br><br>MAIN RESULTS: The main contributions compared to existing reviews are the following: (i) a detailed summary, including a brief mathematical description of each feature extraction algorithm, providing a guide to the basic concepts of the state-of-the-art techniques for SSVEP-based BCIs found in literature; (ii) a categorisation of the training requirements of SSVEP-based methods into three categories, defined as training-free methods, subject-specific and subject-independent training methods; (iii) a comparative review of the training requirements of SSVEP feature extraction methods, providing a reference for future work on SSVEP-based BCIs.<br><br>SIGNIFICANCE: This review highlights the strengths and weaknesses of the three categories of SSVEP training methods. Training-free systems are more practical but their performance is limited due to inter-subject variability resulting from the complex EEG activity. Feature extraction methods that incorporate some training data address this issue and in fact have outperformed training-free methods: subject-specific BCIs are tuned to the individual yielding the best performance at the cost of long, tiring training sessions making these methods unsuitable for everyday use; subject-independent BCIs that make use of training data from various subjects offer a good trade-off between training effort and performance, making these BCIs better suited for practical use.},
}
@article {pmid29867425,
year = {2018},
author = {Dimitriadis, SI and Marimpis, AD},
title = {Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {19},
pmid = {29867425},
issn = {1662-5196},
support = {MR/K004360/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class (N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class (N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class (N = 5) flickering BCI system. Estimating cross-frequency coupling (CFC) and namely δ-θ [δ: (0.5-4 Hz), θ: (4-8 Hz)] phase-to-amplitude coupling (PAC) within sensor and across experimental time, we succeeded in achieving high classification accuracy and Information Transfer Rates (ITR) in the three data sets. Our approach outperformed the originally presented ITR on the three data sets. The bit rates obtained for both the disabled and able-bodied subjects reached the fastest reported level of 324 bits/min with the PAC estimator. Additionally, our approach outperformed alternative signal features such as the relative power (29.73 bits/min) and raw time series analysis (24.93 bits/min) and also the original reported bit rates of 10-25 bits/min. In the second data set, we succeeded in achieving an average ITR of 124.40 ± 11.68 for the slow 60 Hz and an average ITR of 233.99 ± 15.75 for the fast 120 Hz. In the third data set, we succeeded in achieving an average ITR of 106.44 ± 8.94. Current methodology outperforms any previous methodologies applied to each of the three free available BCI datasets.},
}
@article {pmid29867421,
year = {2018},
author = {Pan, J and Xie, Q and Huang, H and He, Y and Sun, Y and Yu, R and Li, Y},
title = {Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {198},
pmid = {29867421},
issn = {1662-5161},
abstract = {For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.},
}
@article {pmid29867411,
year = {2018},
author = {Gateau, T and Ayaz, H and Dehais, F},
title = {In silico vs. Over the Clouds: On-the-Fly Mental State Estimation of Aircraft Pilots, Using a Functional Near Infrared Spectroscopy Based Passive-BCI.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {187},
pmid = {29867411},
issn = {1662-5161},
abstract = {There is growing interest for implementing tools to monitor cognitive performance in naturalistic work and everyday life settings. The emerging field of research, known as neuroergonomics, promotes the use of wearable and portable brain monitoring sensors such as functional near infrared spectroscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. The objective of this study was to implement an on-line passive fNIRS-based brain computer interface to discriminate two levels of working memory load during highly ecological aircraft piloting tasks. Twenty eight recruited pilots were equally split into two groups (flight simulator vs. real aircraft). In both cases, identical approaches and experimental stimuli were used (serial memorization task, consisting in repeating series of pre-recorded air traffic control instructions, easy vs. hard). The results show pilots in the real flight condition committed more errors and had higher anterior prefrontal cortex activation than pilots in the simulator, when completing cognitively demanding tasks. Nevertheless, evaluation of single trial working memory load classification showed high accuracy (>76%) across both experimental conditions. The contributions here are two-fold. First, we demonstrate the feasibility of passively monitoring cognitive load in a realistic and complex situation (live piloting of an aircraft). In addition, the differences in performance and brain activity between the two experimental conditions underscore the need for ecologically-valid investigations.},
}
@article {pmid29867320,
year = {2018},
author = {Dinarès-Ferran, J and Ortner, R and Guger, C and Solé-Casals, J},
title = {A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {308},
pmid = {29867320},
issn = {1662-4548},
abstract = {EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user's brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training. More data can improve the classifier, but also increases the training time, which can be especially problematic for some patients. Training time might be reduced by creating new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing their Intrinsic Mode Function (IMFs). The purpose of this study is to explore the use of artificial EEG frames as replacements for some real ones by comparing classifiers trained with some artificial frames to classifiers trained with only real data. Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 s. In the remaining subjects, at least 12.5% of the real EEG frames could be replaced, reducing the training time by 90 s. Moreover, the method can be used to replace EEG frames that contain artifact, which reduces the impact of rejecting data with artifact. The method was also tested on an out of sample scenario with the best subjects from a public database, who yielded very good results using a frame collection with 87.5% artificial frames. These initial results with healthy users need to be further explored with patients' data, along with research into alternative IMF mixing strategies and using other BCI paradigms.},
}
@article {pmid29867319,
year = {2018},
author = {Hammer, EM and Halder, S and Kleih, SC and Kübler, A},
title = {Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {307},
pmid = {29867319},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor "emotional stability" was negatively correlated (Spearman's rho = -0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. "Emotional stability" was also negatively related to auditory P300-BCI performance (Spearman's rho = -0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. "Emotional stability" was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors.},
}
@article {pmid29867307,
year = {2018},
author = {Zhao, Y and Han, J and Chen, Y and Sun, H and Chen, J and Ke, A and Han, Y and Zhang, P and Zhang, Y and Zhou, J and Wang, C},
title = {Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {272},
pmid = {29867307},
issn = {1662-4548},
abstract = {Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.},
}
@article {pmid29865175,
year = {2018},
author = {Wang, F and Zhang, X and Fu, R and Sun, G},
title = {Study of the Home-Auxiliary Robot Based on BCI.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {6},
pages = {},
pmid = {29865175},
issn = {1424-8220},
abstract = {A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person's field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects' electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.},
}
@article {pmid29862762,
year = {2017},
author = {Cai, M and Hu, P},
title = {[Task Classifcation of Right-hand and Foot Motion Imagery Based on Wavelet Packet Transform].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {41},
number = {3},
pages = {177-180},
doi = {10.3969/j.issn.1671-7104.2017.03.006},
pmid = {29862762},
issn = {1671-7104},
mesh = {*Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Wavelet Analysis ; },
abstract = {Brain-computer interface (BCI) provides a new choice for people who lose communication ability, so the recognition of EEG has been paid attention. In this paper, wavelet packet transform (WPT) and transfer learning (TL) were used to classify right-hand and foot motion imagery tasks. Firstly, based on analyzing the channels and frequency bands closely related to event-related desynchronization (ERD), the EEG signals are decomposed by WPT. Then the relevant nodes were selected to calculate wavelet packet energy. Finally, TL was used to classify the BCI competition Ⅲ data IVa. The ideal classification result was obtained. The results show that the method is simple and effective, and it is valuable for online application of BCI.},
}
@article {pmid29861712,
year = {2018},
author = {Yoon, J and Lee, J and Whang, M},
title = {Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {6058065},
pmid = {29861712},
issn = {1687-5273},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Evoked Potentials ; Female ; Humans ; Learning Curve ; *Literacy ; Machine Learning ; Male ; *Neural Networks, Computer ; Neuropsychological Tests ; ROC Curve ; Semantics ; Signal Processing, Computer-Assisted ; Time Factors ; Visual Perception/physiology ; Young Adult ; },
abstract = {Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.},
}
@article {pmid29860376,
year = {2018},
author = {Pitt, KM and Brumberg, JS},
title = {Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.},
journal = {American journal of speech-language pathology},
volume = {27},
number = {3},
pages = {950-964},
pmid = {29860376},
issn = {1558-9110},
support = {R03 DC011304/DC/NIDCD NIH HHS/United States ; },
mesh = {Adolescent ; Aged ; Auditory Threshold ; Brain/*physiopathology ; Brain Waves ; *Brain-Computer Interfaces ; Clinical Decision-Making ; Cognition ; *Communication ; *Communication Devices for People with Disabilities ; Communication Disorders/diagnosis/physiopathology/psychology/*rehabilitation ; Disability Evaluation ; Equipment Design ; Event-Related Potentials, P300 ; Female ; Humans ; Imagination ; Male ; Motor Activity ; Patient Selection ; Predictive Value of Tests ; Visual Perception ; },
abstract = {PURPOSE: Brain-computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive-sensory-motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals' communication needs.<br><br>METHOD: The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices.<br><br>RESULTS: Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications.<br><br>CONCLUSIONS: This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.},
}
@article {pmid29859878,
year = {2018},
author = {Bridges, NR and Meyers, M and Garcia, J and Shewokis, PA and Moxon, KA},
title = {A rodent brain-machine interface paradigm to study the impact of paraplegia on BMI performance.},
journal = {Journal of neuroscience methods},
volume = {306},
number = {},
pages = {103-114},
pmid = {29859878},
issn = {1872-678X},
support = {F32 NS009697/NS/NINDS NIH HHS/United States ; R01 NS096971/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal ; *Brain-Computer Interfaces ; Disease Models, Animal ; Equipment Design ; Female ; Learning/physiology ; Male ; Neurons/*physiology ; Paraplegia/*physiopathology/rehabilitation ; Psychomotor Performance/*physiology ; Rats, Long-Evans ; Sensorimotor Cortex/*physiopathology ; Spinal Cord Injuries/*physiopathology/rehabilitation ; },
abstract = {BACKGROUND: Most brain machine interfaces (BMI) focus on upper body function in non-injured animals, not addressing the lower limb functional needs of those with paraplegia. A need exists for a novel BMI task that engages the lower body and takes advantage of well-established rodent spinal cord injury (SCI) models to study methods to improve BMI performance.<br><br>NEW METHOD: A tilt BMI task was designed that randomly applies different types of tilts to a platform, decodes the tilt type applied and rights the platform if the decoder correctly classifies the tilt type. The task was tested on female rats and is relatively natural such that it does not require the animal to learn a new skill. It is self-rewarding such that there is no need for additional rewards, eliminating food or water restriction, which can be especially hard on spinalized rats. Finally, task difficulty can be adjusted by making the tilt parameters.<br><br>RESULTS: This novel BMI task bilaterally engages the cortex without visual feedback regarding limb position in space and animals learn to improve their performance both pre and post-SCI.Comparison with Existing Methods: Most BMI tasks primarily engage one hemisphere, are upper-body, rely heavily on visual feedback, do not perform investigations in animal models of SCI, and require nonnaturalistic extrinsic motivation such as water rewarding for performance improvement. Our task addresses these gaps.<br><br>CONCLUSIONS: The BMI paradigm presented here will enable researchers to investigate the interaction of plasticity after SCI and plasticity during BMI training on performance.},
}
@article {pmid29859214,
year = {2018},
author = {Zhang, B and Zhuang, L and Qin, Z and Wei, X and Yuan, Q and Qin, C and Wang, P},
title = {A wearable system for olfactory electrophysiological recording and animal motion control.},
journal = {Journal of neuroscience methods},
volume = {307},
number = {},
pages = {221-229},
doi = {10.1016/j.jneumeth.2018.05.023},
pmid = {29859214},
issn = {1872-678X},
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; Conditioning, Operant ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Male ; Microelectrodes ; *Motion ; Olfactory Pathways/cytology/*physiology ; Rats ; Rats, Sprague-Dawley ; Sensory Receptor Cells/physiology ; Smell/*physiology ; *Wearable Electronic Devices ; },
abstract = {BACKGROUND: Bran-computer interface (BCI) is an important technique used in brain science. However, the large size of equipment and wires severely limit its practical applications.<br><br>NEW METHODS: This study presents a wearable system with bidirectional brain-computer interface based on Wi-Fi technology, which can be used for olfactory electrophysiological recording and animal motion control.<br><br>RESULTS: On the "brain-to-computer" side, the results show that the wireless system can record high-quality olfactory electrophysiological signals for over a month. By analyzing the recorded data, we find that the same mitral/tufted (M/T) cells can be activated by many odorants and different M/T cells can be activated by a single odorant. Further, we find neurons in dorsal lateral OB are highly sensitive to isoamyl acetate. On the "computer-to-brain" side, the results show that we can efficiently control rats' motions by applying electrical stimulations to electrodes implanted in specific brain regions.<br><br>Most existing wireless BCI systems are designed for either recording or stimulating while our system is a bidirectional BCI featured with both functions. Taking advantage of our years of experience in olfactory decoding, we developed the first wireless system for olfactory electrophysiological recording and animal motion control. It provides high-quality recording and efficient motion control for a long time.<br><br>CONCLUSIONS: The system provides possibility of practical BCI applications, such as in vivo bioelectronic nose and "rat-robot".},
}
@article {pmid29853844,
year = {2018},
author = {Kawakami, M and Okuyama, K and Takahashi, Y and Hiramoto, M and Nishimura, A and Ushiba, J and Fujiwara, T and Liu, M},
title = {Change in Reciprocal Inhibition of the Forearm with Motor Imagery among Patients with Chronic Stroke.},
journal = {Neural plasticity},
volume = {2018},
number = {},
pages = {3946367},
pmid = {29853844},
issn = {1687-5443},
mesh = {Adult ; Aged ; Brain/pathology/physiopathology ; Brain Waves ; Brain-Computer Interfaces ; Chronic Disease ; Forearm ; H-Reflex ; Humans ; *Imagination ; Middle Aged ; Motor Activity ; Muscle, Skeletal/*physiopathology ; *Neuronal Plasticity ; *Psychomotor Performance ; Stroke/pathology/*physiopathology/therapy ; Stroke Rehabilitation ; Young Adult ; },
abstract = {We investigated cortically mediated changes in reciprocal inhibition (RI) following motor imagery (MI) in short- and long(er)-term periods. The goals of this study were (1) to describe RI during MI in patients with chronic stroke and (2) to examine the change in RI after MI-based brain-machine interface (BMI) training. Twenty-four chronic stroke patients participated in study 1. All patients imagined wrist extension on the affected side. RI from the extensor carpi radialis to the flexor carpi radialis (FCR) was assessed using a FCR H reflex conditioning-test paradigm. We calculated the "MI effect score on RI" (RI value during MI divided by that at rest) and compared that score according to lesion location. RI during MI showed a significant enhancement compared with RI at rest. The MI effect score on RI in the subcortical lesion group was significantly greater than that in the cortical lesion group. Eleven stroke patients participated in study 2. All patients performed BMI training for 10 days. The MI effect score on RI at a 20 ms interstimulus interval was significantly increased after BMI compared with baseline. In conclusion, mental practice with MI may induce plastic change in spinal reciprocal inhibitory circuits in patients with stroke.},
}
@article {pmid29853833,
year = {2018},
author = {Ozkan, NF and Kahya, E},
title = {Classification of BCI Users Based on Cognition.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {6315187},
pmid = {29853833},
issn = {1687-5273},
mesh = {Accident Prevention ; Adolescent ; Adult ; Blinking ; Brain/*physiology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Event-Related Potentials, P300 ; Female ; Galvanic Skin Response ; Humans ; Male ; Neuropsychological Tests ; Pupil/physiology ; Risk Assessment/methods ; Signal Processing, Computer-Assisted ; Thinking/*physiology ; Visual Perception/physiology ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCI) are systems originally developed to assist paralyzed patients allowing for commands to the computer with brain activities. This study aims to examine cognitive state with an objective, easy-to-use, and easy-to-interpret method utilizing Brain-Computer Interface systems. Seventy healthy participants completed six tasks using a Brain-Computer Interface system and participants' pupil dilation, blink rate, and Galvanic Skin Response (GSR) data were collected simultaneously. Participants filled Nasa-TLX forms following each task and task performances of participants were also measured. Cognitive state clusters were created from the data collected using the K-means method. Taking these clusters and task performances into account, the general cognitive state of each participant was classified as low risk or high risk. Logistic Regression, Decision Tree, and Neural Networks were also used to classify the same data in order to measure the consistency of this classification with other techniques and the method provided a consistency between 87.1% and 100% with other techniques.},
}
@article {pmid29849992,
year = {2018},
author = {Cantillo-Negrete, J and Carino-Escobar, RI and Carrillo-Mora, P and Elias-Vinas, D and Gutierrez-Martinez, J},
title = {Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.},
journal = {Journal of healthcare engineering},
volume = {2018},
number = {},
pages = {1624637},
pmid = {29849992},
issn = {2040-2295},
mesh = {Adult ; Aged ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Hand/*physiology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation/*instrumentation ; Young Adult ; },
abstract = {Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients' average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects' average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System's performance showed that it has a potential to be used for hand rehabilitation of stroke patients.},
}
@article {pmid29849549,
year = {2018},
author = {Leite, HMA and de Carvalho, SN and Costa, TBDS and Attux, R and Hornung, HH and Arantes, DS},
title = {Analysis of User Interaction with a Brain-Computer Interface Based on Steady-State Visually Evoked Potentials: Case Study of a Game.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {4920132},
pmid = {29849549},
issn = {1687-5273},
mesh = {Adult ; Auditory Perception/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Evoked Potentials, Visual ; Fatigue/physiopathology ; Feedback, Psychological/physiology ; Female ; Games, Experimental ; Humans ; Male ; Middle Aged ; Music ; Signal Processing, Computer-Assisted ; Surveys and Questionnaires ; User-Computer Interface ; *Video Games ; Visual Perception/physiology ; Young Adult ; },
abstract = {This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named "Get Coins," through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.},
}
@article {pmid29849104,
year = {2018},
author = {John, SE and Opie, NL and Wong, YT and Rind, GS and Ronayne, SM and Gerboni, G and Bauquier, SH and O'Brien, TJ and May, CN and Grayden, DB and Oxley, TJ},
title = {Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {8427},
pmid = {29849104},
issn = {2045-2322},
mesh = {Animals ; *Blood Vessels ; *Brain-Computer Interfaces ; *Dura Mater ; Electrodes, Implanted ; *Epidural Space ; Evoked Potentials ; Signal-To-Noise Ratio ; },
abstract = {Recent work has demonstrated the feasibility of minimally-invasive implantation of electrodes into a cortical blood vessel. However, the effect of the dura and blood vessel on recording signal quality is not understood and may be a critical factor impacting implementation of a closed-loop endovascular neuromodulation system. The present work compares the performance and recording signal quality of a minimally-invasive endovascular neural interface with conventional subdural and epidural interfaces. We compared bandwidth, signal-to-noise ratio, and spatial resolution of recorded cortical signals using subdural, epidural and endovascular arrays four weeks after implantation in sheep. We show that the quality of the signals (bandwidth and signal-to-noise ratio) of the endovascular neural interface is not significantly different from conventional neural sensors. However, the spatial resolution depends on the array location and the frequency of recording. We also show that there is a direct correlation between the signal-noise-ratio and classification accuracy, and that decoding accuracy is comparable between electrode arrays. These results support the consideration for use of an endovascular neural interface in a clinical trial of a novel closed-loop neuromodulation technology.},
}
@article {pmid29848665,
year = {2018},
author = {Clites, TR and Carty, MJ and Ullauri, JB and Carney, ME and Mooney, LM and Duval, JF and Srinivasan, SS and Herr, HM},
title = {Proprioception from a neurally controlled lower-extremity prosthesis.},
journal = {Science translational medicine},
volume = {10},
number = {443},
pages = {},
doi = {10.1126/scitranslmed.aap8373},
pmid = {29848665},
issn = {1946-6242},
mesh = {Adult ; Ankle/physiopathology ; *Artificial Limbs ; Gait ; Humans ; Joints/physiopathology ; Lower Extremity/*physiopathology ; Male ; Middle Aged ; *Neural Prostheses ; Proprioception/*physiology ; Stair Climbing ; Torque ; },
abstract = {Humans can precisely sense the position, speed, and torque of their body parts. This sense is known as proprioception and is essential to human motor control. Although there have been many attempts to create human-mechatronic interactions, there is still no robust, repeatable methodology to reflect proprioceptive information from a synthetic device onto the nervous system. To address this shortcoming, we present an agonist-antagonist myoneural interface (AMI). The AMI is composed of (i) a surgical construct made up of two muscle-tendons-an agonist and an antagonist-surgically connected in series so that contraction of one muscle stretches the other and (ii) a bidirectional efferent-afferent neural control architecture. The AMI preserves the dynamic muscle relationships that exist within native anatomy, thereby allowing proprioceptive signals from mechanoreceptors within both muscles to be communicated to the central nervous system. We surgically constructed two AMIs within the residual limb of a subject with a transtibial amputation. Each AMI sends control signals to one joint of a two-degree-of-freedom ankle-foot prosthesis and provides proprioceptive information pertaining to the movement of that joint. The AMI subject displayed improved control over the prosthesis compared to a group of four subjects having traditional amputation. We also show natural reflexive behaviors during stair ambulation in the AMI subject that do not appear in the cohort of subjects with traditional amputation. In addition, we demonstrate a system for closed-loop joint torque control in AMI subjects. These results provide a framework for integrating bionic systems with human physiology.},
}
@article {pmid29848301,
year = {2018},
author = {Frølich, L and Andersen, TS and Mørup, M},
title = {Rigorous optimisation of multilinear discriminant analysis with Tucker and PARAFAC structures.},
journal = {BMC bioinformatics},
volume = {19},
number = {1},
pages = {197},
pmid = {29848301},
issn = {1471-2105},
mesh = {Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Humans ; },
abstract = {BACKGROUND: We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant Analysis (MDA) and demonstrate their usage on Electroencephalography (EEG) and simulated data. While existing MDA methods use heuristic optimisation procedures based on an ambiguous Tucker structure, we propose a rigorous approach via optimisation on the cross-product of Stiefel manifolds. We also introduce MDA methods with the PARAFAC structure. We compare the proposed approaches to existing MDA methods and unsupervised multilinear decompositions.<br><br>RESULTS: We find that manifold optimisation substantially improves MDA objective functions relative to existing methods and on simulated data in general improve classification performance. However, we find similar classification performance when applied to the electroencephalography data. Furthermore, supervised approaches substantially outperform unsupervised mulitilinear methods whereas methods with the PARAFAC structure perform similarly to those with Tucker structures. Notably, despite applying the MDA procedures to raw Brain-Computer Interface data, their performances are on par with results employing ample pre-processing and they extract discriminatory patterns similar to the brain activity known to be elicited in the investigated EEG paradigms.<br><br>CONCLUSION: The proposed usage of manifold optimisation constitutes the first rigorous and monotonous optimisation approach for MDA methods and allows for MDA with the PARAFAC structure. Our results show that MDA methods applied to raw EEG data can extract discriminatory patterns when compared to traditional unsupervised multilinear feature extraction approaches, whereas the proposed PARAFAC structured MDA models provide meaningful patterns of activity.},
}
@article {pmid29808111,
year = {2018},
author = {Sweeti, and Joshi, D and Panigrahi, BK and Anand, S and Santhosh, J},
title = {Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features.},
journal = {Journal of healthcare engineering},
volume = {2018},
number = {},
pages = {9213707},
pmid = {29808111},
issn = {2040-2295},
mesh = {Adult ; Algorithms ; Analysis of Variance ; *Attention ; Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; *Electroencephalography ; Female ; Humans ; Male ; Neural Networks, Computer ; *Neurofeedback ; Neuroimaging ; ROC Curve ; Rehabilitation/*methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; *Visual Fields ; Young Adult ; },
abstract = {This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.},
}
@article {pmid29807232,
year = {2018},
author = {Ruijter, BJ and Hofmeijer, J and Tjepkema-Cloostermans, MC and van Putten, MJAM},
title = {The prognostic value of discontinuous EEG patterns in postanoxic coma.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {129},
number = {8},
pages = {1534-1543},
doi = {10.1016/j.clinph.2018.04.745},
pmid = {29807232},
issn = {1872-8952},
mesh = {Adult ; Aged ; Aged, 80 and over ; Cohort Studies ; Coma/*diagnosis/*physiopathology ; Electroencephalography/*methods ; Female ; Heart Arrest/diagnosis/physiopathology ; Humans ; Hypoxia, Brain/*diagnosis/*physiopathology ; Male ; Middle Aged ; Prognosis ; Prospective Studies ; Young Adult ; },
abstract = {OBJECTIVE: To assess the value of background continuity and amplitude fluctuations of the EEG for the prediction of outcome of comatose patients after cardiac arrest.<br><br>METHODS: In a prospective cohort study, we analyzed EEGs recorded in the first 72&#x202f;h after cardiac arrest. We defined the background continuity index (BCI) as the fraction of EEG not spent in suppressions (amplitudes <&#x202f;10&#x202f;&#xb5;V for &#x2265;&#x202f;0.5&#x202f;s), and the burst-suppression amplitude ratio (BSAR) as the mean amplitude ratio between non-suppressed and suppressed segments. Outcome was assessed at 6&#x202f;months and categorized as "good" (Cerebral Performance Category 1-2) or "poor" (CPC 3-5).<br><br>RESULTS: Of the 559 patients included, 46% had a good outcome. Combinations of BCI and BSAR resulted in the highest prognostic accuracies. Good outcome could be predicted at 24&#x202f;h with 57% sensitivity (95% confidence interval (CI): 48-67) at 90% specificity (95%-CI: 86-95). Poor outcome could be predicted at 12&#x202f;h with 50% sensitivity (95%-CI: 42-56) at 100% specificity (95%-CI: 99-100).<br><br>CONCLUSIONS: EEG background continuity and the amplitude ratio between bursts and suppressions reliably predict the outcome of postanoxic coma.<br><br>SIGNIFICANCE: The presented features provide an objective, rapid, and reliable tool to assist in EEG interpretation in the Intensive Care Unit.},
}
@article {pmid29804044,
year = {2018},
author = {Chatelle, C and Spencer, CA and Cash, SS and Hochberg, LR and Edlow, BL},
title = {Feasibility of an EEG-based brain-computer interface in the intensive care unit.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {129},
number = {8},
pages = {1519-1525},
pmid = {29804044},
issn = {1872-8952},
support = {K23 NS094538/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Consciousness Disorders/*diagnosis/*physiopathology ; Electroencephalography/instrumentation/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Auditory/physiology ; Evoked Potentials, Somatosensory/physiology ; Evoked Potentials, Visual/physiology ; Feasibility Studies ; Humans ; *Intensive Care Units ; Middle Aged ; Random Allocation ; },
abstract = {OBJECTIVE: We tested the feasibility of deploying a commercially available EEG-based brain-computer interface (BCI) in the intensive care unit (ICU) to detect consciousness in patients with acute disorders of consciousness (DoC) or locked-in syndrome (LIS).<br><br>METHODS: Ten patients (9 DoC, 1 LIS) and 10 healthy subjects (HS) were enrolled. The BCI utilized oddball auditory evoked potentials, vibrotactile evoked potentials (VTP) and motor imagery (MoI) to assess consciousness. We recorded the assessment completion rate and the time required for assessment, and we calculated the sensitivity and specificity of each paradigm for detecting behavioral signs of consciousness.<br><br>RESULTS: All 10 patients completed the assessment, 9 of whom required less than 1&#x202f;h. The LIS patient reported fatigue before the end of the session. The HS and LIS patient showed more consistent BCI responses than DoC patients, but overall there was no association between BCI responses and behavioral signs of consciousness.<br><br>CONCLUSIONS: The system is feasible to deploy in the ICU and may confirm consciousness in acute LIS, but it was unreliable in acute DoC.<br><br>SIGNIFICANCE: The accuracy of the paradigms for detecting consciousness must be improved and the duration of the protocol should be shortened before this commercially available BCI is ready for clinical implementation in the ICU in patients with acute DoC.},
}
@article {pmid29796437,
year = {2018},
author = {Scripcă, OR and Pădurariu, C and Boricean, NG and Botoș, L},
title = {Leukemic retinophaty, the first manifestation in a case of acute myelogenous leukemia.},
journal = {Romanian journal of ophthalmology},
volume = {62},
number = {1},
pages = {72-77},
pmid = {29796437},
issn = {2457-4325},
mesh = {Adult ; Disseminated Intravascular Coagulation ; Female ; Humans ; Leukemia, Myeloid, Acute ; Leukemia, Promyelocytic, Acute/*complications/diagnosis ; Optic Nerve Diseases/*etiology ; Retinal Hemorrhage ; },
abstract = {A 42-year-old woman, without a specific medical history presented at the Department of Emergency Ophthalmology accusing marked decrease of vision for the left eye (VA 1/ 100). The eye examination revealed an optic neuropathy with multiple retinal hemorrhages at the level of both eyes, but more acutely on the left eye. The brain computer tomography (CT) excluded the suspicion of increasing intracranial pressure. The common blood tests such as complete blood count (CBC), erythrocyte sedimentation rate, and inflammatory markers raised a high suspicion of a malignant haematological disease.},
}
@article {pmid29796060,
year = {2018},
author = {Gursel Ozmen, N and Gumusel, L and Yang, Y},
title = {A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification.},
journal = {Computational and mathematical methods in medicine},
volume = {2018},
number = {},
pages = {9890132},
pmid = {29796060},
issn = {1748-6718},
mesh = {Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Imagination ; Movement ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter "A." Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%), indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.},
}
@article {pmid29790469,
year = {2018},
author = {Campos, AR and Biscoito, L and Gasparinho, MG},
title = {Intraventricular Ganglioglioma Presenting with Spontaneous Hemorrhage.},
journal = {Acta medica portuguesa},
volume = {31},
number = {3},
pages = {170-175},
doi = {10.20344/amp.8943},
pmid = {29790469},
issn = {1646-0758},
mesh = {Adult ; Cerebral Hemorrhage/etiology ; Cerebral Ventricle Neoplasms/complications/*diagnosis/diagnostic imaging/surgery ; Ganglioglioma/complications/*diagnosis/diagnostic imaging/surgery ; Humans ; Male ; },
abstract = {Intraventricular gangliogliomas presenting with spontaneous hemorrhage are rare. Due to high density of important tracts lateral to the ventricular atrium, the intraparietal trans sulcal approach is a good option to remove lesions in this location. These tracts are displaced and sometimes destroyed by the presence of large masses. A 33-year-old male presented with a sudden headache and a generalized seizure. He had a left visual field hemianopia and left visual field neglect. Brain computer tomography and magnetic resonance imaging revealed a hemorrhagic tumor located in his right atrium. With the help of tractography an optimal corridor to the tumor through the intraparietal sulcus was planned. Gross total removal of a ganglioglioma was possible with recovery of visual impairment and control of epilepsy. The efficacy in using tractography as a planning tool for safe tumor removal is demonstrated with clinical, imagiological and histological data, and a surgical video.},
}
@article {pmid29783119,
year = {2018},
author = {Eles, JR and Vazquez, AL and Kozai, TDY and Cui, XT},
title = {In vivo imaging of neuronal calcium during electrode implantation: Spatial and temporal mapping of damage and recovery.},
journal = {Biomaterials},
volume = {174},
number = {},
pages = {79-94},
pmid = {29783119},
issn = {1878-5905},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS094404/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Calcium/*metabolism ; Calcium Signaling/*physiology ; Electric Impedance ; *Electrodes, Implanted ; Fluorescent Dyes/chemistry ; Male ; Mice ; Microelectrodes ; Microscopy, Fluorescence, Multiphoton/methods ; Models, Animal ; Nerve Degeneration/metabolism ; Neurons/*metabolism ; Propidium/administration &amp; dosage ; Silicone Elastomers/metabolism ; Time Factors ; },
abstract = {Implantable electrode devices enable long-term electrophysiological recordings for brain-machine interfaces and basic neuroscience research. Implantation of these devices, however, leads to neuronal damage and progressive neural degeneration that can lead to device failure. The present study uses in vivo two-photon microscopy to study the calcium activity and morphology of neurons before, during, and one month after electrode implantation to determine how implantation trauma injures neurons. We show that implantation leads to prolonged, elevated calcium levels in neurons within 150 μm of the electrode interface. These neurons show signs of mechanical distortion and mechanoporation after implantation, suggesting that calcium influx is related to mechanical trauma. Further, calcium-laden neurites develop signs of axonal injury at 1-3 h post-insert. Over the first month after implantation, physiological neuronal calcium activity increases, suggesting that neurons may be recovering. By defining the mechanisms of neuron damage after electrode implantation, our results suggest new directions for therapies to improve electrode longevity.},
}
@article {pmid29782968,
year = {2018},
author = {Kober, SE and Hinterleitner, V and Bauernfeind, G and Neuper, C and Wood, G},
title = {Trainability of hemodynamic parameters: A near-infrared spectroscopy based neurofeedback study.},
journal = {Biological psychology},
volume = {136},
number = {},
pages = {168-180},
doi = {10.1016/j.biopsycho.2018.05.009},
pmid = {29782968},
issn = {1873-6246},
mesh = {Adaptation, Physiological/*physiology ; Adult ; Deglutition/physiology ; Female ; Hemodynamics/*physiology ; Hemoglobins/metabolism ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Neurofeedback/*physiology ; Oxygen/metabolism ; Prefrontal Cortex/physiology ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {We investigated the trainability of the hemodynamic response as assessed with near-infrared spectroscopy (NIRS) during one neurofeedback (NF) session. Forty-eight participants were randomly assigned to four different groups that tried to either increase or decrease oxygenated (oxy-Hb) or deoxygenated hemoglobin (deoxy-Hb) over the inferior frontal gyrus during imagery of swallowing movements. Deoxy-Hb could be successfully up-regulated while oxy-Hb could be successfully down-regulated during NF. Participants were not able to down-regulate deoxy-Hb or to up-regulate oxy-Hb. These results show that the natural course of oxy- and deoxy-Hb during movement imagery can be reinforced by providing real-time feedback of the corresponding NIRS parameter since deoxy-Hb generally increases and oxy-Hb decreases during imagery of swallowing. Furthermore, signal-to-noise ratio of deoxy-Hb but not of oxy-Hb improved during training. Our results provide new insights into the trainability of the hemodynamic response as assessed with NIRS and have an impact on the application of NIRS-based real-time feedback.},
}
@article {pmid29777826,
year = {2018},
author = {Proulx, N and Samadani, AA and Chau, T},
title = {Quantifying fast optical signal and event-related potential relationships during a visual oddball task.},
journal = {NeuroImage},
volume = {178},
number = {},
pages = {119-128},
doi = {10.1016/j.neuroimage.2018.05.031},
pmid = {29777826},
issn = {1095-9572},
mesh = {Adult ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Functional Neuroimaging/*methods ; Humans ; Male ; Prefrontal Cortex/diagnostic imaging/*physiology ; Spectroscopy, Near-Infrared/*methods ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Event-related potentials (ERPs) have previously been used to confirm the existence of the fast optical signal (FOS) but validation methods have mainly been limited to exploring the temporal correspondence of FOS peaks to those of ERPs. The purpose of this study was to systematically quantify the relationship between FOS and ERP responses to a visual oddball task in both time and frequency domains. Near-infrared spectroscopy (NIRS) and electroencephalography (EEG) sensors were co-located over the prefrontal cortex while participants performed a visual oddball task. Fifteen participants completed 2 data collection sessions each, where they were instructed to keep a mental count of oddball images. The oddball condition produced a positive ERP at 200 ms followed by a negativity 300-500 ms after image onset in the frontal electrodes. In contrast to previous FOS studies, a FOS response was identified only in DC intensity signals and not in phase delay signals. A decrease in DC intensity was found 150-250 ms after oddball image onset with a 400-trial average in 10 of 15 participants. The latency of the positive 200 ms ERP and the FOS DC intensity decrease were significantly correlated for only 6 (out of 15) participants due to the low signal-to-noise ratio of the FOS response. Coherence values between the FOS and ERP oddball responses were found to be significant in the 3-5 Hz frequency band for 10 participants. A significant Granger causal influence of the ERP on the FOS oddball response was uncovered in the 2-6 Hz frequency band for 7 participants. Collectively, our findings suggest that, for a majority of participants, the ERP and the DC intensity signal of the FOS are spectrally coherent, specifically in narrow frequency bands previously associated with event-related oscillations in the prefrontal cortex. However, these electro-optical relationships were only found in a subset of participants. Further research on enhancing the quality of the event-related FOS signal is required before it can be practically exploited in applications such as brain-computer interfacing.},
}
@article {pmid29777674,
year = {2018},
author = {Li, R and Zhang, X and Li, H and Zhang, L and Lu, Z and Chen, J},
title = {An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.},
journal = {Brain research},
volume = {1692},
number = {},
pages = {142-153},
doi = {10.1016/j.brainres.2018.05.018},
pmid = {29777674},
issn = {1872-6240},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Fourier Analysis ; Humans ; Male ; Man-Machine Systems ; Models, Theoretical ; Photic Stimulation ; User-Computer Interface ; Young Adult ; },
abstract = {Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ± 2.64 dB, versus 3.38 ± 0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ± 7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ± 3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach.},
}
@article {pmid29774867,
year = {2018},
author = {Jiang, J and Yin, E and Wang, C and Xu, M and Ming, D},
title = {Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046025},
doi = {10.1088/1741-2552/aac605},
pmid = {29774867},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Electroencephalography (EEG) is a non-linear and non-stationary process, as a result, its features are unstable and often vary in quality across trials, which poses significant challenges to brain-computer interfaces (BCIs). One remedy to this problem is to adaptively collect sufficient EEG evidence using dynamic stopping (DS) strategies. The high-speed steady-state visual evoked potential (SSVEP)-based BCI has experienced tremendous progress in recent years. This study aims to further improve the high-speed SSVEP-based BCI by incorporating the DS strategy.<br><br>APPROACH: This study involves the development of two different DS strategies for a high-speed SSVEP-based BCI, which were based on the Bayes estimation and the discriminant analysis, respectively. To evaluate their performance, they were compared with the conventional fixed stopping (FS) strategy using simulated online tests on both our collected data and a public dataset. Two most effective SSVEP recognition methods were used for comparison, including the extended canonical correlation analysis (CCA) and the ensemble task-related component analysis (TRCA).<br><br>MAIN RESULTS: The DS strategies achieved significantly higher information transfer rates (ITRs) than the FS strategy for both datasets, improving 9.78% for the Bayes-based DS and 6.7% for the discriminant-based DS. Specifically, the discriminant-based DS strategy using ensemble TRCA performed the best for our collected data, reaching an average ITR of 353.3&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;67.1 bits min[-1] with a peak of 460 bits min[-1]. The Bayes-based DS strategy using ensemble TRCA had the highest ITR for the public dataset, reaching an average of 230.2&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;65.8 bits min[-1] with a peak of 304.1 bits min[-1].<br><br>SIGNIFICANCE: This study demonstrates that the proposed dynamic stopping strategies can further improve the performance of a SSVEP-based BCI, and hold promise for practical applications.},
}
@article {pmid29772957,
year = {2019},
author = {Slutzky, MW},
title = {Brain-Machine Interfaces: Powerful Tools for Clinical Treatment and Neuroscientific Investigations.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {25},
number = {2},
pages = {139-154},
pmid = {29772957},
issn = {1089-4098},
support = {R01NS09474/NS/NINDS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; F32 NS009474/NS/NINDS NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology/physiopathology ; *Brain-Computer Interfaces ; Humans ; Learning ; Membrane Potentials ; *Movement ; Nervous System Diseases/*rehabilitation ; },
abstract = {Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment. Because BMIs are controlled directly by the activity of prespecified neurons or cortical areas, they also provide a powerful paradigm with which to investigate fundamental questions about brain physiology, including neuronal behavior, learning, and the role of oscillations. This article reviews the clinical and neuroscientific applications of BMIs, with a primary focus on motor BMIs.},
}
@article {pmid29769435,
year = {2018},
author = {Lotte, F and Jeunet, C},
title = {Defining and quantifying users' mental imagery-based BCI skills: a first step.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046030},
doi = {10.1088/1741-2552/aac577},
pmid = {29769435},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/psychology ; Electroencephalography/*methods/psychology ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: While promising for many applications, electroencephalography (EEG)-based brain-computer interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification algorithm and the BCI user's performances. So far, classification accuracy (CA) is the typical metric used for both aspects. However, we argue in this paper that CA is a poor metric to study BCI users' skills. Here, we propose a definition and new metrics to quantify such BCI skills for mental imagery (MI) BCIs, independently of any classification algorithm.<br><br>APPROACH: We first show in this paper that CA is notably unspecific, discrete, training data and classifier dependent, and as such may not always reflect successful self-modulation of EEG patterns by the user. We then propose a definition of MI-BCI skills that reflects how well the user can self-modulate EEG patterns, and thus how well he could control an MI-BCI. Finally, we propose new performance metrics, classDis, restDist and classStab that specifically measure how distinct and stable the EEG patterns produced by the user are, independently of any classifier.<br><br>MAIN RESULTS: By re-analyzing EEG data sets with such new metrics, we indeed confirmed that CA may hide some increase in MI-BCI skills or hide the user inability to self-modulate a given EEG pattern. On the other hand, our new metrics could reveal such skill improvements as well as identify when a mental task performed by a user was no different than rest EEG.<br><br>SIGNIFICANCE: Our results showed that when studying MI-BCI users' skills, CA should be used with care, and complemented with metrics such as the new ones proposed. Our results also stressed the need to redefine BCI user training by considering the different BCI subskills and their measures. To promote the complementary use of our new metrics, we provide the Matlab code to compute them for free and open-source.},
}
@article {pmid29768990,
year = {2018},
author = {Chen, X and Zhao, B and Wang, Y and Xu, S and Gao, X},
title = {Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.},
journal = {International journal of neural systems},
volume = {28},
number = {8},
pages = {1850018},
doi = {10.1142/S0129065718500181},
pmid = {29768990},
issn = {1793-6462},
mesh = {Adult ; Attention/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; *Evoked Potentials, Visual ; Eye Movements ; Female ; Humans ; Male ; Pattern Recognition, Visual/physiology ; Photic Stimulation ; *Robotics ; Wireless Technology ; Young Adult ; },
abstract = {Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.},
}
@article {pmid29768971,
year = {2019},
author = {Corsi, MC and Chavez, M and Schwartz, D and Hugueville, L and Khambhati, AN and Bassett, DS and De Vico Fallani, F},
title = {Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {29},
number = {1},
pages = {1850014},
doi = {10.1142/S0129065718500144},
pmid = {29768971},
issn = {1793-6462},
mesh = {Adult ; Alpha Rhythm/physiology ; Beta Rhythm/physiology ; Brain-Computer Interfaces/*standards ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Magnetoencephalography/*methods ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.},
}
@article {pmid29765298,
year = {2018},
author = {Miran, S and Akram, S and Sheikhattar, A and Simon, JZ and Zhang, T and Babadi, B},
title = {Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {262},
pmid = {29765298},
issn = {1662-4548},
abstract = {Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurately as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.},
}
@article {pmid29764239,
year = {2018},
author = {Asp, F and Reinfeldt, S},
title = {Horizontal sound localisation accuracy in individuals with conductive hearing loss: effect of the bone conduction implant.},
journal = {International journal of audiology},
volume = {57},
number = {9},
pages = {657-664},
doi = {10.1080/14992027.2018.1470337},
pmid = {29764239},
issn = {1708-8186},
mesh = {Acoustic Stimulation ; Adult ; Aged ; Audiometry, Pure-Tone ; Auditory Perception ; Auditory Threshold ; *Bone Conduction ; Cross-Sectional Studies ; Eye Movements ; Female ; *Hearing Aids ; Hearing Loss, Conductive/diagnosis/physiopathology/psychology/*rehabilitation ; Humans ; Male ; Middle Aged ; Persons with Hearing Disabilities/psychology/*rehabilitation ; Photic Stimulation ; Prospective Studies ; Prosthesis Design ; Prosthesis Implantation/*instrumentation ; *Sound Localization ; Treatment Outcome ; Young Adult ; },
abstract = {OBJECTIVE: The objective of this study is to quantify the effect of the Bone Conduction Implant (BCI) on sound localisation accuracy in subjects with conductive hearing loss (CHL).<br><br>DESIGN: The subjects were tested in a horizontal sound localisation task in which localisation responses were objectively obtained by eye-tracking, in a prospective, cross-sectional design. The tests were performed unaided and unilaterally aided. The stimulus used had a spectrum similar to female speech and was presented at 63 and 73&#x2009;dB SPL. The main outcome measure was the error index (EI), ranging from 0 to 1 (perfect to random performance).<br><br>STUDY SAMPLE: Eleven subjects (aged 21-75 years, five females) with BCI participated in the study. Their mixed/conductive hearing loss was either unilateral (n&#x2009;=&#x2009;5) or bilateral (n&#x2009;=&#x2009;6).<br><br>RESULTS: Three of five subjects (60%) with unilateral CHL, and four of six subjects (67%) with bilateral CHL showed significantly improved sound localisation when using a unilateral BCI (p&#x2009;<&#x2009;.05). For the subjects with bilateral CHL, a distinct linear relation between aided sound localisation and hearing thresholds in the non-implant ear existed at 73&#x2009;dB SPL (18% decrease in the EI per 10&#x2009;dB decrease in pure-tone average, r&#x2009;=&#x2009;0.98, p&#x2009;<&#x2009;.001).<br><br>CONCLUSIONS: Individuals with mixed/conductive hearing loss may benefit from a unilateral BCI in sound localisation.},
}
@article {pmid29761128,
year = {2018},
author = {Cervera, MA and Soekadar, SR and Ushiba, J and Millán, JDR and Liu, M and Birbaumer, N and Garipelli, G},
title = {Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.},
journal = {Annals of clinical and translational neurology},
volume = {5},
number = {5},
pages = {651-663},
pmid = {29761128},
issn = {2328-9503},
abstract = {Brain-computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta-analysis evaluating the clinical effectiveness of BCI-based post-stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post-stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random-effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post-stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta-analysis. Motor improvements, mostly quantified by the upper limb Fugl-Meyer Assessment (FMA-UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA-UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI-induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post-stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.},
}
@article {pmid29755378,
year = {2018},
author = {Pasqualini, I and Blefari, ML and Tadi, T and Serino, A and Blanke, O},
title = {The Architectonic Experience of Body and Space in Augmented Interiors.},
journal = {Frontiers in psychology},
volume = {9},
number = {},
pages = {375},
pmid = {29755378},
issn = {1664-1078},
abstract = {The environment shapes our experience of space in constant interaction with the body. Architectonic interiors amplify the perception of space through the bodily senses; an effect also known as embodiment. The interaction of the bodily senses with the space surrounding the body can be tested experimentally through the manipulation of multisensory stimulation and measured via a range of behaviors related to bodily self-consciousness. Many studies have used Virtual Reality to show that visuotactile conflicts mediated via a virtual body or avatar can disrupt the unified subjective experience of the body and self. In the full-body illusion paradigm, participants feel as if the avatar was their body (ownership, self-identification) and they shift their center of awareness toward the position of the avatar (self-location). However, the influence of non-bodily spatial cues around the body on embodiment remains unclear, and data about the impact of architectonic space on human perception and self-conscious states are sparse. We placed participants into a Virtual Reality arena, where large and narrow virtual interiors were displayed with and without an avatar. We then applied synchronous or asynchronous visuotactile strokes to the back of the participants and avatar, or, to the front wall of the void interiors. During conditions of illusory self-identification with the avatar, participants reported sensations of containment, drift, and touch with the architectonic environment. The absence of the avatar suppressed such feelings, yet, in the large space, we found an effect of continuity between the physical and the virtual interior depending on the full-body illusion. We discuss subjective feelings evoked by architecture and compare the full-body illusion in augmented interiors to architectonic embodiment. A relevant outcome of this study is the potential to dissociate the egocentric, first-person view from the physical point of view through augmented architectonic space.},
}
@article {pmid29752486,
year = {2018},
author = {Goel, R and Ozdemir, RA and Nakagome, S and Contreras-Vidal, JL and Paloski, WH and Parikh, PJ},
title = {Effects of speed and direction of perturbation on electroencephalographic and balance responses.},
journal = {Experimental brain research},
volume = {236},
number = {7},
pages = {2073-2083},
pmid = {29752486},
issn = {1432-1106},
mesh = {Adult ; Brain/diagnostic imaging/*physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials/*physiology ; Feedback, Physiological ; Female ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Postural Balance/*physiology ; Proprioception/*physiology ; Reaction Time/physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {The modulation of perturbation-evoked potential (PEP) N1 as a function of different biomechanical characteristics of perturbation has been investigated before. However, it remains unknown whether the PEP N1 modulation contributes to the shaping of the functional postural response. To improve this understanding, we examined the modulation of functional postural response in relation to the PEP N1 response in ten healthy young subjects during unpredictable perturbations to their upright stance-translations of the support surface in a forward or backward direction at two different amplitudes of constant speed. Using independent components from the fronto-central region, obtained from subject-specific head models created from the MRI, our results show that the latency of onset of the functional postural response after the PEP N1 response was faster for forward than backward perturbations at a constant speed but was not affected by the speed of perturbation. Further, our results reinforce some of the previous findings that suggested that the N1 peak amplitude and peak latency are both modulated by the speed of perturbation but not by the direction of the perturbation. Our results improve the understanding of the relation between characteristics of perturbation and the neurophysiology of reactive balance control and may have implications for the design of brain-machine interfaces for populations with a higher risk of falls.},
}
@article {pmid29752229,
year = {2018},
author = {Zhang, Y and Guo, D and Li, F and Yin, E and Zhang, Y and Li, P and Zhao, Q and Tanaka, T and Yao, D and Xu, P},
title = {Correlated Component Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {5},
pages = {948-956},
doi = {10.1109/TNSRE.2018.2826541},
pmid = {29752229},
issn = {1558-0210},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Principal Component Analysis ; Psychomotor Performance ; Young Adult ; },
abstract = {A new method for steady-state visual evoked potentials (SSVEPs) frequency recognition is proposed to enhance the performance of SSVEP-based brain-computer interface (BCI). Correlated component analysis (CORCA) is introduced, which originally was designed to find linear combinations of electrodes that are consistent across subjects and maximally correlated between them. We propose a CORCA algorithm to learn spatial filters with multiple blocks of individual training data for SSVEP-based BCI scenario. The spatial filters are used to remove background noises by combining the multichannel electroencephalogram signals. We conduct a comparison between the proposed CORCA-based and the task-related component analysis (TRCA) based methods using a 40-class SSVEP benchmark data set recorded from 35 subjects. Our experimental study validates the efficiency of the CORCA-based method, and the extensive comparison results indicate that the CORCA-based method significantly outperforms the TRCA-based method. Superior performance demonstrates that the proposed method holds the promising potential to achieve satisfactory performance for SSVEP-based BCI with a large number of targets.},
}
@article {pmid29752228,
year = {2018},
author = {Edelman, BJ and Meng, J and Gulachek, N and Cline, CC and He, B},
title = {Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {5},
pages = {936-947},
pmid = {29752228},
issn = {1558-0210},
support = {F31 NS096964/NS/NINDS NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Attention/physiology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/statistics &amp; numerical data ; Evoked Potentials, Somatosensory/*physiology ; Evoked Potentials, Visual/*physiology ; Eye Movements/physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.},
}
@article {pmid29749917,
year = {2019},
author = {Romanelli, P and Piangerelli, M and Ratel, D and Gaude, C and Costecalde, T and Puttilli, C and Picciafuoco, M and Benabid, A and Torres, N},
title = {A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface.},
journal = {Journal of neurosurgery},
volume = {130},
number = {4},
pages = {1166-1179},
doi = {10.3171/2017.10.JNS17400},
pmid = {29749917},
issn = {1933-0693},
abstract = {OBJECTIVE: Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans.<br><br>METHODS: The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate (Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 K&#x3a9;. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid.<br><br>RESULTS: The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 K&#x3a9;. Wireless cortical stimulation induced movements of the upper and lower limbs, and elicited fine movements of the digits as well. After the monkey was euthanized, the grid was found to be encapsulated by a newly formed dural sheet. The grid removal was performed easily, and no direct adhesions of the grid to the cortex were found. Conventional histological studies showed no cortical damage in the brain region covered by the grid, except for a single microscopic spot of cortical necrosis (not visible to the naked eye) in a region that had undergone repeated procedures of electrical stimulation. Immunohistological studies of the cortex underlying the grid showed a mild inflammatory process.<br><br>CONCLUSIONS: This preliminary experience in a nonhuman primate shows that a wireless neuroprosthesis, with related long-term ECoG recording (up to 6 months) and multiple DCSs, was tolerated without sequelae. The authors predict that epilepsy surgery could realize great benefit from this novel prosthesis, providing an extended time span for ECoG recording.},
}
@article {pmid29749347,
year = {2018},
author = {Versek, C and Frasca, T and Zhou, J and Chowdhury, K and Sridhar, S},
title = {Electric field encephalography for brain activity monitoring.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046027},
doi = {10.1088/1741-2552/aac3f9},
pmid = {29749347},
issn = {1741-2552},
mesh = {Adult ; Aged ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; *Wireless Technology/instrumentation ; },
abstract = {OBJECTIVE: We describe an early-stage prototype of a new wireless electrophysiological sensor system, called NeuroDot, which can measure neuroelectric potentials and fields at the scalp in a new modality called Electric Field Encephalography (EFEG). We aim to establish the physical validity of the EFEG modality, and examine some of its properties and relative merits compared to EEG.<br><br>APPROACH: We designed a wireless neuroelectric measurement device based on the Texas Instrument ADS1299 Analog Front End platform and a sensor montage, using custom electrodes, to simultaneously measure EFEG and spatially averaged EEG over a localized patch of the scalp (2&#x2009;cm&#x2009;&#x2009;&#xd7;&#x2009;&#x2009;2&#x2009;cm). The signal properties of each modality were compared across tests of noise floor, Berger effect, steady-state visually evoked potential (ssVEP), signal-to-noise ratio (SNR), and others. In order to compare EFEG to EEG modalities in the frequency domain, we use a novel technique to compute spectral power densities and derive narrow-band SNR estimates for ssVEP signals. A simple binary choice brain-computer-interface (BCI) concept based on ssVEP is evaluated. Also, we present examples of high quality recording of transient Visually Evoked Potentials and Fields (tVEPF) that could be used for neurological studies.<br><br>MAIN RESULTS: We demonstrate the capability of the NeuroDot system to record high quality EEG signals comparable to some recent clinical and research grade systems on the market. We show that the locally-referenced EFEG metric is resistant to certain types of movement artifacts. In some ssVEP based measurements, the EFEG modality shows promising results, demonstrating superior signal to noise ratios than the same recording processed as an analogous EEG signal. We show that by using EFEG based ssVEP SNR estimates to perform a binary classification in a model BCI, the optimal information transfer rate (ITR) can be raised from 15 to 30 bits per minute-though these preliminary results are likely sensitive to inter-subject variations and choice of scalp locations, so require further investigation.<br><br>SIGNIFICANCE: Enhancement of ssVEP SNR using EFEG has the potential to improve visually based BCIs and diagnostic paradigms. The time domain analysis of tVEPF signals shows robust features in the electric field components that might have clinical relevance beyond classical VEP approaches.},
}
@article {pmid29747374,
year = {2018},
author = {Pérez-Vidal, AF and Garcia-Beltran, CD and Martínez-Sibaja, A and Posada-Gómez, R},
title = {Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {5},
pages = {},
pmid = {29747374},
issn = {1424-8220},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; Electroencephalography/economics/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wireless Technology ; Young Adult ; },
abstract = {The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC[&amp;reg;] device&amp;mdash;a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75[-]92%) was obtained in identifying P300 evoked potentials.},
}
@article {pmid29747059,
year = {2018},
author = {Wang, J and Feng, Z and Lu, N and Luo, J},
title = {Toward optimal feature and time segment selection by divergence method for EEG signals classification.},
journal = {Computers in biology and medicine},
volume = {97},
number = {},
pages = {161-170},
doi = {10.1016/j.compbiomed.2018.04.022},
pmid = {29747059},
issn = {1879-0534},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods.},
}
@article {pmid29746465,
year = {2018},
author = {Perdikis, S and Tonin, L and Saeedi, S and Schneider, C and Millán, JDR},
title = {The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.},
journal = {PLoS biology},
volume = {16},
number = {5},
pages = {e2003787},
pmid = {29746465},
issn = {1545-7885},
mesh = {*Brain-Computer Interfaces ; Humans ; *Learning ; *Machine Learning ; Quadriplegia/*rehabilitation ; },
abstract = {This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI-user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface-application, BCI output, and electroencephalography (EEG) neuroimaging-with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions.},
}
@article {pmid29745536,
year = {2018},
author = {Xiong, X and Fu, Y and Zhang, X and Li, S and Xu, B and Yin, X},
title = {[Design and experiment of a multi-modal electroencephalogram-near infrared spectroscopy helmet for simultaneously acquiring at the same brain area].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {2},
pages = {290-296},
pmid = {29745536},
issn = {1001-5515},
abstract = {Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combined with EEG was designed and verified by the experiment. According to the 10-20 system or 10-20 extended system, the diameter and spacing of NIRS probe and EEG electrode, NIRS probes were aligned with C3 and C4 as the reference electrodes, and NIRS probes were placed in the middle position between EEG electrodes to simultaneously measure variations of NIRS and the corresponding variation of EEG in the same functional brain area. The clamp holder and near infrared probe were coupled by tightening a screw. To verify the feasibility and effectiveness of the multi-modal EEG-NIRS helmet, NIRS and EEG signals were collected from six healthy subjects during six mental tasks involving the right hand clenching force and speed motor imagery. These signals may reflect brain activity related to hand clenching force and speed motor imagery in a certain extent. The experiment showed that the EEG-NIRS helmet designed in the paper was feasible and effective. It not only could provide support for the multi-modal motor imagery brain-computer interface based on EEG-NIRS, but also was expected to provide support for multi-modal brain functional imaging based on EEG-NIRS.},
}
@article {pmid29745532,
year = {2018},
author = {Li, S and Liu, X and Chen, Y and Wan, H},
title = {[Measurement and performance analysis of functional neural network].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {35},
number = {2},
pages = {258-265},
pmid = {29745532},
issn = {1001-5515},
abstract = {The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.},
}
@article {pmid29743934,
year = {2018},
author = {Dai, M and Zheng, D and Liu, S and Zhang, P},
title = {Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification.},
journal = {Computational and mathematical methods in medicine},
volume = {2018},
number = {},
pages = {9871603},
pmid = {29743934},
issn = {1748-6718},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Learning ; },
abstract = {Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.},
}
@article {pmid29740302,
year = {2018},
author = {Bagarinao, E and Yoshida, A and Ueno, M and Terabe, K and Kato, S and Isoda, H and Nakai, T},
title = {Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {158},
pmid = {29740302},
issn = {1662-5161},
abstract = {Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain-computer/brain-machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants' performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.},
}
@article {pmid29738806,
year = {2018},
author = {Belwafi, K and Romain, O and Gannouni, S and Ghaffari, F and Djemal, R and Ouni, B},
title = {An embedded implementation based on adaptive filter bank for brain-computer interface systems.},
journal = {Journal of neuroscience methods},
volume = {305},
number = {},
pages = {1-16},
doi = {10.1016/j.jneumeth.2018.04.013},
pmid = {29738806},
issn = {1872-678X},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces/economics ; Discriminant Analysis ; Electroencephalography/economics/methods ; Equipment Design ; Humans ; Imagination/physiology ; Linear Models ; Motor Activity/physiology ; Signal Processing, Computer-Assisted ; Software ; Time Factors ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy.<br><br>NEW-METHOD: This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features.<br><br>RESULTS: The proposed system performs fast classification within a time delay of 0.430&#x202f;s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7&#x202f;W.<br><br>Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost.<br><br>CONCLUSIONS: Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates.},
}
@article {pmid29737970,
year = {2018},
author = {Islam, MR and Tanaka, T and Molla, MKI},
title = {Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046021},
doi = {10.1088/1741-2552/aac313},
pmid = {29737970},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/instrumentation/*methods ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Random Allocation ; Young Adult ; },
abstract = {OBJECTIVE: When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks.<br><br>APPROACH: A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM).<br><br>MAIN RESULTS: Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1).<br><br>SIGNIFICANCE: The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.},
}
@article {pmid29734383,
year = {2018},
author = {Shin, J and Müller, KR and Hwang, HJ},
title = {Eyes-closed hybrid brain-computer interface employing frontal brain activation.},
journal = {PloS one},
volume = {13},
number = {5},
pages = {e0196359},
pmid = {29734383},
issn = {1932-6203},
mesh = {Adult ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods/statistics &amp; numerical data ; Female ; Humans ; Male ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted/instrumentation ; Spectroscopy, Near-Infrared/*methods/statistics &amp; numerical data ; },
abstract = {Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9%) and NIRS-based BCI (75.9 ± 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.},
}
@article {pmid29733940,
year = {2018},
author = {Hettiarachchi, IT and Babaei, T and Nguyen, T and Lim, CP and Nahavandi, S},
title = {A fresh look at functional link neural network for motor imagery-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {305},
number = {},
pages = {28-35},
doi = {10.1016/j.jneumeth.2018.05.001},
pmid = {29733940},
issn = {1872-678X},
mesh = {Bayes Theorem ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; Linear Models ; Motor Activity/*physiology ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly applied for BCI classifiers is the multilayer perceptron (MLP). When appropriately designed with optimal number of neuron layers and number of neurons per layer, the ANN can act as a universal approximator. However, due to the low signal-to-noise ratio of EEG signal data, overtraining problem may become an inherent issue, causing these universal approximators to fail in real-time applications.<br><br>NEW METHOD: In this study we introduce a higher order neural network, namely the functional link neural network (FLNN) as a classifier for motor imagery (MI)-based BCI systems, to remedy the drawbacks in MLP.<br><br>RESULTS: We compare the proposed method with competing classifiers such as linear decomposition analysis, na&#xef;ve Bayes, k-nearest neighbours, support vector machine and three MLP architectures. Two multi-class benchmark datasets from the BCI competitions are used. Common spatial pattern algorithm is utilized for feature extraction to build classification models.<br><br>FLNN reports the highest average Kappa value over multiple subjects for both the BCI competition datasets, under similarly preprocessed data and extracted features. Further, statistical comparison results over multiple subjects show that the proposed FLNN classification method yields the best performance among the competing classifiers.<br><br>CONCLUSIONS: Findings from this study imply that the proposed method, which has less computational complexity compared to the MLP, can be implemented effectively in practical MI-based BCI systems.},
}
@article {pmid29729902,
year = {2018},
author = {Roelfsema, PR and Denys, D and Klink, PC},
title = {Mind Reading and Writing: The Future of Neurotechnology.},
journal = {Trends in cognitive sciences},
volume = {22},
number = {7},
pages = {598-610},
doi = {10.1016/j.tics.2018.04.001},
pmid = {29729902},
issn = {1879-307X},
support = {339490/ERC_/European Research Council/International ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Consciousness/physiology ; Humans ; Mental Processes/*physiology ; },
abstract = {Recent advances in neuroscience and technology have made it possible to record from large assemblies of neurons and to decode their activity to extract information. At the same time, available methods to stimulate the brain and influence ongoing processing are also rapidly expanding. These developments pave the way for advanced neurotechnological applications that directly read from, and write to, the human brain. While such technologies are still primarily used in restricted therapeutic contexts, this may change in the future once their performance has improved and they become more widely applicable. Here, we provide an overview of methods to interface with the brain, speculate about potential applications, and discuss important issues associated with a neurotechnologically assisted future.},
}
@article {pmid29726085,
year = {2018},
author = {Mason, SJ and Catto, JWF and Downing, A and Bottomley, SE and Glaser, AW and Wright, P},
title = {Evaluating patient-reported outcome measures (PROMs) for bladder cancer: a systematic review using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist.},
journal = {BJU international},
volume = {122},
number = {5},
pages = {760-773},
pmid = {29726085},
issn = {1464-410X},
mesh = {Adult ; Aged ; Aged, 80 and over ; Checklist ; Consensus ; Databases, Factual ; Female ; Humans ; Male ; Middle Aged ; *Patient Reported Outcome Measures ; Psychometrics/methods/*standards ; Surveys and Questionnaires/*standards ; *Urinary Bladder Neoplasms ; },
abstract = {Patient-reported outcome measures (PROMs) are important tools used to understand patient-focused outcomes from care. Various PROMs have been developed for patients with bladder cancer (BC), although the disease's heterogeneity makes selection difficult. Accurate measurement of health-related quality of life (HRQL) can only be achieved if the PROM chosen is 'fit for purpose' (i.e. psychometrically sound). Systematic reviews of psychometric properties are useful for selecting the best PROM for a specific purpose. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) developed a checklist to improve the selection of health measurement instruments as part of a review process. Our aims were to undertake a systematic review, using the COSMIN criteria, to assess the quality of studies that report the psychometric properties of PROMs used with people with BC and determine the psychometric quality of these PROMs. An electronic search of seven databases including PubMed, MEDLINE and EMBASE (PROSPERO reference CRD42016051974) was undertaken to identify English language publications, published between January 1990 and September 2017 that evaluated psychometric properties of PROMs used in BC research. Two researchers independently screened abstracts and selected full-text papers. Studies were rated on methodological quality using the COSMIN checklist. Overall, 4663 records were screened and 23 studies, reporting outcomes in 3568 patients, were evaluated using the COSMIN checklist. Most PROMs had limited information reported about their psychometric properties. Studies reporting on the Bladder Cancer Index (BCI) and Functional Assessment of Cancer Therapy Vanderbilt Cystectomy Index (FACT-VCI) provided the most detail and these PROMs could be evaluated on the most COSMIN properties. Based on the available evidence, no existing PROM stands out as the most appropriate to measure HRQL in BC populations. This is due to two factors; (i) the heterogeneity of BC and its treatments (ii) no PROM was evaluated on all COSMIN measurement properties due to a lack of validation studies. We suggest future evaluation of generic, cancer generic and BC-specific PROMs to better understand their application with BC populations and propose strategies to help clinicians and researchers.},
}
@article {pmid29718809,
year = {2018},
author = {Mondello, SE and Sunshine, MD and Fischedick, AE and Dreyer, SJ and Horwitz, GD and Anikeeva, P and Horner, PJ and Moritz, CT},
title = {Optogenetic surface stimulation of the rat cervical spinal cord.},
journal = {Journal of neurophysiology},
volume = {120},
number = {2},
pages = {795-811},
doi = {10.1152/jn.00461.2017},
pmid = {29718809},
issn = {1522-1598},
mesh = {Animals ; Cervical Cord/*physiology ; Dependovirus/physiology ; Electromyography ; Female ; Forelimb/innervation/*physiology ; GABAergic Neurons/physiology ; Muscle, Skeletal/innervation/*physiology ; Neurons/*physiology ; *Optogenetics ; Rats, Long-Evans ; },
abstract = {Electrical intraspinal microstimulation (ISMS) at various sites along the cervical spinal cord permits forelimb muscle activation, elicits complex limb movements and may enhance functional recovery after spinal cord injury. Here, we explore optogenetic spinal stimulation (OSS) as a less invasive and cell type-specific alternative to ISMS. To map forelimb muscle activation by OSS in rats, adeno-associated viruses (AAV) carrying the blue-light sensitive ion channels channelrhodopsin-2 (ChR2) and Chronos were injected into the cervical spinal cord at different depths and volumes. Following an AAV incubation period of several weeks, OSS-induced forelimb muscle activation and movements were assessed at 16 sites along the dorsal surface of the cervical spinal cord. Three distinct movement types were observed. We find that AAV injection volume and depth can be titrated to achieve OSS-based activation of several movements. Optical stimulation of the spinal cord is thus a promising method for dissecting the function of spinal circuitry and targeting therapies following injury. NEW &amp; NOTEWORTHY Optogenetics in the spinal cord can be used both for therapeutic treatments and to uncover basic mechanisms of spinal cord physiology. For the first time, we describe the methodology and outcomes of optogenetic surface stimulation of the rat spinal cord. Specifically, we describe the evoked responses of forelimbs and address the effects of different adeno-associated virus injection paradigms. Additionally, we are the first to report on the limitations of light penetration through the rat spinal cord.},
}
@article {pmid29717469,
year = {2019},
author = {Stramondo, JA},
title = {The Distinction Between Curative and Assistive Technology.},
journal = {Science and engineering ethics},
volume = {25},
number = {4},
pages = {1125-1145},
pmid = {29717469},
issn = {1471-5546},
mesh = {Persons with Disabilities/*psychology ; *Ethical Analysis ; Humans ; Identification, Psychological ; Recovery of Function ; Role ; *Self Concept ; Self-Help Devices/*psychology ; Social Identification ; },
abstract = {Disability activists have sometimes claimed their disability has actually increased their well-being. Some even say they would reject a cure to keep these gains. Yet, these same activists often simultaneously propose improvements to the quality and accessibility of assistive technology. However, for any argument favoring assistive over curative technology (or vice versa) to work, there must be a coherent distinction between the two. This line is already vague and will become even less clear with the emergence of novel technologies. This paper asks and tries to answer the question: what is it about the paradigmatic examples of curative and assistive technologies that make them paradigmatic and how can these defining features help us clarify the hard cases? This analysis will begin with an argument that, while the common views of this distinction adequately explain the paradigmatic cases, they fail to accurately pick out the relevant features of those technologies that make them paradigmatic and to provide adequate guidance for parsing the hard cases. Instead, it will be claimed that these categories of curative or assistive technologies are defined by the role the technologies play in establishing a person's relational narrative identity as a member of one of two social groups: disabled people or non-disabled people.},
}
@article {pmid29717203,
year = {2018},
author = {Ciaramidaro, A and Toppi, J and Casper, C and Freitag, CM and Siniatchkin, M and Astolfi, L},
title = {Multiple-Brain Connectivity During Third Party Punishment: an EEG Hyperscanning Study.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {6822},
pmid = {29717203},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Affect/physiology ; *Altruism ; Analysis of Variance ; Brain Waves/*physiology ; Connectome/*psychology ; Cooperative Behavior ; Decision Making/physiology ; Empathy/*physiology ; Games, Experimental ; Healthy Volunteers ; Humans ; Interpersonal Relations ; Male ; Punishment/*psychology ; Self Report ; Stress, Psychological/psychology ; Young Adult ; },
abstract = {Compassion is a particular form of empathic reaction to harm that befalls others and is accompanied by a desire to alleviate their suffering. This altruistic behavior is often manifested through altruistic punishment, wherein individuals penalize a deprecated human's actions, even if they are directed toward strangers. By adopting a dual approach, we provide empirical evidence that compassion is a multifaceted prosocial behavior and can predict altruistic punishment. In particular, in this multiple-brain connectivity study in an EEG hyperscanning setting, compassion was examined during real-time social interactions in a third-party punishment (TPP) experiment. We observed that specific connectivity patterns were linked to behavioral and psychological intra- and interpersonal factors. Thus, our results suggest that an ecological approach based on simultaneous dual-scanning and multiple-brain connectivity is suitable for analyzing complex social phenomena.},
}
@article {pmid29714718,
year = {2018},
author = {Omedes, J and Schwarz, A and Müller-Putz, GR and Montesano, L},
title = {Factors that affect error potentials during a grasping task: toward a hybrid natural movement decoding BCI.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046023},
doi = {10.1088/1741-2552/aac1a1},
pmid = {29714718},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Hand Strength/*physiology ; Humans ; Male ; Movement/*physiology ; Photic Stimulation/*methods ; *Virtual Reality ; Young Adult ; },
abstract = {OBJECTIVE: In this manuscript, we consider factors that may affect the design of a hybrid brain-computer interface (BCI). We combine neural correlates of natural movements and interaction error-related potentials (ErrP) to perform a 3D reaching task, focusing on the impact that such factors have on the evoked ErrP signatures and in their classification.<br><br>APPROACH: Users attempted to control a 3D virtual interface that simulated their own hand, to reach and grasp two different objects. Three factors of interest were modulated during the experimentation: (1) execution speed of the grasping, (2) type of grasping and (3) mental strategy (motor imagery or real motion) used to produce motor commands. Thirteen healthy subjects carried out the protocol. The peaks and latencies of the ErrP were analyzed for the different factors as well as the classification performance.<br><br>MAIN RESULTS: ErrP are evoked for erroneous commands decoded from neural correlates of natural movements. The analysis of variance (ANOVA) analyses revealed that latency and magnitude of the most characteristic ErrP peaks were significantly influenced by the speed at which the grasping was executed, but not the type of grasp. This resulted in an greater accuracy of single-trial decoding of errors for fast movements (75.65%) compared to slow ones (68.99%).<br><br>SIGNIFICANCE: Understanding the effects of combining paradigms is a first step to design hybrid BCI that optimize decoding accuracy and can be deployed in motor substitution and neuro-rehabilitation applications.},
}
@article {pmid29713262,
year = {2018},
author = {Han, J and Zhao, Y and Sun, H and Chen, J and Ke, A and Xu, G and Zhang, H and Zhou, J and Wang, C},
title = {A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {217},
pmid = {29713262},
issn = {1662-4548},
abstract = {Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.},
}
@article {pmid29710754,
year = {2018},
author = {Kim, K and Song, WK and Chong, WS and Yu, CH},
title = {Structural analysis of a rehabilitative training system based on a ceiling rail for safety of hemiplegia patients.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {26},
number = {S1},
pages = {259-268},
pmid = {29710754},
issn = {1878-7401},
mesh = {Body Weight ; Brain-Computer Interfaces ; Equipment Design ; Female ; Gait/*physiology ; Hemiplegia/*rehabilitation ; Humans ; Lower Extremity/*physiology ; Male ; *Patient Safety ; Postural Balance/physiology ; },
abstract = {The body-weight support (BWS) function, which helps to decrease load stresses on a user, is an effective tool for gait and balance rehabilitation training for elderly people with weakened lower-extremity muscular strength, hemiplegic patients, etc. This study conducts structural analysis to secure user safety in order to develop a rail-type gait and balance rehabilitation training system (RRTS). The RRTS comprises a rail, trolley, and brain-machine interface. The rail (platform) is connected to the ceiling structure, bearing the loads of the RRTS and of the user and allowing locomobility. The trolley consists of a smart drive unit (SDU) that assists the user with forward and backward mobility and a body-weight support (BWS) unit that helps the user to control his/her body-weight load, depending on the severity of his/her hemiplegia. The brain-machine interface estimates and measures on a real-time basis the body-weight (load) of the user and the intended direction of his/her movement. Considering the weight of the system and the user, the mechanical safety performance of the system frame under an applied 250-kg static load is verified through structural analysis using ABAQUS (6.14-3) software. The maximum stresses applied on the rail and trolley under the given gravity load of 250 kg, respectively, are 18.52 MPa and 48.44 MPa. The respective safety factors are computed to be 7.83 and 5.26, confirming the RRTS's mechanical safety. An RRTS with verified structural safety could be utilized for gait movement and balance rehabilitation and training for patients with hemiplegia.},
}
@article {pmid29706920,
year = {2018},
author = {Pereira, M and Argelaguet, F and Millán, JDR and Lécuyer, A},
title = {Novice Shooters With Lower Pre-shooting Alpha Power Have Better Performance During Competition in a Virtual Reality Scenario.},
journal = {Frontiers in psychology},
volume = {9},
number = {},
pages = {527},
pmid = {29706920},
issn = {1664-1078},
abstract = {Competition changes the environment for athletes. The difficulty of training for such stressful events can lead to the well-known effect of "choking" under pressure, which prevents athletes from performing at their best level. To study the effect of competition on the human brain, we recorded pilot electroencephalography (EEG) data while novice shooters were immersed in a realistic virtual environment representing a shooting range. We found a differential between-subject effect of competition on mu (8-12 Hz) oscillatory activity during aiming; compared to training, the more the subject was able to desynchronize his mu rhythm during competition, the better was his shooting performance. Because this differential effect could not be explained by differences in simple measures of the kinematics and muscular activity, nor by the effect of competition or shooting performance per se, we interpret our results as evidence that mu desynchronization has a positive effect on performance during competition.},
}
@article {pmid29704221,
year = {2018},
author = {Reynaud, D and Sergent, F and Abi Nahed, R and Brouillet, S and Benharouga, M and Alfaidy, N},
title = {Erratum to: EG-VEGF Maintenance Over Early Gestation to Develop a Pregnancy-Induced Hypertensive Animal Model.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {1710},
number = {},
pages = {E1},
doi = {10.1007/978-1-4939-7498-6_29},
pmid = {29704221},
issn = {1940-6029},
}
@article {pmid29698736,
year = {2018},
author = {Ono, Y and Wada, K and Kurata, M and Seki, N},
title = {Enhancement of motor-imagery ability via combined action observation and motor-imagery training with proprioceptive neurofeedback.},
journal = {Neuropsychologia},
volume = {114},
number = {},
pages = {134-142},
doi = {10.1016/j.neuropsychologia.2018.04.016},
pmid = {29698736},
issn = {1873-3514},
mesh = {Brain/*physiology ; Electroencephalography ; Evoked Potentials/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Imagination/*physiology ; Male ; Motor Activity/physiology ; Neurofeedback/*methods ; Young Adult ; },
abstract = {Varied individual ability to control the sensory-motor rhythms may limit the potential use of motor-imagery (MI) in neurorehabilitation and neuroprosthetics. We employed neurofeedback training of MI under action observation (AO: AOMI) with proprioceptive feedback and examined whether it could enhance MI-induced event-related desynchronization (ERD). Twenty-eight healthy young adults participated in the neurofeedback training. They performed MI while watching a video of hand-squeezing motion from a first-person perspective. Eleven participants received correct proprioceptive feedback of the same hand motion with the video, via an exoskeleton robot attached to their hand, upon their successful generation of ERD. Another nine participants received random feedback. The training lasted for approximately 20 min per day and continued for 6 days within an interval of 2 weeks. MI-ERD power was evaluated separately, without AO, on each experimental day. The MI-ERD power of the participants receiving correct feedback, as opposed to random feedback, was significantly increased after training. An additional experiment in which the remaining eight participants were trained with auditory instead of proprioceptive feedback failed to show statistically significant increase in MI-ERD power. The significant training effect obtained in shorter training time relative to previously proposed methods suggests the superiority of AOMI training and physiologically-congruent proprioceptive feedback to enhance the MI-ERD power. The proposed neurofeedback training could help patients with motor deficits to attain better use of brain-machine interfaces for rehabilitation and/or prosthesis.},
}
@article {pmid29695959,
year = {2018},
author = {Kim, H and Kim, M},
title = {PyMUS: Python-Based Simulation Software for Virtual Experiments on Motor Unit System.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {15},
pmid = {29695959},
issn = {1662-5196},
abstract = {We constructed a physiologically plausible computationally efficient model of a motor unit and developed simulation software that allows for integrative investigations of the input-output processing in the motor unit system. The model motor unit was first built by coupling the motoneuron model and muscle unit model to a simplified axon model. To build the motoneuron model, we used a recently reported two-compartment modeling approach that accurately captures the key cell-type-related electrical properties under both passive conditions (somatic input resistance, membrane time constant, and signal attenuation properties between the soma and the dendrites) and active conditions (rheobase current and afterhyperpolarization duration at the soma and plateau behavior at the dendrites). To construct the muscle unit, we used a recently developed muscle modeling approach that reflects the experimentally identified dependencies of muscle activation dynamics on isometric, isokinetic and dynamic variation in muscle length over a full range of stimulation frequencies. Then, we designed the simulation software based on the object-oriented programing paradigm and developed the software using open-source Python language to be fully operational using graphical user interfaces. Using the developed software, separate simulations could be performed for a single motoneuron, muscle unit and motor unit under a wide range of experimental input protocols, and a hierarchical analysis could be performed from a single channel to the entire system behavior. Our model motor unit and simulation software may represent efficient tools not only for researchers studying the neural control of force production from a cellular perspective but also for instructors and students in motor physiology classroom settings.},
}
@article {pmid29694343,
year = {2018},
author = {, },
title = {Correction: Brain-Computer Interface-Based Communication in the Completely Locked-In State.},
journal = {PLoS biology},
volume = {16},
number = {4},
pages = {e1002629},
pmid = {29694343},
issn = {1545-7885},
abstract = {[This corrects the article DOI: 10.1371/journal.pbio.1002593.].},
}
@article {pmid29694281,
year = {2018},
author = {Salelkar, S and Somasekhar, GM and Ray, S},
title = {Distinct frequency bands in the local field potential are differently tuned to stimulus drift rate.},
journal = {Journal of neurophysiology},
volume = {120},
number = {2},
pages = {681-692},
pmid = {29694281},
issn = {1522-1598},
support = {500145-Z-09-Z/WTDBT_/DBT-Wellcome Trust India Alliance/India ; },
mesh = {Animals ; Female ; *Gamma Rhythm ; Macaca radiata ; Neurons/*physiology ; Photic Stimulation ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Local field potential (LFP) recorded with a microelectrode reflects the activity of several neural processes, including afferent synaptic inputs, microcircuit-level computations, and spiking activity. Objectively probing their contribution requires a design that allows dissociation between these potential contributors. Earlier reports have shown that the primate lateral geniculate nucleus (LGN) has a higher temporal frequency (drift rate) cutoff than the primary visual cortex (V1), such that at higher drift rates inputs into V1 from the LGN continue to persist, whereas output ceases, permitting partial dissociation. Using chronic microelectrode arrays, we recorded spikes and LFP from V1 of passively fixating macaques while presenting sinusoidal gratings drifting over a wide range. We further optimized the gratings to produce strong gamma oscillations, since recent studies in rodent V1 have reported LGN-dependent narrow-band gamma oscillations. Consistent with earlier reports, power in higher LFP frequencies (above ~140 Hz) tracked the population firing rate and were tuned to preferred drift rates similar to those for spikes. Significantly, power in the lower (up to ~40 Hz) frequencies increased transiently in the early epoch after stimulus onset, even at high drift rates, and had preferred drift rates higher than for spikes/high gamma. Narrow-band gamma (50-80 Hz) power was not strongly correlated with power in high or low frequencies and had much lower preferred temporal frequencies. Our results demonstrate that distinct frequency bands of the V1 LFP show diverse tuning profiles, which may potentially convey different attributes of the underlying neural activity. NEW &amp; NOTEWORTHY In recent years the local field potential (LFP) has been increasingly studied, but interpreting its rich frequency content has been difficult. We use a stimulus manipulation that generates different tuning profiles for low, gamma, and high frequencies of the LFP, suggesting contributions from potentially different sources. Our results have possible implications for design of better neural prosthesis systems and brain-machine interfacing applications.},
}
@article {pmid29694279,
year = {2018},
author = {Milekovic, T and Sarma, AA and Bacher, D and Simeral, JD and Saab, J and Pandarinath, C and Sorice, BL and Blabe, C and Oakley, EM and Tringale, KR and Eskandar, E and Cash, SS and Henderson, JM and Shenoy, KV and Donoghue, JP and Hochberg, LR},
title = {Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.},
journal = {Journal of neurophysiology},
volume = {120},
number = {1},
pages = {343-360},
pmid = {29694279},
issn = {1522-1598},
support = {I01 RX002295/RX/RRD VA/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; S10 OD016366/OD/NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/complications/physiopathology/*rehabilitation ; Brain Stem/physiopathology ; *Brain-Computer Interfaces ; *Communication ; Evoked Potentials ; Humans ; Quadriplegia/physiopathology/*rehabilitation ; Stroke/etiology/*physiopathology ; Stroke Rehabilitation/instrumentation/*methods ; },
abstract = {Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brain stem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver. NEW &amp; NOTEWORTHY This study demonstrates, for the first time, stable repeated use of an intracortical brain-computer interface by people with tetraplegia over up to four and a half months. The approach uses local field potentials (LFPs), signals that may be more stable than neuronal action potentials, to decode participants' commands. Throughout the several months of evaluation, the decoder remained unchanged; thus no technical interventions were required to maintain consistent brain-computer interface operation.},
}
@article {pmid29689307,
year = {2018},
author = {Corbet, T and Iturrate, I and Pereira, M and Perdikis, S and Millán, JDR},
title = {Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance.},
journal = {NeuroImage},
volume = {176},
number = {},
pages = {268-276},
doi = {10.1016/j.neuroimage.2018.04.005},
pmid = {29689307},
issn = {1095-9572},
mesh = {Adult ; Axons/*physiology ; Brain-Computer Interfaces ; Cortical Synchronization/physiology ; Electric Stimulation/*methods ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Kinesthesis/*physiology ; Male ; Motor Activity/*physiology ; Motor Neurons/*physiology ; Sensorimotor Cortex/physiology ; Sensory Receptor Cells/*physiology ; Sensory Thresholds/*physiology ; },
abstract = {Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.},
}
@article {pmid29688217,
year = {2018},
author = {He, Y and Luu, TP and Nathan, K and Nakagome, S and Contreras-Vidal, JL},
title = {A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.},
journal = {Scientific data},
volume = {5},
number = {},
pages = {180074},
pmid = {29688217},
issn = {2052-4463},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain/diagnostic imaging/physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neuroimaging ; *Walking ; },
abstract = {We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.},
}
@article {pmid29686644,
year = {2018},
author = {Semprini, M and Laffranchi, M and Sanguineti, V and Avanzino, L and De Icco, R and De Michieli, L and Chiappalone, M},
title = {Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {212},
pmid = {29686644},
issn = {1664-2295},
abstract = {Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.},
}
@article {pmid29686310,
year = {2018},
author = {Lázaro-Muñoz, G and Yoshor, D and Beauchamp, MS and Goodman, WK and McGuire, AL},
title = {Continued access to investigational brain implants.},
journal = {Nature reviews. Neuroscience},
volume = {19},
number = {6},
pages = {317-318},
pmid = {29686310},
issn = {1471-0048},
support = {R01 EY023336/EY/NEI NIH HHS/United States ; R01 MH114854/MH/NIMH NIH HHS/United States ; U01 NS098976/NS/NINDS NIH HHS/United States ; UH3 NS100549/NS/NINDS NIH HHS/United States ; },
mesh = {Brain Diseases/*therapy ; *Brain-Computer Interfaces/economics/ethics ; Clinical Trials as Topic ; *Deep Brain Stimulation/economics/ethics ; Duty to Recontact ; Humans ; *Neural Prostheses/economics/ethics ; *Research Subjects ; },
abstract = {Brain implants are being trialled for their potential to ameliorate treatment-resistant conditions or to restore function. However, there are no clear guidelines for continued access to brain implants for trial participants whose symptoms improve with these devices.},
}
@article {pmid29683431,
year = {2018},
author = {Xu, M and Xiao, X and Wang, Y and Qi, H and Jung, TP and Ming, D},
title = {A Brain-Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {5},
pages = {1166-1175},
doi = {10.1109/TBME.2018.2799661},
pmid = {29683431},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {GOAL: Traditional visual brain-computer interfaces (BCIs) preferred to use large-size stimuli to attract the user's attention and elicit distinct electroencephalography (EEG) features. However, the visual stimuli are of no interest to the users as they just serve as the hidden codes behind the characters. Furthermore, using stronger visual stimuli could cause visual fatigue and other adverse symptoms to users. Therefore, it's imperative for visual BCIs to use small and inconspicuous visual stimuli to code characters.<br><br>METHODS: This study developed a new BCI speller based on miniature asymmetric visual evoked potentials (aVEPs), which encodes 32 characters with a space-code division multiple access scheme and decodes EEG features with a discriminative canonical pattern matching algorithm. Notably, the visual stimulus used in this study only subtended 0.5&#xb0; of visual angle and was placed outside the fovea vision on the lateral side, which could only induce a miniature potential about 0.5 &#x3bc;V in amplitude and about 16.5&#xa0;dB in signal-to-noise rate. A total of 12 subjects were recruited to use the miniature aVEP speller in both offline and online tests.<br><br>RESULTS: Information transfer rates up to 63.33&#xa0;b/min could be achieved from online tests (online demo URL: https://www.youtube.com/edit?o=U&amp;video_id=kC7btB3mvGY).<br><br>CONCLUSION: Experimental results demonstrate the feasibility of using very small and inconspicuous visual stimuli to implement an efficient BCI system, even though the elicited EEG features are very weak.<br><br>SIGNIFICANCE: The proposed innovative technique can broaden the category of BCIs and strengthen the brain-computer communication.},
}
@article {pmid29682000,
year = {2018},
author = {Ma, T and Li, F and Li, P and Yao, D and Zhang, Y and Xu, P},
title = {An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface.},
journal = {Computational and mathematical methods in medicine},
volume = {2018},
number = {},
pages = {9476432},
pmid = {29682000},
issn = {1748-6718},
mesh = {*Brain-Computer Interfaces/standards/statistics &amp; numerical data ; Calibration ; Cluster Analysis ; Computational Biology ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Fuzzy Logic ; Humans ; Male ; Models, Neurological ; Reproducibility of Results ; Support Vector Machine ; Young Adult ; },
abstract = {Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.},
}
@article {pmid29681924,
year = {2018},
author = {Hossain, I and Khosravi, A and Hettiarachchi, I and Nahavandi, S},
title = {Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {6323414},
pmid = {29681924},
issn = {1687-5273},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Electrooculography ; Humans ; *Imagination/physiology ; *Motor Activity/physiology ; *Transfer, Psychology/physiology ; },
abstract = {A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.},
}
@article {pmid29681792,
year = {2018},
author = {Meng, J and Edelman, BJ and Olsoe, J and Jacobs, G and Zhang, S and Beyko, A and He, B},
title = {A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {227},
pmid = {29681792},
issn = {1662-4548},
abstract = {Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.},
}
@article {pmid29677690,
year = {2018},
author = {Emami, Z and Chau, T},
title = {Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {129},
number = {6},
pages = {1268-1275},
doi = {10.1016/j.clinph.2018.03.015},
pmid = {29677690},
issn = {1872-8952},
mesh = {Adult ; Attention/*physiology ; Beta Rhythm/*physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Neurofeedback ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {OBJECTIVES: Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance.<br><br>METHODS: Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy.<br><br>RESULTS: The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5&#x202f;&#xb1;&#x202f;14% for non-distractor trials, and 78.3&#x202f;&#xb1;&#x202f;17% for distractor trials.<br><br>CONCLUSION: This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors.<br><br>SIGNIFICANCE: A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home.},
}
@article {pmid29677475,
year = {2018},
author = {Pavlov, VA and Chavan, SS and Tracey, KJ},
title = {Molecular and Functional Neuroscience in Immunity.},
journal = {Annual review of immunology},
volume = {36},
number = {},
pages = {783-812},
pmid = {29677475},
issn = {1545-3278},
support = {P01 AI102852/AI/NIAID NIH HHS/United States ; R01 GM057226/GM/NIGMS NIH HHS/United States ; R01 GM089807/GM/NIGMS NIH HHS/United States ; R35 GM118182/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Biomarkers ; Disease Susceptibility ; Humans ; Immunity ; Nervous System/anatomy &amp; histology/immunology/metabolism ; Nervous System Physiological Phenomena ; *Neuroimmunomodulation/genetics/immunology ; Signal Transduction ; Translational Research, Biomedical ; },
abstract = {The nervous system regulates immunity and inflammation. The molecular detection of pathogen fragments, cytokines, and other immune molecules by sensory neurons generates immunoregulatory responses through efferent autonomic neuron signaling. The functional organization of this neural control is based on principles of reflex regulation. Reflexes involving the vagus nerve and other nerves have been therapeutically explored in models of inflammatory and autoimmune conditions, and recently in clinical settings. The brain integrates neuro-immune communication, and brain function is altered in diseases characterized by peripheral immune dysregulation and inflammation. Here we review the anatomical and molecular basis of the neural interface with immunity, focusing on peripheral neural control of immune functions and the role of the brain in the model of the immunological homunculus. Clinical advances stemming from this knowledge within the framework of bioelectronic medicine are also briefly outlined.},
}
@article {pmid29676287,
year = {2018},
author = {Kellmeyer, P and Grosse-Wentrup, M and Schulze-Bonhage, A and Ziemann, U and Ball, T},
title = {Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis-implications for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {041003},
doi = {10.1088/1741-2552/aabfa5},
pmid = {29676287},
issn = {1741-2552},
mesh = {Amyotrophic Lateral Sclerosis/diagnostic imaging/*physiopathology/*therapy ; *Brain-Computer Interfaces ; Electrocorticography/methods ; Electroencephalography/methods ; Evoked Potentials, Somatosensory/physiology ; Humans ; Motor Cortex/diagnostic imaging/*physiopathology ; Neurodegenerative Diseases/diagnostic imaging/physiopathology/therapy ; },
abstract = {OBJECTIVE: For patients with amyotrophic lateral sclerosis (ALS) who are suffering from severe communication or motor problems, brain-computer interfaces (BCIs) can improve the quality of life and patient autonomy. However, current BCI systems are not as widely used as their potential and patient demand would let assume. This underutilization is a result of technological as well as user-based limitations but also of the comparatively poor performance of currently existing BCIs in patients with late-stage ALS, particularly in the locked-in state.<br><br>APPROACH: Here we review a broad range of electrophysiological studies in ALS patients with the aim to identify electrophysiological correlates of ALS-related neurodegeneration in motor and non-motor brain regions in to better understand potential neurophysiological limitations of current BCI systems for ALS patients. To this end we analyze studies in ALS patients that investigated basic sensory evoked potentials, resting-state and task-based paradigms using electroencephalography or electrocorticography for basic research purposes as well as for brain-computer interfacing. Main results and significance. Our review underscores that, similarly to mounting evidence from neuroimaging and neuropathology, electrophysiological measures too indicate neurodegeneration in non-motor areas in ALS. Furthermore, we identify an unexpected gap of basic and advanced electrophysiological studies in late-stage ALS patients, particularly in the locked-in state. We propose a research strategy on how to fill this gap in order to improve the design and performance of future BCI systems for this patient group.},
}
@article {pmid29674950,
year = {2018},
author = {Ibayashi, K and Kunii, N and Matsuo, T and Ishishita, Y and Shimada, S and Kawai, K and Saito, N},
title = {Decoding Speech With Integrated Hybrid Signals Recorded From the Human Ventral Motor Cortex.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {221},
pmid = {29674950},
issn = {1662-4548},
abstract = {Restoration of speech communication for locked-in patients by means of brain computer interfaces (BCIs) is currently an important area of active research. Among the neural signals obtained from intracranial recordings, single/multi-unit activity (SUA/MUA), local field potential (LFP), and electrocorticography (ECoG) are good candidates for an input signal for BCIs. However, the question of which signal or which combination of the three signal modalities is best suited for decoding speech production remains unverified. In order to record SUA, LFP, and ECoG simultaneously from a highly localized area of human ventral sensorimotor cortex (vSMC), we fabricated an electrode the size of which was 7 by 13 mm containing sparsely arranged microneedle and conventional macro contacts. We determined which signal modality is the most capable of decoding speech production, and tested if the combination of these signals could improve the decoding accuracy of spoken phonemes. Feature vectors were constructed from spike frequency obtained from SUAs and event-related spectral perturbation derived from ECoG and LFP signals, then input to the decoder. The results showed that the decoding accuracy for five spoken vowels was highest when features from multiple signals were combined and optimized for each subject, and reached 59% when averaged across all six subjects. This result suggests that multi-scale signals convey complementary information for speech articulation. The current study demonstrated that simultaneous recording of multi-scale neuronal activities could raise decoding accuracy even though the recording area is limited to a small portion of cortex, which is advantageous for future implementation of speech-assisting BCIs.},
}
@article {pmid29674949,
year = {2018},
author = {Luo, TJ and Lv, J and Chao, F and Zhou, C},
title = {Effect of Different Movement Speed Modes on Human Action Observation: An EEG Study.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {219},
pmid = {29674949},
issn = {1662-4548},
abstract = {Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities. Six healthy individuals were asked to participate in experiments comprising five different conditions. Four incremental-speed AO tasks and a motor imagery (MI) task involving imaging of the same movement were presented to the individuals. Occipital and sensorimotor regions were selected for BCI analyses. The experimental results showed that hMNS activation was higher in the occipital region but more robust in the sensorimotor region. Since the attended information impacts the activations of the hMNS during AO, the pattern of hMNS activations first rises and subsequently falls to a stable level during incremental-speed modes of AO. The discipline curves suggested that a moderate speed within a decent inter-stimulus interval (ISI) range produced the highest hMNS activations. Since a brain computer/machine interface (BCI) builds a path-way between human and computer/mahcine, the discipline curves will help to construct BCIs made by patterns of action observation (AO-BCI). Furthermore, a new method for constructing non-invasive brain machine brain interfaces (BMBIs) with moderate AO-BCI and motor imagery BCI (MI-BCI) was inspired by this paper.},
}
@article {pmid29670506,
year = {2018},
author = {Colachis, SC and Bockbrader, MA and Zhang, M and Friedenberg, DA and Annetta, NV and Schwemmer, MA and Skomrock, ND and Mysiw, WJ and Rezai, AR and Bresler, HS and Sharma, G},
title = {Dexterous Control of Seven Functional Hand Movements Using Cortically-Controlled Transcutaneous Muscle Stimulation in a Person With Tetraplegia.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {208},
pmid = {29670506},
issn = {1662-4548},
abstract = {Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. In this study, we quantified performance of an intuitive, cortically-controlled, transcutaneous FES system on standardized object manipulation tasks from the Grasp and Release Test (GRT). We found that a tetraplegic individual could use the system to control up to seven functional hand movements, each with >95% individual accuracy. He was able to select one movement from the possible seven movements available to him and use it to appropriately manipulate all GRT objects in real-time using naturalistic grasps. With the use of the system, the participant not only improved his GRT performance over his baseline, demonstrating an increase in number of transfers for all objects except the Block, but also significantly improved transfer times for the heaviest objects (videocassette (VHS), Can). Analysis of underlying motor cortex neural representations associated with the hand grasp states revealed an overlap or non-separability in neural activation patterns for similarly shaped objects that affected BCI-FES performance. These results suggest that motor cortex neural representations for functional grips are likely more related to hand shape and force required to hold objects, rather than to the objects themselves. These results, demonstrating multiple, naturalistic functional hand movements with the BCI-FES, constitute a further step toward translating BCI-FES technologies from research devices to clinical neuroprosthetics.},
}
@article {pmid29669279,
year = {2018},
author = {Filippini, M and Breveglieri, R and Hadjidimitrakis, K and Bosco, A and Fattori, P},
title = {Prediction of Reach Goals in Depth and Direction from the Parietal Cortex.},
journal = {Cell reports},
volume = {23},
number = {3},
pages = {725-732},
doi = {10.1016/j.celrep.2018.03.090},
pmid = {29669279},
issn = {2211-1247},
mesh = {Action Potentials ; Animals ; Hand Strength/physiology ; Macaca fascicularis/*physiology ; Movement ; Parietal Lobe/*physiology ; Photic Stimulation ; Psychomotor Performance ; Space Perception ; },
abstract = {The posterior parietal cortex is well known to mediate sensorimotor transformations during the generation of movement plans, but its ability to control prosthetic limbs in 3D environments has not yet been fully demonstrated. With this aim, we trained monkeys to perform reaches to targets located at various depths and directions and tested whether the reach goal position can be extracted from parietal signals. The reach goal location was reliably decoded with accuracy close to optimal (>90%), and this occurred also well before movement onset. These results, together with recent work showing a reliable decoding of hand grip in the same area, suggest that this is a suitable site to decode the entire prehension action, to be considered in the development of brain-computer interfaces.},
}
@article {pmid29667934,
year = {2018},
author = {Trachel, RE and Brochier, TG and Clerc, M},
title = {Brain-computer interaction for online enhancement of visuospatial attention performance.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046017},
doi = {10.1088/1741-2552/aabf16},
pmid = {29667934},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Male ; Motion Perception/*physiology ; Orientation/physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; Spatial Behavior/*physiology ; Visual Perception/physiology ; Young Adult ; },
abstract = {OBJECTIVE: this study on real-time decoding of visuospatial attention has two objectives: first, to reliably decode self-directed shifts of attention from electroencephalography (EEG) data, and second, to analyze whether this information can be used to enhance visuospatial performance. Visuospatial performance was measured in a target orientation discrimination task, in terms of reaction time, and error rate.<br><br>APPROACH: Our experiment extends the Posner paradigm by introducing a new type of ambiguous cues to indicate upcoming target location. The cues are designed so that their ambiguity is imperceptible to the user. This entails endogenous shifts of attention which are truly self-directed. Two protocols were implemented to exploit the decoding of attention shifts. The first 'adaptive' protocol uses the decoded locus to display the target. In the second 'warning' protocol, the target position is defined in advance, but a warning is flashed when the target mismatches the decoded locus.<br><br>MAIN RESULTS: Both protocols were tested in an online experiment involving ten subjects. The reaction time improved in both the adaptive and the warning protocol. The error rate was improved in the adaptive protocol only.<br><br>SIGNIFICANCE: This proof of concept study brings evidence that visuospatial brain-computer interfaces (BCIs) can be used to enhance improving human-machine interaction in situations where humans must react to off-center events in the visual field.},
}
@article {pmid29666663,
year = {2018},
author = {Piña-Ramirez, O and Valdes-Cristerna, R and Yanez-Suarez, O},
title = {Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen Aimed at Wheelchair Navigation Control.},
journal = {Computational and mathematical methods in medicine},
volume = {2018},
number = {},
pages = {7108906},
pmid = {29666663},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; *Discriminant Analysis ; Electrodes ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual ; Healthy Volunteers ; Humans ; Linear Models ; *Movement ; Photic Stimulation ; Sensitivity and Specificity ; *Wheelchairs ; Young Adult ; },
abstract = {P300 spellers have been widely modified to implement nonspelling tasks. In this work, we propose a "scenario" stimulation screen that is a P300 speller variation to command a wheelchair. Our approach utilized a stimulation screen with an image background (scenario snapshot for a wheelchair) and stimulation markers arranged asymmetrically over relevant landmarks, such as suitable paths, doors, windows, and wall signs. Other scenario stimulation screen features were green/blue stimulation marker color scheme, variable Interstimulus Interval, single marker stimulus mode, and optimized stimulus sequence generator. Eighteen able-bodied subjects participated in the experiment; 78% had no experience in BCI usage. A waveform feature analysis and a Mann-Whitney U test performed over the pairs of target and nontarget coherent averages confirmed that 94% of the subjects elicit P300 (p < .005) on this modified stimulator. Least Absolute Shrinkage and Selection Operator optimization and Linear Discriminant Analysis were utilized for the automatic detection of P300. For evaluation with unseen data, target detection was computed (median sensitivity = 1.00 (0.78-1.00)), together with nontarget discrimination (median specificity = 1.00 (0.98-1.00)). The scenario screen adequately elicits P300 and seems suitable for commanding a wheelchair even when users have no previous experience on the BCI spelling task.},
}
@article {pmid29666632,
year = {2018},
author = {Chen, Y and Liu, X and Li, S and Wan, H},
title = {Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials.},
journal = {Computational intelligence and neuroscience},
volume = {2018},
number = {},
pages = {3505371},
pmid = {29666632},
issn = {1687-5273},
mesh = {Algorithms ; Animals ; Behavior, Animal/*physiology ; Brain/*physiology ; Columbidae ; Cortical Synchronization ; Decision Making/*physiology ; Electrodes, Implanted ; Goals ; Neural Pathways/physiology ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Video Recording ; },
abstract = {Recent studies indicate that the local field potential (LFP) carries information about an animal's behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.},
}
@article {pmid29666575,
year = {2018},
author = {Zhang, X and Elnady, AM and Randhawa, BK and Boyd, LA and Menon, C},
title = {Combining Mental Training and Physical Training With Goal-Oriented Protocols in Stroke Rehabilitation: A Feasibility Case Study.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {125},
pmid = {29666575},
issn = {1662-5161},
abstract = {Stroke is one of the leading causes of permanent disability in adults. The literature suggests that rehabilitation is key to early motor recovery. However, conventional therapy is labor and cost intensive. Robotic and functional electrical stimulation (FES) devices can provide a high dose of repetitions and as such may provide an alternative, or an adjunct, to conventional rehabilitation therapy. Brain-computer interfaces (BCI) could augment neuroplasticity by introducing mental training. However, mental training alone is not enough; but combining mental with physical training could boost outcomes. In the current case study, a portable rehabilitative platform and goal-oriented supporting training protocols were introduced and tested with a chronic stroke participant. A novel training method was introduced with the proposed rehabilitative platform. A 37-year old individual with chronic stroke participated in 6-weeks of training (18 sessions in total, 3 sessions a week, and 1 h per session). In this case study, we show that an individual with chronic stroke can tolerate a 6-week training bout with our system and protocol. The participant was actively engaged throughout the training. Changes in the Wolf Motor Function Test (WMFT) suggest that the training positively affected arm motor function (12% improvement in WMFT score).},
}
@article {pmid29666566,
year = {2018},
author = {Zioga, P and Pollick, F and Ma, M and Chapman, P and Stefanov, K},
title = {"Enheduanna-A Manifesto of Falling" Live Brain-Computer Cinema Performance: Performer and Audience Participation, Cognition and Emotional Engagement Using Multi-Brain BCI Interaction.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {191},
pmid = {29666566},
issn = {1662-4548},
abstract = {The fields of neural prosthetic technologies and Brain-Computer Interfaces (BCIs) have witnessed in the past 15 years an unprecedented development, bringing together theories and methods from different scientific fields, digital media, and the arts. More in particular, artists have been amongst the pioneers of the design of relevant applications since their emergence in the 1960s, pushing the boundaries of applications in real-life contexts. With the new research, advancements, and since 2007, the new low-cost commercial-grade wireless devices, there is a new increasing number of computer games, interactive installations, and performances that involve the use of these interfaces, combining scientific, and creative methodologies. The vast majority of these works use the brain-activity of a single participant. However, earlier, as well as recent examples, involve the simultaneous interaction of more than one participants or performers with the use of Electroencephalography (EEG)-based multi-brain BCIs. In this frame, we discuss and evaluate "Enheduanna-A Manifesto of Falling," a live brain-computer cinema performance that enables for the first time the simultaneous real-time multi-brain interaction of more than two participants, including a performer and members of the audience, using a passive EEG-based BCI system in the context of a mixed-media performance. The performance was realised as a neuroscientific study conducted in a real-life setting. The raw EEG data of seven participants, one performer and two different members of the audience for each performance, were simultaneously recorded during three live events. The results reveal that the majority of the participants were able to successfully identify whether their brain-activity was interacting with the live video projections or not. A correlation has been found between their answers to the questionnaires, the elements of the performance that they identified as most special, and the audience's indicators of attention and emotional engagement. Also, the results obtained from the performer's data analysis are consistent with the recall of working memory representations and the increase of cognitive load. Thus, these results prove the efficiency of the interaction design, as well as the importance of the directing strategy, dramaturgy and narrative structure on the audience's perception, cognitive state, and engagement.},
}
@article {pmid29666460,
year = {2018},
author = {Tian, Y and Xu, W and Yang, L},
title = {Cortical Classification with Rhythm Entropy for Error Processing in Cocktail Party Environment Based on Scalp EEG Recording.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {6070},
pmid = {29666460},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; *Attention ; *Auditory Perception ; Cerebral Cortex/*physiology ; Electroencephalography ; Entropy ; Female ; Humans ; Male ; Reaction Time ; Scalp/physiology ; Support Vector Machine ; Young Adult ; },
abstract = {Using single-trial cortical signals calculated by weighted minimum norm solution estimation (WMNE), the present study explored a feature extraction method based on rhythm entropy to classify the scalp electroencephalography (EEG) signals of error response from that of correct response during performing auditory-track tasks in cocktail party environment. The classification rate achieved 89.7% with single-trial (≈700 ms) when using support vector machine(SVM) with the leave-one-out-cross-validation (LOOCV). And high discriminative regions mainly distributed at the medial frontal cortex (MFC), the left supplementary motor area (lSMA) and the right supplementary motor area (rSMA). The mean entropy value for error trials was significantly lower than that for correct trials in the discriminative cortices. By time-varying network analysis, different information flows changed among these discriminative regions with time, i.e. error processing showed a left-bias information flow, and correct processing presented a right-bias information flow. These findings revealed that the rhythm information based on single cortical signals could be well used to describe characteristics of error-related EEG signals and further provided a novel application about auditory attention for brain computer interfaces (BCIs).},
}
@article {pmid29662908,
year = {2018},
author = {Dehzangi, O and Farooq, M},
title = {Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification.},
journal = {BioMed research international},
volume = {2018},
number = {},
pages = {9796238},
pmid = {29662908},
issn = {2314-6141},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Communication ; Evoked Potentials, Visual/*physiology ; Humans ; *Intensive Care Units ; Signal Processing, Computer-Assisted ; },
abstract = {A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.},
}
@article {pmid29660675,
year = {2018},
author = {Bian, Y and Qi, H and Zhao, L and Ming, D and Guo, T and Fu, X},
title = {Improvements in event-related desynchronization and classification performance of motor imagery using instructive dynamic guidance and complex tasks.},
journal = {Computers in biology and medicine},
volume = {96},
number = {},
pages = {266-273},
doi = {10.1016/j.compbiomed.2018.03.018},
pmid = {29660675},
issn = {1879-0534},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Physical Stimulation ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Task Performance and Analysis ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVE: The motor-imagery based brain-computer interface supplies a potential approach for motor-impaired patients, not only to control rehabilitation facilities but also to promote recovery from motor dysfunctions. To improve event-related desynchronization during motor imagery and obtain improved brain-computer interface classification accuracy, we introduce dynamic video guidance and complex motor tasks to the motor imagery paradigm.<br><br>METHODS: Eleven participants were included in the experiment; 64-channel electroencephalographic data were collected and analyzed during four motor imagery tasks with different guidance. Time-frequency analysis, spectral-time variation analysis, topographical distribution maps, and statistical analysis were utilized to analyze the event-related desynchronization patterns. Common spatial patterns were used to extract spatial pattern features and support vector machines were used to discriminate the offline classification accuracies in three bands (the alpha band, beta band, alpha and beta band) for comparison.<br><br>RESULTS: The experimental outcomes showed that complex motor imagery tasks coupled with dynamic video guidance induced significantly stronger event-related desynchronization than other paradigms, which use simple motor imagery tasks or static guidance. Similar results were obtained during analysis of the motor imagery brain-computer interface classification performance; namely, the highest average classification accuracy in complex and dynamic guidance was improved by approximately 14%, compared with static guidance. For individually specified paradigms, all participants obtained a classification accuracy that exceeded or was equal to 87.5%.<br><br>CONCLUSIONS: This study provides an optional route to enhance the event-related desynchronization activities and classification accuracy of a motor imagery brain-computer interface through optimization of motor imagery tasks and instructive guidance.},
}
@article {pmid29658406,
year = {2021},
author = {Cheng, KS and Lee, JX and Lee, PF},
title = {Designing a neurofeedback device to quantify attention levels using coffee as a reward system.},
journal = {International journal of occupational safety and ergonomics : JOSE},
volume = {27},
number = {1},
pages = {258-266},
doi = {10.1080/10803548.2018.1459348},
pmid = {29658406},
issn = {2376-9130},
mesh = {*Brain Waves ; *Brain-Computer Interfaces ; Coffee ; Electroencephalography ; Humans ; *Neurofeedback ; Reward ; },
abstract = {Purpose. Work performance is closely related to one's attention level. In this study, a brain-computer interface (BCI) device suitable for office usage was chosen to quantify the individual's attention levels. Methods. A BCI system was adopted to interface brainwave signals to a coffee maker via three ascending levels of laser detectors. The preliminary test with this prototype was to characterize the attention level through the collected coffee amount. Here, the preliminary testing was comparing the correlation between the attention level and the participants' cumulative grade point average (CGPA) and scores from the 21-item depression, anxiety, and stress scale (DASS-21) and the attentional control scale (ACS) using ordinal regression. It was assumed that a greater CGPA would generate a greater attention level. Result. The generated coffee amount from the BCI system had a significant positive correlation with the CGPA (p = 0.004), mild depression (p = 0.019) and mild and extremely severe anxiety (p = 0.044 and p = 0.019, respectively) and a negative correlation with the ACS score (p = 0.042). Conclusion. This simple and cost-effective prototype has the potential to enable everyone to know their immediate attention level and predict the possible correlation to their mental state.},
}
@article {pmid29655592,
year = {2018},
author = {Kourouche, S and Buckley, T and Munroe, B and Curtis, K},
title = {Development of a blunt chest injury care bundle: An integrative review.},
journal = {Injury},
volume = {49},
number = {6},
pages = {1008-1023},
doi = {10.1016/j.injury.2018.03.037},
pmid = {29655592},
issn = {1879-0267},
mesh = {Delivery of Health Care/*organization &amp; administration ; Evidence-Based Medicine ; Humans ; Pain Management ; *Patient Care Bundles ; Rib Fractures/*therapy ; Thoracic Injuries/*therapy ; Wounds, Nonpenetrating/*therapy ; },
abstract = {BACKGROUND: Blunt chest injuries (BCI) are associated with high rates of morbidity and mortality. There are many interventions for BCI which may be able to be combined as a care bundle for improved and more consistent outcomes.<br><br>OBJECTIVE: To review and integrate the BCI management interventions to inform the development of a BCI care bundle.<br><br>METHODS: A structured search of the literature was conducted to identify studies evaluating interventions for patients with BCI. Databases MEDLINE, CINAHL, PubMed and Scopus were searched from 1990-April 2017. A two-step data extraction process was conducted using pre-defined data fields, including research quality indicators. Each study was appraised using a quality assessment tool, scored for level of evidence, then data collated into categories. Interventions were also assessed using the APEASE criteria then integrated to develop a BCI care bundle.<br><br>RESULTS: Eighty-one articles were included in the final analysis. Interventions that improved BCI outcomes were grouped into three categories; respiratory intervention, analgesia and surgical intervention. Respiratory interventions included continuous positive airway pressure and high flow nasal oxygen. Analgesia interventions included regular multi-modal analgesia and paravertebral or epidural analgesia. Surgical fixation was supported for use in moderate to severe rib fractures/BCI. Interventions supported by evidence and that met APEASE criteria were combined into a BCI care bundle with four components: respiratory adjuncts, analgesia, complication prevention, and surgical fixation.<br><br>CONCLUSIONS: The key components of a BCI care bundle are respiratory support, analgesia, complication prevention including chest physiotherapy and surgical fixation.},
}
@article {pmid29653130,
year = {2018},
author = {Morikawa, N and Tanaka, T and Islam, MR},
title = {Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.},
journal = {Journal of neuroscience methods},
volume = {304},
number = {},
pages = {1-10},
doi = {10.1016/j.jneumeth.2018.04.001},
pmid = {29653130},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain Mapping ; Brain-Computer Interfaces ; Computer Simulation ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Nontherapeutic Human Experimentation ; Pattern Recognition, Automated ; Photic Stimulation ; Psychophysics ; Visual Perception/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency.<br><br>NEW METHOD: We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving &#x2113;1- and &#x2113;2,1-regularization problems for a mixed-coded SSVEP-BCI. In particular, &#x2113;2,1-regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection.<br><br>RESULTS: A calibration data based canonical correlation analysis (CCA) and CSSF with &#x2113;1- and &#x2113;2,1-regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with &#x2113;1- and &#x2113;2,1-regularization significantly achieved the highest ITR.<br><br>The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs.<br><br>CONCLUSIONS: The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue.},
}
@article {pmid29650005,
year = {2018},
author = {Ssempiira, J and Kissa, J and Nambuusi, B and Kyozira, C and Rutazaana, D and Mukooyo, E and Opigo, J and Makumbi, F and Kasasa, S and Vounatsou, P},
title = {The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda.},
journal = {Malaria journal},
volume = {17},
number = {1},
pages = {162},
pmid = {29650005},
issn = {1475-2875},
support = {IZ01Z0-147286//Swiss Programme for Research on Global Issues for Development (r4d)/ ; 323180//European Research Council (ERC)/ ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Antimalarials/*therapeutic use ; Artemisinins/*therapeutic use ; Bayes Theorem ; *Case Management ; Child ; Child, Preschool ; Drug Combinations ; Humans ; Incidence ; Infant ; Infant, Newborn ; Malaria/*epidemiology ; Middle Aged ; *Mosquito Control ; Plasmodium/drug effects ; Spatio-Temporal Analysis ; Uganda/epidemiology ; Young Adult ; },
abstract = {BACKGROUND: Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016.<br><br>METHODS: Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5&#xa0;years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission.<br><br>RESULTS: The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children&#x2009;<&#x2009;5&#xa0;years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals &#x2265;&#x2009;5&#xa0;years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children&#x2009;<&#x2009;5&#xa0;years and individuals&#x2009;&#x2265;&#x2009;5&#xa0;years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children <&#x2009;5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15.<br><br>CONCLUSION: The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.},
}
@article {pmid29644273,
year = {2017},
author = {Carmichael, SP and Bounds, MC and Shafii, AE and Chang, PK},
title = {Emergency department repair of blunt right atrial rupture utilizing cardiopulmonary bypass.},
journal = {Trauma case reports},
volume = {12},
number = {},
pages = {1-3},
pmid = {29644273},
issn = {2352-6440},
support = {KL2 TR001421/TR/NCATS NIH HHS/United States ; },
abstract = {Blunt cardiac injury (BCI) with free wall rupture carries a high risk of pre-hospital death. Cardiopulmonary bypass (CPB) has been utilized as a bridge to repair of cardiac lesions in select patients. We present an interesting case of emergency department repair of right atrial rupture with cardiopulmonary bypass.},
}
@article {pmid29643430,
year = {2018},
author = {Han, C and Xu, G and Xie, J and Chen, C and Zhang, S},
title = {Highly Interactive Brain-Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {5835},
pmid = {29643430},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; *Motion ; Photic Stimulation/methods ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Visual evoked potential-based brain-computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked "single fundamental peak" responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.},
}
@article {pmid29642493,
year = {2018},
author = {Rodriguez-Ugarte, MS and Iáñez, E and Ortiz-Garcia, M and Azorín, JM},
title = {Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {4},
pages = {},
pmid = {29642493},
issn = {1424-8220},
abstract = {The purpose of this work is to strengthen the cortical excitability over the primary motor cortex (M1) and the cerebro-cerebellar pathway by means of a new transcranial direct current stimulation (tDCS) configuration to detect lower limb motor imagery (MI) in real time using two different cognitive neural states: relax and pedaling MI. The anode is located over the primary motor cortex in Cz, and the cathode over the right cerebro-cerebellum. The real-time brain-computer interface (BCI) designed is based on finding, for each electrode selected, the power at the particular frequency where the most difference between the two mental tasks is observed. Electroencephalographic (EEG) electrodes are placed over the brain's premotor area (PM), M1, supplementary motor area (SMA) and primary somatosensory cortex (S1). A single-blind study is carried out, where fourteen healthy subjects are separated into two groups: sham and active tDCS. Each subject is experimented on for five consecutive days. On all days, the results achieved by the active tDCS group were over 60% in real-time detection accuracy, with a five-day average of 62.6%. The sham group eventually reached those levels of accuracy, but it needed three days of training to do so.},
}
@article {pmid29641396,
year = {2018},
author = {Meena, YK and Cecotti, H and Wong-Lin, K and Dutta, A and Prasad, G},
title = {Toward Optimization of Gaze-Controlled Human-Computer Interaction: Application to Hindi Virtual Keyboard for Stroke Patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {4},
pages = {911-922},
doi = {10.1109/TNSRE.2018.2814826},
pmid = {29641396},
issn = {1558-0210},
mesh = {Adult ; Aged ; Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Eye Movements ; Feedback, Psychological ; Female ; Fixation, Ocular/*physiology ; Healthy Volunteers ; Humans ; Language ; Male ; Middle Aged ; Pupil/physiology ; Reproducibility of Results ; Stroke Rehabilitation/*instrumentation ; User-Computer Interface ; Young Adult ; },
abstract = {Virtual keyboard applications and alternative communication devices provide new means of communication to assist disabled people. To date, virtual keyboard optimization schemes based on script-specific information, along with multimodal input access facility, are limited. In this paper, we propose a novel method for optimizing the position of the displayed items for gaze-controlled tree-based menu selection systems by considering a combination of letter frequency and command selection time. The optimized graphical user interface layout has been designed for a Hindi language virtual keyboard based on a menu wherein 10 commands provide access to type 88 different characters, along with additional text editing commands. The system can be controlled in two different modes: eye-tracking alone and eye-tracking with an access soft-switch. Five different keyboard layouts have been presented and evaluated with ten healthy participants. Furthermore, the two best performing keyboard layouts have been evaluated with eye-tracking alone on ten stroke patients. The overall performance analysis demonstrated significantly superior typing performance, high usability (87% SUS score), and low workload (NASA TLX with 17 scores) for the letter frequency and time-based organization with script specific arrangement design. This paper represents the first optimized gaze-controlled Hindi virtual keyboard, which can be extended to other languages.},
}
@article {pmid29641392,
year = {2018},
author = {Brumberg, JS and Pitt, KM and Burnison, JD},
title = {A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {4},
pages = {874-881},
pmid = {29641392},
issn = {1558-0210},
support = {R03 DC011304/DC/NIDCD NIH HHS/United States ; U54 HD090216/HD/NICHD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Electroencephalography ; *Feedback, Psychological ; Feedback, Sensory ; Female ; Humans ; Imagination ; Learning ; Male ; Mental Fatigue ; Practice, Psychological ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {We conducted a study of a motor imagery brain-computer interface (BCI) using electroencephalography to continuously control a formant frequency speech synthesizer with instantaneous auditory and visual feedback. Over a three-session training period, sixteen participants learned to control the BCI for production of three vowel sounds (/ textipa i/ [heed], / textipa A/ [hot], and / textipa u/ [who'd]) and were split into three groups: those receiving unimodal auditory feedback of synthesized speech, those receiving unimodal visual feedback of formant frequencies, and those receiving multimodal, audio-visual (AV) feedback. Audio feedback was provided by a formant frequency artificial speech synthesizer, and visual feedback was given as a 2-D cursor on a graphical representation of the plane defined by the first two formant frequencies. We found that combined AV feedback led to the greatest performance in terms of percent accuracy, distance to target, and movement time to target compared with either unimodal feedback of auditory or visual information. These results indicate that performance is enhanced when multimodal feedback is meaningful for the BCI task goals, rather than as a generic biofeedback signal of BCI progress.},
}
@article {pmid29641376,
year = {2018},
author = {Vo, K and Pham, T and Nguyen, DN and Kha, HH and Dutkiewicz, E},
title = {Subject-Independent ERP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {4},
pages = {719-728},
doi = {10.1109/TNSRE.2018.2810332},
pmid = {29641376},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Databases, Factual ; Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; Learning ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.},
}
@article {pmid29637955,
year = {2018},
author = {Wangler, A and Canales, R and Held, C and Luong, TQ and Winter, R and Zaitsau, DH and Verevkin, SP and Sadowski, G},
title = {Co-solvent effects on reaction rate and reaction equilibrium of an enzymatic peptide hydrolysis.},
journal = {Physical chemistry chemical physics : PCCP},
volume = {20},
number = {16},
pages = {11317-11326},
doi = {10.1039/c7cp07346a},
pmid = {29637955},
issn = {1463-9084},
mesh = {Animals ; Calcium Chloride/chemistry ; Cattle ; Chymotrypsin/*chemistry ; Dimethyl Sulfoxide/chemistry ; Hydrolysis ; Kinetics ; Methylamines/chemistry ; Phenylalanine/*analogs &amp; derivatives/chemistry ; Sodium Chloride/chemistry ; Solvents/*chemistry ; Thermodynamics ; Urea/chemistry ; Water/chemistry ; },
abstract = {This work presents an approach that expresses the Michaelis constant KaM and the equilibrium constant Kth of an enzymatic peptide hydrolysis based on thermodynamic activities instead of concentrations. This provides KaM and Kth values that are independent of any co-solvent. To this end, the hydrolysis reaction of N-succinyl-l-phenylalanine-p-nitroanilide catalysed by the enzyme α-chymotrypsin was studied in pure buffer and in the presence of the co-solvents dimethyl sulfoxide, trimethylamine-N-oxide, urea, and two salts. A strong influence of the co-solvents on the measured Michaelis constant (KM) and equilibrium constant (Kx) was observed, which was found to be caused by molecular interactions expressed as activity coefficients. Substrate and product activity coefficients were used to calculate the activity-based values KaM and Kth for the co-solvent free reaction. Based on these constants, the co-solvent effect on KM and Kx was predicted in almost quantitative agreement with the experimental data. The approach presented here does not only reveal the importance of understanding the thermodynamic non-ideality of reactions taking place in biological solutions and in many technological applications, it also provides a framework for interpreting and quantifying the multifaceted co-solvent effects on enzyme-catalysed reactions that are known and have been observed experimentally for a long time.},
}
@article {pmid29636660,
year = {2018},
author = {Kirchner, EA and Kim, SK},
title = {Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {188},
pmid = {29636660},
issn = {1662-4548},
abstract = {Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.},
}
@article {pmid29633714,
year = {2018},
author = {Armenta Salas, M and Bashford, L and Kellis, S and Jafari, M and Jo, H and Kramer, D and Shanfield, K and Pejsa, K and Lee, B and Liu, CY and Andersen, RA},
title = {Proprioceptive and cutaneous sensations in humans elicited by intracortical microstimulation.},
journal = {eLife},
volume = {7},
number = {},
pages = {},
pmid = {29633714},
issn = {2050-084X},
support = {U01 NS098975/NS/NINDS NIH HHS/United States ; 5U01NS098975-02/NS/NINDS NIH HHS/United States ; R25 NS099008/NS/NINDS NIH HHS/United States ; NS099008-01/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electric Stimulation/*instrumentation ; *Electrodes, Implanted ; Evoked Potentials, Somatosensory ; Hand/*physiology ; Humans ; *Microelectrodes ; *Proprioception ; Skin/innervation/*physiopathology ; Somatosensory Cortex/*physiology ; Touch Perception ; },
abstract = {Pioneering work with nonhuman primates and recent human studies established intracortical microstimulation (ICMS) in primary somatosensory cortex (S1) as a method of inducing discriminable artificial sensation. However, these artificial sensations do not yet provide the breadth of cutaneous and proprioceptive percepts available through natural stimulation. In a tetraplegic human with two microelectrode arrays implanted in S1, we report replicable elicitations of sensations in both the cutaneous and proprioceptive modalities localized to the contralateral arm, dependent on both amplitude and frequency of stimulation. Furthermore, we found a subset of electrodes that exhibited multimodal properties, and that proprioceptive percepts on these electrodes were associated with higher amplitudes, irrespective of the frequency. These novel results demonstrate the ability to provide naturalistic percepts through ICMS that can more closely mimic the body's natural physiological capabilities. Furthermore, delivering both cutaneous and proprioceptive sensations through artificial somatosensory feedback could improve performance and embodiment in brain-machine interfaces.},
}
@article {pmid29633712,
year = {2018},
author = {de Lafuente, V},
title = {Regaining the senses of touch and movement.},
journal = {eLife},
volume = {7},
number = {},
pages = {},
pmid = {29633712},
issn = {2050-084X},
mesh = {Electric Stimulation ; Humans ; Movement ; Proprioception ; *Somatosensory Cortex ; *Touch ; Touch Perception ; },
abstract = {Artificially activating neurons in the cortex can make a tetraplegic patient feel naturalistic sensations of skin pressure and arm movement.},
}
@article {pmid29633174,
year = {2018},
author = {Gorur, K and Bozkurt, MR and Bascil, MS and Temurtas, F},
title = {Glossokinetic potential based tongue-machine interface for 1-D extraction.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {41},
number = {2},
pages = {379-391},
doi = {10.1007/s13246-018-0635-x},
pmid = {29633174},
issn = {1879-5447},
mesh = {Adult ; *Algorithms ; Biomechanical Phenomena ; Brain Mapping ; Electrodes ; Electroencephalography ; Female ; Humans ; Male ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Tongue/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue-human computer interface or tongue-machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue-machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22-34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain-computer interfaces which have significant inadequacies arisen from the EEG signals.},
}
@article {pmid29630600,
year = {2018},
author = {Dollé, L and Chavarriaga, R and Guillot, A and Khamassi, M},
title = {Interactions of spatial strategies producing generalization gradient and blocking: A computational approach.},
journal = {PLoS computational biology},
volume = {14},
number = {4},
pages = {e1006092},
pmid = {29630600},
issn = {1553-7358},
mesh = {Animals ; Behavior, Animal/physiology ; Cognition/physiology ; Computational Biology ; Computer Simulation ; Cues ; Mammals ; Maze Learning/physiology ; Memory/physiology ; *Models, Psychological ; Reinforcement, Psychology ; Spatial Navigation/*physiology ; },
abstract = {We present a computational model of spatial navigation comprising different learning mechanisms in mammals, i.e., associative, cognitive mapping and parallel systems. This model is able to reproduce a large number of experimental results in different variants of the Morris water maze task, including standard associative phenomena (spatial generalization gradient and blocking), as well as navigation based on cognitive mapping. Furthermore, we show that competitive and cooperative patterns between different navigation strategies in the model allow to explain previous apparently contradictory results supporting either associative or cognitive mechanisms for spatial learning. The key computational mechanism to reconcile experimental results showing different influences of distal and proximal cues on the behavior, different learning times, and different abilities of individuals to alternatively perform spatial and response strategies, relies in the dynamic coordination of navigation strategies, whose performance is evaluated online with a common currency through a modular approach. We provide a set of concrete experimental predictions to further test the computational model. Overall, this computational work sheds new light on inter-individual differences in navigation learning, and provides a formal and mechanistic approach to test various theories of spatial cognition in mammals.},
}
@article {pmid29630571,
year = {2018},
author = {Li, Q and Shi, K and Gao, N and Li, J and Bai, O},
title = {Training set extension for SVM ensemble in P300-speller with familiar face paradigm.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {26},
number = {3},
pages = {469-482},
doi = {10.3233/THC-171074},
pmid = {29630571},
issn = {1878-7401},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; *Support Vector Machine ; Time Factors ; Young Adult ; },
abstract = {BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue.<br><br>OBJECTIVE: This study aimed to develop a method for acquiring more training data based on a collected small training set.<br><br>METHODS: A new method was developed in which two corresponding training datasets in two sequences are superposed and averaged to extend the training set. The proposed method was tested offline on a P300-speller with the familiar face paradigm.<br><br>RESULTS: The SVM ensemble with extended training set achieved 85% classification accuracy for the averaged results of four sequences, and 100% for 11 sequences in the P300-speller. In contrast, the conventional SVM ensemble with non-extended training set achieved only 65% accuracy for four sequences, and 92% for 11 sequences.<br><br>CONCLUSION: The SVM ensemble with extended training set achieves higher classification accuracies than the conventional SVM ensemble, which verifies that the proposed method effectively improves the classification performance of BCI P300-spellers, thus enhancing their practicality.},
}
@article {pmid29628911,
year = {2018},
author = {Dai, C and Zheng, Y and Hu, X},
title = {Estimation of Muscle Force Based on Neural Drive in a Hemispheric Stroke Survivor.},
journal = {Frontiers in neurology},
volume = {9},
number = {},
pages = {187},
pmid = {29628911},
issn = {1664-2295},
abstract = {Robotic assistant-based therapy holds great promise to improve the functional recovery of stroke survivors. Numerous neural-machine interface techniques have been used to decode the intended movement to control robotic systems for rehabilitation therapies. In this case report, we tested the feasibility of estimating finger extensor muscle forces of a stroke survivor, based on the decoded descending neural drive through population motoneuron discharge timings. Motoneuron discharge events were obtained by decomposing high-density surface electromyogram (sEMG) signals of the finger extensor muscle. The neural drive was extracted from the normalized frequency of the composite discharge of the motoneuron pool. The neural-drive-based estimation was also compared with the classic myoelectric-based estimation. Our results showed that the neural-drive-based approach can better predict the force output, quantified by lower estimation errors and higher correlations with the muscle force, compared with the myoelectric-based estimation. Our findings suggest that the neural-drive-based approach can potentially be used as a more robust interface signal for robotic therapies during the stroke rehabilitation.},
}
@article {pmid29627716,
year = {2018},
author = {Spataro, R and Heilinger, A and Allison, B and De Cicco, D and Marchese, S and Gregoretti, C and La Bella, V and Guger, C},
title = {Preserved somatosensory discrimination predicts consciousness recovery in unresponsive wakefulness syndrome.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {129},
number = {6},
pages = {1130-1136},
doi = {10.1016/j.clinph.2018.02.131},
pmid = {29627716},
issn = {1872-8952},
mesh = {Adult ; Aged ; Aged, 80 and over ; Brain/*physiopathology ; Brain-Computer Interfaces ; Consciousness Disorders/*physiopathology ; Discrimination, Psychological/*physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; Touch Perception/*physiology ; Wakefulness/physiology ; Young Adult ; },
abstract = {OBJECTIVE: To assess somatosensory discrimination and command following using a vibrotactile P300-based Brain-Computer Interface (BCI) in Unresponsive Wakefulness Syndrome (UWS), and investigate the predictive role of this cognitive process on the clinical outcomes.<br><br>METHODS: Thirteen UWS patients and six healthy controls each participated in two experimental runs in which they were instructed to count vibrotactile stimuli delivered to the left or right wrist. A BCI determined each subject's task performance based on EEG measures. All of the patients were followed up six months after the BCI assessment, and correlations analysis between accuracy rates and clinical outcome were investigated.<br><br>RESULTS: Four UWS patients demonstrated clear EEG-based indices of task following in one or both paradigms, which did not correlate with clinical factors. The efficacy of somatosensory discrimination strongly correlated (VT2: R&#x202f;=&#x202f;0.89, p&#x202f;=&#x202f;0.0000002, VT3: R&#x202f;=&#x202f;0.81, p&#x202f;=&#x202f;0.002) with the clinical outcome at 6-months. The BCI system also yielded the expected results with healthy controls.<br><br>CONCLUSIONS: Neurophysiological correlates of somatosensory discrimination can be detected in clinically unresponsive patients and are associated with recovery of behavioural responsiveness at six months.<br><br>SIGNIFICANCE: Quantitative measurements of somatosensory discrimination may increase the diagnostic accuracy of persons with DOCs and provide useful prognostic information.},
}
@article {pmid29627003,
year = {2018},
author = {Sokunbi, MO},
title = {Using real-time fMRI brain-computer interfacing to treat eating disorders.},
journal = {Journal of the neurological sciences},
volume = {388},
number = {},
pages = {109-114},
doi = {10.1016/j.jns.2018.03.011},
pmid = {29627003},
issn = {1878-5883},
mesh = {Brain/diagnostic imaging/physiopathology ; Brain-Computer Interfaces ; Feeding and Eating Disorders/*drug therapy/physiopathology/*therapy ; Humans ; *Magnetic Resonance Imaging ; Neurofeedback ; },
abstract = {Real-time functional magnetic resonance imaging based brain-computer interfacing (fMRI neurofeedback) has shown encouraging outcomes in the treatment of psychiatric and behavioural disorders. However, its use in the treatment of eating disorders is very limited. Here, we give a brief overview of how to design and implement fMRI neurofeedback intervention for the treatment of eating disorders, considering the basic and essential components. We also attempt to develop potential adaptations of fMRI neurofeedback intervention for the treatment of anorexia nervosa, bulimia nervosa and binge eating disorder.},
}
@article {pmid29623905,
year = {2018},
author = {Luan, S and Williams, I and Maslik, M and Liu, Y and De Carvalho, F and Jackson, A and Quiroga, RQ and Constandinou, TG},
title = {Compact standalone platform for neural recording with real-time spike sorting and data logging.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046014},
doi = {10.1088/1741-2552/aabc23},
pmid = {29623905},
issn = {1741-2552},
support = {G0801705/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Action Potentials/*physiology ; Animals ; *Computer Systems ; *Data Interpretation, Statistical ; Haplorhini ; Neurons/*physiology ; Printing, Three-Dimensional/*instrumentation ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {OBJECTIVE: Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain-machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: (1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions); (2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation.<br><br>APPROACH: We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data.<br><br>MAIN RESULTS: The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24&#x2009;h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24&#x2009;h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles.<br><br>SIGNIFICANCE: The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.},
}
@article {pmid29623902,
year = {2018},
author = {Astrand, E},
title = {A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {036021},
doi = {10.1088/1741-2552/aaae73},
pmid = {29623902},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Brain Waves/*physiology ; Electroencephalography/methods ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Middle Aged ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved.<br><br>APPROACH: Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, [Formula: see text].<br><br>MAIN RESULTS: The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r&#x2009;&#x2009;=&#x2009;&#x2009;0.47, p&#x2009;&#x2009;<&#x2009;&#x2009;0.05). It is furthermore shown that this measure allows to predict task performance before action (r&#x2009;&#x2009;=&#x2009;&#x2009;0.49, p&#x2009;&#x2009;<&#x2009;&#x2009;0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches.<br><br>SIGNIFICANCE: These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.},
}
@article {pmid29621570,
year = {2018},
author = {Esch, L and Sun, L and Klüber, V and Lew, S and Baumgarten, D and Grant, PE and Okada, Y and Haueisen, J and Hämäläinen, MS and Dinh, C},
title = {MNE Scan: Software for real-time processing of electrophysiological data.},
journal = {Journal of neuroscience methods},
volume = {303},
number = {},
pages = {55-67},
pmid = {29621570},
issn = {1872-678X},
support = {P41 RR014075/RR/NCRR NIH HHS/United States ; S10 RR031599/RR/NCRR NIH HHS/United States ; U01 EB023820/EB/NIBIB NIH HHS/United States ; R01 EB009048/EB/NIBIB NIH HHS/United States ; P41 EB015896/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Child, Preschool ; Electroencephalography/*methods ; Humans ; Infant ; Infant, Newborn ; Magnetoencephalography/*methods ; Neurofeedback/*methods ; Neurosciences/*methods ; *Signal Processing, Computer-Assisted ; *Software Design ; },
abstract = {BACKGROUND: Magnetoencephalography (MEG) and Electroencephalography (EEG) are noninvasive techniques to study the electrophysiological activity of the human brain. Thus, they are well suited for real-time monitoring and analysis of neuronal activity. Real-time MEG/EEG data processing allows adjustment of the stimuli to the subject's responses for optimizing the acquired information especially by providing dynamically changing displays to enable neurofeedback.<br><br>NEW METHOD: We introduce MNE Scan, an acquisition and real-time analysis software based on the multipurpose software library MNE-CPP. MNE Scan allows the development and application of acquisition and novel real-time processing methods in both research and clinical studies. The MNE Scan development follows a strict software engineering process to enable approvals required for clinical software.<br><br>RESULTS: We tested the performance of MNE Scan in several device-independent use cases, including, a clinical epilepsy study, real-time source estimation, and Brain Computer Interface (BCI) application.<br><br>Compared to existing tools we propose a modular software considering clinical software requirements expected by certification authorities. At the same time the software is extendable and freely accessible.<br><br>CONCLUSION: We conclude that MNE Scan is the first step in creating a device-independent open-source software to facilitate the transition from basic neuroscience research to both applied sciences and clinical applications.},
}
@article {pmid29618967,
year = {2018},
author = {Widge, AS and Malone, DA and Dougherty, DD},
title = {Closing the Loop on Deep Brain Stimulation for Treatment-Resistant Depression.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {175},
pmid = {29618967},
issn = {1662-4548},
support = {R03 MH111320/MH/NIMH NIH HHS/United States ; R21 MH109722/MH/NIMH NIH HHS/United States ; UH3 NS100548/NS/NINDS NIH HHS/United States ; },
abstract = {Major depressive episodes are the largest cause of psychiatric disability, and can often resist treatment with medication and psychotherapy. Advances in the understanding of the neural circuit basis of depression, combined with the success of deep brain stimulation (DBS) in movement disorders, spurred several groups to test DBS for treatment-resistant depression. Multiple brain sites have now been stimulated in open-label and blinded studies. Initial open-label results were dramatic, but follow-on controlled/blinded clinical trials produced inconsistent results, with both successes and failures to meet endpoints. Data from follow-on studies suggest that this is because DBS in these trials was not targeted to achieve physiologic responses. We review these results within a technology-lifecycle framework, in which these early trial "failures" are a natural consequence of over-enthusiasm for an immature technology. That framework predicts that from this "valley of disillusionment," DBS may be nearing a "slope of enlightenment." Specifically, by combining recent mechanistic insights and the maturing technology of brain-computer interfaces (BCI), the next generation of trials will be better able to target pathophysiology. Key to that will be the development of closed-loop systems that semi-autonomously alter stimulation strategies based on a patient's individual phenotype. Such next-generation DBS approaches hold great promise for improving psychiatric care.},
}
@article {pmid29616982,
year = {2018},
author = {Abbasi, A and Goueytes, D and Shulz, DE and Ego-Stengel, V and Estebanez, L},
title = {A fast intracortical brain-machine interface with patterned optogenetic feedback.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046011},
doi = {10.1088/1741-2552/aabb80},
pmid = {29616982},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Feedback, Physiological/*physiology ; Mice ; Optogenetics/*methods ; Photic Stimulation/*methods ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: The development of brain-machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback.<br><br>APPROACH: Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1) and delivers feedback simultaneously via a low-latency, high-refresh-rate, spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging.<br><br>MAIN RESULTS: We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying solely on the artificial inputs and outputs of the closed-loop BMI.<br><br>SIGNIFICANCE: This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications.},
}
@article {pmid29616978,
year = {2018},
author = {Zhang, S and Han, X and Chen, X and Wang, Y and Gao, S and Gao, X},
title = {A study on dynamic model of steady-state visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046010},
doi = {10.1088/1741-2552/aabb82},
pmid = {29616978},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Models, Neurological ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Significant progress has been made in the past two decades to considerably improve the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, there are still some unsolved problems that may help us to improve BCI performance, one of which is that our understanding of the dynamic process of SSVEP is still superficial, especially for the transient-state response.<br><br>APPROACH: This study introduced an antiphase stimulation method (antiphase: phase [Formula: see text]), which can simultaneously separate and extract SSVEP and event-related potential (ERP) signals from EEG, and eliminate the interference of ERP to SSVEP. Based on the SSVEP signals obtained by the antiphase stimulation method, the envelope of SSVEP was extracted by the Hilbert transform, and the dynamic model of SSVEP was quantitatively studied by mathematical modeling. The step response of a second-order linear system was used to fit the envelope of SSVEP, and its characteristics were represented by four parameters with physical and physiological meanings: one was amplitude related, one was latency related and two were frequency related. This study attempted to use pre-stimulation paradigms to modulate the dynamic model parameters, and quantitatively analyze the results by applying the dynamic model to further explore the pre-stimulation methods that had the potential to improve BCI performance.<br><br>MAIN RESULTS: The results showed that the dynamic model had good fitting effect with SSVEP under three pre-stimulation paradigms. The test results revealed that the parameters of SSVEP dynamic models could be modulated by the pre-stimulation baseline luminance, and the gray baseline luminance pre-stimulation obtained the highest performance.<br><br>SIGNIFICANCE: This study proposed a dynamic model which was helpful to understand and utilize the transient characteristics of SSVEP. This study also found that pre-stimulation could be used to adjust the parameters of SSVEP model, and had the potential to improve the performance of SSVEP-BCI.},
}
@article {pmid29616456,
year = {2018},
author = {Kumar, S and Sharma, A},
title = {A new parameter tuning approach for enhanced motor imagery EEG signal classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {56},
number = {10},
pages = {1861-1874},
pmid = {29616456},
issn = {1741-0444},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Motor Activity/*physiology ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.},
}
@article {pmid29615848,
year = {2018},
author = {Korik, A and Sosnik, R and Siddique, N and Coyle, D},
title = {Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {130},
pmid = {29615848},
issn = {1662-4548},
abstract = {Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8-12 Hz) and beta (12-28 Hz) bands. Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep). Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28-40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations. Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.},
}
@article {pmid29615797,
year = {2018},
author = {Krauss, P and Metzner, C and Schilling, A and Tziridis, K and Traxdorf, M and Wollbrink, A and Rampp, S and Pantev, C and Schulze, H},
title = {A statistical method for analyzing and comparing spatiotemporal cortical activation patterns.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {5433},
pmid = {29615797},
issn = {2045-2322},
mesh = {Action Potentials ; Algorithms ; Animals ; *Brain Mapping ; Cerebral Cortex/cytology/*physiology ; Electroencephalography ; Humans ; Magnetoencephalography ; Mice ; Somatosensory Cortex/cytology/physiology ; Spatio-Temporal Analysis ; Statistics as Topic/*methods ; },
abstract = {Information in the cortex is encoded in spatiotemporal patterns of neuronal activity, but the exact nature of that code still remains elusive. While onset responses to simple stimuli are associated with specific loci in cortical sensory maps, it is completely unclear how the information about a sustained stimulus is encoded that is perceived for minutes or even longer, when discharge rates have decayed back to spontaneous levels. Using a newly developed statistical approach (multidimensional cluster statistics (MCS)) that allows for a comparison of clusters of data points in n-dimensional space, we here demonstrate that the information about long-lasting stimuli is encoded in the ongoing spatiotemporal activity patterns in sensory cortex. We successfully apply MCS to multichannel local field potential recordings in different rodent models and sensory modalities, as well as to human MEG and EEG data, demonstrating its universal applicability. MCS thus indicates novel ways for the development of powerful read-out algorithms of spatiotemporal brain activity that may be implemented in innovative brain-computer interfaces (BCI).},
}
@article {pmid29614297,
year = {2018},
author = {Khalaf, A and Kersey, J and Eldeeb, S and Alankus, G and Grattan, E and Waterstram, L and Skidmore, E and Akcakaya, M},
title = {EEG-based neglect assessment: A feasibility study.},
journal = {Journal of neuroscience methods},
volume = {303},
number = {},
pages = {169-177},
pmid = {29614297},
issn = {1872-678X},
support = {P20 GM109040/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; Attention/*physiology ; Cerebral Cortex/*physiology ; Diagnosis, Computer-Assisted/instrumentation/*methods ; Electroencephalography/*methods ; Feasibility Studies ; Humans ; Perceptual Disorders/*diagnosis/etiology ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Space Perception/*physiology ; Stroke/complications/*diagnosis ; },
abstract = {BACKGROUND: Spatial neglect (SN) is a neuropsychological syndrome that impairs automatic attention orienting to stimuli in the contralesional visual space of stroke patients. SN is commonly assessed using paper and pencil tests. Recently, computerized tests have been proposed to provide a dynamic assessment of SN. However, both paper- and computer-based methods have limitations.<br><br>NEW METHOD: Electroencephalography (EEG) shows promise for overcoming the limitations of current assessment methods. The aim of this work is to introduce an objective passive BCI system that records EEG signals in response to visual stimuli appearing in random locations on a screen with a dynamically changing background. Our preliminary experimental studies focused on validating the system using healthy participants with intact brains rather than employing it initially in more complex environments with patients having cortical lesions. Therefore, we designed a version of the test in which we simulated SN by hiding target stimuli appearing on the left side of the screen so that the subject's attention is shifted to the right side.<br><br>RESULTS: Results showed that there are statistically significant differences between EEG responses due to right and left side stimuli reflecting different processing and attention levels towards both sides of the screen. The system achieved average accuracy, sensitivity and specificity of 74.24%, 75.17% and 71.36% respectively.<br><br>The proposed test can examine both presence and severity of SN, unlike traditional paper and pencil tests and computer-based methods.<br><br>CONCLUSIONS: The proposed test is a promising objective SN evaluation method.},
}
@article {pmid29604631,
year = {2018},
author = {Hramov, AE and Frolov, NS and Maksimenko, VA and Makarov, VV and Koronovskii, AA and Garcia-Prieto, J and Antón-Toro, LF and Maestú, F and Pisarchik, AN},
title = {Artificial neural network detects human uncertainty.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {28},
number = {3},
pages = {033607},
doi = {10.1063/1.5002892},
pmid = {29604631},
issn = {1089-7682},
mesh = {Adult ; Female ; Humans ; Magnetoencephalography ; Male ; *Neural Networks, Computer ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *Uncertainty ; },
abstract = {Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.},
}
@article {pmid29602255,
year = {2018},
author = {Paret, C and Zähringer, J and Ruf, M and Gerchen, MF and Mall, S and Hendler, T and Schmahl, C and Ende, G},
title = {Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain.},
journal = {Human brain mapping},
volume = {39},
number = {7},
pages = {3018-3031},
pmid = {29602255},
issn = {1097-0193},
mesh = {Adult ; Amygdala/diagnostic imaging/*physiology ; Brain-Computer Interfaces ; Emotions/*physiology ; Female ; Functional Neuroimaging/*methods ; Humans ; Magnetic Resonance Imaging ; Neurofeedback/*methods ; Pattern Recognition, Visual/*physiology ; Prefrontal Cortex/diagnostic imaging/*physiology ; *Self-Control ; Thalamus/diagnostic imaging/*physiology ; Ventral Striatum/diagnostic imaging/*physiology ; Young Adult ; },
abstract = {Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation.},
}
@article {pmid29601538,
year = {2018},
author = {Rezeika, A and Benda, M and Stawicki, P and Gembler, F and Saboor, A and Volosyak, I},
title = {Brain-Computer Interface Spellers: A Review.},
journal = {Brain sciences},
volume = {8},
number = {4},
pages = {},
pmid = {29601538},
issn = {2076-3425},
abstract = {A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.},
}
@article {pmid29596978,
year = {2018},
author = {Ojeda, A and Kreutz-Delgado, K and Mullen, T},
title = {Fast and robust Block-Sparse Bayesian learning for EEG source imaging.},
journal = {NeuroImage},
volume = {174},
number = {},
pages = {449-462},
doi = {10.1016/j.neuroimage.2018.03.048},
pmid = {29596978},
issn = {1095-9572},
mesh = {Algorithms ; Artifacts ; Bayes Theorem ; Brain/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Models, Theoretical ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm. In the first stage, we optimize a simplified non-sparse generative model to get an estimate of the sensor noise covariance and a good initialization of the group-sparsity profile of the sources. Sources obtained at this stage are equivalent to those estimated with the popular inverse method LORETA. In the second stage, we apply a fast SBL algorithm with the noise covariance fixed to the value obtained in the first stage to efficiently shrink to zero groups of sources that are irrelevant for explaining the EEG measurements. In addition, we derive an initialization to the first stage of the algorithm that is optimal in the least squares sense, which prevents delays due to suboptimal initial conditions. We validate our method on both simulated and real EEG data. Simulations show that the method is robust to measurement noise and performs well in real-time, with faster performance than two state of the art SBL solvers. On real error-related negativity EEG data, we obtain source images in agreement with the experimental literature. The method shows promise for real-time neuroimaging and brain-machine interface applications.},
}
@article {pmid29596953,
year = {2018},
author = {Rekleiti, M and Souliotis, K and Sarafis, P and Kyriazis, I and Tsironi, M},
title = {Measuring the reliability and validity of the Greek edition of the Diabetes Quality of Life Brief Clinical Inventory.},
journal = {Diabetes research and clinical practice},
volume = {140},
number = {},
pages = {61-71},
doi = {10.1016/j.diabres.2018.01.019},
pmid = {29596953},
issn = {1872-8227},
mesh = {Aged ; Diabetes Mellitus/*physiopathology ; Female ; Greece ; Humans ; Language ; Male ; Psychometrics/*methods ; Quality of Life/*psychology ; Reproducibility of Results ; Retrospective Studies ; },
abstract = {BACKGROUND: The present study focuses on studying the validity and reliability of the Greek edition of DQOL-BCI. DQOL-BCI includes 15 questions-elements that are evaluated on a 5-grade scale like Likert and two general form-shapes.<br><br>METHODS: The translation process was conducted in conformity with the guidelines of EuroQol group. A non-random sample of 65 people-patients diagnosed with diabetes I and II was selected. The questionnaire that was used to collect the data was the translated version of DQOL-BCI, and included the demographic characteristics of the interviewees. The content validity of DQOL-BCI was re-examined from a team of five experts (expert panel) for qualitative and quantitative performance. The completion of the questionnaire was done via a personal interview.<br><br>RESULTS: The sample consisted of 58 people (35 men and 23 women, 59.9&#x202f;&#xb1;&#x202f;10.9 years). The translation of the questionnaire was found appropriate in accordance to the peculiarities of the Greek language and culture. The largest deviation of values is observed in QOL1 (1.71) in comparison to QOL6 (2.98). The difference between the standard deviations is close to 0.6. The statistics results of the tests showed satisfactory content validity and high construct validity, while the high values for Cronbach alpha index (0.95) reveal high reliability and internal consistency.<br><br>CONCLUSIONS: The Greek version of DQOL-BCI has acceptable psychometric properties and appears to demonstrate high internal reliability and satisfactory construct validity, which allows its use as an important tool in evaluating the quality of life of diabetic patients in relation to their health.},
}
@article {pmid29590508,
year = {2017},
author = {},
title = {Surgical innovations: the bionic eye implant.},
journal = {AORN journal},
volume = {105},
number = {3},
pages = {P13-P14},
doi = {10.1016/S0001-2092(17)30124-2},
pmid = {29590508},
issn = {1878-0369},
mesh = {Adult ; *Bionics ; Blindness/*surgery ; Brain-Computer Interfaces ; Humans ; Patient Selection ; Prosthesis Design ; Retina/surgery ; *Visual Prosthesis ; },
}
@article {pmid29589813,
year = {2018},
author = {Libbrecht, S and Hoffman, L and Welkenhuysen, M and Van den Haute, C and Baekelandt, V and Braeken, D and Haesler, S},
title = {Proximal and distal modulation of neural activity by spatially confined optogenetic activation with an integrated high-density optoelectrode.},
journal = {Journal of neurophysiology},
volume = {120},
number = {1},
pages = {149-161},
pmid = {29589813},
issn = {1522-1598},
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes ; *Evoked Potentials ; Female ; Mice ; Mice, Inbred C57BL ; Neurons/*physiology ; Optogenetics/instrumentation/*methods ; Sensorimotor Cortex/cytology/*physiology ; },
abstract = {Optogenetic manipulations are widely used for investigating the contribution of genetically identified cell types to behavior. Simultaneous electrophysiological recordings are less common, although they are critical for characterizing the specific impact of optogenetic manipulations on neural circuits in vivo. This is at least in part because combining photostimulation with large-scale electrophysiological recordings remains technically challenging, which also poses a limitation for performing extracellular identification experiments. Currently available interfaces that guide light of the appropriate wavelength into the brain combined with an electrophysiological modality suffer from various drawbacks such as a bulky size, low spatial resolution, heat dissipation, or photovoltaic artifacts. To address these challenges, we have designed and fabricated an integrated ultrathin neural interface with 12 optical outputs and 24 electrodes. We used the device to measure the effect of localized stimulation in the anterior olfactory cortex, a paleocortical structure involved in olfactory processing. Our experiments in adult mice demonstrate that because of its small dimensions, our novel tool causes far less tissue damage than commercially available devices. Moreover, optical stimulation and recording can be performed simultaneously, with no measurable electrical artifact during optical stimulation. Importantly, optical stimulation can be confined to small volumes with approximately single-cortical layer thickness. Finally, we find that even highly localized optical stimulation causes inhibition at more distant sites. NEW &amp; NOTEWORTHY In this study, we establish a novel tool for simultaneous extracellular recording and optogenetic photostimulation. Because the device is built using established microchip technology, it can be fabricated with high reproducibility and reliability. We further show that even very localized stimulation affects neural firing far beyond the stimulation site. This demonstrates the difficulty in predicting circuit-level effects of optogenetic manipulations and highlights the importance of closely monitoring neural activity in optogenetic experiments.},
}
@article {pmid29578026,
year = {2018},
author = {Wei, CS and Lin, YP and Wang, YT and Lin, CT and Jung, TP},
title = {A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection.},
journal = {NeuroImage},
volume = {174},
number = {},
pages = {407-419},
doi = {10.1016/j.neuroimage.2018.03.032},
pmid = {29578026},
issn = {1095-9572},
mesh = {Brain/*physiology ; Brain Waves ; Brain-Computer Interfaces ; Calibration ; Cluster Analysis ; Electroencephalography/*methods ; Humans ; *Psychomotor Performance ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *Wakefulness ; },
abstract = {Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient individualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min-1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.},
}
@article {pmid29576963,
year = {2018},
author = {Rimbert, S and Al-Chwa, R and Zaepffel, M and Bougrain, L},
title = {Electroencephalographic modulations during an open- or closed-eyes motor task.},
journal = {PeerJ},
volume = {6},
number = {},
pages = {e4492},
pmid = {29576963},
issn = {2167-8359},
abstract = {There is fundamental knowledge that during the resting state cerebral activity recorded by electroencephalography (EEG) is strongly modulated by the eyes-closed condition compared to the eyes-open condition, especially in the occipital lobe. However, little research has demonstrated the influence of the eyes-closed condition on the motor cortex, particularly during a self-paced movement. This prompted the question: How does the motor cortex activity change between the eyes-closed and eyes-open conditions? To answer this question, we recorded EEG signals from 15 voluntary healthy subjects who performed a simple motor task (i.e., a voluntary isometric flexion of the right-hand index) under two conditions: eyes-closed and eyes-open. Our results confirmed strong modulation in the mu rhythm (7-13 Hz) with a large event-related desynchronisation. However, no significant differences have been observed in the beta band (15-30 Hz). Furthermore, evidence suggests that the eyes-closed condition influences the behaviour of subjects. This study gives us greater insight into the motor cortex and could also be useful in the brain-computer interface (BCI) domain.},
}
@article {pmid29572165,
year = {2018},
author = {Jeunet, C and Lotte, F and Batail, JM and Philip, P and Micoulaud Franchi, JA},
title = {Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review.},
journal = {Neuroscience},
volume = {378},
number = {},
pages = {225-233},
doi = {10.1016/j.neuroscience.2018.03.013},
pmid = {29572165},
issn = {1873-7544},
mesh = {*Brain-Computer Interfaces ; Humans ; Mental Disorders/rehabilitation ; *Neurofeedback ; },
abstract = {In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting recent additions to the BCI literature. The object of this paper is to highlight this literature and discuss its potential relevance and usefulness to better understand the processes underlying NF and further improve the design of clinical trials assessing NF efficacy. Indeed, we are convinced that while NF and BCI are fundamentally different in many ways, both the BCI and NF communities could reach compelling achievements by building upon one another. By reviewing the recent BCI literature, we identified three types of factors that influence BCI performance: task-specific, cognitive/motivational and technology-acceptance-related factors. Since BCIs and NF share a common goal (i.e., learning to modulate specific neurophysiological patterns), similar cognitive and neurophysiological processes are likely to be involved during the training process. Thus, the literature on BCI training may help (1) to deepen our understanding of neurofeedback training processes and (2) to understand the variables that influence the clinical efficacy of NF. This may help to properly assess and/or control the influence of these variables during randomized controlled trials.},
}
@article {pmid29571902,
year = {2018},
author = {Ziouziou, I and Irani, J and Wei, JT and Karmouni, T and El Khader, K and Koutani, A and Iben Attya Andaloussi, A},
title = {Ileal conduit vs orthotopic neobladder: Which one offers the best health-related quality of life in patients undergoing radical cystectomy? A systematic review of literature and meta-analysis.},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {28},
number = {5},
pages = {241-250},
doi = {10.1016/j.purol.2018.02.001},
pmid = {29571902},
issn = {1166-7087},
mesh = {Cystectomy/*methods ; Humans ; *Quality of Life ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Urinary Bladder Neoplasms/*surgery ; *Urinary Diversion/methods ; *Urinary Reservoirs, Continent ; },
abstract = {INTRODUCTION: Orthotopic neobladder (ONB) and ileal conduit (IC) are the most commonly practiced techniques of urinary diversion (UD) after radical cystectomy (RC) in bladder cancer patients. Data in the literature is still discordant regarding which UD technique offers the best HR-QoL.<br><br>OBJECTIVE: The objective was to compare HR-QoL in patients undergoing ONB and IC after RC, through a systematic review of the literature and meta-analysis.<br><br>MATERIAL AND METHODS: We performed a literature search of PubMed, ScienceDirect, CochraneLibrary and ClinicalTrials.Gov in September 2017 according to the Cochrane Handbook and the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes. The studies were evaluated according to the "Oxford Center for Evidence-Based Medicine" criteria. The outcome measures evaluated were subdomains' scores of Bladder Cancer Index BCI: urinary function (UF), urinary bother (UB), bowel function (BF), bowel bother (BB), sexual function (SF) and sexual bother (SB). Continuous outcomes were compared using weighted means differences, with 95% confidence intervals. The presence of publication bias was examined by funnel plots.<br><br>RESULTS: Four studies met the inclusion criteria. The pooled results demonstrated better UF and UB scores in IC patients: differences were -18.17 (95% CI: -27.49, -8.84, P=0.0001) and -3.72 (95% CI: -6.66, -0.79, P=0.01) respectively. There was no significant difference between IC and ONB patients in terms of BF and BB. SF was significantly better in ONB patients: the difference was 12.7 (95% CI, 6.32, 19.08, P<0.0001). However no significant difference was observed regarding SB.<br><br>CONCLUSION: This meta-analysis of non-randomized studies demonstrated a better HR-QoL in urinary outcomes in IC patients compared with ONB patients.},
}
@article {pmid29569635,
year = {2018},
author = {Golovkine, G and Reboud, E and Huber, P},
title = {Corrigendum: Pseudomonas aeruginosa Takes a Multi-Target Approach to Achieve Junction Breach.},
journal = {Frontiers in cellular and infection microbiology},
volume = {8},
number = {},
pages = {52},
doi = {10.3389/fcimb.2018.00052},
pmid = {29569635},
issn = {2235-2988},
abstract = {[This corrects the article on p. 532 in vol. 7, PMID: 29379773.].},
}
@article {pmid29568777,
year = {2018},
author = {Ghani, U and Wasim, M and Khan, US and Mubasher Saleem, M and Hassan, A and Rashid, N and Islam Tiwana, M and Hamza, A and Kashif, A},
title = {Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator.},
journal = {BioMed research international},
volume = {2018},
number = {},
pages = {9861350},
pmid = {29568777},
issn = {2314-6141},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Signal Processing, Computer-Assisted/instrumentation ; Signal-To-Noise Ratio ; User-Computer Interface ; },
abstract = {Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.},
}
@article {pmid29566348,
year = {2018},
author = {Zhang, Y and Chase, SM},
title = {Optimizing the Usability of Brain-Computer Interfaces.},
journal = {Neural computation},
volume = {30},
number = {5},
pages = {1323-1358},
doi = {10.1162/NECO_a_01076},
pmid = {29566348},
issn = {1530-888X},
support = {R01 HD071686/NH/NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; *Formative Feedback ; Humans ; Learning/*physiology ; Neurons/*physiology ; Nonlinear Dynamics ; Time Factors ; },
abstract = {Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.},
}
@article {pmid29566104,
year = {2018},
author = {Royce, M and Bachelot, T and Villanueva, C and Özgüroglu, M and Azevedo, SJ and Cruz, FM and Debled, M and Hegg, R and Toyama, T and Falkson, C and Jeong, J and Srimuninnimit, V and Gradishar, WJ and Arce, C and Ridolfi, A and Lin, C and Cardoso, F},
title = {Everolimus Plus Endocrine Therapy for Postmenopausal Women With Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: A Clinical Trial.},
journal = {JAMA oncology},
volume = {4},
number = {7},
pages = {977-984},
pmid = {29566104},
issn = {2374-2445},
mesh = {Aged ; Antineoplastic Agents/pharmacology/*therapeutic use ; Breast Neoplasms/*drug therapy/pathology ; Everolimus/pharmacology/*therapeutic use ; Female ; Humans ; Middle Aged ; Postmenopause ; },
abstract = {IMPORTANCE: Cotargeting the mammalian target of rapamycin pathway and estrogen receptor may prevent or delay endocrine resistance in patients receiving first-line treatment for advanced breast cancer.<br><br>OBJECTIVE: To investigate the combination of everolimus plus endocrine therapy in first-line and second-line treatment settings for postmenopausal women with estrogen receptor-positive, human epidermal growth receptor 2-negative advanced breast cancer.<br><br>In the multicenter, open-label, single-arm, phase 2 BOLERO-4 (Breast Cancer Trials of Oral Everolimus) clinical trial, 245 patients were screened for eligibility; 202 were enrolled between March 7, 2013, and December 17, 2014. A median follow-up of 29.5 months had been achieved by the data cutoff date (December 17, 2016).<br><br>INTERVENTIONS: Patients received first-line treatment with everolimus, 10 mg/d, plus letrozole, 2.5 mg/d. Second-line treatment with everolimus, 10 mg/d, plus exemestane, 25 mg/d, was offered at the investigator's discretion upon initial disease progression.<br><br>MAIN OUTCOMES AND MEASURES: The primary end point was investigator-assessed progression-free survival in the first-line setting per Response Evaluation Criteria in Solid Tumors, version 1.0. Safety was assessed in patients who received at least 1 dose of study medication and at least 1 postbaseline safety assessment.<br><br>RESULTS: A total of 202 women treated in the first-line setting had a median age of 64.0 years (interquartile range, 58.0-70.0 years) with metastatic (194 [96.0%]) or locally advanced (8 [4.0%]) breast cancer. Median progression-free survival was 22.0 months (95% CI, 18.1-25.1 months) with everolimus and letrozole. Median overall survival was not reached; 24-month estimated overall survival rate was 78.7% (95% CI, 72.1%-83.9%). Fifty patients started second-line treatment; median progression-free survival was 3.7 months (95% CI, 1.9-7.4 months). No new safety signals were observed. In the first-line setting, the most common all-grade adverse event was stomatitis (139 [68.8%]); the most common grade 3 to 4 adverse event was anemia (21 [10.4%]). In the second-line setting, the most common adverse events were stomatitis and decreased weight (10 [20.0%] each); the most common grade 3 to 4 adverse event was hypertension (5 [10.0%]). There were 50 (24.8%) deaths overall during the study; 40 were due to study indication (breast cancer).<br><br>CONCLUSIONS AND RELEVANCE: The results of this trial add to the existing body of evidence suggesting that everolimus plus endocrine therapy is a good first-line treatment option for postmenopausal women with estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer.<br><br>TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01698918.},
}
@article {pmid29565355,
year = {2018},
author = {Shen, H},
title = {Brain Enigma.},
journal = {Scientific American},
volume = {318},
number = {3},
pages = {15},
doi = {10.1038/scientificamerican0318-15},
pmid = {29565355},
issn = {0036-8733},
mesh = {Animals ; Artificial Limbs ; Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; *Movement ; },
}
@article {pmid29565293,
year = {2018},
author = {Bandara, DSV and Arata, J and Kiguchi, K},
title = {Towards Control of a Transhumeral Prosthesis with EEG Signals.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {5},
number = {2},
pages = {},
pmid = {29565293},
issn = {2306-5354},
abstract = {Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis. In the proposed method, the motion intention for arm reaching or hand lifting is identified using classifiers trained with motion-related EEG features. For this purpose, neural network and k-nearest neighbor classifiers are used. Then, elbow motion and hand endpoint motion is estimated using a different set of neural-network-based classifiers, which are trained with motion information recorded using healthy subjects. The predictions from the classifiers are compared with residual limb motion to generate a final prediction of motion intention. This can then be used to realize multi-DoF control of a prosthesis. The experimental results show the feasibility of the proposed method for multi-DoF control of a transhumeral prosthesis. This proof of concept study was performed with healthy subjects.},
}
@article {pmid29563891,
year = {2018},
author = {Osiurak, F and Navarro, J and Reynaud, E},
title = {How Our Cognition Shapes and Is Shaped by Technology: A Common Framework for Understanding Human Tool-Use Interactions in the Past, Present, and Future.},
journal = {Frontiers in psychology},
volume = {9},
number = {},
pages = {293},
pmid = {29563891},
issn = {1664-1078},
abstract = {Over the evolution, humans have constantly developed and improved their technologies. This evolution began with the use of physical tools, those tools that increase our sensorimotor abilities (e.g., first stone tools, modern knives, hammers, pencils). Although we still use some of these tools, we also employ in daily life more sophisticated tools for which we do not systematically understand the underlying physical principles (e.g., computers, cars). Current research is also turned toward the development of brain-computer interfaces directly linking our brain activity to machines (i.e., symbiotic tools). The ultimate goal of research on this topic is to identify the key cognitive processes involved in these different modes of interaction. As a primary step to fulfill this goal, we offer a first attempt at a common framework, based on the idea that humans shape technologies, which also shape us in return. The framework proposed is organized into three levels, describing how we interact when using physical (Past), sophisticated (Present), and symbiotic (Future) technologies. Here we emphasize the role played by technical reasoning and practical reasoning, two key cognitive processes that could nevertheless be progressively suppressed by the proficient use of sophisticated and symbiotic tools. We hope that this framework will provide a common ground for researchers interested in the cognitive basis of human tool-use interactions, from paleoanthropology to neuroergonomics.},
}
@article {pmid29563634,
year = {2018},
author = {Polena, H and Creuzet, J and Dufies, M and Sidibé, A and Khalil-Mgharbel, A and Salomon, A and Deroux, A and Quesada, JL and Roelants, C and Filhol, O and Cochet, C and Blanc, E and Ferlay-Segura, C and Borchiellini, D and Ferrero, JM and Escudier, B and Négrier, S and Pages, G and Vilgrain, I},
title = {The tyrosine-kinase inhibitor sunitinib targets vascular endothelial (VE)-cadherin: a marker of response to antitumoural treatment in metastatic renal cell carcinoma.},
journal = {British journal of cancer},
volume = {118},
number = {9},
pages = {1179-1188},
pmid = {29563634},
issn = {1532-1827},
mesh = {Antineoplastic Agents/*therapeutic use ; *Biomarkers, Pharmacological/metabolism ; Biomarkers, Tumor/metabolism ; Cadherins/*metabolism ; Carcinoma, Renal Cell/*drug therapy/metabolism/pathology ; Cells, Cultured ; Clinical Trials as Topic ; Endothelium, Vascular/*drug effects/metabolism ; Human Umbilical Vein Endothelial Cells ; Humans ; Kidney Neoplasms/*drug therapy/metabolism/pathology ; Molecular Targeted Therapy/methods ; Neoplasm Metastasis ; Retrospective Studies ; Sunitinib/*therapeutic use ; Treatment Outcome ; },
abstract = {BACKGROUND: Vascular endothelial (VE)-cadherin is an endothelial cell-specific protein responsible for endothelium integrity. Its adhesive properties are regulated by post-translational processing, such as tyrosine phosphorylation at site Y[685] in its cytoplasmic domain, and cleavage of its extracellular domain (sVE). In hormone-refractory metastatic breast cancer, we recently demonstrated that sVE levels correlate to poor survival. In the present study, we determine whether kidney cancer therapies had an effect on VE-cadherin structural modifications and their clinical interest to monitor patient outcome.<br><br>METHODS: The effects of kidney cancer biotherapies were tested on an endothelial monolayer model mimicking the endothelium lining blood vessels and on a homotypic and heterotypic 3D cell model mimicking tumour growth. sVE was quantified by ELISA in renal cell carcinoma patients initiating sunitinib (48 patients) or bevacizumab (83 patients) in the first-line metastatic setting (SUVEGIL and TORAVA trials).<br><br>RESULTS: Human VE-cadherin is a direct target for sunitinib which inhibits its VEGF-induced phosphorylation and cleavage on endothelial monolayer and endothelial cell migration in the 3D model. The tumour cell environment modulates VE-cadherin functions through MMPs and VEGF. We demonstrate the presence of soluble VE-cadherin in the sera of mRCC patients (n&#x2009;=&#x2009;131) which level at baseline, is higher than in a healthy donor group (n&#x2009;=&#x2009;96). Analysis of sVE level after 4 weeks of treatment showed that a decrease in sVE level discriminates the responders vs. non-responders to sunitinib, but not bevacizumab.<br><br>CONCLUSIONS: These data highlight the interest for the sVE bioassay in future follow-up of cancer patients treated with targeted therapies such as tyrosine-kinase inhibitors.},
}
@article {pmid29562312,
year = {2018},
author = {Curley, WH and Forgacs, PB and Voss, HU and Conte, MM and Schiff, ND},
title = {Characterization of EEG signals revealing covert cognition in the injured brain.},
journal = {Brain : a journal of neurology},
volume = {141},
number = {5},
pages = {1404-1421},
pmid = {29562312},
issn = {1460-2156},
support = {K23 NS096222/NS/NINDS NIH HHS/United States ; UL1 TR000043/TR/NCATS NIH HHS/United States ; R01 HD051912/HD/NICHD NIH HHS/United States ; UL1 TR002384/TR/NCATS NIH HHS/United States ; UL1 TR000457/TR/NCATS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain/diagnostic imaging/*physiopathology ; Brain Injuries/*complications/diagnostic imaging ; Brain Waves/*physiology ; Child ; Cognition Disorders/*etiology/*physiopathology ; *Electroencephalography ; Female ; Fourier Analysis ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Oxygen/blood ; Young Adult ; },
abstract = {See Boly and Laureys (doi:10.1093/brain/awy080) for a scientific commentary on this article.Patients with severe brain injury are difficult to assess and frequently subject to misdiagnosis. 'Cognitive motor dissociation' is a term used to describe a subset of such patients with preserved cognition as detected with neuroimaging methods but not evident in behavioural assessments. Unlike the locked-in state, cognitive motor dissociation after severe brain injury is prominently marked by concomitant injuries across the cerebrum in addition to limited or no motoric function. In the present study, we sought to characterize the EEG signals used as indicators of cognition in patients with disorders of consciousness and examine their reliability for potential future use to re-establish communication. We compared EEG-based assessments to the results of using similar methods with functional MRI. Using power spectral density analysis to detect EEG evidence of task performance (Two Group Test, P ≤ 0.05, with false discovery rate correction), we found evidence of the capacity to follow commands in 21 of 28 patients with severe brain injury and all 15 healthy individuals studied. We found substantial variability in the temporal and spatial characteristics of significant EEG signals among the patients in contrast to only modest variation in these domains across healthy controls; the majority of healthy controls showed suppression of either 8-12 Hz 'alpha' or 13-40 Hz 'beta' power during task performance, or both. Nine of the 21 patients with EEG evidence of command-following also demonstrated functional MRI evidence of command-following. Nine of the patients with command-following capacity demonstrated by EEG showed no behavioural evidence of a communication channel as detected by a standardized behavioural assessment, the Coma Recovery Scale - Revised. We further examined the potential contributions of fluctuations in arousal that appeared to co-vary with some patients' ability to reliably generate EEG signals in response to command. Five of nine patients with statistically indeterminate responses to one task tested showed a positive response after accounting for variations in overall background state (as visualized in the qualitative shape of the power spectrum) and grouping of trial runs with similar background state characteristics. Our findings reveal signal variations of EEG responses in patients with severe brain injuries and provide insight into the underlying physiology of cognitive motor dissociation. These results can help guide future efforts aimed at re-establishment of communication in such patients who will need customization for brain-computer interfaces.},
}
@article {pmid29559420,
year = {2018},
author = {Griskova-Bulanova, I and Pipinis, E and Voicikas, A and Koenig, T},
title = {Global field synchronization of 40 Hz auditory steady-state response: Does it change with attentional demands?.},
journal = {Neuroscience letters},
volume = {674},
number = {},
pages = {127-131},
doi = {10.1016/j.neulet.2018.03.033},
pmid = {29559420},
issn = {1872-7972},
mesh = {Acoustic Stimulation ; *Attention ; Brain/*physiology ; *Cortical Synchronization ; Electroencephalography ; *Evoked Potentials, Auditory ; Humans ; Male ; },
abstract = {Auditory steady-state responses (ASSRs) are increasingly used in research of neuropsychiatric disorders and for brain-computer interface applications. However, results on attentional modulation of ASSRs are inconclusive. The evaluation of large-scale effects of task-related modulation on ASSRs might give better estimation of the induced changes. The aim of the study was to test global field synchronization - a reference-independent evaluation of the amount of phase-locking among all active regions at a given frequency - during tasks differing in attentional demands to 40 Hz auditory stimulation. Twenty seven healthy young males participated in the EEG study with concurrent 40 Hz binaural click stimulation and three experimental tasks: 1) to count presented stimuli (focused attention); 2) to silently read a text (distraction); 3) to stay awake with closed eyes (resting). We showed that during auditory 40 Hz stimulation, the global field synchronization of the EEG increased as compared to the silent baseline period and the largest increase was observed when subjects counted stimuli or rested with closed eyes. Our results provide insights that depending on the method of assessment, the 40 Hz ASSR might be an indicator of both local and complex synchronization processes that are affected by the state (task performed or psychopathology) of the participants.},
}
@article {pmid29558654,
year = {2018},
author = {Feng, J and Yin, E and Jin, J and Saab, R and Daly, I and Wang, X and Hu, D and Cichocki, A},
title = {Towards correlation-based time window selection method for motor imagery BCIs.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {102},
number = {},
pages = {87-95},
doi = {10.1016/j.neunet.2018.02.011},
pmid = {29558654},
issn = {1879-2782},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Humans ; *Models, Neurological ; *Psychomotor Performance ; Reaction Time ; Stroke/physiopathology ; },
abstract = {The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.},
}
@article {pmid29557346,
year = {2018},
author = {Lopes Dias, C and Sburlea, AI and Müller-Putz, GR},
title = {Masked and unmasked error-related potentials during continuous control and feedback.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {036031},
doi = {10.1088/1741-2552/aab806},
pmid = {29557346},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Feedback, Physiological/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Random Allocation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {UNLABELLED: The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain-computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages.<br><br>OBJECTIVE: We developed a task in which subjects have continuous control of a cursor's position by means of a joystick. The cursor's position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals.<br><br>APPROACH: This paper studies the electroencephalographic (EEG)-measurable signatures caused by a loss of control over the cursor's trajectory, causing a target miss.<br><br>MAIN RESULTS: In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-[Formula: see text], average TPR&#x2009;&#x2009;=&#x2009;&#x2009;81.8% and average TNR&#x2009;&#x2009;=&#x2009;&#x2009;96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-[Formula: see text], average TPR&#x2009;&#x2009;=&#x2009;&#x2009;60.9% and average TNR&#x2009;&#x2009;=&#x2009;&#x2009;58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%.<br><br>SIGNIFICANCE: The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.},
}
@article {pmid29554548,
year = {2018},
author = {Sözer, AT and Fidan, CB},
title = {Novel spatial filter for SSVEP-based BCI: A generated reference filter approach.},
journal = {Computers in biology and medicine},
volume = {96},
number = {},
pages = {98-105},
doi = {10.1016/j.compbiomed.2018.02.019},
pmid = {29554548},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Regression Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems can be realised using only one electrode; however, due to the inter-user and inter-trial differences, the handling of multiple electrode is preferred. This raises the problem of evaluating information from multiple electrode signals. To solve this problem, we developed a novel spatial filtering method (Generated Reference Filter) for SSVEP-based BCIs. In our method an artificial reference signal is generated by a combination of reference electrode signals. Multiple regression analysis (MRA) was used to determine the optimal weight coefficients for signal combination. The filtered signal was obtained by subtraction. The method was tested on a SSVEP dataset and compared with minimum energy combination and common reference methods, namely the surface Laplacian technique and common average referencing. The newly developed method provided more effective filtering and therefore higher SSVEP detection accuracy was obtained. It was also more robust against subject-to-subject and trial-to-trial variability as the artificial reference signal was recalculated for each detection round. No special preparation is required, and the method is easy to implement. These experimental results indicate that the proposed method can be used confidently with SSVEP-based BCI systems.},
}
@article {pmid29553933,
year = {2018},
author = {da Silva, HP},
title = {Physiological Sensing Now Open to the World: New Resources Are Allowing Us to Learn, Experiment, and Create Imaginative Solutions for Biomedical Applications.},
journal = {IEEE pulse},
volume = {9},
number = {2},
pages = {9-11},
doi = {10.1109/MPUL.2018.2790903},
pmid = {29553933},
issn = {2154-2317},
mesh = {Accelerometry/instrumentation ; Biomedical Engineering/*instrumentation ; Brain-Computer Interfaces ; Electrodiagnosis/instrumentation ; Electronics, Medical ; Equipment Design ; Humans ; Monitoring, Physiologic/*instrumentation ; },
abstract = {With the advent of low-cost computing platforms, such as Arduino (http://www.arduino.cc) and Raspberry Pi (http://www.raspberrypi.org), it has become clear that lowering the cost barrier and shortening the learning curve, with the backing of a motivated community, would play a transformational role in the way people learn, experiment, and create imaginative solutions to outstanding problems that can benefit from embedded systems.},
}
@article {pmid29553484,
year = {2018},
author = {Downey, JE and Schwed, N and Chase, SM and Schwartz, AB and Collinger, JL},
title = {Intracortical recording stability in human brain-computer interface users.},
journal = {Journal of neural engineering},
volume = {15},
number = {4},
pages = {046016},
doi = {10.1088/1741-2552/aab7a0},
pmid = {29553484},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Adult ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Male ; Microelectrodes ; Middle Aged ; Motor Cortex/*physiology ; Quadriplegia/physiopathology/*therapy ; },
abstract = {OBJECTIVE: Intracortical brain-computer interfaces (BCIs) are being developed to assist people with motor disabilities in communicating and interacting with the world around them. This technology relies on recordings from the primary motor cortex, which may vary from day to day.<br><br>APPROACH: Here we quantify, in two long-term BCI subjects, the length of time that action potentials from the same neuron, or group of neurons, can be recorded from the motor cortex.<br><br>MAIN RESULTS: These action potentials are identified by their extracellular waveforms and may change within a single day, although some of these identified units can be identified consistently for weeks and even months. Features of the extracellular waveforms allowed us to predict whether a specific unit was more or less likely to remain stable over a prolonged period.<br><br>SIGNIFICANCE: A greater understanding of unit stability and instability can aid the development of motor BCIs, where the goal is to maintain a high level of performance despite changes in the recorded population. BCIs should be able to be operated without technician intervention for hours, and hopefully days, to provide the most benefit to the end-users of this technology.},
}
@article {pmid29551437,
year = {2018},
author = {Torkar, KG and Bedenić, B},
title = {Antimicrobial susceptibility and characterization of metallo-β-lactamases, extended-spectrum β-lactamases, and carbapenemases of Bacillus cereus isolates.},
journal = {Microbial pathogenesis},
volume = {118},
number = {},
pages = {140-145},
doi = {10.1016/j.micpath.2018.03.026},
pmid = {29551437},
issn = {1096-1208},
mesh = {Anti-Bacterial Agents/*pharmacology ; Bacillus cereus/*drug effects/enzymology/*genetics/isolation &amp; purification ; Bacterial Proteins/*genetics ; Drug Resistance, Bacterial ; Genes, Bacterial/*genetics ; Humans ; Microbial Sensitivity Tests ; Phenotype ; beta-Lactamases/*genetics ; },
abstract = {The susceptibility of 66 clinical and environmental B. cereus isolates were tested to selected antimicrobials by a broth microdilution method. All strains were resistant to β-lactams and susceptible to gentamicin and imipenem. Sixty-five (98.5%) isolates were susceptible to meropenem and ciprofloxacin and 74.2% to azithromycin. Significant differences in MIC values between environmental and clinical isolates were not demonstrated (p > 0.05). According to the disc diffusion method, 80.3%-98.5% of the strains were resistant to one or more of four cephalosporins. The presence of genes for B. cereus β-lactamases BCI, BCII, BCIII, extended-spectrum β-lactamases from the CTX and TEM family and the carbapenemases belonging to IMP and VIM family was studied. BlaII genes were expressed in all isolates; the PCR products for blaIII were also detected in two strains, but none of them was positive for blaI. The amplicon of the family blaCTX-M, mostly M-1 and M-15, was confirmed among 68.2% of the isolates, while were blaVIM-like genes determined in 21.2% of the samples.},
}
@article {pmid29546648,
year = {2018},
author = {Kirar, JS and Agrawal, RK},
title = {Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG.},
journal = {Journal of medical systems},
volume = {42},
number = {5},
pages = {78},
pmid = {29546648},
issn = {1573-689X},
support = {3513//University Grants Committee (India)/ ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. In the first phase, EEG signal is segmented into overlapping time segments and bandpass filtered through frequency filter bank of variable size subbands. In the next phase, features are extracted from the segmented and filtered data using stationary common spatial pattern technique (SCSP) that can handle the non- stationarity and artifacts of EEG signal. The univariate feature selection method is used to obtain a relevant subset of features in the third phase. In the final phase, the classifier is used to build adecision model. In this paper, four univariate feature selection methods such as Euclidean distance, correlation, mutual information and Fisher discriminant ratio and two well-known classifiers (LDA and SVM) are investigated. The proposed method has been validated using the publicly available BCI competition IV dataset Ia and BCI Competition III dataset IVa. Experimental results demonstrate that the proposed method significantly outperforms the existing methods in terms of classification error. A reduction of 76.98%, 75.65%, 73.90% and 72.21% in classification error over both datasets and both classifiers can be observed using the proposed CVSTSCSP method in comparison to CSP, SBCSP, FBCSP and CVSCSP respectively.},
}
@article {pmid29544778,
year = {2018},
author = {Acharyya, A and Jadhav, PN and Bono, V and Maharatna, K and Naik, GR},
title = {Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG.},
journal = {Computer methods and programs in biomedicine},
volume = {158},
number = {},
pages = {123-133},
doi = {10.1016/j.cmpb.2018.02.009},
pmid = {29544778},
issn = {1872-7565},
mesh = {Artifacts ; Automation ; *Blinking ; Brain-Computer Interfaces ; Case-Control Studies ; Electroencephalography/*instrumentation/methods/standards ; Equipment Design ; Humans ; Muscles/*physiology ; Reproducibility of Results ; Signal-To-Noise Ratio ; Wavelet Analysis ; },
abstract = {BACKGROUND AND OBJECTIVE: EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode.<br><br>METHODS: In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik f&#xfc;r Epileptologie, Universit&#xe4;t Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform.<br><br>RESULTS: Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power.<br><br>CONCLUSIONS: The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.},
}
@article {pmid29535602,
year = {2018},
author = {Sugi, M and Hagimoto, Y and Nambu, I and Gonzalez, A and Takei, Y and Yano, S and Hokari, H and Wada, Y},
title = {Improving the Performance of an Auditory Brain-Computer Interface Using Virtual Sound Sources by Shortening Stimulus Onset Asynchrony.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {108},
pmid = {29535602},
issn = {1662-4548},
abstract = {Recently, a brain-computer interface (BCI) using virtual sound sources has been proposed for estimating user intention via electroencephalogram (EEG) in an oddball task. However, its performance is still insufficient for practical use. In this study, we examine the impact that shortening the stimulus onset asynchrony (SOA) has on this auditory BCI. While very short SOA might improve its performance, sound perception and task performance become difficult, and event-related potentials (ERPs) may not be induced if the SOA is too short. Therefore, we carried out behavioral and EEG experiments to determine the optimal SOA. In the experiments, participants were instructed to direct attention to one of six virtual sounds (target direction). We used eight different SOA conditions: 200, 300, 400, 500, 600, 700, 800, and 1,100 ms. In the behavioral experiment, we recorded participant behavioral responses to target direction and evaluated recognition performance of the stimuli. In all SOA conditions, recognition accuracy was over 85%, indicating that participants could recognize the target stimuli correctly. Next, using a silent counting task in the EEG experiment, we found significant differences between target and non-target sound directions in all but the 200-ms SOA condition. When we calculated an identification accuracy using Fisher discriminant analysis (FDA), the SOA could be shortened by 400 ms without decreasing the identification accuracies. Thus, improvements in performance (evaluated by BCI utility) could be achieved. On average, higher BCI utilities were obtained in the 400 and 500-ms SOA conditions. Thus, auditory BCI performance can be optimized for both behavioral and neurophysiological responses by shortening the SOA.},
}
@article {pmid29531364,
year = {2018},
author = {Golub, MD and Sadtler, PT and Oby, ER and Quick, KM and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Chase, SM and Yu, BM},
title = {Learning by neural reassociation.},
journal = {Nature neuroscience},
volume = {21},
number = {4},
pages = {607-616},
pmid = {29531364},
issn = {1546-1726},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS105318/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; *Brain Mapping ; Brain-Computer Interfaces ; Learning/*physiology ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/*cytology ; Neurons/*physiology ; Psychomotor Performance/physiology ; Rats ; },
abstract = {Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.},
}
@article {pmid29527584,
year = {2017},
author = {McFarland, DJ and Wolpaw, JR},
title = {EEG-Based Brain-Computer Interfaces.},
journal = {Current opinion in biomedical engineering},
volume = {4},
number = {},
pages = {194-200},
pmid = {29527584},
issn = {2468-4511},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.},
}
@article {pmid29527160,
year = {2018},
author = {Shin, J and Kwon, J and Im, CH},
title = {A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.},
journal = {Frontiers in neuroinformatics},
volume = {12},
number = {},
pages = {5},
pmid = {29527160},
issn = {1662-5196},
abstract = {The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.},
}
@article {pmid29526855,
year = {2018},
author = {Mendoza Laiz, N and Del Valle Díaz, S and Rioja Collado, N and Gomez-Pilar, J and Hornero, R},
title = {Potential benefits of a cognitive training program in mild cognitive impairment (MCI).},
journal = {Restorative neurology and neuroscience},
volume = {36},
number = {2},
pages = {207-213},
doi = {10.3233/RNN-170754},
pmid = {29526855},
issn = {1878-3627},
mesh = {Aged ; Aged, 80 and over ; Attention ; Cognitive Behavioral Therapy/*methods ; Cognitive Dysfunction/*rehabilitation ; Female ; Humans ; Male ; Memory ; Mental Status Schedule ; Middle Aged ; Neuropsychological Tests ; Treatment Outcome ; },
abstract = {BACKGROUND: Dementia is a disease that is constantly evolving in older people. Its diverse symptoms appear with varying degrees of severity affecting the daily life of those who suffer from it. The rate in which dementia progresses depends on different aspects of the treatment, chosen to try to control and slow down the development of the illness.<br><br>OBJECTIVE: The aim of this study is to assess the effectiveness of cognitive training through a Brain Computer Interface (BCI) and the NeuronUp platform in two age groups whose MMSE is between 18-23 MCI (mild dementia).<br><br>METHOD: 32 subjects took part in the study. There were 22 subjects in Group 1 (61-69 years of age) and 10 subjects in Group 2 (70-81 years of age). The criterium for the selection of the groups was to identify the age range with greater improvements due to the training. In order to estimate neuropsychological performance, the subjects were evaluated with the Luria-DNA neuropsychological battery before and after training. This design enables us to evaluate five cognitive areas: visuospatial, spoken language, memory, intellectual processes and attention.<br><br>RESULTS: After training, Group 1 showed significant improvements in almost all the variables measured when compared with Group 2. This reveals a significant increase in cognitive ability, the degree of which depends on the age.<br><br>CONCLUSION: People with mild dementia may delay cognitive impairment with a suitable cognitive training program.},
}
@article {pmid29524123,
year = {2018},
author = {Shin, JW and Kwon, SB and Bak, Y and Lee, SK and Yoon, DY},
title = {BCI induces apoptosis via generation of reactive oxygen species and activation of intrinsic mitochondrial pathway in H1299 lung cancer cells.},
journal = {Science China. Life sciences},
volume = {61},
number = {10},
pages = {1243-1253},
doi = {10.1007/s11427-017-9191-1},
pmid = {29524123},
issn = {1869-1889},
mesh = {A549 Cells ; Apoptosis/*drug effects ; Caspases/metabolism ; Cell Line, Tumor ; Cell Survival/drug effects ; Cytochromes c/metabolism ; Dose-Response Relationship, Drug ; Enzyme Inhibitors/chemistry/*pharmacology ; Gene Expression/drug effects ; Humans ; Lung Neoplasms/genetics/metabolism/pathology ; Mitochondria/*drug effects/genetics/metabolism ; Molecular Structure ; Poly(ADP-ribose) Polymerases/metabolism ; Reactive Oxygen Species/*metabolism ; },
abstract = {The compound (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) is known as an inhibitor of dual specific phosphatase 1/6 and mitogen-activated protein kinase. However, its precise anti-lung cancer mechanism remains unknown. In this study, the effects of BCI on the viability of non-small cell lung cancer cell lines NCI-H1299, A549, and NCI-H460 were evaluated. We confirmed that BCI significantly inhibited the viability of p53(-) NCI-H1299 cells as compared to NCI-H460 and A549 cells, which express wild-type p53. Furthermore, BCI treatment increased the level of cellular reactive oxygen species and pre-treatment of cells with N-acetylcysteine markedly attenuated BCI-mediated apoptosis of NCI-H1299 cells. BCI induced cellular morphological changes, inhibited viability, and produced reactive oxygen species in NCI-H1299 cells in a dose-dependent manner. BCI induced processing of caspase-9, caspase-3, and poly ADP-ribose polymerase as well as the release of cytochrome c from the mitochondria into the cytosol. In addition, BCI downregulated Bcl-2 expression and enhanced Bax expression in a dose-dependent manner in NCI-H1299 cells. However, BCI failed to modulate the expression of the death receptor and extrinsic factor caspase-8 and Bid, a linker between the intrinsic and extrinsic apoptotic pathways in NCI-H1299 cells. Thus, BCI induces apoptosis via generation of reactive oxygen species and activation of the intrinsic pathway in NCI-H1299 cells.},
}
@article {pmid29522413,
year = {2018},
author = {Xie, X and Yu, ZL and Gu, Z and Zhang, J and Cen, L and Li, Y},
title = {Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {698-708},
doi = {10.1109/TNSRE.2018.2794415},
pmid = {29522413},
issn = {1558-0210},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/classification/methods ; Humans ; *Imagination ; Learning ; Machine Learning ; *Movement ; Reproducibility of Results ; },
abstract = {In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold. Then, a new graph embedding algorithm, called bilinear regularized locality preserving (BRLP), is derived upon the Riemannian graph for addressing the problems of high dimensionality frequently arising in BCIs. With a proposed regularization term encoding prior information of EEG channels, the BRLP could obtain more robust performance. Finally, an efficient classification algorithm based on extreme learning machine is proposed to perform on the tangent space of learned embedding. Experimental evaluations on the BCI competition and in-house data sets reveal that the proposed algorithms could obtain significantly higher performance than many competition algorithms after using same filter process.},
}
@article {pmid29522410,
year = {2018},
author = {Penaloza, CI and Alimardani, M and Nishio, S},
title = {Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {666-674},
doi = {10.1109/TNSRE.2018.2792481},
pmid = {29522410},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/classification/methods ; Electromyography ; *Feedback, Sensory ; Female ; Hand ; Healthy Volunteers ; Humans ; Illusions/psychology ; Imagination/*physiology ; Male ; Psychomotor Performance ; Robotics ; Young Adult ; },
abstract = {EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors (i.e., EEG pattern classification algorithm) and human factors (i.e., how well the person can generate good quality EEG patterns), it is the latter that is least investigated. In order to control a motor imagery-based BCI, users need to learn to modulate their sensorimotor brain rhythms by practicing motor imagery using a classical training protocol with an abstract visual feedback. In this paper, we investigate a different BCI training protocol using a human-like android robot (Geminoid HI-2) to provide realistic visual feedback. The proposed training protocol addresses deficiencies of the classical approach and takes the advantage of body-abled user capabilities. Experimental results suggest that android feedback-based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discuss how the influence of body ownership transfer illusion toward the android might have an effect on the modulation of event-related desynchronization/synchronization activity.},
}
@article {pmid29522404,
year = {2018},
author = {Qi, H and Xue, Y and Xu, L and Cao, Y and Jiao, X},
title = {A Speedy Calibration Method Using Riemannian Geometry Measurement and Other-Subject Samples on A P300 Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {602-608},
doi = {10.1109/TNSRE.2018.2801887},
pmid = {29522404},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Calibration ; Communication Devices for People with Disabilities/*statistics &amp; numerical data ; Discriminant Analysis ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Healthy Volunteers ; Humans ; Support Vector Machine ; },
abstract = {P300 spellers are among the most popular brain-computer interface paradigms, and they are used for many clinical applications. However, building the classifier for identifying event-related potential (ERP) responses, i.e., calibrating the P300 speller, is still a time-consuming and user-dependent problem. This paper proposes a novel method to reduce calibration times significantly. In the proposed method, a small number of ERP epochs from the current user were used to build a reference epoch. Based on this reference, the Riemannian distance measurement was used to select similar ERP samples from an existing data pool, which contained other-subject ERP responses. Linear discriminant analysis (LDA), support vector machine, and stepwise LDA were trained as ERP classifiers on the selected database and then were used to identify the user-attended character. With only 12 s of EEG data to calibrate, an average character recognition accuracy for 55 subjects of up to 87.82% was obtained. The LDA that built on other-subject samples that were selected by Riemannian distance outperformed the other classifiers. Compared with other state-of-the-art studies, this method significantly reduces P300 speller calibration times, while maintaining the character recognition accuracy.},
}
@article {pmid29522400,
year = {2018},
author = {Li, J and Yu, ZL and Gu, Z and Wu, W and Li, Y and Jin, L},
title = {A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {563-572},
doi = {10.1109/TNSRE.2018.2803066},
pmid = {29522400},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Humans ; *Neural Networks, Computer ; Prosthesis Design ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET. With hybrid deep structure, the proposed network is able to learn complex spatial and temporal patterns from single-trial ERP signals. To verify the effectiveness of ERP-NET, we carried out a few ERP detection experiments that the proposed model achieved cutting-edge performance. The experimental results demonstrate that the patterns learned by the ERP-NET are discriminative ERP components in which the ERP signals are properly characterized. More importantly, as an effective approach to single-trial analysis, ERP-NET is able to discover new ERP patterns which are significant to neuroscience study as well as BCI applications. Therefore, the proposed ERP-NET is a promising tool for the research on ERP signals.},
}
@article {pmid29522399,
year = {2018},
author = {Zheng, Q and Zhu, F and Heng, PA},
title = {Robust Support Matrix Machine for Single Trial EEG Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {551-562},
doi = {10.1109/TNSRE.2018.2794534},
pmid = {29522399},
issn = {1558-0210},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Software ; *Support Vector Machine ; },
abstract = {Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by adding low-rank constraint to leverage the correlation within each data. However, classification of EEG signals is still challenging, because EEG signals are always contaminated by measurement artifacts, outliers, and non-standard noise sources. As a result, existing matrix classifiers may suffer from performance degradation, because they typically assume that the input EEG signals are clean. In this paper, to account for intra-sample outliers, we propose a novel classifier called a robust support matrix machine (RSMM), for single trial EEG data in matrix form. Inspired by the fact that empirical EEG signals contain strong correlation information, we assume that each EEG matrix can be decomposed into a latent low-rank clean matrix plus a sparse noise matrix. We simultaneously perform signal recovery and train the classifier based on the clean EEG matrices. We formulate our RSMM in a unified framework and present an effective solver based on the alternating direction method of multipliers. To evaluate the proposed method, we conduct extensive classification experiments on real binary EEG signals. The experimental results show that our method has outperformed the state-of-the-art matrix classifiers. This paper may lead to the development of robust brain-computer interfaces (BCIs) with intuitive motor imagery and thus promote the broad use of the noninvasive BCIs technology.},
}
@article {pmid29522398,
year = {2018},
author = {Chavez, M and Grosselin, F and Bussalb, A and De Vico Fallani, F and Navarro-Sune, X},
title = {Surrogate-Based Artifact Removal From Single-Channel EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {540-550},
doi = {10.1109/TNSRE.2018.2794184},
pmid = {29522398},
issn = {1558-0210},
mesh = {Algorithms ; *Artifacts ; Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods/statistics &amp; numerical data ; Eye Movements ; Humans ; Movement ; Muscle, Skeletal ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Wavelet Analysis ; },
abstract = {OBJECTIVE: the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR).<br><br>METHODS: by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG.<br><br>RESULTS: in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques.<br><br>SIGNIFICANCE: in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.},
}
@article {pmid29519457,
year = {2018},
author = {Christopoulos, VN and Andersen, KN and Andersen, RA},
title = {Extinction as a deficit of the decision-making circuitry in the posterior parietal cortex.},
journal = {Handbook of clinical neurology},
volume = {151},
number = {},
pages = {163-182},
doi = {10.1016/B978-0-444-63622-5.00008-5},
pmid = {29519457},
issn = {0072-9752},
mesh = {Animals ; Decision Making/*physiology ; Humans ; Parietal Lobe/*physiopathology ; Perceptual Disorders/*physiopathology ; },
abstract = {Extinction is a common neurologic deficit that often occurs as one of a constellation of symptoms seen with lesions of the posterior parietal cortex (PPC). Although extinction has typically been considered a deficit in the allocation of attention, new findings, particularly from nonhuman primate studies, point to one potential and important source of extinction as damage to decision-making circuits for actions within the PPC. This new understanding provides clues to potential therapies for extinction. Also the finding that the PPC is important for action decisions and action planning has led to new neuroprosthetic applications using PPC recordings as control signals to assist paralyzed patients.},
}
@article {pmid29518363,
year = {2018},
author = {Natraj, N and Ganguly, K},
title = {Shaping Reality through Mental Rehearsal.},
journal = {Neuron},
volume = {97},
number = {5},
pages = {998-1000},
doi = {10.1016/j.neuron.2018.02.017},
pmid = {29518363},
issn = {1097-4199},
mesh = {*Learning ; Movement ; Population Dynamics ; *Transfer, Psychology ; },
abstract = {Previous research has shown that mental rehearsal can improve performance. A new study by Vyas et al. (2018) reveals that direct modulation of neural dynamics using a brain-computer interface can also modify physical movements. The study further demonstrates that "mental practice" and physical movements share a common neural subspace.},
}
@article {pmid29517018,
year = {2018},
author = {Savage, N},
title = {The mind-reading devices that can free paralysed muscles.},
journal = {Nature},
volume = {555},
number = {7695},
pages = {S12-S14},
doi = {10.1038/d41586-018-02478-0},
pmid = {29517018},
issn = {1476-4687},
mesh = {Adult ; Animals ; Brain-Computer Interfaces/*trends ; Electrodes, Implanted ; Epilepsy, Temporal Lobe/physiopathology ; Exoskeleton Device ; Humans ; Male ; Mice ; Middle Aged ; *Movement ; Muscles/*physiology ; Neural Prostheses ; Paralysis/*physiopathology/*rehabilitation ; Robotics/*instrumentation/*trends ; Spinal Cord Injuries/physiopathology ; Stroke/physiopathology ; Touch/physiology ; },
}
@article {pmid29515363,
year = {2018},
author = {Shu, X and Chen, S and Yao, L and Sheng, X and Zhang, D and Jiang, N and Jia, J and Zhu, X},
title = {Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {93},
pmid = {29515363},
issn = {1662-4548},
support = {R01 AA020501/AA/NIAAA NIH HHS/United States ; },
abstract = {Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.},
}
@article {pmid29509785,
year = {2018},
author = {Wairagkar, M and Hayashi, Y and Nasuto, SJ},
title = {Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.},
journal = {PloS one},
volume = {13},
number = {3},
pages = {e0193722},
pmid = {29509785},
issn = {1932-6203},
mesh = {Adult ; Artifacts ; Brain/*physiology ; Electroencephalography ; Equipment Design ; Female ; Fingers/physiology ; Humans ; *Intention ; Male ; Motor Activity/*physiology ; Movement/physiology ; Neuropsychological Tests ; Rest ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention. We investigate the changes in temporal dependencies of the EEG by characterising the decay of autocorrelation during asynchronous voluntary finger tapping movement. The evolution of the autocorrelation function is characterised by its relaxation time, which is used as a robust marker for motion intention. We observed that there was reorganisation of temporal dependencies in EEG during motion intention. The autocorrelation decayed slower during movement intention and faster during the resting state. There was an increase in temporal dependence during movement intention. The relaxation time of the autocorrelation function showed significant (p < 0.05) discrimination between movement and resting state with the mean sensitivity of 78.37 ± 8.83%. The relaxation time provides movement related information that is complementary to the well-known event-related desynchronisation (ERD) by characterising the broad band EEG dynamics which is frequency independent in contrast to ERD. It can also detect motion intention on average 0.51s before the actual movement onset. We have thoroughly compared autocorrelation relaxation time features with ERD in four frequency bands. The relaxation time may therefore, complement the well-known features used in motion-based BCI leading to more robust and intuitive BCI solutions. The results obtained suggest that changes in autocorrelation decay may involve reorganisation of temporal dependencies of brain activity over longer duration during motion intention. This opens the possibilities of investigating further the temporal dynamics of fundamental neural processes underpinning motion intention.},
}
@article {pmid29509691,
year = {2018},
author = {Youssef Ali Amer, A and Wittevrongel, B and Van Hulle, MM},
title = {Accurate Decoding of Short, Phase-Encoded SSVEPs.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {3},
pages = {},
pmid = {29509691},
issn = {1424-8220},
abstract = {Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain-computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target responses. The targets are decoded from the feature scores using the least squares support vector machine (LS-SVM) classifier, and it is shown that some of the proposed features compete with state-of-the-art classifiers when using short (0.5 s) EEG recordings in a binary classification setting.},
}
@article {pmid29509128,
year = {2018},
author = {Gilbert, F and O'Brien, T and Cook, M},
title = {The Effects of Closed-Loop Brain Implants on Autonomy and Deliberation: What are the Risks of Being Kept in the Loop?.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {27},
number = {2},
pages = {316-325},
doi = {10.1017/S0963180117000640},
pmid = {29509128},
issn = {1469-2147},
mesh = {*Attitude to Health ; Brain-Computer Interfaces/*ethics ; Electric Stimulation Therapy/*ethics ; Electrodes, Implanted ; Humans ; Neurosciences/ethics ; *Personal Autonomy ; },
abstract = {Neuroethics Now welcomes articles addressing the ethical application of neuroscience in research and patient care, as well as its impact on society.},
}
@article {pmid29508123,
year = {2018},
author = {Ljungquist, B and Petersson, P and Johansson, AJ and Schouenborg, J and Garwicz, M},
title = {A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup.},
journal = {Neuroinformatics},
volume = {16},
number = {2},
pages = {217-229},
pmid = {29508123},
issn = {1559-0089},
support = {2004.0119//Knut och Alice Wallenbergs Stiftelse/International ; Linnaeus grant (project number 60012701)//Vetenskapsr&#xe5;det/International ; },
mesh = {Action Potentials/*physiology ; Animals ; Brain-Computer Interfaces/statistics &amp; numerical data/*trends ; Databases, Factual/statistics &amp; numerical data/trends ; Electrophysiological Phenomena/*physiology ; Humans ; Neurons/*physiology ; Time Factors ; },
abstract = {Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.},
}
@article {pmid29503189,
year = {2018},
author = {Neely, RM and Koralek, AC and Athalye, VR and Costa, RM and Carmena, JM},
title = {Volitional Modulation of Primary Visual Cortex Activity Requires the Basal Ganglia.},
journal = {Neuron},
volume = {97},
number = {6},
pages = {1356-1368.e4},
doi = {10.1016/j.neuron.2018.01.051},
pmid = {29503189},
issn = {1097-4199},
support = {U19 NS104649/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Basal Ganglia/*physiology ; *Brain-Computer Interfaces ; Male ; Mice ; Mice, Inbred C57BL ; Nerve Net/*physiology ; Rats ; Rats, Long-Evans ; Visual Cortex/*physiology ; Volition/*physiology ; },
abstract = {Animals acquire behaviors through instrumental conditioning. Brain-machine interfaces have used instrumental conditioning to reinforce patterns of neural activity directly, especially in frontal and motor cortices, which are a rich source of signals for voluntary action. However, evidence suggests that activity in primary sensory cortices may also reflect internally driven processes, instead of purely encoding antecedent stimuli. Here, we show that rats and mice can learn to produce arbitrary patterns of neural activity in their primary visual cortex to control an auditory cursor and obtain reward. Furthermore, learning was prevented when neurons in the dorsomedial striatum (DMS), which receives input from visual cortex, were optogenetically inhibited, but not during inhibition of nearby neurons in the dorsolateral striatum. After learning, DMS inhibition did not affect production of the rewarded patterns. These data demonstrate that cortico-basal ganglia circuits play a general role in learning to produce cortical activity that leads to desirable outcomes.},
}
@article {pmid29499468,
year = {2018},
author = {Svendsen, NB and Herzke, D and Harju, M and Bech, C and Gabrielsen, GW and Jaspers, VLB},
title = {Persistent organic pollutants and organophosphate esters in feathers and blood plasma of adult kittiwakes (Rissa tridactyla) from Svalbard - associations with body condition and thyroid hormones.},
journal = {Environmental research},
volume = {164},
number = {},
pages = {158-164},
doi = {10.1016/j.envres.2018.02.012},
pmid = {29499468},
issn = {1096-0953},
mesh = {Animals ; Arctic Regions ; *Environmental Pollutants ; Feathers ; Organophosphates/*adverse effects ; *Polychlorinated Biphenyls ; Svalbard ; Thyroid Hormones ; },
abstract = {Polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), organochlorine pesticides (OCPs) and organophosphate esters (OPEs) were assessed in blood plasma and feathers of 19 adult black-legged kittiwakes (Rissa tridactyla) breeding in two colonies (Blomstrandhalvøya and Krykkjefjellet) at the Arctic archipelago, Svalbard. Potential associations with body condition index (BCI) and thyroid hormones were investigated. All compound classes were detected in both blood plasma and feathers, but due to low sample size and volumes, OPEs could only be quantified in four individuals, warranting larger follow-up studies. Kittiwakes breeding at Blomstrandhalvøya had significantly higher concentrations of organic pollutants in blood plasma than kittiwakes breeding at Krykkjefjellet (p < 0.001). Concentrations in blood plasma and feathers did not significantly correlate for any of the investigated compounds, and feather concentrations did not differ significantly between the colonies. This suggests that pollutant levels in adult kittiwake feathers do not reflect local contamination at breeding sites and are as such not useful to monitor local contamination at Svalbard. Significant negative associations between BCI and most pollutants were found in both populations, whereas significant correlations between the BCI, the ratio of total triiodothyronine to free triiodothyronine (TT3:fT3), and several pollutants were only found for kittiwakes from Blomstrandhalvøya (all r ≥ -0.60 and p ≤ 0.05). This indicates that higher levels of circulating pollutants during the breeding period covary with the TT3: fT3 ratio, and may act as an additional stressor during this period.},
}
@article {pmid29497931,
year = {2018},
author = {Kim, C and Sun, J and Liu, D and Wang, Q and Paek, S},
title = {An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {56},
number = {9},
pages = {1645-1658},
pmid = {29497931},
issn = {1741-0444},
support = {61301012//National Natural Science Foundation of China/ ; 61401117//National Natural Science Foundation of China/ ; 61471140//National Natural Science Foundation of China/ ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Databases as Topic ; *Electroencephalography ; Humans ; *Imagery, Psychotherapy ; *Signal Processing, Computer-Assisted ; },
abstract = {EEG signals have weak intensity, low signal-to-noise ratio, non-stationary, non-linear, time-frequency-spatial characteristics. Therefore, it is important to extract adaptive and robust features that reflect time, frequency and spatial characteristics. This paper proposes an effective feature extraction method WDPSD (feature extraction from the Weighted Difference of Power Spectral Density in an optimal channel couple) that can reflect time, frequency and spatial characteristics for 2-class motor imagery-based BCI system. In the WDPSD method, firstly, Power Spectral Density (PSD) matrices of EEG signals are calculated in all channels, and an optimal channel couple is selected from all possible channel couples by checking non-stationary and class separability, and then a weight matrix which reflects non-stationary of PSD difference matrix in selected channel couple is calculated; finally, the robust and adaptive features are extracted from the PSD difference matrix weighted by the weight matrix. The proposed method is evaluated from EEG signals of BCI Competition IV Dataset 2a and Dataset 2b. The experimental results show a good classification accuracy in single session, session-to-session, and the different types of 2-class motor imagery for different subjects.},
}
@article {pmid29497370,
year = {2018},
author = {Ahn, M and Cho, H and Ahn, S and Jun, SC},
title = {User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {59},
pmid = {29497370},
issn = {1662-5161},
abstract = {Performance variation is a critical issue in motor imagery brain-computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.},
}
@article {pmid29497360,
year = {2018},
author = {Zhao, Y and Tang, J and Cao, Y and Jiao, X and Xu, M and Zhou, P and Ming, D and Qi, H},
title = {Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces-an Offline Study.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {79},
pmid = {29497360},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs), independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP)-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximum r = -0.48, p < 0.001) between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.},
}
@article {pmid29496422,
year = {2018},
author = {Ansari, MS and Nunia, SK and Bansal, A and Singh, P and Sekhon, V and Singh, D and Soni, R and Yadav, P},
title = {Bladder contractility index in posterior urethral valve: A new marker for early prediction of progression to renal failure.},
journal = {Journal of pediatric urology},
volume = {14},
number = {2},
pages = {162.e1-162.e5},
doi = {10.1016/j.jpurol.2017.09.029},
pmid = {29496422},
issn = {1873-4898},
mesh = {Biomarkers ; Child ; Child, Preschool ; Cohort Studies ; Databases, Factual ; Disease Progression ; Female ; Follow-Up Studies ; Humans ; India ; Kidney Failure, Chronic/*etiology/physiopathology ; Male ; Muscle Contraction/physiology ; Predictive Value of Tests ; Proportional Hazards Models ; ROC Curve ; Renal Insufficiency, Chronic/*complications/etiology ; Retrospective Studies ; Risk Assessment ; Time Factors ; Urethral Stricture/*complications/diagnosis ; Urinary Bladder/physiopathology ; Urinary Bladder Neck Obstruction/*complications/diagnosis ; Urodynamics ; Vesico-Ureteral Reflux/complications/diagnosis ; },
abstract = {INTRODUCTION: Posterior urethral valve (PUV) is the most common cause of pediatric end stage renal disease (ESRD), imposing a major health burden on medical community caregivers and adversely affecting the quality of life of patients. Chronic kidney disease (CKD) stage III or estimated GFR of <60 mL/min/1.73 m[2] is known to be associated with more adverse renal, cardiovascular, and clinical outcomes. Thus, it is desirable to identify factors predicting the rapid and early progression of disease. In the present study, baseline characteristics and urodynamic study (UDS) parameters of boys with PUV are correlated with CKD progression to IIIB or more.<br><br>AIMS AND OBJECTIVES: To study the correlation of bladder contractility index (BCI) with development of CKD stage IIIB (eGFR of <45&#xa0;mL/min/1.73&#xa0;m[2]) or more in boys with PUV.<br><br>METHODOLOGY: Baseline characteristics and demographical variables of 270 boys with PUV who underwent valve fulguration at the hospital between 2000 and 2010 were recorded and certain UDS parameters in follow-up were noted such as bladder contractility index (BCI&#xa0;=&#xa0;PdetQmax&#xa0;+&#xa0;5&#xa0;Qmax), end filling pressure (EFP), compliance (&#x394;C), bladder outlet obstruction index (BOOI&#xa0;=&#xa0;Pdet Qmax&#xa0;-&#xa0;2&#xa0;Qmax), and bladder volume efficiency (BVE&#xa0;=&#xa0;Voided volume/total capacity). Fate of patients in follow-up was checked in December 2015.<br><br>RESULTS: Mean follow-up period was 8.5 years (range 5-15) and median age of patients at the time of evaluation was 5.8 years. At the end of the study, 21.8% (59/270) of patients had progressed to CKD stage IIIB or more (primary end point). Cox regression analysis was applied to risk factors predicting development of CKD stage IIIB. In the multivariate model, bladder contractility index (BCI) (HR 0.8; p&#xa0;=&#xa0;0.004), end filling pressure (EFP) (HR 2.1; p&#xa0;=&#xa0;0.010), and compliance (&#x394;C) (p&#xa0;=&#xa0;0.020) were significantly associated with the event (i.e. an eGFR of <45&#xa0;mL/min/1.73&#xa0;m[2]), whereas BOOI (p&#xa0;=&#xa0;0.053) and bladder BVE (p&#xa0;=&#xa0;0.267) were not. ROC cut-off level for BCI predicting the primary end point was 75 (AUC&#xa0;&#xb1;&#xa0;SE, 0.73&#xa0;&#xb1;&#xa0;0.03, sensitivity of 78.2%, and specificity of 62.5%).<br><br>CONCLUSION: In a well performed UDS, BCI may be a useful tool for early detection of boys with PUV who are likely to progress to CKD stage IIIB or more.},
}
@article {pmid29493639,
year = {2018},
author = {Lavermicocca, V and Dellomonaco, AR and Tedesco, A and Notarnicola, M and Di Fede, R and Battaglini, PP},
title = {[Neurofeedback in Parkinson's disease: technologies in speech and language therapy.].},
journal = {Recenti progressi in medicina},
volume = {109},
number = {2},
pages = {130-132},
doi = {10.1701/2865.28908},
pmid = {29493639},
issn = {2038-1840},
mesh = {Aged ; Aged, 80 and over ; Cognition ; Cognitive Dysfunction/etiology/*therapy ; Humans ; Language Therapy/*methods ; Middle Aged ; Neurofeedback/*methods ; Neuropsychological Tests ; Parkinson Disease/physiopathology/psychology/*therapy ; Patient Satisfaction ; Surveys and Questionnaires ; },
abstract = {UNLABELLED: Neurofeedback (NF) is a form of biofeedback based on the self-modulation of brain activity; it aims to enhance mental and behavioral performances. The user modifies his brain functions thanks to EEG-mediated self-regulation and therapist's guidance. Recent advances in Brain-Computer Interfaces (BCI) have provided new evidence on the effectiveness of NF in reinforcing cognitive functions expecially in children with ADHD. The applications on adults with cognitive deficits are still few. The study aims to investigate the possible effect of NF techniques on cognitive performance of patients with Parkinson's disease (PD) in terms of changes in scores at the neurocognitive assessment. Ten PD patients, staged according to Hoehn &amp; Yahr scale and cognitively evaluated, were recruited.<br><br>INCLUSION CRITERIA: age 55-85, correct audio-visual functions, phase-on of dopaminergic therapy, Mild Cognitive Impairment. The rehabilitation program has been structured in 24 sessions. The NeuroSky MindWave headset and related software were used as BCI. At the end of the therapeutic path, the pre and post-treatment test's results were compared. Statistical analyzes were performed with SAS. Cognitive revaluation showed a significant increase in scores and satisfaction questionnaires reported high values. The application of NF techniques in PD patients was promising. The increase in satisfaction levels seems to be due to the perception of a direct control over one's cognitive performances.},
}
@article {pmid29490102,
year = {2018},
author = {Zongrone, AA and Menon, P and Pelto, GH and Habicht, JP and Rasmussen, KM and Constas, MA and Vermeylen, F and Khaled, A and Saha, KK and Stoltzfus, RJ},
title = {The Pathways from a Behavior Change Communication Intervention to Infant and Young Child Feeding in Bangladesh Are Mediated and Potentiated by Maternal Self-Efficacy.},
journal = {The Journal of nutrition},
volume = {148},
number = {2},
pages = {259-266},
pmid = {29490102},
issn = {1541-6100},
support = {001/WHO_/World Health Organization/International ; T32 HD007331/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Bangladesh ; Behavior Therapy ; Child, Preschool ; Cross-Sectional Studies ; Eggs ; Feeding Behavior ; Female ; Humans ; Income ; Infant ; *Infant Nutritional Physiological Phenomena ; Male ; Maternal Behavior/*psychology ; Mothers ; Poverty ; *Self Efficacy ; Vegetables ; },
abstract = {BACKGROUND: Although self-efficacy is a potential determinant of feeding and care behaviors, there is limited empirical analysis of the role of maternal self-efficacy in low- and middle-income countries. In the context of behavior change interventions (BCIs) addressing complementary feeding (CF), it is possible that maternal self-efficacy can mediate or enhance intervention impacts.<br><br>OBJECTIVE: In the context of a BCI in Bangladesh, we studied the role of maternal self-efficacy for CF (MSE-CF) for 2 CF behaviors with the use of a theoretically grounded empirical model of determinants to illustrate the potential roles of MSE-CF.<br><br>METHODS: We developed and tested a locally relevant scale for MSE-CF and included it in a survey (n = 457 mothers of children aged 6-24 mo) conducted as part of a cluster-randomized evaluation. Qualitative research was used to inform the selection of 2 intervention-targeted behaviors: feeding green leafy vegetables in the last 24 h (GLV) and on-time introduction of egg (EGG) between 6 and 8 mo of age. We then examined direct, mediated, and potentiated paths of MSE-CF in relation to the impacts of the BCI on these behaviors with the use of regression and structural equation modeling.<br><br>RESULTS: GLV and EGG were higher in the intensive group than in the nonintensive control group (16.0 percentage points for GLV; P < 0.001; 11.2 percentage points for EGG; P = 0.037). For GLV, MSE-CF mediated (&#x3b2; = 0.345, P = 0.010) and potentiated (&#x3b2; = 0.390, P = 0.038) the effect of the intensive group. In contrast, MSE-CF did not mediate or potentiate the effect of the intervention on EGG.<br><br>CONCLUSIONS: MSE-CF was a significant mediator and potentiator for GLV but not for EGG. The divergent findings highlight the complex determinants of individual specific infant and young child feeding behaviors. The study shows the value of measuring behavioral determinants, such as MSE-CF, that affect a caregiver's capability to adopt intervention-targeted behaviors.},
}
@article {pmid29488902,
year = {2018},
author = {Lotte, F and Bougrain, L and Cichocki, A and Clerc, M and Congedo, M and Rakotomamonjy, A and Yger, F},
title = {A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {031005},
doi = {10.1088/1741-2552/aab2f2},
pmid = {29488902},
issn = {1741-2552},
mesh = {*Algorithms ; Animals ; Brain/*physiology ; Brain-Computer Interfaces/*trends ; Deep Learning/trends ; Electroencephalography/methods/*trends ; Humans ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {OBJECTIVE: Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs.<br><br>APPROACH: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons.<br><br>MAIN RESULTS: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods.<br><br>SIGNIFICANCE: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.},
}
@article {pmid29486778,
year = {2018},
author = {Kim, MK and Sohn, JW and Lee, B and Kim, SP},
title = {A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces.},
journal = {Biomedical engineering online},
volume = {17},
number = {1},
pages = {28},
pmid = {29486778},
issn = {1475-925X},
support = {2016M3C7A1904988//National Research Foundation of Korea (KR)/ ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Movement ; Neurons/*cytology ; Signal-To-Noise Ratio ; },
abstract = {BACKGROUND: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study.<br><br>METHODS: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm.<br><br>RESULTS: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity.<br><br>CONCLUSIONS: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions.},
}
@article {pmid29481376,
year = {2018},
author = {Contreras-Vidal, JL and Bortole, M and Zhu, F and Nathan, K and Venkatakrishnan, A and Francisco, GE and Soto, R and Pons, JL},
title = {Neural Decoding of Robot-Assisted Gait During Rehabilitation After Stroke.},
journal = {American journal of physical medicine &amp; rehabilitation},
volume = {97},
number = {8},
pages = {541-550},
doi = {10.1097/PHM.0000000000000914},
pmid = {29481376},
issn = {1537-7385},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain-Computer Interfaces ; *Electroencephalography ; *Exoskeleton Device ; Feasibility Studies ; Female ; Gait Disorders, Neurologic/*rehabilitation ; Humans ; Male ; Middle Aged ; Paresis/rehabilitation ; Pilot Projects ; Stroke Rehabilitation/*methods ; },
abstract = {OBJECTIVE: Advancements in robot-assisted gait rehabilitation and brain-machine interfaces may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography-based brain-machine interface.<br><br>DESIGN: The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee, and ankle joints. It was integrated with active-electrode electroencephalography and evaluated in hemiparetic stroke survivors for 12 sessions per 4 wks. A continuous-time Kalman decoder operating on delta-band electroencephalography was designed to estimate gait kinematics.<br><br>RESULTS: Five chronic stroke patients completed the study with improvements in walking distance and speed training for 4 wks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an electroencephalography-based brain-machine interface to monitor brain activity or control a rehabilitative exoskeleton.<br><br>CONCLUSIONS: The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during poststroke rehabilitation and represent the first step in developing a brain-machine interface for controlling powered exoskeletons.},
}
@article {pmid29481304,
year = {2018},
author = {Kuek, LE and Griffin, P and Martinello, P and Graham, AN and Kalitsis, P and Robinson, PJ and Mackay, GA},
title = {Identification of an Immortalized Human Airway Epithelial Cell Line with Dyskinetic Cilia.},
journal = {American journal of respiratory cell and molecular biology},
volume = {59},
number = {3},
pages = {375-382},
doi = {10.1165/rcmb.2017-0188OC},
pmid = {29481304},
issn = {1535-4989},
mesh = {Cell Differentiation/*physiology ; Cell Line ; Cells, Cultured ; Cilia/*pathology ; Ciliary Motility Disorders/*pathology ; Dyskinesias/*pathology ; Epithelial Cells/*cytology ; Humans ; },
abstract = {Primary ciliary dyskinesia is an inherited, currently incurable condition. In the respiratory system, primary ciliary dyskinesia causes impaired functioning of the mucociliary escalator, leading to nasal congestion, cough, and recurrent otitis media, and commonly progresses to cause more serious and permanent damage, including hearing deficits, chronic sinusitis, and bronchiectasis. New treatment options for the condition are thus necessary. In characterizing an immortalized human bronchial epithelial cell line (BCi-NS1.1) grown at an air-liquid interface to permit differentiation, we have identified that these cells have dyskinetic motile cilia. The cells had a normal male karyotype, and phenotypic markers of epithelial cell differentiation emerged, as previously shown. Ciliary beat frequency (CBF) as assessed by high-speed videomicroscopy was lower than normal (4.4 Hz). Although changes in CBF induced by known modulators were as expected, the cilia displayed a dyskinetic, circular beat pattern characteristic of central microtubular agenesis with outer doublet transposition. This ultrastructural defect was confirmed by electron microscopy. We propose that the BCi-NS1.1 cell line is a useful model system for examination of modulators of CBF and more specifically could be used to screen for novel drugs with the ability to enhance CBF and perhaps repair a dyskinetic ciliary beat pattern.},
}
@article {pmid29479313,
year = {2018},
author = {Yang, T and Lee, S and Seomoon, E and Kim, SP},
title = {Characteristics of Human Brain Activity during the Evaluation of Service-to-Service Brand Extension.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {44},
pmid = {29479313},
issn = {1662-5161},
abstract = {Brand extension is a marketing strategy to apply the previously established brand name into new goods or service. A number of studies have reported the characteristics of human event-related potentials (ERPs) in response to the evaluation of goods-to-goods brand extension. In contrast, human brain responses to the evaluation of service extension are relatively unexplored. The aim of this study was investigating cognitive processes underlying the evaluation of service-to-service brand extension with electroencephalography (EEG). A total of 56 text stimuli composed of service brand name (S1) followed by extended service name (S2) were presented to participants. The EEG of participants was recorded while participants were asked to evaluate whether a given brand extension was acceptable or not. The behavioral results revealed that participants could evaluate brand extension though they had little knowledge about the extended services, indicating the role of brand in the evaluation of the services. Additionally, we developed a method of grouping brand extension stimuli according to the fit levels obtained from behavioral responses, instead of grouping of stimuli a priori. The ERP analysis identified three components during the evaluation of brand extension: N2, P300, and N400. No difference in the N2 amplitude was found among the different levels of a fit between S1 and S2. The P300 amplitude for the low level of fit was greater than those for higher levels (p < 0.05). The N400 amplitude was more negative for the mid- and high-level fits than the low level. The ERP results of P300 and N400 indicate that the early stage of brain extension evaluation might first detect low-fit brand extension as an improbable target followed by the late stage of the integration of S2 into S1. Along with previous findings, our results demonstrate different cognitive evaluation of service-to-service brand extension from goods-to-goods.},
}
@article {pmid29477593,
year = {2018},
author = {Brockmann, PE and Damiani, F and Pincheira, E and Daiber, F and Ruiz, S and Aboitiz, F and Ferri, R and Bruni, O},
title = {Sleep spindle activity in children with obstructive sleep apnea as a marker of neurocognitive performance: A pilot study.},
journal = {European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society},
volume = {22},
number = {3},
pages = {434-439},
doi = {10.1016/j.ejpn.2018.02.003},
pmid = {29477593},
issn = {1532-2130},
mesh = {Child ; Cognition Disorders/*etiology ; Female ; Humans ; Intelligence Tests ; Male ; Pilot Projects ; Polysomnography ; Sleep Apnea, Obstructive/*complications/*physiopathology ; },
abstract = {STUDY OBJECTIVES: To assess spindle activity as possible markers for neurocognitive consequences in children with mild obstructive sleep apnea.<br><br>METHODS: Children aged 6-11 years diagnosed with mild OSA (i.e., an apnea hypopnea index <5.0) were recruited and compared with age and gender-matched healthy controls. Polysomnographic recordings were analyzed for sleep microstructure and spindle activity. All children completed also an intelligence test battery (i.e., the Wechsler intelligence test for children, 4th version).<br><br>RESULTS: Nineteen children with OSA (13 boys, mean age 7.1&#xa0;&#xb1;&#xa0;1.4&#xa0;y), and 14 controls (7 boys, mean age 8.1&#xa0;&#xb1;&#xa0;1.9&#xa0;y) were included. Mean IQ was 110&#xa0;&#xb1;&#xa0;12 for the complete sample, in children with OSA 111&#xa0;&#xb1;&#xa0;13, and in controls 108&#xa0;&#xb1;&#xa0;12 (p&#xa0;=&#xa0;0.602). Controls showed a higher spindle index in N2 stage than children with OSA: 143.0&#xa0;&#xb1;&#xa0;42.5 vs 89.5&#xa0;&#xb1;&#xa0;56.9, respectively (p&#xa0;=&#xa0;0.003). Spindle index in NREM was strongly and significantly correlated with Verbal Comprehension Index (VCI), Working Memory Index (WMI), Processing Speed Index (PSI), and total IQ in children with OSA.<br><br>CONCLUSIONS: Children with mild OSA demonstrate a different pattern of sleep spindle activity that seems to be linked with neurocognitive performance, especially concerning memory. Sleep spindle activity seems to be involved with mechanisms related with neurocognitive consequences in children with OSA.},
}
@article {pmid29475457,
year = {2018},
author = {Clements, MN and Corstjens, PLAM and Binder, S and Campbell, CH and de Dood, CJ and Fenwick, A and Harrison, W and Kayugi, D and King, CH and Kornelis, D and Ndayishimiye, O and Ortu, G and Lamine, MS and Zivieri, A and Colley, DG and van Dam, GJ},
title = {Latent class analysis to evaluate performance of point-of-care CCA for low-intensity Schistosoma mansoni infections in Burundi.},
journal = {Parasites &amp; vectors},
volume = {11},
number = {1},
pages = {111},
pmid = {29475457},
issn = {1756-3305},
support = {50186//Bill and Melinda Gates Foundation/International ; },
mesh = {Animals ; Antigens, Helminth/*immunology ; Bayes Theorem ; Burundi/epidemiology ; Child ; Clinical Laboratory Techniques/instrumentation/*methods/statistics &amp; numerical data ; Feces/parasitology ; Female ; Glycoproteins/*immunology ; Helminth Proteins/*immunology ; Humans ; Male ; *Point-of-Care Systems ; Prevalence ; Schistosoma mansoni/immunology ; Schistosomiasis mansoni/*diagnosis/*epidemiology/parasitology/urine ; Schools ; Sensitivity and Specificity ; },
abstract = {BACKGROUND: Kato-Katz examination of stool smears is the field-standard method for detecting Schistosoma mansoni infection. However, Kato-Katz misses many active infections, especially of light intensity. Point-of-care circulating cathodic antigen (CCA) is an alternative field diagnostic that is more sensitive than Kato-Katz when intensity is low, but interpretation of CCA-trace results is unclear. To evaluate trace results, we tested urine and stool specimens from 398 pupils from eight schools in Burundi using four approaches: two in Burundi and two in a laboratory in Leiden, the Netherlands. In Burundi, we used Kato-Katz and point-of-care CCA (CCAB). In Leiden, we repeated the CCA (CCAL) and also used Up-Converting Phosphor Circulating Anodic Antigen (CAA).<br><br>METHODS: We applied Bayesian latent class analyses (LCA), first considering CCA traces as negative and then as positive. We used the LCA output to estimate validity of the prevalence estimates of each test in comparison to the population-level infection prevalence and estimated the proportion of trace results that were likely true positives.<br><br>RESULTS: Kato-Katz yielded the lowest prevalence (6.8%), and CCAB with trace considered positive yielded the highest (53.5%). There were many more trace results recorded by CCA in Burundi (32.4%) than in Leiden (2.3%). Estimated prevalence with CAA was 46.5%. LCA indicated that Kato-Katz had the lowest sensitivity: 15.9% [Bayesian Credible Interval (BCI): 9.2-23.5%] with CCA-trace considered negative and 15.0% with trace as positive (BCI: 9.6-21.4%), implying that Kato-Katz missed approximately 85% of infections. CCAB underestimated disease prevalence when trace was considered negative and overestimated disease prevalence when trace was considered positive, by approximately 12 percentage points each way, and CAA overestimated prevalence in both models. Our results suggest that approximately 52.2% (BCI: 37.8-5.8%) of the CCAB trace readings were true infections.<br><br>CONCLUSIONS: Whether measured in the laboratory or the field, CCA outperformed Kato-Katz at the low infection intensities in Burundi. CCA with trace as negative likely missed many infections, whereas CCA with trace as positive overestimated prevalence. In the absence of a field-friendly gold standard diagnostic, the use of a variety of diagnostics with differing properties will become increasingly important as programs move towards elimination of schistosomiasis. It is clear that CCA is a valuable tool for the detection and mapping of S. mansoni infection in the field and CAA may be a valuable field tool in the future.},
}
@article {pmid29472849,
year = {2018},
author = {Lazarou, I and Nikolopoulos, S and Petrantonakis, PC and Kompatsiaris, I and Tsolaki, M},
title = {EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 [st] Century.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {14},
pmid = {29472849},
issn = {1662-5161},
abstract = {People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future.},
}
@article {pmid29472378,
year = {2018},
author = {Young, J and Bertherat, J and Vantyghem, MC and Chabre, O and Senoussi, S and Chadarevian, R and Castinetti, F and , },
title = {Hepatic safety of ketoconazole in Cushing's syndrome: results of a Compassionate Use Programme in France.},
journal = {European journal of endocrinology},
volume = {178},
number = {5},
pages = {447-458},
doi = {10.1530/EJE-17-0886},
pmid = {29472378},
issn = {1479-683X},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Chemical and Drug Induced Liver Injury/diagnosis/*epidemiology/metabolism ; Child ; Cohort Studies ; Compassionate Use Trials/adverse effects/*methods ; Cushing Syndrome/*drug therapy/*epidemiology/metabolism ; Cytochrome P-450 CYP3A Inhibitors/adverse effects/therapeutic use ; Female ; France/epidemiology ; Humans ; Ketoconazole/adverse effects/*therapeutic use ; Liver/drug effects/metabolism ; Longitudinal Studies ; Male ; Middle Aged ; Prospective Studies ; Young Adult ; },
abstract = {OBJECTIVE: Ketoconazole (KTZ) is one of few available treatments for Cushing's syndrome (CS). Although KTZ has been associated with severe hepatotoxicity, little information is available about hepatic safety in CS. The aim of this study was to document changes in liver function in patients with CS treated with KTZ.<br><br>DESIGN: An observational prospective French cohort study (Compassionate Use Programme (CUP)).<br><br>METHODS: Enrolled patients were stratified into a KTZ-naive cohort and a cohort already treated by another formulation of ketoconazole (KTZ-switch cohort). Liver function markers (alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase, &#x3b3;-glutamyltransferase and bilirubin) were monitored at regular intervals. Patients with ALT&#x2009;>&#x2009;3&#x2009;&#xd7;&#x2009;ULN (upper limit of normal), total bilirubin&#x2009;>&#x2009;2&#x2009;&#xd7;&#x2009;ULN or both ALP&#x2009;>&#x2009;2&#x2009;&#xd7;&#x2009;ULN and ALT&#x2009;>&#x2009;ULN were considered to have liver injury.<br><br>RESULTS: Overall, 108 patients were analysed (47 KTZ-na&#xef;ve; 61 KTZ-switch). The median KTZ dose was 600&#x2009;mg/day. Most abnormalities observed were asymptomatic mild increases of liver enzymes. Four patients in the KTZ-na&#xef;ve cohort (8.5%) and two in the KTZ-switch cohort (3.3%) developed liver injury, considered related to KTZ in three cases (all KTZ-na&#xef;ve in the first month of treatment). Five patients had mild liver function abnormalities at baseline and two had proven liver metastases. Two patients recovered on discontinuation of KTZ and the remaining patient died of unrelated causes.<br><br>CONCLUSIONS: These findings highlight the need for close monitoring of liver enzymes especially during the first six months of treatment. Liver enzyme abnormalities usually occurred within four weeks were asymptomatic and could be reversed on timely discontinuation of KTZ.},
}
@article {pmid29471127,
year = {2018},
author = {Bedell, HW and Hermann, JK and Ravikumar, M and Lin, S and Rein, A and Li, X and Molinich, E and Smith, PD and Selkirk, SM and Miller, RH and Sidik, S and Taylor, DM and Capadona, JR},
title = {Targeting CD14 on blood derived cells improves intracortical microelectrode performance.},
journal = {Biomaterials},
volume = {163},
number = {},
pages = {163-173},
pmid = {29471127},
issn = {1878-5905},
support = {I01 RX001495/RX/RRD VA/United States ; P30 CA043703/CA/NCI NIH HHS/United States ; R01 NS082404/NS/NINDS NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Blood Cells/*metabolism ; Brain/cytology/metabolism ; Brain-Computer Interfaces ; Chimera ; Electric Impedance ; *Electrodes, Implanted ; Female ; Humans ; Lipopolysaccharide Receptors/antagonists &amp; inhibitors/genetics/*metabolism ; Macrophages/metabolism ; Male ; Mice, Inbred C57BL ; *Microelectrodes ; Microglia/physiology ; Neurons/metabolism ; Silicon/chemistry ; },
abstract = {Intracortical microelectrodes afford researchers an effective tool to precisely monitor neural spiking activity. Additionally, intracortical microelectrodes have the ability to return function to individuals with paralysis as part of a brain computer interface. Unfortunately, the neural signals recorded by these electrodes degrade over time. Many strategies which target the biological and/or materials mediating failure modes of this decline of function are currently under investigation. The goal of this study is to identify a precise cellular target for future intervention to sustain chronic intracortical microelectrode performance. Previous work from our lab has indicated that the Cluster of Differentiation 14/Toll-like receptor pathway (CD14/TLR) is a viable target to improve chronic laminar, silicon intracortical microelectrode recordings. Here, we use a mouse bone marrow chimera model to selectively knockout CD14, an innate immune receptor, from either brain resident microglia or blood-derived macrophages, in order to understand the most effective targets for future therapeutic options. Using single-unit recordings we demonstrate that inhibiting CD14 from the blood-derived macrophages improves recording quality over the 16 week long study. We conclude that targeting CD14 in blood-derived cells should be part of the strategy to improve the performance of intracortical microelectrodes, and that the daunting task of delivering therapeutics across the blood-brain barrier may not be needed to increase intracortical microelectrode performance.},
}
@article {pmid29467615,
year = {2018},
author = {Wang, Y and Wang, P and Yu, Y},
title = {Decoding English Alphabet Letters Using EEG Phase Information.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {62},
pmid = {29467615},
issn = {1662-4548},
abstract = {Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition.},
}
@article {pmid29467602,
year = {2018},
author = {Padmanaban, S and Baker, J and Greger, B},
title = {Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {22},
pmid = {29467602},
issn = {1662-4548},
abstract = {Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements-similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the feature vector supplied to the SVM. Main results: The average decoding accuracy for multi-unit features and single-unit features using Mutual Information Maximization (MIM) across 47 sessions was 96.74 ± 3.5% and 97.65 ± 3.36% respectively. The reduction in decoding accuracy between using 100% of the features and 10% of features based on MIM was 45.56% (from 93.7 to 51.09%) and 4.75% (from 95.32 to 90.79%) for multi-unit and single-unit features respectively. MIM had best performance compared to other feature selection methods. Significance: These results suggest improved decoding performance can be achieved by using optimally selected features. The results based on clinically relevant performance metrics also suggest that the decoding algorithm can be made robust by using optimal features and feature selection algorithms. We believe that even a few percent increase in performance is important and improves the decoding accuracy of the machine learning algorithm potentially increasing the ease of use of a brain machine interface.},
}
@article {pmid29465895,
year = {2018},
author = {Nagy, I and Fabó, D},
title = {[Clinical neurophysiological methods in diagnosis and treatment of cerebrovascular diseases].},
journal = {Ideggyogyaszati szemle},
volume = {71},
number = {1-02},
pages = {7-14},
doi = {10.18071/isz.71.0007},
pmid = {29465895},
issn = {0019-1442},
mesh = {*Cerebrovascular Disorders/diagnostic imaging/therapy ; *Electroencephalography ; Endarterectomy, Carotid ; Humans ; Monitoring, Physiologic ; Stroke ; *Transcranial Magnetic Stimulation ; },
abstract = {Neurophysiological methods are gaining ground in the diagnosis and therapy of cerebrovascular disease. While the role of the EEG (electroencephalography) in the diagnosis of post-stroke epilepsy is constant, quantitative EEG para-meters, as new indicators of early efficiency after thrombolysis or in prognosis of patient's condition have proved their effectiveness in several clinical studies. In intensive care units, continuous EEG monitoring of critically ill patients became part of neurointenzive care protocols. SSEP (somatosesnsory evoked potencial) and EEG performed during carotid endarterectomy, are early indicative intraoperativ neuromonitoring methods of poor outcome. Neurorehabilitation is a newly discovered area of neurophysiology. Clinical studies have demonstrated the effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the rehabilitation of stroke patients. Brain computer interface mark the onset of modern rehabi-litation, where the function deficit is replaced by robotic tehnology.},
}
@article {pmid29463870,
year = {2018},
author = {Lin, Z and Zhang, C and Zeng, Y and Tong, L and Yan, B},
title = {A novel P300 BCI speller based on the Triple RSVP paradigm.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {3350},
pmid = {29463870},
issn = {2045-2322},
mesh = {*Brain-Computer Interfaces ; Event-Related Potentials, P300 ; Female ; Humans ; Language Disorders/*therapy ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is an advanced human-machine interaction technology. The BCI speller is a typical application that detects the stimulated source-induced EEG signal to identify the expected characters of the subjects. The current mainstream matrix-based BCI speller involves two problems that remain unsolved, namely, gaze-dependent and space-dependent problems. Some scholars have designed gaze-independent and space-independent spelling systems. However, this system still cannot achieve a satisfactory information transfer rate (ITR). In this paper, we propose a novel triple RSVP speller with gaze-independent and space-independent characteristics and higher ITR. The triple RSVP speller uses rapid serial visual presentation (RSVP) paradigm, each time presents three different characters, and each character is presented three times to increase the ITR. The results of the experiments show the triple RSVP speller online average accuracy of 0.790 and average online ITR of 20.259 bit/min, where the system spelled at a speed of 10 s per character, and the stimulus presentation interface is a 90 × 195 pixel rectangle. Thus, the triple RSVP speller can be integrated into mobile smart devices (such as smartphones, smart watches, and others).},
}
@article {pmid29463282,
year = {2018},
author = {Wacharapluesadee, S and Duengkae, P and Chaiyes, A and Kaewpom, T and Rodpan, A and Yingsakmongkon, S and Petcharat, S and Phengsakul, P and Maneeorn, P and Hemachudha, T},
title = {Longitudinal study of age-specific pattern of coronavirus infection in Lyle's flying fox (Pteropus lylei) in Thailand.},
journal = {Virology journal},
volume = {15},
number = {1},
pages = {38},
pmid = {29463282},
issn = {1743-422X},
mesh = {Age Factors ; Animal Diseases/*epidemiology/*virology ; Animals ; Chiroptera/*virology ; *Coronavirus/genetics ; Coronavirus Infections/*veterinary ; Female ; Genome, Viral ; Longitudinal Studies ; Male ; Phylogeny ; Prevalence ; RNA, Viral ; Thailand/epidemiology ; Virus Shedding ; },
abstract = {BACKGROUND: Bats are natural reservoirs for several highly pathogenic and novel viruses including coronaviruses (CoVs) (mainly Alphacoronavirus and Betacoronavirus). Lyle's flying fox (Pteropus lylei)'s roosts and foraging sites are usually in the proximity to humans and animals. Knowledge about age-specific pattern of CoV infection in P. lylei, prevalence, and viral shedding at roosts and foraging sites may have an impact on infection-age-structure model to control CoV outbreak.<br><br>METHODS: P. lylei bats were captured monthly during January-December 2012 for detection of CoV at three areas in Chonburi province; two human dwellings, S1 and S2, where few fruit trees were located with an open pig farm, 0.6&#xa0;km and 5.5&#xa0;km away from the bat roost, S3. Nested RT-PCR of RNA-dependent RNA polymerase (RdRp) gene from rectal swabs was used for CoV detection. The strain of CoV was confirmed by sequencing and phylogenetic analysis.<br><br>RESULTS: CoV infection was found in both juveniles and adult bats between May and October (January, in adults only and April, in juveniles only). Of total rectal swab positives (68/367, 18.5%), ratio was higher in bats captured at S1 (11/44, 25.0%) and S2 (35/99, 35.4%) foraging sites than at roost (S3) (22/224, 9.8%). Juveniles (forearm length&#x2009;&#x2264;&#x2009;136&#xa0;mm) were found with more CoV infection than adults at all three sites; S1 (9/24, 37.5% vs 2/20, 10%), S2 (22/49, 44.9% vs 13/50, 26.0%), and S3 (10/30, 33.3% vs 12/194, 6.2%). The average BCI of CoV infected bats was significantly lower than uninfected bats. No gender difference related to infection was found at the sites. Phylogenetic analysis of conserved RdRp gene revealed that the detected CoVs belonged to group D betacoronavirus (n&#x2009;=&#x2009;64) and alphacoronavirus (n&#x2009;=&#x2009;4).<br><br>CONCLUSIONS: The fact that CoV infection and shedding was found in more juvenile than adult bats may suggest transmission from mother during peripartum period. Whether viral reactivation during parturition period or stress is responsible in maintaining transmission in the bat colony needs to be explored.},
}
@article {pmid29463157,
year = {2018},
author = {López-Larraz, E and Ibáñez, J and Trincado-Alonso, F and Monge-Pereira, E and Pons, JL and Montesano, L},
title = {Comparing Recalibration Strategies for Electroencephalography-Based Decoders of Movement Intention in Neurological Patients with Motor Disability.},
journal = {International journal of neural systems},
volume = {28},
number = {7},
pages = {1750060},
doi = {10.1142/S0129065717500605},
pmid = {29463157},
issn = {1793-6462},
mesh = {Adult ; Aged ; Brain/physiopathology ; *Brain-Computer Interfaces ; Calibration ; Cues ; *Electroencephalography/methods ; Humans ; *Intention ; Male ; Middle Aged ; Motor Activity/*physiology ; Movement Disorders/etiology/physiopathology/*rehabilitation ; Neurological Rehabilitation/*methods ; Pattern Recognition, Automated ; Quadriplegia/etiology/physiopathology/rehabilitation ; Spinal Cord Injuries/complications/physiopathology/rehabilitation ; Stroke/complications/physiopathology ; Stroke Rehabilitation ; },
abstract = {Motor rehabilitation based on the association of electroencephalographic (EEG) activity and proprioceptive feedback has been demonstrated as a feasible therapy for patients with paralysis. To promote long-lasting motor recovery, these interventions have to be carried out across several weeks or even months. The success of these therapies partly relies on the performance of the system decoding movement intentions, which normally has to be recalibrated to deal with the nonstationarities of the cortical activity. Minimizing the recalibration times is important to reduce the setup preparation and maximize the effective therapy time. To date, a systematic analysis of the effect of recalibration strategies in EEG-driven interfaces for motor rehabilitation has not yet been performed. Data from patients with stroke (4 patients, 8 sessions) and spinal cord injury (SCI) (4 patients, 5 sessions) undergoing two different paradigms (self-paced and cue-guided, respectively) are used to study the performance of the EEG-based classification of motor intentions. Four calibration schemes are compared, considering different combinations of training datasets from previous and/or the validated session. The results show significant differences in classifier performances in terms of the true and false positives (TPs) and (FPs). Combining training data from previous sessions with data from the validation session provides the best compromise between the amount of data needed for calibration and the classifier performance. With this scheme, the average true (false) positive rates obtained are 85.3% (17.3%) and 72.9% (30.3%) for the self-paced and the cue-guided protocols, respectively. These results suggest that the use of optimal recalibration schemes for EEG-based classifiers of motor intentions leads to enhanced performances of these technologies, while not requiring long calibration phases prior to starting the intervention.},
}
@article {pmid29462975,
year = {2018},
author = {Floriano, A and F Diez, P and Freire Bastos-Filho, T},
title = {Evaluating the Influence of Chromatic and Luminance Stimuli on SSVEPs from Behind-the-Ears and Occipital Areas.},
journal = {Sensors (Basel, Switzerland)},
volume = {18},
number = {2},
pages = {},
pmid = {29462975},
issn = {1424-8220},
abstract = {This work presents a study of chromatic and luminance stimuli in low-, medium-, and high-frequency stimulation to evoke steady-state visual evoked potential (SSVEP) in the behind-the-ears area. Twelve healthy subjects participated in this study. The electroencephalogram (EEG) was measured on occipital (Oz) and left and right temporal (TP9 and TP10) areas. The SSVEP was evaluated in terms of amplitude, signal-to-noise ratio (SNR), and detection accuracy using power spectral density analysis (PSDA), canonical correlation analysis (CCA), and temporally local multivariate synchronization index (TMSI) methods. It was found that stimuli based on suitable color and luminance elicited stronger SSVEP in the behind-the-ears area, and that the response of the SSVEP was related to the flickering frequency and the color of the stimuli. Thus, green-red stimulus elicited the highest SSVEP in medium-frequency range, and green-blue stimulus elicited the highest SSVEP in high-frequency range, reaching detection accuracy rates higher than 80%. These findings will aid in the development of more comfortable, accurate and stable BCIs with electrodes positioned on the behind-the-ears (hairless) areas.},
}
@article {pmid29456026,
year = {2018},
author = {Vyas, S and Even-Chen, N and Stavisky, SD and Ryu, SI and Nuyujukian, P and Shenoy, KV},
title = {Neural Population Dynamics Underlying Motor Learning Transfer.},
journal = {Neuron},
volume = {97},
number = {5},
pages = {1177-1186.e3},
pmid = {29456026},
issn = {1097-4199},
support = {/HHMI_/Howard Hughes Medical Institute/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; F31 NS103409/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Transfer, Psychology/*physiology ; },
abstract = {Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.},
}
@article {pmid29451067,
year = {2018},
author = {Dong, XW and Zheng, ZH and Ding, J and Luo, X and Li, ZQ and Li, Y and Rong, MY and Fu, YL and Shi, JH and Yu, LC and Wu, ZB and Zhu, P},
title = {Combined detection of uMCP-1 and uTWEAK for rapid discrimination of severe lupus nephritis.},
journal = {Lupus},
volume = {27},
number = {6},
pages = {971-981},
doi = {10.1177/0961203318758507},
pmid = {29451067},
issn = {1477-0962},
mesh = {Adolescent ; Adult ; Aged ; Area Under Curve ; Biomarkers/blood/urine ; Biopsy ; Case-Control Studies ; Chemokine CCL2/*urine ; Cytokine TWEAK/*urine ; Female ; Humans ; Lupus Nephritis/blood/*diagnosis/*urine ; Male ; Middle Aged ; Predictive Value of Tests ; ROC Curve ; Reproducibility of Results ; Severity of Illness Index ; Urinalysis/methods ; Young Adult ; },
abstract = {Reliable markers for the rapid discrimination of severe renal damage remain a vital concern for lupus nephritis (LN). To determine a better tool for kidney damage detection, the present study compared the evaluation ability of novel urinary cytokines and chemokines (namely urinary monocyte chemoattractant protein 1 (uMCP-1), tumor necrosis factor-like weak inducer of apoptosis (uTWEAK)) with traditional serum or urinary markers (namely urinary alpha 1-microgrobulin (uα1-MG), beta 2-microglobulin (uβ2-MG) and serum complement C3 (C3), complement C4 (C4), creatinine (Cr), blood urea nitrogen (BUN) and cystatin C (Cys C)) in discriminating LN renal damage. Correlations between markers with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) renal SLEDAI scores, biopsy activity index (BAI) and biopsy chronicity index (BCI) scores were evaluated. Receiver operating characteristic (ROC) curves were generated to evaluate a single or combined model in discriminating active renal involvement (rSLEDAI scores > 0) and patients with poor pathological outcome (BAI scores ≥ 7). uMCP-1 and uTWEAK possess higher correlation coefficients with renal damage and larger areas under ROC curves (AUCs) than other markers. A combined model of uMCP-1 and uTWEAK showed an AUC of 0.887, sensitivity of 86.67% and specificity of 80.00% to discriminate active LN, and an AUC of 0.778, sensitivity of 75.00% and specificity of 81.82% to discriminate LN with poor outcome, which are better than the utility of any markers individually.},
}
@article {pmid29450494,
year = {2018},
author = {Sestak, I and Buus, R and Cuzick, J and Dubsky, P and Kronenwett, R and Denkert, C and Ferree, S and Sgroi, D and Schnabel, C and Baehner, FL and Mallon, E and Dowsett, M},
title = {Comparison of the Performance of 6 Prognostic Signatures for Estrogen Receptor-Positive Breast Cancer: A Secondary Analysis of a Randomized Clinical Trial.},
journal = {JAMA oncology},
volume = {4},
number = {4},
pages = {545-553},
pmid = {29450494},
issn = {2374-2445},
support = {16891/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Aged ; Anastrozole/therapeutic use ; Antineoplastic Agents, Hormonal/*therapeutic use ; Biomarkers, Pharmacological/analysis ; Biomarkers, Tumor/*genetics ; Breast Neoplasms/*diagnosis/*drug therapy/epidemiology/genetics ; Female ; Follow-Up Studies ; Gene Expression Regulation, Neoplastic/drug effects ; Humans ; Middle Aged ; Neoplasm Recurrence, Local/epidemiology/pathology/prevention &amp; control ; Predictive Value of Tests ; Prognosis ; Receptor, ErbB-2/*genetics/metabolism ; Retrospective Studies ; Tamoxifen/therapeutic use ; *Transcriptome/drug effects ; Treatment Outcome ; },
abstract = {IMPORTANCE: Multiple molecular signatures are available for managing estrogen receptor (ER)-positive breast cancer but with little direct comparative information to guide the patient's choice.<br><br>OBJECTIVE: To conduct a within-patient comparison of the prognostic value of 6 multigene signatures in women with early ER-positive breast cancer who received endocrine therapy for 5 years.<br><br>This retrospective biomarker analysis included 774 postmenopausal women with ER-positive ERBB2 (formerly HER2)-negative breast cancer. This analysis was performed as a preplanned secondary study of data from the Anastrozole or Tamoxifen Alone or Combined randomized clinical trial comparing 5-year treatment with anastrozole vs tamoxifen with 10-year follow-up data. The signatures included the Oncotype Dx recurrence score, PAM50-based Prosigna risk of recurrence (ROR), Breast Cancer Index (BCI), EndoPredict (EPclin), Clinical Treatment Score, and 4-marker immunohistochemical score. Data were collected from January 2009, through April 2015.<br><br>MAIN OUTCOMES AND MEASURES: The primary objective was to compare the prognostic value of these signatures in addition to the Clinical Treatment Score (nodal status, tumor size, grade, age, and endocrine treatment) for distant recurrence for 0 to 10 years and 5 to 10 years after diagnosis. Likelihood ratio (LR) statistics were used with the &#x3c7;2 test and C indexes to assess the prognostic value of each signature.<br><br>RESULTS: In this study of 774 postmenopausal women with ER-positive, ERBB2-negative disease (mean [SD] age, 64.1 [8.1] years), 591 (mean [SD] age, 63.4 [7.9] years) had node-negative disease. The signatures providing the most prognostic information were the ROR (hazard ratio [HR], 2.56; 95% CI, 1.96-3.35), followed by the BCI (HR, 2.46; 95% CI, 1.88-3.23) and EPclin (HR, 2.14; 95% CI, 1.71-2.68). Each provided significantly more information than the Clinical Treatment Score (HR, 1.99; 95% CI, 1.58-2.50), the recurrence score (HR, 1.69; 95% CI, 1.40-2.03), and the 4-marker immunohistochemical score (HR, 1.95; 95% CI, 1.55-2.45). Substantially less information was provided by all 6 molecular tests for the 183 patients with 1 to 3 positive nodes, but the BCI (&#x394;LR &#x3c7;2&#x2009;=&#x2009;9.2) and EPclin (&#x394;LR &#x3c7;2&#x2009;=&#x2009;7.4) provided more additional prognostic information than the other signatures.<br><br>CONCLUSIONS AND RELEVANCE: For women with node-negative disease, the ROR, BCI, and EPclin were significantly more prognostic for overall and late distant recurrence. For women with 1 to 3 positive nodes, limited independent information was available from any test. These data might help oncologists and patients to choose the most appropriate test when considering chemotherapy use and/or extended endocrine therapy.<br><br>TRIAL REGISTRATION: isrctn.com Identifier: ISRCTN18233230.},
}
@article {pmid29450381,
year = {2017},
author = {Avram, E},
title = {Insights in the treatment of congenital nasolacrimal duct obstruction.},
journal = {Romanian journal of ophthalmology},
volume = {61},
number = {2},
pages = {101-106},
pmid = {29450381},
issn = {2457-4325},
mesh = {*Conservative Treatment ; Dacryocystorhinostomy ; Humans ; Infant, Newborn ; Lacrimal Duct Obstruction/*therapy ; Nasal Surgical Procedures ; Nasolacrimal Duct ; Treatment Outcome ; },
abstract = {Introduction: Congenital nasolacrimal duct obstruction is one of the most common causes of epiphora in newborns and the main cause of this condition is the persistence of Hasner membrane. Several treatment options are available, like conservative treatment, probing, irrigation, or more complex techniques. Objective: The objective of this paper is to discuss the efficiency of different treatment options addressing congenital nasolacrimal duct obstruction based on trials reported in literature. Methods: Clinical trials were identified on PubMed. The results were discussed regarding patient age, type of treatment and efficiency of the treatment. Results: 41 trials were reviewed. The rate of resolution according to different treatment options was the following: conservative treatment 14.2-96%, probing 78-100%, irrigation 33-100%, silicon tube intubation 62-100%, inferior turbinate fracture 54.7-97%, balloon dacryocystoplasty 77%, endoscopic intranasal surgery 92.72%, and dacryocystorhinostomy 88.2-93.33%. Conclusions: The first choice in uncomplicated cases should be a conservative treatment, which can be followed until the age of 1 year, while in complicated cases other solutions should be considered. Abbreviations: CNDO = Congenital nasolacrimal duct obstruction, DCR = Dacryocystorhinostomy, MCI = Monocanalicular intubation, BCI = Bicanalicular intubation.},
}
@article {pmid29449799,
year = {2018},
author = {Lisi, G and Rivela, D and Takai, A and Morimoto, J},
title = {Markov Switching Model for Quick Detection of Event Related Desynchronization in EEG.},
journal = {Frontiers in neuroscience},
volume = {12},
number = {},
pages = {24},
pmid = {29449799},
issn = {1662-4548},
abstract = {Quick detection of motor intentions is critical in order to minimize the time required to activate a neuroprosthesis. We propose a Markov Switching Model (MSM) to achieve quick detection of an event related desynchronization (ERD) elicited by motor imagery (MI) and recorded by electroencephalography (EEG). Conventional brain computer interfaces (BCI) rely on sliding window classifiers in order to perform online continuous classification of the rest vs. MI classes. Based on this approach, the detection of abrupt changes in the sensorimotor power suffers from an intrinsic delay caused by the necessity of computing an estimate of variance across several tenths of a second. Here we propose to avoid explicitly computing the EEG signal variance, and estimate the ERD state directly from the voltage information, in order to reduce the detection latency. This is achieved by using a model suitable in situations characterized by abrupt changes of state, the MSM. In our implementation, the model takes the form of a Gaussian observation model whose variance is governed by two latent discrete states with Markovian dynamics. Its objective is to estimate the brain state (i.e., rest vs. ERD) given the EEG voltage, spatially filtered by common spatial pattern (CSP), as observation. The two variances associated with the two latent states are calibrated using the variance of the CSP projection during rest and MI, respectively. The transition matrix of the latent states is optimized by the "quickest detection" strategy that minimizes a cost function of detection latency and false positive rate. Data collected by a dry EEG system from 50 healthy subjects, was used to assess performance and compare the MSM with several logistic regression classifiers of different sliding window lengths. As a result, the MSM achieves a significantly better tradeoff between latency, false positive and true positive rates. The proposed model could be used to achieve a more reactive and stable control of a neuroprosthesis. This is a desirable property in BCI-based neurorehabilitation, where proprioceptive feedback is provided based on the patient's brain signal. Indeed, it is hypothesized that simultaneous contingent association between brain signals and proprioceptive feedback induces superior associative learning.},
}
@article {pmid29448856,
year = {2018},
author = {Sweeti, and Joshi, D and Panigrahi, BK and Anand, S and Santhosh, J},
title = {Study of target and non-target interplay in spatial attention task.},
journal = {Journal of medical engineering &amp; technology},
volume = {42},
number = {2},
pages = {113-120},
doi = {10.1080/03091902.2018.1433244},
pmid = {29448856},
issn = {1464-522X},
mesh = {Evoked Potentials/*physiology ; Female ; Humans ; Male ; Neurological Rehabilitation ; Reaction Time/physiology ; Visual Perception/physiology ; },
abstract = {Selective visual attention is the ability to selectively pay attention to the targets while inhibiting the distractors. This paper aims to study the targets and non-targets interplay in spatial attention task while subject attends to the target object present in one visual hemifield and ignores the distractor present in another visual hemifield. This paper performs the averaged evoked response potential (ERP) analysis and time-frequency analysis. ERP analysis agrees to the left hemisphere superiority over late potentials for the targets present in right visual hemifield. Time-frequency analysis performed suggests two parameters i.e. event-related spectral perturbation (ERSP) and inter-trial coherence (ITC). These parameters show the same properties for the target present in either of the visual hemifields but show the difference while comparing the activity corresponding to the targets and non-targets. In this way, this study helps to visualise the difference between targets present in the left and right visual hemifields and, also the targets and non-targets present in the left and right visual hemifields. These results could be utilised to monitor subjects' performance in brain-computer interface (BCI) and neurorehabilitation.},
}
@article {pmid29446352,
year = {2018},
author = {Chiarelli, AM and Croce, P and Merla, A and Zappasodi, F},
title = {Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {036028},
doi = {10.1088/1741-2552/aaaf82},
pmid = {29446352},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Adult ; *Brain-Computer Interfaces ; *Deep Learning ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Psychomotor Performance/physiology ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures.<br><br>APPROACH: We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1&#x2009;s and overall experiment length of 10&#x2009;min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers.<br><br>MAIN RESULTS: At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect.<br><br>SIGNIFICANCE: BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.},
}
@article {pmid29446329,
year = {2018},
author = {Slutzky, MW and Flint, RD},
title = {Response to "Contribution of EEG signals to brain-machine interfaces".},
journal = {Journal of neurophysiology},
volume = {119},
number = {2},
pages = {763},
pmid = {29446329},
issn = {1522-1598},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Signal Processing, Computer-Assisted ; },
}
@article {pmid29446328,
year = {2018},
author = {Ordikhani-Seyedlar, M and Doser, K},
title = {Contribution of EEG signals to brain-machine interfaces.},
journal = {Journal of neurophysiology},
volume = {119},
number = {2},
pages = {761-762},
doi = {10.1152/jn.00730.2017},
pmid = {29446328},
issn = {1522-1598},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Signal Processing, Computer-Assisted ; },
}
@article {pmid29445036,
year = {2018},
author = {Jayaram, K and Mongeau, JM and Mohapatra, A and Birkmeyer, P and Fearing, RS and Full, RJ},
title = {Transition by head-on collision: mechanically mediated manoeuvres in cockroaches and small robots.},
journal = {Journal of the Royal Society, Interface},
volume = {15},
number = {139},
pages = {},
pmid = {29445036},
issn = {1742-5662},
mesh = {Animals ; *Behavior, Animal ; *Cockroaches ; *Locomotion ; *Robotics ; },
abstract = {Exceptional performance is often considered to be elegant and free of 'errors' or missteps. During the most extreme escape behaviours, neural control can approach or exceed its operating limits in response time and bandwidth. Here we show that small, rapid running cockroaches with robust exoskeletons select head-on collisions with obstacles to maintain the fastest escape speeds possible to transition up a vertical wall. Instead of avoidance, animals use their passive body shape and compliance to negotiate challenging environments. Cockroaches running at over 1 m or 50 body lengths per second transition from the floor to a vertical wall within 75 ms by using their head like an automobile bumper, mechanically mediating the manoeuvre. Inspired by the animal's behaviour, we demonstrate a passive, high-speed, mechanically mediated vertical transitions with a small, palm-sized legged robot. By creating a collision model for animal and human materials, we suggest a size dependence favouring mechanical mediation below 1 kg that we term the 'Haldane limit'. Relying on the mechanical control offered by soft exoskeletons represents a paradigm shift for understanding the control of small animals and the next generation of running, climbing and flying robots where the use of the body can off-load the demand for rapid sensing and actuation.},
}
@article {pmid29444893,
year = {2018},
author = {Missinato, MA and Saydmohammed, M and Zuppo, DA and Rao, KS and Opie, GW and Kühn, B and Tsang, M},
title = {Dusp6 attenuates Ras/MAPK signaling to limit zebrafish heart regeneration.},
journal = {Development (Cambridge, England)},
volume = {145},
number = {5},
pages = {},
pmid = {29444893},
issn = {1477-9129},
support = {R01 HD053287/HD/NICHD NIH HHS/United States ; T32 EB001026/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Animals, Genetically Modified ; Cell Proliferation/genetics ; Down-Regulation/genetics ; Dual Specificity Phosphatase 6/*physiology ; Heart/*physiology ; MAP Kinase Signaling System/*physiology ; Myocytes, Cardiac/physiology ; Proto-Oncogene Proteins p21(ras)/*metabolism ; Regeneration/*genetics ; Signal Transduction/genetics ; Zebrafish/*physiology ; Zebrafish Proteins/physiology ; },
abstract = {Zebrafish regenerate cardiac tissue through proliferation of pre-existing cardiomyocytes and neovascularization. Secreted growth factors such as FGFs, IGF, PDGFs and Neuregulin play essential roles in stimulating cardiomyocyte proliferation. These factors activate the Ras/MAPK pathway, which is tightly controlled by the feedback attenuator Dual specificity phosphatase 6 (Dusp6), an ERK phosphatase. Here, we show that suppressing Dusp6 function enhances cardiac regeneration. Inactivation of Dusp6 by small molecules or by gene inactivation increased cardiomyocyte proliferation, coronary angiogenesis, and reduced fibrosis after ventricular resection. Inhibition of Erbb or PDGF receptor signaling suppressed cardiac regeneration in wild-type zebrafish, but had a milder effect on regeneration in dusp6 mutants. Moreover, in rat primary cardiomyocytes, NRG1-stimulated proliferation can be enhanced upon chemical inhibition of Dusp6 with BCI. Our results suggest that Dusp6 attenuates Ras/MAPK signaling during regeneration and that suppressing Dusp6 can enhance cardiac repair.},
}
@article {pmid29442072,
year = {2018},
author = {Daly, I and Blanchard, C and Holmes, NP},
title = {Cortical excitability correlates with the event-related desynchronization during brain-computer interface control.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026022},
pmid = {29442072},
issn = {1741-2552},
support = {MR/K014250/1/MRC_/Medical Research Council/United Kingdom ; MR/K014250/2/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Cortical Synchronization/physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Transcranial Magnetic Stimulation/methods ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on motor control have been suggested as tools for stroke rehabilitation. Some initial successes have been achieved with this approach, however the mechanism by which they work is not yet fully understood. One possible part of this mechanism is a, previously suggested, relationship between the strength of the event-related desynchronization (ERD), a neural correlate of motor imagination and execution, and corticospinal excitability. Additionally, a key component of BCIs used in neurorehabilitation is the provision of visual feedback to positively reinforce attempts at motor control. However, the ability of visual feedback of the ERD to modulate the activity in the motor system has not been fully explored.<br><br>APPROACH: We investigate these relationships via transcranial magnetic stimulation delivered at different moments in the ongoing ERD related to hand contraction and relaxation during BCI control of a visual feedback bar.<br><br>MAIN RESULTS: We identify a significant relationship between ERD strength and corticospinal excitability, and find that our visual feedback does not affect corticospinal excitability.<br><br>SIGNIFICANCE: Our results imply that efforts to promote functional recovery in stroke by targeting increases in corticospinal excitability may be aided by accounting for the time course of the ERD.},
}
@article {pmid29440388,
year = {2018},
author = {Murty, DVPS and Shirhatti, V and Ravishankar, P and Ray, S},
title = {Large Visual Stimuli Induce Two Distinct Gamma Oscillations in Primate Visual Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {11},
pages = {2730-2744},
pmid = {29440388},
issn = {1529-2401},
support = {//Wellcome Trust/United Kingdom ; 500145-Z-09-Z/WTDBT_/DBT-Wellcome Trust India Alliance/India ; },
mesh = {Algorithms ; Animals ; Electroencephalography ; Evoked Potentials, Visual/physiology ; Female ; Fixation, Ocular ; Gamma Rhythm/*physiology ; Humans ; Macaca radiata/*physiology ; Male ; *Photic Stimulation ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Recent studies have shown the existence of two gamma rhythms in the hippocampus subserving different functions but, to date, primate studies in primary visual cortex have reported a single gamma rhythm. Here, we show that large visual stimuli induce a slow gamma (25-45 Hz) in area V1 of two awake adult female bonnet monkeys and in the EEG of 15 human subjects (7 males and 8 females), in addition to the traditionally known fast gamma (45-70 Hz). The two rhythms had different tuning characteristics for stimulus orientation, contrast, drift speed, and size. Further, fast gamma had short latency, strongly entrained spikes and was coherent over short distances, reflecting short-range processing, whereas slow gamma appeared to reflect long-range processing. Together, two gamma rhythms can potentially provide better coding or communication mechanisms and a more comprehensive biomarker for diagnosis of mental disorders.SIGNIFICANCE STATEMENT Gamma rhythm has been associated with high-level cognitive functions such as attention and feature binding and has been reported to be abnormal in brain disorders such as autism and schizophrenia. Unlike previous studies that have shown a single gamma rhythm in the primate visual cortex, we found that large visual gratings induce two distinct gamma oscillations in both monkey LFP and human EEG. These rhythms, termed slow (25-45 Hz) and fast (45-70 Hz), exhibited distinct tuning preferences, latencies, and coherence profiles, potentially reflecting processing at two different ranges. Multiple gamma oscillations in visual cortex may provide a richer representation of external visual stimuli and could be used for developing brain-machine interfacing applications and screening tests for neuropsychiatric disorders.},
}
@article {pmid29435089,
year = {2018},
author = {Huang, M and Jin, J and Zhang, Y and Hu, D and Wang, X},
title = {Usage of drip drops as stimuli in an auditory P300 BCI paradigm.},
journal = {Cognitive neurodynamics},
volume = {12},
number = {1},
pages = {85-94},
pmid = {29435089},
issn = {1871-4080},
abstract = {Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP (p < 0.05, Wilcoxon signed test; p < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty (p < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.},
}
@article {pmid29432436,
year = {2018},
author = {Tanaka, N and Sano, K and Rahman, MA and Miyata, R and Capi, G and Kawahara, S},
title = {Change in hippocampal theta oscillation associated with multiple lever presses in a bimanual two-lever choice task for robot control in rats.},
journal = {PloS one},
volume = {13},
number = {2},
pages = {e0192593},
pmid = {29432436},
issn = {1932-6203},
mesh = {Animals ; *Choice Behavior ; Electrodes ; Hippocampus/*physiology ; Male ; Rats ; Rats, Wistar ; *Robotics ; },
abstract = {Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in BMI, we investigated hippocampal theta oscillations during a two-lever choice task. During the task body-restrained rats were trained with a food reward to move an e-puck robot towards them by pressing the correct lever, ipsilateral to the robot several times, using the ipsilateral forelimb. The robot carried food and moved along a semicircle track set in front of the rat. We demonstrated that the power of hippocampal theta oscillations gradually increased during a 6-s preparatory period before the start of multiple lever pressing, irrespective of whether the correct lever choice or forelimb side were used. In addition, there was a significant difference in the theta power after the first choice, between correct and incorrect trials. During the correct trials the theta power was highest during the first lever-releasing period, whereas in the incorrect trials it occurred during the second correct lever-pressing period. We also analyzed the hippocampal theta oscillations at the termination of multiple lever pressing during the correct trials. Irrespective of whether the correct forelimb side was used, the power of hippocampal theta oscillations gradually decreased with the termination of multiple lever pressing. The frequency of theta oscillation also demonstrated an increase and decrease, before and after multiple lever pressing, respectively. There was a transient increase in frequency after the first lever press during the incorrect trials, while no such increase was observed during the correct trials. These results suggested that hippocampal theta oscillations reflect some aspects of preparatory and cognitive neural activities during the robot controlling task, which could be used for BMI.},
}
@article {pmid29432199,
year = {2019},
author = {Cheng, J and Jin, J and Daly, I and Zhang, Y and Wang, B and Wang, X and Cichocki, A},
title = {Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {64},
number = {1},
pages = {29-38},
doi = {10.1515/bmt-2017-0082},
pmid = {29432199},
issn = {1862-278X},
mesh = {*Brain-Computer Interfaces/standards ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Visual/*physiology ; Photic Stimulation ; Psychomotor Performance/*physiology ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems can allow their users to communicate with the external world by recognizing intention directly from their brain activity without the assistance of the peripheral motor nervous system. The P300-speller is one of the most widely used visual BCI applications. In previous studies, a flip stimulus (rotating the background area of the character) that was based on apparent motion, suffered from less refractory effects. However, its performance was not improved significantly. In addition, a presentation paradigm that used a "zooming" action (changing the size of the symbol) has been shown to evoke relatively higher P300 amplitudes and obtain a better BCI performance. To extend this method of stimuli presentation within a BCI and, consequently, to improve BCI performance, we present a new paradigm combining both the flip stimulus with a zooming action. This new presentation modality allowed BCI users to focus their attention more easily. We investigated whether such an action could combine the advantages of both types of stimuli presentation to bring a significant improvement in performance compared to the conventional flip stimulus. The experimental results showed that the proposed paradigm could obtain significantly higher classification accuracies and bit rates than the conventional flip paradigm (p<0.01).},
}
@article {pmid29432117,
year = {2018},
author = {Vu, PP and Irwin, ZT and Bullard, AJ and Ambani, SW and Sando, IC and Urbanchek, MG and Cederna, PS and Chestek, CA},
title = {Closed-Loop Continuous Hand Control via Chronic Recording of Regenerative Peripheral Nerve Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {515-526},
doi = {10.1109/TNSRE.2017.2772961},
pmid = {29432117},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; *Artificial Limbs ; Calibration ; Electric Stimulation ; Electromyography/instrumentation/*methods ; Fingers/innervation/physiology ; Macaca mulatta ; *Peripheral Nerves ; Prosthesis Design ; Psychomotor Performance ; Upper Extremity ; *User-Computer Interface ; },
abstract = {Loss of the upper limb imposes a devastating interruption to everyday life. Full restoration of natural arm control requires the ability to simultaneously control multiple degrees of freedom of the prosthetic arm and maintain that control over an extended period of time. Current clinically available myoelectric prostheses do not provide simultaneous control or consistency for transradial amputees. To address this issue, we have implemented a standard Kalman filter for continuous hand control using intramuscular electromyography (EMG) from both regenerative peripheral nerve interfaces (RPNI) and an intact muscle within non-human primates. Seven RPNIs and one intact muscle were implanted with indwelling bipolar intramuscular electrodes in two rhesus macaques. Following recuperations, function-specific EMG signals were recorded and then fed through the Kalman filter during a hand-movement behavioral task to continuously predict the monkey's finger position. We were able to reconstruct continuous finger movement offline with an average correlation of and a root mean squared error (RMSE) of 0.12 between actual and predicted position from two macaques. This finger movement prediction was also performed in real time to enable closed-loop neural control of a virtual hand. Compared with physical hand control, neural control performance was slightly slower but maintained an average target hit success rate of 96.70%. Recalibration longevity measurements maintained consistent average correlation over time but had a significant change in RMSE (). Additionally, extracted single units varied in amplitude by a factor of +18.65% and -25.85% compared with its mean. This is the first demonstration of chronic indwelling electrodes being used for continuous position control via the Kalman filter. Combining these analyses with our novel peripheral nerve interface, we believe that this demonstrates an important step in providing patients with more naturalistic control of their prosthetic limbs.},
}
@article {pmid29432111,
year = {2018},
author = {Wei, CS and Wang, YT and Lin, CT and Jung, TP},
title = {Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {400-406},
doi = {10.1109/TNSRE.2018.2790359},
pmid = {29432111},
issn = {1558-0210},
mesh = {Automobile Driving/psychology ; *Brain-Computer Interfaces ; Cognition/physiology ; Discriminant Analysis ; Electrodes ; Electroencephalography/*methods ; Hair ; Humans ; Pilot Projects ; Reproducibility of Results ; Scalp ; Support Vector Machine ; Wakefulness/*physiology ; },
abstract = {Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects (). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.},
}
@article {pmid29432108,
year = {2018},
author = {Saha, S and Ahmed, KIU and Mostafa, R and Hadjileontiadis, L and Khandoker, A},
title = {Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {371-382},
doi = {10.1109/TNSRE.2017.2778178},
pmid = {29432108},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Cognition/physiology ; Cortical Synchronization ; Electrodes ; Electroencephalography/*statistics &amp; numerical data ; Female ; Foot ; Hand ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Reproducibility of Results ; Tongue ; },
abstract = {Inter-subject and inter-session variabilities pose a significant challenge in electroencephalogram (EEG)-based brain-computer interface (BCI) systems. Furthermore, high dimensional EEG montages introduce huge computational burden due to excessive number of channels involved. Two experimental, i.e., inter-session and inter-subject, variabilities of EEG dynamics during motor imagery (MI) tasks are investigated in this paper. In particular, the effect on the performance of the BCIs due to day-to-day variability in EEG dynamics during the alterations in cognitive stages is explored. In addition, the inter-subject BCIs feasibility between cortically synchronized and desynchronized subject pairs on pairwise performance associativity is further examined. Moreover, the consequences of integrating spatial brain dynamics of varying the number of channels - from specific regions of the brain - are also discussed in case of both the contexts. The proposed approach is validated on real BCI data set containing EEG data from four classes of MI tasks, i.e., left/right hand, both feet, and tongue, subjected prior to a preprocessing of three different spatial filtering techniques. Experimental results have shown that a maximum classification accuracy of around 58% was achieved for the inter-subject experimental case, whereas a 31% deviation was noticed in the classification accuracies across two sessions during the inter-session experimental case. In conclusion, BCIs, without the subject-and session-specific calibration and with lesser number of channels employed, play a vital role while promoting a generic and efficient framework for plug and play use.},
}
@article {pmid29430213,
year = {2017},
author = {Khalighinejad, B and Nagamine, T and Mehta, A and Mesgarani, N},
title = {NAPLIB: AN OPEN SOURCE TOOLBOX FOR REAL-TIME AND OFFLINE NEURAL ACOUSTIC PROCESSING.},
journal = {Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)},
volume = {2017},
number = {},
pages = {846-850},
pmid = {29430213},
issn = {1520-6149},
support = {R01 DC014279/DC/NIDCD NIH HHS/United States ; },
abstract = {In this paper, we introduce the Neural Acoustic Processing Library (NAPLib), a toolbox containing novel processing methods for real-time and offline analysis of neural activity in response to speech. Our method divides the speech signal and resultant neural activity into segmental units (e.g., phonemes), allowing for fast and efficient computations that can be implemented in real-time. NAPLib contains a suite of tools that characterize various properties of the neural representation of speech, which can be used for functionality such as characterizing electrode tuning properties, brain mapping and brain computer interfaces. The library is general and applicable to both invasive and non-invasive recordings, including electroencephalography (EEG), electrocorticography (ECoG) and magnetoecnephalography (MEG). In this work, we describe the structure of NAPLib, as well as demonstrating its use in both EEG and ECoG. We believe NAPLib provides a valuable tool to both clinicians and researchers who are interested in the representation of speech in the brain.},
}
@article {pmid29427847,
year = {2018},
author = {Guler, S and Dannhauer, M and Roig-Solvas, B and Gkogkidis, A and Macleod, R and Ball, T and Ojemann, JG and Brooks, DH},
title = {Computationally optimized ECoG stimulation with local safety constraints.},
journal = {NeuroImage},
volume = {173},
number = {},
pages = {35-48},
pmid = {29427847},
issn = {1095-9572},
support = {P41 GM103545/GM/NIGMS NIH HHS/United States ; },
mesh = {Brain/physiology ; Computer Simulation ; Electrocorticography/*methods ; Electrodes ; Humans ; *Models, Neurological ; },
abstract = {Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice.},
}
@article {pmid29423352,
year = {2017},
author = {Meng, J and Mundahl, J and Streitz, T and Maile, K and Gulachek, N and He, J and He, B},
title = {Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.},
journal = {IEEE access : practical innovations, open solutions},
volume = {5},
number = {},
pages = {18756-18764},
pmid = {29423352},
issn = {2169-3536},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; },
abstract = {Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.},
}
@article {pmid29422842,
year = {2018},
author = {Úbeda, A and Azorín, JM and Farina, D and Sartori, M},
title = {Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces.},
journal = {Frontiers in computational neuroscience},
volume = {12},
number = {},
pages = {3},
pmid = {29422842},
issn = {1662-5188},
abstract = {One of the current challenges in human motor rehabilitation is the robust application of Brain-Machine Interfaces to assistive technologies such as powered lower limb exoskeletons. Reliable decoding of motor intentions and accurate timing of the robotic device actuation is fundamental to optimally enhance the patient's functional improvement. Several studies show that it may be possible to extract motor intentions from electroencephalographic (EEG) signals. These findings, although notable, suggests that current techniques are still far from being systematically applied to an accurate real-time control of rehabilitation or assistive devices. Here we propose the estimation of spinal primitives of multi-muscle control from EEG, using electromyography (EMG) dimensionality reduction as a solution to increase the robustness of the method. We successfully apply this methodology, both to healthy and incomplete spinal cord injury (SCI) patients, to identify muscle contraction during periodical knee extension from the EEG. We then introduce a novel performance metric, which accurately evaluates muscle primitive activations.},
}
@article {pmid29422841,
year = {2018},
author = {Verdière, KJ and Roy, RN and Dehais, F},
title = {Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario.},
journal = {Frontiers in human neuroscience},
volume = {12},
number = {},
pages = {6},
pmid = {29422841},
issn = {1662-5161},
abstract = {Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs.},
}
@article {pmid29421549,
year = {2018},
author = {Cody, PA and Eles, JR and Lagenaur, CF and Kozai, TDY and Cui, XT},
title = {Unique electrophysiological and impedance signatures between encapsulation types: An analysis of biological Utah array failure and benefit of a biomimetic coating in a rat model.},
journal = {Biomaterials},
volume = {161},
number = {},
pages = {117-128},
pmid = {29421549},
issn = {1878-5905},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomimetics/*methods ; *Electric Impedance ; Electrophysiological Phenomena ; Microelectrodes ; Rats ; },
abstract = {Intracortical microelectrode arrays, especially the Utah array, remain the most common choice for obtaining high dimensional recordings of spiking neural activity for brain computer interface and basic neuroscience research. Despite the widespread use and established design, mechanical, material and biological challenges persist that contribute to a steady decline in recording performance (as evidenced by both diminished signal amplitude and recorded cell population over time) or outright array failure. Device implantation injury causes acute cell death and activation of inflammatory microglia and astrocytes that leads to a chronic neurodegeneration and inflammatory glial aggregation around the electrode shanks and often times fibrous tissue growth above the pia along the bed of the array within the meninges. This multifaceted deleterious cascade can result in substantial variability in performance even under the same experimental conditions. We track both impedance signatures and electrophysiological performance of 4 × 4 floating microelectrode Utah arrays implanted in the primary monocular visual cortex (V1m) of Long-Evans rats over a 12-week period. We employ a repeatable visual stimulation method to compare signal-to-noise ratio as well as single- and multi-unit yield from weekly recordings. To explain signal variability with biological response, we compare arrays categorized as either Type 1, partial fibrous encapsulation, or Type 2, complete fibrous encapsulation and demonstrate performance and impedance signatures unique to encapsulation type. We additionally assess benefits of a biomolecule coating intended to minimize distance to recordable units and observe a temporary improvement on multi-unit recording yield and single-unit amplitude.},
}
@article {pmid29409655,
year = {2018},
author = {Murata, A and Fukunaga, D},
title = {Extended Fitts' model of pointing time in eye-gaze input system - Incorporating effects of target shape and movement direction into modeling.},
journal = {Applied ergonomics},
volume = {68},
number = {},
pages = {54-60},
doi = {10.1016/j.apergo.2017.10.019},
pmid = {29409655},
issn = {1872-9126},
mesh = {Brain-Computer Interfaces ; Eye Movement Measurements/*statistics &amp; numerical data ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; *Models, Statistical ; Movement ; Psychomotor Performance ; *Reaction Time ; *Task Performance and Analysis ; Young Adult ; },
abstract = {This study attempted to investigate the effects of the target shape and the movement direction on the pointing time using an eye-gaze input system and extend Fitts' model so that these factors are incorporated into the model and the predictive power of Fitts' model is enhanced. The target shape, the target size, the movement distance, and the direction of target presentation were set as within-subject experimental variables. The target shape included: a circle, and rectangles with an aspect ratio of 1:1, 1:2, 1:3, and 1:4. The movement direction included eight directions: upper, lower, left, right, upper left, upper right, lower left, and lower right. On the basis of the data for identifying the effects of the target shape and the movement direction on the pointing time, an attempt was made to develop a generalized and extended Fitts' model that took into account the movement direction and the target shape. As a result, the generalized and extended model was found to fit better to the experimental data, and be more effective for predicting the pointing time for a variety of human-computer interaction (HCI) task using an eye-gaze input system.},
}
@article {pmid29409449,
year = {2018},
author = {Porcheret, M and Main, C and Croft, P and Dziedzic, K},
title = {Enhancing delivery of osteoarthritis care in the general practice consultation: evaluation of a behaviour change intervention.},
journal = {BMC family practice},
volume = {19},
number = {1},
pages = {26},
pmid = {29409449},
issn = {1471-2296},
support = {G0501798/MRC_/Medical Research Council/United Kingdom ; KMRF-2014-03-002/DH_/Department of Health/United Kingdom ; RP-PG-0407-10386/DH_/Department of Health/United Kingdom ; 18139/ARC_/Arthritis Research UK/United Kingdom ; },
mesh = {*Clinical Competence ; *Education, Medical, Continuing ; General Practice/*standards ; General Practitioners/*education ; Guideline Adherence ; Humans ; Osteoarthritis/diagnosis/*therapy ; *Practice Guidelines as Topic ; United Kingdom ; },
abstract = {BACKGROUND: Professionally-focussed behaviour change intervention (BCI) workshops were utilised in the Management of OsteoArthritis in Consultations (MOSAICS) trial investigating the feasibility of implementing the National Institute for Health and Care Excellence (NICE) Osteoarthritis (OA) Guideline in general practice. The workshops aimed to implement the general practitioner (GP) component of the trial intervention: an enhanced consultation for patients presenting with possible OA. This study presents an evaluation of the BCI workshops on GP competency in conducting these enhanced consultations.<br><br>METHODS: A before-and-after evaluation of the workshops, delivered to GPs participating in the intervention arm of the MOSAICS trial, using video-recorded GP consultations with simulated OA patients. GPs attended four workshops, which had been developed using an implementation framework. Videos were undertaken at three time-points (before workshops and at one- and five-months after) and were assessed by independent observers, blinded to time points, for GP competency in undertaking 14 predetermined consultation tasks.<br><br>RESULTS: Videos of 15 GPs were assessed. GP competency increased from a median of seven consultation tasks undertaken by each GP at baseline to 11 at both time-points after the workshops. Specific tasks which were undertaken more frequently after the workshops related to explaining that OA is treatable and not inevitably progressive, eliciting and addressing patient expectations of the consultation, and providing written OA information. However, the use of the word "osteoarthritis" in giving the diagnosis of OA was not enhanced by the workshops.<br><br>CONCLUSIONS: BCI workshops can enhance GP competency in undertaking consultations for OA. Further initiatives to implement the NICE OA Guideline and enhance the care of people with OA in primary care can be informed by the content and delivery of the workshops evaluated in this study.},
}
@article {pmid29409401,
year = {2018},
author = {Li, M and Xu, G and Xie, J and Chen, C},
title = {A review: Motor rehabilitation after stroke with control based on human intent.},
journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine},
volume = {232},
number = {4},
pages = {344-360},
doi = {10.1177/0954411918755828},
pmid = {29409401},
issn = {2041-3033},
mesh = {Biofeedback, Psychology ; Humans ; *Intention ; *Motor Activity ; Stroke/*physiopathology/*psychology ; Stroke Rehabilitation/*methods ; },
abstract = {Strokes are a leading cause of acquired disability worldwide, and there is a significant need for novel interventions and further research to facilitate functional motor recovery in stroke patients. This article reviews motor rehabilitation methods for stroke survivors with a focus on rehabilitation controlled by human motor intent. The review begins with the neurodevelopmental principles of motor rehabilitation that provide the neuroscientific basis for intuitively controlled rehabilitation, followed by a review of methods allowing human motor intent detection, biofeedback approaches, and quantitative motor rehabilitation assessment. Challenges for future advances in motor rehabilitation after stroke using intuitively controlled approaches are addressed.},
}
@article {pmid29403528,
year = {2017},
author = {Zhang, X and Liu, X and Yuan, SM and Lin, SF},
title = {Eye Tracking Based Control System for Natural Human-Computer Interaction.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {5739301},
pmid = {29403528},
issn = {1687-5273},
mesh = {Adolescent ; Adult ; Attention/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Eye Movement Measurements/*instrumentation ; *Eye Movements ; Female ; Humans ; Male ; Software ; Surveys and Questionnaires ; *User-Computer Interface ; Workload ; Young Adult ; },
abstract = {Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system with integrating both mouse and keyboard functions is proposed in this paper. The proposed system focuses on providing a simple and convenient interactive mode by only using user's eye. The usage flow of the proposed system is designed to perfectly follow human natural habits. Additionally, a magnifier module is proposed to allow the accurate operation. In the experiment, two interactive tasks with different difficulty (searching article and browsing multimedia web) were done to compare the proposed eye control tool with an existing system. The Technology Acceptance Model (TAM) measures are used to evaluate the perceived effectiveness of our system. It is demonstrated that the proposed system is very effective with regard to usability and interface design.},
}
@article {pmid29402310,
year = {2018},
author = {Khan, RA and Naseer, N and Qureshi, NK and Noori, FM and Nazeer, H and Khan, MU},
title = {fNIRS-based Neurorobotic Interface for gait rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {15},
number = {1},
pages = {7},
pmid = {29402310},
issn = {1743-0003},
mesh = {Adult ; *Artificial Limbs ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Exoskeleton Device ; Humans ; Male ; *Neurological Rehabilitation/instrumentation/methods ; *Robotics ; Spectroscopy, Near-Infrared/instrumentation/*methods ; Support Vector Machine ; },
abstract = {BACKGROUND: In this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented.<br><br>METHODS: fNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere's primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Na&#xef;ve Bayes, support vector machine (SVM)) were tested.<br><br>RESULTS: The classification accuracies obtained from SVM using the hrf were significantly higher (p&#x2009;<&#x2009;0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively.<br><br>CONCLUSION: The control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.},
}
@article {pmid29391588,
year = {2018},
author = {López-Bao, JV and Godinho, R and Pacheco, C and Lema, FJ and García, E and Llaneza, L and Palacios, V and Jiménez, J},
title = {Toward reliable population estimates of wolves by combining spatial capture-recapture models and non-invasive DNA monitoring.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {2177},
pmid = {29391588},
issn = {2045-2322},
mesh = {Animals ; *Conservation of Natural Resources ; DNA/*analysis ; Endangered Species ; Female ; Male ; *Models, Theoretical ; *Population Density ; Reproducibility of Results ; *Wolves ; },
abstract = {Decision-makers in wildlife policy require reliable population size estimates to justify interventions, to build acceptance and support in their decisions and, ultimately, to build trust in managing authorities. Traditional capture-recapture approaches present two main shortcomings, namely, the uncertainty in defining the effective sampling area, and the spatially-induced heterogeneity in encounter probabilities. These limitations are overcome using spatially explicit capture-recapture approaches (SCR). Using wolves as case study, and non-invasive DNA monitoring (faeces), we implemented a SCR with a Poisson observation model in a single survey to estimate wolf density and population size, and identify the locations of individual activity centres, in NW Iberia over 4,378 km[2]. During the breeding period, posterior mean wolf density was 2.55 wolves/100 km[2] (95%BCI = 1.87-3.51), and the posterior mean population size was 111.6 ± 18.8 wolves (95%BCI = 81.8-153.6). From simulation studies, addressing different scenarios of non-independence and spatial aggregation of individuals, we only found a slight underestimation in population size estimates, supporting the reliability of SCR for social species. The strategy used here (DNA monitoring combined with SCR) may be a cost-effective way to generate reliable population estimates for large carnivores at regional scales, especially for endangered species or populations under game management.},
}
@article {pmid29387781,
year = {2018},
author = {Perez-Garcia, G and De Gasperi, R and Gama Sosa, MA and Perez, GM and Otero-Pagan, A and Tschiffely, A and McCarron, RM and Ahlers, ST and Elder, GA and Gandy, S},
title = {PTSD-Related Behavioral Traits in a Rat Model of Blast-Induced mTBI Are Reversed by the mGluR2/3 Receptor Antagonist BCI-838.},
journal = {eNeuro},
volume = {5},
number = {1},
pages = {},
pmid = {29387781},
issn = {2373-2822},
support = {I01 RX000996/RX/RRD VA/United States ; I01 RX002333/RX/RRD VA/United States ; },
mesh = {Animals ; Anxiety/drug therapy/metabolism ; Blast Injuries/*complications/drug therapy/psychology ; Brain Concussion/*complications/drug therapy/psychology ; Bridged Bicyclo Compounds/*pharmacology ; Dentate Gyrus/drug effects/metabolism/pathology ; Disease Models, Animal ; Excitatory Amino Acid Antagonists/pharmacology ; Fear/drug effects/physiology ; Male ; Memory, Long-Term/drug effects/physiology ; Neurogenesis/drug effects/physiology ; Neurons/drug effects/metabolism/pathology ; Psychotropic Drugs/*pharmacology ; Rats, Long-Evans ; Receptors, Metabotropic Glutamate/antagonists &amp; inhibitors/metabolism ; Recognition, Psychology/drug effects/physiology ; Stress Disorders, Post-Traumatic/*drug therapy/metabolism/pathology/*psychology ; },
abstract = {Battlefield blast exposure related to improvised explosive devices (IEDs) has become the most common cause of traumatic brain injury (TBI) in the recent conflicts in Iraq and Afghanistan. Mental health problems are common after TBI. A striking feature in the most recent veterans has been the frequency with which mild TBI (mTBI) and posttraumatic stress disorder (PTSD) have appeared together, in contrast to the classical situations in which the presence of mTBI has excluded the diagnosis of PTSD. However, treatment of PTSD-related symptoms that follow blast injury has become a significant problem. BCI-838 (MGS0210) is a Group II metabotropic glutamate receptor (mGluR2/3) antagonist prodrug, and its active metabolite BCI-632 (MGS0039) has proneurogenic, procognitive, and antidepressant activities in animal models. In humans, BCI-838 is currently in clinical trials for refractory depression and suicidality. The aim of the current study was to determine whether BCI-838 could modify the anxiety response and reverse PTSD-related behaviors in rats exposed to a series of low-level blast exposures designed to mimic a human mTBI or subclinical blast exposure. BCI-838 treatment reversed PTSD-related behavioral traits improving anxiety and fear-related behaviors as well as long-term recognition memory. Treatment with BCI-838 also increased neurogenesis in the dentate gyrus (DG) of blast-exposed rats. The safety profile of BCI-838 together with the therapeutic activities reported here, make BCI-838 a promising drug for the treatment of former battlefield Warfighters suffering from PTSD-related symptoms following blast-induced mTBI.},
}
@article {pmid29385839,
year = {2019},
author = {Brumberg, JS and Nguyen, A and Pitt, KM and Lorenz, SD},
title = {Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {14},
number = {3},
pages = {241-249},
pmid = {29385839},
issn = {1748-3115},
support = {R03 DC011304/DC/NIDCD NIH HHS/United States ; U54 HD090216/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Attention/physiology ; Brain Diseases/*physiopathology/*rehabilitation ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Eye Movements/physiology ; Feedback, Sensory/physiology ; Female ; Humans ; Male ; Middle Aged ; Task Performance and Analysis ; User-Computer Interface ; },
abstract = {PURPOSE: We investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments. BCIs are often tested within a single patient population limiting generalization of results. This study focuses on examining individual sensory abilities with an eye toward possible interface adaptations to improve device performance.<br><br>METHODS: Five individuals with a range of neuromotor disorders participated in four-choice BCI control task involving the steady state visually evoked potential. The BCI graphical interface was designed to simulate a commercial AAC device to examine whether an integrated device could be used successfully by individuals with neuromotor impairment.<br><br>RESULTS: All participants were able to interact with the BCI and highest performance was found for participants able to employ an overt visual attention strategy. For participants with visual deficits to due to impaired oculomotor control, effective performance increased after accounting for mismatches between the graphical layout and participant visual capabilities.<br><br>CONCLUSION: As BCIs are translated from research environments to clinical applications, the assessment of BCI-related skills will help facilitate proper device selection and provide individuals who use BCI the greatest likelihood of immediate and long term communicative success. Overall, our results indicate that adaptations can be an effective strategy to reduce barriers and increase access to BCI technology. These efforts should be directed by comprehensive assessments for matching individuals to the most appropriate device to support their complex communication needs. Implications for Rehabilitation Brain computer interfaces using the steady state visually evoked potential can be integrated with an augmentative and alternative communication device to provide access to language and literacy for individuals with neuromotor impairment. Comprehensive assessments are needed to fully understand the sensory, motor, and cognitive abilities of individuals who may use brain-computer interfaces for proper feature matching as selection of the most appropriate device including optimization device layouts and control paradigms. Oculomotor impairments negatively impact brain-computer interfaces that use the steady state visually evoked potential, but modifications to place interface stimuli and communication items in the intact visual field can improve successful outcomes.},
}
@article {pmid29379773,
year = {2017},
author = {Golovkine, G and Reboud, E and Huber, P},
title = {Pseudomonas aeruginosa Takes a Multi-Target Approach to Achieve Junction Breach.},
journal = {Frontiers in cellular and infection microbiology},
volume = {7},
number = {},
pages = {532},
pmid = {29379773},
issn = {2235-2988},
mesh = {Animals ; *Host-Pathogen Interactions ; Humans ; Pseudomonas aeruginosa/*pathogenicity ; Tight Junctions/*microbiology/*physiology ; Virulence Factors/*metabolism ; },
abstract = {Pseudomonas aeruginosa is an opportunistic pathogen which uses a number of strategies to cross epithelial and endothelial barriers at cell-cell junctions. In this review, we describe how the coordinated actions of P. aeruginosa's virulence factors trigger various molecular mechanisms to disarm the junctional gate responsible for tissue integrity.},
}
@article {pmid29379425,
year = {2017},
author = {Toppi, J and Astolfi, L and Risetti, M and Anzolin, A and Kober, SE and Wood, G and Mattia, D},
title = {Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {637},
pmid = {29379425},
issn = {1662-5161},
abstract = {Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could "optimally" promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke.},
}
@article {pmid29379140,
year = {2018},
author = {Utsumi, K and Takano, K and Okahara, Y and Komori, T and Onodera, O and Kansaku, K},
title = {Operation of a P300-based brain-computer interface in patients with Duchenne muscular dystrophy.},
journal = {Scientific reports},
volume = {8},
number = {1},
pages = {1753},
pmid = {29379140},
issn = {2045-2322},
mesh = {Adult ; Brain/metabolism/*physiopathology ; Brain-Computer Interfaces ; Discriminant Analysis ; Dystrophin/metabolism ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Muscular Dystrophy, Duchenne/metabolism/*physiopathology ; Photic Stimulation/methods ; Young Adult ; },
abstract = {A brain-computer interface (BCI) or brain-machine interface is a technology that enables the control of a computer and other external devices using signals from the brain. This technology has been tested in paralysed patients, such as those with cervical spinal cord injuries or amyotrophic lateral sclerosis, but it has not been tested systematically in Duchenne muscular dystrophy (DMD), which is a severe type of muscular dystrophy due to the loss of dystrophin and is often accompanied by progressive muscle weakness and wasting. Here, we investigated the efficacy of a P300-based BCI for patients with DMD. Eight bedridden patients with DMD and eight age- and gender-matched able-bodied controls were instructed to input hiragana characters. We used a region-based, two-step P300-based BCI with green/blue flicker stimuli. EEG data were recorded, and a linear discriminant analysis distinguished the target from other non-targets. The mean online accuracy of inputted characters (accuracy for the two-step procedure) was 71.6% for patients with DMD and 80.6% for controls, with no significant difference between the patients and controls. The P300-based BCI was operated successfully by individuals with DMD in an advanced stage and these findings suggest that this technology may be beneficial for patients with this disease.},
}
@article {pmid29378977,
year = {2018},
author = {Moses, DA and Leonard, MK and Chang, EF},
title = {Real-time classification of auditory sentences using evoked cortical activity in humans.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {036005},
pmid = {29378977},
issn = {1741-2552},
support = {DP2 OD008627/OD/NIH HHS/United States ; F32 DC013486/DC/NIDCD NIH HHS/United States ; R00 NS065120/NS/NINDS NIH HHS/United States ; R01 DC012379/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation/instrumentation/*methods ; Auditory Cortex/*physiology ; *Brain-Computer Interfaces ; *Computer Systems ; Electrodes, Implanted ; Humans ; Speech/*physiology ; Speech Perception/*physiology ; },
abstract = {OBJECTIVE: Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces.<br><br>APPROACH: Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes.<br><br>MAIN RESULTS: We observed single-trial sentence classification accuracies of [Formula: see text] or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting.<br><br>SIGNIFICANCE: Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.},
}
@article {pmid29378317,
year = {2018},
author = {de Cheveigné, A and Wong, DDE and Di Liberto, GM and Hjortkjær, J and Slaney, M and Lalor, E},
title = {Decoding the auditory brain with canonical component analysis.},
journal = {NeuroImage},
volume = {172},
number = {},
pages = {206-216},
doi = {10.1016/j.neuroimage.2018.01.033},
pmid = {29378317},
issn = {1095-9572},
mesh = {Acoustic Stimulation ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/methods ; Evoked Potentials, Auditory/physiology ; Humans ; Magnetoencephalography/methods ; *Signal Processing, Computer-Assisted ; },
abstract = {The relation between a stimulus and the evoked brain response can shed light on perceptual processes within the brain. Signals derived from this relation can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated "decoding" strategies are needed to address continuous stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both the stimulus and the response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves observations of the relation between stimulus and response.},
}
@article {pmid29376885,
year = {2018},
author = {Mu, Z and Yin, J and Hu, J},
title = {Application of a brain-computer interface for person authentication using EEG responses to photo stimuli.},
journal = {Journal of integrative neuroscience},
volume = {17},
number = {1},
pages = {53-60},
doi = {10.31083/JIN-170042},
pmid = {29376885},
issn = {0219-6352},
mesh = {*Biometric Identification ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Neural Networks, Computer ; Pattern Recognition, Visual/physiology ; *Photic Stimulation/methods ; Probability ; },
abstract = {In this paper, a personal authentication system that can effectively identify individuals by generating unique electroencephalogram signal features in response to self-face and non-self-face photos is presented. To achieve performance stability, a sequence of self-face photographs including first-occurrence position and non-first-occurrence position are taken into account in the serial occurrence of visual stimuli. Additionally, a Fisher linear classification method and event-related potential technique for feature analysis is adapted to yield remarkably better outcomes than those obtained by most existing},
}
@article {pmid29376071,
year = {2017},
author = {Rahman, MM and Fattah, SA},
title = {Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {3720589},
pmid = {29376071},
issn = {2314-6141},
mesh = {Brain/*physiopathology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Psychological Tests ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Task Performance and Analysis ; },
abstract = {In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.},
}
@article {pmid29375359,
year = {2017},
author = {Alegre-Cortés, J and Soto-Sánchez, C and Albarracín, AL and Farfán, FD and Val-Calvo, M and Ferrandez, JM and Fernandez, E},
title = {Toward an Improvement of the Analysis of Neural Coding.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {77},
pmid = {29375359},
issn = {1662-5196},
abstract = {Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time-Frequency (T-F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T-F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T-F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain-machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.},
}
@article {pmid29375294,
year = {2017},
author = {Novak, D and Sigrist, R and Gerig, NJ and Wyss, D and Bauer, R and Götz, U and Riener, R},
title = {Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {756},
pmid = {29375294},
issn = {1662-4548},
abstract = {This paper presents a new approach to benchmarking brain-computer interfaces (BCIs) outside the lab. A computer game was created that mimics a real-world application of assistive BCIs, with the main outcome metric being the time needed to complete the game. This approach was used at the Cybathlon 2016, a competition for people with disabilities who use assistive technology to achieve tasks. The paper summarizes the technical challenges of BCIs, describes the design of the benchmarking game, then describes the rules for acceptable hardware, software and inclusion of human pilots in the BCI competition at the Cybathlon. The 11 participating teams, their approaches, and their results at the Cybathlon are presented. Though the benchmarking procedure has some limitations (for instance, we were unable to identify any factors that clearly contribute to BCI performance), it can be successfully used to analyze BCI performance in realistic, less structured conditions. In the future, the parameters of the benchmarking game could be modified to better mimic different applications (e.g., the need to use some commands more frequently than others). Furthermore, the Cybathlon has the potential to showcase such devices to the general public.},
}
@article {pmid29369295,
year = {2017},
author = {Hernández, OH and Hernández-Sánchez, KM},
title = {Omitted Stimulus Potential Depends on the Sensory Modality.},
journal = {Acta neurobiologiae experimentalis},
volume = {77},
number = {4},
pages = {297-304},
pmid = {29369295},
issn = {1689-0035},
mesh = {Adult ; Afferent Pathways/physiology ; Arrestin ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Habituation, Psychophysiologic/physiology ; Humans ; Male ; Physical Stimulation ; Reaction Time/physiology ; Sensation/*physiology ; Time Perception/*physiology ; Young Adult ; },
abstract = {Determining the characteristics of Omitted Stimulus Potential (OSP) parameters using different sensory modalities is important because they reflect timing processes and have a substantial influence on time perception. At the same time, the central mechanisms of time perception associated with sensory processing can modulate cortical brain waves related to cognition. This experiment tested the relationship between parameters of the whole OSP brain wave when trains of auditory, visual or somatosensory stimuli were applied. Twenty healthy young college volunteers completed within‑subjects trials with sensory stimuli at a fixed frequency of 0.5 Hz that ceased unpredictably. These passive trials required no behavioural response and were administered to measure the complete set of OSP (i.e., the rate of rise, amplitude and peak latency). OSPs showed a faster rate of rise for auditory stimuli compared to visual or somatosensory stimuli. Auditory stimuli also produced a shorter time to peak and higher amplitude waves. No significant differences were obtained between visual and somatosensory waves. The results suggest that the brain handles interval timing and expectation with greater efficiency for the auditory system compared to other sensory modalities. This auditory supremacy is congruent with previous behavioural studies using missing stimulus tasks and could be useful for clinical purposes, for example, designing auditory‑based brain‑computer interfaces for patients with motor disabilities and visual impairment. The rate of rise is a dynamic measure that should be included in the ERPs analysis.},
}
@article {pmid29367844,
year = {2017},
author = {Clerico, A and Tiwari, A and Gupta, R and Jayaraman, S and Falk, TH},
title = {Electroencephalography Amplitude Modulation Analysis for Automated Affective Tagging of Music Video Clips.},
journal = {Frontiers in computational neuroscience},
volume = {11},
number = {},
pages = {115},
pmid = {29367844},
issn = {1662-5188},
abstract = {The quantity of music content is rapidly increasing and automated affective tagging of music video clips can enable the development of intelligent retrieval, music recommendation, automatic playlist generators, and music browsing interfaces tuned to the users' current desires, preferences, or affective states. To achieve this goal, the field of affective computing has emerged, in particular the development of so-called affective brain-computer interfaces, which measure the user's affective state directly from measured brain waves using non-invasive tools, such as electroencephalography (EEG). Typically, conventional features extracted from the EEG signal have been used, such as frequency subband powers and/or inter-hemispheric power asymmetry indices. More recently, the coupling between EEG and peripheral physiological signals, such as the galvanic skin response (GSR), have also been proposed. Here, we show the importance of EEG amplitude modulations and propose several new features that measure the amplitude-amplitude cross-frequency coupling per EEG electrode, as well as linear and non-linear connections between multiple electrode pairs. When tested on a publicly available dataset of music video clips tagged with subjective affective ratings, support vector classifiers trained on the proposed features were shown to outperform those trained on conventional benchmark EEG features by as much as 6, 20, 8, and 7% for arousal, valence, dominance and liking, respectively. Moreover, fusion of the proposed features with EEG-GSR coupling features showed to be particularly useful for arousal (feature-level fusion) and liking (decision-level fusion) prediction. Together, these findings show the importance of the proposed features to characterize human affective states during music clip watching.},
}
@article {pmid29364932,
year = {2018},
author = {Kang, BK and Kim, JS and Ryun, S and Chung, CK},
title = {Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.},
journal = {PloS one},
volume = {13},
number = {1},
pages = {e0191480},
pmid = {29364932},
issn = {1932-6203},
mesh = {Adult ; Brain Mapping ; Electrocorticography/*methods ; Epilepsy/diagnostic imaging/physiopathology ; Female ; Humans ; Male ; *Movement ; },
abstract = {Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.},
}
@article {pmid29364119,
year = {2018},
author = {Nurse, ES and John, SE and Freestone, DR and Oxley, TJ and Ung, H and Berkovic, SF and O'Brien, TJ and Cook, MJ and Grayden, DB},
title = {Consistency of Long-Term Subdural Electrocorticography in Humans.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {2},
pages = {344-352},
doi = {10.1109/TBME.2017.2768442},
pmid = {29364119},
issn = {1558-2531},
mesh = {Biomedical Engineering ; Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods/standards ; Humans ; *Neural Prostheses ; Reproducibility of Results ; Seizures/therapy ; Signal Processing, Computer-Assisted/*instrumentation ; Time Factors ; },
abstract = {OBJECTIVE: Subdural electrocorticography (ECoG) can provide a robust control signal for a brain-computer interface (BCI). However, the long-term recording properties of ECoG are poorly understood as most ECoG studies in the BCI field have only used signals recorded for less than 28 days. We assessed human ECoG recordings over durations of many months to investigate changes to recording quality that occur with long-term implantation.<br><br>METHODS: We examined changes in signal properties over time from 15 ambulatory humans who had continuous subdural ECoG monitoring for 184-766 days.<br><br>RESULTS: Individual electrodes demonstrated varying changes in frequency power characteristics over time within individual patients and between patients. Group level analyses demonstrated that there were only small changes in effective signal bandwidth and spectral band power across months. High-gamma signals could be recorded throughout the study, though there was a decline in signal power for some electrodes.<br><br>CONCLUSION: ECoG-based BCI systems can robustly record high-frequency activity over multiple years, albeit with marked intersubject variability.<br><br>SIGNIFICANCE: Group level results demonstrated that ECoG is a promising modality for long-term BCI and neural prosthesis applications.},
}
@article {pmid29363625,
year = {2018},
author = {Brandman, DM and Hosman, T and Saab, J and Burkhart, MC and Shanahan, BE and Ciancibello, JG and Sarma, AA and Milstein, DJ and Vargas-Irwin, CE and Franco, B and Kelemen, J and Blabe, C and Murphy, BA and Young, DR and Willett, FR and Pandarinath, C and Stavisky, SD and Kirsch, RF and Walter, BL and Bolu Ajiboye, A and Cash, SS and Eskandar, EN and Miller, JP and Sweet, JA and Shenoy, KV and Henderson, JM and Jarosiewicz, B and Harrison, MT and Simeral, JD and Hochberg, LR},
title = {Rapid calibration of an intracortical brain-computer interface for people with tetraplegia.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026007},
pmid = {29363625},
issn = {1741-2552},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; I01 RX001155/RX/RRD VA/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; A6779-I/ImVA/Intramural VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces/trends ; Calibration ; Female ; Humans ; *Implantable Neurostimulators/trends ; Male ; Middle Aged ; Motor Cortex/*physiology ; Quadriplegia/physiopathology/*therapy ; Time Factors ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration.<br><br>APPROACH: We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF).<br><br>MAIN RESULTS: Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3&#x2009;min of initializing calibration. We also show that a BCI-na&#xef;ve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37&#x2009;s after initiating calibration.<br><br>SIGNIFICANCE: These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.},
}
@article {pmid29360843,
year = {2018},
author = {İşcan, Z and Nikulin, VV},
title = {Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations.},
journal = {PloS one},
volume = {13},
number = {1},
pages = {e0191673},
pmid = {29360843},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Young Adult ; },
abstract = {Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.},
}
@article {pmid29358903,
year = {2017},
author = {Mejia Tobar, A and Hyoudou, R and Kita, K and Nakamura, T and Kambara, H and Ogata, Y and Hanakawa, T and Koike, Y and Yoshimura, N},
title = {Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {733},
pmid = {29358903},
issn = {1662-4548},
abstract = {The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.},
}
@article {pmid29358571,
year = {2018},
author = {Zanetti, D and Di Berardino, F},
title = {A Bone Conduction Implantable Device as a Functional Treatment Option in Unilateral Microtia with Bilateral Stapes Ankylosis: A Report of Two Cases.},
journal = {The American journal of case reports},
volume = {19},
number = {},
pages = {82-89},
pmid = {29358571},
issn = {1941-5923},
mesh = {Adult ; Ankylosis/*complications ; *Bone Conduction ; Congenital Microtia/*complications ; Female ; *Hearing Aids ; Hearing Loss, Conductive/etiology/*therapy ; Humans ; Male ; *Stapes ; },
abstract = {BACKGROUND Implantable devices have been proposed as an alternative to hearing aids and auditory canal reconstruction in patients with microtia (congenital aural atresia), which includes a malformation of the external and middle ear. This report is of two rare cases of microtia associated with congenital stapes ankylosis treated with an implantable device and describes the treatment outcomes. CASE REPORT Two siblings from Ecuador, a 29-year-old woman, and her 35-year-old brother, were born with unilateral type II microtia with bilateral external auditory canal atresia and conductive hearing loss. Pre-operatively, high-resolution computed tomography (HRCT) imaging was performed using FastView software to allow placement of a bone conduction-floating mass transducer (BC-FMT) to couple a Bonebridge bone conduction implant (BCI) system in both patients. Pure-tone audiometry (PTA) testing and speech audiology were performed. The Abbreviated Profile of Hearing Aid Benefit (APHAB) and the Speech, Spatial and Qualities (SSQ) of hearing scale questionnaires and scoring systems were used. Following activation of the implantable device, both patients achieved improved bilateral conductive hearing with sound-field (field-free) thresholds >25 dB, and speech recognition scores >90%. In both cases, hearing improvement remained at three years following surgery. CONCLUSIONS To our knowledge, these are the first reported cases of microtia with congenital stapes ankylosis successfully treated with a bone conduction implantable device. Patients with microtia and stapes ankylosis who are reluctant to undergo surgery may benefit from unilateral or bilateral, short-term or long-term use of a Bonebridge bone conduction implantable device.},
}
@article {pmid29357477,
year = {2018},
author = {Vaidya, M and Balasubramanian, K and Southerland, J and Badreldin, I and Eleryan, A and Shattuck, K and Gururangan, S and Slutzky, M and Osborne, L and Fagg, A and Oweiss, K and Hatsopoulos, NG},
title = {Emergent coordination underlying learning to reach to grasp with a brain-machine interface.},
journal = {Journal of neurophysiology},
volume = {119},
number = {4},
pages = {1291-1304},
pmid = {29357477},
issn = {1522-1598},
support = {R01 NS093909/NS/NINDS NIH HHS/United States ; },
mesh = {Amputation, Surgical ; Animals ; Arm/*physiopathology ; *Artificial Limbs ; Behavior, Animal/physiology ; *Brain-Computer Interfaces ; Conditioning, Operant/*physiology ; Female ; Macaca mulatta ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Neurons/*physiology ; Psychomotor Performance/*physiology ; *Robotics ; },
abstract = {The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this development is unclear because it is difficult to study in humans. We took the approach of using a brain-machine interface (BMI) paradigm in rhesus macaques with prior therapeutic amputations to examine the emergence of novel, coordinated reach to grasp. Previous research has shown that after amputation, the cortical area previously involved in the control of the lost limb undergoes reorganization, but prior BMI work has largely relied on finding neurons that already encode specific movement-related information. In this study, we taught macaques to cortically control a robotic arm and hand through operant conditioning, using neurons that were not explicitly reach or grasp related. Over the course of training, stereotypical patterns emerged and stabilized in the cross-covariance between the reaching and grasping velocity profiles, between pairs of neurons involved in controlling reach and grasp, and to a comparable, but lesser, extent between other stable neurons in the network. In fact, we found evidence of this structured coordination between pairs composed of all combinations of neurons decoding reach or grasp and other stable neurons in the network. The degree of and participation in coordination was highly correlated across all pair types. Our approach provides a unique model for studying the development of novel, coordinated reach-to-grasp movement at the behavioral and cortical levels. NEW &amp; NOTEWORTHY Given that motor cortex undergoes reorganization after amputation, our work focuses on training nonhuman primates with chronic amputations to use neurons that are not reach or grasp related to control a robotic arm to reach to grasp through the use of operant conditioning, mimicking early development. We studied the development of a novel, coordinated behavior at the behavioral and cortical level, and the neural plasticity in M1 associated with learning to use a brain-machine interface.},
}
@article {pmid29357468,
year = {2018},
author = {Hu, S and Zhang, Q and Wang, J and Chen, Z},
title = {Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity.},
journal = {Journal of neurophysiology},
volume = {119},
number = {4},
pages = {1394-1410},
pmid = {29357468},
issn = {1522-1598},
support = {R01 GM115384/GM/NIGMS NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; R01 NS100016/NS/NINDS NIH HHS/United States ; },
mesh = {Acute Pain/*physiopathology ; *Algorithms ; Animals ; Behavior, Animal/physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Male ; *Models, Neurological ; *Models, Statistical ; Monte Carlo Method ; Neurons/*physiology ; Neurophysiology/*methods ; Rats ; Rats, Sprague-Dawley ; *Signal Processing, Computer-Assisted ; Stochastic Processes ; },
abstract = {Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW &amp; NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.},
}
@article {pmid29352906,
year = {2018},
author = {Hu, S and Bi, S and Yan, D and Zhou, Z and Sun, G and Cheng, X and Chen, X},
title = {Preparation of composite hydroxybutyl chitosan sponge and its role in promoting wound healing.},
journal = {Carbohydrate polymers},
volume = {184},
number = {},
pages = {154-163},
doi = {10.1016/j.carbpol.2017.12.033},
pmid = {29352906},
issn = {1879-1344},
mesh = {Animals ; Anti-Bacterial Agents/chemistry/therapeutic use ; Bandages ; Cell Line ; Cell Survival/drug effects ; Chitosan/*analogs &amp; derivatives/chemistry/therapeutic use ; Male ; Mice ; Rabbits ; Rats ; Rats, Sprague-Dawley ; Spectroscopy, Fourier Transform Infrared ; Wound Healing/*drug effects ; },
abstract = {In this work, a composite sponge was produced by physically mixing hydroxybutyl chitosan with chitosan to form a porous spongy material through vacuum freeze-drying. Hydrophilic and macroporous composite hydroxybutyl chitosan sponge was developed via the incorporation of chitosan into hydroxybutyl chitosan. The composite sponge showed higher porosity (about 85%), greater water absorption (about 25 times), better softness and lower blood-clotting index (BCI) than those of chitosan sponge and hydroxybutyl chitosan sponge. The composite sponge with good hydrophilic could absorb the moisture in the blood to increase blood concentration and viscosity, and become a semi-swelling viscous colloid to clog the capillaries. Cytocompatibility tests with L929 cells and HUVEC cells demonstrated that composite sponge were no cytotoxicity, and could promote the growth of fibroblasts. It made up for the shortcomings of hydroxybutyl chitosan with unfavorable antibacterial effect to achieve a higher level of antibacterial (>99.99% reduction). Eventually, the vivo evaluations in Sprague-Dawley rats revealed that epithelial cells attached to the composite sponge and penetrated into the interior, in addition to this, it was also proved that the composite sponge (HC-1) had a better ability to promote wound healing and helped for faster formation of skin glands and re-epithelialization. The obtained data encourage the use of this composite sponge for wound dressings.},
}
@article {pmid29349070,
year = {2017},
author = {Blum, S and Debener, S and Emkes, R and Volkening, N and Fudickar, S and Bleichner, MG},
title = {EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {3072870},
pmid = {29349070},
issn = {2314-6141},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*instrumentation/methods ; Evoked Potentials/physiology ; Female ; Humans ; Male ; *Mobile Applications ; Signal Processing, Computer-Assisted/*instrumentation ; *Smartphone ; },
abstract = {OBJECTIVE: Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware.<br><br>APPROACH: In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user.<br><br>MAIN RESULTS: We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback.<br><br>SIGNIFICANCE: We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.},
}
@article {pmid29348781,
year = {2017},
author = {Liu, R and Wang, Y and Wu, X and Cheng, J},
title = {Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI.},
journal = {Computational and mathematical methods in medicine},
volume = {2017},
number = {},
pages = {2948742},
pmid = {29348781},
issn = {1748-6718},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Decision Making ; Electroencephalography ; Humans ; *Machine Learning ; Motor Activity ; Movement ; Probability ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Wheelchairs ; },
abstract = {Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time. The properties of the method were illustrated on thirteen subjects' recordings from three datasets. Results showed that our proposed power projective method outperformed two benchmark methods for every subject. Moreover, with sequential classifier, the accuracies across subjects were significantly higher than that with nonsequential ones. The average maximum accuracy of the SPRT method was 84.1%, as compared with 82.3% accuracy for the sequential Bayesian (SB) method. The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off. These results suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI.},
}
@article {pmid29348744,
year = {2017},
author = {Batres-Mendoza, P and Ibarra-Manzano, MA and Guerra-Hernandez, EI and Almanza-Ojeda, DL and Montoro-Sanjose, CR and Romero-Troncoso, RJ and Rostro-Gonzalez, H},
title = {Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {9817305},
pmid = {29348744},
issn = {1687-5273},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Functional Laterality ; Humans ; *Imagination ; Male ; Movement ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; },
abstract = {We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications.},
}
@article {pmid29346750,
year = {2018},
author = {Ienca, M and Jotterand, F and Elger, BS},
title = {From Healthcare to Warfare and Reverse: How Should We Regulate Dual-Use Neurotechnology?.},
journal = {Neuron},
volume = {97},
number = {2},
pages = {269-274},
doi = {10.1016/j.neuron.2017.12.017},
pmid = {29346750},
issn = {1097-4199},
mesh = {Armed Conflicts ; Biomedical Technology/*ethics/legislation &amp; jurisprudence ; Brain-Computer Interfaces ; Computer Security ; Diagnostic Techniques, Neurological/adverse effects/*ethics ; Dual Use Research/*ethics/legislation &amp; jurisprudence ; Humans ; Inventions/*ethics/legislation &amp; jurisprudence ; Lie Detection ; Military Medicine/*ethics/legislation &amp; jurisprudence ; Nervous System Diseases/rehabilitation/therapy ; Neurosciences/*ethics/legislation &amp; jurisprudence ; Self-Help Devices/adverse effects/ethics ; Terrorism ; Torture ; },
abstract = {Recent advances in military-funded neurotechnology and novel opportunities for misusing neurodevices show that the problem of dual use is inherent to neuroscience. This paper discusses how the neuroscience community should respond to these dilemmas and delineates a neuroscience-specific biosecurity framework. This neurosecurity framework involves calibrated regulation, (neuro)ethical guidelines, and awareness-raising activities within the scientific community.},
}
@article {pmid29345632,
year = {2018},
author = {He, Y and Eguren, D and Azorín, JM and Grossman, RG and Luu, TP and Contreras-Vidal, JL},
title = {Brain-machine interfaces for controlling lower-limb powered robotic systems.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {021004},
doi = {10.1088/1741-2552/aaa8c0},
pmid = {29345632},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces/trends ; Electroencephalography/*methods/trends ; Evoked Potentials, Visual/physiology ; *Exoskeleton Device/trends ; Gait/*physiology ; Humans ; Lower Extremity/*physiology ; Robotics/*methods/trends ; Spinal Cord Injuries/physiopathology/therapy ; },
abstract = {OBJECTIVE: Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking.<br><br>APPROACH: To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared.<br><br>MAIN RESULTS: Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others.<br><br>SIGNIFICANCE: We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.},
}
@article {pmid29343650,
year = {2018},
author = {Zhang, P and Ma, X and Chen, L and Zhou, J and Wang, C and Li, W and He, J},
title = {Decoder calibration with ultra small current sample set for intracortical brain-machine interface.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026019},
doi = {10.1088/1741-2552/aaa8a4},
pmid = {29343650},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Calibration ; Cerebral Cortex/*physiology ; Hand Strength/*physiology ; Macaca mulatta ; Male ; Movement/*physiology ; },
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time.<br><br>APPROACH: Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation.<br><br>MAIN RESULTS: The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys.<br><br>SIGNIFICANCE: (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application of intracortical brain-machine interfaces in clinical practice.},
}
@article {pmid29343230,
year = {2018},
author = {Gerchen, MF and Kirsch, M and Bahs, N and Halli, P and Gerhardt, S and Schäfer, A and Sommer, WH and Kiefer, F and Kirsch, P},
title = {The SyBil-AA real-time fMRI neurofeedback study: protocol of a single-blind randomized controlled trial in alcohol use disorder.},
journal = {BMC psychiatry},
volume = {18},
number = {1},
pages = {12},
pmid = {29343230},
issn = {1471-244X},
support = {668863//Horizon 2020 Framework Programme/International ; },
mesh = {Adolescent ; Adult ; Aged ; Alcoholism/*therapy ; Auditory Cortex/physiology ; Brain Mapping/*methods ; Clinical Protocols ; Follow-Up Studies ; Humans ; Magnetic Resonance Imaging/*methods ; Middle Aged ; Neurofeedback/*methods ; Prefrontal Cortex/physiology ; Single-Blind Method ; Treatment Outcome ; Ventral Striatum/physiology ; Young Adult ; },
abstract = {BACKGROUND: Alcohol Use Disorder is a highly prevalent mental disorder which puts a severe burden on individuals, families, and society. The treatment of Alcohol Use Disorder is challenging and novel and innovative treatment approaches are needed to expand treatment options. A promising neuroscience-based intervention method that allows targeting cortical as well as subcortical brain processes is real-time functional magnetic resonance imaging neurofeedback. However, the efficacy of this technique as an add-on treatment of Alcohol Use Disorder in a clinical setting is hitherto unclear and will be assessed in the Systems Biology of Alcohol Addiction (SyBil-AA) neurofeedback study.<br><br>METHODS: N&#x2009;=&#x2009;100 patients with Alcohol Use Disorder will be randomized to 5 parallel groups in a single-blind fashion and receive real-time functional magnetic resonance imaging neurofeedback while they are presented pictures of alcoholic beverages. The groups will either downregulate the ventral striatum, upregulate the right inferior frontal gyrus, negatively modulate the connectivity between these regions, upregulate, or downregulate the auditory cortex as a control region. After receiving 3 sessions of neurofeedback training within a maximum of 2&#xa0;weeks, participants will be followed up monthly for a period of 3&#xa0;months and relapse rates will be assessed as the primary outcome measure.<br><br>DISCUSSION: The results of this study will provide insights into the efficacy of real-time functional magnetic resonance imaging neurofeedback training in the treatment of Alcohol Use Disorder as well as in the involved brain systems. This might help to identify predictors of successful neurofeedback treatment which could potentially be useful in developing personalized treatment approaches.<br><br>TRIAL REGISTRATION: The study was retrospectively registered in the German Clinical Trials Register (trial identifier: DRKS00010253 ; WHO Universal Trial Number (UTN): U1111-1181-4218) on May 10th, 2016.},
}
@article {pmid29339182,
year = {2018},
author = {Joshi, AM and Narayan, EJ and Gramapurohit, NP},
title = {Interrelationship among annual cycles of sex steroids, corticosterone and body condition in Nyctibatrachus humayuni.},
journal = {General and comparative endocrinology},
volume = {260},
number = {},
pages = {151-160},
doi = {10.1016/j.ygcen.2018.01.013},
pmid = {29339182},
issn = {1095-6840},
mesh = {Animals ; *Anura/physiology/urine ; Body Constitution/*physiology ; Corticosterone/*metabolism/urine ; Estradiol/metabolism/urine ; Female ; Gonadal Steroid Hormones/*metabolism/urine ; Male ; Ovary/physiology ; Photoperiod ; Progesterone/metabolism/urine ; Reproduction/*physiology ; Seasons ; Testis/physiology ; Testosterone/metabolism/urine ; },
abstract = {Synergism between extrinsic and intrinsic factors is crucial for the seasonality of reproduction. Environmental factors such as photoperiod and temperature activate the hypothalamus-pituitary-gonadal axis leading to the secretion of steroid hormones that are crucial for reproduction. Sex steroids are not only essential for the maturation of gonads, but also for development of secondary sexual characters in males and reproductive behaviour of both the sexes. In the present study, we quantified the urinary testosterone (UTM) and corticosterone (UCM) metabolites in males and urinary estradiol metabolites (UEM) and UCM in females of Nyctibatrachus humayuni for two consecutive years to determine annual and seasonal variation in the levels of sex steroids, corticosterone and body condition index (BCI). The results show that sex steroids were highest during the breeding season and lowest during the non-breeding season in both the sexes. An increase in UTM and UEM was observed in males and females respectively during the breeding season. Testicular histology showed the presence of all stages of spermatogenesis throughout the year indicating that spermatogenesis is potentially continuous. Ovarian histology showed the presence of vitellogenic follicles only during the breeding season indicating that oogenesis is strictly seasonal. In males, UCM levels were highest during the breeding season, while in females their levels were highest just prior to the breeding season. In males, BCI was highest during the pre-breeding season, declined during the breeding season to increase again during the post-breeding season. In females, BCI was comparable throughout the year. In males, UTM levels were positively correlated with UCM levels but negatively correlated with BCI. Interestingly, UEM, UCM and BCI were not correlated in females. These results indicate that N. humayuni exhibits an associated pattern of reproduction. Quantification of urinary progesterone metabolites (UPM) during the breeding season showed UPM levels were higher in post-spawning females, suggesting the significance of progesterone in ovulation. Further, non-invasive enzyme immunoassay has been successfully standardized in N. humayuni for the quantification of urinary metabolites of steroid hormones.},
}
@article {pmid29335359,
year = {2018},
author = {Kraus, D and Naros, G and Guggenberger, R and Leão, MT and Ziemann, U and Gharabaghi, A},
title = {Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {38},
number = {6},
pages = {1396-1407},
pmid = {29335359},
issn = {1529-2401},
mesh = {Adult ; Association Learning/*physiology ; *Brain-Computer Interfaces ; Cortical Synchronization ; Electroencephalography ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory ; Female ; Hand ; Healthy Volunteers ; Humans ; Imagination/physiology ; Male ; Motor Cortex/physiology ; Neuroimaging/*methods ; Orthotic Devices ; Pyramidal Tracts/*physiology ; Recruitment, Neurophysiological/*physiology ; Robotics ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment. State-dependency was investigated with concurrent, delayed, and independent stimulation relative to the MI task. Specifically, sensorimotor event-related desynchronization (ERD) in the β-band (16-22 Hz) triggered peripheral stimulation through passive hand opening by a robotic orthosis and transcranial magnetic stimulation to the respective cortical motor representation, either synchronously or subsequently. These MI-related paradigms were compared with paired cortical and peripheral input applied independent of the brain state. Cortical stimulation resulted in a significant increase in corticospinal excitability only when applied brain state-dependently and synchronously to peripheral input. These gains were resistant to a depotentiation task, revealed a nonlinear evolution of plasticity, and were mediated via the recruitment of additional corticospinal neurons rather than via synchronization of neuronal firing. Recruitment of additional corticospinal pathways may be achieved when cortical and peripheral inputs are applied concurrently, and during β-ERD. These findings resemble a gating mechanism and are potentially important for developing closed-loop brain stimulation for the treatment of hand paralysis following lesions of the corticospinal tract.SIGNIFICANCE STATEMENT The activity state of the motor system influences the excitability of corticospinal pathways to external input. State-dependent interventions harness this property to increase the connectivity between motor cortex and muscles. These stimulation protocols modulate the gain of the activated pathways, but not the overall corticospinal recruitment. In this study, a brain-machine interface paired peripheral stimulation through passive hand opening with transcranial magnetic stimulation to the respective cortical motor representation during volitional β-band desynchronization. Cortical stimulation resulted in the recruitment of additional corticospinal pathways, but only when applied brain state-dependently and synchronously to peripheral input. These effects resemble a gating mechanism and may be important for the restoration of motor function following lesions of the corticospinal tract.},
}
@article {pmid29331738,
year = {2018},
author = {Neely, RM and Piech, DK and Santacruz, SR and Maharbiz, MM and Carmena, JM},
title = {Recent advances in neural dust: towards a neural interface platform.},
journal = {Current opinion in neurobiology},
volume = {50},
number = {},
pages = {64-71},
doi = {10.1016/j.conb.2017.12.010},
pmid = {29331738},
issn = {1873-6882},
support = {R21 EY027570/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Neural Prostheses ; Neurons/*physiology ; Ultrasonics ; *User-Computer Interface ; *Wireless Technology ; },
abstract = {The neural dust platform uses ultrasonic power and communication to enable a scalable, wireless, and batteryless system for interfacing with the nervous system. Ultrasound offers several advantages over alternative wireless approaches, including a safe method for powering and communicating with sub mm-sized devices implanted deep in tissue. Early studies demonstrated that neural dust motes could wirelessly transmit high-fidelity electrophysiological data in vivo, and that theoretically, this system could be miniaturized well below the mm-scale. Future developments are focused on further minimization of the platform, better encapsulation methods as a path towards truly chronic neural interfaces, improved delivery mechanisms, stimulation capabilities, and finally refinements to enable deployment of neural dust in the central nervous system.},
}
@article {pmid29327652,
year = {2019},
author = {Fujisawa, A and Kasuga, S and Suzuki, T and Ushiba, J},
title = {Acquisition of a mental strategy to control a virtual tail via brain-computer interface.},
journal = {Cognitive neuroscience},
volume = {10},
number = {1},
pages = {30-43},
doi = {10.1080/17588928.2018.1426564},
pmid = {29327652},
issn = {1758-8936},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {The objective of the present study was to clarify the variation in and properties of mental images and policies used to regulate specific image selection when learning to control a brain-computer interface. Healthy volunteers performed a reaching task with a virtually generated monkey tail-like object on a computer monitor by regulating event-related desynchronization (ERD) on the buttock area of the sensorimotor cortex as recorded by electroencephalogram (EEG). Participants were instructed to find a free image by which the tail was well controlled. Seven participants frequently returned to specific images that were mostly unrelated to a tail, and returned to these images on the last day of training. The ERD levels were greater during use of those selected images versus when selected images were not employed. Our results suggest that individuals adopted a mental strategy where they imagine what would reduce the prediction error between the predicted and actual BCI actions.},
}
@article {pmid29326653,
year = {2017},
author = {Murphy, DP and Bai, O and Gorgey, AS and Fox, J and Lovegreen, WT and Burkhardt, BW and Atri, R and Marquez, JS and Li, Q and Fei, DY},
title = {Electroencephalogram-Based Brain-Computer Interface and Lower-Limb Prosthesis Control: A Case Study.},
journal = {Frontiers in neurology},
volume = {8},
number = {},
pages = {696},
pmid = {29326653},
issn = {1664-2295},
abstract = {OBJECTIVE: The purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain-computer interface (BCI) system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control), the electroencephalography (EEG)-based BCI may provide amputees an alternative way to control a prosthesis directly from brain.<br><br>METHODS: A transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences.<br><br>RESULTS: The success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments.<br><br>CONCLUSION: The performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.},
}
@article {pmid29326562,
year = {2017},
author = {Loza, CA and Okun, MS and Príncipe, JC},
title = {A Marked Point Process Framework for Extracellular Electrical Potentials.},
journal = {Frontiers in systems neuroscience},
volume = {11},
number = {},
pages = {95},
pmid = {29326562},
issn = {1662-5137},
abstract = {Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response to external stimuli or internal physiological processes. We propose a novel generative statistical model of a single EEP channel, where the collected signal is regarded as the noisy addition of reoccurring, multi-frequency phasic events over time. One of the main advantages of the proposed framework is the exceptional temporal resolution in the time location of the EEP phasic events, e.g., up to the sampling period utilized in the data collection. Therefore, this allows for the first time a description of neuromodulation in EEPs as a Marked Point Process (MPP), represented by their amplitude, center frequency, duration, and time of occurrence. The generative model for the multi-frequency phasic events exploits sparseness and involves a shift-invariant implementation of the clustering technique known as k-means. The cost function incorporates a robust estimation component based on correntropy to mitigate the outliers caused by the inherent noise in the EEP. Lastly, the background EEP activity is explicitly modeled as the non-sparse component of the collected signal to further improve the delineation of the multi-frequency phasic events in time. The framework is validated using two publicly available datasets: the DREAMS sleep spindles database and one of the Brain-Computer Interface (BCI) competition datasets. The results achieve benchmark performance and provide novel quantitative descriptions based on power, event rates and timing in order to assess behavioral correlates beyond the classical power spectrum-based analysis. This opens the possibility for a unifying point process framework of multiscale brain activity where simultaneous recordings of EEP and the underlying single neuron spike activity can be integrated and regarded as marked and simple point processes, respectively.},
}
@article {pmid29326561,
year = {2017},
author = {Müller, O and Rotter, S},
title = {Neurotechnology: Current Developments and Ethical Issues.},
journal = {Frontiers in systems neuroscience},
volume = {11},
number = {},
pages = {93},
pmid = {29326561},
issn = {1662-5137},
}
@article {pmid29324403,
year = {2018},
author = {Yao, L and Sheng, X and Mrachacz-Kersting, N and Zhu, X and Farina, D and Jiang, N},
title = {Performance of Brain-Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {60-68},
doi = {10.1109/TNSRE.2017.2769686},
pmid = {29324403},
issn = {1558-0210},
mesh = {Adult ; Alpha Rhythm ; Beta Rhythm ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory ; Female ; Functional Laterality ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Somatosensory Cortex/physiology ; Touch/*physiology ; Young Adult ; },
abstract = {A large proportion of users do not achieve adequate control using current non-invasive brain-computer interfaces (BCIs). This issue has being coined "BCI-Illiteracy" and is observed among different BCI modalities. Here, we compare the performance and the BCI-illiteracy rate of a tactile selective sensation (SS) and motor imagery (MI) BCI, for a large subject samples. We analyzed 80 experimental sessions from 57 subjects with two-class SS protocols. For SS, the group average performance was 79.8 ± 10.6%, with 43 out of the 57 subjects (75.4%) exceeding the 70% BCI-illiteracy threshold for left- and right-hand SS discrimination. When compared with previous results, this tactile BCI outperformed all other tactile BCIs currently available. We also analyzed 63 experimental sessions from 43 subjects with two-class MI BCI protocols, where the group average performance was 77.2 ± 13.3%, with 69.7% of the subjects exceeding the 70% performance threshold for left- and right-hand MI. For within-subject comparison, the 24 subjects who participated to both the SS and MI experiments, the BCI performance was superior with SS than MI especially in beta frequency band (p < 0.05), with enhanced R[2] discriminative information in the somatosensory cortex for the SS modality. Both SS and MI showed a functional dissociation between lower alpha ([8 10] Hz) and upper alpha ([10 13] Hz) bands, with BCI performance significantly better in the upper alpha than the lower alpha (p < 0.05) band. In summary, we demonstrated that SS is a promising BCI modality with low BCI illiteracy issue and has great potential in practical applications reaching large population.},
}
@article {pmid29318256,
year = {2018},
author = {Brumberg, JS and Pitt, KM and Mantie-Kozlowski, A and Burnison, JD},
title = {Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.},
journal = {American journal of speech-language pathology},
volume = {27},
number = {1},
pages = {1-12},
pmid = {29318256},
issn = {1558-9110},
support = {R03 DC011304/DC/NIDCD NIH HHS/United States ; U54 HD090216/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces/trends ; Communication ; *Communication Devices for People with Disabilities/trends ; Fatigue ; Humans ; Patient Selection ; Speech Disorders/*rehabilitation ; },
abstract = {PURPOSE: Brain-computer interfaces (BCIs) have the potential to improve communication for people who require but are unable to use traditional augmentative and alternative communication (AAC) devices. As BCIs move toward clinical practice, speech-language pathologists (SLPs) will need to consider their appropriateness for AAC intervention.<br><br>METHOD: This tutorial provides a background on BCI approaches to provide AAC specialists foundational knowledge necessary for clinical application of BCI. Tutorial descriptions were generated based on a literature review of BCIs for restoring communication.<br><br>RESULTS: The tutorial responses directly address 4 major areas of interest for SLPs who specialize in AAC: (a) the current state of BCI with emphasis on SLP scope of practice (including the subareas: the way in which individuals access AAC with BCI, the efficacy of BCI for AAC, and the effects of fatigue), (b) populations for whom BCI is best suited, (c) the future of BCI as an addition to AAC access strategies, and (d) limitations of BCI.<br><br>CONCLUSION: Current BCIs have been designed as access methods for AAC rather than a replacement; therefore, SLPs can use existing knowledge in AAC as a starting point for clinical application. Additional training is recommended to stay updated with rapid advances in BCI.},
}
@article {pmid29317861,
year = {2017},
author = {Alonso-Valerdi, LM and Luz María, AV and Mercado-García, VR and Víctor Rodrigo, MG},
title = {Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {6076913},
pmid = {29317861},
issn = {1687-5273},
mesh = {Attention ; Brain/*physiology ; *Brain-Computer Interfaces ; Emotions ; Humans ; *User-Computer Interface ; },
abstract = {Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities.},
}
@article {pmid29312461,
year = {2017},
author = {Morrison, M and Maia, PD and Kutz, JN},
title = {Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics.},
journal = {Computational and mathematical methods in medicine},
volume = {2017},
number = {},
pages = {6102494},
pmid = {29312461},
issn = {1748-6718},
mesh = {Algorithms ; Brain/*pathology ; Computer Simulation ; Electronics ; Humans ; Memory Disorders/etiology/*prevention &amp; control ; *Models, Biological ; Neural Networks, Computer ; Neurodegenerative Diseases/*complications ; Organoids/*pathology ; },
abstract = {Developing technologies have made significant progress towards linking the brain with brain-machine interfaces (BMIs) which have the potential to aid damaged brains to perform their original motor and cognitive functions. We consider the viability of such devices for mitigating the deleterious effects of memory loss that is induced by neurodegenerative diseases and/or traumatic brain injury (TBI). Our computational study considers the widely used Hopfield network, an autoassociative memory model in which neurons converge to a stable state pattern after receiving an input resembling the given memory. In this study, we connect an auxiliary network of neurons, which models the BMI device, to the original Hopfield network and train it to converge to its own auxiliary memory patterns. Injuries to the original Hopfield memory network, induced through neurodegeneration, for instance, can then be analyzed with the goal of evaluating the ability of the BMI to aid in memory retrieval tasks. Dense connectivity between the auxiliary and Hopfield networks is shown to promote robustness of memory retrieval tasks for both optimal and nonoptimal memory sets. Our computations estimate damage levels and parameter ranges for which full or partial memory recovery is achievable, providing a starting point for novel therapeutic strategies.},
}
@article {pmid29311792,
year = {2017},
author = {Knudsen, EB and Moxon, KA},
title = {Restoration of Hindlimb Movements after Complete Spinal Cord Injury Using Brain-Controlled Functional Electrical Stimulation.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {715},
pmid = {29311792},
issn = {1662-4548},
support = {R01 NS096971/NS/NINDS NIH HHS/United States ; },
abstract = {Single neuron and local field potential signals recorded in the primary motor cortex have been repeatedly demonstrated as viable control signals for multi-degree-of-freedom actuators. Although the primary source of these signals has been fore/upper limb motor regions, recent evidence suggests that neural adaptation underlying neuroprosthetic control is generalizable across cortex, including hindlimb sensorimotor cortex. Here, adult rats underwent a longitudinal study that included a hindlimb pedal press task in response to cues for specific durations, followed by brain machine interface (BMI) tasks in healthy rats, after rats received a complete spinal transection and after the BMI signal controls epidural stimulation (BMI-FES). Over the course of the transition from learned behavior to BMI task, fewer neurons were responsive after the cue, the proportion of neurons selective for press duration increased and these neurons carried more information. After a complete, mid-thoracic spinal lesion that completely severed both ascending and descending connections to the lower limbs, there was a reduction in task-responsive neurons followed by a reacquisition of task selectivity in recorded populations. This occurred due to a change in pattern of neuronal responses not simple changes in firing rate. Finally, during BMI-FES, additional information about the intended press duration was produced. This information was not dependent on the stimulation, which was the same for short and long duration presses during the early phase of stimulation, but instead was likely due to sensory feedback to sensorimotor cortex in response to movement along the trunk during the restored pedal press. This post-cue signal could be used as an error signal in a continuous decoder providing information about the position of the limb to optimally control a neuroprosthetic device.},
}
@article {pmid29311771,
year = {2017},
author = {Thomschewski, A and Ströhlein, A and Langthaler, PB and Schmid, E and Potthoff, J and Höller, P and Leis, S and Trinka, E and Höller, Y},
title = {Imagine There Is No Plegia. Mental Motor Imagery Difficulties in Patients with Traumatic Spinal Cord Injury.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {689},
pmid = {29311771},
issn = {1662-4548},
support = {W 1233/FWF_/Austrian Science Fund FWF/Austria ; },
abstract = {In rehabilitation of patients with spinal cord injury (SCI), imagination of movement is a candidate tool to promote long-term recovery or to control futuristic neuroprostheses. However, little is known about the ability of patients with spinal cord injury to perform this task. It is likely that without the ability to effectively perform the movement, the imagination of movement is also problematic. We therefore examined, whether patients with SCI experience increased difficulties in motor imagery (MI) compared to healthy controls. We examined 7 male patients with traumatic spinal cord injury (aged 23-70 years, median 53) and 20 healthy controls (aged 21-54 years, median 30). All patients had incomplete SCI, with AIS (ASIA Impairment Scale) grades of C or D. All had cervical lesions, except one who had a thoracic injury level. Duration after injury ranged from 3 to 314 months. We performed the Movement Imagery Questionnaire Revised as well as the Beck Depression Inventory in all participants. The self-assessed ability of patients to visually imagine movements ranged from 7 to 36 (Md = 30) and tended to be decreased in comparison to healthy controls (ranged 16-49, Md = 42.5; W = 326.5, p = 0.055). Also, the self-assessed ability of patients to kinesthetically imagine movements (range = 7-35, Md = 31) differed significantly from the control group (range = 23-49, Md = 41; W = 337.5, p = 0.0047). Two patients yielded tendencies for depressive mood and they also reported most problems with movement imagination. Statistical analysis however did not confirm a general relationship between depressive mood and increased difficulty in MI across both groups. Patients with spinal cord injury seem to experience difficulties in imagining movements compared to healthy controls. This result might not only have implications for training and rehabilitation programs, but also for applications like brain-computer interfaces used to control neuroprostheses, which are often based on the brain signals exhibited during the imagination of movements.},
}
@article {pmid29311769,
year = {2017},
author = {Hu, Y and Zhang, L and Chen, M and Li, X and Shi, L},
title = {How Electroencephalogram Reference Influences the Movement Readiness Potential?.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {683},
pmid = {29311769},
issn = {1662-4548},
abstract = {Readiness potential (RP) based on electroencephalograms (EEG) has been studied extensively in recent years, but no studies have investigated the influence of the reference electrode on RP. In order to investigate the reference effect, 10 subjects were recruited and the original vertex reference (Cz) was used to record the raw EEG signal when the subjects performed a motor preparation task. The EEG was then transformed to the common average reference (CAR) and reference electrode standardization technique (REST) reference, and we analyzed the RP waveform and voltage topographies and calculated the classification accuracy of idle and RP EEG segments. Our results showed that the RP waveform and voltage topographies were greatly influenced by the reference, but the classification accuracy was less affected if proper channels were selected as features. Since the Cz channel is near the primary motor cortex, where the source of RP is located, using the REST and CAR references is recommended to get accurate RP waveforms and voltage topographies.},
}
@article {pmid29306638,
year = {2018},
author = {Agarwal, P and Husain, S and Wankhede, S and Sharma, D},
title = {Rectus abdominis detrusor myoplasty (RADM) for acontractile/hypocontractile bladder in spinal cord injury patients: Preliminary report.},
journal = {Journal of plastic, reconstructive &amp; aesthetic surgery : JPRAS},
volume = {71},
number = {5},
pages = {736-742},
doi = {10.1016/j.bjps.2017.12.015},
pmid = {29306638},
issn = {1878-0539},
mesh = {Adult ; Humans ; Male ; Middle Aged ; Quality of Life ; Plastic Surgery Procedures/*methods ; Rectus Abdominis/*surgery ; Spinal Cord Injuries/*complications ; Treatment Outcome ; Urinary Bladder, Neurogenic/etiology/*surgery ; Urodynamics ; },
abstract = {BACKGROUND: Urinary bladder dysfunction in the form of acontractile/hypocontractile bladder is very common after spinal cord injury and it may lead to recurrent urinary tract infection (UTI), stones formation, and deteriorating renal function. Conventionally, these patients evacuate their bladders by life-long clean intermittent catheterization (CIC) or an indwelling catheter (IC). For these patients, another option is to use innervated skeletal muscle wrap around the bladder to augment detrusor function and voluntary evacuation of bladder.<br><br>METHODS: We selected 5 patients with acontractile/hypocontractile bladder following spinal cord trauma. These patients were assessed by urodynamic study for post void residual volume (PVRV), detrusor pressure (Pdet), urine flow rate (Vmax), and bladder contractility index (BCI). All five patients underwent Rectus Abdominis Detrusor Myoplasty (RADM).<br><br>RESULTS: Complete spontaneous voiding was achieved in all patients. Rectus abdominis detrusor myoplasty (RADM) elicits a statistically significant reduction in PVRV and statistically significant increase in urine flow rate, bladder contractility and detrusor pressure after 6 months. Recurrent UTIs ceased in all patients. There were no immediate or late complications.<br><br>CONCLUSION: RADM appears to be a promising option in a patient with acontractile/hypocontractile bladder to restore the bladder function. It avoids CIC in all patients leading to improvement in quality of life in select group of patients.},
}
@article {pmid29306539,
year = {2018},
author = {Hosseini, MP and Pompili, D and Elisevich, K and Soltanian-Zadeh, H},
title = {Random ensemble learning for EEG classification.},
journal = {Artificial intelligence in medicine},
volume = {84},
number = {},
pages = {146-158},
doi = {10.1016/j.artmed.2017.12.004},
pmid = {29306539},
issn = {1873-2860},
mesh = {Automation ; Brain/*physiopathology ; Brain Mapping/*methods ; *Brain Waves ; Cloud Computing ; Electrocorticography ; *Electroencephalography ; False Negative Reactions ; False Positive Reactions ; Humans ; Neural Networks, Computer ; Predictive Value of Tests ; Reproducibility of Results ; Seizures/classification/*diagnosis/physiopathology ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; Time Factors ; Wavelet Analysis ; },
abstract = {Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility.},
}
@article {pmid29306060,
year = {2018},
author = {Bozkurt, AG and Buyukgoz, GG and Soforoglu, M and Tamer, U and Suludere, Z and Boyaci, IH},
title = {Alkaline phosphatase labeled SERS active sandwich immunoassay for detection of Escherichia coli.},
journal = {Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy},
volume = {194},
number = {},
pages = {8-13},
doi = {10.1016/j.saa.2017.12.057},
pmid = {29306060},
issn = {1873-3557},
mesh = {Alkaline Phosphatase/*analysis ; Antibodies/*immunology ; Escherichia coli/enzymology/*isolation &amp; purification ; Gold/chemistry ; Immunoassay/*methods ; Magnetics ; Metal Nanoparticles/*chemistry ; Spectrum Analysis, Raman/*methods ; },
abstract = {In this study, a sandwich immunoassay method utilizing enzymatic activity of alkaline phosphatase (ALP) on 5-bromo-4-chloro-3-indolyl phosphate (BCIP) for Escherichia coli (E. coli) detection was developed using surface enhanced Raman spectroscopy (SERS). For this purpose, spherical magnetic gold coated core-shell nanoparticles (MNPs-Au) and rod shape gold nanoparticles (Au-NRs) were synthesized and modified for immunomagnetic separation (IMS) of E. coli from the solution. In order to specify the developed method to ALP activity, Au-NRs were labeled with this enzyme. After successful construction of the immunoassay, BCIP substrate was added to produce the SERS-active product; 5-bromo-4-chloro-3-indole (BCI). A good linearity (R[2]=0.992) was established between the specific SERS intensity of BCI at 600cm[-1] and logarithmic E. coli concentration in the range of 1.7×10[1]-1.7×10[6]cfumL[-1]. LOD and LOQ values were also calculated and found to be 10cfumL[-1] and 30cfumL[-1], respectively.},
}
@article {pmid29298691,
year = {2018},
author = {Rajasekaran, V and López-Larraz, E and Trincado-Alonso, F and Aranda, J and Montesano, L and Del-Ama, AJ and Pons, JL},
title = {Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {15},
number = {1},
pages = {4},
pmid = {29298691},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Exoskeleton Device ; Female ; Gait/*physiology ; Humans ; Intention ; Lower Extremity/physiopathology ; Male ; Middle Aged ; Spinal Cord Injuries/physiopathology/*rehabilitation ; *Volition ; Young Adult ; },
abstract = {BACKGROUND: Gait training for individuals with neurological disorders is challenging in providing the suitable assistance and more adaptive behaviour towards user needs. The user specific adaptation can be defined based on the user interaction with the orthosis and by monitoring the user intentions. In this paper, an adaptive control model, commanded by the user intention, is evaluated using a lower limb exoskeleton with incomplete spinal cord injury individuals (SCI).<br><br>METHODS: A user intention based adaptive control model has been developed and evaluated with 4 incomplete SCI individuals across 3 sessions of training per individual. The adaptive control model modifies the joint impedance properties of the exoskeleton as a function of the human-orthosis interaction torques and the joint trajectory evolution along the gait sequence, in real time. The volitional input of the user is identified by monitoring the neural signals, pertaining to the user's motor activity. These volitional inputs are used as a trigger to initiate the gait movement, allowing the user to control the initialization of the exoskeleton movement, independently. A Finite-state machine based control model is used in this set-up which helps in combining the volitional orders with the gait adaptation.<br><br>RESULTS: The exoskeleton demonstrated an adaptive assistance depending on the patients' performance without guiding them to follow an imposed trajectory. The exoskeleton initiated the trajectory based on the user intention command received from the brain machine interface, demonstrating it as a reliable trigger. The exoskeleton maintained the equilibrium by providing suitable assistance throughout the experiments. A progressive change in the maximum flexion of the knee joint was observed at the end of each session which shows improvement in the patient performance. Results of the adaptive impedance were evaluated by comparing with the application of a constant impedance value. Participants reported that the movement of the exoskeleton was flexible and the walking patterns were similar to their own distinct patterns.<br><br>CONCLUSIONS: This study demonstrates that user specific adaptive control can be applied on a wearable robot based on the human-orthosis interaction torques and modifying the joints' impedance properties. The patients perceived no external or impulsive force and felt comfortable with the assistance provided by the exoskeleton. The main goal of such a user dependent control is to assist the patients' needs and adapt to their characteristics, thus maximizing their engagement in the therapy and avoiding slacking. In addition, the initiation directly controlled by the brain allows synchronizing the user's intention with the afferent stimulus provided by the movement of the exoskeleton, which maximizes the potentiality of the system in neuro-rehabilitative therapies.},
}
@article {pmid29298521,
year = {2018},
author = {Fernández-Soto, A and Martínez-Rodrigo, A and Moncho-Bogani, J and Latorre, JM and Fernández-Caballero, A},
title = {Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {28},
number = {5},
pages = {1750054},
doi = {10.1142/S012906571750054X},
pmid = {29298521},
issn = {1793-6462},
mesh = {Adult ; Auditory Perception/*physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Male ; *Music ; Periodicity ; Signal Processing, Computer-Assisted ; },
abstract = {For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.},
}
@article {pmid29297303,
year = {2017},
author = {Kumar, S and Sharma, A and Tsunoda, T},
title = {An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.},
journal = {BMC bioinformatics},
volume = {18},
number = {Suppl 16},
pages = {545},
pmid = {29297303},
issn = {1471-2105},
mesh = {Electroencephalography/*classification/methods ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {BACKGROUND: Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent.<br><br>METHODS: In this study, we propose a mutual information based frequency band selection&#xa0;approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30&#xa0;Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine.<br><br>RESULTS: The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets.<br><br>CONCLUSIONS: Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.},
}
@article {pmid29296627,
year = {2017},
author = {Abdalmalak, A and Milej, D and Norton, L and Debicki, DB and Gofton, T and Diop, M and Owen, AM and St Lawrence, K},
title = {Single-session communication with a locked-in patient by functional near-infrared spectroscopy.},
journal = {Neurophotonics},
volume = {4},
number = {4},
pages = {040501},
pmid = {29296627},
issn = {2329-423X},
abstract = {There is a growing interest in the possibility of using functional neuroimaging techniques to aid in detecting covert awareness in patients who are thought to be suffering from a disorder of consciousness. Immerging optical techniques such as time-resolved functional near-infrared spectroscopy (TR-fNIRS) are ideal for such applications due to their low-cost, portability, and enhanced sensitivity to brain activity. The aim of this case study was to investigate for the first time the ability of TR-fNIRS to detect command driven motor imagery (MI) activity in a functionally locked-in patient suffering from Guillain-Barré syndrome. In addition, the utility of using TR-fNIRS as a brain-computer interface (BCI) was also assessed by instructing the patient to perform an MI task as affirmation to three questions: (1) confirming his last name, (2) if he was in pain, and (3) if he felt safe. At the time of this study, the patient had regained limited eye movement, which provided an opportunity to accurately validate a BCI after the fNIRS study was completed. Comparing the two sets of responses showed that fNIRS provided the correct answers to all of the questions. These promising results demonstrate for the first time the potential of using an MI paradigm in combination with fNIRS to communicate with functionally locked-in patients without the need for prior training.},
}
@article {pmid29293572,
year = {2018},
author = {Coutinho, E and Gentsch, K and van Peer, J and Scherer, KR and Schuller, BW},
title = {Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography.},
journal = {PloS one},
volume = {13},
number = {1},
pages = {e0189367},
pmid = {29293572},
issn = {1932-6203},
mesh = {Brain/physiology ; Electroencephalography/*methods ; Electromyography/*methods ; *Emotions ; Face/*physiology ; Humans ; Models, Theoretical ; Support Vector Machine ; },
abstract = {In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks-novelty, intrinsic pleasantness, goal conduciveness, control, and power-in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions.},
}
@article {pmid29286458,
year = {2017},
author = {Mégevand, P and Woodtli, A and Yulzari, A and Cosgrove, GR and Momjian, S and Stimec, BV and Corniola, MV and Fasel, JHD},
title = {Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {129},
pages = {},
pmid = {29286458},
issn = {1940-087X},
mesh = {Cadaver ; *Electrodes, Implanted ; Formaldehyde ; Humans ; *Microelectrodes ; Neocortex/*surgery ; Neurosurgery/education ; Neurosurgical Procedures/instrumentation/*methods ; Tissue Fixation ; },
abstract = {This protocol describes a procedure to assist surgeons in training for the implantation of microelectrode arrays into the neocortex of the human brain. Recent technological progress has enabled the fabrication of microelectrode arrays that allow recording the activity of multiple individual neurons in the neocortex of the human brain. These arrays have the potential to bring unique insight onto the neuronal correlates of cerebral function in health and disease. Furthermore, the identification and decoding of volitional neuronal activity opens the possibility to establish brain-computer interfaces, and thus might help restore lost neurological functions. The implantation of neocortical microelectrode arrays is an invasive procedure requiring a supra-centimetric craniotomy and the exposure of the cortical surface; thus, the procedure must be performed by an adequately trained neurosurgeon. In order to provide an opportunity for surgical training, we designed a procedure based on a human cadaver model. The use of a formaldehyde-fixed human cadaver bypasses the practical, ethical and financial difficulties of surgical practice on animals (especially non-human primates) while preserving the macroscopic structure of the head, skull, meninges and cerebral surface and allowing realistic, operating room-like positioning and instrumentation. Furthermore, the use of a human cadaver is closer to clinical daily practice than any non-human model. The major drawbacks of the cadaveric simulation are the absence of cerebral pulsation and of blood and cerebrospinal fluid circulation. We suggest that a formaldehyde-fixed human cadaver model is an adequate, practical and cost-effective approach to ensure proper surgical training before implanting microelectrode arrays in the living human neocortex.},
}
@article {pmid29283026,
year = {2018},
author = {Raffin, E and Hummel, FC},
title = {Restoring Motor Functions After Stroke: Multiple Approaches and Opportunities.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {24},
number = {4},
pages = {400-416},
doi = {10.1177/1073858417737486},
pmid = {29283026},
issn = {1089-4098},
mesh = {Animals ; Humans ; Motor Activity/physiology ; Movement Disorders/*etiology/physiopathology/*rehabilitation ; Recovery of Function ; Stroke/*complications/physiopathology ; *Stroke Rehabilitation ; },
abstract = {More than 1.5 million people suffer a stroke in Europe per year and more than 70% of stroke survivors experience limited functional recovery of their upper limb, resulting in diminished quality of life. Therefore, interventions to address upper-limb impairment are a priority for stroke survivors and clinicians. While a significant body of evidence supports the use of conventional treatments, such as intensive motor training or constraint-induced movement therapy, the limited and heterogeneous improvements they allow are, for most patients, usually not sufficient to return to full autonomy. Various innovative neurorehabilitation strategies are emerging in order to enhance beneficial plasticity and improve motor recovery. Among them, robotic technologies, brain-computer interfaces, or noninvasive brain stimulation (NIBS) are showing encouraging results. These innovative interventions, such as NIBS, will only provide maximized effects, if the field moves away from the "one-fits all" approach toward a "patient-tailored" approach. After summarizing the most commonly used rehabilitation approaches, we will focus on NIBS and highlight the factors that limit its widespread use in clinical settings. Subsequently, we will propose potential biomarkers that might help to stratify stroke patients in order to identify the individualized optimal therapy. We will discuss future methodological developments, which could open new avenues for poststroke rehabilitation, toward more patient-tailored precision medicine approaches and pathophysiologically motivated strategies.},
}
@article {pmid29282131,
year = {2017},
author = {Statthaler, K and Schwarz, A and Steyrl, D and Kobler, R and Höller, MK and Brandstetter, J and Hehenberger, L and Bigga, M and Müller-Putz, G},
title = {Cybathlon experiences of the Graz BCI racing team Mirage91 in the brain-computer interface discipline.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {129},
pmid = {29282131},
issn = {1743-0003},
support = {643955//Horizon 2020/International ; 681231//European Research Council (BE)/International ; },
mesh = {*Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Humans ; Male ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {BACKGROUND: In this work, we share our experiences made at the world-wide first CYBATHLON, an event organized by the Eidgenössische Technische Hochschule Zürich (ETH Zürich), which took place in Zurich in October 2016. It is a championship for severely motor impaired people using assistive prototype devices to compete against each other. Our team, the Graz BCI Racing Team MIRAGE91 from Graz University of Technology, participated in the discipline "Brain-Computer Interface Race". A brain-computer interface (BCI) is a device facilitating control of applications via the user's thoughts. Prominent applications include assistive technology such as wheelchairs, neuroprostheses or communication devices. In the CYBATHLON BCI Race, pilots compete in a BCI-controlled computer game.<br><br>METHODS: We report on setting up our team, the BCI customization to our pilot including long term training and the final BCI system. Furthermore, we describe CYBATHLON participation and analyze our CYBATHLON result.<br><br>RESULTS: We found that our pilot was compliant over the whole time and that we could significantly reduce the average runtime between start and finish from initially 178&#xa0;s to 143&#xa0;s. After the release of the final championship specifications with shorter track length, the average runtime converged to 120&#xa0;s. We successfully participated in the qualification race at CYBATHLON 2016, but performed notably worse than during training, with a runtime of 196&#xa0;s.<br><br>DISCUSSION: We speculate that shifts in the features, due to the nonstationarities in the electroencephalogram (EEG), but also arousal are possible reasons for the unexpected result. Potential counteracting measures are discussed.<br><br>CONCLUSIONS: The CYBATHLON 2016 was a great opportunity for our student team. We consolidated our theoretical knowledge and turned it into practice, allowing our pilot to play a computer game. However, further research is required to make BCI technology invariant to non-task related changes of the EEG.},
}
@article {pmid29281922,
year = {2018},
author = {Schudlo, LC and Chau, T},
title = {Development of a Ternary Near-Infrared Spectroscopy Brain-Computer Interface: Online Classification of Verbal Fluency Task, Stroop Task and Rest.},
journal = {International journal of neural systems},
volume = {28},
number = {4},
pages = {1750052},
doi = {10.1142/S0129065717500526},
pmid = {29281922},
issn = {1793-6462},
mesh = {Attention/physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Humans ; Memory, Short-Term/physiology ; Rest ; *Spectroscopy, Near-Infrared ; Speech/physiology ; Stroop Test ; Visual Perception/physiology ; },
abstract = {The majority of proposed NIRS-BCIs has considered binary classification. Studies considering high-order classification problems have yielded average accuracies that are less than favorable for practical communication. Consequently, there is a paucity of evidence supporting online classification of more than two mental states using NIRS. We developed an online ternary NIRS-BCI that supports the verbal fluency task (VFT), Stroop task and rest. The system utilized two sessions dedicated solely to classifier training. Additionally, samples were collected prior to each period of online classification to update the classifier. Using a continuous-wave spectrometer, measurements were collected from the prefrontal and parietal cortices while 11 able-bodied adult participants were cued to perform one of the two cognitive tasks or rests. Each task was used to indicate the desire to select a particular letter on a scanning interface, while rest avoided selection. Classification was performed using 25 iteration of bagging with a linear discriminant base classifier. Classifiers were trained on 10-dimensional feature sets. The BCI's classification decision was provided as feedback. An average online classification accuracy of [Formula: see text]% was achieved, representing an ITR of [Formula: see text] bits/min. The results demonstrate that online communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest. Our findings encourage continued efforts to enhance the ITR of NIRS-BCIs.},
}
@article {pmid29279724,
year = {2017},
author = {Tisch, M},
title = {Implantable hearing devices.},
journal = {GMS current topics in otorhinolaryngology, head and neck surgery},
volume = {16},
number = {},
pages = {Doc06},
pmid = {29279724},
issn = {1865-1011},
abstract = {Combined hearing loss is an essential indication for implantable hearing systems. Depending on the bone conduction threshold, various options are available. Patients with mild sensorineural deafness usually benefit from transcutaneous bone conduction implants (BCI), while percutaneous BCI systems are recommended also for moderate hearing loss. For combined hearing losses with moderate and high-grade cochlear hearing loss, active middle ear implants are recommended. For patients with incompatibilities or middle ear surgery, implants are a valuable and proven addition to the therapeutic options.},
}
@article {pmid29277720,
year = {2018},
author = {Lopes-Dos-Santos, V and Rey, HG and Navajas, J and Quian Quiroga, R},
title = {Extracting information from the shape and spatial distribution of evoked potentials.},
journal = {Journal of neuroscience methods},
volume = {296},
number = {},
pages = {12-22},
pmid = {29277720},
issn = {1872-678X},
support = {G1002100/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Auditory Perception/physiology ; Brain/physiology ; Computer Simulation ; Electrodes, Implanted ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; Information Theory ; Pattern Recognition, Automated/methods ; Pattern Recognition, Physiological/physiology ; Recognition, Psychology/physiology ; *Signal Processing, Computer-Assisted ; Supervised Machine Learning ; Visual Perception/physiology ; Wavelet Analysis ; },
abstract = {BACKGROUND: Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections.<br><br>NEW METHOD: To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses.<br><br>RESULTS: Using simulations and real data from four experiments, we show that the proposed approach outperforms standard supervised analyses based on peak amplitude estimation. Moreover, the method can extract information using the raw data from all recorded channels using no a priori knowledge or pre-processing steps.<br><br>We show that traditional approaches often disregard important features of the signal such as the shape of EEG waveforms. Also, other approaches often require some form of a priori knowledge for feature selection and lead to problems of multiple comparisons.<br><br>CONCLUSIONS: This approach offers a new and complementary framework to design experiments that go beyond the traditional analyses of ERPs. Potentially, it allows a wide usage beyond basic research; such as for clinical diagnosis, brain-machine interfaces, and neurofeedback applications requiring single-trial analyses.},
}
@article {pmid29277586,
year = {2018},
author = {Ozdemir, RA and Contreras-Vidal, JL and Paloski, WH},
title = {Cortical control of upright stance in elderly.},
journal = {Mechanisms of ageing and development},
volume = {169},
number = {},
pages = {19-31},
doi = {10.1016/j.mad.2017.12.004},
pmid = {29277586},
issn = {1872-6216},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Delta Rhythm ; *Evoked Potentials ; Female ; *Gamma Rhythm ; Humans ; Lower Extremity/*physiopathology ; Male ; Motor Cortex/*physiopathology ; *Motor Neurons ; *Postural Balance ; },
abstract = {This study examined differences between young and elderly volunteers in cortical involvement to human posture control during quiet stance with normal and altered sensory stimulation (Experiment-1), and biomechanical perturbations (Experiment-2). The primary focus of the first part was to monitor changes in cortical activity when unexpectedly altering the sensory conditions of upright stance, such as switching from stable (eyes open, fixed support surface) to less-stable (eyes closed, sway-referenced support surface) conditions. Our results demonstrate increased cortical activations in delta (0.2-4 Hz) and gamma (30-50 Hz) oscillations, primarily over central-frontal, central, and central parietal cortices during challenging postural conditions. While increased delta rhythms were observed in both groups during challenging sensory conditions, elderly individuals also showed increased gamma band activity over sensorimotor and parietal cortices, when compared to the younger group. To our knowledge, this study is the first to show age differences in balance related cortical activations during continuous postural tasks with challenging sensory conditions. Preliminary correlations also suggest that increased cerebral activity became more relevant to the control of Center of Mass (COM) dynamics when upright stance is threatened. The results of Experiment-2 also showed for the first time that oscillatory rhythms of the cortex are coherent with muscle firing characteristics suggesting increased corticospinal drive from leg motor cortex to lower limb motoneurons following postural perturbations. Finally, perturbation evoked potential (PEP) analyses suggest that, rather than motor system malfunctioning, impairments in perceptual processing of sensory afference forms the basis of prolonged muscle response delays during perturbed balance in the elderly.},
}
@article {pmid29270721,
year = {2018},
author = {Frigerio, M and Manodoro, S and Cola, A and Palmieri, S and Spelzini, F and Milani, R},
title = {Detrusor underactivity in pelvic organ prolapse.},
journal = {International urogynecology journal},
volume = {29},
number = {8},
pages = {1111-1116},
pmid = {29270721},
issn = {1433-3023},
mesh = {Female ; Humans ; Italy ; Pelvic Organ Prolapse/complications/physiopathology/*surgery ; Prevalence ; Retrospective Studies ; Urinary Bladder, Overactive/*diagnosis ; Urinary Bladder, Underactive/complications/*epidemiology ; Urodynamics ; },
abstract = {INTRODUCTION AND HYPOTHESIS: The association between pelvic organ prolapse (POP) and detrusor underactivity (DU) is not well defined. The primary outcome of this study was to evaluate the prevalence of DU in a cohort of patients with POP and its association with symptoms, anatomy. and urodynamic findings. The secondary outcome was to evaluate the evolution of lower urinary tract symptoms after POP repair between DU and non-DU patients.<br><br>METHODS: Consecutive patients who underwent preoperative urodynamic tests were retrospectively analyzed. Detrusor underactivity was evaluated by the Bladder Contractility Index (BCI&#x2009;=&#x2009;pDetQmax&#x2009;+&#x2009;Qmax&#x2009;&#xd7;&#x2009;5) proposed by Abrams. A BCI&#x2009;<&#x2009;100 was considered indicative of an underactive bladder. Patients with underactive bladder were considered group A, whereas the remaining patients were classified as group B.<br><br>RESULTS: A total of 518 patients were studied. According to BCI, detrusor underactivity was identified in 212 (40.9%) patients (group A). Group A showed higher rates of voiding symptoms (59.4% vs 36.3%, p&#x2009;<&#x2009;0.0001) and positive (>100&#xa0;ml) postvoid residual (29.7% vs 9.8%, p&#x2009;<&#x2009;0.0001). Conversely, they displayed lower rates of urge incontinence (15.1% vs 23.2%, p&#x2009;=&#x2009;0.02) and detrusor overactivity (15.6% vs 23.9%, p&#x2009;=&#x2009;0.02). Preoperative Pelvic Organ Prolapse Quantification (POP-Q) demonstrated greater Aa (+1.1&#x2009;&#xb1;&#x2009;1.5 vs +0.9&#x2009;&#xb1;&#x2009;1.5, p&#x2009;=&#x2009;0.03) and Ba (+1.4&#x2009;&#xb1;&#x2009;1.7 vs +1.2&#x2009;&#xb1;&#x2009;1.7, p&#x2009;=&#x2009;0.04) points values in patients in group A. After POP surgery, postoperative voiding symptoms were similar in the two groups (16% vs 15.7%, p&#x2009;=&#x2009;0.91).<br><br>CONCLUSIONS: Our study showed a 40.9% prevalence of DU in POP patients. DU was associated with the presence of voiding symptoms and positive PVR. Moreover, cystocele showed to be more severe in DU group. After surgical repair of POP, voiding symptoms of DU patients became equal to non-DU ones, suggesting that obstruction removal might recover DU in these patients.},
}
@article {pmid29270110,
year = {2017},
author = {Baxter, BS and Edelman, BJ and Sohrabpour, A and He, B},
title = {Anodal Transcranial Direct Current Stimulation Increases Bilateral Directed Brain Connectivity during Motor-Imagery Based Brain-Computer Interface Control.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {691},
pmid = {29270110},
issn = {1662-4548},
support = {RF1 MH114233/MH/NIMH NIH HHS/United States ; R01 EY023101/EY/NEI NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; U01 HL117664/HL/NHLBI NIH HHS/United States ; F31 NS096964/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
abstract = {Transcranial direct current stimulation (tDCS) has been shown to affect motor and cognitive task performance and learning when applied to brain areas involved in the task. Targeted stimulation has also been found to alter connectivity within the stimulated hemisphere during rest. However, the connectivity effect of the interaction of endogenous task specific activity and targeted stimulation is unclear. This study examined the aftereffects of concurrent anodal high-definition tDCS over the left sensorimotor cortex with motor network connectivity during a one-dimensional EEG based sensorimotor rhythm brain-computer interface (SMR-BCI) task. Directed connectivity following anodal tDCS illustrates altered connections bilaterally between frontal and parietal regions, and these alterations occur in a task specific manner; connections between similar cortical regions are altered differentially during left and right imagination trials. During right-hand imagination following anodal tDCS, there was an increase in outflow from the left premotor cortex (PMC) to multiple regions bilaterally in the motor network and increased inflow to the stimulated sensorimotor cortex from the ipsilateral PMC and contralateral sensorimotor cortex. During left-hand imagination following anodal tDCS, there was increased outflow from the stimulated sensorimotor cortex to regions across the motor network. Significant correlations between connectivity and the behavioral measures of total correct trials and time-to-hit (TTH) correct trials were also found, specifically that the input to the left PMC correlated with decreased right hand imagination performance and that flow from the ipsilateral posterior parietal cortex (PPC) to midline sensorimotor cortex correlated with improved performance for both right and left hand imagination. These results indicate that tDCS interacts with task-specific endogenous activity to alter directed connectivity during SMR-BCI. In order to predict and maximize the targeted effect of tDCS, the interaction of stimulation with the dynamics of endogenous activity needs to be examined comprehensively and understood.},
}
@article {pmid29267295,
year = {2017},
author = {Maksimenko, VA and Runnova, AE and Zhuravlev, MO and Makarov, VV and Nedayvozov, V and Grubov, VV and Pchelintceva, SV and Hramov, AE and Pisarchik, AN},
title = {Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface.},
journal = {PloS one},
volume = {12},
number = {12},
pages = {e0188700},
pmid = {29267295},
issn = {1932-6203},
mesh = {Adult ; Alpha Rhythm ; *Attention ; Beta Rhythm ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; *Motivation ; Visual Acuity ; *Visual Perception ; Young Adult ; },
abstract = {The influence of motivation and alertness on brain activity associated with visual perception was studied experimentally using the Necker cube, which ambiguity was controlled by the contrast of its ribs. The wavelet analysis of recorded multichannel electroencephalograms (EEG) allowed us to distinguish two different scenarios while the brain processed the ambiguous stimulus. The first scenario is characterized by a particular destruction of alpha rhythm (8-12 Hz) with a simultaneous increase in beta-wave activity (20-30 Hz), whereas in the second scenario, the beta rhythm is not well pronounced while the alpha-wave energy remains unchanged. The experiments were carried out with a group of financially motivated subjects and another group of unpaid volunteers. It was found that the first scenario occurred mainly in the motivated group. This can be explained by the increased alertness of the motivated subjects. The prevalence of the first scenario was also observed in a group of subjects to whom images with higher ambiguity were presented. We believe that the revealed scenarios can occur not only during the perception of bistable images, but also in other perceptual tasks requiring decision making. The obtained results may have important applications for monitoring and controlling human alertness in situations which need substantial attention. On the base of the obtained results we built a brain-computer interface to estimate and control the degree of alertness in real time.},
}
@article {pmid29257221,
year = {2018},
author = {Tuglu, MM and Bostanabad, SY and Ozyon, G and Dalkiliç, B and Gurdal, H},
title = {The role of dual‑specificity phosphatase 1 and protein phosphatase 1 in β2‑adrenergic receptor‑mediated inhibition of extracellular signal regulated kinase 1/2 in triple negative breast cancer cell lines.},
journal = {Molecular medicine reports},
volume = {17},
number = {1},
pages = {2033-2043},
doi = {10.3892/mmr.2017.8092},
pmid = {29257221},
issn = {1791-3004},
mesh = {Cell Line, Tumor ; Dual Specificity Phosphatase 1/*metabolism ; Female ; Humans ; Mitogen-Activated Protein Kinase 1/*metabolism ; Mitogen-Activated Protein Kinase 3/*metabolism ; Phosphorylation ; Protein Phosphatase 1/*metabolism ; Receptors, Adrenergic, beta-2/*metabolism ; Triple Negative Breast Neoplasms/*metabolism ; },
abstract = {Triple negative breast cancer cell lines express high levels of β2-adrenergic receptor, which have a significant influence on the activity of extracellular signal‑regulated kinase (ERK)1/2. Therefore, it is important to understand the link between β2‑adrenergic receptor signaling and ERK1/2 activity in terms of cancer cell regulation and cancer progression. Although the molecular mechanisms are not completely clarified, β2‑adrenergic receptor stimulation appears to reduce the basal levels of phosphorylated (p)ERK1/2 in MDA‑MB‑231 breast cancer cells. The aim of the current study was to determine the mechanism of β2‑adrenergic receptor‑mediated ERK1/2 dephosphorylation by investigating the role of dual‑specificity phosphatase (DUSP)1/6 and protein phosphatase (PP)1/2, which are established regulators of ERK1/2 phosphorylation, in MDA‑MB‑231 and MDA‑MB‑468 breast cancer cell lines. (E)‑2‑benzylidene‑3‑(cyclohexyl amino)‑2,3‑ dihydro‑1H‑inden‑1‑one (BCI) and calyculin A were employed as DUSP1/6 and PP1/PP2 inhibitors, respectively. Subsequently, the protein levels of DUSP1, PP1, pPP1, ERK1/2 and pERK1/2 were measured by western blot analysis. Cells were transfected with DUSP1 small interfering (si)RNA or PP1 siRNA to inhibit their expression. The results demonstrated that β2‑adrenergic receptor agonists led to the dephosphorylation of basal pERK1/2 in MDA‑MB‑231 and MDA‑MB‑468 cells. The DUSP1/6 inhibitor, BCI, and the PP1/PP2 inhibitor, calyculin A, antagonized the β2‑adrenergic receptor‑mediated dephosphorylation of ERK1/2. Furthermore, β2‑adrenergic receptor stimulation increased the protein expression level of DUSP1, with no effects on DUSP6, PP1 and PP2 expression, and enhanced the expression of the active form of PP1. Downregulation of the expression of DUSP1 or PP1 led to a decline in the β2‑adrenergic receptor‑mediated dephosphorylation of ERK1/2. The results of the present study indicate that β2‑adrenergic receptor‑mediated dephosphorylation of ERK1/2 may be associated with the activity of DUSP1 and PP1 in MDA‑MB‑231 and MDA‑MB‑468 triple negative breast cancer cell lines. The clinical importance of β2‑adrenergic receptor‑mediated inactivation of ERK1/2 as well as the activation of DUSP1 and PP1 should be carefully evaluated in future studies, particularly when β2‑adrenergic blockers are used in patients with triple negative breast cancer.},
}
@article {pmid29255900,
year = {2018},
author = {Skljarevski, V and Oakes, TM and Zhang, Q and Ferguson, MB and Martinez, J and Camporeale, A and Johnson, KW and Shan, Q and Carter, J and Schacht, A and Goadsby, PJ and Dodick, DW},
title = {Effect of Different Doses of Galcanezumab vs Placebo for Episodic Migraine Prevention: A Randomized Clinical Trial.},
journal = {JAMA neurology},
volume = {75},
number = {2},
pages = {187-193},
pmid = {29255900},
issn = {2168-6157},
mesh = {Adolescent ; Adult ; Aged ; Antibodies, Monoclonal/*therapeutic use ; Antibodies, Monoclonal, Humanized ; Calcitonin Gene-Related Peptide/immunology ; Dose-Response Relationship, Drug ; Double-Blind Method ; Female ; Follow-Up Studies ; Humans ; Immunologic Factors/*therapeutic use ; Male ; Middle Aged ; Migraine Disorders/*prevention &amp; control ; *Treatment Outcome ; United States ; Young Adult ; },
abstract = {IMPORTANCE: Galcanezumab (LY2951742), a monoclonal antibody against calcitonin gene-related peptide (CGRP), is one of a novel class of new medicines for migraine prevention.<br><br>OBJECTIVE: To assess whether at least 1 dose of galcanezumab was superior to placebo for episodic migraine prevention.<br><br>A randomized clinical trial was conducted in the United States (July 7, 2014, to August 19, 2015) in clinics of 37 licensed physicians with a specialty including, but not limited to, psychiatry, neurology, internal medicine, and primary care. Subcutaneous injections of galcanezumab, 5, 50, 120, or 300 mg, or placebo were given monthly during the 3-month treatment period. A total of 936 patients were assessed; 526 did not meet study entry or baseline criteria and 410 patients were randomly assigned to receive placebo or galcanezumab. Analyses were conducted on an intent-to-treat population, which included all patients who were randomized and received at least 1 dose of study drug.<br><br>INTERVENTIONS: Short-term migraine treatments were allowed as needed except for opioids or barbiturates.<br><br>MAIN OUTCOMES AND MEASURES: To determine if at least 1 of the 4 doses of galcanezumab tested was superior to placebo for migraine prevention measured by the mean change from baseline in the number of migraine headache days 9 weeks to 12 weeks after randomization.<br><br>RESULTS: Of the 936 patients assessed, 410 met entry criteria (aged 18-65 years with 4-14 migraine headache days per month and migraine onset prior to age 50 years) and were randomized to receive placebo or galcanezumab. For the primary end point, galcanezumab, 120 mg, significantly reduced migraine headache days compared with placebo (99.6% posterior probability -4.8 days; 90% BCI, -5.4 to -4.2 days vs 95% superiority threshold [Bayesian analysis] -3.7 days; 90% BCI, -4.1 to -3.2 days). Adverse events reported by 5% or more of patients in at least 1 galcanezumab dose group and more frequently than placebo included injection-site pain, upper respiratory tract infection, nasopharyngitis, dysmenorrhea, and nausea.<br><br>CONCLUSIONS AND RELEVANCE: Monthly subcutaneous injections of galcanezumab, both 120 mg and 300 mg, demonstrated efficacy (repeated-measures analysis) for the preventive treatment of migraine and support further development in larger phase 3 studies. All dosages were safe and well tolerated for the preventive treatment of episodic migraine.<br><br>TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT02163993.},
}
@article {pmid29255403,
year = {2017},
author = {Hramov, AE and Maksimenko, VA and Pchelintseva, SV and Runnova, AE and Grubov, VV and Musatov, VY and Zhuravlev, MO and Koronovskii, AA and Pisarchik, AN},
title = {Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {674},
pmid = {29255403},
issn = {1662-4548},
abstract = {In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.},
}
@article {pmid29250108,
year = {2017},
author = {Carabez, E and Sugi, M and Nambu, I and Wada, Y},
title = {Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {8163949},
pmid = {29250108},
issn = {1687-5273},
mesh = {Analysis of Variance ; Auditory Perception/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; *Neural Networks, Computer ; Neuropsychological Tests ; Space Perception/physiology ; Time Factors ; Time Perception/physiology ; User-Computer Interface ; },
abstract = {From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on these people as potential users of BCI, we obtained electroencephalogram (EEG) readings from nine healthy subjects who were presented with auditory stimuli via earphones from six different virtual directions. We presented the stimuli following the oddball paradigm to elicit P300 waves within the subject's brain activity for later identification and classification using convolutional neural networks (CNN). The CNN models are given a novel single trial three-dimensional (3D) representation of the EEG data as an input, maintaining temporal and spatial information as close to the experimental setup as possible, a relevant characteristic as eliciting P300 has been shown to cause stronger activity in certain brain regions. Here, we present the results of CNN models using the proposed 3D input for three different stimuli presentation time intervals (500, 400, and 300 ms) and compare them to previous studies and other common classifiers. Our results show >80% accuracy for all the CNN models using the proposed 3D input in single trial P300 classification.},
}
@article {pmid29249952,
year = {2017},
author = {Shu, X and Yao, L and Sheng, X and Zhang, D and Zhu, X},
title = {Enhanced Motor Imagery-Based BCI Performance via Tactile Stimulation on Unilateral Hand.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {585},
pmid = {29249952},
issn = {1662-5161},
support = {R01 AA020501/AA/NIAAA NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) has attracted great interests for its effectiveness in assisting disabled people. However, due to the poor BCI performance, this technique is still far from daily-life applications. One of critical issues confronting BCI research is how to enhance BCI performance. This study aimed at improving the motor imagery (MI) based BCI accuracy by integrating MI tasks with unilateral tactile stimulation (Uni-TS). The effects were tested on both healthy subjects and stroke patients in a controlled study. Twenty-two healthy subjects and four stroke patients were recruited and randomly divided into a control-group and an enhanced-group. In the control-group, subjects performed two blocks of conventional MI tasks (left hand vs. right hand), with 80 trials in each block. In the enhanced-group, subjects also performed two blocks of MI tasks, but constant tactile stimulation was applied on the non-dominant/paretic hand during MI tasks in the second block. We found the Uni-TS significantly enhanced the contralateral cortical activations during MI of the stimulated hand, whereas it had no influence on activation patterns during MI of the non-stimulated hand. The two-class BCI decoding accuracy was significantly increased from 72.5% (MI without Uni-TS) to 84.7% (MI with Uni-TS) in the enhanced-group (p < 0.001, paired t-test). Moreover, stroke patients in the enhanced-group achieved an accuracy >80% during MI with Uni-TS. This novel approach complements the conventional methods for BCI enhancement without increasing source information or complexity of signal processing. This enhancement via Uni-TS may facilitate clinical applications of MI-BCI.},
}
@article {pmid29249949,
year = {2017},
author = {Herrera-Arcos, G and Tamez-Duque, J and Acosta-De-Anda, EY and Kwan-Loo, K and de-Alba, M and Tamez-Duque, U and Contreras-Vidal, JL and Soto, R},
title = {Modulation of Neural Activity during Guided Viewing of Visual Art.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {581},
pmid = {29249949},
issn = {1662-5161},
abstract = {Mobile Brain-Body Imaging (MoBI) technology was deployed to record multi-modal data from 209 participants to examine the brain's response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6-8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E), or given no explanation (Guided-NE). The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada) headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL) control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP) during the guided tour. In this study, we report data related to participants' demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG) in a select subgroup of 18-30 year-old subjects (Nc = 25) that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15-25 Hz) in the prefrontal electrodes (AF7 and AF8) during appreciation of subjects' favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP). No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art appreciation is independent of the artists' intent or original interpretation and related to the individual message that viewers themselves provide to each piece.},
}
@article {pmid29249947,
year = {2017},
author = {Han, CH and Lee, JH and Lim, JH and Kim, YW and Im, CH},
title = {Global Electroencephalography Synchronization as a New Indicator for Tracking Emotional Changes of a Group of Individuals during Video Watching.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {577},
pmid = {29249947},
issn = {1662-5161},
abstract = {In the present study, we investigated whether global electroencephalography (EEG) synchronization can be a new promising index for tracking emotional arousal changes of a group of individuals during video watching. Global field synchronization (GFS), an index known to correlate with human cognitive processes, was evaluated; this index quantified the global temporal synchronization among multichannel EEG data recorded from a group of participants (n = 25) during the plays of two short video clips. The two video clips were each about 5 min long and were designed to evoke negative (fearful) or positive (happy) emotion, respectively. Another group of participants (n = 37) was asked to select the two most emotionally arousing (most touching or most fearful) scenes in each clip. The results of these questionnaire surveys were used as the ground-truth to evaluate whether the GFS could detect emotional highlights of both video clips. The emotional highlights estimated using the grand-averaged GFS waveforms of the first group were also compared with those evaluated from galvanic skin response, photoplethysmography, and multimedia content analysis, which are conventional methods used to estimate temporal changes in emotional arousal during video plays. From our results, we found that beta-band GFS values decreased during high emotional arousal, regardless of the type of emotional stimulus. Moreover, the emotional highlights estimated using the GFS waveforms coincided best with those found by the questionnaire surveys. These findings suggest that GFS might be applicable as a new index for tracking emotional arousal changes of a group of individuals during video watching, and is likely to be used to evaluate or edit movies, TV commercials, and other broadcast products.},
}
@article {pmid29249927,
year = {2017},
author = {Sreekumar, V and Wittig, JH and Sheehan, TC and Zaghloul, KA},
title = {Principled Approaches to Direct Brain Stimulation for Cognitive Enhancement.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {650},
pmid = {29249927},
issn = {1662-4548},
abstract = {In this brief review, we identify key areas of research that inform a systematic and targeted approach for invasive brain stimulation with the goal of modulating higher cognitive functions such as memory. We outline several specific challenges that must be successfully navigated in order to achieve this goal. Specifically, using direct brain stimulation to support memory requires demonstrating that (1) there are reliable neural patterns corresponding to different events and memory states, (2) stimulation can be used to induce these target activity patterns, and (3) inducing such patterns modulates memory in the expected directions. Invasive stimulation studies typically have not taken into account intrinsic brain states and dynamics, nor have they a priori targeted specific neural patterns that have previously been identified as playing an important role in memory. Moreover, the effects of stimulation on neural activity are poorly understood and are sensitive to multiple factors including the specific stimulation parameters, the processing state of the brain at the time of stimulation, and neuroanatomy of the stimulated region. As a result, several studies have reported conflicting results regarding the use of direct stimulation for memory modulation. Here, we review the latest findings relevant to these issues and discuss how we can gain better control over the effects of direct brain stimulation for modulating human memory and cognition.},
}
@article {pmid29249656,
year = {2018},
author = {Mitani, A and Dong, M and Komiyama, T},
title = {Brain-Computer Interface with Inhibitory Neurons Reveals Subtype-Specific Strategies.},
journal = {Current biology : CB},
volume = {28},
number = {1},
pages = {77-83.e4},
pmid = {29249656},
issn = {1879-0445},
support = {P30 EY022589/EY/NEI NIH HHS/United States ; R01 EY025349/EY/NEI NIH HHS/United States ; R01 DC014690/DC/NIDCD NIH HHS/United States ; U01 NS094342/NS/NINDS NIH HHS/United States ; R01 NS091010/NS/NINDS NIH HHS/United States ; R21 DC012641/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Female ; Male ; Mice ; Neocortex/physiology ; Neural Inhibition ; Neurons/*physiology ; Parvalbumins/chemistry ; Somatostatin/chemistry ; Vasoactive Intestinal Peptide/chemistry ; },
abstract = {Brain-computer interfaces have seen an increase in popularity due to their potential for direct neuroprosthetic applications for amputees and disabled individuals. Supporting this promise, animals-including humans-can learn even arbitrary mapping between the activity of cortical neurons and movement of prosthetic devices [1-4]. However, the performance of neuroprosthetic device control has been nowhere near that of limb control in healthy individuals, presenting a dire need to improve the performance. One potential limitation is the fact that previous work has not distinguished diverse cell types in the neocortex, even though different cell types possess distinct functions in cortical computations [5-7] and likely distinct capacities to control brain-computer interfaces. Here, we made a first step in addressing this issue by tracking the plastic changes of three major types of cortical inhibitory neurons (INs) during a neuron-pair operant conditioning task using two-photon imaging of IN subtypes expressing GCaMP6f. Mice were rewarded when the activity of the positive target neuron (N+) exceeded that of the negative target neuron (N-) beyond a set threshold. Mice improved performance with all subtypes, but the strategies were subtype specific. When parvalbumin (PV)-expressing INs were targeted, the activity of N- decreased. However, targeting of somatostatin (SOM)- and vasoactive intestinal peptide (VIP)-expressing INs led to an increase of the N+ activity. These results demonstrate that INs can be individually modulated in a subtype-specific manner and highlight the versatility of neural circuits in adapting to new demands by using cell-type-specific strategies.},
}
@article {pmid29249283,
year = {2018},
author = {Rutishauser, U and Aflalo, T and Rosario, ER and Pouratian, N and Andersen, RA},
title = {Single-Neuron Representation of Memory Strength and Recognition Confidence in Left Human Posterior Parietal Cortex.},
journal = {Neuron},
volume = {97},
number = {1},
pages = {209-220.e3},
pmid = {29249283},
issn = {1097-4199},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; R01 MH110831/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Choice Behavior/*physiology ; Female ; Humans ; Male ; Memory/*physiology ; Middle Aged ; Neurons/*physiology ; Parietal Lobe/*physiology ; Recognition, Psychology/*physiology ; },
abstract = {The human posterior parietal cortex (PPC) is thought to contribute to memory retrieval, but little is known about its specific role. We recorded single PPC neurons of two human tetraplegic subjects implanted with microelectrode arrays, who performed a recognition memory task. We found two groups of neurons that signaled memory-based choices. Memory-selective neurons preferred either novel or familiar stimuli, scaled their response as a function of confidence, and signaled subjective choices regardless of truth. Confidence-selective neurons signaled confidence regardless of stimulus familiarity. Memory-selective signals appeared 553 ms after stimulus onset, but before action onset. Neurons also encoded spoken numbers, but these number-tuned neurons did not carry recognition signals. Together, this functional separation reveals action-independent coding of declarative memory-based familiarity and confidence of choices in human PPC. These data suggest that, in addition to sensory-motor integration, a function of human PPC is to utilize memory signals to make choices.},
}
@article {pmid29247807,
year = {2018},
author = {Ekanayake, J and Hutton, C and Ridgway, G and Scharnowski, F and Weiskopf, N and Rees, G},
title = {Real-time decoding of covert attention in higher-order visual areas.},
journal = {NeuroImage},
volume = {169},
number = {},
pages = {462-472},
pmid = {29247807},
issn = {1095-9572},
support = {MR/J014257/1/MRC_/Medical Research Council/United Kingdom ; MR/J014257/2/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/diagnostic imaging/*physiology ; Eye Movement Measurements ; Female ; Functional Neuroimaging/*methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Male ; Pattern Recognition, Visual/*physiology ; Proof of Concept Study ; Space Perception/*physiology ; Young Adult ; },
abstract = {Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.},
}
@article {pmid29247485,
year = {2018},
author = {Pichiorri, F and Petti, M and Caschera, S and Astolfi, L and Cincotti, F and Mattia, D},
title = {An EEG index of sensorimotor interhemispheric coupling after unilateral stroke: clinical and neurophysiological study.},
journal = {The European journal of neuroscience},
volume = {47},
number = {2},
pages = {158-163},
doi = {10.1111/ejn.13797},
pmid = {29247485},
issn = {1460-9568},
mesh = {Aged ; Aged, 80 and over ; *Brain Waves ; Female ; *Functional Laterality ; Humans ; Male ; Middle Aged ; Pyramidal Tracts/*physiopathology ; Sensorimotor Cortex/*physiopathology ; Stroke/*physiopathology ; },
abstract = {Brain connectivity has been employed to investigate on post-stroke recovery mechanisms and assess the effect of specific rehabilitation interventions. Changes in interhemispheric coupling after stroke have been related to the extent of damage in the corticospinal tract (CST) and thus, to motor impairment. In this study, we aimed at defining an index of interhemispheric connectivity derived from electroencephalography (EEG), correlated with CST integrity and clinical impairment. Thirty sub-acute stroke patients underwent clinical and neurophysiological evaluation: CST integrity was assessed by Transcranial Magnetic Stimulation and high-density EEG was recorded at rest. Connectivity was assessed by means of Partial Directed Coherence and the normalized Inter-Hemispheric Strength (nIHS) was calculated for each patient and frequency band on the whole network and in three sub-networks relative to the frontal, central (sensorimotor) and occipital areas. Interhemipheric coupling as expressed by nIHS on the whole network was significantly higher in patients with preserved CST integrity in beta and gamma bands. The same index estimated for the three sub-networks showed significant differences only in the sensorimotor area in lower beta, with higher values in patients with preserved CST integrity. The sensorimotor lower beta nIHS showed a significant positive correlation with clinical impairment. We propose an EEG-based connectivity index which is a measure of the interhemispheric cross-talking and correlates with functional motor impairment in subacute stroke patients. Such index could be employed to evaluate the effects of training aimed at re-establishing interhemispheric balance and eventually drive the design of future connectivity-driven rehabilitation interventions.},
}
@article {pmid29238531,
year = {2017},
author = {Basset, Y and Lamarre, GPA and Ratz, T and Segar, ST and Decaëns, T and Rougerie, R and Miller, SE and Perez, F and Bobadilla, R and Lopez, Y and Ramirez, JA and Aiello, A and Barrios, H},
title = {The Saturniidae of Barro Colorado Island, Panama: A model taxon for studying the long-term effects of climate change?.},
journal = {Ecology and evolution},
volume = {7},
number = {23},
pages = {9991-10004},
pmid = {29238531},
issn = {2045-7758},
support = {669609/ERC_/European Research Council/International ; },
abstract = {We have little knowledge of the response of invertebrate assemblages to climate change in tropical ecosystems, and few studies have compiled long-term data on invertebrates from tropical rainforests. We provide an updated list of the 72 species of Saturniidae moths collected on Barro Colorado Island (BCI), Panama, during the period 1958-2016. This list will serve as baseline data for assessing long-term changes of saturniids on BCI in the future, as 81% of the species can be identified by their unique DNA Barcode Index Number, including four cryptic species not yet formally described. A local species pool of 60 + species breeding on BCI appears plausible, but more cryptic species may be discovered in the future. We use monitoring data obtained by light trapping to analyze recent population trends on BCI for saturniid species that were relatively common during 2009-2016, a period representing >30 saturniid generations. The abundances of 11 species, of 14 tested, could be fitted to significant time-series models. While the direction of change in abundance was uncertain for most species, two species showed a significant increase over time, and forecast models also suggested continuing increases for most species during 2017-2018, as compared to the 2009 base year. Peaks in saturniid abundance were most conspicuous during El Niño and La Niña years. In addition to a species-specific approach, we propose a reproducible functional classification based on five functional traits to analyze the responses of species sharing similar functional attributes in a fluctuating climate. Our results suggest that the abundances of larger body-size species with good dispersal abilities may increase concomitantly with rising air temperature in the future, because short-lived adults may allocate less time to increasing body temperature for flight, leaving more time available for searching for mating partners or suitable oviposition sites.},
}
@article {pmid29236042,
year = {2017},
author = {Taherisadr, M and Dehzangi, O and Parsaei, H},
title = {Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {12},
pages = {},
pmid = {29236042},
issn = {1424-8220},
abstract = {As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.},
}
@article {pmid29234269,
year = {2017},
author = {Ortiz, M and Rodríguez-Ugarte, M and Iáñez, E and Azorín, JM},
title = {Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {660},
pmid = {29234269},
issn = {1662-4548},
abstract = {The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability. Methods are compared with the Weighted Discriminator, a recently developed comparison index. The tool developed can improve the rehabilitation process associated with lower limb exoskeletons with the help of a Brain-Machine Interface.},
}
@article {pmid29230171,
year = {2017},
author = {Jiang, L and Wang, Y and Cai, B and Wang, Y and Wang, Y},
title = {Spatial-Temporal Feature Analysis on Single-Trial Event Related Potential for Rapid Face Identification.},
journal = {Frontiers in computational neuroscience},
volume = {11},
number = {},
pages = {106},
pmid = {29230171},
issn = {1662-5188},
abstract = {The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification.},
}
@article {pmid29225565,
year = {2017},
author = {Lebedev, MA},
title = {Commentary: Emergence of a Stable Cortical Map for Neuroprosthetic Control.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {642},
pmid = {29225565},
issn = {1662-4548},
}
@article {pmid29224063,
year = {2018},
author = {Rahman, MM and Chowdhury, MA and Fattah, SA},
title = {An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation function of EEG signal.},
journal = {Brain informatics},
volume = {5},
number = {1},
pages = {1-12},
pmid = {29224063},
issn = {2198-4018},
abstract = {Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain-computer interface (BCI) applications. In the design of BCI systems, features extracted from lower frequency bands of scalp-recorded EEG signals are generally considered to classify mental tasks and higher frequency bands are mostly ignored as noise. However, in this paper, it is demonstrated that high frequency components of EEG signal can provide accommodating data for enhancing the classification performance of the mental task-based BCI. Instead of using autoregressive (AR) parameters considering AR modeling of EEG data, reflection coefficients obtained from EEG signal are proposed as potential features. From a given frame of EEG data, reflection coefficients are directly extracted by using the autocorrelation values in a recursive fashion, which avoids matrix inversion and computation of AR parameters. Use of reflection coefficients not only provides an effective feature vector for EEG signal classification but also offers very low computational burden. Support vector machine classifier is deployed in leave-one-out cross-validation manner to carry out classification process. Extensive simulation is done on an openly accessible dataset containing five different mental tasks. It is found that the proposed scheme can classify mental tasks with a very high level of accuracy as well as low time complexity in contrast with some of the existing strategies.},
}
@article {pmid29220327,
year = {2017},
author = {Yu, Y and Zhou, Z and Liu, Y and Jiang, J and Yin, E and Zhang, N and Wang, Z and Liu, Y and Wu, X and Hu, D},
title = {Self-Paced Operation of a Wheelchair Based on a Hybrid Brain-Computer Interface Combining Motor Imagery and P300 Potential.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {12},
pages = {2516-2526},
doi = {10.1109/TNSRE.2017.2766365},
pmid = {29220327},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Female ; Functional Laterality ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; *Wheelchairs ; Young Adult ; },
abstract = {This paper presents a hybrid brain-computer interface (BCI) that combines motor imagery (MI) and P300 potential for the asynchronous operation of a brain-controlled wheelchair whose design is based on a Mecanum wheel. This paradigm is completely user-centric. By sequentially performing MI tasks or paying attention to P300 flashing, the user can use eleven functions to control the wheelchair: move forward/backward, move left/right, move left45/right45, accelerate/decelerate, turn left/right, and stop. The practicality and effectiveness of the proposed approach were validated in eight subjects, all of whom achieved good performance. The preliminary results indicated that the proposed hybrid BCI system with different mental strategies operating sequentially is feasible and has potential applications for practical self-paced control.},
}
@article {pmid29218004,
year = {2017},
author = {Liu, D and Chen, W and Chavarriaga, R and Pei, Z and Millán, JDR},
title = {Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {560},
pmid = {29218004},
issn = {1662-5161},
abstract = {Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was -0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5% and 68.2 ± 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.},
}
@article {pmid29214161,
year = {2017},
author = {de Albuquerque, VHC and Damaševičius, R and Garcia, NM and Pinheiro, PR and Filho, PPR},
title = {Brain Computer Interface Systems for Neurorobotics: Methods and Applications.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {2505493},
doi = {10.1155/2017/2505493},
pmid = {29214161},
issn = {2314-6141},
mesh = {*Brain-Computer Interfaces ; Humans ; Robotics/*methods/*trends ; },
}
@article {pmid29209193,
year = {2017},
author = {Fernández-Caballero, A and Navarro, E and Fernández-Sotos, P and González, P and Ricarte, JJ and Latorre, JM and Rodriguez-Jimenez, R},
title = {Human-Avatar Symbiosis for the Treatment of Auditory Verbal Hallucinations in Schizophrenia through Virtual/Augmented Reality and Brain-Computer Interfaces.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {64},
pmid = {29209193},
issn = {1662-5196},
abstract = {This perspective paper faces the future of alternative treatments that take advantage of a social and cognitive approach with regards to pharmacological therapy of auditory verbal hallucinations (AVH) in patients with schizophrenia. AVH are the perception of voices in the absence of auditory stimulation and represents a severe mental health symptom. Virtual/augmented reality (VR/AR) and brain computer interfaces (BCI) are technologies that are growing more and more in different medical and psychological applications. Our position is that their combined use in computer-based therapies offers still unforeseen possibilities for the treatment of physical and mental disabilities. This is why, the paper expects that researchers and clinicians undergo a pathway toward human-avatar symbiosis for AVH by taking full advantage of new technologies. This outlook supposes to address challenging issues in the understanding of non-pharmacological treatment of schizophrenia-related disorders and the exploitation of VR/AR and BCI to achieve a real human-avatar symbiosis.},
}
@article {pmid29209023,
year = {2017},
author = {Downey, JE and Brane, L and Gaunt, RA and Tyler-Kabara, EC and Boninger, ML and Collinger, JL},
title = {Motor cortical activity changes during neuroprosthetic-controlled object interaction.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {16947},
pmid = {29209023},
issn = {2045-2322},
mesh = {Adult ; Algorithms ; Artificial Limbs ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Female ; Hand Strength ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Quadriplegia/*physiopathology ; },
abstract = {Brain-computer interface (BCI) controlled prosthetic arms are being developed to restore function to people with upper-limb paralysis. This work provides an opportunity to analyze human cortical activity during complex tasks. Previously we observed that BCI control became more difficult during interactions with objects, although we did not quantify the neural origins of this phenomena. Here, we investigated how motor cortical activity changed in the presence of an object independently of the kinematics that were being generated using intracortical recordings from two people with tetraplegia. After identifying a population-wide increase in neural firing rates that corresponded with the hand being near an object, we developed an online scaling feature in the BCI system that operated without knowledge of the task. Online scaling increased the ability of two subjects to control the robotic arm when reaching to grasp and transport objects. This work suggests that neural representations of the environment, in this case the presence of an object, are strongly and consistently represented in motor cortex but can be accounted for to improve BCI performance.},
}
@article {pmid29201621,
year = {2017},
author = {Yan, Z and Pan, T and Xue, M and Chen, C and Cui, Y and Yao, G and Huang, L and Liao, F and Jing, W and Zhang, H and Gao, M and Guo, D and Xia, Y and Lin, Y},
title = {Thermal Release Transfer Printing for Stretchable Conformal Bioelectronics.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {4},
number = {11},
pages = {1700251},
pmid = {29201621},
issn = {2198-3844},
abstract = {Soft neural electrode arrays that are mechanically matched between neural tissues and electrodes offer valuable opportunities for the development of disease diagnose and brain computer interface systems. Here, a thermal release transfer printing method for fabrication of stretchable bioelectronics, such as soft neural electrode arrays, is presented. Due to the large, switchable and irreversible change in adhesion strength of thermal release tape, a low-cost, easy-to-operate, and temperature-controlled transfer printing process can be achieved. The mechanism of this method is analyzed by experiments and fracture-mechanics models. Using the thermal release transfer printing method, a stretchable neural electrode array is fabricated by a sacrificial-layer-free process. The ability of the as-fabricated electrode array to conform different curvilinear surfaces is confirmed by experimental and theoretical studies. High-quality electrocorticography signals of anesthetized rat are collected with the as-fabricated electrode array, which proves good conformal interface between the electrodes and dura mater. The application of the as-fabricated electrode array on detecting the steady-state visual evoked potentials research is also demonstrated by in vivo experiments and the results are compared with those detected by stainless-steel screw electrodes.},
}
@article {pmid29199642,
year = {2018},
author = {Young, D and Willett, F and Memberg, WD and Murphy, B and Walter, B and Sweet, J and Miller, J and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {Signal processing methods for reducing artifacts in microelectrode brain recordings caused by functional electrical stimulation.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026014},
pmid = {29199642},
issn = {1741-2552},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; I01 RX002295/RX/RRD VA/United States ; },
mesh = {Arm/innervation/physiology ; *Artifacts ; Brain-Computer Interfaces ; Deep Brain Stimulation/instrumentation/*methods ; *Electrodes, Implanted ; Humans ; Microelectrodes ; Motor Cortex/*physiology/surgery ; Muscle, Skeletal/innervation/*physiology ; Pilot Projects ; *Signal Processing, Computer-Assisted ; Spinal Cord Injuries/diagnosis/physiopathology/therapy ; Thoracic Vertebrae ; },
abstract = {OBJECTIVE: Functional electrical stimulation (FES) is a promising technology for restoring movement to paralyzed limbs. Intracortical brain-computer interfaces (iBCIs) have enabled intuitive control over virtual and robotic movements, and more recently over upper extremity FES neuroprostheses. However, electrical stimulation of muscles creates artifacts in intracortical microelectrode recordings that could degrade iBCI performance. Here, we investigate methods for reducing the cortically recorded artifacts that result from peripheral electrical stimulation.<br><br>APPROACH: One participant in the BrainGate2 pilot clinical trial had two intracortical microelectrode arrays placed in the motor cortex, and thirty-six stimulating intramuscular electrodes placed in the muscles of the contralateral limb. We characterized intracortically recorded electrical artifacts during both intramuscular and surface stimulation. We compared the performance of three artifact reduction methods: blanking, common average reference (CAR) and linear regression reference (LRR), which creates channel-specific reference signals, composed of weighted sums of other channels.<br><br>MAIN RESULTS: Electrical artifacts resulting from surface stimulation were 175&#x2009;&#x2009;&#xd7;&#x2009;&#x2009;larger than baseline neural recordings (which were 110 &#xb5;V peak-to-peak), while intramuscular stimulation artifacts were only 4&#x2009;&#x2009;&#xd7;&#x2009;&#x2009;larger. The artifact waveforms were highly consistent across electrodes within each array. Application of LRR reduced artifact magnitudes to less than 10 &#xb5;V and largely preserved the original neural feature values used for decoding. Unmitigated stimulation artifacts decreased iBCI decoding performance, but performance was almost completely recovered using LRR, which outperformed CAR and blanking and extracted useful neural information during stimulation artifact periods.<br><br>SIGNIFICANCE: The LRR method was effective at reducing electrical artifacts resulting from both intramuscular and surface FES, and almost completely restored iBCI decoding performance (>90% recovery for surface stimulation and full recovery for intramuscular stimulation). The results demonstrate that FES-induced artifacts can be easily mitigated in FES&#x2009;&#x2009;+&#x2009;&#x2009;iBCI systems by using LRR for artifact reduction, and suggest that the LRR method may also be useful in other noise reduction applications.},
}
@article {pmid29198041,
year = {2018},
author = {Keskinbora, KH and Keskinbora, K},
title = {Ethical considerations on novel neuronal interfaces.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {39},
number = {4},
pages = {607-613},
pmid = {29198041},
issn = {1590-3478},
mesh = {Brain-Computer Interfaces ; Clinical Studies as Topic ; Decision Making/*ethics ; *Ethics, Medical ; Health Care Costs/*statistics &amp; numerical data ; Health Personnel/economics/*ethics/statistics &amp; numerical data ; Humans ; },
abstract = {Wireless powered implants, each smaller than a grain of rice, have the potential to scan and stimulate brain cells. Further research may lead to next-generation brain-machine interfaces for controlling prosthetics, exoskeletons, and robots, as well as "electroceuticals" to treat disorders of the brain and body. In conditions that can be particularly alleviated with brain stimulation, the use of such mini devices may pose certain challenges. Health professionals are becoming increasingly more accountable in decision-making processes that have impacts on the life quality of individuals. It is possible to transmit such stimulation using remote control principles. Perhaps, the most important concern regarding the use of these devices termed as "neural dust" is represented by the possibility of controlling affection and other mental functions via waves reaching the brain using more advanced versions of such devices. This will not only violate the respect for authority principle of ethics, but also medical ethics, and may potentially lead to certain incidents of varying vehemence that may be considered illegal. Therefore, a sound knowledge and implementation of ethical principles is becoming a more important issue on the part of healthcare professionals. In both the ethical decision-making process and in ethical conflicts, it may be useful to re-appraise the principles of medical ethics. In this article, the ethical considerations of these devices are discussed.},
}
@article {pmid29197616,
year = {2018},
author = {Nagel, S and Dreher, W and Rosenstiel, W and Spüler, M},
title = {The effect of monitor raster latency on VEPs, ERPs and Brain-Computer Interface performance.},
journal = {Journal of neuroscience methods},
volume = {295},
number = {},
pages = {45-50},
doi = {10.1016/j.jneumeth.2017.11.018},
pmid = {29197616},
issn = {1872-678X},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Computer Graphics/*instrumentation ; *Computers ; Electroencephalography/methods ; *Evoked Potentials ; Humans ; Movement Disorders/physiopathology ; Photic Stimulation/*instrumentation ; Reaction Time ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Time Factors ; Visual Perception/physiology ; },
abstract = {BACKGROUND: Visual neuroscience experiments and Brain-Computer Interface (BCI) control often require strict timings in a millisecond scale. As most experiments are performed using a personal computer (PC), the latencies that are introduced by the setup should be taken into account and be corrected. As a standard computer monitor uses a rastering to update each line of the image sequentially, this causes a monitor raster latency which depends on the position, on the monitor and the refresh rate.<br><br>NEW METHOD: We technically measured the raster latencies of different monitors and present the effects on visual evoked potentials (VEPs) and event-related potentials (ERPs). Additionally we present a method for correcting the monitor raster latency and analyzed the performance difference of a code-modulated VEP BCI speller by correcting the latency.<br><br>There are currently no other methods validating the effects of monitor raster latency on VEPs and ERPs.<br><br>RESULTS: The timings of VEPs and ERPs are directly affected by the raster latency. Furthermore, correcting the raster latency resulted in a significant reduction of the target prediction error from 7.98% to 4.61% and also in a more reliable classification of targets by significantly increasing the distance between the most probable and the second most probable target by 18.23%.<br><br>CONCLUSIONS: The monitor raster latency affects the timings of VEPs and ERPs, and correcting resulted in a significant error reduction of 42.23%. It is recommend to correct the raster latency for an increased BCI performance and methodical correctness.},
}
@article {pmid29197014,
year = {2018},
author = {Reynaud, D and Sergent, F and Abi Nahed, R and Brouillet, S and Benharouga, M and Alfaidy, N},
title = {EG-VEGF Maintenance Over Early Gestation to Develop a Pregnancy-Induced Hypertensive Animal Model.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {1710},
number = {},
pages = {317-324},
doi = {10.1007/978-1-4939-7498-6_25},
pmid = {29197014},
issn = {1940-6029},
mesh = {Animals ; Disease Models, Animal ; Female ; Humans ; Hypertension, Pregnancy-Induced/*metabolism/pathology ; Male ; Mice ; Pre-Eclampsia/metabolism/pathology ; Pregnancy ; Trophoblasts/metabolism/pathology ; Vascular Endothelial Growth Factor, Endocrine-Gland-Derived/analysis/*metabolism ; },
abstract = {During the last decade, multiple animal models have been developed to mimic hallmarks of pregnancy-induced hypertension (PIH) diseases, which include gestational hypertension, preeclampsia (PE), or eclampsia. Converging in vitro, ex vivo, and clinical studies from our group strongly suggested the potential involvement of the new angiogenic factor EG-VEGF (endocrine gland-derived-VEGF) in the development of PIH. Here, we described the protocol that served to demonstrate that maintenance of EG-VEGF production over 11.5 days post coitus (dpc) in the gravid mice caused the development of PIH. The developed model exhibited most hallmarks of preeclampsia.},
}
@article {pmid29192609,
year = {2018},
author = {Makin, JG and O'Doherty, JE and Cardoso, MMB and Sabes, PN},
title = {Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026010},
doi = {10.1088/1741-2552/aa9e95},
pmid = {29192609},
issn = {1741-2552},
mesh = {*Algorithms ; Animals ; Arm/*physiology ; Electrodes, Implanted ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *Unsupervised Machine Learning ; },
abstract = {OBJECTIVE: The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity-vectors of spike counts in small temporal windows-as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman's (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice.<br><br>APPROACH: To overcome these limitations we introduce a new filter, the 'recurrent exponential-family harmonium' (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip.<br><br>MAIN RESULTS: We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons.<br><br>SIGNIFICANCE: Our algorithm establishes a new state of the art for offline decoding of reaches-in particular, for fingertip velocities, the variable used for control in most online decoders.},
}
@article {pmid29190704,
year = {2017},
author = {Luu, TP and Brantley, JA and Nakagome, S and Zhu, F and Contreras-Vidal, JL},
title = {Electrocortical correlates of human level-ground, slope, and stair walking.},
journal = {PloS one},
volume = {12},
number = {11},
pages = {e0188500},
pmid = {29190704},
issn = {1932-6203},
support = {F99 NS105210/NS/NINDS NIH HHS/United States ; },
mesh = {Electroencephalography/methods ; Female ; Gait ; Humans ; Male ; Walking/*physiology ; },
abstract = {This study investigated electrocortical dynamics of human walking across different unconstrained walking conditions (i.e., level ground (LW), ramp ascent (RA), and stair ascent (SA)). Non-invasive active-electrode scalp electroencephalography (EEG) signals were recorded and a systematic EEG processing method was implemented to reduce artifacts. Source localization combined with independent component analysis and k-means clustering revealed the involvement of four clusters in the brain during the walking tasks: Left and Right Occipital Lobe (LOL, ROL), Posterior Parietal Cortex (PPC), and Central Sensorimotor Cortex (SMC). Results showed that the changes of spectral power in the PPC and SMC clusters were associated with the level of motor task demands. Specifically, we observed α and β suppression at the beginning of the gait cycle in both SA and RA walking (relative to LW) in the SMC. Additionally, we observed significant β rebound (synchronization) at the initial swing phase of the gait cycle, which may be indicative of active cortical signaling involved in maintaining the current locomotor state. An increase of low γ band power in this cluster was also found in SA walking. In the PPC, the low γ band power increased with the level of task demands (from LW to RA and SA). Additionally, our results provide evidence that electrocortical amplitude modulations (relative to average gait cycle) are correlated with the level of difficulty in locomotion tasks. Specifically, the modulations in the PPC shifted to higher frequency bands when the subjects walked in RA and SA conditions. Moreover, low γ modulations in the central sensorimotor area were observed in the LW walking and shifted to lower frequency bands in RA and SA walking. These findings extend our understanding of cortical dynamics of human walking at different level of locomotion task demands and reinforces the growing body of literature supporting a shared-control paradigm between spinal and cortical networks during locomotion.},
}
@article {pmid29187809,
year = {2017},
author = {Wittevrongel, B and Van Hulle, MM},
title = {Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {630},
pmid = {29187809},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection). In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.},
}
@article {pmid29187552,
year = {2018},
author = {Ferrea, E and Suriya-Arunroj, L and Hoehl, D and Thomas, U and Gail, A},
title = {Implantable computer-controlled adaptive multielectrode positioning system.},
journal = {Journal of neurophysiology},
volume = {119},
number = {4},
pages = {1471-1484},
doi = {10.1152/jn.00504.2017},
pmid = {29187552},
issn = {1522-1598},
mesh = {Animals ; Cerebral Cortex/*physiology ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; *Equipment Design ; Macaca mulatta ; *Microelectrodes ; },
abstract = {Acute neuronal recordings performed with metal microelectrodes in nonhuman primates allow investigating the neural substrate of complex cognitive behaviors. Yet the daily reinsertion and positioning of the electrodes prevents recording from many neurons simultaneously, limiting the suitability of these types of recordings for brain-computer interface applications or for large-scale population statistical methods on a trial-by-trial basis. In contrast, chronically implanted multielectrode arrays offer the opportunity to record from many neurons simultaneously, but immovable electrodes prevent optimization of the signal during and after implantation and cause the tissue response to progressively impair the transduced signal quality, thereby limiting the number of different neurons that can be recorded over the lifetime of the implant. Semichronically implanted matrices of electrodes, instead, allow individually movable electrodes in depth and achieve higher channel count compared with acute methods, hence partially overcoming these limitations. Existing semichronic systems with higher channel count lack computerized control of electrode movements, leading to limited user-friendliness and uncertainty in depth positioning. Here we demonstrate a chronically implantable adaptive multielectrode positioning system with detachable drive for computerized depth adjustment of individual electrodes over several millimeters. This semichronic 16-channel system is designed to optimize the simultaneous yield of units in an extended period following implantation since the electrodes can be independently depth adjusted with minimal effort and their signal quality continuously assessed. Importantly, the electrode array is designed to remain within a chronic recording chamber for a prolonged time or can be used for acute recordings with high signal-to-noise ratio in the cerebral cortex of nonhuman primates. NEW &amp; NOTEWORTHY We present a 16-channel motorized, semichronic multielectrode array with individually depth-adjustable electrodes to record in the cerebral cortex of nonhuman primates. Compared with fixed-geometry arrays, this system allows repeated reestablishing of single neuron isolation. Compared with manually adjustable arrays it benefits from computer-controlled positioning. Compared with motorized semichronic systems it allows higher channel counts due to a robotic single actuator approach. Overall the system is designed to optimize the simultaneous yield of units over the course of implantation.},
}
@article {pmid29186848,
year = {2017},
author = {Delisle-Rodriguez, D and Villa-Parra, AC and Bastos-Filho, T and López-Delis, A and Frizera-Neto, A and Krishnan, S and Rocon, E},
title = {Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {12},
pages = {},
pmid = {29186848},
issn = {1424-8220},
abstract = {This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (A C C ≥ 74.79 %) , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.},
}
@article {pmid29185990,
year = {2018},
author = {Olze, K and Jan Wehrmann, C and Mu, L and Schilling, M},
title = {Obstacles in using a computer screen for steady-state visually evoked potential stimulation.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {63},
number = {4},
pages = {377-382},
doi = {10.1515/bmt-2016-0243},
pmid = {29185990},
issn = {1862-278X},
mesh = {Brain-Computer Interfaces/*standards ; Humans ; *Photic Stimulation ; },
abstract = {In brain computer interface (BCI) applications, the use of steady-state visually evoked potentials (SSVEPs) is common. Therefore, a visual stimulation with a constant repetition frequency is necessary. However, using a computer monitor, the set of frequencies that can be used is restricted by the refresh rate of the screen. Frequencies that are not an integer divisor of the refresh rate cannot be displayed correctly. Furthermore, the programming language the stimulation software is written in and the operating system influence the actually generated and presented frequencies. The aim of this paper is to identify the main challenges in generating SSVEP stimulation using a computer screen with and without using DirectX in Windows-based PC systems and to provide solutions for these issues.},
}
@article {pmid29185494,
year = {2017},
author = {Shin, J and Kwon, J and Choi, J and Im, CH},
title = {Performance enhancement of a brain-computer interface using high-density multi-distance NIRS.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {16545},
pmid = {29185494},
issn = {2045-2322},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Hemodynamics/physiology ; Humans ; Male ; Prefrontal Cortex/physiology ; Psychomotor Performance/physiology ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {This study investigated the effectiveness of using a high-density multi-distance source-detector (SD) separations in near-infrared spectroscopy (NIRS), for enhancing the performance of a functional NIRS (fNIRS)-based brain-computer interface (BCI). The NIRS system that was used for the experiment was capable of measuring signals from four SD separations: 15, 21.2, 30, and 33.5 mm, and this allowed the measurement of hemodynamic response alterations at various depths. Fifteen participants were asked to perform mental arithmetic and word chain tasks, to induce task-related hemodynamic response variations, or they were asked to stay relaxed to acquire a baseline signal. To evaluate the degree of BCI performance enhancement by high-density channel configuration, the classification accuracy obtained using a typical low-density lattice SD arrangement, was compared to that obtained using the high-density SD arrangement, while maintaining the SD separation at 30 mm. The analysis results demonstrated that the use of a high-density channel configuration did not result in a noticeable enhancement of classification accuracy. However, the combination of hemodynamic variations, measured by two multi-distance SD separations, resulted in the significant enhancement of overall classification accuracy. The results of this study indicated that the use of high-density multi-distance SD separations can likely provide a new method for enhancing the performance of an fNIRS-BCI.},
}
@article {pmid29182152,
year = {2018},
author = {Xie, Z and Schwartz, O and Prasad, A},
title = {Decoding of finger trajectory from ECoG using deep learning.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {036009},
doi = {10.1088/1741-2552/aa9dbe},
pmid = {29182152},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; Electrocorticography/*methods ; Fingers/*physiology ; Humans ; Movement/*physiology ; Photic Stimulation/methods ; Sensorimotor Cortex/*physiology ; },
abstract = {OBJECTIVE: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance. Here, we used deep neural networks consisting of convolutional neural networks (CNN) and a special kind of recurrent neural network (RNN) called long short term memory (LSTM) to address these needs.<br><br>APPROACH: We used electrocorticography (ECoG) data collected by Kubanek et al. The task consisted of individual finger flexions upon a visual cue. Our model combined a hierarchical feature extractor CNN and a RNN that was able to process sequential data and recognize temporal dynamics in the neural data. CNN was used as the feature extractor and LSTM was used as the regression algorithm to capture the temporal dynamics of the signal.<br><br>MAIN RESULTS: We predicted the finger trajectory using ECoG signals and compared results for the least angle regression (LARS), CNN-LSTM, random forest, LSTM model (LSTM_HC, for using hard-coded features) and a decoding pipeline consisting of band-pass filtering, energy extraction, feature selection and linear regression. The results showed that the deep learning models performed better than the commonly used linear model. The deep learning models not only gave smoother and more realistic trajectories but also learned the transition between movement and rest state.<br><br>SIGNIFICANCE: This study demonstrated a decoding network for BMI that involved a convolutional and recurrent neural network model. It integrated the feature extraction pipeline into the convolution and pooling layer and used LSTM layer to capture the state transitions. The discussed network eliminated the need to separately train the model at each step in the decoding pipeline. The whole system can be jointly optimized using stochastic gradient descent and is capable of online learning.},
}
@article {pmid29182149,
year = {2018},
author = {Michelson, NJ and Vazquez, AL and Eles, JR and Salatino, JW and Purcell, EK and Williams, JJ and Cui, XT and Kozai, TDY},
title = {Multi-scale, multi-modal analysis uncovers complex relationship at the brain tissue-implant neural interface: new emphasis on the biological interface.},
journal = {Journal of neural engineering},
volume = {15},
number = {3},
pages = {033001},
pmid = {29182149},
issn = {1741-2552},
support = {R01 NS094396/NS/NINDS NIH HHS/United States ; R01 NS094404/NS/NINDS NIH HHS/United States ; R21 NS094900/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Female ; Macaca mulatta ; Male ; Mice ; Mice, Inbred C57BL ; Microelectrodes ; Microscopy, Fluorescence, Multiphoton/methods ; Neurons/*physiology ; Rats ; Rats, Long-Evans ; Rats, Sprague-Dawley ; Sensorimotor Cortex/*physiology/surgery ; Visual Cortex/*physiology/surgery ; },
abstract = {OBJECTIVE: Implantable neural electrode devices are important tools for neuroscience research and have an increasing range of clinical applications. However, the intricacies of the biological response after implantation, and their ultimate impact on recording performance, remain challenging to elucidate. Establishing a relationship between the neurobiology and chronic recording performance is confounded by technical challenges related to traditional electrophysiological, material, and histological limitations. This can greatly impact the interpretations of results pertaining to device performance and tissue health surrounding the implant.<br><br>APPROACH: In this work, electrophysiological activity and immunohistological analysis are compared after controlling for motion artifacts, quiescent neuronal activity, and material failure of devices in order to better understand the relationship between histology and electrophysiological outcomes.<br><br>MAIN RESULTS: Even after carefully accounting for these factors, the presence of viable neurons and lack of glial scarring does not convey single unit recording performance.<br><br>SIGNIFICANCE: To better understand the biological factors influencing neural activity, detailed cellular and molecular tissue responses were examined. Decreases in neural activity and blood oxygenation in the tissue surrounding the implant, shift in expression levels of vesicular transporter proteins and ion channels, axon and myelin injury, and interrupted blood flow in nearby capillaries can impact neural activity around implanted neural interfaces. Combined, these tissue changes highlight the need for more comprehensive, basic science research to elucidate the relationship between biology and chronic electrophysiology performance in order to advance neural technologies.},
}
@article {pmid29181021,
year = {2017},
author = {Banville, H and Gupta, R and Falk, TH},
title = {Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {3524208},
pmid = {29181021},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Male ; Mental Processes/*physiology ; Multimodal Imaging ; *Neuropsychological Tests ; *Spectroscopy, Near-Infrared ; Support Vector Machine ; Young Adult ; },
abstract = {Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs.},
}
@article {pmid29180616,
year = {2017},
author = {Balasubramanian, K and Vaidya, M and Southerland, J and Badreldin, I and Eleryan, A and Takahashi, K and Qian, K and Slutzky, MW and Fagg, AH and Oweiss, K and Hatsopoulos, NG},
title = {Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation.},
journal = {Nature communications},
volume = {8},
number = {1},
pages = {1796},
pmid = {29180616},
issn = {2041-1723},
support = {R01 DE023816/DE/NIDCR NIH HHS/United States ; R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS062031/NS/NINDS NIH HHS/United States ; R01 NS093909/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; *Amputation, Surgical ; Animals ; Brain Mapping/instrumentation/*methods ; *Brain-Computer Interfaces ; Electrodes ; Hand Strength/physiology ; Macaca mulatta ; Machine Learning ; Motor Cortex/cytology/*physiology ; Movement/physiology ; Neuronal Plasticity/*physiology ; Neurons/physiology ; Robotics/instrumentation/methods ; Upper Extremity/surgery ; },
abstract = {Studies on neural plasticity associated with brain-machine interface (BMI) exposure have primarily documented changes in single neuron activity, and largely in intact subjects. Here, we demonstrate significant changes in ensemble-level functional connectivity among primary motor cortical (MI) neurons of chronically amputated monkeys exposed to control a multiple-degree-of-freedom robot arm. A multi-electrode array was implanted in M1 contralateral or ipsilateral to the amputation in three animals. Two clusters of stably recorded neurons were arbitrarily assigned to control reach and grasp movements, respectively. With exposure, network density increased in a nearly monotonic fashion in the contralateral monkeys, whereas the ipsilateral monkey pruned the existing network before re-forming a denser connectivity. Excitatory connections among neurons within a cluster were denser, whereas inhibitory connections were denser among neurons across the two clusters. These results indicate that cortical network connectivity can be modified with BMI learning, even among neurons that have been chronically de-efferented and de-afferented due to amputation.},
}
@article {pmid29180483,
year = {2017},
author = {Dong, Y and Raif, KE and Determan, SC and Gai, Y},
title = {Decoding spatial attention with EEG and virtual acoustic space.},
journal = {Physiological reports},
volume = {5},
number = {22},
pages = {},
pmid = {29180483},
issn = {2051-817X},
mesh = {Adult ; *Attention ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; *Sound Localization ; *Spatial Processing ; Visual Perception ; },
abstract = {Decoding spatial attention based on brain signals has wide applications in brain-computer interface (BCI). Previous BCI systems mostly relied on visual patterns or auditory stimulation (e.g., loudspeakers) to evoke synchronous brain signals. There would be difficulties to cover a large range of spatial locations with such a stimulation protocol. The present study explored the possibility of using virtual acoustic space and a visual-auditory matching paradigm to overcome this issue. The technique has the flexibility of generating sound stimulation from virtually any spatial location. Brain signals of eight human subjects were obtained with a 32-channel Electroencephalogram (EEG). Two amplitude-modulated noise or speech sentences carrying distinct spatial information were presented concurrently. Each sound source was tagged with a unique modulation phase so that the phase of the recorded EEG signals indicated the sound being attended to. The phase-tagged sound was further filtered with head-related transfer functions to create the sense of virtual space. Subjects were required to pay attention to the sound source that best matched the location of a visual target. For all the subjects, the phase of a single sound could be accurately reflected over the majority of electrodes based on EEG responses of 90 s or less. The electrodes providing significant decoding performance on auditory attention were fewer and may require longer EEG responses. The reliability and efficiency of decoding with a single electrode varied with subjects. Overall, the virtual acoustic space protocol has the potential of being used in practical BCI systems.},
}
@article {pmid29176943,
year = {2017},
author = {Cruz-Garza, JG and Brantley, JA and Nakagome, S and Kontson, K and Megjhani, M and Robleto, D and Contreras-Vidal, JL},
title = {Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {527},
pmid = {29176943},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.},
}
@article {pmid29176638,
year = {2017},
author = {Eliseyev, A and Auboiroux, V and Costecalde, T and Langar, L and Charvet, G and Mestais, C and Aksenova, T and Benabid, AL},
title = {Recursive Exponentially Weighted N-way Partial Least Squares Regression with Recursive-Validation of Hyper-Parameters in Brain-Computer Interface Applications.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {16281},
pmid = {29176638},
issn = {2045-2322},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrocorticography ; Electroencephalography ; *Least-Squares Analysis ; Magnetoencephalography ; Neurosciences/methods ; Software ; },
abstract = {A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least Squares (REW-NPLS) regression algorithm is proposed for high dimension multi-way (tensor) data treatment and adaptive modeling of complex processes in real-time. The method unites fast and efficient calculation schemes of the Recursive Exponentially Weighted PLS with the robustness of tensor-based approaches. Moreover, contrary to other multi-way recursive algorithms, no loss of information occurs in the REW-NPLS. In addition, the Recursive-Validation method for recursive estimation of the hyper-parameters is proposed instead of conventional cross-validation procedure. The approach was then compared to state-of-the-art methods. The efficiency of the methods was tested in electrocorticography (ECoG) and magnetoencephalography (MEG) datasets. The algorithms are implemented in software suitable for real-time operation. Although the Brain-Computer Interface applications are used to demonstrate the methods, the proposed approaches could be efficiently used in a wide range of tasks beyond neuroscience uniting complex multi-modal data structures, adaptive modeling, and real-time computational requirements.},
}
@article {pmid29170726,
year = {2017},
author = {Miura, S and Takazawa, J and Kobayashi, Y and Fujie, MG},
title = {Accuracy to detection timing for assisting repetitive facilitation exercise system using MRCP and SVM.},
journal = {Robotics and biomimetics},
volume = {4},
number = {1},
pages = {12},
pmid = {29170726},
issn = {2197-3768},
abstract = {This paper presents a feasibility study of a brain-machine interface system to assist repetitive facilitation exercise. Repetitive facilitation exercise is an effective rehabilitation method for patients with hemiplegia. In repetitive facilitation exercise, a therapist stimulates the paralyzed part of the patient while motor commands run along the nerve pathway. However, successful repetitive facilitation exercise is difficult to achieve and even a skilled practitioner cannot detect when a motor command occurs in patient's brain. We proposed a brain-machine interface system for automatically detecting motor commands and stimulating the paralyzed part of a patient. To determine motor commands from patient electroencephalogram (EEG) data, we measured the movement-related cortical potential (MRCP) and constructed a support vector machine system. In this paper, we validated the prediction timing of the system at the highest accuracy by the system using EEG and MRCP. In the experiments, we measured the EEG when the participant bent their elbow when prompted to do so. We analyzed the EEG data using a cross-validation method. We found that the average accuracy was 72.9% and the highest at the prediction timing 280 ms. We conclude that 280 ms is the most suitable to predict the judgment that a patient intends to exercise or not.},
}
@article {pmid29169769,
year = {2017},
author = {Barthélemy, Q and Mayaud, L and Renard, Y and Kim, D and Kang, SW and Gunkelman, J and Congedo, M},
title = {Online denoising of eye-blinks in electroencephalography.},
journal = {Neurophysiologie clinique = Clinical neurophysiology},
volume = {47},
number = {5-6},
pages = {371-391},
doi = {10.1016/j.neucli.2017.10.059},
pmid = {29169769},
issn = {1769-7131},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Blinking/*physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Child ; *Electroencephalography/methods ; Electrooculography/methods ; Humans ; Middle Aged ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: Due to its high temporal resolution, electroencephalography (EEG) has become a broadly-used technology for real-time brain monitoring applications such as neurofeedback (NFB) and brain-computer interfaces (BCI). However, since EEG signals are prone to artifacts, denoising is a crucial step that enables adequate subsequent data processing and interpretation. The aim of this study is to compare manual denoising to unsupervised online denoising, which is essential to real-time applications.<br><br>METHODS: Denoising EEG for real-time applications requires the implementation of unsupervised and online methods. In order to permit genericity, these methods should not rely on electrooculography (EOG) traces nor on temporal/spatial templates of the artifacts. Two blind source separation (BSS) methods are analyzed in this paper with the aim of automatically correcting online eye-blink artifacts: the algorithm for multiple unknown signals extraction (AMUSE) and the approximate joint diagonalization of Fourier cospectra (AJDC). The chosen gold standard is a manual review of the EEG database carried out retrospectively by a human operator. Comparison is carried out using the spectral properties of the continuous EEG and event-related potentials (ERP).<br><br>RESULTS AND CONCLUSION: The AJDC algorithm addresses limitations observed in AMUSE and outperforms it. No statistical difference is found between the manual and automatic approaches on a database composed of 15 healthy individuals, paving the way for an automated, operator-independent, and real-time eye-blink correction technique.},
}
@article {pmid29166301,
year = {2018},
author = {Karim, ME and Pang, M and Platt, RW},
title = {Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?.},
journal = {Epidemiology (Cambridge, Mass.)},
volume = {29},
number = {2},
pages = {191-198},
doi = {10.1097/EDE.0000000000000787},
pmid = {29166301},
issn = {1531-5487},
support = {//CIHR/Canada ; },
mesh = {*Algorithms ; Data Accuracy ; Datasets as Topic/standards ; Empirical Research ; *Machine Learning ; *Propensity Score ; Retrospective Studies ; United Kingdom ; },
abstract = {The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.},
}
@article {pmid29163123,
year = {2017},
author = {Zeng, H and Wang, Y and Wu, C and Song, A and Liu, J and Ji, P and Xu, B and Zhu, L and Li, H and Wen, P},
title = {Closed-Loop Hybrid Gaze Brain-Machine Interface Based Robotic Arm Control with Augmented Reality Feedback.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {60},
pmid = {29163123},
issn = {1662-5218},
abstract = {Brain-machine interface (BMI) can be used to control the robotic arm to assist paralysis people for performing activities of daily living. However, it is still a complex task for the BMI users to control the process of objects grasping and lifting with the robotic arm. It is hard to achieve high efficiency and accuracy even after extensive trainings. One important reason is lacking of sufficient feedback information for the user to perform the closed-loop control. In this study, we proposed a method of augmented reality (AR) guiding assistance to provide the enhanced visual feedback to the user for a closed-loop control with a hybrid Gaze-BMI, which combines the electroencephalography (EEG) signals based BMI and the eye tracking for an intuitive and effective control of the robotic arm. Experiments for the objects manipulation tasks while avoiding the obstacle in the workspace are designed to evaluate the performance of our method for controlling the robotic arm. According to the experimental results obtained from eight subjects, the advantages of the proposed closed-loop system (with AR feedback) over the open-loop system (with visual inspection only) have been verified. The number of trigger commands used for controlling the robotic arm to grasp and lift the objects with AR feedback has reduced significantly and the height gaps of the gripper in the lifting process have decreased more than 50% compared to those trials with normal visual inspection only. The results reveal that the hybrid Gaze-BMI user can benefit from the information provided by the AR interface, improving the efficiency and reducing the cognitive load during the grasping and lifting processes.},
}
@article {pmid29163110,
year = {2017},
author = {Dinov, M and Leech, R},
title = {Modeling Uncertainties in EEG Microstates: Analysis of Real and Imagined Motor Movements Using Probabilistic Clustering-Driven Training of Probabilistic Neural Networks.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {534},
pmid = {29163110},
issn = {1662-5161},
abstract = {Part of the process of EEG microstate estimation involves clustering EEG channel data at the global field power (GFP) maxima, very commonly using a modified K-means approach. Clustering has also been done deterministically, despite there being uncertainties in multiple stages of the microstate analysis, including the GFP peak definition, the clustering itself and in the post-clustering assignment of microstates back onto the EEG timecourse of interest. We perform a fully probabilistic microstate clustering and labeling, to account for these sources of uncertainty using the closest probabilistic analog to KM called Fuzzy C-means (FCM). We train softmax multi-layer perceptrons (MLPs) using the KM and FCM-inferred cluster assignments as target labels, to then allow for probabilistic labeling of the full EEG data instead of the usual correlation-based deterministic microstate label assignment typically used. We assess the merits of the probabilistic analysis vs. the deterministic approaches in EEG data recorded while participants perform real or imagined motor movements from a publicly available data set of 109 subjects. Though FCM group template maps that are almost topographically identical to KM were found, there is considerable uncertainty in the subsequent assignment of microstate labels. In general, imagined motor movements are less predictable on a time point-by-time point basis, possibly reflecting the more exploratory nature of the brain state during imagined, compared to during real motor movements. We find that some relationships may be more evident using FCM than using KM and propose that future microstate analysis should preferably be performed probabilistically rather than deterministically, especially in situations such as with brain computer interfaces, where both training and applying models of microstates need to account for uncertainty. Probabilistic neural network-driven microstate assignment has a number of advantages that we have discussed, which are likely to be further developed and exploited in future studies. In conclusion, probabilistic clustering and a probabilistic neural network-driven approach to microstate analysis is likely to better model and reveal details and the variability hidden in current deterministic and binarized microstate assignment and analyses.},
}
@article {pmid29163103,
year = {2017},
author = {Hommelsen, M and Schneiders, M and Schuld, C and Keyl, P and Rupp, R},
title = {Sensory Feedback Interferes with Mu Rhythm Based Detection of Motor Commands from Electroencephalographic Signals.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {523},
pmid = {29163103},
issn = {1662-5161},
abstract = {Background: Electroencephalogram (EEG)-based brain-computer interfaces (BCI) represent a promising component of restorative motor therapies in individuals with partial paralysis. However, in those patients, sensory functions such as proprioception are at least partly preserved. The aim of this study was to investigate whether afferent feedback interferes with the BCI-based detection of efferent motor commands during execution of movements. Methods: Brain activity of 13 able-bodied subjects (age: 29.1 ± 4.8 years; 11 males) was compared between a motor task (MT) consisting of an isometric, isotonic grip and a somatosensory electrical stimulation (SS) of the fingertips. Modulation of the mu rhythm (8-13 Hz) was investigated to identify changes specifically related to the generation of efferent commands. A linear discriminant analysis (LDA) was used to investigate the activation pattern on a single-trial basis. Classifiers were trained with MT vs. REST (periods without MT/SS) and tested with SS and vice versa to quantify the impact of afferent feedback on the classification results. Results: Few differences in the spatial pattern between MT and SS were found in the modulation of the mu rhythm. All were characterized by event-related desynchronization (ERD) peaks at electrodes C3, C4, and CP3. Execution of the MT was associated with a significantly stronger ERD in the majority of sensorimotor electrodes [C3 (p < 0.01); CP3 (p < 0.05); C4 (p < 0.01)]. Classification accuracy of MT vs. REST was significantly higher than SS vs. REST (77% and 63%; p < 10[-8]). Classifiers trained on MT vs. REST were able to classify SS trials significantly above chance even though no motor commands were present during SS. Classifiers trained on SS performed better in classifying MT instead of SS. Conclusion: Our results challenge the notion that the modulation of the mu rhythm is a robust phenomenon for detecting efferent commands when afferent feedback is present. Instead, they indicate that the mu ERD caused by the processing of afferent feedback generates ERD patterns in the sensorimotor cortex that are masking the ERD patterns caused by the generation of efferent commands. Thus, processing of afferent feedback represents a considerable source of false positives when the mu rhythm is used for the detection of efferent commands.},
}
@article {pmid29163098,
year = {2017},
author = {Athanasiou, A and Klados, MA and Pandria, N and Foroglou, N and Kavazidi, KR and Polyzoidis, K and Bamidis, PD},
title = {A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {517},
pmid = {29163098},
issn = {1662-5161},
abstract = {Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.},
}
@article {pmid29160240,
year = {2018},
author = {Degenhart, AD and Hiremath, SV and Yang, Y and Foldes, S and Collinger, JL and Boninger, M and Tyler-Kabara, EC and Wang, W},
title = {Remapping cortical modulation for electrocorticographic brain-computer interfaces: a somatotopy-based approach in individuals with upper-limb paralysis.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026021},
pmid = {29160240},
issn = {1741-2552},
support = {KL2 TR000146/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Arm/innervation/*physiology ; Brain Mapping/instrumentation/methods ; *Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods ; Electrodes, Implanted ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Paralysis/physiopathology/*therapy ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology aims to provide individuals with paralysis a means to restore function. Electrocorticography (ECoG) uses disc electrodes placed on either the surface of the dura or the cortex to record field potential activity. ECoG has been proposed as a viable neural recording modality for BCI systems, potentially providing stable, long-term recordings of cortical activity with high spatial and temporal resolution. Previously we have demonstrated that a subject with spinal cord injury (SCI) could control an ECoG-based BCI system with up to three degrees of freedom (Wang et al 2013 PLoS One). Here, we expand upon these findings by including brain-control results from two additional subjects with upper-limb paralysis due to amyotrophic lateral sclerosis and brachial plexus injury, and investigate the potential of motor and somatosensory cortical areas to enable BCI control.<br><br>APPROACH: Individuals were implanted with high-density ECoG electrode grids over sensorimotor cortical areas for less than 30 d. Subjects were trained to control a BCI by employing a somatotopic control strategy where high-gamma activity from attempted arm and hand movements drove the velocity of a cursor.<br><br>MAIN RESULTS: Participants were capable of generating robust cortical modulation that was differentiable across attempted arm and hand movements of their paralyzed limb. Furthermore, all subjects were capable of voluntarily modulating this activity to control movement of a computer cursor with up to three degrees of freedom using the somatotopic control strategy. Additionally, for those subjects with electrode coverage of somatosensory cortex, we found that somatosensory cortex was capable of supporting ECoG-based BCI control.<br><br>SIGNIFICANCE: These results demonstrate the feasibility of ECoG-based BCI systems for individuals with paralysis as well as highlight some of the key challenges that must be overcome before such systems are translated to the clinical realm. ClinicalTrials.gov Identifier: NCT01393444.},
}
@article {pmid29152523,
year = {2017},
author = {Huggins, JE and Guger, C and Ziat, M and Zander, TO and Taylor, D and Tangermann, M and Soria-Frisch, A and Simeral, J and Scherer, R and Rupp, R and Ruffini, G and Robinson, DKR and Ramsey, NF and Nijholt, A and Müller-Putz, G and McFarland, DJ and Mattia, D and Lance, BJ and Kindermans, PJ and Iturrate, I and Herff, C and Gupta, D and Do, AH and Collinger, JL and Chavarriaga, R and Chase, SM and Bleichner, MG and Batista, A and Anderson, CW and Aarnoutse, EJ},
title = {Workshops of the Sixth International Brain-Computer Interface Meeting: brain-computer interfaces past, present, and future.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {4},
number = {1-2},
pages = {3-36},
pmid = {29152523},
issn = {2326-263X},
support = {681231/ERC_/European Research Council/International ; R01 NS094748/NS/NINDS NIH HHS/United States ; R13 DC015188/DC/NIDCD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; UL1 TR000457/TR/NCATS NIH HHS/United States ; I01 RX001155/RX/RRD VA/United States ; },
abstract = {The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.},
}
@article {pmid29149855,
year = {2017},
author = {Perez-Marcos, D and Chevalley, O and Schmidlin, T and Garipelli, G and Serino, A and Vuadens, P and Tadi, T and Blanke, O and Millán, JDR},
title = {Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {119},
pmid = {29149855},
issn = {1743-0003},
mesh = {Adult ; Aged ; Exercise Therapy/*methods ; Female ; Humans ; Male ; Middle Aged ; Paresis/*rehabilitation ; Pilot Projects ; Recovery of Function ; Stroke ; Stroke Rehabilitation/*methods ; User-Computer Interface ; *Virtual Reality ; },
abstract = {BACKGROUND: Technology-mediated neurorehabilitation is suggested to enhance training intensity and therefore functional gains. Here, we used a novel virtual reality (VR) system for task-specific upper extremity training after stroke. The system offers interactive exercises integrating motor priming techniques and embodied visuomotor feedback. In this pilot study, we examined (i) rehabilitation dose and training intensity, (ii) functional improvements, and (iii) safety and tolerance when exposed to intensive VR rehabilitation.<br><br>METHODS: Ten outpatient stroke survivors with chronic (>6&#xa0;months) upper extremity paresis participated in a ten-session VR-based upper limb rehabilitation program (2 sessions/week).<br><br>RESULTS: All participants completed all sessions of the treatment. In total, they received a median of 403&#xa0;min of upper limb therapy, with 290&#xa0;min of effective training. Within that time, participants performed a median of 4713 goal-directed movements. Importantly, training intensity increased progressively across sessions from 13.2 to 17.3 movements per minute. Clinical measures show that despite being in the chronic phase, where recovery potential is thought to be limited, participants showed a median improvement rate of 5.3% in motor function (Fugl-Meyer Assessment for Upper Extremity; FMA-UE) post intervention compared to baseline, and of 15.4% at one-month follow-up. For three of them, this improvement was clinically significant. A significant improvement in shoulder active range of motion (AROM) was also observed at follow-up. Participants reported very low levels of pain, stress and fatigue following each session of training, indicating that the intensive VR intervention was well tolerated. No severe adverse events were reported. All participants expressed their interest in continuing the intervention at the hospital or even at home, suggesting high levels of adherence and motivation for the provided intervention.<br><br>CONCLUSIONS: This pilot study showed how a dedicated VR system could deliver high rehabilitation doses and, importantly, intensive training in chronic stroke survivors. FMA-UE and AROM results suggest that task-specific VR training may be beneficial for further functional recovery both in the chronic stage of stroke. Longitudinal studies with higher doses and sample sizes are required to confirm the therapy effectiveness.<br><br>TRIAL REGISTRATION: This trial was retrospectively registered at ClinicalTrials.gov database (registration number NCT03094650) on 14 March 2017.},
}
@article {pmid29148137,
year = {2017},
author = {Irimia, DC and Cho, W and Ortner, R and Allison, BZ and Ignat, BE and Edlinger, G and Guger, C},
title = {Brain-Computer Interfaces With Multi-Sensory Feedback for Stroke Rehabilitation: A Case Study.},
journal = {Artificial organs},
volume = {41},
number = {11},
pages = {E178-E184},
doi = {10.1111/aor.13054},
pmid = {29148137},
issn = {1525-1594},
mesh = {Adult ; Biomechanical Phenomena ; Brain/*physiopathology ; Brain Waves ; *Brain-Computer Interfaces ; Chronic Disease ; Discriminant Analysis ; Electric Stimulation Therapy/*instrumentation/methods ; Electroencephalography ; Equipment Design ; *Feedback, Sensory ; Female ; Hand/*innervation ; Humans ; Linear Models ; Male ; Middle Aged ; *Motor Activity ; Paralysis/diagnosis/physiopathology/*rehabilitation ; Pattern Recognition, Automated ; Recovery of Function ; Signal Processing, Computer-Assisted ; Stroke/diagnosis/physiopathology/*therapy ; Stroke Rehabilitation/*instrumentation/methods ; Time Factors ; Treatment Outcome ; },
abstract = {Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. This work presents the recoveriX system, a hardware and software platform that combines a motor imagery (MI)-based brain-computer interface (BCI), functional electrical stimulation (FES), and visual feedback technologies for a complete sensorimotor closed-loop therapy system for poststroke rehabilitation. The proposed system was tested on two chronic stroke patients in a clinical environment. The patients were instructed to imagine the movement of either the left or right hand in random order. During these two MI tasks, two types of feedback were provided: a bar extending to the left or right side of a monitor as visual feedback and passive hand opening stimulated from FES as proprioceptive feedback. Both types of feedback relied on the BCI classification result achieved using common spatial patterns and a linear discriminant analysis classifier. After 10 sessions of recoveriX training, one patient partially regained control of wrist extension in her paretic wrist and the other patient increased the range of middle finger movement by 1 cm. A controlled group study is planned with a new version of the recoveriX system, which will have several improvements.},
}
@article {pmid29147144,
year = {2017},
author = {Hwang, JH and Nam, KW and Jang, DP and Kim, IY},
title = {Effects of spectral smearing of stimuli on the performance of auditory steady-state response-based brain-computer interface.},
journal = {Cognitive neurodynamics},
volume = {11},
number = {6},
pages = {515-527},
pmid = {29147144},
issn = {1871-4080},
abstract = {There have been few reports that investigated the effects of the degree and pattern of a spectral smearing of stimuli due to deteriorated hearing ability on the performance of auditory brain-computer interface (BCI) systems. In this study, we assumed that such spectral smearing of stimuli may affect the performance of an auditory steady-state response (ASSR)-based BCI system and performed subjective experiments using 10 normal-hearing subjects to verify this assumption. We constructed smearing-reflected stimuli using an 8-channel vocoder with moderate and severe hearing loss setups and, using these stimuli, performed subjective concentration tests with three symmetric and six asymmetric smearing patterns while recording electroencephalogram signals. Then, 56 ratio features were calculated from the recorded signals, and the accuracies of the BCI selections were calculated and compared. Experimental results demonstrated that (1) applying smearing-reflected stimuli decreases the performance of an ASSR-based auditory BCI system, and (2) such negative effects can be reduced by adjusting the feature settings of the BCI algorithm on the basis of results acquired a posteriori. These results imply that by fine-tuning the feature settings of the BCI algorithm according to the degree and pattern of hearing ability deterioration of the recipient, the clinical benefits of a BCI system can be improved.},
}
@article {pmid29147143,
year = {2017},
author = {Khasnobish, A and Datta, S and Bose, R and Tibarewala, DN and Konar, A},
title = {Analyzing text recognition from tactually evoked EEG.},
journal = {Cognitive neurodynamics},
volume = {11},
number = {6},
pages = {501-513},
pmid = {29147143},
issn = {1871-4080},
abstract = {Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2 s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529 bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.},
}
@article {pmid29142267,
year = {2017},
author = {Mols, K and Musa, S and Nuttin, B and Lagae, L and Bonin, V},
title = {In vivo characterization of the electrophysiological and astrocytic responses to a silicon neuroprobe implanted in the mouse neocortex.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {15642},
pmid = {29142267},
issn = {2045-2322},
mesh = {Animals ; Astrocytes/*drug effects ; Brain-Computer Interfaces ; Electrodes, Implanted ; Electrophysiology ; Mice ; Microelectrodes/adverse effects ; Neocortex/*drug effects/physiopathology ; Neurons/*drug effects/physiology ; Silicon/*toxicity ; },
abstract = {Silicon neuroprobes hold great potential for studies of large-scale neural activity and brain computer interfaces, but data on brain response in chronic implants is limited. Here we explored with in vivo cellular imaging the response to multisite silicon probes for neural recordings. We tested a chronic implant for mice consisting of a CMOS-compatible silicon probe rigidly implanted in the cortex under a cranial imaging window. Multiunit recordings of cortical neurons with the implant showed no degradation of electrophysiological signals weeks after implantation (mean spike and noise amplitudes of 186 ± 42 µVpp and 16 ± 3.2 µVrms, respectively, n = 5 mice). Two-photon imaging through the cranial window allowed longitudinal monitoring of fluorescently-labeled astrocytes from the second week post implantation for 8 weeks (n = 3 mice). The imaging showed a local increase in astrocyte-related fluorescence that remained stable from the second to the tenth week post implantation. These results demonstrate that, in a standard electrophysiology protocol in mice, rigidly implanted silicon probes can provide good short to medium term chronic recording performance with a limited astrocyte inflammatory response. The precise factors influencing the response to silicon probe implants remain to be elucidated.},
}
@article {pmid29141030,
year = {2017},
author = {Zhang, X and Xu, G and Xie, J and Zhang, X},
title = {Brain response to luminance-based and motion-based stimulation using inter-modulation frequencies.},
journal = {PloS one},
volume = {12},
number = {11},
pages = {e0188073},
pmid = {29141030},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; *Lighting ; Male ; *Motion ; Young Adult ; },
abstract = {Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) has advantages of high information transfer rate (ITR), less electrodes and little training. So it has been widely investigated. However, the available stimulus frequencies are limited by brain responses. Simultaneous modulation of stimulus luminance is a novel method to resolve this problem. In this study, three experiments were devised to gain a deeper understanding of the brain response to the stimulation using inter-modulation frequencies. First, luminance-based stimulation using one to five inter-modulation frequencies was analyzed for the first time. The characteristics of the brain responses to the proposed stimulation were reported. Second, the motion-based stimulation with equal luminance using inter-modulation frequencies was also proposed for the first time. The response of the brain under these conditions were similar to that of luminance-based stimulation which can induce combination frequencies. And an elementary analysis was conducted to explain the reason of the occurrence of combination frequencies. Finally, the online test demonstrated the efficacy of our proposed two stimulation methods for BCI. The average ITRs reached 34.7836 bits/min and 39.2856 bits/min for luminance-based and motion-based stimulation respectively. This study demonstrated that the simultaneous modulation of stimulus luminance could extend to at least five frequencies to induce SSVEP and the brain response to the stimulus still maintained a certain positive correlation with luminance. And not only luminance-based stimulation, but also motion-based stimulation with equal luminance can elicit inter-modulation frequencies to effectively increase the number of targets for multi-class SSVEP.},
}
@article {pmid29137602,
year = {2017},
author = {Dherani, M and Zehra, SN and Jackson, C and Satyanaryana, V and Huque, R and Chandra, P and Rahman, A and Siddiqi, K},
title = {Behaviour change interventions to reduce second-hand smoke exposure at home in pregnant women - a systematic review and intervention appraisal.},
journal = {BMC pregnancy and childbirth},
volume = {17},
number = {1},
pages = {378},
pmid = {29137602},
issn = {1471-2393},
support = {13007/CRUK_/Cancer Research UK/United Kingdom ; MR/P008941/1/MRC_/Medical Research Council/United Kingdom ; MR/N006224/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Behavior Therapy/*methods ; Female ; Humans ; Maternal Exposure/adverse effects/*prevention &amp; control ; Pregnancy ; Prenatal Care/*methods ; Smoking/psychology ; Smoking Cessation/*methods/psychology ; Tobacco Smoke Pollution/adverse effects/*prevention &amp; control ; },
abstract = {BACKGROUND: Second-hand smoke (SHS) exposure during pregnancy is associated with poor pregnancy and foetal outcomes. Theory-based behaviour change interventions (BCI) have been used successfully to change smoking related behaviours and offer the potential to reduce exposure of SHS in pregnant women. Systematic reviews conducted so far do not evaluate the generalisability and scalability of interventions. The objectives of this review were to (1) report the BCIs for reduction in home exposure to SHS for pregnant women; and (2) critically appraise intervention-reporting, generalisability, feasibility and scalability of the BCIs employed.<br><br>METHODS: Standard methods following PRISMA guidelines were employed. Eight databases were searched from 2000 to 2015 in English. The studies included used BCIs on pregnant women to reduce their home SHS exposure by targeting husbands/partners. The Workgroup for Intervention Development and Evaluation Research (WIDER) guidelines were used to assess intervention reporting. Generalisability, feasibility and scalability were assessed against criteria described by Bonell and Milat.<br><br>RESULTS: Of 3479 papers identified, six studies met the inclusion criteria. These studies found that BCIs led to increased knowledge about SHS harms, reduction or husbands quitting smoking, and increased susceptibility and change in level of actions to reduce SHS at home. Two studies reported objective exposure measures, and one reported objective health outcomes. The studies partially followed WIDER guidelines for reporting, and none met all generalisability, feasibility and scalability criteria.<br><br>CONCLUSIONS: There is a dearth of literature in this area and the quality of studies reviewed was moderate to low. The BCIs appear effective in reducing SHS, however, weak study methodology (self-reported exposure, lack of objective outcome assessment, short follow-up, absence of control group) preclude firm conclusion. Some components of the WIDER checklist were followed for BCI reporting, scalability and feasibility of the studies were not described. More rigorous studies using biochemical and clinical measures for exposures and health outcomes in varied study settings are required. Studies should report interventions in detail using WIDER checklist and assess them for generalisability, feasibility and scalability.<br><br>TRIAL REGISTRATION: CRD40125026666.},
}
@article {pmid29134143,
year = {2017},
author = {Stefano Filho, CA and Attux, R and Castellano, G},
title = {EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches.},
journal = {PeerJ},
volume = {5},
number = {},
pages = {e3983},
pmid = {29134143},
issn = {2167-8359},
abstract = {Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns-that is, variations in the PSD of mu and beta bands-induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.},
}
@article {pmid29130453,
year = {2017},
author = {Xia, B and Cao, L and Maysam, O and Li, J and Xie, H and Su, C and Birbaumer, N},
title = {A binary motor imagery tasks based brain-computer interface for two-dimensional movement control.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066009},
doi = {10.1088/1741-2552/aa7ee9},
pmid = {29130453},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; Random Allocation ; Young Adult ; },
abstract = {OBJECTIVE: Two-dimensional movement control is a popular issue in brain-computer interface (BCI) research and has many applications in the real world. In this paper, we introduce a combined control strategy to a binary class-based BCI system that allows the user to move a cursor in a two-dimensional (2D) plane. Users focus on a single moving vector to control 2D movement instead of controlling vertical and horizontal movement separately.<br><br>APPROACH: Five participants took part in a fixed-target experiment and random-target experiment to verify the effectiveness of the combination control strategy under the fixed and random routine conditions. Both experiments were performed in a virtual 2D dimensional environment and visual feedback was provided on the screen.<br><br>MAIN RESULTS: The five participants achieved an average hit rate of 98.9% and 99.4% for the fixed-target experiment and the random-target experiment, respectively.<br><br>SIGNIFICANCE: The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.},
}
@article {pmid29130452,
year = {2017},
author = {Even-Chen, N and Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Augmenting intracortical brain-machine interface with neurally driven error detectors.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066007},
pmid = {29130452},
issn = {1741-2552},
support = {/HHMI/Howard Hughes Medical Institute/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Acoustic Stimulation/methods ; Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces/standards ; Electrodes, Implanted ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Principal Component Analysis/methods/standards ; *Support Vector Machine/standards ; },
abstract = {OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs.<br><br>APPROACH: We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task.<br><br>MAIN RESULTS: We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF).<br><br>SIGNIFICANCE: Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.},
}
@article {pmid29129011,
year = {2019},
author = {Gilbert, F and Cook, M and O'Brien, T and Illes, J},
title = {Embodiment and Estrangement: Results from a First-in-Human "Intelligent BCI" Trial.},
journal = {Science and engineering ethics},
volume = {25},
number = {1},
pages = {83-96},
pmid = {29129011},
issn = {1471-5546},
mesh = {*Artificial Intelligence ; Brain ; Brain-Computer Interfaces/*adverse effects ; Humans ; Intelligence ; Knowledge ; Prostheses and Implants/*adverse effects ; Qualitative Research ; *Self Concept ; Stress, Psychological/*etiology ; Surveys and Questionnaires ; *Technology ; },
abstract = {While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from the first-in-human clinical trial to study patients with implanted BCI advisory devices. We explored perceptions of self-change across six patients who volunteered to be implanted with artificially intelligent BCI devices. We used qualitative methodological tools grounded in phenomenology to conduct in-depth, semi-structured interviews. Results show that, on the one hand, BCIs can positively increase a sense of the self and control; on the other hand, they can induce radical distress, feelings of loss of control, and a rupture of patient identity. We conclude by offering suggestions for the proactive creation of preparedness protocols specific to intelligent-predictive and advisory-BCI technologies essential to prevent potential iatrogenic harms.},
}
@article {pmid29127346,
year = {2017},
author = {Goss-Varley, M and Dona, KR and McMahon, JA and Shoffstall, AJ and Ereifej, ES and Lindner, SC and Capadona, JR},
title = {Microelectrode implantation in motor cortex causes fine motor deficit: Implications on potential considerations to Brain Computer Interfacing and Human Augmentation.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {15254},
pmid = {29127346},
issn = {2045-2322},
support = {IK1 RX001664/RX/RRD VA/United States ; },
mesh = {Animals ; Brain-Computer Interfaces/*adverse effects ; Electrodes, Implanted/*adverse effects ; Humans ; Microelectrodes/adverse effects ; *Motor Activity ; Motor Cortex/*physiopathology ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Intracortical microelectrodes have shown great success in enabling locked-in patients to interact with computers, robotic limbs, and their own electrically driven limbs. The recent advances have inspired world-wide enthusiasm resulting in billions of dollars invested in federal and industrial sponsorships to understanding the brain for rehabilitative applications. Additionally, private philanthropists have also demonstrated excitement in the field by investing in the use of brain interfacing technologies as a means to human augmentation. While the promise of incredible technologies is real, caution must be taken as implications regarding optimal performance and unforeseen side effects following device implantation into the brain are not fully characterized. The current study is aimed to quantify any motor deficit caused by microelectrode implantation in the motor cortex of healthy rats compared to non-implanted controls. Following electrode insertion, rats were tested on an open-field grid test to study gross motor function and a ladder test to study fine motor function. It was discovered that rats with chronically indwelling intracortical microelectrodes exhibited up to an incredible 527% increase in time to complete the fine motor task. This initial study defines the need for further and more robust behavioral testing of potential unintentional harm caused by microelectrode implantation.},
}
@article {pmid29126946,
year = {2018},
author = {Swan, BD and Gasperson, LB and Krucoff, MO and Grill, WM and Turner, DA},
title = {Sensory percepts induced by microwire array and DBS microstimulation in human sensory thalamus.},
journal = {Brain stimulation},
volume = {11},
number = {2},
pages = {416-422},
pmid = {29126946},
issn = {1876-4754},
support = {R21 NS066115/NS/NINDS NIH HHS/United States ; R25 NS065731/NS/NINDS NIH HHS/United States ; UH3 NS103468/NS/NINDS NIH HHS/United States ; },
mesh = {Deep Brain Stimulation/instrumentation/*methods ; Electrodes, Implanted ; Feedback, Physiological ; Female ; Humans ; Male ; Somatosensory Cortex/physiology ; Thalamus/*physiology ; Touch ; *Touch Perception ; },
abstract = {BACKGROUND: Microstimulation in human sensory thalamus (ventrocaudal, VC) results in focal sensory percepts in the hand and arm which may provide an alternative target site (to somatosensory cortex) for the input of prosthetic sensory information. Sensory feedback to facilitate motor function may require simultaneous or timed responses across separate digits to recreate perceptions of slip as well as encoding of intensity variations in pressure or touch.<br><br>OBJECTIVES: To determine the feasibility of evoking sensory percepts on separate digits with variable intensity through either a microwire array or deep brain stimulation (DBS) electrode, recreating "natural" and scalable percepts relating to the arm and hand.<br><br>METHODS: We compared microstimulation within ventrocaudal sensory thalamus through either a 16-channel microwire array (&#x223c;400&#xa0;k&#x3a9; per channel) or a 4-channel DBS electrode (&#x223c;1.2&#xa0;k&#x3a9; per contact) for percept location, size, intensity, and quality sensation, during thalamic DBS electrode placement in patients with essential tremor.<br><br>RESULTS: Percepts in small hand or finger regions were evoked by microstimulation through individual microwires and in 5/6 patients sensation on different digits could be perceived from stimulation through separate microwires. Microstimulation through DBS electrode contacts evoked sensations over larger areas in 5/5 patients, and the apparent intensity of the perceived response could be modulated with stimulation amplitude. The perceived naturalness of the sensation depended both on the pattern of stimulation as well as intensity of the stimulation.<br><br>CONCLUSIONS: Producing consistent evoked perceptions across separate digits within sensory thalamus is a feasible concept and a compact alternative to somatosensory cortex microstimulation for prosthetic sensory feedback. This approach will require a multi-element low impedance electrode with a sufficient stimulation range to evoke variable intensities of perception and a predictable spread of contacts to engage separate digits.},
}
@article {pmid29125134,
year = {2018},
author = {Golenia, JE and Wenzel, MA and Bogojeski, M and Blankertz, B},
title = {Implicit relevance feedback from electroencephalography and eye tracking in image search.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026002},
doi = {10.1088/1741-2552/aa9999},
pmid = {29125134},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Eye Movements/*physiology ; Feedback, Physiological/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Random Allocation ; Young Adult ; },
abstract = {OBJECTIVE: Methods from brain-computer interfacing (BCI) open a direct access to the mental processes of computer users, which offers particular benefits in comparison to standard methods for inferring user-related information. The signals can be recorded unobtrusively in the background, which circumvents the time-consuming and distracting need for the users to give explicit feedback to questions concerning the individual interest. The obtained implicit information makes it possible to create dynamic user interest profiles in real-time, that can be taken into account by novel types of adaptive, personalised software. In the present study, the potential of implicit relevance feedback from electroencephalography (EEG) and eye tracking was explored with a demonstrator application that simulated an image search engine.<br><br>APPROACH: The participants of the study queried for ambiguous search terms, having in mind one of the two possible interpretations of the respective term. Subsequently, they viewed different images arranged in a grid that were related to the query. The ambiguity of the underspecified search term was resolved with implicit information present in the recorded signals. For this purpose, feature vectors were extracted from the signals and used by multivariate classifiers that estimated the intended interpretation of the ambiguous query.<br><br>MAIN RESULT: The intended interpretation was inferred correctly from a combination of EEG and eye tracking signals in 86% of the cases on average. Information provided by the two measurement modalities turned out to be complementary.<br><br>SIGNIFICANCE: It was demonstrated that BCI methods can extract implicit user-related information in a setting of human-computer interaction. Novelties of the study are the implicit online feedback from EEG and eye tracking, the approximation to a realistic use case in a simulation, and the presentation of a large set of photographies that had to be interpreted with respect to the content.},
}
@article {pmid29124547,
year = {2018},
author = {Steyrl, D and Krausz, G and Koschutnig, K and Edlinger, G and Müller-Putz, GR},
title = {Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).},
journal = {Brain topography},
volume = {31},
number = {1},
pages = {129-149},
pmid = {29124547},
issn = {1573-6792},
support = {681231//H2020 European Research Council/International ; },
mesh = {Adult ; Alpha Rhythm ; *Artifacts ; Brain Mapping/methods ; Computer Simulation ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Visual/physiology ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Male ; Online Systems ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.},
}
@article {pmid29121942,
year = {2017},
author = {Burwell, S and Sample, M and Racine, E},
title = {Ethical aspects of brain computer interfaces: a scoping review.},
journal = {BMC medical ethics},
volume = {18},
number = {1},
pages = {60},
pmid = {29121942},
issn = {1472-6939},
mesh = {Biomedical Research/*ethics ; Brain Diseases/rehabilitation ; Brain-Computer Interfaces/*ethics/trends ; Communication Devices for People with Disabilities/*ethics/trends ; Electroencephalography ; Ethics, Research ; Humans ; Neurosciences/*ethics/*trends ; Personhood ; User-Computer Interface ; },
abstract = {BACKGROUND: Brain-Computer Interface (BCI) is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes (e.g., gaming) as well as health-related purposes (e.g., communication). The unprecedented direct connection created by BCI between human brains and computer hardware raises various ethical, social, and legal challenges that merit further examination and discussion.<br><br>METHODS: To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine.<br><br>RESULTS: Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed.<br><br>CONCLUSIONS: We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers.},
}
@article {pmid29120438,
year = {2017},
author = {Yuste, R and Goering, S and Arcas, BAY and Bi, G and Carmena, JM and Carter, A and Fins, JJ and Friesen, P and Gallant, J and Huggins, JE and Illes, J and Kellmeyer, P and Klein, E and Marblestone, A and Mitchell, C and Parens, E and Pham, M and Rubel, A and Sadato, N and Sullivan, LS and Teicher, M and Wasserman, D and Wexler, A and Whittaker, M and Wolpaw, J},
title = {Four ethical priorities for neurotechnologies and AI.},
journal = {Nature},
volume = {551},
number = {7679},
pages = {159-163},
pmid = {29120438},
issn = {1476-4687},
support = {P30 EY019007/EY/NEI NIH HHS/United States ; R01 EY011787/EY/NEI NIH HHS/United States ; },
mesh = {Alzheimer Disease/diagnosis ; Animals ; Artificial Intelligence/economics/*ethics/trends ; Bioengineering/economics/*ethics/trends ; Biomedical Enhancement/ethics/methods ; Brain-Computer Interfaces/economics/*ethics/trends ; *Codes of Ethics ; Electroencephalography ; Female ; *Guidelines as Topic ; Humans ; Individuality ; Informed Consent/ethics/legislation &amp; jurisprudence ; Male ; Neural Networks, Computer ; Neurosciences/economics/*ethics/trends ; Parkinson Disease/diagnosis ; *Privacy/legislation &amp; jurisprudence ; },
abstract = {Artificial intelligence and brain-computer interfaces must respect and preserve people’s privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.},
}
@article {pmid29118386,
year = {2017},
author = {Wittevrongel, B and Van Wolputte, E and Van Hulle, MM},
title = {Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {15037},
pmid = {29118386},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; *Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; *Support Vector Machine ; Visual Perception/physiology ; Young Adult ; },
abstract = {When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target. Especially for a small number of repetitions of the coding sequence, our beamforming approach significantly outperforms an optimised support vector machine (SVM)-based classifier, which is considered state-of-the-art in cVEP-based BCI. In addition to the traditional 60 Hz stimulus presentation rate for the coding sequence, we also explore the 120 Hz rate, and show that the latter enables faster communication, with a maximal median ITR of 172.87 bits/min. Finally, we also report on a transition effect in the EEG signal following the onset of the stimulus sequence, and recommend to exclude the first 150 ms of the trials from decoding when relying on a single presentation of the stimulus sequence.},
}
@article {pmid29117100,
year = {2017},
author = {Liu, A and Chen, K and Liu, Q and Ai, Q and Xie, Y and Chen, A},
title = {Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {11},
pages = {},
pmid = {29117100},
issn = {1424-8220},
mesh = {*Algorithms ; Automation ; Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Machine Learning ; Signal Processing, Computer-Assisted ; },
abstract = {Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.},
}
@article {pmid29115915,
year = {2017},
author = {Abboud, H and Hill, E and Siddiqui, J and Serra, A and Walter, B},
title = {Neuromodulation in multiple sclerosis.},
journal = {Multiple sclerosis (Houndmills, Basingstoke, England)},
volume = {23},
number = {13},
pages = {1663-1676},
doi = {10.1177/1352458517736150},
pmid = {29115915},
issn = {1477-0970},
mesh = {*Brain-Computer Interfaces ; Deep Brain Stimulation/*methods ; Humans ; *Infusion Pumps, Implantable ; Infusions, Spinal/instrumentation/*methods ; Multiple Sclerosis/*rehabilitation ; Muscle Relaxants, Central/*administration &amp; dosage ; Spinal Cord Stimulation/*methods ; Transcranial Magnetic Stimulation/*methods ; Transcutaneous Electric Nerve Stimulation/*methods ; },
abstract = {Neuromodulation, or the utilization of advanced technology for targeted electrical or chemical neuronal stimulation or inhibition, has been expanding in several neurological subspecialties. In the past decades, immune-modulating therapy has been the main focus of multiple sclerosis (MS) research with little attention to neuromodulation. However, with the recent advances in disease-modifying therapies, it is time to shift the focus of MS research to neuromodulation and restoration of function as with other neurological subspecialties. Preliminary research supports the value of intrathecal baclofen pump and functional electrical stimulation in improving spasticity and motor function in MS patients. Deep brain stimulation can improve MS-related tremor and trigeminal neuralgia. Spinal cord stimulation has been shown to be effective against MS-related pain and bladder dysfunction. Bladder overactivity also responds to sacral neuromodulation and posterior tibial nerve stimulation. Despite limited data in MS, transcranial magnetic stimulation and brain-computer interface are promising neuromodulatory techniques for symptom mitigation and neurorehabilitation of MS patients. In this review, we provide an overview of the available neuromodulatory techniques and the evidence for their use in MS.},
}
@article {pmid29109247,
year = {2017},
author = {Fu, TM and Hong, G and Viveros, RD and Zhou, T and Lieber, CM},
title = {Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {114},
number = {47},
pages = {E10046-E10055},
pmid = {29109247},
issn = {1091-6490},
support = {K99 AG056636/AG/NIA NIH HHS/United States ; R21 DA043985/DA/NIDA NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Behavior, Animal/physiology ; Brain/cytology/*physiology ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electrophysiological Phenomena ; Male ; Mice ; Mice, Inbred C57BL ; Microelectrodes ; Neurons/cytology/*physiology ; Silicon/chemistry ; Stereotaxic Techniques ; Wakefulness/physiology ; },
abstract = {Implantable electrical probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.},
}
@article {pmid29104877,
year = {2017},
author = {Huggins, JE and Müller-Putz, G and Wolpaw, JR},
title = {The Sixth International Brain-Computer Interface Meeting: Advances in Basic and Clinical Research.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {4},
number = {1-2},
pages = {1-2},
pmid = {29104877},
issn = {2326-263X},
support = {R13 DC015188/DC/NIDCD NIH HHS/United States ; },
}
@article {pmid29104374,
year = {2017},
author = {Li, Y and Zhang, S and Jin, Y and Cai, B and Controzzi, M and Zhu, J and Zhang, J and Zheng, X},
title = {Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study.},
journal = {Behavioural neurology},
volume = {2017},
number = {},
pages = {3435686},
pmid = {29104374},
issn = {1875-8584},
mesh = {Adult ; Algorithms ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Electrocorticography/*methods ; Epilepsy ; Female ; Gestures ; Hand/physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Pilot Projects ; Sensorimotor Cortex/physiology ; },
abstract = {Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.},
}
@article {pmid29102712,
year = {2018},
author = {Liu, X and Zhao, K and Wang, D and Ping, Y and Wan, H},
title = {Goal-directed behavior elevates gamma oscillations in nidopallium caudolaterale of pigeon.},
journal = {Brain research bulletin},
volume = {137},
number = {},
pages = {10-16},
doi = {10.1016/j.brainresbull.2017.10.013},
pmid = {29102712},
issn = {1873-2747},
mesh = {Animals ; Brain/*physiology ; Columbidae ; Decision Making/*physiology ; Electrodes, Implanted ; *Gamma Rhythm ; *Goals ; Motor Activity/*physiology ; Neuropsychological Tests ; Signal Processing, Computer-Assisted ; },
abstract = {Avian nidopallium caudolaterale (NCL), a functional analogue of mammalian prefrontal cortex, is thought to be participated to goal-directed behavior. However, few studies so far investigated local field potential (LFP) properties within this area. In this study, we recorded the LFP activity from the NCL of six pigeons when they performed a goal-directed decision-making task in a plus-maze. Spectral analysis revealed a significant LFP-power increase in the gamma-band (40-60Hz) during the decision-making process. Moreover, the LFP activity in the gamma-band was modulated by the behavioral outcomes of pigeons. It could decode effectively the motion directions of animals. These results indicate that the gamma rhythm of LFP recorded from the NCL correlates with the goal-directed behavior of pigeons.},
}
@article {pmid29100117,
year = {2017},
author = {Kumar, S and Mamun, K and Sharma, A},
title = {CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI.},
journal = {Computers in biology and medicine},
volume = {91},
number = {},
pages = {231-242},
doi = {10.1016/j.compbiomed.2017.10.025},
pmid = {29100117},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {BACKGROUND: Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI.<br><br>METHOD: We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices. The proposed method is named CSP-TSM. Spatial filtering is performed on the bandpass filtered MI EEG signal. Riemannian tangent space is utilized for extracting features from the spatial filtered signal. The TSM features are then fused with the CSP variance based features and feature selection is performed using Lasso. Linear discriminant analysis (LDA) is then applied to the selected features and finally classification is done using support vector machine (SVM) classifier.<br><br>RESULTS: The proposed framework gives improved performance for MI EEG signal classification in comparison with several competing methods. Experiments conducted shows that the proposed framework reduces the overall classification error rate for MI-BCI by 3.16%, 5.10% and 1.70% (for BCI Competition III dataset IVa, BCI Competition IV Dataset I and BCI Competition IV Dataset IIb, respectively) compared to the conventional CSP method under the same experimental settings.<br><br>CONCLUSION: The proposed CSP-TSM method produces promising results when compared with several competing methods in this paper. In addition, the computational complexity is less compared to that of TSM method. Our proposed CSP-TSM framework can be potentially used for developing improved MI-BCI systems.},
}
@article {pmid29099388,
year = {2018},
author = {Lees, S and Dayan, N and Cecotti, H and McCullagh, P and Maguire, L and Lotte, F and Coyle, D},
title = {A review of rapid serial visual presentation-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {021001},
doi = {10.1088/1741-2552/aa9817},
pmid = {29099388},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces/trends ; Electroencephalography/*methods/trends ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation/*methods ; Time Factors ; },
abstract = {Rapid serial visual presentation (RSVP) combined with the detection of event-related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited, but significant, literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.},
}
@article {pmid29098907,
year = {2018},
author = {Chang, CT and Huang, C},
title = {A novel method for the detection of VEP signals from frontal region.},
journal = {The International journal of neuroscience},
volume = {128},
number = {6},
pages = {520-529},
doi = {10.1080/00207454.2017.1398749},
pmid = {29098907},
issn = {1563-5279},
mesh = {Adult ; Electroencephalography/*methods ; Electrooculography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Fourier Analysis ; Frontal Lobe/*physiology ; Humans ; Male ; Visual Perception/physiology ; },
abstract = {PURPOSE: This paper studies the feasibility of frontal positioning of electrode to detect electroencephalographic signals.<br><br>METHODS: The experiments were conducted using a board with light-emitting diodes (LEDs) designed to stimulate SSVEP and FVEP. The flashing frequencies were conducted at 15, 23, and 31 Hz. We used the Quick Amp brain wave amplifier to collect brain wave signals at a sampling rate of 1 kHz using a frequency filter band of 10-100 Hz.<br><br>RESULTS: We found that the energy power of VEP will gradually increase from Oz position to Fp2 position. We analyze the data, proving that the Fp2 position can also be used to collect VEP data.<br><br>CONCLUSIONS: Traditional measurements at the Oz location are limited because of the interference from human hair, and an additional electrode is required to detect eye movement and filter this electro-oculogram signal. Our proposed method can effectively acquire critical visually evoked potential and electro-oculogram signals without the electrode at the Oz location, which require extensive preparation of removing hair strands. We also reduced the number of electrodes used in receiving electroencephalographic signals. The frontal electrode positioning could be a remarkable breakthrough to design brain computer interface.},
}
@article {pmid29096552,
year = {2017},
author = {Liu, W and Liu, X and Dai, R and Tang, X},
title = {Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate.},
journal = {Computer assisted surgery (Abingdon, England)},
volume = {22},
number = {sup1},
pages = {258-266},
doi = {10.1080/24699322.2017.1389404},
pmid = {29096552},
issn = {2469-9322},
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Functional Laterality ; Humans ; Imagery, Psychotherapy ; Motor Activity/*physiology ; Movement ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated. The microstate parameters - duration, time coverage and occurrence per second as well as the transition probability of the microstate sequences were obtained with spatio-temporal microstate analysis. The results were shown significant differences (P < 0.05) with paired t-test between the two tasks. Then these microstate parameters were used as features and a linear support vector machine (SVM) was utilized to classify the two tasks with mean accuracy 89.17%, superior performance compared to the other methods. These indicate that the microstate can be a promising feature to improve the performance of the brain-computer interface classification.},
}
@article {pmid29094713,
year = {2017},
author = {Insel, TR},
title = {Join the disruptors of health science.},
journal = {Nature},
volume = {551},
number = {7678},
pages = {23-26},
pmid = {29094713},
issn = {1476-4687},
mesh = {Artificial Intelligence/economics/statistics &amp; numerical data ; Biomedical Technology/economics/trends ; Brain-Computer Interfaces ; Early Detection of Cancer/methods ; Entrepreneurship/*organization &amp; administration ; Fitness Trackers ; Humans ; Mobile Applications ; National Institute of Mental Health (U.S.)/organization &amp; administration ; Privacy ; Public-Private Sector Partnerships/economics/*organization &amp; administration/trends ; Research/*organization &amp; administration/standards/trends ; Software ; United States ; United States Food and Drug Administration/legislation &amp; jurisprudence ; Workforce ; },
}
@article {pmid29093673,
year = {2017},
author = {Ahn, S and Jun, SC},
title = {Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces - Current Limitations and Future Directions.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {503},
pmid = {29093673},
issn = {1662-5161},
abstract = {Multi-modal integration, which combines multiple neurophysiological signals, is gaining more attention for its potential to supplement single modality's drawbacks and yield reliable results by extracting complementary features. In particular, integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is cost-effective and portable, and therefore is a fascinating approach to brain-computer interface (BCI). However, outcomes from the integration of these two modalities have yielded only modest improvement in BCI performance because of the lack of approaches to integrate the two different features. In addition, mismatch of recording locations may hinder further improvement. In this literature review, we surveyed studies of the integration of EEG/fNIRS in BCI thoroughly and discussed its current limitations. We also suggested future directions for efficient and successful multi-modal integration of EEG/fNIRS in BCI systems.},
}
@article {pmid29093672,
year = {2017},
author = {Nicolaou, N and Malik, A and Daly, I and Weaver, J and Hwang, F and Kirke, A and Roesch, EB and Williams, D and Miranda, ER and Nasuto, SJ},
title = {Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {502},
pmid = {29093672},
issn = {1662-5161},
abstract = {Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of "peacefulness". Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5-18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity.},
}
@article {pmid29093545,
year = {2017},
author = {Schurger, A and Faivre, N and Cammoun, L and Trovó, B and Blanke, O},
title = {Entrainment of Voluntary Movement to Undetected Auditory Regularities.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {14867},
pmid = {29093545},
issn = {2045-2322},
mesh = {*Acoustic Stimulation ; Adult ; Auditory Perception ; Female ; Fingers ; Humans ; Male ; Movement/physiology ; Periodicity ; Young Adult ; },
abstract = {In physics "entrainment" refers to the synchronization of two coupled oscillators with similar fundamental frequencies. In behavioral science, entrainment refers to the tendency of humans to synchronize their movements with rhythmic stimuli. Here, we asked whether human subjects performing a tapping task would entrain their tapping to an undetected auditory rhythm surreptitiously introduced in the guise of ambient background noise in the room. Subjects performed two different tasks, one in which they tapped their finger at a steady rate of their own choosing and one in which they performed a single abrupt finger tap on each trial after a delay of their own choosing. In both cases we found that subjects tended to tap in phase with the inducing modulation, with some variability in the preferred phase across subjects, consistent with prior research. In the repetitive tapping task, if the frequency of the inducing stimulus was far from the subject's own self-paced frequency, then entrainment was abolished, consistent with the properties of entrainment in physics. Thus, undetected ambient noise can influence self-generated movements. This suggests that uncued decisions to act are never completely endogenous, but are subject to subtle unnoticed influences from the sensory environment.},
}
@article {pmid29092520,
year = {2017},
author = {Liu, D and Chen, W and Pei, Z and Wang, J},
title = {A brain-controlled lower-limb exoskeleton for human gait training.},
journal = {The Review of scientific instruments},
volume = {88},
number = {10},
pages = {104302},
doi = {10.1063/1.5006461},
pmid = {29092520},
issn = {1089-7623},
mesh = {Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; *Exoskeleton Device ; Feedback ; Gait ; Humans ; *Robotics ; },
abstract = {Brain-computer interfaces have been a novel approach to translate human intentions into movement commands in robotic systems. This paper describes an electroencephalogram-based brain-controlled lower-limb exoskeleton for gait training, as a proof of concept towards rehabilitation with human-in-the-loop. Instead of using conventional single electroencephalography correlates, e.g., evoked P300 or spontaneous motor imagery, we propose a novel framework integrated two asynchronous signal modalities, i.e., sensorimotor rhythms (SMRs) and movement-related cortical potentials (MRCPs). We executed experiments in a biologically inspired and customized lower-limb exoskeleton where subjects (N = 6) actively controlled the robot using their brain signals. Each subject performed three consecutive sessions composed of offline training, online visual feedback testing, and online robot-control recordings. Post hoc evaluations were conducted including mental workload assessment, feature analysis, and statistics test. An average robot-control accuracy of 80.16% ± 5.44% was obtained with the SMR-based method, while estimation using the MRCP-based method yielded an average performance of 68.62% ± 8.55%. The experimental results showed the feasibility of the proposed framework with all subjects successfully controlled the exoskeleton. The current paradigm could be further extended to paraplegic patients in clinical trials.},
}
@article {pmid29090400,
year = {2018},
author = {Binsch, O and Wilschut, ES and Arns, M and Bottenheft, C and Valk, PJL and Vermetten, EHGJM},
title = {No Effects of Successful Bidirectional SMR Feedback Training on Objective and Subjective Sleep in Healthy Subjects.},
journal = {Applied psychophysiology and biofeedback},
volume = {43},
number = {1},
pages = {37-47},
doi = {10.1007/s10484-017-9384-y},
pmid = {29090400},
issn = {1573-3270},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; *Healthy Volunteers ; Heart Rate/physiology ; Humans ; Learning/*physiology ; Male ; Neurofeedback/*physiology ; Sleep/*physiology ; },
abstract = {There is a growing interest in the application of psychophysiological signals in more applied settings. Unidirectional sensory motor rhythm-training (SMR) has demonstrated consistent effects on sleep. In this study the main aim was to analyze to what extent participants could gain voluntary control over sleep-related parameters and secondarily to assess possible influences of this training on sleep metrics. Bidirectional training of SMR as well as heart rate variability (HRV) was used to assess the feasibility of training these parameters as possible brain computer interfaces (BCI) signals, and assess effects normally associated with unidirectional SMR training such as the influence on objective and subjective sleep parameters. Participants (n = 26) received between 11 and 21 training sessions during 7 weeks in which they received feedback on their personalized threshold for either SMR or HRV activity, for both up- and down regulation. During a pre- and post-test a sleep log was kept and participants used a wrist actigraph. Participants were asked to take an afternoon nap on the first day at the testing facility. During napping, sleep spindles were assessed as well as self-reported sleep measures of the nap. Although the training demonstrated successful learning to increase and decrease SMR and HRV activity, no effects were found of bidirectional training on sleep spindles, actigraphy, sleep diaries, and self-reported sleep quality. As such it is concluded that bidirectional SMR and HRV training can be safely used as a BCI and participants were able to improve their control over physiological signals with bidirectional training, whereas the application of bidirectional SMR and HRV training did not lead to significant changes of sleep quality in this healthy population.},
}
@article {pmid29088345,
year = {2018},
author = {Martin, S and Mikutta, C and Leonard, MK and Hungate, D and Koelsch, S and Shamma, S and Chang, EF and Millán, JDR and Knight, RT and Pasley, BN},
title = {Neural Encoding of Auditory Features during Music Perception and Imagery.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {28},
number = {12},
pages = {4222-4233},
pmid = {29088345},
issn = {1460-2199},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; R00 NS065120/NS/NINDS NIH HHS/United States ; R01 DC005779/DC/NIDCD NIH HHS/United States ; R01 DC012379/DC/NIDCD NIH HHS/United States ; K99 DC012804/DC/NIDCD NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; F32 DC013486/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Auditory Perception/*physiology ; Brain Mapping/methods ; Cerebral Cortex/*physiology ; Evoked Potentials, Auditory ; Feedback, Sensory ; *Gamma Rhythm ; Humans ; Imagination/*physiology ; *Music ; Neurons/*physiology ; },
abstract = {Despite many behavioral and neuroimaging investigations, it remains unclear how the human cortex represents spectrotemporal sound features during auditory imagery, and how this representation compares to auditory perception. To assess this, we recorded electrocorticographic signals from an epileptic patient with proficient music ability in 2 conditions. First, the participant played 2 piano pieces on an electronic piano with the sound volume of the digital keyboard on. Second, the participant replayed the same piano pieces, but without auditory feedback, and the participant was asked to imagine hearing the music in his mind. In both conditions, the sound output of the keyboard was recorded, thus allowing precise time-locking between the neural activity and the spectrotemporal content of the music imagery. This novel task design provided a unique opportunity to apply receptive field modeling techniques to quantitatively study neural encoding during auditory mental imagery. In both conditions, we built encoding models to predict high gamma neural activity (70-150 Hz) from the spectrogram representation of the recorded sound. We found robust spectrotemporal receptive fields during auditory imagery with substantial, but not complete overlap in frequency tuning and cortical location compared to receptive fields measured during auditory perception.},
}
@article {pmid29088322,
year = {2017},
author = {Bruurmijn, MLCM and Pereboom, IPL and Vansteensel, MJ and Raemaekers, MAH and Ramsey, NF},
title = {Preservation of hand movement representation in the sensorimotor areas of amputees.},
journal = {Brain : a journal of neurology},
volume = {140},
number = {12},
pages = {3166-3178},
pmid = {29088322},
issn = {1460-2156},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Adult ; Aged ; *Amputation, Surgical ; Case-Control Studies ; Female ; *Forearm ; Functional Neuroimaging ; *Hand ; Humans ; *Machine Learning ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Phantom Limb/*physiopathology ; Sensorimotor Cortex/physiopathology ; Time Factors ; Young Adult ; },
abstract = {Denervation due to amputation is known to induce cortical reorganization in the sensorimotor cortex. Although there is evidence that reorganization does not lead to a complete loss of the representation of the phantom limb, it is unclear to what extent detailed, finger-specific activation patterns are preserved in motor cortex, an issue that is also relevant for development of brain-computer interface solutions for paralysed people. We applied machine learning to obtain a quantitative measure for the functional organization within the motor and adjacent cortices in amputees, using high resolution functional MRI and attempted hand gestures. Subjects with above-elbow arm amputation (n = 8) and non-amputated controls (n = 9) made several gestures with either their right or left hand. Amputees attempted to make gestures with their amputated hand. Images were acquired using 7 T functional MRI. The sensorimotor cortex was divided into four regions, and activity patterns were classified in individual subjects using a support vector machine. Classification scores were significantly above chance for all subjects and all hands, and were highly similar between amputees and controls in most regions. Decodability of phantom movements from primary motor cortex reached the levels of right hand movements in controls. Attempted movements were successfully decoded from primary sensory cortex in amputees, albeit lower than in controls but well above chance level despite absence of somatosensory feedback. There was no significant correlation between decodability and years since amputation, or age. The ability to decode attempted gestures demonstrates that the detailed hand representation is preserved in motor cortex and adjacent regions after denervation. This encourages targeting sensorimotor activity patterns for development of brain-computer interfaces.},
}
@article {pmid29085279,
year = {2017},
author = {Reichert, C and Dürschmid, S and Heinze, HJ and Hinrichs, H},
title = {A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {575},
pmid = {29085279},
issn = {1662-4548},
abstract = {In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithm's ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.},
}
@article {pmid29080913,
year = {2017},
author = {Samuel, OW and Geng, Y and Li, X and Li, G},
title = {Towards Efficient Decoding of Multiple Classes of Motor Imagery Limb Movements Based on EEG Spectral and Time Domain Descriptors.},
journal = {Journal of medical systems},
volume = {41},
number = {12},
pages = {194},
pmid = {29080913},
issn = {1573-689X},
mesh = {Adult ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Machine Learning ; Male ; Middle Aged ; Movement/*physiology ; Neuromuscular Diseases/physiopathology ; Pattern Recognition, Automated ; Upper Extremity/*physiology ; },
abstract = {UNLABELLED: To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition (PR) of electromyogram (EMG) signals has been successfully applied. This technique requires amputees to provide sufficient EMG signals to decode their limb movement intentions (LMIs). However, amputees with neuromuscular disorder/high level amputation often cannot provide sufficient EMG control signals, and thus the applicability of the EMG-PR technique is limited especially to this category of amputees. As an alternative approach, electroencephalograph (EEG) signals recorded non-invasively from the brain have been utilized to decode the LMIs of humans. However, most of the existing EEG based limb movement decoding methods primarily focus on identifying limited classes of upper limb movements. In addition, investigation on EEG feature extraction methods for the decoding of multiple classes of LMIs has rarely been considered. Therefore, 32 EEG feature extraction methods (including 12 spectral domain descriptors (SDDs) and 20 time domain descriptors (TDDs)) were used to decode multiple classes of motor imagery patterns associated with different upper limb movements based on 64-channel EEG recordings. From the obtained experimental results, the best individual TDD achieved an accuracy of 67.05 ± 3.12% as against 87.03 ± 2.26% for the best SDD. By applying a linear feature combination technique, an optimal set of combined TDDs recorded an average accuracy of 90.68% while that of the SDDs achieved an accuracy of 99.55% which were significantly higher than those of the individual TDD and SDD at p < 0.05. Our findings suggest that optimal feature set combination would yield a relatively high decoding accuracy that may improve the clinical robustness of MDoF neuroprosthesis.<br><br>TRIAL REGISTRATION: The study was approved by the ethics committee of Institutional Review Board of Shenzhen Institutes of Advanced Technology, and the reference number is SIAT-IRB-150515-H0077.},
}
@article {pmid29076357,
year = {2018},
author = {Ryan, DB and Colwell, KA and Throckmorton, CS and Collins, LM and Caves, K and Sellers, EW},
title = {Evaluating Brain-Computer Interface Performance in an ALS Population: Checkerboard and Color Paradigms.},
journal = {Clinical EEG and neuroscience},
volume = {49},
number = {2},
pages = {114-121},
doi = {10.1177/1550059417737443},
pmid = {29076357},
issn = {2169-5202},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/methods ; *User-Computer Interface ; },
abstract = {The objective of this study was to investigate the performance of 3 brain-computer interface (BCI) paradigms in an amyotrophic lateral sclerosis (ALS) population (n = 11). Using a repeated-measures design, participants completed 3 BCI conditions: row/column (RCW), checkerboard (CBW), and gray-to-color (CBC). Based on previous studies, it is hypothesized that the CBC and CBW conditions will result in higher accuracy, information transfer rate, waveform amplitude, and user preference over the RCW condition. An offline dynamic stopping simulation will also increase information transfer rate. Higher mean accuracy was observed in the CBC condition (89.7%), followed by the CBW (84.3%) condition, and lowest in the RCW condition (78.7%); however, these differences did not reach statistical significance (P = .062). Eight of the eleven participants preferred the CBC and the remaining three preferred the CBW conditions. The offline dynamic stopping simulation significantly increased information transfer rate (P = .005) and decreased accuracy (P < .000). The findings of this study suggest that color stimuli provide a modest improvement in performance and that participants prefer color stimuli over monochromatic stimuli. Given these findings, BCI paradigms that use color stimuli should be considered for individuals who have ALS.},
}
@article {pmid29075937,
year = {2018},
author = {Zapała, D and Francuz, P and Zapała, E and Kopiś, N and Wierzgała, P and Augustynowicz, P and Majkowski, A and Kołodziej, M},
title = {The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI.},
journal = {Applied psychophysiology and biofeedback},
volume = {43},
number = {1},
pages = {23-35},
pmid = {29075937},
issn = {1573-3270},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions ; *Feedback, Sensory ; Female ; Humans ; *Learning ; Male ; Motivation ; *Neurofeedback ; Young Adult ; },
abstract = {The challenges of research into brain-computer interfaces (BCI) include significant individual differences in learning pace and in the effective operation of BCI devices. The use of neurofeedback training is a popular method of improving the effectiveness BCI operation. The purpose of the present study was to determine to what extent it is possible to improve the effectiveness of operation of sensorimotor rhythm-based brain-computer interfaces (SMR-BCI) by supplementing user training with elements modifying the characteristics of visual feedback. Four experimental groups had training designed to reinforce BCI control by: visual feedback in the form of dummy faces expressing emotions (Group 1); flashing the principal elements of visual feedback (Group 2) and giving both visual feedbacks in one condition (Group 3). The fourth group participated in training with no modifications (Group 4). Training consisted of a series of trials where the subjects directed a ball into a basket located to the right or left side of the screen. In Group 1 a schematic image a face, placed on the controlled object, showed various emotions, depending on the accuracy of control. In Group 2, the cue and targets were flashed with different frequency (4 Hz) than the remaining elements visible on the monitor. Both modifications were also used simultaneously in Group 3. SMR activity during the task was recorded before and after the training. In Group 3 there was a significant improvement in SMR control, compared to subjects in Group 2 and 4 (control). Differences between subjects in Groups 1, 2 and 4 (control) were insignificant. This means that relatively small changes in the training procedure may significantly impact the effectiveness of BCI control. Analysis of behavioural data acquired from all participants at training showed greater effectiveness in directing the object towards the right side of the screen. Subjects with the greatest improvement in SMR control showed a significantly lower difference in the accuracy of rightward and leftward movement than others.},
}
@article {pmid29075429,
year = {2017},
author = {Zeng, X and Zhu, G and Yue, L and Zhang, M and Xie, S},
title = {A Feasibility Study of SSVEP-Based Passive Training on an Ankle Rehabilitation Robot.},
journal = {Journal of healthcare engineering},
volume = {2017},
number = {},
pages = {6819056},
pmid = {29075429},
issn = {2040-2295},
mesh = {Algorithms ; Ankle/*physiology ; Ankle Joint/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Feasibility Studies ; Female ; Fourier Analysis ; Healthy Volunteers ; Humans ; Male ; Motion ; Photic Stimulation ; Rehabilitation/*instrumentation ; Reproducibility of Results ; *Robotics ; Signal Processing, Computer-Assisted ; Virtual Reality ; Young Adult ; },
abstract = {OBJECTIVE: This study aims to establish a steady-state visual evoked potential- (SSVEP-) based passive training protocol on an ankle rehabilitation robot and validate its feasibility.<br><br>METHOD: This paper combines SSVEP signals and the virtual reality circumstance through constructing information transmission loops between brains and ankle robots. The robot can judge motion intentions of subjects and trigger the training when subjects pay their attention on one of the four flickering circles. The virtual reality training circumstance provides real-time visual feedback of ankle rotation.<br><br>RESULT: All five subjects succeeded in conducting ankle training based on the SSVEP-triggered training strategy following their motion intentions. The lowest success rate is 80%, and the highest one is 100%. The lowest information transfer rate (ITR) is 11.5&#x2009;bits/min when the biggest one of the robots for this proposed training is set as 24&#x2009;bits/min.<br><br>CONCLUSION: The proposed training strategy is feasible and promising to be combined with a robot for ankle rehabilitation. Future work will focus on adopting more advanced data process techniques to improve the reliability of intention detection and investigating how patients respond to such a training strategy.},
}
@article {pmid29071340,
year = {2017},
author = {Zheng, Y and Koehnke, J and Besing, J},
title = {Combined Effects of Noise and Reverberation on Sound Localization for Listeners With Normal Hearing and Bilateral Cochlear Implants.},
journal = {American journal of audiology},
volume = {26},
number = {4},
pages = {519-530},
doi = {10.1044/2017_AJA-16-0101},
pmid = {29071340},
issn = {1558-9137},
mesh = {Aged ; Case-Control Studies ; Cochlear Implantation ; Cochlear Implants ; Deafness/*physiopathology/rehabilitation ; Female ; Hearing Loss, Bilateral/*physiopathology/rehabilitation ; Humans ; Male ; Middle Aged ; *Noise ; *Signal-To-Noise Ratio ; Sound Localization/*physiology ; Vibration ; },
abstract = {PURPOSE: This study examined the individual and combined effects of noise and reverberation on the ability of listeners with normal hearing (NH) and with bilateral cochlear implants (BCIs) to localize speech.<br><br>METHOD: Six adults with BCIs and 10 with NH participated. All subjects completed a virtual localization test in quiet and at 0-, -4-, and -8-dB signal-to-noise ratios (SNRs) in simulated anechoic and reverberant (0.2-, 0.6-, and 0.9-s RT60) environments. BCI users were also tested at +8- and +4-dB SNR. A 3-word phrase was presented at 70 dB SPL from 9 simulated locations in the frontal horizontal plane (&#xb1;90&#xb0;), with the noise source at 0&#xb0;.<br><br>RESULTS: BCIs users had significantly poorer localization than listeners with NH in all conditions. BCI users' performance started to decrease at a higher SNR (+4 dB) and shorter RT60 (0.2 s) than listeners with NH (-4 dB and 0.6 s). The combination of noise and reverberation began to degrade localization of BCI users at a higher SNR and a shorter RT60 than listeners with NH.<br><br>CONCLUSION: The clear effect of noise and reverberation on the performance of BCI users provides information that should be useful for refining cochlear implant processing strategies and developing cochlear implant rehabilitation plans to optimize binaural benefit for BCI users in everyday listening situations.},
}
@article {pmid29071301,
year = {2017},
author = {Pereira, M and Sobolewski, A and Millán, JDR},
title = {Action Monitoring Cortical Activity Coupled to Submovements.},
journal = {eNeuro},
volume = {4},
number = {5},
pages = {},
pmid = {29071301},
issn = {2373-2822},
mesh = {Adult ; Analysis of Variance ; Biomechanical Phenomena ; *Brain Mapping ; Cerebral Cortex/*physiology ; Electroencephalography ; Evoked Potentials, Motor/*physiology ; Female ; Hand/physiology ; Humans ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reaction Time ; Young Adult ; },
abstract = {Numerous studies have examined neural correlates of the human brain's action-monitoring system during experimentally segmented tasks. However, it remains unknown how such a system operates during continuous motor output when no experimental time marker is available (such as button presses or stimulus onset). We set out to investigate the electrophysiological correlates of action monitoring when hand position has to be repeatedly monitored and corrected. For this, we recorded high-density electroencephalography (EEG) during a visuomotor tracking task during which participants had to follow a target with the mouse cursor along a visible trajectory. By decomposing hand kinematics into naturally occurring periodic submovements, we found an event-related potential (ERP) time-locked to these submovements and localized in a sensorimotor cortical network comprising the supplementary motor area (SMA) and the precentral gyrus. Critically, the amplitude of the ERP correlated with the deviation of the cursor, 110 ms before the submovement. Control analyses showed that this correlation was truly due to the cursor deviation and not to differences in submovement kinematics or to the visual content of the task. The ERP closely resembled those found in response to mismatch events in typical cognitive neuroscience experiments. Our results demonstrate the existence of a cortical process in the SMA, evaluating hand position in synchrony with submovements. These findings suggest a functional role of submovements in a sensorimotor loop of periodic monitoring and correction and generalize previous results from the field of action monitoring to cases where action has to be repeatedly monitored.},
}
@article {pmid29065590,
year = {2017},
author = {Ko, LW and Ranga, SSK and Komarov, O and Chen, CC},
title = {Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP.},
journal = {Journal of healthcare engineering},
volume = {2017},
number = {},
pages = {3789386},
pmid = {29065590},
issn = {2040-2295},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Female ; Healthy Volunteers ; Humans ; Male ; Task Performance and Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task.},
}
@article {pmid29060891,
year = {2017},
author = {von Luhmann, A and Muller, KR},
title = {Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {4475-4478},
doi = {10.1109/EMBC.2017.8037850},
pmid = {29060891},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Spectroscopy, Near-Infrared ; },
abstract = {OBJECTIVE: In medical applications, neuroscience and brain-computer interface research, bimodal acquisition of brain activity using Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) is at the moment achieved by combining separate commercial devices. We have investigated quantitatively whether dedicated hybrid systems exhibit more advantageous properties.<br><br>METHODS: We studied intermodality electrical crosstalk and timing jitter in two separate and one hybrid EEG-NIRS acquisition device.<br><br>RESULTS: Analysis revealed significantly higher impact of electrical NIRS current crosstalk into the EEG inputs and timing jitters between EEG-NIRS markers in separate devices compared to the hybrid system.<br><br>CONCLUSION: The results support hybrid acquisition systems to be advantageous in setups that require high performance in timing and signal quality.},
}
@article {pmid29060889,
year = {2017},
author = {Cecotti, H and Barachant, A and King, JR and Sanchez Bornot, J and Prasad, G},
title = {Single-trial detection of event-related fields in MEG from the presentation of happy faces: Results of the Biomag 2016 data challenge.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {4467-4470},
doi = {10.1109/EMBC.2017.8037848},
pmid = {29060889},
issn = {2694-0604},
mesh = {Emotions ; Facial Expression ; Fear ; Happiness ; Humans ; *Magnetoencephalography ; },
abstract = {The recognition of brain evoked responses at the single-trial level is a challenging task. Typical non-invasive brain-computer interfaces based on event-related brain responses use eletroencephalograhy. In this study, we consider brain signals recorded with magnetoencephalography (MEG), and we expect to take advantage of the high spatial and temporal resolution for the detection of targets in a series of images. This study was used for the data analysis competition held in the 20th International Conference on Biomagnetism (Biomag) 2016, wherein the goal was to provide a method for single-trial detection of even-related fields corresponding to the presentation of happy faces during the rapid presentation of images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness). The datasets correspond to 204 gradiometers signals obtained from four participants. The best method is based on the combination of several approaches, and mainly based on Riemannian geometry, and it provided an area under the ROC curve of 0.956±0.043. The results show that a high recognition rate of facial expressions can be obtained at the signal-trial level using advanced signal processing and machine learning methodologies.},
}
@article {pmid29060871,
year = {2017},
author = {Junjun Chen, and Kai Xu, and Zaiyue Yang, and Yiwen Wang, },
title = {Detecting abrupt change in neuronal tuning via adaptive point process estimation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {4395-4398},
doi = {10.1109/EMBC.2017.8037830},
pmid = {29060871},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Brain-Computer Interfaces ; Monte Carlo Method ; *Neurons ; },
abstract = {Neuronal tuning property such as preferred direction and modulation depth could change gradually or abruptly in brain machine interface (BMI). The decoding performance will decay in static algorithms where dynamic neuronal tuning property is regarded as stationary. Many adaptive algorithms have been proposed to update the time-varying decoding parameter with main consideration on the decoding performance, but seldom focus on exploring how individual neuronal tuning property changes physiologically. We propose a novel adaptive algorithm based on sequential Monte Carlo point process estimation to capture the abrupt change of neuronal modulation depth and preferred direction. At each time point, the tuning parameter is assumed as static with a large probability and searched within a local area. Meanwhile, the abrupt change is thought to occur with a small probability and explored within a global range. This algorithm is tested on synthetic neural data and compared with a static point process algorithm. The results show that our adaptive algorithm succeeds in detecting the abrupt change in neuronal tuning, which contributes to a better reconstruction of kinematics.},
}
@article {pmid30214988,
year = {2017},
author = {Okahara, Y and Takano, K and Komori, T and Nagao, M and Iwadate, Y and Kansaku, K},
title = {Operation of a P300-based brain-computer interface by patients with spinocerebellar ataxia.},
journal = {Clinical neurophysiology practice},
volume = {2},
number = {},
pages = {147-153},
pmid = {30214988},
issn = {2467-981X},
abstract = {OBJECTIVE: We investigated the efficacy of a P300-based brain-computer interface (BCI) for patients with spinocerebellar ataxia (SCA), which is often accompanied by cerebellar impairment.<br><br>METHODS: Eight patients with SCA and eight age- and gender-matched healthy controls were instructed to input Japanese hiragana characters using the P300-based BCI with green/blue flicker. All patients depended on some assistance in their daily lives (modified Rankin scale: mean 3.5). The chief symptom was cerebellar ataxia; no cognitive deterioration was present. A region-based, two-step P300-based BCI was used. During the P300 task, eight-channel EEG data were recorded, and a linear discriminant analysis distinguished the target from other nontarget regions of the matrix.<br><br>RESULTS: The mean online accuracy in BCI operation was 82.9% for patients with SCA and 83.2% for controls; no significant difference was detected.<br><br>CONCLUSION: The P300-based BCI was operated successfully not only by healthy controls but also by individuals with SCA.<br><br>SIGNIFICANCE: These results suggest that the P300-based BCI may be applicable for patients with SCA.},
}
@article {pmid30603177,
year = {2017},
author = {Lim, H and Ku, J},
title = {Flickering exercise video produces mirror neuron system (MNS) activation and steady state visually evoked potentials (SSVEPs).},
journal = {Biomedical engineering letters},
volume = {7},
number = {4},
pages = {281-286},
pmid = {30603177},
issn = {2093-985X},
abstract = {The action of observing can be used as an effective rehabilitation paradigm, because it activates the mirror neuron system. However, it is difficult to fully use this paradigm because it is difficult to get patients to engage in watching video clips of exercise. In this study, we proposed a steady state visually evoked potential (SSVEP) based paradigm that could be used in a Brain Computer Interface, and examined its feasibility by investigating whether flickering video could activate the mirror neuron system and evoke SSVEPs at the same time. Twenty subjects were recruited and asked to watch the flickering videos at a rate of 20 Hz of upper limb motion and visual white noise, while an EEG signal was recorded. The mu rhythm (8-13 Hz) suppression and the SSVEP (19-21 Hz) evocation were analyzed from recorded EEG. The results showed that SSVEPs, evoked by the flickering stimulus, was observed in both conditions on O1 and O2, but the mu rhythm suppression on C3 and C4 was observed only in the exercise video condition. These results could signify that the flickering video is applicable for the BCI rehabilitation game, activating the mirror neuron system at the same time.},
}
@article {pmid29717597,
year = {2017},
author = {Hu, P and Zhang, L and Zhou, B and Wu, X},
title = {[Online brain-computer interface system based on independent component analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {34},
number = {1},
pages = {106-114},
pmid = {29717597},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination ; Movement ; Online Systems ; },
abstract = {In the research of non-invasive brain-computer interface(BCI), independent component analysis(ICA) has been considered as a promising method of electroencephalogram(EEG) preprocessing and feature enhancement. However, there have been few investigations and implements about online ICA-BCI system up till now.This paper reports the investigation of the ICA-based motor imagery BCI(MIBCI) system, combining the characteristics of unsupervised learning of ICA and event-related desynchronization(ERD) related to motor imagery. We constructed a simple and practical method of ICA spatial filter calculation and discriminate criterion of three-type motor imageries in the study. To validate the online performance of proposed algorithms, an ICA-MIBCI experimental system was fully established based on Neuro Scan EEG amplifier and VC++ platform. Four subjects participated in the experiment of MIBCI testing and two of them took part in the online experiment. The average classification accuracies of the three-type motor imageries reached 89.78% and 89.89% in the offline and online testing, respectively. The experimental results showed that the proposed algorithm produced high classification accuracy and required less time consumption, which would have a prospect of cross platform application.},
}
@article {pmid30603146,
year = {2017},
author = {Xinyu, L and Hong, W and Shan, L and Yan, C and Li, S},
title = {Adaptive common average reference for in vivo multichannel local field potentials.},
journal = {Biomedical engineering letters},
volume = {7},
number = {1},
pages = {7-15},
pmid = {30603146},
issn = {2093-985X},
abstract = {For in vivo neural recording, local field potential (LFP) is often corrupted by spatially correlated artifacts, especially in awake/behaving subjects. A method named adaptive common average reference (ACAR) based on the concept of adaptive noise canceling (ANC) that utilizes the correlative features of common noise sources and implements with common average referencing (CAR), was proposed for removing the spatially correlated artifacts. Moreover, a correlation analysis was devised to automatically select appropriate channels before generating the CAR reference. The performance was evaluated in both synthesized data and real data from the hippocampus of pigeons, and the results were compared with the standard CAR and several previously proposed artifacts removal methods. Comparative testing results suggest that the ACAR performs better than the available algorithms, especially in a low SNR. In addition, feasibility of this method was provided theoretically. The proposed method would be an important pre-processing step for in vivo LFP processing.},
}
@article {pmid29725608,
year = {2017},
author = {Mowla, MR and Huggins, JE and Thompson, DE},
title = {Enhancing P300-BCI performance using latency estimation.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {4},
number = {3},
pages = {137-145},
pmid = {29725608},
issn = {2326-263X},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; },
abstract = {Brain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants.},
}
@article {pmid29629393,
year = {2017},
author = {Chavarriaga, R and Fried-Oken, M and Kleih, S and Lotte, F and Scherer, R},
title = {Heading for new shores! Overcoming pitfalls in BCI design.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {4},
number = {1-2},
pages = {60-73},
pmid = {29629393},
issn = {2326-263X},
support = {90RE5017//ACL HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; 90RE5017//ACL HHS/ ; },
abstract = {Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.},
}
@article {pmid29527538,
year = {2017},
author = {McFarland, DJ and Daly, J and Boulay, C and Parvaz, M},
title = {Therapeutic Applications of BCI Technologies.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {47},
number = {1-2},
pages = {37-52},
pmid = {29527538},
issn = {2326-263X},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. There has been speculation that this technology might also be useful for a variety of diverse therapeutic applications. This survey considers possible ways that BCI technology can be applied to motor rehabilitation following stroke, Parkinson's disease, and psychiatric disorders. We consider potential neural signals as well as the design and goals of BCI-based therapeutic applications. These diverse applications all share a reliance on neuroimaging and signal processing technologies. At the same time, each of these potential applications presents a series of unique challenges.},
}
@article {pmid30404353,
year = {2016},
author = {Weltman, A and Yoo, J and Meng, E},
title = {Flexible, Penetrating Brain Probes Enabled by Advances in Polymer Microfabrication.},
journal = {Micromachines},
volume = {7},
number = {10},
pages = {},
pmid = {30404353},
issn = {2072-666X},
abstract = {The acquisition of high-fidelity, long-term neural recordings in vivo is critically important to advance neuroscience and brain[-]machine interfaces. For decades, rigid materials such as metal microwires and micromachined silicon shanks were used as invasive electrophysiological interfaces to neurons, providing either single or multiple electrode recording sites. Extensive research has revealed that such rigid interfaces suffer from gradual recording quality degradation, in part stemming from tissue damage and the ensuing immune response arising from mechanical mismatch between the probe and brain. The development of "soft" neural probes constructed from polymer shanks has been enabled by advancements in microfabrication; this alternative has the potential to mitigate mismatch-related side effects and thus improve the quality of recordings. This review examines soft neural probe materials and their associated microfabrication techniques, the resulting soft neural probes, and their implementation including custom implantation and electrical packaging strategies. The use of soft materials necessitates careful consideration of surgical placement, often requiring the use of additional surgical shuttles or biodegradable coatings that impart temporary stiffness. Investigation of surgical implantation mechanics and histological evidence to support the use of soft probes will be presented. The review concludes with a critical discussion of the remaining technical challenges and future outlook.},
}
@article {pmid30404352,
year = {2016},
author = {Kook, G and Lee, SW and Lee, HC and Cho, IJ and Lee, HJ},
title = {Neural Probes for Chronic Applications.},
journal = {Micromachines},
volume = {7},
number = {10},
pages = {},
pmid = {30404352},
issn = {2072-666X},
support = {2016R1C1B2009798//National Research Foundation of Korea/ ; 2016M3C7A1904343//National Research Foundation of Korea/ ; },
abstract = {Developed over approximately half a century, neural probe technology is now a mature technology in terms of its fabrication technology and serves as a practical alternative to the traditional microwires for extracellular recording. Through extensive exploration of fabrication methods, structural shapes, materials, and stimulation functionalities, neural probes are now denser, more functional and reliable. Thus, applications of neural probes are not limited to extracellular recording, brain-machine interface, and deep brain stimulation, but also include a wide range of new applications such as brain mapping, restoration of neuronal functions, and investigation of brain disorders. However, the biggest limitation of the current neural probe technology is chronic reliability; neural probes that record with high fidelity in acute settings often fail to function reliably in chronic settings. While chronic viability is imperative for both clinical uses and animal experiments, achieving one is a major technological challenge due to the chronic foreign body response to the implant. Thus, this review aims to outline the factors that potentially affect chronic recording in chronological order of implantation, summarize the methods proposed to minimize each factor, and provide a performance comparison of the neural probes developed for chronic applications.},
}
@article {pmid29714933,
year = {2016},
author = {Li, S and Fu, Y and Yang, Q and Liu, C and Wun, H},
title = {[Pretreatment Research Based on Left and Right Hand Motor Imagery for Single-channel Electroencephalogram].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {5},
pages = {862-866},
pmid = {29714933},
issn = {1001-5515},
mesh = {Algorithms ; Artifacts ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; *Imagination ; *Signal Processing, Computer-Assisted ; },
abstract = {Most of electroencephalogram(EEG)acquired by multi-channels is difficult to be applied to the singlechannel brain-computer interface(BCI)in the EEG analysis method based on left and right hand motor imagery.The present research applied an improved independent component analysis(ICA)method to realize pretreatment of the EEG effectively.Firstly,data drift was removed through linear drift correction.Secondly,the number of virtual channels were increased by applying delayed window data and some EEG artifacts which are namely electrooculogram(EOG)and electrocardiogram(ECG)were removed by ICA.Finally,the average instantaneous energy characteristics were calculated and classified through the instantaneous amplitude which was solved by applying Hilbert-Huang transform(HHT).The experiment proves that the method completes the EEG pretreatment and improves classification ratio of single-channel EEG,and lays a foundation of single-channel and portable BCI.},
}
@article {pmid30404335,
year = {2016},
author = {Ghane Motlagh, B and Choueib, M and Hajhosseini Mesgar, A and Hasanuzzaman, M and Sawan, M},
title = {Direct Growth of Carbon Nanotubes on New High-Density 3D Pyramid-Shaped Microelectrode Arrays for Brain-Machine Interfaces.},
journal = {Micromachines},
volume = {7},
number = {9},
pages = {},
pmid = {30404335},
issn = {2072-666X},
abstract = {Silicon micromachined, high-density, pyramid-shaped neural microelectrode arrays (MEAs) have been designed and fabricated for intracortical 3D recording and stimulation. The novel architecture of this MEA has made it unique among the currently available micromachined electrode arrays, as it has provided higher density contacts between the electrodes and targeted neural tissue facilitating recording from different depths of the brain. Our novel masking technique enhances uniform tip-exposure for variable-height electrodes and improves process time and cost significantly. The tips of the electrodes have been coated with platinum (Pt). We have reported for the first time a selective direct growth of carbon nanotubes (CNTs) on the tips of 3D MEAs using the Pt coating as a catalyzer. The average impedance of the CNT-coated electrodes at 1 kHz is 14 kΩ. The CNT coating led to a 5-fold decrease of the impedance and a 600-fold increase in charge transfer compared with the Pt electrode.},
}
@article {pmid30634214,
year = {2016},
author = {Lin, Y and Zhou, L and Wang, J and Xiao, W and Li, B and Jin, L and Wang, GZ and Liu, JC},
title = {[Effect of Resveratrol on Isoproterenol Induced Cardiomyocyte Apoptosis Rats].},
journal = {Zhongguo Zhong xi yi jie he za zhi Zhongguo Zhongxiyi jiehe zazhi = Chinese journal of integrated traditional and Western medicine},
volume = {36},
number = {7},
pages = {849-853},
pmid = {30634214},
issn = {1003-5370},
mesh = {Animals ; Apoptosis/drug effects ; *Isoproterenol/adverse effects ; *Myocytes, Cardiac/drug effects ; Rats ; Rats, Sprague-Dawley ; *Resveratrol/pharmacology ; bcl-2-Associated X Protein ; },
abstract = {OBJECTIVE: To observe the effect and mechanism of resveratrol (Res) on isoprotere- nol (ISO) induced cardiomyocyte apoptosis rats.<br><br>METHODS: Primary cultured neonatal cardiomyocyte ap- optosis rat model was established using ISO. Apoptosis cells were then randomly divided into 4 groups, i. e., the normal control group (non-serum DMEM culture fluid) , the model group (non-serum DMEM culture fluid + ISO 1 &#x3bc;mol/L for 48 h) , the Res + ISO group (ISO 1 &#x3bc;mol/L + Res 50 &#x3bc;moI/L for 48 h) , the Res control group. (non-serum DMEM culture fluid + Res 50 l_mol/L). The apoptosis rate was measured by Hochest33258 staining. Ultrastructural changes of cardiomyocyte were observed by electron microscope. Leakage of lactate dehydrogenase (LDH) in the culture fluid was measured. Protein expressions of BcI-2 and Bax were detected using Western blot. Results The count of cardiomyocytes were reduced and the nucleus shape was irregular. The apoptosis bodies were visible and the apoptosis rate was increased in the model group. The cell membrane was complete with clear nuclear membrane in the Res + ISO group and the Res control group. Nuclear chromatin was concentrated and cell injured degree was attenuated in the Res +ISO group and the Res control group. Compared with the normal control group, the apoptosis rate and LDH leakage increased, the protein expression of Bcl-2 was down-regulated, and the expression of Bax was up-regulated in the model group (P <0. 05, P <0. 01). Compared with the model group, the apoptosis rate and LDH leakage decreased, the protein expression of Bcl-2 was up-regulated, and the expression of Bax was down-regulated in the Res + ISO group and the Res control group (P <0. 05).<br><br>CONCLUSION: Res could obviously attenuate ISO induced cardiomyocyte apoptosis, and its mechanism might be associated with reversing protein expressions of Bcl-2 and Bax.},
}
@article {pmid29708715,
year = {2016},
author = {Li, J and Wang, J and Li, H},
title = {[Selection and Classification of Elastic Net Feature with Fused Electroencephalogram Features].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {3},
pages = {413-419},
pmid = {29708715},
issn = {1001-5515},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Logistic Models ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Signal classification is a key of brain-computer interface(BCI).In this paper,we present a new method for classifying the electroencephalogram(EEG)signals of which the features are heterogeneous.This method is called wrapped elastic net feature selection and classification.Firstly,we used the joint application of time-domain statistic,power spectral density(PSD),common spatial pattern(CSP)and autoregressive(AR)model to extract high-dimensional fused features of the preprocessed EEG signals.Then we used the wrapped method for feature selection.We fitted the logistic regression model penalized with elastic net on the training data,and obtained the parameter estimation by coordinate descent method.Then we selected best feature subset by using 10-fold cross-validation.Finally,we classified the test sample using the trained model.Data used in the experiment were the EEG data from international BCI CompetitionⅣ.The results showed that the method proposed was suitable for fused feature selection with high-dimension.For identifying EEG signals,it is more effective and faster,and can single out a more relevant subset to obtain a relatively simple model.The average test accuracy reached 81.78%.},
}
@article {pmid30695412,
year = {2016},
author = {Vasilyev, AN and Liburkina, SP and Kaplan, AY},
title = {[Lateralization of EEG Patterns in Humans during Motor Imagery of Arm Movements in the Brain-Computer Interface].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {66},
number = {3},
pages = {302-312},
pmid = {30695412},
issn = {0044-4677},
mesh = {Adult ; Arm/innervation/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Fingers/innervation/*physiology ; *Functional Laterality ; Healthy Volunteers ; Humans ; Male ; Movement/physiology ; Pattern Recognition, Visual/physiology ; Sensorimotor Cortex/diagnostic imaging/*physiology ; Shoulder/innervation/*physiology ; },
abstract = {In this study EEG patterns ofsensorimotor rhythm were examined in 10 healthy subjects while perform- ing motor imagery of upper arm and hand movements. Participants received visual feedback through so called brain-computer interface (BCI) used for detection of user-specific spatio-temporal.EEG patterns associated with performed mental tasks. During the course study,.all of the subjects were able to modulate their sensorimotor EEG by performing motor imagery of shoulder and fingers movements. Patterns during imagery of shoulder movements were found to have more pronounced contralateral localization, than those during hand movements' imagery. That led to significantly better classification accuracies of the most lateralized patterns when discriminating between left and right hand (72 and 58% corresponding to shoulder and hand motor imagery). Value of difference of patterns' lateralization indexes had shown strong correlation with classification accuracy, suggests it could be used as a good ref- erence mark for.choosing optimal motor imagery tasks for BCI application.},
}
@article {pmid29446587,
year = {2016},
author = {Bobrov, PD and Isaev, MR and Korshakov, AV and Oganesyan, VV and Kerechanin, JV and Popodko, AI and Frolov, AA},
title = {[Sources of electrophysiological and foci of hemodynamic brain activity most relevant for controlling a hybrid brain–Computer interface based on classification of EEG patterns and near-infrared spectrography signals during motor imagery].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {3},
pages = {12-24},
pmid = {29446587},
issn = {0131-1646},
mesh = {Brain/*blood supply/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Hemodynamics ; Humans ; Imagination/*physiology ; Spectroscopy, Near-Infrared/methods ; },
abstract = {The method is described for joint use of electroencephalography and near-infrared spectrography for location of sources of electrophysiological and focuses of hemodynamic brain activity during motor execution and imagination. The sources of electrophysiological and focuses of hemodynamic activity the most relevant for controlling the hybrid brain-computer interface based on motor imagery are revealed and discussed.},
}
@article {pmid29446586,
year = {2016},
author = {Atanov, MS and Ivanitsky, GA and Ivanitsky, AM},
title = {[Cognitive brain–Computer interface and probable aspects of its practical application].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {3},
pages = {5-11},
pmid = {29446586},
issn = {0131-1646},
mesh = {Adult ; Biofeedback, Psychology ; *Brain-Computer Interfaces ; *Cognition ; Female ; Humans ; *Learning ; Male ; Young Adult ; },
abstract = {A new type of brain-computer interface was elaborated. It considers a variety of brain activity parameters to determine the type of mental operation being performed at the moment. The corresponding algorithm previously developed in the lab was modified for real-time application. The possibility of interface application for cognitive skills training was investigated. In the proposed paradigm, as soon as the EEG spectral pattern was adequate for the current task, some clue to the solution was presented. As we supposed, such positive biofeedback should facilitate memorization of the current brain state. After just one learning session, the differences in EEG spectra, corresponding two types of tasks, were concentrated in more narrow frequency ranges. It indicates the decrease of mental effort. Moreover, the majority of subjects succeeded to solve the tasks faster, that's an evidence of efficiency increased. The developed interface could be used for the new type of training, based on objective features of brain activity.},
}
@article {pmid29708672,
year = {2016},
author = {Shan, H and Zhu, S},
title = {[A Novel Channel Selection Method for Brain-computer Interface Based on Relief-SBS].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {2},
pages = {350-356},
pmid = {29708672},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination ; Motor Activity ; *Signal Processing, Computer-Assisted ; },
abstract = {Regarding to the channel selection problem during the classification of electroencephalogram(EEG)signals,we proposed a novel method,Relief-SBS,in this paper.Firstly,the proposed method performed EEG channel selection by combining the principles of Relief and sequential backward selection(SBS)algorithms.And then correlation coefficient was used for classification of EEG signals.The selected channels that achieved optimal classification accuracy were considered as optimal channels.The data recorded from motor imagery task experiments were analyzed,and the results showed that the channels selected with our proposed method achieved excellent classification accuracy,and also outperformed other feature selection methods.In addition,the distribution of the optimal channels was proved to be consistent with the neurophysiological knowledge.This demonstrates the effectiveness of our method.It can be well concluded that our proposed method,Relief-SBS,provides a new way for channel selection.},
}
@article {pmid29708318,
year = {2016},
author = {Lu, H},
title = {[Classifying Electroencephalogram Signal Using Under-determined Blind Source Separation and Common Spatial Pattern].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {2},
pages = {216-220},
pmid = {29708318},
issn = {1001-5515},
mesh = {Algorithms ; Artifacts ; Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; *Psychomotor Performance ; Signal Processing, Computer-Assisted ; *Signal-To-Noise Ratio ; },
abstract = {One of the key problems of brain-computer interfaces(BCI)is low signal-to-noise ratio(SNR)of electroencephalogram(EEG)signals.It affects recognition performance.To remove the artifact and noise,block under-determined blind source separation method based on the small number of channels is proposed in this paper.The nonstationary EEG signals are turned into block stationary signals by piecewise.The mixing matrix is estimated by the second-order under-determined blind mixing matrix identification.Then,the beamformer based on minimum mean square error separates the original sources of signals.Eventually,the reconstructed EEG for mixed signals removes the unwanted components of source signals to achieve suppressing artifact.The experiment results on the real motor imagery BCI indicated that the block under-determined blind source separation method could reconstruct signals and remove artifact effectively.The accuracy of motor imagery task of BCI has been greatly improved.},
}
@article {pmid29708317,
year = {2016},
author = {Kang, S and Zhou, B and Wu, X},
title = {[Three-class Motor Imagery Classification Based on Optimal Sub-band Features of Independent Components].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {2},
pages = {208-215},
pmid = {29708317},
issn = {1001-5515},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; Motor Activity ; *Psychomotor Performance ; Signal Processing, Computer-Assisted ; },
abstract = {In the study of the scalp electroencephalogram(EEG)-based brain-computer interface(BCI),individual differences and complex background noise are two main factors which affect the stability of BCI system.For different subjects,therefore,optimization of BCI system parameters is necessary,including the optimal designing of temporal and spatial filters parameters as well as the classifier parameters.In order to improve the accuracy of BCI system,this paper proposes a new BCI information processing method,which combines the optimization design of independent component analysis spatial filter(ICA-SF)with the multiple sub-band features of EEG signals.The four subjects’ three-class motor imagery EEG(MI-EEG)data collected in different periods were analyzed with the proposed method.Experimental results revealed that,during the inner and outer cross-validation of single subject as well as the subject-to-subject validation,the proposed multiple sub-band method always had higher average classification accuracy compared to those with single-band method,and the maximum difference could achieve 6.08% and5.15%,respectively.},
}
@article {pmid29708316,
year = {2016},
author = {Xu, J and Zuo, G},
title = {[Motor Imagery Electroencephalogram Feature Selection Algorithm Based on Mutual Information and Principal Component Analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {33},
number = {2},
pages = {201-207},
pmid = {29708316},
issn = {1001-5515},
mesh = {*Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Imagination ; Principal Component Analysis ; *Psychomotor Performance ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {Aiming at feature selection problem of motor imagery task in brain computer interface(BCI),an algorithm based on mutual information and principal component analysis(PCA)for electroencephalogram(EEG)feature selection is presented.This algorithm introduces the category information,and uses the sum of mutual information matrices between features under different motor imagery category to replace the covariance matrix.The eigenvectors of the sum matrix represent the direction of the principal components and the eigenvalues of the sum matrix are used to determine the dimensionality of principal components.2005 International BCI competition data set was used in our experiments,and four feature extraction methods were adopted,i.e.power spectrum estimation,continuous wavelet transform,wavelet packet decomposition and Hjorth parameters.The proposed feature selection algorithm was adopted to select and combine the most useful features for classification.The results showed that relative to the PCA algorithm,our algorithm had better performance in dimensionality reduction and in classification accuracy with the assistance of support vector machine classifier under the same dimensionality of principal components.},
}
@article {pmid29250562,
year = {2016},
author = {Orhan, U and Nezamfar, H and Akcakaya, M and Erdogmus, D and Higger, M and Moghadamfalahi, M and Fowler, A and Roark, B and Oken, B and Fried-Oken, M},
title = {Probabilistic Simulation Framework for EEG-Based BCI Design.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {3},
number = {4},
pages = {171-185},
pmid = {29250562},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {A simulation framework could decrease the burden of attending long and tiring experimental sessions on the potential users of brain computer interface (BCI) systems. Specifically during the initial design of a BCI, a simulation framework that could replicate the operational performance of the system would be a useful tool for designers to make design choices. In this manuscript, we develop a Monte Carlo based probabilistic simulation framework for electroencephalography (EEG) based BCI design. We employ one event related potential (ERP) based typing and one steady state evoked potential (SSVEP) based control interface as testbeds. We compare the results of simulations with real time experiments. Even though over and under estimation of the performance is possible, the statistical results over the Monte Carlo simulations show that the developed framework generally provides a good approximation of the real time system performance.},
}
@article {pmid29443290,
year = {2012},
author = {Richardson, MD and Parker, JJ and Waziri, A},
title = {Advances in neurosurgery: Five new things.},
journal = {Neurology. Clinical practice},
volume = {2},
number = {3},
pages = {201-207},
pmid = {29443290},
issn = {2163-0402},
abstract = {Surgical options for disease of the nervous system continue to expand in breadth and scope. These advances have been related in large part to progress in technology, translational application of molecular biology, and increasing understanding of the physiologic processes associated with neurologic disease. The current review will outline recent neurosurgical advances in the management of brain tumors, movement disorders, spinal degenerative disease, and neurologic injury. In addition, we include a brief discussion of exciting data from recent trials focusing on the brain-machine interface.},
}
@article {pmid29060868,
year = {2017},
author = {Kha Vo, and Nguyen, DN and Ha Hoang Kha, and Dutkiewicz, E},
title = {Real-time analysis on ensemble SVM scores to reduce P300-Speller intensification time.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {4383-4386},
doi = {10.1109/EMBC.2017.8037827},
pmid = {29060868},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; User-Computer Interface ; },
abstract = {In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a real-time signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers' highest scores. Our experiments were conducted on the dataset of the BCI Competition III and resulted in a successful character rate of over 96% with just approximately 15 to 20 seconds for each character spelling session. As compared with the fixed 31.5 seconds of the best original approach of the competition, our proposed method significantly reduces the required spelling time by over 30% while maintaining the desired classification accuracy.},
}
@article {pmid29060729,
year = {2017},
author = {Kodama, T and Makino, S},
title = {Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3814-3817},
doi = {10.1109/EMBC.2017.8037688},
pmid = {29060729},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrodes ; *Neural Networks, Computer ; },
abstract = {In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full- body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal- training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.},
}
@article {pmid29060725,
year = {2017},
author = {Kinney-Lang, E and Spyrou, L and Ebied, A and Chin, R and Escudero, J},
title = {Elucidating age-specific patterns from background electroencephalogram pediatric datasets via PARAFAC.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3797-3800},
doi = {10.1109/EMBC.2017.8037684},
pmid = {29060725},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Child ; *Electroencephalography ; Factor Analysis, Statistical ; Humans ; },
abstract = {Brain-computer interfaces (BCI) have the potential to provide non-muscular rehabilitation options for children. However, progressive changes in electrophysiology throughout development may pose a potential barrier in the translation of BCI rehabilitation schemes to children. Tensors and multiway analysis could provide tools which help characterize subtle developmental changes in electroencephalogram (EEG) profiles of children, thus supporting translation of BCI paradigms. Spatial, spectral and subject information of age-matched pediatric subjects in two EEG datasets were used to form 3-dimensional tensors for use in parallel factor analysis (PARAFAC) and direct projection comparison. Within dataset cross-validation results indicate PARAFAC can extract age-sensitive factors which accurately predict subject age in 90% of cases. Cross-dataset validation revealed extracted age-dependent factors correctly identified age in 3 of 4 test subjects. These findings demonstrate that tensor analysis can be applied to characterize the age-specific subtleties in EEG, which provide a means for tracking developmental changes in pediatric rehabilitation BCIs.},
}
@article {pmid29060686,
year = {2017},
author = {Bouchard, KE and Bujan, AF and Chang, EF and Sommer, FT},
title = {Sparse coding of ECoG signals identifies interpretable components for speech control in human sensorimotor cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3636-3639},
doi = {10.1109/EMBC.2017.8037645},
pmid = {29060686},
issn = {2694-0604},
mesh = {Brain Mapping ; Brain-Computer Interfaces ; *Electrocorticography ; Humans ; Sensorimotor Cortex ; *Speech ; Tongue ; },
abstract = {The concept of sparsity has proven useful to understanding elementary neural computations in sensory systems. However, the role of sparsity in motor regions is poorly understood. Here, we investigated the functional properties of sparse structure in neural activity collected with high-density electrocorticography (ECoG) from speech sensorimotor cortex (vSMC) in neurosurgical patients. Using independent components analysis (ICA), we found individual components corresponding to individual major oral articulators (i.e., Coronal Tongue, Dorsal Tongue, Lips), which were selectively activated during utterances that engaged that articulator on single trials. Some of the components corresponded to spatially sparse activations. Components with similar properties were also extracted using convolutional sparse coding (CSC), and required less data pre-processing. Finally, individual utterances could be accurately decoded from vSMC ECoG recordings using linear classifiers trained on the high-dimensional sparse codes generated by CSC. Together, these results suggest that sparse coding may be an important framework and tool for understanding sensory-motor activity generating complex behaviors, and may be useful for brain-machine interfaces.},
}
@article {pmid29060683,
year = {2017},
author = {Kai Qian, and Dos Anjos, LA and Balasubramanian, K and Stilson, K and Balcer, C and Hatsopoulos, NG and Kamper, DG},
title = {Using monkey hand exoskeleton to explore finger passive joint movement response in primary motor cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3624-3627},
doi = {10.1109/EMBC.2017.8037642},
pmid = {29060683},
issn = {2694-0604},
support = {UL1 TR000430/TR/NCATS NIH HHS/United States ; R01 NS045853/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Finger Joint ; *Fingers ; Haplorhini ; Motor Cortex ; Movement ; },
abstract = {While neurons in primary motor cortex (M1) have been shown to respond to sensory stimuli, exploration of this phenomenon has proven challenging. Accurate and repeatable presentation of sensory inputs is difficult. Here, we describe a novel paradigm to study response to joint motion and fingertip force. We employed a custom exoskeleton to drive index finger metacarpophalangeal joint (MCP) of a macaque to follow sinusoid trajectories at 4 different frequencies (0.2, 0.5, 1, 2Hz) and 2 movement ranges (68.4, 34.2 degrees). We highlight results of a specific M1 unit that displayed sensitivity to direction (more active during flexion than extension), frequency (greater firing rate at higher frequencies), and movement amplitude (higher rate at larger amplitude). Joint movement trajectories were accurately reconstructed from this single unit with mean R[2] =0.64 ± 0.13. The exoskeleton holds promise for examination of sensory feedback. In addition, it can be used as an external device controlled by a brain-machine interface (BMI) system. The proprioceptive related units in M1 may contribute to improving BMI control performance.},
}
@article {pmid29060682,
year = {2017},
author = {Nakanishi, M and Yijun Wang, and Sheng-Hsiou Hsu, and Yu-Te Wang, and Tzyy-Ping Jung, },
title = {Independent component analysis-based spatial filtering improves template-based SSVEP detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3620-3623},
doi = {10.1109/EMBC.2017.8037641},
pmid = {29060682},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Pattern Recognition, Automated ; Photic Stimulation ; },
abstract = {This study proposes a new algorithm to detect steady-state visual evoked potentials (SSVEPs) based on a template-matching approach combined with independent component analysis (ICA)-based spatial filtering. In recent studies, the effectiveness of the template-based SSVEP detection has been demonstrated in a high-speed brain-computer interface (BCI). Since SSVEPs can be considered as electroencephalogram (EEG) signals generated from underlying brain sources independent from other activities and artifacts, ICA has great potential to enhance the signal-to-noise ratio (SNR) of SSVEPs by separating them from artifacts. This study proposes to apply the ICA-based spatial filters to test data and individual templates obtained by averaging training trials, and then to use the correlation coefficients between the filtered data and templates as features for SSVEP classification. This study applied the proposed method to a 40-class SSVEP dataset to evaluate its classification accuracy against those obtained by conventional canonical correlation analysis (CCA)- and extended CCA-based methods. The study results showed that the ICA-based method outperformed the other methods in terms of the classification accuracy. Furthermore, its computational time was comparable to the CCA-based method, and was much shorter than that of the extended CCA-based method.},
}
@article {pmid29060616,
year = {2017},
author = {Yaguang Jia, and Jun Xie, and Guanghua Xu, and Min Li, and Sicong Zhang, and Ailing Luo, and Xingliang Han, },
title = {A separated feature learning based DBN structure for classification of SSMVEP signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3356-3359},
doi = {10.1109/EMBC.2017.8037575},
pmid = {29060616},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; *Machine Learning ; Neural Networks, Computer ; },
abstract = {Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signals are very complex and valid signals often come with confounded background noise, many current classification methods would lose meaningful information embedded in human EEGs. Otherwise, due to the huge inter-subject variability with respect to characteristics and patterns of BCI signals, there often exists large difference of classification accuracy among different subjects. Since BCI signals have high dimensionality and multi-channel properties, this paper proposes a novel structure of deep belief neural (DBN) network stacked by restricted boltsman machine (RBM) to extract efficient features from steady-state motion visual evoked potential signals and implement further classification. Here DBN extracts local feature from BCI data of each channel separately and fuses the local features, and then input the fused features to the output classifier which is consist of softmax units. Results proved that the proposed algorithm could achieve higher accuracy and lower inter-subject variability in short response time when compared to conventional CCA method.},
}
@article {pmid29060578,
year = {2017},
author = {Congedo, M and Rodrigues, PLC and Bouchard, F and Barachant, A and Jutten, C},
title = {A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3198-3201},
doi = {10.1109/EMBC.2017.8037537},
pmid = {29060578},
issn = {2694-0604},
mesh = {Algorithms ; *Awareness ; Databases, Factual ; Electroencephalography ; },
abstract = {Riemannian geometry has been found accurate and robust for classifying multidimensional data, for instance, in brain-computer interfaces based on electroencephalography. Given a number of data points on the manifold of symmetric positive-definite matrices, it is often of interest to embed these points in a manifold of smaller dimension. This is necessary for large dimensions in order to preserve accuracy and useful in general to speed up computations. Geometry-aware methods try to accomplish this task while respecting as much as possible the geometry of the original data points. We provide a closed-form solution for this problem in a fully unsupervised setting. Through the analysis of three brain-computer interface data bases we show that our method allows substantial dimensionality reduction without affecting the classification accuracy.},
}
@article {pmid29060545,
year = {2017},
author = {Lopez-Larraz, E and Ray, AM and Figueiredo, TC and Bibian, C and Birbaumer, N and Ramos-Murguialday, A},
title = {Stroke lesion location influences the decoding of movement intention from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {3065-3068},
doi = {10.1109/EMBC.2017.8037504},
pmid = {29060545},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Humans ; Intention ; Motor Cortex ; Movement ; *Stroke ; },
abstract = {Recent studies have demonstrated the efficacy of brain-machine interfaces (BMI) for motor rehabilitation after stroke, especially for those patients with severe paralysis. However, a cerebro-vascular accident can affect the brain in many different manners, and lesions in diverse areas, even from significantly different volumes, can lead to similar or equal motor deficits. The location of the insult influences the way the brain activates when moving or attempting to move a paralyzed limb. Since the essence of a rehabilitative BMI is to precisely decode motor commands from the brain, it is crucial to characterize how lesion location affects the measured signals and if and how it influences BMI performance. This paper compares the performances of an electroencephalography (EEG)-based movement intention decoder in two groups of severely paralyzed chronic stroke patients: 14 with subcortical lesions and 14 with mixed (i.e., cortical and subcortical) lesions. We show that the lesion location influences the performance of the BMI when decoding the movement attempts of the paretic arm. The obtained results underline the need for further developments for a better individualization of BMI-based rehabilitative therapies for stroke patients.},
}
@article {pmid29060524,
year = {2017},
author = {Mahmoodi, M and Abadi, BM and Khajepur, H and Harirchian, MH},
title = {A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2980-2983},
doi = {10.1109/EMBC.2017.8037483},
pmid = {29060524},
issn = {2694-0604},
mesh = {Brain ; Brain Mapping ; *Brain-Computer Interfaces ; Contingent Negative Variation ; Electroencephalography ; Humans ; Movement ; },
abstract = {Early detection of intention to move, at self-paced voluntary movements from the activities of neural current sources on the motor cortex, can be an effective approach to brain-computer interface (BCI) systems. Achieving high sensitivity and pre-movement negative latency are important issues for increasing the speed of BCI and other rehabilitation and neurofeedback systems used by disabled and stroke patients and helps enhance their movement abilities. Therefore, developing high-performance extractors or beamformers is a necessary task in this regard. In this paper, for the sake of improving the beamforming performance in well reconstruction of sources of readiness potential, related to hand movement, one kind of surface spatial filter (spherical spline derivative on electrode space) and the linearly constrained minimum variance (LCMV) beamformer are utilized jointly. Moreover, in order to achieve better results, the real head model of each subject was created, using individual head MRI, and was used in beamformer algorithm. Also, few optimizations were done on reconstructed source signal powers to help our template matching classifier with detection of movement onset for five healthy subjects. Our classification results show an average true positive rate (TPR) of 77.1% and 73.1%, false positive rate (FPR) of 28.96% and 28.74% and latency of -512.426 ±396. 7ms and - 360.29 ±252. 16 ms from signals of current sources of motor cortex and sensor space respectively. It can be seen that the proposed method has reliable sensitivity and is faster in prediction of movement onset and more reliable to be used for online BCI in future.},
}
@article {pmid29060522,
year = {2017},
author = {Salehi, SSM and Moghadamfalahi, M and Nezamfar, H and Haghighi, M and Erdogmus, D},
title = {Context-aware recursive bayesian graph traversal in BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2972-2975},
doi = {10.1109/EMBC.2017.8037481},
pmid = {29060522},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Models, Statistical ; Probability ; },
abstract = {Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a "Select" command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.},
}
@article {pmid29060521,
year = {2017},
author = {Mohseni Salehi, SS and Moghadamfalahi, M and Quivira, F and Piers, A and Nezamfar, H and Erdogmus, D},
title = {Decoding complex imagery hand gestures.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2968-2971},
pmid = {29060521},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Gestures ; Hand ; Humans ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy-intended grasp prediction probability-of 64.5% for 8 different hand gestures, more than 5 times the chance level.},
}
@article {pmid29060520,
year = {2017},
author = {Ganeshkumar, M and Kai Keng Ang, and So, RQ},
title = {Reject option to reduce false prediction rates for EEG-motor imagery based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2964-2967},
doi = {10.1109/EMBC.2017.8037479},
pmid = {29060520},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Signal Processing, Computer-Assisted ; },
abstract = {The Filter Bank Common Spatial Pattern (FBCSP) algorithm had been shown to be effective in performing multi-class Electroencephalogram (EEG) decoding of motor imagery using the one-versus-the-rest approach on the BCI Competition IV Dataset IIa. In this paper, we propose a method to reduce false detection rates of decoding through a rejection option based on the difference in the posterior probability computed by the Naïve Bayesian classifier. We applied the proposed approach on the BCI Competition IV Dataset IIa, and the results showed a decrease in the false detection rates from 34.6 % to 6.9%, while average decoded trials decreased from 93.2% to 34.2% using a rejection threshold between 0.1 and 0.9. We subsequently formulated a method to optimize the rejection threshold based on the maximum F0.5 score. The optimal rejection threshold yielded an average decrease in false detection rate to 19.1% with an average of 67.5% of trials decoded. The results showed the feasibility of decreasing false detection rates at a cost of rejection. Nevertheless, the results suggest that the use of reject option (RO) may be used as a training feedback system to train subjects' overt and covert EEG control strategies for better (dexterity and safety) continuous control of external device.},
}
@article {pmid29060519,
year = {2017},
author = {Bibian, C and Lopez-Larraz, E and Irastorza-Landa, N and Birbaumer, N and Ramos-Murguialday, A},
title = {Evaluation of filtering techniques to extract movement intention information from low-frequency EEG activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2960-2963},
doi = {10.1109/EMBC.2017.8037478},
pmid = {29060519},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Humans ; Intention ; *Movement ; },
abstract = {Low-frequency electroencephalographic (EEG) activity provides relevant information for decoding movement commands in healthy subjects and paralyzed patients. Brainmachine interfaces (BMI) exploiting these signals have been developed to provide closed-loop feedback and induce neuroplasticity. Several offline and online studies have already demonstrated that discriminable information related to movement can be decoded from low-frequency EEG activity. However, there is still not a well-established procedure to guarantee that this activity is optimally filtered from the background noise. This work compares different configurations of non-causal (i.e., offline) and causal (i.e., online) filters to classify movement-related cortical potentials (MRCP) with six healthy subjects during reaching movements. Our results reveal important differences in MRCP decoding accuracy dependent on the selected frequency band for both offline and online approaches. In summary, this paper underlines the importance of optimally choosing filter parameters, since their variable response has an impact on the classification of low EEG frequencies for BMI.},
}
@article {pmid29060495,
year = {2017},
author = {Hyeong-Jun Park, and Jongin Kim, and Beomjun Min, and Boreom Lee, },
title = {Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2863-2866},
doi = {10.1109/EMBC.2017.8037454},
pmid = {29060495},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Imagination ; },
abstract = {Performance of motor imagery based brain-computer interfaces (MI BCIs) greatly depends on how to extract the features. Various versions of filter-bank based common spatial pattern have been proposed and used in MI BCIs. Filter-bank based common spatial pattern has more number of features compared with original common spatial pattern. As the number of features increases, the MI BCIs using filter-bank based common spatial pattern can face overfitting problems. In this study, we used eigenvector centrality feature selection method, wavelet packet decomposition common spatial pattern, and kernel extreme learning machine to improve the performance of MI BCIs and avoid overfitting problems. Furthermore, the computational speed was improved by using kernel extreme learning machine.},
}
@article {pmid29060479,
year = {2017},
author = {Chiu-Kuo Chen, and Wai-Chi Fang, },
title = {A reliable brain-computer interface based on SSVEP using online recursive independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2798-2801},
doi = {10.1109/EMBC.2017.8037438},
pmid = {29060479},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Pattern Recognition, Automated ; Photic Stimulation ; },
abstract = {This paper presents a reliable brain-computer interface (BCI) based on a steady-state visually evoked potential (SSEVP) method using online recursive independent component analysis (ORICA) with denoising. The proposed system includes a visual stimulator, a front-end data acquisition module, an ORICA module, a power spectrum density (PSD)-based noise channel detection module, a denoising module, and an EEG reconstruction module, and a detection module using canonical correlation analysis (CCA). The system with the proposed PSD-based denoising mechanism is simulated using test patterns of 9-Hz and 10-Hz SSEVP-based EEG raw data stream with an 8-second sliding window length with a 1-second step size under the condition of 128 Hz sampling rate. The accuracy of the detection is approximately 88% and 95% hit rate for 9-Hz and 10-Hz test patterns, respectively.},
}
@article {pmid29060475,
year = {2017},
author = {Pearce, S and Boger, J and Mrachacz-Kersting, N and Farina, D and Ning Jiang, },
title = {Evaluating the effectiveness of different external cues on non-invasive brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2782-2785},
doi = {10.1109/EMBC.2017.8037434},
pmid = {29060475},
issn = {2694-0604},
mesh = {Autism Spectrum Disorder ; *Brain-Computer Interfaces ; Cues ; Electroencephalography ; Humans ; },
abstract = {Although BCI technology has vastly improved in the last few years, very little research has been done into how different types of cues may affect the resulting signal. We have conducted preliminary work to examine the effects of using auditory versus visual cues on MRCP signal detection. While our sample size was small (n=5), the data for auditory and visual cues were not statistically different for young, healthy participants, suggesting that they are comparable for the parameters analyzed. Our results indicated that audio and visual cues likely produce similar MRCP signals, which is useful information for designing non-invasive BCIs. Future work includes expanding the sample size and conducting work with special needs populations, such as children with Autism Spectrum Disorder (ASD), who are known to have strong preferences for different interfaces.},
}
@article {pmid29060429,
year = {2017},
author = {Samima, S and Sarma, M and Samanta, D},
title = {Correlation of P300 ERPs with visual stimuli and its application to vigilance detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2590-2593},
doi = {10.1109/EMBC.2017.8037387},
pmid = {29060429},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Male ; Wakefulness ; },
abstract = {Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead. This paper aims to pave an alternative solution to the existing techniques using brain computing interface (BCI). EEG device being a non-invasive BCI technique is popular in many applications. In this work, we have utilized P300 component of ERPs of EEG signal for vigilance detection task as it can be detected fast and accurately. Through this work, we aim to establish the correlation between P300 ERP and vigilance. We have performed a number of experiments to substantiate the correctness of our proposal and have also proposed an approach to measure the vigilance level.},
}
@article {pmid29060418,
year = {2017},
author = {Juanhong Yu, and Kai Keng Ang, and Su Hui Ho, and Sia, A and Ho, R},
title = {Prefrontal cortical activation while viewing urban and garden scenes: A pilot fNIRS study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2546-2549},
doi = {10.1109/EMBC.2017.8037376},
pmid = {29060418},
issn = {2694-0604},
mesh = {Brain Mapping ; Gardens ; Humans ; Pilot Projects ; *Prefrontal Cortex ; Spectroscopy, Near-Infrared ; },
abstract = {Recent studies have shown that pleasant and unpleasant emotions could be detected through functional Near-Infrared Spectroscopy (fNIRS). This study investigates the prefrontal cortical activation in human subjects while they were viewing urban and garden scenes. A multi-channel continuous wave fNIRS system was used to record the prefrontal cortical activations from seven subjects. During the data collection, the subjects viewed 40 trials of video clips. In each trial, the subjects viewed a video of randomized urban or garden scenes for 30s followed by 30s of idle scene which showed a dark blue progress bar on black background on the screen. NIRS-SPM is employed to work out the changes of hemoglobin response and the prefrontal cortical activations were generated using group analysis based on the contrasts of urban versus idle, garden versus idle and urban versus garden. The activation for the contrast of urban versus garden showed an increase of oxy-hemoglobin on the right area of the prefrontal cortex with p <; 0.05. This preliminary result showed that the garden scene might provide a pleasant and less stressful experience as compared to the urban scene for subjects. This suggests the possibility of using a NIRS-based Brain-Computer Interface to detect subject preferences of different scenes.},
}
@article {pmid29060417,
year = {2017},
author = {Uzawa, S and Takiguchi, T and Ariki, Y and Nakagawa, S},
title = {Spatiotemporal properties of magnetic fields induced by auditory speech sound imagery and perception.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2542-2545},
doi = {10.1109/EMBC.2017.8037375},
pmid = {29060417},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Auditory Cortex ; Auditory Perception ; Brain Mapping ; Female ; Humans ; Magnetic Fields ; Male ; *Phonetics ; Speech ; Speech Perception ; },
abstract = {Brain computer interface (BCI) technologies, which enable direct communication between the brain and external devices, have been developed. BCI technology can be utilized in neural prosthetics to restore impaired movement, including speech production. However, most of the BCI systems that have been developed are the "P300-speller" type, which can only detect objects that users direct his/her attention at. To develop more versatile BCI systems that can detect a user's intention or thoughts, the brain responses associated with verbal imagery need to be clarified. In this study, the brain magnetic fields associated with auditory verbal imagery and speech hearing were recorded using magnetoencephalography (MEG) carried out on 8 healthy adults. Although the magnetic fields lagged slightly and were long-lasting, significant deflections were observed even for verbal imagery, in the temporal regions, as well as for actual speech hearing. Also, sources for the deflections were localized in the association auditory cortices. Cross-correlations were calculated between envelopes of the imagined/presented speech sound and the evoked brain responses in the temporal areas. Measurable correlations were obtained for the presented speech sound; however, no significant correlations were observed for the imagined speech sound. These results indicate that auditory verbal imagery undoubtedly activates the auditory cortex, at least, and generates some observable neural responses.},
}
@article {pmid29060348,
year = {2017},
author = {Wenqiang Yan, and Guanghua Xu, and Jun Xie, and Min Li, and Sicong Zhang, and Ailing Luo, },
title = {Study on the effects of brightness contrast on steady-state motion visual evoked potential.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2263-2266},
doi = {10.1109/EMBC.2017.8037306},
pmid = {29060348},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Motion ; Photic Stimulation ; },
abstract = {Traditional steady - state visual evoked potential (SSVEP) using light flicker stimulation may easy cause visual fatigue with a consequent reduction of recognition accuracy. In the previous study, we proposed a steady - state motion visual evoked potential (SSMVEP) brain - computer interface (BCI) method. A black - white ring - shaped checkerboard was used as the visual stimulus paradigm in this study. The movement patterns of the checkerboard included contraction and expansion. Based on the signal-to-noise ratio (SNR), recognition accuracy and anti-fatigue properties, the effects of black-white brightness contrast on the brain response were investigated. Experimental results indicated that when the contrast ratio was the highest, it could obtain higher recognition accuracy and SNR, yet it was easy to cause visual fatigue. When the contrast was the lowest, the sensitivity of the eyes against flicker reduced to the lowest, and it could reduce visual fatigue. However the recognition accuracy and SNR were low. In contrast, the appropriate contrast could guarantee high SNR and recognition accuracy, and as much as possible to reduce the visual fatigue.},
}
@article {pmid29060343,
year = {2017},
author = {Leza, C and Puthusserypady, S},
title = {Detection of user independent single trial ERPs in Brain Computer Interfaces: An adaptive spatial filtering approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2243-2246},
doi = {10.1109/EMBC.2017.8037301},
pmid = {29060343},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; *Evoked Potentials ; },
abstract = {Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, specifically suited for single-trial detection of Event-Related Potentials (ERPs) by combining spatial filtering and new feature extraction methods. The adaptive spatial filtering technique, axDAWN, removes the need for calibration of the system thereby improving the overall speed of the system. Besides, axDAWN enhances the P300 response to target stimuli. The wavelet decomposition and entropy of the recorded ERPs are shown to be correlated with the presence of the P300 responses. The proposed scheme is validated thoroughly in a P300 speller and provides a solution to achieve high accuracy results for single-trial detection of ERPs, being the system user independent.},
}
@article {pmid29060317,
year = {2017},
author = {Bingchuan Liu, and Xiaogang Chen, and Chen Yang, and Jian Wu, and Xiaorong Gao, },
title = {Effects of transcranial direct current stimulation on steady-state visual evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2126-2129},
doi = {10.1109/EMBC.2017.8037274},
pmid = {29060317},
issn = {2694-0604},
mesh = {Electrodes ; Evoked Potentials, Motor ; Evoked Potentials, Visual ; Humans ; Motor Cortex ; *Transcranial Direct Current Stimulation ; Transcranial Magnetic Stimulation ; },
abstract = {Recently, transcranial direct current stimulation (tDCS) has attracted increasing attention in the field of neuro-modulation because of its capacity to modulate cortical excitability noninvasively. Previous findings have demonstrated its effectiveness in visual studies. However, few studies have referred to steady-state visual evoked potential (SSVEP), a promising visual evoked potential that has been widely used in brain-computer interfaces. The present work investigated the effects of tDCS on SSVEPs. Sham and 1 mA real tDCS (anodal and cathodal) in a PO7-PO8 montage were administered for 15 min in 12 healthy subjects. Compared with sham conditions, both anodal and cathodal tDCS significantly decreased 7-Hz SSVEP power. Also, anodal tDCS increased 10-Hz SSVEP power. Our study demonstrated that tDCS over occipital areas altered brain activity evoked by visual stimuli.},
}
@article {pmid29060311,
year = {2017},
author = {Lee, D and Hee-Jae Lee, and Sang-Hoon Park, and Woo-Hyuk Jung, and Jae-Ho Kim, and Sang-Goog Lee, },
title = {Speeding up SVM training in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2101-2104},
doi = {10.1109/EMBC.2017.8037268},
pmid = {29060311},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Normal Distribution ; Principal Component Analysis ; Support Vector Machine ; },
abstract = {Traditional Support Vector Machine (SVM) is widely used classification method for brain-computer interface (BCI). However, SVM has a high computational complexity. In this paper, Gaussian Mixture Model (GMM)-based training data reduction is proposed to reduce high computational complexity. The proposed method is configured as follows: First, wavelet-based combined feature vectors are applied for motor imagery electroencephalography (EEG) identification and principal component analysis (PCA) are used to reduce the dimension of feature vectors. Thereafter, the GMM is implemented to reduce training data sizes. Finally, a nonlinear SVM classifier is used to classify the reduced training data. The performance of the proposed method was evaluated using three different motor imagery datasets in terms of accuracy and training time. The results from the study indicate that the proposed method achieves high accuracy with faster computational time in motor imagery EEG classification.},
}
@article {pmid29060310,
year = {2017},
author = {Qureshi, MNI and Dongrae Cho, and Boreom Lee, },
title = {EEG classification for motor imagery BCI using phase-only features extracted by independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2097-2100},
doi = {10.1109/EMBC.2017.8037267},
pmid = {29060310},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; },
abstract = {The accurate classification of the electroencephalography (EEG) signals is the most important task towards the development of a reliable motor imagery brain-computer interface (MI-BCI) system. In this study, we utilized a publically available BCI Competition-IV 2008 dataset IIa. This study address to the binary classification problem of the motor imagery EEG data by using a sigmoid activation function-based extreme learning machines (ELM). We proposed a novel method of extracting the features from the EEG signals by first applying the independent component analysis (ICA) on the time series data and transforming the ICA time series data into Fourier domain and then extract the phase information from the Fourier spectrum. This phase information was further used to calculate the maximized cross-correlation connectivity matrix. The upper diagonal of this matrix was then vectorized and it serves as the basic feature for the ELM classification framework. By using the phase-only features we achieved 97.80% (p <; 0.0022) nested cross-validated classification accuracy. In addition, this process is relatively computationally inexpensive. Thus, it can be an excellent candidate for the motor imagery BCI applications.},
}
@article {pmid29060309,
year = {2017},
author = {Jahangiri, A and Sepulveda, F},
title = {The contribution of different frequency bands in class separability of covert speech tasks for BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2093-2096},
doi = {10.1109/EMBC.2017.8037266},
pmid = {29060309},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Mental Processes ; *Speech ; },
abstract = {Several recent studies demonstrate the possibility of using user initiated covert speech mental tasks in brain computer interfaces with varying degrees of success, but details of the best frequency features had not been investigated. In this work, ten volunteers in the age range of 22-70 years participated in the experiment. Eight of them were neurologically healthy, one user was dyslexic, and another was autistic. The four words "back", "forward", "left", and "right" were shortened into "BA", "FO", "LE", and "RY", which are phonetically dissimilar and cognitively relevant directional commands. Participants were asked to covertly speak each as soon as the letters appeared on a screen. Volunteers completed five recording runs. During each run the four words were presented in random succession to avoid sequence bias. The recorded EEG data from the ten users were analysed to discover the best features within a Gabor Transform of the signals, i.e., those yielding the highest word-pair classification accuracy for this specific type of linguistic mental activity. Using this BCI, suitable class separability of covert speech tasks is confirmed for all, including disabled users, with consistently high classification accuracy from 72% to 88% in all cases. Like motor imagery tasks, Alpha and Beta band activity were found to contain 12% and 31% of the most important features respectively. Gamma band activity, which indicates high mental functions, contains 57% of the most important features in this study.},
}
@article {pmid29060307,
year = {2017},
author = {Omedes, J and Schwarz, A and Montesano, L and Muller-Putz, G},
title = {Hierarchical decoding of grasping commands from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2085-2088},
doi = {10.1109/EMBC.2017.8037264},
pmid = {29060307},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Hand Strength ; Intention ; Movement ; },
abstract = {Brain-Computer Interfaces may present an intuitive way for motor impaired end users to operate assistive devices of daily life. Recent studies showed that complex kinematics like grasping can be successfully decoded from low frequency electroencephalogram. In this work we present a hierarchical method to asynchronously discriminate two different grasps often used in daily life actions (palmar, pincer) from a combined set of motor execution and motor intention. We compared sensorimotor rhythms based features and time features from the low frequency spectrum for best discrimination results. Our results show not only the principle feasibility of the proposed method with detection of asynchronous motor intention at rates of 80% accuracy and subsequent grasping discrimination over 60%, but also that low frequency time domain features provide a more consistent detection pattern. Although the basis of this results is still an off-line analysis we are confident that these results can be transferred to on-line use.},
}
@article {pmid29060306,
year = {2017},
author = {Gauci, N and Falzon, O and Camilleri, T and Camilleri, KP},
title = {Phase-based SSVEPs for real-time control of a motorised bed.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2080-2084},
doi = {10.1109/EMBC.2017.8037263},
pmid = {29060306},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Computer Systems ; Electroencephalography ; *Evoked Potentials ; Photic Stimulation ; },
abstract = {Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.},
}
@article {pmid29060296,
year = {2017},
author = {Zijing Mao, and Wan Xiang Yao, and Yufe Huang, },
title = {Design of deep convolutional networks for prediction of image rapid serial visual presentation events.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {2035-2038},
doi = {10.1109/EMBC.2017.8037252},
pmid = {29060296},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; Brain ; Machine Learning ; *Neural Networks, Computer ; },
abstract = {We report in this paper an investigation of convolutional neural network (CNN) models for target prediction in time-locked image rapid serial visual presentation (RSVP) experiment. We investigated CNN models with 11 different designs of convolution filters in capturing spatial and temporal correlations in EEG data. We showed that for both within-subject and cross-subject predictions, the CNN models outperform the state-of-the-art algorithms: Bayesian linear discriminant analysis (BLDA) and xDAWN spatial filtering and achieved >6% improvement. Among the 11 different CNN models, the global spatial filter and our proposed region of interest (ROI) achieved best performance. We also implemented the deconvolution network to show how we can visualize from activated hidden units for target/nontarget events learned by the ROI-CNN. Our study suggests that deep learning is a powerful tool for RSVP target prediction and the proposed model is applicable for general EEG-based classifications in brain computer interaction research. The code of this project is available at https://github.com/ZijingMao/ROICNN.},
}
@article {pmid29060271,
year = {2017},
author = {Zhihua Tang, and Yijun Wang, and Guoya Dong, and Weihua Pei, and Hongda Chen, },
title = {Learning to control an SSVEP-based BCI speller in naïve subjects.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1934-1937},
doi = {10.1109/EMBC.2017.8037227},
pmid = {29060271},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Learning ; Photic Stimulation ; },
abstract = {High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been demonstrated in several recent studies. This study aimed to investigate some issues regarding feasibility of learning to control an SSVEP-based BCI speller in naïve subjects. An experiment with new BCI users was designed to answer the following questions: (1) How many people can use the SSVEP-BCI speller? (2) How much time is required to train the user? (3) Does continuous system use lead to user fatigue and deteriorated BCI performance? The experiment consisted of three tasks including a 40-class BCI spelling task, a psychomotor vigilance test (PVT) task, and a test of sleepiness scale. Subjects' reaction time (RT) in the PVT task and the fatigue rank in the sleepiness scale test were used as objective and subjective parameters to evaluate subjects' alertness level. Among 11 naïve subjects, 10 of them fulfilled the 9-block experiment. Four of them showed clear learning effects (i.e., an increasing trend of classification accuracy and information transfer rate (ITR)) over time. The remaining subjects showed stable BCI performance during the whole experiment. The results of RT and fatigue rank showed a gradually increasing trend, which is not significant across blocks. In summary, the results of this study suggest that controlling an SSVEP-based BCI speller is in general feasible to learn by naïve subjects after a short training procedure, showing no clear performance deterioration related to fatigue.},
}
@article {pmid29060270,
year = {2017},
author = {Chen Yang, and Xu Han, and Yijun Wang, and Xiaorong Gao, },
title = {A frequency recognition method based on multitaper spectral analysis and SNR estimation for SSVEP-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1930-1933},
doi = {10.1109/EMBC.2017.8037226},
pmid = {29060270},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Neurologic Examination ; Photic Stimulation ; *Signal-To-Noise Ratio ; },
abstract = {Over the past several years, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted wide attention in the field of BCIs research due to high information transfer rate, little user training, and applicability to the majority. In conventional recognition methods for training-free SSVEP-based BCIs, the energy difference between the frequencies of electroencephalogram (EEG) background noise is usually ignored, therefore, there is a significant variance among the recognition accuracy of different stimulus frequencies. In order to improve the performance of training-free SSVEP-based BCIs system and balance the accuracy of recognition between different stimulus frequencies, a recognition method based on multitaper spectral analysis and signal-to-noise ratio estimation (MTSA-SNR) is proposed in this paper. A 40-class SSVEP public benchmark SSVEP dataset recorded from 35 subjects was used to evaluate the performance of the proposed method. Under the condition of 2.25s data length, the accuracy of the three methods were 81.1% (MTSA-SNR), 74.5% (canonical correlation analysis, CCA) and 73.4% (multivariate synchronization index, MSI), and the corresponding ITRs were 101 bits/min (MTSA-SNR), 89 bits/min (CCA), 87 bits/min (MSI). In the low frequency range (8-9.8Hz), the average recognition accuracy of the three methods is 82.9% (MTSA-SNR), 82.0% (CCA), 83.3% (MSI). The average accuracy of the three methods was 78.6% (MTSA-SNR), 64.9% (CCA) and 61.8% (MSI) in the high frequency range (14-15.8Hz). According to the results, the proposed method can effectively improve the performance of training-free SSVEP-based BCI system, and balance the recognition accuracy between different stimulation frequencies.},
}
@article {pmid29060269,
year = {2017},
author = {Tze Hui Koh, and Libedinsky, C and Cuntai Guan, and Kai Keng Ang, and So, RQ},
title = {Stop state classification in intracortical brain-machine-interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1926-1929},
doi = {10.1109/EMBC.2017.8037225},
pmid = {29060269},
issn = {2694-0604},
mesh = {Animals ; Brain ; *Brain-Computer Interfaces ; Discriminant Analysis ; Macaca ; Movement ; },
abstract = {Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.},
}
@article {pmid29060268,
year = {2017},
author = {Huijuan Yang, and Libedinsky, C and Cuntai Guan, and Kai Keng Ang, and So, RQ},
title = {Boosting performance in brain-machine interface by classifier-level fusion based on accumulative training models from multi-day data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1922-1925},
doi = {10.1109/EMBC.2017.8037224},
pmid = {29060268},
issn = {2694-0604},
mesh = {Analysis of Variance ; Animals ; *Brain-Computer Interfaces ; },
abstract = {The nonstationarity of neural signal is still an unresolved issue despite the rapid progress made in brain-machine interface (BMI). This paper investigates how to utilize the rich information and dynamics in multi-day data to address the variability in day-to-day signal quality and neural tuning properties. For this purpose, we propose a classifier-level fusion technique to build a robust decoding model by jointly considering the classifier outputs from multiple base-training models using multi-day data collected prior to test day. The data set used in this study consisted of recordings of 8 days from a non-human primate (NHP) during control of a mobile robot using a joystick. Offline analysis demonstrates the superior performance of the proposed method which results in 4.4% and 13.10% improvements in decoding (significant by one-way ANOVA and post hoc t-test) compared with the two baseline methods: 1) concatenating data from multiple days based on common effective channels, and 2) averaging accuracies across all base-training models. These results further validate the effectiveness of proposed method without recalibration of the model.},
}
@article {pmid29060267,
year = {2017},
author = {Marghi, YM and Farjadian, AB and Sheng-Che Yen, and Erdogmus, D},
title = {EEG-guided robotic mirror therapy system for lower limb rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1917-1921},
doi = {10.1109/EMBC.2017.8037223},
pmid = {29060267},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Lower Extremity ; Robotics ; Stroke Rehabilitation ; },
abstract = {Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.},
}
@article {pmid29060204,
year = {2017},
author = {Ray, AM and Lopez-Larraz, E and Figueiredo, TC and Birbaumer, N and Ramos-Murguialday, A},
title = {Movement-related brain oscillations vary with lesion location in severely paralyzed chronic stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1664-1667},
doi = {10.1109/EMBC.2017.8037160},
pmid = {29060204},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Humans ; Motor Cortex ; Movement ; *Stroke ; Stroke Rehabilitation ; },
abstract = {In the past few years, innovative upper-limb rehabilitation methods have been proposed for chronic stroke patients. These methods aim at functional motor rehabilitation using Brain-machine interfaces to constitute an alternate pathway from the brain to the muscles. Even in patients with absence of residual finger movements, recovery could be achieved. The extent to which these interventions are affected by individual lesion topology is yet to be understood. In this study EEG was measured in 30 chronic stroke patients during movement attempts of the paretic arm. We show that the magnitude of the event-related desynchronization was smaller in patients presenting lesions with involvement of the motor cortex. This could have important implications on the design of new rehabilitation schemes for these patients, which might benefit from carefully tailored interventions.},
}
@article {pmid29060202,
year = {2017},
author = {Lin Yao, and Mei Lin Chen, and Xinjun Sheng, and Mrachacz-Kersting, N and Xiangyang Zhu, and Farina, D and Ning Jiang, },
title = {Cortical oscillatory dynamics of tactile selective sensation - for a novel type of somatosensory Brain-computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1656-1659},
doi = {10.1109/EMBC.2017.8037158},
pmid = {29060202},
issn = {2694-0604},
mesh = {Attention ; Brain Mapping ; Brain-Computer Interfaces ; Humans ; Somatosensory Cortex ; *Touch ; },
abstract = {In this study, cortical oscillatory dynamics with respect to tactile selective sensation tasks were investigated. Subjects were required to perform three tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation of the left hand (SS-L), 2) selective sensation of the right hand (SS-R), 3) bilateral selective sensation (SS-B). Even-related (de)synchronization (ERD/ERS) analysis revealed a clear contralateral activation of somatosensory cortex during the SS-L and SS-R tasks, and a bilateral activation during SS-B tasks. Additionally, we found a clear ERS in occipital region of the brain during all the SS tasks. Diverse activation pattern among SS-L, SS-R and SS-B offers novel brain signals for somatosensory Brain-computer Interfaces.},
}
@article {pmid29060201,
year = {2017},
author = {Chengcheng Han, and Guanghua Xu, and Jun Xie, and Min Li, and Sicong Zhang, and Ailing Luo, },
title = {An eighty-target high-speed Chinese BCI speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1652-1655},
doi = {10.1109/EMBC.2017.8037157},
pmid = {29060201},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials, Visual ; Language ; Photic Stimulation ; },
abstract = {A high-speed Chinese Brain-computer interface (BCI) speller was designed and implemented in this paper. A visual stimulation method called the motion checkerboard stimulation was used to elicit steady-state visual evoked potentials (SSMVEP). With a 10 × 8 high-density matrix interface, phonetic symbols or Chinese characters (sinograms) were presented for selection, and only two selections per character were required. According to experiment results, the ITR of the eighty-target motion checkerboard paradigm achieved 99.1 bits/min, the average accuracy of sinogram spelling system scored above 94.1% and the speed was up to one sinogram per 13.6 s. These results suggest that the proposed Chinese speller can input vast number of characters with higher speed and less operations, providing a high practicability communication method for people with motor disabilities.},
}
@article {pmid29060200,
year = {2017},
author = {Kiral-Kornek, I and Mendis, D and Nurse, ES and Mashford, BS and Freestone, DR and Grayden, DB and Harrer, S},
title = {TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1648-1651},
doi = {10.1109/EMBC.2017.8037156},
pmid = {29060200},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM's TrueNorth Neurosynaptic System. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. This pathway can be adapted to the management of a variety of conditions, including spinal cord injury, epilepsy and Parkinson's disease.},
}
@article {pmid29060078,
year = {2017},
author = {Minsu Song, and Jonghyun Kim, },
title = {Motor imagery enhancement paradigm using moving rubber hand illusion system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1146-1149},
doi = {10.1109/EMBC.2017.8037032},
pmid = {29060078},
issn = {2694-0604},
mesh = {Electroencephalography ; Hand ; Humans ; Illusions ; *Imagery, Psychotherapy ; Movement ; Rubber ; },
abstract = {Motor imagery (MI) has been widely used in neurorehabilitation and brain computer interface. The size of event-related desynchronization (ERD) is a key parameter for successful motor imaginary rehabilitation and BCI adaptation. Many studies have used visual guidance for enhancement/ amplification of motor imagery ERD amplitude, but their enhancements were not significant. We propose a novel ERD enhancing paradigm using body-ownership illusion, or also known as rubber hand illusion (RHI). The system was made by motorized, moving rubber hand which can simulate wrist extension. The amplifying effects of the proposed RHI paradigm were evaluated by comparing ERD sizes of the proposed paradigm with motor imagery and actual motor execution paradigms. The comparison result shows that the improvement of ERD size due to the proposed paradigm was statistically significant (p<;0.05) compared with the other paradigms.},
}
@article {pmid29060065,
year = {2017},
author = {Woo-Ram Lee, and Changkyun Im, and Chin Su Koh, and Jun-Min Kim, and Hyung-Cheul Shin, and Jong-Mo Seo, },
title = {A convex-shaped, PDMS-parylene hybrid multichannel ECoG-electrode array.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1093-1096},
doi = {10.1109/EMBC.2017.8037018},
pmid = {29060065},
issn = {2694-0604},
mesh = {Animals ; Brain-Computer Interfaces ; *Electrocorticography ; Electrodes, Implanted ; Polymers ; Rats ; Xylenes ; },
abstract = {Long-term electrode implant is a challenge for successful brain-computer interfaces (BCIs). It is well known that electrocorticography (ECoG) using flexible planar electrodes is more suitable for long-term implants than intracortical neural recordings using penetrative electrodes. In this study, we propose a convex-shaped, PDMS-parylene hybrid multi-electrode array for long-term stable ECoG recording on the brain or the spinal cord. The electrode array consists of 10 gold recording sites which show impedance values between 50 and 70 kOhm at 1 kHz with a diameter of 100 μm. It is designed like octopus's leg to tightly adhere to the ellipsoidal brain. To assess its performance, epidural ECoG recordings were performed from the main olfactory bulb (MOB) of an anesthetized rat during odor stimulation. The odor-evoked response was shown with an increase of the power in the beta band.},
}
@article {pmid29060064,
year = {2017},
author = {Changhoon Baek, and Jung-Woo Jang, and Sangwan Park, and Yoon-Kyu Song, and Kangmoon Seo, and Jong-Mo Seo, },
title = {3D printed wire electrode carrier for a pilot study of the functional brain mapping.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1090-1092},
doi = {10.1109/EMBC.2017.8037017},
pmid = {29060064},
issn = {2694-0604},
mesh = {Animals ; Brain ; *Brain Mapping ; Electrodes, Implanted ; Movement ; Pilot Projects ; },
abstract = {In this paper, brain-machine interface (BMI) was adopted to investigate the motor area in the animal brain. Stainless steel wire electrodes were implanted to search the brain area for rotation, forward movement, and flapping. In continuation of our search for optimized coordinates for stimulation, we designed electrode case for safer transport and for a more accurate fabrication of the electrode array. The cases are customized for each set of coordinates, and quickly built using a 3D printer. The case reduced the surgery time and increased accuracy in electrode placement. Stimulation of various sites in the brain derived the pigeon to move as we have anticipated.},
}
@article {pmid29060060,
year = {2017},
author = {Tariq, T and Satti, MH and Saeed, M and Kamboh, AM},
title = {Low SNR neural spike detection using scaled energy operators for implantable brain circuits.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1074-1077},
doi = {10.1109/EMBC.2017.8037013},
pmid = {29060060},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Brain ; *Prostheses and Implants ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Real time on-chip spike detection is the first step in decoding neural spike trains in implantable brain machine interface systems. Nonlinear Energy Operator (NEO) is a transform widely used to distinguish neural spikes from background noise. In this paper we define a general form of energy operators, of which NEO is a specific example, which gives better spike-noise separation than NEO and its derivatives. This is because of a non-linear scaling applied to the general discrete energy operator. Using two well-known publically available datasets, the performance of several operators is compared. On data sets that contain multi-unit spikes with low Signal to Noise ratio, the detection accuracy was improved by approximately 15%.},
}
@article {pmid29060050,
year = {2017},
author = {Mahmood, A and Zainab, R and Ahmad, RB and Saeed, M and Kamboh, AM},
title = {Classification of multi-class motor imagery EEG using four band common spatial pattern.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1034-1037},
doi = {10.1109/EMBC.2017.8037003},
pmid = {29060050},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Support Vector Machine ; },
abstract = {Brain Computer Interfaces (BCIs) serve as an integration tool between acquired brain signals and external devices. Precise classification of the acquired brain signals with the least misclassification error is an arduous task. Existing techniques for classification of multi-class motor imagery electroencephalogram (EEG) have low accuracy and are computationally inefficient. This paper introduces a classification algorithm, which uses two frequency ranges, mu and beta rythms, for feature extraction using common spatial pattern (CSP) along with support vector machine (SVM) for classification. The technique uses only four frequency bands with no feature reduction and consequently less computational cost. The implementation of this algorithm on BCI competition III dataset IIIa, resulted in the highest classification accuracy in comparison to existing algorithms. A mean accuracy of 85.5 for offline classification has been achieved using this technique.},
}
@article {pmid29060048,
year = {2017},
author = {Balaji, A and Haldar, A and Patil, K and Ruthvik, TS and Ca, V and Jartarkar, M and Baths, V},
title = {EEG-based classification of bilingual unspoken speech using ANN.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1022-1025},
doi = {10.1109/EMBC.2017.8037000},
pmid = {29060048},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Principal Component Analysis ; *Speech ; Support Vector Machine ; },
abstract = {The ability to interpret unspoken or imagined speech through electroencephalography (EEG) is of therapeutic interest for people suffering from speech disorders and `lockedin' syndrome. It is also useful for brain-computer interface (BCI) techniques not involving articulatory actions. Previous work has involved using particular words in one chosen language and training classifiers to distinguish between them. Such studies have reported accuracies of 40-60% and are not ideal for practical implementation. Furthermore, in today's multilingual society, classifiers trained in one language alone might not always have the desired effect. To address this, we present a novel approach to improve accuracy of the current model by combining bilingual interpretation and decision making. We collect data from 5 subjects with Hindi and English as primary and secondary languages respectively and ask them 20 `Yes'/`No' questions (`Haan'/`Na' in Hindi) in each language. We choose sensors present in regions important to both language processing and decision making. Data is preprocessed, and Principal Component Analysis (PCA) is carried out to reduce dimensionality. This is input to Support Vector Machine (SVM), Random Forest (RF), AdaBoost (AB), and Artificial Neural Networks (ANN) classifiers for prediction. Experimental results reveal best accuracy of 85.20% and 92.18% for decision and language classification respectively using ANN. Overall accuracy of bilingual speech classification is 75.38%.},
}
@article {pmid29060047,
year = {2017},
author = {Toth, J and Arvaneh, M},
title = {Facial expression classification using EEG and gyroscope signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1018-1021},
doi = {10.1109/EMBC.2017.8036999},
pmid = {29060047},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Electromyography ; *Facial Expression ; },
abstract = {In this paper muscle and gyroscope signals provided by a low cost EEG headset were used to classify six different facial expressions. Muscle activities generated by facial expressions are seen in EEG data recorded from scalp. Using the already present EEG device to classify facial expressions allows for a new hybrid brain-computer interface (BCI) system without introducing new hardware such as separate electromyography (EMG) electrodes. To classify facial expressions, time domain and frequency domain EEG data with different sampling rates were used as inputs of the classifiers. The experimental results showed that with sampling rates and classification methods optimized for each participant and feature set, high accuracy classification of facial expressions was achieved. Moreover, adding information extracted from a gyroscope embedded into the used EEG headset increased the performance by an average of 9 to 16%.},
}
@article {pmid29060046,
year = {2017},
author = {Pacheco, K and Acuna, K and Carranza, E and Achanccaray, D and Andreu-Perez, J},
title = {Performance predictors of motor imagery brain-computer interface based on spatial abilities for upper limb rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1014-1017},
doi = {10.1109/EMBC.2017.8036998},
pmid = {29060046},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Extremities ; Humans ; Imagery, Psychotherapy ; Imagination ; Signal Processing, Computer-Assisted ; Spatial Navigation ; },
abstract = {Motor Imagery based BCIs (MI-BCIs) allow the control of devices and communication by imagining different mental tasks. Despite many years of research, BCIs are still not the most accurate systems to control applications, due to two main factors: signal processing with classification, and users. It is admitted that BCI control involves certain characteristics and abilities in its users for optimal results. In this study, spatial abilities are evaluated in relation to MI-BCI control regarding flexion and extension mental tasks. Results show considerable correlation (r=0.49) between block design test (visual motor execution and spatial visualization) and extension-rest tasks. Additionally, rotation test (mental rotation task) presents significant correlation (r=0.56) to flexion-rest tasks.},
}
@article {pmid29060044,
year = {2017},
author = {Shenghong He, and Tianyou Yu, and Zhenghui Gu, and Yuanqing Li, },
title = {A hybrid BCI web browser based on EEG and EOG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1006-1009},
doi = {10.1109/EMBC.2017.8036996},
pmid = {29060044},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Electroencephalography ; *Electrooculography ; Movement ; User-Computer Interface ; Web Browser ; },
abstract = {In this study, we propose a new web browser based on a hybrid brain computer interface (BCI) combining electroencephalographic (EEG) and electrooculography (EOG) signals. Specifically, the user can control the horizontal movement of the mouse by imagining left/right hand motion, and control the vertical movement of the mouse, select/reject a target, or input text in an edit box by blinking eyes in synchrony with the flashes of the corresponding buttons on the GUI. Based on mouse control, target selection and text input, the user can open a web page of interest, select an intended target in the web and read the page content. An online experiment was conducted involving five healthy subjects. The experimental results demonstrated the effectiveness of the proposed method.},
}
@article {pmid29060043,
year = {2017},
author = {Hamad, EM and Al-Gharabli, SI and Saket, MM and Jubran, O},
title = {A Brain Machine Interface for command based control of a wheelchair using conditioning of oscillatory brain activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {1002-1005},
doi = {10.1109/EMBC.2017.8036995},
pmid = {29060043},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; Motion ; User-Computer Interface ; *Wheelchairs ; },
abstract = {In this research a new method of wheelchair control using a Brain Computer Interface (BCI) is proposed, in an attempt to bridge the gap between in-lab and real life applications, we believe it would provide a high level control over the BCI instead of the normal low level commands. It is anticipated to emphasis on mu rhythm to provide the control signals. The wheelchair is equipped with a mapping system, which scans the area and provides a map containing information about the user's current location and next possible destinations, then provides an optimized list of possible trajectories to reach the destination. The paradigm allows users to control the interface using motor imagery and issue commands to switch between possible trajectories and then confirm the choice. Commands trigger the motion of the wheelchair to the intended destination using a user selected path with speed up to 0.5 m/s. The interface also allows the user to interact with different robots through a common robotic system. Evaluation results indicate that this paradigm is indeed usable and could lead to promising outcomes.},
}
@article {pmid29060042,
year = {2017},
author = {Xingwei An, and Yong Cao, and Jinwen Wei, and Shuang Liu, and Xuejun Jiao, and Dong Ming, },
title = {The effect of semantic congruence for visual-auditory bimodal stimuli.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {998-1001},
doi = {10.1109/EMBC.2017.8036994},
pmid = {29060042},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Brain ; Brain-Computer Interfaces ; Evoked Potentials ; *Hearing ; Humans ; Photic Stimulation ; Semantics ; *Vision, Ocular ; },
abstract = {It is commonly believed that brain has faster reaction speed and higher reaction accuracy on visual-auditory bimodal stimuli than single modal stimuli in current neuropsychological researches, while visual-auditory bimodal stimuli (VABS) do not show corresponding superiority in BCI system. This paper aims at investigating whether semantically congruent stimuli could also get better performance than semantically incongruent stimuli in Brain Computer Interface (BCI) system. Two VABS based paradigms (semantically congruent or incongruent) were conducted in this study. 10 healthy subjects participated in the experiment in order to compare the two paradigms. The results indicated that the higher Event-related potential (ERP) amplitude of semantic incongruent paradigm were observed both in target and non-target stimuli. Nevertheless, we didn't observe significant difference of classification accuracy between congruent and incongruent conditions. Most participants showed their preference on semantically congruent condition for less workload needed. This finding demonstrated that semantic congruency has positive effect on behavioral results (less workload) and insignificant effect on system efficiency.},
}
@article {pmid29060041,
year = {2017},
author = {Xingwei An, and Jinwen Wei, and Shuang Liu, and Dong Ming, },
title = {A sLORETA study for gaze-independent BCI speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {994-997},
doi = {10.1109/EMBC.2017.8036993},
pmid = {29060041},
issn = {2694-0604},
mesh = {Attention ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Movement ; },
abstract = {EEG-based BCI (brain-computer-interface) speller, especially gaze-independent BCI speller, has become a hot topic in recent years. It provides direct spelling device by non-muscular method for people with severe motor impairments and with limited gaze movement. Brain needs to conduct both stimuli-driven and stimuli-related attention in fast presented BCI paradigms for such BCI speller applications. Few researchers studied the mechanism of brain response to such fast presented BCI applications. In this study, we compared the distribution of brain activation in visual, auditory, and audio-visual combined stimuli paradigms using sLORETA (standardized low-resolution brain electromagnetic tomography). Between groups comparisons showed the importance of visual and auditory stimuli in audio-visual combined paradigm. They both contribute to the activation of brain regions, with visual stimuli being the predominate stimuli. Visual stimuli related brain region was mainly located at parietal and occipital lobe, whereas response in frontal-temporal lobes might be caused by auditory stimuli. These regions played an important role in audio-visual bimodal paradigms. These new findings are important for future study of ERP speller as well as the mechanism of fast presented stimuli.},
}
@article {pmid29060040,
year = {2017},
author = {Ganesh, S and Timm, D and Moon, KS and Lee, SQ and Youm, W},
title = {Active brainwave pattern generation for brain-to-machine communication.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {990-993},
doi = {10.1109/EMBC.2017.8036992},
pmid = {29060040},
issn = {2694-0604},
mesh = {Brain ; *Brain Waves ; Brain-Computer Interfaces ; Electroencephalography ; User-Computer Interface ; },
abstract = {Over the years of research, Electroencephalogram (EEG) signal study has grown to give promising outcomes. A lot of research has been done on implementing brain-computer-interfaces, and the brain-computer interface (BCI) algorithm as well as the study of the effects of different stimuli on brain signals. This paper intends to make progress toward that goal by developing a responsive real-time EEG-based brain-to-machine communication system by generating distinct EEG signals at will and identification of the explicit pattern that they reflect for the presented self-induced internal visual and auditory stimuli. The brain-to-machine communication system delivers the real-time capture, analysis, and visualization of the brain signal patterns that can be used for smart medical applications such as rehabilitation robotic control, smart wheelchair, etc.},
}
@article {pmid29060039,
year = {2017},
author = {Marjaninejad, A and Taherian, B and Valero-Cuevas, FJ},
title = {Finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {986-989},
doi = {10.1109/EMBC.2017.8036991},
pmid = {29060039},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrocardiography ; Electroencephalography ; Fingers ; Humans ; *Movement ; },
abstract = {Electrocardiogram (ECoG) recordings are very attractive for Brain Machine Interface (BMI) applications due to their balance between good signal to noise ratio and minimal invasiveness. The design of ECoG signal decoders is an open research area to date which requires a better understanding of the nature of these signals and how information is encoded in them. In this study, a linear and a non-linear method, Linear Regression Model (LRM) and Artificial Neural Network (ANN) respectively, were used to decode finger movements from energy in band-specific ECoG signals. It is shown that the ANN only slightly outperformed the LRM, which suggests that finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals. In addition, comparing our results to similar Electroencephalogram (EEG) studies illustrated that the spatio-temporal summation of multiple neural signals is itself linearly correlated with movement, and is not an artifact introduced by the scalp or cranium. Furthermore, a new algorithm was employed to reduce the number of spectral features of the input signals required for either of the decoding methods.},
}
@article {pmid29060035,
year = {2017},
author = {D'Aleo, R and Rouse, A and Schieber, M and Sarma, SV},
title = {An input-output linear time invariant model captures neuronal firing responses to external and behavioral events.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {970-973},
doi = {10.1109/EMBC.2017.8036987},
pmid = {29060035},
issn = {2694-0604},
mesh = {Animals ; Hand Strength ; Linear Models ; Motor Cortex ; Movement ; *Neurons ; },
abstract = {Investigating how neurons in different motor regions respond to external stimuli and behavioral events provides insight into motor control. A recent approach to studying neuronal activity is to construct a zero-input linear time invariant (ZI-LTI) state-space model, wherein the state vector consists of firing rate signals for different populations of neurons across motor regions. This approach allows for the populations to influence each other in a dynamical manner given an initial firing rate condition, and the model can accurately reconstruct firing rates within a limited epoch in the motor task during which no event occurs. Here, we generalize this LTI modeling approach to characterize firing responses of neurons to two events (a go cue and movement onset) in a movement task with a non-zero input LTI state-space model, herein referred to as input-output LTI (IO-LTI). Specifically, responses from 196 neurons in the primary motor (M1), ventral premotor (PMv), and dorsal premotor cortex (PMd) were recorded and modeled in two nonhuman primates executing a reach-to-grasp task. We found that a single IO-LTI model can reconstruct neuronal firing rate patterns of six populations of these neurons across the three areas in the presence of multiple events (go cue, movement onset). This is the first step towards constructing generative models of neuronal firing rates in the presence of multiple events, which then can be used to construct better decoders for brain machine interactive control.},
}
@article {pmid29059985,
year = {2017},
author = {Sadeghi, K and Junghyo Lee, and Banerjee, A and Sohankar, J and Gupta, SKS},
title = {Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {767-770},
doi = {10.1109/EMBC.2017.8036937},
pmid = {29059985},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.},
}
@article {pmid29059912,
year = {2017},
author = {Krell, MM and Su Kyoung Kim, },
title = {Rotational data augmentation for electroencephalographic data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {471-474},
doi = {10.1109/EMBC.2017.8036864},
pmid = {29059912},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Signal Processing, Computer-Assisted ; },
abstract = {MOTIVATION: For deep learning on image data, a common approach is to augment the training data by artificial new images, using techniques like moving windows, scaling, affine distortions, and elastic deformations. In contrast to image data, electroencephalographic (EEG) data suffers even more from the lack of sufficient training data.<br><br>METHODS: We suggest and evaluate rotational distortions similar to affine/rotational distortions of images to generate augmented data.<br><br>RESULTS: Our approach increases the performance of signal processing chains for EEG-based brain-computer interfaces when rotating only around y- and z-axis with an angle around &#xb1;18 degrees to generate new data.<br><br>CONCLUSION: This shows that our processing efficient approach generates meaningful data and encourages to look for further new methods for EEG data augmentation.},
}
@article {pmid29059901,
year = {2017},
author = {Diaz-Parra, A and Canals, S and Moratal, D},
title = {A fully automated method for segmentation and classification of local field potential recordings. Preliminary results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {426-429},
doi = {10.1109/EMBC.2017.8036853},
pmid = {29059901},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Automation ; Brain ; Brain-Computer Interfaces ; Electroencephalography ; Rats ; Theta Rhythm ; },
abstract = {Identification of brain states measured with electrophysiological methods such as electroencephalography and local field potential (LFP) recordings is of great importance in numerous neuroscientific applications. For instance, in Brain Computer Interface, in the diagnosis of neurological disorders as well as to investigate how brain rhythms stem from synchronized physiological mechanisms (e.g., memory and learning). In this work, we propose a fully automated method with the aim of partitioning LFP signals into stationary segments as well as classifying each detected segment into three different classes (delta, regular theta or irregular theta rhythms). Our approach is computationally efficient since the process of detection and partition of signals into stationary segments is only based on two features (the variance and the so-called spectral error measure) and allow the classification at the same time. We developed the algorithm upon analyzing six anesthetized rats, resulting in a true positive rate of 97.5%, 91.8% and 79.1% in detecting delta, irregular theta and regular theta rhythms, respectively. This preliminary quantitative evaluation offers encouraging results for further research.},
}
@article {pmid29059845,
year = {2017},
author = {Abbaspourazad, H and Shanechi, MM},
title = {An unsupervised learning algorithm for multiscale neural activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {201-204},
doi = {10.1109/EMBC.2017.8036797},
pmid = {29059845},
issn = {2694-0604},
mesh = {Action Potentials ; *Algorithms ; Brain-Computer Interfaces ; Likelihood Functions ; Unsupervised Machine Learning ; },
abstract = {Technological advances have enabled the simultaneous recording of multiscale neural activity consisting of spikes, local field potential (LFP), and electrocorticogram (ECoG). Developing models that describe the encoding of behavior within multiscale activity is essential both for understanding neural mechanisms and for various neurotechnologies such as brain-machine interfaces (BMI). Multiscale recordings consist of signals with different statistical profiles and time-scales. While encoding models have been developed for each scale of activity alone, developing statistical models that simultaneously characterize discrete spike and continuous LFP/ECoG recordings and their various time-scales is a major challenge. To address this challenge, we have recently proposed a multiscale state-space encoding model for combined spike/LFP/ECoG recordings. However, methods to learn these state-space models from data are still lacking. Here, we develop an unsupervised learning algorithm for multiscale state-space models. Given a multiscale dataset, our algorithm finds the maximum-likelihood estimate of the state-space model parameters using a new expectation-maximization (EM) technique. We show that the new algorithm can learn the encoding model accurately from simulated multiscale data. We also show that the learned model can be used to decode arm movement trajectories from simulated multiscale activity. These multiscale models have the potential to improve the performance and robustness of various neurotechnologies.},
}
@article {pmid29059844,
year = {2017},
author = {Han-Lin Hsieh, and Wong, YT and Pesaran, B and Shanechi, MM},
title = {Multiscale decoding for reliable brain-machine interface performance over time.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2017},
number = {},
pages = {197-200},
doi = {10.1109/EMBC.2017.8036796},
pmid = {29059844},
issn = {2694-0604},
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motor Cortex ; Movement ; Reproducibility of Results ; },
abstract = {Recordings from invasive implants can degrade over time, resulting in a loss of spiking activity for some electrodes. For brain-machine interfaces (BMI), such a signal degradation lowers control performance. Achieving reliable performance over time is critical for BMI clinical viability. One approach to improve BMI longevity is to simultaneously use spikes and other recording modalities such as local field potentials (LFP), which are more robust to signal degradation over time. We have developed a multiscale decoder that can simultaneously model the different statistical profiles of multi-scale spike/LFP activity (discrete spikes vs. continuous LFP). This decoder can also run at multiple time-scales (millisecond for spikes vs. tens of milliseconds for LFP). Here, we validate the multiscale decoder for estimating the movement of 7 major upper-arm joint angles in a non-human primate (NHP) during a 3D reach-to-grasp task. The multiscale decoder uses motor cortical spike/LFP recordings as its input. We show that the multiscale decoder can improve decoding accuracy by adding information from LFP to spikes, while running at the fast millisecond time-scale of the spiking activity. Moreover, this improvement is achieved using relatively few LFP channels, demonstrating the robustness of the approach. These results suggest that using multiscale decoders has the potential to improve the reliability and longevity of BMIs.},
}
@article {pmid29055718,
year = {2018},
author = {Kato, K and Takahashi, K and Mizuguchi, N and Ushiba, J},
title = {Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm.},
journal = {Journal of neuroscience methods},
volume = {293},
number = {},
pages = {289-298},
doi = {10.1016/j.jneumeth.2017.10.015},
pmid = {29055718},
issn = {1872-678X},
mesh = {*Algorithms ; Brain-Computer Interfaces ; *Cortical Synchronization ; Electroencephalography/*methods ; Fourier Analysis ; Humans ; Imagination/physiology ; Male ; Motor Activity/physiology ; Neurofeedback/methods ; Regression Analysis ; Sensorimotor Cortex/physiology ; *Signal Processing, Computer-Assisted ; Time Factors ; Wavelet Analysis ; Young Adult ; },
abstract = {BACKGROUND: Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain-computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity.<br><br>NEW METHOD: The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT).<br><br>RESULTS: The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89&#xb1;0.032 and 200&#xb1;9.49ms, respectively.<br><br>The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms.<br><br>CONCLUSIONS: These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits.},
}
@article {pmid29054259,
year = {2017},
author = {Chen, Y and Ke, Y and Meng, G and Jiang, J and Qi, H and Jiao, X and Xu, M and Zhou, P and He, F and Ming, D},
title = {Enhancing performance of P300-Speller under mental workload by incorporating dual-task data during classifier training.},
journal = {Computer methods and programs in biomedicine},
volume = {152},
number = {},
pages = {35-43},
doi = {10.1016/j.cmpb.2017.09.002},
pmid = {29054259},
issn = {1872-7565},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Cognition ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Models, Neurological ; Task Performance and Analysis ; *Workload ; Young Adult ; },
abstract = {As one of the most important brain-computer interface (BCI) paradigms, P300-Speller was shown to be significantly impaired once applied in practical situations due to effects of mental workload. This study aims to provide a new method of building training models to enhance performance of P300-Speller under mental workload. Three experiment conditions based on row-column P300-Speller paradigm were performed including speller-only, 3-back-speller and mental-arithmetic-speller. Data under dual-task conditions were introduced to speller-only data respectively to build new training models. Then performance of classifiers with different models was compared under the same testing condition. The results showed that when tasks of imported training data and testing data were the same, character recognition accuracies and round accuracies of P300-Speller with mixed-data training models significantly improved (FDR, p < 0.005). When they were different, performance significantly improved when tested on mental-arithmetic-speller (FDR, p < 0.05) while the improvement was modest when tested on n-back-speller (FDR, p < 0.1). The analysis of ERPs revealed that ERP difference between training data and testing data was significantly diminished when the dual-task data was introduced to training data (FDR, p < 0.05). The new method of training classifier on mixed data proved to be effective in enhancing performance of P300-Speller under mental workload, confirmed the feasibility to build a universal training model and overcome the effects of mental workload in its practical applications.},
}
@article {pmid29051907,
year = {2017},
author = {Speier, W and Chandravadia, N and Roberts, D and Pendekanti, S and Pouratian, N},
title = {Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {4},
number = {1-2},
pages = {114-121},
pmid = {29051907},
issn = {2326-263X},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
abstract = {The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.},
}
@article {pmid29051688,
year = {2017},
author = {Arvaneh, M and Guan, C and Ang, KK and Ward, TE and Chua, KSG and Kuah, CWK and Ephraim Joseph, GJ and Phua, KS and Wang, C},
title = {Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement.},
journal = {Neural computing &amp; applications},
volume = {28},
number = {11},
pages = {3259-3272},
pmid = {29051688},
issn = {0941-0643},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models (p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM.},
}
@article {pmid29051085,
year = {2018},
author = {Griskova-Bulanova, I and Dapsys, K and Melynyte, S and Voicikas, A and Maciulis, V and Andruskevicius, S and Korostenskaja, M},
title = {40Hz auditory steady-state response in schizophrenia: Sensitivity to stimulation type (clicks versus flutter amplitude-modulated tones).},
journal = {Neuroscience letters},
volume = {662},
number = {},
pages = {152-157},
doi = {10.1016/j.neulet.2017.10.025},
pmid = {29051085},
issn = {1872-7972},
mesh = {Acoustic Stimulation ; Adult ; Auditory Perception/*physiology ; Electroencephalography ; *Evoked Potentials, Auditory ; *Gamma Rhythm ; Humans ; Male ; Middle Aged ; Schizophrenia/*physiopathology ; Signal Processing, Computer-Assisted ; Sound Spectrography ; },
abstract = {Auditory steady-state response (ASSR) at 40Hz has been proposed as a potential biomarker for schizophrenia. The ASSR studies in patients have used click stimulation or amplitude-modulated tones. However, the sensitivity of 40Hz ASSRs to different stimulation types in the same group of patients has not been previously evaluated. Two stimulation types for ASSRs were tested in this study: (1) 40Hz clicks and (2) flutter-amplitude modulated tones. The mean phase-locking index, evoked amplitude and event-related spectral perturbation values were compared between schizophrenia patients (n=26) and healthy controls (n=20). Both stimulation types resulted in the observation of impaired phase-locking and power measures of late (200-500ms) 40Hz ASSR in patients compared to healthy controls. The early-latency (0-100ms) 40Hz ASSR part was diminished in the schizophrenia group in response to clicks only. The late-latency 40Hz ASSR parameters obtained through different stimulation types correlated in healthy subjects but not in patients. We conclude that flutter amplitude-modulated tone stimulation, due to its potential to reveal late-latency entrainment deficits, is suitable for use in clinical populations. Careful consideration of experimental stimulation settings can contribute to the interpretation of ASSR deficits and utilization as a potential biomarker.},
}
@article {pmid29050982,
year = {2018},
author = {Wu, QN and Liao, YF and Lu, YX and Wang, Y and Lu, JH and Zeng, ZL and Huang, QT and Sheng, H and Yun, JP and Xie, D and Ju, HQ and Xu, RH},
title = {Pharmacological inhibition of DUSP6 suppresses gastric cancer growth and metastasis and overcomes cisplatin resistance.},
journal = {Cancer letters},
volume = {412},
number = {},
pages = {243-255},
doi = {10.1016/j.canlet.2017.10.007},
pmid = {29050982},
issn = {1872-7980},
mesh = {Adult ; Aged ; Antineoplastic Agents/*pharmacology ; Apoptosis/drug effects ; Cell Line, Tumor ; Cell Proliferation/drug effects ; Cisplatin/*pharmacology ; Drug Resistance, Neoplasm ; Dual Specificity Phosphatase 6/analysis/*antagonists &amp; inhibitors ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Female ; Humans ; Male ; Middle Aged ; Neoplasm Invasiveness ; Neoplasm Metastasis ; Prognosis ; Stomach Neoplasms/*drug therapy/pathology ; Tumor Suppressor Protein p53/physiology ; },
abstract = {Gastric cancer (GC) is the second cause of cancer-related death. Cisplatin (CDDP) is widely used as the standard GC treatment, but relapse and metastasis are common because of intrinsic or acquired drug resistance. The mitogen-activated protein kinase phosphatases (MAPK)-extracellular signal regulated kinases (ERK) pathway contributes to GC progression and drug resistance, but targeting the MAPK-ERK pathway is challenging in GC therapy. Here, we demonstrated that dual-specificity phosphatases 6 (DUSP6) was overexpressed in GC and predicted poor overall survival and progression-free survival. Knockdown DUSP6 inhibited GC proliferation, migration, invasion and induced apoptosis. (E/Z)-BCI hydrochloride (BCI), a DUSP6 small molecule inhibitor, increased the activity of ERK but interestingly decreased the expression of ERK response genes in BGC823, SGC7901 and CDDP-resistant SGC7901/DDP cells. BCI also caused cell death through the DNA damage response (DDR) pathway. Moreover, BCI inhibited cell proliferation, migration and invasion in a receptor-independent manner and enhanced CDDP cytotoxicity at pharmacological concentrations in the GC cells. In vivo experiments further showed that BCI enhances the antitumor effects of CDDP in cell-based xenografts and PDX models. In summary, our findings indicated that disruption of DUSP6 by BCI enhanced CDDP-induced cell death and apoptosis in GC may partly through ERK and DDR pathways. Thus, this study suggests that DUSP6 is a potential prognostic biomarker and a promising target for GC therapy.},
}
@article {pmid29047393,
year = {2017},
author = {Michie, S and Thomas, J and Johnston, M and Aonghusa, PM and Shawe-Taylor, J and Kelly, MP and Deleris, LA and Finnerty, AN and Marques, MM and Norris, E and O'Mara-Eves, A and West, R},
title = {The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.},
journal = {Implementation science : IS},
volume = {12},
number = {1},
pages = {121},
pmid = {29047393},
issn = {1748-5908},
support = {201524/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; /WT_/Wellcome Trust/United Kingdom ; MR/J005037/1/MRC_/Medical Research Council/United Kingdom ; /CRUK_/Cancer Research UK/United Kingdom ; 201,524/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Algorithms ; *Artificial Intelligence ; *Health Behavior ; *Health Policy ; Humans ; Machine Learning ; },
abstract = {BACKGROUND: Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a 'Knowledge System' that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question 'What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?'.<br><br>METHODS: The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility.<br><br>DISCUSSION: The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.},
}
@article {pmid29046625,
year = {2017},
author = {Nicolae, IE and Acqualagna, L and Blankertz, B},
title = {Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {548},
pmid = {29046625},
issn = {1662-4548},
abstract = {Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.},
}
@article {pmid29046428,
year = {2018},
author = {Ho, E and Smith, R and Goetz, G and Lei, X and Galambos, L and Kamins, TI and Harris, J and Mathieson, K and Palanker, D and Sher, A},
title = {Spatiotemporal characteristics of retinal response to network-mediated photovoltaic stimulation.},
journal = {Journal of neurophysiology},
volume = {119},
number = {2},
pages = {389-400},
pmid = {29046428},
issn = {1522-1598},
support = {P30 EY026877/EY/NEI NIH HHS/United States ; R01 EY018608/EY/NEI NIH HHS/United States ; R01 EY027786/EY/NEI NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Rats ; Rats, Long-Evans ; Retinal Ganglion Cells/*physiology ; *Visual Prosthesis ; },
abstract = {Subretinal prostheses aim at restoring sight to patients blinded by photoreceptor degeneration using electrical activation of the surviving inner retinal neurons. Today, such implants deliver visual information with low-frequency stimulation, resulting in discontinuous visual percepts. We measured retinal responses to complex visual stimuli delivered at video rate via a photovoltaic subretinal implant and by visible light. Using a multielectrode array to record from retinal ganglion cells (RGCs) in the healthy and degenerated rat retina ex vivo, we estimated their spatiotemporal properties from the spike-triggered average responses to photovoltaic binary white noise stimulus with 70-μm pixel size at 20-Hz frame rate. The average photovoltaic receptive field size was 194 ± 3 μm (mean ± SE), similar to that of visual responses (221 ± 4 μm), but response latency was significantly shorter with photovoltaic stimulation. Both visual and photovoltaic receptive fields had an opposing center-surround structure. In the healthy retina, ON RGCs had photovoltaic OFF responses, and vice versa. This reversal is consistent with depolarization of photoreceptors by electrical pulses, as opposed to their hyperpolarization under increasing light, although alternative mechanisms cannot be excluded. In degenerate retina, both ON and OFF photovoltaic responses were observed, but in the absence of visual responses, it is not clear what functional RGC types they correspond to. Degenerate retina maintained the antagonistic center-surround organization of receptive fields. These fast and spatially localized network-mediated ON and OFF responses to subretinal stimulation via photovoltaic pixels with local return electrodes raise confidence in the possibility of providing more functional prosthetic vision. NEW &amp; NOTEWORTHY Retinal prostheses currently in clinical use have struggled to deliver visual information at naturalistic frequencies, resulting in discontinuous percepts. We demonstrate modulation of the retinal ganglion cells (RGC) activity using complex spatiotemporal stimuli delivered via subretinal photovoltaic implant at 20 Hz in healthy and in degenerate retina. RGCs exhibit fast and localized ON and OFF network-mediated responses, with antagonistic center-surround organization of their receptive fields.},
}
@article {pmid29046111,
year = {2017},
author = {Liu, R and Wang, Y and Newman, GI and Thakor, NV and Ying, S},
title = {EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI.},
journal = {International journal of neural systems},
volume = {27},
number = {8},
pages = {1750046},
doi = {10.1142/S0129065717500460},
pmid = {29046111},
issn = {1793-6462},
mesh = {Bayes Theorem ; Brain/*physiology ; *Brain-Computer Interfaces ; Decision Making/*physiology ; Electroencephalography/*methods ; Hand/physiology ; Humans ; Imagination/*physiology ; Information Theory ; Motor Activity/*physiology ; Time Factors ; Wavelet Analysis ; },
abstract = {To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects' recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.},
}
@article {pmid29042375,
year = {2017},
author = {Chen, ML and Yao, L and Boger, J and Mercer, K and Thompson, B and Jiang, N},
title = {Non-invasive brain stimulation interventions for management of chronic central neuropathic pain: a scoping review protocol.},
journal = {BMJ open},
volume = {7},
number = {10},
pages = {e016002},
pmid = {29042375},
issn = {2044-6055},
mesh = {Amputation, Surgical ; Deep Brain Stimulation/*methods ; Humans ; Multiple Sclerosis/complications ; Neuralgia/*etiology/*therapy ; *Research Design ; Spinal Cord Injuries/complications ; Stroke/complications ; Scoping Reviews As Topic ; },
abstract = {INTRODUCTION: Pain can affect people regardless of age, gender or ethnicity. Chronic central neuropathic pain (CCNP) is a debilitating condition that affects populations such as stroke survivors, amputees, spinal cord injury patients and patients with multiple sclerosis, with prevalence rates between 30% and 80%. This condition can be caused by a lesion or disease affecting the somatosensory system. CCNP is notoriously drug resistant, and few effective CCNP treatment or management strategies exist. The emergence of non-invasive brain stimulation and neuromodulation techniques provide novel avenues for managing chronic central neuropathic pain. This scoping review aims to systematically identify the methods and effectiveness of non-invasive brain stimulation techniques for treating and managing chronic central neuropathic pain.<br><br>METHODS AND ANALYSIS: The following databases will be searched systematically: PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Institute of Electric and Electronic Engineers (IEEE), Association of Computing Machinary (ACM) and Scopus. Additional literature will be identified by searching the reference lists of identified studies. Studies will include reviews and original research in both published and grey literatures. Two reviewers will independently screen identified studies for final inclusion. A quantitative analysis on the intervention type, application and efficacy will be synthesised along with a qualitative analysis to describe the effectiveness of each intervention.<br><br>ETHICS AND DISSEMINATION: No primary data will be collected and hence formal ethics review is not required. The results of the scoping review will be presented at relevant national and international conferences, published in a peer-reviewed journal and provided to the stakeholders with plain language to be posted on their websites. This scoping review will provide a foundation to guide the development of future primary research on non-invasive brain stimulation and CCNP.},
}
@article {pmid29038516,
year = {2017},
author = {Pirondini, E and Coscia, M and Minguillon, J and Millán, JDR and Van De Ville, D and Micera, S},
title = {EEG topographies provide subject-specific correlates of motor control.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {13229},
pmid = {29038516},
issn = {2045-2322},
mesh = {Adult ; Brain/*physiology ; *Electroencephalography ; Female ; Humans ; Male ; Motor Activity/*physiology ; Muscle, Skeletal/*physiology ; Young Adult ; },
abstract = {Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates' dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.},
}
@article {pmid29035204,
year = {2018},
author = {Yan, W and Xu, G and Xie, J and Li, M and Dan, Z},
title = {Four Novel Motion Paradigms Based on Steady-State Motion Visual Evoked Potential.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {8},
pages = {1696-1704},
doi = {10.1109/TBME.2017.2762690},
pmid = {29035204},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: The purpose of this paper was to study the applicability of paradigms with motion forms for use in a brain-computer interface (BCI). We examined the performances of different paradigms and evaluated the stimulus effects.<br><br>METHODS: We designed four novel stimulus paradigms based on basic motion modes: swing, rotation, spiral, and radial contraction-expansion. Canonical correlation analysis (CCA) was used to analyze the accuracy. Additionally, we optimized CCA template signal harmonic combinations for the different motion paradigms.<br><br>RESULTS: The spiral motion paradigm exhibited the highest average information transfer rate (ITR) and recognition accuracy (41.24&#xa0;bit/min[-1]/95.33%), and the average ITRs and recognition accuracies were lowest for the rotation motion paradigm (31.89 bit/min[-1] /80.89%) and the radial contraction-expansion motion paradigm (32.62&#xa0;bit/min[-1] /80.72%) because they include fewer harmonic components.<br><br>CONCLUSION: Any stimulus paradigms with periodic motion can induce steady-state motion visual evoked potentials (SSMVEPs), but the SSMVEP harmonic components induced by different motion modes differed significantly. The spiral motion paradigm was more suitable for BCI applications.<br><br>SIGNIFICANCE: This study is an important extension to the existing SSMVEP-based BCI literature, and provides new insight to enable future design of the BCI paradigms.},
}
@article {pmid29031463,
year = {2017},
author = {Mizuguchi, N and Kanosue, K},
title = {Changes in brain activity during action observation and motor imagery: Their relationship with motor learning.},
journal = {Progress in brain research},
volume = {234},
number = {},
pages = {189-204},
doi = {10.1016/bs.pbr.2017.08.008},
pmid = {29031463},
issn = {1875-7855},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy/*methods ; Imagination/*physiology ; Learning/*physiology ; Observation ; Psychomotor Performance/*physiology ; },
abstract = {Many studies have demonstrated that training utilizing action observation and/or motor imagery improves motor performance. These two techniques are widely used in sports and in the rehabilitation of movement-related disorders. Motor imagery has also been used for brain-machine/computer interfaces (BMI/BCI). During both action observation and motor imagery, motor-related regions such as the premotor cortex and inferior parietal lobule are activated. This is common to actual execution and are involved with the underlying mechanisms of motor learning without execution. Since it is easier to record brain activity during action observation and motor imagery than that during actual sport movements, action observation, and motor imagery of sports skills or complex whole body movements have been utilized to investigate how neural mechanisms differ across the performance spectrum ranging from beginner to expert. However, brain activity during action observation and motor imagery is influenced by task complexity (i.e., simple vs complex movements). Furthermore, temporal changes in brain activity during actual execution along the long time course of motor learning are likely nonlinear and would be different from that during action observation or motor imagery. Activity in motor-related regions during action observation and motor imagery is typically greater in experts than in nonexperts, while the activity during actual execution is often smaller in experts than in nonexperts.},
}
@article {pmid29029039,
year = {2018},
author = {Li, Y and Wang, F and Chen, Y and Cichocki, A and Sejnowski, T},
title = {The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {28},
number = {10},
pages = {3623-3637},
pmid = {29029039},
issn = {1460-2199},
support = {R01 EB009282/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Auditory Perception/*physiology ; Brain/diagnostic imaging/*physiology ; Brain Mapping ; Crying/psychology ; Emotions ; Humans ; Image Processing, Computer-Assisted ; Laughter/psychology ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Nerve Net/diagnostic imaging/physiology ; Problem Solving/*physiology ; Reproducibility of Results ; Social Environment ; Visual Perception/*physiology ; Young Adult ; },
abstract = {At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem.},
}
@article {pmid29028186,
year = {2018},
author = {Yao, L and Sheng, X and Mrachacz-Kersting, N and Zhu, X and Farina, D and Jiang, N},
title = {Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {8},
pages = {1689-1695},
doi = {10.1109/TBME.2017.2762461},
pmid = {29028186},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Biomedical Engineering ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Psychomotor Performance ; *Signal Processing, Computer-Assisted ; Touch/*physiology ; Wrist/physiology ; Young Adult ; },
abstract = {OBJECTIVE: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation.<br><br>METHODS: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The brain-computer interface (BCI) system was calibrated with the sensation data and then applied for online SAO decoding.<br><br>RESULTS: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to a greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8 &#xb1; 13.1%, with 12 subjects >70% and 4 subject >90%.<br><br>CONCLUSION: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation.<br><br>SIGNIFICANCE: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.},
}
@article {pmid29026344,
year = {2017},
author = {Chajda, I and Länger, H},
title = {Convex congruences.},
journal = {Soft computing},
volume = {21},
number = {19},
pages = {5641-5645},
pmid = {29026344},
issn = {1432-7643},
abstract = {For an algebra [Formula: see text] belonging to a quasivariety [Formula: see text], the quotient [Formula: see text] need not belong to [Formula: see text] for every [Formula: see text]. The natural question arises for which [Formula: see text]. We consider algebras [Formula: see text] of type (2, 0) where a partial order relation is determined by the operations [Formula: see text] and 1. Within these, we characterize congruences on [Formula: see text] for which [Formula: see text] belongs to the same quasivariety as [Formula: see text]. In several particular cases, these congruences are determined by the property that every class is a convex subset of A.},
}
@article {pmid29024794,
year = {2018},
author = {Guy, V and Soriani, MH and Bruno, M and Papadopoulo, T and Desnuelle, C and Clerc, M},
title = {Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral sclerosis.},
journal = {Annals of physical and rehabilitation medicine},
volume = {61},
number = {1},
pages = {5-11},
doi = {10.1016/j.rehab.2017.09.004},
pmid = {29024794},
issn = {1877-0665},
mesh = {Adult ; Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/*rehabilitation ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Patient Satisfaction ; Prospective Studies ; },
abstract = {OBJECTIVES: Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease, restricts patients' communication capacity a few years after onset. A proof-of-concept of brain-computer interface (BCI) has shown promise in ALS and "locked-in" patients, mostly in pre-clinical studies or with only a few patients, but performance was estimated not high enough to support adoption by people with physical limitation of speech. Here, we evaluated a visual BCI device in a clinical study to determine whether disabled people with multiple deficiencies related to ALS would be able to use BCI to communicate in a daily environment.<br><br>METHODS: After clinical evaluation of physical, cognitive and language capacities, 20 patients with ALS were included. The P300 speller BCI system consisted of electroencephalography acquisition connected to real-time processing software and separate keyboard-display control software. It was equipped with original features such as optimal stopping of flashes and word prediction. The study consisted of two 3-block sessions (copy spelling, free spelling and free use) with the system in several modes of operation to evaluate its usability in terms of effectiveness, efficiency and satisfaction.<br><br>RESULTS: The system was effective in that all participants successfully achieved all spelling tasks and was efficient in that 65% of participants selected more than 95% of the correct symbols. The mean number of correct symbols selected per minute ranged from 3.6 (without word prediction) to 5.04 (with word prediction). Participants expressed satisfaction: the mean score was 8.7 on a 10-point visual analog scale assessing comfort, ease of use and utility. Patients quickly learned how to operate the system, which did not require much learning effort.<br><br>CONCLUSION: With its word prediction and optimal stopping of flashes, which improves information transfer rate, the BCI system may be competitive with alternative communication systems such as eye-trackers. Remaining requirements to improve the device for suitable ergonomic use are in progress.},
}
@article {pmid29021985,
year = {2017},
author = {Lührs, M and Goebel, R},
title = {Turbo-Satori: a neurofeedback and brain-computer interface toolbox for real-time functional near-infrared spectroscopy.},
journal = {Neurophotonics},
volume = {4},
number = {4},
pages = {041504},
pmid = {29021985},
issn = {2329-423X},
abstract = {Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications. It communicates directly with common NIRx fNIRS hardware and was tested extensively ensuring that the calculations can be performed in real time without a significant change in calculation times for all sampling intervals during ongoing experiments of up to 6 h of recording. Enabling immediate access to advanced processing features also allows the use of this toolbox for students and nonexperts in the field of fNIRS data acquisition and processing. Flexible network interfaces allow third party stimulus applications to access the processed data and calculated statistics in real time so that this information can be easily incorporated in neurofeedback or BCI presentations.},
}
@article {pmid29019425,
year = {2018},
author = {Johnston, M and Johnston, D and Wood, CE and Hardeman, W and Francis, J and Michie, S},
title = {Communication of behaviour change interventions: Can they be recognised from written descriptions?.},
journal = {Psychology &amp; health},
volume = {33},
number = {6},
pages = {713-723},
doi = {10.1080/08870446.2017.1385784},
pmid = {29019425},
issn = {1476-8321},
support = {MR/K023195/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; *Behavior Therapy ; *Communication ; *Comprehension ; Humans ; *Writing ; },
abstract = {OBJECTIVE: Communication of the content of a behaviour change intervention (BCI) involves clear description followed by appropriate recognition and interpretation. We investigated accuracy of recognition of BCI descriptions and the effects of training in the behaviour change taxonomy BCTTv1.<br><br>METHODS: Materials were 166 written descriptions of two BCIs previously written by 166 separate writers after viewing a video of the BCI. Each of the current participants (12 na&#xef;ve and 12 trained in BCTTv1) was presented with a random sample of the written descriptions and asked to form groups of descriptions they judged to be describing the same intervention. For each participant, we assessed the number of groupings of BCI descriptions, their purity (containing only one BCI) and their differentiation (having a dominant BCI).<br><br>RESULTS: All except one participant classified the descriptions into more than two groupings. Na&#xef;ve participants created significantly more groupings, fewer 'pure' groupings and less differentiated groupings (all Mann-Whitney p&#xa0;<&#xa0;.05).<br><br>CONCLUSIONS: Written communications of BCI contents may not be recognised and interpreted adequately to support implementation. BCT taxonomy training may lead to some progress in interpreting the active content of interventions but, based on this limited study, further progress is needed if BCIs for accurate implementation.},
}
@article {pmid29018336,
year = {2017},
author = {Holdgraf, CR and Rieger, JW and Micheli, C and Martin, S and Knight, RT and Theunissen, FE},
title = {Encoding and Decoding Models in Cognitive Electrophysiology.},
journal = {Frontiers in systems neuroscience},
volume = {11},
number = {},
pages = {61},
pmid = {29018336},
issn = {1662-5137},
support = {P50 MH109429/MH/NIMH NIH HHS/United States ; R01 DC010132/DC/NIDCD NIH HHS/United States ; R01 DC016783/DC/NIDCD NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; },
abstract = {Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.},
}
@article {pmid29017899,
year = {2018},
author = {Khalaf, A and Sybeldon, M and Sejdic, E and Akcakaya, M},
title = {A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines.},
journal = {Journal of neuroscience methods},
volume = {293},
number = {},
pages = {174-182},
doi = {10.1016/j.jneumeth.2017.10.003},
pmid = {29017899},
issn = {1872-678X},
mesh = {Blood Flow Velocity ; Brain/physiology ; *Brain-Computer Interfaces ; Cerebrovascular Circulation/physiology ; Cognition/physiology ; Feasibility Studies ; Female ; Functional Neuroimaging/methods ; Humans ; Imagination/physiology ; Language ; Linear Models ; Male ; Neuropsychological Tests ; Rest ; Rotation ; Space Perception/physiology ; *Support Vector Machine ; *Ultrasonography, Doppler, Transcranial ; *Wavelet Analysis ; Young Adult ; },
abstract = {BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow.<br><br>NEW METHOD: The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals. The Wilcoxon test and support vector machines (SVM), with a linear kernel, were employed for feature reduction and classification.<br><br>RESULTS: The experimental results showed that within approximately 3s of the onset of the cognitive task average accuracies of 80.29%, and 82.35% were obtained for the mental rotation versus resting state and the word generation versus resting state respectively. The mental rotation task versus word generation task achieved an average accuracy of 79.72% within 2.24s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68s.<br><br>The results presented here provide significant improvement compared to the relevant fTCD-based systems presented in literature in terms of accuracy and speed. Specifically, the reported speed in this manuscript is at least 12 and 2.5 times faster than any existing binary and 3-class fTCD-based BCIs, respectively.<br><br>CONCLUSIONS: These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI.},
}
@article {pmid29017508,
year = {2017},
author = {Valero-Cuevas, FJ and Santello, M},
title = {On neuromechanical approaches for the study of biological and robotic grasp and manipulation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {101},
pmid = {29017508},
issn = {1743-0003},
support = {R01 AR050520/AR/NIAMS NIH HHS/United States ; R01 AR052345/AR/NIAMS NIH HHS/United States ; R21 HD081938/HD/NICHD NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; *Hand Strength ; Humans ; *Robotics ; },
abstract = {Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.},
}
@article {pmid28993231,
year = {2018},
author = {Ramsey, NF and Salari, E and Aarnoutse, EJ and Vansteensel, MJ and Bleichner, MG and Freudenburg, ZV},
title = {Decoding spoken phonemes from sensorimotor cortex with high-density ECoG grids.},
journal = {NeuroImage},
volume = {180},
number = {Pt A},
pages = {301-311},
pmid = {28993231},
issn = {1095-9572},
mesh = {Adolescent ; Adult ; Algorithms ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Electrocorticography/methods ; Female ; Humans ; Language ; Male ; Phonetics ; Sensorimotor Cortex/*physiology ; Speech/*physiology ; Support Vector Machine ; Young Adult ; },
abstract = {For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high-density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain-Computer Interfaces are discussed.},
}
@article {pmid28991172,
year = {2017},
author = {Lee, D and Park, SH and Lee, SG},
title = {Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {10},
pages = {},
pmid = {28991172},
issn = {1424-8220},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Models, Biological ; Neurology/*education/*instrumentation ; Normal Distribution ; *Support Vector Machine ; *Wavelet Analysis ; },
abstract = {In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.},
}
@article {pmid28988909,
year = {2018},
author = {Nyangoh Timoh, K and Moszkowicz, D and Zaitouna, M and Lebacle, C and Martinovic, J and Diallo, D and Creze, M and Lavoue, V and Darai, E and Benoit, G and Bessede, T},
title = {Detailed muscular structure and neural control anatomy of the levator ani muscle: a study based on female human fetuses.},
journal = {American journal of obstetrics and gynecology},
volume = {218},
number = {1},
pages = {121.e1-121.e12},
doi = {10.1016/j.ajog.2017.09.021},
pmid = {28988909},
issn = {1097-6868},
mesh = {Female ; Fetus ; Humans ; Microscopy, Electron ; Myocytes, Smooth Muscle/metabolism ; Pelvic Floor/*anatomy &amp; histology/physiology ; },
abstract = {BACKGROUND: Injury to the levator ani muscle or pelvic nerves during pregnancy and vaginal delivery is responsible for pelvic floor dysfunction.<br><br>OBJECTIVE: We sought to demonstrate the presence of smooth muscular cell areas within the levator ani muscle and describe their localization and innervation.<br><br>STUDY DESIGN: Five female human fetuses were studied after approval from the French Biomedicine Agency. Specimens were serially sectioned and stained by Masson trichrome and immunostained for striated and smooth muscle, as well as for somatic, adrenergic, cholinergic, and nitriergic nerve fibers. Slides were digitized for 3-dimensional reconstruction. One fetus was reserved for electron microscopy. We explored the structure and innervation of the levator ani muscle.<br><br>RESULTS: Smooth muscular cell beams were connected externally to the anococcygeal raphe and the levator ani muscle and with the longitudinal anal muscle sphincter. The caudalmost part of the pubovaginal muscle was found to bulge between the rectum and the vagina. This bulging was a smooth muscular interface between the levator ani muscle and the longitudinal anal muscle sphincter. The medial (visceral) part of the levator ani muscle contained smooth muscle cells, in relation to the autonomic nerve fibers of the inferior hypogastric plexus. The lateral (parietal) part of the levator ani muscle contained striated muscle cells only and was innervated by the somatic nerve fibers of levator ani and pudendal nerves. The presence of smooth muscle cells within the medial part of the levator ani muscle was confirmed under electron microscopy in 1 fetus.<br><br>CONCLUSION: We characterized the muscular structure and neural control of the levator ani muscle. The muscle consists of a medial part containing smooth muscle cells under autonomic nerve influence and a lateral part containing striated muscle cells under somatic nerve control. These findings could result in new postpartum rehabilitation techniques.},
}
@article {pmid28983244,
year = {2017},
author = {Dodd, KC and Nair, VA and Prabhakaran, V},
title = {Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {469},
pmid = {28983244},
issn = {1662-5161},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {Following a stroke, the resulting lesion creates contralateral motor impairment and an interhemispheric imbalance involving hyperexcitability of the contralesional hemisphere. Neuronal reorganization may occur on both the ipsilesional and contralesional hemispheres during recovery to regain motor functionality and therefore bilateral activation for the hemiparetic side is often observed. Although ipsilesional hemispheric reorganization is traditionally thought to be most important for successful recovery, definitive conclusions into the role and importance of the contralesional motor cortex remain under debate. Through examining recent research in functional neuroimaging investigating motor cortex changes post-stroke, as well as brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies, this review attempts to clarify the contributions of each hemisphere toward recovery. Several functional magnetic resonance imaging studies suggest that continuation of contralesional hemisphere hyperexcitability correlates with lesser recovery, however a subset of well-recovered patients demonstrate contralesional motor activity and show decreased functional capability when the contralesional hemisphere is inhibited. BCI therapy may beneficially activate either the contralesional or ipsilesional hemisphere, depending on the study design, for chronic stroke patients who are otherwise at a functional plateau. Repetitive TMS used to excite the ipsilesional motor cortex or inhibit the contralesional hemisphere has shown promise in enhancing stroke patients' recovery.},
}
@article {pmid28983235,
year = {2017},
author = {Onishi, A and Takano, K and Kawase, T and Ora, H and Kansaku, K},
title = {Affective Stimuli for an Auditory P300 Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {522},
pmid = {28983235},
issn = {1662-4548},
abstract = {Gaze-independent brain computer interfaces (BCIs) are a potential communication tool for persons with paralysis. This study applies affective auditory stimuli to investigate their effects using a P300 BCI. Fifteen able-bodied participants operated the P300 BCI, with positive and negative affective sounds (PA: a meowing cat sound, NA: a screaming cat sound). Permuted stimuli of the positive and negative affective sounds (permuted-PA, permuted-NA) were also used for comparison. Electroencephalography data was collected, and offline classification accuracies were compared. We used a visual analog scale (VAS) to measure positive and negative affective feelings in the participants. The mean classification accuracies were 84.7% for PA and 67.3% for permuted-PA, while the VAS scores were 58.5 for PA and -12.1 for permuted-PA. The positive affective stimulus showed significantly higher accuracy and VAS scores than the negative affective stimulus. In contrast, mean classification accuracies were 77.3% for NA and 76.0% for permuted-NA, while the VAS scores were -50.0 for NA and -39.2 for permuted NA, which are not significantly different. We determined that a positive affective stimulus with accompanying positive affective feelings significantly improved BCI accuracy. Additionally, an ALS patient achieved 90% online classification accuracy. These results suggest that affective stimuli may be useful for preparing a practical auditory BCI system for patients with disabilities.},
}
@article {pmid28982285,
year = {2018},
author = {Jiao, Y and Zhang, Y and Wang, Y and Wang, B and Jin, J and Wang, X},
title = {A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {28},
number = {4},
pages = {1750039},
doi = {10.1142/S0129065717500393},
pmid = {28982285},
issn = {1793-6462},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Male ; *Models, Theoretical ; Signal Processing, Computer-Assisted ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.},
}
@article {pmid28981448,
year = {2017},
author = {Alcaide-Aguirre, RE and Warschausky, SA and Brown, D and Aref, A and Huggins, JE},
title = {Asynchronous brain-computer interface for cognitive assessment in people with cerebral palsy.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066001},
doi = {10.1088/1741-2552/aa7fc4},
pmid = {28981448},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces/trends ; Cerebral Palsy/*diagnosis/physiopathology/*psychology ; Child ; Cognition/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/physiology ; Female ; Humans ; *Language Tests ; Male ; *Neuropsychological Tests ; Photic Stimulation/methods ; Young Adult ; },
abstract = {OBJECTIVE: Typically, clinical measures of cognition require motor or speech responses. Thus, a significant percentage of people with disabilities are not able to complete standardized assessments. This situation could be resolved by employing a more accessible test administration method, such as a brain-computer interface (BCI). A BCI can circumvent motor and speech requirements by translating brain activity to identify a subject's response. By eliminating the need for motor or speech input, one could use a BCI to assess an individual who previously did not have access to clinical tests.<br><br>APPROACH: We developed an asynchronous, event-related potential BCI-facilitated administration procedure for the peabody picture vocabulary test (PPVT-IV). We then tested our system in typically developing individuals (N&#x2009;&#x2009;=&#x2009;&#x2009;11), as well as people with cerebral palsy (N&#x2009;&#x2009;=&#x2009;&#x2009;19) to compare results to the standardized PPVT-IV format and administration.<br><br>MAIN RESULTS: Standard scores on the BCI-facilitated PPVT-IV, and the standard PPVT-IV were highly correlated (r&#x2009;&#x2009;=&#x2009;&#x2009;0.95, p&#x2009;&#x2009;<&#x2009;&#x2009;0.001), with a mean difference of 2.0&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;6.4 points, which is within the standard error of the PPVT-IV.<br><br>SIGNIFICANCE: Thus, our BCI-facilitated PPVT-IV provided comparable results to the standard PPVT-IV, suggesting that populations for whom standardized cognitive tests are not accessible could benefit from our BCI-facilitated approach.},
}
@article {pmid28981418,
year = {2018},
author = {Zhang, H and Sun, Y and Li, J and Wang, F and Wang, Z},
title = {Covert Verb Reading Contributes to Signal Classification of Motor Imagery in BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {45-50},
doi = {10.1109/TNSRE.2017.2759241},
pmid = {28981418},
issn = {1558-0210},
mesh = {Brain-Computer Interfaces/*classification ; Electroencephalography/classification ; Equipment Design ; Female ; Foot ; Hand ; Healthy Volunteers ; Humans ; *Imagination ; Male ; Movement ; Psychomotor Performance ; *Reading ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Motor imagery is widely used in the brain-computer interface (BCI) systems that can help people actively control devices to directly communicate with the external world, but its training and performance effect is usually poor for normal people. To improve operators' BCI performances, here we proposed a novel paradigm, which combined the covert verb reading in the traditional motor imagery paradigm. In our proposed paradigm, participants were asked to covertly read the presented verbs during imagining right hand or foot movements referred by those verbs. EEG signals were recorded with both our proposed paradigm and the traditional paradigm. By the common spatial pattern method, we, respectively, decomposed these signals into spatial patterns and extracted their features used in the following classification of support vector machine. Compared with the traditional paradigm, our proposed paradigm could generate clearer spatial patterns following a somatotopic distribution, which led to more distinguishable features and higher classification accuracies than those in the traditional paradigm. These results suggested that semantic processing of verbs can influence the brain activity of motor imagery and enhance the mu event-related desynchronisation. The combination of semantic processing with motor imagery is therefore a promising method for the improvement of operators' BCI performances.},
}
@article {pmid28980377,
year = {2017},
author = {Murphy, SJ and Wiegand, T and Comita, LS},
title = {Distance-dependent seedling mortality and long-term spacing dynamics in a neotropical forest community.},
journal = {Ecology letters},
volume = {20},
number = {11},
pages = {1469-1478},
doi = {10.1111/ele.12856},
pmid = {28980377},
issn = {1461-0248},
mesh = {*Biodiversity ; *Forests ; Models, Biological ; Panama ; Population Density ; Population Dynamics ; Seedlings/growth &amp; development/*physiology ; Trees/*physiology ; Tropical Climate ; },
abstract = {Negative distance dependence (NDisD), or reduced recruitment near adult conspecifics, is thought to explain the astounding diversity of tropical forests. While many studies show greater mortality at near vs. far distances from adults, these studies do not seek to track changes in the peak seedling curve over time, thus limiting our ability to link NDisD to coexistence. Using census data collected over 12 years from central Panama in conjunction with spatial mark-connection functions, we show evidence for NDisD for many species, and find that the peak seedling curve shifts away from conspecific adults over time. We find wide variation in the strength of NDisD, which was correlated with seed size and canopy position, but other life-history traits showed no relationship with variation in NDisD mortality. Our results document shifts in peak seedling densities over time, thus providing evidence for the hypothesized spacing mechanism necessary for diversity maintenance in tropical forests.},
}
@article {pmid28969722,
year = {2017},
author = {Schepers, R and Markus, CR},
title = {The interaction between 5-HTTLPR genotype and ruminative thinking on BMI.},
journal = {The British journal of nutrition},
volume = {118},
number = {8},
pages = {629-637},
doi = {10.1017/S0007114517002562},
pmid = {28969722},
issn = {1475-2662},
mesh = {Adolescent ; Adult ; Alleles ; Anxiety Disorders/genetics/psychology ; *Body Mass Index ; Eating/psychology ; Female ; Gene Frequency ; Genotype ; Genotyping Techniques ; Humans ; Male ; Pessimism ; *Polymorphism, Genetic ; Serotonin Plasma Membrane Transport Proteins/*genetics/metabolism ; Stress, Psychological/*genetics ; Surveys and Questionnaires ; Weight Gain ; Young Adult ; },
abstract = {Negative affect or stress is often found to increase energy intake for high palatable energy-rich foods and hence weight gain. Reduced brain serotonin (5-HT) function is known to increase stress vulnerability and the risk for eating-related disturbances. A short (S) allele polymorphism in the serotonin transporter gene (5-HTTLPR) is associated with a less efficient functioning brain serotonin system and therefore higher stress vulnerability. It has been suggested that this genotype may be directly linked to an increased risk for weight gain and/or obesity. However, a high amount of variability has been apparent in replicating such a direct gene on weight gain relationship. A most recent suggestion is that this gene by weight relationship might be moderated by an additional (cognitive) vulnerability factor involving repetitive negative thinking (rumination). Our objective was to investigate whether the S-allele of 5-HTTLPR contributes to weight gain particularly in high cognitive ruminating individuals. A total of 827 healthy young male and female college students (aged 21·3 (sd 3·0) years; BMI 16-41·7 kg/m2) were genotyped for the 5-HTTLPR polymorphism and assessed for rumination (Event Related Ruminative Index) and body weight. In line with the hypothesis, a hierarchical regression model showed that higher BMI scores were observed in specifically high ruminating S'-carriers (P=0·031, f[2]=0·022). These results suggest that cognitive rumination may be a critical moderator of the association between 5-HTTLPR and body mass.},
}
@article {pmid28969378,
year = {2017},
author = {Fovet, T and Micoulaud-Franchi, JA and Vialatte, FB and Lotte, F and Daudet, C and Batail, JM and Mattout, J and Wood, G and Jardri, R and Enriquez-Geppert, S and Ros, T},
title = {On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?.},
journal = {Brain : a journal of neurology},
volume = {140},
number = {10},
pages = {e63},
doi = {10.1093/brain/awx211},
pmid = {28969378},
issn = {1460-2156},
support = {Y 777/FWF_/Austrian Science Fund FWF/Austria ; },
mesh = {*Attention Deficit Disorder with Hyperactivity ; Double-Blind Method ; Electroencephalography ; Humans ; *Neurofeedback ; *Sleep Initiation and Maintenance Disorders ; },
}
@article {pmid28968459,
year = {2017},
author = {Wallet, P and Benaoudia, S and Mosnier, A and Lagrange, B and Martin, A and Lindgren, H and Golovliov, I and Michal, F and Basso, P and Djebali, S and Provost, A and Allatif, O and Meunier, E and Broz, P and Yamamoto, M and Py, BF and Faudry, E and Sjöstedt, A and Henry, T},
title = {IFN-γ extends the immune functions of Guanylate Binding Proteins to inflammasome-independent antibacterial activities during Francisella novicida infection.},
journal = {PLoS pathogens},
volume = {13},
number = {10},
pages = {e1006630},
pmid = {28968459},
issn = {1553-7374},
mesh = {Animals ; Disease Models, Animal ; Enzyme-Linked Immunosorbent Assay ; Female ; Flow Cytometry ; Fluorescent Antibody Technique ; Francisella ; Francisella tularensis/*immunology ; GTP-Binding Proteins/*immunology ; Gene Knockdown Techniques ; Gram-Negative Bacterial Infections/immunology ; Immunoblotting ; Inflammasomes/*immunology ; Interferon-gamma/*immunology ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Knockout ; Tularemia/*immunology ; },
abstract = {Guanylate binding proteins (GBPs) are interferon-inducible proteins involved in the cell-intrinsic immunity against numerous intracellular pathogens. The molecular mechanisms underlying the potent antibacterial activity of GBPs are still unclear. GBPs have been functionally linked to the NLRP3, the AIM2 and the caspase-11 inflammasomes. Two opposing models are currently proposed to explain the GBPs-inflammasome link: i) GBPs would target intracellular bacteria or bacteria-containing vacuoles to increase cytosolic PAMPs release ii) GBPs would directly facilitate inflammasome complex assembly. Using Francisella novicida infection, we investigated the functional interactions between GBPs and the inflammasome. GBPs, induced in a type I IFN-dependent manner, are required for the F. novicida-mediated AIM2-inflammasome pathway. Here, we demonstrate that GBPs action is not restricted to the AIM2 inflammasome, but controls in a hierarchical manner the activation of different inflammasomes complexes and apoptotic caspases. IFN-γ induces a quantitative switch in GBPs levels and redirects pyroptotic and apoptotic pathways under the control of GBPs. Furthermore, upon IFN-γ priming, F. novicida-infected macrophages restrict cytosolic bacterial replication in a GBP-dependent and inflammasome-independent manner. Finally, in a mouse model of tularemia, we demonstrate that the inflammasome and the GBPs are two key immune pathways functioning largely independently to control F. novicida infection. Altogether, our results indicate that GBPs are the master effectors of IFN-γ-mediated responses against F. novicida to control antibacterial immune responses in inflammasome-dependent and independent manners.},
}
@article {pmid28967866,
year = {2018},
author = {Georgiadis, K and Laskaris, N and Nikolopoulos, S and Kompatsiaris, I},
title = {Discriminative codewaves: a symbolic dynamics approach to SSVEP recognition for asynchronous BCI.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026008},
doi = {10.1088/1741-2552/aa904c},
pmid = {28967866},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Random Allocation ; },
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP) is a very popular approach to establishing a communication pathway in brain-computer interfaces (BCIs), without any training requirements for the user. Brain activity recorded over occipital regions, in association with stimuli flickering at distinct frequencies, is used to predict the gaze direction. High performance is achieved when the analysis of multichannel signal is guided by the driving signals. This study introduces an efficient way of identifying the attended stimulus without the need to register the driving signals.<br><br>APPROACH: Regional brain response is described as a dynamical trajectory towards one of the 'attractors' associated with the brainwave entrainment induced by the attended stimulus. A condensed description for each single-trial response is provided by means of discriminative vector quantization, and different trajectories are disentangled based on a simple classification scheme that uses templates and confidence intervals derived from a small training dataset.<br><br>MAIN RESULTS: Experiments, based on two different datasets, provided evidence that the introduced approach compares favorably to well-established alternatives, regarding the information transfer rate.<br><br>SIGNIFICANCE: Our approach relies on (but not restricted to) single sensor traces, incorporates a novel description of brainwaves based on semi-supervised learning, and its great advantage stems from its potential for self-paced BCI.},
}
@article {pmid28966581,
year = {2017},
author = {Li, R and Potter, T and Huang, W and Zhang, Y},
title = {Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {462},
pmid = {28966581},
issn = {1662-5161},
abstract = {Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0-1 s) along with initial hemodynamic dip information from fNIRS (0-2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.},
}
@article {pmid28966573,
year = {2017},
author = {Komatsu, M and Sugano, E and Tomita, H and Fujii, N},
title = {A Chronically Implantable Bidirectional Neural Interface for Non-human Primates.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {514},
pmid = {28966573},
issn = {1662-4548},
abstract = {Optogenetics has potential applications in the study of epilepsy and neuroprostheses, and for studies on neural circuit dynamics. However, to achieve translation to clinical usage, optogenetic interfaces that are capable of chronic stimulation and monitoring with minimal brain trauma are required. We aimed to develop a chronically implantable device for photostimulation of the brain of non-human primates. We used a micro-light-emitting diode (LED) array with a flexible polyimide film. The array was combined with a whole-cortex electrocorticographic (ECoG) electrode array for simultaneous photostimulation and recording. Channelrhodopsin-2 (ChR2) was virally transduced into the cerebral cortex of common marmosets, and then the device was epidurally implanted into their brains. We recorded the neural activity during photostimulation of the awake monkeys for 4 months. The neural responses gradually increased after the virus injection for ~8 weeks and remained constant for another 8 weeks. The micro-LED and ECoG arrays allowed semi-invasive simultaneous stimulation and recording during long-term implantation in the brains of non-human primates. The development of this device represents substantial progress in the field of optogenetic applications.},
}
@article {pmid28964263,
year = {2017},
author = {Ssempiira, J and Nambuusi, B and Kissa, J and Agaba, B and Makumbi, F and Kasasa, S and Vounatsou, P},
title = {The contribution of malaria control interventions on spatio-temporal changes of parasitaemia risk in Uganda during 2009-2014.},
journal = {Parasites &amp; vectors},
volume = {10},
number = {1},
pages = {450},
pmid = {28964263},
issn = {1756-3305},
mesh = {Animals ; Artemisinins/pharmacology ; Child, Preschool ; Culicidae/drug effects/physiology ; Female ; Humans ; Infant ; Insecticide-Treated Bednets/statistics &amp; numerical data ; Insecticides/pharmacology ; Malaria/epidemiology/*prevention &amp; control ; Male ; Mosquito Control ; Parasitemia/epidemiology/*prevention &amp; control ; Spatio-Temporal Analysis ; Uganda/epidemiology ; },
abstract = {BACKGROUND: In Uganda, malaria vector control interventions and case management with Artemisinin Combination Therapies (ACTs) have been scaled up over the last few years as a result of increased funding. Data on parasitaemia prevalence among children less than 5 years old and coverage of interventions was collected during the first two Malaria Indicator Surveys (MIS) conducted in 2009 and 2014, respectively. In this study, we quantify the effects of control interventions on parasitaemia risk changes between the two MIS in a spatio-temporal analysis.<br><br>METHODS: Bayesian geostatistical and temporal models were fitted on the MIS data of 2009 and 2014. The models took into account geographical misalignment in the locations of the two surveys and adjusted for climatic changes and socio-economic differentials. Parasitaemia risk was predicted over a 2 &#xd7; 2&#xa0;km[2] grid and the number of infected children less than 5&#xa0;years old was estimated. Geostatistical variable selection was applied to identify the most important ITN coverage indicators. A spatially varying coefficient model was used to estimate intervention effects at sub-national level.<br><br>RESULTS: The coverage of Insecticide Treated Nets (ITNs) and ACTs more than doubled at country and sub-national levels during the period 2009-2014. The coverage of Indoor Residual Spraying (IRS) remained static at all levels. ITNs, IRS, and ACTs were associated with a reduction in parasitaemia odds of 19% (95% BCI: 18-29%), 78% (95% BCI: 67-84%), and 34% (95% BCI: 28-66%), respectively. Intervention effects varied with region. Higher socio-economic status and living in urban areas were associated with parasitaemia odds reduction of 46% (95% BCI: 0.51-0.57) and 57% (95% BCI: 0.40-0.53), respectively. The probability of parasitaemia risk decline in the country was 85% and varied from 70% in the North-East region to 100% in Kampala region. The estimated number of children infected with malaria declined from 2,480,373 in 2009 to 825,636 in 2014.<br><br>CONCLUSIONS: Interventions have had a strong effect on the decline of parasitaemia risk in Uganda during 2009-2014, albeit with varying magnitude in the regions. This success should be sustained by optimizing ITN coverage to achieve universal coverage.},
}
@article {pmid28964180,
year = {2017},
author = {Miao, M and Zeng, H and Wang, A and Zhao, F and Liu, F},
title = {Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people.},
journal = {The Review of scientific instruments},
volume = {88},
number = {9},
pages = {094305},
doi = {10.1063/1.5001896},
pmid = {28964180},
issn = {1089-7623},
abstract = {Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.},
}
@article {pmid28961119,
year = {2018},
author = {Park, SH and Lee, D and Lee, SG},
title = {Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {498-505},
doi = {10.1109/TNSRE.2017.2757519},
pmid = {28961119},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; *Movement ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP (,), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.},
}
@article {pmid28961117,
year = {2018},
author = {Chabuda, A and Durka, P and Zygierewicz, J},
title = {High Frequency SSVEP-BCI With Hardware Stimuli Control and Phase-Synchronized Comb Filter.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {344-352},
doi = {10.1109/TNSRE.2017.2734164},
pmid = {28961117},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Psychomotor Performance ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {We present an efficient implementation of brain-computer interface (BCI) based on high-frequency steady state visually evoked potentials (SSVEP). Individual shape of the SSVEP response is extracted by means of a feedforward comb filter, which adds delayed versions of the signal to itself. Rendering of the stimuli is controlled by specialized hardware (BCI Appliance). Out of 15 participants of the study, nine were able to produce stable response in at least eight out of ten frequencies from the 30-39 Hz range. They achieved on average 96±4% accuracy and 47±5 bit/min information transfer rate (ITR) for an optimized simple seven-letter speller, while generic full-alphabet speller allowed in this group for 89±9% accuracy and 36±9 bit/min ITR. These values exceed the performances of high-frequency SSVEP-BCI systems reported to date. Classical approach to SSVEP parameterization by relative spectral power in the frequencies of stimulation, implemented on the same data, resulted in significantly lower performance. This suggests that specific shape of the response is an important feature in classification. Finally, we discuss the differences in SSVEP responses of the participants who were able or unable to use the interface, as well as the statistically significant influence of the layout of the speller on the speed of BCI operation.},
}
@article {pmid28961092,
year = {2017},
author = {Bates, M},
title = {From Brain to Body: New Technologies Improve Paralyzed Patients? Quality of Life.},
journal = {IEEE pulse},
volume = {8},
number = {5},
pages = {22-26},
doi = {10.1109/MPUL.2017.2729411},
pmid = {28961092},
issn = {2154-2317},
mesh = {Brain ; *Brain-Computer Interfaces ; Humans ; Paralysis/therapy ; *Quality of Life ; *User-Computer Interface ; },
abstract = {Paralysis, whether caused by spinal cord injury, neurodegenerative disease, or other factors, poses a host of issues for patients. These include not just the inability to move parts of their bodies but potential problems with communication and bladder control as well. Fortunately, the last decade has seen promising technology advances to address these concerns. Although most are still in the research stage, these technologies-including brain-computer interfaces (BCIs), exoskeletons, and robotics-may one day improve the lives of people with paralysis.},
}
@article {pmid28957019,
year = {2017},
author = {Todorova, S and Ventura, V},
title = {Neural Decoding: A Predictive Viewpoint.},
journal = {Neural computation},
volume = {29},
number = {12},
pages = {3290-3310},
pmid = {28957019},
issn = {1530-888X},
support = {R01 MH064537/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*cytology/physiology ; *Brain-Computer Interfaces ; Humans ; Linear Models ; *Models, Neurological ; Neurons/*physiology ; },
abstract = {Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done to various degrees, decoding has not received the attention that the fields of statistics and machine learning have lavished on the prediction problem in the past two decades. Here, we take a more systematic approach to the decoding prediction problem and search for risk-optimized reverse regression, optimal linear estimation (OLE), and Kalman filter models within a large model space composed of several nonlinear transformations of neural spike counts at multiple temporal lags. The reverse regression decoding framework is a standard prediction problem, where penalized methods such as ridge regression or Lasso are routinely used to find minimum risk models. We argue that minimum risk reverse regression is always more efficient than OLE and also happens to be 44% more efficient than a standard Kalman filter in a particular application of offline reconstruction of arm reaches of a rhesus macaque monkey. Yet model selection for tuning curves-based decoding models such as OLE and Kalman filtering is not a standard statistical prediction problem, and no efficient method exists to identify minimum risk models. We apply several methods to build low-risk models and show that in our application, a Kalman filter that includes multiple carefully chosen observation equations per neural unit is 67% more efficient than a standard Kalman filter, but with the drawback that finding such a model is computationally very costly.},
}
@article {pmid28956859,
year = {2017},
author = {Chen, HY and Chen, CC and Hwang, WJ},
title = {An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {10},
pages = {},
pmid = {28956859},
issn = {1424-8220},
abstract = {This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting.},
}
@article {pmid28954333,
year = {2017},
author = {Weng, YF and Qi, RF and Zhang, XD and Zhang, L and Ke, J and Zhong, Y and Chen, F and Xu, Q and Lu, GM},
title = {[The altered topology of brain structural network in patients with acute stress response after traffic accident].},
journal = {Zhonghua yi xue za zhi},
volume = {97},
number = {35},
pages = {2751-2756},
doi = {10.3760/cma.j.issn.0376-2491.2017.35.008},
pmid = {28954333},
issn = {0376-2491},
mesh = {Accidents, Traffic ; Brain ; *Diffusion Tensor Imaging ; Gray Matter ; Humans ; Magnetic Resonance Imaging ; *Stress Disorders, Traumatic, Acute ; },
abstract = {Objective: To explore the changes of brain activities in traffic accident survivors with acute stress response (ASR) within a week by using complex networks analysis method based on graph-theory, and to find out the alteration of topological properties in structural brain network. Method: From January, 2013 to February, 2016, twenty traffic accidents survivors with acute stress disorders (Acute Stress Disorder Interview, ASDI>3)and twenty healthy controls underwent the 3T diffusion tensor imaging (DTI) magnetic resonance imaging scan in Nanjing General Hospital.The graph-theory analysis method was used to compare the structural brain network properties and nodal features between ASR survivors and controls.Statistical analyses were also performed by including anxiety and depression as covariates to evaluate their effect.In additional, Pearson correlation was performed between abnormal parametric values and clinical indices. Results: (1) The brain structural networks had small-world properties in both groups; (2) while compared with healthy controls, patients with ASR showed increased weighted connectivity strength (Si, 1.36±0.47 vs 0.92±0.38, P=0.008) and nodal betweenness centrality (BCi, 20±15 vs 7±6, P=0.002) in left triangular part of inferior frontal (IFG triang_L), increased Si in orbital part of inferior frontal gyrus (1.10±0.31 vs 0.77±0.30, P=0.004) and obviously decreased Si in left caudate (0.75±0.24 vs 1.04±0.35, P=0.004); (3) furthermore, the inclusion of anxiety and depression as covariates abolished nodal parameters differences in IFG triang_L, left caudate, thalamus and inferior temporal gyrus. Conclusions: The brain structure network in ASR patients has small world properties.But nodal parameters change obviously in some nodes compared with healthy controls and mainly locate in prefrontal lobe and striatum. High levels of anxiety and depression in ASR patients may partly account for these alterations.},
}
@article {pmid28951736,
year = {2017},
author = {Jochumsen, M and Rovsing, C and Rovsing, H and Niazi, IK and Dremstrup, K and Kamavuako, EN},
title = {Classification of Hand Grasp Kinetics and Types Using Movement-Related Cortical Potentials and EEG Rhythms.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {7470864},
pmid = {28951736},
issn = {1687-5273},
mesh = {Action Potentials ; Adult ; Brain Waves ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; *Electroencephalography ; Female ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Imagination/physiology ; Kinetics ; Male ; Middle Aged ; Movement/*physiology ; Neurological Rehabilitation ; Support Vector Machine ; Young Adult ; },
abstract = {Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48 ± 0.05 (grasp types), 0.41 ± 0.07 (kinetic profiles, motor execution), and 0.39 ± 0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.},
}
@article {pmid28949829,
year = {2017},
author = {Karinen, HM and Tuomisto, MT},
title = {Performance, Mood, and Anxiety During a Climb of Mount Everest.},
journal = {High altitude medicine &amp; biology},
volume = {18},
number = {4},
pages = {400-410},
doi = {10.1089/ham.2017.0033},
pmid = {28949829},
issn = {1557-8682},
mesh = {Adult ; *Affect ; Altitude Sickness/*psychology ; Anxiety/*etiology ; *Cognition ; Emotions ; Humans ; Male ; Middle Aged ; Mountaineering/*psychology ; Neuropsychological Tests ; Psychiatric Status Rating Scales ; },
abstract = {UNLABELLED: Karinen, Heikki M., and Martti T. Tuomisto. Performance, mood, and anxiety during a climb of Mount Everest. High Alt Med Biol. 18:400-410, 2017.<br><br>BACKGROUND: Various studies have shown the deleterious effects of high-altitude hypoxia on visual, motor, somatosensory, cognitive, and emotional function and also in intelligence tests, reaction time, speech comprehension, hand steadiness, visual contrast discrimination, and word association tests. Because optimal cognitive abilities may be crucial for mountain climbers' safety, this study was intended to evaluate the changes in cognitive performance, mood, and anxiety during an Everest expedition lasting almost 3 months.<br><br>METHODS: A set of physiological (Lake Louise score, oxygen saturation), cognitive (Colorado perceptual speed [CPS] test, number comparison [NC] test), and emotional measurements (Profile of Mood States, anxiety responses, psychological inflexibility) were collected from nine climbers on a partly unsupported Mount Everest expedition at various time points during the course of the expedition at Everest Base Camp (EBC). For confidence intervals we used 95% simultaneous Bonferroni corrected interval (BCI) for the differences.<br><br>RESULTS: During this expedition, the estimates of trait anxiety decreased 13% toward the end of expedition after successful summiting (p&#x2009;=&#x2009;0.004). Simultaneously, fatigue appeared to diminish and the CPS speed results improved 13%. Most expedition members suffered mild symptoms of acute mountain sickness during the first days in the EBC, but this did not affect the speed or the number of mistakes made in the CPS or NC tests. In CPS test the differences between pretest and the physically most demanding period (EBC4, BCI: 0.01, 4.43) and between EBC1 and EBC4 (BCI: 0.57, 4.99), between EBC2 and EBC4 (BCI: 0.45, 4.88), and between EBC3 and EBC4 (BCI: 1.12, 5.55) were significant, showing ever improving results during the expedition.<br><br>CONCLUSION: The most important finding in this study was that well-motivated and trained, self-selected individuals, who volunteer for a long-duration mission, are capable of maintaining high levels of performance, steady mood state, and a good level of vigor on a Mount Everest expedition lasting nearly 3 months.},
}
@article {pmid28949370,
year = {2018},
author = {Nishimoto, A and Kawakami, M and Fujiwara, T and Hiramoto, M and Honaga, K and Abe, K and Mizuno, K and Ushiba, J and Liu, M},
title = {Feasibility of task-specific brain-machine interface training for upper-extremity paralysis in patients with chronic hemiparetic stroke.},
journal = {Journal of rehabilitation medicine},
volume = {50},
number = {1},
pages = {52-58},
doi = {10.2340/16501977-2275},
pmid = {28949370},
issn = {1651-2081},
mesh = {Adult ; Aged ; Aged, 80 and over ; Feasibility Studies ; Female ; Hemiplegia/*complications ; Humans ; Male ; Middle Aged ; Paresis/rehabilitation ; Prospective Studies ; Stroke/*complications ; Stroke Rehabilitation/*methods ; Upper Extremity/*blood supply/pathology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke.<br><br>DESIGN: Prospective before&#xe2;&#x20ac;"after study.<br><br>SUBJECTS: Twenty-six patients with severe chronic hemiparetic stroke.<br><br>METHODS: Participants were trained with the brain-machine interface system to pick up and release pegs during 40-min sessions and 40 min of standard occupational therapy per day for 10 days. Fugl-Meyer upper-extremity motor (FMA) and Motor Activity Log-14 amount of use (MAL-AOU) scores were assessed before and after the intervention. To test its feasibility, 4 occupational therapists who operated the system for the first time assessed it with the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0.<br><br>RESULTS: FMA and MAL-AOU scores improved significantly after brain-machine interface training, with the effect sizes being medium and large, respectively (p<0.01, d=0.55; p<0.01, d=0.88). QUEST effectiveness and safety scores showed feasibility and satisfaction in the clinical setting.<br><br>CONCLUSION: Our newly developed compact brain-machine interface system is feasible for use in real-world clinical settings.},
}
@article {pmid28948168,
year = {2017},
author = {Athanasiou, A and Xygonakis, I and Pandria, N and Kartsidis, P and Arfaras, G and Kavazidi, KR and Foroglou, N and Astaras, A and Bamidis, PD},
title = {Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {5708937},
pmid = {28948168},
issn = {2314-6141},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; Humans ; *Prosthesis Design ; *Rehabilitation/instrumentation/methods ; Robotics/*methods ; },
abstract = {Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.},
}
@article {pmid28945598,
year = {2018},
author = {Cruz, A and Pires, G and Nunes, UJ},
title = {Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {26-36},
doi = {10.1109/TNSRE.2017.2755018},
pmid = {28945598},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces/classification ; Calibration ; *Communication Devices for People with Disabilities ; Electroencephalography/classification ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Feedback, Psychological ; Female ; Healthy Volunteers ; Humans ; Male ; Online Systems ; Reproducibility of Results ; Young Adult ; },
abstract = {Brain-computer interface (BCI) is a useful device for people with severe motor disabilities. However, due to its low speed and low reliability, BCI still has a very limited application in daily real-world tasks. This paper proposes a P300-based BCI speller combined with a double error-related potential (ErrP) detection to automatically correct erroneous decisions. This novel approach introduces a second error detection to infer whether wrong automatic correction also elicits a second ErrP. Thus, two single-trial responses, instead of one, contribute to the final selection, improving the reliability of error detection. Moreover, to increase error detection, the evoked potential detected as target by the P300 classifier is combined with the evoked error potential at a feature-level. Discriminable error and positive potentials (response to correct feedback) were clearly identified. The proposed approach was tested on nine healthy participants and one tetraplegic participant. The online average accuracy for the first and second ErrPs were 88.4% and 84.8%, respectively. With automatic correction, we achieved an improvement around 5% achieving 89.9% in spelling accuracy for an effective 2.92 symbols/min. The proposed approach revealed that double ErrP detection can improve the reliability and speed of BCI systems.},
}
@article {pmid28943849,
year = {2017},
author = {Zhang, Z and Huang, Y and Chen, S and Qu, J and Pan, X and Yu, T and Li, Y},
title = {An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {48},
pmid = {28943849},
issn = {1662-5218},
abstract = {In this study, an intention-driven semi-autonomous intelligent robotic (ID-SIR) system is designed and developed to assist the severely disabled patients to live independently. The system mainly consists of a non-invasive brain-machine interface (BMI) subsystem, a robot manipulator and a visual detection and localization subsystem. Different from most of the existing systems remotely controlled by joystick, head- or eye tracking, the proposed ID-SIR system directly acquires the intention from users' brain. Compared with the state-of-art system only working for a specific object in a fixed place, the designed ID-SIR system can grasp any desired object in a random place chosen by a user and deliver it to his/her mouth automatically. As one of the main advantages of the ID-SIR system, the patient is only required to send one intention command for one drinking task and the autonomous robot would finish the rest of specific controlling tasks, which greatly eases the burden on patients. Eight healthy subjects attended our experiment, which contained 10 tasks for each subject. In each task, the proposed ID-SIR system delivered the desired beverage container to the mouth of the subject and then put it back to the original position. The mean accuracy of the eight subjects was 97.5%, which demonstrated the effectiveness of the ID-SIR system.},
}
@article {pmid28939745,
year = {2017},
author = {Zhang, Y and Schroeder, BE and Jerevall, PL and Ly, A and Nolan, H and Schnabel, CA and Sgroi, DC},
title = {A Novel Breast Cancer Index for Prediction of Distant Recurrence in HR[+] Early-Stage Breast Cancer with One to Three Positive Nodes.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {23},
number = {23},
pages = {7217-7224},
doi = {10.1158/1078-0432.CCR-17-1688},
pmid = {28939745},
issn = {1557-3265},
mesh = {Antineoplastic Agents, Hormonal/therapeutic use ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Breast Neoplasms/drug therapy/genetics/*pathology ; Female ; *Gene Expression Regulation, Neoplastic ; Humans ; Kaplan-Meier Estimate ; Lymph Nodes/*pathology ; Middle Aged ; *Neoplasm Recurrence, Local ; Neoplasm Staging ; Prognosis ; Receptors, Estrogen/metabolism ; Retrospective Studies ; Risk Factors ; Tamoxifen/therapeutic use ; Time Factors ; },
abstract = {Purpose: The study objective was to characterize the prognostic performance of a novel Breast Cancer Index model (BCIN[+]), an integration of BCI gene expression, tumor size, and grade, specifically developed for assessment of distant recurrence (DR) risk in HR[+] breast cancer patients with one to three positive lymph nodes (pN1).Experimental Design: Analysis was conducted in a well-annotated retrospective series of pN1 patients (N = 402) treated with adjuvant endocrine therapy with or without chemotherapy using a prespecified model. The primary endpoint was time-to-DR. Results were determined blinded to clinical outcome. Kaplan-Meier estimates of overall (0-15 years) and late (≥5 years) DR, HRs, and 95% confidence interval (CIs) were estimated. Likelihood ratio statistics assessed relative contributions of prognostic information.Results: BCIN[+] classified 81 patients (20%) as low risk with a 15-year DR rate of 1.3% (95% CI, 0.0%-3.7%) versus 321 patients as high risk with a DR rate of 29.0% (95% CI, 23.2%-34.4%). In patients DR-free for ≥5 years (n = 349), the late DR rate was 1.3% (95% CI, 0.0%-3.7%) and 16.1% (95% CI, 10.6%-21.3%) in low- and high-risk groups, respectively. BCI gene expression alone was significantly prognostic (ΔLR-χ[2] = 20.12; P < 0.0001). Addition of tumor size (ΔLR-χ[2] = 13.29, P = 0.0003) and grade (ΔLR-χ[2] = 12.72; P = 0.0004) significantly improved prognostic performance. BCI added significant prognostic information to tumor size (ΔLR-χ[2] = 17.55; P < 0.0001); addition to tumor grade was incremental (ΔLR-χ[2] = 2.38; P = 0.1) with considerable overlap between prognostic values (ΔLR-χ[2] = 17.74).Conclusions: The integrated BCIN[+] identified 20% of pN1 patients with limited risk of recurrence over 15 years, in whom extended endocrine treatment may be spared. Ongoing studies will characterize combined clinical-genomic risk assessment in node-positive patients. Clin Cancer Res; 23(23); 7217-24. ©2017 AACR.},
}
@article {pmid28938144,
year = {2017},
author = {Suppa, A and Quartarone, A and Siebner, H and Chen, R and Di Lazzaro, V and Del Giudice, P and Paulus, W and Rothwell, JC and Ziemann, U and Classen, J},
title = {The associative brain at work: Evidence from paired associative stimulation studies in humans.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {128},
number = {11},
pages = {2140-2164},
doi = {10.1016/j.clinph.2017.08.003},
pmid = {28938144},
issn = {1872-8952},
mesh = {*Association ; Brain/*physiology ; Electric Stimulation/*methods ; Humans ; Neuronal Plasticity/*physiology ; },
abstract = {The original protocol of Paired Associative Stimulation (PAS) in humans implies repetitive cortical and peripheral nerve stimuli, delivered at specific inter-stimulus intervals, able to elicit non-invasively long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity in the human motor cortex. PAS has been designed to drive cortical LTP/LTD according to the Hebbian rule of associative plasticity. Over the last two decades, a growing number of researchers have increasingly used the PAS technique to assess cortical associative plasticity in healthy humans and in patients with movement disorders and other neuropsychiatric diseases. The present review covers the physiology, pharmacology, pathology and motor effects of PAS. Further sections of the review focus on new protocols of "modified PAS" and possible future application of PAS in neuromorphic circuits designed for brain-computer interface.},
}
@article {pmid28937339,
year = {2017},
author = {Johansson, V and Soekadar, SR and Clausen, J},
title = {Locked Out.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {26},
number = {4},
pages = {555-576},
doi = {10.1017/S0963180117000081},
pmid = {28937339},
issn = {1469-2147},
mesh = {Brain-Computer Interfaces/*ethics ; *Communication ; Persons with Disabilities ; Human Rights ; Humans ; Quadriplegia/*psychology ; *Quality of Life ; *Social Isolation ; *Social Stigma ; },
abstract = {Brain-computer interfaces (BCIs) can enable communication for persons in severe paralysis including locked-in syndrome (LIS); that is, being unable to move or speak while aware. In cases of complete loss of muscle control, termed "complete locked-in syndrome," a BCI may be the only viable solution to restore communication. However, a widespread ignorance regarding quality of life in LIS, current BCIs, and their potential as an assistive technology for persons in LIS, needlessly causes a harmful situation for this cohort. In addition to their medical condition, these persons also face social barriers often perceived as more impairing than their physical condition. Through social exclusion, stigmatization, and frequently being underestimated in their abilities, these persons are being locked out in addition to being locked-in. In this article, we (1) show how persons in LIS are being locked out, including how key issues addressed in the existing literature on ethics, LIS, and BCIs for communication, such as autonomy, quality of life, and advance directives, may reinforce these confinements; (2) show how these practices violate the United Nations Convention on the Rights of Persons with Disabilities, and suggest that we have a moral responsibility to prevent and stop this exclusion; and (3) discuss the role of BCIs for communication as one means to this end and suggest that a novel approach to BCI research is necessary to acknowledge the moral responsibility toward the end users and avoid violating the human rights of persons in LIS.},
}
@article {pmid28933416,
year = {2016},
author = {Naqvi, AA and Zehra, F and Ahmad, R and Ahmad, N},
title = {Developing a Research Instrument to Document Awareness, Knowledge, and Attitudes Regarding Breast Cancer and Early Detection Techniques for Pakistani Women: The Breast Cancer Inventory (BCI).},
journal = {Diseases (Basel, Switzerland)},
volume = {4},
number = {4},
pages = {},
pmid = {28933416},
issn = {2079-9721},
abstract = {There is a general hesitation in participation among Pakistani women when it comes to giving their responses in surveys related to breast cancer which may be due to the associated stigma and conservatism in society. We felt that no research instrument was able to extract information from the respondents to the extent it was needed for the successful execution of our study. The need to develop a research instrument tailored for Pakistani women was based upon the fact that most Pakistani women come from a conservative background and sometimes view this topic as provocative and believe discussing publicly about it as inappropriate. Existing research instruments exhibited a number of weaknesses during literature review. Therefore, using them may not be able to extract information concretely. A research instrument was, thus, developed exclusively. It was coined as, "breast cancer inventory (BCI)" by a panel of experts for executing a study aimed at documenting awareness, knowledge, and attitudes of Pakistani women regarding breast cancer and early detection techniques. The study is still in the data collection phase. The statistical analysis involved the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test for sampling adequacy. In addition, reliability analysis and exploratory factor analysis (EFA) were, also employed. This concept paper focuses on the development, piloting and validation of the BCI. It is the first research instrument which has high acceptability among Pakistani women and is able to extract adequate information from the respondents without causing embarrassment or unease.},
}
@article {pmid28932238,
year = {2017},
author = {Li, W and Jin, J and Duan, F},
title = {Cognitive-Based EEG BCIs and Human Brain-Robot Interactions.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {9471841},
doi = {10.1155/2017/9471841},
pmid = {28932238},
issn = {1687-5273},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Humans ; *Robotics/methods ; },
}
@article {pmid28931749,
year = {2018},
author = {Specker Sullivan, L and Illes, J},
title = {Ethics in published brain-computer interface research.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {013001},
doi = {10.1088/1741-2552/aa8e05},
pmid = {28931749},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*ethics ; Humans ; Informed Consent/*ethics ; *Research Subjects ; },
abstract = {OBJECTIVE: Sophisticated signal processing has opened the doors to more research with human subjects than ever before. The increase in the use of human subjects in research comes with a need for increased human subjects protections.<br><br>APPROACH: We quantified the presence or absence of ethics language in published reports of brain-computer interface (BCI) studies that involved human subjects and qualitatively characterized ethics statements.<br><br>MAIN RESULTS: Reports of BCI studies with human subjects that are published in neural engineering and engineering journals are anchored in the rationale of technological improvement. Ethics language is markedly absent, omitted from 31% of studies published in neural engineering journals and 59% of studies in biomedical engineering journals.<br><br>SIGNIFICANCE: As the integration of technological tools with the capacities of the mind deepens, explicit attention to ethical issues will ensure that broad human benefit is embraced and not eclipsed by technological exclusiveness.},
}
@article {pmid28931054,
year = {2017},
author = {Talakoub, O and Marquez-Chin, C and Popovic, MR and Navarro, J and Fonoff, ET and Hamani, C and Wong, W},
title = {Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.},
journal = {PloS one},
volume = {12},
number = {9},
pages = {e0182542},
pmid = {28931054},
issn = {1932-6203},
mesh = {Adult ; Arm/*physiology ; Biomechanical Phenomena ; Brain-Computer Interfaces ; *Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Electromyography ; Female ; Humans ; Linear Models ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%. An adaptive selection of frequency bands was used for movement classification and prediction. This demonstrates the efficacy of developing a real-time brain-machine interface for arm movements with as few as eight ECoG electrodes.},
}
@article {pmid28930544,
year = {2018},
author = {Sapountzis, P and Gregoriou, GG},
title = {Neural signatures of attention: insights from decoding population activity patterns.},
journal = {Frontiers in bioscience (Landmark edition)},
volume = {23},
number = {2},
pages = {221-246},
doi = {10.2741/4588},
pmid = {28930544},
issn = {2768-6698},
mesh = {Animals ; Attention/*physiology ; Brain/cytology/*physiology ; Cognition/*physiology ; Humans ; Models, Neurological ; Neural Pathways/physiology ; Neurons/*physiology ; Parietal Lobe/cytology/physiology ; Prefrontal Cortex/cytology/physiology ; },
abstract = {Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis. Recent studies have employed machine-learning algorithms in attention and other cognitive tasks to decode the information content of distributed activity patterns across neuronal ensembles on a single trial basis. Here, we review results from studies that have used pattern-classification decoding approaches to explore the population representation of cognitive functions. These studies have offered significant insights into population coding mechanisms. Moreover, we discuss how such advances can aid the development of cognitive brain-computer interfaces.},
}
@article {pmid28928649,
year = {2017},
author = {Sun, R and Wong, WW and Wang, J and Tong, RK},
title = {Changes in Electroencephalography Complexity using a Brain Computer Interface-Motor Observation Training in Chronic Stroke Patients: A Fuzzy Approximate Entropy Analysis.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {444},
pmid = {28928649},
issn = {1662-5161},
abstract = {Entropy-based algorithms have been suggested as robust estimators of electroencephalography (EEG) predictability or regularity. This study aimed to examine possible disturbances in EEG complexity as a means to elucidate the pathophysiological mechanisms in chronic stroke, before and after a brain computer interface (BCI)-motor observation intervention. Eleven chronic stroke subjects and nine unimpaired subjects were recruited to examine the differences in their EEG complexity. The BCI-motor observation intervention was designed to promote functional recovery of the hand in stroke subjects. Fuzzy approximate entropy (fApEn), a novel entropy-based algorithm designed to evaluate complexity in physiological systems, was applied to assess the EEG signals acquired from unimpaired subjects and stroke subjects, both before and after training. The results showed that stroke subjects had significantly lower EEG fApEn than unimpaired subjects (p < 0.05) in the motor cortex area of the brain (C3, C4, FC3, FC4, CP3, and CP4) in both hemispheres before training. After training, motor function of the paretic upper limb, assessed by the Fugl-Meyer Assessment-Upper Limb (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT) improved significantly (p < 0.05). Furthermore, the EEG fApEn in stroke subjects increased considerably in the central area of the contralesional hemisphere after training (p < 0.05). A significant correlation was noted between clinical scales (FMA-UL, ARAT, and WMFT) and EEG fApEn in C3/C4 in the contralesional hemisphere (p < 0.05). This finding suggests that the increase in EEG fApEn could be an estimator of the variance in upper limb motor function improvement. In summary, fApEn can be used to identify abnormal EEG complexity in chronic stroke, when used with BCI-motor observation training. Moreover, these findings based on the fApEn of EEG signals also expand the existing interpretation of training-induced functional improvement in stroke subjects. The entropy-based analysis might serve as a novel approach to understanding the abnormal cortical dynamics of stroke and the neurological changes induced by rehabilitation training.},
}
@article {pmid28927520,
year = {2016},
author = {Yvert, B and Depaulis, A and Delacour, C and Aksenova, T},
title = {Editorial.},
journal = {Journal of physiology, Paris},
volume = {110},
number = {4 Pt A},
pages = {315},
doi = {10.1016/j.jphysparis.2017.09.001},
pmid = {28927520},
issn = {1769-7115},
mesh = {Brain-Computer Interfaces/trends ; Congresses as Topic ; Humans ; *Nanotechnology/standards/trends ; Nerve Net/physiology ; Neural Prostheses/trends ; },
}
@article {pmid28926593,
year = {2017},
author = {Kitahara, K and Hayashi, Y and Yano, S and Kondo, T},
title = {Target-directed motor imagery of the lower limb enhances event-related desynchronization.},
journal = {PloS one},
volume = {12},
number = {9},
pages = {e0184245},
pmid = {28926593},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Knee Joint/physiology ; Lower Extremity/*physiology ; Male ; Movement ; Photic Stimulation ; Young Adult ; },
abstract = {Event-related desynchronization/synchronization (ERD/S) is an electroencephalogram (EEG) feature widely used as control signals for Brain-Computer Interfaces (BCIs). Nevertheless, the underlying neural mechanisms and functions of ERD/S are largely unknown, thus investigating them is crucial to improve the reliability of ERD/S-based BCIs. This study aimed to identify Motor Imagery (MI) conditions that enhance ERD/S. We investigated following three questions: 1) whether target-directed MI affects ERD/S, 2) whether MI with sound imagery affects ERD/S, and 3) whether ERD/S has a body part dependency of MI. Nine participants took part in the experiments of four MI conditions; they were asked to imagine right foot dorsiflexion (F), right foot dorsiflexion and the sound of a bass drum when the sole touched the floor (FS), right leg extension (L), and right leg extension directed toward a soccer ball (LT). Statistical comparison revealed that there were significant differences between conditions L and LT in beta-band ERD and conditions F and L in beta-band ERS. These results suggest that mental rehearsal of target-directed lower limb movement without real sensory stimuli can enhance beta-band ERD; furthermore, MI of foot dorsiflexion induces significantly larger beta-band ERS than that of leg extension. These findings could be exploited for the training of BCIs such as powered prosthetics for disabled person and neurorehabilitation system for stroke patients.},
}
@article {pmid28925374,
year = {2017},
author = {Jayaram, V and Hohmann, M and Just, J and Schölkopf, B and Grosse-Wentrup, M},
title = {Task-induced frequency modulation features for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056015},
doi = {10.1088/1741-2552/aa7778},
pmid = {28925374},
issn = {1741-2552},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Databases, Factual ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation.<br><br>APPROACH: We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering.<br><br>MAIN RESULTS: The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions.<br><br>SIGNIFICANCE: Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.},
}
@article {pmid28924568,
year = {2018},
author = {Trakoolwilaiwan, T and Behboodi, B and Lee, J and Kim, K and Choi, JW},
title = {Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.},
journal = {Neurophotonics},
volume = {5},
number = {1},
pages = {011008},
pmid = {28924568},
issn = {2329-423X},
abstract = {The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.},
}
@article {pmid28923040,
year = {2017},
author = {Mwale, M and Muula, AS},
title = {Systematic review: a review of adolescent behavior change interventions [BCI] and their effectiveness in HIV and AIDS prevention in sub-Saharan Africa.},
journal = {BMC public health},
volume = {17},
number = {1},
pages = {718},
pmid = {28923040},
issn = {1471-2458},
mesh = {Acquired Immunodeficiency Syndrome/prevention &amp; control ; Adolescent ; Adolescent Behavior/*psychology ; Africa South of the Sahara ; HIV Infections/*prevention &amp; control ; *Health Promotion ; Humans ; Program Evaluation ; Randomized Controlled Trials as Topic ; Risk Reduction Behavior ; },
abstract = {BACKGROUND: Despite sub-Saharan Africa [SSA] constituting just 12% of the world's population, the region has the highest burden of HIV with 70% of HIV infection in general and 80% of new infections among young people occuring in the region. Diverse intervention programmes have been implemented among young people but with minimal translation to behavior change. A systematic review of Behavior Change Interventions [BCI] targeting adolescents in SSA was therefore conducted with the objective of delineating this intervention vis-a-vis efficacy gap.<br><br>METHODS: From April to July 2015 searches were made from different journals online. Databases searched included MEDLINE, EBSCOhost, PsychINFO, Cochrane, and Google Scholar; Cambridge and Oxford journal websites, UNAIDS and WHO for studies published between 2000 and 2015. After excluding other studies by review of titles and then abstracts, the studies were reduced to 17. Three of these were randomized trials and five quasi-experimental. Overall interventions included those prescribing life skills, peer education [n&#xa0;=&#xa0;6] and community collaborative programmes. The main study protocol was approved by the University of Malawi College of Medicine Ethics Committee on 30th June 2016 [ref #: P.01/16/1847. The review was registered with PROSPERO [NIH] in 2015.<br><br>RESULTS: The review yielded some 200 titles and abstracts, 20 full text articles were critically analysed and 17 articles reviewed reflecting a dearth in published studies in the area of psychosocial BCI interventions targeting adolescents in SSA. Results show that a number of reviewed interventions [n&#xa0;=&#xa0;8] registered positive outcomes in both knowledge and sexual practices.<br><br>CONCLUSIONS: The review demonstrates a paucity of psychosocial BCI studies targeting adolescents in SSA. There are however mixed findings about the effectiveness of psychosocial BCI targeting adolescents in SSA. Other studies portray intervention effectiveness and others limited efficacy. Peer education as an intervention stands out as being more effective than other psychosocial regimens, like life skills, in facilitating HIV risk reduction. There is therefore need for further research on interventions employing peer education to substantiate their potential efficacy in HIV risk reduction among adolescents.<br><br>PROSPERO REGISTRATION NUMBER: CRD42015019244, available from http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42015019244 .},
}
@article {pmid28922275,
year = {2018},
author = {Drain, JP and Weinberg, DS and Ramey, JS and Moore, TA and Vallier, HA},
title = {Indications for CT-Angiography of the Vertebral Arteries After Trauma.},
journal = {Spine},
volume = {43},
number = {9},
pages = {E520-E524},
doi = {10.1097/BRS.0000000000002420},
pmid = {28922275},
issn = {1528-1159},
mesh = {Adult ; Brain Injuries, Diffuse/diagnostic imaging ; Computed Tomography Angiography/methods/*trends ; Female ; Humans ; Male ; Middle Aged ; Neck Injuries/*diagnostic imaging/therapy ; Spinal Injuries/diagnostic imaging/therapy ; Trauma Centers/*trends ; Vertebral Artery/*diagnostic imaging/*injuries ; Young Adult ; },
abstract = {STUDY DESIGN: Retrospective.<br><br>OBJECTIVE: The purpose of this project is to identify factors that predict vertebral artery injury (VAI) in an effort to assess risks and benefits of computed tomography angiography (CT-A) of the neck in the trauma setting. We seek to develop guidelines for practitioners to stratify patients at medium/high risk of VAI from those who are at low risk.<br><br>SUMMARY OF BACKGROUND DATA: VAI and blunt carotid injury (BCI) together comprise blunt cerebrovascular injury (BCVI). More is known about risk factors for BCI than for VAI, but the neurovascular complications associated with VAI are similarly disastrous. With increasing frequency, trauma providers are using CT-A to screen for BCVI; this test carries risks that include radiation exposure and nephrotoxicity, in addition to higher cost of treatment and longer hospital stay.<br><br>METHODS: Trauma patients seen over 4 months at an urban, level 1 trauma were analyzed. BCVI screening was conducted in 144/1854 (7.77%) patients. Presence of VAI and several clinical characteristics were recorded. Univariate analysis and binomial logistic regression analysis were conducted at a 95% significance level.<br><br>RESULTS: VAI was diagnosed in 0.49% of the study population. Univariate analysis determined six factors associated with positive VAI screening. Regression analysis showed four factors that independently predicted VAI: female sex, decreased Glasgow Coma Scale, cervical spine (c-spine) fracture, and concurrent BCI. A positive c-spine physical examination trended toward predicting VAI without achieving significance.<br><br>CONCLUSION: Several independent predictors of VAI were identified. This study highlights the importance of identifying patients at a higher risk for VAI and indicating CT-A of the neck versus those who are at low risk and can be evaluated without undergoing advanced imaging, as CT-A appears unnecessary for most trauma patients.<br><br>LEVEL OF EVIDENCE: 3.},
}
@article {pmid28920903,
year = {2018},
author = {Khorasani, A and Foodeh, R and Shalchyan, V and Daliri, MR},
title = {Brain Control of an External Device by Extracting the Highest Force-Related Contents of Local Field Potentials in Freely Moving Rats.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {18-25},
doi = {10.1109/TNSRE.2017.2751579},
pmid = {28920903},
issn = {1558-0210},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Artifacts ; Artificial Limbs ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Male ; Models, Theoretical ; Motor Cortex/physiology ; Principal Component Analysis ; Psychomotor Performance ; Rats ; Rats, Wistar ; Reproducibility of Results ; },
abstract = {A local field potential (LFP) signal is an alternative source to neural action potentials for decoding kinematic and kinetic information from the brain. Here, we demonstrate that the better extraction of force-related features from multichannel LFPs improves the accuracy of force decoding. We propose that applying canonical correlation analysis (CCA) filter on the envelopes of separate frequency bands (band-specific CCA) separates non-task related information from the LFPs. The decoding accuracy of the continuous force signal based on the proposed method were compared with three feature reduction methods: 1) band-specific principal component analysis (band-specific PCA) method that extract the components which leads to maximum variance from the envelopes of different frequency bands; 2) correlation coefficient-based (CC-based) feature reduction that selects the best features from the envelopes sorted based on the absolute correlation coefficient between each envelope and the target force signal; and 3) mutual information-based (MI-based) feature reduction that selects the best features from the envelopes sorted based on the mutual information between each envelope and output force signal. The band-specific CCA method outperformed band-specific PCA with 11% improvement, CC-based feature reduction with 16% improvement, and MI-based feature reduction with 18% improvement. In the online brain control experiments, the real-time decoded force signal from the 16-channel LFPs based on the proposed method was used to move a mechanical arm. Two rats performed 88 trials in seven sessions to control the mechanical arm based on the 16-channel LFPs.},
}
@article {pmid28914767,
year = {2017},
author = {Schimpf, PH},
title = {Feasibility of Equivalent Dipole Models for Electroencephalogram-Based Brain Computer Interfaces.},
journal = {Brain sciences},
volume = {7},
number = {9},
pages = {},
pmid = {28914767},
issn = {2076-3425},
abstract = {This article examines the localization errors of equivalent dipolar sources inverted from the surface electroencephalogram in order to determine the feasibility of using their location as classification parameters for non-invasive brain computer interfaces. Inverse localization errors are examined for two head models: a model represented by four concentric spheres and a realistic model based on medical imagery. It is shown that the spherical model results in localization ambiguity such that a number of dipolar sources, with different azimuths and varying orientations, provide a near match to the electroencephalogram of the best equivalent source. No such ambiguity exists for the elevation of inverted sources, indicating that for spherical head models, only the elevation of inverted sources (and not the azimuth) can be expected to provide meaningful classification parameters for brain-computer interfaces. In a realistic head model, all three parameters of the inverted source location are found to be reliable, providing a more robust set of parameters. In both cases, the residual error hypersurfaces demonstrate local minima, indicating that a search for the best-matching sources should be global. Source localization error vs. signal-to-noise ratio is also demonstrated for both head models.},
}
@article {pmid28914232,
year = {2018},
author = {Johnson, NN and Carey, J and Edelman, BJ and Doud, A and Grande, A and Lakshminarayan, K and He, B},
title = {Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {016009},
pmid = {28914232},
issn = {1741-2552},
support = {F31 NS096964/NS/NINDS NIH HHS/United States ; R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Combined Modality Therapy ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Motor Cortex/*physiology ; Recovery of Function/physiology ; Stroke/diagnosis/physiopathology/*therapy ; Stroke Rehabilitation/*methods ; Transcranial Magnetic Stimulation/*methods ; Virtual Reality Exposure Therapy/*methods ; },
abstract = {OBJECTIVE: Combining repetitive transcranial magnetic stimulation (rTMS) with brain-computer interface (BCI) training can address motor impairment after stroke by down-regulating exaggerated inhibition from the contralesional hemisphere and encouraging ipsilesional activation. The objective was to evaluate the efficacy of combined rTMS + BCI, compared to sham rTMS + BCI, on motor recovery after stroke in subjects with lasting motor paresis.<br><br>APPROACH: Three stroke subjects approximately one year post-stroke participated in three weeks of combined rTMS (real or sham) and BCI, followed by three weeks of BCI alone. Behavioral and electrophysiological differences were evaluated at baseline, after three weeks, and after six weeks of treatment.<br><br>MAIN RESULTS: Motor improvements were observed in both real rTMS&#x2009;&#x2009;+&#x2009;&#x2009;BCI and sham groups, but only the former showed significant alterations in inter-hemispheric inhibition in the desired direction and increased relative ipsilesional cortical activation from fMRI. In addition, significant improvements in BCI performance over time and adequate control of the virtual reality BCI paradigm were observed only in the former group.<br><br>SIGNIFICANCE: When combined, the results highlight the feasibility and efficacy of combined rTMS&#x2009;&#x2009;+&#x2009;&#x2009;BCI for motor recovery, demonstrated by increased ipsilesional motor activity and improvements in behavioral function for the real rTMS&#x2009;&#x2009;+&#x2009;&#x2009;BCI condition in particular. Our findings also demonstrate the utility of BCI training alone, as shown by behavioral improvements for the sham rTMS&#x2009;&#x2009;+&#x2009;&#x2009;BCI condition. This study is the first to evaluate combined rTMS and BCI training for motor rehabilitation and provides a foundation for continued work to evaluate the potential of both rTMS and virtual reality BCI training for motor recovery after stroke.},
}
@article {pmid28913349,
year = {2017},
author = {Carelli, L and Solca, F and Faini, A and Meriggi, P and Sangalli, D and Cipresso, P and Riva, G and Ticozzi, N and Ciammola, A and Silani, V and Poletti, B},
title = {Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {1695290},
pmid = {28913349},
issn = {2314-6141},
mesh = {Brain/*physiopathology ; Brain-Computer Interfaces ; Cognition/*physiology ; Humans ; Nervous System Diseases/*physiopathology/*rehabilitation ; Neurofeedback/physiology ; Neuronal Plasticity/physiology ; },
abstract = {Alongside the best-known applications of brain-computer interface (BCI) technology for restoring communication abilities and controlling external devices, we present the state of the art of BCI use for cognitive assessment and training purposes. We first describe some preliminary attempts to develop verbal-motor free BCI-based tests for evaluating specific or multiple cognitive domains in patients with Amyotrophic Lateral Sclerosis, disorders of consciousness, and other neurological diseases. Then we present the more heterogeneous and advanced field of BCI-based cognitive training, which has its roots in the context of neurofeedback therapy and addresses patients with neurological developmental disorders (autism spectrum disorder and attention-deficit/hyperactivity disorder), stroke patients, and elderly subjects. We discuss some advantages of BCI for both assessment and training purposes, the former concerning the possibility of longitudinally and reliably evaluating cognitive functions in patients with severe motor disabilities, the latter regarding the possibility of enhancing patients' motivation and engagement for improving neural plasticity. Finally, we discuss some present and future challenges in the BCI use for the described purposes.},
}
@article {pmid28912701,
year = {2017},
author = {Tian, Y and Zhang, H and Xu, W and Zhang, H and Yang, L and Zheng, S and Shi, Y},
title = {Spectral Entropy Can Predict Changes of Working Memory Performance Reduced by Short-Time Training in the Delayed-Match-to-Sample Task.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {437},
pmid = {28912701},
issn = {1662-5161},
abstract = {Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine classifier with linear kernel to differentiate these two groups. Receiver operating characteristics analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0 and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression prediction model with radial basis function kernel and the root-mean-square error of prediction revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject by short-time training, we found that 16 in 20 subjects' SRT scores were clearly promoted after training and 15 in 20 subjects' SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that spectral entropy could be a promising indicator to predict individual's WM changes by training and further provide a novel application about WM for brain-computer interfaces.},
}
@article {pmid28905090,
year = {2017},
author = {Altuntepe, E and Emel'yanenko, VN and Forster-Rotgers, M and Sadowski, G and Verevkin, SP and Held, C},
title = {Thermodynamics of enzyme-catalyzed esterifications: II. Levulinic acid esterification with short-chain alcohols.},
journal = {Applied microbiology and biotechnology},
volume = {101},
number = {20},
pages = {7509-7521},
doi = {10.1007/s00253-017-8481-4},
pmid = {28905090},
issn = {1432-0614},
support = {Leibniz award to G. Sadowski//Deutsche Forschungsgemeinschaft (DE)/ ; the Cluster of Excellence RESOLV (EXC 1069)//Deutsche Forschungsgemeinschaft (DE)/ ; 14.Z50.31.0038//Government of Russian Federation/ ; },
mesh = {Alcohols/*metabolism ; Enzymes, Immobilized ; Esterification ; Fungal Proteins ; Kinetics ; Levulinic Acids/*metabolism ; Lipase/*metabolism ; Temperature ; *Thermodynamics ; },
abstract = {Levulinic acid was esterified with methanol, ethanol, and 1-butanol with the final goal to predict the maximum yield of these equilibrium-limited reactions as function of medium composition. In a first step, standard reaction data (standard Gibbs energy of reaction Δ [R] g [0]) were determined from experimental formation properties. Unexpectedly, these Δ [R] g [0] values strongly deviated from data obtained with classical group contribution methods that are typically used if experimental standard data is not available. In a second step, reaction equilibrium concentrations obtained from esterification catalyzed by Novozym 435 at 323.15 K were measured, and the corresponding activity coefficients of the reacting agents were predicted with perturbed-chain statistical associating fluid theory (PC-SAFT). The so-obtained thermodynamic activities were used to determine Δ [R] g [0] at 323.15 K. These results could be used to cross-validate Δ [R] g [0] from experimental formation data. In a third step, reaction-equilibrium experiments showed that equilibrium position of the reactions under consideration depends strongly on the concentration of water and on the ratio of levulinic acid: alcohol in the initial reaction mixtures. The maximum yield of the esters was calculated using Δ [R] g [0] data from this work and activity coefficients of the reacting agents predicted with PC-SAFT for varying feed composition of the reaction mixtures. The use of the new Δ [R] g [0] data combined with PC-SAFT allowed good agreement to the measured yields, while predictions based on Δ [R] g [0] values obtained with group contribution methods showed high deviations to experimental yields.},
}
@article {pmid28902895,
year = {2017},
author = {Isaksen, JL and Mohebbi, A and Puthusserypady, S},
title = {Optimal pseudorandom sequence selection for online c-VEP based BCI control applications.},
journal = {PloS one},
volume = {12},
number = {9},
pages = {e0184785},
pmid = {28902895},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation/methods ; },
abstract = {BACKGROUND: In a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process.<br><br>AIMS: This study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials.<br><br>METHODS: A total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score.<br><br>RESULTS: No specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor.<br><br>CONCLUSIONS: The simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.},
}
@article {pmid28899231,
year = {2017},
author = {Garcia-Garcia, MG and Bergquist, AJ and Vargas-Perez, H and Nagai, MK and Zariffa, J and Marquez-Chin, C and Popovic, MR},
title = {Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.},
journal = {The journal of spinal cord medicine},
volume = {40},
number = {6},
pages = {715-722},
pmid = {28899231},
issn = {2045-7723},
support = {0040678//CIHR/Canada ; },
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Motor Cortex/cytology/*physiology ; Neurological Rehabilitation/methods ; Neurons/*physiology ; Pilot Projects ; Rats ; Rats, Long-Evans ; },
abstract = {CONTEXT: Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications.<br><br>FINDINGS: Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43&#xb1;1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73&#xb1;0.23, demonstrating low utility.<br><br>CONCLUSION/CLINICAL RELEVANCE: The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.},
}
@article {pmid28893295,
year = {2017},
author = {Wang, K and Wang, Z and Guo, Y and He, F and Qi, H and Xu, M and Ming, D},
title = {A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {93},
pmid = {28893295},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization ; Electromyography ; Energy Metabolism/*physiology ; Evoked Potentials ; Feasibility Studies ; Female ; Hand/*physiology ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Movement/physiology ; Muscle Contraction/physiology ; Online Systems ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.<br><br>METHODS: Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.<br><br>RESULTS: All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p&#xa0;<&#xa0;0.05) and spatial distribution.<br><br>CONCLUSIONS: This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.},
}
@article {pmid28891513,
year = {2017},
author = {Dai, S and Wei, Q},
title = {Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.},
journal = {Journal of integrative neuroscience},
volume = {16},
number = {3},
pages = {241-254},
doi = {10.3233/JIN-170017},
pmid = {28891513},
issn = {0219-6352},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/instrumentation/*methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Pattern Recognition, Automated/methods ; },
abstract = {Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.},
}
@article {pmid28887545,
year = {2017},
author = {Mashat, MEM and Li, G and Zhang, D},
title = {Human-to-human closed-loop control based on brain-to-brain interface and muscle-to-muscle interface.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {11001},
pmid = {28887545},
issn = {2045-2322},
support = {R01 AA020501/AA/NIAAA NIH HHS/United States ; },
mesh = {Algorithms ; Analysis of Variance ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Neurofeedback ; ROC Curve ; Reproducibility of Results ; },
abstract = {Novel communication techniques have always been fascinating for humankind. This pilot study presents an approach to human interaction by combining direct brain-to-brain interface (BBI) and muscle-to-muscle interface (MMI) in a closed-loop pattern. In this system, artificial paths (data flows) functionally connect natural paths (nerves). The intention from one subject (sender) is recognized using electroencephalography (EEG) based brain-computer interface (BCI), which is sent out to trigger transcranial magnetic stimulation (TMS) on the other subject (receiver) and induce hand motion; meanwhile TMS results in a significant change on the motor evoked potentials (MEP) recorded by electromyography (EMG) of the receiver's arm, which triggers functional electrical stimulation (FES) applied to the sender's arm and generates hand motion. Human-controlled loop and automatic control loop experiments were performed with 6 pairs of healthy subjects to evaluate the performance of the introduced mechanism. The results indicated that response accuracy during human-controlled experiments was 85% which demonstrates the feasibility of the proposed method. During the automatic control test, two subjects could accomplish repetitive and reciprocal hand motion control up to 85 times consecutively.},
}
@article {pmid28883745,
year = {2017},
author = {Takeda, K and Tanino, G and Miyasaka, H},
title = {Review of devices used in neuromuscular electrical stimulation for stroke rehabilitation.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {10},
number = {},
pages = {207-213},
pmid = {28883745},
issn = {1179-1470},
abstract = {Neuromuscular electrical stimulation (NMES), specifically functional electrical stimulation (FES) that compensates for voluntary motion, and therapeutic electrical stimulation (TES) aimed at muscle strengthening and recovery from paralysis are widely used in stroke rehabilitation. The electrical stimulation of muscle contraction should be synchronized with intended motion to restore paralysis. Therefore, NMES devices, which monitor electromyogram (EMG) or electroencephalogram (EEG) changes with motor intention and use them as a trigger, have been developed. Devices that modify the current intensity of NMES, based on EMG or EEG, have also been proposed. Given the diversity in devices and stimulation methods of NMES, the aim of the current review was to introduce some commercial FES and TES devices and application methods, which depend on the condition of the patient with stroke, including the degree of paralysis.},
}
@article {pmid28880131,
year = {2017},
author = {Marquez-Chin, C and Atwell, K and Popovic, MR},
title = {Prediction of specific hand movements using electroencephalographic signals.},
journal = {The journal of spinal cord medicine},
volume = {40},
number = {6},
pages = {696-705},
pmid = {28880131},
issn = {2045-7723},
mesh = {Adult ; Electroencephalography/methods/*standards ; Female ; Hand/innervation/*physiopathology ; Hand Strength ; Humans ; Male ; *Movement ; Neurological Rehabilitation/methods ; Spinal Cord Injuries/diagnosis/*physiopathology/rehabilitation ; },
abstract = {OBJECTIVE: To identify specific hand movements from electroencephalographic activity.<br><br>DESIGN: Proof of concept study.<br><br>SETTING: Rehabilitation hospital in Toronto, Canada.<br><br>PARTICIPANTS: Fifteen healthy individuals with no neurological conditions.<br><br>INTERVENTION: Each individual performed six different hand movements, including four grasps commonly targeted during rehabilitation. All of them used their dominant hand and four of them repeated the experiment with their non-dominant hand. EEG was acquired from 8 different locations (C1, C2, C3, C4, CZ, F3, F4 and Fz). Time-frequency distributions (spectrogram) of the pre-movement EEG activity for each electrode were generated and each of the time-resolved spectral components (1&#x2005;Hz to 50&#x2005;Hz) was correlated with a hyperbolic tangent function to detect power decreases. The spectral components and time ranges with the largest correlation values were identified using a threshold. The resulting features were then used to implement a distance-based classifier.<br><br>OUTCOME MEASURES: Accuracy of classification.<br><br>RESULTS: A minimum of three different dominant hand movements were classified correctly with average accuracies between 65-75% across all 15 participants. Average accuracies between 67-85% for the same three movements were achieved across four of the 15 participants who were tested with their non-dominant hand.<br><br>CONCLUSION: The results suggest that it may be possible to predict specific hand movements from a small number of electroencephalographic electrodes. Further studies including members of the spinal cord injury community are necessary to verify the suitability of the proposed process.},
}
@article {pmid28879007,
year = {2017},
author = {Darvishi, S and Ridding, MC and Hordacre, B and Abbott, D and Baumert, M},
title = {Investigating the impact of feedback update interval on the efficacy of restorative brain-computer interfaces.},
journal = {Royal Society open science},
volume = {4},
number = {8},
pages = {170660},
pmid = {28879007},
issn = {2054-5703},
abstract = {Restorative brain-computer interfaces (BCIs) have been proposed to enhance stroke rehabilitation. Restorative BCIs are able to close the sensorimotor loop by rewarding motor imagery (MI) with sensory feedback. Despite the promising results from early studies, reaching clinically significant outcomes in a timely fashion is yet to be achieved. This lack of efficacy may be due to suboptimal feedback provision. To the best of our knowledge, the optimal feedback update interval (FUI) during MI remains unexplored. There is evidence that sensory feedback disinhibits the motor cortex. Thus, in this study, we explore how shorter than usual FUIs affect behavioural and neurophysiological measures following BCI training for stroke patients using a single-case proof-of-principle study design. The action research arm test was used as the primary behavioural measure and showed a clinically significant increase (36%) over the course of training. The neurophysiological measures including motor evoked potentials and maximum voluntary contraction showed distinctive changes in early and late phases of BCI training. Thus, this preliminary study may pave the way for running larger studies to further investigate the effect of FUI magnitude on the efficacy of restorative BCIs. It may also elucidate the role of early and late phases of motor learning along the course of BCI training.},
}
@article {pmid28878617,
year = {2017},
author = {Werner, T and Vianello, E and Bichler, O and Garbin, D and Cattaert, D and Yvert, B and De Salvo, B and Perniola, L},
title = {Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {486},
doi = {10.3389/fnins.2017.00486},
pmid = {28878617},
issn = {1662-4548},
abstract = {[This corrects the article on p. 474 in vol. 10, PMID: 27857680.].},
}
@article {pmid28877175,
year = {2017},
author = {Pinegger, A and Hiebel, H and Wriessnegger, SC and Müller-Putz, GR},
title = {Composing only by thought: Novel application of the P300 brain-computer interface.},
journal = {PloS one},
volume = {12},
number = {9},
pages = {e0181584},
pmid = {28877175},
issn = {1932-6203},
mesh = {Adult ; Behavior ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Surveys and Questionnaires ; Task Performance and Analysis ; *Thinking ; Visual Analog Scale ; },
abstract = {The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.},
}
@article {pmid28875947,
year = {2018},
author = {Choi, H and Lee, J and Park, J and Lee, S and Ahn, KH and Kim, IY and Lee, KM and Jang, DP},
title = {Improved prediction of bimanual movements by a two-staged (effector-then-trajectory) decoder with epidural ECoG in nonhuman primates.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {016011},
doi = {10.1088/1741-2552/aa8a83},
pmid = {28875947},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Electrocorticography/*methods ; Forecasting ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Random Allocation ; Somatosensory Cortex/*physiology ; Upper Extremity/physiology ; },
abstract = {OBJECTIVE: In arm movement BCIs (brain-computer interfaces), unimanual research has been much more extensively studied than its bimanual counterpart. However, it is well known that the bimanual brain state is different from the unimanual one. Conventional methodology used in unimanual studies does not take the brain stage into consideration, and therefore appears to be insufficient for decoding bimanual movements. In this paper, we propose the use of a two-staged (effector-then-trajectory) decoder, which combines the classification of movement conditions and uses a hand trajectory predicting algorithm for unimanual and bimanual movements, for application in real-world BCIs.<br><br>APPROACH: Two micro-electrode patches (32 channels) were inserted over the dura mater of the left and right hemispheres of two rhesus monkeys, covering the motor related cortex for epidural electrocorticograph (ECoG). Six motion sensors (inertial measurement unit) were used to record the movement signals. The monkeys performed three types of arm movement tasks: left unimanual, right unimanual, bimanual. To decode these movements, we used a two-staged decoder, which combines the effector classifier for four states (left unimanual, right unimanual, bimanual movements, and stationary state) and movement predictor using regression.<br><br>MAIN RESULTS: Using this approach, we successfully decoded both arm positions using the proposed decoder. The results showed that decoding performance for bimanual movements were improved compared to the conventional method, which does not consider the effector, and the decoding performance was significant and stable over a period of four months. In addition, we also demonstrated the feasibility of epidural ECoG signals, which provided an adequate level of decoding accuracy.<br><br>SIGNIFICANCE: These results provide evidence that brain signals are different depending on the movement conditions or effectors. Thus, the two-staged method could be useful if BCIs are used to generalize for both unimanual and bimanual operations in human applications and in various neuro-prosthetics fields.},
}
@article {pmid28874909,
year = {2017},
author = {Liu, R and Zhang, Z and Duan, F and Zhou, X and Meng, Z},
title = {Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {2727856},
pmid = {28874909},
issn = {1687-5273},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Support Vector Machine ; },
abstract = {Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.},
}
@article {pmid28870435,
year = {2017},
author = {Botrel, L and Acqualagna, L and Blankertz, B and Kübler, A},
title = {Short progressive muscle relaxation or motor coordination training does not increase performance in a brain-computer interface based on sensorimotor rhythms (SMR).},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {121},
number = {},
pages = {29-37},
doi = {10.1016/j.ijpsycho.2017.08.007},
pmid = {28870435},
issn = {1872-7697},
mesh = {Adult ; *Autogenic Training ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Neurofeedback/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Brain computer interfaces (BCIs) allow for controlling devices through modulation of sensorimotor rhythms (SMR), yet a profound number of users is unable to achieve sufficient accuracy. Here, we investigated if visuo-motor coordination (VMC) training or Jacobsen's progressive muscle relaxation (PMR) prior to BCI use would increase later performance compared to a control group who performed a reading task (CG). Running the study in two different BCI-labs, we achieved a joint sample size of N=154 naïve participants. No significant effect of either intervention (VMC, PMR, control) was found on resulting BCI performance. Relaxation level and visuo-motor performance were associated with later BCI performance in one BCI-lab but not in the other. These mixed results do not indicate a strong potential of VMC or PMR for boosting performance. Yet further research with different training parameters or experimental designs is needed to complete the picture.},
}
@article {pmid28869537,
year = {2017},
author = {Liu, M and Zhang, F and Huang, HH},
title = {An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {9},
pages = {},
pmid = {28869537},
issn = {1424-8220},
abstract = {Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs.},
}
@article {pmid28863361,
year = {2017},
author = {Ryan, DB and Townsend, G and Gates, NA and Colwell, K and Sellers, EW},
title = {Evaluating brain-computer interface performance using color in the P300 checkerboard speller.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {128},
number = {10},
pages = {2050-2057},
pmid = {28863361},
issn = {1872-8952},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Color Perception/*physiology ; Electroencephalography/methods/standards ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; },
abstract = {OBJECTIVE: Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli.<br><br>METHODS: In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise).<br><br>RESULTS: Online results (n=36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions.<br><br>CONCLUSIONS: Unique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance.<br><br>SIGNIFICANCE: These results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology.},
}
@article {pmid28860986,
year = {2017},
author = {Chmura, J and Rosing, J and Collazos, S and Goodwin, SJ},
title = {Classification of Movement and Inhibition Using a Hybrid BCI.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {38},
pmid = {28860986},
issn = {1662-5218},
abstract = {Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)-when a person imagines a motion without executing it-is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs. left leg) and characteristics (reaching vs. grabbing); and high false positive rates of motion control. Solutions to improve sensorimotor activation and user performance of MI-BCIs have been explored. Virtual reality (VR) motor-execution tasks have replaced simpler visual feedback (smiling faces, arrows) and have solved this problem to an extent. Hybrid BCIs (hBCIs) implementing an additional control signal to MI have improved user control capabilities to a limited extent. These hBCIs either fail to allow the patients to gain asynchronous control of their movements, or have a high false positive rate. We propose an immersive VR environment which provides visual feedback that is both engaging and immediate, but also uniquely engages a different cognitive process in the patient that generates event-related potentials (ERPs). These ERPs provide a key executive function for the users to execute/inhibit movements. Additionally, we propose signal processing strategies and machine learning algorithms to move BCIs toward developing long-term signal stability in patients with distinctive brain signals and capabilities to control motor signals. The hBCI itself and the VR environment we propose would help to move BCI technology outside laboratory environments for motor rehabilitation in hospitals, and potentially for controlling a prosthetic.},
}
@article {pmid28860078,
year = {2017},
author = {Zhang, S and McIntosh, J and Shadli, SM and Neo, PS and Huang, Z and McNaughton, N},
title = {Removing eye blink artefacts from EEG-A single-channel physiology-based method.},
journal = {Journal of neuroscience methods},
volume = {291},
number = {},
pages = {213-220},
doi = {10.1016/j.jneumeth.2017.08.031},
pmid = {28860078},
issn = {1872-678X},
mesh = {Adolescent ; Adult ; *Artifacts ; *Blinking/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Female ; Humans ; Male ; Models, Biological ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: EEG signals are often contaminated with artefacts, particularly with large signals generated by eye blinks. Deletion of artefact can lose valuable data. Current methods of removing the eye blink component to leave residual EEG, such as blind source component removal, require multichannel recording, are computationally intensive, and can alter the original EEG signal.<br><br>NEW METHOD: Here we describe a novel single-channel method using a model based on the ballistic physiological components of the eye blink. This removes the blink component, leaving uncontaminated EEG largely unchanged. Processing time allows its use in real-time applications such as neurofeedback training.<br><br>RESULTS: Blink removal had a success rate of over 90% recovered variance of original EEG when removing synthesised eye blink components. Fronto-lateral sites were poorer (&#x223c;80%) than most other sites (92-96%), with poor fronto-polar results (67%).<br><br>When compared with three popular independent component analysis (ICA) methods, our method was only slightly (1%) better at frontal midline sites but significantly (>20%) better at lateral sites with an overall advantage of &#x223c;10%.<br><br>CONCLUSIONS: With few recording channels and real-time processing, our method shows clear advantages over ICA for removing eye blinks. It should be particularly suited for use in portable brain-computer-interfaces and in neurofeedback training.},
}
@article {pmid28859916,
year = {2017},
author = {Mrachacz-Kersting, N and Voigt, M and Stevenson, AJT and Aliakbaryhosseinabadi, S and Jiang, N and Dremstrup, K and Farina, D},
title = {The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity.},
journal = {Brain research},
volume = {1674},
number = {},
pages = {91-100},
doi = {10.1016/j.brainres.2017.08.025},
pmid = {28859916},
issn = {1872-6240},
mesh = {Adult ; Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Motor/physiology ; Feedback/drug effects ; Female ; Healthy Volunteers ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; Movement/physiology ; Neuronal Plasticity/*physiology ; Neurons, Afferent/*physiology ; Somatosensory Cortex ; Transcranial Magnetic Stimulation/methods ; },
abstract = {A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the movement related cortical potential (MRCP) induces significant plasticity at the cortical level in healthy individuals and chronic stroke patients. Transferring this type of associative brain-computer interface (BCI) intervention into the clinical setting requires that the proprioceptive input is comparable to the techniques implemented during the rehabilitation process. These consist mainly of functional electrical stimulation (FES) and passive movement induced by an actuated orthosis. In this study, we compared these two interventions (BCIFES and BCIpassive) where the afferent input was timed to arrive at the motor cortex during the PN of the MRCP. Twelve healthy participants attended two experimental sessions. They were asked to perform 30 dorsiflexion movements timed to a cue while continuous electroencephalographic (EEG) data were collected from FP1, Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, and Pz, according to the standard international 10-20 system. MRCPs were extracted and the PN time calculated. Next, participants were asked to imagine the same movement 30 times while either FES (frequency: 20Hz, intensity: 8-35mAmp) or a passive ankle movement (amplitude and velocity matched to a normal gait cycle) was applied such that the first afferent inflow would coincide with the PN of the MRCP. The change in the output of the primary motor cortex (M1) was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior (TA) muscle and measuring the motor evoked potential (MEP). Spinal changes were assessed pre and post by eliciting the TA stretch reflex. Both BCIFES and BCIpassive led to significant increases in the excitability of the cortical projections to TA (F(2,22)=4.44, p=0.024) without any concomitant changes at the spinal level. These effects were still present 30min after the cessation of both interventions. There was no significant main effect of intervention, F(1,11)=0.38, p=0.550, indicating that the changes in MEP occurred independently of the type of afferent inflow. An afferent volley generated from a passive movement or an electrical stimulus arrives at the somatosensory cortex at similar times. It is thus likely that the similar effects observed here are strictly due to the tight coupling in time between the afferent inflow and the PN of the MRCP. This provides further support to the associative nature of the proposed BCI system.},
}
@article {pmid28855869,
year = {2017},
author = {Yang, J and Huai, R and Wang, H and Li, W and Wang, Z and Sui, M and Su, X},
title = {Global Positioning System-Based Stimulation for Robo-Pigeons in Open Space.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {40},
pmid = {28855869},
issn = {1662-5218},
abstract = {An evaluation method is described that will enable researchers to study fight control characteristics of robo-pigeons in fully open space. It is not limited by the experimental environment and overcomes environmental interference with flight control in small experimental spaces using a compact system. The system consists of two components: a global positioning system (GPS)-based stimulator with dimensions of 38 mm × 26 mm × 8 mm and a weight of 18 g that can easily be carried by a pigeon as a backpack and a PC-based program developed in Virtual C++. The GPS-based stimulator generates variable stimulation and automatically records the GPS data and stimulus parameters. The PC-based program analyzes the recorded data and displays the flight trajectory of the tested robo-pigeon on a digital map. This method enables quick and clear evaluation of the flight control characteristics of a robo-pigeon in open space based on its visual trajectory, as well as further optimization of the microelectric stimulation parameters to improve the design of robo-pigeons. The functional effectiveness of the method was investigated and verified by performing flight control experiments using a robo-pigeon in open space.},
}
@article {pmid28855183,
year = {2018},
author = {Hespanhol, LC and van Mechelen, W and Verhagen, E},
title = {Effectiveness of online tailored advice to prevent running-related injuries and promote preventive behaviour in Dutch trail runners: a pragmatic randomised controlled trial.},
journal = {British journal of sports medicine},
volume = {52},
number = {13},
pages = {851-858},
doi = {10.1136/bjsports-2016-097025},
pmid = {28855183},
issn = {1473-0480},
mesh = {Adult ; Athletic Injuries/*prevention &amp; control ; Bayes Theorem ; Counseling/*methods ; Female ; Humans ; Internet ; Male ; Middle Aged ; Netherlands ; Running/*injuries ; },
abstract = {BACKGROUND: Trail running is popular worldwide, but there is no preventive intervention for running-related injury (RRI).<br><br>AIM: To evaluate the effectiveness of adding online tailored advice (TrailS6) to general advice on (1) the prevention of RRIs and (2) the determinants and actual preventive behaviour in Dutch trail runners.<br><br>METHODS: Two-arm randomised controlled trial over 6&#x2009;months. 232 trail runners were randomly assigned to an intervention or control group. All participants received online general advice on RRI prevention 1&#x2009;week after baseline. Every 2&#x2009;weeks, participants in the intervention group received specific advice tailored to their RRI status. The control group received no further intervention. Bayesian mixed models were used to analyse the data.<br><br>RESULTS: Trail runners in the intervention group sustained 13% fewer RRIs compared with those in the control group after 6&#x2009;months of follow-up (absolute risk difference -13.1%, 95% Bayesian highest posterior credible interval (95% BCI) -23.3 to -3.1). A preventive benefit was observed in one out of eight trail runners who had received the online tailored advice for 6&#x2009;months (number needed to treat 8, 95% BCI 3 to 22). No significant between-group difference was observed on the determinants and actual preventive behaviours.<br><br>CONCLUSIONS: Online tailored advice prevented RRIs among Dutch trail runners. Therefore, online tailored advice may be used as a preventive component in multicomponent RRI prevention programmes. No effect was observed on determinants and actual preventive behaviours.<br><br>TRIAL REGISTRATION NUMBER: The Netherlands National Trial Register (NTR5431).},
}
@article {pmid28844033,
year = {2017},
author = {Gargiulo, G and Valgimigli, M and Capodanno, D and Bittl, JA},
title = {State of the art: duration of dual antiplatelet therapy after percutaneous coronary intervention and coronary stent implantation - past, present and future perspectives.},
journal = {EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology},
volume = {13},
number = {6},
pages = {717-733},
doi = {10.4244/EIJ-D-17-00468},
pmid = {28844033},
issn = {1969-6213},
mesh = {Coronary Artery Disease/*therapy ; Drug Therapy, Combination/methods ; *Drug-Eluting Stents ; Humans ; Myocardial Infarction/*therapy ; *Percutaneous Coronary Intervention/methods ; Platelet Aggregation Inhibitors/*therapeutic use ; },
abstract = {Evidence from studies published more than 10 years ago suggested that patients receiving first-generation drug-eluting stents (DES) needed dual antiplatelet therapy (DAPT) for at least 12 months. Current evidence from randomised controlled trials (RCT) reported within the past five years suggests that patients with stable ischaemic heart disease who receive newer-generation DES need DAPT for a minimum of three to six months. Patients who undergo stenting for an acute coronary syndrome benefit from DAPT for at least 12 months, but a Bayesian network meta-analysis confirms that extending DAPT beyond 12 months confers a trade-off between reduced ischaemic events and increased bleeding. However, the network meta-analysis finds no credible increase in all-cause mortality if DAPT is lengthened from three to six months to 12 months (posterior median odds ratio [OR] 0.98; 95% Bayesian credible interval [BCI]: 0.73-1.43), from 12 months to 18-48 months (OR 0.87; 95% BCI: 0.64-1.17), or from three to six months to 18-48 months (OR 0.86; 95% BCI: 0.63-1.21). Future investigation should focus on identifying scoring systems that have excellent discrimination and calibration. Although predictive models should be incorporated into systems of care, most decisions about DAPT duration will be based on clinical judgement and patient preference.},
}
@article {pmid28843838,
year = {2017},
author = {Orsborn, AL and Pesaran, B},
title = {Parsing learning in networks using brain-machine interfaces.},
journal = {Current opinion in neurobiology},
volume = {46},
number = {},
pages = {76-83},
pmid = {28843838},
issn = {1873-6882},
support = {R01 EY024067/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; Learning/*physiology ; *Machine Learning ; Neuronal Plasticity/physiology ; },
abstract = {Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.},
}
@article {pmid28843655,
year = {2017},
author = {Blumberg, MS and Dooley, JC},
title = {Phantom Limbs, Neuroprosthetics, and the Developmental Origins of Embodiment.},
journal = {Trends in neurosciences},
volume = {40},
number = {10},
pages = {603-612},
pmid = {28843655},
issn = {1878-108X},
support = {F32 NS101858/NS/NINDS NIH HHS/United States ; R37 HD081168/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Extremities/*growth &amp; development/physiopathology ; Humans ; Movement/physiology ; *Neural Prostheses ; Phantom Limb/physiopathology/*rehabilitation ; Sleep/physiology ; },
abstract = {Amputees who wish to rid themselves of a phantom limb must weaken the neural representation of the absent limb. Conversely, amputees who wish to replace a lost limb must assimilate a neuroprosthetic with the existing neural representation. Whether we wish to remove a phantom limb or assimilate a synthetic one, we will benefit from knowing more about the developmental process that enables embodiment. A potentially critical contributor to that process is the spontaneous activity - in the form of limb twitches - that occurs exclusively and abundantly during active (REM) sleep, a particularly prominent state in early development. The sensorimotor circuits activated by twitching limbs, and the developmental context in which activation occurs, could provide a roadmap for creating neuroprosthetics that feel as if they are part of the body.},
}
@article {pmid28841920,
year = {2017},
author = {Hasegawa, K and Kasuga, S and Takasaki, K and Mizuno, K and Liu, M and Ushiba, J},
title = {Ipsilateral EEG mu rhythm reflects the excitability of uncrossed pathways projecting to shoulder muscles.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {85},
pmid = {28841920},
issn = {1743-0003},
mesh = {Adult ; Brain-Computer Interfaces ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Electromyography ; Evoked Potentials, Motor/physiology ; Female ; Fingers/physiology ; Functional Laterality/physiology ; Humans ; Imagery, Psychotherapy ; Male ; Motor Cortex/physiology ; Muscle, Skeletal/innervation/*physiology ; Nerve Net/*physiology ; Shoulder/innervation/*physiology ; Superficial Back Muscles/innervation/*physiology ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {BACKGROUND: Motor planning, imagery or execution is associated with event-related desynchronization (ERD) of mu rhythm oscillations (8-13 Hz) recordable over sensorimotor areas using electroencephalography (EEG). It was shown that motor imagery involving distal muscles, e.g. finger movements, results in contralateral ERD correlating with increased excitability of the contralateral corticospinal tract (c-CST). Following the rationale that purposefully increasing c-CST excitability might facilitate motor recovery after stroke, ERD recently became an attractive target for brain-computer interface (BCI)-based neurorehabilitation training. It was unclear, however, whether ERD would also reflect excitability of the ipsilateral corticospinal tract (i-CST) that mainly innervates proximal muscles involved in e.g. shoulder movements. Such knowledge would be important to optimize and extend ERD-based BCI neurorehabilitation protocols, e.g. to restore shoulder movements after stroke. Here we used single-pulse transcranial magnetic stimulation (TMS) targeting the ipsilateral primary motor cortex to elicit motor evoked potentials (MEPs) of the trapezius muscle. To assess whether ERD reflects excitability of the i-CST, a correlation analysis between between MEP amplitudes and ipsilateral ERD was performed.<br><br>METHODS: Experiment 1 consisted of a motor execution task during which 10 healthy volunteers performed elevations of the shoulder girdle or finger pinching while a 128-channel EEG was recorded. Experiment 2 consisted of a motor imagery task during which 16 healthy volunteers imagined shoulder girdle elevations or finger pinching while an EEG was recorded; the participants simultaneously received randomly timed, single-pulse TMS to the ipsilateral primary motor cortex. The spatial pattern and amplitude of ERD and the amplitude of the agonist muscle's TMS-induced MEPs were analyzed.<br><br>RESULTS: ERDs occurred bilaterally during both execution and imagery of shoulder girdle elevations, but were lateralized to the contralateral hemisphere during finger pinching. We found that trapezius MEPs increased during motor imagery of shoulder elevations and correlated with ipsilateral ERD amplitudes.<br><br>CONCLUSIONS: Ipsilateral ERD during execution and imagery of shoulder girdle elevations appears to reflect the excitability of uncrossed pathways projecting to the shoulder muscles. As such, ipsilateral ERD could be used for neurofeedback training of shoulder movement, aiming at reanimation of the i-CST.},
}
@article {pmid28841546,
year = {2018},
author = {Zanini, P and Congedo, M and Jutten, C and Said, S and Berthoumieu, Y},
title = {Transfer Learning: A Riemannian Geometry Framework With Applications to Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {5},
pages = {1107-1116},
doi = {10.1109/TBME.2017.2742541},
pmid = {28841546},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Humans ; *Machine Learning ; Models, Theoretical ; },
abstract = {OBJECTIVE: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation.<br><br>METHODS: Data are represented using spatial covariance matrices of the EEG signals, exploiting the recent successful techniques based on the Riemannian geometry of the manifold of symmetric positive definite (SPD) matrices. Cross-session and cross-subject classification can be difficult, due to the many changes intervening between sessions and between subjects, including physiological, environmental, as well as instrumental changes. Here, we propose to affine transform the covariance matrices of every session/subject in order to center them with respect to a reference covariance matrix, making data from different sessions/subjects comparable. Then, classification is performed both using a standard minimum distance to mean classifier, and through a probabilistic classifier recently developed in the literature, based on a density function (mixture of Riemannian Gaussian distributions) defined on the SPD manifold.<br><br>RESULTS: The improvements in terms of classification performances achieved by introducing the affine transformation are documented with the analysis of two BCI datasets.<br><br>CONCLUSION AND SIGNIFICANCE: Hence, we make, through the affine transformation proposed, data from different sessions and subject comparable, providing a significant improvement in the BCI transfer learning problem.},
}
@article {pmid28840164,
year = {2017},
author = {Wyser, D and Lambercy, O and Scholkmann, F and Wolf, M and Gassert, R},
title = {Wearable and modular functional near-infrared spectroscopy instrument with multidistance measurements at four wavelengths.},
journal = {Neurophotonics},
volume = {4},
number = {4},
pages = {041413},
pmid = {28840164},
issn = {2329-423X},
abstract = {With the aim of transitioning functional near-infrared spectroscopy (fNIRS) technology from the laboratory environment to everyday applications, the field has seen a recent push toward the development of wearable/miniaturized, multiwavelength, multidistance, and modular instruments. However, it is challenging to unite all these requirements in a precision instrument with low noise, low drift, and fast sampling characteristics. We present the concept and development of a wearable fNIRS instrument that combines all these key features with the goal of reliably and accurately capturing brain hemodynamics. The proposed instrument consists of a modular network of miniaturized optode modules that include a four-wavelength light source and a highly sensitive silicon photomultiplier detector. Simultaneous measurements with short-separation (7.5 mm; containing predominantly extracerebral signals) and long-separation (20 mm or more; containing both extracerebral and cerebral information) channels are used with short-channel regression filtering methods to increase robustness of fNIRS measurements. Performance of the instrument was characterized with phantom measurements and further validated in human in vivo measurements, demonstrating the good raw signal quality (signal-to-noise ratio of 64 dB for short channels; robust measurements up to 50 mm; dynamic optical range larger than 160 dB), the valid estimation of concentration changes (oxy- and deoxyhemoglobin, and cytochrome-c-oxidase) in muscle and brain, and the detection of task-evoked brain activity. The results of our preliminary tests suggest that the presented fNIRS instrument outperforms existing instruments in many aspects and bears high potential for real-time single-trial fNIRS applications as required for wearable brain-computer interfaces.},
}
@article {pmid28840114,
year = {2017},
author = {Imani, E and Pourmohammad, A and Bagheri, M and Mobasheri, V},
title = {ICA-Based Imagined Conceptual Words Classification on EEG Signals.},
journal = {Journal of medical signals and sensors},
volume = {7},
number = {3},
pages = {130-144},
pmid = {28840114},
issn = {2228-7477},
abstract = {Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25-30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator function, the classification accuracies were almost the same and not very different. Linear discriminant analysis (LDA) in comparison with the neural network yielded higher classification accuracies. ICA is a suitable algorithm for recognizing of the word's concept and its place in the brain. Achieved results from this experiment were the same compared with the results from other methods such as functional magnetic resonance imaging and methods based on the brain signals (EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy was obtained by extracting the target signal from the output of the ICA and extracting the features of coefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classification accuracy more than 60%.},
}
@article {pmid28835651,
year = {2017},
author = {Hong, X and Lu, ZK and Teh, I and Nasrallah, FA and Teo, WP and Ang, KK and Phua, KS and Guan, C and Chew, E and Chuang, KH},
title = {Brain plasticity following MI-BCI training combined with tDCS in a randomized trial in chronic subcortical stroke subjects: a preliminary study.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {9222},
pmid = {28835651},
issn = {2045-2322},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Chronic Disease ; Female ; Humans ; *Imagery, Psychotherapy/methods ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neuronal Plasticity ; Stroke/diagnosis/*physiopathology/*therapy ; *Stroke Rehabilitation/methods ; *Transcranial Direct Current Stimulation ; },
abstract = {Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been used in stroke rehabilitation, though their combinatory effect is unknown. We investigated brain plasticity following a combined MI-BCI and tDCS intervention in chronic subcortical stroke patients with unilateral upper limb disability. Nineteen patients were randomized into tDCS and sham-tDCS groups. Diffusion and perfusion MRI, and transcranial magnetic stimulation were used to study structural connectivity, cerebral blood flow (CBF), and corticospinal excitability, respectively, before and 4 weeks after the 2-week intervention. After quality control, thirteen subjects were included in the CBF analysis. Eleven healthy controls underwent 2 sessions of MRI for reproducibility study. Whereas motor performance showed comparable improvement, long-lasting neuroplasticity can only be detected in the tDCS group, where white matter integrity in the ipsilesional corticospinal tract and bilateral corpus callosum was increased but sensorimotor CBF was decreased, particularly in the ipsilesional side. CBF change in the bilateral parietal cortices also correlated with motor function improvement, consistent with the increased white matter integrity in the corpus callosum connecting these regions, suggesting an involvement of interhemispheric interaction. The preliminary results indicate that tDCS may facilitate neuroplasticity and suggest the potential for refining rehabilitation strategies for stroke patients.},
}
@article {pmid28835183,
year = {2018},
author = {DelPozo-Banos, M and Travieso, CM and Alonso, JB and John, A},
title = {Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram.},
journal = {International journal of neural systems},
volume = {28},
number = {1},
pages = {1750035},
doi = {10.1142/S0129065717500356},
pmid = {28835183},
issn = {1793-6462},
mesh = {Adult ; Biometric Identification/*methods ; Brain/*physiology ; *Electroencephalography/methods ; Emotions/physiology ; Evoked Potentials, Auditory ; Evoked Potentials, Visual ; Female ; Humans ; Linear Models ; Male ; Mental Processes/physiology ; Middle Aged ; Motor Activity/physiology ; Neuropsychological Tests ; Rest ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.},
}
@article {pmid28832671,
year = {2017},
author = {Reboud, E and Bouillot, S and Patot, S and Béganton, B and Attrée, I and Huber, P},
title = {Pseudomonas aeruginosa ExlA and Serratia marcescens ShlA trigger cadherin cleavage by promoting calcium influx and ADAM10 activation.},
journal = {PLoS pathogens},
volume = {13},
number = {8},
pages = {e1006579},
pmid = {28832671},
issn = {1553-7374},
mesh = {ADAM10 Protein/*metabolism ; Animals ; Bacterial Proteins/*metabolism ; Bacterial Toxins/metabolism ; Blotting, Western ; Cadherins/*metabolism ; Calcium/metabolism ; Enzyme Activation ; Female ; Gram-Negative Bacterial Infections/*metabolism ; Hemolysin Proteins/*metabolism ; Humans ; Mice ; Mice, Inbred BALB C ; Microscopy, Confocal ; Pseudomonas aeruginosa/pathogenicity ; Serratia marcescens/pathogenicity ; Virulence/physiology ; Virulence Factors/metabolism ; },
abstract = {Pore-forming toxins are potent virulence factors secreted by a large array of bacteria. Here, we deciphered the action of ExlA from Pseudomonas aeruginosa and ShlA from Serratia marcescens on host cell-cell junctions. ExlA and ShlA are two members of a unique family of pore-forming toxins secreted by a two-component secretion system. Bacteria secreting either toxin induced an ExlA- or ShlA-dependent rapid cleavage of E-cadherin and VE-cadherin in epithelial and endothelial cells, respectively. Cadherin proteolysis was executed by ADAM10, a host cell transmembrane metalloprotease. ADAM10 activation is controlled in the host cell by cytosolic Ca2+ concentration. We show that Ca2+ influx, induced by ExlA or ShlA pore formation in the plasma membrane, triggered ADAM10 activation, thereby leading to cadherin cleavage. Our data suggest that ADAM10 is not a cellular receptor for ExlA and ShlA, further confirming that ADAM10 activation occurred via Ca2+ signalling. In conclusion, ExlA- and ShlA-secreting bacteria subvert a regulation mechanism of ADAM10 to activate cadherin shedding, inducing intercellular junction rupture, cell rounding and loss of tissue barrier integrity.},
}
@article {pmid28832513,
year = {2017},
author = {Alazrai, R and Alwanni, H and Baslan, Y and Alnuman, N and Daoud, MI},
title = {EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {9},
pages = {},
pmid = {28832513},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand ; Humans ; Imagination ; User-Computer Interface ; },
abstract = {This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88 . 8 % and 90 . 2 % , respectively, for the subject-dependent training procedure, and 80 . 8 % and 87 . 8 % , respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.},
}
@article {pmid28832013,
year = {2018},
author = {Couraud, M and Cattaert, D and Paclet, F and Oudeyer, PY and de Rugy, A},
title = {Model and experiments to optimize co-adaptation in a simplified myoelectric control system.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {026006},
doi = {10.1088/1741-2552/aa87cf},
pmid = {28832013},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; Artificial Limbs/trends ; Electromyography/*methods/trends ; Humans ; *Models, Neurological ; Photic Stimulation/*methods ; Prosthesis Design/methods/trends ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls.<br><br>APPROACH: We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner.<br><br>RESULTS: First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations.<br><br>SIGNIFICANCE: The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this approach to more complex and functional myoelectric contexts are discussed.},
}
@article {pmid28830767,
year = {2017},
author = {Aliakbaryhosseinabadi, S and Kamavuako, EN and Jiang, N and Farina, D and Mrachacz-Kersting, N},
title = {Influence of dual-tasking with different levels of attention diversion on characteristics of the movement-related cortical potential.},
journal = {Brain research},
volume = {1674},
number = {},
pages = {10-19},
doi = {10.1016/j.brainres.2017.08.016},
pmid = {28830767},
issn = {1872-6240},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Brain/physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Motor Cortex/physiology ; Movement/physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Dual tasking is defined as performing two tasks concurrently and has been shown to have a significant effect on attention directed to the performance of the main task. In this study, an attention diversion task with two different levels was administered while participants had to complete a cue-based motor task consisting of foot dorsiflexion. An auditory oddball task with two levels of complexity was implemented to divert the user's attention. Electroencephalographic (EEG) recordings were made from nine single channels. Event-related potentials (ERPs) confirmed that the oddball task of counting a sequence of two tones decreased the auditory P300 amplitude more than the oddball task of counting one target tone among three different tones. Pre-movement features quantified from the movement-related cortical potential (MRCP) were changed significantly between single and dual-task conditions in motor and fronto-central channels. There was a significant delay in movement detection for the case of single tone counting in two motor channels only (237.1-247.4ms). For the task of sequence counting, motor cortex and frontal channels showed a significant delay in MRCP detection (232.1-250.5ms). This study investigated the effect of attention diversion in dual-task conditions by analysing both ERPs and MRCPs in single channels. The higher attention diversion lead to a significant reduction in specific MRCP features of the motor task. These results suggest that attention division in dual-tasking situations plays an important role in movement execution and detection. This has important implications in designing real-time brain-computer interface systems.},
}
@article {pmid28830308,
year = {2017},
author = {Sereshkeh, AR and Trott, R and Bricout, A and Chau, T},
title = {Online EEG Classification of Covert Speech for Brain-Computer Interfacing.},
journal = {International journal of neural systems},
volume = {27},
number = {8},
pages = {1750033},
doi = {10.1142/S0129065717500332},
pmid = {28830308},
issn = {1793-6462},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; *Language ; Male ; Support Vector Machine ; Thinking/*physiology ; Time Factors ; Wavelet Analysis ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word "no" and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words "yes" and "no". Twelve participants used these two BCIs to answer yes or no questions. Each participant completed four sessions, comprising two offline training sessions and two online sessions, one for testing each of the BCIs. With a support vector machine and a combination of spectral and time-frequency features, an average accuracy of [Formula: see text] was reached across participants in the online classification of no versus rest, with 10 out of 12 participants surpassing the chance level (60.0% for [Formula: see text]). The online classification of yes versus no yielded an average accuracy of [Formula: see text], with eight participants exceeding the chance level. Task-specific changes in EEG beta and gamma power in language-related brain areas tended to provide discriminatory information. To our knowledge, this is the first report of online EEG classification of covert speech. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.},
}
@article {pmid28827605,
year = {2017},
author = {Friedenberg, DA and Schwemmer, MA and Landgraf, AJ and Annetta, NV and Bockbrader, MA and Bouton, CE and Zhang, M and Rezai, AR and Mysiw, WJ and Bresler, HS and Sharma, G},
title = {Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {8386},
pmid = {28827605},
issn = {2045-2322},
mesh = {Adult ; Arm/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Humans ; Male ; Movement ; *Muscle Contraction ; *Prostheses and Implants ; Quadriplegia/*therapy ; Volition ; },
abstract = {Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients' own paralyzed limbs. To date, human studies have demonstrated an "all-or-none" type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.},
}
@article {pmid28827593,
year = {2017},
author = {Geronimo, A and Sheldon, KE and Broach, JR and Simmons, Z and Schiff, SJ},
title = {Expansion of C9ORF72 in amyotrophic lateral sclerosis correlates with brain-computer interface performance.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {8875},
pmid = {28827593},
issn = {2045-2322},
mesh = {Aged ; Alleles ; Amyotrophic Lateral Sclerosis/*genetics/psychology ; *Brain-Computer Interfaces ; C9orf72 Protein/*genetics ; Case-Control Studies ; Cognitive Dysfunction ; *DNA Repeat Expansion ; Electroencephalography ; Evoked Potentials ; Female ; Frontotemporal Dementia/genetics/psychology ; Humans ; Male ; Middle Aged ; *Models, Biological ; },
abstract = {Abnormal expansion of hexanucleotide GGGGCC (G4C2) in the C9ORF72 gene has been associated with multiple neurodegenerative disorders, with particularly high prevalence in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Repeat expansions of this type have been associated with altered pathology, symptom rate and severity, as well as psychological changes. In this study, we enrolled twenty-five patients with ALS and fifteen neurologically healthy controls in a P300 brain-computer interface (BCI) training procedure. Four of the patients were found to possess an expanded allele, which was associated with a reduction in the quality of evoked potentials that led to reduced performance on the BCI task. Our findings warrant further exploration of the relationship between brain function and G4C2 repeat length. Such a relationship suggests that personalized assessment of suitability of BCI as a communication device in patients with ALS may be feasible.},
}
@article {pmid28827542,
year = {2017},
author = {Luu, TP and Nakagome, S and He, Y and Contreras-Vidal, JL},
title = {Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {8895},
pmid = {28827542},
issn = {2045-2322},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Biomechanical Phenomena ; Brain Mapping ; Brain Waves ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Computer Simulation ; *Electroencephalography ; Electrophysiological Phenomena ; *Exercise Test ; Female ; Humans ; Male ; *Walking ; Young Adult ; },
abstract = {Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users' gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a closed-loop BCI has not been investigated. This study aims to investigate the changes of cortical involvement in human treadmill walking with and without BCI control of a walking avatar. Source localization revealed significant differences in cortical network activity between walking with and without closed-loop BCI control. Our results showed sustained α/µ suppression in the Posterior Parietal Cortex and Inferior Parietal Lobe, indicating increases of cortical involvement during walking with BCI control. We also observed significant increased activity of the Anterior Cingulate Cortex (ACC) in the low frequency band suggesting the presence of a cortical network involved in error monitoring and motor learning. Additionally, the presence of low γ modulations in the ACC and Superior Temporal Gyrus may associate with increases of voluntary control of human gait. This work is a further step toward the development of a novel training paradigm for improving the efficacy of rehabilitation in a top-down approach.},
}
@article {pmid28824400,
year = {2017},
author = {Kosmyna, N and Lécuyer, A},
title = {Designing Guiding Systems for Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {396},
pmid = {28824400},
issn = {1662-5161},
abstract = {Brain-Computer Interface (BCI) community has focused the majority of its research efforts on signal processing and machine learning, mostly neglecting the human in the loop. Guiding users on how to use a BCI is crucial in order to teach them to produce stable brain patterns. In this work, we explore the instructions and feedback for BCIs in order to provide a systematic taxonomy to describe the BCI guiding systems. The purpose of our work is to give necessary clues to the researchers and designers in Human-Computer Interaction (HCI) in making the fusion between BCIs and HCI more fruitful but also to better understand the possibilities BCIs can provide to them.},
}
@article {pmid28821649,
year = {2017},
author = {Haar, S and Dinstein, I and Shelef, I and Donchin, O},
title = {Effector-Invariant Movement Encoding in the Human Motor System.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {37},
number = {37},
pages = {9054-9063},
pmid = {28821649},
issn = {1529-2401},
mesh = {Adult ; Computer Simulation ; Female ; Functional Laterality/*physiology ; Humans ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multivoxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices. We interpret this as evidence for the existence of distributed neural populations in multiple motor system areas that encode ipsilateral and contralateral movements in a similar manner: according to their intrinsic/joint coordinates.SIGNIFICANCE STATEMENT Cortical motor control exhibits clear lateralization: each hemisphere controls the motor output of the contralateral body. Nevertheless, neural populations in ipsilateral areas across the visuomotor hierarchy are active during unilateral movements. We show that fMRI response patterns in the motor cortices are similar for both arms if the movement direction is mirror-reversed across the midline. This suggests that in both ipsilateral and contralateral motor cortices, neural populations have effector-invariant coding of movements in intrinsic coordinates. This not only affects our understanding of motor control, it may serve in the development of brain machine interfaces that also use ipsilateral neural activity.},
}
@article {pmid28819547,
year = {2017},
author = {Shah, SA and Tan, H and Brown, P},
title = {Continuous Force Decoding from Deep Brain Local Field Potentials for Brain Computer Interfacing.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2017},
number = {},
pages = {371-374},
pmid = {28819547},
issn = {1948-3546},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic nucleus (STN) using data from 5 patients with Parkinson's disease, on a continuous basis with low latency. A Wiener-Cascade (WC) model based decoder was proposed using both time-domain and frequency-domain features. The results suggest that high gamma band (300-500Hz) activity, in addition to the beta (13-30Hz) and gamma band (55-90Hz) activity is the most informative for force prediction but combining all features led to better decoding performance. Furthermore, LFP signals preceding the force output by up to 1256 milliseconds were found to be predictive of the force output.},
}
@article {pmid28816702,
year = {2018},
author = {Lindgren, JT},
title = {As above, so below? Towards understanding inverse models in BCI.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {012001},
doi = {10.1088/1741-2552/aa86d0},
pmid = {28816702},
issn = {1741-2552},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Machine Learning ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. We investigate if more accurate BCIs can be obtained by reconstructing the source activities in the volume.<br><br>APPROACH: We contrast the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning. We explain these approaches in a common linear dictionary framework and review the different ways to obtain the dictionary parameters. We consider the effect of source reconstruction on some major difficulties in BCI classification, namely information loss, feature selection and nonstationarity of the EEG.<br><br>MAIN RESULTS: Our analysis suggests that the approaches differ mainly in their parameter estimation. Physiological source reconstruction may thus be expected to improve BCI accuracy if machine learning is not used or where it produces less optimal parameters. We argue that the considered difficulties of surface EEG classification can remain in the reconstructed volume and that data-driven techniques are still necessary. Finally, we provide some suggestions for comparing approaches.<br><br>SIGNIFICANCE: The present work illustrates the relationships between source reconstruction and machine learning-based approaches for EEG data representation. The provided analysis and discussion should help in understanding, applying, comparing and improving such techniques in the future.},
}
@article {pmid28813964,
year = {2017},
author = {Wang, KJ and Zhang, L and Luan, B and Tung, HW and Liu, Q and Wei, J and Sun, M and Mao, ZH},
title = {Brain-computer interface combining eye saccade two-electrode EEG signals and voice cues to improve the maneuverability of wheelchair.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {1073-1078},
doi = {10.1109/ICORR.2017.8009392},
pmid = {28813964},
issn = {1945-7901},
mesh = {Adult ; *Brain-Computer Interfaces ; Cues ; Electroencephalography/*methods ; Equipment Design ; Female ; Humans ; Male ; Saccades/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; *Wheelchairs ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) largely augment human capabilities by translating brain wave signals into feasible commands to operate external devices. However, many issues face the development of BCIs such as the low classification accuracy of brain signals and the tedious human-learning procedures. To solve these problems, we propose to use signals associated with eye saccades and blinks to control a BCI interface. By extracting existing physiological eye signals, the user does not need to adapt his/her brain waves to the device. Furthermore, using saccade signals to control an external device frees the limbs to perform other tasks. In this research, we use two electrodes placed on top of the left and right ears of thirteen participants. Then we use Independent Component Analysis (ICA) to extract meaningful EEG signals associated with eye movements. A sliding-window technique was implemented to collect relevant features. Finally, we classified the features as horizontal or blink eye movements using KNN and SVM. We were able to achieve a mean classification accuracy of about 97%. The two electrodes were then integrated with off-the-shelf earbuds to control a wheelchair. The earbuds can generate voice cues to indicate when to rotate the eyeballs to certain locations (i.e., left or right) or blink, so that the user can select directional commands to drive the wheelchair. In addition, through properly designing the contents of voice menus, we can generate as many commands as possible, even though we only have limited numbers of states of the identified eye saccade movements.},
}
@article {pmid28813949,
year = {2017},
author = {Yazmir, B and Reiner, M},
title = {Monitoring brain potentials to guide neurorehabilitation of tracking impairments.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {983-988},
doi = {10.1109/ICORR.2017.8009377},
pmid = {28813949},
issn = {1945-7901},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Neurological Rehabilitation/*methods ; Video Games ; Virtual Reality ; Young Adult ; },
abstract = {Motor impairments come in different forms. One class of motor impairments, relates to accuracy of tracking a moving object, as, for instance, when chasing in an attempt to catch it. Here we look at neural signals associated with errors in tracking, and the implications for brain-computer-interfaces that target impairment-tailored rehabilitation. As a starting point, we characterized EEG signals evoked by tracking errors during continuous natural motion, in healthy participants. Participants played a virtual 3D, ecologically valid haptic tennis game, and had to track a moving tennis ball in order to hit and send the ball towards the opponent's court. Sudden changes in the motion of the tennis ball elicited error related potentials. These were characterized by a negative peak at 135 msec and two positive peaks at 211 and 336 msec. The negative peak had a parietal scalp distribution, and the positive had a centro-frontal distribution. sLORETA source estimation for the peaks suggested brain activity in the somatosensory, motor, visual and anterior cingulate cortex. Implications are double: changes in the error potential characteristics provide an assessment strategy for rehabilitation; and the identified error potential can be used in the Brain computer interface feedback loop for tailored rehabilitation. Taken together, these results provide a methodology of rehabilitation systems specifically tailored to the unique impairment.},
}
@article {pmid28813935,
year = {2017},
author = {Lopez-Larraz, E and Bibian, C and Birbaumer, N and Ramos-Murguialday, A},
title = {Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {901-906},
doi = {10.1109/ICORR.2017.8009363},
pmid = {28813935},
issn = {1945-7901},
mesh = {Adult ; Arm/physiopathology ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Movement/physiology ; *Paralysis/physiopathology/rehabilitation ; Signal Processing, Computer-Assisted ; Stroke/physiopathology ; Stroke Rehabilitation/*methods ; },
abstract = {Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements. This paper evaluates the effects of removing artifacts from the data used to train a BMI decoder on a dataset of 28 severely paralyzed stroke patients. We show that cleaning the training datasets reduces the global BMI performance for decoding attempts of movement. Further, we demonstrate that this performance drop especially affects the test trials contaminated by artifacts (i.e., trials that might not reflect cortical activity but noise), but not the clean test trials (i.e., trials representing correct cortical activity). This paper underlines the importance of cleaning the datasets used to train BMI systems to improve their efficacy for decoding movement intention and maximize their neurorehabilitative potential.},
}
@article {pmid28813934,
year = {2017},
author = {Sarasola-Sanz, A and Irastorza-Landa, N and Lopez-Larraz, E and Bibian, C and Helmhold, F and Broetz, D and Birbaumer, N and Ramos-Murguialday, A},
title = {A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {895-900},
doi = {10.1109/ICORR.2017.8009362},
pmid = {28813934},
issn = {1945-7901},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/methods ; Electromyography/*instrumentation/methods ; Humans ; Male ; Middle Aged ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation/*instrumentation/methods ; },
abstract = {Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user. Whereas most of the existing non-invasive EEG-EMG-hBMIs have only been subjected to offline testings or are limited to one degree of freedom (DoF), we present an EEG-EMG-hBMI that allows the simultaneous control of 7-DoFs of the upper limb with a robotic exoskeleton. Moreover, it establishes a biologically-inspired hierarchical control flow, requiring the active participation of central and peripheral structures of the nervous system. Contingent visual and proprioceptive feedback about the user's EEG and EMG activity is provided in the form of velocity modulation during functional task training. We believe that training with this closed-loop system may facilitate functional neuroplastic processes and eventually elicit a joint brain and muscle motor rehabilitation. Its usability is validated during a real-time operation session in a healthy participant and a chronic stroke patient, showing encouraging results for its application to a clinical rehabilitation scenario.},
}
@article {pmid28813929,
year = {2017},
author = {Schiatti, L and Tessadori, J and Barresi, G and Mattos, LS and Ajoudani, A},
title = {Soft brain-machine interfaces for assistive robotics: A novel control approach.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {863-869},
doi = {10.1109/ICORR.2017.8009357},
pmid = {28813929},
issn = {1945-7901},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Equipment Design ; Fixation, Ocular/physiology ; Humans ; Robotics/*instrumentation ; *Self-Help Devices ; Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.},
}
@article {pmid28813921,
year = {2017},
author = {Angulo-Sherman, IN and Rodriguez-Ugarte, M and Ianez, E and Ortiz, M and Azorin, JM},
title = {Effect on the classification of motor imagery in EEG after applying anodal tDCS with a 4×1 ring montage over the motor cortex.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {818-822},
doi = {10.1109/ICORR.2017.8009349},
pmid = {28813921},
issn = {1945-7901},
mesh = {Electroencephalography/*classification ; Foot/physiology ; Hand/physiology ; Humans ; Imagination/*classification/physiology ; Motor Cortex/*physiology ; *Transcranial Direct Current Stimulation ; },
abstract = {Transcranial direct stimulation (tDCS) is a technique for modulating brain excitability that has potential to be used in motor neurorehabilitation by enhancing motor activity, such as motor imagery (MI). tDCS effects depend on different factors, like current density and the position of the stimulating electrodes. This study presents preliminary results of the evaluation of the effect of current density on MI performance by measuring right-hand/feet MI accuracy of classification from electroencephalographic (EEG) measurements after anodal tDCS is applied with a 4×1 ring montage over the right-hand or feet motor cortex. Results suggest that there might be an enhancement of feet MI when tDCS is applied over the right-hand motor cortex, but further evaluation is required. If results are confirmed with a larger sample, the montage could be used to optimize feet MI performance and improve the outcome of MI-based brain-computer interfaces, which are used during motor neurorehabilitation.},
}
@article {pmid28813811,
year = {2017},
author = {Lee, J and Mukae, N and Arata, J and Iwata, H and Iramina, K and Iihara, K and Hashizume, M},
title = {A multichannel-near-infrared-spectroscopy-triggered robotic hand rehabilitation system for stroke patients.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {158-163},
doi = {10.1109/ICORR.2017.8009239},
pmid = {28813811},
issn = {1945-7901},
mesh = {Adult ; Algorithms ; Equipment Design ; Hand/*physiopathology ; Humans ; Male ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*instrumentation ; Stroke Rehabilitation/*instrumentation/methods ; },
abstract = {There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation. To examine the feasibility of the prototype, we conducted a preliminary test involving six neurologically intact participants. The test comprised a series of evaluations for two aspects of neurorehabilitation training in a real-time manner: classification accuracy and execution time. The effects of classification-related factors, namely the algorithm, signal type, and number of NIRS channels, were investigated. In the comparison of algorithms, linear discrimination analysis performed better than the support vector machine in terms of both accuracy and training time. The oxyhemoglobin versus deoxyhemoglobin comparison revealed that the two concentrations almost equally contribute to the hand motion estimation. The relationship between the number of NIRS channels and accuracy indicated that a certain number of channels are needed and suggested a need for a method of selecting informative channels. The computation time of 5.84 ms was acceptable for our purpose. Overall, the preliminary prototype showed sufficient feasibility for further development and clinical testing with stroke patients.},
}
@article {pmid28813805,
year = {2017},
author = {Sullivan, JL and Bhagat, NA and Yozbatiran, N and Paranjape, R and Losey, CG and Grossman, RG and Contreras-Vidal, JL and Francisco, GE and O'Malley, MK},
title = {Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2017},
number = {},
pages = {122-127},
pmid = {28813805},
issn = {1945-7901},
support = {R01 NS081854/NS/NINDS NIH HHS/United States ; },
mesh = {Aged ; Brain/*physiology ; *Brain-Computer Interfaces ; Elbow/physiology ; Electroencephalography/instrumentation ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation/*instrumentation/methods ; Upper Extremity/physiology ; },
abstract = {This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control. These results are encouraging and suggest that increasing subject engagement during therapy through the addition of an intent-detecting BMI enhances the effectiveness of standard robotic rehabilitation.},
}
@article {pmid28813231,
year = {2018},
author = {Gong, A and Liu, J and Chen, S and Fu, Y},
title = {Time-Frequency Cross Mutual Information Analysis of the Brain Functional Networks Underlying Multiclass Motor Imagery.},
journal = {Journal of motor behavior},
volume = {50},
number = {3},
pages = {254-267},
doi = {10.1080/00222895.2017.1327417},
pmid = {28813231},
issn = {1940-1027},
mesh = {Brain/*physiology ; Brain Waves/physiology ; Brain-Computer Interfaces ; Electroencephalography ; Foot/physiology ; Hand/physiology ; Humans ; Imagination/*physiology ; Models, Neurological ; Reaction Time/physiology ; Tongue/physiology ; },
abstract = {To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors explored the brain network revealed by these MI tasks using statistical analysis and the analysis of topologic characteristics, and observed significant differences in the reaction level, reaction time, and activated target during 4-class MI tasks. There was a great difference in the reaction level between the execution and resting states during different tasks: the reaction level of the left-hand MI task was the greatest, followed by that of the right-hand, feet, and tongue MI tasks. The reaction time required to perform the tasks also differed: during the left-hand and right-hand MI tasks, the brain networks of subjects reacted promptly and strongly, but there was a delay during the feet and tongue MI task. Statistical analysis and the analysis of network topology revealed the target regions of the brain network during different MI processes. In conclusion, our findings suggest a new way to explain the neural mechanism behind MI.},
}
@article {pmid28810588,
year = {2017},
author = {Xie, Q and Ni, X and Yu, R and Li, Y and Huang, R},
title = {Chronic disorders of consciousness.},
journal = {Experimental and therapeutic medicine},
volume = {14},
number = {2},
pages = {1277-1283},
pmid = {28810588},
issn = {1792-0981},
abstract = {Over the last 20 years, studies have provided greater insight into disorders of consciousness (DOC), also known as altered state of consciousness. Increased brain residual functions have been identified in patients with DOC due to the successful application of novel next-generation imaging technologies. Many unconscious patients have now been confirmed to retain considerable cognitive functions. It is hoped that greater insight regarding the psychological state of patients may be achieved through the use of functional magnetic resonance imaging and brain-computer interfaces. However, issues surrounding the research and treatment of DOC remain problematic. These include differing opinions on the definition of consciousness, difficulties in diagnosis, assessment, prognosis and/or treatment, and newly emerging ethical, legal and social issues. To overcome these, appropriate care must be offered to patients with DOC by clinicians and families, as DOC patients may now be considered to live in more than just a vegetative state. The present article reviews the controversy surrounding the definition of consciousness and the reliability of novel technologies, prognostic prediction, communication with DOC patients and treatment methods. The ethical and social issues surrounding the treatment of DOC and future perspectives are also considered.},
}
@article {pmid28809822,
year = {2017},
author = {Ortner, R and Allison, BZ and Pichler, G and Heilinger, A and Sabathiel, N and Guger, C},
title = {Assessment and Communication for People with Disorders of Consciousness.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {126},
pages = {},
pmid = {28809822},
issn = {1940-087X},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Communication ; Consciousness Disorders/diagnosis/*physiopathology ; Cues ; Electroencephalography ; Equipment Design ; Hand ; Humans ; Imagination ; },
abstract = {In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either 'yes' or 'no'. The suite of paradigms is designed to test basic responses in the first step and to continue to more comprehensive tasks if the first tests are successful. The latter tasks require more cognitive functions, but they could provide communication, which is not possible with the basic tests. All assessment tests produce accuracy plots that show whether the algorithms were able to detect the patient's brain's response to the given tasks. If the accuracy level is beyond the significance level, we assume that the subject understood the task and was able to follow the sequence of commands presented via earphones to the subject. The tasks require users to concentrate on certain stimuli or to imagine moving either the left or right hand. All tasks are designed around the assumption that the user is unable to use the visual modality, and thus, all stimuli presented to the user (including instructions, cues, and feedback) are auditory or tactile.},
}
@article {pmid28809705,
year = {2018},
author = {Barsotti, M and Leonardis, D and Vanello, N and Bergamasco, M and Frisoli, A},
title = {Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {105-114},
doi = {10.1109/TNSRE.2017.2739244},
pmid = {28809705},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Arm/physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography ; Evoked Potentials/physiology ; *Feedback ; Feedback, Sensory/physiology ; Female ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Kinesthesis/*physiology ; Male ; Psychomotor Performance/physiology ; Tendons/*physiology ; Vibration ; Young Adult ; },
abstract = {BACKGROUND AND OBJECTIVES: Feedback plays a crucial role for using brain computer interface systems. This paper proposes the use of vibration-evoked kinaesthetic illusions as part of a novel multisensory feedback for a motor imagery (MI)-based BCI and investigates its contributions in terms of BCI performance and electroencephalographic (EEG) correlates.<br><br>METHODS: sixteen subjects performed two different right arm MI-BCI sessions: with the visual feedback only and with both visual and vibration-evoked kinaesthetic feedback, conveyed by the stimulation of the biceps brachi tendon. In both conditions, the sensory feedback was driven by the MI-BCI. The rich and more natural multisensory feedback was expected to facilitate the execution of MI, and thus to improve the performance of the BCI. The EEG correlates of the proposed feedback were also investigated with and without the performing of MI.<br><br>RESULTS AND CONCLUSIONS: the contribution of vibration-evoked kinaesthetic feedback led to statistically higher BCI performance (Anova, F(1,14) = 18.1, p < .01) and more stable EEG event-related-desynchronization. Obtained results suggest promising application of the proposed method in neuro-rehabilitation scenarios: the advantage of an improved usability could make the MI-BCIs more applicable for those patients having difficulties in performing kinaesthetic imagery.},
}
@article {pmid28809703,
year = {2018},
author = {Won, DO and Hwang, HJ and Kim, DM and Muller, KR and Lee, SW},
title = {Motion-Based Rapid Serial Visual Presentation for Gaze-Independent Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {334-343},
doi = {10.1109/TNSRE.2017.2736600},
pmid = {28809703},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography ; Equipment Design ; Event-Related Potentials, P300 ; Eye Movements ; Female ; Fixation, Ocular/*physiology ; Healthy Volunteers ; Humans ; Male ; Paralysis/rehabilitation ; Psychomotor Performance ; Young Adult ; },
abstract = {Most event-related potential (ERP)-based brain-computer interface (BCI) spellers primarily use matrix layouts and generally require moderate eye movement for successful operation. The fundamental objective of this paper is to enhance the perceptibility of target characters by introducing motion stimuli to classical rapid serial visual presentation (RSVP) spellers that do not require any eye movement, thereby applying them to paralyzed patients with oculomotor dysfunctions. To test the feasibility of the proposed motion-based RSVP paradigm, we implemented three RSVP spellers: 1) fixed-direction motion (FM-RSVP); 2) random-direction motion (RM-RSVP); and 3) (the conventional) non-motion stimulation (NM-RSVP), and evaluated the effect of the three different stimulation methods on spelling performance. The two motion-based stimulation methods, FM- and RM-RSVP, showed shorter P300 latency and higher P300 amplitudes (i.e., 360.4-379.6 ms; 5.5867-) than the NM-RSVP (i.e., 480.4 ms;). This led to higher and more stable performances for FM- and RM-RSVP spellers than NM-RSVP speller (i.e., 79.06±6.45% for NM-RSVP, 90.60±2.98% for RM-RSVP, and 92.74±2.55% for FM-RSVP). In particular, the proposed motion-based RSVP paradigm was significantly beneficial for about half of the subjects who might not accurately perceive rapidly presented static stimuli. These results indicate that the use of proposed motion-based RSVP paradigm is more beneficial for target recognition when developing BCI applications for severely paralyzed patients with complex ocular dysfunctions.},
}
@article {pmid28808655,
year = {2017},
author = {Lin, Z and Zeng, Y and Gao, H and Tong, L and Zhang, C and Wang, X and Wu, Q and Yan, B},
title = {Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {2049094},
pmid = {28808655},
issn = {2314-6141},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Image Processing, Computer-Assisted ; Photic Stimulation ; Visual Perception/*physiology ; },
abstract = {Target image detection based on a rapid serial visual presentation (RSVP) paradigm is a typical brain-computer interface system with various applications, such as image retrieval. In an RSVP paradigm, a P300 component is detected to determine target images. This strategy requires high-precision single-trial P300 detection methods. However, the performance of single-trial detection methods is relatively lower than that of multitrial P300 detection methods. Image retrieval based on multitrial P300 is a new research direction. In this paper, we propose a triple-RSVP paradigm with three images being presented simultaneously and a target image appearing three times. Thus, multitrial P300 classification methods can be used to improve detection accuracy. In this study, these mechanisms were extended and validated, and the characteristics of the multi-RSVP framework were further explored. Two different P300 detection algorithms were also utilized in multi-RSVP to demonstrate that the scheme is universally applicable. Results revealed that the detection accuracy of the multi-RSVP paradigm was higher than that of the standard RSVP paradigm. The results validate the effectiveness of the proposed method, and this method can provide a whole new idea in the field of EEG-based target detection.},
}
@article {pmid28806556,
year = {2018},
author = {Clukey, KE and Lepczyk, CA and Balazs, GH and Work, TM and Li, QX and Bachman, MJ and Lynch, JM},
title = {Persistent organic pollutants in fat of three species of Pacific pelagic longline caught sea turtles: Accumulation in relation to ingested plastic marine debris.},
journal = {The Science of the total environment},
volume = {610-611},
number = {},
pages = {402-411},
doi = {10.1016/j.scitotenv.2017.07.242},
pmid = {28806556},
issn = {1879-1026},
mesh = {Adipose Tissue/*chemistry ; Animals ; California ; China ; Eating ; Environmental Monitoring ; Flame Retardants/pharmacokinetics ; Hydrocarbons, Chlorinated/pharmacokinetics ; Organic Chemicals/*pharmacokinetics ; Pacific Ocean ; *Plastics ; Polychlorinated Biphenyls/pharmacokinetics ; *Turtles ; Waste Products ; },
abstract = {In addition to eating contaminated prey, sea turtles may be exposed to persistent organic pollutants (POPs) from ingesting plastic debris that has absorbed these chemicals. Given the limited knowledge about POPs in pelagic sea turtles and how plastic ingestion influences POP exposure, our objectives were to: 1) provide baseline contaminant levels of three species of pelagic Pacific sea turtles; and 2) assess trends of contaminant levels in relation to species, sex, length, body condition and capture location. In addition, we hypothesized that if ingesting plastic is a significant source of POP exposure, then the amount of ingested plastic may be correlated to POP concentrations accumulated in fat. To address our objectives we compared POP concentrations in fat samples to previously described amounts of ingested plastic from the same turtles. Fat samples from 25 Pacific pelagic sea turtles [2 loggerhead (Caretta caretta), 6 green (Chelonia mydas) and 17 olive ridley (Lepidochelys olivacea) turtles] were analyzed for 81 polychlorinated biphenyls (PCBs), 20 organochlorine pesticides, and 35 brominated flame-retardants. The olive ridley and loggerhead turtles had higher ΣDDTs (dichlorodiphenyltrichloroethane and metabolites) than ΣPCBs, at a ratio similar to biota measured in the South China Sea and southern California. Green turtles had a ratio close to 1:1. These pelagic turtles had lower POP levels than previously reported in nearshore turtles. POP concentrations were unrelated to the amounts of ingested plastic in olive ridleys, suggesting that their exposure to POPs is mainly through prey. In green turtles, concentrations of ΣPCBs were positively correlated with the number of plastic pieces ingested, but these findings were confounded by covariance with body condition index (BCI). Green turtles with a higher BCI had eaten more plastic and also had higher POPs. Taken together, our findings suggest that sea turtles accumulate most POPs through their prey rather than marine debris.},
}
@article {pmid28806144,
year = {2017},
author = {Petito Boyce, C and Sax, SN and Cohen, JM},
title = {Particle size distributions of lead measured in battery manufacturing and secondary smelter facilities and implications in setting workplace lead exposure limits.},
journal = {Journal of occupational and environmental hygiene},
volume = {14},
number = {8},
pages = {594-608},
doi = {10.1080/15459624.2017.1309046},
pmid = {28806144},
issn = {1545-9632},
mesh = {Air Pollutants, Occupational/*analysis ; Environmental Monitoring/methods ; Humans ; Inhalation Exposure/analysis ; Lead/*analysis ; Metallurgy ; Occupational Exposure/*statistics &amp; numerical data ; Particle Size ; Particulate Matter/*analysis ; },
abstract = {Inhalation plays an important role in exposures to lead in airborne particulate matter in occupational settings, and particle size determines where and how much of airborne lead is deposited in the respiratory tract and how much is subsequently absorbed into the body. Although some occupational airborne lead particle size data have been published, limited information is available reflecting current workplace conditions in the U.S. To address this data gap, the Battery Council International (BCI) conducted workplace monitoring studies at nine lead acid battery manufacturing facilities (BMFs) and five secondary smelter facilities (SSFs) across the U.S. This article presents the results of the BCI studies focusing on the particle size distributions calculated from Personal Marple Impactor sampling data and particle deposition estimates in each of the three major respiratory tract regions derived using the Multiple-Path Particle Dosimetry model. The BCI data showed the presence of predominantly larger-sized particles in the work environments evaluated, with average mass median aerodynamic diameters (MMADs) ranging from 21-32 µm for the three BMF job categories and from 15-25 µm for the five SSF job categories tested. The BCI data also indicated that the percentage of lead mass measured at the sampled facilities in the submicron range (i.e., <1 µm, a particle size range associated with enhanced absorption of associated lead) was generally small. The estimated average percentages of lead mass in the submicron range for the tested job categories ranged from 0.8-3.3% at the BMFs and from 0.44-6.1% at the SSFs. Variability was observed in the particle size distributions across job categories and facilities, and sensitivity analyses were conducted to explore this variability. The BCI results were compared with results reported in the scientific literature. Screening-level analyses were also conducted to explore the overall degree of lead absorption potentially associated with the observed particle size distributions and to identify key issues associated with applying such data to set occupational exposure limits for lead.},
}
@article {pmid28805731,
year = {2017},
author = {Xie, J and Xu, G and Luo, A and Li, M and Zhang, S and Han, C and Yan, W},
title = {The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {8},
pages = {},
pmid = {28805731},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal-To-Noise Ratio ; },
abstract = {As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α, θ, θ + α powers, θ/α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications.},
}
@article {pmid28804712,
year = {2017},
author = {Batula, AM and Kim, YE and Ayaz, H},
title = {Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {1463512},
pmid = {28804712},
issn = {2314-6141},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Humans ; Male ; *Motor Activity ; *Robotics ; *Visual Perception ; },
abstract = {Motor-imagery tasks are a popular input method for controlling brain-computer interfaces (BCIs), partially due to their similarities to naturally produced motor signals. The use of functional near-infrared spectroscopy (fNIRS) in BCIs is still emerging and has shown potential as a supplement or replacement for electroencephalography. However, studies often use only two or three motor-imagery tasks, limiting the number of available commands. In this work, we present the results of the first four-class motor-imagery-based online fNIRS-BCI for robot control. Thirteen participants utilized upper- and lower-limb motor-imagery tasks (left hand, right hand, left foot, and right foot) that were mapped to four high-level commands (turn left, turn right, move forward, and move backward) to control the navigation of a simulated or real robot. A significant improvement in classification accuracy was found between the virtual-robot-based BCI (control of a virtual robot) and the physical-robot BCI (control of the DARwIn-OP humanoid robot). Differences were also found in the oxygenated hemoglobin activation patterns of the four tasks between the first and second BCI. These results corroborate previous findings that motor imagery can be improved with feedback and imply that a four-class motor-imagery-based fNIRS-BCI could be feasible with sufficient subject training.},
}
@article {pmid28802870,
year = {2017},
author = {Covic, A and Keitel, C and Porcu, E and Schröger, E and Müller, MM},
title = {Audio-visual synchrony and spatial attention enhance processing of dynamic visual stimulation independently and in parallel: A frequency-tagging study.},
journal = {NeuroImage},
volume = {161},
number = {},
pages = {32-42},
doi = {10.1016/j.neuroimage.2017.08.022},
pmid = {28802870},
issn = {1095-9572},
mesh = {Adolescent ; Adult ; Attention/*physiology ; Auditory Perception/*physiology ; Brain Waves/physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Space Perception/*physiology ; Time Factors ; Young Adult ; },
abstract = {The neural processing of a visual stimulus can be facilitated by attending to its position or by a co-occurring auditory tone. Using frequency-tagging, we investigated whether facilitation by spatial attention and audio-visual synchrony rely on similar neural processes. Participants attended to one of two flickering Gabor patches (14.17 and 17 Hz) located in opposite lower visual fields. Gabor patches further "pulsed" (i.e. showed smooth spatial frequency variations) at distinct rates (3.14 and 3.63 Hz). Frequency-modulating an auditory stimulus at the pulse-rate of one of the visual stimuli established audio-visual synchrony. Flicker and pulsed stimulation elicited stimulus-locked rhythmic electrophysiological brain responses that allowed tracking the neural processing of simultaneously presented Gabor patches. These steady-state responses (SSRs) were quantified in the spectral domain to examine visual stimulus processing under conditions of synchronous vs. asynchronous tone presentation and when respective stimulus positions were attended vs. unattended. Strikingly, unique patterns of effects on pulse- and flicker driven SSRs indicated that spatial attention and audiovisual synchrony facilitated early visual processing in parallel and via different cortical processes. We found attention effects to resemble the classical top-down gain effect facilitating both, flicker and pulse-driven SSRs. Audio-visual synchrony, in turn, only amplified synchrony-producing stimulus aspects (i.e. pulse-driven SSRs) possibly highlighting the role of temporally co-occurring sights and sounds in bottom-up multisensory integration.},
}
@article {pmid28798676,
year = {2017},
author = {Dreyer, AM and Herrmann, CS and Rieger, JW},
title = {Tradeoff between User Experience and BCI Classification Accuracy with Frequency Modulated Steady-State Visual Evoked Potentials.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {391},
pmid = {28798676},
issn = {1662-5161},
abstract = {Steady-state visual evoked potentials (SSVEPs) have been widely employed for the control of brain-computer interfaces (BCIs) because they are very robust, lead to high performance, and allow for a high number of commands. However, such flickering stimuli often also cause user discomfort and fatigue, especially when several light sources are used simultaneously. Different variations of SSVEP driving signals have been proposed to increase user comfort. Here, we investigate the suitability of frequency modulation of a high frequency carrier for SSVEP-BCIs. We compared BCI performance and user experience between frequency modulated (FM) and traditional sinusoidal (SIN) SSVEPs in an offline classification paradigm with four independently flickering light-emitting diodes which were overtly attended (fixated). While classification performance was slightly reduced with the FM stimuli, the user comfort was significantly increased. Comparing the SSVEPs for covert attention to the stimuli (without fixation) was not possible, as no reliable SSVEPs were evoked. Our results reveal that several, simultaneously flickering, light emitting diodes can be used to generate FM-SSVEPs with different frequencies and the resulting occipital electroencephalography (EEG) signals can be classified with high accuracy. While the performance we report could be further improved with adjusted stimuli and algorithms, we argue that the increased comfort is an important result and suggest the use of FM stimuli for future SSVEP-BCI applications.},
}
@article {pmid28798675,
year = {2017},
author = {Liu, Y and Ayaz, H and Shewokis, PA},
title = {Multisubject "Learning" for Mental Workload Classification Using Concurrent EEG, fNIRS, and Physiological Measures.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {389},
pmid = {28798675},
issn = {1662-5161},
abstract = {An accurate measure of mental workload level has diverse neuroergonomic applications ranging from brain computer interfacing to improving the efficiency of human operators. In this study, we integrated electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), and physiological measures for the classification of three workload levels in an n-back working memory task. A significantly better than chance level classification was achieved by EEG-alone, fNIRS-alone, physiological alone, and EEG+fNIRS based approaches. The results confirmed our previous finding that integrating EEG and fNIRS significantly improved workload classification compared to using EEG-alone or fNIRS-alone. The inclusion of physiological measures, however, does not significantly improves EEG-based or fNIRS-based workload classification. A major limitation of currently available mental workload assessment approaches is the requirement to record lengthy calibration data from the target subject to train workload classifiers. We show that by learning from the data of other subjects, workload classification accuracy can be improved especially when the amount of data from the target subject is small.},
}
@article {pmid28798411,
year = {2017},
author = {Valeriani, D and Cinel, C and Poli, R},
title = {Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {7772},
pmid = {28798411},
issn = {2045-2322},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; *Communication ; *Decision Making ; Female ; Humans ; Male ; Visual Perception ; },
abstract = {Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.},
}
@article {pmid28798359,
year = {2017},
author = {Xie, K and Zhang, S and Dong, S and Li, S and Yu, C and Xu, K and Chen, W and Guo, W and Luo, J and Wu, Z},
title = {Portable wireless electrocorticography system with a flexible microelectrodes array for epilepsy treatment.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {7808},
pmid = {28798359},
issn = {2045-2322},
mesh = {Animals ; Brain/*physiopathology ; Brain-Computer Interfaces ; Cell Phone ; Disease Models, Animal ; Electric Stimulation Therapy/*instrumentation ; Electrocorticography/*instrumentation ; Electrodes, Implanted ; Epilepsy/physiopathology/*therapy ; Humans ; Implantable Neurostimulators ; Microelectrodes ; Rats ; Rats, Sprague-Dawley ; Wireless Technology ; },
abstract = {In this paper, we present a portable wireless electrocorticography (ECoG) system. It uses a high resolution 32-channel flexible ECoG electrodes array to collect electrical signals of brain activities and to stimulate the lesions. Electronic circuits are designed for signal acquisition, processing and transmission using Bluetooth Low Energy 4 (LTE4) for wireless communication with cell phone. In-vivo experiments on a rat show that the flexible ECoG system can accurately record electrical signals of brain activities and transmit them to cell phone with a maximal sampling rate of 30 ksampling/s per channel. It demonstrates that the epilepsy lesions can be detected, located and treated through the ECoG system. The wireless ECoG system has low energy consumption and high brain spatial resolution, thus has great prospects for future application.},
}
@article {pmid28797109,
year = {2017},
author = {Ofner, P and Schwarz, A and Pereira, J and Müller-Putz, GR},
title = {Upper limb movements can be decoded from the time-domain of low-frequency EEG.},
journal = {PloS one},
volume = {12},
number = {8},
pages = {e0182578},
pmid = {28797109},
issn = {1932-6203},
mesh = {Adult ; Arm/*physiology ; Brain Waves ; Electroencephalography ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.},
}
@article {pmid28790910,
year = {2017},
author = {Hong, KS and Khan, MJ},
title = {Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {35},
pmid = {28790910},
issn = {1662-5218},
abstract = {In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.},
}
@article {pmid28785973,
year = {2017},
author = {Gong, J and Luo, C and Chang, X and Zhang, R and Klugah-Brown, B and Guo, L and Xu, P and Yao, D},
title = {White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.},
journal = {Brain topography},
volume = {30},
number = {6},
pages = {797-809},
doi = {10.1007/s10548-017-0581-z},
pmid = {28785973},
issn = {1573-6792},
mesh = {Adult ; Brain/*physiology ; Diffusion Magnetic Resonance Imaging ; *Electroencephalography ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Scalp/physiology ; White Matter/*physiology ; Young Adult ; },
abstract = {The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.},
}
@article {pmid28784984,
year = {2017},
author = {Kao, JC and Ryu, SI and Shenoy, KV},
title = {Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {7395},
pmid = {28784984},
issn = {2045-2322},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Algorithms ; Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; Macaca mulatta ; Motor Cortex/*physiology ; Neurons/*physiology ; },
abstract = {Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.},
}
@article {pmid28783638,
year = {2017},
author = {Karimi-Bidhendi, A and Malekzadeh-Arasteh, O and Lee, MC and McCrimmon, CM and Wang, PT and Mahajan, A and Liu, CY and Nenadic, Z and Do, AH and Heydari, P},
title = {CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {5},
pages = {1111-1122},
pmid = {28783638},
issn = {1940-9990},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; *Amplifiers, Electronic ; Brain/diagnostic imaging/*physiology ; Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods ; Electrodes, Implanted ; Equipment Design ; Humans ; Magnetic Resonance Imaging ; Male ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Two brain signal acquisition (BSA) front-ends incorporating two CMOS ultralow power, low-noise amplifier arrays and serializers operating in mosfet weak inversion region are presented. To boost the amplifier's gain for a given current budget, cross-coupled-pair active load topology is used in the first stages of these two amplifiers. These two BSA front-ends are fabricated in 130 and 180 nm CMOS processes, occupying 5.45 mm [2] and 0.352 mm [2] of die areas, respectively (excluding pad rings). The CMOS 130-nm amplifier array is comprised of 64 elements, where each amplifier element consumes 0.216 μW from 0.4 V supply, has input-referred noise voltage (IRNoise) of 2.19 μV[Formula: see text] corresponding to a power efficiency factor (PEF) of 11.7, and occupies 0.044 mm [2] of die area. The CMOS 180 nm amplifier array employs 4 elements, where each element consumes 0.69 μW from 0.6 V supply with IRNoise of 2.3 μV[Formula: see text] (corresponding to a PEF of 31.3) and 0.051 mm [2] of die area. Noninvasive electroencephalographic and invasive electrocorticographic signals were recorded real time directly on able-bodied human subjects, showing feasibility of using these analog front-ends for future fully implantable BSA and brain- computer interface systems.},
}
@article {pmid28782865,
year = {2017},
author = {Schirrmeister, RT and Springenberg, JT and Fiederer, LDJ and Glasstetter, M and Eggensperger, K and Tangermann, M and Hutter, F and Burgard, W and Ball, T},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization.},
journal = {Human brain mapping},
volume = {38},
number = {11},
pages = {5391-5420},
pmid = {28782865},
issn = {1097-0193},
mesh = {Brain/*physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/physiology ; Language ; *Machine Learning ; Motor Activity/physiology ; Neural Pathways/physiology ; Space Perception/physiology ; },
abstract = {Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc.},
}
@article {pmid28777722,
year = {2017},
author = {Dhindsa, K and Carcone, D and Becker, S},
title = {Toward an Open-Ended BCI: A User-Centered Coadaptive Design.},
journal = {Neural computation},
volume = {29},
number = {10},
pages = {2742-2768},
doi = {10.1162/neco_a_01001},
pmid = {28777722},
issn = {1530-888X},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Equipment Design ; Female ; Humans ; Male ; Mental Processes/physiology ; Neurofeedback ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCIs) allow users to control a device by interpreting their brain activity. For simplicity, these devices are designed to be operated by purposefully modulating specific predetermined neurophysiological signals, such as the sensorimotor rhythm. However, the ability to modulate a given neurophysiological signal is highly variable across individuals, contributing to the inconsistent performance of BCIs for different users. These differences suggest that individuals who experience poor BCI performance with one class of brain signals might have good results with another. In order to take advantage of individual abilities as they relate to BCI control, we need to move beyond the current approaches. In this letter, we explore a new BCI design aimed at a more individualized and user-focused experience, which we call open-ended BCI. Individual users were given the freedom to discover their own mental strategies as opposed to being trained to modulate a given brain signal. They then underwent multiple coadaptive training sessions with the BCI. Our first open-ended BCI performed similarly to comparable BCIs while accommodating a wider variety of mental strategies without a priori knowledge of the specific brain signals any individual might use. Post hoc analysis revealed individual differences in terms of which sensory modality yielded optimal performance. We found a large and significant effect of individual differences in background training and expertise, such as in musical training, on BCI performance. Future research should be focused on finding more generalized solutions to user training and brain state decoding methods to fully utilize the abilities of different individuals in an open-ended BCI. Accounting for each individual's areas of expertise could have important implications on BCI training and BCI application design.},
}
@article {pmid28776500,
year = {2017},
author = {Abu-Alqumsan, M and Kapeller, C and Hintermüller, C and Guger, C and Peer, A},
title = {Invariance and variability in interaction error-related potentials and their consequences for classification.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066015},
doi = {10.1088/1741-2552/aa8416},
pmid = {28776500},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces/*classification ; Electroencephalography/classification/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Mental Processes/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: This paper discusses the invariance and variability in interaction error-related potentials (ErrPs), where a special focus is laid upon the factors of (1) the human mental processing required to assess interface actions (2) time (3) subjects.<br><br>APPROACH: Three different experiments were designed as to vary primarily with respect to the mental processes that are necessary to assess whether an interface error has occurred or not. The three experiments were carried out with 11 subjects in a repeated-measures experimental design. To study the effect of time, a subset of the recruited subjects additionally performed the same experiments on different days.<br><br>MAIN RESULTS: The ErrP variability across the different experiments for the same subjects was found largely attributable to the different mental processing required to assess interface actions. Nonetheless, we found that interaction ErrPs are empirically invariant over time (for the same subject and same interface) and to a lesser extent across subjects (for the same interface).<br><br>SIGNIFICANCE: The obtained results may be used to explain across-study variability of ErrPs, as well as to define guidelines for approaches to the ErrP classifier transferability problem.},
}
@article {pmid28775677,
year = {2017},
author = {Frolov, AA and Mokienko, O and Lyukmanov, R and Biryukova, E and Kotov, S and Turbina, L and Nadareyshvily, G and Bushkova, Y},
title = {Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {400},
pmid = {28775677},
issn = {1662-4548},
abstract = {Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group (n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group (n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points (p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points (p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.},
}
@article {pmid28769781,
year = {2017},
author = {Qureshi, NK and Naseer, N and Noori, FM and Nazeer, H and Khan, RA and Saleem, S},
title = {Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain-Computer Interface Using Adaptive Estimation of General Linear Model Coefficients.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {33},
pmid = {28769781},
issn = {1662-5218},
abstract = {In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique. Subsequently, multiple feature combinations of estimated coefficients were used for classification. The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest are 85.5, 85.2, 87.8, 83.7, and 84.8%, respectively, using support vector machine. These results are compared with the best classification accuracies obtained using the conventional hemodynamic response. By means of the proposed methodology, the average classification accuracy obtained was significantly higher (p < 0.05). These results serve to demonstrate the feasibility of developing a high-classification-performance fNIRS-BCI.},
}
@article {pmid28769778,
year = {2017},
author = {Lin, YP and Jao, PK and Yang, YH},
title = {Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis.},
journal = {Frontiers in computational neuroscience},
volume = {11},
number = {},
pages = {64},
pmid = {28769778},
issn = {1662-5188},
abstract = {Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.},
}
@article {pmid28769776,
year = {2017},
author = {Grissmann, S and Zander, TO and Faller, J and Brönstrup, J and Kelava, A and Gramann, K and Gerjets, P},
title = {Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {370},
pmid = {28769776},
issn = {1662-5161},
abstract = {Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.},
}
@article {pmid28769747,
year = {2017},
author = {Ryun, S and Kim, JS and Jeon, E and Chung, CK},
title = {Movement-Related Sensorimotor High-Gamma Activity Mainly Represents Somatosensory Feedback.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {408},
pmid = {28769747},
issn = {1662-4548},
abstract = {Somatosensation plays pivotal roles in the everyday motor control of humans. During active movement, there exists a prominent high-gamma (HG >50 Hz) power increase in the primary somatosensory cortex (S1), and this provides an important feature in relation to the decoding of movement in a brain-machine interface (BMI). However, one concern of BMI researchers is the inflation of the decoding performance due to the activation of somatosensory feedback, which is not elicited in patients who have lost their sensorimotor function. In fact, it is unclear as to how much the HG component activated in S1 contributes to the overall sensorimotor HG power during voluntary movement. With regard to other functional roles of HG in S1, recent findings have reported that these HG power levels increase before the onset of actual movement, which implies neural activation for top-down movement preparation or sensorimotor interaction, i.e., an efference copy. These results are promising for BMI applications but remain inconclusive. Here, we found using electrocorticography (ECoG) from eight patients that HG activation in S1 is stronger and more informative than it is in the primary motor cortex (M1) regardless of the type of movement. We also demonstrate by means of electromyography (EMG) that the onset timing of the HG power in S1 is later (49 ms) than that of the actual movement. Interestingly, we show that the HG power fluctuations in S1 are closely related to subtle muscle contractions, even during the pre-movement period. These results suggest the following: (1) movement-related HG activity in S1 strongly affects the overall sensorimotor HG power, and (2) HG activity in S1 during voluntary movement mainly represents cortical neural processing for somatosensory feedback.},
}
@article {pmid28769745,
year = {2017},
author = {Li, J and Li, Z},
title = {Sums of Spike Waveform Features for Motor Decoding.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {406},
pmid = {28769745},
issn = {1662-4548},
abstract = {Traditionally, the key step before decoding motor intentions from cortical recordings is spike sorting, the process of identifying which neuron was responsible for an action potential. Recently, researchers have started investigating approaches to decoding which omit the spike sorting step, by directly using information about action potentials' waveform shapes in the decoder, though this approach is not yet widespread. Particularly, one recent approach involves computing the moments of waveform features and using these moment values as inputs to decoders. This computationally inexpensive approach was shown to be comparable in accuracy to traditional spike sorting. In this study, we use offline data recorded from two Rhesus monkeys to further validate this approach. We also modify this approach by using sums of exponentiated features of spikes, rather than moments. Our results show that using waveform feature sums facilitates significantly higher hand movement reconstruction accuracy than using waveform feature moments, though the magnitudes of differences are small. We find that using the sums of one simple feature, the spike amplitude, allows better offline decoding accuracy than traditional spike sorting by expert (correlation of 0.767, 0.785 vs. 0.744, 0.738, respectively, for two monkeys, average 16% reduction in mean-squared-error), as well as unsorted threshold crossings (0.746, 0.776; average 9% reduction in mean-squared-error). Our results suggest that the sums-of-features framework has potential as an alternative to both spike sorting and using unsorted threshold crossings, if developed further. Also, we present data comparing sorted vs. unsorted spike counts in terms of offline decoding accuracy. Traditional sorted spike counts do not include waveforms that do not match any template ("hash"), but threshold crossing counts do include this hash. On our data and in previous work, hash contributes to decoding accuracy. Thus, using the comparison between sorted spike counts and threshold crossing counts to evaluate the benefit of sorting is confounded by the presence of hash. We find that when the comparison is controlled for hash, performing sorting is better than not. These results offer a new perspective on the question of to sort or not to sort.},
}
@article {pmid28762027,
year = {2017},
author = {Titus, G and Sudhakar, MS},
title = {A simple and efficient algorithm operating with linear time for MCEEG data compression.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {40},
number = {3},
pages = {759-768},
doi = {10.1007/s13246-017-0575-x},
pmid = {28762027},
issn = {1879-5447},
mesh = {*Algorithms ; *Data Compression ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Popularisation of electroencephalograph (EEG) signals in diversified fields have increased the need for devices capable of operating at lower power and storage requirements. This has led to a great deal of research in data compression, that can address (a) low latency in the coding of the signal, (b) reduced hardware and software dependencies, (c) quantify the system anomalies, and (d) effectively reconstruct the compressed signal. This paper proposes a computationally simple and novel coding scheme named spatial pseudo codec (SPC), to achieve lossy to near lossless compression of multichannel EEG (MCEEG). In the proposed system, MCEEG signals are initially normalized, followed by two parallel processes: one operating on integer part and the other, on fractional part of the normalized data. The redundancies in integer part are exploited using spatial domain encoder, and the fractional part is coded as pseudo integers. The proposed method has been tested on a wide range of databases having variable sampling rates and resolutions. Results indicate that the algorithm has a good recovery performance with an average percentage root mean square deviation (PRD) of 2.72 for an average compression ratio (CR) of 3.16. Furthermore, the algorithm has a complexity of only O(n) with an average encoding and decoding time per sample of 0.3 ms and 0.04 ms respectively. The performance of the algorithm is comparable with recent methods like fast discrete cosine transform (fDCT) and tensor decomposition methods. The results validated the feasibility of the proposed compression scheme for practical MCEEG recording, archiving and brain computer interfacing systems.},
}
@article {pmid28760486,
year = {2017},
author = {Amaral, CP and Simões, MA and Mouga, S and Andrade, J and Castelo-Branco, M},
title = {A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study.},
journal = {Journal of neuroscience methods},
volume = {290},
number = {},
pages = {105-115},
doi = {10.1016/j.jneumeth.2017.07.029},
pmid = {28760486},
issn = {1872-678X},
mesh = {Adult ; Attention Deficit Disorder with Hyperactivity/classification/*etiology ; Autistic Disorder/*complications ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Feasibility Studies ; Female ; Humans ; Male ; *Social Behavior ; User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: We present a novel virtual-reality P300-based Brain Computer Interface (BCI) paradigm using social cues to direct the focus of attention. We combined interactive immersive virtual-reality (VR) technology with the properties of P300 signals in a training tool which can be used in social attention disorders such as autism spectrum disorder (ASD).<br><br>NEW METHOD: We tested the novel social attention training paradigm (P300-based BCI paradigm for rehabilitation of joint-attention skills) in 13 healthy participants, in 3 EEG systems. The more suitable setup was tested online with 4 ASD subjects. Statistical accuracy was assessed based on the detection of P300, using spatial filtering and a Na&#xef;ve-Bayes classifier.<br><br>RESULTS: We compared: 1 - g.Mobilab+ (active dry-electrodes, wireless transmission); 2 - g.Nautilus (active electrodes, wireless transmission); 3 - V-Amp with actiCAP Xpress dry-electrodes. Significant statistical classification was achieved in all systems. g.Nautilus proved to be the best performing system in terms of accuracy in the detection of P300, preparation time, speed and reported comfort. Proof of concept tests in ASD participants proved that this setup is feasible for training joint attention skills in ASD.<br><br>This work provides a unique combination of 'easy-to-use' BCI systems with new technologies such as VR to train joint-attention skills in autism.<br><br>CONCLUSIONS: Our P300 BCI paradigm is feasible for future Phase I/II clinical trials to train joint-attention skills, with successful classification within few trials, online in ASD participants. The g.Nautilus system is the best performing one to use with the developed BCI setup.},
}
@article {pmid28758809,
year = {2018},
author = {Schudlo, LC and Chau, T},
title = {Development and testing an online near-infrared spectroscopy brain-computer interface tailored to an individual with severe congenital motor impairments.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {13},
number = {6},
pages = {581-591},
doi = {10.1080/17483107.2017.1357212},
pmid = {28758809},
issn = {1748-3115},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Congenital Abnormalities/*rehabilitation ; Persons with Disabilities/*rehabilitation ; Equipment Design ; Humans ; *Infrared Rays ; Male ; Psychomotor Performance ; Severity of Illness Index ; },
abstract = {PURPOSE: For non-verbal individuals, brain-computer interfaces (BCIs) are a potential means of communication. Near-infrared spectroscopy (NIRS) is a brain-monitoring modality that has been considered for BCIs. To date, limited NIRS-BCI testing has involved online classification, particularly with individuals with severe motor impairments.<br><br>MATERIALS AND METHODS: We tested an online NIRS-BCI developed for a non-verbal individual with severe congenital motor impairments. The binary BCI differentiated categorical verbal fluency task (VFT) performance and rest using prefrontal measurements. The participant attended five sessions, the last two of which were online with classification feedback.<br><br>RESULTS: An online classification accuracy of 63.33% was achieved using a linear discriminant classifier trained on a four-dimensional feature set. An offline, cross-validation analysis of all data yielded an optimal adjusted classification accuracy of 66.6&#x2009;&#xb1;&#x2009;9.11%. Inconsistent functional responses, contradictory effects of feedback, participant fatigue and motion artefacts were identified as challenges to online classification specific to this participant.<br><br>CONCLUSIONS: Results suggest potential in using an NIRS-BCI controlled by the VFT in instances of severe congenital impairments. Further testing with users with severe disabilities is necessary. Implications for Rehabilitation Brain-computer interfaces (BCIs) can provide a non-motor based means of communication for individuals with severe motor impairments. Near-infrared spectroscopy (NIRS) is a haemodynamic-based brain-imaging modality used in BCIs. To date, NIRS-BCIs have not been thoroughly tested with potential target users. This case study shows that NIRS-BCIs may offer a means of practical communication for individuals with severe congenital impairments and continued exploration is advisable.},
}
@article {pmid28758433,
year = {2017},
author = {Ghirca, MV and Chibelean, C and Frunda, EA and Mártha, O},
title = {[The importance of Bladder Contractility Index in the management of underactive bladder].},
journal = {Orvosi hetilap},
volume = {158},
number = {31},
pages = {1222-1227},
doi = {10.1556/650.2017.30776},
pmid = {28758433},
issn = {0030-6002},
mesh = {Humans ; Hungary ; Lower Urinary Tract Symptoms/*diagnosis/therapy ; Retrospective Studies ; *Severity of Illness Index ; Urinary Bladder/*physiopathology ; Urinary Retention ; Urodynamics ; },
abstract = {AIM: To evaluate the importance of BCI in the management of underactive bladder (UB).<br><br>MATERIAL AND METHOD: A retrospective study over a period of 3 years and 9 months (January 2013-September 2016) in Mure&#x15f; County Hospital, Clinic of Urology, including 91 patients. Detrusor underactivity was defined by BCI less than 100 using the formula: PdetQmax+5Qmax.<br><br>RESULTS: The median of Qmax value was 7 ml/s and the median value of Pdet was 14 cm H2O. The median value of BCI was 55 with extremities between 17 and 110. BCI tends to decrease with age and there is a relation between value of BCI and diabetes (p = 0,003) and neurological diseases (p = 0,015).<br><br>CONCLUSIONS: The UB diagnosis represents a real challenge for the urologist, so that, urodynamical findings such as absence of bladder obstruction, post-void residual urine, Qmax, together with BCI value, helps in setting the proper management. Orv Hetil. 2017; 158(31): 1222-1227.},
}
@article {pmid28758342,
year = {2017},
author = {Rodgers, TW and Xu, CCY and Giacalone, J and Kapheim, KM and Saltonstall, K and Vargas, M and Yu, DW and Somervuo, P and McMillan, WO and Jansen, PA},
title = {Carrion fly-derived DNA metabarcoding is an effective tool for mammal surveys: Evidence from a known tropical mammal community.},
journal = {Molecular ecology resources},
volume = {17},
number = {6},
pages = {e133-e145},
doi = {10.1111/1755-0998.12701},
pmid = {28758342},
issn = {1755-0998},
mesh = {Animal Feed/*analysis ; Animals ; Biodiversity ; DNA/*genetics/isolation &amp; purification ; DNA Barcoding, Taxonomic/*methods ; Diptera/*physiology ; *Feeding Behavior ; Mammals/*classification/genetics ; Metagenomics/*methods ; Panama ; },
abstract = {Metabarcoding of vertebrate DNA derived from carrion flies has been proposed as a promising tool for biodiversity monitoring. To evaluate its efficacy, we conducted metabarcoding surveys of carrion flies on Barro Colorado Island (BCI), Panama, which has a well-known mammal community, and compared our results against diurnal transect counts and camera trapping. We collected 1,084 flies in 29 sampling days, conducted metabarcoding with mammal-specific (16S) and vertebrate-specific (12S) primers, and sequenced amplicons on Illumina MiSeq. For taxonomic assignment, we compared blast with the new program protax, and we found that protax improved species identifications. We detected 20 mammal, four bird, and one lizard species from carrion fly metabarcoding, all but one of which are known from BCI. Fly metabarcoding detected more mammal species than concurrent transect counts (29 sampling days, 13 species) and concurrent camera trapping (84 sampling days, 17 species), and detected 67% of the number of mammal species documented by 8 years of transect counts and camera trapping combined, although fly metabarcoding missed several abundant species. This study demonstrates that carrion fly metabarcoding is a powerful tool for mammal biodiversity surveys and has the potential to detect a broader range of species than more commonly used methods.},
}
@article {pmid28756027,
year = {2016},
author = {Bocquelet, F and Hueber, T and Girin, L and Chabardès, S and Yvert, B},
title = {Key considerations in designing a speech brain-computer interface.},
journal = {Journal of physiology, Paris},
volume = {110},
number = {4 Pt A},
pages = {392-401},
doi = {10.1016/j.jphysparis.2017.07.002},
pmid = {28756027},
issn = {1769-7115},
mesh = {*Brain-Computer Interfaces ; Humans ; Speech/*physiology ; },
abstract = {Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cortical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.},
}
@article {pmid28747007,
year = {2017},
author = {Daoud, F and Pelzer, D and Zuehlke, S and Spiteller, M and Kayser, O},
title = {Ozone pretreatment of process waste water generated in course of fluoroquinolone production.},
journal = {Chemosphere},
volume = {185},
number = {},
pages = {953-963},
doi = {10.1016/j.chemosphere.2017.07.040},
pmid = {28747007},
issn = {1879-1298},
mesh = {Anti-Bacterial Agents/chemistry ; Chromatography, Liquid ; Ciprofloxacin/analysis ; Fluoroquinolones/analysis/*chemistry ; Kinetics ; Mass Spectrometry ; Moxifloxacin ; Ozone/*chemistry ; Waste Disposal, Fluid/*methods ; Wastewater/*chemistry ; Water/analysis ; Water Pollutants, Chemical/analysis/*chemistry ; },
abstract = {During production of active pharmaceutical ingredients, process waste water is generated at several stages of manufacturing. Whenever possible, the resulting waste water will be processed by conventional waste water treatment plants. Currently, incineration of the process waste water is the method to eliminate compounds with high biological activity. Thus, ozone treatment followed by biological waste water treatment was tested as an alternative method. Two prominent representatives of the large group of fluoroquinolone antibiotics (ciprofloxacin and moxifloxacin) were investigated, focussing on waste water of the bulk production. Elimination of the target compounds and generation of their main transformation products were determined by liquid chromatography - high resolution mass spectrometry (LC-HRMS). The obtained results demonstrated, that the concentration of moxifloxacin and its metabolites can be effectively reduced (>99.7%) prior entering the receiving water. On the contrary, the concentration of ciprofloxacin and its metabolites remained too high for safe discharge, necessitating application of prolonged ozonation for its further degradation. The required ozonation time can be estimated based on the determined kinetics. To assure a low biological activity the ecotoxicity of the ozonated waste water was investigated using three trophic levels. By means of multiple-stage mass spectrometry (MS[n]) experiments several new transformation products of the fluoroquinolones were identified. Thus, previously published proposed structures could be corrected or confirmed.},
}
@article {pmid28745300,
year = {2017},
author = {Samek, W and Nakajima, S and Kawanabe, M and Müller, KR},
title = {On robust parameter estimation in brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {061001},
doi = {10.1088/1741-2552/aa8232},
pmid = {28745300},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*statistics &amp; numerical data/trends ; Humans ; Statistics as Topic/*methods/trends ; },
abstract = {OBJECTIVE: The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task.<br><br>APPROACH: This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set.<br><br>MAIN RESULTS: The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters.<br><br>SIGNIFICANCE: Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.},
}
@article {pmid28745299,
year = {2018},
author = {Nguyen, CH and Karavas, GK and Artemiadis, P},
title = {Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {016002},
doi = {10.1088/1741-2552/aa8235},
pmid = {28745299},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*classification ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Speech/*physiology ; Support Vector Machine/*classification ; Young Adult ; },
abstract = {OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications.<br><br>APPROACH: A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects.<br><br>MAIN RESULTS: The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words.<br><br>SIGNIFICANCE: The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.},
}
@article {pmid28744212,
year = {2017},
author = {Rodríguez-Ugarte, M and Iáñez, E and Ortíz, M and Azorín, JM},
title = {Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {45},
pmid = {28744212},
issn = {1662-5196},
abstract = {The aim of this work was to design a personalized BCI model to detect pedaling intention through EEG signals. The approach sought to select the best among many possible BCI models for each subject. The choice was between different processing windows, feature extraction algorithms and electrode configurations. Moreover, data was analyzed offline and pseudo-online (in a way suitable for real-time applications), with a preference for the latter case. A process for selecting the best BCI model was described in detail. Results for the pseudo-online processing with the best BCI model of each subject were on average 76.7% of true positive rate, 4.94 false positives per minute and 55.1% of accuracy. The personalized BCI model approach was also found to be significantly advantageous when compared to the typical approach of using a fixed feature extraction algorithm and electrode configuration. The resulting approach could be used to more robustly interface with lower limb exoskeletons in the context of the rehabilitation of stroke patients.},
}
@article {pmid28743262,
year = {2017},
author = {Cao, Y and An, X and Ke, Y and Jiang, J and Yang, H and Chen, Y and Jiao, X and Qi, H and Ming, D},
title = {The effects of semantic congruency: a research of audiovisual P300-speller.},
journal = {Biomedical engineering online},
volume = {16},
number = {1},
pages = {91},
pmid = {28743262},
issn = {1475-925X},
mesh = {Adult ; *Audiovisual Aids ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; *Semantics ; Young Adult ; },
abstract = {BACKGROUND: Over the past few decades, there have been many studies of aspects of brain-computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm.<br><br>METHODS: The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms.<br><br>RESULTS: The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm.<br><br>CONCLUSIONS: In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.},
}
@article {pmid28742045,
year = {2018},
author = {Yao, L and Chen, ML and Sheng, X and Mrachacz-Kersting, N and Zhu, X and Farina, D and Jiang, N},
title = {A Multi-Class Tactile Brain-Computer Interface Based on Stimulus-Induced Oscillatory Dynamics.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {1},
pages = {3-10},
doi = {10.1109/TNSRE.2017.2731261},
pmid = {28742045},
issn = {1558-0210},
mesh = {Algorithms ; Attention/physiology ; Brain-Computer Interfaces/*classification ; Discrimination, Psychological ; Electroencephalography ; Equipment Design ; Evoked Potentials, Somatosensory ; Female ; Hand/innervation ; Healthy Volunteers ; Humans ; Male ; Reproducibility of Results ; Touch/*physiology ; Young Adult ; },
abstract = {We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile-induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L); 2) selective sensation on right hand (SS-R); 3) bilateral selective sensation; and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9 ± 8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3 ± 4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had an accuracy of 75.2 ± 8.3%, with the best discrimination reached for the classes SS-L, SS-R, and SS-S. Finally, four classes were classified with an accuracy of 59.4 ± 7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.},
}
@article {pmid28740505,
year = {2017},
author = {Mao, X and Li, W and He, H and Xian, B and Zeng, M and Zhou, H and Niu, L and Chen, G},
title = {Object Extraction in Cluttered Environments via a P300-Based IFCE.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {5468208},
pmid = {28740505},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; Color ; Event-Related Potentials, P300 ; Humans ; Lighting ; Robotics/*methods ; },
abstract = {One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.},
}
@article {pmid28740331,
year = {2016},
author = {Atyabi, A and Shic, F and Naples, A},
title = {Mixture of autoregressive modeling orders and its implication on single trial EEG classification.},
journal = {Expert systems with applications},
volume = {65},
number = {},
pages = {164-180},
pmid = {28740331},
issn = {0957-4174},
support = {K01 MH104739/MH/NIMH NIH HHS/United States ; },
abstract = {Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR's modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely &amp; correctly responsiveness of such systems to operator's thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets.},
}
@article {pmid28736662,
year = {2017},
author = {Abdalmalak, A and Milej, D and Diop, M and Shokouhi, M and Naci, L and Owen, AM and St Lawrence, K},
title = {Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently?.},
journal = {Biomedical optics express},
volume = {8},
number = {4},
pages = {2162-2172},
pmid = {28736662},
issn = {2156-7085},
abstract = {Previous functional magnetic resonance imaging (fMRI) studies have shown that a subgroup of patients diagnosed as being in a vegetative state are aware and able to communicate by performing a motor imagery task in response to commands. Due to the fMRI's cost and accessibility, there is a need for exploring different imaging modalities that can be used at the bedside. A promising technique is functional near infrared spectroscopy (fNIRS) that has been successfully applied to measure brain oxygenation in humans. Due to the limited depth sensitivity of continuous-wave NIRS, time-resolved (TR) detection has been proposed as a way of enhancing the sensitivity to the brain, since late arriving photons have a higher probability of reaching the brain. The goal of this study was to assess the feasibility and sensitivity of TR fNIRS in detecting brain activity during motor imagery. Fifteen healthy subjects were recruited in this study, and the fNIRS results were validated using fMRI. The change in the statistical moments of the distribution of times of flight (number of photons, mean time of flight and variance) were calculated for each channel to determine the presence of brain activity. The results indicate up to an 86% agreement between fMRI and TR-fNIRS and the sensitivity ranging from 64 to 93% with the highest value determined for the mean time of flight. These promising results highlight the potential of TR-fNIRS as a portable brain computer interface for patients with disorder of consciousness.},
}
@article {pmid28736135,
year = {2018},
author = {Reiner, M and Lev, DD and Rosen, A},
title = {Theta Neurofeedback Effects on Motor Memory Consolidation and Performance Accuracy: An Apparent Paradox?.},
journal = {Neuroscience},
volume = {378},
number = {},
pages = {198-210},
doi = {10.1016/j.neuroscience.2017.07.022},
pmid = {28736135},
issn = {1873-7544},
mesh = {Adult ; Brain-Computer Interfaces ; Female ; Humans ; Male ; Memory Consolidation/*physiology ; Motor Skills/*physiology ; *Neurofeedback ; Sleep/physiology ; *Theta Rhythm ; },
abstract = {Previous studies have shown that theta neurofeedback enhances motor memory consolidation on an easy-to-learn finger-tapping task. However, the simplicity of the finger-tapping task precludes evaluating the putative effects of elevated theta on performance accuracy. Mastering a motor sequence is classically assumed to entail faster performance with fewer errors. The speed-accuracy tradeoff (SAT) principle states that as action speed increases, motor performance accuracy decreases. The current study investigated whether theta neurofeedback could improve both performance speed and performance accuracy, or would only enhance performance speed at the cost of reduced accuracy. A more complex task was used to study the effects of parietal elevated theta on 45 healthy volunteers The findings confirmed previous results on the effects of theta neurofeedback on memory consolidation. In contrast to the two control groups, in the theta-neurofeedback group the speed-accuracy tradeoff was reversed. The speed-accuracy tradeoff patterns only stabilized after a night's sleep implying enhancement in terms of both speed and accuracy.},
}
@article {pmid28735750,
year = {2017},
author = {Zhang, CY and Aflalo, T and Revechkis, B and Rosario, ER and Ouellette, D and Pouratian, N and Andersen, RA},
title = {Partially Mixed Selectivity in Human Posterior Parietal Association Cortex.},
journal = {Neuron},
volume = {95},
number = {3},
pages = {697-708.e4},
pmid = {28735750},
issn = {1097-4199},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Brain Mapping/methods ; Cognition/*physiology ; Humans ; Motor Cortex/*physiology ; Movement/physiology ; Nerve Net/*physiology ; Parietal Lobe/physiology ; },
abstract = {To clarify the organization of motor representations in posterior parietal cortex, we test how three motor variables (body side, body part, cognitive strategy) are coded in the human anterior intraparietal cortex. All tested movements were encoded, arguing against strict anatomical segregation of effectors. Single units coded for diverse conjunctions of variables, with different dimensions anatomically overlapping. Consistent with recent studies, neurons encoding body parts exhibited mixed selectivity. This mixed selectivity resulted in largely orthogonal coding of body parts, which "functionally segregate" the effector responses despite the high degree of anatomical overlap. Body side and strategy were not coded in a mixed manner as effector determined their organization. Mixed coding of some variables over others, what we term "partially mixed coding," argues that the type of functional encoding depends on the compared dimensions. This structure is advantageous for neuroprosthetics, allowing a single array to decode movements of a large extent of the body.},
}
@article {pmid28730995,
year = {2017},
author = {Omurtag, A and Aghajani, H and Keles, HO},
title = {Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066003},
doi = {10.1088/1741-2552/aa814b},
pmid = {28730995},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Mental Processes/physiology ; Principal Component Analysis/methods ; Psychomotor Performance/*physiology ; Spectroscopy, Near-Infrared/*methods ; Thinking/*physiology ; },
abstract = {OBJECTIVE: Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components.<br><br>APPROACH: We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.<br><br>MAIN RESULTS: EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.<br><br>SIGNIFICANCE: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.},
}
@article {pmid28730911,
year = {2018},
author = {Hsieh, C and Knudson, D},
title = {Important learning factors in high- and low-achieving students in undergraduate biomechanics.},
journal = {Sports biomechanics},
volume = {17},
number = {3},
pages = {361-370},
doi = {10.1080/14763141.2017.1347194},
pmid = {28730911},
issn = {1752-6116},
mesh = {*Academic Success ; *Biomechanical Phenomena ; Female ; Humans ; *Learning ; Male ; Motivation ; Perception ; Self-Control ; Universities ; },
abstract = {The purpose of the present study was to document crucial factors associated with students' learning of biomechanical concepts, particularly between high- and-low achieving students. Students (N = 113) from three introductory biomechanics classes at two public universities volunteered for the study. Two measures of students' learning were obtained, final course grade and improvement on the Biomechanics Concept Inventory version 3 administered before and after the course. Participants also completed a 15-item questionnaire documenting student learning characteristics, effort, and confidence. Partial correlations controlling for all other variables in the study, confirmed previous studies that students' grade point average (p < 0.01), interest in biomechanics, (p < 0.05), and physics credits passed (p < 0.05) are factors uniquely associated with learning biomechanics concepts. Students' confidence when encountering difficult biomechanics concepts was also significantly (p < 0.05) associated with final grade. There were significant differences between top 15% and bottom 15% achievers on these variables (p < 0.05), as well as on readings completed, work to pay for college per week, and learning epistemology. Consequently, instructors should consider strategies to promote students' interest in biomechanics and confidence in solving relevant professional problems in order to improve learning for both low- and high-ability students.},
}
@article {pmid28730182,
year = {2017},
author = {Schneck, N and Haufe, S and Tu, T and Bonanno, GA and Ochsner, K and Sajda, P and Mann, JJ},
title = {Tracking Deceased-Related Thinking with Neural Pattern Decoding of a Cortical-Basal Ganglia Circuit.},
journal = {Biological psychiatry. Cognitive neuroscience and neuroimaging},
volume = {2},
number = {5},
pages = {421-429},
pmid = {28730182},
issn = {2451-9022},
support = {T32 MH015144/MH/NIMH NIH HHS/United States ; },
abstract = {BACKGROUND: Deceased-related thinking is central to grieving and potentially critical to processing of the loss. Self-report measurements might fail to capture important elements of deceased-related thinking and processing. Here, we used a machine learning approach applied to fMRI - known as neural decoding - to develop a measure of ongoing deceased-related processing.<br><br>METHODS: 23 subjects grieving the loss of a first-degree relative, spouse or partner within 14 months underwent two fMRI tasks. They first viewed pictures and stories related to the deceased, a living control and a demographic control figure while providing ongoing valence and arousal ratings. Second, they performed a 10-minute Sustained Attention to Response Task (SART) with thought probes every 25-35 seconds to identify deceased, living and self-related thoughts.<br><br>RESULTS: A conjunction analysis, controlling for valence/arousal, identified neural clusters in basal ganglia, orbital prefrontal cortex and insula associated with both types of deceased-related stimuli vs. the two control conditions in the first task. This pattern was applied to fMRI data collected during the SART, and discriminated deceased-related but not living or self-related thoughts, independently of grief-severity and time since loss. Deceased-related thoughts on the SART correlated with self-reported avoidance. The neural model predicted avoidance over and above deceased-related thoughts.<br><br>CONCLUSIONS: A neural pattern trained to identify mental representations of the deceased tracked deceased-related thinking during a sustained attention task and also predicted subject-level avoidance. This approach provides a new imaging tool to be used as an index of processing the deceased for future studies of complicated grief.},
}
@article {pmid28729830,
year = {2017},
author = {Tsuchimoto, S and Shibusawa, S and Mizuguchi, N and Kato, K and Ebata, H and Liu, M and Hanakawa, T and Ushiba, J},
title = {Resting-State Fluctuations of EEG Sensorimotor Rhythm Reflect BOLD Activities in the Pericentral Areas: A Simultaneous EEG-fMRI Study.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {356},
pmid = {28729830},
issn = {1662-5161},
abstract = {Blockade of the scalp electroencephalographic (EEG) sensorimotor rhythm (SMR) is a well-known phenomenon following attempted or executed motor functions. Such a frequency-specific power attenuation of the SMR occurs in the alpha and beta frequency bands and is spatially registered at primary somatosensory and motor cortices. Here, we hypothesized that resting-state fluctuations of the SMR in the alpha and beta frequency bands also covary with resting-state sensorimotor cortical activity, without involving task-related neural dynamics. The present study employed functional magnetic resonance imaging (fMRI) to investigate the neural regions whose activities were correlated with the simultaneously recorded SMR power fluctuations. The SMR power fluctuations were convolved with a canonical hemodynamic response function and correlated with blood-oxygen-level dependent (BOLD) signals obtained from the entire brain. Our findings show that the alpha and beta power components of the SMR correlate with activities of the pericentral area. Furthermore, brain regions with correlations between BOLD signals and the alpha-band SMR fluctuations were located posterior to those with correlations between BOLD signals and the beta-band SMR. These results are consistent with those of event-related studies of SMR modulation induced by sensory input or motor output. Our findings may help to understand the role of the sensorimotor cortex activity in contributing to the amplitude modulation of SMR during the resting state. This knowledge may be applied to the diagnosis of pathological conditions in the pericentral areas or the refinement of brain-computer interfaces using SMR in the future.},
}
@article {pmid28729822,
year = {2017},
author = {Angulo-Sherman, IN and Rodríguez-Ugarte, M and Iáñez, E and Azorín, JM},
title = {Low Intensity Focused tDCS Over the Motor Cortex Shows Inefficacy to Improve Motor Imagery Performance.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {391},
pmid = {28729822},
issn = {1662-4548},
abstract = {Transcranial direct current stimulation (tDCS) is a brain stimulation technique that can enhance motor activity by stimulating the motor path. Thus, tDCS has the potential of improving the performance of brain-computer interfaces during motor neurorehabilitation. tDCS effects depend on several aspects, including the current density, which usually varies between 0.02 and 0.08 mA/cm[2], and the location of the stimulation electrodes. Hence, testing tDCS montages at several current levels would allow the selection of current parameters for improving stimulation outcomes and the comparison of montages. In a previous study, we found that cortico-cerebellar tDCS shows potential of enhancing right-hand motor imagery. In this paper, we aim to evaluate the effects of the focal stimulation of the motor cortex over motor imagery. In particular, the effect of supplying tDCS with a 4 × 1 ring montage, which consists in placing an anode on the motor cortex and four cathodes around it, over motor imagery was assessed with different current densities. Electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on five healthy subjects for two stimulation schemes: applying tDCS for 10 min on the (1) right-hand or (2) feet motor cortex before EEG recording. Accuracy differences related to the tDCS intensity, as well as μ and β band power changes, were tested for each subject and tDCS modality. In addition, a simulation of the electric field induced by the montage was used to describe its effect on the brain. Results show no improvement trends on classification for the evaluated currents, which is in accordance with the observation of variable EEG band power results despite the focused stimulation. The lack of effects is probably related to the underestimation of the current intensity required to apply a particular current density for small electrodes and the relatively short inter-electrode distance. Hence, higher current intensities should be evaluated in the future for this montage.},
}
@article {pmid28727560,
year = {2017},
author = {Cui, C and Bian, GB and Hou, ZG and Zhao, J and Zhou, H},
title = {A Multimodal Framework Based on Integration of Cortical and Muscular Activities for Decoding Human Intentions About Lower Limb Motions.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {4},
pages = {889-899},
doi = {10.1109/TBCAS.2017.2699189},
pmid = {28727560},
issn = {1940-9990},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; *Electromyography ; Humans ; *Intention ; *Lower Extremity ; Models, Theoretical ; *Movement ; Support Vector Machine ; },
abstract = {In this study, a multimodal fusion framework based on three different modal biosignals is developed to recognize human intentions related to lower limb multi-joint motions which commonly appear in daily life. Electroencephalogram (EEG), electromyogram (EMG) and mechanomyogram (MMG) signals were simultaneously recorded from twelve subjects while performing nine lower limb multi-joint motions. These multimodal data are used as the inputs of the fusion framework for identification of different motion intentions. Twelve fusion techniques are evaluated in this framework and a large number of comparative experiments are carried out. The results show that a support vector machine-based three-modal fusion scheme can achieve average accuracies of 98.61%, 97.78% and 96.85%, respectively, under three different data division forms. Furthermore, the relevant statistical tests reveal that this fusion scheme brings significant accuracy improvement in comparison with the cases of two-modal fusion or only a single modality. These promising results indicate the potential of the multimodal fusion framework for facilitating the future development of human-robot interaction for lower limb rehabilitation.},
}
@article {pmid28727555,
year = {2017},
author = {Breitwieser, C and Tavella, M and Schreuder, M and Cincotti, F and Leeb, R and Muller-Putz, GR},
title = {TiD-Introducing and Benchmarking an Event-Delivery System for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {12},
pages = {2249-2257},
doi = {10.1109/TNSRE.2017.2728199},
pmid = {28727555},
issn = {1558-0210},
mesh = {Algorithms ; *Benchmarking ; *Brain-Computer Interfaces ; Electronic Data Processing ; Equipment Design ; Evoked Potentials, Somatosensory ; Humans ; Software ; Wireless Technology ; },
abstract = {In this paper, we present and analyze an event distribution system for brain-computer interfaces. Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called Tools for brain-computer interaction interface D (TiD), delivers messages in XML format via a buslike system using transmission control protocol connections or shared memory. A dedicated server dispatches TiD messages to distributed or local clients. The TiD message is designed to be flexible and contains time stamps for event synchronization, whereas events describe incidents, which occur during an experiment. TiD was tested extensively toward stability and latency. The effect of an occurring event jitter was analyzed and benchmarked on a reference implementation under different conditions as gigabit and 100-Mb Ethernet or Wi-Fi with a different number of event receivers. A 3-dB signal attenuation, which occurs when averaging jitter influenced trials aligned by events, is starting to become visible at around 1-2 kHz in the case of a gigabit connection. Mean event distribution times across operating systems are ranging from 0.3 to 0.5ms for a gigabit network connection for 10[6] events. Results for other environmental conditions are available in this paper. References already using TiD for event distribution are provided showing the applicability of TiD for event delivery with distributed or local clients.},
}
@article {pmid28727554,
year = {2018},
author = {Sorbello, R and Tramonte, S and Giardina, ME and La Bella, V and Spataro, R and Allison, B and Guger, C and Chella, A},
title = {A Human-Humanoid Interaction Through the Use of BCI for Locked-In ALS Patients Using Neuro-Biological Feedback Fusion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {2},
pages = {487-497},
doi = {10.1109/TNSRE.2017.2728140},
pmid = {28727554},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Attention ; Biofeedback, Psychology/*methods ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; Eye Movements ; Female ; Healthy Volunteers ; Humans ; Male ; Prosthesis Design ; Psychomotor Performance ; Quadriplegia/*rehabilitation ; Robotics ; },
abstract = {This paper illustrates a new architecture for a human-humanoid interaction based on EEG-brain computer interface (EEG-BCI) for patients affected by locked-in syndrome caused by Amyotrophic Lateral Sclerosis (ALS). The proposed architecture is able to recognise users' mental state accordingly to the biofeedback factor , based on users' attention, intention, and focus, that is used to elicit a robot to perform customised behaviours. Experiments have been conducted with a population of eight subjects: four ALS patients in a near locked-in status with normal ocular movement and four healthy control subjects enrolled for age, education, and computer expertise. The results showed as three ALS patients have completed the task with 96.67% success; the healthy controls with 100% success; the fourth ALS has been excluded from the results for his low general attention during the task; the analysis of factor highlights as ALS subjects have shown stronger (81.20%) than healthy controls (76.77%). Finally, a post-hoc analysis is provided to show how robotic feedback helps in maintaining focus on expected task. These preliminary data suggest that ALS patients could successfully control a humanoid robot through a BCI architecture, potentially enabling them to conduct some everyday tasks and extend their presence in the environment.},
}
@article {pmid28723960,
year = {2017},
author = {Falk, BG and Snow, RW and Reed, RN},
title = {A validation of 11 body-condition indices in a giant snake species that exhibits positive allometry.},
journal = {PloS one},
volume = {12},
number = {7},
pages = {e0180791},
pmid = {28723960},
issn = {1932-6203},
mesh = {Adiposity/physiology ; Animals ; Body Composition/*physiology ; Body Weight/*physiology ; Boidae/*anatomy &amp; histology ; },
abstract = {Body condition is a gauge of the energy stores of an animal, and though it has important implications for fitness, survival, competition, and disease, it is difficult to measure directly. Instead, body condition is frequently estimated as a body condition index (BCI) using length and mass measurements. A desirable BCI should accurately reflect true body condition and be unbiased with respect to size (i.e., mean BCI estimates should not change across different length or mass ranges), and choosing the most-appropriate BCI is not straightforward. We evaluated 11 different BCIs in 248 Burmese pythons (Python bivittatus), organisms that, like other snakes, exhibit simple body plans well characterized by length and mass. We found that the length-mass relationship in Burmese pythons is positively allometric, where mass increases rapidly with respect to length, and this allowed us to explore the effects of allometry on BCI verification. We employed three alternative measures of 'true' body condition: percent fat, scaled fat, and residual fat. The latter two measures mostly accommodated allometry in true body condition, but percent fat did not. Our inferences of the best-performing BCIs depended heavily on our measure of true body condition, with most BCIs falling into one of two groups. The first group contained most BCIs based on ratios, and these were associated with percent fat and body length (i.e., were biased). The second group contained the scaled mass index and most of the BCIs based on linear regressions, and these were associated with both scaled and residual fat but not body length (i.e., were unbiased). Our results show that potential differences in measures of true body condition should be explored in BCI verification studies, particularly in organisms undergoing allometric growth. Furthermore, the caveats of each BCI and similarities to other BCIs are important to consider when determining which BCI is appropriate for any particular taxon.},
}
@article {pmid28723386,
year = {2017},
author = {Anic, I and Nath, A and Franco, P and Wichmann, R},
title = {Foam adsorption as an ex situ capture step for surfactants produced by fermentation.},
journal = {Journal of biotechnology},
volume = {258},
number = {},
pages = {181-189},
doi = {10.1016/j.jbiotec.2017.07.015},
pmid = {28723386},
issn = {1873-4863},
mesh = {Adsorption ; Bioreactors/microbiology ; Chromatography, High Pressure Liquid ; Fermentation/*physiology ; Glycolipids/*chemistry/isolation &amp; purification/*metabolism ; Hydrophobic and Hydrophilic Interactions ; Pseudomonas putida/metabolism ; Surface-Active Agents/chemistry/*isolation &amp; purification/*metabolism ; },
abstract = {In this report, a method for a simultaneous production and separation of a microbially synthesized rhamnolipid biosurfactant is presented. During the aerobic cultivation of flagella-free Pseudomonas putida EM383 in a 3.1L stirred tank reactor on glucose as a sole carbon source, rhamnolipids are produced and excreted into the fermentation liquid. Here, a strategy for biosurfactant capture from rhamnolipid enriched fermentation foam using hydrophobic-hydrophobic interaction was investigated. Five adsorbents were tested independently for the application of this capture technique and the best performing adsorbent was tested in a fermentation process. Cell-containing foam was allowed to flow out of the fermentor through the off-gas line and an adsorption packed bed. Foam was observed to collapse instantly, while the resultant liquid flow-through, which was largely devoid of the target biosurfactant, eluted towards the outlet channel of the packed bed column and was subsequently pumped back into the fermentor. After 48h of simultaneous fermentation and ex situ adsorption of rhamnolipids from the foam, 90% out of 5.5g of total rhamnolipids produced were found in ethanol eluate of the adsorbent material, indicating the suitability of this material for ex situ rhamnolipid capture from fermentation processes.},
}
@article {pmid28722685,
year = {2017},
author = {Irwin, ZT and Schroeder, KE and Vu, PP and Bullard, AJ and Tat, DM and Nu, CS and Vaskov, A and Nason, SR and Thompson, DE and Bentley, JN and Patil, PG and Chestek, CA},
title = {Neural control of finger movement via intracortical brain-machine interface.},
journal = {Journal of neural engineering},
volume = {14},
number = {6},
pages = {066004},
pmid = {28722685},
issn = {1741-2552},
support = {R01 GM111293/GM/NIGMS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Fingers/*physiology ; Macaca mulatta ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; },
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques.<br><br>APPROACH: In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex.<br><br>MAIN RESULTS: Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of &#x3c1;&#x2009;&#x2009;=&#x2009;&#x2009;0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s[-1] throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter.<br><br>SIGNIFICANCE: This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step towards full and dexterous control of neural prosthetic devices.},
}
@article {pmid28718781,
year = {2018},
author = {Dagaev, N and Volkova, K and Ossadtchi, A},
title = {Latent variable method for automatic adaptation to background states in motor imagery BCI.},
journal = {Journal of neural engineering},
volume = {15},
number = {1},
pages = {016004},
doi = {10.1088/1741-2552/aa8065},
pmid = {28718781},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Male ; *Models, Statistical ; Movement/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way.<br><br>APPROACH: We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model's parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states.<br><br>MAIN RESULTS: We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects).<br><br>SIGNIFICANCE: Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.},
}
@article {pmid28718779,
year = {2018},
author = {Tahernezhad-Javazm, F and Azimirad, V and Shoaran, M},
title = {A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.},
journal = {Journal of neural engineering},
volume = {15},
number = {2},
pages = {021007},
doi = {10.1088/1741-2552/aa8063},
pmid = {28718779},
issn = {1741-2552},
mesh = {*Algorithms ; Animals ; Brain/*physiology ; Brain-Computer Interfaces/*classification/trends ; Databases, Factual/*classification/trends ; Electroencephalography/*classification/trends ; Humans ; Support Vector Machine/*classification/trends ; },
abstract = {OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used.<br><br>APPROACH: The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared.<br><br>MAIN RESULTS: In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances.<br><br>SIGNIFICANCE: We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.},
}
@article {pmid28715332,
year = {2017},
author = {Rathee, D and Raza, H and Prasad, G and Cecotti, H},
title = {Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {12},
pages = {2461-2471},
doi = {10.1109/TNSRE.2017.2726779},
pmid = {28715332},
issn = {1558-0210},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/statistics &amp; numerical data ; Head ; Humans ; Imagination/*physiology ; Models, Anatomic ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI)-related brain-computer interface systems. Two EEG referencing schemes: common referencing and common average referencing and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks. The study provides three major outcomes: 1) the CSD method performs better than CR, providing a significant improvement of 3.02% and 5.59% across six binary classification tasks and four-class classification task, respectively; 2) the combination of a greater number of channels at the pre-processing stage as compared with the feature extraction stage yields better classification accuracies for all the Laplacian methods; and 3) the efficiency of all the surface Laplacian methods reduced significantly in the case of a fewer number of channels considered during the pre-processing.},
}
@article {pmid28714637,
year = {2017},
author = {Nau, JY},
title = {[Not Available].},
journal = {Revue medicale suisse},
volume = {13},
number = {550},
pages = {434-435},
pmid = {28714637},
issn = {1660-9379},
mesh = {Biocompatible Materials ; *Brain-Computer Interfaces ; Communication ; Humans ; *Quadriplegia/physiopathology ; },
}
@article {pmid28713235,
year = {2017},
author = {Serruya, MD},
title = {Connecting the Brain to Itself through an Emulation.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {373},
pmid = {28713235},
issn = {1662-4548},
support = {U01 NS094340/NS/NINDS NIH HHS/United States ; },
abstract = {Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions.},
}
@article {pmid28713233,
year = {2017},
author = {Carabalona, R},
title = {The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {363},
pmid = {28713233},
issn = {1662-4548},
abstract = {Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on P z and PO7 during the training phase and on PO8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on P z and PO7 (training), whereas the opposite modulation was observed for PO8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience.},
}
@article {pmid28713232,
year = {2017},
author = {Karimi, F and Kofman, J and Mrachacz-Kersting, N and Farina, D and Jiang, N},
title = {Detection of Movement Related Cortical Potentials from EEG Using Constrained ICA for Brain-Computer Interface Applications.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {356},
pmid = {28713232},
issn = {1662-4548},
abstract = {The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches.},
}
@article {pmid28711988,
year = {2017},
author = {Özerdem, MS and Polat, H},
title = {Emotion recognition based on EEG features in movie clips with channel selection.},
journal = {Brain informatics},
volume = {4},
number = {4},
pages = {241-252},
pmid = {28711988},
issn = {2198-4018},
abstract = {Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.},
}
@article {pmid28710639,
year = {2017},
author = {Thygs, FB and Merz, J},
title = {Downstream Process Synthesis for Microbial Steroids.},
journal = {Methods in molecular biology (Clifton, N.J.)},
volume = {1645},
number = {},
pages = {321-345},
doi = {10.1007/978-1-4939-7183-1_23},
pmid = {28710639},
issn = {1940-6029},
mesh = {Adsorption ; Androstenedione/*biosynthesis/chemistry/isolation &amp; purification ; Bacteria/*genetics/metabolism ; Fermentation ; Steroids/*biosynthesis/chemistry/isolation &amp; purification ; },
abstract = {Systematic and holistic process development, considering upstream and downstream processing simultaneously, is crucial for designing effective and economical production processes. Based on a multiphasic fermentation process, where oil is added to improve substrate solubility, a systematic approach for the purification of microbial steroids is presented. The design methodology incorporates expert knowledge in the form of heuristics to generate different downstream processing alternatives for processing the complex, multiphasic fermentation broth, and recovering the target steroid precursor androst-4-ene-3,17-dione (androstenedione; AD). The resulting alternative tree of different purification techniques is evaluated using scouting experiments to check the performance of each technique. Purification steps such as extraction, adsorption-desorption, and precipitation seem most promising and have been investigated in detail. Each process step selected is optimized and connected to a process route for recovering AD.},
}
@article {pmid28709110,
year = {2017},
author = {Kitali, AE and Sando, PET},
title = {A full Bayesian approach to appraise the safety effects of pedestrian countdown signals to drivers.},
journal = {Accident; analysis and prevention},
volume = {106},
number = {},
pages = {327-335},
doi = {10.1016/j.aap.2017.07.004},
pmid = {28709110},
issn = {1879-2057},
mesh = {Accidents, Traffic/prevention &amp; control/*statistics &amp; numerical data ; *Automobile Driving ; Bayes Theorem ; *Cues ; *Environment Design ; Florida ; Humans ; Pedestrians ; *Safety/standards ; },
abstract = {Although they are meant for pedestrians, pedestrian countdown signals (PCSs) give cues to drivers about the length of the remaining green phase, hence affecting drivers' behavior at intersections. This study focuses on the evaluation of the safety effectiveness of PCSs to drivers, in the cities of Jacksonville and Gainesville, Florida, using crash modification factors (CMFs) and crash modification functions (CMFunctions). A full Bayes (FB) before-and-after with comparison group method was used to quantify the safety impacts of PCSs to drivers. The CMFs were established for distinctive categories of crashes based on crash type (rear-end and angle collisions) and severity level (total, fatal and injury (FI), and property damage only (PDO) collisions). The CMFs findings indicated that installing PCSs result in a significant improvement of drivers' safety, at a 95% Bayesian credible interval (BCI), for total, PDO, and rear-end collisions. The results of FI and angle crashes were not significant. The CMFunctions indicate that the treatment effectiveness varies considerably with post-treatment time and traffic volume. Nevertheless, the CMFs on rear-end crashes are observed to decline with post-treatment time. In summary, the results suggest the usefulness of PCSs for drivers. The findings of this study may prompt a need for a broader research to investigate the need to design PCSs that will serve the purpose not only of pedestrians, but drivers as well.},
}
@article {pmid28708963,
year = {2018},
author = {Martin, S and Armstrong, E and Thomson, E and Vargiu, E and Solà, M and Dauwalder, S and Miralles, F and Daly Lynn, J},
title = {A qualitative study adopting a user-centered approach to design and validate a brain computer interface for cognitive rehabilitation for people with brain injury.},
journal = {Assistive technology : the official journal of RESNA},
volume = {30},
number = {5},
pages = {233-241},
doi = {10.1080/10400435.2017.1317675},
pmid = {28708963},
issn = {1949-3614},
mesh = {Adult ; Brain Injuries/*rehabilitation ; *Brain-Computer Interfaces ; Cognitive Dysfunction/*rehabilitation ; Female ; Humans ; Male ; Middle Aged ; Neurological Rehabilitation/*methods ; Qualitative Research ; Telerehabilitation/*methods ; },
abstract = {Cognitive rehabilitation is established as a core intervention within rehabilitation programs following a traumatic brain injury (TBI). Digitally enabled assistive technologies offer opportunities for clinicians to increase remote access to rehabilitation supporting transition into home. Brain Computer Interface (BCI) systems can harness the residual abilities of individuals with limited function to gain control over computers through their brain waves. This paper presents an online cognitive rehabilitation application developed with therapists, to work remotely with people who have TBI, who will use BCI at home to engage in the therapy. A qualitative research study was completed with people who are community dwellers post brain injury (end users), and a cohort of therapists involved in cognitive rehabilitation. A user-centered approach over three phases in the development, design and feasibility testing of this cognitive rehabilitation application included two tasks (Find-a-Category and a Memory Card task). The therapist could remotely prescribe activity with different levels of difficulty. The service user had a home interface which would present the therapy activities. This novel work was achieved by an international consortium of academics, business partners and service users.},
}
@article {pmid28706472,
year = {2017},
author = {Li, L and Xu, G and Zhang, F and Xie, J and Li, M},
title = {Relevant Feature Integration and Extraction for Single-Trial Motor Imagery Classification.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {371},
pmid = {28706472},
issn = {1662-4548},
abstract = {Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spatial projection applied by this algorithm is based on a broad frequency bandpass filter (mainly 5-30 Hz) in which the frequency band is often selected by experience, the CSP is sensitive to noise and the influence of other irrelevant information in the selected broad frequency band. In this paper, to improve the CSP, a novel relevant feature integration and extraction algorithm is proposed. Before projecting, we integrated the motor relevant information to suppress the interference of noise and irrelevant information, as well as to improve the spatial difference for projection. The algorithm was evaluated with public datasets. It showed significantly better classification performance with single trial electroencephalography (EEG) data, increasing by 6.8% compared with the CSP.},
}
@article {pmid28705577,
year = {2017},
author = {Moncrief, TJ and Balaji, P and Lindgren, BB and Weight, CJ and Konety, BR},
title = {Comparative Evaluation of Bladder-specific Health-related Quality of Life Instruments for Bladder Cancer.},
journal = {Urology},
volume = {108},
number = {},
pages = {76-81},
doi = {10.1016/j.urology.2017.06.032},
pmid = {28705577},
issn = {1527-9995},
support = {P30 CA077598/CA/NCI NIH HHS/United States ; },
mesh = {Aged ; Cross-Sectional Studies ; Cystectomy/*psychology ; Female ; *Health Status ; Humans ; Male ; Middle Aged ; Quality of Life/*psychology ; Retrospective Studies ; Surveys and Questionnaires ; Urinary Bladder Neoplasms/physiopathology/*psychology/surgery ; Urination/*physiology ; },
abstract = {OBJECTIVE: To compare 2 bladder cancer-specific health-related quality of life instruments (HRQOL) in the same patient population. Previous HRQOL studies in cystectomy patients have yielded conflicting results. Using a cross-sectional study design, we examined the only 2 validated bladder cancer-specific (HRQOL) measures.<br><br>METHODS: Of the 256 patients who had undergone radical cystectomy from 2009 to 2014, 131 met both inclusion and exclusion criteria. The Functional Assessment Cancer Therapy-Vanderbilt Cystectomy Index (FACT-VCI) and Bladder Cancer Index (BCI) were mailed to these patients. Overall HRQOL and individual domain scores were compared between the 2 instruments with a Spearman correlation coefficient. HRQOL scores were compared by urinary diversion type as well using a non-parametric Wilcoxon rank sum test.<br><br>RESULTS: Our study had a response rate of 49% from 31 ileal conduit (IC) and 33 orthotopic neobladder patients. Overall, there was a moderate correlation between the FACT-VCI and BCI surveys (r&#x2009;=&#x2009;0.57, P&#x2009;<.001). Responses on the BCI domains were strongly correlated with responses on the bladder cancer-specific domain of the FACT-VCI (r&#x2009;=&#x2009;0.74, P&#x2009;<.001). The BCI scores for urinary function were significantly better in the IC group (P&#x2009;=&#x2009;.002). No significant difference was found between IC and orthotopic neobladder using the FACT-VCI.<br><br>CONCLUSION: The FACT-VCI and BCI instruments correlate well within the same patient cohort but capture different aspects of HRQOL. By focusing exclusively on bladder cancer treatment concerns, the BCI appears to be a better tool for assessing and counseling patients on expected treatment-specific changes after diversion type.},
}
@article {pmid28701939,
year = {2017},
author = {Tonin, L and Pitteri, M and Leeb, R and Zhang, H and Menegatti, E and Piccione, F and Millán, JDR},
title = {Behavioral and Cortical Effects during Attention Driven Brain-Computer Interface Operations in Spatial Neglect: A Feasibility Case Study.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {336},
pmid = {28701939},
issn = {1662-5161},
abstract = {During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is able to detect attention task-specific brain patterns in SN patients and can induce significant changes in their abnormal cortical activity (α-power modulation, feature recruitment, and connectivity). The three patients were asked to control online a CVSA BCI by focusing their attention at different spatial locations, including their neglected (left) space. As primary outcome, results show a significant improvement of the reaction time in the neglected space between calibration and online modalities (p < 0.01) for the two out of three patients that had the slowest initial behavioral response. Such an evolution of reaction time negatively correlates (p < 0.05) with an increment of the Individual α-Power computed in the pre-cue interval. Furthermore, all patients exhibited a significant reduction of the inter-hemispheric imbalance (p < 0.05) over time in the parieto-occipital regions. Finally, analysis on the inter-hemispheric functional connectivity suggests an increment across modalities for regions in the affected (right) hemisphere and decrement for those in the healthy. Although preliminary, this feasibility study suggests a possible role of BCI in the therapeutic treatment of lateralized, attention-based visuospatial deficits.},
}
@article {pmid28701938,
year = {2017},
author = {Lin, YP and Jung, TP},
title = {Improving EEG-Based Emotion Classification Using Conditional Transfer Learning.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {334},
pmid = {28701938},
issn = {1662-5161},
abstract = {To overcome the individual differences, an accurate electroencephalogram (EEG)-based emotion-classification system requires a considerable amount of ecological calibration data for each individual, which is labor-intensive and time-consuming. Transfer learning (TL) has drawn increasing attention in the field of EEG signal mining in recent years. The TL leverages existing data collected from other people to build a model for a new individual with little calibration data. However, brute-force transfer to an individual (i.e., blindly leveraged the labeled data from others) may lead to a negative transfer that degrades performance rather than improving it. This study thus proposed a conditional TL (cTL) framework to facilitate a positive transfer (improving subject-specific performance without increasing the labeled data) for each individual. The cTL first assesses an individual's transferability for positive transfer and then selectively leverages the data from others with comparable feature spaces. The empirical results showed that among 26 individuals, the proposed cTL framework identified 16 and 14 transferable individuals who could benefit from the data from others for emotion valence and arousal classification, respectively. These transferable individuals could then leverage the data from 18 and 12 individuals who had similar EEG signatures to attain maximal TL improvements in valence- and arousal-classification accuracy. The cTL improved the overall classification performance of 26 individuals by ~15% for valence categorization and ~12% for arousal counterpart, as compared to their default performance based solely on the subject-specific data. This study evidently demonstrated the feasibility of the proposed cTL framework for improving an individual's default emotion-classification performance given a data repository. The cTL framework may shed light on the development of a robust emotion-classification model using fewer labeled subject-specific data toward a real-life affective brain-computer interface (ABCI).},
}
@article {pmid28701912,
year = {2017},
author = {Jirakittayakorn, N and Wongsawat, Y},
title = {Brain Responses to a 6-Hz Binaural Beat: Effects on General Theta Rhythm and Frontal Midline Theta Activity.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {365},
pmid = {28701912},
issn = {1662-4548},
abstract = {A binaural beat is a beat phenomenon that is generated by the dichotic presentation of two almost equivalent pure tones but with slightly different frequencies. The brain responses to binaural beats remain controversial; therefore, the aim of this study was to investigate theta activity responses to a binaural beat by controlling factors affecting localization, including beat frequency, carrier tone frequency, exposure duration, and recording procedure. Exposure to a 6-Hz binaural beat on a 250 Hz carrier tone for 30 min was utilized in this study. Quantitative electroencephalography (QEEG) was utilized as the recording modality. Twenty-eight participants were divided into experimental and control groups. Emotional states were evaluated by Brunel Mood Scale (BRMUS) before and after exposing to the stimulus. The results showed that theta activity was induced in the entire cortex within 10 min of exposure to the stimulus in the experimental group. Compared to the control group, theta activity was also induced at the frontal and parietal-central regions, which included the Fz position, and left hemisphere dominance was presented for other exposure durations. The pattern recorded for 10 min of exposure appeared to be brain functions of a meditative state. Moreover, tension factor of BRUMS was decreased in experimental group compared to control group which resembled the meditation effect. Thus, a 6-Hz binaural beat on a 250 Hz carrier tone was suggested as a stimulus for inducing a meditative state.},
}
@article {pmid28696536,
year = {2017},
author = {Lim, JH and Kim, YW and Lee, JH and An, KO and Hwang, HJ and Cha, HS and Han, CH and Im, CH},
title = {An emergency call system for patients in locked-in state using an SSVEP-based brain switch.},
journal = {Psychophysiology},
volume = {54},
number = {11},
pages = {1632-1643},
doi = {10.1111/psyp.12916},
pmid = {28696536},
issn = {1469-8986},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Brain/*physiopathology ; *Brain-Computer Interfaces ; *Emergencies ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Quadriplegia/*physiopathology ; Young Adult ; },
abstract = {Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.},
}
@article {pmid28696340,
year = {2017},
author = {Liu, D and Chen, W and Lee, K and Chavarriaga, R and Bouri, M and Pei, Z and Del R Millán, J},
title = {Brain-actuated gait trainer with visual and proprioceptive feedback.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056017},
doi = {10.1088/1741-2552/aa7df9},
pmid = {28696340},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Female ; Gait/*physiology ; Humans ; Lower Extremity/physiology ; Male ; Proprioception/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension.<br><br>APPROACH: We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects.<br><br>MAIN RESULTS: (i) For real-time classification, the average accuracy was [Formula: see text]% and [Formula: see text]% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback ([Formula: see text]%) was significantly better than with visual feedback ([Formula: see text]%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn.<br><br>SIGNIFICANCE: The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.},
}
@article {pmid28693533,
year = {2017},
author = {Nilsson, N and Håkansson, B and Ortiz-Catalan, M},
title = {Classification complexity in myoelectric pattern recognition.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {68},
pmid = {28693533},
issn = {1743-0003},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electromyography/*classification/methods ; Hand Strength/physiology ; Healthy Volunteers ; Humans ; Movement ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Prosthesis Design ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Volition ; },
abstract = {BACKGROUND: Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject's intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition.<br><br>METHODS: CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback-Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec.<br><br>RESULTS: NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers' sensitivity to such redundancy.<br><br>CONCLUSIONS: This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.},
}
@article {pmid28693110,
year = {2017},
author = {Perrault, JR and Stacy, NI and Lehner, AF and Mott, CR and Hirsch, S and Gorham, JC and Buchweitz, JP and Bresette, MJ and Walsh, CJ},
title = {Potential effects of brevetoxins and toxic elements on various health variables in Kemp's ridley (Lepidochelys kempii) and green (Chelonia mydas) sea turtles after a red tide bloom event.},
journal = {The Science of the total environment},
volume = {605-606},
number = {},
pages = {967-979},
doi = {10.1016/j.scitotenv.2017.06.149},
pmid = {28693110},
issn = {1879-1026},
mesh = {Animals ; Florida ; Globulins/analysis ; Gulf of Mexico ; *Harmful Algal Bloom ; Marine Toxins/*toxicity ; Metals, Heavy/blood ; Neoplasms/epidemiology ; Oxidative Stress ; Oxocins/*toxicity ; Turtles/*blood ; },
abstract = {Natural biotoxins and anthropogenic toxicants pose a significant risk to sea turtle health. Documented effects of contaminants include potential disease progression and adverse impacts on development, immune function, and survival in these imperiled species. The shallow seagrass habitats of Florida's northwest coast (Big Bend) serve as an important developmental habitat for Kemp's ridley (Lepidochelys kempii) and green (Chelonia mydas) sea turtles; however, few studies have been conducted in this area. Our objectives were (1) to evaluate plasma analytes (mass, minimum straight carapace length, body condition index [BCI], fibropapilloma tumor score, lysozyme, superoxide dismutase, reactive oxygen/nitrogen species, plasma protein electrophoresis, cholesterol, and total solids) in Kemp's ridleys and green turtles and their correlation to brevetoxins that were released from a red tide bloom event from July-October 2014 in the Gulf of Mexico near Florida's Big Bend, and (2) to analyze red blood cells in Kemp's ridleys and green turtles for toxic elements (arsenic, cadmium, lead, mercury, selenium, thallium) with correlation to the measured plasma analytes. Positive correlations were observed between brevetoxins and α2-globulins in Kemp's ridleys and α2- and γ-globulins in green turtles, indicating potential immunostimulation. Arsenic, cadmium, and lead positively correlated with superoxide dismutase in Kemp's ridleys, suggesting oxidative stress. Lead and mercury in green turtles negatively correlated with BCI, while mercury positively correlated with total tumor score of green turtles afflicted with fibropapillomatosis, suggesting a possible association with mercury and increased tumor growth. The total tumor score of green turtles positively correlated with total protein, total globulins, α2-globulins, and γ-globulins, further suggesting inflammation and immunomodulation as a result of fibropapillomatosis. Lastly, brevetoxin concentrations were positively related to tumor score, indicating potential tumor promotion by brevetoxin. These results signify that brevetoxins and toxic elements elicit various negative effects on sea turtle health, including immune function, oxidative stress, and possibly disease progression.},
}
@article {pmid28692997,
year = {2018},
author = {Sai, CY and Mokhtar, N and Arof, H and Cumming, P and Iwahashi, M},
title = {Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {3},
pages = {664-670},
doi = {10.1109/JBHI.2017.2723420},
pmid = {28692997},
issn = {2168-2208},
mesh = {*Artifacts ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.},
}
@article {pmid28692996,
year = {2018},
author = {Ung, WC and Funane, T and Katura, T and Sato, H and Tang, TB and Hani, AFM and Kiguchi, M and Wei Chun Ung, and Funane, T and Katura, T and Sato, H and Tong Boon Tang, and Hani, AFM and Kiguchi, M},
title = {Effectiveness Evaluation of Real-Time Scalp Signal Separating Algorithm on Near-Infrared Spectroscopy Neurofeedback.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {4},
pages = {1148-1156},
doi = {10.1109/JBHI.2017.2723024},
pmid = {28692996},
issn = {2168-2208},
mesh = {Adult ; *Algorithms ; Brain/physiology ; Female ; Humans ; Male ; Middle Aged ; Neurofeedback/*methods ; Scalp/*physiology ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.},
}
@article {pmid28690513,
year = {2017},
author = {Caceres, CA and Roos, MJ and Rupp, KM and Milsap, G and Crone, NE and Wolmetz, ME and Ratto, CR},
title = {Feature Selection Methods for Zero-Shot Learning of Neural Activity.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {41},
pmid = {28690513},
issn = {1662-5196},
abstract = {Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.},
}
@article {pmid28690497,
year = {2017},
author = {Thomschewski, A and Höller, Y and Höller, P and Leis, S and Trinka, E},
title = {High Amplitude EEG Motor Potential during Repetitive Foot Movement: Possible Use and Challenges for Futuristic BCIs That Restore Mobility after Spinal Cord Injury.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {362},
pmid = {28690497},
issn = {1662-4548},
support = {W 1233/FWF_/Austrian Science Fund FWF/Austria ; },
abstract = {Recent advances in neuroprostheses provide us with promising ideas of how to improve the quality of life in people suffering from impaired motor functioning of upper and lower limbs. Especially for patients after spinal cord injury (SCI), futuristic devices that are controlled by thought via brain-computer interfaces (BCIs) might be of tremendous help in managing daily tasks and restoring at least some mobility. However, there are certain problems arising when trying to implement BCI technology especially in such a heterogenous patient group. A plethora of processes occurring after the injuries change the brain's structure as well as its functionality collectively referred to as neuroplasticity. These changes are very different between individuals, leading to an increasing interest to reveal the exact changes occurring after SCI. In this study we investigated event-related potentials (ERPs) derived from electroencephalography (EEG) signals recorded during the (attempted) execution and imagination of hand and foot movements in healthy subjects and patients with SCI. As ERPs and especially early components are of interest for BCI research we aimed to investigate differences between 22 healthy volunteers and 7 patients (mean age = 51.86, SD = 15.49) suffering from traumatic or non-traumatic SCI since 2-314 months (mean = 116,57, SD = 125,55). We aimed to explore differences in ERP responses as well as the general presence of component that might be of interest to further consider for incorporation into BCI research. In order to match the real-life situation of BCIs for controlling neuroprostheses, we worked on small trial numbers (<25), only. We obtained a focal potential over Pz in ten healthy participants but in none of the patients after lenient artifact rejection. The potential was characterized by a high amplitude, it correlated with the repeated movements (6 times in 6 s) and in nine subjects it significantly differed from a resting condition. Furthermore, there are strong arguments against possible confounding factors leading to the potential's appearance. This phenomenon, occurring when movements are repeatedly conducted, might represent a possible potential to be used in futuristic BCIs and further studies should try to investigate the replicability of its appearance.},
}
@article {pmid28688489,
year = {2017},
author = {Zarei, R and He, J and Siuly, S and Zhang, Y},
title = {A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.},
journal = {Computer methods and programs in biomedicine},
volume = {146},
number = {},
pages = {47-57},
doi = {10.1016/j.cmpb.2017.05.009},
pmid = {28688489},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Least-Squares Analysis ; Logistic Models ; Neural Networks, Computer ; Principal Component Analysis ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND AND OBJECTIVES: Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity.<br><br>METHODS: This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms.<br><br>RESULTS: The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection.<br><br>CONCLUSIONS: The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features.},
}
@article {pmid28687962,
year = {2018},
author = {McWhinney, SR and Tremblay, A and Boe, SG and Bardouille, T},
title = {The impact of goal-oriented task design on neurofeedback learning for brain-computer interface control.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {56},
number = {2},
pages = {201-210},
pmid = {28687962},
issn = {1741-0444},
support = {Knowledge Translation Grant//Brain Repair Centre/ ; },
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Functional Laterality/physiology ; Humans ; *Learning ; Male ; *Neural Networks, Computer ; *Neurofeedback ; Self Report ; Teaching ; Young Adult ; },
abstract = {Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain-computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.},
}
@article {pmid28685918,
year = {2018},
author = {Costecalde, T and Aksenova, T and Torres-Martinez, N and Eliseyev, A and Mestais, C and Moro, C and Benabid, AL},
title = {A Long-Term BCI Study With ECoG Recordings in Freely Moving Rats.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {21},
number = {2},
pages = {149-159},
doi = {10.1111/ner.12628},
pmid = {28685918},
issn = {1525-1403},
mesh = {Algorithms ; Animals ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Longitudinal Studies ; Online Systems ; Physical Stimulation ; Psychomotor Performance/physiology ; Rats ; Time Factors ; User-Computer Interface ; Wakefulness/*physiology ; },
abstract = {BACKGROUND: Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues.<br><br>OBJECTIVE: To develop a simple BCI set-up to easier studies for improving the mathematical tools to process the ECoG to control an effector.<br><br>METHOD: We designed a simple BCI using transcranial electrodes (17 screws, three mechanically linked to create a common reference, 14 used as recording electrodes) to record Electro-Cortico-Graphic (ECoG) neuronal activities in rodents. The data processing is based on an online self-paced non-supervised (asynchronous) BCI paradigm. N-way partial least squares algorithm together with Continuous Wavelet Transformation of ECoG recordings detect signatures related to motor activities. Signature detection in freely moving rats may activate external effectors during a behavioral task, which involved pushing a lever to obtain a reward.<br><br>RESULTS: After routine training, we showed that peak brain activity preceding a lever push (LP) to obtain food reward was located mostly in the cerebellar cortex with a higher correlation coefficient, suggesting a strong postural component and also in the occipital cerebral cortex. Analysis of brain activities provided a stable signature in the high gamma band (&#x223c;180Hz) occurring within 1500 msec before the lever push approximately around -400 msec to -500 msec. Detection of the signature from a single cerebellar cortical electrode triggers the effector with high efficiency (68% Offline and 30% Online) and rare false positives per minute in sessions about 30 minutes and up to one hour (&#x223c;2 online and offline).<br><br>CONCLUSIONS: In summary, our results are original as compared to the rest of the literature, which involves rarely rodents, a simple BCI set-up has been developed in rats, the data show for the first time long-term, up to one year, unsupervised online control of an effector.},
}
@article {pmid28683745,
year = {2017},
author = {Philips, GR and Daly, JJ and Príncipe, JC},
title = {Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {67},
pmid = {28683745},
issn = {1743-0003},
support = {R01 NS063275/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Algorithms ; Biofeedback, Psychology ; Biomarkers ; Biomechanical Phenomena ; Cerebral Cortex/pathology ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Male ; Middle Aged ; Nerve Net/*pathology ; Predictive Value of Tests ; Prognosis ; Recovery of Function ; Stroke/*pathology/*psychology ; *Stroke Rehabilitation ; Survivors ; Treatment Outcome ; Young Adult ; },
abstract = {BACKGROUND: Biomarkers derived from neural activity of the brain present a vital tool for the prediction and evaluation of post-stroke motor recovery, as well as for real-time biofeedback opportunities.<br><br>METHODS: In order to encapsulate recovery-related reorganization of brain networks into such biomarkers, we have utilized the generalized measure of association (GMA) and graph analyses, which include global and local efficiency, as well as hemispheric interdensity and intradensity. These methods were applied to electroencephalogram (EEG) data recorded during a study of 30 stroke survivors (21 male, mean age 57.9 years, mean stroke duration 22.4 months) undergoing 12 weeks of intensive therapeutic intervention.<br><br>RESULTS: We observed that decreases of the intradensity of the unaffected hemisphere are correlated (r s =-0.46;p<0.05) with functional recovery, as measured by the upper-extremity portion of the Fugl-Meyer Assessment (FMUE). In addition, high initial values of local efficiency predict greater improvement in FMUE (R [2]=0.16;p<0.05). In a subset of 17 subjects possessing lesions of the cerebral cortex, reductions of global and local efficiency, as well as the intradensity of the unaffected hemisphere are found to be associated with functional improvement (r s =-0.60,-0.66,-0.75;p<0.05). Within the same subgroup, high initial values of global and local efficiency, are predictive of improved recovery (R [2]=0.24,0.25;p<0.05). All significant findings were specific to the 12.5-25 Hz band.<br><br>CONCLUSIONS: These topological measures show promise for prognosis and evaluation of therapeutic outcomes, as well as potential application to BCI-enabled biofeedback.},
}
@article {pmid28682261,
year = {2017},
author = {Mohammed, A and Zamani, M and Bayford, R and Demosthenous, A},
title = {Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {12},
pages = {2441-2452},
doi = {10.1109/TNSRE.2017.2722986},
pmid = {28682261},
issn = {1558-0210},
mesh = {Algorithms ; Computer Simulation ; Computer Systems ; Deep Brain Stimulation/classification/*methods ; Electroencephalography/classification ; Fourier Analysis ; Humans ; Nonlinear Dynamics ; Normal Distribution ; Parkinson Disease/*rehabilitation ; Reproducibility of Results ; Subthalamic Nucleus ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.},
}
@article {pmid28682260,
year = {2017},
author = {Osuagwu, BA and Zych, M and Vuckovic, A},
title = {Is Implicit Motor Imagery a Reliable Strategy for a Brain-Computer Interface?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {12},
pages = {2239-2248},
doi = {10.1109/TNSRE.2017.2712707},
pmid = {28682260},
issn = {1558-0210},
support = {G0902257/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/classification/statistics &amp; numerical data ; Female ; Hand ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Judgment/physiology ; Linear Models ; Male ; *Movement ; Reproducibility of Results ; Rotation ; Software ; Stroke Rehabilitation/methods ; Young Adult ; },
abstract = {Explicit motor imagery (eMI) is a widely used brain-computer interface (BCI) paradigm, but not everybody can accomplish this task. Here, we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able-bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgment task is known to require mental rotation of a person's own hands, which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left hand and right hand, the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left hand and 78 ± 7% for the right hand, which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special BCI based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement, or for treatment of bodily spatial neglect.},
}
@article {pmid28680906,
year = {2017},
author = {Lapborisuth, P and Zhang, X and Noah, A and Hirsch, J},
title = {Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks.},
journal = {Neurophotonics},
volume = {4},
number = {2},
pages = {021107},
pmid = {28680906},
issn = {2329-423X},
support = {R01 MH107513/MH/NIMH NIH HHS/United States ; R01 MH111629/MH/NIMH NIH HHS/United States ; },
abstract = {Neurofeedback is a method for using neural activity displayed on a computer to regulate one's own brain function and has been shown to be a promising technique for training individuals to interact with brain-machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain-machine interface applications.},
}
@article {pmid28676750,
year = {2017},
author = {Luu, TP and He, Y and Nakagome, S and Nathan, K and Brown, S and Gorges, J and Contreras-Vidal, JL},
title = {Multi-Trial Gait Adaptation of Healthy Individuals during Visual Kinematic Perturbations.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {320},
pmid = {28676750},
issn = {1662-5161},
abstract = {Optimizing rehabilitation strategies requires understanding the effects of contextual cues on adaptation learning. Prior studies have examined these effects on the specificity of split-belt walking adaptation, showing that contextual visual cues can be manipulated to modulate the magnitude, transfer, and washout of split-belt-induced learning in humans. Specifically, manipulating the availability of vision during training or testing phases of learning resulted in differences in adaptive mechanisms for temporal and spatial features of walking. However, multi-trial locomotor training has been rarely explored when using visual kinematic gait perturbations. In this study, we investigated multi-trial locomotor adaptation in ten healthy individuals while applying visual kinematic perturbations. Subjects were instructed to control a moving cursor, which represented the position of their heel, to follow a prescribed heel path profile displayed on a monitor. The perturbations were introduced by scaling all of the lower limb joint angles by a factor of 0.7 (i.e., a gain change), resulting in visual feedback errors between subjects' heel trajectories and the prescribed path profiles. Our findings suggest that, with practice, the subjects learned, albeit with different strategies, to reduce the tracking errors and showed faster response time in later trials. Moreover, the gait symmetry indices, in both the spatial and temporal domains, changed significantly during gait adaptation (P < 0.001). After-effects were present in the temporal gait symmetry index whens the visual perturbations were removed in the post-exposure period (P < 0.001), suggesting adaptation learning. These findings may have implications for developing novel gait rehabilitation interventions.},
}
@article {pmid28676734,
year = {2017},
author = {Liu, YT and Pal, NR and Marathe, AR and Wang, YK and Lin, CT},
title = {Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {332},
pmid = {28676734},
issn = {1662-4548},
abstract = {A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.},
}
@article {pmid28674510,
year = {2017},
author = {van der Schyff, D and Schiavio, A},
title = {The Future of Musical Emotions.},
journal = {Frontiers in psychology},
volume = {8},
number = {},
pages = {988},
pmid = {28674510},
issn = {1664-1078},
}
@article {pmid28671632,
year = {2017},
author = {Wöhrle, H and Tabie, M and Kim, SK and Kirchner, F and Kirchner, EA},
title = {A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {7},
pages = {},
pmid = {28671632},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Humans ; *Movement ; Orthotic Devices ; Self-Help Devices ; },
abstract = {A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.},
}
@article {pmid28671629,
year = {2017},
author = {Liu, YH and Huang, S and Huang, YD},
title = {Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher's Criterion-Based Channel Selection.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {7},
pages = {},
pmid = {28671629},
issn = {1424-8220},
mesh = {*Amyotrophic Lateral Sclerosis ; Brain-Computer Interfaces ; Electroencephalography ; Fractals ; Humans ; Imagination ; },
abstract = {Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain-computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger-Procaccia and Higuchi's methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher's criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher's criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.},
}
@article {pmid28669301,
year = {2017},
author = {Hu, K and Bounni, F and Williams, Z},
title = {Editorial. Advancement in brain-machine interfaces for patients with tetraplegia: neurosurgical perspective.},
journal = {Neurosurgical focus},
volume = {43},
number = {1},
pages = {E5},
doi = {10.3171/2017.5.FOCUS17244},
pmid = {28669301},
issn = {1092-0684},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Neurosurgery ; Quadriplegia/*physiopathology ; Spinal Cord Injuries/*physiopathology ; },
}
@article {pmid28665058,
year = {2017},
author = {Lee, J and Choi, H and Lee, S and Cho, BH and Ahn, KH and Kim, IY and Lee, KM and Jang, DP},
title = {Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates.},
journal = {Journal of Korean medical science},
volume = {32},
number = {8},
pages = {1243-1250},
pmid = {28665058},
issn = {1598-6357},
mesh = {Animals ; Brain-Computer Interfaces ; *Electrocorticography ; Frontal Lobe/physiology ; Macaca mulatta ; Male ; Parietal Lobe/physiology ; Saccades/*physiology ; Support Vector Machine ; },
abstract = {A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The α-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.},
}
@article {pmid28663460,
year = {2017},
author = {Clausen, J and Fetz, E and Donoghue, J and Ushiba, J and Spörhase, U and Chandler, J and Birbaumer, N and Soekadar, SR},
title = {Help, hope, and hype: Ethical dimensions of neuroprosthetics.},
journal = {Science (New York, N.Y.)},
volume = {356},
number = {6345},
pages = {1338-1339},
doi = {10.1126/science.aam7731},
pmid = {28663460},
issn = {1095-9203},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*ethics ; Computer Security ; Humans ; Paralysis/psychology/*rehabilitation ; Privacy ; },
}
@article {pmid28663052,
year = {2017},
author = {Zhang, S and Zheng, Y and Wang, D and Wang, L and Ma, J and Zhang, J and Xu, W and Li, D and Zhang, D},
title = {Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain-computer interface.},
journal = {Neuroscience letters},
volume = {655},
number = {},
pages = {35-40},
doi = {10.1016/j.neulet.2017.06.044},
pmid = {28663052},
issn = {1872-7972},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Humans ; *Imagination ; *Kinesthesis ; Male ; Models, Neurological ; Motor Cortex/physiology ; *Movement ; Psychomotor Performance ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Motor imagery is one of the most investigated paradigms in the field of brain-computer interfaces (BCIs). The present study explored the feasibility of applying a common spatial pattern (CSP)-based algorithm for a functional near-infrared spectroscopy (fNIRS)-based motor imagery BCI. Ten participants performed kinesthetic imagery of their left- and right-hand movements while 20-channel fNIRS signals were recorded over the motor cortex. The CSP method was implemented to obtain the spatial filters specific for both imagery tasks. The mean, slope, and variance of the CSP filtered signals were taken as features for BCI classification. Results showed that the CSP-based algorithm outperformed two representative channel-wise methods for classifying the two imagery statuses using either data from all channels or averaged data from imagery responsive channels only (oxygenated hemoglobin: CSP-based: 75.3±13.1%; all-channel: 52.3±5.3%; averaged: 64.8±13.2%; deoxygenated hemoglobin: CSP-based: 72.3±13.0%; all-channel: 48.8±8.2%; averaged: 63.3±13.3%). Furthermore, the effectiveness of the CSP method was also observed for the motor execution data to a lesser extent. A partial correlation analysis revealed significant independent contributions from all three types of features, including the often-ignored variance feature. To our knowledge, this is the first study demonstrating the effectiveness of the CSP method for fNIRS-based motor imagery BCIs.},
}
@article {pmid28661425,
year = {2017},
author = {Molina-Cantero, AJ and Guerrero-Cubero, J and Gómez-González, IM and Merino-Monge, M and Silva-Silva, JI},
title = {Characterizing Computer Access Using a One-Channel EEG Wireless Sensor.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {7},
pages = {},
pmid = {28661425},
issn = {1424-8220},
mesh = {Attention ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Computers ; *Electroencephalography ; Humans ; Wireless Technology ; },
abstract = {This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high t s c a n ≈ 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI.},
}
@article {pmid28660211,
year = {2017},
author = {Gao, Q and Dou, L and Belkacem, AN and Chen, C},
title = {Noninvasive Electroencephalogram Based Control of a Robotic Arm for Writing Task Using Hybrid BCI System.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {8316485},
pmid = {28660211},
issn = {2314-6141},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Movement/physiology ; Robotics/*instrumentation ; *Writing ; },
abstract = {A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.},
}
@article {pmid28659751,
year = {2017},
author = {Borghini, G and Aricò, P and Di Flumeri, G and Sciaraffa, N and Colosimo, A and Herrero, MT and Bezerianos, A and Thakor, NV and Babiloni, F},
title = {A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {325},
pmid = {28659751},
issn = {1662-4548},
abstract = {Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs.},
}
@article {pmid28656392,
year = {2017},
author = {Ma, Z and Qiu, T},
title = {Performance improvement of ERP-based brain-computer interface via varied geometric patterns.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {12},
pages = {2245-2256},
pmid = {28656392},
issn = {1741-0444},
support = {81241059//National Natural Science Foundation of China/ ; 61172108//National Natural Science Foundation of China/ ; 61139001//National Natural Science Foundation of China/ ; 2012BAJ18B06//National Key Technology R&#xa7;D Program of China/ ; 2014DP173025//Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences/ ; 2016B010108010//Special Program of Guangdong Frontier and Key Technological Innovation/ ; 2016B010125003//Guangdong Technology Project/ ; JSGG20160331185256983//Shenzhen Technology Project/ ; JCYJ20140910003939013//Shenzhen Technology Project/ ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.},
}
@article {pmid28652143,
year = {2017},
author = {Kober, SE and Witte, M and Neuper, C and Wood, G},
title = {Specific or nonspecific? Evaluation of band, baseline, and cognitive specificity of sensorimotor rhythm- and gamma-based neurofeedback.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {120},
number = {},
pages = {1-13},
doi = {10.1016/j.ijpsycho.2017.06.005},
pmid = {28652143},
issn = {1872-7697},
mesh = {Adult ; Analysis of Variance ; Brain Mapping ; Brain Waves/*physiology ; Cognition/*physiology ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Fourier Analysis ; Healthy Volunteers ; Humans ; Male ; Middle Aged ; Neurofeedback/*methods ; Neuropsychological Tests ; },
abstract = {Neurofeedback (NF) is often criticized because of the lack of empirical evidence of its specificity. Our present study thus focused on the specificity of NF on three levels: band specificity, cognitive specificity, and baseline specificity. Ten healthy middle-aged individuals performed ten sessions of SMR (sensorimotor rhythm, 12-15Hz) NF training. A second group (N=10) received feedback of a narrow gamma band (40-43Hz). Effects of NF on EEG resting measurements (tonic EEG) and cognitive functions (memory, intelligence) were evaluated using a pre-post design. Both training groups were able to linearly increase the target training frequencies (either SMR or gamma), indicating the trainability of these EEG frequencies. Both NF training protocols led to nonspecific changes in other frequency bands during NF training. While SMR NF only led to concomitant changes in slower frequencies, gamma training affected nearly the whole power spectrum. SMR NF specifically improved memory functions. Gamma training showed only marginal effects on cognitive functions. SMR power assessed during resting measurements significantly increased after SMR NF training compared to a pre-assessment, indicating specific effects of SMR NF on baseline/tonic EEG. The gamma group did not show any pre-post changes in their EEG resting activity. In conclusion, SMR NF specifically affects cognitive functions (cognitive specificity) and tonic EEG (baseline specificity), while increasing SMR during NF training nonspecifically affects slower EEG frequencies as well (band non-specificity). Gamma NF was associated with nonspecific effects on the EEG power spectrum during training, which did not lead to considerable changes in cognitive functions or baseline EEG activity.},
}
@article {pmid28651628,
year = {2017},
author = {Andrade, J and Cecílio, J and Simões, M and Sales, F and Castelo-Branco, M},
title = {Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {63},
pmid = {28651628},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; Autistic Disorder/therapy ; *Ego ; Electrocorticography ; *Electroencephalography ; Female ; Healthy Volunteers ; Humans ; Imagination/*physiology ; *Intention ; Male ; Movement/*physiology ; Parietal Lobe/physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Temporal Lobe/physiology ; Virtual Reality ; Young Adult ; },
abstract = {BACKGROUND: We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism.<br><br>METHODS: We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines.<br><br>RESULTS: The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non &#x3bc; related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67&#xa0;&#xb1;&#xa0;1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p&#xa0;<&#xa0;0.02).<br><br>CONCLUSIONS: The observed pattern of results show that Self and Third Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts.},
}
@article {pmid28651526,
year = {2017},
author = {Marwood, L and Taylor, R and Goldsmith, K and Romeo, R and Holland, R and Pickles, A and Hutchinson, J and Dietch, D and Cipriani, A and Nair, R and Attenburrow, MJ and Young, AH and Geddes, J and McAllister-Williams, RH and Cleare, AJ},
title = {Study protocol for a randomised pragmatic trial comparing the clinical and cost effectiveness of lithium and quetiapine augmentation in treatment resistant depression (the LQD study).},
journal = {BMC psychiatry},
volume = {17},
number = {1},
pages = {231},
pmid = {28651526},
issn = {1471-244X},
support = {/WT_/Wellcome Trust/United Kingdom ; HTA/14/222/02/DH_/Department of Health/United Kingdom ; },
mesh = {Adult ; Antidepressive Agents/administration &amp; dosage/economics ; Antipsychotic Agents/administration &amp; dosage/economics ; *Cost-Benefit Analysis/methods ; Depressive Disorder, Major/diagnosis/*drug therapy/economics ; Depressive Disorder, Treatment-Resistant/diagnosis/*drug therapy/economics ; Drug Therapy, Combination ; Humans ; Lithium/*administration &amp; dosage/economics ; Quetiapine Fumarate/*administration &amp; dosage/economics ; },
abstract = {BACKGROUND: Approximately 30-50% of patients with major depressive disorder can be classed as treatment resistant, widely defined as a failure to respond to two or more adequate trials of antidepressants in the current episode. Treatment resistant depression is associated with a poorer prognosis and higher mortality rates. One treatment option is to augment an existing antidepressant with a second agent. Lithium and the atypical antipsychotic quetiapine are two such add-on therapies and are currently recommended as first line options for treatment resistant depression. However, whilst neither treatment has been established as superior to the other in short-term studies, they have yet to be compared head-to-head in longer term studies, or with a superiority design in this patient group.<br><br>METHODS: The Lithium versus Quetiapine in Depression (LQD) study is a parallel group, multi-centre, pragmatic, open-label, patient randomised clinical trial designed to address this gap in knowledge. The study will compare the clinical and cost effectiveness of the decision to prescribe lithium or quetiapine add-on therapy to antidepressant medication for patients with treatment resistant depression. Patients will be randomised 1:1 and followed up over 12&#xa0;months, with the hypothesis being that quetiapine will be superior to lithium. The primary outcomes will be: (1) time to all-cause treatment discontinuation over one year, and (2) self-rated depression symptoms rated weekly for one year via the Quick Inventory of Depressive Symptomatology. Other outcomes will include between group differences in response and remission rates, quality of life, social functioning, cost-effectiveness and the frequency of serious adverse events and side effects.<br><br>DISCUSSION: The trial aims to help shape the treatment pathway for patients with treatment resistant depression, by determining whether the decision to prescribe quetiapine is superior to lithium. Strengths of the study include its pragmatic superiority design, broad inclusion criteria (external validity) and longer follow up than previous studies.<br><br>TRIAL REGISTRATION: ISRCTN registry: ISRCTN16387615 , registered 28 February 2016. ClinicalTrials.gov: NCT03004521 , registered 17 November 2016.},
}
@article {pmid28649190,
year = {2017},
author = {Hori, S and Mori, K and Mashimo, T and Seiyama, A},
title = {Effects of Light and Sound on the Prefrontal Cortex Activation and Emotional Function: A Functional Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {321},
pmid = {28649190},
issn = {1662-4548},
abstract = {We constructed a near infrared spectroscopy-based real-time feedback system to estimate the subjects' emotional states using the changes in oxygenated hemoglobin concentration [Δ(oxy-Hb)] in the prefrontal cortex (PFC). Using this system, we investigated the influences of continual mild and equivocal stimuli consisting of lights and a reconstructed waterfall sound on Δ[oxy-Hb] in the PFC. The visual (light) and auditory (sound) stimuli changed randomly and independently, depending on the emotional states of the individual subjects. The emotional states induced by the stimuli were examined via a questionnaire rated on an 11-point scale, from +5 (pleasant) to -5 (unpleasant), through 0 (neutral), after the 5-min experiments. Results from 757 subjects revealed that Δ[oxy-Hb] in the PFC exhibited a weak, but significant, correlation with emotional change, with the given continual and mild stimuli similar to that experienced in response to the intense pleasant/unpleasant stimuli. Based on the results we discuss the generation of pleasant/unpleasant weak emotional change induced by mild and weak stimuli such as light and sound.},
}
@article {pmid28648720,
year = {2017},
author = {Powell, MP and Britz, WR and Harper, JS and Borton, DA},
title = {An engineered home environment for untethered data telemetry from nonhuman primates.},
journal = {Journal of neuroscience methods},
volume = {288},
number = {},
pages = {72-81},
doi = {10.1016/j.jneumeth.2017.06.013},
pmid = {28648720},
issn = {1872-678X},
support = {S10 OD016366/OD/NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal/*physiology ; *Computer Simulation ; Electrodes, Implanted ; *Environment Design ; *Equipment Design/instrumentation/methods ; Neural Prostheses ; Neurons/physiology ; Primates/physiology ; Signal Processing, Computer-Assisted ; *Telemetry/instrumentation/methods ; Wireless Technology/*instrumentation ; },
abstract = {BACKGROUND: Wireless neural recording technologies now provide untethered access to large populations of neurons in the nonhuman primate brain. Such technologies enable long-term, continuous interrogation of neural circuits and importantly open the door for chronic neurorehabilitation platforms. For example, by providing continuous consistent closed loop feedback from a brain machine interface, the nervous system can leverage plasticity to integrate more effectively into the system than would be possible in short experimental sessions. However, to fully realize this opportunity necessitates the development of experimental environments that do not hinder wireless data transmission. Traditional nonhuman primate metal cage construction, while durable and standardized around the world, prevents data transmission at the frequencies necessary for high-bandwidth data transfer.<br><br>NEW METHOD: To overcome this limitation, we have engineered and constructed a radio-frequency transparent home environment for nonhuman primates using primarily non-conductive materials.<br><br>RESULTS: Computational modeling and empirical testing were performed to demonstrate the behavior of transmitted signals passing through the enclosure. In addition, neural data were successfully recorded from a freely behaving nonhuman primate inside the housing system.<br><br>Our design outperforms standard metallic home cages by allowing radiation to transmit beyond its boundaries, without significant interference, while simultaneously maintaining the mechanical and operational integrity of existing commercial home cages.<br><br>CONCLUSIONS: Continuous access to neural signals in combination with other bio-potential and kinematic sensors will empower new insights into unrestrained behavior, aid the development of advanced neural prostheses, and enable neurorehabilitation strategies to be employed outside traditional environments.},
}
@article {pmid28647609,
year = {2017},
author = {Montagna, F and Buiatti, M and Benatti, S and Rossi, D and Farella, E and Benini, L},
title = {A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.},
journal = {Methods (San Diego, Calif.)},
volume = {129},
number = {},
pages = {96-107},
doi = {10.1016/j.ymeth.2017.06.019},
pmid = {28647609},
issn = {1095-9130},
mesh = {Algorithms ; Artifacts ; Electroencephalography/*methods ; Humans ; *Machine Learning ; Photic Stimulation/*methods ; },
abstract = {EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experimental paradigm that efficiently tests brain function by measuring EEG responses to periodic stimulation. Recently, frequency-tagging paradigms have proven successful with low stimulation frequencies (0.5-6Hz), but the EEG signal is intrinsically noisy in this frequency range, requiring heavy signal processing and significant human intervention for response estimation. This limits the possibility to process the EEG on resource-constrained systems and to design smart EEG based devices for automated diagnostic. We propose an algorithm for artifact removal and automated detection of frequency tagging responses in a wide range of stimulation frequencies, which we test on a visual stimulation protocol. The algorithm is rooted on machine learning based pattern recognition techniques and it is tailored for a new generation parallel ultra low power processing platform (PULP), reaching performance of more that 90% accuracy in the frequency detection even for very low stimulation frequencies (<1Hz) with a power budget of 56mW.},
}
@article {pmid28644398,
year = {2017},
author = {Heo, J and Yoon, H and Park, KS},
title = {A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {7},
pages = {},
pmid = {28644398},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Electrooculography ; Eye Movements ; Forehead ; Humans ; User-Computer Interface ; Wearable Electronic Devices ; },
abstract = {Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain-computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles.},
}
@article {pmid28643185,
year = {2018},
author = {Sullivan, LS and Klein, E and Brown, T and Sample, M and Pham, M and Tubig, P and Folland, R and Truitt, A and Goering, S},
title = {Keeping Disability in Mind: A Case Study in Implantable Brain-Computer Interface Research.},
journal = {Science and engineering ethics},
volume = {24},
number = {2},
pages = {479-504},
pmid = {28643185},
issn = {1471-5546},
support = {EEC-1028725//National Science Foundation/International ; },
mesh = {*Attitude ; *Brain-Computer Interfaces ; *Persons with Disabilities ; *Engineering ; Female ; Focus Groups ; Humans ; Male ; Qualitative Research ; *Research ; *Research Personnel ; *Technology ; },
abstract = {Brain-Computer Interface (BCI) research is an interdisciplinary area of study within Neural Engineering. Recent interest in end-user perspectives has led to an intersection with user-centered design (UCD). The goal of user-centered design is to reduce the translational gap between researchers and potential end users. However, while qualitative studies have been conducted with end users of BCI technology, little is known about individual BCI researchers' experience with and attitudes towards UCD. Given the scientific, financial, and ethical imperatives of UCD, we sought to gain a better understanding of practical and principled considerations for researchers who engage with end users. We conducted a qualitative interview case study with neural engineering researchers at a center dedicated to the creation of BCIs. Our analysis generated five themes common across interviews. The thematic analysis shows that participants identify multiple beneficiaries of their work, including other researchers, clinicians working with devices, device end users, and families and caregivers of device users. Participants value experience with device end users, and personal experience is the most meaningful type of interaction. They welcome (or even encourage) end-user input, but are skeptical of limited focus groups and case studies. They also recognize a tension between creating sophisticated devices and developing technology that will meet user needs. Finally, interviewees espouse functional, assistive goals for their technology, but describe uncertainty in what degree of function is "good enough" for individual end users. Based on these results, we offer preliminary recommendations for conducting future UCD studies in BCI and neural engineering.},
}
@article {pmid28643091,
year = {2017},
author = {Rocke, TE and Tripp, DW and Russell, RE and Abbott, RC and Richgels, KLD and Matchett, MR and Biggins, DE and Griebel, R and Schroeder, G and Grassel, SM and Pipkin, DR and Cordova, J and Kavalunas, A and Maxfield, B and Boulerice, J and Miller, MW},
title = {Sylvatic Plague Vaccine Partially Protects Prairie Dogs (Cynomys spp.) in Field Trials.},
journal = {EcoHealth},
volume = {14},
number = {3},
pages = {438-450},
pmid = {28643091},
issn = {1612-9210},
mesh = {Amoxicillin ; Animals ; Arizona ; Colorado ; Montana ; Plague/*immunology/*prevention &amp; control ; Plague Vaccine/*administration &amp; dosage ; Rodent Diseases/*immunology/*prevention &amp; control ; Sciuridae/*immunology ; South Dakota ; Utah ; Yersinia pestis/*immunology ; },
abstract = {Sylvatic plague, caused by Yersinia pestis, frequently afflicts prairie dogs (Cynomys spp.), causing population declines and local extirpations. We tested the effectiveness of bait-delivered sylvatic plague vaccine (SPV) in prairie dog colonies on 29 paired placebo and treatment plots (1-59 ha in size; average 16.9 ha) in 7 western states from 2013 to 2015. We compared relative abundance (using catch per unit effort (CPUE) as an index) and apparent survival of prairie dogs on 26 of the 29 paired plots, 12 with confirmed or suspected plague (Y. pestis positive carcasses or fleas). Even though plague mortality occurred in prairie dogs on vaccine plots, SPV treatment had an overall positive effect on CPUE in all three years, regardless of plague status. Odds of capturing a unique animal were 1.10 (95% confidence interval [C.I.] 1.02-1.19) times higher per trap day on vaccine-treated plots than placebo plots in 2013, 1.47 (95% C.I. 1.41-1.52) times higher in 2014 and 1.19 (95% C.I. 1.13-1.25) times higher in 2015. On pairs where plague occurred, odds of apparent survival were 1.76 (95% Bayesian credible interval [B.C.I.] 1.28-2.43) times higher on vaccine plots than placebo plots for adults and 2.41 (95% B.C.I. 1.72-3.38) times higher for juveniles. Our results provide evidence that consumption of vaccine-laden baits can protect prairie dogs against plague; however, further evaluation and refinement are needed to optimize SPV use as a management tool.},
}
@article {pmid28642168,
year = {2018},
author = {Mohan, H and de Haan, R and Mansvelder, HD and de Kock, CPJ},
title = {The posterior parietal cortex as integrative hub for whisker sensorimotor information.},
journal = {Neuroscience},
volume = {368},
number = {},
pages = {240-245},
doi = {10.1016/j.neuroscience.2017.06.020},
pmid = {28642168},
issn = {1873-7544},
mesh = {Animals ; Parietal Lobe/anatomy &amp; histology/*physiology ; Rats ; Somatosensory Cortex/anatomy &amp; histology/*physiology ; Touch Perception/*physiology ; Vibrissae/*physiology ; },
abstract = {Our daily life consists of a continuous interplay between incoming sensory information and outgoing motor plans. Particularly during goal-directed behavior and active exploration of the sensory environment, brain circuits are merging sensory and motor signals. This is referred to as sensorimotor integration and is relevant for locomotion, vision or tactile exploration. The somatosensory (tactile) system is an attractive modality to study sensorimotor integration in health and disease, motivated by the need for revolutionary technology that builds upon conceptual understanding of sensorimotor integration, such as brain-machine-interfaces and neuro-prosthetics. In this perspective, we focus on the rat whisker system and put forward the posterior parietal cortex as a potential circuit where sensorimotor integration could occur during active somatosensation.},
}
@article {pmid28640544,
year = {2017},
author = {Marcus, M and Baranes, K and Park, M and Choi, IS and Kang, K and Shefi, O},
title = {Interactions of Neurons with Physical Environments.},
journal = {Advanced healthcare materials},
volume = {6},
number = {15},
pages = {},
doi = {10.1002/adhm.201700267},
pmid = {28640544},
issn = {2192-2659},
mesh = {Animals ; Biocompatible Materials/*chemistry ; Biomimetic Materials/*chemistry ; Humans ; Mechanotransduction, Cellular/*physiology ; Neurogenesis/*physiology ; Neurons/cytology/*physiology ; Surface Properties ; Tissue Engineering/*methods ; },
abstract = {Nerve growth strongly relies on multiple chemical and physical signals throughout development and regeneration. Currently, a cure for injured neuronal tissue is an unmet need. Recent advances in fabrication technologies and materials led to the development of synthetic interfaces for neurons. Such engineered platforms that come in 2D and 3D forms can mimic the native extracellular environment and create a deeper understanding of neuronal growth mechanisms, and ultimately advance the development of potential therapies for neuronal regeneration. This progress report aims to present a comprehensive discussion of this field, focusing on physical feature design and fabrication with additional information about considerations of chemical modifications. We review studies of platforms generated with a range of topographies, from micro-scale features down to topographical elements at the nanoscale that demonstrate effective interactions with neuronal cells. Fabrication methods are discussed as well as their biological outcomes. This report highlights the interplay between neuronal systems and the important roles played by topography on neuronal differentiation, outgrowth, and development. The influence of substrate structures on different neuronal cells and parameters including cell fate, outgrowth, intracellular remodeling, gene expression and activity is discussed. Matching these effects to specific needs may lead to the emergence of clinical solutions for patients suffering from neuronal injuries or brain-machine interface (BMI) applications.},
}
@article {pmid28639486,
year = {2017},
author = {Pels, EGM and Aarnoutse, EJ and Ramsey, NF and Vansteensel, MJ},
title = {Estimated Prevalence of the Target Population for Brain-Computer Interface Neurotechnology in the Netherlands.},
journal = {Neurorehabilitation and neural repair},
volume = {31},
number = {7},
pages = {677-685},
pmid = {28639486},
issn = {1552-6844},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Communication Disorders/complications/*epidemiology/*rehabilitation ; Female ; General Practitioners ; Humans ; Male ; Middle Aged ; Netherlands/epidemiology ; *Neurological Rehabilitation ; Neuromuscular Diseases/complications/epidemiology/rehabilitation ; Paralysis/complications/*epidemiology/*rehabilitation ; Prevalence ; Surveys and Questionnaires ; Young Adult ; },
abstract = {BACKGROUND: People who suffer from paralysis have difficulties participating in society. Particularly burdensome is the locked-in syndrome (LIS). LIS patients are not able to move and speak but are cognitively healthy. They rely on assistive technology to interact with the world and may benefit from neurotechnological advances. Optimal research and design of such aids requires a well-defined target population. However, the LIS population is poorly characterized and the number of patients in this condition is unknown.<br><br>OBJECTIVE: Here we estimated and described the LIS patient population in the Netherlands to define the target population for assistive (neuro)technology.<br><br>METHODS: We asked physicians in the Netherlands if they had patients suffering from severe paralysis and communication problems in their files. Physicians responding affirmatively were asked to fill out a questionnaire on the patients' status.<br><br>RESULTS: We sent out 9570 letters to general practitioners (GPs), who reported 83 patients. After first screening, the GPs of 46 patients received the questionnaire. Based on the responses, 26 patients were classified as having LIS. Extrapolation of these numbers resulted in a prevalence of 0.73 patients per 100 000 inhabitants. Notable results from the questionnaire were the percentage of patients with neuromuscular disease (>50%) and living at home (>70%).<br><br>CONCLUSIONS: We revealed an etiologically diverse group of LIS patients. The functioning and needs of these patients were, however, similar and many relied on assistive technology. By characterizing the LIS population, our study may contribute to optimal development of assistive (neuro)technology.},
}
@article {pmid28639320,
year = {2017},
author = {Al-Khudairy, L and Loveman, E and Colquitt, JL and Mead, E and Johnson, RE and Fraser, H and Olajide, J and Murphy, M and Velho, RM and O'Malley, C and Azevedo, LB and Ells, LJ and Metzendorf, MI and Rees, K},
title = {Diet, physical activity and behavioural interventions for the treatment of overweight or obese adolescents aged 12 to 17 years.},
journal = {The Cochrane database of systematic reviews},
volume = {6},
number = {6},
pages = {CD012691},
pmid = {28639320},
issn = {1469-493X},
support = {10/4001/13/DH_/Department of Health/United Kingdom ; MR/K02325X/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adolescent ; *Behavior Therapy ; *Body Mass Index ; Combined Modality Therapy ; *Exercise ; *Feeding Behavior ; Humans ; Overweight/*therapy ; Pediatric Obesity/*therapy ; Quality of Life ; Randomized Controlled Trials as Topic ; },
abstract = {BACKGROUND: Adolescent overweight and obesity has increased globally, and can be associated with short- and long-term health consequences. Modifying known dietary and behavioural risk factors through behaviour changing interventions (BCI) may help to reduce childhood overweight and obesity. This is an update of a review published in 2009.<br><br>OBJECTIVES: To assess the effects of diet, physical activity and behavioural interventions for the treatment of overweight or obese adolescents aged 12 to 17 years.<br><br>SEARCH METHODS: We performed a systematic literature search in: CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, LILACS, and the trial registers ClinicalTrials.gov and ICTRP Search Portal. We checked references of identified studies and systematic reviews. There were no language restrictions. The date of the last search was July 2016 for all databases.<br><br>SELECTION CRITERIA: We selected randomised controlled trials (RCTs) of diet, physical activity and behavioural interventions for treating overweight or obesity in adolescents aged 12 to 17 years.<br><br>DATA COLLECTION AND ANALYSIS: Two review authors independently assessed risk of bias, evaluated the overall quality of the evidence using the GRADE instrument and extracted data following the guidelines of the Cochrane Handbook for Systematic Reviews of Interventions. We contacted trial authors for additional information.<br><br>MAIN RESULTS: We included 44 completed RCTs (4781 participants) and 50 ongoing studies. The number of participants in each trial varied (10 to 521) as did the length of follow-up (6 to 24 months). Participants ages ranged from 12 to 17.5 years in all trials that reported mean age at baseline. Most of the trials used a multidisciplinary intervention with a combination of diet, physical activity and behavioural components. The content and duration of the intervention, its delivery and the comparators varied across trials. The studies contributing most information to outcomes of weight and body mass index (BMI) were from studies at a low risk of bias, but studies with a high risk of bias provided data on adverse events and quality of life.The mean difference (MD) of the change in BMI at the longest follow-up period in favour of BCI was -1.18 kg/m[2] (95% confidence interval (CI) -1.67 to -0.69); 2774 participants; 28 trials; low quality evidence. BCI lowered the change in BMI z score by -0.13 units (95% CI -0.21 to -0.05); 2399 participants; 20 trials; low quality evidence. BCI lowered body weight by -3.67 kg (95% CI -5.21 to -2.13); 1993 participants; 20 trials; moderate quality evidence. The effect on weight measures persisted in trials with 18 to 24 months' follow-up for both BMI (MD -1.49 kg/m[2] (95% CI -2.56 to -0.41); 760 participants; 6 trials and BMI z score MD -0.34 (95% CI -0.66 to -0.02); 602 participants; 5 trials).There were subgroup differences showing larger effects for both BMI and BMI z score in studies comparing interventions with no intervention/wait list control or usual care, compared with those testing concomitant interventions delivered to both the intervention and control group. There were no subgroup differences between interventions with and without parental involvement or by intervention type or setting (health care, community, school) or mode of delivery (individual versus group).The rate of adverse events in intervention and control groups was unclear with only five trials reporting harms, and of these, details were provided in only one (low quality evidence). None of the included studies reported on all-cause mortality, morbidity or socioeconomic effects.BCIs at the longest follow-up moderately improved adolescent's health-related quality of life (standardised mean difference 0.44 ((95% CI 0.09 to 0.79); P = 0.01; 972 participants; 7 trials; 8 comparisons; low quality of evidence) but not self-esteem.Trials were inconsistent in how they measured dietary intake, dietary behaviours, physical activity and behaviour.<br><br>AUTHORS' CONCLUSIONS: We found low quality evidence that multidisciplinary interventions involving a combination of diet, physical activity and behavioural components reduce measures of BMI and moderate quality evidence that they reduce weight in overweight or obese adolescents, mainly when compared with no treatment or waiting list controls. Inconsistent results, risk of bias or indirectness of outcome measures used mean that the evidence should be interpreted with caution. We have identified a large number of ongoing trials (50) which we will include in future updates of this review.},
}
@article {pmid28638316,
year = {2017},
author = {Deshpande, G and Rangaprakash, D and Oeding, L and Cichocki, A and Hu, XP},
title = {A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {246},
pmid = {28638316},
issn = {1662-4548},
abstract = {A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.},
}
@article {pmid28632792,
year = {2017},
author = {Liu, JC and Zacksenhouse, M and Eisen, A and Nofech-Mozes, S and Zacksenhaus, E},
title = {Identification of cell proliferation, immune response and cell migration as critical pathways in a prognostic signature for HER2+:ERα- breast cancer.},
journal = {PloS one},
volume = {12},
number = {6},
pages = {e0179223},
pmid = {28632792},
issn = {1932-6203},
mesh = {Breast Neoplasms/genetics/*immunology/*pathology ; Cell Movement/*genetics ; Cell Proliferation/*genetics ; *Critical Pathways ; Estrogen Receptor alpha/*genetics ; Female ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Immunity, Cellular/*genetics ; Neoplastic Stem Cells/metabolism/pathology ; Prognosis ; Receptor, ErbB-2/*genetics ; },
abstract = {BACKGROUND: Multi-gene prognostic signatures derived from primary tumor biopsies can guide clinicians in designing an appropriate course of treatment. Identifying genes and pathways most essential to a signature performance may facilitate clinical application, provide insights into cancer progression, and uncover potentially new therapeutic targets. We previously developed a 17-gene prognostic signature (HTICS) for HER2+:ERα- breast cancer patients, using genes that are differentially expressed in tumor initiating cells (TICs) versus non-TICs from MMTV-Her2/neu mammary tumors. Here we probed the pathways and genes that underlie the prognostic power of HTICS.<br><br>METHODS: We used Leave-One Out, Data Combination Test, Gene Set Enrichment Analysis (GSEA), Correlation and Substitution analyses together with Receiver Operating Characteristic (ROC) and Kaplan-Meier survival analysis to identify critical biological pathways within HTICS. Publically available cohorts with gene expression and clinical outcome were used to assess prognosis. NanoString technology was used to detect gene expression in formalin-fixed paraffin embedded (FFPE) tissues.<br><br>RESULTS: We show that three major biological pathways: cell proliferation, immune response, and cell migration, drive the prognostic power of HTICS, which is further tuned by Homeostatic and Glycan metabolic signalling. A 6-gene minimal Core that retained a significant prognostic power, albeit less than HTICS, also comprised the proliferation/immune/migration pathways. Finally, we developed NanoString probes that could detect expression of HTICS genes and their substitutions in FFPE samples.<br><br>CONCLUSION: Our results demonstrate that the prognostic power of a signature is driven by the biological processes it monitors, identify cell proliferation, immune response and cell migration as critical pathways for HER2+:ER&#x3b1;- cancer progression, and defines substitutes and Core genes that should facilitate clinical application of HTICS.},
}
@article {pmid28630937,
year = {2016},
author = {Hotson, G and Smith, RJ and Rouse, AG and Schieber, MH and Thakor, NV and Wester, BA},
title = {High Precision Neural Decoding of Complex Movement Trajectories using Recursive Bayesian Estimation with Dynamic Movement Primitives.},
journal = {IEEE robotics and automation letters},
volume = {1},
number = {2},
pages = {676-683},
pmid = {28630937},
issn = {2377-3766},
support = {R01 NS079664/NS/NINDS NIH HHS/United States ; R01 NS088606/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance. Here we present a novel sensor fusion paradigm for BMIs that capitalizes on information able to be extracted from the environment to greatly improve the performance of control. This was accomplished by using dynamic movement primitives to model the 3D endpoint trajectories of manipulating various objects. We then used a switching unscented Kalman filter to continuously arbitrate between the 3D endpoint kinematics predicted by the dynamic movement primitives and control derived from neural signals. We experimentally validated our system by decoding 3D endpoint trajectories executed by a non-human primate manipulating four different objects at various locations. Performance using our system showed a dramatic improvement over using neural signals alone, with median distance between actual and decoded trajectories decreasing from 31.1 cm to 9.9 cm, and mean correlation increasing from 0.80 to 0.98. Our results indicate that our sensor fusion framework can dramatically increase the fidelity of neural prosthetic trajectory decoding.},
}
@article {pmid28627964,
year = {2017},
author = {Eshraghi, B and Jamshidian-Tehrani, M and Mirmohammadsadeghi, A},
title = {Comparison of the success rate between monocanalicular and bicanalicular intubations in incomplete complex congenital nasolacrimal duct obstruction.},
journal = {Orbit (Amsterdam, Netherlands)},
volume = {36},
number = {4},
pages = {215-217},
doi = {10.1080/01676830.2017.1337161},
pmid = {28627964},
issn = {1744-5108},
mesh = {Child ; Child, Preschool ; Disease Progression ; Female ; Humans ; Infant ; Intubation/*methods ; Lacrimal Duct Obstruction/congenital/*therapy ; Male ; Nasolacrimal Duct/*surgery ; Postoperative Complications ; Silicone Elastomers ; Stents ; Treatment Outcome ; },
abstract = {This article compares the success rate between monocanalicular (MCI) and bicanalicular intubations (BCI) in incomplete complex congenital nasolacrimal duct obstruction (CNLDO) and evaluate the factors responsible for the success of intubation. First, 99 patients with incomplete complex CNLDO underwent MCI (Monoka) or BCI (Crawford). Therapeutic success was defined as dye disappearance test grade 0-1 and complete resolution of previous symptoms at 12 months' follow-up. The success rates were compared between two groups. In all cases, the correlation of the age, gender, history of probing, and the presence of purulent discharges with the improvement in CNLDO symptoms were evaluated. 52 cases in the MCI and 47 cases in the BCI group were included. Then, 48 patients (48.5%) had history of probing. 26 patients (26.3%) had purulent discharges. The patients in the MCI group had lower success rate (59.6%) than the patients in the BCI group (74.4%) but the difference was not significant (p = 0.11). No complication occurred in the BCI group. In 4 cases (7.6%) in the MCI group, the tubes were lost before time of planned removal. In all cases, only preoperative absence of the pus was significantly correlated with success (p = 0.09 and OR = 0.39). BCI may be a better treatment for the patients with incomplete complex CNLDO. In silicone intubation for these cases, preoperative absence of purulent discharges could increase the success rate.},
}
@article {pmid28627505,
year = {2017},
author = {Hsu, SH and Jung, TP},
title = {Monitoring alert and drowsy states by modeling EEG source nonstationarity.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056012},
doi = {10.1088/1741-2552/aa7a25},
pmid = {28627505},
issn = {1741-2552},
mesh = {Adult ; *Automobile Driving/psychology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Random Allocation ; Reaction Time/physiology ; Sleep Stages/*physiology ; Wakefulness/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes.<br><br>APPROACH: This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator.<br><br>MAIN RESULTS: Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r&#x2009;&#x2009;=&#x2009;&#x2009;-0.390 with alertness models and r&#x2009;&#x2009;=&#x2009;&#x2009;0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach.<br><br>SIGNIFICANCE: This ICA-based framework provides a new way to study changes of brain states and can be applied to monitoring cognitive or mental states of human operators in attention-critical settings or in passive brain-computer interfaces.},
}
@article {pmid28626393,
year = {2017},
author = {Zhao, X and Zhao, D and Wang, X and Hou, X},
title = {A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP).},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {278},
pmid = {28626393},
issn = {1662-5161},
abstract = {SSVEP is a kind of BCI technology with advantage of high information transfer rate. However, due to its nature, frequencies could be used as stimuli are scarce. To solve such problem, a stimuli encoding method which encodes SSVEP signal using Frequency Shift-Keying (FSK) method is developed. In this method, each stimulus is controlled by a FSK signal which contains three different frequencies that represent "Bit 0," "Bit 1" and "Bit 2" respectively. Different to common BFSK in digital communication, "Bit 0" and "Bit 1" composited the unique identifier of stimuli in binary bit stream form, while "Bit 2" indicates the ending of a stimuli encoding. EEG signal is acquired on channel Oz, O1, O2, Pz, P3, and P4, using ADS1299 at the sample rate of 250 SPS. Before original EEG signal is quadrature demodulated, it is detrended and then band-pass filtered using FFT-based FIR filtering to remove interference. Valid peak of the processed signal is acquired by calculating its derivative and converted into bit stream using window method. Theoretically, this coding method could implement at least 2 [n-1] (n is the length of bit command) stimulus while keeping the ITR the same. This method is suitable to implement stimuli on a monitor and where the frequency and phase could be used to code stimuli is limited as well as implementing portable BCI devices which is not capable of performing complex calculations.},
}
@article {pmid28626358,
year = {2017},
author = {Rodriguez, VJ and Cook, RR and Weiss, SM and Peltzer, K and Jones, DL},
title = {Psychosocial correlates of patient-provider family planning discussions among HIV-infected pregnant women in South Africa.},
journal = {Open access journal of contraception},
volume = {8},
number = {},
pages = {25-33},
pmid = {28626358},
issn = {1179-1527},
support = {P30 AI073961/AI/NIAID NIH HHS/United States ; R01 HD078187/HD/NICHD NIH HHS/United States ; },
abstract = {Patient-provider family planning discussions and preconception counseling can reduce maternal and neonatal risks by increasing adherence to provider recommendations and antiretroviral medication. However, HIV-infected women may not discuss reproductive intentions with providers due to anticipation of negative reactions and stigma. This study aimed to identify correlates of patient-provider family planning discussions among HIV-infected women in rural South Africa, an area with high rates of antenatal HIV and suboptimal rates of prevention of mother-to-child transmission (PMTCT) of HIV. Participants were N=673 pregnant HIV-infected women who completed measures of family planning discussions and knowledge, depression, stigma, intimate partner violence, and male involvement. Participants were, on average, 28 ± 6 years old, and half of them had completed at least 10-11 years of education. Most women were unemployed and had a monthly income of less than ~US$76. Fewer than half of the women reported having family planning discussions with providers. Correlates of patient-provider family planning discussions included younger age, discussions about PMTCT of HIV, male involvement, and decreased stigma (p < 0.05). Depression was indirectly associated with patient-provider family planning discussions through male involvement (b = -0.010, bias-corrected 95% confidence interval [bCI] [-0.019, -0.005]). That is, depression decreased male involvement, and in turn, male involvement increased patient-provider family planning discussions. Therefore, by decreasing male involvement, depression indirectly decreased family planning discussions. Study findings point to the importance of family planning strategies that address depression and facilitate male involvement to enhance communication between patients and providers and optimize maternal and neonatal health outcomes. This study underscores the need for longitudinal assessment of men's impact on family planning discussions both pre- and postpartum. Increasing support for provision of mental health services during pregnancy is merited to ensure the health of pregnant women living with HIV and their infants.},
}
@article {pmid28625485,
year = {2017},
author = {Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions.},
journal = {Neuron},
volume = {95},
number = {1},
pages = {195-208.e9},
pmid = {28625485},
issn = {1097-4199},
support = {/HHMI/Howard Hughes Medical Institute/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Pathways/physiology ; Task Performance and Analysis ; },
abstract = {Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions.},
}
@article {pmid28620273,
year = {2017},
author = {De Feo, V and Boi, F and Safaai, H and Onken, A and Panzeri, S and Vato, A},
title = {State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {269},
pmid = {28620273},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.},
}
@article {pmid28615329,
year = {2017},
author = {Slutzky, MW and Flint, RD},
title = {Physiological properties of brain-machine interface input signals.},
journal = {Journal of neurophysiology},
volume = {118},
number = {2},
pages = {1329-1343},
pmid = {28615329},
issn = {1522-1598},
support = {R01 NS094748/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Evoked Potentials ; Humans ; Movement ; },
abstract = {Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance-including movement-related information, longevity, and stability-of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.},
}
@article {pmid28613237,
year = {2017},
author = {Liao, SC and Wu, CT and Huang, HC and Cheng, WT and Liu, YH},
title = {Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {6},
pages = {},
pmid = {28613237},
issn = {1424-8220},
mesh = {Algorithms ; Brain-Computer Interfaces ; *Depressive Disorder, Major ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.},
}
@article {pmid28611615,
year = {2017},
author = {Walter, C and Rosenstiel, W and Bogdan, M and Gerjets, P and Spüler, M},
title = {Online EEG-Based Workload Adaptation of an Arithmetic Learning Environment.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {286},
pmid = {28611615},
issn = {1662-5161},
abstract = {In this paper, we demonstrate a closed-loop EEG-based learning environment, that adapts instructional learning material online, to improve learning success in students during arithmetic learning. The amount of cognitive workload during learning is crucial for successful learning and should be held in the optimal range for each learner. Based on EEG data from 10 subjects, we created a prediction model that estimates the learner's workload to obtain an unobtrusive workload measure. Furthermore, we developed an interactive learning environment that uses the prediction model to estimate the learner's workload online based on the EEG data and adapt the difficulty of the learning material to keep the learner's workload in an optimal range. The EEG-based learning environment was used by 13 subjects to learn arithmetic addition in the octal number system, leading to a significant learning effect. The results suggest that it is feasible to use EEG as an unobtrusive measure of cognitive workload to adapt the learning content. Further it demonstrates that a promptly workload prediction is possible using a generalized prediction model without the need for a user-specific calibration.},
}
@article {pmid28611611,
year = {2017},
author = {Cao, L and Xia, B and Maysam, O and Li, J and Xie, H and Birbaumer, N},
title = {A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {274},
pmid = {28611611},
issn = {1662-5161},
abstract = {Brain Computer Interface (BCI) speller is a typical BCI-based application to help paralyzed patients express their thoughts. This paper proposed a novel motor imagery based BCI speller with Oct-o-spell paradigm for word input. Furthermore, an intelligent input method was used for improving the performance of the BCI speller. For the English word spelling experiment, we compared synchronous control with previous asynchronous control under the same experimental condition. There were no significant differences between these two control methods in the classification accuracy, information transmission rate (ITR) or letters per minute (LPM). And the accuracy rates of over 70% validated the feasibility for these two control strategies. It was indicated that MI-based synchronous control protocol was feasible for BCI speller. And the efficiency of the predictive text entry (PTE) mode was superior to that of the Non-PTE mode.},
}
@article {pmid28611579,
year = {2017},
author = {Vařeka, L and Mautner, P},
title = {Stacked Autoencoders for the P300 Component Detection.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {302},
pmid = {28611579},
issn = {1662-4548},
abstract = {Novel neural network training methods (commonly referred to as deep learning) have emerged in recent years. Using a combination of unsupervised pre-training and subsequent fine-tuning, deep neural networks have become one of the most reliable classification methods. Since deep neural networks are especially powerful for high-dimensional and non-linear feature vectors, electroencephalography (EEG) and event-related potentials (ERPs) are one of the promising applications. Furthermore, to the authors' best knowledge, there are very few papers that study deep neural networks for EEG/ERP data. The aim of the experiments subsequently presented was to verify if deep learning-based models can also perform well for single trial P300 classification with possible application to P300-based brain-computer interfaces. The P300 data used were recorded in the EEG/ERP laboratory at the Department of Computer Science and Engineering, University of West Bohemia, and are publicly available. Stacked autoencoders (SAEs) were implemented and compared with some of the currently most reliable state-of-the-art methods, such as LDA and multi-layer perceptron (MLP). The parameters of stacked autoencoders were optimized empirically. The layers were inserted one by one and at the end, the last layer was replaced by a supervised softmax classifier. Subsequently, fine-tuning using backpropagation was performed. The architecture of the neural network was 209-130-100-50-20-2. The classifiers were trained on a dataset merged from four subjects and subsequently tested on different 11 subjects without further training. The trained SAE achieved 69.2% accuracy that was higher (p < 0.01) than the accuracy of MLP (64.9%) and LDA (65.9%). The recall of 58.8% was slightly higher when compared with MLP (56.2%) and LDA (58.4%). Therefore, SAEs could be preferable to other state-of-the-art classifiers for high-dimensional event-related potential feature vectors.},
}
@article {pmid28602817,
year = {2017},
author = {Pinotsis, DA and Brincat, SL and Miller, EK},
title = {On memories, neural ensembles and mental flexibility.},
journal = {NeuroImage},
volume = {157},
number = {},
pages = {297-313},
pmid = {28602817},
issn = {1095-9572},
support = {R37 MH087027/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Biophysics/methods ; Brain Waves/*physiology ; *Cues ; Electrocorticography/*methods ; Frontal Lobe/*physiology ; Macaca fascicularis ; Macaca mulatta ; Male ; Memory, Short-Term/*physiology ; *Models, Theoretical ; Nerve Net/*physiology ; Saccades/physiology ; Space Perception/*physiology ; Unsupervised Machine Learning ; Visual Perception/*physiology ; },
abstract = {Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. We find distinct ensembles for different cues and more parsimonious connectivity for cues on the horizontal axis, which may explain the oblique effect in psychophysics. Our approach paves the way for biophysical models with learned parameters that can guide future Brain Computer Interface development.},
}
@article {pmid28600256,
year = {2017},
author = {Haghighi, M and Moghadamfalahi, M and Akcakaya, M and Shinn-Cunningham, BG and Erdogmus, D},
title = {A Graphical Model for Online Auditory Scene Modulation Using EEG Evidence for Attention.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {11},
pages = {1970-1977},
pmid = {28600256},
issn = {1558-0210},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; R01 DC013825/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Attention/*physiology ; Auditory Perception/*physiology ; Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Female ; Humans ; Male ; *Models, Neurological ; Online Systems ; Prosthesis Design ; Signal Processing, Computer-Assisted ; Speech Perception ; Transfer, Psychology ; Wavelet Analysis ; },
abstract = {Recent findings indicate that brain interfaces have the potential to enable attention-guided auditory scene analysis and manipulation in applications, such as hearing aids and augmented/virtual environments. Specifically, noninvasively acquired electroencephalography (EEG) signals have been demonstrated to carry some evidence regarding, which of multiple synchronous speech waveforms the subject attends to. In this paper, we demonstrate that: 1) using data- and model-driven cross-correlation features yield competitive binary auditory attention classification results with at most 20 s of EEG from 16 channels or even a single well-positioned channel; 2) a model calibrated using equal-energy speech waveforms competing for attention could perform well on estimating attention in closed-loop unbalanced-energy speech waveform situations, where the speech amplitudes are modulated by the estimated attention posterior probability distribution; 3) such a model would perform even better if it is corrected (linearly, in this instance) based on EEG evidence dependence on speech weights in the mixture; and 4) calibrating a model based on population EEG could result in acceptable performance for new individuals/users; therefore, EEG-based auditory attention classifiers may generalize across individuals, leading to reduced or eliminated calibration time and effort.},
}
@article {pmid28598279,
year = {2018},
author = {Diesing, D and Wolf, S and Sommerfeld, J and Sarrafzadeh, A and Vajkoczy, P and Dengler, NF},
title = {A novel score to predict shunt dependency after aneurysmal subarachnoid hemorrhage.},
journal = {Journal of neurosurgery},
volume = {128},
number = {5},
pages = {1273-1279},
doi = {10.3171/2016.12.JNS162400},
pmid = {28598279},
issn = {1933-0693},
mesh = {*Cerebrospinal Fluid Shunts ; Female ; Humans ; Male ; Middle Aged ; Prognosis ; Recurrence ; Retrospective Studies ; Risk Factors ; Subarachnoid Hemorrhage/*diagnosis/epidemiology/*surgery ; },
abstract = {OBJECTIVE Feasible clinical scores for predicting shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) are scarce. The chronic hydrocephalus ensuing from SAH score (CHESS) was introduced in 2015 and has a high predictive value for SDHC. Although this score is easy to calculate, several early clinical and radiological factors are required. The authors designed the retrospective analysis described here for external CHESS validation and determination of predictive values for the radiographic Barrow Neurological Institute (BNI) scoring system and a new simplified combined scoring system. METHODS Consecutive data of 314 patients with aSAH were retrospectively analyzed with respect to CHESS parameters and BNI score. A new score, the shunt dependency in aSAH (SDASH) score, was calculated from independent risk factors identified with multivariate analysis. RESULTS Two hundred twenty-five patients survived the initial phase after the hemorrhage, and 27.1% of these patients developed SDHC. The SDASH score was developed from results of multivariate analysis, which revealed acute hydrocephalus (aHP), a BNI score of ≥ 3, and a Hunt and Hess (HH) grade of ≥ 4 to be independent risk factors for SDHC (ORs 5.709 [aHP], 6.804 [BNI], and 4.122 [HH]; p < 0.001). All 3 SDHC scores tested (CHESS, BNI, and SDASH) reliably predicted chronic hydrocephalus (ORs 1.533 [CHESS], 2.021 [BNI], and 2.496 [SDASH]; p ≤ 0.001). Areas under the receiver operating curve (AUROC) for CHESS and SDASH were comparable (0.769 vs 0.785, respectively; p = 0.447), but the CHESS and SDASH scores were superior to the BNI grading system for predicting SDHC (BNI AUROC 0.649; p = 0.014 and 0.001, respectively). In contrast to CHESS and BNI scores, an increase in the SDASH score coincided with a monotonous increase in the risk of developing SDHC. CONCLUSIONS The newly developed SDASH score is a reliable tool for predicting SDHC. It contains fewer factors and is more intuitive than existing scores that were shown to predict SDHC. A prospective score evaluation is needed.},
}
@article {pmid28597847,
year = {2017},
author = {Wang, X and Gkogkidis, CA and Iljina, O and Fiederer, LDJ and Henle, C and Mader, I and Kaminsky, J and Stieglitz, T and Gierthmuehlen, M and Ball, T},
title = {Mapping the fine structure of cortical activity with different micro-ECoG electrode array geometries.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056004},
doi = {10.1088/1741-2552/aa785e},
pmid = {28597847},
issn = {1741-2552},
mesh = {Animals ; Brain Mapping/instrumentation/*methods ; Brain-Computer Interfaces ; Electric Stimulation/methods ; Electrocardiography/methods ; Electrocorticography/instrumentation/*methods ; *Electrodes, Implanted ; Microelectrodes ; Somatosensory Cortex/*physiology ; Swine ; Swine, Miniature ; },
abstract = {OBJECTIVE: Innovations in micro-electrocorticography (µECoG) electrode array manufacturing now allow for intricate designs with smaller contact diameters and/or pitch (i.e. inter-contact distance) down to the sub-mm range. The aims of the present study were: (i) to investigate whether frequency ranges up to 400 Hz can be reproducibly observed in µECoG recordings and (ii) to examine how differences in topographical substructure between these frequency bands and electrode array geometries can be quantified. We also investigated, for the first time, the influence of blood vessels on signal properties and assessed the influence of cortical vasculature on topographic mapping.<br><br>APPROACH: The present study employed two &#xb5;ECoG electrode arrays with different contact diameters and inter-contact distances, which were used to characterize neural activity from the somatosensory cortex of minipigs in a broad frequency range up to 400 Hz. The analysed neural data were recorded in acute experiments under anaesthesia during peripheral electrical stimulation.<br><br>MAIN RESULTS: We observed that &#xb5;ECoG recordings reliably revealed multi-focal cortical somatosensory response patterns, in which response peaks were often less than 1&#x2009;cm apart and would thus not have been resolvable with conventional ECoG. The response patterns differed by stimulation site and intensity, they were distinct for different frequency bands, and the results of functional mapping proved independent of cortical vascular. Our analysis of different frequency bands exhibited differences in the number of activation peaks in topographical substructures. Notably, signal strength and signal-to-noise ratios differed between the two electrode arrays, possibly due to their different sensitivity for variations in spatial patterns and signal strengths.<br><br>SIGNIFICANCE: Our findings that the geometry of &#xb5;ECoG electrode arrays can strongly influence their recording performance can help to make informed decisions that maybe important in number of clinical contexts, including high-resolution brain mapping, advanced epilepsy diagnostics or brain-machine interfacing.},
}
@article {pmid28597846,
year = {2017},
author = {Rathee, D and Cecotti, H and Prasad, G},
title = {Single-trial effective brain connectivity patterns enhance discriminability of mental imagery tasks.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056005},
doi = {10.1088/1741-2552/aa785c},
pmid = {28597846},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Discrimination Learning/*physiology ; Electroencephalography/methods ; Humans ; Imagination/*physiology ; Nerve Net/*physiology ; },
abstract = {OBJECTIVE: The majority of the current approaches of connectivity based brain-computer interface (BCI) systems focus on distinguishing between different motor imagery (MI) tasks. Brain regions associated with MI are anatomically close to each other, hence these BCI systems suffer from low performances. Our objective is to introduce single-trial connectivity feature based BCI system for cognition imagery (CI) based tasks wherein the associated brain regions are located relatively far away as compared to those for MI.<br><br>APPROACH: We implemented time-domain partial Granger causality (PGC) for the estimation of the connectivity features in a BCI setting. The proposed hypothesis has been verified with two publically available datasets involving MI and CI tasks.<br><br>MAIN RESULTS: The results support the conclusion that connectivity based features can provide a better performance than a classical signal processing framework based on bandpass features coupled with spatial filtering for CI tasks, including word generation, subtraction, and spatial navigation. These results show for the first time that connectivity features can provide a reliable performance for imagery-based BCI system.<br><br>SIGNIFICANCE: We show that single-trial connectivity features for mixed imagery tasks (i.e. combination of CI and MI) can outperform the features obtained by current state-of-the-art method and hence can be successfully applied for BCI applications.},
}
@article {pmid28597557,
year = {2017},
author = {Meidahl, AC and Tinkhauser, G and Herz, DM and Cagnan, H and Debarros, J and Brown, P},
title = {Adaptive Deep Brain Stimulation for Movement Disorders: The Long Road to Clinical Therapy.},
journal = {Movement disorders : official journal of the Movement Disorder Society},
volume = {32},
number = {6},
pages = {810-819},
pmid = {28597557},
issn = {1531-8257},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; MR/M014762/1/MRC_/Medical Research Council/United Kingdom ; /DH_/Department of Health/United Kingdom ; },
mesh = {Deep Brain Stimulation/*methods/*standards ; Humans ; Movement Disorders/*therapy ; },
abstract = {Continuous high-frequency DBS is an established treatment for essential tremor and Parkinson's disease. Current developments focus on trying to widen the therapeutic window of DBS. Adaptive DBS (aDBS), where stimulation is dynamically controlled by feedback from biomarkers of pathological brain circuit activity, is one such development. Relevant biomarkers may be central, such as local field potential activity, or peripheral, such as inertial tremor data. Moreover, stimulation may be directed by the amplitude or the phase (timing) of the biomarker signal. In this review, we evaluate existing aDBS studies as proof-of-principle, discuss their limitations, most of which stem from their acute nature, and propose what is needed to take aDBS into a chronic setting. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.},
}
@article {pmid28597018,
year = {2017},
author = {McConnell, AC and Moioli, RC and Brasil, FL and Vallejo, M and Corne, DW and Vargas, PA and Stokes, AA},
title = {Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke.},
journal = {Journal of rehabilitation medicine},
volume = {49},
number = {6},
pages = {449-460},
doi = {10.2340/16501977-2229},
pmid = {28597018},
issn = {1651-2081},
mesh = {Brain-Computer Interfaces/*statistics &amp; numerical data ; Hand Injuries/*rehabilitation ; Humans ; Robotics/*methods ; Stroke/*complications/pathology ; Stroke Rehabilitation/*methods ; },
abstract = {OBJECTIVE: To review the state of the art of robotic-aided hand physiotherapy for post-stroke rehabilitation, including the use of brain-machine interfaces. Each patient has a unique clinical history and, in response to personalized treatment needs, research into individualized and at-home treatment options has expanded rapidly in recent years. This has resulted in the development of many devices and design strategies for use in stroke rehabilitation.<br><br>METHODS: The development progression of robotic-aided hand physiotherapy devices and brain-machine interface systems is outlined, focussing on those with mechanisms and control strategies designed to improve recovery outcomes of the hand post-stroke. A total of 110 commercial and non-commercial hand and wrist devices, spanning the 2 major core designs: end-effector and exoskeleton are reviewed.<br><br>RESULTS: The growing body of evidence on the efficacy and relevance of incorporating brain-machine interfaces in stroke rehabilitation is summarized. The challenges involved in integrating robotic rehabilitation into the healthcare system are discussed.<br><br>CONCLUSION: This review provides novel insights into the use of robotics in physiotherapy practice, and may help system designers to develop new devices.},
}
@article {pmid28596726,
year = {2017},
author = {Kober, SE and Witte, M and Ninaus, M and Koschutnig, K and Wiesen, D and Zaiser, G and Neuper, C and Wood, G},
title = {Ability to Gain Control Over One's Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {271},
pmid = {28596726},
issn = {1662-5161},
abstract = {Spiritual practice, such as prayer or meditation, is associated with focusing attention on internal states and self-awareness processes. As these cognitive control mechanisms presumably are also important for neurofeedback (NF), we investigated whether people who pray frequently (N = 20) show a higher ability of self-control over their own brain activity compared to a control group of individuals who rarely pray (N = 20). All participants underwent structural magnetic resonance imaging (MRI) and one session of sensorimotor rhythm (SMR, 12-15 Hz) based NF training. Individuals who reported a high frequency of prayer showed improved NF performance compared to individuals who reported a low frequency of prayer. The individual ability to control one's own brain activity was related to volumetric aspects of the brain. In the low frequency of prayer group, gray matter volumes in the right insula and inferior frontal gyrus were positively associated with NF performance, supporting prior findings that more general self-control networks are involved in successful NF learning. In contrast, participants who prayed regularly showed a negative association between gray matter volume in the left medial orbitofrontal cortex (Brodmann's area (BA) 10) and NF performance. Due to their regular spiritual practice, they might have been more skillful in gating incoming information provided by the NF system and avoiding task-irrelevant thoughts.},
}
@article {pmid28596725,
year = {2017},
author = {Abou Zeid, E and Rezazadeh Sereshkeh, A and Schultz, B and Chau, T},
title = {A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {254},
pmid = {28596725},
issn = {1662-5161},
abstract = {In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control. Here, we propose a ternary BCI based on single-trial RPs. This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. A pipeline of spatio-temporal filtering with per participant parameter optimization was used for feature extraction. The ternary classification was decomposed into binary classifications using a decision-directed acyclic graph (DDAG). For each class pair in the DDAG structure, an ordered diversified classifier system (ODCS-DDAG) was used to select the best among various classification algorithms or to combine the results of different classification algorithms. Using EEG data from 14 participants performing self-initiated left or right key presses, punctuated with rest periods, we compared the performance of ODCS-DDAG to a ternary classifier and four popular multiclass decomposition methods using only a single classification algorithm. ODCS-DDAG had the highest performance (0.769 Cohen's Kappa score) and was significantly better than the ternary classifier and two of the four multiclass decomposition methods. Our work supports further study of RP-based BCI for intuitive asynchronous environmental control or augmentative communication.},
}
@article {pmid28593170,
year = {2017},
author = {Bellucci, CHS and Ribeiro, WO and Hemerly, TS and de Bessa, J and Antunes, AA and Leite, KRM and Bruschini, H and Srougi, M and Gomes, CM},
title = {Increased detrusor collagen is associated with detrusor overactivity and decreased bladder compliance in men with benign prostatic obstruction.},
journal = {Prostate international},
volume = {5},
number = {2},
pages = {70-74},
pmid = {28593170},
issn = {2287-8882},
abstract = {BACKGROUND: This study aimed to investigate the relationship between detrusor collagen content and urodynamic parameters in men with benign prostatic obstruction.<br><br>MATERIAL AND METHODS: Nineteen consecutive patients undergoing open prostatectomy for bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) were evaluated. Urodynamic tests were performed in all patients. BOO and detrusor contractility were assessed with the BOO index (BOOI) and the bladder contractility index (BCI), respectively. A bladder fragment was obtained during prostatectomy. Eight cadaveric organ donors composed the control group. Bladder sections were stained with picrosirius red and hematoxylin-eosin. The collagen to smooth muscle ratio (C/M) in the detrusor was measured and its relationship with urodynamic parameters was investigated.<br><br>RESULTS: Seven (36.8%) patients were operated on due to lower urinary tract symptoms and 12 (63.2%) had urinary retention. The mean prostate volume was 128.6&#xa0;cm[3]&#xa0;&#xb1;&#xa0;32.3&#xa0;cm[3], the mean BOOI was 76.4&#xa0;&#xb1;&#xa0;33.0, and the mean BCI was 116.1&#xa0;&#xb1;&#xa0;33.7. The mean C/M in BPH patients and controls were 0.43&#xa0;&#xb1;&#xa0;0.13 and 0.33&#xa0;&#xb1;&#xa0;0.09, respectively (P&#xa0;=&#xa0;0.042). A negative correlation was shown between C/M and bladder compliance (r&#xa0;=&#xa0;-0.488, P&#xa0;=&#xa0;0.043). The C/M was increased in BPH patients with detrusor overactivity (DO) compared to those without DO (0.490&#xa0;&#xb1;&#xa0;0.110 and 0.360&#xa0;&#xb1;&#xa0;0.130, respectively; P&#xa0;=&#xa0;0.030) and also in patients with urinary retention (P&#xa0;=&#xa0;0.002). No correlation was shown between C/M and maximum cystometric capacity, BOOI, or BCI.<br><br>CONCLUSION: Men with BOO/BPH have increased detrusor collagen content which is associated with decreased bladder compliance, detrusor overactivity, and urinary retention.},
}
@article {pmid28588442,
year = {2017},
author = {Käthner, I and Halder, S and Hintermüller, C and Espinosa, A and Guger, C and Miralles, F and Vargiu, E and Dauwalder, S and Rafael-Palou, X and Solà, M and Daly, JM and Armstrong, E and Martin, S and Kübler, A},
title = {A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {286},
pmid = {28588442},
issn = {1662-4548},
abstract = {Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.},
}
@article {pmid28586492,
year = {2017},
author = {Huang, KT and Moses, ZB and Chi, JH},
title = {Advances in Implanted Brain-Computer Interfaces Allow for Independent Communication in a Locked-In Patient.},
journal = {Neurosurgery},
volume = {80},
number = {5},
pages = {N30-N31},
doi = {10.1093/neuros/nyx109},
pmid = {28586492},
issn = {1524-4040},
mesh = {Brain ; *Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; User-Computer Interface ; },
}
@article {pmid28585523,
year = {2017},
author = {Kalika, D and Collins, L and Caves, K and Throckmorton, C},
title = {Fusion of P300 and eye-tracker data for spelling using BCI2000.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056010},
doi = {10.1088/1741-2552/aa776b},
pmid = {28585523},
issn = {1741-2552},
mesh = {Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Eye Movements/physiology ; Fixation, Ocular/*physiology ; Humans ; Photic Stimulation/*methods ; Statistics as Topic/methods ; },
abstract = {OBJECTIVE: Various augmentative and alternative communication (AAC) devices have been developed in order to aid communication for individuals with communication disorders. Recently, there has been interest in combining EEG data and eye-gaze data with the goal of developing a hybrid (or 'fused') BCI (hBCI) AAC system. This work explores the effectiveness of a speller that fuses data from an eye-tracker and the P300 speller in order to create a hybrid P300 speller.<br><br>APPROACH: This hybrid speller collects both eye-tracking and EEG data in parallel, and the user spells characters on the screen in the same way that they would if they were only using the P300 speller. Online and offline experiments were performed. The online experiments measured the performance of the speller for sixteen non-disabled participants, while the offline simulations were used to assess the robustness of the hybrid system.<br><br>MAIN RESULTS: Online results showed that for fifteen non-disabled participants, using eye-gaze in a Bayesian framework with EEG data from the P300 speller improved accuracy ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations) and reduced the average number of flashes required to spell a character compared to the standard P300 speller that relies solely on EEG data ([Formula: see text], [Formula: see text], [Formula: see text] for estimated, medium and high variance configurations). Offline simulations indicate that the system provides more robust performance than a standalone eye gaze system.<br><br>SIGNIFICANCE: The results of this work on non-disabled participants shows the potential efficacy of hybrid P300 and eye-tracker speller. Further validation on the amyotrophic lateral sceloris population is needed to assess the benefit of this hybrid system.},
}
@article {pmid28583850,
year = {2017},
author = {Ali, H and Furlanello, F and Lupo, P and Foresti, S and De Ambroggi, G and Epicoco, G and Semprini, L and Fundaliotis, A and Cappato, R},
title = {Clinical and electrocardiographic features of complete heart block after blunt cardiac injury: A systematic review of the literature.},
journal = {Heart rhythm},
volume = {14},
number = {10},
pages = {1561-1569},
doi = {10.1016/j.hrthm.2017.05.040},
pmid = {28583850},
issn = {1556-3871},
mesh = {*Atrioventricular Block/diagnosis/etiology/physiopathology ; *Electrocardiography ; Heart Conduction System/*physiopathology ; Humans ; Myocardial Contusions/*complications ; },
abstract = {The underlying mechanisms and temporal course of complete heart block (CHB) after blunt cardiac injuries (BCIs) are poorly understood, and a systematic analysis of available data is lacking. In this systematic review, PubMed was searched for publications of reported cases of CHB-BCI analyzing clinical findings, electrocardiographic features, temporal course, and outcomes. Case reports on CHB-BCI were available for 50 patients, mainly secondary to traffic or sport accidents. A fatal outcome occurred in 10 of 50 (20%) of patients, while a structural damage of the atrioventricular (AV) conductive system was evident in 4 of 8 (50%) of necropsy studies. Clinical manifestation of CHB-BCI occurred within 72 hours of injury in 38 of 47 (∼80%) of patients, and 1:1 AV conduction was restored within 7-10 days in about half of early survivors. Permanent pacemaker implantation was indicated in 22 of 42 (∼50%) of early survivors because of recurrent or permanent CHB. Cardiac troponins, when analyzed, were elevated in 12 of 13 (∼90%) of patients, and electrocardiographic features of aberrancy were present in 29 of 40 (>70%) of patients. In conclusion, CHB secondary to BCI is associated with 20% mortality mainly occurring in the early posttraumatic period and most of the deaths are due to or triggered by this malignant arrhythmia. Recurrent or permanent CHB requiring pacemaker implantation occurs in ∼50% of survivors. A structural damage of the AV conductive system can be found in 50% of necropsy studies.},
}
@article {pmid28580720,
year = {2017},
author = {Kajal, DS and Braun, C and Mellinger, J and Sacchet, MD and Ruiz, S and Fetz, E and Birbaumer, N and Sitaram, R},
title = {Learned control of inter-hemispheric connectivity: Effects on bimanual motor performance.},
journal = {Human brain mapping},
volume = {38},
number = {9},
pages = {4353-4369},
pmid = {28580720},
issn = {1097-0193},
mesh = {Adult ; Conditioning, Operant/physiology ; Female ; Functional Laterality/*physiology ; Hand/*physiology ; Humans ; Learning/*physiology ; Magnetoencephalography/methods ; Male ; Motor Cortex/*physiology ; Motor Skills/*physiology ; *Neurofeedback/methods/physiology ; Neuronal Plasticity/physiology ; Volition ; },
abstract = {Bimanual movements involve the interactions between both primary motor cortices. These interactions are assumed to involve phase-locked oscillatory brain activity referred to as inter-hemispheric functional coupling. So far, inter-hemispheric functional coupling has been investigated as a function of motor performance. These studies report mostly a negative correlation between the performance in motor tasks and the strength of functional coupling. However, correlation might not reflect a causal relationship. To overcome this limitation, we opted for an alternative approach by manipulating the strength of inter-hemispheric functional coupling and assessing bimanual motor performance as a dependent variable. We hypothesize that an increase/decrease of functional coupling deteriorates/facilitates motor performance in an out-of-phase bimanual finger-tapping task. Healthy individuals were trained to volitionally regulate functional coupling in an operant conditioning paradigm using real-time magnetoencephalography neurofeedback. During operant conditioning, two discriminative stimuli were associated with upregulation and downregulation of functional coupling. Effects of training were assessed by comparing motor performance prior to (pre-test) and after the training (post-test). Participants receiving contingent feedback learned to upregulate and downregulate functional coupling. Comparing motor performance, as indexed by the ratio of tapping speed for upregulation versus downregulation trials, no change was found in the control group between pre- and post-test. In contrast, the group receiving contingent feedback evidenced a significant decrease of the ratio implicating lower tapping speed with stronger functional coupling. Results point toward a causal role of inter-hemispheric functional coupling for the performance in bimanual tasks. Hum Brain Mapp 38:4353-4369, 2017. © 2017 Wiley Periodicals, Inc.},
}
@article {pmid28580648,
year = {2018},
author = {Olsen, S and Signal, N and Niazi, IK and Christensen, T and Jochumsen, M and Taylor, D},
title = {Paired Associative Stimulation Delivered by Pairing Movement-Related Cortical Potentials With Peripheral Electrical Stimulation: An Investigation of the Duration of Neuromodulatory Effects.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {21},
number = {4},
pages = {362-367},
doi = {10.1111/ner.12616},
pmid = {28580648},
issn = {1525-1403},
mesh = {Adult ; Analysis of Variance ; Biophysics ; Brain Waves/*physiology ; Electric Stimulation/*methods ; Electroencephalography ; Electromyography ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Pathways/*physiology ; Outcome Assessment, Health Care ; Time Factors ; },
abstract = {BACKGROUND: Novel paired associative stimulation (novel-PAS), delivered by pairing movement-related cortical potentials (MRCPs) with electrical stimulation of somatosensory afferents, is an innovative neuromodulatory intervention. Novel-PAS results in increased corticomotor excitability and has potential as a rehabilitative adjunct to improve outcomes following stroke. The duration of its excitatory effect has important implications for how this novel PAS intervention might be applied within a traditional therapy session, but previous research has not explored its effects beyond 30 min post-intervention.<br><br>OBJECTIVE: The objective was to explore changes in corticomotor excitability in healthy participants, over a 60-min period following a single session of novel-PAS.<br><br>MATERIALS AND METHOD: Ten healthy adults completed a single session of novel-PAS, delivered by pairing 50 MRCPs with peripheral electrical stimulation. TMS was used to elicit motor evoked potentials (MEPs) of the tibialis anterior (TA) muscle, immediately prior to the intervention, and at 0, 30, 45, and 60 min post-intervention.<br><br>RESULTS: When compared with pre-intervention, there was a statistically significant increase in the mean TA MEP amplitudes at 0 (p&#x2009;=&#x2009;0.006), 30 (p&#x2009;=&#x2009;0.006), 45 (p&#x2009;=&#x2009;0.027), and 60 min post-intervention (p&#x2009;=&#x2009;0.020).<br><br>CONCLUSION: Corticomotor excitability is increased for 60 min following this novel-PAS intervention. Future research could investigate the optimal method of combining this neuromodulatory technique with traditional therapy.},
}
@article {pmid28578663,
year = {2017},
author = {Zhang, Y and Vrancken, B and Feng, Y and Dellicour, S and Yang, Q and Yang, W and Zhang, Y and Dong, L and Pybus, OG and Zhang, H and Tian, H},
title = {Cross-border spread, lineage displacement and evolutionary rate estimation of rabies virus in Yunnan Province, China.},
journal = {Virology journal},
volume = {14},
number = {1},
pages = {102},
pmid = {28578663},
issn = {1743-422X},
mesh = {China/epidemiology ; *Evolution, Molecular ; Humans ; *Molecular Epidemiology ; Nucleocapsid Proteins/genetics ; Rabies/*epidemiology/*virology ; Rabies virus/*classification/*genetics/isolation &amp; purification ; },
abstract = {BACKGROUND: Rabies is an important but underestimated threat to public health, with most cases reported in Asia. Since 2000, a new epidemic wave of rabies has emerged in Yunnan Province, southwestern China, which borders three countries in Southeast Asia.<br><br>METHOD: We estimated gene-specific evolutionary rates for rabies virus using available data in GenBank, then used this information to calibrate the timescale of rabies virus (RABV) spread in Asia. We used 452 publicly available geo-referenced complete nucleoprotein (N) gene sequences, including 52 RABV sequences that were recently generated from samples collected in Yunnan between 2008 and 2012.<br><br>RESULTS: The RABV N gene evolutionary rate was estimated to be 1.88&#x2009;&#xd7;&#x2009;10[-4] (1.37-2.41&#x2009;&#xd7;&#x2009;10[-4], 95% Bayesian credible interval, BCI) substitutions per site per year. Phylogenetic reconstructions show that the currently circulating RABV lineages in Yunnan result from at least seven independent introductions (95% BCI: 6-9 introductions) and represent each of the three main Asian RABV lineages, SEA-1, -2 and -3. We find that Yunnan is a sink location for the domestic spread of RABV and connects RABV epidemics in North China, South China, and Southeast Asia. Cross-border spread from southeast Asia (SEA) into South China, and intermixing of the North and South China epidemics is also well supported. The influx of RABV into Yunnan from SEA was not well-supported, likely due to the poor sampling of SEA RABV diversity. We found evidence for a lineage displacement of the Yunnan SEA-2 and -3 lineages by Yunnan SEA-1 strains, and considered whether this could be attributed to fitness differences.<br><br>CONCLUSION: Overall, our study contributes to a better understanding of the spread of RABV that could facilitate future rabies virus control and prevention efforts.},
}
@article {pmid28573984,
year = {2017},
author = {Li, Z and Jiang, YH and Duan, L and Zhu, CZ},
title = {A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046014},
doi = {10.1088/1741-2552/aa71c0},
pmid = {28573984},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; *Computer Simulation ; Humans ; *Models, Neurological ; Normal Distribution ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {OBJECTIVE: Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC).<br><br>APPROACH: GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers.<br><br>MAIN RESULTS: Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus&#x2009;&#x2009;<54% in two-choice classification accuracy.<br><br>SIGNIFICANCE: We believe GMMAC will be useful for clinical fNIRS-based brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.},
}
@article {pmid28573983,
year = {2017},
author = {Broccard, FD and Joshi, S and Wang, J and Cauwenberghs, G},
title = {Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {041002},
doi = {10.1088/1741-2552/aa67a9},
pmid = {28573983},
issn = {1741-2552},
mesh = {Animals ; Brain/cytology/*physiology ; *Brain-Computer Interfaces ; Humans ; *Neural Networks, Computer ; Neurons/*physiology ; },
abstract = {OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model.<br><br>APPROACH: This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity.<br><br>MAIN RESULTS: Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology.<br><br>SIGNIFICANCE: Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.},
}
@article {pmid28572817,
year = {2017},
author = {Li, T and Zhang, J and Xue, T and Wang, B},
title = {Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {5863512},
pmid = {28572817},
issn = {1687-5273},
mesh = {Brain-Computer Interfaces/*psychology ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Skills/*physiology ; Video Games/*psychology ; Young Adult ; },
abstract = {We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player's BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player's event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players' scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.},
}
@article {pmid28570813,
year = {2017},
author = {Guo, Y and Jiang, S and Grena, BJB and Kimbrough, IF and Thompson, EG and Fink, Y and Sontheimer, H and Yoshinobu, T and Jia, X},
title = {Polymer Composite with Carbon Nanofibers Aligned during Thermal Drawing as a Microelectrode for Chronic Neural Interfaces.},
journal = {ACS nano},
volume = {11},
number = {7},
pages = {6574-6585},
doi = {10.1021/acsnano.6b07550},
pmid = {28570813},
issn = {1936-086X},
support = {R01 NS031234/NS/NINDS NIH HHS/United States ; R01 NS052634/NS/NINDS NIH HHS/United States ; },
abstract = {Microelectrodes provide a direct pathway to investigate brain activities electrically from the external world, which has advanced our fundamental understanding of brain functions and has been utilized for rehabilitative applications as brain-machine interfaces. However, minimizing the tissue response and prolonging the functional durations of these devices remain challenging. Therefore, the development of next-generation microelectrodes as neural interfaces is actively progressing from traditional inorganic materials toward biocompatible and functional organic materials with a miniature footprint, good flexibility, and reasonable robustness. In this study, we developed a miniaturized all polymer-based neural probe with carbon nanofiber (CNF) composites as recording electrodes via the scalable thermal drawing process. We demonstrated that in situ CNF unidirectional alignment can be achieved during the thermal drawing, which contributes to a drastic improvement of electrical conductivity by 2 orders of magnitude compared to a conventional polymer electrode, while still maintaining the mechanical compliance with brain tissues. The resulting neural probe has a miniature footprint, including a recording site with a reduced size comparable to a single neuron and maintained impedance that was able to capture neural activities. Its stable functionality as a chronic implant has been demonstrated with the long-term reliable electrophysiological recording with single-spike resolution and the minimal tissue response over the extended period of implantation in wild-type mice. Technology developed here can be applied to basic chronic electrophysiological studies as well as clinical implementation for neuro-rehabilitative applications.},
}
@article {pmid28566997,
year = {2017},
author = {Courellis, H and Mullen, T and Poizner, H and Cauwenberghs, G and Iversen, JR},
title = {EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {180},
pmid = {28566997},
issn = {1662-4548},
abstract = {Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.},
}
@article {pmid28566464,
year = {2017},
author = {Grosberg, LE and Ganesan, K and Goetz, GA and Madugula, SS and Bhaskhar, N and Fan, V and Li, P and Hottowy, P and Dabrowski, W and Sher, A and Litke, AM and Mitra, S and Chichilnisky, EJ},
title = {Activation of ganglion cells and axon bundles using epiretinal electrical stimulation.},
journal = {Journal of neurophysiology},
volume = {118},
number = {3},
pages = {1457-1471},
pmid = {28566464},
issn = {1522-1598},
support = {R01 EY021271/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Axons/*physiology ; Electric Stimulation ; Evoked Potentials ; Female ; Macaca mulatta ; Male ; *Neural Prostheses ; Retinal Ganglion Cells/*physiology ; Sensory Thresholds ; },
abstract = {Epiretinal prostheses for treating blindness activate axon bundles, causing large, arc-shaped visual percepts that limit the quality of artificial vision. Improving the function of epiretinal prostheses therefore requires understanding and avoiding axon bundle activation. This study introduces a method to detect axon bundle activation on the basis of its electrical signature and uses the method to test whether epiretinal stimulation can directly elicit spikes in individual retinal ganglion cells without activating nearby axon bundles. Combined electrical stimulation and recording from isolated primate retina were performed using a custom multielectrode system (512 electrodes, 10-μm diameter, 60-μm pitch). Axon bundle signals were identified by their bidirectional propagation, speed, and increasing amplitude as a function of stimulation current. The threshold for bundle activation varied across electrodes and retinas, and was in the same range as the threshold for activating retinal ganglion cells near their somas. In the peripheral retina, 45% of electrodes that activated individual ganglion cells (17% of all electrodes) did so without activating bundles. This permitted selective activation of 21% of recorded ganglion cells (7% of expected ganglion cells) over the array. In one recording in the central retina, 75% of electrodes that activated individual ganglion cells (16% of all electrodes) did so without activating bundles. The ability to selectively activate a subset of retinal ganglion cells without axon bundles suggests a possible novel architecture for future epiretinal prostheses.NEW &amp; NOTEWORTHY Large-scale multielectrode recording and stimulation were used to test how selectively retinal ganglion cells can be electrically activated without activating axon bundles. A novel method was developed to identify axon activation on the basis of its unique electrical signature and was used to find that a subset of ganglion cells can be activated at single-cell, single-spike resolution without producing bundle activity in peripheral and central retina. These findings have implications for the development of advanced retinal prostheses.},
}
@article {pmid28566462,
year = {2017},
author = {Prochazka, A},
title = {Neurophysiology and neural engineering: a review.},
journal = {Journal of neurophysiology},
volume = {118},
number = {2},
pages = {1292-1309},
pmid = {28566462},
issn = {1522-1598},
support = {//CIHR/Canada ; },
mesh = {Animals ; Biomedical Engineering/*methods/trends ; Electric Stimulation/methods ; Humans ; *Implantable Neurostimulators ; Nervous System Physiological Phenomena ; Neurophysiology/*methods/trends ; },
abstract = {Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.},
}
@article {pmid28562664,
year = {2017},
author = {Matran-Fernandez, A and Poli, R},
title = {Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0178498},
pmid = {28562664},
issn = {1932-6203},
mesh = {Adult ; *Automation ; *Brain-Computer Interfaces ; Evoked Potentials ; Female ; Humans ; Male ; *Photic Stimulation ; Task Performance and Analysis ; Young Adult ; },
abstract = {The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants' performance. The N2pc that is elicited in our experiments contains information about the position of the target along the horizontal axis. Moreover, combining information from multiple participants provides absolute median improvements in the area under the receiver operating characteristic curve of up to 21% (for groups of size 3) with respect to single-user BCIs. These improvements are bigger when groups are formed by participants with similar individual performance, and much of this effect can be explained using simple theoretical models. Our results suggest that BCIs for automated triaging can be improved by integrating two classification systems: one devoted to target detection and another to detect the attentional shifts associated with lateral targets.},
}
@article {pmid28562624,
year = {2017},
author = {Chen, J and Zhang, D and Engel, AK and Gong, Q and Maye, A},
title = {Application of a single-flicker online SSVEP BCI for spatial navigation.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0178385},
pmid = {28562624},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Flicker Fusion ; Humans ; Male ; *Spatial Navigation ; Video Games ; Young Adult ; },
abstract = {A promising approach for brain-computer interfaces (BCIs) employs the steady-state visual evoked potential (SSVEP) for extracting control information. Main advantages of these SSVEP BCIs are a simple and low-cost setup, little effort to adjust the system parameters to the user and comparatively high information transfer rates (ITR). However, traditional frequency-coded SSVEP BCIs require the user to gaze directly at the selected flicker stimulus, which is liable to cause fatigue or even photic epileptic seizures. The spatially coded SSVEP BCI we present in this article addresses this issue. It uses a single flicker stimulus that appears always in the extrafoveal field of view, yet it allows the user to control four control channels. We demonstrate the embedding of this novel SSVEP stimulation paradigm in the user interface of an online BCI for navigating a 2-dimensional computer game. Offline analysis of the training data reveals an average classification accuracy of 96.9±1.64%, corresponding to an information transfer rate of 30.1±1.8 bits/min. In online mode, the average classification accuracy reached 87.9±11.4%, which resulted in an ITR of 23.8±6.75 bits/min. We did not observe a strong relation between a subject's offline and online performance. Analysis of the online performance over time shows that users can reliably control the new BCI paradigm with stable performance over at least 30 minutes of continuous operation.},
}
@article {pmid28559807,
year = {2017},
author = {Brouwer, AM and Hogervorst, MA and Oudejans, B and Ries, AJ and Touryan, J},
title = {EEG and Eye Tracking Signatures of Target Encoding during Structured Visual Search.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {264},
pmid = {28559807},
issn = {1662-5161},
abstract = {EEG and eye tracking variables are potential sources of information about the underlying processes of target detection and storage during visual search. Fixation duration, pupil size and event related potentials (ERPs) locked to the onset of fixation or saccade (saccade-related potentials, SRPs) have been reported to differ dependent on whether a target or a non-target is currently fixated. Here we focus on the question of whether these variables also differ between targets that are subsequently reported (hits) and targets that are not (misses). Observers were asked to scan 15 locations that were consecutively highlighted for 1 s in pseudo-random order. Highlighted locations displayed either a target or a non-target stimulus with two, three or four targets per trial. After scanning, participants indicated which locations had displayed a target. To induce memory encoding failures, participants concurrently performed an aurally presented math task (high load condition). In a low load condition, participants ignored the math task. As expected, more targets were missed in the high compared with the low load condition. For both conditions, eye tracking features distinguished better between hits and misses than between targets and non-targets (with larger pupil size and shorter fixations for missed compared with correctly encoded targets). In contrast, SRP features distinguished better between targets and non-targets than between hits and misses (with average SRPs showing larger P300 waveforms for targets than for non-targets). Single trial classification results were consistent with these averages. This work suggests complementary contributions of eye and EEG measures in potential applications to support search and detect tasks. SRPs may be useful to monitor what objects are relevant to an observer, and eye variables may indicate whether the observer should be reminded of them later.},
}
@article {pmid28559792,
year = {2017},
author = {Libey, T and Fetz, EE},
title = {Open-Source, Low Cost, Free-Behavior Monitoring, and Reward System for Neuroscience Research in Non-human Primates.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {265},
pmid = {28559792},
issn = {1662-4548},
support = {P51 OD010425/OD/NIH HHS/United States ; },
abstract = {We describe a low-cost system designed to document bodily movement and neural activity and deliver rewards to monkeys behaving freely in their home cage. An important application is to studying brain-machine interface (BMI) systems during free behavior, since brain signals associated with natural movement can differ significantly from those associated with more commonly used constrained conditions. Our approach allows for short-latency (<500 ms) reward delivery and behavior monitoring using low-cost off-the-shelf components. This system interfaces existing untethered recording equipment with a custom hub that controls a cage-mounted feeder. The behavior monitoring system uses a depth camera to provide real-time, easy-to-analyze, gross movement data streams. In a proof-of-concept experiment we demonstrate robust learning of neural activity using the system over 14 behavioral sessions.},
}
@article {pmid28558741,
year = {2017},
author = {Ron-Angevin, R and Velasco-Álvarez, F and Fernández-Rodríguez, &#xc1; and Díaz-Estrella, A and Blanca-Mena, MJ and Vizcaíno-Martín, FJ},
title = {Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {49},
pmid = {28558741},
issn = {1743-0003},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Robotics/*instrumentation ; *Wheelchairs ; },
abstract = {BACKGROUND: Certain diseases affect brain areas that control the movements of the patients' body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair.<br><br>METHOD: A proposal to control a wheelchair through a Brain-Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs.<br><br>RESULTS: Seventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session.<br><br>CONCLUSION: The results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.},
}
@article {pmid28558002,
year = {2017},
author = {Zafar, R and Dass, SC and Malik, AS},
title = {Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0178410},
pmid = {28558002},
issn = {1932-6203},
mesh = {Algorithms ; *Cognition ; Electroencephalography/*methods ; Humans ; Learning ; Likelihood Functions ; *Neural Networks, Computer ; },
abstract = {Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.},
}
@article {pmid28555611,
year = {2017},
author = {Wenzel, MA and Bogojeski, M and Blankertz, B},
title = {Real-time inference of word relevance from electroencephalogram and eye gaze.},
journal = {Journal of neural engineering},
volume = {14},
number = {5},
pages = {056007},
doi = {10.1088/1741-2552/aa7590},
pmid = {28555611},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography/*methods ; Eye Movements/physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Random Allocation ; *Reading ; *Semantics ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time.<br><br>APPROACH: Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time which words and thus which semantic category interested each reader based on the electroencephalogram (EEG) and the eye gaze.<br><br>MAIN RESULTS: Words that were subjectively relevant could be decoded online from the signals. The estimation resulted in an average rank of 1.62 for the category of interest among the five categories after a hundred words had been read.<br><br>SIGNIFICANCE: It was demonstrated that the interest of a reader can be inferred online from EEG and eye tracking signals, which can potentially be used in novel types of adaptive software, which enrich the interaction by adding implicit information about the interest of the user to the explicit interaction. The study is characterised by the following novelties. Interpretation with respect to the word meaning was necessary in contrast to the usual practice in brain-computer interfacing where stimulus recognition is sufficient. The typical counting task was avoided because it would not be sensible for implicit relevance detection. Several words were displayed at the same time, in contrast to the typical sequences of single stimuli. Neural activity was related with eye tracking to the words, which were scanned without restrictions on the eye movements.},
}
@article {pmid28555070,
year = {2017},
author = {Maksimenko, VA and van Heukelum, S and Makarov, VV and Kelderhuis, J and Lüttjohann, A and Koronovskii, AA and Hramov, AE and van Luijtelaar, G},
title = {Absence Seizure Control by a Brain Computer Interface.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {2487},
pmid = {28555070},
issn = {2045-2322},
mesh = {Animals ; *Brain-Computer Interfaces ; Disease Models, Animal ; Electroencephalography/*methods ; Epilepsy, Absence/*diagnosis/diagnostic imaging/physiopathology ; Humans ; Rats ; Seizures/*diagnosis/diagnostic imaging/physiopathology ; },
abstract = {The ultimate goal of epileptology is the complete abolishment of epileptic seizures. This might be achieved by a system that predicts seizure onset combined with a system that interferes with the process that leads to the onset of a seizure. Seizure prediction remains, as of yet, unresolved in absence-epilepsy, due to the sudden onset of seizures. We have developed a real-time absence seizure prediction algorithm, evaluated it and implemented it in an on-line, closed-loop brain stimulation system designed to prevent the spike-wave-discharges (SWDs), typical for absence epilepsy, in a genetic rat model. The algorithm corretly predicted 88% of the SWDs while the remaining were quickly detected. A high number of false-positive detections occurred mainly during light slow-wave-sleep. Inclusion of criteria to prevent false-positives greatly reduced the false alarm rate but decreased the sensitivity of the algoritm. Implementation of the latter version into a closed-loop brain-stimulation-system resulted in a 72% decrease in seizure activity. In contrast to long standing beliefs that SWDs are unpredictable, these results demonstrate that they can be predicted and that the development of closed-loop seizure prediction and prevention systems is a feasable step towards interventions to attain control and freedom from epileptic seizures.},
}
@article {pmid28553694,
year = {2017},
author = {Ludwig, SA and Kong, J},
title = {Investigation of different classifiers and channel configurations of a mobile P300-based brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {12},
pages = {2143-2154},
pmid = {28553694},
issn = {1741-0444},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*classification ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain-computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs.},
}
@article {pmid28553580,
year = {2017},
author = {Seifzadeh, S and Rezaei, M and Faez, K and Amiri, M},
title = {Fast and Efficient Four‑class Motor Imagery Electroencephalography Signal Analysis Using Common Spatial Pattern-Ridge Regression Algorithm for the Purpose of Brain-Computer Interface.},
journal = {Journal of medical signals and sensors},
volume = {7},
number = {2},
pages = {80-85},
pmid = {28553580},
issn = {2228-7477},
abstract = {Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors - the log-band power algorithm and common spatial patterns (CSPs) - are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left-right-hand movement. Finally, three well-known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies.},
}
@article {pmid28551083,
year = {2017},
author = {Koch, AK and Rabsilber, S and Lauche, R and Kümmel, S and Dobos, G and Langhorst, J and Cramer, H},
title = {The effects of yoga and self-esteem on menopausal symptoms and quality of life in breast cancer survivors-A secondary analysis of a randomized controlled trial.},
journal = {Maturitas},
volume = {105},
number = {},
pages = {95-99},
doi = {10.1016/j.maturitas.2017.05.008},
pmid = {28551083},
issn = {1873-4111},
mesh = {Adult ; Breast Neoplasms/*psychology ; Cancer Survivors/*psychology ; Fatigue ; Female ; Humans ; Menopause/*psychology ; Middle Aged ; Quality of Life ; *Self Concept ; *Yoga ; },
abstract = {OBJECTIVES: Previous research has found that yoga can enhance quality of life and ease menopausal symptoms of breast cancer survivors. The study examined whether self-esteem mediated the effects of yoga on quality of life, fatigue and menopausal symptoms, utilizing validated outcome measures.<br><br>STUDY DESIGN: This is a secondary analysis of a randomized controlled trial comparing the effects of yoga with those of usual care in 40 breast cancer survivors who suffered from menopausal symptoms. All participants completed all 3 assessments (week 0, week 12, and week 24) and provided full data.<br><br>MAIN OUTCOME MEASURES: Outcomes were measured using self-rating instruments. Mediation analyses were performed using SPSS.<br><br>RESULTS: Self-esteem mediated the effect of yoga on total menopausal symptoms (B=-2.11, 95% BCI [-5.40 to -0.37]), psychological menopausal symptoms (B=-0.94, 95% BCI [-2.30 to -0.01]), and urogenital menopausal symptoms (B=-0.66, 95% BCI [-1.65 to -0.15]), quality of life (B=8.04, 95% BCI [3.15-17.03]), social well-being (B=1.80, 95% BCI [0.54-4.21]), emotional well-being (B=1.62, 95% BCI [0.70-3.34]), functional well-being (B=1.84, 95% BCI [0.59-4.13]), and fatigue (B=4.34, 95% BCI [1.28-9.55]). Self-esteem had no effect on somatovegetative menopausal symptoms (B=-0.50, 95% BCI n.s.) or on physical well-being (B=0.79, 95% BCI n.s.).<br><br>CONCLUSIONS: Findings support the assumption that self-esteem plays a vital role in the beneficial effect of yoga and that yoga can have long-term benefits for women diagnosed with breast cancer and undergoing menopausal transition.},
}
@article {pmid28550098,
year = {2017},
author = {Bundy, DT and Souders, L and Baranyai, K and Leonard, L and Schalk, G and Coker, R and Moran, DW and Huskey, T and Leuthardt, EC},
title = {Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors.},
journal = {Stroke},
volume = {48},
number = {7},
pages = {1908-1915},
pmid = {28550098},
issn = {1524-4628},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Arm/*physiopathology ; *Brain-Computer Interfaces ; Chronic Disease ; Electroencephalography ; Feasibility Studies ; Humans ; Male ; Middle Aged ; *Outcome Assessment, Health Care ; *Prostheses and Implants ; Recovery of Function/*physiology ; Stroke/*therapy ; Stroke Rehabilitation/instrumentation/*methods ; Survivors ; },
abstract = {BACKGROUND AND PURPOSE: There are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments.<br><br>METHODS: Ten chronic hemiparetic stroke survivors with moderate-to-severe upper-limb motor impairment (mean Action Research Arm Test=13.4) used a powered exoskeleton that opened and closed the affected hand using spectral power from electroencephalographic signals from the unaffected hemisphere associated with imagined hand movements of the paretic limb. Patients used the system at home for 12 weeks. Motor function was evaluated before, during, and after the treatment.<br><br>RESULTS: Across patients, our BCI-driven approach resulted in a statistically significant average increase of 6.2 points in the Action Research Arm Test. This behavioral improvement significantly correlated with improvements in BCI control. Secondary outcomes of grasp strength, Motricity Index, and the Canadian Occupational Performance Measure also significantly improved.<br><br>CONCLUSIONS: The findings demonstrate the therapeutic potential of a BCI-driven neurorehabilitation approach using the unaffected hemisphere in this uncontrolled sample of chronic stroke survivors. They also demonstrate that BCI-driven neurorehabilitation can be effectively delivered in the home environment, thus increasing the probability of future clinical translation.<br><br>CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02552368.},
}
@article {pmid28548052,
year = {2017},
author = {Mainsah, BO and Reeves, G and Collins, LM and Throckmorton, CS},
title = {Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046025},
pmid = {28548052},
issn = {1741-2552},
support = {R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*standards ; *Event-Related Potentials, P300/physiology ; Forecasting ; Humans ; *Pattern Recognition, Visual/physiology ; Photic Stimulation/*methods ; *Psychomotor Performance/physiology ; },
abstract = {OBJECTIVE: The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable.<br><br>APPROACH: We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP.<br><br>MAIN RESULTS: With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm.<br><br>SIGNIFICANCE: By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.},
}
@article {pmid28546809,
year = {2017},
author = {Batula, AM and Mark, JA and Kim, YE and Ayaz, H},
title = {Comparison of Brain Activation during Motor Imagery and Motor Movement Using fNIRS.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {5491296},
pmid = {28546809},
issn = {1687-5273},
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Hand/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Psychomotor Performance ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Motor-activity-related mental tasks are widely adopted for brain-computer interfaces (BCIs) as they are a natural extension of movement intention, requiring no training to evoke brain activity. The ideal BCI aims to eliminate neuromuscular movement, making motor imagery tasks, or imagined actions with no muscle movement, good candidates. This study explores cortical activation differences between motor imagery and motor execution for both upper and lower limbs using functional near-infrared spectroscopy (fNIRS). Four simple finger- or toe-tapping tasks (left hand, right hand, left foot, and right foot) were performed with both motor imagery and motor execution and compared to resting state. Significant activation was found during all four motor imagery tasks, indicating that they can be detected via fNIRS. Motor execution produced higher activation levels, a faster response, and a different spatial distribution compared to motor imagery, which should be taken into account when designing an imagery-based BCI. When comparing left versus right, upper limb tasks are the most clearly distinguishable, particularly during motor execution. Left and right lower limb activation patterns were found to be highly similar during both imagery and execution, indicating that higher resolution imaging, advanced signal processing, or improved subject training may be required to reliably distinguish them.},
}
@article {pmid28542161,
year = {2017},
author = {Albrecht, L and Stallard, RF and Kalko, EKV},
title = {Land use history and population dynamics of free-standing figs in a maturing forest.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0177060},
pmid = {28542161},
issn = {1932-6203},
mesh = {*Ficus/growth &amp; development ; Panama ; *Rainforest ; Tropical Climate ; },
abstract = {Figs (Ficus sp.) are often considered as keystone resources which strongly influence tropical forest ecosystems. We used long-term tree-census data to track the population dynamics of two abundant free-standing fig species, Ficus insipida and F. yoponensis, on Barro Colorado Island (BCI), a 15.6-km2 island in Lake Gatún, Panama. Vegetation cover on BCI consists of a mosaic of old growth (>400 years) and maturing (about 90-150 year old) secondary rainforest. Locations and conditions of fig trees have been mapped and monitored on BCI for more than 35 years (1973-2011), with a focus on the Lutz Catchment area (25 ha). The original distribution of the fig trees shortly after the construction of the Panama Canal was derived from an aerial photograph from 1927 and was compared with previous land use and forest status. The distribution of both fig species (~850 trees) is restricted to secondary forest. Of the original 119 trees observed in Lutz Catchment in 1973, >70% of F. insipida and >90% of F. yoponensis had died by 2011. Observations in other areas on BCI support the trend of declining free-standing figs. We interpret the decline of these figs on BCI as a natural process within a maturing tropical lowland forest. Senescence of the fig trees appears to have been accelerated by severe droughts such as the strong El Niño event in the year 1982/83. Because figs form such an important food resource for frugivores, this shift in resource availability is likely to have cascading effects on frugivore populations.},
}
@article {pmid28541908,
year = {2017},
author = {Yang, Y and Boling, S and Mason, AJ},
title = {A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {4},
pages = {743-754},
doi = {10.1109/TBCAS.2017.2679032},
pmid = {28541908},
issn = {1940-9990},
mesh = {Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; Humans ; Neurons/*physiology ; *Prostheses and Implants ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm[2] area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm[2] electrode array.},
}
@article {pmid28541211,
year = {2017},
author = {Herron, JA and Thompson, MC and Brown, T and Chizeck, HJ and Ojemann, JG and Ko, AL},
title = {Cortical Brain-Computer Interface for Closed-Loop Deep Brain Stimulation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {11},
pages = {2180-2187},
doi = {10.1109/TNSRE.2017.2705661},
pmid = {28541211},
issn = {1558-0210},
mesh = {Beta Rhythm ; *Brain-Computer Interfaces/adverse effects ; Cerebral Cortex/*physiology ; Deep Brain Stimulation/adverse effects/*methods ; Electric Power Supplies ; Electrodes, Implanted ; Essential Tremor/therapy ; Extremities/innervation/physiology ; Humans ; Male ; Middle Aged ; Patient Safety ; Thalamus ; Treatment Outcome ; },
abstract = {Essential tremor is the most common neurological movement disorder. This progressive disease causes uncontrollable rhythmic motions-most often affecting the patient'sdominant upper extremity-thatoccur during volitional movement and make it difficult for the patient to perform everyday tasks. Medication may also become ineffective as the disorder progresses. For many patients, deep brain stimulation (DBS) of the thalamus is an effective means of treating this condition when medication fails. In current use, however, clinicians set the patient's stimulator to apply stimulation at all times-whether it is needed or not. This practice leads to excess power use, and more rapid depletion of batteries that require surgical replacement. In this paper, for the first time, neural sensing of movement (using chronically implanted cortical electrodes) is used to enable or disable stimulation for tremor. Therapeutic stimulation is delivered onlywhen the patient is actively using their effected limb, thereby reducing the total stimulation applied, and potentially extending the lifetime of surgically implanted batteries. This paper, which involves both implanted and external subsystems, paves the way for fully-implanted closed-loop DBS in the future.},
}
@article {pmid28541187,
year = {2017},
author = {Valeriani, D and Poli, R and Cinel, C},
title = {Enhancement of Group Perception via a Collaborative Brain-Computer Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {6},
pages = {1238-1248},
doi = {10.1109/TBME.2016.2598875},
pmid = {28541187},
issn = {1558-2531},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Decision Making/*physiology ; Electroencephalography/*methods ; Eye Movements/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; User-Computer Interface ; },
abstract = {OBJECTIVE: We aimed at improving group performance in a challenging visual search task via a hybrid collaborative brain-computer interface (cBCI).<br><br>METHODS: Ten participants individually undertook a visual search task where a display was presented for 250&#xa0;ms, and they had to decide whether a target was present or not. Local temporal correlation common spatial pattern (LTCCSP) was used to extract neural features from response- and stimulus-locked EEG epochs. The resulting feature vectors were extended by including response times and features extracted from eye movements. A classifier was trained to estimate the confidence of each group member. cBCI-assisted group decisions were then obtained using a confidence-weighted majority vote.<br><br>RESULTS: Participants were combined in groups of different sizes to assess the performance of the cBCI. Results show that LTCCSP neural features, response times, and eye movement features significantly improve the accuracy of the cBCI over what we achieved with previous systems. For most group sizes, our hybrid cBCI yields group decisions that are significantly better than majority-based group decisions.<br><br>CONCLUSION: The visual task considered here was much harder than a task we used in previous research. However, thanks to a range of technological enhancements, our cBCI has delivered a significant improvement over group decisions made by a standard majority vote.<br><br>SIGNIFICANCE: With previous cBCIs, groups may perform better than single non-BCI users. Here, cBCI-assisted groups are more accurate than identically sized non-BCI groups. This paves the way to a variety of real-world applications of cBCIs where reducing decision errors is vital.},
}
@article {pmid28539609,
year = {2017},
author = {Koo, B and Koh, CS and Park, HY and Lee, HG and Chang, JW and Choi, S and Shin, HC},
title = {Manipulation of Rat Movement via Nigrostriatal Stimulation Controlled by Human Visually Evoked Potentials.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {2340},
pmid = {28539609},
issn = {2045-2322},
mesh = {Adult ; Animals ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Maze Learning ; Movement/*physiology ; Rats ; Substantia Nigra/*physiology ; *User-Computer Interface ; },
abstract = {Here, we report that the development of a brain-to-brain interface (BBI) system that enables a human user to manipulate rat movement without any previous training. In our model, the remotely-guided rats (known as ratbots) successfully navigated a T-maze via contralateral turning behaviour induced by electrical stimulation of the nigrostriatal (NS) pathway by a brain- computer interface (BCI) based on the human controller's steady-state visually evoked potentials (SSVEPs). The system allowed human participants to manipulate rat movement with an average success rate of 82.2% and at an average rat speed of approximately 1.9 m/min. The ratbots had no directional preference, showing average success rates of 81.1% and 83.3% for the left- and right-turning task, respectively. This is the first study to demonstrate the use of NS stimulation for developing a highly stable ratbot that does not require previous training, and is the first instance of a training-free BBI for rat navigation. The results of this study will facilitate the development of borderless communication between human and untrained animals, which could not only improve the understanding of animals in humans, but also allow untrained animals to more effectively provide humans with information obtained with their superior perception.},
}
@article {pmid28538681,
year = {2017},
author = {Chien, JH and Korzeniewska, A and Colloca, L and Campbell, C and Dougherty, P and Lenz, F},
title = {Human Thalamic Somatosensory Nucleus (Ventral Caudal, Vc) as a Locus for Stimulation by INPUTS from Tactile, Noxious and Thermal Sensors on an Active Prosthesis.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {6},
pages = {},
pmid = {28538681},
issn = {1424-8220},
support = {R01 NS040059/NS/NINDS NIH HHS/United States ; },
mesh = {Humans ; Neurons ; Pain ; Prostheses and Implants ; *Thalamic Nuclei ; Touch ; },
abstract = {The forebrain somatic sensory locus for input from sensors on the surface of an active prosthesis is an important component of the Brain Machine Interface. We now review the neuronal responses to controlled cutaneous stimuli and the sensations produced by Threshold Stimulation at Microampere current levels (TMIS) in such a locus, the human thalamic Ventral Caudal nucleus (Vc). The responses of these neurons to tactile stimuli mirror those for the corresponding class of tactile mechanoreceptor fiber in the peripheral nerve, and TMIS can evoke sensations like those produced by the stimuli that optimally activate each class. These neuronal responses show a somatotopic arrangement from lateral to medial in the sequence: leg, arm, face and intraoral structures. TMIS evoked sensations show a much more detailed organization into anterior posteriorly oriented rods, approximately 300 microns diameter, that represent smaller parts of the body, such as parts of individual digits. Neurons responding to painful and thermal stimuli are most dense around the posterior inferior border of Vc, and TMIS evoked pain sensations occur in one of two patterns: (i) pain evoked regardless of the frequency or number of spikes in a burst of TMIS; and (ii) the description and intensity of the sensation changes with increasing frequencies and numbers. In patients with major injuries leading to loss of somatic sensory input, TMIS often evokes sensations in the representation of parts of the body with loss of sensory input, e.g., the phantom after amputation. Some patients with these injuries have ongoing pain and pain evoked by TMIS of the representation in those parts of the body. Therefore, thalamic TMIS may produce useful patterned somatotopic feedback to the CNS from sensors on an active prosthesis that is sometimes complicated by TMIS evoked pain in the representation of those parts of the body.},
}
@article {pmid28536269,
year = {2017},
author = {Hernández-González, S and Andreu-Sánchez, C and Martín-Pascual, M&#xc1; and Gruart, A and Delgado-García, JM},
title = {A Cognition-Related Neural Oscillation Pattern, Generated in the Prelimbic Cortex, Can Control Operant Learning in Rats.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {37},
number = {24},
pages = {5923-5935},
pmid = {28536269},
issn = {1529-2401},
mesh = {Animals ; Biological Clocks/*physiology ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Conditioning, Operant/*physiology ; Evoked Potentials/physiology ; Feedback, Physiological/physiology ; Limbic Lobe/*physiology ; Male ; Nerve Net/*physiology ; Rats ; },
abstract = {The prelimbic (PrL) cortex constitutes one of the highest levels of cortical hierarchy dedicated to the execution of adaptive behaviors. We have identified a specific local field potential (LFP) pattern generated in the PrL cortex and associated with cognition-related behaviors. We used this pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected θ to β-γ (θ/β-γ) transition pattern across training sessions. The selected LFP pattern appeared to coincide with a significant decrease in the firing of PrL pyramidal neurons and did not seem to propagate to other cortical or subcortical areas. An indication of the PrL cortex's cognitive nature is that the experimental disruption of this θ/β-γ transition pattern prevented the proper performance of the acquired task without affecting the generation of other motor responses. The use of this LFP pattern to trigger an operant task evoked only minor changes in its electrophysiological properties. Thus, the PrL cortex has the capability of generating an oscillatory pattern for dealing with environmental constraints. In addition, the selected θ/β-γ transition pattern could be a useful tool to activate the presentation of external cues or to modify the current circumstances.SIGNIFICANCE STATEMENT Brain-machine interfaces represent a solution for physically impaired people to communicate with external devices. We have identified a specific local field potential pattern generated in the prelimbic cortex and associated with goal-directed behaviors. We used the pattern to trigger the activation of a visual display on a touch screen as part of an operant conditioning task. Rats learned to increase the presentation rate of the selected field potential pattern across training. The selected pattern was not modified when used to activate the touch screen. Electrical stimulation of the recording site prevented the proper performance of the task. Our findings show that the prelimbic cortex can generate oscillatory patterns that rats can use to control their environment for achieving specific goals.},
}
@article {pmid28534785,
year = {2017},
author = {Chamanzar, A and Shabany, M and Malekmohammadi, A and Mohammadinejad, S},
title = {Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {3},
pages = {585-596},
doi = {10.1109/TBCAS.2017.2669911},
pmid = {28534785},
issn = {1940-9990},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Equipment Design ; Humans ; Intention ; *Movement ; *Wavelet Analysis ; },
abstract = {The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity).},
}
@article {pmid28533700,
year = {2017},
author = {He, Y and Eguren, D and Luu, TP and Contreras-Vidal, JL},
title = {Risk management and regulations for lower limb medical exoskeletons: a review.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {10},
number = {},
pages = {89-107},
pmid = {28533700},
issn = {1179-1470},
abstract = {Gait disability is a major health care problem worldwide. Powered exoskeletons have recently emerged as devices that can enable users with gait disabilities to ambulate in an upright posture, and potentially bring other clinical benefits. In 2014, the US Food and Drug Administration approved marketing of the ReWalk™ Personal Exoskeleton as a class II medical device with special controls. Since then, Indego™ and Ekso™ have also received regulatory approval. With similar trends worldwide, this industry is likely to grow rapidly. On the other hand, the regulatory science of powered exoskeletons is still developing. The type and extent of probable risks of these devices are yet to be understood, and industry standards are yet to be developed. To address this gap, Manufacturer and User Facility Device Experience, Clinicaltrials.gov, and PubMed databases were searched for reports of adverse events and inclusion and exclusion criteria involving the use of lower limb powered exoskeletons. Current inclusion and exclusion criteria, which can determine probable risks, were found to be diverse. Reported adverse events and identified risks of current devices are also wide-ranging. In light of these findings, current regulations, standards, and regulatory procedures for medical device applications in the USA, Europe, and Japan were also compared. There is a need to raise awareness of probable risks associated with the use of powered exoskeletons and to develop adequate countermeasures, standards, and regulations for these human-machine systems. With appropriate risk mitigation strategies, adequate standards, comprehensive reporting of adverse events, and regulatory oversight, powered exoskeletons may one day allow individuals with gait disabilities to safely and independently ambulate.},
}
@article {pmid28533392,
year = {2017},
author = {Zhou, T and Hong, G and Fu, TM and Yang, X and Schuhmann, TG and Viveros, RD and Lieber, CM},
title = {Syringe-injectable mesh electronics integrate seamlessly with minimal chronic immune response in the brain.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {114},
number = {23},
pages = {5894-5899},
pmid = {28533392},
issn = {1091-6490},
mesh = {Animals ; Brain/*immunology/physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Male ; Mice ; Mice, Inbred C57BL ; Microscopy, Fluorescence ; Syringes ; },
abstract = {Implantation of electrical probes into the brain has been central to both neuroscience research and biomedical applications, although conventional probes induce gliosis in surrounding tissue. We recently reported ultraflexible open mesh electronics implanted into rodent brains by syringe injection that exhibit promising chronic tissue response and recording stability. Here we report time-dependent histology studies of the mesh electronics/brain-tissue interface obtained from sections perpendicular and parallel to probe long axis, as well as studies of conventional flexible thin-film probes. Confocal fluorescence microscopy images of the perpendicular and parallel brain slices containing mesh electronics showed that the distribution of astrocytes, microglia, and neurons became uniform from 2-12 wk, whereas flexible thin-film probes yield a marked accumulation of astrocytes and microglia and decrease of neurons for the same period. Quantitative analyses of 4- and 12-wk data showed that the signals for neurons, axons, astrocytes, and microglia are nearly the same from the mesh electronics surface to the baseline far from the probes, in contrast to flexible polymer probes, which show decreases in neuron and increases in astrocyte and microglia signals. Notably, images of sagittal brain slices containing nearly the entire mesh electronics probe showed that the tissue interface was uniform and neurons and neurofilaments penetrated through the mesh by 3 mo postimplantation. The minimal immune response and seamless interface with brain tissue postimplantation achieved by ultraflexible open mesh electronics probes provide substantial advantages and could enable a wide range of opportunities for in vivo chronic recording and modulation of brain activity in the future.},
}
@article {pmid28529762,
year = {2017},
author = {Saha, S and Ahmed, KI and Mostafa, R and Khandoker, AH and Hadjileontiadis, L},
title = {Enhanced inter-subject brain computer interface with associative sensorimotor oscillations.},
journal = {Healthcare technology letters},
volume = {4},
number = {1},
pages = {39-43},
pmid = {28529762},
issn = {2053-3713},
abstract = {Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.},
}
@article {pmid28529473,
year = {2017},
author = {Guger, C and Spataro, R and Allison, BZ and Heilinger, A and Ortner, R and Cho, W and La Bella, V},
title = {Complete Locked-in and Locked-in Patients: Command Following Assessment and Communication with Vibro-Tactile P300 and Motor Imagery Brain-Computer Interface Tools.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {251},
pmid = {28529473},
issn = {1662-4548},
abstract = {Many patients with locked-in syndrome (LIS) or complete locked-in syndrome (CLIS) also need brain-computer interface (BCI) platforms that do not rely on visual stimuli and are easy to use. We investigate command following and communication functions of mindBEAGLE with 9 LIS, 3 CLIS patients and three healthy controls. This tests were done with vibro-tactile stimulation with 2 or 3 stimulators (VT2 and VT3 mode) and with motor imagery (MI) paradigms. In VT2 the stimulators are fixed on the left and right wrist and the participant has the task to count the stimuli on the target hand in order to elicit a P300 response. In VT3 mode an additional stimulator is placed as a distractor on the shoulder and the participant is counting stimuli either on the right or left hand. In motor imagery mode the participant is instructed to imagine left or right hand movement. VT3 and MI also allow the participant to answer yes and no questions. Healthy controls achieved a mean assessment accuracy of 100% in VT2, 93% in VT3, and 73% in MI modes. They were able to communicate with VT3 (86.7%) and MI (83.3%) after 2 training runs. The patients achieved a mean accuracy of 76.6% in VT2, 63.1% in VT3, and 58.2% in MI modes after 1-2 training runs. 9 out of 12 LIS patients could communicate by using the vibro-tactile P300 paradigms (answered on average 8 out of 10 questions correctly) and 3 out of 12 could communicate with the motor imagery paradigm (answered correctly 4,7 out of 5 questions). 2 out of the 3 CLIS patients could use the system to communicate with VT3 (90 and 70% accuracy). The results show that paradigms based on non-visual evoked potentials and motor imagery can be effective for these users. It is also the first study that showed EEG-based BCI communication with CLIS patients and was able to bring 9 out of 12 patients to communicate with higher accuracies than reported before. More importantly this was achieved within less than 15-20 min.},
}
@article {pmid28522850,
year = {2017},
author = {Bouillot, S and Munro, P and Gallet, B and Reboud, E and Cretin, F and Golovkine, G and Schoehn, G and Attrée, I and Lemichez, E and Huber, P},
title = {Pseudomonas aeruginosa Exolysin promotes bacterial growth in lungs, alveolar damage and bacterial dissemination.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {2120},
pmid = {28522850},
issn = {2045-2322},
mesh = {Alveolar Epithelial Cells/*microbiology ; Animals ; Bacteremia/*microbiology ; Bacterial Toxins/*metabolism ; Female ; Mice ; Mice, Inbred BALB C ; Phagocytosis ; Pore Forming Cytotoxic Proteins/*metabolism ; Pseudomonas aeruginosa/*pathogenicity ; },
abstract = {Exolysin (ExlA) is a recently-identified pore-forming toxin secreted by a subset of Pseudomonas aeruginosa strains identified worldwide and devoid of Type III secretion system (T3SS), a major virulence factor. Here, we characterized at the ultrastructural level the lesions caused by an ExlA-secreting strain, CLJ1, in mouse infected lungs. CLJ1 induced necrotic lesions in pneumocytes and endothelial cells, resulting in alveolo-vascular barrier breakdown. Ectopic expression of ExlA in an exlA-negative strain induced similar tissue injuries. In addition, ExlA conferred on bacteria the capacity to proliferate in lungs and to disseminate in secondary organs, similar to bacteria possessing a functional T3SS. CLJ1 did not promote a strong neutrophil infiltration in the alveoli, owing to the weak pro-inflammatory cytokine reaction engendered by the strain. However, CLJ1 was rapidly eliminated from the blood in a bacteremia model, suggesting that it can be promptly phagocytosed by immune cells. Together, our study ascribes to ExlA-secreting bacteria the capacity to proliferate in the lung and to damage pulmonary tissues, thereby promoting metastatic infections, in absence of substantial immune response exacerbation.},
}
@article {pmid28521803,
year = {2017},
author = {Yandell, MB and Quinlivan, BT and Popov, D and Walsh, C and Zelik, KE},
title = {Physical interface dynamics alter how robotic exosuits augment human movement: implications for optimizing wearable assistive devices.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {40},
pmid = {28521803},
issn = {1743-0003},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Exoskeleton Device ; Humans ; Movement ; Robotics/*instrumentation ; },
abstract = {BACKGROUND: Wearable assistive devices have demonstrated the potential to improve mobility outcomes for individuals with disabilities, and to augment healthy human performance; however, these benefits depend on how effectively power is transmitted from the device to the human user. Quantifying and understanding this power transmission is challenging due to complex human-device interface dynamics that occur as biological tissues and physical interface materials deform and displace under load, absorbing and returning power.<br><br>METHODS: Here we introduce a new methodology for quickly estimating interface power dynamics during movement tasks using common motion capture and force measurements, and then apply this method to quantify how a soft robotic ankle exosuit interacts with and transfers power to the human body during walking. We partition exosuit end-effector power (i.e., power output from the device) into power that augments ankle plantarflexion (termed augmentation power) vs. power that goes into deformation and motion of interface materials and underlying soft tissues (termed interface power).<br><br>RESULTS: We provide empirical evidence of how human-exosuit interfaces absorb and return energy, reshaping exosuit-to-human power flow and resulting in three key consequences: (i) During exosuit loading (as applied forces increased), about 55% of exosuit end-effector power was absorbed into the interfaces. (ii) However, during subsequent exosuit unloading (as applied forces decreased) most of the absorbed interface power was returned viscoelastically. Consequently, the majority (about 75%) of exosuit end-effector work over each stride contributed to augmenting ankle plantarflexion. (iii) Ankle augmentation power (and work) was delayed relative to exosuit end-effector power, due to these interface energy absorption and return dynamics.<br><br>CONCLUSIONS: Our findings elucidate the complexities of human-exosuit interface dynamics during transmission of power from assistive devices to the human body, and provide insight into improving the design and control of wearable robots. We conclude that in order to optimize the performance of wearable assistive devices it is important, throughout design and evaluation phases, to account for human-device interface dynamics that affect power transmission and thus human augmentation benefits.},
}
@article {pmid28516901,
year = {2017},
author = {Shenoy Handiru, V and Vinod, AP and Guan, C},
title = {EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046008},
doi = {10.1088/1741-2552/aa6baf},
pmid = {28516901},
issn = {1741-2552},
mesh = {Adult ; Arm/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Eye Movements/physiology ; Factor Analysis, Statistical ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Random Allocation ; },
abstract = {OBJECTIVE: In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions.<br><br>APPROACH: We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method.<br><br>MAIN RESULTS: Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction.<br><br>SIGNIFICANCE: This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.},
}
@article {pmid28513478,
year = {2017},
author = {Egan, JM and Loughnane, GM and Fletcher, H and Meade, E and Lalor, EC},
title = {A gaze independent hybrid-BCI based on visual spatial attention.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046006},
doi = {10.1088/1741-2552/aa6bb2},
pmid = {28513478},
issn = {1741-2552},
mesh = {Attention/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular/*physiology ; Humans ; Occipital Lobe/*physiology ; Photic Stimulation/methods ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) use measures of brain activity to convey a user's intent without the need for muscle movement. Hybrid designs, which use multiple measures of brain activity, have been shown to increase the accuracy of BCIs, including those based on EEG signals reflecting covert attention. Our study examined whether incorporating a measure of the P3 response improved the performance of a previously reported attention-based BCI design that incorporates measures of steady-state visual evoked potentials (SSVEP) and alpha band modulations.<br><br>APPROACH: Subjects viewed stimuli consisting of two bi-laterally located flashing white boxes on a black background. Streams of letters were presented sequentially within the boxes, in random order. Subjects were cued to attend to one of the boxes without moving their eyes, and they were tasked with counting the number of target-letters that appeared within. P3 components evoked by target appearance, SSVEPs evoked by the flashing boxes, and power in the alpha band are modulated by covert attention, and the modulations can be used to classify trials as left-attended or right-attended.<br><br>MAIN RESULTS: We showed that classification accuracy was improved by including a P3 feature along with the SSVEP and alpha features (the inclusion of a P3 feature lead to a 9% increase in accuracy compared to the use of SSVEP and Alpha features alone). We also showed that the design improves the robustness of BCI performance to individual subject differences.<br><br>SIGNIFICANCE: These results demonstrate that incorporating multiple neurophysiological indices of covert attention can improve performance in a gaze-independent BCI.},
}
@article {pmid28512396,
year = {2017},
author = {Bhattacharyya, S and Konar, A and Tibarewala, DN and Hayashibe, M},
title = {A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {226},
pmid = {28512396},
issn = {1662-4548},
abstract = {Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.},
}
@article {pmid28512066,
year = {2017},
author = {Monge-Pereira, E and Ibañez-Pereda, J and Alguacil-Diego, IM and Serrano, JI and Spottorno-Rubio, MP and Molina-Rueda, F},
title = {Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {9},
number = {9},
pages = {918-932},
doi = {10.1016/j.pmrj.2017.04.016},
pmid = {28512066},
issn = {1934-1563},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Hemiplegia/physiopathology/rehabilitation ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Prognosis ; Stroke/*diagnostic imaging/physiopathology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke.<br><br>OBJECTIVES: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review.<br><br>LITERATURE SURVEY: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30,&#xa0;2015.<br><br>METHODOLOGY: This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function.<br><br>SYNTHESIS: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1&#xa0;study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients.<br><br>CONCLUSIONS: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke.<br><br>LEVEL OF EVIDENCE: II.},
}
@article {pmid28507774,
year = {2017},
author = {Kalk, NJ and Young, AH},
title = {Footnotes to Kraepelin: changes in the classification of mood disorders with DSM-5.},
journal = {BJPsych open},
volume = {3},
number = {3},
pages = {e1-e3},
pmid = {28507774},
issn = {2056-4724},
abstract = {SUMMARY: Reliable diagnosis of mood disorders continues to pose a challenge. This is surprising because they have been recognised clinically since classical times. Mood disorders are also common: major depressive disorder affects nearly 300 million people worldwide and bipolar affective disorder nearly 60 million and they are a major cause of disability. Nonetheless, the reliability trials of the updated Diagnostic and Statistical Manual, Fifth Edition (DSM-5) found that the reliability of the diagnosis of major depressive disorder was in the 'questionable' range. Although the reliability of the diagnosis of bipolar I disorder in the same trials was 'good', the sample size of the individuals recruited to validate bipolar II disorder was insufficient to confirm reliability. As the epidemiological prevalences of bipolar I and bipolar II disorders are the same, this alone implies problems in its recognition. Here, we critically evaluate the most recent iteration of DSM mood disorder diagnoses in a historical light and set out the implications for clinical practice and research.<br><br>DECLARATION OF INTEREST: N.J.K. has attended educational activities funded by GlaxoSmithKline (GSK) and by Lundbeck and has worked on data from a study funded by Wyeth; her PhD was jointly funded by the Wellcome Trust and GSK. A.H.Y. has given paid lectures and is on advisory boards for the following companies with drugs used in affective and related disorders: Astrazenaca, Eli Lilly, Janssen, Lundeck, Sunovion, Servier, Livanova. He is Lead Investigator for the Embolden Study (Astrazenaca), BCI Neuroplasticity study and Aripiprazole Mania Study, which are investigator-initiated studies from Astrazenaca, Eli Lilly, Lundbeck, and Wyeth.<br><br>COPYRIGHT AND USAGE: &#xa9; The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.},
}
@article {pmid28506497,
year = {2017},
author = {Krause, J and Merz, J},
title = {Comparison of enzymatic hydrolysis in a centrifugal partition chromatograph and stirred tank reactor.},
journal = {Journal of chromatography. A},
volume = {1504},
number = {},
pages = {64-70},
doi = {10.1016/j.chroma.2017.05.006},
pmid = {28506497},
issn = {1873-3778},
mesh = {Bioreactors ; Candida/enzymology ; Catalysis ; Centrifugation/instrumentation/*methods ; Chromatography, Liquid/*methods ; Enzymes, Immobilized/chemistry ; Fungal Proteins/*chemistry ; Hydrolysis ; Lipase/*chemistry ; },
abstract = {Recently the Centrifugal Partition Chromatography (CPC) device is investigated as a reactor for biocatalytic reactions, as it enables biocatalyst immobilization without solid support and continuous operation of biphasic reaction systems. However, a detailed determination of the enzymes behavior in the CPC reactor and comparison to a classical stirred tank reactor (STR) for biphasic enzyme catalysis is not shown yet. In this study, the performance of an enzymatic biphasic hydrolysis reaction using lipase from Candida rugosa is systematically evaluated using a STR. The results are compared to different experiments conducted in the CPC reactor and used to evaluate the reaction performance in each. The same characteristics and limitations were observed in STR and CPC. At all states the CPC provided a similar reaction performance. However, the reaction in the CPC runs faster into limitations and was not easily scalable due to complex effects of the flow pattern. Although the enzyme was immobilized successfully and the activity of the lipase was preserved during CPC operation optimizations are needed to make the CPC reactor more competitive. For instance, scaling up the chamber geometry seems to be mandatory to increase the reaction performance, which may promote this reactor concept as an alternative to common devices for continuous biphasic biocatalytic reactions.},
}
@article {pmid28504971,
year = {2017},
author = {Schroeder, KE and Irwin, ZT and Bullard, AJ and Thompson, DE and Bentley, JN and Stacey, WC and Patil, PG and Chestek, CA},
title = {Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046016},
pmid = {28504971},
issn = {1741-2552},
support = {K08 NS069783/NS/NINDS NIH HHS/United States ; R01 GM111293/GM/NIGMS NIH HHS/United States ; R01 NS094399/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Artificial Limbs ; *Brain-Computer Interfaces ; Fingers/*physiology ; Hand Strength/*physiology ; Humans ; Macaca mulatta ; Motor Cortex/*physiology ; Touch/*physiology ; },
abstract = {OBJECTIVE: Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception. Here we examined instead how tactile somatosensation of the hand and fingers is represented in M1.<br><br>APPROACH: We recorded multi- and single units and thresholded neural activity from macaque M1 while gently brushing individual finger pads at 2 Hz. We also recorded broadband neural activity from electrocorticogram (ECoG) grids placed on human motor cortex, while applying the same tactile stimulus.<br><br>MAIN RESULTS: Units displaying significant differences in firing rates between individual fingers (p&#x2009;&#x2009;<&#x2009;&#x2009;0.05) represented up to 76.7% of sorted multiunits across four monkeys. After normalizing by the number of channels with significant motor finger responses, the percentage of electrodes with significant tactile responses was 74.9%&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;24.7%. No somatotopic organization of finger preference was obvious across cortex, but many units exhibited cosine-like tuning across multiple digits. Sufficient sensory information was present in M1 to correctly decode stimulus position from multiunit activity above chance levels in all monkeys, and also from ECoG gamma power in two human subjects.<br><br>SIGNIFICANCE: These results provide some explanation for difficulties experienced by motor decoders in clinical trials of cortically controlled prosthetic hands, as well as the general problem of disentangling motor and sensory signals in primate motor cortex during dextrous tasks. Additionally, examination of unit tuning during tactile and proprioceptive inputs indicates cells are often tuned differently in different contexts, reinforcing the need for continued refinement of BMI training and decoding approaches to closed-loop BMI systems for dexterous grasping.},
}
@article {pmid28504943,
year = {2017},
author = {Gui, K and Liu, H and Zhang, D},
title = {Toward Multimodal Human-Robot Interaction to Enhance Active Participation of Users in Gait Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {11},
pages = {2054-2066},
doi = {10.1109/TNSRE.2017.2703586},
pmid = {28504943},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Central Pattern Generators ; Electromyography ; Evoked Potentials, Somatosensory ; Exoskeleton Device ; Gait ; Gait Disorders, Neurologic/*rehabilitation ; Healthy Volunteers ; Humans ; Leg ; Locomotion ; Male ; *Patient Participation ; *Robotics ; Stroke Rehabilitation/instrumentation/methods ; },
abstract = {Robotic exoskeletons for physical rehabilitation have been utilized for retraining patients suffering from paraplegia and enhancing motor recovery in recent years. However, users are not voluntarily involved in most systems. This paper aims to develop a locomotion trainer with multiple gait patterns, which can be controlled by the active motion intention of users. A multimodal human-robot interaction (HRI) system is established to enhance subject's active participation during gait rehabilitation, which includes cognitive HRI (cHRI) and physical HRI (pHRI). The cHRI adopts brain-computer interface based on steady-state visual evoked potential. The pHRI is realized via admittance control based on electromyography. A central pattern generator is utilized to produce rhythmic and continuous lower joint trajectories, and its state variables are regulated by cHRI and pHRI. A custom-made leg exoskeleton prototype with the proposed multimodal HRI is tested on healthy subjects and stroke patients. The results show that voluntary and active participation can be effectively involved to achieve various assistive gait patterns.},
}
@article {pmid28500386,
year = {2017},
author = {Altuntepe, E and Greinert, T and Hartmann, F and Reinhardt, A and Sadowski, G and Held, C},
title = {Thermodynamics of enzyme-catalyzed esterifications: I. Succinic acid esterification with ethanol.},
journal = {Applied microbiology and biotechnology},
volume = {101},
number = {15},
pages = {5973-5984},
doi = {10.1007/s00253-017-8287-4},
pmid = {28500386},
issn = {1432-0614},
mesh = {Biocatalysis ; Enzymes, Immobilized/metabolism ; Esterification ; Ethanol/*metabolism ; Fungal Proteins/chemistry/*metabolism ; Hydrogen-Ion Concentration ; Kinetics ; Lipase/chemistry/*metabolism ; Succinic Acid/*metabolism ; Temperature ; Thermodynamics ; Water ; },
abstract = {Succinic acid (SA) was esterified with ethanol using Candida antarctica lipase B immobilized on acrylic resin at 40 and 50 °C. Enzyme activity in the reaction medium was assured prior to reaction experiments. Reaction-equilibrium experiments were performed for varying initial molalities of SA and water in the reaction mixtures. This allowed calculating the molality-based apparent equilibrium constant K m as function of concentration and temperature. K m was shown to depend strongly on the molality of water and SA as well as on temperature. It could be concluded that increasing the molality of SA shifted the reaction equilibrium towards the products. Water had a strong effect on the activity of the enzyme and on K m . The concentration dependence of K m values was explained by the activity coefficients of the reacting agents. These were predicted with the thermodynamic models Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), NRTL, and Universal Quasichemical Functional Group Activity Coefficients (UNIFAC), yielding the ratio of activity coefficients of products and reactants K γ . All model parameters were taken from literature. The models yielded K γ values between 25 and 115. Thus, activity coefficients have a huge impact on the consistent determination of the thermodynamic equilibrium constants K th. Combining K m and PC-SAFT-predicted K γ allowed determining K th and the standard Gibbs energy of reaction as function of temperature. This value was shown to be in very good agreement with results obtained from group contribution methods for Gibbs energy of formation. In contrast, inconsistencies were observed for K th using K γ values from the classical g[E]-models UNIFAC and NRTL. The importance of activity coefficients opens the door for an optimized reaction setup for enzymatic esterifications.},
}
@article {pmid28499296,
year = {2017},
author = {Tisch, M},
title = {[Implantable Hearing Devices].},
journal = {Laryngo- rhino- otologie},
volume = {96},
number = {S 01},
pages = {S84-S102},
doi = {10.1055/s-0042-118775},
pmid = {28499296},
issn = {1438-8685},
mesh = {Bone Conduction/physiology ; *Cochlear Implants ; Cross-Sectional Studies ; Deafness/epidemiology/*rehabilitation ; Germany ; Humans ; *Ossicular Prosthesis ; Prosthesis Design ; },
abstract = {Combined hearing loss is an essential indication for implantable hearing systems. Depending on the bone conduction threshold, various options are available: Patients with mild sensorineural deafness usually benefit from transcutaneous BCI, while percutaneous BCI systems are recommended also for moderate hearing loss. For combined hearing loss with moderate and high-grade cochlear hearing loss, active middle ear implants are recommended. For patients with incompatibilities or middle ear surgery, implants are a valuable and proven addition to the therapeutic options.},
}
@article {pmid28499043,
year = {2017},
author = {Porter, A},
title = {Bioethics and Transhumanism.},
journal = {The Journal of medicine and philosophy},
volume = {42},
number = {3},
pages = {237-260},
doi = {10.1093/jmp/jhx001},
pmid = {28499043},
issn = {1744-5019},
mesh = {*Bioethical Issues ; Biomedical Enhancement/*ethics ; Genetic Enhancement/ethics ; *Humanism ; Humans ; *Personhood ; Philosophy ; },
abstract = {Transhumanism is a "technoprogressive" socio-political and intellectual movement that advocates for the use of technology in order to transform the human organism radically, with the ultimate goal of becoming "posthuman." To this end, transhumanists focus on and encourage the use of new and emerging technologies, such as genetic engineering and brain-machine interfaces. In support of their vision for humanity, and as a way of reassuring those "bioconservatives" who may balk at the radical nature of that vision, transhumanists claim common ground with a number of esteemed thinkers and traditions, from the ancient philosophy of Plato and Aristotle to the postmodern philosophy of Nietzsche. It is crucially important to give proper scholarly attention to transhumanism now, not only because of its recent and ongoing rise as a cultural and political force (and the concomitant potential ramifications for bioethical discourse and public policy), but because of the imminence of major breakthroughs in the kinds of technologies that transhumanism focuses on. Thus, the articles in this issue of The Journal of Medicine and Philosophy are either explicitly about transhumanism or are on topics, such as the ethics of germline engineering and criteria for personhood, that are directly relevant to the debate between transhumanists (and technoprogressives more broadly) and bioconservatives.},
}
@article {pmid28496406,
year = {2017},
author = {Martínez-Rodrigo, A and Fernández-Sotos, A and Latorre, JM and Moncho-Bogani, J and Fernández-Caballero, A},
title = {Neural Correlates of Phrase Rhythm: An EEG Study of Bipartite vs. Rondo Sonata Form.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {29},
pmid = {28496406},
issn = {1662-5196},
abstract = {This paper introduces the neural correlates of phrase rhythm. In short, phrase rhythm is the rhythmic aspect of phrase construction and the relationships between phrases. For the sake of establishing the neural correlates, a musical experiment has been designed to induce music-evoked stimuli related to phrase rhythm. Brain activity is monitored through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. Our experiment shows statistical differences in theta and alpha bands in the phrase rhythm variations of two classical sonatas, one in bipartite form and the other in rondo form.},
}
@article {pmid28496399,
year = {2017},
author = {Pammi, VSC and Ruiz, S and Lee, S and Noussair, CN and Sitaram, R},
title = {The Effect of Wealth Shocks on Loss Aversion: Behavior and Neural Correlates.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {237},
pmid = {28496399},
issn = {1662-4548},
abstract = {Kahneman and Tversky (1979) first demonstrated that when individuals decide whether or not to accept a gamble, potential losses receive more weight than possible gains in the decision. This phenomenon is referred to as loss aversion. We investigated how loss aversion in risky financial decisions is influenced by sudden changes to wealth, employing both behavioral and neurobiological measures. We implemented an fMRI experimental paradigm, based on that employed by Tom et al. (2007). There are two treatments, called RANDOM and CONTINGENT. In RANDOM, the baseline setting, the changes to wealth, referred to as wealth shocks in economics, are independent of the actual choices participants make. Under CONTINGENT, we induce the belief that the changes in income are a consequence of subjects' own decisions. The magnitudes and sequence of the shocks to wealth are identical between the CONTINGENT and RANDOM treatments. We investigated whether more loss aversion existed in one treatment than another. The behavioral results showed significantly greater loss aversion in CONTINGENT compared to RANDOM after a negative wealth shock. No differences were observed in the response to positive shocks. The fMRI results revealed a neural loss aversion network, comprising the bilateral striatum, amygdala and dorsal anterior cingulate cortex that was common to the CONTINGENT and RANDOM tasks. However, the ventral prefrontal cortex, primary somatosensory cortex and superior occipital cortex, showed greater activation in response to a negative change in wealth due to individual's own decisions than when the change was exogenous. These results indicate that striatum activation correlates with loss aversion independently of the source of the shock, and that the ventral prefrontal cortex (vPFC) codes the experimental manipulation of agency in one's actions influencing loss aversion.},
}
@article {pmid28494052,
year = {2017},
author = {Berry, DA and Zhou, S and Higley, H and Mukundan, L and Fu, S and Reaman, GH and Wood, BL and Kelloff, GJ and Jessup, JM and Radich, JP},
title = {Association of Minimal Residual Disease With Clinical Outcome in Pediatric and Adult Acute Lymphoblastic Leukemia: A Meta-analysis.},
journal = {JAMA oncology},
volume = {3},
number = {7},
pages = {e170580},
pmid = {28494052},
issn = {2374-2445},
support = {UL1 TR000371/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Child ; Humans ; Neoplasm, Residual/*pathology ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/*pathology/therapy ; Prognosis ; Survival Analysis ; Treatment Outcome ; },
abstract = {IMPORTANCE: Minimal residual disease (MRD) refers to the presence of disease in cases deemed to be in complete remission by conventional pathologic analysis. Assessing the association of MRD status following induction therapy in patients with acute lymphoblastic leukemia (ALL) with relapse and mortality may improve the efficiency of clinical trials and accelerate drug development.<br><br>OBJECTIVE: To quantify the relationships between event-free survival (EFS) and overall survival (OS) with MRD status in pediatric and adult ALL using publications of clinical trials and other databases.<br><br>DATA SOURCES: Clinical studies in ALL identified via searches of PubMed, MEDLINE, and clinicaltrials.gov.<br><br>STUDY SELECTION: Our search and study screening process adhered to the PRISMA Guidelines. Studies that addressed EFS or OS by MRD status in patients with ALL were included; reviews, abstracts, and studies with fewer than 30 patients or insufficient MRD description were excluded.<br><br>DATA EXTRACTION AND SYNTHESIS: Study sample size, patient age, follow-up time, timing of MRD assessment (postinduction or consolidation), MRD detection method, phenotype/genotype (B cell, T cell, Philadelphia chromosome), and EFS and OS. Searches of PubMed and MEDLINE identified 566 articles. A parallel search on clinicaltrials.gov found 67 closed trials and 62 open trials as of 2014. Merging results of 2 independent searches and applying exclusions gave 39 publications in 3 arms of patient populations (adult, pediatric, and mixed). We performed separate meta-analyses for each of these 3 subpopulations.<br><br>RESULTS: The 39 publications comprised 13&#x202f;637 patients: 16 adult studies (2076 patients), 20 pediatric (11&#x202f;249 patients), and 3 mixed (312 patients). The EFS hazard ratio (HR) for achieving MRD negativity is 0.23 (95% Bayesian credible interval [BCI] 0.18-0.28) for pediatric patients and 0.28 (95% BCI, 0.24-0.33) for adults. The respective HRs in OS are 0.28 (95% BCI, 0.19-0.41) and 0.28 (95% BCI, 0.20-0.39). The effect was similar across all subgroups and covariates.<br><br>CONCLUSIONS AND RELEVANCE: The value of having achieved MRD negativity is substantial in both pediatric and adult patients with ALL. These results are consistent across therapies, methods of and times of MRD assessment, cutoff levels, and disease subtypes. Minimal residual disease status warrants consideration as an early measure of disease response for evaluating new therapies, improving the efficiency of clinical trials, accelerating drug development, and for regulatory approval. A caveat is that an accelerated approval of a particular new drug using an intermediate end point, such as MRD, would require confirmation using traditional efficacy end points.},
}
@article {pmid28493960,
year = {2017},
author = {Champagne Queloz, A and Klymkowsky, MW and Stern, E and Hafen, E and Köhler, K},
title = {Diagnostic of students' misconceptions using the Biological Concepts Instrument (BCI): A method for conducting an educational needs assessment.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0176906},
pmid = {28493960},
issn = {1932-6203},
mesh = {Adolescent ; Biochemistry/education ; Biology/*education ; Educational Measurement/*methods ; Humans ; *Needs Assessment ; *Students ; },
abstract = {Concept inventories, constructed based on an analysis of students' thinking and their explanations of scientific situations, serve as diagnostics for identifying misconceptions and logical inconsistencies and provide data that can help direct curricular reforms. In the current project, we distributed the Biological Concepts Instrument (BCI) to 17-18-year-old students attending the highest track of the Swiss school system (Gymnasium). Students' performances on many questions related to evolution, genetics, molecular properties and functions were diverse. Important common misunderstandings were identified in the areas of evolutionary processes, molecular properties and an appreciation of stochastic processes in biological systems. Our observations provide further evidence that the BCI is efficient in identifying specific areas where targeted instruction is required. Based on these observations we have initiated changes at several levels to reconsider how biological systems are presented to university biology studies with the goal of improving student's foundational understanding.},
}
@article {pmid28493386,
year = {2017},
author = {Chen, C and Xue, M and Wen, Y and Yao, G and Cui, Y and Liao, F and Yan, Z and Huang, L and Khan, SA and Gao, M and Pan, T and Zhang, H and Jing, W and Guo, D and Zhang, S and Yao, H and Zhou, X and Li, Q and Xia, Y and Lin, Y},
title = {A Ferroelectric Ceramic/Polymer Composite-Based Capacitive Electrode Array for In Vivo Recordings.},
journal = {Advanced healthcare materials},
volume = {6},
number = {16},
pages = {},
doi = {10.1002/adhm.201700305},
pmid = {28493386},
issn = {2192-2659},
mesh = {Animals ; Barium Compounds/chemistry ; Ceramics/chemistry ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Finite Element Analysis ; Male ; *Microelectrodes ; Nanocomposites/*chemistry ; Polymers/chemistry ; Rats ; Rats, Wistar ; Reproducibility of Results ; Titanium/chemistry ; Visual Cortex/physiology/surgery ; },
abstract = {A new implantable capacitive electrode array for electrocorticography signal recording is developed with ferroelectric ceramic/polymer composite. This ultrathin and electrically safe capacitive electrode array is capable of attaching to the biological tissue conformably. The barium titanate/polyimide (BaTiO3 /PI) nanocomposite with high dielectric constant is successfully synthesized and employed as the ultrathin dielectric layer of the capacitive BaTiO3 /PI electrode array. The performance of the capacitive BaTiO3 /PI electrode array is evaluated by electrical characterization and 3D finite-element modeling. In vivo, neural experiments on the visual cortex of rats show the reliability of the capacitive BaTiO3 /PI electrode array. This work shows the potentials of capacitive BaTiO3 /PI electrode array in the field of brain/computer interfaces.},
}
@article {pmid28491030,
year = {2017},
author = {Deuel, TA and Pampin, J and Sundstrom, J and Darvas, F},
title = {The Encephalophone: A Novel Musical Biofeedback Device using Conscious Control of Electroencephalogram (EEG).},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {213},
pmid = {28491030},
issn = {1662-5161},
abstract = {A novel musical instrument and biofeedback device was created using electroencephalogram (EEG) posterior dominant rhythm (PDR) or mu rhythm to control a synthesized piano, which we call the Encephalophone. Alpha-frequency (8-12 Hz) signal power from PDR in the visual cortex or from mu rhythm in the motor cortex was used to create a power scale which was then converted into a musical scale, which could be manipulated by the individual in real time. Subjects could then generate different notes of the scale by activation (event-related synchronization) or de-activation (event-related desynchronization) of the PDR or mu rhythms in visual or motor cortex, respectively. Fifteen novice normal subjects were tested in their ability to hit target notes presented within a 5-min trial period. All 15 subjects were able to perform more accurately (average of 27.4 hits, 67.1% accuracy for visual cortex/PDR signaling; average of 20.6 hits, 57.1% accuracy for mu signaling) than a random note generation (19.03% accuracy). Moreover, PDR control was significantly more accurate than mu control. This shows that novice healthy individuals can control music with better accuracy than random, with no prior training on the device, and that PDR control is more accurate than mu control for these novices. Individuals with more years of musical training showed a moderate positive correlation with more PDR accuracy, but not mu accuracy. The Encephalophone may have potential applications both as a novel musical instrument without requiring movement, as well as a potential therapeutic biofeedback device for patients suffering from motor deficits (e.g., amyotrophic lateral sclerosis (ALS), brainstem stroke, traumatic amputation).},
}
@article {pmid28489913,
year = {2017},
author = {Hiremath, SV and Tyler-Kabara, EC and Wheeler, JJ and Moran, DW and Gaunt, RA and Collinger, JL and Foldes, ST and Weber, DJ and Chen, W and Boninger, ML and Wang, W},
title = {Human perception of electrical stimulation on the surface of somatosensory cortex.},
journal = {PloS one},
volume = {12},
number = {5},
pages = {e0176020},
pmid = {28489913},
issn = {1932-6203},
support = {KL2 TR000146/TR/NCATS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; },
mesh = {Brain Mapping ; Brain-Computer Interfaces ; *Electric Stimulation ; Electrodes ; Electrodes, Implanted ; Humans ; Somatosensory Cortex/*physiology ; },
abstract = {Recent advancement in electrocorticography (ECoG)-based brain-computer interface technology has sparked a new interest in providing somatosensory feedback using ECoG electrodes, i.e., cortical surface electrodes. We conducted a 28-day study of cortical surface stimulation in an individual with arm paralysis due to brachial plexus injury to examine the sensation produced by electrical stimulation of the somatosensory cortex. A high-density ECoG grid was implanted over the somatosensory and motor cortices. Stimulation through cortical surface electrodes over the somatosensory cortex successfully elicited arm and hand sensations in our participant with chronic paralysis. There were three key findings. First, the intensity of perceived sensation increased monotonically with both pulse amplitude and pulse frequency. Second, changing pulse width changed the type of sensation based on qualitative description provided by the human participant. Third, the participant could distinguish between stimulation applied to two neighboring cortical surface electrodes, 4.5 mm center-to-center distance, for three out of seven electrode pairs tested. Taken together, we found that it was possible to modulate sensation intensity, sensation type, and evoke sensations across a range of locations from the fingers to the upper arm using different stimulation electrodes even in an individual with chronic impairment of somatosensory function. These three features are essential to provide effective somatosensory feedback for neuroprosthetic applications.},
}
@article {pmid28487725,
year = {2017},
author = {Cheng, J and Jin, J and Wang, X},
title = {Comparison of the BCI Performance between the Semitransparent Face Pattern and the Traditional Face Pattern.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {1323985},
pmid = {28487725},
issn = {1687-5273},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; *Face ; Facial Expression ; Facial Recognition ; Female ; Humans ; Male ; *Photic Stimulation ; User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems allow users to communicate with the external world by recognizing the brain activity without the assistance of the peripheral motor nervous system. P300-based BCI is one of the most common used BCI systems that can obtain high classification accuracy and information transfer rate (ITR). Face stimuli can result in large event-related potentials and improve the performance of P300-based BCI. However, previous studies on face stimuli focused mainly on the effect of various face types (i.e., face expression, face familiarity, and multifaces) on the BCI performance. Studies on the influence of face transparency differences are scarce. Therefore, we investigated the effect of semitransparent face pattern (STF-P) (the subject could see the target character when the stimuli were flashed) and traditional face pattern (F-P) (the subject could not see the target character when the stimuli were flashed) on the BCI performance from the transparency perspective. Results showed that STF-P obtained significantly higher classification accuracy and ITR than those of F-P (p < 0.05).},
}
@article {pmid28484488,
year = {2017},
author = {Mao, X and Li, M and Li, W and Niu, L and Xian, B and Zeng, M and Chen, G},
title = {Progress in EEG-Based Brain Robot Interaction Systems.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {1742862},
pmid = {28484488},
issn = {1687-5273},
mesh = {Animals ; Brain Waves ; Brain-Computer Interfaces/*trends ; *Electroencephalography ; Feedback ; Humans ; Robotics/*trends ; User-Computer Interface ; },
abstract = {The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.},
}
@article {pmid28479916,
year = {2017},
author = {Liu, JC and Chou, HC and Chen, CH and Lin, YT and Kuo, CH},
title = {Corrigendum to "Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation".},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {8012547},
pmid = {28479916},
issn = {1687-5273},
abstract = {[This corrects the article DOI: 10.1155/2016/3039454.].},
}
@article {pmid28475062,
year = {2017},
author = {Obeidat, QT and Campbell, TA and Kong, J},
title = {Spelling With a Small Mobile Brain-Computer Interface in a Moving Wheelchair.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {11},
pages = {2169-2179},
doi = {10.1109/TNSRE.2017.2700025},
pmid = {28475062},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Cognition ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300 ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation ; Psychomotor Performance/physiology ; Smartphone ; Software ; Visual Perception ; *Wheelchairs ; Young Adult ; },
abstract = {Research into brain-computer interfaces (BCIs), which spell words using brain signals, has revealed that a desktop version of such a speller, the edges paradigm, offers several advantages: This edges paradigm outperforms the benchmark row-column paradigm in terms of accuracy, bitrate, and user experience. It has remained unknown whether these advantages prevailed with a new version of the edges paradigm designed for a mobile device. This paper investigated and evaluated in a rolling wheelchair a mobile BCI, which implemented the edges paradigm on small displays with which visual crowding tends to occur. How the mobile edge paradigm outperforms the mobile row-column paradigm has implications for understanding how principles of visual neurocognition affect BCI speller use in a mobile context. This investigation revealed that all the advantages of the edges paradigm over the row-column paradigm prevailed in this setting. However, the reduction in adjacent errors for the edges paradigm was unprecedentedly limited to horizontal adjacent errors. The interpretation offered is that dimensional constraints of visual interface design on a smartphone thus affected the neurocognitive processes of crowding.},
}
@article {pmid28472337,
year = {2017},
author = {Cho, H and Ahn, M and Ahn, S and Kwon, M and Jun, SC},
title = {EEG datasets for motor imagery brain-computer interface.},
journal = {GigaScience},
volume = {6},
number = {7},
pages = {1-8},
pmid = {28472337},
issn = {2047-217X},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Datasets as Topic/*standards ; Electroencephalography/*methods/standards ; Female ; Humans ; *Imagination ; Male ; *Movement ; Software ; },
abstract = {BACKGROUND: Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states.<br><br>FINDINGS: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information.<br><br>CONCLUSIONS: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.},
}
@article {pmid28467325,
year = {2017},
author = {Shiman, F and López-Larraz, E and Sarasola-Sanz, A and Irastorza-Landa, N and Spüler, M and Birbaumer, N and Ramos-Murguialday, A},
title = {Classification of different reaching movements from the same limb using EEG.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046018},
doi = {10.1088/1741-2552/aa70d2},
pmid = {28467325},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Adult ; Arm/*physiology ; Brain-Computer Interfaces/*classification ; Electroencephalography/*classification/*methods ; *Exoskeleton Device ; Extremities/physiology ; Female ; Humans ; Male ; Movement/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation.<br><br>APPROACH: Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared.<br><br>MAIN RESULTS: Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier.<br><br>SIGNIFICANCE: Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.},
}
@article {pmid28466825,
year = {2017},
author = {Sousa, T and Amaral, C and Andrade, J and Pires, G and Nunes, UJ and Castelo-Branco, M},
title = {Pure visual imagery as a potential approach to achieve three classes of control for implementation of BCI in non-motor disorders.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046026},
doi = {10.1088/1741-2552/aa70ac},
pmid = {28466825},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*classification ; Electroencephalography/*classification/*methods ; Humans ; Imagination/*physiology ; Male ; Motion Perception/*physiology ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: The achievement of multiple instances of control with the same type of mental strategy represents a way to improve flexibility of brain-computer interface (BCI) systems. Here we test the hypothesis that pure visual motion imagery of an external actuator can be used as a tool to achieve three classes of electroencephalographic (EEG) based control, which might be useful in attention disorders.<br><br>APPROACH: We hypothesize that different numbers of imagined motion alternations lead to distinctive signals, as predicted by distinct motion patterns. Accordingly, a distinct number of alternating sensory/perceptual signals would lead to distinct neural responses as previously demonstrated using functional magnetic resonance imaging (fMRI). We anticipate that differential modulations should also be observed in the EEG domain. EEG recordings were obtained from twelve participants using three imagery tasks: imagery of a static dot, imagery of a dot with two opposing motions in the vertical axis (two motion directions) and imagery of a dot with four opposing motions in vertical or horizontal axes (four directions). The data were analysed offline.<br><br>MAIN RESULTS: An increase of alpha-band power was found in frontal and central channels as a result of visual motion imagery tasks when compared with static dot imagery, in contrast with the expected posterior alpha decreases found during simple visual stimulation. The successful classification and discrimination between the three imagery tasks confirmed that three different classes of control based on visual motion imagery can be achieved. The classification approach was based on a support vector machine (SVM) and on the alpha-band relative spectral power of a small group of six frontal and central channels. Patterns of alpha activity, as captured by single-trial SVM closely reflected imagery properties, in particular the number of imagined motion alternations.<br><br>SIGNIFICANCE: We found a new mental task based on visual motion imagery with potential for the implementation of multiclass (3) BCIs. Our results are consistent with the notion that frontal alpha synchronization is related with high internal processing demands, changing with the number of alternation levels during imagery. Together, these findings suggest the feasibility of pure visual motion imagery tasks as a strategy to achieve multiclass control systems with potential for BCI and in particular, neurofeedback applications in non-motor (attentional) disorders.},
}
@article {pmid28463626,
year = {2017},
author = {Cárdenas-Canales, EM and Wolfe, LL and Tripp, DW and Rocke, TE and Abbott, RC and Miller, MW},
title = {Responses of Juvenile Black-tailed Prairie Dogs (Cynomys ludovicianus) to a Commercially Produced Oral Plague Vaccine Delivered at Two Doses.},
journal = {Journal of wildlife diseases},
volume = {53},
number = {4},
pages = {916-920},
doi = {10.7589/2017-02-033},
pmid = {28463626},
issn = {1943-3700},
mesh = {Administration, Oral ; Animals ; Antibodies, Bacterial/blood ; Antigens, Bacterial/immunology ; Plague/prevention &amp; control/*veterinary ; Plague Vaccine/*administration &amp; dosage/immunology ; Random Allocation ; Rodent Diseases/microbiology/*prevention &amp; control ; *Sciuridae ; Yersinia pestis/immunology ; },
abstract = {We confirmed safety and immunogenicity of mass-produced vaccine baits carrying an experimental, commercial-source plague vaccine (RCN-F1/V307) expressing Yersinia pestis V and F1 antigens. Forty-five juvenile black-tailed prairie dogs (Cynomys ludovicianus) were randomly divided into three treatment groups (n=15 animals/group). Animals in the first group received one standard-dose vaccine bait (5×10[7] plaque-forming units [pfu]; STD). The second group received a lower-dose bait (1×10[7] pfu; LOW). In the third group, five animals received two standard-dose baits and 10 were left untreated but in contact. Two vaccine-treated and one untreated prairie dogs died during the study, but laboratory analyses ruled out vaccine involvement. Overall, 17 of 33 (52%; 95% confidence interval for binomial proportion [bCI] 34-69%) prairie dogs receiving vaccine-laden bait showed a positive anti-V antibody response on at least one sampling occasion after bait consumption, and eight (24%; bCI 11-42%) showed sustained antibody responses. The STD and LOW groups did not differ (P≥0.78) in their proportions of overall or sustained antibody responses after vaccine bait consumption. Serum from one of the nine (11%; bCI 0.3-48%) surviving untreated, in-contact prairie dogs also had detectable antibody on one sampling occasion. We did not observe any adverse effects related to oral vaccination.},
}
@article {pmid28463203,
year = {2017},
author = {Wu, D and Lance, BJ and Lawhern, VJ and Gordon, S and Jung, TP and Lin, CT},
title = {EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {11},
pages = {2157-2168},
doi = {10.1109/TNSRE.2017.2699784},
pmid = {28463203},
issn = {1558-0210},
mesh = {Algorithms ; Arousal/physiology ; Artifacts ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Psychomotor Performance/physiology ; Reaction Time/*physiology ; Regression Analysis ; Reproducibility of Results ; Young Adult ; },
abstract = {Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for electroencephalogram (EEG)-based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30%-8.30%, and increase the estimation correlation coefficient by 6.59%-11.13%.},
}
@article {pmid28460597,
year = {2017},
author = {Tamburella, F and Moreno, JC and Iosa, M and Pisotta, I and Cincotti, F and Mattia, D and Pons, JL and Molinari, M},
title = {Boosting the traditional physiotherapist approach for stroke spasticity using a sensorized ankle foot orthosis: a pilot study.},
journal = {Topics in stroke rehabilitation},
volume = {24},
number = {6},
pages = {447-456},
doi = {10.1080/10749357.2017.1318340},
pmid = {28460597},
issn = {1945-5119},
mesh = {Aged ; Ankle Joint/innervation ; Biofeedback, Psychology/*methods ; Electromyography ; Female ; *Foot Orthoses ; Humans ; Male ; Middle Aged ; Muscle Spasticity/*etiology/*rehabilitation ; Muscle Stretching Exercises/instrumentation/*methods ; Pilot Projects ; Range of Motion, Articular ; Retrospective Studies ; Stroke/*complications ; Treatment Outcome ; },
abstract = {BACKGROUND: Spasticity is a motor disorder that is commonly treated manually by a physical therapist (PhT) stretching the muscles. Recent data on learning have demonstrated the importance of human-to-human interaction in improving rehabilitation: cooperative motor behavior engages specific areas of the motor system compared with execution of a task alone.<br><br>OBJECTIVES: We hypothesize that PhT-guided therapy that involves active collaboration with the patient (Pt) through shared biomechanical visual biofeedback (vBFB) positively impacts learning and performance by the Pt during ankle spasticity treatment. A sensorized ankle foot orthosis (AFO) was developed to provide online quantitative data of joint range of motion (ROM), angular velocity, and electromyographic activity to the PhT and Pt during the treatment of ankle spasticity.<br><br>METHODS: Randomized controlled clinical trial. Ten subacute stroke inpatients, randomized into experimental (EXP) and control (CTRL) groups, underwent six&#xa0;weeks of daily treatment. The EXP group was treated with an active AFO, and the CTRL group was given an inactive AFO. Spasticity, ankle ROM, ankle active and passive joint speed, and coactivation index (CI) were assessed at enrollment and after 15-30 sessions.<br><br>RESULTS: Spasticity and CI (p&#xa0;<&#xa0;0.005) decreased significantly after training only in the EXP group, in association with a significant rise in active joint speed and active ROM (p&#xa0;<&#xa0;0.05). Improvements in spasticity (p&#xa0;<&#xa0;0.05), active joint speed (p&#xa0;<&#xa0;0.001), and CI (p&#xa0;<&#xa0;0.001) after treatment differed between the EXP and CTRL groups.<br><br>CONCLUSIONS: PhT-Pt sharing of exercise information, provided by joint sensorization and vBFB, improved the efficacy of the conventional approach for treating ankle spasticity in subacute stroke Pts.},
}
@article {pmid28460127,
year = {2018},
author = {Powers, A and Madan, A and Hilbert, M and Reeves, ST and George, M and Nash, MR and Borckardt, JJ},
title = {Effects of Combining a Brief Cognitive Intervention with Transcranial Direct Current Stimulation on Pain Tolerance: A Randomized Controlled Pilot Study.},
journal = {Pain medicine (Malden, Mass.)},
volume = {19},
number = {4},
pages = {677-685},
doi = {10.1093/pm/pnx098},
pmid = {28460127},
issn = {1526-4637},
mesh = {Adult ; Cognitive Behavioral Therapy/*methods ; Combined Modality Therapy/*methods ; Double-Blind Method ; Female ; Healthy Volunteers ; Humans ; Male ; Pain Management/*methods ; *Pain Threshold ; Pilot Projects ; Transcranial Direct Current Stimulation/*methods ; },
abstract = {OBJECTIVE: Cognitive behavioral therapy has been shown to be effective for treating chronic pain, and a growing literature shows the potential analgesic effects of minimally invasive brain stimulation. However, few studies have systematically investigated the potential benefits associated with combining approaches. The goal of this pilot laboratory study was to investigate the combination of a brief cognitive restructuring intervention and transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex in affecting pain tolerance.<br><br>DESIGN: Randomized, double-blind, placebo-controlled laboratory pilot.<br><br>SETTING: Medical University of South Carolina.<br><br>SUBJECTS: A total of 79 healthy adult volunteers.<br><br>METHODS: Subjects were randomized into one of six groups: 1) anodal tDCS plus a brief cognitive intervention (BCI); 2) anodal tDCS plus pain education; 3) cathodal tDCS plus BCI; 4) cathodal tDCS plus pain education; 5) sham tDCS plus BCI; and 6) sham tDCS plus pain education. Participants underwent thermal pain tolerance testing pre- and postintervention using the Method of Limits.<br><br>RESULTS: A significant main effect for time (pre-post intervention) was found, as well as for baseline thermal pain tolerance (covariate) in the model. A significant time &#xd7; group interaction effect was found on thermal pain tolerance. Each of the five groups that received at least one active intervention outperformed the group receiving sham tDCS and pain education only (i.e., control group), with the exception of the anodal tDCS&#x2009;+&#x2009;education-only group. Cathodal tDCS combined with the BCI produced the largest analgesic effect.<br><br>CONCLUSIONS: Combining cathodal tDCS with BCI yielded the largest analgesic effect of all the conditions tested. Future research might find stronger interactive effects of combined tDCS and a cognitive intervention with larger doses of each intervention. Because this controlled laboratory pilot employed an acute pain analogue and the cognitive intervention did not authentically represent cognitive behavioral therapy per se, the implications of the findings on chronic pain management remain unclear.},
}
@article {pmid28459691,
year = {2017},
author = {Maye, A and Zhang, D and Engel, AK},
title = {Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {7},
pages = {1026-1036},
doi = {10.1109/TNSRE.2017.2666479},
pmid = {28459691},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Attention/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/physiology ; Flicker Fusion/*physiology ; Humans ; Male ; Pattern Recognition, Automated/methods ; Photic Stimulation/methods ; Reproducibility of Results ; Retinal Ganglion Cells/*physiology ; Sensitivity and Specificity ; Visual Cortex/*physiology ; },
abstract = {In brain-computer interfaces (BCIs) that use the steady-state visual evoked response (SSVEP), the user selects a control command by directing attention overtly or covertly to one out of several flicker stimuli. The different control channels are encoded in the frequency, phase, or time domain of the flicker signals. Here, we present a new type of SSVEP BCI, which uses only a single flicker stimulus and yet affords controlling multiple channels. The approach rests on the observation that the relative position between the stimulus and the foci of overt attention result in distinct topographies of the SSVEP response on the scalp. By classifying these topographies, the computer can determine at which position the user is gazing. Offline data analysis in a study on 12 healthy volunteers revealed that 9 targets can be recognized with about 95±3% accuracy, corresponding to an information transfer rate (ITR) of 40.8 ± 3.3 b/min on average. We explored how the classification accuracy is affected by the number of control channels, the trial length, and the number of EEG channels. Our findings suggest that the EEG data from five channels over parieto-occipital brain areas are sufficient for reliably classifying the topographies and that there is a large potential to improve the ITR by optimizing the trial length. The robust performance and the simple stimulation setup suggest that this approach is a prime candidate for applications on desktop and tablet computers.},
}
@article {pmid28453547,
year = {2017},
author = {Choi, I and Rhiu, I and Lee, Y and Yun, MH and Nam, CS},
title = {A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.},
journal = {PloS one},
volume = {12},
number = {4},
pages = {e0176674},
pmid = {28453547},
issn = {1932-6203},
mesh = {Brain-Computer Interfaces/*classification ; Humans ; },
abstract = {A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult to understand the constituent and role of a hybrid BCI system at a glance. Also, the complicated and complex systems make it difficult to evaluate the usability of the systems. We systematically reviewed and analyzed the current state-of-the-art hybrid BCI studies, and proposed a systematic taxonomy for classifying the types of hybrid BCIs with multiple taxonomic criteria. After reviewing 74 journal articles, hybrid BCIs could be categorized with respect to 1) the source of brain signals, 2) the characteristics of the brain signal, and 3) the characteristics of operation in each system. In addition, we exhaustively reviewed recent literature on usability of BCIs. To identify the key evaluation dimensions of usability, we focused on task and measurement characteristics of BCI usability. We classified and summarized 31 BCI usability journal articles according to task characteristics (type and description of task) and measurement characteristics (subjective and objective measures). Afterwards, we proposed usability dimensions for BCI and hybrid BCI systems according to three core-constructs: Satisfaction, effectiveness, and efficiency with recommendations for further research. This paper can help BCI researchers, even those who are new to the field, can easily understand the complex structure of the hybrid systems at a glance. Recommendations for future research can also be helpful in establishing research directions and gaining insight in how to solve ergonomics and HCI design issues surrounding BCI and hybrid BCI systems by usability evaluation.},
}
@article {pmid28452616,
year = {2018},
author = {Oxley, TJ and Opie, NL and Rind, GS and Liyanage, K and John, SE and Ronayne, S and McDonald, AJ and Dornom, A and Lovell, TJH and Mitchell, PJ and Bennett, I and Bauquier, S and Warne, LN and Steward, C and Grayden, DB and Desmond, P and Davis, SM and O'Brien, TJ and May, CN},
title = {An ovine model of cerebral catheter venography for implantation of an endovascular neural interface.},
journal = {Journal of neurosurgery},
volume = {128},
number = {4},
pages = {1020-1027},
doi = {10.3171/2016.11.JNS161754},
pmid = {28452616},
issn = {1933-0693},
mesh = {Animals ; *Brain-Computer Interfaces ; Catheterization/*methods ; Cerebral Veins/*diagnostic imaging/*surgery ; Cranial Sinuses/diagnostic imaging ; Craniotomy/methods ; Electrodes, Implanted ; Endovascular Procedures/*methods ; Female ; Magnetic Resonance Imaging ; Male ; Models, Biological ; Motor Cortex/diagnostic imaging/surgery ; *Neural Prostheses ; Phlebography/*methods ; Prosthesis Implantation/*methods ; *Sheep ; Stents ; },
abstract = {OBJECTIVE Neural interface technology may enable the development of novel therapies to treat neurological conditions, including motor prostheses for spinal cord injury. Intracranial neural interfaces currently require a craniotomy to achieve implantation and may result in chronic tissue inflammation. Novel approaches are required that achieve less invasive implantation methods while maintaining high spatial resolution. An endovascular stent electrode array avoids direct brain trauma and is able to record electrocorticography in local cortical tissue from within the venous vasculature. The motor area in sheep runs in a parasagittal plane immediately adjacent to the superior sagittal sinus (SSS). The authors aimed to develop a sheep model of cerebral venography that would enable validation of an endovascular neural interface. METHODS Cerebral catheter venography was performed in 39 consecutive sheep. Contrast-enhanced MRI of the brain was performed on 13 animals. Multiple telescoping coaxial catheter systems were assessed to determine the largest wide-bore delivery catheter that could be delivered into the anterior SSS. Measurements of SSS diameter and distance from the motor area were taken. The location of the motor area was determined in relation to lateral and superior projections of digital subtraction venography images and confirmed on MRI. RESULTS The venous pathway from the common jugular vein (7.4 mm) to the anterior SSS (1.2 mm) was technically challenging to selectively catheterize. The SSS coursed immediately adjacent to the motor cortex (< 1 mm) for a length of 40 mm, or the anterior half of the SSS. Attempted access with 5-Fr and 6-Fr delivery catheters was associated with longer procedure times and higher complication rates. A 4-Fr catheter (internal lumen diameter 1.1 mm) was successful in accessing the SSS in 100% of cases with no associated complications. Complications included procedure-related venous dissection in two major areas: the torcular herophili, and the anterior formation of the SSS. The bifurcation of the cruciate sulcal veins with the SSS was a reliable predictor of the commencement of the motor area. CONCLUSIONS The ovine model for cerebral catheter venography has generalizability to the human cerebral venous system in relation to motor cortex location. This novel model may facilitate the development of the novel field of endovascular neural interfaces that may include preclinical investigations for cortical recording applications such as paralysis and epilepsy, as well as other potential applications in neuromodulation.},
}
@article {pmid28448641,
year = {2017},
author = {Nakanishi, M and Wang, YT and Jung, TP and Zao, JK and Chien, YY and Diniz-Filho, A and Daga, FB and Lin, YP and Wang, Y and Medeiros, FA},
title = {Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss.},
journal = {JAMA ophthalmology},
volume = {135},
number = {6},
pages = {550-557},
pmid = {28448641},
issn = {2168-6173},
support = {R01 EY021818/EY/NEI NIH HHS/United States ; R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {Aged ; Blindness/*diagnosis/etiology/physiopathology ; *Brain-Computer Interfaces ; Equipment Design ; Evoked Potentials, Visual/*physiology ; Female ; Follow-Up Studies ; Glaucoma/complications/*diagnosis/physiopathology ; Humans ; Intraocular Pressure ; Male ; Prospective Studies ; ROC Curve ; Visual Fields/*physiology ; },
abstract = {IMPORTANCE: The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters.<br><br>OBJECTIVE: To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss.<br><br>This case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals.<br><br>MAIN OUTCOMES AND MEASURES: Receiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability.<br><br>RESULTS: Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P&#x2009;=&#x2009;.03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P&#x2009;=&#x2009;.01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%.<br><br>CONCLUSIONS AND RELEVANCE: The BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.},
}
@article {pmid28446119,
year = {2017},
author = {Rosenfeld, JV and Wong, YT},
title = {Neurobionics and the brain-computer interface: current applications and future horizons.},
journal = {The Medical journal of Australia},
volume = {206},
number = {8},
pages = {363-368},
doi = {10.5694/mja16.01011},
pmid = {28446119},
issn = {1326-5377},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces/ethics ; Electrodes, Implanted ; Electroencephalography ; Epilepsy, Generalized/therapy ; *Exoskeleton Device ; Humans ; *Man-Machine Systems ; Parkinson Disease/therapy ; *Prostheses and Implants ; Quality of Life ; Signal Processing, Computer-Assisted ; },
abstract = {The brain-computer interface (BCI) is an exciting advance in neuroscience and engineering. In a motor BCI, electrical recordings from the motor cortex of paralysed humans are decoded by a computer and used to drive robotic arms or to restore movement in a paralysed hand by stimulating the muscles in the forearm. Simultaneously integrating a BCI with the sensory cortex will further enhance dexterity and fine control. BCIs are also being developed to: provide ambulation for paraplegic patients through controlling robotic exoskeletons; restore vision in people with acquired blindness; detect and control epileptic seizures; and improve control of movement disorders and memory enhancement. High-fidelity connectivity with small groups of neurons requires microelectrode placement in the cerebral cortex. Electrodes placed on the cortical surface are less invasive but produce inferior fidelity. Scalp surface recording using electroencephalography is much less precise. BCI technology is still in an early phase of development and awaits further technical improvements and larger multicentre clinical trials before wider clinical application and impact on the care of people with disabilities. There are also many ethical challenges to explore as this technology evolves.},
}
@article {pmid28442997,
year = {2017},
author = {Kaiju, T and Doi, K and Yokota, M and Watanabe, K and Inoue, M and Ando, H and Takahashi, K and Yoshida, F and Hirata, M and Suzuki, T},
title = {High Spatiotemporal Resolution ECoG Recording of Somatosensory Evoked Potentials with Flexible Micro-Electrode Arrays.},
journal = {Frontiers in neural circuits},
volume = {11},
number = {},
pages = {20},
pmid = {28442997},
issn = {1662-5110},
mesh = {Afferent Pathways/physiology ; Animals ; *Brain Mapping ; Electric Stimulation ; *Electrodes, Implanted ; *Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Fingers/innervation ; Fourier Analysis ; Macaca mulatta ; Nonlinear Dynamics ; Somatosensory Cortex/*physiology ; Support Vector Machine ; Time Factors ; },
abstract = {Electrocorticogram (ECoG) has great potential as a source signal, especially for clinical BMI. Until recently, ECoG electrodes were commonly used for identifying epileptogenic foci in clinical situations, and such electrodes were low-density and large. Increasing the number and density of recording channels could enable the collection of richer motor/sensory information, and may enhance the precision of decoding and increase opportunities for controlling external devices. Several reports have aimed to increase the number and density of channels. However, few studies have discussed the actual validity of high-density ECoG arrays. In this study, we developed novel high-density flexible ECoG arrays and conducted decoding analyses with monkey somatosensory evoked potentials (SEPs). Using MEMS technology, we made 96-channel Parylene electrode arrays with an inter-electrode distance of 700 μm and recording site area of 350 μm[2]. The arrays were mainly placed onto the finger representation area in the somatosensory cortex of the macaque, and partially inserted into the central sulcus. With electrical finger stimulation, we successfully recorded and visualized finger SEPs with a high spatiotemporal resolution. We conducted offline analyses in which the stimulated fingers and intensity were predicted from recorded SEPs using a support vector machine. We obtained the following results: (1) Very high accuracy (~98%) was achieved with just a short segment of data (~15 ms from stimulus onset). (2) High accuracy (~96%) was achieved even when only a single channel was used. This result indicated placement optimality for decoding. (3) Higher channel counts generally improved prediction accuracy, but the efficacy was small for predictions with feature vectors that included time-series information. These results suggest that ECoG signals with high spatiotemporal resolution could enable greater decoding precision or external device control.},
}
@article {pmid28440813,
year = {2017},
author = {Wise, T and Marwood, L and Perkins, AM and Herane-Vives, A and Joules, R and Lythgoe, DJ and Luh, WM and Williams, SCR and Young, AH and Cleare, AJ and Arnone, D},
title = {Instability of default mode network connectivity in major depression: a two-sample confirmation study.},
journal = {Translational psychiatry},
volume = {7},
number = {4},
pages = {e1105},
pmid = {28440813},
issn = {2158-3188},
support = {/WT_/Wellcome Trust/United Kingdom ; MR/N026063/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Brain/diagnostic imaging/*physiopathology ; Brain Mapping/methods ; Depressive Disorder, Major/*physiopathology ; Female ; Functional Neuroimaging/methods ; Gyrus Cinguli/*diagnostic imaging/physiopathology ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Mood Disorders/physiopathology ; Neural Pathways/diagnostic imaging/physiopathology ; Prefrontal Cortex/*diagnostic imaging/physiopathology ; Severity of Illness Index ; },
abstract = {Major depression is associated with altered static functional connectivity in various brain networks, particularly the default mode network (DMN). Dynamic functional connectivity is a novel tool with little application in affective disorders to date, and holds the potential to unravel fluctuations in connectivity strength over time in major depression. We assessed stability of connectivity in major depression between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), key nodes in the DMN that are implicated in ruminative cognitions. Functional connectivity stability between the mPFC and PCC over the course of a resting-state functional magnetic resonance imaging (fMRI) scan was compared between medication-free patients with major depression and healthy controls matched for age, sex and handedness. We tested replicability of the results in an independent sample using multi-echo resting-state fMRI. The primary sample included 20 patients and 19 controls, while the validation sample included 19 patients and 19 controls. Greater connectivity variability was detected in major depression between mPFC and PCC. This was demonstrated in both samples indicating that the results were reliable and were not influenced by the fMRI acquisition approach used. Our results demonstrate that alterations within the DMN in major depression go beyond changes in connectivity strength and extend to reduced connectivity stability within key DMN regions. Findings were robustly replicated across two independent samples. Further research is necessary to better understand the nature of these fluctuations in connectivity and their relationship to the aetiology of major depression.},
}
@article {pmid28436837,
year = {2017},
author = {Arico, P and Borghini, G and Di Flumeri, G and Sciaraffa, N and Colosimo, A and Babiloni, F},
title = {Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {7},
pages = {1431-1436},
doi = {10.1109/TBME.2017.2694856},
pmid = {28436837},
issn = {1558-2531},
mesh = {*Algorithms ; Brain Mapping/*trends ; Brain-Computer Interfaces/*trends ; Electroencephalography/*trends ; Equipment Design ; Forecasting ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*trends ; Software/trends ; Technology Assessment, Biomedical ; },
abstract = {GOAL: This minireview aims to highlight recent important aspects to consider and evaluate when passive brain-computer interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications.<br><br>METHODS: Electroencephalography (EEG) based pBCI has become an important tool for real-time analysis of brain activity since it could potentially provide covertly-without distracting the user from the main task-and objectively-not affected by the subjective judgment of an observer or the user itself-information about the operator cognitive state.<br><br>RESULTS: Different examples of pBCI applications in operational environments and new adaptive interface solutions have been presented and described. In addition, a general overview regarding the correct use of machine learning techniques (e.g., which algorithm to use, common pitfalls to avoid, etc.) in the pBCI field has been provided.<br><br>CONCLUSION: Despite recent innovations on algorithms and neurotechnology, pBCI systems are not completely ready to enter the market yet, mainly due to limitations of the EEG electrodes technology, and algorithms reliability and capability in real settings.<br><br>SIGNIFICANCE: High complexity and safety critical systems (e.g., airplanes, ATM interfaces) should adapt their behaviors and functionality accordingly to the user' actual mental state. Thus, technologies (i.e., pBCIs) able to measure in real time the user's mental states would result very useful in such "high risk" environments to enhance human machine interaction, and so increase the overall safety.},
}
@article {pmid28436836,
year = {2018},
author = {Nakanishi, M and Wang, Y and Chen, X and Wang, YT and Gao, X and Jung, TP},
title = {Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {1},
pages = {104-112},
pmid = {28436836},
issn = {1558-2531},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Task Performance and Analysis ; Young Adult ; },
abstract = {OBJECTIVE: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller.<br><br>METHODS: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects.<br><br>RESULTS: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 &#xb1; 38.17 bits/min with the cue-guided task and 198.67 &#xb1; 50.48 bits/min with the free-spelling task.<br><br>CONCLUSION: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI.<br><br>SIGNIFICANCE: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.},
}
@article {pmid28435098,
year = {2017},
author = {Aspras, I and Jaworska, MM and Górak, A},
title = {Kinetics of chitin deacetylase activation by the ionic liquid [Bmim][Br].},
journal = {Journal of biotechnology},
volume = {251},
number = {},
pages = {94-98},
doi = {10.1016/j.jbiotec.2017.04.015},
pmid = {28435098},
issn = {1873-4863},
mesh = {Amidohydrolases/*chemistry ; Chitosan/chemistry ; Imidazoles/*chemistry ; Ionic Liquids/*chemistry ; Kinetics ; },
abstract = {Chitin deacetylase is the only known enzyme that can deacetylate the N-acetyl-d-glucosamine units in chitin and chitosan to D-glucosamine. Unfortunately, this enzyme, originally obtained from fungi, usually has low activity. Here, we present that it is possible to enhance the activity of chitin deacetylase using the ionic liquid [Bmim][Br]. An increase in activity of up to 160% from the basal chitin deacetylase activity was observed. Kinetic investigations suggest that [Bmim][Br] is a non-essential activator.},
}
@article {pmid28432013,
year = {2017},
author = {Maruyama, Y and Ito, H},
title = {Design of multielectrode arrays for uniform sampling of different orientations of tuned unit populations in the cat visual cortex.},
journal = {Neuroscience research},
volume = {122},
number = {},
pages = {51-63},
doi = {10.1016/j.neures.2017.04.004},
pmid = {28432013},
issn = {1872-8111},
mesh = {Animals ; Cats ; Electrocorticography/*instrumentation/methods ; *Electrodes, Implanted ; Microelectrodes ; Neurons/*physiology ; Visual Cortex/*physiology ; },
abstract = {For better reconstruction of stimulus orientation from a single trial activity of the neuron population in the visual cortex, we need uniform samplings of differently oriented tuned neurons. We recorded multiple neurons simultaneously by using either a four-tetrode array or an eight-microelectrode array, and examined what kinds of electrodes and layouts provided a more homogeneous distribution of the units' optimal orientations. The unit population sampled by a four-tetrode array showed more homogeneous distribution than those sampled by an eight-microelectrode array. We confirmed this property by simulated recording sessions based on the optical imaging data of the orientation map.},
}
@article {pmid28431949,
year = {2017},
author = {Aliakbaryhosseinabadi, S and Kamavuako, EN and Jiang, N and Farina, D and Mrachacz-Kersting, N},
title = {Classification of EEG signals to identify variations in attention during motor task execution.},
journal = {Journal of neuroscience methods},
volume = {284},
number = {},
pages = {27-34},
doi = {10.1016/j.jneumeth.2017.04.008},
pmid = {28431949},
issn = {1872-678X},
mesh = {Algorithms ; Attention/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Perceptual Masking/physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) systems in neuro-rehabilitation use brain signals to control external devices. User status such as attention affects BCI performance; thus detecting the user's attention drift due to internal or external factors is essential for high detection accuracy.<br><br>NEW METHOD: An auditory oddball task was applied to divert the users' attention during a simple ankle dorsiflexion movement. Electroencephalogram signals were recorded from eighteen channels. Temporal and time-frequency features were projected to a lower dimension space and used to analyze the effect of two attention levels on motor tasks in each participant. Then, a global feature distribution was constructed with the projected time-frequency features of all participants from all channels and applied for attention classification during motor movement execution.<br><br>RESULTS: Time-frequency features led to significantly better classification results with respect to the temporal features, particularly for electrodes located over the motor cortex. Motor cortex channels had a higher accuracy in comparison to other channels in the global discrimination of attention level.<br><br>Previous methods have used the attention to a task to drive external devices, such as the P300 speller. However, here we focus for the first time on the effect of attention drift while performing a motor task.<br><br>CONCLUSIONS: It is possible to explore user's attention variation when performing motor tasks in synchronous BCI systems with time-frequency features. This is the first step towards an adaptive real-time BCI with an integrated function to reveal attention shifts from the motor task.},
}
@article {pmid28424601,
year = {2017},
author = {Gramann, K and Fairclough, SH and Zander, TO and Ayaz, H},
title = {Editorial: Trends in Neuroergonomics.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {165},
pmid = {28424601},
issn = {1662-5161},
}
@article {pmid28422647,
year = {2018},
author = {Fan, J and Wade, JW and Key, AP and Warren, ZE and Sarkar, N},
title = {EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.},
journal = {IEEE transactions on bio-medical engineering},
volume = {65},
number = {1},
pages = {43-51},
pmid = {28422647},
issn = {1558-2531},
support = {R01 MH091102/MH/NIMH NIH HHS/United States ; R21 AG050483/AG/NIA NIH HHS/United States ; },
mesh = {Adolescent ; Algorithms ; *Autism Spectrum Disorder/physiopathology/psychology/rehabilitation ; *Automobile Driving ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; *Workload ; },
abstract = {OBJECTIVE: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training.<br><br>METHODS: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins () were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features.<br><br>RESULTS: The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86).<br><br>CONCLUSION: Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development.<br><br>SIGNIFICANCE: The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.},
}
@article {pmid28420954,
year = {2017},
author = {Zhang, Y and Prasad, S and Kilicarslan, A and Contreras-Vidal, JL},
title = {Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {170},
pmid = {28420954},
issn = {1662-4548},
abstract = {With the development of Brain Machine Interface (BMI) systems, people with motor disabilities are able to control external devices to help them restore movement abilities. Longitudinal validation of these systems is critical not only to assess long-term performance reliability but also to investigate adaptations in electrocortical patterns due to learning to use the BMI system. In this paper, we decode the patterns of user's intended gait states (e.g., stop, walk, turn left, and turn right) from scalp electroencephalography (EEG) signals and simultaneously learn the relative importance of different brain areas by using the multiple kernel learning (MKL) algorithm. The region of importance (ROI) is identified during training the MKL for classification. The efficacy of the proposed method is validated by classifying different movement intentions from two subjects-an able-bodied and a spinal cord injury (SCI) subject. The preliminary results demonstrate that frontal and fronto-central regions are the most important regions for the tested subjects performing gait movements, which is consistent with the brain regions hypothesized to be involved in the control of lower-limb movements. However, we observed some regional changes comparing the able-bodied and the SCI subject. Moreover, in the longitudinal experiments, our findings exhibit the cortical plasticity triggered by the BMI use, as the classification accuracy and the weights for important regions-in sensor space-generally increased, as the user learned to control the exoskeleton for movement over multiple sessions.},
}
@article {pmid28420382,
year = {2017},
author = {Angulo-Sherman, IN and Rodríguez-Ugarte, M and Sciacca, N and Iáñez, E and Azorín, JM},
title = {Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {31},
pmid = {28420382},
issn = {1743-0003},
mesh = {Adult ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebellum/*physiology ; Electroencephalography ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination ; Male ; Motor Cortex/*physiology ; Transcranial Direct Current Stimulation/*methods ; },
abstract = {BACKGROUND: Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.<br><br>METHODS: A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm[2]) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare &#x3bc; and &#x3b2; band power when a specific current density is provided against the case of supplying no stimulation.<br><br>RESULTS: The proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on &#x3bc; and/or &#x3b2; band.<br><br>CONCLUSIONS: The proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.},
}
@article {pmid28420129,
year = {2017},
author = {Abtahi, M and Amiri, AM and Byrd, D and Mankodiya, K},
title = {Hand Motion Detection in fNIRS Neuroimaging Data.},
journal = {Healthcare (Basel, Switzerland)},
volume = {5},
number = {2},
pages = {},
pmid = {28420129},
issn = {2227-9032},
abstract = {As the number of people diagnosed with movement disorders is increasing, it becomes vital to design techniques that allow the better understanding of human brain in naturalistic settings. There are many brain imaging methods such as fMRI, SPECT, and MEG that provide the functional information of the brain. However, these techniques have some limitations including immobility, cost, and motion artifacts. One of the most emerging portable brain scanners available today is functional near-infrared spectroscopy (fNIRS). In this study, we have conducted fNIRS neuroimaging of seven healthy subjects while they were performing wrist tasks such as flipping their hand with the periods of rest (no movement). Different models of support vector machine is applied to these fNIRS neuroimaging data and the results show that we could classify the action and rest periods with the accuracy of over 80% for the fNIRS data of individual participants. Our results are promising and suggest that the presented classification method for fNIRS could further be applied to real-time applications such as brain computer interfacing (BCI), and into the future steps of this research to record brain activity from fNIRS and EEG, and fuse them with the body motion sensors to correlate the activities.},
}
@article {pmid28419590,
year = {2017},
author = {Bouton, C},
title = {Cracking the neural code, treating paralysis and the future of bioelectronic medicine.},
journal = {Journal of internal medicine},
volume = {282},
number = {1},
pages = {37-45},
doi = {10.1111/joim.12610},
pmid = {28419590},
issn = {1365-2796},
mesh = {*Biosensing Techniques/trends ; *Biotechnology/trends ; Brain/*physiology ; Electric Stimulation Therapy ; *Electronics, Medical/trends ; Forecasting ; Humans ; Neurons/*physiology ; Paralysis/*physiopathology/*therapy ; Synaptic Transmission ; },
abstract = {The human nervous system is a vast network carrying not only sensory and movement information, but also information to and from our organs, intimately linking it to our overall health. Scientists and engineers have been working for decades to tap into this network and 'crack the neural code' by decoding neural signals and learning how to 'speak' the language of the nervous system. Progress has been made in developing neural decoding methods to decipher brain activity and bioelectronic technologies to treat rheumatoid arthritis, paralysis, epilepsy and for diagnosing brain-related diseases such as Parkinson's and Alzheimer's disease. In a recent first-in-human study involving paralysis, a paralysed male study participant regained movement in his hand, years after his injury, through the use of a bioelectronic neural bypass. This work combined neural decoding and neurostimulation methods to translate and re-route signals around damaged neural pathways within the central nervous system. By extending these methods to decipher neural messages in the peripheral nervous system, status information from our bodily functions and specific organs could be gained. This, one day, could allow real-time diagnostics to be performed to give us a deeper insight into a patient's condition, or potentially even predict disease or allow early diagnosis. The future of bioelectronic medicine is extremely bright and is wide open as new diagnostic and treatment options are developed for patients around the world.},
}
@article {pmid28417848,
year = {2017},
author = {Rasmussen, RG and Schwartz, A and Chase, SM},
title = {Dynamic range adaptation in primary motor cortical populations.},
journal = {eLife},
volume = {6},
number = {},
pages = {},
pmid = {28417848},
issn = {2050-084X},
support = {RC1 NS070311/NS/NINDS NIH HHS/United States ; },
mesh = {*Adaptation, Physiological ; Animals ; Haplorhini ; Models, Neurological ; Motor Cortex/*physiology ; Neurons/*physiology ; },
abstract = {Neural populations from various sensory regions demonstrate dynamic range adaptation in response to changes in the statistical distribution of their input stimuli. These adaptations help optimize the transmission of information about sensory inputs. Here, we show a similar effect in the firing rates of primary motor cortical cells. We trained monkeys to operate a brain-computer interface in both two- and three-dimensional virtual environments. We found that neurons in primary motor cortex exhibited a change in the amplitude of their directional tuning curves between the two tasks. We then leveraged the simultaneous nature of the recordings to test several hypotheses about the population-based mechanisms driving these changes and found that the results are most consistent with dynamic range adaptation. Our results demonstrate that dynamic range adaptation is neither limited to sensory regions nor to rescaling of monotonic stimulus intensity tuning curves, but may rather represent a canonical feature of neural encoding.},
}
@article {pmid28412441,
year = {2017},
author = {Fuglsang, SA and Dau, T and Hjortkjær, J},
title = {Noise-robust cortical tracking of attended speech in real-world acoustic scenes.},
journal = {NeuroImage},
volume = {156},
number = {},
pages = {435-444},
doi = {10.1016/j.neuroimage.2017.04.026},
pmid = {28412441},
issn = {1095-9572},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Auditory Cortex/*physiology ; Electroencephalography ; Female ; Humans ; Male ; Noise ; Speech Perception/*physiology ; Young Adult ; },
abstract = {Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40-50s long blocks of data from 64 scalp electrodes were equally high (80-90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces.},
}
@article {pmid28410052,
year = {2017},
author = {Uehara, T and Sartori, M and Tanaka, T and Fiori, S},
title = {Robust Averaging of Covariances for EEG Recordings Classification in Motor Imagery Brain-Computer Interfaces.},
journal = {Neural computation},
volume = {29},
number = {6},
pages = {1631-1666},
doi = {10.1162/NECO_a_00963},
pmid = {28410052},
issn = {1530-888X},
mesh = {Algorithms ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination/*physiology ; Machine Learning ; Motor Activity/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The estimation of covariance matrices is of prime importance to analyze the distribution of multivariate signals. In motor imagery-based brain-computer interfaces (MI-BCI), covariance matrices play a central role in the extraction of features from recorded electroencephalograms (EEGs); therefore, correctly estimating covariance is crucial for EEG classification. This letter discusses algorithms to average sample covariance matrices (SCMs) for the selection of the reference matrix in tangent space mapping (TSM)-based MI-BCI. Tangent space mapping is a powerful method of feature extraction and strongly depends on the selection of a reference covariance matrix. In general, the observed signals may include outliers; therefore, taking the geometric mean of SCMs as the reference matrix may not be the best choice. In order to deal with the effects of outliers, robust estimators have to be used. In particular, we discuss and test the use of geometric medians and trimmed averages (defined on the basis of several metrics) as robust estimators. The main idea behind trimmed averages is to eliminate data that exhibit the largest distance from the average covariance calculated on the basis of all available data. The results of the experiments show that while the geometric medians show little differences from conventional methods in terms of classification accuracy in the classification of electroencephalographic recordings, the trimmed averages show significant improvement for all subjects.},
}
@article {pmid28409730,
year = {2018},
author = {Swann, NC and de Hemptinne, C and Miocinovic, S and Qasim, S and Ostrem, JL and Galifianakis, NB and Luciano, MS and Wang, SS and Ziman, N and Taylor, R and Starr, PA},
title = {Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease.},
journal = {Journal of neurosurgery},
volume = {128},
number = {2},
pages = {605-616},
pmid = {28409730},
issn = {1933-0693},
support = {R01 NS090913/NS/NINDS NIH HHS/United States ; },
mesh = {Artifacts ; *Brain-Computer Interfaces/adverse effects ; Deep Brain Stimulation/adverse effects/*methods ; Electric Stimulation Therapy ; Electrocorticography ; Electrodes, Implanted ; Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex ; Nerve Net/*physiopathology ; Neurosurgical Procedures/methods ; Parkinson Disease/*physiopathology/psychology/*therapy ; Psychomotor Performance ; Subthalamic Nucleus ; Treatment Outcome ; },
abstract = {OBJECTIVE Dysfunction of distributed neural networks underlies many brain disorders. The development of neuromodulation therapies depends on a better understanding of these networks. Invasive human brain recordings have a favorable temporal and spatial resolution for the analysis of network phenomena but have generally been limited to acute intraoperative recording or short-term recording through temporarily externalized leads. Here, the authors describe their initial experience with an investigational, first-generation, totally implantable, bidirectional neural interface that allows both continuous therapeutic stimulation and recording of field potentials at multiple sites in a neural network. METHODS Under a physician-sponsored US Food and Drug Administration investigational device exemption, 5 patients with Parkinson's disease were implanted with the Activa PC+S system (Medtronic Inc.). The device was attached to a quadripolar lead placed in the subdural space over motor cortex, for electrocorticography potential recordings, and to a quadripolar lead in the subthalamic nucleus (STN), for both therapeutic stimulation and recording of local field potentials. Recordings from the brain of each patient were performed at multiple time points over a 1-year period. RESULTS There were no serious surgical complications or interruptions in deep brain stimulation therapy. Signals in both the cortex and the STN were relatively stable over time, despite a gradual increase in electrode impedance. Canonical movement-related changes in specific frequency bands in the motor cortex were identified in most but not all recordings. CONCLUSIONS The acquisition of chronic multisite field potentials in humans is feasible. The device performance characteristics described here may inform the design of the next generation of totally implantable neural interfaces. This research tool provides a platform for translating discoveries in brain network dynamics to improved neurostimulation paradigms. Clinical trial registration no.: NCT01934296 (clinicaltrials.gov).},
}
@article {pmid28409112,
year = {2017},
author = {Belardinelli, P and Laer, L and Ortiz, E and Braun, C and Gharabaghi, A},
title = {Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis.},
journal = {NeuroImage. Clinical},
volume = {14},
number = {},
pages = {726-733},
pmid = {28409112},
issn = {2213-1582},
mesh = {Adult ; Aged ; Brain Mapping ; Brain-Computer Interfaces ; Electromyography ; Evoked Potentials, Motor/*physiology ; Female ; *Hand ; Humans ; Magnetic Resonance Imaging ; Magnetoencephalography ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Neuronal Plasticity/*physiology ; Quadriplegia/diagnostic imaging/*etiology/rehabilitation ; Stroke/*complications/diagnostic imaging/pathology ; Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {Motor recovery in severely impaired stroke patients is often very limited. To refine therapeutic interventions for regaining motor control in this patient group, the functionally relevant mechanisms of neuronal plasticity need to be detected. Cortico-muscular coherence (CMC) may provide physiological and topographic insights to achieve this goal. Synchronizing limb movements to motor-related brain activation is hypothesized to reestablish cortico-motor control indexed by CMC. In the present study, right-handed, chronic stroke patients with right-hemispheric lesions and left hand paralysis participated in a four-week training for their left upper extremity. A brain-robot interface turned event-related beta-band desynchronization of the lesioned sensorimotor cortex during kinesthetic motor-imagery into the opening of the paralyzed hand by a robotic orthosis. Simultaneous MEG/EMG recordings and individual models from MRIs were used for CMC detection and source reconstruction of cortico-muscular connectivity to the affected finger extensors before and after the training program. The upper extremity-FMA of the patients improved significantly from 16.23 ± 6.79 to 19.52 ± 7.91 (p = 0.0015). All patients showed significantly increased CMC in the beta frequency-band, with a distributed, bi-hemispheric pattern and considerable inter-individual variability. The location of CMC changes was not correlated to the severity of the motor impairment, the motor improvement or the lesion volume. Group analysis of the cortical overlap revealed a common feature in all patients following the intervention: a significantly increased level of ipsilesional premotor CMC that extended from the superior to the middle and inferior frontal gyrus, along with a confined area of increased CMC in the contralesional premotor cortex. In conclusion, functionally relevant modulations of CMC can be detected in patients with long-term, severe motor deficits after a brain-robot assisted rehabilitation training. Premotor beta-band CMC may serve as a biomarker and therapeutic target for novel treatment approaches in this patient group.},
}
@article {pmid28407016,
year = {2017},
author = {Hübner, D and Verhoeven, T and Schmid, K and Müller, KR and Tangermann, M and Kindermans, PJ},
title = {Learning from label proportions in brain-computer interfaces: Online unsupervised learning with guarantees.},
journal = {PloS one},
volume = {12},
number = {4},
pages = {e0175856},
pmid = {28407016},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*standards ; Electroencephalography/methods ; *Evoked Potentials ; Female ; Humans ; Internet ; Male ; Unsupervised Machine Learning/*standards ; User-Computer Interface ; },
abstract = {OBJECTIVE: Using traditional approaches, a brain-computer interface (BCI) requires the collection of calibration data for new subjects prior to online use. Calibration time can be reduced or eliminated e.g., by subject-to-subject transfer of a pre-trained classifier or unsupervised adaptive classification methods which learn from scratch and adapt over time. While such heuristics work well in practice, none of them can provide theoretical guarantees. Our objective is to modify an event-related potential (ERP) paradigm to work in unison with the machine learning decoder, and thus to achieve a reliable unsupervised calibrationless decoding with a guarantee to recover the true class means.<br><br>METHOD: We introduce learning from label proportions (LLP) to the BCI community as a new unsupervised, and easy-to-implement classification approach for ERP-based BCIs. The LLP estimates the mean target and non-target responses based on known proportions of these two classes in different groups of the data. We present a visual ERP speller to meet the requirements of LLP. For evaluation, we ran simulations on artificially created data sets and conducted an online BCI study with 13 subjects performing a copy-spelling task.<br><br>RESULTS: Theoretical considerations show that LLP is guaranteed to minimize the loss function similar to a corresponding supervised classifier. LLP performed well in simulations and in the online application, where 84.5% of characters were spelled correctly on average without prior calibration.<br><br>SIGNIFICANCE: The continuously adapting LLP classifier is the first unsupervised decoder for ERP BCIs guaranteed to find the optimal decoder. This makes it an ideal solution to avoid tedious calibration sessions. Additionally, LLP works on complementary principles compared to existing unsupervised methods, opening the door for their further enhancement when combined with LLP.},
}
@article {pmid28406932,
year = {2017},
author = {Speier, W and Deshpande, A and Cui, L and Chandravadia, N and Roberts, D and Pouratian, N},
title = {A comparison of stimulus types in online classification of the P300 speller using language models.},
journal = {PloS one},
volume = {12},
number = {4},
pages = {e0175382},
pmid = {28406932},
issn = {1932-6203},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Language ; Male ; *Models, Theoretical ; },
abstract = {The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. The goal of this study is therefore twofold. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.},
}
@article {pmid28393761,
year = {2017},
author = {Wang, F and He, Y and Qu, J and Xie, Q and Lin, Q and Ni, X and Chen, Y and Pan, J and Laureys, S and Yu, R and Li, Y},
title = {Enhancing clinical communication assessments using an audiovisual BCI for patients with disorders of consciousness.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046024},
doi = {10.1088/1741-2552/aa6c31},
pmid = {28393761},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adolescent ; Adult ; Brain-Computer Interfaces/*statistics &amp; numerical data ; *Communication ; Consciousness Disorders/*diagnosis/*physiopathology/therapy ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; Random Allocation ; Young Adult ; },
abstract = {OBJECTIVE: The JFK coma recovery scale-revised (JFK CRS-R), a behavioral observation scale, is widely used in the clinical diagnosis/assessment of patients with disorders of consciousness (DOC). However, the JFK CRS-R is associated with a high rate of misdiagnosis (approximately 40%) because DOC patients cannot provide sufficient behavioral responses. A brain-computer interface (BCI) that detects command/intention-specific changes in electroencephalography (EEG) signals without the need for behavioral expression may provide an alternative method.<br><br>APPROACH: In this paper, we proposed an audiovisual BCI communication system based on audiovisual 'yes' and 'no' stimuli to supplement the JFK CRS-R for assessing the communication ability of DOC patients. Specifically, patients were given situation-orientation questions as in the JFK CRS-R and instructed to select the answers using the BCI.<br><br>MAIN RESULTS: Thirteen patients (eight vegetative state (VS) and five minimally conscious state (MCS)) participated in our experiments involving both the BCI- and JFK CRS-R-based assessments. One MCS patient who received a score of 1 in the JFK CRS-R achieved an accuracy of 86.5% in the BCI-based assessment. Seven patients (four VS and three MCS) obtained unresponsive results in the JFK CRS-R-based assessment but responsive results in the BCI-based assessment, and 4 of those later improved scores in the JFK CRS-R-based assessment. Five patients (four VS and one MCS) obtained usresponsive results in both assessments.<br><br>SIGNIFICANCE: The experimental results indicated that the audiovisual BCI could provide more sensitive results than the JFK CRS-R and therefore supplement the JFK CRS-R.},
}
@article {pmid28391211,
year = {2018},
author = {Aydin, EA and Bay, OF and Guler, I},
title = {P300-Based Asynchronous Brain Computer Interface for Environmental Control System.},
journal = {IEEE journal of biomedical and health informatics},
volume = {22},
number = {3},
pages = {653-663},
doi = {10.1109/JBHI.2017.2690801},
pmid = {28391211},
issn = {2168-2208},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Environment, Controlled ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {An asynchronous brain computer interface (A-BCI) determines whether or not a subject is on control state, and produces control commands only in case of subject's being on control state. In this study, we propose a novel P300-based A-BCI algorithm that distinguishes control state and noncontrol state of users. Furthermore, A-BCI algorithm combined with a dynamic stopping function that enables users to select control command independent from a fixed number of intensification sequence. The proposed P300-based A-BCI algorithm uses classification patterns to determine control state and uses optimal operating point of receiver operating characteristics curve for dynamic stopping function. The proposed A-BCI algorithm is also combined with region-based paradigm (RBP) based stimulus interface. The A-BCI algorithm is tested on an internet-based environmental control system. A total of ten nondisabled subjects were participated voluntarily in the experiments. Two-level approach of the RBP-based stimulus interface improves noncontrol state detection accuracy up to 100%. Besides, ratio of incorrect command selection at control state is reduced significantly. At control state, ratio of correct selections, incorrect selections, and missed selections are 93.27%, 1.09%, and 5.63%, respectively. On the other hand, dynamic stopping function enables command selections at least two intensifications. Mean number of intensification sequences to complete the given tasks is 3.15. Thanks to dynamic stopping function, it provides a significant improvement on information transfer rate. The proposed A-BCI algorithm is important in terms of practical BCI systems.},
}
@article {pmid28389030,
year = {2017},
author = {Min, BK and Chavarriaga, R and Millán, JDR},
title = {Harnessing Prefrontal Cognitive Signals for Brain-Machine Interfaces.},
journal = {Trends in biotechnology},
volume = {35},
number = {7},
pages = {585-597},
doi = {10.1016/j.tibtech.2017.03.008},
pmid = {28389030},
issn = {1879-3096},
mesh = {Animals ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Humans ; Prefrontal Cortex/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, here, we identify the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes.},
}
@article {pmid28387616,
year = {2017},
author = {Lanman, TH and Burkus, JK and Dryer, RG and Gornet, MF and McConnell, J and Hodges, SD},
title = {Long-term clinical and radiographic outcomes of the Prestige LP artificial cervical disc replacement at 2 levels: results from a prospective randomized controlled clinical trial.},
journal = {Journal of neurosurgery. Spine},
volume = {27},
number = {1},
pages = {7-19},
doi = {10.3171/2016.11.SPINE16746},
pmid = {28387616},
issn = {1547-5646},
mesh = {Adult ; Aged ; Ceramics ; Cervical Vertebrae/*diagnostic imaging/*surgery ; Disability Evaluation ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Odds Ratio ; Postoperative Complications ; *Prostheses and Implants ; Prosthesis Design ; Prosthesis Failure ; Reoperation ; Spinal Fusion ; Titanium ; Total Disc Replacement/*instrumentation ; Treatment Outcome ; United States ; Young Adult ; },
abstract = {OBJECTIVE The aim of this study was to assess long-term clinical safety and effectiveness in patients undergoing anterior cervical surgery using the Prestige LP artificial disc replacement (ADR) prosthesis to treat degenerative cervical spine disease at 2 adjacent levels compared with anterior cervical discectomy and fusion (ACDF). METHODS A prospective, randomized, controlled, multicenter FDA-approved clinical trial was conducted at 30 US centers, comparing the low-profile titanium ceramic composite-based Prestige LP ADR (n = 209) at 2 levels with ACDF (n = 188). Clinical and radiographic evaluations were completed preoperatively, intraoperatively, and at regular postoperative intervals to 84 months. The primary end point was overall success, a composite variable that included key safety and efficacy considerations. RESULTS At 84 months, the Prestige LP ADR demonstrated statistical superiority over fusion for overall success (observed rate 78.6% vs 62.7%; posterior probability of superiority [PPS] = 99.8%), Neck Disability Index success (87.0% vs 75.6%; PPS = 99.3%), and neurological success (91.6% vs 82.1%; PPS = 99.0%). All other study effectiveness measures were at least noninferior for ADR compared with ACDF. There was no statistically significant difference in the overall rate of implant-related or implant/surgical procedure-related adverse events up to 84 months (26.6% and 27.7%, respectively). However, the Prestige LP group had fewer serious (Grade 3 or 4) implant- or implant/surgical procedure-related adverse events (3.2% vs 7.2%, log hazard ratio [LHR] and 95% Bayesian credible interval [95% BCI] -1.19 [-2.29 to -0.15]). Patients in the Prestige LP group also underwent statistically significantly fewer second surgical procedures at the index levels (4.2%) than the fusion group (14.7%) (LHR -1.29 [95% BCI -2.12 to -0.46]). Angular range of motion at superior- and inferior-treated levels on average was maintained in the Prestige LP ADR group to 84 months. CONCLUSIONS The low-profile artificial cervical disc in this study, Prestige LP, implanted at 2 adjacent levels, maintains improved clinical outcomes and segmental motion 84 months after surgery and is a safe and effective alternative to fusion. Clinical trial registration no.: NCT00637156 (clinicaltrials.gov).},
}
@article {pmid28385624,
year = {2017},
author = {Alimardani, F and Boostani, R and Blankertz, B},
title = {Weighted spatial based geometric scheme as an efficient algorithm for analyzing single-trial EEGS to improve cue-based BCI classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {92},
number = {},
pages = {69-76},
doi = {10.1016/j.neunet.2017.02.014},
pmid = {28385624},
issn = {1879-2782},
mesh = {*Algorithms ; Brain/physiology ; Brain-Computer Interfaces/*classification ; Cues ; Electroencephalography/*methods ; Humans ; },
abstract = {There is a growing interest in analyzing the geometrical behavior of electroencephalogram (EEG) covariance matrix in the context of brain computer interface (BCI). The bottleneck of the current Riemannian framework is the bias of the mean vector of EEG signals to the noisy trials, which deteriorates the covariance matrix in the manifold space. This study presents a spatial weighting scheme to reduce the effect of noisy trials on the mean vector. To assess the proposed method, dataset IIa from BCI competition IV, containing the EEG trials of 9 subjects performing four mental tasks, was utilized. The performance of the proposed method is compared to the classical Riemannian method along with Common Spatial Pattern (CSP) on the dataset. The results show that when considering just two imagery classes, the proposed method performs on par with CSP method, whereas in the multi class scenario, the proposed algorithm outperforms the CSP approach on seven out of nine subjects. Incidentally, the proposed method obtains better accuracy for the majority of subjects compared to the classical Riemannian method.},
}
@article {pmid28384122,
year = {2017},
author = {Chen, Z and Zhang, Q and Tong, APS and Manders, TR and Wang, J},
title = {Deciphering neuronal population codes for acute thermal pain.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036023},
pmid = {28384122},
issn = {1741-2552},
support = {K08 GM102691/GM/NIGMS NIH HHS/United States ; R01 GM115384/GM/NIGMS NIH HHS/United States ; R01 NS100065/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Acute Pain/*physiopathology ; Animals ; Brain Mapping/*methods ; Cerebral Cortex/*physiopathology ; Electrocardiography/methods ; *Hot Temperature ; Hyperalgesia/*physiopathology ; Male ; Nerve Net/physiopathology ; *Pain Perception ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensitivity and Specificity ; Sensory Receptor Cells ; },
abstract = {OBJECTIVE: Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface.<br><br>APPROACH: We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals.<br><br>MAIN RESULTS: The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the 'neuronal threshold' for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes.<br><br>SIGNIFICANCE: Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.},
}
@article {pmid28379187,
year = {2017},
author = {Stawicki, P and Gembler, F and Rezeika, A and Volosyak, I},
title = {A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.},
journal = {Brain sciences},
volume = {7},
number = {4},
pages = {},
pmid = {28379187},
issn = {2076-3425},
abstract = {Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.},
}
@article {pmid28376234,
year = {2017},
author = {Kellner, JR and Hubbell, SP},
title = {Adult mortality in a low-density tree population using high-resolution remote sensing.},
journal = {Ecology},
volume = {98},
number = {6},
pages = {1700-1709},
doi = {10.1002/ecy.1847},
pmid = {28376234},
issn = {0012-9658},
mesh = {Bayes Theorem ; Colorado ; Islands ; Panama ; *Remote Sensing Technology ; Trees/*physiology ; },
abstract = {We developed a statistical framework to quantify mortality rates in canopy trees observed using time series from high-resolution remote sensing. By timing the acquisition of remote sensing data with synchronous annual flowering in the canopy tree species Handroanthus guayacan, we made 2,596 unique detections of 1,006 individual adult trees within 18,883 observation attempts on Barro Colorado Island, Panama (BCI) during an 11-yr period. There were 1,057 observation attempts that resulted in missing data due to cloud cover or incomplete spatial coverage. Using the fraction of 123 individuals from an independent field sample that were detected by satellite data (109 individuals, 88.6%), we estimate that the adult population for this species on BCI was 1,135 individuals. We used a Bayesian state-space model that explicitly accounted for the probability of tree detection and missing observations to compute an annual adult mortality rate of 0.2%·yr[-1] (SE = 0.1, 95% CI = 0.06-0.45). An independent estimate of the adult mortality rate from 260 field-checked trees closely matched the landscape-scale estimate (0.33%·yr[-1] , SE = 0.16, 95% CI = 0.12-0.74). Our proof-of-concept study shows that one can remotely estimate adult mortality rates for canopy tree species precisely in the presence of variable detection and missing observations.},
}
@article {pmid28375650,
year = {2017},
author = {Bassett, DS and Khambhati, AN and Grafton, ST},
title = {Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.},
journal = {Annual review of biomedical engineering},
volume = {19},
number = {},
pages = {327-352},
pmid = {28375650},
issn = {1545-4274},
support = {R01 DC009209/DC/NIDCD NIH HHS/United States ; R01 HD086888/HD/NICHD NIH HHS/United States ; R21 MH106799/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Biomedical Engineering/methods/trends ; Brain/*anatomy &amp; histology/*physiology ; Computer Simulation ; Connectome/*methods/trends ; Forecasting ; Humans ; *Models, Neurological ; Nerve Net/*anatomy &amp; histology/*physiology ; Neuroimaging/*methods/trends ; Neurosciences ; },
abstract = {Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.},
}
@article {pmid28375161,
year = {2017},
author = {Rotermund, D and Pistor, J and Hoeffmann, J and Schellenberg, T and Boll, D and Tolstosheeva, E and Gauck, D and Stemmann, H and Peters-Drolshagen, D and Kreiter, AK and Schneider, M and Paul, S and Lang, W and Pawelzik, KR},
title = {Implications for a Wireless, External Device System to Study Electrocorticography.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {4},
pages = {},
pmid = {28375161},
issn = {1424-8220},
mesh = {Brain ; Brain-Computer Interfaces ; *Electrocorticography ; Prostheses and Implants ; Wireless Technology ; },
abstract = {Implantable neuronal interfaces to the brain are an important keystone for future medical applications. However, entering this field of research is difficult since such an implant requires components from many different areas of technology. Since the complete avoidance of wires is important due to the risk of infections and other long-term problems, means for wirelessly transmitting data and energy are a necessity which adds to the requirements. In recent literature, many high-tech components for such implants are presented with remarkable properties. However, these components are typically not freely available for such a system. Every group needs to re-develop their own solution. This raises the question if it is possible to create a reusable design for an implant and its external base-station, such that it allows other groups to use it as a starting point. In this article, we try to answer this question by presenting a design based exclusively on commercial off-the-shelf components and studying the properties of the resulting system. Following this idea, we present a fully wireless neuronal implant for simultaneously measuring electrocorticography signals at 128 locations from the surface of the brain. All design files are available as open source.},
}
@article {pmid28373984,
year = {2017},
author = {Shin, J and Müller, KR and Schmitz, CH and Kim, DW and Hwang, HJ},
title = {Evaluation of a Compact Hybrid Brain-Computer Interface System.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {6820482},
pmid = {28373984},
issn = {2314-6141},
support = {R21 NS067278/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Mathematics ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/instrumentation ; },
abstract = {We realized a compact hybrid brain-computer interface (BCI) system by integrating a portable near-infrared spectroscopy (NIRS) device with an economical electroencephalography (EEG) system. The NIRS array was located on the subjects' forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA) and baseline (BL) tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG) with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.},
}
@article {pmid28373684,
year = {2017},
author = {Borghini, G and Aricò, P and Di Flumeri, G and Cartocci, G and Colosimo, A and Bonelli, S and Golfetti, A and Imbert, JP and Granger, G and Benhacene, R and Pozzi, S and Babiloni, F},
title = {EEG-Based Cognitive Control Behaviour Assessment: an Ecological study with Professional Air Traffic Controllers.},
journal = {Scientific reports},
volume = {7},
number = {1},
pages = {547},
pmid = {28373684},
issn = {2045-2322},
mesh = {Analysis of Variance ; Arousal ; *Aviation ; *Behavior Control ; Brain/*physiology ; *Cognition ; *Electroencephalography ; Humans ; Knowledge ; Machine Learning ; *Occupations ; Problem Solving ; *Task Performance and Analysis ; },
abstract = {Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic settings.},
}
@article {pmid28368689,
year = {2018},
author = {Koester, HH and Arthanat, S},
title = {Text entry rate of access interfaces used by people with physical disabilities: A systematic review.},
journal = {Assistive technology : the official journal of RESNA},
volume = {30},
number = {3},
pages = {151-163},
doi = {10.1080/10400435.2017.1291544},
pmid = {28368689},
issn = {1949-3614},
mesh = {Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Humans ; Speech Recognition Software ; *User-Computer Interface ; *Writing ; },
abstract = {This study systematically reviewed the research on assistive technology (AT) access interfaces used for text entry, and conducted a quantitative synthesis of text entry rates (TER) associated with common interfaces. We searched 10 databases and included studies in which: typing speed was reported in words per minute (WPM) or equivalent; the access interface was available for public use; and individuals with physical impairments were in the study population. For quantitative synthesis, we used only the TER reported for individuals with physical impairments. Studies also had to report the sample size, and the average and standard deviation for the TER data. Thirty-nine studies met the criteria for quantitative synthesis. Studies involved seven interface types: standard keyboard typing, on-screen keyboard (OSK) with cursor selection, OSK with scanning selection, automatic speech recognition (ASR), Morse code, brain-computer interface (BCI), and other. ASR, standard keyboard, cursor OSK, and scanning OSK had at least four studies and 30 subjects, with TER averaging 15.4, 12.5, 4.2, and 1.7 WPM, respectively. When combined with measurements of a particular client's text entry performance, the TER from this review can be used within an evidence-based decision-making process for selecting control interfaces.},
}
@article {pmid28368083,
year = {2017},
author = {Monge-Pereira, E and Casatorres Perez-Higueras, I and Fernandez-Gonzalez, P and Ibanez-Pereda, J and Serrano, JI and Molina-Rueda, F},
title = {[Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report].},
journal = {Revista de neurologia},
volume = {64},
number = {8},
pages = {362-366},
pmid = {28368083},
issn = {1576-6578},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Male ; Stroke Rehabilitation/*methods ; },
abstract = {INTRODUCTION: In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality.<br><br>CASE REPORT: A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject's motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fugl-Meyer Assessment and the participant's satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty.<br><br>CONCLUSIONS: The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fugl-Meyer Assessment.},
}
@article {pmid28367834,
year = {2017},
author = {Özdenizci, O and Yalçın, M and Erdoğan, A and Patoğlu, V and Grosse-Wentrup, M and Çetin, M},
title = {Electroencephalographic identifiers of motor adaptation learning.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046027},
doi = {10.1088/1741-2552/aa6abd},
pmid = {28367834},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Adaptation, Physiological/*physiology ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Learning/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings.<br><br>APPROACH: Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects.<br><br>MAIN RESULTS: Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates.<br><br>SIGNIFICANCE: Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.},
}
@article {pmid28367109,
year = {2017},
author = {Ibáñez, J and Monge-Pereira, E and Molina-Rueda, F and Serrano, JI and Del Castillo, MD and Cuesta-Gómez, A and Carratalá-Tejada, M and Cano-de-la-Cuerda, R and Alguacil-Diego, IM and Miangolarra-Page, JC and Pons, JL},
title = {Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {126},
pmid = {28367109},
issn = {1662-4548},
abstract = {Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke. Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention. Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol. Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.},
}
@article {pmid28363483,
year = {2017},
author = {Ajiboye, AB and Willett, FR and Young, DR and Memberg, WD and Murphy, BA and Miller, JP and Walter, BL and Sweet, JA and Hoyen, HA and Keith, MW and Peckham, PH and Simeral, JD and Donoghue, JP and Hochberg, LR and Kirsch, RF},
title = {Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration.},
journal = {Lancet (London, England)},
volume = {389},
number = {10081},
pages = {1821-1830},
pmid = {28363483},
issn = {1474-547X},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiopathology/surgery ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Electric Stimulation Therapy/methods ; Electrodes, Implanted/standards ; Feasibility Studies ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Male ; Microelectrodes/adverse effects ; Middle Aged ; Motor Cortex/physiopathology ; Movement/physiology ; Muscle, Skeletal/*physiopathology ; Quadriplegia/*diagnosis/physiopathology/surgery ; Self-Help Devices/statistics &amp; numerical data ; Spinal Cord Injuries/*physiopathology/therapy ; United States ; United States Department of Veterans Affairs ; User-Computer Interface ; },
abstract = {BACKGROUND: People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain-computer interface (iBCI).<br><br>METHODS: We recruited a participant into the BrainGate2 clinical trial, an ongoing study that obtains safety information regarding an intracortical neural interface device, and investigates the feasibility of people with tetraplegia controlling assistive devices using their cortical signals. Surgical procedures were performed at University Hospitals Cleveland Medical Center (Cleveland, OH, USA). Study procedures and data analyses were performed at Case Western Reserve University (Cleveland, OH, USA) and the US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center (Cleveland, OH, USA). The study participant was a 53-year-old man with a spinal cord injury (cervical level 4, American Spinal Injury Association Impairment Scale category A). He received two intracortical microelectrode arrays in the hand area of his motor cortex, and 4 months and 9 months later received a total of 36 implanted percutaneous electrodes in his right upper and lower arm to electrically stimulate his hand, elbow, and shoulder muscles. The participant used a motorised mobile arm support for gravitational assistance and to provide humeral abduction and adduction under cortical control. We assessed the participant's ability to cortically command his paralysed arm to perform simple single-joint arm and hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralysed arm with that of a virtual three-dimensional arm. This study is registered with ClinicalTrials.gov, number NCT00912041.<br><br>FINDINGS: The intracortical implant occurred on Dec 1, 2014, and we are continuing to study the participant. The last session included in this report was Nov 7, 2016. The point-to-point target acquisition sessions began on Oct 8, 2015 (311 days after implant). The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80-100% accuracy), using first a virtual arm and second his own arm animated by FES. Using his paralysed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session 463 days after implant) and feed himself (717 days after implant).<br><br>INTERPRETATION: To our knowledge, this is the first report of a combined implanted FES+iBCI neuroprosthesis for restoring both reaching and grasping movements to people with chronic tetraplegia due to spinal cord injury, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoration of reaching and grasping after paralysis.<br><br>FUNDING: National Institutes of Health, Department of Veterans Affairs.},
}
@article {pmid28361947,
year = {2017},
author = {Nakanishi, Y and Yanagisawa, T and Shin, D and Kambara, H and Yoshimura, N and Tanaka, M and Fukuma, R and Kishima, H and Hirata, M and Koike, Y},
title = {Mapping ECoG channel contributions to trajectory and muscle activity prediction in human sensorimotor cortex.},
journal = {Scientific reports},
volume = {7},
number = {},
pages = {45486},
pmid = {28361947},
issn = {2045-2322},
mesh = {Arm/*physiology ; *Electrocorticography ; Epilepsy/*physiopathology ; Female ; Humans ; Motor Cortex/*physiopathology ; *Movement ; Muscles/*physiology ; },
abstract = {Studies on brain-machine interface techniques have shown that electrocorticography (ECoG) is an effective modality for predicting limb trajectories and muscle activity in humans. Motor control studies have also identified distributions of "extrinsic-like" and "intrinsic-like" neurons in the premotor (PM) and primary motor (M1) cortices. Here, we investigated whether trajectories and muscle activity predicted from ECoG were obtained based on signals derived from extrinsic-like or intrinsic-like neurons. Three participants carried objects of three different masses along the same counterclockwise path on a table. Trajectories of the object and upper arm muscle activity were predicted using a sparse linear regression. Weight matrices for the predictors were then compared to determine if the ECoG channels contributed more information about trajectory or muscle activity. We found that channels over both PM and M1 contributed highly to trajectory prediction, while a channel over M1 was the highest contributor for muscle activity prediction.},
}
@article {pmid28358916,
year = {2017},
author = {Tavakolan, M and Frehlick, Z and Yong, X and Menon, C},
title = {Classifying three imaginary states of the same upper extremity using time-domain features.},
journal = {PloS one},
volume = {12},
number = {3},
pages = {e0174161},
pmid = {28358916},
issn = {1932-6203},
mesh = {Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Models, Theoretical ; Support Vector Machine ; Upper Extremity/*physiology ; },
abstract = {Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.},
}
@article {pmid28357991,
year = {2017},
author = {Chen, YF and Atal, K and Xie, SQ and Liu, Q},
title = {A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046028},
doi = {10.1088/1741-2552/aa6a23},
pmid = {28357991},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*standards ; Electroencephalography/methods/standards ; Empirical Research ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Multivariate Analysis ; Pattern Recognition, Automated/methods/*standards ; Photic Stimulation/*methods ; },
abstract = {OBJECTIVE: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications.<br><br>APPROACH: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition.<br><br>MAIN RESULTS: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5&#x2009;s to 5&#x2009;s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75&#x2009;s to 5&#x2009;s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition.<br><br>SIGNIFICANCE: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.},
}
@article {pmid28350376,
year = {2017},
author = {Mouček, R and Vařeka, L and Prokop, T and Štěbeták, J and Brůha, P},
title = {Event-related potential data from a guess the number brain-computer interface experiment on school children.},
journal = {Scientific data},
volume = {4},
number = {},
pages = {160121},
pmid = {28350376},
issn = {2052-4463},
mesh = {Adolescent ; *Brain-Computer Interfaces ; Child ; Czech Republic ; Electroencephalography ; Female ; Humans ; Male ; },
abstract = {Guess the number is a simple P300-based brain-computer interface experiment. Its aim is to ask the measured participant to pick a number between 1 and 9. Then, he or she is exposed to corresponding visual stimuli and experimenters try to guess the number thought while they are observing event-related potential waveforms on-line. 250 school-age children participated in the experiments that were carried out in elementary and secondary schools in the Czech Republic. Electroencephalographic data from three EEG channels (Fz, Cz, Pz) and stimuli markers were stored. Additional metadata about the participants were collected (gender, age, laterality, the number thought by the participant, the guess of the experimenters, and various interesting additional information). Consequently, we offer the largest publicly available odd-ball paradigm collection of datasets to neuroscientific and brain-computer interface community.},
}
@article {pmid28349140,
year = {2017},
author = {Sim, JY and Haney, MP and Park, SI and McCall, JG and Jeong, JW},
title = {Microfluidic neural probes: in vivo tools for advancing neuroscience.},
journal = {Lab on a chip},
volume = {17},
number = {8},
pages = {1406-1435},
doi = {10.1039/c7lc00103g},
pmid = {28349140},
issn = {1473-0189},
mesh = {Animals ; Brain/cytology/physiology ; Brain-Computer Interfaces ; Equipment Design ; Humans ; Mice ; *Microfluidic Analytical Techniques ; *Neural Prostheses ; Neurons/cytology/physiology ; *Neurosciences ; },
abstract = {Microfluidic neural probes hold immense potential as in vivo tools for dissecting neural circuit function in complex nervous systems. Miniaturization, integration, and automation of drug delivery tools open up new opportunities for minimally invasive implants. These developments provide unprecedented spatiotemporal resolution in fluid delivery as well as multifunctional interrogation of neural activity using combined electrical and optical modalities. Capitalizing on these unique features, microfluidic technology will greatly advance in vivo pharmacology, electrophysiology, optogenetics, and optopharmacology. In this review, we discuss recent advances in microfluidic neural probe systems. In particular, we will highlight the materials and manufacturing processes of microfluidic probes, device configurations, peripheral devices for fluid handling and packaging, and wireless technologies that can be integrated for the control of these microfluidic probe systems. This article summarizes various microfluidic implants and discusses grand challenges and future directions for further developments.},
}
@article {pmid28348648,
year = {2017},
author = {Cheng, M and Lu, Z and Wang, H},
title = {Regularized common spatial patterns with subject-to-subject transfer of EEG signals.},
journal = {Cognitive neurodynamics},
volume = {11},
number = {2},
pages = {173-181},
pmid = {28348648},
issn = {1871-4080},
abstract = {In the context of brain-computer interface (BCI) system, the common spatial patterns (CSP) method has been used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals. However, the classification performance of CSP typically deteriorates when a few training samples are collected from a new BCI user. In this paper, we propose an approach that maintains or improves the recognition accuracy of the system with only a small size of training data set. The proposed approach is formulated by regularizing the classical CSP technique with the strategy of transfer learning. Specifically, we incorporate into the CSP analysis inter-subject information involving the same task, by minimizing the difference between the inter-subject features. Experimental results on two data sets from BCI competitions show that the proposed approach greatly improves the classification performance over that of the conventional CSP method; the transformed variant proved to be successful in almost every case, based on a small number of available training samples.},
}
@article {pmid28348644,
year = {2017},
author = {Puanhvuan, D and Khemmachotikun, S and Wechakarn, P and Wijarn, B and Wongsawat, Y},
title = {Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities.},
journal = {Cognitive neurodynamics},
volume = {11},
number = {2},
pages = {117-134},
pmid = {28348644},
issn = {1871-4080},
abstract = {Currently, electric wheelchairs are commonly used to improve mobility in disabled people. In severe cases, the user is unable to control the wheelchair by themselves because his/her motor functions are disabled. To restore mobility function, a brain-controlled wheelchair (BCW) would be a promising system that would allow the patient to control the wheelchair by their thoughts. P300 is a reliable brain electrical signal, a component of visual event-related potentials (ERPs), that could be used for interpreting user commands. This research aimed to propose a prototype BCW to allowed severe motor disabled patients to practically control a wheelchair for use in their home environment. The users were able to select from 9 possible destination commands in the automatic mode and from 4 directional commands (forward, backward, turn left and right) in the shared-control mode. These commands were selected via the designed P300 processing system. The wheelchair was steered to the desired location by the implemented navigation system. Safety of the user was ensured during wheelchair navigation due to the included obstacle detection and avoidance features. A combination of P300 and EOG was used as a hybrid BCW system. The user could fully operate the system such as enabling P300 detection system, mode shifting and stop/cancelation command by performing a different consecutive blinks to generate eye blinking patterns. The results revealed that the prototype BCW could be operated in either of the proposed modes. With the new design of the LED-based P300 stimulator, the average accuracies of the P300 detection algorithm in the shared-control and automatic modes were 95.31 and 83.42% with 3.09 and 3.79 bits/min, respectively. The P300 classification error was acceptable, as the user could cancel an incorrect command by blinking 2 times. Moreover, the proposed navigation system had a flexible design that could be interfaced with other assistive technologies. This research developed 3 alternative input modules: an eye tracker module and chin and hand controller modules. The user could select the most suitable assistive technology based on his/her level of disability. Other existing assistive technologies could also be connected to the proposed system in the future using the same protocol.},
}
@article {pmid28348527,
year = {2017},
author = {Jiang, Y and Abiri, R and Zhao, X},
title = {Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback.},
journal = {Frontiers in aging neuroscience},
volume = {9},
number = {},
pages = {52},
pmid = {28348527},
issn = {1663-4365},
abstract = {Neurofeedback (NF) is a form of biofeedback that uses real-time (RT) modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training (CT) have provided new tools and evidence that NF improves cognitive functions, such as attention and working memory (WM), beyond what is provided by traditional CT. More published studies have demonstrated the efficacy of NF, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in RT NF training of older brains aiming to match those of younger brains during attention/WM tasks. We also outline potential future advances in RT brainwave-based NF for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that NF, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and CT in the growing older population.},
}
@article {pmid28348511,
year = {2017},
author = {Bauer, R and Gharabaghi, A},
title = {Constraints and Adaptation of Closed-Loop Neuroprosthetics for Functional Restoration.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {111},
pmid = {28348511},
issn = {1662-4548},
abstract = {Closed-loop neuroprosthetics aim to compensate for lost function, e.g., by controlling external devices such as prostheses or wheelchairs. Such assistive approaches seek to maximize speed and classification accuracy for high-dimensional control. More recent approaches use similar technology, but aim to restore lost motor function in the long term. To achieve this goal, restorative neuroprosthetics attempt to facilitate motor re-learning and to strengthen damaged and/or alternative neural connections on the basis of neurofeedback training within rehabilitative environments. Such a restorative approach requires reinforcement learning of self-modulated brain activity which is considered to be beneficial for functional rehabilitation, e.g., improvement of β-power modulation over sensorimotor areas for post-stroke movement restoration. Patients with motor impairments, however, may also have a compromised ability for motor task-related regulation of the targeted brain activity. This would affect the estimation of feature weights and hence the classification accuracy of the feedback device. This, in turn, can frustrate the patients and compromise their motor learning. Furthermore, the feedback training may even become erroneous when unconstrained classifier adaptation-which is often used in assistive approaches-is also applied in this rehabilitation context. In conclusion, the conceptual switch from assistance toward restoration necessitates a methodological paradigm shift from classification accuracy toward instructional efficiency. Furthermore, a constrained feature space, a priori regularized feature weights, and difficulty adaptation present key elements of restorative brain interfaces. These factors need, therefore, to be addressed within a therapeutic framework to facilitate reinforcement learning of brain self-regulation for restorative purposes.},
}
@article {pmid28346712,
year = {2017},
author = {Donkelaar S, CT and Rosier, P and de Kort, L},
title = {Comparison of three methods to analyze detrusor contraction during micturition in men over 50 years of age.},
journal = {Neurourology and urodynamics},
volume = {36},
number = {8},
pages = {2153-2159},
doi = {10.1002/nau.23260},
pmid = {28346712},
issn = {1520-6777},
mesh = {Aged ; *Diagnostic Techniques, Urological ; Humans ; Lower Urinary Tract Symptoms/*diagnosis/physiopathology ; Male ; Middle Aged ; Muscle Contraction/*physiology ; Muscle, Smooth/*physiology ; Prostatectomy ; Urination/*physiology ; Urodynamics/*physiology ; },
abstract = {AIMS: To grade detrusor voiding contraction three parameters are used: the Schäfer pressure-flow nomogram (LinPURR), the bladder contractility index (BCI) and the maximum Watt factor (Wmax). Because these methods to quantify detrusor contraction and/or to diagnose detrusor underactivity (DU) have not yet been mutually compared, this study compares these three methods of grading detrusor contraction.<br><br>MATERIALS AND METHODS: Evaluated were 1420 urodynamic pressure-flow studies from 1222 men (aged >50 years) with lower urinary tract symptoms (LUTS). Excluded were patients with abnormal urinalysis, neurological disorders, surgical correction of congenital anomalies, pelvic surgery, post radical prostatectomy, or with evidence of urethral stricture. Contractility was graded with the LinPURR, the BCI, and Wmax, making a distinction between "strong," "normal," "weak," and "very weak" contractility. We calculated agreement between LinPURR and both BCI and Wmax .<br><br>RESULTS: The contractility groups LinPURR and BCI, as well as LinPURR and Wmax , showed a high agreement of 97.5% and 80.9%, respectively.<br><br>CONCLUSION: This study demonstrates a significant correlation in grading detrusor contractility when comparing LinPURR with the BCI (97.5% agreement) and the Wmax (80.9% agreement). The LinPURR is plausible, and applicable in clinical practice and BCI is (intrinsically) well associating with the LinPURR classes, on a more continuous scale. Both are relevant to define clinically relevant patients groups.},
}
@article {pmid28343333,
year = {2017},
author = {Jian, W and Chen, M and McFarland, DJ},
title = {Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {11},
pages = {1915-1926},
pmid = {28343333},
issn = {1741-0444},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*instrumentation/methods ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {Phase-locking value (PLV) is a potentially useful feature in sensorimotor rhythm-based brain-computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but it is uncertain what impact this might have on PLV. Therefore, the goal of this study was to explore whether zero-phase PLV is meaningful and how it is affected by spatial filtering. Both amplitude and PLV feature were extracted in the frequency band of 10-15 Hz by classical methods using archival EEG data of 18 subjects trained on a two-target BCI task. The results show that with right ear-referenced data, there is meaningful long-range zero-phase synchronization likely involving the primary motor area and the supplementary motor area that cannot be explained by volume conduction. Another novel finding is that the large Laplacian spatial filter enhances the amplitude feature but eliminates most of the phase information seen in ear-referenced data. A bipolar channel using phase-coupled areas also includes both phase and amplitude information and has a significant practical advantage since fewer channels required.},
}
@article {pmid28342747,
year = {2017},
author = {Fischer, P and Pogosyan, A and Cheeran, B and Green, AL and Aziz, TZ and Hyam, J and Little, S and Foltynie, T and Limousin, P and Zrinzo, L and Hariz, M and Samuel, M and Ashkan, K and Brown, P and Tan, H},
title = {Subthalamic nucleus beta and gamma activity is modulated depending on the level of imagined grip force.},
journal = {Experimental neurology},
volume = {293},
number = {},
pages = {53-61},
pmid = {28342747},
issn = {1090-2430},
support = {/WT_/Wellcome Trust/United Kingdom ; MR/P012272/1/MRC_/Medical Research Council/United Kingdom ; MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Aged ; Beta Rhythm/*physiology ; Cues ; Deep Brain Stimulation ; Electroencephalography ; Female ; Gamma Rhythm/*physiology ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Movement/*physiology ; Parkinson Disease/therapy ; Photic Stimulation ; Subthalamic Nucleus/*physiology ; },
abstract = {Motor imagery involves cortical networks similar to those activated by real movements, but the extent to which the basal ganglia are recruited is not yet clear. Gamma and beta oscillations in the subthalamic nucleus (STN) vary with the effort of sustained muscle activity. We recorded local field potentials in Parkinson's disease patients and investigated if similar changes can be observed during imagined gripping at three different 'forces'. We found that beta activity decreased significantly only for imagined grips at the two stronger force levels. Additionally, gamma power significantly scaled with increasing imagined force. Thus, in combination, these two spectral features can provide information about the intended force of an imaginary grip even in the absence of sensory feedback. Modulations in the two frequency bands during imaginary movement may explain the rehabilitating benefit of motor imagery to improve motor performance. The results also suggest that STN LFPs may provide useful information for brain-machine interfaces.},
}
@article {pmid28342407,
year = {2017},
author = {Heo, J and Baek, HJ and Hong, S and Chang, MH and Lee, JS and Park, KS},
title = {Music and natural sounds in an auditory steady-state response based brain-computer interface to increase user acceptance.},
journal = {Computers in biology and medicine},
volume = {84},
number = {},
pages = {45-52},
doi = {10.1016/j.compbiomed.2017.03.011},
pmid = {28342407},
issn = {1879-0534},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Perception/physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Ergonomics ; Female ; Humans ; Male ; *Music ; Quadriplegia/rehabilitation ; Signal Processing, Computer-Assisted ; },
abstract = {Patients with total locked-in syndrome are conscious; however, they cannot express themselves because most of their voluntary muscles are paralyzed, and many of these patients have lost their eyesight. To improve the quality of life of these patients, there is an increasing need for communication-supporting technologies that leverage the remaining senses of the patient along with physiological signals. The auditory steady-state response (ASSR) is an electro-physiologic response to auditory stimulation that is amplitude-modulated by a specific frequency. By leveraging the phenomenon whereby ASSR is modulated by mind concentration, a brain-computer interface paradigm was proposed to classify the selective attention of the patient. In this paper, we propose an auditory stimulation method to minimize auditory stress by replacing the monotone carrier with familiar music and natural sounds for an ergonomic system. Piano and violin instrumentals were employed in the music sessions; the sounds of water streaming and cicadas singing were used in the natural sound sessions. Six healthy subjects participated in the experiment. Electroencephalograms were recorded using four electrodes (Cz, Oz, T7 and T8). Seven sessions were performed using different stimuli. The spectral power at 38 and 42Hz and their ratio for each electrode were extracted as features. Linear discriminant analysis was utilized to classify the selections for each subject. In offline analysis, the average classification accuracies with a modulation index of 1.0 were 89.67% and 87.67% using music and natural sounds, respectively. In online experiments, the average classification accuracies were 88.3% and 80.0% using music and natural sounds, respectively. Using the proposed method, we obtained significantly higher user-acceptance scores, while maintaining a high average classification accuracy.},
}
@article {pmid28340429,
year = {2017},
author = {Buyukturkoglu, K and Porcaro, C and Cottone, C and Cancelli, A and Inglese, M and Tecchio, F},
title = {Simple index of functional connectivity at rest in Multiple Sclerosis fatigue.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {128},
number = {5},
pages = {807-813},
doi = {10.1016/j.clinph.2017.02.010},
pmid = {28340429},
issn = {1872-8952},
mesh = {Adult ; *Beta Rhythm ; Case-Control Studies ; Fatigue/etiology/*physiopathology ; Feedback, Physiological ; Female ; Humans ; Male ; Middle Aged ; Multiple Sclerosis/complications/*physiopathology ; Neural Pathways ; Parietal Lobe/physiology/physiopathology ; Temporal Lobe/physiology/physiopathology ; },
abstract = {OBJECTIVE: To investigate the EEG-derived functional connectivity at rest (FCR) patterns of fatigued Multiple Sclerosis (MS) patients in order to find good parameters for a future EEG-Neurofeedback intervention to reduce their fatigue symptoms.<br><br>METHODS: We evaluated FCR between hemispheric homologous areas, via spectral coherence between pairs of corresponding left and right bipolar derivations, in the Theta, Alpha and Beta bands. We estimated FCR in 18MS patients with different levels of fatigue and minimal clinical severity and in 11 age and gender matched healthy controls. We used correlation analysis to assess the relationship between the fatigue scores and the FCR values differing between fatigued MS patients and controls.<br><br>RESULTS: Among FCR values differing between fatigued MS patients and controls, fatigue symptoms increased with higher Beta temporo-parietal FCR (p=0.00004). Also, positive correlations were found between the fatigue levels and the fronto-frontal FCR in Beta and Theta bands (p=0.0002 and p=0.001 respectively).<br><br>CONCLUSION: We propose that a future EEG-Neurofeedback system against MS fatigue would train patients to decrease voluntarily the beta coherence between the homologous temporo-parietal areas.<br><br>SIGNIFICANCE: We extracted a feature for building an EEG-Neurofeedback system against fatigue in MS.},
}
@article {pmid28336339,
year = {2017},
author = {Noori, FM and Naseer, N and Qureshi, NK and Nazeer, H and Khan, RA},
title = {Optimal feature selection from fNIRS signals using genetic algorithms for BCI.},
journal = {Neuroscience letters},
volume = {647},
number = {},
pages = {61-66},
doi = {10.1016/j.neulet.2017.03.013},
pmid = {28336339},
issn = {1872-7972},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Humans ; Imagination ; Male ; Motor Cortex/*physiology ; Spectroscopy, Near-Infrared/*methods ; Support Vector Machine ; },
abstract = {In this paper, a novel technique for determination of the optimal feature combinations and, thereby, acquisition of the maximum classification performance for a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI), is proposed. After obtaining motor-imagery and rest signals from the motor cortex, filtering is applied to remove the physiological noises. Six features (signal slope, signal mean, signal variance, signal peak, signal kurtosis and signal skewness) are then extracted from the oxygenated hemoglobin (HbO). Afterwards, the hybrid genetic algorithm (GA)-support vector machine (SVM) is applied in order to determine and classify 2- and 3-feature combinations across all subjects. The SVM classifier is applied to classify motor imagery versus rest. Moreover, four time windows (0-20s, 0-10s, 11-20s and 6-15s) are selected, and the hybrid GA-SVM is applied in order to extract the optimal 2- and 3-feature combinations. In the present study, the 11-20s time window showed significantly higher classification accuracies - the minimum accuracy was 91% - than did the other time windows (p<0.05). The proposed hybrid GA-SVM technique, by selecting optimal feature combinations for an fNIRS-based BCI, shows positive classification-performance-enhancing results.},
}
@article {pmid28328506,
year = {2017},
author = {Yao, L and Sheng, X and Zhang, D and Jiang, N and Mrachacz-Kersting, N and Zhu, X and Farina, D},
title = {A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {9},
pages = {1674-1682},
doi = {10.1109/TNSRE.2017.2684084},
pmid = {28328506},
issn = {1558-0210},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Orientation/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Somatosensory Cortex/physiology ; Task Performance and Analysis ; },
abstract = {Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain-computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems. Thirty two subjects were randomly divided into a Control-Group and a Hybrid-Group. In the Control-Group, the subjects performed left and right hand motor imagery (i.e., L-MI and R-MI). In the Hybrid-Group, the subjects performed the four mental tasks (i.e., L-MI, R-MI, L-SAO, and R-SAO). The results indicate that combining two of the tasks in a hybrid manner (such as L-SAO and R-MI) resulted in a significantly greater classification accuracy than when using two MI tasks. The hybrid modality reached 86.1% classification accuracy on average, with a 7.70% increase with respect to MI (), and 7.21% to SAO () alone. Moreover, all 16 subjects in the hybrid modality reached at least 70% accuracy, which is considered the threshold for BCI illiteracy. In addition to the two-class results, the classification accuracy was 68.1% and 54.1% for the three-class and four-class hybrid BCI. Combining the induced brain signals from motor and somatosensory cortex, the proposed stimulus-independent hybrid BCI has shown improved performance with respect to individual modalities, reducing the portion of BCI-illiterate subjects, and provided novel types of multi-class BCIs.},
}
@article {pmid28321973,
year = {2017},
author = {Marchesotti, S and Martuzzi, R and Schurger, A and Blefari, ML and Del Millán, JR and Bleuler, H and Blanke, O},
title = {Cortical and subcortical mechanisms of brain-machine interfaces.},
journal = {Human brain mapping},
volume = {38},
number = {6},
pages = {2971-2989},
pmid = {28321973},
issn = {1097-0193},
mesh = {Adult ; Analysis of Variance ; Area Under Curve ; Biofeedback, Psychology/*physiology ; Brain/diagnostic imaging/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Functional Laterality/physiology ; Humans ; Image Processing, Computer-Assisted ; Imagination/physiology ; Magnetic Resonance Imaging ; Male ; Oxygen/blood ; Photic Stimulation ; Young Adult ; },
abstract = {Technical advances in the field of Brain-Machine Interfaces (BMIs) enable users to control a variety of external devices such as robotic arms, wheelchairs, virtual entities and communication systems through the decoding of brain signals in real time. Most BMI systems sample activity from restricted brain regions, typically the motor and premotor cortex, with limited spatial resolution. Despite the growing number of applications, the cortical and subcortical systems involved in BMI control are currently unknown at the whole-brain level. Here, we provide a comprehensive and detailed report of the areas active during on-line BMI control. We recorded functional magnetic resonance imaging (fMRI) data while participants controlled an EEG-based BMI inside the scanner. We identified the regions activated during BMI control and how they overlap with those involved in motor imagery (without any BMI control). In addition, we investigated which regions reflect the subjective sense of controlling a BMI, the sense of agency for BMI-actions. Our data revealed an extended cortical-subcortical network involved in operating a motor-imagery BMI. This includes not only sensorimotor regions but also the posterior parietal cortex, the insula and the lateral occipital cortex. Interestingly, the basal ganglia and the anterior cingulate cortex were involved in the subjective sense of controlling the BMI. These results inform basic neuroscience by showing that the mechanisms of BMI control extend beyond sensorimotor cortices. This knowledge may be useful for the development of BMIs that offer a more natural and embodied feeling of control for the user. Hum Brain Mapp 38:2971-2989, 2017. © 2017 Wiley Periodicals, Inc.},
}
@article {pmid28320670,
year = {2017},
author = {Casimo, K and Weaver, KE and Wander, J and Ojemann, JG},
title = {BCI Use and Its Relation to Adaptation in Cortical Networks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1697-1704},
pmid = {28320670},
issn = {1558-0210},
support = {K01 MH086118/MH/NIMH NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; R90 DA033461/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electrocorticography ; Electroencephalography ; Equipment Design ; Humans ; Nerve Net/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.},
}
@article {pmid28318537,
year = {2017},
author = {Burrell, AJ and Kaye, DM and Fitzgerald, MC and Cooper, DJ and Hare, JL and Costello, BT and Taylor, AJ},
title = {Cardiac magnetic resonance imaging in suspected blunt cardiac injury: A prospective, pilot, cohort study.},
journal = {Injury},
volume = {48},
number = {5},
pages = {1013-1019},
doi = {10.1016/j.injury.2017.02.025},
pmid = {28318537},
issn = {1879-0267},
mesh = {Adult ; Arrhythmias, Cardiac/*diagnostic imaging/etiology ; Australia/epidemiology ; Biomarkers/blood ; Echocardiography ; Electrocardiography ; Female ; Humans ; Incidence ; Injury Severity Score ; *Magnetic Resonance Imaging/instrumentation/methods ; Male ; *Myocardial Contusions/blood/diagnostic imaging/physiopathology ; Pilot Projects ; Prospective Studies ; Sensitivity and Specificity ; *Thoracic Injuries/complications/diagnostic imaging/physiopathology ; Troponin I/*blood ; },
abstract = {INTRODUCTION: The aim of this study was to evaluate the incidence and severity of blunt cardiac injury (BCI) as determined by cardiac magnetic resonance imaging (CMR), and to compare this to currently used diagnostic methods in severely injured patients.<br><br>MATERIALS AND METHODS: We conducted a prospective, pilot cohort study of 42 major trauma patients from July 2013 to Jan 2015. The cohort underwent CMR within 7 days, enrolling 21 patients with evidence of chest injury and an elevated Troponin I compared to 21 patients without chest injury who acted as controls. Major adverse cardiac events (MACE) including ventricular arrhythmia, unexplained hypotension requiring inotropes, or a requirement for cardiac surgery were recorded.<br><br>RESULTS: 6/21 (28%) patients with chest injuries had abnormal CMR scans, while all 21 control patients had normal scans. CMR abnormalities included myocardial oedema, regional wall motion abnormalities, and myocardial haemorrhage. The left ventricle was the commonest site of injury (5/6), followed by the right ventricle (2/6) and tricuspid valve (1/6). MACE occurred in 5 patients. Sensitivity and specificity values for CMR at predicting MACE were 60% (15-95) and 81% (54-96), which compared favourably with other tests.<br><br>CONCLUSION: In this pilot trial, CMR was found to give detailed anatomic information of myocardial injury in patients with suspected BCI, and may have a role in the diagnosis and management of patients with suspected BCI.},
}
@article {pmid28316617,
year = {2017},
author = {Long, J and Wang, J and Yu, T},
title = {An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300.},
journal = {Computational intelligence and neuroscience},
volume = {2017},
number = {},
pages = {9528097},
pmid = {28316617},
issn = {1687-5273},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.},
}
@article {pmid28301272,
year = {2017},
author = {Ohta, Y and Kawano, A and Kawaguchi, S and Shirai, K and Tsukahara, K},
title = {Speech recognition in bilaterally cochlear implanted adults in Tokyo, Japan.},
journal = {Acta oto-laryngologica},
volume = {137},
number = {8},
pages = {837-841},
doi = {10.1080/00016489.2017.1293293},
pmid = {28301272},
issn = {1651-2251},
mesh = {Adult ; Aged ; Cochlear Implantation ; *Cochlear Implants ; Deafness/rehabilitation ; Female ; Humans ; Japan ; Male ; Middle Aged ; Retrospective Studies ; Speech Discrimination Tests ; *Speech Perception ; },
abstract = {INTRODUCTION: The clinical effects of bilateral cochlear implantation (BCI) include binaural summation and better hearing under noise conditions. This study retrospectively examined the utility of BCI compared to unilateral cochlear implantation (CI) in adults.<br><br>PATIENTS AND METHODS: We investigated 34 adults who underwent BCI, comparing speech recognition between BCI and first CI under silent and noise conditions. We assessed correlations between speech recognition after first and second CIs, and between the interval from first to second CI surgery and speech recognition of second CI.<br><br>RESULTS: Word recognition score (WRS) and sentence recognition score (SRS) were significantly better after BCI than after first CI under conditions of silence and noise. No significant correlation was found between speech recognition after first CI and that after second CI, or between inter-implant interval and speech recognition of second CI for either WRS or SRS.<br><br>CONCLUSIONS: The utility of BCI in Japanese patients was shown. Patients have no need to be pessimistic about hearing after the second implantation even if speech recognition after the first implantation is poor. A long interval from first CI does not necessarily contraindicate contralateral implantation in adults.},
}
@article {pmid28299327,
year = {2017},
author = {Fujii, M and Nakashima, Y and Nakamura, T and Ito, Y and Hara, T},
title = {Minimum Lateral Bone Coverage Required for Securing Fixation of Cementless Acetabular Components in Hip Dysplasia.},
journal = {BioMed research international},
volume = {2017},
number = {},
pages = {4937151},
pmid = {28299327},
issn = {2314-6141},
mesh = {Acetabulum/diagnostic imaging/*surgery ; Aged ; Aged, 80 and over ; Arthroplasty, Replacement, Hip/*methods ; Female ; Follow-Up Studies ; Hip Dislocation/diagnostic imaging/*surgery ; *Hip Prosthesis ; Humans ; Male ; Middle Aged ; Porosity ; Prosthesis Design ; Reoperation ; },
abstract = {Objectives. To determine the minimum lateral bone coverage required for securing stable fixation of the porous-coated acetabular components (cups) in hip dysplasia. Methods. In total, 215 primary total hip arthroplasties in 199 patients were reviewed. The average follow-up period was 49 months (range: 24-77 months). The lateral bone coverage of the cups was assessed by determining the cup center-edge (cup-CE) angle and the bone coverage index (BCI) from anteroposterior pelvic radiographs. Further, cup fixation was determined using the modified DeLee and Charnley classification system. Results. All cups were judged to show stable fixation by bone ingrowth. The cup-CE angle was less than 0° in 7 hips (3.3%) and the minimum cup-CE angle was -9.2° (BCI: 48.8%). Thin radiolucent lines were observed in 5 hips (2.3%), which were not associated with decreased lateral bone coverage. Loosening, osteolysis, dislocation, or revision was not observed in any of the cases during the follow-up period. Conclusion. A cup-CE angle greater than -10° (BCI > 50%) was acceptable for stable bony fixation of the cup. Considering possible errors in manual implantation, we recommend that the cup position be planned such that the cup-CE angle is greater than 0° (BCI > 60%).},
}
@article {pmid28298902,
year = {2017},
author = {Eshaghi, A and Duvvuri, VR and Isabel, S and Banh, P and Li, A and Peci, A and Patel, SN and Gubbay, JB},
title = {Global Distribution and Evolutionary History of Enterovirus D68, with Emphasis on the 2014 Outbreak in Ontario, Canada.},
journal = {Frontiers in microbiology},
volume = {8},
number = {},
pages = {257},
pmid = {28298902},
issn = {1664-302X},
abstract = {Despite its first appearance in 1962, human enterovirus D68 (EV-D68) has been recognized as an emerging respiratory pathogen in the last decade when it caused outbreaks and clusters in several countries including Japan, the Philippines, and the Netherlands. The most recent and largest outbreak of EV-D68 associated with severe respiratory illness took place in North America between August 2014 and January 2015. Between September 1 and October 31 2014, EV-D68 infection was laboratory confirmed among 153/907 (16.9%) persons tested for the virus in Ontario, Canada, using real time RT-PCR and subsequent genotyping by sequencing of partial VP1 gene. In order to understand the evolutionary history of the 2014 North American EV-D68 outbreak, we conducted phylogenetic and phylodynamic analyses using available partial VP1 genes (n = 469) and NCBI available whole genome sequences (WGS) (n = 38). The global EV-D68 phylogenetic tree (n = 469) reconfirms the divergence of three distinct clades A, B, and C from the prototype EV-D68 Fermon strain as previously documented. Two sub-clades (B1 and B2) were identified, with most 2014 EV-D68 outbreak strains belonging to sub-cluster B2b2 (one of the two emerging clusters within sub-clade B2), with two signature substitutions T650A and M700V in BC and DE loops of VP1 gene, respectively. The close homology between WGS of strains from Ontario (n = 2) and USA (n = 21) in the recent EV-D68 outbreak suggests genetic relatedness and also a common source for the outbreak. The time of most recent common ancestor of EV-D68 and the 2014 EV-D68 outbreak strain suggest that the viruses possibly emerged during 1960-1961 and 2012-2013, respectively. We observed lower mean evolutionary rates of global EV-D68 using WGS data than estimated with partial VP1 gene sequences. Based on WGS data, the estimated mean rate of evolution of the EV-D68 B2b cluster was 9.75 × 10[-3] substitutions/site/year (95% BCI 4.11 × 10[-3] to 16 × 10[-3]).},
}
@article {pmid28298888,
year = {2017},
author = {Spataro, R and Chella, A and Allison, B and Giardina, M and Sorbello, R and Tramonte, S and Guger, C and La Bella, V},
title = {Reaching and Grasping a Glass of Water by Locked-In ALS Patients through a BCI-Controlled Humanoid Robot.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {68},
pmid = {28298888},
issn = {1662-5161},
abstract = {Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling. In the present study, locked-in ALS patients used a BCI system to directly control the humanoid robot NAO (Aldebaran Robotics, France) with the aim of reaching and grasping a glass of water. Four ALS patients and four healthy controls were recruited and trained to operate this humanoid robot through a P300-based BCI. A few minutes training was sufficient to efficiently operate the system in different environments. Three out of the four ALS patients and all controls successfully performed the task with a high level of accuracy. These results suggest that BCI-operated robots can be used by locked-in ALS patients as an artificial alter-ego, the machine being able to move, speak and act in his/her place.},
}
@article {pmid28294109,
year = {2017},
author = {Abu-Alqumsan, M and Ebert, F and Peer, A},
title = {Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036024},
doi = {10.1088/1741-2552/aa66e0},
pmid = {28294109},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; *Algorithms ; Biofeedback, Psychology/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Goals ; Humans ; *Man-Machine Systems ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Psychomotor Performance/physiology ; Reproducibility of Results ; Robotics/*methods ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations.<br><br>APPROACH: To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme.<br><br>MAIN RESULTS: Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface.<br><br>SIGNIFICANCE: Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations.},
}
@article {pmid28293184,
year = {2017},
author = {Zander, TO and Andreessen, LM and Berg, A and Bleuel, M and Pawlitzki, J and Zawallich, L and Krol, LR and Gramann, K},
title = {Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {78},
pmid = {28293184},
issn = {1662-5161},
abstract = {We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time. An experiment was conducted inside and outside of a stationary vehicle with running engine, air-conditioning, and muted radio. Signal quality was sufficient for standard EEG analysis in the time and frequency domain as well as for the use in pBCIs. While the influence of vehicle-induced interferences to data quality was insignificant, driving-related movements led to strong shifts in electrode positions. In general, the EEG system used allowed for a fast self-applicability of cap and electrodes. The assessed usability of the system was still acceptable while the wearing comfort decreased strongly over time due to friction and pressure to the head. From these results we conclude that the evaluated system should provide the essential requirements for an application in an autonomous driving context. Nevertheless, further refinement is suggested to reduce shifts of the system due to body movements and increase the headset's usability and wearing comfort.},
}
@article {pmid28293162,
year = {2017},
author = {Ebadi, A and Dalboni da Rocha, JL and Nagaraju, DB and Tovar-Moll, F and Bramati, I and Coutinho, G and Sitaram, R and Rashidi, P},
title = {Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {56},
pmid = {28293162},
issn = {1662-4548},
support = {P30 AG028740/AG/NIA NIH HHS/United States ; },
abstract = {The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.},
}
@article {pmid28291737,
year = {2017},
author = {Sburlea, AI and Montesano, L and Minguez, J},
title = {Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036004},
doi = {10.1088/1741-2552/aa5f2f},
pmid = {28291737},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Cortical Synchronization ; Electroencephalography/*methods ; Female ; *Gait ; Gait Disorders, Neurologic/diagnosis/etiology/*physiopathology ; Humans ; *Intention ; Male ; Middle Aged ; Pattern Recognition, Automated/methods ; Reference Values ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/complications/diagnosis/*physiopathology ; },
abstract = {OBJECTIVE: One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention.<br><br>APPROACH: We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions. Phase features were compared to more conventional amplitude and power features.<br><br>MAIN RESULTS: The neurophysiology analysis showed that phase features have higher signal-to-noise ratio than the other features. Also, BCI detectors of gait intention based on phase, amplitude, and their combination were evaluated under three conditions: session-specific calibration, intersession transfer, and intersubject transfer. Results show that the phase based detector is the most accurate for session-specific calibration (movement intention was correctly detected in 66.5% of trials in healthy subjects, and in 63.3% in stroke patients). However, in intersession and intersubject transfer, the detector that combines amplitude and phase features is the most accurate one and the only that retains its accuracy (62.5% in healthy subjects and 59% in stroke patients) w.r.t. session-specific calibration.<br><br>SIGNIFICANCE: MRCP phase features improve the detection of gait intention and could be used in practice to remove time-consuming BCI recalibration.},
}
@article {pmid28288824,
year = {2016},
author = {Schaeffer, MC and Aksenova, T},
title = {Switching Markov decoders for asynchronous trajectory reconstruction from ECoG signals in monkeys for BCI applications.},
journal = {Journal of physiology, Paris},
volume = {110},
number = {4 Pt A},
pages = {348-360},
doi = {10.1016/j.jphysparis.2017.03.002},
pmid = {28288824},
issn = {1769-7115},
mesh = {Animals ; *Brain-Computer Interfaces ; Haplorhini/*physiology ; Linear Models ; Probability ; },
abstract = {Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article. Most discrete/continuous BCI hybrid decoders rely on static state models which don't exploit the dynamic of NC/IC state succession. A hybrid decoder, referred to as Markov Switching Linear Model (MSLM), is proposed in the present article. The MSLM assumes that the NC/IC state sequence is generated by a first-order Markov chain, and performs dynamic NC/IC state detection. Linear continuous movement models are probabilistically combined using the NC and IC state posterior probabilities yielded by the state decoder. The proposed decoder is evaluated for the task of asynchronous wrist position decoding from high dimensional space-time-frequency ElectroCorticoGraphic (ECoG) features in monkeys. The MSLM is compared with another dynamic hybrid decoder proposed in the literature, namely a Switching Kalman Filter (SKF). A comparison is additionally drawn with a Wiener filter decoder which infers NC states by thresholding trajectory estimates. The MSLM decoder is found to outperform both the SKF and the thresholded Wiener filter decoder in terms of False Positive Ratio and NC/IC state detection error. It additionally surpasses the SKF with respect to the Pearson Correlation Coefficient and Root Mean Squared Error between true and estimated continuous trajectories.},
}
@article {pmid28288807,
year = {2017},
author = {Liu, X and Wan, H and Li, S and Shang, Z and Shi, L},
title = {The role of nidopallium caudolaterale in the goal-directed behavior of pigeons.},
journal = {Behavioural brain research},
volume = {326},
number = {},
pages = {112-120},
doi = {10.1016/j.bbr.2017.02.042},
pmid = {28288807},
issn = {1872-7549},
mesh = {Animals ; Behavior, Animal/*physiology ; Cerebral Cortex/*physiology ; Columbidae/*physiology ; Decision Making/*physiology ; Electrophysiological Phenomena ; *Goals ; Maze Learning/physiology ; Reward ; },
abstract = {Avian nidopallium caudolaterale (NCL) is believed to be analogue to the mammalian prefrontal cortex (PFC), a key brain region for guiding goal-directed behavior. But the role of NCL during goal-directed behavior remains unclear. To investigate whether the pigeon NCL participate in the goal-directed behavior, we recorded single-units from the NCL of four pigeons as they performing goal-directed decision-making task in a plus-maze. During the decision-making process, the firing rates of NCL neurons significantly increased and they are associated with the choice of the upcoming movement. Moreover, both the firing rates and the decoding performance in the correct trials are significantly higher both of that in the error trials. These suggest that the NCL neurons indeed participate in the goal-directed behavior of pigeon and the neural activities may be induced by the rewards. However, the NCL neurons are depend on the rewards received to produce firing patterns discriminating the features of goal-directed behaviors, rather than encode the reward itself. In addition, we found that the functional components of goal-directed behavior are lateralized in the NCL, both the firing rates and the decoding performance in left NCL are significantly higher than both of that in right NCL.},
}
@article {pmid28287076,
year = {2017},
author = {Verhoeven, T and Hübner, D and Tangermann, M and Müller, KR and Dambre, J and Kindermans, PJ},
title = {Improving zero-training brain-computer interfaces by mixing model estimators.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036021},
doi = {10.1088/1741-2552/aa6639},
pmid = {28287076},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Simulation ; Data Interpretation, Statistical ; Evoked Potentials/*physiology ; Female ; Humans ; *Machine Learning ; Male ; *Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration.<br><br>APPROACH: We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP-BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method's strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller.<br><br>MAIN RESULTS: Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable.<br><br>SIGNIFICANCE: Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.},
}
@article {pmid28286317,
year = {2018},
author = {Yoo, PE and John, SE and Farquharson, S and Cleary, JO and Wong, YT and Ng, A and Mulcahy, CB and Grayden, DB and Ordidge, RJ and Opie, NL and O'Brien, TJ and Oxley, TJ and Moffat, BA},
title = {7T-fMRI: Faster temporal resolution yields optimal BOLD sensitivity for functional network imaging specifically at high spatial resolution.},
journal = {NeuroImage},
volume = {164},
number = {},
pages = {214-229},
doi = {10.1016/j.neuroimage.2017.03.002},
pmid = {28286317},
issn = {1095-9572},
mesh = {Adult ; Brain/*diagnostic imaging ; Brain Mapping/*methods ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Male ; Nerve Net/*diagnostic imaging ; Young Adult ; },
abstract = {Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 Tesla (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased net magnetization per voxel and T1-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution. We investigated the effects of varying spatial and temporal resolution on fMRI sensitivity measures and their implications on fMRI-based BCI simulations. We compared temporal signal-to-noise ratio (tSNR), observed percent signal change (%∆S), volumes of significant activation, Z-scores and decoding performance of linear classifiers commonly used in BCIs across a range of spatial and temporal resolution images acquired during an ankle-tapping task. Our results revealed an average increase of 22% in %∆S (p=0.006) and 9% in decoding performance (p=0.015) with temporal resolution only at the highest spatial resolution of 1.5×1.5×1.5mm[3], despite a 29% decrease in tSNR (p<0.001) and plateaued Z-scores. Further, the volume of significant activation was indifferent (p>0.05) across spatial resolution specifically at the highest temporal resolution of 500ms. These results demonstrate that the overall BOLD sensitivity can be increased significantly with temporal resolution, granted an adequately high spatial resolution with minimal physiological noise level. This shows the feasibility of diffuse motor-network imaging at high spatial and temporal resolution with robust BOLD sensitivity with 7T-fMRI. Importantly, we show that this sensitivity improvement could be extended to an fMRI application such as BCIs.},
}
@article {pmid28286237,
year = {2016},
author = {Jarosiewicz, B and Sarma, AA and Saab, J and Franco, B and Cash, SS and Eskandar, EN and Hochberg, LR},
title = {Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces.},
journal = {Journal of physiology, Paris},
volume = {110},
number = {4 Pt A},
pages = {382-391},
pmid = {28286237},
issn = {1769-7115},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces/standards ; Calibration ; Computers/standards ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to "click" during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as "non-click" those periods that the kinematic decoder's retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of "retrospectively labeled" decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.},
}
@article {pmid28285711,
year = {2017},
author = {Schwienheer, C and Krause, J and Schembecker, G and Merz, J},
title = {Modelling centrifugal partition chromatography separation behavior to characterize influencing hydrodynamic effects on separation efficiency.},
journal = {Journal of chromatography. A},
volume = {1492},
number = {},
pages = {27-40},
doi = {10.1016/j.chroma.2017.02.055},
pmid = {28285711},
issn = {1873-3778},
mesh = {Catechols/analysis/isolation &amp; purification ; Centrifugation ; Chromatography, Liquid/instrumentation/*methods ; Hydrodynamics ; Hydroquinones/analysis/isolation &amp; purification ; Solvents/chemistry ; },
abstract = {In addition to the selection or adjustment of phase systems to gain a suitable partition coefficient for the target molecule, the separation efficiency in centrifugal partition chromatography (CPC) is strongly influenced by the hydrodynamic interactions of the mobile and the stationary phase in the chambers. Thus, the hydrodynamic interactions must be investigated and understood in order to enhance a CPC separation run. Different hydrodynamic effects like mass transfer, back mixing and the non-ideal behavior of stationary phase, which cannot be determined directly, are known, but quantifying these effects and their influence on separation performance is barely achieved. In order to understand their influence, a physically detailed mathematical model of a CPC chamber was developed. The model includes a parameter representing the hydrodynamic effects mentioned above and is able to determine the parameters significance by fitting them to separation experiment data. The acquired knowledge is used to correlate the effects of the hydrodynamic influences on the separation performance and can be used to forecast hydrodynamic and separation behavior in a CPC device.},
}
@article {pmid28282626,
year = {2017},
author = {Meurer, F and Do, HT and Sadowski, G and Held, C},
title = {Standard Gibbs energy of metabolic reactions: II. Glucose-6-phosphatase reaction and ATP hydrolysis.},
journal = {Biophysical chemistry},
volume = {223},
number = {},
pages = {30-38},
doi = {10.1016/j.bpc.2017.02.005},
pmid = {28282626},
issn = {1873-4200},
mesh = {Adenosine Triphosphate/*metabolism ; Animals ; Glucose-6-Phosphatase/*metabolism ; Hexokinase/metabolism ; Hydrogen-Ion Concentration ; Hydrolysis ; *Metabolism ; Temperature ; *Thermodynamics ; },
abstract = {ATP (adenosine triphosphate) is a key reaction for metabolism. Tools from systems biology require standard reaction data in order to predict metabolic pathways accurately. However, literature values for standard Gibbs energy of ATP hydrolysis are highly uncertain and differ strongly from each other. Further, such data usually neglect the activity coefficients of reacting agents, and published data like this is apparent (condition-dependent) data instead of activity-based standard data. In this work a consistent value for the standard Gibbs energy of ATP hydrolysis was determined. The activity coefficients of reacting agents were modeled with electrolyte Perturbed-Chain Statistical Associating Fluid Theory (ePC-SAFT). The Gibbs energy of ATP hydrolysis was calculated by combining the standard Gibbs energies of hexokinase reaction and of glucose-6-phosphate hydrolysis. While the standard Gibbs energy of hexokinase reaction was taken from previous work, standard Gibbs energy of glucose-6-phosphate hydrolysis reaction was determined in this work. For this purpose, reaction equilibrium molalities of reacting agents were measured at pH7 and pH8 at 298.15K at varying initial reacting agent molalities. The corresponding activity coefficients at experimental equilibrium molalities were predicted with ePC-SAFT yielding the Gibbs energy of glucose-6-phosphate hydrolysis of -13.72±0.75kJ·mol[-1]. Combined with the value for hexokinase, the standard Gibbs energy of ATP hydrolysis was finally found to be -31.55±1.27kJ·mol[-1]. For both, ATP hydrolysis and glucose-6-phosphate hydrolysis, a good agreement with own and literature values were obtained when influences of pH, temperature, and activity coefficients were explicitly taken into account in order to calculate standard Gibbs energy at pH7, 298.15K and standard state.},
}
@article {pmid28278476,
year = {2017},
author = {Brandman, DM and Cash, SS and Hochberg, LR},
title = {Review: Human Intracortical Recording and Neural Decoding for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1687-1696},
pmid = {28278476},
issn = {1558-0210},
support = {B6453-R//Intramural VA/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; //CIHR/Canada ; },
mesh = {Animals ; *Brain-Computer Interfaces/trends ; Cerebral Cortex/*physiology ; Electroencephalography ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.},
}
@article {pmid28275950,
year = {2017},
author = {Kristensen, E and Guerin-Dugué, A and Rivet, B},
title = {Regularization and a general linear model for event-related potential estimation.},
journal = {Behavior research methods},
volume = {49},
number = {6},
pages = {2255-2274},
doi = {10.3758/s13428-017-0856-z},
pmid = {28275950},
issn = {1554-3528},
mesh = {Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Fixation, Ocular/*physiology ; Humans ; *Linear Models ; },
abstract = {The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response's latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization.},
}
@article {pmid28275048,
year = {2017},
author = {Lebedev, MA and Nicolelis, MA},
title = {Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation.},
journal = {Physiological reviews},
volume = {97},
number = {2},
pages = {767-837},
doi = {10.1152/physrev.00027.2016},
pmid = {28275048},
issn = {1522-1210},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Feedback, Sensory/physiology ; Humans ; Movement/*physiology ; *Neurological Rehabilitation ; },
abstract = {Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.},
}
@article {pmid28270025,
year = {2018},
author = {Zhang, X and Foderaro, G and Henriquez, C and Ferrari, S},
title = {A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks.},
journal = {International journal of neural systems},
volume = {28},
number = {2},
pages = {1750015},
doi = {10.1142/S0129065717500150},
pmid = {28270025},
issn = {1793-6462},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Computer Simulation ; Insecta ; Learning/*physiology ; Locomotion/physiology ; *Models, Neurological ; Nerve Net/*physiology ; Neuronal Plasticity/physiology ; Neurons/*physiology ; User-Computer Interface ; },
abstract = {Recent developments in neural stimulation and recording technologies are providing scientists with the ability of recording and controlling the activity of individual neurons in vitro or in vivo, with very high spatial and temporal resolution. Tools such as optogenetics, for example, are having a significant impact in the neuroscience field by delivering optical firing control with the precision and spatiotemporal resolution required for investigating information processing and plasticity in biological brains. While a number of training algorithms have been developed to date for spiking neural network (SNN) models of biological neuronal circuits, exiting methods rely on learning rules that adjust the synaptic strengths (or weights) directly, in order to obtain the desired network-level (or functional-level) performance. As such, they are not applicable to modifying plasticity in biological neuronal circuits, in which synaptic strengths only change as a result of pre- and post-synaptic neuron firings or biological mechanisms beyond our control. This paper presents a weight-free training algorithm that relies solely on adjusting the spatiotemporal delivery of neuron firings in order to optimize the network performance. The proposed weight-free algorithm does not require any knowledge of the SNN model or its plasticity mechanisms. As a result, this training approach is potentially realizable in vitro or in vivo via neural stimulation and recording technologies, such as optogenetics and multielectrode arrays, and could be utilized to control plasticity at multiple scales of biological neuronal circuits. The approach is demonstrated by training SNNs with hundreds of units to control a virtual insect navigating in an unknown environment.},
}
@article {pmid28269716,
year = {2016},
author = {Elbaz, AM and Ahmed, AT and Mohamed, AM and Oransa, MA and Sayed, KS and Eldeib, AM},
title = {Motor imagery based brain computer interface using transform domain features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6421-6424},
doi = {10.1109/EMBC.2016.7592198},
pmid = {28269716},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Fourier Analysis ; Humans ; *Image Processing, Computer-Assisted ; Male ; *Motor Activity ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements. Different preprocessing, feature selection, and classification schemes were utilized to evaluate the performance of the proposed system for dataset III from BCI competition II. The maximum accuracy achieved was 90.7% while the maximum mutual information was 0.76 bit obtained using the distance series features.},
}
@article {pmid28269708,
year = {2016},
author = {Resquin, F and Ibañez, J and Gonzalez-Vargas, J and Brunetti, F and Dimbwadyo, I and Alves, S and Carrasco, L and Torres, L and Pons, JL},
title = {Combining a hybrid robotic system with a bain-machine interface for the rehabilitation of reaching movements: A case study with a stroke patient.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6381-6384},
doi = {10.1109/EMBC.2016.7592188},
pmid = {28269708},
issn = {2694-0604},
mesh = {Aged ; *Brain-Computer Interfaces ; Exoskeleton Device ; Feedback ; Hand Strength ; Humans ; Learning ; Male ; Movement/*physiology ; Robotics/*methods ; Rotation ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; },
abstract = {Reaching and grasping are two of the most affected functions after stroke. Hybrid rehabilitation systems combining Functional Electrical Stimulation with Robotic devices have been proposed in the literature to improve rehabilitation outcomes. In this work, we present the combined use of a hybrid robotic system with an EEG-based Brain-Machine Interface to detect the user's movement intentions to trigger the assistance. The platform has been tested in a single session with a stroke patient. The results show how the patient could successfully interact with the BMI and command the assistance of the hybrid system with low latencies. Also, the Feedback Error Learning controller implemented in this system could adjust the required FES intensity to perform the task.},
}
@article {pmid28269704,
year = {2016},
author = {Han-Lin Hsieh, and Shanechi, MM},
title = {Multiscale brain-machine interface decoders.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6361-6364},
doi = {10.1109/EMBC.2016.7592183},
pmid = {28269704},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electrophysiological Phenomena ; Models, Neurological ; Motor Cortex/physiology ; Time Factors ; },
abstract = {Brain-machine interfaces (BMI) have vastly used a single scale of neural activity, e.g., spikes or electrocorticography (ECoG), as their control signal. New technology allows for simultaneous recording of multiple scales of neural activity, from spikes to local field potentials (LFP) and ECoG. These advances introduce the new challenge of modeling and decoding multiple scales of neural activity jointly. Such multi-scale decoding is challenging for two reasons. First, spikes are discrete-valued and ECoG/LFP are continuous-valued, resulting in fundamental differences in statistical characteristics. Second, the time-scales of these signals are different, with spikes having a millisecond time-scale and ECoG/LFP having much slower time-scales on the order of tens of milliseconds. Here we develop a new multiscale modeling and decoding framework that addresses these challenges. Our multiscale decoder extracts information from ECoG/LFP in addition to spikes, while operating at the fast time-scale of the spikes. The multiscale decoder specializes to a Kalman filter (KF) or to a point process filter (PPF) when no spikes or ECoG/LFP are available, respectively. Using closed-loop BMI simulations, we show that compared to PPF decoding of spikes alone or KF decoding of LFP/ECoG alone, the multiscale decoder significantly improves the accuracy and error performance of BMI control and runs at the fast millisecond time-scale of the spikes. This new multiscale modeling and decoding framework has the potential to improve BMI control using simultaneous multiscale neural activity.},
}
@article {pmid28269697,
year = {2016},
author = {Holleman, J},
title = {Design considerations for neural amplifiers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6331-6334},
doi = {10.1109/EMBC.2016.7592176},
pmid = {28269697},
issn = {2694-0604},
mesh = {*Amplifiers, Electronic ; Artifacts ; *Brain-Computer Interfaces ; Equipment Design ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {The initial amplification stage is a critical element of a neural signal acquisition system, and the design of low-noise, low-power amplifiers has received a great deal of attention in recent publications. In this paper we discuss practical considerations for the design of amplifiers intended for neural interfaces. Noise is a major issue due to the low amplitude of neural signals. Practical system deployments also require adequate rejection of common-mode interference, such as that due to line power noise or muscle artifacts, and supply noise. This paper attempts to provide some guideance for system and circuit designers and point out opportunities for potential future exploration.},
}
@article {pmid28269694,
year = {2016},
author = {Tokuda, T and Noguchi, S and Iwasaki, S and Takehara, H and Noda, T and Sasagawa, K and Ohta, J},
title = {CMOS-based opto-electronic neural interface devices for optogenetics.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6319-6322},
doi = {10.1109/EMBC.2016.7592173},
pmid = {28269694},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Metals/*chemistry ; *Optical Devices ; Optogenetics/*instrumentation ; *Oxides ; *Semiconductors ; },
abstract = {CMOS-based opto-electronic neural interface devices are presented. The devices are designed with target application of in vitro and in vivo optogenetics. Two types of the opto-electronic neural interface devices are presented. One is single-chip type device for on-chip optogenetics, and the other is multi-chip type device with flexibility and wide-area coverage for in vivo optogenetics on brain. Design, packaging and functional evaluations are presented.},
}
@article {pmid28269693,
year = {2016},
author = {Rezaei, M and Bahrami, H and Mirbozorgi, A and Rusch, LA and Gosselin, B},
title = {A short-impulse UWB BPSK transmitter for large-scale neural recording implants.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {6315-6318},
doi = {10.1109/EMBC.2016.7592172},
pmid = {28269693},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Equipment Design ; *Prostheses and Implants ; Wireless Technology ; },
abstract = {In this paper, a short-impulse ultra-wide band (UWB) transmitter is introduced to enable large-scale neural recordings within miniature brain implants including thousands of channels. The proposed impulse radio UWB transmitter uses a BPSK modulation scheme, the carrier signal of which uses only two delayed impulses to encode the transmitted signal. The proposed UWB transmitter has been implemented into a CMOS 180 nm technology. It occupies 300 μm × 230 μm, and consumes only 6.7 pJ/bit from a 1.8-V supply. Experimental results show that the transmitter has a bandwidth of 2.6 GHz to 5.6 GHz and achieves a maximum data rate of 800 Mbps, which outperforms existing low-power UWB transmitters for similar applications.},
}
@article {pmid28269590,
year = {2016},
author = {Ke Lin, and Yijun Wang, and Xiaorong Gao, },
title = {Time-frequency joint coding method for boosting information transfer rate in an SSVEP based BCI system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5873-5876},
doi = {10.1109/EMBC.2016.7592064},
pmid = {28269590},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Young Adult ; },
abstract = {Steady-State Visual Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) system is an important BCI modality. It has advantages such as ease of use, little training and high Information Transfer Rate (ITR). Traditional SSVEP based BCI systems are based on the Frequency Division Multiple Access (FDMA) approach in telecommunications. Recently, Time Division Multiple Access (TDMA) was also introduced to SSVEP based BCI to enhance the system performance. This study designed a new time-frequency joint coding method to utilize the information coding from both time and frequency domains. TDMA using Different Frequency (DF) mode and Same Frequency (SF) mode were compared to the traditional FDMA mode in the offline experiment. The result showed that the DF mode had better performance than the other two modes. The mean and the standard deviation of accuracy and ITR of the online experiment was 83.3%±5.5% and 130.3 + 14.9 bits/min (trial time: 1.25s) and 92.0%±7.5% and 136.6 + 19.8 bit/min (trial time: 1.5s). The average typing speed for the word-copy spelling task was 14.9 characters per minute (cpm) (trial time: 1.25s) and 14.8 cpm (trial time: 1.5s). The overall results demonstrate the feasibility and advantage of the proposed time-frequency joint coding method.},
}
@article {pmid28269589,
year = {2016},
author = {Yubing Jiang, and Hyeonseok Lee, and Gang Li, and Wan-Young Chung, },
title = {High performance wearable two-channel hybrid BCI system with eye closure assist.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5869-5872},
doi = {10.1109/EMBC.2016.7592063},
pmid = {28269589},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy ; Male ; *Ocular Physiological Phenomena ; Signal Processing, Computer-Assisted ; Wireless Technology ; Young Adult ; },
abstract = {Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state. Additionally, a wearable two-channel EEG device is utilized in order to increase the efficiency of EEG processing and improving the practical utility. Results show that proposed hybrid system can generate four control commands with the average accuracy of 87.72%, which is higher than only using MI. Besides, it is possible to reach the same good accuracy using two-channel EEG as with usual multi-channel EEG.},
}
@article {pmid28269588,
year = {2016},
author = {Ali, SS and Lei Zhang, },
title = {Maximum entropy based common spatial patterns for motor imagery classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5865-5868},
doi = {10.1109/EMBC.2016.7592062},
pmid = {28269588},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Entropy ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy/*classification ; Signal Processing, Computer-Assisted ; },
abstract = {The common spatial pattern (CSP) is extensively used to extract discriminative feature from raw Electroencephalography (EEG) signals for motor imagery classification. The CSP is a statistical signal processing technique, which relies on sample based covariance matrix estimation to give discriminative information from raw EEG signals. The sample based estimation of covariance matrix becomes a problem when the number of training samples is limited, which causes the performance of CSP based brain computer interface (BCI) to degrade significantly. In this paper, we present a maximum entropy based CSP algorithm that incorporates principle of maximum entropy while estimating the sample based covariance matrix. The proposed algorithm is evaluated on publicly available data set samples. The classification results indicate that the proposed algorithm outperforms the traditional CSP algorithm by 13.38% on average.},
}
@article {pmid28269587,
year = {2016},
author = {Nicolae, IE and Stefan, MM and Hurezeanu, B and Taralunga, DD and Strungaru, R and Vasile, TM and Bajenaru, OA and Ungureanu, GM},
title = {Investigating motor imagery tasks by their neural effects - A case study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5861-5864},
doi = {10.1109/EMBC.2016.7592061},
pmid = {28269587},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy/*classification ; Male ; Movement/physiology ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery, one of the first investigated neural process for Brain-Computer Interfaces (BCIs) still provides a great challenge nowadays. Aiming a better and more accurate control, multiple researches have been conducted by the scientific community. Nevertheless, there is still no robust and confident application developed. In order to augment the potential referring to motor imagery, and to attract user's interest, we propose multiple motor imagery tasks in combination with different visual or auditory stimuli. We use multi-class classification for discrimination and we observe confident classification performance for the task related to user's background.},
}
@article {pmid28269586,
year = {2016},
author = {Chikara, RK and Li-Wei Ko, },
title = {Phase modulation-based response-inhibition outcome prediction in translational scenario of stop-signal task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5857-5860},
doi = {10.1109/EMBC.2016.7592060},
pmid = {28269586},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; *Electroencephalography ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation ; Reaction Time ; Young Adult ; },
abstract = {In this paper, a method is proposed to predict the resting-state outcomes of participants based on their electroencephalogram (EEG) signals recorded before the successful /unsuccessful response inhibition. The motivation of this study is to enhance the shooter performance for shooting the target, when their EEG patterns show that they are ready. This method can be used in brain-computer interface (BCI) system. In this study, multi-channel EEG from twenty participants are collected by the electrodes placed at different scalp locations in resting-state time. The EEG trials are used to predict two possible outcomes: successful or unsuccessful stop. Four classifiers (QDC, KNNC, PARZENDC, LDC) are used in this study to evaluation the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from 5-pair electrodes are calculated and then used as the feature input for the classifiers. Our experimental results show that the proposed method prediction accuracy (leave-one-out) was obtained 95% by QDC classifier.},
}
@article {pmid28269585,
year = {2016},
author = {Andreou, D and Poli, R},
title = {Comparing EEG, its time-derivative and their joint use as features in a BCI for 2-D pointer control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5853-5856},
doi = {10.1109/EMBC.2016.7592059},
pmid = {28269585},
issn = {2694-0604},
mesh = {Algorithms ; Area Under Curve ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials/*physiology ; Humans ; ROC Curve ; Signal-To-Noise Ratio ; Support Vector Machine ; },
abstract = {Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse. Features were selected based on the absolute difference of medians of the target and non-target classes. Classification was carried out by an ensemble of linear support vector machines which were optimised using the mutual information criterion. Comparisons were based on the area under the receiver operating characteristics. The Mann-Whitney one-tailed test was used to study significance. Results show that EEG amplitudes are outperformed by both the first derivative and the combined feature vector and that derivatives are better than the combined vector.},
}
@article {pmid28269558,
year = {2016},
author = {Rezaei, M and Maghsoudloo, E and Sawan, M and Gosselin, B},
title = {A 110-nW in-channel sigma-delta converter for large-scale neural recording implants.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5741-5744},
doi = {10.1109/EMBC.2016.7592031},
pmid = {28269558},
issn = {2694-0604},
mesh = {*Analog-Digital Conversion ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Wireless Technology ; },
abstract = {Advancement in wireless and microsystems technology have ushered in new devices that can directly interface with the central nervous system for stimulating and/or monitoring neural circuitry. In this paper, we present an ultra low-power sigma-delta analog-to-digital converter (ADC) intended for utilization into large-scale multi-channel neural recording implants. This proposed design, which provides a resolution of 9 bits using a one-bit oversampled ADC, presents several desirable features that allow for an in-channel ADC scheme, where one sigma-delta converter is provided for each channel, enabling development of scalable systems that can interface with different types of high-density neural microprobes. The proposed circuit, which have been fabricated in a TSMC 180-nm CMOS process, employs a first order noise shaping topology with a passive integrator and a low-supply voltage of 0.6 V to achieve ultra low-power consumption and small size. The proposed ADC clearly outperforms other designs with a power consumption as low as 110 nW for a precision of 9 bits (11-fJ per conversion), a silicon area of only 82 μm × 84 μm and one of the best reported figure of merit among recently published data converters utilized in similar applications.},
}
@article {pmid28269556,
year = {2016},
author = {Sani, OG and Chavarriaga, R and Shamsollahi, MB and Del R Millan, J},
title = {Detection of movement related cortical potential: effects of causal vs. non-causal processing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5733-5736},
doi = {10.1109/EMBC.2016.7592029},
pmid = {28269556},
issn = {2694-0604},
mesh = {Electroencephalography/*methods ; *Evoked Potentials ; Female ; Humans ; Male ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {Movement Related Cortical Potentials (MRCP) have been the subject of numerous studies. They accompany many self-initiated movements and this makes them a good candidate for incorporation in BCI paradigms. In this work we propose a novel experimental protocol involving natural controlling of a computer mouse and based on EEG recordings from 5 subjects, show that it elicits MRCP. We also show the feasibility of online detection of MRCP by implementing a classification based detection framework. Additionally, we discuss the adverse effects of causality restriction on detection performance by implementing an additional offline approach relaxing those restrictions and comparing the results. The best MRCP detection performance achieved on the recorded data with the offline approach has an average maximum accuracy of 0.76 and with the online approach an average AUC of 0.953.},
}
@article {pmid28269555,
year = {2016},
author = {Hadsund, JT and Sorensen, MB and Royo, AC and Niazi, IK and Rovsing, H and Rovsing, C and Jochumsen, M},
title = {Feature domain-specific movement intention detection for stroke rehabilitation with brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5725-5728},
doi = {10.1109/EMBC.2016.7592027},
pmid = {28269555},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Female ; Foot/physiology ; Hand/physiology ; Humans ; Imagination ; *Intention ; Male ; *Movement ; Stroke Rehabilitation/*methods ; Young Adult ; },
abstract = {Brain-computer interface (BCI) driven electrical stimulation has been proposed for neuromodulation for stroke rehabilitation by pairing intentions to move with somatosensory feedback from electrical stimulation. Movement intentions have been detected in several studies using different techniques, with temporal and spectral features being the most common. A few studies have compared temporal and spectral features, but conflicting results have been reported. In this study, the aim was to investigate if complexity measures can be used for movement intention detection and to compare the detection performance based on features extracted from three different domains (time, frequency and complexity) from single-trial EEG. Two data sets were used where four different isometric palmar grasps or dorsiflexions were performed while continuous EEG was recorded. 39 healthy subjects performed or imagined these movements and 11 stroke patients attempted to perform the movements. The EEG was pre-processed and divided into two epoch classes: Background EEG (2 s) and movement intention (2 s). To obtain an estimated detection performance, temporal, spectral and complexity features were extracted and classified (linear discriminant analysis) after the feature vector was reduced using sequential forward selection. The results show that accuracies between 82-87% and 74-80% are obtained for foot and hand movements, respectively. The temporal feature domain was the most dominant for foot movement intention detection, while the spectral features contributed more to the hand movement detection. The complexity features could be used to detect movement intentions, but the performance was much lower compared to temporal and spectral features.},
}
@article {pmid28269554,
year = {2016},
author = {Rosa So, and Libedinsky, C and Kai Keng Ang, and Wee Chiek Clement Lim, and Kyaw Kyar Toe, and Cuntai Guan, },
title = {Adaptive decoding using local field potentials in a brain-machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5721-5724},
doi = {10.1109/EMBC.2016.7592026},
pmid = {28269554},
issn = {2694-0604},
mesh = {*Action Potentials ; Animals ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta/physiology ; Male ; Motor Cortex/*physiology ; *Movement ; },
abstract = {Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model. We implemented a BMI system in a nonhuman primate model that allows brain-controlled movement of a robotic platform. Using this system, we showed that LFP signals alone can be used for decoding in a closed-loop brain-controlled BMI. Further, we performed offline analysis to assess the potential implementation of an adaptive decoding method that does not presume knowledge of the target location. Our results show that with periodic signal and channel selection adaptation, decoding accuracy using LFP alone can be improved by between 5-50%. These results demonstrate the feasibility of implementing unsupervised adaptive methods during asynchronous decoding of LFP signals for long-term usage in a BMI system.},
}
@article {pmid28269553,
year = {2016},
author = {Shah, SA and Huiling Tan, and Brown, P},
title = {Decoding force from deep brain electrodes in Parkinsonian patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5717-5720},
pmid = {28269553},
issn = {2694-0604},
support = {MC_UU_12024/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Biomechanical Phenomena ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Electrodes ; Humans ; Neurons/physiology ; Parkinson Disease/*physiopathology ; },
abstract = {Limitations of many Brain Machine Interface (BMI) systems using invasive electrodes include reliance on single neurons and decoding limited to kinematics only. This study investigates whether force-related information is present in the local field potential (LFP) recorded with deep brain electrodes using data from 14 patients with Parkinson's disease. A classifier based on logistic regression (LR) is developed to classify various force stages, using 10-fold cross validation. Least Absolute and Shrinkage Operator (Lasso) is then employed in order to identify the features with the most predictivity. The results show that force-related information is present in the LFP, and it is possible to distinguish between various force stages using certain frequency-domain (delta, beta, gamma) and time-domain (mobility) features in real-time.},
}
@article {pmid28269552,
year = {2016},
author = {Pailla, T and Jiang, W and Dichter, B and Chang, EF and Gilja, V},
title = {ECoG data analyses to inform closed-loop BCI experiments for speech-based prosthetic applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5713-5716},
doi = {10.1109/EMBC.2016.7592024},
pmid = {28269552},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electrocorticography ; Female ; Humans ; Male ; Prostheses and Implants ; Sensorimotor Cortex/*physiology ; Speech/*physiology ; },
abstract = {Brain Computer Interfaces (BCIs) assist individuals with motor disabilities by enabling them to control prosthetic devices with their neural activity. Performance of closed-loop BCI systems can be improved by using design strategies that leverage structured and task-relevant neural activity. We use data from high density electrocorticography (ECoG) grids implanted in three subjects to study sensory-motor activity during an instructed speech task in which the subjects vocalized three cardinal vowel phonemes. We show how our findings relate to the current understanding of speech physiology and functional organization of human sensory-motor cortex. We investigate the effect of behavioral variations on parameters and performance of the decoding model. Our analyses suggest experimental design strategies that may be critical for speech-based BCI performance.},
}
@article {pmid28269550,
year = {2016},
author = {Fiedler, P and Strohmeier, D and Hunold, A and Griebel, S and Muhle, R and Schreiber, M and Pedrosa, P and Vasconcelos, B and Fonseca, C and Vaz, F and Haueisen, J},
title = {Modular multipin electrodes for comfortable dry EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5705-5708},
doi = {10.1109/EMBC.2016.7592022},
pmid = {28269550},
issn = {2694-0604},
mesh = {Electric Conductivity ; Electrodes ; Electroencephalography/*instrumentation ; *Equipment Design ; Humans ; Metals ; Polymers ; },
abstract = {Electrode and cap concepts for continuous and ubiquitous monitoring of brain activity will open up new fields of application and contribute to increased use of electroencephalography (EEG) in clinical routine, neurosciences, brain-computer-interfacing and out-of-the-lab monitoring. However, mobile and unobtrusive applications are currently hindered by the lack of applicable convenient and reliable electrode and cap systems. We propose a novel modular electrode concept based on a flexible polymer substrate, coated with electrically conductive metallic films. The overall concept enables design adaptation to different head regions and cap designs. We describe the single modules of the system and investigate the influence of electrode pin number, coating material and adduction force on electrode-skin impedance and perceived wearing comfort. Our results contribute to rapid and comfortable multichannel dry EEG.},
}
@article {pmid28269548,
year = {2016},
author = {Fiedler, L and Obleser, J and Lunner, T and Graversen, C},
title = {Ear-EEG allows extraction of neural responses in challenging listening scenarios - A future technology for hearing aids?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5697-5700},
doi = {10.1109/EMBC.2016.7592020},
pmid = {28269548},
issn = {2694-0604},
mesh = {Adult ; Auditory Perception/*physiology ; Brain-Computer Interfaces ; *Ear Canal ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials, Auditory/*physiology ; Hearing Aids ; Humans ; Male ; Middle Aged ; *Speech ; Young Adult ; },
abstract = {Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli. Furthermore, the technology has not yet been tested and validated for ecologically relevant auditory stimuli such as speech. In this study, Ear-EEG and conventional scalp EEG were recorded simultaneously in a discrete-tone as well as a continuous-speech design. The discrete stimuli were applied in a dichotic oddball paradigm, while continuous stimuli were presented diotically as two simultaneous talkers. Cross-correlation of stimulus envelope and Ear-EEG was assessed as a measure of ongoing neural tracking. The extracted ERPs from Ear-EEG revealed typical auditory components yet depended critically on the reference electrode chosen. Reliable neural-tracking responses were extracted from the Ear-EEG for both paradigms, albeit weaker in amplitude than from scalp EEG. In conclusion, this study shows the feasibility of extracting relevant neural features from ear-canal-recorded "Ear-EEG", which might augment future hearing technology.},
}
@article {pmid28269547,
year = {2016},
author = {Xuhong Guo, and Weihua Pei, and Yijun Wang, and Qiang Gui, and He Zhang, and Xiao Xing, and Yong Huang, and Hongda Chen, and Ruicong Liu, and Yuanyuan Liu, },
title = {Developing a one-channel BCI system using a dry claw-like electrode.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {5693-5696},
doi = {10.1109/EMBC.2016.7592019},
pmid = {28269547},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; *Equipment Design ; *Evoked Potentials, Visual ; Fourier Analysis ; Humans ; Signal-To-Noise Ratio ; },
abstract = {An eight-class SSVEP-based BCI system was designed and demonstrated in this study. To minimize the complexity of the traditional equipment and operation, only one work electrode was used. The work electrode was fabricated in our laboratory and designed as a claw-like structure with a diameter of 15 mm, featuring 8 small fingers of 4mm length and 2 mm diameter, and the weight was only 0.1g. The structure and elasticity can help the fingers pass through the hair and contact the scalp when placed on head. The electrode was capable to collect evoked brain activities such as steady-state visual evoked potentials (SSVEPs). This study showed that although the amplitude and SNR of SSVEPs obtained from a dry claw electrode was relatively lower than that from a wet electrode, the difference was not significant. This study further implemented an eight-class SSVEP-based BCI system using a dry claw-like electrode. Three subjects participated in the experiment. Using infinite impulse response (IIR) filtering and a simplified threshold method based on fast Fourier transform (FFT), the average accuracy of the three participants was 89.3% using 4 sec-long SSVEPs, leading to an average information transfer rate (ITR) of 26.5 bits/min. The results suggested the ability of using a dry claw-like electrode to perform practical BCI applications.},
}
@article {pmid28269337,
year = {2016},
author = {Iida, Y and Horie, R},
title = {Implementation of a control system for a power wheelchair with induction of a β/α ratio by visual feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4772-4775},
doi = {10.1109/EMBC.2016.7591794},
pmid = {28269337},
issn = {2694-0604},
mesh = {Acceleration ; Analysis of Variance ; *Electric Power Supplies ; Electroencephalography ; *Feedback, Sensory ; Female ; Humans ; Male ; *Wheelchairs ; },
abstract = {Techniques using electroencephalography (EEG)-based brain computer interfaces (BCIs) have been developed and are eagerly anticipated as novel interfaces for controlling power wheelchairs. In addition to the BCIs, smart glass technology has been developed. In our previous study, we propose a prototype of an intuitive control system for a power wheelchair; this system comprises a simple EEG recorder, smart glass, and a microcomputer. Using this system, the power wheelchair moves straight ahead when a user concentrates, stops when the user blinks, and turns left or right when the user tilt his/her neck to the left or right, respectively. A β/α ratio as an indicator of the concentration and blinks are detected from raw EEG waves, and the tilting of the neck is detected by acceleration sensors in the smart glass. In this study, we proposed a new control system which display the visual feedback of the β/α ratio on the smart glass to induce user's concentration. Our results show that during the experiment, the system successfully worked and induced for the β/α ratio in specific concentrating states.},
}
@article {pmid28269314,
year = {2016},
author = {Chun-Shu Wei, and Yuan-Pin Lin, and Yu-Te Wang, and Chin-Teng Lin, and Tzyy-Ping Jung, },
title = {Transfer learning with large-scale data in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4666-4669},
doi = {10.1109/EMBC.2016.7591768},
pmid = {28269314},
issn = {2694-0604},
mesh = {*Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; },
abstract = {Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.},
}
@article {pmid28269307,
year = {2016},
author = {Fan Zhang, and Ming Liu, and He Huang, },
title = {Tolerance of neural decoding errors for powered artificial legs: A pilot study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4630-4633},
doi = {10.1109/EMBC.2016.7591759},
pmid = {28269307},
issn = {2694-0604},
mesh = {Amputees ; *Artificial Limbs ; *Electric Power Supplies ; Humans ; Neurons/*physiology ; Pilot Projects ; Prosthesis Design ; },
abstract = {Neural-machine interface (NMI) decoding errors challenge the clinical value of neural control of powered artificial legs, because these errors can dangerously disturb the user's walking balance, cause stumbles or falls, and thus threaten the user's confidence and safety in prosthesis use. Although extensive research efforts have been made to minimize the NMI decoding error rate, none of the current approaches can completely eliminate the errors in NMI. This study aimed at improving the robustness of prosthesis control system against neural decoding errors by introducing a fault-tolerant control (FTC) strategy. A novel reconfiguration mechanism, combined with our previously developed NMI decoding error detector, was designed and implemented into our prototypical powered knee prosthesis. The control system with FTC was preliminarily tested on two transfemoral amputees when they walked with the powered prosthesis on different walking terrains. Various NMI errors were simulated when the FTC was enabled and disabled. The preliminary testing results indicated that the FTC strategy was capable of effectively counteracting the disruptive effects of simulated decoding errors by reducing the mechanical work change around the prosthetic knee joint elicited by the NMI error. The outcomes of this study may provide a potential engineering solution to make the neural control of powered artificial legs safer to use.},
}
@article {pmid28269289,
year = {2016},
author = {Handiru, VS and Vinod, AP and Guan, C},
title = {Multi-direction hand movement classification using EEG-based source space analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4551-4554},
doi = {10.1109/EMBC.2016.7591740},
pmid = {28269289},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; Hand/physiology ; Humans ; Models, Statistical ; Movement/*physiology ; Space Perception/*physiology ; },
abstract = {Recent advances in the brain-computer interfaces (BCIs) have demonstrated the inference of movement related activity using non-invasive EEG. However, most of the sensorspace approaches that study sensorimotor rhythms using EEG do not reveal the underlying neurophysiological phenomenon while executing or imagining the movement with finer control. Therefore, there is a need to examine feature extraction techniques in the cortical source space which can provide more information about the task compared to sensor-space. In this study, we extend the traditional sensor-space feature extraction method, Common Spatial Pattern (CSP), to the source space, using various regularization approaches. We use Weighted Minimum Norm Estimate (wMNE) as a source localization technique. We show that for a multi-direction hand movement classification problem, the source space features can result in an increase of over 10% accuracy compared to sensor space features. Fisher's Linear Discriminant (FLD) classifier with the One-versus-rest approach is used for the classification.},
}
@article {pmid28269267,
year = {2016},
author = {Opie, NL and Rind, GS and John, SE and Ronayne, SM and Grayden, DB and Burkitt, AN and May, CN and O'Brien, TJ and Oxley, TJ},
title = {Feasibility of a chronic, minimally invasive endovascular neural interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4455-4458},
doi = {10.1109/EMBC.2016.7591716},
pmid = {28269267},
issn = {2694-0604},
mesh = {Animals ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology/surgery ; Dielectric Spectroscopy ; *Electrodes, Implanted ; *Endovascular Procedures ; Sheep ; Stents ; },
abstract = {Development of a neural interface that can be implanted without risky, open brain surgery will increase the safety and viability of chronic neural recording arrays. We have developed a minimally invasive surgical procedure and an endovascular electrode-array that can be delivered to overlie the cortex through blood vessels. Here, we describe feasibility of the endovascular interface through electrode viability, recording potential and safety. Electrochemical impedance spectroscopy demonstrated that electrode impedance was stable over 91 days and low frequency phase could be used to infer electrode incorporation into the vessel wall. Baseline neural recording were used to identify the maximum bandwidth of the neural interface, which remained stable around 193 Hz for six months. Cross-sectional areas of the implanted vessels were non-destructively measured using the Australian Synchrotron. There was no case of occlusion observed in any of the implanted animals. This work demonstrates the feasibility of an endovascular neural interface to safely and efficaciously record neural information over a chronic time course.},
}
@article {pmid28269262,
year = {2016},
author = {Stock, VN and Balbinot, A},
title = {Movement imagery classification in EMOTIV cap based system by Naïve Bayes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4435-4438},
doi = {10.1109/EMBC.2016.7591711},
pmid = {28269262},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Female ; Hand ; Humans ; Male ; *Movement ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters. The EPOC electrodes cap is used for EEG acquisition, in two test subjects, for two distinct trial formats. The channels picked are FC5, FC6, P7 and P8 of the 10-20 system, and a discussion about the differences of using C3, C4, P3 and P4 positions is proposed. Dataset 3 of the BCI Competition II is also analyzed using the implemented algorithms. The maximum classification results for the proposed experiment and for the BCI Competition dataset were, respectively, 79% and 85% The conclusion of this study is that the picked positions for electrodes may be applied for BCI systems with satisfactory classification rates.},
}
@article {pmid28269172,
year = {2016},
author = {Duque-Munoz, L and Vargas, F and Lopez, JD},
title = {Simplified EEG inverse solution for BCI real-time implementation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4051-4054},
doi = {10.1109/EMBC.2016.7591616},
pmid = {28269172},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; Neuroimaging/*methods ; },
abstract = {EEG brain imaging has become a promising approach in Brain-computer interface applications. However, accurate reconstruction of active regions and computational burden are still open issues. In this paper, we propose to use a simplified forward model that includes the reduction of the cortical dipoles based on Brodmann areas together with state-of-the-art EEG brain imaging techniques. With this approach the well known Beamformers and Greedy Search inverse solutions become feasible for real-time implementation, while guaranteeing lower localization error than previous approaches used in BCI. This methodology was tested with synthetic and real EEG data from a visual attention study. Results show zero localization error in terms of active cortical regions estimation in single 1 s trial datasets, with a computation time of 1.1 s in a non-specialized personal computer. These results open the possibility to obtain in real-time information of active cortical regions in Brain-computer interfaces.},
}
@article {pmid28269099,
year = {2016},
author = {Loza, CA and Principe, JC},
title = {Estimation and modeling of EEG amplitude-temporal characteristics using a marked point process approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3720-3723},
doi = {10.1109/EMBC.2016.7591536},
pmid = {28269099},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Models, Biological ; *Signal Processing, Computer-Assisted ; },
abstract = {We propose a novel interpretation of single channel Electroencephalogram (EEG) traces based on the transient nature of encoded processes in the brain. In particular, the proposed framework models EEG as the output of the noisy addition of temporal, reoccurring, transient patterns known as phasic events. This is not only neurophysiologically sound, but it also provides additional information that classical EEG analysis often disregards. Furthermore, by utilizing sparse decomposition techniques, it is possible to obtain amplitude and timing that is further modeled using estimation and fitting techniques. We model Brain-Computer Interfaces (BCI) competition data features as Gaussian Mixture Model (GMM) samples in order to show the potential of working in the joint space of the parameters. The results not only preserve the topographic discriminant behavior but also expand the realm of possible EEG analysis.},
}
@article {pmid28268984,
year = {2016},
author = {Artoni, F and Pirondini, E and Panarese, A and Micera, S},
title = {Exploring neuro-muscular synergies of reaching movements with unified independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3183-3186},
doi = {10.1109/EMBC.2016.7591405},
pmid = {28268984},
issn = {2694-0604},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/*methods ; Humans ; *Muscle Contraction ; Muscle, Skeletal/*physiology ; Reproducibility of Results ; },
abstract = {The coordinated recruitment of group of muscles through muscles synergies is known to simplify the control of movements. However, how and to what extent such control scheme is encoded at a cortical level is poorly understood. So far, electroencephalography (EEG) and electromyography (EMG) have been used, separately, to investigate the cortical regions of the human brain which may be involved in activating muscle synergies. Here we aim at extending these results by looking for a hierarchical relationship between cortical and muscular sources of activity (neuro-muscular synergies) with a unified analysis of independent components (IC) simultaneously extracted from both EEG and EMG signals. We show for the first time how the direct fusion of EEG and EMG signals to extract unified ICs (unICs) can overcome the limitations of previous approaches, i.e., the difficulty in linking neural with muscular activations, and the lack of reliability of separate preprocessing techniques. Our results show that unified ICs were physiologically meaningful components in agreement with previous works. UNICA (Unified Independent Component Analysis) can also be considered as a solution for estimating overcomplete ICA on EEG and EMG data. These findings are an important step towards an understanding of the cortical control of human muscles synergies, and may have important applications for understanding movement dysfunction and to develop novel approaches for brain-computer interfaces and neuroprostheses.},
}
@article {pmid28268963,
year = {2016},
author = {Friedenberg, DA and Bouton, CE and Annetta, NV and Skomrock, N and Mingming Zhang, and Schwemmer, M and Bockbrader, MA and Mysiw, WJ and Rezai, AR and Bresler, HS and Sharma, G},
title = {Big data challenges in decoding cortical activity in a human with quadriplegia to inform a brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3084-3087},
doi = {10.1109/EMBC.2016.7591381},
pmid = {28268963},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Information Science/*methods ; Male ; Motor Cortex/physiopathology ; Quadriplegia/*physiopathology ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data. We demonstrate an algorithmic approach to analyze such data and present a novel method to evaluate such algorithms. We present our methodology with examples of decoding human brain data in real-time to inform a BCI.},
}
@article {pmid28268959,
year = {2016},
author = {Khanna, P and Athalye, VR and Gowda, S and Costa, RM and Carmena, JM},
title = {Modeling distinct sources of neural variability driving neuroprosthetic control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3068-3071},
doi = {10.1109/EMBC.2016.7591377},
pmid = {28268959},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Models, Neurological ; Motion ; *Prostheses and Implants ; },
abstract = {Many closed-loop, continuous-control brain-machine interface (BMI) architectures rely on decoding via a linear readout of noisy population neural activity. However, recent work has found that decomposing neural population activity into correlated and uncorrelated variability reveals that improvements in cursor control coincide with the emergence of correlated neural variability. In order to address how correlated and uncorrelated neural variability arises and contributes to BMI cursor control, we simulate a neural population receiving combinations of shared inputs affecting all cells and private inputs affecting only individual cells. When simulating BMI cursor-control with different populations, we find that correlated activity generates faster, straighter cursor trajectories, yet sometimes at the cost of inaccuracies. We also find that correlated variability can be generated from either shared inputs or quickly updated private inputs. Overall, our results suggest a role for both correlated and uncorrelated neural activity in cursor control, and potential mechanisms by which correlated activity may emerge.},
}
@article {pmid28268958,
year = {2016},
author = {McNiel, DB and Choi, JS and Hessburg, JP and Francis, JT},
title = {Reward value is encoded in primary somatosensory cortex and can be decoded from neural activity during performance of a psychophysical task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3064-3067},
pmid = {28268958},
issn = {2694-0604},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Macaca radiata ; Neurons/cytology ; Prefrontal Cortex/cytology/physiology ; *Psychophysics ; Reinforcement, Psychology ; *Reward ; Somatosensory Cortex/cytology/*physiology ; Substantia Nigra/cytology/physiology ; },
abstract = {Encoding of reward valence has been shown in various brain regions, including deep structures such as the substantia nigra as well as cortical structures such as the orbitofrontal cortex. While the correlation between these signals and reward valence have been shown in aggregated data comprised of many trials, little work has been done investigating the feasibility of decoding reward valence on a single trial basis. Towards this goal, one non-human primate (macaca radiata) was trained to grip and hold a target level of force in order to earn zero, one, two, or three juice rewards. The animal was informed of the impending result before reward delivery by means of a visual cue. Neural data was recorded from primary somatosensory cortex (S1) during these experiments and firing rate histograms were created following the appearance of the visual cue and used as input to a variety of classifiers. Reward valence was decoded with high levels of accuracy from S1 both in the post-cue and post-reward periods. Additionally, the proportion of units showing significant changes in their firing rates was influenced in a predictable way based on reward valence. The existence of a signal within S1 cortex that encodes reward valence could have utility for implementing reinforcement learning algorithms for brain machine interfaces. The ability to decode this reward signal in real time with limited data is paramount to the usability of such a signal in practical applications.},
}
@article {pmid28268956,
year = {2016},
author = {Hongbao Li, and Fang Wang, and Qiaosheng Zhang, and Shaomin Zhang, and Yiwen Wang, and Xiaoxiang Zheng, and Principe, JC},
title = {Maximum correntropy based attention-gated reinforcement learning designed for brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3056-3059},
doi = {10.1109/EMBC.2016.7591374},
pmid = {28268956},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; *Attention ; *Brain-Computer Interfaces ; Haplorhini ; *Nonlinear Dynamics ; *Reinforcement, Psychology ; },
abstract = {Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions for spatial credit assignment task with better efficiency. However, the outliers existing in the neural signals still make interpret the neural-action mapping difficult. We propose an enhanced AGREL algorithm using correntropy as a criterion, which is more insensitive to noise. Then the algorithm is tested on the neural data where the monkey is trained to do the obstacle avoidance task. The new method converges faster during the training period, and improves from 44.63% to 68.79% on average in success rate compared with the original AGREL. The result indicates that the combination of correntropy criterion and AGREL can reduce the effect of the outliers with better performance when interpreting the mapping between neural signal and kinematics.},
}
@article {pmid28268955,
year = {2016},
author = {Boi, F and Semprini, M and Vato, A},
title = {A non-linear mapping algorithm shaping the control policy of a bidirectional brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3052-3055},
doi = {10.1109/EMBC.2016.7591373},
pmid = {28268955},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Feedback ; Male ; Motor Cortex/physiology ; Movement/physiology ; *Nonlinear Dynamics ; Rats ; },
abstract = {Motor brain-machine interfaces (BMIs) transform neural activities recorded directly from the brain into motor commands to control the movements of an external object by establishing an interface between the central nervous system (CNS) and the device. Bidirectional BMIs are closed-loop systems that add a sensory channel to provide the brain with an artificial feedback signal produced by the interaction between the device and the external world. Taking inspiration from the functioning of the spinal cord in mammalians, in our previous works we designed and developed a bidirectional BMI that uses the neural signals recorded form rats' motor cortex to control the movement of an external object. We implemented a decoding interface based on the approximation of a predefined force field with a central attractor point. Now we consider a non-linear transformation that allows to design a decoder approximating force fields with arbitrary attractors. We describe here the non-linear mapping algorithm and preliminary results of its use with behaving rats.},
}
@article {pmid28268946,
year = {2016},
author = {Khalighinejad, B and Long, LK and Mesgarani, N},
title = {Designing a hands-on brain computer interface laboratory course.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {3010-3014},
pmid = {28268946},
issn = {2694-0604},
support = {R01 DC014279/DC/NIDCD NIH HHS/United States ; },
mesh = {Biomedical Engineering/education ; *Brain-Computer Interfaces ; *Curriculum ; Goals ; Humans ; *Laboratories ; Students ; Surveys and Questionnaires ; Universities ; },
abstract = {Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students. The course provides an effective introduction to the experimental design, neuroscience concepts, data analysis techniques, and technical skills required in the field of BCI.},
}
@article {pmid28268894,
year = {2016},
author = {Schiatti, L and Faes, L and Tessadori, J and Barresi, G and Mattos, L},
title = {Mutual information-based feature selection for low-cost BCIs based on motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {2772-2775},
doi = {10.1109/EMBC.2016.7591305},
pmid = {28268894},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; *Eidetic Imagery ; Electroencephalography ; Humans ; Models, Theoretical ; Reproducibility of Results ; Support Vector Machine ; },
abstract = {In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system. The minimal sub set of features most relevant to task description and less redundant to each other was determined, and the corresponding classification accuracy was assessed offline employing linear support vector machine (SVM) in a 10-fold cross validation scheme. The analysis was performed: (a) on the original full Dataset I from BCI competition IV, (b) on a restricted channels set from Dataset I corresponding to available Emotiv EPOC electrodes locations, and (c) on data recorded with the EPOC system. Results from (a) showed that an offline classification accuracy above 80% can be reached using only 5 features. Limiting the analysis to EPOC channels caused a decrease of classification accuracy, although it still remained above chance level, both for data from (b) and (c). A top accuracy of 70% was achieved using 2 optimal features. These results encourage further research towards the development of portable low cost motor imagery-based BCI systems.},
}
@article {pmid28268893,
year = {2016},
author = {Farmaki, C and Christodoulakis, G and Sakkalis, V},
title = {Applicability of SSVEP-based brain-computer interfaces for robot navigation in real environments.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {2768-2771},
doi = {10.1109/EMBC.2016.7591304},
pmid = {28268893},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Models, Theoretical ; *Neurologic Examination ; *Robotics ; },
abstract = {Brain-computer interfaces have been extensively studied and used in order to aid patients suffering from neuromuscular diseases to communicate and control the surrounding environment. Steady-state visual evoked potentials (SSVEP) constitute a very popular BCI stimulation protocol, due to their efficiency and quick response time. In this study, we developed a SSVEP-based BCI along with a low-cost custom radio-controlled robot-car providing live video feedback from a wireless camera mounted on the robot, serving as our testbed. Our goal was to quantitatively assess the applicability of SSVEPs in real time navigation in realistic environments using a pragmatic approach. In order to assess the additional fatigue that the camera video introduces, we designed a two-session experiment, a control one with no connection to the robot and, thus, no live camera feed, and a realistic one where the users could navigate the robot with the provision of front scenes, captured from the camera. Statistical tests revealed a significant decrease of the accuracy of the system during the realistic session that included live video, in comparison with the session that did not. The results suggest that the moving camera image sequence introduces an extra level of fatigue and/or distraction to the users.},
}
@article {pmid28268892,
year = {2016},
author = {Guofa Shou, and Lei Ding, },
title = {EEG-based single-trial detection of errors from multiple error-related brain activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {2764-2767},
doi = {10.1109/EMBC.2016.7591303},
pmid = {28268892},
issn = {2694-0604},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Reproducibility of Results ; },
abstract = {A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface, where single-trial analysis and classification would provide novel insights on dynamic brain responses to errors. However, procedures of selecting features, as well as procedures of single-trial classification, are not fully investigated for optimal performance. In the present study, we investigated the performance of different configurations of feature extractions in both temporal and frequency domains, for discriminating response errors in a color-word matching Stroop task. Motivated by our previous investigations, we evaluated both temporal and frequency features with component signals, which were obtained from EEG signals via an independent component analysis (ICA). Five component signals (independent components, ICs), originated from the frontal, motor, parietal, and occipital areas, were included in detecting error-related brain activity from single-trial EEG data. The results showed that better performance can be achieved by optimizing time window and frequency range of selected features, sampling scheme of feature-related data, and training of classifiers. However, a simple combination of features from multiple component signals can only slightly improve the detection performance of errors in single-trial data as compared to the frontal IC only. More importantly, it is indicated that four ICs other than the frontal one also carry similar discriminative information about errors in both temporal and frequency domains. The fact suggests flexible means in detecting errors from EEG beyond the frontal brain areas, which might be very valuable in practical applications such that the frontal area is not accessible.},
}
@article {pmid28268891,
year = {2016},
author = {Mercado, L and Rodriguez-Linan, A and Torres-Trevino, LM and Quiroz, G},
title = {Hybrid BCI approach to control an artificial tibio-femoral joint.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {2760-2763},
doi = {10.1109/EMBC.2016.7591302},
pmid = {28268891},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Femur/*physiology ; Humans ; Knee Joint/*physiology ; Models, Theoretical ; Support Vector Machine ; User-Computer Interface ; },
abstract = {Brain-Computer Interfaces (BCIs) for disabled people should allow them to use their remaining functionalities as control possibilities. BCIs connect the brain with external devices to perform the volition or intent of movement, regardless if that individual is unable to perform the task due to body impairments. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in a framework called "Hybrid-BCI" (hBCI) approach to control the movement of a simulated tibio-femoral joint. Two mathematical models of a tibio-femoral joint are used to emulate the kinematic and dynamic behavior of the knee. The interest is to reproduce different velocities of the human gait cycle. The EEG signals are used to classify the user intent, which are the velocity changes, meanwhile the superficial EMG signals are used to estimate the amplitude of such intent. A multi-level controller is used to solve the trajectory tracking problem involved. The lower level consists of an individual controller for each model, it solves the tracking of the desired trajectory even considering different velocities of the human gait cycle. The mid-level uses a combination of a logical operator and a finite state machine for the switching between models. Finally, the highest level consists in a support vector machine to classify the desired activity.},
}
@article {pmid28268643,
year = {2016},
author = {Tobaa, AA and Best, MD and Balasubramanian, K and Takahashi, K and Hatsopoulos, NG},
title = {Properties of primary motor cortical local field potentials in the leg and trunk representations during arm movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1636-1639},
doi = {10.1109/EMBC.2016.7591027},
pmid = {28268643},
issn = {2694-0604},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Arm ; Leg ; Macaca mulatta ; *Motor Cortex ; Motor Neurons ; Movement ; Neurons ; Psychomotor Performance ; Torso ; },
abstract = {Large, spatially-distributed populations of motor cortical neurons are recruited during upper limb movements. Here, we examined how beta attenuation, a mesoscopic reflection of unit engagement, varies across a spatially expansive sampling of primary motor cortex in a non-human primate (macaca mulatta). We found that electrodes in both the trunk and leg representation of motor cortex exhibit qualitatively similar behavior to electrodes in the arm representation during a planar reaching task, despite the fact that there were no overt movements of the trunk or leg. These findings are interpreted in the context of a state-based brain machine interface.},
}
@article {pmid28268631,
year = {2016},
author = {Klosterman, SL and Estepp, JR and Monnin, JW and Christensen, JC},
title = {Day-to-day variability in hybrid, passive brain-computer interfaces: comparing two studies assessing cognitive workload.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1584-1590},
doi = {10.1109/EMBC.2016.7591015},
pmid = {28268631},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Cognition ; Humans ; *Workload ; },
abstract = {As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting from complex cognitive function in comparison to simple, stimulus-based processes. This nonstationarity, referenced as day-to-day variability, results in the inability of many learning algorithms to generalize to new data. In previous work, we have shown that increasing the number of unique testing sessions used to form a learning set can improve the accuracy of classifying mental workload in a binary state paradigm. While this result was very promising, we did not address whether the additional discriminability was the result of a larger learning set or the uniqueness contributed by the testing sessions being spread over multiple days. Further, the simulation task used in this prior analysis was low-fidelity with respect to the task it attempted to model; whether these methods extend to more realistic task simulation environments has not been comparatively investigated. In this work, we compare these previous results to a second study, with a similar multi-day paradigm, that required participants to perform a more realistic simulation task. Comparative analysis of these two studies reveals that the improved generalization of the multi-day learning set is attributable, in large part, to the uniqueness of the multi-day paradigm. Further, this multi-day effect was also observed in the higher fidelity simulation study. These results help to validate the use of the multi-day learning set approach for improving overall system classification accuracy. Future studies should consider the use of multi-day designs for improving generalizability over other interesting dimensions.},
}
@article {pmid28268630,
year = {2016},
author = {Tam, N and Pollonini, L and Zouridakis, G},
title = {Decoding movement direction using phase-space analysis of hemodynamic responses to arm movements based on functional near-infrared spectroscopy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1580-1583},
doi = {10.1109/EMBC.2016.7591014},
pmid = {28268630},
issn = {2694-0604},
mesh = {*Arm ; Brain Mapping ; Hemodynamics ; Humans ; *Movement ; Spectroscopy, Near-Infrared ; },
abstract = {In this study we applied phase-space analysis on the hemodynamic signals recorded from the motor cortex of human subjects using functional near infrared spectroscopy (fNIRS) to decode the direction of intentional hand movements. Our goal is to develop a brain-computer-interface (BCI) based on optical imaging that can control a wheelchair. To establish the relationship between the hemodynamic response and movement direction, participants were asked to perform repetitive arm movements in two orthogonal directions (right-left and front-back) on a horizontal plane, while the time course of the oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) responses were recorded. We applied phase-space analysis on oxy-Hb and deoxy-Hb signals to characterize movement direction. Our results show that movement directions taken pairwise (left vs. right, and forward vs. backward) are mapped onto different quadrants in the oxy-Hb vs. deoxy-Hb phase plane. These findings demonstrate that phase-space analysis can be used to decode the movement direction in a BCI controlling a wheelchair. In conclusion, phase-space analysis can be used to differentiate intentional movement direction without correlating the temporal movement kinematics with the hemodynamic response.},
}
@article {pmid28268629,
year = {2016},
author = {Karimi, F and Kofman, J and Mrachcz-Kersting, N and Farina, D and Ning Jiang, },
title = {Comparison of EEG spatial filters for movement related cortical potential detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1576-1579},
doi = {10.1109/EMBC.2016.7591013},
pmid = {28268629},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Movement ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Movement related cortical potential (MRCP), a slow cortical potential from scalp electroencephalogram (EEG), has been the used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation applications. Detecting MPCPs in real time with high accuracy is essential for a reliable BCI system in these applications. In this study, we investigated the effect of four spatial filters on MRCP detection during executed and imagined dorsiflexion of healthy participants. Our analysis indicated that, in both executed and imagined movement datasets, none of the methods investigated significantly improved trial-to-trial consistency of MRCP. However, all four spatial filters significantly enhanced signal to noise ratio (SNR) of the MRCPs, which is critical to achieve a high true-positive rate in MRCP detection.},
}
@article {pmid28268628,
year = {2016},
author = {Williams, JJ and Tien, RN and Inoue, Y and Schwartz, AB},
title = {Idle state classification using spiking activity and local field potentials in a brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1572-1575},
doi = {10.1109/EMBC.2016.7591012},
pmid = {28268628},
issn = {2694-0604},
support = {F32 NS092430/NS/NINDS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Motor Cortex ; Movement ; },
abstract = {Previous studies of intracortical brain-computer interfaces (BCIs) have often focused on or compared the use of spiking activity and local field potentials (LFPs) for decoding kinematic movement parameters. Conversely, using these signals to detect the initial intention to use a neuroprosthetic device or not has remained a relatively understudied problem. In this study, we examined the relative performance of spiking activity and LFP signals in detecting discrete state changes in attention regarding a user's desire to actively control a BCI device. Preliminary offline results suggest that the beta and high gamma frequency bands of LFP activity demonstrated a capacity for discriminating idle/active BCI control states equal to or greater than firing rate activity on the same channel. Population classifier models using either signal modality demonstrated an indistinguishably high degree of accuracy in decoding rest periods from active BCI reach periods as well as other portions of active BCI task trials. These results suggest that either signal modality may be used to reliably detect discrete state changes on a fine time scale for the purpose of gating neural prosthetic movements.},
}
@article {pmid28268627,
year = {2016},
author = {Merino, LM and Nayak, T and Hall, G and Pack, DJ and Yufei Huang, },
title = {Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1568-1571},
doi = {10.1109/EMBC.2016.7591011},
pmid = {28268627},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; },
abstract = {We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.},
}
@article {pmid28268624,
year = {2016},
author = {Nengneng Peng, and Rui Zhang, and Haihua Zeng, and Fei Wang, and Kai Li, and Yuanqing Li, and Xiaobin Zhuang, },
title = {Control of a nursing bed based on a hybrid brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1556-1559},
doi = {10.1109/EMBC.2016.7591008},
pmid = {28268624},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Female ; Humans ; Male ; User-Computer Interface ; },
abstract = {In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button "back up" in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.},
}
@article {pmid28268623,
year = {2016},
author = {Schildt, CJ and Thomas, SH and Powell, ES and Sawaki, L and Sunderam, S},
title = {Closed-loop afferent electrical stimulation for recovery of hand function in individuals with motor incomplete spinal injury: early clinical results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1552-1555},
doi = {10.1109/EMBC.2016.7591007},
pmid = {28268623},
issn = {2694-0604},
support = {R21 HD079747/HD/NICHD NIH HHS/United States ; },
mesh = {Electric Stimulation ; Evoked Potentials, Motor ; *Hand Strength ; Humans ; Motor Cortex ; Spinal Cord Injuries ; Transcranial Magnetic Stimulation ; },
abstract = {Afferent electrical stimulation is known to augment the effect of rehabilitative therapy through use-dependent cortical plasticity. Experiments pairing transcranial magnetic stimulation (TMS) with peripheral nerve stimulation (PNS) have shown a timing-dependent effect on motor evoked potential (MEP) amplitude suggesting that PNS applied in closed-loop (CL) mode could augment this effect through positive reinforcement. We present early results from a clinical trial in which an EEG brain-machine interface (BMI) was used to apply PNS to two subjects in response to motor intent detected from sensorimotor cortex in a cue-driven hand grip task. Both subjects had stable incomplete cervical spinal cord injury (SCI) with impaired upper limb function commensurate with the injury level. Twelve sessions of CL-PNS applied over a 4-6 week period yielded results suggesting improved hand grip strength and increased task-related modulation of the EEG in one hand of both subjects, and increased TMS-measured motor map area in one. These observations suggest that rehabilitation using such interactive therapies could benefit affected individuals.},
}
@article {pmid28268622,
year = {2016},
author = {Trieu Phat Luu, and Yongtian He, and Nakagame, S and Gorges, J and Nathan, K and Contreras-Vidal, JL},
title = {Unscented Kalman filter for neural decoding of human treadmill walking from non-invasive electroencephalography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1548-1551},
doi = {10.1109/EMBC.2016.7591006},
pmid = {28268622},
issn = {2694-0604},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Exercise Test ; Gait ; Humans ; *Walking ; },
abstract = {The feasibility of decoding lower limb kinematics in human treadmill walking from noninvasive electroencephalography (EEG) has been demonstrated with linear Wiener filter. However, nonlinear relationship between neural activities and limb movements may challenge the linear decoders in real-time brain computer interface (BCI) applications. In this study, we propose a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower limb joint angles from noninvasive scalp EEG signals during human treadmill walking. Our results demonstrate that lower limb joint angles during treadmill walking can be decoded from the fluctuations in the amplitude of slow cortical potentials in the delta band (0.1-3Hz). Overall, the average decoding accuracy were 0.43 ± 0.18 for Pearson's r value and 1.82 ± 3.07 for signal to noise ratio (SNR), and robust to ocular, muscle, or movement artifacts. Moreover, the signal preprocessing scheme and the design of UKF allow the implementation of the proposed EEG-based BCI for real-time applications. This has implications for the development of closed-loop EEG-based BCI systems for gait rehabilitation after stroke.},
}
@article {pmid28268621,
year = {2016},
author = {Yile Jin, and Mingwei Lu, and Xiaotian Wang, and Shaomin Zhang, and Junming Zhu, and Xiaoxiang Zheng, },
title = {Electrocorticographic signals comparison in sensorimotor cortex between contralateral and ipsilateral hand movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1544-1547},
doi = {10.1109/EMBC.2016.7591005},
pmid = {28268621},
issn = {2694-0604},
mesh = {Brain Mapping ; Electroencephalography ; *Hand ; Humans ; *Movement ; *Sensorimotor Cortex ; },
abstract = {Brain machine interfaces (BMIs) have emerged as a technology to restore lost functionality in motor impaired patients. Most BMI systems employed neural signals from contralateral hemisphere. But many studies have also demonstrated the possibility to control hand movement using signals from ipsilateral one. However, the relationship of neural signals in sensorimotor cortex between contralateral and ipsilateral hand movement control is still unclear. In this study, the electrocorticographic signals (ECoG) of sensorimotor cortex were analyzed in two epilepsy participants when they performed a visual guided rock-scissors-paper task by using contralateral and ipsilateral hand respectively. Although typical beta suppression followed increased gamma were observed during the movements of each individual hands, the stronger responses were found in two participants when their contralateral hands were used during the task. We further extracted the power spectrum of high gamma frequency band (70-135Hz) of ECoG signals as neural features to decode the hand movements. The results showed that the classification accuracy of contralateral decoding and ipsilateral decoding were 81% and 78% for participator one (P1) and 84% and 77% for participator two (P2). The accuracy of ipsilateral decoding was only slightly lower than that of contralateral one. The hand movement information contained in ipsilateral sensorimotor cortex suggested that the ipsilateral hemisphere might be also involved in neural modulation as well as contralateral hemisphere did when performing unimanual movement, which would expand the clinical application of BMIs.},
}
@article {pmid28268620,
year = {2016},
author = {Herff, C and Johnson, G and Diener, L and Shih, J and Krusienski, D and Schultz, T},
title = {Towards direct speech synthesis from ECoG: A pilot study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1540-1543},
doi = {10.1109/EMBC.2016.7591004},
pmid = {28268620},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; Pilot Projects ; *Speech ; },
abstract = {Most current Brain-Computer Interfaces (BCIs) achieve high information transfer rates using spelling paradigms based on stimulus-evoked potentials. Despite the success of this interfaces, this mode of communication can be cumbersome and unnatural. Direct synthesis of speech from neural activity represents a more natural mode of communication that would enable users to convey verbal messages in real-time. In this pilot study with one participant, we demonstrate that electrocoticography (ECoG) intracranial activity from temporal areas can be used to resynthesize speech in real-time. This is accomplished by reconstructing the audio magnitude spectrogram from neural activity and subsequently creating the audio waveform from these reconstructed spectrograms. We show that significant correlations between the original and reconstructed spectrograms and temporal waveforms can be achieved. While this pilot study uses audibly spoken speech for the models, it represents a first step towards speech synthesis from speech imagery.},
}
@article {pmid28268619,
year = {2016},
author = {Fei Wang, and Yanbin He, and Jun Qu, and Qiuyou Xie, and Qing Lin, and Xiaoxiao Ni, and Yan Chen, and Ronghao Yu, and Chin-Teng Lin, and Yuanqing Li, },
title = {An audiovisual BCI system for assisting clinical communication assessment in patients with disorders of consciousness: a case study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1536-1539},
doi = {10.1109/EMBC.2016.7591003},
pmid = {28268619},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Coma/diagnosis ; *Consciousness Disorders/diagnosis ; Female ; Humans ; Male ; Persistent Vegetative State ; },
abstract = {The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral scale, is often used for clinical assessments of patients with disorders of consciousness (DOC), such as patients in a vegetative state. However, there has been a high rate of clinical misdiagnosis with the JFK CRS-R because patients with severe brain injures cannot provide sufficient behavioral responses. It is particularly difficult to evaluate the communication function in DOC patients using the JFK CRS-R because a higher level of behavioral responses is needed for communication assessments than for many other assessments, such as an auditory startle assessment. Brain-computer interfaces (BCIs), which provide control and communication by detecting changes in brain signals, can be used to evaluate patients with DOC without the need of behavioral expressions. In this paper, we proposed an audiovisual BCI system to supplement the JFK CRS-R in assessing the communication ability of patients with DOC. In the graphic user interface of the BCI system, two word buttons ("Yes" and "No" in Chinese) were randomly displayed in the left and right sides and flashed in an alternating manner. When a word button flashed, its corresponding spoken word was broadcast from an ipsilateral headphone. The use of semantically congruent audiovisual stimuli improves the detection performance of the BCI system. Similar to the JFK CRS-R, several situation-orientation questions were presented one by one to patients with DOC. For each question, the patient was required to provide his/her answer by selectively focusing on an audiovisual stimulus (audiovisual "Yes" or "No"). As a case study, we applied our BCI system in a patient with DOC who was clinically diagnosed as being in a minimally conscious state (MCS). According to the JFK CRS-R assessment, this patient was unable to communicate consistently. However, he achieved a high accuracy of 86.5% in our BCI experiment. This result indicates his reliable communication ability and demonstrates the effectiveness of our system.},
}
@article {pmid28268618,
year = {2016},
author = {Jiang, W and Pailla, T and Dichter, B and Chang, EF and Gilja, V},
title = {Decoding speech using the timing of neural signal modulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1532-1535},
doi = {10.1109/EMBC.2016.7591002},
pmid = {28268618},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Communication ; Electrocorticography ; Humans ; Language ; *Speech ; },
abstract = {Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects. Our results show that timing-based features of ECoG signals are informative of vowel production and enable decoding accuracies significantly above the level of chance. This suggests that leveraging the temporal structure of neural activity to decode speech could play an important role towards developing highperformance, robust speech BMIs.},
}
@article {pmid28268616,
year = {2016},
author = {Melinscak, F and Montesano, L},
title = {Sample size determination for BCI studies: How many subjects and trials?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1524-1527},
doi = {10.1109/EMBC.2016.7591000},
pmid = {28268616},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Reproducibility of Results ; Research Design ; *Sample Size ; },
abstract = {Sample sizes and, consequently, statistical power have a large influence on the reliability of statistical results, but they are often neglected when planning and reporting studies of brain-computer interfaces (BCIs). This may be in part due to the limitations of classical power calculations, which do not apply to nested experimental designs, that are usually employed in BCI research. In this paper we introduce the methodology of simulation-based sample size determination (SSD) for the planning of BCI studies. We show how the proposed method can be used to determine the necessary number of subjects and trials to obtain a precise estimate of BCI accuracy, when the cost of sampling needs to be constrained by a budget. Furthermore, the method is fully general and can be applied in different experimental designs and in different statistical frameworks.},
}
@article {pmid28268615,
year = {2016},
author = {Mundahl, J and Jianjun Meng, and He, J and Bin He, },
title = {Soft drink effects on sensorimotor rhythm brain computer interface performance and resting-state spectral power.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1520-1523},
doi = {10.1109/EMBC.2016.7590999},
pmid = {28268615},
issn = {2694-0604},
mesh = {*Brain ; Brain Mapping ; Brain-Computer Interfaces ; *Carbonated Beverages ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems allow users to directly control computers and other machines by modulating their brain waves. In the present study, we investigated the effect of soft drinks on resting state (RS) EEG signals and BCI control. Eight healthy human volunteers each participated in three sessions of BCI cursor tasks and resting state EEG. During each session, the subjects drank an unlabeled soft drink with either sugar, caffeine, or neither ingredient. A comparison of resting state spectral power shows a substantial decrease in alpha and beta power after caffeine consumption relative to control. Despite attenuation of the frequency range used for the control signal, caffeine average BCI performance was the same as control. Our work provides a useful characterization of caffeine, the world's most popular stimulant, on brain signal frequencies and their effect on BCI performance.},
}
@article {pmid28268614,
year = {2016},
author = {Ryu, S and Higashi, H and Tanaka, T and Nakauchi, S and Minami, T},
title = {Spatial smoothing of canonical correlation analysis for steady state visual evoked potential based brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1516-1519},
doi = {10.1109/EMBC.2016.7590998},
pmid = {28268614},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Pattern Recognition, Automated ; Photic Stimulation ; },
abstract = {Brain computer interface (BCI) is a system for communication between people and computers via brain activity. Steady-state visual evoked potentials (SSVEPs), a brain response observed in EEG, are evoked by flickering stimuli. SSVEP is one of the promising paradigms for BCI. Canonical correlation analysis (CCA) is widely used for EEG signal processing in SSVEP-based BCIs. However, the classification accuracy of CCA with short signal length is low. In order to solve the problem, we propose a regularization which works in such a way that the CCA spatial filter becomes spatially smooth to give robustness in short signal length condition. The spatial filter is designed in a parameter space spanned by a spatially smooth basis which are given by a graph Fourier transform of three dimensional electrode coordinates. We compared the classification accuracy of the proposed regularized CCA with the standard CCA. The result shows that the proposed CCA outperforms the standard CCA in short signal length condition.},
}
@article {pmid28268613,
year = {2016},
author = {Westergren, N and Bendtsen, RL and Kjaer, TW and Thomsen, CE and Puthusserypady, S and Sorensen, HB},
title = {Steady state visual evoked potential based brain-computer interface for cognitive assessment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1508-1511},
doi = {10.1109/EMBC.2016.7590996},
pmid = {28268613},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Cognition ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Pilot Projects ; User-Computer Interface ; },
abstract = {Cognitive assessment is of growing importance, with the general population getting older and a rapidly growing incidence of dementia, which is a major public health issue. Treatment of dementia must, to be most effective, start early in the disease process. Thus, early detection of cognitive decline is important. Cognitive decline may be detected using fully-automated computerized assessment. Such systems will provide inexpensive and widely available screenings of cognitive ability. The aim of this pilot study is to develop a real time steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) for neurological cognitive assessment. It is intended for use by patients who suffer from diseases impairing their motor skills, but are still able to control their gaze. Results are based on 11 healthy test subjects. The system performance have an average accuracy of 100% - 0%. The test subjects achieved an information transfer rate (ITR) of 14.64 bits/min - 7.63 bits/min and a subject test performance of 47.22% - 34.10%. This study suggests that BCI may be applicable in practice as a computerized cognitive assessment tool. However, many improvements are required for the system to be fully valid and of clinical use.},
}
@article {pmid28268612,
year = {2016},
author = {Irimia, D and Sabathiel, N and Ortner, R and Poboroniuc, M and Coon, W and Allison, BZ and Guger, C},
title = {recoveriX: a new BCI-based technology for persons with stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1504-1507},
doi = {10.1109/EMBC.2016.7590995},
pmid = {28268612},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Movement ; *Stroke ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems have been used primarily to provide communication for persons with severe movement disabilities. This paper presents a new system that extends BCI technology to a new patient group: persons diagnosed with stroke. This system, called recoveriX, is designed to detect changes in motor imagery in real-time to help monitor compliance and provide closed-loop feedback during therapy. We describe recoveriX and present initial results from one patient.},
}
@article {pmid28268609,
year = {2016},
author = {Suefusa, K and Tanaka, T},
title = {Decoding of responses to mixed frequency and phase coded visual stimuli using multiset canonical correlation analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1492-1495},
doi = {10.1109/EMBC.2016.7590992},
pmid = {28268609},
issn = {2694-0604},
mesh = {Algorithms ; Biological Phenomena ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most practical BCIs because of its high recognition accuracies and little training of a user. Mixed frequency and phase coding which can implement a number of commands and achieve a high information transfer rate (ITR) has recently been gaining much attention. In order to implement mixed-coded SSVEP-BCI as a reliable interface, it is important to detect commands fast and accurately. This paper presents a novel method to recognize mixed-coded SSVEPs which achieves high performance. The method employs multiset canonical correlation analysis to obtain spatial filters which enhance SSVEP components. An experiment with a mixed-coded SSVEP-BCI was conducted to evaluate performance of the proposed method compared with the previous work. The experimental results showed that the proposed method achieved significantly higher command recognition accuracy and ITR than the state-of-the-art.},
}
@article {pmid28268608,
year = {2016},
author = {Gembler, F and Stawicki, P and Volosyak, I},
title = {Exploring the possibilities and limitations of multitarget SSVEP-based BCI applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1488-1491},
doi = {10.1109/EMBC.2016.7590991},
pmid = {28268608},
issn = {2694-0604},
mesh = {*Brain ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Healthy Volunteers ; Humans ; Photic Stimulation ; },
abstract = {Steady state visual evoked potentials (SSVEPs) are the brain signals induced by gazing at a constantly flickering target. Frame-based frequency approximation methods can be implemented in order to realize a high number of visual stimuli for SSVEP-based Brain-Computer Interfaces (BCIs) on ordinary computer screens. In this paper, we investigate the possibilities and limitations regarding the number of targets in SSVEP-based BCIs. The BCI-performance of seven healthy subjects was evaluated in an online experiment with six differently sized target matrices. Our results confirm previous observations, according to which BCI accuracy and speed are dependent on the number of simultaneously displayed targets. The peak ITR achieved in the experiment was 130.15 bpm. Interestingly, it was achieved with the 15 target matrix. Generally speaking, the BCI performance dropped with an increasing number of simultaneously displayed targets. Surprisingly, however, one subject even gained control over a system with 84 flickering targets, achieving an accuracy of 91.30%, which verifies that stimulation frequencies separated by less than 0.1 Hz can still be distinguished from each other.},
}
@article {pmid28268607,
year = {2016},
author = {Sato, JI and Washizawa, Y},
title = {Neural decoding of code modulated visual evoked potentials by spatio-temporal inverse filtering for brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1484-1487},
doi = {10.1109/EMBC.2016.7590990},
pmid = {28268607},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neural Networks, Computer ; Neurologic Examination ; },
abstract = {This study addresses neural decoding of a code modulated visual evoked potentials (c-VEPs). c-VEP was recently developed, and applied to brain computer interfaces (BCIs). c-VEP BCI exhibits faster communication speed than existing VEP-based BCIs. In c-VEP BCI, the canonical correlation analysis (CCA) that maximizes the correlation between an averaged signal and single trial signals is often used for the spatial filter. However, CCA does not utilize information of given PN sequence, and hence, the filtered signal may not have properties of PN sequence. In this paper, we propose a decoding method to restore the given PN sequence from the observed VEP. We compare linear and nonlinear spatio-temporal inverse filtering methods. For the linear method, the least mean square error and lasso are used to obtain the filter coefficients. For the non-linear method, the artificial neural network is used. The proposed methods exhibited better decoding performance, and higher classification accuracies than conventional CCA spatial filtered c-VEP BCI.},
}
@article {pmid28268492,
year = {2016},
author = {Paris, A and Atia, G and Vosoughi, A and Berman, SA},
title = {Optimal causal filtering for 1 /fα-type noise in single-electrode EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {997-1001},
doi = {10.1109/EMBC.2016.7590870},
pmid = {28268492},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Understanding the mode of generation and the statistical structure of neurological noise is one of the central problems of biomedical signal processing. We have developed a broad class of abstract biological noise sources we call hidden simplicial tissues. In the simplest cases, such tissue emits what we have named generalized van der Ziel-McWhorter (GVZM) noise which has a roughly 1/fα spectral roll-off. Our previous work focused on the statistical structure of GVZM frequency spectra. However, causality of processing operations (i.e., dependence only on the past) is an essential requirement for real-time applications to seizure detection and brain-computer interfacing. In this paper we outline the theoretical background for optimal causal time-domain filtering of deterministic signals embedded in GVZM noise. We present some of our early findings concerning the optimal filtering of EEG signals for the detection of steady-state visual evoked potential (SSVEP) responses and indicate the next steps in our ongoing research.},
}
@article {pmid28268352,
year = {2016},
author = {Santamaria, L and James, C},
title = {Classification in emotional BCI using phase information from the EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {371-374},
doi = {10.1109/EMBC.2016.7590717},
pmid = {28268352},
issn = {2694-0604},
mesh = {Alpha Rhythm/physiology ; Beta Rhythm/physiology ; *Brain-Computer Interfaces ; Cortical Synchronization ; Electrodes ; Electroencephalography/*methods ; Female ; Humans ; Male ; Time Factors ; },
abstract = {Synchronization and distributed functional networks have been used with success in different areas of engineering. In this paper we use the synchronization information from electroencephalogram (EEG) channels through the Phase Locking Value (PLV) as a potential classification method for a Brain Computer Interface (BCI); this achieved using emotional schematic faces as stimuli in a motor imagery (MI) task. Based on the variations of the PLV values for each proposed task and for each participant, the principal channel pairs are identified. Selected channel pairs, corresponding with the accomplished task, present PLV patterns similarly to Evoked Potentials (ERS/ERD) which are widely used as classification features for MI based BCI.},
}
@article {pmid28268317,
year = {2016},
author = {Younghak Shin, and Heung-No Lee, and Balasingham, I},
title = {Fast L1-based sparse representation of EEG for motor imagery signal classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {223-226},
doi = {10.1109/EMBC.2016.7590680},
pmid = {28268317},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Improvement of classification performance is one of the key challenges in electroencephalogram (EEG) based motor imagery brain-computer interface (BCI). Recently, sparse representation based classification (SRC) method has been shown to provide satisfactory classification accuracy in motor imagery classification. In this paper, we aim to evaluate the performance of the SRC method in terms of not only its classification accuracy but also of its computation time. For this purpose, we investigate the performance of recently developed fast L1 minimization methods for their use in SRC, such as homotopy and fast iterative soft-thresholding algorithm (FISTA). From experimental analysis, we note that the SRC method with the fast L1 minimization algorithms is shown to provide robust classification performance, compared to support vector machine (SVM), both in time and accuracy.},
}
@article {pmid28268272,
year = {2016},
author = {Pilkar, R and Arzouni, N and Ramanujam, A and Chervin, K and Nolan, KJ},
title = {Postural responses after utilization of a computerized biofeedback based intervention aimed at improving static and dynamic balance in traumatic brain injury: a case study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {25-28},
doi = {10.1109/EMBC.2016.7590631},
pmid = {28268272},
issn = {2694-0604},
mesh = {Accidental Falls/prevention &amp; control ; *Biofeedback, Psychology ; Brain Injuries, Traumatic/*rehabilitation ; Brain-Computer Interfaces ; Computers ; Electromyography ; Humans ; Male ; Muscle, Skeletal/*physiology ; Postural Balance/*physiology ; Posture/physiology ; },
abstract = {Balance dysfunction is one of the most disabling aspects of Traumatic Brain Injury (TBI). Without rapid transmission and accurate perception of somatosensory inputs, the automatic postural responses required during standing may be delayed or absent after TBI which can lead to instability. Further, the sensitivity level to which environmental perturbations can be detected is also vital, as the central nervous system will only employ balance control strategies when it perceives a change in equilibrium. Such undetectable perturbations, however small they may be, can result in fatal falls, especially after TBI. In this investigation we used a novel computerized biofeedback based (CBB) intervention aimed at improving perception of external perturbations, and static and dynamic balance in a single male participant with severe TBI. We used an adaptive single interval adjustment matrix (SIAM) protocol to determine the perception of perturbation threshold (PPT) at baseline (1 day pre-intervention) and follow up (1 day post-intervention). External perturbations were provided through sinusoidal translations of 0.5 Hz to the base of support in anterior-posterior direction. Outcome measures included PPT, the Berg balance scale (BBS) and bilateral surface electromyography (EMG) of the lower limbs at baseline and follow up. PPT assessment post intervention showed a decrease in PPT, suggesting an improvement in the ability (gain of 0.42 mm) to detect (even smaller) perturbations which were not perceivable prior to the intervention. There was a significant increase in BBS (6 points) at follow up. The participant demonstrated increased muscle activation for the right gastrocnemius, left soleus, right bicep femoris and left vastus lateralis muscles at follow up. This investigation demonstrate the potential use of the CBB intervention for improving interpretation and organization of multisensory information in a task specific environment to improve balance dysfunction post TBI.},
}
@article {pmid28266930,
year = {2017},
author = {Black, C and Voigts, J and Agrawal, U and Ladow, M and Santoyo, J and Moore, C and Jones, S},
title = {Open Ephys electroencephalography (Open Ephys + EEG): a modular, low-cost, open-source solution to human neural recording.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {035002},
pmid = {28266930},
issn = {1741-2552},
support = {R01 MH106174/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; *Amplifiers, Electronic ; Analog-Digital Conversion ; Brain/*physiology ; Diagnosis, Computer-Assisted/*instrumentation/methods ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Head Protective Devices ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; *Software ; User-Computer Interface ; },
abstract = {OBJECTIVE: Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation.<br><br>APPROACH: Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys&#x2009;&#x2009;+&#x2009;&#x2009;EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments.<br><br>MAIN RESULTS: The Open Ephys&#x2009;&#x2009;+&#x2009;&#x2009;EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys&#x2009;&#x2009;+&#x2009;&#x2009;EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise.<br><br>SIGNIFICANCE: Open Ephys&#x2009;&#x2009;+&#x2009;&#x2009;EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.},
}
@article {pmid28266832,
year = {2017},
author = {Ryu, M and Yang, JH and Ahn, Y and Sim, M and Lee, KH and Kim, K and Lee, T and Yoo, SJ and Kim, SY and Moon, C and Je, M and Choi, JW and Lee, Y and Jang, JE},
title = {Enhancement of Interface Characteristics of Neural Probe Based on Graphene, ZnO Nanowires, and Conducting Polymer PEDOT.},
journal = {ACS applied materials &amp; interfaces},
volume = {9},
number = {12},
pages = {10577-10586},
doi = {10.1021/acsami.7b02975},
pmid = {28266832},
issn = {1944-8252},
mesh = {Bridged Bicyclo Compounds, Heterocyclic ; Graphite ; *Nanowires ; Polymers ; Zinc Oxide ; },
abstract = {In the growing field of brain-machine interface (BMI), the interface between electrodes and neural tissues plays an important role in the recording and stimulation of neural signals. To minimize tissue damage while retaining high sensitivity, a flexible and a smaller electrode with low impedance is required. However, it is a major challenge to reduce electrode size while retaining the conductive characteristics of the electrode. In addition, the mechanical mismatch between stiff electrodes and soft tissues creates damaging reactive tissue responses. Here, we demonstrate a neural probe structure based on graphene, ZnO nanowires, and conducting polymer that provides flexibility and low impedance performance. A hybrid Au and graphene structure was utilized to achieve both flexibility and good conductivity. Using ZnO nanowires to increase the effective surface area drastically decreased the impedance value and enhanced the signal-to-noise ratio (SNR). A poly[3,4-ethylenedioxythiophene] (PEDOT) coating on the neural probe improved the electrical characteristics of the electrode while providing better biocompatibility. In vivo neural signal recordings showed that our neural probe can detect clearer signals.},
}
@article {pmid28261084,
year = {2017},
author = {Khan, MJ and Hong, KS},
title = {Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.},
journal = {Frontiers in neurorobotics},
volume = {11},
number = {},
pages = {6},
pmid = {28261084},
issn = {1662-5218},
abstract = {In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.},
}
@article {pmid28259586,
year = {2017},
author = {Neoh, CF and Samah, S and Wong, YY and Hassali, MA and Shafie, AA and Lim, SM and Ramasamy, K and Mat Nasir, N and Han, YW and Burroughs, T},
title = {Response to Linguistic and psychometric validation of the Malaysian version of Diabetes Quality of Life-Brief Clinical Inventory (DQoL-BCI): Methodological issues to avoid misinterpretation.},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {13},
number = {4},
pages = {884},
doi = {10.1016/j.sapharm.2017.02.004},
pmid = {28259586},
issn = {1934-8150},
mesh = {Databases, Factual/standards ; Diabetes Mellitus/diagnosis/*epidemiology ; Humans ; Linguistics/*standards ; Malaysia/epidemiology ; Psychometrics ; *Quality of Life ; Reproducibility of Results ; Surveys and Questionnaires/*standards ; },
}
@article {pmid28259011,
year = {2017},
author = {Duffy, MJ and Harbeck, N and Nap, M and Molina, R and Nicolini, A and Senkus, E and Cardoso, F},
title = {Clinical use of biomarkers in breast cancer: Updated guidelines from the European Group on Tumor Markers (EGTM).},
journal = {European journal of cancer (Oxford, England : 1990)},
volume = {75},
number = {},
pages = {284-298},
doi = {10.1016/j.ejca.2017.01.017},
pmid = {28259011},
issn = {1879-0852},
mesh = {Biomarkers, Tumor/*metabolism ; Breast Neoplasms/metabolism/*therapy ; Female ; Gene Expression Profiling/methods ; Genetic Testing/methods ; Humans ; Ki-67 Antigen/metabolism ; Neoplasm Proteins/metabolism ; Plasminogen Activator Inhibitor 1/metabolism ; Practice Guidelines as Topic ; Prognosis ; Receptor, ErbB-2/metabolism ; Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Urokinase-Type Plasminogen Activator/metabolism ; },
abstract = {Biomarkers play an essential role in the management of patients with invasive breast cancer. For selecting patients likely to respond to endocrine therapy, both oestrogen receptors (ERs) and progesterone receptors (PRs) should be measured on all newly diagnosed invasive breast cancers. On the other hand, for selecting likely response to all forms of anti-HER2 therapy (trastuzumab, pertuzumab, lapatinib or ado-trastuzumab emtansine), determination of HER2 expression or gene copy number is mandatory. Where feasible, measurement of ER, PR and HER2 should be performed on recurrent lesions and the primary invasive tumour. Although methodological problems exist in the determination of Ki67, because of its clearly established clinical value, wide availability and low costs relative to the available multianalyte signatures, Ki67 may be used for determining prognosis, especially if values are low or high. In oestrogen receptor (ER)-positive, HER2-negative, lymph node-negative patients, multianalyte tests such as urokinase plasminogen activator (uPA)-PAI-1, Oncotype DX, MammaPrint, EndoPredict, Breast Cancer Index (BCI) and Prosigna (PAM50) may be used to predict outcome and aid adjunct therapy decision-making. Oncotype DX, MammaPrint, EndoPredict and Prosigna may be similarly used in patients with 1-3 metastatic lymph nodes. All laboratories measuring biomarkers for patient management should use analytically and clinically validated assays, participate in external quality assurance programs, have established assay acceptance and rejection criteria, perform regular audits and be accredited by an appropriate organisation.},
}
@article {pmid28257073,
year = {2017},
author = {Zhang, X and Li, J and Liu, Y and Zhang, Z and Wang, Z and Luo, D and Zhou, X and Zhu, M and Salman, W and Hu, G and Wang, C},
title = {Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {3},
pages = {},
pmid = {28257073},
issn = {1424-8220},
abstract = {The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.},
}
@article {pmid28252409,
year = {2017},
author = {Cotrina, A and Benevides, AB and Castillo-Garcia, J and Benevides, AB and Rojas-Vigo, D and Ferreira, A and Bastos-Filho, TF},
title = {A SSVEP-BCI Setup Based on Depth-of-Field.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {7},
pages = {1047-1057},
doi = {10.1109/TNSRE.2017.2673242},
pmid = {28252409},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Depth Perception/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain-computer interface, in which two stimuli are presented together in the center of the user's field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa. The evaluationwas conductedwith eight healthy subjects who were asked to focus on just one stimulus at a time. An average accuracy rate of 0.93 was achieved for a time window of 4 s by employing well know SSVEP detection methods. Results show that distinguishable SSVEP can be elicited by the focused stimulus regardless of the non-focused one is also present in the field of view. Finally, this approach allows users to send commands through a stimuli selection by focusing mechanism that does not demand neck, head, and/or eyeball movements.},
}
@article {pmid28245000,
year = {2017},
author = {Szczepaniak, OM and Sawicki, DJ},
title = {Gesture controlled human-computer interface for the disabled.},
journal = {Medycyna pracy},
volume = {68},
number = {1},
pages = {11-21},
doi = {10.13075/mp.5893.00529},
pmid = {28245000},
issn = {0465-5893},
mesh = {Brain-Computer Interfaces ; *Computer-Aided Design ; Persons with Disabilities/*rehabilitation ; Equipment Design ; *Gestures ; Humans ; Occupational Health Services ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {BACKGROUND: The possibility of using a computer by a disabled person is one of the difficult problems of the human-computer interaction (HCI), while the professional activity (employment) is one of the most important factors affecting the quality of life, especially for disabled people. The aim of the project has been to propose a new HCI system that would allow for resuming employment for people who have lost the possibility of a standard computer operation.<br><br>MATERIAL AND METHODS: The basic requirement was to replace all functions of a standard mouse without the need of performing precise hand movements and using fingers. The Microsoft's Kinect motion controller had been selected as a device which would recognize hand movements. Several tests were made in order to create optimal working environment with the new device. The new communication system consisted of the Kinect device and the proper software had been built.<br><br>RESULTS: The proposed system was tested by means of the standard subjective evaluations and objective metrics according to the standard ISO 9241-411:2012. The overall rating of the new HCI system shows the acceptance of the solution. The objective tests show that although the new system is a bit slower, it may effectively replace the computer mouse.<br><br>CONCLUSIONS: The new HCI system fulfilled its task for a specific disabled person. This resulted in the ability to return to work. Additionally, the project confirmed the possibility of effective but nonstandard use of the Kinect device. Med Pr 2017;68(1):1-21.},
}
@article {pmid28241158,
year = {2017},
author = {Bloch, J and Lacour, SP and Courtine, G},
title = {Electronic Dura Mater Meddling in the Central Nervous System.},
journal = {JAMA neurology},
volume = {74},
number = {4},
pages = {470-475},
doi = {10.1001/jamaneurol.2016.5767},
pmid = {28241158},
issn = {2168-6157},
mesh = {Animals ; Biocompatible Materials/therapeutic use ; *Brain-Computer Interfaces ; Central Nervous System/*physiology ; Dura Mater/*physiology ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Silicon ; Spinal Cord Injuries/therapy ; },
abstract = {IMPORTANCE: A growing number of neurologic treatments rely on neural implants capable of delivering electrical and chemical stimulation to targeted regions of the central nervous system for extended periods.<br><br>OBJECTIVE: To assess the potential of a novel class of multimodal neural implants, termed electronic dura mater or e-dura, to fulfill this need.<br><br>EVIDENCE REVIEW: Results from preclinical applications of e-dura implants and clinical evidence.<br><br>FINDINGS: The silicone-based implant e-dura embeds interconnects, electrodes, and chemotrodes that are entirely stretchable. These unique mechanical properties allow e-dura to conform to the circumvolutions of the brain and spinal cord without damaging neural tissues or triggering foreign body reactions.<br><br>CONCLUSIONS AND RELEVANCE: Although challenges lie ahead to reach clinical fruition, the unique mechanical properties and integrated modalities of e-dura provide future opportunities to treat or alleviate neurologic deficits.},
}
@article {pmid28240598,
year = {2017},
author = {Prins, NW and Sanchez, JC and Prasad, A},
title = {Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036016},
doi = {10.1088/1741-2552/aa6317},
pmid = {28240598},
issn = {1741-2552},
mesh = {Biofeedback, Psychology/*methods/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Humans ; Learning/physiology ; *Machine Learning ; *Man-Machine Systems ; *Models, Neurological ; *Reinforcement, Psychology ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance.<br><br>APPROACH: Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries model) to validate proposed controller architecture.<br><br>MAIN RESULTS: In this work, we show how the overall performance of the BMI was improved by using a threshold close to the decision boundary to reject erroneous feedback. Additionally, we show the stability of the system improved when the feedback was used with a threshold.<br><br>SIGNIFICANCE: The result of this study is a step towards making BMIs autonomous. While our method is not fully autonomous, the results demonstrate that extensive training times necessary at the beginning of each BMI session can be significantly decreased. In our approach, decoder training time was only limited to 10 trials in the first BMI session. Subsequent sessions used previous session weights to initialize the decoder. We also present a method where the use of a threshold can be applied to any decoder with a feedback signal that is less than perfect so that erroneous feedback can be avoided and the stability of the system can be increased.},
}
@article {pmid28238175,
year = {2017},
author = {Dong, E and Li, C and Li, L and Du, S and Belkacem, AN and Chen, C},
title = {Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {10},
pages = {1809-1818},
pmid = {28238175},
issn = {1741-0444},
support = {61502340//National Natural Science Foundation of China/ ; 61172185//National Natural Science Foundation of China/ ; 15JCYBJC51800//Natural Science Foundation of Tianjin City/ ; 20120829//Tianjin Higher School Science and Technology Development Fund Planning Project/ ; },
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy/methods ; Imagination/*physiology ; Motor Cortex/*physiology ; Support Vector Machine ; Wavelet Analysis ; },
abstract = {Pattern classification algorithm is the crucial step in developing brain-computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where "OVO" classifiers are used in the first layer and "OVR" in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification.},
}
@article {pmid28232788,
year = {2017},
author = {Darvishi, S and Gharabaghi, A and Boulay, CB and Ridding, MC and Abbott, D and Baumert, M},
title = {Proprioceptive Feedback Facilitates Motor Imagery-Related Operant Learning of Sensorimotor β-Band Modulation.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {60},
pmid = {28232788},
issn = {1662-4548},
abstract = {Motor imagery (MI) activates the sensorimotor system independent of actual movements and might be facilitated by neurofeedback. Knowledge on the interaction between feedback modality and the involved frequency bands during MI-related brain self-regulation is still scarce. Previous studies compared the cortical activity during the MI task with concurrent feedback (MI with feedback condition) to cortical activity during the relaxation task where no feedback was provided (relaxation without feedback condition). The observed differences might, therefore, be related to either the task or the feedback. A proper comparison would necessitate studying a relaxation condition with feedback and a MI task condition without feedback as well. Right-handed healthy subjects performed two tasks, i.e., MI and relaxation, in alternating order. Each of the tasks (MI vs. relaxation) was studied with and without feedback. The respective event-driven oscillatory activity, i.e., sensorimotor desynchronization (during MI) or synchronization (during relaxation), was rewarded with contingent feedback. Importantly, feedback onset was delayed to study the task-related cortical activity in the absence of feedback provision during the delay period. The reward modality was alternated every 15 trials between proprioceptive and visual feedback. Proprioceptive input was superior to visual input to increase the range of task-related spectral perturbations in the α- and β-band, and was necessary to consistently achieve MI-related sensorimotor desynchronization (ERD) significantly below baseline. These effects occurred in task periods without feedback as well. The increased accuracy and duration of learned brain self-regulation achieved in the proprioceptive condition was specific to the β-band. MI-related operant learning of brain self-regulation is facilitated by proprioceptive feedback and mediated in the sensorimotor β-band.},
}
@article {pmid28231470,
year = {2017},
author = {Prsa, M and Galiñanes, GL and Huber, D},
title = {Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons.},
journal = {Neuron},
volume = {93},
number = {4},
pages = {929-939.e6},
pmid = {28231470},
issn = {1097-4199},
mesh = {Animals ; Conditioning, Operant/*physiology ; Feedback, Sensory/*physiology ; Learning/physiology ; Male ; Mice, Transgenic ; Motor Cortex/*physiology ; Motor Neurons/*physiology ; Movement/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {Neuronal motor commands, whether generating real or neuroprosthetic movements, are shaped by ongoing sensory feedback from the displacement being produced. Here we asked if cortical stimulation could provide artificial feedback during operant conditioning of cortical neurons. Simultaneous two-photon imaging and real-time optogenetic stimulation were used to train mice to activate a single neuron in motor cortex (M1), while continuous feedback of its activity level was provided by proportionally stimulating somatosensory cortex. This artificial signal was necessary to rapidly learn to increase the conditioned activity, detect correct performance, and maintain the learned behavior. Population imaging in M1 revealed that learning-related activity changes are observed in the conditioned cell only, which highlights the functional potential of individual neurons in the neocortex. Our findings demonstrate the capacity of animals to use an artificially induced cortical channel in a behaviorally relevant way and reveal the remarkable speed and specificity at which this can occur.},
}
@article {pmid28231460,
year = {2017},
author = {Darie, R and Powell, M and Borton, D},
title = {Delivering the Sense of Touch to the Human Brain.},
journal = {Neuron},
volume = {93},
number = {4},
pages = {728-730},
doi = {10.1016/j.neuron.2017.02.008},
pmid = {28231460},
issn = {1097-4199},
mesh = {Animals ; *Brain Mapping ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; Humans ; Sensation/*physiology ; Somatosensory Cortex/*physiology ; Touch/*physiology ; },
abstract = {Intracortical somatosensory interfaces have now entered the clinical domain. Darie et al. explore the implications of research published in Science Translational Medicine by Flesher et al. (2016), discuss how to design such a system given current technology, and question how to effectively communicate with users about their experience.},
}
@article {pmid28225827,
year = {2017},
author = {Kwak, NS and Müller, KR and Lee, SW},
title = {A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.},
journal = {PloS one},
volume = {12},
number = {2},
pages = {e0172578},
pmid = {28225827},
issn = {1932-6203},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; *Neural Networks, Computer ; Visual Perception/*physiology ; Walking ; },
abstract = {The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN's robust, accurate decoding abilities.},
}
@article {pmid28225794,
year = {2017},
author = {Spüler, M},
title = {A high-speed brain-computer interface (BCI) using dry EEG electrodes.},
journal = {PloS one},
volume = {12},
number = {2},
pages = {e0172400},
pmid = {28225794},
issn = {1932-6203},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electrodes ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; },
abstract = {Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication.},
}
@article {pmid28224972,
year = {2017},
author = {Müller, JS and Vidaurre, C and Schreuder, M and Meinecke, FC and von Bünau, P and Müller, KR},
title = {A mathematical model for the two-learners problem.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036005},
doi = {10.1088/1741-2552/aa620b},
pmid = {28224972},
issn = {1741-2552},
mesh = {Animals ; Computer Simulation ; *Game Theory ; Humans ; Learning/*physiology ; *Linear Models ; *Machine Learning ; *Man-Machine Systems ; *Models, Neurological ; },
abstract = {OBJECTIVE: We present the first generic theoretical formulation of the co-adaptive learning problem and give a simple example of two interacting linear learning systems, a human and a machine.<br><br>APPROACH: After the description of the training protocol of the two learning systems, we define a simple linear model where the two learning agents are coupled by a joint loss function. The simplicity of the model allows us to find learning rules for both human and machine that permit computing theoretical simulations.<br><br>MAIN RESULTS: As seen in simulations, an astonishingly rich structure is found for this eco-system of learners. While the co-adaptive learners are shown to easily stall or get out of sync for some parameter settings, we can find a broad sweet spot of parameters where the learning system can converge quickly. It is defined by mid-range learning rates on the side of the learning machine, quite independent of the human in the loop. Despite its simplistic assumptions the theoretical study could be confirmed by a real-world experimental study where human and machine co-adapt to perform cursor control under distortion. Also in this practical setting the mid-range learning rates yield the best performance and behavioral ratings.<br><br>SIGNIFICANCE: The results presented in this mathematical study allow the computation of simple theoretical simulations and performance of real experimental paradigms. Additionally, they are nicely in line with previous results in the BCI literature.},
}
@article {pmid28224970,
year = {2017},
author = {Jin, J and Zhang, H and Daly, I and Wang, X and Cichocki, A},
title = {An improved P300 pattern in BCI to catch user's attention.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036001},
doi = {10.1088/1741-2552/aa6213},
pmid = {28224970},
issn = {1741-2552},
mesh = {Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) can help patients who have lost control over most muscles but are still conscious and able to communicate or interact with the environment. One of the most popular types of BCI is the P300-based BCI. With this BCI, users are asked to count the number of appearances of target stimuli in an experiment. To date, the majority of visual P300-based BCI systems developed have used the same character or picture as the target for every stimulus presentation, which can bore users. Consequently, users attention may decrease or be negatively affected by adjacent stimuli.<br><br>APPROACH: In this study, a new stimulus is presented to increase user concentration. Honeycomb-shaped figures with 1-3 red dots were used as stimuli. The number and the positions of the red dots in the honeycomb-shaped figure were randomly changed during BCI control. The user was asked to count the number of the dots presented in each flash instead of the number of times they flashed. To assess the performance of this new stimulus, another honeycomb-shaped stimulus, without red dots, was used as a control condition.<br><br>MAIN RESULTS: The results showed that the honeycomb-shaped stimuli with red dots obtained significantly higher classification accuracies and information transfer rates (p&#x2009;&#x2009;<&#x2009;&#x2009;0.05) compared to the honeycomb-shaped stimulus without red dots.<br><br>SIGNIFICANCE: The results indicate that this proposed method can be a promising approach to improve the performance of the BCI system and can be an efficient method in daily application.},
}
@article {pmid28223914,
year = {2017},
author = {Ma, X and Ma, C and Huang, J and Zhang, P and Xu, J and He, J},
title = {Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {44},
pmid = {28223914},
issn = {1662-4548},
abstract = {Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner.},
}
@article {pmid28222570,
year = {2017},
author = {Neto, LL and Constantini, AC and Chun, RYS},
title = {Communication vulnerable in patients with Amyotrophic Lateral Sclerosis: A systematic review.},
journal = {NeuroRehabilitation},
volume = {40},
number = {4},
pages = {561-568},
doi = {10.3233/NRE-171443},
pmid = {28222570},
issn = {1878-6448},
mesh = {Amyotrophic Lateral Sclerosis/epidemiology/*rehabilitation ; Brain-Computer Interfaces/statistics &amp; numerical data ; Communication Devices for People with Disabilities/*statistics &amp; numerical data ; Persons with Disabilities/rehabilitation/statistics &amp; numerical data ; Humans ; Nonverbal Communication ; Periodicals as Topic/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Individuals with Amyotrophic Lateral Sclerosis (ALS) exhibit speech disorders since the early stages that decrease the communication rate and interfere in social participation.<br><br>OBJECTIVE: To conduct a literature review on communication vulnerable and Augmentative and Alternative Communication (AAC) in Amyotrophic Lateral Sclerosis.<br><br>METHOD: Descriptors of the Health Sciences Descriptors (DeCS) were used: Amyotrophic Lateral Sclerosis, Health Vulnerability, Communication Barriers, Nonverbal Communication, and Communication Aids for Disabled. Articles in Portuguese and English from 2010 to 2015, fully available in the Virtual Health Library, PubMed, and Scopus were used. Duplicate articles and those not related to communication/language were excluded.<br><br>RESULTS: Of the 94 articles found, 37 met the criteria. All of them were published in the USA and Europe, none was Brazilian; 27% of 2012 to 2014; 40.5% descriptive studies and 24.3% case studies; 45.9% addressed ALS and 24.3%, other serious motor alterations, including ALS. A large proportion (89.2%) addressed AAC, 70.3% Brain-Computer Interface (BCI).<br><br>CONCLUSION: The results show that the researches recurrently addressed communication vulnerable, although not necessarily in these terms. The device which was most employed was the BCI, mainly in advanced stages of the disease.},
}
@article {pmid28220764,
year = {2017},
author = {Davoudi, A and Ghidary, SS and Sadatnejad, K},
title = {Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036019},
doi = {10.1088/1741-2552/aa61bb},
pmid = {28220764},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/physiology ; *Models, Statistical ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner.<br><br>APPROACH: The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples.<br><br>MAIN RESULTS: We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique-leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data.<br><br>SIGNIFICANCE: The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain-computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.},
}
@article {pmid28220753,
year = {2017},
author = {Pandarinath, C and Nuyujukian, P and Blabe, CH and Sorice, BL and Saab, J and Willett, FR and Hochberg, LR and Shenoy, KV and Henderson, JM},
title = {High performance communication by people with paralysis using an intracortical brain-computer interface.},
journal = {eLife},
volume = {6},
number = {},
pages = {},
pmid = {28220753},
issn = {2050-084X},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication ; Humans ; *Paralysis ; Treatment Outcome ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.},
}
@article {pmid28220071,
year = {2017},
author = {García-Prieto, J and Bajo, R and Pereda, E},
title = {Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {8},
pmid = {28220071},
issn = {1662-5196},
abstract = {Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function.},
}
@article {pmid28212300,
year = {2017},
author = {Luebbert, C and Huxoll, F and Sadowski, G},
title = {Amorphous-Amorphous Phase Separation in API/Polymer Formulations.},
journal = {Molecules (Basel, Switzerland)},
volume = {22},
number = {2},
pages = {},
pmid = {28212300},
issn = {1420-3049},
mesh = {Algorithms ; Calorimetry, Differential Scanning ; Chemical Fractionation ; Drug Compounding ; Models, Chemical ; Molecular Structure ; Pharmaceutical Preparations/*chemistry/*isolation &amp; purification ; *Polymers ; Solubility ; Thermodynamics ; Transition Temperature ; },
abstract = {The long-term stability of pharmaceutical formulations of poorly-soluble drugs in polymers determines their bioavailability and therapeutic applicability. However, these formulations do not only often tend to crystallize during storage, but also tend to undergo unwanted amorphous-amorphous phase separations (APS). Whereas the crystallization behavior of APIs in polymers has been measured and modeled during the last years, the APS phenomenon is still poorly understood. In this study, the crystallization behavior, APS, and glass-transition temperatures formulations of ibuprofen and felodipine in polymeric PLGA excipients exhibiting different ratios of lactic acid and glycolic acid monomers in the PLGA chain were investigated by means of hot-stage microscopy and DSC. APS and recrystallization was observed in ibuprofen/PLGA formulations, while only recrystallization occurred in felodipine/PLGA formulations. Based on a successful modeling of the crystallization behavior using the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT), the occurrence of APS was predicted in agreement with experimental findings.},
}
@article {pmid28211451,
year = {2017},
author = {Fyfe, I},
title = {Motor neuron disease: Communication for completely locked-in patients.},
journal = {Nature reviews. Neurology},
volume = {13},
number = {3},
pages = {130},
doi = {10.1038/nrneurol.2017.25},
pmid = {28211451},
issn = {1759-4766},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Motor Neuron Disease ; },
}
@article {pmid28211450,
year = {2017},
author = {Chaudhary, U and Birbaumer, N and Ramos-Murguialday, A},
title = {Corrigendum: Brain-computer interfaces for communication and rehabilitation.},
journal = {Nature reviews. Neurology},
volume = {13},
number = {3},
pages = {191},
doi = {10.1038/nrneurol.2017.23},
pmid = {28211450},
issn = {1759-4766},
}
@article {pmid28208734,
year = {2017},
author = {Mehmood, RM and Lee, HJ},
title = {Towards Building a Computer Aided Education System for Special Students Using Wearable Sensor Technologies.},
journal = {Sensors (Basel, Switzerland)},
volume = {17},
number = {2},
pages = {},
pmid = {28208734},
issn = {1424-8220},
abstract = {Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain. Nowadays, in special schools, instructors are commonly use some conventional methods for managing special students for educational purposes. In this paper, we proposed a novel computer aided method for instructors at special schools where they can teach special students with the support of our system using wearable technologies.},
}
@article {pmid28207882,
year = {2017},
author = {Stahl, BC and Wakunuma, K and Rainey, S and Hansen, C},
title = {Improving brain computer interface research through user involvement - The transformative potential of integrating civil society organisations in research projects.},
journal = {PloS one},
volume = {12},
number = {2},
pages = {e0171818},
pmid = {28207882},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Community Participation ; Humans ; *Organizations ; Public Health ; Qualitative Research ; Research Design ; },
abstract = {Research on Brain Computer Interfaces (BCI) often aims to provide solutions for vulnerable populations, such as individuals with diseases, conditions or disabilities that keep them from using traditional interfaces. Such research thereby contributes to the public good. This contribution to the public good corresponds to a broader drive of research and funding policy that focuses on promoting beneficial societal impact. One way of achieving this is to engage with the public. In practical terms this can be done by integrating civil society organisations (CSOs) in research. The open question at the heart of this paper is whether and how such CSO integration can transform the research and contribute to the public good. To answer this question the paper describes five detailed qualitative case studies of research projects including CSOs. The paper finds that transformative impact of CSO integration is possible but by no means assured. It provides recommendations on how transformative impact can be promoted.},
}
@article {pmid28207382,
year = {2017},
author = {McCrimmon, CM and Fu, JL and Wang, M and Lopes, LS and Wang, PT and Karimi-Bidhendi, A and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {10},
pages = {2313-2320},
pmid = {28207382},
issn = {1558-2531},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Amplifiers, Electronic/*economics ; Brain Mapping/*economics/*instrumentation ; Brain-Computer Interfaces/*economics ; Cost-Benefit Analysis ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Humans ; Miniaturization ; Reproducibility of Results ; Sensitivity and Specificity ; United States ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.<br><br>METHODS: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states.<br><br>RESULTS: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated (&#x3c1; = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 &#xb1; 0.12 and 0.68 &#xb1; 0.10, respectively, and were not significantly different.<br><br>CONCLUSION: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs.<br><br>SIGNIFICANCE: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.},
}
@article {pmid28203220,
year = {2017},
author = {Gailey, A and Artemiadis, P and Santello, M},
title = {Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.},
journal = {Frontiers in neurology},
volume = {8},
number = {},
pages = {7},
pmid = {28203220},
issn = {1664-2295},
support = {R21 HD081938/HD/NICHD NIH HHS/United States ; },
abstract = {INTRODUCTION: Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution.<br><br>METHODS: We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach's ability to control hand posture and finger forces.<br><br>RESULTS: Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83-99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution.<br><br>DISCUSSION: This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.},
}
@article {pmid28203141,
year = {2017},
author = {Wang, Y and Veluvolu, KC},
title = {Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification.},
journal = {Frontiers in neuroscience},
volume = {11},
number = {},
pages = {28},
pmid = {28203141},
issn = {1662-4548},
abstract = {The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.},
}
@article {pmid28198591,
year = {2017},
author = {Goding, J and Gilmour, A and Martens, P and Poole-Warren, L and Green, R},
title = {Interpenetrating Conducting Hydrogel Materials for Neural Interfacing Electrodes.},
journal = {Advanced healthcare materials},
volume = {6},
number = {9},
pages = {},
doi = {10.1002/adhm.201601177},
pmid = {28198591},
issn = {2192-2659},
mesh = {Bridged Bicyclo Compounds, Heterocyclic/chemistry ; *Electrodes ; Hydrogel, Polyethylene Glycol Dimethacrylate/*chemistry ; Polymers/*chemistry ; Polyvinyl Alcohol/chemistry ; },
abstract = {Conducting hydrogels (CHs) are an emerging technology in the field of medical electrodes and brain-machine interfaces. The greatest challenge to the fabrication of CH electrodes is the hybridization of dissimilar polymers (conductive polymer and hydrogel) to ensure the formation of interpenetrating polymer networks (IPN) required to achieve both soft and electroactive materials. A new hydrogel system is developed that enables tailored placement of covalently immobilized dopant groups within the hydrogel matrix. The role of immobilized dopant in the formation of CH is investigated through covalent linking of sulfonate doping groups to poly(vinyl alcohol) (PVA) macromers. These groups control the electrochemical growth of the conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) and subsequent material properties. The effect of dopant density and interdopant spacing on the physical, electrochemical, and mechanical properties of the resultant CHs is examined. Cytocompatible PVA hydrogels with PEDOT penetration throughout the depth of the electrode are produced. Interdopant spacing is found to be the key factor in the formation of IPNs, with smaller interdopant spacing producing CH electrodes with greater charge storage capacity and lower impedance due to increased PEDOT growth throughout the network. This approach facilitates tailorable, high-performance CH electrodes for next generation, low impedance neuroprosthetic devices.},
}
@article {pmid28198356,
year = {2017},
author = {Suefusa, K and Tanaka, T},
title = {A comparison study of visually stimulated brain-computer and eye-tracking interfaces.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036009},
doi = {10.1088/1741-2552/aa6086},
pmid = {28198356},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Visual/*physiology ; Eye Movements/*physiology ; Humans ; Photic Stimulation/instrumentation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interfacing (BCI) based on visual stimuli detects the target on a screen on which a user is focusing. The detection of the gazing target can be achieved by tracking gaze positions with a video camera, which is called eye-tracking or eye-tracking interfaces (ETIs). The two types of interface have been developed in different communities. Thus, little work on a comprehensive comparison between these two types of interface has been reported. This paper quantitatively compares the performance of these two interfaces on the same experimental platform. Specifically, our study is focused on two major paradigms of BCI and ETI: steady-state visual evoked potential-based BCIs and dwelling-based ETIs.<br><br>APPROACH: Recognition accuracy and the information transfer rate were measured by giving subjects the task of selecting one of four targets by gazing at it. The targets were displayed in three different sizes (with sides 20, 40 and 60&#x2009;mm long) to evaluate performance with respect to the target size.<br><br>MAIN RESULTS: The experimental results showed that the BCI was comparable to the ETI in terms of accuracy and the information transfer rate. In particular, when the size of a target was relatively small, the BCI had significantly better performance than the ETI.<br><br>SIGNIFICANCE: The results on which of the two interfaces works better in different situations would not only enable us to improve the design of the interfaces but would also allow for the appropriate choice of interface based on the situation. Specifically, one can choose an interface based on the size of the screen that displays the targets.},
}
@article {pmid28198354,
year = {2017},
author = {Keshtkaran, MR and Yang, Z},
title = {Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036003},
doi = {10.1088/1741-2552/aa6089},
pmid = {28198354},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; *Algorithms ; *Artifacts ; Computer Simulation ; *Discriminant Analysis ; Humans ; *Machine Learning ; Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering.<br><br>APPROACH: The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters.<br><br>MAIN RESULTS: Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets.<br><br>SIGNIFICANCE: By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.},
}
@article {pmid28197747,
year = {2017},
author = {Kober, SE and Schweiger, D and Reichert, JL and Neuper, C and Wood, G},
title = {Upper Alpha Based Neurofeedback Training in Chronic Stroke: Brain Plasticity Processes and Cognitive Effects.},
journal = {Applied psychophysiology and biofeedback},
volume = {42},
number = {1},
pages = {69-83},
pmid = {28197747},
issn = {1573-3270},
mesh = {Aged ; Alpha Rhythm/*physiology ; Brain/*physiopathology ; Brain Ischemia/physiopathology/psychology/rehabilitation ; Cognition/*physiology ; Electroencephalography ; Female ; Humans ; Intracranial Hemorrhages/physiopathology/psychology/rehabilitation ; Male ; Middle Aged ; Neurofeedback/*methods ; Neuronal Plasticity/*physiology ; Neuropsychological Tests ; Stroke/*physiopathology/psychology ; Stroke Rehabilitation/*methods ; Treatment Outcome ; },
abstract = {In the present study, we investigated the effects of upper alpha based neurofeedback (NF) training on electrical brain activity and cognitive functions in stroke survivors. Therefore, two single chronic stroke patients with memory deficits (subject A with a bilateral subarachnoid hemorrhage; subject B with an ischemic stroke in the left arteria cerebri media) and a healthy elderly control group (N = 24) received up to ten NF training sessions. To evaluate NF training effects, all participants performed multichannel electroencephalogram (EEG) resting measurements and a neuropsychological test battery assessing different cognitive functions before and after NF training. Stroke patients showed improvements in memory functions after successful NF training compared to the pre-assessment. Subject B had a pathological delta (0.5-4 Hz) and upper alpha (10-12 Hz) power maximum over the unaffected hemisphere before NF training. After NF training, he showed a more bilateral and "normalized" topographical distribution of these EEG frequencies. Healthy participants as well as subject A did not show any abnormalities in EEG topography before the start of NF training. Consequently, no changes in the topographical distribution of EEG activity were observed in these participants when comparing the pre- and post-assessment. Hence, our results show that upper alpha based NF training had on the one hand positive effects on memory functions, and on the other hand led to cortical "normalization" in a stroke patient with pathological brain activation patterns, which underlines the potential usefulness of NF as neurological rehabilitation tool.},
}
@article {pmid28197092,
year = {2017},
author = {Cavazza, M and Aranyi, G and Charles, F},
title = {BCI Control of Heuristic Search Algorithms.},
journal = {Frontiers in neuroinformatics},
volume = {11},
number = {},
pages = {6},
pmid = {28197092},
issn = {1662-5196},
abstract = {The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users' mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.},
}
@article {pmid28193497,
year = {2017},
author = {Yazmir, B and Reiner, M},
title = {I act, therefore I err: EEG correlates of success and failure in a virtual throwing game.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {122},
number = {},
pages = {32-41},
doi = {10.1016/j.ijpsycho.2017.02.007},
pmid = {28193497},
issn = {1872-7697},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Electroencephalography ; Evoked Potentials/*physiology ; *Feedback ; Female ; Humans ; Male ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {What are the neural responses to success and failure in a throwing task? To answer this question, we compared Event Related Potentials (ERPs) correlated with success and failure during a highly-ecological-virtual game. Participants played a tennis-like game in an immersive 3D virtual world, against a computer player, by controlling a virtual tennis racket with a force feedback robotic arm. Results showed that success, i.e. hitting the target, and failure, by missing the target, evoked ERP's that differ by peak, latencies, scalp signal distributions, sLORETA source estimation, and time-frequency patterns. The success related grand averaged ERP at the Cz electrode, had two peaks - a negative peak at 244ms and a positive peak at 12ms, prior to the actual successful hit, suggesting a possible process of prediction of success. The grand averaged ERP correlated with failure at Cz, had two peaks, a negative peak at about 107ms and a positive peak at about 311ms post failure. These results suggest different top-down and bottom-up loops for success and failure, which seem to be rooted in the spatial arrangement of the virtual game. Although the latency of the latter is consistent with the error related potentials reported in the literature, the characteristic is unique to this specific error, and differ significantly from other error related potentials in the same environment. These results further provide a basis for EEG based assessment and prediction of user's successful or erroneous movements, and design of the feedback loop in EEG based Brain-Computer Interfaces.},
}
@article {pmid28192282,
year = {2017},
author = {Krell, MM and Wilshusen, N and Seeland, A and Kim, SK},
title = {Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {025003},
doi = {10.1088/1741-2552/aa5166},
pmid = {28192282},
issn = {1741-2552},
mesh = {*Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Data Mining/*methods ; Electroencephalography/*methods ; Humans ; *Models, Neurological ; Online Systems ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Support Vector Machine ; },
abstract = {OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM).<br><br>APPROACH: We focus on data selection strategies which limit the size of the stored training data by different inclusion, exclusion, and further dataset manipulation criteria like handling class imbalance with two new approaches. We provide a comparison of the strategies with linear SVMs on several synthetic datasets with different data shifts as well as on different transfer settings with electroencephalographic (EEG) data.<br><br>MAIN RESULTS: For the synthetic data, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add samples near the decision boundary of the SVM which reduces processing resources.<br><br>SIGNIFICANCE: For brain-computer interfaces based on EEG data, models trained on data from a calibration session, a previous recording session, or even from a recording session with another subject are used. We show, that by using the right combination of data selection criteria, it is possible to adapt the SVM classifier to overcome the performance drop from the transfer.},
}
@article {pmid28190641,
year = {2017},
author = {Athalye, VR and Ganguly, K and Costa, RM and Carmena, JM},
title = {Emergence of Coordinated Neural Dynamics Underlies Neuroprosthetic Learning and Skillful Control.},
journal = {Neuron},
volume = {93},
number = {4},
pages = {955-970.e5},
doi = {10.1016/j.neuron.2017.01.016},
pmid = {28190641},
issn = {1097-4199},
mesh = {Animals ; Brain-Computer Interfaces ; Learning/*physiology ; Models, Neurological ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Movement/*physiology ; Neuronal Plasticity/*physiology ; Neurons/physiology ; },
abstract = {During motor learning, movements and underlying neural activity initially exhibit large trial-to-trial variability that decreases over learning. However, it is unclear how task-relevant neural populations coordinate to explore and consolidate activity patterns. Exploration and consolidation could happen for each neuron independently, across the population jointly, or both. We disambiguated among these possibilities by investigating how subjects learned de novo to control a brain-machine interface using neurons from motor cortex. We decomposed population activity into the sum of private and shared signals, which produce uncorrelated and correlated neural variance, respectively, and examined how these signals' evolution causally shapes behavior. We found that initially large trial-to-trial movement and private neural variability reduce over learning. Concomitantly, task-relevant shared variance increases, consolidating a manifold containing consistent neural trajectories that generate refined control. These results suggest that motor cortex acquires skillful control by leveraging both independent and coordinated variance to explore and consolidate neural patterns.},
}
@article {pmid28188428,
year = {2017},
author = {Arndt, S and Laszig, R and Aschendorff, A and Hassepass, F and Beck, R and Wesarg, T},
title = {Cochlear implant treatment of patients with single-sided deafness or asymmetric hearing loss.},
journal = {HNO},
volume = {65},
number = {Suppl 2},
pages = {98-108},
pmid = {28188428},
issn = {1433-0458},
mesh = {Adult ; Aged ; Audiometry, Pure-Tone ; Bone Conduction ; *Cochlear Implants ; Evidence-Based Medicine ; Female ; Hearing Aids ; Hearing Loss, Unilateral/*rehabilitation ; Humans ; Male ; Middle Aged ; Prospective Studies ; Young Adult ; },
abstract = {BACKGROUND: The rehabilitation of patients with single-sided deafness (SSD) or asymmetric hearing loss can be achieved with conventional (Bi)CROS hearing aids ((Bi)CROS-HA, (Bi)CROS), bone conduction devices (BCI) or with cochlear implants (CI). Unfortunately, only small case series have been published on the treatment outcomes in SSD patients after CI surgery and there are only a few comparative studies evaluating rehabilitation outcomes.<br><br>OBJECTIVE: The aim of this study was to provide evidence of successful treatment of SSD and asymmetric hearing loss with a&#xa0;CI compared to the untreated, monaural hearing condition and the therapy options of BCI and (Bi)CROS in a&#xa0;large number of patients.<br><br>MATERIALS AND METHODS: In a&#xa0;single-centre study, 45&#xa0;patients with SSD and 40&#xa0;patients with asymmetric hearing loss were treated with a&#xa0;CI after careful evaluation for CI candidacy. Monaural speech comprehension in noise and localisation ability were examined with (Bi)CROS-HA and BCI devices (on a&#xa0;test rod) both preoperatively and at 12&#xa0;months after CI switch-on. At the same intervals, subjective evaluation of hearing ability was conducted using the Speech, Spatial and Qualities of Hearing Scale (SSQ).<br><br>RESULTS AND DISCUSSION: This report presents the first evidence of successful binaural rehabilitation with CI in a&#xa0;relatively large patient cohort and the advantages over (Bi)CROS and BCI in smaller subgroups, thus confirming the indication for CI treatment. Moreover, patients with long-term acquired deafness (>10&#xa0;years) show a benefit from the CI comparable to that observed in patients with shorter-term deafness.},
}
@article {pmid28185910,
year = {2017},
author = {Du, ZJ and Kolarcik, CL and Kozai, TDY and Luebben, SD and Sapp, SA and Zheng, XS and Nabity, JA and Cui, XT},
title = {Ultrasoft microwire neural electrodes improve chronic tissue integration.},
journal = {Acta biomaterialia},
volume = {53},
number = {},
pages = {46-58},
pmid = {28185910},
issn = {1878-7568},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Biocompatible Materials/adverse effects/chemistry ; Blood-Brain Barrier ; Electric Conductivity ; Electric Stimulation ; *Electrodes, Implanted/adverse effects ; Foreign-Body Reaction/prevention &amp; control ; Inflammation/prevention &amp; control ; Male ; Materials Testing ; *Microelectrodes/adverse effects ; Neurons/*physiology ; Polymers ; Rats ; Rats, Sprague-Dawley ; Silicone Elastomers ; Subthalamic Nucleus/physiology/surgery ; Tungsten/adverse effects ; },
abstract = {UNLABELLED: Chronically implanted neural multi-electrode arrays (MEA) are an essential technology for recording electrical signals from neurons and/or modulating neural activity through stimulation. However, current MEAs, regardless of the type, elicit an inflammatory response that ultimately leads to device failure. Traditionally, rigid materials like tungsten and silicon have been employed to interface with the relatively soft neural tissue. The large stiffness mismatch is thought to exacerbate the inflammatory response. In order to minimize the disparity between the device and the brain, we fabricated novel ultrasoft electrodes consisting of elastomers and conducting polymers with mechanical properties much more similar to those of brain tissue than previous neural implants. In this study, these ultrasoft microelectrodes were inserted and released using a stainless steel shuttle with polyethyleneglycol (PEG) glue. The implanted microwires showed functionality in acute neural stimulation. When implanted for 1 or 8weeks, the novel soft implants demonstrated significantly reduced inflammatory tissue response at week 8 compared to tungsten wires of similar dimension and surface chemistry. Furthermore, a higher degree of cell body distortion was found next to the tungsten implants compared to the polymer implants. Our results support the use of these novel ultrasoft electrodes for long term neural implants.<br><br>STATEMENT OF SIGNIFICANCE: One critical challenge to the translation of neural recording/stimulation electrode technology to clinically viable devices for brain computer interface (BCI) or deep brain stimulation (DBS) applications is the chronic degradation of device performance due to the inflammatory tissue reaction. While many hypothesize that soft and flexible devices elicit reduced inflammatory tissue responses, there has yet to be a rigorous comparison between soft and stiff implants. We have developed an ultra-soft microelectrode with Young's modulus lower than 1MPa, closely mimicking the brain tissue modulus. Here, we present a rigorous histological comparison of this novel ultrasoft electrode and conventional stiff electrode with the same size, shape and surface chemistry, implanted in rat brains for 1-week and 8-weeks. Significant improvement was observed for ultrasoft electrodes, including inflammatory tissue reaction, electrode-tissue integration as well as mechanical disturbance to nearby neurons. A full spectrum of new techniques were developed in this study, from insertion shuttle to in situ sectioning of the microelectrode to automated cell shape analysis, all of which should contribute new methods to the field. Finally, we showed the electrical functionality of the ultrasoft electrode, demonstrating the potential of flexible neural implant devices for future research and clinical use.},
}
@article {pmid28177925,
year = {2017},
author = {Willett, FR and Murphy, BA and Memberg, WD and Blabe, CH and Pandarinath, C and Walter, BL and Sweet, JA and Miller, JP and Henderson, JM and Shenoy, KV and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026010},
pmid = {28177925},
issn = {1741-2552},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; A6779-I/ImVA/Intramural VA/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; B6453-R/ImVA/Intramural VA/United States ; },
mesh = {*Artifacts ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; *Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; *Models, Neurological ; *Movement ; *Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]).<br><br>APPROACH: Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law.<br><br>MAIN RESULTS: We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder.<br><br>SIGNIFICANCE: The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.},
}
@article {pmid28170407,
year = {2017},
author = {Hagos, S and Hailemariam, D and WoldeHanna, T and Lindtjørn, B},
title = {Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model.},
journal = {PloS one},
volume = {12},
number = {2},
pages = {e0170785},
pmid = {28170407},
issn = {1932-6203},
mesh = {Bayes Theorem ; Child, Preschool ; Cross-Sectional Studies ; Ethiopia/epidemiology ; Female ; Geography, Medical ; Growth Disorders/*epidemiology ; Humans ; Infant ; Infant, Newborn ; Male ; Models, Statistical ; Morbidity ; Nutritional Status ; Risk Factors ; Spatial Analysis ; },
abstract = {BACKGROUND: Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia.<br><br>METHODS: A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area.<br><br>RESULTS: Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and among boys (OR 1.28; 95%BCI; 1.12-1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting.<br><br>CONCLUSION: Stunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.},
}
@article {pmid28167121,
year = {2017},
author = {Vasilyev, A and Liburkina, S and Yakovlev, L and Perepelkina, O and Kaplan, A},
title = {Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates.},
journal = {Neuropsychologia},
volume = {97},
number = {},
pages = {56-65},
doi = {10.1016/j.neuropsychologia.2017.02.005},
pmid = {28167121},
issn = {1873-3514},
mesh = {Adult ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills as well as for rehabilitation therapy of movement disorders. It is believed that MI training efficiency could be improved by using the brain-computer interface (BCI) technology providing real-time feedback on person's mental attempts. While BCI is indeed a convenient and motivating tool for practicing MI, it is not clear whether it could be used for predicting or measuring potential positive impact of the training. In this study, we are trying to establish whether the proficiency in BCI control is associated with any of the neurophysiological or psychological correlates of motor imagery, as well as to determine possible interrelations among them. For that purpose, we studied motor imagery in a group of 19 healthy BCI-trained volunteers and performed a correlation analysis across various quantitative assessment metrics. We examined subjects' sensorimotor event-related EEG events, corticospinal excitability changes estimated with single-pulse transcranial magnetic stimulation (TMS), BCI accuracy and self-assessment reports obtained with specially designed questionnaires and interview routine. Our results showed, expectedly, that BCI performance is dependent on the subject's capability to suppress EEG sensorimotor rhythms, which in turn is correlated with the idle state amplitude of those oscillations. Neither BCI accuracy nor the EEG features associated with MI were found to correlate with the level of corticospinal excitability increase during motor imagery, and with assessed imagery vividness. Finally, a significant correlation was found between the level of corticospinal excitability increase and kinesthetic vividness of imagery (KVIQ-20 questionnaire). Our results suggest that two distinct neurophysiological mechanisms might mediate possible effects of motor imagery: the non-specific cortical sensorimotor disinhibition and the focal corticospinal excitability increase. Acquired data suggests that BCI-based approach is unreliable in assessing motor imagery due to its high dependence on subject's innate EEG features (e.g. resting mu-rhythm amplitude). Therefore, employment of additional assessment protocols, such as TMS and psychological testing, is required for more comprehensive evaluation of the subject's motor imagery training efficiency.},
}
@article {pmid28165359,
year = {2017},
author = {Bestgen, B and Belaid-Choucair, Z and Lomberget, T and Le Borgne, M and Filhol, O and Cochet, C},
title = {In Search of Small Molecule Inhibitors Targeting the Flexible CK2 Subunit Interface.},
journal = {Pharmaceuticals (Basel, Switzerland)},
volume = {10},
number = {1},
pages = {},
pmid = {28165359},
issn = {1424-8247},
abstract = {Protein kinase CK2 is a tetrameric holoenzyme composed of two catalytic (α and/or α') subunits and two regulatory (β) subunits. Crystallographic data paired with fluorescence imaging techniques have suggested that the formation of the CK2 holoenzyme complex within cells is a dynamic process. Although the monomeric CK2α subunit is endowed with a constitutive catalytic activity, many of the plethora of CK2 substrates are exclusively phosphorylated by the CK2 holoenzyme. This means that the spatial and high affinity interaction between CK2α and CK2β subunits is critically important and that its disruption may provide a powerful and selective way to block the phosphorylation of substrates requiring the presence of CK2β. In search of compounds inhibiting this critical protein-protein interaction, we previously designed an active cyclic peptide (Pc) derived from the CK2β carboxy-terminal domain that can efficiently antagonize the CK2 subunit interaction. To understand the functional significance of this interaction, we generated cell-permeable versions of Pc, exploring its molecular mechanisms of action and the perturbations of the signaling pathways that it induces in intact cells. The identification of small molecules inhibitors of this critical interaction may represent the first-choice approach to manipulate CK2 in an unconventional way.},
}
@article {pmid28163677,
year = {2017},
author = {Yeon, J and Kim, J and Ryu, J and Park, JY and Chung, SC and Kim, SP},
title = {Human Brain Activity Related to the Tactile Perception of Stickiness.},
journal = {Frontiers in human neuroscience},
volume = {11},
number = {},
pages = {8},
pmid = {28163677},
issn = {1662-5161},
abstract = {While the perception of stickiness serves as one of the fundamental dimensions for tactile sensation, little has been elucidated about the stickiness sensation and its neural correlates. The present study investigated how the human brain responds to perceived tactile sticky stimuli using functional magnetic resonance imaging (fMRI). To evoke tactile perception of stickiness with multiple intensities, we generated silicone stimuli with varying catalyst ratios. Also, an acrylic sham stimulus was prepared to present a condition with no sticky sensation. From the two psychophysics experiments-the methods of constant stimuli and the magnitude estimation-we could classify the silicone stimuli into two groups according to whether a sticky perception was evoked: the Supra-threshold group that evoked sticky perception and the Infra-threshold group that did not. In the Supra-threshold vs. Sham contrast analysis of the fMRI data using the general linear model (GLM), the contralateral primary somatosensory area (S1) and ipsilateral dorsolateral prefrontal cortex (DLPFC) showed significant activations in subjects, whereas no significant result was found in the Infra-threshold vs. Sham contrast. This result indicates that the perception of stickiness not only activates the somatosensory cortex, but also possibly induces higher cognitive processes. Also, the Supra- vs. Infra-threshold contrast analysis revealed significant activations in several subcortical regions, including the pallidum, putamen, caudate and thalamus, as well as in another region spanning the insula and temporal cortices. These brain regions, previously known to be related to tactile discrimination, may subserve the discrimination of different intensities of tactile stickiness. The present study unveils the human neural correlates of the tactile perception of stickiness and may contribute to broadening the understanding of neural mechanisms associated with tactile perception.},
}
@article {pmid28162776,
year = {2017},
author = {Costa E Silva, JA and Steffen, RE},
title = {The future of psychiatry: brain devices.},
journal = {Metabolism: clinical and experimental},
volume = {69S},
number = {},
pages = {S8-S12},
doi = {10.1016/j.metabol.2017.01.010},
pmid = {28162776},
issn = {1532-8600},
mesh = {Acoustic Stimulation/adverse effects/methods/trends ; Animals ; Biomedical Engineering/methods/trends ; Brain-Computer Interfaces/adverse effects/trends ; Deep Brain Stimulation/adverse effects/instrumentation/trends ; Humans ; Neuropathology/*methods/trends ; Neuropsychiatry/*methods/trends ; Neurosciences/*methods/trends ; Psychiatry/*methods/trends ; Psychotic Disorders/pathology/physiopathology/*therapy ; Therapies, Investigational/adverse effects/*instrumentation/trends ; Transcranial Magnetic Stimulation/adverse effects/trends ; },
abstract = {Recent advances in deep brain stimulators and brain-machine interfaces have greatly expanded the possibilities of neuroprosthetics and neuromodulation. Together with advances in neuroengineering, nanotechnology, molecular biology and material sciences, it is now possible to address fundamental questions in neuroscience in new, more powerful ways. It is now possible to apply these new technologies in ways that range from augmenting and restoring function to neuromodulation modalities that treat neuropsychiatric disorders. Recent developments in neuromodulation methods offer significant advantages and potential clinical benefits for a variety of disorders. Here we describe the current state of the art in neuromodulation methods, and some advances in brain-machine interfaces, describing the advantages and limitations of the clinical applications of each method. The future applications of these new methods and how they will shape the future of psychiatry and medicine, along with safety and ethical implications, are also discussed.},
}
@article {pmid28161876,
year = {2017},
author = {Miao, M and Wang, A and Liu, F},
title = {A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {9},
pages = {1589-1603},
pmid = {28161876},
issn = {1741-0444},
support = {No. BE2012740//Jiangsu Province Science and Technology/ ; },
mesh = {Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagery, Psychotherapy/*methods ; Imagination/physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {Effective feature extraction and classification methods are of great importance for motor imagery (MI)-based brain-computer interface (BCI) systems. The common spatial pattern (CSP) algorithm is a widely used feature extraction method for MI-based BCIs. In this work, we propose a novel spatial-frequency-temporal optimized feature sparse representation-based classification method. Optimal channels are selected based on relative entropy criteria. Significant CSP features on frequency-temporal domains are selected automatically to generate a column vector for sparse representation-based classification (SRC). We analyzed the performance of the new method on two public EEG datasets, namely BCI competition III dataset IVa which has five subjects and BCI competition IV dataset IIb which has nine subjects. Compared to the performance offered by the existing SRC method, the proposed method achieves average classification accuracy improvements of 21.568 and 14.38% for BCI competition III dataset IVa and BCI competition IV dataset IIb, respectively. Furthermore, our approach also shows better classification performance when compared to other competing methods for both datasets.},
}
@article {pmid28161192,
year = {2017},
author = {Jian, W and Chen, M and McFarland, DJ},
title = {EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement.},
journal = {Brain research bulletin},
volume = {130},
number = {},
pages = {156-164},
pmid = {28161192},
issn = {1873-2747},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; *Movement ; Signal Processing, Computer-Assisted ; },
abstract = {Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase.},
}
@article {pmid28157960,
year = {2017},
author = {Rachim, VP and Jiang, Y and Lee, HS and Chung, WY},
title = {Demonstration of long-distance hazard-free wearable EEG monitoring system using mobile phone visible light communication.},
journal = {Optics express},
volume = {25},
number = {2},
pages = {713-719},
doi = {10.1364/OE.25.000713},
pmid = {28157960},
issn = {1094-4087},
abstract = {A wearable electroencephalogram (EEG) is a small mobile device used for long-term brain monitoring systems. Applications of these systems include fatigue monitoring, mental/emotional monitoring, and brain-computer interfaces. However, the usage of wireless wearable EEG systems is limited due to the risks posed by the wireless RF communication radiation in a long-term exposure to the human brain. A novel microwave radiation-free system was developed by integrating visible light communication technology into a wearable EEG device. In this work, we investigated the system's performance in transmitting EEG data at different illuminance level and proposed an algorithm that functions at low illuminance levels for increased transmission distance. Using a 30 Hz smartphone camera, the proposed system was able to transmit 2.4 kbps of error-free EEG data up to 4 meter, which is equal to ~300 lux using an aspheric focus lens.},
}
@article {pmid28157446,
year = {2017},
author = {Groen, SP and Richters, A and Laban, CJ and Devillé, WL},
title = {Implementation of the Cultural Formulation through a newly developed Brief Cultural Interview: Pilot data from the Netherlands.},
journal = {Transcultural psychiatry},
volume = {54},
number = {1},
pages = {3-22},
doi = {10.1177/1363461516678342},
pmid = {28157446},
issn = {1461-7471},
mesh = {Adult ; Culturally Competent Care/*standards ; Ethnopsychology/*methods ; Female ; Humans ; Interview, Psychological/*standards ; Male ; Middle Aged ; Netherlands ; Pilot Projects ; Psychometrics/*instrumentation ; Refugees/*psychology ; Young Adult ; },
abstract = {The Outline for a Cultural Formulation (OCF) has remained underutilized in clinical practice since its publication in the DSM-IV in 1994. In the Netherlands, a Cultural Interview (CI) was developed in 2002 as a tool to facilitate use of the OCF in clinical practice. The time needed to conduct the interview, however, prevented its systematic implementation within mental health institutions. This article presents the development of a shortened and adapted version, the Brief Cultural Interview (BCI), and a pilot study on the feasibility, acceptability, and utility of its implementation with refugee and asylum seeking patients in a Dutch centre for transcultural psychiatry. Results show that the brief version scores better on feasibility and acceptability, while utility for clinical practice remains similar to that of the original CI. These results support the systematic use of the OCF in psychiatric care for a culturally diverse patient population through the application of a relatively brief cultural interview. A secondary finding of the study is that patients' cultural identity was considered by clinicians to be more relevant in the treatment planning sessions than their illness explanations.},
}
@article {pmid28155841,
year = {2017},
author = {Steyrl, D and Krausz, G and Koschutnig, K and Edlinger, G and Müller-Putz, GR},
title = {Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026003},
doi = {10.1088/1741-2552/14/2/026003},
pmid = {28155841},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Artifacts ; Brain Mapping/*methods ; Electroencephalography/*methods ; Female ; Humans ; Magnetic Resonance Imaging/*methods ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Subtraction Technique ; },
abstract = {OBJECTIVE: Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners.<br><br>APPROACH: To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels.<br><br>MAIN RESULTS: The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best.<br><br>SIGNIFICANCE: In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.},
}
@article {pmid28154014,
year = {2017},
author = {Kaltenmeier, CT and Vollmer, LL and Vernetti, LA and Caprio, L and Davis, K and Korotchenko, VN and Day, BW and Tsang, M and Hulkower, KI and Lotze, MT and Vogt, A},
title = {A Tumor Cell-Selective Inhibitor of Mitogen-Activated Protein Kinase Phosphatases Sensitizes Breast Cancer Cells to Lymphokine-Activated Killer Cell Activity.},
journal = {The Journal of pharmacology and experimental therapeutics},
volume = {361},
number = {1},
pages = {39-50},
pmid = {28154014},
issn = {1521-0103},
support = {P30 CA047904/CA/NCI NIH HHS/United States ; R01 CA181450/CA/NCI NIH HHS/United States ; R01 HD053287/HD/NICHD NIH HHS/United States ; R21 CA147985/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; Animals, Genetically Modified ; Antineoplastic Agents/pharmacology/therapeutic use ; Breast Neoplasms/drug therapy/immunology/*metabolism ; Dose-Response Relationship, Drug ; Enzyme Inhibitors/*pharmacology/therapeutic use ; Female ; HeLa Cells ; Hepatocytes/drug effects/immunology/metabolism ; Humans ; JNK Mitogen-Activated Protein Kinases/antagonists &amp; inhibitors/immunology/metabolism ; Killer Cells, Lymphokine-Activated/*drug effects/immunology/*metabolism ; Mitogen-Activated Protein Kinase Phosphatases/*antagonists &amp; inhibitors/immunology/*metabolism ; Rats ; Zebrafish ; },
abstract = {Dual specificity mitogen-activated protein kinase (MAPK) phosphatases [dual specificity phosphatase/MAP kinase phosphatase (DUSP-MKP)] have been hypothesized to maintain cancer cell survival by buffering excessive MAPK signaling caused by upstream activating oncogenic products. A large and diverse body of literature suggests that genetic depletion of DUSP-MKPs can reduce tumorigenicity, suggesting that hyperactivating MAPK signaling by DUSP-MKP inhibitors could be a novel strategy to selectively affect the transformed phenotype. Through in vivo structure-activity relationship studies in transgenic zebrafish we recently identified a hyperactivator of fibroblast growth factor signaling [(E)-2-benzylidene-5-bromo-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI-215)] that is devoid of developmental toxicity and restores defective MAPK activity caused by overexpression of DUSP1 and DUSP6 in mammalian cells. Here, we hypothesized that BCI-215 could selectively affect survival of transformed cells. In MDA-MB-231 human breast cancer cells, BCI-215 inhibited cell motility, caused apoptosis but not primary necrosis, and sensitized cells to lymphokine-activated killer cell activity. Mechanistically, BCI-215 induced rapid and sustained phosphorylation of extracellular signal-regulated kinase (ERK), p38, and c-Jun N-terminal kinase (JNK) in the absence of reactive oxygen species, and its toxicity was partially rescued by inhibition of p38 but not JNK or ERK. BCI-215 also hyperactivated MKK4/SEK1, suggesting activation of stress responses. Kinase phosphorylation profiling documented BCI-215 selectively activated MAPKs and their downstream substrates, but not receptor tyrosine kinases, SRC family kinases, AKT, mTOR, or DNA damage pathways. Our findings support the hypothesis that BCI-215 causes selective cancer cell cytotoxicity in part through non-redox-mediated activation of MAPK signaling, and the findings also identify an intersection with immune cell killing that is worthy of further exploration.},
}
@article {pmid28151957,
year = {2017},
author = {Lajoie, G and Krouchev, NI and Kalaska, JF and Fairhall, AL and Fetz, EE},
title = {Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.},
journal = {PLoS computational biology},
volume = {13},
number = {2},
pages = {e1005343},
pmid = {28151957},
issn = {1553-7358},
support = {P51 OD010425/OD/NIH HHS/United States ; PJT-148844//CIHR/Canada ; R37 NS012542/NS/NINDS NIH HHS/United States ; RGP0049/2009-C//CIHR/Canada ; MOP-84454//CIHR/Canada ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Computer Simulation ; Feedback, Physiological/*physiology ; Humans ; *Models, Neurological ; *Models, Statistical ; Motor Cortex/*physiology ; Neurofeedback/physiology ; Neuronal Plasticity/*physiology ; Statistics as Topic ; },
abstract = {Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.},
}
@article {pmid28145274,
year = {2017},
author = {Ma, T and Li, H and Deng, L and Yang, H and Lv, X and Li, P and Li, F and Zhang, R and Liu, T and Yao, D and Xu, P},
title = {The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026015},
doi = {10.1088/1741-2552/aa5d5f},
pmid = {28145274},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Systems Integration ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control.<br><br>APPROACH: In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor.<br><br>MAIN RESULT: The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands.<br><br>SIGNIFICANCE: The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.},
}
@article {pmid28143603,
year = {2017},
author = {Úbeda, A and Azorín, JM and Chavarriaga, R and R Millán, JD},
title = {Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {14},
number = {1},
pages = {9},
pmid = {28143603},
issn = {1743-0003},
mesh = {Biomechanical Phenomena/physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Upper Extremity ; },
abstract = {BACKGROUND: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements.<br><br>METHODS: The decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories.<br><br>RESULTS: The results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics.<br><br>CONCLUSIONS: This paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.},
}
@article {pmid28141803,
year = {2017},
author = {Chaudhary, U and Xia, B and Silvoni, S and Cohen, LG and Birbaumer, N},
title = {Brain-Computer Interface-Based Communication in the Completely Locked-In State.},
journal = {PLoS biology},
volume = {15},
number = {1},
pages = {e1002593},
pmid = {28141803},
issn = {1545-7885},
mesh = {*Brain-Computer Interfaces ; *Communication ; Electroencephalography ; Humans ; Oxyhemoglobins/metabolism ; Quadriplegia/*physiopathology ; ROC Curve ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared ; },
abstract = {Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called complete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain-computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotrophic lateral sclerosis (ALS)-two of them in permanent CLIS and two entering the CLIS without reliable means of communication-learned to answer personal questions with known answers and open questions all requiring a "yes" or "no" thought using frontocentral oxygenation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a "yes" or "no" response. However, electroencephalogram (EEG) changes in the theta-frequency band correlated with inferior communication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards abolition of complete locked-in states, at least for ALS.},
}
@article {pmid28141525,
year = {2017},
author = {Vouga, T and Zhuang, KZ and Olivier, J and Lebedev, MA and Nicolelis, MA and Bouri, M and Bleuler, H},
title = {EXiO-A Brain-Controlled Lower Limb Exoskeleton for Rhesus Macaques.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {2},
pages = {131-141},
doi = {10.1109/TNSRE.2017.2659654},
pmid = {28141525},
issn = {1558-0210},
mesh = {Animals ; *Artificial Limbs ; *Brain-Computer Interfaces ; Equipment Design ; Equipment Failure Analysis ; *Exoskeleton Device ; Female ; Gait/*physiology ; Macaca mulatta ; Reproducibility of Results ; Robotics/*instrumentation/methods ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake behaving rhesus macaque is presented as a proof-of-concept for a locomotorBMI. A detailed description of the mechanical device, including its innovative features and first experimental results, is provided. During operation, BMI-decoded position and velocity are directly mapped onto the bipedal exoskeleton's motions, which then move the monkey's legs as the monkey remains physicallypassive. To meet the unique requirements of such an application, the exoskeleton's features include: high output torque with backdrivable actuation, size adjustability, and safe user-robot interface. In addition, a novel rope transmission is introduced and implemented. To test the performance of the exoskeleton, a mechanical assessment was conducted, which yielded quantifiable results for transparency, efficiency, stiffness, and tracking performance. Usage under both brain control and automated actuation demonstrates the device's capability to fulfill the demanding needs of this application. These results lay the groundwork for further advancement in BMI-controlled devices for primates including humans.},
}
@article {pmid28140332,
year = {2017},
author = {Seraj, E and Sameni, R},
title = {Robust electroencephalogram phase estimation with applications in brain-computer interface systems.},
journal = {Physiological measurement},
volume = {38},
number = {3},
pages = {501-523},
doi = {10.1088/1361-6579/aa5bba},
pmid = {28140332},
issn = {1361-6579},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations-previously associated to the brain response-are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG.<br><br>APPROACH: With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal's analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin.<br><br>MAIN RESULTS: As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset.<br><br>SIGNIFICANCE: The average performance was improved between 4-7% (in absence of additive noise) and 8-12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test, with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.},
}
@article {pmid28131888,
year = {2017},
author = {Pereira, J and Ofner, P and Schwarz, A and Sburlea, AI and Müller-Putz, GR},
title = {EEG neural correlates of goal-directed movement intention.},
journal = {NeuroImage},
volume = {149},
number = {},
pages = {129-140},
pmid = {28131888},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Motor/physiology ; Female ; Humans ; *Intention ; Male ; Movement/*physiology ; *Neurological Rehabilitation ; Young Adult ; },
abstract = {Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users.},
}
@article {pmid28129143,
year = {2017},
author = {Navarro-Sune, X and Hudson, AL and De Vico Fallani, F and Martinerie, J and Witon, A and Pouget, P and Raux, M and Similowski, T and Chavez, M},
title = {Riemannian Geometry Applied to Detection of Respiratory States From EEG Signals: The Basis for a Brain-Ventilator Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {5},
pages = {1138-1148},
doi = {10.1109/TBME.2016.2592820},
pmid = {28129143},
issn = {1558-2531},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Female ; Humans ; Machine Learning ; Male ; Pattern Recognition, Automated/*methods ; Respiration, Artificial/*methods ; Respiratory Mechanics/*physiology ; },
abstract = {GOAL: During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this study, we propose a brain-computer interface (BCI) to automatically and noninvasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface (BVI).<br><br>METHODS: Our framework exploits the cortical activation provoked by the inspiratory compensation when the subject and the ventilator are desynchronized. Use of a one-class approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory states. The BVI is validated on nine healthy subjects that performed different respiratory tasks that mimic a patient-ventilator disharmony.<br><br>RESULTS: Classification performances, in terms of areas under receiver operating characteristic curves, are significantly improved using EEG signals compared to detection based on air flow. Reduction in the number of electrodes that can achieve discrimination can be often desirable (e.g., for portable BCI systems). By using an iterative channel selection technique, the common highest order ranking, we find that a reduced set of electrodes (n = 6) can slightly improve for an intrasubject configuration, and it still provides fairly good performances for a general intersubject setting.<br><br>CONCLUSION: Results support the discriminant capacity of our approach to identify anomalous respiratory states, by learning from a training set containing only normal respiratory epochs.<br><br>SIGNIFICANCE: The proposed framework opens the door to BVIs for monitoring patient's breathing comfort and adapting ventilator parameters to patient respiratory needs.},
}
@article {pmid28124985,
year = {2017},
author = {Yaacoub, C and Mhanna, G and Rihana, S},
title = {A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.},
journal = {Brain sciences},
volume = {7},
number = {1},
pages = {},
pmid = {28124985},
issn = {2076-3425},
abstract = {Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.},
}
@article {pmid28123630,
year = {2017},
author = {Huang, L and Xu, AM},
title = {SET and MYND domain containing protein 3 in cancer.},
journal = {American journal of translational research},
volume = {9},
number = {1},
pages = {1-14},
pmid = {28123630},
issn = {1943-8141},
abstract = {Lysine methylation plays a vital role in histone modification. Deregulations of lysine methyltransferases and demethylases have been frequently observed in human cancers. The SET and MYND domain containing protein 3 (SMYD3) is a novel histone lysine methyltransferase and it functions by regulating chromatin during the development of myocardial and skeletal muscle. It has been recently unveiled to play significant roles in human cancer genesis and progression via regulating various key cancer-associated genes and pathways and promoting cell proliferation and migration. Upregulation of SMYD3 expression is present in multiple cancer types, suggesting it as a potential prognostic marker. Herein the structure, substrates and targets of SMYD3, and its effects on initiation, invasion and metastasis of diverse tumors (e.g., esophageal squamous cell carcinoma, gastric cancer, hepatocellular carcinoma, cholangiocarcinoma, breast cancer, prostate cancer, and leukemia) are systematically reviewed, providing clues for the development of novel SMYD3-specific personalized anti-cancer therapy. SMYD3 inhibitors (e.g., BCI-121 and novobiocin) could hopefully fight against tumors with efficacy.},
}
@article {pmid28123359,
year = {2016},
author = {Caywood, MS and Roberts, DM and Colombe, JB and Greenwald, HS and Weiland, MZ},
title = {Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {647},
pmid = {28123359},
issn = {1662-5161},
abstract = {There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as "black boxes" that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model's predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy.},
}
@article {pmid28120886,
year = {2017},
author = {Lee, H and Kwon, D and Cho, H and Park, I and Kim, J},
title = {Soft Nanocomposite Based Multi-point, Multi-directional Strain Mapping Sensor Using Anisotropic Electrical Impedance Tomography.},
journal = {Scientific reports},
volume = {7},
number = {},
pages = {39837},
pmid = {28120886},
issn = {2045-2322},
mesh = {*Anisotropy ; Brain-Computer Interfaces ; Electric Impedance ; Electrodes ; Humans ; Nanocomposites/chemistry/*statistics &amp; numerical data ; Nanotubes, Carbon/chemistry ; Silicone Elastomers/chemistry ; Tomography/*instrumentation/methods ; Touch/*physiology ; },
abstract = {The practical utilization of soft nanocomposites as a strain mapping sensor in tactile sensors and artificial skins requires robustness for various contact conditions as well as low-cost fabrication process for large three dimensional surfaces. In this work, we propose a multi-point and multi-directional strain mapping sensor based on multiwall carbon nanotube (MWCNT)-silicone elastomer nanocomposites and anisotropic electrical impedance tomography (aEIT). Based on the anisotropic resistivity of the sensor, aEIT technique can reconstruct anisotropic resistivity distributions using electrodes around the sensor boundary. This strain mapping sensor successfully estimated stretch displacements (error of 0.54 ± 0.53 mm), surface normal forces (error of 0.61 ± 0.62 N), and multi-point contact locations (error of 1.88 ± 0.95 mm in 30 mm × 30 mm area for a planar shaped sensor and error of 4.80 ± 3.05 mm in 40 mm × 110 mm area for a three dimensional contoured sensor). In addition, the direction of lateral stretch was also identified by reconstructing anisotropic distributions of electrical resistivity. Finally, a soft human-machine interface device was demonstrated as a practical application of the developed sensor.},
}
@article {pmid28119594,
year = {2016},
author = {Friesen, CL and Bardouille, T and Neyedli, HF and Boe, SG},
title = {Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {692},
pmid = {28119594},
issn = {1662-5161},
abstract = {Motor imagery (MI) and action observation have proven to be efficacious adjuncts to traditional physiotherapy for enhancing motor recovery following stroke. Recently, researchers have used a combined approach called imagined imitation (II), where an individual watches a motor task being performed, while simultaneously imagining they are performing the movement. While neurofeedback (NFB) has been used extensively with MI to improve patients' ability to modulate sensorimotor activity and enhance motor recovery, the effectiveness of using NFB with II to modulate brain activity is unknown. This project tested the ability of participants to modulate sensorimotor activity during electroencephalography-based II-NFB of a complex, multi-part unilateral handshake, and whether this ability transferred to a subsequent bout of MI. Moreover, given the goal of translating findings from NFB research into practical applications, such as rehabilitation, the II-NFB system was designed with several user interface and user experience features, in an attempt to both drive user engagement and match the level of challenge to the abilities of the subjects. In particular, at easy difficulty levels the II-NFB system incentivized contralateral sensorimotor up-regulation (via event related desynchronization of the mu rhythm), while at higher difficulty levels the II-NFB system incentivized sensorimotor lateralization (i.e., both contralateral up-regulation and ipsilateral down-regulation). Thirty-two subjects, receiving real or sham NFB attended four sessions where they engaged in II-NFB training and subsequent MI. Results showed the NFB group demonstrated more bilateral sensorimotor activity during sessions 2-4 during II-NFB and subsequent MI, indicating mixed success for the implementation of this particular II-NFB system. Here we discuss our findings in the context of the design features included in the II-NFB system, highlighting limitations that should be considered in future designs.},
}
@article {pmid28119472,
year = {2017},
author = {Basso, P and Ragno, M and Elsen, S and Reboud, E and Golovkine, G and Bouillot, S and Huber, P and Lory, S and Faudry, E and Attrée, I},
title = {Pseudomonas aeruginosa Pore-Forming Exolysin and Type IV Pili Cooperate To Induce Host Cell Lysis.},
journal = {mBio},
volume = {8},
number = {1},
pages = {},
pmid = {28119472},
issn = {2150-7511},
mesh = {Cell Survival ; DNA Transposable Elements ; Fimbriae, Bacterial/*metabolism ; Mutagenesis, Insertional ; Pore Forming Cytotoxic Proteins/*metabolism ; Pseudomonas aeruginosa/genetics/*physiology ; Type II Secretion Systems/*metabolism ; },
abstract = {UNLABELLED: Clinical strains of Pseudomonas aeruginosa lacking the type III secretion system genes employ a toxin, exolysin (ExlA), for host cell membrane disruption. Here, we demonstrated that ExlA export requires a predicted outer membrane protein, ExlB, showing that ExlA and ExlB define a new active two-partner secretion (TPS) system of P. aeruginosa In addition to the TPS signals, ExlA harbors several distinct domains, which include one hemagglutinin domain, five arginine-glycine-aspartic acid (RGD) motifs, and a C-terminal region lacking any identifiable sequence motifs. However, this C-terminal region is important for the toxic activity, since its deletion abolishes host cell lysis. Using lipid vesicles and eukaryotic cells, including red blood cells, we demonstrated that ExlA has a pore-forming activity which precedes cell membrane disruption of nucleated cells. Finally, we developed a high-throughput cell-based live-dead assay and used it to screen a transposon mutant library of an ExlA-producing P. aeruginosa clinical strain for bacterial factors required for ExlA-mediated toxicity. The screen resulted in the identification of proteins involved in the formation of type IV pili as being required for ExlA to exert its cytotoxic activity by promoting close contact between bacteria and the host cell. These findings represent the first example of cooperation between a pore-forming toxin of the TPS family and surface appendages in host cell intoxication.<br><br>IMPORTANCE: The course and outcome of acute, toxigenic infections by Pseudomonas aeruginosa clinical isolates rely on the deployment of one of two virulence strategies: delivery of effectors by the well-known type III secretion system or the cytolytic activity of the recently identified two-partner secreted toxin, exolysin. Here, we characterize several features of the mammalian cell intoxication process mediated by exolysin. We found that exolysin requires the outer membrane protein ExlB for export into extracellular medium. Using in vitro recombinant protein and ex vivo assays, we demonstrated a pore-forming activity of exolysin. A cellular cytotoxicity screen of a transposon mutant library, made in an exolysin-producing clinical strain, identified type IV pili as bacterial appendages required for exolysin toxic function. This work deciphers molecular mechanisms underlying the activity of novel virulence factors used by P.&#xa0;aeruginosa clinical strains lacking the type III secretion system, including a requirement for the toxin-producing bacteria to be attached to the targeted cell to induce cytolysis, and defines new targets for developing antivirulence strategies.},
}
@article {pmid28114072,
year = {2017},
author = {Bi, L and Lu, Y and Fan, X and Lian, J and Liu, Y},
title = {Queuing Network Modeling of Driver EEG Signals-Based Steering Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {8},
pages = {1117-1124},
doi = {10.1109/TNSRE.2016.2614003},
pmid = {28114072},
issn = {1558-0210},
abstract = {Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended queuing network-based driver model with a brain-computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving. The brain-controlled steering model has potential values in helping develop a brain-controlled assistive vehicle. Furthermore, this study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots).},
}
@article {pmid28113942,
year = {2017},
author = {Jackson, A and Hall, TM},
title = {Decoding Local Field Potentials for Neural Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1705-1714},
pmid = {28113942},
issn = {1558-0210},
support = {G0802195/MRC_/Medical Research Council/United Kingdom ; 106149//Wellcome Trust/United Kingdom ; K501396//Medical Research Council/United Kingdom ; },
mesh = {Action Potentials/*physiology ; Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Haplorhini ; Humans ; Macaca mulatta ; Signal Processing, Computer-Assisted ; },
abstract = {The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.},
}
@article {pmid28113631,
year = {2017},
author = {Tidoni, E and Gergondet, P and Fusco, G and Kheddar, A and Aglioti, SM},
title = {The Role of Audio-Visual Feedback in a Thought-Based Control of a Humanoid Robot: A BCI Study in Healthy and Spinal Cord Injured People.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {772-781},
doi = {10.1109/TNSRE.2016.2597863},
pmid = {28113631},
issn = {1558-0210},
mesh = {Adult ; Biomimetics/instrumentation ; Brain/physiology ; *Brain-Computer Interfaces ; Persons with Disabilities/rehabilitation ; *Feedback, Sensory ; Female ; Humans ; *Imagination ; Male ; Man-Machine Systems ; *Movement ; Reproducibility of Results ; Robotics/*instrumentation ; Sensitivity and Specificity ; Spinal Cord Injuries/diagnosis/*physiopathology/*rehabilitation ; Task Performance and Analysis ; Treatment Outcome ; Young Adult ; },
abstract = {The efficient control of our body and successful interaction with the environment are possible through the integration of multisensory information. Brain-computer interface (BCI) may allow people with sensorimotor disorders to actively interact in the world. In this study, visual information was paired with auditory feedback to improve the BCI control of a humanoid surrogate. Healthy and spinal cord injured (SCI) people were asked to embody a humanoid robot and complete a pick-and-place task by means of a visual evoked potentials BCI system. Participants observed the remote environment from the robot's perspective through a head mounted display. Human-footsteps and computer-beep sounds were used as synchronous/asynchronous auditory feedback. Healthy participants achieved better placing accuracy when listening to human footstep sounds relative to a computer-generated sound. SCI people demonstrated more difficulty in steering the robot during asynchronous auditory feedback conditions. Importantly, subjective reports highlighted that the BCI mask overlaying the display did not limit the observation of the scenario and the feeling of being in control of the robot. Overall, the data seem to suggest that sensorimotor-related information may improve the control of external devices. Further studies are required to understand how the contribution of residual sensory channels could improve the reliability of BCI systems.},
}
@article {pmid28113591,
year = {2017},
author = {Hong Kai Yap, and Kamaldin, N and Jeong Hoon Lim, and Nasrallah, FA and Goh, JCH and Chen-Hua Yeow, },
title = {A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {782-793},
doi = {10.1109/TNSRE.2016.2602941},
pmid = {28113591},
issn = {1558-0210},
mesh = {Artificial Limbs ; Brain/*physiology ; Brain Mapping/*instrumentation ; *Brain-Computer Interfaces ; Elastic Modulus ; Equipment Design ; Equipment Failure Analysis ; *Exoskeleton Device ; Hand ; Humans ; Magnetic Resonance Imaging/*instrumentation ; Neurological Rehabilitation/*instrumentation ; Reproducibility of Results ; Robotics/*instrumentation ; Sensitivity and Specificity ; },
abstract = {In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.},
}
@article {pmid28113590,
year = {2017},
author = {Khanna, P and Swann, NC and de Hemptinne, C and Miocinovic, S and Miller, A and Starr, PA and Carmena, JM},
title = {Neurofeedback Control in Parkinsonian Patients Using Electrocorticography Signals Accessed Wirelessly With a Chronic, Fully Implanted Device.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1715-1724},
pmid = {28113590},
issn = {1558-0210},
support = {R01 NS090913/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Beta Rhythm ; Brain-Computer Interfaces ; Electrocorticography/*methods ; Electrodes, Implanted ; Equipment Design ; Games, Experimental ; Humans ; Learning ; Male ; Middle Aged ; *Neurofeedback ; Parkinsonian Disorders/*rehabilitation ; Sensorimotor Cortex ; Wireless Technology ; },
abstract = {Parkinson's disease (PD) is characterized by motor symptoms such as rigidity and bradykinesia that prevent normal movement. Beta band oscillations (13-30 Hz) in neural local field potentials (LFPs) have been associated with these motor symptoms. Here, three PD patients implanted with a therapeutic deep brain neural stimulator that can also record and wirelessly stream neural data played a neurofeedback game where they modulated their beta band power from sensorimotor cortical areas. Patients' beta band power was streamed in real-time to update the position of a cursor that they tried to drive into a cued target. After playing the game for 1-2 hours each, all three patients exhibited above chance-level performance regardless of subcortical stimulation levels. This study, for the first time, demonstrates using an invasive neural recording system for at-home neurofeedback training. Future work will investigate chronic neurofeedback training as a potentially therapeutic tool for patients with neurological disorders.},
}
@article {pmid28113512,
year = {2017},
author = {Shahdoost, S and Nudo, R and Mohseni, P},
title = {Generation of Stimulus Triggering from Intracortical Spike Activity for Brain-Machine-Body Interfaces (BMBIs).},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {7},
pages = {998-1008},
doi = {10.1109/TNSRE.2016.2615270},
pmid = {28113512},
issn = {1558-0210},
abstract = {Brain-machine-body interfaces (BMBIs) aim to create an artificial connection in the nervous system by converting neural activity recorded from one cortical region to electrical stimuli delivered to another cortical region, spinal cord, or muscles in real-time. In particular, conditioning-mode BMBIs utilize such activity-dependent stimulation strategies to induce functional re-organization in the nervous system and promote functional recovery after injury by exploiting mechanisms underlying neuroplasticity. This paper reports on reconfigurable, field-programmable gate array (FPGA)-based implementation of a translation algorithm to extract multichannel stimulus trigger signals from intracortical neural spike activity. The approach features digital spike discrimination based on user-set thresholding and time-amplitude windowing, decision making to support different triggering patterns for various stimulation scenarios, as well as trigger-pattern-dependent blanking schemes for robust operation in the presence of stimulus artifacts. Readily lending itself to low-power, low-area implementation for future integration, the algorithm has been synthesized on a Cyclone II FPGA using Altera's Quartus II design software and validated experimentally with prerecorded intracortical neural spike activity from an anesthetized laboratory rat.},
}
@article {pmid28113378,
year = {2017},
author = {Foodeh, R and Khorasani, A and Shalchyan, V and Daliri, MR},
title = {Minimum Noise Estimate filter: a Novel Automated Artifacts Removal method for Field Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {8},
pages = {1143-1152},
doi = {10.1109/TNSRE.2016.2606416},
pmid = {28113378},
issn = {1558-0210},
abstract = {In this paper a novel automated and unsupervised method for removing artifacts from multichannel field potential signals is introduced which can be used in brain computer interface (BCI) applications. The method, which is called minimum noise estimate (MNE) filter is based on an iterative thresholding followed by Rayleigh quotient which tries to find an estimate of the noise and to minimize it over the original signal. MNE filter is capable to operate without any prior information about field potential signals. The performance of the proposed method is evaluated by its application on two different type of signals namely electrocorticogram (ECoG) and electroencephalogram (EEG) datasets through a decoding procedure. The results indicate that the proposed method outperforms well-known artifacts removal techniques such as common average referencing (CAR), Laplacian method, independent component analysis (ICA) and wavelet denoising approach.},
}
@article {pmid28113345,
year = {2017},
author = {Qiu, Z and Allison, BZ and Jin, J and Zhang, Y and Wang, X and Li, W and Cichocki, A},
title = {Optimized Motor Imagery Paradigm Based on Imagining Chinese Characters Writing Movement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {7},
pages = {1009-1017},
doi = {10.1109/TNSRE.2017.2655542},
pmid = {28113345},
issn = {1558-0210},
mesh = {Adult ; Asian People ; Biofeedback, Psychology/*methods ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Language ; Male ; Movement/*physiology ; Task Performance and Analysis ; Word Processing/*methods ; Writing ; Young Adult ; },
abstract = {BACKGROUND: motor imagery (MI) is a mental representation of motor behavior. The MI-based brain computer interfaces (BCIs) can provide communication for the physically impaired. The performance of MI-based BCI mainly depends on the subject's ability to self-modulate electroencephalogram signals. Proper training can help naive subjects learn to modulate brain activity proficiently. However, training subjects typically involve abstract motor tasks and are time-consuming.<br><br>METHODS: to improve the performance of naive subjects during motor imagery, a novel paradigm was presented that would guide naive subjects to modulate brain activity effectively. In this new paradigm, pictures of the left or right hand were used as cues for subjects to finish the motor imagery task. Fourteen healthy subjects (11 male, aged 22-25 years, and mean 23.6&#xb1;1.16) participated in this study. The task was to imagine writing a Chinese character. Specifically, subjects could imagine hand movements corresponding to the sequence of writing strokes in the Chinese character. This paradigm was meant to find an effective and familiar action for most Chinese people, to provide them with a specific, extensively practiced task and help them modulate brain activity.<br><br>RESULTS: results showed that the writing task paradigm yielded significantly better performance than the traditional arrow paradigm (p < 0.001). Questionnaire replies indicated that most subjects thought that the new paradigm was easier.<br><br>CONCLUSION: the proposed new motor imagery paradigm could guide subjects to help them modulate brain activity effectively. Results showed that there were significant improvements using new paradigm, both in classification accuracy and usability.},
}
@article {pmid28113323,
year = {2017},
author = {Shanechi, MM},
title = {Brain-Machine Interface Control Algorithms.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1725-1734},
doi = {10.1109/TNSRE.2016.2639501},
pmid = {28113323},
issn = {1558-0210},
abstract = {Motor brain-machine interfaces (BMI) allow subjects to control external devices by modulating their neural activity. BMIs record the neural activity, use a mathematical algorithm to estimate the subject's intended movement, actuate an external device, and provide visual feedback of the generated movement to the subject. A critical component of a BMI system is the control algorithm, termed decoder. Significant progress has been made in the design of BMI decoders in recent years resulting in proficient control in non-human primates and humans. In this review article, we discuss the decoding algorithms developed in the BMI field, with particular focus on recent designs that are informed by closed-loop control ideas. A motor BMI can be modeled as a closed-loop control system, where the controller is the brain, the plant is the prosthetic, the feedback is the biofeedback, and the control command is the neural activity. Additionally, compared to other closed-loop systems, BMIs have various unique properties. Neural activity is noisy and stochastic, and often consists of a sequence of spike trains. Neural representations of movement could be non-stationary and change over time, for example as a result of learning. We review recent decoder designs that take these unique properties into account. We also discuss the opportunities that exist at the interface of control theory, statistical inference, and neuroscience to devise a control-theoretic framework for BMI design and help develop the next-generation BMI control algorithms.},
}
@article {pmid28113291,
year = {2017},
author = {Xinyang Li, and Cuntai Guan, and Haihong Zhang, and Kai Keng Ang, },
title = {Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {8},
pages = {1906-1913},
doi = {10.1109/TBME.2016.2628958},
pmid = {28113291},
issn = {1558-2531},
mesh = {Algorithms ; *Artifacts ; Brain/*physiology ; Cognition/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Electrooculography/*methods ; Eye Movements/physiology ; Humans ; Machine Learning ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.},
}
@article {pmid28113241,
year = {2017},
author = {von Luhmann, A and Wabnitz, H and Sander, T and Muller, KR},
title = {M3BA: A Mobile, Modular, Multimodal Biosignal Acquisition Architecture for Miniaturized EEG-NIRS-Based Hybrid BCI and Monitoring.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {6},
pages = {1199-1210},
doi = {10.1109/TBME.2016.2594127},
pmid = {28113241},
issn = {1558-2531},
mesh = {Actigraphy/*instrumentation ; Analog-Digital Conversion ; Brain Mapping/*instrumentation ; Electric Power Supplies ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Information Storage and Retrieval/methods ; Miniaturization ; Monitoring, Ambulatory/instrumentation ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; Spectrophotometry, Infrared/*instrumentation/methods ; Systems Integration ; Wireless Technology/*instrumentation ; },
abstract = {OBJECTIVE: For the further development of the fields of telemedicine, neurotechnology, and brain-computer interfaces, advances in hybrid multimodal signal acquisition and processing technology are invaluable. Currently, there are no commonly available hybrid devices combining bioelectrical and biooptical neurophysiological measurements [here electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS)]. Our objective was to design such an instrument in a miniaturized, customizable, and wireless form.<br><br>METHODS: We present here the design and evaluation of a mobile, modular, multimodal biosignal acquisition architecture (M3BA) based on a high-performance analog front-end optimized for biopotential acquisition, a microcontroller, and our openNIRS technology.<br><br>RESULTS: The designed M3BA modules are very small configurable high-precision and low-noise modules (EEG input referred noise @ 500 SPS 1.39 &#x3bc;Vpp, NIRS noise equivalent power NEP750 nm = 5.92 pWpp, and NEP850 nm = 4.77 pWpp) with full input linearity, Bluetooth, 3-D accelerometer, and low power consumption. They support flexible user-specified biopotential reference setups and wireless body area/sensor network scenarios.<br><br>CONCLUSION: Performance characterization and in-vivo experiments confirmed functionality and quality of the designed architecture.<br><br>SIGNIFICANCE: Telemedicine and assistive neurotechnology scenarios will increasingly include wearable multimodal sensors in the future. The M3BA architecture can significantly facilitate future designs for research in these and other fields that rely on customized mobile hybrid biosignal modal biosignal acquisition architecture (M3BA), multimodal, near-infrared spectroscopy (NIRS), wireless body area network (WBAN), wireless body sensor network (WBSN).},
}
@article {pmid28113207,
year = {2017},
author = {Paris, A and Atia, GK and Vosoughi, A and Berman, SA},
title = {A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {8},
pages = {1688-1700},
doi = {10.1109/TBME.2016.2606595},
pmid = {28113207},
issn = {1558-2531},
mesh = {*Algorithms ; *Artifacts ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Computer Systems ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Humans ; *Models, Statistical ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: A characteristic of neurological signal processing is high levels of noise from subcellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel-McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model that has relatively few parameters, matches recorded EEG PSD's with high accuracy from 0 to over 30 Hz, and has approximately 1/f[θ] behavior in the midfrequencies without infinities.<br><br>METHODS: We validate this model using three approaches. First, we show how GVZM PSDs can arise in a population of ion channels at maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically.<br><br>RESULTS: In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely used SSVEP estimators.<br><br>CONCLUSION: The GVZM noise model can be a useful and reliable technique for EEG signal processing.<br><br>SIGNIFICANCE: Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces, which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.},
}
@article {pmid28113203,
year = {2017},
author = {Metsomaa, J and Sarvas, J and Ilmoniemi, RJ},
title = {Blind Source Separation of Event-Related EEG/MEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {9},
pages = {2054-2064},
doi = {10.1109/TBME.2016.2616389},
pmid = {28113203},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; Artifacts ; Brain Mapping/methods ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Magnetoencephalography/*methods ; Male ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain-computer interfaces, artifact removal, and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses, which is challenging because of nonstationary data.<br><br>METHODS: We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA), which is tailored for event-related multitrial data. The method is based on approximate joint diagonalization of multiple covariance matrices estimated from the data at separate latencies. We further show how to extend the methodology for autocovariance matrices and how to apply BSS methods suitable for piecewise stationary data to event-related responses. We compared several BSS approaches by using simulated EEG as well as measured somatosensory and transcranial magnetic stimulation (TMS) evoked EEG.<br><br>RESULTS: Among the compared methods, MUCA was the most tolerant one to noise, TMS artifacts, and other challenges in the data. With measured somatosensory data, over half of the estimated components were found to be similar by MUCA and independent component analysis. MUCA was also stable when tested with several input datasets.<br><br>CONCLUSION: MUCA is based on simple assumptions, and the results suggest that MUCA is robust with nonideal data.<br><br>SIGNIFICANCE: Event-related responses and BSS are valuable and popular tools in neuroscience. Correctly designed BSS is an efficient way of identifying artifactual and neural processes from nonstationary event-related data.},
}
@article {pmid28113192,
year = {2017},
author = {Sengelmann, M and Engel, AK and Maye, A},
title = {Maximizing Information Transfer in SSVEP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {2},
pages = {381-394},
doi = {10.1109/TBME.2016.2559527},
pmid = {28113192},
issn = {1558-2531},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Middle Aged ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Compared to the different brain signals used in brain-computer interface (BCI) paradigms, the s teady-state visually evoked potential (SSVEP) features a high signal to noise ratio, enabling reliable and fast classification of neural activity patterns without extensive training requirements. In this paper, we present methods to further increase the information transfer rates (ITRs) of SSVEP-based BCIs. Starting with stimulus parameter optimizations methods, we develop an improved approach for the use of Canonical correlation analysis and analyze properties of the SSVEP when the user fixates a target and during transitions between targets. These transitions show a negative effect on the system's ITR which we trace back to delays and dead times of the SSVEP. Using two classifier types adapted to continuous and transient SSVEPs and two control modes (fast feedback and fast input), we present a simulated online BCI implementation which addresses the challenges introduced by transient SSVEPs. The resulting system reaches an average ITR of 181 Bits/min and peak ITR values of up to 295 Bits/min for individual users.},
}
@article {pmid28111607,
year = {2016},
author = {White, SW and Richey, JA and Gracanin, D and Coffman, M and Elias, R and LaConte, S and Ollendick, TH},
title = {Psychosocial and Computer-Assisted Intervention for College Students with Autism Spectrum Disorder: Preliminary Support for Feasibility.},
journal = {Education and training in autism and developmental disabilities},
volume = {51},
number = {3},
pages = {307-317},
pmid = {28111607},
issn = {2154-1647},
support = {R21 MH100268/MH/NIMH NIH HHS/United States ; R33 MH100268/MH/NIMH NIH HHS/United States ; },
abstract = {The number of young adults with Autism Spectrum Disorders (ASD) enrolled in higher education institutions has steadily increased over the last decade. Despite this, there has been little research on how to most effectively support this growing population. The current study presents data from a pilot trial of two novel intervention programs developed for college students with ASD. In this small randomized controlled trial, college students with ASD (n = 8) were assigned to one of two new programs - either an intervention based on a virtual reality-Brain-Computer Interface for ASD (BCI-ASD) or a psychosocial intervention, the College and Living Success (CLS) program. Preliminary evidence supports the feasibility and acceptability of both programs, although behavioral outcomes were inconsistent across participants and interventions. Results indicate that expanded research on psychosocial and computer-assisted intervention approaches for this population is warranted, given the preliminary support found in this pilot study.},
}
@article {pmid28109833,
year = {2017},
author = {Passaro, AD and Vettel, JM and McDaniel, J and Lawhern, V and Franaszczuk, PJ and Gordon, SM},
title = {A novel method linking neural connectivity to behavioral fluctuations: Behavior-regressed connectivity.},
journal = {Journal of neuroscience methods},
volume = {279},
number = {},
pages = {60-71},
doi = {10.1016/j.jneumeth.2017.01.010},
pmid = {28109833},
issn = {1872-678X},
mesh = {Behavior/*physiology ; Brain/*physiology ; Brain Mapping/*methods ; Discrimination, Psychological/physiology ; Electroencephalography/*methods ; Fingers/physiology ; Humans ; Neural Pathways/physiology ; Neuropsychological Tests ; Pattern Recognition, Visual/physiology ; Regression Analysis ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {BACKGROUND: During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant.<br><br>NEW METHOD: We introduce a novel method, behavior-regressed connectivity (BRC), to directly examine behavioral fluctuations within an experimental session and capture their relationship to changes in functional connectivity. This method employs the weighted phase lag index (WPLI) applied to a window of trials with a weighting function. Using two datasets, the BRC results are compared to conventional connectivity results during two time windows: the one second before stimulus onset to identify predictive relationships, and the one second after onset to capture task-dependent relationships.<br><br>RESULTS: In both tasks, we replicate the expected results for the conventional connectivity analysis, and extend our understanding of the brain-behavior relationship using the BRC analysis, demonstrating subject-specific BRC maps that correspond to both positive and negative relationships with behavior. Comparison with Existing Method(s): Conventional connectivity analyses assume a consistent relationship between behaviors and functional connectivity, but the BRC method examines performance variability within an experimental session to understand dynamic connectivity and transient behavior.<br><br>CONCLUSION: The BRC approach examines connectivity as it covaries with behavior to complement the knowledge of underlying neural activity derived from conventional connectivity analyses. Within this framework, BRC may be implemented for the purpose of understanding performance variability both within and between participants.},
}
@article {pmid28109832,
year = {2017},
author = {Kaongoen, N and Jo, S},
title = {A novel hybrid auditory BCI paradigm combining ASSR and P300.},
journal = {Journal of neuroscience methods},
volume = {279},
number = {},
pages = {44-51},
doi = {10.1016/j.jneumeth.2017.01.011},
pmid = {28109832},
issn = {1872-678X},
mesh = {Attention/physiology ; Auditory Perception/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; *Evoked Potentials, Auditory ; Feasibility Studies ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is a technology that provides an alternative way of communication by translating brain activities into digital commands. Due to the incapability of using the vision-dependent BCI for patients who have visual impairment, auditory stimuli have been used to substitute the conventional visual stimuli.<br><br>NEW METHOD: This paper introduces a hybrid auditory BCI that utilizes and combines auditory steady state response (ASSR) and spatial-auditory P300 BCI to improve the performance for the auditory BCI system. The system works by simultaneously presenting auditory stimuli with different pitches and amplitude modulation (AM) frequencies to the user with beep sounds occurring randomly between all sound sources. Attention to different auditory stimuli yields different ASSR and beep sounds trigger the P300 response when they occur in the target channel, thus the system can utilize both features for classification.<br><br>RESULTS: The proposed ASSR/P300-hybrid auditory BCI system achieves 85.33% accuracy with 9.11 bits/min information transfer rate (ITR) in binary classification problem.<br><br>The proposed system outperformed the P300 BCI system (74.58% accuracy with 4.18 bits/min ITR) and the ASSR BCI system (66.68% accuracy with 2.01 bits/min ITR) in binary-class problem. The system is completely vision-independent.<br><br>CONCLUSIONS: This work demonstrates that combining ASSR and P300 BCI into a hybrid system could result in a better performance and could help in the development of the future auditory BCI.},
}
@article {pmid28106034,
year = {2017},
author = {Sussillo, D and Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Corrigendum: Making brain-machine interfaces robust to future neural variability.},
journal = {Nature communications},
volume = {8},
number = {},
pages = {14490},
doi = {10.1038/ncomms14490},
pmid = {28106034},
issn = {2041-1723},
}
@article {pmid28102825,
year = {2017},
author = {Barz, F and Livi, A and Lanzilotto, M and Maranesi, M and Bonini, L and Paul, O and Ruther, P},
title = {Versatile, modular 3D microelectrode arrays for neuronal ensemble recordings: from design to fabrication, assembly, and functional validation in non-human primates.},
journal = {Journal of neural engineering},
volume = {14},
number = {3},
pages = {036010},
doi = {10.1088/1741-2552/aa5a90},
pmid = {28102825},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; Electric Impedance ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Macaca mulatta ; Male ; *Microelectrodes ; Motor Cortex/*physiology ; *Printing, Three-Dimensional ; Reproducibility of Results ; Sensitivity and Specificity ; Tissue Array Analysis/*instrumentation ; },
abstract = {OBJECTIVE: Application-specific designs of electrode arrays offer an improved effectiveness for providing access to targeted brain regions in neuroscientific research and brain machine interfaces. The simultaneous and stable recording of neuronal ensembles is the main goal in the design of advanced neural interfaces. Here, we describe the development and assembly of highly customizable 3D microelectrode arrays and demonstrate their recording performance in chronic applications in non-human primates.<br><br>APPROACH: System assembly relies on a microfabricated stacking component that is combined with Michigan-style silicon-based electrode arrays interfacing highly flexible polyimide cables. Based on the novel stacking component, the lead time for implementing prototypes with altered electrode pitches is minimal. Once the fabrication and assembly accuracy of the stacked probes have been characterized, their recording performance is assessed during in vivo chronic experiments in awake rhesus macaques (Macaca mulatta) trained to execute reaching-grasping motor tasks.<br><br>MAIN RESULTS: Using a single set of fabrication tools, we implemented three variants of the stacking component for electrode distances of 250, 300 and 350 &#xb5;m in the stacking direction. We assembled neural probes with up to 96 channels and an electrode density of 98 electrodes mm[-2]. Furthermore, we demonstrate that the shank alignment is accurate to a few &#xb5;m at an angular alignment better than 1&#xb0;. Three 64-channel probes were chronically implanted in two monkeys providing single-unit activity on more than 60% of all channels and excellent recording stability. Histological tissue sections, obtained 52 d after implantation from one of the monkeys, showed minimal tissue damage, in accordance with the high quality and stability of the recorded neural activity.<br><br>SIGNIFICANCE: The versatility of our fabrication and assembly approach should significantly support the development of ideal interface geometries for a broad spectrum of applications. With the demonstrated performance, these probes are suitable for both semi-chronic and chronic applications.},
}
@article {pmid28101424,
year = {2017},
author = {Zafar, A and Hong, KS},
title = {Detection and classification of three-class initial dips from prefrontal cortex.},
journal = {Biomedical optics express},
volume = {8},
number = {1},
pages = {367-383},
pmid = {28101424},
issn = {2156-7085},
abstract = {In this paper, the use of initial dips using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. Features and window sizes for detecting initial dips are also discussed. Three mental tasks including mental arithmetic, mental counting, and puzzle solving are performed in obtaining fNIRS signals from the prefrontal cortex. Vector-based phase analysis method combined with a threshold circle, as a decision criterion, are used to detect the initial dips. Eight healthy subjects participate in experiment. Linear discriminant analysis is used as a classifier. To classify initial dips, five features (signal mean, peak value, signal slope, skewness, and kurtosis) of oxy-hemoglobin (HbO) and four different window sizes (0~1, 0~1.5, 0~2, and 0~2.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 0~2.5 sec provide the best average classification accuracy of 57.5% for three classes. To further validate the result, three-class classification using the conventional hemodynamic response (HR) is also performed, in which two features (signal mean and signal slope) and 2~7 sec window size have yielded the average classification accuracy of 65.9%. This reveals that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks. Further improvement can be made by using deoxy hemoglobin signals in coping with the slow HR problem.},
}
@article {pmid28098561,
year = {2017},
author = {Jia, N and Brincat, SL and Salazar-Gómez, AF and Panko, M and Guenther, FH and Miller, EK},
title = {Decoding of intended saccade direction in an oculomotor brain-computer interface.},
journal = {Journal of neural engineering},
volume = {14},
number = {4},
pages = {046007},
pmid = {28098561},
issn = {1741-2552},
support = {R01 NS035145/NS/NINDS NIH HHS/United States ; R37 MH087027/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Macaca fascicularis ; Macaca mulatta ; Male ; Oculomotor Nerve/*physiology ; Photic Stimulation/*methods ; Prefrontal Cortex/*physiology ; Random Allocation ; Saccades/*physiology ; },
abstract = {OBJECTIVE: To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system.<br><br>APPROACH: We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas.<br><br>MAIN RESULTS: Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex.<br><br>SIGNIFICANCE: Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for 'point and click' computer operation as used in most current AAC systems.},
}
@article {pmid28097128,
year = {2016},
author = {Min, B and Kim, J and Park, HJ and Lee, B},
title = {Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram.},
journal = {BioMed research international},
volume = {2016},
number = {},
pages = {2618265},
pmid = {28097128},
issn = {2314-6141},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/classification/*methods ; Humans ; Imagery, Psychotherapy/*methods ; *Language ; Machine Learning ; Male ; Speech/*physiology ; },
abstract = {The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.},
}
@article {pmid28096809,
year = {2016},
author = {Zhang, W and Sun, F and Tan, C and Liu, S},
title = {Low-Rank Linear Dynamical Systems for Motor Imagery EEG.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {2637603},
pmid = {28096809},
issn = {1687-5273},
mesh = {*Algorithms ; Brain Waves/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; Models, Theoretical ; *Motor Activity ; Nonlinear Dynamics ; *Pattern Recognition, Automated ; },
abstract = {The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.},
}
@article {pmid28096008,
year = {2017},
author = {Najafi Ghezeljeh, T and Kohandany, M and Oskouei, FH and Malek, M},
title = {The effect of progressive muscle relaxation on glycated hemoglobin and health-related quality of life in patients with type 2 diabetes mellitus.},
journal = {Applied nursing research : ANR},
volume = {33},
number = {},
pages = {142-148},
doi = {10.1016/j.apnr.2016.11.008},
pmid = {28096008},
issn = {1532-8201},
mesh = {Adult ; Aged ; Case-Control Studies ; Diabetes Mellitus, Type 2/blood/*physiopathology ; Female ; Glycated Hemoglobin/*metabolism ; Humans ; Male ; Middle Aged ; *Muscle Relaxation ; *Quality of Life ; },
abstract = {AIM: This study aimed to evaluate the effect of Jacobson's progressive muscle relaxation (PMR) on glycated hemoglobin (HbA1c) levels and health-related quality of life (HRQoL) in patients with type 2 diabetes mellitus (DM).<br><br>BACKGROUND: Due to relatively poor HRQoL in patients with type 2 DM, different stress reduction techniques was applied to improve physical and mental health in these patients.<br><br>METHODS: This randomized controlled clinical trial was conducted at the Diabetes and Endocrinology Institute of Firoozgar Hospital, Tehran, Iran, between June and December 2015. Sixty-five patients with type 2 DM were randomly divided into the control (n=35) and PMR (n=30) groups. The patients of the control group only received the conventional care. The PMR group practiced Jacobson's PMR at home for 12 weeks and were monitored by the researcher's phone calls and patient's self-report list. For both groups, Iranian Diabetes Quality of Life Brief Clinical Inventory (IDQoL-BCI) questionnaire was completed and HbA1c levels were measured before and 12 weeks after study entry.<br><br>RESULTS: The results showed that there were no significant differences in terms of HbA1c levels and HRQoL scores between the PMR and control groups 12 weeks after intervention. However, in the PMR group, the intervention led to a significant reduction in HbA1c levels (P=0.04) and a significant increase in total HRQoL score (P=0.045) and its psychosocial dimension (P=0.019).<br><br>CONCLUSION: PMR had no significant impact on HbA1c levels and HRQoL in patients with type 2 DM. Further studies with larger sample size and longer follow-up are needed to improve QoL in patients with type 2 DM.},
}
@article {pmid28092565,
year = {2017},
author = {Myrden, A and Chau, T},
title = {A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {4},
pages = {345-356},
doi = {10.1109/TNSRE.2016.2641956},
pmid = {28092565},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; *Attention ; Brain/*physiopathology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Frustration ; Humans ; Male ; Mental Fatigue/diagnostic imaging/*physiopathology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {Traditional brain-computer interfaces often exhibit unstable performance over time. It has recently been proposed that passive brain-computer interfaces may provide a way to complement and stabilize these traditional systems. In this study, we investigated the feasibility of a passive brain-computer interface that uses electroencephalography to monitor changes in mental state on a single-trial basis. We recorded cortical activity from 15 locations while 11 able-bodied adults completed a series of challenging mental tasks. Using a feature clustering algorithm to account for redundancy in EEG signal features, we classified self-reported changes in fatigue, frustration, and attention levels with 74.8 ± 9.1%, 71.6 ± 5.6%, and 84.8 ± 7.4% accuracy, respectively. Based on the most frequently-selected features across all participants, we note the importance of the frontal and central electrodes for fatigue detection, posterior alpha band and frontal beta band activity for frustration detection, and posterior alpha band activity for attention detection. Future work will focus on integrating these results with an active brain-computer interface.},
}
@article {pmid28092509,
year = {2017},
author = {Zhang, R and Wang, Q and Li, K and He, S and Qin, S and Feng, Z and Chen, Y and Song, P and Yang, T and Zhang, Y and Yu, Z and Hu, Y and Shao, M and Li, Y},
title = {A BCI-based Environmental Control System for Patients with Severe Spinal Cord Injuries.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {8},
pages = {1959-1971},
doi = {10.1109/TBME.2016.2628861},
pmid = {28092509},
issn = {1558-2531},
abstract = {UNLABELLED: This study proposes an event-related potential (ERP) BCI-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs).<br><br>METHODS: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving selfpaced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV).<br><br>RESULTS: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily result in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives per minute, which did not result in any false operations.<br><br>CONCLUSION: All SCI patients could use the proposed ERP BCIbased environmental control system satisfactorily.<br><br>SIGNIFICANCE: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives.},
}
@article {pmid28091397,
year = {2017},
author = {Chen, X and Wang, Y and Zhang, S and Gao, S and Hu, Y and Gao, X},
title = {A novel stimulation method for multi-class SSVEP-BCI using intermodulation frequencies.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026013},
doi = {10.1088/1741-2552/aa5989},
pmid = {28091397},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Color ; Electrocardiography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Flicker Fusion/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; },
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its easy system configuration, high information transfer rate (ITR) and little user training. However, due to the limitations of brain responses and the refresh rate of a monitor, the available stimulation frequencies for practical BCI application are generally restricted.<br><br>APPROACH: This study introduced a novel stimulation method using intermodulation frequencies for SSVEP-BCIs that had targets flickering at the same frequency but with different additional modulation frequencies. The additional modulation frequencies were generated on the basis of choosing desired flickering frequencies. The conventional frame-based 'on/off' stimulation method was used to realize the desired flickering frequencies. All visual stimulation was present on a conventional LCD screen. A 9-target SSVEP-BCI based on intermodulation frequencies was implemented for performance evaluation. To optimize the stimulation design, three approaches (C: chromatic; L: luminance; CL: chromatic and luminance) were evaluated by online testing and offline analysis.<br><br>MAIN RESULTS: SSVEP-BCIs with different paradigms (C, L, and CL) enabled us not only to encode more targets, but also to reliably evoke intermodulation frequencies. The online accuracies for the three paradigms were 91.67% (C), 93.98% (L), and 96.41% (CL). The CL condition achieved the highest classification performance.<br><br>SIGNIFICANCE: These results demonstrated the efficacy of three approaches (C, L, and CL) for eliciting intermodulation frequencies for multi-class SSVEP-BCIs. The combination of chromatic and luminance characteristics of the visual stimuli is the most efficient way for the intermodulation frequency coding method.},
}
@article {pmid28091395,
year = {2017},
author = {Song, Y and Sepulveda, F},
title = {A novel onset detection technique for brain-computer interfaces using sound-production related cognitive tasks in simulated-online system.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016019},
doi = {10.1088/1741-2552/14/1/016019},
pmid = {28091395},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Online Systems ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Young Adult ; },
abstract = {OBJECTIVE: Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues.<br><br>APPROACH: Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies-Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis.<br><br>MAIN RESULTS: Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate).<br><br>SIGNIFICANCE: Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs.},
}
@article {pmid28087767,
year = {2017},
author = {Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {37},
number = {7},
pages = {1721-1732},
pmid = {28087767},
issn = {1529-2401},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; *Adaptation, Physiological ; Animals ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/*cytology ; Movement/*physiology ; Neural Pathways/physiology ; Neurons/*physiology ; Photic Stimulation ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Statistics as Topic ; },
abstract = {Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.},
}
@article {pmid28086889,
year = {2017},
author = {Chai, R and Naik, GR and Ling, SH and Nguyen, HT},
title = {Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.},
journal = {Biomedical engineering online},
volume = {16},
number = {1},
pages = {5},
pmid = {28086889},
issn = {1475-925X},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; Biomedical Research/*instrumentation ; Brain/physiology/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; *Electrophysiological Phenomena ; Female ; Humans ; Male ; Middle Aged ; Spinal Cord Injuries/physiopathology ; *Wireless Technology ; },
abstract = {BACKGROUND: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals.<br><br>METHODS: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia.<br><br>RESULTS: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27&#xa0;bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4&#xa0;s.},
}
@article {pmid28082875,
year = {2016},
author = {Brooks, JR and Garcia, JO and Kerick, SE and Vettel, JM},
title = {Differential Functionality of Right and Left Parietal Activity in Controlling a Motor Vehicle.},
journal = {Frontiers in systems neuroscience},
volume = {10},
number = {},
pages = {106},
pmid = {28082875},
issn = {1662-5137},
abstract = {Driving a motor vehicle is an inherently complex task that requires robust control to avoid catastrophic accidents. Drivers must maintain their vehicle in the middle of the travel lane to avoid high speed collisions with other traffic. Interestingly, while a vehicle's lane deviation (LD) is critical, studies have demonstrated that heading error (HE) is one of the primary variables drivers use to determine a steering response, which directly controls the position of the vehicle in the lane. In this study, we examined how the brain represents the dichotomy between control/response parameters (heading, reaction time (RT), and steering wheel corrections) and task-critical parameters (LD). Specifically, we examined electroencephalography (EEG) alpha band power (8-13 Hz) from estimated sources in right and left parietal regions, and related this activity to four metrics of driving performance. Our results demonstrate differential task involvement between the two hemispheres: right parietal activity was most closely related to LD, whereas left parietal activity was most closely related to HE, RT and steering responses. Furthermore, HE, RT and steering wheel corrections increased over the duration of the experiment while LD did not. Collectively, our results suggest that the brain uses differential monitoring and control strategies in the right and left parietal regions to control a motor vehicle. Our results suggest that the regulation of this control changes over time while maintaining critical task performance. These results are interpreted in two complementary theoretical frameworks: the uncontrolled manifold and compensatory control theories. The central tenet of these frameworks permits performance variability in parameters (i.e., HE, RT and steering) so far as it does not interfere with critical task execution (i.e., LD). Our results extend the existing research by demonstrating potential neural substrates for this phenomenon which may serve as potential targets for brain-computer interfaces that predict poor driving performance.},
}
@article {pmid28082862,
year = {2016},
author = {Guo, L},
title = {The Pursuit of Chronically Reliable Neural Interfaces: A Materials Perspective.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {599},
pmid = {28082862},
issn = {1662-4548},
abstract = {Brain-computer interfaces represent one of the most astonishing technologies in our era. However, the grand challenge of chronic instability and limited throughput of the electrode-tissue interface has significantly hindered the further development and ultimate deployment of such exciting technologies. A multidisciplinary research workforce has been called upon to respond to this engineering need. In this paper, I briefly review this multidisciplinary pursuit of chronically reliable neural interfaces from a materials perspective by analyzing the problem, abstracting the engineering principles, and summarizing the corresponding engineering strategies. I further draw my future perspectives by extending the proposed engineering principles.},
}
@article {pmid28082858,
year = {2016},
author = {Krucoff, MO and Rahimpour, S and Slutzky, MW and Edgerton, VR and Turner, DA},
title = {Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {584},
pmid = {28082858},
issn = {1662-4548},
support = {R25 NS065731/NS/NINDS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; I21 BX003023/BX/BLRD VA/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; I21 RX002223/RX/RRD VA/United States ; R21 AG051103/AG/NIA NIH HHS/United States ; },
abstract = {After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery.},
}
@article {pmid28077887,
year = {2016},
author = {Shahmohammadi, M and Khoshuod, RJ and Zali, A and Seddeghi, AS and Kabir, NM},
title = {Examination of The Predictive Power of Electromyography and Urodynamic Study in Patients with Cauda Equina Syndrome (Horse Tail Syndrome).},
journal = {Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia &amp; Herzegovina : casopis Drustva za medicinsku informatiku BiH},
volume = {24},
number = {5},
pages = {328-331},
pmid = {28077887},
issn = {0353-8109},
abstract = {BACKGROUND: Cauda equina syndrome is a rare disorder that causes loss of Lumbar plexus function (nerve roots) lower than conus medullaris. No risk factor has been defined for this disease yet. Due to the high morbidity of Cauda equina syndrome and lack of sufficient information about the connection between the disease and urodynamic findings and EMG (Electromyography) findings, the need for this comprehensive study is felt.<br><br>OBJECTIVE: The aim is to determine the predictive power of findings resulted from urodynamics and electromyography of perineal region and around sphincter in the clinical cure rate of urination in patients with urinary retention followed by Cauda equina syndrome.<br><br>METHOD: Patients referred to Shohadaye Tajrish Hospital during the years 2009 to 2013, in case of having Cauda equina syndrome symptoms (confirmed with Lumbar MRI), were undergone urodynamic examination and perineal electromyography after surgical decompression action. These both assessments (urodynamic study and electromyography) were repeated during the follow-up of 15 patients in the first and sixth months after surgery and findings were compared with each other.<br><br>RESULTS: Among the Urodynamic findings, Qmax (maximum urine flow) during three studies had a significant relationship with long-term recovery rate of patients (P <0.05). The relationship had been more valuable in follow-ups after one month (P = 0.0001). Also, BCI (Bladder Contractility Index) in all three studies had a significant relationship with clinical improvement in the ability to urinate (P <0.001). The residual urine (PVR) compared to two previous urodynamic findings showed a less significant relationship with clinical cure rate (P = 0.04). Among the findings of muscle-nerve (MUAP Fibrillation, Positive sharp way) none of them had a significant relationship with cure rate.<br><br>CONCLUSION: Urodynamic finding, especially Qmax and bladder contractility index, can be considered as predictive indicators for patients' recovery after surgery.},
}
@article {pmid28071599,
year = {2017},
author = {Rabiul Islam, M and Khademul Islam Molla, M and Nakanishi, M and Tanaka, T},
title = {Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026007},
doi = {10.1088/1741-2552/aa5847},
pmid = {28071599},
issn = {1741-2552},
mesh = {Adult ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Statistics as Topic ; *Unsupervised Machine Learning ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency.<br><br>APPROACH: A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band.<br><br>MAIN RESULTS: A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min[-1] across 10 subjects. The maximum individual ITR is 107.55 bits min[-1] for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min[-1] are achieved with CCA and NCCA respectively.<br><br>SIGNIFICANCE: The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.},
}
@article {pmid28069531,
year = {2018},
author = {Alkoby, O and Abu-Rmileh, A and Shriki, O and Todder, D},
title = {Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning.},
journal = {Neuroscience},
volume = {378},
number = {},
pages = {155-164},
doi = {10.1016/j.neuroscience.2016.12.050},
pmid = {28069531},
issn = {1873-7544},
mesh = {Animals ; Brain/physiology ; Electroencephalography ; Humans ; Learning/physiology ; *Neurofeedback ; Precision Medicine ; Prognosis ; },
abstract = {Despite the success of neurofeedback treatment in many cases, the variability in the efficacy of the treatment is high, and some studies report that a significant proportion of subjects does not benefit from it. Quantifying the extent of this problem is difficult, as many studies do not report the variability among subjects. Nonetheless, the ability to identify in advance those subjects who are - or who are not - likely to benefit from neurofeedback is an important issue, which is only now starting to gain attention. Here, we review the problem of inefficacy in neurofeedback treatment as well as possible psychological and neurophysiological predictors for successful treatment. A possible explanation for treatment ineffectiveness lies in the necessity to adapt the treatment protocol to the individual subject. We therefore discuss the use of personalized neurofeedback protocols as a potential way to reduce the inefficacy problem.},
}
@article {pmid28068295,
year = {2017},
author = {Quitadamo, LR and Cavrini, F and Sbernini, L and Riillo, F and Bianchi, L and Seri, S and Saggio, G},
title = {Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {011001},
doi = {10.1088/1741-2552/14/1/011001},
pmid = {28068295},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/*methods ; Evoked Potentials/physiology ; Humans ; *Man-Machine Systems ; Muscle Contraction/physiology ; Muscle, Skeletal/physiology ; Pattern Recognition, Automated/*methods ; Robotics/methods ; *Support Vector Machine ; },
abstract = {Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.},
}
@article {pmid28068293,
year = {2017},
author = {Kawase, T and Sakurada, T and Koike, Y and Kansaku, K},
title = {A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016015},
doi = {10.1088/1741-2552/aa525f},
pmid = {28068293},
issn = {1741-2552},
mesh = {Adult ; Arm/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Electromyography/instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis ; *Exoskeleton Device ; Female ; Humans ; Male ; Man-Machine Systems ; Middle Aged ; Movement ; Neurological Rehabilitation/*instrumentation/methods ; Paresis/*physiopathology/*rehabilitation ; Systems Integration ; Treatment Outcome ; },
abstract = {OBJECTIVE: Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles.<br><br>APPROACH: Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball.<br><br>MAIN RESULTS: Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;0.09, 0.85&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;0.09, and 0.76&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;0.13, respectively) and the patients (e.g. 0.91&#x2009;&#x2009;&#xb1;&#x2009;&#x2009;0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors&#x2009;&#x2009;<6&#xb0;. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials.<br><br>SIGNIFICANCE: A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.},
}
@article {pmid28066220,
year = {2016},
author = {Manor, R and Mishali, L and Geva, AB},
title = {Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.},
journal = {Frontiers in computational neuroscience},
volume = {10},
number = {},
pages = {130},
pmid = {28066220},
issn = {1662-5188},
abstract = {Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications.},
}
@article {pmid28066170,
year = {2016},
author = {Li, S and Li, J and Li, Z},
title = {An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {587},
pmid = {28066170},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMIs) seek to connect brains with machines or computers directly, for application in areas such as prosthesis control. For this application, the accuracy of the decoding of movement intentions is crucial. We aim to improve accuracy by designing a better encoding model of primary motor cortical activity during hand movements and combining this with decoder engineering refinements, resulting in a new unscented Kalman filter based decoder, UKF2, which improves upon our previous unscented Kalman filter decoder, UKF1. The new encoding model includes novel acceleration magnitude, position-velocity interaction, and target-cursor-distance features (the decoder does not require target position as input, it is decoded). We add a novel probabilistic velocity threshold to better determine the user's intent to move. We combine these improvements with several other refinements suggested by others in the field. Data from two Rhesus monkeys indicate that the UKF2 generates offline reconstructions of hand movements (mean CC 0.851) significantly more accurately than the UKF1 (0.833) and the popular position-velocity Kalman filter (0.812). The encoding model of the UKF2 could predict the instantaneous firing rate of neurons (mean CC 0.210), given kinematic variables and past spiking, better than the encoding models of these two decoders (UKF1: 0.138, p-v Kalman: 0.098). In closed-loop experiments where each monkey controlled a computer cursor with each decoder in turn, the UKF2 facilitated faster task completion (mean 1.56 s vs. 2.05 s) and higher Fitts's Law bit rate (mean 0.738 bit/s vs. 0.584 bit/s) than the UKF1. These results suggest that the modeling and decoder engineering refinements of the UKF2 improve decoding performance. We believe they can be used to enhance other decoders as well.},
}
@article {pmid28065938,
year = {2017},
author = {Yang, S and Deng, B and Wang, J and Li, H and Liu, C and Fietkiewicz, C and Loparo, KA},
title = {Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties.},
journal = {Scientific reports},
volume = {7},
number = {},
pages = {40152},
pmid = {28065938},
issn = {2045-2322},
mesh = {Cerebral Cortex/*physiopathology ; Computer Systems ; Humans ; Membrane Potentials ; *Models, Neurological ; *Neural Networks, Computer ; Neural Pathways/physiopathology ; Neurons/*physiology ; Parkinsonian Disorders/*physiopathology ; Thalamus/*physiopathology ; },
abstract = {Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.},
}
@article {pmid28064315,
year = {2016},
author = {Kharitonova, MA and Kipenskaya, LV and Ilinskaya, ON},
title = {[Activation of biosynthesis of guanyl-specific ribonuclease secreted by Bacillus circulans under salt stress].},
journal = {Molekuliarnaia biologiia},
volume = {50},
number = {6},
pages = {992-998},
doi = {10.7868/S0026898416050086},
pmid = {28064315},
issn = {0026-8984},
mesh = {Bacillus/*enzymology ; Bacterial Proteins/metabolism ; Enzyme Activation/physiology ; Gene Expression Regulation, Bacterial/*physiology ; Gene Expression Regulation, Enzymologic/*physiology ; Regulon/physiology ; Ribonucleases/*metabolism ; Stress, Physiological/*physiology ; },
abstract = {The gene transcription of guanyl-specific ribonucleases (RNases), which provide available phosphate to cells of Bacillus, is controlled by the signal transduction system PhoP-PhoR. However, the biosynthesis of B. circulans RNase does not depend on the signal-transduction regulatory proteins of Pho regulon. It has been found that raising the salt molar concentration in culture medium increases the level of extracellular guanyl-specific ribonuclease Bci synthesized by B. circulans. Sequences homologous to the binding sites of the regulatory protein DegU were found in RNase Bci promoter. The functioning of the DegS-DegU signal transduction system is stimulated by a high salt concentration. Using a strain of B. subtilis that is defective in the DegU regulatory protein, we have shown that the DegS-DegU system participates in the regulation of RNase Bci expression under salt stress.},
}
@article {pmid28061886,
year = {2017},
author = {Erlbeck, H and Mochty, U and Kübler, A and Real, RG},
title = {Circadian course of the P300 ERP in patients with amyotrophic lateral sclerosis - implications for brain-computer interfaces (BCI).},
journal = {BMC neurology},
volume = {17},
number = {1},
pages = {3},
pmid = {28061886},
issn = {1471-2377},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; },
abstract = {BACKGROUND: Accidents or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) can lead to progressing, extensive, and complete paralysis leaving patients aware but unable to communicate (locked-in state). Brain-computer interfaces (BCI) based on electroencephalography represent an important approach to establish communication with these patients. The most common BCI for communication rely on the P300, a positive deflection arising in response to rare events. To foster broader application of BCIs for restoring lost function, also for end-users with impaired vision, we explored whether there were specific time windows during the day in which a P300 driven BCI should be preferably applied.<br><br>METHODS: The present study investigated the influence of time of the day and modality (visual vs. auditory) on P300 amplitude and latency. A sample of 14 patients (end-users) with ALS and 14 healthy age matched volunteers participated in the study and P300 event-related potentials (ERP) were recorded at four different times (10, 12&#xa0;am, 2, &amp; 4&#xa0;pm) during the day.<br><br>RESULTS: Results indicated no differences in P300 amplitudes or latencies between groups (ALS patients v. healthy participants) or time of measurement. In the auditory condition, latencies were shorter and amplitudes smaller as compared to the visual condition.<br><br>CONCLUSION: Our findings suggest applicability of EEG/BCI sessions in patients with ALS throughout normal waking hours. Future studies using actual BCI systems are needed to generalize these findings with regard to BCI effectiveness/efficiency and other times of day.},
}
@article {pmid28060906,
year = {2017},
author = {Yan, W and Xu, G and Li, M and Xie, J and Han, C and Zhang, S and Luo, A and Chen, C},
title = {Steady-State Motion Visual Evoked Potential (SSMVEP) Based on Equal Luminance Colored Enhancement.},
journal = {PloS one},
volume = {12},
number = {1},
pages = {e0169642},
pmid = {28060906},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Color Perception ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Models, Neurological ; *Motion ; Neural Pathways ; Pattern Recognition, Visual ; Photic Stimulation ; Reproducibility of Results ; Visual Cortex/physiology ; },
abstract = {Steady-state visual evoked potential (SSVEP) is one of the typical stimulation paradigms of brain-computer interface (BCI). It has become a research approach to improve the performance of human-computer interaction, because of its advantages including multiple objectives, less recording electrodes for electroencephalogram (EEG) signals, and strong anti-interference capacity. Traditional SSVEP using light flicker stimulation may cause visual fatigue with a consequent reduction of recognition accuracy. To avoid the negative impacts on the brain response caused by prolonged strong visual stimulation for SSVEP, steady-state motion visual evoked potential (SSMVEP) stimulation method was used in this study by an equal-luminance colored ring-shaped checkerboard paradigm. The movement patterns of the checkerboard included contraction and expansion, which produced less discomfort to subjects. Feature recognition algorithms based on power spectrum density (PSD) peak was used to identify the peak frequency on PSD in response to visual stimuli. Results demonstrated that the equal-luminance red-green stimulating paradigm within the low frequency spectrum (lower than 15 Hz) produced higher power of SSMVEP and recognition accuracy than black-white stimulating paradigm. PSD-based SSMVEP recognition accuracy was 88.15±6.56%. There was no statistical difference between canonical correlation analysis (CCA) (86.57±5.37%) and PSD on recognition accuracy. This study demonstrated that equal-luminance colored ring-shaped checkerboard visual stimulation evoked SSMVEP with better SNR on low frequency spectrum of power density and improved the interactive performance of BCI.},
}
@article {pmid28059065,
year = {2017},
author = {Shanechi, MM and Orsborn, AL and Moorman, HG and Gowda, S and Dangi, S and Carmena, JM},
title = {Rapid control and feedback rates enhance neuroprosthetic control.},
journal = {Nature communications},
volume = {8},
number = {},
pages = {13825},
pmid = {28059065},
issn = {2041-1723},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; *Feedback ; Humans ; Macaca mulatta ; Male ; Task Performance and Analysis ; },
abstract = {Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject. We developed a new BMI that allows arbitrarily fast control and feedback rates, and used it to dissociate the effects of each rate in two monkeys. Increasing the control rate significantly improved control even when feedback rate was unchanged. Increasing the feedback rate further facilitated control. We also show that our high-rate BMI significantly outperformed state-of-the-art methods due to higher control and feedback rates, combined with a different point process mathematical encoding model. Our BMI paradigm can dissect the contribution of different elements in the sensorimotor pathway, providing a unique tool for studying neuroprosthetic control mechanisms.},
}
@article {pmid28058044,
year = {2016},
author = {Zhang, J and Wang, B and Zhang, C and Hong, J},
title = {Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {4069790},
pmid = {28058044},
issn = {1687-5273},
mesh = {Adult ; Attention/physiology ; Blinking/*physiology ; Brain-Computer Interfaces ; Electrooculography ; Eye Movements/*physiology ; Female ; Humans ; *Linear Models ; Male ; Online Systems ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG (electrooculogram) data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS (Recursive Least Square) algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.},
}
@article {pmid28056723,
year = {2017},
author = {Coquand-Gandit, M and Jacob, MP and Fhayli, W and Romero, B and Georgieva, M and Bouillot, S and Estève, E and Andrieu, JP and Brasseur, S and Bouyon, S and Garcia-Honduvilla, N and Huber, P and Buján, J and Atanasova, M and Faury, G},
title = {Chronic Treatment with Minoxidil Induces Elastic Fiber Neosynthesis and Functional Improvement in the Aorta of Aged Mice.},
journal = {Rejuvenation research},
volume = {20},
number = {3},
pages = {218-230},
doi = {10.1089/rej.2016.1874},
pmid = {28056723},
issn = {1557-8577},
mesh = {Aging/drug effects/*physiology ; Animals ; Aorta/cytology/drug effects/*physiology/ultrastructure ; Biomechanical Phenomena/drug effects ; Blood Pressure/drug effects ; Body Weight/drug effects ; Collagen/genetics/metabolism ; Elastic Tissue/drug effects/*physiology ; Elastin/metabolism ; Extracellular Matrix/drug effects/metabolism ; Glycation End Products, Advanced/metabolism ; Male ; Mice, Inbred C57BL ; Minoxidil/*pharmacology ; Organ Size/drug effects ; RNA, Messenger/genetics/metabolism ; },
abstract = {Normal arterial aging processes involve vascular cell dysfunction associated with wall stiffening, the latter being due to progressive elastin and elastic fiber degradation, and elastin and collagen cross-linking by advanced glycation end products (AGEs). These processes progressively lead to cardiovascular dysfunction during aging. Elastin is only synthesized during late gestation and childhood, and further degradation occurring throughout adulthood cannot be physiologically compensated by replacement of altered material. However, the ATP-dependent K[+] channel opener minoxidil has been shown to stimulate elastin expression in vitro and in vivo in the aorta of young adult rats. Therefore, we have studied the effect of a 10-week chronic oral treatment with minoxidil (120 mg/L in drinking water) on the aortic structure and function in aged 24-month-old mice. Minoxidil treatment increased tropoelastin, fibulin-5, and lysyl-oxidase messenger RNA levels, reinduced a moderate expression of elastin, and lowered the levels of AGE-related molecules. This was accompanied by the formation of newly synthesized elastic fibers, which had diverse orientations in the wall. A decrease in the glycation capacity of aortic elastin was also produced by minoxidil treatment. The ascending aorta also underwent a minoxidil-induced increase in diameter and decrease in wall thickness, which partly reversed the age-associated thickening and returned the wall thickness value and strain-stress relation closer to those of younger adult animals. In conclusion, our results suggest that minoxidil presents an interesting potential for arterial remodeling in an antiaging perspective, even when treating already aged animals.},
}
@article {pmid28055887,
year = {2017},
author = {Ang, KK and Guan, C},
title = {EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {4},
pages = {392-401},
doi = {10.1109/TNSRE.2016.2646763},
pmid = {28055887},
issn = {1558-0210},
mesh = {Algorithms ; Biofeedback, Psychology/methods/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/*physiology ; Machine Learning ; Motor Cortex/*physiology ; Movement/*physiology ; Neurological Rehabilitation/*methods ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Advances in brain-computer interface (BCI) technology have facilitated the detection of Motor Imagery (MI) from electroencephalography (EEG). First, we present three strategies of using BCI to detect MI from EEG: operant conditioning that employed a fixed model, machine learning that employed a subject-specific model computed from calibration, and adaptive strategy that continuously compute the subject-specific model. Second, we review prevailing works that employed the operant conditioning and machine learning strategies. Third, we present our past work on six stroke patients who underwent a BCI rehabilitation clinical trial with averaged accuracies of 79.8% during calibration and 69.5% across 18 online feedback sessions. Finally, we perform an offline study in this paper on our work employing the adaptive strategy. The results yielded significant improvements of 12% (p < 0.001) and 9% (p < 0.001) using all the data and using limited preceding data respectively in the feedback accuracies. The results showed an increase in the amount of training data yielded improvements. Nevertheless, results of using limited preceding data showed a larger part of the improvement was due to the adaptive strategy and changing subject-specific models did not deteriorate the accuracies. Hence the adaptive strategy is effective in addressing the non-stationarity between calibration and feedback sessions.},
}
@article {pmid28055886,
year = {2017},
author = {Saeedi, S and Chavarriaga, R and Millan, JDR},
title = {Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {4},
pages = {380-391},
doi = {10.1109/TNSRE.2016.2645681},
pmid = {28055886},
issn = {1558-0210},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electrocardiography/*methods ; Evoked Potentials, Motor ; Humans ; Imagination ; Longitudinal Studies ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Quadriplegia/*physiopathology/rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control approaches used for BCIs and we review some of the recent studies at the light of these approaches. We posit that the level of assistance provided to the BCI user should be adjusted in real time in order to enhance BCI reliability over time. This approach has not been extensively studied in the recent literature on BCIs. In addition, we investigate the effectiveness of providing online adaptive assistance in a motor-imagery BCI for a tetraplegic end-user with an incomplete locked-in syndrome in a longitudinal study lasting 11 months. First, we report a reliable estimation of the BCI performance (in terms of command delivery time) using only a window of 1 s in the beginning of trials (AUC ≈ 0.8). Second, we demonstrate how adaptive shared control can exploit the output of the performance estimator to adjust online the level of assistance in a BCI game by regulating its speed. In particular, online adaptive assistance was superior to a fixed condition in terms of success rate (). Remarkably, the results exhibited a stable performance over severalmonths without recalibration of the BCI classifier or the performance estimator.},
}
@article {pmid28053764,
year = {2016},
author = {Yoshida, N and Hashimoto, Y and Shikota, M and Ota, T},
title = {Relief of neuropathic pain after spinal cord injury by brain-computer interface training.},
journal = {Spinal cord series and cases},
volume = {2},
number = {},
pages = {16021},
pmid = {28053764},
issn = {2058-6124},
abstract = {OBJECTIVES: The aim of this study was to report the effects of brain-computer interface (BCI) training, a neurofeedback rehabilitation technique, on persistent neuropathic pain (NP) after cervical spinal cord injury (SCI).<br><br>SUBJECTS AND METHODS: We present the case of a 71-year-old woman with NP in her left upper extremity after SCI (C8). She underwent BCI training as outpatient rehabilitation for 4 months to enhance event-related desynchronization (ERD), which is triggered by the patient's motor intuition. Scalp electroencephalography was recorded to observe the ERD during every BCI training session. The patient's pain was evaluated with the McGill Pain Questionnaire (MPQ) and a visual analog scale (VAS). The MPQ was performed after every BCI training session, and the patient assessed the VAS score on her own, once every few days during the BCI training period.<br><br>RESULTS: After the BCI training started, the patient's ERD during the BCI training period increased significantly, from 15.6-30.3%. Moreover, her VAS score decreased gradually, from 8 to 5, after the BCI training started, although the MPQ did not change significantly.<br><br>CONCLUSION: BCI training has the potential to provide relief for patients with persistent NP via brain plasticity, and to improve their activities of daily living and quality of life.},
}
@article {pmid28041983,
year = {2017},
author = {Erlbeck, H and Real, RG and Kotchoubey, B and Mattia, D and Bargak, J and Kübler, A},
title = {Basic discriminative and semantic processing in patients in the vegetative and minimally conscious state.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {113},
number = {},
pages = {8-16},
doi = {10.1016/j.ijpsycho.2016.12.012},
pmid = {28041983},
issn = {1872-7697},
mesh = {Adult ; Aged ; Auditory Perception/*physiology ; Discrimination, Psychological/*physiology ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Persistent Vegetative State/*physiopathology ; Semantics ; Speech Perception/physiology ; },
abstract = {Patients who survive injuries to the brain following accidents or diseases often acquire a disorder of consciousness (DOC). Assessment of the state of consciousness in these patients is difficult since they are usually incapable of reproducible motor movements. The application of event-related potentials (ERP) recorded via EEG constitutes one promising approach to complement the assessment of cognitive functions in DOC patients. For these assessments, a hierarchical approach was suggested which means that paradigms aiming at higher order ERPs are only presented if early responses were found. In this study, 19 behaviorally unresponsive or low-responsive DOC patients were presented with three auditory paradigms using passive instructions. The paradigms aimed at eliciting the Mismatch Negativity (MMN) and N400 and were applied at two time points. One oddball paradigm (MMN) and two semantic paradigms (word-pairs: N400 Words; sentences: N400 Sentences) were included. The majority of patients (n=15) did not show any response to the stimulation. In the MMN paradigm, an MMN was identified in two patients, in the N400 Words paradigm, only an N1 was identified in one patient, and in the N400 Sentences paradigm, a late positive complex (LPC) was identified in two patients. These data contradict the hierarchical approach since the LPC was identified in patients who did not exhibit an MMN. They further support the notion that even higher information processing as addressed with the N400 paradigms is preserved in a minority of DOC patients. Thus, in this sample, around 10% of the DOC patients exhibited indicators of preserved consciousness.},
}
@article {pmid28041692,
year = {2017},
author = {Arns, M and Batail, JM and Bioulac, S and Congedo, M and Daudet, C and Drapier, D and Fovet, T and Jardri, R and Le-Van-Quyen, M and Lotte, F and Mehler, D and Micoulaud-Franchi, JA and Purper-Ouakil, D and Vialatte, F and , },
title = {Neurofeedback: One of today's techniques in psychiatry?.},
journal = {L'Encephale},
volume = {43},
number = {2},
pages = {135-145},
doi = {10.1016/j.encep.2016.11.003},
pmid = {28041692},
issn = {0013-7006},
mesh = {Brain/physiopathology ; Brain Mapping/methods ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; Mental Disorders/diagnosis/physiopathology/psychology ; Neurofeedback/*methods/physiology ; Psychiatry/*methods/*trends ; },
abstract = {OBJECTIVES: Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry.<br><br>METHOD: Literature on neurofeedback and mental disorders but also on brain computer interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the Neurofeedback evaluation &amp; training (NExT) section of the French Association of biological psychiatry and neuropsychopharmacology (AFPBN).<br><br>RESULTS: Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics) and techniques (EEG, fMRI, signal processing).<br><br>CONCLUSION: The field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain computer interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice.},
}
@article {pmid28031396,
year = {2017},
author = {Eaton, RW and Libey, T and Fetz, EE},
title = {Operant conditioning of neural activity in freely behaving monkeys with intracranial reinforcement.},
journal = {Journal of neurophysiology},
volume = {117},
number = {3},
pages = {1112-1125},
pmid = {28031396},
issn = {1522-1598},
support = {P51 OD010425/OD/NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Conditioning, Operant/*physiology ; Electric Stimulation ; Electromyography ; Macaca nemestrina ; Male ; Motor Cortex/*physiology ; Neurons/*physiology ; Nucleus Accumbens/*physiology ; *Reinforcement, Psychology ; Reward ; Upper Extremity/physiology ; },
abstract = {Operant conditioning of neural activity has typically been performed under controlled behavioral conditions using food reinforcement. This has limited the duration and behavioral context for neural conditioning. To reward cell activity in unconstrained primates, we sought sites in nucleus accumbens (NAc) whose stimulation reinforced operant responding. In three monkeys, NAc stimulation sustained performance of a manual target-tracking task, with response rates that increased monotonically with increasing NAc stimulation. We recorded activity of single motor cortex neurons and documented their modulation with wrist force. We conditioned increased firing rates with the monkey seated in the training booth and during free behavior in the cage using an autonomous head-fixed recording and stimulating system. Spikes occurring above baseline rates triggered single or multiple electrical pulses to the reinforcement site. Such rate-contingent, unit-triggered stimulation was made available for periods of 1-3 min separated by 3-10 min time-out periods. Feedback was presented as event-triggered clicks both in-cage and in-booth, and visual cues were provided in many in-booth sessions. In-booth conditioning produced increases in single neuron firing probability with intracranial reinforcement in 48 of 58 cells. Reinforced cell activity could rise more than five times that of non-reinforced activity. In-cage conditioning produced significant increases in 21 of 33 sessions. In-cage rate changes peaked later and lasted longer than in-booth changes, but were often comparatively smaller, between 13 and 18% above non-reinforced activity. Thus intracranial stimulation reinforced volitional increases in cortical firing rates during both free behavior and a controlled environment, although changes in the latter were more robust.NEW &amp; NOTEWORTHY Closed-loop brain-computer interfaces (BCI) were used to operantly condition increases in muscle and neural activity in monkeys by delivering activity-dependent stimuli to an intracranial reinforcement site (nucleus accumbens). We conditioned increased firing rates with the monkeys seated in a training booth and also, for the first time, during free behavior in a cage using an autonomous head-fixed BCI.},
}
@article {pmid28029630,
year = {2017},
author = {Liu, X and Zhang, M and Richardson, AG and Lucas, TH and Van der Spiegel, J},
title = {Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {4},
pages = {729-742},
doi = {10.1109/TBCAS.2016.2622738},
pmid = {28029630},
issn = {1940-9990},
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Equipment Design ; Neurons/physiology ; Neurophysiology/*instrumentation ; Wireless Technology ; },
abstract = {This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm [2]. The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.},
}
@article {pmid28028495,
year = {2016},
author = {Pourahmad, A and Mahnam, A},
title = {Evaluation of a Low-cost and Low-noise Active Dry Electrode for Long-term Biopotential Recording.},
journal = {Journal of medical signals and sensors},
volume = {6},
number = {4},
pages = {197-202},
pmid = {28028495},
issn = {2228-7477},
abstract = {Wet Ag/AgCl electrodes, although very popular in clinical diagnosis, are not appropriate for expanding applications of wearable biopotential recording systems which are used repetitively and for a long time. Here, the development of a low-cost and low-noise active dry electrode is presented. The performance of the new electrodes was assessed for recording electrocardiogram (ECG) and electroencephalogram (EEG) in comparison with that of typical gel-based electrodes in a series of long-term recording experiments. The ECG signal recorded by these electrodes was well comparable with usual Ag/AgCl electrodes with a correlation up to 99.5% and mean power line noise below 6.0 μVRMS. The active electrodes were also used to measure alpha wave and steady state visual evoked potential by recording EEG. The recorded signals were comparable in quality with signals recorded by standard gel electrodes, suggesting that the designed electrodes can be employed in EEG-based rehabilitation systems and brain-computer interface applications. The mean power line noise in EEG signals recorded by the active electrodes (1.3 μVRMS) was statistically lower than when conventional gold cup electrodes were used (2.0 μVRMS) with a significant level of 0.05, and the new electrodes appeared to be more resistant to the electromagnetic interferences. These results suggest that the developed low-cost electrodes can be used to develop wearable monitoring systems for long-term biopotential recording.},
}
@article {pmid28026782,
year = {2017},
author = {Azghadi, MR and Linares-Barranco, B and Abbott, D and Leong, PH},
title = {A Hybrid CMOS-Memristor Neuromorphic Synapse.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {11},
number = {2},
pages = {434-445},
doi = {10.1109/TBCAS.2016.2618351},
pmid = {28026782},
issn = {1940-9990},
mesh = {Animals ; Electronics ; *Models, Neurological ; *Neural Networks, Computer ; Neurons ; *Semiconductors ; Synapses ; },
abstract = {Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics to realize a brain-inspired platform. This paper proposes a high-performance nano-scale Complementary Metal Oxide Semiconductor (CMOS)-memristive circuit, which mimics a number of essential learning properties of biological synapses. The proposed synaptic circuit that is composed of memristors and CMOS transistors, alters its memristance in response to timing differences among its pre- and post-synaptic action potentials, giving rise to a family of Spike Timing Dependent Plasticity (STDP). The presented design advances preceding memristive synapse designs with regards to the ability to replicate essential behaviours characterised in a number of electrophysiological experiments performed in the animal brain, which involve higher order spike interactions. Furthermore, the proposed hybrid device CMOS area is estimated as [Formula: see text] in a [Formula: see text] process-this represents a factor of ten reduction in area with respect to prior CMOS art. The new design is integrated with silicon neurons in a crossbar array structure amenable to large-scale neuromorphic architectures and may pave the way for future neuromorphic systems with spike timing-dependent learning features. These systems are emerging for deployment in various applications ranging from basic neuroscience research, to pattern recognition, to Brain-Machine-Interfaces.},
}
@article {pmid28026777,
year = {2017},
author = {Tidoni, E and Abu-Alqumsan, M and Leonardis, D and Kapeller, C and Fusco, G and Guger, C and Hintermuller, C and Peer, A and Frisoli, A and Tecchia, F and Bergamasco, M and Aglioti, SM},
title = {Local and Remote Cooperation With Virtual and Robotic Agents: A P300 BCI Study in Healthy and People Living With Spinal Cord Injury.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {9},
pages = {1622-1632},
doi = {10.1109/TNSRE.2016.2626391},
pmid = {28026777},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Female ; Humans ; Imagination ; Male ; *Man-Machine Systems ; Movement ; Reproducibility of Results ; Robotics/*methods ; Sensitivity and Specificity ; Spinal Cord Injuries/*physiopathology/*rehabilitation ; Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {The development of technological applications that allow people to control and embody external devices within social interaction settings represents a major goal for current and future brain-computer interface (BCI) systems. Prior research has suggested that embodied systems may ameliorate BCI end-user's experience and accuracy in controlling external devices. Along these lines, we developed an immersive P300-based BCI application with a head-mounted display for virtual-local and robotic-remote social interactions and explored in a group of healthy participants the role of proprioceptive feedback in the control of a virtual surrogate (Study 1). Moreover, we compared the performance of a small group of people with spinal cord injury (SCI) to a control group of healthy subjects during virtual and robotic social interactions (Study 2), where both groups received a proprioceptive stimulation. Our attempt to combine immersive environments, BCI technologies and neuroscience of body ownership suggests that providing realistic multisensory feedback still represents a challenge. Results have shown that healthy and people living with SCI used the BCI within the immersive scenarios with good levels of performance (as indexed by task accuracy, optimizations calls and Information Transfer Rate) and perceived control of the surrogates. Proprioceptive feedback did not contribute to alter performance measures and body ownership sensations. Further studies are necessary to test whether sensorimotor experience represents an opportunity to improve the use of future embodied BCI applications.},
}
@article {pmid28018162,
year = {2016},
author = {Boi, F and Moraitis, T and De Feo, V and Diotalevi, F and Bartolozzi, C and Indiveri, G and Vato, A},
title = {A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {563},
pmid = {28018162},
issn = {1662-4548},
abstract = {Bidirectional brain-machine interfaces (BMIs) establish a two-way direct communication link between the brain and the external world. A decoder translates recorded neural activity into motor commands and an encoder delivers sensory information collected from the environment directly to the brain creating a closed-loop system. These two modules are typically integrated in bulky external devices. However, the clinical support of patients with severe motor and sensory deficits requires compact, low-power, and fully implantable systems that can decode neural signals to control external devices. As a first step toward this goal, we developed a modular bidirectional BMI setup that uses a compact neuromorphic processor as a decoder. On this chip we implemented a network of spiking neurons built using its ultra-low-power mixed-signal analog/digital circuits. On-chip on-line spike-timing-dependent plasticity synapse circuits enabled the network to learn to decode neural signals recorded from the brain into motor outputs controlling the movements of an external device. The modularity of the BMI allowed us to tune the individual components of the setup without modifying the whole system. In this paper, we present the features of this modular BMI and describe how we configured the network of spiking neuron circuits to implement the decoder and to coordinate it with the encoder in an experimental BMI paradigm that connects bidirectionally the brain of an anesthetized rat with an external object. We show that the chip learned the decoding task correctly, allowing the interfaced brain to control the object's trajectories robustly. Based on our demonstration, we propose that neuromorphic technology is mature enough for the development of BMI modules that are sufficiently low-power and compact, while being highly computationally powerful and adaptive.},
}
@article {pmid28012854,
year = {2017},
author = {Miao, M and Zeng, H and Wang, A and Zhao, C and Liu, F},
title = {Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach.},
journal = {Journal of neuroscience methods},
volume = {278},
number = {},
pages = {13-24},
doi = {10.1016/j.jneumeth.2016.12.010},
pmid = {28012854},
issn = {1872-678X},
mesh = {Bayes Theorem ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Regression Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application.<br><br>NEW METHOD: This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Na&#xef;ve Bayesian Classifier (WNBC) for classification.<br><br>RESULTS: Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance.<br><br>The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature.<br><br>CONCLUSIONS: The proposed approach is a promising candidate for future BCI systems.},
}
@article {pmid28004644,
year = {2017},
author = {Yi, W and Qiu, S and Wang, K and Qi, H and Zhao, X and He, F and Zhou, P and Yang, J and Ming, D},
title = {Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP.},
journal = {Journal of neural engineering},
volume = {14},
number = {2},
pages = {026002},
doi = {10.1088/1741-2552/aa5559},
pmid = {28004644},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Feasibility Studies ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {OBJECTIVE: We proposed a novel simultaneous hybrid brain-computer interface (BCI) by incorporating electrical stimulation into a motor imagery (MI) based BCI system. The goal of this study was to enhance the overall performance of an MI-based BCI. In addition, the brain oscillatory pattern in the hybrid task was also investigated.<br><br>APPROACH: 64-channel electroencephalographic (EEG) data were recorded during MI, selective attention (SA) and hybrid tasks in fourteen healthy subjects. In the hybrid task, subjects performed MI with electrical stimulation which was applied to bilateral median nerve on wrists simultaneously.<br><br>MAIN RESULTS: The hybrid task clearly presented additional steady-state somatosensory evoked potential (SSSEP) induced by electrical stimulation with MI-induced event-related desynchronization (ERD). By combining ERD and SSSEP features, the performance in the hybrid task was significantly better than in both MI and SA tasks, achieving a ~14% improvement in total relative to the MI task alone and reaching ~89% in mean classification accuracy. On the contrary, there was no significant enhancement obtained in performance while separate ERD feature was utilized in the hybrid task. In terms of the hybrid task, the performance using combined feature was significantly better than using separate ERD or SSSEP feature.<br><br>SIGNIFICANCE: The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.},
}
@article {pmid28003656,
year = {2017},
author = {Sitaram, R and Ros, T and Stoeckel, L and Haller, S and Scharnowski, F and Lewis-Peacock, J and Weiskopf, N and Blefari, ML and Rana, M and Oblak, E and Birbaumer, N and Sulzer, J},
title = {Closed-loop brain training: the science of neurofeedback.},
journal = {Nature reviews. Neuroscience},
volume = {18},
number = {2},
pages = {86-100},
pmid = {28003656},
issn = {1471-0048},
mesh = {Animals ; Attention Deficit Disorder with Hyperactivity/therapy ; Brain/*physiology ; Humans ; Learning/*physiology ; Neurofeedback/*physiology ; Neuroimaging/methods ; Neuronal Plasticity/physiology ; Self-Control ; Stroke Rehabilitation/methods ; },
abstract = {Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain-machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.},
}
@article {pmid28003406,
year = {2017},
author = {Mirabella, G and Lebedev, M&#x410;},
title = {Interfacing to the brain's motor decisions.},
journal = {Journal of neurophysiology},
volume = {117},
number = {3},
pages = {1305-1319},
pmid = {28003406},
issn = {1522-1598},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Decision Making/*physiology ; Feedback, Sensory/*physiology ; Humans ; Motor Activity/*physiology ; },
abstract = {It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject's motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components.},
}
@article {pmid28000607,
year = {2017},
author = {Chien, YY and Lin, FC and Zao, JK and Chou, CC and Huang, YP and Kuo, HY and Wang, Y and Jung, TP and Shieh, HD},
title = {Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016018},
doi = {10.1088/1741-2552/aa550d},
pmid = {28000607},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; *Color ; Color Vision/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Flicker Fusion/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures. With the aim of designing visual stimuli that can be embedded into video images, this study developed a novel approach to induce detectable SSVEPs using a composition of red/green/blue flickering lights.<br><br>APPROACH: Based on the opponent theory of colour vision, this study used 32 Hz/40 Hz rectangular red-green or red-blue LED light pulses with a 50% duty cycle, balanced/equal luminance and 0&#xb0;/180&#xb0; phase shifts as the stimulating light sources and tested their efficacy in producing SSVEP responses with high signal-to-noise ratios (SNRs) while reducing the perceived flickering sensation.<br><br>MAIN RESULTS: The empirical results from ten healthy subjects showed that dual-colour lights flickering at 32 Hz/40 Hz with a 50% duty cycle and 180&#xb0; phase shift achieved a greater than 90% detection accuracy with little or no flickering sensation.<br><br>SIGNIFICANCE: As a first step in developing an embedded SSVEP stimulus in commercial displays, this study provides a foundation for developing a combination of three primary colour flickering backlights with adjustable luminance proportions to create a subtle flickering polychromatic light that can elicit SSVEPs at the basic flickering frequency.},
}
@article {pmid28000444,
year = {2017},
author = {Nakhleh, MK and Amal, H and Jeries, R and Broza, YY and Aboud, M and Gharra, A and Ivgi, H and Khatib, S and Badarneh, S and Har-Shai, L and Glass-Marmor, L and Lejbkowicz, I and Miller, A and Badarny, S and Winer, R and Finberg, J and Cohen-Kaminsky, S and Perros, F and Montani, D and Girerd, B and Garcia, G and Simonneau, G and Nakhoul, F and Baram, S and Salim, R and Hakim, M and Gruber, M and Ronen, O and Marshak, T and Doweck, I and Nativ, O and Bahouth, Z and Shi, DY and Zhang, W and Hua, QL and Pan, YY and Tao, L and Liu, H and Karban, A and Koifman, E and Rainis, T and Skapars, R and Sivins, A and Ancans, G and Liepniece-Karele, I and Kikuste, I and Lasina, I and Tolmanis, I and Johnson, D and Millstone, SZ and Fulton, J and Wells, JW and Wilf, LH and Humbert, M and Leja, M and Peled, N and Haick, H},
title = {Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules.},
journal = {ACS nano},
volume = {11},
number = {1},
pages = {112-125},
pmid = {28000444},
issn = {1936-086X},
mesh = {Adult ; Female ; Humans ; Male ; Middle Aged ; Artificial Intelligence ; Biosensing Techniques ; *Breath Tests ; Case-Control Studies ; *Disease/classification ; Gold/chemistry ; *Metal Nanoparticles/chemistry ; *Nanotubes, Carbon/chemistry ; *Pattern Recognition, Automated ; *Volatile Organic Compounds/analysis ; },
abstract = {We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.},
}
@article {pmid27999934,
year = {2017},
author = {Kira, S and Mitsui, T and Kobayashi, H and Haneda, Y and Sawada, N and Takeda, M},
title = {Detrusor pressures in urodynamic studies during voiding in women.},
journal = {International urogynecology journal},
volume = {28},
number = {5},
pages = {783-787},
pmid = {27999934},
issn = {1433-3023},
mesh = {Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Lower Urinary Tract Symptoms/*physiopathology ; Middle Aged ; Muscle Contraction/*physiology ; Pressure ; Retrospective Studies ; Statistics, Nonparametric ; Urethra/*physiology ; Urination/*physiology ; Urodynamics ; },
abstract = {INTRODUCTION AND HYPOTHESIS: To investigate detrusor pressure during voiding in women using urodynamic studies (UDS).<br><br>METHODS: The study group comprised 57 women with non-neurogenic lower urinary tract symptoms. All patients underwent UDS between January 2010 and December 2014. UDS included filling cystometry, pressure flow study (PFS), uroflowmetry for the maximum flow rate (Qmax) and mean flow rate, and postvoid residuals. Existence of voluntary detrusor contraction was defined as a continuous and smooth increase in detrusor pressure (Pdet) after the instruction to micturate in the PFS. The bladder contractility index (BCI) was calculated as Pdet at Qmax&#x2009;+&#x2009;5&#xa0;&#xd7;&#xa0;Qmax. Statistical analyses were performed using the Mann-Whitney U test and p&#x2009;<&#x2009;0.05 was considered statistically significant.<br><br>RESULTS: The PFS showed that 23 patients had detrusor contraction (Pdet+ group) and 34 patients had no detrusor contraction (Pdet- group) during voiding. There were no significant differences in urodynamic parameters between the Pdet+ and Pdet- groups except in Pdet at Qmax and BCI. In the Pdet-&#x2009;group, 21 patients showed an increase in abdominal pressure during voiding (Pabd+ group), while the other 13 patients did not (Pabd- group). There were no differences in any of the urodynamic parameters between the Pabd+ and Pabd- groups.<br><br>CONCLUSIONS: Based on UDS, an increase in detrusor or abdominal pressure may not be necessary in micturition in women. The present study suggests that relaxation of pelvic floor muscles including normal urethral function are important for micturition in women.},
}
@article {pmid27999538,
year = {2016},
author = {Rutkowski, TM},
title = {Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms.},
journal = {Frontiers in neurorobotics},
volume = {10},
number = {},
pages = {20},
pmid = {27999538},
issn = {1662-5218},
abstract = {The paper reviews nine robotic and virtual reality (VR) brain-computer interface (BCI) projects developed by the author, in collaboration with his graduate students, within the BCI-lab research group during its association with University of Tsukuba, Japan. The nine novel approaches are discussed in applications to direct brain-robot and brain-virtual-reality-agent control interfaces using tactile and auditory BCI technologies. The BCI user intentions are decoded from the brainwaves in realtime using a non-invasive electroencephalography (EEG) and they are translated to a symbiotic robot or virtual reality agent thought-based only control. A communication protocol between the BCI output and the robot or the virtual environment is realized in a symbiotic communication scenario using an user datagram protocol (UDP), which constitutes an internet of things (IoT) control scenario. Results obtained from healthy users reproducing simple brain-robot and brain-virtual-agent control tasks in online experiments support the research goal of a possibility to interact with robotic devices and virtual reality agents using symbiotic thought-based BCI technologies. An offline BCI classification accuracy boosting method, using a previously proposed information geometry derived approach, is also discussed in order to further support the reviewed robotic and virtual reality thought-based control paradigms.},
}
@article {pmid27992574,
year = {2016},
author = {Halme, HL and Parkkonen, L},
title = {Comparing Features for Classification of MEG Responses to Motor Imagery.},
journal = {PloS one},
volume = {11},
number = {12},
pages = {e0168766},
pmid = {27992574},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Female ; Functional Laterality ; Humans ; Imagery, Psychotherapy/*methods ; Imagination/*physiology ; Magnetoencephalography/*methods ; Male ; Movement/physiology ; Neurofeedback/methods ; Young Adult ; },
abstract = {BACKGROUND: Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.<br><br>METHODS: MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.<br><br>RESULTS: The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.<br><br>CONCLUSIONS: We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.},
}
@article {pmid27992435,
year = {2016},
author = {van der Kolk, BA and Hodgdon, H and Gapen, M and Musicaro, R and Suvak, MK and Hamlin, E and Spinazzola, J},
title = {A Randomized Controlled Study of Neurofeedback for Chronic PTSD.},
journal = {PloS one},
volume = {11},
number = {12},
pages = {e0166752},
pmid = {27992435},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Neurofeedback ; Research Design ; Stress Disorders, Post-Traumatic/diagnosis/*therapy ; Treatment Outcome ; },
abstract = {INTRODUCTION: Brain/Computer Interaction (BCI) devices are designed to alter neural signals and, thereby, mental activity. This study was a randomized, waitlist (TAU) controlled trial of a BCI, EEG neurofeedback training (NF), in patients with chronic PTSD to explore the capacity of NF to reduce PTSD symptoms and increase affect regulation capacities.<br><br>STUDY DESIGN: 52 individuals with chronic PTSD were randomized to either NF (n = 28) or waitlist (WL) (n = 24). They completed four evaluations, at baseline (T1), after week 6 (T2), at post-treatment (T3), and at one month follow up (T4). Assessment measures were:1. Traumatic Events Screening Inventory (T1); 2. the Clinician Administered PTSD Scale (CAPS; T1, T3, T4); 3. the Davidson Trauma Scale (DTS; T1-T4) and 4. the Inventory of Altered Self-Capacities (IASC; T1-T4). NF training occurred two times per week for 12 weeks and involved a sequential placement with T4 as the active site, P4 as the reference site.<br><br>RESULTS: Participants had experienced an average of 9.29 (SD = 2.90) different traumatic events. Post-treatment a significantly smaller proportion of NF (6/22, 27.3%) met criteria for PTSD than the WL condition (15/22, 68.2%), &#x3c7;2 (n = 44, df = 1) = 7.38, p = .007. There was a significant treatment condition x time interaction (b = -10.45, t = -5.10, p< .001). Measures of tension reduction activities, affect dysregulation, and affect instability exhibited a significant Time x Condition interaction. The effect sizes of NF (d = -2.33 within, d = - 1.71 between groups) are comparable to those reported for the most effective evidence based treatments for PTSD.<br><br>DISCUSSION: Compared with the control group NF produced significant PTSD symptom improvement in individuals with chronic PTSD, as well as in affect regulation capacities. NF deserves further investigation for its potential to ameliorate PTSD and to improve affect regulation, and to clarify its mechanisms of action.},
}
@article {pmid27990247,
year = {2016},
author = {Marquez-Chin, C and Marquis, A and Popovic, MR},
title = {BCI-Triggered Functional Electrical Stimulation Therapy for Upper Limb.},
journal = {European journal of translational myology},
volume = {26},
number = {3},
pages = {6222},
pmid = {27990247},
issn = {2037-7452},
abstract = {We present here the integration of brain-computer interfacing (BCI) technology with functional electrical stimulation therapy to restore voluntary function. The system was tested with a single man with chronic (6 years) severe left hemiplegia resulting from a stroke. The BCI, implemented as a simple "brain-switch" activated by power decreases in the 18 Hz - 28 Hz frequency range of the participant's electroencephalograpic signals, triggered a neuroprosthesis designed to facilitate forward reaching, reaching to the mouth, and lateral reaching movements. After 40 90-minute sessions in which the participant attempted the reaching tasks repeatedly, with the movements assisted by the BCI-triggered neuroprosthesis, the participant's arm function showed a clinically significant six point increase in the Fugl-Meyer Asessment Upper Extermity Sub-Score. These initial results suggest that the combined use of BCI and functional electrical stimulation therapy may restore voluntary reaching function in individuals with chronic severe hemiplegia for whom the rehabilitation alternatives are very limited.},
}
@article {pmid27990240,
year = {2016},
author = {Cho, W and Sabathiel, N and Ortner, R and Lechner, A and Irimia, DC and Allison, BZ and Edlinger, G and Guger, C},
title = {Paired Associative Stimulation Using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot Study.},
journal = {European journal of translational myology},
volume = {26},
number = {3},
pages = {6132},
pmid = {27990240},
issn = {2037-7452},
abstract = {Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses brain-computer interface (BCI) technology to monitor patients' movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar feedback for complete sensorimotor closed loop. To realize this approach, we introduce the recoveriX system, a hardware and software platform for PAS. After 10 sessions of recoveriX training, one stroke patient partially regained control of dorsiflexion in her paretic wrist. A controlled group study is planned with a new version of the recoveriX system, which will use a new FES system and an avatar instead of bar feedback.},
}
@article {pmid27979758,
year = {2017},
author = {Jiang, J and Marathe, AR and Keene, JC and Taylor, DM},
title = {A testbed for optimizing electrodes embedded in the skull or in artificial skull replacement pieces used after injury.},
journal = {Journal of neuroscience methods},
volume = {277},
number = {},
pages = {21-29},
pmid = {27979758},
issn = {1872-678X},
support = {R01 NS058871/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Craniocerebral Trauma/diagnostic imaging/*physiopathology/*surgery ; *Electrodes, Implanted ; Electroencephalography ; Imaging, Three-Dimensional ; Macaca mulatta ; Magnetic Resonance Imaging ; Ossicular Replacement/*methods ; Skull/*physiopathology ; Tomography, X-Ray Computed ; },
abstract = {BACKGROUND: Custom-fitted skull replacement pieces are often used after a head injury or surgery to replace damaged bone. Chronic brain recordings are beneficial after injury/surgery for monitoring brain health and seizure development. Embedding electrodes directly in these artificial skull replacement pieces would be a novel, low-risk way to perform chronic brain monitoring in these patients. Similarly, embedding electrodes directly in healthy skull would be a viable minimally-invasive option for many other neuroscience and neurotechnology applications requiring chronic brain recordings.<br><br>NEW METHOD: We demonstrate a preclinical testbed that can be used for refining electrode designs embedded in artificial skull replacement pieces or for embedding directly into the skull itself. Options are explored to increase the surface area of the contacts without increasing recording contact diameter to maximize recording resolution.<br><br>RESULTS: Embedding electrodes in real or artificial skull allows one to lower electrode impedance without increasing the recording contact diameter by making use of conductive channels that extend into the skull. The higher density of small contacts embedded in the artificial skull in this testbed enables one to optimize electrode spacing for use in real bone.<br><br>For brain monitoring applications, skull-embedded electrodes fill a gap between electroencephalograms recorded on the scalp surface and the more invasive epidural or subdural electrode sheets.<br><br>CONCLUSIONS: Embedding electrodes into the skull or in skull replacement pieces may provide a safe, convenient, minimally-invasive alternative for chronic brain monitoring. The manufacturing methods described here will facilitate further testing of skull-embedded electrodes in animal models.},
}
@article {pmid27966607,
year = {2016},
author = {Golovkine, G and Lemelle, L and Burny, C and Vaillant, C and Palierne, JF and Place, C and Huber, P},
title = {Host cell surfaces induce a Type IV pili-dependent alteration of bacterial swimming.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {38950},
pmid = {27966607},
issn = {2045-2322},
mesh = {Fimbriae, Bacterial/*metabolism ; Host-Pathogen Interactions/*physiology ; Human Umbilical Vein Endothelial Cells/*microbiology ; Humans ; Pseudomonas aeruginosa/*physiology ; },
abstract = {For most pathogenic bacteria, flagellar motility is recognized as a virulence factor. Here, we analysed the swimming behaviour of bacteria close to eukaryotic cellular surfaces, using the major opportunistic pathogen Pseudomonas aeruginosa as a model. We delineated three classes of swimming trajectories on both cellular surfaces and glass that could be differentiated by their speeds and local curvatures, resulting from different levels of hydrodynamic interactions with the surface. Segmentation of the trajectories into linear and curved sections or pause allowed us to precisely describe the corresponding swimming patterns near the two surfaces. We concluded that (i) the trajectory classes were of same nature on cells and glass, however the trajectory distribution was strikingly different between surface types, (ii) on cell monolayers, a larger fraction of bacteria adopted a swimming mode with stronger bacteria-surface interaction mostly dependent upon Type IV pili. Thus, bacteria swim near boundaries with diverse patterns and importantly, Type IV pili differentially influence swimming near cellular and abiotic surfaces.},
}
@article {pmid27966546,
year = {2016},
author = {Meng, J and Zhang, S and Bekyo, A and Olsoe, J and Baxter, B and He, B},
title = {Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {38565},
pmid = {27966546},
issn = {2045-2322},
support = {R01 AT009263/AT/NCCIH NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.},
}
@article {pmid27965067,
year = {2017},
author = {Naros, G and Gharabaghi, A},
title = {Physiological and behavioral effects of β-tACS on brain self-regulation in chronic stroke.},
journal = {Brain stimulation},
volume = {10},
number = {2},
pages = {251-259},
doi = {10.1016/j.brs.2016.11.003},
pmid = {27965067},
issn = {1876-4754},
mesh = {Adult ; Aged ; Beta Rhythm/*physiology ; Brain/*physiology ; Chronic Disease ; Electroencephalography/methods ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Motor Cortex/physiology ; Stroke/physiopathology/*psychology/*therapy ; Transcranial Direct Current Stimulation/*methods ; Treatment Outcome ; },
abstract = {BACKGROUND: Unlike in healthy controls, sensorimotor β-desynchronization (β-ERD) is compromised in stroke patients, i.e., the more severe the patient's motor impairment, the less β-ERD. This, in turn, provides a target substrate for therapeutic brain self-regulation and neurofeedback.<br><br>OBJECTIVE: Transcranial alternating current stimulation (tACS) has been shown to modulate brain oscillations during and after stimulation, and may thus facilitate brain self-regulation during neurofeedback interventions.<br><br>METHODS: Twenty severely impaired, chronic stroke patients performed kinesthetic motor-imagery while a brain-robot interface transformed &#x3b2;-ERD (17-23&#xa0;Hz) of the ipsilesional sensorimotor cortex into opening of the paralyzed hand by a robotic orthosis. In a parallel group design, &#x3b2;-tACS (20&#xa0;Hz, 1.1&#xa0;mA peak-to-peak amplitude) was applied to the lesioned motor cortex either continuously (c-tACS) before or intermittently (i-tACS) during the intervention. Physiological effects of &#x3b2;-tACS were studied using electroencephalography. The patients' ability for brain self-regulation was captured by neurofeedback performance metrics.<br><br>RESULTS: i-tACS - but not c-tACS - improved the classification accuracy of the neurofeedback intervention in comparison to baseline. This effect was mediated via the increased specificity of the classification, i.e., reduced variance of resting oscillations. Neither i-tACS nor c-tACS had aftereffects following the stimulation period.<br><br>CONCLUSION: &#x3b2;-tACS may constitute an adjunct neuromodulation technique during neurofeedback-based interventions for stroke rehabilitation.},
}
@article {pmid27959736,
year = {2016},
author = {Vansteensel, MJ and Pels, EGM and Bleichner, MG and Branco, MP and Denison, T and Freudenburg, ZV and Gosselaar, P and Leinders, S and Ottens, TH and Van Den Boom, MA and Van Rijen, PC and Aarnoutse, EJ and Ramsey, NF},
title = {Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS.},
journal = {The New England journal of medicine},
volume = {375},
number = {21},
pages = {2060-2066},
pmid = {27959736},
issn = {1533-4406},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Amyotrophic Lateral Sclerosis/complications/*rehabilitation ; Aphonia/etiology/*rehabilitation ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electrodes, Implanted ; Female ; Humans ; Middle Aged ; Motor Cortex ; Neurological Rehabilitation/instrumentation ; Quadriplegia/etiology/*rehabilitation ; },
abstract = {Options for people with severe paralysis who have lost the ability to communicate orally are limited. We describe a method for communication in a patient with late-stage amyotrophic lateral sclerosis (ALS), involving a fully implanted brain-computer interface that consists of subdural electrodes placed over the motor cortex and a transmitter placed subcutaneously in the left side of the thorax. By attempting to move the hand on the side opposite the implanted electrodes, the patient accurately and independently controlled a computer typing program 28 weeks after electrode placement, at the equivalent of two letters per minute. The brain-computer interface offered autonomous communication that supplemented and at times supplanted the patient's eye-tracking device. (Funded by the Government of the Netherlands and the European Union; ClinicalTrials.gov number, NCT02224469 .).},
}
@article {pmid27958328,
year = {2016},
author = {Long, J and Xie, Q and Ma, Q and Urbin, MA and Liu, L and Weng, L and Huang, X and Yu, R and Li, Y and Huang, R},
title = {Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {38866},
pmid = {27958328},
issn = {2045-2322},
support = {F32 NS086392/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/physiopathology ; Brain Mapping ; Female ; Frontal Lobe/*physiopathology ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Multivariate Analysis ; Neural Pathways/physiopathology ; Parietal Lobe/*physiopathology ; Persistent Vegetative State/*physiopathology ; },
abstract = {Existing evidence suggests that the default-mode network (DMN) and fronto-pariatal network (FPN) play an important role in altered states of consciousness. However, the brain mechanisms underlying impaired consciousness and the specific network interactions involved are not well understood. We studied the topological properties of brain functional networks using resting-state functional MRI data acquired from 18 patients (11 vegetative state/unresponsive wakefulness syndrome, VS/UWS, and 7 minimally conscious state, MCS) and compared these properties with those of healthy controls. We identified that the topological properties in DMN and FPN are anti-correlated which comes, in part, from the contribution of interactions between dorsolateral prefrontal cortex of the FPN and precuneus of the DMN. Notably, altered nodal connectivity strength was distance-dependent, with most disruptions appearing in long-distance connections within the FPN but in short-distance connections within the DMN. A multivariate pattern-classification analysis revealed that combination of topological patterns between the FPN and DMN could predict conscious state more effectively than connectivity within either network. Taken together, our results imply distinct interactions between the FPN and DMN, which may mediate conscious state.},
}
@article {pmid27958268,
year = {2016},
author = {Sussillo, D and Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Making brain-machine interfaces robust to future neural variability.},
journal = {Nature communications},
volume = {7},
number = {},
pages = {13749},
pmid = {27958268},
issn = {2041-1723},
mesh = {Animals ; Brain Mapping ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; *Nerve Net ; },
abstract = {A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.},
}
@article {pmid27957537,
year = {2016},
author = {Lee, SW and Fallegger, F and Casse, BD and Fried, SI},
title = {Implantable microcoils for intracortical magnetic stimulation.},
journal = {Science advances},
volume = {2},
number = {12},
pages = {e1600889},
pmid = {27957537},
issn = {2375-2548},
support = {I01 RX001663/RX/RRD VA/United States ; R01 EY023651/EY/NEI NIH HHS/United States ; U01 NS099700/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Axons/physiology ; Brain/cytology ; Computer Simulation ; *Electric Stimulation ; *Electrodes, Implanted ; In Vitro Techniques ; *Magnetics ; Mice, Inbred C57BL ; Neurons/*physiology ; },
abstract = {Neural prostheses that stimulate the neocortex have the potential to treat a wide range of neurological disorders. However, the efficacy of electrode-based implants remains limited, with persistent challenges that include an inability to create precise patterns of neural activity as well as difficulties in maintaining response consistency over time. These problems arise from fundamental limitations of electrodes as well as their susceptibility to implantation and have proven difficult to overcome. Magnetic stimulation can address many of these limitations, but coils small enough to be implanted into the cortex were not thought strong enough to activate neurons. We describe a new microcoil design and demonstrate its effectiveness for both activating cortical neurons and driving behavioral responses. The stimulation of cortical pyramidal neurons in brain slices in vitro was reliable and could be confined to spatially narrow regions (<60 μm). The spatially asymmetric fields arising from the coil helped to avoid the simultaneous activation of passing axons. In vivo implantation was safe and resulted in consistent and predictable behavioral responses. The high permeability of magnetic fields to biological substances may yield another important advantage because it suggests that encapsulation and other adverse effects of implantation will not diminish coil performance over time, as happens to electrodes. These findings suggest that a coil-based implant might be a useful alternative to existing electrode-based devices. The enhanced selectivity of microcoil-based magnetic stimulation will be especially useful for visual prostheses as well as for many brain-computer interface applications that require precise activation of the cortex.},
}
@article {pmid27957487,
year = {2016},
author = {Izzidien, A and Ramaraju, S and Roula, MA and McCarthy, PW},
title = {Effect of Anodal-tDCS on Event-Related Potentials: A Controlled Study.},
journal = {BioMed research international},
volume = {2016},
number = {},
pages = {1584947},
pmid = {27957487},
issn = {2314-6141},
mesh = {Analysis of Variance ; Brain/physiopathology ; Double-Blind Method ; Electroencephalography ; *Event-Related Potentials, P300 ; *Evoked Potentials ; Female ; Humans ; Male ; Models, Statistical ; Neurological Rehabilitation ; Signal Processing, Computer-Assisted ; Time Factors ; *Transcranial Direct Current Stimulation ; Treatment Outcome ; Young Adult ; },
abstract = {We aim to measure the postintervention effects of A-tDCS (anodal-tDCS) on brain potentials commonly used in BCI applications, namely, Event-Related Desynchronization (ERD), Event-Related Synchronization (ERS), and P300. Ten subjects were given sham and 1.5 mA A-tDCS for 15 minutes on two separate experiments in a double-blind, randomized order. Postintervention EEG was recorded while subjects were asked to perform a spelling task based on the "oddball paradigm" while P300 power was measured. Additionally, ERD and ERS were measured while subjects performed mental motor imagery tasks. ANOVA results showed that the absolute P300 power exhibited a statistically significant difference between sham and A-tDCS when measured over channel Pz (p = 0.0002). However, the difference in ERD and ERS power was found to be statistically insignificant, in controversion of the the mainstay of the litrature on the subject. The outcomes confirm the possible postintervention effect of tDCS on the P300 response. Heightening P300 response using A-tDCS may help improve the accuracy of P300 spellers for neurologically impaired subjects. Additionally, it may help the development of neurorehabilitation methods targeting the parietal lobe.},
}
@article {pmid27956633,
year = {2016},
author = {Zander, TO and Krol, LR and Birbaumer, NP and Gramann, K},
title = {Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {113},
number = {52},
pages = {14898-14903},
pmid = {27956633},
issn = {1091-6490},
mesh = {Adult ; Brain/physiology ; Computer Systems ; Computers ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; *Machine Learning ; Male ; Prefrontal Cortex/physiology ; *User-Computer Interface ; },
abstract = {The effectiveness of today's human-machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responses were evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from the medial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our understanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator's mindset. Neuroadaptive technology significantly widens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.},
}
@article {pmid27941960,
year = {2016},
author = {Cantillo-Negrete, J and Carino-Escobar, RI and Carrillo-Mora, P and Flores-Rodríguez, TB and Elías-Vinas, D and Gutiérrez-Martínez, J},
title = {Gender Differences in Quantitative Electroencephalogram During a Simple Hand Movement Task in Young Adults.},
journal = {Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion},
volume = {68},
number = {5},
pages = {245-255},
pmid = {27941960},
issn = {0034-8376},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Linear Models ; Male ; Movement/*physiology ; Sex Factors ; Young Adult ; },
abstract = {BACKGROUND: No consensus has been reached regarding the existence of gender differences during motor tasks in electroencephalography. This could lead to misinterpretation of electroencephalography clinical diagnosis and affect the calibration of brain-computer interfaces.<br><br>OBJECTIVE: To assess whether there are statistically significant gender differences in electroencephalography recorded during hand movements.<br><br>METHODS: Electroencephalography data were recorded from 18 women and 18 men while performing hand movements and rest. Electroencephalography power was computed for alpha (8-13 Hz), beta (14-30 Hz), and a broader band including alpha and beta (8-30 Hz) using wavelet transform. Statistical analysis was done using a General Linear Model for repeated measurements (&#x3b1; = 0.05). Additionally, topographic maps were computed for each gender.<br><br>RESULTS: Significant gender differences were found for the rest condition in all analyzed bands. For the hand movement tasks, gender differences were mainly found in the beta band and located in temporoparietal areas. Power decrease observed in topographic maps was located in the centro-parietal areas for females and the centro-frontal areas for males. Additionally, greater power decreases were observed for women in all analyzed frequency bands.<br><br>CONCLUSION: Electroencephalography parameters used for the diagnosis of neuromotor diseases, as well as for brain-computer interface calibration, must take gender into account.},
}
@article {pmid27941631,
year = {2016},
author = {Chang, YJ and Hwang, WJ and Chen, CC},
title = {A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {12},
pages = {},
pmid = {27941631},
issn = {1424-8220},
abstract = {The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.},
}
@article {pmid27940042,
year = {2017},
author = {Joshi, AM and Narayan, EJ and Gramapurohit, NP},
title = {Interrelationship among steroid hormones, energetics and vocalisation in the Bombay night frog (Nyctibatrachus humayuni).},
journal = {General and comparative endocrinology},
volume = {246},
number = {},
pages = {142-149},
doi = {10.1016/j.ygcen.2016.12.003},
pmid = {27940042},
issn = {1095-6840},
mesh = {Androgens/*urine ; Animals ; Breeding ; Corticosterone/*urine ; Energy Metabolism/*physiology ; Male ; Ranidae/*physiology ; *Seasons ; Testosterone/*urine ; Vocalization, Animal/*physiology ; },
abstract = {In vertebrates, the increase in plasma androgens and corticosteroids is essential for the expression of reproductive behaviour. In male anurans, the interaction between hypothalamus-pituitary-gonadal and hypothalamus-pituitary-interrenal axes plays a pivotal role in calling behaviour and energy mobilisation through the secretion of testosterone and corticosterone respectively. To explain the association among body condition, testosterone, corticosterone and calling behaviour the energetic-hormone-vocalisation (EHV) model has been proposed. The model predicts that with continued participation in chorus activity within and across nights, levels of circulating androgens, corticosterone and vocal effort tend to increase and should be positively correlated in calling males. Consequently, decreasing energy reserves should be inversely correlated with corticosterone level in calling males. Depleted energy reserves lead to the peaking of circulating corticosterone, which suppresses androgen production and calling behaviour. In the present study, we used Nyctibatrachus humayuni with unique reproductive behaviour to test the model by quantifying calling behaviour and urinary metabolites of testosterone and corticosterone. We also computed the body condition index (BCI) to assess the association among energetics, levels of testosterone, corticosterone and calling behaviour. The results show that calling males had higher levels of urinary testosterone metabolites (UTM) than non-calling males indicating the importance of testosterone in controlling the calling behaviour. Surprisingly, urinary corticosterone metabolite (UCM) levels were comparable between calling and non-calling males. Further, calling males had higher body condition estimates than non-calling males. The vocal effort was neither associated with UTM, UCM nor BCI. However, a positive association was observed between UTM and UCM levels in calling males indicating the requirement of higher energy for advertisement. Analysis of UTM and UCM levels throughout the breeding season revealed that breeding basal of UTM was significantly higher than that of UCM. Interestingly, UCM levels were maintained at a lower threshold during the breeding season. These observations are in line with some of the predictions of EHV model.},
}
@article {pmid27938804,
year = {2017},
author = {Ling, G and Sanchez, J and Pankratz, K and Clifford, D},
title = {Editorial of Special Issue: Bio-electronics and prosthetics for neurological diseases.},
journal = {Experimental neurology},
volume = {287},
number = {Pt 4},
pages = {435-436},
doi = {10.1016/j.expneurol.2016.11.013},
pmid = {27938804},
issn = {1090-2430},
mesh = {Animals ; *Biotechnology ; *Brain-Computer Interfaces ; *Electronics ; Humans ; Nervous System Diseases/rehabilitation/*surgery ; *Prostheses and Implants ; },
}
@article {pmid27934776,
year = {2017},
author = {McFarland, DJ and Parvaz, MA and Sarnacki, WA and Goldstein, RZ and Wolpaw, JR},
title = {Prediction of subjective ratings of emotional pictures by EEG features.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016009},
pmid = {27934776},
issn = {1741-2552},
support = {R01 DA041528/DA/NIDA NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R21 DA034954/DA/NIDA NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; F32 DA033088/DA/NIDA NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Algorithms ; Arousal/*physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Emotions/*physiology ; Feasibility Studies ; Humans ; Middle Aged ; Pattern Recognition, Automated/*methods ; Photic Stimulation/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli.<br><br>APPROACH: To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings.<br><br>MAIN RESULTS: Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization.<br><br>SIGNIFICANCE: The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.},
}
@article {pmid27930672,
year = {2016},
author = {Ma, X and Huang, X and Ge, Y and Hu, Y and Chen, W and Liu, A and Liu, H and Chen, Y and Li, B and Ning, X},
title = {Brain Connectivity Variation Topography Associated with Working Memory.},
journal = {PloS one},
volume = {11},
number = {12},
pages = {e0165168},
pmid = {27930672},
issn = {1932-6203},
mesh = {Brain/anatomy &amp; histology/*physiology ; *Brain Mapping ; Electroencephalography ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Young Adult ; },
abstract = {Brain connectivity analysis plays an essential role in the research of working memory that involves complex coordination of various brain regions. In this research, we present a comprehensive view of trans-states brain connectivity variation based on continuous scalp EEG, extending beyond traditional stimuli-lock averaging or restriction to short time scales of hundreds of milliseconds after stimulus onset. The scalp EEG was collected under three conditions: quiet, memory, and control. The only difference between the memory and control conditions was that in the memory condition, subjects made an effort to retain information. We started our investigation with calibrations of Pearson correlation in EEG analysis and then derived two indices, link strength and node connectivity, to make comparisons between different states. Finally, we constructed and studied trans-state brain connectivity variation topography. Comparing memory and control states with quiet state, we found that the beta topography highlights links between T5/T6 and O1/O2, which represents the visual ventral stream, and the gamma topography conveys strengthening of inter-hemisphere links and weakening of intra-hemisphere frontal-posterior links, implying parallel inter-hemisphere coordination combined with sequential intra-hemisphere coordination when subjects are confronted with visual stimuli and a motor task. For comparison between memory and control states, we also found that the node connectivity of T6 stands out in gamma topography, which provides strong proof from scalp EEG for the information binding or relational processing function of the temporal lobe in memory formation. To our knowledge, this is the first time for any method to effectively capture brain connectivity variation associated with working memory from a relatively large scale both in time (from a second to a minute) and in space (from the scalp). The method can track brain activity continuously with minimal manual interruptions; therefore, it has promising potential in applications such as brain computer interfaces and brain training.},
}
@article {pmid27929077,
year = {2016},
author = {Eugster, MJA and Ruotsalo, T and Spapé, MM and Barral, O and Ravaja, N and Jacucci, G and Kaski, S},
title = {Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {38580},
pmid = {27929077},
issn = {2045-2322},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; *Databases, Factual ; Electroencephalography ; Evoked Potentials ; Humans ; Internet ; Reading ; },
abstract = {Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual's search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users' interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications.},
}
@article {pmid27926827,
year = {2017},
author = {Branco, MP and Freudenburg, ZV and Aarnoutse, EJ and Bleichner, MG and Vansteensel, MJ and Ramsey, NF},
title = {Decoding hand gestures from primary somatosensory cortex using high-density ECoG.},
journal = {NeuroImage},
volume = {147},
number = {},
pages = {130-142},
pmid = {27926827},
issn = {1095-9572},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Adult ; Brain Mapping ; Brain-Computer Interfaces ; Electrocorticography/*methods ; Electrodes, Implanted ; Epilepsy/surgery ; Female ; Gamma Rhythm ; *Gestures ; Hand ; Humans ; Male ; Middle Aged ; Motor Cortex/physiology ; *Sign Language ; Somatosensory Cortex/*physiology ; Wavelet Analysis ; Young Adult ; },
abstract = {Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) have been proposed as a way to restore and replace motor function or communication in severely paralyzed people. To date, most motor-based BCIs have either focused on the sensorimotor cortex as a whole or on the primary motor cortex (M1) as a source of signals for this purpose. Still, target areas for BCI are not confined to M1, and more brain regions may provide suitable BCI control signals. A logical candidate is the primary somatosensory cortex (S1), which not only shares similar somatotopic organization to M1, but also has been suggested to have a role beyond sensory feedback during movement execution. Here, we investigated whether four complex hand gestures, taken from the American sign language alphabet, can be decoded exclusively from S1 using both spatial and temporal information. For decoding, we used the signal recorded from a small patch of cortex with subdural high-density (HD) grids in five patients with intractable epilepsy. Notably, we introduce a new method of trial alignment based on the increase of the electrophysiological response, which virtually eliminates the confounding effects of systematic and non-systematic temporal differences within and between gestures execution. Results show that S1 classification scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex as a whole (85%), and significantly above chance level (25%). We conclude that S1 offers characteristic spatiotemporal neuronal activation patterns that are discriminative between gestures, and that it is possible to decode gestures with high accuracy from a very small patch of cortex using subdurally implanted HD grids. The feasibility of decoding hand gestures using HD-ECoG grids encourages further investigation of implantable BCI systems for direct interaction between the brain and external devices with multiple degrees of freedom.},
}
@article {pmid27918836,
year = {2017},
author = {Loewe, L and Scheuer, KS and Keel, SA and Vyas, V and Liblit, B and Hanlon, B and Ferris, MC and Yin, J and Dutra, I and Pietsch, A and Javid, CG and Moog, CL and Meyer, J and Dresel, J and McLoone, B and Loberger, S and Movaghar, A and Gilchrist-Scott, M and Sabri, Y and Sescleifer, D and Pereda-Zorrilla, I and Zietlow, A and Smith, R and Pietenpol, S and Goldfinger, J and Atzen, SL and Freiberg, E and Waters, NP and Nusbaum, C and Nolan, E and Hotz, A and Kliman, RM and Mentewab, A and Fregien, N and Loewe, M},
title = {Evolvix BEST Names for semantic reproducibility across code2brain interfaces.},
journal = {Annals of the New York Academy of Sciences},
volume = {1387},
number = {1},
pages = {124-144},
pmid = {27918836},
issn = {1749-6632},
support = {R01 GM086445/GM/NIGMS NIH HHS/United States ; T32 GM007133/GM/NIGMS NIH HHS/United States ; T32 HG002760/HG/NHGRI NIH HHS/United States ; },
mesh = {*Biological Ontologies ; *Brain-Computer Interfaces/standards/trends ; Cloud Computing/standards ; Computational Biology/instrumentation/*methods/standards/trends ; Data Mining/trends ; Humans ; Internet ; Programming Languages ; Reproducibility of Results ; Software ; Software Design ; Terminology as Topic ; },
abstract = {Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general-purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long-term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder-brains to reader-brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.},
}
@article {pmid27918413,
year = {2016},
author = {Tang, Z and Sun, S and Zhang, S and Chen, Y and Li, C and Chen, S},
title = {A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {12},
pages = {},
pmid = {27918413},
issn = {1424-8220},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Foot/physiology ; Hand/physiology ; Humans ; Movement/physiology ; Upper Extremity/physiology ; },
abstract = {To recognize the user's motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.},
}
@article {pmid27917107,
year = {2016},
author = {Blankertz, B and Acqualagna, L and Dähne, S and Haufe, S and Schultze-Kraft, M and Sturm, I and Ušćumlic, M and Wenzel, MA and Curio, G and Müller, KR},
title = {The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {530},
pmid = {27917107},
issn = {1662-4548},
abstract = {The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.},
}
@article {pmid27917105,
year = {2016},
author = {Shishkin, SL and Nuzhdin, YO and Svirin, EP and Trofimov, AG and Fedorova, AA and Kozyrskiy, BL and Velichkovsky, BM},
title = {EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {528},
pmid = {27917105},
issn = {1662-4548},
abstract = {We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200-500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for paralyzed and healthy users, the EBCI "Wish Mouse."},
}
@article {pmid27915127,
year = {2017},
author = {Elsawy, AS and Eldawlatly, S and Taher, M and Aly, GM},
title = {MindEdit: A P300-based text editor for mobile devices.},
journal = {Computers in biology and medicine},
volume = {80},
number = {},
pages = {97-106},
doi = {10.1016/j.compbiomed.2016.11.014},
pmid = {27915127},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; *Mobile Applications ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; *Smartphone ; },
abstract = {Practical application of Brain-Computer Interfaces (BCIs) requires that the whole BCI system be portable. The mobility of BCI systems involves two aspects: making the electroencephalography (EEG) recording devices portable, and developing software applications with low computational complexity to be able to run on low computational-power devices such as tablets and smartphones. This paper addresses the development of MindEdit; a P300-based text editor for Android-based devices. Given the limited resources of mobile devices and their limited computational power, a novel ensemble classifier is utilized that uses Principal Component Analysis (PCA) features to identify P300 evoked potentials from EEG recordings. PCA computations in the proposed method are channel-based as opposed to concatenating all channels as in traditional feature extraction methods; thus, this method has less computational complexity compared to traditional P300 detection methods. The performance of the method is demonstrated on data recorded from MindEdit on an Android tablet using the Emotiv wireless neuroheadset. Results demonstrate the capability of the introduced PCA ensemble classifier to classify P300 data with maximum average accuracy of 78.37±16.09% for cross-validation data and 77.5±19.69% for online test data using only 10 trials per symbol and a 33-character training dataset. Our analysis indicates that the introduced method outperforms traditional feature extraction methods. For a faster operation of MindEdit, a variable number of trials scheme is introduced that resulted in an online average accuracy of 64.17±19.6% and a maximum bitrate of 6.25bit/min. These results demonstrate the efficacy of using the developed BCI application with mobile devices.},
}
@article {pmid27914951,
year = {2017},
author = {Safiri, S and Ayubi, E},
title = {Linguistic and psychometric validation of the Malaysian version of Diabetes Quality of Life-Brief Clinical Inventory (DQoL-BCI): Methodological issues to avoid to misinterpretation.},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {13},
number = {2},
pages = {398},
doi = {10.1016/j.sapharm.2016.11.004},
pmid = {27914951},
issn = {1934-8150},
mesh = {*Linguistics ; Psychometrics ; *Quality of Life ; Surveys and Questionnaires ; },
}
@article {pmid27914171,
year = {2017},
author = {Hwang, HJ and Han, CH and Lim, JH and Kim, YW and Choi, SI and An, KO and Lee, JH and Cha, HS and Hyun Kim, S and Im, CH},
title = {Clinical feasibility of brain-computer interface based on steady-state visual evoked potential in patients with locked-in syndrome: Case studies.},
journal = {Psychophysiology},
volume = {54},
number = {3},
pages = {444-451},
doi = {10.1111/psyp.12793},
pmid = {27914171},
issn = {1469-8986},
mesh = {*Brain-Computer Interfaces ; Cerebral Cortex/*physiopathology ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; Quadriplegia/*physiopathology/*therapy ; Reproducibility of Results ; },
abstract = {Although the feasibility of brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) has been extensively investigated, only a few studies have evaluated its clinical feasibility in patients with locked-in syndrome (LIS), who are the main targets of BCI technology. The main objective of this case report was to share our experiences of SSVEP-based BCI experiments involving five patients with LIS, thereby providing researchers with useful information that can potentially help them to design BCI experiments for patients with LIS. In our experiments, a four-class online SSVEP-based BCI system was implemented and applied to four of five patients repeatedly on multiple days to investigate its test-retest reliability. In the last experiments with two of the four patients, the practical usability of our BCI system was tested using a questionnaire survey. All five patients showed clear and distinct SSVEP responses at all four fundamental stimulation frequencies (6, 6.66, 7.5, 10 Hz), and responses at harmonic frequencies were also observed in three patients. Mean classification accuracy was 76.99% (chance level = 25%). The test-retest reliability experiments demonstrated stable performance of our BCI system over different days even when the initial experimental settings (e.g., electrode configuration, fixation time, visual angle) used in the first experiment were used without significant modifications. Our results suggest that SSVEP-based BCI paradigms might be successfully used to implement clinically feasible BCI systems for severely paralyzed patients.},
}
@article {pmid27912170,
year = {2017},
author = {Aliakbaryhosseinabadi, S and Kostic, V and Pavlovic, A and Radovanovic, S and Nlandu Kamavuako, E and Jiang, N and Petrini, L and Dremstrup, K and Farina, D and Mrachacz-Kersting, N},
title = {Influence of attention alternation on movement-related cortical potentials in healthy individuals and stroke patients.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {128},
number = {1},
pages = {165-175},
doi = {10.1016/j.clinph.2016.11.001},
pmid = {27912170},
issn = {1872-8952},
mesh = {Acoustic Stimulation/methods ; Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Stroke/*diagnosis/physiopathology ; Young Adult ; },
abstract = {OBJECTIVE: In this study, we analyzed the influence of artificially imposed attention variations using the auditory oddball paradigm on the cortical activity associated to motor preparation/execution.<br><br>METHODS: EEG signals from Cz and its surrounding channels were recorded during three sets of ankle dorsiflexion movements. Each set was interspersed with either a complex or a simple auditory oddball task for healthy participants and a complex auditory oddball task for stroke patients.<br><br>RESULTS: The amplitude of the movement-related cortical potentials (MRCPs) decreased with the complex oddball paradigm, while MRCP variability increased. Both oddball paradigms increased the detection latency significantly (p<0.05) and the complex paradigm decreased the true positive rate (TPR) (p=0.04). In patients, the negativity of the MRCP decreased while pre-phase variability increased, and the detection latency and accuracy deteriorated with attention diversion.<br><br>CONCLUSION: Attention diversion has a significant influence on MRCP features and detection parameters, although these changes were counteracted by the application of the laplacian method.<br><br>SIGNIFICANCE: Brain-computer interfaces for neuromodulation that use the MRCP as the control signal are robust to changes in attention. However, attention must be monitored since it plays a key role in plasticity induction. Here we demonstrate that this can be achieved using the single channel Cz.},
}
@article {pmid27909307,
year = {2017},
author = {Wood, H},
title = {Motor neuron disease: Brain-computer interface unlocks the mind of a patient with ALS.},
journal = {Nature reviews. Neurology},
volume = {13},
number = {1},
pages = {6},
pmid = {27909307},
issn = {1759-4766},
mesh = {*Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Humans ; *Motor Neuron Disease ; },
}
@article {pmid27900953,
year = {2017},
author = {Willett, FR and Pandarinath, C and Jarosiewicz, B and Murphy, BA and Memberg, WD and Blabe, CH and Saab, J and Walter, BL and Sweet, JA and Miller, JP and Henderson, JM and Shenoy, KV and Simeral, JD and Hochberg, LR and Kirsch, RF and Ajiboye, AB},
title = {Feedback control policies employed by people using intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016001},
pmid = {27900953},
issn = {1741-2552},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 HD077220/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/methods/*physiology ; Brain/*physiology ; Computer Simulation ; Evoked Potentials, Motor/physiology ; Feedback, Physiological/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; *Models, Neurological ; Movement/*physiology ; Pilot Projects ; *Task Performance and Analysis ; },
abstract = {OBJECTIVE: When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders.<br><br>APPROACH: We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI.<br><br>MAIN RESULTS: We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high.<br><br>SIGNIFICANCE: Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.},
}
@article {pmid27900952,
year = {2017},
author = {Tabar, YR and Halici, U},
title = {A novel deep learning approach for classification of EEG motor imagery signals.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016003},
doi = {10.1088/1741-2560/14/1/016003},
pmid = {27900952},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/*physiology ; *Machine Learning ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; },
abstract = {OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals.<br><br>APPROACH: In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE.<br><br>MAIN RESULTS: The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition.<br><br>SIGNIFICANCE: Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.},
}
@article {pmid27900950,
year = {2017},
author = {Lührs, M and Sorger, B and Goebel, R and Esposito, F},
title = {Automated selection of brain regions for real-time fMRI brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016004},
doi = {10.1088/1741-2560/14/1/016004},
pmid = {27900950},
issn = {1741-2552},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Computer Systems ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Nerve Net/*physiology ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; *Unsupervised Machine Learning ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Word Processing ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps.<br><br>MAIN RESULTS: Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances.&#x2002;Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.},
}
@article {pmid27900947,
year = {2017},
author = {Flint, RD and Rosenow, JM and Tate, MC and Slutzky, MW},
title = {Continuous decoding of human grasp kinematics using epidural and subdural signals.},
journal = {Journal of neural engineering},
volume = {14},
number = {1},
pages = {016005},
pmid = {27900947},
issn = {1741-2552},
support = {KL2 TR001424/TR/NCATS NIH HHS/United States ; R01 NS094748/NS/NINDS NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Dura Mater/physiology ; Electrocorticography/*methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Hand Strength/*physiology ; Humans ; Middle Aged ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Restoring or replacing function in paralyzed individuals will one day be achieved through the use of brain-machine interfaces. Regaining hand function is a major goal for paralyzed patients. Two competing prerequisites for the widespread adoption of any hand neuroprosthesis are accurate control over the fine details of movement, and minimized invasiveness. Here, we explore the interplay between these two goals by comparing our ability to decode hand movements with subdural and epidural field potentials (EFPs).<br><br>APPROACH: We measured the accuracy of decoding continuous hand and finger kinematics during naturalistic grasping motions in five human subjects. We recorded subdural surface potentials (electrocorticography; ECoG) as well as with EFPs, with both standard- and high-resolution electrode arrays.<br><br>MAIN RESULTS: In all five subjects, decoding of continuous kinematics significantly exceeded chance, using either EGoG or EFPs. ECoG decoding accuracy compared favorably with prior investigations of grasp kinematics (mean &#xb1; SD grasp aperture variance accounted for was 0.54&#xa0;&#xb1;&#xa0;0.05 across all subjects, 0.75&#xa0;&#xb1;&#xa0;0.09 for the best subject). In general, EFP decoding performed comparably to ECoG decoding. The 7-20 Hz and 70-115 Hz spectral bands contained the most information about grasp kinematics, with the 70-115 Hz band containing greater information about more subtle movements. Higher-resolution recording arrays provided clearly superior performance compared to standard-resolution arrays.<br><br>SIGNIFICANCE: To approach the fine motor control achieved by an intact brain-body system, it will be necessary to execute motor intent on a continuous basis with high accuracy. The current results demonstrate that this level of accuracy might be achievable not just with ECoG, but with EFPs as well. Epidural placement of electrodes is less invasive, and therefore may incur less risk of encephalitis or stroke than subdural placement of electrodes. Accurately decoding motor commands at the epidural level may be an important step towards a clinically viable brain-machine interface.},
}
@article {pmid27899774,
year = {2016},
author = {Araki, K and Ito, Y},
title = {[A Review Multigene Assays for Clinical Utility in Breast Cancer].},
journal = {Gan to kagaku ryoho. Cancer &amp; chemotherapy},
volume = {43},
number = {11},
pages = {1332-1340},
pmid = {27899774},
issn = {0385-0684},
mesh = {Breast Neoplasms/diagnosis/*genetics ; Gene Expression Profiling ; *Gene Expression Regulation, Neoplastic ; Humans ; Ki-67 Antigen/genetics ; Prognosis ; Recurrence ; },
abstract = {Multigene assays that simultaneously measure the expression of various breast cancer genes have been developed to guide the use of adjuvant chemotherapy in early breast cancer. The efficacy of adjuvant therapies depends on the recurrence risk for an individual patient. As a result, accurate prediction of the recurrence risk is vital for precise adjuvant chemotherapy in individual breast cancer patients. The recurrence risk as typically assessed by conventional examination of histological data of immuno-histological biomarkers(ER, PR, HER2, and Ki-67)is not sufficient to select subsets of patients. Therefore, validated gene expression signatures are useful, in addition to well-established clinico-pathological factors. Available gene expression assay systems, such as MammaPrint®, Oncotype DX®, PAM50 ROR, GGI, EndoPredict®(EP), Breast Cancer IndexSM(BCI), and Curebest®95GC Breast, are recommended. While MammaPrint®and Oncotype DX®are the most predictive of the recurrence risk within the first 5 years of diagnosis, BCI and EPclin have demonstrated utility in predicting late recurrence. In addition, PAM50 provides further biological insights by classifying breast cancers into intrinsic molecular subtypes. These gene expression signatures have become important tools in clinical practice for the identification of low-risk endocrine-positive patients in whom chemotherapy could be avoided. However, there is much work left in the development of a molecular classification considering the biology and novel therapeutic targets in high-risk recurrent disease because chemotherapy has only modest benefits in this population. The recognition of genomic mutations and their relationship to a patient's responsiveness to various therapies will provide important breakthroughs toward precision medicine.},
}
@article {pmid27899311,
year = {2017},
author = {Horesh, SJ and Sivan, J and Rosenstrauch, A and Tesler, I and Degen, AA and Kam, M},
title = {Seasonal biotic and abiotic factors affecting hunting strategy in free-living Saharan sand vipers, Cerastes vipera.},
journal = {Behavioural processes},
volume = {135},
number = {},
pages = {40-44},
doi = {10.1016/j.beproc.2016.11.013},
pmid = {27899311},
issn = {1872-8308},
mesh = {Age Factors ; Animals ; Israel ; *Periodicity ; Predatory Behavior/*physiology ; Viperidae/*physiology ; },
abstract = {Sit-and-wait ambushing and active hunting are two strategies used by predators to capture prey. In snakes, hunting strategy is conserved phylogenetically; most species employ only one strategy. Active hunters encounter and capture more prey but invest more energy in hunting and have higher risks of being predated. This trade-off is important to small predators. The small Cerastes vipera employs both modes of hunting, which is unlike most viperids which use only sit-and wait ambushing. This species hibernates in October and emerges in April. Energy intake should be high prior to hibernation to overcome the non-feeding hibernation period and for reproduction on their emergence. We predicted that more individuals would hunt actively towards hibernation and an abiotic factor would trigger this response. Furthermore, since more energy is required for active hunting, we predicted that snakes in good body condition would use active hunting to a greater extent than snakes in poor body condition. To test our predictions, we tracked free-living snakes year round and determined their hunting strategy, estimated their body condition index (BCI), and calculated circannual parameters of day length as environmental cues known to affect animal behaviour. Two novel findings emerged in this study, namely, hunting strategy was affected significantly by 1) the circannual change in day length and 2) by BCI. The proportion of active hunters increased from 5% in April to over 30% in October and BCI of active foragers was higher than that of sit-and-wait foragers and, therefore, our predictions were supported. The entrainment between the proportion of active hunting and the abiotic factor is indicative of an adaptive function for choosing a hunting strategy. A trend was evident among life stages. When all life stages were present (September-October), the proportion of active foragers increased with age: 0.0% among neonates, 18.2% among juveniles and 31.4% among adults. We concluded that vulnerable small neonates used sit-and-wait ambush not only as a hunting strategy but also as a hiding technique.},
}
@article {pmid27895567,
year = {2016},
author = {Lugo, ZR and Quitadamo, LR and Bianchi, L and Pellas, F and Veser, S and Lesenfants, D and Real, RG and Herbert, C and Guger, C and Kotchoubey, B and Mattia, D and Kübler, A and Laureys, S and Noirhomme, Q},
title = {Cognitive Processing in Non-Communicative Patients: What Can Event-Related Potentials Tell Us?.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {569},
pmid = {27895567},
issn = {1662-5161},
abstract = {Event-related potentials (ERP) have been proposed to improve the differential diagnosis of non-responsive patients. We investigated the potential of the P300 as a reliable marker of conscious processing in patients with locked-in syndrome (LIS). Eleven chronic LIS patients and 10 healthy subjects (HS) listened to a complex-tone auditory oddball paradigm, first in a passive condition (listen to the sounds) and then in an active condition (counting the deviant tones). Seven out of nine HS displayed a P300 waveform in the passive condition and all in the active condition. HS showed statistically significant changes in peak and area amplitude between conditions. Three out of seven LIS patients showed the P3 waveform in the passive condition and five of seven in the active condition. No changes in peak amplitude and only a significant difference at one electrode in area amplitude were observed in this group between conditions. We conclude that, in spite of keeping full consciousness and intact or nearly intact cortical functions, compared to HS, LIS patients present less reliable results when testing with ERP, specifically in the passive condition. We thus strongly recommend applying ERP paradigms in an active condition when evaluating consciousness in non-responsive patients.},
}
@article {pmid27891199,
year = {2016},
author = {Zhang, Y and Guo, D and Xu, P and Zhang, Y and Yao, D},
title = {Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index.},
journal = {Cognitive neurodynamics},
volume = {10},
number = {6},
pages = {505-511},
pmid = {27891199},
issn = {1871-4080},
abstract = {Multivariate synchronization index (MSI) has been proved to be an efficient method for frequency recognition in SSVEP-BCI systems. It measures the correlation according to the entropy of the normalized eigenvalues of the covariance matrix of multichannel signals. In the MSI method, the estimation of covariance matrix omits the temporally local structure of samples. In this study, a new spatio-temporal method, termed temporally local MSI (TMSI), was presented. This new method explicitly exploits temporally local information in modelling the covariance matrix. In order to evaluate the performance of the TMSI, we performs a comparison between the two methods on the real SSVEP datasets from eleven subjects. The results show that the TMSI outperforms the standard MSI. TMSI benefits from exploiting the temporally local structure of EEG signals, and could be a potential method for robust performance of SSVEP-based BCI.},
}
@article {pmid27891083,
year = {2016},
author = {Kleih, SC and Gottschalt, L and Teichlein, E and Weilbach, FX},
title = {Toward a P300 Based Brain-Computer Interface for Aphasia Rehabilitation after Stroke: Presentation of Theoretical Considerations and a Pilot Feasibility Study.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {547},
pmid = {27891083},
issn = {1662-5161},
abstract = {People with post-stroke motor aphasia know what they would like to say but cannot express it through motor pathways due to disruption of cortical circuits. We present a theoretical background for our hypothesized connection between attention and aphasia rehabilitation and suggest why in this context, Brain-Computer Interface (BCI) use might be beneficial for patients diagnosed with aphasia. Not only could BCI technology provide a communication tool, it might support neuronal plasticity by activating language circuits and thereby boost aphasia recovery. However, stroke may lead to heterogeneous symptoms that might hinder BCI use, which is why the feasibility of this approach needs to be investigated first. In this pilot study, we included five participants diagnosed with post-stroke aphasia. Four participants were initially unable to use the visual P300 speller paradigm. By adjusting the paradigm to their needs, participants could successfully learn to use the speller for communication with accuracies up to 100%. We describe necessary adjustments to the paradigm and present future steps to investigate further this approach.},
}
@article {pmid27882256,
year = {2016},
author = {Marquez-Chin, C and Marquis, A and Popovic, MR},
title = {EEG-Triggered Functional Electrical Stimulation Therapy for Restoring Upper Limb Function in Chronic Stroke with Severe Hemiplegia.},
journal = {Case reports in neurological medicine},
volume = {2016},
number = {},
pages = {9146213},
pmid = {27882256},
issn = {2090-6668},
abstract = {We report the therapeutic effects of integrating brain-computer interfacing technology and functional electrical stimulation therapy to restore upper limb reaching movements in a 64-year-old man with severe left hemiplegia following a hemorrhagic stroke he sustained six years prior to this study. He completed 40 90-minute sessions of functional electrical stimulation therapy using a custom-made neuroprosthesis that facilitated 5 different reaching movements. During each session, the participant attempted to reach with his paralyzed arm repeatedly. Stimulation for each of the movement phases (e.g., extending and retrieving the arm) was triggered when the power in the 18 Hz-28 Hz range (beta frequency range) of the participant's EEG activity, recorded with a single electrode, decreased below a predefined threshold. The function of the participant's arm showed a clinically significant improvement in the Fugl-Meyer Assessment Upper Extremity (FMA-UE) subscore (6 points) as well as moderate improvement in Functional Independence Measure Self-Care subscore (7 points). The changes in arm's function suggest that the combination of BCI technology and functional electrical stimulation therapy may restore voluntary motor function in individuals with chronic hemiplegia which results in severe upper limb deficit (FMA-UE ≤ 15), a population that does not benefit from current best-practice rehabilitation interventions.},
}
@article {pmid27881756,
year = {2016},
author = {Mander, L},
title = {A combinatorial approach to angiosperm pollen morphology.},
journal = {Proceedings. Biological sciences},
volume = {283},
number = {1843},
pages = {},
pmid = {27881756},
issn = {1471-2954},
mesh = {Magnoliopsida/*physiology ; Panama ; Pollen/*anatomy &amp; histology ; Tropical Climate ; },
abstract = {Angiosperms (flowering plants) are strikingly diverse. This is clearly expressed in the morphology of their pollen grains, which are characterized by enormous variety in their shape and patterning. In this paper, I approach angiosperm pollen morphology from the perspective of enumerative combinatorics. This involves generating angiosperm pollen morphotypes by algorithmically combining character states and enumerating the results of these combinations. I use this approach to generate 3 643 200 pollen morphotypes, which I visualize using a parallel-coordinates plot. This represents a raw morphospace. To compare real-world and theoretical morphologies, I map the pollen of 1008 species of Neotropical angiosperms growing on Barro Colorado Island (BCI), Panama, onto this raw morphospace. This highlights that, in addition to their well-documented taxonomic diversity, Neotropical rainforests also represent an enormous reservoir of morphological diversity. Angiosperm pollen morphospace at BCI has been filled mostly by pollen morphotypes that are unique to single plant species. Repetition of pollen morphotypes among higher taxa at BCI reflects both constraint and convergence. This combinatorial approach to morphology addresses the complexity that results from large numbers of discrete character combinations and could be employed in any situation where organismal form can be captured by discrete morphological characters.},
}
@article {pmid27880768,
year = {2016},
author = {Bocquelet, F and Hueber, T and Girin, L and Savariaux, C and Yvert, B},
title = {Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces.},
journal = {PLoS computational biology},
volume = {12},
number = {11},
pages = {e1005119},
pmid = {27880768},
issn = {1553-7358},
mesh = {Biofeedback, Psychology/instrumentation/*methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Systems ; Humans ; *Neural Networks, Computer ; Phonetics ; Sound Spectrography/instrumentation/*methods ; Speech Acoustics ; Speech Intelligibility ; Speech Production Measurement/instrumentation/*methods ; },
abstract = {Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer.},
}
@article {pmid27875232,
year = {2017},
author = {Wu, C and Lin, K and Wu, W and Gao, X},
title = {A Novel Algorithm for Learning Sparse Spatio-Spectral Patterns for Event-Related Potentials.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {28},
number = {4},
pages = {862-872},
doi = {10.1109/TNNLS.2015.2496284},
pmid = {27875232},
issn = {2162-2388},
abstract = {Recent years have witnessed brain-computer interface (BCI) as a promising technology for integrating human intelligence and machine intelligence. Currently, event-related potential (ERP)-based BCI is an important branch of noninvasive electroencephalogram (EEG)-based BCIs. Extracting ERPs from a limited number of trials remains challenging due to their low signal-to-noise ratio (SNR) and low spatial resolution caused by volume conduction. In this paper, we propose a probabilistic model for trial-by-trial concatenated EEG, in which the concatenated ERPs are expressed as a linear combination of a set of discrete sine and cosine bases. The bases are simply determined by the data length of a single trial. A sparse prior on the rank of the spatio-spectral pattern matrix is introduced into the model to allow the number of components to be automatically determined. A maximum posterior estimation algorithm based on cyclic descent is then developed to estimate the spatiospectral patterns. A spatial filter can then be obtained by maximizing the SNR of the ERP components. Experiments on both synthetic data and real N170 ERP from 13 subjects were conducted to test the efficacy and efficiency of the algorithm. The results showed that the proposed algorithm can estimate the ERPs more accurately than the several state-of-the-art algorithms.},
}
@article {pmid27867508,
year = {2016},
author = {House, G and Burdea, G and Grampurohit, N and Polistico, K and Roll, D and Damiani, F and Hundal, J and Demesmin, D},
title = {A feasibility study to determine the benefits of upper extremity virtual rehabilitation therapy for coping with chronic pain post-cancer surgery.},
journal = {British journal of pain},
volume = {10},
number = {4},
pages = {186-197},
pmid = {27867508},
issn = {2049-4637},
support = {R43 DA032224/DA/NIDA NIH HHS/United States ; },
abstract = {BACKGROUND: Persistent pain in shoulder and arm following post-surgical breast cancer treatment can lead to cognitive and physical deficits. Depression is also common in breast cancer survivors. Virtual reality therapy with integrative cognitive and physical rehabilitation has not been clinically trialed for this population. The novel BrightArm Duo technology improved cognition and upper extremity (UE) function for other diagnoses and has great potential to benefit individuals coping with post-surgical breast cancer pain.<br><br>OBJECTIVES: The aim of this study was to explore the feasibility of BrightArm Duo therapy for coping with post-surgical chronic pain and associated disability in breast cancer survivors with depression.<br><br>METHODS: BrightArm Duo is a robotic rehabilitation table modulating gravity loading on supported forearms. It tracks arm position and grasping strength while patients play three-dimensional (3D) custom integrative rehabilitation games. Community-dwelling women (N&#x2009;=&#x2009;6) with post-surgical breast cancer pain in the upper arm trained on the system twice a week for 8&#x2009;weeks. Training difficulty increased progressively in game complexity, table tilt and session length (20-50&#x2009;minutes). Standardized assessments were performed before and after therapy for pain, cognition, emotion, UE function and activities of daily living.<br><br>RESULTS: Subjects averaged upwards of 1300 arm repetitions and 850 hand grasps per session. Pain intensity showed a 20% downward trend (p&#x2009;=&#x2009;0.1) that was corroborated by therapist observations and participant feedback. A total of 10 out of 11 cognitive metrics improved post-training (p&#x2009;=&#x2009;0.01) with a significant 8.3-point reduction in depression severity (p&#x2009;=&#x2009;0.04). A total of 17 of 18 range of motion metrics increased (p&#x2009;<&#x2009;0.01), with five affected-side shoulder improvements above the Minimal Clinically Important Difference (8&#xb0;). In all, 13 out of 15 strength and function metrics improved (p&#x2009;=&#x2009;0.02) with lateral deltoid strength increasing 7.4&#x2009;N on the affected side (p&#x2009;=&#x2009;0.05).<br><br>CONCLUSION: This pilot study demonstrated feasibility of using the BrightArm Duo Rehabilitation System to treat cancer survivors coping with upper body chronic pain. Outcomes indicate improvement in cognition, shoulder range, strength, function and depression.},
}
@article {pmid27867090,
year = {2017},
author = {Keitel, C and Thut, G and Gross, J},
title = {Visual cortex responses reflect temporal structure of continuous quasi-rhythmic sensory stimulation.},
journal = {NeuroImage},
volume = {146},
number = {},
pages = {58-70},
pmid = {27867090},
issn = {1095-9572},
support = {098433//Wellcome Trust/United Kingdom ; 098434//Wellcome Trust/United Kingdom ; },
mesh = {Adult ; Attention/physiology ; *Brain Waves ; Electroencephalography ; Female ; Humans ; Male ; Periodicity ; Photic Stimulation ; Reaction Time ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Neural processing of dynamic continuous visual input, and cognitive influences thereon, are frequently studied in paradigms employing strictly rhythmic stimulation. However, the temporal structure of natural stimuli is hardly ever fully rhythmic but possesses certain spectral bandwidths (e.g. lip movements in speech, gestures). Examining periodic brain responses elicited by strictly rhythmic stimulation might thus represent ideal, yet isolated cases. Here, we tested how the visual system reflects quasi-rhythmic stimulation with frequencies continuously varying within ranges of classical theta (4-7Hz), alpha (8-13Hz) and beta bands (14-20Hz) using EEG. Our findings substantiate a systematic and sustained neural phase-locking to stimulation in all three frequency ranges. Further, we found that allocation of spatial attention enhances EEG-stimulus locking to theta- and alpha-band stimulation. Our results bridge recent findings regarding phase locking ("entrainment") to quasi-rhythmic visual input and "frequency-tagging" experiments employing strictly rhythmic stimulation. We propose that sustained EEG-stimulus locking can be considered as a continuous neural signature of processing dynamic sensory input in early visual cortices. Accordingly, EEG-stimulus locking serves to trace the temporal evolution of rhythmic as well as quasi-rhythmic visual input and is subject to attentional bias.},
}
@article {pmid27865859,
year = {2017},
author = {Kress, C and Sadowski, G and Brandenbusch, C},
title = {Solubilization of proteins in aqueous two-phase extraction through combinations of phase-formers and displacement agents.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {112},
number = {},
pages = {38-44},
doi = {10.1016/j.ejpb.2016.11.016},
pmid = {27865859},
issn = {1873-3441},
mesh = {Immunoglobulin G/*chemistry/isolation &amp; purification ; Polyethylene Glycols/chemistry ; Serum Albumin/*chemistry/isolation &amp; purification ; Solubility ; Water ; },
abstract = {The aqueous two-phase extraction (ATPE) of therapeutic proteins is a promising separation alternative to cost-intensive chromatography, still being the workhorse of nowadays downstream processing. As shown in many publications, using NaCl as displacement agent in salt-polymer ATPE allows for a selective purification of the target protein immunoglobulin G (IgG) from human serum albumin (HSA, represents the impurity). However a high yield of the target protein is only achievable as long as the protein is stabilized in solution and not precipitated. In this work the combined influence of NaCl and polyethylene glycol (Mw=2000g/mol) on the IgG-IgG interactions was determined using composition gradient multi-angle light scattering (CG-MALS) demonstrating that NaCl induces a solubilization of IgG in polyethylene glycol 2000 solution. Moreover it is shown that the displacement agent NaCl has a significant and beneficial influence on the IgG solubility in polyethyleneglycol2000-citrate aqueous two-phase system (ATPS) which can also be accessed by these advanced B22 measurements. By simultaneous consideration of IgG solubility data with results of the ATPS phase behavior (especially volume fraction of the respective phases) allows for the selection of process tailored ATPS including identification of the maximum protein feed concentration. Through this approach an ATPS optimization is accessible providing high yields and selectivity of the target protein (IgG).},
}
@article {pmid27861407,
year = {2016},
author = {Liu, J and Abd-El-Barr, M and Chi, JH},
title = {Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients.},
journal = {Neurosurgery},
volume = {79},
number = {6},
pages = {N13-N14},
doi = {10.1227/01.neu.0000508601.15824.39},
pmid = {27861407},
issn = {1524-4040},
mesh = {*Brain-Computer Interfaces ; *Gait ; Humans ; Paraplegia ; },
}
@article {pmid27858227,
year = {2017},
author = {Mora, N and De Munari, I and Ciampolini, P and Del R Millán, J},
title = {Plug&amp;Play Brain-Computer Interfaces for effective Active and Assisted Living control.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {8},
pages = {1339-1352},
pmid = {27858227},
issn = {1741-0444},
mesh = {Brain Mapping/instrumentation/methods ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Visual/*physiology ; Reproducibility of Results ; *Self-Help Devices ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Brain-Computer Interfaces (BCI) rely on the interpretation of brain activity to provide people with disabilities with an alternative/augmentative interaction path. In light of this, BCI could be considered as enabling technology in many fields, including Active and Assisted Living (AAL) systems control. Interaction barriers could be removed indeed, enabling user with severe motor impairments to gain control over a wide range of AAL features. In this paper, a cost-effective BCI solution, targeted (but not limited) to AAL system control is presented. A custom hardware module is briefly reviewed, while signal processing techniques are covered in more depth. Steady-state visual evoked potentials (SSVEP) are exploited in this work as operating BCI protocol. In contrast with most common SSVEP-BCI approaches, we propose the definition of a prediction confidence indicator, which is shown to improve overall classification accuracy. The confidence indicator is derived without any subject-specific approach and is stable across users: it can thus be defined once and then shared between different persons. This allows some kind of Plug&amp;Play interaction. Furthermore, by modelling rest/idle periods with the confidence indicator, it is possible to detect active control periods and separate them from "background activity": this is capital for real-time, self-paced operation. Finally, the indicator also allows to dynamically choose the most appropriate observation window length, improving system's responsiveness and user's comfort. Good results are achieved under such operating conditions, achieving, for instance, a false positive rate of 0.16 min[-1], which outperform current literature findings.},
}
@article {pmid27857680,
year = {2016},
author = {Werner, T and Vianello, E and Bichler, O and Garbin, D and Cattaert, D and Yvert, B and De Salvo, B and Perniola, L},
title = {Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {474},
pmid = {27857680},
issn = {1662-4548},
abstract = {In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.},
}
@article {pmid27852775,
year = {2016},
author = {Seeber, M and Scherer, R and Müller-Putz, GR},
title = {EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {36},
number = {46},
pages = {11671-11681},
pmid = {27852775},
issn = {1529-2401},
mesh = {Adult ; Biological Clocks/*physiology ; Brain Waves/*physiology ; Female ; Fingers/*physiology ; Humans ; Male ; Movement/*physiology ; Nerve Net/physiology ; *Periodicity ; Sensorimotor Cortex/*physiology ; Task Performance and Analysis ; },
abstract = {UNLABELLED: Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior.<br><br>SIGNIFICANCE STATEMENT: EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from movement phase-related amplitude modulations. We separate these two EEG source elements motivated by our previous findings in gait. Here, we found two types of large-scale networks, representing the right fingers in distinction from the time sequence of the movements. These findings suggest that EEG source amplitudes reconstructed in a cortical patch are the superposition of these simultaneously present network activities. Separating these frequency-specific networks is relevant for studying function and possible dysfunction of the cortical sensorimotor system in humans as well as to provide more advanced features for brain-computer interfaces.},
}
@article {pmid27849545,
year = {2017},
author = {Shin, J and von Luhmann, A and Blankertz, B and Kim, DW and Jeong, J and Hwang, HJ and Muller, KR},
title = {Open Access Dataset for EEG+NIRS Single-Trial Classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1735-1745},
doi = {10.1109/TNSRE.2016.2628057},
pmid = {27849545},
issn = {1558-0210},
abstract = {We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we con-ducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be en-hanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.},
}
@article {pmid27849544,
year = {2017},
author = {Fu, Y and Xiong, X and Jiang, C and Xu, B and Li, Y and Li, H},
title = {Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {9},
pages = {1641-1652},
doi = {10.1109/TNSRE.2016.2627809},
pmid = {27849544},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Hand Strength/*physiology ; Hemoglobins/analysis ; Humans ; Imagination/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spectrophotometry, Infrared/*methods ; Stress, Mechanical ; Task Performance and Analysis ; Young Adult ; },
abstract = {Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were placed midway between the NIRS probes. NIRS and EEG signals were acquired from six healthy subjects during six imagined hand clenching force and speed tasks involving the right hand. The results showed that NIRS combined with EEG is effective for simultaneously measuring brain activity of the sensorimotor area. The study also showed that in the duration of (0, 10) s for imagined force and speed of hand clenching, HbO first exhibited a negative variation trend, which was followed by a negative peak. After the negative peak, it exhibited a positive variation trend with a positive peak about 6-8 s after termination of imagined movement. During (-2, 1) s, the EEG may have indicated neural processing during the preparation, execution, and monitoring of a given imagined force and speed of hand clenching. The instantaneous phase, frequency, and amplitude feature of the EEG were calculated by Hilbert transform; HbO and the difference between HbO and Hb concentrations were extracted. The features of NIRS and EEG were combined to classify three levels of imagined force [at 20/50/80% MVGF (maximum voluntary grip force)] and speed (at 0.5/1/2 Hz) of hand clenching by SVM. The average classification accuracy of the NIRS-EEG fusion feature was 0.74 ± 0.02. These results may provide increased control commands of force and speed for a brain-controlled robot based on NIRS-EEG.},
}
@article {pmid27849543,
year = {2017},
author = {Wang, Y and Chen, X and Gao, X and Gao, S},
title = {A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1746-1752},
doi = {10.1109/TNSRE.2016.2627556},
pmid = {27849543},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Algorithms ; Benchmarking ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Communication Devices for People with Disabilities ; Computer Simulation ; Databases, Factual ; Electric Stimulation ; Electrodes, Implanted ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain- computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was . For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available fromhttp://bci.med.tsinghua.edu.cn/download.html.},
}
@article {pmid27845668,
year = {2017},
author = {Formaggio, E and Masiero, S and Bosco, A and Izzi, F and Piccione, F and Del Felice, A},
title = {Quantitative EEG Evaluation During Robot-Assisted Foot Movement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {9},
pages = {1633-1640},
doi = {10.1109/TNSRE.2016.2627058},
pmid = {27845668},
issn = {1558-0210},
mesh = {*Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Equipment Failure Analysis ; Exoskeleton Device ; Female ; Foot/*physiology ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Neurological Rehabilitation/instrumentation/methods ; Range of Motion, Articular/physiology ; Robotics/instrumentation/*methods ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {Passiveand imagined limbmovements induce changes in cerebral oscillatory activity. Central modulatory effects play a role in plastic changes, and are of uttermost importance in rehabilitation. This has extensively been studied for upper limb, but less is known for lower limb. The aim of this study is to investigate the topographical distribution of event-related desynchronization/synchronization(ERD/ERS) and task-relatedcoherence during a robot-assisted and a motor imagery task of lower limb in healthy subjects to inform rehabilitation paradigms. 32-channels electroencephalogram (EEG) was recorded in twenty-one healthy right footed and handed subjects during a robot-assisted single-joint cyclic right ankle movement performed by the BTS ANYMOV robotic hospital bed. Data were acquired with a block protocol for passive and imagined movement at a frequency of 0.2 Hz. ERD/ERS and task related coherence were calculated in alpha1 (8-10 Hz), alpha2 (10.5-12.5 Hz) and beta (13-30 Hz) frequency ranges. During passive movement, alpha2 rhythm desynchronized overC3 and ipsilateral frontal areas (F4, FC2, FC6); betaERD was detected over the bilateral motor areas (Cz, C3, C4). During motor imagery, a significant desynchronization was evident for alpha1 over contralateral sensorimotor cortex (C3), for alpha2 over bilateral motor areas (C3 and C4), and for beta over central scalp areas. Task-related coherence decreased during passive movement in alpha2 band between contralateral central area (C3, CP5, CP1, P3) and ipsilateral frontal area (F8, FC6, T8); beta band coherence decreased between C3-C4 electrodes, and increased between C3-Cz. These data contribute to the understanding of oscillatory activity and functional neuronal interactions during lower limb robot-assisted motor performance. The final output of this line of research is to inform the design and development of neurorehabilitation protocols.},
}
@article {pmid27845666,
year = {2017},
author = {Yger, F and Berar, M and Lotte, F},
title = {Riemannian Approaches in Brain-Computer Interfaces: A Review.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {10},
pages = {1753-1762},
doi = {10.1109/TNSRE.2016.2627016},
pmid = {27845666},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*classification/methods ; Equipment Design ; Humans ; *Models, Theoretical ; Signal Processing, Computer-Assisted ; },
abstract = {Although promising from numerous applications, current brain-computer interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of electroencephalographic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.},
}
@article {pmid27845150,
year = {2017},
author = {Ma, T and Li, H and Yang, H and Lv, X and Li, P and Liu, T and Yao, D and Xu, P},
title = {The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.},
journal = {Journal of neuroscience methods},
volume = {275},
number = {},
pages = {80-92},
doi = {10.1016/j.jneumeth.2016.11.002},
pmid = {27845150},
issn = {1872-678X},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Machine Learning ; Male ; Motion Perception/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control.<br><br>NEW METHOD: In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance.<br><br>RESULTS: The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features.<br><br>Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance.<br><br>CONCLUSIONS: According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300.},
}
@article {pmid27841163,
year = {2016},
author = {Myrden, A and Chau, T},
title = {Towards psychologically adaptive brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066022},
doi = {10.1088/1741-2560/13/6/066022},
pmid = {27841163},
issn = {1741-2552},
mesh = {*Adaptation, Psychological ; Adult ; Algorithms ; Attention/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Equipment Design ; Female ; Frustration ; Humans ; Male ; Maze Learning ; Mental Fatigue/psychology ; Psychomotor Performance ; Reproducibility of Results ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes.<br><br>APPROACH: Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample.<br><br>MAIN RESULTS: Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size.<br><br>SIGNIFICANCE: Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.},
}
@article {pmid27841159,
year = {2016},
author = {Fomina, T and Lohmann, G and Erb, M and Ethofer, T and Schölkopf, B and Grosse-Wentrup, M},
title = {Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066021},
doi = {10.1088/1741-2560/13/6/066021},
pmid = {27841159},
issn = {1741-2552},
mesh = {Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; Artifacts ; *Brain-Computer Interfaces ; Cognition ; *Communication Devices for People with Disabilities ; *Electroencephalography ; Gamma Rhythm ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neurofeedback ; Parietal Lobe/*physiopathology ; Psychomotor Performance ; Theta Rhythm ; },
abstract = {OBJECTIVE: Electroencephalographic (EEG) brain-computer interfaces (BCIs) hold promise in restoring communication for patients with completely locked-in stage amyotrophic lateral sclerosis (ALS). However, these patients cannot use existing EEG-based BCIs, arguably because such systems rely on brain processes that are impaired in the late stages of ALS. In this work, we introduce a novel BCI designed for patients in late stages of ALS based on high-level cognitive processes that are less likely to be affected by ALS.<br><br>APPROACH: We trained two ALS patients via EEG-based neurofeedback to use self-regulation of theta or gamma oscillations in the precuneus for basic communication. Because there is a tight connection between the precuneus and consciousness, precuneus oscillations are arguably generated by high-level cognitive processes, which are less likely to be affected by ALS than processes linked to the peripheral nervous system.<br><br>MAIN RESULTS: Both patients learned to self-regulate their precuneus oscillations and achieved stable online decoding accuracy over the course of disease progression. One patient achieved a mean online decoding accuracy in a binary decision task of 70.55% across 26 training sessions, and the other patient achieved 59.44% across 16 training sessions. We provide empirical evidence that these oscillations were cortical in nature and originated from the intersection of the precuneus, cuneus, and posterior cingulate.<br><br>SIGNIFICANCE: Our results establish that ALS patients can employ self-regulation of precuneus oscillations for communication. Such a BCI is likely to be available to ALS patients as long as their consciousness supports communication.},
}
@article {pmid27837720,
year = {2016},
author = {Ghaderi, F and Kim, SK and Kirchner, EA},
title = {A periodic spatio-spectral filter for event-related potentials.},
journal = {Computers in biology and medicine},
volume = {79},
number = {},
pages = {286-298},
doi = {10.1016/j.compbiomed.2016.10.004},
pmid = {27837720},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {With respect to single trial detection of event-related potentials (ERPs), spatial and spectral filters are two of the most commonly used pre-processing techniques for signal enhancement. Spatial filters reduce the dimensionality of the data while suppressing the noise contribution and spectral filters attenuate frequency components that most likely belong to noise subspace. However, the frequency spectrum of ERPs overlap with that of the ongoing electroencephalogram (EEG) and different types of artifacts. Therefore, proper selection of the spectral filter cutoffs is not a trivial task. In this research work, we developed a supervised method to estimate the spatial and finite impulse response (FIR) spectral filters, simultaneously. We evaluated the performance of the method on offline single trial classification of ERPs in datasets recorded during an oddball paradigm. The proposed spatio-spectral filter improved the overall single-trial classification performance by almost 9% on average compared with the case that no spatial filters were used. We also analyzed the effects of different spectral filter lengths and the number of retained channels after spatial filtering.},
}
@article {pmid27837661,
year = {2017},
author = {Eles, JR and Vazquez, AL and Snyder, NR and Lagenaur, C and Murphy, MC and Kozai, TD and Cui, XT},
title = {Neuroadhesive L1 coating attenuates acute microglial attachment to neural electrodes as revealed by live two-photon microscopy.},
journal = {Biomaterials},
volume = {113},
number = {},
pages = {279-292},
pmid = {27837661},
issn = {1878-5905},
support = {R01 NS062019/NS/NINDS NIH HHS/United States ; R01 NS089688/NS/NINDS NIH HHS/United States ; R01 NS094396/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Cell Adhesion ; Coated Materials, Biocompatible/adverse effects/*chemistry ; *Electrodes, Implanted/adverse effects ; Foreign-Body Reaction/etiology/prevention &amp; control ; Immobilized Proteins/adverse effects/*chemistry ; Mice ; Mice, Transgenic ; Microelectrodes/adverse effects ; Microglia/*cytology/physiology/ultrastructure ; Neural Cell Adhesion Molecule L1/adverse effects/*chemistry ; },
abstract = {Implantable neural electrode technologies for chronic neural recordings can restore functional control to paralysis and limb loss victims through brain-machine interfaces. These probes, however, have high failure rates partly due to the biological responses to the probe which generate an inflammatory scar and subsequent neuronal cell death. L1 is a neuronal specific cell adhesion molecule and has been shown to minimize glial scar formation and promote electrode-neuron integration when covalently attached to the surface of neural probes. In this work, the acute microglial response to L1-coated neural probes was evaluated in vivo by implanting coated devices into the cortex of mice with fluorescently labeled microglia, and tracking microglial dynamics with multi-photon microscopy for the ensuing 6 h in order to understand L1's cellular mechanisms of action. Microglia became activated immediately after implantation, extending processes towards both L1-coated and uncoated control probes at similar velocities. After the processes made contact with the probes, microglial processes expanded to cover 47.7% of the control probes' surfaces. For L1-coated probes, however, there was a statistically significant 83% reduction in microglial surface coverage. This effect was sustained through the experiment. At 6 h post-implant, the radius of microglia activation was reduced for the L1 probes by 20%, shifting from 130.0 to 103.5 μm with the coating. Microglia as far as 270 μm from the implant site displayed significantly lower morphological characteristics of activation for the L1 group. These results suggest that the L1 surface treatment works in an acute setting by microglial mediated mechanisms.},
}
@article {pmid27834567,
year = {2017},
author = {Zhu, S and Nahm, ES and Resnick, B and Friedmann, E and Brown, C and Park, J and Cheon, J and Park, D},
title = {The Moderated Mediating Effect of Self-Efficacy on Exercise Among Older Adults in an Online Bone Health Intervention Study: A Parallel Process Latent Growth Curve Model.},
journal = {Journal of aging and physical activity},
volume = {25},
number = {3},
pages = {378-386},
doi = {10.1123/japa.2016-0216},
pmid = {27834567},
issn = {1543-267X},
mesh = {Age Factors ; Aged ; Exercise/*psychology ; Female ; Growth Charts ; Humans ; Male ; Middle Aged ; Models, Statistical ; Psychomotor Performance ; *Self Efficacy ; },
abstract = {This secondary data analyses of a longitudinal study assessed whether self-efficacy for exercise (SEE) mediated online intervention effects on exercise among older adults and whether age (50-64 vs. ≥65 years) moderated the mediation. Data were from an online bone health intervention study. Eight hundred sixty-six older adults (≥50 years) were randomized to three arms: Bone Power (n = 301), Bone Power Plus (n = 302), or Control (n = 263). Parallel process latent growth curve modeling (LGCM) was used to jointly model growths in SEE and in exercise and to assess the mediating effect of SEE on the effect of intervention on exercise. SEE was a significant mediator in 50- to 64-year-old adults (0.061, 95 BCI: 0.011, 0.163) but not in the ≥65 age group (-0.004, 95% BCI: -0.047, 0.025). Promotion of SEE is critical to improve exercise among 50- to 64-year-olds.},
}
@article {pmid27833542,
year = {2016},
author = {Aricò, P and Borghini, G and Di Flumeri, G and Colosimo, A and Bonelli, S and Golfetti, A and Pozzi, S and Imbert, JP and Granger, G and Benhacene, R and Babiloni, F},
title = {Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {539},
pmid = {27833542},
issn = {1662-5161},
abstract = {Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.},
}
@article {pmid27833537,
year = {2016},
author = {Cui, H},
title = {Forward Prediction in the Posterior Parietal Cortex and Dynamic Brain-Machine Interface.},
journal = {Frontiers in integrative neuroscience},
volume = {10},
number = {},
pages = {35},
pmid = {27833537},
issn = {1662-5145},
abstract = {While remarkable progress has been made in brain-machine interfaces (BMIs) over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC) might play a proactive role in predictive motor control. Here it is proposed that predictive neural activity in PPC could be decoded to provide prosthetic control signals for guiding BMI systems in dynamic environments.},
}
@article {pmid27831885,
year = {2017},
author = {Martinez-Cagigal, V and Gomez-Pilar, J and Alvarez, D and Hornero, R},
title = {An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {8},
pages = {1332-1342},
doi = {10.1109/TNSRE.2016.2623381},
pmid = {27831885},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Electroencephalography/*methods ; Event-Related Potentials, P300 ; Female ; Humans ; Imagination ; Male ; Multiple Sclerosis/*rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; Severity of Illness Index ; *User-Computer Interface ; *Web Browser ; Word Processing/*methods ; },
abstract = {This paper presents an electroencephalographic (EEG) P300-based brain-computer interface (BCI) Internet browser. The system uses the "odd-ball" row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a "read-mode" command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: 1) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and 2) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonomy.},
}
@article {pmid27830790,
year = {2016},
author = {Capogrosso, M and Milekovic, T and Borton, D and Wagner, F and Moraud, EM and Mignardot, JB and Buse, N and Gandar, J and Barraud, Q and Xing, D and Rey, E and Duis, S and Jianzhong, Y and Ko, WK and Li, Q and Detemple, P and Denison, T and Micera, S and Bezard, E and Bloch, J and Courtine, G},
title = {A brain-spine interface alleviating gait deficits after spinal cord injury in primates.},
journal = {Nature},
volume = {539},
number = {7628},
pages = {284-288},
pmid = {27830790},
issn = {1476-4687},
support = {261247/ERC_/European Research Council/International ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Disease Models, Animal ; Electric Stimulation ; Electric Stimulation Therapy/*instrumentation ; Gait/*physiology ; Gait Disorders, Neurologic/*complications/physiopathology/*therapy ; Leg/physiology ; Locomotion/physiology ; Lumbosacral Region ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/physiopathology ; *Neural Prostheses ; Paralysis/complications/physiopathology/therapy ; Reproducibility of Results ; Spinal Cord/physiopathology ; Spinal Cord Injuries/*complications/physiopathology/*therapy ; Wireless Technology/instrumentation ; },
abstract = {Spinal cord injury disrupts the communication between the brain and the spinal circuits that orchestrate movement. To bypass the lesion, brain-computer interfaces have directly linked cortical activity to electrical stimulation of muscles, and have thus restored grasping abilities after hand paralysis. Theoretically, this strategy could also restore control over leg muscle activity for walking. However, replicating the complex sequence of individual muscle activation patterns underlying natural and adaptive locomotor movements poses formidable conceptual and technological challenges. Recently, it was shown in rats that epidural electrical stimulation of the lumbar spinal cord can reproduce the natural activation of synergistic muscle groups producing locomotion. Here we interface leg motor cortex activity with epidural electrical stimulation protocols to establish a brain-spine interface that alleviated gait deficits after a spinal cord injury in non-human primates. Rhesus monkeys (Macaca mulatta) were implanted with an intracortical microelectrode array in the leg area of the motor cortex and with a spinal cord stimulation system composed of a spatially selective epidural implant and a pulse generator with real-time triggering capabilities. We designed and implemented wireless control systems that linked online neural decoding of extension and flexion motor states with stimulation protocols promoting these movements. These systems allowed the monkeys to behave freely without any restrictions or constraining tethered electronics. After validation of the brain-spine interface in intact (uninjured) monkeys, we performed a unilateral corticospinal tract lesion at the thoracic level. As early as six days post-injury and without prior training of the monkeys, the brain-spine interface restored weight-bearing locomotion of the paralysed leg on a treadmill and overground. The implantable components integrated in the brain-spine interface have all been approved for investigational applications in similar human research, suggesting a practical translational pathway for proof-of-concept studies in people with spinal cord injury.},
}
@article {pmid27829062,
year = {2016},
author = {Cheng, YC and Hannaoui, S and John, TR and Dudas, S and Czub, S and Gilch, S},
title = {Early and Non-Invasive Detection of Chronic Wasting Disease Prions in Elk Feces by Real-Time Quaking Induced Conversion.},
journal = {PloS one},
volume = {11},
number = {11},
pages = {e0166187},
pmid = {27829062},
issn = {1932-6203},
mesh = {Animals ; Brain-Computer Interfaces ; *Deer ; Feces/*chemistry ; Prions/*analysis ; Recombinant Proteins ; Wasting Disease, Chronic/*diagnosis ; },
abstract = {Chronic wasting disease (CWD) is a fatal prion disease of wild and captive cervids in North America. Prions are infectious agents composed of a misfolded version of a host-encoded protein, termed PrPSc. Infected cervids excrete and secrete prions, contributing to lateral transmission. Geographical distribution is expanding and case numbers in wild cervids are increasing. Recently, the first European cases of CWD have been reported in a wild reindeer and two moose from Norway. Therefore, methods to detect the infection early in the incubation time using easily available samples are desirable to facilitate effective disease management. We have adapted the real-time quaking induced conversion (RT-QuIC) assay, a sensitive in vitro prion amplification method, for pre-clinical detection of prion seeding activity in elk feces. Testing fecal samples from orally inoculated elk taken at various time points post infection revealed early shedding and detectable prion seeding activity throughout the disease course. Early shedding was also found in two elk encoding a PrP genotype associated with reduced susceptibility for CWD. In summary, we suggest that detection of CWD prions in feces by RT-QuIC may become a useful tool to support CWD surveillance in wild and captive cervids. The finding of early shedding independent of the elk's prion protein genotype raises the question whether prolonged survival is beneficial, considering accumulation of environmental prions and its contribution to CWD transmission upon extended duration of shedding.},
}
@article {pmid27825607,
year = {2017},
author = {Samah, S and Neoh, CF and Wong, YY and Hassali, MA and Shafie, AA and Lim, SM and Ramasamy, K and Mat Nasir, N and Han, YW and Burroughs, T},
title = {Linguistic and psychometric validation of the Malaysian version of Diabetes Quality of Life-Brief Clinical Inventory (DQoL-BCI).},
journal = {Research in social &amp; administrative pharmacy : RSAP},
volume = {13},
number = {6},
pages = {1135-1141},
doi = {10.1016/j.sapharm.2016.10.017},
pmid = {27825607},
issn = {1934-8150},
mesh = {Aged ; *Diabetes Mellitus, Type 2 ; Female ; Humans ; Language ; Malaysia ; Male ; Middle Aged ; Psychometrics ; *Quality of Life ; *Surveys and Questionnaires ; },
abstract = {BACKGROUND: Quality of life (QoL) assessment provides valuable outcome to support clinical decision-making, particularly for patients with chronic diseases that are incurable. A brief, 15-item diabetes-specific tool [i.e. Diabetes Quality of Life-Brief Clinical Inventory (DQoL-BCI)] is known to be developed in English and validated for use in clinical practice. This simplified tool, however, is not readily available for use in the Malaysian setting.<br><br>OBJECTIVE: To translate the DQoL-BCI into a Malaysian version and to assess its construct validity (factorial validity, convergent validity and discriminant validity), reliability (internal consistency) and floor and ceiling effects among the Malaysian diabetic population.<br><br>MATERIAL AND METHODS: A forward-backward translation, involving professional translators and experts with vast experience in translation of patient reported outcome measures, was conducted. A total of 202 patients with Type 2 diabetes mellitus (T2DM) who fulfilled the inclusion criteria were invited to complete the translated DQoL-BCI. Data were analysed using SPSS for exploratory factor analysis (EFA), convergent and discriminant validity, reliability and test-retest, and AMOS software for confirmatory factor analysis (CFA).<br><br>RESULTS: Findings from EFA indicated that the 4-factor structure of the Malaysian version of DQoL-BCI was optimal and explained 50.9% of the variance; CFA confirmed the 4-factor model fit. There was negative, moderate correlation between the scores of DQoL-BCI (Malaysian version) and EQ-5D-3L utility score (r&#xa0;=&#xa0;-0.329, p&#xa0;=&#xa0;0.003). Patients with higher glycated haemoglobin levels (p&#xa0;=&#xa0;0.008), diabetes macrovascular (p&#xa0;=&#xa0;0.017) and microvascular (p&#xa0;=&#xa0;0.013) complications reported poorer QoL. Cronbach's alpha coefficient and intraclass coefficient correlations (range) obtained were 0.703 and 0.86 (0.734-0.934), indicating good reliability and stability of the translated DQoL-BCI.<br><br>CONCLUSION: This study had validated the linguistic and psychometric properties of DQoL-BCI (Malaysian version), thus providing a valid and reliable brief tool for assessing the QoL of Malaysian T2DM patients.},
}
@article {pmid27824089,
year = {2016},
author = {Shin, J and Müller, KR and Hwang, HJ},
title = {Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {36203},
pmid = {27824089},
issn = {2045-2322},
mesh = {Adult ; Brain-Computer Interfaces ; Discriminant Analysis ; Female ; Humans ; Male ; Mathematics ; Mental Processes/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance ; Spectroscopy, Near-Infrared/*instrumentation ; Young Adult ; },
abstract = {We propose a near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) that can be operated in eyes-closed (EC) state. To evaluate the feasibility of NIRS-based EC BCIs, we compared the performance of an eye-open (EO) BCI paradigm and an EC BCI paradigm with respect to hemodynamic response and classification accuracy. To this end, subjects performed either mental arithmetic or imagined vocalization of the English alphabet as a baseline task with very low cognitive loading. The performances of two linear classifiers were compared; resulting in an advantage of shrinkage linear discriminant analysis (LDA). The classification accuracy of EC paradigm (75.6 ± 7.3%) was observed to be lower than that of EO paradigm (77.0 ± 9.2%), which was statistically insignificant (p = 0.5698). Subjects reported they felt it more comfortable (p = 0.057) and easier (p < 0.05) to perform the EC BCI tasks. The different task difficulty may become a cause of the slightly lower classification accuracy of EC data. From the analysis results, we could confirm the feasibility of NIRS-based EC BCIs, which can be a BCI option that may ultimately be of use for patients who cannot keep their eyes open consistently.},
}
@article {pmid27819250,
year = {2016},
author = {Clements, JM and Sellers, EW and Ryan, DB and Caves, K and Collins, LM and Throckmorton, CS},
title = {Applying dynamic data collection to improve dry electrode system performance for a P300-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066018},
pmid = {27819250},
issn = {1741-2552},
support = {R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Communication Disorders/psychology/rehabilitation ; Data Collection/*methods ; *Electrodes ; Electroencephalography/*instrumentation ; Event-Related Potentials, P300/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Signal-To-Noise Ratio ; },
abstract = {OBJECTIVE: Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation.<br><br>APPROACH: We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems.<br><br>MAIN RESULTS: Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems.<br><br>SIGNIFICANCE: Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.},
}
@article {pmid27818001,
year = {2017},
author = {Zich, C and Debener, S and Thoene, AK and Chen, LC and Kranczioch, C},
title = {Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures.},
journal = {Neurobiology of aging},
volume = {49},
number = {},
pages = {183-197},
doi = {10.1016/j.neurobiolaging.2016.10.011},
pmid = {27818001},
issn = {1558-1497},
mesh = {Adult ; Aged ; Aging/*physiology/*psychology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Movement/*physiology ; Multimodal Imaging/*methods ; Neurofeedback/methods/*physiology ; Spectroscopy, Near-Infrared/*methods ; Stroke Rehabilitation/methods ; Young Adult ; },
abstract = {Stroke frequently results in motor impairment. Motor imagery (MI), the mental practice of movements, has been suggested as a promising complement to other therapeutic approaches facilitating motor rehabilitation. Of particular potential is the combination of MI with neurofeedback (NF). However, MI NF protocols have been largely optimized only in younger healthy adults, although strokes occur more frequently in older adults. The present study examined the influence of age on the neural correlates of MI supported by electroencephalogram (EEG)-based NF and on the neural correlates of motor execution. We adopted a multimodal neuroimaging framework focusing on EEG-derived event-related desynchronization (ERD%) and oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations simultaneously acquired using functional near-infrared spectroscopy (fNIRS). ERD%, HbO concentration and HbR concentration were compared between younger (mean age: 24.4 years) and older healthy adults (mean age: 62.6 years). During MI, ERD% and HbR concentration were less lateralized in older adults than in younger adults. The lateralization-by-age interaction was not significant for movement execution. Moreover, EEG-based NF was related to an increase in task-specific activity when compared to the absence of feedback in both older and younger adults. Finally, significant modulation correlations were found between ERD% and hemodynamic measures despite the absence of significant amplitude correlations. Overall, the findings suggest a complex relationship between age and movement-related activity in electrophysiological and hemodynamic measures. Our results emphasize that the age of the actual end-user should be taken into account when designing neurorehabilitation protocols.},
}
@article {pmid27816779,
year = {2017},
author = {Schurger, A and Gale, S and Gozel, O and Blanke, O},
title = {Performance monitoring for brain-computer-interface actions.},
journal = {Brain and cognition},
volume = {111},
number = {},
pages = {44-50},
doi = {10.1016/j.bandc.2016.09.009},
pmid = {27816779},
issn = {1090-2147},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Executive Function/*physiology ; Feedback, Sensory/*physiology ; Humans ; Learning/*physiology ; Male ; Metacognition/*physiology ; Motor Activity/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {When presented with a difficult perceptual decision, human observers are able to make metacognitive judgements of subjective certainty. Such judgements can be made independently of and prior to any overt response to a sensory stimulus, presumably via internal monitoring. Retrospective judgements about one's own task performance, on the other hand, require first that the subject perform a task and thus could potentially be made based on motor processes, proprioceptive, and other sensory feedback rather than internal monitoring. With this dichotomy in mind, we set out to study performance monitoring using a brain-computer interface (BCI), with which subjects could voluntarily perform an action - moving a cursor on a computer screen - without any movement of the body, and thus without somatosensory feedback. Real-time visual feedback was available to subjects during training, but not during the experiment where the true final position of the cursor was only revealed after the subject had estimated where s/he thought it had ended up after 6s of BCI-based cursor control. During the first half of the experiment subjects based their assessments primarily on the prior probability of the end position of the cursor on previous trials. However, during the second half of the experiment subjects' judgements moved significantly closer to the true end position of the cursor, and away from the prior. This suggests that subjects can monitor task performance when the task is performed without overt movement of the body.},
}
@article {pmid27816702,
year = {2018},
author = {Yazmir, B and Reiner, M},
title = {Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.},
journal = {Neuroscience},
volume = {378},
number = {},
pages = {100-112},
doi = {10.1016/j.neuroscience.2016.10.060},
pmid = {27816702},
issn = {1873-7544},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials ; Female ; Humans ; Male ; Motor Activity/*physiology ; Tennis/physiology ; Virtual Reality ; },
abstract = {Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system.},
}
@article {pmid27815182,
year = {2016},
author = {Delhaye, BP and Saal, HP and Bensmaia, SJ},
title = {Key considerations in designing a somatosensory neuroprosthesis.},
journal = {Journal of physiology, Paris},
volume = {110},
number = {4 Pt A},
pages = {402-408},
doi = {10.1016/j.jphysparis.2016.11.001},
pmid = {27815182},
issn = {1769-7115},
mesh = {Activities of Daily Living ; Feedback, Sensory/*physiology ; Humans ; Prosthesis Design/*standards ; },
abstract = {In recent years, a consensus has emerged that somatosensory feedback needs to be provided for upper limb neuroprostheses to be useful. An increasingly promising approach to sensory restoration is to electrically stimulate neurons along the somatosensory neuraxis to convey information about the state of the prosthetic limb and about contact with objects. To date, efforts toward artificial sensory feedback have consisted mainly of demonstrating that some sensory information could be conveyed using a small number of stimulation patterns, generally delivered through single electrodes. However impressive these achievements are, results from different studies are hard to compare, as each research team implements different stimulation patterns and tests the elicited sensations differently. A critical question is whether different stimulation strategies will generalize from contrived laboratory settings to activities of daily living. Here, we lay out some key specifications that an artificial somatosensory channel should meet, discuss how different approaches should be evaluated, and caution about looming challenges that the field of sensory restoration will face.},
}
@article {pmid27814972,
year = {2016},
author = {Crouch, DL and Huang, H},
title = {Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control.},
journal = {Journal of biomechanics},
volume = {49},
number = {16},
pages = {3901-3907},
doi = {10.1016/j.jbiomech.2016.10.035},
pmid = {27814972},
issn = {1873-2380},
mesh = {Adult ; *Artificial Limbs ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Electromyography/*methods ; Female ; Forearm/physiology ; Hand/*physiology ; Humans ; Male ; Metacarpophalangeal Joint/physiology ; *Models, Anatomic ; Movement/physiology ; Range of Motion, Articular/physiology ; Robotics ; Wrist Joint/physiology ; Young Adult ; },
abstract = {Simple, lumped-parameter musculoskeletal models may be more adaptable and practical for clinical real-time control applications, such as prosthesis control. In this study, we determined whether a lumped-parameter, EMG-driven musculoskeletal model with four muscles could predict wrist and metacarpophalangeal (MCP) joint flexion/extension. Forearm EMG signals and joint kinematics were collected simultaneously from 5 able-bodied (AB) subjects. For one subject with unilateral transradial amputation (TRA), joint kinematics were collected from the sound arm during bilateral mirrored motion. Twenty-two model parameters were optimized such that joint kinematics predicted by EMG-driven forward dynamic simulation closely matched measured kinematics. Cross validation was employed to evaluate the model kinematic predictions using Pearson׳s correlation coefficient (r). Model predictions of joint angles were highly to very highly positively correlated with measured values at the wrist (AB mean r=0.94, TRA r=0.92) and MCP (AB mean r=0.88, TRA r=0.93) joints during single-joint wrist and MCP movements, respectively. In simultaneous multi-joint movement, the prediction accuracy for TRA at the MCP joint decreased (r=0.56), while r-values derived from AB subjects and TRA wrist motion were still above 0.75. Though parameters were optimized to match experimental sub-maximal kinematics, passive and maximum isometric joint moments predicted by the model were comparable to reported experimental measures. Our results showed the promise of a lumped-parameter musculoskeletal model for hand/wrist kinematic estimation. Therefore, the model might be useful for EMG control of powered upper limb prostheses, but more work is needed to demonstrate its online performance.},
}
@article {pmid27746229,
year = {2016},
author = {Sturm, I and Lapuschkin, S and Samek, W and Müller, KR},
title = {Interpretable deep neural networks for single-trial EEG classification.},
journal = {Journal of neuroscience methods},
volume = {274},
number = {},
pages = {141-145},
doi = {10.1016/j.jneumeth.2016.10.008},
pmid = {27746229},
issn = {1872-678X},
mesh = {Animals ; Brain/*cytology/*physiology ; Brain Mapping ; Brain Waves/*physiology ; Electroencephalography/*classification/methods ; Humans ; Nerve Net/*physiology ; Neurons/*physiology ; },
abstract = {BACKGROUND: In cognitive neuroscience the potential of deep neural networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise relevance propagation (LRP) has been introduced as a novel method to explain individual network decisions.<br><br>NEW METHOD: We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision.<br><br>RESULTS: DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials.<br><br>We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginary BCI.<br><br>CONCLUSION: We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.},
}
@article {pmid27808125,
year = {2016},
author = {Min, BK and Dähne, S and Ahn, MH and Noh, YK and Müller, KR},
title = {Decoding of top-down cognitive processing for SSVEP-controlled BMI.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {36267},
pmid = {27808125},
issn = {2045-2322},
mesh = {Adult ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Flicker Fusion/physiology ; Humans ; Male ; Photic Stimulation/methods ; Task Performance and Analysis ; Visual Cortex/*physiology ; },
abstract = {We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant's visual cortex uniformly with equal probability, the participant's intention groups the strokes and thus perceives a 'letter Gestalt'. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.},
}
@article {pmid27807349,
year = {2016},
author = {Yanagisawa, T and Fukuma, R and Seymour, B and Hosomi, K and Kishima, H and Shimizu, T and Yokoi, H and Hirata, M and Yoshimine, T and Kamitani, Y and Saitoh, Y},
title = {Induced sensorimotor brain plasticity controls pain in phantom limb patients.},
journal = {Nature communications},
volume = {7},
number = {},
pages = {13209},
pmid = {27807349},
issn = {2041-1723},
mesh = {Adult ; Brachial Plexus Neuropathies/physiopathology ; *Brain-Computer Interfaces ; Humans ; Magnetoencephalography ; Male ; Middle Aged ; Neurofeedback/*methods ; *Neuronal Plasticity ; Pain Management/*methods ; Phantom Limb/physiopathology/*therapy ; Prostheses and Implants ; Sensorimotor Cortex/physiopathology ; },
abstract = {The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain-machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback.},
}
@article {pmid27803662,
year = {2016},
author = {Rana, M and Varan, AQ and Davoudi, A and Cohen, RA and Sitaram, R and Ebner, NC},
title = {Real-Time fMRI in Neuroscience Research and Its Use in Studying the Aging Brain.},
journal = {Frontiers in aging neuroscience},
volume = {8},
number = {},
pages = {239},
pmid = {27803662},
issn = {1663-4365},
support = {P30 AG028740/AG/NIA NIH HHS/United States ; },
abstract = {Cognitive decline is a major concern in the aging population. It is normative to experience some deterioration in cognitive abilities with advanced age such as related to memory performance, attention distraction to interference, task switching, and processing speed. However, intact cognitive functioning in old age is important for leading an independent day-to-day life. Thus, studying ways to counteract or delay the onset of cognitive decline in aging is crucial. The literature offers various explanations for the decline in cognitive performance in aging; among those are age-related gray and white matter atrophy, synaptic degeneration, blood flow reduction, neurochemical alterations, and change in connectivity patterns with advanced age. An emerging literature on neurofeedback and Brain Computer Interface (BCI) reports exciting results supporting the benefits of volitional modulation of brain activity on cognition and behavior. Neurofeedback studies based on real-time functional magnetic resonance imaging (rtfMRI) have shown behavioral changes in schizophrenia and behavioral benefits in nicotine addiction. This article integrates research on cognitive and brain aging with evidence of brain and behavioral modification due to rtfMRI neurofeedback. We offer a state-of-the-art description of the rtfMRI technique with an eye towards its application in aging. We present preliminary results of a feasibility study exploring the possibility of using rtfMRI to train older adults to volitionally control brain activity. Based on these first findings, we discuss possible implementations of rtfMRI neurofeedback as a novel technique to study and alleviate cognitive decline in healthy and pathological aging.},
}
@article {pmid27802344,
year = {2016},
author = {Libedinsky, C and So, R and Xu, Z and Kyar, TK and Ho, D and Lim, C and Chan, L and Chua, Y and Yao, L and Cheong, JH and Lee, JH and Vishal, KV and Guo, Y and Chen, ZN and Lim, LK and Li, P and Liu, L and Zou, X and Ang, KK and Gao, Y and Ng, WH and Han, BS and Chng, K and Guan, C and Je, M and Yen, SC},
title = {Independent Mobility Achieved through a Wireless Brain-Machine Interface.},
journal = {PloS one},
volume = {11},
number = {11},
pages = {e0165773},
pmid = {27802344},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Behavior, Animal ; *Brain-Computer Interfaces ; Macaca fascicularis ; Motor Neurons/cytology ; *Movement ; Software ; *Wireless Technology ; },
abstract = {Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.},
}
@article {pmid27799677,
year = {2016},
author = {Kim, TW and Lee, BH},
title = {Clinical usefulness of brain-computer interface-controlled functional electrical stimulation for improving brain activity in children with spastic cerebral palsy: a pilot randomized controlled trial.},
journal = {Journal of physical therapy science},
volume = {28},
number = {9},
pages = {2491-2494},
pmid = {27799677},
issn = {0915-5287},
abstract = {[Purpose] Evaluating the effect of brain-computer interface (BCI)-based functional electrical stimulation (FES) training on brain activity in children with spastic cerebral palsy (CP) was the aim of this study. [Subjects and Methods] Subjects were randomized into a BCI-FES group (n=9) and a functional electrical stimulation (FES) control group (n=9). Subjects in the BCI-FES group received wrist and hand extension training with FES for 30 minutes per day, 5 times per week for 6 weeks under the BCI-based program. The FES group received wrist and hand extension training with FES for the same amount of time. Sensorimotor rhythms (SMR) and middle beta waves (M-beta) were measured in frontopolar regions 1 and 2 (Fp1, Fp2) to determine the effects of BCI-FES training. [Results] Significant improvements in the SMR and M-beta of Fp1 and Fp2 were seen in the BCI-FES group. In contrast, significant improvement was only seen in the SMR and M-beta of Fp2 in the control group. [Conclusion] The results of the present study suggest that BCI-controlled FES training may be helpful in improving brain activity in patients with cerebral palsy and may be applied as effectively as traditional FES training.},
}
@article {pmid27738096,
year = {2016},
author = {Flesher, SN and Collinger, JL and Foldes, ST and Weiss, JM and Downey, JE and Tyler-Kabara, EC and Bensmaia, SJ and Schwartz, AB and Boninger, ML and Gaunt, RA},
title = {Intracortical microstimulation of human somatosensory cortex.},
journal = {Science translational medicine},
volume = {8},
number = {361},
pages = {361ra141},
doi = {10.1126/scitranslmed.aaf8083},
pmid = {27738096},
issn = {1946-6242},
mesh = {Adult ; *Brain-Computer Interfaces ; Electric Stimulation ; Electrodes, Implanted ; Hand/*physiology ; Humans ; Male ; Man-Machine Systems ; Microelectrodes ; Movement ; Paralysis/rehabilitation ; Signal-To-Noise Ratio ; Somatosensory Cortex/*physiology ; Touch ; Treatment Outcome ; },
abstract = {Intracortical microstimulation of the somatosensory cortex offers the potential for creating a sensory neuroprosthesis to restore tactile sensation. Whereas animal studies have suggested that both cutaneous and proprioceptive percepts can be evoked using this approach, the perceptual quality of the stimuli cannot be measured in these experiments. We show that microstimulation within the hand area of the somatosensory cortex of a person with long-term spinal cord injury evokes tactile sensations perceived as originating from locations on the hand and that cortical stimulation sites are organized according to expected somatotopic principles. Many of these percepts exhibit naturalistic characteristics (including feelings of pressure), can be evoked at low stimulation amplitudes, and remain stable for months. Further, modulating the stimulus amplitude grades the perceptual intensity of the stimuli, suggesting that intracortical microstimulation could be used to convey information about the contact location and pressure necessary to perform dexterous hand movements associated with object manipulation.},
}
@article {pmid27792781,
year = {2016},
author = {Wenzel, MA and Almeida, I and Blankertz, B},
title = {Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?.},
journal = {PloS one},
volume = {11},
number = {10},
pages = {e0165556},
pmid = {27792781},
issn = {1932-6203},
mesh = {Adolescent ; Aged ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials ; Female ; Humans ; Male ; Mathematics ; Memory/physiology ; Middle Aged ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI), because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.<br><br>APPROACH: Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.<br><br>RESULTS: Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG).<br><br>SIGNIFICANCE: The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.},
}
@article {pmid27791704,
year = {2017},
author = {Kim, GH and Seo, JH and Schroff, S and Chen, PC and Lee, KH and Baumgartner, J},
title = {Impact of intraoperative 3-T MRI with diffusion tensor imaging on hemispherectomy.},
journal = {Journal of neurosurgery. Pediatrics},
volume = {19},
number = {1},
pages = {63-69},
doi = {10.3171/2016.4.PEDS15568},
pmid = {27791704},
issn = {1933-0715},
mesh = {Child ; Child, Preschool ; Cross-Sectional Studies ; Diffusion Tensor Imaging/methods/*trends ; Drug Resistant Epilepsy/*diagnosis/physiopathology/*surgery ; Female ; Follow-Up Studies ; Hemispherectomy/methods/*trends ; Humans ; Infant ; Intraoperative Neurophysiological Monitoring/methods/*trends ; Male ; Retrospective Studies ; },
abstract = {OBJECTIVE Hemispherectomy can produce remarkable seizure control of medically intractable hemispheric epilepsy in children, but some patients continue to have seizures after surgery. A frequent cause of treatment failure is incomplete surgical disconnection of the abnormal hemisphere. This study explores whether intraoperative 3-T MRI with diffusion tensor imaging (DTI) during hemispherectomy can identify areas of incomplete disconnection and allow complete disconnection during a single surgery. METHODS The charts of 32 patients with epilepsy who underwent hemispherectomy between January 2012 and July 2014 at the Florida Hospital for Children were reviewed. Patients were grouped as having had curative or palliative hemispherectomy. To assess the completeness of disconnection when the surgeon considered the operation completed, intraoperative 3-T MRI-DTI was performed. If incomplete disconnection was identified, additional surgery was performed until MRI-DTI sequences confirmed satisfactory disconnection. Seizure outcome data were collected via medical records at last follow-up. RESULTS Of 32 patients who underwent hemispherectomy, 23 had curative hemispherectomy and 9 had palliative hemispherectomy. In 11 of 32 surgeries, the first intraoperative MRI-DTI sequences suggested incomplete disconnection and additional surgery followed by repeat MRI-DTI was performed. Complete disconnection was accomplished in 30 of 32 patients (93.8%). Two of 32 disconnections (6.3%) were incomplete on postoperative imaging. Cross-sectional results showed that 21 of 23 patients (91.3%) who had curative hemispherectomy remained free of seizures (International League Against Epilepsy Class 1) at a median follow-up of 1.7 years (range 0.4-2.9 years). The longitudinal seizure freedom after curative hemispherectomy was 95.2% (SE 0.05) at 6 months, 90.5% (SE 0.06) at 1 year, and 90.5% (SE 0.05) at 2 years. CONCLUSIONS Intraoperative 3-T MRI-DTI sequences can identify incomplete disconnection during hemispherectomy and allow higher rates of complete disconnection in a single surgery. Higher rates of complete disconnection seem to achieve better seizure-free outcome following modified functional hemispherectomy.},
}
@article {pmid27790111,
year = {2016},
author = {Zhou, S and Allison, BZ and Kübler, A and Cichocki, A and Wang, X and Jin, J},
title = {Effects of Background Music on Objective and Subjective Performance Measures in an Auditory BCI.},
journal = {Frontiers in computational neuroscience},
volume = {10},
number = {},
pages = {105},
pmid = {27790111},
issn = {1662-5188},
abstract = {Several studies have explored brain computer interface (BCI) systems based on auditory stimuli, which could help patients with visual impairments. Usability and user satisfaction are important considerations in any BCI. Although background music can influence emotion and performance in other task environments, and many users may wish to listen to music while using a BCI, auditory, and other BCIs are typically studied without background music. Some work has explored the possibility of using polyphonic music in auditory BCI systems. However, this approach requires users with good musical skills, and has not been explored in online experiments. Our hypothesis was that an auditory BCI with background music would be preferred by subjects over a similar BCI without background music, without any difference in BCI performance. We introduce a simple paradigm (which does not require musical skill) using percussion instrument sound stimuli and background music, and evaluated it in both offline and online experiments. The result showed that subjects preferred the auditory BCI with background music. Different performance measures did not reveal any significant performance effect when comparing background music vs. no background. Since the addition of background music does not impair BCI performance but is preferred by users, auditory (and perhaps other) BCIs should consider including it. Our study also indicates that auditory BCIs can be effective even if the auditory channel is simultaneously otherwise engaged.},
}
@article {pmid27790085,
year = {2016},
author = {Gharabaghi, A},
title = {What Turns Assistive into Restorative Brain-Machine Interfaces?.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {456},
pmid = {27790085},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMI) may support motor impaired patients during activities of daily living by controlling external devices such as prostheses (assistive BMI). Moreover, BMIs are applied in conjunction with robotic orthoses for rehabilitation of lost motor function via neurofeedback training (restorative BMI). Using assistive BMI in a rehabilitation context does not automatically turn them into restorative devices. This perspective article suggests key features of restorative BMI and provides the supporting evidence: In summary, BMI may be referred to as restorative tools when demonstrating subsequently (i) operant learning and progressive evolution of specific brain states/dynamics, (ii) correlated modulations of functional networks related to the therapeutic goal, (iii) subsequent improvement in a specific task, and (iv) an explicit correlation between the modulated brain dynamics and the achieved behavioral gains. Such findings would provide the rationale for translating BMI-based interventions into clinical settings for reinforcement learning and motor rehabilitation following stroke.},
}
@article {pmid27751787,
year = {2016},
author = {Hu, K and Chen, C and Meng, Q and Williams, Z and Xu, W},
title = {Scientific profile of brain-computer interfaces: Bibliometric analysis in a 10-year period.},
journal = {Neuroscience letters},
volume = {635},
number = {},
pages = {61-66},
doi = {10.1016/j.neulet.2016.10.022},
pmid = {27751787},
issn = {1872-7972},
mesh = {*Bibliometrics ; *Brain-Computer Interfaces ; Periodicals as Topic ; },
abstract = {BACKGROUND: With the tremendous advances in the field of brain-computer interfaces (BCI), the literature in this field has grown exponentially; examination of highly cited articles is a tool that can help identify outstanding scientific studies and landmark papers. This study examined the characteristics of 100 highly cited BCI papers over the past 10 years.<br><br>METHODS: The Web of Science was searched for highly cited papers related to BCI research published from 2006 to 2015. The top 100 highly cited articles were identified. The number of citations and countries, and the corresponding institutions, year of publication, study design, and research area were noted and analyzed.<br><br>RESULTS: The 100 highly cited articles had a mean of 137.1(SE: 15.38) citations. These articles were published in 45 high-impact journals, and mostly in TRANSACTIONS ON BIOMEDICAL ENGINEERING (n=14). Of the 100 articles, 72 were original articles and the rest were review articles. These articles came from 15 countries, with the USA contributing most of the highly cited articles (n=52). Fifty-seven institutions produced these 100 highly cited articles, led by Duke University (n=7).<br><br>CONCLUSIONS: This study provides a historical perspective on the progress in the field of BCI, allows recognition of the most influential reports, and provides useful information that can indicate areas requiring further investigation.},
}
@article {pmid27716564,
year = {2016},
author = {Wolters, N and Schabronath, C and Schembecker, G and Merz, J},
title = {Efficient conversion of pretreated brewer's spent grain and wheat bran by submerged cultivation of Hericium erinaceus.},
journal = {Bioresource technology},
volume = {222},
number = {},
pages = {123-129},
doi = {10.1016/j.biortech.2016.09.121},
pmid = {27716564},
issn = {1873-2976},
mesh = {Animals ; Basidiomycota/chemistry/*physiology ; Biomass ; Biotechnology/*methods ; Dietary Fiber/*metabolism ; Diterpenes/metabolism ; Edible Grain/chemistry/metabolism ; Fermentation ; Waste Products ; },
abstract = {Brewer's spent grain (BSG) and wheat bran (WB) are industrial byproducts that accumulate in millions of tons per year and are typically applied as animal feed. Since both byproducts show a great potential as substrates for fermentation, the approach developed in this study consists of utilizing these lignocellulosic byproducts for biomass production of the medicinal fungus Hericium erinaceus through submerged cultivation. To increase the biological efficiency of the bioconversion, acidic pretreatment was applied yielding a bioconversion of 38.6% for pretreated BSG and 34.8% for pretreated WB. This study shows that the complete degradation of (hemi)cellulose into monosaccharides was not required for an efficient bioconversion. The produced fungal biomass was applied in a second fermentation step to induce the secondary metabolite erinacine C production. Thus, biomass was produced as a functional food ingredient with erinacine C contents of 174.8mg/g for BSG and 99.3mg/g for WB based bioconversions.},
}
@article {pmid27591137,
year = {2016},
author = {Coulibaly, JT and Ouattara, M and Becker, SL and Lo, NC and Keiser, J and N'Goran, EK and Ianniello, D and Rinaldi, L and Cringoli, G and Utzinger, J},
title = {Comparison of sensitivity and faecal egg counts of Mini-FLOTAC using fixed stool samples and Kato-Katz technique for the diagnosis of Schistosoma mansoni and soil-transmitted helminths.},
journal = {Acta tropica},
volume = {164},
number = {},
pages = {107-116},
doi = {10.1016/j.actatropica.2016.08.024},
pmid = {27591137},
issn = {1873-6254},
mesh = {Adolescent ; Animals ; Bayes Theorem ; Child ; Child Health Services ; Child, Preschool ; Cote d'Ivoire/epidemiology ; Feces/*parasitology ; Female ; Humans ; Male ; Prevalence ; Schistosoma mansoni/*isolation &amp; purification ; Schistosomiasis mansoni/*epidemiology/parasitology ; Schools ; Sensitivity and Specificity ; Soil/parasitology ; },
abstract = {Accurate diagnostic tools for human helminthiasis are crucial for epidemiological surveys, improved patient management, and evaluation of community-based intervention studies. However, the diagnosis of intestinal schistosomiasis and soil-transmitted helminthiasis heavily relies on stool microscopy using the Kato-Katz technique, which has a low sensitivity. The Mini-FLOTAC method is an alternative microscopy-based technique, but its diagnostic performance using sodium acetate-acetic acid-formalin-(SAF)-fixed stool specimens has not been validated. The fixation of stool samples for later examination in a laboratory may reduce logistical and financial barriers of prevalence surveys by not requiring field laboratories. We compared the diagnostic accuracy of the Kato-Katz technique using fresh stool samples with the Mini-FLOTAC technique, employing matched stool samples that were fixed in SAF. Three consecutive stool samples from 149 school-aged children in Côte d'Ivoire were subjected to quintuplicate Kato-Katz thick smears examined on the same day. From the remaining stool, approximately 2g was fixed in 10ml of SAF for about 3 months, and then subjected to the Mini-FLOTAC method, using two flotation solutions (FS2 and FS7). The combined results of multiple Kato-Katz and Mini-FLOTAC readings revealed prevalences of Schistosoma mansoni, Trichuris trichiura and hookworm of 99.3%, 72.5% and 7.4%, respectively. Employing a Bayesian latent class analysis to estimate the true sensitivity of the diagnostic approaches, the sensitivity of Mini-FLOTAC using FS2 was 50.1% (95% Bayesian credible interval (BCI): 30.9-70.2%) for hookworm and 68.0% (95% BCI: 34.9-93.5%) for T. trichiura. When applying Mini-FLOTAC using FS7, the sensitivity was 89.9% (95% BCI: 86.9-97.4%) for S. mansoni, 37.2% (95% BCI: 17.2-60.6%) for hookworm and 67.7% (95% BCI: 33.0-93.0%) for T. trichiura. The specificity ranged from 80.1-95.0% in all Mini-FLOTAC tests. Mini-FLOTAC revealed higher arithmetic mean faecal egg counts (FECs) than the Kato-Katz technique. We found a significant correlation in FECs between Kato-Katz and Mini-FLOTAC for S. mansoni and T. trichiura. We conclude that Mini-FLOTAC shows reasonable diagnostic accuracy when using stool samples fixed in SAF for 3 months, and may be an alternative to multiple Kato-Katz thick smears.},
}
@article {pmid27788124,
year = {2016},
author = {Breitwieser, C and Pokorny, C and Müller-Putz, GR},
title = {A hybrid three-class brain-computer interface system utilizing SSSEPs and transient ERPs.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066015},
doi = {10.1088/1741-2560/13/6/066015},
pmid = {27788124},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artifacts ; Brain-Computer Interfaces/*classification ; Cues ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Female ; Fingers/innervation/physiology ; Humans ; Male ; Physical Stimulation ; Touch/physiology ; Young Adult ; },
abstract = {OBJECTIVE: This paper investigates the fusion of steady-state somatosensory evoked potentials (SSSEPs) and transient event-related potentials (tERPs), evoked through tactile simulation on the left and right-hand fingertips, in a three-class EEG based hybrid brain-computer interface. It was hypothesized, that fusing the input signals leads to higher classification rates than classifying tERP and SSSEP individually.<br><br>APPROACH: Fourteen subjects participated in the studies, consisting of a screening paradigm to determine person dependent resonance-like frequencies and a subsequent online paradigm. The whole setup of the BCI system was based on open interfaces, following suggestions for a common implementation platform. During the online experiment, subjects were instructed to focus their attention on the stimulated fingertips as indicated by a visual cue. The recorded data were classified during runtime using a multi-class shrinkage LDA classifier and the outputs were fused together applying a posterior probability based fusion. Data were further analyzed offline, involving a combined classification of SSSEP and tERP features as a second fusion principle. The final results were tested for statistical significance applying a repeated measures ANOVA.<br><br>MAIN RESULTS: A significant classification increase was achieved when fusing the results with a combined classification compared to performing an individual classification. Furthermore, the SSSEP classifier was significantly better in detecting a non-control state, whereas the tERP classifier was significantly better in detecting control states. Subjects who had a higher relative band power increase during the screening session also achieved significantly higher classification results than subjects with lower relative band power increase.<br><br>SIGNIFICANCE: It could be shown that utilizing SSSEP and tERP for hBCIs increases the classification accuracy and also that tERP and SSSEP are not classifying control- and non-control states with the same level of accuracy.},
}
@article {pmid27569365,
year = {2017},
author = {Sokunbi, MO},
title = {Feedback of real-time fMRI signals: From concepts and principles to therapeutic interventions.},
journal = {Magnetic resonance imaging},
volume = {35},
number = {},
pages = {117-124},
doi = {10.1016/j.mri.2016.08.004},
pmid = {27569365},
issn = {1873-5894},
mesh = {Brain/*physiopathology ; Brain Mapping/*methods ; Electroencephalography/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/*methods ; Neurofeedback/*methods ; },
abstract = {The feedback of real-time functional magnetic resonance imaging (rtfMRI) signals, dubbed "neurofeedback", has found applications in the treatment of clinical disorders and enhancement of brain performance. However, knowledge of the basic underlying mechanism on which neurofeedback is based is rather limited. This article introduces the concepts, principles and characteristics of feedback control systems and its applications to electroencephalography (EEG) and rtfMRI signals. Insight into the underlying mechanisms of feedback systems may lead to the development of novel feedback protocols and subsystems for rtfMRI and enhance therapeutic solutions for clinical interventions.},
}
@article {pmid27489369,
year = {2016},
author = {Dubey, A and Ray, S},
title = {Spatial spread of local field potential is band-pass in the primary visual cortex.},
journal = {Journal of neurophysiology},
volume = {116},
number = {4},
pages = {1986-1999},
pmid = {27489369},
issn = {1522-1598},
support = {500145/Z/09/Z//Wellcome Trust/United Kingdom ; },
mesh = {Algorithms ; Animals ; Gamma Rhythm ; Macaca mulatta ; Male ; Microelectrodes ; Neuropsychological Tests ; Signal Processing, Computer-Assisted ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Local field potential (LFP) is a valuable tool in understanding brain function and in brain machine-interfacing applications. However, there is no consensus on the spatial extent of the cortex that contributes to the LFP (its "spatial spread"), with different studies reporting values between a few hundred micrometers and several millimeters. Furthermore, the dependency of the spatial spread on frequency, which could reflect properties of the network architecture and extracellular medium, is not well studied, with theory and models predicting either "all-pass" (frequency-independent) or "low-pass" behavior. Surprisingly, we found the LFP spread to be "band-pass" in the primate primary visual cortex, with the greatest spread in the high-gamma range (60-150 Hz). This was accompanied by an increase in phase coherency across neighboring sites in the same frequency range, consistent with the findings of a recent model that reconciles previous studies by suggesting that spatial spread depends on neuronal correlations.},
}
@article {pmid27780085,
year = {2016},
author = {Ritaccio, AL and Williams, J and Denison, T and Foster, BL and Starr, PA and Gunduz, A and Zijlmans, M and Schalk, G},
title = {Proceedings of the Eighth International Workshop on Advances in Electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {64},
number = {Pt A},
pages = {248-252},
pmid = {27780085},
issn = {1525-5069},
support = {R01 NS096008/NS/NINDS NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R00 MH103479/MH/NIMH NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K99 MH103479/MH/NIMH NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Biomedical Research ; *Brain ; Brain Mapping/*methods ; *Electrocorticography ; Humans ; },
abstract = {Excerpted proceedings of the Eighth International Workshop on Advances in Electrocorticography (ECoG), which convened October 15-16, 2015 in Chicago, IL, are presented. The workshop series has become the foremost gathering to present current basic and clinical research in subdural brain signal recording and analysis.},
}
@article {pmid27779910,
year = {2017},
author = {Grootswagers, T and Wardle, SG and Carlson, TA},
title = {Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.},
journal = {Journal of cognitive neuroscience},
volume = {29},
number = {4},
pages = {677-697},
doi = {10.1162/jocn_a_01068},
pmid = {27779910},
issn = {1530-8898},
mesh = {Brain/*physiology ; Evoked Potentials/*physiology ; Functional Neuroimaging/*methods ; Humans ; Magnetoencephalography/*methods ; *Multivariate Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.},
}
@article {pmid27778225,
year = {2016},
author = {Khilwani, R and Gilgunn, PJ and Kozai, TD and Ong, XC and Korkmaz, E and Gunalan, PK and Cui, XT and Fedder, GK and Ozdoganlar, OB},
title = {Ultra-miniature ultra-compliant neural probes with dissolvable delivery needles: design, fabrication and characterization.},
journal = {Biomedical microdevices},
volume = {18},
number = {6},
pages = {97},
doi = {10.1007/s10544-016-0125-4},
pmid = {27778225},
issn = {1572-8781},
mesh = {Brain-Computer Interfaces ; Elastic Modulus ; *Electrodes, Implanted ; Equipment Design ; *Mechanical Phenomena ; Microtechnology/*instrumentation ; Models, Biological ; *Needles ; },
abstract = {Stable chronic functionality of intracortical probes is of utmost importance toward realizing clinical application of brain-machine interfaces. Sustained immune response from the brain tissue to the neural probes is one of the major challenges that hinder stable chronic functionality. There is a growing body of evidence in the literature that highly compliant neural probes with sub-cellular dimensions may significantly reduce the foreign-body response, thereby enhancing long term stability of intracortical recordings. Since the prevailing commercial probes are considerably larger than neurons and of high stiffness, new approaches are needed for developing miniature probes with high compliance. In this paper, we present design, fabrication, and in vitro evaluation of ultra-miniature (2.7 μm x 10 μm cross section), ultra-compliant (1.4 × 10[-2] μN/μm in the axial direction, and 2.6 × 10[-5] μN/μm and 1.8 × 10[-6] μN/μm in the lateral directions) neural probes and associated probe-encasing biodissolvable delivery needles toward addressing the aforementioned challenges. The high compliance of the probes is obtained by micron-scale cross-section and meandered shape of the parylene-C insulated platinum wiring. Finite-element analysis is performed to compare the strains within the tissue during micromotion when using the ultra-compliant meandered probes with that when using stiff silicon probes. The standard batch microfabrication techniques are used for creating the probes. A dissolvable delivery needle that encases the probe facilitates failure-free insertion and precise placement of the ultra-compliant probes. Upon completion of implantation, the needle gradually dissolves, leaving behind the ultra-compliant neural probe. A spin-casting based micromolding approach is used for the fabrication of the needle. To demonstrate the versatility of the process, needles from different biodissolvable materials, as well as two-dimensional needle arrays with different geometries and dimensions, are fabricated. Further, needles incorporating anti-inflammatory drugs are created to show the co-delivery potential of the needles. An automated insertion device is developed for repeatable and precise implantation of needle-encased probes into brain tissue. Insertion of the needles without mechanical failure, and their subsequent dissolution are demonstrated. It is concluded that ultra-miniature, ultra-compliant probes and associated biodissolvable delivery needles can be successfully fabricated, and the use of the ultra-compliant meandered probes results in drastic reduction in strains imposed in the tissue as compared to stiff probes, thereby showing promise toward chronic applications.},
}
@article {pmid27777953,
year = {2016},
author = {Abiyev, RH and Akkaya, N and Aytac, E and Günsel, I and Çağman, A},
title = {Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks.},
journal = {BioMed research international},
volume = {2016},
number = {},
pages = {9359868},
pmid = {27777953},
issn = {2314-6141},
mesh = {Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Fuzzy Logic ; Machine Learning ; *Man-Machine Systems ; *Neural Networks, Computer ; Pattern Recognition, Automated/methods ; *Wheelchairs ; },
abstract = {The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.},
}
@article {pmid27774048,
year = {2016},
author = {Évain, A and Argelaguet, F and Casiez, G and Roussel, N and Lécuyer, A},
title = {Design and Evaluation of Fusion Approach for Combining Brain and Gaze Inputs for Target Selection.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {454},
pmid = {27774048},
issn = {1662-4548},
abstract = {Gaze-based interfaces and Brain-Computer Interfaces (BCIs) allow for hands-free human-computer interaction. In this paper, we investigate the combination of gaze and BCIs. We propose a novel selection technique for 2D target acquisition based on input fusion. This new approach combines the probabilistic models for each input, in order to better estimate the intent of the user. We evaluated its performance against the existing gaze and brain-computer interaction techniques. Twelve participants took part in our study, in which they had to search and select 2D targets with each of the evaluated techniques. Our fusion-based hybrid interaction technique was found to be more reliable than the previous gaze and BCI hybrid interaction techniques for 10 participants over 12, while being 29% faster on average. However, similarly to what has been observed in hybrid gaze-and-speech interaction, gaze-only interaction technique still provides the best performance. Our results should encourage the use of input fusion, as opposed to sequential interaction, in order to design better hybrid interfaces.},
}
@article {pmid27774046,
year = {2016},
author = {Zhou, S and Jin, J and Daly, I and Wang, X and Cichocki, A},
title = {Optimizing the Face Paradigm of BCI System by Modified Mismatch Negative Paradigm.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {444},
pmid = {27774046},
issn = {1662-4548},
abstract = {Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400) in addition to P300 potentials. Recently, it has been proved that the performance of traditional P300-based BCIs could be improved through a modification of the mismatch pattern. In this paper, a mismatch inverted face pattern (MIF-pattern) was presented to improve the performance of the inverted face pattern (IF-pattern), one of the state of the art patterns used in visual-based BCI systems. Ten subjects attended in this experiment. The result showed that the mismatch inverted face pattern could evoke significantly larger vertex positive potentials (p < 0.05) and N400s (p < 0.05) compared to the inverted face pattern. The classification accuracy (mean accuracy is 99.58%) and ITRs (mean bit rate is 27.88 bit/min) of the mismatch inverted face pattern was significantly higher than that of the inverted face pattern (p < 0.05).},
}
@article {pmid27773679,
year = {2016},
author = {Herweg, A and Gutzeit, J and Kleih, S and Kübler, A},
title = {Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation.},
journal = {Biological psychology},
volume = {121},
number = {Pt A},
pages = {117-124},
doi = {10.1016/j.biopsycho.2016.10.006},
pmid = {27773679},
issn = {1873-6246},
mesh = {Aged ; Brain-Computer Interfaces/*psychology ; Environment ; Evoked Potentials/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Middle Aged ; Physical Stimulation/methods ; Task Performance and Analysis ; Touch/*physiology ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {Tactile event-related potential (ERP) are rarely used as input signal to control brain-computer-interfaces (BCI) due to their low accuracy and speed (information transfer rate, ITR). Age-related loss of tactile sensibility might further decrease their viability for the target population of BCI. In this study we investigated whether training improves tactile ERP-BCI performance within a virtual wheelchair navigation task. Elderly subjects participated in 5 sessions and tactors were placed at legs, abdomen and back. Mean accuracy and ITR increased from 88.43%/4.5bitsmin[-1] in the 1st to 92.56%/4.98bitsmin[-1] in the last session. The mean P300 amplitude increased from 5.46μV to 9.22μV. In an optional task participants achieved an accuracy of 95,56% and a mean ITR of 20,73bitsmin[-1] which is the highest ever achieved with tactile stimulation. Our sample of elderly people further contributed to the external validity of our results.},
}
@article {pmid27632555,
year = {2016},
author = {Wriessnegger, SC and Steyrl, D and Koschutnig, K and Müller-Putz, GR},
title = {Cooperation in mind: Motor imagery of joint and single actions is represented in different brain areas.},
journal = {Brain and cognition},
volume = {109},
number = {},
pages = {19-25},
doi = {10.1016/j.bandc.2016.08.008},
pmid = {27632555},
issn = {1090-2147},
mesh = {Adult ; Brain Mapping/*methods ; *Cooperative Behavior ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Male ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Parietal Lobe/*physiology ; Young Adult ; },
abstract = {In this study brain activity during motor imagery (MI) of joint actions, compared to single actions and rest conditions, was investigated using functional magnetic resonance imaging (fMRI). To the best of our knowledge, this is the first neuroimaging study which directly investigated the neural correlates of joint action motor imagery. Twenty-one healthy participants imagined three different motor tasks (dancing, carrying a box, wiping). Each imagery task was performed at two kinds: alone (single action MI) or with a partner (joint action MI). We hypothesized that to imagine a cooperative task would lead to a stronger cortical activation in motor related areas due to a higher vividness and intensification of the imagery. This would be elicited by the integration of the action simulation of the virtual partner to one's own action. Comparing the joint action and the single action condition with the rest condition, we found significant activation in the precentral gyrus and precuneus respectively. Furthermore the joint action MI showed higher activation patterns in the premotor cortex (inferior and middle frontal gyrus) compared to the single action MI. The imagery of a more vivid and engaging task, like our joint action imagery, could improve rehabilitation processes since a more distributed brain activity is found. Furthermore, the joint action imagery compared to single action imagery might be an appropriate BCI task due to its clear spatial distinction of activation.},
}
@article {pmid27519275,
year = {2017},
author = {Eapen, BC and Murphy, DP and Cifu, DX},
title = {Neuroprosthetics in amputee and brain injury rehabilitation.},
journal = {Experimental neurology},
volume = {287},
number = {Pt 4},
pages = {479-485},
doi = {10.1016/j.expneurol.2016.08.004},
pmid = {27519275},
issn = {1090-2430},
mesh = {Amputation, Surgical/psychology/*rehabilitation ; Brain Injuries/psychology/*rehabilitation ; *Brain-Computer Interfaces ; Cognition Disorders/etiology/therapy ; Consciousness Disorders/etiology/therapy ; Persons with Disabilities/*psychology ; Extremities/surgery ; Feedback, Sensory ; Hearing Loss/etiology/therapy ; Humans ; Memory Disorders/etiology/therapy ; Patient-Centered Care ; Proprioception ; Prostheses and Implants/*psychology ; *Prosthesis Design ; Quality of Life ; Risk Assessment ; Vision Disorders/etiology/therapy ; },
abstract = {The goals of rehabilitation medicine programs are to promote health, restore functional impairments and improve quality of life. The field of neuroprosthetics has evolved over the last decade given an improved understanding of neuroscience and the incorporation of advanced biotechnology and neuroengineering in the rehabilitation setting to develop adaptable applications to help facilitate recovery for individuals with amputations and brain injury. These applications may include a simple cognitive prosthetics aid for impaired memory in brain-injured individuals to myoelectric prosthetics arms with artificial proprioceptive feedback for those with upper extremity amputations. The integration of neuroprosthetics into the existing framework of current rehabilitation approaches not only improves quality-of-care and outcomes but help broadens current rehabilitation treatment paradigms. Although, we are in the infancy of the understanding the true benefit of neuroprosthetics and its clinical applications in the rehabilitation setting there is tremendous amount of promise for future research and development of tools to help facilitate recovery and improve quality of life in individuals with disabilities.},
}
@article {pmid27507089,
year = {2016},
author = {Mondéjar, T and Hervás, R and Johnson, E and Gutierrez, C and Latorre, JM},
title = {Correlation between videogame mechanics and executive functions through EEG analysis.},
journal = {Journal of biomedical informatics},
volume = {63},
number = {},
pages = {131-140},
doi = {10.1016/j.jbi.2016.08.006},
pmid = {27507089},
issn = {1532-0480},
mesh = {Adolescent ; Brain ; Child ; Cognition ; *Electroencephalography ; *Executive Function ; Female ; Humans ; Male ; *Video Games ; },
abstract = {This paper addresses a different point of view of videogames, specifically serious games for health. This paper contributes to that area with a multidisciplinary perspective focus on neurosciences and computation. The experiment population has been pre-adolescents between the ages of 8 and 12 without any cognitive issues. The experiment consisted in users playing videogames as well as performing traditional psychological assessments; during these tasks the frontal brain activity was evaluated. The main goal was to analyse how the frontal lobe of the brain (executive function) works in terms of prominent cognitive skills during five types of game mechanics widely used in commercial videogames. The analysis was made by collecting brain signals during the two phases of the experiment, where the signals were analysed with an electroencephalogram neuroheadset. The validated hypotheses were whether videogames can develop executive functioning and if it was possible to identify which kind of cognitive skills are developed during each kind of typical videogame mechanic. The results contribute to the design of serious games for health purposes on a conceptual level, particularly in support of the diagnosis and treatment of cognitive-related pathologies.},
}
@article {pmid27456271,
year = {2017},
author = {Benz, HL and Civillico, EF},
title = {Neuroprosthetics and the science of patient input.},
journal = {Experimental neurology},
volume = {287},
number = {Pt 4},
pages = {486-491},
pmid = {27456271},
issn = {1090-2430},
support = {FD999999//Intramural FDA HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Device Approval ; Persons with Disabilities/*psychology ; Guidelines as Topic ; Humans ; *Patient Preference ; Patient-Centered Care ; Prostheses and Implants/*psychology ; *Prosthesis Design ; Risk Assessment ; United States ; United States Food and Drug Administration ; },
abstract = {Safe and effective neuroprosthetic systems are of great interest to both DARPA and CDRH, due to their innovative nature and their potential to aid severely disabled populations. By expanding what is possible in human-device interaction, these devices introduce new potential benefits and risks. Therefore patient input, which is increasingly important in weighing benefits and risks, is particularly relevant for this class of devices. FDA has been a significant contributor to an ongoing stakeholder conversation about the inclusion of the patient voice, working collaboratively to create a new framework for a patient-centered approach to medical device development. This framework is evolving through open dialogue with researcher and patient communities, investment in the science of patient input, and policymaking that is responsive to patient-centered data throughout the total product life cycle. In this commentary, we will discuss recent developments in patient-centered benefit-risk assessment and their relevance to the development of neural prosthetic systems.},
}
@article {pmid27425203,
year = {2016},
author = {Vidaurre, C and Klauer, C and Schauer, T and Ramos-Murguialday, A and Müller, KR},
title = {EEG-based BCI for the linear control of an upper-limb neuroprosthesis.},
journal = {Medical engineering &amp; physics},
volume = {38},
number = {11},
pages = {1195-1204},
doi = {10.1016/j.medengphy.2016.06.010},
pmid = {27425203},
issn = {1873-4030},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electric Stimulation ; *Electroencephalography ; Feedback, Physiological ; Humans ; Linear Models ; *Neural Prostheses ; *Upper Extremity ; },
abstract = {Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI-FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.},
}
@article {pmid27659118,
year = {2018},
author = {Sorger, B and Kamp, T and Weiskopf, N and Peters, JC and Goebel, R},
title = {When the Brain Takes 'BOLD' Steps: Real-Time fMRI Neurofeedback Can Further Enhance the Ability to Gradually Self-regulate Regional Brain Activation.},
journal = {Neuroscience},
volume = {378},
number = {},
pages = {71-88},
pmid = {27659118},
issn = {1873-7544},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {Adult ; Brain/*diagnostic imaging/*physiology ; Brain Mapping ; Cerebrovascular Circulation ; Feasibility Studies ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Mental Processes/physiology ; Neurofeedback/*methods/physiology/*radiation effects ; Oxygen/blood ; Time Factors ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i.e., to allow the BCI user to choose from as many options as possible. Recently, the ability to voluntarily modulate spatial and/or temporal blood oxygenation level-dependent (BOLD)-signal features has been explored implementing different mental tasks and/or different encoding time intervals, respectively. Our two-session fMRI feasibility study systematically investigated for the first time the possibility of using magnitudinal BOLD-signal features for intention encoding. Particularly, in our novel paradigm, participants (n=10) were asked to alternately self-regulate their regional brain-activation level to 30%, 60% or 90% of their maximal capacity by applying a selected activation strategy (i.e., performing a mental task, e.g., inner speech) and modulation strategies (e.g., using different speech rates) suggested by the experimenters. In a second step, we tested the hypothesis that the additional availability of feedback information on the current BOLD-signal level within a region of interest improves the gradual-self regulation performance. Therefore, participants were provided with neurofeedback in one of the two fMRI sessions. Our results show that the majority of the participants were able to gradually self-regulate regional brain activation to at least two different target levels even in the absence of neurofeedback. When provided with continuous feedback on their current BOLD-signal level, most participants further enhanced their gradual self-regulation ability. Our findings were observed across a wide variety of mental tasks and across clinical MR field strengths (i.e., at 1.5T and 3T), indicating that these findings are robust and can be generalized across mental tasks and scanner types. The suggested novel parametric activation paradigm enriches the spectrum of current rtfMRI-neurofeedback and BCI methodology and has considerable potential for fundamental and clinical neuroscience applications.},
}
@article {pmid27496574,
year = {2016},
author = {Nasuto, SJ and Hayashi, Y},
title = {Anticipation: Beyond synthetic biology and cognitive robotics.},
journal = {Bio Systems},
volume = {148},
number = {},
pages = {22-31},
doi = {10.1016/j.biosystems.2016.07.011},
pmid = {27496574},
issn = {1872-8324},
mesh = {Animals ; Brain-Computer Interfaces ; Cognition/*physiology ; Computational Biology/*methods/trends ; Cybernetics/methods ; Humans ; Psychophysiology/methods ; Robotics/*methods/trends ; Synthetic Biology/*methods/trends ; },
abstract = {The aim of this paper is to propose that current robotic technologies cannot have intentional states any more than is feasible within the sensorimotor variant of embodied cognition. It argues that anticipation is an emerging concept that can provide a bridge between both the deepest philosophical theories about the nature of life and cognition and the empirical biological and cognitive sciences steeped in reductionist and Newtonian conceptions of causality. The paper advocates that in order to move forward, cognitive robotics needs to embrace new platforms and a conceptual framework that will enable it to pursue, in a meaningful way, questions about autonomy and purposeful behaviour. We suggest that hybrid systems, part robotic and part cultures of neurones, offer experimental platforms where different dimensions of enactivism (sensorimotor, constitutive foundations of biological autonomy, including anticipation), and their relative contributions to cognition, can be investigated in an integrated way. A careful progression, mindful to the deep philosophical concerns but also respecting empirical evidence, will ultimately lead towards unifying theoretical and empirical biological sciences and may offer advancement where reductionist sciences have been so far faltering.},
}
@article {pmid27767063,
year = {2016},
author = {Khorasani, A and Heydari Beni, N and Shalchyan, V and Daliri, MR},
title = {Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {35238},
pmid = {27767063},
issn = {2045-2322},
mesh = {Action Potentials/*physiology ; Animals ; Brain-Computer Interfaces ; Conditioning, Classical/physiology ; Electrodes, Implanted ; Male ; Microelectrodes ; Motor Cortex/anatomy &amp; histology/*physiology ; Movement/*physiology ; Rats ; Rats, Wistar ; Stereotaxic Techniques ; },
abstract = {Local field potential (LFP) signals recorded by intracortical microelectrodes implanted in primary motor cortex can be used as a high informative input for decoding of motor functions. Recent studies show that different kinematic parameters such as position and velocity can be inferred from multiple LFP signals as precisely as spiking activities, however, continuous decoding of the force magnitude from the LFP signals in freely moving animals has remained an open problem. Here, we trained three rats to press a force sensor for getting a drop of water as a reward. A 16-channel micro-wire array was implanted in the primary motor cortex of each trained rat, and obtained LFP signals were used for decoding of the continuous values recorded by the force sensor. Average coefficient of correlation and the coefficient of determination between decoded and actual force signals were r = 0.66 and R[2] = 0.42, respectively. We found that LFP signal on gamma frequency bands (30-120 Hz) had the most contribution in the trained decoding model. This study suggests the feasibility of using low number of LFP channels for the continuous force decoding in freely moving animals resembling BMI systems in real life applications.},
}
@article {pmid27762239,
year = {2016},
author = {Zeid, EA and Sereshkeh, AR and Chau, T},
title = {A pipeline of spatio-temporal filtering for predicting the laterality of self-initiated fine movements from single trial readiness potentials.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066012},
doi = {10.1088/1741-2560/13/6/066012},
pmid = {27762239},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Benchmarking ; *Brain-Computer Interfaces ; Contingent Negative Variation/*physiology ; Electroencephalography/methods ; Functional Laterality/*physiology ; Humans ; Male ; Movement/*physiology ; Reproducibility of Results ; Somatosensory Cortex/physiology ; },
abstract = {OBJECTIVE: In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have attempted single trial classification of RP via spatial and temporal filtering methods, or by combining the RP with event-related desynchronization. However, RP feature extraction remains challenging due to the slow non-oscillatory nature of the potential, its variability among participants and the inherent noise in EEG signals. Here, we propose a participant-specific, individually optimized pipeline of spatio-temporal filtering (PSTF) to improve RP feature extraction for laterality prediction.<br><br>APPROACH: PSTF applies band-pass filtering on RP signals, followed by Fisher criterion spatial filtering to maximize class separation, and finally temporal window averaging for feature dimension reduction. Optimal parameters are simultaneously found by cross-validation for each participant. Using EEG data from 14 participants performing self-initiated left or right key presses as well as two benchmark BCI datasets, we compared the performance of PSTF to two popular methods: common spatial subspace decomposition, and adaptive spatio-temporal filtering.<br><br>MAIN RESULTS: On the BCI benchmark data sets, PSTF performed comparably to both existing methods. With the key press EEG data, PSTF extracted more discriminative features, thereby leading to more accurate (74.99% average accuracy) predictions of RP laterality than that achievable with existing methods.<br><br>SIGNIFICANCE: Naturalistic and volitional interaction with the world is an important capacity that is lost with traditional system-paced BCIs. We demonstrated a significant improvement in fine movement laterality prediction from RP features alone. Our work supports further study of RP-based BCI for intuitive asynchronous control of the environment, such as augmentative communication or wheelchair navigation.},
}
@article {pmid27762234,
year = {2016},
author = {Kinney-Lang, E and Auyeung, B and Escudero, J},
title = {Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain-computer interfaces for motor rehabilitation in children.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {061002},
doi = {10.1088/1741-2560/13/6/061002},
pmid = {27762234},
issn = {1741-2552},
mesh = {Autism Spectrum Disorder ; Brain-Computer Interfaces/*trends ; Child ; Communication Devices for People with Disabilities ; Electroencephalography/methods/*trends ; Humans ; Neural Prostheses ; Prosthesis Design ; Rehabilitation/*methods ; },
abstract = {Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. • BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. • A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. • Indirect studies discovered from the systematic literature search, i.e. neurorehabilitation in children via BCI for autism spectrum disorder, provide insight into translating motor rehabilitation BCI applications to children. • Translating BCI applications to children is a relevant, important area of research which is relatively barren.},
}
@article {pmid27443853,
year = {2016},
author = {Ozdemir, RA and Contreras-Vidal, JL and Lee, BC and Paloski, WH},
title = {Cortical activity modulations underlying age-related performance differences during posture-cognition dual tasking.},
journal = {Experimental brain research},
volume = {234},
number = {11},
pages = {3321-3334},
pmid = {27443853},
issn = {1432-1106},
mesh = {Adult ; Aged ; Aged, 80 and over ; Aging/*physiology ; Analysis of Variance ; Cognition/*physiology ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Parietal Lobe/*physiology ; Postural Balance/*physiology ; *Posture ; Psychomotor Performance/*physiology ; Surveys and Questionnaires ; Young Adult ; },
abstract = {To date, no systematic research investigating cortical correlates of performance changes in dual tasking has been reported in the elderly population. Thus, we monitored whole-scalp cortical activations (EEG) during both single task and posture-cognition dual tasking with the main goal of understanding cortical activity modulations underlying age-related differences on posture-cognition dual tasking conditions. Postural and cognitive data analyses showed that elderly people had decreased cognitive performance even during challenging single cognitive tasks. Working memory impairments in the elderly group can be observed when a challenging cognitive task is performed in any postural condition, while postural control performance differences only became significant during challenging dual task conditions. Behavioral performance results, in general, indicate that elderly subjects may adopt a non-automated conscious control strategy and prioritize postural performance over cognitive performance to maintain upright stance only when the cognitive load is low. EEG analyses showed increased delta, theta and gamma oscillations, primarily over frontal, central-frontal, central and central-parietal cortices during dual tasking conditions. We found that delta oscillations were more responsive to challenging postural conditions presumably related to cortical representations of changing sensory conditions in postural tasks. Theta rhythms, on the other hand, were more responsive to cognitive task difficulty in both groups, with more pronounced increases in younger subjects which may underlie neural correlates of high-level cognitive computations including encoding and retrieval. Gamma oscillations also increased in the elderly primarily over central and central-parietal cortices during challenging postural tasks, indicating increased allocation of attentional sources to postural tasks.},
}
@article {pmid27755745,
year = {2016},
author = {Lunn, NJ and Servanty, S and Regehr, EV and Converse, SJ and Richardson, E and Stirling, I},
title = {Demography of an apex predator at the edge of its range: impacts of changing sea ice on polar bears in Hudson Bay.},
journal = {Ecological applications : a publication of the Ecological Society of America},
volume = {26},
number = {5},
pages = {1302-1320},
doi = {10.1890/15-1256},
pmid = {27755745},
issn = {1051-0761},
mesh = {Animals ; Arctic Regions ; *Bays ; *Climate Change ; *Ecosystem ; Female ; *Ice Cover ; Male ; Models, Biological ; Population Dynamics ; Time Factors ; Ursidae/*physiology ; },
abstract = {Changes in the abundance and distribution of wildlife populations are common consequences of historic and contemporary climate change. Some Arctic marine mammals, such as the polar bear (Ursus maritimus), may be particularly vulnerable to such changes due to the loss of Arctic sea ice. We evaluated the impacts of environmental variation on demographic rates for the Western Hudson Bay (WH), polar bear subpopulation from 1984 to 2011 using live-recapture and dead-recovery data in a Bayesian implementation of multistate capture-recapture models. We found that survival of female polar bears was related to the annual timing of sea ice break-up and formation. Using estimated vital rates (e.g., survival and reproduction) in matrix projection models, we calculated the growth rate of the WH subpopulation and projected population responses under different environmental scenarios while accounting for parametric uncertainty, temporal variation, and demographic stochasticity. Our analysis suggested a long-term decline in the number of bears from 1185 (95% Bayesian credible interval [BCI] = 993-1411) in 1987 to 806 (95% BCI = 653-984) in 2011. In the last 10 yr of the study, the number of bears appeared stable due to temporary stability in sea ice conditions (mean population growth rate for the period 2001-2010 = 1.02, 95% BCI = 0.98-1.06). Looking forward, we estimated long-term growth rates for the WH subpopulation of ~1.02 (95% BCI = 1.00-1.05) and 0.97 (95% BCI = 0.92-1.01) under hypothetical high and low sea ice conditions, respectively. Our findings support previous evidence for a demographic linkage between sea ice conditions and polar bear population dynamics. Furthermore, we present a robust framework for sensitivity analysis with respect to continued climate change (e.g., to inform scenario planning) and for evaluating the combined effects of climate change and management actions on the status of wildlife populations.},
}
@article {pmid27751622,
year = {2016},
author = {Islam, MK and Rastegarnia, A and Yang, Z},
title = {Methods for artifact detection and removal from scalp EEG: A review.},
journal = {Neurophysiologie clinique = Clinical neurophysiology},
volume = {46},
number = {4-5},
pages = {287-305},
doi = {10.1016/j.neucli.2016.07.002},
pmid = {27751622},
issn = {1769-7131},
mesh = {Algorithms ; *Artifacts ; Brain/physiology/physiopathology ; Electroencephalography/*methods ; Humans ; Scalp ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.},
}
@article {pmid27747824,
year = {2017},
author = {Gupta, A and Kumar, D},
title = {Fuzzy clustering-based feature extraction method for mental task classification.},
journal = {Brain informatics},
volume = {4},
number = {2},
pages = {135-145},
pmid = {27747824},
issn = {2198-4018},
abstract = {A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman's statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.},
}
@article {pmid27746716,
year = {2016},
author = {Halder, S and Takano, K and Ora, H and Onishi, A and Utsumi, K and Kansaku, K},
title = {An Evaluation of Training with an Auditory P300 Brain-Computer Interface for the Japanese Hiragana Syllabary.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {446},
pmid = {27746716},
issn = {1662-4548},
abstract = {Gaze-independent brain-computer interfaces (BCIs) are a possible communication channel for persons with paralysis. We investigated if it is possible to use auditory stimuli to create a BCI for the Japanese Hiragana syllabary, which has 46 Hiragana characters. Additionally, we investigated if training has an effect on accuracy despite the high amount of different stimuli involved. Able-bodied participants (N = 6) were asked to select 25 syllables (out of fifty possible choices) using a two step procedure: First the consonant (ten choices) and then the vowel (five choices). This was repeated on 3 separate days. Additionally, a person with spinal cord injury (SCI) participated in the experiment. Four out of six healthy participants reached Hiragana syllable accuracies above 70% and the information transfer rate increased from 1.7 bits/min in the first session to 3.2 bits/min in the third session. The accuracy of the participant with SCI increased from 12% (0.2 bits/min) to 56% (2 bits/min) in session three. Reliable selections from a 10 × 5 matrix using auditory stimuli were possible and performance is increased by training. We were able to show that auditory P300 BCIs can be used for communication with up to fifty symbols. This enables the use of the technology of auditory P300 BCIs with a variety of applications.},
}
@article {pmid27746714,
year = {2016},
author = {Pinegger, A and Wriessnegger, SC and Faller, J and Müller-Putz, GR},
title = {Evaluation of Different EEG Acquisition Systems Concerning Their Suitability for Building a Brain-Computer Interface: Case Studies.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {441},
pmid = {27746714},
issn = {1662-4548},
abstract = {One important aspect in non-invasive brain-computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method. Every system was tested regarding three different aspects, namely, technical, BCI effectiveness and efficiency (P300 communication and control), and user satisfaction (comfort). We found that water-based system had the lowest short circuit noise level, the hydrogel-based system had the highest P300 spelling accuracies, and the dry electrode-based system caused the least inconveniences. Therefore, building a reliable BCI is possible with all the evaluated systems, and it is on the user to decide which system meets the given requirements best.},
}
@article {pmid27701161,
year = {2016},
author = {Torregrosa, T and Koppes, RA},
title = {Bioelectric Medicine and Devices for the Treatment of Spinal Cord Injury.},
journal = {Cells, tissues, organs},
volume = {202},
number = {1-2},
pages = {6-22},
doi = {10.1159/000446698},
pmid = {27701161},
issn = {1422-6421},
mesh = {Brain-Computer Interfaces ; Electric Stimulation ; Electronics, Medical/*instrumentation ; Humans ; Neural Prostheses ; Peripheral Nervous System/pathology ; Spinal Cord Injuries/*therapy ; },
abstract = {Recovery of motor control is paramount for patients living with paralysis following spinal cord injury (SCI). While a cure or regenerative intervention remains on the horizon for the treatment of SCI, a number of neuroprosthetic devices have been employed to treat and mitigate the symptoms of paralysis associated with injuries to the spinal column and associated comorbidities. The recent success of epidural stimulation to restore voluntary motor function in the lower limbs of a small cohort of patients has breathed new life into the promise of electric-based medicine. Recently, a number of new organic and inorganic electronic devices have been developed for brain-computer interfaces to bypass the injury, for neurorehabilitation, bladder and bowel control, and the restoration of motor or sensory control. Herein, we discuss the recent advances in neuroprosthetic devices for treating SCI and highlight future design needs for closed-loop device systems.},
}
@article {pmid27740497,
year = {2016},
author = {Dadarlat, MC and Sabes, PN},
title = {Encoding and Decoding of Multi-Channel ICMS in Macaque Somatosensory Cortex.},
journal = {IEEE transactions on haptics},
volume = {9},
number = {4},
pages = {508-514},
doi = {10.1109/TOH.2016.2616311},
pmid = {27740497},
issn = {2329-4051},
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electrocorticography ; Feedback, Sensory/*physiology ; Macaca ; Male ; Proprioception/*physiology ; Psychomotor Performance/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {Naturalistic control of brain-machine interfaces will require artificial proprioception, potentially delivered via intracortical microstimulation (ICMS). We have previously shown that multi-channel ICMS can guide a monkey reaching to unseen targets in a planar workspace. Here, we expand on that work, asking how ICMS is decoded into target angle and distance by analyzing the performance of a monkey when ICMS feedback was degraded. From the resulting pattern of errors, we found that the animal's estimate of target direction was consistent with a weighted circular-mean strategy-close to the optimal decoding strategy given the ICMS encoding. These results support our previous finding that animals can learn to use this artificial sensory feedback in an efficient and naturalistic manner.},
}
@article {pmid27739405,
year = {2016},
author = {Osuagwu, BC and Wallace, L and Fraser, M and Vuckovic, A},
title = {Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {065002},
doi = {10.1088/1741-2560/13/6/065002},
pmid = {27739405},
issn = {1741-2552},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Electric Stimulation ; Electroencephalography Phase Synchronization ; Evoked Potentials, Somatosensory ; *Hand ; Humans ; Male ; Median Nerve/physiopathology ; Middle Aged ; Movement ; Muscle Strength ; Pilot Projects ; Quadriplegia/physiopathology/*rehabilitation ; Range of Motion, Articular ; Sensorimotor Cortex/physiopathology ; Ulnar Nerve/physiopathology ; Wrist/physiology ; },
abstract = {OBJECTIVE: To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia.<br><br>APPROACH: Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test.<br><br>MAIN RESULTS: Patients in both groups initially had intense ERD during movement attempt that was not restricted to the sensory-motor cortex. Following the treatment, ERD cortical activity restored towards the activity in able-bodied people in BCI-FES group only, remaining wide-spread in FES group. Likewise, SSEP returned in 3 patients in BCI-FES group, having no changes in FES group. The ROM of the wrist improved in both groups. Muscle strength significantly improved for both hands in BCI-FES group. For FES group, a significant improvement was noticed for right hand flexor muscles only.<br><br>SIGNIFICANCE: Combined BCI-FES therapy results in better neurological recovery and better improvement of muscle strength than FES alone. For spinal cord injured patients, BCI-FES should be considered as a therapeutic tool rather than solely a long-term assistive device for the restoration of a lost function.},
}
@article {pmid27739402,
year = {2016},
author = {Petrushin, A and Ferrara, L and Blau, A},
title = {The Si elegans project at the interface of experimental and computational Caenorhabditis elegans neurobiology and behavior.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {065001},
doi = {10.1088/1741-2560/13/6/065001},
pmid = {27739402},
issn = {1741-2552},
mesh = {Animals ; Behavior, Animal/*physiology ; *Brain-Computer Interfaces ; Caenorhabditis elegans/*physiology ; Locomotion/physiology ; *Neural Networks, Computer ; Neurobiology/*methods ; Prosthesis Design ; },
abstract = {OBJECTIVE: In light of recent progress in mapping neural function to behavior, we briefly and selectively review past and present endeavors to reveal and reconstruct nervous system function in Caenorhabditis elegans through simulation.<br><br>APPROACH: Rather than presenting an all-encompassing review on the mathematical modeling of C. elegans, this contribution collects snapshots of pathfinding key works and emerging technologies that recent single- and multi-center simulation initiatives are building on. We thereby point out a few general limitations and problems that these undertakings are faced with and discuss how these may be addressed and overcome.<br><br>MAIN RESULTS: Lessons learned from past and current computational approaches to deciphering and reconstructing information flow in the C. elegans nervous system corroborate the need of refining neural response models and linking them to intra- and extra-environmental interactions to better reflect and understand the actual biological, biochemical and biophysical events that lead to behavior. Together with single-center research efforts, the Si elegans and OpenWorm projects aim at providing the required, in some cases complementary tools for different hardware architectures to support advancement into this direction.<br><br>SIGNIFICANCE: Despite its seeming simplicity, the nervous system of the hermaphroditic nematode C. elegans with just 302 neurons gives rise to a rich behavioral repertoire. Besides controlling vital functions (feeding, defecation, reproduction), it encodes different stimuli-induced as well as autonomous locomotion modalities (crawling, swimming and jumping). For this dichotomy between system simplicity and behavioral complexity, C. elegans has challenged neurobiologists and computational scientists alike. Understanding the underlying mechanisms that lead to a context-modulated functionality of individual neurons would not only advance our knowledge on nervous system function and its failure in pathological states, but have directly exploitable benefits for robotics and the engineering of brain-mimetic computational architectures that are orthogonal to current von-Neumann-type machines.},
}
@article {pmid27739401,
year = {2016},
author = {Fernández-Rodríguez, &#xc1; and Velasco-Álvarez, F and Ron-Angevin, R},
title = {Review of real brain-controlled wheelchairs.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {061001},
doi = {10.1088/1741-2560/13/6/061001},
pmid = {27739401},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*trends ; Persons with Disabilities ; Electroencephalography ; Equipment Design ; Humans ; Wheelchairs/*trends ; },
abstract = {This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface. Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future.},
}
@article {pmid27729844,
year = {2016},
author = {Herff, C and Schultz, T},
title = {Automatic Speech Recognition from Neural Signals: A Focused Review.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {429},
pmid = {27729844},
issn = {1662-4548},
abstract = {Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.},
}
@article {pmid27725827,
year = {2016},
author = {Naseer, N and Qureshi, NK and Noori, FM and Hong, KS},
title = {Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {5480760},
pmid = {27725827},
issn = {1687-5273},
mesh = {Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Humans ; Neuropsychological Tests ; Oxyhemoglobins/*metabolism ; Prefrontal Cortex/*metabolism ; Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared/classification/instrumentation ; Support Vector Machine ; },
abstract = {We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals.},
}
@article {pmid27721741,
year = {2016},
author = {Vassanelli, S and Mahmud, M},
title = {Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {438},
pmid = {27721741},
issn = {1662-4548},
abstract = {Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.},
}
@article {pmid27713685,
year = {2016},
author = {Waytowich, NR and Lawhern, VJ and Bohannon, AW and Ball, KR and Lance, BJ},
title = {Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {430},
pmid = {27713685},
issn = {1662-4548},
abstract = {Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.},
}
@article {pmid27713590,
year = {2017},
author = {Gonzalez-Navarro, P and Moghadamfalahi, M and Akcakaya, M and Erdogmus, D},
title = {Spatio-Temporal EEG Models for Brain Interfaces.},
journal = {Signal processing},
volume = {131},
number = {},
pages = {333-343},
pmid = {27713590},
issn = {0165-1684},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; //United States NIDCD/ ; },
abstract = {Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of parameters is required for BCI inference. However, the relatively high dimensionality of the EEG feature vectors with respect to the number of labeled observations lead to rank deficient covariance matrix estimates. In this manuscript, to overcome ill-conditioned covariance estimation, we propose a structure for the covariance matrices of the multichannel EEG signals. Specifically, we assume that these covariances can be modeled as a Kronecker product of temporal and spatial covariances. Our results over the experimental data collected from the users of a letter-by-letter typing BCI show that with less number of parameter estimations, the system can achieve higher classification accuracies compared to a method that uses full unstructured covariance estimation. Moreover, in order to illustrate that the proposed Kronecker product structure could enable shortening the BCI calibration data collection sessions, using Cramer-Rao bound analysis on simulated data, we demonstrate that a model with structured covariance matrices will achieve the same estimation error as a model with no covariance structure using fewer labeled EEG observations.},
}
@article {pmid27712455,
year = {2017},
author = {Rembado, I and Castagnola, E and Turella, L and Ius, T and Budai, R and Ansaldo, A and Angotzi, GN and Debertoldi, F and Ricci, D and Skrap, M and Fadiga, L},
title = {Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro-Electrocorticography.},
journal = {International journal of neural systems},
volume = {27},
number = {4},
pages = {1650052},
doi = {10.1142/S0129065716500520},
pmid = {27712455},
issn = {1793-6462},
mesh = {Adult ; Brain Neoplasms/physiopathology/surgery ; Electric Stimulation ; Electrocorticography/instrumentation/*methods ; Equipment Design ; *Evoked Potentials, Somatosensory ; Glioma/physiopathology/surgery ; Humans ; Male ; Median Nerve/physiopathology ; Microelectrodes ; Middle Aged ; Motor Cortex/physiopathology ; *Signal Processing, Computer-Assisted ; Somatosensory Cortex/physiopathology ; Touch Perception/physiology ; },
abstract = {High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction - not a negligible phenomenon - is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain-computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.},
}
@article {pmid27706094,
year = {2016},
author = {Krachunov, S and Casson, AJ},
title = {3D Printed Dry EEG Electrodes.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {10},
pages = {},
pmid = {27706094},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; *Electrodes ; Electroencephalography/*methods ; Humans ; *Printing, Three-Dimensional ; },
abstract = {Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise.},
}
@article {pmid27706022,
year = {2016},
author = {Maren, AJ},
title = {The Cluster Variation Method: A Primer for Neuroscientists.},
journal = {Brain sciences},
volume = {6},
number = {4},
pages = {},
pmid = {27706022},
issn = {2076-3425},
abstract = {Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h) for the case of an equiprobable distribution of bistate (neural/neural ensemble) units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution) yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.},
}
@article {pmid27705956,
year = {2016},
author = {Mainsah, BO and Collins, LM and Throckmorton, CS},
title = {Using the detectability index to predict P300 speller performance.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066007},
pmid = {27705956},
issn = {1741-2552},
support = {R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Amyotrophic Lateral Sclerosis/psychology ; Bayes Theorem ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Likelihood Functions ; Models, Statistical ; Models, Theoretical ; Monte Carlo Method ; Reproducibility of Results ; },
abstract = {OBJECTIVE: The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user's performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping (DS) algorithms and other stimulus paradigms is desirable.<br><br>APPROACH: We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian DS algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses.<br><br>MAIN RESULTS: Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method.<br><br>SIGNIFICANCE: The proposed method could serve as a useful tool to initially assess BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level.},
}
@article {pmid27705952,
year = {2016},
author = {Xu, M and Wang, Y and Nakanishi, M and Wang, YT and Qi, H and Jung, TP and Ming, D},
title = {Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features.},
journal = {Journal of neural engineering},
volume = {13},
number = {6},
pages = {066003},
doi = {10.1088/1741-2560/13/6/066003},
pmid = {27705952},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Attention/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Fixation, Ocular ; Humans ; Male ; Photic Stimulation ; Reproducibility of Results ; Space Perception/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP).<br><br>APPROACH: A new paradigm was designed to code the left and right CVSA with the N2pc and SSVEP features, which were then decoded by a classification strategy based on canonical correlation analysis. Eleven subjects were recruited to perform an offline experiment in this study. Temporal waves, amplitudes, and topographies for brain responses related to N2pc and SSVEP were analyzed. The classification accuracy derived from the hybrid EEG features (SSVEP and N2pc) was compared with those using the single EEG features (SSVEP or N2pc).<br><br>MAIN RESULTS: The N2pc could be significantly enhanced under certain conditions of SSVEP modulations. The hybrid EEG features achieved significantly higher accuracy than the single features. It obtained an average accuracy of 72.9% by using a data length of 400 ms after the attention shift. Moreover, the average accuracy reached &#x223c;80% (peak values above 90%) when using 2 s long data.<br><br>SIGNIFICANCE: The results indicate that the combination of N2pc and SSVEP is effective for fast detection of CVSA. The proposed method could be a promising approach for implementing a gaze-independent BCI.},
}
@article {pmid27705860,
year = {2017},
author = {Bi, L and Lu, Y and Fan, X and Lian, J and Liu, Y},
title = {Queuing Network Modeling of Driver EEG Signals-Based Steering Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {8},
pages = {1117-1124},
doi = {10.1109/TNSRE.2016.2614003},
pmid = {27705860},
issn = {1558-0210},
abstract = {Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended queuing network-based driver model with a brain-computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving. The brain-controlled steering model has potential values in helping develop a brain-controlled assistive vehicle. Furthermore, this study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots).},
}
@article {pmid27698660,
year = {2016},
author = {Grandchamp, R and Delorme, A},
title = {The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {4204385},
pmid = {27698660},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Female ; Humans ; Learning/*physiology ; Male ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Recent theoretical and technological advances in neuroimaging techniques now allow brain electrical activity to be recorded using affordable and user-friendly equipment for nonscientist end-users. An increasing number of educators and artists have begun using electroencephalogram (EEG) to control multimedia and live artistic contents. In this paper, we introduce a new concept based on brain computer interface (BCI) technologies: the Brainarium. The Brainarium is a new pedagogical and artistic tool, which can deliver and illustrate scientific knowledge, as well as a new framework for scientific exploration. The Brainarium consists of a portable planetarium device that is being used as brain metaphor. This is done by projecting multimedia content on the planetarium dome and displaying EEG data recorded from a subject in real time using Brain Machine Interface (BMI) technologies. The system has been demonstrated through several performances involving an interaction between the subject controlling the BMI, a musician, and the audience during series of exhibitions and workshops in schools. We report here feedback from 134 participants who filled questionnaires to rate their experiences. Our results show improved subjective learning compared to conventional methods, improved entertainment value, improved absorption into the material being presented, and little discomfort.},
}
@article {pmid27695410,
year = {2016},
author = {Galdo-Alvarez, S and Bonilla, FM and González-Villar, AJ and Carrillo-de-la-Peña, MT},
title = {Functional Equivalence of Imagined vs. Real Performance of an Inhibitory Task: An EEG/ERP Study.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {467},
pmid = {27695410},
issn = {1662-5161},
abstract = {Early neuroimaging and electrophysiological studies suggested that motor imagery recruited a different network than motor execution. However, several studies have provided evidence for the involvement of the same circuits in motor imagery tasks, in the absence of overt responses. The present study aimed to test whether imagined performance of a stop-signal task produces a similar pattern of motor-related EEG activity than that observed during real performance. To this end, mu and beta event-related desynchronization (ERD) and the Lateralized Readiness Potential (LRP) were analyzed. The study also aimed to clarify the functional significance of the Stop-N2 and Stop-P3 event-related potential (ERPs) components, which were also obtained during both real and imagined performance. The results showed a common pattern of brain electrical activity, and with a similar time course, during covert performance and overt execution of the stop-signal task: presence of LRP and Stop-P3 in the imagined condition and identical LRP onset, and similar mu and beta ERD temporal windows for both conditions. These findings suggest that a similar inhibitory network may be activated during both overt and covert execution of the task. Therefore, motor imagery may be useful to improve inhibitory skills and to develop new communicating systems for Brain-Computer Interface (BCI) devices based on inhibitory signals.},
}
@article {pmid27695404,
year = {2016},
author = {Young, BM and Stamm, JM and Song, J and Remsik, AB and Nair, VA and Tyler, ME and Edwards, DF and Caldera, K and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Brain-Computer Interface Training after Stroke Affects Patterns of Brain-Behavior Relationships in Corticospinal Motor Fibers.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {457},
pmid = {27695404},
issn = {1662-5161},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {Background: Brain-computer interface (BCI) devices are being investigated for their application in stroke rehabilitation, but little is known about how structural changes in the motor system relate to behavioral measures with the use of these systems. Objective: This study examined relationships among diffusion tensor imaging (DTI)-derived metrics and with behavioral changes in stroke patients with and without BCI training. Methods: Stroke patients (n = 19) with upper extremity motor impairment were assessed using Stroke Impact Scale (SIS), Action Research Arm Test (ARAT), Nine-Hole Peg Test (9-HPT), and DTI scans. Ten subjects completed four assessments over a control period during which no training was administered. Seventeen subjects, including eight who completed the control period, completed four assessments over an experimental period during which subjects received interventional BCI training. Fractional anisotropy (FA) values were extracted from each corticospinal tract (CST) and transcallosal motor fibers for each scan. Results: No significant group by time interactions were identified at the group level in DTI or behavioral measures. During the control period, increases in contralesional CST FA and in asymmetric FA (aFA) correlated with poorer scores on SIS and 9-HPT. During the experimental period (with BCI training), increases in contralesional CST FA were correlated with improvements in 9-HPT while increases in aFA correlated with improvements in ARAT but with worsening 9-HPT performance; changes in transcallosal motor fibers positively correlated with those in the contralesional CST. All correlations p < 0.05 corrected. Conclusion: These findings suggest that the integrity of the contralesional CST may be used to track individual behavioral changes observed with BCI training after stroke.},
}
@article {pmid27693885,
year = {2016},
author = {Demchenko, I and Katz, R and Pratt, H and Zacksenhouse, M},
title = {Distinct electroencephalographic responses to disturbances and distractors during continuous reaching movements.},
journal = {Brain research},
volume = {1652},
number = {},
pages = {178-187},
doi = {10.1016/j.brainres.2016.09.040},
pmid = {27693885},
issn = {1872-6240},
mesh = {Adult ; Arm/*physiology ; Attention/*physiology ; Biomechanical Phenomena ; Brain/*physiology ; Electroencephalography ; Event-Related Potentials, P300 ; Eye Movement Measurements ; Feedback, Psychological/physiology ; Humans ; Male ; Motor Activity/*physiology ; Neuropsychological Tests ; Psychophysics ; Visual Perception/physiology ; },
abstract = {Discrepancies between actual and appropriate motor commands, dubbed low-level errors, have been shown to elicit a P300 like component. P300 has been studied extensively in cognitive tasks using, in particular, the three-stimulus oddball paradigm. This paradigm revealed two sub-components, known as P3a and P3b, whose relative contributions depend on saliency and task-relevance, respectively. However, the existence and roles of these sub-components in response to low-level errors are poorly understood. Here we investigated responses to low level errors generated by disturbances - including target and cursor jumps, versus responses to distractors, i.e., environmental changes that are irrelevant to the reaching task. Additionally, we examined the response to matching cursor and target jumps (dual jumps), which generate estimation errors, and are thus considered task relevant disturbances, but do not generate low level errors. We found that a significant P3a-like component is evoked by both disturbances and distractors, whereas the P3b-like component is significantly stronger in response to disturbances than distractors. The P3b-like component appears also in response to dual jumps, even though there are no low level errors. We conclude that disturbances and distractors elicit distinct responses, and that the P3b-like component reflects estimation errors rather than low-level errors.},
}
@article {pmid27688746,
year = {2016},
author = {Bastos, NS and Adamatti, DF and Billa, CZ},
title = {Discovering Patterns in Brain Signals Using Decision Trees.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {6391807},
pmid = {27688746},
issn = {1687-5273},
abstract = {Even with emerging technologies, such as Brain-Computer Interfaces (BCI) systems, understanding how our brains work is a very difficult challenge. So we propose to use a data mining technique to help us in this task. As a case of study, we analyzed the brain's behaviour of blind people and sighted people in a spatial activity. There is a common belief that blind people compensate their lack of vision using the other senses. If an object is given to sighted people and we asked them to identify this object, probably the sense of vision will be the most determinant one. If the same experiment was repeated with blind people, they will have to use other senses to identify the object. In this work, we propose a methodology that uses decision trees (DT) to investigate the difference of how the brains of blind people and people with vision react against a spatial problem. We choose the DT algorithm because it can discover patterns in the brain signal, and its presentation is human interpretable. Our results show that using DT to analyze brain signals can help us to understand the brain's behaviour.},
}
@article {pmid27684465,
year = {2016},
author = {Salisbury, DB and Parsons, TD and Monden, KR and Trost, Z and Driver, SJ},
title = {Brain-computer interface for individuals after spinal cord injury.},
journal = {Rehabilitation psychology},
volume = {61},
number = {4},
pages = {435-441},
doi = {10.1037/rep0000099},
pmid = {27684465},
issn = {1939-1544},
mesh = {Adult ; Attitude to Computers ; Brain-Computer Interfaces/*psychology ; Cognition Disorders/psychology/rehabilitation ; Electroencephalography/instrumentation/psychology ; Equipment Design ; Evidence-Based Practice ; Feasibility Studies ; Hospitalization ; Humans ; Male ; Spinal Cord Injuries/*psychology/*rehabilitation ; Video Games ; },
abstract = {PURPOSE/OBJECTIVE: To investigate the feasibility of brain-computer interface (BCI) with patients on an inpatient spinal cord injury (SCI) unit. Research Method/Design: This study included 25 participants aged 18-64 who sustained traumatic or nontraumatic SCI and did not have severe cognitive or psychiatric impairment. Participants completed a variety of screening measures related to cognition, psychological disposition, pain, and technology experience/interest. The Emotiv electroencephalography system was used in conjunction with a cube rotation and manipulation game presented on a laptop computer.<br><br>RESULTS: The majority of participants successfully completed the BCI game and reported enjoyment of the experience. Outside of a mild trend of lower performance among participants with a past or present head injury, there were no demographic variables, injury variables or screening measures significantly associated with BCI performance.<br><br>CONCLUSIONS/IMPLICATIONS: The BCI paradigm demonstrated feasibility and safety across participant age range, educational and vocational background, and level of injury. Despite the rapid integration of technology into rehabilitation health care settings, there are few evidence-based studies regarding the feasibility of technology with specific inpatient populations. Clinical implications and challenges of using this technology in a rehabilitation setting are discussed. (PsycINFO Database Record},
}
@article {pmid27681162,
year = {2016},
author = {Wei, Z and Wei, Q},
title = {The backtracking search optimization algorithm for frequency band and time segment selection in motor imagery-based brain-computer interfaces.},
journal = {Journal of integrative neuroscience},
volume = {15},
number = {3},
pages = {347-364},
doi = {10.1142/S0219635216500229},
pmid = {27681162},
issn = {0219-6352},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Functional Laterality ; Hand/physiology ; Humans ; Imagination/*physiology ; Neuropsychological Tests ; Psychomotor Performance/*physiology ; Time Factors ; },
abstract = {Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8-30[Formula: see text]Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.},
}
@article {pmid27673011,
year = {2016},
author = {Bockbrader, M and Kortes, MJ and Annetta, N and Majstorovic, C and Sharma, G and Friedenberg, DA and Morgan, A and Bresler, H and Mysiw, W and Rezai, AR},
title = {Poster 253 Implanted Brain-Computer Interface Controlling a Neuroprosthetic for Increasing Upper Limb Function in a Human with Tetraparesis.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {8},
number = {9S},
pages = {S242-S243},
doi = {10.1016/j.pmrj.2016.07.427},
pmid = {27673011},
issn = {1934-1563},
}
@article {pmid27669264,
year = {2016},
author = {Su, Y and Routhu, S and Moon, KS and Lee, SQ and Youm, W and Ozturk, Y},
title = {A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {10},
pages = {},
pmid = {27669264},
issn = {1424-8220},
mesh = {Biosensing Techniques/*methods ; Brain-Computer Interfaces ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; *Wireless Technology ; },
abstract = {All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.},
}
@article {pmid27668913,
year = {2018},
author = {Behrle, N and Dyke, P and Dalabih, A},
title = {Interventricular Septal Pseudoaneurysm After Blunt Chest Trauma in a 6 Year Old: An Illustrative Case and Review.},
journal = {Pediatric emergency care},
volume = {34},
number = {2},
pages = {e39-e40},
doi = {10.1097/PEC.0000000000000821},
pmid = {27668913},
issn = {1535-1815},
mesh = {Accidents, Traffic ; Aneurysm, False/*diagnosis/etiology ; Child ; Echocardiography ; Female ; Heart Injuries/*complications/diagnosis ; Heart Septal Defects, Ventricular/diagnosis/*etiology ; Humans ; Ventricular Septum/injuries ; Wounds, Nonpenetrating/*complications ; },
abstract = {Motor vehicle accident is the most common cause of blunt cardiac injury (BCI) in children (85.3%) due to the height of the child in relation to proper restraints and the compliant pediatric rib cage (J Trauma. 1996;40:200-202). Trauma to the chest wall may lead to injury of the myocardium, resulting in myocardial contusion, ventricular septal defect (VSD), ventricular free wall rupture, or valve compromise (J Trauma. 1996;40; 200-202; Heart Lung. 2012;41:200-202; J Inj Violence Res. 2012;4:98-100). There are several proposed mechanisms for the formation of VSD after blunt chest trauma including rupture of ischemic myocardium related to the initial trauma and reopening of a spontaneously closed congenital VSD. Also, chest trauma during isovolumetric contraction of the ventricles may generate enough intraventricular force to cause myocardial rupture (J Trauma. 1996;40:200-202; J Inj Violence Res. 2012;4:98-100; Korean J Pediatr. 2011;54:86-89; Ann Thorac Surg. 2012;94:1714-1716; J Emerg Trauma Shock. 2012;5:184-187). Previous case reports highlight the formation of a true VSD after BCI and the requirement of emergent repair (J Emerg Trauma Shock. 2012;5:184-187; Am Heart J. 1996;131:1039-1041; Korean Circ J. 2011;41:625-628; Ann Thorac Surg 2013;96:297-298; Kardiol Pol. 2013;71:992; Chin Med J. 2013;126:1592-1593). Reported is a case of a 6-year-old girl who developed an interventricular septal pseudoaneurysm after a motor vehicle accident of pedestrian versus car. On the day of presentation, she developed bradycardia after emergent surgical repair for abdominal trauma that required cardiopulmonary resuscitation including 5 minutes of chest compressions. At the time of resuscitation, an emergent transthoracic echocardiogram noted an interventricular pseudoaneurysm. She has been followed with serial transthoracic echocardiograms and has not required surgical intervention. We discuss the risk factors, prevalence, and diagnostic studies and recommended treatment options for structural heart disease after BCI.},
}
@article {pmid27667967,
year = {2016},
author = {May, F and Wiegand, T and Lehmann, S and Huth, A and Fortin, MJ},
title = {Do abundance distributions and species aggregation correctly predict macroecological biodiversity patterns in tropical forests?.},
journal = {Global ecology and biogeography : a journal of macroecology},
volume = {25},
number = {5},
pages = {575-585},
pmid = {27667967},
issn = {1466-822X},
abstract = {AIM: It has been recently suggested that different 'unified theories of biodiversity and biogeography' can be characterized by three common 'minimal sufficient rules': (1) species abundance distributions follow a hollow curve, (2) species show intraspecific aggregation, and (3) species are independently placed with respect to other species. Here, we translate these qualitative rules into a quantitative framework and assess if these minimal rules are indeed sufficient to predict multiple macroecological biodiversity patterns simultaneously.<br><br>LOCATION: Tropical forest plots in Barro Colorado Island (BCI), Panama, and in Sinharaja, Sri Lanka.<br><br>METHODS: We assess the predictive power of the three rules using dynamic and spatial simulation models in combination with census data from the two forest plots. We use two different versions of the model: (1) a neutral model and (2) an extended model that allowed for species differences in dispersal distances. In a first step we derive model parameterizations that correctly represent the three minimal rules (i.e. the model quantitatively matches the observed species abundance distribution and the distribution of intraspecific aggregation). In a second step we applied the parameterized models to predict four additional spatial biodiversity patterns.<br><br>RESULTS: Species-specific dispersal was needed to quantitatively fulfil the three minimal rules. The model with species-specific dispersal correctly predicted the species-area relationship, but failed to predict the distance decay, the relationship between species abundances and aggregations, and the distribution of a spatial co-occurrence index of all abundant species pairs. These results were consistent over the two forest plots.<br><br>MAIN CONCLUSIONS: The three 'minimal sufficient' rules only provide an incomplete approximation of the stochastic spatial geometry of biodiversity in tropical forests. The assumption of independent interspecific placements is most likely violated in many forests due to shared or distinct habitat preferences. Furthermore, our results highlight missing knowledge about the relationship between species abundances and their aggregation.},
}
@article {pmid27664741,
year = {2016},
author = {Seccareccia, I and Kovács, &#xc1;T and Gallegos-Monterrosa, R and Nett, M},
title = {Unraveling the predator-prey relationship of Cupriavidus necator and Bacillus subtilis.},
journal = {Microbiological research},
volume = {192},
number = {},
pages = {231-238},
doi = {10.1016/j.micres.2016.07.007},
pmid = {27664741},
issn = {1618-0623},
mesh = {*Antibiosis ; Bacillus subtilis/*physiology ; Copper ; Cupriavidus necator/*physiology ; Mutation ; Spores, Bacterial ; },
abstract = {Cupriavidus necator is a non-obligate bacterial predator of Gram-negative and Gram-positive bacteria. In this study, we set out to determine the conditions, which are necessary to observe predatory behavior of C. necator. Using Bacillus subtilis as a prey organism, we confirmed that the predatory performance of C. necator is correlated with the available copper level, and that the killing is mediated, at least in part, by secreted extracellular factors. The predatory activity depends on the nutrition status of C. necator, but does not require a quorum of predator cells. This suggests that C. necator is no group predator. Further analyses revealed that sporulation enables B. subtilis to avoid predation by C. necator. In contrast to the interaction with predatory myxobacteria, however, an intact spore coat is not required for resistance. Instead resistance is possibly mediated by quiescence.},
}
@article {pmid27658585,
year = {2016},
author = {Sharma, G and Friedenberg, DA and Annetta, N and Glenn, B and Bockbrader, M and Majstorovic, C and Domas, S and Mysiw, WJ and Rezai, A and Bouton, C},
title = {Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {33807},
pmid = {27658585},
issn = {2045-2322},
abstract = {Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.},
}
@article {pmid27658216,
year = {2016},
author = {Xie, J and Xu, G and Wang, J and Li, M and Han, C and Jia, Y},
title = {Effects of Mental Load and Fatigue on Steady-State Evoked Potential Based Brain Computer Interface Tasks: A Comparison of Periodic Flickering and Motion-Reversal Based Visual Attention.},
journal = {PloS one},
volume = {11},
number = {9},
pages = {e0163426},
pmid = {27658216},
issn = {1932-6203},
abstract = {Steady-state visual evoked potentials (SSVEP) based paradigm is a conventional BCI method with the advantages of high information transfer rate, high tolerance to artifacts and the robust performance across users. But the occurrence of mental load and fatigue when users stare at flickering stimuli is a critical problem in implementation of SSVEP-based BCIs. Based on electroencephalography (EEG) power indices α, θ, θ + α, ratio index θ/α and response properties of amplitude and SNR, this study quantitatively evaluated the mental load and fatigue in both of conventional flickering and the novel motion-reversal visual attention tasks. Results over nine subjects revealed significant mental load alleviation in motion-reversal task rather than flickering task. The interaction between factors of "stimulation type" and "fatigue level" also illustrated the motion-reversal stimulation as a superior anti-fatigue solution for long-term BCI operation. Taken together, our work provided an objective method favorable for the design of more practically applicable steady-state evoked potential based BCIs.},
}
@article {pmid27655447,
year = {2018},
author = {Ma, Z and Qiu, T},
title = {Quasi-periodic fluctuation in Donchin's speller signals and its potential use for asynchronous control.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {63},
number = {2},
pages = {105-112},
doi = {10.1515/bmt-2016-0050},
pmid = {27655447},
issn = {1862-278X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Evoked Potentials ; Humans ; User-Computer Interface ; },
abstract = {When we examine the event-related potential (ERP) responses of Donchin's brain-computer interface (BCI) speller, a type of quasi-periodic fluctuation (FLUC) overlapping with the ERP components can be observed; this fluctuation is traditionally treated as interference. However, if the FLUC is detectable in a working BCI, it can be used for asynchronous control, i.e. to indicate whether the BCI is under the control state (CS) or under the non-control idle state (NC). Asynchronous control is an important issue to address to enable BCI's practical use. In this paper, we examine the characteristics of the FLUC and explore the possibility of using the FLUC for asynchronous control of the BCI. For detecting the FLUC, we propose a method based on the power spectrum and evaluate the detection rates in a simulation. As a result, high true positive rates (TPRs) and low false positive rates (FPRs) are obtained. Our work reveals that the FLUC is of great value for implementing an asynchronous BCI.},
}
@article {pmid27654684,
year = {2016},
author = {Niemeyer, JE},
title = {Brain-machine interfaces: assistive, thought-controlled devices.},
journal = {Lab animal},
volume = {45},
number = {10},
pages = {359-361},
pmid = {27654684},
issn = {1548-4475},
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Humans ; Macaca ; Motor Cortex/physiopathology ; Motor Neurons/physiology ; Paralysis/*rehabilitation ; *Robotics ; },
}
@article {pmid27654174,
year = {2016},
author = {Alimardani, M and Nishio, S and Ishiguro, H},
title = {Removal of proprioception by BCI raises a stronger body ownership illusion in control of a humanlike robot.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {33514},
pmid = {27654174},
issn = {2045-2322},
abstract = {Body ownership illusions provide evidence that our sense of self is not coherent and can be extended to non-body objects. Studying about these illusions gives us practical tools to understand the brain mechanisms that underlie body recognition and the experience of self. We previously introduced an illusion of body ownership transfer (BOT) for operators of a very humanlike robot. This sensation of owning the robot's body was confirmed when operators controlled the robot either by performing the desired motion with their body (motion-control) or by employing a brain-computer interface (BCI) that translated motor imagery commands to robot movement (BCI-control). The interesting observation during BCI-control was that the illusion could be induced even with a noticeable delay in the BCI system. Temporal discrepancy has always shown critical weakening effects on body ownership illusions. However the delay-robustness of BOT during BCI-control raised a question about the interaction between the proprioceptive inputs and delayed visual feedback in agency-driven illusions. In this work, we compared the intensity of BOT illusion for operators in two conditions; motion-control and BCI-control. Our results revealed a significantly stronger BOT illusion for the case of BCI-control. This finding highlights BCI's potential in inducing stronger agency-driven illusions by building a direct communication between the brain and controlled body, and therefore removing awareness from the subject's own body.},
}
@article {pmid27652455,
year = {2016},
author = {Adewole, DO and Serruya, MD and Harris, JP and Burrell, JC and Petrov, D and Chen, HI and Wolf, JA and Cullen, DK},
title = {The Evolution of Neuroprosthetic Interfaces.},
journal = {Critical reviews in biomedical engineering},
volume = {44},
number = {1-2},
pages = {123-152},
pmid = {27652455},
issn = {1943-619X},
support = {U01 NS094340/NS/NINDS NIH HHS/United States ; },
mesh = {Biomedical Research/trends ; Brain-Computer Interfaces/*trends ; Electrodes, Implanted ; Humans ; Neural Prostheses/*trends ; Prosthesis Design/*trends ; Prosthesis Implantation/*methods ; },
abstract = {The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.},
}
@article {pmid27651060,
year = {2017},
author = {Punsawad, Y and Wongsawat, Y},
title = {A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {6},
pages = {965-977},
pmid = {27651060},
issn = {1741-0444},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; Photic Stimulation/methods ; Young Adult ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are widely employed in brain-computer interface (BCI) applications, especially to control machines. However, the use of SSVEPs leads to eye fatigue and causes lower accuracy over the long term, particularly when multi-commands are required. Therefore, this paper proposes a half-field steady-state visual stimulation pattern and paradigm to increase the limited number of commands that can be achieved with existing SSVEP-based BCI methods. Following the theory of vision perception and existing half-field SSVEP-based BCI systems, the new stimulation pattern generates four commands using only one frequency flickering stimulus and has an average classification accuracy of approximately 75 %. According to the proposed stimulus pattern, using only one frequency without requiring users to stare directly at the flickering stimulus allows users to easily focus on the system and experience less visual fatigue compared to existing systems. Furthermore, new half-field SSVEP-based BCI systems are proposed, incorporating our proposed feature extraction and decision-making algorithm. Extracting the signal from the occipital area and using a reference electrode position at the parietal area yielded better results compared to the central area. In addition, we recommend using an LED or LCD as the visual stimulus device (at the recommended size), which yielded comparable results to our proposed feature extraction and decision-making algorithm. Finally, an application of the proposed system is demonstrated for real-time television control.},
}
@article {pmid27651034,
year = {2016},
author = {Rouse, AG and Williams, JJ and Wheeler, JJ and Moran, DW},
title = {Spatial co-adaptation of cortical control columns in a micro-ECoG brain-computer interface.},
journal = {Journal of neural engineering},
volume = {13},
number = {5},
pages = {056018},
doi = {10.1088/1741-2560/13/5/056018},
pmid = {27651034},
issn = {1741-2552},
support = {F31 NS073167/NS/NINDS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG) has been used for a range of applications including electrophysiological mapping, epilepsy monitoring, and more recently as a recording modality for brain-computer interfaces (BCIs). Studies that examine ECoG electrodes designed and implanted chronically solely for BCI applications remain limited. The present study explored how two key factors influence chronic, closed-loop ECoG BCI: (i) the effect of inter-electrode distance on BCI performance and (ii) the differences in neural adaptation and performance when fixed versus adaptive BCI decoding weights are used.<br><br>APPROACH: The amplitudes of epidural micro-ECoG signals between 75 and 105 Hz with 300 &#x3bc;m diameter electrodes were used for one-dimensional and two-dimensional BCI tasks. The effect of inter-electrode distance on BCI control was tested between 3 and 15 mm. Additionally, the performance and cortical modulation differences between constant, fixed decoding using a small subset of channels versus adaptive decoding weights using the entire array were explored.<br><br>MAIN RESULTS: Successful BCI control was possible with two electrodes separated by 9 and 15 mm. Performance decreased and the signals became more correlated when the electrodes were only 3 mm apart. BCI performance in a 2D BCI task improved significantly when using adaptive decoding weights (80%-90%) compared to using constant, fixed weights (50%-60%). Additionally, modulation increased for channels previously unavailable for BCI control under the fixed decoding scheme upon switching to the adaptive, all-channel scheme.<br><br>SIGNIFICANCE: Our results clearly show that neural activity under a BCI recording electrode (which we define as a 'cortical control column') readily adapts to generate an appropriate control signal. These results show that the practical minimal spatial resolution of these control columns with micro-ECoG BCI is likely on the order of 3 mm. Additionally, they show that the combination and interaction between neural adaptation and machine learning are critical to optimizing ECoG BCI performance.},
}
@article {pmid27650629,
year = {2016},
author = {Glonke, S and Sadowski, G and Brandenbusch, C},
title = {Applied catastrophic phase inversion: a continuous non-centrifugal phase separation step in biphasic whole-cell biocatalysis.},
journal = {Journal of industrial microbiology &amp; biotechnology},
volume = {43},
number = {11},
pages = {1527-1535},
pmid = {27650629},
issn = {1476-5535},
mesh = {Biocatalysis ; *Biotransformation ; Centrifugation ; Diethylhexyl Phthalate/chemistry ; Emulsions ; Escherichia coli/metabolism ; Industrial Microbiology ; },
abstract = {Biphasic whole-cell biotransformations are known to be efficient alternatives to common chemical synthesis routes, especially for the production of, e.g. apolar enantiopure organic compounds. They provide high stereoselectivity combined with high product concentrations owing to the presence of an organic phase serving as substrate reservoir and product sink. Industrial implementation suffers from the formation of stable Pickering emulsions caused by the presence of cells. State-of-the-art downstream processing includes inefficient strategies such as excessive centrifugation, use of de-emulsifiers or thermal stress. In contrast, using the catastrophic phase inversion (CPI) phenomenon (sudden switch of emulsion type caused by addition of dispersed phase), Pickering-type emulsions can be destabilized efficiently. Within this work a model system using bis(2-ethylhexyl) phthalate (BEHP) as organic phase in combination with E. coli, JM101 was successfully separated using a continuous mixer settler setup. Compared to the state-of-the-art centrifugal separations, this process allows complete phase separation with no detectable water content or cells in the organic phase with no utilities/additives required. Furthermore, the concentration of the product is not affected by the separation. It is therefore a simple applicable method that can be used for separation of stable Pickering-type emulsions based on the knowledge of the point of inversion.},
}
@article {pmid27647960,
year = {2016},
author = {Myers, MH and Threatt, M and Solies, KM and McFerrin, BM and Hopf, LB and Birdwell, JD and Sillay, KA},
title = {Ambulatory Seizure Monitoring: From Concept to Prototype Device.},
journal = {Annals of neurosciences},
volume = {23},
number = {2},
pages = {100-111},
pmid = {27647960},
issn = {0972-7531},
abstract = {BACKGROUND: The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy.<br><br>PURPOSE: The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques.<br><br>METHODS: The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification.<br><br>RESULT: This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction.<br><br>CONCLUSION: The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient.},
}
@article {pmid27642117,
year = {2016},
author = {Lu, Y and Lyu, H and Richardson, AG and Lucas, TH and Kuzum, D},
title = {Flexible Neural Electrode Array Based-on Porous Graphene for Cortical Microstimulation and Sensing.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {33526},
pmid = {27642117},
issn = {2045-2322},
abstract = {Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue. Porous graphene electrodes show superior impedance and charge injection characteristics making them ideal for high efficiency cortical sensing and stimulation. They exhibit no physical delamination or degradation even after 1 million biphasic stimulation cycles, confirming high endurance. In in vivo experiments with rodents, same array is used to sense brain activity patterns with high spatio-temporal resolution and to control leg muscles with high-precision electrical stimulation from the cortical surface. Flexible porous graphene array offers a minimally invasive but high efficiency neuromodulation scheme with potential applications in cortical mapping, brain-computer interfaces, treatment of neurological disorders, where high resolution and simultaneous recording and stimulation of neural activity are crucial.},
}
@article {pmid27636359,
year = {2016},
author = {Duann, JR and Chiou, JC},
title = {A Comparison of Independent Event-Related Desynchronization Responses in Motor-Related Brain Areas to Movement Execution, Movement Imagery, and Movement Observation.},
journal = {PloS one},
volume = {11},
number = {9},
pages = {e0162546},
pmid = {27636359},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Male ; *Motor Activity ; *Movement ; Young Adult ; },
abstract = {Electroencephalographic (EEG) event-related desynchronization (ERD) induced by movement imagery or by observing biological movements performed by someone else has recently been used extensively for brain-computer interface-based applications, such as applications used in stroke rehabilitation training and motor skill learning. However, the ERD responses induced by the movement imagery and observation might not be as reliable as the ERD responses induced by movement execution. Given that studies on the reliability of the EEG ERD responses induced by these activities are still lacking, here we conducted an EEG experiment with movement imagery, movement observation, and movement execution, performed multiple times each in a pseudorandomized order in the same experimental runs. Then, independent component analysis (ICA) was applied to the EEG data to find the common motor-related EEG source activity shared by the three motor tasks. Finally, conditional EEG ERD responses associated with the three movement conditions were computed and compared. Among the three motor conditions, the EEG ERD responses induced by motor execution revealed the alpha power suppression with highest strengths and longest durations. The ERD responses of the movement imagery and movement observation only partially resembled the ERD pattern of the movement execution condition, with slightly better detectability for the ERD responses associated with the movement imagery and faster ERD responses for movement observation. This may indicate different levels of involvement in the same motor-related brain circuits during different movement conditions. In addition, because the resulting conditional EEG ERD responses from the ICA preprocessing came with minimal contamination from the non-related and/or artifactual noisy components, this result can play a role of the reference for devising a brain-computer interface using the EEG ERD features of movement imagery or observation.},
}
@article {pmid27635965,
year = {2016},
author = {Boone, C and Wojtasiewicz, T and Anderson, WS},
title = {Characterization of a Wearable Dry Electroencephalography System.},
journal = {Neurosurgery},
volume = {79},
number = {4},
pages = {N10-1},
doi = {10.1227/01.neu.0000499703.35843.82},
pmid = {27635965},
issn = {1524-4040},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; },
}
@article {pmid27635129,
year = {2016},
author = {Mondini, V and Mangia, AL and Cappello, A},
title = {EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {4562601},
pmid = {27635129},
issn = {1687-5273},
mesh = {Algorithms ; Analysis of Variance ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagination ; Male ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Time Factors ; },
abstract = {Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's "flexibility" and "customizability," namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.},
}
@article {pmid27634714,
year = {2016},
author = {Kellmeyer, P and Cochrane, T and Müller, O and Mitchell, C and Ball, T and Fins, JJ and Biller-Andorno, N},
title = {The Effects of Closed-Loop Medical Devices on the Autonomy and Accountability of Persons and Systems.},
journal = {Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees},
volume = {25},
number = {4},
pages = {623-633},
doi = {10.1017/S0963180116000359},
pmid = {27634714},
issn = {1469-2147},
mesh = {Brain-Computer Interfaces/*ethics/trends ; Decision Making ; Ethics, Medical ; Humans ; Informed Consent ; Morals ; *Paralysis ; *Personal Autonomy ; *Research Subjects ; },
abstract = {Closed-loop medical devices such as brain-computer interfaces are an emerging and rapidly advancing neurotechnology. The target patients for brain-computer interfaces (BCIs) are often severely paralyzed, and thus particularly vulnerable in terms of personal autonomy, decisionmaking capacity, and agency. Here we analyze the effects of closed-loop medical devices on the autonomy and accountability of both persons (as patients or research participants) and neurotechnological closed-loop medical systems. We show that although BCIs can strengthen patient autonomy by preserving or restoring communicative abilities and/or motor control, closed-loop devices may also create challenges for moral and legal accountability. We advocate the development of a comprehensive ethical and legal framework to address the challenges of emerging closed-loop neurotechnologies like BCIs and stress the centrality of informed consent and refusal as a means to foster accountability. We propose the creation of an international neuroethics task force with members from medical neuroscience, neuroengineering, computer science, medical law, and medical ethics, as well as representatives of patient advocacy groups and the public.},
}
@article {pmid27633205,
year = {2017},
author = {Lacko, D and Vleugels, J and Fransen, E and Huysmans, T and De Bruyne, G and Van Hulle, MM and Sijbers, J and Verwulgen, S},
title = {Ergonomic design of an EEG headset using 3D anthropometry.},
journal = {Applied ergonomics},
volume = {58},
number = {},
pages = {128-136},
doi = {10.1016/j.apergo.2016.06.002},
pmid = {27633205},
issn = {1872-9126},
mesh = {Adult ; *Brain-Computer Interfaces ; Cephalometry/*methods ; Electrodes ; Electroencephalography/*instrumentation ; Equipment Design/*methods ; Ergonomics ; Female ; Head/*anatomy &amp; histology ; Humans ; Male ; Young Adult ; },
abstract = {Although EEG experiments over the past decades have shown numerous applications for brain-computer interfacing (BCI), there is a need for user-friendly BCI devices that can be used in real-world situations. 3D anthropometry and statistical shape modeling have been shown to improve the fit of devices such as helmets and respirators, and thus they might also be suitable to design BCI headgear that better fits the size and shape variation of the human head. In this paper, a new design method for BCI devices is proposed and evaluated. A one-size-fits-all BCI headset frame is designed on the basis of three digital mannequins derived from a shape model of the human head. To verify the design, the geometric fit, stability and repeatability of the prototype were compared to an EEG cap and a commercial BCI headset in a preliminary experiment. Most design specifications were met, and all the results were found to be similar to those of the commercial headset. Therefore, the suggested design method is a feasible alternative to traditional anthropometric design for BCI headsets and similar headgear.},
}
@article {pmid27631828,
year = {2016},
author = {Rigato, C and Reinfeldt, S and Håkansson, B and Jansson, KJ and Hol, MK and Eeg-Olofsson, M},
title = {Audiometric Comparison Between the First Patients With the Transcutaneous Bone Conduction Implant and Matched Percutaneous Bone Anchored Hearing Device Users.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {37},
number = {9},
pages = {1381-1387},
doi = {10.1097/MAO.0000000000001183},
pmid = {27631828},
issn = {1537-4505},
mesh = {Adult ; Aged ; Audiometry ; *Bone Conduction ; Female ; Hearing ; *Hearing Aids ; Hearing Loss, Conductive/*rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/*rehabilitation ; Humans ; Male ; Middle Aged ; Suture Anchors ; },
abstract = {HYPOTHESIS: The transcutaneous bone conduction implant (BCI) is compared with bone-anchored hearing aids (BAHAs) under the hypothesis that the BCI can give similar rehabilitation from an audiological as well as patient-related point of view.<br><br>BACKGROUND: Patients suffering from conductive and mixed hearing losses can often benefit more from rehabilitation using bone conduction devices (BCDs) rather than conventional air conduction devices. The most widely used BCD is the percutaneous BAHA, with a permanent skin-penetrating abutment. To overcome issues related to percutaneous BCDs, the trend today is to develop transcutaneous devices, with intact skin. The BCI is an active transcutaneous device currently in a clinical trial phase. A potential limitation of active transcutaneous devices is the loss of power in the induction link over the skin. To address this issue, countermeasures are taken in the design of the BCI, which is therefore expected to be as effective as percutaneous BCDs.<br><br>METHODS: An early observational study with a matched-pair design was performed to compare BCI and BAHA groups of patients over several audiometric measurements, including speech audiometry and warble tones thresholds with and without the device. Additionally, questionnaires were used to assess the general health condition, benefit, and satisfaction level of patients.<br><br>RESULTS: No statistically significant difference was detected in any of the audiological measurements. The outcome of patient-related measurements was slightly superior for BCI in all subscales.<br><br>CONCLUSION: Results confirm the initial hypothesis of the study: the BCI seems to be capable of providing as good rehabilitation as percutaneous devices for indicated patients.},
}
@article {pmid27631789,
year = {2016},
author = {Zhou, B and Wu, X and Lv, Z and Zhang, L and Guo, X},
title = {A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.},
journal = {PloS one},
volume = {11},
number = {9},
pages = {e0162657},
pmid = {27631789},
issn = {1932-6203},
mesh = {Algorithms ; *Automation ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; },
abstract = {Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.},
}
@article {pmid27630679,
year = {2016},
author = {Guan, J and Hawryluk, GW},
title = {Advancements in the mind-machine interface: towards re-establishment of direct cortical control of limb movement in spinal cord injury.},
journal = {Neural regeneration research},
volume = {11},
number = {7},
pages = {1060-1061},
pmid = {27630679},
issn = {1673-5374},
}
@article {pmid27630547,
year = {2016},
author = {Bremmer, F and Kaminiarz, A and Klingenhoefer, S and Churan, J},
title = {Decoding Target Distance and Saccade Amplitude from Population Activity in the Macaque Lateral Intraparietal Area (LIP).},
journal = {Frontiers in integrative neuroscience},
volume = {10},
number = {},
pages = {30},
pmid = {27630547},
issn = {1662-5145},
abstract = {Primates perform saccadic eye movements in order to bring the image of an interesting target onto the fovea. Compared to stationary targets, saccades toward moving targets are computationally more demanding since the oculomotor system must use speed and direction information about the target as well as knowledge about its own processing latency to program an adequate, predictive saccade vector. In monkeys, different brain regions have been implicated in the control of voluntary saccades, among them the lateral intraparietal area (LIP). Here we asked, if activity in area LIP reflects the distance between fovea and saccade target, or the amplitude of an upcoming saccade, or both. We recorded single unit activity in area LIP of two macaque monkeys. First, we determined for each neuron its preferred saccade direction. Then, monkeys performed visually guided saccades along the preferred direction toward either stationary or moving targets in pseudo-randomized order. LIP population activity allowed to decode both, the distance between fovea and saccade target as well as the size of an upcoming saccade. Previous work has shown comparable results for saccade direction (Graf and Andersen, 2014a,b). Hence, LIP population activity allows to predict any two-dimensional saccade vector. Functional equivalents of macaque area LIP have been identified in humans. Accordingly, our results provide further support for the concept of activity from area LIP as neural basis for the control of an oculomotor brain-machine interface.},
}
@article {pmid27620348,
year = {2016},
author = {Xiao, J and Xie, Q and He, Y and Yu, T and Lu, S and Huang, N and Yu, R and Li, Y},
title = {An Auditory BCI System for Assisting CRS-R Behavioral Assessment in Patients with Disorders of Consciousness.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {32917},
pmid = {27620348},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Aged ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Consciousness/physiology ; Consciousness Disorders/*diagnosis/pathology ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; Reflex, Startle/*physiology ; Young Adult ; },
abstract = {The Coma Recovery Scale-Revised (CRS-R) is a consistent and sensitive behavioral assessment standard for disorders of consciousness (DOC) patients. However, the CRS-R has limitations due to its dependence on behavioral markers, which has led to a high rate of misdiagnosis. Brain-computer interfaces (BCIs), which directly detect brain activities without any behavioral expression, can be used to evaluate a patient's state. In this study, we explored the application of BCIs in assisting CRS-R assessments of DOC patients. Specifically, an auditory passive EEG-based BCI system with an oddball paradigm was proposed to facilitate the evaluation of one item of the auditory function scale in the CRS-R - the auditory startle. The results obtained from five healthy subjects validated the efficacy of the BCI system. Nineteen DOC patients participated in the CRS-R and BCI assessments, of which three patients exhibited no responses in the CRS-R assessment but were responsive to auditory startle in the BCI assessment. These results revealed that a proportion of DOC patients who have no behavioral responses in the CRS-R assessment can generate neural responses, which can be detected by our BCI system. Therefore, the proposed BCI may provide more sensitive results than the CRS-R and thus assist CRS-R behavioral assessments.},
}
@article {pmid27619326,
year = {2016},
author = {Wu, Z and Zheng, N and Zhang, S and Zheng, X and Gao, L and Su, L},
title = {Maze learning by a hybrid brain-computer system.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {31746},
pmid = {27619326},
issn = {2045-2322},
mesh = {Algorithms ; Animals ; *Artificial Intelligence ; Behavior, Animal ; Brain/physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Male ; *Maze Learning ; Memory ; Neural Networks, Computer ; Rats ; Rats, Sprague-Dawley ; Stress, Mechanical ; },
abstract = {The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.},
}
@article {pmid27619224,
year = {2016},
author = {Wronkiewicz, M and Larson, E and Lee, AK},
title = {Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.},
journal = {Journal of neural engineering},
volume = {13},
number = {5},
pages = {056017},
doi = {10.1088/1741-2560/13/5/056017},
pmid = {27619224},
issn = {1741-2552},
mesh = {Artifacts ; Attention/*physiology ; Auditory Perception/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Electric Stimulation ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; Neuroimaging/methods ; *Neurosciences ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research.<br><br>APPROACH: In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention.<br><br>MAIN RESULTS: Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs.<br><br>SIGNIFICANCE: Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.},
}
@article {pmid27618842,
year = {2016},
author = {Das, N and Biesmans, W and Bertrand, A and Francart, T},
title = {The effect of head-related filtering and ear-specific decoding bias on auditory attention detection.},
journal = {Journal of neural engineering},
volume = {13},
number = {5},
pages = {056014},
doi = {10.1088/1741-2560/13/5/056014},
pmid = {27618842},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Algorithms ; Attention/*physiology ; Auditory Perception/*physiology ; Brain-Computer Interfaces ; Cochlear Implants ; Dichotic Listening Tests ; *Ear ; Electroencephalography ; Female ; Functional Laterality/physiology ; *Head ; Humans ; Male ; Neural Prostheses ; Speech Intelligibility ; Speech Perception/physiology ; Young Adult ; },
abstract = {OBJECTIVE: We consider the problem of Auditory Attention Detection (AAD), where the goal is to detect which speaker a person is attending to, in a multi-speaker environment, based on neural activity. This work aims to analyze the influence of head-related filtering and ear-specific decoding on the performance of an AAD algorithm.<br><br>APPROACH: We recorded high-density EEG of 16 normal-hearing subjects as they listened to two speech streams while tasked to attend to the speaker in either their left or right ear. The attended ear was switched between trials. The speech stimuli were administered either dichotically, or after filtering using Head-Related Transfer Functions (HRTFs). A spatio-temporal decoder was trained and used to reconstruct the attended stimulus envelope, and the correlations between the reconstructed and the original stimulus envelopes were used to perform AAD, and arrive at a percentage correct score over all trials.<br><br>MAIN RESULTS: We found that the HRTF condition resulted in significantly higher AAD performance than the dichotic condition. However, speech intelligibility, measured under the same set of conditions, was lower for the HRTF filtered stimuli. We also found that decoders trained and tested for a specific attended ear performed better, compared to decoders trained and tested for both left and right attended ear simultaneously. In the context of the decoders supporting hearing prostheses, the former approach is less realistic, and studies in which each subject always had to attend to the same ear may find over-optimistic results.<br><br>SIGNIFICANCE: This work shows the importance of using realistic binaural listening conditions and training on a balanced set of experimental conditions to obtain results that are more representative for the true AAD performance in practical applications.},
}
@article {pmid27616986,
year = {2016},
author = {Kosmyna, N and Tarpin-Bernard, F and Bonnefond, N and Rivet, B},
title = {Feasibility of BCI Control in a Realistic Smart Home Environment.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {416},
pmid = {27616986},
issn = {1662-5161},
abstract = {Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain Computer Interfaces (BCI) and apply it in the "Domus" smart home platform in order to evaluate the potential interest of users about BCIs at home. We enable users to control lighting, a TV set, a coffee machine and the shutters of the smart home. We evaluate the performance (accuracy, interaction time), usability and feasibility (USE questionnaire) on 12 healthy subjects and 2 disabled subjects. We find that healthy subjects achieve 77% task accuracy. However, disabled subjects achieved a better accuracy (81% compared to 77%).},
}
@article {pmid27615364,
year = {2017},
author = {Simon, DM and Charkhkar, H and St John, C and Rajendran, S and Kang, T and Reit, R and Arreaga-Salas, D and McHail, DG and Knaack, GL and Sloan, A and Grasse, D and Dumas, TC and Rennaker, RL and Pancrazio, JJ and Voit, WE},
title = {Design and demonstration of an intracortical probe technology with tunable modulus.},
journal = {Journal of biomedical materials research. Part A},
volume = {105},
number = {1},
pages = {159-168},
pmid = {27615364},
issn = {1552-4965},
support = {R43 NS084598/NS/NINDS NIH HHS/United States ; U13 NS084752/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain Waves ; Elastic Modulus ; Electrodes ; Frontal Lobe/*physiology ; Mice ; },
abstract = {Intracortical probe technology, consisting of arrays of microelectrodes, offers a means of recording the bioelectrical activity from neural tissue. A major limitation of existing intracortical probe technology pertains to limited lifetime of 6 months to a year of recording after implantation. A major contributor to device failure is widely believed to be the interfacial mechanical mismatch of conventional stiff intracortical devices and the surrounding brain tissue. We describe the design, development, and demonstration of a novel functional intracortical probe technology that has a tunable Young's modulus from ∼2 GPa to ∼50 MPa. This technology leverages advances in dynamically softening materials, specifically thiol-ene/acrylate thermoset polymers, which exhibit minimal swelling of < 3% weight upon softening in vitro. We demonstrate that a shape memory polymer-based multichannel intracortical probe can be fabricated, that the mechanical properties are stable for at least 2 months and that the device is capable of single unit recordings for durations up to 77 days in vivo. This novel technology, which is amenable to processes suitable for manufacturing via standard semiconductor fabrication techniques, offers the capability of softening in vivo to reduce the tissue-device modulus mismatch to ultimately improve long term viability of neural recordings. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 159-168, 2017.},
}
@article {pmid27611070,
year = {2016},
author = {Vasconcelos E Sa, D and Wearden, A and Hartley, S and Emsley, R and Barrowclough, C},
title = {Expressed Emotion and behaviourally controlling interactions in the daily life of dyads experiencing psychosis.},
journal = {Psychiatry research},
volume = {245},
number = {},
pages = {406-413},
doi = {10.1016/j.psychres.2016.08.060},
pmid = {27611070},
issn = {1872-7123},
mesh = {Adolescent ; Adult ; Aged ; Ecological Momentary Assessment ; *Expressed Emotion ; Family Relations/*psychology ; Female ; Humans ; Male ; Middle Aged ; Psychotic Disorders/nursing/*psychology ; Young Adult ; },
abstract = {While research using Experience Sampling Methodology (ESM) suggests that, in general, contact with relatives or friends may be protective for psychotic experiences, contact with high-Expressed Emotion (high-EE) relatives can have adverse consequences for patients. This study investigated whether contact with high-EE relatives, and relatives' behaviourally controlling interactions (BCI) are related to patients' symptoms and to both patients' and relatives' affect when measured using structured diary assessments in the course of everyday life. Twenty-one patients experiencing psychosis and their closest relatives provided synchronized self-reports of symptoms (patients only), affect, dyadic contact and BCI over a 6-days period. Relatives' EE was obtained from Camberwell Family Interviews. Multi-level modeling showed that patients' reports of relatives taking control of them and helping them were associated with increased patient negative affect and symptoms. Relatives' self-reports of nagging, taking control and keeping an eye on the patient were related to fluctuations in relatives' affect. No evidence was found for the moderating effect of EE status on the association between dyadic contact and affect or, in the case of patients, symptoms. When measured using an ecologically valid methodology, momentary behaviourally controlling interactions within dyads experiencing psychosis can impact on patients' affect and symptoms.},
}
@article {pmid27605389,
year = {2016},
author = {Pais-Vieira, M and Yadav, AP and Moreira, D and Guggenmos, D and Santos, A and Lebedev, M and Nicolelis, MAL},
title = {A Closed Loop Brain-machine Interface for Epilepsy Control Using Dorsal Column Electrical Stimulation.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {32814},
pmid = {27605389},
issn = {2045-2322},
support = {R01 DE011451/DE/NIDCR NIH HHS/United States ; R01 NS073125/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Epilepsy/*therapy ; Female ; Male ; Pentylenetetrazole/toxicity ; Rats, Long-Evans ; Seizures/chemically induced/therapy ; Spinal Cord Stimulation/*methods ; },
abstract = {Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.},
}
@article {pmid27601981,
year = {2016},
author = {Armenta Salas, M and Helms Tillery, SI},
title = {Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.},
journal = {Frontiers in systems neuroscience},
volume = {10},
number = {},
pages = {70},
pmid = {27601981},
issn = {1662-5137},
abstract = {The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.},
}
@article {pmid27598310,
year = {2016},
author = {Alimardani, M and Nishio, S and Ishiguro, H},
title = {The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning.},
journal = {PloS one},
volume = {11},
number = {9},
pages = {e0161945},
pmid = {27598310},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Algorithms ; Brain ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Learning/*physiology ; Male ; Motor Skills/physiology ; Movement/*physiology ; Robotics/instrumentation ; },
abstract = {Brain computer interfaces (BCIs) have been developed and implemented in many areas as a new communication channel between the human brain and external devices. Despite their rapid growth and broad popularity, the inaccurate performance and cost of user-training are yet the main issues that prevent their application out of the research and clinical environment. We previously introduced a BCI system for the control of a very humanlike android that could raise a sense of embodiment and agency in the operators only by imagining a movement (motor imagery) and watching the robot perform it. Also using the same setup, we further discovered that the positive bias of subjects' performance both increased their sensation of embodiment and improved their motor imagery skills in a short period. In this work, we studied the shared mechanism between the experience of embodiment and motor imagery. We compared the trend of motor imagery learning when two groups of subjects BCI-operated different looking robots, a very humanlike android's hands and a pair of metallic gripper. Although our experiments did not show a significant change of learning between the two groups immediately during one session, the android group revealed better motor imagery skills in the follow up session when both groups repeated the task using the non-humanlike gripper. This result shows that motor imagery skills learnt during the BCI-operation of humanlike hands are more robust to time and visual feedback changes. We discuss the role of embodiment and mirror neuron system in such outcome and propose the application of androids for efficient BCI training.},
}
@article {pmid27597862,
year = {2016},
author = {Yano, K and Suyama, T},
title = {A Novel Fixed Low-Rank Constrained EEG Spatial Filter Estimation with Application to Movie-Induced Emotion Recognition.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {6734720},
pmid = {27597862},
issn = {1687-5273},
mesh = {Adolescent ; Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions/*physiology ; Female ; Humans ; Male ; Motion Pictures ; Photic Stimulation ; Recognition, Psychology/*physiology ; Young Adult ; },
abstract = {This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives.},
}
@article {pmid27590976,
year = {2016},
author = {McFarland, DJ and Vaughan, TM},
title = {BCI in practice.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {389-404},
doi = {10.1016/bs.pbr.2016.06.005},
pmid = {27590976},
issn = {1875-7855},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Brain-computer interfaces are systems that use signals recorded from the brain to enable communication and control applications for individuals who have impaired function. This technology has developed to the point that it is now being used by individuals who can actually benefit from it. However, there are several outstanding issues that prevent widespread use. These include the ease of obtaining high-quality recordings by home users, the speed, and accuracy of current devices and adapting applications to the needs of the user. In this chapter, we discuss some of these unsolved issues.},
}
@article {pmid27590975,
year = {2016},
author = {Riccio, A and Pichiorri, F and Schettini, F and Toppi, J and Risetti, M and Formisano, R and Molinari, M and Astolfi, L and Cincotti, F and Mattia, D},
title = {Interfacing brain with computer to improve communication and rehabilitation after brain damage.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {357-387},
doi = {10.1016/bs.pbr.2016.04.018},
pmid = {27590975},
issn = {1875-7855},
mesh = {Brain/*physiology ; Brain Injuries/*complications/rehabilitation ; *Brain-Computer Interfaces ; Communicable Diseases/*etiology/*rehabilitation ; Electroencephalography ; Humans ; Neurofeedback/*physiology ; },
abstract = {Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.},
}
@article {pmid27590974,
year = {2016},
author = {Beveridge, R and Wilson, S and Coyle, D},
title = {3D graphics, virtual reality, and motion-onset visual evoked potentials in neurogaming.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {329-353},
doi = {10.1016/bs.pbr.2016.06.006},
pmid = {27590974},
issn = {1875-7855},
mesh = {Adult ; Algorithms ; Brain/diagnostic imaging/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Imaging, Three-Dimensional ; Male ; Motion Perception/*physiology ; *Neuroimaging ; Photic Stimulation ; *User-Computer Interface ; Video Games ; },
abstract = {A brain-computer interface (BCI) offers movement-free control of a computer application and is achieved by reading and translating the cortical activity of the brain into semantic control signals. Motion-onset visual evoked potentials (mVEP) are neural potentials employed in BCIs and occur when motion-related stimuli are attended visually. mVEP dynamics are correlated with the position and timing of the moving stimuli. To investigate the feasibility of utilizing the mVEP paradigm with video games of various graphical complexities including those of commercial quality, we conducted three studies over four separate sessions comparing the performance of classifying five mVEP responses with variations in graphical complexity and style, in-game distractions, and display parameters surrounding mVEP stimuli. To investigate the feasibility of utilizing contemporary presentation modalities in neurogaming, one of the studies compared mVEP classification performance when stimuli were presented using the oculus rift virtual reality headset. Results from 31 independent subjects were analyzed offline. The results show classification performances ranging up to 90% with variations in conditions in graphical complexity having limited effect on mVEP performance; thus, demonstrating the feasibility of using the mVEP paradigm within BCI-based neurogaming.},
}
@article {pmid27590973,
year = {2016},
author = {Aricò, P and Borghini, G and Di Flumeri, G and Colosimo, A and Pozzi, S and Babiloni, F},
title = {A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {295-328},
doi = {10.1016/bs.pbr.2016.04.021},
pmid = {27590973},
issn = {1875-7855},
mesh = {Adult ; Analysis of Variance ; *Aviation ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Male ; Middle Aged ; Neuropsychological Tests ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Self-Assessment ; Signal Detection, Psychological ; *Task Performance and Analysis ; Time Factors ; Workload/*psychology ; },
abstract = {In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.},
}
@article {pmid27590972,
year = {2016},
author = {Gibson, RM and Owen, AM and Cruse, D},
title = {Brain-computer interfaces for patients with disorders of consciousness.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {241-291},
doi = {10.1016/bs.pbr.2016.04.003},
pmid = {27590972},
issn = {1875-7855},
mesh = {Brain Waves/*physiology ; *Brain-Computer Interfaces ; Consciousness Disorders/diagnostic imaging/*rehabilitation ; Databases, Bibliographic/statistics &amp; numerical data ; Electroencephalography ; Humans ; Intention ; Magnetic Resonance Imaging ; Neurofeedback/*methods ; },
abstract = {The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.},
}
@article {pmid27590971,
year = {2016},
author = {Hohmann, MR and Fomina, T and Jayaram, V and Widmann, N and Förster, C and Just, J and Synofzik, M and Schölkopf, B and Schöls, L and Grosse-Wentrup, M},
title = {A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {221-239},
doi = {10.1016/bs.pbr.2016.04.022},
pmid = {27590971},
issn = {1875-7855},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; Alpha Rhythm/physiology ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Brain Mapping ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography ; Electromyography ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Neurofeedback/instrumentation/*methods ; Parietal Lobe/*physiology ; Principal Component Analysis ; Theta Rhythm/physiology ; User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG). Both tasks are designed to modulate activity in the default mode network (DMN) without involving sensorimotor pathways. We find that the two tasks can be distinguished after only one experimental session from the average of the combined bandpower modulations in the theta- (4-7Hz) and alpha-range (8-13Hz), with an average accuracy of 62.5% and 60.8% for healthy subjects and ALS patients, respectively. The spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN.},
}
@article {pmid27590969,
year = {2016},
author = {Ushiba, J and Soekadar, SR},
title = {Brain-machine interfaces for rehabilitation of poststroke hemiplegia.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {163-183},
doi = {10.1016/bs.pbr.2016.04.020},
pmid = {27590969},
issn = {1875-7855},
mesh = {Brain Waves/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Hemiplegia/*etiology/*rehabilitation ; Humans ; Motor Cortex/*physiology ; Movement/physiology ; Neuronal Plasticity/physiology ; Stroke/*complications ; },
abstract = {Noninvasive brain-machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.},
}
@article {pmid27590968,
year = {2016},
author = {Chaudhary, U and Birbaumer, N and Ramos-Murguialday, A},
title = {Brain-computer interfaces in the completely locked-in state and chronic stroke.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {131-161},
doi = {10.1016/bs.pbr.2016.04.019},
pmid = {27590968},
issn = {1875-7855},
mesh = {Brain/*physiology ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Chronic Disease ; *Communication ; Conditioning, Classical/physiology ; Electroencephalography ; Female ; Humans ; Male ; Paralysis/*etiology/*rehabilitation ; Spectroscopy, Near-Infrared ; Stroke/*complications ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost. Previous studies demonstrated successful communication of patients with locked-in state (LIS) using EEG- and invasive electrocorticography-BCI and intracortical recordings when patients still showed residual eye control, but not with patients with complete LIS (ie, complete paralysis). Recently, a NIRS-BCI and classical conditioning procedure was introduced, allowing communication in patients in the complete locked-in state (CLIS). In severe chronic stroke without residual hand function first results indicate a possible superior motor rehabilitation to available treatment using BCI training. Here we present an overview of the available studies and recent results, which open new doors for communication, in the completely paralyzed and rehabilitation in severely affected stroke patients. We also reflect on and describe possible neuronal and learning mechanisms responsible for BCI control and perspective for future BMI research for communication in CLIS and stroke motor recovery.},
}
@article {pmid27590967,
year = {2016},
author = {Agashe, HA and Paek, AY and Contreras-Vidal, JL},
title = {Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {107-128},
doi = {10.1016/bs.pbr.2016.04.016},
pmid = {27590967},
issn = {1875-7855},
mesh = {Aged ; Amputees/*rehabilitation ; *Artificial Limbs ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Follow-Up Studies ; Hand Strength/*physiology ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; Upper Extremity/*physiology ; },
abstract = {Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration. In this study we tested if transradial amputees (A1 and A2) could control grasp preshaping in a prosthetic device using a noninvasive electroencephalography (EEG)-based closed-loop BMI system. Participants attempted to grasp presented objects by controlling two grasping synergies, in 12 sessions performed over 5 weeks. Prior to closed-loop control, the first six sessions included a decoder calibration phase using action observation by the participants; thereafter, the decoder was fixed to examine neuroprosthetic performance in the absence of decoder recalibration. Ability of participants to control the prosthetic was measured by the success rate of grasping; ie, the percentage of trials within a session in which presented objects were successfully grasped. Participant A1 maintained a steady success rate (63±3%) across sessions (significantly above chance [41±5%] for 11 sessions). Participant A2, who was under the influence of pharmacological treatment for depression, hormone imbalance, pain management (for phantom pain as well as shoulder joint inflammation), and drug dependence, achieved a success rate of 32±2% across sessions (significantly above chance [27±5%] in only two sessions). EEG signal quality was stable across sessions, but the decoders created during the first six sessions showed variation, indicating EEG features relevant to decoding at a smaller timescale (100ms) may not be stable. Overall, our results show that (a) an EEG-based BMI for grasping is a feasible strategy for further investigation of prosthetic control by amputees, and (b) factors that may affect brain activity such as medication need further examination to improve accuracy and stability of BMI performance.},
}
@article {pmid27590966,
year = {2016},
author = {Korik, A and Sosnik, R and Siddique, N and Coyle, D},
title = {3D hand motion trajectory prediction from EEG mu and beta bandpower.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {71-105},
doi = {10.1016/bs.pbr.2016.05.001},
pmid = {27590966},
issn = {1875-7855},
mesh = {Adult ; Algorithms ; Biomechanical Phenomena ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Hand/*physiology ; Humans ; *Imagination ; Imaging, Three-Dimensional ; Linear Models ; Male ; Middle Aged ; Movement/*physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {A motion trajectory prediction (MTP) - based brain-computer interface (BCI) aims to reconstruct the three-dimensional (3D) trajectory of upper limb movement using electroencephalography (EEG). The most common MTP BCI employs a time series of bandpass-filtered EEG potentials (referred to here as the potential time-series, PTS, model) for reconstructing the trajectory of a 3D limb movement using multiple linear regression. These studies report the best accuracy when a 0.5-2Hz bandpass filter is applied to the EEG. In the present study, we show that spatiotemporal power distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) bands are more robust for movement trajectory decoding when the standard PTS approach is replaced with time-varying bandpower values of a specified EEG band, ie, with a bandpower time-series (BTS) model. A comprehensive analysis comprising of three subjects performing pointing movements with the dominant right arm toward six targets is presented. Our results show that the BTS model produces significantly higher MTP accuracy (R~0.45) compared to the standard PTS model (R~0.2). In the case of the BTS model, the highest accuracy was achieved across the three subjects typically in the mu (8-12Hz) and low-beta (12-18Hz) bands. Additionally, we highlight a limitation of the commonly used PTS model and illustrate how this model may be suboptimal for decoding motion trajectory relevant information. Although our results, showing that the mu and beta bands are prominent for MTP, are not in line with other MTP studies, they are consistent with the extensive literature on classical multiclass sensorimotor rhythm-based BCI studies (classification of limbs as opposed to motion trajectory prediction), which report the best accuracy of imagined limb movement classification using power values of mu and beta frequency bands. The methods proposed here provide a positive step toward noninvasive decoding of imagined 3D hand movements for movement-free BCIs.},
}
@article {pmid27590965,
year = {2016},
author = {Müller-Putz, GR and Schwarz, A and Pereira, J and Ofner, P},
title = {From classic motor imagery to complex movement intention decoding: The noninvasive Graz-BCI approach.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {39-70},
doi = {10.1016/bs.pbr.2016.04.017},
pmid = {27590965},
issn = {1875-7855},
mesh = {Algorithms ; Brain/*physiology ; *Brain Mapping ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; *Intention ; Movement/*physiology ; },
abstract = {In this chapter, we give an overview of the Graz-BCI research, from the classic motor imagery detection to complex movement intentions decoding. We start by describing the classic motor imagery approach, its application in tetraplegic end users, and the significant improvements achieved using coadaptive brain-computer interfaces (BCIs). These strategies have the drawback of not mirroring the way one plans a movement. To achieve a more natural control-and to reduce the training time-the movements decoded by the BCI need to be closely related to the user's intention. Within this natural control, we focus on the kinematic level, where movement direction and hand position or velocity can be decoded from noninvasive recordings. First, we review movement execution decoding studies, where we describe the decoding algorithms, their performance, and associated features. Second, we describe the major findings in movement imagination decoding, where we emphasize the importance of estimating the sources of the discriminative features. Third, we introduce movement target decoding, which could allow the determination of the target without knowing the exact movement-by-movement details. Aside from the kinematic level, we also address the goal level, which contains relevant information on the upcoming action. Focusing on hand-object interaction and action context dependency, we discuss the possible impact of some recent neurophysiological findings in the future of BCI control. Ideally, the goal and the kinematic decoding would allow an appropriate matching of the BCI to the end users' needs, overcoming the limitations of the classic motor imagery approach.},
}
@article {pmid27590964,
year = {2016},
author = {Jeunet, C and N'Kaoua, B and Lotte, F},
title = {Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates.},
journal = {Progress in brain research},
volume = {228},
number = {},
pages = {3-35},
doi = {10.1016/bs.pbr.2016.04.002},
pmid = {27590964},
issn = {1875-7855},
mesh = {Attention/physiology ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography ; Humans ; Imagery, Psychotherapy/*methods ; *Personality ; Spatial Navigation/physiology ; },
abstract = {While being very promising for a wide range of applications, mental-imagery-based brain-computer interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10-30% of users are unable to control MI-BCIs (so-called BCI illiteracy) while only a small proportion reach acceptable control abilities. This huge interuser variability has led the community to investigate potential predictors of performance related to users' personality and cognitive profile. Based on a literature review, we propose a classification of these MI-BCI performance predictors into three categories representing high-level cognitive concepts: (1) users' relationship with the technology (including the notions of computer anxiety and sense of agency), (2) attention, and (3) spatial abilities. We detail these concepts and their neural correlates in order to better understand their relationship with MI-BCI user-training. Consequently, we propose, by way of future prospects, some guidelines to improve MI-BCI user-training.},
}
@article {pmid27589505,
year = {2016},
author = {Kawakami, M and Fujiwara, T and Ushiba, J and Nishimoto, A and Abe, K and Honaga, K and Nishimura, A and Mizuno, K and Kodama, M and Masakado, Y and Liu, M},
title = {A new therapeutic application of brain-machine interface (BMI) training followed by hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy for patients with severe hemiparetic stroke: A proof of concept study.},
journal = {Restorative neurology and neuroscience},
volume = {34},
number = {5},
pages = {789-797},
doi = {10.3233/RNN-160652},
pmid = {27589505},
issn = {1878-3627},
mesh = {Adult ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; Electroencephalography ; Electromyography ; Evoked Potentials/physiology ; Female ; Follow-Up Studies ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Neuromuscular Junction/*physiology ; Paresis/etiology/*rehabilitation ; Proof of Concept Study ; Severity of Illness Index ; Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy improved paretic upper extremity motor function in patients with severe to moderate hemiparesis. We hypothesized that brain machine interface (BMI) training would be able to increase paretic finger muscle activity enough to apply HANDS therapy in patients with severe hemiparesis, whose finger extensor was absent.<br><br>OBJECTIVE: The aim of this study was to assess the efficacy of BMI training followed by HANDS therapy in patients with severe hemiparesis.<br><br>METHODS: Twenty-nine patients with chronic stroke who could not extend their paretic fingers were participated this study. We applied BMI training for 10 days at 40&#x200a;min per day. The BMI detected the patients' motor imagery of paretic finger extension with event-related desynchronization (ERD) over the affected primary sensorimotor cortex, recorded with electroencephalography. Patients wore a motor-driven orthosis, which extended their paretic fingers and was triggered with ERD. When muscle activity in their paretic fingers was detected with surface electrodes after 10 days of BMI training, we applied HANDS therapy for the following 3 weeks. In HANDS therapy, participants received closed-loop, electromyogram-controlled, neuromuscular electrical stimulation (NMES) combined with a wrist-hand splint for 3 weeks at 8 hours a day. Before BMI training, after BMI training, after HANDS therapy and 3month after HANDS therapy, we assessed Fugl-Meyer Assessment upper extremity motor score (FMA) and the Motor Activity Log14-Amount of Use (MAL-AOU) score.<br><br>RESULTS: After 10 days of BMI training, finger extensor activity had appeared in 21 patients. Eighteen of 21 patients then participated in 3 weeks of HANDS therapy. We found a statistically significant improvement in the FMA and the MAL-AOU scores after the BMI training, and further improvement was seen after the HANDS therapy.<br><br>CONCLUSION: Combining BMI training with HANDS therapy could be an effective therapeutic strategy for severe UE paralysis after stroke.},
}
@article {pmid27587163,
year = {2016},
author = {Gao, L and Cheng, W and Zhang, J and Wang, J},
title = {EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.},
journal = {The Review of scientific instruments},
volume = {87},
number = {8},
pages = {085110},
doi = {10.1063/1.4959983},
pmid = {27587163},
issn = {1089-7623},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Hand/physiology ; Humans ; *Machine Learning ; Male ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.},
}
@article {pmid27579033,
year = {2016},
author = {Liu, JC and Chou, HC and Chen, CH and Lin, YT and Kuo, CH},
title = {Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {3039454},
pmid = {27579033},
issn = {1687-5273},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Feedback ; Female ; Humans ; Male ; *Models, Neurological ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Time Perception/*physiology ; Young Adult ; },
abstract = {A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.},
}
@article {pmid27578310,
year = {2016},
author = {Brandl, S and Frølich, L and Höhne, J and Müller, KR and Samek, W},
title = {Brain-computer interfacing under distraction: an evaluation study.},
journal = {Journal of neural engineering},
volume = {13},
number = {5},
pages = {056012},
doi = {10.1088/1741-2560/13/5/056012},
pmid = {27578310},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials, Somatosensory/physiology ; Female ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {OBJECTIVE: While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research.<br><br>APPROACH: This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments.<br><br>MAIN RESULTS: We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP)&#xa0;+&#xa0;regularized linear discriminant analysis classification pipeline drops significantly in this 'simulated' out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure.<br><br>SIGNIFICANCE: Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.},
}
@article {pmid27567757,
year = {2016},
author = {Fusco, G and Tidoni, E and Barone, N and Pilati, C and Aglioti, SM},
title = {Illusion of arm movement evoked by tendon vibration in patients with spinal cord injury.},
journal = {Restorative neurology and neuroscience},
volume = {34},
number = {5},
pages = {815-826},
doi = {10.3233/RNN-160660},
pmid = {27567757},
issn = {1878-3627},
mesh = {Adult ; Arm/*physiopathology ; Female ; Humans ; Illusions/*physiology ; Male ; Middle Aged ; Movement/*physiology ; Proprioception/physiology ; Spinal Cord Injuries/*rehabilitation ; Surveys and Questionnaires ; Tendons/*innervation ; *Vibration ; },
abstract = {BACKGROUND: Studies in healthy people show that stimulation of muscle spindles through frequency-specific tendon vibration (TV) induces the illusory perception of movement. Following spinal cord injury (SCI), motor and sensory connections between the brain and parts of the body below-the-lesion level are partially or totally impaired.<br><br>OBJECTIVE: The present investigation is a descriptive study aimed to investigate whether people living with SCI may experience movement illusions comparable to a control group.<br><br>METHODS: Healthy and people with SCI were asked to report on three illusion-related features (Vividness, Duration, Illusory Extension) after receiving 70&#x200a;Hz TV on the biceps brachii tendon of both arms. Two different forces of stimulation were applied: 2.4&#x200a;N and 4.2&#x200a;N.<br><br>RESULTS: Both patients and controls were susceptible to the kinesthetic illusion. However patients presented lower sensitivity to TV than healthy subjects. Participants rated stronger illusions of movement after 4.2&#x200a;N than 2.4&#x200a;N stimulation in all the three illusion-related features. Further, patients reported atypical illusory experiences of movement (e.g. as if the arm wanted to extend, or a sensation of pushing against something) that may reflect different reorganization processes following spinal cord injury.<br><br>CONCLUSION: The study provides a preliminary evidence of the possible use of the proprioceptive stimulation in the upper limbs of people living with SCI. Results are discussed in the light of recent advancements of brain-computer applications based on motor imagery for the control of neuroprosthetic and robotic devices in patients with severe sensorimotor deficits.},
}
@article {pmid27567452,
year = {2016},
author = {Ullrich, SF and Averesch, NJ and Castellanos, L and Choi, YH and Rothauer, A and Kayser, O},
title = {Discrimination of wild types and hybrids of Duboisia myoporoides and Duboisia leichhardtii at different growth stages using [1]H NMR-based metabolite profiling and tropane alkaloids-targeted HPLC-MS analysis.},
journal = {Phytochemistry},
volume = {131},
number = {},
pages = {44-56},
doi = {10.1016/j.phytochem.2016.08.008},
pmid = {27567452},
issn = {1873-3700},
mesh = {Alkaloids/chemistry ; Chromatography, High Pressure Liquid ; *Duboisia/chemistry/genetics/growth &amp; development ; Hyoscyamine/*chemistry/isolation &amp; purification ; Metabolomics ; Nuclear Magnetic Resonance, Biomolecular ; Scopolamine/analysis/chemistry ; Tropanes/chemistry ; },
abstract = {Duboisia species, which belong to the family of Solanaceae, are commercially cultivated in large scale, as they are main source of the pharmaceutically-used active compound scopolamine. In this study, [1]H NMR-based metabolite profiling linking primary with secondary metabolism and additional quantification via HPCL-MS with special focus on the tropane alkaloids were applied to compare leaf and root extracts of three wild types and two hybrids of Duboisia myoporoides and D. leichhardtii at different developmental stages grown under controlled conditions in climate chambers and under agricultural field plantation. Based on the leaf extracts, a clear distinction between the Duboisia hybrids and the wild types Duboisia myoporoides and D. leichhardtii using principal component analysis of [1]H NMR data was observed. The average content in scopolamine in the hybrids of Duboisia cultivated in climate chambers increased significantly from month 3-6 after potting of the rooted cuttings, however not so for the examined wild types. The Duboisia hybrids grown in climate chambers showed higher growth and contained more sugars and amino acids than Duboisia hybrids grown in the field, which in contrast showed an enhanced flux towards tropane alkaloids as well as flavonoids. For a more detailed analysis of tropane alkaloids, an appropriate HPLC-MS method was developed and validated. The measurements revealed large differences in the alkaloid pattern within the different genotypes under investigation, especially regarding the last enzymatic step, the conversion from hyoscamine to scopolamine by the hyoscyamine 6β-hydroxylase. Scopolamine was found in highest concentrations in Duboisia hybrids (20.04 ± 4.05 and 17.82 ± 3.52 mg/g dry wt) followed by Duboisia myoporoides (12.71 ± 2.55 mg/g dry wt), both showing a high selectivity for scopolamine in contrast to Duboisia leichhardtii (3.38 ± 0.59 and 5.09 ± 1.24 mg/g dry wt) with hyoscyamine being the predominant alkaloid.},
}
@article {pmid27566453,
year = {2016},
author = {Owora, AH and Carabin, H and Reese, J and Garwe, T},
title = {Summary diagnostic validity of commonly used maternal major depression disorder case finding instruments in the United States: A meta-analysis.},
journal = {Journal of affective disorders},
volume = {205},
number = {},
pages = {335-343},
pmid = {27566453},
issn = {1573-2517},
support = {U54 GM104938/GM/NIGMS NIH HHS/United States ; },
mesh = {Bayes Theorem ; Depressive Disorder, Major/*diagnosis ; Humans ; Mothers/*psychology ; Peripartum Period/psychology ; Psychiatric Status Rating Scales/*statistics &amp; numerical data ; United States ; },
abstract = {INTRODUCTION: Major Depression Disorder (MDD) is common among mothers of young children. However, its detection remains low in primary-care and community-based settings in part due to the uncertainty regarding the validity of existing case-finding instruments. We conducted meta-analyses to estimate the diagnostic validity of commonly used maternal MDD case finding instruments in the United States.<br><br>METHODS: We systematically searched three electronic bibliographic databases PubMed, PsycINFO, and EMBASE from 1994 to 2015 to identify relevant published literature. Study eligibility and quality were evaluated using the Standards for the Reporting of Diagnostic Accuracy studies and Quality Assessment of Diagnostic Accuracy Studies guidelines, respectively. Pooled sensitivity and specificity of case-finding instruments were generated using Bayesian hierarchical summary receiver operating models.<br><br>RESULTS: Overall, 1130 articles were retrieved and 74 articles were selected for full-text review. Twelve articles examining six maternal MDD case-finding instruments met the eligibility criteria and were included in our meta-analyses. Pooled sensitivity and specificity estimates were highest for the BDI-II (91%; 95% Bayesian Credible Interval (BCI): 68%; 99% and 89%; 95% BCI: 62%; 98% respectively) and EPDS10 (74%; 95% BCI: 46%; 91% and 97%; 95% BCI: 84%; 99% respectively) during the antepartum and postpartum periods respectively.<br><br>LIMITATION: No meta-regression was conducted to examine the impact of study-level characteristics on the results.<br><br>DISCUSSION: Diagnostic performance varied among instruments and between peripartum periods. These findings suggest the need for a judicious selection of maternal MDD case-finding instruments depending on the study population and target periods of assessment.},
}
@article {pmid27563927,
year = {2016},
author = {Djemal, R and Bazyed, AG and Belwafi, K and Gannouni, S and Kaaniche, W},
title = {Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique.},
journal = {Brain sciences},
volume = {6},
number = {3},
pages = {},
pmid = {27563927},
issn = {2076-3425},
abstract = {Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies.},
}
@article {pmid27563342,
year = {2016},
author = {Mahapatra, D and Agarwal, K and Khosrowabadi, R and Prasad, DK},
title = {Recent Advances in Statistical Data and Signal Analysis: Application to Real World Diagnostics from Medical and Biological Signals.},
journal = {Computational and mathematical methods in medicine},
volume = {2016},
number = {},
pages = {1643687},
doi = {10.1155/2016/1643687},
pmid = {27563342},
issn = {1748-6718},
mesh = {Bayes Theorem ; Brain-Computer Interfaces ; Computer Graphics ; Computer Simulation ; Electroencephalography ; Humans ; Machine Learning ; Medical Informatics/*instrumentation/methods ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Software ; *Statistics as Topic ; Stochastic Processes ; X-Rays ; },
}
@article {pmid27555805,
year = {2016},
author = {Grimm, F and Walter, A and Spüler, M and Naros, G and Rosenstiel, W and Gharabaghi, A},
title = {Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {367},
pmid = {27555805},
issn = {1662-4548},
abstract = {Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.},
}
@article {pmid27548774,
year = {2016},
author = {Padma, S and Umesh, S and Pant, S and Srinivas, T and Asokan, S},
title = {Fiber Bragg grating sensor-based communication assistance device.},
journal = {Journal of biomedical optics},
volume = {21},
number = {8},
pages = {86012},
doi = {10.1117/1.JBO.21.8.086012},
pmid = {27548774},
issn = {1560-2281},
mesh = {*Communication Devices for People with Disabilities ; *Fiber Optic Technology ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Improvements in emergency medicine in the form of efficient life supporting systems and intensive care have increased the survival rate in critically injured patients; however, in some cases, severe brain and spinal cord injuries can result in a locked-in syndrome or other forms of paralysis, and communication with these patients may become restricted or impossible. The present study proposes a noninvasive, real-time communication assistive methodology for those with restricted communication ability, employing a fiber Bragg grating (FBG) sensor. The communication assistive methodology comprises a breath pattern analyzer using an FBG sensor, which acquires the exhalation force that is converted into strain variations on a cantilever. The FBG breath pattern analyzer along with specific breath patterns, which are programmed to give specific audio output commands, constitutes the proposed fiber Bragg grating sensor-based communication assistive device. The basic communication can be carried out by instructing the patients with restricted communication ability to perform the specific breath patterns. The present approach is intended to be an alternative to the common approach of brain–computer interface in which an instrument is utilized for learning of brain responses.},
}
@article {pmid27547465,
year = {2016},
author = {Ahmed, A and Farhan, B and Vernez, S and Ghoniem, GM},
title = {The challenges in the diagnosis of detrusor underactivity in clinical practice: A mini-review.},
journal = {Arab journal of urology},
volume = {14},
number = {3},
pages = {223-227},
pmid = {27547465},
issn = {2090-598X},
abstract = {OBJECTIVE: To review the current definitions, terminology, epidemiology and aetiology of detrusor underactivity (DU), with specific attention to the diagnostic criteria in use. In addition, we address the relation and the overlap between DU and bladder outlet obstruction (BOO). In this mini-review, we hope to help identify DU patients and facilitate structured clinical evaluation and research.<br><br>METHODS: We searched the English literature using ScienceDirect and PubMed for relevant articles. We used the following terms: 'detrusor underactivity', 'underactive bladder', 'post voiding residual', 'post micturition residual', 'acontractile bladder', 'detrusor failure', and 'detrusor areflexia'.<br><br>RESULT: DU is one of the most common conditions causing lower urinary tract symptoms (LUTS). Unfortunately, it is also the most poorly understood bladder dysfunction with scant research. To our knowledge there is no clear definition and no non-invasive method to characterise this important clinical condition. DU may result from the normal ageing process; however, it has multiple aetiologies including neurogenic and myogenic dysfunction. In many cases the symptoms of DU are similar to those of BOO and it usually requires invasive urodynamic study (UDS) for diagnosis to differentiate the two diagnoses. A number of diagnostic tests may be used including: UDS testing, the Schafer pressure/flow nomogram, linear passive urethral resistance relation, Watts factor, and the bladder contractility index. Of these, UDS testing is the most practical as it determines both the maximum urinary flow rate and the pressure exerted by the detrusor muscle relative to the maximal flow of urine, allowing for precise characterisation of detrusor function.<br><br>CONCLUSION: Currently, the diagnosis of DU is based on invasive urodynamic parameters as defined by the International Continence Society in 2002. There is no consensus for the definition of DU prior to 2002. As there is significant overlap between the symptoms of DU and BOO, it is difficult to diagnose DU clinically.},
}
@article {pmid27544071,
year = {2016},
author = {Yu, Y and Zhou, Z and Yin, E and Jiang, J and Tang, J and Liu, Y and Hu, D},
title = {Toward brain-actuated car applications: Self-paced control with a motor imagery-based brain-computer interface.},
journal = {Computers in biology and medicine},
volume = {77},
number = {},
pages = {148-155},
doi = {10.1016/j.compbiomed.2016.08.010},
pmid = {27544071},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; *Automobile Driving ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Male ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car.},
}
@article {pmid27543971,
year = {2016},
author = {Lutters, B and Koehler, PJ},
title = {Brainwaves in concert: the 20th century sonification of the electroencephalogram.},
journal = {Brain : a journal of neurology},
volume = {139},
number = {Pt 10},
pages = {2809-2814},
doi = {10.1093/brain/aww207},
pmid = {27543971},
issn = {1460-2156},
}
@article {pmid27542114,
year = {2017},
author = {Lu, N and Li, T and Ren, X and Miao, H},
title = {A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {566-576},
doi = {10.1109/TNSRE.2016.2601240},
pmid = {27542114},
issn = {1558-0210},
mesh = {Algorithms ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Fourier Analysis ; Humans ; Imagination/*physiology ; Intention ; *Machine Learning ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform (FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.},
}
@article {pmid27542113,
year = {2017},
author = {Waytowich, NR and Yamani, Y and Krusienski, DJ},
title = {Optimization of Checkerboard Spatial Frequencies for Steady-State Visual Evoked Potential Brain-Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {557-565},
doi = {10.1109/TNSRE.2016.2601013},
pmid = {27542113},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spatio-Temporal Analysis ; Visual Cortex/*physiology ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are oscillations of the electroencephalogram (EEG) which are mainly observed over the occipital area that exhibit a frequency corresponding to a repetitively flashing visual stimulus. SSVEPs have proven to be very consistent and reliable signals for rapid EEG-based brain-computer interface (BCI) control. There is conflicting evidence regarding whether solid or checkerboard-patterned flashing stimuli produce superior BCI performance. Furthermore, the spatial frequency of checkerboard stimuli can be varied for optimal performance. The present study performs an empirical evaluation of performance for a 4-class SSVEP-based BCI when the spatial frequency of the individual checkerboard stimuli is varied over a continuum ranging from a solid background to single-pixel checkerboard patterns. The results indicate that a spatial frequency of 2.4 cycles per degree can maximize the information transfer rate with a reduction in subjective visual irritation compared to lower spatial frequencies. This important finding on stimulus design can lead to improved performance and usability of SSVEP-based BCIs.},
}
@article {pmid27541829,
year = {2016},
author = {Wander, JD and Sarma, D and Johnson, LA and Fetz, EE and Rao, RP and Ojemann, JG and Darvas, F},
title = {Cortico-Cortical Interactions during Acquisition and Use of a Neuroprosthetic Skill.},
journal = {PLoS computational biology},
volume = {12},
number = {8},
pages = {e1004931},
pmid = {27541829},
issn = {1553-7358},
support = {P51 OD010425/OD/NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Child ; Computational Biology ; Electrocorticography ; Female ; Humans ; Learning/*physiology ; Male ; Middle Aged ; Models, Neurological ; Motor Cortex/*physiology ; Nerve Net/physiology ; Task Performance and Analysis ; Young Adult ; },
abstract = {A motor cortex-based brain-computer interface (BCI) creates a novel real world output directly from cortical activity. Use of a BCI has been demonstrated to be a learned skill that involves recruitment of neural populations that are directly linked to BCI control as well as those that are not. The nature of interactions between these populations, however, remains largely unknown. Here, we employed a data-driven approach to assess the interaction between both local and remote cortical areas during the use of an electrocorticographic BCI, a method which allows direct sampling of cortical surface potentials. Comparing the area controlling the BCI with remote areas, we evaluated relationships between the amplitude envelopes of band limited powers as well as non-linear phase-phase interactions. We found amplitude-amplitude interactions in the high gamma (HG, 70-150 Hz) range that were primarily located in the posterior portion of the frontal lobe, near the controlling site, and non-linear phase-phase interactions involving multiple frequencies (cross-frequency coupling between 8-11 Hz and 70-90 Hz) taking place over larger cortical distances. Further, strength of the amplitude-amplitude interactions decreased with time, whereas the phase-phase interactions did not. These findings suggest multiple modes of cortical communication taking place during BCI use that are specialized for function and depend on interaction distance.},
}
@article {pmid27539560,
year = {2016},
author = {Chaudhary, U and Birbaumer, N and Ramos-Murguialday, A},
title = {Brain-computer interfaces for communication and rehabilitation.},
journal = {Nature reviews. Neurology},
volume = {12},
number = {9},
pages = {513-525},
pmid = {27539560},
issn = {1759-4766},
mesh = {Brain-Computer Interfaces/*trends ; Communication Devices for People with Disabilities/*trends ; Electroencephalography/trends ; Humans ; Nervous System Diseases/physiopathology/psychology/*rehabilitation ; },
abstract = {Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.},
}
@article {pmid27536214,
year = {2016},
author = {López-Larraz, E and Trincado-Alonso, F and Rajasekaran, V and Pérez-Nombela, S and Del-Ama, AJ and Aranda, J and Minguez, J and Gil-Agudo, A and Montesano, L},
title = {Control of an Ambulatory Exoskeleton with a Brain-Machine Interface for Spinal Cord Injury Gait Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {359},
pmid = {27536214},
issn = {1662-4548},
abstract = {The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).},
}
@article {pmid27536212,
year = {2016},
author = {Ordikhani-Seyedlar, M and Lebedev, MA and Sorensen, HB and Puthusserypady, S},
title = {Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {352},
pmid = {27536212},
issn = {1662-4548},
abstract = {We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention.},
}
@article {pmid27528864,
year = {2016},
author = {Stawicki, P and Gembler, F and Volosyak, I},
title = {Driving a Semiautonomous Mobile Robotic Car Controlled by an SSVEP-Based BCI.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {4909685},
pmid = {27528864},
issn = {1687-5273},
mesh = {Adolescent ; Adult ; Algorithms ; *Automobile Driving ; Automobiles ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Neurofeedback ; Photic Stimulation ; Psychomotor Performance/physiology ; *Robotics/instrumentation/methods ; Software ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Brain-computer interfaces represent a range of acknowledged technologies that translate brain activity into computer commands. The aim of our research is to develop and evaluate a BCI control application for certain assistive technologies that can be used for remote telepresence or remote driving. The communication channel to the target device is based on the steady-state visual evoked potentials. In order to test the control application, a mobile robotic car (MRC) was introduced and a four-class BCI graphical user interface (with live video feedback and stimulation boxes on the same screen) for piloting the MRC was designed. For the purpose of evaluating a potential real-life scenario for such assistive technology, we present a study where 61 subjects steered the MRC through a predetermined route. All 61 subjects were able to control the MRC and finish the experiment (mean time 207.08 s, SD 50.25) with a mean (SD) accuracy and ITR of 93.03% (5.73) and 14.07 bits/min (4.44), respectively. The results show that our proposed SSVEP-based BCI control application is suitable for mobile robots with a shared-control approach. We also did not observe any negative influence of the simultaneous live video feedback and SSVEP stimulation on the performance of the BCI system.},
}
@article {pmid27528057,
year = {2017},
author = {Tay, A and Di Carlo, D},
title = {Remote Neural Stimulation Using Magnetic Nanoparticles.},
journal = {Current medicinal chemistry},
volume = {24},
number = {5},
pages = {537-548},
doi = {10.2174/0929867323666160814000442},
pmid = {27528057},
issn = {1875-533X},
mesh = {Animals ; Brain/physiology ; Humans ; Magnetic Field Therapy/*methods ; Magnetic Fields ; Magnetite Nanoparticles/*chemistry/*therapeutic use/toxicity ; Mechanotransduction, Cellular ; Nanotechnology/*methods ; Nerve Net/*physiology ; Physical Stimulation/methods ; TRPV Cation Channels/metabolism ; *Wireless Technology ; },
abstract = {Neural stimulation provides a means for scientists to investigate brain functions and neurological diseases. There is also mounting interest in using remote stimulation of neuronal circuits for brain-machine interfaces. In this review, we highlight recently developed technologies utilizing magnetic nanoparticles to generate heat or exert mechanical forces for remote control of brain circuits and compare these with conventional (electrical stimulation and drugs) and second-generation (ultrasound and light) techniques. We also present some of the challenges and progress in areas like genetics, nanoparticle synthesis and energy delivery devices to translate the use of these innovative nanoparticle-based platforms in research and clinical settings.},
}
@article {pmid27525806,
year = {2016},
author = {Arnon, S and Dahan, N and Koren, A and Radiano, O and Ronen, M and Yannay, T and Giron, J and Ben-Ami, L and Amir, Y and Hel-Or, Y and Friedman, D and Bachelet, I},
title = {Thought-Controlled Nanoscale Robots in a Living Host.},
journal = {PloS one},
volume = {11},
number = {8},
pages = {e0161227},
pmid = {27525806},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Cockroaches ; Electroencephalography ; Nanotechnology ; Robotics/*methods ; *Thinking ; },
abstract = {We report a new type of brain-machine interface enabling a human operator to control nanometer-size robots inside a living animal by brain activity. Recorded EEG patterns are recognized online by an algorithm, which in turn controls the state of an electromagnetic field. The field induces the local heating of billions of mechanically-actuating DNA origami robots tethered to metal nanoparticles, leading to their reversible activation and subsequent exposure of a bioactive payload. As a proof of principle we demonstrate activation of DNA robots to cause a cellular effect inside the insect Blaberus discoidalis, by a cognitively straining task. This technology enables the online switching of a bioactive molecule on and off in response to a subject's cognitive state, with potential implications to therapeutic control in disorders such as schizophrenia, depression, and attention deficits, which are among the most challenging conditions to diagnose and treat.},
}
@article {pmid27522166,
year = {2016},
author = {Baxter, BS and Edelman, BJ and Nesbitt, N and He, B},
title = {Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance.},
journal = {Brain stimulation},
volume = {9},
number = {6},
pages = {834-841},
pmid = {27522166},
issn = {1876-4754},
support = {F31 NS096964/NS/NINDS NIH HHS/United States ; R01 EB021027/EB/NIBIB NIH HHS/United States ; R01 EY023101/EY/NEI NIH HHS/United States ; R01 NS096761/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Alpha Rhythm/*physiology ; Beta Rhythm/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Psychomotor Performance/*physiology ; Sensorimotor Cortex/*physiology ; Transcranial Direct Current Stimulation/*methods ; Young Adult ; },
abstract = {BACKGROUND: Transcranial direct current stimulation (tDCS) has been used to alter the excitability of neurons within the cerebral cortex. Improvements in motor learning have been found in multiple studies when tDCS was applied to the motor cortex before or during task learning. The motor cortex is also active during the performance of motor imagination, a cognitive task during which a person imagines, but does not execute, a movement. Motor imagery can be used with noninvasive brain computer interfaces (BCIs) to control virtual objects in up to three dimensions, but to master control of such devices requires long training times.<br><br>OBJECTIVE: To evaluate the effect of high-definition tDCS on the performance and underlying electrophysiology of motor imagery based BCI.<br><br>METHODS: We utilize high-definition tDCS to investigate the effect of stimulation on motor imagery-based BCI performance across and within sessions over multiple training days.<br><br>RESULTS: We report a decreased time-to-hit with anodal stimulation both within and across sessions. We also found differing electrophysiological changes of the stimulated sensorimotor cortex during online BCI task performance for left vs. right trials. Cathodal stimulation led to a decrease in alpha and beta band power during task performance compared to sham stimulation for right hand imagination trials.<br><br>CONCLUSION: These results suggest that unilateral tDCS over the sensorimotor motor cortex differentially affects cortical areas based on task specific neural activation.},
}
@article {pmid27514060,
year = {2018},
author = {Kim, KT and Suk, HI and Lee, SW},
title = {Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {26},
number = {3},
pages = {654-665},
doi = {10.1109/TNSRE.2016.2597854},
pmid = {27514060},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials, Somatosensory/*physiology ; Female ; Foot/physiology ; Hand/physiology ; Healthy Volunteers ; Humans ; Imagination ; Machine Learning ; Male ; Movement ; *Wheelchairs ; Young Adult ; },
abstract = {In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain-machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user's intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.},
}
@article {pmid27513737,
year = {2016},
author = {Hammad, SH and Kamavuako, EN and Farina, D and Jensen, W},
title = {Simulation of a Real-Time Brain Computer Interface for Detecting a Self-Paced Hitting Task.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {19},
number = {8},
pages = {804-811},
doi = {10.1111/ner.12478},
pmid = {27513737},
issn = {1525-1403},
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; *Computer Simulation ; Electroencephalography ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Multivariate Analysis ; Rats ; Rats, Sprague-Dawley ; Self-Control ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVES: An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our objective was to investigate whether a specific motor task could be reliably detected from multiunit intracortical signals from freely moving animals in a simulated, real-time setting.<br><br>MATERIALS AND METHODS: Intracortical signals were first obtained from electrodes placed in the primary motor cortex of four rats that were trained to hit a retractable paddle (defined as a "Hit"). In the simulated real-time setting, the signal-to-noise-ratio was first increased by wavelet denoising. Action potentials were detected, and features were extracted (spike count, mean absolute values, entropy, and combination of these features) within pre-defined time windows (200 ms, 300 ms, and 400 ms) to classify the occurrence of a "Hit."<br><br>RESULTS: We found higher detection accuracy of a "Hit" (73.1%, 73.4%, and 67.9% for the three window sizes, respectively) when the decision was made based on a combination of features rather than on a single feature. However, the duration of the window length was not statistically significant (p&#x2009;=&#x2009;0.5).<br><br>CONCLUSION: Our results showed the feasibility of detecting a motor task in real time in a less restricted environment compared to environments commonly applied within invasive BCI research, and they showed the feasibility of using information extracted from multiunit recordings, thereby avoiding the time-consuming and complex task of extracting and sorting single units.},
}
@article {pmid27513629,
year = {2016},
author = {Donati, AR and Shokur, S and Morya, E and Campos, DS and Moioli, RC and Gitti, CM and Augusto, PB and Tripodi, S and Pires, CG and Pereira, GA and Brasil, FL and Gallo, S and Lin, AA and Takigami, AK and Aratanha, MA and Joshi, S and Bleuler, H and Cheng, G and Rudolph, A and Nicolelis, MA},
title = {Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {30383},
pmid = {27513629},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Sensory ; Female ; Gait/*physiology ; Humans ; Interdisciplinary Communication ; Locomotion ; Lower Extremity ; Male ; Neurological Rehabilitation/*methods ; Paraplegia/physiopathology/*rehabilitation ; Robotics ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Walking/*physiology ; Young Adult ; },
abstract = {Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.},
}
@article {pmid27512364,
year = {2016},
author = {Mirkovic, B and Bleichner, MG and De Vos, M and Debener, S},
title = {Target Speaker Detection with Concealed EEG Around the Ear.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {349},
pmid = {27512364},
issn = {1662-4548},
abstract = {Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids.},
}
@article {pmid27511294,
year = {2017},
author = {O'Shea, DJ and Trautmann, E and Chandrasekaran, C and Stavisky, S and Kao, JC and Sahani, M and Ryu, S and Deisseroth, K and Shenoy, KV},
title = {The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces.},
journal = {Experimental neurology},
volume = {287},
number = {Pt 4},
pages = {437-451},
pmid = {27511294},
issn = {1090-2430},
support = {F31 NS089376/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; /HHMI_/Howard Hughes Medical Institute/United States ; K99 NS092972/NS/NINDS NIH HHS/United States ; T32 MH020016/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Bacterial Proteins/analysis/genetics ; Behavior, Animal ; *Brain-Computer Interfaces ; Calcium/*analysis ; *Calcium Signaling ; Connectome/instrumentation/*methods ; Cytological Techniques/instrumentation ; Electric Stimulation ; Fluorescent Dyes ; Green Fluorescent Proteins/analysis/genetics ; Image Processing, Computer-Assisted/*methods ; Imaging, Three-Dimensional ; Intravital Microscopy/instrumentation/*methods ; Luminescent Proteins/analysis/genetics ; Microscopy, Fluorescence/instrumentation/methods ; Models, Neurological ; Motor Activity ; Motor Cortex/cytology/*physiology ; Nerve Net/*physiology/ultrastructure ; Neurons/chemistry/*physiology/ultrastructure ; Primates/*anatomy &amp; histology/physiology ; *Single-Cell Analysis ; Transduction, Genetic ; Wakefulness ; },
abstract = {A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.},
}
@article {pmid27505099,
year = {2016},
author = {Chen, C and Zhang, G and Huang, H and Wang, J and Tarefder, RA},
title = {Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model.},
journal = {Accident; analysis and prevention},
volume = {96},
number = {},
pages = {79-87},
doi = {10.1016/j.aap.2016.06.015},
pmid = {27505099},
issn = {1879-2057},
mesh = {Accidents, Traffic/*statistics &amp; numerical data ; Automobile Driving/statistics &amp; numerical data ; Bayes Theorem ; Environment ; Female ; Humans ; Logistic Models ; Male ; New Mexico/epidemiology ; Rural Population/statistics &amp; numerical data ; Safety/*statistics &amp; numerical data ; Seat Belts/statistics &amp; numerical data ; Trauma Severity Indices ; Wounds and Injuries/classification/*epidemiology ; },
abstract = {Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention.},
}
@article {pmid27503007,
year = {2016},
author = {Vourvopoulos, A and Bermúdez I Badia, S},
title = {Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {1},
pages = {69},
pmid = {27503007},
issn = {1743-0003},
mesh = {Brain/physiopathology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Female ; Humans ; Male ; Neuronal Plasticity ; Stroke/physiopathology ; Stroke Rehabilitation/*methods ; Upper Extremity/physiopathology ; Virtual Reality Exposure Therapy/*methods ; },
abstract = {BACKGROUND: The use of Brain-Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.<br><br>METHODS: In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence.<br><br>RESULTS: Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience.<br><br>CONCLUSIONS: Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user's profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.},
}
@article {pmid27500200,
year = {2016},
author = {Danna, BJ and Metcalfe, MJ and Wood, EL and Shah, JB},
title = {Assessing Symptom Burden in Bladder Cancer: An Overview of Bladder Cancer Specific Health-Related Quality of Life Instruments.},
journal = {Bladder cancer (Amsterdam, Netherlands)},
volume = {2},
number = {3},
pages = {329-340},
pmid = {27500200},
issn = {2352-3727},
abstract = {Background: A key component to monitoring and investigating patient QOL is through patient reported health related quality of life (HRQOL) outcome measures. Many instruments have been used to assess HRQOL in bladder cancer and each instrument varies in its development, validation, the context of its usage in the literature and its applicability to certain disease states. Objective: In this review, we sought to summarize how clinicians and researchers should most appropriately utilize the available HRQOL instruments for bladder cancer. Methods: We performed a comprehensive literature search of each instrument used in bladder cancer, paying particular attention to the outcomes assessed. We used these outcomes to group the available instruments into categories best reflecting their optimal usage by stage of disease. Results: We found 5 instruments specific to bladder cancer, of which 3 are validated. Only one of the instruments (the EORTC-QLQ-NMIBC24) was involved in a randomized, prospective validation study. The most heavily used instruments are the EORTC-QLQ-BLM30 for muscle-invasive disease and the FACT-Bl which is used across all disease states. Of the 5 available instruments, 4 are automatically administered with general instruments, while the BCI lacks modularity, and requires co-administration with a generalized instrument. Conclusion: There are multiple strong instruments for use in gauging HRQOL in bladder cancer patients. We have divided these instruments into three categories which optimize their usage: instruments for use following NMIBC treatments (EORTC-QLQ-NMIBC24), instruments for use following radical cystectomy (FACT-Bl-Cys and EORTC-QLQ-BLM30) and more inclusive instruments not limited by treatment modality (BCI and FACT-Bl).},
}
@article {pmid27486801,
year = {2016},
author = {Wittevrongel, B and Van Hulle, MM},
title = {Frequency- and Phase Encoded SSVEP Using Spatiotemporal Beamforming.},
journal = {PloS one},
volume = {11},
number = {8},
pages = {e0159988},
pmid = {27486801},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; *Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) the number of selectable targets is rather limited when each target has its own stimulation frequency. One way to remedy this is by combining frequency- with phase encoding. We introduce a new multivariate spatiotemporal filter, based on Linearly Constrained Minimum Variance (LCMV) beamforming, for discriminating between frequency-phase encoded targets more accurately, even when using short signal lengths than with (extended) Canonical Correlation Analysis (CCA), which is traditionally posited for this stimulation paradigm.},
}
@article {pmid27478623,
year = {2016},
author = {Qosa, H and Kaddoumi, A},
title = {Effect of mouse strain as a background for Alzheimer's disease models on the clearance of amyloid-β.},
journal = {Journal of systems and integrative neuroscience},
volume = {2},
number = {2},
pages = {135-140},
pmid = {27478623},
issn = {2059-9781},
support = {P20 GM103424/GM/NIGMS NIH HHS/United States ; R15 NS091934/NS/NINDS NIH HHS/United States ; },
abstract = {Novel animal models of Alzheimer's disease (AD) are relentlessly being developed and existing ones are being fine-tuned; however, these models face multiple challenges associated with the complexity of the disease where most of these models do not reproduce the full phenotypical disease spectrum. Moreover, different AD models express different phenotypes that could affect their validity to recapitulate disease pathogenesis and/or response to a drug. One of the most important and understudied differences between AD models is differences in the phenotypic characteristics of the background species. Here, we used the brain clearance index (BCI) method to investigate the effect of strain differences on the clearance of amyloid β (Aβ) from the brains of four mouse strains. These mouse strains, namely C57BL/6, FVB/N, BALB/c and SJL/J, are widely used as a background for the development of AD mouse models. Findings showed that while Aβ clearance across the blood-brain barrier (BBB) was comparable between the 4 strains, levels of LRP1, an Aβ clearance protein, was significantly lower in SJL/J mice compared to other mouse strains. Furthermore, these mouse strains showed a significantly different response to rifampicin treatment with regard to Aβ clearance and effect on brain level of its clearance-related proteins. Our results provide for the first time an evidence for strain differences that could affect ability of AD mouse models to recapitulate response to a drug, and opens a new research avenue that requires further investigation to successfully develop mouse models that could simulate clinically important phenotypic characteristics of AD.},
}
@article {pmid27478573,
year = {2016},
author = {Bhattacharyya, S and Clerc, M and Hayashibe, M},
title = {A Study on the Effect of Electrical Stimulation as a User Stimuli for Motor Imagery Classification in Brain-Machine Interface.},
journal = {European journal of translational myology},
volume = {26},
number = {2},
pages = {6041},
pmid = {27478573},
issn = {2037-7452},
abstract = {Functional Electrical Stimulation (FES) provides a neuroprosthetic interface to non-recovered muscle groups by stimulating the affected region of the human body. FES in combination with Brain-machine interfacing (BMI) has a wide scope in rehabilitation because this system directly links the cerebral motor intention of the users with its corresponding peripheral muscle activations. In this paper, we examine the effect of FES on the electroencephalography (EEG) during motor imagery (left- and right-hand movement) training of the users. Results suggest a significant improvement in the classification accuracy when the subject was induced with FES stimuli as compared to the standard visual one.},
}
@article {pmid27475796,
year = {2017},
author = {Schmerber, S and Deguine, O and Marx, M and Van de Heyning, P and Sterkers, O and Mosnier, I and Garin, P and Godey, B and Vincent, C and Venail, F and Mondain, M and Deveze, A and Lavieille, JP and Karkas, A},
title = {Safety and effectiveness of the Bonebridge transcutaneous active direct-drive bone-conduction hearing implant at 1-year device use.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {274},
number = {4},
pages = {1835-1851},
pmid = {27475796},
issn = {1434-4726},
mesh = {Adult ; Audiometry ; *Bone Conduction ; Female ; Hearing Aids ; Hearing Loss, Conductive/*surgery ; Hearing Loss, Mixed Conductive-Sensorineural/surgery ; Humans ; Male ; Middle Aged ; *Patient Satisfaction ; Prospective Studies ; *Prostheses and Implants ; Speech Perception ; Surveys and Questionnaires ; Treatment Outcome ; },
abstract = {The objective of this study is to evaluate the safety and efficacy of a new transcutaneous bone-conduction implant (BCI BB) in patients with conductive and mixed hearing loss or with single-sided deafness (SSD), 1 year after surgical implantation. The study design is multicentric prospective, intra-subject measurements. Each subject is his/her own control. The setting is nine university hospitals: 7 French and 2 Belgian. Sixteen subjects with conductive or mixed hearing loss with bone-conduction hearing thresholds under the upper limit of 45 dB HL for each frequency from 500 to 4000 Hz, and 12 subjects with SSD (contralateral hearing within normal range) were enrolled in the study. All subjects were older than 18 years. The intervention is rehabilitative. The main outcome measure is the evaluation of skin safety, audiological measurements, benefit, and satisfaction questionnaires with a 1-year follow up. Skin safety was rated as good or very good. For the mixed or conductive hearing loss groups, the average functional gain (at 500 Hz, 1, 2, 4 kHz) was 26.1 dB HL (SD 13.7), and mean percentage of speech recognition in quiet at 65 dB was 95 % (vs 74 % unaided). In 5/6 SSD subjects, values of SRT in noise were lower with BB. Questionnaires revealed patient benefit and satisfaction. The transcutaneous BCI is very well tolerated at 1-year follow up, improves audiometric thresholds and intelligibility for speech in quiet and noise, and gives satisfaction to both patients with mixed and conductive hearing loss and patients with SSD.},
}
@article {pmid27474965,
year = {2016},
author = {Bauer, R and Fels, M and Royter, V and Raco, V and Gharabaghi, A},
title = {Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {9},
pages = {3156-3164},
doi = {10.1016/j.clinph.2016.06.020},
pmid = {27474965},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiology ; Brain Waves/physiology ; Brain-Computer Interfaces/psychology ; Cross-Over Studies ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Learning/*physiology ; Male ; Neurofeedback/methods/*physiology ; Random Allocation ; Reinforcement, Psychology ; Self-Control/*psychology ; Young Adult ; },
abstract = {OBJECTIVE: Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation.<br><br>METHODS: Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions.<br><br>RESULTS: The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy.<br><br>CONCLUSION: Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training.<br><br>SIGNIFICANCE: Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation.},
}
@article {pmid27472538,
year = {2016},
author = {Bauer, R and Vukelić, M and Gharabaghi, A},
title = {What is the optimal task difficulty for reinforcement learning of brain self-regulation?.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {9},
pages = {3033-3041},
doi = {10.1016/j.clinph.2016.06.016},
pmid = {27472538},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces/*psychology ; Conditioning, Operant/physiology ; Female ; Humans ; Learning/*physiology ; Male ; Neurofeedback/*methods ; Psychomotor Performance/physiology ; *Reinforcement, Psychology ; Self-Control/*psychology ; Young Adult ; },
abstract = {OBJECTIVE: The balance between action and reward during neurofeedback may influence reinforcement learning of brain self-regulation.<br><br>METHODS: Eleven healthy volunteers participated in three runs of motor imagery-based brain-machine interface feedback where a robot passively opened the hand contingent to &#x3b2;-band modulation. For each run, the &#x3b2;-desynchronization threshold to initiate the hand robot movement increased in difficulty (low, moderate, and demanding). In this context, the incentive to learn was estimated by the change of reward per action, operationalized as the change in reward duration per movement onset.<br><br>RESULTS: Variance analysis revealed a significant interaction between threshold difficulty and the relationship between reward duration and number of movement onsets (p<0.001), indicating a negative learning incentive for low difficulty, but a positive learning incentive for moderate and demanding runs. Exploration of different thresholds in the same data set indicated that the learning incentive peaked at higher thresholds than the threshold which resulted in maximum classification accuracy.<br><br>CONCLUSION: Specificity is more important than sensitivity of neurofeedback for reinforcement learning of brain self-regulation.<br><br>SIGNIFICANCE: Learning efficiency requires adequate challenge by neurofeedback interventions.},
}
@article {pmid27471545,
year = {2016},
author = {Yu, B and Ma, L and Li, H and Zhao, L and Bo, H and Wang, X},
title = {Biological Computation Indexes of Brain Oscillations in Unattended Facial Expression Processing Based on Event-Related Synchronization/Desynchronization.},
journal = {Computational and mathematical methods in medicine},
volume = {2016},
number = {},
pages = {8958750},
pmid = {27471545},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Alpha Rhythm ; Artifacts ; Beta Rhythm ; Brain/*physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; *Emotions ; *Facial Expression ; Female ; Humans ; Male ; Oscillometry/*methods ; Pattern Recognition, Automated ; Random Allocation ; Reproducibility of Results ; Sex Factors ; Software ; Young Adult ; },
abstract = {Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in affective Brain Computer Interface (BCI). The present study investigated the different event-related synchronization (ERS) and event-related desynchronization (ERD) of typical brain oscillations in processing Facial Expressions under nonattentional condition. The results show that the lower-frequency bands are mainly used to update Facial Expressions and distinguish the deviant stimuli from the standard ones, whereas the higher-frequency bands are relevant to automatically processing different Facial Expressions. Accordingly, we set up the relations between each brain oscillation and processing unattended Facial Expressions by the measures of ERD and ERS. This research first reveals the contributions of each frequency band for comprehension of Facial Expressions in preattentive stage. It also evidences that participants have emotional experience under nonattentional condition. Therefore, the user's emotional state under nonattentional condition can be recognized in real time by the ERD/ERS computation indexes of different frequency bands of brain oscillations, which can be used in affective BCI to provide the user with more natural and friendly ways.},
}
@article {pmid27470241,
year = {2016},
author = {Crellen, T and Walker, M and Lamberton, PH and Kabatereine, NB and Tukahebwa, EM and Cotton, JA and Webster, JP},
title = {Reduced Efficacy of Praziquantel Against Schistosoma mansoni Is Associated With Multiple Rounds of Mass Drug Administration.},
journal = {Clinical infectious diseases : an official publication of the Infectious Diseases Society of America},
volume = {63},
number = {9},
pages = {1151-1159},
pmid = {27470241},
issn = {1537-6591},
support = {//Wellcome Trust/United Kingdom ; //Medical Research Council/United Kingdom ; 098051//Wellcome Trust/United Kingdom ; },
mesh = {Animals ; Child ; Cross-Sectional Studies ; Drug Resistance ; Female ; Humans ; Male ; *Mass Drug Administration ; Models, Statistical ; Parasite Egg Count ; Praziquantel/administration &amp; dosage/*therapeutic use ; Schistosoma mansoni ; Schistosomiasis mansoni/*drug therapy ; Schistosomicides/administration &amp; dosage/*therapeutic use ; Uganda ; },
abstract = {BACKGROUND: Mass drug administration (MDA) with praziquantel is the cornerstone of schistosomiasis control in sub-Saharan Africa. The effectiveness of this strategy is dependent on the continued high efficacy of praziquantel; however, drug efficacy is rarely monitored using appropriate statistical approaches that can detect early signs of wane.<br><br>METHODS: We conducted a repeated cross-sectional study, examining children infected with Schistosoma mansoni from 6 schools in Uganda that had previously received between 1 and 9 rounds of MDA with praziquantel. We collected up to 12 S. mansoni egg counts from 414 children aged 6-12 years before and 25-27 days after treatment with praziquantel. We estimated individual patient egg reduction rates (ERRs) using a statistical model to explore the influence of covariates, including the number of prior MDA rounds.<br><br>RESULTS: The average ERR among children within schools that had received 8 or 9 previous rounds of MDA (95% Bayesian credible interval [BCI], 88.23%-93.64%) was statistically significantly lower than the average in schools that had received 5 rounds (95% BCI, 96.13%-99.08%) or 1 round (95% BCI, 95.51%-98.96%) of MDA. We estimate that 5.11%, 4.55%, and 16.42% of children from schools that had received 1, 5, and 8-9 rounds of MDA, respectively, had ERRs below the 90% threshold of optimal praziquantel efficacy set by the World Health Organization.<br><br>CONCLUSIONS: The reduced efficacy of praziquantel in schools with a higher exposure to MDA may pose a threat to the effectiveness of schistosomiasis control programs. We call for the efficacy of anthelmintic drugs used in MDA to be closely monitored.},
}
@article {pmid27470173,
year = {2016},
author = {Kletzel, SL and Cary, MP and Ciro, C and Berbrayer, D and Dawson, D and Hoffecker, L and Machtinger, J and Pham, P and Thai, M and Heyn, PC},
title = {Brain Gaming: A User's Product Guide for the Clinician.},
journal = {Archives of physical medicine and rehabilitation},
volume = {97},
number = {8},
pages = {1399-1400},
doi = {10.1016/j.apmr.2016.03.001},
pmid = {27470173},
issn = {1532-821X},
support = {IK1 RX001850/RX/RRD VA/United States ; },
mesh = {Brain/physiopathology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Cognition Disorders/diagnosis/*rehabilitation ; Female ; Humans ; Male ; Physical Therapy Modalities/*instrumentation ; Physical and Rehabilitation Medicine/*education ; *Practice Guidelines as Topic ; Recovery of Function ; },
}
@article {pmid27468316,
year = {2016},
author = {Qin, Y and Zhan, Y and Wang, C and Zhang, J and Yao, L and Guo, X and Wu, X and Hu, B},
title = {Classifying four-category visual objects using multiple ERP components in single-trial ERP.},
journal = {Cognitive neurodynamics},
volume = {10},
number = {4},
pages = {275-285},
pmid = {27468316},
issn = {1871-4080},
abstract = {Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.},
}
@article {pmid27468261,
year = {2016},
author = {Yargholi, E and Hossein-Zadeh, GA},
title = {Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {351},
pmid = {27468261},
issn = {1662-5161},
abstract = {We are frequently exposed to hand written digits 0-9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection.},
}
@article {pmid27467528,
year = {2016},
author = {Robinson, N and Zaidi, AD and Rana, M and Prasad, VA and Guan, C and Birbaumer, N and Sitaram, R},
title = {Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.},
journal = {PloS one},
volume = {11},
number = {7},
pages = {e0159959},
pmid = {27467528},
issn = {1932-6203},
mesh = {Biofeedback, Psychology ; *Brain-Computer Interfaces ; Humans ; *Movement ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.},
}
@article {pmid27466820,
year = {2016},
author = {Morone, G and Paolucci, S and Mattia, D and Pichiorri, F and Tramontano, M and Iosa, M},
title = {The 3Ts of the new millennium neurorehabilitation gym: therapy, technology, translationality.},
journal = {Expert review of medical devices},
volume = {13},
number = {9},
pages = {785-787},
doi = {10.1080/17434440.2016.1218275},
pmid = {27466820},
issn = {1745-2422},
mesh = {Humans ; Neurological Rehabilitation/*methods ; Practice Guidelines as Topic ; *Translational Research, Biomedical ; },
}
@article {pmid27462216,
year = {2016},
author = {Aranyi, G and Pecune, F and Charles, F and Pelachaud, C and Cavazza, M},
title = {Affective Interaction with a Virtual Character Through an fNIRS Brain-Computer Interface.},
journal = {Frontiers in computational neuroscience},
volume = {10},
number = {},
pages = {70},
pmid = {27462216},
issn = {1662-5188},
abstract = {Affective brain-computer interfaces (BCI) harness Neuroscience knowledge to develop affective interaction from first principles. In this article, we explore affective engagement with a virtual agent through Neurofeedback (NF). We report an experiment where subjects engage with a virtual agent by expressing positive attitudes towards her under a NF paradigm. We use for affective input the asymmetric activity in the dorsolateral prefrontal cortex (DL-PFC), which has been previously found to be related to the high-level affective-motivational dimension of approach/avoidance. The magnitude of left-asymmetric DL-PFC activity, measured using functional near infrared spectroscopy (fNIRS) and treated as a proxy for approach, is mapped onto a control mechanism for the virtual agent's facial expressions, in which action units (AUs) are activated through a neural network. We carried out an experiment with 18 subjects, which demonstrated that subjects are able to successfully engage with the virtual agent by controlling their mental disposition through NF, and that they perceived the agent's responses as realistic and consistent with their projected mental disposition. This interaction paradigm is particularly relevant in the case of affective BCI as it facilitates the volitional activation of specific areas normally not under conscious control. Overall, our contribution reconciles a model of affect derived from brain metabolic data with an ecologically valid, yet computationally controllable, virtual affective communication environment.},
}
@article {pmid27462202,
year = {2016},
author = {Wright, J and Macefield, VG and van Schaik, A and Tapson, JC},
title = {A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {312},
pmid = {27462202},
issn = {1662-4548},
abstract = {It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.},
}
@article {pmid27460070,
year = {2016},
author = {Wu, Y and Li, M and Wang, J},
title = {Toward a hybrid brain-computer interface based on repetitive visual stimuli with missing events.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {1},
pages = {66},
pmid = {27460070},
issn = {1743-0003},
mesh = {Adult ; Bayes Theorem ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Support Vector Machine ; },
abstract = {BACKGROUND: Steady-state visually evoked potentials (SSVEPs) can be elicited by repetitive stimuli and extracted in the frequency domain with satisfied performance. However, the temporal information of such stimulus is often ignored. In this study, we utilized repetitive visual stimuli with missing events to present a novel hybrid BCI paradigm based on SSVEP and omitted stimulus potential (OSP).<br><br>METHODS: Four discs flickering from black to white with missing flickers served as visual stimulators to simultaneously elicit subject's SSVEPs and OSPs. Key parameters in the new paradigm, including flicker frequency, optimal electrodes, missing flicker duration and intervals of missing events were qualitatively discussed with offline data. Two omitted flicker patterns including missing black/white disc were proposed and compared. Averaging times were optimized with Information Transfer Rate (ITR) in online experiments, where SSVEPs and OSPs were identified using Canonical Correlation Analysis in the frequency domain and Support Vector Machine (SVM)-Bayes fusion in the time domain, respectively.<br><br>RESULTS AND CONCLUSIONS: The online accuracy and ITR (mean&#x2009;&#xb1;&#x2009;standard deviation) over nine healthy subjects were 79.29&#x2009;&#xb1;&#x2009;18.14&#xa0;% and 19.45&#x2009;&#xb1;&#x2009;11.99 bits/min with missing black disc pattern, and 86.82&#x2009;&#xb1;&#x2009;12.91&#xa0;% and 24.06&#x2009;&#xb1;&#x2009;10.95 bits/min with missing white disc pattern, respectively. The proposed BCI paradigm, for the first time, demonstrated that SSVEPs and OSPs can be simultaneously elicited in single visual stimulus pattern and recognized in real-time with satisfied performance. Besides the frequency features such as SSVEP elicited by repetitive stimuli, we found a new feature (OSP) in the time domain to design a novel hybrid BCI paradigm by adding missing events in repetitive stimuli.},
}
@article {pmid27458384,
year = {2016},
author = {Alonso-Valerdi, LM and Gutiérrez-Begovich, DA and Argüello-García, J and Sepulveda, F and Ramírez-Mendoza, RA},
title = {User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces.},
journal = {Frontiers in physiology},
volume = {7},
number = {},
pages = {279},
pmid = {27458384},
issn = {1664-042X},
abstract = {Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system.},
}
@article {pmid27458376,
year = {2016},
author = {Wen, D and Jia, P and Lian, Q and Zhou, Y and Lu, C},
title = {Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.},
journal = {Frontiers in aging neuroscience},
volume = {8},
number = {},
pages = {172},
pmid = {27458376},
issn = {1663-4365},
abstract = {At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.},
}
@article {pmid27458364,
year = {2016},
author = {Sanchez, G and Lecaignard, F and Otman, A and Maby, E and Mattout, J},
title = {Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {347},
pmid = {27458364},
issn = {1662-5161},
abstract = {The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.},
}
@article {pmid27458349,
year = {2016},
author = {Lukinova, E and Myagkov, M},
title = {Impact of Short Social Training on Prosocial Behaviors: An fMRI Study.},
journal = {Frontiers in systems neuroscience},
volume = {10},
number = {},
pages = {60},
pmid = {27458349},
issn = {1662-5137},
abstract = {Efficient brain-computer interfaces (BCIs) are in need of knowledge about the human brain and how it interacts, plays games, and socializes with other brains. A breakthrough can be achieved by revealing the microfoundations of sociality, an additional component of the utility function reflecting the value of contributing to group success derived from social identity. Building upon our previous behavioral work, we conduct a series of functional magnetic resonance imaging (fMRI) experiments (N = 10 in the Pilot Study and N = 15 in the Main Study) to measure whether and how sociality alters the functional activation of and connectivity between specific systems in the brain. The overarching hypothesis of this study is that sociality, even in a minimal form, serves as a natural mechanism of sustainable cooperation by fostering interaction between brain regions associated with social cognition and those related to value calculation. We use group-based manipulations to induce varying levels of sociality and compare behavior in two social dilemmas: Prisoner's Dilemma and variations of Ultimatum Game. We find that activation of the right inferior frontal gyrus, a region previously associated with cognitive control and modulation of the valuation system, is correlated with activity in the medial prefrontal cortex (mPFC) to a greater degree when participants make economic decisions in a game with an acquaintance, high sociality condition, compared to a game with a random individual, low sociality condition. These initial results suggest a specific biological mechanism through which sociality facilitates cooperation, fairness and provision of public goods at the cost of individual gain. Future research should examine neural dynamics in the brain during the computation of utility in the context of strategic games that involve social interaction for a larger sample of subjects.},
}
@article {pmid27455526,
year = {2017},
author = {Moorman, HG and Gowda, S and Carmena, JM},
title = {Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {750-760},
doi = {10.1109/TNSRE.2016.2593696},
pmid = {27455526},
issn = {1558-0210},
mesh = {Animals ; Artificial Limbs ; Biofeedback, Psychology/methods ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; *Exoskeleton Device ; Feedback, Physiological/*physiology ; Joints/*physiology ; Macaca mulatta ; Male ; Man-Machine Systems ; *Models, Biological ; Robotics/*methods ; Task Performance and Analysis ; },
abstract = {Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single- and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in a way that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal systems or anthropomorphic robotic arms, may benefit from allowing neural control over redundant configuration dimensions as well as the end-effector.},
}
@article {pmid27454876,
year = {2016},
author = {Soria Morillo, LM and Alvarez-Garcia, JA and Gonzalez-Abril, L and Ortega Ramírez, JA},
title = {Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets.},
journal = {Biomedical engineering online},
volume = {15 Suppl 1},
number = {Suppl 1},
pages = {75},
pmid = {27454876},
issn = {1475-925X},
mesh = {Adult ; *Advertising ; Brain/*physiology ; Decision Trees ; Electroencephalography/*economics/*instrumentation ; *Emotions ; Female ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works.<br><br>METHODS: By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad.<br><br>RESULTS: The proposed technique allows to reach more than 75&#xa0;% of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30&#xa0;% compared to other techniques presented in this paper.<br><br>CONCLUSIONS: This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.},
}
@article {pmid27454531,
year = {2016},
author = {Ortega, J and Asensio-Cubero, J and Gan, JQ and Ortiz, A},
title = {Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.},
journal = {Biomedical engineering online},
volume = {15 Suppl 1},
number = {Suppl 1},
pages = {73},
pmid = {27454531},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagery, Psychotherapy/*methods ; *Motor Activity ; *Signal Processing, Computer-Assisted ; Supervised Machine Learning ; },
abstract = {BACKGROUND: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI.<br><br>METHODS: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection.<br><br>RESULTS AND CONCLUSION: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.},
}
@article {pmid27453051,
year = {2017},
author = {Cecotti, H and Ries, AJ},
title = {Best practice for single-trial detection of event-related potentials: Application to brain-computer interfaces.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {111},
number = {},
pages = {156-169},
doi = {10.1016/j.ijpsycho.2016.07.500},
pmid = {27453051},
issn = {1872-7697},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Electroencephalography/*methods/*standards ; Evoked Potentials/*physiology ; Female ; Guidelines as Topic/*standards ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Research Design/*standards ; },
abstract = {The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal is a fundamental component in non-invasive brain-computer interface (BCI) research, and in modern cognitive neuroscience studies. Whereas the grand average response across trials provides an estimation of essential characteristics of a brain-evoked response, an estimation of the differences between trials for a particular type of stimulus can provide key insight about the brain dynamics and possible origins of the brain response. The research in ERP single-trial detection has been mainly driven by applications in biomedical engineering, with an interest from machine learning and signal processing groups that test novel methods on noisy signals. Efficient single-trial detection techniques require processing steps that include temporal filtering, spatial filtering, and classification. In this paper, we review the current state-of-the-art methods for single-trial detection of event-related potentials with applications in BCI. Efficient single-trial detection techniques should embed simple yet efficient functions requiring as few hyper-parameters as possible. The focus of this paper is on methods that do not include a large number of hyper-parameters and can be easily implemented with datasets containing a limited number of trials. A benchmark of different classification methods is proposed on a database recorded from sixteen healthy subjects during a rapid serial visual presentation task. The results support the conclusion that single-trial detection can be achieved with an area under the ROC curve superior to 0.9 with less than ten sensors and 20 trials corresponding to the presentation of a target. Whereas the number of sensors is not a key element for efficient single-trial detection, the number of trials must be carefully chosen for creating a robust classifier.},
}
@article {pmid27449229,
year = {2016},
author = {Kress, C and Sadowski, G and Brandenbusch, C},
title = {Novel Displacement Agents for Aqueous 2-Phase Extraction Can Be Estimated Based on Hybrid Shortcut Calculations.},
journal = {Journal of pharmaceutical sciences},
volume = {105},
number = {10},
pages = {3030-3038},
doi = {10.1016/j.xphs.2016.06.006},
pmid = {27449229},
issn = {1520-6017},
mesh = {Bromides/*chemistry/pharmacology ; *Chemical Precipitation/drug effects ; Humans ; Immunoglobulin G/*analysis ; Liquid-Liquid Extraction/*methods ; Lithium Compounds/*chemistry/pharmacology ; Polyethylene Glycols/chemistry/pharmacology ; Water/*chemistry ; },
abstract = {The purification of therapeutic proteins is a challenging task with immediate need for optimization. Besides other techniques, aqueous 2-phase extraction (ATPE) of proteins has been shown to be a promising alternative to cost-intensive state-of-the-art chromatographic protein purification. Most likely, to enable a selective extraction, protein partitioning has to be influenced using a displacement agent to isolate the target protein from the impurities. In this work, a new displacement agent (lithium bromide [LiBr]) allowing for the selective separation of the target protein IgG from human serum albumin (represents the impurity) within a citrate-polyethylene glycol (PEG) ATPS is presented. In order to characterize the displacement suitability of LiBr on IgG, the mutual influence of LiBr and the phase formers on the aqueous 2-phase system (ATPS) and partitioning is investigated. Using osmotic virial coefficients (B22 and B23) accessible by composition gradient multiangle light-scattering measurements, the precipitating effect of LiBr on both proteins and an estimation of both protein partition coefficients is estimated. The stabilizing effect of LiBr on both proteins was estimated based on B22 and experimentally validated within the citrate-PEG ATPS. Our approach contributes to an efficient implementation of ATPE within the downstream processing development of therapeutic proteins.},
}
@article {pmid27448368,
year = {2016},
author = {Liu, X and Zhang, M and Xiong, T and Richardson, AG and Lucas, TH and Chin, PS and Etienne-Cummings, R and Tran, TD and Van der Spiegel, J},
title = {A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {10},
number = {4},
pages = {874-883},
doi = {10.1109/TBCAS.2016.2574362},
pmid = {27448368},
issn = {1940-9990},
abstract = {Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.},
}
@article {pmid27445783,
year = {2016},
author = {Alonso-Valerdi, LM},
title = {Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.},
journal = {Frontiers in neuroinformatics},
volume = {10},
number = {},
pages = {22},
pmid = {27445783},
issn = {1662-5196},
abstract = {A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application.},
}
@article {pmid27445666,
year = {2016},
author = {Waldert, S},
title = {Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {295},
pmid = {27445666},
issn = {1662-4548},
}
@article {pmid27445663,
year = {2016},
author = {Schroeder, KE and Chestek, CA},
title = {Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {291},
pmid = {27445663},
issn = {1662-4548},
support = {R01 GM111293/GM/NIGMS NIH HHS/United States ; },
abstract = {Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.},
}
@article {pmid27436999,
year = {2016},
author = {Yu, Y and Wu, Z and Xu, K and Gong, Y and Zheng, N and Zheng, X and Pan, G},
title = {Automatic Training of Rat Cyborgs for Navigation.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {6459251},
pmid = {27436999},
issn = {1687-5273},
mesh = {Animals ; *Automation ; Brain/*physiology ; *Brain-Computer Interfaces ; Conditioning, Operant/*physiology ; Electric Stimulation ; Functional Laterality ; Maze Learning ; Medial Forebrain Bundle/physiology ; Microelectrodes ; Rats ; *Spatial Navigation ; Time Factors ; },
abstract = {A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.},
}
@article {pmid27436902,
year = {2016},
author = {Khalighinejad, N and Haggard, P},
title = {Extending experiences of voluntary action by association.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {113},
number = {31},
pages = {8867-8872},
pmid = {27436902},
issn = {1091-6490},
support = {323943/ERC_/European Research Council/International ; },
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Hand/*physiology ; Humans ; Intention ; Models, Neurological ; Movement/physiology ; Psychomotor Performance/physiology ; Time Perception/*physiology ; Volition/*physiology ; Young Adult ; },
abstract = {"Sense of agency" refers to the experience that links one's voluntary actions to their external outcomes. It remains unclear whether this ubiquitous experience is hardwired, arising from specific signals within the brain's motor systems, or rather depends on associative learning, through repeated cooccurrence of voluntary movements and their outcomes. To distinguish these two models, we asked participants to trigger a tone by a voluntary keypress action. The voluntary action was always associated with an involuntary movement of the other hand. We then tested whether the combination of the involuntary movement and tone alone might now suffice to produce a sense of agency, even when the voluntary action was omitted. Sense of agency was measured using an implicit marker based on time perception, namely a shift in the perceived time of the outcome toward the action that caused it. Across two experiments, repeatedly pairing an involuntary movement with a voluntary action induced key temporal features of agency, with the outcome now perceived as shifted toward the involuntary movement. This shift required involuntary movements to have been previously associated with voluntary actions. We show that some key aspects of agency may be transferred from voluntary actions to involuntary movements. An internal volitional signal is required for the primary acquisition of agency but, with repeated association, the involuntary movement in itself comes to produce some key temporal features of agency over the subsequent outcome. This finding may explain how humans can develop an enduring sense of agency in nonnatural cases, like brain-machine interfaces.},
}
@article {pmid27432803,
year = {2016},
author = {Lee, GW and Zambetta, F and Li, X and Paolini, AG},
title = {Utilising reinforcement learning to develop strategies for driving auditory neural implants.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046027},
doi = {10.1088/1741-2560/13/4/046027},
pmid = {27432803},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Brain-Computer Interfaces ; *Cochlear Implants ; Cochlear Nucleus/physiology ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Inferior Colliculi/physiology ; Learning/*physiology ; Machine Learning ; Prosthesis Design ; Rats ; Rats, Wistar ; Reinforcement, Psychology ; Software ; },
abstract = {OBJECTIVE: In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies.<br><br>APPROACH: By recording a comprehensive set of acoustic frequency presentations and neural responses from a set of animals we created a large database of neural responses to acoustic stimulation. Extensive electrical stimulation in the CN and the recording of neural responses in the IC provides a mapping of how the auditory system responds to electrical stimuli. The combined dataset is used as the foundation for the simulator, which is used to implement and test learning algorithms.<br><br>MAIN RESULTS: Reinforcement learning, utilising a modified n-Armed Bandit solution, is implemented to demonstrate the model's function. We show the ability to effectively learn stimulation patterns which mimic the cochlea's ability to covert acoustic frequencies to neural activity. Time taken to learn effective replication using neural stimulation takes less than 20 min under continuous testing.<br><br>SIGNIFICANCE: These results show the utility of reinforcement learning in the field of neural stimulation. These results can be coupled with existing sound processing technologies to develop new auditory prosthetics that are adaptable to the recipients current auditory pathway. The same process can theoretically be abstracted to other sensory and motor systems to develop similar electrical replication of neural signals.},
}
@article {pmid27429995,
year = {2016},
author = {Mihara, M and Miyai, I},
title = {Review of functional near-infrared spectroscopy in neurorehabilitation.},
journal = {Neurophotonics},
volume = {3},
number = {3},
pages = {031414},
pmid = {27429995},
issn = {2329-423X},
abstract = {We provide a brief overview of the research and clinical applications of near-infrared spectroscopy (NIRS) in the neurorehabilitation field. NIRS has several potential advantages and shortcomings as a neuroimaging tool and is suitable for research application in the rehabilitation field. As one of the main applications of NIRS, we discuss its application as a monitoring tool, including investigating the neural mechanism of functional recovery after brain damage and investigating the neural mechanisms for controlling bipedal locomotion and postural balance in humans. In addition to being a monitoring tool, advances in signal processing techniques allow us to use NIRS as a therapeutic tool in this field. With a brief summary of recent studies investigating the clinical application of NIRS using motor imagery task, we discuss the possible clinical usage of NIRS in brain-computer interface and neurofeedback.},
}
@article {pmid27429448,
year = {2016},
author = {Cronin, JA and Wu, J and Collins, KL and Sarma, D and Rao, RP and Ojemann, JG and Olson, JD},
title = {Task-Specific Somatosensory Feedback via Cortical Stimulation in Humans.},
journal = {IEEE transactions on haptics},
volume = {9},
number = {4},
pages = {515-522},
pmid = {27429448},
issn = {2329-4051},
support = {K12 HD001097/HD/NICHD NIH HHS/United States ; R25 NS079200/NS/NINDS NIH HHS/United States ; U10 NS086525/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electrocorticography ; Feedback, Sensory/*physiology ; Hand/*physiology ; Humans ; Motor Activity/*physiology ; Psychomotor Performance/*physiology ; Psychophysics ; Somatosensory Cortex/*physiology ; },
abstract = {Cortical stimulation through electrocorticographic (ECoG) electrodes is a potential method for providing sensory feedback in future prosthetic and rehabilitative applications. Here, we evaluate human subjects' ability to continuously modulate their motor behavior based on feedback from direct surface stimulation of the somatosensory cortex. Subjects wore a dataglove that measured their hand aperture position and received one of three stimuli over the hand sensory cortex based on their current hand position as compared to a target aperture position. Using cortical stimulation feedback, subjects adjusted their hand aperture to move towards the target aperture region. One subject was able to achieve accuracies and R[2] values well above chance (best performance: R[2] = 0.93; accuracy = 0.76/1). Performance dropped during the catch trial (same stimulus independent of the position) to below chance levels, suggesting that the subject had been using the varied sensory feedback to modulate their motor behavior. To our knowledge, this study represents one of the first demonstrations of using direct cortical surface stimulation of the human sensory cortex to perform a motor task, and is a first step towards developing closed-loop human sensorimotor brain-computer interfaces.},
}
@article {pmid27428387,
year = {2016},
author = {Huber, P and Basso, P and Reboud, E and Attrée, I},
title = {Pseudomonas aeruginosa renews its virulence factors.},
journal = {Environmental microbiology reports},
volume = {8},
number = {5},
pages = {564-571},
doi = {10.1111/1758-2229.12443},
pmid = {27428387},
issn = {1758-2229},
support = {ANR-10-LABX-49-01//CEA, Inserm, University Grenoble-Alpes and CNRS, and from ANR grants/ ; ANR-15-CE11-0018//CEA, Inserm, University Grenoble-Alpes and CNRS, and from ANR grants/ ; },
abstract = {Highly divergent strains of the major human opportunistic pathogen Pseudomonas aeruginosa have been isolated around the world by different research laboratories. They came from patients with various types of infectious diseases or from the environment. These strains are devoid of the major virulence factor used by classical strains, the Type III secretion system, but possess additional putative virulence factors, including a novel two-partner secretion system, ExlBA, responsible for the hypervirulent behavior of some clinical isolates. Here, we review the genetic and phenotypic characteristics of these recently-discovered P. aeruginosa outliers.},
}
@article {pmid27416603,
year = {2017},
author = {He, S and Zhang, R and Wang, Q and Chen, Y and Yang, T and Feng, Z and Zhang, Y and Shao, M and Li, Y},
title = {A P300-Based Threshold-Free Brain Switch and Its Application in Wheelchair Control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {715-725},
doi = {10.1109/TNSRE.2016.2591012},
pmid = {27416603},
issn = {1558-0210},
mesh = {Adult ; Biofeedback, Psychology/*instrumentation/methods ; *Brain-Computer Interfaces ; Diagnosis, Differential ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; *Event-Related Potentials, P300 ; Humans ; Male ; Man-Machine Systems ; Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; Spinal Cord Injuries/*physiopathology/*rehabilitation ; User-Computer Interface ; *Wheelchairs ; Young Adult ; },
abstract = {The key issue of electroencephalography (EEG)-based brain switches is to detect the control and idle states in an asynchronous manner. Most existing methods rely on a threshold. However, it is often time consuming to select a satisfactory threshold, and the chosen threshold might be inappropriate over a long period of time due to the variability of the EEG signals. This paper presents a new P300-based threshold-free brain switch. Specifically, one target button and three pseudo buttons, which are intensified in a random order to produce P300 potential, are set in the graphical user interface. The user can issue a switch command by focusing on the target button. Two support vector machine (SVM) classifiers, namely, SVM1 and SVM2, are used in the detection algorithm. During detection, we first obtained four SVM scores, corresponding to the four flashing buttons, by applying SVM1 to the ongoing EEG. If the SVM score corresponding to the target button was negative or not at the maximum, then an idle state was determined. Moreover, if the target button had a maximum and positive score, then we fed the four SVM scores as features into SVM2 to further discriminate the control and idle states. As an application, this brain switch was used to produce a start/stop command for an intelligent wheelchair, of which the left, right, forward, backward functions were carried out by an autonomous navigation system. Several experiments were conducted with eight healthy subjects and five patients with spinal cord injuries (SCIs). The experimental results not only demonstrated the effectiveness of our approach but also illustrated the potential application for patients with SCIs.},
}
@article {pmid27416602,
year = {2017},
author = {Higger, M and Quivira, F and Akcakaya, M and Moghadamfalahi, M and Nezamfar, H and Cetin, M and Erdogmus, D},
title = {Recursive Bayesian Coding for BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {704-714},
pmid = {27416602},
issn = {1558-0210},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Movement/physiology ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; Visual Perception/*physiology ; Word Processing/*methods ; },
abstract = {Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.},
}
@article {pmid27416601,
year = {2017},
author = {Yang, Y and Mason, AJ},
title = {Frequency Band Separability Feature Extraction Method With Weighted Haar Wavelet Implementation for Implantable Spike Sorting.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {530-538},
doi = {10.1109/TNSRE.2016.2590560},
pmid = {27416601},
issn = {1558-0210},
mesh = {Action Potentials/*physiology ; *Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Machine Learning ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Wavelet Analysis ; },
abstract = {Hardware-efficient feature extraction is an important step for real-time and on-chip spike sorting. Based on an analysis of spike energy spectrum, a new feature set is developed using the positive and negative spike peaks in low and high frequency bands. A separability metric that evaluates the informativeness and noise sensitivity of features is introduced to optimize the cutoff frequency of each band. Haar-based discrete wavelet transform was chosen to implement memory- and hardware-efficient filters for extracting frequency band separability features. Specifically, peaks from the first level detail and the fourth level approximation were used to represent a spike. To improve clustering performance, the detail features were weighted into the same dynamic range as the approximation features. The new feature extraction method was tested at different signal-to-noise ratios using synthesized datasets consisting of considerable and various spike shapes extracted from real neural recordings. The results show that the new method has 3%-10% better spike sorting performance than other hardware-efficient methods while consuming comparable hardware resources.},
}
@article {pmid27402598,
year = {2016},
author = {Samek, W and Blythe, DAJ and Curio, G and Müller, KR and Blankertz, B and Nikulin, VV},
title = {Multiscale temporal neural dynamics predict performance in a complex sensorimotor task.},
journal = {NeuroImage},
volume = {141},
number = {},
pages = {291-303},
doi = {10.1016/j.neuroimage.2016.06.056},
pmid = {27402598},
issn = {1095-9572},
mesh = {Adult ; Alpha Rhythm/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Prognosis ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Time Factors ; Visual Perception/*physiology ; },
abstract = {Ongoing neuronal oscillations are pivotal in brain functioning and are known to influence subjects' performance. This modulation is usually studied on short time scales whilst multiple time scales are rarely considered. In our study we show that Long-Range Temporal Correlations (LRTCs) estimated from the amplitude of EEG oscillations over a range of time-scales predict performance in a complex sensorimotor task, based on Brain-Computer Interfacing (BCI). Our paradigm involved eighty subjects generating covert motor responses to dynamically changing visual cues and thus controlling a computer program through the modulation of neuronal oscillations. The neuronal dynamics were estimated with multichannel EEG. Our results show that: (a) BCI task accuracy may be predicted on the basis of LRTCs measured during the preceding training session, and (b) this result was not due to signal-to-noise ratio of the ongoing neuronal oscillations. Our results provide direct empirical evidence in addition to previous theoretical work suggesting that scale-free neuronal dynamics are important for optimal brain functioning.},
}
@article {pmid27401122,
year = {2016},
author = {Li, Y and Wang, F and Huang, B and Yang, W and Yu, T and Talsma, D},
title = {The modulatory effect of semantic familiarity on the audiovisual integration of face-name pairs.},
journal = {Human brain mapping},
volume = {37},
number = {12},
pages = {4333-4348},
pmid = {27401122},
issn = {1097-0193},
mesh = {Adult ; Brain/diagnostic imaging/*physiology ; Brain Mapping ; Facial Recognition/*physiology ; Female ; Humans ; Linear Models ; Magnetic Resonance Imaging ; Male ; *Names ; Neuropsychological Tests ; Recognition, Psychology/*physiology ; Reproducibility of Results ; *Semantics ; Speech Perception/*physiology ; },
abstract = {To recognize individuals, the brain often integrates audiovisual information from familiar or unfamiliar faces, voices, and auditory names. To date, the effects of the semantic familiarity of stimuli on audiovisual integration remain unknown. In this functional magnetic resonance imaging (fMRI) study, we used familiar/unfamiliar facial images, auditory names, and audiovisual face-name pairs as stimuli to determine the influence of semantic familiarity on audiovisual integration. First, we performed a general linear model analysis using fMRI data and found that audiovisual integration occurred for familiar congruent and unfamiliar face-name pairs but not for familiar incongruent pairs. Second, we decoded the familiarity categories of the stimuli (familiar vs. unfamiliar) from the fMRI data and calculated the reproducibility indices of the brain patterns that corresponded to familiar and unfamiliar stimuli. The decoding accuracy rate was significantly higher for familiar congruent versus unfamiliar face-name pairs (83.2%) than for familiar versus unfamiliar faces (63.9%) and for familiar versus unfamiliar names (60.4%). This increase in decoding accuracy was not observed for familiar incongruent versus unfamiliar pairs. Furthermore, compared with the brain patterns associated with facial images or auditory names, the reproducibility index was significantly improved for the brain patterns of familiar congruent face-name pairs but not those of familiar incongruent or unfamiliar pairs. Our results indicate the modulatory effect that semantic familiarity has on audiovisual integration. Specifically, neural representations were enhanced for familiar congruent face-name pairs compared with visual-only faces and auditory-only names, whereas this enhancement effect was not observed for familiar incongruent or unfamiliar pairs. Hum Brain Mapp 37:4333-4348, 2016. © 2016 Wiley Periodicals, Inc.},
}
@article {pmid27396478,
year = {2016},
author = {Daly, I and Williams, D and Kirke, A and Weaver, J and Malik, A and Hwang, F and Miranda, E and Nasuto, SJ},
title = {Affective brain-computer music interfacing.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046022},
doi = {10.1088/1741-2560/13/4/046022},
pmid = {27396478},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Adult ; Affect/*physiology ; Algorithms ; Artifacts ; Artificial Intelligence ; Brain-Computer Interfaces/*psychology ; Electroencephalography ; Female ; Humans ; Male ; Music/*psychology ; Young Adult ; },
abstract = {OBJECTIVE: We aim to develop and evaluate an affective brain-computer music interface (aBCMI) for modulating the affective states of its users.<br><br>APPROACH: An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions.<br><br>MAIN RESULTS: The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, [Formula: see text]) and modulate its user's affective states significantly above chance level [Formula: see text].<br><br>SIGNIFICANCE: Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.},
}
@article {pmid27392601,
year = {2016},
author = {Kray, JE and Dombrovskiy, VY and Vogel, TR},
title = {Carotid artery dissection and motor vehicle trauma: patient demographics, associated injuries and impact of treatment on cost and length of stay.},
journal = {BMC emergency medicine},
volume = {16},
number = {1},
pages = {23},
pmid = {27392601},
issn = {1471-227X},
mesh = {Accidents, Traffic/statistics &amp; numerical data ; Adolescent ; Adult ; Age Factors ; Carotid Artery Injuries/*economics/*epidemiology/therapy ; Comorbidity ; Costs and Cost Analysis ; Female ; Humans ; Injury Severity Score ; Length of Stay/statistics &amp; numerical data ; Male ; Middle Aged ; Retrospective Studies ; Socioeconomic Factors ; Wounds and Injuries/epidemiology ; Wounds, Nonpenetrating/*economics/*epidemiology/therapy ; Young Adult ; },
abstract = {BACKGROUND: Blunt carotid arterial injury (BCI) is a rare injury associated with motor vehicle collision (MVC). There are few population based analyses evaluating carotid injury associated with blunt trauma and their associated injuries as well as outcomes.<br><br>METHODS: The Nationwide Inpatient Sample (NIS) 2003-2010 data was queried to identify patients after MVC who had documented BCI during their hospitalizations utilizing ICD-9-CM codes. Demographics, associated injuries, interventions performed, length of stay, and cost were evaluated.<br><br>RESULTS: 1,686,867 patients were estimated having sustained MVC; 1,168 BCI were estimated. No patients with BCI had open repair, 4.24&#xa0;% had a carotid artery stent (CAS), and 95.76&#xa0;% of patients had no operative intervention. Age groups associated with BCI were: 18-24 (27.8&#xa0;%), 47-60 (22.3&#xa0;%), 35-46 (20.6&#xa0;%), 25-34 (19.1&#xa0;%), >61 (10.2&#xa0;%). Associated injuries included long bone fractures (28.5&#xa0;%), stroke and intracranial hemorrhage (28.5&#xa0;%), cranial injuries (25.6&#xa0;%), thoracic injuries (23.6&#xa0;%), cervical fractures (21.8&#xa0;%), facial fractures (19.9&#xa0;%), skull fractures (18.8&#xa0;%), pelvic fractures (18.5&#xa0;%), hepatic (13.3&#xa0;%) and splenic (9.2&#xa0;%) injuries. Complications included respiratory (44.2&#xa0;%), bleeding (16.1&#xa0;%), urinary tract infections (8.9&#xa0;%), and sepsis (4.9&#xa0;%). Overall mortality was 14.1&#xa0;% without differences with regard to intervention (18.5&#xa0;% vs. 13.9&#xa0;%; P&#x2009;=&#x2009;0.36). Stroke and intracranial hemorrhage was associated with a 2.7 times greater risk of mortality. Mean length of stay for patients with BCI undergoing stenting compared to no intervention were similar (13.1&#xa0;days vs. 15.9&#xa0;days) but had a greater mean cost ($83,030 vs. $63,200, p&#x2009;=&#x2009;0.3).<br><br>CONCLUSION: BCI is a rare injury associated with MVC, most frequently reported in younger patients. Frequently associated injuries were long bone fractures, stroke and intracranial hemorrhage, thoracic injuries, and pelvic fractures which are likely associated with the force/mechanism of injury. The majority of patients were treated without intervention, but when CAS was utilized, it did not impact mortality and trended toward increased costs.},
}
@article {pmid27392361,
year = {2017},
author = {Xie, X and Yu, ZL and Lu, H and Gu, Z and Li, Y},
title = {Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {504-516},
doi = {10.1109/TNSRE.2016.2587939},
pmid = {27392361},
issn = {1558-0210},
mesh = {Algorithms ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Linear Models ; *Machine Learning ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG signals by exploiting the fact that the space of SPD matrices endowed with Riemannian distance is a high-dimensional Riemannian manifold. To alleviate the overfitting and heavy computation problems associated with conventional classification methods on high-dimensional manifold, we propose a framework for intrinsic sub-manifold learning from a high-dimensional Riemannian manifold. Considering a special case of SPD space, a simple yet efficient bilinear sub-manifold learning (BSML) algorithm is derived to learn the intrinsic sub-manifold by identifying a bilinear mapping that maximizes the preservation of the local geometry and global structure of the original manifold. Two BSML-based classification algorithms are further proposed to classify the data on a learned intrinsic sub-manifold. Experimental evaluation of the classification of EEG revealed that the BSML method extracts the intrinsic sub-manifold approximately 5× faster and with higher classification accuracy compared with competing algorithms. The BSML also exhibited strong robustness against a small training dataset, which often occurs in BCI studies.},
}
@article {pmid27392339,
year = {2017},
author = {Van Eyndhoven, S and Francart, T and Bertrand, A},
title = {EEG-Informed Attended Speaker Extraction From Recorded Speech Mixtures With Application in Neuro-Steered Hearing Prostheses.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {5},
pages = {1045-1056},
doi = {10.1109/TBME.2016.2587382},
pmid = {27392339},
issn = {1558-2531},
mesh = {Algorithms ; Attention/physiology ; Auditory Perception/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Auditory/physiology ; *Hearing Aids ; Humans ; Pattern Recognition, Automated/methods ; Pattern Recognition, Physiological/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Sound Spectrography/*methods ; *Speech Recognition Software ; },
abstract = {OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest.<br><br>METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multichannel speech separation and denoising algorithm.<br><br>RESULTS: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals.<br><br>CONCLUSIONS: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD.<br><br>SIGNIFICANCE: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain-computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.},
}
@article {pmid27390179,
year = {2017},
author = {Matlack, CB and Chizeck, HJ and Moritz, CT},
title = {Empirical Movement Models for Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {6},
pages = {694-703},
doi = {10.1109/TNSRE.2016.2584101},
pmid = {27390179},
issn = {1558-0210},
mesh = {Animals ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Macaca nemestrina ; *Models, Neurological ; Models, Statistical ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {For brain-computer interfaces (BCIs) which provide the user continuous position control, there is little standardization of performance metrics or evaluative tasks. One candidate metric is Fitts's law, which has been used to describe aimed movements across a range of computer interfaces, and has recently been applied to BCI tasks. Reviewing selected studies, we identify two basic problems with Fitts's law: its predictive performance is fragile, and the estimation of 'information transfer rate' from the model is unsupported. Our main contribution is the adaptation and validation of an alternative model to Fitts's law in the BCI context. We show that the Shannon-Welford model outperforms Fitts's law, showing robust predictive power when target distance and width have disproportionate effects on difficulty. Building on a prior study of the Shannon-Welford model, we show that identified model parameters offer a novel approach to quantitatively assess the role of control-display gain in speed/accuracy performance tradeoffs during brain control.},
}
@article {pmid27388598,
year = {2016},
author = {Kress, C and Sadowski, G and Brandenbusch, C},
title = {Protein partition coefficients can be estimated efficiently by hybrid shortcut calculations.},
journal = {Journal of biotechnology},
volume = {233},
number = {},
pages = {151-159},
doi = {10.1016/j.jbiotec.2016.06.032},
pmid = {27388598},
issn = {1873-4863},
mesh = {Chemical Fractionation/*methods ; Chemical Precipitation ; Humans ; Immunoglobulin G/chemistry/isolation &amp; purification ; *Models, Chemical ; Polyethylene Glycols/chemistry ; Proteins/*chemistry/*isolation &amp; purification ; Serum Albumin/chemistry/isolation &amp; purification ; },
abstract = {The extraction of therapeutic proteins like monoclonal antibodies in aqueous two-phase systems (ATPS) is a suitable alternative to common cost intensive chromatographic purification steps within the downstream processing. Thereby the protein partitioning can be selectively changed using a displacement agent (additional salt) in order to allow for a successful purification of the target protein. Within this work a new shortcut strategy for the calculation of protein partition coefficients in polymer-salt ATPS is presented. The required protein-solute (phase-forming component, displacement agent) interactions are covered by the cross virial coefficient B23 measured by composition gradient multi-angle light scattering (CG-MALS). Using this shortcut calculation allows for an efficient determination of the partition coefficients of the target protein immunoglobulin G (IgG) and the impurity human serum albumin (HSA) within PEG-citrate and PEG-phosphate ATPS independently on the protein concentration. We demonstrate that the selection of a suitable displacement agent allowing for a selective purification of IgG from HSA is accessible by B23. Based on the determination of the protein-protein interactions via CG-MALS covered by the second osmotic virial coefficient B22 a further optimization of ATPS preventing protein precipitation is enabled. The results show that our approach contributes to an efficient downstream processing development.},
}
@article {pmid27383408,
year = {2016},
author = {Shehada, N and Cancilla, JC and Torrecilla, JS and Pariente, ES and Brönstrup, G and Christiansen, S and Johnson, DW and Leja, M and Davies, MP and Liran, O and Peled, N and Haick, H},
title = {Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath.},
journal = {ACS nano},
volume = {10},
number = {7},
pages = {7047-7057},
doi = {10.1021/acsnano.6b03127},
pmid = {27383408},
issn = {1936-086X},
mesh = {Asthma/diagnosis ; *Breath Tests ; Humans ; Lung Diseases/*diagnosis ; *Nanowires ; *Silicon ; Volatile Organic Compounds/*analysis ; },
abstract = {Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations.},
}
@article {pmid27379234,
year = {2016},
author = {Moorjani, S},
title = {Erratum: Addendum: Miniaturized Technologies for Enhancement of Motor Plasticity.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {4},
number = {},
pages = {51},
doi = {10.3389/fbioe.2016.00051},
pmid = {27379234},
issn = {2296-4185},
support = {P51 RR000166/RR/NCRR NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
abstract = {[This corrects the article on p. 30 in vol. 4, PMID: 27148525.].},
}
@article {pmid27378836,
year = {2016},
author = {Guhathakurta, D and Dutta, A},
title = {Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses-An Application in Ischemic Stroke.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {261},
pmid = {27378836},
issn = {1662-4548},
abstract = {Transcranial direct current stimulation (tDCS) modulates cortical neural activity and hemodynamics. Electrophysiological methods (electroencephalography-EEG) measure neural activity while optical methods (near-infrared spectroscopy-NIRS) measure hemodynamics coupled through neurovascular coupling (NVC). Assessment of NVC requires development of NIRS-EEG joint-imaging sensor montages that are sensitive to the tDCS affected brain areas. In this methods paper, we present a software pipeline incorporating freely available software tools that can be used to target vascular territories with tDCS and develop a NIRS-EEG probe for joint imaging of tDCS-evoked responses. We apply this software pipeline to target primarily the outer convexity of the brain territory (superficial divisions) of the middle cerebral artery (MCA). We then present a computational method based on Empirical Mode Decomposition of NIRS and EEG time series into a set of intrinsic mode functions (IMFs), and then perform a cross-correlation analysis on those IMFs from NIRS and EEG signals to model NVC at the lesional and contralesional hemispheres of an ischemic stroke patient. For the contralesional hemisphere, a strong positive correlation between IMFs of regional cerebral hemoglobin oxygen saturation and the log-transformed mean-power time-series of IMFs for EEG with a lag of about -15 s was found after a cumulative 550 s stimulation of anodal tDCS. It is postulated that system identification, for example using a continuous-time autoregressive model, of this coupling relation under tDCS perturbation may provide spatiotemporal discriminatory features for the identification of ischemia. Furthermore, portable NIRS-EEG joint imaging can be incorporated into brain computer interfaces to monitor tDCS-facilitated neurointervention as well as cortical reorganization.},
}
@article {pmid27378253,
year = {2016},
author = {Wang, F and Li, G and Chen, J and Duan, Y and Zhang, D},
title = {Novel semi-dry electrodes for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046021},
doi = {10.1088/1741-2560/13/4/046021},
pmid = {27378253},
issn = {1741-2552},
mesh = {Adult ; Biocompatible Materials ; *Brain-Computer Interfaces ; Ceramics ; Communication Devices for People with Disabilities ; *Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Imagination ; Male ; Prosthesis Design ; Reproducibility of Results ; Scalp ; Young Adult ; },
abstract = {OBJECTIVES: Modern applications of brain-computer interfaces (BCIs) based on electroencephalography rely heavily on the so-called wet electrodes (e.g. Ag/AgCl electrodes) which require gel application and skin preparation to operate properly. Recently, alternative 'dry' electrodes have been developed to increase ease of use, but they often suffer from higher electrode-skin impedance and signal instability. In the current paper, we have proposed a novel porous ceramic-based 'semi-dry' electrode. The key feature of the semi-dry electrodes is that their tips can slowly and continuously release a tiny amount of electrolyte liquid to the scalp, which provides an ionic conducting path for detecting neural signals.<br><br>APPROACH: The performance of the proposed electrode was evaluated by simultaneous recording of the wet and semi-dry electrodes pairs in five classical BCI paradigms: eyes open/closed, the motor imagery BCI, the P300 speller, the N200 speller and the steady-state visually evoked potential-based BCI.<br><br>MAIN RESULTS: The grand-averaged temporal cross-correlation was 0.95 &#xb1; 0.07 across the subjects and the nine recording positions, and these cross-correlations were stable throughout the whole experimental protocol. In the spectral domain, the semi-dry/wet coherence was greater than 0.80 at all frequencies and greater than 0.90 at frequencies above 10 Hz, with the exception of a dip around 50 Hz (i.e. the powerline noise). More importantly, the BCI classification accuracies were also comparable between the two types of electrodes.<br><br>SIGNIFICANCE: Overall, these results indicate that the proposed semi-dry electrode can effectively capture the electrophysiological responses and is a feasible alternative to the conventional dry electrode in BCI applications.},
}
@article {pmid27377663,
year = {2016},
author = {Lopez-Gordo, MA and Grima Murcia, MD and Padilla, P and Pelayo, F and Fernandez, E},
title = {Asynchronous Detection of Trials Onset from Raw EEG Signals.},
journal = {International journal of neural systems},
volume = {26},
number = {7},
pages = {1650034},
doi = {10.1142/S0129065716500349},
pmid = {27377663},
issn = {1793-6462},
mesh = {Acoustic Stimulation ; Adult ; Attention/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography/*methods ; *Evoked Potentials ; Female ; Humans ; Male ; Neuropsychological Tests ; Signal Processing, Computer-Assisted ; Speech Perception/physiology ; Telemetry/*methods ; Time Factors ; Young Adult ; },
abstract = {Clinical processing of event-related potentials (ERPs) requires a precise synchrony between the stimulation and the acquisition units that are guaranteed by means of a physical link between them. This precise synchrony is needed since temporal misalignments during trial averaging can lead to high deviations of peak times, thus causing error in diagnosis or inefficiency in classification in brain-computer interfaces (BCIs). Out of the laboratory, mobile EEG systems and BCI headsets are not provided with the physical link, thus being inadequate for acquisition of ERPs. In this study, we propose a method for the asynchronous detection of trials onset from raw EEG without physical links. We validate it with a BCI application based on the dichotic listening task. The user goal was to attend the cued auditory message and to report three keywords contained in it while ignoring the other message. The BCI goal was to detect the attended message from the analysis of auditory ERPs. The rate of successful onset detection in both synchronous (using the real onset) and asynchronous (blind detection of trial onset from raw EEG) was 73% with a synchronization error of less than 1[Formula: see text]ms. The level of synchronization provided by this proposal would allow home-based acquisition of ERPs with low cost BCI headsets and any media player unit without physical links between them.},
}
@article {pmid27377661,
year = {2017},
author = {Zhang, Y and Wang, Y and Jin, J and Wang, X},
title = {Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.},
journal = {International journal of neural systems},
volume = {27},
number = {2},
pages = {1650032},
doi = {10.1142/S0129065716500325},
pmid = {27377661},
issn = {1793-6462},
mesh = {Algorithms ; *Bayes Theorem ; Brain/*physiology ; Brain-Computer Interfaces ; Datasets as Topic ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Linear Models ; *Machine Learning ; Motor Activity/*physiology ; Time Factors ; },
abstract = {Effective common spatial pattern (CSP) feature extraction for motor imagery (MI) electroencephalogram (EEG) recordings usually depends on the filter band selection to a large extent. Subband optimization has been suggested to enhance classification accuracy of MI. Accordingly, this study introduces a new method that implements sparse Bayesian learning of frequency bands (named SBLFB) from EEG for MI classification. CSP features are extracted on a set of signals that are generated by a filter bank with multiple overlapping subbands from raw EEG data. Sparse Bayesian learning is then exploited to implement selection of significant features with a linear discriminant criterion for classification. The effectiveness of SBLFB is demonstrated on the BCI Competition IV IIb dataset, in comparison with several other competing methods. Experimental results indicate that the SBLFB method is promising for development of an effective classifier to improve MI classification.},
}
@article {pmid27377299,
year = {2019},
author = {Martin, S and Millán, JDR and Knight, RT and Pasley, BN},
title = {The use of intracranial recordings to decode human language: Challenges and opportunities.},
journal = {Brain and language},
volume = {193},
number = {},
pages = {73-83},
pmid = {27377299},
issn = {1090-2155},
support = {K99 DC012804/DC/NIDCD NIH HHS/United States ; L30 DC011179/DC/NIDCD NIH HHS/United States ; R01 NS021135/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Electrocorticography/instrumentation/*methods ; Electrodes, Implanted ; Humans ; *Language ; Phonetics ; Semantics ; Speech/*physiology ; Speech Perception/physiology ; },
abstract = {Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and temporal resolution, and thus offer a unique opportunity to investigate and decode the electrophysiological dynamics underlying speech processing. In this review article, we describe current approaches to decoding different features of speech perception and production - such as spectrotemporal, phonetic, phonotactic, semantic, and articulatory components - using intracranial recordings. A specific section is devoted to the decoding of imagined speech, and potential applications to speech prosthetic devices. We outline the challenges in decoding human language, as well as the opportunities in scientific and neuroengineering applications.},
}
@article {pmid27376723,
year = {2016},
author = {Bascil, MS and Tesneli, AY and Temurtas, F},
title = {Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {39},
number = {3},
pages = {665-676},
doi = {10.1007/s13246-016-0462-x},
pmid = {27376723},
issn = {1879-5447},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Least-Squares Analysis ; Machine Learning ; Male ; *Neural Networks, Computer ; *Pattern Recognition, Automated ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; *Task Performance and Analysis ; },
abstract = {Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha-beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.},
}
@article {pmid27376685,
year = {2016},
author = {Chortos, A and Liu, J and Bao, Z},
title = {Pursuing prosthetic electronic skin.},
journal = {Nature materials},
volume = {15},
number = {9},
pages = {937-950},
pmid = {27376685},
issn = {1476-4660},
mesh = {Animals ; Biomimetics/*instrumentation ; *Electrical Equipment and Supplies ; Humans ; Mechanical Phenomena ; *Skin, Artificial ; },
abstract = {Skin plays an important role in mediating our interactions with the world. Recreating the properties of skin using electronic devices could have profound implications for prosthetics and medicine. The pursuit of artificial skin has inspired innovations in materials to imitate skin's unique characteristics, including mechanical durability and stretchability, biodegradability, and the ability to measure a diversity of complex sensations over large areas. New materials and fabrication strategies are being developed to make mechanically compliant and multifunctional skin-like electronics, and improve brain/machine interfaces that enable transmission of the skin's signals into the body. This Review will cover materials and devices designed for mimicking the skin's ability to sense and generate biomimetic signals.},
}
@article {pmid27375458,
year = {2016},
author = {Kamran, MA and Mannan, MM and Jeong, MY},
title = {Cortical Signal Analysis and Advances in Functional Near-Infrared Spectroscopy Signal: A Review.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {261},
pmid = {27375458},
issn = {1662-5161},
abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that measures the concentration changes of oxy-hemoglobin (HbO) and de-oxy hemoglobin (HbR) at the same time. It is an emerging cortical imaging modality with a good temporal resolution that is acceptable for brain-computer interface applications. Researchers have developed several methods in last two decades to extract the neuronal activation related waveform from the observed fNIRS time series. But still there is no standard method for analysis of fNIRS data. This article presents a brief review of existing methodologies to model and analyze the activation signal. The purpose of this review article is to give a general overview of variety of existing methodologies to extract useful information from measured fNIRS data including pre-processing steps, effects of differential path length factor (DPF), variations and attributes of hemodynamic response function (HRF), extraction of evoked response, removal of physiological noises, instrumentation, and environmental noises and resting/activation state functional connectivity. Finally, the challenges in the analysis of fNIRS signal are summarized.},
}
@article {pmid27375410,
year = {2016},
author = {Hettich, DT and Bolinger, E and Matuz, T and Birbaumer, N and Rosenstiel, W and Spüler, M},
title = {EEG Responses to Auditory Stimuli for Automatic Affect Recognition.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {244},
pmid = {27375410},
issn = {1662-4548},
abstract = {Brain state classification for communication and control has been well established in the area of brain-computer interfaces over the last decades. Recently, the passive and automatic extraction of additional information regarding the psychological state of users from neurophysiological signals has gained increased attention in the interdisciplinary field of affective computing. We investigated how well specific emotional reactions, induced by auditory stimuli, can be detected in EEG recordings. We introduce an auditory emotion induction paradigm based on the International Affective Digitized Sounds 2nd Edition (IADS-2) database also suitable for disabled individuals. Stimuli are grouped in three valence categories: unpleasant, neutral, and pleasant. Significant differences in time domain domain event-related potentials are found in the electroencephalogram (EEG) between unpleasant and neutral, as well as pleasant and neutral conditions over midline electrodes. Time domain data were classified in three binary classification problems using a linear support vector machine (SVM) classifier. We discuss three classification performance measures in the context of affective computing and outline some strategies for conducting and reporting affect classification studies.},
}
@article {pmid27374247,
year = {2017},
author = {Young, KL and Koppel, S and Charlton, JL},
title = {Toward best practice in Human Machine Interface design for older drivers: A review of current design guidelines.},
journal = {Accident; analysis and prevention},
volume = {106},
number = {},
pages = {460-467},
doi = {10.1016/j.aap.2016.06.010},
pmid = {27374247},
issn = {1879-2057},
mesh = {Accidents, Traffic/prevention &amp; control ; Age Factors ; Aged ; Aging/*physiology ; *Automobile Driving ; *Automobiles ; Brain-Computer Interfaces ; Cognitive Dysfunction ; Guidelines as Topic ; Humans ; Male ; },
abstract = {Older adults are the fastest growing segment of the driving population. While there is a strong emphasis for older people to maintain their mobility, the safety of older drivers is a serious community concern. Frailty and declines in a range of age-related sensory, cognitive, and physical impairments can place older drivers at an increased risk of crash-related injuries and death. A number of studies have indicated that in-vehicle technologies such as Advanced Driver Assistance Systems (ADAS) and In-Vehicle Information Systems (IVIS) may provide assistance to older drivers. However, these technologies will only benefit older drivers if their design is congruent with the complex needs and diverse abilities of this driving cohort. The design of ADAS and IVIS is largely informed by automotive Human Machine Interface (HMI) guidelines. However, it is unclear to what extent the declining sensory, cognitive and physical capabilities of older drivers are addressed in the current guidelines. This paper provides a review of key current design guidelines for IVIS and ADAS with respect to the extent they address age-related changes in functional capacities. The review revealed that most of the HMI guidelines do not address design issues related to older driver impairments. In fact, in many guidelines driver age and sensory cognitive and physical impairments are not mentioned at all and where reference is made, it is typically very broad. Prescriptive advice on how to actually design a system so that it addresses the needs and limitations of older drivers is not provided. In order for older drivers to reap the full benefits that in-vehicle technology can afford, it is critical that further work establish how older driver limitations and capabilities can be supported by the system design process, including their inclusion into HMI design guidelines.},
}
@article {pmid27358520,
year = {2016},
author = {Acharya, SR and Dasgupta, P and Das, S and Halder, S and Panda, N},
title = {Retropancreatic Ovarian Tumor.},
journal = {The Indian journal of surgery},
volume = {78},
number = {3},
pages = {232-234},
pmid = {27358520},
issn = {0972-2068},
abstract = {Retroperitoneal mucinous cystadenomas are rare lesions (less than 50 reported) characterized by presence of ovary like stroma of unknown origin. However, germinal component of ovary has never been found in them. The pancreas occasionally gives rise to mucinous cystadenomas, but they are always intrapancreatic. We report a unique case of a rare retroperitoneal mucinous cystadenomas with presence of ovarian follicles in a 45-year-old lady who presented with an abdominal mass. This was successfully excised. Though retroperitoneal mucinous cystadenomas are rare, presence of ovarian follicle (germ cell) in them has never been reported before.},
}
@article {pmid27354552,
year = {2016},
author = {Li, AY and Filson, CP and Hollingsworth, JM and He, C and Weizer, AZ and Hollenbeck, BK and Gilbert, SM and Hafez, KS and Lee, CT and Dunn, RL and Montgomery, JS},
title = {Patient-Reported Convalescence and Quality of Life Recovery: A Comparison of Open and Robotic-Assisted Radical Cystectomy.},
journal = {Surgical innovation},
volume = {23},
number = {6},
pages = {598-605},
doi = {10.1177/1553350616656284},
pmid = {27354552},
issn = {1553-3514},
mesh = {Adult ; Aged ; Cohort Studies ; Convalescence/*psychology ; Cystectomy/adverse effects/*methods ; Databases, Factual ; Female ; Humans ; Laparotomy/methods ; Male ; Middle Aged ; Pain Measurement ; Pain, Postoperative/physiopathology ; *Patient Reported Outcome Measures ; *Quality of Life ; Recovery of Function ; Retrospective Studies ; Risk Assessment ; Robotic Surgical Procedures/*methods ; Urinary Bladder Neoplasms/mortality/pathology/*surgery ; },
abstract = {Background Robotic-assisted radical cystectomy (RARC) is gaining traction as a surgical approach, but there are limited data on patient-reported outcomes for this technique compared to open radical cystectomy (ORC). Objective To compare health-related quality of life (HRQoL) and short-term convalescence among bladder cancer patients who underwent ORC and RARC. Methods Review of a single-institution bladder cancer database was conducted. Baseline and postoperative HRQoL was evaluated using the Bladder Cancer Index (BCI) for 324 patients who had ORC (n = 267) or RARC (n = 57) between 2008 and 2012. The BCI assesses function and bother in urinary, bowel, and sexual domains. Among 87 distinct patients (ORC n = 67, RARC n = 20), we also evaluated short-term postoperative convalescence using the Convalescence and Recovery Evaluation (CARE) questionnaire. Our primary outcomes were HRQoL within 12 months and short-term convalescence within 6 weeks following cystectomy. We fit generalized estimating equation regression models to estimate longitudinal changes in BCI scores within domains, and CARE domain score differences were tested with Wilcoxon rank-sum tests. Results Clinical characteristics and baseline BCI/CARE scores were similar between the 2 groups (all P > .05). Within 1 year after surgery, recovery of HRQoL across all BCI domains was comparable, with scores nearly returning to baseline at 1 year for all patients. CARE scores at 4 weeks revealed that patients treated with ORC had better pain (29.1 vs 20.0, P = .02) domain scores compared to RARC. These differences abated by week 6. Conclusions HRQoL recovery and short-term convalescence were similar in this cohort following ORC and RARC.},
}
@article {pmid27354191,
year = {2016},
author = {Hortal, E and Úbeda, A and Iáñez, E and Azorín, JM and Fernández, E},
title = {EEG-Based Detection of Starting and Stopping During Gait Cycle.},
journal = {International journal of neural systems},
volume = {26},
number = {7},
pages = {1650029},
doi = {10.1142/S0129065716500295},
pmid = {27354191},
issn = {1793-6462},
mesh = {Adolescent ; Adult ; Biomechanical Phenomena ; Brain/*physiology/physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Exoskeleton Device ; False Positive Reactions ; Female ; Gait/*physiology ; Humans ; Leg/*physiology/physiopathology ; Male ; Muscle Spasticity/physiopathology/rehabilitation ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Stroke Rehabilitation/methods ; Support Vector Machine ; Time Factors ; Young Adult ; },
abstract = {Walking is for humans an essential task in our daily life. However, there is a huge (and growing) number of people who have this ability diminished or are not able to walk due to motor disabilities. In this paper, a system to detect the start and the stop of the gait through electroencephalographic signals has been developed. The system has been designed in order to be applied in the future to control a lower limb exoskeleton to help stroke or spinal cord injured patients during the gait. The brain-machine interface (BMI) training has been optimized through a preliminary analysis using the brain information recorded during the experiments performed by three healthy subjects. Afterward, the system has been verified by other four healthy subjects and three patients in a real-time test. In both preliminary optimization analysis and real-time tests, the results obtained are very similar. The true positive rates are [Formula: see text] and [Formula: see text] respectively. Regarding the false positive per minute, the values are also very similar, decreasing from 2.66 in preliminary tests to 1.90 in real-time. Finally, the average latencies in the detection of the movement intentions are 794 and 798[Formula: see text]ms, preliminary and real-time tests respectively.},
}
@article {pmid27351722,
year = {2016},
author = {Degenhart, AD and Eles, J and Dum, R and Mischel, JL and Smalianchuk, I and Endler, B and Ashmore, RC and Tyler-Kabara, EC and Hatsopoulos, NG and Wang, W and Batista, AP and Cui, XT},
title = {Histological evaluation of a chronically-implanted electrocorticographic electrode grid in a non-human primate.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046019},
pmid = {27351722},
issn = {1741-2552},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; T32 NS086749/NS/NINDS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; R01 NS062019/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Collagen Type I/metabolism ; Electrocorticography/*adverse effects/*instrumentation ; Electrodes, Implanted/*adverse effects ; Granuloma, Foreign-Body/pathology ; Hand/innervation/physiology ; Immunohistochemistry ; Macaca mulatta ; Macrophages/pathology ; Male ; Microelectrodes ; Microscopy, Confocal ; Motor Cortex/*pathology/physiology ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG), used as a neural recording modality for brain-machine interfaces (BMIs), potentially allows for field potentials to be recorded from the surface of the cerebral cortex for long durations without suffering the host-tissue reaction to the extent that it is common with intracortical microelectrodes. Though the stability of signals obtained from chronically implanted ECoG electrodes has begun receiving attention, to date little work has characterized the effects of long-term implantation of ECoG electrodes on underlying cortical tissue.<br><br>APPROACH: We implanted and recorded from a high-density ECoG electrode grid subdurally over cortical motor areas of a Rhesus macaque for 666 d.<br><br>MAIN RESULTS: Histological analysis revealed minimal damage to the cortex underneath the implant, though the grid itself was encapsulated in collagenous tissue. We observed macrophages and foreign body giant cells at the tissue-array interface, indicative of a stereotypical foreign body response. Despite this encapsulation, cortical modulation during reaching movements was observed more than 18 months post-implantation.<br><br>SIGNIFICANCE: These results suggest that ECoG may provide a means by which stable chronic cortical recordings can be obtained with comparatively little tissue damage, facilitating the development of clinically viable BMI systems.},
}
@article {pmid27351459,
year = {2016},
author = {Zink, R and Hunyadi, B and Huffel, SV and Vos, MD},
title = {Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046017},
doi = {10.1088/1741-2560/13/4/046017},
pmid = {27351459},
issn = {1741-2552},
mesh = {Adult ; Artifacts ; Attention/*physiology ; Auditory Perception/*physiology ; Bicycling/*psychology ; Brain-Computer Interfaces ; Cognition/physiology ; Electroencephalography/*instrumentation ; Electrooculography ; Environment ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Muscle, Skeletal/physiology ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings.<br><br>APPROACH: We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts.<br><br>MAIN RESULTS: A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences.<br><br>SIGNIFICANCE: Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.},
}
@article {pmid27337711,
year = {2017},
author = {Matran-Fernandez, A and Poli, R},
title = {Brain-Computer Interfaces for Detection and Localization of Targets in Aerial Images.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {4},
pages = {959-969},
doi = {10.1109/TBME.2016.2583200},
pmid = {27337711},
issn = {1558-2531},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Machine Learning ; Male ; Pattern Recognition, Visual/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Space Perception/*physiology ; Task Performance and Analysis ; Young Adult ; },
abstract = {OBJECTIVE: The N2pc event-related potential (ERP) appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located. We explored the feasibility of using it to extract information on the spatial location of targets in aerial images shown by means of a rapid serial visual presentation (RSVP) protocol using single-trial classification.<br><br>METHODS: Images were shown to 11 participants at a presentation rate of 5&#xa0;Hz while recording electroencephalographic signals. With the resulting ERPs, we trained linear classifiers for single-trial detection of target presence and location. We analyzed the classifiers' decisions and their raw output scores on independent test sets as well as the averages and voltage distributions of the ERPs.<br><br>RESULTS: The N2pc is elicited in RSVP presentation of complex images and can be recognized in single trials (the median area under the receiver operating characteristic curve was 0.76 for left versus right classification). Moreover, the peak amplitude of this ERP correlates with the horizontal position of the target within an image. The N2pc varies significantly depending on handedness, and these differences can be used for discriminating participants in terms of their preferred hand.<br><br>CONCLUSION AND SIGNIFICANCE: The N2pc is elicited during RSVP presentation of real complex images and contains analogue information that can be used to roughly infer the horizontal position of targets. Furthermore, differences in the N2pc due to handedness should be taken into account when creating collaborative brain-computer interfaces.},
}
@article {pmid27337709,
year = {2017},
author = {Kao, JC and Nuyujukian, P and Ryu, SI and Shenoy, KV},
title = {A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {4},
pages = {935-945},
doi = {10.1109/TBME.2016.2582691},
pmid = {27337709},
issn = {1558-2531},
support = {/HHMI/Howard Hughes Medical Institute/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; R01 NS064318/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/instrumentation/*methods ; Male ; Markov Chains ; *Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys (). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.},
}
@article {pmid27337706,
year = {2017},
author = {Opie, NL and van der Nagel, NR and John, SE and Vessey, K and Rind, GS and Ronayne, SM and Fletcher, EL and May, CN and OBrien, TJ and Oxley, TJ},
title = {Micro-CT and Histological Evaluation of an Neural Interface Implanted Within a Blood Vessel.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {4},
pages = {928-934},
doi = {10.1109/TBME.2016.2552226},
pmid = {27337706},
issn = {1558-2531},
mesh = {Animals ; Blood Vessel Prosthesis ; Cerebral Arteries/*cytology/*diagnostic imaging/surgery ; Diagnostic Techniques, Neurological/*instrumentation ; *Electrodes, Implanted ; Endovascular Procedures/instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Female ; Prosthesis Implantation ; Sheep ; *Stents ; Tomography, X-Ray Computed/methods ; },
abstract = {OBJECTIVE: Recently, we reported the development of a stent-mounted electrode array (Stentrode) capable of chronically recording neural signals from within a blood vessel with high fidelity. Preliminary data suggested incorporation of the Stentrode into the blood vessel wall was associated with improved recording sensitivity. We now investigate neointimal incorporation of the Stentrode, implanted in a cohort of sheep for up to 190 days.<br><br>METHODS: Micro-CT, obtained from the Imaging and Medical Beamline at the Australian Synchrotron, and histomorphometic techniques developed specifically for evaluation of cerebral vasculature implanted with a stent-electrode array were compared as measures to assess device incorporation and vessel patency.<br><br>RESULTS: Both micro-CT analysis and histomorphometry, revealed a strong correlation between implant duration and the number of incorporated stent struts. <10% (26/268) of stent struts were covered in neointima in sheep implanted for <2 weeks, increasing to >78% (191/243) between 2 and 4 weeks. Average strut-to-lumen thickness from animals implanted >12 weeks was comparable across both modalities, 339 &#xb1;15 &#x3bc;m measured using micro-CT and 331 &#xb1;19 &#x3bc;m (n = 292) measured histologically. There was a strong correlation between lumen areas measured using the two modalities (), with no observation of vessel occlusion observed from any of the 12 animals implanted for up to 190&#xa0;days.<br><br>CONCLUSION: Micro-CT and the histomorphometric techniques we developed are comparable and can both be used to identify incorporation of a Stentrode implanted in cerebral vessels.<br><br>SIGNIFICANCE: This study demonstrates preliminary safety of a stent-electrode array implanted in cerebral vasculature, which may facilitate technological advances in minimally invasive brain-computer interfaces.},
}
@article {pmid27335578,
year = {2016},
author = {Giroldini, W and Pederzoli, L and Bilucaglia, M and Melloni, S and Tressoldi, P},
title = {A new method to detect event-related potentials based on Pearson's correlation.},
journal = {EURASIP journal on bioinformatics &amp; systems biology},
volume = {2016},
number = {1},
pages = {11},
pmid = {27335578},
issn = {1687-4145},
abstract = {Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP's waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase-in consonance with the stimuli-in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.},
}
@article {pmid27329006,
year = {2016},
author = {Horki, P and Bauernfeind, G and Schippinger, W and Pichler, G and Müller-Putz, GR},
title = {Evaluation of induced and evoked changes in EEG during selective attention to verbal stimuli.},
journal = {Journal of neuroscience methods},
volume = {270},
number = {},
pages = {165-176},
doi = {10.1016/j.jneumeth.2016.06.015},
pmid = {27329006},
issn = {1872-678X},
mesh = {Adult ; Aged ; Attention/*physiology ; Brain/*physiology/physiopathology ; Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Female ; Humans ; Imagination/physiology ; Language Tests ; Male ; Mathematical Concepts ; Motor Activity/physiology ; Neuropsychological Tests ; Persistent Vegetative State/physiopathology ; Problem Solving/physiology ; *Signal Processing, Computer-Assisted ; Speech/physiology ; Speech Perception/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Two challenges need to be addressed before bringing non-motor mental tasks for brain-computer interface (BCI) control to persons in a minimally conscious state (MCS), who can be behaviorally unresponsive even when proven to be consciously aware: first, keeping the cognitive demands as low as possible so that they could be fulfilled by persons with MCS. Second, increasing the control of experimental protocol (i.e. type and timing of the task performance).<br><br>NEW METHOD: The goal of this study is twofold: first goal is to develop an experimental paradigm that can facilitate the performance of brain-teasers (e.g. mental subtraction and word generation) on the one hand, and can increase the control of experimental protocol on the other hand. The second goal of this study is to exploit the similar findings for mentally attending to someone else's verbal performance of brain-teaser tasks and self-performing the same tasks to setup an online BCI, and to compare it in healthy participants to the current "state-of-the-art" motor imagery (MI, sports).<br><br>RESULTS: The response accuracies for the best performing healthy participants indicate that selective attention to verbal performance of mental subtraction (SUB) is a viable alternative to the MI. Time-frequency analysis of the SUB task in one participant with MCS did not reveal any significant (p<0.05) EEG changes, whereas imagined performance of one sport of participants' choice (SPORT) revealed task-related EEG changes over neurophysiological plausible cortical areas.<br><br>We found that mentally attending to someone else's verbal performance of brain-teaser tasks leads to similar results as in self-performing the same tasks.<br><br>CONCLUSIONS: In this work we demonstrated that a single auditory selective attention task (i.e. mentally attending to someone else's verbal performance of mental subtraction) can modulate both induced and evoked changes in EEG, and be used for yes/no communication in an auditory scanning paradigm.},
}
@article {pmid27323368,
year = {2017},
author = {Li, W and Guo, Y and Fan, J and Ma, C and Ma, X and Chen, X and He, J},
title = {The Neural Mechanism Exploration of Adaptive Motor Control: Dynamical Economic Cell Allocation in the Primary Motor Cortex.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {5},
pages = {492-501},
doi = {10.1109/TNSRE.2016.2580620},
pmid = {27323368},
issn = {1558-0210},
mesh = {Animals ; Brain Mapping/methods ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials, Motor/physiology ; Feedback, Physiological/*physiology ; Humans ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; Neurons/*physiology ; },
abstract = {Adaptive flexibility is of significance for the smooth and efficient movements in goal attainment. However, the underlying work mechanism of the cerebral cortex in adaptive motor control still remains unclear. How does the cerebral cortex organize and coordinate the activity of a large population of cells in the implementation of various motor strategies? To explore this issue, single-unit activities from the M1 region and kinematic data were recorded simultaneously in monkeys performing 3D reach-to-grasp tasks with different perturbations. Varying motor control strategies were employed and achieved in different perturbed tasks, via the dynamic allocation of cells to modulate specific movement parameters. An economic principle was proposed for the first time to describe a basic rule for cell allocation in the primary motor cortex. This principle, defined as the Dynamic Economic Cell Allocation Mechanism (DECAM), guarantees benefit maximization in cell allocation under limited neuronal resources, and avoids committing resources to uneconomic investments for unreliable factors with no or little revenue. That is to say, the cells recruited are always preferentially allocated to those factors with reliable return; otherwise, the cells are dispatched to respond to other factors about task. The findings of this study might partially reveal the working mechanisms underlying the role of the cerebral cortex in adaptive motor control, wherein is also of significance for the design of future intelligent brain-machine interfaces and rehabilitation device.},
}
@article {pmid27323367,
year = {2017},
author = {Khasnobish, A and Konar, A and Tibarewala, DN and Nagar, AK},
title = {Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {1},
pages = {88-102},
doi = {10.1109/TNSRE.2016.2580580},
pmid = {27323367},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiopathology ; Electroencephalography/*methods ; Fuzzy Logic ; Humans ; Man-Machine Systems ; Middle Aged ; *Movement ; Movement Disorders/*physiopathology/rehabilitation ; *Neural Pathways ; Neurological Rehabilitation/instrumentation/methods ; Pattern Recognition, Automated/methods ; *Psychomotor Performance ; Reproducibility of Results ; Robotics/instrumentation/methods ; Sensitivity and Specificity ; },
abstract = {In visual-motor coordination, the human brain processes visual stimuli representative of complex motion-related tasks at the occipital lobe to generate the necessary neuronal signals for the parietal and pre-frontal lobes, which in turn generates movement related plans to excite the motor cortex to execute the actual tasks. The paper introduces a novel approach to provide rehabilitative support to patients suffering from neurological damage in their pre-frontal, parietal and/or motor cortex regions. An attempt to bypass the natural visual-motor pathway is undertaken using interval type-2 fuzzy sets to generate the approximate EEG response of the damaged pre-frontal/parietal/motor cortex from the occipital EEG signals. The approximate EEG response is used to trigger a pre-trained joint coordinate generator to obtain the desired joint coordinates of the link end-points of a robot imitating the human subject. The robot arm is here employed as a rehabilitative aid in order to move each link end-points to the desired locations in the reference coordinate system by appropriately activating its links using the well-known inverse kinematics approach. The mean-square positional errors obtained for each link end-points is found within acceptable limits for all experimental subjects including subjects with partial parietal damage, indicating a possible impact of the proposed approach in rehabilitative robotics. Subjective variation in EEG features over different sessions of experimental trials is modeled here using interval type-2 fuzzy sets for its inherent power to handle uncertainty. Experiments undertaken confirm that interval type-2 fuzzy realization outperforms its classical type-1 counterpart and back-propagation neural approaches in all experimental cases, considering link positional error as a metric. The proposed research offers a new opening for the development of possible rehabilitative aids for people with partial impairment in visual-motor coordination.},
}
@article {pmid27322267,
year = {2016},
author = {Kim, K and Lim, SH and Lee, J and Kang, WS and Moon, C and Choi, JW},
title = {Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {6},
pages = {},
pmid = {27322267},
issn = {1424-8220},
mesh = {Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; *Likelihood Functions ; Models, Theoretical ; Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain's response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.},
}
@article {pmid27317498,
year = {2016},
author = {Melinscak, F and Montesano, L},
title = {Beyond p-values in the evaluation of brain-computer interfaces: A Bayesian estimation approach.},
journal = {Journal of neuroscience methods},
volume = {270},
number = {},
pages = {30-45},
doi = {10.1016/j.jneumeth.2016.06.008},
pmid = {27317498},
issn = {1872-678X},
mesh = {Alpha Rhythm ; Bayes Theorem ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/methods ; *Evaluation Studies as Topic ; Evoked Potentials, Visual ; Humans ; Imagination/physiology ; Lignans ; Linear Models ; Mathematical Concepts ; Motor Activity/physiology ; Motor Cortex/physiology ; Music ; Problem Solving/physiology ; Rest ; Visual Perception/physiology ; },
abstract = {BACKGROUND: To statistically evaluate the performance of brain-computer interfaces (BCIs), researchers usually rely on null hypothesis significance testing (NHST), i.e. p-values. However, over-reliance on NHST is often identified as one of the causes of the recent reproducibility crisis in psychology and neuroscience.<br><br>NEW METHOD: In this paper we propose Bayesian estimation as an alternative to NHST in the analysis of BCI performance data. For the three most common experimental designs in BCI research - which would usually be analyzed using a t-test, a linear regression, or an ANOVA - we develop hierarchical models and estimate their parameters using Bayesian inference. Furthermore, we show that the described models are special cases of the hierarchical generalized linear model (HGLM), which we propose as a general framework for the analysis of BCI performance.<br><br>RESULTS: We demonstrate the effectiveness of the proposed models on three real datasets and show how the results obtained with Bayesian estimation can give a nuanced insight into BCI performance data. Additionally, we provide all the data and code necessary to reproduce the presented results.<br><br>Compared to NHST, Bayesian estimation with the HGLM allows more flexibility in the analysis of BCI performance data from nested experimental designs, and the obtained results have a more straightforward interpretation.<br><br>CONCLUSIONS: Besides gains in flexibility and interpretability, a wider adoption of the Bayesian estimation approach in BCI studies could bring about greater transparency in data analysis, allow accumulation of knowledge across studies, and reduce questionable practices such as "p-hacking".},
}
@article {pmid27313959,
year = {2016},
author = {Vidal, GW and Rynes, ML and Kelliher, Z and Goodwin, SJ},
title = {Review of Brain-Machine Interfaces Used in Neural Prosthetics with New Perspective on Somatosensory Feedback through Method of Signal Breakdown.},
journal = {Scientifica},
volume = {2016},
number = {},
pages = {8956432},
pmid = {27313959},
issn = {2090-908X},
abstract = {The brain-machine interface (BMI) used in neural prosthetics involves recording signals from neuron populations, decoding those signals using mathematical modeling algorithms, and translating the intended action into physical limb movement. Recently, somatosensory feedback has become the focus of many research groups given its ability in increased neural control by the patient and to provide a more natural sensation for the prosthetics. This process involves recording data from force sensitive locations on the prosthetics and encoding these signals to be sent to the brain in the form of electrical stimulation. Tactile sensation has been achieved through peripheral nerve stimulation and direct stimulation of the somatosensory cortex using intracortical microstimulation (ICMS). The initial focus of this paper is to review these principles and link them to modern day applications such as restoring limb use to those who lack such control. With regard to how far the research has come, a new perspective for the signal breakdown concludes the paper, offering ideas for more real somatosensory feedback using ICMS to stimulate particular sensations by differentiating touch sensors and filtering data based on unique frequencies.},
}
@article {pmid27313507,
year = {2016},
author = {Mahmud, M and Vassanelli, S},
title = {Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {248},
pmid = {27313507},
issn = {1662-4548},
abstract = {In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.},
}
@article {pmid27305277,
year = {2016},
author = {Buczinski, S and Ménard, J and Timsit, E},
title = {Incremental Value (Bayesian Framework) of Thoracic Ultrasonography over Thoracic Auscultation for Diagnosis of Bronchopneumonia in Preweaned Dairy Calves.},
journal = {Journal of veterinary internal medicine},
volume = {30},
number = {4},
pages = {1396-1401},
pmid = {27305277},
issn = {1939-1676},
mesh = {Animals ; Auscultation/*veterinary ; Bayes Theorem ; Bronchopneumonia/diagnosis/*veterinary ; Cattle ; Cattle Diseases/*diagnosis ; Cross-Sectional Studies ; Ultrasonography/*veterinary ; },
abstract = {BACKGROUND: Thoracic ultrasonography (TUS) is a specific and relatively sensitive method to diagnose bronchopneumonia (BP) in dairy calves. Unfortunately, as it requires specific training and equipment, veterinarians typically base their diagnosis on thoracic auscultation (AUSC), which is rapid and easy to perform.<br><br>HYPOTHESIS/OBJECTIVES: We hypothesized that the use of TUS, in addition to AUSC, can significantly increase accuracy of BP diagnosis. Therefore, the objectives were to (i) determine the incremental value of TUS over AUSC for diagnosis of BP in preweaned dairy calves and (ii) assess diagnostic accuracy of AUSC.<br><br>ANIMALS: Two hundred and nine dairy calves (<1 month of age) were enrolled in this cross-sectional study.<br><br>METHODS: Prospective cross-sectional study. All calves from a veal calves unit were examined (independent operators) using the Wisconsin Calf Respiratory Scoring Criteria (CRSC), AUSC, and TUS. A Bayesian latent class approach was used to estimate the incremental value of AUSC over TUS (integrated discrimination improvement [IDI]) and the diagnostic accuracy of AUSC.<br><br>RESULTS: Abnormal CRSC, AUSC, and TUS were recorded in 3.3, 53.1, and 23.9% of calves, respectively. AUSC was sensitive (72.9%; 95% Bayesian credible interval [BCI]: 50.1-96.4%), but not specific (53.3%; 95% BCI: 43.3-64.0%) to diagnose BP. Compared to AUSC, TUS was more specific (92.9%; 95% BCI: 86.5-97.1%), but had similar sensitivity (76.5%; 95% BCI: 60.2-88.8%). The incremental value of TUS over AUSC was high (IDI = 43.7%; 5% BCI: 22.0-63.0%) significantly improving proportions of sick and healthy calves appropriately classified.<br><br>The use of TUS over AUSC significantly improved accuracy of BP diagnosis in dairy calves.},
}
@article {pmid27303809,
year = {2016},
author = {Deliano, M and Tabelow, K and König, R and Polzehl, J},
title = {Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis.},
journal = {PloS one},
volume = {11},
number = {6},
pages = {e0157355},
pmid = {27303809},
issn = {1932-6203},
mesh = {*Algorithms ; Animals ; Behavior, Animal/physiology ; Brain/physiology ; Electrocorticography ; Gerbillinae ; Learning/*physiology ; *Learning Curve ; *Models, Neurological ; },
abstract = {Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.},
}
@article {pmid27303808,
year = {2016},
author = {Bashford, L and Mehring, C},
title = {Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface.},
journal = {PloS one},
volume = {11},
number = {6},
pages = {e0156591},
pmid = {27303808},
issn = {1932-6203},
mesh = {Adult ; Analysis of Variance ; Body Image ; *Brain-Computer Interfaces ; Female ; Hand/*physiology ; Humans ; *Illusions ; Imagination/physiology ; Male ; Movement/*physiology ; Proprioception/physiology ; Rubber ; Self Concept ; Surveys and Questionnaires ; Visual Perception/physiology ; Young Adult ; },
abstract = {To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands.},
}
@article {pmid27303595,
year = {2016},
author = {Resalat, SN and Saba, V},
title = {A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System.},
journal = {Basic and clinical neuroscience},
volume = {7},
number = {1},
pages = {13-19},
pmid = {27303595},
issn = {2008-126X},
abstract = {INTRODUCTION: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications.<br><br>METHODS: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery.<br><br>RESULTS: The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features.<br><br>DISCUSSION: These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system; however, the Power Spectral Density (PSD) based &#x3b1;-BP feature had the highest averaged accuracy.},
}
@article {pmid27297044,
year = {2016},
author = {Krumpe, T and Walter, C and Rosenstiel, W and Spüler, M},
title = {Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046015},
doi = {10.1088/1741-2560/13/4/046015},
pmid = {27297044},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization ; Electrooculography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Observation ; Online Systems ; Psychomotor Performance ; Young Adult ; },
abstract = {OBJECTIVE: In this study, the feasibility of detecting a P300 via an asynchronous classification mode in a reactive EEG-based brain-computer interface (BCI) was evaluated. The P300 is one of the most popular BCI control signals and therefore used in many applications, mostly for active communication purposes (e.g. P300 speller). As the majority of all systems work with a stimulus-locked mode of classification (synchronous), the field of applications is limited. A new approach needs to be applied in a setting in which a stimulus-locked classification cannot be used due to the fact that the presented stimuli cannot be controlled or predicted by the system.<br><br>APPROACH: A continuous observation task requiring the detection of outliers was implemented to test such an approach. The study was divided into an offline and an online part.<br><br>MAIN RESULTS: Both parts of the study revealed that an asynchronous detection of the P300 can successfully be used to detect single events with high specificity. It also revealed that no significant difference in performance was found between the synchronous and the asynchronous approach.<br><br>SIGNIFICANCE: The results encourage the use of an asynchronous classification approach in suitable applications without a potential loss in performance.},
}
@article {pmid27296902,
year = {2016},
author = {Štrbac, M and Belić, M and Isaković, M and Kojić, V and Bijelić, G and Popović, I and Radotić, M and Došen, S and Marković, M and Farina, D and Keller, T},
title = {Integrated and flexible multichannel interface for electrotactile stimulation.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046014},
doi = {10.1088/1741-2560/13/4/046014},
pmid = {27296902},
issn = {1741-2552},
mesh = {Adult ; Amputees ; Brain-Computer Interfaces ; *Computer Simulation ; Discrimination, Psychological ; Electric Stimulation ; Female ; Hand Strength ; Humans ; Male ; *Neural Prostheses ; Proprioception ; Prosthesis Design ; Psychometrics ; Touch/*physiology ; Wrist/innervation/physiology ; Young Adult ; },
abstract = {OBJECTIVE: The aim of the present work was to develop and test a flexible electrotactile stimulation system to provide real-time feedback to the prosthesis user. The system requirements were to accommodate the capabilities of advanced multi-DOF myoelectric hand prostheses and transmit the feedback variables (proprioception and force) using intuitive coding, with high resolution and after minimal training.<br><br>APPROACH: We developed a fully-programmable and integrated electrotactile interface supporting time and space distributed stimulation over custom designed flexible array electrodes. The system implements low-level access to individual stimulation channels as well as a set of high-level mapping functions translating the state of a multi-DoF prosthesis (aperture, grasping force, wrist rotation) into a set of predefined dynamic stimulation profiles. The system was evaluated using discrimination tests employing spatial and frequency coding (10 able-bodied subjects) and dynamic patterns (10 able-bodied and 6 amputee subjects). The outcome measure was the success rate (SR) in discrimination.<br><br>MAIN RESULTS: The more practical electrode with the common anode configuration performed similarly to the more usual concentric arrangement. The subjects could discriminate six spatial and four frequency levels with SR >90% after a few minutes of training, whereas the performance significantly deteriorated for more levels. The dynamic patterns were intuitive for the subjects, although amputees showed lower SR than able-bodied individuals (86% &#xb1; 10% versus 99% &#xb1; 3%).<br><br>SIGNIFICANCE: The tests demonstrated that the system was easy to setup and apply. The design and resolution of the multipad electrode was evaluated. Importantly, the novel dynamic patterns, which were successfully tested, can be superimposed to transmit multiple feedback variables intuitively and simultaneously. This is especially relevant for closing the loop in modern multifunction prostheses. Therefore, the proposed system is convenient for practical applications and can be used to implement sensory perception training and/or closed-loop control of myoelectric prostheses, providing grasping force and proprioceptive feedback.},
}
@article {pmid27287429,
year = {2016},
author = {Siddiqi, K and Dogar, O and Rashid, R and Jackson, C and Kellar, I and O'Neill, N and Hassan, M and Ahmed, F and Irfan, M and Thomson, H and Khan, J},
title = {Behaviour change intervention for smokeless tobacco cessation: its development, feasibility and fidelity testing in Pakistan and in the UK.},
journal = {BMC public health},
volume = {16},
number = {},
pages = {501},
pmid = {27287429},
issn = {1471-2458},
support = {MC_PC_13081/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Asian People ; *Behavior Therapy ; Feasibility Studies ; Female ; *Health Behavior ; Humans ; Interviews as Topic ; Male ; Pakistan ; *Patient Compliance ; Self Efficacy ; Tobacco Use Cessation/*psychology ; United Kingdom ; },
abstract = {BACKGROUND: People of South Asian-origin are responsible for more than three-quarters of all the smokeless tobacco (SLT) consumption worldwide; yet there is little evidence on the effect of SLT cessation interventions in this population. South Asians use highly addictive and hazardous SLT products that have a strong socio-cultural dimension. We designed a bespoke behaviour change intervention (BCI) to support South Asians in quitting SLT and then evaluated its feasibility in Pakistan and in the UK.<br><br>METHODS: We conducted two literature reviews to identify determinants of SLT use among South Asians and behaviour change techniques (BCTs) likely to modify these, respectively. Iterative consensus development workshops helped in selecting potent BCTs for BCI and designing activities and materials to deliver these. We piloted the BCI in 32 SLT users. All BCI sessions were audiotaped and analysed for adherence to intervention content and the quality of interaction (fidelity index). In-depth interviews with16 participants and five advisors assessed acceptability and feasibility of delivering the BCI, respectively. Quit success was assessed at 6 months by saliva/urine cotinine.<br><br>RESULTS: The BCI included 23 activities and an interactive pictorial resource that supported these. Activities included raising awareness of the harms of SLT use and benefits of quitting, boosting clients' motivation and self-efficacy, and developing strategies to manage their triggers, withdrawal symptoms, and relapse should that occur. Betel quid and Guthka were the common forms of SLT used. Pakistani clients were more SLT dependent than those in the UK. Out of 32, four participants had undetectable cotinine at 6 months. Fidelity scores for each site varied between 11.2 and 42.6 for adherence to content - maximum score achievable 44; and between 1.4 and 14 for the quality of interaction - maximum score achievable was 14. Interviews with advisors highlighted the need for additional training on BCTs, integrating nicotine replacement and reducing duration of the pre-quit session. Clients were receptive to health messages but most reported SLT reduction rather than complete cessation.<br><br>CONCLUSION: We developed a theory-based BCI that was also acceptable and feasible to deliver with moderate fidelity scores. It now needs to be evaluated in an effectiveness trial.},
}
@article {pmid27282228,
year = {2016},
author = {Liang, S and Choi, KS and Qin, J and Pang, WM and Wang, Q and Heng, PA},
title = {Improving the discrimination of hand motor imagery via virtual reality based visual guidance.},
journal = {Computer methods and programs in biomedicine},
volume = {132},
number = {},
pages = {63-74},
doi = {10.1016/j.cmpb.2016.04.023},
pmid = {27282228},
issn = {1872-7565},
mesh = {Adult ; Female ; Hand/*physiology ; Humans ; Male ; *Psychomotor Performance ; *User-Computer Interface ; },
abstract = {While research on the brain-computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications.},
}
@article {pmid27280400,
year = {2016},
author = {, },
title = {Correction: Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications.},
journal = {PloS one},
volume = {11},
number = {6},
pages = {e0157581},
pmid = {27280400},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0137910.].},
}
@article {pmid27276551,
year = {2016},
author = {Held, C and Sadowski, G},
title = {Thermodynamics of Bioreactions.},
journal = {Annual review of chemical and biomolecular engineering},
volume = {7},
number = {},
pages = {395-414},
doi = {10.1146/annurev-chembioeng-080615-034704},
pmid = {27276551},
issn = {1947-5446},
mesh = {Biocatalysis ; Electrolytes/chemistry ; Enzymes/chemistry/*metabolism ; Hydrogen-Ion Concentration ; Kinetics ; Solvents/chemistry ; Temperature ; Thermodynamics ; },
abstract = {Thermodynamic principles have been applied to enzyme-catalyzed reactions since the beginning of the 1930s in an attempt to understand metabolic pathways. Currently, thermodynamics is also applied to the design and analysis of biotechnological processes. The key thermodynamic quantity is the Gibbs energy of reaction, which must be negative for a reaction to occur spontaneously. However, the application of thermodynamic feasibility studies sometimes yields positive Gibbs energies of reaction even for reactions that are known to occur spontaneously, such as glycolysis. This article reviews the application of thermodynamics in enzyme-catalyzed reactions. It summarizes the basic thermodynamic relationships used for describing the Gibbs energy of reaction and also refers to the nonuniform application of these relationships in the literature. The review summarizes state-of-the-art approaches that describe the influence of temperature, pH, electrolytes, solvents, and concentrations of reacting agents on the Gibbs energy of reaction and, therefore, on the feasibility and yield of biological reactions.},
}
@article {pmid27275376,
year = {2016},
author = {Huang, M and Daly, I and Jin, J and Zhang, Y and Wang, X and Cichocki, A},
title = {An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps.},
journal = {Cognitive neurodynamics},
volume = {10},
number = {3},
pages = {201-209},
pmid = {27275376},
issn = {1871-4080},
abstract = {Visual brain-computer interfaces (BCIs) are not suitable for people who cannot reliably maintain their eye gaze. Considering that this group usually maintains audition, an auditory based BCI may be a good choice for them. In this paper, we explore two auditory patterns: (1) a pattern utilizing symmetrical spatial cues with multiple frequency beeps [called the high low medium (HLM) pattern], and (2) a pattern utilizing non-symmetrical spatial cues with six tones derived from the diatonic scale [called the diatonic scale (DS) pattern]. These two patterns are compared to each other in terms of accuracy to determine which auditory pattern is better. The HLM pattern uses three different frequency beeps and has a symmetrical spatial distribution. The DS pattern uses six spoken stimuli, which are six notes solmizated as "do", "re", "mi", "fa", "sol" and "la", and derived from the diatonic scale. These six sounds are distributed to six, spatially distributed, speakers. Thus, we compare a BCI paradigm using beeps with another BCI paradigm using tones on the diatonic scale, when the stimuli are spatially distributed. Although no significant differences are found between the ERPs, the HLM pattern performs better than the DS pattern: the online accuracy achieved with the HLM pattern is significantly higher than that achieved with the DS pattern (p = 0.0028).},
}
@article {pmid27273682,
year = {2016},
author = {Livi, F and Ndoro, S and Caird, J and Crimmins, D},
title = {Indirect cavernous carotid fistula in a 12-year-old girl.},
journal = {Journal of surgical case reports},
volume = {2016},
number = {6},
pages = {},
pmid = {27273682},
issn = {2042-8812},
abstract = {We present a very rare case of indirect cavernous carotid fistula (CCF) in a 12-year-old girl. Indirect CCF is extremely rare in the paediatric population. A 12-year-old girl presented with a 7-month history of frontal headaches and intermittent left-sided proptosis. On examination, she had dilated and engorged scleral veins on the left eye, mild dysdiadochokinesia and past pointing on the left side. A brain computer tomography with contrast, brain magnetic resonance imaging (MRI) and interventional radiography (IR) cerebral angiogram confirmed the diagnosis of CCF. The CCF was embolized and a follow-up brain MRI and an IR cerebral angiogram were conducted over the course of 8 months that revealed no evidence of residual CCF. CCF, though rare in the paediatric population, should be highly considered in the differential diagnosis when dilated scleral veins, proptosis and dysdiadokinesis are present in the clinical setting. Prompt treatment has good prognostic results.},
}
@article {pmid27272616,
year = {2016},
author = {Sepulveda, P and Sitaram, R and Rana, M and Montalba, C and Tejos, C and Ruiz, S},
title = {How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI.},
journal = {Human brain mapping},
volume = {37},
number = {9},
pages = {3153-3171},
pmid = {27272616},
issn = {1097-0193},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain Mapping ; Humans ; Imagery, Psychotherapy/*methods ; Learning/*physiology ; Magnetic Resonance Imaging ; Male ; Neurofeedback/*methods ; *Reward ; Young Adult ; },
abstract = {The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.},
}
@article {pmid27265671,
year = {2017},
author = {Fry, CH and Gammie, A and Drake, MJ and Abrams, P and Kitney, DG and Vahabi, B},
title = {Estimation of bladder contractility from intravesical pressure-volume measurements.},
journal = {Neurourology and urodynamics},
volume = {36},
number = {4},
pages = {1009-1014},
pmid = {27265671},
issn = {1520-6777},
support = {R01 DK098361/DK/NIDDK NIH HHS/United States ; },
mesh = {Adult ; Aged ; Biomechanical Phenomena ; Female ; Humans ; Male ; Middle Aged ; Muscle Contraction/*physiology ; Muscle, Smooth/*physiology ; Pressure ; Urinary Bladder/*physiology ; Urination/*physiology ; *Urodynamics ; },
abstract = {AIMS: To describe parameters from urodynamic pressure recordings that describe urinary bladder contractility through the use of principles of muscle mechanics.<br><br>METHODS: Subtracted detrusor pressure and voided flow were recorded from patients undergoing filling cystometry. The isovolumetric increase of detrusor pressure, P, of a voluntary bladder contraction before voiding was used to generate a plot of (dP/dt)/P versus P. Extrapolation of the plot to the y-axis and the x-axis generated a contractility parameter, vCE (the maximum rate of pressure development) and the maximum isovolumetric pressure, P0 , respectively. Similar curves were obtained in ex vivo pig bladders with different concentrations of the inotropic agent carbachol and shown in a supplement.<br><br>RESULTS: Values of vCE , but not P0 , diminished with age in female subjects. vCE was most significantly associated with the 20-80% duration of isovolumetric contraction t20-80 ; and a weaker association with maximum flow rate and BCI in women. P0 was not associated with any urodynamic variable in women, but in men was with t20-80 and isovolumetric pressure indices.<br><br>CONCLUSIONS: The rate of isovolumetric subtracted detrusor pressure (t20-80) increase shows a very significant association with indices of bladder contractility as derived from a derived force-velocity curve. We propose that t20-80 is a detrusor contractility parameter (DCP). Neurourol. Urodynam. 36:1009-1014, 2017. &#xa9; 2016 Wiley Periodicals, Inc.},
}
@article {pmid27259085,
year = {2016},
author = {Liang, S and Choi, KS and Qin, J and Wang, Q and Pang, WM and Heng, PA},
title = {Discrimination of motor imagery tasks via information flow pattern of brain connectivity.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {24 Suppl 2},
number = {},
pages = {S795-801},
doi = {10.3233/THC-161212},
pmid = {27259085},
issn = {1878-7401},
mesh = {Algorithms ; Electroencephalography ; Humans ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {BACKGROUND: The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain.<br><br>OBJECTIVE: This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks.<br><br>METHODS: The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern.<br><br>RESULTS: Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs).<br><br>CONCLUSION: The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).},
}
@article {pmid27257872,
year = {2016},
author = {, },
title = {Correction: Exploring Combinations of Auditory and Visual Stimuli for Gaze-Independent Brain-Computer Interfaces.},
journal = {PloS one},
volume = {11},
number = {6},
pages = {e0157284},
pmid = {27257872},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0111070.].},
}
@article {pmid27255798,
year = {2016},
author = {Xu, F and Zhou, W and Zhen, Y and Yuan, Q and Wu, Q},
title = {Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.},
journal = {International journal of neural systems},
volume = {26},
number = {6},
pages = {1650022},
doi = {10.1142/S0129065716500222},
pmid = {27255798},
issn = {1793-6462},
mesh = {Brain-Computer Interfaces ; Datasets as Topic ; Electrocorticography/*methods ; Epilepsies, Partial/physiopathology/surgery ; Fingers/physiology ; *Fractals ; Functional Laterality ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; Motor Cortex/*physiology/physiopathology/surgery ; Neuropsychological Tests ; Pattern Recognition, Automated/methods ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; Time Factors ; Tongue/physiology ; },
abstract = {The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.},
}
@article {pmid27254871,
year = {2017},
author = {Wang, YT and Nakanishi, M and Wang, Y and Wei, CS and Cheng, CK and Jung, TP},
title = {An Online Brain-Computer Interface Based on SSVEPs Measured From Non-Hair-Bearing Areas.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {1},
pages = {11-18},
doi = {10.1109/TNSRE.2016.2573819},
pmid = {27254871},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Visual/*physiology ; Hair ; Humans ; Male ; Online Systems ; Photic Stimulation/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.},
}
@article {pmid27253616,
year = {2016},
author = {Luo, J and Feng, Z and Zhang, J and Lu, N},
title = {Dynamic frequency feature selection based approach for classification of motor imageries.},
journal = {Computers in biology and medicine},
volume = {75},
number = {},
pages = {45-53},
doi = {10.1016/j.compbiomed.2016.03.004},
pmid = {27253616},
issn = {1879-0534},
mesh = {*Algorithms ; Electroencephalography/*methods ; Electronic Data Processing/*methods ; Female ; Humans ; Male ; *Software ; },
abstract = {Electroencephalography (EEG) is one of the most popular techniques to record the brain activities such as motor imagery, which is of low signal-to-noise ratio and could lead to high classification error. Therefore, selection of the most discriminative features could be crucial to improve the classification performance. However, the traditional feature selection methods employed in brain-computer interface (BCI) field (e.g. Mutual Information-based Best Individual Feature (MIBIF), Mutual Information-based Rough Set Reduction (MIRSR) and cross-validation) mainly focus on the overall performance on all the trials in the training set, and thus may have very poor performance on some specific samples, which is not acceptable. To address this problem, a novel sequential forward feature selection approach called Dynamic Frequency Feature Selection (DFFS) is proposed in this paper. The DFFS method emphasized the importance of the samples that got misclassified while only pursuing high overall classification performance. In the DFFS based classification scheme, the EEG data was first transformed to frequency domain using Wavelet Packet Decomposition (WPD), which is then employed as the candidate set for further discriminatory feature selection. The features are selected one by one in a boosting manner. After one feature being selected, the importance of the correctly classified samples based on the feature will be decreased, which is equivalent to increasing the importance of the misclassified samples. Therefore, a complement feature to the current features could be selected in the next run. The selected features are then fed to a classifier trained by random forest algorithm. Finally, a time series voting-based method is utilized to improve the classification performance. Comparisons between the DFFS-based approach and state-of-art methods on BCI competition IV data set 2b have been conducted, which have shown the superiority of the proposed algorithm.},
}
@article {pmid27252637,
year = {2016},
author = {Naseer, N and Noori, FM and Qureshi, NK and Hong, KS},
title = {Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {237},
pmid = {27252637},
issn = {1662-5161},
abstract = {In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.},
}
@article {pmid28626356,
year = {2016},
author = {Bataineh, M and McNiel, D and Choi, J and Hessburg, J and Francis, J},
title = {Pilot Study for Grip Force Prediction Using Neural Signals from Different Brain Regions.},
journal = {Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference},
volume = {2016},
number = {},
pages = {19-20},
pmid = {28626356},
issn = {1086-4105},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
abstract = {The design of brain machine interfaces (BMI) has been improving over the past decade. Such improvements have led to advanced capability in terms of restoring the functionality of a paralyzed/amputated limb and producing fine controlled movements of a robotic arm and hand. However, there is still more to be invested towards producing advanced BMI features such as producing appropriate forces when gripping and carrying an object using an artificial limb. This feature requires direct supervision and control from the brain to produce accurate results. Toward this goal, this work investigates the processing of neural signals from four brain regions in a nonhuman primate to predict maximum grip force. The signals received from each of the primary motor (M1) cortex, primary somatosensory (S1) cortex, dorsal premotor (PmD) cortex, and ventral premotor (PmV) cortex are used to build regression models to predict the applied maximum grip force. Comparisons of model prediction results are presented. The relative prediction accuracy from all brain regions would assist in further investigation to build robust approaches for controlling the force values. The brain regions and their interactions could eventually be summed in a weighted manner to complete the targeted approach.},
}
@article {pmid28626355,
year = {2016},
author = {McNiel, D and Bataineh, M and Choi, J and Hessburg, J and Francis, J},
title = {Classifier Performance in Primary Somatosensory Cortex Towards Implementation of a Reinforcement Learning Based Brain Machine Interface.},
journal = {Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference},
volume = {2016},
number = {},
pages = {17-18},
pmid = {28626355},
issn = {1086-4105},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
abstract = {Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist in cortical structures simultaneously used for BMI control. This work evaluates the ability of several common classifiers to detect impending reward delivery within primary somatosensory (S1) cortex during a grip force match to sample task performed by a nonhuman primate. The accuracy of these classifiers was further evaluated over a range of conditions to identify parameters that provide maximum classification accuracy. S1 cortex was found to provide highly accurate classification of the reinforcement signal across many classifiers and a wide variety of data input parameters. The classification accuracy in S1 cortex between rewarding and non-rewarding trials was apparent when the animal was expecting an impending delivery or an impending withholding of reward following trial completion. The high accuracy of classification in S1 cortex can be used to adapt an RL based BMI towards a user's intent. Real-time implementation of these classifiers in an RL based BMI could be used to adapt control of a prosthesis dynamically to match the intent of its user.},
}
@article {pmid28556642,
year = {2016},
author = {Azieva, AZ and Dudanov, IP and Kozlov, KL and Ordynets, SV and Abuazab, BS and Stafeeva, IV and Shakhgirieva, MR},
title = {[Bilateral stenoses of carotids. The surgical treatment in acute period of ischemic stroke in patients of different age groups].},
journal = {Advances in gerontology = Uspekhi gerontologii},
volume = {29},
number = {5},
pages = {737-741},
pmid = {28556642},
issn = {1561-9125},
mesh = {Aged ; *Brain Ischemia/diagnosis/etiology/surgery ; *Carotid Stenosis/diagnostic imaging/surgery ; Cerebrovascular Circulation ; *Endarterectomy, Carotid/adverse effects/methods ; Female ; Humans ; Male ; Middle Aged ; Outcome and Process Assessment, Health Care ; Perfusion Imaging/methods ; Risk Adjustment ; Risk Factors ; Severity of Illness Index ; Tomography, X-Ray Computed/methods ; Ultrasonography, Doppler, Duplex/methods ; },
abstract = {The study included 138 patients who had 193 reconstructive operations on carotid arteries (CA) in the acute period of ischemic stroke. The 1st group included 22 patients with bilateral stenosis, age under 60 years; the 2nd group - 33 patients with a bilateral stenosis at the age more than 60 years; the 3rd group consisted of 83 patients with bilateral stenosis of the ipsilateral CA were operated in an acute period of ischemic stroke, but with contraindications and/or refused the surgery on the contralateral CA. Brain computer tomography, ultrasound duplex scanning of the CA with the assessment degree of stenosis and structure of plaques, also MSCT-AG of the head and neck vessels were performed for all patients. Cerebral oximeter was used for assessing adequate cerebral perfusion during cross-clamping of the CA. Indications for the installation of a temporary intraluminal shunt during cross-clamping of the CA performed according to the method.},
}
@article {pmid28325008,
year = {2016},
author = {Wang, PT and Gandasetiawan, K and McCrimmon, CM and Karimi-Bidhendi, A and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {Feasibility of an ultra-low power digital signal processor platform as a basis for a fully implantable brain-computer interface system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {4491-4494},
pmid = {28325008},
issn = {2694-0604},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electrocorticography ; *Electrodes, Implanted ; Fourier Analysis ; Humans ; Male ; Principal Component Analysis ; Signal Processing, Computer-Assisted/*instrumentation ; Spinal Cord Injuries/*therapy ; Young Adult ; },
abstract = {A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.},
}
@article {pmid28324971,
year = {2016},
author = {McCrimmon, CM and Ming Wang, and Silva Lopes, L and Wang, PT and Karimi-Bidhendi, A and Liu, CY and Heydari, P and Nenadic, Z and Do, AH},
title = {A small, portable, battery-powered brain-computer interface system for motor rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {2776-2779},
pmid = {28324971},
issn = {2694-0604},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Cost-Benefit Analysis ; *Electric Power Supplies ; Electroencephalography ; *Equipment Design ; Humans ; Recovery of Function ; Spinal Cord Injuries/*rehabilitation ; Stroke ; *Stroke Rehabilitation ; },
abstract = {Motor rehabilitation using brain-computer interface (BCI) systems may facilitate functional recovery in individuals after stroke or spinal cord injury. Nevertheless, these systems are typically ill-suited for widespread adoption due to their size, cost, and complexity. In this paper, a small, portable, and extremely cost-efficient (<;$200) BCI system has been developed using a custom electroencephalographic (EEG) amplifier array, and a commercial microcontroller and touchscreen. The system's performance was tested using a movement-related BCI task in 3 able-bodied subjects with minimal previous BCI experience. Specifically, subjects were instructed to alternate between relaxing and dorsiflexing their right foot, while their EEG was acquired and analyzed in real-time by the BCI system to decode their underlying movement state. The EEG signals acquired by the custom amplifier array were similar to those acquired by a commercial amplifier (maximum correlation coefficient ρ=0.85). During real-time BCI operation, the average correlation between instructional cues and decoded BCI states across all subjects (ρ=0.70) was comparable to that of full-size BCI systems. Small, portable, and inexpensive BCI systems such as the one reported here may promote a widespread adoption of BCI-based movement rehabilitation devices in stroke and spinal cord injury populations.},
}
@article {pmid28324945,
year = {2016},
author = {Isaksen, J and Mohebbi, A and Puthusserypady, S},
title = {A comparative study of pseudorandom sequences used in a c-VEP based BCI for online wheelchair control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2016},
number = {},
pages = {1512-1515},
doi = {10.1109/EMBC.2016.7590997},
pmid = {28324945},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; *Wheelchairs ; },
abstract = {In this study, a c-VEP based BCI system was developed to run on three distinctive pseudorandom sequences, namely the m-code, the Gold-code, and the Barker-code. The Visual Evoked Potentials (VEPs) were provoked using these codes. In the online session, subjects controlled a LEGO[®] Mindstorms[®] robot around a fixed track. Choosing the optimal code proved a significant increase in accuracy (p<;0.00001) over the average performance. No single code proved significantly more accurate than the others (p=0.81), suggesting that the term "optimal code" is subject-dependent. However, the Gold-code was significantly faster than both alternatives (p=0.006, p=0.016). When choosing the optimal code for accuracy, no significant decrease in Time Per Identification (TPI) was found (p=0.67). Thus, when creating an online c-VEP based BCI system, it is recommended to use multiple random sequences for increased performance.},
}
@article {pmid28261630,
year = {2016},
author = {Huggins, JE and Alcaide-Aguirre, RE and Hill, K},
title = {Effects of text generation on P300 brain-computer interface performance.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {3},
number = {2},
pages = {112-120},
pmid = {28261630},
issn = {2326-263X},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8)=2.59 p=0.0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7)=-2.68, p=0.0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.},
}
@article {pmid27713915,
year = {2015},
author = {Luu, TP and He, Y and Brown, S and Nakagome, S and Contreras-Vidal, JL},
title = {A Closed-loop Brain Computer Interface to a Virtual Reality Avatar: Gait Adaptation to Visual Kinematic Perturbations.},
journal = {... International Conference on Virtual Rehabilitation. International Conference on Virtual Rehabilitation},
volume = {2015},
number = {},
pages = {30-37},
pmid = {27713915},
issn = {2331-9542},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; //United States NINDS/ ; },
abstract = {The control of human bipedal locomotion is of great interest to the field of lower-body brain computer interfaces (BCIs) for rehabilitation of gait. While the feasibility of a closed-loop BCI system for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a virtual reality (BCI-VR) environment has yet to be demonstrated. In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control the walking movements of a virtual avatar. Moreover, virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days. Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. These findings have implications for the development of BCI-VR systems for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI system.},
}
@article {pmid28435796,
year = {2014},
author = {Sanghvi, GV and Ghevariya, D and Gosai, S and Langa, R and Dhaduk, N and Kunjadia, PD and Vaishnav, DJ and Dave, GS},
title = {Isolation and partial purification of erythromycin from alkaliphilic Streptomyces werraensis isolated from Rajkot, India.},
journal = {Biotechnology reports (Amsterdam, Netherlands)},
volume = {1-2},
number = {},
pages = {2-7},
pmid = {28435796},
issn = {2215-017X},
abstract = {An alkaliphilic actinomycete, BCI-1, was isolated from soil samples collected from Saurashtra University campus, Gujarat. Isolated strain was identified as Streptomyces werraensis based on morphological, biochemical and phylogenetic analysis. Maximum antibiotic production was obtained in media containing sucrose 2%, Yeast extract 1.5%, and NaCl 2.5% at pH 9.0 for 7 days at 30 °C. Maximum inhibitory compound was produced at pH 9 and at 30 °C. FTIR revealed imine, amine, alkane (C[bond, double bond]C) of aromatic ring and p-di substituted benzene, whereas HPLC analysis of partially purified compound and library search confirmed 95% peaks matches with erythromycin. Chloroform extracted isolated compound showed MIC values 1 μg/ml against Bacillus subtilis, ≤0.5 μg/ml against Staphylococcus aureus, ≤0.5 μg/ml against Escherichia coli and 2.0 μg/ml against Serretia GSD2 sp., which is more effective in comparison to ehtylacetate and methanol extracted compounds. The study holds significance as only few alkaliphilic actinomycetes have been explored for their antimicrobial potential.},
}
@article {pmid27429129,
year = {2013},
author = {Wolbring, G and Diep, L and Yumakulov, S and Ball, N and Leopatra, V and Yergens, D},
title = {Emerging Therapeutic Enhancement Enabling Health Technologies and Their Discourses: What Is Discussed within the Health Domain?.},
journal = {Healthcare (Basel, Switzerland)},
volume = {1},
number = {1},
pages = {20-52},
pmid = {27429129},
issn = {2227-9032},
abstract = {So far, the very meaning of health and therefore, treatment and rehabilitation is benchmarked to the normal or species-typical body. We expect certain abilities in members of a species; we expect humans to walk but not to fly, but a bird we expect to fly. However, increasingly therapeutic interventions have the potential to give recipients beyond species-typical body related abilities (therapeutic enhancements, TE). We believe that the perfect storm of TE, the shift in ability expectations toward beyond species-typical body abilities, and the increasing desire of health consumers to shape the health system will increasingly influence various aspects of health care practice, policy, and scholarship. We employed qualitative and quantitative methods to investigate among others how human enhancement, neuro/cognitive enhancement, brain machine interfaces, and social robot discourses cover (a) healthcare, healthcare policy, and healthcare ethics, (b) disability and (c) health consumers and how visible various assessment fields are within Neuro/Cogno/ Human enhancement and within the BMI and social robotics discourse. We found that health care, as such, is little discussed, as are health care policy and ethics; that the term consumers (but not health consumers) is used; that technology, impact and needs assessment is absent; and that the imagery of disabled people is primarily a medical one. We submit that now, at this early stage, is the time to gain a good understanding of what drives the push for the enhancement agenda and enhancement-enabling devices, and the dynamics around acceptance and diffusion of therapeutic enhancements.},
}
@article {pmid27873894,
year = {2008},
author = {HajjHassan, M and Chodavarapu, V and Musallam, S},
title = {NeuroMEMS: Neural Probe Microtechnologies.},
journal = {Sensors (Basel, Switzerland)},
volume = {8},
number = {10},
pages = {6704-6726},
pmid = {27873894},
issn = {1424-8220},
abstract = {Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.},
}
@article {pmid28307577,
year = {1997},
author = {Fincke, OM and Yanoviak, SP and Hanschu, RD},
title = {Predation by odonates depresses mosquito abundance in water-filled tree holes in Panama.},
journal = {Oecologia},
volume = {112},
number = {2},
pages = {244-253},
doi = {10.1007/s004420050307},
pmid = {28307577},
issn = {1432-1939},
abstract = {In the lowland moist forest of Barro Colorado Island (BCI), Panama, larvae of four common species of odonates, a mosquito, and a tadpole are the major predators in water-filled tree holes. Mosquito larvae are their most common prey. Holes colonized naturally by predators and prey had lower densities of mosquitoes if odonates were present than if they were absent. Using artificial tree holes placed in the field, we tested the effects of odonates on their mosquito prey while controlling for the quantity and species of predator, hole volume, and nutrient input. In large and small holes with low nutrient input, odonates depressed the number of mosquitoes present and the number that survived to pupation. Increasing nutrient input (and consequently, mosquito abundance) to abnormally high levels dampened the effect of predation when odonates were relatively small. However, the predators grew faster with higher nutrients, and large larvae in all three genera reduced the number of mosquitoes surviving to pupation, even though the abundance of mosquito larvae remained high. Size-selective predation by the odonates is a likely explanation for this result; large mosquito larvae were less abundant in the predator treatment than in the controls. Because species assemblages were similar between natural and artificial tree holes, our results suggest that odonates are keystone species in tree holes on BCI, where they are the most common large predators.},
}
@article {pmid28314040,
year = {1993},
author = {Forget, PM},
title = {Post-dispersal predation and scatterhoarding of Dipteryx panamensis (Papilionaceae) seeds by rodents in Panama.},
journal = {Oecologia},
volume = {94},
number = {2},
pages = {255-261},
pmid = {28314040},
issn = {1432-1939},
abstract = {In tropical rain forests of Central America, the canopy tree Dipteryx panamensis (Papilionaceae) fruits when overall fruit biomass is low for mammals. Flying and arboreal consumers feed on D. panamensis and drop seeds under the parent or disperse them farther away. Seeds on the ground attract many vertebrate seed-eaters, some of them potential secondary seed dispersers. The fate of seeds artificially distributed to simulate bat dispersal was studied in relation to fruitfall periodicity and the visiting frequency of diurnal rodents at Barro Colorado Island (BCI), Panama. The frequency of visits by agoutis is very high at the beginning of fruitfall, but in the area close (<50 m) to fruiting trees (Dipteryx-rich area) it declines throughout fruiting, whereas it remains unchanged farther (>50 m) away (Dipteryx-poor and Gustavia-rich area). Squirrels were usually observed in the Dipteryx-rich area. Along with intense post-dispersal seed predation by rodents in the Dipteryx-rich area, a significant proportion of seeds were cached by rodents in the Dipteryx-poor area. Post-dispersal seed predation rate was inversely related to hoarding rate. A significantly greater proportion of seeds was cached in March, especially more than 100 m from the nearest fruiting tree. This correlates with the mid-fruiting period, i.e. during the height of D. panamensis fruiting, when rodents seem to be temporarily satiated with the food supply at parent trees. Hoarding remained high toward April, i.e. late in the fruiting season of D. panamensis. Low survival of scatterhoarded seeds suggests that the alternative food supply over the animal's home-ranges in May-June 1990 was too low to promote survival of cached seeds. Seedlings are assumed to establish in the less-used area of the rodents' home-range when overall food supply is sufficient to satiate post-dispersal predators.},
}
@article {pmid28312142,
year = {1991},
author = {Mulkey, SS and Smith, AP and Wright, SJ},
title = {Comparative life history and physiology of two understory Neotropical herbs.},
journal = {Oecologia},
volume = {88},
number = {2},
pages = {263-273},
pmid = {28312142},
issn = {1432-1939},
abstract = {Demography and physiology of two broad-leaved understory tropical herbs (Marantaceae) were studied in gaps and shaded understory in large-scale irrigated and control treatments during the dry season at Barro Colorado Island (BCI), Panama. Because photosynthetic acclimation potential may not predict light environments where tropical species are found, we studied a suite of physiological features to determine if they uniquely reflect the distribution of each species. Calathea inocephala and Pleiostachya pruinosa grow and reproduce in gaps, persist in shade, and have equivalent rates of leaf production. Calathea leaves survived 2 to 3 times as long as leaves of Pleiostachya and plants of Pleiostachya were 6 to 8 times more likely to die as plants of Calathea during 3.5 years of study. Pleiostachya had lowest survival in shade and when not irrigated during the dry season, while Calathea survived well in both habitats and both treatments. Pleiostachya had higher photosynthetic capacity and stomatal conductance than Calathea and acclimated to gaps by producing leaves with higher photosynthetic capacity. Calathea had lower mesophyll CO2 concentrations than Pleiostachya. Both species had similar dark respiration rates and light compensation points, and water-use and nitrogen-use efficiencies were inversely related between species. Species showed no differences in leaf osmotic potentials at full turgor. Calathea roots were deeper and had tuberous swellings.Leaf-level assimilation and potential water loss are consistent with where these species are found, but photosynthetic acclimation to high light does not reflect both species' abilities to grow and reproduce in gaps. Pleiostachya's gap-dependent, rapid growth and reproduction require high rates of carbon gain in short-lived leaves, which can amortize their cost quickly. High rates of water loss are associated with reduced longevity during drought. Calathea's roots may confer greater capacitance, while its leaves are durable, long-lived and have lower water loss, permitting persistence long after gap closure.},
}
@article {pmid28312826,
year = {1989},
author = {Wolda, H},
title = {Seasonal cues in tropical organisms. Rainfall? Not necessarily!.},
journal = {Oecologia},
volume = {80},
number = {4},
pages = {437-442},
pmid = {28312826},
issn = {1432-1939},
abstract = {Activity seasons of tropical organisms often start, on the average, at or about the beginning of the rainy or dry seasons. The hypothesis that the onset or cessation of the wet season provides the seasonal cues necessary of the initiation of the activity season of some tropical organisms is tested with data on Panamanian cicadas. Seasonal adult activity patterns are described for cicada species in Panama, mostly from Barro Colorado Island (BCI), some from Las Cumbres. In all species the correlation between the timing of the beginning and the end of the cicada season was low and not significant, so that the actual beginning data of a cicada season in a particular year had little or no predictive value for the end date. Seven out of eleven species on BCI started their average activity season at the average beginning of the dry season (one species) or rainy season (six species). Nevertheless, in 13 years, correlations between the start or end of the cicada seasons and that of the meteorological seasons were low and not significant. At best, the beginning and end of the rains played a minor role as seasonal cues governing cicada emergence or the termination of the cicada season. It is speculated that photoperiod might be a major seasonal cue governing emergence, through its effects on the host plants.},
}
@article {pmid28312898,
year = {1987},
author = {Harrison, S},
title = {Treefall gaps versus forest understory as environments for a defoliating moth on a tropical forest shrub.},
journal = {Oecologia},
volume = {72},
number = {1},
pages = {65-68},
pmid = {28312898},
issn = {1432-1939},
abstract = {The moth Zunacetha annulata (Dioptidae) is a specialist on the understory shrub Hybanthus prunifolius (Violaceae) in the forest of Barro Colorado Island (BCI), Panama. The larvae, which are capable of defoliating entire shrubs, concentrate their attack upon the small minority of H. prunifolius individuals that grow in treefall gaps. Field experiments demonstrated that larval growth rates were 37% higher, and weights at pupation 25% higher, on shrubs in gaps than on shrubs in the understory. In a common environment in the laboratory, growth rates of larvae were 23% higher on foliage taken from shrubs in gaps than on foliage from shrubs in the understory.However, larvae grown in a temperature regime simulating that of gaps did not grow faster than larvae in an understory regime, when the two groups were reared in growth chambers on foliage taken from the same shrubs. In the field, predation appeared higher in gaps: experimental groups of larvae survived at rates of 65% per day in gaps and 78% per day in the understory. Quality of foliage, and not direct effects of the environment, appears to be responsible for the observed pattern of defoliation by this moth.},
}
@article {pmid28311625,
year = {1986},
author = {Greenberg, R and Gradwohl, J},
title = {Constant density and stable territoriality in some tropical insectivorous birds.},
journal = {Oecologia},
volume = {69},
number = {4},
pages = {618-625},
pmid = {28311625},
issn = {1432-1939},
abstract = {Four species of understory antbirds (Formicariidae: Myrmotherula fulviventris, M. axillaris, Microrhopias quixensis, and Thamnophilus punctatus) had stable populations over eight rainy seasons on Barro Colorado Island, Panama. The co-defended territories of M. fulviventris and Microrhopias quixensis, were essentially identical from year to year on our intensive study site, despite a moderate turnover of territory owners. The location of the territories of T. punctatus was also similar between years. This stability occurred in the face of considerable annual variation in the survivorship of adult M. fulviventris and T. punctatus. This variation was not significantly correlated with patterns of rainfall. Stable territoriality has rarely been reported from relatively-short-lived insectivorous birds. The annual production of young was significantly variable only in M. axillaris. Because BCI is an island comprised of one habitat (tropical forest) and so supports a closed population of antbirds, and because it is unlikely that natality equaled mortality on our study site during the entire eight years of the study, we suggest that these breeding populations are socially regulated at a constant level below the limits directly set by food supply.},
}
@article {pmid29010919,
year = {1932},
author = {Banker, SS},
title = {A Plea for the Use of Suction.},
journal = {The Indian medical gazette},
volume = {67},
number = {1},
pages = {18-21},
pmid = {29010919},
issn = {0019-5863},
}
@article {pmid29006876,
year = {1919},
author = {Eadon-Clarke, C},
title = {Surgical Treatment of Tubercular Abscesses.},
journal = {The Indian medical gazette},
volume = {54},
number = {6},
pages = {235},
pmid = {29006876},
issn = {0019-5863},
}
@article {pmid26513804,
year = {2016},
author = {Zeng, H and Song, A},
title = {Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {27},
number = {11},
pages = {2301-2313},
doi = {10.1109/TNNLS.2015.2475618},
pmid = {26513804},
issn = {2162-2388},
abstract = {In addition to the noisy and limited spatial resolution characteristics of the electroencephalography (EEG) signal, the intrinsic nonstationarity in the EEG data makes the single-trial EEG classification an even more challenging problem in brain-computer interface (BCI). Variations of the signal properties within a session often result in deteriorated classification performance. This is mainly attributed to the reason that the routine feature extraction or classification method does not take the changes in the signal into account. Although several extensions to the standard feature extraction method have been proposed to reduce the sensitivity to nonstationarity in data, they optimize different objective functions from that of the subsequent classification model, and thereby, the extracted features may not be optimized for the classification. In this paper, we propose an approach that directly optimizes the classifier's discriminativity and robustness against the within-session nonstationarity of the EEG data through a single optimization paradigm, and show that it can greatly improve the performance, in particular for the subjects who have difficulty in controlling a BCI. Moreover, the experimental results on two benchmark data sets demonstrate that our approach significantly outperforms the compared approaches in reducing classification error rates.},
}
@article {pmid26415189,
year = {2016},
author = {Zhang, Y and Zhou, G and Jin, J and Zhao, Q and Wang, X and Cichocki, A},
title = {Sparse Bayesian Classification of EEG for Brain-Computer Interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {27},
number = {11},
pages = {2256-2267},
doi = {10.1109/TNNLS.2015.2476656},
pmid = {26415189},
issn = {2162-2388},
mesh = {Algorithms ; *Bayes Theorem ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Neural Networks, Computer ; },
abstract = {Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount of training data is required from the user and 2) it takes a relatively long time to calibrate the classifier. These limitations substantially deteriorate the system's practicability and may cause a user to be reluctant to use BCIs. In this paper, we introduce a sparse Bayesian method by exploiting Laplace priors, namely, SBLaplace, for EEG classification. A sparse discriminant vector is learned with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework. All required model parameters are automatically estimated from training data without the need of CV. Extensive comparisons are carried out between the SBLaplace algorithm and several other competing methods based on two EEG data sets. The experimental results demonstrate that the SBLaplace algorithm achieves better overall performance than the competing algorithms for EEG classification.},
}
@article {pmid26906278,
year = {2016},
author = {Gomez-Pilar, J and Corralejo, R and Nicolas-Alonso, LF and Álvarez, D and Hornero, R},
title = {Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {54},
number = {11},
pages = {1655-1666},
pmid = {26906278},
issn = {1741-0444},
mesh = {Aged ; Aged, 80 and over ; *Brain-Computer Interfaces ; Case-Control Studies ; Cognition/*physiology ; *Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Middle Aged ; Motor Activity/*physiology ; *Neurofeedback ; Task Performance and Analysis ; },
abstract = {Neurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain-computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with an NFT. In our study, we hypothesized that an NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging effects. To assess the effectiveness of our MI-BCI application, 63 subjects (older than 60 years) were recruited. This novel application was used by 31 subjects (NFT group). Their Luria neuropsychological test scores were compared with the remaining 32 subjects, who did not perform NFT (control group). Electroencephalogram changes measured by relative power (RP) endorsed cognitive potential findings under study: visuospatial, oral language, memory, intellectual and attention functions. Three frequency bands were selected to assess cognitive changes: 12, 18, and 21 Hz (bandwidth 3 Hz). Significant increases (p < 0.01) in the RP of these frequency bands were found. Moreover, results from cognitive tests showed significant improvements (p < 0.01) in four cognitive functions after performing five NFT sessions: visuospatial, oral language, memory, and intellectual. This established evidence in the association between NFT performed by a MI-BCI and enhanced cognitive performance. Therefore, it could be a novel approach to help elderly people.},
}
@article {pmid27252417,
year = {2016},
author = {Sestak, I and Zhang, Y and Schroeder, BE and Schnabel, CA and Dowsett, M and Cuzick, J and Sgroi, D},
title = {Cross-Stratification and Differential Risk by Breast Cancer Index and Recurrence Score in Women with Hormone Receptor-Positive Lymph Node-Negative Early-Stage Breast Cancer.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {22},
number = {20},
pages = {5043-5048},
doi = {10.1158/1078-0432.CCR-16-0155},
pmid = {27252417},
issn = {1557-3265},
support = {16891/CRUK_/Cancer Research UK/United Kingdom ; 16893/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {Anastrozole ; Antineoplastic Agents, Hormonal/therapeutic use ; Breast Neoplasms/*diagnosis/drug therapy/*genetics/pathology ; Female ; Gene Expression Profiling ; *Health Status Indicators ; Humans ; Lymph Nodes/pathology ; Neoplasm Recurrence, Local/*diagnosis/pathology ; Nitriles/therapeutic use ; Prognosis ; Receptors, Estrogen/*metabolism ; Receptors, Progesterone/*metabolism ; Retrospective Studies ; Risk Factors ; Tamoxifen/therapeutic use ; Triazoles/therapeutic use ; },
abstract = {PURPOSE: Previous results from the TransATAC study demonstrated that both the Breast Cancer Index (BCI) and the OncotypeDX Recurrence Score (RS) added significant prognostic information to clinicopathologic factors over a 10-year period. Here, we examined cross-stratification between BCI and RS to directly compare their prognostic accuracy at the individual patient level.<br><br>EXPERIMENTAL DESIGN: A total of 665 patients with hormone receptor-positive (HR[+]) and lymph node-negative disease were included in this retrospective analysis. BCI and RS risk groups were determined using predefined clinical cut-off points. Kaplan-Meier estimates of 10-year risk of distant recurrence (DR) and log-rank tests were used to examine cross-stratification between BCI and RS.<br><br>RESULTS: As previously reported, both RS and BCI were significantly prognostic in years 0 to 10. BCI provided significant additional prognostic information to the Clinical Treatment Score (CTS) plus RS (&#x394;LR-&#x3c7;[2] = 11.09; P < 0.001), whereas no additional prognostic information was provided by RS to CTS plus BCI (&#x394;LR-&#x3c7;[2] = 2.22; P = 0.1). Restratification by BCI of the low and intermediate RS risk groups led to subgroups with significantly different DR rates (P < 0.001 and P = 0.003, respectively). In contrast, restratified subgroups created by RS of BCI risk groups did not differ significantly.<br><br>CONCLUSIONS: In this retrospective analysis, BCI demonstrated increased prognostic accuracy versus RS. Notably, BCI identified subsets of RS low and RS intermediate risk patients with significant and clinically relevant rates of DR. These results indicate that additional subsets of women with HR[+], lymph node-negative breast cancer identified by BCI may be suitable candidates for adjuvant chemotherapy or extended endocrine therapy. Clin Cancer Res; 22(20); 5043-8. &#xa9;2016 AACRSee related commentary by Brufsky and Davidson, p. 4963.},
}
@article {pmid27250872,
year = {2016},
author = {Ohata, R and Ogawa, K and Imamizu, H},
title = {Single-trial prediction of reaction time variability from MEG brain activity.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {27416},
pmid = {27250872},
issn = {2045-2322},
mesh = {Adult ; Brain Mapping/methods ; Brain-Computer Interfaces ; Hand/physiology ; Humans ; Magnetoencephalography/methods ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/physiology ; Parietal Lobe/*physiology ; Reaction Time/*physiology ; Young Adult ; },
abstract = {Neural activity prior to movement onset contains essential information for predictive assistance for humans using brain-machine-interfaces (BMIs). Even though previous studies successfully predicted different goals for upcoming movements, it is unclear whether non-invasive recording signals contain the information to predict trial-by-trial behavioral variability under the same movement. In this paper, we examined the predictability of subsequent short or long reaction times (RTs) from magnetoencephalography (MEG) signals in a delayed-reach task. The difference in RTs was classified significantly above chance from 550 ms before the go-signal onset using the cortical currents in the premotor cortex. Significantly above-chance classification was performed in the lateral prefrontal and the right inferior parietal cortices at the late stage of the delay period. Thus, inter-trial variability in RTs is predictable information. Our study provides a proof-of-concept of the future development of non-invasive BMIs to prevent delayed movements.},
}
@article {pmid27247280,
year = {2016},
author = {Ethier, C and Acuna, D and Solla, SA and Miller, LE},
title = {Adaptive neuron-to-EMG decoder training for FES neuroprostheses.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046009},
pmid = {27247280},
issn = {1741-2552},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electrodes, Implanted ; Electromyography/*methods ; Haplorhini ; Isometric Contraction ; *Neural Prostheses ; Neurons/*physiology ; Online Systems ; Perception ; Prosthesis Design ; },
abstract = {OBJECTIVE: We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals.<br><br>APPROACH: Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns.<br><br>MAIN RESULTS: We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals.<br><br>SIGNIFICANCE: This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by including data from multiple grasping tasks in the training of the neuron-to-EMG decoder. Our approach would make it possible for persons with SCI to grasp objects with their own hands, using near-normal motor intent.},
}
@article {pmid27247140,
year = {2016},
author = {Klein, E and Ojemann, J},
title = {Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {043001},
doi = {10.1088/1741-2560/13/4/043001},
pmid = {27247140},
issn = {1741-2552},
mesh = {Animals ; Brain-Computer Interfaces/adverse effects/ethics/*standards ; Humans ; Informed Consent/*standards ; Research ; Risk ; },
abstract = {OBJECTIVE: Implantable brain-computer interface (BCI) research promises improvements in human health and enhancements in quality of life. Informed consent of subjects is a central tenet of this research. Rapid advances in neuroscience, and the intimate connection between functioning of the brain and conceptions of the self, make informed consent particularly challenging in BCI research. Identification of safety and research-related risks associated with BCI devices is an important step in ensuring meaningful informed consent.<br><br>APPROACH: This paper highlights a number of BCI research risks, including safety concerns, cognitive and communicative impairments, inappropriate subject expectations, group vulnerabilities, privacy and security, and disruptions of identity.<br><br>MAIN RESULTS: Based on identified BCI research risks, best practices are needed for understanding and incorporating BCI-related risks into informed consent protocols.<br><br>SIGNIFICANCE: Development of best practices should be guided by processes that are: multidisciplinary, systematic and transparent, iterative, relational and exploratory.},
}
@article {pmid27246601,
year = {2016},
author = {Riener, R},
title = {The Cybathlon promotes the development of assistive technology for people with physical disabilities.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {1},
pages = {49},
pmid = {27246601},
issn = {1743-0003},
mesh = {Activities of Daily Living ; Artificial Limbs ; Persons with Disabilities/*rehabilitation ; Electric Stimulation ; Humans ; Robotics ; *Self-Help Devices ; *Sports ; Wheelchairs ; },
abstract = {BACKGROUND: The Cybathlon is a new kind of championship, where people with physical disabilities compete against each other at tasks of daily life, with the aid of advanced assistive devices including robotic technologies. The first championship will take place at the Swiss Arena Kloten, Zurich, on 8 October 2016.<br><br>THE IDEA: Six disciplines are part of the competition comprising races with powered leg prostheses, powered arm prostheses, functional electrical stimulation driven bikes, powered wheelchairs, powered exoskeletons and brain-computer interfaces. This commentary describes the six disciplines and explains the current technological deficiencies that have to be addressed by the competing teams. These deficiencies at present often lead to disappointment or even rejection of some of the related technologies in daily applications.<br><br>CONCLUSION: The Cybathlon aims to promote the development of useful technologies that facilitate the lives of people with disabilities. In the long run, the developed devices should become affordable and functional for all relevant activities in daily life.},
}
@article {pmid27245594,
year = {2016},
author = {Anderson, AM and Lennox, JL and Nguyen, ML and Waldrop-Valverde, D and Tyor, WR and Loring, DW},
title = {Preliminary study of a novel cognitive assessment device for the evaluation of HIV-associated neurocognitive impairment.},
journal = {Journal of neurovirology},
volume = {22},
number = {6},
pages = {816-822},
pmid = {27245594},
issn = {1538-2443},
support = {K23 MH095679/MH/NIMH NIH HHS/United States ; P30 AI050409/AI/NIAID NIH HHS/United States ; U01 AI069418/AI/NIAID NIH HHS/United States ; UM1 AI069418/AI/NIAID NIH HHS/United States ; },
mesh = {Adult ; Area Under Curve ; *Brain-Computer Interfaces ; CD4 Lymphocyte Count ; Cognitive Dysfunction/complications/*diagnosis/physiopathology/psychology ; Equipment Design ; Female ; Georgia ; HIV Infections/complications/*diagnosis/physiopathology/psychology ; Humans ; Male ; Middle Aged ; *Neuropsychological Tests ; RNA, Viral/blood ; Sensitivity and Specificity ; Severity of Illness Index ; },
abstract = {Given the high prevalence of HIV-associated neurocognitive disorders (HAND), we examined the performance of a novel computerized cognitive assessment device (NCAD) for the evaluation of neurocognitive impairment in the setting of HIV. In addition to a standard 8-test neuropsychological battery, each participant underwent testing with the NCAD, which requires approximately 20 min and has been shown to accurately measure neurocognition in elderly individuals. The NCAD yields seven subtest scores in addition to an overall predictive score that is calculated based on subtest results. Thirty-nine HIV-infected participants were included in this study; the majority of which (71.8 %) had undetectable plasma HIV RNA levels and a history of significant immunocompromise (median nadir CD4+ count 34 cells/μl). The mean composite neuropsychological score (NPT-8) was 46.07, and mean global deficit score (GDS) was 0.59. NCAD total subtest accuracy correlated significantly with NPT-8 (Pearson correlation r = 0.59, p < 0.0001) as well as GDS (Spearman's rho = -0.36, p = 0.02). NCAD predictive score also correlated significantly with NPT-8 (Spearman's rho = -0.5601, p = 0.0016) and GDS (Spearman's rho = 0.45, p = 0.0144). When using the most recent nosology of HAND criteria for neurocognitive impairment, the area under the curve (AUC) for NCAD total subtest accuracy was 0.7562 (p = 0.012), while the AUC for the HIV dementia scale was 0.508 (p = 0.930). While not as comprehensive as a full neuropsychological battery, the NCAD shows promise as a rapid screening tool for HIV-infected individuals, and additional research of this device is indicated.},
}
@article {pmid27244745,
year = {2017},
author = {Yao, L and Sheng, X and Zhang, D and Jiang, N and Farina, D and Zhu, X},
title = {A BCI System Based on Somatosensory Attentional Orientation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {25},
number = {1},
pages = {78-87},
doi = {10.1109/TNSRE.2016.2572226},
pmid = {27244745},
issn = {1558-0210},
mesh = {Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Orientation/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Touch/*physiology ; Young Adult ; },
abstract = {We propose and test a novel brain-computer interface (BCI) based on imagined tactile sensation. During an imagined tactile sensation, referred to as somatosensory attentional orientation (SAO), the subject shifts and maintains somatosensory attention on a body part, e.g., left or right hand. The SAO can be detected from EEG recordings for establishing a communication channel. To test for the hypothesis that SAO on different body parts can be discriminated from EEG, 14 subjects were assigned to a group who received an actual sensory stimulation (STE-Group), and 18 subjects were assigned to the SAO only group (SAO-Group). In single trials, the STE-Group received tactile stimulation first (both wrists simultaneously stimulated), and then maintained the attention on the selected body part (without stimulation). The same group also performed the SAO task first and then received the tactile stimulation. Conversely, the SAO-Group performed SAO without any stimulation, neither before nor after the SAO. In both the STE-Group and SAO-Group, it was possible to identify the SAO-related oscillatory activation that corresponded to a contralateral event-related desynchronization (ERD) stronger than the ipsilateral ERD. Discriminative information, represented as R[2] , was found mainly on the somatosensory area of the cortex. In the STE-Group, the average classification accuracy of SAO was 83.6%, and it was comparable with tactile BCI based on selective sensation (paired-t test, P > 0.05). In the SAO-Group the average online performance was 75.7%. For this group, after frequency band selection the offline performance reached 82.5% on average, with ≥ 80% for 12 subjects and ≥ 95% for four subjects. Complementary to tactile sensation, the SAO does not require sensory stimulation, with the advantage of being completely independent from the stimulus.},
}
@article {pmid27243454,
year = {2016},
author = {Wei, Q and Feng, S and Lu, Z},
title = {Stimulus Specificity of Brain-Computer Interfaces Based on Code Modulation Visual Evoked Potentials.},
journal = {PloS one},
volume = {11},
number = {5},
pages = {e0156416},
pmid = {27243454},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Color ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {A brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP) is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the method for target recognition. Five experiments were devised to investigate the effect of stimulus specificity on target recognition and we made an effort to find the optimal stimulus parameters for size, color and proximity of the stimuli, length of modulation sequence and its lag between two adjacent stimuli. By changing the values of these parameters and measuring classification accuracy of the c-VEP BCI, an optimal value of each parameter can be attained. Experimental results of ten subjects showed that stimulus size of visual angle 3.8°, white, spatial proximity of visual angle 4.8° center to center apart, modulation sequence of length 63 bits and the lag of 4 bits between adjacent stimuli yield individually superior performance. These findings provide a basis for determining stimulus presentation of a high-performance c-VEP based BCI system.},
}
@article {pmid27242429,
year = {2016},
author = {Royter, V and Gharabaghi, A},
title = {Brain State-Dependent Closed-Loop Modulation of Paired Associative Stimulation Controlled by Sensorimotor Desynchronization.},
journal = {Frontiers in cellular neuroscience},
volume = {10},
number = {},
pages = {115},
pmid = {27242429},
issn = {1662-5102},
abstract = {BACKGROUND: Pairing peripheral electrical stimulation (ES) and transcranial magnetic stimulation (TMS) increases corticospinal excitability when applied with a specific temporal pattern. When the two stimulation techniques are applied separately, motor imagery (MI)-related oscillatory modulation amplifies both ES-related cortical effects-sensorimotor event-related desynchronization (ERD), and TMS-induced peripheral responses-motor-evoked potentials (MEP). However, the influence of brain self-regulation on the associative pairing of these stimulation techniques is still unclear.<br><br>OBJECTIVE: The aim of this pilot study was to investigate the effects of MI-related ERD during associative ES and TMS on subsequent corticospinal excitability.<br><br>METHOD: The paired application of functional electrical stimulation (FES) of the extensor digitorum communis (EDC) muscle and subsequent single-pulse TMS (110% resting motor threshold (RMT)) of the contralateral primary motor cortex (M1) was controlled by beta-band (16-22 Hz) ERD during MI of finger extension and applied within a brain-machine interface environment in six healthy subjects. Neural correlates were probed by acquiring the stimulus-response curve (SRC) of both MEP peak-to-peak amplitude and area under the curve (AUC) before and after the intervention.<br><br>RESULT: The application of approximately 150 pairs of associative FES and TMS resulted in a significant increase of MEP amplitudes and AUC, indicating that the induced increase of corticospinal excitability was mediated by the recruitment of additional neuronal pools. MEP increases were brain state-dependent and correlated with beta-band ERD, but not with the background EDC muscle activity; this finding was independent of the FES intensity applied.<br><br>CONCLUSION: These results could be relevant for developing closed-loop therapeutic approaches such as the application of brain state-dependent, paired associative stimulation (PAS) in the context of neurorehabilitation.},
}
@article {pmid27237390,
year = {2016},
author = {Matt, M and Nordentoft, S and Kopacka, I and Pölzler, T and Lassnig, H and Jelovcan, S and Stüger, HP},
title = {Estimating sensitivity and specificity of a PCR for boot socks to detect Campylobacter in broiler primary production using Bayesian latent class analysis.},
journal = {Preventive veterinary medicine},
volume = {128},
number = {},
pages = {51-57},
doi = {10.1016/j.prevetmed.2016.03.015},
pmid = {27237390},
issn = {1873-1716},
mesh = {Animal Husbandry/*methods ; Animals ; Austria/epidemiology ; Bayes Theorem ; Campylobacter/genetics/*isolation &amp; purification ; Campylobacter Infections/diagnosis/epidemiology/microbiology/*veterinary ; Denmark/epidemiology ; Feces/microbiology ; Polymerase Chain Reaction/standards/*veterinary ; Poultry Diseases/diagnosis/*epidemiology/microbiology ; Sensitivity and Specificity ; },
abstract = {The present study compares three different assays for sample collection and detection of Campylobacter spp. in broiler flocks, based on (i) the collection of faecal samples from intestinal organs (caecum), (ii) individual faecal droppings collected from the bedding and (iii) faecal material collected by socks placed on the outside of a pair of boots (boot socks) and used for walking around in the flock. The two first methods are examined for Campylobacter using a culture method (ISO-10272-2:2006), while the boot socks are tested using PCR. The PCR-assay is a genus specific multiplex PCR with primers targeting 16S rDNA in Campylobacter and primers targeting Yersinia ruckerii. Sixty-seven broiler flocks from Austria and 83 broiler flocks from Denmark were included in this prospective study and 89 of these were found to be positive in at least one method (AT: 49 samples, DK: 40 samples) whereas 61 of these were negative in all assays. In Austria samples for the three assays were collected simultaneously, which facilitates a direct comparison of the diagnostic test performance. In Denmark, however, boot socks and faecal droppings were collected three days before slaughter while caecum samples were collected at slaughter. The results were evaluated in the absence of a gold standard using a Bayesian latent class model. Austrian results showed higher sensitivity for PCR detection in sock samples (0.98; Bayesian credible interval (BCI) [0.93-1]) than for culture of faecal droppings (0.86; BCI [0.76-0.91]) or caecal samples (0.92; BCI [0.85-0.97]). The potential impact of Campylobacter introduction within the final three days before slaughter was observed in Denmark, where four flocks were tested negative three days before slaughter, but were detected positive at the slaughterhouse. Therefore the model results for the PCR sensitivity (0.88; BCI [0.83-0.97]) and cultural ISO-method in faecal samples (0.84; BCI [0.76-0.92]) are lower than for caecal samples (0.93; BCI [0.85-0.98]). In our study, PCR detection on boot sock samples is more sensitive than conventional culture. In view of the advantage of rapid results before slaughter and low costs for sampling, especially in combination with existing Salmonella surveillance systems (just another pair of boot socks needed), this method-matrix combination could be a valuable surveillance tool in the broiler primary production.},
}
@article {pmid27232952,
year = {2016},
author = {Franz, EA and Fu, Y and Moore, M and Winter, T and Mayne, T and Debnath, R and Stringer, C},
title = {Fooling the brain by mirroring the hand: Brain correlates of the perceptual capture of limb ownership.},
journal = {Restorative neurology and neuroscience},
volume = {34},
number = {5},
pages = {721-732},
doi = {10.3233/RNN-150622},
pmid = {27232952},
issn = {1878-3627},
mesh = {Adolescent ; Body Image/*psychology ; Brain/*physiology ; *Brain Mapping ; Brain Waves/*physiology ; Electroencephalography ; Female ; *Hand ; Healthy Volunteers ; Humans ; Male ; Motion Perception/*physiology ; Online Systems ; Psychomotor Performance ; Surveys and Questionnaires ; Young Adult ; },
abstract = {BACKGROUND: Mirror therapy (MT) is an increasingly employed method aimed at reducing phantom pain and other negative sensations following loss of a limb or damage to sensorimotor systems. However, the brain processes associated with the perception of limb ownership, a key correlate of MT, are unknown.<br><br>OBJECTIVE: To examine whether transient perceptions of limb ownership together with associated neural activity can be elucidated using a purpose-developed mirror reflection task combined with electrophysiological (EEG) measures and cutting-edge analyses.<br><br>METHODS: Brain activity was measured online using EEG in 20 healthy controls while they produced opening-closing movements of one hand in control conditions or while viewing the mirror reflection of the movements. The key experimental condition required participants to make a foot pedal response whenever a change in perception of ownership (of a mirror-reflected limb) occurred (Mirror condition). Control conditions and a strict epoching regime were employed using standard subtractive logic to isolate the perception of limb ownership (which was further verified by self-reports).<br><br>RESULTS: Data from 15 participants were suitable for complete analysis; the remaining reported no experience of ownership. Significant spectral power increases were found in central-parietal regions in association with perceptions of ownership, with the most prominent effect specific to the alpha frequency band (8-13&#x200a;Hz) measured at the right parietal area. Source localization analyses further identified brain networks associated with the mirror reflection condition in the alpha frequency (parietal lobe) and the beta frequency (middle temporal areas). These were distinct from localized networks associated with the foot pedal response.<br><br>CONCLUSION: Transient perceptions of ownership can be captured experimentally, and are associated with localized sites of neural activation. This is an initial step toward eventual development of therapeutic targets for interventions including brain computer interfaces (BCIs) aimed at ameliorating the negative effects associated with impaired or missing limbs.},
}
@article {pmid27221469,
year = {2016},
author = {Perruchoud, D and Pisotta, I and Carda, S and Murray, MM and Ionta, S},
title = {Biomimetic rehabilitation engineering: the importance of somatosensory feedback for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {041001},
doi = {10.1088/1741-2560/13/4/041001},
pmid = {27221469},
issn = {1741-2552},
mesh = {Biomedical Engineering/trends ; Biomimetics/*trends ; Brain-Computer Interfaces/*trends ; *Feedback, Physiological ; Humans ; Rehabilitation/*instrumentation ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMIs) re-establish communication channels between the nervous system and an external device. The use of BMI technology has generated significant developments in rehabilitative medicine, promising new ways to restore lost sensory-motor functions. However and despite high-caliber basic research, only a few prototypes have successfully left the laboratory and are currently home-deployed.<br><br>APPROACH: The failure of this laboratory-to-user transfer likely relates to the absence of BMI solutions for providing naturalistic feedback about the consequences of the BMI's actions. To overcome this limitation, nowadays cutting-edge BMI advances are guided by the principle of biomimicry; i.e. the artificial reproduction of normal neural mechanisms.<br><br>MAIN RESULTS: Here, we focus on the importance of somatosensory feedback in BMIs devoted to reproducing movements with the goal of serving as a reference framework for future research on innovative rehabilitation procedures. First, we address the correspondence between users' needs and BMI solutions. Then, we describe the main features of invasive and non-invasive BMIs, including their degree of biomimicry and respective advantages and drawbacks. Furthermore, we explore the prevalent approaches for providing quasi-natural sensory feedback in BMI settings. Finally, we cover special situations that can promote biomimicry and we present the future directions in basic research and clinical applications.<br><br>SIGNIFICANCE: The continued incorporation of biomimetic features into the design of BMIs will surely serve to further ameliorate the realism of BMIs, as well as tremendously improve their actuation, acceptance, and use.},
}
@article {pmid27217826,
year = {2016},
author = {Gudiño-Mendoza, B and Sanchez-Ante, G and Antelis, JM},
title = {Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals.},
journal = {Computational and mathematical methods in medicine},
volume = {2016},
number = {},
pages = {3195373},
pmid = {27217826},
issn = {1748-6718},
mesh = {Brain/*physiopathology ; Brain Mapping/*methods ; Electrodes ; *Electroencephalography ; Electromyography ; Female ; Humans ; Male ; Movement ; Oscillometry ; Rehabilitation ; Robotics ; Signal Processing, Computer-Assisted ; Upper Extremity/physiopathology ; Young Adult ; },
abstract = {Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.},
}
@article {pmid27216571,
year = {2016},
author = {Alam, M and Rodrigues, W and Pham, BN and Thakor, NV},
title = {Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives.},
journal = {Brain research},
volume = {1646},
number = {},
pages = {25-33},
doi = {10.1016/j.brainres.2016.05.039},
pmid = {27216571},
issn = {1872-6240},
mesh = {Animals ; Brain/physiopathology ; *Brain-Computer Interfaces ; Humans ; *Man-Machine Systems ; Neurological Rehabilitation/instrumentation/*methods ; Neuronal Plasticity ; Paraplegia/physiopathology/therapy ; Prostheses and Implants ; Quadriplegia/physiopathology/therapy ; Recovery of Function ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/physiopathology/*therapy ; Spinal Cord Stimulation/*methods ; },
abstract = {Restoration of motor function is one of the highest priorities in individuals afflicted with spinal cord injury (SCI). The application of brain-machine interfaces (BMIs) to neuroprostheses provides an innovative approach to treat patients with sensorimotor impairments. A BMI decodes motor intent from cortical signals to control external devices such as a computer cursor or a robotic arm. Recent BMI systems can now use these motor intent signals to directly activate paretic muscles or to modulate the spinal cord in a way that reengage dormant neuromuscular systems below the level of injury. In this perspective, we review the progress made in the development of brain-machine-spinal-cord interfaces (BMSCIs) and highlight their potential for neurorehabilitation after SCI. The advancement and application of these neuroprostheses goes beyond improved motor control. The use of BMSCI may combine repetitive physical training along with intent-driven neuromodulation to promote neurorehabilitation by facilitating activity-dependent plasticity. Strong evidence suggests that proper timing of volitional neuromodulation facilitates long-term potentiation in the neuronal circuits that can promote permanent functional recovery in SCI subjects. However, the effectiveness of these implantable neuroprostheses must take into account the fact that there will be continuous changes in the interface between the signals of intent and the actual trigger to initiate the motor action.},
}
@article {pmid27214131,
year = {2016},
author = {Sousa, T and Direito, B and Lima, J and Ferreira, C and Nunes, U and Castelo-Branco, M},
title = {Control of Brain Activity in hMT+/V5 at Three Response Levels Using fMRI-Based Neurofeedback/BCI.},
journal = {PloS one},
volume = {11},
number = {5},
pages = {e0155961},
pmid = {27214131},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/*methods ; Male ; Middle Aged ; Neurofeedback/*methods ; Psychomotor Performance ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {A major challenge in brain-computer interface (BCI) research is to increase the number of command classes and levels of control. BCI studies often use binary control level approaches (level 0 and 1 of brain activation for each class of control). Different classes may often be achieved but not different levels of activation for the same class. The increase in the number of levels of control in BCI applications may allow for larger efficiency in neurofeedback applications. In this work we test the hypothesis whether more than two modulation levels can be achieved in a single brain region, the hMT+/V5 complex. Participants performed three distinct imagery tasks during neurofeedback training: imagery of a stationary dot, imagery of a dot with two opposing motions in the vertical axis and imagery of a dot with four opposing motions in vertical or horizontal axes (imagery of 2 or 4 motion directions). The larger the number of motion alternations, the higher the expected hMT+/V5 response. A substantial number (17 of 20) of participants achieved successful binary level of control and 12 were able to reach even 3 significant levels of control within the same session, confirming the whole group effects at the individual level. With this simple approach we suggest that it is possible to design a parametric system of control based on activity modulation of a specific brain region with at least 3 different levels. Furthermore, we show that particular imagery task instructions, based on different number of motion alternations, provide feasible achievement of different control levels in BCI and/or neurofeedback applications.},
}
@article {pmid27199714,
year = {2016},
author = {Mannan, MM and Jeong, MY and Kamran, MA},
title = {Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {193},
pmid = {27199714},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.},
}
@article {pmid27199710,
year = {2016},
author = {Callan, DE and Terzibas, C and Cassel, DB and Sato, MA and Parasuraman, R},
title = {The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {187},
pmid = {27199710},
issn = {1662-5161},
abstract = {The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in which the participant recovered from the perturbation (motor intention) vs. just passively viewing the perturbation. The BCI-decoder was tested on the third session of the same simple task and found to be able to significantly distinguish motor intention trials from passive viewing trials (mean = 69.8%). The same BCI-decoder was then used to test the fourth session on the complex task. The BCI-decoder significantly classified perturbation from no perturbation trials (73.3%) with a significant time savings of 72.3 ms (Original response time of 425.0-352.7 ms for BCI-decoder). The BCI-decoder model of the best subject was shown to generalize for both performance and time savings to the other subjects. The results of our off-line open loop simulation demonstrate that BCI based neuroadaptive automation has the potential to decode motor intention faster than manual control in response to a hazardous perturbation in flight attitude while ignoring ongoing motor and visual induced activity related to piloting the airplane.},
}
@article {pmid27199638,
year = {2016},
author = {Yoshimura, N and Nishimoto, A and Belkacem, AN and Shin, D and Kambara, H and Hanakawa, T and Koike, Y},
title = {Decoding of Covert Vowel Articulation Using Electroencephalography Cortical Currents.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {175},
pmid = {27199638},
issn = {1662-4548},
abstract = {With the goal of providing assistive technology for the communication impaired, we proposed electroencephalography (EEG) cortical currents as a new approach for EEG-based brain-computer interface spellers. EEG cortical currents were estimated with a variational Bayesian method that uses functional magnetic resonance imaging (fMRI) data as a hierarchical prior. EEG and fMRI data were recorded from ten healthy participants during covert articulation of Japanese vowels /a/ and /i/, as well as during a no-imagery control task. Applying a sparse logistic regression (SLR) method to classify the three tasks, mean classification accuracy using EEG cortical currents was significantly higher than that using EEG sensor signals and was also comparable to accuracies in previous studies using electrocorticography. SLR weight analysis revealed vertices of EEG cortical currents that were highly contributive to classification for each participant, and the vertices showed discriminative time series signals according to the three tasks. Furthermore, functional connectivity analysis focusing on the highly contributive vertices revealed positive and negative correlations among areas related to speech processing. As the same findings were not observed using EEG sensor signals, our results demonstrate the potential utility of EEG cortical currents not only for engineering purposes such as brain-computer interfaces but also for neuroscientific purposes such as the identification of neural signaling related to language processing.},
}
@article {pmid27199630,
year = {2016},
author = {Geronimo, A and Kamrunnahar, M and Schiff, SJ},
title = {Single Trial Predictors for Gating Motor-Imagery Brain-Computer Interfaces Based on Sensorimotor Rhythm and Visual Evoked Potentials.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {164},
pmid = {27199630},
issn = {1662-4548},
abstract = {For brain-computer interfaces (BCIs) that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of ongoing processes, visual evoked responses, and voluntary modulation. We proposed to use three brain signatures for predicting success on a single trial of a BCI task. The first two features, the amplitude and phase of the pre-trial mu amplitude, were chosen as a correlate for cortical excitability. The remaining feature, related to the visually evoked response to the cue, served as a possible measure of fixation and attention to the task. Of these three features, mu rhythm amplitude over the central electrodes at the time of cue presentation and to a lesser extent the single trial visual evoked response were correlated with the success on the subsequent imagery task. Despite the potential for gating trials using these features, an offline gating simulation was limited in its ability to produce an increase in device throughput. This discrepancy highlights a distinction between the identification of predictive features, and the use of this knowledge in an online BCI. Using such a system, we cannot assume that the user will respond similarly when faced with a scenario where feedback is altered by trials that are gated on a regular basis. The results of this study suggest the possibility of using individualized, pre-task neural signatures for personalized, and asynchronous (self-paced) BCI applications, although these effects need to be quantified in a real-time adaptive scenario in a future study.},
}
@article {pmid27196543,
year = {2017},
author = {Kryger, M and Wester, B and Pohlmeyer, EA and Rich, M and John, B and Beaty, J and McLoughlin, M and Boninger, M and Tyler-Kabara, EC},
title = {Flight simulation using a Brain-Computer Interface: A pilot, pilot study.},
journal = {Experimental neurology},
volume = {287},
number = {Pt 4},
pages = {473-478},
doi = {10.1016/j.expneurol.2016.05.013},
pmid = {27196543},
issn = {1090-2430},
mesh = {*Aviation ; *Brain-Computer Interfaces ; *Computer Simulation ; Deep Brain Stimulation/instrumentation/*methods ; Electrodes, Implanted ; Female ; Humans ; Microelectrodes ; Motor Cortex/*physiology ; Pilot Projects ; Pilots/*psychology ; Quadriplegia/etiology/psychology/therapy ; Spinocerebellar Degenerations/complications/psychology/*therapy ; },
abstract = {As Brain-Computer Interface (BCI) systems advance for uses such as robotic arm control it is postulated that the control paradigms could apply to other scenarios, such as control of video games, wheelchair movement or even flight. The purpose of this pilot study was to determine whether our BCI system, which involves decoding the signals of two 96-microelectrode arrays implanted into the motor cortex of a subject, could also be used to control an aircraft in a flight simulator environment. The study involved six sessions in which various parameters were modified in order to achieve the best flight control, including plane type, view, control paradigm, gains, and limits. Successful flight was determined qualitatively by evaluating the subject's ability to perform requested maneuvers, maintain flight paths, and avoid control losses such as dives, spins and crashes. By the end of the study, it was found that the subject could successfully control an aircraft. The subject could use both the jet and propeller plane with different views, adopting an intuitive control paradigm. From the subject's perspective, this was one of the most exciting and entertaining experiments she had performed in two years of research. In conclusion, this study provides a proof-of-concept that traditional motor cortex signals combined with a decoding paradigm can be used to control systems besides a robotic arm for which the decoder was developed. Aside from possible functional benefits, it also shows the potential for a new recreational activity for individuals with disabilities who are able to master BCI control.},
}
@article {pmid27196417,
year = {2016},
author = {Eliseyev, A and Aksenova, T},
title = {Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording.},
journal = {PloS one},
volume = {11},
number = {5},
pages = {e0154878},
pmid = {27196417},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electrocorticography/*methods ; Humans ; Imaging, Three-Dimensional ; *Least-Squares Analysis ; Models, Statistical ; Movement ; Neurosciences ; Online Systems ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience.},
}
@article {pmid27195788,
year = {2016},
author = {Lamti, HA and Gorce, P and Ben Khelifa, MM and Alimi, AM},
title = {When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {19},
number = {16},
pages = {1749-1759},
doi = {10.1080/10255842.2016.1183198},
pmid = {27195788},
issn = {1476-8259},
mesh = {Electrodes ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Mental Fatigue/*physiopathology ; Neural Networks, Computer ; Support Vector Machine ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn't show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.},
}
@article {pmid27194241,
year = {2016},
author = {Rutkowski, TM},
title = {Data-Driven Multimodal Sleep Apnea Events Detection : Synchrosquezing Transform Processing and Riemannian Geometry Classification Approaches.},
journal = {Journal of medical systems},
volume = {40},
number = {7},
pages = {162},
pmid = {27194241},
issn = {1573-689X},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electrocardiography ; Electroencephalography ; Eye Movements/physiology ; Humans ; Multimodal Imaging/*methods ; Oxygen/blood ; Pulmonary Ventilation/physiology ; *Signal Processing, Computer-Assisted ; Sleep/*physiology ; Sleep Apnea Syndromes/classification/*diagnosis ; Snoring/physiopathology ; },
abstract = {A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification.},
}
@article {pmid27191387,
year = {2016},
author = {Merel, J and Carlson, D and Paninski, L and Cunningham, JP},
title = {Neuroprosthetic Decoder Training as Imitation Learning.},
journal = {PLoS computational biology},
volume = {12},
number = {5},
pages = {e1004948},
pmid = {27191387},
issn = {1553-7358},
mesh = {*Algorithms ; Arm ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Computer Simulation ; Humans ; Learning ; Robotics ; Supervised Machine Learning ; Task Performance and Analysis ; },
abstract = {Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In practice, the decoder is often learned by updating its parameters while the user performs a task. When the user's intention is not directly observable, recent methods have demonstrated value in training the decoder against a surrogate for the user's intended movement. Here we show that training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available. Specifically, we describe how a generic imitation learning meta-algorithm, dataset aggregation (DAgger), can be adapted to train a generic brain-computer interface. By deriving existing learning algorithms for brain-computer interfaces in this framework, we provide a novel analysis of regret (an important metric of learning efficacy) for brain-computer interfaces. This analysis allows us to characterize the space of algorithmic variants and bounds on their regret rates. Existing approaches for decoder learning have been performed in the cursor control setting, but the available design principles for these decoders are such that it has been impossible to scale them to naturalistic settings. Leveraging our findings, we then offer an algorithm that combines imitation learning with optimal control, which should allow for training of arbitrary effectors for which optimal control can generate goal-oriented control. We demonstrate this novel and general BCI algorithm with simulated neuroprosthetic control of a 26 degree-of-freedom model of an arm, a sophisticated and realistic end effector.},
}
@article {pmid27188155,
year = {2016},
author = {Badakva, AM and Miller, NV and Zobova, LN},
title = {[Artificial Feedback for Invasive Brain-Computer Interfaces].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {1},
pages = {128-136},
pmid = {27188155},
issn = {0131-1646},
mesh = {*Brain-Computer Interfaces ; *Feedback, Physiological ; Humans ; Motor Cortex/*physiology ; *Touch ; },
abstract = {During the last two decades, considerable progress has been made in the studies of brain-computer interfaces (BCIs)--devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for articial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes which depend heavily on the afferent innervation of skin, muscles and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multi-channel micro-electrodes implanted in motor areas, while tactile, proprioceptive and other useful signals are transported back to the brain through spatial-temporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with articial sensory feedback will not only help patients, but will also expand knowledge base in the field of human cortical functions.},
}
@article {pmid27188154,
year = {2016},
author = {Kaplan, AY},
title = {[Neurophysiological Foundations and Practical Realizations of the Brain-Machine Interfaces the Technology in Neurological Rehabilitation].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {1},
pages = {118-127},
pmid = {27188154},
issn = {0131-1646},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Motor Disorders/rehabilitation ; *Neurological Rehabilitation ; Speech Disorders/rehabilitation ; },
abstract = {Technology brain-computer interface (BCI) based on the registration and interpretation of EEG has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that IMC--is a completely new paradigm in psychophysiology, allowing test hypotheses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e. only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state and prospects of development of training, communicational and assisting complexes based on BCI to control them without muscular effort on the basis of mental commands detected in the EEG of patients with severely impaired speech and motor system.},
}
@article {pmid27188145,
year = {2016},
author = {Mokienko, OA and Lyukmanov, RKh and Chernikova, LA and Suponeva, NA and Piradov, MA and Frolov, AA},
title = {[Brain-Computer Interface: the First Clinical Experience in Russia].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {1},
pages = {31-39},
pmid = {27188145},
issn = {0131-1646},
mesh = {Brain/physiopathology ; Brain Damage, Chronic/*rehabilitation ; *Brain-Computer Interfaces ; Electroencephalography ; Exoskeleton Device ; Humans ; *Imagination ; Kinesthesis ; *Movement ; Paresis/*rehabilitation ; Russia ; },
abstract = {Motor imagery is suggested to stimulate the same plastic mechanisms in the brain as a real movement. The brain-computer interface (BCI) controls motor imagery by converting EEG during this process into the commands for an external device. This article presents the results of two-stage study of the clinical use of non-invasive BCI in the rehabilitation of patients with severe hemiparesis caused by focal brain damage. It was found that the ability to control BCI did not depend on the duration of a disease, brain lesion localization and the degree of neurological deficit. The first step of the study involved 36 patients; it showed that the efficacy of rehabilitation was higher in the group with the use of BCI (the score on the Action Research Arm Test (ARAT) improved from 1 [0; 2] to 5 [0; 16] points, p = 0.012; no significant improvement was observed in control group). The second step of the study involved 19 patients; the complex BCI-exoskeleton (i.e. with the kinesthetic feedback) was used for motor imagery trainings. The improvement of the motor function of hands was proved by ARAT (the score improved from 2 [0; 37] to 4 [1; 45:5] points, p = 0.005) and Fugl-Meyer scale (from 72 [63; 110 ] to 79 [68; 115] points, p = 0.005).},
}
@article {pmid27188144,
year = {2016},
author = {Biryukova, EV and Pavlova, OG and Kurganskaya, ME and Bobrov, PD and Turbina, LG and Frolov, AA and Davydov, VI and Sil'tchenko, AV and Mokienko, OA},
title = {[Arm Motor Function Recovery during Rehabilitation with the Use of Hand Exoskeleton Controlled by Brain-Computer Interface: a Patient with Severe Brain Damage].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {1},
pages = {19-30},
pmid = {27188144},
issn = {0131-1646},
mesh = {*Arm ; Brain/physiopathology ; Brain Damage, Chronic/physiopathology/*rehabilitation ; *Brain-Computer Interfaces ; *Exoskeleton Device ; Hand ; Humans ; *Recovery of Function ; },
abstract = {We studied the dynamics of motor function recovery in a patient with severe brain damage in the course of neurorehabilitation using hand exoskeleton controlled by brain-computer interface. For estimating the motor function of paretic arm, we used the biomechanical analysis of movements registered during the course of rehabilitation. After 15 weekly sessions of hand exoskeleton control, the following results were obtained: a) the velocity profile of goal-directed movements of paretic hand became bell-shaped, b) the patient began to extend and abduct the hand which was flexed and adducted in the beginning of rehabilitation, and c) the patient began to supinate the forearm which was pronated in the beginning of rehabilitation. The first result is an evidence of the general improvement of the quality of motor control, while the second and third results prove that the spasticity of paretic arm has decreased.},
}
@article {pmid27188143,
year = {2016},
author = {Frolov, AA and Husek, D and Silchenko, AV and Tintera, Y and Rydlo, J},
title = {[The Changes in the Hemodynamic Activity of the Brain during Moroe Imagery Training with the Use of Brain-Computer Interface].},
journal = {Fiziologiia cheloveka},
volume = {42},
number = {1},
pages = {5-18},
pmid = {27188143},
issn = {0131-1646},
mesh = {Arm ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Hemodynamics ; Humans ; *Imagination ; Leg ; *Movement ; },
abstract = {With the use of functional MRI (fMRI), we studied the changes in brain hemodynamic activity of healthy subjects during motor imagery training with the use brain-computer interface (BCI), which is based on the recognition of EEG patterns of imagined movements. ANOVA dispersion analysis showed there are 14 areas of the brain where statistically sgnificant changes were registered. Detailed analysis of the activity in these areas before and after training (Student's and Mann-Whitney tests) reduced the amount of areas with significantly changed activity to five; these are Brodmann areas 44 and 45, insula, middle frontal gyrus, and anterior cingulate gyrus. We suggest that these changes are caused by the formation of memory traces of those brain activity patterns which are most accurately recognized by BCI classifiers as correspondent with limb movements. We also observed a tendency of increase in the activity of motor imagery after training. The hemodynamic activity in all these 14 areas during real movements was either approximatly the same or significantly higher than during motor imagery; activity during imagined leg movements was higher that that during imagined arm movements, except for the areas of representation of arms.},
}
@article {pmid27187530,
year = {2016},
author = {Sannelli, C and Vidaurre, C and Müller, KR and Blankertz, B},
title = {Ensembles of adaptive spatial filters increase BCI performance: an online evaluation.},
journal = {Journal of neural engineering},
volume = {13},
number = {4},
pages = {046003},
doi = {10.1088/1741-2560/13/4/046003},
pmid = {27187530},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Waves ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/statistics &amp; numerical data ; Humans ; Imagination/physiology ; Machine Learning ; Movement/physiology ; },
abstract = {OBJECTIVE: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem.<br><br>APPROACH: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches.<br><br>MAIN RESULTS: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency.<br><br>SIGNIFICANCE: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.},
}
@article {pmid27184959,
year = {2016},
author = {Belleudi, V and Sciattella, P and Agabiti, N and Di Martino, M and Di Domenicantonio, R and Davoli, M and Fusco, D},
title = {Socioeconomic differences in one-year survival after ischemic stroke: the effect of acute and post-acute care-pathways in a cohort study.},
journal = {BMC public health},
volume = {16},
number = {},
pages = {408},
pmid = {27184959},
issn = {1471-2458},
mesh = {Adult ; Aged ; Aged, 80 and over ; Cohort Studies ; *Educational Status ; Female ; Health Services Accessibility/statistics &amp; numerical data ; Humans ; Male ; Middle Aged ; Odds Ratio ; Patient Discharge/statistics &amp; numerical data ; Quality of Health Care/*statistics &amp; numerical data ; Severity of Illness Index ; Socioeconomic Factors ; Stroke/*mortality ; Subacute Care/*statistics &amp; numerical data ; },
abstract = {BACKGROUND: The reasons for socioeconomic inequity in stroke mortality are not well understood. The aim of this study was to explore the role of ischemic stroke care-pathways on the association between education level and one-year survival after hospital admission.<br><br>METHODS: Hospitalizations for ischemic stroke during 2011/12 were selected from Lazio health data. Patients' clinical history was defined by retrieving previous hospitalizations and drugs prescriptions. The association between education level and mortality after stroke was studied for acute and post-acute phases using multilevel logistic models (Odds Ratio (OR)). Different scenarios of quality care-pathways were identified considering hospital performance, access to rehabilitation and drug treatment post-discharge. The probability to survive to acute and post-acute phases according to education level and care-pathway scenarios was estimated for a "mean-severity" patient. One-year survival probability was calculated as the product of two probabilities. For each scenario, the 1-year survival probability ratio, university versus elementary education, and its Bootstrap Confidence Intervals (95 % BCI) were calculated.<br><br>RESULTS: We identified 9,958 patients with ischemic stroke, 53.3&#xa0;% with elementary education level and 3.2&#xa0;% with university. The mortality was 14.9&#xa0;% in acute phase and 14.3&#xa0;% in post-acute phase among survived to the acute phase. The adjusted mortality in acute and post-acute phases decreased with an increase in educational level (OR&#x2009;=&#x2009;0.90 p-trend&#x2009;<&#x2009;0.001; OR&#x2009;=&#x2009;0.85 p-trend&#x2009;<&#x2009;0.001). For the best care-pathway, the one-year survival probability ratio was 1.06 (95 % BCI&#x2009;=&#x2009;1.03-1.10), while it was 1.17 (95 % BCI&#x2009;=&#x2009;1.09-1.25) for the worst.<br><br>CONCLUSIONS: Education level was inversely associated with mortality both in acute and post-acute phases. The care-pathway reduces but does not eliminate 1-year survival inequity.},
}
@article {pmid27179577,
year = {2016},
author = {Pancak, J and Wagnerova, H and Škultéty Szárazová, A and Blaho, A and Durovsky, O and Durovska, J},
title = {Multi-infarct dementia and Alzheimer disease, contribution of cerebral circulation ultrasonography to pathogenesis and differential diagnosis. Value of microembolisation.},
journal = {Neuro endocrinology letters},
volume = {37},
number = {2},
pages = {137-140},
pmid = {27179577},
issn = {0172-780X},
mesh = {Aged ; Aged, 80 and over ; Alzheimer Disease/*diagnostic imaging ; Cerebrovascular Circulation/*physiology ; Dementia, Multi-Infarct/*diagnostic imaging ; Diagnosis, Differential ; Female ; Humans ; Male ; Ultrasonography/*methods ; },
abstract = {OBJECTIVES: Dementias are one of the most serious health and socioeconomic issues. Multi-infarct dementia (MID) and Alzheimer´s type dementia (AD) exhibit differences in cerebrovascular blood flow velocity profiles and in presence of microemboli, detected by transcranial Doppler sonography.<br><br>MATERIAL AND METHODS: A group of 77 persons was divided into 4 subgroups: 1. subgroup of patients with MID (n=19; 10 male and 9 female, mean age was 74.32&#xb1;8.30 years); 2. subgroup of patients with AD (n=19; 11 male and 8 female, mean age was 70.37&#xb1;87.85 years); 3. subgroup of patients with hypertension (n=19; 11 male and 8 female, age adjusted) and 4. sex and age adjusted control group (CG) of 20 persons without hypertension or other serious risk factors. The duplex ultrasonographic examination of extracranial and intracranial circulation was preceded by neurologic, neuropsychological and psychiatric examination. The presence of microemboli was determined using Multi Dop X2 device (maker DWL), 60 minutes monitoring. All patients underwent brain computer tomography (CT) or magnetic resonance imaging (MRI).<br><br>RESULTS: We found significantly higher incidence (68.4%, p=0.5267) of asymptomatic microemboli in ACM in the group of patients with MID compared to the AD group, the group of patients with hypertension and CG.<br><br>CONCLUSION: The occurrence of "asymptomatic" emboli in the middle cerebral artery in patients with multi-infarct dementia is higher in the current study. Although these microemboli do not cause immediate symptoms, the evidence suggests, that they may be a risk factor for cognitive impairment, especially for multi-infarct dementia.},
}
@article {pmid27174504,
year = {2016},
author = {Okano, JT and Robbins, D and Palk, L and Gerstoft, J and Obel, N and Blower, S},
title = {Testing the hypothesis that treatment can eliminate HIV: a nationwide, population-based study of the Danish HIV epidemic in men who have sex with men.},
journal = {The Lancet. Infectious diseases},
volume = {16},
number = {7},
pages = {789-796},
pmid = {27174504},
issn = {1474-4457},
support = {R01 AI116493/AI/NIAID NIH HHS/United States ; R21 AI114478/AI/NIAID NIH HHS/United States ; R56 AI041935/AI/NIAID NIH HHS/United States ; },
mesh = {Cohort Studies ; Denmark ; Epidemics/*prevention &amp; control ; HIV Infections/drug therapy/*epidemiology/prevention &amp; control ; *Homosexuality, Male ; Humans ; Incidence ; Male ; Models, Statistical ; Viral Load/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Worldwide, approximately 35 million individuals are infected with HIV; about 25 million of these live in sub-Saharan Africa. WHO proposes using treatment as prevention (TasP) to eliminate HIV. Treatment suppresses viral load, decreasing the probability an individual transmits HIV. The elimination threshold is one new HIV infection per 1000 individuals. Here, we test the hypothesis that TasP can substantially reduce epidemics and eliminate HIV. We estimate the impact of TasP, between 1996 and 2013, on the Danish HIV epidemic in men who have sex with men (MSM), an epidemic UNAIDS has identified as a priority for elimination.<br><br>METHODS: We use a CD4-staged Bayesian back-calculation approach to estimate incidence, and the hidden epidemic (the number of HIV-infected undiagnosed MSM). To develop the back-calculation model, we use data from an ongoing nationwide population-based study: the Danish HIV Cohort Study.<br><br>FINDINGS: Incidence, and the hidden epidemic, decreased substantially after treatment was introduced in 1996. By 2013, incidence was close to the elimination threshold: 1&#xb7;4 (median, 95% Bayesian credible interval [BCI] 0&#xb7;4-2&#xb7;1) new HIV infections per 1000 MSM and there were only 617 (264-858) undiagnosed MSM. Decreasing incidence and increasing treatment coverage were highly correlated; a treatment threshold effect was apparent.<br><br>INTERPRETATION: Our study is the first to show that TasP can substantially reduce a country's HIV epidemic, and bring it close to elimination. However, we have shown the effectiveness of TasP under optimal conditions: very high treatment coverage, and exceptionally high (98%) viral suppression rate. Unless these extremely challenging conditions can be met in sub-Saharan Africa, the WHO's global elimination strategy is unlikely to succeed.<br><br>FUNDING: National Institute of Allergy and Infectious Diseases.},
}
@article {pmid27172246,
year = {2016},
author = {Jeunet, C and Jahanpour, E and Lotte, F},
title = {Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036024},
doi = {10.1088/1741-2560/13/3/036024},
pmid = {27172246},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/physiology ; *Learning ; Male ; Motor Skills ; Psychomotor Performance/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Space Perception/physiology ; Spatial Navigation ; Young Adult ; },
abstract = {OBJECTIVE: While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' motor imagery based BCI (MI-BCI) control performance.<br><br>APPROACH: We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants (N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users' spatial ability and pre-training &#x3bc; rhythm amplitude, as both have been related to MI-BCI performance in the literature.<br><br>MAIN RESULTS: Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not.<br><br>SIGNIFICANCE: These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.},
}
@article {pmid27171896,
year = {2016},
author = {Rouse, AG},
title = {A four-dimensional virtual hand brain-machine interface using active dimension selection.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036021},
pmid = {27171896},
issn = {1741-2552},
support = {R01 NS065902/NS/NINDS NIH HHS/United States ; R01 NS079664/NS/NINDS NIH HHS/United States ; R01 NS092626/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Color Perception/physiology ; Computer Simulation ; Conditioning, Operant/physiology ; Form Perception/physiology ; *Hand ; Hand Strength/physiology ; Macaca mulatta ; Male ; Motor Cortex/physiology ; *Neural Prostheses ; Posture/physiology ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored.<br><br>APPROACH: ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures.<br><br>MAIN RESULTS: Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s(-1) for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control.<br><br>SIGNIFICANCE: ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.},
}
@article {pmid27169693,
year = {2016},
author = {Poletti, B and Carelli, L and Solca, F and Lafronza, A and Pedroli, E and Faini, A and Zago, S and Ticozzi, N and Meriggi, P and Cipresso, P and Lulé, D and Ludolph, AC and Riva, G and Silani, V},
title = {Cognitive assessment in Amyotrophic Lateral Sclerosis by means of P300-Brain Computer Interface: a preliminary study.},
journal = {Amyotrophic lateral sclerosis &amp; frontotemporal degeneration},
volume = {17},
number = {7-8},
pages = {473-481},
doi = {10.1080/21678421.2016.1181182},
pmid = {27169693},
issn = {2167-9223},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*complications ; Brain/*physiopathology ; Case-Control Studies ; *Cognition Disorders/diagnosis/etiology/pathology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Neuropsychological Tests ; Statistics, Nonparametric ; Surveys and Questionnaires ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To investigate the use of P300-based Brain Computer Interface (BCI) technology for the administration of motor-verbal free cognitive tests in Amyotrophic Lateral Sclerosis (ALS).<br><br>METHODS: We recruited 15 ALS patients and 15 age- and education-matched healthy subjects. All participants underwent a BCI-based neuropsychological assessment, together with two standard cognitive screening tools (FAB, MoCA), two psychological questionnaires (BDI, STAI-Y) and a usability questionnaire. For patients, clinical and respiratory examinations were also performed, together with a behavioural assessment (FBI).<br><br>RESULTS: Correlations were observed between standard cognitive and BCI-based neuropsychological assessment, mainly concerning execution times in the ALS group. Moreover, patients provided positive rates concerning the BCI perceived usability and subjective experience. Finally, execution times at the BCI-based neuropsychological assessment were useful to discriminate patients from controls, with patients achieving lower processing speed than controls regarding executive functions.<br><br>CONCLUSIONS: The developed motor-verbal free neuropsychological battery represents an innovative approach, that could provide relevant information for clinical practice and ethical issues. Its use for cognitive evaluation throughout the course of ALS, currently not available by means of standard assessment, must be addressed in further longitudinal validation studies. Further work will be aimed at refining the developed system and enlarging the cognitive spectrum investigated.},
}
@article {pmid27169387,
year = {2016},
author = {Bareket, L and Inzelberg, L and Rand, D and David-Pur, M and Rabinovich, D and Brandes, B and Hanein, Y},
title = {Temporary-tattoo for long-term high fidelity biopotential recordings.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {25727},
pmid = {27169387},
issn = {2045-2322},
mesh = {Electrochemistry ; Electrodes ; Electromyography/*methods ; Glass/chemistry ; Humans ; *Ink ; Photoelectron Spectroscopy ; Printing ; Skin ; *Tattooing ; Thiophenes/chemistry ; },
abstract = {Electromyography is a non-invasive method widely used to map muscle activation. For decades, it was commonly accepted that dry metallic electrodes establish poor electrode-skin contact, making them impractical for skin electromyography applications. Gelled electrodes are therefore the standard in electromyography with their use confined, almost entirely, to laboratory settings. Here we present novel dry electrodes, exhibiting outstanding electromyography recording along with excellent user comfort. The electrodes were realized using screen-printing of carbon ink on a soft support. The conformity of the electrodes helps establish direct contact with the skin, making the use of a gel superfluous. Plasma polymerized 3,4-ethylenedioxythiophene was used to enhance the impedance of the electrodes. Cyclic voltammetry measurements revealed an increase in electrode capacitance by a factor of up to 100 in wet conditions. Impedance measurements show a reduction factor of 10 in electrode impedance on human skin. The suitability of the electrodes for long-term electromyography recordings from the hand and from the face is demonstrated. The presented electrodes are ideally-suited for many applications, such as brain-machine interfacing, muscle diagnostics, post-injury rehabilitation, and gaming.},
}
@article {pmid27165452,
year = {2016},
author = {Martin, S and Brunner, P and Iturrate, I and Millán, Jdel R and Schalk, G and Knight, RT and Pasley, BN},
title = {Word pair classification during imagined speech using direct brain recordings.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {25803},
pmid = {27165452},
issn = {2045-2322},
support = {R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K99 DC012804/DC/NIDCD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Auditory Perception/physiology ; Brain/*physiology ; *Brain Mapping ; Discrimination, Psychological ; Electrodes ; *Electroencephalography ; Gamma Rhythm/physiology ; Humans ; *Imagination ; ROC Curve ; *Speech ; Time Factors ; *Vocabulary ; },
abstract = {People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.},
}
@article {pmid27164075,
year = {2016},
author = {Lange, L and Heisel, S and Sadowski, G},
title = {Predicting the Solubility of Pharmaceutical Cocrystals in Solvent/Anti-Solvent Mixtures.},
journal = {Molecules (Basel, Switzerland)},
volume = {21},
number = {5},
pages = {},
pmid = {27164075},
issn = {1420-3049},
mesh = {Acetonitriles/*chemistry ; Crystallization ; Ethanol/*chemistry ; Models, Molecular ; Niacinamide/*chemistry ; Powder Diffraction ; Solubility ; Solvents ; Succinic Acid/*chemistry ; Technology, Pharmaceutical/*methods ; Thermodynamics ; Water/*chemistry ; X-Ray Diffraction ; },
abstract = {In this work, the solubilities of pharmaceutical cocrystals in solvent/anti-solvent systems were predicted using PC-SAFT in order to increase the efficiency of cocrystal formation processes. Modeling results and experimental data were compared for the cocrystal system nicotinamide/succinic acid (2:1) in the solvent/anti-solvent mixtures ethanol/water, ethanol/acetonitrile and ethanol/ethyl acetate at 298.15 K and in the ethanol/ethyl acetate mixture also at 310.15 K. The solubility of the investigated cocrystal slightly increased when adding small amounts of anti-solvent to the solvent, but drastically decreased for high anti-solvent amounts. Furthermore, the solubilities of nicotinamide, succinic acid and the cocrystal in the considered solvent/anti-solvent mixtures showed strong deviations from ideal-solution behavior. However, by accounting for the thermodynamic non-ideality of the components, PC-SAFT is able to predict the solubilities in all above-mentioned solvent/anti-solvent systems in good agreement with the experimental data.},
}
@article {pmid27163316,
year = {2016},
author = {Wei, Q and Huang, Y and Li, M and Lu, Z},
title = {VEP-based brain-computer interfaces modulated by Golay complementary series for improving performance.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {24 Suppl 2},
number = {},
pages = {S541-9},
doi = {10.3233/THC-161180},
pmid = {27163316},
issn = {1878-7401},
mesh = {Algorithms ; Brain-Computer Interfaces/*standards ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Software Design ; },
abstract = {BACKGROUND: The goal of a brain-computer interface (BCI) is to enable communication by pure brain activity without neural and muscle control. However, the practical use of BCIs is limited by low information transfer rate. Recently, code modulation visual evoked potential (c-VEP) based BCIs have exhibited great potential in establishing high-rate communication between the brain and the external world.<br><br>OBJECTIVE: This study aims at exploring a more effective modulation code than the commonly used pseudorandom M sequence for c-VEP based BCIs (c-VEP BCIs) in order to increase the detection accuracy of stimulus targets and the resulting information transfer rate.<br><br>METHOD: Golay complementary sequence pair is used for constructing the modulation code of c-VEP BCIs due to their superior autocorrelation property. The modulation code is created by concatenating a pair of Golay complementary sequences. Sixteen target stimuli are modulated by the Golay code and its time shift versions.<br><br>RESULTS: Through offline analysis on data recorded from seven subjects and online test on five subjects, the Golay code modulated BCI yielded higher detection accuracy and information transfer rate than those achieved by M sequence.<br><br>CONCLUSION: The Golay code modulated BCI demonstrates a high performance compared with the M sequence modulated systems, and it is applicable to persons with motor disabilities.},
}
@article {pmid27153565,
year = {2016},
author = {Speier, W and Arnold, C and Pouratian, N},
title = {Integrating language models into classifiers for BCI communication: a review.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {031002},
pmid = {27153565},
issn = {1741-2552},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain-Computer Interfaces/*classification ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; *Language ; Models, Theoretical ; Natural Language Processing ; },
abstract = {OBJECTIVE: The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems.<br><br>APPROACH: The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models.<br><br>MAIN RESULTS: Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation.<br><br>SIGNIFICANCE: Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.},
}
@article {pmid27152666,
year = {2016},
author = {Wan, F and da Cruz, JN and Nan, W and Wong, CM and Vai, MI and Rosa, A},
title = {Alpha neurofeedback training improves SSVEP-based BCI performance.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036019},
doi = {10.1088/1741-2560/13/3/036019},
pmid = {27152666},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Alpha Rhythm/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Neurofeedback/classification/*physiology ; Photic Stimulation ; Reproducibility of Results ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user's SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs.<br><br>APPROACH: An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with 'low' performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison.<br><br>MAIN RESULTS: The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group.<br><br>SIGNIFICANCE: These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects' performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.},
}
@article {pmid27152498,
year = {2016},
author = {Perdikis, S and Leeb, R and Millán, JD},
title = {Context-aware adaptive spelling in motor imagery BCI.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036018},
doi = {10.1088/1741-2560/13/3/036018},
pmid = {27152498},
issn = {1741-2552},
mesh = {Adaptation, Physiological ; Adult ; *Algorithms ; Awareness ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Discriminant Analysis ; Electroencephalography ; Female ; Humans ; Language ; Male ; Psychomotor Performance ; Reaction Time ; Reproducibility of Results ; },
abstract = {OBJECTIVE: This work presents a first motor imagery-based, adaptive brain-computer interface (BCI) speller, which is able to exploit application-derived context for improved, simultaneous classifier adaptation and spelling. Online spelling experiments with ten able-bodied users evaluate the ability of our scheme, first, to alleviate non-stationarity of brain signals for restoring the subject's performances, second, to guide naive users into BCI control avoiding initial offline BCI calibration and, third, to outperform regular unsupervised adaptation.<br><br>APPROACH: Our co-adaptive framework combines the BrainTree speller with smooth-batch linear discriminant analysis adaptation. The latter enjoys contextual assistance through BrainTree's language model to improve online expectation-maximization maximum-likelihood estimation.<br><br>MAIN RESULTS: Our results verify the possibility to restore single-sample classification and BCI command accuracy, as well as spelling speed for expert users. Most importantly, context-aware adaptation performs significantly better than its unsupervised equivalent and similar to the supervised one. Although no significant differences are found with respect to the state-of-the-art PMean approach, the proposed algorithm is shown to be advantageous for 30% of the users.<br><br>SIGNIFICANCE: We demonstrate the possibility to circumvent supervised BCI recalibration, saving time without compromising the adaptation quality. On the other hand, we show that this type of classifier adaptation is not as efficient for BCI training purposes.},
}
@article {pmid27148525,
year = {2016},
author = {Moorjani, S},
title = {Miniaturized Technologies for Enhancement of Motor Plasticity.},
journal = {Frontiers in bioengineering and biotechnology},
volume = {4},
number = {},
pages = {30},
pmid = {27148525},
issn = {2296-4185},
support = {P51 RR000166/RR/NCRR NIH HHS/United States ; },
abstract = {The idea that the damaged brain can functionally reorganize itself - so when one part fails, there lies the possibility for another to substitute - is an exciting discovery of the twentieth century. We now know that motor circuits once presumed to be hardwired are not, and motor-skill learning, exercise, and even mental rehearsal of motor tasks can turn genes on or off to shape brain architecture, function, and, consequently, behavior. This is a very significant alteration from our previously static view of the brain and has profound implications for the rescue of function after a motor injury. Presentation of the right cues, applied in relevant spatiotemporal geometries, is required to awaken the dormant plastic forces essential for repair. The focus of this review is to highlight some of the recent progress in neural interfaces designed to harness motor plasticity, and the role of miniaturization in development of strategies that engage diverse elements of the neuronal machinery to synergistically facilitate recovery of function after motor damage.},
}
@article {pmid27148018,
year = {2016},
author = {Wang, NX and Olson, JD and Ojemann, JG and Rao, RP and Brunton, BW},
title = {Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations.},
journal = {Frontiers in human neuroscience},
volume = {10},
number = {},
pages = {165},
pmid = {27148018},
issn = {1662-5161},
abstract = {Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings.},
}
@article {pmid27147955,
year = {2016},
author = {Panzeri, S and Safaai, H and De Feo, V and Vato, A},
title = {Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {165},
pmid = {27147955},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.},
}
@article {pmid27142358,
year = {2016},
author = {Chen, PC and Castillo, EM and Baumgartner, J and Seo, JH and Korostenskaja, M and Lee, KH},
title = {Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals.},
journal = {Brain topography},
volume = {29},
number = {5},
pages = {728-737},
doi = {10.1007/s10548-016-0493-3},
pmid = {27142358},
issn = {1573-6792},
mesh = {Brain/*physiopathology ; Child ; Child, Preschool ; Corpus Callosum/surgery ; Drug Resistant Epilepsy/*physiopathology/surgery ; Electrocorticography ; Epilepsies, Partial/*physiopathology/surgery ; Epilepsy, Generalized/*physiopathology/surgery ; Female ; Humans ; Infant ; Male ; Neural Pathways/physiopathology ; },
abstract = {Simultaneous bilateral onset and bi-synchrony epileptiform discharges in electroencephalogram (EEG) remain hallmarks for generalized seizures. However, the possibility of an epileptogenic focus triggering rapidly generalized epileptiform discharges has been documented in several studies. Previously, a new multi-stage surgical procedure using bilateral intracranial EEG (iEEG) prior to and post complete corpus callosotomy (CC) was developed to uncover seizure focus in non-lateralizing focal epilepsy. Five patients with drug-resistant generalized epilepsy who underwent this procedure were included in the study. Their bilateral iEEG findings prior to complete CC showed generalized epileptiform discharges with no clear lateralization. Nonetheless, the bilateral ictal iEEG findings post complete CC indicated lateralized or localized seizure onset. This study hypothesized that brain functional connectivity analysis, applied to the pre CC bilateral iEEG recordings, could help identify focal epileptogenic networks in generalized epilepsy. The results indicated that despite diffuse epileptiform discharges, focal features can still be observed in apparent generalized seizures through brain connectivity analysis. The seizure onset localization/lateralization from connectivity analysis demonstrated a good agreement with the bilateral iEEG findings post complete CC and final surgical outcomes. Our study supports the role of focal epileptic networks in generalized seizures.},
}
@article {pmid27138273,
year = {2016},
author = {Delgado Saa, JF and Pesters, Ad and Cetin, M},
title = {Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036017},
doi = {10.1088/1741-2560/13/3/036017},
pmid = {27138273},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; Brain Mapping ; Brain-Computer Interfaces ; Electrocorticography/*methods ; Fingers/*physiology ; Humans ; Models, Statistical ; Movement/*physiology ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings.<br><br>APPROACH: The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addition, the proposed method models the dynamics of the task executed by the subject and uses information about these dynamics as prior information during the classification stage.<br><br>MAIN RESULTS: The results show that incorporating temporal information about the executed task as well as incorporating long-range dependencies between the brain signals and the labels effectively increases the system's classification performance compared to methods in the state of art.<br><br>SIGNIFICANCE: The method proposed in this work makes use of probabilistic graphical models to incorporate temporal information in the classification of finger movements from electrocorticographic recordings. The proposed method highlights the importance of including prior information about the task that the subjects execute. As the results show, the combination of these two features effectively produce a significant improvement of the system's classification performance.},
}
@article {pmid27137671,
year = {2016},
author = {Hamedi, M and Salleh, ShH and Noor, AM},
title = {Electroencephalographic Motor Imagery Brain Connectivity Analysis for BCI: A Review.},
journal = {Neural computation},
volume = {28},
number = {6},
pages = {999-1041},
doi = {10.1162/NECO_a_00838},
pmid = {27137671},
issn = {1530-888X},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces/trends ; Electroencephalography/*methods/trends ; Humans ; Imagery, Psychotherapy/methods/trends ; Imagination/*physiology ; Motor Skills/physiology ; Nerve Net/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.},
}
@article {pmid27135037,
year = {2016},
author = {Peters, B and Mooney, A and Oken, B and Fried-Oken, M},
title = {SOLICITING BCI USER EXPERIENCE FEEDBACK FROM PEOPLE WITH SEVERE SPEECH AND PHYSICAL IMPAIRMENTS.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {3},
number = {1},
pages = {47-58},
pmid = {27135037},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) researchers have shown increasing interest in soliciting user experience (UX) feedback, but the severe speech and physical impairments (SSPI) of potential users create barriers to effective implementation with existing feedback instruments. This article describes augmentative and alternative communication (AAC)-based techniques for obtaining feedback from this population, and presents results from administration of a modified questionnaire to 12 individuals with SSPI after trials with a BCI spelling system. The proposed techniques facilitated successful questionnaire completion and provision of narrative feedback for all participants. Questionnaire administration required less than five minutes and minimal effort from participants. Results indicated that individual users may have very different reactions to the same system, and that ratings of workload and comfort provide important information not available through objective performance measures. People with SSPI are critical stakeholders in the future development of BCI, and appropriate adaptation of feedback questionnaires and administration techniques allows them to participate in shaping this assistive technology.},
}
@article {pmid27132528,
year = {2016},
author = {Sefcik, RK and Opie, NL and John, SE and Kellner, CP and Mocco, J and Oxley, TJ},
title = {The evolution of endovascular electroencephalography: historical perspective and future applications.},
journal = {Neurosurgical focus},
volume = {40},
number = {5},
pages = {E7},
doi = {10.3171/2016.3.FOCUS15635},
pmid = {27132528},
issn = {1092-0684},
support = {TL1 TR001434/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Deep Brain Stimulation/*methods ; *Electroencephalography/history/methods/trends ; Endovascular Procedures/*methods/trends ; Epilepsy/*therapy ; History, 20th Century ; History, 21st Century ; Humans ; },
abstract = {Current standard practice requires an invasive approach to the recording of electroencephalography (EEG) for epilepsy surgery, deep brain stimulation (DBS), and brain-machine interfaces (BMIs). The development of endovascular techniques offers a minimally invasive route to recording EEG from deep brain structures. This historical perspective aims to describe the technical progress in endovascular EEG by reviewing the first endovascular recordings made using a wire electrode, which was followed by the development of nanowire and catheter recordings and, finally, the most recent progress in stent-electrode recordings. The technical progress in device technology over time and the development of the ability to record chronic intravenous EEG from electrode arrays is described. Future applications for the use of endovascular EEG in the preoperative and operative management of epilepsy surgery are then discussed, followed by the possibility of the technique's future application in minimally invasive operative approaches to DBS and BMI.},
}
@article {pmid27132523,
year = {2016},
author = {Azad, TD and Veeravagu, A and Steinberg, GK},
title = {Neurorestoration after stroke.},
journal = {Neurosurgical focus},
volume = {40},
number = {5},
pages = {E2},
pmid = {27132523},
issn = {1092-0684},
support = {R01 NS058784/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Cell Transplantation ; Electric Stimulation Therapy ; Humans ; Neuronal Plasticity/physiology ; Recovery of Function/*physiology ; Stroke/physiopathology/*therapy ; },
abstract = {Recent advancements in stem cell biology and neuromodulation have ushered in a battery of new neurorestorative therapies for ischemic stroke. While the understanding of stroke pathophysiology has matured, the ability to restore patients' quality of life remains inadequate. New therapeutic approaches, including cell transplantation and neurostimulation, focus on reestablishing the circuits disrupted by ischemia through multidimensional mechanisms to improve neuroplasticity and remodeling. The authors provide a broad overview of stroke pathophysiology and existing therapies to highlight the scientific and clinical implications of neurorestorative therapies for stroke.},
}
@article {pmid27125475,
year = {2016},
author = {Roberts, T and De Graaf, JB and Nicol, C and Hervé, T and Fiocchi, M and Sanaur, S},
title = {Flexible Inkjet-Printed Multielectrode Arrays for Neuromuscular Cartography.},
journal = {Advanced healthcare materials},
volume = {5},
number = {12},
pages = {1462-1470},
doi = {10.1002/adhm.201600108},
pmid = {27125475},
issn = {2192-2659},
mesh = {*Bridged Bicyclo Compounds, Heterocyclic/administration &amp; dosage/chemistry ; *Dielectric Spectroscopy/instrumentation/methods ; Electrodes ; *Electromyography/instrumentation/methods ; Female ; Humans ; Male ; *Polymers/administration &amp; dosage/chemistry ; *Polystyrenes/administration &amp; dosage/chemistry ; *Skin ; },
abstract = {UNLABELLED: Flexible Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (<br><br>PEDOT: PSS) conductive-polymer multielectrode arrays (MEAs) are fabricated without etching or aggressive lift-off processes, only by additive solution processes. Inkjet printing technology has several advantages, such as a customized design and a rapid realization time, adaptability to different patients and to different applications. In particular, inkjet printing technology, as additive and "contactless" technology, can be easily inserted into various technological fabrication steps on different substrates at low cost. In vivo electrochemical impedance spectroscopy measurements show the time stability of such MEAs. An equivalent circuit model is established for such flexible cutaneous MEAs. It is shown that the charge transfer resistance remains the same, even two months after fabrication. Surface electromyography and electrocardiography measurements show that the<br><br>PEDOT: PSS MEAs record electrophysiological activity signals that are comparable to those obtained with unitary Ag/AgCl commercial electrodes. Additionally, such MEAs offer parallel and simultaneous recordings on multiple locations at high surface density. It also proves its suitability to reconstruct an innervation zone map and opens new perspectives for a better control of amputee's myoelectric prostheses. The employment of additive technologies such as inkjet printing suggests that the integration of multifunctional sensors can improve the performances of ultraflexible brain-computer interfaces.},
}
@article {pmid27124558,
year = {2016},
author = {Toppi, J and Borghini, G and Petti, M and He, EJ and De Giusti, V and He, B and Astolfi, L and Babiloni, F},
title = {Investigating Cooperative Behavior in Ecological Settings: An EEG Hyperscanning Study.},
journal = {PloS one},
volume = {11},
number = {4},
pages = {e0154236},
pmid = {27124558},
issn = {1932-6203},
mesh = {Adult ; Aviation ; Brain Mapping ; Computer Simulation ; *Cooperative Behavior ; Ecology ; Electroencephalography/*methods ; Female ; Frontal Lobe/anatomy &amp; histology/*physiology ; Humans ; Male ; Middle Aged ; Parietal Lobe/anatomy &amp; histology/*physiology ; Pilots/*psychology ; Signal Processing, Computer-Assisted ; Workforce ; },
abstract = {The coordinated interactions between individuals are fundamental for the success of the activities in some professional categories. We reported on brain-to-brain cooperative interactions between civil pilots during a simulated flight. We demonstrated for the first time how the combination of neuroelectrical hyperscanning and intersubject connectivity could provide indicators sensitive to the humans' degree of synchronization under a highly demanding task performed in an ecological environment. Our results showed how intersubject connectivity was able to i) characterize the degree of cooperation between pilots in different phases of the flight, and ii) to highlight the role of specific brain macro areas in cooperative behavior. During the most cooperative flight phases pilots showed, in fact, dense patterns of interbrain connectivity, mainly linking frontal and parietal brain areas. On the contrary, the amount of interbrain connections went close to zero in the non-cooperative phase. The reliability of the interbrain connectivity patterns was verified by means of a baseline condition represented by formal couples, i.e. pilots paired offline for the connectivity analysis but not simultaneously recorded during the flight. Interbrain density was, in fact, significantly higher in real couples with respect to formal couples in the cooperative flight phases. All the achieved results demonstrated how the description of brain networks at the basis of cooperation could effectively benefit from a hyperscanning approach. Interbrain connectivity was, in fact, more informative in the investigation of cooperative behavior with respect to established EEG signal processing methodologies applied at a single subject level.},
}
@article {pmid27116730,
year = {2017},
author = {Tadipatri, VA and Tewfik, AH and Pellizzer, G and Ashe, J},
title = {Overcoming Long-Term Variability in Local Field Potentials Using an Adaptive Decoder.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {2},
pages = {319-328},
doi = {10.1109/TBME.2016.2557070},
pmid = {27116730},
issn = {1558-2531},
support = {I01 CX000437/CX/CSRD VA/United States ; },
mesh = {*Algorithms ; Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Male ; *Models, Theoretical ; *Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {Long-term variability remains one of the major hurdles in using intracortical recordings like local field potentials for brain computer interfaces (BCI). Practical neural decoders need to overcome time instability of neural signals to estimate subject behavior accurately and faithfully over the long term. This paper presents a novel decoder that 1) characterizes each behavioral task (i.e., different movement directions under different force conditions) with multiple neural patterns and 2) adapts to the long-term variations in neural features by identifying the stable neural patterns. This adaptation can be performed in both an unsupervised and a semisupervised learning framework requiring minimal feedback from the user. To achieve generalization over time, the proposed decoder uses redundant sparse regression models that adapt to day-to-day variations in neural patterns. While this update requires no explicit feedback from the BCI user, any feedback (explicit or derived) to the BCI improves its performance. With this adaptive decoder, we investigated the effects of long-term neural modulation especially when subjects encountered new external forces against movement. The proposed decoder predicted eight hand-movement directions with an accuracy of 95% over two weeks (when there was no external forces); and 85% in later acquisition sessions spanning up to 42 days (when the monkeys countered external field forces). Since the decoder can operate with or without manual intervention, it could alleviate user frustration associated with BCI.},
}
@article {pmid27115740,
year = {2016},
author = {Costa, &#xc1; and Iáñez, E and Úbeda, A and Hortal, E and Del-Ama, AJ and Gil-Agudo, &#xc1; and Azorín, JM},
title = {Decoding the Attentional Demands of Gait through EEG Gamma Band Features.},
journal = {PloS one},
volume = {11},
number = {4},
pages = {e0154136},
pmid = {27115740},
issn = {1932-6203},
mesh = {Adult ; Attention ; Brain/*physiology ; Brain-Computer Interfaces ; Cognition ; Electroencephalography/*methods ; Exercise Therapy/*methods ; Female ; *Gait ; Humans ; Male ; Spinal Cord Injuries/*rehabilitation ; Support Vector Machine ; Walking ; Young Adult ; },
abstract = {Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient's involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical information obtained from the scalp is acquired with the goal of studying the cognitive mechanisms related to the users' attention to the gait. Data from 10 healthy users and 3 incomplete Spinal Cord Injury patients are acquired during treadmill walking. During gait, users are asked to perform 4 attentional tasks. Data obtained are treated to reduce movement artifacts. Features from δ(1 - 4Hz), θ(4 - 8Hz), α(8 - 12Hz), β(12 - 30Hz), γlow(30 - 50Hz), γhigh(50 - 90Hz) frequency bands are extracted and analyzed to find which ones provide more information related to attention. The selected bands are tested with 5 classifiers to distinguish between tasks. Classification results are also compared with chance levels to evaluate performance. Results show success rates of ∼67% for healthy users and ∼59% for patients. These values are obtained using features from γ band suggesting that the attention mechanisms are related to selective attention mechanisms, meaning that, while the attention on gait decreases the level of attention on the environment and external visual information increases. Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine classifiers provide the best results for all users. Results from patients are slightly lower, but significantly different, than those obtained from healthy users supporting the idea that the patients pay more attention to gait during non-attentional tasks due to the inherent difficulties they have during normal gait. This study provides evidence of the existence of classifiable cortical information related to the attention level on the gait. This fact could allow the development of a real-time system that obtains the attention level during lower limb rehabilitation. This information could be used as feedback to adapt the rehabilitation strategy.},
}
@article {pmid27112893,
year = {2016},
author = {Fan, L and Yang, H and Yang, J and Peng, M and Hu, J},
title = {Preparation and characterization of chitosan/gelatin/PVA hydrogel for wound dressings.},
journal = {Carbohydrate polymers},
volume = {146},
number = {},
pages = {427-434},
doi = {10.1016/j.carbpol.2016.03.002},
pmid = {27112893},
issn = {1879-1344},
mesh = {*Bandages ; Chitosan/*chemistry ; Gelatin/*chemistry ; Hydrogels/*chemistry ; Polyvinyl Alcohol/*chemistry ; Tensile Strength ; *Wounds and Injuries ; },
abstract = {Chitosan (CS)/gelatin (Gel)/polyvinyl alcohol (PVA) hydrogels were prepared by the gamma irradiation method for usage in wound dressing applications. Chitosan and gelatin solution was mixed with poly(vinyl alcohol) (PVA) solution at different weight ratios of CS/Gel of 1:3, 1:2, 1:1, 2:1 and 3:1. The hydrogels irradiated at 40kGy. The structure of the hydrogels was characterized by using FT-IR and SEM. The CS/Gel/PVA hydrogels were characterized for physical properties and blood clotting activity. The tensile strength of CS/Gel/PVA hydrogel enhanced than on the basis of the Gel/PVA hydrogel. The highest tensile strength reached the 2.2Mpa. All hydrogels have shown a good coagulation effect. It takes only 5min for the BCI index to reached 0.032 only 5min when the weight ratio of CS/Gel was 1:1. It means that the hemostatic effect of hydrogels were optimal. And the hydrogrls also showed good pH-sensitivity, swelling ability and water evaporation rate. Therefore, this hydrogel showed a promising potential to be applied as wound dressing.},
}
@article {pmid27112213,
year = {2016},
author = {Remsik, A and Young, B and Vermilyea, R and Kiekhoefer, L and Abrams, J and Evander Elmore, S and Schultz, P and Nair, V and Edwards, D and Williams, J and Prabhakaran, V},
title = {A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke.},
journal = {Expert review of medical devices},
volume = {13},
number = {5},
pages = {445-454},
pmid = {27112213},
issn = {1745-2422},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Motor Activity/*physiology ; Recovery of Function/*physiology ; Stroke/*physiopathology/*therapy ; *Stroke Rehabilitation ; Upper Extremity/*physiopathology ; },
abstract = {Stroke is a leading cause of acquired disability resulting in distal upper extremity functional motor impairment. Stroke mortality rates continue to decline with advances in healthcare and medical technology. This has led to an increased demand for advanced, personalized rehabilitation. Survivors often experience some level of spontaneous recovery shortly after their stroke event, yet reach a functional plateau after which there is exiguous motor recovery. Nevertheless, studies have demonstrated the potential for recovery beyond this plateau. Non-traditional neurorehabilitation techniques, such as those incorporating the brain-computer interface (BCI), are being investigated for rehabilitation. BCIs may offer a gateway to the brain's plasticity and revolutionize how humans interact with the world. Non-invasive BCIs work by closing the proprioceptive feedback loop with real-time, multi-sensory feedback allowing for volitional modulation of brain signals to assist hand function. BCI technology potentially promotes neuroplasticity and Hebbian-based motor recovery by rewarding cortical activity associated with sensory-motor rhythms through use with a variety of self-guided and assistive modalities.},
}
@article {pmid27108404,
year = {2016},
author = {Knothe Tate, ML and Detamore, M and Capadona, JR and Woolley, A and Knothe, U},
title = {Engineering and commercialization of human-device interfaces, from bone to brain.},
journal = {Biomaterials},
volume = {95},
number = {},
pages = {35-46},
doi = {10.1016/j.biomaterials.2016.03.038},
pmid = {27108404},
issn = {1878-5905},
mesh = {Animals ; Bone and Bones/physiopathology ; Brain/physiopathology ; Brain-Computer Interfaces ; Humans ; Knee/physiopathology ; *Musculoskeletal Physiological Phenomena ; Nanocomposites/therapeutic use ; *Nervous System Physiological Phenomena ; Prostheses and Implants ; *Regeneration ; *Technology Transfer ; *Tissue Engineering ; Tissue Scaffolds ; Wound Healing ; },
abstract = {Cutting edge developments in engineering of tissues, implants and devices allow for guidance and control of specific physiological structure-function relationships. Yet the engineering of functionally appropriate human-device interfaces represents an intractable challenge in the field. This leading opinion review outlines a set of current approaches as well as hurdles to design of interfaces that modulate transfer of information, i.a. forces, electrical potentials, chemical gradients and haptotactic paths, between endogenous and engineered body parts or tissues. The compendium is designed to bridge across currently separated disciplines by highlighting specific commonalities between seemingly disparate systems, e.g. musculoskeletal and nervous systems. We focus on specific examples from our own laboratories, demonstrating that the seemingly disparate musculoskeletal and nervous systems share common paradigms which can be harnessed to inspire innovative interface design solutions. Functional barrier interfaces that control molecular and biophysical traffic between tissue compartments of joints are addressed in an example of the knee. Furthermore, we describe the engineering of gradients for interfaces between endogenous and engineered tissues as well as between electrodes that physically and electrochemically couple the nervous and musculoskeletal systems. Finally, to promote translation of newly developed technologies into products, protocols, and treatments that benefit the patients who need them most, regulatory and technical challenges and opportunities are addressed on hand from an example of an implant cum delivery device that can be used to heal soft and hard tissues, from brain to bone.},
}
@article {pmid27103137,
year = {2016},
author = {Zhang, T and Liu, T and Li, F and Li, M and Liu, D and Zhang, R and He, H and Li, P and Gong, J and Luo, C and Yao, D and Xu, P},
title = {Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.},
journal = {NeuroImage},
volume = {134},
number = {},
pages = {475-485},
doi = {10.1016/j.neuroimage.2016.04.030},
pmid = {27103137},
issn = {1095-9572},
mesh = {Adult ; Attention/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Frontal Lobe/*anatomy &amp; histology/*physiology ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Male ; Neural Pathways/physiology ; Parietal Lobe/*anatomy &amp; histology/*physiology ; *Psychomotor Performance ; Young Adult ; },
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.},
}
@article {pmid27099159,
year = {2017},
author = {Tan, P and Tan, GZ and Cai, ZX and Sa, WP and Zou, YQ},
title = {Using ELM-based weighted probabilistic model in the classification of synchronous EEG BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {55},
number = {1},
pages = {33-43},
pmid = {27099159},
issn = {1741-0444},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Databases as Topic ; *Electroencephalography ; Humans ; *Machine Learning ; *Models, Statistical ; },
abstract = {Extreme learning machine (ELM) is an effective machine learning technique with simple theory and fast implementation, which has gained increasing interest from various research fields recently. A new method that combines ELM with probabilistic model method is proposed in this paper to classify the electroencephalography (EEG) signals in synchronous brain-computer interface (BCI) system. In the proposed method, the softmax function is used to convert the ELM output to classification probability. The Chernoff error bound, deduced from the Bayesian probabilistic model in the training process, is adopted as the weight to take the discriminant process. Since the proposed method makes use of the knowledge from all preceding training datasets, its discriminating performance improves accumulatively. In the test experiments based on the datasets from BCI competitions, the proposed method is compared with other classification methods, including the linear discriminant analysis, support vector machine, ELM and weighted probabilistic model methods. For comparison, the mutual information, classification accuracy and information transfer rate are considered as the evaluation indicators for these classifiers. The results demonstrate that our method shows competitive performance against other methods.},
}
@article {pmid27097901,
year = {2016},
author = {Oby, ER and Perel, S and Sadtler, PT and Ruff, DA and Mischel, JL and Montez, DF and Cohen, MR and Batista, AP and Chase, SM},
title = {Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036009},
pmid = {27097901},
issn = {1741-2552},
support = {T32 NS007391/NS/NINDS NIH HHS/United States ; R00 EY020844/EY/NEI NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; R01 EY022930/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Extracellular Space/*physiology ; Macaca mulatta ; Male ; Motor Cortex/physiology ; *Movement ; Neural Prostheses ; Photic Stimulation ; Psychomotor Performance ; Signal-To-Noise Ratio ; Visual Cortex/physiology ; },
abstract = {OBJECTIVE: A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1).<br><br>APPROACH: We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings.<br><br>MAIN RESULTS: The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography.<br><br>SIGNIFICANCE: How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.},
}
@article {pmid27092051,
year = {2016},
author = {Pokorny, C and Breitwieser, C and Müller-Putz, GR},
title = {The Role of Transient Target Stimuli in a Steady-State Somatosensory Evoked Potential-Based Brain-Computer Interface Setup.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {152},
pmid = {27092051},
issn = {1662-4548},
abstract = {In earlier literature, so-called twitches were used to support a user in a steady-state somatosensory evoked potential (SSSEP) based brain-computer interface (BCI) to focus attention on the requested targets. Within this work, we investigate the impact of these transient target stimuli on SSSEPs in a real-life BCI setup. A hybrid BCI was designed which combines SSSEPs and P300 potentials evoked by twitches randomly embedded into the streams of tactile stimuli. The EEG of fourteen healthy subjects was recorded, while their left and right index fingers were simultaneously stimulated using frequencies selected in a screening procedure. The subjects were randomly instructed by a cue on a screen to focus attention on one or none of the fingers. Feature for SSSEPs and P300 potentials were extracted and classified using separately trained multi-class shrinkage LDA classifiers. Three-class classification accuracies significantly better than random could be reached by nine subjects using SSSEP features and by 12 subjects using P300 features respectively. The average classification accuracies were 48.6% using SSSEP and 50.7% using P300 features. By means of a Monte Carlo permutation test it could be shown that twitches have an attenuation effect on the SSSEP. Significant SSSEP blocking effects time-locked to twitch positions were found in seven subjects. Our findings suggest that the attempt to combine different types of stimulation signals like repetitive signals and twitches has a mutual influence on each other, which may be the main reason for the rather moderate BCI performance. This influence is originated at the level of stimulus generation but becomes apparent as physiological effect in the SSSEP. When designing a hybrid BCI based on SSSEPs and P300 potentials, one has to find an optimal tradeoff depending on the overall design goals or individual subjects' performance. Our results give therefore some new insights that may be useful for the successful design of hybrid BCIs.},
}
@article {pmid27090947,
year = {2016},
author = {Congedo, M and Korczowski, L and Delorme, A and Lopes da Silva, F},
title = {Spatio-temporal common pattern: A companion method for ERP analysis in the time domain.},
journal = {Journal of neuroscience methods},
volume = {267},
number = {},
pages = {74-88},
doi = {10.1016/j.jneumeth.2016.04.008},
pmid = {27090947},
issn = {1872-678X},
mesh = {*Algorithms ; Artifacts ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; Multivariate Analysis ; Neuropsychological Tests ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; Time Factors ; Visual Perception/physiology ; },
abstract = {BACKGROUND: Already used at the incept of research on event-related potentials (ERP) over half a century ago, the arithmetic mean is still the benchmark for ERP estimation. Such estimation, however, requires a large number of sweeps and/or a careful rejection of artifacts affecting the electroencephalographic recording.<br><br>NEW METHOD: In this article we propose a method for estimating ERPs as they are naturally contaminated by biological and instrumental artifacts. The proposed estimator makes use of multivariate spatio-temporal filtering to increase the signal-to-noise ratio. This approach integrates a number of relevant advances in ERP data analysis, such as single-sweep adaptive estimation of amplitude and latency and the use of multivariate regression to account for ERP overlapping in time.<br><br>RESULTS: We illustrate the effectiveness of the proposed estimator analyzing a dataset comprising 24 subjects involving a visual odd-ball paradigm, without performing any artifact rejection.<br><br>As compared to the arithmetic average, a lower number of sweeps is needed. Furthermore, artifact rejection can be performed roughly using permissive automatic procedures.<br><br>CONCLUSION: The proposed ensemble average estimator yields a reference companion to the arithmetic ensemble average estimation, suitable both in clinical and research settings. The method can be applied equally to event related fields (ERF) recorded by means of magnetoencephalography. In this article we describe all necessary methodological details to promote testing and comparison of this proposed method by peers. Furthermore, we release a MATLAB toolbox, a plug-in for the EEGLAB software suite and a stand-alone executable application.},
}
@article {pmid27090735,
year = {2016},
author = {Sugata, H and Hirata, M and Yanagisawa, T and Matsushita, K and Yorifuji, S and Yoshimine, T},
title = {Common neural correlates of real and imagined movements contributing to the performance of brain-machine interfaces.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {24663},
pmid = {27090735},
issn = {2045-2322},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Feedback, Sensory ; Female ; Humans ; *Imagination ; Male ; *Movement ; Psychomotor Performance ; },
abstract = {The relationship between M1 activity representing motor information in real and imagined movements have not been investigated with high spatiotemporal resolution using non-invasive measurements. We examined the similarities and differences in M1 activity during real and imagined movements. Ten subjects performed or imagined three types of right upper limb movements. To infer the movement type, we used 40 virtual channels in the M1 contralateral to the movement side (cM1) using a beamforming approach. For both real and imagined movements, cM1 activities increased around response onset, after which their intensities were significantly different. Similarly, although decoding accuracies surpassed the chance level in both real and imagined movements, these were significantly different after the onset. Single virtual channel-based analysis showed that decoding accuracy significantly increased around the hand and arm areas during real and imagined movements and that these are spatially correlated. The temporal correlation of decoding accuracy significantly increased around the hand and arm areas, except for the period immediately after response onset. Our results suggest that cM1 is involved in similar neural activities related to the representation of motor information during real and imagined movements, except for presence or absence of sensory-motor integration induced by sensory feedback.},
}
@article {pmid27084317,
year = {2016},
author = {Yang, B and Li, H and Wang, Q and Zhang, Y},
title = {Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.},
journal = {Computer methods and programs in biomedicine},
volume = {129},
number = {},
pages = {21-28},
doi = {10.1016/j.cmpb.2016.02.020},
pmid = {27084317},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Humans ; },
abstract = {BACKGROUND AND OBJECTIVE: Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper.<br><br>METHODS: The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained.<br><br>RESULTS: Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7&#xb1;0.9%, and specificity: 91&#xb1;1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively.<br><br>CONCLUSIONS: The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate helpless sub-bands for each subject and make remaining fewer sub-bands keep better separability by fisher distance, which leads to a higher classification accuracy than WPD-CSP method.},
}
@article {pmid27079082,
year = {2015},
author = {Xu, L and Xiao, G and Li, M},
title = {[Study on Electroencephalogram Recognition Framework by Common Spatial Pattern and Fuzzy Fusion].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {6},
pages = {1173-1178},
pmid = {27079082},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Humans ; },
abstract = {Common spatial pattern (CSP) is a very popular method for spatial filtering to extract the features from electroencephalogram (EEG) signals, but it may cause serious over-fitting issue. In this paper, after the extraction and recognition of feature, we present a new way in which the recognition results are fused to overcome the over-fitting and improve recognition accuracy. And then a new framework for EEG recognition is proposed by using CSP to extract features from EEG signals, using linear discriminant analysis (LDA) classifiers to identify the user's mental state from such features, and using Choquet fuzzy integral to fuse classifiers results. Brain-computer interface (BCI) competition 2005 data sets IVa was used to validate the framework. The results demonstrated that it effective ly improved recognition and to some extent overcome the over-fitting problem of CSP. It showed the effectiveness of this framework for dealing with EEG.},
}
@article {pmid27078889,
year = {2016},
author = {Schultze-Kraft, M and Dähne, S and Gugler, M and Curio, G and Blankertz, B},
title = {Unsupervised classification of operator workload from brain signals.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036008},
doi = {10.1088/1741-2560/13/3/036008},
pmid = {27078889},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artifacts ; Brain/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Electroencephalography ; Galvanic Skin Response ; Heart Rate ; Humans ; Male ; Models, Statistical ; Psychomotor Performance ; Reproducibility of Results ; Respiratory Rate ; *Signal Processing, Computer-Assisted ; Workload ; },
abstract = {OBJECTIVE: In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches.<br><br>APPROACH: Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance.<br><br>MAIN RESULTS: Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features.<br><br>SIGNIFICANCE: Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.},
}
@article {pmid27071192,
year = {2016},
author = {Smith, WA and Mogen, BJ and Fetz, EE and Sathe, VS and Otis, BP},
title = {Exploiting Electrocorticographic Spectral Characteristics for Optimized Signal Chain Design: A 1.08 Analog Front End With Reduced ADC Resolution Requirements.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {10},
number = {6},
pages = {1171-1180},
pmid = {27071192},
issn = {1940-9990},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Amplifiers, Electronic ; Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods ; Equipment Design ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Electrocorticography (ECoG) is an important area of research for Brain-Computer Interface (BCI) development. ECoG, along with some other biopotentials, has spectral characteristics that can be exploited for more optimal front-end performance than is achievable with conventional techniques. This paper optimizes noise performance of such a system and discusses an equalization technique that reduces the analog-to-digital converter (ADC) dynamic range requirements and eliminates the need for a variable gain amplifier (VGA). We demonstrate a fabricated prototype in 1p9m 65 nm CMOS that takes advantage of the presented findings to achieve high-fidelity, full-spectrum ECoG recording. It requires 1.08 μW over a 150 Hz bandwidth for the entire analog front end and only 7 bits of ADC resolution.},
}
@article {pmid27069460,
year = {2016},
author = {Tavakolan, M and Yong, X and Zhang, X and Menon, C},
title = {Classification Scheme for Arm Motor Imagery.},
journal = {Journal of medical and biological engineering},
volume = {36},
number = {},
pages = {12-21},
pmid = {27069460},
issn = {1609-0985},
abstract = {Facilitating independent living of individuals with upper extremity impairment is a compelling goal for our society. The degree of disability of these individuals could potentially be reduced by using robotic devices that assist their movements in activities of daily living. One approach to control such robotic systems is the use of a brain-computer interface, which detects the user's intention. This study proposes a method for estimating the user's intention using electroencephalographic (EEG) signals. The proposed method is capable of discriminating rest from various imagined arm movements, including grasping and elbow flexion. The features extracted from EEG signals are autoregressive model coefficients, root-mean-square amplitude, and waveform length. Support vector machine was used as a classifier, distinguishing class labels corresponding to rest and imagined arm movements. The performance of the proposed method was evaluated using cross-validation. Average accuracies of 91.8 ± 5.8 and 90 ± 4.1 % were obtained for distinguishing rest versus grasping and rest versus elbow flexion. The results show that the proposed scheme provides 18.9, 17.1, and 16.5 % higher classification accuracies for distinguishing rest versus grasping and 21.9, 17.6, and 18.1 % higher classification accuracies for distinguishing rest versus elbow flexion compared with those obtained using filter bank common spatial pattern, band power, and common spatial pattern methods, respectively, which are widely used in the literature.},
}
@article {pmid27066153,
year = {2016},
author = {Chew, LH and Teo, J and Mountstephens, J},
title = {Aesthetic preference recognition of 3D shapes using EEG.},
journal = {Cognitive neurodynamics},
volume = {10},
number = {2},
pages = {165-173},
pmid = {27066153},
issn = {1871-4080},
abstract = {Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.},
}
@article {pmid27065787,
year = {2016},
author = {Bhagat, NA and Venkatakrishnan, A and Abibullaev, B and Artz, EJ and Yozbatiran, N and Blank, AA and French, J and Karmonik, C and Grossman, RG and O'Malley, MK and Francisco, GE and Contreras-Vidal, JL},
title = {Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {122},
pmid = {27065787},
issn = {1662-4548},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS081854/NS/NINDS NIH HHS/United States ; },
abstract = {This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: (1) an adaptive time window was used for extracting features during BMI calibration; (2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and (3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 ± 21.4% on day 4 and 67.1 ± 14.6% on day 5. The overall false positive rate (FPR) across subjects was 27.74 ± 37.46% on day 4 and 27.5 ± 35.64% on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10%). On average, motor intent was detected -367 ± 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.},
}
@article {pmid27065338,
year = {2016},
author = {Paggiaro, A and Birbaumer, N and Cavinato, M and Turco, C and Formaggio, E and Del Felice, A and Masiero, S and Piccione, F},
title = {Magnetoencephalography in Stroke Recovery and Rehabilitation.},
journal = {Frontiers in neurology},
volume = {7},
number = {},
pages = {35},
pmid = {27065338},
issn = {1664-2295},
abstract = {Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility.},
}
@article {pmid27065087,
year = {2016},
author = {Ruiz, S and Birbaumer, N and Sitaram, R},
title = {Editorial: Learned Brain Self-Regulation for Emotional Processing and Attentional Modulation: From Theory to Clinical Applications.},
journal = {Frontiers in behavioral neuroscience},
volume = {10},
number = {},
pages = {62},
pmid = {27065087},
issn = {1662-5153},
}
@article {pmid27064824,
year = {2016},
author = {Luu, TP and He, Y and Brown, S and Nakagame, S and Contreras-Vidal, JL},
title = {Gait adaptation to visual kinematic perturbations using a real-time closed-loop brain-computer interface to a virtual reality avatar.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036006},
pmid = {27064824},
issn = {1741-2552},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological/*physiology ; Adult ; Algorithms ; Biomechanical Phenomena/*physiology ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography ; Gait/*physiology ; Humans ; Imagination/physiology ; Male ; Motor Cortex/physiology ; Photic Stimulation ; Psychomotor Performance/physiology ; Stroke Rehabilitation/methods ; *Virtual Reality ; Walking/physiology ; Young Adult ; },
abstract = {OBJECTIVE: The control of human bipedal locomotion is of great interest to the field of lower-body brain-computer interfaces (BCIs) for gait rehabilitation. While the feasibility of closed-loop BCI systems for the control of a lower body exoskeleton has been recently shown, multi-day closed-loop neural decoding of human gait in a BCI virtual reality (BCI-VR) environment has yet to be demonstrated. BCI-VR systems provide valuable alternatives for movement rehabilitation when wearable robots are not desirable due to medical conditions, cost, accessibility, usability, or patient preferences.<br><br>APPROACH: In this study, we propose a real-time closed-loop BCI that decodes lower limb joint angles from scalp electroencephalography (EEG) during treadmill walking to control a walking avatar in a virtual environment. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1-3 Hz) were used for prediction; thus, the EEG features correspond to time-domain amplitude modulated potentials in the delta band. Virtual kinematic perturbations resulting in asymmetric walking gait patterns of the avatar were also introduced to investigate gait adaptation using the closed-loop BCI-VR system over a period of eight days.<br><br>MAIN RESULTS: Our results demonstrate the feasibility of using a closed-loop BCI to learn to control a walking avatar under normal and altered visuomotor perturbations, which involved cortical adaptations. The average decoding accuracies (Pearson's r values) in real-time BCI across all subjects increased from (Hip: 0.18&#xa0;&#xb1;&#xa0;0.31; Knee: 0.23&#xa0;&#xb1;&#xa0;0.33; Ankle: 0.14&#xa0;&#xb1;&#xa0;0.22) on Day 1 to (Hip: 0.40&#xa0;&#xb1;&#xa0;0.24; Knee: 0.55&#xa0;&#xb1;&#xa0;0.20; Ankle: 0.29&#xa0;&#xb1;&#xa0;0.22) on Day 8.<br><br>SIGNIFICANCE: These findings have implications for the development of a real-time closed-loop EEG-based BCI-VR system for gait rehabilitation after stroke and for understanding cortical plasticity induced by a closed-loop BCI-VR system.},
}
@article {pmid27064728,
year = {2016},
author = {Abu-Alqumsan, M and Peer, A},
title = {Advancing the detection of steady-state visual evoked potentials in brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {036005},
doi = {10.1088/1741-2560/13/3/036005},
pmid = {27064728},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization ; Evoked Potentials, Somatosensory/physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {OBJECTIVE: Spatial filtering has proved to be a powerful pre-processing step in detection of steady-state visual evoked potentials and boosted typical detection rates both in offline analysis and online SSVEP-based brain-computer interface applications. State-of-the-art detection methods and the spatial filters used thereby share many common foundations as they all build upon the second order statistics of the acquired Electroencephalographic (EEG) data, that is, its spatial autocovariance and cross-covariance with what is assumed to be a pure SSVEP response. The present study aims at highlighting the similarities and differences between these methods.<br><br>APPROACH: We consider the canonical correlation analysis (CCA) method as a basis for the theoretical and empirical (with real EEG data) analysis of the state-of-the-art detection methods and the spatial filters used thereby. We build upon the findings of this analysis and prior research and propose a new detection method (CVARS) that combines the power of the canonical variates and that of the autoregressive spectral analysis in estimating the signal and noise power levels.<br><br>MAIN RESULTS: We found that the multivariate synchronization index method and the maximum contrast combination method are variations of the CCA method. All three methods were found to provide relatively unreliable detections in low signal-to-noise ratio (SNR) regimes. CVARS and the minimum energy combination methods were found to provide better estimates for different SNR levels.<br><br>SIGNIFICANCE: Our theoretical and empirical results demonstrate that the proposed CVARS method outperforms other state-of-the-art detection methods when used in an unsupervised fashion. Furthermore, when used in a supervised fashion, a linear classifier learned from a short training session is able to estimate the hidden user intention, including the idle state (when the user is not attending to any stimulus), rapidly, accurately and reliably.},
}
@article {pmid27064508,
year = {2016},
author = {Contreras-Vidal, JL and A Bhagat, N and Brantley, J and Cruz-Garza, JG and He, Y and Manley, Q and Nakagome, S and Nathan, K and Tan, SH and Zhu, F and Pons, JL},
title = {Powered exoskeletons for bipedal locomotion after spinal cord injury.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {031001},
doi = {10.1088/1741-2560/13/3/031001},
pmid = {27064508},
issn = {1741-2552},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Exoskeleton Device ; Female ; Humans ; *Locomotion ; Male ; Middle Aged ; Paralysis/psychology/rehabilitation ; Prosthesis Design ; Quality of Life ; Spinal Cord Injuries/psychology/*rehabilitation ; Walking ; Young Adult ; },
abstract = {OBJECTIVE: Powered exoskeletons promise to increase the quality of life of people with lower-body paralysis or weakened legs by assisting or restoring legged mobility while providing health benefits across multiple physiological systems. Here, a systematic review of the literature on powered exoskeletons addressed critical questions: What is the current evidence of clinical efficacy for lower-limb powered exoskeletons? What are the benefits and risks for individuals with spinal cord injury (SCI)? What are the levels of injury considered in such studies? What are their outcome measures? What are the opportunities for the next generation exoskeletons?<br><br>APPROACH: A systematic search of online databases was performed to identify clinical trials and safety or efficacy studies with lower-limb powered exoskeletons for individuals with SCI. Twenty-two studies with eight powered exoskeletons thus selected, were analyzed based on the protocol design, subject demographics, study duration, and primary/secondary outcome measures for assessing exoskeleton's performance in SCI subjects.<br><br>MAIN RESULTS: Findings show that the level of injury varies across studies, with T10 injuries being represented in 45.4% of the studies. A categorical breakdown of outcome measures revealed 63% of these measures were gait and ambulation related, followed by energy expenditure (16%), physiological improvements (13%), and usability and comfort (8%). Moreover, outcome measures varied across studies, and none had measures spanning every category, making comparisons difficult.<br><br>SIGNIFICANCE: This review of the literature shows that a majority of current studies focus on thoracic level injury as well as there is an emphasis on ambulatory-related primary outcome measures. Future research should: 1) develop criteria for optimal selection and training of patients most likely to benefit from this technology, 2) design multimodal gait intention detection systems that engage and empower the user, 3) develop real-time monitoring and diagnostic capabilities, and 4) adopt comprehensive metrics for assessing safety, benefits, and usability.},
}
@article {pmid27061243,
year = {2016},
author = {Fry, A and Mullinger, KJ and O'Neill, GC and Barratt, EL and Morris, PG and Bauer, M and Folland, JP and Brookes, MJ},
title = {Modulation of post-movement beta rebound by contraction force and rate of force development.},
journal = {Human brain mapping},
volume = {37},
number = {7},
pages = {2493-2511},
pmid = {27061243},
issn = {1097-0193},
support = {G0901321/MRC_/Medical Research Council/United Kingdom ; MR/K005464/1/MRC_/Medical Research Council/United Kingdom ; MR/M006301/1/MRC_/Medical Research Council/United Kingdom ; MR/M009122/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Beta Rhythm/*physiology ; Brain/*physiology ; Female ; Humans ; Isometric Contraction/*physiology ; Magnetoencephalography ; Male ; Movement/*physiology ; Muscle Strength Dynamometer ; Time Factors ; Wrist/physiology ; },
abstract = {Movement induced modulation of the beta rhythm is one of the most robust neural oscillatory phenomena in the brain. In the preparation and execution phases of movement, a loss in beta amplitude is observed [movement related beta decrease (MRBD)]. This is followed by a rebound above baseline on movement cessation [post movement beta rebound (PMBR)]. These effects have been measured widely, and recent work suggests that they may have significant importance. Specifically, they have potential to form the basis of biomarkers for disease, and have been used in neuroscience applications ranging from brain computer interfaces to markers of neural plasticity. However, despite the robust nature of both MRBD and PMBR, the phenomena themselves are poorly understood. In this study, we characterise MRBD and PMBR during a carefully controlled isometric wrist flexion paradigm, isolating two fundamental movement parameters; force output, and the rate of force development (RFD). Our results show that neither altered force output nor RFD has a significant effect on MRBD. In contrast, PMBR was altered by both parameters. Higher force output results in greater PMBR amplitude, and greater RFD results in a PMBR which is higher in amplitude and shorter in duration. These findings demonstrate that careful control of movement parameters can systematically change PMBR. Further, for temporally protracted movements, the PMBR can be over 7 s in duration. This means accurate control of movement and judicious selection of paradigm parameters are critical in future clinical and basic neuroscientific studies of sensorimotor beta oscillations. Hum Brain Mapp 37:2493-2511, 2016. © 2016 Wiley Periodicals, Inc.},
}
@article {pmid27059999,
year = {2016},
author = {Broniec, A},
title = {Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {54},
number = {12},
pages = {1935-1947},
pmid = {27059999},
issn = {1741-0444},
mesh = {Adult ; Electrodes ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Humans ; *Imagination ; Male ; *Movement ; Signal Processing, Computer-Assisted ; *Spectrum Analysis ; Young Adult ; },
abstract = {Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain-computer interface.},
}
@article {pmid27052520,
year = {2016},
author = {Marchesotti, S and Bassolino, M and Serino, A and Bleuler, H and Blanke, O},
title = {Quantifying the role of motor imagery in brain-machine interfaces.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {24076},
pmid = {27052520},
issn = {2045-2322},
mesh = {Adult ; Behavior ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Motor Activity/*physiology ; Psychomotor Performance/physiology ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the behavioral accuracy of MI, and in electroencephalographic variables, were able to differentiate between high- versus low-aptitude BMI users. High-aptitude BMI users showed higher MI accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. Additionally, for the first time, we applied mental chronometry, a measure quantifying the degree to which imagined and executed movements share a similar temporal profile. We also identified enhanced lateralized μ-band oscillations over sensorimotor cortices during MI in high- versus low-aptitude BMI users. These findings reveal that subjective, behavioral, and EEG measurements of MI are intimately linked to BMI control. We propose that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).},
}
@article {pmid27051414,
year = {2016},
author = {Martišius, I and Damaševičius, R},
title = {A Prototype SSVEP Based Real Time BCI Gaming System.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {3861425},
pmid = {27051414},
issn = {1687-5273},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials/physiology ; Humans ; Machine Learning ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; User-Computer Interface ; *Video Games ; },
abstract = {Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.},
}
@article {pmid27050535,
year = {2016},
author = {Hwang, HJ and Choi, H and Kim, JY and Chang, WD and Kim, DW and Kim, K and Jo, S and Im, CH},
title = {Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.},
journal = {Journal of biomedical optics},
volume = {21},
number = {9},
pages = {091303},
doi = {10.1117/1.JBO.21.9.091303},
pmid = {27050535},
issn = {1560-2281},
mesh = {Adult ; Brain/*blood supply/*diagnostic imaging/physiology ; *Brain-Computer Interfaces ; Hemoglobins/analysis ; Humans ; Male ; Neuropsychological Tests ; Oxyhemoglobins/analysis ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/instrumentation/*methods ; Young Adult ; },
abstract = {In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to “yes” or “no” intentions (e.g., mental arithmetic calculation for “yes”). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient’s internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an “fNIRS-based direct intention decoding” paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing “yes” or “no” intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.},
}
@article {pmid27046883,
year = {2017},
author = {Ge, S and Wang, R and Leng, Y and Wang, H and Lin, P and Iramina, K},
title = {A Double-Partial Least-Squares Model for the Detection of Steady-State Visual Evoked Potentials.},
journal = {IEEE journal of biomedical and health informatics},
volume = {21},
number = {4},
pages = {897-903},
doi = {10.1109/JBHI.2016.2546311},
pmid = {27046883},
issn = {2168-2208},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Least-Squares Analysis ; Male ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Establishing a high-accuracy and training-free brain-computer interface (BCI) system is essential for improving BCI practicality. In this study, we propose for the first time a training-free double-partial least-squares (D-PLS) model for steady-state visual evoked potential (SSVEP) detection that consists of double-layer PLS, a PLS spatial filter, and a PLS feature extractor. Electroencephalographic data from 11 healthy volunteers under four different visual stimulation frequencies were used to test the proposed method. Compared with commonly used spatial filters, minimum energy combination and average maximum contrast combination, the classification accuracies could be improved 2-10% by our proposed PLS spatial filter. Furthermore, our proposed PLS feature extractor achieved better performance than current feature extraction methods, namely power spectral density analysis, canonical correlation analysis, and the use of the least absolute shrinkage and selection operator. The average classification accuracy for our proposed D-PLS model exceeded [Formula: see text] when the signal time window was longer than 3.5 s and reached as high as [Formula: see text] when the time window was 5 s. Moreover, the D-PLS model can be easily set without training data, so it can be used widely in SSVEP-based BCI systems.},
}
@article {pmid27046866,
year = {2017},
author = {Antelis, JM and Montesano, L and Ramos-Murguialday, A and Birbaumer, N and Minguez, J},
title = {Decoding Upper Limb Movement Attempt From EEG Measurements of the Contralesional Motor Cortex in Chronic Stroke Patients.},
journal = {IEEE transactions on bio-medical engineering},
volume = {64},
number = {1},
pages = {99-111},
doi = {10.1109/TBME.2016.2541084},
pmid = {27046866},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Arm ; *Brain-Computer Interfaces ; Chronic Disease ; Electroencephalography/*methods ; Female ; Humans ; Imagination ; Intention ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Movement Disorders/diagnosis/etiology/*physiopathology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/complications/diagnosis/*physiopathology ; },
abstract = {GOAL: Stroke survivors usually require motor rehabilitation therapy as, due to the lesion, they completely or partially loss mobility in the limbs. Brain-computer interface technology offers the possibility of decoding the attempt to move paretic limbs in real time to improve existing motor rehabilitation. However, a major difficulty for the practical application of the BCI to stroke survivors is that the brain rhythms that encode the motor states might be diminished due to the lesion. This study investigates the continuous decoding of natural attempt to move the paralyzed upper limb in stroke survivors from electroencephalographic signals of the unaffected contralesional motor cortex.<br><br>RESULTS: Experiments were carried out with the aid of six severely affected chronic stroke patients performing/attempting self-selected reaching movements of the unaffected/affected upper limb. The electroencephalographic (EEG) analysis showed significant cortical activation on the uninjured motor cortex when moving the contralateral unaffected arm and in the attempt to move the ipsilateral affected arm. Using this activity, significant continuous decoding of movement was obtained in six out of six participants in movements of the unaffected limb, and in four out of six participants in the attempt to move the affected limb.<br><br>CONCLUSION: This study showed that it is possible to construct a decoder of the attempt to move the paretic arm for chronic stroke patients using the EEG activity of the healthy contralesional motor cortex.<br><br>SIGNIFICANCE: This decoding model could provide to stroke survivors with a natural, easy, and intuitive way to achieve control of BCIs or robot-assisted rehabilitation devices.},
}
@article {pmid27046109,
year = {2016},
author = {Naros, G and Naros, I and Grimm, F and Ziemann, U and Gharabaghi, A},
title = {Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance.},
journal = {NeuroImage},
volume = {134},
number = {},
pages = {142-152},
doi = {10.1016/j.neuroimage.2016.03.016},
pmid = {27046109},
issn = {1095-9572},
mesh = {Adult ; Beta Rhythm/*physiology ; Biological Clocks/physiology ; Brain Mapping ; Female ; Humans ; Male ; Movement/*physiology ; Neurofeedback/*methods/*physiology ; Psychomotor Performance/*physiology ; *Reinforcement, Psychology ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; },
abstract = {Self-regulation of sensorimotor oscillations is currently researched in neurorehabilitation, e.g. for priming subsequent physiotherapy in stroke patients, and may be modulated by neurofeedback or transcranial brain stimulation. It has still to be demonstrated, however, whether and under which training conditions such brain self-regulation could also result in motor gains. Thirty-two right-handed, healthy subjects participated in a three-day intervention during which they performed 462 trials of kinesthetic motor-imagery while a brain-robot interface (BRI) turned event-related β-band desynchronization of the left sensorimotor cortex into the opening of the right hand by a robotic orthosis. Different training conditions were compared in a parallel-group design: (i) adaptive classifier thresholding and contingent feedback, (ii) adaptive classifier thresholding and non-contingent feedback, (iii) non-adaptive classifier thresholding and contingent feedback, and (iv) non-adaptive classifier thresholding and non-contingent feedback. We studied the task-related cortical physiology with electroencephalography and the behavioral performance in a subsequent isometric motor task. Contingent neurofeedback and adaptive classifier thresholding were critical for learning brain self-regulation which, in turn, led to behavioral gains after the intervention. The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement. Operant learning of brain self-regulation with a BRI may offer a therapeutic perspective for severely affected stroke patients lacking residual hand function.},
}
@article {pmid27035820,
year = {2016},
author = {Shanechi, MM and Orsborn, AL and Carmena, JM},
title = {Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.},
journal = {PLoS computational biology},
volume = {12},
number = {4},
pages = {e1004730},
pmid = {27035820},
issn = {1553-7358},
mesh = {Action Potentials ; Adaptation, Physiological ; Animals ; Behavior, Animal ; Biomechanical Phenomena ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Computer Simulation ; Feedback, Sensory ; Humans ; Macaca mulatta/physiology/psychology ; Male ; Models, Neurological ; Motor Cortex/physiology ; Software Design ; Task Performance and Analysis ; },
abstract = {Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.},
}
@article {pmid27035169,
year = {2017},
author = {Wilson, CD and Safavi-Abbasi, S and Sun, H and Kalani, MY and Zhao, YD and Levitt, MR and Hanel, RA and Sauvageau, E and Mapstone, TB and Albuquerque, FC and McDougall, CG and Nakaji, P and Spetzler, RF},
title = {Meta-analysis and systematic review of risk factors for shunt dependency after aneurysmal subarachnoid hemorrhage.},
journal = {Journal of neurosurgery},
volume = {126},
number = {2},
pages = {586-595},
doi = {10.3171/2015.11.JNS152094},
pmid = {27035169},
issn = {1933-0693},
mesh = {*Cerebrospinal Fluid Shunts ; Humans ; Hydrocephalus/etiology/*therapy ; Intracranial Aneurysm/complications/*therapy ; Risk Factors ; Subarachnoid Hemorrhage/complications/*therapy ; },
abstract = {OBJECTIVE Aneurysmal subarachnoid hemorrhage (aSAH) may be complicated by hydrocephalus in 6.5%-67% of cases. Some patients with aSAH develop shunt dependency, which is often managed by ventriculoperitoneal shunt placement. The objectives of this study were to review published risk factors for shunt dependency in patients with aSAH, determine the level of evidence for each factor, and calculate the magnitude of each risk factor to better guide patient management. METHODS The authors searched PubMed and MEDLINE databases for Level A and Level B articles published through December 31, 2014, that describe factors affecting shunt dependency after aSAH and performed a systematic review and meta-analysis, stratifying the existing data according to level of evidence. RESULTS On the basis of the results of the meta-analysis, risk factors for shunt dependency included high Fisher grade (OR 7.74, 95% CI 4.47-13.41), acute hydrocephalus (OR 5.67, 95% CI 3.96-8.12), in-hospital complications (OR 4.91, 95% CI 2.79-8.64), presence of intraventricular blood (OR 3.93, 95% CI 2.80-5.52), high Hunt and Hess Scale score (OR 3.25, 95% CI 2.51-4.21), rehemorrhage (OR 2.21, 95% CI 1.24-3.95), posterior circulation location of the aneurysm (OR 1.85, 95% CI 1.35-2.53), and age ≥ 60 years (OR 1.81, 95% CI 1.50-2.19). The only risk factor included in the meta-analysis that did not reach statistical significance was female sex (OR 1.13, 95% CI 0.77-1.65). CONCLUSIONS The authors identified several risk factors for shunt dependency in aSAH patients that help predict which patients are likely to require a permanent shunt. Although some of these risk factors are not independent of each other, this information assists clinicians in identifying at-risk patients and managing their treatment.},
}
@article {pmid27034541,
year = {2016},
author = {Salisbury, DB and Dahdah, M and Driver, S and Parsons, TD and Richter, KM},
title = {Virtual reality and brain computer interface in neurorehabilitation.},
journal = {Proceedings (Baylor University. Medical Center)},
volume = {29},
number = {2},
pages = {124-127},
pmid = {27034541},
issn = {0899-8280},
abstract = {The potential benefit of technology to enhance recovery after central nervous system injuries is an area of increasing interest and exploration. The primary emphasis to date has been motor recovery/augmentation and communication. This paper introduces two original studies to demonstrate how advanced technology may be integrated into subacute rehabilitation. The first study addresses the feasibility of brain computer interface with patients on an inpatient spinal cord injury unit. The second study explores the validity of two virtual environments with acquired brain injury as part of an intensive outpatient neurorehabilitation program. These preliminary studies support the feasibility of advanced technologies in the subacute stage of neurorehabilitation. These modalities were well tolerated by participants and could be incorporated into patients' inpatient and outpatient rehabilitation regimens without schedule disruptions. This paper expands the limited literature base regarding the use of advanced technologies in the early stages of recovery for neurorehabilitation populations and speaks favorably to the potential integration of brain computer interface and virtual reality technologies as part of a multidisciplinary treatment program.},
}
@article {pmid27032003,
year = {2016},
author = {Cipolle, MD and Ingraham Lopresto, BC and Pirrung, JM and Meyer, EM and Manta, C and Nightingale, AS and Robinson, EJ and Tinkoff, GH},
title = {Embedding a trauma hospitalist in the trauma service reduces mortality and 30-day trauma-related readmissions.},
journal = {The journal of trauma and acute care surgery},
volume = {81},
number = {1},
pages = {178-183},
doi = {10.1097/TA.0000000000001062},
pmid = {27032003},
issn = {2163-0763},
mesh = {Aged ; Comorbidity ; Delaware ; Female ; *Hospital Mortality ; *Hospitalists ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Patient Care Team/*organization &amp; administration ; Patient Readmission/*statistics &amp; numerical data ; Propensity Score ; Retrospective Studies ; *Trauma Centers ; Workforce ; },
abstract = {BACKGROUND: Recognizing the increasing age and comorbid conditions of patients admitted to our trauma service, we embedded a hospitalist on the trauma service at our Level I trauma center.This program was initiated in January 2013. This study was designed to investigate differences in outcomes between trauma patients who received care from the trauma hospitalist (THOSP) program and similarly medically complex trauma patients who did not receive THOSP care.<br><br>METHODS: There were 566 patients comanaged with THOSP between December 2013 and November 2014. These patients were matched (1:2) with propensity scores to a contemporaneous control group based on age, Injury Severity Score (ISS), and comorbid conditions. Outcomes examined included mortality, trauma-related readmissions, upgrades to the intensive care unit, hospital length of stay, the development of in-hospital complications, and the frequency of obtaining medical subspecialist consultation. Differences in outcomes were compared with Mann-Whitney U-test or &#x3c7; test as appropriate.<br><br>RESULTS: High-quality matching resulted in the loss of 97 THOSP patients for the final analysis. Table 1 shows the balance between the two groups after matching. While there was a 1-day increase in hospital length of stay and an increase in upgrades to the intensive care unit, there was a reduction in mortality, trauma-related readmissions, and the development of renal failure after implementation of the THOSP program (Table 2). Implementation of this program made no significant difference in the frequency of cardiology, nephrology, neurology, or endocrinology consultations. There was also no difference in the development of the complications of venous thromboembolism, pneumonia, stroke, urinary tract infection, bacteremia, or alcohol withdrawal.<br><br>CONCLUSION: Our study provides evidence that embedding a hospitalist on the trauma service reduces mortality and trauma-related readmissions. A reason for these improved outcomes may be related to THOSP "vigilance."<br><br>LEVEL OF EVIDENCE: Therapeutic/care management study, level IV.},
}
@article {pmid27024937,
year = {2015},
author = {Raby, M},
title = {Ark and Archive: Making a Place for Long-Term Research on Barro Colorado Island, Panama.},
journal = {Isis; an international review devoted to the history of science and its cultural influences},
volume = {106},
number = {4},
pages = {798-824},
doi = {10.1086/684610},
pmid = {27024937},
issn = {0021-1753},
mesh = {Ecology/*history/organization &amp; administration ; History, 20th Century ; Humans ; Natural History/*history ; Panama ; Research/*history/organization &amp; administration ; *Tropical Climate ; },
abstract = {Barro Colorado Island (BCI), Panama, may be the most studied tropical forest in the world. A 1,560-hectare island created by the flooding of the Panama Canal, BCI became a nature reserve and biological research station in 1923. Contemporaries saw the island as an "ark" preserving a sample of primeval tropical nature for scientific study. BCI was not simply "set aside," however. The project of making it a place for science significantly reshaped the island through the twentieth century. This essay demonstrates that BCI was constructed specifically to allow long-term observation of tropical organisms--their complex behaviors, life histories, population dynamics, and changing species composition. An evolving system of monitoring and information technology transformed the island into a living scientific "archive," in which the landscape became both an object and a repository of scientific knowledge. As a research site, BCI enabled a long-term, place-based form of collective empiricism, focused on the study of the ecology of a single tropical island. This essay articulates tropical ecology as a "science of the archive" in order to examine the origins of practices of environmental surveillance that have become central to debates about global change and conservation.},
}
@article {pmid27014511,
year = {2016},
author = {Maskeliunas, R and Damasevicius, R and Martisius, I and Vasiljevas, M},
title = {Consumer-grade EEG devices: are they usable for control tasks?.},
journal = {PeerJ},
volume = {4},
number = {},
pages = {e1746},
pmid = {27014511},
issn = {2167-8359},
abstract = {We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.},
}
@article {pmid27014102,
year = {2016},
author = {Dyck, MS and Mathiak, KA and Bergert, S and Sarkheil, P and Koush, Y and Alawi, EM and Zvyagintsev, M and Gaebler, AJ and Shergill, SS and Mathiak, K},
title = {Targeting Treatment-Resistant Auditory Verbal Hallucinations in Schizophrenia with fMRI-Based Neurofeedback - Exploring Different Cases of Schizophrenia.},
journal = {Frontiers in psychiatry},
volume = {7},
number = {},
pages = {37},
pmid = {27014102},
issn = {1664-0640},
abstract = {Auditory verbal hallucinations (AVHs) are a hallmark of schizophrenia and can significantly impair patients' emotional, social, and occupational functioning. Despite progress in psychopharmacology, over 25% of schizophrenia patients suffer from treatment-resistant hallucinations. In the search for alternative treatment methods, neurofeedback (NF) emerges as a promising therapy tool. NF based on real-time functional magnetic resonance imaging (rt-fMRI) allows voluntarily change of the activity in a selected brain region - even in patients with schizophrenia. This study explored effects of NF on ongoing AVHs. The selected participants were trained in the self-regulation of activity in the anterior cingulate cortex (ACC), a key monitoring region involved in generation and intensity modulation of AVHs. Using rt-fMRI, three right-handed patients, suffering from schizophrenia and ongoing, treatment-resistant AVHs, learned control over ACC activity on three separate days. The effect of NF training on hallucinations' severity was assessed with the Auditory Vocal Hallucination Rating Scale (AVHRS) and on the affective state - with the Positive and Negative Affect Schedule (PANAS). All patients yielded significant upregulation of the ACC and reported subjective improvement in some aspects of AVHs (AVHRS) such as disturbance and suffering from the voices. In general, mood (PANAS) improved during NF training, though two patients reported worse mood after NF on the third day. ACC and reward system activity during NF learning and specific effects on mood and symptoms varied across the participants. None of them profited from the last training set in the prolonged three-session training. Moreover, individual differences emerged in brain networks activated with NF and in symptom changes, which were related to the patients' symptomatology and disease history. NF based on rt-fMRI seems a promising tool in therapy of AVHs. The patients, who suffered from continuous hallucinations for years, experienced symptom changes that may be attributed to the NF training. In order to assess the effectiveness of NF as a therapeutic method, this effect has to be studied systematically in larger groups; further, long-term effects need to be assessed. Particularly in schizophrenia, future NF studies should take into account the individual differences in reward processing, fatigue, and motivation to develop individualized training protocols.},
}
@article {pmid27014048,
year = {2016},
author = {Bigdely-Shamlo, N and Makeig, S and Robbins, KA},
title = {Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.},
journal = {Frontiers in neuroinformatics},
volume = {10},
number = {},
pages = {7},
pmid = {27014048},
issn = {1662-5196},
support = {R01 MH084819/MH/NIMH NIH HHS/United States ; R01 NS047293/NS/NINDS NIH HHS/United States ; },
abstract = {Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org).},
}
@article {pmid27013690,
year = {2016},
author = {Flint, RD and Scheid, MR and Wright, ZA and Solla, SA and Slutzky, MW},
title = {Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {36},
number = {12},
pages = {3623-3632},
pmid = {27013690},
issn = {1529-2401},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; R25 GM079300/GM/NIGMS NIH HHS/United States ; T32 HD057845/HD/NICHD NIH HHS/United States ; R25 GM79300/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Biofeedback, Psychology/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials, Motor/*physiology ; Female ; Haplorhini ; Longitudinal Studies ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {UNLABELLED: The human motor system is capable of remarkably precise control of movements--consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3 d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the "task-relevant space") were significantly more stable than were projections into the task-irrelevant (or "task-null") space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the task-irrelevant space to vary substantially from trial to trial. We found that the brain appears capable of maintaining stable movement representations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions.<br><br>SIGNIFICANCE STATEMENT: It is unknown whether cortical signals are stable for more than a few weeks. Here, we demonstrate that motor cortical signals can exhibit high stability over several years. This result is particularly important to brain-machine interfaces because it could enable stable performance with infrequent recalibration. Although we can maintain movement accuracy over time, movement components that are unrelated to the goals of a task (such as elbow position during reaching) often vary from trial to trial. This is consistent with the minimum intervention principle of optimal feedback control. We provide evidence that the motor cortex acts according to this principle: cortical activity is more stable in the task-relevant space and more variable in the task-irrelevant space.},
}
@article {pmid27008670,
year = {2016},
author = {Wu, D and Lawhern, VJ and Hairston, WD and Lance, BJ},
title = {Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {11},
pages = {1125-1137},
doi = {10.1109/TNSRE.2016.2544108},
pmid = {27008670},
issn = {1558-0210},
mesh = {*Algorithms ; Brain-Computer Interfaces/*standards ; Electrodes ; Electroencephalography/*instrumentation/*standards ; Equipment Design ; Equipment Failure Analysis ; Humans ; *Machine Learning ; Online Systems ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Electroencephalography (EEG) headsets are the most commonly used sensing devices for brain-computer interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these advantages are limited if the data arise from different hardware systems, which often vary between application spaces. Currently, this creates a need to recalibrate classifiers, which negatively affects people's interest in using such systems. In this paper, we employ active weighted adaptation regularization (AwAR), which integrates weighted adaptation regularization (wAR) and active learning, to expedite the calibration process. wAR makes use of labeled data from the previous headset and handles class-imbalance, and active learning selects the most informative samples from the new headset to label. Experiments on single-trial event-related potential classification show that AwAR can significantly increase the classification accuracy, given the same number of labeled samples from the new headset. In other words, AwAR can effectively reduce the number of labeled samples required from the new headset, given a desired classification accuracy, suggesting value in collating data for use in wide scale transfer-learning applications.},
}
@article {pmid27008657,
year = {2016},
author = {Moritz, CT and Ruther, P and Goering, S and Stett, A and Ball, T and Burgard, W and Chudler, EH and Rao, RP},
title = {New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {7},
pages = {1354-1367},
doi = {10.1109/TBME.2016.2543662},
pmid = {27008657},
issn = {1558-2531},
mesh = {*Biomedical Engineering ; *Brain/physiology/surgery ; *Brain-Computer Interfaces ; Electrocorticography ; Humans ; Models, Biological ; *Neurosciences ; *Prosthesis Design ; },
abstract = {GOAL: To identify and overcome barriers to creating new neurotechnologies capable of restoring both motor and sensory function in individuals with neurological conditions.<br><br>METHODS: This report builds upon the outcomes of a joint workshop between the US National Science Foundation and the German Research Foundation on New Perspectives in Neuroengineering and Neurotechnology convened in Arlington, VA, USA, November 13-14, 2014.<br><br>RESULTS: The participants identified key technological challenges for recording and manipulating neural activity, decoding, and interpreting brain data in the presence of plasticity, and early considerations of ethical and social issues pertinent to the adoption of neurotechnologies.<br><br>CONCLUSIONS: The envisaged progress in neuroengineering requires tightly integrated hardware and signal processing efforts, advances in understanding of physiological adaptations to closed-loop interactions with neural devices, and an open dialog with stakeholders and potential end-users of neurotechnology.<br><br>SIGNIFICANCE: The development of new neurotechnologies (e.g., bidirectional brain-computer interfaces) could significantly improve the quality of life of people living with the effects of brain or spinal cord injury, or other neurodegenerative diseases. Focused efforts aimed at overcoming the remaining barriers at the electrode tissue interface, developing implantable hardware with on-board computation, and refining stimulation methods to precisely activate neural tissue will advance both our understanding of brain function and our ability to treat currently intractable disorders of the nervous system.},
}
@article {pmid27006652,
year = {2016},
author = {Petti, M and Toppi, J and Babiloni, F and Cincotti, F and Mattia, D and Astolfi, L},
title = {EEG Resting-State Brain Topological Reorganization as a Function of Age.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {6243694},
pmid = {27006652},
issn = {1687-5273},
mesh = {Adult ; Aging/*physiology ; Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Rest ; Support Vector Machine ; Young Adult ; },
abstract = {Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20-63 years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.},
}
@article {pmid27005002,
year = {2016},
author = {Xu, M and Liu, J and Chen, L and Qi, H and He, F and Zhou, P and Wan, B and Ming, D},
title = {Incorporation of Inter-Subject Information to Improve the Accuracy of Subject-Specific P300 Classifiers.},
journal = {International journal of neural systems},
volume = {26},
number = {3},
pages = {1650010},
doi = {10.1142/S0129065716500106},
pmid = {27005002},
issn = {1793-6462},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Datasets as Topic ; Electroencephalography/*methods ; *Event-Related Potentials, P300/physiology ; Female ; Humans ; Machine Learning ; Male ; Young Adult ; },
abstract = {Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain-computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject's data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra- and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject's data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms.},
}
@article {pmid27001943,
year = {2016},
author = {Lancashire, HT and Vanhoestenberghe, A and Pendegrass, CJ and Ajam, YA and Magee, E and Donaldson, N and Blunn, GW},
title = {Microchannel neural interface manufacture by stacking silicone and metal foil laminae.},
journal = {Journal of neural engineering},
volume = {13},
number = {3},
pages = {034001},
doi = {10.1088/1741-2560/13/3/034001},
pmid = {27001943},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Impedance ; Electrodes ; Electrodes, Implanted ; Equipment Failure Analysis ; Interferometry ; Male ; Metals/*chemistry ; Neurons/*physiology ; Peripheral Nerves/physiology ; Prosthesis Design ; Rats ; Rats, Inbred Lew ; Silicones/*chemistry ; },
abstract = {OBJECTIVE: Microchannel neural interfaces (MNIs) overcome problems with recording from peripheral nerves by amplifying signals independent of node of Ranvier position. Selective recording and stimulation using an MNI requires good insulation between microchannels and a high electrode density. We propose that stacking microchannel laminae will improve selectivity over single layer MNI designs due to the increase in electrode number and an improvement in microchannel sealing.<br><br>APPROACH: This paper describes a manufacturing method for creating MNIs which overcomes limitations on electrode connectivity and microchannel sealing. Laser cut silicone-metal foil laminae were stacked using plasma bonding to create an array of microchannels containing tripolar electrodes. Electrodes were DC etched and electrode impedance and cyclic voltammetry were tested.<br><br>MAIN RESULTS: MNIs with 100 &#x3bc;m and 200 &#x3bc;m diameter microchannels were manufactured. High electrode density MNIs are achievable with electrodes present in every microchannel. Electrode impedances of 27.2&#xa0;&#xb1;&#xa0;19.8 k&#x3a9; at 1 kHz were achieved. Following two months of implantation in Lewis rat sciatic nerve, micro-fascicles were observed regenerating through the MNI microchannels.<br><br>SIGNIFICANCE: Selective MNIs with the peripheral nervous system may allow upper limb amputees to control prostheses intuitively.},
}
@article {pmid26996601,
year = {2016},
author = {Keynan, JN and Meir-Hasson, Y and Gilam, G and Cohen, A and Jackont, G and Kinreich, S and Ikar, L and Or-Borichev, A and Etkin, A and Gyurak, A and Klovatch, I and Intrator, N and Hendler, T},
title = {Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging-Inspired Electroencephalography Improves Implicit Emotion Regulation.},
journal = {Biological psychiatry},
volume = {80},
number = {6},
pages = {490-496},
doi = {10.1016/j.biopsych.2015.12.024},
pmid = {26996601},
issn = {1873-2402},
mesh = {Adult ; Amygdala/*physiology ; Brain-Computer Interfaces/*psychology ; Down-Regulation/physiology ; Electroencephalography/*methods ; Emotions/*physiology ; Humans ; Machine Learning ; Magnetic Resonance Imaging/*methods ; Neurofeedback/physiology ; Photic Stimulation ; Young Adult ; },
abstract = {The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level-dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level-dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.},
}
@article {pmid26994876,
year = {2016},
author = {Gilmour, AD and Woolley, AJ and Poole-Warren, LA and Thomson, CE and Green, RA},
title = {A critical review of cell culture strategies for modelling intracortical brain implant material reactions.},
journal = {Biomaterials},
volume = {91},
number = {},
pages = {23-43},
doi = {10.1016/j.biomaterials.2016.03.011},
pmid = {26994876},
issn = {1878-5905},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Cell Culture Techniques/*methods ; *Electrodes, Implanted ; Humans ; Wound Healing ; },
abstract = {The capacity to predict in vivo responses to medical devices in humans currently relies greatly on implantation in animal models. Researchers have been striving to develop in vitro techniques that can overcome the limitations associated with in vivo approaches. This review focuses on a critical analysis of the major in vitro strategies being utilized in laboratories around the world to improve understanding of the biological performance of intracortical, brain-implanted microdevices. Of particular interest to the current review are in vitro models for studying cell responses to penetrating intracortical devices and their materials, such as electrode arrays used for brain computer interface (BCI) and deep brain stimulation electrode probes implanted through the cortex. A background on the neural interface challenge is presented, followed by discussion of relevant in vitro culture strategies and their advantages and disadvantages. Future development of 2D culture models that exhibit developmental changes capable of mimicking normal, postnatal development will form the basis for more complex accurate predictive models in the future. Although not within the scope of this review, innovations in 3D scaffold technologies and microfluidic constructs will further improve the utility of in vitro approaches.},
}
@article {pmid26992723,
year = {2016},
author = {Shin, YS and On, JW and Kim, MK},
title = {Clinical significance of diabetes mellitus on detrusor functionality on stress urinary incontinent women without bladder outlet obstruction.},
journal = {International urogynecology journal},
volume = {27},
number = {10},
pages = {1557-1561},
pmid = {26992723},
issn = {1433-3023},
mesh = {Aged ; Case-Control Studies ; Diabetes Complications/*physiopathology ; Female ; Humans ; Middle Aged ; Muscle Contraction ; Retrospective Studies ; Urinary Bladder Neck Obstruction/*complications ; Urinary Bladder, Overactive/*complications ; Urinary Incontinence, Stress/*complications ; Urination/*physiology ; },
abstract = {INTRODUCTION AND HYPOTHESIS: Our aim was to evaluate the effect of diabetes mellitus (DM) on detrusor contractility (DC) in women without bladder outlet obstruction (BOO) by using urodynamic study (UDS).<br><br>METHODS: We reviewed the clinical records of 863 consecutive women without BOO, each of whom was diagnosed with stress urinary incontinence (SUI) by UDS. Uroflowmetry measurements included maximal flow rate (Qmax), time to Qmax, voided volume, and postvoid residual urine volume (PVR). Data from filling cystometry included the first strong desire to void and the Valsalva leak-point pressure (VLPP). For voiding cystometry data, detrusor pressure at Qmax (Pdet@Qmax) and bladder contractility index (BCI) were analyzed. In the DM group, the level of glycosylated hemoglobin (HbA1c) and DM duration were measured.<br><br>RESULTS: After the application of exclusion criteria, complete UDS data of 708 patients were available. The cohort was divided into two groups according to DM status. The DM group comprised 92 (12.9&#xa0;%) patients, the non-DM group 616 (87.0&#xa0;%). Mean maximal flow rate and Pdet@Qmax and bladder contractility index were lower in the DM group, in whom mean DM duration was 9.24&#x2009;&#xb1;&#x2009;7.63&#xa0;years and mean HbA1c level 7.27&#x2009;&#xb1;&#x2009;1.43&#xa0;%. DM duration was significantly correlated with Qmax (-0.309, p&#x2009;=&#x2009;0.003), Pdet@Qmax (-0.369, p&#x2009;<&#x2009;0.001), and BCI (-0.409, p&#x2009;<&#x2009;0.001). Moreover, the HbA1c level was significantly correlated with Qmax (-0.256, p&#x2009;=&#x2009;0.016), Pdet@Qmax (-0.231, p&#x2009;=&#x2009;0.026), and BCI (-0.308, p&#x2009;=&#x2009;0.002).<br><br>CONCLUSIONS: Our UDS data revealed that DM is associated with impaired DC in women without BOO. Moreover, longer DM duration and poor glycemic control were associated with impaired DC.},
}
@article {pmid26987662,
year = {2016},
author = {Downey, JE and Weiss, JM and Muelling, K and Venkatraman, A and Valois, JS and Hebert, M and Bagnell, JA and Schwartz, AB and Collinger, JL},
title = {Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {},
pages = {28},
pmid = {26987662},
issn = {1743-0003},
mesh = {Adult ; Brain/physiopathology ; *Brain-Computer Interfaces ; Female ; Hand/physiopathology ; Hand Strength ; Humans ; Male ; Middle Aged ; Movement ; Neurological Rehabilitation/*instrumentation ; *Quadriplegia/physiopathology ; Robotics/*methods ; *Upper Extremity/physiopathology ; },
abstract = {BACKGROUND: Recent studies have shown that brain-machine interfaces (BMIs) offer great potential for restoring upper limb function. However, grasping objects is a complicated task and the signals extracted from the brain may not always be capable of driving these movements reliably. Vision-guided robotic assistance is one possible way to improve BMI performance. We describe a method of shared control where the user controls a prosthetic arm using a BMI and receives assistance with positioning the hand when it approaches an object.<br><br>METHODS: Two human subjects with tetraplegia used a robotic arm to complete object transport tasks with and without shared control. The shared control system was designed to provide a balance between BMI-derived intention and computer assistance. An autonomous robotic grasping system identified and tracked objects and defined stable grasp positions for these objects. The system identified when the user intended to interact with an object based on the BMI-controlled movements of the robotic arm. Using shared control, BMI controlled movements and autonomous grasping commands were blended to ensure secure grasps.<br><br>RESULTS: Both subjects were more successful on object transfer tasks when using shared control compared to BMI control alone. Movements made using shared control were more accurate, more efficient, and less difficult. One participant attempted a task with multiple objects and successfully lifted one of two closely spaced objects in 92&#xa0;% of trials, demonstrating the potential for users to accurately execute their intention while using shared control.<br><br>CONCLUSIONS: Integration of BMI control with vision-guided robotic assistance led to improved performance on object transfer tasks. Providing assistance while maintaining generalizability will make BMI systems more attractive to potential users.<br><br>TRIAL REGISTRATION: NCT01364480 and NCT01894802 .},
}
@article {pmid26987533,
year = {2016},
author = {Ma, CX and Bose, R and Ellis, MJ},
title = {Prognostic and Predictive Biomarkers of Endocrine Responsiveness for Estrogen Receptor Positive Breast Cancer.},
journal = {Advances in experimental medicine and biology},
volume = {882},
number = {},
pages = {125-154},
doi = {10.1007/978-3-319-22909-6_5},
pmid = {26987533},
issn = {0065-2598},
mesh = {Animals ; Antineoplastic Agents, Hormonal/*therapeutic use ; Biomarkers, Tumor/*metabolism ; Breast Neoplasms/*drug therapy/genetics/*metabolism/mortality/pathology ; Chemotherapy, Adjuvant ; DNA Mutational Analysis ; Disease Progression ; Disease-Free Survival ; Female ; Gene Expression Profiling ; Humans ; Mutation ; Neoadjuvant Therapy ; Neoplasm Recurrence, Local ; Neoplasms, Hormone-Dependent/*drug therapy/genetics/*metabolism/mortality/pathology ; Predictive Value of Tests ; Receptors, Estrogen/*drug effects/genetics/*metabolism ; Risk Assessment ; Risk Factors ; Signal Transduction/drug effects ; Time Factors ; Treatment Outcome ; },
abstract = {The estrogen-dependent nature of breast cancer is the fundamental basis for endocrine therapy. The presence of estrogen receptor (ER), the therapeutic target of endocrine therapy, is a prerequisite for this therapeutic approach. However, estrogen-independent growth often exists de novo at diagnosis or develops during the course of endocrine therapy. Therefore ER alone is insufficient in predicting endocrine therapy efficacy. Several RNA-based multigene assays are now available in clinical practice to assess distant recurrence risk, with majority of these assays evaluated in patients treated with 5 years of adjuvant endocrine therapy. While MammaPrint and Oncotype Dx are most predictive of recurrence risk within the first 5 years of diagnosis, Prosigna, Breast Cancer Index (BCI), and EndoPredict Clin have also demonstrated utility in predicting late recurrence. In addition, PAM50, or Prosigna, provides further biological insights by classifying breast cancers into intrinsic molecular subtypes. Additional strategies are under investigation in prospective clinical trials to differentiate endocrine sensitive and resistant tumors and include on-treatment Ki-67 and Preoperative Endocrine Prognostic Index (PEPI) score in the setting of neoadjuvant endocrine therapy. These biomarkers have become important tools in clinical practice for the identification of low risk patients for whom chemotherapy could be avoided. However, there is much work ahead toward the development of a molecular classification that informs the biology and novel therapeutic targets in high-risk disease as chemotherapy has only modest benefit in this population. The recognition of somatic mutations and their relationship to endocrine therapy responsiveness opens important opportunities toward this goal.},
}
@article {pmid26984895,
year = {2016},
author = {Hammer, J and Pistohl, T and Fischer, J and Kršek, P and Tomášek, M and Marusič, P and Schulze-Bonhage, A and Aertsen, A and Ball, T},
title = {Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {26},
number = {6},
pages = {2863-2881},
pmid = {26984895},
issn = {1460-2199},
mesh = {Adolescent ; Adult ; Arm/physiology ; Biomechanical Phenomena ; Electrocorticography ; Female ; Humans ; Male ; Middle Aged ; Models, Neurological ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Neurons/*physiology ; Neuropsychological Tests ; Young Adult ; },
abstract = {How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600-1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain-machine interfaces exploiting population signals.},
}
@article {pmid26982717,
year = {2016},
author = {Li, G and Zhang, D},
title = {Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.},
journal = {PloS one},
volume = {11},
number = {3},
pages = {e0150667},
pmid = {26982717},
issn = {1932-6203},
support = {R01 AA020501/AA/NIAAA NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Animals ; Brain/*physiology ; Cockroaches/*physiology ; Evoked Potentials, Visual ; Humans ; Male ; *Man-Machine Systems ; Young Adult ; },
abstract = {An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.},
}
@article {pmid26978853,
year = {2016},
author = {Zahran, M},
title = {Brain-Inspired Machines: What, Exactly, Are We Looking For?.},
journal = {IEEE pulse},
volume = {7},
number = {2},
pages = {48-51},
doi = {10.1109/MPUL.2015.2513728},
pmid = {26978853},
issn = {2154-2317},
mesh = {Animals ; *Brain ; *Brain-Computer Interfaces ; Humans ; *Models, Theoretical ; },
abstract = {In the computing community, people look at the brain as the ultimate computer. Brain-inspired machines are believed to be more efficient than the traditional Von Neumann computing paradigm, which has been the dominant computing model since the dawn of computing. More recently, however, there have been many claims made regarding attempts to build brain-inspired machines. But one question, in particular, needs to be thoroughly considered before we embark on creating these so-called brain-inspired machines: Inspired by what, exactly? Do we want to build a full replica of the human brain, assuming we have the required technology?},
}
@article {pmid26973112,
year = {2016},
author = {Kober, SE and Reichert, JL and Neuper, C and Wood, G},
title = {Interactive effects of age and gender on EEG power and coherence during a short-term memory task in middle-aged adults.},
journal = {Neurobiology of aging},
volume = {40},
number = {},
pages = {127-137},
doi = {10.1016/j.neurobiolaging.2016.01.015},
pmid = {26973112},
issn = {1558-1497},
mesh = {Adult ; Aging/*psychology ; Brain/*physiology ; Brain Waves ; *Electroencephalography ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Middle Aged/*psychology ; *Sex Characteristics ; Young Adult ; },
abstract = {The effects of age and gender on electroencephalographic (EEG) activity during a short-term memory task were assessed in a group of 40 healthy participants aged 22-63 years. Multi-channel EEG was recorded in 20 younger (mean = 24.65-year-old, 10 male) and 20 middle-aged participants (mean = 46.40-year-old, 10 male) during performance of a Sternberg task. EEG power and coherence measures were analyzed in different frequency bands. Significant interactions emerged between age and gender in memory performance and concomitant EEG parameters, suggesting that the aging process differentially influences men and women. Middle-aged women showed a lower short-term memory performance compared to young women, which was accompanied by decreasing delta and theta power and increasing brain connectivity with age in women. In contrast, men showed no age-related decline in short-term memory performance and no changes in EEG parameters. These results provide first evidence of age-related alterations in EEG activity underlying memory processes, which were already evident in the middle years of life in women but not in men.},
}
@article {pmid26971787,
year = {2016},
author = {Wittevrongel, B and Van Hulle, MM},
title = {Faster P300 Classifier Training Using Spatiotemporal Beamforming.},
journal = {International journal of neural systems},
volume = {26},
number = {3},
pages = {1650014},
doi = {10.1142/S0129065716500143},
pmid = {26971787},
issn = {1793-6462},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Linear Models ; Male ; Neuropsychological Tests ; Support Vector Machine ; Time Factors ; Visual Perception/physiology ; Young Adult ; },
abstract = {The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.},
}
@article {pmid26971785,
year = {2016},
author = {Hong, KS and Naseer, N},
title = {Reduction of Delay in Detecting Initial Dips from Functional Near-Infrared Spectroscopy Signals Using Vector-Based Phase Analysis.},
journal = {International journal of neural systems},
volume = {26},
number = {3},
pages = {1650012},
doi = {10.1142/S012906571650012X},
pmid = {26971785},
issn = {1793-6462},
mesh = {Algorithms ; Cerebrovascular Circulation/physiology ; Functional Neuroimaging/*methods ; Hand/physiology ; Hemodynamics/physiology ; Humans ; Male ; Mathematical Concepts ; Motor Activity/physiology ; Motor Cortex/*physiology ; Oxygen/blood ; Prefrontal Cortex/*physiology ; Problem Solving/physiology ; Rest ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Time Factors ; },
abstract = {In this paper, we present a systematic method to reduce the time lag in detecting initial dips using a vector-based phase diagram and an autoregressive moving average with exogenous signals (ARMAX) model-based q-step-ahead prediction algorithm. With functional near-infrared spectroscopy (fNIRS), signals related to mental arithmetic and right-hand clenching are acquired from the prefrontal and left primary motor cortices, respectively. The interrelationship between oxygenated hemoglobin, deoxygenated hemoglobin, total hemoglobin and cerebral oxygen exchange are related to initial dips. Specifically, a threshold value from the resting state hemodynamics is incorporated, as a decision criterion, into the vector-based phase diagram to determine the occurrence of initial dips. To further reduce the time lag, a [Formula: see text]-step-ahead prediction method is applied to predict the occurrence of the dips. A combination of the threshold criterion and the prediction method resulted in the delay time of about 0.9[Formula: see text]s. The results demonstrate that rapid detection of initial dip is possible and therefore can be used for real-time brain-computer interfacing.},
}
@article {pmid26970878,
year = {2016},
author = {Kraus, D and Naros, G and Bauer, R and Khademi, F and Leão, MT and Ziemann, U and Gharabaghi, A},
title = {Brain State-Dependent Transcranial Magnetic Closed-Loop Stimulation Controlled by Sensorimotor Desynchronization Induces Robust Increase of Corticospinal Excitability.},
journal = {Brain stimulation},
volume = {9},
number = {3},
pages = {415-424},
doi = {10.1016/j.brs.2016.02.007},
pmid = {26970878},
issn = {1876-4754},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Fingers ; Humans ; Long-Term Synaptic Depression ; Male ; Motor Cortex/physiology ; Neuronal Plasticity/physiology ; Periodicity ; Pyramidal Tracts/*physiology ; Sensorimotor Cortex/*physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {BACKGROUND: Desynchronization of sensorimotor rhythmic activity increases instantaneous corticospinal excitability, as indexed by amplitudes of motor-evoked potentials (MEP) elicited by transcranial magnetic stimulation (TMS). The accumulative effect of cortical stimulation in conjunction with sensorimotor desynchronization is, however, unclear.<br><br>OBJECTIVE: The aim of this study was to investigate the effects of repetitive pairing event-related desynchronization (ERD) with TMS of the precentral gyrus on corticospinal excitability.<br><br>METHODS: Closed-loop single-pulse TMS was controlled by beta-band (16-22&#x2009;Hz) ERD during motor-imagery of finger extension and applied within a brain-computer interface environment in eleven healthy subjects. The same number and pattern of stimuli were applied in a control group of eleven subjects during rest, i.e. independent of ERD. To probe for plasticity resistant to depotentiation, stimulation protocols were followed by a depotentiation task.<br><br>RESULTS: Brain state-dependent application of approximately 300 TMS pulses during beta-ERD resulted in a significant increase of corticospinal excitability. By contrast, the identical stimulation pattern applied independent of beta-ERD in the control experiment resulted in a decrease of corticospinal excitability. These effects persisted beyond the period of stimulation and the depotentiation task.<br><br>CONCLUSION: These results could be instrumental in developing new therapeutic approaches such as the application of closed-loop stimulation in the context of neurorehabilitation.},
}
@article {pmid26968838,
year = {2016},
author = {Klingberg, K and Srivastava, D},
title = {Restart the heart.},
journal = {BMJ case reports},
volume = {2016},
number = {},
pages = {},
pmid = {26968838},
issn = {1757-790X},
mesh = {Cardiopulmonary Resuscitation/*methods ; Heart ; Humans ; Male ; Myocardial Contusions/*complications/mortality ; Out-of-Hospital Cardiac Arrest/etiology/mortality/*therapy ; Treatment Outcome ; Ventricular Fibrillation/etiology/mortality ; Young Adult ; },
abstract = {Early bystander cardiopulmonary resuscitation and rapid defibrillation are the most important factors for favourable outcomes after out of hospital cardiac arrest (OHCA)-as the new American Heart Association/European Resuscitation Council (AHA/ERC) guidelines emphasise. The patient in our case was a healthy young man who had a witnessed cardiac arrest due to a chest collision with the goalkeeper during a football match. Basic life support was immediately provided by his teammates until an automated external defibrillator was brought to the scene. Blunt cardiac injury (BCI) may result in injured myocardium or arrhythmias. Ventricular fibrillation due to BCI in absence of structural cardiac disease is one of the main causes of OHCA in young healthy athletes with high mortality rates. We demonstrate important aspects of the recently released guidelines on cardiac arrest and the chain of survival by the leading societies.},
}
@article {pmid26967140,
year = {2016},
author = {Allender, MC and Phillips, CA and Baker, SJ and Wylie, DB and Narotsky, A and Dreslik, MJ},
title = {HEMATOLOGY IN AN EASTERN MASSASAUGA (SISTRURUS CATENATUS) POPULATION AND THE EMERGENCE OF OPHIDIOMYCES IN ILLINOIS, USA.},
journal = {Journal of wildlife diseases},
volume = {52},
number = {2},
pages = {258-269},
doi = {10.7589/2015-02-049},
pmid = {26967140},
issn = {1943-3700},
mesh = {Animals ; Ascomycota/*isolation &amp; purification ; Illinois/epidemiology ; Models, Biological ; Mycoses/epidemiology/microbiology/*veterinary ; Retrospective Studies ; Viperidae/*blood/microbiology ; },
abstract = {Disease events are threatening wildlife populations across North America. Specifically, mortality events due to Ophidiomyces (snake fungal disease; SFD) have been observed recently in snakes in Illinois, US. We investigated the health of a population of eastern massasaugas (Sistrurus catenatus) in south-central Illinois using 1) a meta-analysis of hematologic findings from 2004, 2011, 2013, and 2014; 2) a determination of the prevalence of SFD in snakes examined in 2013 and 2014; and 3) the examination of 184 museum specimens collected from 1999-2013 for signs and presence of SFD. For the meta-analysis and prevalence of SFD, hematologic analytes were reduced to three principle components that explained 67.5% of the cumulative variance. There were significant differences among one principle component (total white blood cell counts, monocytes, lymphocytes, and basophils) across years when it was highest in 2004 and 2014. The top general linear model explaining the difference in principle components included the main effects of year and stage, body condition index (BCI), and the interaction between stage and BCI. The prevalence of SFD was 18% (n=7) in 2013 and 24% (n=11) in 2014, and no hematologic analytes were associated with SFD. In museum specimens, Ophidiomyces DNA was first detected from an individual collected in 2000. Studies such as these, integrating multiple modalities of health, can elucidate the epidemiology of diseases that may pose conservation threats.},
}
@article {pmid26961426,
year = {2016},
author = {Bravo-Esteban, E and López-Larraz, E},
title = {[Enhancement of motor relearning and functional recovery in stroke patients: non-invasive strategies for modulating the central nervous system].},
journal = {Revista de neurologia},
volume = {62},
number = {6},
pages = {273-281},
pmid = {26961426},
issn = {1576-6578},
mesh = {Central Nervous System/physiopathology ; Humans ; *Motor Skills ; Neuronal Plasticity ; Recovery of Function ; Stroke/*physiopathology ; Stroke Rehabilitation/*methods ; *Transcranial Direct Current Stimulation/methods ; *Transcranial Magnetic Stimulation/methods ; },
abstract = {INTRODUCTION: Most of the stroke survivors do not recover the basal state of the affected upper limb, suffering from a severe disability which remains during the chronic phase of the illness. This has an extremely negative impact in the quality of life of these patients. Hence, neurorehabilitation strategies aim at the minimization of the sensorimotor dysfunctions associated to stroke, by promoting neuroplasticity in the central nervous system.<br><br>DEVELOPMENT: Brain reorganization can facilitate motor and functional recovery in stroke subjects. None-theless, after the insult, maladaptive neuroplastic changes can also happen, which may lead to the appearance of certain sensori-motor disorders such as spasticity. Noninvasive brain stimulation strategies, like transcranial direct current stimulation or transcranial magnetic stimulation, are widely used techniques that, when applied over the primary motor cortex, can modify neural networks excitability, as well as cognitive functions, both in healthy subjects and individuals with neurological disorders. Similarly, brain-machine-interface systems also have the potential to induce a brain reorganization by the contingent and simultaneous association between the brain activation and the peripheral stimulation.<br><br>CONCLUSION: This review describes the positive effects of the previously mentioned neurorehabilitation strategies for the enhancement of cortical reorganization after stroke, and how they can be used to alleviate the symptoms of the spasticity syndrome.},
}
@article {pmid26960227,
year = {2016},
author = {Soltani, N and Aliroteh, MS and Salam, MT and Perez Velazquez, JL and Genov, R},
title = {Low-Radiation Cellular Inductive Powering of Rodent Wireless Brain Interfaces: Methodology and Design Guide.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {10},
number = {4},
pages = {920-932},
doi = {10.1109/TBCAS.2015.2502840},
pmid = {26960227},
issn = {1940-9990},
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Power Supplies ; Electromagnetic Radiation ; Electrophysiological Phenomena ; Equipment Design ; Rats ; Rats, Wistar ; Wireless Technology ; },
abstract = {This paper presents a general methodology of inductive power delivery in wireless chronic rodent electrophysiology applications. The focus is on such systems design considerations under the following key constraints: maximum power delivery under the allowable specific absorption rate (SAR), low cost and spatial scalability. The methodology includes inductive coil design considerations within a low-frequency ferrite-core-free power transfer link which includes a scalable coil-array power transmitter floor and a single-coil implanted or worn power receiver. A specific design example is presented that includes the concept of low-SAR cellular single-transmitter-coil powering through dynamic tracking of a magnet-less receiver spatial location. The transmitter coil instantaneous supply current is monitored using a small number of low-cost electronic components. A drop in its value indicates the proximity of the receiver due to the reflected impedance of the latter. Only the transmitter coil nearest to the receiver is activated. Operating at the low frequency of 1.5 MHz, the inductive powering floor delivers a maximum of 15.9 W below the IEEE C95 SAR limit, which is over three times greater than that in other recently reported designs. The power transfer efficiency of 39% and 13% at the nominal and maximum distances of 8 cm and 11 cm, respectively, is maintained.},
}
@article {pmid26959050,
year = {2016},
author = {Yuan, M and Webb, E and Lemoine, NR and Wang, Y},
title = {CRISPR-Cas9 as a Powerful Tool for Efficient Creation of Oncolytic Viruses.},
journal = {Viruses},
volume = {8},
number = {3},
pages = {72},
pmid = {26959050},
issn = {1999-4915},
support = {C16420/A18066//Cancer Research UK/United Kingdom ; MR/M015696/1//Medical Research Council/United Kingdom ; },
mesh = {*CRISPR-Cas Systems ; Molecular Biology/*methods ; Oncolytic Viruses/*genetics/*isolation &amp; purification ; *Recombination, Genetic ; Virology/*methods ; },
abstract = {The development of oncolytic viruses has led to an emerging new class of cancer therapeutics. Although the safety profile has been encouraging, the transition of oncolytic viruses to the clinical setting has been a slow process due to modifications. Therefore, a new generation of more potent oncolytic viruses needs to be exploited, following our better understanding of the complex interactions between the tumor, its microenvironment, the virus, and the host immune response. The conventional method for creation of tumor-targeted oncolytic viruses is based on homologous recombination. However, the creation of new mutant oncolytic viruses with large genomes remains a challenge due to the multi-step process and low efficiency of homologous recombination. The CRISPR-associated endonuclease Cas9 has hugely advanced the potential to edit the genomes of various organisms due to the ability of Cas9 to target a specific genomic site by a single guide RNA. In this review, we discuss the CRISPR-Cas9 system as an efficient viral editing method for the creation of new oncolytic viruses, as well as its potential future applications in the development of oncolytic viruses. Further, this review discusses the potential of off-target effects as well as CRISPR-Cas9 as a tool for basic research into viral biology.},
}
@article {pmid26959029,
year = {2016},
author = {Batres-Mendoza, P and Montoro-Sanjose, CR and Guerra-Hernandez, EI and Almanza-Ojeda, DL and Rostro-Gonzalez, H and Romero-Troncoso, RJ and Ibarra-Manzano, MA},
title = {Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {3},
pages = {},
pmid = {26959029},
issn = {1424-8220},
mesh = {Brain/physiology ; Brain Mapping/*instrumentation ; *Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/*instrumentation ; Humans ; Movement/physiology ; Support Vector Machine ; },
abstract = {Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.},
}
@article {pmid26959021,
year = {2016},
author = {Singh, S and Lo, MC and Damodaran, VB and Kaplan, HM and Kohn, J and Zahn, JD and Shreiber, DI},
title = {Modeling the Insertion Mechanics of Flexible Neural Probes Coated with Sacrificial Polymers for Optimizing Probe Design.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {3},
pages = {},
pmid = {26959021},
issn = {1424-8220},
support = {P41 EB001046/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; Biosensing Techniques/*methods ; *Brain-Computer Interfaces ; Electrodes ; Finite Element Analysis ; Humans ; *Nerve Net ; Polymers/*chemistry ; Rats ; Xylenes/chemistry ; },
abstract = {Single-unit recording neural probes have significant advantages towards improving signal-to-noise ratio and specificity for signal acquisition in brain-to-computer interface devices. Long-term effectiveness is unfortunately limited by the chronic injury response, which has been linked to the mechanical mismatch between rigid probes and compliant brain tissue. Small, flexible microelectrodes may overcome this limitation, but insertion of these probes without buckling requires supporting elements such as a stiff coating with a biodegradable polymer. For these coated probes, there is a design trade-off between the potential for successful insertion into brain tissue and the degree of trauma generated by the insertion. The objective of this study was to develop and validate a finite element model (FEM) to simulate insertion of coated neural probes of varying dimensions and material properties into brain tissue. Simulations were performed to predict the buckling and insertion forces during insertion of coated probes into a tissue phantom with material properties of brain. The simulations were validated with parallel experimental studies where probes were inserted into agarose tissue phantom, ex vivo chick embryonic brain tissue, and ex vivo rat brain tissue. Experiments were performed with uncoated copper wire and both uncoated and coated SU-8 photoresist and Parylene C probes. Model predictions were found to strongly agree with experimental results (<10% error). The ratio of the predicted buckling force-to-predicted insertion force, where a value greater than one would ideally be expected to result in successful insertion, was plotted against the actual success rate from experiments. A sigmoidal relationship was observed, with a ratio of 1.35 corresponding to equal probability of insertion and failure, and a ratio of 3.5 corresponding to a 100% success rate. This ratio was dubbed the "safety factor", as it indicated the degree to which the coating should be over-designed to ensure successful insertion. Probability color maps were generated to visually compare the influence of design parameters. Statistical metrics derived from the color maps and multi-variable regression analysis confirmed that coating thickness and probe length were the most important features in influencing insertion potential. The model also revealed the effects of manufacturing flaws on insertion potential.},
}
@article {pmid26955040,
year = {2016},
author = {Lin, C and Wang, BH and Jiang, N and Xu, R and Mrachacz-Kersting, N and Farina, D},
title = {Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {9},
pages = {921-927},
doi = {10.1109/TNSRE.2016.2531118},
pmid = {26955040},
issn = {1558-0210},
mesh = {Adult ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/physiology ; Intention ; Machine Learning ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {The detection of voluntary motor intention from EEG has been applied to closed-loop brain-computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements. The online and offline experimental results demonstrated that the proposed LSDA approach for MRCP detection outperformed the Locality Preserving Projection (LPP) approach, which was previously shown to be the most accurate algorithm so far tested for MRCP detection. For example, in the online tests, the performance of LSDA was superior than LPP in terms of a significant reduction in false positives (FP) (passive FP: 1.6 ±0.9/min versus 2.9 ±1.0/min, p = 0.002, active FP: 2.2 ±0.8/min versus 2.7 ±0.6/min , p = 0.03), for a similar rate of true positives. In conclusion, the proposed LSDA based MRCP detection method is superior to previous approaches and is promising for developing patient-driven BCI systems for motor function rehabilitation as well as for neuroscience research.},
}
@article {pmid26953174,
year = {2016},
author = {Hemakom, A and Goverdovsky, V and Looney, D and Mandic, DP},
title = {Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications.},
journal = {Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
volume = {374},
number = {2065},
pages = {20150199},
pmid = {26953174},
issn = {1364-503X},
mesh = {Algorithms ; Biomedical Engineering ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain-computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.},
}
@article {pmid26941954,
year = {2016},
author = {Dal Negro, RW and Celli, BR},
title = {The BODECOST Index (BCI): a composite index for assessing the impact of COPD in real life.},
journal = {Multidisciplinary respiratory medicine},
volume = {11},
number = {},
pages = {10},
pmid = {26941954},
issn = {1828-695X},
abstract = {BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a progressive condition which is characterized by a dramatic socio-economic impact. Several indices were extensively investigated in order to asses the mortality risk in COPD, but the utilization of health care resources was never included in calculations. The aim of this study was to assess the predictive value of annual cost of care on COPD mortality at three years, and to develop a comprehensive index for easy calculation of mortality risk in real life.<br><br>METHODS: COPD patients were anonymously and automatically selected from the local institutional Data Base. Selection criteria were: COPD diagnosis; both genders; age&#x2009;&#x2265;&#x2009;40&#xa0;years; availability of at least one complete clinical record/year, including history; clinical signs; complete lung function, therapeutic strategy, health BODE index; Charlson Comorbidity Index, and outcomes, collected at the first visit, and over the following 3-years. At the first visit, the health annual cost of care was calculated in each patient for the previous 12&#xa0;months, and the survival rate was also measured over the following 3 years. The hospitalization and the exacerbation rate were implemented to the BODE index and the novel index thus obtained was called BODECOST index (BCI), ranging from 0 to 10 points. The mean cost for each BCI step was calculated and then compared to the corresponding patients' survival duration. Parametrical, non parametrical tests, and linear regression&#xa0;were used; p&#x2009;<&#x2009;0.05 was accepted as the lower limit of significance.<br><br>RESULTS: At the first visit, the selected 275 patients were well matched for all variables by gender. The overall mortality over the 3 year survey was 40.4&#xa0;% (n&#x2009;=&#x2009;111/275). When compared to that of BODE index (r&#x2009;=&#x2009;0.22), the total annual cost of care and the number of exacerbations showed the highest regression value vs the survival time (r&#x2009;=&#x2009;0.58 and r&#x2009;=&#x2009;0.44, respectively). BCI score proved strictly proportional to both the cost of care and the survival time in our sample of COPD patients.<br><br>DISCUSSION: BCI takes origin from the implementation of the BODE index with the two main components of the annual cost of care, such as the number of hospitalizations and of exacerbations occurring yearly in COPD patients, and their corresponding economic impact. In other words, higher the BCI score, shorter the survival and higher the cost, these trends being strictly linked.<br><br>CONCLUSIONS: BCI is a novel composite index which helps in predicting the impact of COPD at 3 years in real life, both in terms of patients' survival and of COPD economic burden.},
}
@article {pmid26941601,
year = {2016},
author = {Costa, &#xc1; and Salazar-Varas, R and Úbeda, A and Azorín, JM},
title = {Characterization of Artifacts Produced by Gel Displacement on Non-invasive Brain-Machine Interfaces during Ambulation.},
journal = {Frontiers in neuroscience},
volume = {10},
number = {},
pages = {60},
pmid = {26941601},
issn = {1662-4548},
abstract = {So far, Brain-Machine Interfaces (BMIs) have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals). To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training) and one movement-free condition (BMI motor imagery task) are compared to perform this study. Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD) of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions.},
}
@article {pmid26938468,
year = {2016},
author = {Rajangam, S and Tseng, PH and Yin, A and Lehew, G and Schwarz, D and Lebedev, MA and Nicolelis, MA},
title = {Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {22170},
pmid = {26938468},
issn = {2045-2322},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; DP1MH099903/DP/NCCDPHP CDC HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Microelectrodes/*statistics &amp; numerical data ; Motor Cortex/*physiology ; Motor Neurons/*physiology ; Paralysis/*rehabilitation ; Robotics ; Wheelchairs ; Wireless Technology ; },
abstract = {Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair's translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.},
}
@article {pmid26936596,
year = {2018},
author = {Emge, DK and Vialatte, FB and Dreyfus, G and Adalı, T},
title = {Independent Vector Analysis for SSVEP Signal Enhancement, Detection, and Topographical Mapping.},
journal = {Brain topography},
volume = {31},
number = {1},
pages = {117-124},
doi = {10.1007/s10548-016-0478-2},
pmid = {26936596},
issn = {1573-6792},
mesh = {Algorithms ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Functional Laterality ; Healthy Volunteers ; Humans ; Signal Detection, Psychological/*physiology ; },
abstract = {Steady state visual evoked potentials (SSVEPs) have been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations. SSVEPs can be observed in the scalp-based recordings of electroencephalogram signals, and are one component buried amongst the normal brain signals and complex noise. We present a novel method for enhancing and improving detection of SSVEPs by leveraging the rich joint blind source separation framework using independent vector analysis (IVA). IVA exploits the diversity within each dataset while preserving dependence across all the datasets. This approach is shown to enhance the detection of SSVEP signals across a range of frequencies and subjects for BCI systems. Furthermore, we show that IVA enables improved topographic mapping of the SSVEP propagation providing a promising new tool for neuroscience and neurocognitive research.},
}
@article {pmid26935548,
year = {2016},
author = {Milton, K and Nolin, DA and Ellis, K and Lozier, J and Sandel, B and Lacey, EA},
title = {Genetic, spatial, and social relationships among adults in a group of howler monkeys (Alouatta palliata) from Barro Colorado Island, Panama.},
journal = {Primates; journal of primatology},
volume = {57},
number = {2},
pages = {253-265},
pmid = {26935548},
issn = {1610-7365},
mesh = {Alouatta/genetics/*physiology ; *Animal Distribution ; Animals ; Female ; *Genetic Variation ; Male ; Microsatellite Repeats ; Panama ; *Social Behavior ; },
abstract = {Kinship plays an important role in the social behavior of many primate species, including patterns of intra-group affiliation and cooperation. Within social groups, kinship is strongly affected by dispersal patterns, with the degree of relatedness among group-mates expected to decrease as the tendency to disperse increases. In primate species characterized by bisexual dispersal, relatedness among adult group-mates is predicted to be low, with social interactions shaped largely by factors other than kinship. To date, however, few studies have examined the role of kinship in social interactions in bisexually dispersing species. Accordingly, we collected genetic, spatial and behavioral data on all adult members (three males, six females) in a group of free-ranging mantled howler monkeys (Alouatta palliata)--a bisexually dispersing species of atelid primate--from Barro Colorado Island (BCI), Panama. Analyses of microsatellite variation revealed that relatedness was greater among adult males in this group (mean pairwise relatedness = 0.32 for males versus 0.09 for females). Relatedness among individuals, however, was not associated with either spatial proximity or frequency of social interactions. Instead, sex was a better predictor of both of these aspects of social behavior. While relatedness among adults had no discernible effect on the intra-group social interactions documented in this study, we postulate that kinship may facilitate affiliative and cooperative behaviors among male group-mates when interacting competitively with neighboring howler groups over access to food or potential mates.},
}
@article {pmid26935245,
year = {2016},
author = {Longo, N and Imbimbo, C and Fusco, F and Ficarra, V and Mangiapia, F and Di Lorenzo, G and Creta, M and Imperatore, V and Mirone, V},
title = {Complications and quality of life in elderly patients with several comorbidities undergoing cutaneous ureterostomy with single stoma or ileal conduit after radical cystectomy.},
journal = {BJU international},
volume = {118},
number = {4},
pages = {521-526},
doi = {10.1111/bju.13462},
pmid = {26935245},
issn = {1464-410X},
mesh = {Aged ; *Cystectomy/methods ; Female ; Humans ; Male ; Postoperative Complications/*etiology ; *Quality of Life ; Retrospective Studies ; Ureterostomy/*adverse effects ; Urinary Bladder Neoplasms/*complications/*surgery ; Urinary Diversion/*adverse effects ; },
abstract = {OBJECTIVES: To compare peri-operative outcomes and quality of life (QoL) in a series of elderly patients with high comorbidity status who underwent single stoma cutaneous ureterostomy (CU) or ileal conduit (IC) after radical cystectomy (RC).<br><br>PATIENTS AND METHODS: The clinical records of patients aged >75 years with an American Society of Anesthesiologists (ASA) score >2 who underwent RC at a single institution between March 2009 and March 2014 were retrospectively analysed. After RC, all patients included in the study received an IC urinary diversion or a CU with single stoma urinary diversion. Preoperative clinical characteristics as well as intra- and postoperative outcomes were evaluated and compared between the two groups. In addition, the Bladder Cancer Index (BCI) was used to assess QoL.<br><br>RESULTS: A total of 70 patients were included in the final comparative analyses. Of these, 35 underwent IC diversion and 35 CU single stoma diversion. The two groups were similar with regard to age, gender, ASA score, type of indication and pathological features. Operating times (P < 0.001), estimated blood loss (P < 0.001), need for intensive care unit stay (P = 0.01), time to drain removal (P < 0.001) and length of hospital stay (P < 0.001) were significantly higher in patients undergoing IC diversion. The number of patients with intra- (P = 0.04) and early postoperative (P = 0.02) complications was also significantly higher among those undergoing IC diversion. Interestingly, the mean BCI scores were overlapping in the two groups.<br><br>CONCLUSIONS: The present results show that CU with a single stoma can represent a valid alternative to IC in elderly patients with relevant comorbidities, reducing peri-operative complications without a significant impairment of QoL.},
}
@article {pmid26935230,
year = {2016},
author = {Park, W and Kwon, GH and Kim, YH and Lee, JH and Kim, L},
title = {EEG response varies with lesion location in patients with chronic stroke.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {},
pages = {21},
pmid = {26935230},
issn = {1743-0003},
mesh = {Adult ; Aged ; Brain/pathology/physiopathology ; Brain-Computer Interfaces ; Chronic Disease ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Female ; Functional Laterality ; Hand Strength ; Humans ; Imagination ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/physiopathology ; Movement ; Psychomotor Performance ; Stroke/*pathology/*physiopathology ; Supination ; },
abstract = {BACKGROUND: Brain activation differs according to lesion location in functional magnetic resonance imaging (fMRI) studies, but lesion location-dependent electroencephalographic (EEG) alterations are unclear. Because of the increasing use of EEG-based brain-computer-interface rehabilitation, we examined lesion location-dependent EEG patterns in patients with stroke while they performed motor tasks.<br><br>METHODS: Twelve patients with chronic stroke were divided into three subgroups according to their lesion locations: supratentorial lesions that included M1 (SM1+), supratentorial lesions that excluded M1 (SM1-), and infratentorial (INF) lesions. Participants performed three motor tasks [active, passive, and motor imagery (MI)] with supination and grasping movements. The hemispheric asymmetric indexes, which were calculated with laterality coefficients (LCs), the temporal changes in the event-related desynchronization (ERD) patterns in the bilateral motor cortex, and the topographical distributions in the 28-channel EEG patterns around the supplementary motor area and bilateral motor cortex of the three participant subgroups were compared with those of the 12 age-matched healthy controls.<br><br>RESULTS: The SM1+ group exhibited negative LC values in the active and MI motor tasks, while the other patient subgroups exhibited positive LC values. Negative LC values indicate that the ERD/ERS intensity of the ipsilateral hemisphere is higher than the contralateral hemisphere, whereas positive LC values indicate that the ERD/ERS intensity of the contralateral hemisphere is higher than the ipsilateral hemisphere. The LC values of SM1+ and healthy controls differed significantly (rank-sum test, p&#x2009;<&#x2009;0.05) in both the supination and grasping movements in the active task. The three patient subgroups differed distinctly from each other in the topography analysis.<br><br>CONCLUSIONS: The hemispheric asymmetry and topographic characteristics of the beta band power patterns in the patients with stroke differed according to the location of the lesion, which suggested that EEG analyses of neurorehabilitation should be implemented according to lesion location.},
}
@article {pmid26930692,
year = {2016},
author = {Ando, H and Takizawa, K and Yoshida, T and Matsushita, K and Hirata, M and Suzuki, T},
title = {Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {10},
number = {6},
pages = {1068-1078},
doi = {10.1109/TBCAS.2016.2514522},
pmid = {26930692},
issn = {1940-9990},
mesh = {*Brain-Computer Interfaces ; Electrocorticography/instrumentation/*methods ; Electrodes, Implanted ; Equipment Design ; Humans ; Neurons/*physiology ; Signal Processing, Computer-Assisted ; Wireless Technology ; },
abstract = {Simultaneous recordings of neural activity at large scale, in the long term and under bio-safety conditions, can provide essential data. These data can be used to advance the technology for brain-machine interfaces in clinical applications, and to understand brain function. For this purpose, we present a new multichannel neural recording system that can record up to 4096-channel (ch) electrocorticogram data by multiple connections of customized application-specific integrated circuits (ASICs). The ASIC includes 64-ch low-noise amplifiers, analog time-division multiplexers, and 12-bit successive approximation register ADCs. Recorded data sampled at a rate of 1 kS/s are multiplexed with time division via an integrated multiplex board, and in total 51.2 Mbps of raw data for 4096 ch are generated. This system has an ultra-wideband (UWB) wireless unit for transmitting the recorded neural signals. The ASICs, multiplex boards, and UWB transmitter unit are designed with the aim of implanting them. From preliminary experiments with a human body-equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps for distances below 20 mm .},
}
@article {pmid26924826,
year = {2016},
author = {Bowsher, K and Civillico, EF and Coburn, J and Collinger, J and Contreras-Vidal, JL and Denison, T and Donoghue, J and French, J and Getzoff, N and Hochberg, LR and Hoffmann, M and Judy, J and Kleitman, N and Knaack, G and Krauthamer, V and Ludwig, K and Moynahan, M and Pancrazio, JJ and Peckham, PH and Pena, C and Pinto, V and Ryan, T and Saha, D and Scharen, H and Shermer, S and Skodacek, K and Takmakov, P and Tyler, D and Vasudevan, S and Wachrathit, K and Weber, D and Welle, CG and Ye, M},
title = {Brain-computer interface devices for patients with paralysis and amputation: a meeting report.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {023001},
doi = {10.1088/1741-2560/13/2/023001},
pmid = {26924826},
issn = {1741-2552},
mesh = {Amputation, Surgical ; *Amputees ; Brain-Computer Interfaces/standards/*trends ; Communication Devices for People with Disabilities/standards/*trends ; *Device Approval/standards ; Humans ; Maryland ; Paralysis/epidemiology/*therapy ; United States/epidemiology ; },
abstract = {OBJECTIVE: The Food and Drug Administration's (FDA) Center for Devices and Radiological Health (CDRH) believes it is important to help stakeholders (e.g., manufacturers, health-care professionals, patients, patient advocates, academia, and other government agencies) navigate the regulatory landscape for medical devices. For innovative devices involving brain-computer interfaces, this is particularly important.<br><br>APPROACH: Towards this goal, on 21 November, 2014, CDRH held an open public workshop on its White Oak, MD campus with the aim of fostering an open discussion on the scientific and clinical considerations associated with the development of brain-computer interface (BCI) devices, defined for the purposes of this workshop as neuroprostheses that interface with the central or peripheral nervous system to restore lost motor or sensory capabilities.<br><br>MAIN RESULTS: This paper summarizes the presentations and discussions from that workshop.<br><br>SIGNIFICANCE: CDRH plans to use this information to develop regulatory considerations that will promote innovation while maintaining appropriate patient protections. FDA plans to build on advances in regulatory science and input provided in this workshop to develop guidance that provides recommendations for premarket submissions for BCI devices. These proceedings will be a resource for the BCI community during the development of medical devices for consumers.},
}
@article {pmid26917709,
year = {2016},
author = {Zhu, Y and Xu, K and Xu, C and Zhang, J and Ji, J and Zheng, X and Zhang, H and Tian, M},
title = {PET Mapping for Brain-Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes.},
journal = {Journal of nuclear medicine : official publication, Society of Nuclear Medicine},
volume = {57},
number = {7},
pages = {1141-1145},
doi = {10.2967/jnumed.115.171868},
pmid = {26917709},
issn = {1535-5667},
mesh = {Animals ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Computer Simulation ; *Electrodes, Implanted ; Fluorodeoxyglucose F18 ; Male ; Orientation ; Positron-Emission Tomography/*methods ; Psychomotor Performance ; Radiopharmaceuticals ; Rats ; Rats, Sprague-Dawley ; Ventral Thalamic Nuclei/*diagnostic imaging ; },
abstract = {UNLABELLED: Brain-computer interface (BCI) technology has great potential for improving the quality of life for neurologic patients. This study aimed to use PET mapping for BCI-based stimulation in a rat model with electrodes implanted in the ventroposterior medial (VPM) nucleus of the thalamus.<br><br>METHODS: PET imaging studies were conducted before and after stimulation of the right VPM.<br><br>RESULTS: Stimulation induced significant orienting performance. (18)F-FDG uptake increased significantly in the paraventricular thalamic nucleus, septohippocampal nucleus, olfactory bulb, left crus II of the ansiform lobule of the cerebellum, and bilaterally in the lateral septum, amygdala, piriform cortex, endopiriform nucleus, and insular cortex, but it decreased in the right secondary visual cortex, right simple lobule of the cerebellum, and bilaterally in the somatosensory cortex.<br><br>CONCLUSION: This study demonstrated that PET mapping after VPM stimulation can identify specific brain regions associated with orienting performance. PET molecular imaging may be an important approach for BCI-based research and its clinical applications.},
}
@article {pmid26914644,
year = {2016},
author = {Reboud, E and Elsen, S and Bouillot, S and Golovkine, G and Basso, P and Jeannot, K and Attrée, I and Huber, P},
title = {Phenotype and toxicity of the recently discovered exlA-positive Pseudomonas aeruginosa strains collected worldwide.},
journal = {Environmental microbiology},
volume = {18},
number = {10},
pages = {3425-3439},
doi = {10.1111/1462-2920.13262},
pmid = {26914644},
issn = {1462-2920},
mesh = {Animals ; Bacterial Proteins/genetics/*metabolism ; Cichorium intybus/microbiology ; Female ; Humans ; Mice ; Mice, Inbred BALB C ; Phenotype ; Phylogeny ; Pseudomonas Infections/*microbiology ; Pseudomonas aeruginosa/genetics/*isolation &amp; purification/metabolism/*pathogenicity ; Virulence ; Virulence Factors/genetics/metabolism ; },
abstract = {We recently identified a hypervirulent strain of Pseudomonas aeruginosa, differing significantly from the classical strains in that it lacks the type 3 secretion system (T3SS), a major determinant of P. aeruginosa virulence. This new strain secretes a novel toxin, called ExlA, which induces plasma membrane rupture in host cells. For this study, we collected 18 other exlA-positive T3SS-negative strains, analyzed their main virulence factors and tested their toxicity in various models. Phylogenetic analysis revealed two groups. The strains were isolated on five continents from patients with various pathologies or in the environment. Their proteolytic activity and their motion abilities were highly different, as well as their capacity to infect epithelial, endothelial, fibroblastic and immune cells, which correlated directly with ExlA secretion levels. In contrast, their toxicity towards human erythrocytes was limited. Some strains were hypervirulent in a mouse pneumonia model and others on chicory leaves. We conclude that (i) exlA-positive strains can colonize different habitats and may induce various infection types, (ii) the strains secreting significant amounts of ExlA are cytotoxic for most cell types but are poorly hemolytic, (iii) toxicity in planta does not correlate with ExlA secretion.},
}
@article {pmid26913648,
year = {2016},
author = {Townsend, G and Platsko, V},
title = {Pushing the P300-based brain-computer interface beyond 100 bpm: extending performance guided constraints into the temporal domain.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026024},
doi = {10.1088/1741-2560/13/2/026024},
pmid = {26913648},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; Reaction Time/*physiology ; Time Factors ; },
abstract = {OBJECTIVE: A new presentation paradigm for the P300-based brain-computer interface (BCI) referred to as the 'asynchronous paradigm' (ASP) is introduced and studied. It is based on the principle of performance guided constraints (Townsend et al 2012 Neurosci. Lett. 531 63-8) extended from the spatial domain into the temporal domain. The traditional constraint of flashing targets in predefined constant epochs of time is eliminated and targets flash asynchronously with timing based instead on constraints intended to improve performance.<br><br>APPROACH: We propose appropriate temporal constraints to derive the ASP and compare its performance to that of the 'checkerboard paradigm' (CBP), which has previously been shown to be superior to the standard 'row/column paradigm' introduced by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). Ten participants were tested in the ASP and CBP conditions both with traditional flashing items and with flashing faces in place of the targets (see Zhang et al 2012 J. Neural Eng. 9 026018; Kaufmann and K&#xfc;bler 2014 J. Neural Eng. 11 ; Chen et al 2015 J. Neurosci. Methods 239 18-27). Eleven minutes of calibration data were used as input to a stepwise linear discriminant analysis to derive classification coefficients used for online classification.<br><br>MAIN RESULTS: Accuracy was consistently high for both paradigms (87% and 93%) while information transfer rate was 45% higher for the ASP than the CBP. In a free spelling task, one subject spelled a 66 character sentence (from a 72 item matrix) with 100% accuracy in 3 min and 24 s demonstrating a practical throughput of 120 bits per minute (bpm) with a theoretical upper bound of 258 bpm. The subject repeated the task three times in a row without error.<br><br>SIGNIFICANCE: This work represents an advance in P300 speller technology and raises the ceiling that was being reached on P300-based BCIs. Most importantly, the research presented here is a novel and effective general strategy for organising timing for flashing items. The ASP is only one possible implementation of this work since in general it can be used to describe all previous existing presentation paradigms as well as any possible new ones. This may be especially important for people with neuromuscular disabilities.},
}
@article {pmid26907278,
year = {2016},
author = {Zhang, Z and Luo, D and Rasim, Y and Li, Y and Meng, G and Xu, J and Wang, C},
title = {A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {242},
pmid = {26907278},
issn = {1424-8220},
mesh = {Accidents, Traffic ; Algorithms ; *Automobile Driving ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Models, Theoretical ; User-Computer Interface ; Wakefulness ; },
abstract = {In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.},
}
@article {pmid26907276,
year = {2016},
author = {Mannan, MM and Kim, S and Jeong, MY and Kamran, MA},
title = {Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {241},
pmid = {26907276},
issn = {1424-8220},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography/*methods ; Eye Movements/physiology ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.},
}
@article {pmid26904980,
year = {2016},
author = {Hessburg, PC and Rizzo, J and O'Malley, ER},
title = {Preface: The Eye and The Chip world research congress on visual neuro-prosthetics.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {020401},
doi = {10.1088/1741-2560/13/2/020401},
pmid = {26904980},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/trends ; *Congresses as Topic ; Eye ; Humans ; *Internationality ; Neural Prostheses/*trends ; Vision Disorders/surgery/therapy ; Vision, Ocular/*physiology ; Visual Prosthesis/*trends ; },
}
@article {pmid26904967,
year = {2016},
author = {Fukuma, R and Yanagisawa, T and Saitoh, Y and Hosomi, K and Kishima, H and Shimizu, T and Sugata, H and Yokoi, H and Hirata, M and Kamitani, Y and Yoshimine, T},
title = {Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {21781},
pmid = {26904967},
issn = {2045-2322},
mesh = {Adult ; *Artificial Limbs ; Brain-Computer Interfaces ; Hand ; Humans ; Magnetoencephalography ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Movement ; Paralysis/physiopathology ; Signal Processing, Computer-Assisted ; },
abstract = {Neuroprosthetic arms might potentially restore motor functions for severely paralysed patients. Invasive measurements of cortical currents using electrocorticography have been widely used for neuroprosthetic control. Moreover, magnetoencephalography (MEG) exhibits characteristic brain signals similar to those of invasively measured signals. However, it remains unclear whether non-invasively measured signals convey enough motor information to control a neuroprosthetic hand, especially for severely paralysed patients whose sensorimotor cortex might be reorganized. We tested an MEG-based neuroprosthetic system to evaluate the accuracy of using cortical currents in the sensorimotor cortex of severely paralysed patients to control a prosthetic hand. The patients attempted to grasp with or open their paralysed hand while the slow components of MEG signals (slow movement fields; SMFs) were recorded. Even without actual movements, the SMFs of all patients indicated characteristic spatiotemporal patterns similar to actual movements, and the SMFs were successfully used to control a neuroprosthetic hand in a closed-loop condition. These results demonstrate that the slow components of MEG signals carry sufficient information to classify movement types. Successful control by paralysed patients suggests the feasibility of using an MEG-based neuroprosthetic hand to predict a patient's ability to control an invasive neuroprosthesis via the same signal sources as the non-invasive method.},
}
@article {pmid26903886,
year = {2016},
author = {Cicchese, JJ and Berry, SD},
title = {Hippocampal Non-Theta-Contingent Eyeblink Classical Conditioning: A Model System for Neurobiological Dysfunction.},
journal = {Frontiers in psychiatry},
volume = {7},
number = {},
pages = {1},
pmid = {26903886},
issn = {1664-0640},
abstract = {Typical information processing is thought to depend on the integrity of neurobiological oscillations that may underlie coordination and timing of cells and assemblies within and between structures. The 3-7 Hz bandwidth of hippocampal theta rhythm is associated with cognitive processes essential to learning and depends on the integrity of cholinergic, GABAergic, and glutamatergic forebrain systems. Since several significant psychiatric disorders appear to result from dysfunction of medial temporal lobe (MTL) neurochemical systems, preclinical studies on animal models may be an important step in defining and treating such syndromes. Many studies have shown that the amount of hippocampal theta in the rabbit strongly predicts the acquisition rate of classical eyeblink conditioning and that impairment of this system substantially slows the rate of learning and attainment of asymptotic performance. Our lab has developed a brain-computer interface that makes eyeblink training trials contingent upon the explicit presence or absence of hippocampal theta. The behavioral benefit of theta-contingent training has been demonstrated in both delay and trace forms of the paradigm with a two- to fourfold increase in learning speed over non-theta states. The non-theta behavioral impairment is accompanied by disruption of the amplitude and synchrony of hippocampal local field potentials, multiple-unit excitation, and single-unit response patterns dependent on theta state. Our findings indicate a significant electrophysiological and behavioral impact of the pretrial state of the hippocampus that suggests an important role for this MTL system in associative learning and a significant deleterious impact in the absence of theta. Here, we focus on the impairments in the non-theta state, integrate them into current models of psychiatric disorders, and suggest how improvement in our understanding of neurobiological oscillations is critical for theories and treatment of psychiatric pathology.},
}
@article {pmid26902372,
year = {2016},
author = {Bundy, DT and Pahwa, M and Szrama, N and Leuthardt, EC},
title = {Decoding three-dimensional reaching movements using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026021},
pmid = {26902372},
issn = {1741-2552},
support = {TL1 TR000449/TR/NCATS NIH HHS/United States ; UL1 TR000448/TR/NCATS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electrocorticography/*methods ; Electrodes, Implanted ; Electroencephalography/methods ; Hand/*physiology ; Humans ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Random Allocation ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space.<br><br>APPROACH: To investigate this, we designed a 3D center-out reaching task that was performed by five epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares (PLS) regression model to perform offline prediction of hand speed, velocity, and position.<br><br>MAIN RESULTS: The hierarchical PLS regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters.<br><br>SIGNIFICANCE: We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.},
}
@article {pmid26902294,
year = {2016},
author = {Lin, K and Cinetto, A and Wang, Y and Chen, X and Gao, S and Gao, X},
title = {An online hybrid BCI system based on SSVEP and EMG.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026020},
doi = {10.1088/1741-2560/13/2/026020},
pmid = {26902294},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Choice Behavior/physiology ; Electroencephalography/*methods ; Electromyography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Online Systems ; Photic Stimulation/*methods ; Random Allocation ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance.<br><br>APPROACH: A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct.<br><br>MAIN RESULTS: The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min(-1) respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min(-1), SSVEP: 60.2 bits min(-1)). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min(-1).<br><br>SIGNIFICANCE: These results suggest that the hybrid system proposed here is suitable for practical use.},
}
@article {pmid26891350,
year = {2016},
author = {Acqualagna, L and Botrel, L and Vidaurre, C and Kübler, A and Blankertz, B},
title = {Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface.},
journal = {PloS one},
volume = {11},
number = {2},
pages = {e0148886},
pmid = {26891350},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Biofeedback, Psychology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Reproducibility of Results ; Young Adult ; },
abstract = {In the last years Brain Computer Interface (BCI) technology has benefited from the development of sophisticated machine leaning methods that let the user operate the BCI after a few trials of calibration. One remarkable example is the recent development of co-adaptive techniques that proved to extend the use of BCIs also to people not able to achieve successful control with the standard BCI procedure. Especially for BCIs based on the modulation of the Sensorimotor Rhythm (SMR) these improvements are essential, since a not negligible percentage of users is unable to operate SMR-BCIs efficiently. In this study we evaluated for the first time a fully automatic co-adaptive BCI system on a large scale. A pool of 168 participants naive to BCIs operated the co-adaptive SMR-BCI in one single session. Different psychological interventions were performed prior the BCI session in order to investigate how motor coordination training and relaxation could influence BCI performance. A neurophysiological indicator based on the Power Spectral Density (PSD) was extracted by the recording of few minutes of resting state brain activity and tested as predictor of BCI performances. Results show that high accuracies in operating the BCI could be reached by the majority of the participants before the end of the session. BCI performances could be significantly predicted by the neurophysiological indicator, consolidating the validity of the model previously developed. Anyway, we still found about 22% of users with performance significantly lower than the threshold of efficient BCI control at the end of the session. Being the inter-subject variability still the major problem of BCI technology, we pointed out crucial issues for those who did not achieve sufficient control. Finally, we propose valid developments to move a step forward to the applicability of the promising co-adaptive methods.},
}
@article {pmid26891487,
year = {2016},
author = {Norman, S and Dennison, M and Wolbrecht, E and Cramer, S and Srinivasan, R and Reinkensmeyer, D},
title = {Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {8},
pages = {911-919},
pmid = {26891487},
issn = {1558-0210},
support = {K24 HD074722/HD/NICHD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 HD062744/HD/NICHD NIH HHS/United States ; UL1 TR000153/TR/NCATS NIH HHS/United States ; },
abstract = {Brain-computer interfacing is a technology that has the potential to improve patient engagement in robot-assisted rehabilitation therapy. For example, movement intention reduces mu (8-13 Hz) oscillation amplitude over the sensorimotor cortex, a phenomenon referred to as event-related desynchronization (ERD). In an ERD-contingent assistance paradigm, initial BCI-enhanced robotic therapy studies have used ERD to provide robotic assistance for movement. Here we investigated how ERD changed as a function of audio-visual stimuli, overt movement from the participant, and robotic assistance. Twelve unimpaired subjects played a computer game designed for rehabilitation therapy with their fingers using the FINGER robotic exoskeleton. In the game, the participant and robot matched movement timing to audio-visual stimuli in the form of notes approaching a target on the screen set to the consistent beat of popular music. The audio-visual stimulation of the game alone did not cause ERD, before or after training. In contrast, overt movement by the subject caused ERD, whether or not the robot assisted the finger movement. Notably, ERD was also present when the subjects remained passive and the robot moved their fingers to play the game. This ERD occurred in anticipation of the passive finger movement with similar onset timing as for the overt movement conditions. These results demonstrate that ERD can be contingent on expectation of robotic assistance; that is, the brain generates an anticipatory ERD in expectation of a robot-imposed but predictable movement. This is a caveat that should be considered in designing BCIs for enhancing patient effort in robotically-assisted therapy.},
}
@article {pmid26888931,
year = {2016},
author = {Wagner, J and Makeig, S and Gola, M and Neuper, C and Müller-Putz, G},
title = {Distinct β Band Oscillatory Networks Subserving Motor and Cognitive Control during Gait Adaptation.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {36},
number = {7},
pages = {2212-2226},
pmid = {26888931},
issn = {1529-2401},
mesh = {Acoustic Stimulation ; Adaptation, Physiological/*physiology ; Adult ; Beta Rhythm/*physiology ; Brain Mapping ; Cerebral Cortex/physiology ; Cognition/*physiology ; Cues ; Electroencephalography ; Evoked Potentials/physiology ; Executive Function/physiology ; Functional Laterality/physiology ; Gait/*physiology ; Humans ; Male ; Movement/*physiology ; Nerve Net/*physiology ; Young Adult ; },
abstract = {UNLABELLED: Everyday locomotion and obstacle avoidance requires effective gait adaptation in response to sensory cues. Many studies have shown that efficient motor actions are associated with μ rhythm (8-13 Hz) and β band (13-35 Hz) local field desynchronizations in sensorimotor and parietal cortex, whereas a number of cognitive task studies have reported higher behavioral accuracy to be associated with increases in β band power in prefrontal and sensory cortex. How these two distinct patterns of β band oscillations interplay during gait adaptation, however, has not been established. Here we recorded 108 channel EEG activity from 18 participants (10 males, 22-35 years old) attempting to walk on a treadmill in synchrony with a series of pacing cue tones, and quickly adapting their step rate and length to sudden shifts in pacing cue tempo. Independent component analysis parsed each participant's EEG data into maximally independent component (IC) source processes, which were then grouped across participants into distinct spatial/spectral clusters. Following cue tempo shifts, mean β band power was suppressed for IC sources in central midline and parietal regions, whereas mean β band power increased in IC sources in or near medial prefrontal and dorsolateral prefrontal cortex. In the right dorsolateral prefrontal cortex IC cluster, the β band power increase was stronger during (more effortful) step shortening than during step lengthening. These results thus show that two distinct patterns of β band activity modulation accompany gait adaptations: one likely serving movement initiation and execution; and the other, motor control and inhibition.<br><br>SIGNIFICANCE STATEMENT: Understanding brain dynamics supporting gait adaptation is crucial for understanding motor deficits in walking, such as those associated with aging, stroke, and Parkinson's. Only a few electromagnetic brain imaging studies have examined neural correlates of human upright walking. Here, application of independent component analysis to EEG data recorded during treadmill walking allowed us to uncover two distinct &#x3b2; band oscillatory cortical networks that are active during gait adaptation to shifts in the tempo of an auditory pacing cue: (8-13 Hz) &#x3bc; rhythm and (13-35 Hz) &#x3b2; band power decreases in central and parietal cortex and (14-20 Hz) &#x3b2; band power increases in frontal brain areas. These results provide a fuller framework for electrophysiological studies of cortical gait control and its disorders.},
}
@article {pmid26886302,
year = {2016},
author = {Reschke, T and Zherikova, KV and Verevkin, SP and Held, C},
title = {Benzoic Acid and Chlorobenzoic Acids: Thermodynamic Study of the Pure Compounds and Binary Mixtures With Water.},
journal = {Journal of pharmaceutical sciences},
volume = {105},
number = {3},
pages = {1050-1058},
doi = {10.1016/j.xphs.2015.12.020},
pmid = {26886302},
issn = {1520-6017},
mesh = {Benzoic Acid/*chemistry ; Chlorobenzoates/*chemistry ; Drug Stability ; Organic Chemicals/*chemistry ; Solubility ; Solvents/chemistry ; Thermodynamics ; Water/*chemistry ; },
abstract = {Benzoic acid is a model compound for drug substances in pharmaceutical research. Process design requires information about thermodynamic phase behavior of benzoic acid and its mixtures with water and organic solvents. This work addresses phase equilibria that determine stability and solubility. In this work, Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) was used to model the phase behavior of aqueous and organic solutions containing benzoic acid and chlorobenzoic acids. Absolute vapor pressures of benzoic acid and 2-, 3-, and 4-chlorobenzoic acid from literature and from our own measurements were used to determine pure-component PC-SAFT parameters. Two binary interaction parameters between water and/or benzoic acid were used to model vapor-liquid and liquid-liquid equilibria of water and/or benzoic acid between 280 and 413 K. The PC-SAFT parameters and 1 binary interaction parameter were used to model aqueous solubility of the chlorobenzoic acids. Additionally, solubility of benzoic acid in organic solvents was predicted without using binary parameters. All results showed that pure-component parameters for benzoic acid and for the chlorobenzoic acids allowed for satisfying modeling phase equilibria. The modeling approach established in this work is a further step to screen solubility and to predict the whole phase region of mixtures containing pharmaceuticals.},
}
@article {pmid26881010,
year = {2016},
author = {Alvarado-González, M and Garduño, E and Bribiesca, E and Yáñez-Suárez, O and Medina-Bañuelos, V},
title = {P300 Detection Based on EEG Shape Features.},
journal = {Computational and mathematical methods in medicine},
volume = {2016},
number = {},
pages = {2029791},
pmid = {26881010},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Area Under Curve ; Brain-Computer Interfaces ; Calibration ; Computer Simulation ; Electrodes ; *Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Humans ; Likelihood Functions ; Male ; Models, Statistical ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification.},
}
@article {pmid26881008,
year = {2015},
author = {Shakeel, A and Navid, MS and Anwar, MN and Mazhar, S and Jochumsen, M and Niazi, IK},
title = {A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials.},
journal = {Computational and mathematical methods in medicine},
volume = {2015},
number = {},
pages = {346217},
pmid = {26881008},
issn = {1748-6718},
mesh = {Brain-Computer Interfaces ; Computational Biology ; Electroencephalography/*methods/statistics &amp; numerical data ; Humans ; Intention ; Motor Cortex/*physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; },
abstract = {The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signal's acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can also induce plasticity in brain. Along with different training schedules, this can lead to restoration of motor control in stroke patients.},
}
@article {pmid26880873,
year = {2016},
author = {Ji, H and Li, J and Lu, R and Gu, R and Cao, L and Gong, X},
title = {EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {1732836},
pmid = {26880873},
issn = {1687-5273},
mesh = {*Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Data Compression ; Electroencephalography/*classification ; Humans ; Support Vector Machine ; },
abstract = {Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.},
}
@article {pmid26880543,
year = {2016},
author = {Leo, A and Handjaras, G and Bianchi, M and Marino, H and Gabiccini, M and Guidi, A and Scilingo, EP and Pietrini, P and Bicchi, A and Santello, M and Ricciardi, E},
title = {A synergy-based hand control is encoded in human motor cortical areas.},
journal = {eLife},
volume = {5},
number = {},
pages = {},
pmid = {26880543},
issn = {2050-084X},
mesh = {Biomechanical Phenomena ; Electromyography ; Hand/*physiology ; Humans ; *Locomotion ; Magnetic Resonance Imaging ; Models, Neurological ; Motor Cortex/*physiology ; *Posture ; },
abstract = {How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses.},
}
@article {pmid26879418,
year = {2016},
author = {Joseph, B and Jokar, TO and Khalil, M and Haider, AA and Kulvatunyou, N and Zangbar, B and Tang, A and Zeeshan, M and O'Keeffe, T and Abbas, D and Latifi, R and Rhee, P},
title = {Identifying the broken heart: predictors of mortality and morbidity in suspected blunt cardiac injury.},
journal = {American journal of surgery},
volume = {211},
number = {6},
pages = {982-988},
doi = {10.1016/j.amjsurg.2015.10.027},
pmid = {26879418},
issn = {1879-1883},
mesh = {Academic Medical Centers ; Adult ; Aged ; *Cause of Death ; Cohort Studies ; Echocardiography/methods ; Female ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Morbidity ; Multimodal Imaging/methods ; Myocardial Contusions/*diagnosis/*mortality ; Positron-Emission Tomography/methods ; Predictive Value of Tests ; Retrospective Studies ; Risk Assessment ; Survival Rate ; Trauma Centers ; Troponin I/analysis ; Wounds, Nonpenetrating/*diagnosis/*mortality ; },
abstract = {BACKGROUND: Blunt cardiac injury (BCI) is an infrequent but potentially fatal finding in thoracic trauma. Its clinical presentation is highly variable and patient characteristics and injury pattern have never been described in trauma patients. The aim of this study was to identify predictors of mortality in BCI patients.<br><br>METHODS: We performed an 8-year retrospective analysis of all trauma patients diagnosed with BCI at our Level 1 trauma center. Patients older than 18&#xa0;years, blunt chest trauma, and a suspected diagnosis of BCI were included. BCI was diagnosed based on the presence of electrocardiography (EKG), echocardiography, biochemical cardiac markers, and/or radionuclide imaging studies. Elevated troponin I was defined as more than 2 recordings of greater than or equal to .2. Abnormal EKG findings were defined as the presence of bundle branch block, ST segment, and t-wave abnormalities. Univariate and multivariate regression analyses were performed.<br><br>RESULTS: A total of 117 patients with BCI were identified. The mean age was 51 &#xb1; 22&#xa0;years, 65% were male, mean systolic blood pressure was 93 &#xb1; 65, and overall mortality rate was 44%. Patients who died were more likely to have a lactate greater than 2.5 (68% vs 31%, P = .02), hypotension (systolic blood pressure < 90) (86% vs 14%, P = .001), and elevated troponin I (86% vs 11%, P = .01). There was no difference in the rib fracture (58% vs 56%, P = .8), sternal fracture (11% vs 21%, P = .2), and abnormal EKG (89% vs 90%, P = .6) findings. Hypotension and lactate greater than 2.5 were the strongest predictors of mortality in BCI.<br><br>CONCLUSIONS: BCI remains an important diagnostic and management challenge. However, once diagnosed resuscitative therapy focused on correction of hypotension and lactate may prove beneficial. Although the role of troponin in diagnosing BCI remains controversial, elevated troponin may have prognostic significance.},
}
@article {pmid26876690,
year = {2016},
author = {Jang, YY and Kim, TH and Lee, BH},
title = {Effects of Brain-Computer Interface-controlled Functional Electrical Stimulation Training on Shoulder Subluxation for Patients with Stroke: A Randomized Controlled Trial.},
journal = {Occupational therapy international},
volume = {23},
number = {2},
pages = {175-185},
doi = {10.1002/oti.1422},
pmid = {26876690},
issn = {1557-0703},
mesh = {Aged ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*methods ; Humans ; Male ; Middle Aged ; Occupational Therapy/*methods ; Paresis/etiology/rehabilitation ; Recovery of Function ; Stroke/*physiopathology ; Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity/*physiopathology ; },
abstract = {The purpose of this study was to investigate the effects of brain-computer interface (BCI)-controlled functional electrical stimulation (FES) training on shoulder subluxation of patients with stroke. Twenty subjects were randomly divided into two groups: the BCI-FES group (n = 10) and the FES group (n = 10). Patients in the BCI-FES group were administered conventional therapy with the BCI-FES on the shoulder subluxation area of the paretic upper extremity, five times per week during 6 weeks, while the FES group received conventional therapy with FES only. All patients were assessed for shoulder subluxation (vertical distance, VD; horizontal distance, HD), pain (visual analogue scale, VAS) and the Manual Function Test (MFT) at the time of recruitment to the study and after 6 weeks of the intervention. The BCI-FES group demonstrated significant improvements in VD, HD, VAS and MFT after the intervention period, while the FES group demonstrated significant improvements in HD, VAS and MFT. There were also significant differences in the VD and two items (shoulder flexion and abduction) of the MFT between the two groups. The results of this study suggest that BCI-FES training may be effective in improving shoulder subluxation of patients with stroke by facilitating motor recovery. Copyright © 2016 John Wiley &amp; Sons, Ltd.},
}
@article {pmid26873900,
year = {2016},
author = {Landa-Jiménez, MA and González-Gaspar, P and Pérez-Estudillo, C and López-Meraz, ML and Morgado-Valle, C and Beltran-Parrazal, L},
title = {Open-box muscle-computer interface: introduction to human-computer interactions in bioengineering, physiology, and neuroscience courses.},
journal = {Advances in physiology education},
volume = {40},
number = {1},
pages = {119-122},
doi = {10.1152/advan.00009.2015},
pmid = {26873900},
issn = {1522-1229},
mesh = {Bioengineering/instrumentation/*methods ; Biomedical Engineering/instrumentation/*methods ; *Brain-Computer Interfaces ; Electromyography/instrumentation/*methods ; Female ; Humans ; Male ; Muscle Fatigue/physiology ; Muscle, Skeletal/*physiology ; Neurosciences/instrumentation/*methods ; Students ; Young Adult ; },
}
@article {pmid26871553,
year = {2016},
author = {Stohs, SJ and Kaats, GR and Preuss, HG},
title = {Safety and Efficacy of Banaba-Moringa oleifera-Green Coffee Bean Extracts and Vitamin D3 in a Sustained Release Weight Management Supplement.},
journal = {Phytotherapy research : PTR},
volume = {30},
number = {4},
pages = {681-688},
pmid = {26871553},
issn = {1099-1573},
mesh = {Blood Pressure ; *Body Composition ; Body Weight Maintenance/*drug effects ; Bone Density/drug effects ; Cholecalciferol/chemistry ; Coffee/*chemistry ; Delayed-Action Preparations/chemistry ; *Dietary Supplements ; Humans ; Moringa oleifera/*chemistry ; Pilot Projects ; Plant Extracts/*pharmacology ; Plant Leaves/chemistry ; Quality of Life ; Tablets ; },
abstract = {This 60-day, 30-subject pilot study examined a novel combination of ingredients in a unique sustained release (Carbopol matrix) tablet consumed twice daily. The product was composed of extracts of banaba leaf, green coffee bean, and Moringa oleifera leaf and vitamin D3. Safety was assessed using a 45-measurement blood chemistry panel, an 86-item self-reported Quality of Life Inventory, bone mineral density, and cardiovascular changes. Efficacy was assessed by calculating a body composition improvement index (BCI) based on changes in dual energy X-ray absorptiometry measured fat mass (FM) and fat-free mass (FFM) as well as between the study group (SG) and a historical placebo group. No changes occurred in any blood chemistry measurements. Positive changes were found in the Quality of Life (QOL) inventory composite scores. No adverse effects were observed. Decreases occurred in FM (p = 0.004) and increases in FFM (p = 0.009). Relative to the historical placebo group, the SG lost more FM (p < 0.0001), gained more FFM (p = <0.0001), and had a negative BCI of -2.7 lb. compared with a positive BCI in the SG of 3.4 lb., a 6.1 discordance (p = 0.0009). The data support the safety and efficacy of this unique product and demonstrate importance of using changes in body composition versus scale weight and BMI.},
}
@article {pmid26864771,
year = {2016},
author = {Hudson, AL and Navarro-Sune, X and Martinerie, J and Pouget, P and Raux, M and Chavez, M and Similowski, T},
title = {Electroencephalographic detection of respiratory-related cortical activity in humans: from event-related approaches to continuous connectivity evaluation.},
journal = {Journal of neurophysiology},
volume = {115},
number = {4},
pages = {2214-2223},
pmid = {26864771},
issn = {1522-1598},
mesh = {Adult ; Cerebral Cortex/*physiology ; *Evoked Potentials, Motor ; Female ; Humans ; Male ; Reaction Time ; *Respiration ; },
abstract = {The presence of a respiratory-related cortical activity during tidal breathing is abnormal and a hallmark of respiratory difficulties, but its detection requires superior discrimination and temporal resolution. The aim of this study was to validate a computational method using EEG covariance (or connectivity) matrices to detect a change in brain activity related to breathing. In 17 healthy subjects, EEG was recorded during resting unloaded breathing (RB), voluntary sniffs, and breathing against an inspiratory threshold load (ITL). EEG were analyzed by the specially developed covariance-based classifier, event-related potentials, and time-frequency (T-F) distributions. Nine subjects repeated the protocol. The classifier could accurately detect ITL and sniffs compared with the reference period of RB. For ITL, EEG-based detection was superior to airflow-based detection (P < 0.05). A coincident improvement in EEG-airflow correlation in ITL compared with RB (P < 0.05) confirmed that EEG detection relates to breathing. Premotor potential incidence was significantly higher before inspiration in sniffs and ITL compared with RB (P < 0.05), but T-F distributions revealed a significant difference between sniffs and RB only (P < 0.05). Intraclass correlation values ranged from poor (-0.2) to excellent (1.0). Thus, as for conventional event-related potential analysis, the covariance-based classifier can accurately predict a change in brain state related to a change in respiratory state, and given its capacity for near "real-time" detection, it is suitable to monitor the respiratory state in respiratory and critically ill patients in the development of a brain-ventilator interface.},
}
@article {pmid26863526,
year = {2016},
author = {Petropolis, DB and Faust, DM and Tolle, M and Rivière, L and Valentin, T and Neuveut, C and Hernandez-Cuevas, N and Dufour, A and Olivo-Marin, JC and Guillen, N},
title = {Human Liver Infection in a Dish: Easy-To-Build 3D Liver Models for Studying Microbial Infection.},
journal = {PloS one},
volume = {11},
number = {2},
pages = {e0148667},
pmid = {26863526},
issn = {1932-6203},
mesh = {Cell Culture Techniques ; Cell Line, Tumor ; Coculture Techniques ; Entamoeba histolytica/*physiology ; Hepatocytes/*parasitology ; Host-Parasite Interactions ; Humans ; Liver Abscess, Amebic/*parasitology ; },
abstract = {Human liver infection is a major cause of death worldwide, but fundamental studies on infectious diseases affecting humans have been hampered by the lack of robust experimental models that accurately reproduce pathogen-host interactions in an environment relevant for the human disease. In the case of liver infection, one consequence of this absence of relevant models is a lack of understanding of how pathogens cross the sinusoidal endothelial barrier and parenchyma. To fill that gap we elaborated human 3D liver in vitro models, composed of human liver sinusoidal endothelial cells (LSEC) and Huh-7 hepatoma cells as hepatocyte model, layered in a structure mimicking the hepatic sinusoid, which enable studies of key features of early steps of hepatic infection. Built with established cell lines and scaffold, these models provide a reproducible and easy-to-build cell culture approach of reduced complexity compared to animal models, while preserving higher physiological relevance compared to standard 2D systems. For proof-of-principle we challenged the models with two hepatotropic pathogens: the parasitic amoeba Entamoeba histolytica and hepatitis B virus (HBV). We constructed four distinct setups dedicated to investigating specific aspects of hepatic invasion: 1) pathogen 3D migration towards hepatocytes, 2) hepatocyte barrier crossing, 3) LSEC and subsequent hepatocyte crossing, and 4) quantification of human hepatic virus replication (HBV). Our methods comprise automated quantification of E. histolytica migration and hepatic cells layer crossing in the 3D liver models. Moreover, replication of HBV virus occurs in our virus infection 3D liver model, indicating that routine in vitro assays using HBV or others viruses can be performed in this easy-to-build but more physiological hepatic environment. These results illustrate that our new 3D liver infection models are simple but effective, enabling new investigations on infectious disease mechanisms. The better understanding of these mechanisms in a human-relevant environment could aid the discovery of drugs against pathogenic liver infection.},
}
@article {pmid26863276,
year = {2016},
author = {Hotson, G and McMullen, DP and Fifer, MS and Johannes, MS and Katyal, KD and Para, MP and Armiger, R and Anderson, WS and Thakor, NV and Wester, BA and Crone, NE},
title = {Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026017-026017},
pmid = {26863276},
issn = {1741-2552},
support = {R01 NS088606/NS/NINDS NIH HHS/United States ; 1R01NS088606-01/NS/NINDS NIH HHS/United States ; },
mesh = {*Artificial Limbs ; Brain-Computer Interfaces ; Electrocorticography/*methods ; *Electrodes, Implanted ; Fingers/*physiology ; Humans ; Male ; Movement/*physiology ; Sensorimotor Cortex/*physiology ; User-Computer Interface ; Vibration ; Young Adult ; },
abstract = {OBJECTIVE: We used native sensorimotor representations of fingers in a brain-machine interface (BMI) to achieve immediate online control of individual prosthetic fingers.<br><br>APPROACH: Using high gamma responses recorded with a high-density electrocorticography (ECoG) array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: (1) if any finger was moving, and, if so, (2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory modular prosthetic limb.<br><br>MAIN RESULTS: The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control.<br><br>SIGNIFICANCE: Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.},
}
@article {pmid26863159,
year = {2016},
author = {Kilicarslan, A and Grossman, RG and Contreras-Vidal, JL},
title = {A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026013},
doi = {10.1088/1741-2560/13/2/026013},
pmid = {26863159},
issn = {1741-2552},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889-01/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Artifacts ; Blinking/*physiology ; Electroencephalography/*methods/standards ; Exercise Test/methods/standards ; Eye Movements/*physiology ; Female ; Humans ; Male ; Scalp/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Non-invasive measurement of human neural activity based on the scalp electroencephalogram (EEG) allows for the development of biomedical devices that interface with the nervous system for scientific, diagnostic, therapeutic, or restorative purposes. However, EEG recordings are often considered as prone to physiological and non-physiological artifacts of different types and frequency characteristics. Among them, ocular artifacts and signal drifts represent major sources of EEG contamination, particularly in real-time closed-loop brain-machine interface (BMI) applications, which require effective handling of these artifacts across sessions and in natural settings.<br><br>APPROACH: We extend the usage of a robust adaptive noise cancelling (ANC) scheme ([Formula: see text] filtering) for removal of eye blinks, eye motions, amplitude drifts and recording biases simultaneously. We also characterize the volume conduction, by estimating the signal propagation levels across all EEG scalp recording areas due to ocular artifact generators. We find that the amplitude and spatial distribution of ocular artifacts vary greatly depending on the electrode location. Therefore, fixed filtering parameters for all recording areas would naturally hinder the true overall performance of an ANC scheme for artifact removal. We treat each electrode as a separate sub-system to be filtered, and without the loss of generality, they are assumed to be uncorrelated and uncoupled.<br><br>MAIN RESULTS: Our results show over 95-99.9% correlation between the raw and processed signals at non-ocular artifact regions, and depending on the contamination profile, 40-70% correlation when ocular artifacts are dominant. We also compare our results with the offline independent component analysis and artifact subspace reconstruction methods, and show that some local quantities are handled better by our sample-adaptive real-time framework. Decoding performance is also compared with multi-day experimental data from 2 subjects, totaling 19 sessions, with and without [Formula: see text] filtering of the raw data.<br><br>SIGNIFICANCE: The proposed method allows real-time adaptive artifact removal for EEG-based closed-loop BMI applications and mobile EEG studies in general, thereby increasing the range of tasks that can be studied in action and context while reducing the need for discarding data due to artifacts. Significant increase in decoding performances also justify the effectiveness of the method to be used in real-time closed-loop BMI applications.},
}
@article {pmid26861347,
year = {2016},
author = {Lo, CC and Chien, TY and Chen, YC and Tsai, SH and Fang, WC and Lin, BS},
title = {A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.},
journal = {Sensors (Basel, Switzerland)},
volume = {16},
number = {2},
pages = {213},
pmid = {26861347},
issn = {1424-8220},
mesh = {Algorithms ; Biosensing Techniques/*instrumentation ; Brain/physiology ; *Brain-Computer Interfaces ; *Electrodes ; *Electroencephalography ; Humans ; },
abstract = {Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.},
}
@article {pmid26861029,
year = {2016},
author = {Zeyl, T and Yin, E and Keightley, M and Chau, T},
title = {Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior to error-related potential score as an uncertain label.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026008},
doi = {10.1088/1741-2560/13/2/026008},
pmid = {26861029},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces/standards ; Electroencephalography/*methods/standards ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Time Factors ; *Uncertainty ; Young Adult ; },
abstract = {OBJECTIVE: Error-related potentials (ErrPs) have the potential to guide classifier adaptation in BCI spellers, for addressing non-stationary performance as well as for online optimization of system parameters, by providing imperfect or partial labels. However, the usefulness of ErrP-based labels for BCI adaptation has not been established in comparison to other partially supervised methods. Our objective is to make this comparison by retraining a two-step P300 speller on a subset of confident online trials using naïve labels taken from speller output, where confidence is determined either by (i) ErrP scores, (ii) posterior target scores derived from the P300 potential, or (iii) a hybrid of these scores. We further wish to evaluate the ability of partially supervised adaptation and retraining methods to adjust to a new stimulus-onset asynchrony (SOA), a necessary step towards online SOA optimization.<br><br>APPROACH: Eleven consenting able-bodied adults attended three online spelling sessions on separate days with feedback in which SOAs were set at 160 ms (sessions 1 and 2) and 80 ms (session 3). A post hoc offline analysis and a simulated online analysis were performed on sessions two and three to compare multiple adaptation methods. Area under the curve (AUC) and symbols spelled per minute (SPM) were the primary outcome measures.<br><br>MAIN RESULTS: Retraining using supervised labels confirmed improvements of 0.9 percentage points (session 2, p < 0.01) and 1.9 percentage points (session 3, p < 0.05) in AUC using same-day training data over using data from a previous day, which supports classifier adaptation in general.<br><br>SIGNIFICANCE: Using posterior target score alone as a confidence measure resulted in the highest SPM of the partially supervised methods, indicating that ErrPs are not necessary to boost the performance of partially supervised adaptive classification. Partial supervision significantly improved SPM at a novel SOA, showing promise for eventual online SOA optimization.},
}
@article {pmid26860004,
year = {2015},
author = {Kiroy, VN and Bakhtin, OM and Minyaeva, NR and Lazurenko, DM and Aslanyan, EV and Kiroy, RI},
title = {[Electrographic Correlations of Inner Speech].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {65},
number = {5},
pages = {616-625},
pmid = {26860004},
issn = {0044-4677},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Male ; Speech/*physiology ; User-Computer Interface ; },
abstract = {On the purpose to detect in EEG specific patterns associated with any verbal performance the gamma activity were investigated. The technique which allows the subject to initiate the mental pronunciation of words and phrases (inner speech) was created. Wavelet analysis of EEG has been experimentally demonstrated that the preparation and implementation stages are related to the specific spatio-temporal patterns in frequency range 64-68 Hz. Sustainable reproduction and efficient identification of such patterns can solve the fundamentally problem of alphabet control commands formation for Brain Computer Interface and Brain to Braine Interface systems.},
}
@article {pmid26859831,
year = {2016},
author = {Pfeiffer, T and Heinze, N and Frysch, R and Deouell, LY and Schoenfeld, MA and Knight, RT and Rose, G},
title = {Extracting duration information in a picture category decoding task using hidden Markov Models.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026010},
pmid = {26859831},
issn = {1741-2552},
support = {R37 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Electrocorticography/methods ; Electroencephalography/methods ; Humans ; Information Storage and Retrieval/*methods ; Magnetoencephalography/methods ; Male ; *Markov Chains ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; },
abstract = {OBJECTIVE: Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed.<br><br>APPROACH: Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths.<br><br>MAIN RESULTS: Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only.<br><br>SIGNIFICANCE: The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.},
}
@article {pmid26859756,
year = {2016},
author = {Glannon, W},
title = {Ethical issues in neuroprosthetics.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {021002},
doi = {10.1088/1741-2560/13/2/021002},
pmid = {26859756},
issn = {1741-2552},
abstract = {OBJECTIVE: Neuroprosthetics are artificial devices or systems designed to generate, restore or modulate a range of neurally mediated functions. These include sensorimotor, visual, auditory, cognitive affective and volitional functions that have been impaired or lost from congenital anomalies, traumatic brain injury, infection, amputation or neurodevelopmental and neurodegenerative disorders. Cochlear implants, visual prosthetics, deep brain stimulation, brain-computer interfaces, brain-to-brain interfaces and hippocampal prosthetics can bypass, replace or compensate for dysfunctional neural circuits, brain injury and limb loss. They can enable people with these conditions to gain or regain varying degrees of control of thought and behavior. These direct and indirect interventions in the brain raise general ethical questions about weighing the potential benefit of altering neural circuits against the potential harm from neurophysiological and psychological sequelae. Other ethical questions are more specific to the therapeutic goals of particular neuroprosthetics and the conditions for which they are indicated. These include informed consent, agency, autonomy (free will) and identity.<br><br>APPROACH: This review is an analysis and discussion of these questions. It also includes consideration of social justice issues such as how to establish and implement fair selection criteria in providing access to neuroprosthetic research and balancing technological innovation with patients' best interests.<br><br>MAIN RESULTS: Neuroprosthetics can restore or improve motor and mental functions in bypassing areas of injury or modulating dysregulation in neural circuits. As enabling devices that integrate with these circuits, neuroprosthetics can restore varying degrees of autonomous agency for people affected by neurological and psychiatric disorders. They can also re-establish the connectedness and continuity of the psychological properties they had before injury or disease onset and thereby re-establish their identity. Neuroprosthetics can maximize benefit and minimize harm for people affected by damaged or dysfunctional brains and improve the quality of their lives.<br><br>SIGNIFICANCE: Provided that adequate protections are in place for research subjects and patients, the probable benefit of research into and therapeutic applications of neuroprosthetics outweighs the risk and therefore can be ethically justified. Depending on their neurogenerative potential, there may be an ethical obligation to conduct this research. Advances in neuroscience will generate new ethical and philosophical questions about people and their brains. These questions should shape the evolution and application of novel techniques to better understand and treat brain disorders.},
}
@article {pmid26859341,
year = {2016},
author = {Wang, PT and King, CE and McCrimmon, CM and Lin, JJ and Sazgar, M and Hsu, FP and Shaw, SJ and Millet, DE and Chui, LA and Liu, CY and Do, AH and Nenadic, Z},
title = {Comparison of decoding resolution of standard and high-density electrocorticogram electrodes.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026016},
pmid = {26859341},
issn = {1741-2552},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; Electrocorticography/instrumentation/*methods/*standards ; Electrodes, Implanted/*standards ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed.<br><br>APPROACH: Three subjects implanted with standard grids (4 mm diameter, 10 mm spacing) and three with HD grids (2 mm diameter, 4 mm spacing) had ECoG signals recorded while performing the following movements: (1) pincer grasp/release, (2) wrist flexion/extension, (3) pronation/supination, (4) elbow flexion/extension, (5) shoulder internal/external rotation, and (6) shoulder forward flexion/extension. Data from the primary motor cortex were used to train a state decoder to detect the presence/absence of movement, and a six-class decoder to distinguish between these movements.<br><br>MAIN RESULTS: The average performances of the state decoders trained on HD ECoG data were superior (p&#xa0;=&#xa0;3.05 &#xd7; 10(-5)) to those of their standard grid counterparts across all combinations of the &#x3bc;, &#x3b2;, low-&#x3b3;, and high-&#x3b3; frequency bands. The average best decoding error for HD grids was 2.6%, compared to 8.5% of standard grids (chance 50%). The movement decoders trained on HD ECoG data were superior (p&#xa0;=&#xa0;3.05 &#xd7; 10(-5)) to those based on standard ECoG across all band combinations. The average best decoding errors of 11.9% and 33.1% were obtained for HD and standard grids, respectively (chance error 83.3%). These improvements can be attributed to higher electrode density and signal quality of HD grids.<br><br>SIGNIFICANCE: Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.},
}
@article {pmid26859192,
year = {2016},
author = {Blokland, Y and Farquhar, J and Lerou, J and Mourisse, J and Scheffer, GJ and Geffen, GJ and Spyrou, L and Bruhn, J},
title = {Decoding motor responses from the EEG during altered states of consciousness induced by propofol.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026014},
doi = {10.1088/1741-2560/13/2/026014},
pmid = {26859192},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Adolescent ; Adult ; Anesthetics, Intravenous/*administration &amp; dosage ; Awareness/drug effects/physiology ; *Brain-Computer Interfaces ; Consciousness/drug effects/*physiology ; Electroencephalography/drug effects/*methods ; Female ; Humans ; Male ; Propofol/*administration &amp; dosage ; Psychomotor Performance/drug effects/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Patients undergoing general anesthesia may awaken and become aware of the surgical procedure. Due to neuromuscular blocking agents, patients could be conscious yet unable to move. Using brain-computer interface (BCI) technology, it may be possible to detect movement attempts from the EEG. However, it is unknown how an anesthetic influences the brain response to motor tasks.<br><br>APPROACH: We tested the offline classification performance of a movement-based BCI in 12 healthy subjects at two effect-site concentrations of propofol. For each subject a second classifier was trained on the subject's data obtained before sedation, then tested on the data obtained during sedation ('transfer classification').<br><br>MAIN RESULTS: At concentration 0.5 &#x3bc;g ml(-1), despite an overall propofol EEG effect, the mean single trial classification accuracy was 85% (95% CI 81%-89%), and 83% (79%-88%) for the transfer classification. At 1.0 &#x3bc;g ml(-1), the accuracies were 81% (76%-86%), and 72% (66%-79%), respectively. At the highest propofol concentration for four subjects, unlike the remaining subjects, the movement-related brain response had been largely diminished, and the transfer classification accuracy was not significantly above chance. These subjects showed a slower and more erratic task response, indicating an altered state of consciousness distinct from that of the other subjects.<br><br>SIGNIFICANCE: The results show the potential of using a BCI to detect intra-operative awareness and justify further development of this paradigm. At the same time, the relationship between motor responses and consciousness and its clinical relevance for intraoperative awareness requires further investigation.},
}
@article {pmid26858634,
year = {2016},
author = {Chen, L and Jin, J and Daly, I and Zhang, Y and Wang, X and Cichocki, A},
title = {Exploring Combinations of Different Color and Facial Expression Stimuli for Gaze-Independent BCIs.},
journal = {Frontiers in computational neuroscience},
volume = {10},
number = {},
pages = {5},
pmid = {26858634},
issn = {1662-5188},
abstract = {BACKGROUND: Some studies have proven that a conventional visual brain computer interface (BCI) based on overt attention cannot be used effectively when eye movement control is not possible. To solve this problem, a novel visual-based BCI system based on covert attention and feature attention has been proposed and was called the gaze-independent BCI. Color and shape difference between stimuli and backgrounds have generally been used in examples of gaze-independent BCIs. Recently, a new paradigm based on facial expression changes has been presented, and obtained high performance. However, some facial expressions were so similar that users couldn't tell them apart, especially when they were presented at the same position in a rapid serial visual presentation (RSVP) paradigm. Consequently, the performance of the BCI is reduced.<br><br>NEW METHOD: In this paper, we combined facial expressions and colors to optimize the stimuli presentation in the gaze-independent BCI. This optimized paradigm was called the colored dummy face pattern. It is suggested that different colors and facial expressions could help users to locate the target and evoke larger event-related potentials (ERPs). In order to evaluate the performance of this new paradigm, two other paradigms were presented, called the gray dummy face pattern and the colored ball pattern.<br><br>The key point that determined the value of the colored dummy faces stimuli in BCI systems was whether the dummy face stimuli could obtain higher performance than gray faces or colored balls stimuli. Ten healthy participants (seven male, aged 21-26 years, mean 24.5 &#xb1; 1.25) participated in our experiment. Online and offline results of four different paradigms were obtained and comparatively analyzed.<br><br>RESULTS: The results showed that the colored dummy face pattern could evoke higher P300 and N400 ERP amplitudes, compared with the gray dummy face pattern and the colored ball pattern. Online results showed that the colored dummy face pattern had a significant advantage in terms of classification accuracy (p < 0.05) and information transfer rate (p < 0.05) compared to the other two patterns.<br><br>CONCLUSIONS: The stimuli used in the colored dummy face paradigm combined color and facial expressions. This had a significant advantage in terms of the evoked P300 and N400 amplitudes and resulted in high classification accuracies and information transfer rates. It was compared with colored ball and gray dummy face stimuli.},
}
@article {pmid26852113,
year = {2016},
author = {Urbanek, H and van der Smagt, P},
title = {iEMG: Imaging electromyography.},
journal = {Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology},
volume = {27},
number = {},
pages = {1-9},
doi = {10.1016/j.jelekin.2016.01.001},
pmid = {26852113},
issn = {1873-5711},
mesh = {Adult ; Electrodes ; Electromyography/instrumentation/*methods ; Forearm/diagnostic imaging/physiology ; Humans ; Male ; Muscle, Skeletal/*diagnostic imaging/*physiology ; Ultrasonography ; },
abstract = {Advanced data analysis and visualization methodologies have played an important role in making surface electromyography both a valuable diagnostic methodology of neuromuscular disorders and a robust brain-machine interface, usable as a simple interface for prosthesis control, arm movement analysis, stiffness control, gait analysis, etc. But for diagnostic purposes, as well as for interfaces where the activation of single muscles is of interest, surface EMG suffers from severe crosstalk between deep and superficial muscle activation, making the reliable detection of the source of the signal, as well as reliable quantification of deeper muscle activation, prohibitively difficult. To address these issues we present a novel approach for processing surface electromyographic data. Our approach enables the reconstruction of 3D muscular activity location, making the depth of muscular activity directly visible. This is even possible when deep muscles are overlaid with superficial muscles, such as seen in the human forearm. The method, which we call imaging EMG (iEMG), is based on using the crosstalk between a sufficiently large number of surface electromyographic electrodes to reconstruct the 3D generating electrical potential distribution within a given area. Our results are validated by in vivo measurements of iEMG and ultrasound on the human forearm.},
}
@article {pmid26849869,
year = {2016},
author = {Xu, R and Jiang, N and Dosen, S and Lin, C and Mrachacz-Kersting, N and Dremstrup, K and Farina, D},
title = {Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {8},
pages = {901-910},
doi = {10.1109/TNSRE.2016.2523565},
pmid = {26849869},
issn = {1558-0210},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback, Sensory/physiology ; Humans ; Imagination/physiology ; Information Storage and Retrieval/*methods ; Male ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Touch/*physiology ; },
abstract = {In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.},
}
@article {pmid26848745,
year = {2016},
author = {Mathôt, S and Melmi, JB and van der Linden, L and Van der Stigchel, S},
title = {The Mind-Writing Pupil: A Human-Computer Interface Based on Decoding of Covert Attention through Pupillometry.},
journal = {PloS one},
volume = {11},
number = {2},
pages = {e0148805},
pmid = {26848745},
issn = {1932-6203},
mesh = {Adult ; Attention ; Female ; Humans ; Male ; Photic Stimulation ; *Pupil ; Reflex, Pupillary ; *User-Computer Interface ; Visual Perception ; },
abstract = {We present a new human-computer interface that is based on decoding of attention through pupillometry. Our method builds on the recent finding that covert visual attention affects the pupillary light response: Your pupil constricts when you covertly (without looking at it) attend to a bright, compared to a dark, stimulus. In our method, participants covertly attend to one of several letters with oscillating brightness. Pupil size reflects the brightness of the selected letter, which allows us-with high accuracy and in real time-to determine which letter the participant intends to select. The performance of our method is comparable to the best covert-attention brain-computer interfaces to date, and has several advantages: no movement other than pupil-size change is required; no physical contact is required (i.e. no electrodes); it is easy to use; and it is reliable. Potential applications include: communication with totally locked-in patients, training of sustained attention, and ultra-secure password input.},
}
@article {pmid26846163,
year = {2016},
author = {Tong, J and Lin, Q and Xiao, R and Ding, L},
title = {Combining multiple features for error detection and its application in brain-computer interface.},
journal = {Biomedical engineering online},
volume = {15},
number = {},
pages = {17},
pmid = {26846163},
issn = {1475-925X},
mesh = {Adult ; Area Under Curve ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Female ; Humans ; Male ; Nerve Net/physiology ; ROC Curve ; Research Design ; *Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; Young Adult ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) is an assistive technology that conveys users' intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings.<br><br>METHODS: This study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 &#xb1; 5.4, range 20-37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing.<br><br>RESULTS: The total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively.<br><br>CONCLUSIONS: The proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.},
}
@article {pmid26841382,
year = {2016},
author = {An, X and Tang, J and Liu, S and He, F and Qi, H and Wan, B and Ming, D},
title = {Effects of Temporal Congruity Between Auditory and Visual Stimuli Using Rapid Audio-Visual Serial Presentation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {10},
pages = {2125-2132},
doi = {10.1109/TBME.2015.2511539},
pmid = {26841382},
issn = {1558-2531},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Auditory/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Time Factors ; Visual Perception/physiology ; Young Adult ; },
abstract = {GOAL: Combining visual and auditory stimuli in event-related potential (ERP)-based spellers gained more attention in recent years. Few of these studies notice the difference of ERP components and system efficiency caused by the shifting of visual and auditory onset. Here, we aim to study the effect of temporal congruity of auditory and visual stimuli onset on bimodal brain-computer interface (BCI) speller.<br><br>METHODS: We designed five visual and auditory combined paradigms with different visual-to-auditory delays (-33 to +100&#xa0;ms). Eleven participants attended in this study. ERPs were acquired and aligned according to visual and auditory stimuli onset, respectively. ERPs of Fz, Cz, and PO7 channels were studied through the statistical analysis of different conditions both from visual-aligned ERPs and audio-aligned ERPs. Based on the visual-aligned ERPs, classification accuracy was also analyzed to seek the effects of visual-to-auditory delays.<br><br>RESULTS: The latencies of ERP components depended mainly on the visual stimuli onset. Auditory stimuli onsets influenced mainly on early component accuracies, whereas visual stimuli onset determined later component accuracies. The latter, however, played a dominate role in overall classification.<br><br>SIGNIFICANCE: This study is important for further studies to achieve better explanations and ultimately determine the way to optimize the bimodal BCI application.},
}
@article {pmid26834703,
year = {2015},
author = {Cherradi, N},
title = {microRNAs as Potential Biomarkers in Adrenocortical Cancer: Progress and Challenges.},
journal = {Frontiers in endocrinology},
volume = {6},
number = {},
pages = {195},
pmid = {26834703},
issn = {1664-2392},
abstract = {Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis and limited therapeutic options. Over the last decade, pan-genomic analyses of genetic and epigenetic alterations and genome-wide expression profile studies allowed major advances in the understanding of the molecular genetics of ACC. Besides the well-known dysfunctional molecular pathways in adrenocortical tumors, such as the IGF2 pathway, the Wnt pathway, and TP53, high-throughput technologies enabled a more comprehensive genomic characterization of adrenocortical cancer. Integration of expression profile data with exome sequencing, SNP array analysis, methylation, and microRNA (miRNA) profiling led to the identification of subgroups of malignant tumors with distinct molecular alterations and clinical outcomes. miRNAs post-transcriptionally silence their target gene expression either by degrading mRNA or by inhibiting translation. Although our knowledge of the contribution of deregulated miRNAs to the pathogenesis of ACC is still in its infancy, recent studies support their relevance in gene expression alterations in these tumors. Some miRNAs have been shown to carry potential diagnostic and prognostic values, while others may be good candidates for therapeutic interventions. With the emergence of disease-specific blood-borne miRNAs signatures, analyses of small cohorts of patients with ACC suggest that circulating miRNAs represent promising non-invasive biomarkers of malignancy or recurrence. However, some technical challenges still remain, and most of the miRNAs reported in the literature have not yet been validated in sufficiently powered and longitudinal studies. In this review, we discuss the current knowledge regarding the deregulation of tumor-associated and circulating miRNAs in ACC patients, while emphasizing their potential significance in pathogenic pathways in light of recent insights into the role of miRNAs in shaping the tumor microenvironment.},
}
@article {pmid26834611,
year = {2015},
author = {Ahn, S and Kim, K and Jun, SC},
title = {Steady-State Somatosensory Evoked Potential for Brain-Computer Interface-Present and Future.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {716},
pmid = {26834611},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) performance has achieved continued improvement over recent decades, and sensorimotor rhythm-based BCIs that use motor function have been popular subjects of investigation. However, it remains problematic to introduce them to the public market because of their low reliability. As an alternative resolution to this issue, visual-based BCIs that use P300 or steady-state visually evoked potentials (SSVEPs) seem promising; however, the inherent visual fatigue that occurs with these BCIs may be unavoidable. For these reasons, steady-state somatosensory evoked potential (SSSEP) BCIs, which are based on tactile selective attention, have gained increasing attention recently. These may reduce the fatigue induced by visual attention and overcome the low reliability of motor activity. In this literature survey, recent findings on SSSEP and its methodological uses in BCI are reviewed. Further, existing limitations of SSSEP BCI and potential future directions for the technique are discussed.},
}
@article {pmid26834607,
year = {2015},
author = {Durantin, G and Scannella, S and Gateau, T and Delorme, A and Dehais, F},
title = {Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {707},
pmid = {26834607},
issn = {1662-5161},
abstract = {Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.},
}
@article {pmid26834551,
year = {2015},
author = {Xu, R and Jiang, N and Mrachacz-Kersting, N and Dremstrup, K and Farina, D},
title = {Factors of Influence on the Performance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {527},
pmid = {26834551},
issn = {1662-4548},
abstract = {Brain-computer interfacing (BCI) has recently been applied as a rehabilitation approach for patients with motor disorders, such as stroke. In these closed-loop applications, a brain switch detects the motor intention from brain signals, e.g., scalp EEG, and triggers a neuroprosthetic device, either to deliver sensory feedback or to mimic real movements, thus re-establishing the compromised sensory-motor control loop and promoting neural plasticity. In this context, single trial detection of motor intention with short latency is a prerequisite. The performance of the event detection from EEG recordings is mainly determined by three factors: the type of motor imagery (e.g., repetitive, ballistic), the frequency band (or signal modality) used for discrimination (e.g., alpha, beta, gamma, and MRCP, i.e., movement-related cortical potential), and the processing technique (e.g., time-series analysis, sub-band power estimation). In this study, we investigated single trial EEG traces during movement imagination on healthy individuals, and provided a comprehensive analysis of the performance of a short-latency brain switch when varying these three factors. The morphological investigation showed a cross-subject consistency of a prolonged negative phase in MRCP, and a delayed beta rebound in sensory-motor rhythms during repetitive tasks. The detection performance had the greatest accuracy when using ballistic MRCP with time-series analysis. In this case, the true positive rate (TPR) was ~70% for a detection latency of ~200 ms. The results presented here are of practical relevance for designing BCI systems for motor function rehabilitation.},
}
@article {pmid26834546,
year = {2015},
author = {Cellot, G and Lagonegro, P and Tarabella, G and Scaini, D and Fabbri, F and Iannotta, S and Prato, M and Salviati, G and Ballerini, L},
title = {PEDOT:PSS Interfaces Support the Development of Neuronal Synaptic Networks with Reduced Neuroglia Response In vitro.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {521},
pmid = {26834546},
issn = {1662-4548},
abstract = {UNLABELLED: The design of electrodes based on conductive polymers in brain-machine interface technology offers the opportunity to exploit variably manufactured materials to reduce gliosis, indeed the most common brain response to chronically implanted neural electrodes. In fact, the use of conductive polymers, finely tailored in their physical-chemical properties, might result in electrodes with improved adaptability to the brain tissue and increased charge-transfer efficiency. Here we interfaced poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (<br><br>PEDOT: PSS) doped with different amounts of ethylene glycol (EG) with rat hippocampal primary cultures grown for 3 weeks on these synthetic substrates. We used immunofluorescence and scanning electron microscopy (SEM) combined to single cell electrophysiology to assess the biocompatibility of<br><br>PEDOT: PSS in terms of neuronal growth and synapse formation. We investigated neuronal morphology, density and electrical activity. We reported the novel observation that opposite to neurons, glial cell density was progressively reduced, hinting at the ability of this material to down regulate glial reaction. Thus,<br><br>PEDOT: PSS is an attractive candidate for the design of new implantable electrodes, controlling the extent of glial reactivity without affecting neuronal viability and function.},
}
@article {pmid26833918,
year = {2016},
author = {Paret, C and Kluetsch, R and Zaehringer, J and Ruf, M and Demirakca, T and Bohus, M and Ende, G and Schmahl, C},
title = {Alterations of amygdala-prefrontal connectivity with real-time fMRI neurofeedback in BPD patients.},
journal = {Social cognitive and affective neuroscience},
volume = {11},
number = {6},
pages = {952-960},
pmid = {26833918},
issn = {1749-5024},
mesh = {Adult ; Amygdala/*physiopathology ; Borderline Personality Disorder/*physiopathology/rehabilitation ; *Brain-Computer Interfaces ; Humans ; Magnetic Resonance Imaging/*methods ; Nerve Net/*physiopathology ; Neurofeedback/methods/*physiology ; Prefrontal Cortex/*physiopathology ; },
abstract = {With the use of real-time functional magnetic resonance imaging neurofeedback (NF), amygdala activitiy can be visualized in real time. In this study, continuous amygdala NF was provided to patients with borderline personality disorder (BPD) with the instruction to down-regulate. During four sessions of NF training, patients viewed aversive pictures and received feedback from a thermometer display, which showed the amygdala blood oxygenation level-dependent signal. Conditions of regulation and viewing without regulation were presented. Each session started with a resting-state scan and was followed by a transfer run without NF. Amygdala regulation, task-related and resting-state functional brain connectivity were analyzed. Self-ratings of dissociation and difficulty in emotion regulation were collected. BPD patients down-regulated right amygdala activation but there were no improvements over time. Task-related amygdala-ventromedial prefrontal cortex connectivity was altered across the four sessions, with an increased connectivity when regulating vs viewing pictures. Resting-state amygdala-lateral prefrontal cortex connectivity was altered and dissociation, as well as scores for 'lack of emotional awareness', decreased with training. Results demonstrated that amygdala NF may improve healthy brain connectivity, as well as emotion regulation. A randomized-controlled trial is needed to investigate whether amygdala NF is instrumental for improving neural regulation and emotion regulation in BPD patients.},
}
@article {pmid26831487,
year = {2016},
author = {Chen, K and Liu, Q and Ai, Q and Zhou, Z and Xie, SQ and Meng, W},
title = {A MUSIC-based method for SSVEP signal processing.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {39},
number = {1},
pages = {71-84},
doi = {10.1007/s13246-015-0398-6},
pmid = {26831487},
issn = {1879-5447},
mesh = {*Algorithms ; Amplifiers, Electronic ; Computer Simulation ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Statistics as Topic ; Time Factors ; },
abstract = {The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.},
}
@article {pmid26828741,
year = {2016},
author = {Hong, KS and Santosa, H},
title = {Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy.},
journal = {Hearing research},
volume = {333},
number = {},
pages = {157-166},
doi = {10.1016/j.heares.2016.01.009},
pmid = {26828741},
issn = {1878-5891},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Cortex/blood supply/*physiology ; Auditory Pathways/physiology ; *Auditory Perception ; Biomarkers/blood ; Brain Mapping/*methods ; *Cerebrovascular Circulation ; Cerebrum/blood supply/*physiology ; Discriminant Analysis ; *Discrimination, Psychological ; Female ; Functional Laterality ; Hemodynamics ; Humans ; Irritable Mood ; Linear Models ; Male ; Noise/adverse effects ; Oxyhemoglobins/metabolism ; Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared ; Speech Perception ; Support Vector Machine ; Young Adult ; },
abstract = {The ability of the auditory cortex in the brain to distinguish different sounds is important in daily life. This study investigated whether activations in the auditory cortex caused by different sounds can be distinguished using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses (HRs) in both hemispheres using fNIRS were measured in 18 subjects while exposing them to four sound categories (English-speech, non-English-speech, annoying sounds, and nature sounds). As features for classifying the different signals, the mean, slope, and skewness of the oxy-hemoglobin (HbO) signal were used. With regard to the language-related stimuli, the HRs evoked by understandable speech (English) were observed in a broader brain region than were those evoked by non-English speech. Also, the magnitudes of the HbO signals evoked by English-speech were higher than those of non-English speech. The ratio of the peak values of non-English and English speech was 72.5%. Also, the brain region evoked by annoying sounds was wider than that by nature sounds. However, the signal strength for nature sounds was stronger than that for annoying sounds. Finally, for brain-computer interface (BCI) purposes, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were applied to the four sound categories. The overall classification performance for the left hemisphere was higher than that for the right hemisphere. Therefore, for decoding of auditory commands, the left hemisphere is recommended. Also, in two-class classification, the annoying vs. nature sounds comparison provides a higher classification accuracy than the English vs. non-English speech comparison. Finally, LDA performs better than SVM.},
}
@article {pmid26824883,
year = {2016},
author = {Zink, R and Hunyadi, B and Huffel, SV and Vos, MD},
title = {Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026005},
doi = {10.1088/1741-2560/13/2/026005},
pmid = {26824883},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*classification/methods/standards ; Adult ; Auditory Cortex/*physiology ; Brain-Computer Interfaces/*classification/standards ; Calibration ; Female ; Humans ; Male ; Young Adult ; },
abstract = {OBJECTIVE: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification.<br><br>APPROACH: We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm.<br><br>MAIN RESULTS: The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach.<br><br>SIGNIFICANCE: The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.},
}
@article {pmid26824791,
year = {2016},
author = {Best, MD and Takahashi, K and Suminski, AJ and Ethier, C and Miller, LE and Hatsopoulos, NG},
title = {Comparing offline decoding performance in physiologically defined neuronal classes.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026004},
pmid = {26824791},
issn = {1741-2552},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motor Cortex/cytology/*physiology ; Neurons/classification/*physiology ; Normal Distribution ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Recently, several studies have documented the presence of a bimodal distribution of spike waveform widths in primary motor cortex. Although narrow and wide spiking neurons, corresponding to the two modes of the distribution, exhibit different response properties, it remains unknown if these differences give rise to differential decoding performance between these two classes of cells.<br><br>APPROACH: We used a Gaussian mixture model to classify neurons into narrow and wide physiological classes. Using similar-size, random samples of neurons from these two physiological classes, we trained offline decoding models to predict a variety of movement features. We compared offline decoding performance between these two physiologically defined populations of cells.<br><br>MAIN RESULTS: We found that narrow spiking neural ensembles decode motor parameters better than wide spiking neural ensembles including kinematics, kinetics, and muscle activity.<br><br>SIGNIFICANCE: These findings suggest that the utility of neural ensembles in brain machine interfaces may be predicted from their spike waveform widths.},
}
@article {pmid26824590,
year = {2016},
author = {Geronimo, A and Simmons, Z and Schiff, SJ},
title = {Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026002},
doi = {10.1088/1741-2560/13/2/026002},
pmid = {26824590},
issn = {1741-2552},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*diagnosis/physiopathology/*therapy ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Photic Stimulation/*methods ; Predictive Value of Tests ; Psychomotor Performance/*physiology ; Surveys and Questionnaires ; },
abstract = {OBJECTIVE: Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder.<br><br>APPROACH: In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery.<br><br>RESULTS: The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age.<br><br>SIGNIFICANCE: These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.},
}
@article {pmid26824461,
year = {2016},
author = {Bashashati, H and Ward, RK and Bashashati, A},
title = {User-customized brain computer interfaces using Bayesian optimization.},
journal = {Journal of neural engineering},
volume = {13},
number = {2},
pages = {026001},
doi = {10.1088/1741-2560/13/2/026001},
pmid = {26824461},
issn = {1741-2552},
mesh = {*Algorithms ; Bayes Theorem ; Brain/physiology ; Brain-Computer Interfaces/*standards/trends ; Electroencephalography/methods ; Humans ; Pattern Recognition, Automated/methods/*standards ; *User-Computer Interface ; },
abstract = {OBJECTIVE: The brain characteristics of different people are not the same. Brain computer interfaces (BCIs) should thus be customized for each individual person. In motor-imagery based synchronous BCIs, a number of parameters (referred to as hyper-parameters) including the EEG frequency bands, the channels and the time intervals from which the features are extracted should be pre-determined based on each subject's brain characteristics.<br><br>APPROACH: To determine the hyper-parameter values, previous work has relied on manual or semi-automatic methods that are not applicable to high-dimensional search spaces. In this paper, we propose a fully automatic, scalable and computationally inexpensive algorithm that uses Bayesian optimization to tune these hyper-parameters. We then build different classifiers trained on the sets of hyper-parameter values proposed by the Bayesian optimization. A final classifier aggregates the results of the different classifiers.<br><br>MAIN RESULTS: We have applied our method to 21 subjects from three BCI competition datasets. We have conducted rigorous statistical tests, and have shown the positive impact of hyper-parameter optimization in improving the accuracy of BCIs. Furthermore, We have compared our results to those reported in the literature.<br><br>SIGNIFICANCE: Unlike the best reported results in the literature, which are based on more sophisticated feature extraction and classification methods, and rely on prestudies to determine the hyper-parameter values, our method has the advantage of being fully automated, uses less sophisticated feature extraction and classification methods, and yields similar or superior results compared to the best performing designs in the literature.},
}
@article {pmid26824212,
year = {2015},
author = {Monini, S and Filippi, C and Atturo, F and Biagini, M and Lazzarino, AI and Barbara, M},
title = {Individualised headband simulation test for predicting outcome after percutaneous bone conductive implantation.},
journal = {Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale},
volume = {35},
number = {4},
pages = {258-264},
pmid = {26824212},
issn = {1827-675X},
mesh = {Auditory Threshold ; *Bone Conduction ; *Hearing Aids ; Hearing Loss, Conductive/*surgery ; Humans ; Reproducibility of Results ; Speech Perception ; Treatment Outcome ; },
abstract = {Trans-cutaneous bone conduction (BC) stimulators, when coupled to the HB (BC-HB), are generally used to predict the results that could be achieved after bone conductive implant (BCI) surgery, and their performance is generally considered inferior to that provided by the definitive percutaneous system. The aim of the present study was to compare the performances between BC-HB and BCI of the same typology, when the former's sound processor is fitted in accordance to the individual auditory situation. Twenty-two patients selected for surgical application of a BCI were evaluated and the same audiological protocol was used to select the candidate and assess the final outcome. The BC-HB was properly fitted based on individual hearing loss and personal auditory targets, and tested as primary step of the protocol to obtain the most reliable predictive value. The BAHA Divino and BP100 sound processors were applied in 12 patients with conductive/mixed hearing loss (CMHL) and in 10 subjects with single sided deafness (SSD). Audiometric evaluation included the pure tone average (PTA3) threshold between 250-1000 Hz; the PTA thresholds at 2000 and 4000 Hz; intelligibility scores as percentage of word recognition (WRS) in quiet and in noise; and subjective evaluation of perceived sound quality by a visual analogue scale (VAS). Statistical evaluation with a student's t test was used for assessment of efficacy of BC-HB and BCI compared with the unaided condition. Spearman's Rho coefficient was used to confirm the reliability of the BC-HB simulation test as a predictor of definitive outcome. The results showed that the mean PTA difference between BCI and BC-HB ranged from 2.54 to 8.27 decibels in the CMHL group and from 1.27 to 3.9 decibels in the SSD group. Compared with the BC-HB, BCI showed a better WRS both in CMHL (16% in quiet and 12% in noise) and in SSD (5% in quiet and a 1% in noise) groups. Spearman's Rho coefficient, calculated for PTA, WRS in quiet and in noise and VAS in the two aided conditions, showed a significant correlation between BC-HB and BCI, between PTA and VAS and between WRS in quiet and VAS. It is possible to conclude that the headband test, when the sound processor of the selected bone conductive implant is fitted and personalised for individual hearing loss and auditory targets of the candidate, may provide highly predictive data of the definitive outcome after BCI implant surgery.},
}
@article {pmid26822502,
year = {2016},
author = {Gaide, T and Dreimann, JM and Behr, A and Vorholt, AJ},
title = {Overcoming Phase-Transfer Limitations in the Conversion of Lipophilic Oleo Compounds in Aqueous Media-A Thermomorphic Approach.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {55},
number = {8},
pages = {2924-2928},
doi = {10.1002/anie.201510738},
pmid = {26822502},
issn = {1521-3773},
abstract = {A new process concept has been developed for recycling transition-metal catalysts in the synthesis of moderately polar products via aqueous thermomorphic multicomponent solvent systems. This work focuses on the use of "green" solvents (1-butanol and water) in the hydroformylation of the bio-based substrate methyl 10-undecenoate. Following the successful development of a biphasic reaction system on the laboratory scale, the reaction was transferred to a continuously operated miniplant to demonstrate the robustness of this innovative recycling concept for homogenous catalysts.},
}
@article {pmid26822435,
year = {2016},
author = {Yi, W and Qiu, S and Wang, K and Qi, H and He, F and Zhou, P and Zhang, L and Ming, D},
title = {EEG oscillatory patterns and classification of sequential compound limb motor imagery.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {13},
number = {},
pages = {11},
pmid = {26822435},
issn = {1743-0003},
mesh = {Adult ; Beta Rhythm ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization ; Extremities/*physiology ; Female ; Foot/physiology ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: A number of studies have been done on movement imagination of motor sequences with a single limb. However, brain oscillatory patterns induced by movement imagination of motor sequences involving multiple limbs have not been reported in recent years. The goal of the present study was to verify the feasibility of application of motor sequences involving multiple limbs to brain-computer interface (BCI) systems based on motor imagery (MI). The changes of EEG patterns and the inter-influence between movements associated with the imagination of motor sequences were also investigated.<br><br>METHODS: The experiment, where 12 healthy subjects participated, involved one motor sequence with a single limb and three kinds of motor sequences with two or three limbs. The activity involved mental simulation, imagining playing drums with two conditions (60 and 30 beats per minute for the first and second conditions, respectively).<br><br>RESULTS: Movement imagination of different limbs in the sequence contributed to time-variant event-related desynchronization (ERD) patterns within both mu and beta rhythms, which was more obvious for the second condition compared with the first condition. The ERD values of left/right hand imagery with prior hand imagery were significantly larger than those with prior foot imagery, while the phase locking values (PLVs) between central electrodes and the mesial frontocentral electrode of non-initial movement were significantly larger than those of the initial movement during imagination of motor sequences for both conditions. Classification results showed that the power spectral density (PSD) based method outperformed the multi-class common spatial patterns (multi-CSP) based method: The highest accuracies were 82.86&#xa0;% and 91.43&#xa0;%, and the mean values were 65&#xa0;% and 74.14&#xa0;% for the first and second conditions, respectively.<br><br>CONCLUSIONS: This work implies that motor sequences involving multiple limbs can be utilized to build a multimodal classification paradigm in MI-based BCI systems, and that prior movement imagination can result in the changes of neural activities in motor areas during subsequent movement imagination in the process of limb switching.},
}
@article {pmid26820903,
year = {2016},
author = {Chernov, MM and Chen, G and Torre-Healy, LA and Friedman, RM and Roe, AW},
title = {Microelectrode array stimulation combined with intrinsic optical imaging: A novel tool for functional brain mapping.},
journal = {Journal of neuroscience methods},
volume = {263},
number = {},
pages = {7-14},
pmid = {26820903},
issn = {1872-678X},
support = {R01 NS044375/NS/NINDS NIH HHS/United States ; R01 NS093998/NS/NINDS NIH HHS/United States ; R56 NS044375/NS/NINDS NIH HHS/United States ; NS 044375/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain Mapping ; Cerebral Cortex/*diagnostic imaging/*physiology ; *Electric Stimulation ; Forelimb/physiology ; Macaca mulatta ; Microelectrodes ; Movement/physiology ; *Optical Imaging ; Wakefulness ; },
abstract = {BACKGROUND: Functional brain mapping via cortical microstimulation is a widely used clinical and experimental tool. However, data are traditionally collected point by point, making the technique very time consuming. Moreover, even in skilled hands, consistent penetration depths are difficult to achieve. Finally, the effects of microstimulation are assessed behaviorally, with no attempt to capture the activity of the local cortical circuits being stimulated.<br><br>NEW METHOD: We propose a novel method for functional brain mapping, which combines the use of a microelectrode array with intrinsic optical imaging. The precise spacing of electrodes allows for fast, accurate mapping of the area of interest in a regular grid. At the same time, the optical window allows for visualization of local neural connections when stimulation is combined with intrinsic optical imaging.<br><br>RESULTS: We demonstrate the efficacy of our technique using the primate motor cortex as a sample application, using a combination of microstimulation, imaging and electrophysiological recordings during wakefulness and under anesthesia. Comparison with current method: We find the data collected with our method is consistent with previous data published by others. We believe that our approach enables data to be collected faster and in a more consistent fashion and makes possible a number of studies that would be difficult to carry out with the traditional approach.<br><br>CONCLUSIONS: Our technique allows for simultaneous modulation and imaging of cortical sensorimotor networks in wakeful subjects over multiple sessions which is highly desirable for both the study of cortical organization and the design of brain machine interfaces.},
}
@article {pmid26819595,
year = {2016},
author = {Cavrini, F and Bianchi, L and Quitadamo, LR and Saggio, G},
title = {A Fuzzy Integral Ensemble Method in Visual P300 Brain-Computer Interface.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {9845980},
pmid = {26819595},
issn = {1687-5273},
mesh = {Biomechanical Phenomena ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; *Fuzzy Logic ; Humans ; *Models, Biological ; Photic Stimulation ; Robotics ; Spatial Navigation/*physiology ; },
abstract = {We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.},
}
@article {pmid26819580,
year = {2016},
author = {Frey, J and Appriou, A and Lotte, F and Hachet, M},
title = {Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort.},
journal = {Computational intelligence and neuroscience},
volume = {2016},
number = {},
pages = {2758103},
pmid = {26819580},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain Mapping ; Brain Waves/*physiology ; Brain-Computer Interfaces ; Computer Simulation ; Depth Perception/*physiology ; *Electroencephalography ; Female ; Humans ; Male ; Monte Carlo Method ; Photic Stimulation ; Statistics, Nonparametric ; Surveys and Questionnaires ; User-Computer Interface ; Young Adult ; },
abstract = {With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users' states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials' (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users' state by modifying the overlap of left and right images according to the classifier output.},
}
@article {pmid26812728,
year = {2016},
author = {Wang, H and Zhang, Y and Waytowich, NR and Krusienski, DJ and Zhou, G and Jin, J and Wang, X and Cichocki, A},
title = {Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {5},
pages = {532-541},
doi = {10.1109/TNSRE.2016.2519350},
pmid = {26812728},
issn = {1558-0210},
mesh = {Adult ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Computer Simulation ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Linear Models ; Machine Learning ; Male ; Multivariate Analysis ; Pattern Recognition, Automated/*methods ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Many of the most widely accepted methods for reliable detection of steady-state visual evoked potentials (SSVEPs) in the electroencephalogram (EEG) utilize canonical correlation analysis (CCA). CCA uses pure sine and cosine reference templates with frequencies corresponding to the visual stimulation frequencies. These generic reference templates may not optimally reflect the natural SSVEP features obscured by the background EEG. This paper introduces a new approach that utilizes spatio-temporal feature extraction with multivariate linear regression (MLR) to learn discriminative SSVEP features for improving the detection accuracy. MLR is implemented on dimensionality-reduced EEG training data and a constructed label matrix to find optimally discriminative subspaces. Experimental results show that the proposed MLR method significantly outperforms CCA as well as several other competing methods for SSVEP detection, especially for time windows shorter than 1 second. This demonstrates that the MLR method is a promising new approach for achieving improved real-time performance of SSVEP-BCIs.},
}
@article {pmid26812487,
year = {2016},
author = {Finke, A and Essig, K and Marchioro, G and Ritter, H},
title = {Toward FRP-Based Brain-Machine Interfaces-Single-Trial Classification of Fixation-Related Potentials.},
journal = {PloS one},
volume = {11},
number = {1},
pages = {e0146848},
pmid = {26812487},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Young Adult ; },
abstract = {The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant's body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction.},
}
@article {pmid26807789,
year = {2016},
author = {Rawool, VW},
title = {Emerging technologies with potential for objectively evaluating speech recognition skills.},
journal = {International journal of audiology},
volume = {55 Suppl 1},
number = {},
pages = {S41-50},
doi = {10.3109/14992027.2015.1128570},
pmid = {26807789},
issn = {1708-8186},
mesh = {Biomedical Technology/ethics/*trends ; Hearing Loss/*physiopathology ; Humans ; Noise/*adverse effects ; *Speech Perception ; Speech Reception Threshold Test/ethics/methods/*trends ; },
abstract = {Work-related exposure to noise and other ototoxins can cause damage to the cochlea, synapses between the inner hair cells, the auditory nerve fibers, and higher auditory pathways, leading to difficulties in recognizing speech. Procedures designed to determine speech recognition scores (SRS) in an objective manner can be helpful in disability compensation cases where the worker claims to have poor speech perception due to exposure to noise or ototoxins. Such measures can also be helpful in determining SRS in individuals who cannot provide reliable responses to speech stimuli, including patients with Alzheimer's disease, traumatic brain injuries, and infants with and without hearing loss. Cost-effective neural monitoring hardware and software is being rapidly refined due to the high demand for neurogaming (games involving the use of brain-computer interfaces), health, and other applications. More specifically, two related advances in neuro-technology include relative ease in recording neural activity and availability of sophisticated analysing techniques. These techniques are reviewed in the current article and their applications for developing objective SRS procedures are proposed. Issues related to neuroaudioethics (ethics related to collection of neural data evoked by auditory stimuli including speech) and neurosecurity (preservation of a person's neural mechanisms and free will) are also discussed.},
}
@article {pmid26804778,
year = {2016},
author = {Lorenz, R and Monti, RP and Violante, IR and Anagnostopoulos, C and Faisal, AA and Montana, G and Leech, R},
title = {The Automatic Neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI.},
journal = {NeuroImage},
volume = {129},
number = {},
pages = {320-334},
pmid = {26804778},
issn = {1095-9572},
support = {/WT_/Wellcome Trust/United Kingdom ; 103045/Z/13/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain/physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Machine Learning ; Magnetic Resonance Imaging/*methods ; Male ; Neurosciences/methods ; },
abstract = {Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.},
}
@article {pmid26802188,
year = {2016},
author = {Myrden, A and Chau, T},
title = {Feature clustering for robust frequency-domain classification of EEG activity.},
journal = {Journal of neuroscience methods},
volume = {262},
number = {},
pages = {77-84},
doi = {10.1016/j.jneumeth.2016.01.014},
pmid = {26802188},
issn = {1872-678X},
mesh = {*Algorithms ; Brain Waves/*physiology ; Cerebral Cortex/*physiology ; *Cluster Analysis ; Electroencephalography/*classification/*methods ; Humans ; Spectrum Analysis ; },
abstract = {BACKGROUND: The analysis of electroencephalograms is often performed in the frequency-domain. These analyses typically involve the computation of spectral power either over pre-defined frequency bands (e.g. theta, delta, alpha, and beta bands) or over a large number of narrow frequency ranges. However, the former technique ignores variability in these frequency bands over time and between participants while the latter ignores the significant redundancy between these powers.<br><br>NEW METHOD: This paper details an unsupervised feature extraction method for EEG data that uses a clustering of features to agglomerate narrow-band spectral powers based on their similarities. This method computes a set of analogues to the traditional frequency bands that are data-driven and participant-specific. A fast correlation-based filter was used to identify which of these agglomerated features were most useful for each investigated classification problem.<br><br>RESULTS: The new feature clustering algorithm was used to detect changes in three mental states and to detect the performance of three mental tasks. Balanced classification accuracies approaching or exceeding 70% were attained for all classification problems.<br><br>Classification accuracies attained by this algorithm were compared to those attained by two frequency-domain algorithms that did not employ clustering--a wide-band algorithm based on the spectral power within the theta, delta, alpha, and beta bands and a narrow-band algorithm based on the spectral power within 1-Hz ranges. Overall, the feature clustering algorithm was statistically superior to both alternative algorithms.<br><br>CONCLUSIONS: The new feature clustering algorithm provides a promising alternative to conventional frequency-domain EEG analysis.},
}
@article {pmid26800319,
year = {2017},
author = {Scandola, M and Aglioti, SM and Pozeg, P and Avesani, R and Moro, V},
title = {Motor imagery in spinal cord injured people is modulated by somatotopic coding, perspective taking, and post-lesional chronic pain.},
journal = {Journal of neuropsychology},
volume = {11},
number = {3},
pages = {305-326},
doi = {10.1111/jnp.12098},
pmid = {26800319},
issn = {1748-6653},
mesh = {Adult ; Aged ; Case-Control Studies ; Chronic Pain/*complications/*psychology ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; *Movement ; Paraplegia/psychology ; Personality ; Quadriplegia/psychology ; Spinal Cord Injuries/*complications/*psychology ; Young Adult ; },
abstract = {Motor imagery (MI) allows one to mentally represent an action without necessarily performing it. Importantly, however, MI is profoundly influenced by the ability to actually execute actions, as demonstrated by the impairment of this ability as a consequence of lesions in motor cortices, limb amputations, movement limiting chronic pain, and spinal cord injury. Understanding MI and its deficits in patients with motor limitations is fundamentally important as development of some brain-computer interfaces and daily life strategies for coping with motor disorders are based on this ability. We explored MI in a large sample of patients with spinal cord injury (SCI) using a comprehensive battery of questionnaires to assess the ability to imagine actions from a first-person or a third-person perspective and also imagine the proprioceptive components of actions. Moreover, we correlated MI skills with personality measures and clinical variables such as the level and completeness of the lesion and the presence of chronic pain. We found that the MI deficits (1) concerned the body parts affected by deafferentation and deefferentation, (2) were present in first- but not in third-person perspectives, and (3) were more altered in the presence of chronic pain. MI is thus closely related to bodily perceptions and representations. Every attempt to devise tools and trainings aimed at improving autonomy needs to consider the cognitive changes due to the body-brain disconnection.},
}
@article {pmid26798330,
year = {2015},
author = {She, Q and Ma, Y and Meng, M and Luo, Z},
title = {Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {251945},
pmid = {26798330},
issn = {1687-5273},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*classification ; Humans ; Imagination/*physiology ; Models, Neurological ; Models, Statistical ; Movement/*physiology ; Probability ; *Support Vector Machine ; },
abstract = {Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.},
}
@article {pmid26796630,
year = {2016},
author = {Smulders, YE and van Zon, A and Stegeman, I and Rinia, AB and Van Zanten, GA and Stokroos, RJ and Hendrice, N and Free, RH and Maat, B and Frijns, JH and Briaire, JJ and Mylanus, EA and Huinck, WJ and Smit, AL and Topsakal, V and Tange, RA and Grolman, W},
title = {Comparison of Bilateral and Unilateral Cochlear Implantation in Adults: A Randomized Clinical Trial.},
journal = {JAMA otolaryngology-- head &amp; neck surgery},
volume = {142},
number = {3},
pages = {249-256},
doi = {10.1001/jamaoto.2015.3305},
pmid = {26796630},
issn = {2168-619X},
mesh = {Adolescent ; Adult ; Aged ; Auditory Perception/*physiology ; Cochlear Implantation/*methods ; Deafness/diagnosis/physiopathology/*surgery ; Female ; Hearing/*physiology ; Hearing Loss/diagnosis/physiopathology/*surgery ; Hearing Tests ; Humans ; Male ; Middle Aged ; *Self Report ; Speech Perception ; Surveys and Questionnaires ; Treatment Outcome ; Young Adult ; },
abstract = {IMPORTANCE: The cost of bilateral cochlear implantation (BCI) is usually not reimbursed by insurance companies because of a lack of well-designed studies reporting the benefits of a second cochlear implant.<br><br>OBJECTIVE: To determine the benefits of simultaneous BCI compared with unilateral cochlear implantation (UCI) in adults with postlingual deafness.<br><br>A multicenter randomized clinical trial was performed. The study took place in 5 Dutch tertiary referral centers: the University Medical Centers of Utrecht, Maastricht, Groningen, Leiden, and Nijmegen. Forty patients eligible for cochlear implantation met the study criteria and were included from January 12, 2010, through November 2, 2012. The main inclusion criteria were postlingual onset of hearing loss, age of 18 to 70 years, duration of hearing loss of less than 20 years, and a marginal hearing aid benefit. Two participants withdrew from the study before implantation. Nineteen participants were randomized to undergo UCI and 19 to undergo BCI.<br><br>INTERVENTIONS: The BCI group received 2 cochlear implants during 1 surgery. The UCI group received 1 cochlear implant.<br><br>MAIN OUTCOMES AND MEASURES: The primary outcome was the Utrecht Sentence Test with Adaptive Randomized Roving levels (speech in noise, both presented from straight ahead). Secondary outcomes were consonant-vowel-consonant words in silence, speech-intelligibility test with spatially separated sources (speech in noise from different directions), sound localization, and quality of hearing questionnaires. Before any data were collected, the hypothesis was that the BCI group would perform better on the objective and subjective tests that concerned speech intelligibility in noise and spatial hearing.<br><br>RESULTS: Thirty-eight patients were included in the study. Fifteen patients in the BCI group used hearing aids before implantation compared with 19 in the UCI group. Otherwise, there were no significant differences between the groups' baseline characteristics. At 1-year follow-up, there were no significant differences between groups on the Utrecht Sentence Test with Adaptive Randomized Roving levels (9.1 dB, UCI group; 8.2 dB, BCI group; P&#x2009;= .39) or the consonant-vowel-consonant test (median percentage correct score 85.0% in the UCI group and 86.8% in the BCI group; P = .21). The BCI group performed significantly better than the UCI group when noise came from different directions (median speech reception threshold in noise, 14.4 dB, BCI group; 5.6 dB, BCI group; P <.001). The BCI group was better able to localize sounds (median correct score of 50.0% at 60&#xb0;, UCI group; 96.7%, BCI group; P <.001). These results were consistent with the patients' self-reported hearing capabilities.<br><br>CONCLUSIONS AND RELEVANCE: This randomized clinical trial demonstrates a significant benefit of simultaneous BCI above UCI in daily listening situations for adults with postlingual deafness.<br><br>TRIAL REGISTRATION: trialregister.nl Identifier: NTR1722.},
}
@article {pmid26796293,
year = {2016},
author = {Golub, MD and Chase, SM and Batista, AP and Yu, BM},
title = {Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.},
journal = {Current opinion in neurobiology},
volume = {37},
number = {},
pages = {53-58},
pmid = {26796293},
issn = {1873-6882},
support = {P30 NS076405/NS/NINDS NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Cognition/*physiology ; Feedback, Sensory/*physiology ; Neurology/trends ; },
abstract = {Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain-computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.},
}
@article {pmid26795195,
year = {2016},
author = {Goyal, A and Samadani, AA and Guerguerian, AM and Chau, T},
title = {An online three-class Transcranial Doppler ultrasound brain computer interface.},
journal = {Neuroscience research},
volume = {107},
number = {},
pages = {47-56},
doi = {10.1016/j.neures.2015.12.013},
pmid = {26795195},
issn = {1872-8111},
mesh = {Adult ; *Brain-Computer Interfaces ; *Cognition ; Female ; Humans ; Imagination ; Male ; Middle Cerebral Artery/physiology ; Online Systems ; Photic Stimulation ; *Ultrasonography, Doppler, Transcranial ; Verbal Behavior ; Young Adult ; },
abstract = {Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues.},
}
@article {pmid26793103,
year = {2015},
author = {Trebbastoni, A and Pichiorri, F and D'Antonio, F and Campanelli, A and Onesti, E and Ceccanti, M and de Lena, C and Inghilleri, M},
title = {Altered Cortical Synaptic Plasticity in Response to 5-Hz Repetitive Transcranial Magnetic Stimulation as a New Electrophysiological Finding in Amnestic Mild Cognitive Impairment Converting to Alzheimer's Disease: Results from a 4-year Prospective Cohort Study.},
journal = {Frontiers in aging neuroscience},
volume = {7},
number = {},
pages = {253},
pmid = {26793103},
issn = {1663-4365},
abstract = {INTRODUCTION: To investigate cortical excitability and synaptic plasticity in amnestic mild cognitive impairment (aMCI) using 5 Hz repetitive transcranial magnetic stimulation (5 Hz-rTMS) and to assess whether specific TMS parameters predict conversion time to Alzheimer's disease (AD).<br><br>MATERIALS AND METHODS: Forty aMCI patients (single- and multi-domain) and 20 healthy controls underwent, at baseline, a neuropsychological examination and 5&#x2009;Hz-rTMS delivered in trains of 10 stimuli and 120% of resting motor threshold (rMT) intensity over the dominant motor area. The rMT and the ratio between amplitude of the 1st and the 10th motor-evoked potential elicited by the train (X/I-MEP ratio) were calculated as measures of cortical excitability and synaptic plasticity, respectively. Patients were followed up annually over a period of 48&#x2009;months. Analysis of variance for repeated measures was used to compare TMS parameters in patients with those in controls. Spearman's correlation was performed by considering demographic variables, aMCI subtype, neuropsychological test scores, TMS parameters, and conversion time.<br><br>RESULTS: Thirty-five aMCI subjects completed the study; 60% of these converted to AD. The baseline rMT and X/I-MEP ratio were significantly lower in patients than in controls (p&#x2009;=&#x2009;0.04 and p&#x2009;=&#x2009;0.01). Spearman's analysis showed that conversion time correlated with the rMT (0.40) and X/I-MEP ratio (0.51).<br><br>DISCUSSION: aMCI patients displayed cortical hyperexcitability and altered synaptic plasticity to 5&#x2009;Hz-rTMS when compared with healthy subjects. The extent of these changes correlated with conversion time. These alterations, which have previously been observed in AD, are thus present in the early stages of disease and may be considered as potential neurophysiological markers of conversion from aMCI to AD.},
}
@article {pmid26793089,
year = {2015},
author = {Nathan, K and Contreras-Vidal, JL},
title = {Negligible Motion Artifacts in Scalp Electroencephalography (EEG) During Treadmill Walking.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {708},
pmid = {26793089},
issn = {1662-5161},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; },
abstract = {Recent mobile brain/body imaging (MoBI) techniques based on active electrode scalp electroencephalogram (EEG) allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h) using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject's head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects' motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.},
}
@article {pmid26791225,
year = {2016},
author = {Magdesian, MH and Lopez-Ayon, GM and Mori, M and Boudreau, D and Goulet-Hanssens, A and Sanz, R and Miyahara, Y and Barrett, CJ and Fournier, AE and De Koninck, Y and Grütter, P},
title = {Rapid Mechanically Controlled Rewiring of Neuronal Circuits.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {36},
number = {3},
pages = {979-987},
pmid = {26791225},
issn = {1529-2401},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Animals ; Axons/physiology ; Cells, Cultured ; Cerebral Cortex/cytology/*physiology ; Female ; Hippocampus/cytology/*physiology ; Male ; Nerve Net/cytology/*physiology ; Nerve Regeneration/*physiology ; Neurites/physiology ; Neurons/*physiology ; Patch-Clamp Techniques/methods ; Rats ; Rats, Sprague-Dawley ; Time Factors ; },
abstract = {CNS injury may lead to permanent functional deficits because it is still not possible to regenerate axons over long distances and accurately reconnect them with an appropriate target. Using rat neurons, microtools, and nanotools, we show that new, functional neurites can be created and precisely positioned to directly (re)wire neuronal networks. We show that an adhesive contact made onto an axon or dendrite can be pulled to initiate a new neurite that can be mechanically guided to form new synapses at up to 0.8 mm distance in <1 h. Our findings challenge current understanding of the limits of neuronal growth and have direct implications for the development of new therapies and surgical techniques to achieve functional regeneration. Significance statement: Brain and spinal cord injury may lead to permanent disability and death because it is still not possible to regenerate neurons over long distances and accurately reconnect them with an appropriate target. Using microtools and nanotools we have developed a new method to rapidly initiate, elongate, and precisely connect new functional neuronal circuits over long distances. The extension rates achieved are ≥60 times faster than previously reported. Our findings have direct implications for the development of new therapies and surgical techniques to achieve functional regeneration after trauma and in neurodegenerative diseases. It also opens the door for the direct wiring of robust brain-machine interfaces as well as for investigations of fundamental aspects of neuronal signal processing and neuronal function.},
}
@article {pmid26790614,
year = {2016},
author = {Márquez-Ruiz, J and Ammann, C and Leal-Campanario, R and Ruffini, G and Gruart, A and Delgado-García, JM},
title = {Synthetic tactile perception induced by transcranial alternating-current stimulation can substitute for natural sensory stimulus in behaving rabbits.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {19753},
pmid = {26790614},
issn = {2045-2322},
mesh = {Animals ; *Behavior, Animal ; Brain-Computer Interfaces ; Conditioning, Classical ; Electric Stimulation ; Evoked Potentials, Somatosensory ; Motor Cortex/physiology ; Rabbits ; Somatosensory Cortex/physiology ; *Touch Perception ; *Transcranial Direct Current Stimulation ; },
abstract = {The use of brain-derived signals for controlling external devices has long attracted the attention from neuroscientists and engineers during last decades. Although much effort has been dedicated to establishing effective brain-to-computer communication, computer-to-brain communication feedback for "closing the loop" is now becoming a major research theme. While intracortical microstimulation of the sensory cortex has already been successfully used for this purpose, its future application in humans partly relies on the use of non-invasive brain stimulation technologies. In the present study, we explore the potential use of transcranial alternating-current stimulation (tACS) for synthetic tactile perception in alert behaving animals. More specifically, we determined the effects of tACS on sensory local field potentials (LFPs) and motor output and tested its capability for inducing tactile perception using classical eyeblink conditioning in the behaving animal. We demonstrated that tACS of the primary somatosensory cortex vibrissa area could indeed substitute natural stimuli during training in the associative learning paradigm.},
}
@article {pmid26780814,
year = {2016},
author = {Yu, YH and Lu, SW and Chuang, CH and King, JT and Chang, CL and Chen, SA and Chen, SF and Lin, CT},
title = {An Inflatable and Wearable Wireless System for Making 32-Channel Electroencephalogram Measurements.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {7},
pages = {806-813},
doi = {10.1109/TNSRE.2016.2516029},
pmid = {26780814},
issn = {1558-0210},
mesh = {Amplifiers, Electronic ; Analog-Digital Conversion ; Computer Communication Networks/*instrumentation ; *Electric Power Supplies ; *Electrodes ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Feasibility Studies ; Humans ; Monitoring, Ambulatory/*instrumentation ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications.},
}
@article {pmid26778962,
year = {2015},
author = {Murphy, MD and Guggenmos, DJ and Bundy, DT and Nudo, RJ},
title = {Current Challenges Facing the Translation of Brain Computer Interfaces from Preclinical Trials to Use in Human Patients.},
journal = {Frontiers in cellular neuroscience},
volume = {9},
number = {},
pages = {497},
pmid = {26778962},
issn = {1662-5102},
support = {R01 NS030853/NS/NINDS NIH HHS/United States ; T32 HD057850/HD/NICHD NIH HHS/United States ; },
abstract = {Current research in brain computer interface (BCI) technology is advancing beyond preclinical studies, with trials beginning in human patients. To date, these trials have been carried out with several different types of recording interfaces. The success of these devices has varied widely, but different factors such as the level of invasiveness, timescale of recorded information, and ability to maintain stable functionality of the device over a long period of time all must be considered in addition to accuracy in decoding intent when assessing the most practical type of device moving forward. Here, we discuss various approaches to BCIs, distinguishing between devices focusing on control of operations extrinsic to the subject (e.g., prosthetic limbs, computer cursors) and those focusing on control of operations intrinsic to the brain (e.g., using stimulation or external feedback), including closed-loop or adaptive devices. In this discussion, we consider the current challenges facing the translation of various types of BCI technology to eventual human application.},
}
@article {pmid26772662,
year = {2016},
author = {Chinnadurai, S and Fonnesbeck, C and Snyder, KM and Sathe, NA and Morad, A and Likis, FE and McPheeters, ML},
title = {Pharmacologic Interventions for Infantile Hemangioma: A Meta-analysis.},
journal = {Pediatrics},
volume = {137},
number = {2},
pages = {e20153896},
doi = {10.1542/peds.2015-3896},
pmid = {26772662},
issn = {1098-4275},
support = {HHSA290-2012-00009-I//PHS HHS/United States ; },
mesh = {Administration, Oral ; Administration, Topical ; Adrenergic beta-Antagonists/therapeutic use ; Glucocorticoids/therapeutic use ; Hemangioma/*drug therapy ; Humans ; Infant ; Infant, Newborn ; Propranolol/therapeutic use ; Timolol/therapeutic use ; },
abstract = {CONTEXT: Infantile hemangiomas (IH) may be associated with significant functional impact.<br><br>OBJECTIVE: The objective of this study was to meta-analyze studies of pharmacologic interventions for children with IH.<br><br>DATA SOURCES: Data sources were Medline and other databases from 1982 to June 2015.<br><br>STUDY SELECTION: Two reviewers assessed studies using predetermined inclusion criteria.<br><br>DATA EXTRACTION: One reviewer extracted data with review by a second.<br><br>RESULTS: We included 18 studies in a network meta-analysis assessing relative expected rates of IH clearance associated with &#x3b2;-blockers and steroids. Oral propranolol had the largest mean estimate of expected clearance (95%; 95% Bayesian credible interval [BCI]: 88%-99%) relative to oral corticosteroids (43%, 95% BCI: 21%-66%) and control (6%, 95% BCI: 1%-11%). Strength of evidence (SOE) was high for propranolol's effects on reducing lesion size compared with observation/placebo. Corticosteroids demonstrated moderate effectiveness at reducing size/volume (moderate SOE for improvement in IH). SOE was low for effects of topical timolol versus placebo.<br><br>LIMITATIONS: Methodologic limitations of available evidence may compromise SOE. Validity of meta-analytic estimates relies on the assumption of exchangeability among studies, conditional on effects of the intervention. Results rely on assumed lack of reporting bias.<br><br>CONCLUSIONS: Propranolol is effective at reducing IH size compared with placebo, observation, and other treatments including steroids in most studies. Corticosteroids demonstrate moderate effectiveness at reducing IH size/volume. The meta-analysis estimates provide a relative ranking of anticipated rates of lesion clearance among treatments. Families and clinicians making treatment decisions should also factor in elements such as lesion size, location, number, and type, and patient and family preferences.},
}
@article {pmid26763021,
year = {2016},
author = {Arba, F and Palumbo, V and Boulanger, JM and Pracucci, G and Inzitari, D and Buchan, AM and Hill, MD and , },
title = {Leukoaraiosis and lacunes are associated with poor clinical outcomes in ischemic stroke patients treated with intravenous thrombolysis.},
journal = {International journal of stroke : official journal of the International Stroke Society},
volume = {11},
number = {1},
pages = {62-67},
doi = {10.1177/1747493015607517},
pmid = {26763021},
issn = {1747-4949},
mesh = {Administration, Intravenous ; Aged ; Brain/*diagnostic imaging ; Brain Ischemia/*complications/diagnosis/diagnostic imaging/*drug therapy ; Canada ; Female ; Fibrinolytic Agents/therapeutic use ; Humans ; Leukoaraiosis/*complications/diagnosis/diagnostic imaging ; Logistic Models ; Male ; Prognosis ; Sensitivity and Specificity ; Severity of Illness Index ; Stroke/*complications/diagnosis/diagnostic imaging/*drug therapy ; Thrombolytic Therapy ; Tissue Plasminogen Activator/therapeutic use ; Tomography, X-Ray Computed ; },
abstract = {BACKGROUND: The effect of preexisting small vessel disease on outcomes of patients with ischemic stroke treated with i.v. thrombolysis is not fully understood.<br><br>AIM: We aim to investigate the effect of combined leukoaraiosis and lacunes as detected on unenhanced brain computer tomography at baseline on clinical outcomes after i.v. thrombolysis.<br><br>METHODS: We analyzed data from the Canadian Alteplase for Stroke Effectiveness Study. Small vessel disease was assessed on baseline computer tomography rating for leukoaraiosis and lacunes. We dichotomized the burden of small vessel disease to "absent or moderate" and "severe." Clinical outcomes at 90 days included excellent outcome (mRS&#x2009;=&#x2009;0-1), good outcome (mRS&#x2009;=&#x2009;0-2), and the occurrence of symptomatic intracerebral hemorrhage. Sensitivity analysis was performed on two age groups (&#x2264;80 versus >80). We ran logistic regression adjusting for confounders to evaluate independent effect of small vessel disease on outcomes.<br><br>RESULTS: There were 820 patients with available brain computer tomography with mean age (&#xb1;SD) of 71.3 (&#xb1;13.2), 455 (55.5%) were male. Of these, 123 (15%) patients had severe small vessel disease at baseline. Age group analysis revealed significant associations of small vessel disease only in patients aged &#x2264;80. After adjustment for confounders, presence of severe small vessel disease reduced the chances of both excellent (OR&#x2009;=&#x2009;0.42, 95% CI&#x2009;=&#x2009;0.24-0.74) and good outcome (OR&#x2009;=&#x2009;0.35, 95% CI&#x2009;=&#x2009;0.21-0.58) and with an increased risk of symptomatic intracerebral hemorrhage (OR&#x2009;=&#x2009;5.91; 95% CI&#x2009;=&#x2009;2.40-14.57).<br><br>CONCLUSION: When considered together as radiological expressions of small vessel disease, presence and severity of severe leukoaraiosis and lacunes on baseline computer tomography scan are associated with poor clinical outcomes in patients treated with i.v. thrombolysis.},
}
@article {pmid26759193,
year = {2016},
author = {Li, Y and Long, J and Huang, B and Yu, T and Wu, W and Li, P and Fang, F and Sun, P},
title = {Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.},
journal = {Scientific reports},
volume = {6},
number = {},
pages = {18914},
pmid = {26759193},
issn = {2045-2322},
mesh = {*Acoustic Stimulation ; Adult ; *Attention ; Brain/physiology ; Brain Mapping ; Emotions ; Female ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Middle Aged ; *Photic Stimulation ; Reproducibility of Results ; *Semantics ; Young Adult ; },
abstract = {An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.},
}
@article {pmid26758821,
year = {2016},
author = {Pavone, EF and Tieri, G and Rizza, G and Tidoni, E and Grisoni, L and Aglioti, SM},
title = {Embodying Others in Immersive Virtual Reality: Electro-Cortical Signatures of Monitoring the Errors in the Actions of an Avatar Seen from a First-Person Perspective.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {36},
number = {2},
pages = {268-279},
pmid = {26758821},
issn = {1529-2401},
mesh = {Adult ; *Brain Mapping ; Brain Waves ; Cerebral Cortex/*physiology ; Electroencephalography ; Evoked Potentials ; Female ; Fourier Analysis ; Humans ; *Interpersonal Relations ; Male ; Movement/*physiology ; Observation ; Photic Stimulation ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Time Factors ; User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {UNLABELLED: Brain monitoring of errors in one's own and other's actions is crucial for a variety of processes, ranging from the fine-tuning of motor skill learning to important social functions, such as reading out and anticipating the intentions of others. Here, we combined immersive virtual reality and EEG recording to explore whether embodying the errors of an avatar by seeing it from a first-person perspective may activate the error monitoring system in the brain of an onlooker. We asked healthy participants to observe, from a first- or third-person perspective, an avatar performing a correct or an incorrect reach-to-grasp movement toward one of two virtual mugs placed on a table. At the end of each trial, participants reported verbally how much they embodied the avatar's arm. Ratings were maximal in first-person perspective, indicating that immersive virtual reality can be a powerful tool to induce embodiment of an artificial agent, even through mere visual perception and in the absence of any cross-modal boosting. Observation of erroneous grasping from a first-person perspective enhanced error-related negativity and medial-frontal theta power in the trials where human onlookers embodied the virtual character, hinting at the tight link between early, automatic coding of error detection and sense of embodiment. Error positivity was similar in 1PP and 3PP, suggesting that conscious coding of errors is similar for self and other. Thus, embodiment plays an important role in activating specific components of the action monitoring system when others' errors are coded as if they are one's own errors.<br><br>SIGNIFICANCE STATEMENT: Detecting errors in other's actions is crucial for social functions, such as reading out and anticipating the intentions of others. Using immersive virtual reality and EEG recording, we explored how the brain of an onlooker reacted to the errors of an avatar seen from a first-person perspective. We found that mere observation of erroneous actions enhances electrocortical markers of error detection in the trials where human onlookers embodied the virtual character. Thus, the cerebral system for action monitoring is maximally activated when others' errors are coded as if they are one's own errors. The results have important implications for understanding how the brain can control the external world and thus creating new brain-computer interfaces.},
}
@article {pmid26758353,
year = {2016},
author = {Delgado-Fernandez, J and Penanes, JR and Torres, CV and Gordillo-Velez, CH and Manzanares-Soler, R and Sola, RG},
title = {Infratentorial angioleiomyoma: case report and review of the literature.},
journal = {Revista de neurologia},
volume = {62},
number = {2},
pages = {68-74},
pmid = {26758353},
issn = {1576-6578},
mesh = {Adult ; *Angiomyoma/diagnosis/surgery ; Humans ; *Infratentorial Neoplasms/diagnosis/surgery ; Male ; },
abstract = {INTRODUCTION: Intracranial angioleiomyomas are extremely rare lesions. Only 22 intracranial angioleiomyomas have been described in the literature and only three were infratentorial.<br><br>CASE REPORT: We report a case of an infratentorial angioleiomyoma in a 43 year-old-man, who underwent a brain computer tomography because of hearing loss. The MRI showed a 1.4 cm tumor, initially described as a meningioma, with progressive enhancement after gadolinium injection, an augmented apparent diffusion coefficient and a generalized metabolite decreased in the spectroscopy. The lesion was surgically removed through a suboccipital approach with a good evolution and without postoperative complications. In the immunohistological study, the lesion was mainly composed of multiple vessels and the immunohistochemistry was positive for actin and caldesmon. Two years after surgery, no recurrence has been found in the MRI.<br><br>CONCLUSION: Angioleiomyomas diagnostic may be complex, but some radiological features could help in the differential diagnostic. Angioleiomyomas are benign tumors associated with favorable outcomes after total resection, that in our case, did not show a significant bleeding risk.},
}
@article {pmid26752711,
year = {2016},
author = {Kabbara, A and Khalil, M and El-Falou, W and Eid, H and Hassan, M},
title = {Functional Brain Connectivity as a New Feature for P300 Speller.},
journal = {PloS one},
volume = {11},
number = {1},
pages = {e0146282},
pmid = {26752711},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Nerve Net/*physiology ; Young Adult ; },
abstract = {The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of 'P300 speller'. The proposed approach was compared to the well-known methods proposed in the state of the art of "P300 Speller", mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.},
}
@article {pmid26748791,
year = {2016},
author = {Lin, BS and Pan, JS and Chu, TY and Lin, BS},
title = {Development of a Wearable Motor-Imagery-Based Brain-Computer Interface.},
journal = {Journal of medical systems},
volume = {40},
number = {3},
pages = {71},
pmid = {26748791},
issn = {1573-689X},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Hand/*physiology ; Humans ; Monitoring, Ambulatory/instrumentation ; Movement/*physiology ; Wireless Technology ; },
abstract = {A motor-imagery-based brain-computer interface (BCI) is a translator that converts the motor intention of the brain into a control command to control external machines without muscles. Numerous motor-imagery-based BCIs have been successfully proposed in previous studies. However, several electroencephalogram (EEG) channels are typically required for providing sufficient information to maintain a specific accuracy and bit rate, and the bulk volume of these EEG machines is also inconvenient. A wearable motor imagery-based BCI system was proposed and implemented in this study. A wearable mechanical design with novel active comb-shaped dry electrodes was developed to measure EEG signals without conductive gels at hair sites, which is easy and convenient for users wearing the EEG machine. In addition, a wireless EEG acquisition module was also designed to measure EEG signals, which provides a user with more freedom of motion. The proposed wearable motor-imagery-based BCI system was validated using an electrical specifications test and a hand motor imagery experiment. Experimental results showed that the proposed wearable motor-imagery-based BCI system provides favorable signal quality for measuring EEG signals and detecting motor imagery.},
}
@article {pmid26739384,
year = {2016},
author = {Nielsen, CB},
title = {Visualization: A Mind-Machine Interface for Discovery.},
journal = {Trends in genetics : TIG},
volume = {32},
number = {2},
pages = {73-75},
doi = {10.1016/j.tig.2015.12.002},
pmid = {26739384},
issn = {0168-9525},
mesh = {Computer Graphics ; *Genetics/trends ; Genomics/*methods ; Image Processing, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {Computation is critical for enabling us to process data volumes and model data complexities that are unthinkable by manual means. However, we are far from automating the sense-making process. Human knowledge and reasoning are critical for discovery. Visualization offers a powerful interface between mind and machine that should be further exploited in future genome analysis tools.},
}
@article {pmid26738174,
year = {2015},
author = {Korik, A and Siddique, N and Sosnik, R and Coyle, D},
title = {E3D hand movement velocity reconstruction using power spectral density of EEG signals and neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {8103-8106},
doi = {10.1109/EMBC.2015.7320274},
pmid = {26738174},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Hand ; Humans ; Movement ; Neural Networks, Computer ; },
abstract = {Three dimensional (3D) limb motion trajectory is predictable with a non-invasive brain-computer interface (BCI). To date, most non-invasive motion trajectory prediction BCIs use potential values of electroencephalographic (EEG) signals as the input to a multiple linear regression (mLR) based kinetic data estimator. We investigated the possible improvement in accuracy of 3D hand movement prediction (i.e., the correlation of registered and reconstructed hand velocities) by replacing raw EEG potentials with spectrum power values of specific EEG bands. We also investigated if a non-linear neural network based estimator outperformed the mLR approach. The spectrum power model provided significantly higher accuracy (R~0.60) compared to the similar EEG potentials based approach (R~0.45). Additionally, when replacing the mLR based kinetic data estimation module with a feed-forward neural network (NN) we found the NN based spectrum power model provided higher accuracy (R~0.70) compared to the similar mLR based approach (R~0.60).},
}
@article {pmid26738144,
year = {2015},
author = {Ruyi Foong, and Kai Keng Ang, and Chai Quek, and Cuntai Guan, and Aung Aung Phyo Wai, },
title = {An analysis on driver drowsiness based on reaction time and EEG band power.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7982-7985},
doi = {10.1109/EMBC.2015.7320244},
pmid = {26738144},
issn = {2694-0604},
mesh = {Automobile Driving ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Reaction Time ; *Sleep Stages ; },
abstract = {Falling asleep during driving is a serious problem that has resulted in fatal accidents worldwide. Thus, there is a need to detect driver drowsiness to counter it. This study analyzes the changes in the electroencephalography (EEG) collected from 4 subjects driving under monotonous road conditions using a driving simulator. The drowsiness level of the subjects is inferred from the time taken to react to events. The results from the analysis of the reaction time shows that drowsiness occurs in cycles, which correspond to short sleep cycles known as `microsleeps'. The results from a time-frequency analysis of the four frequency bands' power reveals differences between trials with fast and slow reaction times; greater beta band power is present in all subjects, greater alpha power in 2 subjects, greater theta power in 2 subjects, and greater delta power in 3 subjects, for fast reaction trials. Overall, this study shows that reaction time can be used to infer the drowsiness, and subject-specific changes in the EEG band power may be used to infer drowsiness. Thus the study shows a promising prospect of developing Brain-Computer Interface to detect driver drowsiness.},
}
@article {pmid26738110,
year = {2015},
author = {Camilleri, TA and Camilleri, KP and Fabri, SG},
title = {Semi-supervised segmentation of EEG data in BCI systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7845-7848},
doi = {10.1109/EMBC.2015.7320210},
pmid = {26738110},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Eye ; Humans ; Models, Theoretical ; Signal Processing, Computer-Assisted ; },
abstract = {This work investigates the use of a semi-supervised, autoregressive switching multiple model (AR-SMM) framework for the segmentation of EEG data applied to brain computer interface (BCI) systems. This gives the possibility of identifying and learning novel modes within the data, giving insight on the changing dynamics of the EEG data and possibly also offering a solution for shorter training periods in BCIs. Furthermore it is shown that the semi-supervised model allocation process is robust to different starting positions and gives consistent results.},
}
@article {pmid26738039,
year = {2015},
author = {Zhen Qin, and Bin Zhang, and Ning Hu, and Ping Wang, },
title = {Detection and classification of tastants in vivo using a novel bioelectronic tongue in combination with brain-machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7550-7553},
doi = {10.1109/EMBC.2015.7320139},
pmid = {26738039},
issn = {2694-0604},
mesh = {Animals ; Brain-Computer Interfaces ; Female ; Rats ; Rats, Sprague-Dawley ; *Signal Processing, Computer-Assisted ; Taste Perception ; Tongue/physiology ; },
abstract = {The mammalian gustatory system is acknowledged as one of the most valid chemosensing systems. The sense of taste particularly provides critical information about ingestion of toxic and noxious chemicals. Thus the potential of utilizing rats' gustatory system is investigated in detecting sapid substances. By recording electrical activities of neurons in gustatory cortex, a novel bioelectronic tongue system is developed in combination with brain-machine interface technology. Features are extracted in both spikes and local field potentials. By visualizing these features, classification is performed and the responses to different tastants can be prominently separated from each other. The results suggest that this in vivo bioelectronic tongue is capable of detecting tastants and will provide a promising platform for potential applications in evaluating palatability of food and beverages.},
}
@article {pmid26738010,
year = {2015},
author = {Rejer, I and Gorski, P},
title = {Benefits of ICA in the Case of a Few Channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7434-7437},
doi = {10.1109/EMBC.2015.7320110},
pmid = {26738010},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; Databases, Factual ; Electroencephalography/*methods ; Models, Biological ; *Principal Component Analysis ; Signal-To-Noise Ratio ; },
abstract = {Independent Component Analysis (ICA) is often used at the signal preprocessing stage in EEG analysis for its ability to filter out artifacts from the signal. The benefits of using ICA are the most apparent when multi-channel signal is recorded. The question is, however, what kind of benefits (if any) can be obtained when ICA is applied for a few channel recording. We addressed this question in this paper by setting up the hypothesis that even in the case of only three channels, ICA can rearrange the sources to new mixtures in such a way that the true brain sources will be enhanced in some components, and the artifacts will be enhanced in others. To verify our hypothesis we applied three popular ICA algorithms to preprocess data from a benchmark file (motor imagery file from the II BCI Competition). Our results, presented in terms of classification precision, show that all ICA algorithms enhanced the signal to noise ratio for components correlating with signals recorded over C3 and C4 channels (the classification precision was higher in their case) and lessened the signal to noise ratio for components correlating with signals recorded over Cz channels.},
}
@article {pmid26737966,
year = {2015},
author = {Roy, RN and Bonnet, S and Charbonnier, S and Jallon, P and Campagne, A},
title = {A comparison of ERP spatial filtering methods for optimal mental workload estimation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7254-7257},
doi = {10.1109/EMBC.2015.7320066},
pmid = {26737966},
issn = {2694-0604},
support = {DP2-OD006454/OD/NIH HHS/United States ; K23-NS090900/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Memory ; Principal Component Analysis ; Workload ; },
abstract = {Mental workload estimation is of crucial interest for user adaptive interfaces and neuroergonomics. Its estimation can be performed using event-related potentials (ERPs) extracted from electroencephalographic recordings (EEG). Several ERP spatial filtering methods have been designed to enhance relevant EEG activity for active brain-computer interfaces. However, to our knowledge, they have not yet been used and compared for mental state monitoring purposes. This paper presents a thorough comparison of three ERP spatial filtering methods: principal component analysis (PCA), canonical correlation analysis (CCA) and the xDAWN algorithm. Those methods are compared in their performance to allow for an accurate classification of mental workload when applied in an otherwise similar processing chain. The data of 20 healthy participants that performed a memory task for 10 minutes each was used for classification. Two levels of mental workload were considered depending on the number of digits participants had to memorize (2/6). The highest performances were obtained using the CCA filtering and the xDAWN algorithm respectively with 98% and 97% of correct classification. Their performances were significantly higher than that obtained using the PCA filtering (88%).},
}
@article {pmid26737964,
year = {2015},
author = {Li, Y and Ma, S and Hu, Z and Chen, J and Su, G and Dou, W},
title = {Single trial EEG classification applied to a face recognition experiment using different feature extraction methods.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7246-7249},
doi = {10.1109/EMBC.2015.7320064},
pmid = {26737964},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; Brain-Computer Interfaces ; Cognition/physiology ; *Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Principal Component Analysis ; Recognition, Psychology/*physiology ; Support Vector Machine ; Wavelet Analysis ; Young Adult ; },
abstract = {Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.},
}
@article {pmid26737962,
year = {2015},
author = {Hsieh, CH and Huang, YH},
title = {Low-complexity EEG-based eye movement classification using extended moving difference filter and pulse width demodulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7238-7241},
doi = {10.1109/EMBC.2015.7320062},
pmid = {26737962},
issn = {2694-0604},
mesh = {Algorithms ; Blinking ; *Brain-Computer Interfaces ; *Electroencephalography ; *Eye Movements ; Humans ; Monitoring, Physiologic ; Signal Processing, Computer-Assisted ; },
abstract = {This paper presents an eye movement classification algorithm for EEG-based brain-computer interface. The proposed system first used a low-complexity extended moving difference filter to acquire clean pulse waveform of eye-movement events. Then, a pulse width demodulation algorithm was designed to identify eye-movement events of left/right/up/down directions. The eye blinking events can be easily eliminated by excluding the pulses with small pulse-width, and thus the detection rate can be improved. Besides, the pulse width demodulation requires only addition operations to achieve a near 90% averaged detection. The computation complexity is much lower than those of other works in the literature.},
}
@article {pmid26737908,
year = {2015},
author = {Placidi, G and Petracca, A and Spezialetti, M and Iacoviello, D},
title = {Classification strategies for a single-trial binary Brain Computer Interface based on remembering unpleasant odors.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {7019-7022},
doi = {10.1109/EMBC.2015.7320008},
pmid = {26737908},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/metabolism ; *Brain-Computer Interfaces ; Electroencephalography ; Emotions/physiology ; Humans ; Male ; Odorants/*analysis ; },
abstract = {A Brain Computer Interface (BCI) is a useful instrument to support human communication. In recent years, BCI systems have been frequently implemented by using EEG. Regarding the communication paradigm used, there exists a very large number of strategies and, recently, the remembering of unpleasant odors has been also defined. However, the quality of the signals collected by this last paradigm is very poor, due to the absence of a real stimulus (the stimulus consists in remembering a disgusting situation). For this reason, a crucial node is the choice of a very efficient classification algorithm to improve the accuracy of the BCI. The present paper describes a and compares classification strategies for such type of BCI systems. The proposed methods and the experimental setup are described and experimental measurements are presented and discussed.},
}
@article {pmid26737898,
year = {2015},
author = {Jayaram, V and Widmann, N and Förster, C and Fomina, T and Hohmann, M and Müller Vom Hagen, J and Synofzik, M and Schölkopf, B and Schöls, L and Grosse-Wentrup, M},
title = {Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6979-6982},
doi = {10.1109/EMBC.2015.7319998},
pmid = {26737898},
issn = {2694-0604},
mesh = {Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/*diagnosis ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Motor Cortex/metabolism ; },
abstract = {Despite decades of research on EEG-based brain-computer interfaces (BCIs) in patients with amyotrophic lateral sclerosis (ALS), there is still little known about how the disease affects the electromagnetic field of the brain. This may be one reason for the present failure of EEG-based BCI paradigms for completely locked-in ALS patients. In order to help understand this failure, we have recorded resting state data from six ALS patients and thirty-two healthy controls to investigate for group differences. While similar studies have been attempted in the past, none have used high-density EEG or tried to distinguish between physiological and non-physiological sources of the EEG. We find an ALS-specific global increase in gamma power (30-90 Hz) that is not specific to the motor cortex, suggesting that the mechanism behind ALS affects non-motor cortical regions even in the absence of comorbid cognitive deficits.},
}
@article {pmid26737819,
year = {2015},
author = {Kamikawa, Y and Tanaka, T},
title = {Responses in posterior parietal cortex to movement intention task with visual and tactile cues.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6654-6657},
doi = {10.1109/EMBC.2015.7319919},
pmid = {26737819},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Cues ; Electroencephalography ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Humans ; *Intention ; Movement/physiology ; Parietal Lobe/*physiology ; Touch/*physiology ; },
abstract = {Posterior parietal cortex (PPC) is considered to be related to forming of motor intention. The detection of the direction intended movements and the type of intended movement is a challenging goal in neuroscience and engineering applications such as brain-computer interfacing (BCI). In previous studies, it has been reported that EEG signals extracted from PPC can be used to decode intended movement direction. However, it is not clear whether extracted EEG signals are related to motor intention, because visually evoked potential (VEP) which evoked by visual cue in their experiment may be included in their extracted EEG signals. The purpose of this study is to investigate the possibility of VEP mixed into extracted EEG signals. Therefore experiments with not only visual but also tactile cues were conducted. EEG components that could be related to PPC were extracted by using independent component analysis (ICA). We compared event related potential (ERP) waveforms between two experiments. In the result, ERP waveforms of the experiment with tactile cue were significantly different from that of the experiment with visual cue. This result suggests that VEP was included in the EEG signals extracted from PPC in the experiment with visual cue.},
}
@article {pmid26737816,
year = {2015},
author = {Lin, CT and Wang, YK and Fang, CN and Yu, YH and King, JT},
title = {Extracting patterns of single-trial EEG using an adaptive learning algorithm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6642-6645},
doi = {10.1109/EMBC.2015.7319916},
pmid = {26737816},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {The improvement of brain imaging technique brings about an opportunity for developing and investigating brain-computer interface (BCI) which is a way to interact with computer and environment. The measured brain activities usually constitute the signals of interest and noises. Applying the portable device and removing noise are the benefits to real-world BCI. In this study, one portable electroencephalogram (EEG) system non-invasively acquired brain dynamics through wireless transmission while six subjects participated in the rapid serial visual presentation (RSVP) paradigm. The event-related potential (ERP) was traditionally estimated by ensemble averaging (EA) to increase the signal-to-noise ratio. One adaptive filter of data-reusing radial basis function network (DR-RBFN) was also utilized as the estimator. The results showed that this portable EEG system stably acquired brain activities. Furthermore, the task-related potentials could be clearly explored from the limited samples of EEG data through DR-RBFN. According to the artifact-free data from the portable device, this study demonstrated the potential to move the BCI from laboratory research to real-life application in the near future.},
}
@article {pmid26737815,
year = {2015},
author = {Wei, CS and Wang, YT and Lin, CT and Jung, TP},
title = {Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6638-6641},
doi = {10.1109/EMBC.2015.7319915},
pmid = {26737815},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Evoked Potentials, Visual/*physiology ; Hair/*physiology ; Humans ; Scalp/*physiology ; },
abstract = {Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.},
}
@article {pmid26737813,
year = {2015},
author = {Kitahara, K and Kondo, T},
title = {Modulation of ERD/S by having a conscious target during lower-extremity motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6630-6633},
doi = {10.1109/EMBC.2015.7319913},
pmid = {26737813},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Consciousness ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Intention ; *Lower Extremity ; Male ; *Motor Activity ; Young Adult ; },
abstract = {In the field of neurorehabilitation with brain-computer interfaces (BCIs) technology, an EEG feature, event-related desynchronization/synchronization (ERD/S) caused by motor imagery (MI) is widely used for estimating human motor intention. However, sufficient neurofeedback training is required for the use of the MI-based BCI system, because the ability to generate ERD/S is highly dependent on individuals. To find an effective MI condition for the BCI system, we hypothesize that having a conscious target during MI would enhance the extent of ERD/S. In the experiments, we investigated the individual effect of two types of MIs: leg extension (L) and leg extension with a conscious target (i.e., kicking a ball (KB)) on the resultant ERD/S. We evaluated time-frequency maps of ERD/S and statistically compared these two conditions (i.e., L and KB). As a result, a significant difference was found in beta-ERD (paired t-test, p <; 0.01), while there were no significant differences in mu-ERD and beta-ERS. These results suggest that having a conscious target during lower extremity MI would strengthen the ERD in beta frequency band compared to the case without target.},
}
@article {pmid26737711,
year = {2015},
author = {Flores Vega, C and Murray, V},
title = {Multiscale AM-FM methods on EEG signals for motor task classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6210-6214},
doi = {10.1109/EMBC.2015.7319811},
pmid = {26737711},
issn = {2694-0604},
mesh = {Bayes Theorem ; Brain/*pathology ; Discriminant Analysis ; Electrodes ; Electroencephalography/*methods ; Hand/physiology ; Humans ; Least-Squares Analysis ; Linear Models ; Motor Skills/*physiology ; Neural Networks, Computer ; Probability ; ROC Curve ; Regression Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {In this manuscript, we present the use of customized, multiscale amplitude-modulation frequency-modulation (AMFM) methods on electroencephalography (EEG) brain signals during the subject development a motor task: right hand and left hand. This approach is compared to various non-linear patterns and methods that have been applied in order to characterize and understand the dynamic behavior of the EEG signals. The AM-FM methods have been optimized in terms of multiscale filters for the mu band (8-12 Hz). The instantaneous AM-FM values are processed using their probability density function and classified using multiple layer perceptron (MLP) and the partial least squares regression (PLS). The system is tested using the standard BCI dataset with results with a precision to 89% and an area under the ROC to 91%.},
}
@article {pmid26737706,
year = {2015},
author = {Jiang, T and Ince, NF and Jiang, T and Wang, T and Mei, S and Li, Y and Wang, X and Sha, Z},
title = {Local spatial correlation analysis of hand flexion/extension using intraoperative high-density ECoG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6190-6193},
doi = {10.1109/EMBC.2015.7319806},
pmid = {26737706},
issn = {2694-0604},
mesh = {Adult ; Brain ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electrocorticography ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials ; Female ; Hand/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {We recorded motor cortical activity using highdensity electrocorticogram (ECoG) from three patients during awake craniotomy. Subjects repeatedly executed hand flexion/extension tasks according to auditory instructions. Clear event-related desynchronization (ERD) in beta band (8-32) Hz and event-related synchronization (ERS) in gamma band (60-200) Hz were observed. High frequency band (HFB: 60-200 Hz) activation was found to be more localized compared to low frequency band (LFB: 8-32 Hz) activation in all subjects. Local spatial correlation maps in LFB and HFB were constructed by computing the correlation between channels. Local spatial correlation dropped more in the ERD/ERS areas consistently in two subjects. The results indicate that ERD/ERS patterns are more spatially uncorrelated and denser ECoG electrode is necessary within these areas to map uncorrelated `sources'. High resolution electrodes might improve both clinical functional mapping and brain machine interface outcomes in the near future.},
}
@article {pmid26737703,
year = {2015},
author = {Schettini, F and Risetti, M and Arico, P and Formisano, R and Babiloni, F and Mattia, D and Cincotti, F},
title = {P300 latency Jitter occurrence in patients with disorders of consciousness: Toward a better design for Brain Computer Interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6178-6181},
doi = {10.1109/EMBC.2015.7319803},
pmid = {26737703},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; *Brain-Computer Interfaces ; Case-Control Studies ; Consciousness Disorders/*physiopathology ; Electroencephalography/*methods ; Electrooculography/*methods ; Female ; Humans ; Male ; Middle Aged ; Persistent Vegetative State/physiopathology ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {In this study the P300 latency jitter has been explored in an EEG data set collected from a group of patients with disorders of consciousness (DOC; n=13) that was administered with an auditory Oddball paradigm under passive and active conditions. A method based on wavelet transform was applied to estimate single trial P300 waveforms. Preliminary results showed that 5 Vegetative State (VS) and 8 Minimally Conscious Staten (MCS) patients exhibited significantly higher values of P300 latency jitter as compared to those obtained from a control group of 12 healthy subjects. In addition, the magnitude of the P300 latency jitter negatively correlated with patients' clinical status. The existence of such phenomenon might substantially limit an effective use of Brain Computer Interface systems for communication.},
}
@article {pmid26737702,
year = {2015},
author = {Tello, R and Pouryazdian, S and Ferreira, A and Beheshti, S and Krishnan, S and Bastos, T},
title = {A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6174-6177},
doi = {10.1109/EMBC.2015.7319802},
pmid = {26737702},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Male ; Models, Theoretical ; Nontherapeutic Human Experimentation ; Photic Stimulation ; Signal Processing, Computer-Assisted ; },
abstract = {This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.},
}
@article {pmid26737701,
year = {2015},
author = {Mora, N and De Munari, I and Ciampolini, P},
title = {SSVEP-based BCI: A "Plug &amp; play" approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {6170-6173},
doi = {10.1109/EMBC.2015.7319801},
pmid = {26737701},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Middle Aged ; Nontherapeutic Human Experimentation ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potentials (SSVEP) paradigm has many appealing features, aiming at implementing BCI-enabled communication-control applications. In this paper, we present a complete signal processing chain for a self-paced, SSVEP-based BCI. The proposed approach mostly focuses at reducing the user effort in dealing with BCI, featuring no need of user-specific calibration or training. In this paper, the classification algorithm is introduced and first validated on offline waveforms, aiming at improving classification accuracy and minimizing the false positive rate. Then, implementation of an online, self-paced SSVEP BCI is illustrated. The scheme refers to a four-way choice and exploits discrimination between intentional control states and nocontrol ones. Good performance is achieved, both in terms of true positive rate (>94%), as well as low false positive rate (0.26 min(-1)), even in experiments carried out outside lab-controlled conditions.},
}
@article {pmid26737596,
year = {2015},
author = {O'Sullivan, JA and Reilly, RB and Lalor, EC},
title = {Improved decoding of attentional selection in a cocktail party environment with EEG via automatic selection of relevant independent components.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {5740-5743},
doi = {10.1109/EMBC.2015.7319696},
pmid = {26737596},
issn = {2694-0604},
mesh = {Algorithms ; *Attention ; Brain ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Recently it has been shown to be possible to ascertain which speaker a subject is attending to in a cocktail party environment from single-trial (~60s) electroencephalography (EEG) data. The attentional selection of most of subjects could be decoded with a very high accuracy (>90%). However, the performance of many subjects fell below what would be required for a potential brain computer interface (BCI). One potential reason for this is that activity related to the stimuli may have a lower signal-to-noise ratio on the scalp for some subjects than others. Independent component analysis (ICA) is a commonly used method for denoising EEG data. However, its effective use often requires the subjective choice of the experimenter to determine which independent components (ICs) to retain and which to reject. Algorithms do exist to automatically determine the reliability of ICs, however they provide no information as to their relevance for the task at hand. Here we introduce a novel method for automatically selecting ICs which are relevant for decoding attentional selection. In doing so, we show a significant increase in classification accuracy at all test data durations from 60s to 10s. These findings have implications for the future development of naturalistic and user-friendly BCIs, as well as for smart hearing aids.},
}
@article {pmid26737497,
year = {2015},
author = {Mugler, EM and Goldrick, M and Rosenow, JM and Tate, MC and Slutzky, MW},
title = {Decoding of articulatory gestures during word production using speech motor and premotor cortical activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {5339-5342},
doi = {10.1109/EMBC.2015.7319597},
pmid = {26737497},
issn = {2694-0604},
support = {UL1TR000150/TR/NCATS NIH HHS/United States ; },
mesh = {Anatomic Landmarks ; Electrodes ; *Gestures ; Humans ; *Language ; Motor Activity/*physiology ; Motor Cortex/*physiology ; *Phonetics ; Speech/*physiology ; Speech Perception/physiology ; },
abstract = {Brain-machine interfaces that directly translate attempted speech from the speech motor areas could change the lives of people with complete paralysis. However, it remains uncertain exactly how speech production is encoded in cortex. Improving this understanding could greatly improve brain-machine interface design. Specifically, it is not clear to what extent the different levels of speech production (phonemes, or speech sounds, and articulatory gestures, which describe the movements of the articulator muscles) are represented in the motor cortex. Using electrocorticographic (ECoG) electrodes on the cortical surface, we recorded neural activity from speech motor and premotor areas during speech production. We decoded both gestures and phonemes using the neural signals. Overall classification accuracy was higher for gestures than phonemes. In particular, gestures were better represented in the primary sensorimotor cortices, while phonemes were better represented in more anterior areas.},
}
@article {pmid26737449,
year = {2015},
author = {Tam, WK and So, R and Guan, C and Yang, Z},
title = {EC-PC spike detection for high performance brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {5142-5145},
doi = {10.1109/EMBC.2015.7319549},
pmid = {26737449},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Haplorhini ; Neurons/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Spike detection is often the first step in neural signal processing. It has profound effects on subsequent steps down the signal processing pipeline. Most existing spike detection algorithms require manual setting of detection threshold, which is very inconvenient for long-term neural interface. Furthermore, these algorithms are usually only evaluated using simulated dataset. Few studies are devoted to evaluating how different spike detection algorithms affect decoding performance in brain-computer interface. We have proposed a new spike detection algorithm called "exponential component - power component" (EC-PC) that offers fully automatic unsupervised spike detection. In this study, we compared the performance of a motor decoding task when different spike detection algorithms were used. EC-PC is shown to produce a higher decoding accuracy compared with other existing algorithms. Our results suggest that EC-PC can help improve motor decoding performance of brain-computer interface.},
}
@article {pmid26737437,
year = {2015},
author = {Gazziro, M and Braga, CF and Moreira, DA and Carvalho, AC and Rodrigues, JF and Navarro, JS and Ardila, JC and Mioni, DP and Pessatti, M and Fabbro, P and Freewin, C and Saddow, SE},
title = {Transmission of wireless neural signals through a 0.18 µm CMOS low-power amplifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {5094-5097},
doi = {10.1109/EMBC.2015.7319537},
pmid = {26737437},
issn = {2694-0604},
mesh = {*Amplifiers, Electronic ; Biocompatible Materials ; *Brain-Computer Interfaces ; Carbon Compounds, Inorganic ; Electric Power Supplies ; *Electrodes ; Electronics ; Humans ; Silicon Compounds ; Telemetry/*instrumentation ; },
abstract = {In the field of Brain Machine Interfaces (BMI) researchers still are not able to produce clinically viable solutions that meet the requirements of long-term operation without the use of wires or batteries. Another problem is neural compatibility with the electrode probes. One of the possible ways of approaching these problems is the use of semiconductor biocompatible materials (silicon carbide) combined with an integrated circuit designed to operate with low power consumption. This paper describes a low-power neural signal amplifier chip, named Cortex, fabricated using 0.18 μm CMOS process technology with all electronics integrated in an area of 0.40 mm(2). The chip has 4 channels, total power consumption of only 144 μW, and is impedance matched to silicon carbide biocompatible electrodes.},
}
@article {pmid26737422,
year = {2015},
author = {Brennan, CP and McCullagh, PJ and Galway, L and Lightbody, G},
title = {Promoting autonomy in a smart home environment with a smarter interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {5032-5035},
doi = {10.1109/EMBC.2015.7319522},
pmid = {26737422},
issn = {2694-0604},
mesh = {Adult ; Automation ; *Brain-Computer Interfaces ; Electroretinography ; Eye Movements/physiology ; Humans ; Middle Aged ; Radio Frequency Identification Device ; Remote Sensing Technology ; User-Computer Interface ; },
abstract = {In the not too distant future, the median population age will tend towards 65; an age at which the need for dependency increases. Most older people want to remain autonomous and self-sufficient for as long as possible. As environments become smarter home automation solutions can be provided to support this aspiration. The technology discussed within this paper focuses on providing a home automation system that can be controlled by most users regardless of mobility restrictions, and hence it may be applicable to older people. It comprises a hybrid Brain-Computer Interface, home automation user interface and actuators. In the first instance, our system is controlled with conventional computer input, which is then replaced with eye tracking and finally a BCI and eye tracking collaboration. The systems have been assessed in terms of information throughput; benefits and limitations are evaluated.},
}
@article {pmid26737395,
year = {2015},
author = {Olivieri, E and Barresi, G and Mattos, LS},
title = {BCI-based user training in surgical robotics.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4918-4921},
doi = {10.1109/EMBC.2015.7319495},
pmid = {26737395},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Computer User Training/*methods ; Equipment Design ; Feedback, Psychological ; Humans ; Male ; Phantoms, Imaging ; Robotic Surgical Procedures/*education/instrumentation/methods ; Software ; User-Computer Interface ; },
abstract = {Human error is a critical risk in surgery, so an important aim of surgical robotic systems is to improve the performance and the safety of surgical operations. Such systems can be potentially enhanced by a brain-computer interface (BCI) able to monitor the user's mental focus and use this information to improve the level of safety of the procedures. In order to evaluate such potential usage of BCIs, this paper describes a novel framework for training the user to regulate his/her own mental state while performing surgery-like tasks using a robotic system. This self-regulation is based on augmented reality (AR) feedback representing the BCI-monitored mental state, which helps the user's effort in maintaining a high level of mental focus during the task. A comparison between a BCI-based training and a training without a BCI highlighted a reduction of post-training trial times as a result of the enhanced training setup, without any loss in performance or in user experience. Such finding allows the identification of further improvements and novel potential applications of this training and interaction paradigm.},
}
@article {pmid26737362,
year = {2015},
author = {Chen, B and Wang, Q},
title = {Combining human volitional control with intrinsic controller on robotic prosthesis: A case study on adaptive slope walking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4777-4780},
doi = {10.1109/EMBC.2015.7319462},
pmid = {26737362},
issn = {2694-0604},
mesh = {Adult ; Amputees ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electromyography/instrumentation/*methods ; Exercise Test ; Humans ; Male ; Muscle, Skeletal/physiology ; Prosthesis Design ; Robotics/instrumentation/*methods ; Volition ; Walking/*physiology ; },
abstract = {Affording lower-limb amputees the ability to volitionally control robotic prostheses can improve the adaptability to terrain changes as well as enhancing proprioception. However, it also increases amputees' conscious burdens for prosthesis control. Therefore, in this paper, we aim to propose a hybrid controller which combines human volitional control with the intrinsic controller on the robotic transtibial prosthesis, enabling the amputee actively controlling prosthesis with little conscious attention. In this preliminary study, a hybrid controller for adaptive slope walking was designed. A slope estimator was embedded in the intrinsic controller to estimate the ground slope of the previous step using signals measured by prosthetic sensors. And a myoelectric controller allows the amputee subject to convey slope changes to prosthetic controller by volitionally contract his residual muscles, whose electromyography signals were mapped to the slope increment. The hybrid controller combined these two results to obtain the estimated slope. One male transtibial amputee subject was recruited in this research. Experiment results showed that the intrinsic slope estimator produced satisfactory estimation results with an average absolute error of 0.70 ± 0.54 degrees. By adding amputee's volitional control, the hybrid controller is able to predict the upcoming slope changes.},
}
@article {pmid26737349,
year = {2015},
author = {Hu, Z and Sun, Y and Lim, J and Thakor, N and Bezerianos, A},
title = {Investigating the correlation between the neural activity and task performance in a psychomotor vigilance test.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4725-4728},
doi = {10.1109/EMBC.2015.7319449},
pmid = {26737349},
issn = {2694-0604},
mesh = {Attention/physiology ; Brain Waves ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Mental Fatigue/physiopathology ; Models, Biological ; *Models, Neurological ; Nontherapeutic Human Experimentation ; Psychomotor Performance/*physiology ; Reaction Time/*physiology ; Regression Analysis ; Task Performance and Analysis ; Young Adult ; },
abstract = {Neural activity is known to correlate with decrements in task performance as individuals enter the state of mental fatigue which might lead to lowered productivity and increased safety risks. Incorporating a passive brain computer interface (BCI) technique that detects changes in subject's neural activity and predicts the behavioral performance when the subject is underperforming might be a promising approach to reduce human error in real-world situations. Here, we developed a reliable model using EEG power spectrum to estimate time-on-task performance in a psychomotor vigilance test (PVT) which can fit across individuals. High correlation between the estimated and actual reaction time was achieved. Hence, our results illustrate the feasibility for modeling time-on-task decrements in performance among different individuals from their brainwave activity, with potential applications in several domains, including traffic and industrial safety.},
}
@article {pmid26737347,
year = {2015},
author = {He, Y and Contreras-Vidal, JL},
title = {Classification of finger vibrotactile input using scalp EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4717-4720},
doi = {10.1109/EMBC.2015.7319447},
pmid = {26737347},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Fingers/*physiology ; Humans ; Male ; Models, Neurological ; Nontherapeutic Human Experimentation ; Pilot Projects ; Scalp/physiology ; *Signal Processing, Computer-Assisted ; Vibration ; },
abstract = {While there are many output brain-computer interface (output BCIs) studies, few have examined the input pathway, namely decoding the sensory input. To examine the possibility of building a BCI with sensory input using scalp electroencephalography (EEG), this study builds a classifier based on Local Fisher Discriminant Analysis (LFDA) and Gaussian Mixture Model (GMM) to classify neural activity generated by vibrotactile sensory stimuli delivered to the fingers. Small vibrators were placed on the fingertips of the participant. They vibrated one by one in a random sequence while the participant sat still with eyes closed. EEG data were recorded and later used to classify which finger was vibrated. There were two tasks: one focusing on differentiating between ipsilateral fingers, the other one focusing on differentiating contralateral fingers. Decoding accuracies were high in both tasks: 97.6% and 99.3% respectively. Event-related EEG features in both amplitude and power domain are discussed.},
}
@article {pmid26737314,
year = {2015},
author = {Xin Zhang, and Guanghua Xu, and Jun Xie, and Min Li, and Wei Pei, and Jinhua Zhang, },
title = {An EEG-driven Lower Limb Rehabilitation Training System for Active and Passive Co-stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4582-4585},
doi = {10.1109/EMBC.2015.7319414},
pmid = {26737314},
issn = {2694-0604},
mesh = {Electroencephalography ; Humans ; *Lower Extremity ; Recovery of Function ; Stroke ; Stroke Rehabilitation ; },
abstract = {With the advent of an aging society, stroke makes a heavy burden for our society. Stroke can damage the motor and sensory neural system and block the closed loop between the brain and the body. Due to the neural plasticity, this closed loop can be rebuilt through training. Users' actively engagement can help expedite functional recovery. Therefore, we propose an EEG-driven Lower Limb Rehabilitation Training System (LLRTS) that can achieve Active and Passive Co-stimulation (APC). Virtual Reality (VR), BCI and robot are introduced into the system. When users have motor intentions, this system could automatically support corresponding visual and somatic sensory feedback. That is to say active and passive stimulations are controlled by user's mind. This paper reports the idea and construction of this rehabilitation system. Preliminary experimental results support the concept.},
}
@article {pmid26737197,
year = {2015},
author = {Hsu, SH and Pion-Tonachini, L and Jung, TP and Cauwenberghs, G},
title = {Tracking non-stationary EEG sources using adaptive online recursive independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4106-4109},
pmid = {26737197},
issn = {2694-0604},
support = {R01 NS047293/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/physiology ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However, its adaptation performance has not been fully explored due to the difficulty in choosing an appropriate forgetting factor: the weight applied to new data in a recursive update which determines the trade-off between the adaptation capability and convergence quality. This study proposes an adaptive forgetting factor for ORICA (adaptive ORICA) to learn and adapt to non-stationarity in the EEG data. Using a realistically simulated non-stationary EEG dataset, we empirically show adaptive forgetting factors outperform other commonly-used non-adaptive rules when underlying source dynamics are changing. Standard offline ICA can only extract a subset of the changing sources while adaptive ORICA can recover all. Applied to actual EEG data recorded from a task-switching experiments, adaptive ORICA can learn and re-learn the task-related components as they change. With an adaptive forgetting factor, adaptive ORICA can track non-stationary EEG sources, opening many new online applications in brain-computer interfaces and in monitoring of brain dynamics.},
}
@article {pmid26737195,
year = {2015},
author = {Ando, H and Takizawa, K and Yoshida, T and Matsushita, K and Hirata, M and Suzuki, T},
title = {Multichannel neural recording with a 128 Mbps UWB wireless transmitter for implantable brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {4097-4100},
doi = {10.1109/EMBC.2015.7319295},
pmid = {26737195},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrocorticography/*instrumentation ; Electrodes ; Humans ; *Neural Prostheses ; Wireless Technology/*instrumentation ; },
abstract = {To realize a low-invasive and high accuracy BMI (Brain-machine interface) system, we have already developed a fully-implantable wireless BMI system which consists of ECoG neural electrode arrays, neural recording ASICs, a Wi-Fi based wireless data transmitter and a wireless power receiver with a rechargeable battery. For accurate estimation of movement intentions, it is important for a BMI system to have a large number of recording channels. In this paper, we report a new multi-channel BMI system which is able to record up to 4096-ch ECoG data by multiple connections of 64-ch ASICs and time division multiplexing of recorded data. This system has an ultra-wide-band (UWB) wireless unit for transmitting the recorded neural signals to outside the body. By preliminary experiments with a human body equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps mode below 20 mm distance.},
}
@article {pmid26737159,
year = {2015},
author = {Lindig-León, C and Bougrain, L},
title = {Comparison of sensorimotor rhythms in EEG signals during simple and combined motor imageries over the contra and ipsilateral hemispheres.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3953-3956},
doi = {10.1109/EMBC.2015.7319259},
pmid = {26737159},
issn = {2694-0604},
mesh = {Brain/physiology ; Brain Mapping ; Brain-Computer Interfaces ; *Electroencephalography ; Hand/physiology ; Humans ; Movement/physiology ; Sensorimotor Cortex/*physiology ; },
abstract = {Imaginary motor tasks cause brain oscillations that can be detected through the analysis of electroencephalographic (EEG) recordings. This article aims at studying whether or not the characteristics of the brain activity induced by the combined motor imagery (MI) of both hands can be assumed as the superposition of the activity generated during simple hand MIs. After analyzing the sensorimotor rhythms in EEG signals of five healthy subjects, results show that the imagination of both hands movement generates in each brain hemisphere similar activity as the one produced by each simple hand MI in the contralateral side. Furthermore, during simple hand MIs, brain activity over the ipsilateral hemisphere presents similar characteristics as those observed during the rest condition. Thus, it is shown that the proposed scheme is valid and promising for brain-computer interfaces (BCI) control, allowing to easily detect patterns induced by combined MIs.},
}
@article {pmid26737158,
year = {2015},
author = {Chou, Z and Lim, J and Brown, S and Keller, M and Bugbee, J and Broccard, F and Khraiche, ML and Silva, GA and Cauwenberghs, G},
title = {Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3949-3952},
doi = {10.1109/EMBC.2015.7319258},
pmid = {26737158},
issn = {2694-0604},
mesh = {Animals ; Membrane Potentials ; Microelectrodes ; Neural Networks, Computer ; Neurons/*physiology ; Retina/physiology ; Software ; },
abstract = {Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.},
}
@article {pmid26737113,
year = {2015},
author = {Fan, J and Wade, JW and Bian, D and Key, AP and Warren, ZE and Mion, LC and Sarkar, N},
title = {A Step towards EEG-based brain computer interface for autism intervention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3767-3770},
pmid = {26737113},
issn = {2694-0604},
support = {R01 MH091102/MH/NIMH NIH HHS/United States ; 1R01MH091102-01A1/MH/NIMH NIH HHS/United States ; },
mesh = {Adolescent ; Autism Spectrum Disorder/physiopathology/psychology/*therapy ; Automobile Driving/education ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Emotions ; Female ; Humans ; Male ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Teaching ; User-Computer Interface ; },
abstract = {Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention.},
}
@article {pmid26737088,
year = {2015},
author = {Rutkowski, TM},
title = {Student teaching and research laboratory focusing on brain-computer interface paradigms--A creative environment for computer science students.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3667-3670},
doi = {10.1109/EMBC.2015.7319188},
pmid = {26737088},
issn = {2694-0604},
mesh = {Awards and Prizes ; *Brain-Computer Interfaces ; Creativity ; Information Science/*education ; Japan ; Research ; *Students ; Teaching ; },
abstract = {This paper presents an applied concept of a brain-computer interface (BCI) student research laboratory (BCI-LAB) at the Life Science Center of TARA, University of Tsukuba, Japan. Several successful case studies of the student projects are reviewed together with the BCI Research Award 2014 winner case. The BCI-LAB design and project-based teaching philosophy is also explained. Future teaching and research directions summarize the review.},
}
@article {pmid26737075,
year = {2015},
author = {Gemignani, J and Gheysens, T and Summerer, L},
title = {Beyond astronaut's capabilities: The current state of the art.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3615-3618},
doi = {10.1109/EMBC.2015.7319175},
pmid = {26737075},
issn = {2694-0604},
mesh = {*Astronauts ; *Biomedical Engineering ; Humans ; *Space Flight ; *Spacecraft ; },
abstract = {Space agencies have developed extensive expertise with sustaining human presence in low earth orbits and microgravity. Prolonged human presence in space beyond EarthâĂŹs orbit presents additional, some still unsolved issues. These are linked to the distance to Earth (impossibility of effective tele-operation, psychological effects linked to remoteness from Earth, required autonomy, the handling of emergencies, long mission durations), and to the environments beyond the Earth magnetosphere (radiation levels, local environments including atmospheres, dust, gravity, day-night cycles). These issues have impacts on the spacecraft design, the mission operations, astronaut selection and preparation and required supporting/ enabling technologies. This paper builds upon previous work by Rossini et al. , in critically reviewing and updating the current state of scientific research on enhancing astronaut's capabilities to face some of these challenges. In particular, it discusses the pertinence and feasibility of two approaches aiming at enhancing the chances of success of human missions: induced hibernation state and brain-machine interfaces.},
}
@article {pmid26736965,
year = {2015},
author = {Guermandi, M and Bigucci, A and Franchi Scarselli, E and Guerrieri, R},
title = {EEG acquisition system based on active electrodes with common-mode interference suppression by Driving Right Leg circuit.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {3169-3172},
doi = {10.1109/EMBC.2015.7319065},
pmid = {26736965},
issn = {2694-0604},
mesh = {Algorithms ; Electrodes ; Electroencephalography/*methods ; Humans ; Leg/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {We present a system for the acquisition of EEG signals based on active electrodes and implementing a Driving Right Leg circuit (DgRL). DgRL allows for single-ended amplification and analog-to-digital conversion, still guaranteeing a common mode rejection in excess of 110 dB. This allows the system to acquire high-quality EEG signals essentially removing network interference for both wet and dry-contact electrodes. The front-end amplification stage is integrated on the electrode, minimizing the system's sensitivity to electrode contact quality, cable movement and common mode interference. The A/D conversion stage can be either integrated in the remote back-end or placed on the head as well, allowing for an all-digital communication to the back-end. Noise integrated in the band from 0.5 to 100 Hz is comprised between 0.62 and 1.3 μV, depending on the configuration. Current consumption for the amplification and A/D conversion of one channel is 390 μA. Thanks to its low noise, the high level of interference suppression and its quick setup capabilities, the system is particularly suitable for use outside clinical environments, such as in home care, brain-computer interfaces or consumer-oriented applications.},
}
@article {pmid26736893,
year = {2015},
author = {Darvishi, S and Abbott, D and Baumert, M},
title = {Prediction of motor imagery based brain computer interface performance using a reaction time test.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2880-2883},
doi = {10.1109/EMBC.2015.7318993},
pmid = {26736893},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination ; Reaction Time ; },
abstract = {Brain computer interfaces (BCIs) enable human brains to interact directly with machines. Motor imagery based BCI (MI-BCI) encodes the motor intentions of human agents and provides feedback accordingly. However, 15-30% of people are not able to perform vivid motor imagery. To save time and monetary resources, a number of predictors have been proposed to screen for users with low BCI aptitude. While the proposed predictors provide some level of correlation with MI-BCI performance, simple, objective and accurate predictors are currently not available. Thus, in this study we have examined the utility of a simple reaction time (SRT) test for predicting MI-BCI performance. We enrolled 10 subjects and measured their motor imagery performance with either visual or proprioceptive feedback. Their reaction time was also measured using a SRT test. The results show a significant negative correlation (r ≈ -0.67) between SRT and MI-BCI performance. Therefore SRT may be used as a simple and reliable predictor of MI-BCI performance.},
}
@article {pmid26736884,
year = {2015},
author = {Hennrich, J and Herff, C and Heger, D and Schultz, T},
title = {Investigating deep learning for fNIRS based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2844-2847},
doi = {10.1109/EMBC.2015.7318984},
pmid = {26736884},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Machine Learning ; Neural Networks, Computer ; Spectroscopy, Near-Infrared ; },
abstract = {Functional Near infrared Spectroscopy (fNIRS) is a relatively young modality for measuring brain activity which has recently shown promising results for building Brain Computer Interfaces (BCI). Due to its infancy, there are still no standard approaches for meaningful features and classifiers for single trial analysis of fNIRS. Most studies are limited to established classifiers from EEG-based BCIs and very simple features. The feasibility of more complex and powerful classification approaches like Deep Neural Networks has, to the best of our knowledge, not been investigated for fNIRS based BCI. These networks have recently become increasingly popular, as they outperformed conventional machine learning methods for a variety of tasks, due in part to advances in training methods for neural networks. In this paper, we show how Deep Neural Networks can be used to classify brain activation patterns measured by fNIRS and compare them with previously used methods.},
}
@article {pmid26736829,
year = {2015},
author = {Yang, H and Sakhavi, S and Ang, KK and Guan, C},
title = {On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2620-2623},
doi = {10.1109/EMBC.2015.7318929},
pmid = {26736829},
issn = {2694-0604},
mesh = {*Algorithms ; Electrodes ; Electroencephalography ; Humans ; Movement/physiology ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; },
abstract = {Learning the deep structures and unknown correlations is important for the detection of motor imagery of EEG signals (MI-EEG). This study investigates the use of convolutional neural networks (CNNs) for the classification of multi-class MI-EEG signals. Augmented common spatial pattern (ACSP) features are generated based on pair-wise projection matrices, which covers various frequency ranges. We propose a frequency complementary feature map selection (FCMS) scheme by constraining the dependency among frequency bands. Experiments are conducted on BCI competition IV dataset IIa with 9 subjects. Averaged cross-validation accuracy of 68.45% and 69.27% is achieved for FCMS and all feature maps, respectively, which is significantly higher (4.53% and 5.34%) than random map selection and higher (1.44% and 2.26%) than filter-bank CSP (FBCSP). The results demonstrate that the CNNs are capable of learning discriminant, deep structure features for EEG classification without relying on the handcrafted features.},
}
@article {pmid26736760,
year = {2015},
author = {Wriessnegger, SC and Hackhofer, D and Muller-Putz, GR},
title = {Classification of unconscious like/dislike decisions: First results towards a novel application for BCI technology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2331-2334},
doi = {10.1109/EMBC.2015.7318860},
pmid = {26736760},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; *Decision Making ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Photic Stimulation ; *Unconscious, Psychology ; Young Adult ; },
abstract = {More and more applications for BCI technology emerge that are not restricted to communication or control, like gaming, rehabilitation, Neuro-IS research, neuro-economics or security. In this context a so called passive BCI, a system that derives its outputs from arbitrary brain activity for enriching a human-machine interaction with implicit information on the actual user state will be used. Concretely EEG-based BCI technology enables the use of signals related to attention, intentions and mental state, without relying on indirect measures based on overt behavior or other physiological signals which is an important point e.g. in Neuromarketing research. The scope of this pilot EEG-study was to detect like/dislike decisions on car stimuli just by means of ERP analysis. Concretely to define user preferences concerning different car designs by implementing an offline BCI based on shrinkage LDA classification. Although classification failed in the majority of participants the elicited early (sub) conscious ERP components reflect user preferences for cars. In a broader sense this study should pave the way towards a "product design BCI" suitable for neuromarketing research.},
}
@article {pmid26736759,
year = {2015},
author = {Wilaiprasitporn, T and Yagi, T},
title = {Orientation-modulated attention effect on visual evoked potential: Application for PIN system using brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2327-2330},
doi = {10.1109/EMBC.2015.7318859},
pmid = {26736759},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; *Orientation ; Photic Stimulation ; *Records ; Statistics as Topic ; Young Adult ; },
abstract = {This research demonstrates the orientation-modulated attention effect on visual evoked potential. We combined this finding with our previous findings about the motion-modulated attention effect and used the result to develop novel visual stimuli for a personal identification number (PIN) application based on a brain-computer interface (BCI) framework. An electroencephalography amplifier with a single electrode channel was sufficient for our application. A computationally inexpensive algorithm and small datasets were used in processing. Seven healthy volunteers participated in experiments to measure offline performance. Mean accuracy was 83.3% at 13.9 bits/min. Encouraged by these results, we plan to continue developing the BCI-based personal identification application toward real-time systems.},
}
@article {pmid26736758,
year = {2015},
author = {Scherer, R and Faller, J and Opisso, E and Costa, U and Steyrl, D and Muller-Putz, GR},
title = {Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2323-2326},
doi = {10.1109/EMBC.2015.7318858},
pmid = {26736758},
issn = {2694-0604},
mesh = {Adult ; Aged ; Brain/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Middle Aged ; *Online Systems ; *Task Performance and Analysis ; },
abstract = {Non-stationarity and inherent variability of the noninvasive electroencephalogram (EEG) makes robust recognition of spontaneous EEG patterns challenging. Reliable modulation of EEG patterns that a BCI can robustly detect is a skill that users must learn. In this paper, we present a novel online co-adaptive BCI training paradigm. The system autonomously screens users for their ability to modulate EEG patterns in a predictive way and adapts its model parameters online. Results of a supporting study in seven first-time BCI users with disability are very encouraging. Three of 7 users achieved online accuracy > 70% for 2-class BCI control after 24 minutes of training. Online performance in 6 of 7 users was significantly higher than chance level. Online control was based on one single bipolar EEG channel. Beta band activity carried most discriminant information. Our fully automatic co-adaptive online approach allows to evaluate whether user benefit from current BCI technology within a reasonable timescale.},
}
@article {pmid26736757,
year = {2015},
author = {Pereira, M and Sobolewski, A and Millan, Jdel R},
title = {Modulation of the inter-hemispheric asymmetry of motor-related brain activity using brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2319-2322},
doi = {10.1109/EMBC.2015.7318857},
pmid = {26736757},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Cerebrum ; Electroencephalography ; Functional Laterality ; Humans ; Motor Activity/*physiology ; Pilot Projects ; Task Performance and Analysis ; },
abstract = {Non-invasive brain stimulation has shown promising results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used to successfully facilitate movement-related brain activity spared by the infarct. We propose to merge both approaches by using BCI to train stroke patients to rebalance their motor-related brain activity during motor tasks, through the use of online feedback. In this pilot study, we report results showing that some healthy subjects were able to learn to spontaneously up- and/or down-regulate their ipsilateral brain activity during a single session.},
}
@article {pmid26736756,
year = {2015},
author = {Arvaneh, M and Ward, TE and Robertson, IH},
title = {Effects of feedback latency on P300-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2315-2318},
doi = {10.1109/EMBC.2015.7318856},
pmid = {26736756},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; *Feedback ; Female ; Humans ; Male ; Signal-To-Noise Ratio ; Surveys and Questionnaires ; Task Performance and Analysis ; Young Adult ; },
abstract = {Feedback has been shown to affect performance when using a Brain-Computer Interface (BCI) based on sensorimotor rhythms. In contrast, little is known about the influence of feedback on P300-based BCIs. There is still an open question whether feedback affects the regulation of P300 and consequently the operation of P300-based BCIs. In this paper, for the first time, the influence of feedback on the P300-based BCI speller task is systematically assessed. For this purpose, 24 healthy participants performed the classic P300-based BCI speller task, while only half of them received feedback. Importantly, the number of flashes per letter was reduced on a regular basis in order to increase the frequency of providing feedback. Experimental results showed that feedback could significantly improve the P300-based BCI speller performance, if it was provided in short time intervals (e.g. in sequences as short as 4 to 6 flashes per row/column). Moreover, our offline analysis showed that providing feedback remarkably enhanced the relevant ERP patterns and attenuated the irrelevant ERP patterns, such that the discrimination between target and non-target EEG trials increased.},
}
@article {pmid26736755,
year = {2015},
author = {Wang, Y and Mohanarangam, K and Mallipeddi, R and Veluvolu, KC},
title = {Spatial filter and feature selection optimization based on EA for multi-channel EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2311-2314},
doi = {10.1109/EMBC.2015.7318855},
pmid = {26736755},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {The EEG signals employed for BCI systems are generally band-limited. The band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter was developed to obtain amplitude estimates of the EEG signal in a pre-fixed frequency band in real-time. However, the high-dimensionality of the feature vector caused by the application of BMFLC to multi-channel EEG based BCI deteriorates the performance of the classifier. In this work, we apply evolutionary algorithm (EA) to tackle this problem. The real-valued EA encodes both the spatial filter and the feature selection into its solution and optimizes it with respect to the classification error. Three BMFLC based BCI configurations are proposed. Our results show that the BMFLC-KF with covariance matrix adaptation evolution strategy (CMAES) has the best overall performance.},
}
@article {pmid26736745,
year = {2015},
author = {Wang, YT and Nakanishi, M and Kappel, SL and Kidmose, P and Mandic, DP and Wang, Y and Cheng, CK and Jung, TP},
title = {Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2271-2274},
doi = {10.1109/EMBC.2015.7318845},
pmid = {26736745},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Ear/physiology ; *Electroencephalography/instrumentation/methods ; Evoked Potentials, Visual/*physiology ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the EarEEG relatively low. Recently, filter bank- and training data-based canonical correlation analysis algorithms have shown significant performance improvement in terms of accuracy of target detection and information transfer rate (ITR). This study implemented an online four-class SSVEP-based BCI system using EarEEG. Four subjects participated in offline and online BCI experiments. For the offline classification, an average accuracy of 82.71±11.83 % was obtained using 4 sec-long SSVEPs acquired from earpieces. In the online experiment, all subjects successfully completed the tasks with an average accuracy of 87.92±12.10 %, leading to an average ITR of 16.60±6.55 bits/min. The results suggest that EarEEG can be used to perform practical BCI applications. The EarEEG has the potential to be used as a portable EEG recordings platform, that could enable real-world BCI applications.},
}
@article {pmid26736717,
year = {2015},
author = {Shahdoost, S and Frost, S and Dunham, C and DeJong, S and Barbay, S and Nudo, R and Mohseni, P},
title = {Cortical control of intraspinal microstimulation: Toward a new approach for restoration of function after spinal cord injury.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {2159-2162},
doi = {10.1109/EMBC.2015.7318817},
pmid = {26736717},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Equipment Design ; Motor Cortex/*physiology ; Paralysis/etiology/physiopathology ; Rats ; Signal Processing, Computer-Assisted ; Spinal Cord/physiology ; Spinal Cord Injuries/complications/physiopathology/*therapy ; },
abstract = {Approximately 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress toward developing a miniaturized brain-machine-spinal cord interface (BMSI) that converts in real time the neural command signals recorded from the cortical motor regions to electrical stimuli delivered to the spinal cord below the injury level. Using a combination of custom integrated circuit (IC) technology for corticospinal interfacing and field-programmable gate array (FPGA)-based technology for embedded signal processing, we demonstrate proof-of-concept of distinct muscle pattern activation via intraspinal microstimulation (ISMS) controlled in real time by intracortical neural spikes in an anesthetized laboratory rat.},
}
@article {pmid26736664,
year = {2015},
author = {Frolich, L and Winkler, I and Muller, KR and Samek, W},
title = {Investigating effects of different artefact types on motor imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1942-1945},
doi = {10.1109/EMBC.2015.7318764},
pmid = {26736664},
issn = {2694-0604},
mesh = {*Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Eye Movements ; Humans ; Imagination ; Motor Activity ; Muscle, Skeletal/physiology ; },
abstract = {Artefacts in recordings of the electroencephalogram (EEG) are a common problem in Brain-Computer Interfaces (BCIs). Artefacts make it difficult to calibrate from training sessions, resulting in low test performance, or lead to artificially high performance when unintentionally used for BCI control. We investigate different artefacts' effects on motor-imagery based BCI relying on Common Spatial Patterns (CSP). Data stem from an 80-subject BCI study. We use the recently developed classifier IC_MARC to classify independent components of EEG data into neural and five classes of artefacts. We find that muscle, but not ocular, artefacts adversely affect BCI performance when all 119 EEG channels are used. Artefacts have little influence when using 48 centrally located EEG channels in a configuration previously found to be optimal.},
}
@article {pmid26736659,
year = {2015},
author = {Shiman, F and Irastorza-Landa, N and Sarasola-Sanz, A and Spuler, M and Birbaumer, N and Ramos-Murguialday, A},
title = {Towards decoding of functional movements from the same limb using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1922-1925},
doi = {10.1109/EMBC.2015.7318759},
pmid = {26736659},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Extremities ; Female ; Humans ; Male ; *Movement ; Young Adult ; },
abstract = {In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement to induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have been controlled using EEG activity. However, all studies have used a 2-class or one-dimensional decoding scheme. In this study we investigated EEG decoding of 5 functional movements of the same limb towards an online scenario. Six healthy participants performed a three-dimensional center-out reaching task based on direction movements (four directions and rest) wearing a 32-channel EEG cap. A BCI design based on multiclass extensions of Spectrally Weighted Common Spatial Patterns (Spec-CSP) and a linear discriminant analysis (LDA) classifier was developed and tested offline. The decoding accuracy was 5-fold cross-validated. A decoding accuracy of 39.5% on average for all the six subjects was obtained (chance level being 20%). The results of the current study demonstrate multiple functional movements decoding (significantly higher than chance level) from the same limb using EEG data. This study represents first steps towards a same limb multi degree of freedom (DOF) online EEG based BCI for motor restoration.},
}
@article {pmid26736658,
year = {2015},
author = {Nishifuji, S and Sugita, Y and Hirano, H},
title = {Event-related modulation of steady-state visual evoked potentials for eyes-closed brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1918-1921},
doi = {10.1109/EMBC.2015.7318758},
pmid = {26736658},
issn = {2694-0604},
mesh = {Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Persons with Disabilities ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Male ; Young Adult ; },
abstract = {Brain computer interfaces (BCIs), also be referred to be as brain machine interfaces, transform modulations of electroencephalogram (EEG) into user's intents to communicate with others without voice and physical movement. BCIs have been studied and developed as one of the important means for communication-aid between disabled with severe motor disabilities such as amyotrophic lateral sclerosis and muscular dystrophy patients and their caregivers. State-of-art BCIs have achieved the outstanding performance in information transfer rate and classification accuracy. However, most of conventional BCIs are still unavailable for patients with impaired oculomotor control due to requirement of visual modality. The present study aimed at developing a novel 2-class BCI which was independent of oculomotor control including eye-opening using event-related modulation of steady state visual evoked potential (SSVEP) associated with mental tasks under eyes-closed condition. Eleven healthy subjects aged 21-24 years old were recruited and directed to perform each of two mental tasks under an eyes-closed condition; mental focus on flicker stimuli and image recall of their favorite animals, respectively. The magnitudes of SSVEP in the posterior regions of almost all the subjects were seen to be modulated by performing the mental tasks under the conditions of the flickering frequency of 10 Hz and stimulus intensity of 3-5 lx, which was used to express a user's binary intent, namely, performing one of the mental tasks or not (rest). The classification performance on the mental focus, 80 %, was larger than that on the image recall, 75 %, in average across all the subjects. Shortening of the data length used for classification would improve the information transfer rate of the proposed BCI.},
}
@article {pmid26736657,
year = {2015},
author = {Chamanzar, A and Malekmohammadi, A and Bahrani, M and Shabany, M},
title = {Accurate single-trial detection of movement intention made possible using adaptive wavelet transform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1914-1917},
doi = {10.1109/EMBC.2015.7318757},
pmid = {26736657},
issn = {2694-0604},
mesh = {Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography ; Electrooculography/*methods ; Humans ; *Intention ; *Movement ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; *Wavelet Analysis ; },
abstract = {The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML), electrooculography (EOG) artifact removal method to remove eye-related artifacts to significantly improve the detection performance. We have applied this technique to our locally recorded Emotiv(®) data set of 6 healthy subjects, where an average detection selectivity of 85 ± 6% and sensitivity of 88 ± 7.7% is achieved with a temporal precision in the range of -1250 to 367 ms in onset detections of single-trials.},
}
@article {pmid26736656,
year = {2015},
author = {Shimizu, K and Makino, S and Rutkowski, TM},
title = {Inter-stimulus interval study for the tactile point-pressure brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1910-1913},
doi = {10.1109/EMBC.2015.7318756},
pmid = {26736656},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; *Evoked Potentials ; Humans ; Male ; *Touch ; Young Adult ; },
abstract = {The paper presents a study of an inter-stimulus interval (ISI) influence on a tactile point-pressure stimulus-based brain-computer interface's (tpBCI) classification accuracy. A novel tactile pressure generating tpBCI stimulator is also discussed, which is based on a three-by-three pins' matrix prototype. The six pin-linear patterns are presented to the user's palm during the online tpBCI experiments in an oddball style paradigm allowing for "the aha-responses" elucidation, within the event related potential (ERP). A subsequent classification accuracies' comparison is discussed based on two ISI settings in an online tpBCI application. A research hypothesis of classification accuracies' non-significant differences with various ISIs is confirmed based on the two settings of 120 ms and 300 ms, as well as with various numbers of ERP response averaging scenarios.},
}
@article {pmid26736655,
year = {2015},
author = {Aminaka, D and Makino, S and Rutkowski, TM},
title = {Chromatic and high-frequency cVEP-based BCI paradigm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1906-1909},
doi = {10.1109/EMBC.2015.7318755},
pmid = {26736655},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {We present results of an approach to a code-modulated visual evoked potential (cVEP) based brain-computer interface (BCI) paradigm using four high-frequency flashing stimuli. To generate higher frequency stimulation compared to the state-of-the-art cVEP-based BCIs, we propose to use the light-emitting diodes (LEDs) driven from a small micro-controller board hardware generator designed by our team. The high-frequency and green-blue chromatic flashing stimuli are used in the study in order to minimize a danger of a photosensitive epilepsy (PSE). We compare the the green-blue chromatic cVEP-based BCI accuracies with the conventional white-black flicker based interface. The high-frequency cVEP responses are identified using a canonical correlation analysis (CCA) method.},
}
@article {pmid26736654,
year = {2015},
author = {Bauernfeind, G and Horki, P and Kurz, EM and Schippinger, W and Pichler, G and Muller-Putz, GR},
title = {Improved concept and first results of an auditory single-switch BCI for the future use in disorders of consciousness patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1902-1905},
doi = {10.1109/EMBC.2015.7318754},
pmid = {26736654},
issn = {2694-0604},
mesh = {Adult ; *Auditory Perception ; *Brain-Computer Interfaces ; *Communication ; *Consciousness Disorders ; Electroencephalography ; Female ; Humans ; Male ; Young Adult ; },
abstract = {A promising approach to establish basic communication for disorders of consciousness (DOC) patients, is the application of Brain-Computer Interface (BCI) systems, especially the use of single-switch BCIs (ssBCIs). Recently we proposed the concept of a novel auditory ssBCI paradigm and presented first classification results. In this study we report on the evaluation of four different modifications of the original paradigm with the intention to increase the suitability. Therefore we investigated different sound types and the inclusion of additional spatial information. Finally, the classification investigation with the most encouraging modifications shows an enhancement compared to our original paradigm, within healthy subjects, implicating better results for the future use in DOC patients.},
}
@article {pmid26736623,
year = {2015},
author = {Zink, R and Hunyádi, B and Van Huffel, S and De Vos, M},
title = {Classifying the auditory P300 using mobile EEG recordings without calibration phase.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1777-1780},
doi = {10.1109/EMBC.2015.7318723},
pmid = {26736623},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; *Evoked Potentials, Auditory ; Humans ; Models, Theoretical ; Reproducibility of Results ; Young Adult ; },
abstract = {One of the major drawbacks in mobile EEG Brain Computer Interfaces (BCI) is the need for subject specific training data to train a classifier. By removing the need for supervised classification and calibration phase, new users could start immediate interaction with a BCI. We propose a solution to exploit the structural difference by means of canonical polyadic decomposition (CPD) for three-class auditory oddball data without the need for subject-specific information. We achieve this by adding average event-related-potential (ERP) templates to the CPD model. This constitutes a novel similarity measure between single-trial pairs and known-templates, which results in a fast and interpretable classifier. These results have similar accuracy to those of the supervised and cross-validated stepwise LDA approach but without the need for having subject-dependent data. Therefore the described CPD method has a significant practical advantage over the traditional and widely used approach.},
}
@article {pmid26736622,
year = {2015},
author = {Zaitcev, A and Cook, G and Wei Liu, and Paley, M and Milne, E},
title = {Feature Extraction for BCIs Based on Electromagnetic Source Localization and Multiclass Filter Bank Common Spatial Patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1773-1776},
doi = {10.1109/EMBC.2015.7318722},
pmid = {26736622},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Electromagnetic Phenomena ; Humans ; Models, Theoretical ; Signal Processing, Computer-Assisted ; },
abstract = {Brain-Computer Interfaces (BCIs) provide means for communication and control without muscular movement and, therefore, can offer significant clinical benefits. Electrical brain activity recorded by electroencephalography (EEG) can be interpreted into software commands by various classification algorithms according to the descriptive features of the signal. In this paper we propose a novel EEG BCI feature extraction method employing EEG source reconstruction and Filter Bank Common Spatial Patterns (FBCSP) based on Joint Approximate Diagonalization (JAD). The proposed method is evaluated by the commonly used reference EEG dataset yielding an average classification accuracy of 77.1 ± 10.1 %. It is shown that FBCSP feature extraction applied to reconstructed source components outperforms conventional CSP and FBCSP feature extraction methods applied to signals in the sensor domain.},
}
@article {pmid26736621,
year = {2015},
author = {Korczowski, L and Congedo, M and Jutten, C},
title = {Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1769-1772},
doi = {10.1109/EMBC.2015.7318721},
pmid = {26736621},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; *Evoked Potentials ; Female ; Humans ; Male ; Models, Theoretical ; Pilot Projects ; Young Adult ; },
abstract = {The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.},
}
@article {pmid26736620,
year = {2015},
author = {Kapeller, C and Gergondet, P and Kamada, K and Ogawa, H and Takeuchi, F and Ortner, R and Pruckl, R and Kheddar, A and Scharinger, J and Guger, C},
title = {Online control of a humanoid robot through hand movement imagination using CSP and ECoG based features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1765-1768},
doi = {10.1109/EMBC.2015.7318720},
pmid = {26736620},
issn = {2694-0604},
mesh = {Aged ; *Brain-Computer Interfaces ; *Electrocorticography ; Electroencephalography ; Equipment Design ; Female ; *Hand ; Humans ; *Imagination ; Models, Theoretical ; *Robotics ; },
abstract = {Intention recognition through decoding brain activity could lead to a powerful and independent Brain-Computer-Interface (BCI) allowing for intuitive control of devices like robots. A common strategy for realizing such a system is the motor imagery (MI) BCI using electroencephalography (EEG). Changing to invasive recordings like electrocorticography (ECoG) allows extracting very robust features and easy introduction of an idle state, which might simplify the mental task and allow the subject to focus on the environment. Especially for multi-channel recordings like ECoG, common spatial patterns (CSP) provide a powerful tool for feature optimization and dimensionality reduction. This work focuses on an invasive and independent MI BCI that allows triggering from an idle state, and therefore facilitates tele-operation of a humanoid robot. The task was to lift a can with the robot's hand. One subject participated and reached 95.4 % mean online accuracy after six runs of 40 trials. To our knowledge, this is the first online experiment with a MI BCI using CSPs from ECoG signals.},
}
@article {pmid26736619,
year = {2015},
author = {Boubchir, L and Touati, Y and Daachi, B and Chérif, AA},
title = {EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1761-1764},
doi = {10.1109/EMBC.2015.7318719},
pmid = {26736619},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Models, Theoretical ; Robotics/*instrumentation ; },
abstract = {In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.},
}
@article {pmid26736618,
year = {2015},
author = {Dehzangi, O and Jafari, R},
title = {Time-varying and simultaneous frequency stimulation for multi-class SSVEP-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1757-1760},
doi = {10.1109/EMBC.2015.7318718},
pmid = {26736618},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Computer Simulation ; Equipment Design ; *Evoked Potentials, Visual ; Humans ; Neurologic Examination/*methods ; },
abstract = {In recent years, there has been increasing interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems for their high signal to noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are limited. The goal of this paper is to investigate time-varying and simultaneous frequency stimulation in order to increase the number of visual stimuli with a fixed number of stimulation frequencies in multiclass SSVEP-based BCI systems. This study analyzes the SSVEPs induced by groups of light-emitting diodes (LEDs). The proposed method produces more selections than the number of stimulation frequencies through an efficient combination of time-varying and simultaneous frequencies for stimulation. The feasibility and effectiveness of our proposed method was confirmed by a set of experiments conducted on six subjects. The results confirmed that our proposed stimulation is a promising method to increase the number of stimuli using a fixed number of frequencies for multi-class SSVEP-based BCI tasks.},
}
@article {pmid26736596,
year = {2015},
author = {Hsieh, HL and Shanechi, MM},
title = {Optimal calibration of the learning rate in closed-loop adaptive brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1667-1670},
doi = {10.1109/EMBC.2015.7318696},
pmid = {26736596},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Humans ; Machine Learning ; Models, Statistical ; *Signal Processing, Computer-Assisted ; },
abstract = {Closed-loop decoder adaptation (CLDA) can improve brain-machine interface (BMI) performance. CLDA methods use batches of data to refit the decoder parameters in closed-loop operation. Recently, dynamic state-space algorithms have also been designed to fit the parameters of a point process decoder (PPF). A main design parameter that needs to be selected in any CLDA algorithm is the learning rate, i.e., how fast should the decoder parameters be updated on the basis of new neural observations. So far, the learning rate of CLDA algorithms has been selected empirically using ad-hoc methods. Here we develop a principled framework to calibrate the learning rate in adaptive state-space algorithms. The learning rate introduces a trade-off between the convergence rate and the steady-state error covariance of the estimated decoder parameters. Hence our algorithm first finds an analytical upper-bound on the steady-state error covariance as a function of the learning rate. It then finds the inverse mapping to select the optimal learning rate based on the maximum allowable steady-state error. Using numerical BMI experiments, we show that the calibration algorithm selects the optimal learning rate that meets the requirement on steady-state error level while achieving the fastest convergence rate possible corresponding to this steady-state level.},
}
@article {pmid26736552,
year = {2015},
author = {Pereira, J and Ofner, P and Muller-Putz, GR},
title = {Goal-directed or aimless? EEG differences during the preparation of a reach-and-touch task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1488-1491},
doi = {10.1109/EMBC.2015.7318652},
pmid = {26736552},
issn = {2694-0604},
mesh = {Activities of Daily Living ; Brain-Computer Interfaces ; *Electroencephalography ; Goals ; Humans ; Movement ; Touch ; },
abstract = {The natural control of neuroprostheses is currently a challenge in both rehabilitation engineering and brain-computer interfaces (BCIs) research. One of the recurrent problems is to know exactly when to activate such devices. For the execution of the most common activities of daily living, these devices only need to be active when in the presence of a goal. Therefore, we believe that the distinction between the planning of goal-directed and aimless movements, using non-invasive recordings, can be useful for the implementation of a simple and effective activation method for these devices. We investigated whether those differences are detectable during a reach-and-touch task, using electroencephalography (EEG). Event-related potentials and oscillatory activity changes were studied. Our results show that there are statistically significant differences between both types of movement. Combining this information with movement decoding would allow a natural control strategy for BCIs, exclusively relying on the cognitive processes behind movement preparation and execution.},
}
@article {pmid26736551,
year = {2015},
author = {Nicolae, IE and Acqualagna, L and Blankertz, B},
title = {Neural indicators of the depth of cognitive processing for user-adaptive neurotechnological applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1484-1487},
doi = {10.1109/EMBC.2015.7318651},
pmid = {26736551},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; *Cognition ; Electroencephalography ; Evoked Potentials ; Humans ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {The ability to infer implicit user variables in realtime and in an unobtrusive way would open a broad variety of applications such as adapting the user interface in human-computer interaction or developing safety assistance systems in industrial workplaces. Such information may be extracted from behavior, peripheral physiology and brain activity. Each of these sensors has its advantages and disadvantages suggesting that finally all available features should be fused. While in Brain-Computer Interface (BCI) research powerful methods for the real-time extraction of information from brain signals have been developed, comparatively little effort was spent on the extraction of hidden user states. As a further step in this direction, we propose a novel experimental paradigm to study the feasibility of quantifying how deeply presented information is processed in the brain. An investigation of event-related potentials (ERPs) demonstrates the effectiveness of our task in inducing different levels of cognitive processing and shows which features of brain activity provide discriminative information.},
}
@article {pmid26736550,
year = {2015},
author = {Nejati, H and Tsourides, K and Pomponiu, V and Ehrenberg, EC and Ngai-Man Cheung, and Sinha, P},
title = {Towards perception awareness: Perceptual event detection for Brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1480-1483},
doi = {10.1109/EMBC.2015.7318650},
pmid = {26736550},
issn = {2694-0604},
mesh = {Adaptation, Physiological ; *Awareness ; Brain ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Perception ; },
abstract = {Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting perceptual events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given perceptual event. As this "signature" is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.},
}
@article {pmid26736549,
year = {2015},
author = {Haofei Wang, and Xujiong Dong, and Zhaokang Chen, and Shi, BE},
title = {Hybrid gaze/EEG brain computer interface for robot arm control on a pick and place task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1476-1479},
doi = {10.1109/EMBC.2015.7318649},
pmid = {26736549},
issn = {2694-0604},
mesh = {Bayes Theorem ; Brain ; Brain-Computer Interfaces ; *Electroencephalography ; Robotics ; User-Computer Interface ; },
abstract = {We describe a hybrid brain computer interface that integrates gaze information from an eye tracker with brain activity information measured by electroencephalography (EEG). Users explicitly control the end effector of a robot arm to move in one of four directions using motor imagery to perform a pick and place task. Measurements of the natural eye gaze behavior of subjects is used to infer the instantaneous intent of the users based on the past gaze trajectory. This information is integrated with the output of the EEG classifier and contextual information about the environment probabilistically using Bayesian inference. Our experiments demonstrate that subjects can achieve 100% task completion within three minutes and that the integration of EEG and gaze information significantly improves performance over either cue in isolation.},
}
@article {pmid26736547,
year = {2015},
author = {Ofner, P and Muller-Putz, GR},
title = {Movement target decoding from EEG and the corresponding discriminative sources: A preliminary study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1468-1471},
doi = {10.1109/EMBC.2015.7318647},
pmid = {26736547},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Imagination ; Motor Cortex ; Movement ; },
abstract = {Brain-computer interfaces (BCIs) can detect movement imaginations (MI) which can act as a control signal for a neuroprosthesis of a paralyzed person. However, today's non-invasive BCIs can only detect simply qualities of MI, like what body part is subjected to MI. More advanced future non-invasive BCIs should be able to detect many qualities of MI to allow a natural control of a neuroprosthesis. In this preliminary study, we decoded movement targets during a self-paced center-out reaching task, and calculated corresponding spatial patterns in the source space. We were able to decode the movement targets with significant classification accuracy from one out of three subjects during the movement planning phase. This subject showed a distinct spatial pattern over the central motor area.},
}
@article {pmid26736470,
year = {2015},
author = {Long Chen, and Zhongpeng Wang, and Feng He, and Jiajia Yang, and Hongzhi Qi, and Peng Zhou, and Baikun Wan, and Dong Ming, },
title = {An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1152-1155},
doi = {10.1109/EMBC.2015.7318570},
pmid = {26736470},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; User-Computer Interface ; },
abstract = {The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.},
}
@article {pmid26736461,
year = {2015},
author = {Randazzo, L and Iturrate, I and Chavarriaga, R and Leeb, R and Del Millan, JR},
title = {Detecting intention to grasp during reaching movements from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1115-1118},
doi = {10.1109/EMBC.2015.7318561},
pmid = {26736461},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Hand Strength ; Humans ; *Intention ; Movement ; },
abstract = {Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user's intentions into motor actions in order to support the user's impaired neuro-muscular system. Recently, it has been suggested that slow cortical potentials (SCPs), negative deflections in the electroencephalographic (EEG) signals peaking around one second before the initiation of movements, might be of interest because they offer an accurate time resolution for the provided feedback. Many state-of-the-art studies exploiting SCPs have focused on decoding intention of movements related to walking and arm reaching, but up to now few studies have focused on decoding the intention to grasp, which is of fundamental importance in upper-limb tasks. In this work, we present a technique that exploits EEG to decode grasping correlates during reaching movements. Results obtained with four subjects show the existence of SCPs prior to the execution of grasping movements and how they can be used to classify, with accuracy rates greater than 70% across all subjects, the intention to grasp. Using a sliding window approach, we have also demonstrated how this intention can be decoded on average around 400 ms before the grasp movements for two out of four subjects, and after the onset of grasp itself for the two other subjects.},
}
@article {pmid26736460,
year = {2015},
author = {Chavarriaga, R and Iturrate, I and Wannebroucq, Q and Del Millan, JR},
title = {Decoding fast-paced error-related potentials in monitoring protocols.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1111-1114},
doi = {10.1109/EMBC.2015.7318560},
pmid = {26736460},
issn = {2694-0604},
mesh = {Brain ; Brain Mapping ; Electroencephalography ; *Evoked Potentials ; Feedback ; },
abstract = {Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI > 2 s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1 s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.},
}
@article {pmid26736459,
year = {2015},
author = {Ogawa, T and Hirayama, J and Gupta, P and Moriya, H and Yamaguchi, S and Ishikawa, A and Inoue, Y and Kawanabe, M and Ishii, S},
title = {Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1107-1110},
doi = {10.1109/EMBC.2015.7318559},
pmid = {26736459},
issn = {2694-0604},
mesh = {Activities of Daily Living ; *Brain-Computer Interfaces ; Feasibility Studies ; Humans ; Spectroscopy, Near-Infrared ; Support Vector Machine ; },
abstract = {Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies.},
}
@article {pmid26736458,
year = {2015},
author = {Bonkon Koo, and Hwan-Gon Lee, and Yunjun Nam, and Seungjin Choi, },
title = {Immersive BCI with SSVEP in VR head-mounted display.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1103-1106},
doi = {10.1109/EMBC.2015.7318558},
pmid = {26736458},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Photic Stimulation ; User-Computer Interface ; },
abstract = {In this paper we present an immersive brain computer interface (BCI) where we use a virtual reality head-mounted display (VRHMD) to invoke SSVEP responses. Compared to visual stimuli in monitor display, we demonstrate that visual stimuli in VRHMD indeed improve the user engagement for BCI. To this end, we validate our method with experiments on a VR maze game, the goal of which is to guide a ball into the destination in a 2D grid map in a 3D space, successively choosing one of four neighboring cells using SSVEP evoked by visual stimuli on neighboring cells. Experiments indicate that the averaged information transfer rate is improved by 10% for VRHMD, compared to the case in monitor display and the users feel easier to play the game with the proposed system.},
}
@article {pmid26736457,
year = {2015},
author = {Yuxiao Yang, and Shanechi, MM},
title = {A generalizable adaptive brain-machine interface design for control of anesthesia.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1099-1102},
doi = {10.1109/EMBC.2015.7318557},
pmid = {26736457},
issn = {2694-0604},
mesh = {Anesthesia, General ; Anesthetics ; *Brain-Computer Interfaces ; Coma ; Feedback ; Humans ; },
abstract = {Brain-machine interfaces (BMIs) for closed-loop control of anesthesia have the potential to automatically monitor and control brain states under anesthesia. Since a variety of anesthetic states are needed in different clinical scenarios, designing a generalizable BMI architecture that can control a wide range of anesthetic states is essential. In addition, drug dynamics are non-stationary over time and could change with the depth of anesthesia. Hence for precise control, a BMI needs to track these non-stationarities online. Here we design a BMI architecture that generalizes to control of various anesthetic states and their associated neural signatures, and is adaptive to time-varying drug dynamics. We provide a systematic approach to build general parametric models that quantify the anesthetic state and describe the drug dynamics. Based on these models, we develop an adaptive closed-loop controller within the framework of stochastic optimal feedback control. This controller tracks the non-stationarities in drug dynamics, achieves tight control in a time-varying environment, and removes the need for an offline system identification session. For robustness, the BMI also ensures small drug infusion rate variations at steady state. We test the BMI architecture for control of two common anesthetic states, i.e., burst suppression in medically-induced coma and unconsciousness in general anesthesia. Using numerical experiments, we find that the BMI generalizes to control of both these anesthetic states; in a time-varying environment, even without initial knowledge of model parameters, the BMI accurately controls these two different anesthetic states, reducing bias and error more than 70 times and 9 times, respectively, compared with a non-adaptive system.},
}
@article {pmid26736456,
year = {2015},
author = {Kaczmarek, P and Salomon, P},
title = {Towards SSVEP-based, portable, responsive Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1095-1098},
doi = {10.1109/EMBC.2015.7318556},
pmid = {26736456},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual ; Photic Stimulation ; User-Computer Interface ; },
abstract = {A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.},
}
@article {pmid26736455,
year = {2015},
author = {Chew, G and Kai Keng Ang, and So, RQ and Zhiming Xu, and Cuntai Guan, },
title = {Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1091-1094},
doi = {10.1109/EMBC.2015.7318555},
pmid = {26736455},
issn = {2694-0604},
mesh = {Action Potentials ; Brain-Computer Interfaces ; Electrodes, Implanted ; Humans ; *Motor Cortex ; },
abstract = {Decoding of directional information in the motor cortex traditionally utilizes only firing rate information. However, information from other features could be extracted and combined with firing rate in order to increase classification accuracy. This study proposes the combination of firing rate and spike-train synchrony information in the decoding of motor cortical activity. Synchrony measures used are Event Synchronization (ES), SPIKE-Distance, and ISI-Distance. All data used for analyses were obtained from implanted electrode recordings of the primary motor cortex of a monkey that was trained to manipulate a motorized vehicle with 4 degrees of freedom (left, right, front and stop) via joystick control. Firstly, synchrony features could decode time periods, which were otherwise incorrectly decoded by firing rate alone, above chance levels. Secondly, using an ensemble classifier design for offline analysis, combining firing rate and ISI-distance information increases overall decoding accuracy by 1.1%. These results show that synchrony features in spike-trains do contain information not carried in firing rate. In addition, these results also demonstrate the feasibility of combining synchrony and firing rate for improving the classification accuracy of invasive brain-machine interface (BMI) in the control of neural prosthetics.},
}
@article {pmid26736454,
year = {2015},
author = {Spuler, M},
title = {A Brain-Computer Interface (BCI) system to use arbitrary Windows applications by directly controlling mouse and keyboard.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1087-1090},
doi = {10.1109/EMBC.2015.7318554},
pmid = {26736454},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Computers ; Electroencephalography ; Evoked Potentials, Visual ; User-Computer Interface ; Writing ; },
abstract = {A Brain-Computer Interface (BCI) allows to control a computer by brain activity only, without the need for muscle control. In this paper, we present an EEG-based BCI system based on code-modulated visual evoked potentials (c-VEPs) that enables the user to work with arbitrary Windows applications. Other BCI systems, like the P300 speller or BCI-based browsers, allow control of one dedicated application designed for use with a BCI. In contrast, the system presented in this paper does not consist of one dedicated application, but enables the user to control mouse cursor and keyboard input on the level of the operating system, thereby making it possible to use arbitrary applications. As the c-VEP BCI method was shown to enable very fast communication speeds (writing more than 20 error-free characters per minute), the presented system is the next step in replacing the traditional mouse and keyboard and enabling complete brain-based control of a computer.},
}
@article {pmid26736453,
year = {2015},
author = {Spuler, M and Sarasola-Sanz, A and Birbaumer, N and Rosenstiel, W and Ramos-Murguialday, A},
title = {Comparing metrics to evaluate performance of regression methods for decoding of neural signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1083-1086},
doi = {10.1109/EMBC.2015.7318553},
pmid = {26736453},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Regression Analysis ; },
abstract = {The use of regression methods for decoding of neural signals has become popular, with its main applications in the field of Brain-Machine Interfaces (BMIs) for control of prosthetic devices or in the area of Brain-Computer Interfaces (BCIs) for cursor control. When new methods for decoding are being developed or the parameters for existing methods should be optimized to increase performance, a metric is needed that gives an accurate estimate of the prediction error. In this paper, we evaluate different performance metrics regarding their robustness for assessing prediction errors. Using simulated data, we show that different kinds of prediction error (noise, scaling error, bias) have different effects on the different metrics and evaluate which methods are best to assess the overall prediction error, as well as the individual types of error. Based on the obtained results we can conclude that the most commonly used metrics correlation coefficient (CC) and normalized root-mean-squared error (NRMSE) are well suited for evaluation of cross-validated results, but should not be used as sole criterion for cross-subject or cross-session evaluations.},
}
@article {pmid26736452,
year = {2015},
author = {Ibarra Chaoul, A and Grosse-Wentrup, M},
title = {Is breathing rate a confounding variable in brain-computer interfaces (BCIs) based on EEG spectral power?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1079-1082},
doi = {10.1109/EMBC.2015.7318552},
pmid = {26736452},
issn = {2694-0604},
mesh = {Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Confounding Factors, Epidemiologic ; Electroencephalography ; Humans ; },
abstract = {Brain-computer interfaces (BCIs) enable paralyzed patients to interact with the world by directly decoding brain activity. We investigated if systematic changes in breathing rate affect EEG bandpower features that are commonly used in BCIs. This is of particular interest for the development of cognitive BCIs for patients with artificial ventilation, e.g. for those in late stages of amyotrophic lateral sclerosis (ALS). If subjects can alter the spectrum of the EEG by changing their breathing rate, decoding results obtained with healthy subjects may not generalize to this patient population. We recorded a high-density EEG from twelve healthy subjects, who were instructed to alternate between fast and slow breathing. We do not find any statistically significant modulation of EEG bandpower. As such, changes in breathing rate are unlikely to substantially bias the performance of BCIs based on EEG bandpower features.},
}
@article {pmid26736451,
year = {2015},
author = {Akman Aydin, E and Bay, OF and Guler, I},
title = {Region based Brain Computer Interface for a home control application.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1075-1078},
doi = {10.1109/EMBC.2015.7318551},
pmid = {26736451},
issn = {2694-0604},
mesh = {Brain ; *Brain-Computer Interfaces ; Persons with Disabilities ; Electroencephalography ; Humans ; User-Computer Interface ; },
abstract = {Environment control is one of the important challenges for disabled people who suffer from neuromuscular diseases. Brain Computer Interface (BCI) provides a communication channel between the human brain and the environment without requiring any muscular activation. The most important expectation for a home control application is high accuracy and reliable control. Region-based paradigm is a stimulus paradigm based on oddball principle and requires selection of a target at two levels. This paper presents an application of region based paradigm for a smart home control application for people with neuromuscular diseases. In this study, a region based stimulus interface containing 49 commands was designed. Five non-disabled subjects were attended to the experiments. Offline analysis results of the experiments yielded 95% accuracy for five flashes. This result showed that region based paradigm can be used to select commands of a smart home control application with high accuracy in the low number of repetitions successfully. Furthermore, a statistically significant difference was not observed between the level accuracies.},
}
@article {pmid26736450,
year = {2015},
author = {Nakaizumi, C and Makino, S and Rutkowski, TM},
title = {Head-related impulse response cues for spatial auditory brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1071-1074},
doi = {10.1109/EMBC.2015.7318550},
pmid = {26736450},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Cues ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials, Auditory ; Humans ; Pilot Projects ; User-Computer Interface ; },
abstract = {This study provides a comprehensive test of a head-related impulse response (HRIR) cues for a spatial auditory brain-computer interface (saBCI) speller paradigm. We present a comparison with the conventional virtual sound headphone-based spatial auditory modality. We propose and optimize the three types of sound spatialization settings using a variable elevation in order to evaluate the HRIR efficacy for the saBCI. Three experienced and seven naive BCI users participated in the three experimental setups based on ten presented Japanese syllables. The obtained EEG auditory evoked potentials (AEP) resulted with encouragingly good and stable P300 responses in online BCI experiments. Our case study indicated that users could perceive elevation in the saBCI experiments generated using the HRIR measured from a general head model. The saBCI accuracy and information transfer rate (ITR) scores have been improved comparing to the classical horizontal plane-based virtual spatial sound reproduction modality, as far as the healthy users in the current pilot study are concerned.},
}
@article {pmid26736449,
year = {2015},
author = {Almajidy, RK and Boudria, Y and Hofmann, UG and Besio, W and Mankodiya, K},
title = {Multimodal 2D Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1067-1070},
doi = {10.1109/EMBC.2015.7318549},
pmid = {26736449},
issn = {2694-0604},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; Electroencephalography ; Humans ; Imagination ; Multimodal Imaging ; User-Computer Interface ; },
abstract = {In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.},
}
@article {pmid26736448,
year = {2015},
author = {Kao, JC and Ryu, SI and Shenoy, KV},
title = {Leveraging historical knowledge of neural dynamics to rescue decoder performance as neural channels are lost: "Decoder hysteresis".},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1061-1066},
doi = {10.1109/EMBC.2015.7318548},
pmid = {26736448},
issn = {2694-0604},
support = {R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Motor Cortex ; Neurons ; },
abstract = {An intracortical brain-machine interface (BMI) decodes spiking activity recorded from motor cortical neurons to drive a prosthetic device (e.g., a computer cursor or robotic arm). As the number of recorded neurons decreases over time due to decay in recording quality, the performance of a BMI decreases. We asked: can degrading BMI performance be rescued by using prior information from when more neurons were observed? This would entail augmenting a decoder by using previously learned knowledge about motor cortex (at an earlier point in the array lifetime). We implemented this idea by modeling low-dimensional dynamics of the neural population, which describe how the population evolves through time. We posit that if the neural dynamics accurately reflect properties of motor cortex, then having a better estimate of these dynamics should result in a better decoder. Using previously collected (offline) experimental data, we found that a decoder using dynamics inferred in the past (when more neural channels were available) outperformed the same decoder using dynamics inferred from the (fewer) remaining neural channels. These results suggest that neural dynamics capture important features of the neural population responses in motor cortex, and that knowledge of these dynamics may rescue BMI performance even as array signal quality degrades.},
}
@article {pmid26736447,
year = {2015},
author = {Nakanishi, M and Yijun Wang, and Yu-Te Wang, and Tzyy-Ping Jung, },
title = {A dynamic stopping method for improving performance of steady-state visual evoked potential based brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1057-1060},
doi = {10.1109/EMBC.2015.7318547},
pmid = {26736447},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Eye Diseases ; Humans ; Neurologic Examination ; Photic Stimulation ; },
abstract = {The performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been drastically improved in the past few years. In conventional SSVEP-based BCIs, the speed of a selection is fixed towards high performance based on preliminary offline analysis. However, due to inter-trial variability, the optimal selection time to achieve sufficient accuracy is different for each trial. To optimize the performance of SSVEP-based BCIs, this study proposed a dynamic stopping method that can adaptively determine a selection time in each trial by applying a threshold to the probability of detecting a target. A 12-class SSVEP dataset recorded from 10 subjects was used to evaluate the performance of the proposed method. Compared to the conventional method with a fixed selection time towards the highest accuracy, the proposed method could significantly reduce the averaged selection time (0.84±0.39 s vs. 1.44±0.63 s, p<;0.05) with comparable accuracy (99.44±1.57 % vs. 99.55±1.22 %). As a result, the simulated online information transfer rate (ITR) with the dynamic stopping method achieved a significant improvement compared to the conventional method (125.30±21.55 bits/min vs. 92.75±23.77 bits/min). These results suggest that the proposed dynamic stopping method is effective for improving the performance of SSVEP-based BCI systems.},
}
@article {pmid26736446,
year = {2015},
author = {Pinegger, A and Wriessnegger, SC and Muller-Putz, GR},
title = {Sheet music by mind: Towards a brain-computer interface for composing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1053-1056},
doi = {10.1109/EMBC.2015.7318546},
pmid = {26736446},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; *Music ; Software ; Surveys and Questionnaires ; },
abstract = {Providing brain-computer interface (BCI) users engaging applications should be one of the main targets in BCI research. A painting application, a web browser and other applications can already be controlled via BCI. Another engaging application would be a music composer for self-expression. In this work, we describe Brain Composing: A BCI controlled music composing software. We tested and evaluated the implemented brain composing system with five volunteers. Using a tap water-based electrode biosignal amplifier further improved the usability of the system. Three participants reached accuracies above 77% and were able to copy-compose a given melody. Results of questionnaires support that our brain composing system is an attractive and easy way to compose music via a BCI.},
}
@article {pmid26736445,
year = {2015},
author = {Schwarz, A and Scherer, R and Steyrl, D and Faller, J and Muller-Putz, GR},
title = {A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1049-1052},
doi = {10.1109/EMBC.2015.7318545},
pmid = {26736445},
issn = {2694-0604},
mesh = {Adaptation, Physiological ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; Imagination ; Periodicity ; User-Computer Interface ; },
abstract = {Sensorimotor rhythm (SMR) based Brain-Computer Interfaces (BCI) typically require lengthy user training. This can be exhausting and fatiguing for the user as data collection may be monotonous and typically without any feedback for user motivation. Hence new ways to reduce user training and improve performance are needed. We recently introduced a two class motor imagery BCI system which continuously adapted with increasing run-time to the brain patterns of the user. The system was designed to provide visual feedback to the user after just five minutes. The aim of the current work was to improve user-specific online adaptation, which was expected to lead to higher performances. To maximize SMR discrimination, the method of filter-bank common spatial patterns (fbCSP) and Random Forest (RF) classifier were combined. In a supporting online study, all volunteers performed significantly better than chance. Overall peak accuracy of 88.6 ± 6.1 (SD) % was reached, which significantly exceeded the performance of our previous system by 13%. Therefore, we consider this system the next step towards fully auto-calibrating motor imagery BCIs.},
}
@article {pmid26736440,
year = {2015},
author = {Foerster, M and Burdin, F and Safont, F and Bernert, M and Dehaene, D and Lambert, A and Charvet, G},
title = {KDI: A wireless ECoG recording platform with impedance spectroscopy, electrical stimulation and real-time, lossless data compression.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {1029-1032},
doi = {10.1109/EMBC.2015.7318540},
pmid = {26736440},
issn = {2694-0604},
mesh = {Data Compression ; Dielectric Spectroscopy ; Electric Stimulation ; *Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Equipment Design ; Wireless Technology ; },
abstract = {A power-efficient modular wireless platform has been designed for prototyping and pre-clinical evaluations of neural recording implants. This Kit for Designing Implants (KDI) is separated in function specific modules of 34×34mm which can be assembled as needed. This paper presents the design of new modules for this existing wireless KDI platform. These modules cover the functionalities of electrical stimulation for BCI neurofeedback, impedance spectroscopy for monitoring tissue reaction around implanted electrodes and a real-time lossless data compression algorithm for ECoG signals. This algorithm has been implemented using two different hardware solutions and its performances compared. The design and evaluation of these modules are a first step towards the inclusion of these functionalities into the next generation of WIMAGINE(®) implants.},
}
@article {pmid26736334,
year = {2015},
author = {Mohebbi, A and Engelsholm, SK and Puthusserypady, S and Kjaer, TW and Thomsen, CE and Sorensen, HB},
title = {A brain computer interface for robust wheelchair control application based on pseudorandom code modulated Visual Evoked Potential.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {602-605},
doi = {10.1109/EMBC.2015.7318434},
pmid = {26736334},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Pilot Projects ; User-Computer Interface ; Wheelchairs ; },
abstract = {In this pilot study, a novel and minimalistic Brain Computer Interface (BCI) based wheelchair control application was developed. The system was based on pseudorandom code modulated Visual Evoked Potentials (c-VEPs). The visual stimuli in the scheme were generated based on the Gold code, and the VEPs were recognized and classified using subject-specific algorithms. The system provided the ability of controlling a wheelchair model (LEGO(®) MINDSTORM(®) EV3 robot) in 4 different directions based on the elicited c-VEPs. Ten healthy subjects were evaluated in testing the system where an average accuracy of 97% was achieved. The promising results illustrate the potential of this approach when considering a real wheelchair application.},
}
@article {pmid26736326,
year = {2015},
author = {Ping-Keng Jao, and Yuan-Pin Lin, and Yi-Hsuan Yang, and Tzyy-Ping Jung, },
title = {Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {570-573},
doi = {10.1109/EMBC.2015.7318426},
pmid = {26736326},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Emotions ; Humans ; Principal Component Analysis ; },
abstract = {An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.},
}
@article {pmid26736325,
year = {2015},
author = {Tong Jijun, and Zhang Peng, and Xiao Ran, and Ding Lei, },
title = {The portable P300 dialing system based on tablet and Emotiv Epoc headset.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {566-569},
doi = {10.1109/EMBC.2015.7318425},
pmid = {26736325},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300 ; *Tablets ; },
abstract = {A Brain-computer interface (BCI) is a novel communication system that translates brain signals into a control signal. Now with the appearance of the commercial EEG headsets and mobile smart platforms (tablet, smartphone), it is possible to develop the mobile BCI system, which can greatly improve the life quality of patients suffering from motor disease, such as amyotrophic lateral scleroses (ALS), multiple sclerosis, cerebral palsy and head trauma. This study adopted a 14-channel Emotiv EPOC headset and Microsoft surface pro 3 to realize a dialing system, which was represented by 4×3 matrices of alphanumeric characters. The performance of the online portable dialing system based on P300 is satisfying. The average classification accuracy reaches 88.75±10.57% in lab and 73.75±16.94% in metro, while the information transfer rate (ITR) reaches 7.17±1.80 and 5.05±2.17 bits/min respectively. This means the commercial EEG headset and tablet has good prospect in developing real time BCI system in realistic environments.},
}
@article {pmid26736324,
year = {2015},
author = {Sato, J and Washizawa, Y},
title = {Reliability-based automatic repeat request for short code modulation visual evoked potentials in brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {562-565},
doi = {10.1109/EMBC.2015.7318424},
pmid = {26736324},
issn = {2694-0604},
mesh = {Brain ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Neurologic Examination ; Reproducibility of Results ; },
abstract = {We propose two methods to improve code modulation visual evoked potential brain computer interfaces (cVEP BCIs). Most of BCIs average brain signals from several trials in order to improve the classification performance. The number of averaging defines the trade-off between input speed and accuracy, and the optimal averaging number depends on individual, signal acquisition system, and so forth. Firstly, we propose a novel dynamic method to estimate the averaging number for cVEP BCIs. The proposed method is based on the automatic repeat request (ARQ) that is used in communication systems. The existing cVEP BCIs employ rather longer code, such as 63-bit M-sequence. The code length also defines the trade-off between input speed and accuracy. Since the reliability of the proposed BCI can be controlled by the proposed ARQ method, we introduce shorter codes, 32-bit M-sequence and the Kasami-sequence. Thanks to combine the dynamic averaging number estimation method and the shorter codes, the proposed system exhibited higher information transfer rate compared to existing cVEP BCIs.},
}
@article {pmid26736310,
year = {2015},
author = {Meena, YK and Cecotti, H and KongFatt Wong-Lin, and Prasad, G},
title = {Towards increasing the number of commands in a hybrid brain-computer interface with combination of gaze and motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {506-509},
doi = {10.1109/EMBC.2015.7318410},
pmid = {26736310},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Fixation, Ocular ; Humans ; Imagery, Psychotherapy ; Imagination ; User-Computer Interface ; },
abstract = {Non-invasive brain-computer interface (BCI) provides a novel means of communication. This can be achieved by measuring electroencephalogram (EEG) signal over the sensory motor cortex of a person performing motor imagery (MI) tasks. However, the performance of BCI remains currently too low to be of wide practical use. A hybrid BCI system could improve the performance by combining two or more modalities such as eye tracking, and the detection of brain activity responses. In this paper, first, we propose a simultaneous hybrid BCI that combines an event-related de-synchronization (ERD) BCI and an eye tracker. Second, we aim to further improve performance by increasing the number of commands (i.e., the number of choices accessible to the user). In particular, we show a significant improvement in performance for a simultaneous gaze-MI system using a total of eight commands. The experimental task requires subjects to search for spatially located items using gaze, and select an item using MI signals. This experimental task studied visuomotor compatible and incompatible conditions. As incorporating incompatible conditions between gaze direction and MI can increase the number of choices in the hybrid BCI, our experimental task includes single-trial detection for average, compatible and incompatible conditions, using seven different classification methods. The mean accuracy for MI, and the information transfer rate (ITR) for the compatible condition is found to be higher than the average and the incompatible conditions. The results suggest that gaze-MI hybrid BCI systems can increase the number of commands, and the location of the items should be taken into account for designing the system.},
}
@article {pmid26736308,
year = {2015},
author = {Sburlea, AI and Montesano, L and Minguez, J},
title = {Intersession adaptation of the EEG-based detector of self-paced walking intention in stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {498-501},
doi = {10.1109/EMBC.2015.7318408},
pmid = {26736308},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Humans ; Intention ; *Stroke ; Stroke Rehabilitation ; Walking ; },
abstract = {Brain-computer interfaces (BCIs) have been used in patients with motor impairments as a rehabilitation tool, allowing the control of prosthetic devices with their brain signals. Typically, before each rehabilitation session a calibration phase is recorded to account for session-specific signal changes. Calibration is often an inconvenient process due to its length and patients' fatigue-proneness. This paper focuses on improving the performance of an EEG-based detector of walking intention for intersession transfer. Nine stroke subjects executed a self-paced walking task during three sessions, with one week between sessions. We performed an intersession adaptation by using 80% of one session's data and an additional 20% of a next session for training, and then we tested the detection model on the remaining part of the next session. In practice, this would constitute a longer initial calibration (40 minutes) and a shorter recalibration in subsequent sessions (10 minutes). After training set adaption we attain an average increase in performance of 13.5% over non-adaptive training. Furthermore, we used an approximation of Kullback-Leibler (KL) divergence to quantify the difference between training and testing sets for the non-adaptive and adaptive transfer. As a potential explanation for the improvement of intersession performance, we found a significant decrease in KL-divergence in the case of adaptive transfer.},
}
@article {pmid26736302,
year = {2015},
author = {Zhaokang Chen, and Shi, BE},
title = {A Two-stage model for inference of target identity during 2D cursor control from natural gaze trajectories.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {474-477},
doi = {10.1109/EMBC.2015.7318402},
pmid = {26736302},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Psychomotor Performance ; },
abstract = {We describe a two stage hidden Markov model based algorithm for inferring target identity in a 2D cursor control task where subjects are instructed to use a joystick to steer a cursor towards a target while avoiding obstacles. The first stage of the model converts a regularly sampled gaze trajectory into a sequence of fixations. The second stage then makes a determination of the end target in the cursor control task based on this sequence of fixations. In contrast to prior work, this two stage model allows for more accurate modelling of the natural eye gaze behavior of subjects, which in turn leads to increased accuracy and speed of target identification. This work demonstrates the importance of accurate gaze modelling, and paves the way for more natural and reliable hybrid Brain Computer Interfaces.},
}
@article {pmid26736203,
year = {2015},
author = {Even-Chen, N and Stavisky, SD and Kao, JC and Ryu, SI and Shenoy, KV},
title = {Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {71-75},
doi = {10.1109/EMBC.2015.7318303},
pmid = {26736203},
issn = {2694-0604},
support = {8DPIHD075623//PHS HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Feedback ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.},
}
@article {pmid26736202,
year = {2015},
author = {Ali, A and Puthusserypady, S},
title = {A 3D learning playground for potential attention training in ADHD: A brain computer interface approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {67-70},
doi = {10.1109/EMBC.2015.7318302},
pmid = {26736202},
issn = {2694-0604},
mesh = {Adult ; Attention Deficit Disorder with Hyperactivity/*therapy ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Female ; Humans ; Learning ; Male ; *Video Games ; },
abstract = {This paper presents a novel brain-computer-interface (BCI) system that could potentially be used for enhancing the attention ability of subjects with attention deficit hyperactivity disorder (ADHD). It employs the steady state visual evoked potential (SSVEP) paradigm. The developed system consists of a 3D classroom environment with active 3D distractions and 2D games executed on the blackboard. The system is concealed as a game (with stages of varying difficulty) with an underlying story to motivate the subjects. It was tested on eleven healthy subjects and the results undeniably establish that by moving to a higher stage in the game where the 2D environment is changed to 3D along with the added 3D distractions, the difficulty level in keeping attention on the main task increases for the subjects. Results also show a mean accuracy of 92.26 ± 7.97% and a mean average selection time of 3.07 ± 1.09 seconds.},
}
@article {pmid26736201,
year = {2015},
author = {Fukami, T and Ishihara, K and Ishikawa, F},
title = {Preliminary study for extraction of P300 and SSVEP by stimulus presentation using phase inversion technique in hybrid BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {63-66},
doi = {10.1109/EMBC.2015.7318301},
pmid = {26736201},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; *Evoked Potentials, Visual ; Humans ; Male ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; },
abstract = {In this study, we propose a novel stimulation presentation method for the hybrid BCI of the P300 and steady state visual evoked potential (SSVEP) to separate the two components efficiently. The method produces the separation by generating the P300 at two time points whose phase difference is π radians in the SSVEP component corresponding to stimulus frequency. Assuming that the consecutive two P300 responses are identical and the SSVEP is sinusoidal, the P300 can be extracted as a summation of the above two responses by suppressing the SSVEP. Also, the SSVEP can be detected by the subtraction of the above two responses. Accordingly, this method is realized by a stimulus pair consisting of the above two stimuli. In an EEG experiment, we used a checkerboard stimulus and character presentation for obtaining the SSVEP and P300, respectively. The stimulus frequencies of the checkerboard were assigned to 5 Hz and 3 Hz to classify the target character from the two given characters. The results showed the appearance of a prominent P300 component from only one pair of stimuli, even though the fundamental and harmonic frequency components of the SSVEP for lower stimulus frequencies are not very stable. This is because of the asymmetry of the positive and negative potentials for the SSVEP. It is a good idea to use a stimulus frequency that overlaps with the P300 frequency band, because this method does not separate the P300 and SSVEP by EEG frequency difference. Moreover, it reduces the measurement time (i.e., it shortens the number of averagings required for P300 estimation) because the SSVEP cancels out if it is sinusoidal. We consider that this will be a useful method to estimate the P300 and SSVEP simultaneously from these aspects.},
}
@article {pmid26736200,
year = {2015},
author = {Balasubramanian, K and Takahashi, K and Hatsopoulos, NG},
title = {Causal network in a deafferented non-human primate brain.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {59-62},
pmid = {26736200},
issn = {2694-0604},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Equipment Design ; Hand Strength ; Humans ; Macaca mulatta ; Motor Cortex/physiology ; Motor Neurons/*physiology ; Nerve Net/*physiology ; Robotics/instrumentation ; },
abstract = {De-afferented/efferented neural ensembles can undergo causal changes when interfaced to neuroprosthetic devices. These changes occur via recruitment or isolation of neurons, alterations in functional connectivity within the ensemble and/or changes in the role of neurons, i.e., excitatory/inhibitory. In this work, emergence of a causal network and changes in the dynamics are demonstrated for a deafferented brain region exposed to BMI (brain-machine interface) learning. The BMI was controlling a robot for reach-and-grasp behavior. And, the motor cortical regions used for the BMI were deafferented due to chronic amputation, and ensembles of neurons were decoded for velocity control of the multi-DOF robot. A generalized linear model-framework based Granger causality (GLM-GC) technique was used in estimating the ensemble connectivity. Model selection was based on the AIC (Akaike Information Criterion).},
}
@article {pmid26736198,
year = {2015},
author = {Boi, F and Semprini, M and Mussa Ivaldi, FA and Panzeri, S and Vato, A},
title = {A bidirectional brain-machine interface connecting alert rodents to a dynamical system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2015},
number = {},
pages = {51-54},
doi = {10.1109/EMBC.2015.7318298},
pmid = {26736198},
issn = {2694-0604},
mesh = {Animals ; Brain/physiology ; *Brain-Computer Interfaces ; *Equipment Design ; Rats ; Spinal Cord/physiology ; },
abstract = {We present a novel experimental framework that implements a bidirectional brain-machine interface inspired by the operation of the spinal cord in vertebrates that generates a control policy in the form of a force field. The proposed experimental set-up allows connecting the brain of freely moving rats to an external device. We tested this apparatus in a preliminary experiment with an alert rat that used the interface for acquiring a food reward. The goal of this approach to bidirectional interfaces is to explore the role of voluntary neural commands in controlling a dynamical system represented by a small cart moving on vertical plane and connected to a water/pellet dispenser.},
}
@article {pmid26735705,
year = {2016},
author = {Melinscak, F and Montesano, L and Minguez, J},
title = {Asynchronous detection of kinesthetic attention during mobilization of lower limbs using EEG measurements.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016018},
doi = {10.1088/1741-2560/13/1/016018},
pmid = {26735705},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Kinesthesis/*physiology ; Leg/*physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {OBJECTIVE: Attention is known to modulate the plasticity of the motor cortex, and plasticity is crucial for recovery in motor rehabilitation. This study addresses the possibility of using an EEG-based brain-computer interface (BCI) to detect kinesthetic attention to movement.<br><br>APPROACH: A novel experiment emulating physical rehabilitation was designed to study kinesthetic attention. The protocol involved continuous mobilization of lower limbs during which participants reported levels of attention to movement-from focused kinesthetic attention to mind wandering. For this protocol an asynchronous BCI detector of kinesthetic attention and deliberate mind wandering was designed.<br><br>MAIN RESULTS: EEG analysis showed significant differences in theta, alpha, and beta bands, related to the attentional state. These changes were further pinpointed to bands relative to the frequency of the individual alpha peak. The accuracy of the designed BCI ranged between 60.8% and 68.4% (significantly above chance level), depending on the used analysis window length, i.e. acceptable detection delay.<br><br>SIGNIFICANCE: This study shows it is possible to use self-reporting to study attention-related changes in EEG during continuous mobilization. Such a protocol is used to develop an asynchronous BCI detector of kinesthetic attention, with potential applications to motor rehabilitation.},
}
@article {pmid26735396,
year = {2016},
author = {Foley, KE},
title = {Ideas in movement: The next wave of brain-computer interfaces.},
journal = {Nature medicine},
volume = {22},
number = {1},
pages = {2-5},
pmid = {26735396},
issn = {1546-170X},
mesh = {Brain-Computer Interfaces/*trends ; Humans ; Paralysis/*rehabilitation ; },
}
@article {pmid26733788,
year = {2015},
author = {Gembler, F and Stawicki, P and Volosyak, I},
title = {Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {474},
pmid = {26733788},
issn = {1662-4548},
abstract = {Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase "RHINE WAAL UNIVERSITY" with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy).},
}
@article {pmid26730580,
year = {2016},
author = {Buccino, AP and Keles, HO and Omurtag, A},
title = {Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.},
journal = {PloS one},
volume = {11},
number = {1},
pages = {e0146610},
pmid = {26730580},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Arm/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Functional Laterality/physiology ; Hand/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/physiology ; Photic Stimulation ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes.},
}
@article {pmid26728744,
year = {2016},
author = {Sgroi, DC and Chapman, JA and Badovinac-Crnjevic, T and Zarella, E and Binns, S and Zhang, Y and Schnabel, CA and Erlander, MG and Pritchard, KI and Han, L and Shepherd, LE and Goss, PE and Pollak, M},
title = {Assessment of the prognostic and predictive utility of the Breast Cancer Index (BCI): an NCIC CTG MA.14 study.},
journal = {Breast cancer research : BCR},
volume = {18},
number = {1},
pages = {1},
pmid = {26728744},
issn = {1465-542X},
mesh = {Adult ; Aged ; Breast Neoplasms/*drug therapy/genetics/pathology ; Disease-Free Survival ; Female ; Gene Expression Regulation, Neoplastic/drug effects ; Homeodomain Proteins/*biosynthesis/genetics ; Humans ; Kaplan-Meier Estimate ; Lymph Nodes/pathology ; Middle Aged ; Octreotide/administration &amp; dosage ; *Prognosis ; Randomized Controlled Trials as Topic ; Receptors, Interleukin/*biosynthesis/genetics ; Receptors, Interleukin-17 ; Tamoxifen/administration &amp; dosage ; },
abstract = {BACKGROUND: Biomarkers that can be used to accurately assess the residual risk of disease recurrence in women with hormone receptor-positive breast cancer are clinically valuable. We evaluated the prognostic value of the Breast Cancer Index (BCI), a continuous risk index based on a combination of HOXB13:IL17BR and molecular grade index, in women with early breast cancer treated with either tamoxifen alone or tamoxifen plus octreotide in the NCIC MA.14 phase III clinical trial (ClinicalTrials.gov Identifier NCT00002864; registered 1 November 1999).<br><br>METHODS: Gene expression analysis of BCI by real-time polymerase chain reaction was performed blinded to outcome on RNA extracted from archived formalin-fixed, paraffin-embedded tumor samples of 299 patients with both lymph node-negative (LN-) and lymph node-positive (LN+) disease enrolled in the MA.14 trial. Our primary objective was to determine the prognostic performance of BCI based on relapse-free survival (RFS). MA.14 patients experienced similar RFS on both treatment arms. Association of gene expression data with RFS was evaluated in univariate analysis with a stratified log-rank test statistic, depicted with a Kaplan-Meier plot and an adjusted Cox survivor plot. In the multivariate assessment, we used stratified Cox regression. The prognostic performance of an emerging, optimized linear BCI model was also assessed in a post hoc analysis.<br><br>RESULTS: Of 299 samples, 292 were assessed successfully for BCI for 146 patients accrued in each MA.14 treatment arm. BCI risk groups had a significant univariate association with RFS (stratified log-rank p&#x2009;=&#x2009;0.005, unstratified log-rank p&#x2009;=&#x2009;0.007). Adjusted 10-year RFS in BCI low-, intermediate-, and high-risk groups was 87.5&#xa0;%, 83.9&#xa0;%, and 74.7&#xa0;%, respectively. BCI had a significant prognostic effect [hazard ratio (HR) 2.34, 95&#xa0;% confidence interval (CI) 1.33-4.11; p&#x2009;=&#x2009;0.004], although not a predictive effect, on RFS in stratified multivariate analysis, adjusted for pathological tumor stage (HR 2.22, 95&#xa0;% CI 1.22-4.07; p&#x2009;=&#x2009;0.01). In the post hoc multivariate analysis, higher linear BCI was associated with shorter RFS (p&#x2009;=&#x2009;0.002).<br><br>CONCLUSIONS: BCI had a strong prognostic effect on RFS in patients with early-stage breast cancer treated with tamoxifen alone or with tamoxifen and octreotide. BCI was prognostic in both LN- and LN+ patients. This retrospective study is an independent validation of the prognostic performance of BCI in a prospective trial.},
}
@article {pmid26728269,
year = {2016},
author = {Pichiorri, F and Onesti, E and Tartaglia, G and Inghilleri, M},
title = {Foot drop of central origin: a misleading alteration of nerve conduction study.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {37},
number = {5},
pages = {811-813},
pmid = {26728269},
issn = {1590-3478},
mesh = {Electric Stimulation ; Female ; Gait Disorders, Neurologic/*diagnosis ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Neural Conduction/*physiology ; Neurologic Examination ; Peroneal Nerve/*physiopathology ; Reaction Time/physiology ; },
}
@article {pmid26727615,
year = {2016},
author = {Golovkine, G and Faudry, E and Bouillot, S and Elsen, S and Attrée, I and Huber, P},
title = {Pseudomonas aeruginosa Transmigrates at Epithelial Cell-Cell Junctions, Exploiting Sites of Cell Division and Senescent Cell Extrusion.},
journal = {PLoS pathogens},
volume = {12},
number = {1},
pages = {e1005377},
pmid = {26727615},
issn = {1553-7374},
mesh = {Animals ; Cell Division/physiology ; Cell Line ; Cellular Senescence/physiology ; Dogs ; Epithelial Cells/*metabolism ; Humans ; Immunohistochemistry ; Intercellular Junctions/metabolism ; Madin Darby Canine Kidney Cells ; Microscopy, Confocal ; Pseudomonas Infections/*virology ; Pseudomonas aeruginosa/*pathogenicity ; Transfection ; },
abstract = {To achieve systemic infection, bacterial pathogens must overcome the critical and challenging step of transmigration across epithelial barriers. This is particularly true for opportunistic pathogens such as Pseudomonas aeruginosa, an agent which causes nosocomial infections. Despite extensive study, details on the mechanisms used by this bacterium to transmigrate across epithelial tissues, as well as the entry sites it uses, remain speculative. Here, using real-time microscopy and a model epithelial barrier, we show that P. aeruginosa employs a paracellular transmigration route, taking advantage of altered cell-cell junctions at sites of cell division or when senescent cells are expelled from the cell layer. Once a bacterium transmigrates, it is followed by a cohort of bacteria using the same entry point. The basal compartment is then invaded radially from the initial penetration site. Effective transmigration and propagation require type 4 pili, the type 3 secretion system (T3SS) and a flagellum, although flagellum-deficient bacteria can occasionally invade the basal compartment from wounded areas. In the basal compartment, the bacteria inject the T3SS toxins into host cells, disrupting the cytoskeleton and focal contacts to allow their progression under the cells. Thus, P. aeruginosa exploits intrinsic host cell processes to breach the epithelium and invade the subcellular compartment.},
}
@article {pmid26726921,
year = {2016},
author = {Ušćumlić, M and Blankertz, B},
title = {Active visual search in non-stationary scenes: coping with temporal variability and uncertainty.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016015},
doi = {10.1088/1741-2560/13/1/016015},
pmid = {26726921},
issn = {1741-2552},
mesh = {Adaptation, Physiological/physiology ; Adult ; Attention/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Eye Movements/*physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Middle Aged ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spatio-Temporal Analysis ; Young Adult ; },
abstract = {OBJECTIVE: State-of-the-art experiments for studying neural processes underlying visual cognition often constrain sensory inputs (e.g., static images) and our behavior (e.g., fixed eye-gaze, long eye fixations), isolating or simplifying the interaction of neural processes. Motivated by the non-stationarity of our natural visual environment, we investigated the electroencephalography (EEG) correlates of visual recognition while participants overtly performed visual search in non-stationary scenes. We hypothesized that visual effects (such as those typically used in human-computer interfaces) may increase temporal uncertainty (with reference to fixation onset) of cognition-related EEG activity in an active search task and therefore require novel techniques for single-trial detection.<br><br>APPROACH: We addressed fixation-related EEG activity in an active search task with respect to stimulus-appearance styles and dynamics. Alongside popping-up stimuli, our experimental study embraces two composite appearance styles based on fading-in, enlarging, and motion effects. Additionally, we explored whether the knowledge obtained in the pop-up experimental setting can be exploited to boost the EEG-based intention-decoding performance when facing transitional changes of visual content.<br><br>MAIN RESULTS: The results confirmed our initial hypothesis that the dynamic of visual content can increase temporal uncertainty of the cognition-related EEG activity in active search with respect to fixation onset. This temporal uncertainty challenges the pivotal aim to keep the decoding performance constant irrespective of visual effects. Importantly, the proposed approach for EEG decoding based on knowledge transfer between the different experimental settings gave a promising performance.<br><br>SIGNIFICANCE: Our study demonstrates that the non-stationarity of visual scenes is an important factor in the evolution of cognitive processes, as well as in the dynamic of ocular behavior (i.e., dwell time and fixation duration) in an active search task. In addition, our method to improve single-trial detection performance in this adverse scenario is an important step in making brain-computer interfacing technology available for human-computer interaction applications.},
}
@article {pmid26724935,
year = {2016},
author = {Liang, S and Choi, KS and Qin, J and Pang, WM and Heng, PA},
title = {Enhancing training performance for brain-computer interface with object-directed 3D visual guidance.},
journal = {International journal of computer assisted radiology and surgery},
volume = {11},
number = {11},
pages = {2129-2137},
pmid = {26724935},
issn = {1861-6429},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Patient Education as Topic ; Reproducibility of Results ; },
abstract = {PURPOSE: The accuracy of the classification of user intentions is essential for motor imagery (MI)-based brain-computer interface (BCI). Effective and appropriate training for users could help us produce the high reliability of mind decision making related with MI tasks. In this study, we aimed to investigate the effects of visual guidance on the classification performance of MI-based BCI.<br><br>METHODS: In this study, leveraging both the single-subject and the multi-subject BCI paradigms, we train and classify MI tasks with three different scenarios in a 3D virtual environment, including non-object-directed scenario, static-object-directed scenario, and dynamic object-directed scenario. Subjects are required to imagine left-hand or right-hand movement with the visual guidance.<br><br>RESULTS: We demonstrate that the classification performances of left-hand and right-hand MI task have differences on these three scenarios, and confirm that both static-object-directed and dynamic object-directed scenarios could provide better classification accuracy than the non-object-directed case. We further indicate that both static-object-directed and dynamic object-directed scenarios could shorten the response time as well as be suitable applied in the case of small training data. In addition, experiment results demonstrate that the multi-subject BCI paradigm could improve the classification performance comparing with the single-subject paradigm. These results suggest that it is possible to improve the classification performance with the appropriate visual guidance and better BCI paradigm.<br><br>CONCLUSION: We believe that our findings would have the potential for improving classification performance of MI-based BCI and being applied in the practical applications.},
}
@article {pmid26719088,
year = {2016},
author = {Mrachacz-Kersting, N and Jiang, N and Stevenson, AJ and Niazi, IK and Kostic, V and Pavlovic, A and Radovanovic, S and Djuric-Jovicic, M and Agosta, F and Dremstrup, K and Farina, D},
title = {Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.},
journal = {Journal of neurophysiology},
volume = {115},
number = {3},
pages = {1410-1421},
pmid = {26719088},
issn = {1522-1598},
mesh = {Adult ; Aged ; *Association ; *Brain-Computer Interfaces ; *Evoked Potentials, Motor ; Female ; Foot/physiology ; Hand/physiology ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/physiology ; *Neuronal Plasticity ; Pyramidal Tracts/physiology ; Recovery of Function ; Stroke/*physiopathology ; Stroke Rehabilitation ; },
abstract = {Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.},
}
@article {pmid26710441,
year = {2015},
author = {Zhou, J and Tang, X},
title = {[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {4},
pages = {735-739},
pmid = {26710441},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Fourier Analysis ; Humans ; Support Vector Machine ; },
abstract = {In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.},
}
@article {pmid26702394,
year = {2014},
author = {Lee, G and Matsunaga, A and Dura-Bernal, S and Zhang, W and Lytton, WW and Francis, JT and Fortes, JA},
title = {Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models.},
journal = {Journal of computational surgery},
volume = {3},
number = {12},
pages = {1-23},
pmid = {26702394},
issn = {2194-3990},
support = {R01 MH086638/MH/NIMH NIH HHS/United States ; U01 EB017695/EB/NIBIB NIH HHS/United States ; },
abstract = {Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.},
}
@article {pmid26701866,
year = {2016},
author = {Kim, SK and Kirchner, EA},
title = {Handling Few Training Data: Classifier Transfer Between Different Types of Error-Related Potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {3},
pages = {320-332},
doi = {10.1109/TNSRE.2015.2507868},
pmid = {26701866},
issn = {1558-0210},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*classification ; *Evoked Potentials ; Female ; Humans ; Male ; Photic Stimulation ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {This paper proposes an application oriented approach that enables to transfer a classifier trained within an experimental scenario into a more complex application scenario or a specific rehabilitation situation which do not allow to collect sufficient training data within a reasonable amount of time. The proposed transfer approach is not limited to be applied to the same type of event-related potential. We show that a classifier trained to detect a certain brain pattern can be used successfully to detect another brain pattern, which is expected to be similar to the first one. In particular a classifier is transferred between two different types of error-related potentials (ErrPs) within the same subject. The classifier trained on observation ErrPs is used to detect interaction ErrPs, since twice as much training data is collected for observation ErrPs compared to interaction ErrPs during the same calibration time. Our results show that the proposed transfer approach is feasible and outperforms another approach, in which a classifier is transferred between different subjects but the same type of ErrP is used to train and test the classifier. The proposed approach is a promising way to handle few training data and to reduce calibration time in ErrP-based brain-computer interfaces.},
}
@article {pmid26699697,
year = {2017},
author = {Taherian, S and Selitskiy, D and Pau, J and Claire Davies, T},
title = {Are we there yet? Evaluating commercial grade brain-computer interface for control of computer applications by individuals with cerebral palsy.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {12},
number = {2},
pages = {165-174},
doi = {10.3109/17483107.2015.1111943},
pmid = {26699697},
issn = {1748-3115},
mesh = {Adolescent ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Cerebral Palsy/physiopathology/*rehabilitation ; Electroencephalography ; Fatigue/physiopathology ; Female ; Health Status ; Humans ; Motivation ; Self-Help Devices/*statistics &amp; numerical data ; Young Adult ; },
abstract = {PURPOSE: Using a commercial electroencephalography (EEG)-based brain-computer interface (BCI), the training and testing protocol for six individuals with spastic quadriplegic cerebral palsy (GMFCS and MACS IV and V) was evaluated.<br><br>METHOD: A customised, gamified training paradigm was employed. Over three weeks, the participants spent two sessions exploring the system, and up to six sessions playing the game which focussed on EEG feedback of left and right arm motor imagery.<br><br>RESULTS: The participants showed variable inconclusive results in the ability to produce two distinct EEG patterns. Participant performance was influenced by physical illness, motivation, fatigue and concentration.<br><br>CONCLUSIONS: The results from this case study highlight the infancy of BCIs as a form of assistive technology for people with cerebral palsy. Existing commercial BCIs are not designed according to the needs of end-users. Implications for Rehabilitation Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces are not designed for practical assistive technology use for people with cerebral palsy. Practical brain-computer interface assistive technologies may need to be flexible to suit individual needs.},
}
@article {pmid26696875,
year = {2015},
author = {Manor, R and Geva, AB},
title = {Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI.},
journal = {Frontiers in computational neuroscience},
volume = {9},
number = {},
pages = {146},
pmid = {26696875},
issn = {1662-5188},
abstract = {Brain computer interfaces rely on machine learning (ML) algorithms to decode the brain's electrical activity into decisions. For example, in rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. Here, we continue our previous work, presenting a deep neural network model for the use of single trial EEG classification in RSVP tasks. Deep neural networks have shown state of the art performance in computer vision and speech recognition and thus have great promise for other learning tasks, like classification of EEG samples. In our model, we introduce a novel spatio-temporal regularization for EEG data to reduce overfitting. We show improved classification performance compared to our earlier work on a five categories RSVP experiment. In addition, we compare performance on data from different sessions and validate the model on a public benchmark data set of a P300 speller task. Finally, we discuss the advantages of using neural network models compared to manually designing feature extraction algorithms.},
}
@article {pmid26696872,
year = {2015},
author = {Naseer, N},
title = {Commentary: Correlation of prefrontal cortical activation with changing vehicle speeds in actual driving: a vector-based functional near-infrared spectroscopy study.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {665},
pmid = {26696872},
issn = {1662-5161},
}
@article {pmid26695712,
year = {2016},
author = {Won, DO and Hwang, HJ and Dähne, S and Müller, KR and Lee, SW},
title = {Effect of higher frequency on the classification of steady-state visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016014},
doi = {10.1088/1741-2560/13/1/016014},
pmid = {26695712},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Flicker Fusion/physiology ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system.<br><br>APPROACH: We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view.<br><br>MAIN RESULTS: The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers.<br><br>SIGNIFICANCE: These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.},
}
@article {pmid26690500,
year = {2015},
author = {Belkacem, AN and Saetia, S and Zintus-art, K and Shin, D and Kambara, H and Yoshimura, N and Berrached, N and Koike, Y},
title = {Real-Time Control of a Video Game Using Eye Movements and Two Temporal EEG Sensors.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {653639},
pmid = {26690500},
issn = {1687-5273},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Computer Systems ; *Electroencephalography ; Electrooculography ; Eye Movements/*physiology ; Feedback, Sensory/physiology ; Female ; Humans ; Male ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Video Games/*psychology ; Wavelet Analysis ; Young Adult ; },
abstract = {EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control.},
}
@article {pmid26689462,
year = {2016},
author = {Charkhkar, H and Knaack, GL and McHail, DG and Mandal, HS and Peixoto, N and Rubinson, JF and Dumas, TC and Pancrazio, JJ},
title = {Chronic intracortical neural recordings using microelectrode arrays coated with PEDOT-TFB.},
journal = {Acta biomaterialia},
volume = {32},
number = {},
pages = {57-67},
doi = {10.1016/j.actbio.2015.12.022},
pmid = {26689462},
issn = {1878-7568},
mesh = {Animals ; Borates ; Boric Acids/*pharmacology ; Bridged Bicyclo Compounds, Heterocyclic/*pharmacology ; Cerebral Cortex/*cytology ; Coated Materials, Biocompatible/*pharmacology ; Dielectric Spectroscopy ; Electric Impedance ; Electrodes, Implanted ; Female ; Gold ; Microelectrodes ; Neurons/drug effects/*physiology ; Polymers/*pharmacology ; Rats, Long-Evans ; },
abstract = {UNLABELLED: Microelectrode arrays have been extensively utilized to record extracellular neuronal activity for brain-machine interface applications. Modifying the microelectrodes with conductive polymers such as poly(3,4-ethylenedioxythiophene) (PEDOT) has been reported to be advantageous because it increases the effective surface area of the microelectrodes, thereby decreasing impedance and enhancing charge transfer capacity. However, the long term stability and integrity of such coatings for chronic recordings remains unclear. Previously, our group has demonstrated that use of the smaller counter ion tetrafluoroborate (TFB) during electrodeposition increased the stability of the PEDOT coatings in vitro compared to the commonly used counter ion poly(styrenesulfonate) (PSS). In the current work, we examined the long-term in vivo performance of PEDOT-TFB coated microelectrodes. To do so, we selectively modified half of the microelectrodes on NeuroNexus single shank probes with PEDOT-TFB while the other half of the microelectrodes were modified with gold as a control. The modified probes were then implanted into the primary motor cortex of rats. Single unit recordings were observed on both PEDOT-TFB and gold control microelectrodes for more than 12 weeks. Compared to the gold-coated microelectrodes, the PEDOT-TFB coated microelectrodes exhibited an overall significantly lower impedance and higher number of units per microelectrode specifically for the first four weeks. The majority of PEDOT-TFB microelectrodes with activity had an impedance magnitude lower than 400 kΩ at 1 kHz. Our equivalent circuit modeling of the impedance data suggests stability in the polymer-related parameters for the duration of the study. In addition, when comparing PEDOT-TFB microelectrodes with and without long-term activity, we observed a distinction in certain circuit parameters for these microelectrodes derived from equivalent circuit modeling prior to implantation. This observation may prove useful in qualifying PEDOT-TFB microelectrodes with a greater likelihood of registering long-term activity. Overall, our findings confirm that PEDOT-TFB is a chronically stable coating for microelectrodes to enable neural recording.<br><br>STATEMENT OF SIGNIFICANCE: Microelectrode arrays have been extensively utilized to record extracellular neuronal activity for brain-machine interface applications. Poly(3,4-ethylenedioxythiophene) (PEDOT) has gained interest because of its unique electrochemical characteristics and its excellent intrinsic electrical conductivity. However, the long-term stability of the PEDOT film, especially for chronic neural applications, is unclear. In this manuscript, we report for the first time the use of highly stable PEDOT doped with tetrafluoroborate (TFB) for long-term neural recordings. We show that PEDOT-TFB coated microelectrodes on average register more units compared to control gold microelectrodes for at least first four weeks post implantation. We collected the in vivo impedance data over a wide frequency spectrum and developed an equivalent circuit model which helped us determine certain parameters to distinguish between PEDOT-TFB microelectrodes with and without long-term activity. Our findings suggest that PEDOT-TFB is a chronically stable coating for neural recording microelectrodes. As such, PEDOT-TFB could facilitate chronic recordings with ultra-small and high-density neural arrays.},
}
@article {pmid26687642,
year = {2016},
author = {Barbosa, S and Pires, G and Nunes, U},
title = {Toward a reliable gaze-independent hybrid BCI combining visual and natural auditory stimuli.},
journal = {Journal of neuroscience methods},
volume = {261},
number = {},
pages = {47-61},
doi = {10.1016/j.jneumeth.2015.11.026},
pmid = {26687642},
issn = {1872-678X},
mesh = {Acoustic Stimulation ; Adult ; Auditory Perception/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Event-Related Potentials, P300/*physiology ; Eye Movements ; Feasibility Studies ; Female ; Humans ; Male ; Photic Stimulation ; Quadriplegia/physiopathology/therapy ; Visual Perception/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Brain computer interfaces (BCIs) are one of the last communication options for patients in the locked-in state (LIS). For complete LIS patients, interfaces must be gaze-independent due to their eye impairment. However, unimodal gaze-independent approaches typically present levels of performance substantially lower than gaze-dependent approaches. The combination of multimodal stimuli has been pointed as a viable way to increase users' performance.<br><br>NEW METHOD: A hybrid visual and auditory (HVA) P300-based BCI combining simultaneously visual and auditory stimulation is proposed. Auditory stimuli are based on natural meaningful spoken words, increasing stimuli discrimination and decreasing user's mental effort in associating stimuli to the symbols. The visual part of the interface is covertly controlled ensuring gaze-independency.<br><br>RESULTS: Four conditions were experimentally tested by 10 healthy participants: visual overt (VO), visual covert (VC), auditory (AU) and covert HVA. Average online accuracy for the hybrid approach was 85.3%, which is more than 32% over VC and AU approaches. Questionnaires' results indicate that the HVA approach was the less demanding gaze-independent interface. Interestingly, the P300 grand average for HVA approach coincides with an almost perfect sum of P300 evoked separately by VC and AU tasks.<br><br>The proposed HVA-BCI is the first solution simultaneously embedding natural spoken words and visual words to provide a communication lexicon. Online accuracy and task demand of the approach compare favorably with state-of-the-art.<br><br>CONCLUSIONS: The proposed approach shows that the simultaneous combination of visual covert control and auditory modalities can effectively improve the performance of gaze-independent BCIs.},
}
@article {pmid26685257,
year = {2016},
author = {Hsu, SH and Mullen, TR and Jung, TP and Cauwenberghs, G},
title = {Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {3},
pages = {309-319},
pmid = {26685257},
issn = {1558-0210},
support = {R01 MH084819/MH/NIMH NIH HHS/United States ; 1R01MH084819-03/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Artifacts ; Brain/physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation/statistics &amp; numerical data ; Humans ; Least-Squares Analysis ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Independent component analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: 1) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; 2) capability to detect and adapt to nonstationarity in 64-ch simulated EEG data; and 3) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces.},
}
@article {pmid26684888,
year = {2015},
author = {Hsu, WY},
title = {Single-trial EEG analysis using similarity measure.},
journal = {Bio-medical materials and engineering},
volume = {26},
number = {3-4},
pages = {161-168},
doi = {10.3233/BME-151554},
pmid = {26684888},
issn = {1878-3619},
mesh = {Brain/ultrastructure ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Models, Theoretical ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Single-trial electroencephalogram (EEG) data are analyzed with similarity measure. Time-frequency representation is constructed from EEG signals. It is then weighted with t-statistics. Finally, the test data are discriminated with similarity measure. Compared with non-weighted version, the experimental results indicate that the proposed method obtains better results in classification accuracy.},
}
@article {pmid26683698,
year = {2015},
author = {Min, JO and Kim, SY and Shin, US and Yoon, BE},
title = {Multi-walled carbon nanotubes change morpho-functional and GABA characteristics of mouse cortical astrocytes.},
journal = {Journal of nanobiotechnology},
volume = {13},
number = {},
pages = {92},
pmid = {26683698},
issn = {1477-3155},
mesh = {Animals ; Animals, Newborn ; Astrocytes/drug effects/*metabolism/ultrastructure ; Brain-Computer Interfaces/statistics &amp; numerical data ; Cell Communication ; Cerebral Cortex/drug effects/metabolism/ultrastructure ; Intercellular Junctions/drug effects/*metabolism/ultrastructure ; Mice ; Mice, Inbred BALB C ; Nanotubes, Carbon/*chemistry ; Polylysine/chemistry/pharmacology ; Primary Cell Culture ; gamma-Aminobutyric Acid/*metabolism ; },
abstract = {BACKGROUND: Multi-walled carbon nanotubes (MW-CNTs) have been extensively explored for their possible beneficial use in the nervous system. CNTs have shown to modulate neuronal growth and electrical properties, but its effect that varying length of MW-CNTs on primary astrocyte roles have not been clearly demonstrated yet.<br><br>RESULTS: We investigate here the effect of MW-CNTs on astrocytic morphology, cell-cell interaction and the distribution of intracellular GABA (gamma-amino butyric acid). Primary cultured cortical astrocytes on MW-CNT-coated glass coverslips grow rounder and make more cell-cell interactions, with many cell processes, compared to astrocytes on poly-D-lysine (PDL) coverslips. In addition, intracellular GABA spreads into the cell processes of astrocytes on MW-CNT coverslips. When this GABA spreads into cell processes from the cell body GABA can be released more easily and in larger quantities compared to astrocytes on PDL coverslips.<br><br>CONCLUSIONS: Our result confirm that MW-CNTs modulate astrocytic morphology, the distribution of astrocytic GABA, cell-cell interactions and the extension of cell processes. CNTs look to be a promising material for use neuroprosthetics such as brain-machine interface technologies.},
}
@article {pmid26683062,
year = {2016},
author = {Bradford, JC and Lukos, JR and Ferris, DP},
title = {Electrocortical activity distinguishes between uphill and level walking in humans.},
journal = {Journal of neurophysiology},
volume = {115},
number = {2},
pages = {958-966},
doi = {10.1152/jn.00089.2015},
pmid = {26683062},
issn = {1522-1598},
mesh = {Adult ; *Brain Waves ; Cerebral Cortex/*physiology ; Female ; Functional Laterality ; Gait ; Humans ; Male ; Walking/*physiology ; },
abstract = {The objective of this study was to determine if electrocortical activity is different between walking on an incline compared with level surface. Subjects walked on a treadmill at 0% and 15% grades for 30 min while we recorded electroencephalography (EEG). We used independent component (IC) analysis to parse EEG signals into maximally independent sources and then computed dipole estimations for each IC. We clustered cortical source ICs and analyzed event-related spectral perturbations synchronized to gait events. Theta power fluctuated across the gait cycle for both conditions, but was greater during incline walking in the anterior cingulate, sensorimotor and posterior parietal clusters. We found greater gamma power during level walking in the left sensorimotor and anterior cingulate clusters. We also found distinct alpha and beta fluctuations, depending on the phase of the gait cycle for the left and right sensorimotor cortices, indicating cortical lateralization for both walking conditions. We validated the results by isolating movement artifact. We found that the frequency activation patterns of the artifact were different than the actual EEG data, providing evidence that the differences between walking conditions were cortically driven rather than a residual artifact of the experiment. These findings suggest that the locomotor pattern adjustments necessary to walk on an incline compared with level surface may require supraspinal input, especially from the left sensorimotor cortex, anterior cingulate, and posterior parietal areas. These results are a promising step toward the use of EEG as a feed-forward control signal for ambulatory brain-computer interface technologies.},
}
@article {pmid26678392,
year = {2016},
author = {Kiernan, MC},
title = {35,000 Days on Earth.},
journal = {Journal of neurology, neurosurgery, and psychiatry},
volume = {87},
number = {1},
pages = {1-2},
doi = {10.1136/jnnp-2015-312686},
pmid = {26678392},
issn = {1468-330X},
mesh = {Brain-Computer Interfaces ; History, 20th Century ; Humans ; Memory/*physiology ; Neurosciences/history/*trends ; Precision Medicine ; },
}
@article {pmid26678249,
year = {2016},
author = {Yin, E and Zeyl, T and Saab, R and Hu, D and Zhou, Z and Chau, T},
title = {An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface.},
journal = {International journal of neural systems},
volume = {26},
number = {1},
pages = {1650001},
doi = {10.1142/S0129065716500015},
pmid = {26678249},
issn = {1793-6462},
mesh = {Acoustic Stimulation/*methods ; Adult ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Physical Stimulation/*methods ; Saccades ; Young Adult ; },
abstract = {Most P300 event-related potential (ERP)-based brain-computer interface (BCI) studies focus on gaze shift-dependent BCIs, which cannot be used by people who have lost voluntary eye movement. However, the performance of visual saccade-independent P300 BCIs is generally poor. To improve saccade-independent BCI performance, we propose a bimodal P300 BCI approach that simultaneously employs auditory and tactile stimuli. The proposed P300 BCI is a vision-independent system because no visual interaction is required of the user. Specifically, we designed a direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction. Furthermore, the channels and number of trials were tailored to each user to improve online performance. With 12 participants, the average online information transfer rate (ITR) of the bimodal approach improved by 45.43% and 51.05% over that attained, respectively, with the auditory and tactile approaches individually. Importantly, the average online ITR of the bimodal approach, including the break time between selections, reached 10.77 bits/min. These findings suggest that the proposed bimodal system holds promise as a practical visual saccade-independent P300 BCI.},
}
@article {pmid26675472,
year = {2015},
author = {García-Cossio, E and Severens, M and Nienhuis, B and Duysens, J and Desain, P and Keijsers, N and Farquhar, J},
title = {Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications.},
journal = {PloS one},
volume = {10},
number = {12},
pages = {e0137910},
pmid = {26675472},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; *Exercise Test ; Female ; Healthy Volunteers ; Humans ; Male ; Middle Aged ; *Psychomotor Performance ; Stroke/physiopathology/psychology ; *Walking ; Young Adult ; },
abstract = {Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.},
}
@article {pmid26672048,
year = {2016},
author = {Chen, Y and Yao, E and Basu, A},
title = {A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {10},
number = {3},
pages = {679-692},
doi = {10.1109/TBCAS.2015.2483618},
pmid = {26672048},
issn = {1940-9990},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Electronics, Medical/*instrumentation ; Fingers/physiology ; Haplorhini ; Humans ; Machine Learning ; Neurons/*physiology ; },
abstract = {Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.},
}
@article {pmid26671217,
year = {2016},
author = {Liew, SL and Rana, M and Cornelsen, S and Fortunato de Barros Filho, M and Birbaumer, N and Sitaram, R and Cohen, LG and Soekadar, SR},
title = {Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback.},
journal = {Neurorehabilitation and neural repair},
volume = {30},
number = {7},
pages = {671-675},
pmid = {26671217},
issn = {1552-6844},
support = {K12 HD055929/HD/NICHD NIH HHS/United States ; Z99 NS999999//Intramural NIH HHS/United States ; },
mesh = {Adult ; Brain/*diagnostic imaging ; Brain-Computer Interfaces ; Female ; Humans ; Image Processing, Computer-Assisted ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; Neural Pathways/*diagnostic imaging ; Neurofeedback/*methods ; Online Systems ; Oxygen/blood ; Stroke/*diagnostic imaging ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Two thirds of stroke survivors experience motor impairment resulting in long-term disability. The anatomical substrate is often the disruption of cortico-subcortical pathways. It has been proposed that reestablishment of cortico-subcortical communication relates to functional recovery.<br><br>OBJECTIVE: In this study, we applied a novel training protocol to augment ipsilesional cortico-subcortical connectivity after stroke. Chronic stroke patients with severe motor impairment were provided online feedback of blood-oxygenation level dependent signal connectivity between cortical and subcortical regions critical for motor function using real-time functional magnetic resonance imaging neurofeedback.<br><br>RESULTS: In this proof of principle study, 3 out of 4 patients learned to voluntarily modulate cortico-subcortical connectivity as intended.<br><br>CONCLUSIONS: Our results document for the first time the feasibility and safety for patients with chronic stroke and severe motor impairment to self-regulate and augment ipsilesional cortico-subcortical connectivity through neurofeedback using real-time functional magnetic resonance imaging.},
}
@article {pmid26670376,
year = {2015},
author = {Li, Y and Pan, J and He, Y and Wang, F and Laureys, S and Xie, Q and Yu, R},
title = {Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system.},
journal = {BMC neurology},
volume = {15},
number = {},
pages = {259},
pmid = {26670376},
issn = {1471-2377},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Mathematical Concepts ; Middle Aged ; Persistent Vegetative State/*physiopathology ; Problem Solving/*physiology ; },
abstract = {BACKGROUND: For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication.<br><br>METHODS: In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback.<br><br>RESULTS: Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients.<br><br>CONCLUSIONS: Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses.},
}
@article {pmid26668390,
year = {2016},
author = {Schultze-Kraft, M and Birman, D and Rusconi, M and Allefeld, C and Görgen, K and Dähne, S and Blankertz, B and Haynes, JD},
title = {The point of no return in vetoing self-initiated movements.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {113},
number = {4},
pages = {1080-1085},
pmid = {26668390},
issn = {1091-6490},
mesh = {Adult ; Brain-Computer Interfaces ; Contingent Negative Variation/*physiology ; Electroencephalography ; Electromyography ; Female ; Humans ; Male ; *Movement ; },
abstract = {In humans, spontaneous movements are often preceded by early brain signals. One such signal is the readiness potential (RP) that gradually arises within the last second preceding a movement. An important question is whether people are able to cancel movements after the elicitation of such RPs, and if so until which point in time. Here, subjects played a game where they tried to press a button to earn points in a challenge with a brain-computer interface (BCI) that had been trained to detect their RPs in real time and to emit stop signals. Our data suggest that subjects can still veto a movement even after the onset of the RP. Cancellation of movements was possible if stop signals occurred earlier than 200 ms before movement onset, thus constituting a point of no return.},
}
@article {pmid26664810,
year = {2015},
author = {Karadottir, H and Kulkarni, NN and Gudjonsson, T and Karason, S and Gudmundsson, GH},
title = {Cyclic mechanical stretch down-regulates cathelicidin antimicrobial peptide expression and activates a pro-inflammatory response in human bronchial epithelial cells.},
journal = {PeerJ},
volume = {3},
number = {},
pages = {e1483},
pmid = {26664810},
issn = {2167-8359},
abstract = {Mechanical ventilation (MV) of patients can cause damage to bronchoalveolar epithelium, leading to a sterile inflammatory response, infection and in severe cases sepsis. Limited knowledge is available on the effects of MV on the innate immune defense system in the human lung. In this study, we demonstrate that cyclic stretch of the human bronchial epithelial cell lines VA10 and BCi NS 1.1 leads to down-regulation of cathelicidin antimicrobial peptide (CAMP) gene expression. We show that treatment of VA10 cells with vitamin D3 and/or 4-phenyl butyric acid counteracted cyclic stretch mediated down-regulation of CAMP mRNA and protein expression (LL-37). Further, we observed an increase in pro-inflammatory responses in the VA10 cell line subjected to cyclic stretch. The mRNA expression of the genes encoding pro-inflammatory cytokines IL-8 and IL-1β was increased after cyclic stretching, where as a decrease in gene expression of chemokines IP-10 and RANTES was observed. Cyclic stretch enhanced oxidative stress in the VA10 cells. The mRNA expression of toll-like receptor (TLR) 3, TLR5 and TLR8 was reduced, while the gene expression of TLR2 was increased in VA10 cells after cyclic stretch. In conclusion, our in vitro results indicate that cyclic stretch may differentially modulate innate immunity by down-regulation of antimicrobial peptide expression and increase in pro-inflammatory responses.},
}
@article {pmid26657958,
year = {2016},
author = {Widge, AS and Sahay, A},
title = {Closing the Loop in Deep Brain Stimulation for Psychiatric Disorders: Lessons from Motor Neural Prosthetics.},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
volume = {41},
number = {1},
pages = {379-380},
pmid = {26657958},
issn = {1740-634X},
support = {R01 MH104175/MH/NIMH NIH HHS/United States ; 1-R01MH104175/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Brain/*metabolism/pathology ; Brain-Computer Interfaces/trends ; Deep Brain Stimulation/*methods/trends ; Humans ; Mental Disorders/*metabolism/pathology/*therapy ; Nerve Net/*metabolism/pathology ; *Neural Prostheses/trends ; },
}
@article {pmid26657920,
year = {2016},
author = {Fukami, T and Watanabe, J and Ishikawa, F},
title = {Robust estimation of event-related potentials via particle filter.},
journal = {Computer methods and programs in biomedicine},
volume = {125},
number = {},
pages = {26-36},
doi = {10.1016/j.cmpb.2015.11.006},
pmid = {26657920},
issn = {1872-7565},
mesh = {Algorithms ; Electroencephalography ; *Event-Related Potentials, P300 ; },
abstract = {BACKGROUND AND OBJECTIVE: In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution.<br><br>METHODS: We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller.<br><br>RESULTS: A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging.<br><br>CONCLUSIONS: We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller.},
}
@article {pmid26656579,
year = {2016},
author = {Mottaghi, R and Fidler, S and Yuille, A and Urtasun, R and Parikh, D},
title = {Human-Machine CRFs for Identifying Bottlenecks in Scene Understanding.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {38},
number = {1},
pages = {74-87},
doi = {10.1109/TPAMI.2015.2437377},
pmid = {26656579},
issn = {1939-3539},
mesh = {Algorithms ; Artificial Intelligence/*statistics &amp; numerical data ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computer Simulation ; Databases, Factual ; Humans ; Pattern Recognition, Automated/statistics &amp; numerical data ; Pattern Recognition, Visual ; },
abstract = {Recent trends in image understanding have pushed for scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we "plug-in" human subjects for each of the various components in a conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much "head room" there is to improve scene understanding by focusing research efforts on various individual tasks.},
}
@article {pmid26655766,
year = {2016},
author = {Sachs, NA and Ruiz-Torres, R and Perreault, EJ and Miller, LE},
title = {Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016009},
pmid = {26655766},
issn = {1741-2552},
support = {UL1 RR025741/RR/NCRR NIH HHS/United States ; 3UL1 RR025741/RR/NCRR NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; NS048845/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; *Computer Peripherals ; Electroencephalography/*methods ; Macaca mulatta ; Male ; Motion ; Pattern Recognition, Automated/methods ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Robotics ; Sensitivity and Specificity ; Visual Perception/*physiology ; Word Processing/methods ; },
abstract = {OBJECTIVE: It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed.<br><br>APPROACH: We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state.<br><br>MAIN RESULTS: We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor's proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder.<br><br>SIGNIFICANCE: We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.},
}
@article {pmid26655609,
year = {2016},
author = {Yuan, M and Yang, Y and Yan, H and Li, J and Liu, R and Li, T and Li, Y and Liang, X and Ding, X and Lu, L},
title = {INCREASED POSTERIOR RETINAL VESSELS IN MILD ASYMPTOMATIC FAMILIAL EXUDATIVE VITREORETINOPATHY EYES.},
journal = {Retina (Philadelphia, Pa.)},
volume = {36},
number = {6},
pages = {1209-1215},
doi = {10.1097/IAE.0000000000000830},
pmid = {26655609},
issn = {1539-2864},
mesh = {Eye Diseases, Hereditary ; Familial Exudative Vitreoretinopathies ; Female ; Fluorescein Angiography ; Humans ; Male ; Middle Aged ; Observer Variation ; Optic Disk/blood supply ; Reproducibility of Results ; Retinal Diseases/classification/*diagnosis ; Retinal Vessels/*pathology ; Visual Acuity ; },
abstract = {PURPOSE: To document a new clinical manifestation in familial exudative vitreoretinopathy (FEVR) eyes, especially in mild asymptomatic eyes with normal vision.<br><br>METHODS: Twenty individuals with mild Stage I or II FEVR with a conventional "normal-appearing" posterior pole and 20 healthy control eyes were recruited. The crossing numbers of retinal vessels with peripapillary inner reference circle, peripapillary outer reference circle, peripapillary temporal inner arc, peripapillary temporal outer arc, and branching points between the peripapillary outer reference circle and peripapillary inner reference circle were counted. Vessel bifurcation was evaluated by B/CI (defined as the branching number divided by the crossing number on peripapillary inner reference circle) and CO/B (crossing number on peripapillary outer reference circle divided by the branching number) ratios. The inter- and intraobservers' agreements were analyzed. All these parameters were compared between FEVR and control groups.<br><br>RESULTS: The coefficient of repeatability for the parameters ranged from 2.597 to 5.096, and the intraclass correlation coefficients were all above 0.85. All the parameters showed good interobserver agreement with a narrow range of 95% limit of agreement (from -3.16 to 3.37) and high Pearson correlation (P < 0.001). The mean crossing numbers on peripapillary inner reference circle, peripapillary outer reference circle, peripapillary temporal inner arc, peripapillary temporal outer arc, and the branching numbers were larger in the FEVR group. No significant differences were found in CO/B and B/CI ratios.<br><br>CONCLUSION: Patients with FEVR have more vessels radiated from the optic disk in the posterior pole. Unlike the increased vessels in the peripheral retina, the increasing pattern of peripapillary vascularity in patients with FEVR does not appear to have a component of overbifurcation. This is a new documented clinical manifestation in patients with FEVR. Attention to an increased or arrangement pattern of retinal vessels may aid in the screening of FEVR.},
}
@article {pmid26655346,
year = {2016},
author = {Ke, Y and Wang, P and Chen, Y and Gu, B and Qi, H and Zhou, P and Ming, D},
title = {Training and testing ERP-BCIs under different mental workload conditions.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016007},
doi = {10.1088/1741-2560/13/1/016007},
pmid = {26655346},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Cognition/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Event-Related Potentials, P300/physiology ; Female ; Humans ; *Machine Learning ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Word Processing/methods ; *Workload ; Young Adult ; },
abstract = {OBJECTIVE: As one of the most popular and extensively studied paradigms of brain-computer interfaces (BCIs), event-related potential-based BCIs (ERP-BCIs) are usually built and tested in ideal laboratory settings in most existing studies, with subjects concentrating on stimuli and intentionally avoiding possible distractors. This study is aimed at examining the effect of simultaneous mental activities on ERP-BCIs by manipulating various levels of mental workload during the training and/or testing of an ERP-BCI.<br><br>APPROACH: Mental workload was manipulated during the training or testing of a row-column P300-speller to investigate how and to what extent the spelling performance and the ERPs evoked by the oddball stimuli are affected by simultaneous mental workload.<br><br>MAIN RESULTS: Responses of certain ERP components, temporal-occipital N200 and the late reorienting negativity evoked by the oddball stimuli and the classifiability of ERP features between targets and non-targets decreased with the increase of mental workload encountered by the subject. However, the effect of mental workload on the performance of ERP-BCI was not always negative but depended on the conditions where the ERP-BCI was built and applied. The performance of ERP-BCI built under an ideal lab setting without any irrelevant mental activities declined with the increasing mental workload of the testing data. However, the performance was significantly improved when an ERP-BCI was built under an appropriate mental workload level, compared to that built under speller-only conditions.<br><br>SIGNIFICANCE: The adverse effect of concurrent mental activities may present a challenge for ERP-BCIs trained in ideal lab settings but which are to be used in daily work, especially when users are performing demanding mental processing. On the other hand, the positive effects of the mental workload of the training data suggest that introducing appropriate mental workload during training ERP-BCIs is of potential benefit to the performance in practical applications.},
}
@article {pmid26655176,
year = {2016},
author = {Qin, Z and Zhang, B and Hu, L and Zhuang, L and Hu, N and Wang, P},
title = {A novel bioelectronic tongue in vivo for highly sensitive bitterness detection with brain-machine interface.},
journal = {Biosensors &amp; bioelectronics},
volume = {78},
number = {},
pages = {374-380},
doi = {10.1016/j.bios.2015.11.078},
pmid = {26655176},
issn = {1873-4235},
mesh = {Animals ; *Biosensing Techniques ; Brain-Computer Interfaces ; Electrophysiology/*methods ; Nerve Net/chemistry/physiology ; Neurons/chemistry/physiology ; Quaternary Ammonium Compounds/chemistry/*isolation &amp; purification ; Rats ; Taste/physiology ; *Taste Perception ; },
abstract = {Animals' gustatory system has been widely acknowledged as one of the most sensitive chemosensing systems, especially for its ability to detect bitterness. Since bitterness usually symbolizes inedibility, the potential to use rodent's gustatory system is investigated to detect bitter compounds. In this work, the extracellular potentials of a group of neurons are recorded by chronically coupling microelectrode array to rat's gustatory cortex with brain-machine interface (BMI) technology. Local field potentials (LFPs), which represent the electrophysiological activity of neural networks, are chosen as target signals due to stable response patterns across trials and are further divided into different oscillations. As a result, different taste qualities yield quality-specific LFPs in time domain which suggests the selectivity of this in vivo bioelectronic tongue. Meanwhile, more quantitative study in frequency domain indicates that the post-stimulation power of beta and low gamma oscillations shows dependence with concentrations of denatonium benzoate, a prototypical bitter compound, and the limit of detection is deduced to be 0.076 μM, which is two orders lower than previous in vitro bioelectronic tongues and conventional electronic tongues. According to the results, this in vivo bioelectronic tongue in combination with BMI presents a promising method in highly sensitive bitterness detection and is supposed to provide new platform in measuring bitterness degree.},
}
@article {pmid26654594,
year = {2015},
author = {Sburlea, AI and Montesano, L and Cano de la Cuerda, R and Alguacil Diego, IM and Miangolarra-Page, JC and Minguez, J},
title = {Detecting intention to walk in stroke patients from pre-movement EEG correlates.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {113},
pmid = {26654594},
issn = {1743-0003},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Motivation/physiology ; Stroke/psychology ; *Stroke Rehabilitation ; Walking/physiology/psychology ; },
abstract = {BACKGROUND: Most studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI.<br><br>METHODS: We investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy. Nine chronic stroke patients performed a self-initiated walking task during three sessions, with an intersession interval of one week.<br><br>RESULTS: Using a decoder that combines temporal and spectral sparse classifiers we detected pre-movement state with an accuracy of 64 % in a range between 18 % and 85.2 %, with the chance level at 4 %. Furthermore, we found a significantly strong positive correlation (r = 0.561, p = 0.048) between the motivation of the patients to perform the rehabilitation related task and the accuracy of the BCI detector of their intention to walk.<br><br>CONCLUSIONS: We show that a detector based on temporal and spectral features can be used to classify pre-movement state in stroke patients. Additionally, we found that patients' motivation to perform the task showed a strong correlation to the attained detection rate of their walking intention.},
}
@article {pmid26648858,
year = {2015},
author = {Snyder, KL and Kline, JE and Huang, HJ and Ferris, DP},
title = {Independent Component Analysis of Gait-Related Movement Artifact Recorded using EEG Electrodes during Treadmill Walking.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {639},
pmid = {26648858},
issn = {1662-5161},
support = {R01 NS073649/NS/NINDS NIH HHS/United States ; },
abstract = {There has been a recent surge in the use of electroencephalography (EEG) as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA) and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals) with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.},
}
@article {pmid26646729,
year = {2016},
author = {Hammer, MM and Raptis, DA and Cummings, KW and Mellnick, VM and Bhalla, S and Schuerer, DJ and Raptis, CA},
title = {Imaging in blunt cardiac injury: Computed tomographic findings in cardiac contusion and associated injuries.},
journal = {Injury},
volume = {47},
number = {5},
pages = {1025-1030},
doi = {10.1016/j.injury.2015.11.008},
pmid = {26646729},
issn = {1879-0267},
mesh = {Adult ; Aged ; *Echocardiography ; Female ; Humans ; Injury Severity Score ; Lung Injury/*diagnostic imaging/etiology/physiopathology ; Male ; Middle Aged ; Myocardial Contusions/*diagnostic imaging/etiology/physiopathology ; Practice Guidelines as Topic ; Retrospective Studies ; Rib Fractures/complications/*diagnostic imaging ; *Tomography, X-Ray Computed ; Wounds, Nonpenetrating/complications/*diagnostic imaging/physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Blunt cardiac injury (BCI) may manifest as cardiac contusion or, more rarely, as pericardial or myocardial rupture. Computed tomography (CT) is performed in the vast majority of blunt trauma patients, but the imaging features of cardiac contusion are not well described.<br><br>PURPOSE: To evaluate CT findings and associated injuries in patients with clinically diagnosed BCI.<br><br>MATERIALS AND METHODS: We identified 42 patients with blunt cardiac injury from our institution's electronic medical record. Clinical parameters, echocardiography results, and laboratory tests were recorded. Two blinded reviewers analyzed chest CTs performed in these patients for myocardial hypoenhancement and associated injuries.<br><br>RESULTS: CT findings of severe thoracic trauma are commonly present in patients with severe BCI; 82% of patients with ECG, cardiac enzyme, and echocardiographic evidence of BCI had abnormalities of the heart or pericardium on CT; 73% had anterior rib fractures, and 64% had pulmonary contusions. Sternal fractures were only seen in 36% of such patients. However, myocardial hypoenhancement on CT is poorly sensitive for those patients with cardiac contusion: 0% of right ventricular contusions and 22% of left ventricular contusions seen on echocardiography were identified on CT.<br><br>CONCLUSION: CT signs of severe thoracic trauma are frequently present in patients with severe BCI and should be regarded as indirect evidence of potential BCI. Direct CT findings of myocardial contusion, i.e. myocardial hypoenhancement, are poorly sensitive and should not be used as a screening tool. However, some left ventricular contusions can be seen on CT, and these patients could undergo echocardiography or cardiac MRI to evaluate for wall motion abnormalities.},
}
@article {pmid26646183,
year = {2015},
author = {Golub, MD and Yu, BM and Chase, SM},
title = {Internal models for interpreting neural population activity during sensorimotor control.},
journal = {eLife},
volume = {4},
number = {},
pages = {},
pmid = {26646183},
issn = {2050-084X},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01-HD-071686/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Extremities/physiology ; *Feedback, Sensory ; Macaca mulatta ; *Models, Neurological ; *Movement ; *Psychomotor Performance ; Sensorimotor Cortex/*physiology ; },
abstract = {To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.},
}
@article {pmid26645694,
year = {2016},
author = {Vecchiato, G and Borghini, G and Aricò, P and Graziani, I and Maglione, AG and Cherubino, P and Babiloni, F},
title = {Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {54},
number = {10},
pages = {1503-1513},
pmid = {26645694},
issn = {1741-0444},
mesh = {Adult ; Attention ; *Aviation ; Brain-Computer Interfaces ; *Computer Simulation ; Electroencephalography/*methods ; Humans ; Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) are widely used for clinical applications and exploited to design robotic and interactive systems for healthy people. We provide evidence to control a sensorimotor electroencephalographic (EEG) BCI system while piloting a flight simulator and attending a double attentional task simultaneously. Ten healthy subjects were trained to learn how to manage a flight simulator, use the BCI system, and answer to the attentional tasks independently. Afterward, the EEG activity was collected during a first flight where subjects were required to concurrently use the BCI, and a second flight where they were required to simultaneously use the BCI and answer to the attentional tasks. Results showed that the concurrent use of the BCI system during the flight simulation does not affect the flight performances. However, BCI performances decrease from the 83 to 63 % while attending additional alertness and vigilance tasks. This work shows that it is possible to successfully control a BCI system during the execution of multiple tasks such as piloting a flight simulator with an extra cognitive load induced by attentional tasks. Such framework aims to foster the knowledge on BCI systems embedded into vehicles and robotic devices to allow the simultaneous execution of secondary tasks.},
}
@article {pmid26644071,
year = {2016},
author = {Wenzel, MA and Schultze-Kraft, R and Meinecke, FC and Fabien Cardinaux, and Kemp, T and Klaus-Robert Müller, and Gabriel Curio, and Benjamin Blankertz, },
title = {EEG-based usability assessment of 3D shutter glasses.},
journal = {Journal of neural engineering},
volume = {13},
number = {1},
pages = {016003},
doi = {10.1088/1741-2560/13/1/016003},
pmid = {26644071},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Equipment Failure Analysis/methods ; Ergonomics/instrumentation/methods ; Evoked Potentials, Visual/*physiology ; *Eyeglasses ; Female ; Flicker Fusion/*physiology ; Humans ; Imaging, Three-Dimensional/*instrumentation ; Male ; Middle Aged ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Television/instrumentation ; User-Computer Interface ; Vision, Binocular/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Neurotechnology can contribute to the usability assessment of products by providing objective measures of neural workload and can uncover usability impediments that are not consciously perceived by test persons. In this study, the neural processing effort imposed on the viewer of 3D television by shutter glasses was quantified as a function of shutter frequency. In particular, we sought to determine the critical shutter frequency at which the 'neural flicker' vanishes, such that visual fatigue due to this additional neural effort can be prevented by increasing the frequency of the system.<br><br>APPROACH: Twenty-three participants viewed an image through 3D shutter glasses, while multichannel electroencephalogram (EEG) was recorded. In total ten shutter frequencies were employed, selected individually for each participant to cover the range below, at and above the threshold of flicker perception. The source of the neural flicker correlate was extracted using independent component analysis and the flicker impact on the visual cortex was quantified by decoding the state of the shutter from the EEG.<br><br>MAIN RESULT: Effects of the shutter glasses were traced in the EEG up to around 67&#xa0;Hz-about 20&#xa0;Hz over the flicker perception threshold-and vanished at the subsequent frequency level of 77&#xa0;Hz.<br><br>SIGNIFICANCE: The impact of the shutter glasses on the visual cortex can be detected by neurotechnology even when a flicker is not reported by the participants. Potential impact. Increasing the shutter frequency from the usual 50&#xa0;Hz or 60&#xa0;Hz to 77&#xa0;Hz reduces the risk of visual fatigue and thus improves shutter-glass-based 3D usability.},
}
@article {pmid26643516,
year = {2015},
author = {Slutzky, M},
title = {Brain machine interfaces: state of the art and challenges to translation.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {152-153},
doi = {10.1016/j.nbd.2015.06.009},
pmid = {26643516},
issn = {1095-953X},
mesh = {Brain/*physiopathology ; Brain-Computer Interfaces/*trends ; Humans ; Neuronal Plasticity ; Translational Research, Biomedical/*trends ; },
}
@article {pmid26641261,
year = {2016},
author = {Rahne, T and Schilde, S and Seiwerth, I and Radetzki, F and Stoevesandt, D and Plontke, SK},
title = {Mastoid Dimensions in Children and Young Adults: Consequences for the Geometry of Transcutaneous Bone-Conduction Implants.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {37},
number = {1},
pages = {57-61},
doi = {10.1097/MAO.0000000000000881},
pmid = {26641261},
issn = {1537-4505},
mesh = {Adolescent ; Adult ; Aging ; *Bone Conduction ; Child ; Child, Preschool ; Cross-Sectional Studies ; Equipment Design ; Female ; Hearing Loss, Conductive/therapy ; Humans ; Infant ; Male ; Mastoid/*anatomy &amp; histology/growth &amp; development/surgery ; Prostheses and Implants ; Sex Characteristics ; Tomography, X-Ray Computed ; Treatment Outcome ; Young Adult ; },
abstract = {OBJECTIVES: Bone-conduction implants (BCI) are available for adults and children who are aged 5 years or more. Because a transcutaneous bone-conduction implant introduced in 2013 does not completely fit into all adult mastoids, we investigated mastoid dimensions and the possibility of fitting the implant in children.<br><br>DESIGN: Computed tomography scans of 151 mastoids from 80 children and young adolescents from the age of 5 months to 20 years and 52 control mastoids from 33 adults were retrospectively analyzed. After three-dimensional reconstruction, mastoid volume was measured. The chances of fitting the Bonebridge or a novel BCI were determined as a function of age. Implant diameter and implantation depths were virtually varied to identify the most advantageous dimensions for reducing the minimum age for implantation.<br><br>RESULTS: Mastoid volume increased to 13.8 ml in female and 16.4 ml in male adult mastoids at ages 18.9 years (male) and 19.0 years (female). Without compromising the middle fossa dura or the sinus and without lifts, the Bonebridge implant fit in 81% of male adult mastoids and 77% of the female adult mastoids. For children, the 50% chance of fitting a Bonebridge in the mastoids was reached at age 12 years; with a protrusion of 4 mm (4-mm lifts), this age was reduced to >6 years. The novel BCI fit in 100% of male and 94% of female adult mastoids.<br><br>CONCLUSIONS: Casing diameter is the most limiting factor for Bonebridge implantation in children. A modified implant casing with a truncated cone and reduced diameter and volume would increase the number of hearing impaired children who can be rehabilitated with a Bonebridge implant. Radiological planning for Bonebridge implantation is necessary in all children.},
}
@article {pmid26641241,
year = {2015},
author = {Li, L and Wang, J and Xu, G and Li, M and Xie, J},
title = {The Study of Object-Oriented Motor Imagery Based on EEG Suppression.},
journal = {PloS one},
volume = {10},
number = {12},
pages = {e0144256},
pmid = {26641241},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Electromyography/methods ; Female ; Humans ; Leg ; Male ; Nontherapeutic Human Experimentation ; Young Adult ; },
abstract = {Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI), object-oriented motor imagery (OI), non-object-oriented motor imagery (NI) and visual observation (VO) was recorded. Study results showed that OI and NI presented significant contralateral suppression in mu rhythm (p < 0.05). Besides, OI exhibited significant contralateral suppression in beta rhythm (p < 0.05). While no significant mu or beta contralateral suppression could be found during VO or SI (p > 0.05). Compared with NI, OI showed significant difference (p < 0.05) in mu rhythm and weak significant difference (p = 0.0612) in beta rhythm over the contralateral hemisphere. The ability of motor imagery can be reflected by the suppression degree of mu and beta frequencies which are the motor related rhythms. Thus, greater enhancement of activation in mirror neuron system is involved in response to object-oriented motor imagery. The object-oriented motor imagery is favorable for improvement of motor imagery ability.},
}
@article {pmid26639017,
year = {2016},
author = {Jochumsen, M and Niazi, IK and Dremstrup, K and Kamavuako, EN},
title = {Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {54},
number = {10},
pages = {1491-1501},
pmid = {26639017},
issn = {1741-0444},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Hand/*physiopathology ; Humans ; Male ; *Neurological Rehabilitation ; Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; Young Adult ; },
abstract = {Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention are crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior to the movement onset. Fourteen healthy subjects performed the three grasps 100 times, while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA). The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79 ± 8 %) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features: 76 ± 9 % (2-class) and 63 ± 10 % (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset, which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.},
}
@article {pmid26635598,
year = {2015},
author = {Dura-Bernal, S and Zhou, X and Neymotin, SA and Przekwas, A and Francis, JT and Lytton, WW},
title = {Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.},
journal = {Frontiers in neurorobotics},
volume = {9},
number = {},
pages = {13},
pmid = {26635598},
issn = {1662-5218},
support = {U01 EB017695/EB/NIBIB NIH HHS/United States ; },
abstract = {Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics.},
}
@article {pmid26635587,
year = {2015},
author = {Alonso-Valerdi, LM and Sepulveda, F and Ramírez-Mendoza, RA},
title = {Perception and Cognition of Cues Used in Synchronous Brain-Computer Interfaces Modify Electroencephalographic Patterns of Control Tasks.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {636},
pmid = {26635587},
issn = {1662-5161},
abstract = {A motor imagery (MI)-based brain-computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain-computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest.},
}
@article {pmid26635579,
year = {2015},
author = {Kontson, KL and Megjhani, M and Brantley, JA and Cruz-Garza, JG and Nakagome, S and Robleto, D and White, M and Civillico, E and Contreras-Vidal, JL},
title = {Your Brain on Art: Emergent Cortical Dynamics During Aesthetic Experiences.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {626},
pmid = {26635579},
issn = {1662-5161},
abstract = {The brain response to conceptual art was studied with mobile electroencephalography (EEG) to examine the neural basis of aesthetic experiences. In contrast to most studies of perceptual phenomena, participants were moving and thinking freely as they viewed the exhibit The Boundary of Life is Quietly Crossed by Dario Robleto at the Menil Collection-Houston. The brain activity of over 400 subjects was recorded using dry-electrode and one reference gel-based EEG systems over a period of 3 months. Here, we report initial findings based on the reference system. EEG segments corresponding to each art piece were grouped into one of three classes (complex, moderate, and baseline) based on analysis of a digital image of each piece. Time, frequency, and wavelet features extracted from EEG were used to classify patterns associated with viewing art, and ranked based on their relevance for classification. The maximum classification accuracy was 55% (chance = 33%) with delta and gamma features the most relevant for classification. Functional analysis revealed a significant increase in connection strength in localized brain networks while subjects viewed the most aesthetically pleasing art compared to viewing a blank wall. The direction of signal flow showed early recruitment of broad posterior areas followed by focal anterior activation. Significant differences in the strength of connections were also observed across age and gender. This work provides evidence that EEG, deployed on freely behaving subjects, can detect selective signal flow in neural networks, identify significant differences between subject groups, and report with greater-than-chance accuracy the complexity of a subject's visual percept of aesthetically pleasing art. Our approach, which allows acquisition of neural activity "in action and context," could lead to understanding of how the brain integrates sensory input and its ongoing internal state to produce the phenomenon which we term aesthetic experience.},
}
@article {pmid26635547,
year = {2015},
author = {Jarvis, S and Schultz, SR},
title = {Prospects for Optogenetic Augmentation of Brain Function.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {157},
pmid = {26635547},
issn = {1662-5137},
abstract = {The ability to optically control neural activity opens up possibilities for the restoration of normal function following neurological disorders. The temporal precision, spatial resolution, and neuronal specificity that optogenetics offers is unequalled by other available methods, so will it be suitable for not only restoring but also extending brain function? As the first demonstrations of optically "implanted" novel memories emerge, we examine the suitability of optogenetics as a technique for extending neural function. While optogenetics is an effective tool for altering neural activity, the largest impediment for optogenetics in neural augmentation is our systems level understanding of brain function. Furthermore, a number of clinical limitations currently remain as substantial hurdles for the applications proposed. While neurotechnologies for treating brain disorders and interfacing with prosthetics have advanced rapidly in the past few years, partially addressing some of these critical problems, optogenetics is not yet suitable for use in humans. Instead we conclude that for the immediate future, optogenetics is the neurological equivalent of the 3D printer: its flexibility providing an ideal tool for testing and prototyping solutions for treating brain disorders and augmenting brain function.},
}
@article {pmid26631144,
year = {2016},
author = {Kato, K and Sasada, S and Nishimura, Y},
title = {Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man.},
journal = {Journal of neurophysiology},
volume = {115},
number = {2},
pages = {978-991},
doi = {10.1152/jn.00143.2015},
pmid = {26631144},
issn = {1522-1598},
mesh = {*Adaptation, Physiological ; Adult ; Female ; Hand/innervation/physiology ; Humans ; Male ; Muscle, Skeletal/innervation/*physiology ; Ulnar Nerve/*physiology ; },
abstract = {Controlling a neuroprosthesis requires learning a novel input-output transformation; however, how subjects incorporate this into limb control remains obscure. To elucidate the underling mechanisms, we investigated the motor adaptation process to a novel artificial recurrent connection (ARC) from a muscle to a peripheral nerve in healthy humans. In this paradigm, the ulnar nerve was electrically stimulated in proportion to the activation of the flexor carpi ulnaris (FCU), which is ulnar-innervated and monosynaptically innervated from Ia afferents of the FCU, defined as the "homonymous muscle," or the palmaris longus (PL), which is not innervated by the ulnar nerve and produces similar movement to the FCU, defined as the "synergist muscle." The ARC boosted the activity of the homonymous muscle and wrist joint movement during a visually guided reaching task. Participants could control muscle activity to utilize the ARC for the volitional control of wrist joint movement and then readapt to the absence of the ARC to either input muscle. Participants reduced homonymous muscle recruitment with practice, regardless of the input muscle. However, the adaptation process in the synergist muscle was dependent on the input muscle. The activity of the synergist muscle decreased when the input was the homonymous muscle, whereas it increased when it was the synergist muscle. This reorganization of the neuromotor map, which was maintained as an aftereffect of the ARC, was observed only when the input was the synergist muscle. These findings demonstrate that the ARC induced reorganization of neuromotor map in a targeted and sustainable manner.},
}
@article {pmid26625906,
year = {2015},
author = {Kober, SE and Schweiger, D and Witte, M and Reichert, JL and Grieshofer, P and Neuper, C and Wood, G},
title = {Specific effects of EEG based neurofeedback training on memory functions in post-stroke victims.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {107},
pmid = {26625906},
issn = {1743-0003},
mesh = {Adult ; Aged ; Electroencephalography/*methods ; Female ; Humans ; Male ; Memory Disorders/etiology/*rehabilitation ; Middle Aged ; Neurofeedback/*methods ; Stroke/psychology ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Using EEG based neurofeedback (NF), the activity of the brain is modulated directly and, therefore, the cortical substrates of cognitive functions themselves. In the present study, we investigated the ability of stroke patients to control their own brain activity via NF and evaluated specific effects of different NF protocols on cognition, in particular recovery of memory.<br><br>METHODS: N&#x2009;=&#x2009;17 stroke patients received up to ten sessions of either SMR (N&#x2009;=&#x2009;11, 12-15&#xa0;Hz) or Upper Alpha (N&#x2009;=&#x2009;6, e.g. 10-12&#xa0;Hz) NF training. N&#x2009;=&#x2009;7 stroke patients received treatment as usual as control condition. Furthermore, N&#x2009;=&#x2009;40 healthy controls performed NF training as well. To evaluate the NF training outcome, a test battery assessing different cognitive functions was performed before and after NF training.<br><br>RESULTS: About 70&#xa0;% of both patients and controls achieved distinct gains in NF performance leading to improvements in verbal short- and long-term memory, independent of the used NF protocol. The SMR patient group showed specific improvements in visuo-spatial short-term memory performance, whereas the Upper Alpha patient group specifically improved their working memory performance. NF training effects were even stronger than effects of traditional cognitive training methods in stroke patients. NF training showed no effects on other cognitive functions than memory.<br><br>CONCLUSIONS: Post-stroke victims with memory deficits could benefit from NF training as much as healthy controls. The used NF training protocols (SMR, Upper Alpha) had specific as well as unspecific effects on memory. Hence, NF might offer an effective cognitive rehabilitation tool improving memory deficits of stroke survivors.},
}
@article {pmid26625440,
year = {2016},
author = {Iacoviello, D and Petracca, A and Spezialetti, M and Placidi, G},
title = {A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli.},
journal = {IEEE transactions on cybernetics},
volume = {46},
number = {12},
pages = {3171-3180},
doi = {10.1109/TCYB.2015.2498974},
pmid = {26625440},
issn = {2168-2275},
abstract = {The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.},
}
@article {pmid26625423,
year = {2017},
author = {Wang, F and Wang, Y and Xu, K and Li, H and Liao, Y and Zhang, Q and Zhang, S and Zheng, X and Principe, JC},
title = {Quantized Attention-Gated Kernel Reinforcement Learning for Brain-Machine Interface Decoding.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {28},
number = {4},
pages = {873-886},
doi = {10.1109/TNNLS.2015.2493079},
pmid = {26625423},
issn = {2162-2388},
abstract = {Reinforcement learning (RL)-based decoders in brain-machine interfaces (BMIs) interpret dynamic neural activity without patients' real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require finding a good policy in very few trials, which impose a limit on the complexity of the tasks that can be learned before the animal quits. Therefore, this paper explores the possibility of letting the agent infer potential goals through actions over space with multiple objects, using the instantaneous reward to assign credit spatially. A previous method, attention-gated RL employs a multilayer perceptron trained with backpropagation, but it is prone to local minima entrapment. We propose a quantized attention-gated kernel RL (QAGKRL) to avoid the local minima adaptation in spatial credit assignment and sparsify the network topology. The experimental results show that the QAGKRL achieves higher successful rates and more stable performance, indicating its powerful decoding ability for more sophisticated BMI tasks as required in clinical applications.},
}
@article {pmid26625417,
year = {2016},
author = {Hsu, HT and Lee, IH and Tsai, HT and Chang, HC and Shyu, KK and Hsu, CC and Chang, HH and Yeh, TK and Chang, CY and Lee, PL},
title = {Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {5},
pages = {603-615},
doi = {10.1109/TNSRE.2015.2496184},
pmid = {26625417},
issn = {1558-0210},
mesh = {Adult ; Aging ; Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Feasibility Studies ; Frontal Lobe ; Humans ; Middle Aged ; Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiopathology ; *Visual Perception ; },
abstract = {This paper studies the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps, were measured in eight young subjects, eight elders and seven ALS patients. Each subject was requested to participate in a calibration study and an application study. The calibration study was designed to find the amplitude-frequency characteristics of SSVEPs recorded from Oz and Fpz positions, while the application study was designed to test the feasibility of using frontal SSVEP to control a two-command SSVEP-based BCI. The SSVEP amplitude was detected by an epoch-average process which enables artifact-contaminated epochs can be removed. The seven ALS patients were severely impaired, and four patients, who were incapable of completing our BCI task, were excluded from calculation of BCI performance. The averaged accuracies, command transfer intervals and information transfer rates in operating frontal SSVEP-based BCI were 96.1%, 3.43 s/command, and 14.42 bits/min in young subjects; 91.8%, 6.22 s/command, and 6.16 bits/min in elders; 81.2%, 12.14 s/command, and 1.51 bits/min in ALS patients, respectively. The frontal SSVEP could be an alternative choice to design SSVEP-based BCI.},
}
@article {pmid26625261,
year = {2015},
author = {Jeunet, C and N'Kaoua, B and Subramanian, S and Hachet, M and Lotte, F},
title = {Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.},
journal = {PloS one},
volume = {10},
number = {12},
pages = {e0143962},
pmid = {26625261},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces/*psychology ; Cognition/*physiology ; Electroencephalography/*psychology ; Female ; Humans ; Imagery, Psychotherapy/methods ; Learning/physiology ; Male ; Neurophysiology/methods ; Personality/*physiology ; Personality Disorders/physiopathology ; Reproducibility of Results ; Young Adult ; },
abstract = {Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants' BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants' performance with a mean error of less than 3 points. This study determined how users' profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user.},
}
@article {pmid26620822,
year = {2016},
author = {Sprague, SA and McBee, MT and Sellers, EW},
title = {The effects of working memory on brain-computer interface performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {2},
pages = {1331-1341},
pmid = {26620822},
issn = {1872-8952},
support = {R33 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470-03/DC/NIDCD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Brain-Computer Interfaces ; Child ; Child, Preschool ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Middle Aged ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: The purpose of the present study is to evaluate the relationship between working memory and BCI performance.<br><br>METHODS: Participants took part in two separate sessions. The first session consisted of three computerized tasks. The List Sorting Working Memory Task was used to measure working memory, the Picture Vocabulary Test was used to measure general intelligence, and the Dimensional Change Card Sort Test was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task.<br><br>RESULTS: The results indicate that both working memory and general intelligence are significant predictors of BCI performance.<br><br>CONCLUSIONS: This suggests that working memory training could be used to improve performance on a BCI task.<br><br>SIGNIFICANCE: Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users.},
}
@article {pmid26617510,
year = {2015},
author = {von Lühmann, A and Herff, C and Heger, D and Schultz, T},
title = {Toward a Wireless Open Source Instrument: Functional Near-infrared Spectroscopy in Mobile Neuroergonomics and BCI Applications.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {617},
pmid = {26617510},
issn = {1662-5161},
abstract = {Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument's hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects.},
}
@article {pmid26617321,
year = {2016},
author = {Santostasi, G and Malkani, R and Riedner, B and Bellesi, M and Tononi, G and Paller, KA and Zee, PC},
title = {Phase-locked loop for precisely timed acoustic stimulation during sleep.},
journal = {Journal of neuroscience methods},
volume = {259},
number = {},
pages = {101-114},
pmid = {26617321},
issn = {1872-678X},
support = {P01 AG011412/AG/NIA NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; P01AG11412/AG/NIA NIH HHS/United States ; UL1TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {Acoustic Stimulation/*methods ; Brain Waves/*physiology ; Delta Rhythm/physiology ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization/*physiology ; Humans ; Sleep Stages/*physiology ; },
abstract = {BACKGROUND: A brain-computer interface could potentially enhance the various benefits of sleep.<br><br>NEW METHOD: We describe a strategy for enhancing slow-wave sleep (SWS) by stimulating the sleeping brain with periodic acoustic stimuli that produce resonance in the form of enhanced slow-wave activity in the electroencephalogram (EEG). The system delivers each acoustic stimulus at a particular phase of an electrophysiological rhythm using a phase-locked loop (PLL).<br><br>RESULTS: The PLL is computationally economical and well suited to follow and predict the temporal behavior of the EEG during slow-wave sleep.<br><br>Acoustic stimulation methods may be able to enhance SWS without the risks inherent in electrical stimulation or pharmacological methods. The PLL method differs from other acoustic stimulation methods that are based on detecting a single slow wave rather than modeling slow-wave activity over an extended period of time.<br><br>CONCLUSIONS: By providing real-time estimates of the phase of ongoing EEG oscillations, the PLL can rapidly adjust to physiological changes, thus opening up new possibilities to study brain dynamics during sleep. Future application of these methods hold promise for enhancing sleep quality and associated daytime behavior and improving physiologic function.},
}
@article {pmid26616450,
year = {2016},
author = {Wang, X and Hu, L and Li, C and Gan, L and He, M and He, X and Tian, W and Li, M and Xu, L and Li, Y and Chen, Y},
title = {Improvement in physical and biological properties of chitosan/soy protein films by surface grafted heparin.},
journal = {International journal of biological macromolecules},
volume = {83},
number = {},
pages = {19-29},
doi = {10.1016/j.ijbiomac.2015.11.052},
pmid = {26616450},
issn = {1879-0003},
mesh = {Animals ; Anticoagulants/chemistry/pharmacology ; Blood Coagulation/drug effects ; Cell Line ; Chitosan/*chemistry/*pharmacology ; Coated Materials, Biocompatible/chemistry ; Hemolysis/drug effects ; Heparin/*chemistry/*pharmacology ; Hydrophobic and Hydrophilic Interactions ; Materials Testing/methods ; Mice ; Platelet Adhesiveness/drug effects ; Soybean Proteins/*chemistry/*pharmacology ; Spectroscopy, Fourier Transform Infrared/methods ; Surface Properties ; Thrombosis/metabolism ; },
abstract = {A series of chitosan/soy protein isolate (SPI) composite films (CS-n, n=0, 10 and 30, corresponding to SPI content in the composites) were prepared. Heparin was grafted onto the surface of CS-n to fabricate a series of heparinized films (HCS-n). CS-n and HCS-n were characterized by ATR-Fourier transform infrared spectroscopy and water contact angle. The surface heparin density was measured by toluidine blue assay. The results showed that heparin has been successfully grafted onto the surface of CS-n. Heparin evenly distributed on the surface of the films and the heparin content increased with the increase of SPI content, and the hydrophilicity of the films was enhanced due to the grafted heparin. The cytocompatibility and hemocompatibility of CS-n and HCS-n were evaluated by cell culture (MTT assay, live/dead assay, cell morphology and cell density observation), platelet adhesion test, plasma recalcification time (PRT) measurement, hemolysis assay and thrombus formation test. HCS-n showed higher cell adhesion rate and improved cytocompatibility compared to the corresponding CS-n. HCS-n also exhibited lower platelet adhesion, longer PRT, higher blood anticoagulant indexes (BCI) and lower hemolysis rate than the corresponding CS-n. The improved cytocompatibility and hemocompatibility of HCS-n would shed light on the potential applications of chitosan/soy protein-based biomaterials that may come into contact with blood.},
}
@article {pmid26615754,
year = {2015},
author = {Wolters, N and Schembecker, G and Merz, J},
title = {Erinacine C: A novel approach to produce the secondary metabolite by submerged cultivation of Hericium erinaceus.},
journal = {Fungal biology},
volume = {119},
number = {12},
pages = {1334-1344},
doi = {10.1016/j.funbio.2015.10.005},
pmid = {26615754},
issn = {1878-6146},
mesh = {Basidiomycota/genetics/*growth &amp; development/*metabolism ; Biomass ; Culture Media/*metabolism ; Diterpenes/*metabolism ; Industrial Microbiology/*methods ; Mycelium/genetics/growth &amp; development/metabolism ; Nitrogen/metabolism ; Secondary Metabolism ; },
abstract = {Erinacine C is a cyathane scaffold-based secondary metabolite, which is naturally produced by the filamentous fungus Hericium erinaceus and has a high potential to treat nervous diseases such as Alzheimer's disease. The investigated approach consists of combining an optimised precultivation of H. erinaceus with an enhanced erinacine C production by developing a suitable main cultivation medium enabling the utilisation of high biomass contents. The final erinacine C production medium is buffered by 100 mM HEPES to ensure a stable pH value of 7.5 during main cultivation at inoculation ratios of up to 5:10 (v/v). The medium components, such as 5.0 g L(-1) oatmeal, 1.5 g L(-1) calcium carbonate, and 0.5 g L(-1) Edamin(®) K are crucial for an increased erinacine C production. Besides, different carbon to nitrogen ratios of 25, 64, and 103 do not affect the erinacine C synthesis. The investigated approach enables the production of 2.73 g erinacine C per litre main cultivation broth, which is tenfold higher than published data. In addition, erinacine C biosynthesis is determined to occur mainly in the first six days of main cultivation.},
}
@article {pmid26610351,
year = {2015},
author = {Huang, Y and Pan, X and Zhou, Q and Huang, H and Li, L and Cui, X and Wang, G and Jizhong, R and Yin, L and Xu, D and Hong, Y},
title = {Quality-of-life outcomes and unmet needs between ileal conduit and orthotopic ileal neobladder after radical cystectomy in a Chinese population: a 2-to-1 matched-pair analysis.},
journal = {BMC urology},
volume = {15},
number = {},
pages = {117},
pmid = {26610351},
issn = {1471-2490},
mesh = {Aged ; China/epidemiology ; Combined Modality Therapy ; Cystectomy/*psychology/*rehabilitation/statistics &amp; numerical data ; Female ; Humans ; Ileum/*transplantation ; Male ; Matched-Pair Analysis ; Middle Aged ; Needs Assessment/*statistics &amp; numerical data ; Patient Satisfaction/statistics &amp; numerical data ; Prevalence ; Quality of Life/*psychology ; Retrospective Studies ; Risk Factors ; Treatment Outcome ; Urinary Diversion/*psychology/statistics &amp; numerical data ; },
abstract = {BACKGROUND: Health-related quality-of-life (HRQoL) is an important consideration after radical cystectomy (RC). Lack of effective ways to assess HRQoL after RC and unawareness of disease-specific problems related to ileal conduit (IC) and orthotopic ileal neobladder (OIN) are serious problems. The present study was to evaluate and compare morbidity and HRQoL between IC and OIN after RC, and examine their unmet needs in the two groups.<br><br>METHODS: A retrospective analysis was made of 294 patients treated with RC in our hospital between 2007 and 2013. Matched pair analysis was used to determine the patients of IC and OIN groups. Patient HRQoL between IC and OIN groups was assessed using the bladder-specific bladder cancer index (BCI) and European Organization for Research and Treatment of Cancer Body Image scale (BIS) questionnaires. Unmet information of patients undergoing these two urinary diversions was recorded through individual interviews.<br><br>RESULTS: Of the 117 included patients, 39 patients were treated with OIN and the other 78 matched patients with IC as controls for matched pair analysis. There was no significant difference in baseline characteristics between the two groups. OIN patients showed significantly better BIS scores in terms of HRQoL outcomes after RC at a short-term (<1 year) follow-up level, but there was no significant difference at a long-term (>1 year) follow-up level between the two groups. Interestingly, urinary bother (UB) and urinary function (UF) were poor in OIN patients at the one-year follow-up level, but there was no significant difference in UB between the two groups at the long term follow-up level. Unmet needs analysis showed that OIN patients had a more positive attitude towards treatment and participated in physical and social activities more positively, although they may have more urine leakage problems.<br><br>CONCLUSIONS: The mean BIS score in OIN group patients was significantly better than that in IC group patients at the one-year follow-up level, but there was no significant difference at the long-term follow-up level. Due attention should be paid to some particular unmet needs in individual patients in managing the two UD modalities.},
}
@article {pmid26604836,
year = {2015},
author = {Jansson, KJ and Håkansson, B and Reinfeldt, S and Rigato, C and Eeg-Olofsson, M},
title = {Magnetic resonance imaging investigation of the bone conduction implant - a pilot study at 1.5 Tesla.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {8},
number = {},
pages = {413-423},
pmid = {26604836},
issn = {1179-1470},
abstract = {PURPOSE: The objective of this pilot study was to investigate if an active bone conduction implant (BCI) used in an ongoing clinical study withstands magnetic resonance imaging (MRI) of 1.5 Tesla. In particular, the MRI effects on maximum power output (MPO), total harmonic distortion (THD), and demagnetization were investigated. Implant activation and image artifacts were also evaluated.<br><br>METHODS AND MATERIALS: One implant was placed on the head of a test person at the position corresponding to the normal position of an implanted BCI and applied with a static pressure using a bandage and scanned in a 1.5 Tesla MRI camera. Scanning was performed both with and without the implant, in three orthogonal planes, and for one spin-echo and one gradient-echo pulse sequence. Implant functionality was verified in-between the scans using an audio processor programmed to generate a sequence of tones when attached to the implant. Objective verification was also carried out by measuring MPO and THD on a skull simulator as well as retention force, before and after MRI.<br><br>RESULTS: It was found that the exposure of 1.5 Tesla MRI only had a minor effect on the MPO, ie, it decreased over all frequencies with an average of 1.1&#xb1;2.1 dB. The THD remained unchanged above 300 Hz and was increased only at lower frequencies. The retention magnet was demagnetized by 5%. The maximum image artifacts reached a distance of 9 and 10 cm from the implant in the coronal plane for the spin-echo and the gradient-echo sequence, respectively. The test person reported no MRI induced sound from the implant.<br><br>CONCLUSION: This pilot study indicates that the present BCI may withstand 1.5 Tesla MRI with only minor effects on its performance. No MRI induced sound was reported, but the head image was highly distorted near the implant.},
}
@article {pmid26602980,
year = {2016},
author = {Hanakawa, T},
title = {Organizing motor imageries.},
journal = {Neuroscience research},
volume = {104},
number = {},
pages = {56-63},
doi = {10.1016/j.neures.2015.11.003},
pmid = {26602980},
issn = {1872-8111},
mesh = {Animals ; Brain/*physiology ; Brain-Computer Interfaces ; Humans ; *Imagination ; Motor Cortex/physiology ; *Movement ; Parietal Lobe/physiology ; Prefrontal Cortex/physiology ; },
abstract = {Over the last few decades, motor imagery has attracted the attention of researchers as a prototypical example of 'embodied cognition' and also as a basis for neuro-rehabilitation and brain-machine interfaces. The current definition of motor imagery is widely accepted, but it is important to note that various abilities rather than a single cognitive entity are dealt with under a single term. Here, motor imagery has been characterized based on four factors: (1) motor control, (2) explicitness, (3) sensory modalities, and (4) agency. Sorting out these factors characterizing motor imagery may explain some discrepancies and variability in the findings from previous studies and will help to optimize a study design in accordance with the purpose of each study in the future.},
}
@article {pmid26601225,
year = {2015},
author = {Gerber, S and Horenko, I},
title = {Improving clustering by imposing network information.},
journal = {Science advances},
volume = {1},
number = {7},
pages = {e1500163},
pmid = {26601225},
issn = {2375-2548},
abstract = {Cluster analysis is one of the most popular data analysis tools in a wide range of applied disciplines. We propose and justify a computationally efficient and straightforward-to-implement way of imposing the available information from networks/graphs (a priori available in many application areas) on a broad family of clustering methods. The introduced approach is illustrated on the problem of a noninvasive unsupervised brain signal classification. This task is faced with several challenging difficulties such as nonstationary noisy signals and a small sample size, combined with a high-dimensional feature space and huge noise-to-signal ratios. Applying this approach results in an exact unsupervised classification of very short signals, opening new possibilities for clustering methods in the area of a noninvasive brain-computer interface.},
}
@article {pmid26600162,
year = {2016},
author = {Marathe, AR and Lawhern, VJ and Wu, D and Slayback, D and Lance, BJ},
title = {Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {3},
pages = {333-343},
doi = {10.1109/TNSRE.2015.2502323},
pmid = {26600162},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/statistics &amp; numerical data ; Female ; Humans ; *Machine Learning ; Male ; Middle Aged ; Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems.},
}
@article {pmid26600160,
year = {2016},
author = {Irwin, ZT and Thompson, DE and Schroeder, KE and Tat, DM and Hassani, A and Bullard, AJ and Woo, SL and Urbanchek, MG and Sachs, AJ and Cederna, PS and Stacey, WC and Patil, PG and Chestek, CA},
title = {Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {5},
pages = {521-531},
doi = {10.1109/TNSRE.2015.2501752},
pmid = {26600160},
issn = {1558-0210},
mesh = {Amplifiers, Electronic ; Analog-Digital Conversion ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Data Compression/methods ; *Electric Power Supplies ; Electrocorticography/*instrumentation ; Electromyography/*instrumentation ; Energy Transfer ; Equipment Design ; Equipment Failure Analysis ; Humans ; Macaca mulatta ; Signal Processing, Computer-Assisted/instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.},
}
@article {pmid26599827,
year = {2016},
author = {Asensio-Cubero, J and Gan, JQ and Palaniappan, R},
title = {Multiresolution analysis over graphs for a motor imagery based online BCI game.},
journal = {Computers in biology and medicine},
volume = {68},
number = {},
pages = {21-26},
doi = {10.1016/j.compbiomed.2015.10.016},
pmid = {26599827},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; Humans ; *Imagination ; *Video Games ; },
abstract = {Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes.},
}
@article {pmid26595929,
year = {2016},
author = {Liu, YT and Lin, YY and Wu, SL and Chuang, CH and Lin, CT},
title = {Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {27},
number = {2},
pages = {347-360},
doi = {10.1109/TNNLS.2015.2496330},
pmid = {26595929},
issn = {2162-2388},
mesh = {Adult ; *Automobile Driving/psychology ; Brain/*physiology ; Electroencephalography/methods ; Fatigue/*diagnosis/psychology ; Female ; Forecasting ; *Fuzzy Logic ; Humans ; Male ; *Neural Networks, Computer ; Reaction Time/physiology ; Young Adult ; },
abstract = {This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.},
}
@article {pmid26595103,
year = {2015},
author = {Zhang, H and Chavarriaga, R and Khaliliardali, Z and Gheorghe, L and Iturrate, I and Millán, Jd},
title = {EEG-based decoding of error-related brain activity in a real-world driving task.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066028},
doi = {10.1088/1741-2560/12/6/066028},
pmid = {26595103},
issn = {1741-2552},
mesh = {Adult ; *Automobile Driving/psychology ; Brain/*physiology ; *Brain-Computer Interfaces/psychology ; *Computer Simulation ; Electroencephalography/*methods ; Female ; Humans ; Male ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVES: Recent studies have started to explore the implementation of brain-computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections.<br><br>APPROACH: We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests.<br><br>RESULTS: An average classification accuracy of 0.698 &#xb1; 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 &#xb1; 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car.<br><br>SIGNIFICANCE: The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.},
}
@article {pmid26594147,
year = {2015},
author = {Hayashibe, M and Guiraud, D and Pons, JL and Farina, D},
title = {Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {434},
pmid = {26594147},
issn = {1662-4548},
}
@article {pmid26591445,
year = {2015},
author = {Fricker, GA and Wolf, JA and Saatchi, SS and Gillespie, TW},
title = {Predicting spatial variations of tree species richness in tropical forests from high-resolution remote sensing.},
journal = {Ecological applications : a publication of the Ecological Society of America},
volume = {25},
number = {7},
pages = {1776-1789},
doi = {10.1890/14-1593.1},
pmid = {26591445},
issn = {1051-0761},
mesh = {*Biodiversity ; Demography ; Environmental Monitoring ; *Forests ; Islands ; Panama ; *Remote Sensing Technology ; Trees/*classification ; *Tropical Climate ; },
abstract = {There is an increasing interest in identifying theories, empirical data sets, and remote-sensing metrics that can quantify tropical forest alpha diversity at a landscape scale. Quantifying patterns of tree species richness in the field is time consuming, especially in regions with over 100 tree species/ha. We examine species richness in a 50-ha plot in Barro Colorado Island in Panama and test if biophysical measurements of canopy reflectance from high-resolution satellite imagery and detailed vertical forest structure and topography from light detection and ranging (lidar) are associated with species richness across four tree size classes (>1, 1-10, >10, and >20 cm dbh) and three spatial scales (1, 0.25, and 0.04 ha). We use the 2010 tree inventory, including 204,757 individuals belonging to 301 species of freestanding woody plants or 166 ± 1.5 species/ha (mean ± SE), to compare with remote-sensing data. All remote-sensing metrics became less correlated with species richness as spatial resolution decreased from 1.0 ha to 0.04 ha and tree size increased from 1 cm to 20 cm dbh. When all stems with dbh > 1 cm in 1-ha plots were compared to remote-sensing metrics, standard deviation in canopy reflectance explained 13% of the variance in species richness. The standard deviations of canopy height and the topographic wetness index (TWI) derived from lidar were the best metrics to explain the spatial variance in species richness (15% and 24%, respectively). Using multiple regression models, we made predictions of species richness across Barro Colorado Island (BCI) at the 1-ha spatial scale for different tree size classes. We predicted variation in tree species richness among all plants (adjusted r[2] = 0.35) and trees with dbh > 10 cm (adjusted r[2] = 0.25). However, the best model results were for understory trees and shrubs (dbh 1-10 cm) (adjusted r[2] = 0.52) that comprise the majority of species richness in tropical forests. Our results indicate that high-resolution remote sensing can predict a large percentage of variance in species richness and potentially provide a framework to map and predict alpha diversity among trees in diverse tropical forests.},
}
@article {pmid26590895,
year = {2015},
author = {Lan, G and Lu, B and Wang, T and Wang, L and Chen, J and Yu, K and Liu, J and Dai, F and Wu, D},
title = {Chitosan/gelatin composite sponge is an absorbable surgical hemostatic agent.},
journal = {Colloids and surfaces. B, Biointerfaces},
volume = {136},
number = {},
pages = {1026-1034},
doi = {10.1016/j.colsurfb.2015.10.039},
pmid = {26590895},
issn = {1873-4367},
mesh = {Animals ; Biocompatible Materials ; Blood Platelets/cytology ; Bombyx ; Cell Adhesion ; Cell Line ; Chitosan/*chemistry ; Gelatin/*chemistry ; *Hemostasis ; Mice ; Microscopy, Electron, Scanning ; Rabbits ; *Surgical Sponges ; },
abstract = {Chitosan is a versatile biological material that is very well known for its hemostatic properties. The purpose of this study was to test the hemostatic properties of a chitosan composite obtained from silkworm pupae and gelatin. This spongy porous material was cross-linked with tannins and then freeze-dried under vacuum to obtain composites containing chitosan and gelatin in different proportions. Results showed that the best blood-clotting index (BCI) was achieved in vitro by a chitosan/gelatin sponge (CG) ratio of 5/5 (W/W). Furthermore, CG had the best hemostatic effect in rabbit artery bleeding and liver model tests compared to the two components separately. The better hemostatic effect of CG may be due to its ability to absorb blood platelets easily and to the higher liquid adsorption ratio. However, no obvious differences were observed in thrombin generation with both aPTT and PT tests. Cell toxicity tests with L929 cells showed that CG caused no obvious cytotoxicity. In addition, subcutaneous transplantation of CG into rabbits resulted in almost complete degradation of CG after 6 weeks, together with rich vascular generation and proliferation in the transplanted region. Thus, CG can be considered an effective absorbable hemostatic material.},
}
@article {pmid26586832,
year = {2015},
author = {Klaes, C and Kellis, S and Aflalo, T and Lee, B and Pejsa, K and Shanfield, K and Hayes-Jackson, S and Aisen, M and Heck, C and Liu, C and Andersen, RA},
title = {Hand Shape Representations in the Human Posterior Parietal Cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {46},
pages = {15466-15476},
pmid = {26586832},
issn = {1529-2401},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; P50 MH942581A/MH/NIMH NIH HHS/United States ; EY015545/EY/NEI NIH HHS/United States ; EY013337/EY/NEI NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; *Brain Mapping ; Cues ; Hand Strength/*physiology ; Humans ; Image Processing, Computer-Assisted ; Imagination/*physiology ; Magnetic Resonance Imaging ; Models, Neurological ; Movement ; Neurons/physiology ; Oxygen/blood ; Parietal Lobe/blood supply/cytology/*physiology ; Photic Stimulation ; },
abstract = {UNLABELLED: Humans shape their hands to grasp, manipulate objects, and to communicate. From nonhuman primate studies, we know that visual and motor properties for grasps can be derived from cells in the posterior parietal cortex (PPC). Are non-grasp-related hand shapes in humans represented similarly? Here we show for the first time how single neurons in the PPC of humans are selective for particular imagined hand shapes independent of graspable objects. We find that motor imagery to shape the hand can be successfully decoded from the PPC by implementing a version of the popular Rock-Paper-Scissors game and its extension Rock-Paper-Scissors-Lizard-Spock. By simultaneous presentation of visual and auditory cues, we can discriminate motor imagery from visual information and show differences in auditory and visual information processing in the PPC. These results also demonstrate that neural signals from human PPC can be used to drive a dexterous cortical neuroprosthesis.<br><br>SIGNIFICANCE STATEMENT: This study shows for the first time hand-shape decoding from human PPC. Unlike nonhuman primate studies in which the visual stimuli are the objects to be grasped, the visually cued hand shapes that we use are independent of the stimuli. Furthermore, we can show that distinct neuronal populations are activated for the visual cue and the imagined hand shape. Additionally we found that auditory and visual stimuli that cue the same hand shape are processed differently in PPC. Early on in a trial, only the visual stimuli and not the auditory stimuli can be decoded. During the later stages of a trial, the motor imagery for a particular hand shape can be decoded for both modalities.},
}
@article {pmid26586270,
year = {2016},
author = {Korostenskaja, M and Ruksenas, O and Pipinis, E and Griskova-Bulanova, I},
title = {Phase-locking index and power of 40-Hz auditory steady-state response are not related to major personality trait dimensions.},
journal = {Experimental brain research},
volume = {234},
number = {3},
pages = {711-719},
pmid = {26586270},
issn = {1432-1106},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Cortex/*physiology ; Electroencephalography/methods ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; Personality/*physiology ; Personality Inventory ; Young Adult ; },
abstract = {Although a number of studies have demonstrated state-related dependence of auditory steady-state responses (ASSRs), the investigations assessing trait-related ASSR changes are limited. Five consistently identified major trait dimensions, also referred to as "big five" (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness), are considered to account for virtually all personality variances in both healthy people and those with psychiatric disorders. The purpose of the present study was, for the first time, to establish the link between 40-Hz ASSR and "big five" major personality trait dimensions in young healthy adults. Ninety-four young healthy volunteers participated (38 males and 56 females; mean age ± SD 22.180 ± 2.75). The 40-Hz click trains were presented for each subject 30 times with an inter-train interval of 1-1.5 s. The EEG responses were recorded from F3, Fz, F4, C3, Cz, C4, P3, Pz and P4 locations according to 10/20 electrode placement system. Phase-locking index (PLI) and event-related power perturbation (ERSP) were calculated, each providing the following characteristics: peak time, entrainment frequency, peak value and mean value. For assessing "big five" personality traits, NEO Personality Inventory Revised (NEO-PI-R) was used. No significant correlation between 40-Hz ASSR PLI or ERSP and "big five" personality traits was observed. Our results indicate that there is no dependence between 40-Hz ASSR entrainment and personality traits, demonstrating low individual 40-Hz variability in this domain. Our results support further development of 40-Hz ASSR as a neurophysiological marker allowing distinguishing between healthy population and patients with psychiatric disorders.},
}
@article {pmid26584583,
year = {2015},
author = {Hsu, WY},
title = {Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy.},
journal = {International journal of neural systems},
volume = {25},
number = {8},
pages = {1550037},
doi = {10.1142/S0129065715500379},
pmid = {26584583},
issn = {1793-6462},
mesh = {Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography ; Entropy ; Female ; Fuzzy Logic ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Sensorimotor Cortex/*physiology ; Support Vector Machine ; *Wavelet Analysis ; },
abstract = {An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain-computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals. Several potential features, including amplitude modulation, spectral power and asymmetry ratio, adaptive autoregressive model, and wavelet fuzzy approximate entropy (wfApEn) that can measure and quantify the complexity or irregularity of EEG signals, are then extracted for subsequent classification. Finally, the significant sub-features are selected from feature combination by quantum-behaved particle swarm optimization and then classified by support vector machine (SVM). Compared with feature extraction without wfApEn on MI data from two data sets for nine subjects, the results indicate that the proposed system including wfApEn obtains better performance in average classification accuracy of 88.2% and average number of commands per minute of 12.1, which is promising in the BCI work applications.},
}
@article {pmid26584486,
year = {2016},
author = {Wang, Y and She, X and Liao, Y and Li, H and Zhang, Q and Zhang, S and Zheng, X and Principe, J},
title = {Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain-Machine Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {8},
pages = {1728-1741},
doi = {10.1109/TBME.2015.2500585},
pmid = {26584486},
issn = {1558-2531},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; *Models, Neurological ; Monte Carlo Method ; Neuronal Plasticity/*physiology ; Neurons/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Classic brain-machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.},
}
@article {pmid26582986,
year = {2015},
author = {Nakamura, M and Yanagisawa, T and Okamura, Y and Fukuma, R and Hirata, M and Araki, T and Kamitani, Y and Yorifuji, S},
title = {Categorical discrimination of human body parts by magnetoencephalography.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {609},
pmid = {26582986},
issn = {1662-5161},
abstract = {Humans recognize body parts in categories. Previous studies have shown that responses in the fusiform body area (FBA) and extrastriate body area (EBA) are evoked by the perception of the human body, when presented either as whole or as isolated parts. These responses occur approximately 190 ms after body images are visualized. The extent to which body-sensitive responses show specificity for different body part categories remains to be largely clarified. We used a decoding method to quantify neural responses associated with the perception of different categories of body parts. Nine subjects underwent measurements of their brain activities by magnetoencephalography (MEG) while viewing 14 images of feet, hands, mouths, and objects. We decoded categories of the presented images from the MEG signals using a support vector machine (SVM) and calculated their accuracy by 10-fold cross-validation. For each subject, a response that appeared to be a body-sensitive response was observed and the MEG signals corresponding to the three types of body categories were classified based on the signals in the occipitotemporal cortex. The accuracy in decoding body-part categories (with a peak at approximately 48%) was above chance (33.3%) and significantly higher than that for random categories. According to the time course and location, the responses are suggested to be body-sensitive and to include information regarding the body-part category. Finally, this non-invasive method can decode category information of a visual object with high temporal and spatial resolution and this result may have a significant impact in the field of brain-machine interface research.},
}
@article {pmid26580120,
year = {2015},
author = {Verhoeven, T and Buteneers, P and Wiersema, JR and Dambre, J and Kindermans, PJ},
title = {Towards a symbiotic brain-computer interface: exploring the application-decoder interaction.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066027},
doi = {10.1088/1741-2560/12/6/066027},
pmid = {26580120},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces/trends ; Electroencephalography/*methods/trends ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Machine Learning/trends ; Male ; Photic Stimulation/*methods ; },
abstract = {OBJECTIVE: State of the art brain-computer interface (BCI) research focuses on improving individual components such as the application or the decoder that converts the user's brain activity to control signals. In this study, we investigate the interaction between these components in the P300 speller, a BCI for communication. We introduce a synergistic approach in which the stimulus presentation sequence is modified to enhance the machine learning decoding. In this way we aim for an improved overall BCI performance.<br><br>APPROACH: First, a new stimulus presentation paradigm is introduced which provides us flexibility in tuning the sequence of visual stimuli presented to the user. Next, an experimental setup in which this paradigm is compared to other paradigms uncovers the underlying mechanism of the interdependence between the application and the performance of the decoder.<br><br>MAIN RESULTS: Extensive analysis of the experimental results reveals the changing requirements of the decoder concerning the data recorded during the spelling session. When few data is recorded, the balance in the number of target and non-target stimuli shown to the user is more important than the signal-to-noise rate (SNR) of the recorded response signals. Only when more data has been collected, the SNR becomes the dominant factor.<br><br>SIGNIFICANCE: For BCIs in general, knowing the dominant factor that affects the decoder performance and being able to respond to it is of utmost importance to improve system performance. For the P300 speller, the proposed tunable paradigm offers the possibility to tune the application to the decoder's needs at any time and, as such, fully exploit this application-decoder interaction.},
}
@article {pmid26579972,
year = {2015},
author = {Bleichner, MG and Jansma, JM and Salari, E and Freudenburg, ZV and Raemaekers, M and Ramsey, NF},
title = {Classification of mouth movements using 7 T fMRI.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066026},
doi = {10.1088/1741-2560/12/6/066026},
pmid = {26579972},
issn = {1741-2552},
mesh = {Adolescent ; Brain Mapping/classification/methods ; Brain-Computer Interfaces ; Female ; Humans ; Magnetic Resonance Imaging/*classification/methods ; Male ; Mouth/*physiology ; Movement/*physiology ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements.<br><br>APPROACH: In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design.<br><br>MAIN RESULTS: Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%).<br><br>SIGNIFICANCE: The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.},
}
@article {pmid26579880,
year = {2016},
author = {Herhut, M and Brandenbusch, C and Sadowski, G},
title = {Non-monotonic course of protein solubility in aqueous polymer-salt solutions can be modeled using the sol-mxDLVO model.},
journal = {Biotechnology journal},
volume = {11},
number = {2},
pages = {282-289},
doi = {10.1002/biot.201500123},
pmid = {26579880},
issn = {1860-7314},
mesh = {Animals ; Chickens/metabolism ; Isomerases/*chemistry/isolation &amp; purification ; *Models, Chemical ; Muramidase/*chemistry/isolation &amp; purification ; Polyethylene Glycols/*chemistry ; Salinity ; Solubility ; },
abstract = {Protein purification is often performed using cost-intensive chromatographic steps. To discover economic alternatives (e.g., crystallization), knowledge on protein solubility as a function of temperature, pH, and additives in solution as well as their concentration is required. State-of-the-art models for predicting protein solubility almost exclusively consider aqueous salt systems, whereas "salting-in" and "salting-out" effects induced by the presence of an additional polymer are not considered. Thus, we developed the sol-mxDLVO model. Using this newly developed model, protein solubility in the presence of one salt and one polymer, especially the non-monotonic course of protein solubility, could be predicted. Systems considered included salts (NaCl, Na-p-Ts, (NH(4))(2) SO(4)) and the polymer polyethylene glycol (MW: 2000 g/mol, 12000 g/mol) and proteins lysozyme from chicken egg white (pH 4 to 5.5) and D-xylose ketol-isomerase (pH 7) at 298.15 K. The results show that by using the sol-mxDLVO model, protein solubility in polymer-salt solutions can be modeled in good agreement with the experimental data for both proteins considered. The sol-mxDLVO model can describe the non-monotonic course of protein solubility as a function of polymer concentration and salt concentration, previously not covered by state-of-the-art models.},
}
@article {pmid26578401,
year = {2015},
author = {Sanft, T and Aktas, B and Schroeder, B and Bossuyt, V and DiGiovanna, M and Abu-Khalaf, M and Chung, G and Silber, A and Hofstatter, E and Mougalian, S and Epstein, L and Hatzis, C and Schnabel, C and Pusztai, L},
title = {Prospective assessment of the decision-making impact of the Breast Cancer Index in recommending extended adjuvant endocrine therapy for patients with early-stage ER-positive breast cancer.},
journal = {Breast cancer research and treatment},
volume = {154},
number = {3},
pages = {533-541},
pmid = {26578401},
issn = {1573-7217},
mesh = {Aged ; Aged, 80 and over ; Antineoplastic Agents, Hormonal/therapeutic use ; Anxiety/psychology ; Breast Neoplasms/*drug therapy/pathology/psychology ; Chemotherapy, Adjuvant ; *Decision Making ; Female ; Gene Expression Regulation, Neoplastic ; Genetic Testing ; Humans ; Middle Aged ; Neoplasm Recurrence, Local/drug therapy ; Prognosis ; Prospective Studies ; Receptors, Estrogen/metabolism ; Surveys and Questionnaires ; Tamoxifen/therapeutic use ; },
abstract = {Extended adjuvant endocrine therapy (10 vs. 5 years) trials have demonstrated improved outcomes in early-stage estrogen receptor (ER)-positive breast cancer; however, the absolute benefit is modest, and toxicity and tolerability challenges remain. Predictive and prognostic information from genomic analysis may help inform this clinical decision. The purpose of this study was to assess the impact of the Breast Cancer Index (BCI) on physician recommendations for extended endocrine therapy and on patient anxiety and decision conflict. Patients with stage I-III, ER-positive breast cancer who completed at least 3.5 years of adjuvant endocrine therapy were offered participation. Genomic classification with BCI was performed on archived tumor tissues and the results were reported to the treating physician who discussed results with the patient. Patients and physicians completed pre- and post-test questionnaires regarding preferences for extended endocrine therapy. Patients also completed the validated traditional Decisional Conflict Scale (DCS) and State Trait Anxiety Inventory forms (STAI-Y1) pre- and post-test. 96 patients were enrolled at the Yale Cancer Center [median age 60.5 years (range 45-87), 79% postmenopausal, 60% stage I). BCI predicted a low risk of late recurrence in 59% of patients versus intermediate/high in 24 and 17%, respectively. Physician recommendations for extended endocrine therapy changed for 26% of patients after considering BCI results, with a net decrease in recommendations for extended endocrine therapy from 74 to 54%. After testing, fewer patients wanted to continue extended therapy and decision conflict and anxiety also decreased. Mean STAI and DCS scores were 31.3 versus 29.1 (p = 0.031) and 20.9 versus 10.8 (p < 0.001) pre- and post-test, respectively. Incorporation of BCI into risk/benefit discussions regarding extended endocrine therapy resulted in changes in treatment recommendations and improved patient satisfaction.},
}
@article {pmid26578354,
year = {2016},
author = {Reichert, JL and Kober, SE and Witte, M and Neuper, C and Wood, G},
title = {Age-related effects on verbal and visuospatial memory are mediated by theta and alpha II rhythms.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {99},
number = {},
pages = {67-78},
doi = {10.1016/j.ijpsycho.2015.11.004},
pmid = {26578354},
issn = {1872-7697},
mesh = {Adult ; Aged ; Aged, 80 and over ; Aging/*physiology ; Alpha Rhythm/*physiology ; Female ; Humans ; Male ; Memory/physiology ; Middle Aged ; Photic Stimulation/methods ; *Reading ; Rest/physiology ; Spatial Memory/*physiology ; Theta Rhythm/*physiology ; Verbal Learning/*physiology ; Young Adult ; },
abstract = {Both electrical brain activity during rest and memory functions change across the lifespan. Moreover, electrical brain activity is associated with memory functions. However, the interplay between all these effects has been investigated only scarcely. The present study investigated the extent to which the power of resting-state electroencephalographic (EEG) frequencies mediates the impact of aging on verbal and visuospatial memory. Seventy healthy participants with 22 to 83years of age completed a visuospatial and verbal learning and memory test and provided eyes-open and eyes-closed resting-state EEG data. Robust age-related effects on behavioral and EEG data were observed. Mediation analyses showed that the relative power of the theta (4-8Hz) frequency band in fronto-central locations partly explained the negative age-related effect on delayed recall in the verbal memory task. The relative power of the alpha II (10-12Hz) frequency band in mainly parietal locations partly explained the negative impact of age on immediate and delayed recall in the visuospatial task. Results indicate that spontaneous brain activity carries specific information about aging processes and predicts the level of competence in verbal and visuospatial memory tasks.},
}
@article {pmid26577345,
year = {2015},
author = {Salazar-Varas, R and Costa, &#xc1; and Iáñez, E and Úbeda, A and Hortal, E and Azorín, JM},
title = {Analyzing EEG signals to detect unexpected obstacles during walking.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {101},
pmid = {26577345},
issn = {1743-0003},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Walking/physiology ; },
abstract = {BACKGROUND: When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons.<br><br>METHODS: In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates.<br><br>RESULTS: From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively.<br><br>CONCLUSIONS: An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking.},
}
@article {pmid26575886,
year = {2015},
author = {Chen, LH and Chen, SS and Liang, L and Li, CC},
title = {[Effects of asthma and inhaled corticosteroids in children on the final adult height: a systemic review and Meta analysis].},
journal = {Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics},
volume = {17},
number = {11},
pages = {1242-1247},
pmid = {26575886},
issn = {1008-8830},
mesh = {Administration, Inhalation ; Adrenal Cortex Hormones/*adverse effects ; Adult ; Asthma/*drug therapy ; Body Height/*drug effects ; Child ; Humans ; },
abstract = {OBJECTIVE: To evaluate the effects of asthma and inhaled corticosteroids (ICS) in children on the final adult height.<br><br>METHODS: A search was performed to collect studies evaluating the relationship between asthma and ICS in children and the final adult height in PubMed, BCI, EMbase, Web of Science, CNKI and Wanfang databases, then a systemic review and Meta analysis were conducted.<br><br>RESULTS: Six studies evaluating the relationship between childhood asthma and the final adult height were enrolled. Three of them indicated that the final adult height was not influenced by childhood asthma. Two of them suggested a mild effect, and the effect was correlated with severity of childhood asthma. One of them indicated that a lower final adult height related to childhhod asthma was found only in black females without a high school education. Four studies evaluating the relationship between ICS and the final adult height were included. Compared with the non-ICS treatment group, healthy control group and the target height, ICS treatment had no effects on the final adult height.<br><br>CONCLUSIONS: Childhood asthma does not or only mildly decrease the final adult height. ICS treatment does not significantly affect the final adult height.},
}
@article {pmid26575032,
year = {2015},
author = {Kober, SE and Gressenberger, B and Kurzmann, J and Neuper, C and Wood, G},
title = {Voluntary Modulation of Hemodynamic Responses in Swallowing Related Motor Areas: A Near-Infrared Spectroscopy-Based Neurofeedback Study.},
journal = {PloS one},
volume = {10},
number = {11},
pages = {e0143314},
pmid = {26575032},
issn = {1932-6203},
mesh = {Adult ; Brain/metabolism/*pathology ; Brain Mapping ; Deglutition/*physiology ; Female ; *Hemodynamics ; Humans ; Male ; Oxyhemoglobins/metabolism ; Prefrontal Cortex/metabolism ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {In the present study, we show for the first time that motor imagery of swallowing, which is defined as the mental imagination of a specific motor act without overt movements by muscular activity, can be successfully used as mental strategy in a neurofeedback training paradigm. Furthermore, we demonstrate its effects on cortical correlates of swallowing function. Therefore, N = 20 healthy young adults were trained to voluntarily increase their hemodynamic response in swallowing related brain areas as assessed with near-infrared spectroscopy (NIRS). During seven training sessions, participants received either feedback of concentration changes in oxygenated hemoglobin (oxy-Hb group, N = 10) or deoxygenated hemoglobin (deoxy-Hb group, N = 10) over the inferior frontal gyrus (IFG) during motor imagery of swallowing. Before and after the training, we assessed cortical activation patterns during motor execution and imagery of swallowing. The deoxy-Hb group was able to voluntarily increase deoxy-Hb over the IFG during imagery of swallowing. Furthermore, swallowing related cortical activation patterns were more pronounced during motor execution and imagery after the training compared to the pre-test, indicating cortical reorganization due to neurofeedback training. The oxy-Hb group could neither control oxy-Hb during neurofeedback training nor showed any cortical changes. Hence, successful modulation of deoxy-Hb over swallowing related brain areas led to cortical reorganization and might be useful for future treatments of swallowing dysfunction.},
}
@article {pmid26573655,
year = {2016},
author = {Placidi, G and Petracca, A and Spezialetti, M and Iacoviello, D},
title = {A Modular Framework for EEG Web Based Binary Brain Computer Interfaces to Recover Communication Abilities in Impaired People.},
journal = {Journal of medical systems},
volume = {40},
number = {1},
pages = {34},
pmid = {26573655},
issn = {1573-689X},
mesh = {*Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography/*instrumentation ; Humans ; *Internet ; },
abstract = {A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel.},
}
@article {pmid26562524,
year = {2015},
author = {Zhao, J and Li, W and Li, M},
title = {Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots.},
journal = {PloS one},
volume = {10},
number = {11},
pages = {e0142168},
pmid = {26562524},
issn = {1932-6203},
mesh = {Adult ; *Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Reproducibility of Results ; Robotics/instrumentation/*methods ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot-a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject's mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.},
}
@article {pmid26562013,
year = {2015},
author = {Pahwa, M and Kusner, M and Hacker, CD and Bundy, DT and Weinberger, KQ and Leuthardt, EC},
title = {Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications.},
journal = {PloS one},
volume = {10},
number = {11},
pages = {e0142947},
pmid = {26562013},
issn = {1932-6203},
support = {F30 MH099877/MH/NIMH NIH HHS/United States ; TL1 TR000449/TR/NCATS NIH HHS/United States ; UL1 TR000448/TR/NCATS NIH HHS/United States ; 1F30MH099877-01-A1/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Brain/*anatomy &amp; histology/*physiology ; *Brain-Computer Interfaces ; Electrocorticography/*methods ; Female ; Humans ; Logistic Models ; Male ; Middle Aged ; Models, Anatomic ; *Sleep ; *Wakefulness ; Young Adult ; },
abstract = {Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.},
}
@article {pmid26561770,
year = {2016},
author = {Chang, MH and Lee, JS and Heo, J and Park, KS},
title = {Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI.},
journal = {Journal of neuroscience methods},
volume = {258},
number = {},
pages = {104-113},
doi = {10.1016/j.jneumeth.2015.11.001},
pmid = {26561770},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) generate weak SSVEP with a monitor and cannot use harmonic frequencies, whereas P300-based BCIs need multiple stimulation sequences. These issues can decrease the information transfer rate (ITR).<br><br>NEW METHOD: In this paper, we introduce a novel hybrid SSVEP-P300 speller that generates dual-frequency SSVEP, allowing it to overcome the abovementioned limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, while the oddball stimulus of the target character evokes the P300. A canonical correlation analysis (CCA) and a step-wise linear discriminant analysis (SWLDA) classified SSVEP and P300, respectively. Ten subjects participated in offline and online experiments, in which accuracy and ITR were compared with those of conventional SSVEP and P300 spellers.<br><br>RESULTS: The offline analysis revealed not only the P300 potential but also SSVEP with peaks at sub-harmonic frequencies, demonstrating that the proposed speller elicited dual-frequency SSVEP. This dual-frequency stimulation improved SSVEP recognition, increased the number of targets by employing harmonic frequencies, reduced the stimulation time for P300, and consequently improved ITR as compared to the conventional spellers.<br><br>The new method reduces the stimulation time and allows harmonic frequencies to be employed for different stimuli.<br><br>CONCLUSIONS: The results indicate that this study provides a promising approach to make the BCI speller more reliable and efficient.},
}
@article {pmid26561608,
year = {2016},
author = {Boulay, CB and Pieper, F and Leavitt, M and Martinez-Trujillo, J and Sachs, AJ},
title = {Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles.},
journal = {Journal of neurophysiology},
volume = {115},
number = {1},
pages = {486-499},
pmid = {26561608},
issn = {1522-1598},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {*Action Potentials ; Algorithms ; Animals ; Information Theory ; *Intention ; Macaca fascicularis ; Male ; *Models, Neurological ; Neurons/*physiology ; Photic Stimulation ; Prefrontal Cortex/*physiology ; Psychomotor Performance/*physiology ; *Saccades ; Signal Processing, Computer-Assisted ; },
abstract = {Neurons in the lateral prefrontal cortex (LPFC) encode sensory and cognitive signals, as well as commands for goal-directed actions. Therefore, the LPFC might be a good signal source for a goal-selection brain-computer interface (BCI) that decodes the intended goal of a motor action previous to its execution. As a first step in the development of a goal-selection BCI, we set out to determine if we could decode simple behavioral intentions to direct gaze to eight different locations in space from single-trial LPFC neural activity. We recorded neuronal spiking activity from microelectrode arrays implanted in area 8A of the LPFC of two adult macaques while they made visually guided saccades to one of eight targets in a center-out task. Neuronal activity encoded target location immediately after target presentation, during a delay epoch, during the execution of the saccade, and every combination thereof. Many (40%) of the neurons that encoded target location during multiple epochs preferred different locations during different epochs. Despite heterogeneous and dynamic responses, the neuronal feature set that best predicted target location was the averaged firing rates from the entire trial and it was best classified using linear discriminant analysis (63.6-96.9% in 12 sessions, mean 80.3%; information transfer rate: 21-59, mean 32.8 bits/min). Our results demonstrate that it is possible to decode intended saccade target location from single-trial LPFC activity and suggest that the LPFC is a suitable signal source for a goal-selection cognitive BCI.},
}
@article {pmid26560852,
year = {2016},
author = {Chang, WD and Cha, HS and Kim, K and Im, CH},
title = {Detection of eye blink artifacts from single prefrontal channel electroencephalogram.},
journal = {Computer methods and programs in biomedicine},
volume = {124},
number = {},
pages = {19-30},
doi = {10.1016/j.cmpb.2015.10.011},
pmid = {26560852},
issn = {1872-7565},
mesh = {Adult ; *Algorithms ; *Artifacts ; Blinking/*physiology ; Brain/*physiology ; Diagnosis, Computer-Assisted ; Electroencephalography/*methods ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reference Values ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.},
}
@article {pmid26560357,
year = {2015},
author = {Jarosiewicz, B and Sarma, AA and Bacher, D and Masse, NY and Simeral, JD and Sorice, B and Oakley, EM and Blabe, C and Pandarinath, C and Gilja, V and Cash, SS and Eskandar, EN and Friehs, G and Henderson, JM and Shenoy, KV and Donoghue, JP and Hochberg, LR},
title = {Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface.},
journal = {Science translational medicine},
volume = {7},
number = {313},
pages = {313ra179},
pmid = {26560357},
issn = {1946-6242},
support = {N01HD10018/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; R01 DC014034/DC/NIDCD NIH HHS/United States ; R01NS066311-S1/NS/NINDS NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01DC014034/DC/NIDCD NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; I01 RX001155/RX/RRD VA/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/complications ; *Brain-Computer Interfaces ; Calibration ; Female ; Humans ; Male ; Motor Cortex/physiopathology ; Quadriplegia/*physiopathology/*rehabilitation ; *Self-Help Devices ; Stroke/complications ; },
abstract = {Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.},
}
@article {pmid26555555,
year = {2015},
author = {Stuart, KE and Houssami, N and Taylor, R and Hayen, A and Boyages, J},
title = {Long-term outcomes of ductal carcinoma in situ of the breast: a systematic review, meta-analysis and meta-regression analysis.},
journal = {BMC cancer},
volume = {15},
number = {},
pages = {890},
pmid = {26555555},
issn = {1471-2407},
mesh = {Breast Neoplasms/*epidemiology/pathology/therapy ; Carcinoma, Intraductal, Noninfiltrating/*drug therapy/pathology/radiotherapy/surgery ; Female ; Humans ; Neoplasm Recurrence, Local/*drug therapy/pathology/radiotherapy/surgery ; Radiotherapy, Adjuvant ; Regression Analysis ; Retrospective Studies ; *Treatment Outcome ; },
abstract = {BACKGROUND: To summarize data on long-term ipsilateral local recurrence (LR) and breast cancer death rate (BCDR) for patients with ductal carcinoma in situ (DCIS) who received different treatments.<br><br>METHODS: Systematic review and study-level meta-analysis of prospective (n&#x2009;=&#x2009;5) and retrospective (n&#x2009;=&#x2009;21) studies of patients with pure DCIS and with median or mean follow-up time of &#x2265;10 years. Meta-regression was performed to assess and adjust for effects of potential confounders - the average age of women, period of initial treatment, and of bias - follow-up duration on recurrence- and death-rates in each treatment group. LR and BCDR rates by local treatment used were reported. Outside of randomized trials, remaining studies were likely to have tailored patient treatment according to the clinical situation.<br><br>RESULTS: Nine thousand four hundred and four DCIS cases in 9391 patients with 10-year follow-up were included. The adjusted meta-regression LR rate for mastectomy was 2.6 % (95 % CI, 0.8-4.5); breast-conserving surgery with radiotherapy (RT), 13.6 % (95 % CI, 9.8-17.4); breast-conserving surgery without RT, 25.5 % (95 % CI, 18.1-32.9); and biopsy-only (residual predominately low-grade DCIS following inadequate excision), 27.8 % (95 % CI, 8.4-47.1). RT&#x2009;+&#x2009;tamoxifen (TAM) in conservation surgery (CS) patients resulted in lower LR compared to one or no adjuvant treatments: LR rate for CS&#x2009;+&#x2009;RT&#x2009;+&#x2009;TAM, 9.7 %; CS&#x2009;+&#x2009;RT(no TAM), 14.1 %; CS&#x2009;+&#x2009;TAM(no RT), 24.7 %; CS(alone), 25.1 % (linear trend for treatment P&#x2009;<&#x2009;0.0001). Compared to CS&#x2009;+&#x2009;RT&#x2009;+&#x2009;TAM, a significantly higher invasive LR was observed for CS(alone), odds ratio (OR) 2.61 (P&#x2009;<&#x2009;0.0001); CS&#x2009;+&#x2009;TAM(no RT), OR 2.52 (P&#x2009;=&#x2009;0.001); CS&#x2009;+&#x2009;RT(no TAM), OR 1.59 (P&#x2009;=&#x2009;0.022). BCDR was similar for mastectomy, breast-conserving surgery with or without RT (1.3-2.0 %) and non-significantly higher for biopsy-only (2.7 %). Additionally, the 15-year follow-up was reported where all like-studies had&#x2009;&#x2265;&#x2009;15-year data sets; the biopsy-only patients had a meta-analysed total LR rate of 40.2 % and the invasive LR rate was 28.1 %. The biopsy-only patients had a&#x2009;&#x2265;&#x2009;15-year BCDR (that included women with metastatic disease) of 17.9 %; the&#x2009;&#x2265;&#x2009;15-year BCDR was 55.2 % for those with invasive LR.<br><br>CONCLUSIONS: More local intervention was associated with greater local control for patients with DCIS at long-term follow-up. For patients undergoing breast-conservation, invasive LR was significantly lower when two rather than one adjuvant treatment modalities were given.},
}
@article {pmid26550023,
year = {2015},
author = {Gao, D and Zhang, R and Liu, T and Li, F and Ma, T and Lv, X and Li, P and Yao, D and Xu, P},
title = {Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems.},
journal = {Computational and mathematical methods in medicine},
volume = {2015},
number = {},
pages = {680769},
pmid = {26550023},
issn = {1748-6718},
mesh = {Bayes Theorem ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Computer Simulation ; Databases, Factual/statistics &amp; numerical data ; Discriminant Analysis ; Electroencephalography/statistics &amp; numerical data ; Evoked Potentials, Visual ; Female ; Humans ; Linear Models ; Machine Learning ; Male ; Online Systems ; Probability ; Sample Size ; Young Adult ; },
abstract = {BACKGROUND: Usually the training set of online brain-computer interface (BCI) experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing.<br><br>METHODS: In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the reliable samples from the testing set to enlarge the training set, aiming to mine the additional information from testing set to adjust the biased classification boundary obtained from the small training set. The proposed approach is an extension of previous Z-LDA and is named enhanced Z-LDA (EZ-LDA).<br><br>RESULTS: We evaluated the classification performance of LDA, Z-LDA, and EZ-LDA on simulation and real BCI datasets with different sizes of training samples, and classification results showed EZ-LDA achieved the best classification performance.<br><br>CONCLUSIONS: EZ-LDA is promising to deal with the small sample size training problem usually existing in online BCI system.},
}
@article {pmid26547847,
year = {2016},
author = {Aydın, EA and Bay, &#xd6;F and Güler, &#x130;},
title = {Implementation of an Embedded Web Server Application for Wireless Control of Brain Computer Interface Based Home Environments.},
journal = {Journal of medical systems},
volume = {40},
number = {1},
pages = {27},
pmid = {26547847},
issn = {1573-689X},
mesh = {*Brain-Computer Interfaces ; *Home Care Services ; Humans ; *Internet ; Signal Processing, Computer-Assisted/*instrumentation ; Wireless Technology/economics/*instrumentation ; },
abstract = {Brain Computer Interface (BCI) based environment control systems could facilitate life of people with neuromuscular diseases, reduces dependence on their caregivers, and improves their quality of life. As well as easy usage, low-cost, and robust system performance, mobility is an important functionality expected from a practical BCI system in real life. In this study, in order to enhance users' mobility, we propose internet based wireless communication between BCI system and home environment. We designed and implemented a prototype of an embedded low-cost, low power, easy to use web server which is employed in internet based wireless control of a BCI based home environment. The embedded web server provides remote access to the environmental control module through BCI and web interfaces. While the proposed system offers to BCI users enhanced mobility, it also provides remote control of the home environment by caregivers as well as the individuals in initial stages of neuromuscular disease. The input of BCI system is P300 potentials. We used Region Based Paradigm (RBP) as stimulus interface. Performance of the BCI system is evaluated on data recorded from 8 non-disabled subjects. The experimental results indicate that the proposed web server enables internet based wireless control of electrical home appliances successfully through BCIs.},
}
@article {pmid26541673,
year = {2015},
author = {Bostanov, V},
title = {Multivariate assessment of event-related potentials with the t-CWT method.},
journal = {BMC neuroscience},
volume = {16},
number = {},
pages = {73},
pmid = {26541673},
issn = {1471-2202},
mesh = {*Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Multivariate Analysis ; *Principal Component Analysis ; *Wavelet Analysis ; },
abstract = {BACKGROUND: Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test.<br><br>RESULTS: This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed.<br><br>CONCLUSIONS: Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.},
}
@article {pmid26539089,
year = {2015},
author = {Ray, AM and Sitaram, R and Rana, M and Pasqualotto, E and Buyukturkoglu, K and Guan, C and Ang, KK and Tejos, C and Zamorano, F and Aboitiz, F and Birbaumer, N and Ruiz, S},
title = {A subject-independent pattern-based Brain-Computer Interface.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {269},
pmid = {26539089},
issn = {1662-5153},
abstract = {While earlier Brain-Computer Interface (BCI) studies have mostly focused on modulating specific brain regions or signals, new developments in pattern classification of brain states are enabling real-time decoding and modulation of an entire functional network. The present study proposes a new method for real-time pattern classification and neurofeedback of brain states from electroencephalographic (EEG) signals. It involves the creation of a fused classification model based on the method of Common Spatial Patterns (CSPs) from data of several healthy individuals. The subject-independent model is then used to classify EEG data in real-time and provide feedback to new individuals. In a series of offline experiments involving training and testing of the classifier with individual data from 27 healthy subjects, a mean classification accuracy of 75.30% was achieved, demonstrating that the classification system at hand can reliably decode two types of imagery used in our experiments, i.e., happy emotional imagery and motor imagery. In a subsequent experiment it is shown that the classifier can be used to provide neurofeedback to new subjects, and that these subjects learn to "match" their brain pattern to that of the fused classification model in a few days of neurofeedback training. This finding can have important implications for future studies on neurofeedback and its clinical applications on neuropsychiatric disorders.},
}
@article {pmid26536851,
year = {2015},
author = {Heidrich, RO and Jensen, E and Rebelo, F and Oliveira, T},
title = {A comparative study: use of a Brain-computer Interface (BCI) device by people with cerebral palsy in interaction with computers.},
journal = {Anais da Academia Brasileira de Ciencias},
volume = {87},
number = {4},
pages = {1929-1937},
doi = {10.1590/0001-3765201520130413},
pmid = {26536851},
issn = {1678-2690},
mesh = {*Brain-Computer Interfaces ; Brazil ; Case-Control Studies ; Cerebral Palsy/*physiopathology ; Humans ; Portugal ; Reaction Time ; *User-Computer Interface ; },
abstract = {This article presents a comparative study among people with cerebral palsy and healthy controls, of various ages, using a Brain-computer Interface (BCI) device. The research is qualitative in its approach. Researchers worked with Observational Case Studies. People with cerebral palsy and healthy controls were evaluated in Portugal and in Brazil. The study aimed to develop a study for product evaluation in order to perceive whether people with cerebral palsy could interact with the computer and compare whether their performance is similar to that of healthy controls when using the Brain-computer Interface. Ultimately, it was found that there are no significant differences between people with cerebral palsy in the two countries, as well as between populations without cerebral palsy (healthy controls).},
}
@article {pmid26529768,
year = {2016},
author = {Spyrou, L and Blokland, Y and Farquhar, J and Bruhn, J},
title = {Optimal Multitrial Prediction Combination and Subject-Specific Adaptation for Minimal Training Brain Switch Designs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {6},
pages = {700-709},
doi = {10.1109/TNSRE.2015.2494378},
pmid = {26529768},
issn = {1558-0210},
mesh = {Adaptation, Physiological/*physiology ; *Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Equipment Design ; Equipment Failure Analysis ; Humans ; *Models, Statistical ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; },
abstract = {Brain-Computer Interface (BCI) systems are traditionally designed by taking into account user-specific data to enable practical use. More recently, subject independent (SI) classification algorithms have been developed which bypass the subject specific adaptation and enable rapid use of the system. A brain switch is a particular BCI system where the system is required to distinguish from two separate mental tasks corresponding to the on-off commands of a switch. Such applications require a low false positive rate (FPR) while having an acceptable response time (RT) until the switch is activated. In this work, we develop a methodology that produces optimal brain switch behavior through subject specific (SS) adaptation of: a) a multitrial prediction combination model and b) an SI classification model. We propose a statistical model of combining classifier predictions that enables optimal FPR calibration through a short calibration session. We trained an SI classifier on a training synchronous dataset and tested our method on separate holdout synchronous and asynchronous brain switch experiments. Although our SI model obtained similar performance between training and holdout datasets, 86% and 85% for the synchronous and 69% and 66% for the asynchronous the between subject FPR and TPR variability was high (up to 62%). The short calibration session was then employed to alleviate that problem and provide decision thresholds that achieve when possible a target FPR=1% with good accuracy for both datasets.},
}
@article {pmid26529439,
year = {2015},
author = {Zhang, R and Yao, D and Valdés-Sosa, PA and Li, F and Li, P and Zhang, T and Ma, T and Li, Y and Xu, P},
title = {Efficient resting-state EEG network facilitates motor imagery performance.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066024},
doi = {10.1088/1741-2560/12/6/066024},
pmid = {26529439},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Nerve Net/*physiology ; Psychomotor Performance/*physiology ; Rest/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Motor imagery-based brain-computer interface (MI-BCI) systems hold promise in motor function rehabilitation and assistance for motor function impaired people. But the ability to operate an MI-BCI varies across subjects, which becomes a substantial problem for practical BCI applications beyond the laboratory.<br><br>APPROACH: Several previous studies have demonstrated that individual MI-BCI performance is related to the resting state of brain. In this study, we further investigate offline MI-BCI performance variations through the perspective of resting-state electroencephalography (EEG) network.<br><br>MAIN RESULTS: Spatial topologies and statistical measures of the network have close relationships with MI classification accuracy. Specifically, mean functional connectivity, node degrees, edge strengths, clustering coefficient, local efficiency and global efficiency are positively correlated with MI classification accuracy, whereas the characteristic path length is negatively correlated with MI classification accuracy. The above results indicate that an efficient background EEG network may facilitate MI-BCI performance. Finally, a multiple linear regression model was adopted to predict subjects' MI classification accuracy based on the efficiency measures of the resting-state EEG network, resulting in a reliable prediction.<br><br>SIGNIFICANCE: This study reveals the network mechanisms of the MI-BCI and may help to find new strategies for improving MI-BCI performance.},
}
@article {pmid26529119,
year = {2015},
author = {McFarland, DJ and Sarnacki, WA and Wolpaw, JR},
title = {Effects of training pre-movement sensorimotor rhythms on behavioral performance.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066021},
pmid = {26529119},
issn = {1741-2552},
support = {1P41EB018783/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Sensorimotor Cortex/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects.<br><br>APPROACH: Eight individuals first performed a joystick-based cursor-movement task with variable warning periods. Targets appeared randomly on a video monitor and subjects moved the cursor to the target and pressed a select button within 2 s. SMR features in the pre-movement EEG that correlated with performance speed and accuracy were identified. The subjects then learned to increase or decrease these features to control a two-target BCI task. Following successful BCI training, they were asked to increase or decrease SMR amplitude in order to initiate the joystick task.<br><br>MAIN RESULTS: After BCI training, pre-movement SMR amplitude was correlated with performance in subjects with initial poor performance: lower amplitude was associated with faster and more accurate movement. The beneficial effect on performance of lower SMR amplitude was greater in subjects with lower initial performance levels.<br><br>SIGNIFICANCE: These results indicate that BCI-based SMR training can affect a standard motor behavior. They provide a rationale for studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful motor function.},
}
@article {pmid26528168,
year = {2015},
author = {Brauchle, D and Vukelić, M and Bauer, R and Gharabaghi, A},
title = {Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {564},
pmid = {26528168},
issn = {1662-5161},
abstract = {While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns-but different aptitudes-of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e., when the orthosis was actually attached to the subject's arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces (BRIs) may successfully link three-dimensional robotic training to the participants' efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration, a proposal that warrants further investigation in a larger cohort of stroke patients.},
}
@article {pmid26518530,
year = {2015},
author = {Liberati, G and Federici, S and Pasqualotto, E},
title = {Extracting neurophysiological signals reflecting users' emotional and affective responses to BCI use: A systematic literature review.},
journal = {NeuroRehabilitation},
volume = {37},
number = {3},
pages = {341-358},
doi = {10.3233/NRE-151266},
pmid = {26518530},
issn = {1878-6448},
mesh = {*Affect ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Emotions ; Humans ; Magnetic Resonance Imaging ; Male ; Neurophysiology ; Thinking ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) allow persons with impaired mobility to communicate and interact with the environment, supporting goal-directed thinking and cognitive function. Ideally, a BCI should be able to recognize a user's internal state and adapt to it in real-time, to improve interaction.<br><br>OBJECTIVE: Our aim was to examine studies investigating the recognition of affective states from neurophysiological signals, evaluating how current achievements can be applied to improve BCIs.<br><br>METHODS: Following the PRISMA guidelines, we performed a literature search using PubMed and ProQuest databases. We considered peer-reviewed research articles in English, focusing on the recognition of emotions from neurophysiological signals in view of enhancing BCI use.<br><br>RESULTS: Of the 526 identified records, 30 articles comprising 32 studies were eligible for review. Their analysis shows that the affective BCI field is developing, with a variety of combinations of neuroimaging techniques, selected neurophysiological features, and classification algorithms currently being tested. Nevertheless, there is a gap between laboratory experiments and their translation to everyday situations.<br><br>CONCLUSIONS: BCI developers should focus on testing emotion classification with patients in ecological settings and in real-time, with more precise definitions of what they are investigating, and communicating results in a standardized way.},
}
@article {pmid26513801,
year = {2015},
author = {Corradi, F and Indiveri, G},
title = {A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {9},
number = {5},
pages = {699-709},
doi = {10.1109/TBCAS.2015.2479256},
pmid = {26513801},
issn = {1940-9990},
mesh = {Acoustic Stimulation ; Amplifiers, Electronic ; Animals ; Biomedical Engineering/*instrumentation ; Birds ; Brain/physiology ; *Brain-Computer Interfaces ; Equipment Design ; Neurons/*physiology ; Neurosciences/*instrumentation ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45-54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 μV, and consumes 90 μW . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition.},
}
@article {pmid26512264,
year = {2015},
author = {Jeon, H and Shin, DA},
title = {Experimental Set Up of P300 Based Brain Computer Interface Using a Bioamplifier and BCI2000 System for Patients with Spinal Cord Injury.},
journal = {Korean Journal of Spine},
volume = {12},
number = {3},
pages = {119-123},
pmid = {26512264},
issn = {1738-2262},
abstract = {OBJECTIVE: Brain computer interface (BCI) is one of the most promising technologies for helping people with neurological disorders. Most current BCI systems are relatively expensive and difficult to set up. Therefore, we developed a P300-based BCI system with a cheap bioamplifier and open source software. The purpose of this study was to describe the setup process of the system and preliminary experimental results.<br><br>METHODS: Ten spinal cord-injured patients were recruited. We used a sixteen-channel EEG(KT88-1016, Contec, China) and BCI2000 software (Wadsworth center, NY, USA). Subjects were asked to spell a 5-character word using the P300-based BCI system with 10 minutes of training. EEG data were acquired during the experiment. After subjects spelled the word for ten trials, the spelling accuracy and information transfer rate (ITR) were obtained in each patients.<br><br>RESULTS: All subjects performed the experiment without difficulty. The mean accuracy was 59.4&#xb1;22.8%. The spelling accuracy reversely correlated with the age. Younger subjects spelled with higher accuracy than older subjects (p=0.018). However, sex, injury level, time since injury and ASIA scale were not correlated with the accuracy. The mean of ITR was 2.26&#xb1;1.22 bit/min.<br><br>CONCLUSION: This study showed that a BCI system can be set up inexpensively with a low-price bioamplifier and open-source software. The spelling accuracy was moderately achieved with our system. P300-based BCI is useful in young patients, but modification is necessary in old patients who have low ability of recognition and concentration.},
}
@article {pmid26510583,
year = {2015},
author = {Hwang, HJ and Ferreria, VY and Ulrich, D and Kilic, T and Chatziliadis, X and Blankertz, B and Treder, M},
title = {A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {15890},
pmid = {26510583},
issn = {2045-2322},
mesh = {Adult ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; *Eyelids ; Female ; Humans ; Male ; *Photic Stimulation ; },
abstract = {A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition, and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli, and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.},
}
@article {pmid26505298,
year = {2016},
author = {Kraus, D and Naros, G and Bauer, R and Leão, MT and Ziemann, U and Gharabaghi, A},
title = {Brain-robot interface driven plasticity: Distributed modulation of corticospinal excitability.},
journal = {NeuroImage},
volume = {125},
number = {},
pages = {522-532},
doi = {10.1016/j.neuroimage.2015.09.074},
pmid = {26505298},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization/physiology ; Electromyography ; Evoked Potentials, Motor/physiology ; Feedback, Sensory/physiology ; Female ; Humans ; Male ; Neuronal Plasticity/*physiology ; Pyramidal Tracts/*physiology ; Robotics/methods ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {Brain-robot interfaces (BRI) are studied as novel interventions to facilitate functional restoration in patients with severe and persistent motor deficits following stroke. They bridge the impaired connection in the sensorimotor loop by providing brain-state dependent proprioceptive feedback with orthotic devices attached to the hand or arm of the patients. The underlying neurophysiology of this BRI neuromodulation is still largely unknown. We investigated changes of corticospinal excitability with transcranial magnetic stimulation in thirteen right-handed healthy subjects who performed 40min of kinesthetic motor imagery receiving proprioceptive feedback with a robotic orthosis attached to the left hand contingent to event-related desynchronization of the right sensorimotor cortex in the β-band (16-22Hz). Neural correlates of this BRI intervention were probed by acquiring the stimulus-response curve (SRC) of both motor evoked potential (MEP) peak-to-peak amplitudes and areas under the curve. In addition, a motor mapping was obtained. The specificity of the effects was studied by comparing two neighboring hand muscles, one BRI-trained and one control muscle. Robust changes of MEP amplitude but not MEP area occurred following the BRI intervention, but only in the BRI-trained muscle. The steep part of the SRC showed an MEP increase, while the plateau of the SRC showed an MEP decrease. MEP mapping revealed a distributed pattern with a decrease of excitability in the hand area of the primary motor cortex, which controlled the BRI, but an increase of excitability in the surrounding somatosensory and premotor cortex. In conclusion, the BRI intervention induced a complex pattern of modulated corticospinal excitability, which may boost subsequent motor learning during physiotherapy.},
}
@article {pmid26504654,
year = {2015},
author = {Khan, MJ and Hong, KS},
title = {Passive BCI based on drowsiness detection: an fNIRS study.},
journal = {Biomedical optics express},
volume = {6},
number = {10},
pages = {4063-4078},
pmid = {26504654},
issn = {2156-7085},
abstract = {We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left- and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.},
}
@article {pmid26504211,
year = {2015},
author = {Kim, S and Callier, T and Tabot, GA and Gaunt, RA and Tenore, FV and Bensmaia, SJ},
title = {Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {112},
number = {49},
pages = {15202-15207},
pmid = {26504211},
issn = {1091-6490},
mesh = {Animals ; *Electric Stimulation ; Macaca mulatta/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.},
}
@article {pmid26501230,
year = {2015},
author = {Robinson, N and Guan, C and Vinod, AP},
title = {Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066019},
doi = {10.1088/1741-2560/12/6/066019},
pmid = {26501230},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Hand/*physiology ; Humans ; Linear Models ; Male ; Movement/*physiology ; },
abstract = {OBJECTIVE: The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings.<br><br>APPROACH: EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables.<br><br>MAIN RESULTS: The proposed method yielded correlation of (0.60 &#xb1; 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 &#xb1; 0.07, p < 0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time.<br><br>SIGNIFICANCE: The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.},
}
@article {pmid26500476,
year = {2015},
author = {Kleih, SC and Herweg, A and Kaufmann, T and Staiger-Sälzer, P and Gerstner, N and Kübler, A},
title = {The WIN-speller: a new intuitive auditory brain-computer interface spelling application.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {346},
pmid = {26500476},
issn = {1662-4548},
abstract = {The objective of this study was to test the usability of a new auditory Brain-Computer Interface (BCI) application for communication. We introduce a word based, intuitive auditory spelling paradigm the WIN-speller. In the WIN-speller letters are grouped by words, such as the word KLANG representing the letters A, G, K, L, and N. Thereby, the decoding step between perceiving a code and translating it to the stimuli it represents becomes superfluous. We tested 11 healthy volunteers and four end-users with motor impairment in the copy spelling mode. Spelling was successful with an average accuracy of 84% in the healthy sample. Three of the end-users communicated with average accuracies of 80% or higher while one user was not able to communicate reliably. Even though further evaluation is required, the WIN-speller represents a potential alternative for BCI based communication in end-users.},
}
@article {pmid26497727,
year = {2016},
author = {Klein, E},
title = {Informed Consent in Implantable BCI Research: Identifying Risks and Exploring Meaning.},
journal = {Science and engineering ethics},
volume = {22},
number = {5},
pages = {1299-1317},
pmid = {26497727},
issn = {1471-5546},
mesh = {Brain-Computer Interfaces/*ethics ; Electrodes, Implanted/ethics ; *Ethics, Research ; Humans ; *Informed Consent ; Risk ; },
abstract = {Implantable brain-computer interface (BCI) technology is an expanding area of engineering research now moving into clinical application. Ensuring meaningful informed consent in implantable BCI research is an ethical imperative. The emerging and rapidly evolving nature of implantable BCI research makes identification of risks, a critical component of informed consent, a challenge. In this paper, 6 core risk domains relevant to implantable BCI research are identified-short and long term safety, cognitive and communicative impairment, inappropriate expectations, involuntariness, affective impairment, and privacy and security. Work in deep brain stimulation provides a useful starting point for understanding this core set of risks in implantable BCI. Three further risk domains-risks pertaining to identity, agency, and stigma-are identified. These risks are not typically part of formalized consent processes. It is important as informed consent practices are further developed for implantable BCI research that attention be paid not just to disclosing core research risks but exploring the meaning of BCI research with potential participants.},
}
@article {pmid26495971,
year = {2015},
author = {Curado, MR and Cossio, EG and Broetz, D and Agostini, M and Cho, W and Brasil, FL and Yilmaz, O and Liberati, G and Lepski, G and Birbaumer, N and Ramos-Murguialday, A},
title = {Residual Upper Arm Motor Function Primes Innervation of Paretic Forearm Muscles in Chronic Stroke after Brain-Machine Interface (BMI) Training.},
journal = {PloS one},
volume = {10},
number = {10},
pages = {e0140161},
pmid = {26495971},
issn = {1932-6203},
mesh = {Adult ; Aged ; Arm/*physiopathology ; Brain-Computer Interfaces ; Chronic Disease ; Electromyography ; Female ; Forearm/innervation/*physiopathology ; Hand/physiopathology ; Humans ; Male ; Middle Aged ; Movement ; Muscle, Skeletal/innervation/*physiopathology ; Paresis/physiopathology/*rehabilitation ; *Physical Therapy Modalities ; Shoulder/physiopathology ; Stroke/physiopathology ; *Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {BACKGROUND: Abnormal upper arm-forearm muscle synergies after stroke are poorly understood. We investigated whether upper arm function primes paralyzed forearm muscles in chronic stroke patients after Brain-Machine Interface (BMI)-based rehabilitation. Shaping upper arm-forearm muscle synergies may support individualized motor rehabilitation strategies.<br><br>METHODS: Thirty-two chronic stroke patients with no active finger extensions were randomly assigned to experimental or sham groups and underwent daily BMI training followed by physiotherapy during four weeks. BMI sessions included desynchronization of ipsilesional brain activity and a robotic orthosis to move the paretic limb (experimental group, n = 16). In the sham group (n = 16) orthosis movements were random. Motor function was evaluated with electromyography (EMG) of forearm extensors, and upper arm and hand Fugl-Meyer assessment (FMA) scores. Patients performed distinct upper arm (e.g., shoulder flexion) and hand movements (finger extensions). Forearm EMG activity significantly higher during upper arm movements as compared to finger extensions was considered facilitation of forearm EMG activity. Intraclass correlation coefficient (ICC) was used to test inter-session reliability of facilitation of forearm EMG activity.<br><br>RESULTS: Facilitation of forearm EMG activity ICC ranges from 0.52 to 0.83, indicating fair to high reliability before intervention in both limbs. Facilitation of forearm muscles is higher in the paretic as compared to the healthy limb (p<0.001). Upper arm FMA scores predict facilitation of forearm muscles after intervention in both groups (significant correlations ranged from R = 0.752, p = 0.002 to R = 0.779, p = 0.001), but only in the experimental group upper arm FMA scores predict changes in facilitation of forearm muscles after intervention (R = 0.709, p = 0.002; R = 0.827, p<0.001).<br><br>CONCLUSIONS: Residual upper arm motor function primes recruitment of paralyzed forearm muscles in chronic stroke patients and predicts changes in their recruitment after BMI training. This study suggests that changes in upper arm-forearm synergies contribute to stroke motor recovery, and provides candidacy guidelines for similar BMI-based clinical practice.},
}
@article {pmid26493715,
year = {2015},
author = {Halasa, TK and Surapaneni, L and Sattur, MG and Pines, AR and Aoun, RJ and Bendok, BR},
title = {Human Brain-to-Brain Interface: Prelude to Telepathy.},
journal = {World neurosurgery},
volume = {84},
number = {6},
pages = {1507-1508},
doi = {10.1016/j.wneu.2015.10.031},
pmid = {26493715},
issn = {1878-8769},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Humans ; Telepathy/*physiology ; },
}
@article {pmid26491482,
year = {2015},
author = {Zernotti, ME and Sarasty, AB},
title = {Active Bone Conduction Prosthesis: Bonebridge(TM).},
journal = {International archives of otorhinolaryngology},
volume = {19},
number = {4},
pages = {343-348},
pmid = {26491482},
issn = {1809-9777},
abstract = {Introduction Bone conduction implants are indicated for patients with conductive and mixed hearing loss, as well as for patients with single-sided deafness (SSD). The transcutaneous technology avoids several complications of the percutaneous bone conduction implants including skin reaction, skin growth over the abutment, and wound infection. The Bonebridge (MED-EL, Austria) prosthesis is a semi-implantable hearing system: the BCI (Bone Conduction Implant) is the implantable part that contains the Bone Conduction-Floating Mass Transducer (BC-FMT), which applies the vibrations directly to the bone; the external component is the audio processor Amadé BB (MED-EL, Austria), which digitally processes the sound and sends the information through the coil to the internal part. Bonebridge may be implanted through three different approaches: the transmastoid, the retrosigmoid, or the middle fossa approach. Objective This systematic review aims to describe the world́s first active bone conduction implant system, Bonebridge, as well as describe the surgical techniques in the three possible approaches, showing results from implant centers in the world in terms of functional gain, speech reception thresholds and word recognition scores. Data Synthesis The authors searched the MEDLINE database using the key term Bonebridge. They selected only five publications to include in this systematic review. The review analyzes 20 patients that received Bonebridge implants with different approaches and pathologies. Conclusion Bonebridge is a solution for patients with conductive/mixed hearing loss and SSD with different surgical approaches, depending on their anatomy. The system imparts fewer complications than percutaneous bone conduction implants and shows proven benefits in speech discrimination and functional gain.},
}
@article {pmid26489759,
year = {2015},
author = {Shan, H and Xu, H and Zhu, S and He, B},
title = {A novel channel selection method for optimal classification in different motor imagery BCI paradigms.},
journal = {Biomedical engineering online},
volume = {14},
number = {},
pages = {93},
pmid = {26489759},
issn = {1475-925X},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; EB006433/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; *Imagination ; *Motor Activity ; },
abstract = {BACKGROUND: For sensorimotor rhythms based brain-computer interface (BCI) systems, classification of different motor imageries (MIs) remains a crucial problem. An important aspect is how many scalp electrodes (channels) should be used in order to reach optimal performance classifying motor imaginations. While the previous researches on channel selection mainly focus on MI tasks paradigms without feedback, the present work aims to investigate the optimal channel selection in MI tasks paradigms with real-time feedback (two-class control and four-class control paradigms).<br><br>METHODS: In the present study, three datasets respectively recorded from MI tasks experiment, two-class control and four-class control experiments were analyzed offline. Multiple frequency-spatial synthesized features were comprehensively extracted from every channel, and a new enhanced method IterRelCen was proposed to perform channel selection. IterRelCen was constructed based on Relief algorithm, but was enhanced from two aspects: change of target sample selection strategy and adoption of the idea of iterative computation, and thus performed more robust in feature selection. Finally, a multiclass support vector machine was applied as the classifier. The least number of channels that yield the best classification accuracy were considered as the optimal channels. One-way ANOVA was employed to test the significance of performance improvement among using optimal channels, all the channels and three typical MI channels (C3, C4, Cz).<br><br>RESULTS: The results show that the proposed method outperformed other channel selection methods by achieving average classification accuracies of 85.2, 94.1, and 83.2&#xa0;% for the three datasets, respectively. Moreover, the channel selection results reveal that the average numbers of optimal channels were significantly different among the three MI paradigms.<br><br>CONCLUSIONS: It is demonstrated that IterRelCen has a strong ability for feature selection. In addition, the results have shown that the numbers of optimal channels in the three different motor imagery BCI paradigms are distinct. From a MI task paradigm, to a two-class control paradigm, and to a four-class control paradigm, the number of required channels for optimizing the classification accuracy increased. These findings may provide useful information to optimize EEG based BCI systems, and further improve the performance of noninvasive BCI.},
}
@article {pmid26485972,
year = {2015},
author = {Wang, J and Yang, L},
title = {[Tensor Feature Extraction Using Multi-linear Principal Component Analysis for Brain Computer Interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {3},
pages = {526-530},
pmid = {26485972},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Principal Component Analysis ; },
abstract = {The brain computer interface (BCI) can be used to control external devices directly through electroencephalogram (EEG) information. A multi-linear principal component analysis (MPCA) framework was used for the limitations of tensor form of multichannel EEG signals processing based on traditional principal component analysis (PCA) and two-dimensional principal component analysis (2DPCA). Based on MPCA, we used the projection of tensor-matrix to achieve the goal of dimensionality reduction and features exaction. Then we used the Fisher linear classifier to classify the features. Furthermore, we used this novel method on the BCI competition II dataset 4 and BCI competition N dataset 3 in the experiment. The second-order tensor representation of time-space EEG data and the third-order tensor representation of time-space-frequency BEG data were used. The best results that were superior to those from other dimensionality reduction methods were obtained by much debugging on parameter P and testQ. For two-order tensor, the highest accuracy rates could be achieved as 81.0% and 40.1%, and for three-order tensor, the highest accuracy rates were 76.0% and 43.5%, respectively.},
}
@article {pmid26485971,
year = {2015},
author = {Zhou, B and Wu, X and Lu, Z and Zhang, L and Guo, X and Zhang, C},
title = {[Channel Selection for Multi-class Motor Imagery Based on Common Spatial Pattern].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {3},
pages = {520-525},
pmid = {26485971},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Pattern Recognition, Physiological ; },
abstract = {High-density channels are often used to acquire electroencephalogram (EEG) spatial information in different cortical regions of the brain in brain-computer interface (BCI) systems. However, applying excessive channels is inconvenient for signal acquisition, and it may bring artifacts. To avoid these defects, the common spatial pattern (CSP) algorithm was used for channel selection and a selection criteria based on norm-2 is proposed in this paper. The channels with the highest M scores were selected for the purpose of using fewer channels to acquire similar rate with high density channels. The Dataset III a from BCI competition 2005 were used for comparing the classification accuracies of three motor imagery between whole channels and the selected channels with the present proposed method. The experimental results showed that the classification accuracies of three subjects using the 20 channels selected with the present method were all higher than the classification accuracies using all 60 channels, which convinced that our method could be more effective and useful.},
}
@article {pmid26485969,
year = {2015},
author = {Yang, C and Huang, L and Wen, N and Yang, J},
title = {[Study on Steady State Visual Evoked Potential Target Detection Based on Two-dimensional Ensemble Empirical Mode Decomposition].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {3},
pages = {508-513},
pmid = {26485969},
issn = {1001-5515},
mesh = {Algorithms ; Brain/physiology ; *Brain Mapping ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Neural Pathways ; },
abstract = {Brain computer interface is a control system between brain and outside devices by transforming electroencephalogram (EEG) signal. The brain computer interface system does not depend on the normal output pathways, such as peripheral nerve and muscle tissue, so it can provide a new way of the communication control for paralysis or nerve muscle damaged disabled persons. Steady state visual evoked potential (SSVEP) is one of non-invasive EEG signals, and it has been widely used in research in recent years. SSVEP is a kind of rhythmic brain activity simulated by continuous visual stimuli. SSVEP frequency is composed of a fixed visual stimulation frequency and its harmonic frequencies. The two-dimensional ensemble empirical mode decomposition (2D-EEMD) is an improved algorithm of the classical empirical mode decomposition (EMD) algorithm which extended the decomposition to two-dimensional direction. 2D-EEMD has been widely used in ocean hurricane, nuclear magnetic resonance imaging (MRI), Lena image and other related image processing fields. The present study shown in this paper initiatively applies 2D-EEMD to SSVEP. The decomposition, the 2-D picture of intrinsic mode function (IMF), can show the SSVEP frequency clearly. The SSVEP IMFs which had filtered noise and artifacts were mapped into the head picture to reflect the time changing trend of brain responding visual stimuli, and to reflect responding intension based on different brain regions. The results showed that the occipital region had the strongest response. Finally, this study used short-time Fourier transform (STFT) to detect SSVEP frequency of the 2D-EEMD reconstructed signal, and the accuracy rate increased by 16%.},
}
@article {pmid26485727,
year = {2017},
author = {Li, J and Li, C and Cichocki, A},
title = {Canonical Polyadic Decomposition With Auxiliary Information for Brain-Computer Interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {21},
number = {1},
pages = {263-271},
doi = {10.1109/JBHI.2015.2491645},
pmid = {26485727},
issn = {2168-2208},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Magnetoencephalography/methods ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Physiological signals are often organized in the form of multiple dimensions (e.g., channel, time, task, and 3-D voxel), so it is better to preserve original organization structure when processing. Unlike vector-based methods that destroy data structure, canonical polyadic decomposition (CPD) aims to process physiological signals in the form of multiway array, which considers relationships between dimensions and preserves structure information contained by the physiological signal. Nowadays, CPD is utilized as an unsupervised method for feature extraction in a classification problem. After that, a classifier, such as support vector machine, is required to classify those features. In this manner, classification task is achieved in two isolated steps. We proposed supervised CPD by directly incorporating auxiliary label information during decomposition, by which a classification task can be achieved without an extra step of classifier training. The proposed method merges the decomposition and classifier learning together, so it reduces procedure of classification task compared with that of respective decomposition and classification. In order to evaluate the performance of the proposed method, three different kinds of signals, synthetic signal, EEG signal, and MEG signal, were used. The results based on evaluations of synthetic and real signals demonstrated that the proposed method is effective and efficient.},
}
@article {pmid26485409,
year = {2015},
author = {Cruz-Garza, JG and Hernandez, ZR and Tse, T and Caducoy, E and Abibullaev, B and Contreras-Vidal, JL},
title = {A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {104},
pages = {},
pmid = {26485409},
issn = {1940-087X},
support = {P01 HD064653/HD/NICHD NIH HHS/United States ; P01 HD064653-01/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Child ; Cognition/physiology ; Electroencephalography/*methods ; Female ; Humans ; *Infant ; Interpersonal Relations ; Male ; Movement/physiology ; Multimodal Imaging ; *Social Behavior ; },
abstract = {Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing "in action and in context". This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.},
}
@article {pmid26484828,
year = {2016},
author = {Salari, N and Rose, M},
title = {Dissociation of the functional relevance of different pre-stimulus oscillatory activity for memory formation.},
journal = {NeuroImage},
volume = {125},
number = {},
pages = {1013-1021},
doi = {10.1016/j.neuroimage.2015.10.037},
pmid = {26484828},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Memory/*physiology ; Photic Stimulation ; *User-Computer Interface ; },
abstract = {The state of a neural assembly preceding an incoming stimulus modulates the processing of that subsequently presented stimuli. For human memory formation, the role of oscillatory brain activity within different frequency ranges has been discussed but a more functional relation could not be established. In the present Experiment I, an increase of pre-stimulus theta- (3-7Hz) and beta- (13-17Hz) band oscillations during encoding for later remembered stimuli was observed. To establish a more direct functional relation, we adopted a novel brain-computer-interface (BCI) method to selectively detect oscillatory activity in real-time combined with an adaptive stimulus presentation at different levels of activity. Therefore, in the second experiment the BCI was used to present the visual stimuli with a high temporal resolution directly within defined brain states of beta- or theta-band activity. The quality of the subsequent processing of the stimuli was assessed at the behavioral level with a surprise recognition task. Results revealed a variation of memory performance in direct relation to the amount of pre-stimulus beta- but not theta-band oscillations, suggesting a functional relevance of beta-band oscillations for memory encoding. Thus, the BCI method enabled a more functional differentiation of the effective role of ongoing oscillatory activity.},
}
@article {pmid26483659,
year = {2015},
author = {Seeber, M and Scherer, R and Wagner, J and Solis-Escalante, T and Müller-Putz, GR},
title = {Corrigendum: EEG beta suppression and low gamma modulation are different elements of human upright walking.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {542},
pmid = {26483659},
issn = {1662-5161},
abstract = {[This corrects the article on p. 485 in vol. 8, PMID: 25071515.].},
}
@article {pmid26483657,
year = {2015},
author = {Weyand, S and Chau, T},
title = {Correlates of Near-Infrared Spectroscopy Brain-Computer Interface Accuracy in a Multi-Class Personalization Framework.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {536},
pmid = {26483657},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) provide individuals with a means of interacting with a computer using only neural activity. To date, the majority of near-infrared spectroscopy (NIRS) BCIs have used prescribed tasks to achieve binary control. The goals of this study were to evaluate the possibility of using a personalized approach to establish control of a two-, three-, four-, and five-class NIRS-BCI, and to explore how various user characteristics correlate to accuracy. Ten able-bodied participants were recruited for five data collection sessions. Participants performed six mental tasks and a personalized approach was used to select each individual's best discriminating subset of tasks. The average offline cross-validation accuracies achieved were 78, 61, 47, and 37% for the two-, three-, four-, and five-class problems, respectively. Most notably, all participants exceeded an accuracy of 70% for the two-class problem, and two participants exceeded an accuracy of 70% for the three-class problem. Additionally, accuracy was found to be strongly positively correlated (Pearson's) with perceived ease of session (ρ = 0.653), ease of concentration (ρ = 0.634), and enjoyment (ρ = 0.550), but strongly negatively correlated with verbal IQ (ρ = -0.749).},
}
@article {pmid26483648,
year = {2015},
author = {, },
title = {Erratum: Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {137},
doi = {10.3389/fnsys.2015.00137},
pmid = {26483648},
issn = {1662-5137},
abstract = {[This corrects the article on p. 71 in vol. 9, PMID: 26042002.].},
}
@article {pmid26483627,
year = {2015},
author = {Xie, T and Zhang, D and Wu, Z and Chen, L and Zhu, X},
title = {Classifying multiple types of hand motions using electrocorticography during intraoperative awake craniotomy and seizure monitoring processes-case studies.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {353},
pmid = {26483627},
issn = {1662-4548},
abstract = {In this work, some case studies were conducted to classify several kinds of hand motions from electrocorticography (ECoG) signals during intraoperative awake craniotomy &amp; extraoperative seizure monitoring processes. Four subjects (P1, P2 with intractable epilepsy during seizure monitoring and P3, P4 with brain tumor during awake craniotomy) participated in the experiments. Subjects performed three types of hand motions (Grasp, Thumb-finger motion and Index-finger motion) contralateral to the motor cortex covered with ECoG electrodes. Two methods were used for signal processing. Method I: autoregressive (AR) model with burg method was applied to extract features, and additional waveform length (WL) feature has been considered, finally the linear discriminative analysis (LDA) was used as the classifier. Method II: stationary subspace analysis (SSA) was applied for data preprocessing, and the common spatial pattern (CSP) was used for feature extraction before LDA decoding process. Applying method I, the three-class accuracy of P1~P4 were 90.17, 96.00, 91.77, and 92.95% respectively. For method II, the three-class accuracy of P1~P4 were 72.00, 93.17, 95.22, and 90.36% respectively. This study verified the possibility of decoding multiple hand motion types during an awake craniotomy, which is the first step toward dexterous neuroprosthetic control during surgical implantation, in order to verify the optimal placement of electrodes. The accuracy during awake craniotomy was comparable to results during seizure monitoring. This study also indicated that ECoG was a promising approach for precise identification of eloquent cortex during awake craniotomy, and might form a promising BCI system that could benefit both patients and neurosurgeons.},
}
@article {pmid26483479,
year = {2015},
author = {Chen, X and Wang, Y and Nakanishi, M and Gao, X and Jung, TP and Gao, S},
title = {High-speed spelling with a noninvasive brain-computer interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {112},
number = {44},
pages = {E6058-67},
pmid = {26483479},
issn = {1091-6490},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials, Visual ; Humans ; *Language ; },
abstract = {The past 20 years have witnessed unprecedented progress in brain-computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.},
}
@article {pmid26481030,
year = {2015},
author = {Lesenfants, D and Chatelle, C and Laureys, S and Noirhomme, Q},
title = {[Brain-computer interfaces, Locked-In syndrome, and disorders of consciousness].},
journal = {Medecine sciences : M/S},
volume = {31},
number = {10},
pages = {904-911},
doi = {10.1051/medsci/20153110017},
pmid = {26481030},
issn = {1958-5381},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Coma/diagnosis/psychology/therapy ; Consciousness/physiology ; Diagnostic Errors ; Functional Neuroimaging ; Humans ; Quadriplegia/diagnosis/etiology/*pathology/*therapy ; Unconsciousness/diagnosis/psychology/*therapy ; User-Computer Interface ; Vision, Ocular/physiology ; },
abstract = {Detecting signs of consciousness in patients with severe brain injury constitutes a real challenge for clinicians. The current gold standard in clinical diagnosis is the behavioral scale relying on motor abilities, which are often impaired or nonexistent in these patients. In this context, brain-computer interfaces (BCIs) could offer a potential complementary tool to detect signs of consciousness whilst bypassing the usual motor pathway. In addition to complementing behavioral assessments and potentially reducing error rate, BCIs could also serve as a communication tool for paralyzed but conscious patients, e.g., suffering from Locked-In Syndrome. In this paper, we report on recent work conducted by the Coma Science Group on BCI technology, aiming to optimize diagnosis and communication in patients with disorders of consciousness and Locked-In syndrome.},
}
@article {pmid26479067,
year = {2015},
author = {Nakanishi, M and Wang, Y and Wang, YT and Jung, TP},
title = {A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials.},
journal = {PloS one},
volume = {10},
number = {10},
pages = {e0140703},
pmid = {26479067},
issn = {1932-6203},
support = {R21 EY025056/EY/NEI NIH HHS/United States ; 1R21EY025056-01/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Statistics as Topic/*methods ; },
abstract = {Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.},
}
@article {pmid26479469,
year = {2015},
author = {Tsoneva, T and Garcia-Molina, G and Desain, P},
title = {Neural dynamics during repetitive visual stimulation.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066017},
doi = {10.1088/1741-2560/12/6/066017},
pmid = {26479469},
issn = {1741-2552},
mesh = {Adult ; Cerebral Cortex/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Gamma Rhythm/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands.<br><br>APPROACH: We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain.<br><br>MAIN RESULTS: Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation.<br><br>SIGNIFICANCE: These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function.},
}
@article {pmid26477360,
year = {2015},
author = {Goodman, G and Poznanski, RR and Cacha, L and Bercovich, D},
title = {The Two-Brains Hypothesis: Towards a guide for brain-brain and brain-machine interfaces.},
journal = {Journal of integrative neuroscience},
volume = {14},
number = {3},
pages = {281-293},
doi = {10.1142/S0219635215500235},
pmid = {26477360},
issn = {0219-6352},
mesh = {Aging/physiology ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Consciousness/physiology ; Electroencephalography/methods ; Humans ; Mental Recall/physiology ; *Models, Neurological ; Motor Activity/physiology ; Neurons/physiology ; Quantum Theory ; Transcranial Magnetic Stimulation/methods ; },
abstract = {Great advances have been made in signaling information on brain activity in individuals, or passing between an individual and a computer or robot. These include recording of natural activity using implants under the scalp or by external means or the reverse feeding of such data into the brain. In one recent example, noninvasive transcranial magnetic stimulation (TMS) allowed feeding of digitalized information into the central nervous system (CNS). Thus, noninvasive electroencephalography (EEG) recordings of motor signals at the scalp, representing specific motor intention of hand moving in individual humans, were fed as repetitive transcranial magnetic stimulation (rTMS) at a maximum intensity of 2.0[Formula: see text]T through a circular magnetic coil placed flush on each of the heads of subjects present at a different location. The TMS was said to induce an electric current influencing axons of the motor cortex causing the intended hand movement: the first example of the transfer of motor intention and its expression, between the brains of two remote humans. However, to date the mechanisms involved, not least that relating to the participation of magnetic induction, remain unclear. In general, in animal biology, magnetic fields are usually the poor relation of neuronal current: generally "unseen" and if apparent, disregarded or just given a nod. Niels Bohr searched for a biological parallel to complementary phenomena of physics. Pertinently, the two-brains hypothesis (TBH) proposed recently that advanced animals, especially man, have two brains i.e., the animal CNS evolved as two fundamentally different though interdependent, complementary organs: one electro-ionic (tangible, known and accessible), and the other, electromagnetic (intangible and difficult to access) - a stable, structured and functional 3D compendium of variously induced interacting electro-magnetic (EM) fields. Research on the CNS in health and disease progresses including that on brain-brain, brain-computer and brain-robot engineering. As they grow even closer, these disciplines involve their own unique complexities, including direction by the laws of inductive physics. So the novel TBH hypothesis has wide fundamental implications, including those related to TMS. These require rethinking and renewed research engaging the fully complementary equivalence of mutual magnetic and electric field induction in the CNS and, within this context, a new mathematics of the brain to decipher higher cognitive operations not possible with current brain-brain and brain-machine interfaces. Bohr may now rest.},
}
@article {pmid26476869,
year = {2015},
author = {Hortal, E and Planelles, D and Resquin, F and Climent, JM and Azorín, JM and Pons, JL},
title = {Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {92},
pmid = {26476869},
issn = {1743-0003},
mesh = {*Brain-Computer Interfaces ; *Exoskeleton Device ; Female ; Humans ; Male ; Middle Aged ; Movement/physiology ; Nervous System Diseases/*rehabilitation ; Upper Extremity/physiology ; },
abstract = {BACKGROUND: As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes.<br><br>METHODS: In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user's brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection.<br><br>RESULTS: Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 &#xb1; 14.5 %, and patients obtained an accuracy of 65.3 &#xb1; 9.0 %, with a low False Positives rate (FP) (19.2 &#xb1; 10.4 % and 15.0 &#xb1; 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 &#xb1; 13.2 % for healthy users and 71.6 &#xb1; 15.8 % for patients, with 28.7 &#xb1; 19.9 % and 21.2 &#xb1; 13.3 % of FP rate (healthy users and patients, respectively).<br><br>CONCLUSIONS: The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.},
}
@article {pmid26469340,
year = {2016},
author = {Riechmann, H and Finke, A and Ritter, H},
title = {Using a cVEP-Based Brain-Computer Interface to Control a Virtual Agent.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {6},
pages = {692-699},
doi = {10.1109/TNSRE.2015.2490621},
pmid = {26469340},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; Visual Perception/*physiology ; },
abstract = {Brain-computer interfaces provide a means for controlling a device by brain activity alone. One major drawback of noninvasive BCIs is their low information transfer rate, obstructing a wider deployment outside the lab. BCIs based on codebook visually evoked potentials (cVEP) outperform all other state-of-the-art systems in that regard. Previous work investigated cVEPs for spelling applications. We present the first cVEP-based BCI for use in real-world settings to accomplish everyday tasks such as navigation or action selection. To this end, we developed and evaluated a cVEP-based on-line BCI that controls a virtual agent in a simulated, but realistic, 3-D kitchen scenario. We show that cVEPs can be reliably triggered with stimuli in less restricted presentation schemes, such as on dynamic, changing backgrounds. We introduce a novel, dynamic repetition algorithm that allows for optimizing the balance between accuracy and speed individually for each user. Using these novel mechanisms in a 12-command cVEP-BCI in the 3-D simulation results in ITRs of 50 bits/min on average and 68 bits/min maximum. Thus, this work supports the notion of cVEP-BCIs as a particular fast and robust approach suitable for real-world use.},
}
@article {pmid26468607,
year = {2015},
author = {Liao, Y and She, X and Wang, Y and Zhang, S and Zhang, Q and Zheng, X and Principe, JC},
title = {Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066014},
doi = {10.1088/1741-2560/12/6/066014},
pmid = {26468607},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Electromyography/*methods ; *Monte Carlo Method ; Motor Cortex/*physiology ; Movement/*physiology ; Rats ; },
abstract = {OBJECTIVE: Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true.<br><br>APPROACH: In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat's motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties.<br><br>MAIN RESULTS: Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average.<br><br>SIGNIFICANCE: These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.},
}
@article {pmid26465339,
year = {2015},
author = {Little, MP and Kwon, D and Zablotska, LB and Brenner, AV and Cahoon, EK and Rozhko, AV and Polyanskaya, ON and Minenko, VF and Golovanov, I and Bouville, A and Drozdovitch, V},
title = {Impact of Uncertainties in Exposure Assessment on Thyroid Cancer Risk among Persons in Belarus Exposed as Children or Adolescents Due to the Chernobyl Accident.},
journal = {PloS one},
volume = {10},
number = {10},
pages = {e0139826},
pmid = {26465339},
issn = {1932-6203},
support = {K07 CA132918/CA/NCI NIH HHS/United States ; N01CP21178/CP/NCI NIH HHS/United States ; 5K07CA132918/CA/NCI NIH HHS/United States ; N01-CP-21178/CP/NCI NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Bayes Theorem ; *Chernobyl Nuclear Accident ; Child ; Disasters ; Dose-Response Relationship, Radiation ; Environmental Exposure ; Female ; Humans ; Iodine Radioisotopes/*adverse effects ; Male ; Markov Chains ; Models, Statistical ; Monte Carlo Method ; Neoplasms, Radiation-Induced/*epidemiology ; Nuclear Reactors ; Prevalence ; Reproducibility of Results ; Republic of Belarus ; Risk Factors ; Stochastic Processes ; Thyroid Neoplasms/*epidemiology/*etiology ; Ukraine ; Uncertainty ; Young Adult ; },
abstract = {BACKGROUND: The excess incidence of thyroid cancer in Ukraine and Belarus observed a few years after the Chernobyl accident is considered to be largely the result of 131I released from the reactor. Although the Belarus thyroid cancer prevalence data has been previously analyzed, no account was taken of dose measurement error.<br><br>METHODS: We examined dose-response patterns in a thyroid screening prevalence cohort of 11,732 persons aged under 18 at the time of the accident, diagnosed during 1996-2004, who had direct thyroid 131I activity measurement, and were resident in the most radio-actively contaminated regions of Belarus. Three methods of dose-error correction (regression calibration, Monte Carlo maximum likelihood, Bayesian Markov Chain Monte Carlo) were applied.<br><br>RESULTS: There was a statistically significant (p<0.001) increasing dose-response for prevalent thyroid cancer, irrespective of regression-adjustment method used. Without adjustment for dose errors the excess odds ratio was 1.51 Gy- (95% CI 0.53, 3.86), which was reduced by 13% when regression-calibration adjustment was used, 1.31 Gy- (95% CI 0.47, 3.31). A Monte Carlo maximum likelihood method yielded an excess odds ratio of 1.48 Gy- (95% CI 0.53, 3.87), about 2% lower than the unadjusted analysis. The Bayesian method yielded a maximum posterior excess odds ratio of 1.16 Gy- (95% BCI 0.20, 4.32), 23% lower than the unadjusted analysis. There were borderline significant (p = 0.053-0.078) indications of downward curvature in the dose response, depending on the adjustment methods used. There were also borderline significant (p = 0.102) modifying effects of gender on the radiation dose trend, but no significant modifying effects of age at time of accident, or age at screening as modifiers of dose response (p>0.2).<br><br>CONCLUSIONS: In summary, the relatively small contribution of unshared classical dose error in the current study results in comparatively modest effects on the regression parameters.},
}
@article {pmid26465036,
year = {2015},
author = {Koons, DN and Colchero, F and Hersey, K and Gimenez, O},
title = {Disentangling the effects of climate, density dependence, and harvest on an iconic large herbivore's population dynamics.},
journal = {Ecological applications : a publication of the Ecological Society of America},
volume = {25},
number = {4},
pages = {956-967},
doi = {10.1890/14-0932.1},
pmid = {26465036},
issn = {1051-0761},
mesh = {Animals ; Bison/*physiology ; *Climate ; Models, Biological ; Population Density ; Population Dynamics ; Time Factors ; },
abstract = {Understanding the relative effects of climate, harvest, and density dependence on population dynamics is critical for guiding sound population management, especially for ungulates in arid and semiarid environments experiencing climate change. To address these issues for bison in southern Utah, USA, we applied a Bayesian state-space model to a 72-yr time series of abundance counts. While accounting for known harvest (as well as live removal) from the population, we found that the bison population in southern Utah exhibited a strong potential to grow from low density (β0 = 0.26; Bayesian credible interval based on 95% of the highest posterior density [BCI] = 0.19-0.33), and weak but statistically significant density dependence (β1 = -0.02, BCI = -0.04 to -0.004). Early spring temperatures also had strong positive effects on population growth (Pfat1 = 0.09, BCI = 0.04-0.14), much more so than precipitation and other temperature-related variables (model weight > three times more than that for other climate variables). Although we hypothesized that harvest is the primary driving force of bison population dynamics in southern Utah, our elasticity analysis indicated that changes in early spring temperature could have a greater relative effect on equilibrium abundance than either harvest or. the strength of density dependence. Our findings highlight the utility of incorporating elasticity analyses into state-space population models, and the need to include climatic processes in wildlife management policies and planning.},
}
@article {pmid26457507,
year = {2017},
author = {Weyand, S and Chau, T},
title = {Challenges of implementing a personalized mental task near-infrared spectroscopy brain-computer interface for a non-verbal young adult with motor impairments.},
journal = {Developmental neurorehabilitation},
volume = {20},
number = {2},
pages = {99-107},
doi = {10.3109/17518423.2015.1087436},
pmid = {26457507},
issn = {1751-8431},
mesh = {*Brain-Computer Interfaces ; Humans ; Male ; *Mental Processes ; Motor Disorders/psychology/*rehabilitation ; Neurological Rehabilitation/instrumentation/*methods ; *Nonverbal Communication ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {PURPOSE: Near-infrared spectroscopy brain-computer interfaces (NIRS-BCIs) have been proposed as potential motor-free communication pathways. This paper documents the challenges of implementing an NIRS-BCI with a non-verbal, severely and congenitally impaired, but cognitively intact young adult.<br><br>METHODS: A 5-session personalized mental task NIRS-BCI training paradigm was invoked, whereby participant-specific mental tasks were selected either by the researcher or by the user, on the basis of prior performance or user preference.<br><br>RESULTS: Although the personalized mental task selection and training framework had been previously demonstrated with able-bodied participants, the participant was not able to exceed chance-level accuracies. Challenges to the acquisition of BCI control may have included disinclination to BCI training, structural or functional brain atypicalities, heightened emotional arousal and confounding haemodynamic patterns associated with novelty and reward processing.<br><br>CONCLUSIONS: Overall, we stress the necessity for further clinical NIRS-BCI research involving non-verbal individuals with severe motor impairments.},
}
@article {pmid26456594,
year = {2015},
author = {Li, F and Liu, T and Wang, F and Li, H and Gong, D and Zhang, R and Jiang, Y and Tian, Y and Guo, D and Yao, D and Xu, P},
title = {Relationships between the resting-state network and the P3: Evidence from a scalp EEG study.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {15129},
pmid = {26456594},
issn = {2045-2322},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Frontal Lobe/physiology ; Humans ; Male ; Nerve Net/*physiology ; Occipital Lobe/physiology ; Parietal Lobe/physiology ; Rest/*physiology ; Scalp/physiology ; Task Performance and Analysis ; },
abstract = {The P3 is an important event-related potential that can be used to identify neural activity related to the cognitive processes of the human brain. However, the relationships, especially the functional correlations, between resting-state brain activity and the P3 have not been well established. In this study, we investigated the relationships between P3 properties (i.e., amplitude and latency) and resting-state brain networks. The results indicated that P3 amplitude was significantly correlated with resting-state network topology, and in general, larger P3 amplitudes could be evoked when the resting-state brain network was more efficient. However, no significant relationships were found for the corresponding P3 latency. Additionally, the long-range connections between the prefrontal/frontal and parietal/occipital brain regions, which represent the synchronous activity of these areas, were functionally related to the P3 parameters, especially P3 amplitude. The findings of the current study may help us better understand inter-subject variation in the P3, which may be instructive for clinical diagnosis, cognitive neuroscience studies, and potential subject selection for brain-computer interface applications.},
}
@article {pmid26453287,
year = {2016},
author = {Baltus, A and Herrmann, CS},
title = {The importance of individual frequencies of endogenous brain oscillations for auditory cognition - A short review.},
journal = {Brain research},
volume = {1640},
number = {Pt B},
pages = {243-250},
doi = {10.1016/j.brainres.2015.09.030},
pmid = {26453287},
issn = {1872-6240},
mesh = {Animals ; Auditory Perception/*physiology ; Brain/*physiology ; Cognition/*physiology ; Gamma Rhythm/*physiology ; Humans ; Memory/*physiology ; },
abstract = {Oscillatory EEG activity in the human brain with frequencies in the gamma range (approx. 30-80Hz) is known to be relevant for a large number of cognitive processes. Interestingly, each subject reveals an individual frequency of the auditory gamma-band response (GBR) that coincides with the peak in the auditory steady state response (ASSR). A common resonance frequency of auditory cortex seems to underlie both the individual frequency of the GBR and the peak of the ASSR. This review sheds light on the functional role of oscillatory gamma activity for auditory processing. For successful processing, the auditory system has to track changes in auditory input over time and store information about past events in memory which allows the construction of auditory objects. Recent findings support the idea of gamma oscillations being involved in the partitioning of auditory input into discrete samples to facilitate higher order processing. We review experiments that seem to suggest that inter-individual differences in the resonance frequency are behaviorally relevant for gap detection and speech processing. A possible application of these resonance frequencies for brain computer interfaces is illustrated with regard to optimized individual presentation rates for auditory input to correspond with endogenous oscillatory activity. This article is part of a Special Issue entitled SI: Auditory working memory.},
}
@article {pmid26452197,
year = {2016},
author = {Aghaei, AS and Mahanta, MS and Plataniotis, KN},
title = {Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {1},
pages = {15-29},
doi = {10.1109/TBME.2015.2487738},
pmid = {26452197},
issn = {1558-2531},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*classification/*methods ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Feature extraction is one of the most important steps in any brain-computer interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs (MI-BCI) has been the focus of several works in the past decade. This paper proposes a novel method, called separable common spatio-spectral patterns (SCSSP), for extraction of discriminant spatio-spectral EEG features in MI-BCIs.<br><br>METHODS: Assuming a binary classification problem, SCSSP uses a heteroscedastic matrix-variate Gaussian model for the multiband EEG rhythms, and seeks the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, SCSSP can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm.<br><br>RESULTS: The experimental results on two-class and multiclass motor-imagery data from publicly available BCI Competition datasets demonstrate that the proposed computationally efficient method competes closely with filter-bank CSP (FBCSP), and can even outperform the FBCSP if enough training data are available. Furthermore, SCSSP provides us with a simple measure for ranking the discriminant power of extracted spatio-spectral features, which is not possible in FBCSP.<br><br>CONCLUSION: The matrix-variate Gaussian assumption allows the SCSSP method to jointly process the EEG data in both spatial and spectral domains. As a result, compared to the similar solutions in the literature such as FBCSP, the proposed SCSSP method requires significantly lower computations.<br><br>SIGNIFICANCE: The proposed computationally efficient spatio-spectral feature extractor is particularly suitable for applications in which the computational power is limited, such as emerging wearable mobile BCI systems.},
}
@article {pmid26451817,
year = {2015},
author = {Xin, Y and Li, WX and Zhang, Z and Cheung, RC and Song, D and Berger, TW},
title = {An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.},
journal = {IEEE/ACM transactions on computational biology and bioinformatics},
volume = {12},
number = {5},
pages = {1034-1047},
doi = {10.1109/TCBB.2015.2440248},
pmid = {26451817},
issn = {1557-9964},
mesh = {Action Potentials/*physiology ; Algorithms ; Brain/*physiology ; Brain Mapping/*instrumentation ; Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; *Neural Prostheses ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.},
}
@article {pmid26447843,
year = {2015},
author = {Cho, H and Ahn, M and Kim, K and Jun, SC},
title = {Increasing session-to-session transfer in a brain-computer interface with on-site background noise acquisition.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066009},
doi = {10.1088/1741-2560/12/6/066009},
pmid = {26447843},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces/*standards ; *Electricity/adverse effects ; Electrocardiography/methods/*standards ; Humans ; Male ; Young Adult ; },
abstract = {OBJECTIVE: A brain-computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems.<br><br>APPROACH: In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF.<br><br>MAIN RESULTS: We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers.<br><br>SIGNIFICANCE: We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.},
}
@article {pmid26447770,
year = {2015},
author = {C Schudlo, L and Chau, T},
title = {Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066008},
doi = {10.1088/1741-2560/12/6/066008},
pmid = {26447770},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Parietal Lobe/physiology ; Prefrontal Cortex/physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Rest/*physiology ; Spectroscopy, Near-Infrared/*methods ; Speech/*physiology ; *Stroop Test ; Young Adult ; },
abstract = {OBJECTIVE: The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements.<br><br>APPROACH: Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set.<br><br>MAIN RESULTS: VFT, Stroop task and rest were classified at an average accuracy of 71.7% &#xb1; 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 &#xb1; 0.35 bits/min versus 0.73 &#xb1; 0.24 bits/min).<br><br>SIGNIFICANCE: These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.},
}
@article {pmid26441568,
year = {2015},
author = {Mateo, S and Di Rienzo, F and Bergeron, V and Guillot, A and Collet, C and Rode, G},
title = {Motor imagery reinforces brain compensation of reach-to-grasp movement after cervical spinal cord injury.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {234},
pmid = {26441568},
issn = {1662-5153},
abstract = {Individuals with cervical spinal cord injury (SCI) that causes tetraplegia are challenged with dramatic sensorimotor deficits. However, certain rehabilitation techniques may significantly enhance their autonomy by restoring reach-to-grasp movements. Among others, evidence of motor imagery (MI) benefits for neurological rehabilitation of upper limb movements is growing. This literature review addresses MI effectiveness during reach-to-grasp rehabilitation after tetraplegia. Among articles from MEDLINE published between 1966 and 2015, we selected ten studies including 34 participants with C4 to C7 tetraplegia and 22 healthy controls published during the last 15 years. We found that MI of possible non-paralyzed movements improved reach-to-grasp performance by: (i) increasing both tenodesis grasp capabilities and muscle strength; (ii) decreasing movement time (MT), and trajectory variability; and (iii) reducing the abnormally increased brain activity. MI can also strengthen motor commands by potentiating recruitment and synchronization of motoneurons, which leads to improved recovery. These improvements reflect brain adaptations induced by MI. Furthermore, MI can be used to control brain-computer interfaces (BCI) that successfully restore grasp capabilities. These results highlight the growing interest for MI and its potential to recover functional grasping in individuals with tetraplegia, and motivate the need for further studies to substantiate it.},
}
@article {pmid26441505,
year = {2015},
author = {Agorelius, J and Tsanakalis, F and Friberg, A and Thorbergsson, PT and Pettersson, LM and Schouenborg, J},
title = {An array of highly flexible electrodes with a tailored configuration locked by gelatin during implantation-initial evaluation in cortex cerebri of awake rats.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {331},
pmid = {26441505},
issn = {1662-4548},
abstract = {BACKGROUND: A major challenge in the field of neural interfaces is to overcome the problem of poor stability of neuronal recordings, which impedes long-term studies of individual neurons in the brain. Conceivably, unstable recordings reflect relative movements between electrode and tissue. To address this challenge, we have developed a new ultra-flexible electrode array and evaluated its performance in awake non-restrained animals.<br><br>METHODS: An array of eight separated gold leads (4 &#xd7; 10 &#x3bc;m), individually flexible in 3D, were cut from a gold sheet using laser milling and insulated with Parylene C. To provide structural support during implantation into rat cortex, the electrode array was embedded in a hard gelatin based material, which dissolves after implantation. Recordings were made during 3 weeks. At termination, the animals were perfused with fixative and frozen to prevent dislocation of the implanted electrodes. A thick slice of brain tissue, with the electrode array still in situ, was made transparent using methyl salicylate to evaluate the conformation of the implanted electrode array.<br><br>RESULTS: Median noise levels and signal/noise remained relatively stable during the 3 week observation period; 4.3-5.9 &#x3bc;V and 2.8-4.2, respectively. The spike amplitudes were often quite stable within recording sessions and for 15% of recordings where single-units were identified, the highest-SNR unit had an amplitude higher than 150 &#x3bc;V. In addition, high correlations (>0.96) between unit waveforms recorded at different time points were obtained for 58% of the electrode sites. The structure of the electrode array was well preserved 3 weeks after implantation.<br><br>CONCLUSIONS: A new implantable multichannel neural interface, comprising electrodes individually flexible in 3D that retain its architecture and functionality after implantation has been developed. Since the new neural interface design is adaptable, it offers a versatile tool to explore the function of various brain structures.},
}
@article {pmid26441454,
year = {2016},
author = {Vigaru, B and Sulzer, J and Gassert, R},
title = {Design and Evaluation of a Cable-Driven fMRI-Compatible Haptic Interface to Investigate Precision Grip Control.},
journal = {IEEE transactions on haptics},
volume = {9},
number = {1},
pages = {20-32},
pmid = {26441454},
issn = {2329-4051},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; },
abstract = {Our hands and fingers are involved in almost all activities of daily living and, as such, have a disproportionately large neural representation. Functional magnetic resonance imaging investigations into the neural control of the hand have revealed great advances, but the harsh MRI environment has proven to be a challenge to devices capable of delivering a large variety of stimuli necessary for well-controlled studies. This paper presents a fMRI-compatible haptic interface to investigate the neural mechanisms underlying precision grasp control. The interface, located at the scanner bore, is controlled remotely through a shielded electromagnetic actuation system positioned at the end of the scanner bed and then through a high stiffness, low inertia cable transmission. We present the system design, taking into account requirements defined by the biomechanics and dynamics of the human hand, as well as the fMRI environment. Performance evaluation revealed a structural stiffness of 3.3 N/mm, renderable forces up to 94 N, and a position control bandwidth of at least 19 Hz. MRI-compatibility tests showed no degradation in the operation of the haptic interface or the image quality. A preliminary fMRI experiment during a pilot study validated the usability of the haptic interface, illustrating the possibilities offered by this device.},
}
@article {pmid26437432,
year = {2015},
author = {McClay, WA and Yadav, N and Ozbek, Y and Haas, A and Attias, HT and Nagarajan, SS},
title = {A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem.},
journal = {Brain sciences},
volume = {5},
number = {4},
pages = {419-440},
pmid = {26437432},
issn = {2076-3425},
abstract = {Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user's intent for specific keyboard strikes or mouse button presses. The BCI's data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject's MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.},
}
@article {pmid26435497,
year = {2015},
author = {Ham, JE and Oh, EK and Kim, DH and Choi, SH},
title = {Differential expression profiles and roles of inducible DUSPs and ERK1/2-specific constitutive DUSP6 and DUSP7 in microglia.},
journal = {Biochemical and biophysical research communications},
volume = {467},
number = {2},
pages = {254-260},
doi = {10.1016/j.bbrc.2015.09.180},
pmid = {26435497},
issn = {1090-2104},
mesh = {Animals ; Animals, Newborn ; Dual Specificity Phosphatase 1/antagonists &amp; inhibitors/*genetics/metabolism ; Dual Specificity Phosphatase 6/antagonists &amp; inhibitors/*genetics/metabolism ; Flavonoids/pharmacology ; Gene Expression Regulation ; Isoenzymes/genetics/metabolism ; Lipopolysaccharides/pharmacology ; Microglia/cytology/drug effects/*metabolism ; Mitogen-Activated Protein Kinase 1/*genetics/metabolism ; Mitogen-Activated Protein Kinase 3/*genetics/metabolism ; Phosphorylation/drug effects ; Primary Cell Culture ; Protein Kinase Inhibitors/pharmacology ; Pyrazoles/pharmacology ; Pyridazines/pharmacology ; Rats ; Rats, Sprague-Dawley ; Signal Transduction ; p38 Mitogen-Activated Protein Kinases/genetics/metabolism ; },
abstract = {Dual-specificity phosphatases (DUSPs) show distinct substrate preferences for specific MAPKs. DUSPs sharing a substrate preference for ERK1/2 may be classified as inducible or constitutive. In contrast to the inducible DUSPs which also dephosphorylate p38 MAPK and JNK in the major inflammatory pathways, constitutive DUSP6 and DUSP7 are specific to ERK1/2 and have not been studied in microglia and other immune cells to date. In the present study, we differentiated mRNA expression profiles of inducible and constitutive DUSPs that dephosphorylate ERK1/2 in microglia. Lipopolysaccharide (LPS) at 1 ng/ml induced prompt phosphorylation of ERK1/2 with peak induction at 30 min. LPS induced expression of DUSP1, DUSP2, and DUSP5 within 60 min, whereas DUSP4 expression was induced more slowly. DUSP6 and DUSP7 exhibited constitutive basal expression, which decreased immediately after LPS stimulation but subsequently returned to basal levels. The expression of DUSP6 and DUSP7 was regulated inverse to the phosphorylation of ERK1/2 in LPS-stimulated microglia. Therefore, we next investigated the correlation between DUSP6 and DUSP7 expression and ERK1/2 phosphorylation in resting and LPS-stimulated microglia. Inhibition of the ERK1/2 pathway by PD98059 and FR180204 resulted in a decrease in DUSP6 and DUSP7 expression, both in resting and LPS-stimulated microglia. These inhibitors partially blocked the LPS-induced expression of DUSP1, DUSP2, and DUSP4, but had no effect on DUSP5. Finally, we examined the role of DUSP6 activity in the downregulation of ERK1/2 phosphorylation. BCI, an inhibitor of DUSP6, increased the phosphorylation of ERK1/2. However, pretreatment with BCI inhibited the LPS-induced phosphorylation of ERK1/2. These results demonstrate that constitutive DUPS6 and DUSP7 expression was downregulated inverse to the expression of inducible DUSPs and the phosphorylation of ERK1/2 in LPS-stimulated microglia. The expression of DUPS6 and DUSP7 was mediated by ERK1/2 activity both in resting and LPS-stimulated microglia. In turn, DUSP6 suppressed the basal phosphorylation of ERK1/2, but exerted no suppressive effect on LPS-induced phosphorylation. Although DUSP6 is acknowledged as a negative regulator of the ERK1/2 pathway, such roles of DUSP6 need to be examined further in activated microglia.},
}
@article {pmid26420660,
year = {2015},
author = {Duvvuri, VR and Granados, A and Rosenfeld, P and Bahl, J and Eshaghi, A and Gubbay, JB},
title = {Genetic diversity and evolutionary insights of respiratory syncytial virus A ON1 genotype: global and local transmission dynamics.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {14268},
pmid = {26420660},
issn = {2045-2322},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; *Evolution, Molecular ; Female ; *Genetic Variation ; *Genotype ; Global Health ; Humans ; Infant ; Infant, Newborn ; Male ; Middle Aged ; Ontario/epidemiology ; Phylogeny ; Respiratory Syncytial Virus Infections/epidemiology/transmission/*virology ; Respiratory Syncytial Virus, Human/*genetics ; Selection, Genetic ; Young Adult ; },
abstract = {Human respiratory syncytial virus (RSV) A ON1 genotype, first detected in 2010 in Ontario, Canada, has been documented in 21 countries to date. This study investigated persistence and transmission dynamics of ON1 by grouping 406 randomly selected RSV-positive specimens submitted to Public Health Ontario from August 2011 to August 2012; RSV-A-positive specimens were genotyped. We identified 370 RSV-A (181 NA1, 135 NA2, 51 ON1 3 GA5) and 36 RSV-B positive specimens. We aligned time-stamped second hypervariable region (330 bp) of G-gene sequence data (global, n = 483; and Ontario, n = 60) to evaluate transmission dynamics. Global data suggests that the most recent common ancestor of ON1 emerged during the 2008-2009 season. Mean evolutionary rate of the global ON1 was 4.10 × 10(-3) substitutions/site/year (95% BCI 3.1-5.0 × 10(-3)), not significantly different to that of Ontario ON1. The estimated mean reproductive number (R0 = ∼ 1.01) from global and Ontario sequences showed no significant difference and implies stability among global RSV-A ON1. This study suggests that local epidemics exhibit similar underlying evolutionary and epidemiological dynamics to that of the persistent global RSV-A ON1 population. These findings underscore the importance of continual molecular surveillance of RSV in order to gain a better understanding of epidemics.},
}
@article {pmid26415149,
year = {2015},
author = {Mullen, TR and Kothe, CA and Chi, YM and Ojeda, A and Kerth, T and Makeig, S and Jung, TP and Cauwenberghs, G},
title = {Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {11},
pages = {2553-2567},
pmid = {26415149},
issn = {1558-2531},
support = {R01 MH084819/MH/NIMH NIH HHS/United States ; 1R01MH084819-03/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/*instrumentation/methods ; Humans ; Male ; Neuroimaging/*instrumentation/methods ; Task Performance and Analysis ; Young Adult ; },
abstract = {GOAL: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.<br><br>METHODS: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system.<br><br>RESULTS: Simulations yielded high accuracy (AUC = 0.97 &#xb1; 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 &#xb1;0.09) and LCMV (0.72 &#xb1;0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 &#xb1;0.16) but significantly better for LCMV (0.82 &#xb1;0.12) .<br><br>CONCLUSION: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG.<br><br>SIGNIFICANCE: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.},
}
@article {pmid26411502,
year = {2016},
author = {Beudel, M and Brown, P},
title = {Adaptive deep brain stimulation in Parkinson's disease.},
journal = {Parkinsonism &amp; related disorders},
volume = {22 Suppl 1},
number = {Suppl 1},
pages = {S123-6},
pmid = {26411502},
issn = {1873-5126},
mesh = {Animals ; Brain-Computer Interfaces/trends ; Deep Brain Stimulation/*methods/trends ; Humans ; Hypokinesia/diagnosis/therapy ; Parkinson Disease/*diagnosis/*therapy ; Subthalamic Nucleus/*pathology ; Tremor/diagnosis/therapy ; },
abstract = {Although Deep Brain Stimulation (DBS) is an established treatment for Parkinson's disease (PD), there are still limitations in terms of effectivity, side-effects and battery consumption. One of the reasons for this may be that not only pathological but also physiological neural activity can be suppressed whilst stimulating. For this reason, adaptive DBS (aDBS), where stimulation is applied according to the level of pathological activity, might be advantageous. Initial studies of aDBS demonstrate effectiveness in PD, but there are still many questions to be answered before aDBS can be applied clinically. Here we discuss the feedback signals and stimulation algorithms involved in adaptive stimulation in PD and sketch a potential road-map towards clinical application.},
}
@article {pmid26410490,
year = {2015},
author = {Wang, H and Song, A and Li, B and Xu, B and Li, Y},
title = {Psychophysiological classification and experiment study for spontaneous EEG based on two novel mental tasks.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {23 Suppl 2},
number = {},
pages = {S249-62},
doi = {10.3233/THC-150960},
pmid = {26410490},
issn = {1878-7401},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Consciousness/*physiology ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Meditation ; Occipital Lobe/metabolism ; Parietal Lobe/metabolism ; Relaxation/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Study of imagination offers a perfect setting for study of a large variety of states of consciousness.<br><br>OBJECTIVE: Here, we studied the characteristics of two electroencephalographic (EEG) patterns evoked by two different imaginary tasks and evaluated the binary classification performance.<br><br>METHODS: Fifteen individuals (11 male and 4 female, age range of 22 to 33) participated in five sessions of 32-channel EEG recordings. Only by analyzing the subjects' output EEG signals from the central parieto-occipital region of PZ electrode, under the circumstances of consciousness of relaxation-meditation or tension-imagination, we carried out the experiment of feature extraction for spontaneous EEG, as the subjects were blindfolded but asked to open their eyes all the same. The Hilbert-Huang Transform (HHT) was utilized to obtain the Hilbert time-frequency amplitude spectrum, and then with the feature vector set extracted, a two-class Fisher linear discriminant analysis classifier was trained for classification of data epochs of those two tasks.<br><br>RESULTS: The overall result was that about 90% (&#xb1; 5%) of the epochs could be correctly classified to their originating task.<br><br>CONCLUSION: This study not only brings new opportunities for consciousness studies, but also provides a new classification paradigm for achieving control of robots based on the brain-computer interface (BCI).},
}
@article {pmid26410210,
year = {2015},
author = {Lee, M and Park, CH and Im, CH and Kim, JH and Kwon, GH and Kim, L and Chang, WH and Kim, YH},
title = {Motor imagery learning across a sequence of trials in stroke patients.},
journal = {Restorative neurology and neuroscience},
volume = {34},
number = {4},
pages = {635-645},
doi = {10.3233/RNN-150534},
pmid = {26410210},
issn = {1878-3627},
mesh = {Aged ; Brain Waves/*physiology ; Brain-Computer Interfaces/*standards ; Female ; Humans ; Imagination/*physiology ; Learning/*physiology ; Male ; Middle Aged ; Motor Activity/*physiology ; Stroke/*physiopathology ; Stroke Rehabilitation/*standards ; },
abstract = {PURPOSE: In brain-computer interfaces (BCIs), electrical brain signals during motor imagery are utilized as commands connecting the brain to a computer. To use BCI in patients with stroke, unique brain signal changes should be characterized during motor imagery process. This study aimed to examine the trial-dependent motor-imagery-related activities in stroke patients.<br><br>METHODS: During the recording of electroencephalography (EEG) signals, 12 chronic stroke patients and 11 age-matched healthy controls performed motor imagery finger tapping at 1.3&#x200a;sec intervals. Trial-dependent brain signal changes were assessed by analysis of the mu and beta bands.<br><br>RESULTS: Neuronal activity in healthy controls was observed over bilateral hemispheres at the mu and beta bands regardless of changes in the trials, whereas neuronal activity in stroke patients was mainly seen over the ipsilesional hemisphere at the beta band. With progression to repeated trials, healthy controls displayed a decrease in cortical activity in the contralateral hemisphere at the mu band and in bilateral hemispheres at the beta band. In contrast, stroke patients showed a decreasing trend in cortical activity only over the ipsilesional hemisphere at the beta band.<br><br>CONCLUSIONS: Trial-dependent changes during motor imagery learning presented in a different manner in stroke patients. Understanding motor imagery learning in stroke patients is crucial for enhancing the effectiveness of motor-imagery-based BCIs.},
}
@article {pmid26409339,
year = {2015},
author = {Lancioni, GE and Simone, IL and De Caro, MF and Singh, NN and O'Reilly, MF and Sigafoos, J and Ferlisi, G and Zullo, V and Schirone, S and Denitto, F and Zonno, N},
title = {Assisting persons with advanced amyotrophic lateral sclerosis in their leisure engagement and communication needs with a basic technology-aided program.},
journal = {NeuroRehabilitation},
volume = {36},
number = {3},
pages = {355-365},
doi = {10.3233/NRE-151224},
pmid = {26409339},
issn = {1878-6448},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/diagnosis/psychology/*rehabilitation ; *Brain-Computer Interfaces/psychology ; Communication ; *Communication Devices for People with Disabilities/psychology/trends ; Eye Movements/physiology ; Female ; *Health Services Needs and Demand/trends ; Humans ; *Leisure Activities/psychology ; Male ; Middle Aged ; Music/psychology ; Photic Stimulation/methods ; Text Messaging/trends ; },
abstract = {BACKGROUND: Eye-tracking communication devices and brain-computer interfaces are the two resources available to help people with advanced amyotrophic lateral sclerosis (ALS) avoid isolation and passivity.<br><br>OBJECTIVE: This study was aimed at assessing a technology-aided program (i.e., a third possible resource) for five patients with advanced ALS who needed support for communication and leisure activities.<br><br>METHODS: The participants were exposed to baseline and intervention conditions. The technology-aided program, which was used during the intervention, (a) included the communication and leisure options that each participant considered important for him or her (e.g., music, videos, statements/requests, and text messaging) and (b) allowed the participant to access those options with minimal responses (e.g., finger movement or eyelid closure) monitored via microswitches.<br><br>RESULTS: The participants started leisure and communication engagement independently only during the intervention (i.e., when the program was used). The mean percentages of session time spent in those forms of engagement were between about 60 and 80. Preference checks and brief interviews indicated that participants and families liked the program.<br><br>CONCLUSIONS: The program might be viewed as an additional approach/resource for patients with advanced ALS.},
}
@article {pmid26407815,
year = {2016},
author = {Höhne, J and Bartz, D and Hebart, MN and Müller, KR and Blankertz, B},
title = {Analyzing neuroimaging data with subclasses: A shrinkage approach.},
journal = {NeuroImage},
volume = {124},
number = {Pt A},
pages = {740-751},
doi = {10.1016/j.neuroimage.2015.09.031},
pmid = {26407815},
issn = {1095-9572},
mesh = {Brain Mapping ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Motion Perception ; Neuroimaging/*statistics &amp; numerical data ; Photic Stimulation ; Reproducibility of Results ; },
abstract = {Among the numerous methods used to analyze neuroimaging data, Linear Discriminant Analysis (LDA) is commonly applied for binary classification problems. LDAs popularity derives from its simplicity and its competitive classification performance, which has been reported for various types of neuroimaging data. Yet the standard LDA approach proves less than optimal for binary classification problems when additional label information (i.e. subclass labels) is present. Subclass labels allow to model structure in the data, which can be used to facilitate the classification task. In this paper, we illustrate how neuroimaging data exhibit subclass labels that may contain valuable information. We also show that the standard LDA classifier is unable to exploit subclass labels. We introduce a novel method that allows subclass labels to be incorporated efficiently into the classifier. The novel method, which we call Relevance Subclass LDA (RSLDA), computes an individual classification hyperplane for each subclass. It is based on regularized estimators of the subclass mean and uses other subclasses as regularization targets. We demonstrate the applicability and performance of our method on data drawn from two different neuroimaging modalities: (I) EEG data from brain-computer interfacing with event-related potentials, and (II) fMRI data in response to different levels of visual motion. We show that RSLDA outperforms the standard LDA approach for both types of datasets. These findings illustrate the benefits of exploiting subclass structure in neuroimaging data. Finally, we show that our classifier also outputs regularization profiles, enabling researchers to interpret the subclass structure in a meaningful way. RSLDA therefore yields increased classification accuracy as well as a better interpretation of neuroimaging data. Since both results are highly favorable, we suggest to apply RSLDA for various classification problems within neuroimaging and beyond.},
}
@article {pmid26406024,
year = {2015},
author = {Yang, J and Huai, R and Wang, H and Lv, C and Su, X},
title = {A robo-pigeon based on an innovative multi-mode telestimulation system.},
journal = {Bio-medical materials and engineering},
volume = {26 Suppl 1},
number = {},
pages = {S357-63},
doi = {10.3233/BME-151323},
pmid = {26406024},
issn = {1878-3619},
mesh = {Animals ; Behavior, Animal/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Columbidae/*physiology ; Deep Brain Stimulation/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Man-Machine Systems ; Robotics/*instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {In this paper, we describe a new multi-mode telestimulation system for brain-microstimulation for the navigation of a robo-pigeon, a new type of bio-robot based on Brain-Computer Interface (BCI) techniques. The multi-mode telestimulation system overcomes neuron adaptation that was a key shortcoming of the previous single-mode stimulation by the use of non-steady TTL biphasic pulses accomplished by randomly alternating pulse modes. To improve efficiency, a new behavior model ("virtual fear") is proposed and applied to the robo-pigeon. Unlike the previous "virtual reward" model, the "virtual fear" behavior model does not require special training. The performance and effectiveness of the system to alleviate the adaptation of neurons was verified by a robo-pigeon navigation test, simultaneously confirming the practicality of the "virtual fear" behavioral model.},
}
@article {pmid26405916,
year = {2015},
author = {Wei, Q and Wei, Z},
title = {Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.},
journal = {Bio-medical materials and engineering},
volume = {26 Suppl 1},
number = {},
pages = {S1523-32},
doi = {10.3233/BME-151451},
pmid = {26405916},
issn = {1878-3619},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.},
}
@article {pmid26405870,
year = {2015},
author = {Yu, W and Feng, H and Feng, Y and Madani, K and Sabourin, C},
title = {Nonholonomic mobile system control by combining EEG-based BCI with ANFIS.},
journal = {Bio-medical materials and engineering},
volume = {26 Suppl 1},
number = {},
pages = {S1125-33},
doi = {10.3233/BME-151409},
pmid = {26405870},
issn = {1878-3619},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrocardiography/*methods ; Evoked Potentials, Motor/physiology ; Fuzzy Logic ; Humans ; Imagination/*physiology ; Male ; *Man-Machine Systems ; Motor Cortex/*physiology ; Movement/physiology ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Robotics/*methods ; },
abstract = {Motor imagery EEG-based BCI has advantages in the assistance of human control of peripheral devices, such as the mobile robot or wheelchair, because the subject is not exposed to any stimulation and suffers no risk of fatigue. However, the intensive training necessary to recognize the numerous classes of data makes it hard to control these nonholonomic mobile systems accurately and effectively. This paper proposes a new approach which combines motor imagery EEG with the Adaptive Neural Fuzzy Inference System. This approach fuses the intelligence of humans based on motor imagery EEG with the precise capabilities of a mobile system based on ANFIS. This approach realizes a multi-level control, which makes the nonholonomic mobile system highly controllably without stopping or relying on sensor information. Also, because the ANFIS controller can be trained while performing the control task, control accuracy and efficiency is increased for the user. Experimental results of the nonholonomic mobile robot verify the effectiveness of this approach.},
}
@article {pmid26405856,
year = {2015},
author = {Duan, L and Ge, H and Ma, W and Miao, J},
title = {EEG feature selection method based on decision tree.},
journal = {Bio-medical materials and engineering},
volume = {26 Suppl 1},
number = {},
pages = {S1019-25},
doi = {10.3233/BME-151397},
pmid = {26405856},
issn = {1878-3619},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Decision Trees ; Electroencephalography/*methods ; Humans ; Principal Component Analysis ; Support Vector Machine ; },
abstract = {This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.},
}
@article {pmid26403657,
year = {2015},
author = {Reichelt, AC and Westbrook, RF and Morris, MJ},
title = {Integration of reward signalling and appetite regulating peptide systems in the control of food-cue responses.},
journal = {British journal of pharmacology},
volume = {172},
number = {22},
pages = {5225-5238},
pmid = {26403657},
issn = {1476-5381},
mesh = {Animals ; Appetite/*physiology ; Cues ; Feeding Behavior/*physiology ; Food ; Humans ; Peptides/*physiology ; *Reward ; },
abstract = {Understanding the neurobiological substrates that encode learning about food-associated cues and how those signals are modulated is of great clinical importance especially in light of the worldwide obesity problem. Inappropriate or maladaptive responses to food-associated cues can promote over-consumption, leading to excessive energy intake and weight gain. Chronic exposure to foods rich in fat and sugar alters the reinforcing value of foods and weakens inhibitory neural control, triggering learned, but maladaptive, associations between environmental cues and food rewards. Thus, responses to food-associated cues can promote cravings and food-seeking by activating mesocorticolimbic dopamine neurocircuitry, and exert physiological effects including salivation. These responses may be analogous to the cravings experienced by abstaining drug addicts that can trigger relapse into drug self-administration. Preventing cue-triggered eating may therefore reduce the over-consumption seen in obesity and binge-eating disorder. In this review we discuss recent research examining how cues associated with palatable foods can promote reward-based feeding behaviours and the potential involvement of appetite-regulating peptides including leptin, ghrelin, orexin and melanin concentrating hormone. These peptide signals interface with mesolimbic dopaminergic regions including the ventral tegmental area to modulate reactivity to cues associated with palatable foods. Thus, a novel target for anti-obesity therapeutics is to reduce non-homeostatic, reward driven eating behaviour, which can be triggered by environmental cues associated with highly palatable, fat and sugar rich foods.},
}
@article {pmid26401885,
year = {2015},
author = {Khaliliardali, Z and Chavarriaga, R and Gheorghe, LA and Millán, Jdel R},
title = {Action prediction based on anticipatory brain potentials during simulated driving.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066006},
doi = {10.1088/1741-2560/12/6/066006},
pmid = {26401885},
issn = {1741-2552},
mesh = {Adult ; *Automobile Driving/psychology ; Brain/*physiology ; *Brain-Computer Interfaces/psychology ; *Computer Simulation ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Female ; Forecasting ; Humans ; *Intention ; Male ; Young Adult ; },
abstract = {OBJECTIVE: The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating.<br><br>APPROACH: We investigated anticipatory slow cortical potentials in electroencephalogram recorded from 18 healthy participants in a driving simulator using a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions: count-down numbers followed by 'Start'/'Stop' cue. We report decoding performance before the action onset using a quadratic discriminant analysis classifier based on temporal features.<br><br>MAIN RESULTS: (i) Despite the visual and driving related cognitive distractions, we show the presence of anticipatory event related potentials locked to the stimuli onset similar to the widely reported CNV signal (with an average peak value of -8 &#x3bc;V at electrode Cz). (ii) We demonstrate the discrimination between cases requiring to perform an action upon imperative subsequent stimulus (Go condition, e.g. a 'Red' traffic light) versus events that do not require such action (No-go condition; e.g. a 'Yellow' light); with an average single trial classification performance of 0.83 &#xb1; 0.13 for braking and 0.79 &#xb1; 0.12 for accelerating (area under the curve). (iii) We show that the centro-medial anticipatory potentials are observed as early as 320 &#xb1; 200 ms before the action with a detection rate of 0.77 &#xb1; 0.12 in offline analysis.<br><br>SIGNIFICANCE: We show for the first time the feasibility of predicting the driver's intention through decoding anticipatory related potentials during simulated car driving with high recognition rates.},
}
@article {pmid26401684,
year = {2015},
author = {Banca, P and Sousa, T and Duarte, IC and Castelo-Branco, M},
title = {Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions.},
journal = {Journal of neural engineering},
volume = {12},
number = {6},
pages = {066003},
doi = {10.1088/1741-2560/12/6/066003},
pmid = {26401684},
issn = {1741-2552},
mesh = {Acoustic Stimulation/methods ; Adult ; Cerebellum/*physiology ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/methods ; Male ; Motion Perception/*physiology ; Neocortex/*physiology ; Nerve Net/physiology ; Neurofeedback/*methods ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies.<br><br>APPROACH: We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5.<br><br>MAIN RESULTS: We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs.<br><br>SIGNIFICANCE: Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.},
}
@article {pmid26401590,
year = {2016},
author = {Alfaidy, N and Hoffmann, P and Gillois, P and Gueniffey, A and Lebayle, C and Garçin, H and Thomas-Cadi, C and Bessonnat, J and Coutton, C and Villaret, L and Quenard, N and Bergues, U and Feige, JJ and Hennebicq, S and Brouillet, S},
title = {PROK1 Level in the Follicular Microenvironment: A New Noninvasive Predictive Biomarker of Embryo Implantation.},
journal = {The Journal of clinical endocrinology and metabolism},
volume = {101},
number = {2},
pages = {435-444},
doi = {10.1210/jc.2015-1988},
pmid = {26401590},
issn = {1945-7197},
mesh = {Adult ; Biomarkers/analysis ; Cryopreservation ; Culture Media/analysis ; *Embryo Implantation ; Female ; Fertilization in Vitro ; Follicular Fluid/*chemistry ; Gastrointestinal Hormones/*analysis ; Genetic Markers ; Granulosa Cells ; Humans ; Infertility, Female ; Oocyte Retrieval ; Ovary/metabolism ; Pregnancy ; Prospective Studies ; Treatment Outcome ; Vascular Endothelial Growth Factor, Endocrine-Gland-Derived/*analysis ; },
abstract = {CONTEXT: Prokineticin 1 (PROK1), also called endocrine gland-derived vascular endothelial growth factor, is a well-established regulator of endometrial receptivity and placental development. However, its clinical usefulness as a noninvasive predictive biomarker of embryo implantation is yet to be validated.<br><br>OBJECTIVE: The main objective of this article was to determine the relationship between PROK1 levels in the follicular fluid (FF) and fertilization culture media (FCM) and the reproductive outcome in patients who received a first conventional in vitro fertilization-embryo transfer. The secondary objective was to characterize the expression of PROK1 and its receptors (PROKRs) in the human follicular microenvironment.<br><br>DESIGN AND SETTING: We conducted a prospective study between January 2013 and June 2015 at the University Hospital of Grenoble.<br><br>PATIENTS: A total of 135 infertile in vitro fertilization patients and 10 women undergoing ovarian tissue cryopreservation were included.<br><br>INTERVENTIONS: The PROK1 concentration was measured by ELISA in FF and FCM collected on the day of oocyte retrieval and the day of the oocyte denudation step, respectively. Follicular expression of the PROK1/PROKR system was determined by immunohistochemistry, RT-quantitative PCR, and ELISA.<br><br>MAIN OUTCOME MEASURE: Assessment of the clinical pregnancy rates was the main outcome.<br><br>RESULTS: FF and FCM PROK1 levels were significantly higher in the embryo implantation group (P < .001) and were predictive of subsequent embryo implantation (area under the receiver operating characteristic curve, 0.91 [95% confidence interval, 0.81-1.00], P = .001; and 0.88 [0.72-1.00], P = .001, respectively). FF and FCM PROK1 levels remain similar irrespective of the embryo morphokinetic parameters (P = .71 and P = .83, respectively). The PROK1/PROKR system is expressed during human folliculogenesis.<br><br>CONCLUSIONS: PROK1 levels in FF and FCM could constitute new predictive noninvasive markers of successful embryo implantation in conventional in vitro fertilization-embryo transfer.},
}
@article {pmid26400061,
year = {2015},
author = {King, CE and Wang, PT and McCrimmon, CM and Chou, CC and Do, AH and Nenadic, Z},
title = {The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {80},
pmid = {26400061},
issn = {1743-0003},
mesh = {Brain/physiopathology ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*instrumentation/*methods ; Electroencephalography/methods ; Feasibility Studies ; Humans ; Male ; Paraplegia/etiology/*rehabilitation ; Prostheses and Implants ; Spinal Cord Injuries/complications/*rehabilitation ; Walking/physiology ; },
abstract = {BACKGROUND: Direct brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI.<br><br>METHODS: An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the participant engaged in several real-time walking tests using the BCI-FES system. This was done in both a suspended, off-the-ground condition, and an overground walking condition. BCI states, gyroscope, laser distance meter, and video recording data were used to assess the BCI performance.<br><br>RESULTS: During the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled overground walking tests, and demonstrated the ability to purposefully operate the BCI-FES system by following verbal cues. Based on the comparison between the ground truth and decoded BCI states, he achieved information transfer rates >3 bit/s and correlations >0.9. No adverse events directly related to the study were observed.<br><br>CONCLUSION: This proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible. Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI. If this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified. In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.},
}
@article {pmid26392353,
year = {2015},
author = {Foldes, ST and Weber, DJ and Collinger, JL},
title = {MEG-based neurofeedback for hand rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {85},
pmid = {26392353},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Magnetoencephalography/*methods ; Male ; Neurofeedback/*methods ; Spinal Cord Injuries/*rehabilitation ; },
abstract = {BACKGROUND: Providing neurofeedback (NF) of motor-related brain activity in a biologically-relevant and intuitive way could maximize the utility of a brain-computer interface (BCI) for promoting therapeutic plasticity. We present a BCI capable of providing intuitive and direct control of a video-based grasp.<br><br>METHODS: Utilizing magnetoencephalography's (MEG) high temporal and spatial resolution, we recorded sensorimotor rhythms (SMR) that were modulated by grasp or rest intentions. SMR modulation controlled the grasp aperture of a stop motion video of a human hand. The displayed hand grasp position was driven incrementally towards a closed or opened state and subjects were required to hold the targeted position for a time that was adjusted to change the task difficulty.<br><br>RESULTS: We demonstrated that three individuals with complete hand paralysis due to spinal cord injury (SCI) were able to maintain brain-control of closing and opening a virtual hand with an average of 63 % success which was significantly above the average chance rate of 19 %. This level of performance was achieved without pre-training and less than 4 min of calibration. In addition, successful grasp targets were reached in 1.96&#x2009;&#xb1;&#x2009;0.15 s. Subjects performed 200 brain-controlled trials in approximately 30 min excluding breaks. Two of the three participants showed a significant improvement in SMR indicating that they had learned to change their brain activity within a single session of NF.<br><br>CONCLUSIONS: This study demonstrated the utility of a MEG-based BCI system to provide realistic, efficient, and focused NF to individuals with paralysis with the goal of using NF to induce neuroplasticity.},
}
@article {pmid26390443,
year = {2016},
author = {Cecotti, H},
title = {Single-Trial Detection With Magnetoencephalography During a Dual-Rapid Serial Visual Presentation Task.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {1},
pages = {220-227},
doi = {10.1109/TBME.2015.2478695},
pmid = {26390443},
issn = {1558-2531},
mesh = {Adult ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Magnetoencephalography/*methods ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {GOAL: The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in brain-machine interface. Current systems based on the detection of brain responses during rapid serial visual presentation (RSVP) tasks possess advantages for both healthy and disabled people, as they are gaze independent and can offer a high throughput.<br><br>METHODS: We propose a novel paradigm based on a dual-RSVP task that assumes a low target probability. Two streams of images are presented simultaneously on the screen, the second stream is identical to the first one, but delayed in time. Participants were asked to detect images containing a person. They follow the first stream until they see a target image, then change their attention to the second stream until the target image reappears, finally they change their attention back to the first stream.<br><br>RESULTS: The performance of single-trial detection was evaluated on both streams and their combination of the decisions with signal recorded with magnetoencephalography (MEG) during the dual-RSVP task. We compare classification performance across different sets of channels (magnetometers, gradiometers) with a BLDA classifier with inputs obtained after spatial filtering.<br><br>CONCLUSION: The results suggest that single-trial detection can be obtained with an area under the ROC curve superior to 0.95, and that an almost perfect accuracy can be obtained with some subjects thanks to the combination of the decisions from two trials, without doubling the duration of the experiment.<br><br>SIGNIFICANCE: The present results show that a reliable accuracy can be obtained with the MEG for target detection during a dual-RSVP task.},
}
@article {pmid26390442,
year = {2016},
author = {Lee, SM and Kim, JH and Park, C and Hwang, JY and Hong, JS and Lee, KH and Lee, SH},
title = {Self-Adhesive and Capacitive Carbon Nanotube-Based Electrode to Record Electroencephalograph Signals From the Hairy Scalp.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {1},
pages = {138-147},
doi = {10.1109/TBME.2015.2478406},
pmid = {26390442},
issn = {1558-2531},
mesh = {Adhesives/*chemistry ; Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation/methods ; Evoked Potentials, Auditory ; Hair/physiology ; Humans ; Nanotubes, Carbon/*chemistry ; Scalp/*physiology ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {We fabricated a carbon nanotube (CNT)/adhesive polydimethylsiloxane (aPDMS) composite-based dry electroencephalograph (EEG) electrode for capacitive measuring of EEG signals. As research related to brain-computer interface applications has advanced, the presence of hairs on a patient's scalp has continued to present an obstacle to recorder EEG signals using dry electrodes. The CNT/aPDMS electrode developed here is elastic, highly conductive, self-adhesive, and capable of making conformal contact with and attaching to a hairy scalp. Onto the conductive disk, hundreds of conductive pillars coated with Parylene C insulation layer were fabricated. A CNT/aPDMS layer was attached on the disk to transmit biosignals to the pillar. The top of disk was designed to be solderable, which enables the electrode to connect with a variety of commercial EEG acquisition systems. The mechanical and electrical characteristics of the electrode were tested, and the performances of the electrodes were evaluated by recording EEGs, including alpha rhythms, auditory-evoked potentials, and steady-state visually-evoked potentials. The results revealed that the electrode provided a high signal-to-noise ratio with good tolerance for motion. Almost no leakage current was observed. Although preamplifiers with ultrahigh input impedance have been essential for previous capacitive electrodes, the EEGs were recorded here by directly connecting a commercially available EEG acquisition system to the electrode to yield high-quality signals comparable to those obtained using conventional wet electrodes.},
}
@article {pmid26388720,
year = {2015},
author = {Xiao, R and Ding, L},
title = {EEG resolutions in detecting and decoding finger movements from spectral analysis.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {308},
pmid = {26388720},
issn = {1662-4548},
abstract = {Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g., hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of non-invasive BCI and neuroprosthesis with intuitive and flexible controls.},
}
@article {pmid26386750,
year = {2016},
author = {Murzabaev, M and Kojima, T and Mizoguchi, T and Kobayashi, I and DeKosky, BJ and Georgiou, G and Nakano, H},
title = {Handmade microfluidic device for biochemical applications in emulsion.},
journal = {Journal of bioscience and bioengineering},
volume = {121},
number = {4},
pages = {471-476},
doi = {10.1016/j.jbiosc.2015.08.001},
pmid = {26386750},
issn = {1347-4421},
mesh = {Cell-Free System ; DNA/genetics/metabolism ; DNA-Directed RNA Polymerases/metabolism ; Emulsions ; Escherichia coli/metabolism ; Flow Cytometry ; Fluorescence ; In Vitro Techniques/economics/instrumentation/methods ; *Lab-On-A-Chip Devices/economics ; Ligases/analysis/biosynthesis/genetics ; Magnetics ; Microspheres ; *Protein Biosynthesis ; RNA, Catalytic/analysis/biosynthesis/genetics ; Repressor Proteins/analysis/biosynthesis/genetics ; *Transcription, Genetic ; Viral Proteins/metabolism ; Viral Regulatory and Accessory Proteins/analysis/biosynthesis/genetics ; },
abstract = {A simple, inexpensive flow-focusing device has been developed to make uniform droplets for biochemical reactions, such as in vitro transcription and cell-free protein synthesis. The device was fabricated from commercially available components without special equipment. Using the emulsion droplets formed by the device, a class I ligase ribozyme, bcI 23, was successfully synthesized from DNA attached to magnetic microbeads by T7 RNA polymerase. It was also ligated with an RNA substrate on the same microbeads, and detected using flow cytometry with a fluorescent probe. In addition, a single-chain derivative of the lambda Cro protein was expressed using an Escherichia coli cell-free protein synthesis system in emulsion, which was prepared using the flow-focusing device. In both emulsified reactions, usage of the flow-focusing device was able to greatly reduce the coefficient of variation for the amount of RNA or protein displayed on the microbeads, demonstrating the device is advantageous for quantitative analysis in high-throughput screening.},
}
@article {pmid26382749,
year = {2015},
author = {Alonso-Valerdi, LM and Salido-Ruiz, RA and Ramirez-Mendoza, RA},
title = {Motor imagery based brain-computer interfaces: An emerging technology to rehabilitate motor deficits.},
journal = {Neuropsychologia},
volume = {79},
number = {Pt B},
pages = {354-363},
doi = {10.1016/j.neuropsychologia.2015.09.012},
pmid = {26382749},
issn = {1873-3514},
mesh = {Biofeedback, Psychology ; Brain/physiopathology ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy/*methods ; Motor Activity/*physiology ; Movement Disorders/*rehabilitation ; },
abstract = {When the sensory-motor integration system is malfunctioning provokes a wide variety of neurological disorders, which in many cases cannot be treated with conventional medication, or via existing therapeutic technology. A brain-computer interface (BCI) is a tool that permits to reintegrate the sensory-motor loop, accessing directly to brain information. A potential, promising and quite investigated application of BCI has been in the motor rehabilitation field. It is well-known that motor deficits are the major disability wherewith the worldwide population lives. Therefore, this paper aims to specify the foundation of motor rehabilitation BCIs, as well as to review the recent research conducted so far (specifically, from 2007 to date), in order to evaluate the suitability and reliability of this technology. Although BCI for post-stroke rehabilitation is still in its infancy, the tendency is towards the development of implantable devices that encompass a BCI module plus a stimulation system.},
}
@article {pmid26379800,
year = {2015},
author = {Zhang, L and Gan, JQ and Wang, H},
title = {Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.},
journal = {Cognitive neurodynamics},
volume = {9},
number = {5},
pages = {495-508},
pmid = {26379800},
issn = {1871-4080},
abstract = {Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.},
}
@article {pmid26378500,
year = {2015},
author = {Shin, Y and Lee, S and Ahn, M and Cho, H and Jun, SC and Lee, HN},
title = {Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.},
journal = {Computers in biology and medicine},
volume = {66},
number = {},
pages = {29-38},
doi = {10.1016/j.compbiomed.2015.08.017},
pmid = {26378500},
issn = {1879-0534},
mesh = {Algorithms ; Brain ; *Brain-Computer Interfaces ; Databases, Factual ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagery, Psychotherapy ; Linear Models ; Motor Skills ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation.},
}
@article {pmid26375967,
year = {2015},
author = {Zeitler, DM and Dorman, MF and Natale, SJ and Loiselle, L and Yost, WA and Gifford, RH},
title = {Sound Source Localization and Speech Understanding in Complex Listening Environments by Single-sided Deaf Listeners After Cochlear Implantation.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {36},
number = {9},
pages = {1467-1471},
pmid = {26375967},
issn = {1537-4505},
support = {R01 DC009404/DC/NIDCD NIH HHS/United States ; R01 DC010821/DC/NIDCD NIH HHS/United States ; U54 HD083211/HD/NICHD NIH HHS/United States ; R01-DC010821/DC/NIDCD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Auditory Perception ; Case-Control Studies ; Child ; *Cochlear Implantation ; Cochlear Implants ; Deafness/*rehabilitation ; Environment ; Female ; Hearing Loss, Unilateral/*rehabilitation ; Humans ; Male ; Middle Aged ; Noise ; Persons with Hearing Disabilities ; Prospective Studies ; *Sound Localization ; Speech ; *Speech Perception ; Treatment Outcome ; Young Adult ; },
abstract = {OBJECTIVE: To assess improvements in sound source localization and speech understanding in complex listening environments after unilateral cochlear implantation for single-sided deafness (SSD).<br><br>STUDY DESIGN: Nonrandomized, open, prospective case series.<br><br>SETTING: Tertiary referral center.<br><br>PATIENTS: Nine subjects with a unilateral cochlear implant (CI) for SSD (SSD-CI) were tested. Reference groups for the task of sound source localization included young (n&#x200a;=&#x200a;45) and older (n&#x200a;=&#x200a;12) normal-hearing (NH) subjects and 27 bilateral CI (BCI) subjects.<br><br>INTERVENTION: Unilateral cochlear implantation.<br><br>MAIN OUTCOME MEASURES: Sound source localization was tested with 13 loudspeakers in a 180 arc in front of the subject. Speech understanding was tested with the subject seated in an 8-loudspeaker sound system arrayed in a 360-degree pattern. Directionally appropriate noise, originally recorded in a restaurant, was played from each loudspeaker. Speech understanding in noise was tested using the Azbio sentence test and sound source localization quantified using root mean square error.<br><br>RESULTS: All CI subjects showed poorer-than-normal sound source localization. SSD-CI subjects showed a bimodal distribution of scores: six subjects had scores near the mean of those obtained by BCI subjects, whereas three had scores just outside the 95th percentile of NH listeners. Speech understanding improved significantly in the restaurant environment when the signal was presented to the side of the CI.<br><br>CONCLUSION: Cochlear implantation for SSD can offer improved speech understanding in complex listening environments and improved sound source localization in both children and adults. On tasks of sound source localization, SSD-CI patients typically perform as well as BCI patients and, in some cases, achieve scores at the upper boundary of normal performance.},
}
@article {pmid26372428,
year = {2016},
author = {Roijendijk, L and Gielen, S and Farquhar, J},
title = {Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {8},
pages = {893-900},
doi = {10.1109/TNSRE.2015.2477687},
pmid = {26372428},
issn = {1558-0210},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; *Machine Learning ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; },
abstract = {Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class- relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters.},
}
@article {pmid26371006,
year = {2015},
author = {Gagnon-Turcotte, G and Kisomi, AA and Ameli, R and Camaro, CO and LeChasseur, Y and Néron, JL and Bareil, PB and Fortier, P and Bories, C and de Koninck, Y and Gosselin, B},
title = {A Wireless Optogenetic Headstage with Multichannel Electrophysiological Recording Capability.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {9},
pages = {22776-22797},
pmid = {26371006},
issn = {1424-8220},
mesh = {Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; Electrophysiology/*instrumentation ; Equipment Design ; Mice ; Microelectrodes ; Optogenetics/*instrumentation ; Telemetry/*instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {We present a small and lightweight fully wireless optogenetic headstage capable of optical neural stimulation and electrophysiological recording. The headstage is suitable for conducting experiments with small transgenic rodents, and features two implantable fiber-coupled light-emitting diode (LED) and two electrophysiological recording channels. This system is powered by a small lithium-ion battery and is entirely built using low-cost commercial off-the-shelf components for better flexibility, reduced development time and lower cost. Light stimulation uses customizable stimulation patterns of varying frequency and duty cycle. The optical power that is sourced from the LED is delivered to target light-sensitive neurons using implantable optical fibers, which provide a measured optical power density of 70 mW/mm[2] at the tip. The headstage is using a novel foldable rigid-flex printed circuit board design, which results into a lightweight and compact device. Recording experiments performed in the cerebral cortex of transgenic ChR2 mice under anesthetized conditions show that the proposed headstage can trigger neuronal activity using optical stimulation, while recording microvolt amplitude electrophysiological signals.},
}
@article {pmid26367470,
year = {2016},
author = {Christoffersen, GRJ and Schachtman, TR},
title = {Electrophysiological CNS-processes related to associative learning in humans.},
journal = {Behavioural brain research},
volume = {296},
number = {},
pages = {211-232},
doi = {10.1016/j.bbr.2015.09.011},
pmid = {26367470},
issn = {1872-7549},
mesh = {Association Learning/*physiology ; Brain Waves/*physiology ; Cerebral Cortex/*physiology ; Conditioning, Psychological/*physiology ; Evoked Potentials/*physiology ; Humans ; },
abstract = {The neurophysiology of human associative memory has been studied with electroencephalographic techniques since the 1930s. This research has revealed that different types of electrophysiological processes in the human brain can be modified by conditioning: sensory evoked potentials, sensory induced gamma-band activity, periods of frequency-specific waves (alpha and beta waves, the sensorimotor rhythm and the mu-rhythm) and slow cortical potentials. Conditioning of these processes has been studied in experiments that either use operant conditioning or repeated contingent pairings of conditioned and unconditioned stimuli (classical conditioning). In operant conditioning, the appearance of a specific brain process is paired with an external stimulus (neurofeedback) and the feedback enables subjects to obtain varying degrees of control of the CNS-process. Such acquired self-regulation of brain activity has found practical uses for instance in the amelioration of epileptic seizures, Autism Spectrum Disorders (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). It has also provided communicative means of assistance for tetraplegic patients through the use of brain computer interfaces. Both extra and intracortically recorded signals have been coupled with contingent external feedback. It is the aim for this review to summarize essential results on all types of electromagnetic brain processes that have been modified by classical or operant conditioning. The results are organized according to type of conditioned EEG-process, type of conditioning, and sensory modalities of the conditioning stimuli.},
}
@article {pmid26364287,
year = {2015},
author = {Dietrich, WD},
title = {Protection and Repair After Spinal Cord Injury: Accomplishments and Future Directions.},
journal = {Topics in spinal cord injury rehabilitation},
volume = {21},
number = {2},
pages = {174-187},
pmid = {26364287},
issn = {1945-5763},
mesh = {Animals ; Cell- and Tissue-Based Therapy/methods ; Humans ; Locomotion ; Neuroprotection ; Quality of Life ; Spinal Cord Injuries/physiopathology/rehabilitation/*therapy ; },
abstract = {It was an honor for me to present the 2014 G. Heiner Sell Memorial Lecture at the annual American Spinal Injury Association (ASIA) meeting in San Antonio. For this purpose, I provided a comprehensive review of the scope of research targeting discovery and translational and clinical investigations into spinal cord injury (SCI) research. Indeed, these are exciting times in the area of spinal cord research and clinical initiatives. Many laboratories and clinical programs throughout the world are publishing data related to the pathophysiology of SCI and new strategies for protecting and promoting recovery in both animal models and humans. For this lecture, several topics were discussed including neuroprotective and reparative strategies, neurorehabilitation, quality of life issues, and future directions. In the area of neuroprotection, pathophysiological events that may be targeted with therapeutic strategies, including pharmacological and targeted temperature management were reviewed. For reparative approaches, the importance of both intrinsic and extrinsic mechanisms of axonal regeneration was highlighted. Various cell therapies currently being tested in preclinical and clinical arenas were reviewed as well as ongoing US Food and Drug Administration approved trials for SCI patients. Neurorehabilitation is an evolving research field with locomotive training strategies, electrical stimulation, and brain-machine interface programs targeting various types of SCI. The importance of testing combination approaches including neuroprotective, reparative, and rehabilitative strategies to maximize recovery mechanisms was therefore emphasized. Finally, quality of life issues that affect thousands of individuals living with paralysis were also presented. Future directions and specific obstacles that require attention as we continue to move the SCI field forward were discussed.},
}
@article {pmid26363348,
year = {2016},
author = {Toppi, J and Astolfi, L and Poudel, GR and Innes, CRH and Babiloni, F and Jones, RD},
title = {Time-varying effective connectivity of the cortical neuroelectric activity associated with behavioural microsleeps.},
journal = {NeuroImage},
volume = {124},
number = {Pt A},
pages = {421-432},
doi = {10.1016/j.neuroimage.2015.08.059},
pmid = {26363348},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping ; Brain Waves ; *Cerebral Cortex/physiology ; Electroencephalography/*methods ; Female ; Frontal Lobe/physiology ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neural Pathways/physiology ; Parietal Lobe/physiology ; Signal Processing, Computer-Assisted ; *Sleep Stages ; Time Factors ; Young Adult ; },
abstract = {An episode of complete failure to respond during an attentive task accompanied by behavioural signs of sleep is called a behavioural microsleep. We proposed a combination of high-resolution EEG and an advanced method for time-varying effective connectivity estimation for reconstructing the temporal evolution of the causal relations between cortical regions when microsleeps occur during a continuous visuomotor task. We found connectivity patterns involving left-right frontal, left-right parietal, and left-frontal/right-parietal connections commencing in the interval [-500; -250] ms prior to the onset of microsleeps and disappearing at the end of the microsleeps. Our results from global graph indices derived from effective connectivity analysis have revealed EEG-based biomarkers of all stages of microsleeps (preceding, onset, pre-recovery, recovery). In particular, this raises the possibility of being able to predict microsleeps in real-world tasks and initiate a 'wake-up' intervention to avert the microsleeps and, hence, prevent injurious and even multi-fatality accidents.},
}
@article {pmid26360225,
year = {2016},
author = {da Silva-Sauer, L and Valero-Aguayo, L and de la Torre-Luque, A and Ron-Angevin, R and Varona-Moya, S},
title = {Concentration on performance with P300-based BCI systems: a matter of interface features.},
journal = {Applied ergonomics},
volume = {52},
number = {},
pages = {325-332},
doi = {10.1016/j.apergo.2015.08.002},
pmid = {26360225},
issn = {1872-9126},
mesh = {Adult ; *Brain-Computer Interfaces/psychology/standards ; Persons with Disabilities ; Electroencephalography ; *Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Surveys and Questionnaires ; User-Computer Interface ; },
abstract = {People who suffer from severe motor disabilities have difficulties to communicate with others or to interact with their environment using natural, i.e., muscular channels. These limitations can be overcome to some extent by using brain-computer interfaces (BCIs), because such systems allow users to communicate on the basis of their brain activity only. Among the several types of BCIs for spelling purposes, those that rely on the P300 event related potential-P300-based spellers-are chosen preferentially due to their high reliability. However, they demand from the user to sustain his/her attention to the desired character over a relatively long period of time. Therefore, the user's capacity to concentrate can affect his/her performance with a P300-based speller. The aim of this study was to test this hypothesis using three different interfaces: one based on the classic P300 speller paradigm, another also based on that speller but including a word predictor, and a third one that was based on the T9 interface developed for mobile phones. User performance was assessed by measuring the time to complete a spelling task and the accuracy of character selection. The d2 test was applied to assess attention and concentration. Sample (N = 14) was divided into two groups basing on of concentration scores. As a result, performance was better with the predictor-enriched interfaces: less time was needed to solve the task and participants made fewer errors (p < .05). There were also significant effects of concentration (p < .05) on performance with the standard P300 speller. In conclusion, the performance of those users with lower concentration level can be improved by providing BCIs with more interactive interfaces. These findings provide substantial evidence in order to highlight the impact of psychological features on BCI performance and should be taken into account for future assistive technology systems.},
}
@article {pmid26358366,
year = {2016},
author = {Kulkarni, NN and Gunnarsson, HI and Yi, Z and Gudmundsdottir, S and Sigurjonsson, OE and Agerberth, B and Gudmundsson, GH},
title = {Glucocorticoid dexamethasone down-regulates basal and vitamin D3 induced cathelicidin expression in human monocytes and bronchial epithelial cell line.},
journal = {Immunobiology},
volume = {221},
number = {2},
pages = {245-252},
doi = {10.1016/j.imbio.2015.09.001},
pmid = {26358366},
issn = {1878-3279},
mesh = {Antimicrobial Cationic Peptides/agonists/antagonists &amp; inhibitors/*genetics/immunology ; Cell Differentiation ; Cell Line ; Chemokine CXCL10/genetics/immunology ; Cholecalciferol/antagonists &amp; inhibitors/*pharmacology ; Dexamethasone/*pharmacology ; Epithelial Cells/cytology/*drug effects/immunology ; Gene Expression Regulation ; Glucocorticoids/*pharmacology ; Humans ; Immunity, Innate/drug effects ; Interleukin-1beta/genetics/immunology ; Macrophages/cytology/*drug effects/immunology ; Mifepristone/pharmacology ; Monocytes/cytology/drug effects/immunology ; Muramidase/antagonists &amp; inhibitors/genetics/immunology ; Poly I-C/pharmacology ; Receptors, Glucocorticoid/antagonists &amp; inhibitors/genetics/immunology ; Respiratory Mucosa/cytology/drug effects/immunology ; Signal Transduction ; beta-Defensins/antagonists &amp; inhibitors/genetics/immunology ; Cathelicidins ; },
abstract = {Glucocorticoids (GCs) have been extensively used as the mainstream treatment for chronic inflammatory disorders. The persistent use of steroids in the past decades and the association with secondary infections warrants for detailed investigation into their effects on the innate immune system and the therapeutic outcome. In this study, we analyse the effect of GCs on antimicrobial polypeptide (AMP) expression. We hypothesize that GC related side effects, including secondary infections are a result of compromised innate immune responses. Here, we show that treatment with dexamethasone (Dex) inhibits basal mRNA expression of the following AMPs; human cathelicidin, human beta defensin 1, lysozyme and secretory leukocyte peptidase 1 in the THP-1 monocytic cell-line (THP-1 monocytes). Furthermore, pre-treatment with Dex inhibits vitamin D3 induced cathelicidin expression in THP-1 monocytes, primary monocytes and in the human bronchial epithelial cell line BCi NS 1.1. We also demonstrate that treatment with the glucocorticoid receptor (GR) inhibitor RU486 counteracts Dex mediated down-regulation of basal and vitamin D3 induced cathelicidin expression in THP-1 monocytes. Moreover, we confirmed the anti-inflammatory effect of Dex. Pre-treatment with Dex inhibits dsRNA mimic poly IC induction of the inflammatory chemokine IP10 (CXCL10) and cytokine IL1B mRNA expression in THP-1 monocytes. These results suggest that GCs inhibit innate immune responses, in addition to exerting beneficial anti-inflammatory effects.},
}
@article {pmid26358282,
year = {2015},
author = {Iacoviello, D and Petracca, A and Spezialetti, M and Placidi, G},
title = {A real-time classification algorithm for EEG-based BCI driven by self-induced emotions.},
journal = {Computer methods and programs in biomedicine},
volume = {122},
number = {3},
pages = {293-303},
doi = {10.1016/j.cmpb.2015.08.011},
pmid = {26358282},
issn = {1872-7565},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces/statistics &amp; numerical data ; Computer Systems ; *Electroencephalography ; Emotions/*physiology ; Humans ; Male ; Principal Component Analysis ; },
abstract = {BACKGROUND AND OBJECTIVE: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed.<br><br>METHOD: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM.<br><br>RESULTS: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels.<br><br>CONCLUSIONS: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities.},
}
@article {pmid26357416,
year = {2016},
author = {Liu, R and Wang, YX and Zhang, L},
title = {An FDES-Based Shared Control Method for Asynchronous Brain-Actuated Robot.},
journal = {IEEE transactions on cybernetics},
volume = {46},
number = {6},
pages = {1452-1462},
doi = {10.1109/TCYB.2015.2469278},
pmid = {26357416},
issn = {2168-2275},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; *Robotics ; },
abstract = {The asynchronous brain-computer interface (BCI) offers more natural human-machine interaction. However, it is still considered insufficient to control rapid and complex sequences of movements for a robot without any advanced control method. This paper proposes a new shared controller based on the supervisory theory of fuzzy discrete event system (FDES) for brain-actuated robot control. The developed supervisory theory allows the more reliable control mode to play a dominant role in the robot control which is beneficial to reduce misoperation and improve the robustness of the system. The experimental procedures consist of real-time direct manual control and BCI control tests from ten volunteers. Both tests have shown that the proposed method significantly improves the performance and robustness of the robotic control. In an online BCI experiment, eight of the participants successfully controlled the robot to circumnavigate obstacles and reached the target with a three mental states asynchronous BCI while the other two participants failed in all the BCI control sessions. Furthermore, the FDES-based shared control method also helps to reduce the workload. It can be stated that the asynchronous BCI, in combination with FDES-based shared controller, is feasible for the real-time and robust control of robotics.},
}
@article {pmid26354145,
year = {2015},
author = {Iturrate, I and Chavarriaga, R and Montesano, L and Minguez, J and Millán, Jdel R},
title = {Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {13893},
pmid = {26354145},
issn = {2045-2322},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Learning ; Male ; Movement ; Psychomotor Performance ; Young Adult ; },
abstract = {Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. Here we demonstrate an alternative and complementary BMI paradigm that overcomes that limitation by decoding cognitive brain signals associated with monitoring processes relevant for achieving goals. In our approach the neuroprosthesis executes actions that the subject evaluates as erroneous or correct, and exploits the brain correlates of this assessment to learn suitable motor behaviours. Results show that, after a short user's training period, this teaching BMI paradigm operated three different neuroprostheses and generalized across several targets. Our results further support that these error-related signals reflect a task-independent monitoring mechanism in the brain, making this teaching paradigm scalable. We anticipate this BMI approach to become a key component of any neuroprosthesis that mimics natural motor control as it enables continuous adaptation in the absence of explicit information about goals. Furthermore, our paradigm can seamlessly incorporate other cognitive signals and conventional neuroprosthetic approaches, invasive or non-invasive, to enlarge the range and complexity of tasks that can be accomplished.},
}
@article {pmid26353236,
year = {2015},
author = {Severens, M and Perusquia-Hernandez, M and Nienhuis, B and Farquhar, J and Duysens, J},
title = {Using Actual and Imagined Walking Related Desynchronization Features in a BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {877-886},
doi = {10.1109/TNSRE.2014.2371391},
pmid = {26353236},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Walking/*physiology ; },
abstract = {Recently, brain-computer interface (BCI) research has extended to investigate its possible use in motor rehabilitation. Most of these investigations have focused on the upper body. Only few studies consider gait because of the difficulty of recording EEG during gross movements. However, for stroke patients the rehabilitation of gait is of crucial importance. Therefore, this study investigates if a BCI can be based on walking related desynchronization features. Furthermore, the influence of complexity of the walking movements on the classification performance is investigated. Two BCI experiments were conducted in which healthy subjects performed a cued walking task, a more complex walking task (backward or adaptive walking), and imagination of the same tasks. EEG data during these tasks was classified into walking and no-walking. The results from both experiments show that despite the automaticity of walking and recording difficulties, brain signals related to walking could be classified rapidly and reliably. Classification performance was higher for actual walking movements than for imagined walking movements. There was no significant increase in classification performance for both the backward and adaptive walking tasks compared with the cued walking tasks. These results are promising for developing a BCI for the rehabilitation of gait.},
}
@article {pmid26351023,
year = {2015},
author = {Semework, M},
title = {Microstimulation: Principles, Techniques, and Approaches to Somatosensory Neuroprosthesis.},
journal = {Critical reviews in biomedical engineering},
volume = {43},
number = {1},
pages = {61-95},
doi = {10.1615/critrevbiomedeng.2015012287},
pmid = {26351023},
issn = {0278-940X},
support = {P30 EY019007/EY/NEI NIH HHS/United States ; 1R01 EY014978-06/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Brain/anatomy &amp; histology/physiology ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*instrumentation/*methods ; Humans ; Microelectrodes ; Neural Pathways/physiology ; *Neural Prostheses ; Somatosensory Disorders/*therapy ; },
abstract = {The power of movement of electrically charged particles has been used to alleviate an array of illnesses and help control some human body parts. Microstimulation, the electrical current-driven excitation of neural elements, is now being aimed at brain-machine interfaces (BMIs), brain-controlled external devices that improve quality of life for people such as those who have lost the ability to use their limbs. This effort is motivated by behavioral experiments that indicate a direct link between microstimulation-induced sensory experience and behavior, pointing to the possibility of optimizing and controlling the outputs of BMIs. Several laboratories have focused on using electrical stimulation to return somatosensory feedback from prosthetic limbs directly to the user's central nervous system. However, the difficulty of the problem has led to limited success thus far, and there is a need for a better understanding of the basic principles of neural microstimulation. This article provides a review of the available literature and some recent work at Downstate Medical Center and Columbia University on microstimulation of the primate and rodent somatosensory (S1) cortex and the ventral posterolateral thalamus. It is aimed at contributing to the existing knowledge base to generate good behavioral responses and effective, BMI-appropriate somatosensory feedback. In general, the threshold for the particular brain tissue in response to current-amplitude has to be determined by rigorous experimentation. For consistently reproducible results, hardware and thresholds for microstimulation have to be specified. In addition, effects on motor functions, including unwanted side effects in response to the microstimulation of brain tissue, must be examined to take the field from bench to bedside.},
}
@article {pmid26350406,
year = {2016},
author = {Halder, S and Käthner, I and Kübler, A},
title = {Training leads to increased auditory brain-computer interface performance of end-users with motor impairments.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {2},
pages = {1288-1296},
doi = {10.1016/j.clinph.2015.08.007},
pmid = {26350406},
issn = {1872-8952},
mesh = {Acoustic Stimulation/*methods ; Aged ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Skills Disorders/diagnosis/*physiopathology/psychology ; *Practice, Psychological ; },
abstract = {OBJECTIVE: Auditory brain-computer interfaces are an assistive technology that can restore communication for motor impaired end-users. Such non-visual brain-computer interface paradigms are of particular importance for end-users that may lose or have lost gaze control. We attempted to show that motor impaired end-users can learn to control an auditory speller on the basis of event-related potentials.<br><br>METHODS: Five end-users with motor impairments, two of whom with additional visual impairments, participated in five sessions. We applied a newly developed auditory brain-computer interface paradigm with natural sounds and directional cues.<br><br>RESULTS: Three of five end-users learned to select symbols using this method. Averaged over all five end-users the information transfer rate increased by more than 1800% from the first session (0.17 bits/min) to the last session (3.08 bits/min). The two best end-users achieved information transfer rates of 5.78 bits/min and accuracies of 92%.<br><br>CONCLUSIONS: Our results show that an auditory BCI with a combination of natural sounds and directional cues, can be controlled by end-users with motor impairment. Training improves the performance of end-users to the level of healthy controls.<br><br>SIGNIFICANCE: To our knowledge, this is the first time end-users with motor impairments controlled an auditory brain-computer interface speller with such high accuracy and information transfer rates. Further, our results demonstrate that operating a BCI with event-related potentials benefits from training and specifically end-users may require more than one session to develop their full potential.},
}
@article {pmid26348987,
year = {2015},
author = {Song, W and Semework, M},
title = {Tactile representation in somatosensory thalamus (VPL) and cortex (S1) of awake primate and the plasticity induced by VPL neuroprosthetic stimulation.},
journal = {Brain research},
volume = {1625},
number = {},
pages = {301-313},
doi = {10.1016/j.brainres.2015.08.046},
pmid = {26348987},
issn = {1872-6240},
mesh = {Action Potentials/*physiology ; Animals ; Electric Stimulation ; Haplorhini ; Magnetic Resonance Imaging ; Male ; Microelectrodes ; Neural Pathways ; Neurons/*physiology ; Physical Stimulation ; Somatosensory Cortex/*physiology ; Statistics, Nonparametric ; Thalamus/*physiology ; Touch/*physiology ; *Wakefulness ; },
abstract = {To further understand how tactile information is carried in somatosensory cortex (S1) and the thalamus (VPL), and how neuronal plasticity after neuroprosthetic stimulation affects sensory encoding, we chronically implanted microelectrode arrays across hand areas in both S1 and VPL, where neuronal activities were simultaneously recorded during tactile stimulation on the finger pad of awake monkeys. Tactile information encoded in the firing rate of individual units (rate coding) or in the synchrony of unit pairs (synchrony coding) was quantitatively assessed within the information theoretic-framework. We found that tactile information encoded in VPL was higher than that encoded in S1 for both rate coding and synchrony coding; rate coding carried greater information than synchrony coding for the same recording area. With the aim for neuroprosthetic stimulation, plasticity of the circuit was tested after 30 min of VPL electrical stimulation, where stimuli were delivered either randomly or contingent on the spiking of an S1 unit. We showed that neural encoding in VPL was more stable than in S1, which depends not only on the thalamic input but also on recurrent feedback. The percent change of mutual-information after stimulation was increased with closed-loop stimulation, but decreased with random stimulation. The underlying mechanisms during closed-loop stimulation might be spike-timing-dependent plasticity, while frequency-dependent synaptic plasticity might play a role in random stimulation. Our results suggest that VPL could be a promising target region for somatosensory stimulation with closed-loop brain-machine-interface applications.},
}
@article {pmid26348986,
year = {2016},
author = {Lewis, PM and Rosenfeld, JV},
title = {Electrical stimulation of the brain and the development of cortical visual prostheses: An historical perspective.},
journal = {Brain research},
volume = {1630},
number = {},
pages = {208-224},
doi = {10.1016/j.brainres.2015.08.038},
pmid = {26348986},
issn = {1872-6240},
mesh = {Animals ; Electric Stimulation Therapy/*history/instrumentation/methods ; History, 18th Century ; History, 19th Century ; History, 20th Century ; History, 21st Century ; Humans ; Prosthesis Design ; *Visual Cortex/physiology/physiopathology ; Visual Prosthesis/*history ; },
abstract = {Rapid advances are occurring in neural engineering, bionics and the brain-computer interface. These milestones have been underpinned by staggering advances in micro-electronics, computing, and wireless technology in the last three decades. Several cortically-based visual prosthetic devices are currently being developed, but pioneering advances with early implants were achieved by Brindley followed by Dobelle in the 1960s and 1970s. We have reviewed these discoveries within the historical context of the medical uses of electricity including attempts to cure blindness, the discovery of the visual cortex, and opportunities for cortex stimulation experiments during neurosurgery. Further advances were made possible with improvements in electrode design, greater understanding of cortical electrophysiology and miniaturisation of electronic components. Human trials of a new generation of prototype cortical visual prostheses for the blind are imminent. This article is part of a Special Issue entitled Hold Item.},
}
@article {pmid26347642,
year = {2015},
author = {Sollfrank, T and Hart, D and Goodsell, R and Foster, J and Tan, T},
title = {3D visualization of movements can amplify motor cortex activation during subsequent motor imagery.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {463},
pmid = {26347642},
issn = {1662-5161},
abstract = {A repetitive movement practice by motor imagery (MI) can influence motor cortical excitability in the electroencephalogram (EEG). This study investigated if a realistic visualization in 3D of upper and lower limb movements can amplify motor related potentials during subsequent MI. We hypothesized that a richer sensory visualization might be more effective during instrumental conditioning, resulting in a more pronounced event related desynchronization (ERD) of the upper alpha band (10-12 Hz) over the sensorimotor cortices thereby potentially improving MI based brain-computer interface (BCI) protocols for motor rehabilitation. The results show a strong increase of the characteristic patterns of ERD of the upper alpha band components for left and right limb MI present over the sensorimotor areas in both visualization conditions. Overall, significant differences were observed as a function of visualization modality (VM; 2D vs. 3D). The largest upper alpha band power decrease was obtained during MI after a 3-dimensional visualization. In total in 12 out of 20 tasks the end-user of the 3D visualization group showed an enhanced upper alpha ERD relative to 2D VM group, with statistical significance in nine tasks.With a realistic visualization of the limb movements, we tried to increase motor cortex activation during subsequent MI. The feedback and the feedback environment should be inherently motivating and relevant for the learner and should have an appeal of novelty, real-world relevance or aesthetic value (Ryan and Deci, 2000; Merrill, 2007). Realistic visual feedback, consistent with the participant's MI, might be helpful for accomplishing successful MI and the use of such feedback may assist in making BCI a more natural interface for MI based BCI rehabilitation.},
}
@article {pmid26347597,
year = {2015},
author = {Marathe, AR and Ries, AJ and Lawhern, VJ and Lance, BJ and Touryan, J and McDowell, K and Cecotti, H},
title = {The effect of target and non-target similarity on neural classification performance: a boost from confidence.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {270},
pmid = {26347597},
issn = {1662-4548},
abstract = {Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world environments there are likely to be many shared features between targets and non-targets resulting in similar neural activity between the two classes. It is unknown how current neural-based target classification algorithms perform when qualitatively similar target and non-target images are presented. This study address this question by comparing behavioral and neural classification performance across two conditions: first, when targets were the only infrequent stimulus presented amongst frequent background distracters; and second when targets were presented together with infrequent non-targets containing similar visual features to the targets. The resulting findings show that behavior is slower and less accurate when targets are presented together with similar non-targets; moreover, single-trial classification yielded high levels of misclassification when infrequent non-targets are included. Furthermore, we present an approach to mitigate the image misclassification. We use confidence measures to assess the quality of single-trial classification, and demonstrate that a system in which low confidence trials are reclassified through a secondary process can result in improved performance.},
}
@article {pmid26341935,
year = {2015},
author = {Pierella, C and Abdollahi, F and Farshchiansadegh, A and Pedersen, J and Thorp, EB and Mussa-Ivaldi, FA and Casadio, M},
title = {Remapping residual coordination for controlling assistive devices and recovering motor functions.},
journal = {Neuropsychologia},
volume = {79},
number = {Pt B},
pages = {364-376},
pmid = {26341935},
issn = {1873-3514},
support = {R01 HD072080/HD/NICHD NIH HHS/United States ; 1R01HD072080/HD/NICHD NIH HHS/United States ; },
mesh = {Female ; Humans ; Learning/*physiology ; Male ; Middle Aged ; Motor Skills/*physiology ; Musculoskeletal Physiological Phenomena ; Principal Component Analysis ; Recovery of Function/*physiology ; *Self-Help Devices ; Spinal Cord Injuries/*rehabilitation ; Treatment Outcome ; *User-Computer Interface ; Young Adult ; },
abstract = {The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human-machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user's residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies.},
}
@article {pmid26340647,
year = {2015},
author = {Jiang, J and Zhou, Z and Yin, E and Yu, Y and Liu, Y and Hu, D},
title = {A novel Morse code-inspired method for multiclass motor imagery brain-computer interface (BCI) design.},
journal = {Computers in biology and medicine},
volume = {66},
number = {},
pages = {11-19},
doi = {10.1016/j.compbiomed.2015.08.011},
pmid = {26340647},
issn = {1879-0534},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback ; Female ; Healthy Volunteers ; Humans ; Imagery, Psychotherapy ; Male ; *Motor Skills ; ROC Curve ; Reproducibility of Results ; Robotics ; Signal Processing, Computer-Assisted ; *Software ; Young Adult ; },
abstract = {Motor imagery (MI)-based brain-computer interfaces (BCIs) allow disabled individuals to control external devices voluntarily, helping us to restore lost motor functions. However, the number of control commands available in MI-based BCIs remains limited, limiting the usability of BCI systems in control applications involving multiple degrees of freedom (DOF), such as control of a robot arm. To address this problem, we developed a novel Morse code-inspired method for MI-based BCI design to increase the number of output commands. Using this method, brain activities are modulated by sequences of MI (sMI) tasks, which are constructed by alternately imagining movements of the left or right hand or no motion. The codes of the sMI task was detected from EEG signals and mapped to special commands. According to permutation theory, an sMI task with N-length allows 2 × (2(N)-1) possible commands with the left and right MI tasks under self-paced conditions. To verify its feasibility, the new method was used to construct a six-class BCI system to control the arm of a humanoid robot. Four subjects participated in our experiment and the averaged accuracy of the six-class sMI tasks was 89.4%. The Cohen's kappa coefficient and the throughput of our BCI paradigm are 0.88 ± 0.060 and 23.5bits per minute (bpm), respectively. Furthermore, all of the subjects could operate an actual three-joint robot arm to grasp an object in around 49.1s using our approach. These promising results suggest that the Morse code-inspired method could be used in the design of BCIs for multi-DOF control.},
}
@article {pmid26338101,
year = {2015},
author = {Käthner, I and Kübler, A and Halder, S},
title = {Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {76},
pmid = {26338101},
issn = {1743-0003},
mesh = {Amyotrophic Lateral Sclerosis/rehabilitation ; *Brain-Computer Interfaces ; Caregivers/psychology ; *Communication Devices for People with Disabilities ; Electrodes, Implanted ; Electroencephalography ; Electrooculography/*methods ; Eye Movements/*physiology ; Humans ; Male ; Middle Aged ; Oculomotor Muscles/physiology ; Patient Satisfaction ; Psychomotor Performance/*physiology ; Quadriplegia/psychology/*rehabilitation ; Self-Help Devices ; },
abstract = {BACKGROUND: In this study, we evaluated electrooculography (EOG), an eye tracker and an auditory brain-computer interface (BCI) as access methods to augmentative and alternative communication (AAC). The participant of the study has been in the locked-in state (LIS) for 6 years due to amyotrophic lateral sclerosis. He was able to communicate with slow residual eye movements, but had no means of partner independent communication. We discuss the usability of all tested access methods and the prospects of using BCIs as an assistive technology.<br><br>METHODS: Within four days, we tested whether EOG, eye tracking and a BCI would allow the participant in LIS to make simple selections. We optimized the parameters in an iterative procedure for all systems.<br><br>RESULTS: The participant was able to gain control over all three systems. Nonetheless, due to the level of proficiency previously achieved with his low-tech AAC method, he did not consider using any of the tested systems as an additional communication channel. However, he would consider using the BCI once control over his eye muscles would no longer be possible. He rated the ease of use of the BCI as the highest among the tested systems, because no precise eye movements were required; but also as the most tiring, due to the high level of attention needed to operate the BCI.<br><br>CONCLUSIONS: In this case study, the partner based communication was possible due to the good care provided and the proficiency achieved by the interlocutors. To ease the transition from a low-tech AAC method to a BCI once control over all muscles is lost, it must be simple to operate. For persons, who rely on AAC and are affected by a progressive neuromuscular disease, we argue that a complementary approach, combining BCIs and standard assistive technology, can prove valuable to achieve partner independent communication and ease the transition to a purely BCI based approach. Finally, we provide further evidence for the importance of a user-centered approach in the design of new assistive devices.},
}
@article {pmid26336895,
year = {2016},
author = {Aas, S and Wasserman, D},
title = {Brain-computer interfaces and disability: extending embodiment, reducing stigma?.},
journal = {Journal of medical ethics},
volume = {42},
number = {1},
pages = {37-40},
doi = {10.1136/medethics-2015-102807},
pmid = {26336895},
issn = {1473-4257},
mesh = {*Brain-Computer Interfaces ; *Cognition ; Concept Formation ; *Persons with Disabilities ; Humans ; *Social Stigma ; },
abstract = {Brain-Computer Interfaces (BCIs) now enable an individual without limb function to "move" a detached mechanical arm to perform simple actions, such as feeding herself. This technology may eventually offer almost everyone a way to move objects at a distance, by exercising cognitive control of a mechanical device. At that point, BCIs may be seen less as an assistive technology for disabled people, and more as a tool, like the internet, which can benefit all users. We will argue that BCIs will have a significant but uncertain impact on attitudes toward disabilities and on norms of bodily form and function. It may be liberating, oppressive, or both. Its impact, we argue, will depend - though not in any simple way - on whether BCIs come to be seen as parts of the body itself or as external tools.},
}
@article {pmid26336136,
year = {2016},
author = {Wang, H and Li, X},
title = {Regularized Filters for L1-Norm-Based Common Spatial Patterns.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {2},
pages = {201-211},
doi = {10.1109/TNSRE.2015.2474141},
pmid = {26336136},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Equipment Design ; Humans ; Immunohistochemistry ; Models, Statistical ; Pattern Recognition, Automated ; Reproducibility of Results ; Signal-To-Noise Ratio ; Wavelet Analysis ; },
abstract = {The l1 -norm-based common spatial patterns (CSP-L1) approach is a recently developed technique for optimizing spatial filters in the field of electroencephalogram (EEG)-based brain computer interfaces. The l1 -norm-based expression of dispersion in CSP-L1 alleviates the negative impact of outliers. In this paper, we further improve the robustness of CSP-L1 by taking into account noise which does not necessarily have as large a deviation as with outliers. The noise modelling is formulated by using the waveform length of the EEG time course. With the noise modelling, we then regularize the objective function of CSP-L1, in which the l1-norm is used in two folds: one is the dispersion and the other is the waveform length. An iterative algorithm is designed to resolve the optimization problem of the regularized objective function. A toy illustration and the experiments of classification on real EEG data sets show the effectiveness of the proposed method.},
}
@article {pmid26336135,
year = {2016},
author = {Aghagolzadeh, M and Truccolo, W},
title = {Inference and Decoding of Motor Cortex Low-Dimensional Dynamics via Latent State-Space Models.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {2},
pages = {272-282},
pmid = {26336135},
issn = {1558-0210},
support = {K01 NS057389/NS/NINDS NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; S10 OD016366/OD/NIH HHS/United States ; R01 NS25074/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Behavior, Animal/*physiology ; Biomechanical Phenomena ; Brain-Computer Interfaces/*psychology ; Electrodes, Implanted ; Hand Strength/physiology ; Macaca mulatta ; Microelectrodes ; Models, Neurological ; Motor Cortex/*physiology ; Movement/physiology ; Poisson Distribution ; Psychomotor Performance/physiology ; },
abstract = {Motor cortex neuronal ensemble spiking activity exhibits strong low-dimensional collective dynamics (i.e., coordinated modes of activity) during behavior. Here, we demonstrate that these low-dimensional dynamics, revealed by unsupervised latent state-space models, can provide as accurate or better reconstruction of movement kinematics as direct decoding from the entire recorded ensemble. Ensembles of single neurons were recorded with triple microelectrode arrays (MEAs) implanted in ventral and dorsal premotor (PMv, PMd) and primary motor (M1) cortices while nonhuman primates performed 3-D reach-to-grasp actions. Low-dimensional dynamics were estimated via various types of latent state-space models including, for example, Poisson linear dynamic system (PLDS) models. Decoding from low-dimensional dynamics was implemented via point process and Kalman filters coupled in series. We also examined decoding based on a predictive subsampling of the recorded population. In this case, a supervised greedy procedure selected neuronal subsets that optimized decoding performance. When comparing decoding based on predictive subsampling and latent state-space models, the size of the neuronal subset was set to the same number of latent state dimensions. Overall, our findings suggest that information about naturalistic reach kinematics present in the recorded population is preserved in the inferred low-dimensional motor cortex dynamics. Furthermore, decoding based on unsupervised PLDS models may also outperform previous approaches based on direct decoding from the recorded population or on predictive subsampling.},
}
@article {pmid26331532,
year = {2015},
author = {Rigosa, J and Panarese, A and Dominici, N and Friedli, L and van den Brand, R and Carpaneto, J and DiGiovanna, J and Courtine, G and Micera, S},
title = {Decoding bipedal locomotion from the rat sensorimotor cortex.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056014},
doi = {10.1088/1741-2560/12/5/056014},
pmid = {26331532},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Gait/*physiology ; Hindlimb/*physiology ; Locomotion/*physiology ; Pattern Recognition, Automated/*methods ; Rats ; Rats, Inbred Lew ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; },
abstract = {OBJECTIVE: Decoding forelimb movements from the firing activity of cortical neurons has been interfaced with robotic and prosthetic systems to replace lost upper limb functions in humans. Despite the potential of this approach to improve locomotion and facilitate gait rehabilitation, decoding lower limb movement from the motor cortex has received comparatively little attention. Here, we performed experiments to identify the type and amount of information that can be decoded from neuronal ensemble activity in the hindlimb area of the rat motor cortex during bipedal locomotor tasks.<br><br>APPROACH: Rats were trained to stand, step on a treadmill, walk overground and climb staircases in a bipedal posture. To impose this gait, the rats were secured in a robotic interface that provided support against the direction of gravity and in the mediolateral direction, but behaved transparently in the forward direction. After completion of training, rats were chronically implanted with a micro-wire array spanning the left hindlimb motor cortex to record single and multi-unit activity, and bipolar electrodes into 10 muscles of the right hindlimb to monitor electromyographic signals. Whole-body kinematics, muscle activity, and neural signals were simultaneously recorded during execution of the trained tasks over multiple days of testing. Hindlimb kinematics, muscle activity, gait phases, and locomotor tasks were decoded using offline classification algorithms.<br><br>MAIN RESULTS: We found that the stance and swing phases of gait and the locomotor tasks were detected with accuracies as robust as 90% in all rats. Decoded hindlimb kinematics and muscle activity exhibited a larger variability across rats and tasks.<br><br>SIGNIFICANCE: Our study shows that the rodent motor cortex contains useful information for lower limb neuroprosthetic development. However, brain-machine interfaces estimating gait phases or locomotor behaviors, instead of continuous variables such as limb joint positions or speeds, are likely to provide more robust control strategies for the design of such neuroprostheses.},
}
@article {pmid26330746,
year = {2014},
author = {Contreras-Vidal, JL},
title = {Identifying Engineering, Clinical and Patient's Metrics for Evaluating and Quantifying Performance of Brain-Machine Interface (BMI) Systems.},
journal = {Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics},
volume = {2014},
number = {},
pages = {1489-1492},
pmid = {26330746},
issn = {1062-922X},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01 NS081854/NS/NINDS NIH HHS/United States ; R13 NS082045/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-machine interface (BMI) devices have unparalleled potential to restore functional movement capabilities to stroke, paralyzed and amputee patients. Although BMI systems have achieved success in a handful of investigative studies, translation of closed-loop neuroprosthetic devices from the laboratory to the market is challenged by gaps in the scientific data regarding long-term device reliability and safety, uncertainty in the regulatory, market and reimbursement pathways, lack of metrics for evaluating and quantifying performance in BMI systems, as well as patient-acceptance challenges that impede their fast and effective translation to the end user. This review focuses on the identification of engineering, clinical and user's BMI metrics for new and existing BMI applications.},
}
@article {pmid26329625,
year = {2015},
author = {Minakawa, M and Yu, Z and Kowatari, R and Kondo, N and Miyata, S and Maeda, T and Suzuki, Y and Fukuda, I},
title = {[Heparin-induced Thrombocytopenia after Total Arch Replacement].},
journal = {Kyobu geka. The Japanese journal of thoracic surgery},
volume = {68},
number = {10},
pages = {826-831},
pmid = {26329625},
issn = {0021-5252},
mesh = {Aged ; Aorta, Thoracic/*surgery ; Aortic Aneurysm/surgery ; Heparin/*adverse effects ; Humans ; Male ; Postoperative Complications ; Thrombocytopenia/*chemically induced ; },
abstract = {A 71-year-old man underwent total arch replacement for the true aortic arch aneurysm. On the post-operative day (POD) 10, right hemiplegia and motor aphasia occurred, and it was revealed that there were multiple cerebral infarction in brain computer tomography scan and magnetic resonanse imaging. Furthermore, platelet count has declined significantly from POD 15, so we suspected that heparin-induced thrombocytopenia might occurred. Then, we stopped continuous injection of heparin and administered argatroban and warfarin. In blood examinations, anti-platelet factor 4(PF4)/ heparin antibody measured by latex turbidimetry significantly increased at 5.2 U/ml, and specific immunoglobulin G for PF4/ heparin was also significantly high(optical density 2.334, cut off 0.400). Measurement of platelet derived microparticles produced by stimulation using various dose of heparin(functional assay) indicated typical pattern observed in heparin-induced thrombocytopenia. Thereafter, platelet count recovered and the patient recovered without another thromboembolic event.},
}
@article {pmid26327405,
year = {2015},
author = {Horowitz, J and Patton, J},
title = {I Meant to Do That: Determining the Intentions of Action in the Face of Disturbances.},
journal = {PloS one},
volume = {10},
number = {9},
pages = {e0137289},
pmid = {26327405},
issn = {1932-6203},
support = {R01 NS053606/NS/NINDS NIH HHS/United States ; 1 R01 NS053606/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces ; Face/*physiology ; Feedback, Sensory/*physiology ; Female ; Hand/physiology ; Humans ; Intention ; Learning/physiology ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Our actions often do not match our intentions when there are external disturbances such as turbulence. We derived a novel modeling approach for determining this motor intent from targeted reaching motions that are disturbed by an unexpected force. First, we demonstrated how to mathematically invert both feedforward (predictive) and feedback controls to obtain an intended trajectory. We next examined the model's sensitivity to a realistic range of parameter uncertainties, and found that the expected inaccuracy due to all possible parameter mis-estimations was less than typical movement-to-movement variations seen when humans reach to similar targets. The largest sensitivity arose mainly from uncertainty in joint stiffnesses. Humans cannot change their intent until they acquire sensory feedback, therefore we tested the hypothesis that a straight-line intent should be evident for at least the first 120 milliseconds following the onset of a disturbance. As expected, the intended trajectory showed no change from undisturbed reaching for more than 150 milliseconds after the disturbance onset. Beyond this point, however, we detected a change in intent in five out of eight subjects, surprisingly even when the hand is already near the target. Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms. In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.},
}
@article {pmid26322594,
year = {2015},
author = {Ritaccio, A and Matsumoto, R and Morrell, M and Kamada, K and Koubeissi, M and Poeppel, D and Lachaux, JP and Yanagisawa, Y and Hirata, M and Guger, C and Schalk, G},
title = {Proceedings of the Seventh International Workshop on Advances in Electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {51},
number = {},
pages = {312-320},
pmid = {26322594},
issn = {1525-5069},
support = {R21-EB006356/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01-EB00856/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; P41-EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Electrocorticography/trends ; Electroencephalography/*trends ; Epilepsy/diagnosis/therapy ; Humans ; },
abstract = {The Seventh International Workshop on Advances in Electrocorticography (ECoG) convened in Washington, DC, on November 13-14, 2014. Electrocorticography-based research continues to proliferate widely across basic science and clinical disciplines. The 2014 workshop highlighted advances in neurolinguistics, brain-computer interface, functional mapping, and seizure termination facilitated by advances in the recording and analysis of the ECoG signal. The following proceedings document summarizes the content of this successful multidisciplinary gathering.},
}
@article {pmid26321943,
year = {2015},
author = {Kocaturk, M and Gulcur, HO and Canbeyli, R},
title = {Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.},
journal = {Frontiers in neurorobotics},
volume = {9},
number = {},
pages = {8},
pmid = {26321943},
issn = {1662-5218},
abstract = {In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain-machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.},
}
@article {pmid26321898,
year = {2015},
author = {Faghih, RT and Dahleh, MA and Brown, EN},
title = {An optimization formulation for characterization of pulsatile cortisol secretion.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {228},
pmid = {26321898},
issn = {1662-4548},
abstract = {Cortisol is released to relay information to cells to regulate metabolism and reaction to stress and inflammation. In particular, cortisol is released in the form of pulsatile signals. This low-energy method of signaling seems to be more efficient than continuous signaling. We hypothesize that there is a controller in the anterior pituitary that leads to pulsatile release of cortisol, and propose a mathematical formulation for such controller, which leads to impulse control as opposed to continuous control. We postulate that this controller is minimizing the number of secretory events that result in cortisol secretion, which is a way of minimizing the energy required for cortisol secretion; this controller maintains the blood cortisol levels within a specific circadian range while complying with the first order dynamics underlying cortisol secretion. We use an ℓ0-norm cost function for this controller, and solve a reweighed ℓ1-norm minimization algorithm for obtaining the solution to this optimization problem. We use four examples to illustrate the performance of this approach: (i) a toy problem that achieves impulse control, (ii) two examples that achieve physiologically plausible pulsatile cortisol release, (iii) an example where the number of pulses is not within the physiologically plausible range for healthy subjects while the cortisol levels are within the desired range. This novel approach results in impulse control where the impulses and the obtained blood cortisol levels have a circadian rhythm and an ultradian rhythm that are in agreement with the known physiology of cortisol secretion. The proposed formulation is a first step in developing intermittent controllers for curing cortisol deficiency. This type of bio-inspired pulse controllers can be employed for designing non-continuous controllers in brain-machine interface design for neuroscience applications.},
}
@article {pmid26320626,
year = {2015},
author = {Lv, X and Hou, J and Xia, YL and Ning, J and He, GY and Wang, P and Ge, GB and Xiu, ZL and Yang, L},
title = {Glucuronidation of bavachinin by human tissues and expressed UGT enzymes: Identification of UGT1A1 and UGT1A8 as the major contributing enzymes.},
journal = {Drug metabolism and pharmacokinetics},
volume = {30},
number = {5},
pages = {358-365},
doi = {10.1016/j.dmpk.2015.07.001},
pmid = {26320626},
issn = {1880-0920},
mesh = {Animals ; Estradiol/metabolism ; Flavonoids/*pharmacokinetics ; Glucuronides/metabolism ; Glucuronosyltransferase/antagonists &amp; inhibitors/*metabolism ; Humans ; In Vitro Techniques ; Intestinal Mucosa/metabolism ; Isoenzymes/metabolism ; Kinetics ; Mice ; Microsomes/metabolism ; Microsomes, Liver/metabolism ; Uridine Diphosphate Glucuronic Acid/metabolism ; },
abstract = {Bavachinin (BCI), a major bioactive compound in Chinese herbal Psoralea corylifolia, possesses a wide range of biological activities. In this study, the glucuronidation pathway of BCI was characterized for the first time, by using pooled human liver microsomes (HLM), pooled human intestine microsomes (HIM) and recombinant human UDP-glucosyltransferases (UGTs). One mono-glucuronide was detected in HLM in the presence of uridine-diphosphate glucuronic acid (UDPGA), and it was biosynthesized and well-characterized as BCI-4'-O-glucuronide (BCIG). Reaction phenotyping assay showed that UGT1A1, UGT1A3 and UGT1A8 were involved in BCI-4'-O-glucuronidation, while UGT1A1 and UGT1A8 displayed the higher catalytic ability among all tested UGT isoforms. Kinetic analysis demonstrated that BCI-4'-O-glucuronidation in both HLM and UGT1A1 followed sigmoidal kinetic behaviors and displayed much close Km values (12.4 μM in HLM &amp; 9.7 μM in UGT1A1). Both chemical inhibition assays and correlation analysis demonstrated that UGT1A1 displayed a predominant role in BCI-4'-O-glucuronidation in HLM. Both HIM and UGT1A8 exhibited substrate inhibition at high concentrations, and Km values of HIM and UGT1A8 were 3.6 and 2.3 μM, respectively. Similar catalytic efficiencies were observed for HIM (199.3 μL/min/mg) and UGT1A8 (216.2 μL/min/mg). These findings suggested that UGT1A1 and UGT1A8 were the primary isoforms involved in BCI-4'-O-glucuronidation in HLM, and HIM, respectively.},
}
@article {pmid26319545,
year = {2015},
author = {Tehovnik, EJ and Chen, LL},
title = {Brain control and information transfer.},
journal = {Experimental brain research},
volume = {233},
number = {12},
pages = {3335-3347},
pmid = {26319545},
issn = {1432-1106},
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Humans ; *Information Theory ; Motor Activity/*physiology ; Volition/*physiology ; },
abstract = {In this review, we examine the importance of having a body as essential for the brain to transfer information about the outside world to generate appropriate motor responses. We discuss the context-dependent conditioning of the motor control neural circuits and its dependence on the completion of feedback loops, which is in close agreement with the insights of Hebb and colleagues, who have stressed that for learning to occur the body must be intact and able to interact with the outside world. Finally, we apply information theory to data from published studies to evaluate the robustness of the neuronal signals obtained by bypassing the body (as used for brain-machine interfaces) versus via the body to move in the world. We show that recording from a group of neurons that bypasses the body exhibits a vastly degraded level of transfer of information as compared to that of an entire brain using the body to engage in the normal execution of behaviour. We conclude that body sensations provide more than just feedback for movements; they sustain the necessary transfer of information as animals explore their environment, thereby creating associations through learning. This work has implications for the development of brain-machine interfaces used to move external devices.},
}
@article {pmid26318085,
year = {2015},
author = {Yang, S and Wang, J and Li, S and Deng, B and Wei, X and Yu, H and Li, H},
title = {Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {71},
number = {},
pages = {62-75},
doi = {10.1016/j.neunet.2015.07.017},
pmid = {26318085},
issn = {1879-2782},
mesh = {Algorithms ; Basal Ganglia/*physiopathology ; Brain-Computer Interfaces ; Computer Systems ; Computers ; Humans ; Models, Statistical ; *Neural Networks, Computer ; Neurons ; Parkinson Disease/*physiopathology ; Robotics ; },
abstract = {The basal ganglia (BG) comprise multiple subcortical nuclei, which are responsible for cognition and other functions. Developing a brain-machine interface (BMI) demands a suitable solution for the real-time implementation of a portable BG. In this study, we used a digital hardware implementation of a BG network containing 256 modified Izhikevich neurons and 2048 synapses to reliably reproduce the biological characteristics of BG on a single field programmable gate array (FPGA) core. We also highlighted the role of Parkinsonian analysis by considering neural dynamics in the design of the hardware-based architecture. Thus, we developed a multi-precision architecture based on a precise analysis using the FPGA-based platform with fixed-point arithmetic. The proposed embedding BG network can be applied to intelligent agents and neurorobotics, as well as in BMI projects with clinical applications. Although we only characterized the BG network with Izhikevich models, the proposed approach can also be extended to more complex neuron models and other types of functional networks.},
}
@article {pmid26317826,
year = {2015},
author = {Emel'yanenko, VN and Zaitsau, DH and Shoifet, E and Meurer, F and Verevkin, SP and Schick, C and Held, C},
title = {Benchmark Thermochemistry for Biologically Relevant Adenine and Cytosine. A Combined Experimental and Theoretical Study.},
journal = {The journal of physical chemistry. A},
volume = {119},
number = {37},
pages = {9680-9691},
doi = {10.1021/acs.jpca.5b04753},
pmid = {26317826},
issn = {1520-5215},
mesh = {Adenine/*chemistry ; *Benchmarking ; Computer Simulation ; Cytosine/*chemistry ; *Models, Theoretical ; Molecular Structure ; *Quantum Theory ; Thermodynamics ; },
abstract = {The thermochemical properties available in the literature for adenine and cytosine are in disarray. A new condensed phase standard (p° = 0.1 MPa) molar enthalpy of formation at T = 298.15 K was measured by using combustion calorimetry. New molar enthalpies of sublimation were derived from the temperature dependence of vapor pressure measured by transpiration and by the quarz-crystal microbalance technique. The heat capacities of crystalline adenine and cytosine were measured by temperature-modulated DSC. Thermodynamic data on adenine and cytosine available in the literature were collected, evaluated, and combined with our experimental results. Thus, the evaluated collection of data together with the new experimental results reported here has helped to resolve contradictions in the available enthalpies of formation. A set of reliable thermochemical data is recommended for adenine and cytosine for further thermochemical calculations. Quantum-chemical calculations of the gas phase molar enthalpies of formation of adenine and cytosine have been performed by using the G4 method and results were in excellent agreement with the recommended experimental data. The standard molar entropies of formation and the standard molar Gibbs functions of formation in crystal and gas state have been calculated. Experimental vapor-pressure data measured in this work were used to estimate pure-component PC-SAFT parameters. This allowed modeling solubility of adenine and cytosine in water over the temperature interval 278-310 K.},
}
@article {pmid26308002,
year = {2015},
author = {Li, G and Chung, WY},
title = {A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {8},
pages = {20873-20893},
pmid = {26308002},
issn = {1424-8220},
mesh = {*Automobile Driving ; *Awareness ; Electrodes ; Electroencephalography/*instrumentation ; Humans ; Signal Processing, Computer-Assisted ; *Sleep Stages ; Smartphone ; },
abstract = {Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.},
}
@article {pmid26307849,
year = {2015},
author = {Boakye, CH and Shah, PP and Doddapaneni, R and Patel, AR and Safe, S and Singh, M},
title = {Enhanced Percutaneous Delivery of 1,1-bis(3'-indolyl)-1-(p-chlorophenyl) Methane for Skin Cancer Chemoprevention.},
journal = {Journal of biomedical nanotechnology},
volume = {11},
number = {7},
pages = {1269-1281},
doi = {10.1166/jbn.2015.2064},
pmid = {26307849},
issn = {1550-7033},
support = {5SC1CA161676-03/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; Antineoplastic Agents/administration &amp; dosage/chemistry ; Cell Line, Tumor ; Diffusion ; Drug Synergism ; Female ; Indoles/*administration &amp; dosage/chemistry ; Mice ; Mice, Nude ; Nanocapsules/*chemistry/ultrastructure ; Neoplasms, Radiation-Induced/etiology/*metabolism/*prevention &amp; control ; Particle Size ; Skin Absorption ; Skin Neoplasms/etiology/*metabolism/*prevention &amp; control ; Ultraviolet Rays/adverse effects ; },
abstract = {Skin cancer has high incidence in the United States and is mainly caused by ultraviolet B (UVB) radiation. In this study, we demonstrated the role of 1,1-bis(3'-indolyl)-1-(p-chlorophenyl) methane (DIM-D) in the prevention of skin photocarcinogenesis using an in vivo UVB-induced skin cancer model. We also evaluated the efficiency of oleic acid-modified nanostructured lipid carriers to deliver DIM-D across the skin barrier into the epidermis for chemopreventive activity. Nanocarriers were 203.00 ± 21.21 nm in diameter with polydispersity, zeta potential and entrapment efficiency of 0.33 ± 0.01, 37.17 ± 0.90 mV and 93.64 ± 0.65%, respectively. Oleic acid-modified nanocarriers were incorporated into Hydroxypropyl methylcellulose to form DIM-D-Nanogel (DIM-D-N). DIM-D-N pretreatment prior to UVB exposure delayed tumor initiation and reduced tumor multiplicity (p < 0.05) at the end of the study compared to Epigallocatechin gallate (EGCG) gel pretreatment. DIM-D-N pretreatment decreased UVB-induced damage to skin lipids and proteins (p < 0.05), respectively by 7.63 and 2.56-fold less than EGCG gel pretreatment and by 17.86 and 11.92-fold less than UVB-only treatment. Histology showed rete-ridge extension, epidermal thickening and hyperkeratosis for UVB-only treatment and EGCG gel pretreatment; DIM-D-N pretreatment showed similar features as the negative control. Western blot analysis showed increased Nurr1 expression (p < 0.05) for DIM-D-N pretreated group compared to EGCG gel (4.68-fold). DIM-D-N pretreatment reduced BCI-2 expression (p < 0.05) but increased Bax and cPARP. Knock down studies with Nurr1 siRNA reduced the expressions of Nurr1 and cPARP by 8.18 and 1.45-fold, respectively (p < 0.05). Our results suggest the role of DIM-D in skin cancer chemoprevention mediated by possible molecular therapeutic targets such as Nurr1.},
}
@article {pmid26305233,
year = {2015},
author = {Jochumsen, M and Niazi, IK and Taylor, D and Farina, D and Dremstrup, K},
title = {Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056013},
doi = {10.1088/1741-2560/12/5/056013},
pmid = {26305233},
issn = {1741-2552},
mesh = {Adult ; Aged ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor ; Female ; Hand/*physiopathology ; Humans ; *Imagination ; Machine Learning ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Pattern Recognition, Automated/methods ; Reference Values ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/complications/*physiopathology ; },
abstract = {OBJECTIVE: To detect movement intention from executed and imaginary palmar grasps in healthy subjects and attempted executions in stroke patients using one EEG channel. Moreover, movement force and speed were also decoded.<br><br>APPROACH: Fifteen healthy subjects performed motor execution and imagination of four types of palmar grasps. In addition, five stroke patients attempted to perform the same movements. The movements were detected from the continuous EEG using a single electrode/channel overlying the cortical representation of the hand. Four features were extracted from the EEG signal and classified with a support vector machine (SVM) to decode the level of force and speed associated with the movement. The system performance was evaluated based on both detection and classification.<br><br>MAIN RESULTS: &#x223c; 75% of all movements (executed, imaginary and attempted) were detected 100 ms before the onset of the movement. &#x223c; 60% of the movements were correctly classified according to the intended level of force and speed. When detection and classification were combined, &#x223c; 45% of the movements were correctly detected and classified in both the healthy and stroke subjects, although the performance was slightly better in healthy subjects.<br><br>SIGNIFICANCE: The results indicate that it is possible to use a single EEG channel for detecting movement intentions that may be combined with assistive technologies. The simple setup may lead to a smoother transition from laboratory tests to the clinic.},
}
@article {pmid26305124,
year = {2015},
author = {Mamun, KA and Mace, M and Lutman, ME and Stein, J and Liu, X and Aziz, T and Vaidyanathan, R and Wang, S},
title = {Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056011},
doi = {10.1088/1741-2560/12/5/056011},
pmid = {26305124},
issn = {1741-2552},
mesh = {Adult ; Aged ; Algorithms ; Basal Ganglia/*physiopathology ; Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Movement ; Movement Disorders/*physiopathology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Correlating electrical activity within the human brain to movement is essential for developing and refining interventions (e.g. deep brain stimulation (DBS)) to treat central nervous system disorders. It also serves as a basis for next generation brain-machine interfaces (BMIs). This study highlights a new decoding strategy for capturing movement and its corresponding laterality from deep brain local field potentials (LFPs).<br><br>APPROACH: LFPs were recorded with surgically implanted electrodes from the subthalamic nucleus or globus pallidus interna in twelve patients with Parkinson's disease or dystonia during a visually cued finger-clicking task. We introduce a method to extract frequency dependent neural synchronization and inter-hemispheric connectivity features based upon wavelet packet transform (WPT) and Granger causality approaches. A novel weighted sequential feature selection algorithm has been developed to select optimal feature subsets through a feature contribution measure. This is particularly useful when faced with limited trials of high dimensionality data as it enables estimation of feature importance during the decoding process.<br><br>MAIN RESULTS: This novel approach was able to accurately and informatively decode movement related behaviours from the recorded LFP activity. An average accuracy of 99.8% was achieved for movement identification, whilst subsequent laterality classification was 81.5%. Feature contribution analysis highlighted stronger contralateral causal driving between the basal ganglia hemispheres compared to ipsilateral driving, with causality measures considerably improving laterality discrimination.<br><br>SIGNIFICANCE: These findings demonstrate optimally selected neural synchronization alongside causality measures related to inter-hemispheric connectivity can provide an effective control signal for augmenting adaptive BMIs. In the case of DBS patients, acquiring such signals requires no additional surgery whilst providing a relatively stable and computationally inexpensive control signal. This has the potential to extend invasive BMI, based on recordings within the motor cortex, by providing additional information from subcortical regions.},
}
@article {pmid26305111,
year = {2015},
author = {Basset, Y and Barrios, H and Segar, S and Srygley, RB and Aiello, A and Warren, AD and Delgado, F and Coronado, J and Lezcano, J and Arizala, S and Rivera, M and Perez, F and Bobadilla, R and Lopez, Y and Ramirez, JA},
title = {The Butterflies of Barro Colorado Island, Panama: Local Extinction since the 1930s.},
journal = {PloS one},
volume = {10},
number = {8},
pages = {e0136623},
pmid = {26305111},
issn = {1932-6203},
mesh = {Animals ; Butterflies/*genetics/physiology ; *DNA Barcoding, Taxonomic ; Ecosystem ; *Extinction, Biological ; Islands ; Panama ; *Phylogeny ; Tropical Climate ; },
abstract = {Few data are available about the regional or local extinction of tropical butterfly species. When confirmed, local extinction was often due to the loss of host-plant species. We used published lists and recent monitoring programs to evaluate changes in butterfly composition on Barro Colorado Island (BCI, Panama) between an old (1923-1943) and a recent (1993-2013) period. Although 601 butterfly species have been recorded from BCI during the 1923-2013 period, we estimate that 390 species are currently breeding on the island, including 34 cryptic species, currently only known by their DNA Barcode Index Number. Twenty-three butterfly species that were considered abundant during the old period could not be collected during the recent period, despite a much higher sampling effort in recent times. We consider these species locally extinct from BCI and they conservatively represent 6% of the estimated local pool of resident species. Extinct species represent distant phylogenetic branches and several families. The butterfly traits most likely to influence the probability of extinction were host growth form, wing size and host specificity, independently of the phylogenetic relationships among butterfly species. On BCI, most likely candidates for extinction were small hesperiids feeding on herbs (35% of extinct species). However, contrary to our working hypothesis, extinction of these species on BCI cannot be attributed to loss of host plants. In most cases these host plants remain extant, but they probably subsist at lower or more fragmented densities. Coupled with low dispersal power, this reduced availability of host plants has probably caused the local extinction of some butterfly species. Many more bird than butterfly species have been lost from BCI recently, confirming that small preserves may be far more effective at conserving invertebrates than vertebrates and, therefore, should not necessarily be neglected from a conservation viewpoint.},
}
@article {pmid26303933,
year = {2015},
author = {Hänselmann, S and Schneiders, M and Weidner, N and Rupp, R},
title = {Transcranial magnetic stimulation for individual identification of the best electrode position for a motor imagery-based brain-computer interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {71},
pmid = {26303933},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology ; Brain Mapping ; *Brain-Computer Interfaces ; *Electrodes ; Electroencephalography ; Female ; Foot/innervation ; Hand/innervation ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/anatomy &amp; histology/physiology ; Movement/physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {BACKGROUND: For the translation of noninvasive motor imagery (MI)-based brain-computer interfaces (BCIs) from the lab environment to end users at their homes, their handling must be improved. As a key component, the number of electroencephalogram (EEG)-recording electrodes has to be kept at a minimum. However, due to inter-individual anatomical and physiological variations, reducing the number of electrodes bares the risk of electrode misplacement, which will directly translate into a limited BCI performance of end users. The aim of the study is to evaluate the use of focal transcranial magnetic stimulation (TMS) as an easy tool to individually optimize electrode positioning for a MI-based BCI. For this, the area of MI-induced mu-rhythm modulation was compared with the motor hand representation area in respect to their localization and to the control performance of a MI-based BCI.<br><br>METHODS: Focal TMS was applied to map the motor hand areas and a 48-channel high-resolution EEG was used to localize MI-induced mu-rhythm modulations in 11 able-bodied, right-handed subjects (5 male, age: 23-31). The online BCI performances of the study participants were assessed with a single next-neighbor Laplace channel consecutively placed over the motor hand area and over the area of the strongest mu-modulation.<br><br>RESULTS: For most subjects, a consistent deviation between the position of the mu-modulation center and the corresponding motor hand areas well above the localization error could be observed in mediolateral and to a lesser degree in anterior-posterior direction. On an individual level, the MI-induced mu-rhythm modulation was at average found 1.6 cm (standard deviation (SD)&#x2009;=&#x2009;1.30 cm) lateral and 0.31 cm anterior (SD&#x2009;=&#x2009;1.39 cm) to the motor hand area and enabled a significantly better online BCI performance than the motor hand areas.<br><br>CONCLUSION: On an individual level a trend towards a consistent average spatial distance between motor hand area and mu-rhythm modulation center was found indicating that TMS may be used as a simple tool for quick individual optimization of EEG-recording electrode positions of MI-based BCIs. The study results indicate that motor hand areas of the primary motor cortex determined by TMS are not the main generators of the cortical mu-rhythm.},
}
@article {pmid26302519,
year = {2016},
author = {Liu, Y and Zhang, H and Chen, M and Zhang, L},
title = {A Boosting-Based Spatial-Spectral Model for Stroke Patients' EEG Analysis in Rehabilitation Training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {1},
pages = {169-179},
doi = {10.1109/TNSRE.2015.2466079},
pmid = {26302519},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/physiology ; *Models, Neurological ; Models, Statistical ; Motor Cortex/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spatio-Temporal Analysis ; Stroke/*diagnosis/physiopathology ; Stroke Rehabilitation ; },
abstract = {Studies have shown that a motor imagery electro encephalogram (EEG)-based brain-computer interface (BCI) system can be used as a rehabilitation tool for stroke patients. Efficient classification of EEG from stroke patients is fundamental in the BCI-based stroke rehabilitation systems. One of the most successful algorithms for EEG classification is the common spatial patterns (CSP). However, studies have reported that the performance of CSP heavily relies on its operational frequency band and channels configuration. To the best of our knowledge, there is no agreed upon clinical conclusion about motor imagery patterns of stroke patients. In this case, it is not available to obtain the active channels and frequency bands related to brain activities of stroke patients beforehand. Hence, for using the CSP algorithm, we usually set a relatively broad frequency range and channels, or try to find subject-related frequency bands and channels. To address this problem, we propose an adaptive boosting algorithm to perform autonomous selection of key channels and frequency band. In the proposed method, the spatial-spectral configurations are divided into multiple preconditions, and a new heuristic supervisor of stochastic gradient boost strategy is utilized to train weak classifiers under these preconditions. Extensive experiment comparisons have been performed on three datasets including two benchmark datasets from the famous BCI competition III and BCI competition IV as well as one self-acquired dataset from stroke patients. Results show that our algorithm yields relatively higher classification accuracies compared with seven state-of-the-art approaches. In addition, the spatial patterns (spatial weights) and spectral patterns (bandpass filters) determined by the algorithm can also be used for further analysis of the data, e.g., for brain source localization and physiological knowledge exploration.},
}
@article {pmid26302518,
year = {2016},
author = {Zeyl, T and Yin, E and Keightley, M and Chau, T},
title = {Adding Real-Time Bayesian Ranks to Error-Related Potential Scores Improves Error Detection and Auto-Correction in a P300 Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {1},
pages = {46-56},
doi = {10.1109/TNSRE.2015.2461495},
pmid = {26302518},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Word Processing/*methods ; Young Adult ; },
abstract = {Brain-computer interface (BCI) spellers could improve access to communication for people with profound physical disabilities; however, improved speed and accuracy of these spellers is required to make them practical for everyday use. Here we introduce the combination of P300-speller confidence with the error-related potential (ErrP) to improve online single-trial error detection and correction accuracies in a BCI speller. First, we present a mechanism for obtaining P300-confidence using a real-time Bayesian dynamic stopping framework that makes novel use of additional stimuli that occur due to epoch and filter delays. Second, we propose an ensemble of decision trees to combine ErrP and P300-confidence features. Third, we describe the unique attentional differences between error and correct feedback in our spelling interface and discuss how these differences affect ErrP physiology. We tested online error detection on 11 typically developed adults using a BCI system trained on a previous day and found an average sensitivity of 86.67% and specificity of 96.59%. Automatic correction increased selection accuracy by 13.67% and utility grew by a factor of 4.48. We found, however, that the improved performance was primarily attributable to the inclusion of P300 confidence in error detection, calling into question the significance of single-trial ErrP detection.},
}
@article {pmid26301829,
year = {2015},
author = {Buyukturkoglu, K and Roettgers, H and Sommer, J and Rana, M and Dietzsch, L and Arikan, EB and Veit, R and Malekshahi, R and Kircher, T and Birbaumer, N and Sitaram, R and Ruiz, S},
title = {Self-Regulation of Anterior Insula with Real-Time fMRI and Its Behavioral Effects in Obsessive-Compulsive Disorder: A Feasibility Study.},
journal = {PloS one},
volume = {10},
number = {8},
pages = {e0135872},
pmid = {26301829},
issn = {1932-6203},
mesh = {Adult ; Anxiety/*physiopathology/psychology ; Cerebral Cortex/diagnostic imaging/*physiopathology ; Emotions/physiology ; Fear/*physiology/psychology ; Feasibility Studies ; Female ; Heart Rate ; Humans ; Learning/physiology ; Magnetic Resonance Imaging ; Male ; Neuroimaging ; Obsessive-Compulsive Disorder/diagnostic imaging/*physiopathology/psychology ; Radiography ; Self-Control ; Skin Physiological Phenomena ; Visual Perception/physiology ; },
abstract = {INTRODUCTION: Obsessive-compulsive disorder (OCD) is a common and chronic condition that can have disabling effects throughout the patient's lifespan. Frequent symptoms among OCD patients include fear of contamination and washing compulsions. Several studies have shown a link between contamination fears, disgust over-reactivity, and insula activation in OCD. In concordance with the role of insula in disgust processing, new neural models based on neuroimaging studies suggest that abnormally high activations of insula could be implicated in OCD psychopathology, at least in the subgroup of patients with contamination fears and washing compulsions.<br><br>METHODS: In the current study, we used a Brain Computer Interface (BCI) based on real-time functional magnetic resonance imaging (rtfMRI) to aid OCD patients to achieve down-regulation of the Blood Oxygenation Level Dependent (BOLD) signal in anterior insula. Our first aim was to investigate whether patients with contamination obsessions and washing compulsions can learn to volitionally decrease (down-regulate) activity in the insula in the presence of disgust/anxiety provoking stimuli. Our second aim was to evaluate the effect of down-regulation on clinical, behavioural and physiological changes pertaining to OCD symptoms. Hence, several pre- and post-training measures were performed, i.e., confronting the patient with a disgust/anxiety inducing real-world object (Ecological Disgust Test), and subjective rating and physiological responses (heart rate, skin conductance level) of disgust towards provoking pictures.<br><br>RESULTS: Results of this pilot study, performed in 3 patients (2 females), show that OCD patients can gain self-control of the BOLD activity of insula, albeit to different degrees. In two patients positive changes in behaviour in the EDT were observed following the rtfMRI trainings. Behavioural changes were also confirmed by reductions in the negative valence and in the subjective perception of disgust towards symptom provoking images.<br><br>CONCLUSION: Although preliminary, results of this study confirmed that insula down-regulation is possible in patients suffering from OCD, and that volitional decreases of insula activation could be used for symptom alleviation in this disorder.},
}
@article {pmid26301519,
year = {2016},
author = {Kim, T and Kim, S and Lee, B},
title = {Effects of Action Observational Training Plus Brain-Computer Interface-Based Functional Electrical Stimulation on Paretic Arm Motor Recovery in Patient with Stroke: A Randomized Controlled Trial.},
journal = {Occupational therapy international},
volume = {23},
number = {1},
pages = {39-47},
doi = {10.1002/oti.1403},
pmid = {26301519},
issn = {1557-0703},
mesh = {Aged ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Feedback ; Female ; Humans ; Male ; Middle Aged ; Occupational Therapy/*methods ; Paresis/etiology/*rehabilitation ; Range of Motion, Articular ; Recovery of Function ; Single-Blind Method ; Stroke/complications ; *Stroke Rehabilitation ; Upper Extremity/*physiopathology ; Wrist Joint/physiopathology ; },
abstract = {The purpose of this study was to investigate whether action observational training (AOT) plus brain-computer interface-based functional electrical stimulation (BCI-FES) has a positive influence on motor recovery of paretic upper extremity in patients with stroke. This was a hospital-based, randomized controlled trial with a blinded assessor. Thirty patients with a first-time stroke were randomly allocated to one of two groups: the BCI-FES group (n = 15) and the control group (n = 15). The BCI-FES group administered to AOT plus BCI-FES on the paretic upper extremity five times per week during 4 weeks while both groups received conventional therapy. The primary outcomes were the Fugl-Meyer Assessment of the Upper Extremity, Motor Activity Log (MAL), Modified Barthel Index and range of motion of paretic arm. A blinded assessor evaluated the outcomes at baseline and 4 weeks. All baseline outcomes did not differ significantly between the two groups. After 4 weeks, the Fugl-Meyer Assessment of the Upper Extremity sub-items (total, shoulder and wrist), MAL (MAL-Activity of Use and Quality of Movement), Modified Barthel Index and wrist flexion range of motion were significantly higher in the BCI-FES group (p < 0.05). AOT plus BCI-based FES is effective in paretic arm rehabilitation by improving the upper extremity performance. The motor improvements suggest that AOT plus BCI-based FES can be used as a therapeutic tool for stroke rehabilitation. The limitations of the study are that subjects had a certain limited level of upper arm function, and the sample size was comparatively small; hence, it is recommended that future large-scale trials should consider stratified and lager populations according to upper arm function.},
}
@article {pmid26300760,
year = {2015},
author = {Meltzer, B and Reichenbach, CS and Braiman, C and Schiff, ND and Hudspeth, AJ and Reichenbach, T},
title = {The steady-state response of the cerebral cortex to the beat of music reflects both the comprehension of music and attention.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {436},
pmid = {26300760},
issn = {1662-5161},
abstract = {The brain's analyses of speech and music share a range of neural resources and mechanisms. Music displays a temporal structure of complexity similar to that of speech, unfolds over comparable timescales, and elicits cognitive demands in tasks involving comprehension and attention. During speech processing, synchronized neural activity of the cerebral cortex in the delta and theta frequency bands tracks the envelope of a speech signal, and this neural activity is modulated by high-level cortical functions such as speech comprehension and attention. It remains unclear, however, whether the cortex also responds to the natural rhythmic structure of music and how the response, if present, is influenced by higher cognitive processes. Here we employ electroencephalography to show that the cortex responds to the beat of music and that this steady-state response reflects musical comprehension and attention. We show that the cortical response to the beat is weaker when subjects listen to a familiar tune than when they listen to an unfamiliar, non-sensical musical piece. Furthermore, we show that in a task of intermodal attention there is a larger neural response at the beat frequency when subjects attend to a musical stimulus than when they ignore the auditory signal and instead focus on a visual one. Our findings may be applied in clinical assessments of auditory processing and music cognition as well as in the construction of auditory brain-machine interfaces.},
}
@article {pmid26300129,
year = {2015},
author = {Wang, F and He, Y and Pan, J and Xie, Q and Yu, R and Zhang, R and Li, Y},
title = {Erratum: A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {12592},
doi = {10.1038/srep12592},
pmid = {26300129},
issn = {2045-2322},
}
@article {pmid26296548,
year = {2015},
author = {Krist, AH and Baumann, LJ and Holtrop, JS and Wasserman, MR and Stange, KC and Woo, M},
title = {Evaluating Feasible and Referable Behavioral Counseling Interventions.},
journal = {American journal of preventive medicine},
volume = {49},
number = {3 Suppl 2},
pages = {S138-49},
doi = {10.1016/j.amepre.2015.05.009},
pmid = {26296548},
issn = {1873-2607},
mesh = {Advisory Committees/*organization &amp; administration ; Behavior Therapy/*classification/trends ; Counseling/*methods ; Evidence-Based Medicine ; Humans ; Primary Health Care/*organization &amp; administration ; United States ; },
abstract = {The U.S. Preventive Services Task Force (USPTF) recognizes that behaviors have a major impact on health and well-being. Currently, the USPSTF has 11 behavioral counseling intervention (BCI) recommendations. These BCIs can be delivered in a primary care setting or patients can be referred to other clinical or community programs. Unfortunately, many recommended BCIs are infrequently and ineffectually delivered, suggesting that more evidence is needed to understand which BCIs are feasible and referable. In response, the USPSTF convened an expert forum in 2013 to inform the evaluation of BCI feasibility. This manuscript reports on findings from the forum and proposes that researchers use several frameworks to help clinicians and the USPSTF evaluate which BCIs work under usual conditions. A key recommendation for BCI researchers is to use frameworks whose components can support dissemination and implementation efforts. These frameworks include the Template for Intervention Description and Replication (TIDieR), which helps describe the essential components of an intervention, and pragmatic frameworks like Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) or Pragmatic-Explanatory Continuum Indicator Summary (PRECIS), which help to report study design elements and outcomes. These frameworks can both guide the design of more-feasible BCIs and produce clearer feasibility evidence. Critical evidence gaps include a better understanding of which patients will benefit from a BCI, how flexible interventions can be without compromising effectiveness, required clinician expertise, necessary intervention intensity and follow-up, impact of patient and clinician intervention adherence, optimal conditions for BCI delivery, and how new care models will influence BCI feasibility.},
}
@article {pmid26295695,
year = {2015},
author = {Schwienheer, C and Prinz, A and Zeiner, T and Merz, J},
title = {Separation of active laccases from Pleurotus sapidus culture supernatant using aqueous two-phase systems in centrifugal partition chromatography.},
journal = {Journal of chromatography. B, Analytical technologies in the biomedical and life sciences},
volume = {1002},
number = {},
pages = {1-7},
doi = {10.1016/j.jchromb.2015.07.050},
pmid = {26295695},
issn = {1873-376X},
mesh = {Centrifugation ; Chromatography, Liquid/*methods ; Laccase/*isolation &amp; purification ; Pleurotus/*enzymology ; },
abstract = {For the production of bio active compounds, e.g., active enzymes or antibodies, a conserved purification process with a minimum loss of active compounds is necessary. In centrifugal partition chromatography (CPC), the separation effect is based on the different distribution of the components to be separated between two immiscible liquid phases. Thereby, one liquid phase is kept stationary in chambers by a centrifugal field and the mobile phase is pumped through via connecting ducts. Aqueous two phase systems (ATPS) are known to provide benign conditions for biochemical products and seem to be promising when used in CPC for purification tasks. However, it is not known if active biochemical compounds can "survive" the conditions in a CPC where strong shear forces can occur due to the two-phasic flow under centrifugal forces. Therefore, this aspect has been faced within this study by the separation of active laccases from a fermentation broth of Pleurotus sapidus. After selecting a suitable ATPS and operating conditions, the activity yield was calculated and the preservation of the active enzymes could be observed. Therefore, CPC could be shown as potentially suitable for the purification of bio-active compounds.},
}
@article {pmid26294467,
year = {2015},
author = {Mora, N and De Munari, I and Ciampolini, P},
title = {A plug&amp;play Brain Computer Interface solution for AAL systems.},
journal = {Studies in health technology and informatics},
volume = {217},
number = {},
pages = {152-158},
pmid = {26294467},
issn = {1879-8365},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; },
abstract = {We present a complete BCI-enabled (Brain Computer Interface) solution for Ambient Assisted Living system control. BCI are alternative, augmentative communication means capable of exploiting just the brain waveforms to infer intent, thus potentially posing as a technological bridge capable of overcoming limitations in the usual neuromuscular pathways. The module was completely developed in a customized way, encompassing hardware and software components. We demonstrate the effectiveness of the approach on a practical control scenario in which the user can issue 4 different commands, at his own pace and will, in real-time. No initial calibration is necessary, in line with the aimed plug&amp;play approach. Results are very promising, especially in false positives rejection, well improving over literature.},
}
@article {pmid26292651,
year = {2016},
author = {Fosu-Nyarko, J and Tan, JA and Gill, R and Agrez, VG and Rao, U and Jones, MG},
title = {De novo analysis of the transcriptome of Pratylenchus zeae to identify transcripts for proteins required for structural integrity, sensation, locomotion and parasitism.},
journal = {Molecular plant pathology},
volume = {17},
number = {4},
pages = {532-552},
pmid = {26292651},
issn = {1364-3703},
mesh = {Animals ; Base Sequence ; Caenorhabditis elegans/genetics ; Carbohydrates/chemistry ; Computer Simulation ; Expressed Sequence Tags ; Feeding Behavior ; *Gene Expression Profiling ; Genes, Helminth ; Helminth Proteins/*genetics/metabolism ; Locomotion/*genetics ; Molecular Sequence Annotation ; Parasites/*genetics ; Pharynx/physiology ; Proteome/metabolism ; RNA, Messenger/genetics/metabolism ; Sensation/*genetics ; Transcriptome/*genetics ; Tylenchoidea/*genetics ; },
abstract = {The root lesion nematode Pratylenchus zeae, a migratory endoparasite, is an economically important pest of major crop plants (e.g. cereals, sugarcane). It enters host roots, migrates through root tissues and feeds from cortical cells, and defends itself against biotic and abiotic stresses in the soil and in host tissues. We report de novo sequencing of the P. zeae transcriptome using 454 FLX, and the identification of putative transcripts encoding proteins required for movement, response to stimuli, feeding and parasitism. Sequencing generated 347,443 good quality reads which were assembled into 10,163 contigs and 139,104 singletons: 65% of contigs and 28% of singletons matched sequences of free-living and parasitic nematodes. Three-quarters of the annotated transcripts were common to reference nematodes, mainly representing genes encoding proteins for structural integrity and fundamental biochemical processes. Over 15,000 transcripts were similar to Caenorhabditis elegans genes encoding proteins with roles in mechanical and neural control of movement, responses to chemicals, mechanical and thermal stresses. Notably, 766 transcripts matched parasitism genes employed by both migratory and sedentary endoparasites in host interactions, three of which hybridized to the gland cell region, suggesting that they might be secreted. Conversely, transcripts for effectors reported to be involved in feeding site formation by sedentary endoparasites were conspicuously absent. Transcripts similar to those encoding some secretory-excretory products at the host interface of Brugia malayi, the secretome of Meloidogyne incognita and products of gland cells of Heterodera glycines were also identified. This P. zeae transcriptome provides new information for genome annotation and functional analysis of possible targets for control of pratylenchid nematodes.},
}
@article {pmid26291321,
year = {2015},
author = {Kwak, NS and Müller, KR and Lee, SW},
title = {A lower limb exoskeleton control system based on steady state visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056009},
doi = {10.1088/1741-2560/12/5/056009},
pmid = {26291321},
issn = {1741-2552},
mesh = {Adult ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Evoked Potentials, Visual ; *Exoskeleton Device ; Feedback ; Gait Disorders, Neurologic/*physiopathology/*rehabilitation ; Humans ; Lower Extremity ; Male ; Robotics/instrumentation ; Visual Perception ; },
abstract = {OBJECTIVE: We have developed an asynchronous brain-machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs).<br><br>APPROACH: By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors.<br><br>MAIN RESULTS: Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 &#xb1; 5.73%, a response time of 3.28 &#xb1; 1.82 s, an information transfer rate of 32.9 &#xb1; 9.13 bits/min, and a completion time of 1100 &#xb1; 154.92 s for the experimental parcour studied.<br><br>SIGNIFICANCE: The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.},
}
@article {pmid26290661,
year = {2015},
author = {Angulo-Sherman, IN and Gutiérrez, D},
title = {A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a Brain-Computer Interface.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {824175},
pmid = {26290661},
issn = {1687-5273},
mesh = {Adult ; Analysis of Variance ; Auditory Perception/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; *Electroencephalography ; Evoked Potentials/*physiology ; Feedback ; Female ; Humans ; Male ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Visual Perception/physiology ; Volunteers/psychology ; Young Adult ; },
abstract = {We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stimulation (FES). After each training session, the volunteers' accuracy in operating the BCI was assessed, and the event-related coherence (ErCoh) was calculated for all possible combinations of pairs of EEG sensors. After at least four training sessions, we searched for significant differences in accuracy and ErCoh using one-way analysis of variance (ANOVA) and multiple comparison tests. Our results show that there exists a high correlation between an increase in ErCoh and performance improvement, and this effect is mainly localized in the centrofrontal and centroparietal brain regions for the case of our motor imagery task. This result has a direct implication with the development of new techniques to evaluate BCI performance and the process of selecting a feedback modality that better enhances the volunteer's capacity to operate a BCI system.},
}
@article {pmid26290504,
year = {2015},
author = {de Deus Santos, MR and Silva Martins, A and Baptistotte, C and Work, TM},
title = {Health condition of juvenile Chelonia mydas related to fibropapillomatosis in southeast Brazil.},
journal = {Diseases of aquatic organisms},
volume = {115},
number = {3},
pages = {193-201},
doi = {10.3354/dao02883},
pmid = {26290504},
issn = {0177-5103},
mesh = {Animals ; Atlantic Ocean ; Brazil/epidemiology ; Hematocrit ; Papilloma/blood/epidemiology/*veterinary ; Turtles/*blood ; },
abstract = {Packed cell volume (PCV), plasma biochemistry, visual body condition (BC), and calculated body condition index (BCI) were evaluated in 170 wild juvenile green sea turtles Chelonia mydas from an aggregation in the effluent canal of a steel mill in Brazil. Occurrence of cutaneous fibropapillomatosis (FP) was observed in 44.1% of the animals examined. BCI alone did not differ significantly between healthy animals and those afflicted with FP. However, all turtles with low BCI were severely afflicted and were uremic, hypoglycemic, and anemic in relation to healthy animals. Severe FP was not always reflected by a poor health condition of the individual. Clinical evaluation and plasma biochemistry indicated that most animals afflicted with FP were in good health condition. Differences in FP manifestations and associated health conditions in different geographic regions must be assessed by long-term health monitoring programs to help define priorities for conservation efforts.},
}
@article {pmid26290069,
year = {2015},
author = {Kim, YJ and Park, SW and Yeom, HG and Bang, MS and Kim, JS and Chung, CK and Kim, S},
title = {A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.},
journal = {Biomedical engineering online},
volume = {14},
number = {},
pages = {81},
pmid = {26290069},
issn = {1475-925X},
mesh = {Adult ; *Arm ; *Brain ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; *Hand ; Hand Strength/physiology ; Humans ; *Magnetoencephalography ; Male ; *Robotics ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified.<br><br>METHODS: The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories.<br><br>RESULTS: The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50&#xa0;% with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75&#xa0;% respectively.<br><br>CONCLUSIONS: A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because the non-invasive neural signal is less accurate. However the success rate could be sufficiently improved for practical applications by using additional sensors. Robot arm control based on hand trajectories predicted from EEG would allow for portability, and the performance with EEG was comparable to that with MEG.},
}
@article {pmid26287193,
year = {2015},
author = {Chen, YL and Hwang, WJ and Ke, CE},
title = {An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {8},
pages = {19830-19851},
pmid = {26287193},
issn = {1424-8220},
mesh = {Action Potentials/*physiology ; *Algorithms ; *Electronics ; Software ; },
abstract = {A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.},
}
@article {pmid26284992,
year = {2015},
author = {Wang, YF and Dai, GS and Liu, F and Long, ZL and Yan, JH and Chen, HF},
title = {Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations.},
journal = {Journal of cognitive neuroscience},
volume = {27},
number = {12},
pages = {2406-2415},
doi = {10.1162/jocn_a_00864},
pmid = {26284992},
issn = {1530-8898},
mesh = {Adolescent ; Brain/*physiology ; Brain Mapping ; Brain Waves/*physiology ; Cerebrovascular Circulation/*physiology ; Cognition/physiology ; Comprehension/physiology ; Humans ; Male ; Neuropsychological Tests ; Oxygen/*blood ; Pattern Recognition, Visual/*physiology ; Periodicity ; Reaction Time ; Reading ; *Semantics ; Young Adult ; },
abstract = {Steady-state responses (SSRs) reflect the synchronous neural oscillations evoked by noninvasive and consistently repeated stimuli at the fundamental or harmonic frequencies. The steady-state evoked potentials (SSEPs; the representative form of the SSRs) have been widely used in the cognitive and clinical neurosciences and brain-computer interface research. However, the steady-state evoked potentials have limitations in examining high-frequency neural oscillations and basic cognition. In addition, synchronous neural oscillations in the low frequency range (<1 Hz) and in higher-order cognition have received a little attention. Therefore, we examined the SSRs in the low frequency range using a new index, the steady-state BOLD responses (SSBRs) evoked by semantic stimuli. Our results revealed that the significant SSBRs were induced at the fundamental frequency of stimuli and the first harmonic in task-related regions, suggesting the enhanced variability of neural oscillations entrained by exogenous stimuli. The SSBRs were independent of neurovascular coupling and characterized by sensorimotor bias, an indication of regional-dependent neuroplasticity. Furthermore, the amplitude of SSBRs may predict behavioral performance and show the psychophysiological relevance. Our findings provide valuable insights into the understanding of the SSRs evoked by higher-order cognition and how the SSRs modulate low-frequency neural oscillations.},
}
@article {pmid26284171,
year = {2015},
author = {Jamaloo, F and Mikaeili, M},
title = {Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-computer Interface.},
journal = {Journal of medical signals and sensors},
volume = {5},
number = {3},
pages = {156-161},
pmid = {26284171},
issn = {2228-7477},
abstract = {Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems. In the present study, a novel CSP sub-band feature selection has been proposed based on the discriminative information of the features. Besides, a distinction sensitive learning vector quantization based weighting of the selected features has been considered. Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets. The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.},
}
@article {pmid26283932,
year = {2015},
author = {Rouse, AG and Schieber, MH},
title = {Advancing brain-machine interfaces: moving beyond linear state space models.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {108},
pmid = {26283932},
issn = {1662-5137},
support = {R01 NS065902/NS/NINDS NIH HHS/United States ; R01 NS079664/NS/NINDS NIH HHS/United States ; },
abstract = {Advances in recent years have dramatically improved output control by Brain-Machine Interfaces (BMIs). Such devices nevertheless remain robotic and limited in their movements compared to normal human motor performance. Most current BMIs rely on transforming recorded neural activity to a linear state space composed of a set number of fixed degrees of freedom. Here we consider a variety of ways in which BMI design might be advanced further by applying non-linear dynamics observed in normal motor behavior. We consider (i) the dynamic range and precision of natural movements, (ii) differences between cortical activity and actual body movement, (iii) kinematic and muscular synergies, and (iv) the implications of large neuronal populations. We advance the hypothesis that a given population of recorded neurons may transmit more useful information than can be captured by a single, linear model across all movement phases and contexts. We argue that incorporating these various non-linear characteristics will be an important next step in advancing BMIs to more closely match natural motor performance.},
}
@article {pmid26279141,
year = {2015},
author = {Zhao, J and Farhatnia, Y and Kalaskar, DM and Zhang, Y and Bulter, PE and Seifalian, AM},
title = {The influence of porosity on the hemocompatibility of polyhedral oligomeric silsesquioxane poly (caprolactone-urea) urethane.},
journal = {The international journal of biochemistry &amp; cell biology},
volume = {68},
number = {},
pages = {176-186},
doi = {10.1016/j.biocel.2015.08.007},
pmid = {26279141},
issn = {1878-5875},
mesh = {Biocompatible Materials/chemical synthesis/chemistry/*pharmacology ; Blood Platelets/cytology/*drug effects/physiology ; Humans ; Materials Testing ; Nanocomposites/chemistry/ultrastructure ; Organosilicon Compounds/chemical synthesis/chemistry/*pharmacology ; Platelet Activation/drug effects ; Platelet Adhesiveness/drug effects ; Polyesters/chemical synthesis/chemistry/*pharmacology ; Polytetrafluoroethylene/pharmacology ; Polyurethanes/chemical synthesis/chemistry/*pharmacology ; Porosity ; Surface Properties ; },
abstract = {BACKGROUND: The physio-chemical properties of blood contacting biomaterials play an important role in determining their hemocompatibility. It is shown in literature that surface roughness and porosity have significant effect on hemocompatibility. In this study, we use a biocompatible, low thrombogenic nanocomposite polymer called POSS-PCU to test this hypothesis: would porosity compromise the hemocompatibility of POSS-PCU. We compared the hemocompatibility of POSS-PCU films of various pore sizes with PTFE, which is a commercially available material used in most blood contacting devices.<br><br>METHODS: Sterilized POSS-PCU films with different size pores were prepared as samples and porous PTFE film were selected as control. And all samples were subjected to SEM for topograpgy, mechanical test for characterization and hemocompatibility tests to evaluate contact activation, platelet adhesion and activation, as well as whole blood clotting response to the samples.<br><br>RESULTS: WCA significantly increased with the pore size of POSS-PCU film, whereas both tensile stress and strain decreased significantly as the sizes of pores increased. However, when compared to PTFE film with same size pores, POSS-PCU films showed both higher tensile stress and strain. Pore size had little impact over POSS-PCU's surface chemistry groups as tested by FTIR analysis. Contact activation and platelet adhesion essay also showed no significant difference between different POSS-PCU samples. However, in whole blood reactions, POSS-PCU with pores size around 2-5&#x3bc;m showed higher BCI than plain films and those with pores size around 35-45&#x3bc;m. POSS-PCU showed lower thrombogencity and higher hemocompatibility comparing with porous PTFE on the aspects of platelet activation, adhesion and whole blood reaction.<br><br>SUMMARY AND CONCLUSIONS: POSS-PCU polymer films as a biomaterial in chronic blood contacting implants show significant lower thrombogencity and higher hemocompatibility than porous PTFE film. It is desirable as a coating or covering material in small diameter stents for treating cardiovascular diseases, cerebral vascular diseases and peripheral arterial diseases.},
}
@article {pmid26277421,
year = {2015},
author = {Zhang, Y and Zhou, G and Jin, J and Wang, X and Cichocki, A},
title = {Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {255},
number = {},
pages = {85-91},
doi = {10.1016/j.jneumeth.2015.08.004},
pmid = {26277421},
issn = {1872-678X},
mesh = {Access to Information ; Brain/*physiology ; *Brain-Computer Interfaces ; Datasets as Topic ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {BACKGROUND: Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually.<br><br>NEW METHOD: This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification.<br><br>RESULTS: Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI.<br><br>The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods.<br><br>CONCLUSIONS: The proposed SFBCSP is a potential method for improving the performance of MI-based BCI.},
}
@article {pmid26276986,
year = {2016},
author = {Edelman, BJ and Baxter, B and He, B},
title = {EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {1},
pages = {4-14},
pmid = {26276986},
issn = {1558-2531},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Humans ; Imagination/*physiology ; Male ; Young Adult ; },
abstract = {GOAL: Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.<br><br>METHODS: We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.<br><br>RESULTS: We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.<br><br>CONCLUSION: ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.<br><br>SIGNIFICANCE: This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.},
}
@article {pmid26269633,
year = {2015},
author = {Scholl, J and Kolling, N and Nelissen, N and Wittmann, MK and Harmer, CJ and Rushworth, MF},
title = {The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {32},
pages = {11233-11251},
pmid = {26269633},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; 100973/WT_/Wellcome Trust/United Kingdom ; G0700399/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Brain/*physiology ; Brain-Computer Interfaces ; Choice Behavior/*physiology ; Female ; Humans ; Learning/*physiology ; Magnetic Resonance Imaging ; Male ; Models, Neurological ; *Reward ; Young Adult ; },
abstract = {UNLABELLED: Natural environments are complex, and a single choice can lead to multiple outcomes. Agents should learn which outcomes are due to their choices and therefore relevant for future decisions and which are stochastic in ways common to all choices and therefore irrelevant for future decisions between options. We designed an experiment in which human participants learned the varying reward and effort magnitudes of two options and repeatedly chose between them. The reward associated with a choice was randomly real or hypothetical (i.e., participants only sometimes received the reward magnitude associated with the chosen option). The real/hypothetical nature of the reward on any one trial was, however, irrelevant for learning the longer-term values of the choices, and participants ought to have only focused on the informational content of the outcome and disregarded whether it was a real or hypothetical reward. However, we found that participants showed an irrational choice bias, preferring choices that had previously led, by chance, to a real reward in the last trial. Amygdala and ventromedial prefrontal activity was related to the way in which participants' choices were biased by real reward receipt. By contrast, activity in dorsal anterior cingulate cortex, frontal operculum/anterior insula, and especially lateral anterior prefrontal cortex was related to the degree to which participants resisted this bias and chose effectively in a manner guided by aspects of outcomes that had real and more sustained relationships with particular choices, suppressing irrelevant reward information for more optimal learning and decision making.<br><br>SIGNIFICANCE STATEMENT: In complex natural environments, a single choice can lead to multiple outcomes. Human agents should only learn from outcomes that are due to their choices, not from outcomes without such a relationship. We designed an experiment to measure learning about reward and effort magnitudes in an environment in which other features of the outcome were random and had no relationship with choice. We found that, although people could learn about reward magnitudes, they nevertheless were irrationally biased toward repeating certain choices as a function of the presence or absence of random reward features. Activity in different brain regions in the prefrontal cortex either reflected the bias or reflected resistance to the bias.},
}
@article {pmid26268353,
year = {2015},
author = {Diez, PF and Garcés Correa, A and Orosco, L and Laciar, E and Mut, V},
title = {Attention-level transitory response: a novel hybrid BCI approach.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056007},
doi = {10.1088/1741-2560/12/5/056007},
pmid = {26268353},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/methods ; Reaction Time/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI).<br><br>APPROACH: Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets.<br><br>MAIN RESULTS: The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed.<br><br>SIGNIFICANCE: A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.},
}
@article {pmid26266424,
year = {2015},
author = {Powers, JC and Bieliaieva, K and Wu, S and Nam, CS},
title = {The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces.},
journal = {Brain sciences},
volume = {5},
number = {3},
pages = {318-356},
pmid = {26266424},
issn = {2076-3425},
abstract = {Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs) show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use-i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE) of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1) alternative signal evocation methods within the oddball paradigm; (2) environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3) measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.},
}
@article {pmid26263829,
year = {2016},
author = {Juarez-Hernandez, LJ and Cornella, N and Pasquardini, L and Battistoni, S and Vidalino, L and Vanzetti, L and Caponi, S and Dalla Serra, M and Iannotta, S and Pederzolli, C and Macchi, P and Musio, C},
title = {Bio-hybrid interfaces to study neuromorphic functionalities: New multidisciplinary evidences of cell viability on poly(anyline) (PANI), a semiconductor polymer with memristive properties.},
journal = {Biophysical chemistry},
volume = {208},
number = {},
pages = {40-47},
doi = {10.1016/j.bpc.2015.07.008},
pmid = {26263829},
issn = {1873-4200},
mesh = {Aniline Compounds/*chemistry ; Cell Adhesion ; Cell Survival ; Electrolytes/chemistry ; HEK293 Cells ; HeLa Cells ; Humans ; Semiconductors ; Surface Properties ; },
abstract = {The interfacing of artificial devices with biological systems is a challenging field that crosses several disciplines ranging from fundamental research (biophysical chemistry, neurobiology, material and surface science) to frontier technological application (nanotechnology, bioelectronics). The memristor is the fourth fundamental circuit element, whose electrical properties favor applications in signal processing, neural networks, and brain-computer interactions and it represents a new frontier for technological applications in many fields including the nanotechnologies, bioelectronics and the biosensors. Using multidisciplinary approaches, covering surface science, cell biology and electrophysiology, we successfully implemented a living bio-hybrid system constituted by cells adhering to films of poly(aniline) (PANI), a semiconductor polymer having memristive properties assembled with polyelectrolytes. Here we tested whether the PANI devices could support survivor, adhesion and differentiation of several cell lines, including the neuron-like SHSY5Y cells. Moreover, we performed electrophysiology on these cells showing that the biophysical properties are retained with differences occurring in the recorded ion currents. Taken together, the cell viability here reported is the key requirement to design and develop a reliable functional memristor-based bio-hybrid able to mimic neuronal activity and plasticity.},
}
@article {pmid26259213,
year = {2016},
author = {Kreilinger, A and Hiebel, H and Müller-Putz, GR},
title = {Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.},
journal = {IEEE transactions on bio-medical engineering},
volume = {63},
number = {3},
pages = {519-529},
doi = {10.1109/TBME.2015.2465866},
pmid = {26259213},
issn = {1558-2531},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Electroencephalography/*standards ; *Feedback ; Female ; Humans ; Imagination ; Male ; *Signal Processing, Computer-Assisted ; *Video Games ; Young Adult ; },
abstract = {OBJECTIVE: This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection.<br><br>METHODS: In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users.<br><br>RESULTS: We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game.<br><br>CONCLUSION: Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs.<br><br>SIGNIFICANCE: This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.},
}
@article {pmid26256069,
year = {2015},
author = {Lin, CS and Lin, JC and Huang, YC and Lai, YC and Chang, HC},
title = {The designs and applications of a scanning interface with electrical signal detection on the scalp for the severely disabled.},
journal = {Computer methods and programs in biomedicine},
volume = {122},
number = {2},
pages = {207-214},
doi = {10.1016/j.cmpb.2015.07.007},
pmid = {26256069},
issn = {1872-7565},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation/*methods ; Electrooculography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Eye Movements/physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {This study discussed a computer-aided program development that meets the requirements of people with physical disabilities. A number of control modes, such as electrode signal recorded on the scalp and blink control, were combined with the scanning human-machine interface to improve the external input/output device. Moreover, a novel and precise algorithm, which filters noise and reduces misrecognition of the system, was proposed. A convenient assistive device can assist people with physical disabilities to meet their requirements for independent living and communication with the outside. The traditional scanning keyboard is changed, and only the phonetic notations are typed instead of characters, thus the time of tone and function selection could be saved, and the typing time could be also reduced. Barrier-free computer assistive devices and interface for people with physical disabilities in typing or speech could allow them to use a scanning keyboard to select phonetic symbols instead of Chinese characters to express their thoughts. The human-machine interface controls can obtain more reliable results as 99.8% connection success rate and 95% typing success rate.},
}
@article {pmid26255740,
year = {2015},
author = {Hayn, L and Koch, M},
title = {Suppression of excitotoxicity and foreign body response by memantine in chronic cannula implantation into the rat brain.},
journal = {Brain research bulletin},
volume = {117},
number = {},
pages = {54-68},
doi = {10.1016/j.brainresbull.2015.08.001},
pmid = {26255740},
issn = {1873-2747},
mesh = {Animals ; Antigens, Nuclear/metabolism ; Astrocytes/drug effects/pathology/physiology ; Biomechanical Phenomena ; Calcium-Binding Proteins/metabolism ; Catheters, Indwelling/*adverse effects ; Cell Survival/drug effects ; Cicatrix/drug therapy/pathology/physiopathology ; Excitatory Amino Acid Antagonists/pharmacology ; Forelimb/physiopathology ; Glial Fibrillary Acidic Protein/metabolism ; Glutamic Acid/metabolism ; Macrophages/drug effects/pathology/physiology ; Male ; Memantine/*pharmacology ; Microfilament Proteins/metabolism ; Microglia/drug effects/pathology/physiology ; Motor Activity/drug effects/physiology ; Motor Cortex/*drug effects/pathology/physiopathology/*surgery ; Nerve Tissue Proteins/metabolism ; Neurons/drug effects/pathology/physiology ; Neuroprotective Agents/*pharmacology ; Rats ; },
abstract = {Chronic brain implants are accompanied by a tissue response that causes the loss of neurons in the vicinity of the implant and the formation of a glial scar that is also referred to as foreign body response. Despite immense progress in the field of brain-computer interface (BCI) research the biocompatibility of chronic brain implants remains a primary concern in device design. Excitotoxic overstimulation of NMDA-receptors by extrasynaptic glutamate plays a pivotal role in cell death in the acute phase of the tissue reaction. In this study, we examined the effect of the uncompetitive NMDA-receptor antagonist memantine locally applied during cannula implantation in the caudal forelimb area (CFA) of the motor cortex (M1) in Lister Hooded rats on their behavioural performance in a skilled reaching and a rung-ladder task as well as in the open field. Moreover, the distribution of neurons and glial cells in the vicinity of the implant were assessed. Memantine improved the performance in the behavioural paradigms compared to controls and increased the number of surviving neurons in the vicinity of the implant even above basal levels accompanied by a reduction in astrocytic scar formation directly around the implant with no effect on the microglia/macrophage activation two and six weeks after cannula implantation. These findings suggest that memantine is a potential therapeutic agent in the acute phase of chronic foreign body implantation in the motor cortex in terms of increasing the viability of neurons adjacent to the implant and of improving the behavioural outcome after surgery.},
}
@article {pmid26253606,
year = {2015},
author = {Tsu, AP and Burish, MJ and GodLove, J and Ganguly, K},
title = {Cortical neuroprosthetics from a clinical perspective.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {154-160},
pmid = {26253606},
issn = {1095-953X},
support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*trends ; Cerebral Cortex/*physiopathology ; Electrodes, Implanted ; Feedback, Sensory ; Humans ; Movement Disorders/physiopathology/*rehabilitation ; Prostheses and Implants ; Recovery of Function ; Translational Research, Biomedical/*trends ; User-Computer Interface ; },
abstract = {Recent pilot clinical studies have demonstrated that subjects with severe disorders of movement and communication can exert direct neural control over assistive devices using invasive Brain-Machine Interface (BMI) technology, also referred to as 'cortical neuroprosthetics'. These important proof-of-principle studies have generated great interest among those with disability and clinicians who provide general medical, neurological and/or rehabilitative care. Taking into account the perspective of providers who may be unfamiliar with the field, we first review the clinical goals and fundamentals of invasive BMI technology, and then briefly summarize the vast body of basic science research demonstrating its feasibility. We emphasize recent translational progress in the target clinical populations and discuss translational challenges and future directions.},
}
@article {pmid26250594,
year = {2016},
author = {Herhut, M and Brandenbusch, C and Sadowski, G},
title = {Inclusion of mPRISM potential for polymer-induced protein interactions enables modeling of second osmotic virial coefficients in aqueous polymer-salt solutions.},
journal = {Biotechnology journal},
volume = {11},
number = {1},
pages = {146-154},
doi = {10.1002/biot.201500086},
pmid = {26250594},
issn = {1860-7314},
mesh = {Isomerases/chemistry/metabolism ; Models, Theoretical ; Muramidase/chemistry/metabolism ; Osmosis ; Polyethylene Glycols/chemistry ; Polymers/*chemistry ; Proteins/chemistry/*metabolism ; Salts/*chemistry ; Scattering, Radiation ; gamma-Globulins/chemistry/metabolism ; },
abstract = {The downstream processing of therapeutic proteins is a challenging task. Key information needed to estimate applicable workup strategies (e.g. crystallization) are the interactions of the proteins with other components in solution. This information can be deduced from the second osmotic virial coefficient B22 , measurable by static light scattering. Thermodynamic models are very valuable for predicting B22 data for different process conditions and thus decrease the experimental effort. Available B22 models consider aqueous salt solutions but fail for the prediction of B22 if an additional polymer is present in solution. This is due to the fact that depending on the polymer concentration protein-protein interactions are not rectified as assumed within these models. In this work, we developed an extension of the xDLVO model to predict B22 data of proteins in aqueous polymer-salt solutions. To show the broad applicability of the model, lysozyme, γ-globulin and D-xylose ketol isomerase in aqueous salt solution containing polyethylene glycol were considered. For all proteins considered, the modified xDLVO model was able to predict the experimentally observed non-monotonical course in B22 data with high accuracy. When used in an early stage in process development, the model will contribute to an efficient and cost effective downstream processing development.},
}
@article {pmid26248679,
year = {2015},
author = {Blokland, Y and Spyrou, L and Lerou, J and Mourisse, J and Jan Scheffer, G and van Geffen, GJ and Farquhar, J and Bruhn, J},
title = {Detection of attempted movement from the EEG during neuromuscular block: proof of principle study in awake volunteers.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {12815},
pmid = {26248679},
issn = {2045-2322},
mesh = {Adult ; Brain/*drug effects/*physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Male ; Movement/*drug effects/*physiology ; Neuromuscular Blockade/methods ; Neuromuscular Blocking Agents/*administration &amp; dosage ; Paralysis/physiopathology ; User-Computer Interface ; Volunteers ; Wakefulness/*drug effects/*physiology ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) have the potential to detect intraoperative awareness during general anaesthesia. Traditionally, BCI research is aimed at establishing or improving communication and control for patients with permanent paralysis. Patients experiencing intraoperative awareness also lack the means to communicate after administration of a neuromuscular blocker, but may attempt to move. This study evaluates the principle of detecting attempted movements from the electroencephalogram (EEG) during local temporary neuromuscular blockade. EEG was obtained from four healthy volunteers making 3-second hand movements, both before and after local administration of rocuronium in one isolated forearm. Using offline classification analysis we investigated whether the attempted movements the participants made during paralysis could be distinguished from the periods when they did not move or attempt to move. Attempted movement trials were correctly identified in 81 (68-94)% (mean (95% CI)) and 84 (74-93)% of the cases using 30 and 9 EEG channels, respectively. Similar accuracies were obtained when training the classifier on the participants' actual movements. These results provide proof of the principle that a BCI can detect movement attempts during neuromuscular blockade. Based on this, in the future a BCI may serve as a communication channel between a patient under general anaesthesia and the anaesthesiologist.},
}
@article {pmid26246229,
year = {2015},
author = {Zhang, D and Huang, B and Wu, W and Li, S},
title = {An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.},
journal = {International journal of neural systems},
volume = {25},
number = {7},
pages = {1550030},
doi = {10.1142/S0129065715500306},
pmid = {26246229},
issn = {1793-6462},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Models, Neurological ; Pattern Recognition, Automated/*methods ; Photic Stimulation ; ROC Curve ; Visual Perception/physiology ; Young Adult ; },
abstract = {Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.},
}
@article {pmid26244781,
year = {2015},
author = {Witte, M and Ninaus, M and Kober, SE and Neuper, C and Wood, G},
title = {Neuronal Correlates of Cognitive Control during Gaming Revealed by Near-Infrared Spectroscopy.},
journal = {PloS one},
volume = {10},
number = {8},
pages = {e0134816},
pmid = {26244781},
issn = {1932-6203},
mesh = {Adult ; Brain Mapping ; Cerebral Cortex/cytology/*physiology ; *Cognition ; Female ; Hemoglobins/analysis ; Humans ; *Learning ; Male ; Neurons/*physiology ; Psychomotor Performance ; Spectroscopy, Near-Infrared/*methods ; *Video Games ; Young Adult ; },
abstract = {In everyday life we quickly build and maintain associations between stimuli and behavioral responses. This is governed by rules of varying complexity and past studies have identified an underlying fronto-parietal network involved in cognitive control processes. However, there is only limited knowledge about the neuronal activations during more natural settings like game playing. We thus assessed whether near-infrared spectroscopy recordings can reflect different demands on cognitive control during a simple game playing task. Sixteen healthy participants had to catch falling objects by pressing computer keys. These objects either fell randomly (RANDOM task), according to a known stimulus-response mapping applied by players (APPLY task) or according to a stimulus-response mapping that had to be learned (LEARN task). We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex. Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY. Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping. This study therefore demonstrates a neuronal marker of cognitive control during gaming revealed by near-infrared spectroscopy recordings.},
}
@article {pmid26239819,
year = {2015},
author = {Kress, C and Brandenbusch, C},
title = {Osmotic Virial Coefficients as Access to the Protein Partitioning in Aqueous Two-Phase Systems.},
journal = {Journal of pharmaceutical sciences},
volume = {104},
number = {11},
pages = {3703-3709},
doi = {10.1002/jps.24602},
pmid = {26239819},
issn = {1520-6017},
mesh = {*Chemical Precipitation ; Immunoglobulin G/*chemistry ; Light ; Osmosis ; Phase Transition ; Phosphates/*chemistry ; Polyethylene Glycols/*chemistry ; Scattering, Radiation ; Thermodynamics ; Water/chemistry ; },
abstract = {A promising alternative to state of the art chromatographic separations of therapeutic proteins is the extraction of the target protein using an aqueous two-phase system (ATPS). The use of an additional salt working as a displacement agent can influence the protein partitioning behavior in ATPS and thus enable a selective purification of the target protein. The selection of a suitable ATPS for protein extraction requires information concerning the protein-protein interactions (second osmotic virial coefficient B22) as well as the interactions between protein and solute (displacement agent and phase-forming components) (cross virial coefficient B23). In this work, the partitioning behavior and the precipitation affinity of immunoglobulin G (IgG) is considered within a polyethylene glycol (PEG)-phosphate ATPS. The influence on IgG partitioning upon addition of NaCl and (NH4)2 SO4 was investigated. In order to access the IgG precipitation affinity and the IgG partitioning behavior, the B22 and B23 values were determined for several combinations of solute [PEG, phosphate buffer, NaCl, and (NH4)2 SO4 ] and IgG based on static light scattering measurements. A qualitative estimation of the IgG precipitation affinity and the suitability of a solute as potential displacement agent within the PEG-phosphate ATPS on the basis of the measured B22 and B23 values is presented.},
}
@article {pmid26238437,
year = {2015},
author = {Hayes, DF},
title = {Clinical utility of genetic signatures in selecting adjuvant treatment: Risk stratification for early vs. late recurrences.},
journal = {Breast (Edinburgh, Scotland)},
volume = {24 Suppl 2},
number = {},
pages = {S6-S10},
doi = {10.1016/j.breast.2015.07.002},
pmid = {26238437},
issn = {1532-3080},
mesh = {Antineoplastic Agents, Hormonal/adverse effects/*therapeutic use ; Aromatase Inhibitors/adverse effects/*therapeutic use ; Breast Neoplasms/chemistry/*drug therapy/*genetics/pathology ; Chemotherapy, Adjuvant/adverse effects ; Female ; Gene Expression Profiling ; Humans ; Neoplasm Metastasis ; Neoplasm Recurrence, Local ; Receptors, Estrogen/analysis ; Receptors, Progesterone/analysis ; Risk Assessment ; Tamoxifen/adverse effects/*therapeutic use ; },
abstract = {Adjuvant endocrine therapy (ET) reduces the odds of distant recurrence and mortality by nearly one-half in women with hormone receptor (HR) positive early stage breast cancer. While the risk of recurrence is lower for HR positive than negative patients during the first 5-7 years, HR positive patients suffer ongoing recurrences between 0.5 and 2% year over subsequent years. Extended adjuvant ET further reduces recurrence during this late phase of follow-up. ET is associated with post-menopausal side effects (hot flashes, sexual dysfunction, mood changes, and weight gain), and occasional major toxicities (thrombosis and endometrial cancer with tamoxifen; bone mineral loss and possibly heart disease with AIs) persist throughout therapy. Accurate and reliable estimates of the risk of recurrence after five years of ET for women with prior HR positive breast cancer would permit appropriate extended ET decisions. The risk of long-term relapse is related to lymph node status and size of tumor, but these are relatively crude. Several groups have investigated whether multi-parameter tumor biomarker tests might identify those patients whose risk of recurrence is so low that extended ET is not justified. These assays include IHC4, the 21-gene "OncotypeDX", the 12-gene "Endopredict," the PAM50, and the 2-gene "Breast Cancer Index (BCI)" assays. The clinical validity of all these tests for this use context have been established, with at least one paper for each that shows a statistically significant difference in risk of distant recurrence during the 5-10 years after the initial five years of adjuvant endocrine therapy. However, the stakes are high, and although each of these represents a "prospective retrospective" study, they require further validation in subsequent datasets before they should be considered to have "clinical utility" and are used to withhold potentially life-saving treatment. Perhaps more importantly, the clinical breast cancer community, and especially the patient, need to determine how low the risk of late recurrence needs to be to forego the toxicities and side effects of extended adjuvant ET.},
}
@article {pmid26236225,
year = {2015},
author = {Liao, JY and Kirsch, RF},
title = {Velocity neurons improve performance more than goal or position neurons do in a simulated closed-loop BCI arm-reaching task.},
journal = {Frontiers in computational neuroscience},
volume = {9},
number = {},
pages = {84},
pmid = {26236225},
issn = {1662-5188},
support = {T32 GM007250/GM/NIGMS NIH HHS/United States ; TL1 TR000441/TR/NCATS NIH HHS/United States ; },
abstract = {Brain-Computer Interfaces (BCIs) that convert brain-recorded neural signals into intended movement commands could eventually be combined with Functional Electrical Stimulation to allow individuals with Spinal Cord Injury to regain effective and intuitive control of their paralyzed limbs. To accelerate the development of such an approach, we developed a model of closed-loop BCI control of arm movements that (1) generates realistic arm movements (based on experimentally measured, visually-guided movements with real-time error correction), (2) simulates cortical neurons with firing properties consistent with literature reports, and (3) decodes intended movements from the noisy neural ensemble. With this model we explored (1) the relative utility of neurons tuned for different movement parameters (position, velocity, and goal) and (2) the utility of recording from larger numbers of neurons-critical issues for technology development and for determining appropriate brain areas for recording. We simulated arm movements that could be practically restored to individuals with severe paralysis, i.e., movements from an armrest to a volume in front of the person. Performance was evaluated by calculating the smallest movement endpoint target radius within which the decoded cursor position could dwell for 1 s. Our results show that goal, position, and velocity neurons all contribute to improve performance. However, velocity neurons enabled smaller targets to be reached in shorter amounts of time than goal or position neurons. Increasing the number of neurons also improved performance, although performance saturated at 30-50 neurons for most neuron types. Overall, our work presents a closed-loop BCI simulator that models error corrections and the firing properties of various movement-related neurons that can be easily modified to incorporate different neural properties. We anticipate that this kind of tool will be important for development of future BCIs.},
}
@article {pmid26236210,
year = {2015},
author = {Galán, F and Baker, SN},
title = {Deafferented controllers: a fundamental failure mechanism in cortical neuroprosthetic systems.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {186},
pmid = {26236210},
issn = {1662-5153},
support = {/WT_/Wellcome Trust/United Kingdom ; 101002/WT_/Wellcome Trust/United Kingdom ; },
abstract = {Brain-machine interface (BMI) research assumes that patients with disconnected neural pathways could naturally control a prosthetic device by volitionally modulating sensorimotor cortical activity usually responsible for movement coordination. However, computational approaches to motor control challenge this view. This article examines the predictions of optimal feedback control (OFC) theory on the effects that loss of motor output and sensory feedback have on the normal generation of motor commands. Example simulations of unimpaired, totally disconnected, and deafferented controllers illustrate that by neglecting the dynamic interplay between motor commands, state estimation, feedback and behavior, current BMI systems face translational challenges rooted in a debatable assumption and experimental models of limited validity.},
}
@article {pmid26236207,
year = {2015},
author = {Vukelić, M and Gharabaghi, A},
title = {Self-regulation of circumscribed brain activity modulates spatially selective and frequency specific connectivity of distributed resting state networks.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {181},
pmid = {26236207},
issn = {1662-5153},
abstract = {The mechanisms of learning involved in brain self-regulation have still to be unveiled to exploit the full potential of this methodology for therapeutic interventions. This skill of volitionally changing brain activity presumably resembles motor skill learning which in turn is accompanied by plastic changes modulating resting state networks. Along these lines, we hypothesized that brain regulation and neurofeedback would similarly modify intrinsic networks at rest while presenting a distinct spatio-temporal pattern. High-resolution electroencephalography preceded and followed a single neurofeedback training intervention of modulating circumscribed sensorimotor low β-activity by kinesthetic motor imagery in eleven healthy participants. The participants were kept in the deliberative phase of skill acquisition with high demands for learning self-regulation through stepwise increases of task difficulty. By applying the corrected imaginary part of the coherency function, we observed increased functional connectivity of both the primary motor and the primary somatosensory cortex with their respective contralateral homologous cortices in the low β-frequency band which was self-regulated during feedback. At the same time, the primary motor cortex-but none of the surrounding cortical areas-showed connectivity to contralateral supplementary motor and dorsal premotor areas in the high β-band. Simultaneously, the neurofeedback target displayed a specific increase of functional connectivity with an ipsilateral fronto-parietal network in the α-band while presenting a de-coupling with contralateral primary and secondary sensorimotor areas in the very same frequency band. Brain self-regulation modifies resting state connections spatially selective to the neurofeedback target of the dominant hemisphere. These are anatomically distinct with regard to the cortico-cortical connectivity pattern and are functionally specific with regard to the time domain of coherent activity consistent with a Hebbian-like sharpening concept.},
}
@article {pmid26233942,
year = {2015},
author = {Kübler, A and Müller-Putz, G and Mattia, D},
title = {User-centred design in brain-computer interface research and development.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {5},
pages = {312-314},
doi = {10.1016/j.rehab.2015.06.003},
pmid = {26233942},
issn = {1877-0665},
mesh = {*Brain-Computer Interfaces ; Humans ; *Research ; User-Computer Interface ; },
}
@article {pmid26231620,
year = {2015},
author = {Ferdowsi, S and Abolghasemi, V and Sanei, S},
title = {A new informed tensor factorization approach to EEG-fMRI fusion.},
journal = {Journal of neuroscience methods},
volume = {254},
number = {},
pages = {27-35},
doi = {10.1016/j.jneumeth.2015.07.018},
pmid = {26231620},
issn = {1872-678X},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain Mapping/*methods ; Cerebrovascular Circulation/physiology ; Electroencephalography/*methods ; Factor Analysis, Statistical ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Middle Aged ; Multimodal Imaging/*methods ; Oxygen/blood ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: In this paper exploitation of correlation between post-movement beta rebound in EEG and blood oxygenation level dependent (BOLD) in fMRI is addressed. Brain studies do not reveal any clear relationship between synchronous neuronal activity and BOLD signal. Simultaneous recording of EEG and fMRI provides a great opportunity to recognize different areas of the brain involved in EEG events.<br><br>NEW METHOD: In order to incorporate information derived from EEG signals into fMRI analysis a specific constraint is introduced in this paper. Here, PARAFAC as a variant of tensor factorization, exploits the data changes in more than two modes in order to reveal the information about the fMRI BOLD and its time course simultaneously. In addition, various constraints can be applied during the alternating process for estimation of its parameters.<br><br>RESULTS: The achieved results from extensive set of experiments confirm effectiveness of the proposed method to detect the brain regions responsible for beta rebound. Moreover, fMRI-only and EEG-fMRI analysis using PARAFAC2 illustrate correct expected activities in the brain area.<br><br>The advantages of the proposed method are revealed when comparing the results with those of obtained using general linear model (GLM) which is a well-known model-based approach.<br><br>CONCLUSIONS: The proposed method is a semi-blind decomposition technique which employs PARAFAC2 without relying on a predefined time course. The achieved results indicate that this approach can pave the path for multi-task analysis in BCI applications.},
}
@article {pmid26226930,
year = {2016},
author = {Keitel, C and Müller, MM},
title = {Audio-visual synchrony and feature-selective attention co-amplify early visual processing.},
journal = {Experimental brain research},
volume = {234},
number = {5},
pages = {1221-1231},
pmid = {26226930},
issn = {1432-1106},
mesh = {Acoustic Stimulation ; Adult ; *Association ; Attention/*physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Reaction Time ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Our brain relies on neural mechanisms of selective attention and converging sensory processing to efficiently cope with rich and unceasing multisensory inputs. One prominent assumption holds that audio-visual synchrony can act as a strong attractor for spatial attention. Here, we tested for a similar effect of audio-visual synchrony on feature-selective attention. We presented two superimposed Gabor patches that differed in colour and orientation. On each trial, participants were cued to selectively attend to one of the two patches. Over time, spatial frequencies of both patches varied sinusoidally at distinct rates (3.14 and 3.63 Hz), giving rise to pulse-like percepts. A simultaneously presented pure tone carried a frequency modulation at the pulse rate of one of the two visual stimuli to introduce audio-visual synchrony. Pulsed stimulation elicited distinct time-locked oscillatory electrophysiological brain responses. These steady-state responses were quantified in the spectral domain to examine individual stimulus processing under conditions of synchronous versus asynchronous tone presentation and when respective stimuli were attended versus unattended. We found that both, attending to the colour of a stimulus and its synchrony with the tone, enhanced its processing. Moreover, both gain effects combined linearly for attended in-sync stimuli. Our results suggest that audio-visual synchrony can attract attention to specific stimulus features when stimuli overlap in space.},
}
@article {pmid26220660,
year = {2015},
author = {Kao, JC and Nuyujukian, P and Ryu, SI and Churchland, MM and Cunningham, JP and Shenoy, KV},
title = {Single-trial dynamics of motor cortex and their applications to brain-machine interfaces.},
journal = {Nature communications},
volume = {6},
number = {},
pages = {7759},
pmid = {26220660},
issn = {2041-1723},
support = {R01NS076460/NS/NINDS NIH HHS/United States ; 5R01MH09964703/MH/NIMH NIH HHS/United States ; 8DP1HD075623-04/DP/NCCDPHP CDC HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; //Howard Hughes Medical Institute/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/*physiology ; },
abstract = {Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain-machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs.},
}
@article {pmid26219602,
year = {2015},
author = {Ninaus, M and Kober, SE and Witte, M and Koschutnig, K and Neuper, C and Wood, G},
title = {Brain volumetry and self-regulation of brain activity relevant for neurofeedback.},
journal = {Biological psychology},
volume = {110},
number = {},
pages = {126-133},
doi = {10.1016/j.biopsycho.2015.07.009},
pmid = {26219602},
issn = {1873-6246},
mesh = {Adult ; Brain/*anatomy &amp; histology/*physiology ; Feedback, Sensory ; Female ; Gamma Rhythm/*physiology ; Humans ; Learning/physiology ; Male ; Middle Aged ; Neurofeedback/methods/*physiology ; Regression Analysis ; *Self-Control ; },
abstract = {Neurofeedback is a technique to learn to control brain signals by means of real time feedback. In the present study, the individual ability to learn two EEG neurofeedback protocols - sensorimotor rhythm and gamma rhythm - was related to structural properties of the brain. The volumes in the anterior insula bilaterally, left thalamus, right frontal operculum, right putamen, right middle frontal gyrus, and right lingual gyrus predicted the outcomes of sensorimotor rhythm training. Gray matter volumes in the supplementary motor area and left middle frontal gyrus predicted the outcomes of gamma rhythm training. These findings combined with further evidence from the literature are compatible with the existence of a more general self-control network, which through self-referential and self-control processes regulates neurofeedback learning.},
}
@article {pmid26217298,
year = {2015},
author = {Kober, SE and Bauernfeind, G and Woller, C and Sampl, M and Grieshofer, P and Neuper, C and Wood, G},
title = {Hemodynamic Signal Changes Accompanying Execution and Imagery of Swallowing in Patients with Dysphagia: A Multiple Single-Case Near-Infrared Spectroscopy Study.},
journal = {Frontiers in neurology},
volume = {6},
number = {},
pages = {151},
pmid = {26217298},
issn = {1664-2295},
abstract = {In the present multiple case study, we examined hemodynamic changes in the brain in response to motor execution (ME) and motor imagery (MI) of swallowing in dysphagia patients compared to healthy matched controls using near-infrared spectroscopy (NIRS). Two stroke patients with cerebral lesions in the right hemisphere, two stroke patients with lesions in the brainstem, and two neurologically healthy control subjects actively swallowed saliva (ME) and mentally imagined to swallow saliva (MI) in a randomized order while changes in concentration of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) were assessed. In line with recent findings in healthy young adults, MI and ME of swallowing led to the strongest NIRS signal change in the inferior frontal gyrus in stroke patients as well as in healthy elderly. We found differences in the topographical distribution and time course of the hemodynamic response in dependence on lesion location. Dysphagia patients with lesions in the brainstem showed bilateral hemodynamic signal changes in the inferior frontal gyrus during active swallowing comparable to healthy controls. In contrast, dysphagia patients with cerebral lesions in the right hemisphere showed more unilateral activation patterns during swallowing. Furthermore, patients with cerebral lesions showed a prolonged time course of the hemodynamic response during MI and ME of swallowing compared to healthy controls and patients with brainstem lesions. Brain activation patterns associated with ME and MI of swallowing were largely comparable, especially for changes in deoxy-Hb. Hence, the present results provide new evidence regarding timing and topographical distribution of the hemodynamic response during ME and MI of swallowing in dysphagia patients and may have practical impact on future dysphagia treatment.},
}
@article {pmid26216789,
year = {2016},
author = {Yao, J and Sheaff, C and Carmona, C and Dewald, JP},
title = {Impact of Shoulder Abduction Loading on Brain-Machine Interface in Predicting Hand Opening and Closing in Individuals With Chronic Stroke.},
journal = {Neurorehabilitation and neural repair},
volume = {30},
number = {4},
pages = {363-372},
pmid = {26216789},
issn = {1552-6844},
support = {R01 HD039343/HD/NICHD NIH HHS/United States ; R01 HD047569/HD/NICHD NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; UL1 TR001422/TR/NCATS NIH HHS/United States ; },
mesh = {Arm/*physiopathology ; *Brain-Computer Interfaces ; Chronic Disease ; Electroencephalography ; Electromyography ; Female ; Hand/*physiopathology ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/*physiopathology ; Shoulder/*physiopathology ; Stroke/*physiopathology ; },
abstract = {BACKGROUND: Many individuals with moderate and severe stroke are unable to use their paretic hand. Currently, the effect of conventional therapy on regaining meaningful hand function in this population is limited. Efforts have been made to use brain-machine interfaces (BMIs) to control hand function. To date, almost all BMI classification algorithms are designed for detecting hand movements with a resting arm. However, many functional movements require simultaneous movements of the arm and hand. Arm movement will possibly affect the detection of intended hand movements, specifically for individuals with chronic stroke who have muscle synergies. The most prevalent upper-extremity synergy-flexor synergy-is expressed as an abnormal coupling between shoulder abductors and elbow/wrist/finger flexors.<br><br>OBJECTIVE: We hypothesized that because of flexor synergy, shoulder abductor activity would affect the detection of the hand-opening (a movement inhibited by flexion synergy) but not the hand-closing task (a movement facilitated by the flexion synergy).<br><br>METHODS: We evaluated the accuracy of a BMI classification algorithm in detecting hand-opening versus closing after reaching a target with 2 different shoulder-abduction loads in 6 individuals with stroke.<br><br>RESULTS: We found a decreased accuracy in detecting hand opening when an individual with stroke intends to open the hand while activating shoulder abductors. However, such decreased accuracy with increased shoulder loading was not shown while detecting a hand-closing task.<br><br>CONCLUSIONS: This study supports the idea that one should consider the effect of shoulder abduction activity when designing BMI classification algorithms for the purpose of restoring hand function in individuals with moderate to severe stroke.},
}
@article {pmid26214339,
year = {2015},
author = {Jochumsen, M and Niazi, IK and Mrachacz-Kersting, N and Jiang, N and Farina, D and Dremstrup, K},
title = {Comparison of spatial filters and features for the detection and classification of movement-related cortical potentials in healthy individuals and stroke patients.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056003},
doi = {10.1088/1741-2560/12/5/056003},
pmid = {26214339},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aged ; Algorithms ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; Stroke/diagnosis/*physiopathology ; },
abstract = {OBJECTIVE: The possibility of detecting movement-related cortical potentials (MRCPs) at the single trial level has been explored for closing the motor control loop with brain-computer interfaces (BCIs) for neurorehabilitation. A distinct feature of MRCPs is that the movement kinetic information is encoded in the brain potential prior to the onset of the movement, which makes it possible to timely drive external devices to provide sensory feedback according to the efferent activity from the brain. The aim of this study was to compare methods for the detection (different spatial filters) and classification (features extracted from various domains) of MRCPs from continuous electroencephalography recordings from executed and imagined movements from healthy subjects (n = 24) and attempted movements from stroke patients (n = 6) to optimize the performance of MRCP-based BCIs for neurorehabilitation.<br><br>APPROACH: The MRCPs from four cue-based tasks were detected with a template matching approach and a set of spatial filters, and classified with a linear support vector machine using the combination of temporal, spectral, time-scale, or entropy-based features.<br><br>MAIN RESULTS: The best spatial filter (large Laplacian spatial filter (LLSF)) resulted in a true positive rate of 82 &#xb1; 9%, 78 &#xb1; 12% and 72 &#xb1; 9% (with detections occurring &#x223c; 200 ms before the onset of the movement) for executed, imagined and attempted movements (stroke patients). The best feature combination (temporal and spectral) led to pairwise classification of 73 &#xb1; 9%, 64 &#xb1; 10% and 80 &#xb1; 12%. When the detection was combined with classification, 60 &#xb1; 10%, 49 &#xb1; 10% and 58 &#xb1; 10% of the movements were both correctly detected and classified for executed, imagined and attempted movements. A similar performance for detection and classification was obtained with optimized spatial filtering.<br><br>SIGNIFICANCE: A simple setup with an LLSF is useful for detecting cued movements while the combination of features from the time and frequency domain can optimize the decoding of kinetic information from MRCPs; this may be used in neuromodulatory BCIs.},
}
@article {pmid26213933,
year = {2015},
author = {Solé-Casals, J and Vialatte, FB},
title = {Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer's Disease Screening from EEG Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {8},
pages = {17963-17976},
pmid = {26213933},
issn = {1424-8220},
mesh = {Aged ; Alzheimer Disease/*diagnosis ; *Artifacts ; Automation ; Case-Control Studies ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Statistics as Topic ; },
abstract = {A large number of studies have analyzed measurable changes that Alzheimer's disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer's disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.},
}
@article {pmid26211270,
year = {2015},
author = {Wang, J and Liu, Y},
title = {[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {2},
pages = {451-4, 464},
pmid = {26211270},
issn = {1001-5515},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Magnetoencephalography ; Principal Component Analysis ; },
abstract = {This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.},
}
@article {pmid26209283,
year = {2016},
author = {Silvoni, S and Konicar, L and Prats-Sedano, MA and Garcia-Cossio, E and Genna, C and Volpato, C and Cavinato, M and Paggiaro, A and Veser, S and De Massari, D and Birbaumer, N},
title = {Tactile event-related potentials in amyotrophic lateral sclerosis (ALS): Implications for brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {1},
pages = {936-945},
doi = {10.1016/j.clinph.2015.06.029},
pmid = {26209283},
issn = {1872-8952},
mesh = {Adult ; Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/*diagnosis/*physiopathology ; *Brain-Computer Interfaces/trends ; Electric Stimulation/methods ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Touch/*physiology ; *Vibration ; },
abstract = {OBJECTIVE: We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed.<br><br>METHODS: We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping.<br><br>RESULTS: A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively.<br><br>CONCLUSIONS: Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs.<br><br>SIGNIFICANCE: Implications for brain-computer interface implementation of the proposed method for patients in critical conditions, such as the late stage of ALS and the (completely) locked-in state, are discussed.},
}
@article {pmid26208328,
year = {2015},
author = {Thielen, J and van den Broek, P and Farquhar, J and Desain, P},
title = {Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.},
journal = {PloS one},
volume = {10},
number = {7},
pages = {e0133797},
pmid = {26208328},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Female ; Humans ; Internet ; Male ; Photic Stimulation ; Reproducibility of Results ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.},
}
@article {pmid26205425,
year = {2015},
author = {Weyand, S and Takehara-Nishiuchi, K and Chau, T},
title = {Exploring methodological frameworks for a mental task-based near-infrared spectroscopy brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {254},
number = {},
pages = {36-45},
doi = {10.1016/j.jneumeth.2015.07.007},
pmid = {26205425},
issn = {1872-678X},
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Mental Processes/*physiology ; Neurofeedback/physiology ; *Neuropsychological Tests ; Spectroscopy, Near-Infrared/*methods ; Time Factors ; Young Adult ; },
abstract = {BACKGROUND: Near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs) enable users to interact with their environment using only cognitive activities. This paper presents the results of a comparison of four methodological frameworks used to select a pair of tasks to control a binary NIRS-BCI; specifically, three novel personalized task paradigms and the state-of-the-art prescribed task framework were explored.<br><br>NEW METHODS: Three types of personalized task selection approaches were compared, including: user-selected mental tasks using weighted slope scores (WS-scores), user-selected mental tasks using pair-wise accuracy rankings (PWAR), and researcher-selected mental tasks using PWAR. These paradigms, along with the state-of-the-art prescribed mental task framework, where mental tasks are selected based on the most commonly used tasks in literature, were tested by ten able-bodied participants who took part in five NIRS-BCI sessions.<br><br>RESULTS: The frameworks were compared in terms of their accuracy, perceived ease-of-use, computational time, user preference, and length of training. Most notably, researcher-selected personalized tasks resulted in significantly higher accuracies, while user-selected personalized tasks resulted in significantly higher perceived ease-of-use. It was also concluded that PWAR minimized the amount of data that needed to be collected; while, WS-scores maximized user satisfaction and minimized computational time.<br><br>In comparison to the state-of-the-art prescribed mental tasks, our findings show that overall, personalized tasks appear to be superior to prescribed tasks with respect to accuracy and perceived ease-of-use.<br><br>CONCLUSIONS: The deployment of personalized rather than prescribed mental tasks ought to be considered and further investigated in future NIRS-BCI studies.},
}
@article {pmid26193332,
year = {2015},
author = {Omedes, J and Iturrate, I and Minguez, J and Montesano, L},
title = {Analysis and asynchronous detection of gradually unfolding errors during monitoring tasks.},
journal = {Journal of neural engineering},
volume = {12},
number = {5},
pages = {056001},
doi = {10.1088/1741-2560/12/5/056001},
pmid = {26193332},
issn = {1741-2552},
mesh = {Adult ; Attention/physiology ; Brain/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Humans ; Machine Learning ; Male ; Motion Perception/*physiology ; Pattern Recognition, Automated/methods ; Reaction Time/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; },
abstract = {Human studies on cognitive control processes rely on tasks involving sudden-onset stimuli, which allow the analysis of these neural imprints to be time-locked and relative to the stimuli onset. Human perceptual decisions, however, comprise continuous processes where evidence accumulates until reaching a boundary. Surpassing the boundary leads to a decision where measured brain responses are associated to an internal, unknown onset. The lack of this onset for gradual stimuli hinders both the analyses of brain activity and the training of detectors. This paper studies electroencephalographic (EEG)-measurable signatures of human processing for sudden and gradual cognitive processes represented as a trajectory mismatch under a monitoring task. Time-locked potentials and brain-source analysis of the EEG of sudden mismatches revealed the typical components of event-related potentials and the involvement of brain structures related to cognitive control processing. For gradual mismatch events, time-locked analyses did not show any discernible EEG scalp pattern, despite related brain areas being, to a lesser extent, activated. However, and thanks to the use of non-linear pattern recognition algorithms, it is possible to train an asynchronous detector on sudden events and use it to detect gradual mismatches, as well as obtaining an estimate of their unknown onset. Post-hoc time-locked scalp and brain-source analyses revealed that the EEG patterns of detected gradual mismatches originated in brain areas related to cognitive control processing. This indicates that gradual events induce latency in the evaluation process but that similar brain mechanisms are present in sudden and gradual mismatch events. Furthermore, the proposed asynchronous detection model widens the scope of applications of brain-machine interfaces to other gradual processes.},
}
@article {pmid26190995,
year = {2015},
author = {Naros, G and Gharabaghi, A},
title = {Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {391},
pmid = {26190995},
issn = {1662-5161},
abstract = {Neurofeedback training of Motor imagery (MI)-related brain-states with brain-computer/brain-machine interfaces (BCI/BMI) is currently being explored as an experimental intervention prior to standard physiotherapy to improve the motor outcome of stroke rehabilitation. The use of BCI/BMI technology increases the adherence to MI training more efficiently than interventions with sham or no feedback. Moreover, pilot studies suggest that such a priming intervention before physiotherapy might-like some brain stimulation techniques-increase the responsiveness of the brain to the subsequent physiotherapy, thereby improving the general clinical outcome. However, there is little evidence up to now that these BCI/BMI-based interventions have achieved operate conditioning of specific brain states that facilitate task-specific functional gains beyond the practice of primed physiotherapy. In this context, we argue that BCI/BMI technology provides a valuable neurofeedback tool for rehabilitation but needs to aim at physiological features relevant for the targeted behavioral gain. Moreover, this therapeutic intervention has to be informed by concepts of reinforcement learning to develop its full potential. Such a refined neurofeedback approach would need to address the following issues: (1) Defining a physiological feedback target specific to the intended behavioral gain, e.g., β-band oscillations for cortico-muscular communication. This targeted brain state could well be different from the brain state optimal for the neurofeedback task, e.g., α-band oscillations for differentiating MI from rest; (2) Selecting a BCI/BMI classification and thresholding approach on the basis of learning principles, i.e., balancing challenge and reward of the neurofeedback task instead of maximizing the classification accuracy of the difficulty level device; and (3) Adjusting the difficulty level in the course of the training period to account for the cognitive load and the learning experience of the participant. Here, we propose a comprehensive neurofeedback strategy for motor restoration after stroke that addresses these aspects, and provide evidence for the feasibility of the suggested approach by demonstrating that dynamic threshold adaptation based on reinforcement learning may lead to frequency-specific operant conditioning of β-band oscillations paralleled by task-specific motor improvement; a proposal that requires investigation in a larger cohort of stroke patients.},
}
@article {pmid26186053,
year = {2015},
author = {Bianchi, AM and Baselli, G and Babiloni, F and Rizzo, G},
title = {Multidimensional Processes: In Italy, biomedical signal and image processing embraces a multiparametric, multimodal, multiscale paradigm.},
journal = {IEEE pulse},
volume = {6},
number = {4},
pages = {44-49},
doi = {10.1109/MPUL.2015.2428680},
pmid = {26186053},
issn = {2154-2317},
mesh = {*Biomedical Engineering ; *Biomedical Research ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Image Processing, Computer-Assisted ; Italy ; Models, Neurological ; *Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; },
abstract = {Biomedical studies, both in research and in clinical applications, deal with the management of large amounts of data. Different sensors and transducers, advances in technologies, and the availability of innovative medical equipment and instrumentation all contribute to the ability to make biological measurements at different scales, ranging from systems, to organs, to tissues, to cells, right down to proteins and genes. Biomedical signals and data carry important information about the system or the organ that generated them.},
}
@article {pmid26183126,
year = {2015},
author = {Bojorges-Valdez, E and Echeverría, JC and Yanez-Suarez, O},
title = {Evaluation of the continuous detection of mental calculation episodes as a BCI control input.},
journal = {Computers in biology and medicine},
volume = {64},
number = {},
pages = {155-162},
doi = {10.1016/j.compbiomed.2015.06.014},
pmid = {26183126},
issn = {1879-0534},
mesh = {Adult ; Area Under Curve ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Mental Processes/*physiology ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Time Factors ; Young Adult ; },
abstract = {This paper presents an evaluation of the continuous detection of mental calculation episodes, which may be useful for users who strive to operate current BCI paradigms or even for augmenting degrees of freedom. The experimentation consisted in the alternated realization of basic arithmetic mental calculations and resting periods. EEG data were analyzed using sliding windows of 2s length. The experimental population was comprised of fifteen healthy subjects who participated in three sessions on different days. The features used for the classification process were the power spectral density over the beta band ([14-35] Hz) and the scaling exponent obtained via detrended fluctuation analysis. Both indices were estimated over four channels, specifically selected for each subject. The performance was evaluated using the Area Under the ROC Curve (AUC) by measuring the overall classification performance of each experimental session with a cross-validation procedure, and by transferring the model obtained from one session to the others called inter Session Validation (iSV). The best AUC values computed in each cross-validation session were: 0.87±0.067, 0.89±0.056 and 0.88±0.040 respectively; and the iSV provided a value of 0.67±0.122. These high values indicate that a mental calculation paradigm and a combination of features can efficiently control a BCI system. Notwithstanding that several days passed between sessions, the AUC mean value estimated for the iSV is similar to the performance of a motor imagery-BCI calibrated on the same day.},
}
@article {pmid26182228,
year = {2015},
author = {Lugo, ZR and Bruno, MA and Gosseries, O and Demertzi, A and Heine, L and Thonnard, M and Blandin, V and Pellas, F and Laureys, S},
title = {Beyond the gaze: Communicating in chronic locked-in syndrome.},
journal = {Brain injury},
volume = {29},
number = {9},
pages = {1056-1061},
doi = {10.3109/02699052.2015.1004750},
pmid = {26182228},
issn = {1362-301X},
mesh = {Adult ; Brain Stem Infarctions/rehabilitation ; Chronic Disease ; *Communication ; Eye Movements/physiology ; Female ; Humans ; Male ; Middle Aged ; Quadriplegia/*physiopathology/psychology/*rehabilitation ; Self-Help Devices ; Speech ; Surveys and Questionnaires ; },
abstract = {OBJECTIVE: Locked-in syndrome (LIS) usually follows a brainstem stroke and is characterized by paralysis of all voluntary muscles (except eyes' movements or blinking) and lack of speech with preserved consciousness. Several tools have been developed to promote communication with these patients. The aim of the study was to evaluate the current status regarding communication in a cohort of LIS patients.<br><br>DESIGN: A survey was conducted in collaboration with the French Association of Locked-in syndrome (ALIS).<br><br>SUBJECTS AND METHODS: Two hundred and four patients, members of ALIS, were invited to fill in a questionnaire on communication issues and clinical evolution (recovery of verbal language and movements, presence of visual and/or auditory deficits).<br><br>RESULTS: Eighty-eight responses were processed. All respondents (35% female, mean age&#x2009;=&#x2009;52&#x2009;&#xb1;&#x2009;12 years, mean time in LIS&#x2009;=&#x2009;10&#x2009;&#xb1;&#x2009;6 years) reported using a yes/no communication code using mainly eyes' movements and 62% used assisting technology; 49% could communicate through verbal language and 73% have recovered some functional movements within the years.<br><br>CONCLUSION: The results highlight the possibility to recover non-eye dependent communication, speech production and some functional movement in the majority of chronic LIS patients.},
}
@article {pmid26172951,
year = {2015},
author = {, },
title = {Correction: Improving the Performance of an EEG-Based Motor Imagery Brain Computer Interface Using Task Evoked Changes in Pupil Diameter.},
journal = {PloS one},
volume = {10},
number = {7},
pages = {e0133095},
pmid = {26172951},
issn = {1932-6203},
}
@article {pmid26170261,
year = {2015},
author = {Marathe, AR and Taylor, DM},
title = {The impact of command signal power distribution, processing delays, and speed scaling on neurally-controlled devices.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046031},
pmid = {26170261},
issn = {1741-2552},
support = {R01 NS058871/NS/NINDS NIH HHS/United States ; R01NS058871/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Energy Transfer ; Feedback, Sensory/*physiology ; Female ; Humans ; Information Storage and Retrieval/methods ; Male ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reaction Time/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Time Factors ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Decoding algorithms for brain-machine interfacing (BMI) are typically only optimized to reduce the magnitude of decoding errors. Our goal was to systematically quantify how four characteristics of BMI command signals impact closed-loop performance: (1) error magnitude, (2) distribution of different frequency components in the decoding errors, (3) processing delays, and (4) command gain.<br><br>APPROACH: To systematically evaluate these different command features and their interactions, we used a closed-loop BMI simulator where human subjects used their own wrist movements to command the motion of a cursor to targets on a computer screen. Random noise with three different power distributions and four different relative magnitudes was added to the ongoing cursor motion in real time to simulate imperfect decoding. These error characteristics were tested with four different visual feedback delays and two velocity gains.<br><br>MAIN RESULTS: Participants had significantly more trouble correcting for errors with a larger proportion of low-frequency, slow-time-varying components than they did with jittery, higher-frequency errors, even when the error magnitudes were equivalent. When errors were present, a movement delay often increased the time needed to complete the movement by an order of magnitude more than the delay itself. Scaling down the overall speed of the velocity command can actually speed up target acquisition time when low-frequency errors and delays are present.<br><br>SIGNIFICANCE: This study is the first to systematically evaluate how the combination of these four key command signal features (including the relatively-unexplored error power distribution) and their interactions impact closed-loop performance independent of any specific decoding method. The equations we derive relating closed-loop movement performance to these command characteristics can provide guidance on how best to balance these different factors when designing BMI systems. The equations reported here also provide an efficient way to compare a diverse range of decoding options offline.},
}
@article {pmid26170164,
year = {2015},
author = {Fels, M and Bauer, R and Gharabaghi, A},
title = {Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046029},
doi = {10.1088/1741-2560/12/4/046029},
pmid = {26170164},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electromyography/methods ; Female ; Humans ; Male ; Middle Aged ; Nerve Net/physiology ; Neurofeedback/*methods ; Physical Exertion/physiology ; Psychomotor Performance/*physiology ; Rest/physiology ; Robotics/*methods ; *Workload ; Young Adult ; },
abstract = {OBJECTIVE: Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns.<br><br>APPROACH: We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task.<br><br>MAIN RESULTS: The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise.<br><br>SIGNIFICANCE: Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants' ability and task difficulty of neurofeedback interventions.},
}
@article {pmid26170163,
year = {2015},
author = {Paus, R and Hart, E and Ji, Y},
title = {A novel approach for predicting the dissolution profiles of pharmaceutical tablets.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {96},
number = {},
pages = {53-64},
doi = {10.1016/j.ejpb.2015.06.029},
pmid = {26170163},
issn = {1873-3441},
mesh = {Hydrogen-Ion Concentration ; Kinetics ; *Models, Chemical ; Molecular Structure ; Naproxen/*chemistry ; Powder Diffraction ; Solubility ; Surface Properties ; Tablets ; Temperature ; Trimethoprim/*chemistry ; X-Ray Diffraction ; },
abstract = {In this paper, the intrinsic dissolution profiles of naproxen (NAP) at pH values of 1.5 and 3.0 and of trimethoprim (TMP) at pH values of 1.5, 3.0, 5.0, 6.5 and 7.2 were measured. Meanwhile, the dissolution profiles of NAP and TMP from cylindrical tablets were measured at different temperatures (298.15K, 305.15K, 301.15K and 310.15K) and stirring speeds (50rpm, 100rpm and 150rpm) as well as at different pH values (1.5, 3.0, 5.0, 6.5 and 7.2). Additionally the pH-dependent solubilities of both APIs were measured and modeled. The chemical-potential-gradient model combined with the perturbed-chain statistical associating fluid theory (PC-SAFT) was applied to predict the dissolution profiles of the cylindrical tablets of NAP and TMP under different conditions based on the analysis of their intrinsic dissolution profiles as well as on the determination of the surface-area reduction of the API tablets during dissolution. It was shown that the predicted dissolution profiles of the tablets under different conditions were in a good accordance with the experimental findings.},
}
@article {pmid26169961,
year = {2015},
author = {Wronkiewicz, M and Larson, E and Lee, AK},
title = {Leveraging anatomical information to improve transfer learning in brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046027},
pmid = {26169961},
issn = {1741-2552},
support = {R00 DC010196/DC/NIDCD NIH HHS/United States ; R00DC010196/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*anatomy &amp; histology/*physiology ; Computer Simulation ; Electroencephalography/methods ; Humans ; Information Storage and Retrieval/*methods ; *Machine Learning ; Models, Anatomic ; Models, Neurological ; Pattern Recognition, Automated/methods ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) represent a technology with the potential to rehabilitate a range of traumatic and degenerative nervous system conditions but require a time-consuming training process to calibrate. An area of BCI research known as transfer learning is aimed at accelerating training by recycling previously recorded training data across sessions or subjects. Training data, however, is typically transferred from one electrode configuration to another without taking individual head anatomy or electrode positioning into account, which may underutilize the recycled data.<br><br>APPROACH: We explore transfer learning with the use of source imaging, which estimates neural activity in the cortex. Transferring estimates of cortical activity, in contrast to scalp recordings, provides a way to compensate for variability in electrode positioning and head morphologies across subjects and sessions.<br><br>MAIN RESULTS: Based on simulated and measured electroencephalography activity, we trained a classifier using data transferred exclusively from other subjects and achieved accuracies that were comparable to or surpassed a benchmark classifier (representative of a real-world BCI). Our results indicate that classification improvements depend on the number of trials transferred and the cortical region of interest.<br><br>SIGNIFICANCE: These findings suggest that cortical source-based transfer learning is a principled method to transfer data that improves BCI classification performance and provides a path to reduce BCI calibration time.},
}
@article {pmid26169880,
year = {2015},
author = {Blabe, CH and Gilja, V and Chestek, CA and Shenoy, KV and Anderson, KD and Henderson, JM},
title = {Assessment of brain-machine interfaces from the perspective of people with paralysis.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {043002},
pmid = {26169880},
issn = {1741-2552},
support = {R01 NS066311/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities/psychology/*statistics &amp; numerical data ; Electroencephalography/psychology/*statistics &amp; numerical data ; Female ; Health Care Surveys ; Humans ; Male ; Middle Aged ; *Needs Assessment ; Patient Preference/psychology/*statistics &amp; numerical data ; Quadriplegia/epidemiology/psychology/*rehabilitation ; Robotics/*statistics &amp; numerical data ; Technology ; United States/epidemiology ; Young Adult ; },
abstract = {OBJECTIVE: One of the main goals of brain-machine interface (BMI) research is to restore function to people with paralysis. Currently, multiple BMI design features are being investigated, based on various input modalities (externally applied and surgically implantable sensors) and output modalities (e.g. control of computer systems, prosthetic arms, and functional electrical stimulation systems). While these technologies may eventually provide some level of benefit, they each carry associated burdens for end-users. We sought to assess the attitudes of people with paralysis toward using various technologies to achieve particular benefits, given the burdens currently associated with the use of each system.<br><br>APPROACH: We designed and distributed a technology survey to determine the level of benefit necessary for people with tetraplegia due to spinal cord injury to consider using different technologies, given the burdens currently associated with them. The survey queried user preferences for 8 BMI technologies including electroencephalography, electrocorticography, and intracortical microelectrode arrays, as well as a commercially available eye tracking system for comparison. Participants used a 5-point scale to rate their likelihood to adopt these technologies for 13 potential control capabilities.<br><br>MAIN RESULTS: Survey respondents were most likely to adopt BMI technology to restore some of their natural upper extremity function, including restoration of hand grasp and/or some degree of natural arm movement. High speed typing and control of a fast robot arm were also of interest to this population. Surgically implanted wireless technologies were twice as 'likely' to be adopted as their wired equivalents.<br><br>SIGNIFICANCE: Assessing end-user preferences is an essential prerequisite to the design and implementation of any assistive technology. The results of this survey suggest that people with tetraplegia would adopt an unobtrusive, autonomous BMI system for both restoration of upper extremity function and control of external devices such as communication interfaces.},
}
@article {pmid26169755,
year = {2015},
author = {Lahr, J and Schwartz, C and Heimbach, B and Aertsen, A and Rickert, J and Ball, T},
title = {Invasive brain-machine interfaces: a survey of paralyzed patients' attitudes, knowledge and methods of information retrieval.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {043001},
doi = {10.1088/1741-2560/12/4/043001},
pmid = {26169755},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Attitude to Health ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Electrodes, Implanted ; Female ; Germany/epidemiology ; Health Care Surveys ; Health Knowledge, Attitudes, Practice ; Humans ; Male ; Middle Aged ; Paralysis/epidemiology/*rehabilitation ; Patient Education as Topic/*statistics &amp; numerical data ; Patient Satisfaction/*statistics &amp; numerical data ; Self-Help Devices/*statistics &amp; numerical data ; Young Adult ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment.<br><br>APPROACH: Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options.<br><br>MAIN RESULTS: The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs.<br><br>SIGNIFICANCE: Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.},
}
@article {pmid26169320,
year = {2015},
author = {Zhang, H and Chavarriaga, R and del R Millán, J},
title = {Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels.},
journal = {NeuroImage},
volume = {120},
number = {},
pages = {64-74},
doi = {10.1016/j.neuroimage.2015.07.012},
pmid = {26169320},
issn = {1095-9572},
mesh = {Adult ; Alpha Rhythm/physiology ; Beta Rhythm/physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Executive Function/*physiology ; Female ; Humans ; Male ; Motion Perception/*physiology ; Nerve Net/*physiology ; Theta Rhythm/physiology ; Young Adult ; },
abstract = {Electrophysiological and neuroimaging evidence suggest the existence of common mechanisms for monitoring erroneous events, independent of the source of errors. Previous works have described modulations of theta activity in the medial frontal cortex elicited by either self-generated errors or erroneous feedback. In turn, similar patterns have recently been reported to appear after the observation of external errors. We report cross-regional interactions after observation of errors at both average and single-trial levels. We recorded scalp electroencephalography (EEG) signals from 15 subjects while monitoring the movement of a cursor on a computer screen. Connectivity patterns, estimated using multivariate auto-regressive models, show increased error-related modulations of the information transfer in the theta and alpha bands between frontocentral and frontolateral areas. Conversely, a decrease of connectivity in the beta band is also observed. These network patterns are similar to those elicited by self-generated errors. However, since no motor response is required, they appear to be related to intrinsic mechanisms of error processing, instead of being linked to co-activation of motor areas. Noticeably, we demonstrate that cross-regional interaction patterns can be estimated on a trial-by-trial basis. These trial-specific patterns, consistent with the multi-trial analysis, convey discriminant information on whether a trial was elicited by observation of an erroneous action. Overall, our study supports the role of frequency-specific modulations in the medial frontal cortex in coordinating cross-regional activity during cognitive monitoring at a single-trial basis.},
}
@article {pmid26167712,
year = {2015},
author = {Charland, P and Léger, PM and Sénécal, S and Courtemanche, F and Mercier, J and Skelling, Y and Labonté-Lemoyne, E},
title = {Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {101},
pages = {e52627},
pmid = {26167712},
issn = {1940-087X},
mesh = {Behavior/physiology ; Brain/physiology ; Cognition/physiology ; Electroencephalography ; Humans ; Learning/*physiology ; Neurophysiology/instrumentation/*methods ; Software ; },
abstract = {In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.},
}
@article {pmid26167530,
year = {2015},
author = {Miralles, F and Vargiu, E and Dauwalder, S and Solà, M and Müller-Putz, G and Wriessnegger, SC and Pinegger, A and Kübler, A and Halder, S and Käthner, I and Martin, S and Daly, J and Armstrong, E and Guger, C and Hintermüller, C and Lowish, H},
title = {Brain Computer Interface on Track to Home.},
journal = {TheScientificWorldJournal},
volume = {2015},
number = {},
pages = {623896},
pmid = {26167530},
issn = {1537-744X},
mesh = {*Brain-Computer Interfaces ; *Computer Systems ; Persons with Disabilities ; Electrodes ; Electroencephalography ; Humans ; Internet ; Software ; Telerehabilitation ; User-Computer Interface ; Wireless Technology ; },
abstract = {The novel BackHome system offers individuals with disabilities a range of useful services available via brain-computer interfaces (BCIs), to help restore their independence. This is the time such technology is ready to be deployed in the real world, that is, at the target end users' home. This has been achieved by the development of practical electrodes, easy to use software, and delivering telemonitoring and home support capabilities which have been conceived, implemented, and tested within a user-centred design approach. The final BackHome system is the result of a 3-year long process involving extensive user engagement to maximize effectiveness, reliability, robustness, and ease of use of a home based BCI system. The system is comprised of ergonomic and hassle-free BCI equipment; one-click software services for Smart Home control, cognitive stimulation, and web browsing; and remote telemonitoring and home support tools to enable independent home use for nonexpert caregivers and users. BackHome aims to successfully bring BCIs to the home of people with limited mobility to restore their independence and ultimately improve their quality of life.},
}
@article {pmid26164417,
year = {2016},
author = {Satkunasivam, R and Santomauro, M and Chopra, S and Plotner, E and Cai, J and Miranda, G and Salibian, S and Aron, M and Ginsberg, D and Daneshmand, S and Desai, M and Gill, IS},
title = {Robotic Intracorporeal Orthotopic Neobladder: Urodynamic Outcomes, Urinary Function, and Health-related Quality of Life.},
journal = {European urology},
volume = {69},
number = {2},
pages = {247-253},
pmid = {26164417},
issn = {1873-7560},
support = {P30 CA014089/CA/NCI NIH HHS/United States ; },
mesh = {Aged ; Aged, 80 and over ; Compliance ; Cystectomy ; Humans ; Incontinence Pads ; Intermittent Urethral Catheterization ; Male ; Middle Aged ; Organ Size ; Quality of Life ; Retrospective Studies ; *Robotic Surgical Procedures ; Surgically-Created Structures/adverse effects/*pathology/*physiology ; Urinary Bladder/*pathology/*physiopathology/surgery ; Urinary Bladder Neoplasms/*surgery ; Urinary Incontinence/etiology ; Urination ; Urodynamics ; },
abstract = {BACKGROUND: Intracorporeal orthotopic neobladder (iONB) creation following robotic radical cystectomy is an emerging procedure and robust functional data are required.<br><br>OBJECTIVE: To evaluate urodynamic features of iONB and bladder cancer-specific and general health-related quality-of-life (HRQOL) outcomes.<br><br>We retrospectively assessed 28 men who underwent iONB creation (January 2012 to October 2013) and compared results to a previously characterized cohort of 79 of open ONB procedures.<br><br>iONB pressure-volume properties were characterized using multichannel urodynamics (UDS). The Bladder Cancer Index (BCI) questionnaire, modified with mucus- and pad-related questions, and the Short Form Health Survey (SF-36) were used to evaluate urinary function and HRQOL. ONB cohorts were compared for functional outcomes and BCI score. Multivariable linear regression was used to assess predictors of BCI score.<br><br>RESULTS AND LIMITATIONS: The median follow-up was 9.4 mo for the iONB and 62.1 mo for the open ONB group (p<0.0001); &#x2265;2-yr follow-up had been completed for one (4%) patient in the iONB group compared to 75 (95%) patients in the open ONB group (p<0.0001). In UDS tests, the iONB group had minimal postvoid residual volume, normal compliance, and a mean capacity of 514 cm(3) (range 339-1001). BCI mean scores for urinary function (p=0.58) and urinary bother (p=0.31) were comparable between the groups. The surgical approach was not associated with the BCI score on multivariable analysis. Rates of 24-h pad use were comparable between iONB and open ONB groups (pad-free 17% vs. 19%; &#x2264;2 pads 84% vs. 79%), as reflected by total pad usage (p=0.1); pad size and daytime wetness were worse in the iONB group. The clean intermittent catheterization rate was 10.7% in the iONB and 6.3% in the open ONB group. Limitations include the retrospective comparison, small number of patients and short follow-up for the iONB group.<br><br>CONCLUSIONS: iONB had adequate UDS characteristics and comparable bladder cancer-specific HRQOL scores to open ONB. However, pad size and daytime wetness were worse for iONB, albeit over significantly shorter follow-up.<br><br>PATIENT SUMMARY: We demonstrate that the volumetric and pressure characteristics are acceptable for a neobladder created using an entirely robot-assisted laparoscopic technique after bladder removal for cancer. Urinary function and quality-of-life outcomes related to the robotic technique were compared to those for neobladders created via an open surgical technique. We found that urinary function and bother indices were comparable; however, the robotic group required larger incontinence pads that were wetter during the daytime. This may be explained by the significantly shorter duration of recovery after surgery in the robotic group.},
}
@article {pmid26163771,
year = {2015},
author = {Faulkner, HG and Myrden, A and Li, M and Mamun, K and Chau, T},
title = {Sequential hypothesis testing for automatic detection of task-related changes in cerebral perfusion in a brain-computer interface.},
journal = {Neuroscience research},
volume = {100},
number = {},
pages = {29-38},
doi = {10.1016/j.neures.2015.06.007},
pmid = {26163771},
issn = {1872-8111},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*blood supply/*physiology ; Cognition/*physiology ; Decision Making/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; *Ultrasonography, Doppler, Transcranial ; Young Adult ; },
abstract = {Evidence suggests that the cerebral blood flow patterns accompanying cognitive activity are retained in many locked-in patients. These patterns can be monitored using transcranial Doppler ultrasound (TCD), a medical imaging technique that measures bilateral cerebral blood flow velocities. Recently, TCD has been proposed as an alternative imaging modality for brain-computer interfaces (BCIs). However, most previous TCD-BCI studies have performed offline analyses with impractically lengthy tasks. In this study, we designed a BCI that automatically differentiates between counting and verbal fluency tasks using sequential hypothesis testing to make decisions as quickly as possible. Ten able-bodied participants silently alternated between counting and verbal fluency tasks within the paradigm of a simulated on-screen keyboard. During this experiment, blood flow velocities were recorded within the left and right middle cerebral arteries using bilateral TCD. Twelve features were used to characterize TCD signals. In a simulated online analysis, sequential hypothesis testing was used to update estimates of class probability every 250 ms as TCD data were processed. Classification was terminated once a threshold level of certainty was reached. Mean classification accuracy across all participants was 72% after an average of 23s, compared to an offline analysis which obtained a classification accuracy of 80% after 45 s. This represents a substantial gain in data transmission rate, while maintaining classification accuracies exceeding 70%. Furthermore, a range of decision times between 19 and 28s was observed, suggesting that the ability of sequential hypothesis testing to adapt the task duration for each individual participant is critical to achieving consistent performance across participants. These results indicate that sequential hypothesis testing is a promising alternative for online TCD-BCIs.},
}
@article {pmid26162751,
year = {2015},
author = {McCrimmon, CM and King, CE and Wang, PT and Cramer, SC and Nenadic, Z and Do, AH},
title = {Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {57},
pmid = {26162751},
issn = {1743-0003},
support = {K24 HD074722/HD/NICHD NIH HHS/United States ; UL1 TR000153/TR/NCATS NIH HHS/United States ; UL1 TR001414/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; *Brain-Computer Interfaces ; Chronic Disease ; Electric Stimulation Therapy/instrumentation/*methods ; Electroencephalography ; Feasibility Studies ; Female ; Foot/physiopathology ; Gait Disorders, Neurologic/*etiology/*rehabilitation ; Humans ; Male ; Middle Aged ; Patient Safety ; Physical Therapy Modalities/*adverse effects/*instrumentation ; Range of Motion, Articular ; Reproducibility of Results ; Stroke/*complications ; *Stroke Rehabilitation ; Treatment Outcome ; Walking ; },
abstract = {BACKGROUND: Many stroke survivors have significant long-term gait impairment, often involving foot drop. Current physiotherapies provide limited recovery. Orthoses substitute for ankle strength, but they provide no lasting therapeutic effect. Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) is a novel rehabilitative approach that may generate permanent neurological improvements. This study explores the safety and feasibility of a foot-drop-targeted BCI-FES physiotherapy in chronic stroke survivors.<br><br>METHODS: Subjects (n&#x2009;=&#x2009;9) operated an electroencephalogram-based BCI-FES system for foot dorsiflexion in 12 one-hour sessions over four weeks. Gait speed, dorsiflexion active range of motion (AROM), six-minute walk distance (6MWD), and Fugl-Meyer leg motor (FM-LM) scores were assessed before, during, and after therapy. The primary safety outcome measure was the proportion of subjects that deteriorated in gait speed by &#x2265;0.16 m/s at one week or four weeks post-therapy. The secondary outcome measures were the proportion of subjects that experienced a clinically relevant decrease in dorsiflexion AROM (&#x2265;2.5&#xb0;), 6MWD (&#x2265;20 %), and FM-LM score (&#x2265;10 %) at either post-therapy assessment.<br><br>RESULTS: No subjects (0/9) experienced a clinically significant deterioration in gait speed, dorsiflexion AROM, 6MWT distance, or FM-LM score at either post-therapy assessment. Five subjects demonstrated a detectable increase (&#x2265;0.06 m/s) in gait speed, three subjects demonstrated a detectable increase (&#x2265;2.5&#xb0;) in dorsiflexion AROM, five subjects demonstrated a detectable increase (&#x2265;10 %) in 6MWD, and three subjects demonstrated a detectable increase (&#x2265;10 %) in FM-LM. Five of the six subjects that exhibited a detectable increase in either post-therapy gait speed or 6MWD also exhibited significant (p&#x2009;<&#x2009;0.01 using a Mann-Whitney U test) increases in electroencephalogram event-related synchronization/desynchronization. Additionally, two subjects experienced a clinically important increase (&#x2265;0.16 m/s) in gait speed, and four subjects experienced a clinically important increase (&#x2265;20 %) in 6MWD. Linear mixed models of gait speed, dorsiflexion AROM, 6MWD, and FM-LM scores suggest that BCI-FES therapy is associated with an increase in lower motor performance at a statistically, yet not clinically, significant level.<br><br>CONCLUSION: BCI-FES therapy is safe. If it is shown to improve post-stroke gait function in future studies, it could provide a new gait rehabilitation option for severely impaired patients. Formal clinical trials are warranted.},
}
@article {pmid26161073,
year = {2015},
author = {Cordes, JS and Mathiak, KA and Dyck, M and Alawi, EM and Gaber, TJ and Zepf, FD and Klasen, M and Zvyagintsev, M and Gur, RC and Mathiak, K},
title = {Cognitive and neural strategies during control of the anterior cingulate cortex by fMRI neurofeedback in patients with schizophrenia.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {169},
pmid = {26161073},
issn = {1662-5153},
abstract = {Cognitive functioning is impaired in patients with schizophrenia, leading to significant disabilities in everyday functioning. Its improvement is an important treatment target. Neurofeedback (NF) seems a promising method to address the neural dysfunctions underlying those cognitive impairments. The anterior cingulate cortex (ACC), a central hub for cognitive processing, is one of the brain regions known to be dysfunctional in schizophrenia. Here we conducted NF training based on real-time functional magnetic resonance imaging (fMRI) in patients with schizophrenia to enable them to control their ACC activity. Training was performed over 3 days in a group of 11 patients with schizophrenia and 11 healthy controls. Social feedback was provided in accordance with the evoked activity in the selected region of interest (ROI). Neural and cognitive strategies were examined off-line. Both groups learned to control the activity of their ACC but used different neural strategies: patients activated the dorsal and healthy controls the rostral subdivision. Patients mainly used imagination of music to elicit activity and the control group imagination of sports. In a stepwise regression analysis, the difference in neural control did not result from the differences in cognitive strategies but from diagnosis alone. Based on social reinforcers, patients with schizophrenia can learn to regulate localized brain activity. However, cognitive strategies and neural network location differ from healthy controls. These data emphasize that for therapeutic interventions in patients with schizophrenia compensatory strategies may emerge. Specific cognitive skills or specific dysfunctional networks should be addressed to train impaired skills. Social NF based on fMRI may be one method to accomplish precise learning targets.},
}
@article {pmid26158523,
year = {2015},
author = {Ramakrishnan, A and Ifft, PJ and Pais-Vieira, M and Byun, YW and Zhuang, KZ and Lebedev, MA and Nicolelis, MAL},
title = {Computing Arm Movements with a Monkey Brainet.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {10767},
pmid = {26158523},
issn = {2045-2322},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; DP1MH099903/DP/NCCDPHP CDC HHS/United States ; },
mesh = {Animals ; Arm/*physiology ; Behavior, Animal ; Brain/*physiology ; Brain-Computer Interfaces ; Electrodes, Implanted ; Haplorhini ; Movement/physiology ; Neurons/physiology ; },
abstract = {Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.},
}
@article {pmid26158005,
year = {2015},
author = {Weyand, S and Schudlo, L and Takehara-Nishiuchi, K and Chau, T},
title = {Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain-computer interface.},
journal = {Neurophotonics},
volume = {2},
number = {2},
pages = {025001},
pmid = {26158005},
issn = {2329-423X},
abstract = {Brain-computer interfaces (BCIs) allow individuals to use only cognitive activities to interact with their environment. The widespread use of BCIs is limited, due in part to their lack of user-friendliness. The main goal of this work was to develop a more user-centered BCI and determine if: (1) individuals can acquire control of an online near-infrared spectroscopy BCI via usability and performance-informed selection of mental tasks without compromising classification accuracy and (2) the combination of usability and performance-informed selection of mental tasks yields subjective ease-of-use ratings that exceed those attainable with prescribed mental tasks. Twenty able-bodied participants were recruited. Half of the participants served as a control group, using the state-of-the-art prescribed mental strategies. The other half of the participants comprised the study group, choosing their own personalized mental strategies out of eleven possible tasks. It was concluded that users were, in fact, able to acquire control of the more user-centered BCI without a significant change in accuracy compared to the prescribed task BCI. Furthermore, the personalized BCI yielded higher subjective ease-of-use ratings than the prescribed BCI. Average online accuracies of [Formula: see text] and [Formula: see text] were achieved by the personalized and prescribed mental task groups, respectively.},
}
@article {pmid26157378,
year = {2015},
author = {Young, BM and Nigogosyan, Z and Walton, LM and Remsik, A and Song, J and Nair, VA and Tyler, ME and Edwards, DF and Caldera, K and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Dose-response relationships using brain-computer interface technology impact stroke rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {361},
pmid = {26157378},
issn = {1662-5161},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) are an emerging novel technology for stroke rehabilitation. Little is known about how dose-response relationships for BCI therapies affect brain and behavior changes. We report preliminary results on stroke patients (n = 16, 11 M) with persistent upper extremity motor impairment who received therapy using a BCI system with functional electrical stimulation of the hand and tongue stimulation. We collected MRI scans and behavioral data using the Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) before, during, and after the therapy period. Using anatomical and functional MRI, we computed Laterality Index (LI) for brain activity in the motor network during impaired hand finger tapping. Changes from baseline LI and behavioral scores were assessed for relationships with dose, intensity, and frequency of BCI therapy. We found that gains in SIS Strength were directly responsive to BCI therapy: therapy dose and intensity correlated positively with increased SIS Strength (p ≤ 0.05), although no direct relationships were identified with ARAT or 9-HPT scores. We found behavioral measures that were not directly sensitive to differences in BCI therapy administration but were associated with concurrent brain changes correlated with BCI therapy administration parameters: therapy dose and intensity showed significant (p ≤ 0.05) or trending (0.05 < p < 0.1) negative correlations with LI changes, while therapy frequency did not affect LI. Reductions in LI were then correlated (p ≤ 0.05) with increased SIS Activities of Daily Living scores and improved 9-HPT performance. Therefore, some behavioral changes may be reflected by brain changes sensitive to differences in BCI therapy administration, while others such as SIS Strength may be directly responsive to BCI therapy administration. Data preliminarily suggest that when using BCI in stroke rehabilitation, therapy frequency may be less important than dose and intensity.},
}
@article {pmid26154513,
year = {2015},
author = {Kovacevic, N and Ritter, P and Tays, W and Moreno, S and McIntosh, AR},
title = {'My Virtual Dream': Collective Neurofeedback in an Immersive Art Environment.},
journal = {PloS one},
volume = {10},
number = {7},
pages = {e0130129},
pmid = {26154513},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Art ; Brain/physiology ; *Brain-Computer Interfaces ; Cognition ; Electroencephalography/*methods ; Female ; Humans ; Imagination ; Learning ; Male ; Middle Aged ; Multivariate Analysis ; *Music ; Neurofeedback/*methods ; Relaxation ; Software ; Video Games ; Young Adult ; },
abstract = {While human brains are specialized for complex and variable real world tasks, most neuroscience studies reduce environmental complexity, which limits the range of behaviours that can be explored. Motivated to overcome this limitation, we conducted a large-scale experiment with electroencephalography (EEG) based brain-computer interface (BCI) technology as part of an immersive multi-media science-art installation. Data from 523 participants were collected in a single night. The exploratory experiment was designed as a collective computer game where players manipulated mental states of relaxation and concentration with neurofeedback targeting modulation of relative spectral power in alpha and beta frequency ranges. Besides validating robust time-of-night effects, gender differences and distinct spectral power patterns for the two mental states, our results also show differences in neurofeedback learning outcome. The unusually large sample size allowed us to detect unprecedented speed of learning changes in the power spectrum (~ 1 min). Moreover, we found that participants' baseline brain activity predicted subsequent neurofeedback beta training, indicating state-dependent learning. Besides revealing these training effects, which are relevant for BCI applications, our results validate a novel platform engaging art and science and fostering the understanding of brains under natural conditions.},
}
@article {pmid26150780,
year = {2015},
author = {Trachel, RE and Clerc, M and Brochier, TG},
title = {Decoding covert shifts of attention induced by ambiguous visuospatial cues.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {358},
pmid = {26150780},
issn = {1662-5161},
abstract = {Simple and unambiguous visual cues (e.g., an arrow) can be used to trigger covert shifts of visual attention away from the center of gaze. The processing of visual stimuli is enhanced at the attended location. Covert shifts of attention modulate the power of cerebral oscillations in the alpha band over parietal and occipital regions. These modulations are sufficiently robust to be decoded on a single trial basis from electroencephalography (EEG) signals. It is often assumed that covert attention shifts are under voluntary control, and that they also occur in more natural and complex environments, but there is no direct evidence to support this assumption. We address this important issue by using random-dot stimuli to cue one of two opposite locations, where a visual target is presented. We contrast two conditions, one in which the random-dot motion is predictive of the target location, and the other, in which it provides ambiguous information. Behavioral results show attention shifts in anticipation of the visual target, in both conditions. In addition, using the common spatial patterns (CSPs) algorithm, we extract EEG power features in the alpha-band (around 10 Hz) that best discriminate the attended location in single trials. We obtain a significant decoding accuracy in 7/10 subjects using a cross-validation procedure applied in the predictive condition. Interestingly, similar accuracy (significant in 5/10 subjects) is obtained when the CSPs trained in the predictive condition are tested in the ambiguous condition. In agreement with this result, we find that the CSPs show very similar topographies in both conditions. These results shed a new light on the behavioral and EEG correlates of visuospatial attention in complex visual environments. This study demonstrates that alpha-power features could be used in brain-computer interfaces to decode covert attention shifts in an environment containing ambiguous spatial information.},
}
@article {pmid26146459,
year = {2015},
author = {Pardos, M and Korostenskaja, M and Xiang, J and Fujiwara, H and Lee, KH and Horn, PS and Byars, A and Vannest, J and Wang, Y and Hemasilpin, N and Rose, DF},
title = {Physical Feature Encoding and Word Recognition Abilities Are Altered in Children with Intractable Epilepsy: Preliminary Neuromagnetic Evidence.},
journal = {Behavioural neurology},
volume = {2015},
number = {},
pages = {237436},
pmid = {26146459},
issn = {1875-8584},
mesh = {Adolescent ; Brain Mapping ; Cerebral Cortex/*pathology/physiopathology ; Child ; Epilepsy/*pathology/physiopathology/therapy ; Female ; Humans ; *Language ; Magnetoencephalography/methods ; Male ; Memory/*physiology ; Treatment Outcome ; },
abstract = {Objective evaluation of language function is critical for children with intractable epilepsy under consideration for epilepsy surgery. The purpose of this preliminary study was to evaluate word recognition in children with intractable epilepsy by using magnetoencephalography (MEG). Ten children with intractable epilepsy (M/F 6/4, mean ± SD 13.4 ± 2.2 years) were matched on age and sex to healthy controls. Common nouns were presented simultaneously from visual and auditory sensory inputs in "match" and "mismatch" conditions. Neuromagnetic responses M1, M2, M3, M4, and M5 with latencies of ~100 ms, ~150 ms, ~250 ms, ~350 ms, and ~450 ms, respectively, elicited during the "match" condition were identified. Compared to healthy children, epilepsy patients had both significantly delayed latency of the M1 and reduced amplitudes of M3 and M5 responses. These results provide neurophysiologic evidence of altered word recognition in children with intractable epilepsy.},
}
@article {pmid26142906,
year = {2015},
author = {Zhang, Y and Chase, SM},
title = {Recasting brain-machine interface design from a physical control system perspective.},
journal = {Journal of computational neuroscience},
volume = {39},
number = {2},
pages = {107-118},
pmid = {26142906},
issn = {1573-6873},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Humans ; Linear Models ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/*physiology ; },
abstract = {With the goal of improving the quality of life for people suffering from various motor control disorders, brain-machine interfaces provide direct neural control of prosthetic devices by translating neural signals into control signals. These systems act by reading motor intent signals directly from the brain and using them to control, for example, the movement of a cursor on a computer screen. Over the past two decades, much attention has been devoted to the decoding problem: how should recorded neural activity be translated into the movement of the cursor? Most approaches have focused on this problem from an estimation standpoint, i.e., decoders are designed to return the best estimate of motor intent possible, under various sets of assumptions about how the recorded neural signals represent motor intent. Here we recast the decoder design problem from a physical control system perspective, and investigate how various classes of decoders lead to different types of physical systems for the subject to control. This framework leads to new interpretations of why certain types of decoders have been shown to perform better than others. These results have implications for understanding how motor neurons are recruited to perform various tasks, and may lend insight into the brain's ability to conceptualize artificial systems.},
}
@article {pmid26138148,
year = {2016},
author = {Sollfrank, T and Ramsay, A and Perdikis, S and Williamson, J and Murray-Smith, R and Leeb, R and Millán, JDR and Kübler, A},
title = {The effect of multimodal and enriched feedback on SMR-BCI performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {1},
pages = {490-498},
doi = {10.1016/j.clinph.2015.06.004},
pmid = {26138148},
issn = {1872-8952},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: This study investigated the effect of multimodal (visual and auditory) continuous feedback with information about the uncertainty of the input signal on motor imagery based BCI performance. A liquid floating through a visualization of a funnel (funnel feedback) provided enriched visual or enriched multimodal feedback.<br><br>METHODS: In a between subject design 30 healthy SMR-BCI naive participants were provided with either conventional bar feedback (CB), or visual funnel feedback (UF), or multimodal (visual and auditory) funnel feedback (MF). Subjects were required to imagine left and right hand movement and were trained to control the SMR based BCI for five sessions on separate days.<br><br>RESULTS: Feedback accuracy varied largely between participants. The MF feedback lead to a significantly better performance in session 1 as compared to the CB feedback and could significantly enhance motivation and minimize frustration in BCI use across the five training sessions.<br><br>CONCLUSION: The present study demonstrates that the BCI funnel feedback allows participants to modulate sensorimotor EEG rhythms. Participants were able to control the BCI with the funnel feedback with better performance during the initial session and less frustration compared to the CB feedback.<br><br>SIGNIFICANCE: The multimodal funnel feedback provides an alternative to the conventional cursorbar feedback for training subjects to modulate their sensorimotor rhythms.},
}
@article {pmid26138146,
year = {2016},
author = {Kellis, S and Sorensen, L and Darvas, F and Sayres, C and O'Neill, K and Brown, RB and House, P and Ojemann, J and Greger, B},
title = {Multi-scale analysis of neural activity in humans: Implications for micro-scale electrocorticography.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {1},
pages = {591-601},
doi = {10.1016/j.clinph.2015.06.002},
pmid = {26138146},
issn = {1872-8952},
mesh = {Electrocorticography/*instrumentation/standards ; *Electrodes, Implanted/standards ; Humans ; Male ; Microelectrodes/standards ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Electrocorticography grids have been used to study and diagnose neural pathophysiology for over 50 years, and recently have been used for various neural prosthetic applications. Here we provide evidence that micro-scale electrodes are better suited for studying cortical pathology and function, and for implementing neural prostheses.<br><br>METHODS: This work compares dynamics in space, time, and frequency of cortical field potentials recorded by three types of electrodes: electrocorticographic (ECoG) electrodes, non-penetrating micro-ECoG (&#x3bc;ECoG) electrodes that use microelectrodes and have tighter interelectrode spacing; and penetrating microelectrodes (MEA) that penetrate the cortex to record single- or multiunit activity (SUA or MUA) and local field potentials (LFP).<br><br>RESULTS: While the finest spatial scales are found in LFPs recorded intracortically, we found that LFP recorded from &#x3bc;ECoG electrodes demonstrate scales of linear similarity (i.e., correlation, coherence, and phase) closer to the intracortical electrodes than the clinical ECoG electrodes.<br><br>CONCLUSIONS: We conclude that LFPs can be recorded intracortically and epicortically at finer scales than clinical ECoG electrodes are capable of capturing.<br><br>SIGNIFICANCE: Recorded with appropriately scaled electrodes and grids, field potentials expose a more detailed representation of cortical network activity, enabling advanced analyses of cortical pathology and demanding applications such as brain-computer interfaces.},
}
@article {pmid26134845,
year = {2015},
author = {Fukuma, R and Yanagisawa, T and Yorifuji, S and Kato, R and Yokoi, H and Hirata, M and Saitoh, Y and Kishima, H and Kamitani, Y and Yoshimine, T},
title = {Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals.},
journal = {PloS one},
volume = {10},
number = {7},
pages = {e0131547},
pmid = {26134845},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; *Artificial Limbs ; *Brain-Computer Interfaces ; Female ; Hand/*physiology ; Humans ; *Magnetoencephalography ; Male ; Motor Cortex ; Movement ; Normal Distribution ; Prosthesis Design ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {OBJECTIVE: A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis.<br><br>METHOD: Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition.<br><br>RESULTS: The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 &#xb1; 12.9% (mean &#xb1; SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 &#xb1; 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 &#xb1; 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition.<br><br>CONCLUSIONS: Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.},
}
@article {pmid26133797,
year = {2015},
author = {Perel, S and Sadtler, PT and Oby, ER and Ryu, SI and Tyler-Kabara, EC and Batista, AP and Chase, SM},
title = {Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics.},
journal = {Journal of neurophysiology},
volume = {114},
number = {3},
pages = {1500-1512},
pmid = {26133797},
issn = {1522-1598},
support = {R01 NS065065/NS/NINDS NIH HHS/United States ; 5R01-HD-071686/HD/NICHD NIH HHS/United States ; 5R01-NS-065065/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Biomechanical Phenomena ; Macaca mulatta ; Motor Cortex/cytology/*physiology ; *Motor Skills ; Neurons/physiology ; },
abstract = {A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the "beta band" (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.},
}
@article {pmid26132298,
year = {2015},
author = {Rubio, G and López-Rodríguez, JA and Zuluaga, P and Ponce, G and Martínez-Gras, I and Jiménez-Arriero, M&#xc1;},
title = {Clinical and Demographic Characteristics of Binge Drinkers Associated with Lack of Efficacy of Brief Intervention and Medical Advice.},
journal = {Adicciones},
volume = {27},
number = {2},
pages = {90-98},
pmid = {26132298},
issn = {0214-4840},
mesh = {Adult ; Binge Drinking/diagnosis/epidemiology/*therapy ; *Directive Counseling ; Female ; Humans ; Male ; *Psychotherapy ; Socioeconomic Factors ; Treatment Failure ; },
abstract = {UNLABELLED: Brief Counseling Intervention (BCI) and Medical advice (MA) are psychotherapeutic approaches used for the treatment of binge drinkers in Primary Care. Although binge drinking is a common pattern of alcohol misuse in Europe and in the US, no studies have evaluated those subjects who do not respond to Brief Counseling Interventions or Medical Advice.<br><br>OBJECTIVE: To determine the clinical and demographic characteristics of binge drinkers in whom BCI or MA are not effective in reducing harmful alcohol use.<br><br>METHODS: This is a secondary analysis of data from a randomized alcohol brief intervention trial with a 12-month follow-up period. A total of 674 subjects (89%) participated right through to the end of the study. The primary outcome measure was change in harmful alcohol use from baseline to 12 months.<br><br>RESULTS: The strongest baseline predictors of harmful alcohol use during follow-up were educational status, young adults, and high number of cigarettes smoked, present family history of alcoholism, treatment condition and number of drinks per episode of binge drinking.<br><br>CONCLUSIONS: Binge drinkers are a heterogeneous group that responds to brief intervention or MA but in a subgroup of them these interventions fail to prevent harmful alcohol use. Other interventions should be implemented for these subjects.},
}
@article {pmid26131890,
year = {2015},
author = {Iturrate, I and Grizou, J and Omedes, J and Oudeyer, PY and Lopes, M and Montesano, L},
title = {Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.},
journal = {PloS one},
volume = {10},
number = {7},
pages = {e0131491},
pmid = {26131890},
issn = {1932-6203},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Brain-Computer Interfaces/*standards ; Calibration ; *Evoked Potentials ; Humans ; Likelihood Functions ; },
abstract = {This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.},
}
@article {pmid26129730,
year = {2015},
author = {Williamson, A and Ferro, M and Leleux, P and Ismailova, E and Kaszas, A and Doublet, T and Quilichini, P and Rivnay, J and Rózsa, B and Katona, G and Bernard, C and Malliaras, GG},
title = {Localized Neuron Stimulation with Organic Electrochemical Transistors on Delaminating Depth Probes.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {27},
number = {30},
pages = {4405-4410},
doi = {10.1002/adma.201500218},
pmid = {26129730},
issn = {1521-4095},
abstract = {Organic electrochemical transistors are integrated on depth probes to achieve localized electrical stimulation of neurons. The probes feature a mechanical delamination process which leaves only a 4 μm thick film with embedded transistors inside the brain. This considerably reduces probe invasiveness and correspondingly improves future brain-machine interfaces.},
}
@article {pmid26124702,
year = {2015},
author = {Herff, C and Heger, D and de Pesters, A and Telaar, D and Brunner, P and Schalk, G and Schultz, T},
title = {Brain-to-text: decoding spoken phrases from phone representations in the brain.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {217},
pmid = {26124702},
issn = {1662-4548},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; },
abstract = {It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech.},
}
@article {pmid26123281,
year = {2015},
author = {Wang, F and He, Y and Pan, J and Xie, Q and Yu, R and Zhang, R and Li, Y},
title = {A Novel Audiovisual Brain-Computer Interface and Its Application in Awareness Detection.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {9962},
pmid = {26123281},
issn = {2045-2322},
mesh = {Acoustic Stimulation ; Adult ; Awareness/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; },
abstract = {Currently, detecting awareness in patients with disorders of consciousness (DOC) is a challenging task, which is commonly addressed through behavioral observation scales such as the JFK Coma Recovery Scale-Revised. Brain-computer interfaces (BCIs) provide an alternative approach to detect awareness in patients with DOC. However, these patients have a much lower capability of using BCIs compared to healthy individuals. This study proposed a novel BCI using temporally, spatially, and semantically congruent audiovisual stimuli involving numbers (i.e., visual and spoken numbers). Subjects were instructed to selectively attend to the target stimuli cued by instruction. Ten healthy subjects first participated in the experiment to evaluate the system. The results indicated that the audiovisual BCI system outperformed auditory-only and visual-only systems. Through event-related potential analysis, we observed audiovisual integration effects for target stimuli, which enhanced the discriminability between brain responses for target and nontarget stimuli and thus improved the performance of the audiovisual BCI. This system was then applied to detect the awareness of seven DOC patients, five of whom exhibited command following as well as number recognition. Thus, this audiovisual BCI system may be used as a supportive bedside tool for awareness detection in patients with DOC.},
}
@article {pmid26117187,
year = {2015},
author = {Prudic, A and Ji, Y and Luebbert, C and Sadowski, G},
title = {Influence of humidity on the phase behavior of API/polymer formulations.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {94},
number = {},
pages = {352-362},
doi = {10.1016/j.ejpb.2015.06.009},
pmid = {26117187},
issn = {1873-3441},
mesh = {Absorption, Physicochemical ; Chemistry, Pharmaceutical ; Humidity ; Indomethacin/*chemistry ; *Models, Chemical ; Molecular Structure ; Naproxen/*chemistry ; Phase Transition ; Povidone/*chemistry ; Pyrrolidines/*chemistry ; Vinyl Compounds/*chemistry ; Water/chemistry ; },
abstract = {Amorphous formulations of APIs in polymers tend to absorb water from the atmosphere. This absorption of water can induce API recrystallization, leading to reduced long-term stability during storage. In this work, the phase behavior of different formulations was investigated as a function of relative humidity. Indomethacin and naproxen were chosen as model APIs and poly(vinyl pyrrolidone) (PVP) and poly(vinyl pyrrolidone-co-vinyl acetate) (PVPVA64) as excipients. The formulations were prepared by spray drying. The water sorption in pure polymers and in formulations was measured at 25°C and at different values of relative humidity (RH=25%, 50% and 75%). Most water was absorbed in PVP-containing systems, and water sorption was decreasing with increasing API content. These trends could also be predicted in good agreement with the experimental data using the thermodynamic model PC-SAFT. Furthermore, the effect of absorbed water on API solubility in the polymer and on the glass-transition temperature of the formulations was predicted with PC-SAFT and the Gordon-Taylor equation, respectively. The absorbed water was found to significantly decrease the API solubility in the polymer as well as the glass-transition temperature of the formulation. Based on a quantitative modeling of the API/polymer phase diagrams as a function of relative humidity, appropriate API/polymer compositions can now be selected to ensure long-term stable amorphous formulations at given storage conditions.},
}
@article {pmid26116078,
year = {2015},
author = {Marques, EM and Blom, AW and Lenguerrand, E and Wylde, V and Noble, SM},
title = {Local anaesthetic wound infiltration in addition to standard anaesthetic regimen in total hip and knee replacement: long-term cost-effectiveness analyses alongside the APEX randomised controlled trials.},
journal = {BMC medicine},
volume = {13},
number = {},
pages = {151},
pmid = {26116078},
issn = {1741-7015},
support = {MR/K025643/1/MRC_/Medical Research Council/United Kingdom ; RP-PG-0407-10070/DH_/Department of Health/United Kingdom ; },
mesh = {Aged ; Anesthesia, Local/methods ; Anesthetics, Local/therapeutic use ; Arthroplasty, Replacement, Knee/adverse effects/*economics/methods ; *Cost-Benefit Analysis ; Female ; Humans ; Male ; Outcome Assessment, Health Care ; Pain Management/*economics/methods ; Pain, Postoperative/*prevention &amp; control ; Quality-Adjusted Life Years ; Surveys and Questionnaires ; Wound Closure Techniques/*economics ; },
abstract = {BACKGROUND: The Arthroplasty Pain Experience (APEX) studies are two randomised controlled trials in primary total hip (THR) and total knee replacement (TKR) at a large UK orthopaedics centre. APEX investigated the effect of local anaesthetic wound infiltration (LAI), administered before wound closure, in addition to standard analgesia, on pain severity at 12 months. This article reports results of the within-trial economic evaluations.<br><br>METHODS: Cost-effectiveness was assessed from the health and social care payer perspective in relation to quality adjusted life years (QALYs) and the primary clinical outcome, the WOMAC Pain score at 12-months follow-up. Resource use was collected from hospital records and patient-completed postal questionnaires, and valued using unit cost estimates from local NHS Trust finance department and national tariffs. Missing data were addressed using multiple imputation chained equations. Costs and outcomes were compared per trial arm and plotted in cost-effectiveness planes. If no arm was dominant (i.e., more effective and less expensive than the other), incremental cost-effectiveness ratios were estimated. The economic results were bootstrapped incremental net monetary benefit statistics (INMB) and cost-effectiveness acceptability curves. One-way deterministic sensitivity analyses explored any methodological uncertainty.<br><br>RESULTS: In both the THR and TKR trials, LAI was the dominant treatment: cost-saving and more effective than standard care, in relation to QALYs and WOMAC Pain. Using the &#xa3;20,000 per QALY threshold, in THR, the INMB was &#xa3;1,125 (95 % BCI, &#xa3;183 to &#xa3;2,067) and the probability of being cost-effective was over 98 %. In TKR, the INMB was &#xa3;264 (95 % BCI, -&#xa3;710 to &#xa3;1,238), but there was only 62 % probability of being cost-effective. When considering an NHS perspective only, LAI was no longer dominant in THR, but still highly cost-effective, with an INMB of &#xa3;961 (95 % BCI, &#xa3;50 to &#xa3;1,873).<br><br>CONCLUSIONS: Administering LAI is a cost-effective treatment option in THR and TKR surgeries. The evidence, because of larger QALY gain, is stronger for THR. In TKR, there is more uncertainty around the economic result, and smaller QALY gains. Results, however, point to LAI being cheaper than standard analgesia, which includes a femoral nerve block.<br><br>TRIAL REGISTRATION: ISRCTN96095682 , 29/04/2010.},
}
@article {pmid26115603,
year = {2015},
author = {Tidoni, E and Tieri, G and Aglioti, SM},
title = {Re-establishing the disrupted sensorimotor loop in deafferented and deefferented people: The case of spinal cord injuries.},
journal = {Neuropsychologia},
volume = {79},
number = {Pt B},
pages = {301-309},
doi = {10.1016/j.neuropsychologia.2015.06.029},
pmid = {26115603},
issn = {1873-3514},
mesh = {Brain-Computer Interfaces ; Cerebral Cortex/*pathology ; Humans ; Neural Pathways/*physiology ; Neuronal Plasticity/*physiology ; Recovery of Function/*physiology ; Spinal Cord Injuries/pathology/*rehabilitation ; },
abstract = {Acting efficiently in the world depends on the activity of motor and somatosensory systems, the integration of which is necessary for the proper functioning of the sensorimotor loop (SL). Profound alterations of SL functioning follow spinal cord injury (SCI), a condition that brings about a disconnection of the body from the brain. Such disconnection creates a substantial deprivation of somatosensorial inputs and motor outputs. Consequent somatic deficits and motor paralysis affect the body below the lesion level. A complete restoration of normal functions of the SL cannot be expected until basic neuroscience has found a way to re-establish the interrupted neural connectivity. Meanwhile, studies should focus on the development of technical solutions for dealing with the disruption of the sensorimotor loop. This review discusses the structural and functional adaptive reorganization of the brain after SCI, and the maladaptive mechanisms that impact on the processing of body related information, which alter motor imagery strategies and EEG signals. Studies that show how residual functions (e.g. face tactile sensitivity) may help people to restore a normal body image are also reviewed. Finally, data on how brain and residual body signals may be used to improve brain computer interface systems is discussed in relation to the issue of how such systems may help SCI people to re-enter the world and interact with objects and other individuals.},
}
@article {pmid26114954,
year = {2015},
author = {Nurse, ES and Karoly, PJ and Grayden, DB and Freestone, DR},
title = {A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0131328},
pmid = {26114954},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Humans ; *Machine Learning ; *Neural Networks, Computer ; },
abstract = {This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI), based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific domain knowledge. Instead of pre-defining features, the time-domain signal is input to a population of multi-layer perceptrons (MLPs) in order to perform a stochastic search for the best structure. The results showed that the average performance of the new algorithm outperformed other published methods using the Berlin BCI IV (2008) competition dataset and was comparable to the best results in the Berlin BCI II (2002-3) competition dataset. The new method was also applied to electroencephalography (EEG) data recorded from five subjects undertaking a hand squeeze task and demonstrated high levels of accuracy with a mean classification accuracy of 78.9% after five-fold cross-validation. Our new approach has been shown to give accurate results across different motor tasks and signal types as well as between subjects.},
}
@article {pmid26113812,
year = {2015},
author = {Hiremath, SV and Chen, W and Wang, W and Foldes, S and Yang, Y and Tyler-Kabara, EC and Collinger, JL and Boninger, ML},
title = {Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.},
journal = {Frontiers in integrative neuroscience},
volume = {9},
number = {},
pages = {40},
pmid = {26113812},
issn = {1662-5145},
support = {KL2 TR000146/TR/NCATS NIH HHS/United States ; P30 AG024827/AG/NIA NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; UL1 TR000005/TR/NCATS NIH HHS/United States ; },
abstract = {A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.},
}
@article {pmid26112335,
year = {2015},
author = {Wilson, JC and Kesler, M and Pelegrin, SL and Kalvi, L and Gruber, A and Steenland, HW},
title = {Watching from a distance: A robotically controlled laser and real-time subject tracking software for the study of conditioned predator/prey-like interactions.},
journal = {Journal of neuroscience methods},
volume = {253},
number = {},
pages = {78-89},
doi = {10.1016/j.jneumeth.2015.06.015},
pmid = {26112335},
issn = {1872-678X},
mesh = {Animals ; Avoidance Learning/*physiology ; Brain-Computer Interfaces ; *Lasers ; Male ; *Models, Biological ; *Predatory Behavior ; Rats ; Rats, Inbred F344 ; Rats, Long-Evans ; Reward ; *Robotics ; *Software ; Time Factors ; },
abstract = {BACKGROUND: The physical distance between predator and prey is a primary determinant of behavior, yet few paradigms exist to study this reliably in rodents.<br><br>NEW METHOD: The utility of a robotically controlled laser for use in a predator-prey-like (PPL) paradigm was explored for use in rats. This involved the construction of a robotic two-dimensional gimbal to dynamically position a laser beam in a behavioral test chamber. Custom software was used to control the trajectory and final laser position in response to user input on a console. The software also detected the location of the laser beam and the rodent continuously so that the dynamics of the distance between them could be analyzed. When the animal or laser beam came within a fixed distance the animal would either be rewarded with electrical brain stimulation or shocked subcutaneously.<br><br>RESULTS: Animals that received rewarding electrical brain stimulation could learn to chase the laser beam, while animals that received aversive subcutaneous shock learned to actively avoid the laser beam in the PPL paradigm. Mathematical computations are presented which describe the dynamic interaction of the laser and rodent.<br><br>The robotic laser offers a neutral stimulus to train rodents in an open field and is the first device to be versatile enough to assess distance between predator and prey in real time.<br><br>CONCLUSIONS: With ongoing behavioral testing this tool will permit the neurobiological investigation of predator/prey-like relationships in rodents, and may have future implications for prosthetic limb development through brain-machine interfaces.},
}
@article {pmid26111226,
year = {2015},
author = {Gerson, SA and Schiavio, A and Timmers, R and Hunnius, S},
title = {Active Drumming Experience Increases Infants' Sensitivity to Audiovisual Synchrony during Observed Drumming Actions.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0130960},
pmid = {26111226},
issn = {1932-6203},
mesh = {Acoustic Stimulation ; Auditory Perception/*physiology ; Female ; Humans ; Infant ; Male ; *Music ; Photic Stimulation ; Psychomotor Performance/*physiology ; Visual Perception/*physiology ; },
abstract = {In the current study, we examined the role of active experience on sensitivity to multisensory synchrony in six-month-old infants in a musical context. In the first of two experiments, we trained infants to produce a novel multimodal effect (i.e., a drum beat) and assessed the effects of this training, relative to no training, on their later perception of the synchrony between audio and visual presentation of the drumming action. In a second experiment, we then contrasted this active experience with the observation of drumming in order to test whether observation of the audiovisual effect was as effective for sensitivity to multimodal synchrony as active experience. Our results indicated that active experience provided a unique benefit above and beyond observational experience, providing insights on the embodied roots of (early) music perception and cognition.},
}
@article {pmid26109672,
year = {2015},
author = {Agarwal, R and Thakor, NV and Sarma, SV and Massaquoi, SG},
title = {PMv Neuronal Firing May Be Driven by a Movement Command Trajectory within Multidimensional Gaussian Fields.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {25},
pages = {9508-9525},
pmid = {26109672},
issn = {1529-2401},
mesh = {Animals ; Electrophysiology ; Hand Strength/physiology ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Neurons/*physiology ; Principal Component Analysis ; Psychomotor Performance/*physiology ; },
abstract = {The premotor cortex (PM) is known to be a site of visuo-somatosensory integration for the production of movement. We sought to better understand the ventral PM (PMv) by modeling its signal encoding in greater detail. Neuronal firing data was obtained from 110 PMv neurons in two male rhesus macaques executing four reach-grasp-manipulate tasks. We found that in the large majority of neurons (∼90%) the firing patterns across the four tasks could be explained by assuming that a high-dimensional position/configuration trajectory-like signal evolving ∼250 ms before movement was encoded within a multidimensional Gaussian field (MGF). Our findings are consistent with the possibility that PMv neurons process a visually specified reference command for the intended arm/hand position trajectory with respect to a proprioceptively or visually sensed initial configuration. The estimated MGF were (hyper) disc-like, such that each neuron's firing modulated strongly only with commands that evolved along a single direction within position/configuration space. Thus, many neurons appeared to be tuned to slices of this input signal space that as a collection appeared to well cover the space. The MGF encoding models appear to be consistent with the arm-referent, bell-shaped, visual target tuning curves and target selectivity patterns observed in PMV visual-motor neurons. These findings suggest that PMv may implement a lookup table-like mechanism that helps translate intended movement trajectory into time-varying patterns of activation in motor cortex and spinal cord. MGFs provide an improved nonlinear framework for potentially decoding visually specified, intended multijoint arm/hand trajectories well in advance of movement.},
}
@article {pmid26103603,
year = {2015},
author = {Hatamikia, S and Nasrabadi, AM},
title = {Subject transfer BCI based on Composite Local Temporal Correlation Common Spatial Pattern.},
journal = {Computers in biology and medicine},
volume = {64},
number = {},
pages = {1-11},
doi = {10.1016/j.compbiomed.2015.06.001},
pmid = {26103603},
issn = {1879-0534},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/instrumentation ; Equipment Design ; Humans ; Imagery, Psychotherapy ; Machine Learning ; Pattern Recognition, Automated/*methods ; Self-Help Devices ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {In this paper, a subject transfer framework is proposed for the classification of Electroencephalogram (EEG) signals in brain-computer interfaces (BCIs). This study introduces a modification of Common Spatial Pattern (CSP) for subject transfer BCIs, where similar characteristics are considered to transfer knowledge from other subjects׳ data. With this aim, we proposed a new approach based on Composite Local Temporal Correlation CSP, namely Composite LTCCSP with selected subjects, which considers the similarity between subjects using Frobenius distance. The performance of the proposed method is compared with different methods like traditional CSP, Composite CSP, LTCCSP and Composite LTCCSP. Experimental results have shown that our proposed method has increased the performance compared to all these different methods. Furthermore, our results suggest that it is worth emphasizing the data of subjects with similar characteristics in a subject transfer diagram. The suggested framework, as demonstrated by experimental results, can obtain a positive knowledge transfer for enhancing the performance of BCIs.},
}
@article {pmid26099149,
year = {2016},
author = {Hu, S and Wang, H and Zhang, J and Kong, W and Cao, Y and Kozma, R},
title = {Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {27},
number = {7},
pages = {1429-1444},
doi = {10.1109/TNNLS.2015.2441137},
pmid = {26099149},
issn = {2162-2388},
mesh = {*Brain-Computer Interfaces ; Cerebral Cortex ; Electroencephalography ; Humans ; },
abstract = {In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.},
}
@article {pmid26098896,
year = {2015},
author = {Freire, MA and Faber, J and Lemos, NA and Santos, JR and Cavalcanti, PF and Lima, RH and Morya, E},
title = {Distribution and Morphology of Calcium-Binding Proteins Immunoreactive Neurons following Chronic Tungsten Multielectrode Implants.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0130354},
pmid = {26098896},
issn = {1932-6203},
mesh = {Animals ; Brain Waves/physiology ; Brain-Computer Interfaces/adverse effects ; Calbindin 1/*metabolism ; Calbindin 2/*metabolism ; Electrodes, Implanted/*adverse effects ; Implants, Experimental/*adverse effects ; Male ; Microglia/metabolism ; Motor Cortex/physiology/surgery ; Parvalbumins/*metabolism ; Rats ; Rats, Wistar ; },
abstract = {The development of therapeutic approaches to improve the life quality of people suffering from different types of body paralysis is a current major medical challenge. Brain-machine interface (BMI) can potentially help reestablishing lost sensory and motor functions, allowing patients to use their own brain activity to restore sensorimotor control of paralyzed body parts. Chronic implants of multielectrodes, employed to record neural activity directly from the brain parenchyma, constitute the fundamental component of a BMI. However, before this technique may be effectively available to human clinical trials, it is essential to characterize its long-term impact on the nervous tissue in animal models. In the present study we evaluated how chronic implanted tungsten microelectrode arrays impact the distribution and morphology of interneurons reactive to calcium-binding proteins calbindin (CB), calretinin (CR) and parvalbumin (PV) across the rat's motor cortex. Our results revealed that chronic microelectrode arrays were well tolerated by the nervous tissue, with recordings remaining viable for up to 6 months after implantation. Furthermore, neither the morphology nor the distribution of inhibitory neurons were broadly impacted. Moreover, restricted microglial activation was observed on the implanted sites. On the whole, our results confirm and expand the notion that tungsten multielectrodes can be deemed as a feasible candidate to future human BMI studies.},
}
@article {pmid26097447,
year = {2015},
author = {Käthner, I and Kübler, A and Halder, S},
title = {Rapid P300 brain-computer interface communication with a head-mounted display.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {207},
pmid = {26097447},
issn = {1662-4548},
abstract = {Visual ERP (P300) based brain-computer interfaces (BCIs) allow for fast and reliable spelling and are intended as a muscle-independent communication channel for people with severe paralysis. However, they require the presentation of visual stimuli in the field of view of the user. A head-mounted display could allow convenient presentation of visual stimuli in situations, where mounting a conventional monitor might be difficult or not feasible (e.g., at a patient's bedside). To explore if similar accuracies can be achieved with a virtual reality (VR) headset compared to a conventional flat screen monitor, we conducted an experiment with 18 healthy participants. We also evaluated it with a person in the locked-in state (LIS) to verify that usage of the headset is possible for a severely paralyzed person. Healthy participants performed online spelling with three different display methods. In one condition a 5 × 5 letter matrix was presented on a conventional 22 inch TFT monitor. Two configurations of the VR headset were tested. In the first (glasses A), the same 5 × 5 matrix filled the field of view of the user. In the second (glasses B), single letters of the matrix filled the field of view of the user. The participant in the LIS tested the VR headset on three different occasions (glasses A condition only). For healthy participants, average online spelling accuracies were 94% (15.5 bits/min) using three flash sequences for spelling with the monitor and glasses A and 96% (16.2 bits/min) with glasses B. In one session, the participant in the LIS reached an online spelling accuracy of 100% (10 bits/min) using the glasses A condition. We also demonstrated that spelling with one flash sequence is possible with the VR headset for healthy users (mean: 32.1 bits/min, maximum reached by one user: 71.89 bits/min at 100% accuracy). We conclude that the VR headset allows for rapid P300 BCI communication in healthy users and may be a suitable display option for severely paralyzed persons.},
}
@article {pmid26093541,
year = {2015},
author = {van Helden, J and Weiskirchen, R},
title = {Performance of the two new fully automated anti-Müllerian hormone immunoassays compared with the clinical standard assay.},
journal = {Human reproduction (Oxford, England)},
volume = {30},
number = {8},
pages = {1918-1926},
doi = {10.1093/humrep/dev127},
pmid = {26093541},
issn = {1460-2350},
mesh = {Adult ; Anti-Mullerian Hormone/*blood ; Female ; Humans ; Immunoassay/*methods ; Infertility/blood ; Middle Aged ; Polycystic Ovary Syndrome/blood ; Young Adult ; },
abstract = {STUDY QUESTION: How do the two new fully automated anti-Müllerian hormone (AMH) assays released in September 2014 by two different diagnostic companies perform compared with the clinical standard assay, namely the AMH Gen II enzyme-linked immunosorbent assay (ELISA)?<br><br>SUMMARY ANSWER: Both fully automated AMH assays perform in a nearly identical fashion compared with the AMH Gen II assay, with a higher analytical sensitivity.<br><br>WHAT IS KNOWN ALREADY: Owing to the lack of standardization, the results of AMH ELISA assays are sometimes difficult to compare. The BCI AMH Gen II assay became the clinical reference assay over the last few years. Two newly developed fully automated, highly sensitive AMH immunoassays, based on the AMH Gen II antibody composition have become available since September 2014.<br><br>STUDY DESIGN, SIZE, DURATION: Previously characterized serum samples from 155 women were used to measure AMH with the three immunoassays, focusing on the aspect of predicting ovarian reserve.<br><br>Samples from 94 women with an unfilled desire for a child diagnosed as infertile/subfertile, 29 samples women with polycystic ovary syndrome and 32 women approaching menopause were included to the study. The precision and the linearity in dilutions of the two new AMH assays were determined and the assay results were compared with the clinical reference (the modified version of the BCI AMH Gen II assay) and to the antral follicle counts of the study participants. Cutoff values for the discrimination between each of two predefined groups were calculated using receiver operating characteristic analysis.<br><br>The performance evaluation of the fully automated AMH assays resulted in a within-run and intermediate precision of 0.9-1.9% and 2.5-6.5% with the one and 0.9-3.6% or 4.4-10.7% with the other immunoassay, respectively. Pearson's coefficient of correlation was 0.991 for the method comparison between both assays with a bias of 0.003 ng/ml and a slope of 0.97. The discrimination of the new immunoassays between subfertile women and women approaching menopause was significantly better compared with the BCI Gen II assay (87.5 versus 68.8%, P < 0.05).<br><br>Owing to the low number of study subjects in each group, the results have to be confirmed in further studies.<br><br>The findings of the study are in good agreement with studies that used the Ultra Sensitivite AMH and the pico AMH ELISA assays. The application of AMH measurement onto an automated immunoassay platform is a major step forward, allowing health care providers rapid access to the AMH result and facilitating the adoption of AMH measurement into daily clinical practice.<br><br>We declare no financial relationships or competing interests.},
}
@article {pmid26093326,
year = {2015},
author = {Dyson, M and Thomas, E and Casini, L and Burle, B},
title = {Online extraction and single trial analysis of regions contributing to erroneous feedback detection.},
journal = {NeuroImage},
volume = {121},
number = {},
pages = {146-158},
doi = {10.1016/j.neuroimage.2015.06.041},
pmid = {26093326},
issn = {1095-9572},
support = {241077/ERC_/European Research Council/International ; },
mesh = {Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Executive Function/*physiology ; *Feedback ; Humans ; Imagination ; Motor Activity ; Psychomotor Performance/*physiology ; },
abstract = {Understanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain-Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity.},
}
@article {pmid26091773,
year = {2015},
author = {Klutz, S and Magnus, J and Lobedann, M and Schwan, P and Maiser, B and Niklas, J and Temming, M and Schembecker, G},
title = {Developing the biofacility of the future based on continuous processing and single-use technology.},
journal = {Journal of biotechnology},
volume = {213},
number = {},
pages = {120-130},
doi = {10.1016/j.jbiotec.2015.06.388},
pmid = {26091773},
issn = {1873-4863},
mesh = {Antibodies, Monoclonal/*biosynthesis ; *Biotechnology ; Chromatography ; *Drug Industry ; *Facility Design and Construction ; Filtration ; Hydrogen-Ion Concentration ; Staphylococcal Protein A/chemistry ; Virus Inactivation ; },
abstract = {To maintain or strengthen their market position, biopharmaceutical producers have to adapt their production facilities to a drastically changed market environment. Contrary to currently used large scale batch-wise operated production facilities, where stainless steel equipment is widely applied, small scale and flexible production processes are desired. Consequently, the concept of the "biofacility of the future" has been developed, which combines the attributes fast, flexible, small, inexpensive and sustainable. Four design principles build the facility's basis and are presented within this work: continuous processing, 100% single-use equipment, closed processing and adopting the ballroom concept. However, no publication presents a completely continuously operated platform process for the production of monoclonal antibodies up to now. Therefore, this work establishes the proof of concept regarding continuous antibody manufacturing. A pilot plant for the production of monoclonal antibodies has been built 100% in single-use equipment. It was operated fully continuous and automated in the upstream and the downstream part. The concepts that allow continuously operating the pilot plant are presented within this work, i.e., continuously operated filtration, continuously operated viral inactivation, continuously operated chromatography and a continuously operated formulation. Analytics showed that the produced product was within specification limits of industrial bulk drug substances.},
}
@article {pmid26090799,
year = {2015},
author = {Bashashati, H and Ward, RK and Birch, GE and Bashashati, A},
title = {Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0129435},
pmid = {26090799},
issn = {1932-6203},
mesh = {Algorithms ; Area Under Curve ; *Brain-Computer Interfaces ; Datasets as Topic ; Discriminant Analysis ; Electroencephalography ; Humans ; *Psychomotor Performance ; Reproducibility of Results ; Support Vector Machine ; },
abstract = {A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems.},
}
@article {pmid26087308,
year = {2015},
author = {Li, Q and Liu, S and Li, J and Bai, O},
title = {Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0130325},
pmid = {26087308},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Color ; *Event-Related Potentials, P300 ; Face ; Female ; Humans ; Male ; Photic Stimulation ; *Recognition, Psychology ; User-Computer Interface ; Writing ; Young Adult ; },
abstract = {BACKGROUND: A recent study showed improved performance of the P300-speller when the flashing row or column was overlaid with translucent pictures of familiar faces (FF spelling paradigm). However, the performance of the P300-speller is not yet satisfactory due to its low classification accuracy and information transfer rate.<br><br>OBJECTIVE: To investigate whether P300-speller performance is further improved when the chromatic property and the FF spelling paradigm are combined.<br><br>METHODS: We proposed a new spelling paradigm in which the flashing row or column is overlaid with translucent green pictures of familiar faces (GFF spelling paradigm). We analyzed the ERP waveforms elicited by the FF and proposed GFF spelling paradigms and compared P300-speller performance between the two paradigms.<br><br>RESULTS: Significant differences in the amplitudes of four ERP components (N170, VPP, P300, and P600f) were observed between both spelling paradigms. Compared to the FF spelling paradigm, the GFF spelling paradigm elicited ERP waveforms of higher amplitudes and resulted in improved P300-speller performance.<br><br>CONCLUSIONS: Combining the chromatic property (green color) and the FF spelling paradigm led to better classification accuracy and an increased information transfer rate. These findings demonstrate a promising new approach for improving the performance of the P300-speller.},
}
@article {pmid26086029,
year = {2014},
author = {Cassady, K and You, A and Doud, A and He, B},
title = {The impact of mind-body awareness training on the early learning of a brain-computer interface.},
journal = {Technology},
volume = {2},
number = {3},
pages = {254-260},
pmid = {26086029},
issn = {2339-5478},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EY023101/EY/NEI NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) systems allow users to interact with their environment by bypassing muscular control to tap directly into the users' thoughts. In the present study, we investigate the role of prior experience with yoga and meditation, examples of formalized mind-body awareness training (MBAT), in learning to use a one-dimensional sensorimotor rhythm based BCI. Thirty-six human subjects volunteered to participate in two different cohorts based on past experience with MBAT - experienced MBAT practitioners and controls. All subjects participated in three BCI experiments to achieve competency in controlling the BCI system. The MBAT cohort achieved BCI competency significantly faster than the control cohort. In addition, the MBAT cohort demonstrated enhanced ability to control the system on various measures of BCI performance and improved significantly more over time when compared to control. Our work provides insight into valuable strategies for reducing barriers to BCI fluency that limit the more widespread use of these systems.},
}
@article {pmid26083683,
year = {2015},
author = {Ron-Angevin, R and Varona-Moya, S and da Silva-Sauer, L},
title = {Initial test of a T9-like P300-based speller by an ALS patient.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046023},
doi = {10.1088/1741-2560/12/4/046023},
pmid = {26083683},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology/rehabilitation ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Language ; Male ; Pilot Projects ; Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; *Visual Perception ; Word Processing/methods ; Young Adult ; },
abstract = {OBJECTIVE: Visual P300-based brain-computer interface spellers offer a useful communication channel for locked-in patients, who are completely dependent in their daily lives. One of the research goals for these systems is to achieve greater communication rates by means of modifying some features of their interfaces, e.g., reducing the matrix size. However, such modifications may not work well with disabled end-users, such as patients of amyotrophic lateral sclerosis (ALS), due to a supposed reduction of their cognitive resources. The purpose of the present study was to provide a proof of concept that ALS patients could efficiently use a P300-based speller with a 4 × 3 symbol matrix based on the T9 interface developed for mobile phones.<br><br>APPROACH: We conducted an experiment with a sample of 11 able-bodied participants and one locked-in patient with ALS. All participants tested our T9-like visual P300-based speller and also two different 7 &#xd7; 6 matrix spellers based on Farwell and Donchin's classic proposal-one of them included a word predictor system like the T9-like speller did.<br><br>MAIN RESULTS: The performance analyses indicated that the locked-in patient benefited from using a reduced matrix size as much as healthy users did, spelling words almost 1.6 times faster and equally accurately when using the T9-like speller than when using the alternative spellers.<br><br>SIGNIFICANCE: Due to counting on only one locked-in patient, the current work constitutes a feasibility study. The actual usability of systems such as the one proposed in this paper should be determined by means of studies with a greater number of end-users in real-life conditions.},
}
@article {pmid26083480,
year = {2015},
author = {Xia, B and Maysam, O and Veser, S and Cao, L and Li, J and Jia, J and Xie, H and Birbaumer, N},
title = {A combination strategy based brain-computer interface for two-dimensional movement control.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046021},
doi = {10.1088/1741-2560/12/4/046021},
pmid = {26083480},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Feedback, Physiological/physiology ; Female ; Humans ; Male ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Word Processing/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Two-dimensional (2D) movement control is an important issue in brain-computer interfaces (BCIs) research because being able to move, for example, a cursor with the brain will enable patients with motor disabilities to control their environment. However, it is still a challenge to continuously control 2D movement with a non-invasive BCI system. In this paper, we developed a 2D cursor control with motor imagery BCI tasks allowing users to move a cursor to any position by using a combination strategy. With this strategy, a user can combine multiple motor imagery tasks, alternatively or simultaneously, to control 2D movements.<br><br>APPROACH: After a training session, six participants took part in the first control strategy experiment (the center-out experiment) to verify the effectiveness of the cursor control. Three of the six participants performed an additional experiment, in which they were required to move the cursor to hit five targets in a given sequence.<br><br>MAIN RESULTS: The average hit rate was more than [Formula: see text] and the trajectories were close to the shortest path. The average hit rate was more than 95.6% and the trajectories were close to the shortest path in the center-out experiment. In the additional experiment, three participants achieved a 100% hit rate with a short trajectory.<br><br>SIGNIFICANCE: The results demonstrated that users were able to effectively control the 2D movement using the proposed strategy. The present system may be used as a tool to interact with the external world.},
}
@article {pmid26082705,
year = {2015},
author = {Myrden, A and Chau, T},
title = {Effects of user mental state on EEG-BCI performance.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {308},
pmid = {26082705},
issn = {1662-5161},
abstract = {Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states-fatigue, frustration, and attention-on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.},
}
@article {pmid26078350,
year = {2015},
author = {Hughes, MA and Shipston, MJ and Murray, AF},
title = {Towards a 'siliconeural computer': technological successes and challenges.},
journal = {Philosophical transactions. Series A, Mathematical, physical, and engineering sciences},
volume = {373},
number = {2046},
pages = {},
doi = {10.1098/rsta.2014.0217},
pmid = {26078350},
issn = {1364-503X},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Cell Membrane/metabolism ; Computer Simulation ; Computers ; Electronics ; Humans ; Materials Testing ; Models, Neurological ; Nerve Net ; Neural Networks, Computer ; Neurons/metabolism/*physiology ; Polymers/chemistry ; Silicon/chemistry ; Silicon Dioxide/chemistry ; Xylenes/chemistry ; },
abstract = {Electronic signals govern the function of both nervous systems and computers, albeit in different ways. As such, hybridizing both systems to create an iono-electric brain-computer interface is a realistic goal; and one that promises exciting advances in both heterotic computing and neuroprosthetics capable of circumventing devastating neuropathology. 'Neural networks' were, in the 1980s, viewed naively as a potential panacea for all computational problems that did not fit well with conventional computing. The field bifurcated during the 1990s into a highly successful and much more realistic machine learning community and an equally pragmatic, biologically oriented 'neuromorphic computing' community. Algorithms found in nature that use the non-synchronous, spiking nature of neuronal signals have been found to be (i) implementable efficiently in silicon and (ii) computationally useful. As a result, interest has grown in techniques that could create mixed 'siliconeural' computers. Here, we discuss potential approaches and focus on one particular platform using parylene-patterned silicon dioxide.},
}
@article {pmid26076696,
year = {2015},
author = {Bortole, M and Venkatakrishnan, A and Zhu, F and Moreno, JC and Francisco, GE and Pons, JL and Contreras-Vidal, JL},
title = {The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {54},
pmid = {26076696},
issn = {1743-0003},
support = {UL1 TR000371/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Biomechanical Phenomena ; *Exoskeleton Device ; Gait Disorders, Neurologic/*rehabilitation ; Humans ; Lower Extremity ; Male ; Middle Aged ; Paresis/etiology/rehabilitation ; Patient Safety ; Physical Therapy Modalities ; Pilot Projects ; Prosthesis Design ; *Robotics ; Stroke/complications ; Stroke Rehabilitation ; Survivors ; Treatment Outcome ; },
abstract = {BACKGROUND: Stroke significantly affects thousands of individuals annually, leading to considerable physical impairment and functional disability. Gait is one of the most important activities of daily living affected in stroke survivors. Recent technological developments in powered robotics exoskeletons can create powerful adjunctive tools for rehabilitation and potentially accelerate functional recovery. Here, we present the development and evaluation of a novel lower limb robotic exoskeleton, namely H2 (Technaid S.L., Spain), for gait rehabilitation in stroke survivors.<br><br>METHODS: H2 has six actuated joints and is designed to allow intensive overground gait training. An assistive gait control algorithm was developed to create a force field along a desired trajectory, only applying torque when patients deviate from the prescribed movement pattern. The device was evaluated in 3 hemiparetic stroke patients across 4 weeks of training per individual (approximately 12 sessions). The study was approved by the Institutional Review Board at the University of Houston. The main objective of this initial pre-clinical study was to evaluate the safety and usability of the exoskeleton. A Likert scale was used to measure patient's perception about the easy of use of the device.<br><br>RESULTS: Three stroke patients completed the study. The training was well tolerated and no adverse events occurred. Early findings demonstrate that H2 appears to be safe and easy to use in the participants of this study. The overground training environment employed as a means to enhance active patient engagement proved to be challenging and exciting for patients. These results are promising and encourage future rehabilitation training with a larger cohort of patients.<br><br>CONCLUSIONS: The developed exoskeleton enables longitudinal overground training of walking in hemiparetic patients after stroke. The system is robust and safe when applied to assist a stroke patient performing an overground walking task. Such device opens the opportunity to study means to optimize a rehabilitation treatment that can be customized for individuals.<br><br>TRIAL REGISTRATION: This study was registered at ClinicalTrials.gov (https://clinicaltrials.gov/show/NCT02114450).},
}
@article {pmid26074763,
year = {2015},
author = {van Erp, JB and Brouwer, AM and Zander, TO},
title = {Editorial: Using neurophysiological signals that reflect cognitive or affective state.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {193},
pmid = {26074763},
issn = {1662-4548},
}
@article {pmid26073720,
year = {2016},
author = {Sprinzl, GM and Wolf-Magele, A},
title = {The Bonebridge Bone Conduction Hearing Implant: indication criteria, surgery and a systematic review of the literature.},
journal = {Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology &amp; Cervico-Facial Surgery},
volume = {41},
number = {2},
pages = {131-143},
doi = {10.1111/coa.12484},
pmid = {26073720},
issn = {1749-4486},
mesh = {Bone Conduction/*physiology ; *Hearing Aids ; Hearing Loss, Mixed Conductive-Sensorineural/physiopathology/*surgery ; Humans ; Prosthesis Design ; Prosthesis Implantation ; },
abstract = {BACKGROUND: Hearing aids and implants employing bone conduction (BC) stimulation have a long tradition in the treatment of conductive or mixed hearing loss, with their indications being extended in the 2000s to include single-sided deafness (SSD). Existing percutaneous bone conduction implants (BCI) provide significant audiological gain but are associated with a high rate of complications. This has led to the development of passive transcutaneous BCIs; however, audiological benefit may be compromised. An active transcutaneous BCI, the Bonebridge, was recently introduced and first implanted in 2011 as part of a clinical trial.<br><br>OBJECTIVE OF REVIEW: To introduce and assess the safety and effectiveness of the Bonebridge for individuals with conductive or mixed hearing loss, and SSD.<br><br>TYPE OF REVIEW: Systematic review.<br><br>SEARCH STRATEGY: The Cochrane Library, PubMed and OVIDSP (MEDLINE) and EMBASE were searched to identify papers on the Bonebridge published as of June 2014. No exclusion criteria were set on publication language, study design or reported outcomes. The literature found was supplemented by presentations from relevant conferences.<br><br>EVALUATION METHOD: Study selection, data extraction and study quality assessment were carried out by a single reviewer with any uncertainties resolved with consulting a second reviewer. Studies were synthesised narratively and results were tabulated.<br><br>RESULTS: A total of 29 studies, 17 published and 12 presentations, were identified. The highest quality evidence was from three single-arm trials. In those assessing the safety of implantation, 6 of 117 patients experienced a minor adverse event with superficial revision surgery being required in one case. Studies demonstrated improved hearing thresholds and speech recognition with the Bonebridge when compared to no aiding in adults and children with either type of hearing loss. This was reflected in high device satisfaction rates. Data collected in the second year of device use further suggest the benefit to remain constant.<br><br>CONCLUSION: The transcutaneous BCI system Bonebridge provides a valuable and stable audiological benefit to patients suffering from conductive or mixed hearing loss and SSD. With its active transcutaneous design, the Bonebridge offers a lower complication rate to percutaneous systems and higher and more reliable hearing gain compared to other transcutaneous or percutaneous systems. Moreover, the fast activation of the implant system enables the recipient of the system to benefit in a short time frame postoperatively from the intervention.},
}
@article {pmid26071402,
year = {2015},
author = {Engdahl, SM and Christie, BP and Kelly, B and Davis, A and Chestek, CA and Gates, DH},
title = {Surveying the interest of individuals with upper limb loss in novel prosthetic control techniques.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {12},
number = {},
pages = {53},
pmid = {26071402},
issn = {1743-0003},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; K12HD073945/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; Amputees ; *Artificial Limbs ; Brain-Computer Interfaces ; Cerebral Cortex ; Educational Status ; Female ; Hand ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/innervation ; Neural Prostheses ; Patient Satisfaction ; Peripheral Nerves ; Prosthesis Design ; Surveys and Questionnaires ; *Upper Extremity ; User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: Novel techniques for the control of upper limb prostheses may allow users to operate more complex prostheses than those that are currently available. Because many of these techniques are surgically invasive, it is important to understand whether individuals with upper limb loss would accept the associated risks in order to use a prosthesis.<br><br>METHODS: An online survey of individuals with upper limb loss was conducted. Participants read descriptions of four prosthetic control techniques. One technique was noninvasive (myoelectric) and three were invasive (targeted muscle reinnervation, peripheral nerve interfaces, cortical interfaces). Participants rated how likely they were to try each technique if it offered each of six different functional features. They also rated their general interest in each of the six features. A two-way repeated measures analysis of variance with Greenhouse-Geisser corrections was used to examine the effect of the technique type and feature on participants' interest in each technique.<br><br>RESULTS: Responses from 104 individuals were analyzed. Many participants were interested in trying the techniques - 83 % responded positively toward myoelectric control, 63 % toward targeted muscle reinnervation, 68 % toward peripheral nerve interfaces, and 39 % toward cortical interfaces. Common concerns about myoelectric control were weight, cost, durability, and difficulty of use, while the most common concern about the invasive techniques was surgical risk. Participants expressed greatest interest in basic prosthesis features (e.g., opening and closing the hand slowly), as opposed to advanced features like fine motor control and touch sensation.<br><br>CONCLUSIONS: The results of these investigations may be used to inform the development of future prosthetic technologies that are appealing to individuals with upper limb loss.},
}
@article {pmid26069961,
year = {2015},
author = {Gonzalez-Vargas, J and Dosen, S and Amsuess, S and Yu, W and Farina, D},
title = {Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0127528},
pmid = {26069961},
issn = {1932-6203},
mesh = {Adult ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electromyography ; Female ; Hand/*physiology ; Hand Strength ; Humans ; Male ; *Man-Machine Systems ; Middle Aged ; *User-Computer Interface ; },
abstract = {Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.},
}
@article {pmid26068536,
year = {2015},
author = {Taghavi, H and Håkansson, B and Reinfeldt, S and Eeg-Olofsson, M and Jansson, KJ and Håkansson, E and Nasri, B},
title = {Technical design of a new bone conduction implant (BCI) system.},
journal = {International journal of audiology},
volume = {54},
number = {10},
pages = {736-744},
doi = {10.3109/14992027.2015.1051665},
pmid = {26068536},
issn = {1708-8186},
mesh = {Acoustic Stimulation ; Audiometry, Speech ; Auditory Threshold ; *Bone Conduction ; *Hearing Aids ; Hearing Loss, Sensorineural/diagnosis/physiopathology/psychology/*rehabilitation ; Humans ; Materials Testing ; Models, Anatomic ; Persons with Hearing Disabilities/psychology/*rehabilitation ; Prosthesis Design ; *Prosthesis Implantation ; Signal Processing, Computer-Assisted ; Speech Intelligibility ; *Speech Perception ; },
abstract = {OBJECTIVE: The objective of this study is to describe the technical design and verify the technical performance of a new bone conduction implant (BCI) system.<br><br>DESIGN: The BCI consists of an external audio processor and an implanted unit called the bridging bone conductor. These two units use an inductive link to communicate with each other through the intact skin in order to drive an implanted transducer.<br><br>STUDY SAMPLE: In this study, the design of the full BCI system has been described and verified on a skull simulator and on real patients.<br><br>RESULTS: It was found that the maximum output force (peak 107 dB re 1 &#x3bc;N) of the BCI is robust for skin thickness range of 2-8 mm and that the total harmonic distortion is below 8% in the speech frequency range for 70 dB input sound pressure level. The current consumption is 7.5 mA, which corresponds to 5-7 days use with a single battery.<br><br>CONCLUSIONS: This study shows that the BCI is a robust design that gives a sufficiently high output and an excellent sound quality for the hearing rehabilitation of indicated patients.},
}
@article {pmid26067346,
year = {2015},
author = {Porbadnigk, AK and Görnitz, N and Sannelli, C and Binder, A and Braun, M and Kloft, M and Müller, KR},
title = {Extracting latent brain states--Towards true labels in cognitive neuroscience experiments.},
journal = {NeuroImage},
volume = {120},
number = {},
pages = {225-253},
doi = {10.1016/j.neuroimage.2015.05.078},
pmid = {26067346},
issn = {1095-9572},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Cognitive Neuroscience/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual ; *Unsupervised Machine Learning ; Young Adult ; },
abstract = {Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N=20 participants).},
}
@article {pmid26066840,
year = {2015},
author = {Evans, N and Gale, S and Schurger, A and Blanke, O},
title = {Visual Feedback Dominates the Sense of Agency for Brain-Machine Actions.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0130019},
pmid = {26066840},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Motion Perception/*physiology ; Movement/*physiology ; },
abstract = {Recent advances in neuroscience and engineering have led to the development of technologies that permit the control of external devices through real-time decoding of brain activity (brain-machine interfaces; BMI). Though the feeling of controlling bodily movements (sense of agency; SOA) has been well studied and a number of well-defined sensorimotor and cognitive mechanisms have been put forth, very little is known about the SOA for BMI-actions. Using an on-line BMI, and verifying that our subjects achieved a reasonable level of control, we sought to describe the SOA for BMI-mediated actions. Our results demonstrate that discrepancies between decoded neural activity and its resultant real-time sensory feedback are associated with a decrease in the SOA, similar to SOA mechanisms proposed for bodily actions. However, if the feedback discrepancy serves to correct a poorly controlled BMI-action, then the SOA can be high and can increase with increasing discrepancy, demonstrating the dominance of visual feedback on the SOA. Taken together, our results suggest that bodily and BMI-actions rely on common mechanisms of sensorimotor integration for agency judgments, but that visual feedback dominates the SOA in the absence of overt bodily movements or proprioceptive feedback, however erroneous the visual feedback may be.},
}
@article {pmid26061188,
year = {2015},
author = {Speier, W and Arnold, CW and Deshpande, A and Knall, J and Pouratian, N},
title = {Incorporating advanced language models into the P300 speller using particle filtering.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046018},
pmid = {26061188},
issn = {1741-2552},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T32 EB016640/EB/NIBIB NIH HHS/United States ; K23EB014326/EB/NIBIB NIH HHS/United States ; T32-EB016640/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Simulation ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Machine Learning ; Models, Statistical ; *Natural Language Processing ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; Word Processing/methods ; },
abstract = {OBJECTIVE: The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity.<br><br>APPROACH: Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model.<br><br>MAIN RESULT: This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method.<br><br>SIGNIFICANCE: These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.},
}
@article {pmid26054072,
year = {2016},
author = {Zhang, R and Li, Y and Yan, Y and Zhang, H and Wu, S and Yu, T and Gu, Z},
title = {Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {24},
number = {1},
pages = {128-139},
doi = {10.1109/TNSRE.2015.2439298},
pmid = {26054072},
issn = {1558-0210},
mesh = {Adult ; *Brain-Computer Interfaces ; *Ecosystem ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Female ; Humans ; Machine Learning ; Male ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Robotics/*instrumentation ; Sensitivity and Specificity ; Spatial Navigation ; *Wheelchairs ; Young Adult ; },
abstract = {The concept of controlling a wheelchair using brain signals is promising. However, the continuous control of a wheelchair based on unstable and noisy electroencephalogram signals is unreliable and generates a significant mental burden for the user. A feasible solution is to integrate a brain-computer interface (BCI) with automated navigation techniques. This paper presents a brain-controlled intelligent wheelchair with the capability of automatic navigation. Using an autonomous navigation system, candidate destinations and waypoints are automatically generated based on the existing environment. The user selects a destination using a motor imagery (MI)-based or P300-based BCI. According to the determined destination, the navigation system plans a short and safe path and navigates the wheelchair to the destination. During the movement of the wheelchair, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be substantially alleviated. Furthermore, our system can adapt to changes in the environment. Two experiments based on MI and P300 were conducted to demonstrate the effectiveness of our system.},
}
@article {pmid26051753,
year = {2016},
author = {Baykara, E and Ruf, CA and Fioravanti, C and Käthner, I and Simon, N and Kleih, SC and Kübler, A and Halder, S},
title = {Effects of training and motivation on auditory P300 brain-computer interface performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {1},
pages = {379-387},
doi = {10.1016/j.clinph.2015.04.054},
pmid = {26051753},
issn = {1872-8952},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Cortex/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Motivation/*physiology ; Young Adult ; },
abstract = {OBJECTIVES: Brain-computer interface (BCI) technology aims at helping end-users with severe motor paralysis to communicate with their environment without using the natural output pathways of the brain. For end-users in complete paralysis, loss of gaze control may necessitate non-visual BCI systems. The present study investigated the effect of training on performance with an auditory P300 multi-class speller paradigm. For half of the participants, spatial cues were added to the auditory stimuli to see whether performance can be further optimized. The influence of motivation, mood and workload on performance and P300 component was also examined.<br><br>METHODS: In five sessions, 16 healthy participants were instructed to spell several words by attending to animal sounds representing the rows and columns of a 5 &#xd7; 5 letter matrix.<br><br>RESULTS: 81% of the participants achieved an average online accuracy of &#x2a7e; 70%. From the first to the fifth session information transfer rates increased from 3.72 bits/min to 5.63 bits/min. Motivation significantly influenced P300 amplitude and online ITR. No significant facilitative effect of spatial cues on performance was observed.<br><br>CONCLUSIONS: Training improves performance in an auditory BCI paradigm. Motivation influences performance and P300 amplitude.<br><br>SIGNIFICANCE: The described auditory BCI system may help end-users to communicate independently of gaze control with their environment.},
}
@article {pmid26048549,
year = {2015},
author = {Chun, H and Chung, TD},
title = {Iontronics.},
journal = {Annual review of analytical chemistry (Palo Alto, Calif.)},
volume = {8},
number = {},
pages = {441-462},
doi = {10.1146/annurev-anchem-071114-040202},
pmid = {26048549},
issn = {1936-1335},
mesh = {Animals ; *Electronics ; Humans ; Ion Transport ; *Ions ; },
abstract = {Iontronics is an emerging technology based on sophisticated control of ions as signal carriers that bridges solid-state electronics and biological system. It is found in nature, e.g., information transduction and processing of brain in which neurons are dynamically polarized or depolarized by ion transport across cell membranes. It suggests the operating principle of aqueous circuits made of predesigned structures and functional materials that characteristically interact with ions of various charge, mobility, and affinity. Working in aqueous environments, iontronic devices offer profound implications for biocompatible or biodegradable logic circuits for sensing, ecofriendly monitoring, and brain-machine interfacing. Furthermore, iontronics based on multi-ionic carriers sheds light on futuristic biomimic information processing. In this review, we overview the historical achievements and the current state of iontronics with regard to theory, fabrication, integration, and applications, concluding with comments on where the technology may advance.},
}
@article {pmid26046075,
year = {2015},
author = {Chaudhary, U and Birbaumer, N},
title = {Communication in locked-in state after brainstem stroke: a brain-computer-interface approach.},
journal = {Annals of translational medicine},
volume = {3},
number = {Suppl 1},
pages = {S29},
pmid = {26046075},
issn = {2305-5839},
}
@article {pmid26044230,
year = {2015},
author = {Wang, PQ and Ding, ZG and Zhang, GB and Wang, Y and Liu, JZ},
title = {A study on lesion pattern of bilateral cerebellar infarct.},
journal = {European review for medical and pharmacological sciences},
volume = {19},
number = {10},
pages = {1845-1851},
pmid = {26044230},
issn = {2284-0729},
mesh = {Aged ; Cerebellum/metabolism/*pathology ; Cerebral Arteries/metabolism/*pathology ; Cerebral Infarction/complications/*diagnosis/metabolism ; Cerebrovascular Circulation ; *Diffusion Magnetic Resonance Imaging ; Female ; Humans ; Male ; Middle Aged ; Stroke/*diagnosis/etiology/metabolism ; },
abstract = {OBJECTIVE: To explore the lesion patterns and stroke mechanism of the acute bilateral cerebellar infarct.<br><br>PATIENTS AND METHODS: Patients admitted to Xiangyang Hospital with acute cerebellar infarcts, confirmed by diffusion-weighted imaging (DWI), were investigated. Patients were divided into two groups by lesions: unilateral cerebellar infarct (UCI) and bilateral cerebellar infarct (BCI). The demographic features, involved territories and concomitant lesions outside the cerebellum (CLOC). The causes were analyzed.<br><br>RESULTS: Amongst the 115 patients hospitalized with posterior circulation cerebral infarct due to acute stroke, 56 patients had cerebellar infarct. There were 36 (64.3%) cases of unilateral cerebellar infarct and 20 (35.7%) cases of the BCI. The baseline information shows that stroke history (p = 0.002), fibrinogen (p = 0.036) and admission NIHSS score (M) (p = 0.001) for the BCI group are higher than the unilateral cerebellar infarct group. The incidence rate of cerebellar infarct in a posterior inferior cerebellar artery (PICA) blood supplying territory is the highest by divisions of vascular distribution. Unilateral cerebellar infarct occurs more often (p = 0.006); BCI is more common in PICA+SCA blood supplying territory (p = 0.004). The incidence rate of BCI merged with CLOC is much higher than the unilateral cerebellar infarct (p = 0.002). Merged infratentorial lesions are more common (p = 0.022) than BCI with atherosclerosis (p = 0.041). Offending artery diseases are mainly in the V4 segment of the vertebral artery, and in the severe stenosis or occlusion of V4 and BA junction.<br><br>CONCLUSIONS: BCI was frequently involved in the PICA + SCA territory. Our results support the fact that embolism resulted from large artery atherosclerosis is the important stroke mechanism in the BCI.},
}
@article {pmid26042003,
year = {2015},
author = {Zehr, EP},
title = {Future think: cautiously optimistic about brain augmentation using tissue engineering and machine interface.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {72},
pmid = {26042003},
issn = {1662-5137},
}
@article {pmid26042002,
year = {2015},
author = {Benyamini, M and Zacksenhouse, M},
title = {Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {71},
pmid = {26042002},
issn = {1662-5137},
abstract = {Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.},
}
@article {pmid26041930,
year = {2015},
author = {Gulati, T and Won, SJ and Ramanathan, DS and Wong, CC and Bodepudi, A and Swanson, RA and Ganguly, K},
title = {Robust neuroprosthetic control from the stroke perilesional cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {22},
pages = {8653-8661},
pmid = {26041930},
issn = {1529-2401},
support = {R01 NS081149/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Analysis of Variance ; Animals ; Brain-Computer Interfaces ; Male ; Motor Cortex/pathology/*physiopathology ; Motor Skills/*physiology ; Neurons/*physiology ; Rats ; Rats, Long-Evans ; Stroke/*pathology ; User-Computer Interface ; },
abstract = {Intracortical brain-machine interfaces (BMIs) may eventually restore function in those with motor disability after stroke. However, current research into the development of intracortical BMIs has focused on subjects with largely intact cortical structures, such as those with spinal cord injury. Although the stroke perilesional cortex (PLC) has been hypothesized as a potential site for a BMI, it remains unclear whether the injured motor cortical network can support neuroprosthetic control directly. Using chronic electrophysiological recordings in a rat stroke model, we demonstrate here the PLC's capacity for neuroprosthetic control and physiological plasticity. We initially found that the perilesional network demonstrated abnormally increased slow oscillations that also modulated neural firing. Despite these striking abnormalities, neurons in the perilesional network could be modulated volitionally to learn neuroprosthetic control. The rate of learning was surprisingly similar regardless of the electrode distance from the stroke site and was not significantly different from intact animals. Moreover, neurons achieved similar task-related modulation and, as an ensemble, formed cell assemblies with learning. Such control was even achieved in animals with poor motor recovery, suggesting that neuroprosthetic control is possible even in the absence of motor recovery. Interestingly, achieving successful control also reduced locking to abnormal oscillations significantly. Our results thus suggest that, despite the disrupted connectivity in the PLC, it may serve as an effective target for neuroprosthetic control in those with poor motor recovery after stroke.},
}
@article {pmid26041914,
year = {2015},
author = {Waldert, S and Vigneswaran, G and Philipp, R and Lemon, RN and Kraskov, A},
title = {Modulation of the Intracortical LFP during Action Execution and Observation.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {22},
pages = {8451-8461},
pmid = {26041914},
issn = {1529-2401},
support = {/WT_/Wellcome Trust/United Kingdom ; 102849/Z/13/Z/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Action Potentials/*physiology ; Animals ; Electroencephalography ; Electromyography ; Evoked Potentials, Motor/*physiology ; Hand Strength ; Macaca mulatta ; Male ; Mirror Neurons/*physiology ; Motor Cortex/*cytology ; Movement/*physiology ; Observation ; Psychomotor Performance ; Reaction Time/physiology ; },
abstract = {The activity of mirror neurons in macaque ventral premotor cortex (PMv) and primary motor cortex (M1) is modulated by the observation of another's movements. This modulation could underpin well documented changes in EEG/MEG activity indicating the existence of a mirror neuron system in humans. Because the local field potential (LFP) represents an important link between macaque single neuron and human noninvasive studies, we focused on mirror properties of intracortical LFPs recorded in the PMv and M1 hand regions in two macaques while they reached, grasped and held different objects, or observed the same actions performed by an experimenter. Upper limb EMGs were recorded to control for covert muscle activity during observation.The movement-related potential (MRP), investigated as intracortical low-frequency LFP activity (<9 Hz), was modulated in both M1 and PMv, not only during action execution but also during action observation. Moreover, the temporal LFP modulations during execution and observation were highly correlated in both cortical areas. Beta power in both PMv and M1 was clearly modulated in both conditions. Although the MRP was detected only during dynamic periods of the task (reach/grasp/release), beta decreased during dynamic and increased during static periods (hold).Comparison of LFPs for different grasps provided evidence for partially nonoverlapping networks being active during execution and observation, which might be related to different inputs to motor areas during these conditions. We found substantial information about grasp in the MRP corroborating its suitability for brain-machine interfaces, although information about grasp was generally low during action observation.},
}
@article {pmid26039315,
year = {2015},
author = {Santaliestra-Pasías, AM and Mouratidou, T and Reisch, L and Pigeot, I and Ahrens, W and Mårild, S and Molnár, D and Siani, A and Sieri, S and Tornatiris, M and Veidebaum, T and Verbestel, V and De Bourdeaudhuij, I and Moreno, LA},
title = {Clustering of lifestyle behaviours and relation to body composition in European children. The IDEFICS study.},
journal = {European journal of clinical nutrition},
volume = {69},
number = {7},
pages = {811-816},
pmid = {26039315},
issn = {1476-5640},
mesh = {Body Composition ; Body Mass Index ; Child ; *Child Behavior/ethnology ; *Child Nutritional Physiological Phenomena/ethnology ; Child, Preschool ; Cluster Analysis ; Cross-Sectional Studies ; Diet/*adverse effects/ethnology ; Europe/epidemiology ; Female ; Humans ; *Life Style/ethnology ; Male ; *Motor Activity ; Nutrition Surveys ; Overweight/epidemiology/ethnology/*etiology ; Parents ; Pediatric Obesity/epidemiology/ethnology/*etiology ; Risk Factors ; Sedentary Behavior/ethnology ; Waist Circumference ; },
abstract = {BACKGROUND: Dietary patterns, physical activity (PA) and sedentary behaviours are some of the main behavioural determinants of obesity; their combined influence in children has been addressed in a limited number of studies.<br><br>SUBJECTS/METHODS: Children (16,228) aged 2-9 years old from eight European countries participated in the baseline survey of the IDEFICS study. A subsample of 11,674 children (50.8% males) were included in the present study. Children's food and beverage consumption (fruit and vegetables (F&amp;V) and sugar-sweetened beverages (SSBs)), PA and sedentary behaviours were assessed via parental questionnaires. Sex-specific cluster analysis was applied to identify behavioural clusters. Analysis of covariance and logistic regression were applied to examine the association between behavioural clusters and body composition indicators (BCIs).<br><br>RESULTS: Six behavioural clusters were identified (C1-C6) both in boys and girls. In both sexes, clusters characterised by high level of PA (C1 and C3) included a large proportion of older children, whereas clusters characterised by low SSB consumption (C5 and C6) included a large proportion of younger children. Significant associations between derived clusters and BCI were observed only in boys; those boys in the cluster with the highest time spent in sedentary activities and low PA had increased odds of having a body mass index z-score (odds ratio (OR)=1.33; 95% confidence interval (CI)=(1.01, 1.74)) and a waist circumference z-score (OR=1.41; 95%CI=(1.06, 1.86)) greater than one.<br><br>CONCLUSION: Clusters characterised by high sedentary behaviour, low F&amp;V and SSB consumption and low PA turned out to be the most obesogenic factors in this sample of European children.},
}
@article {pmid26039073,
year = {2015},
author = {Chang, XY and Chen, BM and Liu, G and Zhou, T and Jia, XR and Peng, SL},
title = {Effects of climate change on plant population growth rate and community composition change.},
journal = {PloS one},
volume = {10},
number = {6},
pages = {e0126228},
pmid = {26039073},
issn = {1932-6203},
mesh = {*Biodiversity ; *Climate Change ; *Models, Biological ; *Plants ; Population Dynamics ; },
abstract = {The impacts of climate change on forest community composition are still not well known. Although directional trends in climate change and community composition change were reported in recent years, further quantitative analyses are urgently needed. Previous studies focused on measuring population growth rates in a single time period, neglecting the development of the populations. Here we aimed to compose a method for calculating the community composition change, and to testify the impacts of climate change on community composition change within a relatively short period (several decades) based on long-term monitoring data from two plots-Dinghushan Biosphere Reserve, China (DBR) and Barro Colorado Island, Panama (BCI)-that are located in tropical and subtropical regions. We proposed a relatively more concise index, Slnλ, which refers to an overall population growth rate based on the dominant species in a community. The results indicated that the population growth rate of a majority of populations has decreased over the past few decades. This decrease was mainly caused by population development. The increasing temperature had a positive effect on population growth rates and community change rates. Our results promote understanding and explaining variations in population growth rates and community composition rates, and are helpful to predict population dynamics and population responses to climate change.},
}
@article {pmid26035476,
year = {2015},
author = {Chen, X and Wang, Y and Gao, S and Jung, TP and Gao, X},
title = {Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046008},
doi = {10.1088/1741-2560/12/4/046008},
pmid = {26035476},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Machine Learning ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Statistics as Topic ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Word Processing/methods ; },
abstract = {OBJECTIVE: Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established.<br><br>APPROACH: This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects.<br><br>MAIN RESULTS: The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of &#x223c;33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 &#xb1; 20.34 bits min(-1).<br><br>SIGNIFICANCE: By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.},
}
@article {pmid26029919,
year = {2015},
author = {Merel, J and Pianto, DM and Cunningham, JP and Paninski, L},
title = {Encoder-decoder optimization for brain-computer interfaces.},
journal = {PLoS computational biology},
volume = {11},
number = {6},
pages = {e1004288},
pmid = {26029919},
issn = {1553-7358},
support = {T32 NS064929/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computational Biology ; Computer Simulation ; Humans ; *Models, Neurological ; Neural Prostheses ; Psychophysics ; *Signal Processing, Computer-Assisted ; },
abstract = {Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen. It has long been recognized that both the user and decoding system can adapt to increase the accuracy of the end effector. Co-adaptation is the process whereby a user learns to control the system in conjunction with the decoder adapting to learn the user's neural patterns. We provide a mathematical framework for co-adaptation and relate co-adaptation to the joint optimization of the user's control scheme ("encoding model") and the decoding algorithm's parameters. When the assumptions of that framework are respected, co-adaptation cannot yield better performance than that obtainable by an optimal initial choice of fixed decoder, coupled with optimal user learning. For a specific case, we provide numerical methods to obtain such an optimized decoder. We demonstrate our approach in a model brain-computer interface system using an online prosthesis simulator, a simple human-in-the-loop pyschophysics setup which provides a non-invasive simulation of the BCI setting. These experiments support two claims: that users can learn encoders matched to fixed, optimal decoders and that, once learned, our approach yields expected performance advantages.},
}
@article {pmid26029091,
year = {2015},
author = {Sakamoto, T and Kondo, T},
title = {Visuomotor learning by passive motor experience.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {279},
pmid = {26029091},
issn = {1662-5161},
abstract = {Humans can adapt to unfamiliar dynamic and/or kinematic transformations through the active motor experience. Recent studies of neurorehabilitation using robots or brain-computer interface (BCI) technology suggest that passive motor experience would play a measurable role in motor recovery, however our knowledge of passive motor learning is limited. To clarify the effects of passive motor experience on human motor learning, we performed arm reaching experiments guided by a robotic manipulandum. The results showed that the passive motor experience had an anterograde transfer effect on the subsequent motor execution, whereas no retrograde interference was confirmed in the ABA paradigm experiment. This suggests that the passive experience of the error between visual and proprioceptive sensations leads to the limited but actual compensation of behavior, although it is fragile and cannot be consolidated as a persistent motor memory.},
}
@article {pmid26029061,
year = {2015},
author = {Zippo, AG and Romanelli, P and Torres Martinez, NR and Caramenti, GC and Benabid, AL and Biella, GE},
title = {A novel wireless recording and stimulating multichannel epicortical grid for supplementing or enhancing the sensory-motor functions in monkey (Macaca fascicularis).},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {73},
pmid = {26029061},
issn = {1662-5137},
abstract = {Artificial brain-machine interfaces (BMIs) represent a prospective step forward supporting or replacing faulty brain functions. So far, several obstacles, such as the energy supply, the portability and the biocompatibility, have been limiting their effective translation in advanced experimental or clinical applications. In this work, a novel 16 channel chronically implantable epicortical grid has been proposed. It provides wireless transmission of cortical recordings and stimulations, with induction current recharge. The grid has been chronically implanted in a non-human primate (Macaca fascicularis) and placed over the somato-motor cortex such that 13 electrodes recorded or stimulated the primary motor cortex and three the primary somatosensory cortex, in the deeply anaesthetized animal. Cortical sensory and motor recordings and stimulations have been performed within 3 months from the implant. In detail, by delivering motor cortex epicortical single spot stimulations (1-8 V, 1-10 Hz, 500 ms, biphasic waves), we analyzed the motor topographic precision, evidenced by tunable finger or arm movements of the anesthetized animal. The responses to light mechanical peripheral sensory stimuli (blocks of 100 stimuli, each single stimulus being <1 ms and interblock intervals of 1.5-4 s) have been analyzed. We found 150-250 ms delayed cortical responses from fast finger touches, often spread to nearby motor stations. We also evaluated the grid electrical stimulus interference with somatotopic natural tactile sensory processing showing no suppressing interference with sensory stimulus detection. In conclusion, we propose a chronically implantable epicortical grid which can accommodate most of current technological restrictions, representing an acceptable candidate for BMI experimental and clinical uses.},
}
@article {pmid26028259,
year = {2015},
author = {Yuan, P and Chen, X and Wang, Y and Gao, X and Gao, S},
title = {Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information.},
journal = {Journal of neural engineering},
volume = {12},
number = {4},
pages = {046006},
doi = {10.1088/1741-2560/12/4/046006},
pmid = {26028259},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; },
abstract = {OBJECTIVE: A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding.<br><br>APPROACH: The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation.<br><br>MAIN RESULTS: The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%.<br><br>SIGNIFICANCE: The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.},
}
@article {pmid26024821,
year = {2015},
author = {Meier, R and Thommes, M and Rasenack, N and Krumme, M and Moll, KP and Kleinebudde, P},
title = {Simplified formulations with high drug loads for continuous twin-screw granulation.},
journal = {International journal of pharmaceutics},
volume = {496},
number = {1},
pages = {12-23},
doi = {10.1016/j.ijpharm.2015.05.060},
pmid = {26024821},
issn = {1873-3476},
mesh = {Carboxymethylcellulose Sodium/chemistry ; Chemistry, Pharmaceutical/*methods ; Delayed-Action Preparations ; Drug Compounding/*methods ; Drug Liberation ; Excipients/*chemistry ; Ibuprofen/*administration &amp; dosage/chemistry ; Particle Size ; Povidone/chemistry ; Solubility ; Starch/analogs &amp; derivatives/chemistry ; Tablets ; Tensile Strength ; },
abstract = {As different batches of the same excipients will be intermixed during continuous processes, the traceability of batches is complicated. Simplified formulations may help to reduce problems related to batch intermixing and traceability. Twin-screw granulation with subsequent tableting was used to produce granules and tablets, containing drug, disintegrant and binder (binary and ternary mixtures), only. Drug loads up to 90% were achieved and five different disintegrants were screened for keeping their disintegration suitability after wetting. Granule size distributions were consistently mono-modal and narrow. Granule strength reached higher values, using ternary mixtures. Tablets containing croscarmellose-Na as disintegrant displayed tensile strengths up to 3.1MPa and disintegration times from 400 to 466s, resulting in the most robust disintegrant. Dissolution was overall complete and above 96% within 30 min. Na-starch glycolate offers tensile strengths up to 2.8MPa at disintegration times from 25s to 1031s, providing the broadest application window, as it corresponds in some parts to different definitions of orodispersible tablets. Tablets containing micronized crospovidone are not suitable for immediate release, but showed possibilities to produce highly drug loaded, prolonged release tablets. Tablets and granules from simplified formulations offer great opportunities to improve continuous processes, present performances comparable to more complicated formulations and are able to correspond to requirements of the authorities.},
}
@article {pmid26020525,
year = {2015},
author = {Úbeda, A and Hortal, E and Iáñez, E and Perez-Vidal, C and Azorín, JM},
title = {Assessing movement factors in upper limb kinematics decoding from EEG signals.},
journal = {PloS one},
volume = {10},
number = {5},
pages = {e0128456},
pmid = {26020525},
issn = {1932-6203},
mesh = {Adult ; Biomechanical Phenomena ; *Electroencephalography ; Humans ; Male ; *Models, Biological ; Movement/*physiology ; Upper Extremity/*physiology ; },
abstract = {The past decades have seen the rapid development of upper limb kinematics decoding techniques by performing intracortical recordings of brain signals. However, the use of non-invasive approaches to perform similar decoding procedures is still in its early stages. Recent studies show that there is a correlation between electroencephalographic (EEG) signals and hand-reaching kinematic parameters. From these studies, it could be concluded that the accuracy of upper limb kinematics decoding depends, at least partially, on the characteristics of the performed movement. In this paper, we have studied upper limb movements with different speeds and trajectories in a controlled environment to analyze the influence of movement variability in the decoding performance. To that end, low frequency components of the EEG signals have been decoded with linear models to obtain the position of the volunteer's hand during performed trajectories grasping the end effector of a planar manipulandum. The results confirm that it is possible to obtain kinematic information from low frequency EEG signals and show that decoding performance is significantly influenced by movement variability and tracking accuracy as continuous and slower movements improve the accuracy of the decoder. This is a key factor that should be taken into account in future experimental designs.},
}
@article {pmid26019952,
year = {2014},
author = {Elmissiry, MM and Ali, AG and Abulfotooh, A and Moussa, AA and Ali, GA},
title = {Factors determining the amount of residual urine in men with bladder outlet obstruction: Could it be a predictor for bladder contractility?.},
journal = {Arab journal of urology},
volume = {12},
number = {3},
pages = {214-218},
pmid = {26019952},
issn = {2090-598X},
abstract = {OBJECTIVE: To determine from urodynamic data what causes an increased postvoid residual urine volume (PVR) in men with bladder outlet obstruction (BOO), urethral resistance or bladder failure, and to determine how to predict bladder contractility from the PVR.<br><br>PATIENTS AND METHODS: We analysed retrospectively the pressure-flow studies (PFS) of 90 men with BOO. Nine patients could not void and the remaining 81 were divided into three groups, i.e. A (30 men, PVR&#xa0;<&#xa0;100&#xa0;mL), B (30 men, PVR 100-450&#xa0;mL) and C (21 men, PVR&#xa0;>&#xa0;450&#xa0;mL). The division was made according to a receiver operating characteristic curve, showing that using a threshold PVR of 450&#xa0;mL had the best sensitivity and specificity for detecting the start of bladder failure.<br><br>RESULTS: The filling phase showed an increase in bladder capacity with the increase in PVR and a significantly lower incidence of detrusor overactivity in group C. The voiding phase showed a significant decrease in voided volume and maximum urinary flow rate (Q max) as the PVR increased, while the urethral resistance factor (URF) increased from group A to B to C. The detrusor pressure at Q max (PdetQ max) and opening pressure were significantly higher in group B, which had the highest bladder contractility index (BCI) and longest duration of contraction. Group C had the lowest BCI and the lowest PdetQ max.<br><br>CONCLUSIONS: In men with BOO, PVR results from increasing outlet resistance at the start and up to a PVR of 450&#xa0;mL, where the bladder reaches its maximum compensation. At volumes of >450&#xa0;mL, both the outlet resistance and bladder failure are working together, leading to detrusor decompensation.},
}
@article {pmid26017599,
year = {2015},
author = {Nghiem, BT and Sando, IC and Gillespie, RB and McLaughlin, BL and Gerling, GJ and Langhals, NB and Urbanchek, MG and Cederna, PS},
title = {Providing a sense of touch to prosthetic hands.},
journal = {Plastic and reconstructive surgery},
volume = {135},
number = {6},
pages = {1652-1663},
doi = {10.1097/PRS.0000000000001289},
pmid = {26017599},
issn = {1529-4242},
mesh = {Amputation, Traumatic/*rehabilitation/surgery ; Artificial Limbs ; Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Female ; Forecasting ; Hand/innervation/*surgery ; Humans ; Male ; *Prosthesis Design ; Prosthesis Fitting ; Psychomotor Performance/physiology ; Sensory Thresholds/*physiology ; Touch/physiology ; Touch Perception/physiology ; Treatment Outcome ; },
abstract = {Each year, approximately 185,000 Americans suffer the devastating loss of a limb. The effects of upper limb amputations are profound because a person's hands are tools for everyday functioning, expressive communication, and other uniquely human attributes. Despite the advancements in prosthetic technology, current upper limb prostheses are still limited in terms of complex motor control and sensory feedback. Sensory feedback is critical to restoring full functionality to amputated patients because it would relieve the cognitive burden of relying solely on visual input to monitor motor commands and provide tremendous psychological benefits. This article reviews the latest innovations in sensory feedback and argues in favor of peripheral nerve interfaces. First, the authors examine the structure of the peripheral nerve and its importance in the development of a sensory interface. Second, the authors discuss advancements in targeted muscle reinnervation and direct neural stimulation by means of intraneural electrodes. The authors then explore the future of prosthetic sensory feedback using innovative technologies for neural signaling, specifically, the sensory regenerative peripheral nerve interface and optogenetics. These breakthroughs pave the way for the development of a prosthetic limb with the ability to feel.},
}
@article {pmid26014663,
year = {2015},
author = {Cao, L and Ju, Z and Li, J and Jian, R and Jiang, C},
title = {Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {253},
number = {},
pages = {10-17},
doi = {10.1016/j.jneumeth.2015.05.014},
pmid = {26014663},
issn = {1872-678X},
mesh = {*Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Automated ; Photic Stimulation ; Recognition, Psychology ; Signal Detection, Psychological ; },
abstract = {BACKGROUND: Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum.<br><br>NEW METHOD: In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition.<br><br>RESULTS: The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants.<br><br>CONCLUSIONS: These conclusions demonstrated that our proposed method was promising for a high-speed online BCI.},
}
@article {pmid26013877,
year = {2016},
author = {Christophe, V and Duprez, C and Congard, A and Fournier, E and Lesur, A and Antoine, P and Vanlemmens, L},
title = {Evaluate the subjective experience of the disease and its treatment in the partners of young women with non-metastatic breast cancer.},
journal = {European journal of cancer care},
volume = {25},
number = {5},
pages = {734-743},
doi = {10.1111/ecc.12327},
pmid = {26013877},
issn = {1365-2354},
mesh = {Adult ; Aged ; Antineoplastic Agents, Hormonal/administration &amp; dosage ; Antineoplastic Combined Chemotherapy Protocols/*therapeutic use ; Breast Neoplasms/drug therapy/*psychology ; Female ; France ; Humans ; Interpersonal Relations ; Male ; Middle Aged ; Reproducibility of Results ; Self Concept ; Sexual Partners/*psychology ; Surveys and Questionnaires/*standards ; Trastuzumab/administration &amp; dosage ; },
abstract = {The impact of the disease experience on the quality of life of the relatives of patients with cancer is now well documented. However, few scales specifically address the partners' subjective quality of life. This study aims to validate a questionnaire assessing the impact of cancer on the quality of life of the partners of young women with breast cancer. Partners (n = 499) of women aged <45 when diagnosed with a non-metastatic breast cancer completed a self-reported questionnaire generated from non-directive interviews led in an initial study. The structure of the scale was examined by exploratory and confirmatory factor analyses. Internal consistency, test-retest reliability and concurrent validity were assessed. The final Partner-YW-BCI contained 36 items and assessed eight dimensions of the subjective experience of partners: (1) feeling of couple cohesion, (2) negative affectivity and apprehension about the future, (3) body image and sexuality, (4) career management, (5) deterioration of the relationships with close relatives, (6) management of child(ren) and of everyday life, (7) financial difficulties, and (8) sharing and support from close relatives. The scale showed adequate psychometric properties, and will help clinicians to identify the problems of partners and to respond to them by an optimal care management.},
}
@article {pmid26012371,
year = {2015},
author = {Brandenbusch, C and Glonke, S and Collins, J and Hoffrogge, R and Grunwald, K and Bühler, B and Schmid, A and Sadowski, G},
title = {Process boundaries of irreversible scCO2 -assisted phase separation in biphasic whole-cell biocatalysis.},
journal = {Biotechnology and bioengineering},
volume = {112},
number = {11},
pages = {2316-2323},
doi = {10.1002/bit.25655},
pmid = {26012371},
issn = {1097-0290},
mesh = {Adsorption ; *Biocatalysis ; Biotechnology/*methods ; *Carbon Dioxide ; Chromatography, Supercritical Fluid/*methods ; Emulsions/*chemistry ; Membrane Proteins/*chemistry ; },
abstract = {The formation of stable emulsions in biphasic biotransformations catalyzed by microbial cells turned out to be a major hurdle for industrial implementation. Recently, a cost-effective and efficient downstream processing approach, using supercritical carbon dioxide (scCO2) for both irreversible emulsion destabilization (enabling complete phase separation within minutes of emulsion treatment) and product purification via extraction has been proposed by Brandenbusch et al. (2010). One of the key factors for a further development and scale-up of the approach is the understanding of the mechanism underlying scCO2 -assisted phase separation. A systematic approach was applied within this work to investigate the various factors influencing phase separation during scCO2 treatment (that is pressure, exposure of the cells to CO2 , and changes of cell surface properties). It was shown that cell toxification and cell disrupture are not responsible for emulsion destabilization. Proteins from the aqueous phase partially adsorb to cells present at the aqueous-organic interface, causing hydrophobic cell surface characteristics, and thus contribute to emulsion stabilization. By investigating the change in cell-surface hydrophobicity of these cells during CO2 treatment, it was found that a combination of catastrophic phase inversion and desorption of proteins from the cell surface is responsible for irreversible scCO2 mediated phase separation. These findings are essential for the definition of process windows for scCO2 -assisted phase separation in biphasic whole-cell biocatalysis.},
}
@article {pmid26011877,
year = {2015},
author = {Pashaie, R and Baumgartner, R and Richner, TJ and Brodnick, SK and Azimipour, M and Eliceiri, KW and Williams, JC},
title = {Closed-Loop Optogenetic Brain Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {10},
pages = {2327-2337},
doi = {10.1109/TBME.2015.2436817},
pmid = {26011877},
issn = {1558-2531},
mesh = {Algorithms ; Animals ; Brain/*physiology/surgery ; Brain-Computer Interfaces ; Cerebrovascular Circulation/physiology ; Electrocorticography/instrumentation/methods ; Equipment Design ; Hemodynamics/physiology ; Mice ; Mice, Transgenic ; Optical Imaging/*methods ; Optogenetics/instrumentation/*methods ; Signal Processing, Computer-Assisted ; },
abstract = {This paper presents a new approach for implementation of closed-loop brain-machine interface algorithms by combining optogenetic neural stimulation with electrocorticography and fluorescence microscopy. We used a new generation of microfabricated electrocorticography (micro-ECoG) devices in which electrode arrays are embedded within an optically transparent biocompatible substrate that provides optical access to the brain tissue during electrophysiology recording. An optical setup was designed capable of projecting arbitrary patterns of light for optogenetic stimulation and performing fluorescence microscopy through the implant. For realization of a closed-loop system using this platform, the feedback can be taken from electrophysiology data or fluorescence imaging. In the closed-loop systems discussed in this paper, the feedback signal was taken from the micro-ECoG. In these algorithms, the electrophysiology data are continuously transferred to a computer and compared with some predefined spatial-temporal patterns of neural activity. The computer which processes the data also readjusts the duration and distribution of optogenetic stimulating pulses to minimize the difference between the recorded activity and the predefined set points so that after a limited period of transient response the recorded activity follows the set points. Details of the system design and implementation of typical closed-loop paradigms are discussed in this paper.},
}
@article {pmid26011638,
year = {2015},
author = {Bittl, JA and Tamis-Holland, JE and Lang, CD and He, Y},
title = {Outcomes after multivessel or culprit-Vessel intervention for ST-elevation myocardial infarction in patients with multivessel coronary disease: a Bayesian cross-design meta-analysis.},
journal = {Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography &amp; Interventions},
volume = {86 Suppl 1},
number = {},
pages = {S15-22},
doi = {10.1002/ccd.26025},
pmid = {26011638},
issn = {1522-726X},
mesh = {*Bayes Theorem ; Coronary Artery Disease/*complications ; Coronary Vessels/*surgery ; *Electrocardiography ; Humans ; Myocardial Infarction/etiology/*surgery ; Myocardial Revascularization/*methods ; },
abstract = {INTRODUCTION: During primary percutaneous coronary intervention (PCI), patients with ST-elevation myocardial infarction (STEMI) and multivessel coronary disease can undergo either multivessel intervention (MVI) or culprit-vessel intervention (CVI) only.<br><br>BACKGROUND: Randomized controlled trials (RCTs) support the use of MVI, but cohort studies support the use of CVI.<br><br>METHODS: We developed Bayesian models that incorporated parameters for study type and study outcome after MVI or CVI.<br><br>RESULTS: A total of 18 studies (4 RCTs, 3 matched cohort studies, and 11 unmatched observational studies) enrolled 48,398 patients with STEMI and multivessel CAD and reported outcomes after MVI or CVI-only at the time of primary PCI. Using a Bayesian hierarchical model, we found that the point estimates replicated previously reported trends, but the wide Bayesian credible intervals (BCI) excluded any plausible mortality difference between MVI versus CVI in all three study types: RCTs (odds ratio [OR] 0.60, 95% BCI 0.31-1.20), matched cohort studies (OR 1.37, 95% BCI 0.86-2.24), or unmatched cohort studies (OR 1.16, 95% BCI 0.70-1.89). Both the global summary (OR 1.10, 95% BCI 0.74-1.51) and a sensitivity analysis that weighted the RCTs 1-5 times as much as observational studies revealed no credible advantage of one PCI strategy over the other (OR 1.05, 95% BCI 0.64-1.48).<br><br>CONCLUSIONS: Bayesian approaches contextualize the comparison of different strategies by study type and suggest that neither MVI nor CVI emerges as a preferred strategy in an analysis that accounts mortality differences.},
}
@article {pmid26002787,
year = {2015},
author = {Nistor, PA and May, PW and Tamagnini, F and Randall, AD and Caldwell, MA},
title = {Long-term culture of pluripotent stem-cell-derived human neurons on diamond--A substrate for neurodegeneration research and therapy.},
journal = {Biomaterials},
volume = {61},
number = {},
pages = {139-149},
doi = {10.1016/j.biomaterials.2015.04.050},
pmid = {26002787},
issn = {1878-5905},
support = {G1100623/MRC_/Medical Research Council/United Kingdom ; H-1002/PUK_/Parkinson's UK/United Kingdom ; NC/C014103/1/NC3RS_/National Centre for the Replacement, Refinement and Reduction of Animals in Research/United Kingdom ; },
mesh = {Batch Cell Culture Techniques/*methods ; Biocompatible Materials/chemical synthesis ; Cell Differentiation/physiology ; Cell Proliferation/physiology ; Cell Survival/physiology ; Cells, Cultured ; Humans ; Materials Testing ; Nanodiamonds/*chemistry/ultrastructure ; Neurodegenerative Diseases/pathology/therapy ; Neurons/*cytology/*physiology ; Particle Size ; Pluripotent Stem Cells/*cytology/*physiology ; Surface Properties ; },
abstract = {Brain Computer Interfaces (BCI) currently represent a field of intense research aimed both at understanding neural circuit physiology and at providing functional therapy for traumatic or degenerative neurological conditions. Due to its chemical inertness, biocompatibility and stability, diamond is currently being actively investigated as a potential substrate material for culturing cells and for use as the electrically active component of a neural sensor. Here we provide a protocol for the differentiation of mature, electrically active neurons on microcrystalline synthetic thin-film diamond substrates starting from undifferentiated pluripotent stem cells. Furthermore, we investigate the optimal characteristics of the diamond microstructure for long-term neuronal sustainability. We also analyze the effect of boron as a dopant for such a culture. We found that the diamond crystalline structure has a significant influence on the neuronal culture unlike the boron doping. Specifically, small diamond microcrystals promote higher neurite density formation. We find that boron incorporated into the diamond does not influence the neurite density and has no deleterious effect on cell survival.},
}
@article {pmid26001643,
year = {2015},
author = {Venthur, B and Dähne, S and Höhne, J and Heller, H and Blankertz, B},
title = {Wyrm: A Brain-Computer Interface Toolbox in Python.},
journal = {Neuroinformatics},
volume = {13},
number = {4},
pages = {471-486},
pmid = {26001643},
issn = {1559-0089},
mesh = {Algorithms ; Animals ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy ; Machine Learning ; *Programming Languages ; *Software ; },
abstract = {In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm (https://github.com/bbci/wyrm), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.},
}
@article {pmid25999824,
year = {2015},
author = {Sexton, CA},
title = {The overlooked potential for social factors to improve effectiveness of brain-computer interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {70},
pmid = {25999824},
issn = {1662-5137},
}
@article {pmid25999506,
year = {2015},
author = {Aflalo, T and Kellis, S and Klaes, C and Lee, B and Shi, Y and Pejsa, K and Shanfield, K and Hayes-Jackson, S and Aisen, M and Heck, C and Liu, C and Andersen, RA},
title = {Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human.},
journal = {Science (New York, N.Y.)},
volume = {348},
number = {6237},
pages = {906-910},
pmid = {25999506},
issn = {1095-9203},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; P50 MH942581A/MH/NIMH NIH HHS/United States ; EY015545/EY/NEI NIH HHS/United States ; EY013337/EY/NEI NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Cognition ; Electrodes, Implanted ; Functional Neuroimaging/*methods ; Humans ; Microelectrodes ; Motor Activity ; Movement ; *Neural Prostheses ; Neurons/*physiology ; Parietal Lobe/*physiopathology ; Quadriplegia/*physiopathology/*therapy ; },
abstract = {Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.},
}
@article {pmid25997260,
year = {2015},
author = {Wang, J and Zhang, Y},
title = {[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {32},
number = {1},
pages = {19-24},
pmid = {25997260},
issn = {1001-5515},
mesh = {Bayes Theorem ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Magnetoencephalography ; Multivariate Analysis ; Principal Component Analysis ; },
abstract = {Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.},
}
@article {pmid25996702,
year = {2015},
author = {Embrandiri, SS and Reddy, MR},
title = {Optimal channels election for multi-Channels SVEP Detection and Classification in BCIS.},
journal = {Biomedical sciences instrumentation},
volume = {51},
number = {},
pages = {77-84},
pmid = {25996702},
issn = {0067-8856},
abstract = {Many multi-channel techniques for Steady-State Visual-Evoked Potential (SSVEP) detection from EEG have shown significant improvement in the performance of Brain-Computer Interfaces (BCIs). Multi-channel methods, generally involve deriving a spatial filter to linearly combine the EEG channels so as to minimize the noise energy and enhance the SSVEP response. In this paper, three state of the art multi-channel techniques are studied and compared. The performance of the classifiers for varying number and combination of the EEG channels is studied to determine the optimal choice of channels that yield maximum classification accuracy. The correlation of different channel parameters with the net montage performance is also investigated. Results indicate that Minimum Energy Channel (MEC) based classifier yields the highest accuracy values using 6 channels for all the 3 subjects. Significance of non-occipital locations for signal acquisition has been observed. Further, results indicate that the choice of channels to be used in the montage is to be made keeping in mind their effectivesignal strength, co-channel noise correlation values and signal to noise ratios. This ensures that a particular montage has effectively assimilated the signal and noise components.},
}
@article {pmid25992718,
year = {2015},
author = {Scherer, R and Faller, J and Friedrich, EV and Opisso, E and Costa, U and Kübler, A and Müller-Putz, GR},
title = {Individually adapted imagery improves brain-computer interface performance in end-users with disability.},
journal = {PloS one},
volume = {10},
number = {5},
pages = {e0123727},
pmid = {25992718},
issn = {1932-6203},
mesh = {Adult ; Brain/physiopathology ; *Brain-Computer Interfaces/psychology ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy ; Imagination ; Male ; Middle Aged ; Movement ; Quadriplegia/physiopathology/psychology/rehabilitation ; Reproducibility of Results ; Spinal Cord Injuries/physiopathology/psychology/*rehabilitation ; Stroke/physiopathology/psychology ; *Stroke Rehabilitation ; User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.},
}
@article {pmid25990358,
year = {2015},
author = {Siikamaki, H and Kivela, P and Fotopoulos, M and Ollgren, J and Kantele, A},
title = {Illness and injury of travellers abroad: Finnish nationwide data from 2010 to 2012, with incidences in various regions of the world.},
journal = {Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin},
volume = {20},
number = {19},
pages = {15-26},
pmid = {25990358},
issn = {1560-7917},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; Databases as Topic ; Europe/epidemiology ; Female ; Finland/epidemiology ; Humans ; Incidence ; Infant ; Infant, Newborn ; *Internationality ; Male ; Middle Aged ; Travel/*statistics &amp; numerical data ; Wounds and Injuries/*epidemiology ; Young Adult ; },
abstract = {The number of international tourist arrivals reached 1,000 million in 2012. Assessment of travellers' health problems has relied on proportionate morbidity data.Given the lack of data on number of visitors to each region, incidences have been impossible to calculate.This study, largest yet reporting travellers' health problems, is the first to present incidence of illness and injury. Data on Finnish travellers with health problems abroad during 2010 to 2012 were retrieved from the database of an assistance organisation,SOS International, covering 95% of those requiring aid abroad. The numbers were compared with those of Finnish travellers in the database of the Official Statistics of Finland. The SOS International database included 50,710 cases: infections constituted the most common health problem (60%), followed by injuries(14%), diseases of skin (5%), musculoskeletal system and connective tissue (5%), digestive tract (3%),and vascular system (2%). Gastroenteritis (23%) and respiratory infections (21%) proved the most frequent diagnoses. Overall incidence of illness or injury was high in Africa (97.9/100,000 travel days; 95% Bayesian credible interval (BCI): 53.1–145.5), southern Europe plus the eastern Mediterranean (92.3; 95% BCI: 75.4–110.1) and Asia (65.0; 95% BCI: 41.5–87.9). The data show significant differences between geographical regions, indicating the main risks and thus providing destination-specific tools for travelers' healthcare.},
}
@article {pmid25986750,
year = {2015},
author = {Liu, Y and Zhao, Q and Zhang, L},
title = {Uncorrelated multiway discriminant analysis for motor imagery EEG classification.},
journal = {International journal of neural systems},
volume = {25},
number = {4},
pages = {1550013},
doi = {10.1142/S0129065715500136},
pmid = {25986750},
issn = {1793-6462},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {Motor imagery-based brain-computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usually loses effects due to individual variability among different subjects in practical applications. In this study, a robust tensor-based method is proposed for a multiway discriminative subspace extraction from tensor-represented EEG data, which performs well in motor imagery EEG classification without the prior neurophysiologic knowledge like channels configuration and active frequency bands. Motor imagery EEG patterns in spatial-spectral-temporal domain are detected directly from the multidimensional EEG, which may provide insights to the underlying cortical activity patterns. Extensive experiment comparisons have been performed on a benchmark dataset from the famous BCI competition III as well as self-acquired data from healthy subjects and stroke patients. The experimental results demonstrate the superior performance of the proposed method over the contemporary methods.},
}
@article {pmid25985333,
year = {2015},
author = {Gøtzsche, PC and Young, AH and Crace, J},
title = {Does long term use of psychiatric drugs cause more harm than good?.},
journal = {BMJ (Clinical research ed.)},
volume = {350},
number = {},
pages = {h2435},
pmid = {25985333},
issn = {1756-1833},
support = {//Canadian Institutes of Health Research/Canada ; //Medical Research Council/United Kingdom ; //Wellcome Trust/United Kingdom ; },
mesh = {Europe ; Humans ; Mental Disorders/*drug therapy ; Psychotropic Drugs/*adverse effects/therapeutic use ; Randomized Controlled Trials as Topic ; Suicide/*statistics &amp; numerical data ; United States ; },
abstract = {We could stop almost all psychotropic drug use without deleterious effect, says Peter C Gøtzsche, questioning trial designs that underplay harms and overplay benefits. Allan H Young and John Crace disagree, arguing that evidence supports long term use},
}
@article {pmid25983676,
year = {2015},
author = {Brouwer, AM and Zander, TO and van Erp, JB and Korteling, JE and Bronkhorst, AW},
title = {Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {136},
pmid = {25983676},
issn = {1662-4548},
abstract = {Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic "Using neurophysiological signals that reflect cognitive or affective state" we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently "cheating" with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications.},
}
@article {pmid25982878,
year = {2015},
author = {Serdar Bascil, M and Tesneli, AY and Temurtas, F},
title = {Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface.},
journal = {Australasian physical &amp; engineering sciences in medicine},
volume = {38},
number = {2},
pages = {229-239},
doi = {10.1007/s13246-015-0345-6},
pmid = {25982878},
issn = {1879-5447},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Imagination ; Movement ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; },
abstract = {Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha &amp; beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.},
}
@article {pmid25980505,
year = {2015},
author = {Ibáñez, J and Serrano, JI and del Castillo, MD and Minguez, J and Pons, JL},
title = {Predictive classification of self-paced upper-limb analytical movements with EEG.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {53},
number = {11},
pages = {1201-1210},
pmid = {25980505},
issn = {1741-0444},
mesh = {Adult ; Algorithms ; Electroencephalography/*methods ; Humans ; Male ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Upper Extremity/*physiology ; },
abstract = {The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.},
}
@article {pmid25979668,
year = {2015},
author = {Zich, C and Debener, S and De Vos, M and Frerichs, S and Maurer, S and Kranczioch, C},
title = {Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.},
journal = {NeuroImage},
volume = {116},
number = {},
pages = {80-91},
doi = {10.1016/j.neuroimage.2015.05.009},
pmid = {25979668},
issn = {1095-9572},
mesh = {Adult ; Age Factors ; Brain Waves ; Cerebral Cortex/*physiology ; Electroencephalography/methods ; Female ; Functional Laterality/*physiology ; Hand ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; *Movement ; *Neurofeedback ; Young Adult ; },
abstract = {The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (<30 years). The present study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end-users.},
}
@article {pmid25977685,
year = {2015},
author = {Martinez-Leon, JA and Cano-Izquierdo, JM and Ibarrola, J},
title = {Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {781207},
pmid = {25977685},
issn = {1687-5273},
mesh = {Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Fuzzy Logic ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {This paper presents an investigation aimed at drastically reducing the processing burden required by motor imagery brain-computer interface (BCI) systems based on electroencephalography (EEG). In this research, the focus has moved from the channel to the feature paradigm, and a 96% reduction of the number of features required in the process has been achieved maintaining and even improving the classification success rate. This way, it is possible to build cheaper, quicker, and more portable BCI systems. The data set used was provided within the framework of BCI Competition III, which allows it to compare the presented results with the classification accuracy achieved in the contest. Furthermore, a new three-step methodology has been developed which includes a feature discriminant character calculation stage; a score, order, and selection phase; and a final feature selection step. For the first stage, both statistics method and fuzzy criteria are used. The fuzzy criteria are based on the S-dFasArt classification algorithm which has shown excellent performance in previous papers undertaking the BCI multiclass motor imagery problem. The score, order, and selection stage is used to sort the features according to their discriminant nature. Finally, both order selection and Group Method Data Handling (GMDH) approaches are used to choose the most discriminant ones.},
}
@article {pmid25976925,
year = {2015},
author = {Ossmy, O and Fried, I and Mukamel, R},
title = {Decoding speech perception from single cell activity in humans.},
journal = {NeuroImage},
volume = {117},
number = {},
pages = {151-159},
doi = {10.1016/j.neuroimage.2015.05.001},
pmid = {25976925},
issn = {1095-9572},
mesh = {Acoustic Stimulation ; Action Potentials ; Adult ; Auditory Cortex/*physiology ; Brain Waves ; Female ; Humans ; Male ; Neurons/*physiology ; Signal Processing, Computer-Assisted ; Speech Perception/*physiology ; Young Adult ; },
abstract = {Deciphering the content of continuous speech is a challenging task performed daily by the human brain. Here, we tested whether activity of single cells in auditory cortex could be used to support such a task. We recorded neural activity from auditory cortex of two neurosurgical patients while presented with a short video segment containing speech. Population spiking activity (~20 cells per patient) allowed detection of word onset and decoding the identity of perceived words with significantly high accuracy levels. Oscillation phase of local field potentials (8-12Hz) also allowed decoding word identity although with lower accuracy levels. Our results provide evidence that the spiking activity of a relatively small population of cells in human primary auditory cortex contains significant information for classification of words in ongoing speech. Given previous evidence for overlapping neural representation during speech perception and production, this may have implications for developing brain-machine interfaces for patients with deficits in speech production.},
}
@article {pmid25975145,
year = {2014},
author = {Vasilyeva, LN and Badakva, AM and Miller, NV and Zobova, LN and Roschin, VY and Bondar, IV},
title = {[Long-term recording of single unit activity and criteria for estimation of stability].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {64},
number = {6},
pages = {693-701},
pmid = {25975145},
issn = {0044-4677},
mesh = {Action Potentials/*physiology ; Animals ; Brain/cytology/*physiology ; Electrodes, Implanted ; Haplorhini/*physiology ; Microelectrodes ; Neuronal Plasticity/*physiology ; Neurons/cytology/*physiology ; Single-Cell Analysis ; Stereotaxic Techniques ; Time Factors ; },
abstract = {Stable single-unit recording in the brain of vertebrates allows to investigate processes underlying neural plasticity. In applied aspect long-term single-unit recording can be useful for development of invasive brain--computer interface. Here we propose a criterion for identification of neurons that were recorded for more than one day. Based only on the spike forms classification yields ambiguous result. Additional parameters (such as form of interspike interval histogram or certain parameters of that histogram) decreased misclassification probability considerably. Using proposed criterion we were able to identify 82 neurons that were recoded for more than one day. In extreme case activity of one neuron was observed for 94 days.},
}
@article {pmid25973676,
year = {2015},
author = {Ohmae, S and Takahashi, T and Lu, X and Nishimori, Y and Kodaka, Y and Takashima, I and Kitazawa, S},
title = {Decoding the timing and target locations of saccadic eye movements from neuronal activity in macaque oculomotor areas.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036014},
doi = {10.1088/1741-2560/12/3/036014},
pmid = {25973676},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Macaca ; Motor Cortex/*physiology ; Neurons/*physiology ; Reproducibility of Results ; Saccades/*physiology ; Sensitivity and Specificity ; Time Factors ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: The control of movement timing has been a significant challenge for brain-machine interfaces (BMIs). As a first step toward developing a timing-based BMI, we aimed to decode movement timing and target locations in a visually guided saccadic eye movement task using the activity of neurons in the primate frontal eye field (FEF) and supplementary eye field (SEF).<br><br>APPROACH: For this purpose, we developed a template-matching method that could recruit a variety of neurons in these areas.<br><br>MAIN RESULTS: As a result, we were able to achieve a favorable estimation of saccade onset: for example, data from 20 randomly sampled FEF neurons or 40 SEF neurons achieved a median estimation error of &#x223c;10 ms with an interquartile range less than 50 ms (&#xb1; &#x223c;25 ms). In the best case, seven simultaneously recorded SEF neurons using a multi-electrode array achieved a comparable accuracy (10&#xa0;&#xb1;&#xa0;30 ms). The method was significantly better than a heuristic method that used only a group of movement cells with sharp discharges at the onset of saccades. The estimation of target location was less accurate but still favorable, especially when we estimated target location at a timing of 200 ms after the onset of saccade: the method was able to discriminate 16 targets with an accuracy of 90%, which differed not only in their directions (eight directions) but also in amplitude (10/20&#xb0;) when we used data from 61 randomly sampled FEF neurons.<br><br>SIGNIFICANCE: The results show that the timing, amplitude and direction of saccades can be decoded from neuronal activity in the FEF and SEF and further suggest that timing-based BMIs can be developed by decoding timing information using the template-matching method.},
}
@article {pmid25973635,
year = {2015},
author = {Spinnato, J and Roubaud, MC and Burle, B and Torrésani, B},
title = {Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036013},
doi = {10.1088/1741-2560/12/3/036013},
pmid = {25973635},
issn = {1741-2552},
support = {241077/ERC_/European Research Council/International ; },
mesh = {*Algorithms ; *Artifacts ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Linear Models ; Magnetoencephalography/methods ; *Models, Neurological ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {OBJECTIVE: The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments.<br><br>APPROACH: The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model.<br><br>MAIN RESULTS: Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context.<br><br>SIGNIFICANCE: The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.},
}
@article {pmid25973549,
year = {2015},
author = {Deng, X and Liu, DF and Kay, K and Frank, LM and Eden, UT},
title = {Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter.},
journal = {Neural computation},
volume = {27},
number = {7},
pages = {1438-1460},
pmid = {25973549},
issn = {1530-888X},
support = {T32 GM007618/GM/NIGMS NIH HHS/United States ; R01 MH0901188/MH/NIMH NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; R01 MH105174/MH/NIMH NIH HHS/United States ; R01 NS073118/NS/NINDS NIH HHS/United States ; },
mesh = {Acrylates ; *Action Potentials ; *Algorithms ; Animals ; Bayes Theorem ; CA1 Region, Hippocampal/physiology ; CA2 Region, Hippocampal/physiology ; Computer Simulation ; Electrophysiology/instrumentation/methods ; Models, Neurological ; Motor Activity/physiology ; Neurons/physiology ; Phenyl Ethers ; Rats, Long-Evans ; Signal Processing, Computer-Assisted ; Space Perception/physiology ; },
abstract = {Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision, such as real-time decoding for brain-computer interfaces. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes's rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat's position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalent to or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.},
}
@article {pmid25972896,
year = {2015},
author = {Suraj, and Tiwari, P and Ghosh, S and Sinha, RK},
title = {Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {945729},
pmid = {25972896},
issn = {1687-5273},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; *Cluster Analysis ; Discriminant Analysis ; Electroencephalography ; Female ; Humans ; *Imagination ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.},
}
@article {pmid25972167,
year = {2015},
author = {Marsh, BT and Tarigoppula, VS and Chen, C and Francis, JT},
title = {Toward an autonomous brain machine interface: integrating sensorimotor reward modulation and reinforcement learning.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {35},
number = {19},
pages = {7374-7387},
pmid = {25972167},
issn = {1529-2401},
support = {R01 NS092894/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Brain/cytology/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electromyography ; Evoked Potentials/physiology ; Female ; Linear Models ; Macaca radiata ; Male ; Models, Neurological ; Neurons/physiology ; *Reinforcement, Psychology ; *Reward ; },
abstract = {For decades, neurophysiologists have worked on elucidating the function of the cortical sensorimotor control system from the standpoint of kinematics or dynamics. Recently, computational neuroscientists have developed models that can emulate changes seen in the primary motor cortex during learning. However, these simulations rely on the existence of a reward-like signal in the primary sensorimotor cortex. Reward modulation of the primary sensorimotor cortex has yet to be characterized at the level of neural units. Here we demonstrate that single units/multiunits and local field potentials in the primary motor (M1) cortex of nonhuman primates (Macaca radiata) are modulated by reward expectation during reaching movements and that this modulation is present even while subjects passively view cursor motions that are predictive of either reward or nonreward. After establishing this reward modulation, we set out to determine whether we could correctly classify rewarding versus nonrewarding trials, on a moment-to-moment basis. This reward information could then be used in collaboration with reinforcement learning principles toward an autonomous brain-machine interface. The autonomous brain-machine interface would use M1 for both decoding movement intention and extraction of reward expectation information as evaluative feedback, which would then update the decoding algorithm as necessary. In the work presented here, we show that this, in theory, is possible.},
}
@article {pmid25968934,
year = {2015},
author = {Oweiss, KG and Badreldin, IS},
title = {Neuroplasticity subserving the operation of brain-machine interfaces.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {161-171},
pmid = {25968934},
issn = {1095-953X},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; R01 NS093909/NS/NINDS NIH HHS/United States ; NS062031/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Homeostasis ; Humans ; Learning/*physiology ; *Models, Neurological ; Motor Skills ; *Neuronal Plasticity ; *Psychomotor Performance ; User-Computer Interface ; },
abstract = {Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications.},
}
@article {pmid25964753,
year = {2015},
author = {Song, J and Nair, VA and Young, BM and Walton, LM and Nigogosyan, Z and Remsik, A and Tyler, ME and Farrar-Edwards, D and Caldera, KE and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {195},
pmid = {25964753},
issn = {1662-5161},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; P50 AG033514/AG/NIA NIH HHS/United States ; },
abstract = {Tracking and predicting motor outcomes is important in determining effective stroke rehabilitation strategies. Diffusion tensor imaging (DTI) allows for evaluation of the underlying structural integrity of brain white matter tracts and may serve as a potential biomarker for tracking and predicting motor recovery. In this study, we examined the longitudinal relationship between DTI measures of the posterior limb of the internal capsule (PLIC) and upper-limb motor outcomes in 13 stroke patients (median 20-month post-stroke) who completed up to 15 sessions of intervention using brain-computer interface (BCI) technology. Patients' upper-limb motor outcomes and PLIC DTI measures including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were assessed longitudinally at four time points: pre-, mid-, immediately post- and 1-month-post intervention. DTI measures and ratios of each DTI measure comparing the ipsilesional and contralesional PLIC were correlated with patients' motor outcomes to examine the relationship between structural integrity of the PLIC and patients' motor recovery. We found that lower diffusivity and higher FA values of the ipsilesional PLIC were significantly correlated with better upper-limb motor function. Baseline DTI ratios were significantly correlated with motor outcomes measured immediately post and 1-month-post BCI interventions. A few patients achieved improvements in motor recovery meeting the minimum clinically important difference (MCID). These findings suggest that upper-limb motor recovery in stroke patients receiving BCI interventions relates to the microstructural status of the PLIC. Lower diffusivity and higher FA measures of the ipsilesional PLIC contribute toward better motor recovery in the stroke-affected upper-limb. DTI-derived measures may be a clinically useful biomarker in tracking and predicting motor recovery in stroke patients receiving BCI interventions.},
}
@article {pmid25964745,
year = {2015},
author = {Schicktanz, S and Amelung, T and Rieger, JW},
title = {Qualitative assessment of patients' attitudes and expectations toward BCIs and implications for future technology development.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {64},
pmid = {25964745},
issn = {1662-5137},
abstract = {Brain-computer-interfaces (BCIs) are important for the next generation of neuro-prosthesis innovations. Only few pilot projects have tested patients' abilities to control BCIs as well as their satisfaction with the offered technologies. On the one hand, little is known about patients' moral attitudes toward the benefit-risk-ratio of BCIs as well as their needs, priorities, and expectations. On the other hand, ethics experts intensively discuss the general risks of BCIs as well as the limits of neuro-enhancement. To our knowledge, we present here the first qualitative interview study with ten chronic patients matching the potential user categories for motor and communication BCIs to assess their practical and moral attitudes toward this technology. The interviews reveal practical and moral attitudes toward motor BCIs that can impact future technology development. We discuss our empirical findings on patients' perspectives and compare them to neuroscientists' and ethicists' perspectives. Our analysis indicates only partial overlap between the potential users' and the experts' assessments of BCI-technology. It points out the importance of considering the needs and desires of the targeted patient group. Based on our findings, we suggest a multi-fold approach to the development of clinical BCIs, rooted in the participatory technology-development. We conclude that clinical BCI development needs to be explored in a disease-related and culturally sensitive way.},
}
@article {pmid25962718,
year = {2016},
author = {Lee, J},
title = {Cochlear Implantation, Enhancements, Transhumanism and Posthumanism: Some Human Questions.},
journal = {Science and engineering ethics},
volume = {22},
number = {1},
pages = {67-92},
pmid = {25962718},
issn = {1471-5546},
mesh = {Biomedical Engineering/*ethics ; *Biomedical Enhancement/ethics ; *Brain-Computer Interfaces/ethics ; *Cochlear Implantation ; *Cochlear Implants ; Human Characteristics ; Humans ; },
abstract = {Biomedical engineering technologies such as brain-machine interfaces and neuroprosthetics are advancements which assist human beings in varied ways. There are exciting yet speculative visions of how the neurosciences and bioengineering may influence human nature. However, these could be preparing a possible pathway towards an enhanced and even posthuman future. This article seeks to investigate several ethical themes and wider questions of enhancement, transhumanism and posthumanism. Four themes of interest are: autonomy, identity, futures, and community. Three larger questions can be asked: will everyone be enhanced? Will we be "human" if we are not, one day, transhuman? Should we be enhanced or not? The article proceeds by concentrating on a widespread and sometimes controversial application: the cochlear implant, an auditory prosthesis implanted into Deaf patients. Cochlear implantation and its reception in both the deaf and hearing communities have a distinctive moral discourse, which can offer surprising insights. The paper begins with several points about the enhancement of human beings, transhumanism's reach beyond the human, and posthuman aspirations. Next it focuses on cochlear implants on two sides. Firstly, a shorter consideration of what technologies may do to humans in a transhumanist world. Secondly, a deeper analysis of cochlear implantation's unique socio-political movement, its ethical explanations and cultural experiences linked with pediatric cochlear implantation-and how those wary of being thrust towards posthumanism could marshal such ideas by analogy. As transhumanism approaches, the issues and questions merit continuing intense analysis.},
}
@article {pmid25960315,
year = {2015},
author = {Schudlo, LC and Chau, T},
title = {Single-trial classification of near-infrared spectroscopy signals arising from multiple cortical regions.},
journal = {Behavioural brain research},
volume = {290},
number = {},
pages = {131-142},
doi = {10.1016/j.bbr.2015.04.053},
pmid = {25960315},
issn = {1872-7549},
mesh = {Adult ; Attention/*physiology ; Brain-Computer Interfaces ; Functional Neuroimaging/*methods ; Humans ; Memory, Short-Term/*physiology ; Parietal Lobe/*physiology ; Prefrontal Cortex/*physiology ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have primarily made use of measurements taken from a single cortical area. In particular, the anterior prefrontal cortex has been the key area used for detecting higher-level cognitive task performance. However, mental task execution typically requires coordination between several, spatially-distributed brain regions. We investigated the value of expanding the area of interrogation to include NIRS measurements from both the prefrontal and parietal cortices to decode mental states. Hemodynamic activity was monitored at 46 locations over the prefrontal and parietal cortices using a continuous-wave near-infrared spectrometer while 11 able-bodied adults rested or performed either the verbal fluency task (VFT) or Stroop task. Offline classification was performed for the three possible binary problems using 25 iterations of bagging with a linear discriminant base classifier. Classifiers were trained on a 10 dimensional feature set. When all 46 measurement locations were considered for classification, average accuracies of 80.4±7.0%, 82.4±7.6%, and 82.8±5.9% in differentiating VFT vs rest, Stroop vs rest and VFT vs Stroop, respectively, were obtained. Relative to using measurements from the anterior PFC alone, an overall average improvement of 11.3% was achieved. Utilizing NIRS measurements from the prefrontal and parietal cortices can be of value in classifying mental states involving working memory and attention. NIRS-BCI accuracies may be improved by incorporating measurements from several, distinct cortical regions, rather than a single area alone. Further development of an NIRS-BCI supporting combinations of VFT, Stroop task and rest states is also warranted.},
}
@article {pmid25959328,
year = {2015},
author = {Liberati, G and Pizzimenti, A and Simione, L and Riccio, A and Schettini, F and Inghilleri, M and Mattia, D and Cincotti, F},
title = {Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals.},
journal = {Applied ergonomics},
volume = {50},
number = {},
pages = {139-146},
doi = {10.1016/j.apergo.2015.03.012},
pmid = {25959328},
issn = {1872-9126},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/psychology/*therapy ; Brain-Computer Interfaces/psychology/*standards ; Caregivers/psychology ; Emotions ; Female ; Focus Groups ; *Health Services Needs and Demand ; Humans ; Male ; Middle Aged ; Sense of Coherence ; },
abstract = {By focus group methodology, we examined the opinions and requirements of persons with ALS, their caregivers, and health care assistants with regard to developing a brain-computer interface (BCI) system that fulfills the user's needs. Four overarching topics emerged from this analysis: 1) lack of information on BCI and its everyday applications; 2) importance of a customizable system that supports individuals throughout the various stages of the disease; 3) relationship between affectivity and technology use; and 4) importance of individuals retaining a sense of agency. These findings should be considered when developing new assistive technology. Moreover, the BCI community should acknowledge the need to bridge experimental results and its everyday application.},
}
@article {pmid25958761,
year = {2015},
author = {Barette, C and Soleilhac, E and Charavay, C and Cochet, C and Fauvarque, MO},
title = {[Strength and specificity of the CMBA screening platform for bioactive molecules discovery].},
journal = {Medecine sciences : M/S},
volume = {31},
number = {4},
pages = {423-431},
doi = {10.1051/medsci/20153104017},
pmid = {25958761},
issn = {0767-0974},
mesh = {Data Interpretation, Statistical ; Database Management Systems ; *Drug Discovery/instrumentation/methods/standards ; *Drug Evaluation, Preclinical/instrumentation/methods ; France ; Humans ; Molecular Targeted Therapy/methods ; Sensitivity and Specificity ; *Small Molecule Libraries/standards/supply &amp; distribution ; },
abstract = {Used as powerful chemical probes in Life science fundamental research, the application potential of new bioactive molecular entities includes but extends beyond their development as therapeutic drugs in pharmacology. In this review, we wish to point out the methodology of chemical libraries screening on living cells or purified proteins at the CMBA academic platform of Grenoble Alpes University, and strategies employed to further characterize the selected bioactive molecules by phenotypic profiling on human cells. Multiple application fields are concerned by the screening activity developed at CMBA with bioactive molecules previously selected for their potential as tools for fundamental research purpose, therapeutic candidates to treat cancer or infection, or promising compounds for production of bioenergy.},
}
@article {pmid25955587,
year = {2015},
author = {Seri, E and Shtilerman, E and Shnerb, NM},
title = {The glocal forest.},
journal = {PloS one},
volume = {10},
number = {5},
pages = {e0126117},
pmid = {25955587},
issn = {1932-6203},
mesh = {Biodiversity ; Cluster Analysis ; Forests ; *Models, Biological ; Trees/growth &amp; development ; },
abstract = {Spatial ecological patterns reflect the underlying processes that shape the structure of species and communities. Mechanisms like intra- and inter-specific competition, dispersal and host-pathogen interactions can act over a wide range of scales. Yet, the inference of such processes from patterns is a challenging task. Here we call attention to a quite unexpected phenomenon in the extensively studied tropical forest at the Barro-Colorado Island (BCI): the spatial deployment of (almost) all tree species is statistically equivalent, once distances are normalized by ℓ0, the typical distance between neighboring conspecific trees. Correlation function, cluster statistics and nearest-neighbor distance distribution become species-independent after this rescaling. Global observables (species frequencies) and local spatial structure appear to be interrelated. This "glocality" suggests a radical interpretation of recent experiments that show a correlation between species' abundance and the negative feedback among conspecifics. For the forest to be glocal, the negative feedback must govern spatial patterns over all scales.},
}
@article {pmid25954857,
year = {2015},
author = {Massaro, S},
title = {Neurofeedback in the workplace: from neurorehabilitation hope to neuroleadership hype?.},
journal = {International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation},
volume = {38},
number = {3},
pages = {276-278},
doi = {10.1097/MRR.0000000000000119},
pmid = {25954857},
issn = {1473-5660},
mesh = {Brain-Computer Interfaces ; Humans ; *Neurofeedback ; *Neurological Rehabilitation ; Workplace ; },
abstract = {Brain-computer interface neurofeedback has rapidly become an engaging topic for occupational research at large. Notwithstanding some criticism, research and practice have begun converging on the efficacy of brain-computer interface neurofeedback as a part of holistic interventions in rehabilitation. Yet, its use in vocational contexts has recently blossomed into wider attributes, beyond rehabilitation practice per se, additionally targeting performance enhancements and leadership interventions in healthy individuals. By exploring this emerging scenario, this paper aims to provide an interdisciplinary forum of analysis on the deriving implications for rehabilitation professionals, signaling how these may invite both possible threats for the field and opportunities to engage in novel translational partnerships.},
}
@article {pmid25946198,
year = {2015},
author = {Stavisky, SD and Kao, JC and Nuyujukian, P and Ryu, SI and Shenoy, KV},
title = {A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036009},
pmid = {25946198},
issn = {1741-2552},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; T32 MH020016/MH/NIMH NIH HHS/United States ; 8DP1HD075623/DP/NCCDPHP CDC HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; *Brain-Computer Interfaces ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.<br><br>APPROACH: Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.<br><br>MAIN RESULTS: LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.<br><br>SIGNIFICANCE: These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.},
}
@article {pmid25945382,
year = {2015},
author = {Shurkhay, VA and Aleksandrova, EV and Potapov, AA and Goryainov, SA},
title = {The current state of the brain-computer interface problem.},
journal = {Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko},
volume = {79},
number = {1},
pages = {97-104},
doi = {10.17116/neiro201579197-104},
pmid = {25945382},
issn = {0042-8817},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/*trends ; Humans ; },
abstract = {It was only 40 years ago that the first PC appeared. Over this period, rather short in historical terms, we have witnessed the revolutionary changes in lives of individuals and the entire society. Computer technologies are tightly connected with any field, either directly or indirectly. We can currently claim that computers are manifold superior to a human mind in terms of a number of parameters; however, machines lack the key feature: they are incapable of independent thinking (like a human). However, the key to successful development of humankind is collaboration between the brain and the computer rather than competition. Such collaboration when a computer broadens, supplements, or replaces some brain functions is known as the brain-computer interface. Our review focuses on real-life implementation of this collaboration.},
}
@article {pmid25937458,
year = {2016},
author = {Xiang, J and Korostenskaja, M and Molloy, C and deGrauw, X and Leiken, K and Gilman, C and Meinzen-Derr, J and Fujiwara, H and Rose, DF and Mitchell, T and Murray, DS},
title = {Multi-frequency localization of aberrant brain activity in autism spectrum disorder.},
journal = {Brain &amp; development},
volume = {38},
number = {1},
pages = {82-90},
doi = {10.1016/j.braindev.2015.04.007},
pmid = {25937458},
issn = {1872-7131},
mesh = {Adolescent ; Autism Spectrum Disorder/pathology/*physiopathology ; Brain/pathology/*physiopathology ; Brain Mapping/methods ; Brain Waves ; Child ; Female ; Humans ; Magnetic Resonance Imaging ; Magnetoencephalography ; Male ; Pilot Projects ; },
abstract = {OBJECTIVE: The abnormality of intrinsic brain activity in autism spectrum disorders (ASDs) is still inconclusive. Contradictory results have been found pointing towards hyper-activity or hypo-activity in various brain regions. The present research aims to investigate the spatial and spectral signatures of aberrant brain activity in an unprecedented frequency range of 1-2884 Hz at source levels in ASD using newly developed methods.<br><br>MATERIALS AND METHODS: Seven ASD subjects and age- and gender-matched controls were studied using a high-sampling rate magnetoencephalography (MEG) system. Brain activity in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low gamma (30-55 Hz), high gamma (65-90 Hz), ripples (90-200 Hz), high-frequency oscillations (HFOs, 200-1000 Hz), and very high-frequency oscillations (VHFOs, 1000-2884 Hz) was volumetrically localized and measured using wavelet and beamforming.<br><br>RESULTS: In comparison to controls, ASD subjects had significantly higher odds of alpha activity (8-12 Hz) in the sensorimotor cortex (mu rhythm), and generally high-frequency activity (90-2884 Hz) in the frontal cortex. The source power of HFOs (200-1000 Hz) in the frontal cortex in ASD was significantly elevated as compared with controls.<br><br>CONCLUSION: The results suggest that ASD has significantly altered intrinsic brain activity in both low- and high-frequency ranges. Increased intrinsic high-frequency activity in the frontal cortex may play a key role in ASD.},
}
@article {pmid25935543,
year = {2015},
author = {Sahana, G and Höglund, JK and Guldbrandtsen, B and Lund, MS},
title = {Loci associated with adult stature also affect calf birth survival in cattle.},
journal = {BMC genetics},
volume = {16},
number = {},
pages = {47},
pmid = {25935543},
issn = {1471-2156},
mesh = {Animals ; Body Height/*genetics ; Cattle ; *Genetic Association Studies ; Genomics ; Humans ; Live Birth/*genetics ; Polymorphism, Single Nucleotide ; *Quantitative Trait Loci ; },
abstract = {BACKGROUND: Understanding the underlying pleiotropic relationships among quantitative traits is necessary in order to predict correlated responses to artificial selection. The availability of large-scale next-generation sequence data in cattle has provided an opportunity to examine whether pleiotropy is responsible for overlapping QTL in multiple economic traits. In the present study, we examined QTL affecting cattle stillbirth, calf size, and adult stature located in the same genomic region.<br><br>RESULTS: A genome scan using imputed whole genome sequence variants revealed one QTL with large effects on the service sire calving index (SCI), and body conformation index (BCI) at the same location (~39&#xa0;Mb) on chromosome 6 in Nordic Red cattle. The targeted region was analyzed for SCI and BCI component traits. The QTL peak included LCORL and NCAPG genes, which had been reported to influence fetal growth and adult stature in several species. The QTL exhibited large effects on calf size and stature in Nordic Red cattle. Two deviant haplotypes (HAP1 and HAP2) were resolved which increased calf size at birth, and affected adult body conformation. However, the haplotypes also resulted in increased calving difficulties and calf mortality due to increased calf size at birth. Haplotype locations overlapped, however linkage disequilibrium (LD) between the sites was low, suggesting that two independent mutations were responsible for similar effects. The difference in prevalence between the two haplotypes in Nordic Red subpopulations suggested independent origins in different populations.<br><br>CONCLUSIONS: Results of our study identified QTL with large effects on body conformation and service sire calving traits on chromosome 6 in cattle. We present robust evidence that variation at the LCORL and NCAPG locus affects calf size at birth and adult stature. We suggest the two deviant haplotypes within the QTL were due to two independent mutations.},
}
@article {pmid25933101,
year = {2015},
author = {Yuksel, A and Olmez, T},
title = {A neural network-based optimal spatial filter design method for motor imagery classification.},
journal = {PloS one},
volume = {10},
number = {5},
pages = {e0125039},
pmid = {25933101},
issn = {1932-6203},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Motor Activity/*physiology ; *Neural Networks, Computer ; },
abstract = {In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.},
}
@article {pmid25931680,
year = {2015},
author = {Chung, E and Kim, JH and Park, DS and Lee, BH},
title = {Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial.},
journal = {Journal of physical therapy science},
volume = {27},
number = {3},
pages = {559-562},
pmid = {25931680},
issn = {0915-5287},
abstract = {[Purpose] This study sought to determine the effects of brain-computer interface-based functional electrical stimulation (BCI-FES) on brain activation in patients with stroke. [Subjects] The subjects were randomized to in a BCI-FES group (n=5) and a functional electrical stimulation (FES) group (n=5). [Methods] Patients in the BCI-FES group received ankle dorsiflexion training with FES for 30 minutes per day, 5 times under the brain-computer interface-based program. The FES group received ankle dorsiflexion training with FES for the same amount of time. [Results] The BCI-FES group demonstrated significant differences in the frontopolar regions 1 and 2 attention indexes, and frontopolar 1 activation index. The FES group demonstrated no significant differences. There were significant differences in the frontopolar 1 region activation index between the two groups after the interventions. [Conclusion] The results of this study suggest that BCI-FES training may be more effective in stimulating brain activation than only FES training in patients recovering from stroke.},
}
@article {pmid25929619,
year = {2015},
author = {Karch, JD and Sander, MC and von Oertzen, T and Brandmaier, AM and Werkle-Bergner, M},
title = {Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance.},
journal = {NeuroImage},
volume = {118},
number = {},
pages = {538-552},
doi = {10.1016/j.neuroimage.2015.04.038},
pmid = {25929619},
issn = {1095-9572},
mesh = {Adolescent ; Adult ; Aged ; Aging/*physiology ; Attention/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Child ; Electroencephalography ; Female ; Humans ; Machine Learning ; Male ; Memory, Short-Term/*physiology ; *Models, Neurological ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain-computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults.},
}
@article {pmid25926777,
year = {2015},
author = {Serruya, MD},
title = {As we may think and be: brain-computer interfaces to expand the substrate of mind.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {53},
pmid = {25926777},
issn = {1662-5137},
}
@article {pmid25918501,
year = {2015},
author = {Hoffmann, LC and Cicchese, JJ and Berry, SD},
title = {Harnessing the power of theta: natural manipulations of cognitive performance during hippocampal theta-contingent eyeblink conditioning.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {50},
pmid = {25918501},
issn = {1662-5137},
abstract = {Neurobiological oscillations are regarded as essential to normal information processing, including coordination and timing of cells and assemblies within structures as well as in long feedback loops of distributed neural systems. The hippocampal theta rhythm is a 3-12 Hz oscillatory potential observed during cognitive processes ranging from spatial navigation to associative learning. The lower range, 3-7 Hz, can occur during immobility and depends upon the integrity of cholinergic forebrain systems. Several studies have shown that the amount of pre-training theta in the rabbit strongly predicts the acquisition rate of classical eyeblink conditioning and that impairment of this system substantially slows the rate of learning. Our lab has used a brain-computer interface (BCI) that delivers eyeblink conditioning trials contingent upon the explicit presence or absence of hippocampal theta. A behavioral benefit of theta-contingent training has been demonstrated in both delay and trace forms of the paradigm with a two- to four-fold increase in learning speed. This behavioral effect is accompanied by enhanced amplitude and synchrony of hippocampal local field potential (LFP)s, multi-unit excitation, and single-unit response patterns that depend on theta state. Additionally, training in the presence of hippocampal theta has led to increases in the salience of tone-induced unit firing patterns in the medial prefrontal cortex, followed by persistent multi-unit activity during the trace interval. In cerebellum, rhythmicity and precise synchrony of stimulus time-locked LFPs with those of hippocampus occur preferentially under the theta condition. Here we review these findings, integrate them into current models of hippocampal-dependent learning and suggest how improvement in our understanding of neurobiological oscillations is critical for theories of medial temporal lobe processes underlying intact and pathological learning.},
}
@article {pmid25915773,
year = {2015},
author = {Sburlea, AI and Montesano, L and Minguez, J},
title = {Continuous detection of the self-initiated walking pre-movement state from EEG correlates without session-to-session recalibration.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036007},
doi = {10.1088/1741-2560/12/3/036007},
pmid = {25915773},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Anticipation, Psychological/*physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods/standards ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; Volition/*physiology ; Walking/*physiology ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) as a rehabilitation tool have been used to restore functions in patients with motor impairments by actively involving the central nervous system and triggering prosthetic devices according to the detected pre-movement state. However, since EEG signals are highly variable between subjects and recording sessions, typically a BCI is calibrated at the beginning of each session. This process is inconvenient especially for patients suffering locomotor disabilities in maintaining a bipedal position for a longer time. This paper presents a continuous EEG decoder of a pre-movement state in self-initiated walking and the usage of this decoder from session to session without recalibrating.<br><br>APPROACH: Ten healthy subjects performed a self-initiated walking task during three sessions, with an intersession interval of one week. The implementation of our continuous decoder is based on the combination of movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features with sparse classification models.<br><br>MAIN RESULTS: During intrasession our technique detects the pre-movement state with 70% accuracy. Moreover this decoder can be applied from session to session without recalibration, with a decrease in performance of about 4% on a one- or two-week intersession interval.<br><br>SIGNIFICANCE: Our detection model operates in a continuous manner, which makes it a straightforward asset for rehabilitation scenarios. By using both temporal and spectral information we attained higher detection rates than the ones obtained with the MRCP and ERD detection models, both during the intrasession and intersession conditions.},
}
@article {pmid25914616,
year = {2015},
author = {Agashe, HA and Paek, AY and Zhang, Y and Contreras-Vidal, JL},
title = {Global cortical activity predicts shape of hand during grasping.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {121},
pmid = {25914616},
issn = {1662-4548},
abstract = {Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural "symphony" as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs.},
}
@article {pmid25909828,
year = {2015},
author = {Song, X and Yoon, SC},
title = {Improving brain-computer interface classification using adaptive common spatial patterns.},
journal = {Computers in biology and medicine},
volume = {61},
number = {},
pages = {150-160},
doi = {10.1016/j.compbiomed.2015.03.023},
pmid = {25909828},
issn = {1879-0534},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP's performance by adding regularization terms into the training. Most of them require target subjects' training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data's class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications.},
}
@article {pmid25907415,
year = {2016},
author = {Olson, JD and Wander, JD and Johnson, L and Sarma, D and Weaver, K and Novotny, EJ and Ojemann, JG and Darvas, F},
title = {Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {127},
number = {1},
pages = {277-284},
pmid = {25907415},
issn = {1872-8952},
support = {NIH K01MH08611805/MH/NIMH NIH HHS/United States ; 5T90DA03243602/DA/NIDA NIH HHS/United States ; K12 HD001097/HD/NICHD NIH HHS/United States ; K01 MH086118/MH/NIMH NIH HHS/United States ; K12 2K12HD001097/HD/NICHD NIH HHS/United States ; NIH R01 NS065186/NS/NINDS NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiology ; Child ; Child, Preschool ; Electrocorticography/instrumentation/*methods ; *Electrodes, Implanted ; Electroencephalography/methods ; Female ; Humans ; Male ; Subdural Space/*physiology ; },
abstract = {OBJECTIVE: The purpose of this study is to determine the relationship between cortical electrophysiological (CE) signals recorded from the surface of the brain (subdural electrocorticography, or ECoG) and signals recorded extracranially from the subgaleal (SG) space.<br><br>METHODS: We simultaneously recorded several hours of continuous ECoG and SG signals from 3 human pediatric subjects, and compared power spectra of signals between a differential SG montage and several differential ECoG montages to determine the nature of the transfer function between them.<br><br>RESULTS: We demonstrate the presence of CE signals in the SG montage in the high-gamma range (HG, 70-110 Hz), and the transfer function between 70 and 110 Hz is best characterized as a linear function of frequency. We also test an alternative transfer function, i.e. a single pole filter, to test the hypothesis of frequency dependent attenuation in that range, but find this model to be inferior to the linear model.<br><br>CONCLUSIONS: Our findings indicate that SG electrodes are capable of recording HG signals without frequency distortion compared with ECoG electrodes.<br><br>SIGNIFICANCE: HG signals could be recorded minimally invasively from outside the skull, which could be important for clinical care or brain-computer interface applications.},
}
@article {pmid25903067,
year = {2015},
author = {Kalyuzhny, M and Kadmon, R and Shnerb, NM},
title = {A neutral theory with environmental stochasticity explains static and dynamic properties of ecological communities.},
journal = {Ecology letters},
volume = {18},
number = {6},
pages = {572-580},
doi = {10.1111/ele.12439},
pmid = {25903067},
issn = {1461-0248},
mesh = {*Biodiversity ; *Biota ; Ecology/methods ; Forests ; *Models, Theoretical ; Population Density ; Population Dynamics ; Stochastic Processes ; Time Factors ; },
abstract = {Understanding the forces shaping ecological communities is crucial to basic science and conservation. Neutral theory has made considerable progress in explaining static properties of communities, like species abundance distributions (SADs), with a simple and generic model, but was criticised for making unrealistic predictions of fundamental dynamic patterns and for being sensitive to interspecific differences in fitness. Here, we show that a generalised neutral theory incorporating environmental stochasticity may resolve these limitations. We apply the theory to real data (the tropical forest of Barro Colorado Island) and demonstrate that it much better explains the properties of short-term population fluctuations and the decay of compositional similarity with time, while retaining the ability to explain SADs. Furthermore, the predictions are considerably more robust to interspecific fitness differences. Our results suggest that this integration of niches and stochasticity may serve as a minimalistic framework explaining fundamental static and dynamic characteristics of ecological communities.},
}
@article {pmid25900287,
year = {2015},
author = {Salisbury, DB and Driver, S and Parsons, TD},
title = {Brain-computer interface targeting non-motor functions after spinal cord injury: a case report.},
journal = {Spinal cord},
volume = {53 Suppl 1},
number = {},
pages = {S25-6},
doi = {10.1038/sc.2014.230},
pmid = {25900287},
issn = {1476-5624},
mesh = {Adult ; *Brain-Computer Interfaces/psychology ; Humans ; Male ; Neuropsychological Tests ; *Spinal Cord Injuries/physiopathology/psychology/rehabilitation ; },
abstract = {STUDY DESIGN: This is a case report.<br><br>OBJECTIVES: The objective of this study was to report on a brain-computer interface (BCI) paradigm that is successfully used with an inpatient spinal cord injury patient.<br><br>SETTING: This study was conducted in an inpatient rehabilitation hospital.<br><br>METHODS: A 25-year-old man with a C5 burst fracture and subsequent tetraplegia (The American Spinal Injury Association) participated in this case study. He completed a brief battery of psychological, pain, cognitive and other screening measures at points before and after the BCI paradigm during his rehabilitation hospitalization.<br><br>RESULTS: The paradigm was easily learned and well tolerated with no adverse effects.<br><br>CONCLUSIONS: This case is reflective of the trends in our ongoing feasibility study evaluating BCI technology in the inpatient rehabilitation setting. Clinical implications and challenges of using this technology in a busy hospital unit are reviewed.},
}
@article {pmid25890770,
year = {2015},
author = {Nolta, NF and Christensen, MB and Crane, PD and Skousen, JL and Tresco, PA},
title = {BBB leakage, astrogliosis, and tissue loss correlate with silicon microelectrode array recording performance.},
journal = {Biomaterials},
volume = {53},
number = {},
pages = {753-762},
doi = {10.1016/j.biomaterials.2015.02.081},
pmid = {25890770},
issn = {1878-5905},
mesh = {Animals ; Astrocytes/*pathology ; *Blood-Brain Barrier ; Gliosis/*physiopathology ; Male ; *Microelectrodes ; Rats ; Rats, Sprague-Dawley ; *Silicon ; },
abstract = {The clinical usefulness of brain machine interfaces that employ penetrating silicon microelectrode arrays is limited by inconsistent performance at chronic time points. While it is widely believed that elements of the foreign body response (FBR) contribute to inconsistent single unit recording performance, the relationships between the FBR and recording performance have not been well established. To address this shortfall, we implanted 4X4 Utah Electrode Arrays into the cortex of 28 young adult rats, acquired electrophysiological recordings weekly for up to 12 weeks, used quantitative immunohistochemical methods to examine the intensity and spatial distribution of neural and FBR biomarkers, and examined whether relationships existed between biomarker distribution and recording performance. We observed that the FBR was characterized by persistent inflammation and consisted of typical biomarkers, including presumptive activated macrophages and activated microglia, astrogliosis, and plasma proteins indicative of blood-brain-barrier disruption, as well as general decreases in neuronal process distribution. However, unlike what has been described for recording electrodes that create only a single penetrating injury, substantial brain tissue loss generally in the shape of a pyramidal lesion cavity was observed at the implantation site. Such lesions were also observed in stab wounded animals indicating that the damage was caused by vascular disruption at the time of implantation. Using statistical approaches, we found that blood-brain barrier leakiness and astrogliosis were both associated with reduced recording performance, and that tissue loss was negatively correlated with recording performance. Taken together, our data suggest that a reduction of vascular damage at the time of implantation either by design changes or use of hemostatic coatings coupled to a reduction of chronic inflammatory sequela will likely improve the recording performance of high density intracortical silicon microelectrode arrays over long indwelling periods and lead to enhanced clinical use of this promising technology.},
}
@article {pmid25890141,
year = {2015},
author = {Fetz, EE},
title = {Restoring motor function with bidirectional neural interfaces.},
journal = {Progress in brain research},
volume = {218},
number = {},
pages = {241-252},
doi = {10.1016/bs.pbr.2015.01.001},
pmid = {25890141},
issn = {1875-7855},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Deep Brain Stimulation/*methods ; Humans ; Motor Activity/physiology ; Movement Disorders/etiology/*therapy ; Neuronal Plasticity/physiology ; Recovery of Function/*physiology ; },
abstract = {Closed-loop brain-computer interfaces have bidirectional connections that allow activity-dependent stimulation of the brain, spinal cord, or muscles. Such bidirectional brain-computer interfaces (BBCI) have three major applications that can be used to restore lost motor function. First, the brain could learn to incorporate a long-term artificial recurrent connection into normal behavior, exploiting the brain's ability to adapt to consistent sensorimotor conditions. The obvious clinical application for restoring motor function is to use an artificial recurrent connection to bridge a lost biological connection. Second, activity-dependent stimulation can generate synaptic plasticity on the cellular level. The corresponding clinical application is to strengthen weakened neural connections, such as occur in stroke. A third application involves delivery of activity-dependent deep brain stimulation at subcortical reward sites, which can operantly reinforce the activity that generates the stimulation. The BBCI paradigm has numerous specific applications, depending on the source of the signals and the stimulated targets.},
}
@article {pmid25889840,
year = {2015},
author = {Ly, J and McGrath, JJ and Gouin, JP},
title = {Poor sleep as a pathophysiological pathway underlying the association between stressful experiences and the diurnal cortisol profile among children and adolescents.},
journal = {Psychoneuroendocrinology},
volume = {57},
number = {},
pages = {51-60},
pmid = {25889840},
issn = {1873-3360},
support = {89886-1//Canadian Institutes of Health Research/Canada ; MOP89886//Canadian Institutes of Health Research/Canada ; OCO79897//Canadian Institutes of Health Research/Canada ; 79897-1//Canadian Institutes of Health Research/Canada ; 95353-1//Canadian Institutes of Health Research/Canada ; 89886-2//Canadian Institutes of Health Research/Canada ; },
mesh = {Adolescent ; Child ; Circadian Rhythm/*physiology ; Female ; Humans ; Hydrocortisone/*metabolism ; Male ; Saliva/metabolism ; Self Report ; Sleep/physiology ; Sleep Initiation and Maintenance Disorders/*metabolism ; Stress, Psychological/*metabolism ; },
abstract = {Recent evidence suggests that poor sleep is a potential pathway underlying the association between stressful experiences and the diurnal cortisol profile. However, existing findings are largely limited to adults. The present study examines whether poor sleep (duration, quality) mediates the relation between stressful experiences and the diurnal cortisol profile in children and adolescents. Children and adolescents (N = 220, M(age) = 12.62) provided six saliva samples over two days to derive cortisol indices (bedtime, AUCAG, AUCTG, slope(MAX)). Perceived stress, stressful life events, self-reported sleep duration, and sleep quality were measured. Using bootstrapping analyses, sleep quality mediated the relation between perceived stress and AUCTG (R(2) = 0.10, F(7, 212) = 3.55, p = .001; 95% BCI[0.09, 1.15]), as well as the relation between stressful life events and AUCTG (R(2) = 0.11, F(7, 212) = 3.69, p = .001; 95% BCI[0.40, 3.82]). These mediation models remained significant after adjusting for sleep duration, suggesting that poor sleep quality underlies the association between stressful experiences and the diurnal cortisol profile in children and adolescents. Longitudinal data combined with objectively-measured sleep is essential to further disentangle the complex association between sleep and stress.},
}
@article {pmid25887263,
year = {2015},
author = {Zich, C and Debener, S and Kranczioch, C and Bleichner, MG and Gutberlet, I and De Vos, M},
title = {Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery.},
journal = {NeuroImage},
volume = {114},
number = {},
pages = {438-447},
doi = {10.1016/j.neuroimage.2015.04.020},
pmid = {25887263},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Male ; *Movement ; *Neurofeedback ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain-computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.},
}
@article {pmid25885059,
year = {2015},
author = {Chen, Y and Yang, W and Long, J and Zhang, Y and Feng, J and Li, Y and Huang, B},
title = {Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.},
journal = {PloS one},
volume = {10},
number = {4},
pages = {e0124153},
pmid = {25885059},
issn = {1932-6203},
mesh = {Aged ; Case-Control Studies ; Cerebellum/physiopathology ; Cerebral Cortex/physiopathology ; *Connectome ; *Discriminant Analysis ; Early Diagnosis ; Female ; Head Movements ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Nerve Net/physiopathology ; Parkinson Disease/diagnosis/*physiopathology ; Sensitivity and Specificity ; *Support Vector Machine ; },
abstract = {Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain resting-state functional connectivity patterns of Parkinson's disease (PD), which are expected to provide additional information for the clinical diagnosis and treatment of this disease. First, we computed the functional connectivity between each pair of 116 regions of interest derived from a prior atlas. The most discriminative features based on Kendall tau correlation coefficient were then selected. A support vector machine classifier was employed to classify 21 PD patients with 26 demographically matched healthy controls. This method achieved a classification accuracy of 93.62% using leave-one-out cross-validation, with a sensitivity of 90.47% and a specificity of 96.15%. The majority of the most discriminative functional connections were located within or across the default mode, cingulo-opercular and frontal-parietal networks and the cerebellum. These disease-related resting-state network alterations might play important roles in the pathophysiology of this disease. Our results suggest that analyses of whole-brain resting-state functional connectivity patterns have the potential to improve the clinical diagnosis and treatment evaluation of PD.},
}
@article {pmid25882342,
year = {2015},
author = {Lande, RG and Pourzand, M},
title = {WITHDRAWN: Brain computer interface technology: Usability and applications in psychiatry.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {},
number = {},
pages = {},
doi = {10.3233/THC-150909},
pmid = {25882342},
issn = {1878-7401},
abstract = {Ahead of Print article withdrawn by publisher.},
}
@article {pmid25881063,
year = {2015},
author = {Ma, C and Ma, X and Zhang, H and Xu, J and He, J},
title = {Neuronal representation of stand and squat in the primary motor cortex of monkeys.},
journal = {Behavioral and brain functions : BBF},
volume = {11},
number = {},
pages = {15},
pmid = {25881063},
issn = {1744-9081},
mesh = {Animals ; Cues ; Electrodes, Implanted ; Electromyography ; Lower Extremity/innervation/physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Motor Neurons/physiology ; Movement/*physiology ; Neural Pathways/physiology ; Neural Prostheses ; Neurons/physiology ; Photic Stimulation ; Physical Conditioning, Animal ; Posture/*physiology ; },
abstract = {BACKGROUND: Determining neuronal topographical information in the cerebral cortex is of fundamental importance for developing neuroprosthetics. Significant progress has been achieved in decoding hand voluntary movement with cortical neuronal activity in nonhuman primates. However, there are few successful reports in scientific literature for decoding lower limb voluntary movement with the cortical neuronal firing. We once reported an experimental system, which consists of a specially designed chair, a visually guided stand and squat task training paradigm and an acute neuron recording setup. With this system, we can record high quality cortical neuron activity to investigate the correlation between these neuronal signals and stand/squat movement.<br><br>METHODS/RESULTS: In this research, we train two monkeys to perform the visually guided stand and squat task, and record neuronal activity in the vast areas targeted to M1 hind-limb region, at a distance of 1&#xa0;mm. We find that 76.9% of recorded neurons (1230 out of 1598 neurons) showing task-firing modulation, including 294 (18.4%) during the pre-response window; 310 (19.4%) for standing up; 104 (6.5%) for the holding stand phase; and 205 (12.8%) during the sitting down. The distributions of different type neurons have a high degree of overlap. They are mainly ranged from +7.0 to 13&#xa0;mm in the Posterior-Anterior dimension, and from +0.5 to 4.0&#xa0;mm in Dosal-lateral dimension, very close to the midline, and just anterior of the central sulcus.<br><br>CONCLUSIONS/SIGNIFICANCE: The present study examines the neuronal activity related to lower limb voluntary movements in M1 and find topographical information of various neurons tuned to different stages of the stand and squat task. This work may contribute to understanding the fundamental principles of neural control of lower limb movements. Especially, the topographical information suggests us where to implant the chronic microelectrode arrays to harvest the most quantity and highest quality neurons related to lower limb movements, which may accelerate to develop cortically controlled lower limb neuroprosthetics for spinal cord injury subjects.},
}
@article {pmid25875047,
year = {2015},
author = {Waytowich, NR and Krusienski, DJ},
title = {Spatial decoupling of targets and flashing stimuli for visual brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036006},
doi = {10.1088/1741-2560/12/3/036006},
pmid = {25875047},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Cues ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Recently, paradigms using code-modulated visual evoked potentials (c-VEPs) have proven to achieve among the highest information transfer rates for noninvasive brain-computer interfaces (BCIs). One issue with current c-VEP paradigms, and visual-evoked paradigms in general, is that they require direct foveal fixation of the flashing stimuli. These interfaces are often visually unpleasant and can be irritating and fatiguing to the user, thus adversely impacting practical performance. In this study, a novel c-VEP BCI paradigm is presented that attempts to perform spatial decoupling of the targets and flashing stimuli using two distinct concepts: spatial separation and boundary positioning.<br><br>APPROACH: For the paradigm, the flashing stimuli form a ring that encompasses the intended non-flashing targets, which are spatially separated from the stimuli. The user fixates on the desired target, which is classified using the changes to the EEG induced by the flashing stimuli located in the non-foveal visual field. Additionally, a subset of targets is also positioned at or near the stimulus boundaries, which decouples targets from direct association with a single stimulus. This allows a greater number of target locations for a fixed number of flashing stimuli.<br><br>MAIN RESULTS: Results from 11 subjects showed practical classification accuracies for the non-foveal condition, with comparable performance to the direct-foveal condition for longer observation lengths. Online results from 5 subjects confirmed the offline results with an average accuracy across subjects of 95.6% for a 4-target condition. The offline analysis also indicated that targets positioned at or near the boundaries of two stimuli could be classified with the same accuracy as traditional superimposed (non-boundary) targets.<br><br>SIGNIFICANCE: The implications of this research are that c-VEPs can be detected and accurately classified to achieve comparable BCI performance without requiring potentially irritating direct foveation of flashing stimuli. Furthermore, this study shows that it is possible to increase the number of targets beyond the number of stimuli without degrading performance. Given the superior information transfer rate of c-VEP paradigms, these results can lead to the development of more practical and ergonomic BCIs.},
}
@article {pmid25870554,
year = {2015},
author = {Elnady, AM and Zhang, X and Xiao, ZG and Yong, X and Randhawa, BK and Boyd, L and Menon, C},
title = {A Single-Session Preliminary Evaluation of an Affordable BCI-Controlled Arm Exoskeleton and Motor-Proprioception Platform.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {168},
pmid = {25870554},
issn = {1662-5161},
abstract = {Traditional, hospital-based stroke rehabilitation can be labor-intensive and expensive. Furthermore, outcomes from rehabilitation are inconsistent across individuals and recovery is hard to predict. Given these uncertainties, numerous technological approaches have been tested in an effort to improve rehabilitation outcomes and reduce the cost of stroke rehabilitation. These techniques include brain-computer interface (BCI), robotic exoskeletons, functional electrical stimulation (FES), and proprioceptive feedback. However, to the best of our knowledge, no studies have combined all these approaches into a rehabilitation platform that facilitates goal-directed motor movements. Therefore, in this paper, we combined all these technologies to test the feasibility of using a BCI-driven exoskeleton with FES (robotic training device) to facilitate motor task completion among individuals with stroke. The robotic training device operated to assist a pre-defined goal-directed motor task. Because it is hard to predict who can utilize this type of technology, we considered whether the ability to adapt skilled movements with proprioceptive feedback would predict who could learn to control a BCI-driven robotic device. To accomplish this aim, we developed a motor task that requires proprioception for completion to assess motor-proprioception ability. Next, we tested the feasibility of robotic training system in individuals with chronic stroke (n = 9) and found that the training device was well tolerated by all the participants. Ability on the motor-proprioception task did not predict the time to completion of the BCI-driven task. Both participants who could accurately target (n = 6) and those who could not (n = 3), were able to learn to control the BCI device, with each BCI trial lasting on average 2.47 min. Our results showed that the participants' ability to use proprioception to control motor output did not affect their ability to use the BCI-driven exoskeleton with FES. Based on our preliminary results, we show that our robotic training device has potential for use as therapy for a broad range of individuals with stroke.},
}
@article {pmid25870550,
year = {2015},
author = {Zuberer, A and Brandeis, D and Drechsler, R},
title = {Are treatment effects of neurofeedback training in children with ADHD related to the successful regulation of brain activity? A review on the learning of regulation of brain activity and a contribution to the discussion on specificity.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {135},
pmid = {25870550},
issn = {1662-5161},
abstract = {While issues of efficacy and specificity are crucial for the future of neurofeedback training, there may be alternative designs and control analyses to circumvent the methodological and ethical problems associated with double-blind placebo studies. Surprisingly, most NF studies do not report the most immediate result of their NF training, i.e., whether or not children with ADHD gain control over their brain activity during the training sessions. For the investigation of specificity, however, it seems essential to analyze the learning and adaptation processes that take place in the course of the training and to relate improvements in self-regulated brain activity across training sessions to behavioral, neuropsychological and electrophysiological outcomes. To this aim, a review of studies on neurofeedback training with ADHD patients which include the analysis of learning across training sessions or relate training performance to outcome is presented. Methods on how to evaluate and quantify learning of EEG regulation over time are discussed. "Non-learning" has been reported in a small number of ADHD-studies, but has not been a focus of general methodological discussion so far. For this reason, selected results from the brain-computer interface (BCI) research on the so-called "brain-computer illiteracy", the inability to gain control over one's brain activity, are also included. It is concluded that in the discussion on specificity, more attention should be devoted to the analysis of EEG regulation performance in the course of the training and its impact on clinical outcome. It is necessary to improve the knowledge on characteristic cross-session and within-session learning trajectories in ADHD and to provide the best conditions for learning.},
}
@article {pmid25869861,
year = {2015},
author = {Milekovic, T and Truccolo, W and Grün, S and Riehle, A and Brochier, T},
title = {Local field potentials in primate motor cortex encode grasp kinetic parameters.},
journal = {NeuroImage},
volume = {114},
number = {},
pages = {338-355},
pmid = {25869861},
issn = {1095-9572},
support = {K01 NS057389/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; Brain Waves ; Female ; *Hand Strength ; Macaca ; Motor Cortex/*physiology ; *Movement ; },
abstract = {Reach and grasp kinematics are known to be encoded in the spiking activity of neuronal ensembles and in local field potentials (LFPs) recorded from primate motor cortex during movement planning and execution. However, little is known, especially in LFPs, about the encoding of kinetic parameters, such as forces exerted on the object during the same actions. We implanted two monkeys with microelectrode arrays in the motor cortical areas MI and PMd to investigate encoding of grasp-related parameters in motor cortical LFPs during planning and execution of reach-and-grasp movements. We identified three components of the LFP that modulated during grasps corresponding to low (0.3-7Hz), intermediate (~10-~40Hz) and high (~80-250Hz) frequency bands. We show that all three components can be used to classify not only grip types but also object loads during planning and execution of a grasping movement. In addition, we demonstrate that all three components recorded during planning or execution can be used to continuously decode finger pressure forces and hand position related to the grasping movement. Low and high frequency components provide similar classification and decoding accuracies, which were substantially higher than those obtained from the intermediate frequency component. Our results demonstrate that intended reach and grasp kinetic parameters are encoded in multiple LFP bands during both movement planning and execution. These findings also suggest that the LFP is a reliable signal for the control of parameters related to object load and applied pressure forces in brain-machine interfaces.},
}
@article {pmid25868229,
year = {2014},
author = {Wang, J and Zhang, H and Wang, L and Xu, G},
title = {[Research on the methods for electroencephalogram feature extraction based on blind source separation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {6},
pages = {1195-1201},
pmid = {25868229},
issn = {1001-5515},
mesh = {Algorithms ; Artifacts ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Foot ; Hand ; Humans ; Imagination ; Movement ; Signal Processing, Computer-Assisted ; Tongue ; },
abstract = {In the present investigation, we studied four methods of blind source separation/independent component analysis (BSS/ICA), AMUSE, SOBI, JADE, and FastICA. We did the feature extraction of electroencephalogram (EEG) signals of brain computer interface (BCI) for classifying spontaneous mental activities, which contained four mental tasks including imagination of left hand, right hand, foot and tongue movement. Different methods of extract physiological components were studied and achieved good performance. Then, three combined methods of SOBI and FastICA for extraction of EEG features of motor imagery were proposed. The results showed that combining of SOBI and ICA could not only reduce various artifacts and noise but also localize useful source and improve accuracy of BCI. It would improve further study of physiological mechanisms of motor imagery.},
}
@article {pmid25866504,
year = {2015},
author = {Bae, J and Sanchez Giraldo, LG and Pohlmeyer, EA and Francis, JT and Sanchez, JC and Príncipe, JC},
title = {Kernel temporal differences for neural decoding.},
journal = {Computational intelligence and neuroscience},
volume = {2015},
number = {},
pages = {481375},
pmid = {25866504},
issn = {1687-5273},
mesh = {*Algorithms ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Computer Simulation ; *Models, Neurological ; Reinforcement, Psychology ; },
abstract = {We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces.},
}
@article {pmid25861942,
year = {2015},
author = {Razavipour, F and Sameni, R},
title = {A study of event related potential frequency domain coherency using multichannel electroencephalogram subspace analysis.},
journal = {Journal of neuroscience methods},
volume = {249},
number = {},
pages = {22-28},
doi = {10.1016/j.jneumeth.2015.03.037},
pmid = {25861942},
issn = {1872-678X},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Event related potentials (ERP) are time-locked electrical activities of the brain in direct response to a specific sensory, cognitive, or motor stimulus. ERP components, such as the P300 wave, which are involved in the process of decision-making, help scientists diagnose specific cognitive disabilities.<br><br>NEW METHOD: In this study, we utilize the angles between multichannel electroencephalogram (EEG) subspaces in different frequency bands, as a similarity factor for studying the spatial coherency between ERP frequency responses. A matched filter is used to enhance the ERP from background EEG.<br><br>RESULTS: While previous researches have focused on frequencies below 10 Hz, as the major frequency band of ERP, it is shown that by using the proposed method, significant ERP-related information can also be found in the 25-40 Hz band. These frequency bands are selected by calculating the correlation coefficient between P300 response segments and synthetic EEG, and ERP segments without P300 waves, and by rejecting the bands having the most association with background EEG and non-P300 components.<br><br>The significance of the results is assessed by real EEG acquired in brain computer interface experiments versus synthetic EEG produced by existing methods in the literature, to assure that the results are not systematic side effects of the proposed framework.<br><br>CONCLUSIONS: The overall results show that the equivalent dipoles corresponding to narrow-band events in the brain are spatially coherent within different (not necessarily adjacent) frequency bands. The results of this study can lead into novel perspectives in ERP studies.},
}
@article {pmid25859210,
year = {2015},
author = {Naseer, N and Hong, KS},
title = {Corrigendum "fNIRS-based brain-computer interfaces: a review".},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {172},
pmid = {25859210},
issn = {1662-5161},
abstract = {[This corrects the article on p. 3 in vol. 9, PMID: 25674060.].},
}
@article {pmid25859204,
year = {2015},
author = {Spüler, M and Niethammer, C},
title = {Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {155},
pmid = {25859204},
issn = {1662-5161},
abstract = {When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback. With this study, we wanted to answer three different questions: (i) Can ErrPs be measured in electroencephalography (EEG) recordings during a task with continuous cursor control? (ii) Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii) Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action). We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible. Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG.},
}
@article {pmid25857625,
year = {2015},
author = {Noonan, MJ and Rahman, MA and Newman, C and Buesching, CD and Macdonald, DW},
title = {Avoiding verisimilitude when modelling ecological responses to climate change: the influence of weather conditions on trapping efficiency in European badgers (Meles meles).},
journal = {Global change biology},
volume = {21},
number = {10},
pages = {3575-3585},
doi = {10.1111/gcb.12942},
pmid = {25857625},
issn = {1365-2486},
mesh = {Animals ; *Climate Change ; England ; Feeding Behavior ; Female ; Male ; Models, Biological ; Mustelidae/*physiology ; Population Dynamics ; Seasons ; Weather ; },
abstract = {The signal for climate change effects can be abstruse; consequently, interpretations of evidence must avoid verisimilitude, or else misattribution of causality could compromise policy decisions. Examining climatic effects on wild animal population dynamics requires ability to trap, observe or photograph and to recapture study individuals consistently. In this regard, we use 19 years of data (1994-2012), detailing the life histories on 1179 individual European badgers over 3288 (re-) trapping events, to test whether trapping efficiency was associated with season, weather variables (both contemporaneous and time lagged), body-condition index (BCI) and trapping efficiency (TE). PCA factor loadings demonstrated that TE was affected significantly by temperature and precipitation, as well as time lags in these variables. From multi-model inference, BCI was the principal driver of TE, where badgers in good condition were less likely to be trapped. Our analyses exposed that this was enacted mechanistically via weather variables driving BCI, affecting TE. Notably, the very conditions that militated for poor trapping success have been associated with actual survival and population abundance benefits in badgers. Using these findings to parameterize simulations, projecting best-/worst-case scenario weather conditions and BCI resulted in 8.6% ± 4.9 SD difference in seasonal TE, leading to a potential 55.0% population abundance under-estimation under the worst-case scenario; 38.6% over-estimation under the best case. Interestingly, simulations revealed that while any single trapping session might prove misrepresentative of the true population abundance, due to weather effects, prolonging capture-mark-recapture studies under sub-optimal conditions decreased the accuracy of population estimates significantly. We also use these projection scenarios to explore how weather could impact government-led trapping of badgers in the UK, in relation to TB management. We conclude that population monitoring must be calibrated against the likelihood that weather conditions could be altering trap success directly, and therefore biasing model design.},
}
@article {pmid25856486,
year = {2015},
author = {Moxon, KA and Foffani, G},
title = {Brain-machine interfaces beyond neuroprosthetics.},
journal = {Neuron},
volume = {86},
number = {1},
pages = {55-67},
doi = {10.1016/j.neuron.2015.03.036},
pmid = {25856486},
issn = {1097-4199},
mesh = {Animals ; Brain/cytology/*physiology ; *Brain-Computer Interfaces ; Humans ; *Neural Prostheses ; Neurons/*physiology ; },
abstract = {The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.},
}
@article {pmid25852778,
year = {2015},
author = {Han, L and Liang, Z and Jiacai, Z and Changming, W and Li, Y and Xia, W and Xiaojuan, G},
title = {Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations.},
journal = {Cognitive neurodynamics},
volume = {9},
number = {2},
pages = {103-112},
pmid = {25852778},
issn = {1871-4080},
abstract = {A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time-frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.},
}
@article {pmid25847919,
year = {2015},
author = {Bleichner, MG and Lundbeck, M and Selisky, M and Minow, F and Jäger, M and Emkes, R and Debener, S and De Vos, M},
title = {Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?.},
journal = {Physiological reports},
volume = {3},
number = {4},
pages = {},
pmid = {25847919},
issn = {2051-817X},
abstract = {Electroencephalography (EEG) allows the study of the brain-behavior relationship in humans. Most of what we have learned with EEG was through observing the brain-behavior relationship under well-controlled laboratory conditions. However, by reducing "normal" behavior to a minimum the ecological validity of the results can be limited. Recent developments toward mobile EEG solutions allow to study the brain-behavior relationship outside the laboratory in more natural situations. Besides mobility and robustness with respect to motion, mobile EEG systems should also interfere as little as possible with the participant's behavior. For example, natural interaction with other people could be hindered when it is obvious that a participant wears an EEG cap. This study evaluates the signal quality obtained with an unobtrusive solution for EEG monitoring through the integration of miniaturized EEG ton-electrodes into both a discreet baseball cap and an individualized ear piece. We show that such mini electrodes located at scalp and ear locations can reliably record event related potentials in a P300 brain-computer-interface application.},
}
@article {pmid25845481,
year = {2015},
author = {Lu, N and Yin, T},
title = {Motor imagery classification via combinatory decomposition of ERP and ERSP using sparse nonnegative matrix factorization.},
journal = {Journal of neuroscience methods},
volume = {249},
number = {},
pages = {41-49},
doi = {10.1016/j.jneumeth.2015.03.031},
pmid = {25845481},
issn = {1872-678X},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/physiology ; Movement/*physiology ; },
abstract = {BACKGROUND: Brain activities could be measured by devices like EEG, MEG, MRI etc. in terms of electric or magnetic signal, which could provide information from three domains, i.e., time, frequency and space. Combinatory analysis of these features could definitely help to improve the classification performance on brain activities. NMF (nonnegative matrix factorization) has been widely applied in pattern extraction tasks (e.g., face recognition, gene data analysis) which could provide physically meaningful explanation of the data. However, brain signals also take negative values, so only spectral feature has been employed in existing NMF studies for brain computer interface. In addition, sparsity is an intrinsic characteristic of electric signals.<br><br>NEW METHOD: To incorporate sparsity constraint and enable analysis of time domain feature using NMF, a new solution for motor imagery classification is developed, which combinatorially analyzes the ERP (event related potential, time domain) and ERSP (event related spectral perturbation, frequency domain) features via a modified mixed alternating least square based NMF method (MALS-NMF for short).<br><br>RESULTS: Extensive experiments have verified the effectivity the proposed method. The results also showed that imposing sparsity constraint on the coefficient matrix in ERP factorization and basis matrix in ERSP factorization could better improve the algorithm performance.<br><br>Comparisons with other eight representative methods have further verified the superiority of the proposed method.<br><br>CONCLUSIONS: The MALS-NMF method is an effective solution for motor imagery classification and has shed some new light into the field of brain dynamics pattern analysis.},
}
@article {pmid25834118,
year = {2015},
author = {Yin, X and Xu, B and Jiang, C and Fu, Y and Wang, Z and Li, H and Shi, G},
title = {A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching.},
journal = {Journal of neural engineering},
volume = {12},
number = {3},
pages = {036004},
doi = {10.1088/1741-2560/12/3/036004},
pmid = {25834118},
issn = {1741-2552},
mesh = {Adult ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Multimodal Imaging/methods ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*methods ; Stress, Mechanical ; Systems Integration ; },
abstract = {OBJECTIVE: In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching.<br><br>APPROACH: The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature was extracted from EEG, whereas the HbD [the difference of oxy-hemoglobin (HbO) and deoxy-hemoglobin (Hb)] feature was used to improve the classification accuracy of fNIRS. The EEG and fNIRS features were combined and optimized using the joint mutual information (JMI) feature selection criterion; then the extracted features were classified with the extreme learning machines (ELMs).<br><br>MAIN RESULTS: In this study, the averaged classification accuracy of EEG signals achieved by the time-phase-frequency feature improved by 7%, to 18%, more than the single-type feature, and improved by 15% more than common spatial pattern (CSP) feature. The HbD feature of fNIRS signals improved the accuracy by 1%, to 4%, more than Hb, HbO, or HbT (total hemoglobin). The EEG-fNIRS feature for decoding motor imagery of both force and speed of hand clenching achieved an accuracy of 89%&#xa0;&#xb1;&#xa0;2%, and improved the accuracy by 1% to 5% more than the sole EEG or fNIRS feature.<br><br>SIGNIFICANCE: Our novel motor imagery paradigm improves BCI performance by increasing the number of extracted commands. Both the time-phase-frequency and the HbD feature improve the classification accuracy of EEG and fNIRS signals, respectively, and the hybrid EEG-fNIRS technique achieves a higher decoding accuracy for two-class motor imagery, which may provide the framework for future multi-modal online BCI systems.},
}
@article {pmid25830903,
year = {2016},
author = {Steyrl, D and Scherer, R and Faller, J and Müller-Putz, GR},
title = {Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {61},
number = {1},
pages = {77-86},
doi = {10.1515/bmt-2014-0117},
pmid = {25830903},
issn = {1862-278X},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Discriminant Analysis ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Humans ; Imagination/physiology ; *Machine Learning ; Male ; *Nonlinear Dynamics ; Oscillometry/methods ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.},
}
@article {pmid25830611,
year = {2015},
author = {Yong, X and Menon, C},
title = {EEG classification of different imaginary movements within the same limb.},
journal = {PloS one},
volume = {10},
number = {4},
pages = {e0121896},
pmid = {25830611},
issn = {1932-6203},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Arm/physiology ; Brain-Computer Interfaces ; Electroencephalography ; Fingers/*physiology ; Hand Strength ; Humans ; Imagination ; Motor Cortex ; Movement ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; },
abstract = {The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.},
}
@article {pmid25827275,
year = {2015},
author = {Ethier, C and Gallego, JA and Miller, LE},
title = {Brain-controlled neuromuscular stimulation to drive neural plasticity and functional recovery.},
journal = {Current opinion in neurobiology},
volume = {33},
number = {},
pages = {95-102},
pmid = {25827275},
issn = {1873-6882},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Evoked Potentials, Motor ; Humans ; Neuromuscular Junction/*physiology ; Neuronal Plasticity/*physiology ; *Recovery of Function ; },
abstract = {There is mounting evidence that appropriately timed neuromuscular stimulation can induce neural plasticity and generate functional recovery from motor disorders. This review addresses the idea that coordinating stimulation with a patient's voluntary effort might further enhance neurorehabilitation. Studies in cell cultures and behaving animals have delineated the rules underlying neural plasticity when single neurons are used as triggers. However, the rules governing more complex stimuli and larger networks are less well understood. We argue that functional recovery might be optimized if stimulation were modulated by a brain machine interface, to match the details of the patient's voluntary intent. The potential of this novel approach highlights the need for a better understanding of the complex rules underlying this form of plasticity.},
}
@article {pmid25824573,
year = {2015},
author = {Marques, VL and Guariento, A and Simões, MS and Blay, G and Lotito, AP and Silva, CA},
title = {[Childhood-onset systemic polyarteritis nodosa and systemic lupus erythematosus: an overlap syndrome?].},
journal = {Revista brasileira de reumatologia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.rbr.2015.01.004},
pmid = {25824573},
issn = {1809-4570},
abstract = {We described herein a patient who presented an overlap syndrome of childhood-onset systemic polyarteritis nodosa (c-PAN) and childhood-onset systemic lupus erythematosus (c-SLE). A 9-year-old girl presented tender subcutaneous nodules on feet, arterial hypertension, right hemiplegia and dysarthric speech. She was hospitalized due to stroke and left foot drop. Brain computer tomography showed ischemic stroke. Magnetic resonance angiography revealed stenosis in the middle cerebral and internal carotid arteries. Electroneuromyography identified a mononeuropathy of left posterior tibial nerve and she fulfilled the c-PAN validated criteria. She was treated with intravenous methylprednisolone pulse therapy followed by prednisone, that was progressively tapered, six months of intravenous cyclophosphamide and after that she received azathioprine for 19 months. At the age of 14 years and 9 months, she presented malar rash, photosensitivity, edema in lower limbs and arterial hypertension. The proteinuria was 1.7g/day. Antinuclear antibodies (ANA) were 1/1280 (homogeneous nuclear pattern) and anti-dsDNA antibodies were positive. Renal biopsy showed focal proliferative and membranous glomerulonephritis. Therefore, she fulfilled the American College of Rheumatology classification criteria for SLE and she was treated with prednisone, hydroxychloroquine and mycophenolate mofetil. In conclusion, we described herein a possible overlap syndrome of two autoimmune diseases, where c-PAN occurred five years before the c-SLE diagnosis.},
}
@article {pmid25823050,
year = {2015},
author = {Kuan, YC and Lo, YK and Kim, Y and Chang, MC and Liu, W},
title = {Wireless gigabit data telemetry for large-scale neural recording.},
journal = {IEEE journal of biomedical and health informatics},
volume = {19},
number = {3},
pages = {949-957},
doi = {10.1109/JBHI.2015.2416202},
pmid = {25823050},
issn = {2168-2208},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Equipment Design ; Humans ; Models, Theoretical ; Prostheses and Implants ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {Implantable wireless neural recording from a large ensemble of simultaneously acting neurons is a critical component to thoroughly investigate neural interactions and brain dynamics from freely moving animals. Recent researches have shown the feasibility of simultaneously recording from hundreds of neurons and suggested that the ability of recording a larger number of neurons results in better signal quality. This massive recording inevitably demands a large amount of data transfer. For example, recording 2000 neurons while keeping the signal fidelity (> 12 bit, > 40 KS/s per neuron) needs approximately a 1-Gb/s data link. Designing a wireless data telemetry system to support such (or higher) data rate while aiming to lower the power consumption of an implantable device imposes a grand challenge on neuroscience community. In this paper, we present a wireless gigabit data telemetry for future large-scale neural recording interface. This telemetry comprises of a pair of low-power gigabit transmitter and receiver operating at 60 GHz, and establishes a short-distance wireless link to transfer the massive amount of neural signals outward from the implanted device. The transmission distance of the received neural signal can be further extended by an externally rendezvous wireless transceiver, which is less power/heat-constraint since it is not at the immediate proximity of the cortex and its radiated signal is not seriously attenuated by the lossy tissue. The gigabit data link has been demonstrated to achieve a high data rate of 6 Gb/s with a bit-error-rate of 10(-12) at a transmission distance of 6 mm, an applicable separation between transmitter and receiver. This high data rate is able to support thousands of recording channels while ensuring a low energy cost per bit of 2.08 pJ/b.},
}
@article {pmid25823030,
year = {2015},
author = {Cecotti, H and Marathe, AR and Ries, AJ},
title = {Optimization of Single-Trial Detection of Event-Related Potentials Through Artificial Trials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {9},
pages = {2170-2176},
doi = {10.1109/TBME.2015.2417054},
pmid = {25823030},
issn = {1558-2531},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {GOAL: Many brain-computer interface (BCI) classification techniques rely on a large number of labeled brain responses to create efficient classifiers. A large database representing all of the possible variability in the signal is impossible to obtain in a short period of time, and prolonged calibration times prevent efficient BCI use. We propose to improve BCIs based on the detection of event-related potentials (ERPs) in two ways.<br><br>METHODS: First, we increase the size of the training database by considering additional deformed trials. The creation of the additional deformed trials is based on the addition of Gaussian noise, and on the variability of the ERP latencies. Second, we exploit the variability of the ERP latencies by combining decisions across multiple deformed trials. These new methods are evaluated on data from 16 healthy subjects participating in a rapid serial visual presentation task.<br><br>RESULTS: The results show a significant increase in the performance of single-trial detection with the addition of artificial trials, and the combination of decisions obtained from altered trials. When the number of trials to train a classifier is low, the proposed approach allows us improve performance from an AUC of 0.533&#xb1;0.080 to 0.905&#xb1;0.053. This improvement represents approximately an 80% reduction in classification error.<br><br>CONCLUSION: These results demonstrate that artificially increasing the training dataset leads to improved single-trial detection.<br><br>SIGNIFICANCE: Calibration sessions can be shortened for BCIs based on ERP detection.},
}
@article {pmid25818687,
year = {2015},
author = {Seeber, M and Scherer, R and Wagner, J and Solis-Escalante, T and Müller-Putz, GR},
title = {High and low gamma EEG oscillations in central sensorimotor areas are conversely modulated during the human gait cycle.},
journal = {NeuroImage},
volume = {112},
number = {},
pages = {318-326},
doi = {10.1016/j.neuroimage.2015.03.045},
pmid = {25818687},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Artifacts ; Brain Mapping ; *Electroencephalography ; Female ; Gait/*physiology ; Gamma Rhythm/*physiology ; Humans ; Image Processing, Computer-Assisted ; Male ; Muscle, Skeletal/physiology ; Nerve Net/physiology ; Neuroimaging ; Robotics ; Sensorimotor Cortex/*physiology ; Walking/physiology ; Young Adult ; },
abstract = {Investigating human brain function is essential to develop models of cortical involvement during walking. Such models could advance the analysis of motor impairments following brain injuries (e.g., stroke) and may lead to novel rehabilitation approaches. In this work, we applied high-density EEG source imaging based on individual anatomy to enable neuroimaging during walking. To minimize the impact of muscular influence on EEG recordings we introduce a novel artifact correction method based on spectral decomposition. High γ oscillations (>60Hz) were previously reported to play an important role in motor control. Here, we investigate high γ amplitudes while focusing on two different aspects of a walking experiment, namely the fact that a person walks and the rhythmicity of walking. We found that high γ amplitudes (60-80Hz), located focally in central sensorimotor areas, were significantly increased during walking compared to standing. Moreover, high γ (70-90Hz) amplitudes in the same areas are modulated in relation to the gait cycle. Since the spectral peaks of high γ amplitude increase and modulation do not match, it is plausible that these two high γ elements represent different frequency-specific network interactions. Interestingly, we found high γ (70-90Hz) amplitudes to be coupled to low γ (24-40Hz) amplitudes, which both are modulated in relation to the gait cycle but conversely to each other. In summary, our work is a further step towards modeling cortical involvement during human upright walking.},
}
@article {pmid25816742,
year = {2015},
author = {Janzen, T and Haegeman, B and Etienne, RS},
title = {A sampling formula for ecological communities with multiple dispersal syndromes.},
journal = {Journal of theoretical biology},
volume = {374},
number = {},
pages = {94-106},
doi = {10.1016/j.jtbi.2015.03.018},
pmid = {25816742},
issn = {1095-8541},
mesh = {*Biodiversity ; Ecology/methods ; Ecosystem ; Likelihood Functions ; *Models, Biological ; Panama ; *Plant Dispersal ; Population Dynamics ; Probability ; Reproducibility of Results ; Species Specificity ; Trees/*physiology ; Tropical Climate ; },
abstract = {Over the past decade, the neutral theory of biodiversity has stirred up community assembly theory considerably by suggesting that stochasticity in the form of ecological drift is an important factor determining community composition and community turnover. The neutral theory assumes that all species within a community are functionally equivalent (the neutrality assumption), and therefore applies best to communities of trophically similar species. Evidently, trophically similar species may still differ in dispersal ability, and therefore may not be completely functionally equivalent. Here we present a new sampling formula that takes into account the partitioning of a community into two guilds that differ in immigration rate. We show that, using this sampling formula, we can accurately detect a subdivision into guilds from species abundance distributions, given ecological data about dispersal ability. We apply our sampling formula to tropical tree data from Barro Colorado Island, Panama. Tropical trees are divided depending on their dispersal mode, where biotically dispersed trees are grouped as one guild, and abiotically dispersed trees represent another guild. We find that breaking neutrality by adding guild structure to the neutral model significantly improves the fit to data and provides a better understanding of community assembly on BCI. Our findings are thus an important step towards an integration of neutral and niche theory.},
}
@article {pmid25816347,
year = {2015},
author = {Gateau, T and Durantin, G and Lancelot, F and Scannella, S and Dehais, F},
title = {Real-time state estimation in a flight simulator using fNIRS.},
journal = {PloS one},
volume = {10},
number = {3},
pages = {e0121279},
pmid = {25816347},
issn = {1932-6203},
mesh = {Adult ; *Aerospace Medicine ; Aircraft ; Aviation ; Brain-Computer Interfaces ; *Computer Simulation ; Female ; Hemodynamics ; Humans ; Male ; Memory, Short-Term/*physiology ; Prefrontal Cortex/*physiology ; Spectroscopy, Near-Infrared ; Workload ; },
abstract = {Working memory is a key executive function for flying an aircraft. This function is particularly critical when pilots have to recall series of air traffic control instructions. However, working memory limitations may jeopardize flight safety. Since the functional near-infrared spectroscopy (fNIRS) method seems promising for assessing working memory load, our objective is to implement an on-line fNIRS-based inference system that integrates two complementary estimators. The first estimator is a real-time state estimation MACD-based algorithm dedicated to identifying the pilot's instantaneous mental state (not-on-task vs. on-task). It does not require a calibration process to perform its estimation. The second estimator is an on-line SVM-based classifier that is able to discriminate task difficulty (low working memory load vs. high working memory load). These two estimators were tested with 19 pilots who were placed in a realistic flight simulator and were asked to recall air traffic control instructions. We found that the estimated pilot's mental state matched significantly better than chance with the pilot's real state (62% global accuracy, 58% specificity, and 72% sensitivity). The second estimator, dedicated to assessing single trial working memory loads, led to 80% classification accuracy, 72% specificity, and 89% sensitivity. These two estimators establish reusable blocks for further fNIRS-based passive brain computer interface development.},
}
@article {pmid25816285,
year = {2015},
author = {Rozado, D and Duenser, A and Howell, B},
title = {Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter.},
journal = {PloS one},
volume = {10},
number = {3},
pages = {e0121262},
pmid = {25816285},
issn = {1932-6203},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor ; Humans ; Motor Activity/*physiology ; Pupil/*physiology ; Task Performance and Analysis ; },
abstract = {For individuals with high degrees of motor disability or locked-in syndrome, it is impractical or impossible to use mechanical switches to interact with electronic devices. Brain computer interfaces (BCIs) can use motor imagery to detect interaction intention from users but lack the accuracy of mechanical switches. Hence, there exists a strong need to improve the accuracy of EEG-based motor imagery BCIs attempting to implement an on/off switch. Here, we investigate how monitoring the pupil diameter of a person as a psycho-physiological parameter in addition to traditional EEG channels can improve the classification accuracy of a switch-like BCI. We have recently noticed in our lab (work not yet published) how motor imagery is associated with increases in pupil diameter when compared to a control rest condition. The pupil diameter parameter is easily accessible through video oculography since most gaze tracking systems report pupil diameter invariant to head position. We performed a user study with 30 participants using a typical EEG based motor imagery BCI. We used common spatial patterns to separate motor imagery, signaling movement intention, from a rest control condition. By monitoring the pupil diameter of the user and using this parameter as an additional feature, we show that the performance of the classifier trying to discriminate motor imagery from a control condition improves over the traditional approach using just EEG derived features. Given the limitations of EEG to construct highly robust and reliable BCIs, we postulate that multi-modal approaches, such as the one presented here that monitor several psycho-physiological parameters, can be a successful strategy in making BCIs more accurate and less vulnerable to constraints such as requirements for long training sessions or high signal to noise ratio of electrode channels.},
}
@article {pmid25815815,
year = {2015},
author = {Combaz, A and Van Hulle, MM},
title = {Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.},
journal = {PloS one},
volume = {10},
number = {3},
pages = {e0121481},
pmid = {25815815},
issn = {1932-6203},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; *Evoked Potentials, Visual ; Feasibility Studies ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI.<br><br>APPROACH: We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline.<br><br>MAIN RESULTS: Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.},
}
@article {pmid25810487,
year = {2015},
author = {Martinez, CA and Wang, C},
title = {Structural constraints on learning in the neural network.},
journal = {Journal of neurophysiology},
volume = {114},
number = {5},
pages = {2555-2557},
pmid = {25810487},
issn = {1522-1598},
support = {T32 HD064578/HD/NICHD NIH HHS/United States ; R01-HD-065438/HD/NICHD NIH HHS/United States ; T32-HD-064578/HD/NICHD NIH HHS/United States ; },
mesh = {Brain ; *Brain Mapping ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Learning ; Neural Networks, Computer ; User-Computer Interface ; },
abstract = {Recent research suggests the brain can learn almost any brain-computer interface (BCI) configuration; however, contrasting behavioral evidence from structural learning theory argues that previous experience facilitates, or impedes, future learning. A study by Sadtler and colleagues (Nature 512: 423-426, 2014) used BCI to demonstrate that neural network structural characteristics constrain learning, a finding that might also provide insight into how the brain responds to and recovers after injury.},
}
@article {pmid25810484,
year = {2015},
author = {Ramos-Murguialday, A and Birbaumer, N},
title = {Brain oscillatory signatures of motor tasks.},
journal = {Journal of neurophysiology},
volume = {113},
number = {10},
pages = {3663-3682},
pmid = {25810484},
issn = {1522-1598},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Brain Waves/*physiology ; Brain-Computer Interfaces ; Cues ; Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Orthotic Devices ; Young Adult ; },
abstract = {Noninvasive brain-computer-interfaces (BCI) coupled with prosthetic devices were recently introduced in the rehabilitation of chronic stroke and other disorders of the motor system. These BCI systems and motor rehabilitation in general involve several motor tasks for training. This study investigates the neurophysiological bases of an EEG-oscillation-driven BCI combined with a neuroprosthetic device to define the specific oscillatory signature of the BCI task. Controlling movements of a hand robotic orthosis with motor imagery of the same movement generates sensorimotor rhythm oscillation changes and involves three elements of tasks also used in stroke motor rehabilitation: passive and active movement, motor imagery, and motor intention. We recorded EEG while nine healthy participants performed five different motor tasks consisting of closing and opening of the hand as follows: 1) motor imagery without any external feedback and without overt hand movement, 2) motor imagery that moves the orthosis proportional to the produced brain oscillation change with online proprioceptive and visual feedback of the hand moving through a neuroprosthetic device (BCI condition), 3) passive and 4) active movement of the hand with feedback (seeing and feeling the hand moving), and 5) rest. During the BCI condition, participants received contingent online feedback of the decrease of power of the sensorimotor rhythm, which induced orthosis movement and therefore proprioceptive and visual information from the moving hand. We analyzed brain activity during the five conditions using time-frequency domain bootstrap-based statistical comparisons and Morlet transforms. Activity during rest was used as a reference. Significant contralateral and ipsilateral event-related desynchronization of sensorimotor rhythm was present during all motor tasks, largest in contralateral-postcentral, medio-central, and ipsilateral-precentral areas identifying the ipsilateral precentral cortex as an integral part of motor regulation. Changes in task-specific frequency power compared with rest were similar between motor tasks, and only significant differences in the time course and some narrow specific frequency bands were observed between motor tasks. We identified EEG features representing active and passive proprioception (with and without muscle contraction) and active intention and passive involvement (with and without voluntary effort) differentiating brain oscillations during motor tasks that could substantially support the design of novel motor BCI-based rehabilitation therapies. The BCI task induced significantly different brain activity compared with the other motor tasks, indicating neural processes unique to the use of body actuators control in a BCI context.},
}
@article {pmid25807525,
year = {2015},
author = {Amaral, CP and Simões, MA and Castelo-Branco, MS},
title = {Neural signals evoked by stimuli of increasing social scene complexity are detectable at the single-trial level and right lateralized.},
journal = {PloS one},
volume = {10},
number = {3},
pages = {e0121970},
pmid = {25807525},
issn = {1932-6203},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Eye Movements/physiology ; Female ; Functional Laterality/*physiology ; Humans ; Male ; Pattern Recognition, Visual/physiology ; Photic Stimulation ; Social Perception ; Young Adult ; },
abstract = {Classification of neural signals at the single-trial level and the study of their relevance in affective and cognitive neuroscience are still in their infancy. Here we investigated the neurophysiological correlates of conditions of increasing social scene complexity using 3D human models as targets of attention, which may also be important in autism research. Challenging single-trial statistical classification of EEG neural signals was attempted for detection of oddball stimuli with increasing social scene complexity. Stimuli had an oddball structure and were as follows: 1) flashed schematic eyes, 2) simple 3D faces flashed between averted and non-averted gaze (only eye position changing), 3) simple 3D faces flashed between averted and non-averted gaze (head and eye position changing), 4) animated avatar alternated its gaze direction to the left and to the right (head and eye position), 5) environment with 4 animated avatars all of which change gaze and one of which is the target of attention. We found a late (> 300 ms) neurophysiological oddball correlate for all conditions irrespective of their complexity as assessed by repeated measures ANOVA. We attempted single-trial detection of this signal with automatic classifiers and obtained a significant balanced accuracy classification of around 79%, which is noteworthy given the amount of scene complexity. Lateralization analysis showed a specific right lateralization only for more complex realistic social scenes. In sum, complex ecological animations with social content elicit neurophysiological events which can be characterized even at the single-trial level. These signals are right lateralized. These finding paves the way for neuroscientific studies in affective neuroscience based on complex social scenes, and given the detectability at the single trial level this suggests the feasibility of brain computer interfaces that can be applied to social cognition disorders such as autism.},
}
@article {pmid25806719,
year = {2016},
author = {Andresen, EM and Fried-Oken, M and Peters, B and Patrick, DL},
title = {Initial constructs for patient-centered outcome measures to evaluate brain-computer interfaces.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {11},
number = {7},
pages = {548-557},
pmid = {25806719},
issn = {1748-3115},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; *Brain-Computer Interfaces ; Communication ; Persons with Disabilities/*rehabilitation ; Family Relations ; Female ; Humans ; International Classification of Functioning, Disability and Health ; Interviews as Topic ; Male ; Mental Health ; Middle Aged ; *Patient Reported Outcome Measures ; Quality of Life ; *Research Design ; Self-Help Devices ; Social Participation ; Young Adult ; },
abstract = {PURPOSE: The authors describe preliminary work toward the creation of patient-centered outcome (PCO) measures to evaluate brain-computer interface (BCI) as an assistive technology (AT) for individuals with severe speech and physical impairments (SSPI).<br><br>METHOD: In Phase 1, 591 items from 15 existing measures were mapped to the International Classification of Functioning, Disability and Health (ICF). In Phase 2, qualitative interviews were conducted with eight people with SSPI and seven caregivers. Resulting text data were coded in an iterative analysis.<br><br>RESULTS: Most items (79%) were mapped to the ICF environmental domain; over half (53%) were mapped to more than one domain. The ICF framework was well suited for mapping items related to body functions and structures, but less so for items in other areas, including personal factors. Two constructs emerged from qualitative data: quality of life (QOL) and AT. Component domains and themes were identified for each.<br><br>CONCLUSIONS: Preliminary constructs, domains and themes were generated for future PCO measures relevant to BCI. Existing instruments are sufficient for initial items but do not adequately match the values of people with SSPI and their caregivers. Field methods for interviewing people with SSPI were successful, and support the inclusion of these individuals in PCO research. Implications for Rehabilitation Adapted interview methods allow people with severe speech and physical impairments to participate in patient-centered outcomes research. Patient-centered outcome measures are needed to evaluate the clinical implementation of brain-computer interface as an assistive technology.},
}
@article {pmid25805963,
year = {2015},
author = {Estepp, JR and Christensen, JC},
title = {Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {54},
pmid = {25805963},
issn = {1662-4548},
abstract = {The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface) on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral) may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of) effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors.},
}
@article {pmid25804352,
year = {2015},
author = {Jin, J and Sellers, EW and Zhou, S and Zhang, Y and Wang, X and Cichocki, A},
title = {A P300 brain-computer interface based on a modification of the mismatch negativity paradigm.},
journal = {International journal of neural systems},
volume = {25},
number = {3},
pages = {1550011},
doi = {10.1142/S0129065715500112},
pmid = {25804352},
issn = {1793-6462},
mesh = {*Algorithms ; Brain-Computer Interfaces/*standards ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Humans ; Signal-To-Noise Ratio ; *User-Computer Interface ; },
abstract = {The P300-based brain-computer interface (BCI) is an extension of the oddball paradigm, and can facilitate communication for people with severe neuromuscular disorders. It has been shown that, in addition to the P300, other event-related potential (ERP) components have been shown to contribute to successful operation of the P300 BCI. Incorporating these components into the classification algorithm can improve the classification accuracy and information transfer rate (ITR). In this paper, a single character presentation paradigm was compared to a presentation paradigm that is based on the visual mismatch negativity. The mismatch negativity paradigm showed significantly higher classification accuracy and ITRs than a single character presentation paradigm. In addition, the mismatch paradigm elicited larger N200 and N400 components than the single character paradigm. The components elicited by the presentation method were consistent with what would be expected from a mismatch paradigm and a typical P300 was also observed. The results show that increasing the signal-to-noise ratio by increasing the amplitude of ERP components can significantly improve BCI speed and accuracy. The mismatch presentation paradigm may be considered a viable option to the traditional P300 BCI paradigm.},
}
@article {pmid25803728,
year = {2015},
author = {Vitale, F and Summerson, SR and Aazhang, B and Kemere, C and Pasquali, M},
title = {Neural stimulation and recording with bidirectional, soft carbon nanotube fiber microelectrodes.},
journal = {ACS nano},
volume = {9},
number = {4},
pages = {4465-4474},
doi = {10.1021/acsnano.5b01060},
pmid = {25803728},
issn = {1936-086X},
mesh = {Animals ; *Carbon ; Carbon Fiber ; Electric Stimulation/*instrumentation ; *Electrodes, Implanted ; Male ; Materials Testing ; *Mechanical Phenomena ; Microelectrodes ; *Nanotubes, Carbon ; Neurons/*cytology ; Rats ; },
abstract = {The development of microelectrodes capable of safely stimulating and recording neural activity is a critical step in the design of many prosthetic devices, brain-machine interfaces, and therapies for neurologic or nervous-system-mediated disorders. Metal electrodes are inadequate prospects for the miniaturization needed to attain neuronal-scale stimulation and recording because of their poor electrochemical properties, high stiffness, and propensity to fail due to bending fatigue. Here we demonstrate neural recording and stimulation using carbon nanotube (CNT) fiber electrodes. In vitro characterization shows that the tissue contact impedance of CNT fibers is remarkably lower than that of state-of-the-art metal electrodes, making them suitable for recording single-neuron activity without additional surface treatments. In vivo chronic studies in parkinsonian rodents show that CNT fiber microelectrodes stimulate neurons as effectively as metal electrodes with 10 times larger surface area, while eliciting a significantly reduced inflammatory response. The same CNT fiber microelectrodes can record neural activity for weeks, paving the way for the development of novel multifunctional and dynamic neural interfaces with long-term stability.},
}
@article {pmid25802861,
year = {2015},
author = {Wang, JJ and Xue, F and Li, H},
title = {Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso.},
journal = {BioMed research international},
volume = {2015},
number = {},
pages = {703768},
pmid = {25802861},
issn = {2314-6141},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Databases as Topic ; *Electroencephalography ; Humans ; },
abstract = {Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs). Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.},
}
@article {pmid25800212,
year = {2015},
author = {Park, CH and Chang, WH and Lee, M and Kwon, GH and Kim, L and Kim, ST and Kim, YH},
title = {Which motor cortical region best predicts imagined movement?.},
journal = {NeuroImage},
volume = {113},
number = {},
pages = {101-110},
doi = {10.1016/j.neuroimage.2015.03.033},
pmid = {25800212},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain Mapping ; Brain-Computer Interfaces ; Female ; Functional Laterality/physiology ; Hand/innervation/physiology ; Hand Strength/physiology ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; Rotation ; Space Perception/physiology ; },
abstract = {In brain-computer interfacing (BCI), motor imagery is used to provide a gateway to an effector action or behavior. However, in contrast to the main functional role of the primary motor cortex (M1) in motor execution, the M1's involvement in motor imagery has been debated, while the roles of secondary motor areas such as the premotor cortex (PMC) and supplementary motor area (SMA) in motor imagery have been proposed. We examined which motor cortical region had the greatest predictive ability for imagined movement among the primary and secondary motor areas. For two modes of motor performance, executed movement and imagined movement, in 12 healthy subjects who performed two types of motor task, hand grasping and hand rotation, we used the multivariate Bayes method to compare predictive ability between the primary and secondary motor areas (M1, PMC, and SMA) contralateral to the moved hand. With the distributed representation of activation, executed movement was best predicted from the M1 while imagined movement from the SMA, among the three motor cortical regions, in both types of motor task. In addition, the most predictive information about the distinction between executed movement and imagined movement was contained in the M1. The greater predictive ability of the SMA for imagined movement suggests its functional role that could be applied to motor imagery-based BCI.},
}
@article {pmid25796980,
year = {2015},
author = {Kiene, FE and Pein, M and Thommes, M},
title = {Orientation to determine quality attributes of flavoring excipients containing volatile molecules.},
journal = {Journal of pharmaceutical and biomedical analysis},
volume = {110},
number = {},
pages = {20-26},
doi = {10.1016/j.jpba.2015.01.033},
pmid = {25796980},
issn = {1873-264X},
mesh = {Chemistry, Pharmaceutical ; *Chromatography, Gas/standards ; Excipients/*analysis/standards ; Flavoring Agents/*analysis/standards ; Odorants/*analysis ; Quality Control ; Technology, Pharmaceutical/*methods/standards ; Time Factors ; Volatilization ; },
abstract = {Pharmaceutical excipients containing volatile odor-active molecules can be used in pharmaceutical development to increase patients' compliance. However, capturing the molecular composition of these odor-active substances is challenging. Therefore, guidance for the analytical investigation of these excipients should be developed. Using a model flavor, lead molecules were chosen and a gas chromatographic method was validated according to pharmaceutical guidelines. Changes during storage as well as batch homogeneity and conformity were investigated. The knowledge gained could be used to understand molecular differences between batches caused by aging. A suitable attempt to capture the volatile molecular composition of flavoring substance was presented and the found results could be used for the determination and interpretation of quality attributes.},
}
@article {pmid25794405,
year = {2016},
author = {Georgieva, P and Bouaynaya, N and Silva, F and Mihaylova, L and Jain, LC},
title = {A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources.},
journal = {IEEE journal of biomedical and health informatics},
volume = {20},
number = {3},
pages = {880-892},
doi = {10.1109/JBHI.2015.2413752},
pmid = {25794405},
issn = {2168-2208},
support = {R01 GM096191/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Electroencephalography (EEG)-based brain computer interface (BCI) is the most studied noninvasive interface to build a direct communication pathway between the brain and an external device. However, correlated noises in EEG measurements still constitute a significant challenge. Alternatively, building BCIs based on filtered brain activity source signals instead of using their surface projections, obtained from the noisy EEG signals, is a promising and not well-explored direction. In this context, finding the locations and waveforms of inner brain sources represents a crucial task for advancing source-based noninvasive BCI technologies. In this paper, we propose a novel multicore beamformer particle filter (multicore BPF) to estimate the EEG brain source spatial locations and their corresponding waveforms. In contrast to conventional (single-core) beamforming spatial filters, the developed multicore BPF considers explicitly temporal correlation among the estimated brain sources by suppressing activation from regions with interfering coherent sources. The hybrid multicore BPF brings together the advantages of both deterministic and Bayesian inverse problem algorithms in order to improve the estimation accuracy. It solves the brain activity localization problem without prior information about approximate areas of source locations. Moreover, the multicore BPF reduces the dimensionality of the problem to half compared with the PF solution, thus alleviating the curse of dimensionality problem. The results, based on generated and real EEG data, show that the proposed framework recovers correctly the dominant sources of brain activity.},
}
@article {pmid25794393,
year = {2015},
author = {Yu, T and Yu, Z and Gu, Z and Li, Y},
title = {Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {6},
pages = {1068-1077},
doi = {10.1109/TNSRE.2015.2413943},
pmid = {25794393},
issn = {1558-0210},
mesh = {Algorithms ; Automation ; Bayes Theorem ; *Brain-Computer Interfaces ; Data Collection ; Databases, Factual ; Discriminant Analysis ; Electrodes ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Machine Learning ; Models, Statistical ; Normal Distribution ; Reproducibility of Results ; },
abstract = {During the development of a brain-computer interface, it is beneficial to exploit information in multiple electrode signals. However, a small channel subset is favored for not only machine learning feasibility, but also practicality in commercial and clinical BCI applications. An embedded channel selection approach based on grouped automatic relevance determination is proposed. The proposed Gaussian conjugate group-sparse prior and the embedded nature of the concerned Bayesian linear model enable simultaneous channel selection and feature classification. Moreover, with the marginal likelihood (evidence) maximization technique, hyper-parameters that determine the sparsity of the model are directly estimated from the training set, avoiding time-consuming cross-validation. Experiments have been conducted on P300 speller BCIs. The results for both public and in-house datasets show that the channels selected by our techniques yield competitive classification performance with the state-of-the-art and are biologically relevant to P300.},
}
@article {pmid25794340,
year = {2015},
author = {Scott, WW and Sharp, S and Figueroa, SA and Eastman, AL and Hatchette, CV and Madden, CJ and Rickert, KL},
title = {Clinical and radiographic outcomes following traumatic Grade 1 and 2 carotid artery injuries: a 10-year retrospective analysis from a Level I trauma center. The Parkland Carotid and Vertebral Artery Injury Survey.},
journal = {Journal of neurosurgery},
volume = {122},
number = {5},
pages = {1196-1201},
doi = {10.3171/2015.1.JNS14642},
pmid = {25794340},
issn = {1933-0693},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Carotid Artery Injuries/complications/*diagnostic imaging/*therapy ; Cerebral Infarction/etiology ; Female ; Health Care Surveys ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Radiography ; Retrospective Studies ; Time Factors ; Trauma Centers ; Treatment Outcome ; Vertebral Artery/injuries ; Wounds, Nonpenetrating/complications/*diagnostic imaging/*therapy ; Young Adult ; },
abstract = {OBJECT: Proper screening, management, and follow-up of Grade 1 and 2 blunt carotid artery injuries (BCIs) remains controversial. These low-grade BCIs were analyzed to define their natural history and establish a rational management plan based on lesion progression and cerebral infarction.<br><br>METHODS: A retrospective review of a prospectively maintained database of all blunt traumatic carotid and vertebral artery injuries treated between August 2003 and April 2013 was performed and Grade 1 and 2 BCIs were identified. Grade 1 injuries are defined as a vessel lumen stenosis of less than 25%, and Grade 2 injuries are defined as a stenosis of the vessel lumen between 25% and 50%. Demographic information, radiographic imaging, number of imaging sessions performed per individual, length of radiographic follow-up, radiographic outcome at end of follow-up, treatment(s) provided, and documentation of ischemic stroke or transient ischemic attack were recorded.<br><br>RESULTS: One hundred seventeen Grade 1 and 2 BCIs in 100 patients were identified and available for follow-up. The mean follow-up duration was 60 days. Final imaging of Grade 1 and 2 BCIs demonstrated that 64% of cases had resolved, 13% of cases were radiographically stable, and 9% were improved, whereas 14% radiographically worsened. Of the treatments received, 54% of cases were treated with acetylsalicylic acid (ASA), 31% received no treatment, and 15% received various medications and treatments, including endovascular stenting. There was 1 cerebral infarction that was thought to be related to bilateral Grade 2 BCI, which developed soon after hospital admission.<br><br>CONCLUSIONS: The majority of Grade 1 and 2 BCIs remained stable or improved at final follow-up. Despite a 14% rate of radiographic worsening in the Grade 1 and 2 BCIs cohort, there were no adverse clinical outcomes associated with these radiographic changes. The stroke rate was 1% in this low-grade BCIs cohort, which may be an overestimate. The use of ASA or other antiplatelet or anticoagulant medications in these low-grade BCIs did not appear to correlate with radiographic injury stability, nor with a decreased rate of cerebral infarction. Although these data suggest that these Grade 1 and 2 BCIs may require less intensive radiographic follow-up, future prospective studies are needed to make conclusive changes related to treatment and management.},
}
@article {pmid25791211,
year = {2015},
author = {Prudic, A and Lesniak, AK and Ji, Y and Sadowski, G},
title = {Thermodynamic phase behaviour of indomethacin/PLGA formulations.},
journal = {European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V},
volume = {93},
number = {},
pages = {88-94},
doi = {10.1016/j.ejpb.2015.01.029},
pmid = {25791211},
issn = {1873-3441},
mesh = {Calorimetry, Differential Scanning ; Chemistry, Pharmaceutical ; *Drug Carriers ; Indomethacin/*chemistry ; Lactic Acid/*chemistry ; Models, Chemical ; Molecular Weight ; Phase Transition ; Polyglycolic Acid/*chemistry ; Polylactic Acid-Polyglycolic Acid Copolymer ; Solubility ; Technology, Pharmaceutical/methods ; *Thermodynamics ; Transition Temperature ; },
abstract = {In the current study, the phase behaviour of indomethacin and poly(lactic-co-glycolic acid) (PLGA) formulations was investigated as a function of the molecular weight and the copolymer composition of PLGA. The formulations were prepared by ball milling, and the phase behaviour, comprised of the glass-transition temperature of the formulations and the solubility of indomethacin in PLGA, was measured using modulated differential scanning calorimetry (mDSC). The results determined that the solubility of indomethacin in PLGA at room temperature was very low and increased with a corresponding decrease in the molecular weight of PLGA. The copolymer composition of PLGA had a minor effect on the indomethacin solubility. The effect of PLGA's molecular weight and copolymer composition on the solubility of indomethacin could be modelled using the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) with a high degree of accuracy when compared with the experimental data. The glass-transition temperatures had a negative deviation from the weighted mean of the glass-transition temperatures of the pure substances, which could be described by the Kwei-equation.},
}
@article {pmid25790172,
year = {2015},
author = {Köhler, P and Wolff, A and Ejserholm, F and Wallman, L and Schouenborg, J and Linsmeier, CE},
title = {Influence of probe flexibility and gelatin embedding on neuronal density and glial responses to brain implants.},
journal = {PloS one},
volume = {10},
number = {3},
pages = {e0119340},
pmid = {25790172},
issn = {1932-6203},
mesh = {Animals ; Astrocytes/drug effects/pathology ; *Brain-Computer Interfaces ; Cerebral Cortex/drug effects/pathology ; Electrodes, Implanted ; Gelatin/*therapeutic use ; Neuroglia/drug effects/pathology ; Neurons/*drug effects/pathology ; Polymers/therapeutic use ; Prostheses and Implants/*adverse effects ; Rats ; },
abstract = {To develop long-term high quality communication between brain and computer, a key issue is how to reduce the adverse foreign body responses. Here, the impact of probe flexibility and gelatine embedding on long-term (6w) tissue responses, was analyzed. Probes of same polymer material, size and shape, flexible mainly in one direction, were implanted in rat cerebral cortex (nimplants = 3 x 8) in two orientations with respect to the major movement direction of the brain relative to the skull: parallel to (flex mode) or transverse to (rigid mode). Flex mode implants were either embedded in gelatin or non-embedded. Neurons, activated microglia and astrocytes were visualized using immunohistochemistry. The astrocytic reactivity, but not microglial response, was significantly lower to probes implanted in flex mode as compared to rigid mode. The microglial response, but not astrocytic reactivity, was significantly smaller to gelatin embedded probes (flex mode) than non-embedded. Interestingly, the neuronal density was preserved in the inner zone surrounding gelatin embedded probes. This contrasts to the common reports of reduced neuronal density close to implanted probes. In conclusion, sheer stress appears to be an important factor for astrocytic reactivity to implanted probes. Moreover, gelatin embedding can improve the neuronal density and reduce the microglial response close to the probe.},
}
@article {pmid25788102,
year = {2015},
author = {Zhang, R and Xu, P and Chen, R and Li, F and Guo, L and Li, P and Zhang, T and Yao, D},
title = {Predicting Inter-session Performance of SMR-Based Brain-Computer Interface Using the Spectral Entropy of Resting-State EEG.},
journal = {Brain topography},
volume = {28},
number = {5},
pages = {680-690},
doi = {10.1007/s10548-015-0429-3},
pmid = {25788102},
issn = {1573-6792},
mesh = {Adult ; Biomarkers ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Entropy ; Feedback, Sensory/*physiology ; Female ; Forecasting ; Hand/physiology ; Humans ; Imagination ; Male ; Motor Activity ; ROC Curve ; Reproducibility of Results ; },
abstract = {Currently most subjects can control the sensorimotor rhythm-based brain-computer interface (SMR-BCI) successfully after several training procedures. However, 15-30% of subjects cannot achieve SMR-BCI control even after long-term training, and they are termed as "BCI inefficiency". This study focuses on the investigation of reliable SMR-BCI performance predictor. 40 subjects participated in the first experimental session and 26 of them returned in the second session, each session consists of an eyes closed/open resting-state EEG recording run and four EEG recording runs with hand motor imagery. We found spectral entropy derived from eyes closed resting-state EEG of channel C3 has a high correlation with SMR-BCI performance (r = 0.65). Thus, we proposed to use it as a biomarker to predict individual SMR-BCI performance. Receiver operating characteristics analysis and leave-one-out cross-validation demonstrated that the spectral entropy predictor provide outstanding classification capability for high and low aptitude BCI users. To our knowledge, there has been no discussion about the reliability of inter-session prediction in previous studies. We further evaluated the inter-session prediction performance of the spectral entropy predictor, and the results showed that the average classification accuracy of inter-session prediction up to 89%. The proposed predictor is convenient to obtain because it derived from single channel resting-state EEG, it could be used to identify potential SMR-BCI inefficiency subjects from novel users. But there are still limitations because Kübler et al. have shown that some BCI users may need eight or more sessions before they develop classifiable SMR activity.},
}
@article {pmid25775550,
year = {2015},
author = {Norton, JJ and Lee, DS and Lee, JW and Lee, W and Kwon, O and Won, P and Jung, SY and Cheng, H and Jeong, JW and Akce, A and Umunna, S and Na, I and Kwon, YH and Wang, XQ and Liu, Z and Paik, U and Huang, Y and Bretl, T and Yeo, WH and Rogers, JA},
title = {Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {112},
number = {13},
pages = {3920-3925},
pmid = {25775550},
issn = {1091-6490},
mesh = {*Brain-Computer Interfaces ; Cognition ; Computers ; *Ear, External ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Electronics ; Equipment Design ; Event-Related Potentials, P300 ; Fractals ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain-computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain-computer interface and elicitation of an event-related potential (P300 wave).},
}
@article {pmid25775495,
year = {2015},
author = {Moghadamfalahi, M and Orhan, U and Akcakaya, M and Nezamfar, H and Fried-Oken, M and Erdogmus, D},
title = {Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {910-920},
doi = {10.1109/TNSRE.2015.2411574},
pmid = {25775495},
issn = {1558-0210},
support = {R01DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Language ; Machine Learning ; Male ; Models, Neurological ; *Natural Language Processing ; Pattern Recognition, Automated/methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; *Word Processing ; },
abstract = {Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been utilized to induce ERPs. In this manuscript, through an experimental study, we assess the speed, recorded signal quality, and system accuracy of a language-model-assisted BCI typing system using three different presentation paradigms: a 4 × 7 matrix paradigm of a 28-character alphabet with row-column presentation (RCP) and single-character presentation (SCP), and rapid serial visual presentation (RSVP) of the same. Our analyses show that signal quality and classification accuracy are comparable between the two visual stimulus presentation paradigms. In addition, we observe that while the matrix-based paradigm can be generally employed with lower inter-trial-interval (ITI) values, the best presentation paradigm and ITI value configuration is user dependent. This potentially warrants offering both presentation paradigms and variable ITI options to users of BCI typing systems.},
}
@article {pmid25774541,
year = {2015},
author = {Ventura, V and Todorova, S},
title = {A computationally efficient method for incorporating spike waveform information into decoding algorithms.},
journal = {Neural computation},
volume = {27},
number = {5},
pages = {1033-1050},
pmid = {25774541},
issn = {1530-888X},
support = {R01 MH064537/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Data Interpretation, Statistical ; Evoked Potentials, Motor/*physiology ; Humans ; Linear Models ; *Models, Neurological ; Movement/physiology ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.},
}
@article {pmid25773879,
year = {2015},
author = {Harris, AR and Molino, PJ and Kapsa, RM and Clark, GM and Paolini, AG and Wallace, GG},
title = {Correlation of the impedance and effective electrode area of doped PEDOT modified electrodes for brain-machine interfaces.},
journal = {The Analyst},
volume = {140},
number = {9},
pages = {3164-3174},
doi = {10.1039/c4an02362e},
pmid = {25773879},
issn = {1364-5528},
mesh = {*Brain-Computer Interfaces ; Dextrans/*chemistry ; Dielectric Spectroscopy ; Electric Impedance ; Electrodes ; Humans ; Polystyrenes/*chemistry ; Sulfonamides/*chemistry ; Thiophenes/*chemistry ; },
abstract = {Electrode impedance is used to assess the thermal noise and signal-to-noise ratio for brain-machine interfaces. An intermediate frequency of 1 kHz is typically measured, although other frequencies may be better predictors of device performance. PEDOT-PSS, PEDOT-DBSA and PEDOT-pTs conducting polymer modified electrodes have reduced impedance at 1 kHz compared to bare metal electrodes, but have no correlation with the effective electrode area. Analytical solutions to impedance indicate that all low-intermediate frequencies can be used to compare the electrode area at a series RC circuit, typical of an ideal metal electrode in a conductive solution. More complex equivalent circuits can be used for the modified electrodes, with a simplified Randles circuit applied to PEDOT-PSS and PEDOT-pTs and a Randles circuit including a Warburg impedance element for PEDOT-DBSA at 0 V. The impedance and phase angle at low frequencies using both equivalent circuit models is dependent on the electrode area. Low frequencies may therefore provide better predictions of the thermal noise and signal-to-noise ratio at modified electrodes. The coefficient of variation of the PEDOT-pTs impedance at low frequencies was lower than the other conducting polymers, consistent with linear and steady-state electroactive area measurements. There are poor correlations between the impedance and the charge density as they are not ideal metal electrodes.},
}
@article {pmid25773726,
year = {2015},
author = {Krause, J and Oeldorf, T and Schembecker, G and Merz, J},
title = {Enzymatic hydrolysis in an aqueous organic two-phase system using centrifugal partition chromatography.},
journal = {Journal of chromatography. A},
volume = {1391},
number = {},
pages = {72-79},
doi = {10.1016/j.chroma.2015.02.071},
pmid = {25773726},
issn = {1873-3778},
mesh = {Biocatalysis ; Bioreactors ; Candida/enzymology ; Countercurrent Distribution ; Fungal Proteins/*chemistry ; Hydrolysis ; Kinetics ; Lipase/*chemistry ; Palmitates/chemistry ; Water/chemistry ; },
abstract = {Multi-phase reaction systems, mostly aqueous organic systems, are used in enzyme catalysis to convert hydrophobic substrates which are almost insoluble in aqueous media. In this study, a Centrifugal Partition Chromatograph is used as a compact device for enzymatic multi-phase reaction that combines efficient substrate supply to the aqueous phase and separation of both phases in one apparatus. A process design procedure to systematically select the aqueous and organic phase to achieve stable and efficient reaction rates and operation conditions in Centrifugal Partition Chromatography for efficient mixing and separation of the phases is presented. The procedure is applied to the hydrolysis of 4-nitrophenyl palmitate with a lipase derived from Candida rugosa. It was found that the hydrolysis rate of 4-nitrophenyl palmitate was two times higher in Centrifugal Partition Chromatography than in comparable stirred tank reactor experiments.},
}
@article {pmid25769276,
year = {2015},
author = {Pisotta, I and Perruchoud, D and Ionta, S},
title = {Hand-in-hand advances in biomedical engineering and sensorimotor restoration.},
journal = {Journal of neuroscience methods},
volume = {246},
number = {},
pages = {22-29},
doi = {10.1016/j.jneumeth.2015.03.003},
pmid = {25769276},
issn = {1872-678X},
mesh = {*Biomedical Engineering ; Brain/*physiology ; *Brain-Computer Interfaces ; Hand/*physiology ; Humans ; Robotics ; User-Computer Interface ; },
abstract = {BACKGROUND: Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown.<br><br>NEW METHOD: The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function.<br><br>RESULTS: Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed.<br><br>Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation).<br><br>CONCLUSIONS: Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities.},
}
@article {pmid25769171,
year = {2015},
author = {Liu, X and Zhang, M and Subei, B and Richardson, AG and Lucas, TH and Van der Spiegel, J},
title = {The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {9},
number = {2},
pages = {248-258},
doi = {10.1109/TBCAS.2015.2392555},
pmid = {25769171},
issn = {1940-9990},
support = {K12NS080223/NS/NINDS NIH HHS/United States ; },
mesh = {Amplifiers, Electronic ; *Brain-Computer Interfaces ; Electrodes, Implanted ; *Equipment Design ; Humans ; Signal Processing, Computer-Assisted/instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system.},
}
@article {pmid25766495,
year = {2015},
author = {Schwienheer, C and Merz, J and Schembecker, G},
title = {Investigation, comparison and design of chambers used in centrifugal partition chromatography on the basis of flow pattern and separation experiments.},
journal = {Journal of chromatography. A},
volume = {1390},
number = {},
pages = {39-49},
doi = {10.1016/j.chroma.2015.01.085},
pmid = {25766495},
issn = {1873-3778},
mesh = {Centrifugation ; Countercurrent Distribution/instrumentation/*methods ; Hydrodynamics ; },
abstract = {In centrifugal partition chromatography (CPC) the separation efficiency is mainly influenced by the hydrodynamic of mobile and stationary phase in the chambers. Thus, the hydrodynamic has to be investigated and understood in order to enhance a CPC separation run. Different chamber geometries have been developed in the past and the influence of several phase systems and CPC operating conditions were investigated for these chambers. However, a direct comparison between the different chamber types has not been performed yet. In order to investigate the direct influence of the chamber design on the hydrodynamic, several chamber designs - partially similar in geometry to commercial available designs - are investigated under standardized conditions in the present study. The results show the influence of geometrical aspects of the chamber design on the hydrodynamic and therewith, on the separation efficiency. As a conclusion of the present study, some ideas for an optimal chamber design for laboratory and industrial purpose are proposed.},
}
@article {pmid25764705,
year = {2014},
author = {Zhao, L and Xing, X and Guo, X and Liu, Z and He, Y},
title = {[A wireless smart home system based on brain-computer interface of steady state visual evoked potential].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {5},
pages = {967-970},
pmid = {25764705},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Microcomputers ; *Wireless Technology ; },
abstract = {Brain-computer interface (BCI) system is a system that achieves communication and control among humans and computers and other electronic equipment with the electroencephalogram (EEG) signals. This paper describes the working theory of the wireless smart home system based on the BCI technology. We started to get the steady-state visual evoked potential (SSVEP) using the single chip microcomputer and the visual stimulation which composed by LED lamp to stimulate human eyes. Then, through building the power spectral transformation on the LabVIEW platform, we processed timely those EEG signals under different frequency stimulation so as to transfer them to different instructions. Those instructions could be received by the wireless transceiver equipment to control the household appliances and to achieve the intelligent control towards the specified devices. The experimental results showed that the correct rate for the 10 subjects reached 100%, and the control time of average single device was 4 seconds, thus this design could totally achieve the original purpose of smart home system.},
}
@article {pmid25764703,
year = {2014},
author = {Wang, Y and Li, X and Li, H and Shao, C and Ying, L and Wu, S},
title = {[Feature extraction of motor imagery electroencephalography based on time-frequency-space domains].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {5},
pages = {955-961},
pmid = {25764703},
issn = {1001-5515},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; },
abstract = {The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.},
}
@article {pmid25762908,
year = {2015},
author = {Bauer, R and Gharabaghi, A},
title = {Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {21},
pmid = {25762908},
issn = {1662-5153},
abstract = {Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject's ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject's cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance.},
}
@article {pmid25762907,
year = {2015},
author = {Blefari, ML and Sulzer, J and Hepp-Reymond, MC and Kollias, S and Gassert, R},
title = {Improvement in precision grip force control with self-modulation of primary motor cortex during motor imagery.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {18},
pmid = {25762907},
issn = {1662-5153},
support = {K12 HD073945/HD/NICHD NIH HHS/United States ; },
abstract = {Motor imagery (MI) has shown effectiveness in enhancing motor performance. This may be due to the common neural mechanisms underlying MI and motor execution (ME). The main region of the ME network, the primary motor cortex (M1), has been consistently linked to motor performance. However, the activation of M1 during motor imagery is controversial, which may account for inconsistent rehabilitation therapy outcomes using MI. Here, we examined the relationship between contralateral M1 (cM1) activation during MI and changes in sensorimotor performance. To aid cM1 activity modulation during MI, we used real-time fMRI neurofeedback-guided MI based on cM1 hand area blood oxygen level dependent (BOLD) signal in healthy subjects, performing kinesthetic MI of pinching. We used multiple regression analysis to examine the correlation between cM1 BOLD signal and changes in motor performance during an isometric pinching task of those subjects who were able to activate cM1 during motor imagery. Activities in premotor and parietal regions were used as covariates. We found that cM1 activity was positively correlated to improvements in accuracy as well as overall performance improvements, whereas other regions in the sensorimotor network were not. The association between cM1 activation during MI with performance changes indicates that subjects with stronger cM1 activation during MI may benefit more from MI training, with implications toward targeted neurotherapy.},
}
@article {pmid25761881,
year = {2015},
author = {Feng, X and Zhang, Y and Shao, N and Wang, Y and Zhuang, Z and Wu, P and Lee, MJ and Liu, Y and Wang, X and Zhuang, J and Delpire, E and Gu, D and Cai, H},
title = {Aldosterone modulates thiazide-sensitive sodium chloride cotransporter abundance via DUSP6-mediated ERK1/2 signaling pathway.},
journal = {American journal of physiology. Renal physiology},
volume = {308},
number = {10},
pages = {F1119-27},
pmid = {25761881},
issn = {1522-1466},
support = {DK093501/DK/NIDDK NIH HHS/United States ; K08 DK068226S-1/DK/NIDDK NIH HHS/United States ; R01 GM074771/GM/NIGMS NIH HHS/United States ; GM074771/GM/NIGMS NIH HHS/United States ; I01 BX000994/BX/BLRD VA/United States ; },
mesh = {Aldosterone/metabolism/*pharmacology ; Animals ; Dual Specificity Phosphatase 6/*metabolism ; Electrolytes/metabolism ; MAP Kinase Signaling System/*drug effects/physiology ; Mice ; Mice, Knockout ; Models, Animal ; Phosphorylation/drug effects/physiology ; Protein Serine-Threonine Kinases/*deficiency/genetics/metabolism ; Signal Transduction/drug effects/physiology ; Sodium Chloride Symporters/*drug effects/*metabolism ; Sodium Chloride, Dietary/pharmacology ; Thiazides/*pharmacology ; Ubiquitination/drug effects/physiology ; },
abstract = {Thiazide-sensitive sodium chloride cotransporter (NCC) plays an important role in maintaining blood pressure. Aldosterone is known to modulate NCC abundance. Previous studies reported that dietary salts modulated NCC abundance through either WNK4 [with no lysine (k) kinase 4]-SPAK (Ste20-related proline alanine-rich kinase) or WNK4-extracellular signal-regulated kinase-1 and -2 (ERK1/2) signaling pathways. To exclude the influence of SPAK signaling pathway on the role of the aldosterone-mediated ERK1/2 pathway in NCC regulation, we investigated the effects of dietary salt changes and aldosterone on NCC abundance in SPAK knockout (KO) mice. We found that in SPAK KO mice low-salt diet significantly increased total NCC abundance while reducing ERK1/2 phosphorylation, whereas high-salt diet decreased total NCC while increasing ERK1/2 phosphorylation. Importantly, exogenous aldosterone administration increased total NCC abundance in SPAK KO mice while increasing DUSP6 expression, an ERK1/2-specific phosphatase, and led to decreasing ERK1/2 phosphorylation without changing the ratio of phospho-T53-NCC/total NCC. In mouse distal convoluted tubule (mDCT) cells, aldosterone increased DUSP6 expression while reducing ERK1/2 phosphorylation. DUSP6 Knockdown increased ERK1/2 phosphorylation while reducing total NCC expression. Inhibition of DUSP6 by (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one increased ERK1/2 phosphorylation and reversed the aldosterone-mediated increments of NCC partly by increasing NCC ubiquitination. Therefore, these data suggest that aldosterone modulates NCC abundance via altering NCC ubiquitination through a DUSP6-dependent ERK1/2 signal pathway in SPAK KO mice and part of the effects of dietary salt changes may be mediated by aldosterone in the DCTs.},
}
@article {pmid25761260,
year = {2015},
author = {Kapeller, C and Korostenskaja, M and Prueckl, R and Chen, PC and Lee, KH and Westerveld, M and Salinas, CM and Cook, JC and Baumgartner, JE and Guger, C},
title = {CortiQ-based Real-Time Functional Mapping for Epilepsy Surgery.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {32},
number = {3},
pages = {e12-22},
doi = {10.1097/WNP.0000000000000131},
pmid = {25761260},
issn = {1537-1603},
mesh = {Adolescent ; Adult ; Brain Mapping/*methods ; *Computer Systems ; Electrocorticography/*methods ; Epilepsy/*surgery ; Humans ; Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; Patient-Specific Modeling ; Software ; Tomography, X-Ray Computed ; Young Adult ; },
abstract = {PURPOSE: To evaluate the use of the cortiQ-based mapping system (g.tec medication engineering GmbH, Austria) for real-time functional mapping (RTFM) and to compare it to results from electrical cortical stimulation mapping (ESM) and functional magnetic resonance imaging (fMRI).<br><br>METHODS: Electrocorticographic activity was recorded in 3 male patients with intractable epilepsy by using cortiQ mapping system and analyzed in real time. Activation related to motor, sensory, and receptive language tasks was determined by evaluating the power of the high gamma frequency band (60-170 Hz). The sensitivity and specificity of RTFM were tested against ESM and fMRI results.<br><br>RESULTS: "Next-neighbor" approach demonstrated [sensitivity/specificity %] (1) RTFM against ESM: 100.00/79.70 for hand motor; 100.00/73.87 for hand sensory; -/87 for language (it was not identified by the ESM); (2) RTFM against fMRI: 100.00/84.4 for hand motor; 66.70/85.35 for hand sensory; and 87.85/77.70 for language.<br><br>CONCLUSIONS: The results of the quantitative "next-neighbor" RTFM evaluation were concordant to those from ESM and fMRI. The RTFM correlates well with localization of hand motor function provided by ESM and fMRI, which may offer added localization in the operating room and guidance for extraoperative ESM mapping. Real-time functional mapping correlates with fMRI language activation when ESM findings are negative. It has fewer limitations than ESM and greater flexibility in activation paradigms and measuring responses.},
}
@article {pmid25756862,
year = {2015},
author = {Tseng, KC and Lin, BS and Wong, AM and Lin, BS},
title = {Design of a mobile brain computer interface-based smart multimedia controller.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {3},
pages = {5518-5530},
pmid = {25756862},
issn = {1424-8220},
mesh = {Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Music/*psychology ; },
abstract = {Music is a way of expressing our feelings and emotions. Suitable music can positively affect people. However, current multimedia control methods, such as manual selection or automatic random mechanisms, which are now applied broadly in MP3 and CD players, cannot adaptively select suitable music according to the user's physiological state. In this study, a brain computer interface-based smart multimedia controller was proposed to select music in different situations according to the user's physiological state. Here, a commercial mobile tablet was used as the multimedia platform, and a wireless multi-channel electroencephalograph (EEG) acquisition module was designed for real-time EEG monitoring. A smart multimedia control program built in the multimedia platform was developed to analyze the user's EEG feature and select music according his/her state. The relationship between the user's state and music sorted by listener's preference was also examined in this study. The experimental results show that real-time music biofeedback according a user's EEG feature may positively improve the user's attention state.},
}
@article {pmid25753951,
year = {2015},
author = {Pasqualotto, E and Matuz, T and Federici, S and Ruf, CA and Bartl, M and Olivetti Belardinelli, M and Birbaumer, N and Halder, S},
title = {Usability and Workload of Access Technology for People With Severe Motor Impairment: A Comparison of Brain-Computer Interfacing and Eye Tracking.},
journal = {Neurorehabilitation and neural repair},
volume = {29},
number = {10},
pages = {950-957},
doi = {10.1177/1545968315575611},
pmid = {25753951},
issn = {1552-6844},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Disability Evaluation ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Eye Movements/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Disorders/*complications/*diagnosis ; Neuropsychological Tests ; Statistics, Nonparametric ; User-Computer Interface ; *Workload ; },
abstract = {BACKGROUND: Eye trackers are widely used among people with amyotrophic lateral sclerosis, and their benefits to quality of life have been previously shown. On the contrary, Brain-computer interfaces (BCIs) are still quite a novel technology, which also serves as an access technology for people with severe motor impairment.<br><br>OBJECTIVE: To compare a visual P300-based BCI and an eye tracker in terms of information transfer rate (ITR), usability, and cognitive workload in users with motor impairments.<br><br>METHODS: Each participant performed 3 spelling tasks, over 4 total sessions, using an Internet browser, which was controlled by a spelling interface that was suitable for use with either the BCI or the eye tracker. At the end of each session, participants evaluated usability and cognitive workload of the system.<br><br>RESULTS: ITR and System Usability Scale (SUS) score were higher for the eye tracker (Wilcoxon signed-rank test: ITR T = 9, P = .016; SUS T = 12.50, P = .035). Cognitive workload was higher for the BCI (T = 4; P = .003).<br><br>CONCLUSIONS: Although BCIs could be potentially useful for people with severe physical disabilities, we showed that the usability of BCIs based on the visual P300 remains inferior to eye tracking. We suggest that future research on visual BCIs should use eye tracking-based control as a comparison to evaluate performance or focus on nonvisual paradigms for persons who have lost gaze control.},
}
@article {pmid25749073,
year = {2015},
author = {Paus, R and Prudic, A and Ji, Y},
title = {Influence of excipients on solubility and dissolution of pharmaceuticals.},
journal = {International journal of pharmaceutics},
volume = {485},
number = {1-2},
pages = {277-287},
doi = {10.1016/j.ijpharm.2015.03.004},
pmid = {25749073},
issn = {1873-3476},
mesh = {Chemistry, Pharmaceutical ; Diffusion ; Excipients/*chemistry ; Indomethacin/*chemistry ; Kinetics ; Mannitol/chemistry ; Models, Chemical ; Models, Statistical ; Naproxen/*chemistry ; Polyethylene Glycols/chemistry ; Povidone/chemistry ; Solubility ; Technology, Pharmaceutical/methods ; Viscosity ; Water/chemistry ; },
abstract = {In this work, solubilities and dissolution profiles of the active pharmaceutical ingredients (APIs) indomethacin and naproxen were measured in water in the presence of one excipient out of polyethylene glycol (PEG) 2000, 6000 and 12000, polyvinylpyrrolidone (PVP) K 25 and mannitol. It was found that the solubility of indomethacin and naproxen was increased with an addition of the selected excipients, which was also predicted by the perturbed-chain statistical associating fluid theory (PC-SAFT). The two-step chemical-potential-gradient model was applied to investigate the dissolution mechanism of indomethacin and naproxen in water in the presence of the excipient. It was found that the dissolution mechanisms of indomethacin and naproxen were changed by the presence of excipients. Although the solubility of the API was increased by the addition of excipients, the dissolution rate of the API was decreased in some cases. This was mainly due to the combination of the molecular interactions between the API and the polymer with the influence of the excipients on the kinetic part (rate constant of the surface reaction or diffusion of the API or both) of API dissolution as function of PEG molar mass as well as of the API type. Based upon the determined rate constants, the dissolution profiles were modeled with a high accuracy compared with the experimental data.},
}
@article {pmid25747342,
year = {2015},
author = {Lu, N and Li, T and Pan, J and Ren, X and Feng, Z and Miao, H},
title = {Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.},
journal = {Computers in biology and medicine},
volume = {60},
number = {},
pages = {32-39},
doi = {10.1016/j.compbiomed.2015.02.010},
pmid = {25747342},
issn = {1879-0534},
mesh = {Algorithms ; Brain/*pathology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Medical Informatics/methods ; Models, Statistical ; Pattern Recognition, Automated ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Time Factors ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio.<br><br>NEW METHOD: In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification.<br><br>RESULTS: Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method.<br><br>Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF.<br><br>CONCLUSIONS: The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint.},
}
@article {pmid25743268,
year = {2015},
author = {Reichert, JL and Kober, SE and Neuper, C and Wood, G},
title = {Resting-state sensorimotor rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {11},
pages = {2068-2077},
doi = {10.1016/j.clinph.2014.09.032},
pmid = {25743268},
issn = {1872-8952},
mesh = {Adult ; Aging/physiology ; Brain Mapping ; Cognition/physiology ; Electroencephalography/*methods ; Female ; Humans ; Learning/*physiology ; Male ; Middle Aged ; Neurofeedback/physiology ; Predictive Value of Tests ; Rest/*physiology ; Sensorimotor Cortex/*physiology ; Up-Regulation/*physiology ; },
abstract = {OBJECTIVE: Instrumental conditioning of EEG activity (EEG-IC) is a promising method for improvement and rehabilitation of cognitive functions. However, it has been found that even healthy adults are not always able to learn how to regulate their brain activity during EEG-IC. In the present study, the role of a neurophysiological predictor of EEG-IC learning performance, the resting-state power of sensorimotor rhythm (rs-SMR, 12-15Hz), was investigated.<br><br>METHODS: Eyes-open and eyes-closed rs-SMR power was assessed before N=28 healthy adults underwent 10 training sessions of instrumental SMR conditioning (ISC), in which participants should learn to voluntarily increase their SMR power by means of audio-visual feedback. A control group of N=19 participants received gamma (40-43Hz) or sham EEG-IC.<br><br>RESULTS: N=19 of the ISC participants could be classified as "responders" as they were able to increase SMR power during training sessions, while N=9 participants ("non-responders") were not able to increase SMR power. Rs-SMR power in responders before start of ISC was higher in widespread parieto-occipital areas than in non-responders. A discriminant analysis indicated that eyes-open rs-SMR power in a central brain region specifically predicted later ISC performance, but not an increase of SMR in the control group.<br><br>CONCLUSIONS: Together, these findings indicate that rs-SMR power is a specific and easy-to-measure predictor of later ISC learning performance.<br><br>SIGNIFICANCE: The assessment of factors that influence the ability to regulate brain activity is of high relevance, as it could be used to avoid potentially frustrating and expensive EEG-IC training sessions for participants who have a low chance of success.},
}
@article {pmid25741249,
year = {2015},
author = {Callan, DE and Durantin, G and Terzibas, C},
title = {Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight.},
journal = {Frontiers in systems neuroscience},
volume = {9},
number = {},
pages = {11},
pmid = {25741249},
issn = {1662-5137},
abstract = {Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces.},
}
@article {pmid25741005,
year = {2015},
author = {Yuan, M and Zhang, W and Wang, J and Al Yaghchi, C and Ahmed, J and Chard, L and Lemoine, NR and Wang, Y},
title = {Efficiently editing the vaccinia virus genome by using the CRISPR-Cas9 system.},
journal = {Journal of virology},
volume = {89},
number = {9},
pages = {5176-5179},
pmid = {25741005},
issn = {1098-5514},
support = {12008/CRUK_/Cancer Research UK/United Kingdom ; },
mesh = {*CRISPR-Cas Systems ; *Genome, Viral ; Molecular Biology/*methods ; *Recombination, Genetic ; Technology, Pharmaceutical/methods ; Vaccinia virus/*genetics ; },
abstract = {Vaccinia virus (VACV) continues to be used in immunotherapy for the prevention of infectious diseases and treatment of cancer since its use for the eradication of smallpox. However, the current method of editing the VACV genome is not efficient. Here, we demonstrate that the CRISPR-Cas9 system can be used to edit the VACV genome rapidly and efficiently. Additionally, a set of 8,964 computationally designed unique guide RNAs (gRNAs) targeting all VACV genes will be valuable for the study of VACV gene functions.},
}
@article {pmid25734647,
year = {2015},
author = {Kautz, T and Eskofier, BM},
title = {A robust Kalman framework with resampling and optimal smoothing.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {3},
pages = {4975-4995},
doi = {10.3390/s150304975},
pmid = {25734647},
issn = {1424-8220},
abstract = {The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.},
}
@article {pmid25732084,
year = {2015},
author = {Yin, X and Xu, B and Jiang, C and Fu, Y and Wang, Z and Li, H and Shi, G},
title = {Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.},
journal = {Journal of medical systems},
volume = {39},
number = {5},
pages = {53},
pmid = {25732084},
issn = {1573-689X},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Hemodynamics/*physiology ; Humans ; Image Processing, Computer-Assisted ; Imagination/*physiology ; *Machine Learning ; Male ; Motor Cortex/*physiology ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7%, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.},
}
@article {pmid25730850,
year = {2015},
author = {Flinker, A and Korzeniewska, A and Shestyuk, AY and Franaszczuk, PJ and Dronkers, NF and Knight, RT and Crone, NE},
title = {Redefining the role of Broca's area in speech.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {112},
number = {9},
pages = {2871-2875},
pmid = {25730850},
issn = {1091-6490},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS091139/NS/NINDS NIH HHS/United States ; 2R37NS21135/NS/NINDS NIH HHS/United States ; NS40596/NS/NINDS NIH HHS/United States ; F32 MH075317/MH/NIMH NIH HHS/United States ; F31 NS065656/NS/NINDS NIH HHS/United States ; F31NS065656/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; F32MH075317/MH/NIMH NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Broca Area/*physiology ; Female ; Humans ; Male ; Motor Cortex/physiology ; Nerve Net/*physiology ; Speech/*physiology ; },
abstract = {For over a century neuroscientists have debated the dynamics by which human cortical language networks allow words to be spoken. Although it is widely accepted that Broca's area in the left inferior frontal gyrus plays an important role in this process, it was not possible, until recently, to detail the timing of its recruitment relative to other language areas, nor how it interacts with these areas during word production. Using direct cortical surface recordings in neurosurgical patients, we studied the evolution of activity in cortical neuronal populations, as well as the Granger causal interactions between them. We found that, during the cued production of words, a temporal cascade of neural activity proceeds from sensory representations of words in temporal cortex to their corresponding articulatory gestures in motor cortex. Broca's area mediates this cascade through reciprocal interactions with temporal and frontal motor regions. Contrary to classic notions of the role of Broca's area in speech, while motor cortex is activated during spoken responses, Broca's area is surprisingly silent. Moreover, when novel strings of articulatory gestures must be produced in response to nonword stimuli, neural activity is enhanced in Broca's area, but not in motor cortex. These unique data provide evidence that Broca's area coordinates the transformation of information across large-scale cortical networks involved in spoken word production. In this role, Broca's area formulates an appropriate articulatory code to be implemented by motor cortex.},
}
@article {pmid25730834,
year = {2015},
author = {Qi, F and Li, Y and Wu, W},
title = {RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {26},
number = {12},
pages = {3070-3082},
doi = {10.1109/TNNLS.2015.2402694},
pmid = {25730834},
issn = {2162-2388},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain-computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an l2 -regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.},
}
@article {pmid25729347,
year = {2015},
author = {Bauer, R and Gharabaghi, A},
title = {Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.},
journal = {Frontiers in neuroscience},
volume = {9},
number = {},
pages = {36},
pmid = {25729347},
issn = {1662-4548},
abstract = {Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.},
}
@article {pmid25729205,
year = {2015},
author = {Chung, E and Park, SI and Jang, YY and Lee, BH},
title = {Effects of brain-computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results.},
journal = {Journal of physical therapy science},
volume = {27},
number = {2},
pages = {513-516},
pmid = {25729205},
issn = {0915-5287},
abstract = {[Purpose] The purpose of this study was to determine the effects of brain-computer interface (BCI)-based functional electrical stimulation (FES) on balance and gait function in patients with stroke. [Subjects] Subjects were randomly allocated to a BCI-FES group (n=5) and a FES group (n=5). [Methods] The BCI-FES group received ankle dorsiflexion training with FES according to a BCI-based program for 30 minutes per day for 5 days. The FES group received ankle dorsiflexion training with FES for the same duration. [Results] Following the intervention, the BCI-FES group showed significant differences in Timed Up and Go test value, cadence, and step length on the affected side. The FES group showed no significant differences after the intervention. However, there were no significant differences between the 2 groups after the intervention. [Conclusion] The results of this study suggest that BCI-based FES training is a more effective exercise for balance and gait function than FES training alone in patients with stroke.},
}
@article {pmid25728514,
year = {2015},
author = {Peserico, A and Germani, A and Sanese, P and Barbosa, AJ and Di Virgilio, V and Fittipaldi, R and Fabini, E and Bertucci, C and Varchi, G and Moyer, MP and Caretti, G and Del Rio, A and Simone, C},
title = {A SMYD3 Small-Molecule Inhibitor Impairing Cancer Cell Growth.},
journal = {Journal of cellular physiology},
volume = {230},
number = {10},
pages = {2447-2460},
pmid = {25728514},
issn = {1097-4652},
support = {14-0149/AICR_/Worldwide Cancer Research/United Kingdom ; },
mesh = {Animals ; Cell Line, Tumor ; Cell Movement/drug effects ; Cell Proliferation/*drug effects ; Cell Transformation, Neoplastic/genetics ; Gene Expression Regulation, Neoplastic/drug effects ; Histone-Lysine N-Methyltransferase/*antagonists &amp; inhibitors/*metabolism ; Humans ; Liver Neoplasms/pathology ; Mice ; RNA Interference/drug effects ; Transcriptional Activation/drug effects ; Up-Regulation ; },
abstract = {SMYD3 is a histone lysine methyltransferase that plays an important role in transcriptional activation as a member of an RNA polymerase complex, and its oncogenic role has been described in different cancer types. We studied the expression and activity of SMYD3 in a preclinical model of colorectal cancer (CRC) and found that it is strongly upregulated throughout tumorigenesis both at the mRNA and protein level. Our results also showed that RNAi-mediated SMYD3 ablation impairs CRC cell proliferation indicating that SMYD3 is required for proper cancer cell growth. These data, together with the importance of lysine methyltransferases as a target for drug discovery, prompted us to carry out a virtual screening to identify new SMYD3 inhibitors by testing several candidate small molecules. Here we report that one of these compounds (BCI-121) induces a significant reduction in SMYD3 activity both in vitro and in CRC cells, as suggested by the analysis of global H3K4me2/3 and H4K5me levels. Of note, the extent of cell growth inhibition by BCI-121 was similar to that observed upon SMYD3 genetic ablation. Most of the results described above were obtained in CRC; however, when we extended our observations to tumor cell lines of different origin, we found that SMYD3 inhibitors are also effective in other cancer types, such as lung, pancreatic, prostate, and ovarian. These results represent the proof of principle that SMYD3 is a druggable target and suggest that new compounds capable of inhibiting its activity may prove useful as novel therapeutic agents in cancer treatment.},
}
@article {pmid25726268,
year = {2015},
author = {Guenther, FH and Hickok, G},
title = {Role of the auditory system in speech production.},
journal = {Handbook of clinical neurology},
volume = {129},
number = {},
pages = {161-175},
doi = {10.1016/B978-0-444-62630-1.00009-3},
pmid = {25726268},
issn = {0072-9752},
support = {R01 DC002852/DC/NIDCD NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R01 DC03681/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Auditory Pathways/*physiology ; Brain/*physiology ; Brain Mapping ; Humans ; *Speech ; Speech Perception/*physiology ; },
abstract = {This chapter reviews evidence regarding the role of auditory perception in shaping speech output. Evidence indicates that speech movements are planned to follow auditory trajectories. This in turn is followed by a description of the Directions Into Velocities of Articulators (DIVA) model, which provides a detailed account of the role of auditory feedback in speech motor development and control. A brief description of the higher-order brain areas involved in speech sequencing (including the pre-supplementary motor area and inferior frontal sulcus) is then provided, followed by a description of the Hierarchical State Feedback Control (HSFC) model, which posits internal error detection and correction processes that can detect and correct speech production errors prior to articulation. The chapter closes with a treatment of promising future directions of research into auditory-motor interactions in speech, including the use of intracranial recording techniques such as electrocorticography in humans, the investigation of the potential roles of various large-scale brain rhythms in speech perception and production, and the development of brain-computer interfaces that use auditory feedback to allow profoundly paralyzed users to learn to produce speech using a speech synthesizer.},
}
@article {pmid25721552,
year = {2015},
author = {Hill, K and Kovacs, T and Shin, S},
title = {Critical issues using brain-computer interfaces for augmentative and alternative communication.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S8-15},
doi = {10.1016/j.apmr.2014.01.034},
pmid = {25721552},
issn = {1532-821X},
support = {R123-DE-01274401/DE/NIDCR NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/rehabilitation ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Humans ; Language ; Physical Therapy Modalities ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) may potentially be of significant practical value to patients in advanced stages of amyotrophic lateral sclerosis and locked-in syndrome for whom conventional augmentative and alternative communication (AAC) systems, which require some measure of consistent voluntary muscle control, are not satisfactory options. However, BCIs have primarily been used for communication in laboratory research settings. This article discusses 4 critical issues that should be addressed as BCIs are translated out of laboratory settings to become fully functional BCI/AAC systems that may be implemented clinically. These issues include (1) identification of primary, secondary, and tertiary system features; (2) integrating BCI/AAC systems in the World Health Organization's International Classification of Functioning, Disability and Health framework; (3) implementing language-based assessment and intervention; and (4) performance measurement. A clinical demonstration project is presented as an example of research beginning to address these critical issues.},
}
@article {pmid25721551,
year = {2015},
author = {Ang, KK and Guan, C and Phua, KS and Wang, C and Zhao, L and Teo, WP and Chen, C and Ng, YS and Chew, E},
title = {Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S79-87},
doi = {10.1016/j.apmr.2014.08.008},
pmid = {25721551},
issn = {1532-821X},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Middle Aged ; Physical Therapy Modalities ; Recovery of Function ; Robotics ; *Stroke Rehabilitation ; Transcranial Direct Current Stimulation/*methods ; *Upper Extremity ; },
abstract = {OBJECTIVE: To investigate the efficacy and effects of transcranial direct current stimulation (tDCS) on motor imagery brain-computer interface (MI-BCI) with robotic feedback for stroke rehabilitation.<br><br>DESIGN: A sham-controlled, randomized controlled trial.<br><br>SETTING: Patients recruited through a hospital stroke rehabilitation program.<br><br>PARTICIPANTS: Subjects (N=19) who incurred a stroke 0.8 to 4.3 years prior, with moderate to severe upper extremity functional impairment, and passed BCI screening.<br><br>INTERVENTIONS: Ten sessions of 20 minutes of tDCS or sham before 1 hour of MI-BCI with robotic feedback upper limb stroke rehabilitation for 2 weeks. Each rehabilitation session comprised 8 minutes of evaluation and 1 hour of therapy.<br><br>MAIN OUTCOME MEASURES: Upper extremity Fugl-Meyer Motor Assessment (FMMA) scores measured end-intervention at week 2 and follow-up at week 4, online BCI accuracies from the evaluation part, and laterality coefficients of the electroencephalogram (EEG) from the therapy part of the 10 rehabilitation sessions.<br><br>RESULTS: FMMA score improved in both groups at week 4, but no intergroup differences were found at any time points. Online accuracies of the evaluation part from the tDCS group were significantly higher than those from the sham group. The EEG laterality coefficients from the therapy part of the tDCS group were significantly higher than those of the sham group.<br><br>CONCLUSIONS: The results suggest a role for tDCS in facilitating motor imagery in stroke.},
}
@article {pmid25721550,
year = {2015},
author = {Morone, G and Pisotta, I and Pichiorri, F and Kleih, S and Paolucci, S and Molinari, M and Cincotti, F and Kübler, A and Mattia, D},
title = {Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S71-8},
doi = {10.1016/j.apmr.2014.05.026},
pmid = {25721550},
issn = {1532-821X},
mesh = {*Brain-Computer Interfaces ; Humans ; *Inpatients ; Paresis/etiology/*rehabilitation ; Stroke/complications ; *Stroke Rehabilitation ; *Upper Extremity ; },
abstract = {OBJECTIVE: To evaluate the feasibility of brain-computer interface (BCI)-assisted motor imagery training to support hand/arm motor rehabilitation after stroke during hospitalization.<br><br>DESIGN: Proof-of-principle study.<br><br>SETTING: Neurorehabilitation hospital.<br><br>PARTICIPANTS: Convenience sample of patients (N=8) with new-onset arm plegia or paresis caused by unilateral stroke.<br><br>INTERVENTIONS: The BCI-based intervention was administered as an "add-on" to usual care and lasted 4 weeks. Under the supervision of a therapist, patients were asked to practice motor imagery of their affected hand and received as a discrete feedback the movements of a "virtual" hand superimposed on their own. Such a BCI-based device was installed in a rehabilitation hospital ward.<br><br>MAIN OUTCOME MEASURES: Following a user-centered design, we assessed system usability in terms of motivation, satisfaction (by means of visual analog scales), and workload (National Aeronautics and Space Administration-Task Load Index). The usability of the BCI-based system was also evaluated by 15 therapists who participated in a focus group.<br><br>RESULTS: All patients successfully accomplished the BCI training. Significant positive correlations were found between satisfaction and motivation (P=.001, r=.393). BCI performance correlated with interest (P=.027, r=.257) and motivation (P=.012, r=.289). During the focus group, professionals positively acknowledged the opportunity offered by BCI-assisted training to measure patients' adherence to rehabilitation.<br><br>CONCLUSIONS: An ecological BCI-based device to assist motor imagery practice was found to be feasible as an add-on intervention and tolerable by patients who were exposed to the system in the rehabilitation environment.},
}
@article {pmid25721549,
year = {2015},
author = {Coyle, D and Stow, J and McCreadie, K and McElligott, J and Carroll, &#xc1;},
title = {Sensorimotor modulation assessment and brain-computer interface training in disorders of consciousness.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S62-70},
doi = {10.1016/j.apmr.2014.08.024},
pmid = {25721549},
issn = {1532-821X},
mesh = {Adult ; Awareness ; *Brain-Computer Interfaces ; Consciousness Disorders/*rehabilitation ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Physical Therapy Modalities ; User-Computer Interface ; },
abstract = {OBJECTIVES: To assess awareness in subjects who are in a minimally conscious state by using an electroencephalogram-based brain-computer interface (BCI), and to determine whether these patients may learn to modulate sensorimotor rhythms with visual feedback, stereo auditory feedback, or both.<br><br>DESIGN: Initial assessment included imagined hand movement or toe wiggling to activate sensorimotor areas and modulate brain rhythms in 90 trials (4 subjects). Within-subject and within-group analyses were performed to evaluate significant activations. A within-subject analysis was performed involving multiple BCI technology training sessions to improve the capacity of the user to modulate sensorimotor rhythms through visual and auditory feedback.<br><br>SETTING: Hospital, homes of subjects, and a primary care facility.<br><br>PARTICIPANTS: Subjects (N=4; 3 men, 1 woman) who were in a minimally conscious state (age range, 27-53 y; 1-12 y after brain injury).<br><br>INTERVENTIONS: Not applicable.<br><br>MAIN OUTCOME MEASURES: Awareness detection was determined from sensorimotor patterns that differed for each motor imagery task. BCI performance was determined from the mean classification accuracy of brain patterns by using a BCI signal processing framework and assessment of performance in multiple sessions.<br><br>RESULTS: All subjects demonstrated significant and appropriate brain activation during the initial assessment, and real-time feedback was provided to improve arousal. Consistent activation was observed in multiple sessions.<br><br>CONCLUSIONS: The electroencephalogram-based assessment showed that patients in a minimally conscious state may have the capacity to operate a simple BCI-based communication system, even without any detectable volitional control of movement.},
}
@article {pmid25721548,
year = {2015},
author = {Riccio, A and Holz, EM and Aricò, P and Leotta, F and Aloise, F and Desideri, L and Rimondini, M and Kübler, A and Mattia, D and Cincotti, F},
title = {Hybrid P300-based brain-computer interface to improve usability for people with severe motor disability: electromyographic signals for error correction during a spelling task.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S54-61},
doi = {10.1016/j.apmr.2014.05.029},
pmid = {25721548},
issn = {1532-821X},
mesh = {Adult ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Nervous System Diseases/*rehabilitation ; Patient Satisfaction ; Pilot Projects ; Rehabilitation Centers ; Self-Help Devices ; User-Computer Interface ; },
abstract = {OBJECTIVE: To evaluate the impact of a hybrid control on usability of a P300-based brain-computer interface (BCI) system that was designed to control an assistive technology software and was integrated with an electromyographic channel for error correction.<br><br>DESIGN: Proof-of-principle study with a convenience sample.<br><br>SETTING: Neurologic rehabilitation hospital.<br><br>PARTICIPANTS: Participants (N=11) in this pilot study included healthy (n=8) and severely motor impaired (n=3) persons. The 3 people with severe motor disability were identified as potential candidates to benefit from the proposed hybrid BCI system for communication and environmental interaction.<br><br>INTERVENTIONS: To eventually investigate the improvement in usability, we compared 2 modalities of BCI system control: a P300-based and a hybrid P300 electromyographic-based mode of control.<br><br>MAIN OUTCOME MEASURES: System usability was evaluated according to the following outcome measures within 3 domains: (1) effectiveness (overall system accuracy and P300-based BCI accuracy); (2) efficiency (throughput time and users' workload); and (3) satisfaction (users' satisfaction). We also considered the information transfer rate and time for selection.<br><br>RESULTS: Findings obtained in healthy participants were in favor of a higher usability of the hybrid control as compared with the nonhybrid. A similar trend was indicated by the observational results gathered from each of the 3 potential end-users.<br><br>CONCLUSIONS: The proposed hybrid BCI control modality could provide end-users with severe motor disability with an option to exploit some residual muscular activity, which could not be fully reliable for properly controlling an assistive technology device. The findings reported in this pilot study encourage the implementation of a clinical trial involving a large cohort of end-users.},
}
@article {pmid25721547,
year = {2015},
author = {Schettini, F and Riccio, A and Simione, L and Liberati, G and Caruso, M and Frasca, V and Calabrese, B and Mecella, M and Pizzimenti, A and Inghilleri, M and Mattia, D and Cincotti, F},
title = {Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S46-53},
doi = {10.1016/j.apmr.2014.05.027},
pmid = {25721547},
issn = {1532-821X},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*rehabilitation ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Environment ; Female ; Humans ; Male ; Middle Aged ; Rehabilitation Centers ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {OBJECTIVE: To evaluate the feasibility and usability of an assistive technology (AT) prototype designed to be operated with conventional/alternative input channels and a P300-based brain-computer interface (BCI) in order to provide users who have different degrees of muscular impairment resulting from amyotrophic lateral sclerosis (ALS) with communication and environmental control applications.<br><br>DESIGN: Proof-of-principle study with a convenience sample.<br><br>SETTING: An apartment-like space designed to be fully accessible by people with motor disabilities for occupational therapy, placed in a neurologic rehabilitation hospital.<br><br>PARTICIPANTS: End-users with ALS (N=8; 5 men, 3 women; mean age &#xb1; SD, 60 &#xb1; 12 y) recruited by a clinical team from an ALS center.<br><br>INTERVENTIONS: Three experimental conditions based on (1) a widely validated P300-based BCI alone; (2) the AT prototype operated by a conventional/alternative input device tailored to the specific end-user's residual motor abilities; and (3) the AT prototype accessed by a P300-based BCI. These 3 conditions were presented to all participants in 3 different sessions.<br><br>MAIN OUTCOME MEASURES: System usability was evaluated in terms of effectiveness (accuracy), efficiency (written symbol rate, time for correct selection, workload), and end-user satisfaction (overall satisfaction) domains. A comparison of the data collected in the 3 conditions was performed.<br><br>RESULTS: Effectiveness and end-user satisfaction did not significantly differ among the 3 experimental conditions. Condition III was less efficient than condition II as expressed by the longer time for correct selection.<br><br>CONCLUSIONS: A BCI can be used as an input channel to access an AT by persons with ALS, with no significant reduction of usability.},
}
@article {pmid25721546,
year = {2015},
author = {Huggins, JE and Moinuddin, AA and Chiodo, AE and Wren, PA},
title = {What would brain-computer interface users want: opinions and priorities of potential users with spinal cord injury.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S38-45.e1-5},
doi = {10.1016/j.apmr.2014.05.028},
pmid = {25721546},
issn = {1532-821X},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Caregivers ; Communication Devices for People with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Physical Therapy Modalities ; Socioeconomic Factors ; Spinal Cord Injuries/psychology/*rehabilitation ; Trauma Severity Indices ; User-Computer Interface ; },
abstract = {OBJECTIVES: To identify perceptions among people with spinal cord injury (SCI) of the priorities for brain-computer interface (BCI) applications and design features along with the time investment and risk acceptable to obtain a BCI.<br><br>DESIGN: Survey.<br><br>SETTING: Research registry participants surveyed via telephone and BCI usage study participants surveyed in person before BCI use.<br><br>PARTICIPANTS: Convenience sample of people with SCI (N=40), consisting of persons from the registry (n=30) and from the BCI study (n=10). Participants were classified as those with low function (n=24) and those with high function (n=16).<br><br>INTERVENTIONS: Not applicable.<br><br>MAIN OUTCOME MEASURES: Descriptive statistics of functional independence, living situations and support structures, ratings of importance of different task and design features, and acceptable levels of performance, risk, and time investment.<br><br>RESULTS: BCIs were of interest to 96% of the low-function group. Emergency communication was the top priority task (ranked in the top 2 by 43%). The most important design features were "functions the BCI provides" and "simplicity of BCI setup." Desired performance was 90% accuracy, with standby mode errors no more than once every 4 hours and speeds of more than 20 letters per minute. Dry electrodes were preferred over gel or implanted electrodes (P<.05). Median acceptable setup time was 10 to 20 minutes, satisfying 65% of participants.<br><br>CONCLUSIONS: People with low functional independence resulting from SCI have a strong interest in BCIs. Advances in speed and setup time will be required for BCIs to meet the desired performance. Creating BCI functions appropriate to the needs of those with SCI will be of ultimate importance for BCI acceptance with this population.},
}
@article {pmid25721545,
year = {2015},
author = {Peters, B and Bieker, G and Heckman, SM and Huggins, JE and Wolf, C and Zeitlin, D and Fried-Oken, M},
title = {Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S33-7},
pmid = {25721545},
issn = {1532-821X},
support = {R13DC012744/DC/NIDCD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R13 DC012744/DC/NIDCD NIH HHS/United States ; 1R01DC009834/DC/NIDCD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; Quality of Life ; User-Computer Interface ; },
abstract = {More than 300 researchers gathered at the 2013 International Brain-Computer Interface (BCI) Meeting to discuss current practice and future goals for BCI research and development. The authors organized the Virtual Users' Forum at the meeting to provide the BCI community with feedback from users. We report on the Virtual Users' Forum, including initial results from ongoing research being conducted by 2 BCI groups. Online surveys and in-person interviews were used to solicit feedback from people with disabilities who are expert and novice BCI users. For the Virtual Users' Forum, their responses were organized into 4 major themes: current (non-BCI) communication methods, experiences with BCI research, challenges of current BCIs, and future BCI developments. Two authors with severe disabilities gave presentations during the Virtual Users' Forum, and their comments are integrated with the other results. While participants' hopes for BCIs of the future remain high, their comments about available systems mirror those made by consumers about conventional assistive technology. They reflect concerns about reliability (eg, typing accuracy/speed), utility (eg, applications and the desire for real-time interactions), ease of use (eg, portability and system setup), and support (eg, technical support and caregiver training). People with disabilities, as target users of BCI systems, can provide valuable feedback and input on the development of BCI as an assistive technology. To this end, participatory action research should be considered as a valuable methodology for future BCI research.},
}
@article {pmid25721544,
year = {2015},
author = {Kübler, A and Holz, EM and Sellers, EW and Vaughan, TM},
title = {Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S27-32},
doi = {10.1016/j.apmr.2014.03.036},
pmid = {25721544},
issn = {1532-821X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Cognition ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Environment ; Humans ; *Patient Selection ; Physical Therapy Modalities ; },
abstract = {Noninvasive brain-computer interfaces (BCIs) use scalp-recorded electrical activity from the brain to control an application. Over the past 20 years, research demonstrating that BCIs can provide communication and control to individuals with severe motor impairment has increased almost exponentially. Although considerable effort has been dedicated to offline analysis for improving signal detection and translation, far less effort has been made to conduct online studies with target populations. Thus, there remains a great need for both long-term and translational BCI studies that include individuals with disabilities in their own homes. Completing these studies is the only sure means to answer questions about BCI utility and reliability. Here we suggest an algorithm for candidate selection for electroencephalographic (EEG)-based BCI home studies. This algorithm takes into account BCI end-users and their environment and should assist in study design and substantially improve subject retention rates, thereby improving the overall efficacy of BCI home studies. It is the result of a workshop at the Fifth International BCI Meeting that allowed us to leverage the expertise of multiple research laboratories and people from multiple backgrounds in BCI research.},
}
@article {pmid25721543,
year = {2015},
author = {Holz, EM and Botrel, L and Kaufmann, T and Kübler, A},
title = {Long-term independent brain-computer interface home use improves quality of life of a patient in the locked-in state: a case study.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S16-26},
doi = {10.1016/j.apmr.2014.03.035},
pmid = {25721543},
issn = {1532-821X},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*rehabilitation ; *Brain-Computer Interfaces ; Female ; Humans ; Interpersonal Relations ; *Paintings ; *Patient Satisfaction ; *Quality of Life ; User-Computer Interface ; },
abstract = {OBJECTIVE: Despite intense brain-computer interface (BCI) research for >2 decades, BCIs have hardly been established at patients' homes. The current study aimed at demonstrating expert independent BCI home use by a patient in the locked-in state and the effect it has on quality of life.<br><br>DESIGN: In this case study, the P300 BCI-controlled application Brain Painting was facilitated and installed at the patient's home. Family and caregivers were trained in setting up the BCI system. After every BCI session, the end user indicated subjective level of control, loss of control, level of exhaustion, satisfaction, frustration, and enjoyment. To monitor BCI home use, evaluation data of every session were automatically sent and stored on a remote server. Satisfaction with the BCI as an assistive device and subjective workload was indicated by the patient. In accordance with the user-centered design, usability of the BCI was evaluated in terms of its effectiveness, efficiency, and satisfaction. The influence of the BCI on quality of life of the end user was assessed.<br><br>SETTING: At the patient's home.<br><br>PARTICIPANT: A 73-year-old patient with amyotrophic lateral sclerosis in the locked-in state.<br><br>INTERVENTIONS: Not applicable.<br><br>MAIN OUTCOME MEASURE: The BCI has been used by the patient independent of experts for >14 months. The patient painted in about 200 BCI sessions (1-3 times per week) with a mean painting duration of 81.86 minutes (SD=52.15, maximum: 230.41). BCI improved quality of life of the patient.<br><br>RESULTS: In most of the BCI sessions the end user's satisfaction was high (mean=7.4, SD=3.24; range, 0-10). Dissatisfaction occurred mostly because of technical problems at the beginning of the study or varying BCI control. The subjective workload was moderate (mean=40.61; range, 0-100). The end user was highy satisfied with all components of the BCI (mean 4.42-5.0; range, 1-5). A perfect match between the user and the BCI technology was achieved (mean: 4.8; range, 1-5). Brain Painting had a positive impact on the patient's life on all three dimensions: competence (1.5), adaptability (2.17) and self-esteem (1.5); (range: -3 = maximum negative impact; 3 maximum positive impact). The patient had her first public art exhibition in July 2013; future exhibitions are in preparation.<br><br>CONCLUSIONS: Independent BCI home use is possible with high satisfaction for the end user. The BCI indeed positively influenced quality of life of the patient and supports social inclusion. Results demonstrate that visual P300 BCIs can be valuable for patients in the locked-in state even if other means of communication are still available (eye tracker).},
}
@article {pmid25721542,
year = {2015},
author = {Daly, JJ and Huggins, JE},
title = {Brain-computer interface: current and emerging rehabilitation applications.},
journal = {Archives of physical medicine and rehabilitation},
volume = {96},
number = {3 Suppl},
pages = {S1-7},
pmid = {25721542},
issn = {1532-821X},
support = {R13 DC012744/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Humans ; *Leisure Activities ; Physical Therapy Modalities/*instrumentation ; *Recovery of Function ; },
abstract = {A formal definition of brain-computer interface (BCI) is as follows: a system that acquires brain signal activity and translates it into an output that can replace, restore, enhance, supplement, or improve the existing brain signal, which can, in turn, modify or change ongoing interactions between the brain and its internal or external environment. More simply, a BCI can be defined as a system that translates "brain signals into new kinds of outputs." After brain signal acquisition, the BCI evaluates the brain signal and extracts signal features that have proven useful for task performance. There are 2 broad categories of BCIs: implantable and noninvasive, distinguished by invasively and noninvasively acquired brain signals, respectively. For this supplement, we will focus on BCIs that use noninvasively acquired brain signals.},
}
@article {pmid25720582,
year = {2015},
author = {Monini, S and Musy, I and Filippi, C and Atturo, F and Barbara, M},
title = {Bone conductive implants in single-sided deafness.},
journal = {Acta oto-laryngologica},
volume = {135},
number = {4},
pages = {381-388},
doi = {10.3109/00016489.2014.990057},
pmid = {25720582},
issn = {1651-2251},
mesh = {Adult ; Aged ; Audiometry ; *Bone Conduction ; Deafness/*rehabilitation ; Equipment Design ; Female ; *Hearing Aids ; Hearing Loss, Unilateral/*rehabilitation ; Humans ; Male ; Middle Aged ; Noise ; Reproducibility of Results ; Sound Localization/physiology ; Speech Perception/physiology ; },
abstract = {CONCLUSION: Bone conduction implants (BCIs) have been shown to partially restore some of the functions lost when binaural hearing is missing, such as in subjects with single-sided deafness (SSD). The use of a single BCI needs to be recommended by a clinician based on thorough counselling with the SSD subject.<br><br>OBJECTIVES: To perform an overview of the present capabilities of BCIs for SSD and to evaluate the reliability of the audiological evaluation for assessing speech recognition in noise and sound localization cues, which are major problems related to the loss of binaural hearing.<br><br>METHODS: Nine subjects with SSD who received BCI implants underwent a preoperative audiological evaluation that included sound field speech audiometry, word recognition score (WRS) testing and sound localization testing in quiet and in noise. They were also tested for the accuracy of their directional word recognition in noise and their subjective perceptions of their hearing difficulties using the APHAB questionnaire.<br><br>RESULTS: The mean maximum accuracy of word discrimination was 65.5% in the unaided condition and 78.9% in the BCI-aided condition. Sound localization in noise was better with the BCI than in the unaided condition, especially when the stimulus and noise were presented on the same side as the implanted ear. The accuracy of directional word recognition showed an improvement with the BCI with respect to the unaided condition on the BCI side, with either the stimulus in the implanted ear and the noise in the contralateral ear or with both the stimulus and noise presented to the implanted ear.},
}
@article {pmid25715696,
year = {2015},
author = {Ji, Y and Paus, R and Prudic, A and Lübbert, C and Sadowski, G},
title = {A Novel Approach for Analyzing the Dissolution Mechanism of Solid Dispersions.},
journal = {Pharmaceutical research},
volume = {32},
number = {8},
pages = {2559-2578},
pmid = {25715696},
issn = {1573-904X},
mesh = {Calorimetry, Differential Scanning ; Chemistry, Pharmaceutical ; Desiccation ; Excipients/*chemistry ; Hydrogen-Ion Concentration ; Indomethacin/*chemistry ; Naproxen/*chemistry ; Povidone/*chemistry ; *Solubility ; Spectrophotometry, Ultraviolet ; Surface Properties ; Temperature ; Thermodynamics ; X-Ray Diffraction ; },
abstract = {PURPOSE: To analyze the dissolution mechanism of solid dispersions of poorly water-soluble active pharmaceutical ingredients (APIs), to predict the dissolution profiles of the APIs and to find appropriate ways to improve their dissolution rate.<br><br>METHODS: The dissolution profiles of indomethacin and naproxen from solid dispersions in PVP K25 were measured in vitro using a rotating-disk system (USP II). A chemical-potential-gradient model combined with the thermodynamic model PC-SAFT was developed to investigate the dissolution mechanism of indomethacin and naproxen from their solid dispersions at different conditions and to predict the dissolution profiles of these APIs.<br><br>RESULTS: The results show that the dissolution of the investigated solid dispersions is controlled by dissolution of both, API and PVP K25 as they codissolve according to the initial API loading. Moreover, the dissolution of indomethacin and naproxen was improved by decreasing the API loading in polymer (leading to amorphous solid dispersions) and increasing stirring speed, temperature and pH of the dissolution medium. The dissolution of indomethacin and naproxen from their amorphous solid dispersions is mainly controlled by the surface reaction, which implies that indomethacin and naproxen dissolution can be effectively improved by formulation design and by improving their solvation performance.<br><br>CONCLUSIONS: The chemical-potential-gradient model combined with PC-SAFT can be used to analyze the dissolution mechanism of solid dispersions and to describe and predict the dissolution profiles of API as function of stirring speed, temperature and pH value of the medium. This work helps to find appropriate ways to improve the dissolution rate of poorly-soluble APIs.},
}
@article {pmid25713866,
year = {2014},
author = {Basiul, IA and Kaplan, AIa},
title = {[Dependence N200 and P300 ERPs in P300-based brain-computer interface on the variations of voluntary attention].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {64},
number = {2},
pages = {159-165},
pmid = {25713866},
issn = {0044-4677},
mesh = {Adolescent ; Adult ; Attention/*physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation/instrumentation ; Spatio-Temporal Analysis ; Task Performance and Analysis ; },
abstract = {Hypothesis about dependence P300 and N200 potentials evoked by flashes of rows and columns of the stimulation matrix on type of the task and voluntary attention was evaluated. We tested three types of the task: 1) just look at target symbol; 2) look at the target symbol, count its flashes and report the amount of flashes after finishing the task; 3) type target symbol in P300-based brain-computer interface (BCI). In 17 subjects research we showed that maximum amplitudes of P300 and N200 ERPs was occurred in the second type of the task ("look at and count flashes"). Also in this type of task we observed most of all cases statistically reliable difference between target and nontarget P300 and N200 ERPs. Lowest amplitudes of ERPs and number of cases of statistically reliable differences between target and nontarget were showed in the first type of the task ("just look at the symbol"). So we assume that succesful working in P300-based BCI doesn't need the maximum amplitudes of the relevent ERPs but most depend on spatiotemporal complex of these potentials.},
}
@article {pmid25712802,
year = {2015},
author = {Pichiorri, F and Morone, G and Petti, M and Toppi, J and Pisotta, I and Molinari, M and Paolucci, S and Inghilleri, M and Astolfi, L and Cincotti, F and Mattia, D},
title = {Brain-computer interface boosts motor imagery practice during stroke recovery.},
journal = {Annals of neurology},
volume = {77},
number = {5},
pages = {851-865},
doi = {10.1002/ana.24390},
pmid = {25712802},
issn = {1531-8249},
mesh = {Aged ; Brain-Computer Interfaces/*psychology ; *Evoked Potentials, Motor ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Pilot Projects ; *Recovery of Function ; Stroke/physiopathology/*psychology/*therapy ; },
abstract = {OBJECTIVE: Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain-computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients.<br><br>METHODS: Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n&#x2009;=&#x2009;14) and 1-month MI training without BCI support (control group; n&#x2009;=&#x2009;14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl-Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings.<br><br>RESULTS: Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p&#x2009;<&#x2009;0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p&#x2009;<&#x2009;0.05).<br><br>INTERPRETATION: The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments.},
}
@article {pmid25710243,
year = {2015},
author = {Ceballos, GA and Hernández, LF},
title = {Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface.},
journal = {Journal of neural engineering},
volume = {12},
number = {2},
pages = {026009},
doi = {10.1088/1741-2560/12/2/026009},
pmid = {25710243},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Machine Learning ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; Word Processing/*methods ; },
abstract = {OBJECTIVE: The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency.<br><br>APPROACH: Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II).<br><br>MAIN RESULTS: The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved.<br><br>SIGNIFICANCE: We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.},
}
@article {pmid25710068,
year = {2014},
author = {Bondar', IV and Vasil'eva, LN and Badakva, AM and Miller, NV and Zobova, LN and Roshchin, VIu},
title = {[Quality of neuronal signal registered in the monkey motor cortex with chronically implanted multiple microwires].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {64},
number = {1},
pages = {101-112},
pmid = {25710068},
issn = {0044-4677},
mesh = {Action Potentials/*physiology ; Animals ; Brain-Computer Interfaces/*veterinary ; Cerebral Cortex/cytology/*physiology ; Electrodes, Implanted ; Electrophysiology/instrumentation ; Haplorhini/*physiology ; Microelectrodes ; Neurons/cytology/*physiology ; Wakefulness/physiology ; },
abstract = {Disconnection of central and peripheral parts of motor system leads to severe forms of disability. However, current research of brain-computer interfaces will solve the problem of rehabilitation of patients with motor disorders in future. Chronic recordings of single-unit activity in specialized areas of cerebral cortex could provide appropriate control signal for effectors with multiple degrees of freedom. In present article we evaluated the quality of chronic single-unit recordings in the primary motor cortex of awake behaving monkeys obtained with bundles of multiple microwires. Action potentials of proper quality were recorded from single units during three months. In some cases up to 7 single units could be extracted on a channel. Recording quality stabilized after 40 days since electrodes were implanted. Ultimately, functionality of multiple electrodes bundle makes it highly usable and reliable instrument for obtaining of control neurophysiologic signal from populations of neurons for brain-computer interfaces.},
}
@article {pmid25707256,
year = {2014},
author = {Ganin, IP and Kaplan, AIa},
title = {[The P300-based brain-computer interface: presentation of the complex "flash + movement" stimuli].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {64},
number = {1},
pages = {32-40},
pmid = {25707256},
issn = {0044-4677},
mesh = {Adult ; Attention/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Motion ; Pattern Recognition, Visual/*physiology ; Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {The P300 based brain-computer interface requires the detection of P300 wave of brain event-related potentials. Most of its users learn the BCI control in several minutes and after the short classifier training they can type a text on the computer screen or assemble an image of separate fragments in simple BCI-based video games. Nevertheless, insufficient attractiveness for users and conservative stimuli organization in this BCI may restrict its integration into real information processes control. At the same time initial movement of object (motion-onset stimuli) may be an independent factor that induces P300 wave. In current work we checked the hypothesis that complex "flash + movement" stimuli together with drastic and compact stimuli organization on the computer screen may be much more attractive for user while operating in P300 BCI. In 20 subjects research we showed the effectiveness of our interface. Both accuracy and P300 amplitude were higher for flashing stimuli and complex "flash + movement" stimuli compared to motion-onset stimuli. N200 amplitude was maximal for flashing stimuli, while for "flash + movement" stimuli and motion-onset stimuli it was only a half of it. Similar BCI with complex stimuli may be embedded into compact control systems requiring high level of user attention under impact of negative external effects obstructing the BCI control.},
}
@article {pmid25707215,
year = {2014},
author = {Saltykov, KA and Bark, ED and Kulikov, MA},
title = {[Comparison of event-related potentials components characteristics obtained during stimulation of symbolical and alphabetic matrixes used in brain-computer interface paradigm].},
journal = {Fiziologiia cheloveka},
volume = {40},
number = {4},
pages = {18-26},
pmid = {25707215},
issn = {0131-1646},
mesh = {Adult ; Brain/physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Language ; Male ; Photic Stimulation ; Visual Perception/*physiology ; },
abstract = {In order to create a brain-computer interface (BCI) on the basis of paradigm P300 (the so-called Farwell-Donchin paradigm, FD) with a symbolical matrix used as stimuli, there were compared characteristics of event-related potentials (ERP) obtained from stimulation both by symbolical and alphabetical matrixes. The matrixes contained 6 x 6 signs (cyrillic letters or symbols-pictograms). Nine healthy adults were examined in 18 experiments during which 28 channel EEG were recorded while matrixes of two types (containing either cyrillic letters or symbols-pictograms) were used for stimulation. The obtained ERP data, i.e. amplitudes and peak latencies of the following components of ERP: N1, P3 with sub-components P3a and P3b, N4 were compared and analized for different types of the stimulation matrixes. Similar changes in amplitude or peak latency received from 7 or more out of 9 examinees were taken into consideration, matching the criteria of significance. It was discovered that for components P3a, P3b and N4 the amplitudes of ERP in response to a symbolic matrix were bigger than to a letter matrix, the opposite being true for component N1. Latent periods of ERP components were shorter for a symbolic matrix than for a letter matrix in case of components N1 and P3a, and longer in case of P3b and N4. In order to find out which zones of the brain react to stimulation the most, there was conducted a pair t-test (series of pair t-tests) to analize the topography of variety of ERP responses to different types of stimuli, and, through comparing the amplitudes of ERP components, a topographical map detailing the variety of responses to the different types of matrixes was obtained. The data about the differences were analized separately for each of 28 channels, then the (absolute magnitude t-test) were summed up algebraically for all the nine examinees. Thus, it was shown, for amplitudes of all the tested ERP components in the case of pair "significant-insignificant letters", the topography of the t-test is represented by two separate areas with distinct lateralization. In the case of pair "significant-insignificant symbols", the field of maximum responses streanch in croas direction. In the case of pair "significant letters-significant symbols" the topography is elaboratly orgenised.},
}
@article {pmid25706721,
year = {2015},
author = {Yin, E and Zeyl, T and Saab, R and Chau, T and Hu, D and Zhou, Z},
title = {A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {4},
pages = {693-701},
doi = {10.1109/TNSRE.2015.2403270},
pmid = {25706721},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Learning Curve ; Male ; Psychomotor Performance ; Support Vector Machine ; Young Adult ; },
abstract = {The present study proposes a hybrid brain-computer interface (BCI) with 64 selectable items based on the fusion of P300 and steady-state visually evoked potential (SSVEP) brain signals. With this approach, row/column (RC) P300 and two-step SSVEP paradigms were integrated to create two hybrid paradigms, which we denote as the double RC (DRC) and 4-D spellers. In each hybrid paradigm, the target is simultaneously detected based on both P300 and SSVEP potentials as measured by the electroencephalogram. We further proposed a maximum-probability estimation (MPE) fusion approach to combine the P300 and SSVEP on a score level and compared this approach to other approaches based on linear discriminant analysis, a naïve Bayes classifier, and support vector machines. The experimental results obtained from thirteen participants indicated that the 4-D hybrid paradigm outperformed the DRC paradigm and that the MPE fusion achieved higher accuracy compared with the other approaches. Importantly, 12 of the 13 participants, using the 4-D paradigm achieved an accuracy of over 90% and the average accuracy was 95.18%. These promising results suggest that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard.},
}
@article {pmid25705995,
year = {2015},
author = {Reinfeldt, S and Håkansson, B and Taghavi, H and Fredén Jansson, KJ and Eeg-Olofsson, M},
title = {The bone conduction implant: Clinical results of the first six patients.},
journal = {International journal of audiology},
volume = {54},
number = {6},
pages = {408-416},
pmid = {25705995},
issn = {1708-8186},
mesh = {Acoustic Stimulation/instrumentation/methods ; Adolescent ; Adult ; Aged ; Audiometry, Pure-Tone ; Auditory Threshold ; Bone Conduction/*physiology ; Correction of Hearing Impairment/*instrumentation ; Female ; Hearing Loss, Conductive/physiopathology/psychology/*rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/physiopathology/psychology/*rehabilitation ; Humans ; Male ; Middle Aged ; *Neural Prostheses ; Noise ; *Quality of Life ; Signal-To-Noise Ratio ; Speech Perception/physiology ; Treatment Outcome ; Young Adult ; },
abstract = {OBJECTIVE: To investigate audiological and quality of life outcomes for a new active transcutaneous device, called the bone conduction implant (BCI), where the transducer is implanted under intact skin.<br><br>DESIGN: A clinical study with sound field audiometry and questionnaires at six-month follow-up was conducted with a bone-anchored hearing aid on a softband as reference device.<br><br>STUDY SAMPLE: Six patients (age 18-67 years) with mild-to-moderate conductive or mixed hearing loss.<br><br>RESULTS: The surgical procedure was found uneventful with no adverse events. The first hypothesis that BCI had a statistically significant improvement over the unaided condition was proven by a pure-tone-average improvement of 31.0 dB, a speech recognition threshold improvement in quiet (27.0 dB), and a speech recognition score improvement in noise (51.2 %). At speech levels, the signal-to-noise ratio threshold for BCI was - 5.5 dB. All BCI results were better than, or similar to the reference device results, and the APHAB and GBI questionnaires scores showed statistically significant improvements versus the unaided situation, supporting the second and third hypotheses.<br><br>CONCLUSIONS: The BCI provides significant hearing rehabilitation for patients with mild-to-moderate conductive or mixed hearing impairments, and can be easily and safely implanted under intact skin.},
}
@article {pmid25703940,
year = {2015},
author = {McCane, LM and Heckman, SM and McFarland, DJ and Townsend, G and Mak, JN and Sellers, EW and Zeitlin, D and Tenteromano, LM and Wolpaw, JR and Vaughan, TM},
title = {P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {11},
pages = {2124-2131},
pmid = {25703940},
issn = {1872-8952},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aging/*physiology ; Amyotrophic Lateral Sclerosis/*physiopathology ; Brain Mapping ; *Brain-Computer Interfaces ; Case-Control Studies ; Communication ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Reaction Time/physiology ; Vision, Ocular/physiology ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.<br><br>METHODS: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (&#xb1;9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6&#xd7;6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.<br><br>RESULTS: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).<br><br>CONCLUSIONS: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).<br><br>SIGNIFICANCE: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.},
}
@article {pmid25702460,
year = {2014},
author = {Kazennikov, OV and Kireeva, TB and Shlykov, VIu},
title = {[Influence of the movable support under one leg on human vertical posture during standing with asymmetric load on legs].},
journal = {Fiziologiia cheloveka},
volume = {40},
number = {3},
pages = {57-65},
pmid = {25702460},
issn = {0131-1646},
mesh = {Adult ; Aged ; Body Weight ; *Brain Waves ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Leg/physiology ; Male ; Middle Aged ; Movement/*physiology ; Posture ; },
abstract = {The posture in standing subjects was studied when the legs were placed on supports of different degrees of mobility, as well as, when a part of body weight was voluntary transferred to one leg. The aim of these experiments was to explore how the mobility of support under the feet affects the balance and how this influence could be changed by the load distribution between the legs during standing. When both legs were on rigid immovable supports, the posture maintaining was accomplished by control of center of pressure (CP) of both legs. When the subject transferred the weight on one foot the posture was maintained mainly due to the control of CP of loaded leg. When the legs were on the supports of different mobility, the balance was maintained by the control of CP of the leg on the immovable support. This result was observed both when the subject stood with symmetric load on the legs and when the load was transferred to one leg. Even when the leg was unloaded but was placed on immovable support its CP moved more in comparison with the CP of loaded leg on movable support. The results show that the support mobility under the legs is a factor that determines the mechanisms of the posture maintenance, and this factor is more significant than the load distribution between the legs. So, we can conclude that the upright posture is maintained accounting the physical properties of the supports under the feet.},
}
@article {pmid25702459,
year = {2014},
author = {Frolov, AA and Gusek, D and Bobrov, PD and Mokienko, OA and Chernikova, LA and Konovalov, RN},
title = {[Localization of brain electrical activity sources and hemodynamic activity foci during motor imagery].},
journal = {Fiziologiia cheloveka},
volume = {40},
number = {3},
pages = {45-56},
pmid = {25702459},
issn = {0131-1646},
mesh = {Brain Mapping ; Brain Waves/*physiology ; *Electroencephalography ; Hemodynamics/*physiology ; Humans ; Imagination/*physiology ; Movement/physiology ; Nervous System Physiological Phenomena ; },
abstract = {Studied are sources of brain activity contributing to EEG patterns which correspond to motor imagery. The accuracy of their classification determines the efficiency of brain-computer interface (BCI) allowing for controlling external technical devices directly by brain signals without involving muscle activity. Sources of brain activity are identified by Independent Component Analysis. Those independent components for which the BCI classification accuracy are at maximum are treated as relevant for motor imagery task. Two of the most relevant sources demonstrate strictly exposed event related desynchronization and synchronization of mu--rhythm during imagery of contra--and ipsilateral hands. These sources are localized by solving inverse EEG problem taking into account individual geometry of brain and its covers provided by anatomical MRI images. The sources are shown to be localized in BA 3A relating to proprioceptive sensitivity of the contralateral hand. Their positions are closed to foci of BOLD activity obtained by fMRI.},
}
@article {pmid25700572,
year = {2016},
author = {Naoi, Y and Noguchi, S},
title = {Multi-gene classifiers for prediction of recurrence in breast cancer patients.},
journal = {Breast cancer (Tokyo, Japan)},
volume = {23},
number = {1},
pages = {12-18},
doi = {10.1007/s12282-015-0596-9},
pmid = {25700572},
issn = {1880-4233},
mesh = {Breast Neoplasms/*genetics/therapy ; Chemotherapy, Adjuvant ; Female ; Humans ; Ki-67 Antigen/*genetics ; Mastectomy ; Neoplasm Recurrence, Local/*genetics/therapy ; Oligonucleotide Array Sequence Analysis ; Prognosis ; Receptor, ErbB-2/*genetics ; Receptors, Estrogen/*genetics ; Receptors, Progesterone/*genetics ; },
abstract = {Accurate prediction of recurrence risk is of vital importance for tailoring adjuvant chemotherapy for individual breast cancer patients. Although recurrence risk has been assessed by means of examination of histological data and biomarkers (ER, PR, HER2, Ki67), such conventional examinations are not accurate enough to select subsets of patients who are at sufficiently low risk of recurrence to be spared adjuvant chemotherapy without comprising the prognosis. In the past two decades or so, comprehensive gene expression analysis technology has rapidly developed and made it possible to construct recurrence prediction models for breast cancer based on multi-gene expression in tumor tissues. These models include MammaPrint, Oncotype DX, PAM50 ROR, GGI, EndoPredict, BCI, and Curebest 95GC. In clinical practice, these multi-gene classifiers are mostly used for ER-positive and node-negative breast cancer patients for whom deciding the indication of adjuvant chemotherapy based on conventional histological examination findings alone is often difficult. This article briefly reviews these multi-gene expression-based classifiers with special emphasis on Curebest™ 95GC, which was developed by us for ER-positive and node-negative breast cancer patients.},
}
@article {pmid25698307,
year = {2015},
author = {Vuckovic, A and Hasan, MA and Osuagwu, B and Fraser, M and Allan, DB and Conway, BA and Nasseroleslami, B},
title = {The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {11},
pages = {2170-2180},
pmid = {25698307},
issn = {1872-8952},
support = {G0902257/MRC_/Medical Research Council/United Kingdom ; G0902257/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Comorbidity ; Cortical Synchronization/physiology ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Foot/innervation/physiology ; Hand/innervation/physiology ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Motor Activity/*physiology ; Motor Cortex/physiology ; Neuralgia/epidemiology/*physiopathology/*psychology ; Paraplegia/epidemiology/*physiopathology/*psychology ; },
abstract = {OBJECTIVE: The aim of this study was to test how the presence of central neuropathic pain (CNP) influences the performance of a motor imagery based Brain Computer Interface (BCI).<br><br>METHODS: In this electroencephalography (EEG) based study, we tested BCI classification accuracy and analysed event related desynchronisation (ERD) in 3 groups of volunteers during imagined movements of their arms and legs. The groups comprised of nine able-bodied people, ten paraplegic patients with CNP (lower abdomen and legs) and nine paraplegic patients without CNP. We tested two types of classifiers: a 3 channel bipolar montage and classifiers based on common spatial patterns (CSPs), with varying number of channels and CSPs.<br><br>RESULTS: Paraplegic patients with CNP achieved higher classification accuracy and had stronger ERD than paraplegic patients with no pain for all classifier configurations. Highest 2-class classification accuracy was achieved for CSP classifier covering wider cortical area: 82&#xb1;7% for patients with CNP, 82&#xb1;4% for able-bodied and 78&#xb1;5% for patients with no pain.<br><br>CONCLUSION: Presence of CNP improves BCI classification accuracy due to stronger and more distinct ERD.<br><br>SIGNIFICANCE: Results of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD.},
}
@article {pmid25698176,
year = {2015},
author = {Williams, NJ and Nasuto, SJ and Saddy, JD},
title = {Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.},
journal = {Journal of neuroscience methods},
volume = {250},
number = {},
pages = {22-33},
doi = {10.1016/j.jneumeth.2015.02.007},
pmid = {25698176},
issn = {1872-678X},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Cluster Analysis ; Computer Simulation ; Datasets as Topic ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; Language ; Language Tests ; Models, Neurological ; Neuropsychological Tests ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Visual Perception/physiology ; },
abstract = {BACKGROUND: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data.<br><br>NEW METHOD: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA).<br><br>RESULTS: After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.<br><br>Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation.<br><br>CONCLUSIONS: Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging.},
}
@article {pmid25690551,
year = {2015},
author = {Tamez-Duque, J and Cobian-Ugalde, R and Kilicarslan, A and Venkatakrishnan, A and Soto, R and Contreras-Vidal, JL},
title = {Real-time strap pressure sensor system for powered exoskeletons.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {2},
pages = {4550-4563},
pmid = {25690551},
issn = {1424-8220},
mesh = {Humans ; *Orthotic Devices ; Paraplegia/*therapy ; Pressure ; Quality of Life ; Spinal Cord Injuries/therapy ; },
abstract = {Assistive and rehabilitative powered exoskeletons for spinal cord injury (SCI) and stroke subjects have recently reached the clinic. Proper tension and joint alignment are critical to ensuring safety. Challenges still exist in adjustment and fitting, with most current systems depending on personnel experience for appropriate individual fastening. Paraplegia and tetraplegia patients using these devices have impaired sensation and cannot signal if straps are uncomfortable or painful. Excessive pressure and blood-flow restriction can lead to skin ulcers, necrotic tissue and infections. Tension must be just enough to prevent slipping and maintain posture. Research in pressure dynamics is extensive for wheelchairs and mattresses, but little research has been done on exoskeleton straps. We present a system to monitor pressure exerted by physical human-machine interfaces and provide data about levels of skin/body pressure in fastening straps. The system consists of sensing arrays, signal processing hardware with wireless transmission, and an interactive GUI. For validation, a lower-body powered exoskeleton carrying the full weight of users was used. Experimental trials were conducted with one SCI and one able-bodied subject. The system can help prevent skin injuries related to excessive pressure in mobility-impaired patients using powered exoskeletons, supporting functionality, independence and better overall quality of life.},
}
@article {pmid25690098,
year = {2015},
author = {Bouillot, S and Attrée, I and Huber, P},
title = {Pharmacological activation of Rap1 antagonizes the endothelial barrier disruption induced by exotoxins ExoS and ExoT of Pseudomonas aeruginosa.},
journal = {Infection and immunity},
volume = {83},
number = {5},
pages = {1820-1829},
pmid = {25690098},
issn = {1098-5522},
mesh = {ADP Ribose Transferases/*antagonists &amp; inhibitors ; Bacterial Toxins/*antagonists &amp; inhibitors ; Cells, Cultured ; Colforsin/*metabolism ; Endothelial Cells/drug effects/*microbiology ; Enzyme Activators/metabolism ; GTPase-Activating Proteins/*antagonists &amp; inhibitors ; Humans ; Pseudomonas aeruginosa/*physiology ; Shelterin Complex ; Telomere-Binding Proteins/*metabolism ; },
abstract = {Most clinical strains of Pseudomonas aeruginosa, a leading agent of nosocomial infections, are multiresistant to antibiotherapy. Because of the paucity of new available antibiotics, the investigation of strategies aimed at limiting the action of its major virulence factors has gained much interest. The type 3 secretion system of P. aeruginosa and its effectors are known to be major determinants of toxicity and are required for bacterial dissemination in the host. Bacterial transmigration across the vascular wall is considered to be an important step in the infectious process. Using human endothelial primary cells, we demonstrate that forskolin (FSK), a drug inducing cyclic AMP (cAMP) elevation in eukaryotic cells, strikingly reduced the cell retraction provoked by two type 3 toxins, ExoS and ExoT, found in the majority of clinical strains. Conversely, cytotoxicity of a strain carrying the type 3 effector ExoU was unaffected by FSK. In addition, FSK altered the capacity of two ExoS/ExoT strains to transmigrate across cell monolayers. In agreement with these findings, other drugs and a cytokine inducing the increase of cAMP intracellular levels have also protected cells from retraction. cAMP is an activator of both protein kinase A and EPAC, a GTPase exchange factor of Rap1. Using activators or inhibitors of either pathway, we show that the beneficial effect of FSK is exerted by the activation of the EPAC/Rap1 axis, suggesting that its protective effect is mediated by reinforcing cell-cell and cell-substrate adhesion.},
}
@article {pmid25688193,
year = {2015},
author = {Basilio, R and Garrido, GJ and Sato, JR and Hoefle, S and Melo, BR and Pamplona, FA and Zahn, R and Moll, J},
title = {FRIEND Engine Framework: a real time neurofeedback client-server system for neuroimaging studies.},
journal = {Frontiers in behavioral neuroscience},
volume = {9},
number = {},
pages = {3},
pmid = {25688193},
issn = {1662-5153},
support = {G0902304/MRC_/Medical Research Council/United Kingdom ; },
abstract = {In this methods article, we present a new implementation of a recently reported FSL-integrated neurofeedback tool, the standalone version of "Functional Real-time Interactive Endogenous Neuromodulation and Decoding" (FRIEND). We will refer to this new implementation as the FRIEND Engine Framework. The framework comprises a client-server cross-platform solution for real time fMRI and fMRI/EEG neurofeedback studies, enabling flexible customization or integration of graphical interfaces, devices, and data processing. This implementation allows a fast setup of novel plug-ins and frontends, which can be shared with the user community at large. The FRIEND Engine Framework is freely distributed for non-commercial, research purposes.},
}
@article {pmid25686293,
year = {2015},
author = {Saa, JF and Pesters, Ad and McFarland, D and Çetin, M},
title = {Word-level language modeling for P300 spellers based on discriminative graphical models.},
journal = {Journal of neural engineering},
volume = {12},
number = {2},
pages = {026007},
pmid = {25686293},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00085605/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Computer Graphics ; Computer Simulation ; Electrocardiography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Language ; Models, Statistical ; *Natural Language Processing ; Task Performance and Analysis ; Word Processing/*methods ; },
abstract = {OBJECTIVE: In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers.<br><br>APPROACH: This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller.<br><br>MAIN RESULTS: Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system.<br><br>SIGNIFICANCE: The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.},
}
@article {pmid25683533,
year = {2015},
author = {Lacko, D and Huysmans, T and Parizel, PM and De Bruyne, G and Verwulgen, S and Van Hulle, MM and Sijbers, J},
title = {Evaluation of an anthropometric shape model of the human scalp.},
journal = {Applied ergonomics},
volume = {48},
number = {},
pages = {70-85},
doi = {10.1016/j.apergo.2014.11.008},
pmid = {25683533},
issn = {1872-9126},
mesh = {Adult ; Anthropometry ; Brain-Computer Interfaces ; Computer-Aided Design ; Female ; Head/anatomy &amp; histology ; Humans ; Magnetic Resonance Imaging ; Male ; *Models, Anatomic ; Scalp/*anatomy &amp; histology ; Young Adult ; },
abstract = {This paper presents the evaluation a 3D shape model of the human head. A statistical shape model of the head is created from a set of 100 MRI scans. The ability of the shape model to predict new head shapes is evaluated by considering the prediction error distributions. The effect of using intuitive anthropometric measurements as parameters is examined and the sensitivity to measurement errors is determined. Using all anthropometric measurements, the average prediction error is 1.60 ± 0.36 mm, which shows the feasibility of the new parameters. The most sensitive measurement is the ear height, the least sensitive is the arc length. Finally, two applications of the anthropometric shape model are considered: the study of the male and female population and the design of a brain-computer interface headset. The results show that an anthropometric shape model can be a valuable tool for both research and design.},
}
@article {pmid25682943,
year = {2015},
author = {Morioka, H and Kanemura, A and Hirayama, J and Shikauchi, M and Ogawa, T and Ikeda, S and Kawanabe, M and Ishii, S},
title = {Learning a common dictionary for subject-transfer decoding with resting calibration.},
journal = {NeuroImage},
volume = {111},
number = {},
pages = {167-178},
doi = {10.1016/j.neuroimage.2015.02.015},
pmid = {25682943},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Functional Neuroimaging/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments.},
}
@article {pmid25681971,
year = {2015},
author = {Lo, MC and Wang, S and Singh, S and Damodaran, VB and Kaplan, HM and Kohn, J and Shreiber, DI and Zahn, JD},
title = {Coating flexible probes with an ultra fast degrading polymer to aid in tissue insertion.},
journal = {Biomedical microdevices},
volume = {17},
number = {2},
pages = {34},
pmid = {25681971},
issn = {1572-8781},
support = {P41 EB001046/EB/NIBIB NIH HHS/United States ; T32 EB005583/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Biocompatible Materials/*chemistry/metabolism ; Brain/embryology ; Brain-Computer Interfaces ; Chick Embryo ; Epoxy Compounds/chemistry ; Materials Testing/*methods ; Microtechnology ; Polycarboxylate Cement/chemistry ; Polymers/*chemistry/metabolism ; *Prostheses and Implants ; Rats, Sprague-Dawley ; Sepharose/chemistry ; Temperature ; Tyrosine/chemistry ; },
abstract = {We report a fabrication process for coating neural probes with an ultrafast degrading polymer to create consistent and reproducible devices for neural tissue insertion. The rigid polymer coating acts as a probe insertion aid, but resorbs within hours post-implantation. Despite the feasibility for short term neural recordings from currently available neural prosthetic devices, most of these devices suffer from long term gliosis, which isolates the probes from adjacent neurons, increasing the recording impedance and stimulation threshold. The size and stiffness of implanted probes have been identified as critical factors that lead to this long term gliosis. Smaller, more flexible probes that match the mechanical properties of brain tissue could allow better long term integration by limiting the mechanical disruption of the surrounding tissue during and after probe insertion, while being flexible enough to deform with the tissue during brain movement. However, these small flexible probes inherently lack the mechanical strength to penetrate the brain on their own. In this work, we have developed a micromolding method for coating a non-functional miniaturized SU-8 probe with an ultrafast degrading tyrosine-derived polycarbonate (E5005(2K)). Coated, non-functionalized probes of varying dimensions were reproducibly fabricated with high yields. The polymer erosion/degradation profiles of the probes were characterized in vitro. The probes were also mechanically characterized in ex vivo brain tissue models by measuring buckling and insertion forces during probe insertion. The results demonstrate the ability to produce polymer coated probes of consistent quality for future in vivo use, for example to study the effects of different design parameters that may affect tissue response during long term chronic intra-cortical microelectrode neural recordings.},
}
@article {pmid25681017,
year = {2015},
author = {Masse, NY and Jarosiewicz, B and Simeral, JD and Bacher, D and Stavisky, SD and Cash, SS and Oakley, EM and Berhanu, E and Eskandar, E and Friehs, G and Hochberg, LR and Donoghue, JP},
title = {Reprint of "Non-causal spike filtering improves decoding of movement intention for intracortical BCIs".},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {94-103},
pmid = {25681017},
issn = {1872-678X},
support = {N01 HD053403/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; },
abstract = {BACKGROUND: Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials.<br><br>NEW METHOD: We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals.<br><br>RESULTS: Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7&#xb0; for participant S3, who was implanted 5.4 years prior to this study, and by 3.5&#xb0; for participant T2, who was implanted 3 months prior to this study.<br><br>CONCLUSIONS: Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.},
}
@article {pmid25680948,
year = {2015},
author = {Sun, H and Blakely, TM and Darvas, F and Wander, JD and Johnson, LA and Su, DK and Miller, KJ and Fetz, EE and Ojemann, JG},
title = {Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {11},
pages = {2150-2161},
pmid = {25680948},
issn = {1872-8952},
support = {R01 NS065186/NS/NINDS NIH HHS/United States ; R25 NS079200/NS/NINDS NIH HHS/United States ; R01 NS065186-01/NS/NINDS NIH HHS/United States ; T90 DA023436-02/DA/NIDA NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain Mapping ; Brain-Computer Interfaces ; Efferent Pathways/physiology ; Electrocorticography ; Electrophysiological Phenomena/physiology ; Feedback, Sensory/physiology ; Female ; Fingers/innervation/*physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Retrospective Studies ; Somatosensory Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Human voluntary movements are a final product of complex interactions between multiple sensory, cognitive and motor areas of central nervous system. The objective was to investigate temporal sequence of activation of premotor (PM), primary motor (M1) and somatosensory (S1) areas during cued finger movements.<br><br>METHODS: Electrocorticography (ECoG) was used to measure activation timing in human PM, S1, and M1 neurons in preparation for finger movements in 5 subjects with subdural grids for seizure localization. Cortical activation was determined by the onset of high gamma (HG) oscillation (70-150Hz). The three cortical regions were mapped anatomically using a common brain atlas and confirmed independently with direct electrical cortical stimulation, somatosensory evoked potentials and detection of HG response to tactile stimulation. Subjects were given visual cues to flex each finger or pinch the thumb and index finger. Movements were captured with a dataglove and time-locked with ECoG. A windowed covariance metric was used to identify the rising slope of HG power between two electrodes and compute time lag. Statistical constraints were applied to the time estimates to combat the noise. Rank sum testing was used to verify the sequential activation of cortical regions across 5 subjects.<br><br>RESULTS: In all 5 subjects, HG activation in PM preceded S1 by an average of 53&#xb1;13ms (P=0.03), PM preceded M1 by 180&#xb1;40ms (P=0.001) and S1 activation preceded M1 by 136&#xb1;40ms (P=0.04).<br><br>CONCLUSIONS: Sequential HG activation of PM, S1 and M1 regions in preparation for movements is reported. Activity in S1 prior to any overt body movements supports the notion that these neurons may encode sensory information in anticipation of movements, i.e., an efference copy. Our analysis suggests that S1 modulation likely originates from PM.<br><br>SIGNIFICANCE: First electrophysiological evidence of efference copy in humans.},
}
@article {pmid25680208,
year = {2015},
author = {Nicolas-Alonso, LF and Corralejo, R and Gomez-Pilar, J and Álvarez, D and Hornero, R},
title = {Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {4},
pages = {702-712},
doi = {10.1109/TNSRE.2015.2398573},
pmid = {25680208},
issn = {1558-0210},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Foot/physiology ; Functional Laterality/physiology ; Hand/physiology ; Humans ; Imagination/*physiology ; Movement ; Tongue/physiology ; },
abstract = {Practical motor imagery-based brain computer interface (MI-BCI) applications are limited by the difficult to decode brain signals in a reliable way. In this paper, we propose a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks. Stacked generalization is used to exploit the power of classifier ensembles for combining information coming from multiple sources and reducing the existing uncertainty in EEG signals. The outputs of several regularized linear discriminant analysis (RLDA) models are combined to account for temporal, spatial, and spectral information. The resultant algorithm is called stacked RLDA (SRLDA). Additionally, an adaptive processing stage is introduced before classification to reduce the harmful effect of intersession non-stationarity. The benefits of the proposed method are evaluated on the BCI Competition IV dataset 2a. We demonstrate its effectiveness in binary and multiclass settings with four different motor imagery tasks: left-hand, right-hand, both feet, and tongue movements. The results show that adaptive SRLDA outperforms the winner of the competition and other approaches tested on this multiclass dataset.},
}
@article {pmid25680205,
year = {2015},
author = {Yu, T and Xiao, J and Wang, F and Zhang, R and Gu, Z and Cichocki, A and Li, Y},
title = {Enhanced Motor Imagery Training Using a Hybrid BCI With Feedback.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {7},
pages = {1706-1717},
doi = {10.1109/TBME.2015.2402283},
pmid = {25680205},
issn = {1558-2531},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; *Feedback ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Young Adult ; },
abstract = {GOAL: Motor imagery-related mu/beta rhythms, which can be voluntarily modulated by subjects, have been widely used in EEG-based brain computer interfaces (BCIs). Moreover, it has been suggested that motor imagery-specific EEG differences can be enhanced by feedback training. However, the differences observed in the EEGs of naive subjects are typically not sufficient to provide reliable EEG control and thus result in unintended feedback. Such feedback can frustrate subjects and impede training. In this study, a hybrid BCI paradigm combining motor imagery and steady-state visually evoked potentials (SSVEPs) has been proposed to provide effective continuous feedback for motor imagery training.<br><br>METHODS: During the initial training sessions, subjects must focus on flickering buttons to evoke SSVEPs as they perform motor imagery tasks. The output/feedback of the hybrid BCI is based on hybrid features consisting of motor imagery- and SSVEP-related brain signals. In this context, the SSVEP plays a more important role than motor imagery in generating feedback. As the training progresses, the subjects can gradually decrease their visual attention to the flickering buttons, provided that the feedback is still effective. In this case, the feedback is mainly based on motor imagery.<br><br>RESULTS: Our experimental results demonstrate that subjects generate distinguishable brain patterns of hand motor imagery after only five training sessions lasting approximately 1.5 h each.<br><br>CONCLUSION: The proposed hybrid feedback paradigm can be used to enhance motor imagery training.<br><br>SIGNIFICANCE: This hybrid BCI system with feedback can effectively identify the intentions of the subjects.},
}
@article {pmid25680204,
year = {2015},
author = {Woehrle, H and Krell, MM and Straube, S and Kim, SK and Kirchner, EA and Kirchner, F},
title = {An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {7},
pages = {1696-1705},
doi = {10.1109/TBME.2015.2402252},
pmid = {25680204},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Humans ; Machine Learning ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {GOAL: Current brain-computer interfaces (BCIs) are usually based on various, often supervised, signal processing methods. The disadvantage of supervised methods is the requirement to calibrate them with recently acquired subject-specific training data. Here, we present a novel algorithm for dimensionality reduction (spatial filter), that is ideally suited for single-trial detection of event-related potentials (ERPs) and can be adapted online to a new subject to minimize or avoid calibration time.<br><br>METHODS: The algorithm is based on the well-known xDAWN filter, but uses generalized eigendecomposition to allow an incremental training by recursive least squares (RLS) updates of the filter coefficients. We analyze the effectiveness of the spatial filter in different transfer scenarios and combinations with adaptive classifiers.<br><br>RESULTS: The results show that it can compensate changes due to switching between different users, and therefore allows to reuse training data that has been previously recorded from other subjects.<br><br>CONCLUSIONS: The presented approach allows to reduce or completely avoid a calibration phase and to instantly use the BCI system with only a minor decrease of performance.<br><br>SIGNIFICANCE: The novel filter can adapt a precomputed spatial filter to a new subject and make a BCI system user independent.},
}
@article {pmid25678455,
year = {2015},
author = {Schubauer-Berigan, MK and Anderson, JL and Hein, MJ and Little, MP and Sigurdson, AJ and Pinkerton, LE},
title = {Breast cancer incidence in a cohort of U.S. flight attendants.},
journal = {American journal of industrial medicine},
volume = {58},
number = {3},
pages = {252-266},
pmid = {25678455},
issn = {1097-0274},
support = {CC999999//Intramural CDC HHS/United States ; },
mesh = {Age Factors ; *Air Travel ; Breast Neoplasms/*epidemiology/etiology ; Chronobiology Disorders/*complications ; Cohort Studies ; Cosmic Radiation/*adverse effects ; Female ; Humans ; Incidence ; Middle Aged ; Occupational Exposure/*adverse effects ; Surveys and Questionnaires ; United States/epidemiology ; },
abstract = {BACKGROUND: Flight attendants may have elevated breast cancer incidence (BCI). We evaluated BCI's association with cosmic radiation dose and circadian rhythm disruption among 6,093 female former U.S. flight attendants.<br><br>METHODS: We collected questionnaire data on BCI and risk factors for breast cancer from 2002-2005. We conducted analyses to evaluate (i) BCI in the cohort compared to the U.S. population; and (ii) exposure-response relations. We applied an indirect adjustment to estimate whether parity and age at first birth (AFB) differences between the cohort and U.S. population could explain BCI that differed from expectation.<br><br>RESULTS: BCI was elevated but may be explained by lower parity and older AFB in the cohort than among U.S. women. BCI was not associated with exposure metrics in the cohort overall. Significant positive associations with both were observed only among women with parity of three or more.<br><br>CONCLUSIONS: Future cohort analyses may be informative on the role of these occupational exposures and non-occupational risk factors.},
}
@article {pmid25674060,
year = {2015},
author = {Naseer, N and Hong, KS},
title = {fNIRS-based brain-computer interfaces: a review.},
journal = {Frontiers in human neuroscience},
volume = {9},
number = {},
pages = {3},
pmid = {25674060},
issn = {1662-5161},
abstract = {A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.},
}
@article {pmid25672521,
year = {2015},
author = {Baecke, S and Lützkendorf, R and Mallow, J and Luchtmann, M and Tempelmann, C and Stadler, J and Bernarding, J},
title = {A proof-of-principle study of multi-site real-time functional imaging at 3T and 7T: Implementation and validation.},
journal = {Scientific reports},
volume = {5},
number = {},
pages = {8413},
pmid = {25672521},
issn = {2045-2322},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Humans ; Magnetic Resonance Imaging/*methods/standards ; Male ; Young Adult ; },
abstract = {Real-time functional Magnetic Resonance Imaging (rtfMRI) is used mainly for neurofeedback or for brain-computer interfaces (BCI). But multi-site rtfMRI could in fact help in the application of new interactive paradigms such as the monitoring of mutual information flow or the controlling of objects in shared virtual environments. For that reason, a previously developed framework that provided an integrated control and data analysis of rtfMRI experiments was extended to enable multi-site rtfMRI. Important new components included a data exchange platform for analyzing the data of both MR scanners independently and/or jointly. Information related to brain activation can be displayed separately or in a shared view. However, a signal calibration procedure had to be developed and integrated in order to permit the connecting of sites that had different hardware and to account for different inter-individual brain activation levels. The framework was successfully validated in a proof-of-principle study with twelve volunteers. Thus the overall concept, the calibration of grossly differing signals, and BCI functionality on each site proved to work as required. To model interactions between brains in real-time, more complex rules utilizing mutual activation patterns could easily be implemented to allow for new kinds of social fMRI experiments.},
}
@article {pmid25671095,
year = {2014},
author = {Vareka, L and Bruha, P and Moucek, R},
title = {Event-related potential datasets based on a three-stimulus paradigm.},
journal = {GigaScience},
volume = {3},
number = {1},
pages = {35},
pmid = {25671095},
issn = {2047-217X},
abstract = {BACKGROUND: The event-related potentials technique is widely used in cognitive neuroscience research. The P300 waveform has been explored in many research articles because of its wide applications, such as lie detection or brain-computer interfaces (BCI). However, very few datasets are publicly available. Therefore, most researchers use only their private datasets for their analysis. This leads to minimally comparable results, particularly in brain-computer research interfaces. Here we present electroencephalography/event-related potentials (EEG/ERP) data. The data were obtained from 20 healthy subjects and was acquired using an odd-ball hardware stimulator. The visual stimulation was based on a three-stimulus paradigm and included target, non-target and distracter stimuli. The data and collected metadata are shared in the EEG/ERP Portal.<br><br>FINDINGS: The paper also describes the process and validation results of the presented data. The data were validated using two different methods. The first method evaluated the data by measuring the percentage of artifacts. The second method tested if the expectation of the experimental results was fulfilled (i.e., if the target trials contained the P300 component). The validation proved that most datasets were suitable for subsequent analysis.<br><br>CONCLUSIONS: The presented datasets together with their metadata provide researchers with an opportunity to study the P300 component from different perspectives. Furthermore, they can be used for BCI research.},
}
@article {pmid25668430,
year = {2015},
author = {Ahn, M and Jun, SC},
title = {Performance variation in motor imagery brain-computer interface: a brief review.},
journal = {Journal of neuroscience methods},
volume = {243},
number = {},
pages = {103-110},
doi = {10.1016/j.jneumeth.2015.01.033},
pmid = {25668430},
issn = {1872-678X},
mesh = {Brain/anatomy &amp; histology/*physiology/physiopathology ; *Brain-Computer Interfaces ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Neural Pathways/anatomy &amp; histology/physiology/physiopathology ; Reproducibility of Results ; },
abstract = {Brain-computer interface (BCI) technology has attracted significant attention over recent decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in that performance varies greatly across and even within subjects, an obstacle that degrades the reliability of BCI systems. Understanding the causes of these problems is important if we are to create more stable systems. In this short review, we report the most recent studies and findings on performance variation, especially in motor imagery-based BCI, which has found that low-performance groups have a less-developed brain network that is incapable of motor imagery. Further, psychological and physiological states influence performance variation within subjects. We propose a possible strategic approach to deal with this variation, which may contribute to improving the reliability of BCI. In addition, the limitations of current work and opportunities for future studies are discussed.},
}
@article {pmid25667356,
year = {2015},
author = {Weyand, S and Takehara-Nishiuchi, K and Chau, T},
title = {Weaning Off Mental Tasks to Achieve Voluntary Self-Regulatory Control of a Near-Infrared Spectroscopy Brain-Computer Interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {4},
pages = {548-561},
doi = {10.1109/TNSRE.2015.2399392},
pmid = {25667356},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Algorithms ; Biofeedback, Psychology ; Brain/*physiology ; Brain Chemistry ; *Brain-Computer Interfaces ; Female ; Hemodynamics/physiology ; Hemoglobins/chemistry ; Humans ; Infrared Rays ; Male ; Mental Processes/*physiology ; Psychomotor Performance ; Surveys and Questionnaires ; Young Adult ; },
abstract = {As a noninvasive and safe optical measure of hemodynamic brain activity, near-infrared spectroscopy (NIRS) has emerged as a potential brain-computer interface (BCI) access modality. Currently, to the best of our knowledge, all NIRS BCIs use mental tasks to elicit changes in regional hemodynamic activity. One of the limitations of using mental tasks is that they can be cognitively demanding, and unintuitive. The goal of this work was to explore the development of a neurofeedback-based NIRS BCI that weans users off mental tasks, to instead use voluntary self-regulation. Ten able-bodied participants were recruited for this study. After ten sessions of using two personalized mental tasks to increase and decrease the participant's hemodynamic activity, the users were asked, for the remaining sessions, to stop performing their tasks and instead use only a desire to modulate their hemodynamic activity. By the final online session, participants were able to exclusively use voluntary self-regulation with an average accuracy of 79 ±13%. Additionally, the majority of participants indicated that BCI control via self-regulation was less taxing and more intuitive than BCI operation using mental tasks.},
}
@article {pmid25665968,
year = {2015},
author = {Vukelić, M and Gharabaghi, A},
title = {Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality.},
journal = {NeuroImage},
volume = {111},
number = {},
pages = {1-11},
doi = {10.1016/j.neuroimage.2015.01.058},
pmid = {25665968},
issn = {1095-9572},
mesh = {Adult ; Beta Rhythm/*physiology ; Brain-Computer Interfaces ; Cross-Over Studies ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Proprioception/*physiology ; Theta Rhythm/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain-computer interface (BCI) and (II) proprioceptive feedback with a brain-robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity.},
}
@article {pmid25661447,
year = {2015},
author = {Scherer, R and Billinger, M and Wagner, J and Schwarz, A and Hettich, DT and Bolinger, E and Lloria Garcia, M and Navarro, J and Müller-Putz, G},
title = {Thought-based row-column scanning communication board for individuals with cerebral palsy.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {14-22},
doi = {10.1016/j.rehab.2014.11.005},
pmid = {25661447},
issn = {1877-0665},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Palsy/physiopathology/*rehabilitation ; *Communication Devices for People with Disabilities ; Electroencephalography ; Equipment Design ; Female ; Humans ; Male ; Middle Aged ; Neurological Rehabilitation/*instrumentation ; Thinking ; Young Adult ; },
abstract = {Impairment of an individual's ability to communicate is a major hurdle for active participation in education and social life. A lot of individuals with cerebral palsy (CP) have normal intelligence, however, due to their inability to communicate, they fall behind. Non-invasive electroencephalogram (EEG) based brain-computer interfaces (BCIs) have been proposed as potential assistive devices for individuals with CP. BCIs translate brain signals directly into action. Motor activity is no longer required. However, translation of EEG signals may be unreliable and requires months of training. Moreover, individuals with CP may exhibit high levels of spontaneous and uncontrolled movement, which has a large impact on EEG signal quality and results in incorrect translations. We introduce a novel thought-based row-column scanning communication board that was developed following user-centered design principles. Key features include an automatic online artifact reduction method and an evidence accumulation procedure for decision making. The latter allows robust decision making with unreliable BCI input. Fourteen users with CP participated in a supporting online study and helped to evaluate the performance of the developed system. Users were asked to select target items with the row-column scanning communication board. The results suggest that seven among eleven remaining users performed better than chance and were consequently able to communicate by using the developed system. Three users were excluded because of insufficient EEG signal quality. These results are very encouraging and represent a good foundation for the development of real-world BCI-based communication devices for users with CP.},
}
@article {pmid25659465,
year = {2015},
author = {Kasahara, K and DaSalla, CS and Honda, M and Hanakawa, T},
title = {Neuroanatomical correlates of brain-computer interface performance.},
journal = {NeuroImage},
volume = {110},
number = {},
pages = {95-100},
doi = {10.1016/j.neuroimage.2015.01.055},
pmid = {25659465},
issn = {1095-9572},
mesh = {Brain/*anatomy &amp; histology ; Brain Mapping ; *Brain-Computer Interfaces ; Cortical Synchronization ; Data Interpretation, Statistical ; Electroencephalography ; Female ; Gray Matter/anatomy &amp; histology/physiology ; Humans ; Image Processing, Computer-Assisted ; Imagination ; Magnetic Resonance Imaging ; Male ; Motor Cortex/physiology ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) offer a potential means to replace or restore lost motor function. However, BCI performance varies considerably between users, the reasons for which are poorly understood. Here we investigated the relationship between sensorimotor rhythm (SMR)-based BCI performance and brain structure. Participants were instructed to control a computer cursor using right- and left-hand motor imagery, which primarily modulated their left- and right-hemispheric SMR powers, respectively. Although most participants were able to control the BCI with success rates significantly above chance level even at the first encounter, they also showed substantial inter-individual variability in BCI success rate. Participants also underwent T1-weighted three-dimensional structural magnetic resonance imaging (MRI). The MRI data were subjected to voxel-based morphometry using BCI success rate as an independent variable. We found that BCI performance correlated with gray matter volume of the supplementary motor area, supplementary somatosensory area, and dorsal premotor cortex. We suggest that SMR-based BCI performance is associated with development of non-primary somatosensory and motor areas. Advancing our understanding of BCI performance in relation to its neuroanatomical correlates may lead to better customization of BCIs based on individual brain structure.},
}
@article {pmid25659136,
year = {2015},
author = {, },
title = {Correction: a supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.},
journal = {PloS one},
volume = {10},
number = {2},
pages = {e0118257},
pmid = {25659136},
issn = {1932-6203},
abstract = {[This corrects the article DOI: 10.1371/journal.pone.0112352.].},
}
@article {pmid25658806,
year = {2015},
author = {Tahirovic, A and Matteucci, M and Mainardi, L},
title = {An averaging technique for the P300 spatial distribution.},
journal = {Methods of information in medicine},
volume = {54},
number = {3},
pages = {215-220},
doi = {10.3414/ME13-02-0037},
pmid = {25658806},
issn = {2511-705X},
mesh = {Algorithms ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; Principal Component Analysis ; Scalp ; *Signal Processing, Computer-Assisted ; },
abstract = {INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images".<br><br>OBJECTIVES: The main objectives of the paper regard the analysis of amplitude spatial distribution of the P300 evoked potential over a scalp of a particular subject and finding an averaged spatial distribution template for that subject. This template, which may differ for two different subjects, can help in getting a more accurate P300 detection for all BCIs that inherently use spatial filtering to detect P300 signal. Finally, the proposed averaging technique for a particular subject obtains an averaged spatial distribution template through only several epochs, which makes the proposed averaging technique fast and possible to use without applying any prior training data as in case of data enhancement technique.<br><br>METHODS: The method used in the proposed framework for the averaging of spatial distribution of P300 evoked potentials is based on the statistical properties of independent components (ICs). These components are obtained by using independent component analysis (ICA) from different target epochs.<br><br>RESULTS: This paper gives a novel averaging technique for the spatial distribution of P300 evoked potentials, which is based on the P300 signals obtained from different target epochs using the ICA algorithm. Such a technique provides a more reliable P300 spatial distribution for a subject of interest, which can be used either for an improved spatial selection of ICs, or more accurate P300 detection and extraction. In addition, the experiments demonstrate that the values of spatial intensity computed by the proposed technique for P300 signal converge after only several target epochs for each electrode allocation. Such a speed of convergence allows the proposed algorithm to easily adapt to a subject of interest without any additional artificial data preparation prior the algorithm execution such in case of data enhancement technique.<br><br>CONCLUSION: The proposed technique averages the P300 spatial distribution for a particular subject over all electrode allocations. First, the technique combines P300-like components obtained by the ICA run within a target epoch in order to obtainan averaged P300 spatial distribution. Second, the technique averages spatial distributions of P300 signals obtained from different target epochs in order to get the final averaged template. Such an template can be useful for any BCI technique where spatial selection is used to detect evoked potentials.},
}
@article {pmid25655381,
year = {2015},
author = {Kasashima-Shindo, Y and Fujiwara, T and Ushiba, J and Matsushika, Y and Kamatani, D and Oto, M and Ono, T and Nishimoto, A and Shindo, K and Kawakami, M and Tsuji, T and Liu, M},
title = {Brain-computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: Proof of concept study.},
journal = {Journal of rehabilitation medicine},
volume = {47},
number = {4},
pages = {318-324},
doi = {10.2340/16501977-1925},
pmid = {25655381},
issn = {1651-2081},
mesh = {Brain-Computer Interfaces/*statistics &amp; numerical data ; Female ; Humans ; Male ; Middle Aged ; Paresis/physiopathology/*rehabilitation/*therapy ; Stroke/physiopathology/*therapy ; *Stroke Rehabilitation ; Transcranial Direct Current Stimulation/*methods ; Upper Extremity/*physiopathology ; },
abstract = {OBJECTIVE: Brain-computer interface technology has been applied to stroke patients to improve their motor function. Event-related desynchronization during motor imagery, which is used as a brain-computer interface trigger, is sometimes difficult to detect in stroke patients. Anodal transcranial direct current stimulation (tDCS) is known to increase event-related desynchronization. This study investigated the adjunctive effect of anodal tDCS for brain-computer interface training in patients with severe hemiparesis.<br><br>SUBJECTS: Eighteen patients with chronic stroke.<br><br>DESIGN: A non-randomized controlled study.<br><br>METHODS: Subjects were divided between a brain-computer interface group and a tDCS- brain-computer interface group and participated in a 10-day brain-computer interface training. Event-related desynchronization was detected in the affected hemisphere during motor imagery of the affected fingers. The tDCS-brain-computer interface group received anodal tDCS before brain-computer interface training. Event-related desynchronization was evaluated before and after the intervention. The Fugl-Meyer Assessment upper extremity motor score (FM-U) was assessed before, immediately after, and 3 months after, the intervention.<br><br>RESULTS: Event-related desynchronization was significantly increased in the tDCS- brain-computer interface group. The FM-U was significantly increased in both groups. The FM-U improvement was maintained at 3 months in the tDCS-brain-computer interface group.<br><br>CONCLUSION: Anodal tDCS can be a conditioning tool for brain-computer interface training in patients with severe hemiparetic stroke.},
}
@article {pmid25653605,
year = {2014},
author = {Burke, JF and Merkow, MB and Jacobs, J and Kahana, MJ and Zaghloul, KA},
title = {Brain computer interface to enhance episodic memory in human participants.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1055},
pmid = {25653605},
issn = {1662-5161},
support = {R01 MH061975/MH/NIMH NIH HHS/United States ; },
abstract = {Recent research has revealed that neural oscillations in the theta (4-8 Hz) and alpha (9-14 Hz) bands are predictive of future success in memory encoding. Because these signals occur before the presentation of an upcoming stimulus, they are considered stimulus-independent in that they correlate with enhanced memory encoding independent of the item being encoded. Thus, such stimulus-independent activity has important implications for the neural mechanisms underlying episodic memory as well as the development of cognitive neural prosthetics. Here, we developed a brain computer interface (BCI) to test the ability of such pre-stimulus activity to modulate subsequent memory encoding. We recorded intracranial electroencephalography (iEEG) in neurosurgical patients as they performed a free recall memory task, and detected iEEG theta and alpha oscillations that correlated with optimal memory encoding. We then used these detected oscillatory changes to trigger the presentation of items in the free recall task. We found that item presentation contingent upon the presence of pre-stimulus theta and alpha oscillations modulated memory performance in more sessions than expected by chance. Our results suggest that an electrophysiological signal may be causally linked to a specific behavioral condition, and contingent stimulus presentation has the potential to modulate human memory encoding.},
}
@article {pmid25646900,
year = {2015},
author = {Vincent, C and Barré, Y and Vincent, T and Taulemesse, JM and Robitzer, M and Guibal, E},
title = {Chitin-Prussian blue sponges for Cs(I) recovery: from synthesis to application in the treatment of accidental dumping of metal-bearing solutions.},
journal = {Journal of hazardous materials},
volume = {287},
number = {},
pages = {171-179},
doi = {10.1016/j.jhazmat.2015.01.041},
pmid = {25646900},
issn = {1873-3336},
mesh = {Accidents ; Adsorption ; Cellulose/chemistry ; Cesium/*chemistry ; Chitin/*chemistry ; Ferrocyanides/*chemistry ; Freezing ; Porosity ; Solutions ; Water/chemistry ; Water Pollutants, Chemical/*chemistry ; Water Purification/methods ; },
abstract = {Prussian blue (i.e., iron[III] hexacyanoferrate[II], PB) has been synthesized by reaction of iron(III) chloride with potassium hexacyanoferrate and further immobilized in chitosan sponge (cellulose fibers were added in some samples to evaluate their impact on mechanical resistance). The composite was finally re-acetylated to produce a chitin-PB sponge. Experimental conditions such as the freezing temperature, the content of PB, the concentration of the biopolymer and the presence of cellulose fibers have been varied in order to evaluate their effect on the porous structure of the sponge, its water absorption properties and finally its use for cesium(I) recovery. The concept developed with this system consists in the absorption of contaminated water by the composite sponge, the in situ binding of target metal on Prussian blue load and the centrifugation of the material to remove treated water from soaked sponge. This material is supposed to be useful for the fast treatment of accidental dumping of Cs-contaminated water.},
}
@article {pmid25642177,
year = {2014},
author = {Yilmaz, O and Birbaumer, N and Ramos-Murguialday, A},
title = {Movement related slow cortical potentials in severely paralyzed chronic stroke patients.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1033},
pmid = {25642177},
issn = {1662-5161},
abstract = {Movement-related slow cortical potentials (SCPs) are proposed as reliable and immediate indicators of cortical reorganization in motor learning. SCP amplitude and latency have been reported as markers for the brain's computational effort, attention and movement planning. SCPs have been used as an EEG signature of motor control and as a main feature in Brain-Machine-Interfaces (BMIs). Some reports suggest SCPs are modified following stroke. In this study, we investigated movement-related SCPs in severe chronic stroke patients with no residual paretic hand movements preceding and during paretic (when they try to move) and healthy hand movements. The aim was to identify SCP signatures related to cortex integrity and complete paralysis due to stroke in the chronic stage. Twenty severely impaired (no residual finger extension) chronic stoke patients, of whom ten presented subcortical and ten cortical and subcortical lesions, underwent EEG and EMG recordings during a cue triggered hand movement (open/close) paradigm. SCP onset appeared and peaked significantly earlier during paretic hand movements than during healthy hand movements. Amplitudes were significantly larger over the midline (Cz, Fz) for paretic hand movements while contralateral (C4, F4) and midline (Cz, Fz) amplitudes were significantly larger than ipsilateral activity for healthy hand movements. Dividing the participants into subcortical only and mixed lesioned patient groups, no significant differences observed in SCP amplitude and latency between groups. This suggests lesions in the thalamocortical loop as the main factor in SCP changes after stroke. Furthermore, we demonstrated how, after long-term complete paralysis, post-stroke intention to move a paralyzed hand resulted in longer and larger SCPs originating in the frontal areas. These results suggest SCP are a valuable feature that should be incorporated in the design of new neurofeedback strategies for motor neurorehabilitation.},
}
@article {pmid25640806,
year = {2015},
author = {Yin, X and Xu, B and Jiang, C and Fu, Y and Wang, Z and Li, H and Shi, G},
title = {NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI.},
journal = {Medical engineering &amp; physics},
volume = {37},
number = {3},
pages = {280-286},
doi = {10.1016/j.medengphy.2015.01.005},
pmid = {25640806},
issn = {1873-4030},
mesh = {*Algorithms ; Brain/blood supply ; *Brain-Computer Interfaces ; Hand/*physiology ; Hemodynamics ; Humans ; *Movement ; *Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared ; },
abstract = {Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain-computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodynamic measurements, empirical mode decomposition (EMD) was applied to reduce the physiological noise overwhelmed in the task-relevant NIRS signals. Compared with continuous wavelet decomposition, EMD does not require a pre-determined basis function. EMD decomposes the original signals into a set of intrinsic mode functions (IMFs). In this study, joint mutual information was applied to select the optimal features, and support vector machine was used as a classifier. Offline and pseudo-online analyses showed that the most feasible classification accuracy can be obtained using IMFs as input features. Accordingly, an alternative feature is provided to develop the NIRS-BCI system.},
}
@article {pmid25632076,
year = {2015},
author = {Boulay, CB and Chen, XY and Wolpaw, JR},
title = {Electrocorticographic activity over sensorimotor cortex and motor function in awake behaving rats.},
journal = {Journal of neurophysiology},
volume = {113},
number = {7},
pages = {2232-2241},
pmid = {25632076},
issn = {1522-1598},
support = {NS-22189/NS/NINDS NIH HHS/United States ; NS-061823/NS/NINDS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; HD-36020/HD/NICHD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal/physiology ; Electrocorticography/methods ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Somatosensory/*physiology ; H-Reflex/*physiology ; Male ; Muscle Contraction/*physiology ; Muscle, Skeletal/innervation/*physiology ; Rats ; Rats, Sprague-Dawley ; Sensorimotor Cortex/*physiology ; Wakefulness/physiology ; },
abstract = {Sensorimotor cortex exerts both short-term and long-term control over the spinal reflex pathways that serve motor behaviors. Better understanding of this control could offer new possibilities for restoring function after central nervous system trauma or disease. We examined the impact of ongoing sensorimotor cortex (SMC) activity on the largely monosynaptic pathway of the H-reflex, the electrical analog of the spinal stretch reflex. In 41 awake adult rats, we measured soleus electromyographic (EMG) activity, the soleus H-reflex, and electrocorticographic activity over the contralateral SMC while rats were producing steady-state soleus EMG activity. Principal component analysis of electrocorticographic frequency spectra before H-reflex elicitation consistently revealed three frequency bands: μβ (5-30 Hz), low γ (γ1; 40-85 Hz), and high γ (γ2; 100-200 Hz). Ongoing (i.e., background) soleus EMG amplitude correlated negatively with μβ power and positively with γ1 power. In contrast, H-reflex size correlated positively with μβ power and negatively with γ1 power, but only when background soleus EMG amplitude was included in the linear model. These results support the hypothesis that increased SMC activation (indicated by decrease in μβ power and/or increase in γ1 power) simultaneously potentiates the H-reflex by exciting spinal motoneurons and suppresses it by decreasing the efficacy of the afferent input. They may help guide the development of new rehabilitation methods and of brain-computer interfaces that use SMC activity as a substitute for lost or impaired motor outputs.},
}
@article {pmid25628522,
year = {2014},
author = {Talakoub, O and Popovic, MR and Navaro, J and Hamani, C and Fonoff, ET and Wong, W},
title = {Temporal alignment of electrocorticographic recordings for upper limb movement.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {431},
pmid = {25628522},
issn = {1662-4548},
abstract = {The detection of movement-related components of the brain activity is useful in the design of brain-machine interfaces. A common approach is to classify the brain activity into a number of templates or states. To find these templates, the neural responses are averaged over each movement task. For averaging to be effective, one must assume that the neural components occur at identical times over repeated trials. However, complex arm movements such as reaching and grasping are prone to cross-trial variability due to the way movements are performed. Typically initiation time, duration of movement and movement speed are variable even as a subject tries to reproduce the same task identically across trials. Therefore, movement-related neural activity will tend to occur at different times across the trials. Due to this mismatch, the averaging of neural activity will not bring into salience movement-related components. To address this problem, we present a method of alignment that accounts for the variabilities in the way the movements are conducted. In this study, arm speed was used to align neural activity. Four subjects had electrocorticographic (ECoG) electrodes implanted over their primary motor cortex and were asked to perform reaching and retrieving tasks using the upper limb contralateral to the site of electrode implantation. The arm speeds were aligned using a non-linear transformation of the temporal axes resulting in average spectrograms with superior visualization of movement-related neural activity when compared to averaging without alignment.},
}
@article {pmid25627426,
year = {2015},
author = {Takmakov, P and Ruda, K and Scott Phillips, K and Isayeva, IS and Krauthamer, V and Welle, CG},
title = {Rapid evaluation of the durability of cortical neural implants using accelerated aging with reactive oxygen species.},
journal = {Journal of neural engineering},
volume = {12},
number = {2},
pages = {026003},
pmid = {25627426},
issn = {1741-2552},
support = {FD999999//Intramural FDA HHS/United States ; },
mesh = {Brain/*physiology ; Coated Materials, Biocompatible/*chemistry ; *Electrodes, Implanted ; Equipment Design ; Equipment Failure Analysis/methods ; Humans ; Materials Testing/methods ; Microelectrodes ; Reactive Oxygen Species/*chemistry ; Resins, Synthetic/*chemistry ; Time Factors ; Tungsten/*chemistry ; },
abstract = {OBJECTIVE: A challenge for implementing high bandwidth cortical brain-machine interface devices in patients is the limited functional lifespan of implanted recording electrodes. Development of implant technology currently requires extensive non-clinical testing to demonstrate device performance. However, testing the durability of the implants in vivo is time-consuming and expensive. Validated in vitro methodologies may reduce the need for extensive testing in animal models.<br><br>APPROACH: Here we describe an in vitro platform for rapid evaluation of implant stability. We designed a reactive accelerated aging (RAA) protocol that employs elevated temperature and reactive oxygen species (ROS) to create a harsh aging environment. Commercially available microelectrode arrays (MEAs) were placed in a solution of hydrogen peroxide at 87 &#xb0;C for a period of 7 days. We monitored changes to the implants with scanning electron microscopy and broad spectrum electrochemical impedance spectroscopy (1 Hz-1 MHz) and correlated the physical changes with impedance data to identify markers associated with implant failure.<br><br>MAIN RESULTS: RAA produced a diverse range of effects on the structural integrity and electrochemical properties of electrodes. Temperature and ROS appeared to have different effects on structural elements, with increased temperature causing insulation loss from the electrode microwires, and ROS concentration correlating with tungsten metal dissolution. All array types experienced impedance declines, consistent with published literature showing chronic (>30 days) declines in array impedance in vivo. Impedance change was greatest at frequencies <10 Hz, and smallest at frequencies 1 kHz and above. Though electrode performance is traditionally characterized by impedance at 1 kHz, our results indicate that an impedance change at 1 kHz is not a reliable predictive marker of implant degradation or failure.<br><br>SIGNIFICANCE: ROS, which are known to be present in vivo, can create structural damage and change electrical properties of MEAs. Broad-spectrum electrical impedance spectroscopy demonstrates increased sensitivity to electrode damage compared with single-frequency measurements. RAA can be a useful tool to simulate worst-case in vivo damage resulting from chronic electrode implantation, simplifying the device development lifecycle.},
}
@article {pmid25624754,
year = {2015},
author = {Lee, TS and Quek, SY and Goh, SJ and Phillips, R and Guan, C and Cheung, YB and Feng, L and Wang, CC and Chin, ZY and Zhang, H and Lee, J and Ng, TP and Krishnan, KR},
title = {A pilot randomized controlled trial using EEG-based brain-computer interface training for a Chinese-speaking group of healthy elderly.},
journal = {Clinical interventions in aging},
volume = {10},
number = {},
pages = {217-227},
pmid = {25624754},
issn = {1178-1998},
mesh = {Aged ; Asian People ; Attention ; *Brain-Computer Interfaces ; *Cognition ; Electroencephalography ; Humans ; *Learning ; Memory ; *Neuropsychological Tests ; Patient Satisfaction ; Quality of Life ; Singapore ; },
abstract = {BACKGROUND: There is growing evidence that cognitive training (CT) can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG)-based brain-computer interface (BCI) CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the acceptability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly.<br><br>METHODS: Thirty-nine elderly participants were randomized into intervention (n=21) and wait-list control (n=18) arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), which was translated and culturally adapted for the Chinese-speaking local population. Users were asked about any adverse events experienced after each session as a safety measure.<br><br>RESULTS: The training was deemed easily usable and acceptable by senior users. The median difference in the change scores pre- and post-training of the modified RBANS total score was 8.0 (95% confidence interval [CI]: 0.0-16.0, P=0.042) higher in the intervention arm than waitlist control, while the mean difference was 9.0 (95% CI: 1.7-16.2, P=0.017). Ten (30.3%) participants reported a total of 16 adverse events - all of which were graded "mild" except for one graded "moderate".<br><br>CONCLUSION: Our BCI training system shows potential in improving cognition in both English- and Chinese-speaking elderly, and deserves further evaluation in a Phase III trial. Overall, participants responded positively on the usability and acceptability questionnaire.},
}
@article {pmid25623294,
year = {2015},
author = {Chaudhary, U and Birbaumer, N and Curado, MR},
title = {Brain-machine interface (BMI) in paralysis.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {9-13},
doi = {10.1016/j.rehab.2014.11.002},
pmid = {25623294},
issn = {1877-0665},
mesh = {Amyotrophic Lateral Sclerosis/*rehabilitation ; Brain-Computer Interfaces/*trends ; Humans ; Neurological Rehabilitation/*instrumentation ; Paralysis/etiology/*rehabilitation ; Recovery of Function ; *Stroke Rehabilitation ; },
abstract = {INTRODUCTION: Brain-machine interfaces (BMIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this review, we focus on the current advances of non-invasive BMIs for communication in patients with amyotrophic lateral sclerosis (ALS) and for restoration of motor impairment after severe stroke.<br><br>BMI FOR ALS PATIENTS: BMI represents a promising strategy to establish communication with paralyzed ALS patients as it does not need muscle engagement for its use. Distinct techniques have been explored to assess brain neurophysiology to control BMI for patients' communication, especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS). Previous studies demonstrated successful communication with ALS patients using EEG-BMI when patients still showed residual eye control, but patients with complete paralysis were unable to communicate with this system. We recently introduced functional NIRS (fNIRS)-BMI for communication in ALS patients in the complete locked-in syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for communication in ALS patients after complete paralysis.<br><br>BMI FOR STROKE MOTOR RECOVERY: In addition to assisted communication, BMI is also being extensively studied for motor recovery after stroke. BMI for stroke motor recovery includes intensive BMI training linking brain activity related to patient's intention to move the paretic limb with the contingent sensory feedback of the paretic limb movement guided by assistive devices. BMI studies in this area are mainly focused on EEG- or magnetoencephalography (MEG)-BMI systems due to their high temporal resolution, which facilitates online contingency between intention to move and sensory feedback of the intended movement. EEG-BMI training was recently demonstrated in a controlled study to significantly improve motor performance in stroke patients with severe paresis. Neural basis for BMI-induced restoration of motor function and perspectives for future BMI research for stroke motor recovery are discussed.},
}
@article {pmid25623293,
year = {2015},
author = {Mattout, J and Perrin, M and Bertrand, O and Maby, E},
title = {Improving BCI performance through co-adaptation: applications to the P300-speller.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {23-28},
doi = {10.1016/j.rehab.2014.10.006},
pmid = {25623293},
issn = {1877-0665},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Healthy Volunteers ; Humans ; Language ; Neurological Rehabilitation/*instrumentation ; *User-Computer Interface ; },
abstract = {A well-known neurophysiological marker that can easily be captured with electroencephalography (EEG) is the so-called P300: a positive signal deflection occurring at about 300 ms after a relevant stimulus. This brain response is particularly salient when the target stimulus is rare among a series of distracting stimuli, whatever the type of sensory input. Therefore, it has been proposed and extensively studied as a possible feature for direct brain-computer communication. The most advanced non-invasive BCI application based on this principle is the P300-speller. However, it is still a matter of debate whether this application will prove relevant to any population of patients. In a series of recent theoretical and empirical studies, we have been using this P300-based paradigm to push forward the performance of non-invasive BCI. This paper summarizes the proposed improvements and obtained results. Importantly, those could be generalized to many kinds of BCI, beyond this particular application. Indeed, they relate to most of the key components of a closed-loop BCI, namely: improving the accuracy of the system by trying to detect and correct for errors automatically; optimizing the computer's speed-accuracy trade-off by endowing it with adaptive behavior; but also simplifying the hardware and time for set-up in the aim of routine use in patients. Our results emphasize the importance of the closed-loop interaction and of the ensuing co-adaptation between the user and the machine whenever possible. Most of our evaluations have been conducted in healthy subjects. We conclude with perspectives for clinical applications.},
}
@article {pmid25620924,
year = {2014},
author = {Simon, N and Käthner, I and Ruf, CA and Pasqualotto, E and Kübler, A and Halder, S},
title = {An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1039},
pmid = {25620924},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g., in the completely locked-in state) or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on 2 consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS) in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min) that increased to 90% (4.23 bits/min) on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.},
}
@article {pmid25617126,
year = {2015},
author = {Gutowski, SM and Shoemaker, JT and Templeman, KL and Wei, Y and Latour, RA and Bellamkonda, RV and LaPlaca, MC and García, AJ},
title = {Protease-degradable PEG-maleimide coating with on-demand release of IL-1Ra to improve tissue response to neural electrodes.},
journal = {Biomaterials},
volume = {44},
number = {},
pages = {55-70},
pmid = {25617126},
issn = {1878-5905},
support = {T32EB006343-01A2/EB/NIBIB NIH HHS/United States ; T32 GM008433/GM/NIGMS NIH HHS/United States ; EB-002027/EB/NIBIB NIH HHS/United States ; UL RR025008/RR/NCRR NIH HHS/United States ; F31NS073358/NS/NINDS NIH HHS/United States ; UL1 TR000454/TR/NCATS NIH HHS/United States ; UL1 RR025008/RR/NCRR NIH HHS/United States ; P41 EB002027/EB/NIBIB NIH HHS/United States ; T32 EB006343/EB/NIBIB NIH HHS/United States ; F31 NS073358/NS/NINDS NIH HHS/United States ; },
mesh = {Amino Acid Sequence ; Animals ; Astrocytes/drug effects/pathology ; Blood-Brain Barrier/drug effects/pathology ; Cell Adhesion/drug effects ; Cell Survival/drug effects ; Cells, Cultured ; Chondroitin Sulfates/metabolism ; Coated Materials, Biocompatible/*pharmacology ; *Electrodes, Implanted ; Gene Expression Regulation/drug effects ; Glial Fibrillary Acidic Protein/metabolism ; Hydrogel, Polyethylene Glycol Dimethacrylate/chemistry ; Immunoglobulin G/metabolism ; Inflammation Mediators/metabolism ; Interleukin 1 Receptor Antagonist Protein/*metabolism ; Lipopolysaccharides/pharmacology ; Male ; Maleimides/*pharmacology ; Microglia/drug effects ; Molecular Sequence Data ; Neurons/*drug effects ; Peptide Hydrolases/*metabolism ; Polyethylene Glycols/chemistry/*pharmacology ; Rats, Sprague-Dawley ; Surface Properties ; },
abstract = {Neural electrodes are an important part of brain-machine interface devices that can restore functionality to patients with sensory and movement disorders. Chronically implanted neural electrodes induce an unfavorable tissue response which includes inflammation, scar formation, and neuronal cell death, eventually causing loss of electrode function. We developed a poly(ethylene glycol) hydrogel coating for neural electrodes with non-fouling characteristics, incorporated an anti-inflammatory agent, and engineered a stimulus-responsive degradable portion for on-demand release of the anti-inflammatory agent in response to inflammatory stimuli. This coating reduces in vitro glial cell adhesion, cell spreading, and cytokine release compared to uncoated controls. We also analyzed the in vivo tissue response using immunohistochemistry and microarray qRT-PCR. Although no differences were observed among coated and uncoated electrodes for inflammatory cell markers, lower IgG penetration into the tissue around PEG+IL-1Ra coated electrodes indicates an improvement in blood-brain barrier integrity. Gene expression analysis showed higher expression of IL-6 and MMP-2 around PEG+IL-1Ra samples, as well as an increase in CNTF expression, an important marker for neuronal survival. Importantly, increased neuronal survival around coated electrodes compared to uncoated controls was observed. Collectively, these results indicate promising findings for an engineered coating to increase neuronal survival and improve tissue response around implanted neural electrodes.},
}
@article {pmid25616606,
year = {2015},
author = {Luauté, J and Morlet, D and Mattout, J},
title = {BCI in patients with disorders of consciousness: clinical perspectives.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {29-34},
doi = {10.1016/j.rehab.2014.09.015},
pmid = {25616606},
issn = {1877-0665},
mesh = {Awareness ; Brain Injuries/physiopathology/rehabilitation ; Brain-Computer Interfaces/*trends ; Communication Devices for People with Disabilities ; Consciousness Disorders/physiopathology/*rehabilitation ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; Neurological Rehabilitation/*instrumentation ; },
abstract = {The reestablishment of communication is one of the main goals for patients with disorders of consciousness (DOC). It is now established that many DOC patients retain the ability to process stimuli of varying complexity even in the absence of behavioural response. Motor impairment, fatigue, attention disorders might contribute to the difficulty of communication in this population. Brain-computer interfaces (BCI) might be helpful in restoring some communication ability in these patients. After a definition of the different disorders of consciousness that might benefit from BCI, brain markers able to detect cognitive processes and awareness in the absence of behavioural manifestation are described. Can these markers be willfully modulated and used to restore communication in DOC patients? In order to answer this question, three paradigmatic articles using either functional imaging or electrophysiology are critically analysed with regard to clinical applications. It appears that the use of fMRI is limited from a clinical point of view, whereas the EEG seems more feasible with possible BCI applications at the patient's bedside. However, at this stage, several limitations are pointed out: the lack of awareness in itself, the lack of sensitivity of the technique, atypical pattern of brain activity in brain damaged patients. The challenge is now to select the best candidates, to improve the efficiency, portability and cost of these techniques. Although this innovative technology may concern a minority of DOC patients, it might offer the possibility to restore or improve communication to heavily disabled patients and meanwhile detect a signature of awareness.},
}
@article {pmid25616605,
year = {2015},
author = {Luauté, J and Laffont, I},
title = {BCIs and physical medicine and rehabilitation: the future is now.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {1-2},
doi = {10.1016/j.rehab.2014.12.002},
pmid = {25616605},
issn = {1877-0665},
mesh = {Brain-Computer Interfaces/*trends ; Forecasting ; Humans ; Neurological Rehabilitation/*instrumentation/trends ; },
}
@article {pmid25614021,
year = {2015},
author = {van Dokkum, LEH and Ward, T and Laffont, I},
title = {Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {3-8},
doi = {10.1016/j.rehab.2014.09.016},
pmid = {25614021},
issn = {1877-0665},
mesh = {Brain-Computer Interfaces/*trends ; Humans ; Neurological Rehabilitation/*instrumentation ; *Stroke Rehabilitation ; },
abstract = {The idea of using brain computer interfaces (BCI) for rehabilitation emerged relatively recently. Basically, BCI for neurorehabilitation involves the recording and decoding of local brain signals generated by the patient, as he/her tries to perform a particular task (even if imperfect), or during a mental imagery task. The main objective is to promote the recruitment of selected brain areas involved and to facilitate neural plasticity. The recorded signal can be used in several ways: (i) to objectify and strengthen motor imagery-based training, by providing the patient feedback on the imagined motor task, for example, in a virtual environment; (ii) to generate a desired motor task via functional electrical stimulation or rehabilitative robotic orthoses attached to the patient's limb – encouraging and optimizing task execution as well as "closing" the disrupted sensorimotor loop by giving the patient the appropriate sensory feedback; (iii) to understand cerebral reorganizations after lesion, in order to influence or even quantify plasticity-induced changes in brain networks. For example, applying cerebral stimulation to re-equilibrate inter-hemispheric imbalance as shown by functional recording of brain activity during movement may help recovery. Its potential usefulness for a patient population has been demonstrated on various levels and its diverseness in interface applications makes it adaptable to a large population. The position and status of these very new rehabilitation systems should now be considered with respect to our current and more or less validated traditional methods, as well as in the light of the wide range of possible brain damage. The heterogeneity in post-damage expression inevitably complicates the decoding of brain signals and thus their use in pathological conditions, asking for controlled clinical trials.},
}
@article {pmid25611124,
year = {2015},
author = {Yang, Z and Fripp, J and Chandra, SS and Neubert, A and Xia, Y and Strudwick, M and Paproki, A and Engstrom, C and Crozier, S},
title = {Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images.},
journal = {Physics in medicine and biology},
volume = {60},
number = {4},
pages = {1441-1459},
doi = {10.1088/0031-9155/60/4/1441},
pmid = {25611124},
issn = {1361-6560},
mesh = {*Algorithms ; Cartilage, Articular/pathology ; Humans ; Humeral Head/pathology ; Imaging, Three-Dimensional/*methods ; Magnetic Resonance Imaging/*methods ; Models, Statistical ; Scapula/pathology ; Shoulder Joint/*pathology ; },
abstract = {We present a statistical shape model approach for automated segmentation of the proximal humerus and scapula with subsequent bone-cartilage interface (BCI) extraction from 3D magnetic resonance (MR) images of the shoulder region. Manual and automated bone segmentations from shoulder MR examinations from 25 healthy subjects acquired using steady-state free precession sequences were compared with the Dice similarity coefficient (DSC). The mean DSC scores between the manual and automated segmentations of the humerus and scapula bone volumes surrounding the BCI region were 0.926 ± 0.050 and 0.837 ± 0.059, respectively. The mean DSC values obtained for BCI extraction were 0.806 ± 0.133 for the humerus and 0.795 ± 0.117 for the scapula. The current model-based approach successfully provided automated bone segmentation and BCI extraction from MR images of the shoulder. In future work, this framework appears to provide a promising avenue for automated segmentation and quantitative analysis of cartilage in the glenohumeral joint.},
}
@article {pmid25605627,
year = {2015},
author = {Gore, RK and Choi, Y and Bellamkonda, R and English, A},
title = {Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016017},
pmid = {25605627},
issn = {1741-2552},
support = {R01 NS065109/NS/NINDS NIH HHS/United States ; R25 NS065739/NS/NINDS NIH HHS/United States ; NS065109/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Electrodes, Implanted ; Equipment Failure Analysis ; Female ; Guided Tissue Regeneration/*instrumentation ; Monitoring, Ambulatory/*instrumentation ; Nerve Regeneration/*physiology ; Neural Conduction/physiology ; Prosthesis Design ; Rats ; Rats, Inbred Lew ; Reproducibility of Results ; Sensitivity and Specificity ; *Tissue Scaffolds ; },
abstract = {OBJECTIVE: Neural interface technologies could provide controlling connections between the nervous system and external technologies, such as limb prosthetics. The recording of efferent, motor potentials is a critical requirement for a peripheral neural interface, as these signals represent the user-generated neural output intended to drive external devices. Our objective was to evaluate structural and functional neural regeneration through a microchannel neural interface and to characterize potentials recorded from electrodes placed within the microchannels in awake and behaving animals.<br><br>APPROACH: Female rats were implanted with muscle EMG electrodes and, following unilateral sciatic nerve transection, the cut nerve was repaired either across a microchannel neural interface or with end-to-end surgical repair. During a 13 week recovery period, direct muscle responses to nerve stimulation proximal to the transection were monitored weekly. In two rats repaired with the neural interface, four wire electrodes were embedded in the microchannels and recordings were obtained within microchannels during proximal stimulation experiments and treadmill locomotion.<br><br>MAIN RESULTS: In these proof-of-principle experiments, we found that axons from cut nerves were capable of functional reinnervation of distal muscle targets, whether regenerating through a microchannel device or after direct end-to-end repair. Discrete stimulation-evoked and volitional potentials were recorded within interface microchannels in a small group of awake and behaving animals and their firing patterns correlated directly with intramuscular recordings during locomotion. Of 38 potentials extracted, 19 were identified as motor axons reinnervating tibialis anterior or soleus muscles using spike triggered averaging.<br><br>SIGNIFICANCE: These results are evidence for motor axon regeneration through microchannels and are the first report of in vivo recordings from regenerated motor axons within microchannels in a small group of awake and behaving animals. These unique findings provide preliminary evidence that efferent, volitional motor potentials can be recorded from the microchannel-based peripheral neural interface; a critical requirement for any neural interface intended to facilitate direct neural control of external technologies.},
}
@article {pmid25605498,
year = {2015},
author = {Sadtler, PT and Ryu, SI and Tyler-Kabara, EC and Yu, BM and Batista, AP},
title = {Brain-computer interface control along instructed paths.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016015},
pmid = {25605498},
issn = {1741-2552},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; P30-NS076405/NS/NINDS NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; R01-HD071686/HD/NICHD NIH HHS/United States ; R01-NS065065/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Computer Peripherals ; Cues ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire.<br><br>APPROACH: We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics.<br><br>MAIN RESULTS: We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task.<br><br>SIGNIFICANCE: The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.},
}
@article {pmid25600602,
year = {2015},
author = {Pineda-Peña, AC and Faria, NR and Mina, T and Amini-Bavil-Olyaee, S and Alavian, SM and Lemey, P and Maes, P and Van Ranst, M and Pourkarim, MR},
title = {Epidemiological history and genomic characterization of non-D1 HBV strains identified in Iran.},
journal = {Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology},
volume = {63},
number = {},
pages = {38-41},
doi = {10.1016/j.jcv.2014.12.010},
pmid = {25600602},
issn = {1873-5967},
mesh = {Adult ; Carrier State/epidemiology/virology ; Cluster Analysis ; DNA, Viral/chemistry/genetics ; *Evolution, Molecular ; Female ; Genetic Variation ; *Genome, Viral ; *Genotype ; Hepatitis B/*epidemiology/virology ; Hepatitis B virus/*classification/*genetics/isolation &amp; purification ; Humans ; Iran/epidemiology ; Male ; Middle Aged ; Molecular Epidemiology ; Molecular Sequence Data ; Phylogeny ; Sequence Analysis, DNA ; Sequence Homology ; },
abstract = {BACKGROUND: Hepatitis B virus (HBV) has been classified into eight genotypes and forty subgenotypes. Genotype D of HBV is the most worldwide distributed genotype and HBV subgenotype D1 has been isolated from Iranian patients.<br><br>OBJECTIVE: To characterize for the first time complete genomes of recently emerged non-D1 strains in Iran.<br><br>STUDY DESIGN: HBV complete genomes isolated from 9 Iranian HBV carriers were sequenced. Different diversities of the ORFs were mapped and evolutionary history relationships were investigated.<br><br>RESULTS: Phylogenetic analysis identified four D2 subgenotypes and five D3 subgenotypes of HBV in the studied patients. Of note, D2 strains clustered with strains from Lebanon and Syria. The time of the most recent common ancestor (TMRCA) of the first cluster of D2 was dated at 1953 (BCI=1926, 1976) while the second cluster was dated at 1947 (BCI=1911, 1978). All five Iranian D3 strains formed a monophyletic cluster with Indian strain and dated back to 1967 (BCI=1946, 1987). Surprisingly, two D3 strains had an adw2 subtype. Interestingly, more than 80% of the present strains showed precore mutations, while two isolates carried basal core promoter variation.<br><br>CONCLUSION: Iranian D2 and D3 isolates were introduced on at least two and one occasion in Iran and diverged from west and south Asian HBV strains, respectively. Considering the impact of the different (sub) genotypes on clinical outcome, exploring the distinct mutational patterns of Iranian D1 and non-D1 strains is of clinical importance.},
}
@article {pmid25599177,
year = {2015},
author = {Canales, A and Jia, X and Froriep, UP and Koppes, RA and Tringides, CM and Selvidge, J and Lu, C and Hou, C and Wei, L and Fink, Y and Anikeeva, P},
title = {Multifunctional fibers for simultaneous optical, electrical and chemical interrogation of neural circuits in vivo.},
journal = {Nature biotechnology},
volume = {33},
number = {3},
pages = {277-284},
pmid = {25599177},
issn = {1546-1696},
mesh = {Animals ; Blood-Brain Barrier/drug effects ; Drug Delivery Systems ; Electrodes ; *Electrophysiological Phenomena/drug effects ; Foreign-Body Reaction/pathology ; Implants, Experimental ; Male ; Metals/pharmacology ; Mice, Inbred C57BL ; Mice, Transgenic ; Nerve Net/drug effects/*physiology ; *Optical Fibers ; Optogenetics ; },
abstract = {Brain function depends on simultaneous electrical, chemical and mechanical signaling at the cellular level. This multiplicity has confounded efforts to simultaneously measure or modulate these diverse signals in vivo. Here we present fiber probes that allow for simultaneous optical stimulation, neural recording and drug delivery in behaving mice with high resolution. These fibers are fabricated from polymers by means of a thermal drawing process that allows for the integration of multiple materials and interrogation modalities into neural probes. Mechanical, electrical, optical and microfluidic measurements revealed high flexibility and functionality of the probes under bending deformation. Long-term in vivo recordings, optogenetic stimulation, drug perturbation and analysis of tissue response confirmed that our probes can form stable brain-machine interfaces for at least 2 months. We expect that our multifunctional fibers will permit more detailed manipulation and analysis of neural circuits deep in the brain of behaving animals than achievable before.},
}
@article {pmid25599079,
year = {2014},
author = {Blakely, TM and Olson, JD and Miller, KJ and Rao, RP and Ojemann, JG},
title = {Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {1},
number = {3-4},
pages = {147-157},
pmid = {25599079},
issn = {2326-263X},
support = {K12 HD001097/HD/NICHD NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; T32 NS007144/NS/NINDS NIH HHS/United States ; },
abstract = {Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75-200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms.},
}
@article {pmid25596422,
year = {2015},
author = {Combrisson, E and Jerbi, K},
title = {Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.},
journal = {Journal of neuroscience methods},
volume = {250},
number = {},
pages = {126-136},
doi = {10.1016/j.jneumeth.2015.01.010},
pmid = {25596422},
issn = {1872-678X},
mesh = {Algorithms ; Bayes Theorem ; Brain/*physiology/physiopathology ; Computer Simulation ; Discriminant Analysis ; Electrodes, Implanted ; Electroencephalography/*methods ; Epilepsy/physiopathology ; Humans ; Linear Models ; Magnetoencephalography/*methods ; Models, Neurological ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naïve Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings.},
}
@article {pmid25595535,
year = {2015},
author = {Nijboer, F},
title = {Technology transfer of brain-computer interfaces as assistive technology: barriers and opportunities.},
journal = {Annals of physical and rehabilitation medicine},
volume = {58},
number = {1},
pages = {35-38},
doi = {10.1016/j.rehab.2014.11.001},
pmid = {25595535},
issn = {1877-0665},
mesh = {*Brain-Computer Interfaces ; Equipment Design ; Humans ; Neurological Rehabilitation/*instrumentation ; *Self-Help Devices ; *Technology Transfer ; },
abstract = {This paper provides an analysis of perspectives from different stakeholders on the state-of-the-art of BCI. Three barriers for technology transfer of BCIs as access technologies are identified. First, BCIs are developed with a narrow focus on creating a reliable technology, while a broader focus on creating a usable technology is needed. Second, the potential target group, which could benefit from BCIs as access technologies is expected to be very small. Development costs are therefore high, while reimbursements are expected to be low, which challenges the commercial viability. Third, potential target users should be much more included in the design process of BCIs to ensure that the end-products meet technical, ethical, legal and social requirements. These three issues need to be urgently addressed so that target users may benefit from this promising technology.},
}
@article {pmid25595414,
year = {2015},
author = {Tong, J and Zhu, D},
title = {Multi-phase cycle coding for SSVEP based brain-computer interfaces.},
journal = {Biomedical engineering online},
volume = {14},
number = {},
pages = {5},
pmid = {25595414},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potential (SSVEP) have attracted more and more attentions for their short time response and high information transfer rate (ITR). The use of a high stimulation frequency (from 30 Hz to 40 Hz) is more comfortable for users and can avoid the amplitude-frequency problem, but the number of available phases for stimulation source is limited. To circumvent this deficiency, a novel protocol named Multi-Phase Cycle Coding (MPCC) for SSVEP-based BCIs was proposed in the present study.<br><br>METHODS: In MPCC, each target is coded by a block word that includes a series of cyclic codewords, and each block word is corresponding to a certain flickering visual stimulus, which is a combination of multiple phases from an available phase set and flickers at single frequency. The methods of generating block code and extracting phase were presented and experiments were performed to investigate the feasibility of MPCC.<br><br>RESULTS: The optimal stimulation frequency was subject-specific, and the optimal duration was longer than 0.5 s. The BCI system with MPCC could achieve average discrimination accuracy 93.51&#x2009;&#xb1;&#x2009;5.62% and information transfer rate 33.77&#x2009;&#xb1;&#x2009;8.67%.<br><br>CONCLUSIONS: The MPCC has the error correction ability, can effectively increase the encoded targets and improve the performance of the system. Therefore, the MPCC is promising for practical BCIs.},
}
@article {pmid25590063,
year = {2014},
author = {},
title = {Proceedings of the Fifth International Brain–Computer Interface Meeting, June 3-7, 2013, Pacific Grove, CA.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {030301-035016},
pmid = {25590063},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; },
}
@article {pmid25589568,
year = {2015},
author = {Morimoto, J and Kawato, M},
title = {Creating the brain and interacting with the brain: an integrated approach to understanding the brain.},
journal = {Journal of the Royal Society, Interface},
volume = {12},
number = {104},
pages = {20141250},
pmid = {25589568},
issn = {1742-5662},
mesh = {Algorithms ; Animals ; Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; Cognition ; Computer Simulation ; Electroencephalography/methods ; Humans ; Man-Machine Systems ; Models, Theoretical ; Motor Skills/physiology ; Neurosciences/*trends ; Robotics ; },
abstract = {In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.},
}
@article {pmid25588137,
year = {2015},
author = {Mainsah, BO and Collins, LM and Colwell, KA and Sellers, EW and Ryan, DB and Caves, K and Throckmorton, CS},
title = {Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016013},
pmid = {25588137},
issn = {1741-2552},
support = {R33 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470-03/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; Brain Mapping/methods ; Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Computer Peripherals ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Information Storage and Retrieval/*methods ; Male ; Middle Aged ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Visual Cortex/physiopathology ; Visual Perception ; Word Processing ; },
abstract = {OBJECTIVE: The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred.<br><br>APPROACH: We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation.<br><br>MAIN RESULTS: Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms.<br><br>SIGNIFICANCE: We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.},
}
@article {pmid25587889,
year = {2015},
author = {Pinegger, A and Faller, J and Halder, S and Wriessnegger, SC and Müller-Putz, GR},
title = {Control or non-control state: that is the question! An asynchronous visual P300-based BCI approach.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {014001},
doi = {10.1088/1741-2560/12/1/014001},
pmid = {25587889},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) were proven to be a reliable synchronous communication method. For everyday life situations, however, this synchronous mode is impractical because the system will deliver a selection even if the user is not paying attention to the stimulation. So far, research into attention-aware visual ERP-BCIs (i.e., asynchronous ERP-BCIs) has led to variable success. In this study, we investigate new approaches for detection of user engagement.<br><br>APPROACH: Classifier output and frequency-domain features of electroencephalogram signals as well as the hybridization of them were used to detect the user's state. We tested their capabilities for state detection in different control scenarios on offline data from 21 healthy volunteers.<br><br>MAIN RESULTS: The hybridization of classifier output and frequency-domain features outperformed the results of the single methods, and allowed building an asynchronous P300-based BCI with an average correct state detection accuracy of more than 95%.<br><br>SIGNIFICANCE: Our results show that all introduced approaches for state detection in an asynchronous P300-based BCI can effectively avoid involuntary selections, and that the hybrid method is the most effective approach.},
}
@article {pmid25587704,
year = {2015},
author = {Gunasekera, B and Saxena, T and Bellamkonda, R and Karumbaiah, L},
title = {Intracortical recording interfaces: current challenges to chronic recording function.},
journal = {ACS chemical neuroscience},
volume = {6},
number = {1},
pages = {68-83},
doi = {10.1021/cn5002864},
pmid = {25587704},
issn = {1948-7193},
mesh = {Animals ; Electrodes, Implanted ; Humans ; Motor Cortex/*cytology/*physiology ; Neurons/*physiology ; Online Systems ; Time Factors ; *User-Computer Interface ; },
abstract = {Brain Computer Interfaces (BCIs) offer significant hope to tetraplegic and paraplegic individuals. This technology relies on extracting and translating motor intent to facilitate control of a computer cursor or to enable fine control of an external assistive device such as a prosthetic limb. Intracortical recording interfaces (IRIs) are critical components of BCIs and consist of arrays of penetrating electrodes that are implanted into the motor cortex of the brain. These multielectrode arrays (MEAs) are responsible for recording and conducting neural signals from local ensembles of neurons in the motor cortex with the high speed and spatiotemporal resolution that is required for exercising control of external assistive prostheses. Recent design and technological innovations in the field have led to significant improvements in BCI function. However, long-term (chronic) BCI function is severely compromised by short-term (acute) IRI recording failure. In this review, we will discuss the design and function of current IRIs. We will also review a host of recent advances that contribute significantly to our overall understanding of the cellular and molecular events that lead to acute recording failure of these invasive implants. We will also present recent improvements to IRI design and provide insights into the futuristic design of more chronically functional IRIs.},
}
@article {pmid25580443,
year = {2014},
author = {Widge, AS and Dougherty, DD and Moritz, CT},
title = {Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {1},
number = {2},
pages = {126-136},
pmid = {25580443},
issn = {2326-263X},
support = {R01 NS066357/NS/NINDS NIH HHS/United States ; },
abstract = {There is a pressing clinical need for responsive neurostimulators, which sense a patient's brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patient's intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation.},
}
@article {pmid25577407,
year = {2015},
author = {Sakurada, T and Kawase, T and Komatsu, T and Kansaku, K},
title = {Use of high-frequency visual stimuli above the critical flicker frequency in a SSVEP-based BMI.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {10},
pages = {1972-1978},
doi = {10.1016/j.clinph.2014.12.010},
pmid = {25577407},
issn = {1872-8952},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Flicker Fusion/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: This study presents a new steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) using flickering visual stimuli at frequencies greater than the critical flicker frequency (CFF).<br><br>METHODS: We first asked participants to fixate on a green/blue flicker (30-70Hz), and SSVEP amplitude was evaluated. Participants were asked to indicate whether the stimulus was visibly flickering and to report their subjective level of discomfort. We then assessed visibly (41, 43, and 45Hz) vs. invisibly (61, 63, and 65Hz) flickering stimulus in an SSVEP-based BMI. Visual fatigue was assessed via the flicker test before and after operation of the BMI.<br><br>RESULTS: Higher frequency stimuli reduced participants' subjective discomfort. Participants successfully controlled the SSVEP-based BMI using both the visibly and invisibly flickering stimuli (93.1% and 88.0%, respectively); the flicker test revealed a decrease in CFF (i.e., visual fatigue) under the visible condition only (-5.7%, P<0.001).<br><br>CONCLUSIONS: The use of high-frequency visual stimuli above the CFF led to high classification accuracy and decreased visual fatigue in an SSVEP-based BMI.<br><br>SIGNIFICANCE: High-frequency flicker stimuli above the CFF were able to induce SSVEPs and may prove useful in the development of BMI-based assistive products.},
}
@article {pmid25576427,
year = {2015},
author = {Mozos, OM and Galindo, C and Tapus, A},
title = {Guest-editorial: Computer-based intelligent technologies for improving the quality of life.},
journal = {IEEE journal of biomedical and health informatics},
volume = {19},
number = {1},
pages = {4-5},
doi = {10.1109/JBHI.2014.2350651},
pmid = {25576427},
issn = {2168-2208},
mesh = {*Brain-Computer Interfaces ; Diagnosis, Computer-Assisted ; *Quality Improvement ; *Quality of Life ; *Self-Help Devices ; *Telemedicine ; Therapy, Computer-Assisted ; },
}
@article {pmid25575224,
year = {2015},
author = {Zehr, EP},
title = {The potential transformation of our species by neural enhancement.},
journal = {Journal of motor behavior},
volume = {47},
number = {1},
pages = {73-78},
pmid = {25575224},
issn = {1940-1027},
mesh = {Biomedical Enhancement/*ethics/methods ; Humans ; *Neuronal Plasticity ; },
abstract = {Neural enhancement represents recovery of function that has been lost due to injury or disease pathology. Restoration of functional ability is the objective. For example, a neuroprosthetic to replace a forearm and hand lost to the ravages of war or industrial accident. However, the same basic constructs used for neural enhancement after injury could amplify abilities that are already in the natural normal range. That is, neural enhancement technologies to restore function and improve daily abilities for independent living could be used to improve so-called normal function to ultimate function. Approaching that functional level by use and integration of technology takes us toward the concept of a new species. This new subspecies--homo sapiens technologicus--is one that uses technology not just to assist but to change its own inherent biological function. The author uses examples from prosthetics and neuroprosthetics to address the issue of the limitations of constructs on the accepted range of human performance ability and aims to provide a cautionary view toward reflection on where our science may take the entire species.},
}
@article {pmid25574019,
year = {2015},
author = {Minev, IR and Musienko, P and Hirsch, A and Barraud, Q and Wenger, N and Moraud, EM and Gandar, J and Capogrosso, M and Milekovic, T and Asboth, L and Torres, RF and Vachicouras, N and Liu, Q and Pavlova, N and Duis, S and Larmagnac, A and Vörös, J and Micera, S and Suo, Z and Courtine, G and Lacour, SP},
title = {Biomaterials. Electronic dura mater for long-term multimodal neural interfaces.},
journal = {Science (New York, N.Y.)},
volume = {347},
number = {6218},
pages = {159-163},
doi = {10.1126/science.1260318},
pmid = {25574019},
issn = {1095-9203},
mesh = {Animals ; Biocompatible Materials/therapeutic use ; Brain-Computer Interfaces ; Drug Delivery Systems/*methods ; *Dura Mater ; Elasticity ; Electric Stimulation/*methods ; Electrochemotherapy/*methods ; *Electrodes, Implanted ; Locomotion ; Mice ; Mice, Inbred Strains ; Motor Cortex/physiopathology ; Multimodal Imaging ; Neurons/physiology ; Paralysis/etiology/physiopathology/*therapy ; Platinum ; *Prostheses and Implants ; Silicon ; Spinal Cord/physiopathology ; Spinal Cord Injuries/complications/physiopathology/*therapy ; },
abstract = {The mechanical mismatch between soft neural tissues and stiff neural implants hinders the long-term performance of implantable neuroprostheses. Here, we designed and fabricated soft neural implants with the shape and elasticity of dura mater, the protective membrane of the brain and spinal cord. The electronic dura mater, which we call e-dura, embeds interconnects, electrodes, and chemotrodes that sustain millions of mechanical stretch cycles, electrical stimulation pulses, and chemical injections. These integrated modalities enable multiple neuroprosthetic applications. The soft implants extracted cortical states in freely behaving animals for brain-machine interface and delivered electrochemical spinal neuromodulation that restored locomotion after paralyzing spinal cord injury.},
}
@article {pmid25571555,
year = {2014},
author = {Mugler, EM and Goldrick, M and Slutzky, MW},
title = {Cortical encoding of phonemic context during word production.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6790-6793},
doi = {10.1109/EMBC.2014.6945187},
pmid = {25571555},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Gamma Rhythm ; Humans ; *Language ; Motor Cortex/*physiopathology ; Software ; *Speech ; },
abstract = {Brain-computer interfaces that directly decode speech could restore communication to locked-in individuals. However, decoding speech from brain signals still faces many challenges. We investigated decoding of phonemes - the smallest separable parts of speech - from ECoG signals during word production. We expanded on previous efforts to identify specific phoneme by identifying phonemes by where in the word they were formed. We evaluated how the context of phonemes in words affects classification results using linear discriminant analysis. The decoding accuracy of our linear classifier indicated the degree to which the context of a phoneme can be determined from the cortical signal significantly greater than chance. Further, we identified the spectrotemporal features that contributed most to successful decoding of phonemic classes. Finally, we discuss how this can augment speech decoding for neural interfaces.},
}
@article {pmid25571554,
year = {2014},
author = {Toppi, J and Mattia, D and Anzolin, A and Risetti, M and Petti, M and Cincotti, F and Babiloni, F and Astolfi, L},
title = {Time varying effective connectivity for describing brain network changes induced by a memory rehabilitation treatment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6786-6789},
doi = {10.1109/EMBC.2014.6945186},
pmid = {25571554},
issn = {2694-0604},
mesh = {Aged ; Brain/physiopathology ; Brain Waves ; Cognition ; Cognition Disorders/physiopathology/psychology/rehabilitation ; Electroencephalography ; Female ; Humans ; Male ; Memory ; Memory Disorders/physiopathology/psychology/rehabilitation ; Nerve Net/physiopathology ; Neurofeedback ; Neuropsychological Tests ; Stroke/physiopathology/*psychology ; Stroke Rehabilitation ; Treatment Outcome ; Young Adult ; },
abstract = {In clinical practice, cognitive impairment is often observed after stroke. The efficacy of rehabilitative interventions is routinely assessed by means of a neuropsychological test battery. Nowadays, more evidences indicate that the neuroplasticity which occurs after stroke can be better understood by investigating changes in brain networks. In this study we applied advanced methodologies for effective connectivity estimation in combination with graph theory approach, to define EEG derived descriptors of brain networks underlying memory tasks. In particular, we proposed such descriptors to identify substrates of efficacy of a Brain-Computer Interface (BCI) controlled neurofeedback intervention to improve cognitive function after stroke. Electroencephalographic (EEG) data were collected from two stroke patients before and after a neurofeedback-based training for memory deficits. We show that the estimated brain connectivity indices were sensitive to different training intervention outcomes, thus suggesting an effective support to the neuropsychological assessment in the evaluation of the changes induced by the BCI-based cognitive rehabilitative intervention.},
}
@article {pmid25571522,
year = {2014},
author = {Bou Assi, E and Rihana, S and Sawan, M},
title = {Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6655-6658},
doi = {10.1109/EMBC.2014.6945154},
pmid = {25571522},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; Blinking ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Electrooculography ; Humans ; Motor Activity ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.},
}
@article {pmid25571488,
year = {2014},
author = {Liao, Y and Li, H and Zhang, Q and Fan, G and Wang, Y and Zheng, X},
title = {Decoding the non-stationary neuron spike trains by dual Monte Carlo point process estimation in motor Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6513-6516},
doi = {10.1109/EMBC.2014.6945120},
pmid = {25571488},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Haplorhini ; Humans ; Monte Carlo Method ; Motor Cortex/cytology/physiology ; Movement/physiology ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.},
}
@article {pmid25571487,
year = {2014},
author = {Loza, CA and Philips, GR and Hazrati, MKh and Daly, JJ and Principe, JC},
title = {Classification of hand movement direction based on EEG high-gamma activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6509-6512},
doi = {10.1109/EMBC.2014.6945119},
pmid = {25571487},
issn = {2694-0604},
support = {R01-NS063275/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Gamma Rhythm ; Hand/physiology ; Humans ; Male ; *Motor Activity ; Motor Cortex/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The Electroencephalogram (EEG) is a non-invasive technique used in the medical field to record and analyze brain activity. In particular, Brain Machine Interfaces (BMI) create this bridge between brain signals and the external world through prosthesis, visual interfaces and other physical devices. This paper investigates the relation between particular hand movement directions while using a BMI and the EEG recordings during such movement. The Common Spatial Pattern method (CSP) over the high-γ frequency band is utilized in order to discriminate opposite hand movement directions. The experiment is performed with three subjects and the average classification accuracy is obtained for two different cases.},
}
@article {pmid25571486,
year = {2014},
author = {Gibson, A and Artemiadis, P},
title = {Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6505-6508},
doi = {10.1109/EMBC.2014.6945118},
pmid = {25571486},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; *Artificial Limbs ; Brain-Computer Interfaces ; Feedback, Sensory ; Hand/physiology ; Humans ; Learning ; Robotics ; },
abstract = {As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.},
}
@article {pmid25571485,
year = {2014},
author = {Arvaneh, M and Robertson, I and Ward, TE},
title = {Subject-to-subject adaptation to reduce calibration time in motor imagery-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6501-6504},
doi = {10.1109/EMBC.2014.6945117},
pmid = {25571485},
issn = {2694-0604},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; Imagination ; *Signal Processing, Computer-Assisted ; },
abstract = {In order to enhance the usability of a motor imagery-based brain-computer interface (BCI), it is highly desirable to reduce the calibration time. Due to inter-subject variability, typically a new subject has to undergo a 20-30 minutes calibration session to collect sufficient data for training a BCI model based on his/her brain patterns. This paper proposes a new subject-to-subject adaptation algorithm to reliably reduce the calibration time of a new subject to only 3-4 minutes. To reduce the calibration time, unlike several past studies, the proposed algorithm does not require a large pool of historic sessions. In the proposed algorithm, using only a few trials from the new subject, first, the new subject's data is adapted to each available historic session separately. This is done by a linear transformation minimizing the distribution difference between the two groups of EEG data. Thereafter, among the available historic sessions, the one matched the most to the new subject's adapted data is selected as the calibration session. Consequently, the previously trained model based on the selected historic session is entirely used for the classification of the new subject's data after adaptation. The proposed algorithm is evaluated on a publicly available dataset with 9 subjects. For each subject, the calibration session is selected only from the calibration sessions of the eight other subjects. The experimental results showed that our proposed algorithm not only reduced the calibration time by 85%, but also performed on average only 1.7% less accurate than the subject-dependent calibration results.},
}
@article {pmid25571484,
year = {2014},
author = {Philips, GR and Hazrati, MKh and Daly, JJ and Principe, JC},
title = {Addressing low frequency movement artifacts in EEG signal recorded during center-out reaching tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6497-6500},
doi = {10.1109/EMBC.2014.6945116},
pmid = {25571484},
issn = {2694-0604},
support = {R01-NS063275/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Artifacts ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Humans ; Male ; Middle Aged ; Movement ; *Signal Processing, Computer-Assisted ; },
abstract = {The successful application of noninvasive brain-computer interfaces (BCI) to neurological rehabilitation requires examination of low frequency movement artifacts and development of accurate new methods for their correction. To this end, this study applies an adaptive trend extraction method to electroencephalogram (EEG) signals recorded during active and passive center-out reaching tasks. Distinct patterns are discovered, which correlate to arm kinematics, but are shown to be largely artifactual in nature. Notably, these patterns are found to be similar to features currently used for discrimination of movement direction, indicating a necessity for caution and precise signal processing methods when utilizing low frequency content of EEG signals in such applications.},
}
@article {pmid25571483,
year = {2014},
author = {Shanechi, MM and Orsborn, A and Moorman, H and Gowda, S and Carmena, JM},
title = {High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6493-6496},
doi = {10.1109/EMBC.2014.6945115},
pmid = {25571483},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Feedback ; Humans ; Macaca mulatta ; Movement ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-machine interface (BMI) performance has been improved using Kalman filters (KF) combined with closed-loop decoder adaptation (CLDA). CLDA fits the decoder parameters during closed-loop BMI operation based on the neural activity and inferred user velocity intention. These advances have resulted in the recent ReFIT-KF and SmoothBatch-KF decoders. Here we demonstrate high-performance and robust BMI control using a novel closed-loop BMI architecture termed adaptive optimal feedback-controlled (OFC) point process filter (PPF). Adaptive OFC-PPF allows subjects to issue neural commands and receive feedback with every spike event and hence at a faster rate than the KF. Moreover, it adapts the decoder parameters with every spike event in contrast to current CLDA techniques that do so on the time-scale of minutes. Finally, unlike current methods that rotate the decoded velocity vector, adaptive OFC-PPF constructs an infinite-horizon OFC model of the brain to infer velocity intention during adaptation. Preliminary data collected in a monkey suggests that adaptive OFC-PPF improves BMI control. OFC-PPF outperformed SmoothBatch-KF in a self-paced center-out movement task with 8 targets. This improvement was due to both the PPF's increased rate of control and feedback compared with the KF, and to the OFC model suggesting that the OFC better approximates the user's strategy. Also, the spike-by-spike adaptation resulted in faster performance convergence compared to current techniques. Thus adaptive OFC-PPF enabled proficient BMI control in this monkey.},
}
@article {pmid25571451,
year = {2014},
author = {Youssofzadeh, V and Zanotto, D and Stegall, P and Naeem, M and Wong-Lin, K and Agrawal, SK and Prasad, G},
title = {Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {6361-6364},
doi = {10.1109/EMBC.2014.6945083},
pmid = {25571451},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; Electroencephalography ; Gait/*physiology ; Humans ; Male ; Nerve Net/*physiology ; Rest ; Robotics/*methods ; Task Performance and Analysis ; },
abstract = {Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A frontoparietal connection was found in all robot-assisted training sessions. Following training, a causal "top-down" cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.},
}
@article {pmid25571359,
year = {2014},
author = {Alharbi, M and Sabouni, A and Noghanian, S},
title = {Miniature antenna for sensor network on human head.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5980-5983},
doi = {10.1109/EMBC.2014.6944991},
pmid = {25571359},
issn = {2694-0604},
mesh = {Adult ; Brain/diagnostic imaging ; Brain Mapping ; Brain-Computer Interfaces ; Equipment Design ; Healthy Volunteers ; Humans ; Magnetic Resonance Imaging ; Miniaturization ; Monitoring, Physiologic/instrumentation/*methods ; Radiography ; Wireless Technology ; },
abstract = {This paper proposes a novel miniaturized antenna for sensor network with focus on placement on human head. The antenna is within the volume of 3.5×3.5×1.5 mm(3). It provides directive gain in the direction outward the body.},
}
@article {pmid25571230,
year = {2014},
author = {Ordikhani-Seyedlar, M and Sorensen, HB and Kjaer, TW and Siebner, HR and Puthusserypady, S},
title = {SSVEP-modulation by covert and overt attention: Novel features for BCI in attention neuro-rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5462-5465},
doi = {10.1109/EMBC.2014.6944862},
pmid = {25571230},
issn = {2694-0604},
mesh = {Adult ; Area Under Curve ; Attention/*physiology ; Attention Deficit Disorder with Hyperactivity/rehabilitation ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Eye Movements ; Humans ; Models, Statistical ; Pilot Projects ; Rehabilitation/*methods ; Signal Processing, Computer-Assisted ; },
abstract = {In this pilot study the effect of attention (covert and overt) on the signal detection and classification of steady-state visual-evoked potential (SSVEP) were investigated. Using the SSVEP-based paradigm, data were acquired from 4 subjects using 3 scalp electroencephalography (EEG) electrodes located on the visual area. Subjects were instructed to perform the attention task in which they attended covertly or overtly to either of the stimuli flickering with different frequencies (6, 7, 8 and 9Hz). We observed a decrease in signal power in covert compared to the overt attention. However, there was a consistent pattern in covert attention causing an increase in the power of the 2(nd) harmonic of the attended frequency. Encouraging results of this preliminary study indicates that it can be adapted and implemented in the brain-computer interface (BCI) system which could potentially be used as a neuro-rehabilitation tool for individuals with attention deficit.},
}
@article {pmid25571229,
year = {2014},
author = {Castillo-Garcia, J and Cotrina, A and Benevides, A and Delisle-Rodriguez, D and Longo, B and Caicedo, E and Ferreira, A and Bastos, T},
title = {Adaptive BCI based on software agents.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5458-5461},
doi = {10.1109/EMBC.2014.6944861},
pmid = {25571229},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Databases, Factual ; *Electroencephalography ; Humans ; Models, Neurological ; Neurons ; Pattern Recognition, Automated ; Reproducibility of Results ; *Software ; Support Vector Machine ; User-Computer Interface ; },
abstract = {The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users. How should this topology be in order to implement a system that can be used by large number of users with an optimal set of features? The proposal presented in this paper allows for obtaining feature reduction and classifier selection based on software agents. The software agents contain Genetic Algorithms (GA) and a cost function. GA used entropy and mutual information to choose the number of features. For the classifier selection a cost function was defined. Success rate and Cohen's Kappa coefficient are used as parameters to evaluate the classifiers performance. The obtained results allow finding a topology represented as a neural model for an adaptive BCI, where the number of the channels, features and the classifier are interrelated. The minimal subset of features and the optimal classifier were obtained with the adaptive BCI. Only three EEG channels were needed to obtain a success rate of 93% for the BCI competition III data set IVa.},
}
@article {pmid25571225,
year = {2014},
author = {Białas, P and Milanowski, P},
title = {A high frequency steady-state visually evoked potential based brain computer interface using consumer-grade EEG headset.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5442-5445},
doi = {10.1109/EMBC.2014.6944857},
pmid = {25571225},
issn = {2694-0604},
mesh = {Algorithms ; Brain/pathology ; *Brain-Computer Interfaces ; Calibration ; Electrodes ; *Electroencephalography ; Equipment Design ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Neurologic Examination ; Pattern Recognition, Automated ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {This work evaluates a possibility of creating a high-frequency, SSVEP-based brain computer interface using a low cost EEG recording hardware - an Emotiv EEG Neuro-headset. Both above aspects are crucial to enable deploying the BCI technology in the consumer market. High frequencies can be used to create a non-tiring and more pleasant interface. Commercial EEG systems, as the Emotiv EEG, although demonstrating large underperformance, are much more affordable than standard, clinical-grade EEG amplifiers. A system classifying between two stimuli and rest is designed and tested in two experiments: on five and ten subject respectively. First, the accuracy of the system is compared for frequencies in lower range (17Hz, 19Hz, 23Hz, 25Hz) and higher range (31Hz, 33Hz, 37Hz, 40Hz). The mean online accuracy is 80%±15% for the former and 67%±12% for the latter. Second, a more thorough investigation is done by evaluating the system for frequencies within a set of 35Hz-40Hz. Although the mean accuracy, 64% ± 22%, is relatively low, most of the users were able to achieve satisfying accuracy, with the mean reaching 82%±5%, which would allow for an efficient, and yet pleasant, usage of the BCI system. In each case a user dependent approach is applied, with a calibration session lasting about five minutes. EEG feature extraction is done using common spatial pattern (CSP) filtering, canonical correlation analysis (CCA), and linear discrimination analysis (LDA).},
}
@article {pmid25571208,
year = {2014},
author = {Crk, I and Kluthe, T},
title = {Toward using alpha and theta brain waves to quantify programmer expertise.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5373-5376},
doi = {10.1109/EMBC.2014.6944840},
pmid = {25571208},
issn = {2694-0604},
mesh = {Alpha Rhythm ; Brain/*physiology ; Cognition ; Comprehension ; Electroencephalography ; Humans ; Learning ; Programming Languages ; Theta Rhythm ; },
abstract = {Empirical studies of programming language learnability and usability have thus far depended on indirect measures of human cognitive performance, attempting to capture what is at its essence a purely cognitive exercise through various indicators of comprehension, such as the correctness of coding tasks or the time spent working out the meaning of code and producing acceptable solutions. Understanding program comprehension is essential to understanding the inherent complexity of programming languages, and ultimately, having a measure of mental effort based on direct observation of the brain at work will illuminate the nature of the work of programming. We provide evidence of direct observation of the cognitive effort associated with programming tasks, through a carefully constructed empirical study using a cross-section of undergraduate computer science students and an inexpensive, off-the-shelf brain-computer interface device. This study presents a link between expertise and programming language comprehension, draws conclusions about the observed indicators of cognitive effort using recent cognitive theories, and proposes directions for future work that is now possible.},
}
@article {pmid25571168,
year = {2014},
author = {Thakor, NV and Fifer, MS and Hotson, G and Benz, HL and Newman, GI and Milsap, GW and Crone, NE},
title = {Neuroprosthetic limb control with electrocorticography: approaches and challenges.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5212-5215},
doi = {10.1109/EMBC.2014.6944800},
pmid = {25571168},
issn = {2694-0604},
support = {3R01NS040596-09S1/NS/NINDS NIH HHS/United States ; 5T32EB003383-08/EB/NIBIB NIH HHS/United States ; R01NS40596/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electrocorticography/*methods ; *Electrodes ; Humans ; Movement ; *Prostheses and Implants ; *Upper Extremity ; },
abstract = {Advanced upper limb prosthetics, such as the Johns Hopkins Applied Physics Lab Modular Prosthetic Limb (MPL), are now available for research and preliminary clinical applications. Research attention has shifted to developing means of controlling these prostheses. Penetrating microelectrode arrays are often used in animal and human models to decode action potentials for cortical control. These arrays may suffer signal loss over the long-term and therefore should not be the only implant type investigated for chronic BMI use. Electrocorticographic (ECoG) signals from electrodes on the cortical surface may provide more stable long-term recordings. Several studies have demonstrated ECoG's potential for decoding cortical activity. As a result, clinical studies are investigating ECoG encoding of limb movement, as well as its use for interfacing with and controlling advanced prosthetic arms. This overview presents the technical state of the art in the use of ECoG in controlling prostheses. Technical limitations of the current approach and future directions are also presented.},
}
@article {pmid25571167,
year = {2014},
author = {Suzuki, T and Ando, H and Yoshida, T and Sawahata, H and Kawasaki, K and Hasegawa, I and Matsushita, K and Hirata, M and Yoshimine, T and Takizawa, K},
title = {Super multi-channel recording systems with UWB wireless transmitter for BMI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5208-5211},
doi = {10.1109/EMBC.2014.6944799},
pmid = {25571167},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrocorticography/*instrumentation ; *Electrodes, Implanted ; Humans ; Wireless Technology/*instrumentation ; },
abstract = {In order to realize a low-invasive and high accuracy Brain-Machine Interface (BMI) system for clinical applications, a super multi-channel recording system was developed in which 4096 channels of Electrocorticogram (ECoG) signal can be amplified and transmitted to outside the body by using an Ultra Wide Band (UWB) wireless system. Also, a high density, flexible electrode array made by using a Parylene-C substrate was developed that is composed of units of 32-ch recording arrays. We have succeeded in an evaluation test of UWB wireless transmitting using a body phantom system.},
}
@article {pmid25571166,
year = {2014},
author = {Hirata, M and Morris, S and Sugata, H and Matsushita, K and Yanagisawa, T and Kishima, H and Yoshimine, T},
title = {Patient-specific contour-fitting sheet electrodes for electrocorticographic brain machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5204-5207},
doi = {10.1109/EMBC.2014.6944798},
pmid = {25571166},
issn = {2694-0604},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology ; Animals ; Brain/*physiopathology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electrocorticography/*methods ; *Electrodes, Implanted ; Humans ; Male ; Precision Medicine ; Rats ; },
abstract = {Non-invasive localization of certain brain functions may be mapped on a millimeter level. However, the inter-electrode spacing of common clinical brain surface electrodes still remains around 10 mm, and some electrodes fail to measure cortical activity due to unconformable plain electrode sheets. Here, we present details on development of implantable electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interfaces. We produced personalized sheet electrodes after the creation of individualized molds using a 3D-printer. We created arrays to fit the surface curvature of the brain and inside the central sulcus, with inter-electrode distances of 2.5 mm. Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high gamma-band information in an amyotrophic lateral sclerosis patient. This sheet electrode may contribute to the higher performance of BMI's.},
}
@article {pmid25571163,
year = {2014},
author = {Balasubramanian, K and Takahashi, K and Slutzky, M and Hatsopoulos, NG},
title = {Multi-modal decoding: longitudinal coherency changes between spike trains, local field potentials and electrocorticogram signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5192-5195},
pmid = {25571163},
issn = {2694-0604},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Animals ; *Brain-Computer Interfaces ; *Electrocorticography ; Macaca mulatta/*physiology ; Motor Cortex/*physiology ; },
abstract = {Neural information degeneracy in chronic implants due to signal instabilities affects optimal performance of brain-machine interfaces (BMIs). Spike-decoders are more vulnerable compared to those using LFPs and ECoG signals. In order for BMIs to perform reliably across years, decoders should be able to use neural information contained in various signal modalities. Hence, it is important to identify information redundancy among signal types. In this work, spikes, LFPs and ECoGs were recorded simultaneously from motor cortex of a rhesus monkey, while the animal was learning to control a multi-DOF robot with a spike-decoder. As the behavioral performance increased, the linear association among the signal types increased. Coherency of these signals increased in specific frequency bands as learning occurred. These results suggest the possibility of substituting the information lost in one modality by another.},
}
@article {pmid25571123,
year = {2014},
author = {Elsawy, AS and Eldawlatly, S and Taher, M and Aly, GM},
title = {Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {5032-5035},
doi = {10.1109/EMBC.2014.6944755},
pmid = {25571123},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Humans ; Language ; Male ; Principal Component Analysis ; },
abstract = {The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.},
}
@article {pmid25571090,
year = {2014},
author = {Wilaiprasitporn, T and Yagi, T},
title = {Investigation of P300 response characteristics through human color vision-based visual stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4900-4903},
doi = {10.1109/EMBC.2014.6944722},
pmid = {25571090},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Color ; *Color Vision ; Electrodes ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Healthy Volunteers ; Humans ; Male ; *Photic Stimulation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this study, we propose visual stimulation based on the primary colors (red, green, and blue) in order to investigate the characteristics of the P300 response. Eleven healthy volunteers participated in our experiment, and their brain signals were recorded by electroencephalography (EEG). Using two basic measures referred to as `on-peak' and `off-peak' for comparison of the P300 response among the participants, we found that the P300 response varies depending on the color of the stimulus. The results of this investigation are expected to contribute to various existing and future EEG-based applications.},
}
@article {pmid25571083,
year = {2014},
author = {Vaidya, M and Dickey, A and Best, MD and Coles, J and Balasubramanian, K and Suminski, AJ and Hatsopoulos, NG},
title = {Ultra-long term stability of single units using chronically implanted multielectrode arrays.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4872-4875},
pmid = {25571083},
issn = {2694-0604},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Algorithms ; Animals ; Behavior, Animal ; Brain-Computer Interfaces ; Cell Survival ; *Electrodes, Implanted ; Electrophysiology ; Female ; Macaca mulatta ; Motor Cortex/physiology ; Neurons/*physiology ; Reproducibility of Results ; Robotics ; Signal Processing, Computer-Assisted ; },
abstract = {Recordings from chronically implanted multielectrode arrays have become prevalent in both neuroscience and neural engineering experiments. To date, however, the extent to which populations of single-units remain stable over long periods of time has not been well characterized. In this study, neural activity was recorded from a Utah multielectrode array implanted in the primary motor cortex of a rhesus macaque during 18 recording sessions spanning nine months. We found that 67% of the units were stable through the first 15 days, 31% of units were stable through 47 days, 21% of units were stable through 106 days, and 8% of units were stable over 9 months. Thus not only were units stable over a timescale of several months, but units stable over 2 months were more likely to remain stable in the next 2 months.},
}
@article {pmid25571082,
year = {2014},
author = {Best, MD and Suminski, AJ and Takahashi, K and Hatsopoulos, NG},
title = {Consideration of the functional relationship between cortex and motor periphery improves offline decoding performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4868-4871},
pmid = {25571082},
issn = {2694-0604},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Behavior ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Computer Simulation ; Electric Stimulation ; Electrodes ; Electrophysiology ; Macaca ; Male ; Motor Cortex/*physiology ; *Neural Prostheses ; Neurons/*physiology ; Software ; Spinal Cord/physiology ; },
abstract = {Decoding neural activity to control prosthetic devices or computer interfaces is a promising avenue for rehabilitating individuals with amputation or severe spinal cord injury. In most cases, however, the local functionality of the neural tissue is not considered when designing a decoding algorithm. One way to characterize the functional specificity of a local region of motor cortex, and its output effects, is to use intracortical microstimulation. In this study, we examined how the results of an ICMS experiment relate to the performance of various offline decoders. We found evidence that units from electrodes with stimulation effects decode kinematics better than units from electrodes without stimulation effects.},
}
@article {pmid25571024,
year = {2014},
author = {Davies, SR and James, CJ},
title = {Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4631-4634},
doi = {10.1109/EMBC.2014.6944656},
pmid = {25571024},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; *Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; },
abstract = {This paper introduces a new signal processing method called Spatio-Temporal Multivariate Empirical Mode Decomposition (ST-MEMD). It is a new variation of Empirical Mode Decomposition (EMD) that takes spatial and temporal information into account simultaneously rather than processing each signal source in isolation. The original and new methods were tested on single-trial electroencephalogram data with a two-class problem, in this case data using the Motor Imagery paradigm in brain-computer interfacing. However, whilst ST-MEMD retained the increase in sensitivity and specificity from adding spatial data, the new temporal data made no meaningful difference in terms of performance.},
}
@article {pmid25571016,
year = {2014},
author = {Kapeller, C and Schneider, C and Kamada, K and Ogawa, H and Kunii, N and Ortner, R and Pruckl, R and Guger, C},
title = {Single trial detection of hand poses in human ECoG using CSP based feature extraction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4599-4602},
doi = {10.1109/EMBC.2014.6944648},
pmid = {25571016},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Epilepsy/physiopathology ; Female ; Fingers/*physiopathology ; Hand Strength ; Humans ; Male ; Motor Cortex/physiopathology ; Movement/physiology ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Decoding brain activity of corresponding highlevel tasks may lead to an independent and intuitively controlled Brain-Computer Interface (BCI). Most of today's BCI research focuses on analyzing the electroencephalogram (EEG) which provides only limited spatial and temporal resolution. Derived electrocorticographic (ECoG) signals allow the investigation of spatially highly focused task-related activation within the high-gamma frequency band, making the discrimination of individual finger movements or complex grasping tasks possible. Common spatial patterns (CSP) are commonly used for BCI systems and provide a powerful tool for feature optimization and dimensionality reduction. This work focused on the discrimination of (i) three complex hand movements, as well as (ii) hand movement and idle state. Two subjects S1 and S2 performed single `open', `peace' and `fist' hand poses in multiple trials. Signals in the high-gamma frequency range between 100 and 500 Hz were spatially filtered based on a CSP algorithm for (i) and (ii). Additionally, a manual feature selection approach was tested for (i). A multi-class linear discriminant analysis (LDA) showed for (i) an error rate of 13.89 % / 7.22 % and 18.42 % / 1.17 % for S1 and S2 using manually / CSP selected features, where for (ii) a two class LDA lead to a classification error of 13.39 % and 2.33 % for S1 and S2, respectively.},
}
@article {pmid25570900,
year = {2014},
author = {Bhagat, NA and French, J and Venkatakrishnan, A and Yozbatiran, N and Francisco, GE and O'Malley, MK and Contreras-Vidal, JL},
title = {Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4127-4130},
pmid = {25570900},
issn = {2694-0604},
support = {R01 NS081854/NS/NINDS NIH HHS/United States ; R01NS081854-02/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Male ; Middle Aged ; Movement ; Paresis/physiopathology/rehabilitation ; *Robotics ; Signal Processing, Computer-Assisted ; Stroke/physiopathology ; *Stroke Rehabilitation ; Support Vector Machine ; Upper Extremity/physiopathology ; Young Adult ; },
abstract = {Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.},
}
@article {pmid25570897,
year = {2014},
author = {Cecotti, H and Rivet, B},
title = {Improving single-trial detection of event-related potentials through artificial deformed signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4115-4118},
doi = {10.1109/EMBC.2014.6944529},
pmid = {25570897},
issn = {2694-0604},
mesh = {Adult ; Area Under Curve ; Bayes Theorem ; Brain/physiology ; Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; ROC Curve ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {To propose a reliable and robust Brain-Computer Interface (BCI), efficient machine learning and signal processing methods have to be used. However, it is often necessary to have a sufficient number of labeled brain responses to create a model. A large database that would represent all of the possible variabilities of the signal is not always possible to obtain, because calibration sessions have to be short. In the case of BCIs based on the detection of event-related potentials (ERPs), we propose to tackle this problem by including additional deformed patterns in the training database to increase the number of labeled brain responses. The creation of the additional deformed patterns is based on two approaches: (i) smooth deformation fields, and (ii) right and left shifted signals. The evaluation is performed with data from 10 healthy subjects participating in a P300 speller experiment. The results show that small shifts of the signal allow a better estimation of both spatial filters, and a linear classifier. The best performance, AUC=0.828 ± 0.061, is obtained by combining the smooth deformation fields and the shifts, after spatial filtering, compared to AUC=0.543 ± 0.025, without additional deformed patterns. The results support the conclusion that adding signals with small deformations can significantly improve the performance of single-trial detection when the amount of training data is limited.},
}
@article {pmid25570870,
year = {2014},
author = {Suefusa, K and Tanaka, T},
title = {Visually stimulated brain-computer interfaces compete with eye tracking interfaces when using small targets.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4005-4008},
doi = {10.1109/EMBC.2014.6944502},
pmid = {25570870},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/physiology ; Evoked Potentials, Visual/physiology ; Eye Movements/*physiology ; Female ; Humans ; Male ; },
abstract = {Visually stimulated brain-computer interfacing detects which target on a screen a user is gazing at; however, this is also accomplished by tracking gaze points with a camera. These two approaches have been independently investigated and sometimes doubts about BCI with visual stimuli are raised in terms of usability compared to eye tracking interfaces (ETI). This paper answers this question by investigating information transfer rates (ITR) and recognition accuracies of BCI and ETI having a similar interface design, where subjects were asked to gaze at one of four targets on a screen. Experimental results revealed that BCI is comparable in ITR to ETI and had better performance for relatively small targets on the screen.},
}
@article {pmid25570869,
year = {2014},
author = {Si, X and Zhou, W and Hong, B},
title = {Neural distance amplification of lexical tone in human auditory cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {4001-4004},
doi = {10.1109/EMBC.2014.6944501},
pmid = {25570869},
issn = {2694-0604},
mesh = {Adult ; Auditory Cortex/*physiology ; Brain/physiology ; Brain Mapping ; Brain-Computer Interfaces ; Electrocorticography ; Electrodes ; Female ; Humans ; Language ; Male ; Middle Aged ; Speech Perception/*physiology ; },
abstract = {In tonal languages, like Chinese, lexical tone serves as a key feature to provide contrast in word meaning. Behavior studies suggest that Mandarin Chinese tone is categorically perceived. However, the neural mechanism underlying Mandarin tone perception is still poorly understood. In this study, an Oddball paradigm was designed by selecting two standard-deviant stimulus pairs with same physical distance but different category labels, among the synthesized tones with continuously varying pitch contours. Using electrocorticography (ECoG) recording over human auditory cortex, high temporal and spatial resolution cortical neural signals were used for the first time to investigate the cortical processing of lexical tone. Here, we found different neural responses to the two standard-deviant tone pairs, and the difference increased from low to high level along the hierarchy of human auditory cortex. In the two dimensional neural space, cross-category neural distance of lexical tones is selectively amplified on those high level electrodes. These findings support a hierarchical and categorical model of Mandarin tone perception, and favor the using of high-level electrodes for a better performance of lexical tone discrimination in speech brain computer interface.},
}
@article {pmid25570868,
year = {2014},
author = {Omedes, J and Iturrate, I and Montesano, L},
title = {Brain connectivity in continuous error tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3997-4000},
doi = {10.1109/EMBC.2014.6944500},
pmid = {25570868},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials/*physiology ; Humans ; },
abstract = {Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection. This paper studies a different characteristic that may carry additional information to be exploited by asynchronous ErrP detectors: brain connectivity coherence patterns appearing while the user monitors the continuous operation of a device. The results obtained with five subject revealed the presence of an error potential in an asynchronous reaching task an showed an increase in the coherency within the theta band.},
}
@article {pmid25570867,
year = {2014},
author = {Chen, X and Wang, Y and Nakanishi, M and Jung, TP and Gao, X},
title = {Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3993-3996},
doi = {10.1109/EMBC.2014.6944499},
pmid = {25570867},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller.},
}
@article {pmid25570866,
year = {2014},
author = {Agashe, HA and Contreras-Vidal, JL},
title = {Observation-based training for neuroprosthetic control of grasping by amputees.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3989-3992},
doi = {10.1109/EMBC.2014.6944498},
pmid = {25570866},
issn = {2694-0604},
support = {P01 HD064653/HD/NICHD NIH HHS/United States ; },
mesh = {Aged ; Amputees/*rehabilitation ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Electroencephalography ; Female ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; },
abstract = {Current brain-machine interfaces (BMIs) allow upper limb amputees to position robotic arms with a high degree of accuracy, but lack the ability to control hand pre-shaping for grasping different objects. We have previously shown that low frequency (0.1-1 Hz) time domain cortical activity recorded at the scalp via electroencephalography (EEG) encodes information about grasp pre-shaping. To transfer this technology to clinical populations such as amputees, the challenge lies in constructing BMI models in the absence of overt training hand movements. Here we show that it is possible to train BMI models using observed grasping movements performed by a robotic hand attached to amputees' residual limb. Three transradial amputees controlled the grasping motion of an attached robotic hand via their EEG, following the action-observation training phase. Over multiple sessions, subjects successfully grasped the presented object (a bottle or a credit card) in 53±16 % of trials, demonstrating the validity of the BMI models. Importantly, the validation of the BMI model was through closed-loop performance, which demonstrates generalization of the model to unseen data. These results suggest `mirror neuron system' properties captured by delta band EEG that allows neural representation for action observation to be used for action control in an EEG-based BMI system.},
}
@article {pmid25570865,
year = {2014},
author = {He, Y and Nathan, K and Venkatakrishnan, A and Rovekamp, R and Beck, C and Ozdemir, R and Francisco, GE and Contreras-Vidal, JL},
title = {An integrated neuro-robotic interface for stroke rehabilitation using the NASA X1 powered lower limb exoskeleton.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3985-3988},
doi = {10.1109/EMBC.2014.6944497},
pmid = {25570865},
issn = {2694-0604},
mesh = {Algorithms ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Electroencephalography ; Exoskeleton Device ; Gait/physiology ; Humans ; Leg ; Male ; Middle Aged ; Quality of Life ; *Robotics ; Signal Processing, Computer-Assisted ; *Stroke Rehabilitation ; },
abstract = {Stroke remains a leading cause of disability, limiting independent ambulation in survivors, and consequently affecting quality of life (QOL). Recent technological advances in neural interfacing with robotic rehabilitation devices are promising in the context of gait rehabilitation. Here, the X1, NASA's powered robotic lower limb exoskeleton, is introduced as a potential diagnostic, assistive, and therapeutic tool for stroke rehabilitation. Additionally, the feasibility of decoding lower limb joint kinematics and kinetics during walking with the X1 from scalp electroencephalographic (EEG) signals--the first step towards the development of a brain-machine interface (BMI) system to the X1 exoskeleton--is demonstrated.},
}
@article {pmid25570864,
year = {2014},
author = {Kohler, F and Kiele, P and Ordonez, JS and Stieglitz, T and Schuettler, M},
title = {A polymer-metal two step sealing concept for hermetic neural implant packages.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3981-3984},
doi = {10.1109/EMBC.2014.6944496},
pmid = {25570864},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Helium/chemistry ; Humans ; Humidity ; Metals/*chemistry ; Miniaturization ; Polymers/*chemistry ; Prostheses and Implants ; Zinc Oxide-Eugenol Cement/*chemistry ; },
abstract = {In this paper, we introduce a technique for double-sealed ceramic packages for the long-term protection of implanted electronics against body fluids. A sequential sealing procedure consisting of a first step, during which the package is sealed with epoxy, protecting the implant electronics from aggressive flux fumes. These result from the application of the actual moisture barrier which is a metal seal applied in a second step by soft soldering. Epoxy sealing is carried out in helium atmosphere for later fine leak testing. The solder seal is applied on the laboratory bench. After the first sealing step, a satisfactory barrier for moisture is already achieved with values for helium leakage of usually LHe = 6·10(-8) mbar 1 s(-1). After solder sealing, a very low leakage rate of LHe ≤ 1·10(-12) mbar 1 s(-1) was found, which was the lower detection limit of the measurement setup, suggesting excellent hermeticity and hence moisture barrier. Presuming an implant package volume of V ≥ 0.5 cm(3), the time to reach a critical humidity of p = 5000 ppm H2O inside the package will be longer than any anticipated average life of human patients.},
}
@article {pmid25570834,
year = {2014},
author = {Gabsteiger, F and Leutheuser, H and Reis, P and Lochmann, M and Eskofier, BM},
title = {ICA-based reduction of electromyogenic artifacts in EEG data: comparison with and without EMG data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3861-3864},
doi = {10.1109/EMBC.2014.6944466},
pmid = {25570834},
issn = {2694-0604},
mesh = {Adult ; Artifacts ; Brain/*physiology ; Electroencephalography/*methods ; Electromyography ; Female ; Humans ; Male ; Movement ; Principal Component Analysis ; Young Adult ; },
abstract = {Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA). ICA estimates statistically independent components (ICs) that, when linearly combined, closely match the input (sensor) data. Removing the ICs that represent artifact sources and re-mixing the sources returns the input data with reduced noise activity. ICs of real-world data are usually not perfectly separated, actual sources, but a mixture of these sources. Adding additional input signals, predominantly generated by a single IC that is already part of the original sensor data, should increase that IC's separability. We conducted this study to evaluate this concept for ICA-based electromyogenic artifact reduction in EEG using EMG signals as additional inputs. To acquire the appropriate data we worked with nine human volunteers. The EEG and EMG were recorded while the study volunteers performed seven exercises designed to produce a wide range of representative myogenic artifacts. To evaluate the effect of the EMG signals we estimated the sources of each dataset once with and once without the EMG data. The ICs were automatically classified as either `myogenic' or `non-myogenic'. We removed the former before back projection. Afterwards we calculated an objective measure to quantify the artifact reduction and assess the effect of including EMG signals. Our study showed that the ICA-based reduction of electromyogenic artifacts can be improved by including the EMG data of artifact-inducing muscles. This approach could prove beneficial for locomotor disorder research, brain-computer interfaces, neurofeedback, and most other areas where brain activity during movement has to be analyzed.},
}
@article {pmid25570830,
year = {2014},
author = {Hsu, SH and Mullen, T and Jung, TP and Cauwenberghs, G},
title = {Online recursive independent component analysis for real-time source separation of high-density EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3845-3848},
doi = {10.1109/EMBC.2014.6944462},
pmid = {25570830},
issn = {2694-0604},
support = {1R01MH084819-03/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/physiopathology ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Least-Squares Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Online Independent Component Analysis (ICA) algorithms have recently seen increasing development and application across a range of fields, including communications, biosignal processing, and brain-computer interfaces. However, prior work in this domain has primarily focused on algorithmic proofs of convergence, with application limited to small `toy' examples or to relatively low channel density EEG datasets. Furthermore, there is limited availability of computationally efficient online ICA implementations, suitable for real-time application. This study describes an optimized online recursive ICA algorithm (ORICA), with online recursive least squares (RLS) whitening, for blind source separation of high-density EEG data. It is implemented as an online-capable plugin within the open-source BCILAB (EEGLAB) framework. We further derive and evaluate a block-update modification to the ORICA learning rule. We demonstrate the algorithm's suitability for accurate and efficient source identification in high density (64-channel) realistically-simulated EEG data, as well as real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment.},
}
@article {pmid25570808,
year = {2014},
author = {Nathan, V and Jafari, R},
title = {Characterizing contact impedance, signal quality and robustness as a function of the cardinality and arrangement of fingers on dry contact EEG electrodes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3755-3758},
doi = {10.1109/EMBC.2014.6944440},
pmid = {25570808},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electric Impedance ; Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Visual ; Humans ; Scalp/physiology ; },
abstract = {Continuous monitoring of patients' electroencephalography (EEG) outside of clinical settings will be valuable for detecting the onset of medical conditions such as epilepsy, as well as for enabling patients with physically disabling conditions like amyotrophic lateral sclerosis (ALS) to communicate using a brain-computer interface (BCI). This requires the development of a wearable dry-contact EEG system that takes into account not only the signal quality but also the robustness of the system for everyday use. To this end, we investigate whether certain designs of dry electrodes lend themselves to better characteristics overall with respect to these factors. Five different metallic finger-based dry electrodes were designed and scalp electrode impedance was used to compare them under varying capping conditions, followed by an evaluation of how well they captured steady state visually evoked potentials (SSVEP). Our findings indicate that configurations with a relatively low density of fingers can more effectively penetrate through hair on the scalp and are more robust to varying conditions. This was confirmed to be a statistically significant observation through a one-sided paired t-test that resulted in a p-value <; 0.004.},
}
@article {pmid25570777,
year = {2014},
author = {Gomez-Pilar, J and Corralejo, R and Nicolas-Alonso, LF and Álvarez, D and Hornero, R},
title = {Assessment of neurofeedback training by means of motor imagery based-BCI for cognitive rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3630-3633},
doi = {10.1109/EMBC.2014.6944409},
pmid = {25570777},
issn = {2694-0604},
mesh = {Aged ; Aging/physiology ; *Brain-Computer Interfaces ; Cognition Disorders/physiopathology/*rehabilitation ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; *Neurofeedback ; Psychomotor Performance/*physiology ; Quality of Life ; },
abstract = {The age-related impairment is an increasing problem due to the aging suffered by the population, especially in developed countries. It is usual to use electroencephalogram (EEG)-based Brain Computer Interface (BCI) systems by means of the signal in order to assist and to improve the quality of life of people with disabilities. However, a parallel research line addresses the problem by the use of BCI systems as a way to train cognitive areas to achieve a deceleration of cognitive impairment or even an improvement. In this regard, a neurofeedback training (NFT) tool using motor imagery-based BCI, was developed. Training consists on imagery motor exercises combined with memory and logical relation tasks. In order to assess the effectiveness of the application 40 subjects, older than 59 years old, took part in this study. Our NFT application was tested by 20 subjects and their scores of a neuropsychological test were compared with the remaining 20 subjects who did not perform the NFT. Results show a significant improvement of three cognitive features after performing the NFT: visual perception, expressive speech, and immediate memory. Therefore, evidences show that the performance of a NFT tool based on motor imagery tasks could be a positive activity for slow down the aging effects.},
}
@article {pmid25570775,
year = {2014},
author = {Coffey, AL and Leamy, DJ and Ward, TE},
title = {A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3622-3625},
doi = {10.1109/EMBC.2014.6944407},
pmid = {25570775},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Clothing ; Equipment Design ; Feedback, Sensory ; Hand/innervation/*physiology ; *Home Care Services ; Humans ; Rehabilitation/economics/*instrumentation ; Robotics ; Software ; *Stroke Rehabilitation ; },
abstract = {Commercially available devices for Brain-Computer Interface (BCI)-controlled robotic stroke rehabilitation are prohibitively expensive for many researchers who are interested in the topic and physicians who would utilize such a device. Additionally, they are cumbersome and require a technician to operate, increasing the inaccessibility of such devices for home-based robotic stroke rehabilitation therapy. Presented here is the design, implementation and test of an inexpensive, portable and adaptable BCI-controlled hand therapy device. The system utilizes a soft, flexible, pneumatic glove which can be used to deflect the subject's wrist and fingers. Operation is provided by a custom-designed pneumatic circuit. Air flow is controlled by an embedded system, which receives serial port instruction from a PC running real-time BCI software. System tests demonstrate that glove control can be successfully driven by a real-time BCI. A system such as the one described here may be used to explore closed loop neurofeedback rehabilitation in stroke relatively inexpensively and potentially in home environments.},
}
@article {pmid25570771,
year = {2014},
author = {Rohani, DA and Sorensen, HB and Puthusserypady, S},
title = {Brain-computer interface using P300 and virtual reality: a gaming approach for treating ADHD.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3606-3609},
doi = {10.1109/EMBC.2014.6944403},
pmid = {25570771},
issn = {2694-0604},
mesh = {Algorithms ; Attention ; Attention Deficit Disorder with Hyperactivity/psychology/*therapy ; Brain-Computer Interfaces ; Computer Simulation ; Event-Related Potentials, P300 ; Feedback, Psychological ; Humans ; ROC Curve ; User-Computer Interface ; Video Games ; Virtual Reality Exposure Therapy ; },
abstract = {This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses. In an experimental setup using five subjects, an average error below 30% was achieved. To make it more challenging the BCI system has been embedded inside an immersive 3D virtual reality (VR) classroom with simulated distractions, which was created by combining a low-cost infrared camera and an "off-axis perspective projection" algorithm. This system is intended for kids by operating with four electrodes, as well as a non-intrusive VR setting. With the promising results, and considering the simplicity of the scheme, we hope to encourage future studies to adapt the techniques presented in this study.},
}
@article {pmid25570669,
year = {2014},
author = {Valenza, G and Vanello, N and Milanesi, M and Scilingo, EP and Landini, L},
title = {Decoding underlying brain activities in time and frequency domains through complex independent component analysis of EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3192-3195},
doi = {10.1109/EMBC.2014.6944301},
pmid = {25570669},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/methods ; Humans ; Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; },
abstract = {Brain activities are often investigated through Electroencephalographic (EEG) data analysis using time-domain Independent Component Analysis (ICA). Nevertheless, the instantaneous mixing model of ICA cannot properly describe spatio-temporal dynamics, such as those related to traveling waves of neural activity. In this work, we exploit the application of the Complex ICA (cICA) to describe the underlying brain activities in time and frequency domain. In particular, we show how to effectively extract the most significant time-frequency structure of cortical activity in order to solve a compelling EEG-based pattern classification problem. The crucial step of independent component selection among frequencies is performed using an objective computational method based on template matching techniques with physiologically-plausible activations. Experimental results are obtained using on-line EEG data from the BCI Competition 2003 and are expressed in terms of confusion matrix after leave-one-out validation procedure. A comparative analysis between ICA and cICA models reveals that cICA estimation gives powerful information and allows to achieve a higher classification accuracy with respect to instantaneous ICA.},
}
@article {pmid25570635,
year = {2014},
author = {Nakanishi, M and Wang, Y and Wang, YT and Mitsukura, Y and Jung, TP},
title = {Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3053-3056},
doi = {10.1109/EMBC.2014.6944267},
pmid = {25570635},
issn = {2694-0604},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.},
}
@article {pmid25570634,
year = {2014},
author = {Xu, Z and So, RQ and Toe, KK and Ang, KK and Guan, C},
title = {On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3049-3052},
doi = {10.1109/EMBC.2014.6944266},
pmid = {25570634},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *Robotics ; Support Vector Machine ; },
abstract = {This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subject's self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.},
}
@article {pmid25570632,
year = {2014},
author = {Stavisky, SD and Kao, JC and Nuyujukian, P and Ryu, SI and Shenoy, KV},
title = {Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3041-3044},
doi = {10.1109/EMBC.2014.6944264},
pmid = {25570632},
issn = {2694-0604},
support = {8DP1HD075623/DP/NCCDPHP CDC HHS/United States ; NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes ; *Evoked Potentials, Motor ; Macaca mulatta ; Motor Cortex/physiology ; Movement/physiology ; },
abstract = {The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.},
}
@article {pmid25570631,
year = {2014},
author = {Wang, Y and Nakanishi, M and Wang, YT and Jung, TP},
title = {Enhancing detection of steady-state visual evoked potentials using individual training data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {3037-3040},
doi = {10.1109/EMBC.2014.6944263},
pmid = {25570631},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Nontherapeutic Human Experimentation ; },
abstract = {Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.},
}
@article {pmid25570621,
year = {2014},
author = {Santana, E and Brockmeier, AJ and Principe, JC},
title = {Joint optimization of algorithmic suites for EEG analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2997-3000},
doi = {10.1109/EMBC.2014.6944253},
pmid = {25570621},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Databases as Topic ; Electroencephalography/*methods ; Humans ; Imagery, Psychotherapy ; Learning ; Motor Activity ; Neural Networks, Computer ; Time Factors ; },
abstract = {Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.},
}
@article {pmid25570537,
year = {2014},
author = {Pluta, T and Bernardo, R and Shin, HW and Bernardo, DR},
title = {Unsupervised learning of electrocorticography motifs with binary descriptors of wavelet features and hierarchical clustering.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2657-2660},
doi = {10.1109/EMBC.2014.6944169},
pmid = {25570537},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography/*methods ; Humans ; *Learning ; *Wavelet Analysis ; },
abstract = {We describe a novel method for data mining spectro-spatiotemporal network motifs from electrocorticographic (ECoG) data. The method utilizes wavelet feature extraction from ECoG data, generation of compact binary vectors from these features, and binary vector hierarchical clustering. The potential utility of this method in the discovery of recurring neural patterns is demonstrated in an example showing clustering of ictal and post-ictal gamma activity patterns. The method allows for the efficient and scalable retrieval and clustering of neural motifs occurring in massive amounts of neural data, such as in prolonged EEG/ECoG recordings and in brain computer interfaces.},
}
@article {pmid25570530,
year = {2014},
author = {Chen, W and Liu, X and Litt, B},
title = {Logistic-weighted regression improves decoding of finger flexion from electrocorticographic signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2629-2632},
doi = {10.1109/EMBC.2014.6944162},
pmid = {25570530},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography ; Fingers/*physiology ; Humans ; Logistic Models ; Motor Activity ; Signal Processing, Computer-Assisted ; },
abstract = {One of the most interesting applications of brain computer interfaces (BCIs) is movement prediction. With the development of invasive recording techniques and decoding algorithms in the past ten years, many single neuron-based and electrocorticography (ECoG)-based studies have been able to decode trajectories of limb movements. As the output variables are continuous in these studies, a regression model is commonly used. However, the decoding of limb movements is not a pure regression problem, because the trajectories can be apparently classified into a motion state and a resting state, which result in a binary property overlooked by previous studies. In this paper, we propose an algorithm called logistic-weighted regression to make use of the property, and apply the algorithm to a BCI system decoding flexion of human fingers from ECoG signals. Our results show that the application of logistic-weighted regression improves decoding performance compared to the application of linear regression or pace regression. The proposed algorithm is also immensely valuable in the other BCIs decoding continuous movements.},
}
@article {pmid25570520,
year = {2014},
author = {Quick, KM and Card, NS and Whaite, SM and Mischel, J and Loughlin, P and Batista, AP},
title = {Assessing vibrotactile feedback strategies by controlling a cursor with unstable dynamics.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2589-2592},
pmid = {25570520},
issn = {2694-0604},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Female ; Hand/*physiology ; Hand Strength/physiology ; Humans ; Male ; *Task Performance and Analysis ; Touch/*physiology ; Young Adult ; },
abstract = {Brain computer interface (BCI) control predominately uses visual feedback. Real arm movements, however, are controlled under a diversity of feedback mechanisms. The lack of additional BCI feedback modalities forces users to maintain visual contact while performing tasks. Such stringent requirements result in poor BCI control during tasks that inherently lack visual feedback, such as grasping, or when visual attention is diverted. Using a modified version of the Critical Tracking Task which we call the Critical Stability Task (CST), we tested the ability of 9 human subjects to control an unstable system using either free arm movements or pinch force. The subjects were provided either visual feedback, 'proportional' vibrotactile feedback, or 'on-off' vibrotactile feedback about the state of the unstable system. We increased the difficulty of the control task by making the virtual system more unstable. We judged the effectiveness of a particular form of feedback as the maximal instability the system could reach before the subject lost control of it. We found three main results. First, subjects can use solely vibrotactile feedback to control an unstable system, although control was better using visual feedback. Second, 'proportional' vibrotactile feedback provided slightly better control than 'on-off' vibrotactile feedback. Third, there was large intra-subject variability in terms of the most effective input and feedback methods. This highlights the need to tailor the input and feedback methods to the subject when a high degree of control is desired. Our new task can provide a complement to traditional center-out paradigms to help boost the real-world relevance of BCI research in the lab.},
}
@article {pmid25570511,
year = {2014},
author = {Matlack, C and Haddock, A and Moritz, CT and Chizeck, HJ},
title = {Motor cortical decoding performance depends on controlled system order.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2553-2556},
doi = {10.1109/EMBC.2014.6944143},
pmid = {25570511},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Macaca ; *Models, Neurological ; Motor Cortex/*physiology ; Proprioception ; },
abstract = {Recent advances in intracortical brain-machine interfaces (BMIs) for position control have leveraged state estimators to decode intended movements from cortical activity. We revisit the underlying assumptions behind the use of Kalman filters in this context, focusing on the fact that identified cortical coding models capture closed-loop task dynamics. We show that closed-loop models can be partitioned, exposing feedback policies of the brain which are separate from interface and task dynamics. Changing task dynamics may cause the brain to change its control policy, and consequently the closed-loop dynamics. This may degrade performance of decoders upon switching from manual tasks to velocity-controlled BMI-mediated tasks. We provide experimental results showing that for the same manual cursor task, changing system order affects neural coding of movement. In one experimental condition force determines position directly, and in the other force determines cursor velocity. From this we draw an analogy to subjects transitioning from manual reaching tasks to velocity-controlled BMI tasks. We conclude with suggested principles for improving BMI decoder performance, including matching the controlled system order between manual and brain control, and identifying the brain's controller dynamics rather than complete closed-loop dynamics.},
}
@article {pmid25570400,
year = {2014},
author = {Durantin, G and Scannella, S and Gateau, T and Delorme, A and Dehais, F},
title = {Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : application to fNIRS signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2107-2110},
doi = {10.1109/EMBC.2014.6944032},
pmid = {25570400},
issn = {2694-0604},
mesh = {*Algorithms ; Brain/physiology ; *Computer Systems ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Time Factors ; Young Adult ; },
abstract = {Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.},
}
@article {pmid25570378,
year = {2014},
author = {Heger, D and Herff, C and Schultz, T},
title = {Combining feature extraction and classification for fNIRS BCIs by regularized least squares optimization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2012-2015},
doi = {10.1109/EMBC.2014.6944010},
pmid = {25570378},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Female ; Humans ; Least-Squares Analysis ; Male ; Models, Theoretical ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {In this paper, we show that multiple operations of the typical pattern recognition chain of an fNIRS-based BCI, including feature extraction and classification, can be unified by solving a convex optimization problem. We formulate a regularized least squares problem that learns a single affine transformation of raw HbO(2) and HbR signals. We show that this transformation can achieve competitive results in an fNIRS BCI classification task, as it significantly improves recognition of different levels of workload over previously published results on a publicly available n-back data set. Furthermore, we visualize the learned models and analyze their spatio-temporal characteristics.},
}
@article {pmid25570377,
year = {2014},
author = {Ang, KK and Yu, J and Guan, C},
title = {Single-trial classification of NIRS data from prefrontal cortex during working memory tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2008-2011},
doi = {10.1109/EMBC.2014.6944009},
pmid = {25570377},
issn = {2694-0604},
mesh = {Algorithms ; Humans ; Memory, Short-Term/*physiology ; Prefrontal Cortex/*physiology ; Reproducibility of Results ; Spectroscopy, Near-Infrared/*methods ; Task Performance and Analysis ; },
abstract = {This study presents single-trial classification performance on high density Near Infrared Spectroscopy (NIRS) data collected from the prefrontal cortex of 11 healthy subjects while performing working memory tasks and idle condition. The NIRS data collected comprised a total of 40 trials of n-back tasks for 2 difficulty levels: n=1 for easy and n=3 for hard. The single-trial classification was performed on features extracted using common average reference spatial filtering and single-trial baseline reference. The single-trial classification was performed using 5×5-fold cross-validations on the NIRS data collected by using mutual information-based feature selection and the support vector machine classifier. The results yielded average accuracies of 72.7%, 68.0% and 84.0% in classifying hard versus easy tasks, easy versus idle tasks and hard versus idle tasks respectively. Hence the results demonstrated a potential feasibility of using high density NIRS-based BCI for assessing working memory load.},
}
@article {pmid25570376,
year = {2014},
author = {Bauernfeind, G and Steyrl, D and Brunner, C and Muller-Putz, GR},
title = {Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: a comparison between different classifiers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2004-2007},
doi = {10.1109/EMBC.2014.6944008},
pmid = {25570376},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Confidence Intervals ; Discriminant Analysis ; Humans ; Spectroscopy, Near-Infrared/*methods ; *Statistics as Topic ; Support Vector Machine ; },
abstract = {Functional near infrared spectroscopy (fNIRS) is an emerging technique for the in-vivo assessment of functional activity of the cerebral cortex as well as in the field of brain-computer-interface (BCI) research. A common challenge for the utilization of fNIRS for BCIs is a stable and reliable single trial classification of the recorded spatio-temporal hemodynamic patterns. Many different classification methods are available, but up to now, not more than two different classifiers were evaluated and compared on one data set. In this work, we overcome this issue by comparing five different classification methods on mental arithmetic fNIRS data: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVM), analytic shrinkage regularized LDA (sLDA), and analytic shrinkage regularized QDA (sQDA). Depending on the used method and feature type (oxy-Hb or deoxy-Hb), achieved classification results vary between 56.1 % (deoxy-Hb/QDA) and 86.6% (oxy-Hb/SVM). We demonstrated that regularized classifiers perform significantly better than non-regularized ones. Considering simplicity and computational effort, we recommend the use of sLDA for fNIRS-based BCIs.},
}
@article {pmid25570375,
year = {2014},
author = {Batula, AM and Ayaz, H and Kim, YE},
title = {Evaluating a four-class motor-imagery-based optical brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {2000-2003},
doi = {10.1109/EMBC.2014.6944007},
pmid = {25570375},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Magnetic Resonance Imaging ; Motor Activity/*physiology ; *Optical Phenomena ; Task Performance and Analysis ; Young Adult ; },
abstract = {This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface, with two subjects achieving 50% accuracy. fNIRS is a noninvasive, safe, portable, and affordable optical brain imaging technique used to monitor cortical hemodynamic changes. Because of its portability and ease of use, fNIRS is amenable to deployment in more natural settings. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) BCIs have already been used with up to four motor-imagery-based commands. While fNIRS-based BCIs are relatively new, success with EEG and fMRI systems, as well as signal characteristics similar to fMRI and complementary to EEG, suggest that fNIRS could serve to build or augment future BCIs.},
}
@article {pmid25570374,
year = {2014},
author = {Schudlo, LC and Weyand, S and Chau, T},
title = {A review of past and future near-infrared spectroscopy brain computer interface research at the PRISM lab.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1996-1999},
doi = {10.1109/EMBC.2014.6944006},
pmid = {25570374},
issn = {2694-0604},
mesh = {Brain/physiopathology ; *Brain-Computer Interfaces ; Humans ; Research/*trends ; Spectroscopy, Near-Infrared/*trends ; Task Performance and Analysis ; },
abstract = {Single-trial classification of near-infrared spectroscopy (NIRS) signals for brain-computer interface (BCI) applications has recently gained much attention. This paper reviews research in this area conducted at the PRISM lab (University of Toronto) to date, as well as directions for future work. Thus far, research has included classification of hemodynamic changes induced by the performance of various mental tasks in both offline and online settings, as well as offline classification of cortical changes evoked by different affective states. The majority of NIRS-BCI work has only involved able-bodied individuals. However, preliminary work involving individuals from target BCI-user populations is also underway. In addition to further testing with users with severe disabilities, ongoing and future research will focus on enhancing classification accuracies, communication speed and user experience.},
}
@article {pmid25570337,
year = {2014},
author = {Xinyang Li, and Cuntai Guan, and Kai Keng Ang, and Haihong Zhang, and Sim Heng Ong, },
title = {Spatial filter adaptation based on the divergence framework for motor imagery EEG classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1847-1850},
doi = {10.1109/EMBC.2014.6943969},
pmid = {25570337},
issn = {2694-0604},
mesh = {Adaptation, Physiological ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Computer Simulation ; Electroencephalography/*methods ; Humans ; *Imagery, Psychotherapy ; Models, Statistical ; Motor Skills/*physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance.},
}
@article {pmid25570292,
year = {2014},
author = {Tanwani, AK and del R Millan, J and Billard, A},
title = {Rewards-driven control of robot arm by decoding EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1658-1661},
doi = {10.1109/EMBC.2014.6943924},
pmid = {25570292},
issn = {2694-0604},
mesh = {Arm/*physiology ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Movement ; Reward ; *Robotics ; Stroke/physiopathology ; },
abstract = {Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.},
}
@article {pmid25570290,
year = {2014},
author = {Akhtar, A and Norton, JJ and Kasraie, M and Bretl, T},
title = {Playing checkers with your mind: an interactive multiplayer hardware game platform for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1650-1653},
doi = {10.1109/EMBC.2014.6943922},
pmid = {25570290},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Computers ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; *Play and Playthings ; Young Adult ; },
abstract = {In this paper we describe a multiplayer brain-computer interface (BCI) based on the classic game of checkers using steady-state visually evoked potentials (SSVEPs). Previous research in BCI gaming focuses mainly on the production of software-based games using a computer screen--few hardware-based BCI games using a physical board have been developed. Hardware-based games can present a unique set of challenges when compared to software-based games. Depending on where the user is sitting, some stimuli might be farther away from the player, at a steeper viewing angle, conflated with competing stimuli, or occluded by physical barriers. In our game, we light squares on a checkerboard with flickering LEDs to elicit SSVEP responses in the subjects. When a subject attends to a particular square, the resulting SSVEPs are classified and a robot arm moves the selected piece. In a set of pilot experiments we investigated the ability of two subjects to use the SSVEP-based hardware game platform, and assessed how interstimulus distance, interstimulus angle, distance between target stimulus and subject, number of competing stimuli, and visual occlusions of the stimuli influence classification accuracy.},
}
@article {pmid25570288,
year = {2014},
author = {Tadipatri, VA and Tewfik, AH and Ashe, J},
title = {Long-term decoding of arm movement using Spatial Distribution of Neural Patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1642-1645},
doi = {10.1109/EMBC.2014.6943920},
pmid = {25570288},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; Arm/*physiology ; Brain-Computer Interfaces ; Macaca mulatta ; Male ; *Movement ; Neurons/*physiology ; Reproducibility of Results ; Time Factors ; },
abstract = {Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.},
}
@article {pmid25570287,
year = {2014},
author = {Yuxiao Yang, and Shanechi, MM},
title = {An adaptive brain-machine interface algorithm for control of burst suppression in medical coma.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1638-1641},
doi = {10.1109/EMBC.2014.6943919},
pmid = {25570287},
issn = {2694-0604},
mesh = {*Action Potentials ; *Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Coma/*physiopathology ; *Computer Simulation ; Humans ; },
abstract = {Burst suppression is an electroencephalogram (EEG) indicator of profound brain inactivation in which bursts of electrical activity alternate with periods of isoelectricity termed suppression. Specified time-varying levels of burst suppression are targeted in medical coma, a drug-induced brain state used for example to treat uncontrollable seizures. A brain-machine interface (BMI) that observes the EEG could automate the control of drug infusion rate to track a desired target burst suppression trajectory. Such a BMI needs to use models of drug dynamics and burst suppression observations, whose parameters could change with the burst suppression level and the environment over time. Currently, these parameters are fit prior to real-time control, requiring a separate system identification session. Moreover, this approach cannot track parameter variations over time. In addition, small variations in drug infusion rate may be desired at steady state. Here we develop a novel adaptive algorithm for robust control of medical coma in face of unknown and time-varying system parameters. We design an adaptive recursive Bayesian estimator to jointly estimate drug concentrations and system parameters in real time. We construct a controller using the linear-quadratic-regulator strategy that explicitly penalizes large infusion rate variations at steady state and uses the estimates as feedback to generate robust control. Using simulations, we show that the adaptive algorithm achieves precise control of time-varying target levels of burst suppression even when model parameters are initialized randomly, and reduces the infusion rate variation at steady state.},
}
@article {pmid25570286,
year = {2014},
author = {Kick, C and Volosyak, I},
title = {Evaluation of different spelling layouts for SSVEP based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1634-1637},
doi = {10.1109/EMBC.2014.6943918},
pmid = {25570286},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Demography ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Language ; Male ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems enable humans to communicate with their environment by directly using brain signals. This way, body movement is not explicitly required for communication making this technology especially useful for people with limited mobility. In this study, the system performance and well-being of 38 subjects are investigated using two different layouts of graphical user interfaces (GUI) presented on a computer screen. A steady state visual evoked potential (SSVEP) based BCI speller is used. Furthermore, three different predefined stimulus frequency sets are tested. Results show that the system works best for 55 % of the test subjects using visual stimuli in the range of 8.57 Hz-15 Hz. The majority of subjects (71 %), prefers the graphical user interface layout called Layout 2. Main advantage of this layout is that each desired letter or symbol can be selected with only two commands in contrast to Layout 1, where usually more than two commands are needed to select a desired object.},
}
@article {pmid25570284,
year = {2014},
author = {Minho Won, and Albalawi, H and Xin Li, and Thomas, DE},
title = {Low-power hardware implementation of movement decoding for brain computer interface with reduced-resolution discrete cosine transform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1626-1629},
doi = {10.1109/EMBC.2014.6943916},
pmid = {25570284},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Computers ; *Electric Power Supplies ; Electrocorticography ; Equipment Design ; Humans ; *Movement ; },
abstract = {This paper describes a low-power hardware implementation for movement decoding of brain computer interface. Our proposed hardware design is facilitated by two novel ideas: (i) an efficient feature extraction method based on reduced-resolution discrete cosine transform (DCT), and (ii) a new hardware architecture of dual look-up table to perform discrete cosine transform without explicit multiplication. The proposed hardware implementation has been validated for movement decoding of electrocorticography (ECoG) signal by using a Xilinx FPGA Zynq-7000 board. It achieves more than 56× energy reduction over a reference design using band-pass filters for feature extraction.},
}
@article {pmid25570252,
year = {2014},
author = {Müller-Putz, GR and Steyrl, D and Faller, J},
title = {Adaptive hybrid brain-computer interaction: ask a trainer for assistance!.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1493-1496},
doi = {10.1109/EMBC.2014.6943884},
pmid = {25570252},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Female ; Healthy Volunteers ; Humans ; *Imagery, Psychotherapy ; Learning ; Male ; Online Systems ; Reproducibility of Results ; Young Adult ; },
abstract = {In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.},
}
@article {pmid25570246,
year = {2014},
author = {Martínez-Vargas, JD and Castro-Hoyos, C and Castellanos-Dominguez, G},
title = {Entropy-based multichannel measure of stationarity for characterization of motor imagery patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1469-1472},
doi = {10.1109/EMBC.2014.6943878},
pmid = {25570246},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Entropy ; Humans ; Imagination ; *Movement ; Neuroimaging ; Signal Processing, Computer-Assisted ; },
abstract = {We propose a novel approach for measuring the stationarity level of multichannel time-series. This measure is based on stationarity definition over time-varying spectra and aims to quantify the relationship between local (single-channel dynamics) and global (multichannel dynamics) stationarity. With the purpose of separate among several motor/imagery tasks, we asssume that movement imagination implies an increase on the EEG variability, consequently, as discriminant features, we first compute the non-stationary components of input signals, and we further obtain its stationary level throughout the proposed measure. To assess the separability level of the proposed features, we employ the t-student test. Obtained results evidence that our measure is able to accurately detect brain areas projected on the scalp where motor tasks are performed.},
}
@article {pmid25570215,
year = {2014},
author = {Cotrina, A and Benevides, A and Ferreira, A and Bastos, T and Castillo, J and Menezes, ML and Pereira, C},
title = {Towards an architecture of a hybrid BCI based on SSVEP-BCI and passive-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1342-1345},
doi = {10.1109/EMBC.2014.6943847},
pmid = {25570215},
issn = {2694-0604},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation ; },
abstract = {Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decrease when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the user's intentions, the situational interpretations and mainly the emotional state. In this work, an architecture composed by passive-BCI co-working with SSVEP-BCI is proposed, with the aim of improving the performance of the reactive-BCI. The possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. In this sense, two ways to recover the accuracy of SSVEP are presented in this paper: 1) Adjusting of Amplitude of the SSVEP and 2) Adjusting of Frequency of the SSVEP response. The results are promising, because accuracy of SSVEP-BCI can be recovered in the case that it was reduced by the BCI user's emotional state.},
}
@article {pmid25570214,
year = {2014},
author = {Willett, FR and Suminski, AJ and Fagg, AH and Hatsopoulos, NG},
title = {Differences in motor cortical representations of kinematic variables between action observation and action execution and implications for brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1334-1337},
doi = {10.1109/EMBC.2014.6943845},
pmid = {25570214},
issn = {2694-0604},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Exoskeleton Device ; Macaca mulatta ; Motor Cortex/*physiology ; Movement ; Neurons/physiology ; },
abstract = {Observing an action being performed and executing the same action cause similar patterns of neural activity to emerge in the primary motor cortex (MI). Previous work has shown that the neural activity evoked during action observation (AO) is informative as to both the kinematics and muscle activation patterns of the action being performed, although the neural activity recorded during action observation contains less information than the activity recorded during action execution (AE). In this study, we extend these results by comparing the representation of different kinematic variables in MI single /multi unit activity between AO and AE conditions in three rhesus macaques. We show that the representation of acceleration decreases more significantly than that of position and velocity in AO (population decoding performance for acceleration decreases more steeply, and fewer neurons in AO encode acceleration significantly as compared to AE). We discuss the relevance of these results to brain-machine interfaces that make use of neural activity during AO to initialize a mapping function between neural activity and motor commands.},
}
@article {pmid25570213,
year = {2014},
author = {Yeom, SK and Fazli, S and Müller, KR and Lee, SW},
title = {Towards an enhanced ERP speller based on the visual processing of face familiarity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1330-1333},
doi = {10.1109/EMBC.2014.6943844},
pmid = {25570213},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Language ; Male ; Photic Stimulation ; Recognition, Psychology ; },
abstract = {In this study, a novel P300 based brain-computer interface (BCI) system using random set presentation pattern and employing the effect of face familiarity has been proposed and developed. While the effect of face familiarity is widely studied in the cognitive neurosciences, it has so far not been addressed for the purpose of BCI. We compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our novel approach. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli and thereby improving P300-based spelling performance. This leads to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.},
}
@article {pmid25570211,
year = {2014},
author = {Loughnane, GM and Meade, E and Reilly, RB and Lalor, EC},
title = {Towards a gaze-independent hybrid-BCI based on SSVEPs, alpha-band modulations and the P300.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1322-1325},
doi = {10.1109/EMBC.2014.6943842},
pmid = {25570211},
issn = {2694-0604},
mesh = {Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular/*physiology ; Humans ; },
abstract = {In recent years it has been shown to be possible to create a Brain Computer Interface (BCI) using non-invasive electroencephalographic (EEG) measurements of covert visual spatial attention. For example, that both Steady-State Visual Evoked Potentials (SSVEP) and parieto-occipital alpha band activity have been shown to be sensitive to covert attention and this has been exploited to provide simple communication control without the need for any physical movement. In this study, potential improvements in the speed and accuracy of such a BCI are investigated by exploring the possibility of incorporating a P300 task into an SSVEP covert attention paradigm. Should this be possible it would pave the way for a gaze-independent hybrid BCI based on three somewhat independent EEG signals. Within a well-established SSVEP-based attention paradigm we show that it is possible to make a binary classification of covert attention using just the P300 with an average accuracy of 71% across three subjects. We also validate previously published research by showing robust attention effects on the SSVEP and alpha band activity within this paradigm. In future work, it is hoped that by integrating the three signals into a hybrid BCI a significant improvement in performance will be forthcoming leading to an easily usable real time communication device for patients with severe disabilities such as Locked-In Syndrome (LIS).},
}
@article {pmid25570209,
year = {2014},
author = {Lauteslager, T and O'Sullivan, JA and Reilly, RB and Lalor, EC},
title = {Decoding of attentional selection in a cocktail party environment from single-trial EEG is robust to task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1318-1321},
doi = {10.1109/EMBC.2014.6943841},
pmid = {25570209},
issn = {2694-0604},
mesh = {Attention/*physiology ; Auditory Perception/*physiology ; Electroencephalography/*methods ; Humans ; Task Performance and Analysis ; },
abstract = {Recently it has been shown to be possible to ascertain the target of a subject's attention in a cocktail party environment from single-trial (~60 s) electroencephalography (EEG) data. Specifically, this was shown in the context of a dichotic listening paradigm where subjects were cued to attend to a story in one ear while ignoring a different story in the other and were required to answer questions on both stories. This paradigm resulted in a high decoding accuracy that correlated with task performance across subjects. Here, we extend this finding by showing that the ability to accurately decode attentional selection in a dichotic speech paradigm is robust to the particular attention task at hand. Subjects attended to one of two dichotically presented stories under four task conditions. These conditions required subjects to 1) answer questions on the content of both stories, 2) detect irregular frequency fluctuations in the voice of the attended speaker 3) answer questions on both stories and detect frequency fluctuations in the attended story, and 4) detect target words in the attended story. All four tasks led to high decoding accuracy (~89%). These results offer new possibilities for creating user-friendly brain computer interfaces (BCIs).},
}
@article {pmid25570208,
year = {2014},
author = {Edelman, B and Baxter, B and He, B},
title = {Discriminating hand gesture motor imagery tasks using cortical current density estimation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1314-1317},
doi = {10.1109/EMBC.2014.6943840},
pmid = {25570208},
issn = {2694-0604},
support = {EB006433/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Hand/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/physiology ; },
abstract = {Current EEG based brain computer interface (BCI) systems have achieved successful control in up to 3 dimensions; however the current paradigm may be unnatural for many rehabilitative and recreational applications. Therefore there is a great need to find motor imagination (MI) tasks that are realistic for output device control. In this paper we present our results on classifying hand gesture MI tasks, including right hand flexion, extension, supination and pronation using a novel EEG inverse imaging approach. By using both temporal and spatial specificity in the source domain we were able to separate MI tasks with up to 95% accuracy for binary classification of any two tasks compared to a maximum of only 79% in the sensor domain.},
}
@article {pmid25570207,
year = {2014},
author = {Chin, ZY and Ang, KK and Wang, C and Guan, C},
title = {Discriminative channel addition and reduction for filter bank common spatial pattern in motor imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1310-1313},
doi = {10.1109/EMBC.2014.6943839},
pmid = {25570207},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {An electroencephalography (EEG)-based Motor Imagery Brain-Computer Interface (MI-BCI) requires a long setup time if a large number of channels is used, and EEG from noisy or irrelevant channels may adversely affect the classification performance. To address this issue, this paper proposed 2 approaches to systematically select discriminative channels for EEG-based MI-BCI. The proposed Discriminative Channel Addition (DCA) approach and the Discriminative Channel Reduction (DCR) approach selects subject-specific discriminative channels by iteratively adding or removing channels based on the cross-validation classification accuracies obtained using the Filter Bank Common Spatial Pattern algorithm. The performances of the proposed approaches were evaluated on the BCI Competition IV Dataset 2a. The results on 2-class and 4-class MI data showed that DCA, which iteratively adds channels, selected 13~14 channels that consistently yielded better cross-validation accuracies on the training data and session-to-session transfer accuracies on the evaluation data compared to the use of a full 22-channel setup. Hence, this results in a reduced channel setup that could improve the classification accuracy of the MI-BCI after removing less discriminative channels.},
}
@article {pmid25570205,
year = {2014},
author = {Zhang, R and Li, Y and Yan, Y and Zhang, H and Wu, S},
title = {An intelligent wheelchair based on automated navigation and BCI techniques.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1302-1305},
doi = {10.1109/EMBC.2014.6943837},
pmid = {25570205},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Wheelchairs ; },
abstract = {In this paper, we propose an intelligent wheelchair system that relies on a brain computer interface (BCI) and automatic navigation. When in operation, candidate destinations and waypoints are automatically generated on the basis of the current environment. Then, the user selects a destination using a P300-based BCI. Finally, the navigation system plans a path and navigates the wheelchair to the determined destination. While the wheelchair is in motion, the user can issue a stop command with the BCI. Using our system, the mental burden of the user can be alleviated to a large degree. Furthermore, our system can adapt to changes in the environment. The experimental results demonstrated the effectiveness of our system.},
}
@article {pmid25570204,
year = {2014},
author = {Rupp, KM and Schieber, MH and Thakor, NV},
title = {Local field potentials mitigate decline in motor decoding performance caused by loss of spiking units.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1298-1301},
pmid = {25570204},
issn = {2694-0604},
support = {R01 NS079664/NS/NINDS NIH HHS/United States ; T32 EB003383/EB/NIBIB NIH HHS/United States ; 5T32EB003383/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {The technology underlying brain computer interfaces has recently undergone rapid development, though a variety of issues remain that are currently preventing it from becoming a viable clinical assistive tool. Though decoding of motor output has been shown to be particularly effective when using spikes, these decoders tend to degrade with the loss of subsets of these signals. One potential solution to this problem is to include features derived from LFP signals in the decoder to mitigate these negative effects. We explored this solution and found that the decline in decoding performance that accompanies spiking unit dropout was significantly reduced when LFP power features were included in the decoder. Additionally, high frequency LFP features in the 100-170 Hz band were more effective than low frequency LFP features in the 2-4 Hz band at protecting the decoder from a dropoff in performance. LFP power appears to be an effective signal to improve the robustness of spiking unit decoders. Future studies will explore online classification and performance improvements in chronic implants by the proposed method.},
}
@article {pmid25570201,
year = {2014},
author = {Pinegger, A and Deckert, L and Halder, S and Barry, N and Faller, J and Käthner, I and Hintermüller, C and Wriessnegger, SC and Kübler, A and Müller-Putz, GR},
title = {Write, read and answer emails with a dry 'n' wireless brain-computer interface system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1286-1289},
doi = {10.1109/EMBC.2014.6943833},
pmid = {25570201},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electrodes ; *Electronic Mail ; Female ; Humans ; Male ; *Reading ; User-Computer Interface ; *Wireless Technology ; *Writing ; Young Adult ; },
abstract = {Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this paper we evaluated one of the latest innovations in the area of BCI: A wireless EEG amplifier with dry electrode technology combined with a web browser which enables BCI users to use standard webmail. With this system ten volunteers performed a daily life task: Write, read and answer an email. Experimental results of this study demonstrate the power of the introduced BCI system.},
}
@article {pmid25570200,
year = {2014},
author = {Cecotti, H and Eckstein, MP and Giesbrecht, B},
title = {Single-trial classification of neural responses evoked in rapid serial visual presentation: effects of stimulus onset asynchrony and stimulus repetition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1282-1285},
doi = {10.1109/EMBC.2014.6943832},
pmid = {25570200},
issn = {2694-0604},
mesh = {Area Under Curve ; Brain/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Photic Stimulation ; Young Adult ; },
abstract = {Rapid serial visual presentation (RSVP) tasks, in which participants are presented with a continuous sequence of images in one location, have been used in combination with electroencephalography (EEG) in a variety of Brain-Machine Interface (BMI) applications. The RSVP task is advantageous because it can be performed at a high temporal rate. The rate of the RSVP sequence is controlled by the stimulus onset asynchrony (SOA) between subsequent stimuli. When used within the context of a BMI, an RSVP task with short SOA could increase the information throughput of the system while also allowing for stimulus repetitions. However, reducing the SOA also increases the perceptual degradation caused by presenting two stimuli in close succession, and it decreases the target-to-target interval (TTI), which can increase the cognitive demands of the task. These negative consequences of decreasing the SOA could affect on the EEG signal measured in the task and degrade the performance of the BMI. Here we systematically investigate the effects of SOA and stimulus repetition (r) on single-trial target detection in an RSVP task. Ten healthy volunteers participated in an RSVP task in four conditions that varied in SOA and repetitions (SOA=500 ms, r=1; SOA=250 ms, r=2; SOA=166 ms, r=3; and SOA=100 ms, r=5) while processing time across conditions was controlled. There were two key results: First, when controlling for the number of repetitions, single-trial performance increases when the SOA decreases. Second, when the repetitions were combined, the best performance (AUC=0.967) was obtained with the shortest SOA (100 ms). These results suggest that shortening the SOA in an RSVP task has the benefit of increasing the performance relative to longer SOAs, and it also allows a higher number of repetitions of the stimuli in a limited amount of time.},
}
@article {pmid25570199,
year = {2014},
author = {Song, C and Xu, R and Hong, B},
title = {Decoding of Chinese phoneme clusters using ECoG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1278-1281},
doi = {10.1109/EMBC.2014.6943831},
pmid = {25570199},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; Algorithms ; Child ; Electrocorticography/*methods ; Electrodes ; Female ; Humans ; *Language ; Male ; *Phonetics ; Time Factors ; Young Adult ; },
abstract = {A finite set of phonetic units is used in human speech, but how our brain recognizes these units from speech streams is still largely unknown. The revealing of this neural mechanism may lead to the development of new types of speech brain computer interfaces (BCI) and computer speech recognition systems. In this study, we used electrocorticography (ECoG) signal from human cortex to decode phonetic units during the perception of continuous speech. By exploring the wavelet time-frequency features, we identified ECoG electrodes that have selective response to specific Chinese phonemes. Gamma and high-gamma power of these electrodes were further combined to separate sets of phonemes into clusters. The clustered organization largely coincided with phonological categories defined by the place of articulation and manner of articulation. These findings were incorporated into a decoding framework of Chinese phonemes clusters. Using support vector machine (SVM) classifier, we achieved consistent accuracies higher than chance level across five patients discriminating specific phonetic clusters, which suggests a promising direction of implementing a speech BCI.},
}
@article {pmid25570197,
year = {2014},
author = {He, W and Wei, P and Zhou, Y and Wang, L},
title = {Modulation effect of transcranial direct current stimulation on phase synchronization in motor imagery brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1270-1273},
doi = {10.1109/EMBC.2014.6943829},
pmid = {25570197},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Humans ; *Imagery, Psychotherapy ; Male ; *Motor Activity ; Task Performance and Analysis ; *Transcranial Direct Current Stimulation ; },
abstract = {Transcranial direct current stimulation (tDCS) has been demonstrated that it can enhance the cortex excitability and modulate the event-related desynchronization (ERD) in motor imagery (MI). Phase synchronization is an important signature in the brain that reflects the neural interaction and integration, which has been adopted as an important EEG pattern for Brian-Computer Interface (BCI) control. In this study, we designed an experiment paradigm and investigated whether the tDCS can modulate the phase synchronization between the primary motor cortex (M1) and the supplementary motor area (SMA) in MI. Ten healthy subjects were selected and separated into two groups randomly. They performed the left and right hand MI task in two successive sessions. According to the different groups, anodal or sham stimulation were administrated to the right side of the M1. The phase locking value (PLV), which is a reliable measurement of phase synchronization in MI, was calculated. The pre and post-stimulation normalized PLV in the left hand MI task were compared. The result manifests that the normalized PLV of the entire subjects in anodal stimulation group increases after the stimulation, which shows a statistically significant difference (paired t-test p = 0.0371, n = 5). Our study reveals that the tDCS can impact the neural coupling between different brain regions and modulate phase synchronization in MI. Moreover, intervention of phase synchronization by tDCS might contribute to the rehabilitation of people with motor disorder and neurological disorders.},
}
@article {pmid25570196,
year = {2014},
author = {Petti, M and Mattia, D and Pichiorri, F and Toppi, J and Salinari, S and Babiloni, F and Astolfi, L and Cincotti, F},
title = {A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1267-1269},
doi = {10.1109/EMBC.2014.6943828},
pmid = {25570196},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Electrophysiological Phenomena ; Humans ; Imagery, Psychotherapy/*methods ; Middle Aged ; *Motor Activity ; Stroke/*physiopathology ; *Stroke Rehabilitation ; },
abstract = {In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.},
}
@article {pmid25570195,
year = {2014},
author = {Xiao, R and Ding, L},
title = {Classification of finger pairs from one hand based on spectral features in human EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1263-1266},
doi = {10.1109/EMBC.2014.6943827},
pmid = {25570195},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Electroencephalography/*methods ; Female ; Fingers/*physiology ; Hand/*physiology ; Humans ; Male ; Principal Component Analysis ; },
abstract = {Individual finger movements are well-articulated movements of fine body parts, the successful decoding of which can provide extra degrees of freedom to drive brain computer interface (BCI) applications. Past studies present some unique features revealed from spectral principal component analysis (PCA) on electrophysiological data recorded in both the surface of the brain (electrocorticography, ECoG) and the scalp (electroencephalography, EEG). These features contain discriminable information about fine individual finger movements from one hand. However, the efficacy of these spectral features has not been well investigated under the application of various classifiers. In the present study, we set out to investigate the topic using noninvasive human EEG. Several classifiers were chosen to explore their capability in capturing the spectral PC features to decode individual finger movements pairwisely from one hand using noninvasive EEG, aiming to investigate the efficacy of these spectral features in a decoding task.},
}
@article {pmid25570194,
year = {2014},
author = {Perdikis, S and Leeb, R and Millán, Jdel R},
title = {Subject-oriented training for motor imagery brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1259-1262},
doi = {10.1109/EMBC.2014.6943826},
pmid = {25570194},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; *Motor Activity ; },
abstract = {Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec.},
}
@article {pmid25570193,
year = {2014},
author = {Lamti, HA and Ben Khelifa, MM and Alimi, AM and Gorce, P},
title = {Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1255-1258},
doi = {10.1109/EMBC.2014.6943825},
pmid = {25570193},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Electrodes ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Mental Fatigue/*physiopathology ; *Wheelchairs ; },
abstract = {The aim of this paper is to investigate the influence of mental fatigue on Positive 300 (P300) and Steady State Visual Evoked Potentials (SSVEP) during virtual wheelchair navigation. For this purpose, experimental protocols were setup in order to induce mental fatigue, P300 and SSVEP. Next, the correlation between mental fatigue and P300/SSVEP parameters were investigated. At the end, the best correlated features from both modalities were used as inputs for three classification techniques. Depending on the subject samples (healthy vs palsy), The best overall classification rate reached 80% for P300 modality. The results of this investigation constitute the first steps towards an anticipatory system that can assist the wheelchair driver during navigation, depending on his mental fatigue level.},
}
@article {pmid25570191,
year = {2014},
author = {McCrimmon, CM and King, CE and Wang, PT and Cramer, SC and Nenadic, Z and Do, AH},
title = {Brain-controlled functional electrical stimulation for lower-limb motor recovery in stroke survivors.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1247-1250},
pmid = {25570191},
issn = {2694-0604},
support = {K24 HD074722/HD/NICHD NIH HHS/United States ; T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Electric Stimulation ; Electroencephalography ; Gait/physiology ; Humans ; Leg/*physiology ; Physical Therapy Modalities ; Range of Motion, Articular ; Signal-To-Noise Ratio ; Stroke/*physiopathology ; },
abstract = {Despite the prevalence of stroke-induced gait impairment due to foot drop, current rehabilitative practices to improve gait function are limited, and orthoses can be uncomfortable and do not provide long-lasting benefits. Therefore, novel modalities that may facilitate lasting neurological and functional improvements, such as brain-computer interfaces (BCIs), have been explored. In this article, we assess the feasibility of BCI-controlled functional electrical stimulation (FES) as a novel physiotherapy for post-stroke foot drop. Three chronic stroke survivors with foot drop received three, 1-hour sessions of therapy during 1 week. All subjects were able to purposefully operate the BCI-FES system in real time. Furthermore, the salient electroencephalographic (EEG) features used for classification by the data-driven methodology were determined to be physiologically relevant. Over the course of this short therapy, the subjects' dorsiflexion active range of motion (AROM) improved by 3°, 4°, and 8°, respectively. These results indicate that chronic stroke survivors can operate the BCI-FES system, and that BCI-FES intervention may promote functional improvements.},
}
@article {pmid25570190,
year = {2014},
author = {Wang, PT and King, CE and McCrimmon, CM and Shaw, SJ and Millett, DE and Liu, CY and Chui, LA and Nenadic, Z and Do, AH},
title = {Electrocorticogram encoding of upper extremity movement duration.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1243-1246},
pmid = {25570190},
issn = {2694-0604},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Elbow/physiology ; Electrocorticography ; Electrodes, Implanted ; *Electroencephalography ; Hand Strength/physiology ; Humans ; Linear Models ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Shoulder/physiology ; },
abstract = {Electrocorticogram (ECoG) is a promising long-term signal acquisition platform for brain-computer interface (BCI) systems such as upper extremity prostheses. Several studies have demonstrated decoding of arm and finger trajectories from ECoG high-gamma band (80-160 Hz) signals. In this study, we systematically vary the velocity of three elementary movement types (pincer grasp, elbow and shoulder flexion/extension) to test whether the high-gamma band encodes for the entirety of the movements, or merely the movement onset. To this end, linear regression models were created for the durations and amplitudes of high-gamma power bursts and velocity deflections. One subject with 8×8 high-density ECoG grid (4 mm center-to-center electrode spacing) participated in the experiment. The results of the regression models indicated that the power burst durations varied directly with the movement durations (e.g. R(2)=0.71 and slope=1.0 s/s for elbow). The persistence of power bursts for the duration of the movement suggests that the primary motor cortex (M1) is likely active for the entire duration of a movement, instead of providing a marker for the movement onset. On the other hand, the amplitudes were less co-varied. Furthermore, the electrodes of maximum R(2) conformed to somatotopic arrangement of the brain. Also, electrodes responsible for flexion and extension movements could be resolved on the high-density grid. In summary, these findings suggest that M1 may be directly responsible for activating the individual muscle motor units, and future BCI may be able to utilize them for better control of prostheses.},
}
@article {pmid25570189,
year = {2014},
author = {King, CE and Wang, PT and McCrimmon, CM and Chou, CC and Do, AH and Nenadic, Z},
title = {Brain-computer interface driven functional electrical stimulation system for overground walking in spinal cord injury participant.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1238-1242},
pmid = {25570189},
issn = {2694-0604},
support = {T32 GM008620/GM/NIGMS NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Electric Stimulation ; Electroencephalography ; Humans ; Male ; Paraplegia ; Spinal Cord Injuries/*physiopathology ; Walking/*physiology ; },
abstract = {The current treatment for ambulation after spinal cord injury (SCI) is to substitute the lost behavior with a wheelchair; however, this can result in many co-morbidities. Thus, novel solutions for the restoration of walking, such as brain-computer interfaces (BCI) and functional electrical stimulation (FES) devices, have been sought. This study reports on the first electroencephalogram (EEG) based BCI-FES system for overground walking, and its performance assessment in an individual with paraplegia due to SCI. The results revealed that the participant was able to purposefully operate the system continuously in real time. If tested in a larger population of SCI individuals, this system may pave the way for the restoration of overground walking after SCI.},
}
@article {pmid25570188,
year = {2014},
author = {Smith, DJ and Varghese, LA and Stepp, CE and Guenther, FH},
title = {Comparison of steady-state visual and somatosensory evoked potentials for brain-computer interface control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1234-1237},
doi = {10.1109/EMBC.2014.6943820},
pmid = {25570188},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Photic Stimulation ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {Many proposed EEG-based brain-computer interfaces (BCIs) make use of visual stimuli to elicit steady-state visual evoked potentials (SSVEP), the frequency of which can be mapped to a computer input. However, such a control scheme can be ineffective if a user has no motor control over their eyes and cannot direct their gaze towards a flashing stimulus to generate such a signal. Tactile-based methods, such as somatosensory steady-state evoked potentials (SSSEP), are a potentially attractive alternative in these scenarios. Here, we compare the neural signals elicited by SSSEP to those elicited by SSVEP in naïve BCI users towards evaluating the feasibility of SSSEP-based control of an EEG BCI.},
}
@article {pmid25570187,
year = {2014},
author = {Munoz, JE and Rios, LH and Henao, OA},
title = {Low cost implementation of a Motor Imagery experiment with BCI system and its use in neurorehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1230-1233},
doi = {10.1109/EMBC.2014.6943819},
pmid = {25570187},
issn = {2694-0604},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Humans ; Neurological Rehabilitation/*economics ; Stroke Rehabilitation ; },
abstract = {The application of rehabilitation programs based on videogames with brain-computer interfaces (BCI) allows to provide feedback to the user with the expectation of stimulate the brain plasticity that will restore the motor control. The use of specific mental strategies such as Motor Imagery (MI) in neuroscientific experiments with BCI systems often requires the acquisition of sophisticated interfaces and specialized software for execution, which usually have a high implementation costs. We present a combination of low-cost hardware and open-source software for the implementation of videogame based on virtual reality with MI and its potential use as neurotherapy for stroke patients. Three machine learning algorithms for the BCI signals classification are shown: LDA (Linear Discriminant Analysis) and two Support Vector Machines (SVM) in order to determine which task of MI is being performed by the user in a particular moment of the experiment. All classification algorithms was evaluated in 8 healthy subjects, the average accuracy of the best classifier was 96.7%, which shows that it is possible to carry out serious neuroscientific experiments with MI using low-cost BCI systems and achieve comparable accuracies with more sophisticated and expensive devices.},
}
@article {pmid25570186,
year = {2014},
author = {An, X and Ming, D and Sterling, D and Qi, H and Blankertz, B},
title = {Optimizing visual-to-auditory delay for multimodal BCI speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1226-1229},
doi = {10.1109/EMBC.2014.6943818},
pmid = {25570186},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; Attention/physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Language ; },
abstract = {Multimodal spellers combining visual and auditory stimulation have recently gained more attention in ERP-based Brain-Computer Interfaces (BCIs). Most studies found an improved efficiency compared to unimodal paradigms while few have explored the effect of the visual-to-auditory delays on the spelling performance. Here, we study five conditions with different visual-to-auditory delays, in order to find the paradigm that provides the best overall BCI performance. We compared the temporal and spatial binary classification accuracy as well as the grand-averaged classification accuracies over repetitions. Results show that long delays may cause better performance in early time intervals corresponding to negative ERP components, but better overall performance is achieved with short visual-to-auditory delays.},
}
@article {pmid25570185,
year = {2014},
author = {Eliseyev, A and Mestais, C and Charvet, G and Sauter, F and Abroug, N and Arizumi, N and Cokgungor, S and Costecalde, T and Foerster, M and Korczowski, L and Moriniere, B and Porcherot, J and Pradal, J and Ratel, D and Tarrin, N and Torres-Martinez, N and Verney, A and Aksenova, T and Benabid, AL},
title = {CLINATEC® BCI platform based on the ECoG-recording implant WIMAGINE® and the innovative signal-processing: preclinical results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1222-1225},
doi = {10.1109/EMBC.2014.6943817},
pmid = {25570185},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; *Electrocorticography ; Electrodes, Implanted ; Electroencephalography ; Exoskeleton Device ; Macaca mulatta ; Quality of Life ; Signal Processing, Computer-Assisted ; },
abstract = {The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). Innovative ECoG signal decoding algorithms will allow the control of the exoskeleton by the subject's brain activity. Currently, the whole BCI platform was tested in real-time in preclinical experiments carried out in nonhuman primates. In these experiments, the exoskeleton arm was controlled by means of the decoded neuronal activity.},
}
@article {pmid25570134,
year = {2014},
author = {Hachmeister, N and Finke, A and Ritter, H},
title = {Does machine-mediated interaction induce inter-brain synchrony?--A hyperscanning study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {1018-1021},
doi = {10.1109/EMBC.2014.6943766},
pmid = {25570134},
issn = {2694-0604},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Robotics ; },
abstract = {We present a study in which participants were trained in several sessions to control a (comparatively simple) robot via an EEG-/motor imagery-based Brain-Computer Interface (BCI). In the final (experiment) session pairs of participants were formed and each participant controlled one of two robots in a shared space. EEG data was recorded synchronously from both participants. We performed a joint data analysis on the datasets and found increases of phase-locking in μ- and θ-band. One such phase-locking effect appears to be time-locked to the start of the robotic action.},
}
@article {pmid25570052,
year = {2014},
author = {Gaume, A and Vialatte, F and Dreyfus, G},
title = {Transient brain activity explains the spectral content of steady-state visual evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {688-692},
doi = {10.1109/EMBC.2014.6943684},
pmid = {25570052},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are widely used in the design of brain-computer interfaces (BCIs). A lot of effort has therefore been devoted to find a fast and reliable way to detect SSVEPs. We study the link between transient and steady-state VEPs and show that it is possible to predict the spectral content of a subject's SSVEPs by simulating trains of transient VEPs. This could lead to a better understanding of evoked potentials as well as to better performances of SSVEP-based BCIs, by providing a tool to improve SSVEP detection algorithms.},
}
@article {pmid25570048,
year = {2014},
author = {Yang, H and Guan, C and Ang, KK and Phua, KS and Wang, C},
title = {Selection of effective EEG channels in brain computer interfaces based on inconsistencies of classifiers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {672-675},
doi = {10.1109/EMBC.2014.6943680},
pmid = {25570048},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Hand/physiology ; Hand Strength ; Humans ; Motor Activity ; },
abstract = {This paper proposed a novel method to select the effective Electroencephalography (EEG) channels for the motor imagery tasks based on the inconsistencies from multiple classifiers. The inconsistency criterion for channel selection was designed based on the fluctuation of the classification accuracies among different classifiers when the noisy channels were included. These noisy channels were then identified and removed till a required number of channels was selected or a predefined classification accuracy with reference to baseline was obtained. Experiments conducted on a data set of 13 healthy subjects performing hand grasping and idle revealed that the EEG channels from the motor area were most frequently selected. Furthermore, the mean increases of 4.07%, 3.10% and 1.77% of the averaged accuracies in comparison with the four existing channel selection methods were achieved for the non-feedback, feedback and calibration sessions, respectively, by selecting as low as seven channels. These results further validated the effectiveness of our proposed method.},
}
@article {pmid25570047,
year = {2014},
author = {Tomida, N and Yamagishi, M and Yamada, I and Tanaka, T},
title = {A reduced rank approach for covariance matrix estimation in EEG signal classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {668-671},
doi = {10.1109/EMBC.2014.6943679},
pmid = {25570047},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; },
abstract = {Common Spatial Pattern (CSP) methods are widely used to extract the brain activity for brain machine interfacing (BMI) based on electroencephalogram (EEG). For each mental task, CSP methods estimate a covariance matrix of EEG signals and adopt the uniform average of the sample covariance matrices over trials. However, the uniform average is sensitive to outliers caused by e.g. unrelated brain activity. In this paper, we propose an improvement of the estimated covariance matrix utilized in CSP methods by reducing the influence of the outliers as well as guaranteeing positive definiteness. More precisely, our estimation is the projection of the uniform average onto the intersection of two convex sets: the first set is a special reduced dimensional subspace which alleviates the influence of the outliers; the second is the positive definite cone. A numerical experiment supports the effectiveness of the proposed technique.},
}
@article {pmid25570044,
year = {2014},
author = {Reagor, MK and Zong, C and Jafari, R},
title = {Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {654-657},
doi = {10.1109/EMBC.2014.6943676},
pmid = {25570044},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation ; Evoked Potentials, Visual/*physiology ; Humans ; Visual Cortex/physiology ; },
abstract = {Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without and one with performance feedback. Analysis was performed off-line. A ratio of the canonical correlation analysis (CCA) coefficients was used to construct a Bayesian probability model and a thresholding method for the ratio of the posterior probability of the target frequency over maximal posterior probability of non-target frequencies was used as classification criteria. Moreover, the probability thresholds were optimized for each frequency, subject to maximizing the ITR. We achieved a maximum ITR of 39.82 bit/min. Although the performance feedback did not improve the overall ITR, it did improve the accuracy measure. Possible applications in the medical industry are discussed.},
}
@article {pmid25570002,
year = {2014},
author = {Shahdoost, S and Frost, S and Van Acker, G and DeJong, S and Dunham, C and Barbay, S and Nudo, R and Mohseni, P},
title = {Towards a miniaturized brain-machine-spinal cord interface (BMSI) for restoration of function after spinal cord injury.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {486-489},
doi = {10.1109/EMBC.2014.6943634},
pmid = {25570002},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Hindlimb/physiology ; Male ; Movement ; Rats ; *Recovery of Function ; Spinal Cord Injuries/*therapy ; },
abstract = {Nearly 6 million people in the United States are currently living with paralysis in which 23% of the cases are related to spinal cord injury (SCI). Miniaturized closed-loop neural interfaces have the potential for restoring function and mobility lost to debilitating neural injuries such as SCI by leveraging recent advancements in bioelectronics and a better understanding of the processes that underlie functional and anatomical reorganization in an injured nervous system. This paper describes our current progress towards developing a miniaturized brain-machine-spinal cord interface (BMSI) that is envisioned to convert in real time the neural command signals recorded from the brain to electrical stimuli delivered to the spinal cord below the injury level. Specifically, the paper reports on a corticospinal interface integrated circuit (IC) as a core building block for such a BMSI that is capable of low-noise recording of extracellular neural spikes from the cerebral cortex as well as muscle activation using intraspinal microstimulation (ISMS) in a rat with contusion injury to the thoracic spinal cord. The paper further presents results from a neurobiological study conducted in both normal and SCI rats to investigate the effect of various ISMS parameters on movement thresholds in the rat hindlimb. Coupled with proper signal-processing algorithms in the future for the transformation between the cortically recorded data and ISMS parameters, such a BMSI has the potential to facilitate functional recovery after an SCI by re-establishing corticospinal communication channels lost due to the injury.},
}
@article {pmid25570001,
year = {2014},
author = {Kimtan, T and Thupmongkol, J and Williams, JC and Thongpang, S},
title = {Printable and transparent micro-electrocorticography (μECoG) for optogenetic applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {482-485},
doi = {10.1109/EMBC.2014.6943633},
pmid = {25570001},
issn = {2694-0604},
mesh = {Electrocorticography/*instrumentation ; Electrodes ; Equipment Design ; Optogenetics/*instrumentation ; Printing ; },
abstract = {Micro-electrocorticography (μECoG) displays advantages over traditional invasive methods. The μECoG electrode can record neural activity with high spatial-temporal resolution and it can reduce implantation side effects (e.g. vascular and local-neuronal damage, tissue encapsulation, infection). In this study, we propose a printable transparent μECoG electrode for optogenetic applications by using ultrasonic microfluid printing technique. The device is based on poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT: PSS) as a conductive polymer, polydimethylsiloxane (PDMS) as an insulating polymer and poly(chloro-para-xylylene) (Parylene-C) as the device substrate. We focus on ultrasonic microfluid printing due to its low production cost, excellent material handling capability, and its customizable film thickness (down to 5-20 microns). The ultrasonic fluid-printed μECoG displays high spatial resolution and records simulated signal (0-200 Hz sine wave) effectively with low electrode impedance (50-200 kOhms@1kHz). The μECoG also shows good biocompatibility suitable for customizable chronic implants. This new neural interfacing device could be combined with optogenetics and Brain-Computer Interface (BCI) applications for a possible future use in neurological disease diagnosis and rehabilitations.},
}
@article {pmid25569985,
year = {2014},
author = {Kohler, F and Stieglitz, T and Schuettler, M},
title = {Morphological and electrochemical properties of an explanted PtIr electrode array after 15 months in vivo.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {418-421},
doi = {10.1109/EMBC.2014.6943617},
pmid = {25569985},
issn = {2694-0604},
mesh = {Animals ; Brain-Computer Interfaces ; Dielectric Spectroscopy ; *Electrochemistry ; *Electrodes, Implanted ; Electroencephalography ; Iridium/*chemistry ; Microscopy, Electron, Scanning ; Platinum/*chemistry ; Sheep ; },
abstract = {We investigated the morphological and electrochemical properties of an explanted laser-machined 32 channel electrocorticogram (ECoG) electrode array made of platinum-iridium and silicone rubber. It was connected to a wireless brain-computer interface (BCI) and implanted in a sheep for more than 15 months. Recordings and stimulations of cortical activity were conducted over the whole period on a regularly basis. Currently, this is the longest in vivo study for this type of ECoG electrode array. Results were compared with an unused electrode array of same dimensions, material and production method. Visual inspections revealed no significant material alterations, despite organic residuals which could be easily removed though. Electrochemical impedance measurements also attested proper long-term stability of magnitude and phase, the difference between explanted electrode contacts and those of the unused array were found negligible.},
}
@article {pmid25569911,
year = {2014},
author = {Dehzangi, O and Nathan, V and Zong, C and Lee, C and Kim, I and Jafari, R},
title = {A novel stimulation for multi-class SSVEP-based brain-computer interface using patterns of time-varying frequencies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {118-121},
doi = {10.1109/EMBC.2014.6943543},
pmid = {25569911},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Visual ; Photic Stimulation/*methods ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Steady-state visual evoked potential (SSVEP) has become one of the most widely employed modalities in online brain computer interface (BCI) because of its high signal-to-noise ratio. However, due to the limitations of brain physiology and the refresh rate of the display devices, the available stimulation frequencies that evoke strong SSVEPs are generally limited for practical applications. In this paper, we introduce a novel stimulation method using patterns of time-varying frequencies that can increase the number of visual stimuli with a fixed number of stimulation frequencies for use in multi-class SSVEP-based BCI systems. We then propose a probabilistic framework and investigate three approaches to detect different patterns of time-varying frequencies. The results confirmed that our proposed stimulation is a promising method for multi-class SSVEP-based BCI tasks. Our pattern detection approaches improved the detection performance significantly by extracting higher quality discriminative information from the input signal.},
}
@article {pmid25569888,
year = {2014},
author = {Onishi, A and Natsume, K},
title = {Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {26-29},
doi = {10.1109/EMBC.2014.6943520},
pmid = {25569888},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/methods ; *Evoked Potentials ; Humans ; },
abstract = {Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.},
}
@article {pmid25569887,
year = {2014},
author = {Tello, RM and Müller, SM and Bastos-Filho, T and Ferreira, A},
title = {Comparison between wire and wireless EEG acquisition systems based on SSVEP in an Independent-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2014},
number = {},
pages = {22-25},
doi = {10.1109/EMBC.2014.6943519},
pmid = {25569887},
issn = {2694-0604},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; Male ; Neurologic Examination ; Photic Stimulation ; Wireless Technology ; },
abstract = {This paper presents a comparison between two different technologies of acquisition systems (BrainNet36 and Emotiv Epoc) for an Independent-BCI based on Steady-State Visual Evoked Potential (SSVEP). Two stimuli separated by a viewing angle <; 1° were used. Multivariate Synchronization Index (MSI) technique was used as feature extractor and five subjects participated in the experiments. The class is obtained through a criterion of maxima. The left and right flicker stimuli were modulated at frequencies of 8.0 and 13.0 Hz, respectively. Acquisition via BrainNet system showed better results, obtaining the highest value for accuracy (100%) and the highest ITR (35.18 bits/min). This Independent-BCI is based on covert attention.},
}
@article {pmid25569757,
year = {2015},
author = {Tolstosheeva, E and Gordillo-González, V and Biefeld, V and Kempen, L and Mandon, S and Kreiter, AK and Lang, W},
title = {A multi-channel, flex-rigid ECoG microelectrode array for visual cortical interfacing.},
journal = {Sensors (Basel, Switzerland)},
volume = {15},
number = {1},
pages = {832-854},
pmid = {25569757},
issn = {1424-8220},
mesh = {Animals ; *Brain-Computer Interfaces ; Dielectric Spectroscopy ; Electroencephalography/*instrumentation ; Electrophysiological Phenomena ; Macaca mulatta ; *Microelectrodes ; Microtechnology ; Signal Processing, Computer-Assisted ; Sodium Chloride ; Time Factors ; Visual Cortex/*physiology ; },
abstract = {High-density electrocortical (ECoG) microelectrode arrays are promising signal-acquisition platforms for brain-computer interfaces envisioned, e.g., as high-performance communication solutions for paralyzed persons. We propose a multi-channel microelectrode array capable of recording ECoG field potentials with high spatial resolution. The proposed array is of a 150 mm2 total recording area; it has 124 circular electrodes (100, 300 and 500 µm in diameter) situated on the edges of concentric hexagons (min. 0.8 mm interdistance) and a skull-facing reference electrode (2.5 mm2 surface area). The array is processed as a free-standing device to enable monolithic integration of a rigid interposer, designed for soldering of fine-pitch SMD-connectors on a minimal assembly area. Electrochemical characterization revealed distinct impedance spectral bands for the 100, 300 and 500 µm-type electrodes, and for the array's own reference. Epidural recordings from the primary visual cortex (V1) of an awake Rhesus macaque showed natural electrophysiological signals and clear responses to standard visual stimulation. The ECoG electrodes of larger surface area recorded signals with greater spectral power in the gamma band, while the skull-facing reference electrode provided higher average gamma power spectral density (γPSD) than the common average referencing technique.},
}
@article {pmid25569368,
year = {2015},
author = {Reinfeldt, S and Östli, P and Håkansson, B and Taghavi, H and Eeg-Olofsson, M and Stalfors, J},
title = {Study of the feasible size of a bone conduction implant transducer in the temporal bone.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {36},
number = {4},
pages = {631-637},
doi = {10.1097/MAO.0000000000000682},
pmid = {25569368},
issn = {1537-4505},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Algorithms ; Bone Conduction/physiology ; Ear Canal/diagnostic imaging ; Feasibility Studies ; Female ; *Hearing Aids ; Humans ; Male ; Middle Aged ; *Prosthesis Design ; Radiographic Image Interpretation, Computer-Assisted ; Temporal Bone/*diagnostic imaging/*surgery ; Tomography, X-Ray Computed ; Transducers ; },
abstract = {HYPOTHESIS: The aim was to assess the temporal bone volume to determine the suitable size and position of a bone conduction implant (BCI) transducer.<br><br>BACKGROUND: A BCI transducer needs to be sufficiently small to fit in the mastoid portion of the temporal bone for a majority of patients. The anatomical geometry limits both the dimension of an implanted transducer and its positions in the temporal bone to provide a safe and simple surgery.<br><br>METHODS: Computed tomography (CT) scans of temporal bones from 22 subjects were virtually reconstructed. With an algorithm in MATLAB, the maximum transducer diameter as function of the maximum transducer depth in the temporal bone, and the most suitable position were calculated in all subjects.<br><br>RESULTS: An implanted transducer diameter of 16 mm inserted at a depth of 4 mm statistically fitted 95% of the subjects. If changing the transducer diameter to 12 mm, a depth of 6 mm would fit in 95% of the subjects. The most suitable position was found to be around 20 mm behind the ear canal.<br><br>CONCLUSION: The present BCI transducer casing, used in ongoing clinical trials, was designed from the results in this study, demonstrating that the present BCI transducer casing (largest diameter [diagonal]: 15.5 mm, height: 6.4 mm) will statistically fit more than 95% of the subjects. Hence, the present BCI transducer is concluded to be sufficiently small to fit most normal-sized temporal bones and should be placed approximately 20 mm behind the ear canal.},
}
@article {pmid25566035,
year = {2014},
author = {Charland-Verville, V and Lesenfants, D and Sela, L and Noirhomme, Q and Ziegler, E and Chatelle, C and Plotkin, A and Sobel, N and Laureys, S},
title = {Detection of response to command using voluntary control of breathing in disorders of consciousness.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1020},
pmid = {25566035},
issn = {1662-5161},
abstract = {BACKGROUND: Detecting signs of consciousness in patients in a vegetative state/unresponsive wakefulness syndrome (UWS/VS) or minimally conscious state (MCS) is known to be very challenging. Plotkin et al. (2010) recently showed the possibility of using a breathing-controlled communication device in patients with locked in syndrome. We here aim to test a breathing-based "sniff controller" that could be used as an alternative diagnostic tool to evaluate response to command in severely brain damaged patients with chronic disorders of consciousness (DOC).<br><br>METHODS: Twenty-five DOC patients were included. Patients' resting breathing-amplitude was measured during a 5 min resting condition. Next, they were instructed to end the presentation of a music sequence by sniffing vigorously. An automated detection of changes in breathing amplitude (i.e., >1.5 SD of resting) ended the music and hence provided positive feedback to the patient.<br><br>RESULTS: None of the 11 UWS/VS patients showed a sniff-based response to command. One out of 14 patients with MCS was able to willfully modulate his breathing pattern to answer the command on 16/19 trials (accuracy 84%). Interestingly, this patient failed to show any other motor response to command.<br><br>DISCUSSION: We here illustrate the possible interest of using breathing-dependent response to command in the detection of residual cognition in patients with DOC after severe brain injury.},
}
@article {pmid25566029,
year = {2014},
author = {Horki, P and Bauernfeind, G and Klobassa, DS and Pokorny, C and Pichler, G and Schippinger, W and Müller-Putz, GR},
title = {Detection of mental imagery and attempted movements in patients with disorders of consciousness using EEG.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1009},
pmid = {25566029},
issn = {1662-5161},
abstract = {Further development of an EEG based communication device for patients with disorders of consciousness (DoC) could benefit from addressing the following gaps in knowledge-first, an evaluation of different types of motor imagery; second, an evaluation of passive feet movement as a mean of an initial classifier setup; and third, rapid delivery of biased feedback. To that end we investigated whether complex and/or familiar mental imagery, passive, and attempted feet movement can be reliably detected in patients with DoC using EEG recordings, aiming to provide them with a means of communication. Six patients in a minimally conscious state (MCS) took part in this study. The patients were verbally instructed to perform different mental imagery tasks (sport, navigation), as well as attempted feet movements, to induce distinctive event-related (de)synchronization (ERD/S) patterns in the EEG. Offline classification accuracies above chance level were reached in all three tasks (i.e., attempted feet, sport, and navigation), with motor tasks yielding significant (p < 0.05) results more often than navigation (sport: 10 out of 18 sessions; attempted feet: 7 out of 14 sessions; navigation: 4 out of 12 sessions). The passive feet movements, evaluated in one patient, yielded mixed results: whereas time-frequency analysis revealed task-related EEG changes over neurophysiological plausible cortical areas, the classification results were not significant enough (p < 0.05) to setup an initial classifier for the detection of attempted movements. Concluding, the results presented in this study are consistent with the current state of the art in similar studies, to which we contributed by comparing different types of mental tasks, notably complex motor imagery and attempted feet movements, within patients. Furthermore, we explored new venues, such as an evaluation of passive feet movement as a mean of an initial classifier setup, and rapid delivery of biased feedback.},
}
@article {pmid25566028,
year = {2014},
author = {Ros, T and J Baars, B and Lanius, RA and Vuilleumier, P},
title = {Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {1008},
pmid = {25566028},
issn = {1662-5161},
abstract = {Neurofeedback (NFB) is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB. The objective is to provide a firmly neurophysiological account of NFB, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a "black box". To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from "bottom-up" mechanisms of neural synchronization, followed by "top-down" regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of NFB in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape(s). The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability (i.e., self-organised criticality (SOC)).},
}
@article {pmid25565984,
year = {2014},
author = {Kennedy, P},
title = {Brain-machine interfaces as a challenge to the "moment of singularity".},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {213},
pmid = {25565984},
issn = {1662-5137},
}
@article {pmid25565947,
year = {2014},
author = {Morishita, S and Sato, K and Watanabe, H and Nishimura, Y and Isa, T and Kato, R and Nakamura, T and Yokoi, H},
title = {Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {417},
pmid = {25565947},
issn = {1662-4548},
abstract = {Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.},
}
@article {pmid27170898,
year = {2015},
author = {Baali, H and Khorshidtalab, A and Mesbah, M and Salami, MJ},
title = {A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {3},
number = {},
pages = {2100108},
pmid = {27170898},
issn = {2168-2372},
abstract = {In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain-computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling's [Formula: see text] statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.},
}
@article {pmid27148554,
year = {2015},
author = {Milovanovic, I and Robinson, R and Fetz, EE and Moritz, CT},
title = {Simultaneous and independent control of a brain-computer interface and contralateral limb movement.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {2},
number = {4},
pages = {174-185},
pmid = {27148554},
issn = {2326-263X},
support = {P51 RR000166/RR/NCRR NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; },
abstract = {Toward expanding the population of potential BCI users to the many individuals with lateralized cortical stroke, here we examined whether the cortical hemisphere controlling ongoing movements of the contralateral limb can simultaneously generate signals to control a BCI. A monkey was trained to perform a simultaneous BCI and manual control task designed to test whether one hemisphere could effectively differentiate its output and provide independent control of two tasks. Pairs of well-isolated single units were used to control a BCI cursor in one dimension, while isometric wrist torque of the contralateral forelimb controlled the cursor in a second dimension. The monkey could independently modulate cortical units and contralateral wrist torque regardless of the strength of directional tuning of the units controlling the BCI. When the presented targets required explicit decoupling of unit activity and wrist torque, directionally tuned units exhibited significantly less efficient cursor trajectories compared to when unit activity and wrist torque could remain correlated. The results indicate that neural activity from a single hemisphere can be effectively decoupled to simultaneously control a BCI and ongoing limb movement, suggesting that BCIs may be a viable future treatment for individuals with lateralized cortical stroke.},
}
@article {pmid26949710,
year = {2015},
author = {Dijkstra, K and Brunner, P and Gunduz, A and Coon, W and Ritaccio, AL and Farquhar, J and Schalk, G},
title = {Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {2},
number = {4},
pages = {161-173},
pmid = {26949710},
issn = {2326-263X},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70-170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech.},
}
@article {pmid27202134,
year = {2014},
author = {Kosaner, M and Urban, M},
title = {The Decision Making Process In Receiving Bone Conduction Implants (Bci) For Single Sided Deafness.},
journal = {Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research},
volume = {17},
number = {7},
pages = {A611},
doi = {10.1016/j.jval.2014.08.2143},
pmid = {27202134},
issn = {1524-4733},
}
@article {pmid26709323,
year = {2014},
author = {Dura-Bernal, S and Chadderdon, GL and Neymotin, SA and Francis, JT and Lytton, WW},
title = {Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm.},
journal = {Pattern recognition letters},
volume = {36},
number = {},
pages = {204-212},
pmid = {26709323},
issn = {0167-8655},
support = {R01 MH086638/MH/NIMH NIH HHS/United States ; },
abstract = {Brain-machine interfaces can greatly improve the performance of prosthetics. Utilizing biomimetic neuronal modeling in brain machine interfaces (BMI) offers the possibility of providing naturalistic motor-control algorithms for control of a robotic limb. This will allow finer control of a robot, while also giving us new tools to better understand the brain's use of electrical signals. However, the biomimetic approach presents challenges in integrating technologies across multiple hardware and software platforms, so that the different components can communicate in real-time. We present the first steps in an ongoing effort to integrate a biomimetic spiking neuronal model of motor learning with a robotic arm. The biomimetic model (BMM) was used to drive a simple kinematic two-joint virtual arm in a motor task requiring trial-and-error convergence on a single target. We utilized the output of this model in real time to drive mirroring motion of a Barrett Technology WAM robotic arm through a user datagram protocol (UDP) interface. The robotic arm sent back information on its joint positions, which was then used by a visualization tool on the remote computer to display a realistic 3D virtual model of the moving robotic arm in real time. This work paves the way towards a full closed-loop biomimetic brain-effector system that can be incorporated in a neural decoder for prosthetic control, to be used as a platform for developing biomimetic learning algorithms for controlling real-time devices.},
}
@article {pmid27170884,
year = {2014},
author = {Yu, YH and Lu, SW and Liao, LD and Lin, CT},
title = {Design, Fabrication, and Experimental Validation of Novel Flexible Silicon-Based Dry Sensors for Electroencephalography Signal Measurements.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {2},
number = {},
pages = {2700307},
pmid = {27170884},
issn = {2168-2372},
abstract = {Many commercially available electroencephalography (EEG) sensors, including conventional wet and dry sensors, can cause skin irritation and user discomfort owing to the foreign material. The EEG products, especially sensors, highly prioritize the comfort level during devices wear. To overcome these drawbacks for EEG sensors, this paper designs Societe Generale de Surveillance S [Formula: see text] A [Formula: see text] (SGS)-certified, silicon-based dry-contact EEG sensors (SBDSs) for EEG signal measurements. According to the SGS testing report, SBDSs extract does not irritate skin or induce noncytotoxic effects on L929 cells according to ISO10993-5. The SBDS is also lightweight, flexible, and nonirritating to the skin, as well as capable of easily fitting to scalps without any skin preparation or use of a conductive gel. For forehead and hairy sites, EEG signals can be measured reliably with the designed SBDSs. In particular, for EEG signal measurements at hairy sites, the acicular and flexible design of SBDS can push the hair aside to achieve satisfactory scalp contact, as well as maintain low skin-electrode interface impedance. Results of this paper demonstrate that the proposed sensors perform well in the EEG measurements and are feasible for practical applications.},
}
@article {pmid27170864,
year = {2014},
author = {Ejserholm, F and Köhler, P and Granmo, M and Schouenborg, J and Bengtsson, M and Wallman, L},
title = {μ-Foil Polymer Electrode Array for Intracortical Neural Recordings.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {2},
number = {},
pages = {1500207},
pmid = {27170864},
issn = {2168-2372},
abstract = {We have developed a multichannel electrode array-termed [Formula: see text]-foil-that comprises ultrathin and flexible electrodes protruding from a thin foil at fixed distances. In addition to allowing some of the active sites to reach less compromised tissue, the barb-like protrusions that also serves the purpose of anchoring the electrode array into the tissue. This paper is an early evaluation of technical aspects and performance of this electrode array in acute in vitro/in vivo experiments. The interface impedance was reduced by up to two decades by electroplating the active sites with platinum black. The platinum black also allowed for a reduced phase lag for higher frequency components. The distance between the protrusions of the electrode array was tailored to match the architecture of the rat cerebral cortex. In vivo acute measurements confirmed a high signal-to-noise ratio for the neural recordings, and no significant crosstalk between recording channels.},
}
@article {pmid26271130,
year = {2014},
author = {Park, C and Took, CC and Mandic, DP},
title = {Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {1},
pages = {1-10},
doi = {10.1109/TNSRE.2013.2294903},
pmid = {26271130},
issn = {1558-0210},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {A novel augmented complex-valued common spatial pattern (CSP) algorithm is introduced in order to cater for general complex signals with noncircular probability distributions. This is a typical case in multichannel electroencephalogram (EEG), due to the power difference or correlation between the data channels, yet current methods only cater for a very restrictive class of circular data. The proposed complex-valued CSP algorithms account for the generality of complex noncircular data, by virtue of the use of augmented complex statistics and the strong-uncorrelating transform (SUT). Depending on the degree of power difference of complex signals, the analysis and simulations show that the SUT based algorithm maximizes the inter-class difference between two motor imagery tasks. Simulations on both synthetic noncircular sources and motor imagery experiments using real-world EEG support the approach.},
}
@article {pmid26150963,
year = {2014},
author = {Little, G and Boe, S and Bardouille, T},
title = {Head movement compensation in real-time magnetoencephalographic recordings.},
journal = {MethodsX},
volume = {1},
number = {},
pages = {275-282},
pmid = {26150963},
issn = {2215-0161},
abstract = {Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.•Data acquisition•Head position estimation•Source localization•Real-time source estimation This work explains the technical details and validates each of these steps.},
}
@article {pmid26056608,
year = {2014},
author = {Mikołajewska, E and Mikołajewski, D},
title = {Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness.},
journal = {Military Medical Research},
volume = {1},
number = {},
pages = {14},
pmid = {26056608},
issn = {2095-7467},
abstract = {Disorders of consciousness (DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces (BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.},
}
@article {pmid25726806,
year = {2014},
author = {Kotov, SV and Turbina, LG and Bobrov, PD and Frolov, AA and Pavlova, OG and Kurganskaia, ME and Biriukova, EV},
title = {[Rehabilitation of post stroke patients using a bioengineering system "brain-computer interface + exoskeleton"].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {114},
number = {12 Pt 2},
pages = {66-72},
doi = {10.17116/jnevro201411412266-71},
pmid = {25726806},
issn = {1997-7298},
abstract = {Objective. To investigate the possibility of using a bioengineering system, which includes an electroencephalograph and a personal computer with a software for synchronous data transmission, recognition and classification of EEG signals, development of directions for intended actions in real time in the combination with the hand exoskeleton (the bioengineering system "brain-computer interface + exoskeleton"), in motor rehabilitation of post stroke patients with paresis of the upper extremity. Material and methods. Brain-computer interface is a promising field of neurorehabilitation. Rehabilitation treatment, including 8-10 sessions, was conducted in 5 patients with paresis of the upper extremity. All patients had large MRI lesions in cortical/subcortical areas. Results. Positive changes in neurological status measured with the NIHSS, a significant increase in the volume and power of movements in the paretic hand, improvement of coordination and slight decrease in the level of spasticity were found after the treatment. There was an increase in daily activities measured with the Barthel index, mostly due to the improvement of fine motor skills. The level of disability assessed by the modified Rankin scale was changed significantly. Conclusion. The use of "brain-computer interface + exoskeleton" in the rehabilitation of post stroke patients with hand paresis provided positive results that would need to be verified in further studies.},
}
@article {pmid26317095,
year = {2013},
author = {Sarmah, E and Kennedy, P},
title = {Detecting Silent Vocalizations in a Locked-In Subject.},
journal = {Neuroscience journal},
volume = {2013},
number = {},
pages = {594624},
pmid = {26317095},
issn = {2314-4262},
support = {R44 DC007050/DC/NIDCD NIH HHS/United States ; },
abstract = {Problem Addressed. Decoding of silent vocalization would be enhanced by detecting vocalization onset. This is necessary in order to improve decoding of neural firings and thus synthesize near conversational speech in locked-in subjects implanted with brain computer interfacing devices. Methodology. Cortical recordings were obtained during attempts at inner speech in a mute and paralyzed subject (ER) implanted with a recording electrode to detect and analyze lower beta band peaks meeting the criterion of a minimum 0.2% increase in the power spectrum density (PSD). To provide supporting data, three speaking subjects were used in a similar testing paradigm using EEG signals recorded over the speech area. Results. Conspicuous lower beta band peaks were identified around the time of assumed speech onset. The correlations between single unit firings, recorded at the same time as the continuous neural signals, were found to increase after the lower beta band peaks as compared to before the peaks. Studies in the nonparalyzed control individuals suggested that the lower beta band peaks were related to the movement of the articulators of speech (tongue, jaw, and lips), not to higher order speech processes. Significance and Potential Impact. The results indicate that the onset of silent and overt speech is associated with a sharp peak in lower beta band activity-an important step in the development of a speech prosthesis. This raises the possibility of using these peaks in online applications to assist decoding paradigms being developed to decode speech from neural signal recordings in mute humans.},
}
@article {pmid26816225,
year = {2010},
author = {Emet, M and Akoz, A and Aslan, S and Saritas, A and Cakir, Z and Acemoglu, H},
title = {Assessment of cardiac injury in patients with blunt chest trauma.},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {36},
number = {5},
pages = {441-447},
pmid = {26816225},
issn = {1863-9933},
abstract = {BACKGROUND: There has been difficulty in the appropriate determination of blunt cardiac injury (BCI) related to blunt thoracic trauma (BTT). The aim of this study is to assess BCI and the effectiveness of diagnostic tests in BTT in patients admitted to the emergency department (ED).<br><br>METHODS: Eighty-eight patients with suspected myocardial injury in BTT were enrolled. The diagnostic criteria for BCI were: elevation of troponin&#xa0;I, arrhythmia requiring treatment, unexplained low voltage on electrocardiography (ECG), unexplained hypotension requiring vasopressor, cardiogenic shock requiring inotropes, and transthoracic echocardiographic (TTE) findings suggestive of BCI. Patients with arrhythmia in the medical history, congestive heart failure, ischemic heart disease, history of cardiac surgery, and those <16&#xa0;years of age were excluded.<br><br>RESULTS: The BCI rate was 25% in thoracic injuries. The sensitivity and specificity of troponin&#xa0;I, creatine kinase-MB fraction (CK-MB)/creatine kinase (CK) ratio, ECG, and CK-MB/CK ratio plus ECG were 68% and 100%, 50% and 53%, 54.5% and 72%, and 59% and 33%, respectively. Frequency of palpitation, initial CK-MB levels, initial heart rate, frequency of pulmonary contusion, abnormal ECG, and mortality rate were significantly higher in patients with BCI compared with patients without BCI. Pulmonary contusion, accompanying palpitation, Glasgow coma scale (GCS) &#x2264;13, and abnormal ECG findings were important independent parameters increasing the likelihood of BCI on univariate analysis comparing patients with and without BCI.<br><br>CONCLUSION: Indicators such as cardiac enzymes and ECG have low sensitivity and specificity when used alone. The reliability of ECG in the diagnosis of BCI decreases in the later hours of trauma.},
}
@article {pmid26815865,
year = {2010},
author = {Ismailov, RM},
title = {Trauma Associated with Cardiac Conduction Abnormalities: Population-Based Perspective, Mechanism and Review of Literature.},
journal = {European journal of trauma and emergency surgery : official publication of the European Trauma Society},
volume = {36},
number = {3},
pages = {227-232},
pmid = {26815865},
issn = {1863-9933},
abstract = {OBJECTIVES: Various cardiac conduction abnormalities have been described as being a result of trauma in many case reports. The aim of this research was to look at the association between trauma (thoracic and cardiac) and conduction abnormalities in a large hospitalized population.<br><br>METHODS: Cases diagnosed with trauma and various cardiac conduction disorders were identified based on ICD-9-CM discharge diagnoses from 986 acute general hospitals across 33 states in 2001.<br><br>RESULTS: Independent of potential confounding factors, discharge for blunt cardiac injury (BCI) was associated with a threefold increased risk for cardiac conduction abnormalities (95% confidence interval 2.45-4.51) during hospitalization in 2001. Both BCI and thoracic trauma had a significant association with right bundle branch block (RBBB) in this study (OR 6.04; 95% confidence interval (CI) 3.77-9.67 and OR 1.75; 95% CI 1.38-2.23 respectively).<br><br>CONCLUSIONS: The results of this study demonstrate the impact of trauma on cardiac conduction abnormalities. This study represents an attempt to consider a mechanism of a complex traumatic cardiac event from a population-based perspective, and may improve the prognosis for patients diagnosed with cardiac or thoracic injuries.},
}
@article {pmid25565945,
year = {2014},
author = {Roset, SA and Gant, K and Prasad, A and Sanchez, JC},
title = {An adaptive brain actuated system for augmenting rehabilitation.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {415},
pmid = {25565945},
issn = {1662-4548},
abstract = {For people living with paralysis, restoration of hand function remains the top priority because it leads to independence and improvement in quality of life. In approaches to restore hand and arm function, a goal is to better engage voluntary control and counteract maladaptive brain reorganization that results from non-use. Standard rehabilitation augmented with developments from the study of brain-computer interfaces could provide a combined therapy approach for motor cortex rehabilitation and to alleviate motor impairments. In this paper, an adaptive brain-computer interface system intended for application to control a functional electrical stimulation (FES) device is developed as an experimental test bed for augmenting rehabilitation with a brain-computer interface. The system's performance is improved throughout rehabilitation by passive user feedback and reinforcement learning. By continuously adapting to the user's brain activity, similar adaptive systems could be used to support clinical brain-computer interface neurorehabilitation over multiple days.},
}
@article {pmid25565937,
year = {2014},
author = {Dutta, A and Lahiri, U and Das, A and Nitsche, MA and Guiraud, D},
title = {Post-stroke balance rehabilitation under multi-level electrotherapy: a conceptual review.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {403},
pmid = {25565937},
issn = {1662-4548},
abstract = {Stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to respond to intrinsic or extrinsic stimuli by reorganizing its structure, function, and connections is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. It has been shown that active cortical participation in a closed-loop brain machine interface (BMI) can induce neuroplasticity in cortical networks where the brain acts as a controller, e.g., during a visuomotor task. Here, the motor task can be assisted with neuromuscular electrical stimulation (NMES) where the BMI will act as a real-time decoder. However, the cortical control and induction of neuroplasticity in a closed-loop BMI is also dependent on the state of brain, e.g., visuospatial attention during visuomotor task performance. In fact, spatial neglect is a hidden disability that is a common complication of stroke and is associated with prolonged hospital stays, accidents, falls, safety problems, and chronic functional disability. This hypothesis and theory article presents a multi-level electrotherapy paradigm toward motor rehabilitation in virtual reality that postulates that while the brain acts as a controller in a closed-loop BMI to drive NMES, the state of brain can be can be altered toward improvement of visuomotor task performance with non-invasive brain stimulation (NIBS). This leads to a multi-level electrotherapy paradigm where a virtual reality-based adaptive response technology is proposed for post-stroke balance rehabilitation. In this article, we present a conceptual review of the related experimental findings.},
}
@article {pmid25560222,
year = {2015},
author = {Bamdad, M and Zarshenas, H and Auais, MA},
title = {Application of BCI systems in neurorehabilitation: a scoping review.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {10},
number = {5},
pages = {355-364},
doi = {10.3109/17483107.2014.961569},
pmid = {25560222},
issn = {1748-3115},
mesh = {*Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Evoked Potentials, Visual ; Humans ; Mobility Limitation ; Movement ; Neurological Rehabilitation/*instrumentation ; Speech Disorders/rehabilitation ; User-Computer Interface ; },
abstract = {PURPOSE: To review various types of electroencephalographic activities of the brain and present an overview of brain-computer interface (BCI) systems' history and their applications in rehabilitation.<br><br>METHODS: A scoping review of published English literature on BCI application in the field of rehabilitation was undertaken. IEEE Xplore, ScienceDirect, Google Scholar and Scopus databases were searched since inception up to August 2012. All experimental studies published in English and discussed complete cycle of the BCI process was included in the review.<br><br>RESULTS AND DISCUSSION: In total, 90 articles met the inclusion criteria and were reviewed. Various approaches that improve the accuracy and performance of BCI systems were discussed. Based on BCI's clinical application, reviewed articles were categorized into three groups: motion rehabilitation, speech rehabilitation and virtual reality control (VRC). Almost half of the reviewed papers (48%) concentrated on VRC. Speech rehabilitation and motion rehabilitation made up 33% and 19% of the reviewed papers, respectively. Among different types of electroencephalography signals, P300, steady state visual evoked potentials and motor imagery signals were the most common.<br><br>CONCLUSIONS: This review discussed various applications of BCI in rehabilitation and showed how BCI can be used to improve the quality of life for people with neurological disabilities. It will develop and promote new models of communication and finally, will create an accurate, reliable, online communication between human brain and computer and reduces the negative effects of external stimuli on BCI performance. Implications for Rehabilitation The field of brain-computer interfaces (BCI) is rapidly advancing and it is expected to fulfill a critical role in rehabilitation of neurological disorders and in movement restoration in the forthcoming years. In the near future, BCI has notable potential to become a major tool used by people with disabilities to control locomotion and communicate with surrounding environment and, consequently, improve the quality of life for many affected persons. Electrical field recording at the scalp (i.e. electroencephalography) is the most likely method to be of practical value for clinical use as it is simple and non-invasive. However, some aspects need future improvements, such as the ability to separate close imagery signal (motion of extremities and phalanges that are close together).},
}
@article {pmid25551155,
year = {2015},
author = {Butler, VJ and Branicky, R and Yemini, E and Liewald, JF and Gottschalk, A and Kerr, RA and Chklovskii, DB and Schafer, WR},
title = {A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans.},
journal = {Journal of the Royal Society, Interface},
volume = {12},
number = {102},
pages = {20140963},
pmid = {25551155},
issn = {1742-5662},
support = {103784/WT_/Wellcome Trust/United Kingdom ; MC_U105185857/MRC_/Medical Research Council/United Kingdom ; /HHMI/Howard Hughes Medical Institute/United States ; T32 MH015174/MH/NIMH NIH HHS/United States ; P40 OD010440/OD/NIH HHS/United States ; MC-A022-5PB91/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Alleles ; Animals ; Behavior, Animal ; Biomechanical Phenomena ; Caenorhabditis elegans/*physiology ; Calcium/metabolism ; Crosses, Genetic ; Electrophysiological Phenomena ; Green Fluorescent Proteins/metabolism ; Image Processing, Computer-Assisted ; Linear Models ; Microscopy, Fluorescence ; Models, Biological ; Motor Neurons/metabolism ; Movement ; Muscles/*physiology ; Neurons/metabolism ; Plasmids/metabolism ; Proprioception ; *Swimming ; },
abstract = {Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.},
}
@article {pmid25548890,
year = {2015},
author = {Rainsbury, JW and Williams, BA and Gulliver, M and Morris, DP},
title = {Preoperative headband assessment for semi-implantable bone conduction hearing devices in conductive hearing loss: is it useful or misleading?.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {36},
number = {2},
pages = {e58-62},
doi = {10.1097/MAO.0000000000000695},
pmid = {25548890},
issn = {1537-4505},
mesh = {Adult ; *Bone Conduction ; Female ; *Hearing Aids ; Hearing Loss, Conductive/*surgery ; Hearing Tests ; Humans ; Male ; Middle Aged ; Ossicular Replacement ; Otitis Media/*surgery ; *Preoperative Care ; Retrospective Studies ; Speech Perception ; Treatment Outcome ; },
abstract = {OBJECTIVE: To establish whether preoperative assessment using a conventional, percutaneous bone conducting implant (pBCI) processor on a headband accurately represents postoperative performance of a semi-implantable BCI (siBCI).<br><br>STUDY DESIGN: Retrospective case series.<br><br>SETTING: Tertiary otology unit.<br><br>PATIENTS: Five patients with chronic otitis media (implanted unilaterally) and one with bilateral congenital ossicular fixation (implanted bilaterally).<br><br>INTERVENTION(S): Semi-implantable bone conduction hearing implant.<br><br>MAIN OUTCOME MEASURE(S): Functional hearing gain; preoperative (headband) versus postoperative (aided) speech discrimination; unaided bone conduction (BC) versus postoperative (aided) soundfield threshold.<br><br>RESULTS: Significant functional gain was seen at all frequencies (one-tailed t test p G 0.01; n = 7). There was a 50 dB improvement in median speech reception threshold (SRT) from 70 dB unaided to 20 dB aided. Compared to the preoperative BC, aided siBCI thresholds were worse at 0.5 kHz, but at frequencies from 1 to 6 kHz, the siBCI closely matched the bone curve (p G 0.01). The siBCI performed better than both pBCI processors on a headband at 3 to 4 kHz, except 1 kHz (p G 0.01).<br><br>CONCLUSIONS: BC thresholds may be a better indicator of implant performance than headband assessment. Candidacy assessment for siBCI implantation that relies on headband testing with pBCI processors should be interpreted with caution because the headband may under-represent the implanted device. This seems to be especially true at 3 kHz and above and may make it difficult for surgeons to conduct accurate informed consent discussions with patients about the realistic anticipated outcomes and benefits of the procedure.},
}
@article {pmid25547267,
year = {2015},
author = {Sonkin, KM and Stankevich, LA and Khomenko, JG and Nagornova, ZV and Shemyakina, NV},
title = {Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand.},
journal = {Artificial intelligence in medicine},
volume = {63},
number = {2},
pages = {107-117},
doi = {10.1016/j.artmed.2014.12.006},
pmid = {25547267},
issn = {1873-2860},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Fingers ; Humans ; Imagination/*physiology ; Male ; Movement ; *Neural Networks, Computer ; *Support Vector Machine ; Thumb ; },
abstract = {OBJECTIVE: This study aimed to find effective approaches to electroencephalographic (EEG) signal analysis and resolve problems of real and imaginary finger movement pattern recognition and categorization for one hand.<br><br>METHODS AND MATERIALS: Eight right-handed subjects (mean age 32.8 [SD=3.3] years) participated in the study, and activity from sensorimotor zones (central and contralateral to the movements/imagery) was recorded for EEG data analysis. In our study, we explored the decoding accuracy of EEG signals using real and imagined finger (thumb/index of one hand) movements using artificial neural network (ANN) and support vector machine (SVM) algorithms for future brain-computer interface (BCI) applications.<br><br>RESULTS: The decoding accuracy of the SVM based on a Gaussian radial basis function linearly increased with each trial accumulation (mean: 45%, max: 62% with 20 trial summarizations), and the decoding accuracy of the ANN was higher when single-trial discrimination was applied (mean: 38%, max: 42%). The chosen approaches of EEG signal discrimination demonstrated differential sensitivity to data accumulation. Additionally, the time responses varied across subjects and inside sessions but did not influence the discrimination accuracy of the algorithms.<br><br>CONCLUSION: This work supports the feasibility of the approach, which is presumed suitable for one-hand finger movement (real and imaginary) decoding. These results could be applied in the elaboration of multiclass BCI systems.},
}
@article {pmid25546343,
year = {2015},
author = {Varadharajan, S and Winiwarter, S and Carlsson, L and Engkvist, O and Anantha, A and Kogej, T and Fridén, M and Stålring, J and Chen, H},
title = {Exploring in silico prediction of the unbound brain-to-plasma drug concentration ratio: model validation, renewal, and interpretation.},
journal = {Journal of pharmaceutical sciences},
volume = {104},
number = {3},
pages = {1197-1206},
doi = {10.1002/jps.24301},
pmid = {25546343},
issn = {1520-6017},
mesh = {Animals ; Blood-Brain Barrier/*metabolism ; *Capillary Permeability ; *Computer Simulation ; Humans ; *Models, Biological ; Pharmaceutical Preparations/administration &amp; dosage/*blood ; *Pharmacokinetics ; Protein Binding ; Reproducibility of Results ; Support Vector Machine ; },
abstract = {Recently, we built an in silico model to predict the unbound brain-to-plasma concentration ratio (Kp,uu,brain), a measure of the distribution of a compound between the blood plasma and the brain. Here, we validate the previous model with new additional data points expanding the chemical space and use that data also to renew the model. The model building process was similar to our previous approach; however, a new set of descriptors, molecular signatures, was included to facilitate the model interpretation from a structure perspective. The best consensus model shows better predictive power than the previous model (R(2) = 0.6 vs. R(2) = 0.53, when the same 99 compounds were used as test set). The two-class classification accuracy increased from 76% using the previous model to 81%. Furthermore, the atom-summarized gradient based on molecular signature descriptors was proposed as an interesting new approach to interpret the Kp,uu,brain machine learning model and scrutinize structure Kp,uu,brain relationships for investigated compounds.},
}
@article {pmid25545500,
year = {2014},
author = {Hotson, G and Fifer, MS and Acharya, S and Benz, HL and Anderson, WS and Thakor, NV and Crone, NE},
title = {Coarse electrocorticographic decoding of ipsilateral reach in patients with brain lesions.},
journal = {PloS one},
volume = {9},
number = {12},
pages = {e115236},
pmid = {25545500},
issn = {1932-6203},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; T32 EB003383/EB/NIBIB NIH HHS/United States ; 5T32EB003383-08/EB/NIBIB NIH HHS/United States ; 3R01NS0405956-09S1/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Arm/innervation/physiopathology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Motor Cortex/physiopathology ; *Movement ; Paralysis/*physiopathology ; Somatosensory Cortex/physiopathology ; Stroke/*physiopathology ; },
abstract = {In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control. We found that the first principal component (PC) of unconstrained, naturalistic reaching movements of the upper limb could be decoded from ipsilateral ECoG using a linear model. ECoG signal features yielding the best decoding accuracy were different across subjects. Performance saturated with very few input features. Decoding performances of 0.77, 0.73, and 0.66 (median Pearson's r between the predicted and actual first PC of movement using nine signal features) were achieved in the three subjects. The performance achieved here with small numbers of electrodes and computationally simple decoding algorithms suggests that it may be possible to control a BMI using ECoG recorded from damaged sensorimotor brain systems.},
}
@article {pmid25542350,
year = {2015},
author = {Mulliken, GH and Bichot, NP and Ghadooshahy, A and Sharma, J and Kornblith, S and Philcock, M and Desimone, R},
title = {Custom-fit radiolucent cranial implants for neurophysiological recording and stimulation.},
journal = {Journal of neuroscience methods},
volume = {241},
number = {},
pages = {146-154},
pmid = {25542350},
issn = {1872-678X},
support = {EY017291/EY/NEI NIH HHS/United States ; EY017292/EY/NEI NIH HHS/United States ; R01 EY017292/EY/NEI NIH HHS/United States ; EY020692/EY/NEI NIH HHS/United States ; R01 EY017921/EY/NEI NIH HHS/United States ; F32 EY020692/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Benzophenones ; *Biocompatible Materials/chemistry ; Electric Stimulation/instrumentation/methods ; *Ketones/chemistry ; Macaca mulatta ; Magnetic Resonance Imaging/*instrumentation/methods ; *Polyethylene Glycols/chemistry ; Polymers ; *Prostheses and Implants ; Skull/anatomy &amp; histology ; },
abstract = {BACKGROUND: Recording and manipulating neural activity in awake behaving animal models requires long-term implantation of cranial implants that must address a variety of design considerations, which include preventing infection, minimizing tissue damage, mechanical strength of the implant, and MRI compatibility.<br><br>NEW METHOD: Here we address these issues by designing legless, custom-fit cranial implants using structural MRI-based reconstruction of the skull and that are made from carbon-reinforced PEEK.<br><br>RESULTS: We report several novel custom-fit radiolucent implant designs, which include a legless recording chamber, a legless stimulation chamber, a multi-channel microdrive and a head post. The fit to the skull was excellent in all cases, with no visible gaps between the base of the implants and the skull. The wound margin was minimal in size and showed no sign of infection or skin recession.<br><br>Cranial implants used for neurophysiological investigation in awake behaving animals often employ methyl methacrylate (MMA) to serve as a bonding agent to secure the implant to the skull. Other designs rely on radially extending legs to secure the implant. Both of these methods have significant drawbacks. MMA is toxic to bone and frequently leads to infection while radially extending legs cause the skin to recede away from the implant, ultimately exposing bone and proliferating granulation tissue.<br><br>CONCLUSIONS: These radiolucent implants constitute a set of technologies suitable for reliable long-term recording, which minimize infection and tissue damage.},
}
@article {pmid25541564,
year = {2014},
author = {Sundararajan, L},
title = {Mind, Machine, and Creativity: An Artist's Perspective.},
journal = {The Journal of creative behavior},
volume = {48},
number = {2},
pages = {136-151},
pmid = {25541564},
issn = {0022-0175},
abstract = {Harold Cohen is a renowned painter who has developed a computer program, AARON, to create art. While AARON has been hailed as one of the most creative AI programs, Cohen consistently rejects the claims of machine creativity. Questioning the possibility for AI to model human creativity, Cohen suggests in so many words that the human mind takes a different route to creativity, a route that privileges the relational, rather than the computational, dimension of cognition. This unique perspective on the tangled web of mind, machine, and creativity is explored by an application of three relational models of the mind to an analysis of Cohen's talks and writings, which are available on his website: www.aaronshome.com.},
}
@article {pmid25541187,
year = {2015},
author = {Engemann, DA and Gramfort, A},
title = {Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.},
journal = {NeuroImage},
volume = {108},
number = {},
pages = {328-342},
doi = {10.1016/j.neuroimage.2014.12.040},
pmid = {25541187},
issn = {1095-9572},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Magnetoencephalography/*methods ; *Models, Neurological ; *Signal Processing, Computer-Assisted ; },
abstract = {Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals.},
}
@article {pmid25541081,
year = {2015},
author = {Patel, VL and Kannampallil, TG},
title = {Cognitive informatics in biomedicine and healthcare.},
journal = {Journal of biomedical informatics},
volume = {53},
number = {},
pages = {3-14},
doi = {10.1016/j.jbi.2014.12.007},
pmid = {25541081},
issn = {1532-0480},
mesh = {Brain-Computer Interfaces ; *Cognition ; Computational Biology/*methods/*trends ; Decision Making ; Delivery of Health Care ; Humans ; Intensive Care Units ; Interdisciplinary Communication ; Medical Informatics ; Operating Rooms ; Problem Solving ; Reproducibility of Results ; Research Design ; Workflow ; },
abstract = {Cognitive Informatics (CI) is a burgeoning interdisciplinary domain comprising of the cognitive and information sciences that focuses on human information processing, mechanisms and processes within the context of computing and computer applications. Based on a review of articles published in the Journal of Biomedical Informatics (JBI) between January 2001 and March 2014, we identified 57 articles that focused on topics related to cognitive informatics. We found that while the acceptance of CI into the mainstream informatics research literature is relatively recent, its impact has been significant - from characterizing the limits of clinician problem-solving and reasoning behavior, to describing coordination and communication patterns of distributed clinical teams, to developing sustainable and cognitively-plausible interventions for supporting clinician activities. Additionally, we found that most research contributions fell under the topics of decision-making, usability and distributed team activities with a focus on studying behavioral and cognitive aspects of clinical personnel, as they performed their activities or interacted with health information systems. We summarize our findings within the context of the current areas of CI research, future research directions and current and future challenges for CI researchers.},
}
@article {pmid25540133,
year = {2015},
author = {Mottaz, A and Solcà, M and Magnin, C and Corbet, T and Schnider, A and Guggisberg, AG},
title = {Neurofeedback training of alpha-band coherence enhances motor performance.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {9},
pages = {1754-1760},
doi = {10.1016/j.clinph.2014.11.023},
pmid = {25540133},
issn = {1872-8952},
support = {129679/SNSF_/Swiss National Science Foundation/Switzerland ; 146639/SNSF_/Swiss National Science Foundation/Switzerland ; },
mesh = {Adult ; Alpha Rhythm/*physiology ; Brain Mapping/*methods ; Female ; Humans ; Imagination/physiology ; Male ; Motor Cortex/*physiology ; Neurofeedback/*methods/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Neurofeedback training of motor cortex activations with brain-computer interface systems can enhance recovery in stroke patients. Here we propose a new approach which trains resting-state functional connectivity associated with motor performance instead of activations related to movements.<br><br>METHODS: Ten healthy subjects and one stroke patient trained alpha-band coherence between their hand motor area and the rest of the brain using neurofeedback with source functional connectivity analysis and visual feedback.<br><br>RESULTS: Seven out of ten healthy subjects were able to increase alpha-band coherence between the hand motor cortex and the rest of the brain in a single session. The patient with chronic stroke learned to enhance alpha-band coherence of his affected primary motor cortex in 7 neurofeedback sessions applied over one month. Coherence increased specifically in the targeted motor cortex and in alpha frequencies. This increase was associated with clinically meaningful and lasting improvement of motor function after stroke.<br><br>CONCLUSIONS: These results provide proof of concept that neurofeedback training of alpha-band coherence is feasible and behaviorally useful.<br><br>SIGNIFICANCE: The study presents evidence for a role of alpha-band coherence in motor learning and may lead to new strategies for rehabilitation.},
}
@article {pmid25538591,
year = {2014},
author = {Gharabaghi, A and Naros, G and Khademi, F and Jesser, J and Spüler, M and Walter, A and Bogdan, M and Rosenstiel, W and Birbaumer, N},
title = {Learned self-regulation of the lesioned brain with epidural electrocorticography.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {429},
pmid = {25538591},
issn = {1662-5153},
abstract = {INTRODUCTION: Different techniques for neurofeedback of voluntary brain activations are currently being explored for clinical application in brain disorders. One of the most frequently used approaches is the self-regulation of oscillatory signals recorded with electroencephalography (EEG). Many patients are, however, unable to achieve sufficient voluntary control of brain activity. This could be due to the specific anatomical and physiological changes of the patient's brain after the lesion, as well as to methodological issues related to the technique chosen for recording brain signals.<br><br>METHODS: A patient with an extended ischemic lesion of the cortex did not gain volitional control of sensorimotor oscillations when using a standard EEG-based approach. We provided him with neurofeedback of his brain activity from the epidural space by electrocorticography (ECoG).<br><br>RESULTS: Ipsilesional epidural recordings of field potentials facilitated self-regulation of brain oscillations in an online closed-loop paradigm and allowed reliable neurofeedback training for a period of 4 weeks.<br><br>CONCLUSION: Epidural implants may decode and train brain activity even when the cortical physiology is distorted following severe brain injury. Such practice would allow for reinforcement learning of preserved neural networks and may well provide restorative tools for those patients who are severely afflicted.},
}
@article {pmid25538577,
year = {2014},
author = {Pfurtscheller, G and Andrade, A and Koschutnig, K and Brunner, C and da Silva, FL},
title = {Initiation of voluntary movements at free will and ongoing 0.1-Hz BOLD oscillations in the insula-a pilot study.},
journal = {Frontiers in integrative neuroscience},
volume = {8},
number = {},
pages = {93},
pmid = {25538577},
issn = {1662-5145},
abstract = {Recently we hypothesized that the intention to initiate a voluntary movement at free will may be related to the dynamics of hemodynamic variables, which may be supported by the intertwining of networks for the timing of voluntary movements and the control of cardiovascular variables in the insula. In the present study voluntary movements of 3 healthy subjects were analyzed using fMRI scans at 1.83-s intervals along with the time course of slow hemodynamic changes in sensorimotor networks. For the analyses of BOLD time courses the Wavelet transform coherence (WTC) and calculation of phase-locking values were used. Analyzed was the frequency band between 0.07 and 0.13 Hz in the supplementary motor area (SMA) and insula, two widely separated regions co-active in motor behavior. BOLD signals displayed slow fluctuations, concentrated around 0.1 Hz whereby the intrinsic oscillations in the insula preceded those in the SMA by 0.5-1 s. These preliminary results suggest that slow hemodynamic changes in SMA and insula may condition the initiation of a voluntary movement at free will.},
}
@article {pmid25538544,
year = {2014},
author = {Gerjets, P and Walter, C and Rosenstiel, W and Bogdan, M and Zander, TO},
title = {Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {385},
pmid = {25538544},
issn = {1662-4548},
abstract = {According to Cognitive Load Theory (CLT), one of the crucial factors for successful learning is the type and amount of working-memory load (WML) learners experience while studying instructional materials. Optimal learning conditions are characterized by providing challenges for learners without inducing cognitive over- or underload. Thus, presenting instruction in a way that WML is constantly held within an optimal range with regard to learners' working-memory capacity might be a good method to provide these optimal conditions. The current paper elaborates how digital learning environments, which achieve this goal can be developed by combining approaches from Cognitive Psychology, Neuroscience, and Computer Science. One of the biggest obstacles that needs to be overcome is the lack of an unobtrusive method of continuously assessing learners' WML in real-time. We propose to solve this problem by applying passive Brain-Computer Interface (BCI) approaches to realistic learning scenarios in digital environments. In this paper we discuss the methodological and theoretical prospects and pitfalls of this approach based on results from the literature and from our own research. We present a strategy on how several inherent challenges of applying BCIs to WML and learning can be met by refining the psychological constructs behind WML, by exploring their neural signatures, by using these insights for sophisticated task designs, and by optimizing algorithms for analyzing electroencephalography (EEG) data. Based on this strategy we applied machine-learning algorithms for cross-task classifications of different levels of WML to tasks that involve studying realistic instructional materials. We obtained very promising results that yield several recommendations for future work.},
}
@article {pmid25533310,
year = {2015},
author = {Halder, S and Pinegger, A and Käthner, I and Wriessnegger, SC and Faller, J and Pires Antunes, JB and Müller-Putz, GR and Kübler, A},
title = {Brain-controlled applications using dynamic P300 speller matrices.},
journal = {Artificial intelligence in medicine},
volume = {63},
number = {1},
pages = {7-17},
doi = {10.1016/j.artmed.2014.12.001},
pmid = {25533310},
issn = {1873-2860},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Case-Control Studies ; Persons with Disabilities/psychology/*rehabilitation ; Equipment Design ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; *Motor Activity ; Motor Disorders/physiopathology/psychology/*rehabilitation ; Reaction Time ; *Self-Help Devices ; Severity of Illness Index ; Time Factors ; *User-Computer Interface ; *Web Browser ; Young Adult ; },
abstract = {OBJECTIVES: Access to the world wide web and multimedia content is an important aspect of life. We present a web browser and a multimedia user interface adapted for control with a brain-computer interface (BCI) which can be used by severely motor impaired persons.<br><br>METHODS: The web browser dynamically determines the most efficient P300 BCI matrix size to select the links on the current website. This enables control of the web browser with fewer commands and smaller matrices. The multimedia player was based on an existing software. Both applications were evaluated with a sample of ten healthy participants and three end-users. All participants used a visual P300 BCI with face-stimuli for control.<br><br>RESULTS: The healthy participants completed the multimedia player task with 90% accuracy and the web browsing task with 85% accuracy. The end-users completed the tasks with 62% and 58% accuracy. All healthy participants and two out of three end-users reported that they felt to be in control of the system.<br><br>CONCLUSIONS: In this study we presented a multimedia application and an efficient web browser implemented for control with a BCI.<br><br>SIGNIFICANCE: Both applications provide access to important areas of modern information retrieval and entertainment.},
}
@article {pmid25530479,
year = {2015},
author = {Gentili, RJ and Bradberry, TJ and Oh, H and Costanzo, ME and Kerick, SE and Contreras-Vidal, JL and Hatfield, BD},
title = {Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge.},
journal = {Biological psychology},
volume = {105},
number = {},
pages = {51-65},
doi = {10.1016/j.biopsycho.2014.12.003},
pmid = {25530479},
issn = {1873-6246},
mesh = {Adaptation, Physiological/*physiology ; Adult ; Brain Mapping ; Cerebral Cortex/*physiology ; Cognition/physiology ; Electroencephalography ; Executive Function/*physiology ; Humans ; Movement/*physiology ; Nerve Net/*physiology ; Neural Pathways/physiology ; Psychomotor Performance/*physiology ; },
abstract = {Cortical dynamics were examined during a cognitive-motor adaptation task that required inhibition of a familiar motor plan. EEG coherence between the motor planning (Fz) and left hemispheric region was progressively reduced over trials (low-beta, high-beta, gamma bands) along with faster, straighter reaching movements during both planning and execution. The major reduction in coherence (delta, low/high-theta, low/high-alpha bands) between Fz and the left prefrontal region during both movement planning and execution suggests gradual disengagement of frontal executive following its initial role in the suppression of established visuomotor maps. Also, change in the directionality of phase lags (delta, high-alpha, high-beta, gamma bands) reflects a progressive shift from feedback to feedforward motor control. The reduction of cortico-cortical communication, particularly in the frontal region, and the strategic feedback/feedforward mode shift translated as higher quality motor performance. This study extends our understanding of the role of frontal executive beyond purely cognitive tasks to cognitive-motor tasks.},
}
@article {pmid25529197,
year = {2015},
author = {Hong, KS and Naseer, N and Kim, YH},
title = {Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.},
journal = {Neuroscience letters},
volume = {587},
number = {},
pages = {87-92},
doi = {10.1016/j.neulet.2014.12.029},
pmid = {25529197},
issn = {1872-7972},
mesh = {Adult ; Brain-Computer Interfaces ; Discriminant Analysis ; Feasibility Studies ; Functional Laterality ; Humans ; Imagination ; Linear Models ; Male ; Motor Activity ; Motor Cortex/*physiology ; Prefrontal Cortex/*physiology ; Problem Solving ; Spectroscopy, Near-Infrared ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.},
}
@article {pmid25529142,
year = {2015},
author = {Bokken, GC and Portengen, L and Cornelissen, JB and Bergwerff, AA and van Knapen, F},
title = {Bayesian estimation of diagnostic accuracy of a new bead-based antibody detection test to reveal Toxoplasma gondii infections in pig populations.},
journal = {Veterinary parasitology},
volume = {207},
number = {1-2},
pages = {1-6},
doi = {10.1016/j.vetpar.2014.11.020},
pmid = {25529142},
issn = {1873-2550},
mesh = {Animals ; Antibodies, Protozoan/*blood ; Bayes Theorem ; Enzyme-Linked Immunosorbent Assay/methods/veterinary ; Immunomagnetic Separation/methods/*veterinary ; Netherlands ; Reproducibility of Results ; Sensitivity and Specificity ; Swine ; Swine Diseases/*diagnosis/parasitology ; Toxoplasma/*immunology ; Toxoplasmosis, Animal/*diagnosis/parasitology ; },
abstract = {The success of a Toxoplasma gondii surveillance program in European pig production systems depends partly on the quality of the test to detect infection in the population. The test accuracy of a recently developed serological bead-based assay (BBA) was investigated earlier using sera from experimentally infected animals. In this study, the accuracy of the BBA was determined by the use of sera from animals from two field subpopulations. As no T. gondii infection information of these animals was available, test accuracy was determined through a Bayesian approach allowing for conditional dependency between BBA and an ELISA test. The priors for prevalence were based on available information from literature, whereas for specificity vague non-informative priors were used. Priors for sensitivity were based either on available information or specified as non-informative. Posterior estimates for BBA sensitivity and specificity were (mode) 0.855 (Bayesian 95% credibility interval (bCI) 0.702-0.960) and 0.913 (bCI 0.893-0.931), respectively. Comparing the results of BBA and ELISA, sensitivity was higher for the BBA while specificity was higher for ELISA. Alternative priors for the sensitivity affected posterior estimates for sensitivity of both BBA and ELISA, but not for specificity. Because the difference in prevalence between the two subpopulations is small, and the number of infected animals is small as well, the precision of the posterior estimates for sensitivity may be less accurate in comparison to the estimates for specificity. The estimated value for specificity of BBA is at least optimally defined for testing pigs from conventional and organic Dutch farms.},
}
@article {pmid25527239,
year = {2015},
author = {Bauer, R and Fels, M and Vukelić, M and Ziemann, U and Gharabaghi, A},
title = {Bridging the gap between motor imagery and motor execution with a brain-robot interface.},
journal = {NeuroImage},
volume = {108},
number = {},
pages = {319-327},
doi = {10.1016/j.neuroimage.2014.12.026},
pmid = {25527239},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Motor Activity/*physiology ; Neural Pathways/*physiology ; Principal Component Analysis ; *Robotics ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {According to electrophysiological studies motor imagery and motor execution are associated with perturbations of brain oscillations over spatially similar cortical areas. By contrast, neuroimaging and lesion studies suggest that at least partially distinct cortical networks are involved in motor imagery and execution. We sought to further disentangle this relationship by studying the role of brain-robot interfaces in the context of motor imagery and motor execution networks. Twenty right-handed subjects performed several behavioral tasks as indicators for imagery and execution of movements of the left hand, i.e. kinesthetic imagery, visual imagery, visuomotor integration and tonic contraction. In addition, subjects performed motor imagery supported by haptic/proprioceptive feedback from a brain-robot-interface. Principal component analysis was applied to assess the relationship of these indicators. The respective cortical resting state networks in the α-range were investigated by electroencephalography using the phase slope index. We detected two distinct abilities and cortical networks underlying motor control: a motor imagery network connecting the left parietal and motor areas with the right prefrontal cortex and a motor execution network characterized by transmission from the left to right motor areas. We found that a brain-robot-interface might offer a way to bridge the gap between these networks, opening thereby a backdoor to the motor execution system. This knowledge might promote patient screening and may lead to novel treatment strategies, e.g. for the rehabilitation of hemiparesis after stroke.},
}
@article {pmid25522824,
year = {2015},
author = {Dreyer, AM and Herrmann, CS},
title = {Frequency-modulated steady-state visual evoked potentials: a new stimulation method for brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {241},
number = {},
pages = {1-9},
doi = {10.1016/j.jneumeth.2014.12.004},
pmid = {25522824},
issn = {1872-678X},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {BACKGROUND: Steady-state visual evoked potentials (SSVEPs) are widely used for brain-computer interfaces. However, users experience fatigue due to exposure to flickering stimuli. High-frequency stimulation has been proposed to reduce this problem. We adapt frequency-modulated (FM) stimulation from the auditory domain, where it is commonly used to evoke steady-state responses, and compare the EEG as well as behavioral flicker perceptibility ratings.<br><br>NEW METHOD: We evoke SSVEPs with a green light-emitting diode (LED) driven by FM signals.<br><br>RESULTS: FM-SSVEPs with different carrier and modulation frequencies can reliably be evoked with spectral peaks at the lower FM sideband. Subjective perceptibility ratings decrease with increasing FM carrier frequencies, while the peak amplitude and signal-to-noise ratio (SNR) remain the same.<br><br>There are neither amplitude nor SNR differences between SSVEPs evoked rectangularly, sinusoidally or via FM. Perceptibility ratings were lower for FM-SSVEPs with carrier frequencies of 20Hz and above than for sinusoidally evoked SSVEPs.<br><br>CONCLUSIONS: FM-SSVEPs seem to be beneficial for BCI usage. Reduced flicker perceptibility in FM-SSVEPs suggests reduced fatigue, which leads to an enhanced user experience and performance.},
}
@article {pmid25520632,
year = {2014},
author = {Serruya, MD},
title = {Bottlenecks to clinical translation of direct brain-computer interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {226},
pmid = {25520632},
issn = {1662-5137},
abstract = {Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two-cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders-have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion), lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.},
}
@article {pmid25520608,
year = {2014},
author = {Casson, AJ},
title = {Artificial Neural Network classification of operator workload with an assessment of time variation and noise-enhancement to increase performance.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {372},
pmid = {25520608},
issn = {1662-4548},
abstract = {Workload classification-the determination of whether a human operator is in a high or low workload state to allow their working environment to be optimized-is an emerging application of passive Brain-Computer Interface (BCI) systems. Practical systems must not only accurately detect the current workload state, but also have good temporal performance: requiring little time to set up and train the classifier, and ensuring that the reported performance level is consistent and predictable over time. This paper investigates the temporal performance of an Artificial Neural Network based classification system. For networks trained on little EEG data good classification accuracies (86%) are achieved over very short time frames, but substantial decreases in accuracy are found as the time gap between the network training and the actual use is increased. Noise-enhanced processing, where artificially generated noise is deliberately added to the testing signals, is investigated as a potential technique to mitigate this degradation without requiring the network to be re-trained using more data. Small stochastic resonance effects are demonstrated whereby the classification process gets better in the presence of more noise. The effect is small and does not eliminate the need for re-training, but it is consistent, and this is the first demonstration of such effects for non-evoked/free-running EEG signals suitable for passive BCI.},
}
@article {pmid25514553,
year = {2015},
author = {Zapała, D and Zabielska-Mendyk, E and Cudo, A and Krzysztofiak, A and Augustynowicz, P and Francuz, P},
title = {Short-term kinesthetic training for sensorimotor rhythms: effects in experts and amateurs.},
journal = {Journal of motor behavior},
volume = {47},
number = {4},
pages = {312-318},
doi = {10.1080/00222895.2014.982067},
pmid = {25514553},
issn = {1940-1027},
mesh = {Adult ; Brain Waves/*physiology ; Electroencephalography ; Hand/*physiology ; Humans ; Imagination/physiology ; *Kinesthesis ; Male ; Psychomotor Performance/*physiology ; Transfer, Psychology/*physiology ; Young Adult ; },
abstract = {The authors' aim was to examine whether short-term kinesthetic training affects the level of sensorimotor rhythm (SMR) in different frequency band: alpha (8-12 Hz), lower beta (12.5-16 Hz) and beta (16.5-20 Hz) during the execution of a motor imagery task of closing and opening the right and the left hand by experts (jugglers, practicing similar exercises on an everyday basis) and amateurs (individuals not practicing any sports). It was found that the performance of short kinesthetic training increases the power of alpha rhythm when executing imagery tasks only in the group of amateurs. Therefore, kinesthetic training may be successfully used as a method increasing the vividness of motor imagery, for example, in tasks involving the control of brain-computer interfaces based on SMR.},
}
@article {pmid25514519,
year = {2015},
author = {Okazaki, YO and Horschig, JM and Luther, L and Oostenveld, R and Murakami, I and Jensen, O},
title = {Real-time MEG neurofeedback training of posterior alpha activity modulates subsequent visual detection performance.},
journal = {NeuroImage},
volume = {107},
number = {},
pages = {323-332},
doi = {10.1016/j.neuroimage.2014.12.014},
pmid = {25514519},
issn = {1095-9572},
mesh = {Adolescent ; Adult ; Alpha Rhythm/*physiology ; Attention/physiology ; Biofeedback, Psychology/*methods ; Cognition/physiology ; Cues ; Face ; Female ; Functional Laterality/physiology ; Humans ; Learning/physiology ; Magnetoencephalography/*methods ; Male ; Photic Stimulation ; Psychomotor Performance/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {It has been demonstrated that alpha activity is lateralized when attention is directed to the left or right visual hemifield. We investigated whether real-time neurofeedback training of the alpha lateralization enhances participants' ability to modulate posterior alpha lateralization and causes subsequent short-term changes in visual detection performance. The experiment consisted of three phases: (i) pre-training assessment, (ii) neurofeedback phase and (iii) post-training assessment. In the pre- and post-training phases we measured the threshold to covertly detect a cued faint Gabor stimulus presented in the left or right hemifield. During magnetoencephalography (MEG) neurofeedback, two face stimuli superimposed with noise were presented bilaterally. Participants were cued to attend to one of the hemifields. The transparency of the superimposed noise and thus the visibility of the stimuli were varied according to the momentary degree of hemispheric alpha lateralization. In a double-blind procedure half of the participants were provided with sham feedback. We found that hemispheric alpha lateralization increased with the neurofeedback training; this was mainly driven by an ipsilateral alpha increase. Surprisingly, comparing pre- to post-training, detection performance decreased for a Gabor stimulus presented in the hemifield that was un-attended during neurofeedback. This effect was not observed in the sham group. Thus, neurofeedback training alters alpha lateralization, which in turn decreases performances in the untrained hemifield. Our findings suggest that alpha oscillations play a causal role for the allocation of attention. Furthermore, our neurofeedback protocol serves to reduce the detection of unattended visual information and could therefore be of potential use for training to reduce distractibility in attention deficit patients, but also highlights that neurofeedback paradigms can have negative impact on behavioral performance and should be applied with caution.},
}
@article {pmid25514320,
year = {2015},
author = {Wodlinger, B and Downey, JE and Tyler-Kabara, EC and Schwartz, AB and Boninger, ML and Collinger, JL},
title = {Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016011},
doi = {10.1088/1741-2560/12/1/016011},
pmid = {25514320},
issn = {1741-2552},
mesh = {Adult ; Arm/*physiopathology ; *Artificial Limbs ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/methods ; Equipment Failure Analysis ; Evoked Potentials, Motor ; Feedback, Physiological ; Female ; Humans ; Imagination ; Joints/*physiopathology ; Models, Biological ; Prosthesis Design ; Quadriplegia/*physiopathology/rehabilitation ; Robotics/*instrumentation ; },
abstract = {OBJECTIVE: In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex.<br><br>APPROACH: Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously.<br><br>MAIN RESULTS: Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping.<br><br>SIGNIFICANCE: Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.},
}
@article {pmid25513800,
year = {2014},
author = {Lu, S and Peng, H and Gao, P},
title = {A body characteristic index to evaluate the level of risk of heat strain for a group of workers with a test.},
journal = {International journal of occupational safety and ergonomics : JOSE},
volume = {20},
number = {4},
pages = {647-659},
doi = {10.1080/10803548.2014.11077074},
pmid = {25513800},
issn = {1080-3548},
mesh = {Adult ; Body Temperature Regulation/*physiology ; Body Weights and Measures ; Climate ; Exercise Test ; Female ; Health Status Indicators ; Heat Stress Disorders/*physiopathology/prevention &amp; control ; *Hot Temperature ; Humans ; Male ; Occupational Exposure ; Occupational Health ; Oxygen Consumption ; },
abstract = {The purpose of this study was to develop a body characteristic index (BCI) based on the distribution of maximal oxygen uptake per body mass (VO2max/mass), body surface area per body mass (BSA/mass), and percentage of body fat (Fat%) to evaluate the relative level of individual physiological responses to heat strain in a group of workers. BCI was based upon the data obtained from 10 males and 10 females exercising for 60 min on a treadmill at 2 relative exercise intensities of 25% and 45% VO2max in mild, warm wet, and hot dry climate condition, separately. BCI was developed into 2 formulas, which were proved to be better predictors for heat strain responses than each individual characteristic, and more sensitive than body type to describe the distributions of individual characteristics and distinguish the differences in physiological responses to heat.},
}
@article {pmid25510922,
year = {2014},
author = {Witkowski, M and Cortese, M and Cempini, M and Mellinger, J and Vitiello, N and Soekadar, SR},
title = {Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG).},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {165},
pmid = {25510922},
issn = {1743-0003},
mesh = {Adult ; Artifacts ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography/*methods ; *Exoskeleton Device ; Eye Movements/physiology ; Female ; Hand/*physiology ; Humans ; Male ; Movement/physiology ; Reproducibility of Results ; },
abstract = {BACKGROUND: Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions.<br><br>FINDINGS: 12 healthy volunteers (8 male, mean age 28.1 &#xb1; 3.63y) used EEG (condition #1) and hybrid EEG/EOG (condition #2) signals to control a hand exoskeleton. Motor imagery-related brain activity was translated into exoskeleton-driven hand closing motions. Unintended motions could be interrupted by eye movement-related EOG signals. In order to evaluate BNCI control and safety, participants were instructed to follow a visual cue indicating either to move or not to move the hand exoskeleton in a random order. Movements exceeding 25% of a full grasping motion when the device was not supposed to be moved were defined as safety violation. While participants reached comparable control under both conditions, safety was frequently violated under condition #1 (EEG), but not under condition #2 (EEG/EOG).<br><br>CONCLUSION: EEG/EOG biosignal fusion can substantially enhance safety of assistive BNCI systems improving their applicability in daily life environments.},
}
@article {pmid25505878,
year = {2014},
author = {De Massari, D and Pacheco, D and Malekshahi, R and Betella, A and Verschure, PF and Birbaumer, N and Caria, A},
title = {Fast mental states decoding in mixed reality.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {415},
pmid = {25505878},
issn = {1662-5153},
abstract = {The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.},
}
@article {pmid25505392,
year = {2014},
author = {Sokunbi, MO and Linden, DE and Habes, I and Johnston, S and Ihssen, N},
title = {Real-time fMRI brain-computer interface: development of a "motivational feedback" subsystem for the regulation of visual cue reactivity.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {392},
pmid = {25505392},
issn = {1662-5153},
support = {G1100629/MRC_/Medical Research Council/United Kingdom ; MR/L010305/1/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Here we present a novel neurofeedback subsystem for the presentation of motivationally relevant visual feedback during the self-regulation of functional brain activation. Our "motivational neurofeedback" approach uses functional magnetic resonance imaging (fMRI) signals elicited by visual cues (pictures) and related to motivational processes such as craving or hunger. The visual feedback subsystem provides simultaneous feedback through these images as their size corresponds to the magnitude of fMRI signal change from a target brain area. During self-regulation of cue-evoked brain responses, decreases and increases in picture size thus provide real motivational consequences in terms of cue approach vs. cue avoidance, which increases face validity of the approach in applied settings. Further, the outlined approach comprises of neurofeedback (regulation) and "mirror" runs that allow to control for non-specific and task-unrelated effects, such as habituation or neural adaptation. The approach was implemented in the Python programming language. Pilot data from 10 volunteers showed that participants were able to successfully down-regulate individually defined target areas, demonstrating feasibility of the approach. The newly developed visual feedback subsystem can be integrated into protocols for imaging-based brain-computer interfaces (BCI) and may facilitate neurofeedback research and applications into healthy and dysfunctional motivational processes, such as food craving or addiction.},
}
@article {pmid25505377,
year = {2014},
author = {Bulea, TC and Prasad, S and Kilicarslan, A and Contreras-Vidal, JL},
title = {Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {376},
pmid = {25505377},
issn = {1662-4548},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; },
abstract = {Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function.},
}
@article {pmid25505104,
year = {2015},
author = {Seyedhosseini, M and Shushruth, S and Davis, T and Ichida, JM and House, PA and Greger, B and Angelucci, A and Tasdizen, T},
title = {Informative features of local field potential signals in primary visual cortex during natural image stimulation.},
journal = {Journal of neurophysiology},
volume = {113},
number = {5},
pages = {1520-1532},
pmid = {25505104},
issn = {1522-1598},
support = {R01 EY019363/EY/NEI NIH HHS/United States ; R01 MH100635/MH/NIMH NIH HHS/United States ; EY019363/EY/NEI NIH HHS/United States ; EY022757/EY/NEI NIH HHS/United States ; EY019743/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Evoked Potentials, Visual ; Macaca fascicularis ; Male ; Photic Stimulation ; Visual Cortex/*physiology ; *Visual Perception ; },
abstract = {The local field potential (LFP) is of growing importance in neurophysiology as a metric of network activity and as a readout signal for use in brain-machine interfaces. However, there are uncertainties regarding the kind and visual field extent of information carried by LFP signals, as well as the specific features of the LFP signal conveying such information, especially under naturalistic conditions. To address these questions, we recorded LFP responses to natural images in V1 of awake and anesthetized macaques using Utah multielectrode arrays. First, we have shown that it is possible to identify presented natural images from the LFP responses they evoke using trained Gabor wavelet (GW) models. Because GW models were devised to explain the spiking responses of V1 cells, this finding suggests that local spiking activity and LFPs (thought to reflect primarily local synaptic activity) carry similar visual information. Second, models trained on scalar metrics, such as the evoked LFP response range, provide robust image identification, supporting the informative nature of even simple LFP features. Third, image identification is robust only for the first 300 ms following image presentation, and image information is not restricted to any of the spectral bands. This suggests that the short-latency broadband LFP response carries most information during natural scene viewing. Finally, best image identification was achieved by GW models incorporating information at the scale of ∼ 0.5° in size and trained using four different orientations. This suggests that during natural image viewing, LFPs carry stimulus-specific information at spatial scales corresponding to few orientation columns in macaque V1.},
}
@article {pmid25504690,
year = {2015},
author = {Christie, BP and Tat, DM and Irwin, ZT and Gilja, V and Nuyujukian, P and Foster, JD and Ryu, SI and Shenoy, KV and Thompson, DE and Chestek, CA},
title = {Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016009},
pmid = {25504690},
issn = {1741-2552},
support = {DP1 HD075623/HD/NICHD NIH HHS/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; DP1HD075623/DP/NCCDPHP CDC HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Macaca mulatta ; Motor Cortex/*physiology ; Nerve Net/physiology ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials.<br><br>APPROACH: We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Na&#xef;ve Bayes classifier for reaching direction and a linear regression to evaluate hand position.<br><br>MAIN RESULTS: We found the highest performance for thresholding when placing a threshold between -3 and -4.5 &#xd7; Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Na&#xef;ve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded.<br><br>SIGNIFICANCE: For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.},
}
@article {pmid25494495,
year = {2015},
author = {Ofner, P and Müller-Putz, GR},
title = {Using a noninvasive decoding method to classify rhythmic movement imaginations of the arm in two planes.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {3},
pages = {972-981},
doi = {10.1109/TBME.2014.2377023},
pmid = {25494495},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; Male ; Movement/physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {A brain-computer interface (BCI) can help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a control signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means of invasive recording techniques, and executed movements have been reconstructed using noninvasive decoding techniques. However, it is unclear if detailed imagined movements can be decoded noninvasively using electroencephalography (EEG). We made progress toward imagined movement decoding and successfully classified horizontal and vertical imagined rhythmic movements of the right arm in healthy subjects using EEG. Notably, we used an experimental design which avoided muscle and eye movements to prevent classification results being affected. To classify imagined movements of the same limb, we decoded the movement trajectories and correlated them with assumed movement trajectories (horizontal and vertical). We then assigned the decoded movements to the assumed movements with the higher correlation. To train the decoder, we applied partial least squares, which allowed us to interpret the classifier weights although channels were highly correlated. To conclude, we showed the classification of imagined movements of one limb in two different movement planes in seven out of nine subjects. Furthermore, we found a strong involvement of the supplementary motor area. Finally, as our classifier was based on the decoding approach, we indirectly showed the decoding of imagined movements.},
}
@article {pmid25490027,
year = {2015},
author = {Soekadar, SR and Witkowski, M and Vitiello, N and Birbaumer, N},
title = {An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {60},
number = {3},
pages = {199-205},
doi = {10.1515/bmt-2014-0126},
pmid = {25490027},
issn = {1862-278X},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography/*methods ; Electrooculography/*methods ; *Exoskeleton Device ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; Paralysis ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted ; },
abstract = {The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5 ± 3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24 ± 16.71%, BPI: 65.93 ± 24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65 ± 11.28, BPI: 76.03 ± 18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.},
}
@article {pmid25489973,
year = {2015},
author = {Soekadar, SR and Birbaumer, N and Slutzky, MW and Cohen, LG},
title = {Brain-machine interfaces in neurorehabilitation of stroke.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {172-179},
doi = {10.1016/j.nbd.2014.11.025},
pmid = {25489973},
issn = {1095-953X},
mesh = {Brain/*physiopathology ; Brain Waves ; Brain-Computer Interfaces/*trends ; Electric Stimulation Therapy ; Feedback, Sensory ; Humans ; Learning/physiology ; Movement Disorders/etiology/*rehabilitation ; Neurofeedback ; Neurological Rehabilitation/instrumentation/methods/*trends ; Neuronal Plasticity ; Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.},
}
@article {pmid25485610,
year = {2015},
author = {Burdea, GC and Polistico, K and House, GP and Liu, RR and Muñiz, R and Macaro, NA and Slater, LM},
title = {Novel integrative virtual rehabilitation reduces symptomatology of primary progressive aphasia--a case report.},
journal = {The International journal of neuroscience},
volume = {125},
number = {12},
pages = {949-958},
pmid = {25485610},
issn = {1563-5279},
support = {R44 AG044639/AG/NIA NIH HHS/United States ; 9R44AG044639-02A1/AG/NIA NIH HHS/United States ; },
mesh = {Aphasia, Primary Progressive/*physiopathology/*rehabilitation ; Executive Function ; Humans ; Male ; Middle Aged ; Neuropsychological Tests ; Telerehabilitation/*methods ; Treatment Outcome ; },
abstract = {PURPOSE: BrightBrainer™ integrative cognitive rehabilitation system evaluation in an Adult Day Program by a subject with Primary Progressive Aphasia (PPA) assumed to be of the mixed nonfluent/logopenic variant, and for determination of potential benefits.<br><br>METHODS: The subject was a 51-year-old Caucasian male diagnosed with PPA who had attended an Adult Day Program for 18 months prior to BrightBrainer training. The subject interacted with therapeutic games using a controller that measured 3D hand movements and flexion of both index fingers. The computer simulations adapted difficulty level based on task performance; results were stored on a remote server. The clinical trial consisted of 16 sessions, twice/week for 8 weeks. The subject was evaluated through neuropsychological measures, therapy notes and caregiver feedback forms.<br><br>RESULTS: Neuropsychological testing indicated no depression (BDI 0) and severe dementia (BIMS 1 and MMSE 3). The 6.5 h of therapy consisted of games targeting Language comprehension; Executive functions; Focusing; Short-term memory; and Immediate/working memory. The subject attained the highest difficulty level in all-but-one game, while averaging 1300-arm task-oriented active movement repetitions and 320 index finger flexion movements per session. While neuropsychological testing showed no benefits, the caregiver reported strong improvements in verbal responses, vocabulary use, speaking in complete sentences, following one-step directions and participating in daily activities. This corroborated well with therapy notes.<br><br>CONCLUSIONS: Preliminary findings demonstrate a meaningful reduction of PPA symptoms for the subject, suggesting follow-up imaging studies to detail neuronal changes induced by BrightBrainer system and controlled studies with a sufficiently large number of PPA subjects.},
}
@article {pmid25485284,
year = {2014},
author = {Huggins, JE and Guger, C and Allison, B and Anderson, CW and Batista, A and Brouwer, AM and Brunner, C and Chavarriaga, R and Fried-Oken, M and Gunduz, A and Gupta, D and Kübler, A and Leeb, R and Lotte, F and Miller, LE and Müller-Putz, G and Rutkowski, T and Tangermann, M and Thompson, DE},
title = {Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.},
journal = {Brain computer interfaces (Abingdon, England)},
volume = {1},
number = {1},
pages = {27-49},
pmid = {25485284},
issn = {2326-263X},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; R13 DC012744/DC/NIDCD NIH HHS/United States ; },
abstract = {The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7[th], 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.},
}
@article {pmid25477814,
year = {2014},
author = {Vuckovic, A and Pineda, JA and LaMarca, K and Gupta, D and Guger, C},
title = {Interaction of BCI with the underlying neurological conditions in patients: pros and cons.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {42},
pmid = {25477814},
issn = {1662-6443},
}
@article {pmid25477777,
year = {2014},
author = {Putze, F and Hesslinger, S and Tse, CY and Huang, Y and Herff, C and Guan, C and Schultz, T},
title = {Hybrid fNIRS-EEG based classification of auditory and visual perception processes.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {373},
pmid = {25477777},
issn = {1662-4548},
abstract = {For multimodal Human-Computer Interaction (HCI), it is very useful to identify the modalities on which the user is currently processing information. This would enable a system to select complementary output modalities to reduce the user's workload. In this paper, we develop a hybrid Brain-Computer Interface (BCI) which uses Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) to discriminate and detect visual and auditory stimulus processing. We describe the experimental setup we used for collection of our data corpus with 12 subjects. On this data, we performed cross-validation evaluation, of which we report accuracy for different classification conditions. The results show that the subject-dependent systems achieved a classification accuracy of 97.8% for discriminating visual and auditory perception processes from each other and a classification accuracy of up to 94.8% for detecting modality-specific processes independently of other cognitive activity. The same classification conditions could also be discriminated in a subject-independent fashion with accuracy of up to 94.6 and 86.7%, respectively. We also look at the contributions of the two signal types and show that the fusion of classifiers using different features significantly increases accuracy.},
}
@article {pmid25476924,
year = {2014},
author = {Cantillo-Negrete, J and Gutierrez-Martinez, J and Carino-Escobar, RI and Carrillo-Mora, P and Elias-Vinas, D},
title = {An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender.},
journal = {Biomedical engineering online},
volume = {13},
number = {},
pages = {158},
pmid = {25476924},
issn = {1475-925X},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Discriminant Analysis ; Electroencephalography/methods ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Motor Skills ; Neurology/methods ; Rehabilitation/*methods ; Reproducibility of Results ; Sex Factors ; Signal Processing, Computer-Assisted ; *Stroke Rehabilitation ; Young Adult ; },
abstract = {BACKGROUND: One of the difficulties for the implementation of Brain-Computer Interface (BCI) systems for motor impaired patients is the time consumed in the system design process, since patients do not have the adequate physical nor psychological conditions to complete the process. For this reason most of BCIs are designed in a subject-dependent approach using data of healthy subjects. The developing of subject-independent systems is an option to decrease the required training sessions to design a BCI with patient functionality. This paper presents a proof-of-concept study to evaluate subject-independent system based on hand motor imagery taking gender into account.<br><br>METHODS: Subject-Independent BCIs are proposed using Common Spatial Patterns and log variance features of two groups of healthy subjects; one of the groups was composed by people of male gender and the other one by people of female gender. The performance of the developed gender-specific BCI designs was evaluated with respect to a subject-independent BCI designed without taking gender into account, and afterwards its performance was evaluated with data of two healthy subjects that were not included in the initial sample. As an additional test to probe the potential use for subcortical stroke patients we applied the methodology to two patients with right hand weakness. T-test was employed to determine the significance of the difference between traditional approach and the proposed gender-specific approach.<br><br>RESULTS: For most of the tested conditions, the gender-specific BCIs have a statistically significant better performance than those that did not take gender into account. It was also observed that with a BCI designed with log-variance features in the alpha and beta band of healthy subjects' data, it was possible to classify hand motor imagery of subcortical stroke patients above the practical level of chance.<br><br>CONCLUSIONS: A larger subjects' sample test may be necessary to improve the performances of the gender-specific BCIs and to further test this methodology on different patients. The reduction of complexity in the implementation of BCI systems could bring these systems closer to applications such as controlling devices for the motor rehabilitation of stroke patients, and therefore, contribute to a more effective neurological rehabilitation.},
}
@article {pmid25474811,
year = {2015},
author = {Kim, JH and Bießmann, F and Lee, SW},
title = {Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements From Electroencephalogram Signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {867-876},
doi = {10.1109/TNSRE.2014.2375879},
pmid = {25474811},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Arm/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Decoding motor commands from noninvasively measured neural signals has become important in brain-computer interface (BCI) research. Applications of BCI include neurorehabilitation after stroke and control of limb prostheses. Until now, most studies have tested simple movement trajectories in two dimensions by using constant velocity profiles. However, most real-world scenarios require much more complex movement trajectories and velocity profiles. In this study, we decoded motor commands in three dimensions from electroencephalography (EEG) recordings while the subjects either executed or observed/imagined complex upper limb movement trajectories. We compared the accuracy of simple linear methods and nonlinear methods. In line with previous studies our results showed that linear decoders are an efficient and robust method for decoding motor commands. However, while we took the same precautions as previous studies to suppress eye-movement related EEG contamination, we found that subtracting residual electro-oculogram activity from the EEG data resulted in substantially lower motor decoding accuracy for linear decoders. This effect severely limits the transfer of previous results to practical applications in which neural activation is targeted. We observed that nonlinear methods showed no such drop in decoding performance. Our results demonstrate that eye-movement related contamination of brain signals constitutes a severe problem for decoding motor signals from EEG data. These results are important for developing accurate decoders of motor signal from neural signals for use with BCI-based neural prostheses and neurorehabilitation in real-world scenarios.},
}
@article {pmid25474810,
year = {2015},
author = {Akce, A and Norton, JJ and Bretl, T},
title = {An SSVEP-Based Brain-Computer Interface for Text Spelling With Adaptive Queries That Maximize Information Gain Rates.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {857-866},
doi = {10.1109/TNSRE.2014.2373338},
pmid = {25474810},
issn = {1558-0210},
mesh = {Adolescent ; *Brain-Computer Interfaces ; Child ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Information Storage and Retrieval/methods ; Machine Learning ; Male ; *Natural Language Processing ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Word Processing/*methods ; Young Adult ; },
abstract = {This paper presents a brain-computer interface for text entry using steady-state visually evoked potentials (SSVEP). Like other SSVEP-based spellers, ours identifies the desired input character by posing questions (or queries) to users through a visual interface. Each query defines a mapping from possible characters to steady-state stimuli. The user responds by attending to one of these stimuli. Unlike other SSVEP-based spellers, ours chooses from a much larger pool of possible queries-on the order of ten thousand instead of ten. The larger query pool allows our speller to adapt more effectively to the inherent structure of what is being typed and to the input performance of the user, both of which make certain queries provide more information than others. In particular, our speller chooses queries from this pool that maximize the amount of information to be received per unit of time, a measure of mutual information that we call information gain rate. To validate our interface, we compared it with two other state-of-the-art SSVEP-based spellers, which were re-implemented to use the same input mechanism. Results showed that our interface, with the larger query pool, allowed users to spell multiple-word texts nearly twice as fast as they could with the compared spellers.},
}
@article {pmid25469774,
year = {2014},
author = {Kübler, A and Holz, EM and Riccio, A and Zickler, C and Kaufmann, T and Kleih, SC and Staiger-Sälzer, P and Desideri, L and Hoogerwerf, EJ and Mattia, D},
title = {The user-centered design as novel perspective for evaluating the usability of BCI-controlled applications.},
journal = {PloS one},
volume = {9},
number = {12},
pages = {e112392},
pmid = {25469774},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces/economics ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Movement Disorders/*physiopathology ; Patient Satisfaction ; Surveys and Questionnaires ; User-Computer Interface ; },
abstract = {Albeit research on brain-computer interfaces (BCI) for controlling applications has expanded tremendously, we still face a translational gap when bringing BCI to end-users. To bridge this gap, we adapted the user-centered design (UCD) to BCI research and development which implies a shift from focusing on single aspects, such as accuracy and information transfer rate (ITR), to a more holistic user experience. The UCD implements an iterative process between end-users and developers based on a valid evaluation procedure. Within the UCD framework usability of a device can be defined with regard to its effectiveness, efficiency, and satisfaction. We operationalized these aspects to evaluate BCI-controlled applications. Effectiveness was regarded equivalent to accuracy of selections and efficiency to the amount of information transferred per time unit and the effort invested (workload). Satisfaction was assessed with questionnaires and visual-analogue scales. These metrics have been successfully applied to several BCI-controlled applications for communication and entertainment, which were evaluated by end-users with severe motor impairment. Results of four studies, involving a total of N = 19 end-users revealed: effectiveness was moderate to high; efficiency in terms of ITR was low to high and workload low to medium; depending on the match between user and technology, and type of application satisfaction was moderate to high. The here suggested evaluation metrics within the framework of the UCD proved to be an applicable and informative approach to evaluate BCI controlled applications, and end-users with severe impairment and in the locked-in state were able to participate in this process.},
}
@article {pmid25467185,
year = {2015},
author = {Kondo, T and Saeki, M and Hayashi, Y and Nakayashiki, K and Takata, Y},
title = {Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface.},
journal = {Human movement science},
volume = {43},
number = {},
pages = {239-249},
doi = {10.1016/j.humov.2014.08.014},
pmid = {25467185},
issn = {1872-7646},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Motion Perception/physiology ; Motor Cortex/*physiology ; Motor Skills/physiology ; Neurofeedback/*physiology ; Pattern Recognition, Visual/*physiology ; Young Adult ; },
abstract = {Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills.},
}
@article {pmid25464809,
year = {2014},
author = {Pu, X and Liu, T and Wu, Q and Zhang, R and Xu, P and Li, K and Xia, Y and Yao, D},
title = {[Study on neurofeedback system based on electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {4},
pages = {894-898},
pmid = {25464809},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Neurofeedback ; },
abstract = {Neurofeedback, as an alternative treatment method of behavioral medicine, is a technique which translates the electroencephalogram (EEG) signals to styles as sounds or animation to help people understand their own physical status and learn to enhance or suppress certain EEG signals to regulate their own brain functions after several repeated trainings. This paper develops a neurofeedback system on the foundation of brain-computer interface technique. The EEG features are extracted through real-time signal process and then translated to feedback information. Two feedback screens are designed for relaxation training and attention training individually. The veracity and feasibility of the neurofeedback system are validated through system simulation and preliminary experiment.},
}
@article {pmid25464783,
year = {2014},
author = {Wang, J and Yang, C},
title = {[Research of controlling of smart home system based on P300 brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {4},
pages = {762-766},
pmid = {25464783},
issn = {1001-5515},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; },
abstract = {Using electroencephalogram (EEG) signal to control external devices has always been the research focus in the field of brain-computer interface (BCI). This is especially significant for those disabilities who have lost capacity of movements. In this paper, the P300-based BCI and the microcontroller-based wireless radio frequency (RF) technology are utilized to design a smart home control system, which can be used to control household appliances, lighting system, and security devices directly. Experiment results showed that the system was simple, reliable and easy to be populirised.},
}
@article {pmid25464346,
year = {2015},
author = {Akram, F and Han, SM and Kim, TS},
title = {An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier.},
journal = {Computers in biology and medicine},
volume = {56},
number = {},
pages = {30-36},
doi = {10.1016/j.compbiomed.2014.10.021},
pmid = {25464346},
issn = {1879-0534},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Male ; *Writing ; },
abstract = {BACKGROUND: A typical P300-based spelling brain computer interface (BCI) system types a single character with a character presentation paradigm and a P300 classification system. Lately, a few attempts have been made to type a whole word with the help of a smart dictionary that suggests some candidate words with the input of a few initial characters.<br><br>METHODS: In this paper, we propose a novel paradigm utilizing initial character typing with word suggestions and a novel P300 classifier to increase word typing speed and accuracy. The novel paradigm involves modifying the Text on 9 keys (T9) interface, which is similar to the keypad of a mobile phone used for text messaging. Users can type the initial characters using a 3&#xd7;3 matrix interface and an integrated custom-built dictionary that suggests candidate words as the user types the initials. Then the user can select one of the given suggestions to complete word typing. We have adopted a random forest classifier, which significantly improves P300 classification accuracy by combining multiple decision trees.<br><br>RESULTS AND DISCUSSION: We conducted experiments with 10 subjects using the proposed BCI system. Our proposed paradigms significantly reduced word typing time and made word typing more convenient by outputting complete words with only a few initial character inputs. The conventional spelling system required an average time of 3.47 min per word while typing 10 random words, whereas our proposed system took an average time of 1.67 min per word, a 51.87% improvement, for the same words under the same conditions.},
}
@article {pmid25461477,
year = {2015},
author = {Yao, L and Meng, J and Sheng, X and Zhang, D and Zhu, X},
title = {A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {016005},
doi = {10.1088/1741-2560/12/1/016005},
pmid = {25461477},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Calibration ; Electroencephalography/*standards ; Female ; Humans ; Illusions/*physiology ; Imagination/*physiology ; Internationality ; Male ; Movement/*physiology ; Physical Stimulation/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Tendons/*physiology ; Vibration ; },
abstract = {OBJECTIVE: Lack of efficient calibration and task guidance in motor imagery (MI) based brain-computer interface (BCI) would result in the failure of communication or control, especially in patients, such as a stroke with motor impairment and intact sensation, locked-in state amyotrophic lateral sclerosis, in which the sources of data for calibration may worsen the subsequent decoding. In addition, enhancing the proprioceptive experience in MI might improve the BCI performance.<br><br>APPROACH: In this work, we propose a new calibrating and task guidance methodology to further improve the MI BCI, exploiting the afferent nerve system through tendon vibration stimulation to induce a sensation with kinesthesia illusion. A total of 30 subjects' experiments were carried out, and randomly divided into a control group (control-group) and calibration and task guidance group (CTG-group).<br><br>MAIN RESULTS: Online experiments have shown that MI could be decoded by classifier calibrated solely using sensation data, with 8 of the 15 subjects in the CTG-Group above 80%, 3 above 95% and all above 65%. Offline chronological cross-validation analysis shows that it has reached a comparable performance with the traditional calibration method (F (1, 14) = 0.14, P = 0.7176). In addition, the discrimination accuracy of MI in the CTG-Group is significantly 12.17% higher on average than that in the control-group (unpaired-T test, P = 0.0086), and illusory sensation indicates no significant difference (unpaired-T test, p = 0.3412). The finding of the existed similarity of the discriminative brain patterns and grand averaged ERD/ERS between imagined movement (actively induced) and illusory movement (passively evoked) also validates the proposed calibration and task guidance framework.<br><br>SIGNIFICANCE: The cognitive complexity of the illusory sensation task is much lower and more objective than that of MI. In addition, subjects' kinesthetic experience mentally simulated during the MI task might be enhanced by accessing sensory experiences from the illusory stimulation. This sensory stimulation aided BCI design could help make MI BCI more applicable.},
}
@article {pmid25461213,
year = {2014},
author = {Ritaccio, A and Brunner, P and Gunduz, A and Hermes, D and Hirsch, LJ and Jacobs, J and Kamada, K and Kastner, S and Knight, RT and Lesser, RP and Miller, K and Sejnowski, T and Worrell, G and Schalk, G},
title = {Proceedings of the Fifth International Workshop on Advances in Electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {41},
number = {},
pages = {183-192},
pmid = {25461213},
issn = {1525-5069},
support = {R01-EB000856/EB/NIBIB NIH HHS/United States ; R01-EY017699/EY/NEI NIH HHS/United States ; R01-NS63039/NS/NINDS NIH HHS/United States ; R01 EY023656/EY/NEI NIH HHS/United States ; R21-EY023656/EY/NEI NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R37-NS21135/NS/NINDS NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; R01 NS063039/NS/NINDS NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; R01-NS065186/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Brain Mapping/*methods ; *Cerebral Cortex/physiology/physiopathology ; *Congresses as Topic ; Electroencephalography/instrumentation/*methods ; Humans ; },
abstract = {The Fifth International Workshop on Advances in Electrocorticography convened in San Diego, CA, on November 7-8, 2013. Advancements in methodology, implementation, and commercialization across both research and in the interval year since the last workshop were the focus of the gathering. Electrocorticography (ECoG) is now firmly established as a preferred signal source for advanced research in functional, cognitive, and neuroprosthetic domains. Published output in ECoG fields has increased tenfold in the past decade. These proceedings attempt to summarize the state of the art.},
}
@article {pmid25460808,
year = {2015},
author = {Jorfi, M and Skousen, JL and Weder, C and Capadona, JR},
title = {Progress towards biocompatible intracortical microelectrodes for neural interfacing applications.},
journal = {Journal of neural engineering},
volume = {12},
number = {1},
pages = {011001},
pmid = {25460808},
issn = {1741-2552},
support = {I01 RX000334/RX/RRD VA/United States ; I01 RX001495/RX/RRD VA/United States ; R01 NS082404/NS/NINDS NIH HHS/United States ; 1R01NS082404-01A1/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Coated Materials, Biocompatible/*chemical synthesis ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Microelectrodes ; Neurons/*physiology ; },
abstract = {To ensure long-term consistent neural recordings, next-generation intracortical microelectrodes are being developed with an increased emphasis on reducing the neuro-inflammatory response. The increased emphasis stems from the improved understanding of the multifaceted role that inflammation may play in disrupting both biologic and abiologic components of the overall neural interface circuit. To combat neuro-inflammation and improve recording quality, the field is actively progressing from traditional inorganic materials towards approaches that either minimizes the microelectrode footprint or that incorporate compliant materials, bioactive molecules, conducting polymers or nanomaterials. However, the immune-privileged cortical tissue introduces an added complexity compared to other biomedical applications that remains to be fully understood. This review provides a comprehensive reflection on the current understanding of the key failure modes that may impact intracortical microelectrode performance. In addition, a detailed overview of the current status of various materials-based approaches that have gained interest for neural interfacing applications is presented, and key challenges that remain to be overcome are discussed. Finally, we present our vision on the future directions of materials-based treatments to improve intracortical microelectrodes for neural interfacing.},
}
@article {pmid25459407,
year = {2014},
author = {Shenoy, KV and Carmena, JM},
title = {Combining decoder design and neural adaptation in brain-machine interfaces.},
journal = {Neuron},
volume = {84},
number = {4},
pages = {665-680},
doi = {10.1016/j.neuron.2014.08.038},
pmid = {25459407},
issn = {1097-4199},
support = {DP1-OD006409/OD/NIH HHS/United States ; TRO1-NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; Motor Cortex/physiology ; Movement/*physiology ; Neurons/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system.},
}
@article {pmid25459058,
year = {2015},
author = {Zhuang, L and Guo, T and Cao, D and Ling, L and Su, K and Hu, N and Wang, P},
title = {Detection and classification of natural odors with an in vivo bioelectronic nose.},
journal = {Biosensors &amp; bioelectronics},
volume = {67},
number = {},
pages = {694-699},
doi = {10.1016/j.bios.2014.09.102},
pmid = {25459058},
issn = {1873-4235},
mesh = {Animals ; *Biosensing Techniques ; Brain-Computer Interfaces ; Male ; Microelectrodes ; Neurons/cytology/physiology ; Nose/physiology ; Odorants/*analysis ; Olfactory Bulb/cytology/*physiology ; Rats ; Smell/physiology ; },
abstract = {The mammalian olfactory system is recognized as one of the most effective chemosensing systems. We thus investigated the potential of utilizing the rat's olfactory system to detect odors. By chronically coupling multiple microelectrodes to olfactory bulb of behaving rats, we extract an array of mitral/tufted cells (M/Ts) which could generate odor-specific temporal patterns of neural discharge. We performed multidimensional analysis of recorded M/Ts, finding that natural odors released from different fruit lead to distinct odor response patterns. Thus an array of M/Ts carried sufficient information to discriminate odors. This novel brain-machine interface using rat's olfaction presents a promising method for odor detection and discrimination, and it is the first step towards in vivo bioelectronic nose equipped with biological olfaction and artificial devices.},
}
@article {pmid25454278,
year = {2015},
author = {Reynolds, C and Osuagwu, BA and Vuckovic, A},
title = {Influence of motor imagination on cortical activation during functional electrical stimulation.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {7},
pages = {1360-1369},
pmid = {25454278},
issn = {1872-8952},
mesh = {Adult ; Afferent Pathways/physiology ; *Brain-Computer Interfaces ; Cortical Synchronization/physiology ; Efferent Pathways/physiology ; Electric Stimulation/*methods ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; },
abstract = {OBJECTIVE: Motor imagination (MI) and functional electrical stimulation (FES) can activate the sensory-motor cortex through efferent and afferent pathways respectively. Motor imagination can be used as a control strategy to activate FES through a brain-computer interface as the part of a rehabilitation therapy. It is believed that precise timing between the onset of MI and FES is important for strengthening the cortico-spinal pathways but it is not known whether prolonged MI during FES influences cortical response.<br><br>METHODS: Electroencephalogram was measured in ten able-bodied participants using MI strategy to control FES through a BCI system. Event related synchronisation/desynchronisation (ERS/ERD) over the sensory-motor cortex was analysed and compared in three paradigms: MI before FES, MI before and during FES and FES alone activated automatically.<br><br>RESULTS: MI practiced both before and during FES produced strongest ERD. When MI only preceded FES it resulted in a weaker beta ERD during FES than when FES was activated automatically. Following termination of FES, beta ERD returns to the baseline level within 0.5s while alpha ERD took longer than 1s.<br><br>CONCLUSIONS: When MI and FES are combined for rehabilitation purposes it is recommended that MI is practiced throughout FES activation period.<br><br>SIGNIFICANCE: The study is relevant for neurorehabilitation of movement.},
}
@article {pmid25451480,
year = {2015},
author = {Naselaris, T and Olman, CA and Stansbury, DE and Ugurbil, K and Gallant, JL},
title = {A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes.},
journal = {NeuroImage},
volume = {105},
number = {},
pages = {215-228},
pmid = {25451480},
issn = {1095-9572},
support = {P41 EB015894/EB/NIBIB NIH HHS/United States ; R01 EY019684/EY/NEI NIH HHS/United States ; P30 EY011374/EY/NEI NIH HHS/United States ; NEI R01-EY023384/EY/NEI NIH HHS/United States ; R01 EY023384/EY/NEI NIH HHS/United States ; P30 NS076408/NS/NINDS NIH HHS/United States ; NEI R01-EY019684/EY/NEI NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Pattern Recognition, Visual/*physiology ; Visual Cortex/*physiology ; },
abstract = {Recent multi-voxel pattern classification (MVPC) studies have shown that in early visual cortex patterns of brain activity generated during mental imagery are similar to patterns of activity generated during perception. This finding implies that low-level visual features (e.g., space, spatial frequency, and orientation) are encoded during mental imagery. However, the specific hypothesis that low-level visual features are encoded during mental imagery is difficult to directly test using MVPC. The difficulty is especially acute when considering the representation of complex, multi-object scenes that can evoke multiple sources of variation that are distinct from low-level visual features. Therefore, we used a voxel-wise modeling and decoding approach to directly test the hypothesis that low-level visual features are encoded in activity generated during mental imagery of complex scenes. Using fMRI measurements of cortical activity evoked by viewing photographs, we constructed voxel-wise encoding models of tuning to low-level visual features. We also measured activity as subjects imagined previously memorized works of art. We then used the encoding models to determine if putative low-level visual features encoded in this activity could pick out the imagined artwork from among thousands of other randomly selected images. We show that mental images can be accurately identified in this way; moreover, mental image identification accuracy depends upon the degree of tuning to low-level visual features in the voxels selected for decoding. These results directly confirm the hypothesis that low-level visual features are encoded during mental imagery of complex scenes. Our work also points to novel forms of brain-machine interaction: we provide a proof-of-concept demonstration of an internet image search guided by mental imagery.},
}
@article {pmid25449558,
year = {2015},
author = {Marchetti, M and Priftis, K},
title = {Brain-computer interfaces in amyotrophic lateral sclerosis: A metanalysis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {6},
pages = {1255-1263},
doi = {10.1016/j.clinph.2014.09.017},
pmid = {25449558},
issn = {1872-8952},
mesh = {Amyotrophic Lateral Sclerosis/diagnosis/*physiopathology/*therapy ; *Brain-Computer Interfaces/trends ; *Communication Devices for People with Disabilities/trends ; Electroencephalography/methods/trends ; Humans ; },
abstract = {OBJECTIVE: Despite recent groundbreaking findings on the genetic causes of amyotrophic lateral sclerosis (ALS), and improvements on neuroimaging techniques for ALS diagnosis have been reported, the main clinical intervention in ALS remains palliative care. Brain-computer interfaces (BCIs) have been proposed as a channel of communication and control for ALS patients. The present metanalysis was performed to test the evidence of BCI effectiveness in ALS, and to investigate whether the promising aims emerged from the first studies have been reached.<br><br>METHODS: Studies on ALS patients tested with BCIs, until June 2013, were searched in PubMed and PsychInfo. The random-effect approach was used to compute the pooled effectiveness of BCI in ALS. A meta-regression was performed to test whether there was a BCI performance improvement as a function of time. Finally, BCI effectiveness for complete paralyzed ALS patients was tested. Twenty-seven studies were eligible for metanalysis.<br><br>RESULTS: The pooled classification accuracy (C.A.) of ALS patients with BCI was about 70%, but this estimation was affected by significant heterogeneity and inconsistency. C.A. did not significantly increase as a function of time. C.A. of completely paralyzed ALS patients with BCI did not differ from that obtained by chance.<br><br>CONCLUSIONS: After 15 years of studies, it is as yet not possible to reliably establish the effectiveness of BCIs.<br><br>SIGNIFICANCE: Methodological issues among the retrieved studies should be addressed and new well-powered studies should be conducted to confirm BCI effectiveness for ALS patients.},
}
@article {pmid25447224,
year = {2015},
author = {Ethier, C and Miller, LE},
title = {Brain-controlled muscle stimulation for the restoration of motor function.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {180-190},
pmid = {25447224},
issn = {1095-953X},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; #NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; Brain-Computer Interfaces/*trends ; Electric Stimulation Therapy/instrumentation/methods/*trends ; Electrocorticography/instrumentation/methods ; Electroencephalography/instrumentation/methods ; Humans ; Motor Cortex/physiopathology ; Muscle, Skeletal/innervation/*physiopathology ; Neurons/physiology ; Paralysis/etiology/physiopathology/*rehabilitation ; *Psychomotor Performance ; Recovery of Function ; Spinal Cord Injuries/*complications ; },
abstract = {Loss of the ability to move, as a consequence of spinal cord injury or neuromuscular disorder, has devastating consequences for the paralyzed individual, and great economic consequences for society. Functional electrical stimulation (FES) offers one means to restore some mobility to these individuals, improving not only their autonomy, but potentially their general health and well-being as well. FES uses electrical stimulation to cause the paralyzed muscles to contract. Existing clinical systems require the stimulation to be preprogrammed, with the patient typically using residual voluntary movement of another body part to trigger and control the patterned stimulation. The rapid development of neural interfacing in the past decade offers the promise of dramatically improved control for these patients, potentially allowing continuous control of FES through signals recorded from motor cortex, as the patient attempts to control the paralyzed body part. While application of these 'brain-machine interfaces' (BMIs) has undergone dramatic development for control of computer cursors and even robotic limbs, their use as an interface for FES has been much more limited. In this review, we consider both FES and BMI technologies and discuss the prospect for combining the two to provide important new options for paralyzed individuals.},
}
@article {pmid25438320,
year = {2015},
author = {Mainsah, BO and Morton, KD and Collins, LM and Sellers, EW and Throckmorton, CS},
title = {Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {737-743},
pmid = {25438320},
issn = {1558-0210},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R33DC010470-03/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Humans ; Machine Learning ; *Natural Language Processing ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Word Processing/*methods ; },
abstract = {P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies (> 70%) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behavior or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform offline simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that flagged, deleted and replaced erroneous characters (-47-0%) . Providing additional information about the likelihood of characters to a dictionary-based correction further improves accuracy. Our Bayesian dictionary-based correction algorithm that utilizes P300 classifier confidences performed comparably (44-416%) to an oracle ErrP dictionary-based method that assumed perfect ErrP classification (43-433%).},
}
@article {pmid25437633,
year = {2014},
author = {Kiguchi, M and Funane, T},
title = {Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes.},
journal = {Journal of biomedical optics},
volume = {19},
number = {11},
pages = {110505},
doi = {10.1117/1.JBO.19.11.110505},
pmid = {25437633},
issn = {1560-2281},
mesh = {*Algorithms ; Cerebrovascular Circulation/physiology ; Hemoglobins/analysis ; Humans ; Models, Biological ; Phantoms, Imaging ; Scalp/*blood supply ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {A real-time algorithm for removing scalp-blood signals from functional near-infrared spectroscopy signals is proposed. Scalp and deep signals have different dependencies on the source-detector distance. These signals were separated using this characteristic. The algorithm was validated through an experiment using a dynamic phantom in which shallow and deep absorptions were independently changed. The algorithm for measurement of oxygenated and deoxygenated hemoglobins using two wavelengths was explicitly obtained. This algorithm is potentially useful for real-time systems, e.g., brain-computer interfaces and neuro-feedback systems.},
}
@article {pmid25435815,
year = {2014},
author = {Bennett, A and Yukich, J and Miller, JM and Vounatsou, P and Hamainza, B and Ingwe, MM and Moonga, HB and Kamuliwo, M and Keating, J and Smith, TA and Steketee, RW and Eisele, TP},
title = {A methodological framework for the improved use of routine health system data to evaluate national malaria control programs: evidence from Zambia.},
journal = {Population health metrics},
volume = {12},
number = {1},
pages = {30},
pmid = {25435815},
issn = {1478-7954},
abstract = {BACKGROUND: Due to challenges in laboratory confirmation, reporting completeness, timeliness, and health access, routine incidence data from health management information systems (HMIS) have rarely been used for the rigorous evaluation of malaria control program scale-up in Africa.<br><br>METHODS: We used data from the Zambia HMIS for 2009-2011, a period of rapid diagnostic and reporting scale-up, to evaluate the association between insecticide-treated net (ITN) program intensity and district-level monthly confirmed outpatient malaria incidence using a dose-response national platform approach with district-time units as the unit of analysis. A Bayesian geostatistical model was employed to estimate longitudinal district-level ITN coverage from household survey and programmatic data, and a conditional autoregressive model (CAR) was used to impute missing HMIS data. The association between confirmed malaria case incidence and ITN program intensity was modeled while controlling for known confounding factors, including climate variability, reporting, testing, treatment-seeking, and access to health care, and additionally accounting for spatial and temporal autocorrelation.<br><br>RESULTS: An increase in district level ITN coverage of one ITN per household was associated with an estimated 27% reduction in confirmed case incidence overall (incidence rate ratio (IRR): 0&#x2009;&#xb7;&#x2009;73, 95% Bayesian Credible Interval (BCI): 0&#x2009;&#xb7;&#x2009;65-0&#x2009;&#xb7;&#x2009;81), and a 41% reduction in areas of lower malaria burden.<br><br>CONCLUSIONS: When improved through comprehensive parasitologically confirmed case reporting, HMIS data can become a valuable tool for evaluating malaria program scale-up. Using this approach we provide further evidence that increased ITN coverage is associated with decreased malaria morbidity and use of health services for malaria illness in Zambia. These methods and results are broadly relevant for malaria program evaluations currently ongoing in sub-Saharan Africa, especially as routine confirmed case data improve.},
}
@article {pmid25434791,
year = {2015},
author = {Hevér, NV and Péntek, M and Balló, A and Gulácsi, L and Baji, P and Brodszky, V and Damásdi, M and Bognár, Z and Tóth, G and Buzogány, I and Szántó, &#xc1;},
title = {Health related quality of life in patients with bladder cancer: a cross-sectional survey and validation study of the Hungarian version of the Bladder Cancer Index.},
journal = {Pathology oncology research : POR},
volume = {21},
number = {3},
pages = {619-627},
pmid = {25434791},
issn = {1532-2807},
mesh = {Aged ; Cross-Sectional Studies ; Female ; Follow-Up Studies ; *Health Status ; Humans ; Hungary ; Male ; *Models, Statistical ; Prognosis ; Psychometrics ; *Quality Indicators, Health Care ; *Quality of Life ; Surveys and Questionnaires ; Urinary Bladder Neoplasms/*psychology/therapy ; },
abstract = {Health-related quality of life (HRQoL) is an important outcome in oncology care although an underexplored area in bladder cancer (BC). Our aims were to assess HRQoL of patients with BC, analyse relationships between diverse HRQoL measures and validate the Hungarian version of the Bladder Cancer Index (BCI) questionnaire. A cross-sectional survey was performed among patients with BC (N = 151). Validated Hungarian versions of the FACT-Bl, SF-36 and EQ-5D were applied and SF-6D was derived. Psychometric analysis of the Hungarian BCI was performed. Pearson correlations between the five measures were analysed. Deterioration in SF-36 Physical Functioning was detected among patients aged 45-64 years. The EQ-5D score did not differ significantly from the age-matched population norm. Correlations between the FACT-Bl, EQ-5D and SF-6D utility measures were strong (r > 0.6). Cronbach alpha coefficients of the Hungarian BCI ranged from 0.75 to 0.97 and factor analysis confirmed that data fit to the six predefined subdomains. Test-retest correlations (reliability, N = 50) ranged from 0.67 to 0.87 and interscale correlations between urinary, bowel and sexual BCI domains were weak or moderate (r = 0.29 to 0.49). Convergent validity revealed a stronger correlation with FACT-Bl (r = 0.126 to 0.719) than with generic health state scores (r = 0.096 to 0.584). Results of divergent validity of the Hungarian BCI by treatment groups by Kruskal Wallis test were promising although limited by low sample sizes in cystectomy subgroups. Generic health state measures have limited capacity to capture HRQoL impact of BC. Validity tests yielded favourable results for the Hungarian BCI. Mapping studies to estimate utility scores from FACT-Bl are encouraged but less recommendable with the BCI.},
}
@article {pmid25424701,
year = {2015},
author = {Lend, AK and Kazantseva, A and Kivil, A and Valvere, V and Palm, K},
title = {Diagnostic significance of alternative splice variants of REST and DOPEY1 in the peripheral blood of patients with breast cancer.},
journal = {Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine},
volume = {36},
number = {4},
pages = {2473-2480},
pmid = {25424701},
issn = {1423-0380},
mesh = {Adult ; Aged ; Aged, 80 and over ; Alternative Splicing/genetics ; Biomarkers, Tumor/blood/*genetics ; Breast Neoplasms/*blood/drug therapy/*genetics/pathology ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Middle Aged ; Neoadjuvant Therapy ; Neoplasm Staging ; Nerve Tissue Proteins/*genetics ; Repressor Proteins/blood/*genetics ; },
abstract = {Changes in alternative splicing have been linked to cancer development. We hypothesized that changes occurring in tumor tissue can also be detected in the peripheral blood of cancer patients leading to discovery of blood biomarkers of breast cancer. Alternative splicing profiles of 94 genes were examined in cancerous breast tissue. Discriminating splice variants were analyzed in the peripheral blood of early stage (BCI/II) (stage I-II; n = 26), neoadjuvant receiving locally advanced breast cancer patients (LABC) (stage IIb-IIIa, b; n = 10) and healthy volunteers (n = 26) using qRT-PCR analysis. Changes in marker expression during neoadjuvant therapy were analyzed at 15 timepoints. High expression of REST-N50, the alternatively spliced variant of REST, was detected in the blood of LABC patients but not in BCI/II and healthy controls (p = 0.0032 and p = 0.0029, respectively). Expression levels of DOPEY1v2, the alternative splice variant of DOPEY1, in the blood could differentiate cancer from healthy controls (p = 0.024) and discriminate between patient groups (BCI/II vs LABC, p = 0.002). Positive response to neoadjuvant therapy of REST-N50-positive LABC patients correlated with a decrease in REST-N50 levels (p < 0.0001). Assessment of REST-N50 and DOPEY1v2 may prove useful in diagnostic blood tests of breast cancer. REST-N50 shows a high potential as a blood biomarker for evaluating the effectiveness of therapy in the neoadjuvant setting.},
}
@article {pmid25422454,
year = {2014},
author = {Graf, AB and Andersen, RA},
title = {Brain-machine interface for eye movements.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {111},
number = {49},
pages = {17630-17635},
pmid = {25422454},
issn = {1091-6490},
support = {R01 EY005522/EY/NEI NIH HHS/United States ; EY005522/EY/NEI NIH HHS/United States ; EY013337/EY/NEI NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; EY007492/EY/NEI NIH HHS/United States ; R01 EY007492/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Bayes Theorem ; Behavior ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Eye Movements/*physiology ; Haplorhini ; Humans ; Learning ; Male ; Neurodegenerative Diseases/immunology ; Neurons/physiology ; Paralysis/rehabilitation ; Parietal Lobe/physiology ; Reproducibility of Results ; Saccades/*physiology ; Time Factors ; },
abstract = {A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain-machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.},
}
@article {pmid25421909,
year = {2015},
author = {Fyfe, I},
title = {Neural repair and rehabilitation: Implant helps patient with incomplete locked-in syndrome.},
journal = {Nature reviews. Neurology},
volume = {11},
number = {1},
pages = {2},
pmid = {25421909},
issn = {1759-4766},
mesh = {*Brain-Computer Interfaces ; *Communication ; Female ; Humans ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
}
@article {pmid25420246,
year = {2014},
author = {Lee, JH and Delbruck, T and Pfeiffer, M and Park, PK and Shin, CW and Ryu, HE and Kang, BC},
title = {Real-time gesture interface based on event-driven processing from stereo silicon retinas.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {25},
number = {12},
pages = {2250-2263},
doi = {10.1109/TNNLS.2014.2308551},
pmid = {25420246},
issn = {2162-2388},
mesh = {*Brain-Computer Interfaces/trends ; *Computer Systems/trends ; *Gestures ; Humans ; Pattern Recognition, Automated/*methods/trends ; Photic Stimulation/methods ; *Retina/physiology ; Silicon/*chemistry ; },
abstract = {We propose a real-time hand gesture interface based on combining a stereo pair of biologically inspired event-based dynamic vision sensor (DVS) silicon retinas with neuromorphic event-driven postprocessing. Compared with conventional vision or 3-D sensors, the use of DVSs, which output asynchronous and sparse events in response to motion, eliminates the need to extract movements from sequences of video frames, and allows significantly faster and more energy-efficient processing. In addition, the rate of input events depends on the observed movements, and thus provides an additional cue for solving the gesture spotting problem, i.e., finding the onsets and offsets of gestures. We propose a postprocessing framework based on spiking neural networks that can process the events received from the DVSs in real time, and provides an architecture for future implementation in neuromorphic hardware devices. The motion trajectories of moving hands are detected by spatiotemporally correlating the stereoscopically verged asynchronous events from the DVSs by using leaky integrate-and-fire (LIF) neurons. Adaptive thresholds of the LIF neurons achieve the segmentation of trajectories, which are then translated into discrete and finite feature vectors. The feature vectors are classified with hidden Markov models, using a separate Gaussian mixture model for spotting irrelevant transition gestures. The disparity information from stereovision is used to adapt LIF neuron parameters to achieve recognition invariant of the distance of the user to the sensor, and also helps to filter out movements in the background of the user. Exploiting the high dynamic range of DVSs, furthermore, allows gesture recognition over a 60-dB range of scene illuminance. The system achieves recognition rates well over 90% under a variety of variable conditions with static and dynamic backgrounds with naïve users.},
}
@article {pmid25420067,
year = {2015},
author = {Dadarlat, MC and O'Doherty, JE and Sabes, PN},
title = {A learning-based approach to artificial sensory feedback leads to optimal integration.},
journal = {Nature neuroscience},
volume = {18},
number = {1},
pages = {138-144},
pmid = {25420067},
issn = {1546-1726},
support = {R01 EY015679/EY/NEI NIH HHS/United States ; T32 EY007120/EY/NEI NIH HHS/United States ; EY007120/EY/NEI NIH HHS/United States ; EY015679/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal/physiology ; Brain-Computer Interfaces ; Conditioning, Operant/physiology ; Feedback, Psychological/*physiology ; Learning/*physiology ; Macaca mulatta ; Male ; Photic Stimulation ; Psychomotor Performance/physiology ; Sensation/*physiology ; Somatosensory Cortex/physiology ; Visual Perception/physiology ; },
abstract = {Proprioception-the sense of the body's position in space-is important to natural movement planning and execution and will likewise be necessary for successful motor prostheses and brain-machine interfaces (BMIs). Here we demonstrate that monkeys were able to learn to use an initially unfamiliar multichannel intracortical microstimulation signal, which provided continuous information about hand position relative to an unseen target, to complete accurate reaches. Furthermore, monkeys combined this artificial signal with vision to form an optimal, minimum-variance estimate of relative hand position. These results demonstrate that a learning-based approach can be used to provide a rich artificial sensory feedback signal, suggesting a new strategy for restoring proprioception to patients using BMIs, as well as a powerful new tool for studying the adaptive mechanisms of sensory integration.},
}
@article {pmid25416371,
year = {2014},
author = {Ee, H},
title = {Business continuity 2014: From traditional to integrated Business Continuity Management.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {8},
number = {2},
pages = {102-105},
pmid = {25416371},
issn = {1749-9216},
mesh = {Commerce/*organization &amp; administration ; Disaster Planning/*organization &amp; administration ; Humans ; Risk Management/*organization &amp; administration ; },
abstract = {As global change continues to generate new challenges and potential threats to businesses, traditional business continuity management (BCM) slowly reveals its limitations and weak points to ensuring 'business resiliency' today. Consequently, BCM professionals also face the challenge of re-evaluating traditional concepts and introducing new strategies and industry best practices. This paper points to why traditional BCM is no longer sufficient in terms of enabling businesses to survive in today's high-risk environment. It also looks into some of the misconceptions about BCM and other stumbling blocks to establishing effective BCM today. Most importantly, however, this paper provides tips based on the Business Continuity Institute's (BCI) Good Practices Guideline (GPG) and the latest international BCM standard ISO 22301 on how to overcome the issues and challenges presented.},
}
@article {pmid25416195,
year = {2015},
author = {Chard, LS and Maniati, E and Wang, P and Zhang, Z and Gao, D and Wang, J and Cao, F and Ahmed, J and El Khouri, M and Hughes, J and Wang, S and Li, X and Denes, B and Fodor, I and Hagemann, T and Lemoine, NR and Wang, Y},
title = {A vaccinia virus armed with interleukin-10 is a promising therapeutic agent for treatment of murine pancreatic cancer.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {21},
number = {2},
pages = {405-416},
doi = {10.1158/1078-0432.CCR-14-0464},
pmid = {25416195},
issn = {1557-3265},
support = {12008/CRUK_/Cancer Research UK/United Kingdom ; MR/M015696/1/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Animals ; Cell Line, Tumor ; Interleukin-10/*genetics ; Lymphocytes/immunology ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Transgenic ; Neoplasm Transplantation ; Oncolytic Virotherapy ; Oncolytic Viruses/*genetics ; Pancreatic Neoplasms/immunology/*therapy ; Vaccinia virus/*genetics ; Virus Replication ; },
abstract = {PURPOSE: Vaccinia virus has strong potential as a novel therapeutic agent for treatment of pancreatic cancer. We investigated whether arming vaccinia virus with interleukin-10 (IL10) could enhance the antitumor efficacy with the view that IL10 might dampen the host immunity to the virus, increasing viral persistence, thus maximizing the oncolytic effect and antitumor immunity associated with vaccinia virus.<br><br>EXPERIMENTAL DESIGN: The antitumor efficacy of IL10-armed vaccinia virus (VVL&#x394;TK-IL10) and control VV&#x394;TK was assessed in pancreatic cancer cell lines, mice bearing subcutaneous pancreatic cancer tumors and a pancreatic cancer transgenic mouse model. Viral persistence within the tumors was examined and immune depletion experiments as well as immunophenotyping of splenocytes were carried out to dissect the functional mechanisms associated with the viral efficacy.<br><br>RESULTS: Compared with unarmed VVL&#x394;TK, VVL&#x394;TK-IL10 had a similar level of cytotoxicity and replication in vitro in murine pancreatic cancer cell lines, but rendered a superior antitumor efficacy in the subcutaneous pancreatic cancer model and a K-ras-p53 mutant-transgenic pancreatic cancer model after systemic delivery, with induction of long-term antitumor immunity. The antitumor efficacy of VVL&#x394;TK-IL10 was dependent on CD4(+) and CD8(+), but not NK cells. Clearance of VVL&#x394;TK-IL10 was reduced at early time points compared with the control virus. Treatment with VVL&#x394;TK-IL10 resulted in a reduction in virus-specific, but not tumor-specific CD8(+) cells compared with VVL&#x394;TK.<br><br>CONCLUSIONS: These results suggest that VVL&#x394;TK-IL10 has strong potential as an antitumor therapeutic for pancreatic cancer.},
}
@article {pmid25415989,
year = {2015},
author = {Dragas, J and Jackel, D and Hierlemann, A and Franke, F},
title = {Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {2},
pages = {149-158},
pmid = {25415989},
issn = {1558-0210},
support = {267351/ERC_/European Research Council/International ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Electrocardiography/*instrumentation/methods ; *Electrodes ; Equipment Design ; Equipment Failure Analysis ; Nerve Net/*physiology ; Neurons/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.},
}
@article {pmid25415550,
year = {2015},
author = {Vučković, A and Wallace, L and Allan, DB},
title = {Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study.},
journal = {Journal of neurologic physical therapy : JNPT},
volume = {39},
number = {1},
pages = {3-14},
doi = {10.1097/NPT.0000000000000063},
pmid = {25415550},
issn = {1557-0584},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Electroencephalography ; Hand/*physiopathology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Movement/physiology ; Muscle Strength/physiology ; Psychomotor Performance/*physiology ; Quadriplegia/physiopathology/*rehabilitation ; Quality of Life ; },
abstract = {BACKGROUND AND PURPOSE: Impaired hand function decreases quality of life in persons with tetraplegia. We tested functional electrical stimulation (FES) controlled by a hybrid brain-computer interface (BCI) for improving hand function in participants with tetraplegia.<br><br>METHODS: Two participants with subacute tetraplegia (participant 1: C5 Brown-Sequard syndrome, participant 2: complete C5 lesion) took part in this proof-of-concept study. The goal was to determine whether the BCI system could drive the FES device by accurately classifying participants' intent (open or close the hand). Participants 1 and 2 received 10 sessions and 4 sessions of BCI-FES, respectively. A novel time-switch BCI strategy based on motor imagery was used to activate the FES. In one session, we tested a hybrid BCI-FES based on 2 spontaneously generated brain rhythms: a sensory-motor rhythm during motor imagery to activate a stimulator and occipital alpha rhythms to deactivate the stimulator. Participants received BCI-FES therapy 2 to 3 times a week in addition to conventional therapy. Imagery ability and muscle strength were measured before and after treatment.<br><br>RESULTS: Visual feedback was associated with a 4-fold increase of brain response during motor imagery in both participants. For participant 1, classification accuracy (open/closed) for motor imagery-based BCI was 83.5% (left hand) and 83.8% (right hand); participant 2 had a classification accuracy of 83.8% for the right hand. Participant 1 had moderate improvement in muscle strength, while there was no change for participant 2.<br><br>DISCUSSION AND CONCLUSION: We demonstrated feasibility of BCI-FES, using 2 naturally generated brain rhythms. Studies on a larger number of participants are needed to separate the effects of BCI training from effects of conventional therapy.Video Abstract available. (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A84) for more insights from the authors.},
}
@article {pmid25411486,
year = {2014},
author = {Knudsen, EB and Powers, ME and Moxon, KA},
title = {Dissociating movement from movement timing in the rat primary motor cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {34},
number = {47},
pages = {15576-15586},
pmid = {25411486},
issn = {1529-2401},
support = {R01 NS057419/NS/NINDS NIH HHS/United States ; NS057419/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Decerebrate State/physiopathology ; Electromyography ; Hindlimb/innervation/physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/physiology ; Rats ; Rats, Long-Evans ; Reward ; Stereotyped Behavior/physiology ; },
abstract = {Neural encoding of the passage of time to produce temporally precise movements remains an open question. Neurons in several brain regions across different experimental contexts encode estimates of temporal intervals by scaling their activity in proportion to the interval duration. In motor cortex the degree to which this scaled activity relies upon afferent feedback and is guided by motor output remains unclear. Using a neural reward paradigm to dissociate neural activity from motor output before and after complete spinal transection, we show that temporally scaled activity occurs in the rat hindlimb motor cortex in the absence of motor output and after transection. Context-dependent changes in the encoding are plastic, reversible, and re-established following injury. Therefore, in the absence of motor output and despite a loss of afferent feedback, thought necessary for timed movements, the rat motor cortex displays scaled activity during a broad range of temporally demanding tasks similar to that identified in other brain regions.},
}
@article {pmid25405234,
year = {2014},
author = {Jun, YB and Ahn, SS and Muhiuddin, G},
title = {Hesitant fuzzy soft subalgebras and ideals in BCK/BCI-algebras.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {763929},
pmid = {25405234},
issn = {1537-744X},
mesh = {Algorithms ; Decision Making ; *Fuzzy Logic ; *Models, Statistical ; Uncertainty ; },
abstract = {As a link between classical soft sets and hesitant fuzzy sets, the notion of hesitant fuzzy soft sets is introduced and applied to a decision making problem in the papers by Babitha and John (2013) and Wang et al. (2014). The aim of this paper is to apply hesitant fuzzy soft set for dealing with several kinds of theories in BCK/BCI-algebras. The notions of hesitant fuzzy soft subalgebras and (closed) hesitant fuzzy soft ideals are introduced, and related properties are investigated. Relations between a hesitant fuzzy soft subalgebra and a (closed) hesitant fuzzy soft ideal are discussed. Conditions for a hesitant fuzzy soft set to be a hesitant fuzzy soft subalgebra are given, and conditions for a hesitant fuzzy soft subalgebra to be a hesitant fuzzy soft ideal are provided. Characterizations of a (closed) hesitant fuzzy soft ideal are considered.},
}
@article {pmid25404753,
year = {2015},
author = {Hsu, WY},
title = {Enhancing the performance of motor imagery EEG classification using phase features.},
journal = {Clinical EEG and neuroscience},
volume = {46},
number = {2},
pages = {113-118},
doi = {10.1177/1550059414555123},
pmid = {25404753},
issn = {1550-0594},
mesh = {Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; },
abstract = {An electroencephalogram recognition system considering phase features is proposed to enhance the performance of motor imagery classification in this study. It mainly consists of feature extraction, feature selection and classification. Surface Laplacian filter is used for background removal. Several potential features, including phase features, are then extracted to enhance the classification accuracy. Next, genetic algorithm is used to select sub-features from feature combination. Finally, selected features are classified by extreme learning machine. Compared with "without phase features" and linear discriminant analysis on motor imagery data from 2 data sets, the results denote that the proposed system achieves enhanced performance, which is suitable for the brain-computer interface applications.},
}
@article {pmid25401520,
year = {2014},
author = {Chen, CC and Syue, KS and Li, KC and Yeh, SC},
title = {Neuronal correlates of a virtual-reality-based passive sensory P300 network.},
journal = {PloS one},
volume = {9},
number = {11},
pages = {e112228},
pmid = {25401520},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; *Computer Simulation ; *Event-Related Potentials, P300 ; Humans ; Male ; *Models, Neurological ; Young Adult ; },
abstract = {P300, a positive event-related potential (ERP) evoked at around 300 ms after stimulus, can be elicited using an active or passive oddball paradigm. Active P300 requires a person's intentional response, whereas passive P300 does not require an intentional response. Passive P300 has been used in incommunicative patients for consciousness detection and brain computer interface. Active and passive P300 differ in amplitude, but not in latency or scalp distribution. However, no study has addressed the mechanism underlying the production of passive P300. In particular, it remains unclear whether the passive P300 shares an identical active P300 generating network architecture when no response is required. This study aims to explore the hierarchical network of passive sensory P300 production using dynamic causal modelling (DCM) for ERP and a novel virtual reality (VR)-based passive oddball paradigm. Moreover, we investigated the causal relationship of this passive P300 network and the changes in connection strength to address the possible functional roles. A classical ERP analysis was performed to verify that the proposed VR-based game can reliably elicit passive P300. The DCM results suggested that the passive and active P300 share the same parietal-frontal neural network for attentional control and, underlying the passive network, the feed-forward modulation is stronger than the feed-back one. The functional role of this forward modulation may indicate the delivery of sensory information, automatic detection of differences, and stimulus-driven attentional processes involved in performing this passive task. To our best knowledge, this is the first study to address the passive P300 network. The results of this study may provide a reference for future clinical studies on addressing the network alternations under pathological states of incommunicative patients. However, caution is required when comparing patients' analytic results with this study. For example, the task presented here is not applicable to incommunicative patients.},
}
@article {pmid25398273,
year = {2014},
author = {López-Larraz, E and Montesano, L and Gil-Agudo, &#xc1; and Minguez, J},
title = {Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {153},
pmid = {25398273},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Intention ; Male ; Middle Aged ; Motor Cortex/physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/*rehabilitation ; Upper Extremity/*physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Brain-machine interfaces (BMI) have recently been integrated within motor rehabilitation therapies by actively involving the central nervous system (CNS) within the exercises. For instance, the online decoding of intention of motion of a limb from pre-movement EEG correlates is being used to convert passive rehabilitation strategies into active ones mediated by robotics. As early stages of upper limb motor rehabilitation usually focus on analytic single-joint mobilizations, this paper investigates the feasibility of building BMI decoders for these specific types of movements.<br><br>METHODS: Two different experiments were performed within this study. For the first one, six healthy subjects performed seven self-initiated upper-limb analytic movements, involving from proximal to distal articulations. For the second experiment, three spinal cord injury patients performed two of the previously studied movements with their healthy elbow and paralyzed wrist. In both cases EEG neural correlates such as the event-related desynchronization (ERD) and movement related cortical potentials (MRCP) were analyzed, as well as the accuracies of continuous decoders built using the pre-movement features of these correlates (i.e., the intention of motion was decoded before movement onset).<br><br>RESULTS: The studied movements could be decoded in both healthy subjects and patients. For healthy subjects there were significant differences in the EEG correlates and decoding accuracies, dependent on the moving joint. Percentages of correctly anticipated trials ranged from 75% to 40% (with chance level being around 20%), with better performances for proximal than for distal movements. For the movements studied for the SCI patients the accuracies were similar to the ones of the healthy subjects.<br><br>CONCLUSIONS: This paper shows how it is possible to build continuous decoders to detect movement intention from EEG correlates for seven different upper-limb analytic movements. Furthermore we report differences in accuracies among movements, which might have an impact on the design of the rehabilitation technologies that will integrate this new type of information. The applicability of the decoders was shown in a clinical population, with similar performances between healthy subjects and patients.},
}
@article {pmid25398134,
year = {2014},
author = {Duszyk, A and Bierzyńska, M and Radzikowska, Z and Milanowski, P and Kuś, R and Suffczyński, P and Michalska, M and Łabęcki, M and Zwoliński, P and Durka, P},
title = {Towards an optimization of stimulus parameters for brain-computer interfaces based on steady state visual evoked potentials.},
journal = {PloS one},
volume = {9},
number = {11},
pages = {e112099},
pmid = {25398134},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Color ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular ; Humans ; Male ; *Photic Stimulation ; Time Factors ; },
abstract = {Efforts to construct an effective brain-computer interface (BCI) system based on Steady State Visual Evoked Potentials (SSVEP) commonly focus on sophisticated mathematical methods for data analysis. The role of different stimulus features in evoking strong SSVEP is less often considered and the knowledge on the optimal stimulus properties is still fragmentary. The goal of this study was to provide insight into the influence of stimulus characteristics on the magnitude of SSVEP response. Five stimuli parameters were tested: size, distance, colour, shape, and presence of a fixation point in the middle of each flickering field. The stimuli were presented on four squares on LCD screen, with each square highlighted by LEDs flickering with different frequencies. Brighter colours and larger dimensions of flickering fields resulted in a significantly stronger SSVEP response. The distance between stimulation fields and the presence or absence of the fixation point had no significant effect on the response. Contrary to a popular belief, these results suggest that absence of the fixation point does not reduce the magnitude of SSVEP response. However, some parameters of the stimuli such as colour and the size of the flickering field play an important role in evoking SSVEP response, which indicates that stimuli rendering is an important factor in building effective SSVEP based BCI systems.},
}
@article {pmid25397998,
year = {2015},
author = {Gorsich, EE and Bengis, RG and Ezenwa, VO and Jolles, AE},
title = {Evaluation of the sensitivity and specificity of an enzyme-linked immunosorbent assay for diagnosing brucellosis in African buffalo (Syncerus caffer).},
journal = {Journal of wildlife diseases},
volume = {51},
number = {1},
pages = {9-18},
doi = {10.7589/2013-12-334},
pmid = {25397998},
issn = {1943-3700},
mesh = {Animals ; Brucellosis/diagnosis/*veterinary ; *Buffaloes ; Enzyme-Linked Immunosorbent Assay/methods/*veterinary ; Sensitivity and Specificity ; },
abstract = {Brucellosis is a disease of veterinary and public health importance worldwide. In sub-Saharan Africa, where the bacterium Brucella abortus has been identified in several free-ranging wildlife species, successful disease control may be dependent on accurate detection in wildlife reservoirs, including African buffalo (Syncerus caffer). We estimated the sensitivity and specificity of a commercial enzyme-linked immunosorbent assay (ELISA) (IDEXX Brucellosis Serum Ab test, IDEXX Laboratories, Westbrook, Maine, USA) for B. abortus based on a data set of 571 serum samples from 258 buffalo in the Kruger National Park, South Africa. We defined a pseudogold standard test result as those buffalo that were consistently positive or negative on two additional serologic tests, namely, the rose bengal test (RBT) and the complement fixation test (CFT). The ELISA's cutoff value was selected using receiver operating characteristics analysis, the pseudogold standard, and a threshold criterion that maximizes the total sensitivity and specificity. Then, we estimated the sensitivity and specificity of all three tests using Bayesian inference and latent class analysis. The ELISA had an estimated sensitivity of 0.928 (95% Bayesian posterior credibility interval [95% BCI] = 0.869-0.974) and specificity of 0.870 (95% BCI = 0.836-0.900). Compared with the ELISA, the RBT had a higher estimated sensitivity of 0.986 (95% BCI = 0.928-0.999), and both the RBT and CFT had higher specificities, estimated to be 0.992 (95% BCI = 0.971-0.996) and 0.998 (95% BCI = 0.992-0.999), respectively. Therefore, no single serologic test perfectly detected the antibody. However, after adjustment of cutoff values for South African conditions, the IDEXX Brucellosis Serum Ab Test may be a valuable additional screening test for brucellosis in Kruger National Park's African buffalo.},
}
@article {pmid25395940,
year = {2014},
author = {Dudzińska, M and Tarach, JS and Burroughs, TE and Zwolak, A and Matuszek, B and Smoleń, A and Nowakowski, A},
title = {Validation of the Polish version of Diabetes Quality of Life - Brief Clinical Inventory (DQL-BCI) among patients with type 2 diabetes.},
journal = {Archives of medical science : AMS},
volume = {10},
number = {5},
pages = {891-898},
pmid = {25395940},
issn = {1734-1922},
abstract = {INTRODUCTION: The aim of the study was to develop a Polish version of the Diabetes Quality of Life Brief Clinical Inventory (DQL-BCI) and to perform validating evaluation of selected psychometric aspects.<br><br>MATERIAL AND METHODS: The translation process was performed in accordance with generally accepted international principles of translation and cultural adaptation of measurement tools. Two hundred and seventy-four subjects with type 2 diabetes completed the Polish version of DQL-BCI, the generic EQ-5D questionnaire and the diabetes-specific DSC-R. The examination provides information about the reliability (internal consistency, test-retest) and the construct validity of the studied tool (the relationship between the DQL-BCI score and EQ-5D and DSC-R scales, as well as selected clinical patient characteristics).<br><br>RESULTS: Cronbach's &#x3b1; (internal consistency) for the translated version of DQL-BCI was 0.76. Test-retest Pearson correlation coefficient was 0.96. Spearman's coefficient correlation between DQL-BCI score and EQ-5D index and EQ-VAS were 0.6 (p = 0.0000001) and 0.61 (p = 0.0000001) respectively. The correlation between scores of the examined tool and DSC-R total score was -0.6 (p = 0.0000001). Quality of life was lower among patients with microvascular as well as macrovascular complications and with occurring hypoglycemic episodes.<br><br>CONCLUSIONS: The result of this study is the Polish scale used to test the quality of life of patients with diabetes, which includes the range of problems faced by patients while maintaining a patient-friendly form. High reliability of the scale and good construct validity qualify the Polish version of DQL-BCI as a reliable tool in both research and individual diagnostics.},
}
@article {pmid25394671,
year = {2015},
author = {Durgan, J and Tao, G and Walters, MS and Florey, O and Schmidt, A and Arbelaez, V and Rosen, N and Crystal, RG and Hall, A},
title = {SOS1 and Ras regulate epithelial tight junction formation in the human airway through EMP1.},
journal = {EMBO reports},
volume = {16},
number = {1},
pages = {87-96},
pmid = {25394671},
issn = {1469-3178},
support = {16337/CRUK_/Cancer Research UK/United Kingdom ; R01 GM081435/GM/NIGMS NIH HHS/United States ; HL107882/HL/NHLBI NIH HHS/United States ; C47718/A16337/CRUK_/Cancer Research UK/United Kingdom ; GM081435/GM/NIGMS NIH HHS/United States ; R01 HL107882/HL/NHLBI NIH HHS/United States ; P30 CA008748/CA/NCI NIH HHS/United States ; CA008748/CA/NCI NIH HHS/United States ; },
mesh = {Bronchi/*cytology ; Cell Line ; Epithelial Cells/metabolism ; Gene Expression Regulation ; Humans ; Lung Neoplasms/genetics ; MAP Kinase Signaling System ; Neoplasm Proteins/genetics/*metabolism ; Oligonucleotide Array Sequence Analysis ; Receptors, Cell Surface/genetics/*metabolism ; SOS1 Protein/genetics/*metabolism ; Tight Junctions/*metabolism ; ras Proteins/genetics/*metabolism ; },
abstract = {The human airway is lined with respiratory epithelial cells, which create a critical barrier through the formation of apical tight junctions. To investigate the molecular mechanisms underlying this process, an RNAi screen for guanine nucleotide exchange factors (GEFs) was performed in human bronchial epithelial cells (16HBE). We report that SOS1, acting through the Ras/MEK/ERK pathway, is essential for tight junction formation. Global microarray analysis identifies epithelial membrane protein 1 (EMP1), an integral tetraspan membrane protein, as a major transcriptional target. EMP1 is indispensable for tight junction formation and function in 16HBE cells and in a human airway basal progenitor-like cell line (BCi-NS1.1). Furthermore, EMP1 is significantly downregulated in human lung cancers. Together, these data identify important roles for SOS1/Ras and EMP1 in tight junction assembly during airway morphogenesis.},
}
@article {pmid25394574,
year = {2014},
author = {Hall, TM and Nazarpour, K and Jackson, A},
title = {Real-time estimation and biofeedback of single-neuron firing rates using local field potentials.},
journal = {Nature communications},
volume = {5},
number = {},
pages = {5462},
pmid = {25394574},
issn = {2041-1723},
support = {086561//Wellcome Trust/United Kingdom ; G0802195/MRC_/Medical Research Council/United Kingdom ; K501396//Medical Research Council/United Kingdom ; },
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Female ; Macaca mulatta ; *Neurofeedback/methods ; Neurons/*physiology ; },
abstract = {The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain-machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.},
}
@article {pmid25394419,
year = {2014},
author = {Revechkis, B and Aflalo, TN and Kellis, S and Pouratian, N and Andersen, RA},
title = {Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066014},
pmid = {25394419},
issn = {1741-2552},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Macaca mulatta ; Male ; *Neural Prostheses ; Parietal Lobe/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding.<br><br>APPROACH: A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality.<br><br>MAIN RESULTS: Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position.<br><br>SIGNIFICANCE: Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.},
}
@article {pmid25392006,
year = {2015},
author = {Hsu, WY and Hu, YP},
title = {Artificial bee colony algorithm for single-trial electroencephalogram analysis.},
journal = {Clinical EEG and neuroscience},
volume = {46},
number = {2},
pages = {119-125},
doi = {10.1177/1550059414538808},
pmid = {25392006},
issn = {1550-0594},
mesh = {*Algorithms ; Animals ; Bees ; Biomimetics/methods ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; },
abstract = {In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications.},
}
@article {pmid25390372,
year = {2014},
author = {Costa, &#xc1; and Hortal, E and Iáñez, E and Azorín, JM},
title = {A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.},
journal = {PloS one},
volume = {9},
number = {11},
pages = {e112352},
pmid = {25390372},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Electromyography ; Humans ; Jaw/innervation/*physiology ; Male ; *Man-Machine Systems ; Robotics/*instrumentation ; },
abstract = {Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.},
}
@article {pmid25388661,
year = {2015},
author = {Horschig, JM and Oosterheert, W and Oostenveld, R and Jensen, O},
title = {Modulation of Posterior Alpha Activity by Spatial Attention Allows for Controlling A Continuous Brain-Computer Interface.},
journal = {Brain topography},
volume = {28},
number = {6},
pages = {852-864},
doi = {10.1007/s10548-014-0401-7},
pmid = {25388661},
issn = {1573-6792},
mesh = {Adult ; Alpha Rhythm/*physiology ; Attention/*physiology ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Magnetoencephalography ; Male ; Online Systems ; Photic Stimulation ; Space Perception/*physiology ; Young Adult ; },
abstract = {Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.},
}
@article {pmid25386727,
year = {2014},
author = {Folcher, M and Oesterle, S and Zwicky, K and Thekkottil, T and Heymoz, J and Hohmann, M and Christen, M and Daoud El-Baba, M and Buchmann, P and Fussenegger, M},
title = {Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant.},
journal = {Nature communications},
volume = {5},
number = {},
pages = {5392},
pmid = {25386727},
issn = {2041-1723},
support = {321381/ERC_/European Research Council/International ; },
mesh = {Alkaline Phosphatase/biosynthesis ; Animals ; *Brain-Computer Interfaces ; Cyclic GMP/analogs &amp; derivatives/metabolism ; Electroencephalography ; Female ; *Gene Expression ; Humans ; *Implants, Experimental ; Mice ; Optogenetics/*methods ; Signal Transduction ; Transcription, Genetic ; *Transgenes ; Wireless Technology ; },
abstract = {Synthetic devices for traceless remote control of gene expression may provide new treatment opportunities in future gene- and cell-based therapies. Here we report the design of a synthetic mind-controlled gene switch that enables human brain activities and mental states to wirelessly programme the transgene expression in human cells. An electroencephalography (EEG)-based brain-computer interface (BCI) processing mental state-specific brain waves programs an inductively linked wireless-powered optogenetic implant containing designer cells engineered for near-infrared (NIR) light-adjustable expression of the human glycoprotein SEAP (secreted alkaline phosphatase). The synthetic optogenetic signalling pathway interfacing the BCI with target gene expression consists of an engineered NIR light-activated bacterial diguanylate cyclase (DGCL) producing the orthogonal second messenger cyclic diguanosine monophosphate (c-di-GMP), which triggers the stimulator of interferon genes (STING)-dependent induction of synthetic interferon-β promoters. Humans generating different mental states (biofeedback control, concentration, meditation) can differentially control SEAP production of the designer cells in culture and of subcutaneous wireless-powered optogenetic implants in mice.},
}
@article {pmid25386113,
year = {2014},
author = {Tang-Schomer, MD and Hu, X and Tupaj, M and Tien, LW and Whalen, M and Omenetto, F and Kaplan, DL},
title = {Film-based Implants for Supporting Neuron-Electrode Integrated Interfaces for The Brain.},
journal = {Advanced functional materials},
volume = {24},
number = {13},
pages = {1938-1948},
pmid = {25386113},
issn = {1616-301X},
support = {P41 EB002520/EB/NIBIB NIH HHS/United States ; R01 AR061988/AR/NIAMS NIH HHS/United States ; R01 NS061255/NS/NINDS NIH HHS/United States ; },
abstract = {Neural engineering provides promise for cell therapy by integrating the host brain with brain-machine-interface technologies in order to externally modulate functions. Long-term interfaces with the host brain remain a critical challenge due to insufficient graft cell survivability and loss of brain electrode sensitivity over time. Here, integrated neuron-electrode interfaces were developed on thin flexible and transparent silk films as brain implants. Mechanical properties and surface topography of silk films were optimized to promote cell survival and alignment of primary rat cortical cells. Compartmentalized cultures of living neural circuit and co-patterned electrode arrays were incorporated on the silk films with built-in wire connections. Electrical stimulation via electrodes embedded in the films activated surrounding neurons evoked calcium responses. In mice brains the silk film implants showed conformal contact capable of modulating host brain cells with minimal inflammatory response and stable indwelling for weeks. The approach of combining cell therapy and brain electrodes could provide sustained functional brain-machine interfaces with ex vivo control of neuron-electrode interface with spatial and temporal precision.},
}
@article {pmid25385765,
year = {2015},
author = {Bacher, D and Jarosiewicz, B and Masse, NY and Stavisky, SD and Simeral, JD and Newell, K and Oakley, EM and Cash, SS and Friehs, G and Hochberg, LR},
title = {Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome.},
journal = {Neurorehabilitation and neural repair},
volume = {29},
number = {5},
pages = {462-471},
pmid = {25385765},
issn = {1552-6844},
support = {N01HD10018/HD/NICHD NIH HHS/United States ; HHSN275201100018C/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication ; Communication Devices for People with Disabilities ; Female ; Humans ; Middle Aged ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
abstract = {A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome.},
}
@article {pmid25384045,
year = {2014},
author = {Yeom, SK and Fazli, S and Müller, KR and Lee, SW},
title = {An efficient ERP-based brain-computer interface using random set presentation and face familiarity.},
journal = {PloS one},
volume = {9},
number = {11},
pages = {e111157},
pmid = {25384045},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; *Face ; Humans ; Male ; Models, Theoretical ; Pattern Recognition, Visual/*physiology ; Photic Stimulation ; Recognition, Psychology/*physiology ; },
abstract = {Event-related potential (ERP)-based P300 spellers are commonly used in the field of brain-computer interfaces as an alternative channel of communication for people with severe neuro-muscular diseases. This study introduces a novel P300 based brain-computer interface (BCI) stimulus paradigm using a random set presentation pattern and exploiting the effects of face familiarity. The effect of face familiarity is widely studied in the cognitive neurosciences and has recently been addressed for the purpose of BCI. In this study we compare P300-based BCI performances of a conventional row-column (RC)-based paradigm with our approach that combines a random set presentation paradigm with (non-) self-face stimuli. Our experimental results indicate stronger deflections of the ERPs in response to face stimuli, which are further enhanced when using the self-face images, and thereby improving P300-based spelling performance. This lead to a significant reduction of stimulus sequences required for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-based BCI setup.},
}
@article {pmid25380169,
year = {2014},
author = {Hao, Y and Zhang, Q and Controzzi, M and Cipriani, C and Li, Y and Li, J and Zhang, S and Wang, Y and Chen, W and Chiara Carrozza, M and Zheng, X},
title = {Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066011},
doi = {10.1088/1741-2560/11/6/066011},
pmid = {25380169},
issn = {1741-2552},
mesh = {Animals ; Brain Mapping/*methods ; Electrodes, Implanted ; Hand Strength/*physiology ; Haplorhini ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {OBJECTIVE: Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear.<br><br>APPROACH: To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects.<br><br>MAIN RESULTS: Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies.<br><br>SIGNIFICANCE: This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.},
}
@article {pmid25380071,
year = {2014},
author = {Alcaide-Aguirre, RE and Huggins, JE},
title = {Novel hold-release functionality in a P300 brain-computer interface.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066010},
pmid = {25380071},
issn = {1741-2552},
support = {R21 HD054913/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces/*trends ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Young Adult ; },
abstract = {Assistive technology control interface theory describes interface activation and interface deactivation as distinct properties of any control interface. Separating control of activation and deactivation allows precise timing of the duration of the activation. Objective. We propose a novel P300 brain-computer interface (BCI) functionality with separate control of the initial activation and the deactivation (hold-release) of a selection. Approach. Using two different layouts and off-line analysis, we tested the accuracy with which subjects could (1) hold their selection and (2) quickly change between selections. Main results. Mean accuracy across all subjects for the hold-release algorithm was 85% with one hold-release classification and 100% with two hold-release classifications. Using a layout designed to lower perceptual errors, accuracy increased to a mean of 90% and the time subjects could hold a selection was 40% longer than with the standard layout. Hold-release functionality provides improved response time (6-16 times faster) over the initial P300 BCI selection by allowing the BCI to make hold-release decisions from very few flashes instead of after multiple sequences of flashes. Significance. For the BCI user, hold-release functionality allows for faster, more continuous control with a P300 BCI, creating new options for BCI applications.},
}
@article {pmid25376049,
year = {2016},
author = {Kristina Yanti, D and Zuki Yusoff, M and Sagayan Asirvadam, V},
title = {Single-Trial Visual Evoked Potential Extraction Using Partial Least-Squares-Based Approach.},
journal = {IEEE journal of biomedical and health informatics},
volume = {20},
number = {1},
pages = {82-90},
doi = {10.1109/JBHI.2014.2367152},
pmid = {25376049},
issn = {2168-2208},
mesh = {Algorithms ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Least-Squares Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {A single-trial extraction of a visual evoked potential (VEP) signal based on the partial least-squares (PLS) regression method has been proposed in this paper. This paper has focused on the extraction and estimation of the latencies of P100, P200, P300, N75, and N135 in the artificial electroencephalograph (EEG) signal. The real EEG signal obtained from the hospital was only concentrated on the P100. The performance of the PLS has been evaluated mainly on the basis of latency error rate of the peaks for the artificial EEG signal, and the mean peak detection and standard deviation for the real EEG signal. The simulation results show that the proposed PLS algorithm is capable of reconstructing the EEG signal into its desired shape of the ideal VEP. For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. It has also shown better performance for later peaks (P200 and P300). The PLS outperformed not only in positive peaks but also in N75. In P100, the PLS was comparable with the GEVD although N135 was better estimated by GEVD. The proposed PLS algorithm is comparable to GEVD given that PLS does not alter the EEG input signal. The PLS algorithm gives the best estimate to multitrial ensemble averaging. This research offers benefits such as avoiding patient's fatigue during VEP test measurement in the hospital, in BCI applications and in EEG-fMRI integration.},
}
@article {pmid25376041,
year = {2015},
author = {Kim, M and Kim, BH and Jo, S},
title = {Quantitative evaluation of a low-cost noninvasive hybrid interface based on EEG and eye movement.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {2},
pages = {159-168},
doi = {10.1109/TNSRE.2014.2365834},
pmid = {25376041},
issn = {1558-0210},
mesh = {*Brain-Computer Interfaces ; *Computer Peripherals ; Electrocardiography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Eye Movements/*physiology ; Female ; Humans ; Male ; Photography/*instrumentation ; Pupil/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Systems Integration ; Young Adult ; },
abstract = {This paper describes a low-cost noninvasive brain-computer interface (BCI) hybridized with eye tracking. It also discusses its feasibility through a Fitts' law-based quantitative evaluation method. Noninvasive BCI has recently received a lot of attention. To bring the BCI applications into real life, user-friendly and easily portable devices need to be provided. In this work, as an approach to realize a real-world BCI, electroencephalograph (EEG)-based BCI combined with eye tracking is investigated. The two interfaces can be complementary to attain improved performance. Especially to consider public availability, a low-cost interface device is intentionally used for test. A low-cost commercial EEG recording device is integrated with an inexpensive custom-built eye tracker. The developed hybrid interface is evaluated through target pointing and selection experiments. Eye movement is interpreted as cursor movement and noninvasive BCI selects a cursor point with two selection confirmation schemes. Using Fitts' law, the proposed interface scheme is compared with other interface schemes such as mouse, eye tracking with dwell time, and eye tracking with keyboard. In addition, the proposed hybrid BCI system is discussed with respect to a practical interface scheme. Although further advancement is required, the proposed hybrid BCI system has the potential to be practically useful in a natural and intuitive manner.},
}
@article {pmid25376032,
year = {2015},
author = {Sagha, H and Perdikis, S and Millán, Jdel R and Chavarriaga, R},
title = {Quantifying electrode reliability during brain-computer interface operation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {3},
pages = {858-864},
doi = {10.1109/TBME.2014.2366554},
pmid = {25376032},
issn = {1558-2531},
mesh = {*Artifacts ; *Brain-Computer Interfaces ; Electrodes/standards ; Electroencephalography/*classification ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {One of the problems of noninvasive brain-computer interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator's attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can for instance be the result of electrode misplacement, degrading impedance or loss of connectivity. Since this problem can develop at run time, there is a need of a systematic approach to evaluate electrode reliability during online BCI operation. In this paper, we propose two metrics detecting how much each channel is deviating from its expected behavior. This quantifies electrode reliability at run time which could be embedded into BCI data processing to increase performance. We assess the effectiveness of these metrics in quantifying signal degradation by conducting three experiments: Electrode swap, electrode manipulation, and offline artificially degradation of P300 signals.},
}
@article {pmid25374532,
year = {2014},
author = {Martens, S and Bensch, M and Halder, S and Hill, J and Nijboer, F and Ramos-Murguialday, A and Schoelkopf, B and Birbaumer, N and Gharabaghi, A},
title = {Epidural electrocorticography for monitoring of arousal in locked-in state.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {861},
pmid = {25374532},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) often fails to assess both the level (i.e., arousal) and the content (i.e., awareness) of pathologically altered consciousness in patients without motor responsiveness. This might be related to a decline of awareness, to episodes of low arousal and disturbed sleep patterns, and/or to distorting and attenuating effects of the skull and intermediate tissue on the recorded brain signals. Novel approaches are required to overcome these limitations. We introduced epidural electrocorticography (ECoG) for monitoring of cortical physiology in a late-stage amytrophic lateral sclerosis patient in completely locked-in state (CLIS). Despite long-term application for a period of six months, no implant-related complications occurred. Recordings from the left frontal cortex were sufficient to identify three arousal states. Spectral analysis of the intrinsic oscillatory activity enabled us to extract state-dependent dominant frequencies at <4, ~7 and ~20 Hz, representing sleep-like periods, and phases of low and elevated arousal, respectively. In the absence of other biomarkers, ECoG proved to be a reliable tool for monitoring circadian rhythmicity, i.e., avoiding interference with the patient when he was sleeping and exploiting time windows of responsiveness. Moreover, the effects of interventions addressing the patient's arousal, e.g., amantadine medication, could be evaluated objectively on the basis of physiological markers, even in the absence of behavioral parameters. Epidural ECoG constitutes a feasible trade-off between surgical risk and quality of recorded brain signals to gain information on the patient's present level of arousal. This approach enables us to optimize the timing of interactions and medical interventions, all of which should take place when the patient is in a phase of high arousal. Furthermore, avoiding low-responsiveness periods will facilitate measures to implement alternative communication pathways involving brain-computer interfaces (BCI).},
}
@article {pmid25372874,
year = {2015},
author = {Geronimo, A and Stephens, HE and Schiff, SJ and Simmons, Z},
title = {Acceptance of brain-computer interfaces in amyotrophic lateral sclerosis.},
journal = {Amyotrophic lateral sclerosis &amp; frontotemporal degeneration},
volume = {16},
number = {3-4},
pages = {258-264},
doi = {10.3109/21678421.2014.969275},
pmid = {25372874},
issn = {2167-9223},
mesh = {Adult ; Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/complications/*psychology/*rehabilitation ; Brain-Computer Interfaces/*psychology ; Caregivers/psychology ; Cognition Disorders/*etiology/rehabilitation ; *Communication Devices for People with Disabilities/psychology ; Persons with Disabilities/rehabilitation ; Female ; Health Surveys ; Humans ; Logistic Models ; Male ; Mental Disorders/etiology/rehabilitation ; Middle Aged ; },
abstract = {Brain-computer interfaces (BCI) have the potential to permit patients with amyotrophic lateral sclerosis (ALS) to communicate even when locked in. Although as many as half of patients with ALS develop cognitive or behavioral dysfunction, the impact of these factors on acceptance of and ability to use a BCI has not been studied. We surveyed patients with ALS and their caregivers about BCIs used as assistive communication tools. The survey focused on the features of a BCI system, the desired end-use functions, and requirements. Functional, cognitive, and behavioral data were collected from patients and analyzed for their influence over decisions about BCI device use. Results showed that behavioral impairment was associated with decreased receptivity to the use of BCI technology. In addition, the operation of a BCI system during a pilot study altered patients' opinions of the utility of the system, generally in line with their perceived performance at controlling the device. In conclusion, these two findings have implications for the engineering design and clinical care phases of assistive device deployment.},
}
@article {pmid25368546,
year = {2014},
author = {Faller, J and Scherer, R and Friedrich, EV and Costa, U and Opisso, E and Medina, J and Müller-Putz, GR},
title = {Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {320},
pmid = {25368546},
issn = {1662-4548},
abstract = {Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).},
}
@article {pmid25358531,
year = {2014},
author = {Spüler, M and Walter, A and Ramos-Murguialday, A and Naros, G and Birbaumer, N and Gharabaghi, A and Rosenstiel, W and Bogdan, M},
title = {Decoding of motor intentions from epidural ECoG recordings in severely paralyzed chronic stroke patients.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066008},
doi = {10.1088/1741-2560/11/6/066008},
pmid = {25358531},
issn = {1741-2552},
mesh = {Aged ; Chronic Disease ; Electrodes, Implanted ; Electroencephalography/*methods ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Paralysis/diagnosis/*physiopathology ; Severity of Illness Index ; Stroke/diagnosis/*physiopathology ; },
abstract = {OBJECTIVE: Recently, there have been several approaches to utilize a brain-computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients.<br><br>APPROACH: In a screening session we recorded epidural ECoG data over the ipsilesional motor cortex from four chronic stroke patients who had no residual hand movement. Data was analyzed offline using a support vector machine (SVM) to decode different movement intentions.<br><br>MAIN RESULTS: We showed that up to seven hand movement intentions can be decoded with an average accuracy of 61% (chance level 15.6%). When reducing the number of classes, average accuracies up to 88% can be achieved for decoding three different movement intentions.<br><br>SIGNIFICANCE: The findings suggest that ipsilesional epidural ECoG can be used as a viable control signal for BCI-driven neuroprosthesis. Although patients showed no sign of residual hand movement, brain activity at the ipsilesional motor cortex still shows enough intention-related activity to decode different movement intentions with sufficient accuracy.},
}
@article {pmid25352793,
year = {2014},
author = {Sitaram, R and Caria, A and Veit, R and Gaber, T and Ruiz, S and Birbaumer, N},
title = {Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {344},
pmid = {25352793},
issn = {1662-5153},
abstract = {This pilot study aimed to explore whether criminal psychopaths can learn volitional regulation of the left anterior insula with real-time fMRI neurofeedback. Our previous studies with healthy volunteers showed that learned control of the blood oxygenation-level dependent (BOLD) signal was specific to the target region, and not a result of general arousal and global unspecific brain activation, and also that successful regulation modulates emotional responses, specifically to aversive picture stimuli but not neutral stimuli. In this pilot study, four criminal psychopaths were trained to regulate the anterior insula by employing negative emotional imageries taken from previous episodes in their lives, in conjunction with contingent feedback. Only one out of the four participants learned to increase the percent differential BOLD in the up-regulation condition across training runs. Subjects with higher Psychopathic Checklist-Revised (PCL:SV) scores were less able to increase the BOLD signal in the anterior insula than their lower PCL:SV counterparts. We investigated functional connectivity changes in the emotional network due to learned regulation of the successful participant, by employing multivariate Granger Causality Modeling (GCM). Learning to up-regulate the left anterior insula not only increased the number of connections (causal density) in the emotional network in the single successful participant but also increased the difference between the number of outgoing and incoming connections (causal flow) of the left insula. This pilot study shows modest potential for training psychopathic individuals to learn to control brain activity in the anterior insula.},
}
@article {pmid25352773,
year = {2014},
author = {Wang, YT and Huang, KC and Wei, CS and Huang, TY and Ko, LW and Lin, CT and Cheng, CK and Jung, TP},
title = {Developing an EEG-based on-line closed-loop lapse detection and mitigation system.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {321},
pmid = {25352773},
issn = {1662-4548},
abstract = {In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.},
}
@article {pmid25350547,
year = {2014},
author = {An, X and Höhne, J and Ming, D and Blankertz, B},
title = {Exploring combinations of auditory and visual stimuli for gaze-independent brain-computer interfaces.},
journal = {PloS one},
volume = {9},
number = {10},
pages = {e111070},
pmid = {25350547},
issn = {1932-6203},
mesh = {*Acoustic Stimulation ; Adult ; Analysis of Variance ; Behavior ; *Brain-Computer Interfaces ; Decision Making ; Electroencephalography ; Evoked Potentials ; Eye Movements ; Female ; Humans ; Male ; *Photic Stimulation ; Reproducibility of Results ; Treatment Outcome ; },
abstract = {For Brain-Computer Interface (BCI) systems that are designed for users with severe impairments of the oculomotor system, an appropriate mode of presenting stimuli to the user is crucial. To investigate whether multi-sensory integration can be exploited in the gaze-independent event-related potentials (ERP) speller and to enhance BCI performance, we designed a visual-auditory speller. We investigate the possibility to enhance stimulus presentation by combining visual and auditory stimuli within gaze-independent spellers. In this study with N = 15 healthy users, two different ways of combining the two sensory modalities are proposed: simultaneous redundant streams (Combined-Speller) and interleaved independent streams (Parallel-Speller). Unimodal stimuli were applied as control conditions. The workload, ERP components, classification accuracy and resulting spelling speed were analyzed for each condition. The Combined-speller showed a lower workload than uni-modal paradigms, without the sacrifice of spelling performance. Besides, shorter latencies, lower amplitudes, as well as a shift of the temporal and spatial distribution of discriminative information were observed for Combined-speller. These results are important and are inspirations for future studies to search the reason for these differences. For the more innovative and demanding Parallel-Speller, where the auditory and visual domains are independent from each other, a proof of concept was obtained: fifteen users could spell online with a mean accuracy of 87.7% (chance level <3%) showing a competitive average speed of 1.65 symbols per minute. The fact that it requires only one selection period per symbol makes it a good candidate for a fast communication channel. It brings a new insight into the true multisensory stimuli paradigms. Novel approaches for combining two sensory modalities were designed here, which are valuable for the development of ERP-based BCI paradigms.},
}
@article {pmid25347884,
year = {2015},
author = {Kim, YH and Thakor, NV and Schieber, MH and Kim, HN},
title = {Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {3},
pages = {374-384},
pmid = {25347884},
issn = {1558-0210},
support = {R01 NS027686/NS/NINDS NIH HHS/United States ; R01 NS079664/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Bayes Theorem ; *Brain-Computer Interfaces ; Equipment Design ; Fingers/*innervation/*physiology ; Macaca mulatta ; Male ; Models, Neurological ; Models, Theoretical ; Motor Cortex/cytology/*physiology ; Motor Neurons/*physiology ; *Neural Prostheses ; Psychomotor Performance ; Reproducibility of Results ; },
abstract = {Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.},
}
@article {pmid25341256,
year = {2014},
author = {Eliseyev, A and Aksenova, T},
title = {Stable and artifact-resistant decoding of 3D hand trajectories from ECoG signals using the generalized additive model.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066005},
doi = {10.1088/1741-2560/11/6/066005},
pmid = {25341256},
issn = {1741-2552},
mesh = {Animals ; *Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Hand ; Haplorhini ; *Models, Neurological ; Movement ; },
abstract = {OBJECTIVE: The key criterion for reliability of brain-computer interface (BCI) devices is their stability and robustness in natural environments in the presence of spurious signals and artifacts.<br><br>APPROACH: To improve stability and robustness, a generalized additive model (GAM) is proposed for BCI decoder identification. Together with partial least squares (PLS), GAM can be applied to treat high-dimensional data and it is compatible with real-time applications. For evaluation of prediction quality, along with standard criteria such as Pearson correlation, root mean square error (RMSE), mean absolute error (MAE), additional criteria, mean absolute differential error (MADE) and dynamic time warping (DTW) distance, are chosen. These criteria reflect the smoothness and dissimilarity of the predicted and observed signals in the presence of phase desynchronization.<br><br>MAIN RESULTS: The efficiency of the GAM-PLS model is tested on the publicly available database of simultaneous recordings of the continuous three-dimensional hand trajectories and epidural electrocorticogram signals of the Japanese macaque. GAM-PLS outperforms the generic PLS and improves the evaluation criteria: 22% (Pearson correlation), 8% (RMSE), 13% (MAE), 31% (MADE), 20% (DTW).<br><br>SIGNIFICANCE: Motor-related BCIs are systems to improve the quality of life of individuals with severe motor disabilities. The improvement of the reliability of the BCI decoder is an important step toward real-life applications of BCI technologies.},
}
@article {pmid25339687,
year = {2014},
author = {Fu, C and Suzuki, Y and Kiyono, K and Morasso, P and Nomura, T},
title = {An intermittent control model of flexible human gait using a stable manifold of saddle-type unstable limit cycle dynamics.},
journal = {Journal of the Royal Society, Interface},
volume = {11},
number = {101},
pages = {20140958},
pmid = {25339687},
issn = {1742-5662},
mesh = {Ankle Joint/*physiology ; Gait/*physiology ; Hip Joint/*physiology ; Humans ; Knee Joint/*physiology ; *Models, Biological ; Postural Balance/*physiology ; },
abstract = {Stability of human gait is the ability to maintain upright posture during walking against external perturbations. It is a complex process determined by a number of cross-related factors, including gait trajectory, joint impedance and neural control strategies. Here, we consider a control strategy that can achieve stable steady-state periodic gait while maintaining joint flexibility with the lowest possible joint impedance. To this end, we carried out a simulation study of a heel-toe footed biped model with hip, knee and ankle joints and a heavy head-arms-trunk element, working in the sagittal plane. For simplicity, the model assumes a periodic desired joint angle trajectory and joint torques generated by a set of feed-forward and proportional-derivative feedback controllers, whereby the joint impedance is parametrized by the feedback gains. We could show that a desired steady-state gait accompanied by the desired joint angle trajectory can be established as a stable limit cycle (LC) for the feedback controller with an appropriate set of large feedback gains. Moreover, as the feedback gains are decreased for lowering the joint stiffness, stability of the LC is lost only in a few dimensions, while leaving the remaining large number of dimensions quite stable: this means that the LC becomes saddle-type, with a low-dimensional unstable manifold and a high-dimensional stable manifold. Remarkably, the unstable manifold remains of low dimensionality even when the feedback gains are decreased far below the instability point. We then developed an intermittent neural feedback controller that is activated only for short periods of time at an optimal phase of each gait stride. We characterized the robustness of this design by showing that it can better stabilize the unstable LC with small feedback gains, leading to a flexible gait, and in particular we demonstrated that such an intermittent controller performs better if it drives the state point to the stable manifold, rather than directly to the LC. The proposed intermittent control strategy might have a high affinity for the inverted pendulum analogy of biped gait, providing a dynamic view of how the step-to-step transition from one pendular stance to the next can be achieved stably in a robust manner by a well-timed neural intervention that exploits the stable modes embedded in the unstable dynamics.},
}
@article {pmid25338503,
year = {2015},
author = {Goodrich, E and Wahbeh, H and Mooney, A and Miller, M and Oken, BS},
title = {Teaching mindfulness meditation to adults with severe speech and physical impairments: An exploratory study.},
journal = {Neuropsychological rehabilitation},
volume = {25},
number = {5},
pages = {708-732},
pmid = {25338503},
issn = {1464-0694},
support = {K24 AT005121/AT/NCCIH NIH HHS/United States ; R01 DC009834/DC/NIDCD NIH HHS/United States ; AT005121/AT/NCCIH NIH HHS/United States ; DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/complications ; Brain Diseases/etiology/*therapy ; Brain Stem Infarctions/complications ; Cerebral Palsy/complications ; Female ; Humans ; Male ; Meditation/*methods ; Middle Aged ; Mindfulness/*methods ; Muscular Dystrophies/complications ; Parkinson Disease/complications ; Speech Disorders/*therapy ; Spinocerebellar Ataxias/complications ; Treatment Outcome ; },
abstract = {People with severe speech and physical impairments may benefit from mindfulness meditation training because it has the potential to enhance their ability to cope with anxiety, depression and pain and improve their attentional capacity to use brain-computer interface systems. Seven adults with severe speech and physical impairments (SSPI) - defined as speech that is understood less than 25% of the time and/or severely reduced hand function for writing/typing - participated in this exploratory, uncontrolled intervention study. The objectives were to describe the development and implementation of a six-week mindfulness meditation intervention and to identify feasible outcome measures in this population. The weekly intervention was delivered by an instructor in the participant's home, and participants were encouraged to practise daily using audio recordings. The objective adherence to home practice was 10.2 minutes per day. Exploratory outcome measures were an n-back working memory task, the Attention Process Training-II Attention Questionnaire, the Pittsburgh Sleep Quality Index, the Perceived Stress Scale, the Positive and Negative Affect Schedule, and a qualitative feedback survey. There were no statistically significant pre-post results in this small sample, yet administration of the measures proved feasible, and qualitative reports were overall positive. Obstacles to teaching mindfulness meditation to persons with SSPI are reported, and solutions are proposed.},
}
@article {pmid25324768,
year = {2014},
author = {Joyal, CC and Jacob, L and Cigna, MH and Guay, JP and Renaud, P},
title = {Virtual faces expressing emotions: an initial concomitant and construct validity study.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {787},
pmid = {25324768},
issn = {1662-5161},
abstract = {BACKGROUND: Facial expressions of emotions represent classic stimuli for the study of social cognition. Developing virtual dynamic facial expressions of emotions, however, would open-up possibilities, both for fundamental and clinical research. For instance, virtual faces allow real-time Human-Computer retroactions between physiological measures and the virtual agent.<br><br>OBJECTIVES: The goal of this study was to initially assess concomitants and construct validity of a newly developed set of virtual faces expressing six fundamental emotions (happiness, surprise, anger, sadness, fear, and disgust). Recognition rates, facial electromyography (zygomatic major and corrugator supercilii muscles), and regional gaze fixation latencies (eyes and mouth regions) were compared in 41 adult volunteers (20 &#x2642;, 21 &#x2640;) during the presentation of video clips depicting real vs. virtual adults expressing emotions.<br><br>RESULTS: Emotions expressed by each set of stimuli were similarly recognized, both by men and women. Accordingly, both sets of stimuli elicited similar activation of facial muscles and similar ocular fixation times in eye regions from man and woman participants.<br><br>CONCLUSION: Further validation studies can be performed with these virtual faces among clinical populations known to present social cognition difficulties. Brain-Computer Interface studies with feedback-feedforward interactions based on facial emotion expressions can also be conducted with these stimuli.},
}
@article {pmid25324735,
year = {2014},
author = {Wood, G and Kober, SE and Witte, M and Neuper, C},
title = {On the need to better specify the concept of "control" in brain-computer-interfaces/neurofeedback research.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {171},
pmid = {25324735},
issn = {1662-5137},
abstract = {Aiming at a better specification of the concept of "control" in brain-computer-interfaces (BCIs) and neurofeedback (NF) research, we propose to distinguish "self-control of brain activity" from the broader concept of "BCI control", since the first describes a neurocognitive phenomenon and is only one of the many components of "BCI control". Based on this distinction, we developed a framework based on dual-processes theory that describes the cognitive determinants of self-control of brain activity as the interplay of automatic vs. controlled information processing. Further, we distinguish between cognitive processes that are necessary and sufficient to achieve a given level of self-control of brain activity and those which are not. We discuss that those cognitive processes which are not necessary for the learning process can hamper self-control because they cannot be completely turned-off at any time. This framework aims at a comprehensive description of the cognitive determinants of the acquisition of self-control of brain activity underlying those classes of BCI which require the user to achieve regulation of brain activity as well as NF learning.},
}
@article {pmid25324734,
year = {2014},
author = {Saniotis, A and Henneberg, M and Kumaratilake, J and Grantham, JP},
title = {"Messing with the mind": evolutionary challenges to human brain augmentation.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {152},
pmid = {25324734},
issn = {1662-5137},
abstract = {The issue of brain augmentation has received considerable scientific attention over the last two decades. A key factor to brain augmentation that has been widely overlooked are the complex evolutionary processes which have taken place in evolving the human brain to its current state of functioning. Like other bodily organs, the human brain has been subject to the forces of biological adaptation. The structure and function of the brain, is very complex and only now we are beginning to understand some of the basic concepts of cognition. Therefore, this article proposes that brain-machine interfacing and nootropics are not going to produce "augmented" brains because we do not understand enough about how evolutionary pressures have informed the neural networks which support human cognitive faculties.},
}
@article {pmid25321447,
year = {2014},
author = {Wood, DE and Nader, DA and Springmeyer, SC and Elstad, MR and Coxson, HO and Chan, A and Rai, NS and Mularski, RA and Cooper, CB and Wise, RA and Jones, PW and Mehta, AC and Gonzalez, X and Sterman, DH and , },
title = {The IBV Valve trial: a multicenter, randomized, double-blind trial of endobronchial therapy for severe emphysema.},
journal = {Journal of bronchology &amp; interventional pulmonology},
volume = {21},
number = {4},
pages = {288-297},
doi = {10.1097/LBR.0000000000000110},
pmid = {25321447},
issn = {1948-8270},
support = {K08 HL091127/HL/NHLBI NIH HHS/United States ; },
mesh = {Aged ; Bayes Theorem ; Bronchoscopy/adverse effects/*methods ; Double-Blind Method ; Female ; Humans ; Male ; Middle Aged ; Pneumonectomy/methods ; *Prostheses and Implants ; Pulmonary Emphysema/physiopathology/surgery/*therapy ; Quality of Life ; Surveys and Questionnaires ; Treatment Outcome ; },
abstract = {BACKGROUND: Lung volume reduction surgery improves quality of life, exercise capacity, and survival in selected patients but is accompanied by significant morbidity. Bronchoscopic approaches may provide similar benefits with less morbidity.<br><br>METHODS: In a randomized, sham procedure controlled, double-blind trial, 277 subjects were enrolled at 36 centers. Patients had emphysema, airflow obstruction, hyperinflation, and severe dyspnea. The primary effectiveness measure was a significant improvement in disease-related quality of life (St. George's Respiratory Questionnaire) and changes in lobar lung volumes. The primary safety measure was a comparison of serious adverse events.<br><br>RESULTS: There were 6/121 (5.0%) responders in the treatment group at 6 months, significantly >1/134 (0.7%) in the control group [Bayesian credible intervals (BCI), 0.05%, 9.21%]. Lobar volume changes were significantly different with an average decrease in the treated lobes of -224 mL compared with -17 mL for the control group (BCI, -272, -143). The proportion of responders in St. George's Respiratory Questionnaire was not greater in the treatment group. There were significantly more subjects with a serious adverse event in the treatment group (n=20 or 14.1%) compared with the control group (n=5 or 3.7%) (BCI, 4.0, 17.1), but most were neither procedure nor device related.<br><br>CONCLUSIONS: This trial had technical and statistical success but partial-bilateral endobronchial valve occlusion did not obtain clinically meaningful results. Safety results were acceptable and compare favorably to lung volume reduction surgery and other bronchial valve studies. Further studies need to focus on improved patient selection and a different treatment algorithm.<br><br>TRIAL REGISTRY: ClinicalTrials.gov NCT00475007.},
}
@article {pmid25316166,
year = {2015},
author = {Speier, W and Deshpande, A and Pouratian, N},
title = {A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {6},
pages = {1171-1177},
pmid = {25316166},
issn = {1872-8952},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T32 EB016640/EB/NIBIB NIH HHS/United States ; K23EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces/economics ; Electrodes/economics ; Electroencephalography/economics/instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Prospective Studies ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {OBJECTIVE: The P300 speller is intended to restore communication to patients with advanced neuromuscular disorders, but clinical implementation may be hindered by several factors, including system setup, burden, and cost. Our goal was to develop a method that can overcome these barriers by optimizing EEG electrode number and placement for P300 studies within a population of subjects.<br><br>METHODS: A Gibbs sampling method was developed to find the optimal electrode configuration given a set of P300 speller data. The method was tested on a set of data from 15 healthy subjects using an established 32-electrode pattern. Resulting electrode configurations were then validated using online prospective testing with a na&#xef;ve Bayes classifier in 15 additional healthy subjects.<br><br>RESULTS: The method yielded a set of four posterior electrodes (PO&#x2088;, PO&#x2087;, POZ, CPZ), which produced results that are likely sufficient to be clinically effective. In online prospective validation testing, no significant difference was found between subjects' performances using the reduced and the full electrode configurations.<br><br>CONCLUSIONS: The proposed method can find reduced sets of electrodes within a subject population without reducing performance.<br><br>SIGNIFICANCE: Reducing the number of channels may reduce costs, set-up time, signal bandwidth, and computation requirements for practical online P300 speller implementation.},
}
@article {pmid25316017,
year = {2014},
author = {Goodrick, S},
title = {Man and machine.},
journal = {The Lancet. Neurology},
volume = {13},
number = {11},
pages = {1080},
doi = {10.1016/S1474-4422(14)70247-9},
pmid = {25316017},
issn = {1474-4465},
mesh = {Brain Injuries/*therapy ; Brain-Computer Interfaces/*trends ; Humans ; Memory Disorders/*therapy ; United States ; United States Department of Defense/*trends ; },
}
@article {pmid25314905,
year = {2015},
author = {Chen, L and Jin, J and Zhang, Y and Wang, X and Cichocki, A},
title = {A survey of the dummy face and human face stimuli used in BCI paradigm.},
journal = {Journal of neuroscience methods},
volume = {239},
number = {},
pages = {18-27},
doi = {10.1016/j.jneumeth.2014.10.002},
pmid = {25314905},
issn = {1872-678X},
mesh = {Adult ; Bayes Theorem ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; *Face ; Female ; Fourier Analysis ; Humans ; Male ; Online Systems ; Pattern Recognition, Visual/*physiology ; Photic Stimulation ; Reaction Time/physiology ; Young Adult ; },
abstract = {BACKGROUND: It was proved that the human face stimulus were superior to the flash only stimulus in BCI system. However, human face stimulus may lead to copyright infringement problems and was hard to be edited according to the requirement of the BCI study. Recently, it was reported that facial expression changes could be done by changing a curve in a dummy face which could obtain good performance when it was applied to visual-based P300 BCI systems.<br><br>NEW METHOD: In this paper, four different paradigms were presented, which were called dummy face pattern, human face pattern, inverted dummy face pattern and inverted human face pattern, to evaluate the performance of the dummy faces stimuli compared with the human faces stimuli.<br><br>The key point that determined the value of dummy faces in BCI systems were whether dummy faces stimuli could obtain as good performance as human faces stimuli. Online and offline results of four different paradigms would have been obtained and comparatively analyzed.<br><br>RESULTS: Online and offline results showed that there was no significant difference among dummy faces and human faces in ERPs, classification accuracy and information transfer rate when they were applied in BCI systems.<br><br>CONCLUSIONS: Dummy faces stimuli could evoke large ERPs and obtain as high classification accuracy and information transfer rate as the human faces stimuli. Since dummy faces were easy to be edited and had no copyright infringement problems, it would be a good choice for optimizing the stimuli of BCI systems.},
}
@article {pmid25313662,
year = {2014},
author = {Boninger, ML and Wechsler, LR and Stein, J},
title = {Robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery from stroke: updates and advances.},
journal = {American journal of physical medicine &amp; rehabilitation},
volume = {93},
number = {11 Suppl 3},
pages = {S145-54},
pmid = {25313662},
issn = {1537-7385},
support = {P30 AG024827/AG/NIA NIH HHS/United States ; R01 NS072342/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Combined Modality Therapy ; Female ; Forecasting ; Humans ; Male ; Prognosis ; Quality Improvement ; Recovery of Function ; Regenerative Medicine/methods/trends ; Robotics/*methods ; Stem Cell Transplantation/*methods/trends ; Stroke/diagnosis/*surgery ; *Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {OBJECTIVE: The aim of this study was to describe the current state and latest advances in robotics, stem cells, and brain-computer interfaces in rehabilitation and recovery for stroke.<br><br>DESIGN: The authors of this summary recently reviewed this work as part of a national presentation. The article represents the information included in each area.<br><br>RESULTS: Each area has seen great advances and challenges as products move to market and experiments are ongoing.<br><br>CONCLUSIONS: Robotics, stem cells, and brain-computer interfaces all have tremendous potential to reduce disability and lead to better outcomes for patients with stroke. Continued research and investment will be needed as the field moves forward. With this investment, the potential for recovery of function is likely substantial.},
}
@article {pmid25311388,
year = {2015},
author = {Worssam, CJ and Meade, LC and Connolly, JD},
title = {Non-obstructing 3D depth cues influence reach-to-grasp kinematics.},
journal = {Experimental brain research},
volume = {233},
number = {2},
pages = {385-396},
pmid = {25311388},
issn = {1432-1106},
mesh = {Adult ; Analysis of Variance ; Biomechanical Phenomena ; *Cues ; Depth Perception/*physiology ; Feedback, Sensory/*physiology ; Female ; Functional Laterality ; Hand Strength/*physiology ; Humans ; Male ; Movement/physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Young Adult ; },
abstract = {It has been demonstrated that both visual feedback and the presence of certain types of non-target objects in the workspace can affect kinematic measures and the trajectory path of the moving hand during reach-to-grasp movements. Yet no study to date has examined the possible effect of providing non-obstructing three-dimensional (3D) depth cues within the workspace and with consistent retinal inputs and whether or not these alter manual prehension movements. Participants performed a series of reach-to-grasp movements in both open- (without visual feedback) and closed-loop (with visual feedback) conditions in the presence of one of three possible 3D depth cues. Here, it is reported that preventing online visual feedback (or not) and the presence of a particular depth cue had a profound effect on kinematic measures for both the reaching and grasping components of manual prehension-despite the fact that the 3D depth cues did not act as a physical obstruction at any point. The depth cues modulated the trajectory of the reaching hand when the target block was located on the left side of the workspace but not on the right. These results are discussed in relation to previous reports and implications for brain-computer interface decoding algorithms are provided.},
}
@article {pmid25309420,
year = {2014},
author = {Rupp, R},
title = {Challenges in clinical applications of brain computer interfaces in individuals with spinal cord injury.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {38},
pmid = {25309420},
issn = {1662-6443},
abstract = {Brain computer interfaces (BCIs) are devices that measure brain activities and translate them into control signals used for a variety of applications. Among them are systems for communication, environmental control, neuroprostheses, exoskeletons, or restorative therapies. Over the last years the technology of BCIs has reached a level of matureness allowing them to be used not only in research experiments supervised by scientists, but also in clinical routine with patients with neurological impairments supervised by clinical personnel or caregivers. However, clinicians and patients face many challenges in the application of BCIs. This particularly applies to high spinal cord injured patients, in whom artificial ventilation, autonomic dysfunctions, neuropathic pain, or the inability to achieve a sufficient level of control during a short-term training may limit the successful use of a BCI. Additionally, spasmolytic medication and the acute stress reaction with associated episodes of depression may have a negative influence on the modulation of brain waves and therefore the ability to concentrate over an extended period of time. Although BCIs seem to be a promising assistive technology for individuals with high spinal cord injury systematic investigations are highly needed to obtain realistic estimates of the percentage of users that for any reason may not be able to operate a BCI in a clinical setting.},
}
@article {pmid25307730,
year = {2014},
author = {Ahn, S and Ahn, M and Cho, H and Chan Jun, S},
title = {Achieving a hybrid brain-computer interface with tactile selective attention and motor imagery.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066004},
doi = {10.1088/1741-2560/11/6/066004},
pmid = {25307730},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Brain/physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Photic Stimulation/*methods ; Touch/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: We propose a new hybrid brain-computer interface (BCI) system that integrates two different EEG tasks: tactile selective attention (TSA) using a vibro-tactile stimulator on the left/right finger and motor imagery (MI) of left/right hand movement. Event-related desynchronization (ERD) from the MI task and steady-state somatosensory evoked potential (SSSEP) from the TSA task are retrieved and combined into two hybrid senses.<br><br>APPROACH: One hybrid approach is to measure two tasks simultaneously; the features of each task are combined for testing. Another hybrid approach is to measure two tasks consecutively (TSA first and MI next) using only MI features. For comparison with the hybrid approaches, the TSA and MI tasks are measured independently.<br><br>MAIN RESULTS: Using a total of 16 subject datasets, we analyzed the BCI classification performance for MI, TSA and two hybrid approaches in a comparative manner; we found that the consecutive hybrid approach outperformed the others, yielding about a 10% improvement in classification accuracy relative to MI alone. It is understood that TSA may play a crucial role as a prestimulus in that it helps to generate earlier ERD prior to MI and thus sustains ERD longer and to a stronger degree; this ERD may give more discriminative information than ERD in MI alone.<br><br>SIGNIFICANCE: Overall, our proposed consecutive hybrid approach is very promising for the development of advanced BCI systems.},
}
@article {pmid25307561,
year = {2014},
author = {Nuyujukian, P and Kao, JC and Fan, JM and Stavisky, SD and Ryu, SI and Shenoy, KV},
title = {Performance sustaining intracortical neural prostheses.},
journal = {Journal of neural engineering},
volume = {11},
number = {6},
pages = {066003},
doi = {10.1088/1741-2560/11/6/066003},
pmid = {25307561},
issn = {1741-2552},
support = {8DP1HD075623/DP/NCCDPHP CDC HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; R01-NS064318/NS/NINDS NIH HHS/United States ; R01-NS066311/NS/NINDS NIH HHS/United States ; TR01NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; *Neural Prostheses ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time.<br><br>APPROACH: We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time.<br><br>MAIN RESULTS: We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements.<br><br>SIGNIFICANCE: The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.},
}
@article {pmid25307551,
year = {2015},
author = {Galán, F and Baker, MR and Alter, K and Baker, SN},
title = {Degraded EEG decoding of wrist movements in absence of kinaesthetic feedback.},
journal = {Human brain mapping},
volume = {36},
number = {2},
pages = {643-654},
pmid = {25307551},
issn = {1097-0193},
support = {//Wellcome Trust/United Kingdom ; 101002//Wellcome Trust/United Kingdom ; },
mesh = {Brain/*physiology ; Electroencephalography ; Feedback, Sensory/*physiology ; Humans ; Imagination/physiology ; Ischemia ; Male ; Motor Activity/*physiology ; Nerve Block ; Signal Processing, Computer-Assisted ; Wrist/*physiology ; },
abstract = {A major assumption of brain-machine interface research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here, we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in the absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.},
}
@article {pmid25298968,
year = {2014},
author = {Sultana, N and Rani, N and Ali, MI and Hussain, A},
title = {Soft translations and soft extensions of BCI/BCK-algebras.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {536709},
pmid = {25298968},
issn = {1537-744X},
mesh = {*Algorithms ; Fuzzy Logic ; *Mathematical Computing ; Mathematics/*methods ; *Models, Theoretical ; },
abstract = {The concept of soft translations of soft subalgebras and soft ideals over BCI/BCK-algebras is introduced and some related properties are studied. Notions of Soft extensions of soft subalgebras and soft ideals over BCI/BCK-algebras are also initiated. Relationships between soft translations and soft extensions are explored.},
}
@article {pmid25298385,
year = {2015},
author = {Witham, CL and Baker, SN},
title = {Information theoretic analysis of proprioceptive encoding during finger flexion in the monkey sensorimotor system.},
journal = {Journal of neurophysiology},
volume = {113},
number = {1},
pages = {295-306},
pmid = {25298385},
issn = {1522-1598},
support = {101002//Wellcome Trust/United Kingdom ; },
mesh = {Action Potentials ; Animals ; Cerebellar Nuclei/*physiology ; Female ; Fingers/*physiology ; Ganglia, Spinal/*physiology ; Information Theory ; Macaca mulatta ; Microelectrodes ; Models, Neurological ; Motor Activity/physiology ; Neurons/*physiology ; Proprioception/*physiology ; Sensorimotor Cortex/*physiology ; Signal Processing, Computer-Assisted ; Time Factors ; Torque ; },
abstract = {There is considerable debate over whether the brain codes information using neural firing rate or the fine-grained structure of spike timing. We investigated this issue in spike discharge recorded from single units in the sensorimotor cortex, deep cerebellar nuclei, and dorsal root ganglia in macaque monkeys trained to perform a finger flexion task. The task required flexion to four different displacements against two opposing torques; the eight possible conditions were randomly interleaved. We used information theory to assess coding of task condition in spike rate, discharge irregularity, and spectral power in the 15- to 25-Hz band during the period of steady holding. All three measures coded task information in all areas tested. Information coding was most often independent between irregularity and 15-25 Hz power (60% of units), moderately redundant between spike rate and irregularity (56% of units redundant), and highly redundant between spike rate and power (93%). Most simultaneously recorded unit pairs coded using the same measure independently (86%). Knowledge of two measures often provided extra information about task, compared with knowledge of only one alone. We conclude that sensorimotor systems use both rate and temporal codes to represent information about a finger movement task. As well as offering insights into neural coding, this work suggests that incorporating spike irregularity into algorithms used for brain-machine interfaces could improve decoding accuracy.},
}
@article {pmid25298323,
year = {2014},
author = {Sellers, EW and Ryan, DB and Hauser, CK},
title = {Noninvasive brain-computer interface enables communication after brainstem stroke.},
journal = {Science translational medicine},
volume = {6},
number = {257},
pages = {257re7},
pmid = {25298323},
issn = {1946-6242},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470-03/DC/NIDCD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain Stem Infarctions/*rehabilitation ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Diffusion Magnetic Resonance Imaging ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Male ; Neuropsychological Tests ; Quadriplegia/*rehabilitation ; Task Performance and Analysis ; Translational Research, Biomedical ; },
abstract = {Brain-computer interfaces (BCIs) provide communication that is independent of muscle control, and can be especially important for individuals with severe neuromuscular disease who cannot use standard communication pathways or other assistive technology. It has previously been shown that people with amyotrophic lateral sclerosis (ALS) can successfully use BCI after all other means of independent communication have failed. The BCI literature has asserted that brainstem stroke survivors can also benefit from BCI use. This study used a P300-based event-related potential spelling system. This case study demonstrates that an individual locked-in owing to brainstem stroke was able to use a noninvasive BCI to communicate volitional messages. Over a period of 13 months, the participant was able to successfully operate the system during 40 of 62 recording sessions. He was able to accurately spell words provided by the experimenter and to initiate dialogues with his family. The results broadly suggest that, regardless of the precipitating event, BCI use may be of benefit to those with locked-in syndrome.},
}
@article {pmid25298322,
year = {2014},
author = {Ortiz-Catalan, M and Håkansson, B and Brånemark, R},
title = {An osseointegrated human-machine gateway for long-term sensory feedback and motor control of artificial limbs.},
journal = {Science translational medicine},
volume = {6},
number = {257},
pages = {257re6},
doi = {10.1126/scitranslmed.3008933},
pmid = {25298322},
issn = {1946-6242},
mesh = {Activities of Daily Living ; Amputees/rehabilitation ; *Artificial Limbs ; Electrodes, Implanted ; Electromyography ; *Feedback, Sensory ; Humans ; *Osseointegration ; Prosthesis Design ; Range of Motion, Articular ; Touch Perception ; Translational Research, Biomedical ; },
abstract = {A major challenge since the invention of implantable devices has been a reliable and long-term stable transcutaneous communication. In the case of prosthetic limbs, existing neuromuscular interfaces have been unable to address this challenge and provide direct and intuitive neural control. Although prosthetic hardware and decoding algorithms are readily available, there is still a lack of appropriate and stable physiological signals for controlling the devices. We developed a percutaneous osseointegrated (bone-anchored) interface that allows for permanent and unlimited bidirectional communication with the human body. With this interface, an artificial limb can be chronically driven by implanted electrodes in the peripheral nerves and muscles of an amputee, outside of controlled environments and during activities of daily living, thus reducing disability and improving quality of life. We demonstrate in one subject, for more than 1 year, that implanted electrodes provide a more precise and reliable control than surface electrodes, regardless of limb position and environmental conditions, and with less effort. Furthermore, long-term stable myoelectric pattern recognition and appropriate sensory feedback elicited via neurostimulation was demonstrated. The opportunity to chronically record and stimulate the neuromuscular system allows for the implementation of intuitive control and naturally perceived sensory feedback, as well as opportunities for the prediction of complex limb motions and better understanding of sensory perception. The permanent bidirectional interface presented here is a critical step toward more natural limb replacement, by combining stable attachment with permanent and reliable human-machine communication.},
}
@article {pmid25296369,
year = {2014},
author = {Fischer, S},
title = {Forecast 2014: this year will be a good one for biomedical engineering--thanks to breakthrough technologies and unprecedented funding opportunities.},
journal = {IEEE pulse},
volume = {5},
number = {1},
pages = {18-27},
doi = {10.1109/MPUL.2013.2289460},
pmid = {25296369},
issn = {2154-2317},
mesh = {*Biomedical Engineering/economics/methods/trends ; Brain-Computer Interfaces ; Diabetes Mellitus/therapy ; Humans ; Nanotechnology ; Tissue Engineering ; },
}
@article {pmid25295793,
year = {2014},
author = {Gustavsen, G and Schroeder, B and Kennedy, P and Pothier, KC and Erlander, MG and Schnabel, CA and Ali, H},
title = {Health economic analysis of Breast Cancer Index in patients with ER+, LN- breast cancer.},
journal = {The American journal of managed care},
volume = {20},
number = {8},
pages = {e302-10},
pmid = {25295793},
issn = {1936-2692},
mesh = {Breast Neoplasms/diagnosis/economics/*genetics/pathology ; Cost Savings ; Cost-Benefit Analysis ; Female ; Gene Expression Profiling/*economics/methods ; Humans ; Lymphatic Metastasis ; Models, Economic ; Neoplasm Recurrence, Local/diagnosis/economics/genetics ; Predictive Value of Tests ; Receptors, Estrogen/*metabolism ; Time Factors ; },
abstract = {OBJECTIVES: Breast Cancer Index (BCI) is a novel gene expression-based test for patients with estrogen receptor positive (ER+), lymph node negative (LN-) breast cancer that predicts risk of recurrence over 10 years, and also specifically predicts risk of late (≥5 y) recurrences and likelihood of benefit from extended (≥5 y) endocrine therapy. The objective of this study was to evaluate cost utility of BCI from a US third-party payer perspective.<br><br>STUDY DESIGN: Two fact-based economic models were developed to project the cost and effectiveness of BCI in a hypothetical population of patients with ER+, LN- breast cancer compared with standard clinicopathologic diagnostic modalities.<br><br>METHODS: Costs associated with adjuvant chemotherapy, toxicity, followup, endocrine therapy, and recurrence were modeled over 10 years. The models examined cost utility compared with standard practice when used at diagnosis and in patients disease-free at 5 years post diagnosis.<br><br>RESULTS: Use of BCI was projected to be cost saving in both models. In the newly diagnosed population, net cost savings were $3803 per patient tested. In the 5 years post diagnosis population, BCI was projected to yield a net cost savings of $1803 per patient tested. Sensitivity analyses demonstrated that BCI was cost saving across a wide range of clinically relevant input assumptions.<br><br>CONCLUSIONS: BCI was projected to be cost saving when used either at diagnosis or at 5 years post diagnosis. Cost savings are achieved through projected impact on adjuvant chemotherapy use, extended endocrine therapy use, and endocrine therapy compliance. These findings require validation in additional cohorts, including studies of real-world clinical practice.},
}
@article {pmid25294988,
year = {2014},
author = {Potter, SM and El Hady, A and Fetz, EE},
title = {Closed-loop neuroscience and neuroengineering.},
journal = {Frontiers in neural circuits},
volume = {8},
number = {},
pages = {115},
pmid = {25294988},
issn = {1662-5110},
support = {P51 OD010425/OD/NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Bioengineering ; Humans ; Models, Neurological ; Nerve Net/*physiology ; Nervous System/*cytology ; Neurons/physiology ; *Neurosciences ; },
}
@article {pmid25291525,
year = {2015},
author = {Tzovara, A and Chavarriaga, R and De Lucia, M},
title = {Quantifying the time for accurate EEG decoding of single value-based decisions.},
journal = {Journal of neuroscience methods},
volume = {250},
number = {},
pages = {114-125},
doi = {10.1016/j.jneumeth.2014.09.029},
pmid = {25291525},
issn = {1872-678X},
mesh = {Adult ; Brain/*physiology ; Decision Making/*physiology ; Electroencephalography/*methods ; Evoked Potentials ; Female ; Gambling ; Humans ; Logistic Models ; Male ; Models, Psychological ; Multivariate Analysis ; Neuropsychological Tests ; Psychomotor Performance/physiology ; Reaction Time/physiology ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Time Factors ; Visual Perception/physiology ; },
abstract = {BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process.<br><br>NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers.<br><br>RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at &#x223c;500 ms for 'easy' vs. &#x223c;700 ms for 'hard' decisions, well before subjects' response (&#x223c;340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level.<br><br>We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making.<br><br>CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.},
}
@article {pmid25278847,
year = {2014},
author = {Zarka, D and Cevallos, C and Petieau, M and Hoellinger, T and Dan, B and Cheron, G},
title = {Neural rhythmic symphony of human walking observation: Upside-down and Uncoordinated condition on cortical theta, alpha, beta and gamma oscillations.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {169},
pmid = {25278847},
issn = {1662-5137},
abstract = {Biological motion observation has been recognized to produce dynamic change in sensorimotor activation according to the observed kinematics. Physical plausibility of the spatial-kinematic relationship of human movement may play a major role in the top-down processing of human motion recognition. Here, we investigated the time course of scalp activation during observation of human gait in order to extract and use it on future integrated brain-computer interface using virtual reality (VR). We analyzed event related potentials (ERP), the event related spectral perturbation (ERSP) and the inter-trial coherence (ITC) from high-density EEG recording during video display onset (-200-600 ms) and the steady state visual evoked potentials (SSVEP) inside the video of human walking 3D-animation in three conditions: Normal; Upside-down (inverted images); and Uncoordinated (pseudo-randomly mixed images). We found that early visual evoked response P120 was decreased in Upside-down condition. The N170 and P300b amplitudes were decreased in Uncoordinated condition. In Upside-down and Uncoordinated conditions, we found decreased alpha power and theta phase-locking. As regards gamma oscillation, power was increased during the Upside-down animation and decreased during the Uncoordinated animation. An SSVEP-like response oscillating at about 10 Hz was also described showing that the oscillating pattern is enhanced 300 ms after the heel strike event only in the Normal but not in the Upside-down condition. Our results are consistent with most of previous point-light display studies, further supporting possible use of virtual reality for neurofeedback applications.},
}
@article {pmid25278830,
year = {2014},
author = {Grahn, PJ and Mallory, GW and Berry, BM and Hachmann, JT and Lobel, DA and Lujan, JL},
title = {Restoration of motor function following spinal cord injury via optimal control of intraspinal microstimulation: toward a next generation closed-loop neural prosthesis.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {296},
pmid = {25278830},
issn = {1662-4548},
support = {R21 NS087320/NS/NINDS NIH HHS/United States ; R25 GM055252/GM/NIGMS NIH HHS/United States ; },
abstract = {Movement is planned and coordinated by the brain and carried out by contracting muscles acting on specific joints. Motor commands initiated in the brain travel through descending pathways in the spinal cord to effector motor neurons before reaching target muscles. Damage to these pathways by spinal cord injury (SCI) can result in paralysis below the injury level. However, the planning and coordination centers of the brain, as well as peripheral nerves and the muscles that they act upon, remain functional. Neuroprosthetic devices can restore motor function following SCI by direct electrical stimulation of the neuromuscular system. Unfortunately, conventional neuroprosthetic techniques are limited by a myriad of factors that include, but are not limited to, a lack of characterization of non-linear input/output system dynamics, mechanical coupling, limited number of degrees of freedom, high power consumption, large device size, and rapid onset of muscle fatigue. Wireless multi-channel closed-loop neuroprostheses that integrate command signals from the brain with sensor-based feedback from the environment and the system's state offer the possibility of increasing device performance, ultimately improving quality of life for people with SCI. In this manuscript, we review neuroprosthetic technology for improving functional restoration following SCI and describe brain-machine interfaces suitable for control of neuroprosthetic systems with multiple degrees of freedom. Additionally, we discuss novel stimulation paradigms that can improve synergy with higher planning centers and improve fatigue-resistant activation of paralyzed muscles. In the near future, integration of these technologies will provide SCI survivors with versatile closed-loop neuroprosthetic systems for restoring function to paralyzed muscles.},
}
@article {pmid25274409,
year = {2015},
author = {Carabin, H and McGARVEY, ST and Sahlu, I and Tarafder, MR and Joseph, L and DE Andrade, BB and Balolong, E and Olveda, R},
title = {Schistosoma japonicum in Samar, the Philippines: infection in dogs and rats as a possible risk factor for human infection.},
journal = {Epidemiology and infection},
volume = {143},
number = {8},
pages = {1767-1776},
pmid = {25274409},
issn = {1469-4409},
support = {R01 TW001582/TW/FIC NIH HHS/United States ; R01 TW01582/TW/FIC NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Animals ; Anthelmintics/therapeutic use ; Buffaloes ; Cats ; Child ; Cohort Studies ; Disease Reservoirs/*parasitology ; Dogs ; Feces/parasitology ; Female ; Humans ; Incidence ; Male ; Philippines/epidemiology ; Praziquantel/therapeutic use ; Rats ; Risk Factors ; *Schistosoma japonicum ; Schistosomiasis japonica/drug therapy/epidemiology/*veterinary ; Swine ; Young Adult ; },
abstract = {The role that animals play in the transmission of Schistosoma japonicum to humans in the Philippines remains uncertain and prior studies have not included several species, adjustment for misclassification error and clustering, or used a cohort design. A cohort study of 2468 people providing stool samples at 12 months following praziquantel treatment in 50 villages of Western Samar, the Philippines, was conducted. Stool samples from dogs, cats, rats, and water buffaloes were collected at baseline (2003-2004) and follow-up (2005). Latent-class hierarchical Bayesian log-binomial models adjusting for misclassification errors in diagnostic tests were used. The village-level baseline and follow-up prevalences of cat, dog, and rat S. japonicum infection were associated with the 12-month cumulative incidence of human S. japonicum infection, with similar magnitude and precision of effect, but correlation between infection levels made it difficult to divide their respective effects. The cumulative incidence ratios associated with a 1% increase in the prevalence of infection in dogs at baseline and in rats at follow-up were 1·04 [95% Bayesian credible interval (BCI) 1·02-1·07] and 1·02 (95% BCI 1·01-1·04), respectively, when both species were entered in the model. Dogs appear to play a role in human schistosomiasis infection while rats could be used as schistosomiasis sentinels.},
}
@article {pmid25273279,
year = {2016},
author = {Bleichner, MG and Freudenburg, ZV and Jansma, JM and Aarnoutse, EJ and Vansteensel, MJ and Ramsey, NF},
title = {Give me a sign: decoding four complex hand gestures based on high-density ECoG.},
journal = {Brain structure &amp; function},
volume = {221},
number = {1},
pages = {203-216},
pmid = {25273279},
issn = {1863-2661},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrocorticography/*methods ; Female ; *Gestures ; Hand/*physiology ; Humans ; Middle Aged ; Pattern Recognition, Automated/*methods ; Sensorimotor Cortex/*physiology ; Sign Language ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The increasing understanding of human brain functions makes it possible to directly interact with the brain for therapeutic purposes. Implantable brain computer interfaces promise to replace or restore motor functions in patients with partial or complete paralysis. We postulate that neuronal states associated with gestures, as they are used in the finger spelling alphabet of sign languages, provide an excellent signal for implantable brain computer interfaces to restore communication. To test this, we evaluated decodability of four gestures using high-density electrocorticography in two participants. The electrode grids were located subdurally on the hand knob area of the sensorimotor cortex covering a surface of 2.5-5.2 cm(2). Using a pattern-matching classification approach four types of hand gestures were classified based on their pattern of neuronal activity. In the two participants the gestures were classified with 97 and 74% accuracy. The high frequencies (>65 Hz) allowed for the best classification results. This proof-of-principle study indicates that the four gestures are associated with a reliable and discriminable spatial representation on a confined area of the sensorimotor cortex. This robust representation on a small area makes hand gestures an interesting control feature for an implantable BCI to restore communication for severely paralyzed people.},
}
@article {pmid25270615,
year = {2016},
author = {Taherian, S and Selitskiy, D and Pau, J and Davies, TC and Owens, RG},
title = {Training to use a commercial brain-computer interface as access technology: a case study.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {11},
number = {4},
pages = {345-350},
doi = {10.3109/17483107.2014.967313},
pmid = {25270615},
issn = {1748-3115},
mesh = {*Brain-Computer Interfaces ; Cerebral Palsy/*rehabilitation ; Persons with Disabilities/*rehabilitation ; Electroencephalography ; Fatigue ; Female ; Health Status ; Humans ; Motivation ; *Self-Help Devices ; Young Adult ; },
abstract = {PURPOSE: This case study describes how an individual with spastic quadriplegic cerebral palsy was trained over a period of four weeks to use a commercial electroencephalography (EEG)-based brain-computer interface (BCI).<br><br>METHOD: The participant spent three sessions exploring the system, and seven sessions playing a game focused on EEG feedback training of left and right arm motor imagery and a customised, training game paradigm was employed.<br><br>RESULTS: The participant showed improvement in the production of two distinct EEG patterns. The participant's performance was influenced by motivation, fatigue and concentration. Six weeks post-training the participant could still control the BCI and used this to type a sentence using an augmentative and alternative communication application on a wirelessly linked device.<br><br>CONCLUSIONS: The results from this case study highlight the importance of creating a dynamic, relevant and engaging training environment for BCIs. Implications for Rehabilitation Customising a training paradigm to suit the users' interests can influence adherence to assistive technology training. Mood, fatigue, physical illness and motivation influence the usability of a brain-computer interface. Commercial brain-computer interfaces, which require little set up time, may be used as access technology for individuals with severe disabilities.},
}
@article {pmid25268915,
year = {2014},
author = {Planelles, D and Hortal, E and Costa, A and Ubeda, A and Iáez, E and Azorín, JM},
title = {Evaluating classifiers to detect arm movement intention from EEG signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {14},
number = {10},
pages = {18172-18186},
pmid = {25268915},
issn = {1424-8220},
mesh = {Adult ; Arm/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials ; Female ; Humans ; Intention ; Male ; Movement/*physiology ; },
abstract = {This paper presents a methodology to detect the intention to make a reaching movement with the arm in healthy subjects before the movement actually starts. This is done by measuring brain activity through electroencephalographic (EEG) signals that are registered by electrodes placed over the scalp. The preparation and performance of an arm movement generate a phenomenon called event-related desynchronization (ERD) in the mu and beta frequency bands. A novel methodology to characterize this cognitive process based on three sums of power spectral frequencies involved in ERD is presented. The main objective of this paper is to set the benchmark for classifiers and to choose the most convenient. The best results are obtained using an SVM classifier with around 72% accuracy. This classifier will be used in further research to generate the control commands to move a robotic exoskeleton that helps people suffering from motor disabilities to perform the movement. The final aim is that this brain-controlled robotic exoskeleton improves the current rehabilitation processes of disabled people.},
}
@article {pmid25266261,
year = {2014},
author = {Bhattacharyya, S and Konar, A and Tibarewala, DN},
title = {Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {52},
number = {12},
pages = {1007-1017},
pmid = {25266261},
issn = {1741-0444},
mesh = {Adult ; Arm ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Rehabilitation/*instrumentation ; Robotics/*instrumentation ; Support Vector Machine ; Task Performance and Analysis ; Young Adult ; },
abstract = {The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8% and 44 s, respectively, and the average rate of reaching the target is 95%. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.},
}
@article {pmid25264802,
year = {2014},
author = {Gutiérrez-Martínez, J and Núñez-Gaona, MA and Carrillo-Mora, P},
title = {[Technological advances in neurorehabilitation].},
journal = {Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion},
volume = {66 Suppl 1},
number = {},
pages = {S8-23},
pmid = {25264802},
issn = {0034-8376},
mesh = {Electric Stimulation Therapy/methods ; Humans ; Neuronal Plasticity/physiology ; Physical Therapy Modalities/*trends ; Recovery of Function ; Robotics/trends ; Spinal Cord Injuries/*rehabilitation ; *Stroke Rehabilitation ; Therapy, Computer-Assisted/trends ; },
abstract = {Neurological rehabilitation arose as formal method in the 60's, for the therapeutic treatment of patients with stroke or spinal cord injury, which develop severe sequelae that affect their motor and sensory abilities. Although the Central Nervous System has plasticity mechanisms for spontaneous recovery, a high percentage of patients should receive specialized therapies to regain motor function, such as Constraint Induced Movement Therapy or Upright physical Therapy. The neurorehabilitation has undergone drastic changes over the last two decades due to the incorporation of computer and robotic electronic devices, designed to produce positive changes in cortical excitability of the cerebral hemisphere damaged and so to improve neuroplasticity. Among equipment, we can mention those for electrotherapy devices, apparatus for transcranial magnetic stimulation, the robotic lower limb orthoses, robot for upper limb training, systems for functional electrical stimulation, neuroprosthesis and brain computer interfaces. These devices have caused controversy because of its application and benefits reported in the literature. The aim of Neurorehabilitation technologies is to take advantage of the functional neuromuscular structures preserved, and they compensate or re-learn the functions that previously made the damaged areas. The purpose of this article is to mention some clinical applications and benefits that these technologies offer to patients with neuronal injury.},
}
@article {pmid25264791,
year = {2014},
author = {Cantillo-Negrete, J and Gutiérrez-Martínez, J and Flores-Rodríguez, TB and Cariño-Escobar, RI and Elías-Viñas, D},
title = {[Characterization of electrical brain activity related to hand motor imagery in healthy subjects].},
journal = {Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion},
volume = {66 Suppl 1},
number = {},
pages = {S111-21},
pmid = {25264791},
issn = {0034-8376},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Hand ; Humans ; Imagination/physiology ; Male ; Mexico ; Movement/*physiology ; Psychomotor Performance/physiology ; Statistics, Nonparametric ; Time Factors ; Young Adult ; },
abstract = {Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann- Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.},
}
@article {pmid25264354,
year = {2015},
author = {Skinner, DL and Laing, GL and Rodseth, RN and Ryan, L and Hardcastle, TC and Muckart, DJ},
title = {Blunt cardiac injury in critically ill trauma patients: a single centre experience.},
journal = {Injury},
volume = {46},
number = {1},
pages = {66-70},
doi = {10.1016/j.injury.2014.08.051},
pmid = {25264354},
issn = {1879-0267},
mesh = {Adult ; Biomarkers/blood ; *Critical Care ; Critical Illness ; *Electrocardiography ; Female ; Heart Injuries/blood/*diagnosis/mortality ; Hospital Mortality ; Humans ; Injury Severity Score ; Lactic Acid/*blood ; Length of Stay/*statistics &amp; numerical data ; Male ; Predictive Value of Tests ; Retrospective Studies ; Risk Factors ; Trauma Centers ; Troponin I/*blood ; Wounds, Nonpenetrating/blood/*diagnosis/mortality ; },
abstract = {PURPOSE: This study describes the incidence and outcomes of blunt cardiac injury (BCI) in a single trauma intensive care unit (TICU), together with the spectrum of thoracic injuries and cardiac abnormalities seen in BCI.<br><br>METHODS: We performed a retrospective observational study of 169 patients with blunt thoracic trauma admitted from January 2010 to April 2013. BCI was diagnosed using an elevated serum troponin in the presence of either clinical, ECG or transthoracic echocardiography (TTE) abnormalities in keeping with BCI. The mechanism of injury, associated thoracic injuries and TTE findings in these patients are reported.<br><br>RESULTS: The incidence of BCI among patients with blunt thoracic trauma was 50% (n=84). BCI patients had higher injury severity scores (ISS) (median 37 [IQR 29-47]; p=0.001) and higher admission serum lactate levels (median 3.55 [IQR 2.4-6.2], p=0.008). In patients with BCI, the median serum TnI level was 2823ng/L (IQR 1353-6833), with the highest measurement of 64950ng/L. TTEs were performed on 38 (45%) patients with BCI, of whom 30 (79%) had abnormalities. Patients with BCI had a higher mortality (32% vs. 16%; p=0.028) and trended towards a longer length of stay (17.0 days [standard deviation (SD) 13.5] vs. 13.6 days [SD 12.0]; p=0.084).<br><br>CONCLUSIONS: BCI was associated with an increased mortality and a trend towards a longer length of stay in this study. It is a clinically relevant diagnosis which requires a high index of suspicion. Screening of high risk patients with significant blunt thoracic trauma for BCI with serum troponins should be routine practise. Patients diagnosed with BCI should undergo more advanced imaging such as TTE or TOE to exclude significant cardiac structural injury.},
}
@article {pmid25258708,
year = {2014},
author = {Hu, X and Wang, Y and Zhao, T and Gunduz, A},
title = {Neural coding for effective rehabilitation.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {286505},
pmid = {25258708},
issn = {2314-6141},
mesh = {Brain/*physiology ; Electromyography ; Humans ; Nervous System Diseases/physiopathology/*rehabilitation ; Neuroimaging ; Robotics ; User-Computer Interface ; },
abstract = {Successful neurological rehabilitation depends on accurate diagnosis, effective treatment, and quantitative evaluation. Neural coding, a technology for interpretation of functional and structural information of the nervous system, has contributed to the advancements in neuroimaging, brain-machine interface (BMI), and design of training devices for rehabilitation purposes. In this review, we summarized the latest breakthroughs in neuroimaging from microscale to macroscale levels with potential diagnostic applications for rehabilitation. We also reviewed the achievements in electrocorticography (ECoG) coding with both animal models and human beings for BMI design, electromyography (EMG) interpretation for interaction with external robotic systems, and robot-assisted quantitative evaluation on the progress of rehabilitation programs. Future rehabilitation would be more home-based, automatic, and self-served by patients. Further investigations and breakthroughs are mainly needed in aspects of improving the computational efficiency in neuroimaging and multichannel ECoG by selection of localized neuroinformatics, validation of the effectiveness in BMI guided rehabilitation programs, and simplification of the system operation in training devices.},
}
@article {pmid25253909,
year = {2014},
author = {Elfström, M and Zedrosser, A and Jerina, K and Støen, OG and Kindberg, J and Budic, L and Jonozovič, M and Swenson, JE},
title = {Does despotic behavior or food search explain the occurrence of problem brown bears in Europe?.},
journal = {The Journal of wildlife management},
volume = {78},
number = {5},
pages = {881-893},
pmid = {25253909},
issn = {0022-541X},
abstract = {Bears foraging near human developments are often presumed to be responding to food shortage, but this explanation ignores social factors, in particular despotism in bears. We analyzed the age distribution and body condition index (BCI) of shot brown bears in relation to densities of bears and people, and whether the shot bears were killed by managers (i.e., problem bears; n = 149), in self-defense (n = 51), or were hunter-killed nonproblem bears (n = 1,896) during 1990-2010. We compared patterns between areas with (Slovenia) and without supplemental feeding (Sweden) of bears relative to 2 hypotheses. The food-search/food-competition hypothesis predicts that problem bears should have a higher BCI (e.g., exploiting easily accessible and/or nutritious human-derived foods) or lower BCI (e.g., because of food shortage) than nonproblem bears, that BCI and human density should have a positive correlation, and problem bear occurrence and seasonal mean BCI of nonproblem bears should have a negative correlation (i.e., more problem bears during years of low food availability). Food competition among bears additionally predicts an inverse relationship between BCI and bear density. The safety-search/naivety hypothesis (i.e., avoiding other bears or lack of human experience) predicts no relationship between BCI and human density, provided no dietary differences due to spatiotemporal habitat use among bears, no relationship between problem bear occurrence and seasonal mean BCI of nonproblem bears, and does not necessarily predict a difference between BCI for problem/nonproblem bears. If food competition or predation avoidance explained bear occurrence near settlements, we predicted younger problem than nonproblem bears and a negative correlation between age and human density. However, if only food search explained bear occurrence near settlements, we predicted no relation between age and problem or nonproblem bear status, or between age and human density. We found no difference in BCI or its variability between problem and nonproblem bears, no relation between BCI and human density, and no correlation between numbers of problem bears shot and seasonal mean BCI for either country. The peak of shot problem bears occurred from April to June in Slovenia and in June in Sweden (i.e., during the mating period when most intraspecific predation occurs and before fall hyperphagia). Problem bears were younger than nonproblem bears, and both problem and nonproblem bears were younger in areas of higher human density. These age differences, in combination with similarities in BCI between problem and nonproblem bears and lack of correlation between BCI and human density, suggested safety-search and naïve dispersal to be the primary mechanisms responsible for bear occurrence near settlements. Younger bears are less competitive, more vulnerable to intraspecific predation, and lack human experience, compared to adults. Body condition was inversely related to the bear density index in Sweden, whereas we found no correlation in Slovenia, suggesting that supplemental feeding may have reduced food competition, in combination with high bear harvest rates. Bears shot in self-defense were older and their BCI did not differ from that of nonproblem bears. Reasons other than food shortage apparently explained why most bears were involved in encounters with people or viewed as problematic near settlements in our study.},
}
@article {pmid25250058,
year = {2014},
author = {Zhang, Y and Dong, L and Zhang, R and Yao, D and Zhang, Y and Xu, P},
title = {An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI.},
journal = {Computational and mathematical methods in medicine},
volume = {2014},
number = {},
pages = {908719},
pmid = {25250058},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*psychology ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Likelihood Functions ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation ; Young Adult ; },
abstract = {An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI.},
}
@article {pmid25249973,
year = {2014},
author = {Semework, M and DiStasio, M},
title = {Short-term dynamics of causal information transfer in thalamocortical networks during natural inputs and microstimulation for somatosensory neuroprosthesis.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {36},
pmid = {25249973},
issn = {1662-6443},
abstract = {Recording the activity of large populations of neurons requires new methods to analyze and use the large volumes of time series data thus created. Fast and clear methods for finding functional connectivity are an important step toward the goal of understanding neural processing. This problem presents itself readily in somatosensory neuroprosthesis (SSNP) research, which uses microstimulation (MiSt) to activate neural tissue to mimic natural stimuli, and has the capacity to potentiate, depotentiate, or even destroy functional connections. As the aim of SSNP engineering is artificially creating neural responses that resemble those observed during natural inputs, a central goal is describing the influence of MiSt on activity structure among groups of neurons, and how this structure may be altered to affect perception or behavior. In this paper, we demonstrate the concept of Granger causality, combined with maximum likelihood methods, applied to neural signals recorded before, during, and after natural and electrical stimulation. We show how these analyses can be used to evaluate the changing interactions in the thalamocortical somatosensory system in response to repeated perturbation. Using LFPs recorded from the ventral posterolateral thalamus (VPL) and somatosensory cortex (S1) in anesthetized rats, we estimated pair-wise functional interactions between functional microdomains. The preliminary results demonstrate input-dependent modulations in the direction and strength of information flow during and after application of MiSt. Cortico-cortical interactions during cortical MiSt and baseline conditions showed the largest causal influence differences, while there was no statistically significant difference between pre- and post-stimulation baseline causal activities. These functional connectivity changes agree with physiologically accepted communication patterns through the network, and their particular parameters have implications for both rehabilitation and brain-machine interface SSNP applications.},
}
@article {pmid25249967,
year = {2014},
author = {Ke, Y and Qi, H and He, F and Liu, S and Zhao, X and Zhou, P and Zhang, L and Ming, D},
title = {An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {703},
pmid = {25249967},
issn = {1662-5161},
abstract = {Mental workload (MW)-based adaptive system has been found to be an effective approach to enhance the performance of human-machine interaction and to avoid human error caused by overload. However, MW estimated from the spontaneously generated electroencephalogram (EEG) was found to be task-specific. In existing studies, EEG-based MW classifier can work well under the task used to train the classifier (within-task) but crash completely when used to classify MW of a task that is similar to but not included in the training data (cross-task). The possible causes have been considered to be the task-specific EEG patterns, the mismatched workload across tasks and the temporal effects. In this study, cross-task performance-based feature selection (FS) and regression model were tried to cope with these challenges, in order to make EEG-based MW estimator trained on working memory tasks work well under a complex simulated multi-attribute task (MAT). The results show that the performance of regression model trained on working memory task and tested on multi-attribute task with the feature subset picked-out were significantly improved (correlation coefficient (COR): 0.740 ± 0.147 and 0.598 ± 0.161 for FS data and validation data respectively) when compared to the performance in the same condition with all features (chance level). It can be inferred that there do exist some MW-related EEG features can be picked out and there are something in common between MW of a relatively simple task and a complex task. This study provides a promising approach to measure MW across tasks.},
}
@article {pmid25249947,
year = {2014},
author = {Thurlings, ME and Brouwer, AM and Van Erp, JB and Werkhoven, P},
title = {Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {143},
pmid = {25249947},
issn = {1662-5137},
abstract = {Gaze-independent event-related potential (ERP) based brain-computer interfaces (BCIs) yield relatively low BCI performance and traditionally employ unimodal stimuli. Bimodal ERP-BCIs may increase BCI performance due to multisensory integration or summation in the brain. An additional advantage of bimodal BCIs may be that the user can choose which modality or modalities to attend to. We studied bimodal, visual-tactile, gaze-independent BCIs and investigated whether or not ERP components' tAUCs and subsequent classification accuracies are increased for (1) bimodal vs. unimodal stimuli; (2) location-congruent vs. location-incongruent bimodal stimuli; and (3) attending to both modalities vs. to either one modality. We observed an enhanced bimodal (compared to unimodal) P300 tAUC, which appeared to be positively affected by location-congruency (p = 0.056) and resulted in higher classification accuracies. Attending either to one or to both modalities of the bimodal location-congruent stimuli resulted in differences between ERP components, but not in classification performance. We conclude that location-congruent bimodal stimuli improve ERP-BCIs, and offer the user the possibility to switch the attended modality without losing performance.},
}
@article {pmid25248189,
year = {2015},
author = {Park, W and Kwon, GH and Kim, DH and Kim, YH and Kim, SP and Kim, L},
title = {Assessment of cognitive engagement in stroke patients from single-trial EEG during motor rehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {3},
pages = {351-362},
doi = {10.1109/TNSRE.2014.2356472},
pmid = {25248189},
issn = {1558-0210},
mesh = {Aged ; Beta Rhythm ; Brain-Computer Interfaces ; Cognition/*physiology ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization ; Female ; Hand Strength ; Humans ; Male ; Middle Aged ; Motor Cortex/physiopathology ; Movement Disorders/etiology/*psychology/*rehabilitation ; Pronation/physiology ; Robotics ; Stroke/complications/*psychology ; *Stroke Rehabilitation ; Supination ; },
abstract = {We propose a novel method for monitoring cognitive engagement in stroke patients during motor rehabilitation. Active engagement reflects implicit motivation and can enhance motor recovery. In this study, we used electroencephalography (EEG) to assess cognitive engagement in 11 chronic stroke patients while they executed active and passive motor tasks involving grasping and supination hand movements. We observed that the active motor task induced larger event-related desynchronization (ERD) than the passive task in the bilateral motor cortex and supplementary motor area (SMA). ERD differences between tasks were observed during both initial and post-movement periods . Additionally, differences in beta band activity were larger than differences in mu band activity . EEG data was used to help classify each trial as involving the active or passive motor task. Average classification accuracy was 80.7 ±0.1% for grasping movement and 82.8 ±0.1% for supination movement. Classification accuracy using a combination of movement and post-movement periods was higher than in other cases . Our results support using EEG to assess cognitive engagement in stroke patients during motor rehabilitation.},
}
@article {pmid25248173,
year = {2015},
author = {Tomida, N and Tanaka, T and Ono, S and Yamagishi, M and Higashi, H},
title = {Active data selection for motor imagery EEG classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {2},
pages = {458-467},
doi = {10.1109/TBME.2014.2358536},
pmid = {25248173},
issn = {1558-2531},
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Rejecting or selecting data from multiple trials of electroencephalography (EEG) recordings is crucial. We propose a sparsity-aware method to data selection from a set of multiple EEG recordings during motor-imagery tasks, aiming at brain machine interfaces (BMIs). Instead of empirical averaging over sample covariance matrices for multiple trials including low-quality data, which can lead to poor performance in BMI classification, we introduce weighted averaging with weight coefficients that can reject such trials. The weight coefficients are determined by the l1-minimization problem that lead to sparse weights such that almost zero-values are allocated to low-quality trials. The proposed method was successfully applied for estimating covariance matrices for the so-called common spatial pattern (CSP) method, which is widely used for feature extraction from EEG in the two-class classification. Classification of EEG signals during motor imagery was examined to support the proposed method. It should be noted that the proposed data selection method can be applied to a number of variants of the original CSP method.},
}
@article {pmid25247368,
year = {2014},
author = {Andersen, RA and Kellis, S and Klaes, C and Aflalo, T},
title = {Toward more versatile and intuitive cortical brain-machine interfaces.},
journal = {Current biology : CB},
volume = {24},
number = {18},
pages = {R885-R897},
pmid = {25247368},
issn = {1879-0445},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; R01 EY005522/EY/NEI NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY007492/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials, Somatosensory ; Humans ; *Prostheses and Implants ; *Robotics ; },
abstract = {Brain-machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain-machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics.},
}
@article {pmid25245096,
year = {2014},
author = {Liu, J and Li, X and Marciniak, C and Rymer, WZ and Zhou, P},
title = {Extraction of neural control commands using myoelectric pattern recognition: a novel application in adults with cerebral palsy.},
journal = {International journal of neural systems},
volume = {24},
number = {7},
pages = {1450022},
doi = {10.1142/S0129065714500221},
pmid = {25245096},
issn = {1793-6462},
support = {R24 HD050821/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Cerebral Palsy/*physiopathology/rehabilitation ; Electromyography/*methods ; Female ; Forearm/*physiopathology ; Hand/*physiopathology ; Humans ; Linear Models ; Male ; Motor Activity/*physiology ; Pattern Recognition, Automated/*methods ; Severity of Illness Index ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Young Adult ; },
abstract = {This study investigates an electromyogram (EMG)-based neural interface toward hand rehabilitation for patients with cerebral palsy (CP). Forty-eight channels of surface EMG signals were recorded from the forearm of eight adult subjects with CP, while they tried to perform six different hand grasp patterns. A series of myoelectric pattern recognition analyses were performed to identify the movement intention of each subject with different EMG feature sets and classifiers. Our results indicate that across all subjects high accuracies (average overall classification accuracy > 98%) can be achieved in classification of six different hand movements, suggesting that there is substantial motor control information contained in paretic muscles of the CP subjects. Furthermore, with a feature selection analysis, it was found that a small number of ranked EMG features can maintain high classification accuracies comparable to those obtained using all the EMG features (average overall classification accuracy > 96% with 16 selected EMG features). The findings of the study suggest that myoelectric pattern recognition may be a useful control strategy for promoting hand rehabilitation in CP patients.},
}
@article {pmid25243772,
year = {2014},
author = {Cecotti, H and Rivet, B},
title = {Correction: cecotti, h. And rivet, B. Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials. Brain sci. 2014, 4, 335-355.},
journal = {Brain sciences},
volume = {4},
number = {3},
pages = {488-508},
pmid = {25243772},
issn = {2076-3425},
abstract = {The authors wish to make the following correction to this paper (Cecotti, H.; Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials. Brain Sci. 2014, 4, 335-355). Dut to an error the reference number in the original published paper were not shown. The former main text should be replaced as below.},
}
@article {pmid25242561,
year = {2014},
author = {Seáñez-González, I and Mussa-Ivaldi, FA},
title = {Cursor control by Kalman filter with a non-invasive body-machine interface.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056026},
pmid = {25242561},
issn = {1741-2552},
support = {R01 HD072080/HD/NICHD NIH HHS/United States ; },
mesh = {Actigraphy/*methods ; *Algorithms ; *Computer Peripherals ; Female ; Humans ; Male ; *Man-Machine Systems ; *Range of Motion, Articular ; Shoulder Joint/*physiology ; Task Performance and Analysis ; Word Processing/*methods ; Young Adult ; },
abstract = {OBJECTIVE: We describe a novel human-machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user's upper-body.<br><br>APPROACH: A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor.<br><br>MAIN RESULTS: Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body-machine interface systems as an alternative or complement to brain-machine interfaces for accomplishing cursor control in 2D space.<br><br>SIGNIFICANCE: The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick.},
}
@article {pmid25242377,
year = {2014},
author = {Klaes, C and Shi, Y and Kellis, S and Minxha, J and Revechkis, B and Andersen, RA},
title = {A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056024},
pmid = {25242377},
issn = {1741-2552},
support = {P50 MH094258/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; *Artificial Limbs ; *Brain-Computer Interfaces ; Cognition/physiology ; Electric Stimulation/*instrumentation ; *Electrodes, Implanted ; Equipment Failure Analysis ; Feedback, Sensory/*physiology ; Macaca ; Male ; Prosthesis Design ; Somatosensory Cortex/*physiology ; Touch/*physiology ; },
abstract = {OBJECTIVE: Present day cortical brain-machine interfaces (BMIs) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closed-loop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available.<br><br>APPROACH: To overcome this limitation, this study tested a closed-loop BMI that utilizes intracortical microstimulation to provide 'tactile' sensation to a non-human primate.<br><br>MAIN RESULT: Using stimulation electrodes in Brodmann area 1 of somatosensory cortex (BA1) and recording electrodes in the anterior intraparietal area, the parietal reach region and dorsal area 5 (area 5d), it was found that this form of feedback can be used in BMI tasks.<br><br>SIGNIFICANCE: Providing somatosensory feedback has the poyential to greatly improve the performance of cognitive neuroprostheses especially for fine control and object manipulation. Adding stimulation to a BMI system could therefore improve the quality of life for severely paralyzed patients.},
}
@article {pmid25242018,
year = {2014},
author = {Li, X and Guan, C and Zhang, H and Ang, KK and Ong, SH},
title = {Adaptation of motor imagery EEG classification model based on tensor decomposition.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056020},
doi = {10.1088/1741-2560/11/5/056020},
pmid = {25242018},
issn = {1741-2552},
mesh = {Algorithms ; Brain Mapping/*methods ; Computer Simulation ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; *Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Session-to-session nonstationarity is inherent in brain-computer interfaces based on electroencephalography. The objective of this paper is to quantify the mismatch between the training model and test data caused by nonstationarity and to adapt the model towards minimizing the mismatch.<br><br>APPROACH: We employ a tensor model to estimate the mismatch in a semi-supervised manner, and the estimate is regularized in the discriminative objective function.<br><br>MAIN RESULTS: The performance of the proposed adaptation method was evaluated on a dataset recorded from 16 subjects performing motor imagery tasks on different days. The classification results validated the advantage of the proposed method in comparison with other regularization-based or spatial filter adaptation approaches. Experimental results also showed that there is a significant correlation between the quantified mismatch and the classification accuracy.<br><br>SIGNIFICANCE: The proposed method approached the nonstationarity issue from the perspective of data-model mismatch, which is more direct than data variation measurement. The results also demonstrated that the proposed method is effective in enhancing the performance of the feature extraction model.},
}
@article {pmid25236674,
year = {2014},
author = {Long, J and Huang, X and Liao, Y and Hu, X and Hu, J and Lui, S and Zhang, R and Li, Y and Gong, Q},
title = {Prediction of post-earthquake depressive and anxiety symptoms: a longitudinal resting-state fMRI study.},
journal = {Scientific reports},
volume = {4},
number = {},
pages = {6423},
pmid = {25236674},
issn = {2045-2322},
mesh = {Adult ; Anxiety Disorders/*diagnostic imaging/physiopathology ; Brain/physiopathology ; Brain Mapping ; China ; Depression/*diagnostic imaging/physiopathology ; *Earthquakes ; Female ; Humans ; Longitudinal Studies ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Radiography ; Stress Disorders, Post-Traumatic/*diagnostic imaging/physiopathology ; Survivors ; },
abstract = {Neurobiological markers of stress symptom progression for healthy survivors from a disaster (e.g., an earthquake) would greatly help with early intervention to prevent the development of stress-related disorders. However, the relationship between the neurobiological alterations and the symptom progression over time is unclear. Here, we examined 44 healthy survivors of the Wenchuan earthquake in China in a longitudinal resting-state fMRI study to observe the alterations of brain functions related to depressive or anxiety symptom progression. Using multi-variate pattern analysis to the fMRI data, we successfully predicted the depressive or anxiety symptom severity for these survivors in short- (25 days) and long-term (2 years) and the symptom severity changes over time. Several brain areas (e.g., the frontolimbic and striatal areas) and the functional connectivities located within the fronto-striato-thalamic and default-mode networks were found to be correlated with the symptom progression and might play important roles in the adaptation to trauma.},
}
@article {pmid25230468,
year = {2014},
author = {Ledo, A and Schnitzer, SA},
title = {Disturbance and clonal reproduction determine liana distribution and maintain liana diversity in a tropical forest.},
journal = {Ecology},
volume = {95},
number = {8},
pages = {2169-2178},
doi = {10.1890/13-1775.1},
pmid = {25230468},
issn = {0012-9658},
mesh = {Demography ; Panama ; Plants/*classification ; Population Density ; Reproduction/physiology ; *Trees ; *Tropical Climate ; },
abstract = {Negative density dependence (NDD) and habitat specialization have received strong empirical support as mechanisms that explain tree species diversity maintenance and distribution in tropical forests. In contrast, disturbance appears to play only a minor role. Previous studies have rarely examined the relative strengths of these diversity maintenance mechanisms concurrently, and few studies have included plant groups other than trees. Here we used a large, spatially explicit data set from Barro Colorado Island, Panama (BCI) to test whether liana and tree species distribution patterns are most consistent with NDD, habitat specialization, or disturbance. We found compelling evidence that trees responded to habitat specialization and NDD; however, only disturbance explained the distribution of the majority of liana species and maintained liana diversity. Lianas appear to respond to disturbance with high vegetative (clonal) reproduction, and liana species' ability to produce clonal stems following disturbance results in a clumped spatial distribution. Thus, clonal reproduction following disturbance explains local liana spatial distribution and diversity maintenance on BCI, whereas negative density dependence and habitat specialization, two prominent mechanisms contributing to tree species diversity and distribution, do not.},
}
@article {pmid25227081,
year = {2014},
author = {Yang, B and Han, Z and Zan, P and Wang, Q},
title = {New KF-PP-SVM classification method for EEG in brain-computer interfaces.},
journal = {Bio-medical materials and engineering},
volume = {24},
number = {6},
pages = {3665-3673},
doi = {10.3233/BME-141194},
pmid = {25227081},
issn = {1878-3619},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/physiology ; Models, Statistical ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Support Vector Machine ; },
abstract = {Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.},
}
@article {pmid25227000,
year = {2014},
author = {Wei, Y and Jun, Y and Lin, S and Hong, L},
title = {Improving classification accuracy using fuzzy method for BCI signals.},
journal = {Bio-medical materials and engineering},
volume = {24},
number = {6},
pages = {2937-2943},
doi = {10.3233/BME-141113},
pmid = {25227000},
issn = {1878-3619},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; *Fuzzy Logic ; Humans ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Electroencephalograph (EEG) signals feature extraction and processing is one of the most difficult and important part in the brain-computer interface (BCI) research field. EEG signals are generally unstable, complex and have low signal-noise ratio, which is difficult to be analyzed and processed. To solve this problem, this paper disassembles EEG signals with the empirical mode decomposition (EMD) algorithm, extracts the characteristic values of the major intrinsic mode function (IMF) components, and then classifies them with fuzzy C-means (FCM) method. Also, comparison research is done between the proposed method and several current EEG classification methods. Experimental results show that the classification accuracy based on the EEG signals of the second BCI competition in 2003 is up to 78%, which is superior to those of the comparative EEG classification methods.},
}
@article {pmid25226998,
year = {2014},
author = {Jiang, J and Zhou, Z and Yin, E and Yu, Y and Hu, D},
title = {Hybrid Brain-Computer Interface (BCI) based on the EEG and EOG signals.},
journal = {Bio-medical materials and engineering},
volume = {24},
number = {6},
pages = {2919-2925},
doi = {10.3233/BME-141111},
pmid = {25226998},
issn = {1878-3619},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography/*methods ; Evoked Potentials, Motor/*physiology ; Eye Movements/physiology ; Fixation, Ocular/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/physiology ; Pattern Recognition, Automated/methods ; Task Performance and Analysis ; },
abstract = {Recently, the integration of different electrophysiological signals into an electroencephalogram (EEG) has become an effective approach to improve the practicality of brain-computer interface (BCI) systems, referred to as hybrid BCIs. In this paper, a hybrid BCI was designed by combining an EEG with electrocardiograph (EOG) signals and tested using a target selection experiment. Gaze direction from the EOG and the event-related (de)synchronization (ERD/ERS) induced by motor imagery from the EEG were simultaneously detected as the output of the BCI system. The target selection mechanism was based on the synthesis of the gaze direction and ERD activity. When an ERD activity was detected, the target corresponding to the gaze direction was selected; without ERD activity, no target was selected, even when a subjects gaze was directed at the target. With this mechanism, the operation of the BCI system is more flexible and voluntary. The accuracy and completion time of the target selection tasks during the online testing were 89.3% and 2.4 seconds, respectively. These results show the feasibility and practicality of this hybrid BCI system, which can potentially be used in the rehabilitation of disabled individuals.},
}
@article {pmid25226996,
year = {2014},
author = {Tian, Y and Li, F and Xu, P and Yuan, Z and Zhao, D and Zhang, H},
title = {Combining canonical correlation analysis and infinite reference for frequency recognition of steady-state visual evoked potential recordings: a comparison with periodogram method.},
journal = {Bio-medical materials and engineering},
volume = {24},
number = {6},
pages = {2901-2908},
doi = {10.3233/BME-141109},
pmid = {25226996},
issn = {1878-3619},
mesh = {Adult ; Algorithms ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Oscillometry/*methods ; Pattern Recognition, Automated/*methods ; Photic Stimulation/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Statistics as Topic ; },
abstract = {Steady-state visual evoked potentials (SSVEP) are the visual system responses to a repetitive visual stimulus flickering with the constant frequency and of great importance in the study of brain activity using scalp electroencephalography (EEG) recordings. However, the reference influence for the investigation of SSVEP is generally not considered in previous work. In this study a new approach that combined the canonical correlation analysis with infinite reference (ICCA) was proposed to enhance the accuracy of frequency recognition of SSVEP recordings. Compared with the widely used periodogram method (PM), ICCA is able to achieve higher recognition accuracy when extracts frequency within a short span. Further, the recognition results suggested that ICCA is a very robust tool to study the brain computer interface (BCI) based on SSVEP.},
}
@article {pmid25222949,
year = {2015},
author = {Shaeri, MA and Sodagar, AM},
title = {A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {3},
pages = {485-497},
doi = {10.1109/TNSRE.2014.2355139},
pmid = {25222949},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Computer Simulation ; Data Compression ; Electrophysiological Phenomena ; Equipment Design ; Humans ; Microcomputers ; *Neural Prostheses ; Prostheses and Implants ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Wavelet Analysis ; },
abstract = {This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.},
}
@article {pmid25221505,
year = {2014},
author = {Xu, R and Jiang, N and Vuckovic, A and Hasan, M and Mrachacz-Kersting, N and Allan, D and Fraser, M and Nasseroleslami, B and Conway, B and Dremstrup, K and Farina, D},
title = {Movement-related cortical potentials in paraplegic patients: abnormal patterns and considerations for BCI-rehabilitation.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {35},
pmid = {25221505},
issn = {1662-6443},
support = {G0902257/MRC_/Medical Research Council/United Kingdom ; },
abstract = {Non-invasive EEG-based Brain-Computer Interfaces (BCI) can be promising for the motor neuro-rehabilitation of paraplegic patients. However, this shall require detailed knowledge of the abnormalities in the EEG signatures of paraplegic patients. The association of abnormalities in different subgroups of patients and their relation to the sensorimotor integration are relevant for the design, implementation and use of BCI systems in patient populations. This study explores the patterns of abnormalities of movement related cortical potentials (MRCP) during motor imagery tasks of feet and right hand in patients with paraplegia (including the subgroups with/without central neuropathic pain (CNP) and complete/incomplete injury patients) and the level of distinctiveness of abnormalities in these groups using pattern classification. The most notable observed abnormalities were the amplified execution negativity and its slower rebound in the patient group. The potential underlying mechanisms behind these changes and other minor dissimilarities in patients' subgroups, as well as the relevance to BCI applications, are discussed. The findings are of interest from a neurological perspective as well as for BCI-assisted neuro-rehabilitation and therapy.},
}
@article {pmid25219223,
year = {2014},
author = {Huang, L and Wang, H},
title = {[EEG feature extraction based on quantum particle swarm optimizer and independent component analysis].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {3},
pages = {502-505},
pmid = {25219223},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {Feature extraction is a very crucial step in P300-based brain-computer interface (BCI) and independent component analysis (ICA) is a suitable P300 feature extraction method. But at present the convergence performance of the general ICA iteration methods are not very satisfactory. In this paper, a method based on quantum particle swarm optimizer (QPSO) algorithm and ICA technique is put forward for P300 extraction. In this method, quantum computing is used to impel ICA iteration to globally converge faster. It achieved the purpose of extracting P300 rapidly and efficiently. The method was tested on two public datasets of BCI Competition II and III, and a simple linear classifier was employed to classify the extracted P300 features. The recognition accuracy reached 94.4% with 15 times averaged. The results showed that the proposed method could extract P300 rapidly and the extraction effect did not reduce. It provides an experimental basis for further study of real-time BCI system.},
}
@article {pmid25218113,
year = {2015},
author = {Chen, PC and Baumgartner, J and Seo, JH and Korostenskaja, M and Lee, KH},
title = {Bilateral intracranial EEG with corpus callosotomy may uncover seizure focus in nonlocalizing focal epilepsy.},
journal = {Seizure},
volume = {24},
number = {},
pages = {63-69},
doi = {10.1016/j.seizure.2014.08.011},
pmid = {25218113},
issn = {1532-2688},
mesh = {Adolescent ; Brain Mapping ; Child ; Child, Preschool ; Corpus Callosum/*surgery ; Craniotomy/*methods ; *Electroencephalography ; Epilepsies, Partial/*surgery ; Female ; Humans ; Infant ; Magnetic Resonance Imaging ; Male ; Retrospective Studies ; Treatment Outcome ; },
abstract = {PURPOSE: To evaluate the value of a new multi-stage surgical procedure using bilateral intracranial electroencephalogram (iEEG) prior and post complete corpus callosotomy (CC) for epileptogenic focus localization.<br><br>METHOD: Thirty patients with drug-resistant epilepsy underwent bilateral iEEG monitoring to localize epileptogenic focus for surgical treatment. Among them, bisynchronous epileptogenic activities were found in 9 pediatric patients. These 9 patients then received complete CC and continued bilateral iEEG monitoring for further seizure localization. Final surgical treatment decisions were made based on the bilateral iEEG findings post complete CC. The entire multi-stage procedure was performed during the same hospital stay. We retrospectively studied the data from the 9 patients.<br><br>RESULTS: Seizure onset was lateralized in 3 patients who later received functional hemispherectomy. In another 4 patients, seizure onset was localized, resulting in resective surgery. Bilateral multiple subpial transection was performed on 1 patient with identified bilateral independent seizure onset. One patient did not have seizures following complete CC leading to removal of electrodes without any further resection. Subsequent follow-up showed favorable outcome in all patients: seizure-free in 7, more than 90% reduction in 2. None of the patients experienced surgery related complications during the procedure and follow-up period.<br><br>CONCLUSION: The multi-stage surgical procedure utilizing iEEG monitoring with CC is a viable option for select patients with catastrophic non-localizing epilepsy. Further study is necessary to find the optimal selection criteria for use of this novel approach.},
}
@article {pmid25216478,
year = {2014},
author = {Mustafa, M and Magnor, M},
title = {ElectroEncephaloGraphics: Making waves in computer graphics research.},
journal = {IEEE computer graphics and applications},
volume = {34},
number = {6},
pages = {46-56},
doi = {10.1109/MCG.2014.107},
pmid = {25216478},
issn = {1558-1756},
mesh = {*Computer Graphics ; Electroencephalography/*methods ; Humans ; },
abstract = {Electroencephalography (EEG) is a novel modality for investigating perceptual graphics problems. Until recently, EEG has predominantly been used for clinical diagnosis, in psychology, and by the brain-computer-interface community. Researchers are extending it to help understand the perception of visual output from graphics applications and to create approaches based on direct neural feedback. Researchers have applied EEG to graphics to determine perceived image and video quality by detecting typical rendering artifacts, to evaluate visualization effectiveness by calculating the cognitive load, and to automatically optimize rendering parameters for images and videos on the basis of implicit neural feedback.},
}
@article {pmid25210153,
year = {2014},
author = {Zhuang, KZ and Lebedev, MA and Nicolelis, MA},
title = {Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms.},
journal = {Journal of neurophysiology},
volume = {112},
number = {11},
pages = {2865-2887},
pmid = {25210153},
issn = {1522-1598},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; DP1-MH-099903/DP/NCCDPHP CDC HHS/United States ; R01-NS-073952/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Arm/*innervation/physiology ; Data Interpretation, Statistical ; Electromyography/methods ; *Evoked Potentials, Motor ; Female ; Macaca mulatta ; Male ; Motor Cortex/cytology/*physiology ; Movement ; Muscle, Skeletal/innervation/physiology ; Neurons/*physiology ; *Reaction Time ; Somatosensory Cortex/cytology/physiology ; },
abstract = {Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.},
}
@article {pmid25206933,
year = {2014},
author = {Wang, H and Li, Y and Long, J and Yu, T and Gu, Z},
title = {An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface.},
journal = {Cognitive neurodynamics},
volume = {8},
number = {5},
pages = {399-409},
pmid = {25206933},
issn = {1871-4080},
abstract = {Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain-computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).},
}
@article {pmid25206321,
year = {2014},
author = {Kashihara, K},
title = {A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {244},
pmid = {25206321},
issn = {1662-4548},
abstract = {Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600-700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In accordance with this classification, a face on a computer morphs into a sad or displeased countenance. The proposed method could be incorporated as a part of non-verbal communication tools to enable emotional expression.},
}
@article {pmid25204774,
year = {2014},
author = {Kageyama, Y and Hirata, M and Yanagisawa, T and Shimokawa, T and Sawada, J and Morris, S and Mizushima, N and Kishima, H and Sakura, O and Yoshimine, T},
title = {Severely affected ALS patients have broad and high expectations for brain-machine interfaces.},
journal = {Amyotrophic lateral sclerosis &amp; frontotemporal degeneration},
volume = {15},
number = {7-8},
pages = {513-519},
doi = {10.3109/21678421.2014.951943},
pmid = {25204774},
issn = {2167-9223},
mesh = {Adult ; Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/*nursing/*psychology ; *Brain-Computer Interfaces ; *Communication ; *Communication Devices for People with Disabilities ; Female ; Humans ; Male ; Middle Aged ; Quality of Life ; Retrospective Studies ; Severity of Illness Index ; Surveys and Questionnaires ; Tracheostomy ; },
abstract = {Brain-machine interfaces (BMIs) may provide new communication channels and motor function to individuals with severe neurodegenerative diseases, but little is known about their interests in such devices. We investigated the interests of severely affected ALS patients in BMIs, and examined factors that might influence these interests. We conducted an anonymous, mail-back questionnaire survey of severely disabled ALS patients diagnosed using the revised El Escorial criteria. Thirty-seven patients responded to the questionnaire. Twenty-nine (78.4%) had undergone tracheostomy positive pressure ventilation. More than 80% of the patients were interested in communication support. Thirty-three (89.2%) felt stressed during communication. Among those using assistive communication devices (17 patients), 15 (88.2%) were not satisfied with them. More than 50% of the patients expressed an interest in BMIs. Their expectations of BMIs ranged widely from emergency alarm to postural change. The frequent use of personal computers tended to be correlated with an interest in invasive BMIs (p = 0.07). In conclusion, this was the first questionnaire survey demonstrating that severely affected ALS patients have broad and high expectations for BMIs. Communication was the most desired support from BMIs for such patients. We need to meet their widely ranging expectations of BMIs.},
}
@article {pmid25203982,
year = {2015},
author = {Nuyujukian, P and Fan, JM and Kao, JC and Ryu, SI and Shenoy, KV},
title = {A high-performance keyboard neural prosthesis enabled by task optimization.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {1},
pages = {21-29},
pmid = {25203982},
issn = {1558-2531},
support = {R01 NS064318/NS/NINDS NIH HHS/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01-NS066311/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; 1DP1OD006409/OD/NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01-NS064318/NS/NINDS NIH HHS/United States ; T-R01NS076460/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Equipment Design ; Equipment Failure Analysis ; Humans ; Macaca mulatta ; Man-Machine Systems ; Movement Disorders/physiopathology/*rehabilitation ; Pattern Recognition, Visual/*physiology ; Task Performance and Analysis ; User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously moving cursor neural prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject-specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this paper, we present the highest performing free-paced neural prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits/s. These findings demonstrate that meaningful high performance can be achieved using an intracortical neural prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.},
}
@article {pmid25203496,
year = {2014},
author = {Colwell, K and Throckmorton, C and Collins, L and Morton, K},
title = {Projected accuracy metric for the P300 Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {5},
pages = {921-925},
doi = {10.1109/TNSRE.2014.2324892},
pmid = {25203496},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Models, Statistical ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {The P300 Speller brain-computer interface (BCI) is a virtual keyboard that allows users to type without requiring neuromuscular control. P300 Speller research commonly aims to improve the system accuracy, which is typically estimated by spelling a small number of characters and calculating the percent spelled correctly. In this paper we introduce a new method for estimating the long-term ("projected") accuracy, which utilizes all available flash data and a probabilistic model of the Speller system to produce an estimate with lower variance and lower granularity than the standard measure. We apply the new method to 110 previously-collected P300 Speller runs to confirm its consistency, and simulate spelling runs from real subject data to demonstrate lower variance on the accuracy estimate for any given amount of data.},
}
@article {pmid25202300,
year = {2014},
author = {Piangerelli, M and Ciavarro, M and Paris, A and Marchetti, S and Cristiani, P and Puttilli, C and Torres, N and Benabid, AL and Romanelli, P},
title = {A fully integrated wireless system for intracranial direct cortical stimulation, real-time electrocorticography data transmission, and smart cage for wireless battery recharge.},
journal = {Frontiers in neurology},
volume = {5},
number = {},
pages = {156},
pmid = {25202300},
issn = {1664-2295},
abstract = {Wireless transmission of cortical signals is an essential step to improve the safety of epilepsy procedures requiring seizure focus localization and to provide chronic recording of brain activity for Brain Computer Interface (BCI) applications. Our group developed a fully implantable and externally rechargeable device, able to provide wireless electrocorticographic (ECoG) recording and cortical stimulation (CS). The first prototype of a wireless multi-channel very low power ECoG system was custom-designed to be implanted on non-human primates. The device, named ECOGIW-16E, is housed in a compact hermetically sealed Polyether ether ketone (PEEK) enclosure, allowing seamless battery recharge. ECOGIW-16E is recharged in a wireless fashion using a special cage designed to facilitate the recharge process in monkeys and developed in accordance with guidelines for accommodation of animals by Council of Europe (ETS123). The inductively recharging cage is made up of nylon and provides a thoroughly novel experimental setting on freely moving animals. The combination of wireless cable-free ECoG and external seamless battery recharge solves the problems and shortcomings caused by the presence of cables leaving the skull, providing a safer and easier way to monitor patients and to perform ECoG recording on primates. Data transmission exploits the newly available Medical Implant Communication Service band (MICS): 402-405 MHz. ECOGIW-16E was implanted over the left sensorimotor cortex of a macaca fascicularis to assess the feasibility of wireless ECoG monitoring and brain mapping through CS. With this device, we were able to record the everyday life ECoG signal from a monkey and to deliver focal brain stimulation with movement elicitation.},
}
@article {pmid25202231,
year = {2014},
author = {Rosenboom, D},
title = {Active imaginative listening-a neuromusical critique.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {251},
pmid = {25202231},
issn = {1662-4548},
abstract = {The parallel study of music in science and creative practice can be traced back to the ancients; and paralleling the emergence of music neuroscience, creative musical practitioners have employed neurobiological phenomena extensively in music composition and performance. Several examples from the author's work in this area, which began in the 1960s, are cited and briefly described. From this perspective, the author also explores questions pertinent to current agendas evident in music neuroscience and speculates on potentially potent future directions.},
}
@article {pmid25201560,
year = {2015},
author = {Tabot, GA and Kim, SS and Winberry, JE and Bensmaia, SJ},
title = {Restoring tactile and proprioceptive sensation through a brain interface.},
journal = {Neurobiology of disease},
volume = {83},
number = {},
pages = {191-198},
pmid = {25201560},
issn = {1095-953X},
support = {R01 NS082865/NS/NINDS NIH HHS/United States ; R01 NS08285/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological ; Amputees/rehabilitation ; Animals ; Biomimetics ; *Brain-Computer Interfaces ; Electric Stimulation ; Feedback, Sensory ; Humans ; Neuronal Plasticity ; Neurons/physiology ; Proprioception/*physiology ; *Prostheses and Implants ; Psychomotor Performance/*physiology ; Self Concept ; Somatosensory Cortex/*physiology ; Touch/*physiology ; Touch Perception/*physiology ; Upper Extremity/innervation/physiopathology ; },
abstract = {Somatosensation plays a critical role in the dexterous manipulation of objects, in emotional communication, and in the embodiment of our limbs. For upper-limb neuroprostheses to be adopted by prospective users, prosthetic limbs will thus need to provide sensory information about the position of the limb in space and about objects grasped in the hand. One approach to restoring touch and proprioception consists of electrically stimulating neurons in somatosensory cortex in the hopes of eliciting meaningful sensations to support the dexterous use of the hands, promote their embodiment, and perhaps even restore the affective dimension of touch. In this review, we discuss the importance of touch and proprioception in everyday life, then describe approaches to providing artificial somatosensory feedback through intracortical microstimulation (ICMS). We explore the importance of biomimicry--the elicitation of naturalistic patterns of neuronal activation--and that of adaptation--the brain's ability to adapt to novel sensory input, and argue that both biomimicry and adaptation will play a critical role in the artificial restoration of somatosensation. We also propose that the documented re-organization that occurs after injury does not pose a significant obstacle to brain interfaces. While still at an early stage of development, sensory restoration is a critical step in transitioning upper-limb neuroprostheses from the laboratory to the clinic.},
}
@article {pmid25193343,
year = {2014},
author = {Burns, A and Adeli, H and Buford, JA},
title = {Brain-computer interface after nervous system injury.},
journal = {The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry},
volume = {20},
number = {6},
pages = {639-651},
doi = {10.1177/1073858414549015},
pmid = {25193343},
issn = {1089-4098},
mesh = {Amyotrophic Lateral Sclerosis/rehabilitation ; Brain/*physiopathology ; *Brain-Computer Interfaces/trends ; Consciousness Disorders/rehabilitation ; Humans ; Parkinson Disease/rehabilitation ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/rehabilitation ; Stroke Rehabilitation ; Trauma, Nervous System/*rehabilitation ; },
abstract = {Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson's disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.},
}
@article {pmid25192573,
year = {2014},
author = {Chai, R and Ling, SH and Hunter, GP and Tran, Y and Nguyen, HT},
title = {Brain-computer interface classifier for wheelchair commands using neural network with fuzzy particle swarm optimization.},
journal = {IEEE journal of biomedical and health informatics},
volume = {18},
number = {5},
pages = {1614-1624},
doi = {10.1109/JBHI.2013.2295006},
pmid = {25192573},
issn = {2168-2208},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; *Fuzzy Logic ; Humans ; Middle Aged ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted ; *Wheelchairs ; },
abstract = {This paper presents the classification of a three-class mental task-based brain-computer interface (BCI) that uses the Hilbert-Huang transform for the features extractor and fuzzy particle swarm optimization with cross-mutated-based artificial neural network (FPSOCM-ANN) for the classifier. The experiments were conducted on five able-bodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different time-windows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubik's cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for on-off commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the able-bodied subjects; however, this was improved by increasing the duration of the time-windows. The FPSOCM-ANN provides improved accuracies compared to genetic algorithm-based artificial neural network (GA-ANN) for three mental tasks-based BCI classifications with the best classification accuracy achieved for a 7-s time-window: 84.4% (FPSOCM-ANN) compared to 77.4% (GA-ANN). More comparisons on feature extractors and classifiers were included. For two-channel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.},
}
@article {pmid25191263,
year = {2014},
author = {Zacksenhouse, M and Lebedev, MA and Nicolelis, MA},
title = {Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.},
journal = {Frontiers in computational neuroscience},
volume = {8},
number = {},
pages = {91},
pmid = {25191263},
issn = {1662-5188},
abstract = {What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.},
}
@article {pmid25190480,
year = {2014},
author = {Hoffmann, P and Held, C and Maskow, T and Sadowski, G},
title = {A thermodynamic investigation of the glucose-6-phosphate isomerization.},
journal = {Biophysical chemistry},
volume = {195},
number = {},
pages = {22-31},
doi = {10.1016/j.bpc.2014.08.002},
pmid = {25190480},
issn = {1873-4200},
mesh = {Glucose-6-Phosphate/*chemistry ; Glutamic Acid/chemistry ; Hydrogen-Ion Concentration ; Isomerism ; Kinetics ; Temperature ; Thermodynamics ; },
abstract = {In this work, Δ(R)g(+) values for the enzymatic G6P isomerization were determined as a function of the G6P equilibrium molality between 25 °C and 37 °C. The reaction mixtures were buffered at pH=8.5. In contrast to standard literature work, Δ(R)g(+) values were determined from activity-based equilibrium constants instead of molality-based apparent values. This yielded a Δ(R)g(+) value of 2.55±0.05 kJ mol(-1) at 37 °C, independent of the solution pH between 7.5 and 8.5. Furthermore, Δ(R)h(+) was measured at pH=8.5 and 25 °C yielding 12.05±0.2 kJ mol(-1). Accounting for activity coefficients turned out to influence Δ(R)g(+) up to 30% upon increasing the G6P molality. This result was confirmed by predictions using the thermodynamic model ePC-SAFT. Finally, the influence of the buffer and of potassium glutamate as an additive on the reaction equilibrium was measured and predicted with ePC-SAFT in good agreement.},
}
@article {pmid25188730,
year = {2014},
author = {Aziz, F and Arof, H and Mokhtar, N and Mubin, M},
title = {HMM based automated wheelchair navigation using EOG traces in EEG.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056018},
doi = {10.1088/1741-2560/11/5/056018},
pmid = {25188730},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Electrooculography/*methods ; Female ; Humans ; Male ; *Man-Machine Systems ; *Markov Chains ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; *Wheelchairs ; Young Adult ; },
abstract = {This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.},
}
@article {pmid25182191,
year = {2014},
author = {Jin, J and Allison, BZ and Zhang, Y and Wang, X and Cichocki, A},
title = {An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical functions.},
journal = {International journal of neural systems},
volume = {24},
number = {8},
pages = {1450027},
doi = {10.1142/S0129065714500270},
pmid = {25182191},
issn = {1793-6462},
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Electroencephalography/*methods ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; *Face ; Female ; Functional Neuroimaging/*methods ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Recognition, Psychology/*physiology ; Young Adult ; },
abstract = {Recent research has shown that a new face paradigm is superior to the conventional "flash only" approach that has dominated P300 brain-computer interfaces (BCIs) for over 20 years. However, these face paradigms did not study the repetition effects and the stability of evoked event related potentials (ERPs), which would decrease the performance of P300 BCI. In this paper, we explored whether a new "multi-faces (MF)" approach would yield more distinct ERPs than the conventional "single face (SF)" approach. To decrease the repetition effects and evoke large ERPs, we introduced a new stimulus approach called the "MF" approach, which shows different familiar faces randomly. Fifteen subjects participated in runs using this new approach and an established "SF" approach. The result showed that the MF pattern enlarged the N200 and N400 components, evoked stable P300 and N400, and yielded better BCI performance than the SF pattern. The MF pattern can evoke larger N200 and N400 components and more stable P300 and N400, which increase the classification accuracy compared to the face pattern.},
}
@article {pmid25179667,
year = {2014},
author = {Hashimoto, Y and Ota, T and Mukaino, M and Liu, M and Ushiba, J},
title = {Functional recovery from chronic writer's cramp by brain-computer interface rehabilitation: a case report.},
journal = {BMC neuroscience},
volume = {15},
number = {},
pages = {103},
pmid = {25179667},
issn = {1471-2202},
mesh = {Aged ; Beta Rhythm/physiology ; *Brain-Computer Interfaces ; Dystonic Disorders/physiopathology/*rehabilitation ; Electroencephalography/*methods ; Electromyography ; Feasibility Studies ; Female ; Handwriting ; Humans ; Motor Activity/physiology ; Neurofeedback/*methods ; Pilot Projects ; Recovery of Function/physiology ; Sensorimotor Cortex/physiopathology ; Treatment Outcome ; },
abstract = {BACKGROUND: Dystonia is often currently treated with botulinum toxin injections to spastic muscles, or deep brain stimulation to the basal ganglia. In addition to these pharmacological or neurosurgical measures, a new noninvasive treatment concept, functional modulation using a brain-computer interface, was tested for feasibility. We recorded electroencephalograms (EEGs) over the bilateral sensorimotor cortex from a patient suffering from chronic writer's cramp. The patient was asked to suppress an exaggerated beta frequency component in the EEG during hand extension.<br><br>RESULTS: The patient completed biweekly one-hour training for 5 months without any adverse effects. Significant decrease of the beta frequency component during handwriting was confirmed, and was associated with clear functional improvement.<br><br>CONCLUSION: The current pilot study suggests that a brain-computer Interface can give explicit feedback of ongoing cortical excitability to patients with dystonia and allow them to suppress exaggerated neural activity, resulting in functional recovery.},
}
@article {pmid25164754,
year = {2014},
author = {Sadtler, PT and Quick, KM and Golub, MD and Chase, SM and Ryu, SI and Tyler-Kabara, EC and Yu, BM and Batista, AP},
title = {Neural constraints on learning.},
journal = {Nature},
volume = {512},
number = {7515},
pages = {423-426},
pmid = {25164754},
issn = {1476-4687},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; P30-NS076405/NS/NINDS NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; R01-HD071686/HD/NICHD NIH HHS/United States ; R01-NS065065/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Computers ; Learning/*physiology ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/cytology/physiology ; Motor Skills/*physiology ; Nerve Net/cytology/physiology ; Neurons/physiology ; },
abstract = {Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.},
}
@article {pmid25163823,
year = {2014},
author = {Corralejo, R and Nicolás-Alonso, LF and Alvarez, D and Hornero, R},
title = {A P300-based brain-computer interface aimed at operating electronic devices at home for severely disabled people.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {52},
number = {10},
pages = {861-872},
pmid = {25163823},
issn = {1741-0444},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Electronics/*instrumentation ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Surveys and Questionnaires ; },
abstract = {The present study aims at developing and assessing an assistive tool for operating electronic devices at home by means of a P300-based brain-computer interface (BCI). Fifteen severely impaired subjects participated in the study. The developed tool allows users to interact with their usual environment fulfilling their main needs. It allows for navigation through ten menus and to manage up to 113 control commands from eight electronic devices. Ten out of the fifteen subjects were able to operate the proposed tool with accuracy above 77 %. Eight out of them reached accuracies higher than 95 %. Moreover, bitrates up to 20.1 bit/min were achieved. The novelty of this study lies in the use of an environment control application in a real scenario: real devices managed by potential BCI end-users. Although impaired users might not be able to set up this system without aid of others, this study takes a significant step to evaluate the degree to which such populations could eventually operate a stand-alone system. Our results suggest that neither the type nor the degree of disability is a relevant issue to suitably operate a P300-based BCI. Hence, it could be useful to assist disabled people at home improving their personal autonomy.},
}
@article {pmid25162231,
year = {2014},
author = {Höhne, J and Holz, E and Staiger-Sälzer, P and Müller, KR and Kübler, A and Tangermann, M},
title = {Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.},
journal = {PloS one},
volume = {9},
number = {8},
pages = {e104854},
pmid = {25162231},
issn = {1932-6203},
mesh = {*Artificial Intelligence ; Brain/*physiology ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography ; Humans ; *Imagination ; Middle Aged ; Reaction Time ; *Self-Help Devices ; Time Factors ; },
abstract = {Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.},
}
@article {pmid25161891,
year = {2014},
author = {Stoeckel, LE and Garrison, KA and Ghosh, S and Wighton, P and Hanlon, CA and Gilman, JM and Greer, S and Turk-Browne, NB and deBettencourt, MT and Scheinost, D and Craddock, C and Thompson, T and Calderon, V and Bauer, CC and George, M and Breiter, HC and Whitfield-Gabrieli, S and Gabrieli, JD and LaConte, SM and Hirshberg, L and Brewer, JA and Hampson, M and Van Der Kouwe, A and Mackey, S and Evins, AE},
title = {Optimizing real time fMRI neurofeedback for therapeutic discovery and development.},
journal = {NeuroImage. Clinical},
volume = {5},
number = {},
pages = {245-255},
pmid = {25161891},
issn = {2213-1582},
support = {R21 DA030523/DA/NIDA NIH HHS/United States ; R01 AA02152901A1/AA/NIAAA NIH HHS/United States ; P50 DA009241/DA/NIDA NIH HHS/United States ; K12 DA00167/DA/NIDA NIH HHS/United States ; P41 RR014075/RR/NCRR NIH HHS/United States ; K01 DA034093/DA/NIDA NIH HHS/United States ; R21/33DA026104/DA/NIDA NIH HHS/United States ; K12 DA000167/DA/NIDA NIH HHS/United States ; K24DA029262/DA/NIDA NIH HHS/United States ; R33 DA026104/DA/NIDA NIH HHS/United States ; R03 DA029163/DA/NIDA NIH HHS/United States ; L30 DA030089/DA/NIDA NIH HHS/United States ; R01 AT007922-01/AT/NCCIH NIH HHS/United States ; R33 DA026085/DA/NIDA NIH HHS/United States ; R01 AT007922/AT/NCCIH NIH HHS/United States ; K23 DA032612/DA/NIDA NIH HHS/United States ; K23DA032612/DA/NIDA NIH HHS/United States ; K24 DA029262/DA/NIDA NIH HHS/United States ; R03 DA029163-01A1/DA/NIDA NIH HHS/United States ; K24 DA030443/DA/NIDA NIH HHS/United States ; R21DA030523/DA/NIDA NIH HHS/United States ; K01 DA027756/DA/NIDA NIH HHS/United States ; R01 MH095789/MH/NIMH NIH HHS/United States ; P50 DA09241/DA/NIDA NIH HHS/United States ; 5R21DA026085/DA/NIDA NIH HHS/United States ; R21 DA026085/DA/NIDA NIH HHS/United States ; P41RR14075/RR/NCRR NIH HHS/United States ; K24DA030443/DA/NIDA NIH HHS/United States ; },
mesh = {Brain Mapping/*methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Neurofeedback/*methods ; },
abstract = {While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health, the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain-behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.},
}
@article {pmid25161613,
year = {2014},
author = {Astrand, E and Wardak, C and Ben Hamed, S},
title = {Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {144},
pmid = {25161613},
issn = {1662-5137},
abstract = {Brain-machine interfaces (BMIs) using motor cortical activity to drive an external effector like a screen cursor or a robotic arm have seen enormous success and proven their great rehabilitation potential. An emerging parallel effort is now directed to BMIs controlled by endogenous cognitive activity, also called cognitive BMIs. While more challenging, this approach opens new dimensions to the rehabilitation of cognitive disorders. In the present work, we focus on BMIs driven by visuospatial attention signals and we provide a critical review of these studies in the light of the accumulated knowledge about the psychophysics, anatomy, and neurophysiology of visual spatial attention. Importantly, we provide a unique comparative overview of the several studies, ranging from non-invasive to invasive human and non-human primates studies, that decode attention-related information from ongoing neuronal activity. We discuss these studies in the light of the challenges attention-driven cognitive BMIs have to face. In a second part of the review, we discuss past and current attention-based neurofeedback studies, describing both the covert effects of neurofeedback onto neuronal activity and its overt behavioral effects. Importantly, we compare neurofeedback studies based on the amplitude of cortical activity to studies based on the enhancement of cortical information content. Last, we discuss several lines of future research and applications for attention-driven cognitive brain-computer interfaces (BCIs), including the rehabilitation of cognitive deficits, restored communication in locked-in patients, and open-field applications for enhanced cognition in normal subjects. The core motivation of this work is the key idea that the improvement of current cognitive BMIs for therapeutic and open field applications needs to be grounded in a proper interdisciplinary understanding of the physiology of the cognitive function of interest, be it spatial attention, working memory or any other cognitive signal.},
}
@article {pmid25160566,
year = {2015},
author = {Schnakers, C and Giacino, JT and Løvstad, M and Habbal, D and Boly, M and Di, H and Majerus, S and Laureys, S},
title = {Preserved covert cognition in noncommunicative patients with severe brain injury?.},
journal = {Neurorehabilitation and neural repair},
volume = {29},
number = {4},
pages = {308-317},
doi = {10.1177/1545968314547767},
pmid = {25160566},
issn = {1552-6844},
mesh = {Adult ; Attention/*physiology ; Auditory Perception/physiology ; Brain/*physiopathology ; Brain Injuries/complications/*physiopathology ; Cognition/*physiology ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials, Auditory ; Female ; Humans ; Male ; Middle Aged ; Persistent Vegetative State/etiology/*physiopathology ; },
abstract = {BACKGROUND: Despite recent evidence suggesting that some severely brain-injured patients retain some capacity for top-down processing (covert cognition), the degree of sparing is unknown.<br><br>OBJECTIVE: Top-down attentional processing was assessed in patients in minimally conscious (MCS) and vegetative states (VS) using an active event-related potential (ERP) paradigm.<br><br>METHODS: A total of 26 patients were included (38 &#xb1; 12 years old, 9 traumatic, 21 patients >1 year postonset): 8 MCS+, 8 MCS-, and 10 VS patients. There were 14 healthy controls (30 &#xb1; 8 years old). The ERP paradigm included (1) a passive condition and (2) an active condition, wherein the participant was instructed to voluntarily focus attention on his/her own name. In each condition, the participant's own name was presented 100 times (ie, 4 blocks of 25 stimuli).<br><br>RESULTS: In 5 MCS+ patients as well as in 3 MCS- patients and 1 VS patient, an enhanced P3 amplitude was observed in the active versus passive condition. Relative to controls, patients showed a response that was (1) widely distributed over frontoparietal areas and (2) not present in all blocks (3 of 4). In patients with covert cognition, the amplitude of the response was lower in frontocentral electrodes compared with controls but did not differ from that in the MCS+ group.<br><br>CONCLUSION: The results indicate that volitional top-down attention is impaired in patients with covert cognition. Further investigation is crucially needed to better understand top-down cognitive functioning in this population because this may help refine brain-computer interface-based communication strategies.},
}
@article {pmid25160220,
year = {2014},
author = {Sato, H and Washizawa, Y},
title = {A novel EEG-based spelling system using N100 and P300.},
journal = {Studies in health technology and informatics},
volume = {205},
number = {},
pages = {428-432},
pmid = {25160220},
issn = {1879-8365},
mesh = {Adult ; *Algorithms ; Brain Waves/*physiology ; Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Word Processing ; Writing ; Young Adult ; },
abstract = {P300-speller is one of the most popular EEG-based spelling systems proposed by Farwell and Donchin. P300-speller has a 6 × 6 character matrix and requires at least 12 flashes to input one character. This restricts increasing of the information transfer rate (ITR) by decreasing the number of flashes. In this paper, a novel spelling system is proposed to reduce the number of flashes by sequential stimulation of images. In order to determine the command, the proposed system utilizes two kinds of the event-related brain potentials (ERP), N100 and P300 whereas P300-speller utilizes only P300. Thanks to using both N100 and P300, the proposed system achieves higher accuracy and faster spelling speed than P300-speller. Our experiment by ten subjects showed that ITR of the proposed system is an average of 0.25 bit/sec improved compared to P300-speller.},
}
@article {pmid25159737,
year = {2015},
author = {Takeuchi, N and Mori, T and Nishijima, K and Kondo, T and Izumi, S},
title = {Inhibitory transcranial direct current stimulation enhances weak beta event-related synchronization after foot motor imagery in patients with lower limb amputation.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {32},
number = {1},
pages = {44-50},
doi = {10.1097/WNP.0000000000000123},
pmid = {25159737},
issn = {1537-1603},
mesh = {Aged ; Amputees/*rehabilitation ; Cortical Synchronization/*physiology ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/physiology ; Leg ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Transcranial Direct Current Stimulation ; },
abstract = {PURPOSE: Sensorimotor rhythm patterns in patients with lower limb amputations might be altered because of reorganization of the sensorimotor cortices. The authors evaluated the sensorimotor rhythm of motor imagery (MI) in healthy subjects and patients with lower limb amputations. In addition, the authors investigated whether transcranial direct current stimulation (tDCS) could modulate sensorimotor rhythm control.<br><br>METHODS: Six healthy subjects and six patients with lower limb amputations were assigned to receive anodal, cathodal, or sham tDCS over the foot motor area in a randomized order. The authors evaluated event-related desynchronization and event-related synchronization (ERS) of unilateral hand and bilateral foot MI before and after tDCS.<br><br>RESULTS: Beta ERS of foot MI in patients with lower limb amputations was significantly lesser than that in healthy subjects. Compared with sham stimulation, cathodal tDCS enhanced beta ERS of foot MI in patients with lower limb amputations. In contrast, anodal tDCS decreased beta ERS of foot MI in healthy subjects.<br><br>CONCLUSIONS: This is the first study to demonstrate that cathodal tDCS can enhance a weak beta ERS of foot MI in patients with lower limb amputations. These findings might contribute in improving the effectiveness of sensorimotor rhythm-based brain computer interface for gait restoration after lower limb amputation.},
}
@article {pmid25153352,
year = {2014},
author = {Verbeeck, N and Yang, J and De Moor, B and Caprioli, RM and Waelkens, E and Van de Plas, R},
title = {Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases.},
journal = {Analytical chemistry},
volume = {86},
number = {18},
pages = {8974-8982},
pmid = {25153352},
issn = {1520-6882},
support = {R01 GM058008/GM/NIGMS NIH HHS/United States ; P41 GM103391/GM/NIGMS NIH HHS/United States ; P41 GM103391-03/GM/NIGMS NIH HHS/United States ; R01 GM058008-14/GM/NIGMS NIH HHS/United States ; P41 RR031461/RR/NCRR NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Automation ; Brain/*anatomy &amp; histology/metabolism ; Brain-Computer Interfaces ; Humans ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Ions/chemistry/metabolism ; Mice ; *Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization ; },
abstract = {Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments.},
}
@article {pmid25153207,
year = {2014},
author = {Stieglitz, T and Neves, H and Ruther, P},
title = {Neural probes--microsystems to interface with the brain.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {59},
number = {4},
pages = {269-271},
doi = {10.1515/bmt-2012-0094},
pmid = {25153207},
issn = {1862-278X},
mesh = {Action Potentials/*physiology ; Biosensing Techniques/instrumentation ; Brain/*physiology ; Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; *Infusion Pumps, Implantable ; *Microelectrodes ; Microfluidic Analytical Techniques/*instrumentation ; Microinjections/instrumentation ; },
}
@article {pmid25152729,
year = {2014},
author = {Sugata, H and Hirata, M and Yanagisawa, T and Shayne, M and Matsushita, K and Goto, T and Yorifuji, S and Yoshimine, T},
title = {Alpha band functional connectivity correlates with the performance of brain-machine interfaces to decode real and imagined movements.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {620},
pmid = {25152729},
issn = {1662-5161},
abstract = {Brain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain-machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.},
}
@article {pmid25147518,
year = {2014},
author = {Hammer, EM and Kaufmann, T and Kleih, SC and Blankertz, B and Kübler, A},
title = {Visuo-motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR).},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {574},
pmid = {25147518},
issn = {1662-5161},
abstract = {Modulation of sensorimotor rhythms (SMR) was suggested as a control signal for brain-computer interfaces (BCI). Yet, there is a population of users estimated between 10 to 50% not able to achieve reliable control and only about 20% of users achieve high (80-100%) performance. Predicting performance prior to BCI use would facilitate selection of the most feasible system for an individual, thus constitute a practical benefit for the user, and increase our knowledge about the correlates of BCI control. In a recent study, we predicted SMR-BCI performance from psychological variables that were assessed prior to the BCI sessions and BCI control was supported with machine-learning techniques. We described two significant psychological predictors, namely the visuo-motor coordination ability and the ability to concentrate on the task. The purpose of the current study was to replicate these results thereby validating these predictors within a neurofeedback based SMR-BCI that involved no machine learning.Thirty-three healthy BCI novices participated in a calibration session and three further neurofeedback training sessions. Two variables were related with mean SMR-BCI performance: (1) a measure for the accuracy of fine motor skills, i.e., a trade for a person's visuo-motor control ability; and (2) subject's "attentional impulsivity". In a linear regression they accounted for almost 20% in variance of SMR-BCI performance, but predictor (1) failed significance. Nevertheless, on the basis of our prior regression model for sensorimotor control ability we could predict current SMR-BCI performance with an average prediction error of M = 12.07%. In more than 50% of the participants, the prediction error was smaller than 10%. Hence, psychological variables played a moderate role in predicting SMR-BCI performance in a neurofeedback approach that involved no machine learning. Future studies are needed to further consolidate (or reject) the present predictors.},
}
@article {pmid25147509,
year = {2014},
author = {Kapeller, C and Kamada, K and Ogawa, H and Prueckl, R and Scharinger, J and Guger, C},
title = {An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {139},
pmid = {25147509},
issn = {1662-5137},
abstract = {A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed.},
}
@article {pmid25146416,
year = {2015},
author = {Farisco, M and Laureys, S and Evers, K},
title = {Externalization of consciousness. Scientific possibilities and clinical implications.},
journal = {Current topics in behavioral neurosciences},
volume = {19},
number = {},
pages = {205-222},
doi = {10.1007/7854_2014_338},
pmid = {25146416},
issn = {1866-3370},
mesh = {Brain-Computer Interfaces/*standards ; *Communication ; Consciousness/*physiology ; Consciousness Disorders/*physiopathology/rehabilitation ; Functional Neuroimaging/*standards ; Humans ; Informed Consent/*ethics ; },
abstract = {The paper starts by analyzing recent advancements in neurotechnological assessment of residual consciousness in patients with disorders of consciousness and in neurotechnology-mediated communication with them. Ethical issues arising from these developments are described, with particular focus on informed consent. Against this background, we argue for the necessity of further scientific efforts and ethical reflection in neurotechnological assessment of consciousness and 'cerebral communication' with verbally non-communicative patients.},
}
@article {pmid25144252,
year = {2014},
author = {Brown, CA},
title = {Binaural enhancement for bilateral cochlear implant users.},
journal = {Ear and hearing},
volume = {35},
number = {5},
pages = {580-584},
pmid = {25144252},
issn = {1538-4667},
support = {R01 DC008329/DC/NIDCD NIH HHS/United States ; R01 DC010494/DC/NIDCD NIH HHS/United States ; R01DC008329/DC/NIDCD NIH HHS/United States ; },
mesh = {Aged ; Aged, 80 and over ; Cochlear Implantation/*methods ; Cochlear Implants ; Cues ; Deafness/*rehabilitation ; Humans ; Middle Aged ; *Sound Localization ; Speech Intelligibility ; *Speech Perception ; },
abstract = {OBJECTIVES: Bilateral cochlear implant (BCI) users receive limited binaural cues and, thus, show little improvement to speech intelligibility from spatial cues. The feasibility of a method for enhancing the binaural cues available to BCI users is investigated. This involved extending interaural differences of levels, which typically are restricted to high frequencies, into the low-frequency region.<br><br>DESIGN: Speech intelligibility was measured in BCI users listening over headphones and with direct stimulation, with a target talker presented to one side of the head in the presence of a masker talker on the other side. Spatial separation was achieved by applying either naturally occurring binaural cues or enhanced cues.<br><br>RESULTS: In this listening configuration, BCI patients showed greater speech intelligibility with the enhanced binaural cues than with naturally occurring binaural cues.<br><br>CONCLUSIONS: In some situations, it is possible for BCI users to achieve greater speech intelligibility when binaural cues are enhanced by applying interaural differences of levels in the low-frequency region.},
}
@article {pmid25144171,
year = {2014},
author = {Hill, K and Kovacs, T and Shin, S},
title = {Reliability of brain-computer interface language sample transcription procedures.},
journal = {Journal of rehabilitation research and development},
volume = {51},
number = {4},
pages = {579-590},
doi = {10.1682/JRRD.2013.05.0102},
pmid = {25144171},
issn = {1938-1352},
mesh = {Amyotrophic Lateral Sclerosis/*rehabilitation ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Humans ; Language ; *Nonverbal Communication ; Reproducibility of Results ; *Software ; },
abstract = {We tested the reliability of transcribing language samples of daily brain-computer interface (BCI) communication recorded as language activity monitoring (LAM) logfiles. This study determined interrater reliability and interjudge agreement for transcription of communication of veterans with amyotrophic lateral sclerosis using a P300-based BCI as an augmentative and alternative communication (AAC) system. KeyLAM software recorded logfiles in a universal logfile format during use of BCI-controlled email and word processing applications. These logfiles were encrypted and sent to our laboratory for decryption, transcription, and analysis. The study reports reliability results on transcription of 345 daily logfile samples. The procedure was found to be accurate across transcribers/raters. Frequency of agreement ratios of 97.6% for total number of words and 93.5% for total utterances were found as measures of interrater reliability. Interjudge agreement was 100% for both measures. The results indicated that transcribing language samples using LAM data is highly reliable and the fidelity of the process can be maintained. LAM data supported the transcription of a large number of samples that could not have been completed using audio and video recordings of AAC speakers. This demonstrated efficiency of LAM tools to measure language performance benefits to BCI research and clinical communities.},
}
@article {pmid25140144,
year = {2014},
author = {Stenzel, A and Dolk, T and Colzato, LS and Sellaro, R and Hommel, B and Liepelt, R},
title = {The joint Simon effect depends on perceived agency, but not intentionality, of the alternative action.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {595},
pmid = {25140144},
issn = {1662-5161},
abstract = {A co-actor's intentionality has been suggested to be a key modulating factor for joint action effects like the joint Simon effect (JSE). However, in previous studies intentionality has often been confounded with agency defined as perceiving the initiator of an action as being the causal source of the action. The aim of the present study was to disentangle the role of agency and intentionality as modulating factors of the JSE. In Experiment 1, participants performed a joint go/nogo Simon task next to a co-actor who either intentionally controlled a response button with own finger movements (agency+/intentionality+) or who passively placed the hand on a response button that moved up and down on its own as triggered by computer signals (agency-/intentionality-). In Experiment 2, we included a condition in which participants believed that the co-actor intentionally controlled the response button with a Brain-Computer Interface (BCI) while placing the response finger clearly besides the response button, so that the causal relationship between agent and action effect was perceptually disrupted (agency-/intentionality+). As a control condition, the response button was computer controlled while the co-actor placed the response finger besides the response button (agency-/intentionality-). Experiment 1 showed that the JSE is present with an intentional co-actor and causality between co-actor and action effect, but absent with an unintentional co-actor and a lack of causality between co-actor and action effect. Experiment 2 showed that the JSE is absent with an intentional co-actor, but no causality between co-actor and action effect. Our findings indicate an important role of the co-actor's agency for the JSE. They also suggest that the attribution of agency has a strong perceptual basis.},
}
@article {pmid25140134,
year = {2014},
author = {Bouyarmane, K and Vaillant, J and Sugimoto, N and Keith, F and Furukawa, J and Morimoto, J},
title = {Brain-machine interfacing control of whole-body humanoid motion.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {138},
pmid = {25140134},
issn = {1662-5137},
abstract = {We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.},
}
@article {pmid25137064,
year = {2014},
author = {Grau, C and Ginhoux, R and Riera, A and Nguyen, TL and Chauvat, H and Berg, M and Amengual, JL and Pascual-Leone, A and Ruffini, G},
title = {Conscious brain-to-brain communication in humans using non-invasive technologies.},
journal = {PloS one},
volume = {9},
number = {8},
pages = {e105225},
pmid = {25137064},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Middle Aged ; Nonverbal Communication ; Phosphenes ; Transcranial Magnetic Stimulation ; Visual Cortex/physiology ; },
abstract = {Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI) has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI). These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B) communication between subjects (hyperinteraction). Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG) changes with a CBI inducing the conscious perception of phosphenes (light flashes) through neuronavigated, robotized transcranial magnetic stimulation (TMS), with special care taken to block sensory (tactile, visual or auditory) cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues.},
}
@article {pmid25136290,
year = {2014},
author = {Lew, EY and Chavarriaga, R and Silvoni, S and Millán, Jdel R},
title = {Single trial prediction of self-paced reaching directions from EEG signals.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {222},
pmid = {25136290},
issn = {1662-4548},
abstract = {Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.},
}
@article {pmid25134085,
year = {2015},
author = {Daly, I and Scherer, R and Billinger, M and Müller-Putz, G},
title = {FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {5},
pages = {725-736},
doi = {10.1109/TNSRE.2014.2346621},
pmid = {25134085},
issn = {1558-0210},
mesh = {Algorithms ; *Artifacts ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Cerebral Palsy/*physiopathology ; Electroencephalography/*methods ; Evoked Potentials ; Female ; Humans ; Internet ; Male ; Online Systems ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Software ; Wavelet Analysis ; },
abstract = {A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.},
}
@article {pmid25129921,
year = {2014},
author = {Song, HJ and Ye, J and Shi, S and Zhang, ZC and Kuang, X and Xing, DL and Yuan, ZQ and Lin, F and Wang, XG and Hao, ZQ},
title = {[Woody plant species composition and community structure in residual fragments of broad-leaved Korean pine mixed forests in Changbai Mountains area].},
journal = {Ying yong sheng tai xue bao = The journal of applied ecology},
volume = {25},
number = {5},
pages = {1239-1249},
pmid = {25129921},
issn = {1001-9332},
mesh = {*Biodiversity ; China ; *Forests ; Pinus ; *Trees ; },
abstract = {The broad-leaved Korean pine mixed forest represents the typical vegetation type of the eastern mountain area in Northeast China. However, due to the interference of human activities, the natural broad-leaved Korean pine forest only distributes in some residual fragments with unequal areas in Changbai Mountains and Small Hinggan Mountains. To compare and analyze the similarities and differences of broad-leaved Korean pine mixed forests in the different areas, we established six forest plots following the field protocol of the 50 hm2 forest plot in Panama (Barro Colorado Island, BCI) in 2012 in Changbai Mountain National Nature Reserve in Jilin Province and the eastern mountain area in Liaoning Province. All free-standing plant species with DBH (diameter at breast height) > or = 1 cm were mapped, tagged and identified to species. The results showed that there were 69 woody species in the six plots, comprising 42 genera and24 families. Aceraceae was the most species-rich family in all six plots. Most species belonged to the plant type of North Temperate Zone, with a minor subtropical plant species component. The statistics of species abundance, basal area, mean DBH, and importance value showed that there were obviously dominant species in each community. The DBH distribution of all individuals showed a reversed "J" type. However, the percentage of individuals in small size-class and large size-class varied in the six communities, which indicated that these communities were at different successional stages. Ranked by the importance value, the DBH distribution of the top three species in the six plots showed four distribution types: reversed "J" distribution, reversed "L" distribution, unimodal distribution, and partial peak distribution. Spatial distribution patterns of the main species in the six plots changed differently with species and size-class, and the distribution patterns of the same species varied in the different plots.},
}
@article {pmid25128338,
year = {2014},
author = {Volmer, J and Neumann, C and Bühler, B and Schmid, A},
title = {Engineering of Pseudomonas taiwanensis VLB120 for constitutive solvent tolerance and increased specific styrene epoxidation activity.},
journal = {Applied and environmental microbiology},
volume = {80},
number = {20},
pages = {6539-6548},
pmid = {25128338},
issn = {1098-5336},
mesh = {Bacterial Proteins/genetics/metabolism ; Biocatalysis ; Drug Resistance, Bacterial ; Epoxy Compounds/metabolism/pharmacology ; Gene Deletion ; Genetic Engineering/*methods ; Molecular Sequence Data ; Pseudomonas/drug effects/*genetics/*metabolism ; Solvents/pharmacology ; Styrene/*metabolism/pharmacology ; },
abstract = {The application of whole cells as biocatalysts is often limited by the toxicity of organic solvents, which constitute interesting substrates/products or can be used as a second phase for in situ product removal and as tools to control multistep biocatalysis. Solvent-tolerant bacteria, especially Pseudomonas strains, are proposed as promising hosts to overcome such limitations due to their inherent solvent tolerance mechanisms. However, potential industrial applications suffer from tedious, unproductive adaptation processes, phenotypic variability, and instable solvent-tolerant phenotypes. In this study, genes described to be involved in solvent tolerance were identified in Pseudomonas taiwanensis VLB120, and adaptive solvent tolerance was proven by cultivation in the presence of 1% (vol/vol) toluene. Deletion of ttgV, coding for the specific transcriptional repressor of solvent efflux pump TtgGHI gene expression, led to constitutively solvent-tolerant mutants of P. taiwanensis VLB120 and VLB120ΔC. Interestingly, the increased amount of solvent efflux pumps enhanced not only growth in the presence of toluene and styrene but also the biocatalytic performance in terms of stereospecific styrene epoxidation, although proton-driven solvent efflux is expected to compete with the styrene monooxygenase for metabolic energy. Compared to that of the P. taiwanensis VLB120ΔC parent strain, the maximum specific epoxidation activity of P. taiwanensis VLB120ΔCΔttgV doubled to 67 U/g of cells (dry weight). This study shows that solvent tolerance mechanisms, e.g., the solvent efflux pump TtgGHI, not only allow for growth in the presence of organic compounds but can also be used as tools to improve redox biocatalysis involving organic solvents.},
}
@article {pmid25128256,
year = {2014},
author = {Masse, NY and Jarosiewicz, B and Simeral, JD and Bacher, D and Stavisky, SD and Cash, SS and Oakley, EM and Berhanu, E and Eskandar, E and Friehs, G and Hochberg, LR and Donoghue, JP},
title = {Non-causal spike filtering improves decoding of movement intention for intracortical BCIs.},
journal = {Journal of neuroscience methods},
volume = {236},
number = {},
pages = {58-67},
pmid = {25128256},
issn = {1872-678X},
support = {N01HD10018/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; },
mesh = {*Action Potentials ; Aged ; Biomechanical Phenomena ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Female ; Humans ; Male ; Middle Aged ; Motor Activity/*physiology ; Neuropsychological Tests ; Pilot Projects ; Quadriplegia/physiopathology/therapy ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials.<br><br>NEW METHOD: We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals.<br><br>RESULTS: Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7&#xb0; for participant S3, who was implanted 5.4 years prior to this study, and by 3.5&#xb0; for participant T2, who was implanted 3 months prior to this study.<br><br>CONCLUSIONS: Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.},
}
@article {pmid25126578,
year = {2014},
author = {Ryun, S and Kim, JS and Lee, SH and Jeong, S and Kim, SP and Chung, CK},
title = {Movement type prediction before its onset using signals from prefrontal area: an electrocorticography study.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {783203},
pmid = {25126578},
issn = {2314-6141},
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Elbow/physiopathology ; *Electroencephalography ; Electromyography ; Epilepsy/*physiopathology ; Female ; Hand/physiopathology ; Humans ; Male ; Motor Cortex/*physiopathology ; Movement/physiology ; },
abstract = {Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (-2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13-30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.},
}
@article {pmid25126061,
year = {2014},
author = {Glannon, W},
title = {Ethical issues with brain-computer interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {136},
pmid = {25126061},
issn = {1662-5137},
}
@article {pmid25125446,
year = {2014},
author = {Grosse-Wentrup, M and Schölkopf, B},
title = {A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056015},
doi = {10.1088/1741-2560/11/5/056015},
pmid = {25125446},
issn = {1741-2552},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology/rehabilitation ; Biological Clocks/*physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electrocardiography/*methods ; Feedback, Physiological/*physiology ; Female ; Gamma Rhythm/*physiology ; Humans ; Male ; Middle Aged ; Parietal Lobe/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems are often based on motor- and/or sensory processes that are known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a novel BCI designed for patients in late stages of ALS that only requires high-level cognitive processes to transmit information from the user to the BCI.<br><br>APPROACH: We trained subjects via EEG-based neurofeedback to self-regulate the amplitude of gamma-oscillations in the superior parietal cortex (SPC). We argue that parietal gamma-oscillations are likely to be associated with high-level attentional processes, thereby providing a communication channel that does not rely on the integrity of sensory- and/or motor-pathways impaired in late stages of ALS.<br><br>MAIN RESULTS: Healthy subjects quickly learned to self-regulate gamma-power in the SPC by alternating between states of focused attention and relaxed wakefulness, resulting in an average decoding accuracy of 70.2%. One locked-in ALS patient (ALS-FRS-R score of zero) achieved an average decoding accuracy significantly above chance-level though insufficient for communication (55.8%).<br><br>SIGNIFICANCE: Self-regulation of gamma-power in the SPC is a feasible paradigm for brain-computer interfacing and may be preserved in late stages of ALS. This provides a novel approach to testing whether completely locked-in ALS patients retain the capacity for goal-directed thinking.},
}
@article {pmid25124925,
year = {2014},
author = {Han, P and Zhou, XH and Chang, N and Xiao, CL and Yan, S and Ren, H and Yang, XZ and Zhang, ML and Wu, Q and Tang, B and Diao, JP and Zhu, X and Zhang, C and Li, CY and Cheng, H and Xiong, JW},
title = {Hydrogen peroxide primes heart regeneration with a derepression mechanism.},
journal = {Cell research},
volume = {24},
number = {9},
pages = {1091-1107},
pmid = {25124925},
issn = {1748-7838},
mesh = {Animals ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Heart/drug effects/*physiology ; Hydrogen Peroxide/*pharmacology ; In Vitro Techniques ; Leukocytes/drug effects/metabolism ; Models, Biological ; Myocardium/enzymology ; Proteasome Endopeptidase Complex/metabolism ; Proteolysis/drug effects ; Regeneration/*drug effects ; Repressor Proteins/metabolism ; Signal Transduction/drug effects ; Ubiquitination/drug effects ; Zebrafish ; Zebrafish Proteins/metabolism ; },
abstract = {While the adult human heart has very limited regenerative potential, the adult zebrafish heart can fully regenerate after 20% ventricular resection. Although previous reports suggest that developmental signaling pathways such as FGF and PDGF are reused in adult heart regeneration, the underlying intracellular mechanisms remain largely unknown. Here we show that H2O2 acts as a novel epicardial and myocardial signal to prime the heart for regeneration in adult zebrafish. Live imaging of intact hearts revealed highly localized H2O2 (~30 μM) production in the epicardium and adjacent compact myocardium at the resection site. Decreasing H2O2 formation with the Duox inhibitors diphenyleneiodonium (DPI) or apocynin, or scavenging H2O2 by catalase overexpression markedly impaired cardiac regeneration while exogenous H2O2 rescued the inhibitory effects of DPI on cardiac regeneration, indicating that H2O2 is an essential and sufficient signal in this process. Mechanistically, elevated H2O2 destabilized the redox-sensitive phosphatase Dusp6 and hence increased the phosphorylation of Erk1/2. The Dusp6 inhibitor BCI achieved similar pro-regenerative effects while transgenic overexpression of dusp6 impaired cardiac regeneration. H2O2 plays a dual role in recruiting immune cells and promoting heart regeneration through two relatively independent pathways. We conclude that H2O2 potentially generated from Duox/Nox2 promotes heart regeneration in zebrafish by unleashing MAP kinase signaling through a derepression mechanism involving Dusp6.},
}
@article {pmid25122909,
year = {2014},
author = {Sasada, S and Kato, K and Kadowaki, S and Groiss, SJ and Ugawa, Y and Komiyama, T and Nishimura, Y},
title = {Volitional walking via upper limb muscle-controlled stimulation of the lumbar locomotor center in man.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {34},
number = {33},
pages = {11131-11142},
pmid = {25122909},
issn = {1529-2401},
mesh = {Adult ; Biomechanical Phenomena ; Electric Stimulation ; Electromyography ; Healthy Volunteers ; Humans ; Locomotion/*physiology ; Male ; Middle Aged ; Muscle, Skeletal/*physiology ; Posture/*physiology ; Spinal Cord Injuries/physiopathology ; Walking/*physiology ; },
abstract = {Gait disturbance in individuals with spinal cord lesion is attributed to the interruption of descending pathways to the spinal locomotor center, whereas neural circuits below and above the lesion maintain their functional capability. An artificial neural connection (ANC), which bridges supraspinal centers and locomotor networks in the lumbar spinal cord beyond the lesion site, may restore the functional impairment. To achieve an ANC that sends descending voluntary commands to the lumbar locomotor center and bypasses the thoracic spinal cord, upper limb muscle activity was converted to magnetic stimuli delivered noninvasively over the lumbar vertebra. Healthy participants were able to initiate and terminate walking-like behavior and to control the step cycle through an ANC controlled by volitional upper limb muscle activity. The walking-like behavior stopped just after the ANC was disconnected from the participants even when the participant continued to swing arms. Furthermore, additional simultaneous peripheral electrical stimulation to the foot via the ANC enhanced this walking-like behavior. Kinematics of the induced behaviors were identical to those observed in voluntary walking. These results demonstrate that the ANC induces volitionally controlled, walking-like behavior of the legs. This paradigm may be able to compensate for the dysfunction of descending pathways by sending commands to the preserved locomotor center at the lumbar spinal cord and may enable individuals with paraplegia to regain volitionally controlled walking.},
}
@article {pmid25122834,
year = {2015},
author = {Meng, J and Yao, L and Sheng, X and Zhang, D and Zhu, X},
title = {Simultaneously optimizing spatial spectral features based on mutual information for EEG classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {1},
pages = {227-240},
doi = {10.1109/TBME.2014.2345458},
pmid = {25122834},
issn = {1558-2531},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {High performance of the brain-computer interface (BCI) needs efficient algorithms to extract discriminative features from raw electroencephalography (EEG) signals. In this paper, we present a novel scheme to extract spatial spectral features for the motor imagery-based BCI. The learning task is formulated by maximizing the mutual information between spatial spectral features (MMISS) and class labels, by which a unique objective function directly related to Bayes classification error is optimized. The spatial spectral features are assumed to follow a parametric Gaussian distribution, which has been validated by the normal distribution Mardia's test, and under this assumption the estimation of mutual information is derived. We propose a gradient based alternative and iterative learning algorithm to optimize the cost function and derive the spatial and spectral filters simultaneously. The experimental results on dataset IVa of BCI competition III and dataset IIa of BCI competition IV show that the proposed MMISS is able to efficiently extract discriminative features from motor imagery-based EEG signals to enhance the classification accuracy compared to other existing algorithms.},
}
@article {pmid25122063,
year = {2014},
author = {Dunand, C and Hoffmann, P and Sapin, V and Blanchon, L and Salomon, A and Sergent, F and Benharouga, M and Sabra, S and Guibourdenche, J and Lye, SJ and Feige, JJ and Alfaidy, N},
title = {Endocrine gland-derived endothelial growth factor (EG-VEGF) is a potential novel regulator of human parturition.},
journal = {Biology of reproduction},
volume = {91},
number = {3},
pages = {73},
doi = {10.1095/biolreprod.114.119990},
pmid = {25122063},
issn = {1529-7268},
mesh = {Adult ; Amnion/metabolism ; Cells, Cultured ; Cesarean Section ; Chorion/cytology/*metabolism ; *Down-Regulation ; Female ; Humans ; Labor, Obstetric/blood/*metabolism ; Placenta/metabolism ; Placentation ; Pregnancy ; Pregnancy Trimester, Second ; Pregnancy Trimester, Third ; Receptors, G-Protein-Coupled/metabolism ; Receptors, Peptide/metabolism ; Receptors, Vascular Endothelial Growth Factor/metabolism ; Tissue Culture Techniques ; Up-Regulation ; Vascular Endothelial Growth Factor A/blood/*metabolism ; },
abstract = {EG-VEGF is an angiogenic factor that we identified as a new placental growth factor during human pregnancy. EG-VEGF is also expressed in the mouse fetal membrane (FM) by the end of gestation, suggesting a local role for this protein in the mechanism of parturition. However, injection of EG-VEGF to gravid mice did not induce labor, suggesting a different role for EG-VEGF in parturition. Here, we searched for its role in the FM in relation to human parturition. Human pregnant sera and total FM, chorion, and amnion were collected during the second and third trimesters from preterm no labor, term no labor, and term labor patients. Primary human chorion trophoblast and FM explants cultures were also used. We demonstrate that circulating EG-VEGF increased toward term and significantly decreased at the time of labor. EG-VEGF production was higher in the FM compared to placentas matched for gestational age. Within the FM, the chorion was the main source of EG-VEGF. EG-VEGF receptors, PROKR1 and PROKR2, were differentially expressed within the FM with increased expression toward term and an abrupt decrease with the onset of labor. In chorion trophoblast and FM explants collected from nonlaboring patients, EG-VEGF decreased metalloproteinase-2 and -9 activities and increased PGDH (prostaglandin-metabolizing enzyme) expression. Altogether these data demonstrate that EG-VEGF is a new cytokine that acts locally to ensure FM protection in late pregnancy. Its fine contribution to the initiation of human labor is exhibited by the abrupt decrease in its levels as well as a reduction in its receptors.},
}
@article {pmid25120466,
year = {2014},
author = {Song, J and Young, BM and Nigogosyan, Z and Walton, LM and Nair, VA and Grogan, SW and Tyler, ME and Farrar-Edwards, D and Caldera, KE and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Characterizing relationships of DTI, fMRI, and motor recovery in stroke rehabilitation utilizing brain-computer interface technology.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {31},
pmid = {25120466},
issn = {1662-6443},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {The relationship of the structural integrity of white matter tracts and cortical activity to motor functional outcomes in stroke patients is of particular interest in understanding mechanisms of brain structural and functional changes while recovering from stroke. This study aims to probe these underlying mechanisms using diffusion tensor imaging (DTI) and fMRI measures. We examined the structural integrity of the posterior limb of the internal capsule (PLIC) using DTI and corticomotor activity using motor-task fMRI in stroke patients who completed up to 15 sessions of rehabilitation therapy using Brain-Computer Interface (BCI) technology. We hypothesized that (1) the structural integrity of PLIC and corticomotor activity are affected by stroke; (2) changes in structural integrity and corticomotor activity following BCI intervention are related to motor recovery; (3) there is a potential relationship between structural integrity and corticomotor activity. We found that (1) the ipsilesional PLIC showed significantly decreased fractional anisotropy (FA) values when compared to the contralesional PLIC; (2) lower ipsilesional PLIC-FA values were significantly associated with worse motor outcomes (i.e., ipsilesional PLIC-FA and motor outcomes were positively correlated.); (3) lower ipsilesional PLIC-FA values were significantly associated with greater ipsilesional corticomotor activity during impaired-finger-tapping-task fMRI (i.e., ipsilesional PLIC-FA and ipsilesional corticomotor activity were negatively correlated), with an overall bilateral pattern of corticomotor activity observed; and (4) baseline FA values predicted motor recovery assessed after BCI intervention. These findings suggest that (1) greater vs. lesser microstructural integrity of the ipsilesional PLIC may contribute toward better vs. poor motor recovery respectively in the stroke-affected limb and demand lesser vs. greater cortical activity respectively from the ipsilesional motor cortex; and that (2) PLIC-FA is a promising biomarker in tracking and predicting motor functional recovery in stroke patients receiving BCI intervention.},
}
@article {pmid25120465,
year = {2014},
author = {Ang, KK and Guan, C and Phua, KS and Wang, C and Zhou, L and Tang, KY and Ephraim Joseph, GJ and Kuah, CW and Chua, KS},
title = {Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {30},
pmid = {25120465},
issn = {1662-6443},
abstract = {The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10-50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.},
}
@article {pmid25120417,
year = {2014},
author = {Arduin, PJ and Frégnac, Y and Shulz, DE and Ego-Stengel, V},
title = {Bidirectional control of a one-dimensional robotic actuator by operant conditioning of a single unit in rat motor cortex.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {206},
pmid = {25120417},
issn = {1662-4548},
abstract = {The design of efficient neuroprosthetic devices has become a major challenge for the long-term goal of restoring autonomy to motor-impaired patients. One approach for brain control of actuators consists in decoding the activity pattern obtained by simultaneously recording large neuronal ensembles in order to predict in real-time the subject's intention, and move the prosthesis accordingly. An alternative way is to assign the output of one or a few neurons by operant conditioning to control the prosthesis with rules defined by the experimenter, and rely on the functional adaptation of these neurons during learning to reach the desired behavioral outcome. Here, several motor cortex neurons were recorded simultaneously in head-fixed awake rats and were conditioned, one at a time, to modulate their firing rate up and down in order to control the speed and direction of a one-dimensional actuator carrying a water bottle. The goal was to maintain the bottle in front of the rat's mouth, allowing it to drink. After learning, all conditioned neurons modulated their firing rate, effectively controlling the bottle position so that the drinking time was increased relative to chance. The mean firing rate averaged over all bottle trajectories depended non-linearly on position, so that the mouth position operated as an attractor. Some modifications of mean firing rate were observed in the surrounding neurons, but to a lesser extent. Notably, the conditioned neuron reacted faster and led to a better control than surrounding neurons, as calculated by using the activity of those neurons to generate simulated bottle trajectories. Our study demonstrates the feasibility, even in the rodent, of using a motor cortex neuron to control a prosthesis in real-time bidirectionally. The learning process includes modifications of the activity of neighboring cortical neurons, while the conditioned neuron selectively leads the activity patterns associated with the prosthesis control.},
}
@article {pmid25116904,
year = {2014},
author = {Ahn, M and Lee, M and Choi, J and Jun, SC},
title = {A review of brain-computer interface games and an opinion survey from researchers, developers and users.},
journal = {Sensors (Basel, Switzerland)},
volume = {14},
number = {8},
pages = {14601-14633},
pmid = {25116904},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/physiology ; Humans ; Research Personnel ; *User-Computer Interface ; *Video Games ; },
abstract = {In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to "the easiness of playing" and the "development platform" as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.},
}
@article {pmid25113378,
year = {2014},
author = {Kim, SH and Steele, JW and Lee, SW and Clemenson, GD and Carter, TA and Treuner, K and Gadient, R and Wedel, P and Glabe, C and Barlow, C and Ehrlich, ME and Gage, FH and Gandy, S},
title = {Proneurogenic Group II mGluR antagonist improves learning and reduces anxiety in Alzheimer Aβ oligomer mouse.},
journal = {Molecular psychiatry},
volume = {19},
number = {11},
pages = {1235-1242},
pmid = {25113378},
issn = {1476-5578},
support = {I01 BX000348/BX/BLRD VA/United States ; K12 GM068524/GM/NIGMS NIH HHS/United States ; U01 AG046170/AG/NIA NIH HHS/United States ; T32 GM062754/GM/NIGMS NIH HHS/United States ; T32GM062754/GM/NIGMS NIH HHS/United States ; I01 RX000684/RX/RRD VA/United States ; },
mesh = {Alzheimer Disease/*drug therapy/physiopathology/*psychology ; Amyloid beta-Peptides/metabolism ; Amyloid beta-Protein Precursor/genetics/metabolism ; Animals ; Anxiety/*drug therapy/physiopathology ; Disease Models, Animal ; Hippocampus/drug effects/physiopathology ; Humans ; Learning/*drug effects/physiology ; Neurogenesis/drug effects/physiology ; Psychotropic Drugs/chemistry/*pharmacology ; Receptors, Metabotropic Glutamate/*antagonists &amp; inhibitors/metabolism ; },
abstract = {Proneurogenic compounds have recently shown promise in some mouse models of Alzheimer's pathology. Antagonists at Group II metabotropic glutamate receptors (Group II mGluR: mGlu2, mGlu3) are reported to stimulate neurogenesis. Agonists at those receptors trigger γ-secretase-inhibitor-sensitive biogenesis of Aβ42 peptides from isolated synaptic terminals, which is selectively suppressed by antagonist pretreatment. We have assessed the therapeutic potential of chronic pharmacological inhibition of Group II mGluR in Dutch APP (Alzheimer's amyloid precursor protein E693Q) transgenic mice that accumulate Dutch amyloid-β (Aβ) oligomers but never develop Aβ plaques. BCI-838 is a clinically well-tolerated, orally bioavailable, investigational prodrug that delivers to the brain BCI-632, the active Group II mGluR antagonist metabolite. Dutch Aβ-oligomer-forming APP transgenic mice (APP E693Q) were dosed with BCI-838 for 3 months. Chronic treatment with BCI-838 was associated with reversal of transgene-related amnestic behavior, reduction in anxiety, reduction in levels of brain Aβ monomers and oligomers, and stimulation of hippocampal neurogenesis. Group II mGluR inhibition may offer a unique package of relevant properties as an Alzheimer's disease therapeutic or prophylactic by providing both attenuation of neuropathology and stimulation of repair.},
}
@article {pmid25112683,
year = {2014},
author = {Kang, M and Jung, S and Zhang, H and Kang, T and Kang, H and Yoo, Y and Hong, JP and Ahn, JP and Kwak, J and Jeon, D and Kotov, NA and Kim, B},
title = {Subcellular neural probes from single-crystal gold nanowires.},
journal = {ACS nano},
volume = {8},
number = {8},
pages = {8182-8189},
pmid = {25112683},
issn = {1936-086X},
support = {R21 CA121841/CA/NCI NIH HHS/United States ; },
mesh = {Animals ; Brain/*cytology ; Brain-Computer Interfaces ; Electrodes, Implanted ; Gold/*chemistry ; Intracellular Space/*metabolism ; Male ; Mice ; Mice, Inbred C57BL ; Molecular Probes/*chemistry/*metabolism ; *Nanowires ; },
abstract = {Size reduction of neural electrodes is essential for improving the functionality of neuroprosthetic devices, developing potent therapies for neurological and neurodegenerative diseases, and long-term brain–computer interfaces. Typical neural electrodes are micromanufactured devices with dimensions ranging from tens to hundreds of micrometers. Their further miniaturization is necessary to reduce local tissue damage and chronic immunological reactions of the brain. Here we report the neural electrode with subcellular dimensions based on single-crystalline gold nanowires (NWs) with a diameter of ∼100 nm. Unique mechanical and electrical properties of defect-free gold NWs enabled their implantation and recording of single neuron-activities in a live mouse brain despite a ∼50× reduction of the size compared to the closest analogues. Reduction of electrode dimensions enabled recording of neural activity with improved spatial resolution and differentiation of brain activity in response to different social situations for mice. The successful localization of the epileptic seizure center was also achieved using a multielectrode probe as a demonstration of the diagnostics potential of NW electrodes. This study demonstrated the realism of single-neuron recording using subcellular-sized electrodes that may be considered a pivotal point for use in diverse studies of chronic brain diseases.},
}
@article {pmid25111822,
year = {2014},
author = {Bauernfeind, G and Wriessnegger, SC and Daly, I and Müller-Putz, GR},
title = {Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056010},
doi = {10.1088/1741-2560/11/5/056010},
pmid = {25111822},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Artifacts ; Brain Mapping/methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Oxygen/*blood ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the in vivo assessment of functional activity of the cerebral cortex as well as in the field of brain-computer interface (BCI) research. A common challenge for the utilization of fNIRS in these areas is a stable and reliable investigation of the spatio-temporal hemodynamic patterns. However, the recorded patterns may be influenced and superimposed by signals generated from physiological processes, resulting in an inaccurate estimation of the cortical activity. Up to now only a few studies have investigated these influences, and still less has been attempted to remove/reduce these influences. The present study aims to gain insights into the reduction of physiological rhythms in hemodynamic signals (oxygenated hemoglobin (oxy-Hb), deoxygenated hemoglobin (deoxy-Hb)).<br><br>APPROACH: We introduce the use of three different signal processing approaches (spatial filtering, a common average reference (CAR) method; independent component analysis (ICA); and transfer function (TF) models) to reduce the influence of respiratory and blood pressure (BP) rhythms on the hemodynamic responses.<br><br>MAIN RESULTS: All approaches produce large reductions in BP and respiration influences on the oxy-Hb signals and, therefore, improve the contrast-to-noise ratio (CNR). In contrast, for deoxy-Hb signals CAR and ICA did not improve the CNR. However, for the TF approach, a CNR-improvement in deoxy-Hb can also be found.<br><br>SIGNIFICANCE: The present study investigates the application of different signal processing approaches to reduce the influences of physiological rhythms on the hemodynamic responses. In addition to the identification of the best signal processing method, we also show the importance of noise reduction in fNIRS data.},
}
@article {pmid25110624,
year = {2014},
author = {Venkatakrishnan, A and Francisco, GE and Contreras-Vidal, JL},
title = {Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation.},
journal = {Current physical medicine and rehabilitation reports},
volume = {2},
number = {2},
pages = {93-105},
pmid = {25110624},
issn = {2167-4833},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS081854/NS/NINDS NIH HHS/United States ; },
abstract = {Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.},
}
@article {pmid25109901,
year = {2015},
author = {Seo, D and Carmena, JM and Rabaey, JM and Maharbiz, MM and Alon, E},
title = {Model validation of untethered, ultrasonic neural dust motes for cortical recording.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {114-122},
doi = {10.1016/j.jneumeth.2014.07.025},
pmid = {25109901},
issn = {1872-678X},
mesh = {Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Humans ; *Models, Biological ; Prostheses and Implants ; Reproducibility of Results ; *Ultrasonics ; *User-Computer Interface ; Wireless Technology ; },
abstract = {A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10-100 μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127 μm)(3) neural dust motes immersed in water 3 cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246 ppm backscatter, resulting in a maximum received power of ∼0.5 μW with ∼1 nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions.},
}
@article {pmid25108604,
year = {2014},
author = {Lin, YP and Wang, Y and Jung, TP},
title = {Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {119},
pmid = {25108604},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Female ; Humans ; Male ; *Online Systems ; Software ; *User-Computer Interface ; Walking/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.<br><br>METHODS: This study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.<br><br>RESULTS: Despite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89&#xa0;m/s).<br><br>CONCLUSIONS: SSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.},
}
@article {pmid25107852,
year = {2015},
author = {Miranda, RA and Casebeer, WD and Hein, AM and Judy, JW and Krotkov, EP and Laabs, TL and Manzo, JE and Pankratz, KG and Pratt, GA and Sanchez, JC and Weber, DJ and Wheeler, TL and Ling, GS},
title = {DARPA-funded efforts in the development of novel brain-computer interface technologies.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {52-67},
doi = {10.1016/j.jneumeth.2014.07.019},
pmid = {25107852},
issn = {1872-678X},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The Defense Advanced Research Projects Agency (DARPA) has funded innovative scientific research and technology developments in the field of brain-computer interfaces (BCI) since the 1970s. This review highlights some of DARPA's major advances in the field of BCI, particularly those made in recent years. Two broad categories of DARPA programs are presented with respect to the ultimate goals of supporting the nation's warfighters: (1) BCI efforts aimed at restoring neural and/or behavioral function, and (2) BCI efforts aimed at improving human training and performance. The programs discussed are synergistic and complementary to one another, and, moreover, promote interdisciplinary collaborations among researchers, engineers, and clinicians. Finally, this review includes a summary of some of the remaining challenges for the field of BCI, as well as the goals of new DARPA efforts in this domain.},
}
@article {pmid25104969,
year = {2014},
author = {Goljahani, A and D'Avanzo, C and Silvoni, S and Tonin, P and Piccione, F and Sparacino, G},
title = {Preprocessing by a Bayesian single-trial event-related potential estimation technique allows feasibility of an assistive single-channel P300-based brain-computer interface.},
journal = {Computational and mathematical methods in medicine},
volume = {2014},
number = {},
pages = {731046},
pmid = {25104969},
issn = {1748-6718},
mesh = {Adult ; Aged ; Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology ; Bayes Theorem ; *Brain-Computer Interfaces ; Case-Control Studies ; *Event-Related Potentials, P300 ; Humans ; Middle Aged ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Software ; User-Computer Interface ; },
abstract = {A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.},
}
@article {pmid25104385,
year = {2014},
author = {Merolla, PA and Arthur, JV and Alvarez-Icaza, R and Cassidy, AS and Sawada, J and Akopyan, F and Jackson, BL and Imam, N and Guo, C and Nakamura, Y and Brezzo, B and Vo, I and Esser, SK and Appuswamy, R and Taba, B and Amir, A and Flickner, MD and Risk, WP and Manohar, R and Modha, DS},
title = {Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface.},
journal = {Science (New York, N.Y.)},
volume = {345},
number = {6197},
pages = {668-673},
doi = {10.1126/science.1254642},
pmid = {25104385},
issn = {1095-9203},
mesh = {*Brain ; *Brain-Computer Interfaces ; *Computer Simulation ; *Neural Networks, Computer ; *Neurons ; Software ; Synapses ; },
abstract = {Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non-von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.},
}
@article {pmid25104367,
year = {2014},
author = {Service, RF},
title = {The brain chip.},
journal = {Science (New York, N.Y.)},
volume = {345},
number = {6197},
pages = {614-616},
doi = {10.1126/science.345.6197.614},
pmid = {25104367},
issn = {1095-9203},
mesh = {*Brain ; *Brain-Computer Interfaces ; *Computer Simulation ; *Neural Networks, Computer ; *Neurons ; },
}
@article {pmid25100958,
year = {2014},
author = {van der Heiden, L and Liberati, G and Sitaram, R and Kim, S and Jaśkowski, P and Raffone, A and Olivetti Belardinelli, M and Birbaumer, N and Veit, R},
title = {Insula and inferior frontal triangularis activations distinguish between conditioned brain responses using emotional sounds for basic BCI communication.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {247},
pmid = {25100958},
issn = {1662-5153},
abstract = {In order to enable communication through a brain-computer interface (BCI), it is necessary to discriminate between distinct brain responses. As a first step, we probed the possibility to discriminate between affirmative ("yes") and negative ("no") responses using a semantic classical conditioning paradigm, within an fMRI setting. Subjects were presented with congruent and incongruent word-pairs as conditioned stimuli (CS), respectively eliciting affirmative and negative responses. Incongruent word-pairs were associated to an unpleasant unconditioned stimulus (scream, US1) and congruent word-pairs were associated to a pleasant unconditioned stimulus (baby-laughter, US2), in order to elicit emotional conditioned responses (CR). The aim was to discriminate between affirmative and negative responses, enabled by their association with the positive and negative affective stimuli. In the late acquisition phase, when the US were not present anymore, there was a strong significant differential activation for incongruent and congruent word-pairs in a cluster comprising the left insula and the inferior frontal triangularis. This association was not found in the habituation phase. These results suggest that the difference in affirmative and negative brain responses was established as an effect of conditioning, allowing to further investigate the possibility of using this paradigm for a binary choice BCI.},
}
@article {pmid25100937,
year = {2014},
author = {Chavarriaga, R and Sobolewski, A and Millán, Jdel R},
title = {Errare machinale est: the use of error-related potentials in brain-machine interfaces.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {208},
pmid = {25100937},
issn = {1662-4548},
abstract = {The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches.},
}
@article {pmid25100038,
year = {2014},
author = {Wu, Z and Su, S},
title = {A dynamic selection method for reference electrode in SSVEP-based BCI.},
journal = {PloS one},
volume = {9},
number = {8},
pages = {e104248},
pmid = {25100038},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Signal-To-Noise Ratio ; },
abstract = {In SSVEP-based Brain-Computer Interface (BCI), it is very important to get an evoked EEG with a high signal to noise ratio (SNR). The SNR of SSVEP is fundamentally related to the characteristics of stimulus, such as its intensity and frequency, and it is also related to both the reference electrode and the active electrode. In the past, with SSVEP-based BCI, often the potential at 'Cz', the average potential at all electrodes or the average mastoid potential, were statically selected as the reference. In conjunction, a certain electrode in the occipital area was statically selected as the active electrode for all stimuli. This work proposed a dynamic selection method for the reference electrode, in which all electrodes can be looked upon as active electrodes, while an electrode which can result in the maximum sum relative-power of a specific frequency SSVEP can be confirmed dynamically and considered as the optimum reference electrode for that specific frequency stimulus. Comparing this dynamic selection method with previous methods, in which 'Cz', the average potential at all electrodes or the average mastoid potential were selected as the reference electrode, it is demonstrated that the SNR of SSVEP is improved significantly as is the accuracy of SSVEP detection.},
}
@article {pmid25096831,
year = {2014},
author = {Angotzi, GN and Boi, F and Zordan, S and Bonfanti, A and Vato, A},
title = {A programmable closed-loop recording and stimulating wireless system for behaving small laboratory animals.},
journal = {Scientific reports},
volume = {4},
number = {},
pages = {5963},
pmid = {25096831},
issn = {2045-2322},
mesh = {Action Potentials/physiology ; Animals ; Behavior, Animal/*physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Electrodes, Implanted ; Equipment Design ; Male ; Rats ; Rats, Long-Evans ; Remote Sensing Technology/*instrumentation ; Signal Processing, Computer-Assisted/instrumentation ; Somatosensory Cortex/physiology/surgery ; Stereotaxic Techniques ; Wireless Technology/*instrumentation ; },
abstract = {A portable 16-channels microcontroller-based wireless system for a bi-directional interaction with the central nervous system is presented in this work. The device is designed to be used with freely behaving small laboratory animals and allows recording of spontaneous and evoked neural activity wirelessly transmitted and stored on a personal computer. Biphasic current stimuli with programmable duration, frequency and amplitude may be triggered in real-time on the basis of the recorded neural activity as well as by the animal behavior within a specifically designed experimental setup. An intuitive graphical user interface was developed to configure and to monitor the whole system. The system was successfully tested through bench tests and in vivo measurements on behaving rats chronically implanted with multi-channels microwire arrays.},
}
@article {pmid25094020,
year = {2014},
author = {Flint, RD and Wang, PT and Wright, ZA and King, CE and Krucoff, MO and Schuele, SU and Rosenow, JM and Hsu, FP and Liu, CY and Lin, JJ and Sazgar, M and Millett, DE and Shaw, SJ and Nenadic, Z and Do, AH and Slutzky, MW},
title = {Extracting kinetic information from human motor cortical signals.},
journal = {NeuroImage},
volume = {101},
number = {},
pages = {695-703},
doi = {10.1016/j.neuroimage.2014.07.049},
pmid = {25094020},
issn = {1095-9572},
support = {UL1 RR025741/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Electrodes, Implanted ; Electroencephalography/*methods ; Electromyography ; Female ; Gamma Rhythm/physiology ; Hand/physiology ; Humans ; Isometric Contraction/*physiology ; Kinetics ; Male ; Middle Aged ; Motor Cortex/*physiology ; Muscle, Skeletal/*physiology ; Young Adult ; },
abstract = {Brain machine interfaces (BMIs) have the potential to provide intuitive control of neuroprostheses to restore grasp to patients with paralyzed or amputated upper limbs. For these neuroprostheses to function, the ability to accurately control grasp force is critical. Grasp force can be decoded from neuronal spikes in monkeys, and hand kinematics can be decoded using electrocorticogram (ECoG) signals recorded from the surface of the human motor cortex. We hypothesized that kinetic information about grasping could also be extracted from ECoG, and sought to decode continuously-graded grasp force. In this study, we decoded isometric pinch force with high accuracy from ECoG in 10 human subjects. The predicted signals explained from 22% to 88% (60 ± 6%, mean ± SE) of the variance in the actual force generated. We also decoded muscle activity in the finger flexors, with similar accuracy to force decoding. We found that high gamma band and time domain features of the ECoG signal were most informative about kinetics, similar to our previous findings with intracortical LFPs. In addition, we found that peak cortical representations of force applied by the index and little fingers were separated by only about 4mm. Thus, ECoG can be used to decode not only kinematics, but also kinetics of movement. This is an important step toward restoring intuitively-controlled grasp to impaired patients.},
}
@article {pmid25091344,
year = {2015},
author = {Zich, C and De Vos, M and Kranczioch, C and Debener, S},
title = {Wireless EEG with individualized channel layout enables efficient motor imagery training.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {4},
pages = {698-710},
doi = {10.1016/j.clinph.2014.07.007},
pmid = {25091344},
issn = {1872-8952},
mesh = {Adolescent ; Adult ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Imagination/*physiology ; Learning/physiology ; Male ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: The study compared two channel-reduction approaches in order to investigate the effects of systematic motor imagery (MI) neurofeedback practice in an everyday environment using a very user-friendly EEG system consisting of individualized caps and highly portable hardware.<br><br>METHODS: Sixteen BCI novices were trained over four consecutive days to imagine left and right hand movements while receiving feedback. The most informative bipolar channels for use on the subsequent days were identified on the first day for each individual based on a high-density online MI recording.<br><br>RESULTS: Online classification accuracy on the first day was 85.1% on average (range: 64.7-97.7%). Offline an individually-selected bipolar channel pair based on common spatial patterns significantly outperformed a pair informed by independent component analysis and a standard 10-20 pair. From day 2 to day 4 online MI accuracy increased significantly (day 2: 69.1%; day 4: 73.3%), which was mostly caused by a reduction in ipsilateral event-related desynchronization of sensorimotor rhythms.<br><br>CONCLUSION: The present study demonstrates that systematic MI practice in an everyday environment with a user-friendly EEG system results in MI learning effects.<br><br>SIGNIFICANCE: These findings help to bridge the gap between elaborate laboratory studies with healthy participants and efficient home or hospital based MI neurofeedback protocols.},
}
@article {pmid25091286,
year = {2015},
author = {McFarland, DJ},
title = {The advantages of the surface Laplacian in brain-computer interface research.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {97},
number = {3},
pages = {271-276},
pmid = {25091286},
issn = {1872-7697},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Online Systems ; *Wavelet Analysis ; },
abstract = {Brain-computer interface (BCI) systems frequently use signal processing methods, such as spatial filtering, to enhance performance. The surface Laplacian can reduce spatial noise and aid in identification of sources. In BCI research, these two functions of the surface Laplacian correspond to prediction accuracy and signal orthogonality. In the present study, an off-line analysis of data from a sensorimotor rhythm-based BCI task dissociated these functions of the surface Laplacian by comparing nearest-neighbor and next-nearest neighbor Laplacian algorithms. The nearest-neighbor Laplacian produced signals that were more orthogonal while the next-nearest Laplacian produced signals that resulted in better accuracy. Both prediction and signal identification are important for BCI research. Better prediction of user's intent produces increased speed and accuracy of communication and control. Signal identification is important for ruling out the possibility of control by artifacts. Identifying the nature of the control signal is relevant both to understanding exactly what is being studied and in terms of usability for individuals with limited motor control.},
}
@article {pmid25088691,
year = {2014},
author = {Chen, CH and Ho, MS and Shyu, KK and Hsu, KC and Wang, KW and Lee, PL},
title = {A noninvasive brain computer interface using visually-induced near-infrared spectroscopy responses.},
journal = {Neuroscience letters},
volume = {580},
number = {},
pages = {22-26},
doi = {10.1016/j.neulet.2014.07.042},
pmid = {25088691},
issn = {1872-7972},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Photic Stimulation ; *Spectroscopy, Near-Infrared ; *Visual Perception ; Young Adult ; },
abstract = {Visually-induced near-infrared spectroscopy (NIRS) response was utilized to design a brain computer interface (BCI) system. Four circular checkerboards driven by distinct flickering sequences were displayed on a LCD screen as visual stimuli to induce subjects' NIRS responses. Each flickering sequence was a concatenated sequence of alternative flickering segments and resting segments. The flickering segment was designed with fixed duration of 3s whereas the resting segment was chosen randomly within 15-20s to create the mutual independencies among different flickering sequences. Six subjects were recruited in this study and subjects were requested to gaze at the four visual stimuli one-after-one in a random order. Since visual responses in human brain are time-locked to the onsets of visual stimuli and the flicker sequences of distinct visual stimuli were designed mutually independent, the NIRS responses induced by user's gazed targets can be discerned from non-gazed targets by applying a simple averaging process. The accuracies for the six subjects were higher than 90% after 10 or more epochs being averaged.},
}
@article {pmid25088378,
year = {2014},
author = {Käthner, I and Wriessnegger, SC and Müller-Putz, GR and Kübler, A and Halder, S},
title = {Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface.},
journal = {Biological psychology},
volume = {102},
number = {},
pages = {118-129},
doi = {10.1016/j.biopsycho.2014.07.014},
pmid = {25088378},
issn = {1873-6246},
mesh = {Adult ; Alpha Rhythm/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Fatigue ; Female ; Humans ; Male ; Mental Fatigue/*physiopathology ; Theta Rhythm/physiology ; Workload ; Young Adult ; },
abstract = {The study aimed at revealing electrophysiological indicators of mental workload and fatigue during prolonged usage of a P300 brain-computer interface (BCI). Mental workload was experimentally manipulated with dichotic listening tasks. Medium and high workload conditions alternated. Behavioral measures confirmed that the manipulation of mental workload was successful. Reduced P300 amplitude was found for the high workload condition. Along with lower performance and an increase in the subjective level of fatigue, an increase of power in the alpha band was found for the last as compared to the first run of both conditions. The study confirms that a combination of signals derived from the time and frequency domain of the electroencephalogram is promising for the online detection of workload and fatigue. It also demonstrates that satisfactory accuracies can be achieved by healthy participants with the P300 speller, despite constant distraction and when pursuing the task for a long time.},
}
@article {pmid25086297,
year = {2015},
author = {Fetterhoff, D and Opris, I and Simpson, SL and Deadwyler, SA and Hampson, RE and Kraft, RA},
title = {Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ[9]-tetrahydrocannabinol administration.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {136-153},
pmid = {25086297},
issn = {1872-678X},
support = {DA006634/DA/NIDA NIH HHS/United States ; P50 DA006634/DA/NIDA NIH HHS/United States ; K25 EB012236/EB/NIBIB NIH HHS/United States ; DA07625/DA/NIDA NIH HHS/United States ; K25 EB012236-01A1/EB/NIBIB NIH HHS/United States ; R01 DA007625/DA/NIDA NIH HHS/United States ; },
mesh = {Action Potentials/drug effects ; Algorithms ; Analysis of Variance ; Animals ; Computer Simulation ; Dose-Response Relationship, Drug ; Dronabinol/*administration &amp; dosage ; *Electronic Data Processing ; Hippocampus/*cytology ; Memory, Short-Term/*drug effects ; Models, Neurological ; Neurons/cytology/*drug effects ; Nonlinear Dynamics ; Psychotropic Drugs/*administration &amp; dosage ; Rats ; Rats, Long-Evans ; },
abstract = {BACKGROUND: Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing.<br><br>NEW METHOD: Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models.<br><br>RESULTS: Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons.<br><br>WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events.<br><br>CONCLUSION: z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.},
}
@article {pmid25084446,
year = {2014},
author = {Alam, M and Chen, X and Zhang, Z and Li, Y and He, J},
title = {A brain-machine-muscle interface for restoring hindlimb locomotion after complete spinal transection in rats.},
journal = {PloS one},
volume = {9},
number = {8},
pages = {e103764},
pmid = {25084446},
issn = {1932-6203},
mesh = {Animals ; *Brain-Computer Interfaces ; Female ; Hindlimb/*physiology ; Locomotion/*physiology ; Motor Cortex/physiology ; Physical Conditioning, Animal ; Rats ; Rats, Sprague-Dawley ; },
abstract = {A brain-machine interface (BMI) is a neuroprosthetic device that can restore motor function of individuals with paralysis. Although the feasibility of BMI control of upper-limb neuroprostheses has been demonstrated, a BMI for the restoration of lower-limb motor functions has not yet been developed. The objective of this study was to determine if gait-related information can be captured from neural activity recorded from the primary motor cortex of rats, and if this neural information can be used to stimulate paralysed hindlimb muscles after complete spinal cord transection. Neural activity was recorded from the hindlimb area of the primary motor cortex of six female Sprague Dawley rats during treadmill locomotion before and after mid-thoracic transection. Before spinal transection there was a strong association between neural activity and the step cycle. This association decreased after spinal transection. However, the locomotive state (standing vs. walking) could still be successfully decoded from neural recordings made after spinal transection. A novel BMI device was developed that processed this neural information in real-time and used it to control electrical stimulation of paralysed hindlimb muscles. This system was able to elicit hindlimb muscle contractions that mimicked forelimb stepping. We propose this lower-limb BMI as a future neuroprosthesis for human paraplegics.},
}
@article {pmid25082789,
year = {2014},
author = {Ibáñez, J and Serrano, JI and del Castillo, MD and Monge-Pereira, E and Molina-Rueda, F and Alguacil-Diego, I and Pons, JL},
title = {Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056009},
doi = {10.1088/1741-2560/11/5/056009},
pmid = {25082789},
issn = {1741-2552},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Algorithms ; Arm/*physiopathology ; Biological Clocks ; Electrocardiography/*methods ; *Evoked Potentials, Somatosensory ; Humans ; Male ; Middle Aged ; *Movement ; Paralysis/*physiopathology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiopathology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity 'number of misclassifications' and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied.<br><br>APPROACH: Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a na&#xef;ve Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed.<br><br>MAIN RESULTS: On average, 74.5 &#xb1; 13.8% and 82.2 &#xb1; 10.4% of the movements were detected with 1.32 &#xb1; 0.87 and 1.50 &#xb1; 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083).<br><br>SIGNIFICANCE: A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain-computer interface applications with this group of patients.},
}
@article {pmid25082743,
year = {2014},
author = {Pan, J and Xie, Q and He, Y and Wang, F and Di, H and Laureys, S and Yu, R and Li, Y},
title = {Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056007},
doi = {10.1088/1741-2560/11/5/056007},
pmid = {25082743},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aged ; *Awareness ; *Brain-Computer Interfaces ; Consciousness Disorders/*diagnosis/*physiopathology ; *Diagnostic Techniques, Neurological ; *Event-Related Potentials, P300 ; *Evoked Potentials, Somatosensory ; Female ; Humans ; Male ; Middle Aged ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {OBJECTIVE: The bedside detection of potential awareness in patients with disorders of consciousness (DOC) currently relies only on behavioral observations and tests; however, the misdiagnosis rates in this patient group are historically relatively high. In this study, we proposed a visual hybrid brain-computer interface (BCI) combining P300 and steady-state evoked potential (SSVEP) responses to detect awareness in severely brain injured patients.<br><br>APPROACH: Four healthy subjects, seven DOC patients who were in a vegetative state (VS, n = 4) or minimally conscious state (MCS, n = 3), and one locked-in syndrome (LIS) patient attempted a command-following experiment. In each experimental trial, two photos were presented to each patient; one was the patient's own photo, and the other photo was unfamiliar. The patients were instructed to focus on their own or the unfamiliar photos. The BCI system determined which photo the patient focused on with both P300 and SSVEP detections.<br><br>MAIN RESULTS: Four healthy subjects, one of the 4 VS, one of the 3 MCS, and the LIS patient were able to selectively attend to their own or the unfamiliar photos (classification accuracy, 66-100%). Two additional patients (one VS and one MCS) failed to attend the unfamiliar photo (50-52%) but achieved significant accuracies for their own photo (64-68%). All other patients failed to show any significant response to commands (46-55%).<br><br>SIGNIFICANCE: Through the hybrid BCI system, command following was detected in four healthy subjects, two of 7 DOC patients, and one LIS patient. We suggest that the hybrid BCI system could be used as a supportive bedside tool to detect awareness in patients with DOC.},
}
@article {pmid25082508,
year = {2014},
author = {Todorova, S and Sadtler, P and Batista, A and Chase, S and Ventura, V},
title = {To sort or not to sort: the impact of spike-sorting on neural decoding performance.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056005},
pmid = {25082508},
issn = {1741-2552},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 MH064537/MH/NIMH NIH HHS/United States ; 2R01MH064537/MH/NIMH NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Computer Simulation ; Data Interpretation, Statistical ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Models, Neurological ; Models, Statistical ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity.<br><br>APPROACH: We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step.<br><br>MAIN RESULTS: Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior.<br><br>SIGNIFICANCE: Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive methods used here. Even basic spike-sorting adds value to the low-threshold waveform-crossing methods often employed in BCI decoding.},
}
@article {pmid25081427,
year = {2014},
author = {Nakanishi, M and Wang, Y and Wang, YT and Mitsukura, Y and Jung, TP},
title = {A high-speed brain speller using steady-state visual evoked potentials.},
journal = {International journal of neural systems},
volume = {24},
number = {6},
pages = {1450019},
doi = {10.1142/S0129065714500191},
pmid = {25081427},
issn = {1793-6462},
mesh = {Algorithms ; Animals ; Biophysics ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation ; Reaction Time/physiology ; Time Factors ; },
abstract = {Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.},
}
@article {pmid25080406,
year = {2014},
author = {Kaufmann, T and Kübler, A},
title = {Beyond maximum speed--a novel two-stimulus paradigm for brain-computer interfaces based on event-related potentials (P300-BCI).},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056004},
doi = {10.1088/1741-2560/11/5/056004},
pmid = {25080406},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrocardiography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Research Design ; *Task Performance and Analysis ; Visual Perception/*physiology ; Writing ; Young Adult ; },
abstract = {OBJECTIVE: The speed of brain-computer interfaces (BCI), based on event-related potentials (ERP), is inherently limited by the commonly used one-stimulus paradigm. In this paper, we introduce a novel paradigm that can increase the spelling speed by a factor of 2, thereby extending the one-stimulus paradigm to a two-stimulus paradigm. Two different stimuli (a face and a symbol) are presented at the same time, superimposed on different characters and ERPs are classified using a multi-class classifier. Here, we present the proof-of-principle that is achieved with healthy participants.<br><br>APPROACH: Eight participants were confronted with the novel two-stimulus paradigm and, for comparison, with two one-stimulus paradigms that used either one of the stimuli. Classification accuracies (percentage of correctly predicted letters) and elicited ERPs from the three paradigms were compared in a comprehensive offline analysis.<br><br>MAIN RESULTS: The accuracies slightly decreased with the novel system compared to the established one-stimulus face paradigm. However, the use of two stimuli allowed for spelling at twice the maximum speed of the one-stimulus paradigms, and participants still achieved an average accuracy of 81.25%. This study introduced an alternative way of increasing the spelling speed in ERP-BCIs and illustrated that ERP-BCIs may not yet have reached their speed limit. Future research is needed in order to improve the reliability of the novel approach, as some participants displayed reduced accuracies. Furthermore, a comparison to the most recent BCI systems with individually adjusted, rapid stimulus timing is needed to draw conclusions about the practical relevance of the proposed paradigm.<br><br>SIGNIFICANCE: We introduced a novel two-stimulus paradigm that might be of high value for users who have reached the speed limit with the current one-stimulus ERP-BCI systems.},
}
@article {pmid25080373,
year = {2014},
author = {Zhou, Z and Yin, E and Liu, Y and Jiang, J and Hu, D},
title = {A novel task-oriented optimal design for P300-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056003},
doi = {10.1088/1741-2560/11/5/056003},
pmid = {25080373},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrocardiography/instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; *Research Design ; *Task Performance and Analysis ; Visual Perception/*physiology ; Writing ; Young Adult ; },
abstract = {Objective. The number of items of a P300-based brain-computer interface (BCI) should be adjustable in accordance with the requirements of the specific tasks. To address this issue, we propose a novel task-oriented optimal approach aimed at increasing the performance of general P300 BCIs with different numbers of items. Approach. First, we proposed a stimulus presentation with variable dimensions (VD) paradigm as a generalization of the conventional single-character (SC) and row-column (RC) stimulus paradigms. Furthermore, an embedding design approach was employed for any given number of items. Finally, based on the score-P model of each subject, the VD flash pattern was selected by a linear interpolation approach for a certain task. Main results. The results indicate that the optimal BCI design consistently outperforms the conventional approaches, i.e., the SC and RC paradigms. Specifically, there is significant improvement in the practical information transfer rate for a large number of items. Significance. The results suggest that the proposed optimal approach would provide useful guidance in the practical design of general P300-based BCIs.},
}
@article {pmid25080297,
year = {2014},
author = {Llera, A and Gómez, V and Kappen, HJ},
title = {Quantitative analysis of task selection for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {11},
number = {5},
pages = {056002},
doi = {10.1088/1741-2560/11/5/056002},
pmid = {25080297},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Neuronal Plasticity/physiology ; *Task Performance and Analysis ; },
abstract = {OBJECTIVE: To assess quantitatively the impact of task selection in the performance of brain-computer interfaces (BCI).<br><br>APPROACH: We consider the task-pairs derived from multi-class BCI imagery movement tasks in three different datasets. We analyze for the first time the benefits of task selection on a large-scale basis (109 users) and evaluate the possibility of transferring task-pair information across days for a given subject.<br><br>MAIN RESULTS: Selecting the subject-dependent optimal task-pair among three different imagery movement tasks results in approximately 20% potential increase in the number of users that can be expected to control a binary BCI. The improvement is observed with respect to the best task-pair fixed across subjects. The best task-pair selected for each subject individually during a first day of recordings is generally a good task-pair in subsequent days. In general, task learning from the user side has a positive influence in the generalization of the optimal task-pair, but special attention should be given to inexperienced subjects.<br><br>SIGNIFICANCE: These results add significant evidence to existing literature that advocates task selection as a necessary step towards usable BCIs. This contribution motivates further research focused on deriving adaptive methods for task selection on larger sets of mental tasks in practical online scenarios.},
}
@article {pmid25079941,
year = {2014},
author = {Zhao, Y and Rennaker, RL and Hutchens, C and Ibrahim, TS},
title = {Implanted miniaturized antenna for brain computer interface applications: analysis and design.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e103945},
pmid = {25079941},
issn = {1932-6203},
support = {R01 NS062065/NS/NINDS NIH HHS/United States ; R01NS062065/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Implants, Experimental ; Phantoms, Imaging ; Wireless Technology ; },
abstract = {Implantable Brain Computer Interfaces (BCIs) are designed to provide real-time control signals for prosthetic devices, study brain function, and/or restore sensory information lost as a result of injury or disease. Using Radio Frequency (RF) to wirelessly power a BCI could widely extend the number of applications and increase chronic in-vivo viability. However, due to the limited size and the electromagnetic loss of human brain tissues, implanted miniaturized antennas suffer low radiation efficiency. This work presents simulations, analysis and designs of implanted antennas for a wireless implantable RF-powered brain computer interface application. The results show that thin (on the order of 100 micrometers thickness) biocompatible insulating layers can significantly impact the antenna performance. The proper selection of the dielectric properties of the biocompatible insulating layers and the implantation position inside human brain tissues can facilitate efficient RF power reception by the implanted antenna. While the results show that the effects of the human head shape on implanted antenna performance is somewhat negligible, the constitutive properties of the brain tissues surrounding the implanted antenna can significantly impact the electrical characteristics (input impedance, and operational frequency) of the implanted antenna. Three miniaturized antenna designs are simulated and demonstrate that maximum RF power of up to 1.8 milli-Watts can be received at 2 GHz when the antenna implanted around the dura, without violating the Specific Absorption Rate (SAR) limits.},
}
@article {pmid25079449,
year = {2014},
author = {Zhang, F and Liu, M and Harper, S and Lee, M and Huang, H},
title = {Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {89},
pages = {},
pmid = {25079449},
issn = {1940-087X},
support = {R21 HD064968/HD/NICHD NIH HHS/United States ; RHD064968A//PHS HHS/United States ; },
mesh = {Amputees/*rehabilitation ; *Artificial Limbs ; Biomechanical Phenomena ; Electromyography/instrumentation/*methods ; Humans ; Male ; Middle Aged ; Software ; Thigh/innervation/physiology ; Walking/physiology ; },
abstract = {To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs.},
}
@article {pmid25076886,
year = {2014},
author = {Young, BM and Nigogosyan, Z and Walton, LM and Song, J and Nair, VA and Grogan, SW and Tyler, ME and Edwards, DF and Caldera, K and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {26},
pmid = {25076886},
issn = {1662-6443},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; },
abstract = {This study aims to examine the changes in task-related brain activity induced by rehabilitative therapy using brain-computer interface (BCI) technologies and whether these changes are relevant to functional gains achieved through the use of these therapies. Stroke patients with persistent upper-extremity motor deficits received interventional rehabilitation therapy using a closed-loop neurofeedback BCI device (n = 8) or no therapy (n = 6). Behavioral assessments using the Stroke Impact Scale, the Action Research Arm Test (ARAT), and the Nine-Hole Peg Test (9-HPT) as well as task-based fMRI scans were conducted before, during, after, and 1 month after therapy administration or at analogous intervals in the absence of therapy. Laterality Index (LI) values during finger tapping of each hand were calculated for each time point and assessed for correlation with behavioral outcomes. Brain activity during finger tapping of each hand shifted over the course of BCI therapy, but not in the absence of therapy, to greater involvement of the non-lesioned hemisphere (and lesser involvement of the stroke-lesioned hemisphere) as measured by LI. Moreover, changes from baseline LI values during finger tapping of the impaired hand were correlated with gains in both objective and subjective behavioral measures. These findings suggest that the administration of interventional BCI therapy can induce differential changes in brain activity patterns between the lesioned and non-lesioned hemispheres and that these brain changes are associated with changes in specific motor functions.},
}
@article {pmid25076875,
year = {2014},
author = {Li, Z},
title = {Decoding methods for neural prostheses: where have we reached?.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {129},
pmid = {25076875},
issn = {1662-5137},
abstract = {This article reviews advances in decoding methods for brain-machine interfaces (BMIs). Recent work has focused on practical considerations for future clinical deployment of prosthetics. This review is organized by open questions in the field such as what variables to decode, how to design neural tuning models, which neurons to select, how to design models of desired actions, how to learn decoder parameters during prosthetic operation, and how to adapt to changes in neural signals and neural tuning. The concluding discussion highlights the need to design and test decoders within the context of their expected use and the need to answer the question of how much control accuracy is good enough for a prosthetic.},
}
@article {pmid25076487,
year = {2014},
author = {Yeom, HG and Kim, JS and Chung, CK},
title = {High-accuracy brain-machine interfaces using feedback information.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e103539},
pmid = {25076487},
issn = {1932-6203},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Feedback ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {Sensory feedback is very important for movement control. However, feedback information has not been directly used to update movement prediction model in the previous BMI studies, although the closed-loop BMI system provides the visual feedback to users. Here, we propose a BMI framework combining image processing as the feedback information with a novel prediction method. The feedback-prediction algorithm (FPA) generates feedback information from the positions of objects and modifies movement prediction according to the information. The FPA predicts a target among objects based on the movement direction predicted from the neural activity. After the target selection, the FPA modifies the predicted direction toward the target and modulates the magnitude of the predicted vector to easily reach the target. The FPA repeats the modification in every prediction time points. To evaluate the improvements of prediction accuracy provided by the feedback, we compared the prediction performances with feedback (FPA) and without feedback. We demonstrated that accuracy of movement prediction can be considerably improved by the FPA combining feedback information. The accuracy of the movement prediction was significantly improved for all subjects (P<0.001) and 32.1% of the mean error was reduced. The BMI performance will be improved by combining feedback information and it will promote the development of a practical BMI system.},
}
@article {pmid25073174,
year = {2015},
author = {Castagnola, E and Maiolo, L and Maggiolini, E and Minotti, A and Marrani, M and Maita, F and Pecora, A and Angotzi, GN and Ansaldo, A and Boffini, M and Fadiga, L and Fortunato, G and Ricci, D},
title = {PEDOT-CNT-Coated Low-Impedance, Ultra-Flexible, and Brain-Conformable Micro-ECoG Arrays.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {3},
pages = {342-350},
doi = {10.1109/TNSRE.2014.2342880},
pmid = {25073174},
issn = {1558-0210},
mesh = {Animals ; Brain/physiology ; Brain Mapping ; Brain-Computer Interfaces ; *Bridged Bicyclo Compounds, Heterocyclic ; Electric Impedance ; Electrochemical Techniques ; Electrodes ; Electroencephalography/*instrumentation ; Electrophysiological Phenomena ; Evoked Potentials, Somatosensory ; Male ; Microelectrodes ; *Nanotubes, Carbon ; Physical Stimulation ; *Polymers ; Rats ; Rats, Long-Evans ; Signal-To-Noise Ratio ; Vibrissae/physiology ; },
abstract = {Electrocorticography (ECoG) is becoming a common tool for clinical applications, such as preparing patients for epilepsy surgery or localizing tumor boundaries, as it successfully balances invasiveness and information quality. Clinical ECoG arrays use millimeter-scale electrodes and centimeter-scale pitch and cannot precisely map neural activity. Higher-resolution electrodes are of interest for both current clinical applications, providing access to more precise neural activity localization and novel applications, such as neural prosthetics, where current information density and spatial resolution is insufficient to suitably decode signals for a chronic brain-machine interface. Developing such electrodes is not trivial because their small contact area increases the electrode impedance, which seriously affects the signal-to-noise ratio, and adhering such an electrode to the brain surface becomes critical. The most straightforward approach requires increasing the array conformability with flexible substrates while improving the electrode performance using materials with superior electrochemical properties. In this paper, we propose an ultra-flexible and conformable polyimide-based micro-ECoG array of submillimeter recording sites electrochemically coated with high surface area conductive polymer-carbon nanotube composites to improve their brain-electrical coupling capabilities. We characterized our devices both electrochemically and by recording from rat somatosensory cortex in vivo. The performance of the coated and uncoated electrodes was directly compared by simultaneously recording the same neuronal activity during multiwhisker deflection stimulation. Finally, we assessed the effect of electrode size on the extraction of somatosensory evoked potentials and found that in contrast to the normal high-impedance microelectrodes, the recording capabilities of our low-impedance microelectrodes improved upon reducing their size from 0.2 to 0.1 mm.},
}
@article {pmid25073173,
year = {2015},
author = {Wang, Y and Wang, F and Xu, K and Zhang, Q and Zhang, S and Zheng, X},
title = {Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {3},
pages = {458-467},
doi = {10.1109/TNSRE.2014.2341275},
pmid = {25073173},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; Attention/*physiology ; *Brain-Computer Interfaces ; Equipment Design ; Learning/*physiology ; Macaca mulatta ; Male ; Motor Cortex/physiology ; Movement ; Psychomotor Performance/*physiology ; *Reinforcement, Psychology ; Software ; },
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.},
}
@article {pmid25072739,
year = {2014},
author = {Poli, R and Valeriani, D and Cinel, C},
title = {Collaborative brain-computer interface for aiding decision-making.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e102693},
pmid = {25072739},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Decision Making ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Models, Theoretical ; Photic Stimulation ; Reaction Time ; Young Adult ; },
abstract = {We look at the possibility of integrating the percepts from multiple non-communicating observers as a means of achieving better joint perception and better group decisions. Our approach involves the combination of a brain-computer interface with human behavioural responses. To test ideas in controlled conditions, we asked observers to perform a simple matching task involving the rapid sequential presentation of pairs of visual patterns and the subsequent decision as whether the two patterns in a pair were the same or different. We recorded the response times of observers as well as a neural feature which predicts incorrect decisions and, thus, indirectly indicates the confidence of the decisions made by the observers. We then built a composite neuro-behavioural feature which optimally combines the two measures. For group decisions, we uses a majority rule and three rules which weigh the decisions of each observer based on response times and our neural and neuro-behavioural features. Results indicate that the integration of behavioural responses and neural features can significantly improve accuracy when compared with the majority rule. An analysis of event-related potentials indicates that substantial differences are present in the proximity of the response for correct and incorrect trials, further corroborating the idea of using hybrids of brain-computer interfaces and traditional strategies for improving decision making.},
}
@article {pmid25071547,
year = {2014},
author = {Young, BM and Nigogosyan, Z and Remsik, A and Walton, LM and Song, J and Nair, VA and Grogan, SW and Tyler, ME and Edwards, DF and Caldera, K and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {25},
pmid = {25071547},
issn = {1662-6443},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) technology is being incorporated into new stroke rehabilitation devices, but little is known about brain changes associated with its use. We collected anatomical and functional MRI of nine stroke patients with persistent upper extremity motor impairment before, during, and after therapy using a BCI system. Subjects were asked to perform finger tapping of the impaired hand during fMRI. Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) domains of Hand Function (HF) and Activities of Daily Living (ADL) were also assessed. Group-level analyses examined changes in whole-brain task-based functional connectivity (FC) to seed regions in the motor network observed during and after BCI therapy. Whole-brain FC analyses seeded in each thalamus showed FC increases from baseline at mid-therapy and post-therapy (p < 0.05). Changes in FC between seeds at both the network and the connection levels were examined for correlations with changes in behavioral measures. Average motor network FC was increased post-therapy, and changes in average network FC correlated (p < 0.05) with changes in performance on ARAT (R (2) = 0.21), 9-HPT (R (2) = 0.41), SIS HF (R (2) = 0.27), and SIS ADL (R (2) = 0.40). Multiple individual connections within the motor network were found to correlate in change from baseline with changes in behavioral measures. Many of these connections involved the thalamus, with change in each of four behavioral measures significantly correlating with change from baseline FC of at least one thalamic connection. These preliminary results show changes in FC that occur with the administration of rehabilitative therapy using a BCI system. The correlations noted between changes in FC measures and changes in behavioral outcomes indicate that both adaptive and maladaptive changes in FC may develop with this therapy and also suggest a brain-behavior relationship that may be stimulated by the neuromodulatory component of BCI therapy.},
}
@article {pmid25071546,
year = {2014},
author = {Eleryan, A and Vaidya, M and Southerland, J and Badreldin, IS and Balasubramanian, K and Fagg, AH and Hatsopoulos, N and Oweiss, K},
title = {Tracking single units in chronic, large scale, neural recordings for brain machine interface applications.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {23},
pmid = {25071546},
issn = {1662-6443},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS062031/NS/NINDS NIH HHS/United States ; UL1 TR000430/TR/NCATS NIH HHS/United States ; },
abstract = {In the study of population coding in neurobiological systems, tracking unit identity may be critical to assess possible changes in the coding properties of neuronal constituents over prolonged periods of time. Ensuring unit stability is even more critical for reliable neural decoding of motor variables in intra-cortically controlled brain-machine interfaces (BMIs). Variability in intrinsic spike patterns, tuning characteristics, and single-unit identity over chronic use is a major challenge to maintaining this stability, requiring frequent daily calibration of neural decoders in BMI sessions by an experienced human operator. Here, we report on a unit-stability tracking algorithm that efficiently and autonomously identifies putative single-units that are stable across many sessions using a relatively short duration recording interval at the start of each session. The algorithm first builds a database of features extracted from units' average spike waveforms and firing patterns across many days of recording. It then uses these features to decide whether spike occurrences on the same channel on one day belong to the same unit recorded on another day or not. We assessed the overall performance of the algorithm for different choices of features and classifiers trained using human expert judgment, and quantified it as a function of accuracy and execution time. Overall, we found a trade-off between accuracy and execution time with increasing data volumes from chronically implanted rhesus macaques, with an average of 12 s processing time per channel at ~90% classification accuracy. Furthermore, 77% of the resulting putative single-units matched those tracked by human experts. These results demonstrate that over the span of a few months of recordings, automated unit tracking can be performed with high accuracy and used to streamline the calibration phase during BMI sessions. Our findings may be useful to the study of population coding during learning, and to improve the reliability of BMI systems and accelerate their deployment in clinical applications.},
}
@article {pmid25071545,
year = {2014},
author = {Friedrich, EV and Suttie, N and Sivanathan, A and Lim, T and Louchart, S and Pineda, JA},
title = {Brain-computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {21},
pmid = {25071545},
issn = {1662-6443},
abstract = {Individuals with autism spectrum disorder (ASD) show deficits in social and communicative skills, including imitation, empathy, and shared attention, as well as restricted interests and repetitive patterns of behaviors. Evidence for and against the idea that dysfunctions in the mirror neuron system are involved in imitation and could be one underlying cause for ASD is discussed in this review. Neurofeedback interventions have reduced symptoms in children with ASD by self-regulation of brain rhythms. However, cortical deficiencies are not the only cause of these symptoms. Peripheral physiological activity, such as the heart rate and its variability, is closely linked to neurophysiological signals and associated with social engagement. Therefore, a combined approach targeting the interplay between brain, body, and behavior could be more effective. Brain-computer interface applications for combined neurofeedback and biofeedback treatment for children with ASD are currently nonexistent. To facilitate their use, we have designed an innovative game that includes social interactions and provides neural- and body-based feedback that corresponds directly to the underlying significance of the trained signals as well as to the behavior that is reinforced.},
}
@article {pmid25071544,
year = {2014},
author = {Daly, I and Faller, J and Scherer, R and Sweeney-Reed, CM and Nasuto, SJ and Billinger, M and Müller-Putz, GR},
title = {Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {20},
pmid = {25071544},
issn = {1662-6443},
abstract = {Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.},
}
@article {pmid25071543,
year = {2014},
author = {Ono, T and Shindo, K and Kawashima, K and Ota, N and Ito, M and Ota, T and Mukaino, M and Fujiwara, T and Kimura, A and Liu, M and Ushiba, J},
title = {Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {19},
pmid = {25071543},
issn = {1662-6443},
abstract = {Recent studies have shown that scalp electroencephalogram (EEG) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients with severe hemiplegia. However, key elements in BCI architecture for functional recovery has yet to be clear. We in this study focused on the type of feedback to the patients, which is given contingently to their motor-related EEG in a BCI context. The efficacy of visual and somatosensory feedbacks was compared by a two-group study with the chronic stroke patients who are suffering with severe motor hemiplegia. Twelve patients were asked an attempt of finger opening in the affected side repeatedly, and the event-related desynchronization (ERD) in EEG of alpha and beta rhythms was monitored over bilateral parietal regions. Six patients were received a simple visual feedback in which the hand open/grasp picture on screen was animated at eye level, following significant ERD. Six patients were received a somatosensory feedback in which the motor-driven orthosis was triggered to extend the paralyzed fingers from 90 to 50°. All the participants received 1-h BCI treatment with 12-20 training days. After the training period, while no changes in clinical scores and electromyographic (EMG) activity were observed in visual feedback group after training, voluntary EMG activity was newly observed in the affected finger extensors in four cases and the clinical score of upper limb function in the affected side was also improved in three participants in somatosensory feedback group. Although the present study was conducted with a limited number of patients, these results imply that BCI training with somatosensory feedback could be more effective for rehabilitation than with visual feedback. This pilot trial positively encouraged further clinical BCI research using a controlled design.},
}
@article {pmid25071515,
year = {2014},
author = {Seeber, M and Scherer, R and Wagner, J and Solis-Escalante, T and Müller-Putz, GR},
title = {EEG beta suppression and low gamma modulation are different elements of human upright walking.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {485},
pmid = {25071515},
issn = {1662-5161},
abstract = {Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG) recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source modeling to high-density (120 channels) EEG recordings using individual anatomy reconstructed from structural magnetic resonance imaging scans. First, we analyzed amplitude differences between the conditions, walking and upright standing. Second, we investigated amplitude modulations related to the gait phase. During active walking upper μ (10-12 Hz) and β (18-30 Hz) oscillations were suppressed [event-related desynchronization (ERD)] compared to upright standing. Significant β ERD activity was located focally in central sensorimotor areas for 9/10 subjects. Additionally, we found that low γ (24-40 Hz) amplitudes were modulated related to the gait phase. Because there is a certain frequency band overlap between sustained β ERD and gait phase related modulations in the low γ range, these two phenomena are superimposed. Thus, we observe gait phase related amplitude modulations at a certain ERD level. We conclude that sustained μ and β ERD reflect a movement related state change of cortical excitability while gait phase related modulations in the low γ represent the motion sequence timing during gait. Interestingly, the center frequencies of sustained β ERD and gait phase modulated amplitudes were identified to be different. They may therefore be caused by different neuronal rhythms, which should be taken under consideration in future studies.},
}
@article {pmid25071505,
year = {2014},
author = {Wriessnegger, SC and Steyrl, D and Koschutnig, K and Müller-Putz, GR},
title = {Short time sports exercise boosts motor imagery patterns: implications of mental practice in rehabilitation programs.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {469},
pmid = {25071505},
issn = {1662-5161},
abstract = {Motor imagery (MI) is a commonly used paradigm for the study of motor learning or cognitive aspects of action control. The rationale for using MI training to promote the relearning of motor function arises from research on the functional correlates that MI shares with the execution of physical movements. While most of the previous studies investigating MI were based on simple movements in the present study a more attractive mental practice was used to investigate cortical activation during MI. We measured cerebral responses with functional magnetic resonance imaging (fMRI) in twenty three healthy volunteers as they imagined playing soccer or tennis before and after a short physical sports exercise. Our results demonstrated that only 10 min of training are enough to boost MI patterns in motor related brain regions including premotor cortex and supplementary motor area (SMA) but also fronto-parietal and subcortical structures. This supports previous findings that MI has beneficial effects especially in combination with motor execution when used in motor rehabilitation or motor learning processes. We conclude that sports MI combined with an interactive game environment could be a promising additional tool in future rehabilitation programs aiming to improve upper or lower limb functions or support neuroplasticity.},
}
@article {pmid25068464,
year = {2014},
author = {Kindermans, PJ and Schreuder, M and Schrauwen, B and Müller, KR and Tangermann, M},
title = {True zero-training brain-computer interfacing--an online study.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e102504},
pmid = {25068464},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Female ; Humans ; Male ; Young Adult ; },
abstract = {Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.},
}
@article {pmid25065437,
year = {2014},
author = {Harris, KD},
title = {Sleep replay meets brain-machine interface.},
journal = {Nature neuroscience},
volume = {17},
number = {8},
pages = {1019-1021},
pmid = {25065437},
issn = {1546-1726},
support = {095668//Wellcome Trust/United Kingdom ; },
mesh = {Animals ; Learning/*physiology ; Male ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Neurons/*physiology ; Sleep/*physiology ; },
}
@article {pmid25064189,
year = {2014},
author = {Jangraw, DC and Johri, A and Gribetz, M and Sajda, P},
title = {NEDE: an open-source scripting suite for developing experiments in 3D virtual environments.},
journal = {Journal of neuroscience methods},
volume = {235},
number = {},
pages = {245-251},
doi = {10.1016/j.jneumeth.2014.06.033},
pmid = {25064189},
issn = {1872-678X},
mesh = {Brain/physiology ; Calibration ; Electroencephalography/methods ; Eye Movement Measurements ; Humans ; Internet ; *Software ; *User-Computer Interface ; Video Games ; },
abstract = {BACKGROUND: As neuroscientists endeavor to understand the brain's response to ecologically valid scenarios, many are leaving behind hyper-controlled paradigms in favor of more realistic ones. This movement has made the use of 3D rendering software an increasingly compelling option. However, mastering such software and scripting rigorous experiments requires a daunting amount of time and effort.<br><br>NEW METHOD: To reduce these startup costs and make virtual environment studies more accessible to researchers, we demonstrate a naturalistic experimental design environment (NEDE) that allows experimenters to present realistic virtual stimuli while still providing tight control over the subject's experience. NEDE is a suite of open-source scripts built on the widely used Unity3D game development software, giving experimenters access to powerful rendering tools while interfacing with eye tracking and EEG, randomizing stimuli, and providing custom task prompts.<br><br>RESULTS: Researchers using NEDE can present a dynamic 3D virtual environment in which randomized stimulus objects can be placed, allowing subjects to explore in search of these objects. NEDE interfaces with a research-grade eye tracker in real-time to maintain precise timing records and sync with EEG or other recording modalities.<br><br>Python offers an alternative for experienced programmers who feel comfortable mastering and integrating the various toolboxes available. NEDE combines many of these capabilities with an easy-to-use interface and, through Unity's extensive user base, a much more substantial body of assets and tutorials.<br><br>CONCLUSIONS: Our flexible, open-source experimental design system lowers the barrier to entry for neuroscientists interested in developing experiments in realistic virtual environments.},
}
@article {pmid25063602,
year = {2014},
author = {Onyishi, SE and Twiss, CO},
title = {Pressure flow studies in men and women.},
journal = {The Urologic clinics of North America},
volume = {41},
number = {3},
pages = {453-67, ix},
doi = {10.1016/j.ucl.2014.04.007},
pmid = {25063602},
issn = {1558-318X},
mesh = {Female ; Humans ; Male ; Muscle Contraction/physiology ; Nomograms ; Urethra/physiopathology ; Urethral Obstruction/physiopathology ; Urinary Bladder/physiopathology ; Urinary Bladder Neck Obstruction/diagnosis/physiopathology ; Urination/*physiology ; *Urodynamics ; },
abstract = {There are well established pressure flow criteria and nomograms for urinary obstruction in men. The pressure flow criteria for female urinary obstruction are not well established due to differences in female voiding dynamics as compared to men. Typically, other information such as radiographic data and clinical symptoms are needed to facilitate the diagnosis. Detrusor underactivity remains a poorly studied clinical condition without definitive urodynamic diagnostic criteria. Modalities proposed for objective analysis of detrusor function such as power (watt) factor, linear passive urethral resistance relation and BCI nomogram were all developed to analyze male voiding dysfunction. Overall, further investigation is needed to establish acceptable urodynamic criteria for defining detrusor underactivity in women.},
}
@article {pmid25061837,
year = {2014},
author = {Liu, YH and Wu, CT and Cheng, WT and Hsiao, YT and Chen, PM and Teng, JT},
title = {Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.},
journal = {Sensors (Basel, Switzerland)},
volume = {14},
number = {8},
pages = {13361-13388},
pmid = {25061837},
issn = {1424-8220},
mesh = {Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*instrumentation/*methods ; Emotions/*physiology ; Humans ; Software ; *Support Vector Machine ; },
abstract = {Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.},
}
@article {pmid25060658,
year = {2014},
author = {Young, BM and Williams, J and Prabhakaran, V},
title = {BCI-FES: could a new rehabilitation device hold fresh promise for stroke patients?.},
journal = {Expert review of medical devices},
volume = {11},
number = {6},
pages = {537-539},
pmid = {25060658},
issn = {1745-2422},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; R01EB009103/EB/NIBIB NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; R01EB000856-06/EB/NIBIB NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; T32GM008692/GM/NIGMS NIH HHS/United States ; K23NS086852/NS/NINDS NIH HHS/United States ; UL1TR000427/TR/NCATS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; RC1MH090912-01/MH/NIMH NIH HHS/United States ; 1T32EB011434-01A1/EB/NIBIB NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*instrumentation ; Brain-Computer Interfaces/*trends ; Electric Stimulation Therapy/*instrumentation/trends ; Equipment Design ; Humans ; Paralysis/etiology/*rehabilitation ; Stroke/complications ; *Stroke Rehabilitation ; },
abstract = {It has been known that stroke constitutes a major source of acquired disability, with nearly 800,000 new strokes each year in the USA alone. While advances in public and preventative health have helped reduce stroke incidence in high-income countries in recent decades, growth of the aging population, increasing stroke rates in low- to middle-income countries and medical advances that have reduced stroke mortality are all contributing to an increase in stroke survivors worldwide. Large numbers of stroke survivors have residual motor deficits. This editorial will provide an introduction to a class of new therapies being investigated with the aim of improving motor outcomes in stroke patients that uses what is known as brain-computer interface technology.},
}
@article {pmid25058014,
year = {2014},
author = {Kober, SE and Wood, G and Kampl, C and Neuper, C and Ischebeck, A},
title = {Electrophysiological correlates of mental navigation in blind and sighted people.},
journal = {Behavioural brain research},
volume = {273},
number = {},
pages = {106-115},
doi = {10.1016/j.bbr.2014.07.022},
pmid = {25058014},
issn = {1872-7549},
mesh = {Adult ; Blindness/*physiopathology ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Occipital Lobe/*physiopathology ; Spatial Navigation/*physiology ; Young Adult ; },
abstract = {The aim of the present study was to investigate functional reorganization of the occipital cortex for a mental navigation task in blind people. Eight completely blind adults and eight sighted matched controls performed a mental navigation task, in which they mentally imagined to walk along familiar routes of their hometown during a multi-channel EEG measurement. A motor imagery task was used as control condition. Furthermore, electrophysiological activation patterns during a resting measurement with open and closed eyes were compared between blind and sighted participants. During the resting measurement with open eyes, no differences in EEG power were observed between groups, whereas sighted participants showed higher alpha (8-12Hz) activity at occipital sites compared to blind participants during an eyes-closed resting condition. During the mental navigation task, blind participants showed a stronger event-related desynchronization in the alpha band over the visual cortex compared to sighted controls indicating a stronger activation in this brain region in the blind. Furthermore, groups showed differences in functional brain connectivity between fronto-central and parietal-occipital brain networks during mental navigation indicating stronger visuo-spatial processing in sighted than in blind people during mental navigation. Differences in electrophysiological parameters between groups were specific for mental navigation since no group differences were observed during motor imagery. These results indicate that in the absence of vision the visual cortex takes over other functions such as spatial navigation.},
}
@article {pmid25057968,
year = {2014},
author = {Liao, JY and Kirsch, RF},
title = {Characterizing and predicting submovements during human three-dimensional arm reaches.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e103387},
pmid = {25057968},
issn = {1932-6203},
support = {T32 GM007250/GM/NIGMS NIH HHS/United States ; UL1 TR000439/TR/NCATS NIH HHS/United States ; N01HD10018/HD/NICHD NIH HHS/United States ; T32GM007250/GM/NIGMS NIH HHS/United States ; },
mesh = {Algorithms ; Arm/anatomy &amp; histology/*physiology ; Computer Simulation ; Female ; Humans ; Imaging, Three-Dimensional/*methods ; Male ; Movement ; *Neural Networks, Computer ; Predictive Value of Tests ; },
abstract = {We have demonstrated that 3D target-oriented human arm reaches can be represented as linear combinations of discrete submovements, where the submovements are a set of minimum-jerk basis functions for the reaches. We have also demonstrated the ability of deterministic feed-forward Artificial Neural Networks (ANNs) to predict the parameters of the submovements. ANNs were trained using kinematic data obtained experimentally from five human participants making target-directed movements that were decomposed offline into minimum-jerk submovements using an optimization algorithm. Under cross-validation, the ANNs were able to accurately predict the parameters (initiation-time, amplitude, and duration) of the individual submovements. We also demonstrated that the ANNs can together form a closed-loop model of human reaching capable of predicting 3D trajectories with VAF >95.9% and RMSE ≤4.32 cm relative to the actual recorded trajectories. This closed-loop model is a step towards a practical arm trajectory generator based on submovements, and should be useful for the development of future arm prosthetic devices that are controlled by brain computer interfaces or other user interfaces.},
}
@article {pmid25055835,
year = {2015},
author = {Park, CH and Chang, WH and Lee, M and Kwon, GH and Kim, L and Kim, ST and Kim, YH},
title = {Predicting the performance of motor imagery in stroke patients: multivariate pattern analysis of functional MRI data.},
journal = {Neurorehabilitation and neural repair},
volume = {29},
number = {3},
pages = {247-254},
doi = {10.1177/1545968314543308},
pmid = {25055835},
issn = {1552-6844},
mesh = {Brain Mapping/*methods ; Female ; Fingers ; Functional Laterality/physiology ; Humans ; Image Processing, Computer-Assisted/methods ; Imagination/*physiology ; Magnetic Resonance Imaging/*methods ; Male ; Middle Aged ; Motor Activity/*physiology ; Motor Cortex/*physiopathology ; Multivariate Analysis ; Stroke/*physiopathology ; },
abstract = {BACKGROUND: In a brain-computer interface for stroke rehabilitation, motor imagery is a preferred means for providing a gateway to an effector action or behavior. However, stroke patients often exhibit failure to comply with motor imagery, and therefore their motor imagery performance is highly variable.<br><br>OBJECTIVE: We sought to identify motor cortical areas responsible for motor imagery performance in stroke patients, specifically by using a multivariate pattern analysis of functional magnetic resonance imaging data.<br><br>METHODS: We adopted an imaginary finger tapping task in which motor imagery performance could be monitored for 12 chronic stroke patients with subcortical infarcts and 12 age- and sex-matched healthy controls. We identified the typical activation pattern elicited for motor imagery in healthy controls, as computed over the voxels within each searchlight in the motor cortex. Then we measured the similarity of each individual's activation pattern to the typical activation pattern.<br><br>RESULTS: In terms of activation levels, the stroke patients showed no activation in the ipsilesional primary motor cortex (M1); in terms of activation patterns, they showed lower similarity to the typical activation pattern in the area than the healthy controls. Furthermore, the stroke patients were better able to perform motor imagery if their activation patterns in the bilateral supplementary motor areas and ipsilesional M1 were close to the typical activation pattern.<br><br>CONCLUSIONS: These findings suggest functional roles of the motor cortical areas for compliance with motor imagery in stroke, which can be applied to the implementation of motor imagery-based brain-computer interface for stroke rehabilitation.},
}
@article {pmid25053224,
year = {2015},
author = {Ono, T and Tomita, Y and Inose, M and Ota, T and Kimura, A and Liu, M and Ushiba, J},
title = {Multimodal sensory feedback associated with motor attempts alters BOLD responses to paralyzed hand movement in chronic stroke patients.},
journal = {Brain topography},
volume = {28},
number = {2},
pages = {340-351},
doi = {10.1007/s10548-014-0382-6},
pmid = {25053224},
issn = {1573-6792},
mesh = {Aged ; Brain/*physiopathology ; Cerebrovascular Circulation/physiology ; Chronic Disease ; Electroencephalography ; Feedback, Sensory/*physiology ; Fingers/*physiopathology ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Motor Activity/*physiology ; Oxygen/blood ; Paralysis/etiology/*physiopathology ; Stroke/complications/*physiopathology ; },
abstract = {Electroencephalogram-based brain-computer interfaces (BCI) have been used as a potential tool for training volitional regulation of corticomuscular drive in patients who have severe hemiplegia due to stroke. However, it is unclear whether ERD observed while attempting motor execution can be regarded as a neural marker that represents M1 excitability in survivors of severe stroke. Therefore we investigated the association between ERD and the blood-oxygen-level-dependent (BOLD) fMRI signal during attempted movement of a paralyzed finger in stroke patients. Nine chronic stroke patients received BCI training for finger extension movement 1 h daily for a duration of 1 month. The sensorimotor rhythm was recorded from the sensorimotor area of the damaged hemisphere, and ongoing amplitude variations were monitored using a BCI system. Either a visual alert or the action of a motor-driven orthosis was triggered in response to ERD of the sensorimotor rhythm while patients attempted extension movements of the paralyzed fingers. Inter-subject covariance between ERD and the BOLD response in the sensorimotor areas was calculated. After BCI training, an increased ERD over the damaged hemisphere was confirmed in all participants while they attempted extension of the affected finger and this increase was associated with a BOLD response in primary sensorimotor area. Whole-brain MRI revealed that the primary sensorimotor area and supplementary motor area were activated in the damaged hemisphere after 1 month of BCI training. ERD reflects the BOLD responses of the primary motor areas in either hemisphere while patients who have severe chronic hemiplegia due to a stroke attempt an extension movement of the paralyzed fingers. One month of BCI can alter motor-related brain area activation. Combining BCI with other methods to facilitate such changes may help to implement BCI for motor rehabilitation after stroke.},
}
@article {pmid25050324,
year = {2014},
author = {Yeom, HG and Hong, W and Kang, DY and Chung, CK and Kim, JS and Kim, SP},
title = {A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {176857},
pmid = {25050324},
issn = {2314-6141},
mesh = {Adult ; *Algorithms ; Female ; Hand/*physiology ; Humans ; *Image Processing, Computer-Assisted ; *Magnetoencephalography ; Male ; *Models, Theoretical ; Movement ; Photic Stimulation ; Young Adult ; },
abstract = {Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.},
}
@article {pmid25045733,
year = {2014},
author = {Xu, B and Fu, Y and Shi, G and Yin, X and Wang, Z and Li, H and Jiang, C},
title = {Enhanced performance by time-frequency-phase feature for EEG-based BCI systems.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {420561},
pmid = {25045733},
issn = {1537-744X},
mesh = {Algorithms ; *Artificial Intelligence ; *Electroencephalography ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using "MIFS" feature selection criterion, scaled feature using "MIFS" feature selection criterion, and scaled feature using "mRMR" feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the "mRMR" feature selection criterion can get higher classification rate than the "MIFS" feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.},
}
@article {pmid25036334,
year = {2014},
author = {Li, X and Chen, X and Yan, Y and Wei, W and Wang, ZJ},
title = {Classification of EEG signals using a multiple kernel learning support vector machine.},
journal = {Sensors (Basel, Switzerland)},
volume = {14},
number = {7},
pages = {12784-12802},
pmid = {25036334},
issn = {1424-8220},
mesh = {Adult ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Case-Control Studies ; Cognition/physiology ; Electroencephalography/*instrumentation ; Female ; Humans ; Learning ; Male ; Middle Aged ; Signal Processing, Computer-Assisted/*instrumentation ; Software ; *Support Vector Machine ; Young Adult ; },
abstract = {In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.},
}
@article {pmid25036216,
year = {2014},
author = {Hwang, HJ and Lim, JH and Kim, DW and Im, CH},
title = {Evaluation of various mental task combinations for near-infrared spectroscopy-based brain-computer interfaces.},
journal = {Journal of biomedical optics},
volume = {19},
number = {7},
pages = {77005},
doi = {10.1117/1.JBO.19.7.077005},
pmid = {25036216},
issn = {1560-2281},
mesh = {Adult ; Brain/blood supply ; Brain Mapping/instrumentation/*methods ; *Brain-Computer Interfaces ; Female ; Hemodynamics/physiology ; Humans ; Male ; Psychomotor Performance/*physiology ; Spectroscopy, Near-Infrared/instrumentation/*methods ; Young Adult ; },
abstract = {A number of recent studies have demonstrated that near-infrared spectroscopy (NIRS) is a promisingneuroimaging modality for brain-computer interfaces (BCIs). So far, most NIRS-based BCI studies have focusedon enhancing the accuracy of the classification of different mental tasks. In the present study, we evaluated theperformances of a variety of mental task combinations in order to determine the mental task pairs that are bestsuited for customized NIRS-based BCIs. To this end, we recorded event-related hemodynamic responses whileseven participants performed eight different mental tasks. Classification accuracies were then estimated for allpossible pairs of the eight mental tasks (8C2 = 28). Based on this analysis, mental task combinations with relatively high classification accuracies frequently included the following three mental tasks: “mental multiplication,” “mental rotation,” and “right-hand motor imagery.” Specifically, mental task combinations consisting of two of these three mental tasks showed the highest mean classification accuracies. It is expected that our results will be a useful reference to reduce the time needed for preliminary tests when discovering individual-specific mental task combinations.},
}
@article {pmid25035965,
year = {2014},
author = {Paraskevopoulou, SE and Wu, D and Eftekhar, A and Constandinou, TG},
title = {Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.},
journal = {Journal of neuroscience methods},
volume = {235},
number = {},
pages = {145-156},
doi = {10.1016/j.jneumeth.2014.07.004},
pmid = {25035965},
issn = {1872-678X},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Basal Ganglia/physiology ; Cerebral Cortex/physiology ; Cluster Analysis ; Computer Simulation ; Computers ; Databases, Factual ; Female ; Humans ; Macaca ; Models, Neurological ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; },
abstract = {This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.},
}
@article {pmid25035941,
year = {2014},
author = {Ladkau, N and Schmid, A and Bühler, B},
title = {The microbial cell-functional unit for energy dependent multistep biocatalysis.},
journal = {Current opinion in biotechnology},
volume = {30},
number = {},
pages = {178-189},
doi = {10.1016/j.copbio.2014.06.003},
pmid = {25035941},
issn = {1879-0429},
mesh = {Bacteria/genetics/metabolism ; Biocatalysis ; *Energy Metabolism ; Escherichia coli/genetics/metabolism ; *Genetic Engineering ; Recombinant Proteins/genetics/*metabolism ; },
abstract = {Whole-cell biocatalysis has emerged as an important tool for the synthesis of value-added fine and bulk chemicals as well as pharmaceuticals. Especially, the rapid development of recombinant DNA technologies resulted in a shift from the exploitation of natural enzymes and pathways to the design of recombinant cell factories comprising heterologous enzymes and/or synthetic, orthologous pathways for the synthesis of industrially relevant compounds. This review discusses recent developments and concepts applied in the frame of multistep whole-cell biocatalysis along with representative examples.},
}
@article {pmid25030984,
year = {2014},
author = {Getzin, S and Wiegand, T and Hubbell, SP},
title = {Stochastically driven adult-recruit associations of tree species on Barro Colorado Island.},
journal = {Proceedings. Biological sciences},
volume = {281},
number = {1790},
pages = {},
pmid = {25030984},
issn = {1471-2954},
mesh = {Ecosystem ; Panama ; Population Density ; Seed Dispersal/*physiology ; Seedlings/*physiology ; Trees/*physiology ; Tropical Climate ; },
abstract = {The spatial placement of recruits around adult conspecifics represents the accumulated outcome of several pattern-forming processes and mechanisms such as primary and secondary seed dispersal, habitat associations or Janzen-Connell effects. Studying the adult-recruit relationship should therefore allow the derivation of specific hypotheses on the processes shaping population and community dynamics. We analysed adult-recruit associations for 65 tree species taken from six censuses of the 50 ha neotropical forest plot on Barro Colorado Island (BCI), Panama. We used point pattern analysis to test, at a range of neighbourhood scales, for spatial independence between recruits and adults, to assess the strength and type of departure from independence, and its relationship with species properties. Positive associations expected to prevail due to dispersal limitation occurred only in 16% of all cases; instead a majority of species showed spatial independence (≈73%). Independence described the placement of recruits around conspecific adults in good approximation, although we found weak and noisy signals of species properties related to seed dispersal. We hypothesize that spatial mechanisms with strong stochastic components such as animal seed dispersal overpower the pattern-forming effects of dispersal limitation, density dependence and habitat association, or that some of the pattern-forming processes cancel out each other.},
}
@article {pmid25029330,
year = {2015},
author = {Morris, S and Hirata, M and Sugata, H and Goto, T and Matsushita, K and Yanagisawa, T and Saitoh, Y and Kishima, H and Yoshimine, T},
title = {Patient-specific cortical electrodes for sulcal and gyral implantation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {4},
pages = {1034-1041},
doi = {10.1109/TBME.2014.2329812},
pmid = {25029330},
issn = {1558-2531},
mesh = {Amyotrophic Lateral Sclerosis/surgery ; Animals ; Biocompatible Materials ; *Brain-Computer Interfaces ; Cell Line ; Cerebral Cortex/*physiology/*surgery ; Cricetinae ; Electrocorticography/*instrumentation ; Electrodes ; Equipment Design ; Female ; Humans ; Male ; Materials Testing ; Middle Aged ; *Models, Biological ; Precision Medicine/*instrumentation ; },
abstract = {PURPOSE: Noninvasive localization of certain brain functions may be mapped on a millimetre level. However, the interelectrode spacing of common clinical brain surface electrodes still remains around 10 mm. Here, we present details on development of electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interface (BMI) technologies.<br><br>METHODS: We used platinum-plate-electrodes of 1-mm diameter to produce sheet electrodes after the creation of individualized molds using a 3-D printer and a press system that sandwiched the electrodes between personalized silicone sheets.<br><br>RESULTS: We created arrays to fit the surface curvature of the brain and inside the central sulcus, with interelectrode distances of 2.5 mm (a density of 16 times previous standard types). Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant, and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high-gamma-band information in an amyotrophic lateral sclerosis patient.<br><br>CONCLUSION: We developed cortical sheet electrodes with a high-spatial resolution, tailor-made to match an individual's brain.<br><br>SIGNIFICANCE: This sheet electrode may contribute to the higher performance of BMI's.},
}
@article {pmid25028989,
year = {2014},
author = {Godlove, JM and Whaite, EO and Batista, AP},
title = {Comparing temporal aspects of visual, tactile, and microstimulation feedback for motor control.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046025},
pmid = {25028989},
issn = {1741-2552},
support = {R01NS065065/NS/NINDS NIH HHS/United States ; R01HD071686/HD/NICHD NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; T90DA022761/DA/NIDA NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; T90 DA022761/DA/NIDA NIH HHS/United States ; },
mesh = {Adult ; Animals ; Electric Stimulation ; Feedback, Physiological/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Macaca mulatta ; Male ; Movement/*physiology ; Physical Stimulation ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Touch/*physiology ; Young Adult ; },
abstract = {OBJECTIVES: Current brain-computer interfaces (BCIs) rely on visual feedback, requiring sustained visual attention to use the device. Improvements to BCIs may stem from the development of an effective way to provide quick feedback independent of vision. Tactile stimuli, either delivered on the skin surface, or directly to the brain via microstimulation in somatosensory cortex, could serve that purpose. We examined the effectiveness of vibrotactile stimuli and microstimulation as a means of non-visual feedback by using a fundamental element of feedback: the ability to react to a stimulus while already in motion.<br><br>APPROACH: Human and monkey subjects performed a center-out reach task which was, on occasion, interrupted with a stimulus cue that instructed a change in reach target.<br><br>MAIN RESULTS: Subjects generally responded faster to tactile cues than to visual cues. However, when we delivered cues via microstimuation in a monkey, its response was slower on average than for both tactile and visual cues.<br><br>SIGNIFICANCE: Tactile and microstimulation feedback can be used to rapidly adjust movements mid-flight. The relatively slow speed of microstimulation is surprising and warrants further investigation. Overall, these results highlight the importance of considering temporal aspects of feedback when designing alternative forms of feedback for BCIs.},
}
@article {pmid25024292,
year = {2014},
author = {Baek, DH and Lee, J and Byeon, HJ and Choi, H and Young Kim, I and Lee, KM and Jungho Pak, J and Pyo Jang, D and Lee, SH},
title = {A thin film polyimide mesh microelectrode for chronic epidural electrocorticography recording with enhanced contactability.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046023},
doi = {10.1088/1741-2560/11/4/046023},
pmid = {25024292},
issn = {1741-2552},
mesh = {Animals ; Brain/physiology ; Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Epidural Space/*physiology ; *Imides ; Macaca mulatta ; Male ; *Microelectrodes ; Models, Anatomic ; *Neural Prostheses ; *Polymers ; Psychomotor Performance/physiology ; Saccades/physiology ; Surgical Mesh ; },
abstract = {OBJECTIVE: Epidural electrocorticography (ECoG) activity may be more reliable and stable than single-unit-activity or local field potential. Invasive brain computer interface (BCI) devices are limited by mechanical mismatching and cellular reactive responses due to differences in the elastic modulus and the motion of stiff electrodes. We propose a mesh-shaped electrode to enhance the contactability between surface of dura and electrode.<br><br>APPROACH: We designed a polyimide (PI) electrode with a mesh pattern for more conformal contact with a curved surface. We compared the contact capability of mesh PI electrodes with conventionally used sheet PI electrode. The electrical properties of the mesh PI electrode were evaluated for four weeks. We recorded the epidural ECoG (eECoG) activity on the surface of rhesus monkey brains while they performed a saccadic task for four months.<br><br>MAIN RESULTS: The mesh PI electrode showed good contact with the agarose brain surface, as evaluated by visual inspection and signal measurement. It was about 87% accurate in predicting the direction of saccade eye movement.<br><br>SIGNIFICANCE: Our results indicate that the mesh PI electrode was flexible and good contact on the curved surface and can record eECoG activity maintaining close contact to dura, which was proved by in vivo and in vitro test.},
}
@article {pmid25018706,
year = {2014},
author = {Horschig, JM and Zumer, JM and Bahramisharif, A},
title = {Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {119},
pmid = {25018706},
issn = {1662-5137},
abstract = {Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.},
}
@article {pmid25015699,
year = {2014},
author = {Gharabaghi, A and Naros, G and Walter, A and Grimm, F and Schuermeyer, M and Roth, A and Bogdan, M and Rosenstiel, W and Birbaumer, N},
title = {From assistance towards restoration with epidural brain-computer interfacing.},
journal = {Restorative neurology and neuroscience},
volume = {32},
number = {4},
pages = {517-525},
doi = {10.3233/RNN-140387},
pmid = {25015699},
issn = {1878-3627},
mesh = {Aged ; Brain/pathology/*physiology ; Electroencephalography ; Epidural Space ; Humans ; Magnetic Resonance Imaging ; Male ; Movement/physiology ; Movement Disorders/etiology/pathology/*rehabilitation ; Neurofeedback/*methods ; Prostheses and Implants ; Stroke/complications ; *User-Computer Interface ; },
abstract = {PURPOSE: Today's implanted brain-computer interfaces make direct contact with the brain or even penetrate the tissue, bearing additional risks with regard to safety and stability. What is more, these approaches aim to control prosthetic devices as assistive tools and do not yet strive to become rehabilitative tools for restoring lost motor function.<br><br>METHODS: We introduced a less invasive, implantable interface by applying epidural electrocorticography in a chronic stroke survivor with a persistent motor deficit. He was trained to modulate his natural motor-related oscillatory brain activity by receiving online feedback.<br><br>RESULTS: Epidural recordings of field potentials in the beta-frequency band projecting onto the anatomical hand knob proved most successful in discriminating between the attempt to move the paralyzed hand and to rest. These spectral features allowed for fast and reliable control of the feedback device in an online closed-loop paradigm. Only seven training sessions were required to significantly improve maximum wrist extension.<br><br>CONCLUSIONS: For patients suffering from severe motor deficits, epidural implants may decode and train the brain activity generated during attempts to move with high spatial resolution, thus facilitating specific and high-intensity practice even in the absence of motor control. This would thus transform them from pure assistive devices to restorative tools in the context of reinforcement learning and neurorehabilitation.},
}
@article {pmid25014972,
year = {2015},
author = {Horki, P and Klobassa, DS and Pokorny, C and Müller-Putz, GR},
title = {Evaluation of healthy EEG responses for spelling through listener-assisted scanning.},
journal = {IEEE journal of biomedical and health informatics},
volume = {19},
number = {1},
pages = {29-36},
doi = {10.1109/JBHI.2014.2328494},
pmid = {25014972},
issn = {2168-2208},
mesh = {Auditory Perception/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Female ; Humans ; Imagination/physiology ; Male ; Movement/physiology ; Reference Values ; Task Performance and Analysis ; *Word Processing ; Young Adult ; },
abstract = {We investigated whether listener-assisted scanning, an alternative communication method for persons with severe motor and visual impairments but preserved cognitive skills, could be used for spelling with EEG. To that end spoken letters were presented sequentially, and the participants made selections by performing motor execution/imagery or a cognitive task. The motor task was a brisk dorsiflexion of both feet, and the cognitive task was related to working memory and perception of human voice. The motor imagery task yielded the most promising results with respect to letter selection accuracy, albeit with a large variation in individual performance. The cognitive task yielded significant (p = 0.05) albeit moderate results. Closer inspection of grand average ERPs for the cognitive task revealed task-related modulation of a late negative component, which is novel in the auditory BCI literature. Guidelines for further development are presented.},
}
@article {pmid25014960,
year = {2015},
author = {Mestais, CS and Charvet, G and Sauter-Starace, F and Foerster, M and Ratel, D and Benabid, AL},
title = {WIMAGINE: wireless 64-channel ECoG recording implant for long term clinical applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {1},
pages = {10-21},
doi = {10.1109/TNSRE.2014.2333541},
pmid = {25014960},
issn = {1558-0210},
mesh = {Animals ; Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Electronics ; Equipment Design ; Humans ; Macaca fascicularis ; Macaca mulatta ; Materials Testing ; Neural Prostheses ; Signal Processing, Computer-Assisted ; Software ; Wireless Technology/*instrumentation ; },
abstract = {A wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE has been designed for various clinical applications. The device is aimed at interfacing a cortical electrode array to an external computer for neural recording and control applications. This active implantable medical device is able to record neural activity on 64 electrodes with selectable gain and sampling frequency, with less than 1 μV(RMS) input referred noise in the [0.5 Hz - 300 Hz] band. It is powered remotely through an inductive link at 13.56 MHz which provides up to 100 mW. The digitized data is transmitted wirelessly to a custom designed base station connected to a PC. The hermetic housing and the antennae have been designed and optimized to ease the surgery. The design of this implant takes into account all the requirements of a clinical trial, in particular safety, reliability, and compliance with the regulations applicable to class III AIMD. The main features of this WIMAGINE implantable device and its architecture are presented, as well as its functional performances and long-term biocompatibility results.},
}
@article {pmid25014055,
year = {2014},
author = {Faller, J and Scherer, R and Costa, U and Opisso, E and Medina, J and Müller-Putz, GR},
title = {A co-adaptive brain-computer interface for end users with severe motor impairment.},
journal = {PloS one},
volume = {9},
number = {7},
pages = {e101168},
pmid = {25014055},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Aged ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Motor Activity/physiology ; Movement Disorders/*physiopathology ; Young Adult ; },
abstract = {Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired volunteers. The co-adaptive BCI supports a non-control state, which is an important step toward intuitive, self-paced control. A secondary aim was to have the same participants operate a specifically designed self-paced BCI training paradigm based on the auto-calibrated classifier. The co-adaptive BCI analyzed the electroencephalogram from three bipolar derivations (C3, Cz, and C4) online, while the 22 end users alternately performed right hand movement imagery (MI), left hand MI and relax with eyes open (non-control state). After less than five minutes, the BCI auto-calibrated and proceeded to provide visual feedback for the MI task that could be classified better against the non-control state. The BCI continued to regularly recalibrate. In every calibration step, the system performed trial-based outlier rejection and trained a linear discriminant analysis classifier based on one auto-selected logarithmic band-power feature. In 24 minutes of training, the co-adaptive BCI worked significantly (p = 0.01) better than chance for 18 of 22 end users. The self-paced BCI training paradigm worked significantly (p = 0.01) better than chance in 11 of 20 end users. The presented co-adaptive BCI complements existing approaches in that it supports a non-control state, requires very little setup time, requires no BCI expert and works online based on only two electrodes. The preliminary results from the self-paced BCI paradigm compare favorably to previous studies and the collected data will allow to further improve self-paced BCI systems for disabled users.},
}
@article {pmid25012465,
year = {2014},
author = {King, CE and Dave, KR and Wang, PT and Mizuta, M and Reinkensmeyer, DJ and Do, AH and Moromugi, S and Nenadic, Z},
title = {Performance assessment of a brain-computer interface driven hand orthosis.},
journal = {Annals of biomedical engineering},
volume = {42},
number = {10},
pages = {2095-2105},
doi = {10.1007/s10439-014-1066-9},
pmid = {25012465},
issn = {1573-9686},
mesh = {Adult ; *Brain-Computer Interfaces ; Calibration ; Electrodes ; Electroencephalography/instrumentation ; Female ; Hand/*physiology ; Humans ; Male ; *Orthotic Devices ; Young Adult ; },
abstract = {Stroke survivors are typically affected by hand motor impairment. Despite intensive rehabilitation and spontaneous recovery, improvements typically plateau a year after a stroke. Therefore, novel approaches capable of restoring or augmenting lost motor behaviors are needed. Brain-computer interfaces (BCIs) may offer one such approach by using neurophysiological activity underlying hand movements to control an upper extremity orthosis. To test the performance of such a system, we developed an electroencephalogram-based BCI controlled electrically actuated hand orthosis. Six able-bodied participants voluntarily grasped/relaxed one hand to elicit BCI-mediated closing/opening of the orthosis mounted on the opposite hand. Following a short training/calibration procedure, participants demonstrated real-time, online control of the orthosis by following computer cues. Their performances resulted in an average of 1.15 (standard deviation: 0.85) false alarms and 0.22 (0.36) omissions per minute. Analysis of signals from electrogoniometers mounted on both hands revealed an average correlation between voluntary and BCI-mediated movements of 0.58 (0.13), with all but one online performance being statistically significant. This suggests that a BCI driven hand orthosis is feasible, and therefore should be tested in stroke individuals with hand weakness. If proven viable, this technology may provide a novel approach to the neuro-rehabilitation of hand function after stroke.},
}
@article {pmid25011062,
year = {2014},
author = {Watanabe, H and Sakatani, T and Suzuki, T and Sato, MA and Nishimura, Y and Nambu, A and Kawato, M and Isa, T},
title = {Reconstruction of intracortical whisker-evoked local field potential from electrocorticogram using a model trained for spontaneous activity in the rat barrel cortex.},
journal = {Neuroscience research},
volume = {87},
number = {},
pages = {40-48},
doi = {10.1016/j.neures.2014.06.010},
pmid = {25011062},
issn = {1872-8111},
mesh = {Animals ; Electroencephalography/*methods ; *Evoked Potentials, Somatosensory ; Linear Models ; Male ; Models, Neurological ; Physical Stimulation ; Rats ; Rats, Wistar ; Somatosensory Cortex/*physiology ; Touch Perception/*physiology ; Vibrissae/physiology ; },
abstract = {Electrocorticogram (ECoG) has provided neural information from the cortical surfaces, is widely used in clinical applications, and expected to be useful for brain-machine interfaces. Recent studies have defined the relationship between neural activity in deep layers of the cerebral cortex and ECoG. However, it is still unclear whether this relationship is shared across different brain states. In this study, spontaneous activity and whisker-evoked responses in the barrel cortex of anesthetized rats were recorded with a 32-channel ECoG electrode array and 32-channel linear silicon probe electrodes, respectively. Spontaneous local field potentials (LFPs) at various depths could be reconstructed with high accuracy (R>0.9) by a linear weighted summation of spontaneous ECoG. Current source density analysis revealed that the reconstructed LFPs correctly represented laminar profiles of current sinks and sources as well as the raw LFP. Moreover, when we applied the spontaneous activity model to reconstruction of LFP from the whisker-related ECoG, high accuracy of reconstruction could be obtained (R>0.9). Our results suggest that the ECoG carried rich information about synaptic currents in the deep layers of the cortex, and the same reconstruction model can be applied to estimate both spontaneous activity and whisker-evoked responses.},
}
@article {pmid25009491,
year = {2014},
author = {Young, BM and Nigogosyan, Z and Nair, VA and Walton, LM and Song, J and Tyler, ME and Edwards, DF and Caldera, K and Sattin, JA and Williams, JC and Prabhakaran, V},
title = {Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {18},
pmid = {25009491},
issn = {1662-6443},
support = {RC1 MH090912/MH/NIMH NIH HHS/United States ; UL1 TR000427/TR/NCATS NIH HHS/United States ; TL1 TR000429/TR/NCATS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; K23 NS086852/NS/NINDS NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; },
abstract = {Therapies involving new technologies such as brain-computer interfaces (BCI) are being studied to determine their potential for interventional rehabilitation after acute events such as stroke produce lasting impairments. While studies have examined the use of BCI devices by individuals with disabilities, many such devices are intended to address a specific limitation and have been studied when this limitation or disability is present in isolation. Little is known about the therapeutic potential of these devices for individuals with multiple disabilities with an acquired impairment overlaid on a secondary long-standing disability. We describe a case in which a male patient with congenital deafness suffered a right pontine ischemic stroke, resulting in persistent weakness of his left hand and arm. This patient volunteer completed four baseline assessments beginning at 4 months after stroke onset and subsequently underwent 6 weeks of interventional rehabilitation therapy using a closed-loop neurofeedback BCI device with visual, functional electrical stimulation, and tongue stimulation feedback modalities. Additional assessments were conducted at the midpoint of therapy, upon completion of therapy, and 1 month after completing all BCI therapy. Anatomical and functional MRI scans were obtained at each assessment, along with behavioral measures including the Stroke Impact Scale (SIS) and the Action Research Arm Test (ARAT). Clinically significant improvements in behavioral measures were noted over the course of BCI therapy, with more than 10 point gains in both the ARAT scores and scores for the SIS hand function domain. Neuroimaging during finger tapping of the impaired hand also showed changes in brain activation patterns associated with BCI therapy. This case study demonstrates the potential for individuals who have preexisting disability or possible atypical brain organization to learn to use a BCI system that may confer some rehabilitative benefit.},
}
@article {pmid25007993,
year = {2014},
author = {Oelke, M and Rademakers, KL and van Koeveringe, GA},
title = {Detrusor contraction power parameters (BCI and W max) rise with increasing bladder outlet obstruction grade in men with lower urinary tract symptoms: results from a urodynamic database analysis.},
journal = {World journal of urology},
volume = {32},
number = {5},
pages = {1177-1183},
pmid = {25007993},
issn = {1433-8726},
mesh = {Aged ; Databases, Factual ; Humans ; Lower Urinary Tract Symptoms/complications/*physiopathology ; Male ; Middle Aged ; *Muscle Contraction ; Urinary Bladder/*physiopathology ; Urinary Bladder Neck Obstruction/complications/*physiopathology ; *Urodynamics ; },
abstract = {PURPOSE: To investigate to what extent detrusor work during voiding is influenced by bladder outlet obstruction (BOO) in adult men with lower urinary tract symptoms (LUTS).<br><br>MATERIALS AND METHODS: We reviewed data of patients with LUTS suggestive of benign prostatic hyperplasia who received computer-urodynamic investigations as part of their baseline clinical assessment. BOO was defined by the Sch&#xe4;fer classification and detrusor work during voiding was quantified by calculation of the bladder contractility index (BCI) and maximum Watt factor (W max) obtained by pressure-flow analysis.<br><br>RESULTS: A total of 786 men with medians of 64 years, IPSS 16 and prostate volume of 35 ml, were included in the study. A total of 462 patients (58.8 %) had BOO (Sch&#xe4;fer 2-6). Both detrusor contraction power parameters continuously increased with rising BOO grade. Median BCI increased from 73.3 in Sch&#xe4;fer 0 to 188.0 in Sch&#xe4;fer 6, whereas W max increased from 9.6 to 23.4 W/m(2) (p < 0.001). Results of BCI and W max correlated well (p < 0.001). With increasing BOO grade, there was a significant decrease of voiding efficiency (p < 0.001).<br><br>CONCLUSIONS: In adult male LUTS patients, detrusor contraction power parameters-BCI and W max-continuously increase with rising BOO grade. According to our results, it is impossible to determine a single threshold value for detrusor contraction power to diagnose detrusor underactivity in a group of LUTS patients with different BOO grades. The study is limited to men with non-neurogenic LUTS. Future studies should evaluate exact threshold values for BCI and W max in BOO subgroups to adequately define detrusor underactivity and investigate men with other bladder conditions.},
}
@article {pmid25002460,
year = {2014},
author = {Theim, TJ and Shirk, RY and Givnish, TJ},
title = {Spatial genetic structure in four understory Psychotria species (Rubiaceae) and implications for tropical forest diversity.},
journal = {American journal of botany},
volume = {101},
number = {7},
pages = {1189-1199},
doi = {10.3732/ajb.1300460},
pmid = {25002460},
issn = {1537-2197},
abstract = {• Premise of the study: Tropical forests are the most species-rich terrestrial communities on Earth, and understory trees and shrubs comprise a large fraction of their plant species diversity, especially at high rainfalls. The mechanisms responsible for generating such high levels of diversity remain unknown. One hypothesis is that fleshy-fruited understory species should have limited seed dispersal due to the sedentary nature of their avian dispersers, resulting in restricted gene flow, population differentiation at small spatial scales, and ultimately, high rates of allopatric speciation.• Methods: We sampled four species of the hyperdiverse tropical shrub genus Psychotria (Rubiaceae) on Barro Colorado Island (BCI) and two nearby sites in Panama. We genotyped each species with AFLPs, assessed genetic differentiation among populations, and determined patterns of fine-scale spatial genetic structure in the BCI population. Measures of spatial autocorrelation and population density were used to estimate the dispersal distance parameter σ.• Key results: Regionally, ΦPT values ranged from 0.13 to 0.28, reflecting local population differentiation and suggesting that Lake Gatun/Rio Chagres has posed a relatively strong barrier to gene flow. Fine-scale spatial genetic structure on BCI was stronger than in most canopy trees, and estimated distances of gene flow were unusually low for endozoochorous tropical woody plants, with dispersal distance σ = 9-113 m.• Conclusions: These results demonstrate comparatively limited gene flow in bird-dispersed understory species, supporting a hypothesized mechanism for generating high levels of plant species diversity in tropical rain forests, in one of the largest genera of flowering plants on Earth.},
}
@article {pmid24997761,
year = {2014},
author = {Gulati, T and Ramanathan, DS and Wong, CC and Ganguly, K},
title = {Reactivation of emergent task-related ensembles during slow-wave sleep after neuroprosthetic learning.},
journal = {Nature neuroscience},
volume = {17},
number = {8},
pages = {1107-1113},
pmid = {24997761},
issn = {1546-1726},
support = {R25 MH060482/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Learning/*physiology ; Male ; Microelectrodes ; Motor Cortex/cytology/*physiology/surgery ; Motor Skills/*physiology ; Neural Prostheses/standards ; Neurons/cytology/*physiology ; Patch-Clamp Techniques/instrumentation/methods ; Rats ; Rats, Long-Evans ; Sleep/*physiology ; Task Performance and Analysis ; },
abstract = {Brain-machine interfaces can allow neural control over assistive devices. They also provide an important platform for studying neural plasticity. Recent studies have suggested that optimal engagement of learning is essential for robust neuroprosthetic control. However, little is known about the neural processes that may consolidate a neuroprosthetic skill. On the basis of the growing body of evidence linking slow-wave activity (SWA) during sleep to consolidation, we examined whether there is 'offline' processing after neuroprosthetic learning. Using a rodent model, we found that, after successful learning, task-related units specifically experienced increased locking and coherency to SWA during sleep. Moreover, spike-spike coherence among these units was substantially enhanced. These changes were not present with poor skill acquisition or after control awake periods, demonstrating the specificity of our observations to learning. Notably, the time spent in SWA predicted the performance gains. Thus, SWA appears to be involved in offline processing after neuroprosthetic learning.},
}
@article {pmid24997343,
year = {2015},
author = {Wang, M and Daly, I and Allison, BZ and Jin, J and Zhang, Y and Chen, L and Wang, X},
title = {A new hybrid BCI paradigm based on P300 and SSVEP.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {16-25},
doi = {10.1016/j.jneumeth.2014.06.003},
pmid = {24997343},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Analysis of Variance ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Fourier Analysis ; Humans ; Male ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain-computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored.<br><br>NEW METHOD: The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength.<br><br>RESULT: The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm.<br><br>A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification.<br><br>CONCLUSIONS: The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm.},
}
@article {pmid24997342,
year = {2014},
author = {Putze, F and Schultz, T},
title = {Adaptive cognitive technical systems.},
journal = {Journal of neuroscience methods},
volume = {234},
number = {},
pages = {108-115},
doi = {10.1016/j.jneumeth.2014.06.029},
pmid = {24997342},
issn = {1872-678X},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; Databases, Bibliographic/statistics &amp; numerical data ; Electroencephalography ; Humans ; *Man-Machine Systems ; *Workload ; },
abstract = {Adaptive cognitive technical systems are capable of sensing the internal state of its user and of adapting its behavior appropriately to those measurements to improve the usability of the system. One important example of such user state is the user's mental workload level. This paper gives an introduction to the topic of workload recognition and adaptation. It reviews the literature on recognition of workload from physiological signals and on how those user state estimates are employed to improve human-machine interaction.},
}
@article {pmid24995815,
year = {2014},
author = {Korostenskaja, M and Chen, PC and Salinas, CM and Westerveld, M and Brunner, P and Schalk, G and Cook, JC and Baumgartner, J and Lee, KH},
title = {Real-time functional mapping: potential tool for improving language outcome in pediatric epilepsy surgery.},
journal = {Journal of neurosurgery. Pediatrics},
volume = {14},
number = {3},
pages = {287-295},
pmid = {24995815},
issn = {1933-0715},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Anticonvulsants/therapeutic use ; Brain Mapping/*methods ; Cerebral Cortex/*physiopathology ; *Electric Stimulation ; *Electroencephalography ; Epilepsies, Partial/drug therapy/physiopathology/*surgery ; Female ; Humans ; *Language ; Neuropsychological Tests ; Sensitivity and Specificity ; *Speech ; },
abstract = {Accurate language localization expands surgical treatment options for epilepsy patients and reduces the risk of postsurgery language deficits. Electrical cortical stimulation mapping (ESM) is considered to be the clinical gold standard for language localization. While ESM affords clinically valuable results, it can be poorly tolerated by children, requires active participation and compliance, carries a risk of inducing seizures, is highly time consuming, and is labor intensive. Given these limitations, alternative and/or complementary functional localization methods such as analysis of electrocorticographic (ECoG) activity in high gamma frequency band in real time are needed to precisely identify eloquent cortex in children. In this case report, the authors examined 1) the use of real-time functional mapping (RTFM) for language localization in a high gamma frequency band derived from ECoG to guide surgery in an epileptic pediatric patient and 2) the relationship of RTFM mapping results to postsurgical language outcomes. The authors found that RTFM demonstrated relatively high sensitivity (75%) and high specificity (90%) when compared with ESM in a "next-neighbor" analysis. While overlapping with ESM in the superior temporal region, RTFM showed a few other areas of activation related to expressive language function, areas that were eventually resected during the surgery. The authors speculate that this resection may be associated with observed postsurgical expressive language deficits. With additional validation in more subjects, this finding would suggest that surgical planning and associated assessment of the risk/benefit ratio would benefit from information provided by RTFM mapping.},
}
@article {pmid24995476,
year = {2014},
author = {Foster, JD and Nuyujukian, P and Freifeld, O and Gao, H and Walker, R and I Ryu, S and H Meng, T and Murmann, B and J Black, M and Shenoy, KV},
title = {A freely-moving monkey treadmill model.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046020},
doi = {10.1088/1741-2560/11/4/046020},
pmid = {24995476},
issn = {1741-2552},
support = {DP1-OD006409/OD/NIH HHS/United States ; R01-NS064318/NS/NINDS NIH HHS/United States ; R01-NS066311/NS/NINDS NIH HHS/United States ; R01-NS0666311/NS/NINDS NIH HHS/United States ; T-R01NS076460/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal/physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electrophysiological Phenomena/physiology ; Macaca mulatta ; Microelectrodes ; Models, Neurological ; Motor Cortex/physiology ; Movement/*physiology ; Rotation ; Visual Prosthesis ; Walking/physiology ; },
abstract = {OBJECTIVE: Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement.<br><br>APPROACH: We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill.<br><br>MAIN RESULTS: Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions.<br><br>SIGNIFICANCE: Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.},
}
@article {pmid24994968,
year = {2014},
author = {Touryan, J and Apker, G and Lance, BJ and Kerick, SE and Ries, AJ and McDowell, K},
title = {Estimating endogenous changes in task performance from EEG.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {155},
pmid = {24994968},
issn = {1662-4548},
abstract = {Brain wave activity is known to correlate with decrements in behavioral performance as individuals enter states of fatigue, boredom, or low alertness.Many BCI technologies are adversely affected by these changes in user state, limiting their application and constraining their use to relatively short temporal epochs where behavioral performance is likely to be stable. Incorporating a passive BCI that detects when the user is performing poorly at a primary task, and adapts accordingly may prove to increase overall user performance. Here, we explore the potential for extending an established method to generate continuous estimates of behavioral performance from ongoing neural activity; evaluating the extended method by applying it to the original task domain, simulated driving; and generalizing the method by applying it to a BCI-relevant perceptual discrimination task. Specifically, we used EEG log power spectra and sequential forward floating selection (SFFS) to estimate endogenous changes in behavior in both a simulated driving task and a perceptual discrimination task. For the driving task the average correlation coefficient between the actual and estimated lane deviation was 0.37 ± 0.22 (μ ± σ). For the perceptual discrimination task we generated estimates of accuracy, reaction time, and button press duration for each participant. The correlation coefficients between the actual and estimated behavior were similar for these three metrics (accuracy = 0.25 ± 0.37, reaction time = 0.33 ± 0.23, button press duration = 0.36 ± 0.30). These findings illustrate the potential for modeling time-on-task decrements in performance from concurrent measures of neural activity.},
}
@article {pmid24987350,
year = {2014},
author = {Tidoni, E and Gergondet, P and Kheddar, A and Aglioti, SM},
title = {Audio-visual feedback improves the BCI performance in the navigational control of a humanoid robot.},
journal = {Frontiers in neurorobotics},
volume = {8},
number = {},
pages = {20},
pmid = {24987350},
issn = {1662-5218},
abstract = {Advancement in brain computer interfaces (BCI) technology allows people to actively interact in the world through surrogates. Controlling real humanoid robots using BCI as intuitively as we control our body represents a challenge for current research in robotics and neuroscience. In order to successfully interact with the environment the brain integrates multiple sensory cues to form a coherent representation of the world. Cognitive neuroscience studies demonstrate that multisensory integration may imply a gain with respect to a single modality and ultimately improve the overall sensorimotor performance. For example, reactivity to simultaneous visual and auditory stimuli may be higher than to the sum of the same stimuli delivered in isolation or in temporal sequence. Yet, knowledge about whether audio-visual integration may improve the control of a surrogate is meager. To explore this issue, we provided human footstep sounds as audio feedback to BCI users while controlling a humanoid robot. Participants were asked to steer their robot surrogate and perform a pick-and-place task through BCI-SSVEPs. We found that audio-visual synchrony between footsteps sound and actual humanoid's walk reduces the time required for steering the robot. Thus, auditory feedback congruent with the humanoid actions may improve motor decisions of the BCI's user and help in the feeling of control over it. Our results shed light on the possibility to increase robot's control through the combination of multisensory feedback to a BCI user.},
}
@article {pmid24982944,
year = {2014},
author = {Gonzalez, A and Nambu, I and Hokari, H and Wada, Y},
title = {EEG channel selection using particle swarm optimization for the classification of auditory event-related potentials.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {350270},
pmid = {24982944},
issn = {1537-744X},
mesh = {*Algorithms ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; },
abstract = {Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy.},
}
@article {pmid24979726,
year = {2014},
author = {Bamdadian, A and Guan, C and Ang, KK and Xu, J},
title = {The predictive role of pre-cue EEG rhythms on MI-based BCI classification performance.},
journal = {Journal of neuroscience methods},
volume = {235},
number = {},
pages = {138-144},
doi = {10.1016/j.jneumeth.2014.06.011},
pmid = {24979726},
issn = {1872-678X},
mesh = {Alpha Rhythm ; Brain/*physiology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography/*methods ; Feasibility Studies ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; Signal Processing, Computer-Assisted ; Theta Rhythm ; Visual Perception/physiology ; },
abstract = {BACKGROUND: One of the main issues in motor imagery-based (MI-based) brain-computer interface (BCI) systems is a large variation in the classification performance of BCI users. However, the exact reason of low performance of some users is still under investigation. Having some prior knowledge about the performance of users may be helpful in understanding possible reasons of performance variations.<br><br>NEW METHOD: In this study a novel coefficient from pre-cue EEG rhythms is proposed. The proposed coefficient is computed from the spectral power of pre-cue EEG data for specific rhythms over different regions of the brain. The feasibility of predicting the classification performance of the MI-based BCI users from the proposed coefficient is investigated.<br><br>RESULTS: Group level analysis on N=17 healthy subjects showed that there is a significant correlation r=0.53 (p=0.02) between the proposed coefficient and the cross-validation accuracies of the subjects in performing MI. The results showed that subjects with higher cross-validation accuracies have yielded significantly higher values of the proposed coefficient and vice versa.<br><br>In comparison with other previous predictors, this coefficient captures spatial information from the brain in addition to spectral information.<br><br>CONCLUSION: The result of using the proposed coefficient suggests that having higher frontal theta and lower posterior alpha prior to performing MI may enhance the BCI classification performance. This finding reveals prospect of designing a novel experiment to prepare the user towards improved motor imagery performance.},
}
@article {pmid24979704,
year = {2014},
author = {Esghaei, M and Daliri, MR},
title = {Decoding of visual attention from LFP signals of macaque MT.},
journal = {PloS one},
volume = {9},
number = {6},
pages = {e100381},
pmid = {24979704},
issn = {1932-6203},
mesh = {*Algorithms ; Animals ; Attention/*physiology ; Brain-Computer Interfaces ; Craniotomy ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Macaca mulatta/*physiology ; Male ; Microelectrodes ; Photic Stimulation ; Visual Cortex/anatomy &amp; histology/*physiology ; },
abstract = {The local field potential (LFP) has recently been widely used in brain computer interfaces (BCI). Here we used power of LFP recorded from area MT of a macaque monkey to decode where the animal covertly attended. Support vector machines (SVM) were used to learn the pattern of power at different frequencies for attention to two possible positions. We found that LFP power at both low (<9 Hz) and high (31-120 Hz) frequencies contains sufficient information to decode the focus of attention. Highest decoding performance was found for gamma frequencies (31-120 Hz) and reached 82%. In contrast low frequencies (<9 Hz) could help the classifier reach a higher decoding performance with a smaller amount of training data. Consequently, we suggest that low frequency LFP can provide fast but coarse information regarding the focus of attention, while higher frequencies of the LFP deliver more accurate but less timely information about the focus of attention.},
}
@article {pmid24977218,
year = {2014},
author = {Zhang, X},
title = {On some fuzzy filters in pseudo-BCI algebras.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {718972},
pmid = {24977218},
issn = {1537-744X},
mesh = {*Algorithms ; *Fuzzy Logic ; Mathematics/*methods ; *Numerical Analysis, Computer-Assisted ; },
abstract = {Some new properties of fuzzy associative filters (also known as fuzzy associative pseudo-filters), fuzzy p-filter (also known as fuzzy pseudo-p-filters), and fuzzy a-filter (also known as fuzzy pseudo-a-filters) in pseudo-BCI algebras are investigated. By these properties, the following important results are proved: (1) a fuzzy filter (also known as fuzzy pseudo-filters) of a pseudo-BCI algebra is a fuzzy associative filter if and only if it is a fuzzy a-filter; (2) a filter (also known as pseudo-filter) of a pseudo-BCI algebra is associative if and only if it is an a-filter (also call it pseudo-a filter); (3) a fuzzy filter of a pseudo-BCI algebra is fuzzy a-filter if and only if it is both a fuzzy p-filter and a fuzzy q-filter.},
}
@article {pmid24975290,
year = {2015},
author = {Bai, L and Yu, T and Li, Y},
title = {A brain computer interface-based explorer.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {2-7},
doi = {10.1016/j.jneumeth.2014.06.015},
pmid = {24975290},
issn = {1872-678X},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Computers ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Imagery, Psychotherapy/instrumentation/*methods ; Online Systems ; *User-Computer Interface ; },
abstract = {In recent years, various applications of brain computer interfaces (BCIs) have been studied. In this paper, we present a hybrid BCI combining P300 and motor imagery to operate an explorer. Our system is mainly composed of a BCI mouse, a BCI speller and an explorer. Through this system, the user can access his computer and manipulate (open, close, copy, paste, and delete) files such as documents, pictures, music, movies and so on. The system has been tested with five subjects, and the experimental results show that the explorer can be successfully operated according to subjects' intentions.},
}
@article {pmid24971199,
year = {2014},
author = {Khare, M and Singh, A and Zamboni, P},
title = {Prospect of brain-machine interface in motor disabilities: the future support for multiple sclerosis patient to improve quality of life.},
journal = {Annals of medical and health sciences research},
volume = {4},
number = {3},
pages = {305-312},
pmid = {24971199},
issn = {2141-9248},
abstract = {Multiple sclerosis (MS) is an autoimmune neurological disorder, which has impacted health related quality of life (HRQoL) more intensively than any other neurological disorder. The approaches to improve the health standard in MS patient are still a subject of primary importance in medical practice and seek a lot of experimental exploration. The present review briefly explains the anomaly in neuron anatomy and dysfunction in signal transmission arising in the context with the chronic cerebrospinal venous insufficiency (CCSVI), a recent hypothesis related with MS pathophysiology. Subsequently, it insights brain-machine interface (BMI) as an alternative approach to improve the HRQoL of MS subjects. Information sources were searched from peer-reviewed data bases (Medline, BioMed Central, PubMed) and grey-literature databases for data published in 2000 or later. We also did systemic search in edited books, articles in seminar papers, reports extracted from newspapers and scientific magazines, articles accessed from internet; mostly using PubMed, Google search engine and Wikipedia. Out of approximately 178, 240 research articles obtained using selected keywords, those articles were included in the present study which addresses the latest definitions of HRQol and latest scientific and ethical developments in the research of MS and BMI. The article presented a brief survey of CCSVI mediated MS and BMI-approach as a treatment to serve the patients suffering from disabilities as a result of MS, followed by successful precedence of BMI approach. Apart from these, the major findings of selected research articles including the development of parameters to define HRQoL, types and development of BMIs and its role in interconnecting brain with actuators, along with CCSVI being a possible cause of MS have formed the foundations to conclude the findings of the present review article. We propose a perspective BMI approach and promises it holds for future research to improve HRQoL in MS patients. In addition, we propose that brain-computer interfaces will be the core of new treatment modalities in the future for MS disabilities.},
}
@article {pmid24971046,
year = {2014},
author = {Mühl, C and Jeunet, C and Lotte, F},
title = {EEG-based workload estimation across affective contexts.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {114},
pmid = {24971046},
issn = {1662-4548},
abstract = {Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive tool to adapt the human-computer interaction to the user state. To create systems that reliably work in the complexity of the real world, a robustness against contextual changes (e.g., mood), has to be achieved. To study the resilience of state-of-the-art EEG-based workload classification against stress we devise a novel experimental protocol, in which we manipulated the affective context (stressful/non-stressful) while the participant solved a task with two workload levels. We recorded self-ratings, behavior, and physiology from 24 participants to validate the protocol. We test the capability of different, subject-specific workload classifiers using either frequency-domain, time-domain, or both feature varieties to generalize across contexts. We show that the classifiers are able to transfer between affective contexts, though performance suffers independent of the used feature domain. However, cross-context training is a simple and powerful remedy allowing the extraction of features in all studied feature varieties that are more resilient to task-unrelated variations in signal characteristics. Especially for frequency-domain features, across-context training is leading to a performance comparable to within-context training and testing. We discuss the significance of the result for neurophysiology-based workload detection in particular and for the construction of reliable passive brain-computer interfaces in general.},
}
@article {pmid24970835,
year = {2014},
author = {Feeney, A and Nilsson, E and Skinner, MK},
title = {Cytokine (IL16) and tyrphostin actions on ovarian primordial follicle development.},
journal = {Reproduction (Cambridge, England)},
volume = {148},
number = {3},
pages = {321-331},
pmid = {24970835},
issn = {1741-7899},
support = {R01 ES012974/ES/NIEHS NIH HHS/United States ; R01 HD048898/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; CD4 Antigens/metabolism ; Female ; Interleukin-16/*metabolism ; Ovarian Follicle/growth &amp; development/*metabolism ; Ovary/growth &amp; development/*metabolism ; Rats ; Rats, Sprague-Dawley ; Tyrphostins/*metabolism ; },
abstract = {An ovarian follicle is composed of an oocyte and surrounding theca and granulosa cells. Oocytes are stored in an arrested state within primordial follicles until they are signaled to re-initiate development by undergoing primordial-to-primary follicle transition. Previous gene bionetwork analyses of primordial follicle development identified a number of critical cytokine signaling pathways and genes potentially involved in the process. In the current study, candidate regulatory genes and pathways from the gene network analyses were tested for their effects on the formation of primordial follicles (follicle assembly) and on primordial follicle transition using whole ovary organ culture experiments. Observations indicate that the tyrphostin inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one increased follicle assembly significantly, supporting a role for the MAPK signaling pathway in follicle assembly. The cytokine interleukin 16 (IL16) promotes primordial-to-primary follicle transition as compared with the controls, where as Delta-like ligand 4 (DLL4) and WNT-3A treatments have no effect. Immunohistochemical experiments demonstrated the localization of both the cytokine IL16 and its receptor CD4 in the granulosa cells surrounding each oocyte within the ovarian follicle. The tyrphostin LDN193189 (LDN) is an inhibitor of the bone morphogenic protein receptor 1 within the TGFB signaling pathway and was found to promote the primordial-to-primary follicle transition. Observations support the importance of cytokines (i.e., IL16) and cytokine signaling pathways in the regulation of early follicle development. Insights into regulatory factors affecting early primordial follicle development are provided that may associate with ovarian disease and translate to improved therapy in the future.},
}
@article {pmid24967916,
year = {2014},
author = {Shin, J and Jeong, J},
title = {Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain-computer interface.},
journal = {Journal of biomedical optics},
volume = {19},
number = {6},
pages = {067009},
doi = {10.1117/1.JBO.19.6.067009},
pmid = {24967916},
issn = {1560-2281},
mesh = {Adult ; Algorithms ; Arm/physiology ; Bayes Theorem ; *Brain-Computer Interfaces ; Computer Graphics ; Computer Simulation ; Female ; *Hemodynamics ; Hemoglobins/analysis ; Humans ; Knee/physiology ; Male ; Movement/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {We improved the performance of a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface based on relatively short task duration and multiclass classification. A custom-built eight-channel fNIRS system was used over the motor cortex areas in both hemispheres to measure the hemodynamic responses evoked by four different motor tasks (overt execution of arm lifting and knee extension for both sides) instead of finger tapping. The hemodynamic responses were classified using the naive Bayes classifier. Among the mean, max, slope, variance, and median of the signal amplitude and the time lag of the signal, several signal features are chosen to obtain highest classification accuracy. Ten runs of threefold cross-validation were conducted, which yielded classification accuracies of 87.1%±2.4% to 95.5%±2.4%, 77.5%±1.9% to 92.4%±3.2%, and 73.8%±3.5% to 91.5%±1.4% for the binary, ternary, and quaternary classifications, respectively. Eight seconds of task duration for obtaining sufficient quaternary classification accuracy was suggested. The bit transfer rate per minute (BPM) based on the quaternary classification accuracy was investigated. A BPM can be achieved from 2.81 to 5.40 bits/min.},
}
@article {pmid24967357,
year = {2014},
author = {Brouillet, S and Dufour, A and Prot, F and Feige, JJ and Equy, V and Alfaidy, N and Gillois, P and Hoffmann, P},
title = {Influence of the umbilical cord insertion site on the optimal individual birth weight achievement.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {341251},
pmid = {24967357},
issn = {2314-6141},
mesh = {Birth Weight/*physiology ; Cohort Studies ; Female ; Gestational Age ; Humans ; Infant, Newborn ; Pregnancy/*physiology ; Umbilical Cord/*physiology ; },
abstract = {STUDY QUESTION: To determine whether the umbilical cord insertion site of singleton pregnancies could be linked to the newborn birth weight at term and its individual growth potential achievement.<br><br>MATERIAL AND METHODS: A cohort study including 528 records of term neonates was performed. Each neonate was assessed for growth adjusted for gestational age according to the infant's growth potential using the AUDIPOG module. We considered two categories of umbilical cord insertions: central and peripheral. Intrauterine growth restriction was defined as birth weight below the 10th percentile. Statistical analysis was performed using Chi-square, Student's t test, Wilcoxon test, ANOVA, and logistic regression.<br><br>RESULTS: We observed a total of 343 centrally inserted cords versus 185 peripheral cords. There were twice as many smokers in the mothers of the peripheral category compared to the centrally inserted ones. More importantly, we demonstrated that only 17/343 (5.0%) of infants with central cord insertion were growth restricted, compared to 37/185 (20.0%) of the infants born with a peripheral insertion. Neonates with centrally inserted cord were significantly heavier.<br><br>CONCLUSION: The umbilical cord insertion site of singleton pregnancies is associated with the newborn's birth weight at term and its individual growth potential achievement.},
}
@article {pmid24967316,
year = {2014},
author = {Al-Fahoum, AS and Al-Fraihat, AA},
title = {Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains.},
journal = {ISRN neuroscience},
volume = {2014},
number = {},
pages = {730218},
pmid = {24967316},
issn = {2314-4661},
abstract = {Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.},
}
@article {pmid24966816,
year = {2014},
author = {Di Pino, G and Maravita, A and Zollo, L and Guglielmelli, E and Di Lazzaro, V},
title = {Augmentation-related brain plasticity.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {109},
pmid = {24966816},
issn = {1662-5137},
abstract = {Today, the anthropomorphism of the tools and the development of neural interfaces require reconsidering the concept of human-tools interaction in the framework of human augmentation. This review analyses the plastic process that the brain undergoes when it comes into contact with augmenting artificial sensors and effectors and, on the other hand, the changes that the use of external augmenting devices produces in the brain. Hitherto, few studies investigated the neural correlates of augmentation, but clues on it can be borrowed from logically-related paradigms: sensorimotor training, cognitive enhancement, cross-modal plasticity, sensorimotor functional substitution, use and embodiment of tools. Augmentation modifies function and structure of a number of areas, i.e., primary sensory cortices shape their receptive fields to become sensitive to novel inputs. Motor areas adapt the neuroprosthesis representation firing-rate to refine kinematics. As for normal motor outputs, the learning process recruits motor and premotor cortices and the acquisition of proficiency decreases attentional recruitment, focuses the activity on sensorimotor areas and increases the basal ganglia drive on the cortex. Augmentation deeply relies on the frontoparietal network. In particular, premotor cortex is involved in learning the control of an external effector and owns the tool motor representation, while the intraparietal sulcus extracts its visual features. In these areas, multisensory integration neurons enlarge their receptive fields to embody supernumerary limbs. For operating an anthropomorphic neuroprosthesis, the mirror system is required to understand the meaning of the action, the cerebellum for the formation of its internal model and the insula for its interoception. In conclusion, anthropomorphic sensorized devices can provide the critical sensory afferences to evolve the exploitation of tools through their embodiment, reshaping the body representation and the sense of the self.},
}
@article {pmid24963838,
year = {2014},
author = {Horton, C and Srinivasan, R and D'Zmura, M},
title = {Envelope responses in single-trial EEG indicate attended speaker in a 'cocktail party'.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046015},
pmid = {24963838},
issn = {1741-2552},
support = {R01 MH068004/MH/NIMH NIH HHS/United States ; 2R01-MH68004/MH/NIMH NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Alpha Rhythm ; Attention/*physiology ; Auditory Cortex/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Functional Laterality/physiology ; Humans ; Individuality ; Male ; Reproducibility of Results ; Sensory Gating/*physiology ; Speech Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Recent studies have shown that auditory cortex better encodes the envelope of attended speech than that of unattended speech during multi-speaker ('cocktail party') situations. We investigated whether these differences were sufficiently robust within single-trial electroencephalographic (EEG) data to accurately determine where subjects attended. Additionally, we compared this measure to other established EEG markers of attention.<br><br>APPROACH: High-resolution EEG was recorded while subjects engaged in a two-speaker 'cocktail party' task. Cortical responses to speech envelopes were extracted by cross-correlating the envelopes with each EEG channel. We also measured steady-state responses (elicited via high-frequency amplitude modulation of the speech) and alpha-band power, both of which have been sensitive to attention in previous studies. Using linear classifiers, we then examined how well each of these features could be used to predict the subjects' side of attention at various epoch lengths.<br><br>MAIN RESULTS: We found that the attended speaker could be determined reliably from the envelope responses calculated from short periods of EEG, with accuracy improving as a function of sample length. Furthermore, envelope responses were far better indicators of attention than changes in either alpha power or steady-state responses.<br><br>SIGNIFICANCE: These results suggest that envelope-related signals recorded in EEG data can be used to form robust auditory BCI's that do not require artificial manipulation (e.g., amplitude modulation) of stimuli to function.},
}
@article {pmid24963747,
year = {2014},
author = {Hoon Lee, J and Min Lee, S and Jin Byeon, H and Sook Hong, J and Suk Park, K and Lee, SH},
title = {CNT/PDMS-based canal-typed ear electrodes for inconspicuous EEG recording.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046014},
doi = {10.1088/1741-2560/11/4/046014},
pmid = {24963747},
issn = {1741-2552},
mesh = {Adult ; Alpha Rhythm/physiology ; Biocompatible Materials ; Brain-Computer Interfaces ; *Dimethylpolysiloxanes ; *Ear Canal ; *Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Auditory/physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Materials Testing ; *Nanotubes, Carbon ; Prosthesis Design ; Young Adult ; },
abstract = {OBJECTIVE: Current electroencephalogram (EEG) monitoring systems typically require cumbersome electrodes that must be pasted on a scalp, making a private recording of an EEG in a public place difficult. We have developed a small, user friendly, biocompatible electrode with a good appearance for inconspicuous EEG monitoring.<br><br>APPROACH: We fabricated carbon nanotube polydimethylsiloxane (CNT/PDMS)-based canal-type ear electrodes (CEE) for EEG recording. These electrodes have an additional function, triggering sound stimulation like earphones and recording EEG simultaneously for auditory brain-computer interface (BCI). The electrode performance was evaluated by a standard EEG measurement paradigm, including the detection of alpha rhythms and measurements of N100 auditory evoked potential (AEP), steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR). Furthermore, the bio- and skin-compatibility of CNT/PDMS were tested.<br><br>MAIN RESULTS: All feasibility studies were successfully recorded with the fabricated electrodes, and the biocompatibility of CNT/PDMS was also proved.<br><br>SIGNIFICANCE: These electrodes could be used to monitor EEG clinically, in ubiquitous health care and in brain-computer interfaces.},
}
@article {pmid24961765,
year = {2014},
author = {Cecotti, H and Rivet, B},
title = {Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials.},
journal = {Brain sciences},
volume = {4},
number = {2},
pages = {335-355},
pmid = {24961765},
issn = {2076-3425},
abstract = {New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject's will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal. Similarly as combining trials over time improves performance, combining trials across subjects can significantly improve performance compared with when only a single user is involved. Yet, cooperative BCIs may only be used in particular settings, and new paradigms must be proposed to efficiently use this approach. The possible benefits of using several subjects are addressed, and compared with current single-subject BCI paradigms. To show the advantages of a cooperative BCI, we evaluate the performance of combining decisions across subjects with data from an event-related potentials (ERP) based experiment where each subject observed the same sequence of visual stimuli. Furthermore, we show that it is possible to achieve a mean AUC superior to 0.95 with 10 subjects and 3 electrodes on each subject, or with 4 subjects and 6 electrodes on each subject. Several emerging challenges and possible applications are proposed to highlight how cooperative BCIs could be efficiently used with current technologies and leverage BCI applications.},
}
@article {pmid24961700,
year = {2014},
author = {Sanchez, G and Daunizeau, J and Maby, E and Bertrand, O and Bompas, A and Mattout, J},
title = {Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing.},
journal = {Brain sciences},
volume = {4},
number = {1},
pages = {49-72},
pmid = {24961700},
issn = {2076-3425},
abstract = {Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself.},
}
@article {pmid24955357,
year = {2014},
author = {Alfaidy, N and Hoffmann, P and Boufettal, H and Samouh, N and Aboussaouira, T and Benharouga, M and Feige, JJ and Brouillet, S},
title = {The multiple roles of EG-VEGF/PROK1 in normal and pathological placental angiogenesis.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {451906},
pmid = {24955357},
issn = {2314-6141},
mesh = {Female ; Gastrointestinal Hormones/*genetics/metabolism ; Humans ; Neovascularization, Pathologic/*genetics ; Neovascularization, Physiologic/*genetics ; Placenta/metabolism/pathology ; Pregnancy ; Vascular Endothelial Growth Factor, Endocrine-Gland-Derived/*genetics/metabolism ; },
abstract = {Placentation is associated with several steps of vascular adaptations throughout pregnancy. These vascular changes occur both on the maternal and fetal sides, consisting of maternal uterine spiral arteries remodeling and placental vasculogenesis and angiogenesis, respectively. Placental angiogenesis is a pivotal process for efficient fetomaternal exchanges and placental development. This process is finely controlled throughout pregnancy, and it involves ubiquitous and pregnancy-specific angiogenic factors. In the last decade, endocrine gland derived vascular endothelial growth factor (EG-VEGF), also called prokineticin 1 (PROK1), has emerged as specific placental angiogenic factor that controls many aspects of normal and pathological placental angiogenesis such as recurrent pregnancy loss (RPL), gestational trophoblastic diseases (GTD), fetal growth restriction (FGR), and preeclampsia (PE). This review recapitulates EG-VEGF mediated-angiogenesis within the placenta and at the fetomaternal interface and proposes that its deregulation might contribute to the pathogenesis of several placental diseases including FGR and PE. More importantly this paper argues for EG-VEGF clinical relevance as a potential biomarker of the onset of pregnancy pathologies and discusses its potential usefulness for future therapeutic directions.},
}
@article {pmid24954713,
year = {2015},
author = {Opris, I and Fuqua, JL and Gerhardt, GA and Hampson, RE and Deadwyler, SA},
title = {Prefrontal cortical recordings with biomorphic MEAs reveal complex columnar-laminar microcircuits for BCI/BMI implementation.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {104-113},
pmid = {24954713},
issn = {1872-678X},
support = {R01 DA023573/DA/NIDA NIH HHS/United States ; DA06634/DA/NIDA NIH HHS/United States ; R01 DA026487/DA/NIDA NIH HHS/United States ; DA023573/DA/NIDA NIH HHS/United States ; P50 DA006634/DA/NIDA NIH HHS/United States ; R01 CA155293/CA/NCI NIH HHS/United States ; DA026487/DA/NIDA NIH HHS/United States ; },
mesh = {Action Potentials/drug effects/*physiology ; Analysis of Variance ; Animals ; *Brain-Computer Interfaces ; Discrimination, Psychological/*physiology ; Glutamic Acid/pharmacology ; Macaca mulatta ; *Microelectrodes ; Nerve Net/*physiology ; Neurons/drug effects/*physiology ; Photic Stimulation ; Prefrontal Cortex/*cytology ; },
abstract = {The mammalian prefrontal cortex known as the seat of high brain functions uses a six layer distribution of minicolumnar neurons to coordinate the integration of sensory information and the selection of relevant signals for goal driven behavior. To reveal the complex functionality of these columnar microcircuits we employed simultaneous recordings with several configurations of biomorphic microelectrode arrays (MEAs) within cortical layers in adjacent minicolumns, in four nohuman primates (NHPs) performing a delayed match-to-sample (DMS) visual discrimination task. We examined: (1) the functionality of inter-laminar, and inter-columnar interactions between pairs of cells in the same or different minicolumns by use of normalized cross-correlation histograms (CCH), (2) the modulation of glutamate concentration in layer 2/3, and (3) the potential interactions within these microcircuits. The results demonstrate that neurons in both infra-granular and supra-granular layers interact through inter-laminar loops, as well as through intra-laminar to produce behavioral response signals. These results provide new insights into the manner in which prefrontal cortical microcircuitry integrates sensory stimuli used to provide behaviorally relevant signals that may be implemented in brain computer/machine interfaces (BCI/BMIs) during performance of the task.},
}
@article {pmid24954539,
year = {2014},
author = {Breuer, L and Dammers, J and Roberts, TP and Shah, NJ},
title = {Ocular and cardiac artifact rejection for real-time analysis in MEG.},
journal = {Journal of neuroscience methods},
volume = {233},
number = {},
pages = {105-114},
doi = {10.1016/j.jneumeth.2014.06.016},
pmid = {24954539},
issn = {1872-678X},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Algorithms ; *Artifacts ; Auditory Perception/physiology ; Brain/physiology ; Child ; Electrocardiography/methods ; Electrooculography/methods ; Eye Movements/*physiology ; Heart/*physiology ; Humans ; Magnetoencephalography/instrumentation/*methods ; Middle Aged ; Pattern Recognition, Automated/methods ; *Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {BACKGROUND: Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process.<br><br>NEW METHOD: The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity.<br><br>RESULTS: OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M).<br><br>The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods.<br><br>CONCLUSION: Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.},
}
@article {pmid24952749,
year = {2014},
author = {Lanzani, G},
title = {Materials for bioelectronics: organic electronics meets biology.},
journal = {Nature materials},
volume = {13},
number = {8},
pages = {775-776},
pmid = {24952749},
issn = {1476-4660},
mesh = {Biocompatible Materials/chemistry ; Biology/*methods ; Brain-Computer Interfaces ; *Electronics ; Humans ; Nanomedicine/trends ; Nanowires ; Nerve Net ; Neurons/*physiology ; Oxygen/chemistry ; Polymers/chemistry ; Semiconductors ; },
}
@article {pmid24951791,
year = {2014},
author = {Sudarsan, S and Dethlefsen, S and Blank, LM and Siemann-Herzberg, M and Schmid, A},
title = {The functional structure of central carbon metabolism in Pseudomonas putida KT2440.},
journal = {Applied and environmental microbiology},
volume = {80},
number = {17},
pages = {5292-5303},
pmid = {24951791},
issn = {1098-5336},
mesh = {Benzoates/metabolism ; Carbon/*metabolism ; Citric Acid Cycle/*genetics ; Fructose/metabolism ; Glucose/metabolism ; Glycolysis/*genetics ; Metabolic Flux Analysis ; Pentose Phosphate Pathway/*genetics ; Pseudomonas putida/*genetics/*metabolism ; },
abstract = {What defines central carbon metabolism? The classic textbook scheme of central metabolism includes the Embden-Meyerhof-Parnas (EMP) pathway of glycolysis, the pentose phosphate pathway, and the citric acid cycle. The prevalence of this definition of central metabolism is, however, equivocal without experimental validation. We address this issue using a general experimental approach that combines the monitoring of transcriptional and metabolic flux changes between steady states on alternative carbon sources. This approach is investigated by using the model bacterium Pseudomonas putida with glucose, fructose, and benzoate as carbon sources. The catabolic reactions involved in the initial uptake and metabolism of these substrates are expected to show a correlated change in gene expressions and metabolic fluxes. However, there was no correlation for the reactions linking the 12 biomass precursor molecules, indicating a regulation mechanism other than mRNA synthesis for central metabolism. This result substantiates evidence for a (re)definition of central carbon metabolism including all reactions that are bound to tight regulation and transcriptional invariance. Contrary to expectations, the canonical Entner-Doudoroff and EMP pathways sensu stricto are not a part of central carbon metabolism in P. putida, as they are not regulated differently from the aromatic degradation pathway. The regulatory analyses presented here provide leads on a qualitative basis to address the use of alternative carbon sources by deregulation and overexpression at the transcriptional level, while rate improvements in central carbon metabolism require careful adjustment of metabolite concentrations, as regulation resides to a large extent in posttranslational and/or metabolic regulation.},
}
@article {pmid24951704,
year = {2015},
author = {Yousefi, S and Wein, A and Kowalski, KC and Richardson, AG and Srinivasan, L},
title = {Smoothness as a failure mode of Bayesian mixture models in brain-machine interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {23},
number = {1},
pages = {128-137},
doi = {10.1109/TNSRE.2014.2329698},
pmid = {24951704},
issn = {1558-0210},
mesh = {Algorithms ; Arm/physiology ; Bayes Theorem ; *Brain-Computer Interfaces ; Computer Simulation ; Equipment Design ; Humans ; Motor Cortex/physiology ; Movement/physiology ; Neural Prostheses ; *Signal Processing, Computer-Assisted ; },
abstract = {Various recursive Bayesian filters based on reach state equations (RSE) have been proposed to convert neural signals into reaching movements in brain-machine interfaces. When the target is known, RSE produce exquisitely smooth trajectories relative to the random walk prior in the basic Kalman filter. More realistically, the target is unknown, and gaze analysis or other side information is expected to provide a discrete set of potential targets. In anticipation of this scenario, various groups have implemented RSE-based mixture (hybrid) models, which define a discrete random variable to represent target identity. While principled, this approach sacrifices the smoothness of RSE with known targets. This paper combines empirical spiking data from primary motor cortex and mathematical analysis to explain this loss in performance. We focus on angular velocity as a meaningful and convenient measure of smoothness. Our results demonstrate that angular velocity in the trajectory is approximately proportional to change in target probability. The constant of proportionality equals the difference in heading between parallel filters from the two most probable targets, suggesting a smoothness benefit to more narrowly spaced targets. Simulation confirms that measures to smooth the data likelihood also improve the smoothness of hybrid trajectories, including increased ensemble size and uniformity in preferred directions. We speculate that closed-loop training or neuronal subset selection could be used to shape the user's tuning curves towards this end.},
}
@article {pmid24950192,
year = {2014},
author = {Ge, S and Wang, R and Yu, D},
title = {Classification of four-class motor imagery employing single-channel electroencephalography.},
journal = {PloS one},
volume = {9},
number = {6},
pages = {e98019},
pmid = {24950192},
issn = {1932-6203},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Fourier Analysis ; Hand/physiology ; Humans ; Models, Theoretical ; Motor Cortex/*physiology ; Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.},
}
@article {pmid24949462,
year = {2014},
author = {Xu, K and Wang, Y and Wang, F and Liao, Y and Zhang, Q and Li, H and Zheng, X},
title = {Neural decoding using a parallel sequential Monte Carlo method on point processes with ensemble effect.},
journal = {BioMed research international},
volume = {2014},
number = {},
pages = {685492},
pmid = {24949462},
issn = {2314-6141},
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; Humans ; *Models, Neurological ; Models, Theoretical ; *Monte Carlo Method ; *Psychomotor Performance ; },
abstract = {Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.},
}
@article {pmid24945767,
year = {2014},
author = {Rose, N},
title = {The Human Brain Project: social and ethical challenges.},
journal = {Neuron},
volume = {82},
number = {6},
pages = {1212-1215},
doi = {10.1016/j.neuron.2014.06.001},
pmid = {24945767},
issn = {1097-4199},
mesh = {Animals ; Biomedical Research/*ethics/trends ; Brain/anatomy &amp; histology/pathology/*physiology ; Brain Diseases/diagnosis/genetics/therapy ; Brain-Computer Interfaces/*ethics/trends ; Confidentiality/ethics/trends ; Humans ; Psychophysiology/ethics/trends ; *Social Responsibility ; },
abstract = {Focusing on the Human Brain Project, I discuss some social and ethical challenges raised by such programs of research: the possibility of a unified knowledge of "the brain," balancing privacy and the public good, dilemmas of "dual use," brain-computer interfaces, and "responsible research and innovation" in governance of emerging technologies.},
}
@article {pmid24936420,
year = {2014},
author = {Noirhomme, Q and Lesenfants, D and Gomez, F and Soddu, A and Schrouff, J and Garraux, G and Luxen, A and Phillips, C and Laureys, S},
title = {Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions.},
journal = {NeuroImage. Clinical},
volume = {4},
number = {},
pages = {687-694},
pmid = {24936420},
issn = {2213-1582},
mesh = {Adult ; Aged ; *Bias ; Brain/*pathology ; Brain Injuries/*diagnosis ; Brain-Computer Interfaces ; *Computer Simulation ; Humans ; Middle Aged ; *Models, Statistical ; Young Adult ; },
abstract = {Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain-computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.},
}
@article {pmid24936414,
year = {2014},
author = {Bentley, P and Ganesalingam, J and Carlton Jones, AL and Mahady, K and Epton, S and Rinne, P and Sharma, P and Halse, O and Mehta, A and Rueckert, D},
title = {Prediction of stroke thrombolysis outcome using CT brain machine learning.},
journal = {NeuroImage. Clinical},
volume = {4},
number = {},
pages = {635-640},
pmid = {24936414},
issn = {2213-1582},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {Area Under Curve ; *Artificial Intelligence ; Brain/*diagnostic imaging/drug effects ; Female ; Fibrinolytic Agents/*therapeutic use ; Humans ; Male ; *Outcome Assessment, Health Care ; Predictive Value of Tests ; Retrospective Studies ; Severity of Illness Index ; Stroke/*drug therapy ; Tissue Plasminogen Activator/*therapeutic use ; *Tomography, X-Ray Computed ; },
abstract = {A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis - a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626-0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1-5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.},
}
@article {pmid24933017,
year = {2014},
author = {Yu, K and Ai-Nashash, H and Thakor, N and Li, X},
title = {The analytic bilinear discrimination of single-trial EEG signals in rapid image triage.},
journal = {PloS one},
volume = {9},
number = {6},
pages = {e100097},
pmid = {24933017},
issn = {1932-6203},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping ; Brain-Computer Interfaces ; Computer Simulation ; *Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Triage/*methods ; },
abstract = {The linear discriminant analysis (LDA) method is a classical and commonly utilized technique for dimensionality reduction and classification in brain-computer interface (BCI) systems. Being a first-order discriminator, LDA is usually preceded by the feature extraction of electroencephalogram (EEG) signals, as multi-density EEG data are of second order. In this study, an analytic bilinear classification method which inherits and extends LDA is proposed. This method considers 2-dimentional EEG signals as the feature input and performs classification using the optimized complex-valued bilinear projections. Without being transformed into frequency domain, the complex-valued bilinear projections essentially spatially and temporally modulate the phases and magnitudes of slow event-related potentials (ERPs) elicited by distinct brain states in the sense that they become more separable. The results show that the proposed method has demonstrated its discriminating capability in the development of a rapid image triage (RIT) system, which is a challenging variant of BCIs due to the fast presentation speed and consequently overlapping of ERPs.},
}
@article {pmid24927041,
year = {2014},
author = {Kang, H and Choi, S},
title = {Bayesian common spatial patterns for multi-subject EEG classification.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {57},
number = {},
pages = {39-50},
doi = {10.1016/j.neunet.2014.05.012},
pmid = {24927041},
issn = {1879-2782},
mesh = {*Algorithms ; Bayes Theorem ; *Brain Waves ; Data Interpretation, Statistical ; Electroencephalography/classification/*methods ; Humans ; *Models, Neurological ; },
abstract = {Multi-subject electroencephalography (EEG) classification involves algorithm development for automatically categorizing brain waves measured from multiple subjects who undergo the same mental task. Common spatial patterns (CSP) or its probabilistic counterpart, PCSP, is a popular discriminative feature extraction method for EEG classification. Models in CSP or PCSP are trained on a subject-by-subject basis so that inter-subject information is neglected. In the case of multi-subject EEG classification, however, it is desirable to capture inter-subject relatedness in learning a model. In this paper we present a nonparametric Bayesian model for a multi-subject extension of PCSP where subject relatedness is captured by assuming that spatial patterns across subjects share a latent subspace. Spatial patterns and the shared latent subspace are jointly learned by variational inference. We use an infinite latent feature model to automatically infer the dimension of the shared latent subspace, placing Indian Buffet process (IBP) priors on our model. Numerical experiments on BCI competition III IVa and IV 2a dataset demonstrate the high performance of our method, compared to PCSP and existing Bayesian multi-task CSP models.},
}
@article {pmid24922501,
year = {2014},
author = {Dangi, S and Gowda, S and Moorman, HG and Orsborn, AL and So, K and Shanechi, M and Carmena, JM},
title = {Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces.},
journal = {Neural computation},
volume = {26},
number = {9},
pages = {1811-1839},
doi = {10.1162/NECO_a_00632},
pmid = {24922501},
issn = {1530-888X},
mesh = {Action Potentials ; *Algorithms ; Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Calibration ; Electrodes, Implanted ; Likelihood Functions ; Macaca ; Male ; Motor Activity/physiology ; Time Factors ; },
abstract = {Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every time step during these procedures, rather than waiting to update the decoder at periodic intervals in a more batch-based process. Here, we present recursive maximum likelihood (RML), a CLDA algorithm that performs continuous adaptation of a Kalman filter decoder's parameters. We demonstrate that RML possesses a variety of useful properties and practical algorithmic advantages. First, we show how RML leverages the accuracy of updates based on a batch of data while still adapting parameters on every time step. Second, we illustrate how the RML algorithm is parameterized by a single, intuitive half-life parameter that can be used to adjust the rate of adaptation in real time. Third, we show how even when the number of neural features is very large, RML's memory-efficient recursive update rules can be reformulated to also be computationally fast so that continuous adaptation is still feasible. To test the algorithm in closed-loop experiments, we trained three macaque monkeys to perform a center-out reaching task by using either spiking activity or local field potentials to control a 2D computer cursor. RML achieved higher levels of performance more rapidly in comparison to a previous CLDA algorithm that adapts parameters on a more intermediate timescale. Overall, our results indicate that RML is an effective CLDA algorithm for achieving rapid performance acquisition using continuous adaptation.},
}
@article {pmid24921388,
year = {2014},
author = {Perge, JA and Zhang, S and Malik, WQ and Homer, ML and Cash, S and Friehs, G and Eskandar, EN and Donoghue, JP and Hochberg, LR},
title = {Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046007},
pmid = {24921388},
issn = {1741-2552},
support = {N01HD10018/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; NCMRR-N01HD53403/HD/NICHD NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Brain-Computer Interfaces ; Calibration ; Electroencephalography/*statistics &amp; numerical data ; Humans ; Imagination/physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Action potentials and local field potentials (LFPs) recorded in primary motor cortex contain information about the direction of movement. LFPs are assumed to be more robust to signal instabilities than action potentials, which makes LFPs, along with action potentials, a promising signal source for brain-computer interface applications. Still, relatively little research has directly compared the utility of LFPs to action potentials in decoding movement direction in human motor cortex.<br><br>APPROACH: We conducted intracortical multi-electrode recordings in motor cortex of two persons (T2 and [S3]) as they performed a motor imagery task. We then compared the offline decoding performance of LFPs and spiking extracted from the same data recorded across a one-year period in each participant.<br><br>MAIN RESULTS: We obtained offline prediction accuracy of movement direction and endpoint velocity in multiple LFP bands, with the best performance in the highest (200-400 Hz) LFP frequency band, presumably also containing low-pass filtered action potentials. Cross-frequency correlations of preferred directions and directional modulation index showed high similarity of directional information between action potential firing rates (spiking) and high frequency LFPs (70-400 Hz), and increasing disparity with lower frequency bands (0-7, 10-40 and 50-65 Hz). Spikes predicted the direction of intended movement more accurately than any individual LFP band, however combined decoding of all LFPs was statistically indistinguishable from spike-based performance. As the quality of spiking signals (i.e. signal amplitude) and the number of significantly modulated spiking units decreased, the offline decoding performance decreased 3.6[5.65]%/month (for T2 and [S3] respectively). The decrease in the number of significantly modulated LFP signals and their decoding accuracy followed a similar trend (2.4[2.85]%/month, ANCOVA, p = 0.27[0.03]).<br><br>SIGNIFICANCE: Field potentials provided comparable offline decoding performance to unsorted spikes. Thus, LFPs may provide useful external device control using current human intracortical recording technology. (<br><br>NCT00912041.).},
}
@article {pmid24920030,
year = {2014},
author = {Law, AJ and Rivlis, G and Schieber, MH},
title = {Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons.},
journal = {Journal of neurophysiology},
volume = {112},
number = {6},
pages = {1528-1548},
pmid = {24920030},
issn = {1522-1598},
support = {F31 NS065628/NS/NINDS NIH HHS/United States ; R01 NS065902/NS/NINDS NIH HHS/United States ; R01-NS-065902/NS/NINDS NIH HHS/United States ; F31-NS-065628/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/cytology/*physiology ; *Motor Skills ; Neurons/*physiology ; Synapses/*physiology ; },
abstract = {Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture-such as the similarity of preferred directions-had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill.},
}
@article {pmid24918435,
year = {2014},
author = {Nakanishi, M and Wang, Y and Wang, YT and Mitsukura, Y and Jung, TP},
title = {Generating visual flickers for eliciting robust steady-state visual evoked potentials at flexible frequencies using monitor refresh rate.},
journal = {PloS one},
volume = {9},
number = {6},
pages = {e99235},
pmid = {24918435},
issn = {1932-6203},
support = {D11PC20183/PC/NCI NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; *Flicker Fusion ; Humans ; },
abstract = {In the study of steady-state visual evoked potentials (SSVEPs), it remains a challenge to present visual flickers at flexible frequencies using monitor refresh rate. For example, in an SSVEP-based brain-computer interface (BCI), it is difficult to present a large number of visual flickers simultaneously on a monitor. This study aims to explore whether or how a newly proposed frequency approximation approach changes signal characteristics of SSVEPs. At 10 Hz and 12 Hz, the SSVEPs elicited using two refresh rates (75 Hz and 120 Hz) were measured separately to represent the approximation and constant-period approaches. This study compared amplitude, signal-to-noise ratio (SNR), phase, latency, scalp distribution, and frequency detection accuracy of SSVEPs elicited using the two approaches. To further prove the efficacy of the approximation approach, this study implemented an eight-target BCI using frequencies from 8-15 Hz. The SSVEPs elicited by the two approaches were found comparable with regard to all parameters except amplitude and SNR of SSVEPs at 12 Hz. The BCI obtained an averaged information transfer rate (ITR) of 95.0 bits/min across 10 subjects with a maximum ITR of 120 bits/min on two subjects, the highest ITR reported in the SSVEP-based BCIs. This study clearly showed that the frequency approximation approach can elicit robust SSVEPs at flexible frequencies using monitor refresh rate and thereby can largely facilitate various SSVEP-related studies in neural engineering and visual neuroscience.},
}
@article {pmid24917804,
year = {2014},
author = {Zao, JK and Gan, TT and You, CK and Chung, CE and Wang, YT and Rodríguez Méndez, SJ and Mullen, T and Yu, C and Kothe, C and Hsiao, CT and Chu, SL and Shieh, CK and Jung, TP},
title = {Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {370},
pmid = {24917804},
issn = {1662-5161},
abstract = {EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.},
}
@article {pmid24916738,
year = {2015},
author = {Rossi-Izquierdo, M and Santos-Pérez, S and Del-Río-Valeiras, M and Lirola-Delgado, A and Faraldo-García, A and Vaamonde-Sánchez-Andrade, I and Gayoso-Diz, P and Soto-Varela, A},
title = {Is there a relationship between objective and subjective assessment of balance in elderly patients with instability?.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {272},
number = {9},
pages = {2201-2206},
pmid = {24916738},
issn = {1434-4726},
mesh = {Accidental Falls ; Aged ; Aged, 80 and over ; Aging/*physiology ; Cross-Sectional Studies ; Disability Evaluation ; Dizziness/physiopathology ; Female ; Humans ; Male ; Postural Balance/*physiology ; Surveys and Questionnaires ; },
abstract = {To assess whether a subjective questionnaire that measures the disability caused by balance disorders in daily life activities is correlated to objective assessment of balance in elderly patients with age-related instability. We included 37 subjects aged 65 years or more who presented balance disorders induced solely by age. Balance assessment was through the sensory organisation test and limits of stability of computerised dynamic posturography, the SwayStar system and the modified timed up and go test. The patients also completed the dizziness handicap inventory (DHI) questionnaire. The SwayStar balance control index (BCI) was most significantly correlated to the DHI score and the score of its different scales. When we divided the patients into subgroups according to DHI score, we only found statistically significant differences in the BCI and number of falls. In our population of elderly patients with instability, there is practically no correlation between the DHI and the static balance assessment. However, there is greater correlation with the BCI, which could show that dynamic balance is perceived as more disabling for these patients. In this case, when designing a rehabilitation protocol we should focus more on dynamic activities such as gait.},
}
@article {pmid24910595,
year = {2014},
author = {Lebedev, MA},
title = {How to read neuron-dropping curves?.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {102},
pmid = {24910595},
issn = {1662-5137},
}
@article {pmid24910150,
year = {2015},
author = {Jiang, N and Gizzi, L and Mrachacz-Kersting, N and Dremstrup, K and Farina, D},
title = {A brain-computer interface for single-trial detection of gait initiation from movement related cortical potentials.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {1},
pages = {154-159},
doi = {10.1016/j.clinph.2014.05.003},
pmid = {24910150},
issn = {1872-8952},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Female ; Gait/*physiology ; Humans ; *Intention ; Male ; Movement/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Applications of brain-computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detection of gait initiation using movement related cortical potentials (MRCP).<br><br>METHODS: The templates of MRCP were extracted from 9-channel scalp EEG during gait initiation in 9 healthy subjects. Independent component analysis (ICA) was used to remove artifacts, and the Laplacian spatial filter was applied to enhance the signal-to-noise ratio of MRCP. Following these pre-processing steps, a matched filter was used to perform single-trial detection of gait initiation.<br><br>RESULTS: ICA preprocessing was shown to significantly improve the detection performance. With ICA preprocessing, across all subjects, the true positive rate (TPR) of the detection was 76.9&#xb1;8.97%, and the false positive rate was 2.93&#xb1;1.09 per minute.<br><br>CONCLUSION: The results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis.<br><br>SIGNIFICANCE: The results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation.},
}
@article {pmid24909879,
year = {2014},
author = {Korotchenko, VN and Saydmohammed, M and Vollmer, LL and Bakan, A and Sheetz, K and Debiec, KT and Greene, KA and Agliori, CS and Bahar, I and Day, BW and Vogt, A and Tsang, M},
title = {In vivo structure-activity relationship studies support allosteric targeting of a dual specificity phosphatase.},
journal = {Chembiochem : a European journal of chemical biology},
volume = {15},
number = {10},
pages = {1436-1445},
pmid = {24909879},
issn = {1439-7633},
support = {HL088016/HL/NHLBI NIH HHS/United States ; P01 CA078039/CA/NCI NIH HHS/United States ; P30 CA047904/CA/NCI NIH HHS/United States ; P30A047904//PHS HHS/United States ; HD053287/HD/NICHD NIH HHS/United States ; P41 GM103712/GM/NIGMS NIH HHS/United States ; CA78039/CA/NCI NIH HHS/United States ; R01 HD053287/HD/NICHD NIH HHS/United States ; R01 HL088016/HL/NHLBI NIH HHS/United States ; R01 GM099738/GM/NIGMS NIH HHS/United States ; },
mesh = {Allosteric Regulation ; Animals ; Drug Design ; Dual Specificity Phosphatase 6/*antagonists &amp; inhibitors/metabolism ; Embryo, Nonmammalian/drug effects/metabolism ; Enzyme Inhibitors/*chemistry/*pharmacology ; Extracellular Signal-Regulated MAP Kinases/metabolism ; Fibroblast Growth Factors/metabolism ; Indenes/*chemistry/*pharmacology ; Models, Molecular ; Signal Transduction/drug effects ; Structure-Activity Relationship ; Zebrafish/embryology ; },
abstract = {Dual specificity phosphatase 6 (DUSP6) functions as a feedback attenuator of fibroblast growth factor signaling during development. In vitro high throughput chemical screening attempts to discover DUSP6 inhibitors have yielded limited success. However, in vivo whole-organism screens of zebrafish identified compound 1 (BCI) as an allosteric inhibitor of DUSP6. Here we designed and synthesized a panel of analogues to define the structure-activity relationship (SAR) of DUSP6 inhibition. In vivo high-content analysis in transgenic zebrafish, coupled with cell-based chemical complementation assays, identified structural features of the pharmacophore of 1 that were essential for biological activity. In vitro assays of DUSP hyperactivation corroborated the results from in vivo and cellular SAR. The results reinforce the notion that DUSPs are druggable through allosteric mechanisms and illustrate the utility of zebrafish as a model organism for in vivo SAR analyses.},
}
@article {pmid24904384,
year = {2014},
author = {Friedrich, EV and Wood, G and Scherer, R and Neuper, C},
title = {Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {348},
pmid = {24904384},
issn = {1662-5161},
}
@article {pmid24904323,
year = {2014},
author = {Sakurai, Y},
title = {Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {104},
pmid = {24904323},
issn = {1662-5137},
abstract = {This perspective emphasizes that the brain-machine interface (BMI) research has the potential to clarify major mysteries of the brain and that such clarification of the mysteries by neuroscience is needed to develop BMIs. I enumerate five principal mysteries. The first is "how is information encoded in the brain?" This is the fundamental question for understanding what our minds are and is related to the verification of Hebb's cell assembly theory. The second is "how is information distributed in the brain?" This is also a reconsideration of the functional localization of the brain. The third is "what is the function of the ongoing activity of the brain?" This is the problem of how the brain is active during no-task periods and what meaning such spontaneous activity has. The fourth is "how does the bodily behavior affect the brain function?" This is the problem of brain-body interaction, and obtaining a new "body" by a BMI leads to a possibility of changes in the owner's brain. The last is "to what extent can the brain induce plasticity?" Most BMIs require changes in the brain's neuronal activity to realize higher performance, and the neuronal operant conditioning inherent in the BMIs further enhances changes in the activity.},
}
@article {pmid24904261,
year = {2014},
author = {Strait, M and Scheutz, M},
title = {What we can and cannot (yet) do with functional near infrared spectroscopy.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {117},
pmid = {24904261},
issn = {1662-4548},
abstract = {Functional near infrared spectroscopy (NIRS) is a relatively new technique complimentary to EEG for the development of brain-computer interfaces (BCIs). NIRS-based systems for detecting various cognitive and affective states such as mental and emotional stress have already been demonstrated in a range of adaptive human-computer interaction (HCI) applications. However, before NIRS-BCIs can be used reliably in realistic HCI settings, substantial challenges oncerning signal processing and modeling must be addressed. Although many of those challenges have been identified previously, the solutions to overcome them remain scant. In this paper, we first review what can be currently done with NIRS, specifically, NIRS-based approaches to measuring cognitive and affective user states as well as demonstrations of passive NIRS-BCIs. We then discuss some of the primary challenges these systems would face if deployed in more realistic settings, including detection latencies and motion artifacts. Lastly, we investigate the effects of some of these challenges on signal reliability via a quantitative comparison of three NIRS models. The hope is that this paper will actively engage researchers to acilitate the advancement of NIRS as a more robust and useful tool to the BCI community.},
}
@article {pmid24904257,
year = {2014},
author = {Prins, NW and Sanchez, JC and Prasad, A},
title = {A confidence metric for using neurobiological feedback in actor-critic reinforcement learning based brain-machine interfaces.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {111},
pmid = {24904257},
issn = {1662-4548},
abstract = {Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.},
}
@article {pmid24904251,
year = {2014},
author = {Zimmermann, JB and Jackson, A},
title = {Closed-loop control of spinal cord stimulation to restore hand function after paralysis.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {87},
pmid = {24904251},
issn = {1662-4548},
abstract = {As yet, no cure exists for upper-limb paralysis resulting from the damage to motor pathways after spinal cord injury or stroke. Recently, neural activity from the motor cortex of paralyzed individuals has been used to control the movements of a robot arm but restoring function to patients' actual limbs remains a considerable challenge. Previously we have shown that electrical stimulation of the cervical spinal cord in anesthetized monkeys can elicit functional upper-limb movements like reaching and grasping. Here we show that stimulation can be controlled using cortical activity in awake animals to bypass disruption of the corticospinal system, restoring their ability to perform a simple upper-limb task. Monkeys were trained to grasp and pull a spring-loaded handle. After temporary paralysis of the hand was induced by reversible inactivation of primary motor cortex using muscimol, grasp-related single-unit activity from the ventral premotor cortex was converted into stimulation patterns delivered in real-time to the cervical spinal gray matter. During periods of closed-loop stimulation, task-modulated electromyogram, movement amplitude, and task success rate were improved relative to interleaved control periods without stimulation. In some sessions, single motor unit activity from weakly active muscles was also used successfully to control stimulation. These results are the first use of a neural prosthesis to improve the hand function of primates after motor cortex disruption, and demonstrate the potential for closed-loop cortical control of spinal cord stimulation to reanimate paralyzed limbs.},
}
@article {pmid24904131,
year = {2014},
author = {Nicolelis, M and Servick, K},
title = {Interview. Kickoff looms for demo of brain-controlled machine.},
journal = {Science (New York, N.Y.)},
volume = {344},
number = {6188},
pages = {1069-1070},
doi = {10.1126/science.344.6188.1069},
pmid = {24904131},
issn = {1095-9203},
mesh = {Arm/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Brazil ; Electrodes, Implanted ; Electroencephalography ; Humans ; Neurology/*trends ; North Carolina ; Robotics/*methods ; Soccer ; },
}
@article {pmid24898872,
year = {2014},
author = {Chimoyi, LA and Musenge, E},
title = {Spatial analysis of factors associated with HIV infection among young people in Uganda, 2011.},
journal = {BMC public health},
volume = {14},
number = {},
pages = {555},
pmid = {24898872},
issn = {1471-2458},
mesh = {Adolescent ; Adult ; Alcohol Drinking ; Bayes Theorem ; Cluster Analysis ; Cross-Sectional Studies ; Female ; HIV Infections/*epidemiology ; Humans ; Logistic Models ; Male ; Marital Status ; Prevalence ; Risk Factors ; Sexual Behavior/statistics &amp; numerical data ; Sexually Transmitted Diseases/epidemiology ; Uganda/epidemiology ; Young Adult ; },
abstract = {BACKGROUND: The HIV epidemic in East Africa is of public health importance with an increasing number of young people getting infected. This study sought to identify spatial clusters and examine the geographical variation of HIV infection at a regional level while accounting for risk factors associated with HIV/AIDS among young people in Uganda.<br><br>METHODS: A secondary data analysis was conducted on a survey cross-sectional design whose data were obtained from the 2011 Uganda Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) for 7 518 young people aged 15-24 years. The analysis was performed in three stages while incorporating population survey sampling weights. Maximum likelihood-based logistic regression models were used to explore the non-spatially adjusted factors associated with HIV infection. Spatial scan statistic was used to identify geographical clusters of elevated HIV infections which justified modelling using a spatial random effects model by Bayesian-based logistic regression models.<br><br>RESULTS: In this study, 309/533 HIV sero-positive female participants were selected with majority residing in the rural areas [386(72%)]. Compared to singles, those currently [Adjusted Odds Ratio (AOR) =3.64; (95% CI; 1.25-10.27)] and previously married [AOR&#x2009;=&#x2009;5.62; (95% CI: 1.52-20.75)] participants had significantly higher likelihood of HIV infections. Sexually Transmitted Infections [AOR&#x2009;=&#x2009;2.21; (95% CI: 1.35-3.60)] were more than twice likely associated with HIV infection. One significant (p&#x2009;<&#x2009;0.05) primary cluster of HIV prevalence around central Uganda emerged from the SaTScan cluster analysis. Spatial analysis disclosed behavioural factors associated with greater odds of HIV infection such as; alcohol use before sexual intercourse [Posterior Odds Ratio (POR) =1.32; 95% (BCI: 1.11-1.63)]. Condom use [POR&#x2009;=&#x2009;0.54; (95% BCI: 0.41-0.69)] and circumcision [POR&#x2009;=&#x2009;0.66; (95% BCI: 0.45-0.99)] provided a protective effect against HIV.<br><br>CONCLUSIONS: The study revealed associations between high-risk sexual behaviour and HIV infection. Behavioural change interventions should therefore be pertinent to the prevention of HIV. Spatial analysis further revealed a significant HIV cluster towards the Central and Eastern areas of Uganda. We propose that interventions targeting young people should initially focus on these regions and subsequently spread out across Uganda.},
}
@article {pmid24895652,
year = {2014},
author = {Muhiuddin, G and Feng, F and Jun, YB},
title = {Subalgebras of BCK/BCI-algebras based on cubic soft sets.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {458638},
doi = {10.1155/2014/458638},
pmid = {24895652},
issn = {1537-744X},
mesh = {*Models, Theoretical ; },
abstract = {Operations of cubic soft sets including "AND" operation and "OR" operation based on P-orders and R-orders are introduced and some related properties are investigated. An example is presented to show that the R-union of two internal cubic soft sets might not be internal. A sufficient condition is provided, which ensure that the R-union of two internal cubic soft sets is also internal. Moreover, some properties of cubic soft subalgebras of BCK/BCI-algebras based on a given parameter are discussed.},
}
@article {pmid24891497,
year = {2014},
author = {Iolov, A and Ditlevsen, S and Longtin, A},
title = {Stochastic optimal control of single neuron spike trains.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046004},
doi = {10.1088/1741-2560/11/4/046004},
pmid = {24891497},
issn = {1741-2552},
mesh = {Algorithms ; Biophysical Phenomena ; Computer Simulation ; Electrophysiological Phenomena ; Feedback, Physiological ; Models, Neurological ; Neurons/*physiology ; Software ; Stochastic Processes ; Synaptic Transmission/physiology ; },
abstract = {OBJECTIVE: External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain-machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue.<br><br>APPROACH: We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access to the spike times (open-loop control).<br><br>MAIN RESULTS: We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris-Lecar spiking neuron model, for which an LIF approximation is first obtained from a spike sequence using a previously published method. We further show that a related control strategy based on the assumption that there is no noise performs poorly in comparison to our noise-based strategies. The algorithms are numerically intensive and may require efficiency refinements to achieve real-time control; in particular, the open-loop context is more numerically demanding than the closed-loop one.<br><br>SIGNIFICANCE: Our main contribution is the online feedback control of a noisy neuron through modulation of the input, taking into account physiological constraints on the control. A precise and robust targeting of neural activity based on stochastic optimal control has great potential for regulating neural activity in e.g. prosthetic applications and to improve our understanding of the basic mechanisms by which neuronal firing patterns can be controlled in vivo.},
}
@article {pmid24891496,
year = {2014},
author = {Jangraw, DC and Wang, J and Lance, BJ and Chang, SF and Sajda, P},
title = {Neurally and ocularly informed graph-based models for searching 3D environments.},
journal = {Journal of neural engineering},
volume = {11},
number = {4},
pages = {046003},
doi = {10.1088/1741-2560/11/4/046003},
pmid = {24891496},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Computer Graphics ; Computer Simulation ; Electroencephalography ; Electrooculography ; Environment ; Humans ; *Models, Neurological ; Orientation/physiology ; Pupil/physiology ; Saccades/physiology ; },
abstract = {OBJECTIVE: As we move through an environment, we are constantly making assessments, judgments and decisions about the things we encounter. Some are acted upon immediately, but many more become mental notes or fleeting impressions-our implicit 'labeling' of the world. In this paper, we use physiological correlates of this labeling to construct a hybrid brain-computer interface (hBCI) system for efficient navigation of a 3D environment.<br><br>APPROACH: First, we record electroencephalographic (EEG), saccadic and pupillary data from subjects as they move through a small part of a 3D virtual city under free-viewing conditions. Using machine learning, we integrate the neural and ocular signals evoked by the objects they encounter to infer which ones are of subjective interest to them. These inferred labels are propagated through a large computer vision graph of objects in the city, using semi-supervised learning to identify other, unseen objects that are visually similar to the labeled ones. Finally, the system plots an efficient route to help the subjects visit the 'similar' objects it identifies.<br><br>MAIN RESULTS: We show that by exploiting the subjects' implicit labeling to find objects of interest instead of exploring naively, the median search precision is increased from 25% to 97%, and the median subject need only travel 40% of the distance to see 84% of the objects of interest. We also find that the neural and ocular signals contribute in a complementary fashion to the classifiers' inference of subjects' implicit labeling.<br><br>SIGNIFICANCE: In summary, we show that neural and ocular signals reflecting subjective assessment of objects in a 3D environment can be used to inform a graph-based learning model of that environment, resulting in an hBCI system that improves navigation and information delivery specific to the user's interests.},
}
@article {pmid24887604,
year = {2014},
author = {Choi, K},
title = {Electroencephalography-based real-time cortical monitoring system that uses hierarchical Bayesian estimations for the brain-machine interface.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {31},
number = {3},
pages = {218-228},
doi = {10.1097/WNP.0000000000000064},
pmid = {24887604},
issn = {1537-1603},
mesh = {Adult ; Bayes Theorem ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; *Computer Systems ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/methods ; Male ; Photic Stimulation/methods ; Young Adult ; },
abstract = {In this study, a real-time cortical activity monitoring system was constructed, which could estimate cortical activities every 125 milliseconds over 2,240 vertexes from 64 channel electroencephalography signals through the Hierarchical Bayesian estimation that uses functional magnetic resonance imaging data as its prior information. Recently, functional magnetic resonance imaging has mostly been used in the neurofeedback field because it allows for high spatial resolution. However, in functional magnetic resonance imaging, the time for the neurofeedback information to reach the patient is delayed several seconds because of its poor temporal resolution. Therefore, a number of problems need to be solved to effectively implement feedback training paradigms in patients. To address this issue, this study used a new cortical activity monitoring system that improved both spatial and temporal resolution by using both functional magnetic resonance imaging data and electroencephalography signals in conjunction with one another. This system is advantageous as it can improve applications in the fields of real-time diagnosis, neurofeedback, and the brain-machine interface.},
}
@article {pmid24886978,
year = {2014},
author = {Höhne, J and Tangermann, M},
title = {Towards user-friendly spelling with an auditory brain-computer interface: the CharStreamer paradigm.},
journal = {PloS one},
volume = {9},
number = {6},
pages = {e98322},
pmid = {24886978},
issn = {1932-6203},
mesh = {Artifacts ; Auditory Cortex/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Humans ; *Writing ; },
abstract = {Realizing the decoding of brain signals into control commands, brain-computer interfaces (BCI) aim to establish an alternative communication pathway for locked-in patients. In contrast to most visual BCI approaches which use event-related potentials (ERP) of the electroencephalogram, auditory BCI systems are challenged with ERP responses, which are less class-discriminant between attended and unattended stimuli. Furthermore, these auditory approaches have more complex interfaces which imposes a substantial workload on their users. Aiming for a maximally user-friendly spelling interface, this study introduces a novel auditory paradigm: "CharStreamer". The speller can be used with an instruction as simple as "please attend to what you want to spell". The stimuli of CharStreamer comprise 30 spoken sounds of letters and actions. As each of them is represented by the sound of itself and not by an artificial substitute, it can be selected in a one-step procedure. The mental mapping effort (sound stimuli to actions) is thus minimized. Usability is further accounted for by an alphabetical stimulus presentation: contrary to random presentation orders, the user can foresee the presentation time of the target letter sound. Healthy, normal hearing users (n = 10) of the CharStreamer paradigm displayed ERP responses that systematically differed between target and non-target sounds. Class-discriminant features, however, varied individually from the typical N1-P2 complex and P3 ERP components found in control conditions with random sequences. To fully exploit the sequential presentation structure of CharStreamer, novel data analysis approaches and classification methods were introduced. The results of online spelling tests showed that a competitive spelling speed can be achieved with CharStreamer. With respect to user rating, it clearly outperforms a control setup with random presentation sequences.},
}
@article {pmid24886610,
year = {2014},
author = {Nakayashiki, K and Saeki, M and Takata, Y and Hayashi, Y and Kondo, T},
title = {Modulation of event-related desynchronization during kinematic and kinetic hand movements.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {90},
pmid = {24886610},
issn = {1743-0003},
mesh = {Biomechanical Phenomena ; Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Imagination/physiology ; Kinetics ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Event-related desynchronization/synchronization (ERD/ERS) is a relative power decrease/increase of electroencephalogram (EEG) in a specific frequency band during physical motor execution and mental motor imagery, thus it is widely used for the brain-computer interface (BCI) purpose. However what the ERD really reflects and its frequency band specific role have not been agreed and are under investigation. Understanding the underlying mechanism which causes a significant ERD would be crucial to improve the reliability of the ERD-based BCI. We systematically investigated the relationship between conditions of actual repetitive hand movements and resulting ERD.<br><br>METHODS: Eleven healthy young participants were asked to close/open their right hand repetitively at three different speeds (Hold, 1/3 Hz, and 1 Hz) and four distinct motor loads (0, 2, 10, and 15 kgf). In each condition, participants repeated 20 experimental trials, each of which consisted of rest (8-10 s), preparation (1 s) and task (6 s) periods. Under the Hold condition, participants were instructed to keep clenching their hand (i.e., isometric contraction) during the task period. Throughout the experiment, EEG signals were recorded from left and right motor areas for offline data analysis. We obtained time courses of EEG power spectrum to discuss the modulation of mu and beta-ERD/ERS due to the task conditions.<br><br>RESULTS: We confirmed salient mu-ERD (8-13 Hz) and slightly weak beta-ERD (14-30 Hz) on both hemispheres during repetitive hand grasping movements. According to a 3 &#xd7; 4 ANOVA (speed &#xd7; motor load), both mu and beta-ERD during the task period were significantly weakened under the Hold condition, whereas no significant difference in the kinetics levels and interaction effect was observed.<br><br>CONCLUSIONS: This study investigates the effect of changes in kinematics and kinetics on resulting ERD during repetitive hand grasping movements. The experimental results suggest that the strength of ERD may reflect the time differentiation of hand postures in motor planning process or the variation of proprioception resulting from hand movements, rather than the motor command generated in the down stream, which recruits a group of motor neurons.},
}
@article {pmid24880998,
year = {2014},
author = {Kim, BH and Kim, M and Jo, S},
title = {Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking.},
journal = {Computers in biology and medicine},
volume = {51},
number = {},
pages = {82-92},
doi = {10.1016/j.compbiomed.2014.04.020},
pmid = {24880998},
issn = {1879-0534},
mesh = {*Aircraft ; *Brain-Computer Interfaces ; *Electroencephalography ; Eye Movement Measurements ; Eye Movements/*physiology ; Humans ; },
abstract = {We propose a wearable hybrid interface where eye movements and mental concentration directly influence the control of a quadcopter in three-dimensional space. This noninvasive and low-cost interface addresses limitations of previous work by supporting users to complete their complicated tasks in a constrained environment in which only visual feedback is provided. The combination of the two inputs augments the number of control commands to enable the flying robot to travel in eight different directions within the physical environment. Five human subjects participated in the experiments to test the feasibility of the hybrid interface. A front view camera on the hull of the quadcopter provided the only visual feedback to each remote subject on a laptop display. Based on the visual feedback, the subjects used the interface to navigate along pre-set target locations in the air. The flight performance was evaluated by comparing with a keyboard-based interface. We demonstrate the applicability of the hybrid interface to explore and interact with a three-dimensional physical space through a flying robot.},
}
@article {pmid24880133,
year = {2014},
author = {Nakanishi, Y and Yanagisawa, T and Shin, D and Chen, C and Kambara, H and Yoshimura, N and Fukuma, R and Kishima, H and Hirata, M and Koike, Y},
title = {Decoding fingertip trajectory from electrocorticographic signals in humans.},
journal = {Neuroscience research},
volume = {85},
number = {},
pages = {20-27},
doi = {10.1016/j.neures.2014.05.005},
pmid = {24880133},
issn = {1872-8111},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Epilepsy/physiopathology ; Female ; Fingers/innervation/*physiology ; Humans ; Movement/*physiology ; Sensorimotor Cortex/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Seeking to apply brain-machine interface technology in neuroprosthetics, a number of methods for predicting trajectory of the elbow and wrist have been proposed and have shown remarkable results. Recently, the prediction of hand trajectory and classification of hand gestures or grasping types have attracted considerable attention. However, trajectory prediction for precise finger motion has remained a challenge. We proposed a method for the prediction of fingertip motions from electrocorticographic signals in human cortex. A patient performed extension/flexion tasks with three fingers. Average Pearson's correlation coefficients and normalized root-mean-square errors between decoded and actual trajectories were 0.83-0.90 and 0.24-0.48, respectively. To confirm generalizability to other users, we applied our method to the BCI Competition IV open data sets. Our method showed that the prediction accuracy of fingertip trajectory could be equivalent to that of other results in the competition.},
}
@article {pmid24880046,
year = {2014},
author = {Cutini, S and Brigadoi, S},
title = {Unleashing the future potential of functional near-infrared spectroscopy in brain sciences.},
journal = {Journal of neuroscience methods},
volume = {232},
number = {},
pages = {152-156},
doi = {10.1016/j.jneumeth.2014.05.024},
pmid = {24880046},
issn = {1872-678X},
mesh = {Animals ; Brain/*metabolism ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; *Spectroscopy, Near-Infrared ; *Wireless Technology ; },
abstract = {The wondrous innovations bound to the introduction of functional near-infrared spectroscopy in cognitive neuroscience are characterized by a multifaceted nature, ranging from technological improvements to sophisticated signal processing methods; the outstanding progress enabled scientists to investigate a variety of hard-to-test clinical populations and to successfully employ optical imaging in fields that were almost unimaginable twenty years ago. Here we illustrate how the emerging use of fNIRS methodologies might represent a drawing power in a variety of challenging experimental and medical contexts; we expect in the near future a wide increase of the use of wireless fNIRS, especially in children and in particular clinical populations, as well as a striking progress of fNIRS-BCI and hybrid BCI systems for neurofeedback and neurorehabilitation. These emerging trends might dramatically foster the future potential of fNIRS in brain sciences, provided that they are properly supported by a significant progress in signal processing and cognitive neuroscience.},
}
@article {pmid24880045,
year = {2014},
author = {Liparas, D and Dimitriadis, SI and Laskaris, NA and Tzelepi, A and Charalambous, K and Angelis, L},
title = {Exploiting the temporal patterning of transient VEP signals: a statistical single-trial methodology with implications to brain-computer interfaces (BCIs).},
journal = {Journal of neuroscience methods},
volume = {232},
number = {},
pages = {189-198},
doi = {10.1016/j.jneumeth.2014.04.032},
pmid = {24880045},
issn = {1872-678X},
mesh = {Attention/physiology ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Discrimination, Psychological ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Functional Laterality ; Humans ; Male ; Photic Stimulation ; User-Computer Interface ; Visual Perception/physiology ; },
abstract = {BACKGROUND: When visual evoked potentials (VEPs) are deployed in brain-computer interfaces (BCIs), the emphasis is put on stimulus design. In the case of transient VEPs (TVEPs) brain responses are never treated individually, i.e. on a single-trial (ST) basis, due to their poor signal quality. Therefore their main characteristic, which is the emergence during early latencies, remains unexplored.<br><br>NEW METHOD: Following a pattern-analytic methodology, we investigated the possibility of using single-trial TVEP responses to differentiate between the different spatial locations where a particular visual stimulus appeared and decide whether it was attended or unattended by the subject.<br><br>RESULTS: Covert spatial attention modulates the temporal patterning of TVEPs in such a way that a brief ST-segment, from a single synthesized sensor, is sufficient for a Mahalanobis-Taguchi (MT) system to decode subject's intention.<br><br>In contrast to previous VEP-based approaches, stimulus-related information and user's intention are being decoded from transient ST-signals via exploiting aspects of brain response in the temporal domain.<br><br>CONCLUSIONS: We demonstrated that in the TVEP signals there is sufficient discriminative information, coming in the form of a temporal code. We were able to introduce an efficient scheme that can fully exploit this information for the benefit of online classification. The measured performance brings high expectations for incorporating these ideas in BCI-control.},
}
@article {pmid24868021,
year = {2016},
author = {Pulvirenti, T and Hong, A and Clements, A and Forstner, D and Suchowersky, A and Guminski, A and McNeil, C and Hersey, P and Fogarty, G and Kefford, R and Long, GV and Wang, W},
title = {Acute Radiation Skin Toxicity Associated With BRAF Inhibitors.},
journal = {Journal of clinical oncology : official journal of the American Society of Clinical Oncology},
volume = {34},
number = {3},
pages = {e17-20},
doi = {10.1200/JCO.2013.49.0565},
pmid = {24868021},
issn = {1527-7755},
mesh = {Adult ; Aged ; Female ; Humans ; Male ; Melanoma/drug therapy/enzymology/genetics/radiotherapy ; Protein Kinase Inhibitors/*adverse effects ; Proto-Oncogene Proteins B-raf/*antagonists &amp; inhibitors/genetics ; Radiation Injuries/*chemically induced/enzymology/genetics ; Skin Diseases/chemically induced/enzymology/*etiology/genetics ; },
}
@article {pmid24866035,
year = {2014},
author = {Yu, BM and Chase, SM},
title = {Shedding light on learning.},
journal = {Nature neuroscience},
volume = {17},
number = {6},
pages = {746-747},
pmid = {24866035},
issn = {1546-1726},
mesh = {Animals ; *Brain-Computer Interfaces ; Calcium Signaling/*physiology ; Cerebral Cortex/*physiology ; Learning/*physiology ; Male ; *Microscopy, Fluorescence, Multiphoton ; Volition/*physiology ; },
}
@article {pmid24862002,
year = {2014},
author = {Bertagnolli, C and Uhart, A and Dupin, JC and da Silva, MG and Guibal, E and Desbrieres, J},
title = {Biosorption of chromium by alginate extraction products from Sargassum filipendula: investigation of adsorption mechanisms using X-ray photoelectron spectroscopy analysis.},
journal = {Bioresource technology},
volume = {164},
number = {},
pages = {264-269},
doi = {10.1016/j.biortech.2014.04.103},
pmid = {24862002},
issn = {1873-2976},
mesh = {Adsorption ; Alginates/*chemistry ; Biodegradation, Environmental ; Chromium/*isolation &amp; purification ; Glucuronic Acid/chemistry ; Hexuronic Acids/chemistry ; Kinetics ; Photoelectron Spectroscopy/*methods ; Sargassum/*chemistry ; Thermodynamics ; Time Factors ; },
abstract = {The alginate extraction products from Brazilian brown seaweed Sargassum filipendula were studied for chromium biosorption. Batch experiments were conducted at pH 2 and 3 and 20°C to determine the sorption capacity of this biosorbents for chromium (VI) and (III). The biomass was characterized before and after metal binding by X-ray photoelectron spectroscopy (XPS) in order to determine the mechanisms of chromium biosorption. The residue has a high adsorption capacity, close the value obtained with seaweed and higher than that of alginate for both Cr(III) and Cr(VI). XPS analysis of the biosorbents revealed that carboxyl, amino and sulfonate groups are responsible for the binding of the metal ions. The analysis also indicated that the Cr(VI) bound to the biomass was reduced to Cr(III).},
}
@article {pmid24860614,
year = {2014},
author = {Flamary, R and Jrad, N and Phlypo, R and Congedo, M and Rakotomamonjy, A},
title = {Mixed-norm regularization for brain decoding.},
journal = {Computational and mathematical methods in medicine},
volume = {2014},
number = {},
pages = {317056},
pmid = {24860614},
issn = {1748-6718},
mesh = {Algorithms ; Area Under Curve ; Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Computer Simulation ; Evoked Potentials ; Humans ; Models, Statistical ; Reproducibility of Results ; Software ; },
abstract = {This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.},
}
@article {pmid24860546,
year = {2014},
author = {Takano, K and Ora, H and Sekihara, K and Iwaki, S and Kansaku, K},
title = {Coherent Activity in Bilateral Parieto-Occipital Cortices during P300-BCI Operation.},
journal = {Frontiers in neurology},
volume = {5},
number = {},
pages = {74},
pmid = {24860546},
issn = {1664-2295},
abstract = {The visual P300 brain-computer interface (BCI), a popular system for electroencephalography (EEG)-based BCI, uses the P300 event-related potential to select an icon arranged in a flicker matrix. In earlier studies, we used green/blue (GB) luminance and chromatic changes in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray (WG) luminance flicker matrix. To highlight areas involved in improved P300-BCI performance, we used simultaneous EEG-fMRI recordings and showed enhanced activities in bilateral and right lateralized parieto-occipital areas. Here, to capture coherent activities of the areas during P300-BCI, we collected whole-head 306-channel magnetoencephalography data. When comparing functional connectivity between the right and left parieto-occipital channels, significantly greater functional connectivity in the alpha band was observed under the GB flicker matrix condition than under the WG flicker matrix condition. Current sources were estimated with a narrow-band adaptive spatial filter, and mean imaginary coherence was computed in the alpha band. Significantly greater coherence was observed in the right posterior parietal cortex under the GB than under the WG condition. Re-analysis of previous EEG-based P300-BCI data showed significant correlations between the power of the coherence of the bilateral parieto-occipital cortices and their performance accuracy. These results suggest that coherent activity in the bilateral parieto-occipital cortices plays a significant role in effectively driving the P300-BCI.},
}
@article {pmid24860445,
year = {2014},
author = {Attiah, MA and Farah, MJ},
title = {Minds, motherboards, and money: futurism and realism in the neuroethics of BCI technologies.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {86},
pmid = {24860445},
issn = {1662-5137},
}
@article {pmid24860416,
year = {2014},
author = {Umeda, T and Watanabe, H and Sato, MA and Kawato, M and Isa, T and Nishimura, Y},
title = {Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {97},
pmid = {24860416},
issn = {1662-4548},
abstract = {Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface.},
}
@article {pmid24860130,
year = {2014},
author = {Hutchinson, DT},
title = {The quest for the bionic arm.},
journal = {The Journal of the American Academy of Orthopaedic Surgeons},
volume = {22},
number = {6},
pages = {346-351},
doi = {10.5435/JAAOS-22-06-346},
pmid = {24860130},
issn = {1067-151X},
mesh = {Amputees/*rehabilitation ; *Artificial Limbs ; Bionics/*instrumentation ; Brain-Computer Interfaces ; Humans ; Military Personnel ; Prosthesis Design ; *Upper Extremity/innervation/surgery ; },
abstract = {The current state of research of upper extremity prosthetic devices is focused on creating a complete prosthesis with full motor and sensory function that will provide amputees with a near-normal human arm. Although advances are being made rapidly, many hurdles remain to be overcome before a functional, so-called bionic arm is a reality. Acquiring signals via nerve or muscle inputs will require either a reliable wireless device or direct wiring through an osseous-integrated implant. The best way to tap into the "knowledge" present in the peripheral nerve is yet to be determined.},
}
@article {pmid24860041,
year = {2014},
author = {Lin, CT and Chuang, CH and Huang, CS and Tsai, SF and Lu, SW and Chen, YH and Ko, LW},
title = {Wireless and wearable EEG system for evaluating driver vigilance.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {2},
pages = {165-176},
doi = {10.1109/TBCAS.2014.2316224},
pmid = {24860041},
issn = {1940-9990},
mesh = {Attention/physiology ; *Automobile Driving ; Brain-Computer Interfaces ; Clothing ; Electroencephalography/*instrumentation/methods ; Humans ; Signal Processing, Computer-Assisted ; Sleep Stages ; Wireless Technology/*instrumentation ; },
abstract = {Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.},
}
@article {pmid24851798,
year = {2014},
author = {Gierthmuehlen, M and Wang, X and Gkogkidis, A and Henle, C and Fischer, J and Fehrenbacher, T and Kohler, F and Raab, M and Mader, I and Kuehn, C and Foerster, K and Haberstroh, J and Freiman, TM and Stieglitz, T and Rickert, J and Schuettler, M and Ball, T},
title = {Mapping of sheep sensory cortex with a novel microelectrocorticography grid.},
journal = {The Journal of comparative neurology},
volume = {522},
number = {16},
pages = {3590-3608},
doi = {10.1002/cne.23631},
pmid = {24851798},
issn = {1096-9861},
mesh = {Afferent Pathways/physiology ; Animals ; *Brain Mapping ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Face/innervation ; Female ; Fourier Analysis ; Imaging, Three-Dimensional ; Magnetic Resonance Imaging ; Male ; *Microelectrodes ; Physical Stimulation ; Sheep/*anatomy &amp; histology ; Somatosensory Cortex/*physiology ; Time Factors ; },
abstract = {Microelectrocorticography (µECoG) provides insights into the cortical organization with high temporal and spatial resolution desirable for better understanding of neural information processing. Here we evaluated the use of µECoG for detailed cortical recording of somatosensory evoked potentials (SEPs) in an ovine model. The approach to the cortex was planned using an MRI-based 3D model of the sheep's brain. We describe a minimally extended surgical procedure allowing placement of two different µECoG grids on the somatosensory cortex. With this small craniotomy, the frontal sinus was kept intact, thus keeping the surgical site sterile and making this approach suitable for chronic implantations. We evaluated the procedure for chronic implantation of an encapsulated µECoG recording system. During acute and chronic recordings, significant SEP responses in the triangle between the ansate, diagonal, and coronal sulcus were identified in all animals. Stimulation of the nose, upper lip, lower lip, and chin caused a somatotopic lateral-to-medial, ipsilateral response pattern. With repetitive recordings of SEPs, this somatotopic pattern was reliably recorded for up to 16 weeks. The findings of this study confirm the previously postulated ipsilateral, somatotopic organization of the sheep's sensory cortex. High gamma band activity was spatially most specific in the comparison of different frequency components of the somatosensory evoked response. This study provides a basis for further acute and chronic investigations of the sheep's sensory cortex by characterizing its exact position, its functional properties, and the surgical approach with respect to macroanatomical landmarks.},
}
@article {pmid24847247,
year = {2014},
author = {Marchetti, M and Priftis, K},
title = {Effectiveness of the P3-speller in brain-computer interfaces for amyotrophic lateral sclerosis patients: a systematic review and meta-analysis.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {12},
pmid = {24847247},
issn = {1662-6443},
abstract = {A quarter of century ago, Farwell and Donchin (1988) described their mental prosthesis for "talking off the top of your head." This innovative communication system, later named P3-speller, has been the most investigated and tested brain-computer interface (BCI) system, to date. A main goal of the research on P3-spellers was the development of an effective assistive device for patients with severe motor diseases. Among these patients are those affected by amyotrophic lateral sclerosis (ALS). ALS patients have become a target population in P3-speller (and more generally in BCI) research. The P3-speller relies on the visual sensory modality, and it can be controlled by requiring users to actively move their eyes. Unfortunately, eye-movement control is usually not spared in the last stages of ALS, and, then, it is definitively lost in the case of complete paralysis. We reviewed the literature on ALS patients tested by means of P3-speller systems. Our aim was to investigate the evidence available to date of the P3-spellers effectiveness in ALS patients. To address this goal, a meta-analytic approach was adopted. The pooled classification accuracy performance, among retrieved studies, was about 74%. This estimation, however, was affected by significant heterogeneity and inconsistency among studies. This fact makes this percentage estimation (i.e., 74%) unreliable. Nowadays, the conclusion is that the initial hopes posed on P3-speller for ALS patients have not been met yet. In addition, no trials in which the P3-speller has been compared to current assistive technologies for communication (e.g., eye-trackers) are available. In conclusion, further studies are required to obtain a reliable index of P3-speller effectiveness in ALS. Furthermore, comparisons of P3-speller systems with the available assistive technologies are needed to assess the P3-speller usefulness with non-completely paralyzed ALS-patients.},
}
@article {pmid24847223,
year = {2014},
author = {Glannon, W},
title = {Prostheses for the will.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {79},
pmid = {24847223},
issn = {1662-5137},
}
@article {pmid24847198,
year = {2014},
author = {Guo, Y and Foulds, RA and Adamovich, SV and Sahin, M},
title = {Encoding of forelimb forces by corticospinal tract activity in the rat.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {62},
pmid = {24847198},
issn = {1662-4548},
support = {R01 HD058301/HD/NICHD NIH HHS/United States ; R01 NS072385/NS/NINDS NIH HHS/United States ; },
abstract = {In search of a solution to the long standing problems encountered in traditional brain computer interfaces (BCI), the lateral descending tracts of the spinal cord present an alternative site for taping into the volitional motor signals. Due to the convergence of the cortical outputs into a final common pathway in the descending tracts of the spinal cord, neural interfaces with the spinal cord can potentially acquire signals richer with volitional information in a smaller anatomical region. The main objective of this study was to evaluate the feasibility of extracting motor control signals from the corticospinal tract (CST) of the rat spinal cord. Flexible substrate, multi-electrode arrays (MEA) were implanted in the CST of rats trained for a lever pressing task. This novel use of flexible substrate MEAs allowed recording of CST activity in behaving animals for up to three weeks with the current implantation technique. Time-frequency and principal component analyses (PCA) were applied to the neural signals to reconstruct isometric forelimb forces. Computed regression coefficients were then used to predict isometric forces in additional trials. The correlation between measured and predicted forces in the vertical direction averaged across six animals was 0.67 and R (2) value was 0.44. Force regression in the horizontal directions was less successful, possibly due to the small amplitude of forces. Neural signals above and near the high gamma band made the largest contributions to prediction of forces. The results of this study support the feasibility of a spinal cord computer interface (SCCI) for generation of command signals in paralyzed individuals.},
}
@article {pmid24845299,
year = {2014},
author = {Jansson, KJ and Håkansson, B and Reinfeldt, S and Taghavi, H and Eeg-Olofsson, M},
title = {MRI induced torque and demagnetization in retention magnets for a bone conduction implant.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {6},
pages = {1887-1893},
doi = {10.1109/TBME.2014.2309978},
pmid = {24845299},
issn = {1558-2531},
mesh = {Bone Conduction ; *Cochlear Implants ; Humans ; Magnetic Resonance Imaging/*adverse effects ; *Magnets ; *Models, Theoretical ; *Torque ; },
abstract = {Performing magnetic resonance imaging (MRI) examinations in patients who use implantable medical devices involve safety risks both for the patient and the implant. Hearing implants often use two permanent magnets, one implanted and one external, for the retention of the external transmitter coil to the implanted receiver coil to achieve an optimal signal transmission. The implanted magnet is subjected to both demagnetization and torque, magnetically induced by the MRI scanner. In this paper, demagnetization and a comparison between measured and simulated induced torque is studied for the retention magnet used in a bone conduction implant (BCI) system. The torque was measured and simulated in a uniform static magnetic field of 1.5 T. The magnetic field was generated by a dipole electromagnet and permanent magnets with two different types of coercive fields were tested. Demagnetization and maximum torque for the high coercive field magnets was 7.7% ± 2.5% and 0.20 ± 0.01 Nm, respectively and 71.4% ± 19.1% and 0.18 ± 0.01 Nm for the low coercive field magnets, respectively. The simulated maximum torque was 0.34 Nm, deviating from the measured torque in terms of amplitude, mainly related to an insufficient magnet model. The BCI implant with high coercive field magnets is believed to be magnetic resonance (MR) conditional up to 1.5 T if a compression band is used around the skull to fix the implant. This is not approved and requires further investigations, and if removal of the implant is needed, the surgical operation is expected to be simple.},
}
@article {pmid24844742,
year = {2014},
author = {Yang, Z and Huang, Z and Gonzalez-Castillo, J and Dai, R and Northoff, G and Bandettini, P},
title = {Using fMRI to decode true thoughts independent of intention to conceal.},
journal = {NeuroImage},
volume = {99},
number = {},
pages = {80-92},
pmid = {24844742},
issn = {1095-9572},
support = {Z99 MH999999//Intramural NIH HHS/United States ; },
mesh = {Adult ; Brain/physiology ; Cerebral Cortex/physiology ; Female ; Humans ; Image Processing, Computer-Assisted ; Intention ; Lie Detection/*psychology ; Magnetic Resonance Imaging/*methods ; Male ; Photic Stimulation ; Reproducibility of Results ; Young Adult ; },
abstract = {Multi-variate pattern analysis (MVPA) applied to BOLD-fMRI has proven successful at decoding complicated fMRI signal patterns associated with a variety of cognitive processes. One cognitive process, not yet investigated, is the mental representation of "Yes/No" thoughts that precede the actual overt response to a binary "Yes/No" question. In this study, we focus on examining: (1) whether spatial patterns of the hemodynamic response carry sufficient information to allow reliable decoding of "Yes/No" thoughts; and (2) whether decoding of "Yes/No" thoughts is independent of the intention to respond honestly or dishonestly. To achieve this goal, we conducted two separate experiments. Experiment 1, collected on a 3T scanner, examined the whole brain to identify regions that carry sufficient information to permit significantly above-chance prediction of "Yes/No" thoughts at the group level. In Experiment 2, collected on a 7T scanner, we focused on the regions identified in Experiment 1 to examine the capability of achieving high decoding accuracy at the single subject level. A set of regions--namely right superior temporal gyrus, left supra-marginal gyrus, and left middle frontal gyrus--exhibited high decoding power. Decoding accuracy for these regions increased with trial averaging. When 18 trials were averaged, the median accuracies were 82.5%, 77.5%, and 79.5%, respectively. When trials were separated according to deceptive intentions (set via experimental cues), and classifiers were trained on honest trials, but tested on trials where subjects were asked to deceive, the median accuracies of these regions still reached 66%, 75%, and 78.5%. These results provide evidence that concealed "Yes/No" thoughts are encoded in the BOLD signal, retaining some level of independence from the subject's intentions to answer honestly or dishonestly. These findings also suggest the theoretical possibility for more efficient brain-computer interfaces where subjects only need to think their answers to communicate.},
}
@article {pmid24844082,
year = {2013},
author = {Moreno, JD},
title = {Mind wars. Brain science and the military.},
journal = {Monash bioethics review},
volume = {31},
number = {2},
pages = {83-99},
pmid = {24844082},
issn = {1321-2753},
mesh = {*Bioethical Issues ; Brain/*physiology ; Brain Mapping/ethics ; Brain-Computer Interfaces/ethics ; Ethics, Research ; Human Experimentation/ethics ; Humans ; *Military Personnel ; Morals ; Neurosciences/*ethics ; Robotics/ethics ; Security Measures/*ethics ; Terrorism/ethics ; United States ; },
abstract = {This article is based on a public lecture hosted by the Monash University Centre for Human Bioethics in Melbourne, Australia on 11 April 2013. The lecture recording was transcribed by Vicky Ryan; and, the original transcript has been edited--for clarity and brevity--by Vicky Ryan, Michael Selgelid and Jonathan Moreno.},
}
@article {pmid24842488,
year = {2014},
author = {Liu, L and Doran, S and Xu, Y and Manwani, B and Ritzel, R and Benashski, S and McCullough, L and Li, J},
title = {Inhibition of mitogen-activated protein kinase phosphatase-1 (MKP-1) increases experimental stroke injury.},
journal = {Experimental neurology},
volume = {261},
number = {},
pages = {404-411},
pmid = {24842488},
issn = {1090-2430},
support = {R01 NS078446/NS/NINDS NIH HHS/United States ; R21 NS079137/NS/NINDS NIH HHS/United States ; R21NS079137/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain Injuries/etiology/prevention &amp; control ; Cyclohexylamines/adverse effects ; Disease Models, Animal ; Dose-Response Relationship, Drug ; Dual Specificity Phosphatase 1/*deficiency/genetics ; Encephalitis/*etiology ; Enzyme Activation/drug effects/genetics ; Enzyme Inhibitors/pharmacology ; Gene Expression Regulation, Enzymologic/drug effects/*genetics ; Indenes/adverse effects ; Infarction, Middle Cerebral Artery/*complications/*etiology/genetics ; MAP Kinase Kinase 4/metabolism ; Male ; Mice, Knockout ; Neurologic Examination ; p38 Mitogen-Activated Protein Kinases/metabolism ; },
abstract = {BACKGROUND AND PURPOSE: Activation of mitogen-activated protein kinases (MAPKs), particularly c-jun-N-terminal kinases (JNK) and p38 exacerbates stroke injury by provoking pro-apoptotic and pro-inflammatory cellular signaling. MAPK phosphatase-1 (MKP-1) restrains the over-activation of MAPKs via rapid de-phosphorylation of the MAPKs. We therefore examined the role of MKP-1 in stroke and studied its inhibitory effects on MAPKs after experimental stroke.<br><br>METHODS: Male mice were subjected to transient middle cerebral artery occlusion (MCAO). MKP-1 knockout (KO) mice and a MKP-1 pharmacological inhibitor were utilized. We utilized flow cytometry, immunohistochemistry (IHC), and Western blots analysis to explore MKP-1 signaling and its effects on apoptosis/inflammation in the brain and specifically in microglia after stroke.<br><br>RESULTS: MKP-1 was highly expressed in the nuclei of both neurons and microglia after stroke. MKP-1 genetic deletion exacerbated stroke outcome by increasing infarct, neurological deficits and hemorrhagic transformation. Additionally, delayed treatment of the MKP-1 pharmacological inhibitor worsened stroke outcome in wild type (WT) mice but had no effect in MKP-1 KO mice. Furthermore, MKP-1 deletion led to increased c-jun-N-terminal kinase (JNK) activation and microglial p38 activation after stroke. Finally, MKP-1 deletion or inhibition increased inflammatory and apoptotic response as evidenced by the increased levels of interleukin-6 (IL-6), tumor necrosis factor &#x3b1; (TNF&#x3b1;), ratio of p-c-jun/c-jun and cleaved caspase-3 following ischemia.<br><br>CONCLUSIONS: We have demonstrated that MKP-1 signaling is an endogenous protective mechanism in stroke. Our data imply that MKP-1 possesses anti-apoptotic and anti-inflammatory properties by simultaneously controlling the activities of JNK and microglial p38.},
}
@article {pmid24839893,
year = {2014},
author = {Pfeifer, R and Iida, F and Lungarella, M},
title = {Cognition from the bottom up: on biological inspiration, body morphology, and soft materials.},
journal = {Trends in cognitive sciences},
volume = {18},
number = {8},
pages = {404-413},
doi = {10.1016/j.tics.2014.04.004},
pmid = {24839893},
issn = {1879-307X},
mesh = {Animals ; *Artificial Intelligence ; Brain/*physiology ; Brain-Computer Interfaces ; Cognition/*physiology ; Humans ; Mind-Body Relations, Metaphysical ; *Models, Biological ; Perception ; Robotics ; Software ; },
abstract = {Traditionally, in cognitive science the emphasis is on studying cognition from a computational point of view. Studies in biologically inspired robotics and embodied intelligence, however, provide strong evidence that cognition cannot be analyzed and understood by looking at computational processes alone, but that physical system-environment interaction needs to be taken into account. In this opinion article, we review recent progress in cognitive developmental science and robotics, and expand the notion of embodiment to include soft materials and body morphology in the big picture. We argue that we need to build our understanding of cognition from the bottom up; that is, all the way from how our body is physically constructed.},
}
@article {pmid24839840,
year = {2014},
author = {Qiao, J and Hu, P and Hong, J},
title = {[Research of classification about BCI based on the signals energy].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {38},
number = {1},
pages = {14-18},
pmid = {24839840},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; },
abstract = {Aiming at the issue of motor imagery electroencephalography (EEG) pattern recognition in the research of brain-computer interface (BCI), a power feature method based on discrete wavelet packet decomposition is proposed for the channels C3 and C4. Firstly, a six-border Butterworth filter is used to denoise the two-channel EEG signals. Secondly, two-channel EEG signals are decomposed to five levels using Daubechies wavelet and the fourth level and the fifth level are chosen to reconstruct the signals and compute its power feature. Finally, linear discriminant analysis (LDA) is utilized to classify the feature and the Kappa value is utilized to measure the accuracy of the classifier. This method is applied to the standard dataset BCICIV_2b-gdf of BCI Competition 2008, and experimental results show that this method reflect the feature of event-related synchronization and event-related desynchronization obviously and it is an effective way to classify the EEG patterns in the research of BCI.},
}
@article {pmid24838816,
year = {2015},
author = {Taghizadeh-Sarabi, M and Daliri, MR and Niksirat, KS},
title = {Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines.},
journal = {Brain topography},
volume = {28},
number = {1},
pages = {33-46},
doi = {10.1007/s10548-014-0371-9},
pmid = {24838816},
issn = {1573-6792},
mesh = {Adolescent ; Adult ; Artifacts ; Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Photic Stimulation ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; *Wavelet Analysis ; Young Adult ; },
abstract = {Decoding and classification of objects through task-oriented electroencephalographic (EEG) signals are the most crucial goals of recent researches conducted mainly for brain-computer interface applications. In this study we aimed to classify single-trial 12 categories of recorded EEG signals. Ten subjects participated in this study. The task was to select target images among 12 basic object categories including animals, flowers, fruits, transportation devices, body organs, clothing, food, stationery, buildings, electronic devices, dolls and jewelry. In order to decode object categories, we have considered several units namely artifact removing, feature extraction, feature selection, and classification. Data were divided into training, validation, and test sets following the artifact removal process. Features were extracted using three different wavelets namely Daubechies4, Haar, and Symlet2. Features were selected among training data and were reduced afterward via scalar feature selection using three criteria including T test, entropy, and Bhattacharyya distance. Selected features were classified by the one-against-one support vector machine (SVM) multi-class classifier. The parameters of SVM were optimized based on training and validation sets. The classification performance (measured by means of accuracy) was obtained approximately 80 % for animal and stationery categories. Moreover, Symlet2 and T test were selected as better wavelet and selection criteria, respectively.},
}
@article {pmid24838347,
year = {2014},
author = {Schettini, F and Aloise, F and Aricò, P and Salinari, S and Mattia, D and Cincotti, F},
title = {Self-calibration algorithm in an asynchronous P300-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035004},
doi = {10.1088/1741-2560/11/3/035004},
pmid = {24838347},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain-Computer Interfaces/*standards ; Calibration ; Communication Devices for People with Disabilities/*standards ; Electroencephalography/*instrumentation/*standards ; Equipment Design ; Equipment Failure Analysis ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Female ; Humans ; Italy ; Male ; Reference Values ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Reliability is a desirable characteristic of brain-computer interface (BCI) systems when they are intended to be used under non-experimental operating conditions. In addition, their overall usability is influenced by the complex and frequent procedures that are required for configuration and calibration. Earlier studies examined the issue of asynchronous control in P300-based BCIs, introducing dynamic stopping and automatic control suspension features. This report proposes and evaluates an algorithm for the automatic recalibration of the classifier's parameters using unsupervised data.<br><br>APPROACH: Ten healthy subjects participated in five P300-based BCI sessions throughout a single day. First, we examined whether continuous adaptation of control parameters improved the accuracy of the asynchronous system over time. Then, we assessed the performance of the self-calibration algorithm with respect to the no-recalibration and supervised calibration conditions with regard to system accuracy and communication efficiency.<br><br>MAIN RESULTS: Offline tests demonstrated that continuous adaptation of the control parameters significantly increased the communication efficiency of asynchronous P300-based BCIs. The self-calibration algorithm correctly assigned labels to unsupervised data with 95% accuracy, effecting communication efficiency that was comparable with that of supervised repeated calibration.<br><br>SIGNIFICANCE: Although additional online tests that involve end-users under non-experimental conditions are needed, these preliminary results are encouraging, from which we conclude that the self-calibration algorithm is a promising solution to improve P300-based BCI usability and reliability.},
}
@article {pmid24838278,
year = {2014},
author = {Hill, NJ and Ricci, E and Haider, S and McCane, LM and Heckman, S and Wolpaw, JR and Vaughan, TM},
title = {A practical, intuitive brain-computer interface for communicating 'yes' or 'no' by listening.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035003},
pmid = {24838278},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Algorithms ; *Auditory Perception ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Female ; Humans ; Male ; *Man-Machine Systems ; Middle Aged ; Quadriplegia/*physiopathology/*rehabilitation ; Treatment Outcome ; User-Computer Interface ; },
abstract = {OBJECTIVE: Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words 'yes' and 'no') without loss of performance, and whether the system could be used by people in the locked-in state.<br><br>APPROACH: We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions.<br><br>MAIN RESULTS: The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects' performance with word stimuli (mean 77% &#xb1; 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73% &#xb1; 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects.<br><br>SIGNIFICANCE: Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.},
}
@article {pmid24838215,
year = {2014},
author = {Lesenfants, D and Habbal, D and Lugo, Z and Lebeau, M and Horki, P and Amico, E and Pokorny, C and Gómez, F and Soddu, A and Müller-Putz, G and Laureys, S and Noirhomme, Q},
title = {An independent SSVEP-based brain-computer interface in locked-in syndrome.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035002},
doi = {10.1088/1741-2560/11/3/035002},
pmid = {24838215},
issn = {1741-2552},
mesh = {Adult ; Aged ; *Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Humans ; Man-Machine Systems ; Middle Aged ; Neurofeedback/instrumentation ; Photic Stimulation/instrumentation/methods ; Quadriplegia/*physiopathology/*rehabilitation ; Speech Disorders/physiopathology/*rehabilitation ; Support Vector Machine ; Treatment Outcome ; User-Computer Interface ; *Visual Perception ; Young Adult ; },
abstract = {OBJECTIVE: Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS).<br><br>APPROACH: Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based 'interlaced squares' stimulation pattern.<br><br>MAIN RESULTS: Mean offline and online accuracies on healthy subjects were respectively 85 &#xb1; 2% and 74 &#xb1; 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 &#xb1; 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online.<br><br>SIGNIFICANCE: We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.},
}
@article {pmid24838070,
year = {2014},
author = {Thompson, DE and Quitadamo, LR and Mainardi, L and Laghari, KU and Gao, S and Kindermans, PJ and Simeral, JD and Fazel-Rezai, R and Matteucci, M and Falk, TH and Bianchi, L and Chestek, CA and Huggins, JE},
title = {Performance measurement for brain-computer or brain-machine interfaces: a tutorial.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035001},
pmid = {24838070},
issn = {1741-2552},
support = {R13 DC012744/DC/NIDCD NIH HHS/United States ; #1R13DC12744-1/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*standards ; Electroencephalography/*instrumentation/*standards ; Equipment Failure Analysis/*standards ; Guideline Adherence ; Neurofeedback/*instrumentation ; *Practice Guidelines as Topic ; United States ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research.<br><br>APPROACH: A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop.<br><br>MAIN RESULTS: Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories.<br><br>SIGNIFICANCE: Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.},
}
@article {pmid24837824,
year = {2014},
author = {Huggins, JE and Wolpaw, JR},
title = {Papers from the fifth international brain-computer interface meeting. Preface.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {030301},
pmid = {24837824},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R13 DC012744/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*ethics/*trends ; Communication Devices for People with Disabilities/*ethics/*trends ; Persons with Disabilities/*rehabilitation ; Humans ; Neurofeedback/*ethics ; Neuromuscular Diseases/*rehabilitation ; },
}
@article {pmid24836742,
year = {2014},
author = {Yang, H and Guan, C and Chua, KS and Chok, SS and Wang, CC and Soon, PK and Tang, CK and Ang, KK},
title = {Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035016},
doi = {10.1088/1741-2560/11/3/035016},
pmid = {24836742},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; *Deglutition ; Deglutition Disorders/etiology/*physiopathology/*rehabilitation ; Electroencephalography/methods ; Evoked Potentials, Motor ; Feasibility Studies ; Feedback, Physiological ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; Models, Neurological ; *Movement ; Neurofeedback/*methods ; Stroke/complications/physiopathology ; Stroke Rehabilitation ; Task Performance and Analysis ; User-Computer Interface ; Wavelet Analysis ; },
abstract = {OBJECTIVE: Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing.<br><br>APPROACH: Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data.<br><br>MAIN RESULTS: Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model.<br><br>SIGNIFICANCE: These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.},
}
@article {pmid24836588,
year = {2014},
author = {Mugler, EM and Patton, JL and Flint, RD and Wright, ZA and Schuele, SU and Rosenow, J and Shih, JJ and Krusienski, DJ and Slutzky, MW},
title = {Direct classification of all American English phonemes using signals from functional speech motor cortex.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035015},
pmid = {24836588},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; NIBIB/NINDS EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Female ; Humans ; *Language ; Male ; Motor Cortex ; Pattern Recognition, Automated/methods ; Sound Spectrography/*methods ; Speech/*physiology ; Speech Acoustics ; Speech Production Measurement/*methods ; *Speech Recognition Software ; Translating ; United States ; User-Computer Interface ; },
abstract = {OBJECTIVE: Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we sought to decode elements of speech production using ECoG.<br><br>APPROACH: We investigated words that contain the entire set of phonemes in the general American accent using ECoG with four subjects. Using a linear classifier, we evaluated the degree to which individual phonemes within each word could be correctly identified from cortical signal.<br><br>MAIN RESULTS: We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success.<br><br>SIGNIFICANCE: We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits s(-1) (33.6 words min(-1)), supporting pursuit of speech articulation for BCI control.},
}
@article {pmid24836436,
year = {2014},
author = {Boudria, Y and Feltane, A and Besio, W},
title = {Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035014},
doi = {10.1088/1741-2560/11/3/035014},
pmid = {24836436},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; *Computer Peripherals ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on electroencephalography (EEG) have been shown to accurately detect mental activities, but the acquisition of high levels of control require extensive user training. Furthermore, EEG has low signal-to-noise ratio and low spatial resolution. The objective of the present study was to compare the accuracy between two types of BCIs during the first recording session. EEG and tripolar concentric ring electrode (TCRE) EEG (tEEG) brain signals were recorded and used to control one-dimensional cursor movements.<br><br>APPROACH: Eight human subjects were asked to imagine either 'left' or 'right' hand movement during one recording session to control the computer cursor using TCRE and disc electrodes.<br><br>MAIN RESULTS: The obtained results show a significant improvement in accuracies using TCREs (44%-100%) compared to disc electrodes (30%-86%).<br><br>SIGNIFICANCE: This study developed the first tEEG-based BCI system for real-time one-dimensional cursor movements and showed high accuracies with little training.},
}
@article {pmid24836294,
year = {2014},
author = {Winkler, I and Brandl, S and Horn, F and Waldburger, E and Allefeld, C and Tangermann, M},
title = {Robust artifactual independent component classification for BCI practitioners.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035013},
doi = {10.1088/1741-2560/11/3/035013},
pmid = {24836294},
issn = {1741-2552},
mesh = {*Algorithms ; *Artifacts ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Humans ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {OBJECTIVE: EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain-computer interfaces (BCIs).<br><br>APPROACH: Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms.<br><br>MAIN RESULTS: We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control.<br><br>SIGNIFICANCE: Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.},
}
@article {pmid24836095,
year = {2014},
author = {Kaur, K and Shih, JJ and Krusienski, DJ},
title = {Empirical models of scalp-EEG responses using non-concurrent intracranial responses.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035012},
pmid = {24836095},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; NIBIB/NINDS EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Computer Simulation ; Electroencephalography/*methods ; Epilepsy/*physiopathology ; *Evoked Potentials ; Humans ; *Models, Neurological ; Scalp/physiopathology ; *Visual Perception ; },
abstract = {OBJECTIVE: This study presents inter-subject models of scalp-recorded electroencephalographic (sEEG) event-related potentials (ERPs) using intracranially recorded ERPs from electrocorticography and stereotactic depth electrodes in the hippocampus, generally termed as intracranial EEG (iEEG).<br><br>APPROACH: The participants were six patients with medically-intractable epilepsy that underwent temporary placement of intracranial electrode arrays to localize seizure foci. Participants performed one experimental session using a brain-computer interface matrix spelling paradigm controlled by sEEG prior to the iEEG electrode implantation, and one or more identical sessions controlled by iEEG after implantation. All participants were able to achieve excellent spelling accuracy using sEEG, four of the participants achieved roughly equivalent performance in the iEEG sessions, and all participants were significantly above chance accuracy for the iEEG sessions. The sERPs were modeled using a linear combination of iERPs using two different optimization criteria.<br><br>MAIN RESULTS: The results indicate that sERPs can be accurately estimated from the iERPs for the patients that exhibited stable ERPs over the respective sessions, and that the transformed iERPs can be accurately classified with an sERP-derived classifier.<br><br>SIGNIFICANCE: The resulting models provide a new empirical representation of the formation and distribution of sERPs from underlying composite iERPs. These new insights provide a better understanding of ERP relationships and can potentially lead to the development of more robust signal processing methods for noninvasive EEG applications.},
}
@article {pmid24835837,
year = {2014},
author = {Müller-Putz, GR and Daly, I and Kaiser, V},
title = {Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain-computer interface accuracy.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035011},
doi = {10.1088/1741-2560/11/3/035011},
pmid = {24835837},
issn = {1741-2552},
mesh = {Adult ; Aged ; Algorithms ; *Brain-Computer Interfaces ; Cervical Vertebrae/physiopathology ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Humans ; *Imagination ; Middle Aged ; Motor Cortex/*physiopathology ; Paralysis/etiology/*physiopathology/rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; Spinal Cord Injuries/complications/*physiopathology/rehabilitation ; Task Performance and Analysis ; Thoracic Vertebrae/physiopathology ; User-Computer Interface ; },
abstract = {OBJECTIVE: Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no 'cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI.<br><br>APPROACH: Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups.<br><br>MAIN RESULTS: It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%).<br><br>SIGNIFICANCE: The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.},
}
@article {pmid24835634,
year = {2014},
author = {Toppi, J and Risetti, M and Quitadamo, LR and Petti, M and Bianchi, L and Salinari, S and Babiloni, F and Cincotti, F and Mattia, D and Astolfi, L},
title = {Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035010},
doi = {10.1088/1741-2560/11/3/035010},
pmid = {24835634},
issn = {1741-2552},
mesh = {Adaptation, Physiological/physiology ; Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Learning/*physiology ; Male ; Neurofeedback/*methods/*physiology ; Periodicity ; Reproducibility of Results ; Sensitivity and Specificity ; Sensorimotor Cortex/*physiology ; Somatosensory Cortex/*physiopathology ; },
abstract = {OBJECTIVE: It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks.<br><br>APPROACH: Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms.<br><br>MAIN RESULTS: Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training.<br><br>SIGNIFICANCE: The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.},
}
@article {pmid24835495,
year = {2014},
author = {Martel, A and Dähne, S and Blankertz, B},
title = {EEG predictors of covert vigilant attention.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035009},
doi = {10.1088/1741-2560/11/3/035009},
pmid = {24835495},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Arousal/*physiology ; Attention/*physiology ; Brain/*physiology ; Brain Mapping/methods ; Cues ; Electroencephalography/*methods ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Motion Perception/*physiology ; Reaction Time/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: The present study addressed the question whether neurophysiological signals exhibit characteristic modulations preceding a miss in a covert vigilant attention task which mimics a natural environment in which critical stimuli may appear in the periphery of the visual field.<br><br>APPROACH: Subjective, behavioural and encephalographic (EEG) data of 12 participants performing a modified Mackworth Clock task were obtained and analysed offline. The stimulus consisted of a pointer performing regular ticks in a clockwise sequence across 42 dots arranged in a circle. Participants were requested to covertly attend to the pointer and press a response button as quickly as possible in the event of a jump, a rare and random event.<br><br>MAIN RESULTS: Significant increases in response latencies and decreases in the detection rates were found as a function of time-on-task, a characteristic effect of sustained attention tasks known as the vigilance decrement. Subjective sleepiness showed a significant increase over the duration of the experiment. Increased activity in the &#x3b1;-frequency range (8-14 Hz) was observed emerging and gradually accumulating 10 s before a missed target. Additionally, a significant gradual attenuation of the P3 event-related component was found to antecede misses by 5 s.<br><br>SIGNIFICANCE: The results corroborate recent findings that behavioural errors are presaged by specific neurophysiological activity and demonstrate that lapses of attention can be predicted in a covert setting up to 10 s in advance reinforcing the prospective use of brain-computer interface (BCI) technology for the detection of waning vigilance in real-world scenarios. Combining these findings with real-time single-trial analysis from BCI may pave the way for cognitive states monitoring systems able to determine the current, and predict the near-future development of the brain's attentional processes.},
}
@article {pmid24835331,
year = {2014},
author = {Aricò, P and Aloise, F and Schettini, F and Salinari, S and Mattia, D and Cincotti, F},
title = {Influence of P300 latency jitter on event related potential-based brain-computer interface performance.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035008},
doi = {10.1088/1741-2560/11/3/035008},
pmid = {24835331},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Language ; Male ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; User-Computer Interface ; Word Processing ; },
abstract = {OBJECTIVE: Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality.<br><br>APPROACH: We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed.<br><br>MAIN RESULTS: The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant.<br><br>SIGNIFICANCE: The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.},
}
@article {pmid24835132,
year = {2014},
author = {Lorenz, R and Pascual, J and Blankertz, B and Vidaurre, C},
title = {Towards a holistic assessment of the user experience with hybrid BCIs.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035007},
doi = {10.1088/1741-2560/11/3/035007},
pmid = {24835132},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces/*psychology ; Ergonomics/*methods ; Female ; Holistic Health ; Humans ; Male ; *Man-Machine Systems ; Patient Participation/*methods/*psychology ; *Patient Satisfaction ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: In recent years, brain-computer interfaces (BCIs) have become mature enough to immensely benefit from the expertise and tools established in the field of human-computer interaction (HCI). One of the core objectives in HCI research is the design of systems that provide a pleasurable user experience (UX). While the majority of BCI studies exclusively evaluate common efficiency measures such as classification accuracy and speed, single research groups have begun to look at further usability aspects such as ease of use, workload and learnability. However, these evaluation metrics only cover pragmatic aspects of UX while still not considering the hedonic quality of UX. In order to gain a holistic perspective on UX, hedonic quality aspects such as motivation and frustration were also taken into account for our evaluation of three BCI-driven interfaces, which were proposed to be used as a two-stage neuroprosthetic control within the EU project MUNDUS.<br><br>APPROACH: At the first stage, one of six possible actions was selected and either confirmed or cancelled at the second stage. For the experiment, a solely event-related-potential-based interface (ERP-ERP) and two hybrid solutions were tested that were controlled by ERP and motor imagery (MI)--resulting in the two possible combinations: ERP selection/MI confirmation (ERP-MI) or MI selection/ERP confirmation (MI-ERP). Behavioural, subjective and encephalographic (EEG) data of 12 healthy subjects were collected during an online experiment with the three graphical user interfaces (GUIs).<br><br>MAIN RESULTS: Results showed a significantly greater pragmatic quality (in terms of accuracy, efficiency, workload, use quality and learnability) for the ERP-ERP and ERP-MI GUIs in contrast to the MI-ERP GUI. Consequently, the MI-ERP GUI is least suited for use as a neuroprosthetic control. With respect to the comparison of the ERP-ERP and ERP-MI GUIs, no significant differences in pragmatic and hedonic quality of UX were found. Since throughout better results were obtained for the conventional approach and it was most preferred by the subjects, the ERP-ERP GUI seems more suitable for its deployment in actual end-users. Nevertheless, for individuals with stable MI patterns, the hybrid interface can be provided as an additional option of choice within the MUNDUS framework.<br><br>SIGNIFICANCE: Although the paramount goal in BCI research still remains the improvement of classification accuracy and communication speed, it is of significance to note that it is equally important for end-users to keep up their motivation and prevent frustration. By including pragmatic as well as hedonic quality aspects, this study is the first effort to gain a holistic perspective of the UX while interacting with BCI-driven assistive technology aimed at actual end-users. The broad-scale methodology provided valuable insights into the underlying dynamics causing the users' experience to differ across the GUIs. The results will be used to refine a BCI-driven neuroprosthesis and test it with end-users.},
}
@article {pmid24834973,
year = {2014},
author = {Cohen, O and Koppel, M and Malach, R and Friedman, D},
title = {Controlling an avatar by thought using real-time fMRI.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035006},
doi = {10.1088/1741-2560/11/3/035006},
pmid = {24834973},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Computer Systems ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Imagination/*physiology ; Magnetic Resonance Imaging/methods ; Male ; Movement ; Robotics/*methods ; Spatial Navigation/*physiology ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: We have developed a brain-computer interface (BCI) system based on real-time functional magnetic resonance imaging (fMRI) with virtual reality feedback. The advantage of fMRI is the relatively high spatial resolution and the coverage of the whole brain; thus we expect that it may be used to explore novel BCI strategies, based on new types of mental activities. However, fMRI suffers from a low temporal resolution and an inherent delay, since it is based on a hemodynamic response rather than electrical signals. Thus, our objective in this paper was to explore whether subjects could perform a BCI task in a virtual environment using our system, and how their performance was affected by the delay.<br><br>APPROACH: The subjects controlled an avatar by left-hand, right-hand and leg motion or imagery. The BCI classification is based on locating the regions of interest (ROIs) related with each of the motor classes, and selecting the ROI with maximum average values online. The subjects performed a cue-based task and a free-choice task, and the analysis includes evaluation of the performance as well as subjective reports.<br><br>MAIN RESULTS: Six subjects performed the task with high accuracy when allowed to move their fingers and toes, and three subjects achieved high accuracy using imagery alone. In the cue-based task the accuracy was highest 8-12 s after the trigger, whereas in the free-choice task the subjects performed best when the feedback was provided 6 s after the trigger.<br><br>SIGNIFICANCE: We show that subjects are able to perform a navigation task in a virtual environment using an fMRI-based BCI, despite the hemodynamic delay. The same approach can be extended to other mental tasks and other brain areas.},
}
@article {pmid24834896,
year = {2014},
author = {Kindermans, PJ and Tangermann, M and Müller, KR and Schrauwen, B},
title = {Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {035005},
doi = {10.1088/1741-2560/11/3/035005},
pmid = {24834896},
issn = {1741-2552},
mesh = {Algorithms ; *Artificial Intelligence ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Language ; Man-Machine Systems ; *Models, Theoretical ; Systems Integration ; User-Computer Interface ; },
abstract = {OBJECTIVE: Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping.<br><br>APPROACH: A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated.<br><br>MAIN RESULTS: Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation.<br><br>SIGNIFICANCE: A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.},
}
@article {pmid24834047,
year = {2014},
author = {Gharabaghi, A and Naros, G and Walter, A and Roth, A and Bogdan, M and Rosenstiel, W and Mehring, C and Birbaumer, N},
title = {Epidural electrocorticography of phantom hand movement following long-term upper-limb amputation.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {285},
pmid = {24834047},
issn = {1662-5161},
abstract = {INTRODUCTION: Prostheses for upper-limb amputees are currently controlled by either myoelectric or peripheral neural signals. Performance and dexterity of these devices is still limited, particularly when it comes to controlling hand function. Movement-related brain activity might serve as a complementary bio-signal for motor control of hand prosthesis.<br><br>METHODS: We introduced a methodology to implant a cortical interface without direct exposure of the brain surface in an upper-limb amputee. This bi-directional interface enabled us to explore the cortical physiology following long-term transhumeral amputation. In addition, we investigated neurofeedback of electrocorticographic brain activity related to the patient's motor imagery to open his missing hand, i.e., phantom hand movement, for real-time control of a virtual hand prosthesis.<br><br>RESULTS: Both event-related brain activity and cortical stimulation revealed mutually overlapping cortical representations of the phantom hand. Phantom hand movements could be robustly classified and the patient required only three training sessions to gain reliable control of the virtual hand prosthesis in an online closed-loop paradigm that discriminated between hand opening and rest.<br><br>CONCLUSION: Epidural implants may constitute a powerful and safe alternative communication pathway between the brain and external devices for upper-limb amputees, thereby facilitating the integrated use of different signal sources for more intuitive and specific control of multi-functional devices in clinical use.},
}
@article {pmid24834030,
year = {2014},
author = {Mandonnet, E and Duffau, H},
title = {Understanding entangled cerebral networks: a prerequisite for restoring brain function with brain-computer interfaces.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {82},
pmid = {24834030},
issn = {1662-5137},
abstract = {Historically, cerebral processing has been conceptualized as a framework based on statically localized functions. However, a growing amount of evidence supports a hodotopical (delocalized) and flexible organization. A number of studies have reported absence of a permanent neurological deficit after massive surgical resections of eloquent brain tissue. These results highlight the tremendous plastic potential of the brain. Understanding anatomo-functional correlates underlying this cerebral reorganization is a prerequisite to restore brain functions through brain-computer interfaces (BCIs) in patients with cerebral diseases, or even to potentiate brain functions in healthy individuals. Here, we review current knowledge of neural networks that could be utilized in the BCIs that enable movements and language. To this end, intraoperative electrical stimulation in awake patients provides valuable information on the cerebral functional maps, their connectomics and plasticity. Overall, these studies indicate that the complex cerebral circuitry that underpins interactions between action, cognition and behavior should be throughly investigated before progress in BCI approaches can be achieved.},
}
@article {pmid24833254,
year = {2014},
author = {Broccard, FD and Mullen, T and Chi, YM and Peterson, D and Iversen, JR and Arnold, M and Kreutz-Delgado, K and Jung, TP and Makeig, S and Poizner, H and Sejnowski, T and Cauwenberghs, G},
title = {Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders.},
journal = {Annals of biomedical engineering},
volume = {42},
number = {8},
pages = {1573-1593},
pmid = {24833254},
issn = {1573-9686},
support = {NS065701/NS/NINDS NIH HHS/United States ; R01-NS047293-09A1/NS/NINDS NIH HHS/United States ; U54 NS065701/NS/NINDS NIH HHS/United States ; /HHMI_/Howard Hughes Medical Institute/United States ; R01 NS047293/NS/NINDS NIH HHS/United States ; U54 TR001456/TR/NCATS NIH HHS/United States ; },
mesh = {Animals ; Brain/physiology ; Feedback, Physiological ; Humans ; Movement Disorders/*therapy ; Neuronal Plasticity ; },
abstract = {Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.},
}
@article {pmid24829569,
year = {2014},
author = {Li, L and Brockmeier, AJ and Choi, JS and Francis, JT and Sanchez, JC and Príncipe, JC},
title = {A tensor-product-kernel framework for multiscale neural activity decoding and control.},
journal = {Computational intelligence and neuroscience},
volume = {2014},
number = {},
pages = {870160},
pmid = {24829569},
issn = {1687-5273},
mesh = {Action Potentials/*physiology ; Animals ; Brain/*cytology ; Brain-Computer Interfaces ; Computer Simulation ; Electric Stimulation ; Evoked Potentials/physiology ; Female ; Fingers/innervation ; Humans ; *Models, Neurological ; Neurons/*physiology ; Rats ; Rats, Long-Evans ; *Signal Processing, Computer-Assisted ; Touch ; },
abstract = {Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.},
}
@article {pmid24829038,
year = {2014},
author = {Maes, IH and Cima, RF and Anteunis, LJ and Scheijen, DJ and Baguley, DM and El Refaie, A and Vlaeyen, JW and Joore, MA},
title = {Cost-effectiveness of specialized treatment based on cognitive behavioral therapy versus usual care for tinnitus.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {35},
number = {5},
pages = {787-795},
doi = {10.1097/MAO.0000000000000331},
pmid = {24829038},
issn = {1537-4505},
mesh = {Adult ; Aged ; Cognitive Behavioral Therapy/economics/*methods ; Cost-Benefit Analysis ; Female ; Health Care Costs ; Humans ; Male ; Middle Aged ; *Quality of Life ; Quality-Adjusted Life Years ; Tinnitus/economics/psychology/*therapy ; Treatment Outcome ; },
abstract = {OBJECTIVE: To evaluate the cost-effectiveness of specialized multidisciplinary tinnitus treatment based on cognitive behavioral therapy, compared with care as usual.<br><br>DESIGN: Randomized controlled trial including an economic evaluation from a health-care and societal perspective, using a one-year time horizon.<br><br>SETTING: Audiologic center.<br><br>PATIENTS: A referred sample of 626 patients with tinnitus were eligible for participation. Approximately 492 patients were included in the study. Eighty-six (35%) of 247 patients in the usual care group, and 74 (30%) of 245 patients in the specialized care group were lost to follow-up by month 12.<br><br>MAIN OUTCOME MEASURES: Quality adjusted life years (QALYs) as measured with the Health Utilities Index Mark III and cost in US dollars.<br><br>RESULTS: Compared with patients receiving usual care, patients who received specialized care gained on average 0.015 QALYs (95% bootstrapped confidence interval [BCI], -0.03 to 0.06). The incremental costs from a societal perspective are $357 (95% BCI,-$1,034 to $1,785). The incremental cost per QALY from a societal perspective amounted to $24,580. The probability that SC is cost-effective from a societal perspective is 58% for a willingness to pay for a QALY of $45,000.<br><br>CONCLUSION: Specialized multidisciplinary tinnitus treatment based on cognitive behavioral therapy is cost-effective as compared with usual care. Although uncertainty surrounding the incremental costs and effects is considerable, sensitivity analysis indicated that cost-effectiveness results were robust.},
}
@article {pmid24828450,
year = {2014},
author = {Nasseroleslami, B and Vossoughi, G and Boroushaki, M and Parnianpour, M},
title = {Simulation of movement in three-dimensional musculoskeletal human lumbar spine using directional encoding-based neurocontrollers.},
journal = {Journal of biomechanical engineering},
volume = {136},
number = {9},
pages = {091010},
doi = {10.1115/1.4027664},
pmid = {24828450},
issn = {1528-8951},
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Humans ; Lumbar Vertebrae/*physiology ; *Models, Biological ; *Movement ; Muscles/*physiology ; },
abstract = {Despite development of accurate musculoskeletal models for human lumbar spine, the methods for prediction of muscle activity patterns in movements lack proper association with corresponding sensorimotor integrations. This paper uses the directional information of the Jacobian of the musculoskeletal system to orchestrate adaptive critic-based fuzzy neural controller modules for controlling a complex nonlinear redundant musculoskeletal system. The proposed controller is used to control a 3D 3-degree of freedom (DOF) musculoskeletal model of trunk, actuated by 18 muscles. The controller is capable of learning to control from sensory information, without relying on pre-assumed model parameters. Simulation results show satisfactory tracking of movements and the simulated muscle activation patterns conform to previous EMG experiments and optimization studies. The proposed controller can be used as a computationally inexpensive muscle activity generator to distinguish between neural and mechanical contributions to movement and for study of sensory versus motor origins of motor function and dysfunction in human spine.},
}
@article {pmid24828128,
year = {2014},
author = {Xie, J and Xu, G and Wang, J and Zhang, S and Zhang, F and Li, Y and Han, C and Li, L},
title = {Addition of visual noise boosts evoked potential-based brain-computer interface.},
journal = {Scientific reports},
volume = {4},
number = {},
pages = {4953},
pmid = {24828128},
issn = {2045-2322},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; *Noise ; *Photic Stimulation ; Stochastic Processes ; Young Adult ; },
abstract = {Although noise has a proven beneficial role in brain functions, there have not been any attempts on the dedication of stochastic resonance effect in neural engineering applications, especially in researches of brain-computer interfaces (BCIs). In our study, a steady-state motion visual evoked potential (SSMVEP)-based BCI with periodic visual stimulation plus moderate spatiotemporal noise can achieve better offline and online performance due to enhancement of periodic components in brain responses, which was accompanied by suppression of high harmonics. Offline results behaved with a bell-shaped resonance-like functionality and 7-36% online performance improvements can be achieved when identical visual noise was adopted for different stimulation frequencies. Using neural encoding modeling, these phenomena can be explained as noise-induced input-output synchronization in human sensory systems which commonly possess a low-pass property. Our work demonstrated that noise could boost BCIs in addressing human needs.},
}
@article {pmid24822035,
year = {2014},
author = {Lin, YP and Yang, YH and Jung, TP},
title = {Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening.},
journal = {Frontiers in neuroscience},
volume = {8},
number = {},
pages = {94},
pmid = {24822035},
issn = {1662-4548},
abstract = {Electroencephalography (EEG)-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI), neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications), the music modality would play a complementary role and augment the EEG results from around 61-67% in valence classification and from around 58-67% in arousal classification. The musical timber appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.},
}
@article {pmid24819489,
year = {2014},
author = {Hughes, MA},
title = {Engineering brain-computer interfaces: past, present and future.},
journal = {Journal of neurosurgical sciences},
volume = {58},
number = {2},
pages = {117-123},
pmid = {24819489},
issn = {0390-5616},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {Biomedical Engineering/*history ; Brain-Computer Interfaces/*history ; History, 18th Century ; History, 19th Century ; History, 20th Century ; History, 21st Century ; History, Ancient ; Humans ; Nervous System Diseases/*history/rehabilitation ; Neurosurgery/*history ; },
abstract = {Electricity governs the function of both nervous systems and computers. Whilst ions move in polar fluids to depolarize neuronal membranes, electrons move in the solid-state lattices of microelectronic semiconductors. Joining these two systems together, to create an iono-electric brain-computer interface, is an immense challenge. However, such interfaces offer (and in select clinical contexts have already delivered) a method of overcoming disability caused by neurological or musculoskeletal pathology. To fulfill their theoretical promise, several specific challenges demand consideration. Rate-limiting steps cover a diverse range of disciplines including microelectronics, neuro-informatics, engineering, and materials science. As those who work at the tangible interface between brain and outside world, neurosurgeons are well placed to contribute to, and inform, this cutting edge area of translational research. This article explores the historical background, status quo, and future of brain-computer interfaces; and outlines the challenges to progress and opportunities available to the clinical neurosciences community.},
}
@article {pmid24815034,
year = {2014},
author = {Li, J and Peng, Y and Liu, Y and Li, W and Jin, Y and Tang, Z and Duan, Y},
title = {Investigation of potential breath biomarkers for the early diagnosis of breast cancer using gas chromatography-mass spectrometry.},
journal = {Clinica chimica acta; international journal of clinical chemistry},
volume = {436},
number = {},
pages = {59-67},
doi = {10.1016/j.cca.2014.04.030},
pmid = {24815034},
issn = {1873-3492},
mesh = {Aldehydes/*analysis ; Biomarkers, Tumor/*analysis ; Breast Neoplasms/*diagnosis ; *Breath Tests ; Case-Control Studies ; Early Detection of Cancer/*methods ; Female ; *Gas Chromatography-Mass Spectrometry ; Humans ; },
abstract = {BACKGROUND: Breast cancer (BC) remains the most commonly diagnosed malignancy in women. We investigated 4 straight aldehydes in the exhaled breath as potential early BC diagnostic biomarkers.<br><br>METHODS: End-tailed breath were collected by Bio-VOC&#xae; sampler and assayed by gas chromatography-mass spectrometry. Kruskal-Wallis one-way analysis of variance test and binary logistic regression were used for data analysis. The diagnostic accuracies were evaluated by receiver operating characteristic curves. A predictive model/equation was generated using the 4 biomarkers and validated by leave-one-out cross-validation.<br><br>RESULTS: All four potential biomarkers demonstrated significant differences in concentrations between BC and healthy controls (HC) (p<0.05). The areas under the curves (AUCs) in HC vs BCI-II model using hexanal, heptanal, octanal, and nonanal were 0.816, 0.809, 0.731, and 0.830, respectively. The AUC for their combined use was 0.934 (sensitivity 91.7%, specificity 95.8%) in the early diagnosis of BC. The predictive model/equation exhibited good sensitivity (72.7%) and specificity (91.7%) in distinguishing between HC and BC (cross-validation: sensitivity 68.2% and specificity 91.7%).<br><br>CONCLUSIONS: The diagnostic values of 4 exhaled straight aldehydes as early diagnostic biomarkers for BC were successfully verified and the diagnostic accuracy improved in their combined use.},
}
@article {pmid24809722,
year = {2014},
author = {Kim, J and Lee, SK and Lee, B},
title = {EEG classification in a single-trial basis for vowel speech perception using multivariate empirical mode decomposition.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036010},
doi = {10.1088/1741-2560/11/3/036010},
pmid = {24809722},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Auditory/*physiology ; Humans ; Korea ; Male ; Multivariate Analysis ; Pattern Recognition, Automated/methods ; Psychomotor Performance/physiology ; *Semantics ; Speech Perception/*physiology ; Speech Production Measurement/methods ; Speech Recognition Software ; },
abstract = {OBJECTIVE: The objective of this study is to find components that might be related to phoneme representation in the brain and to discriminate EEG responses for each speech sound on a trial basis.<br><br>APPROACH: We used multivariate empirical mode decomposition (MEMD) and common spatial pattern for feature extraction. We chose three vowel stimuli, /a/, /i/ and /u/, based on previous findings, such that the brain can detect change in formant frequency (F2) of vowels. EEG activity was recorded from seven native Korean speakers at Gwangju Institute of Science and Technology. We applied MEMD over EEG channels to extract speech-related brain signal sources, and looked for the intrinsic mode functions which were dominant in the alpha bands. After the MEMD procedure, we applied the common spatial pattern algorithm for enhancing the classification performance, and used linear discriminant analysis (LDA) as a classifier.<br><br>MAIN RESULTS: The brain responses to the three vowels could be classified as one of the learned phonemes on a single-trial basis with our approach.<br><br>SIGNIFICANCE: The results of our study show that brain responses to vowels can be classified for single trials using MEMD and LDA. This approach may not only become a useful tool for the brain-computer interface but it could also be used for discriminating the neural correlates of categorical speech perception.},
}
@article {pmid24809544,
year = {2014},
author = {Wang, D and Zhang, Q and Li, Y and Wang, Y and Zhu, J and Zhang, S and Zheng, X},
title = {Long-term decoding stability of local field potentials from silicon arrays in primate motor cortex during a 2D center out task.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036009},
doi = {10.1088/1741-2560/11/3/036009},
pmid = {24809544},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Longitudinal Studies ; Macaca ; Male ; Microarray Analysis/instrumentation ; *Microelectrodes ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Silicon ; },
abstract = {OBJECTIVE: Many serious concerns exist in the long-term stability of brain-machine interfaces (BMIs) based on spike signals (single unit activity, SUA; multi unit activity, MUA). Some studies showed local field potentials (LFPs) could offer a stable decoding performance. However, the decoding stability of LFPs was examined only when high quality spike signals were recorded. Here we aim to examine the long-term decoding stability of LFPs over a larger time scale when the quality of spike signals was from good to poor or even no spike was recorded.<br><br>APPROACH: Neural signals were collected from motor cortex of three monkeys via silicon arrays over 230, 290 and 690 days post-implantation when they performed 2D center out task. To compare long-term stability between LFPs and spike signals, we examined them in neural signals characteristics, directional tuning properties and offline decoding performance, respectively.<br><br>MAIN RESULTS: We observed slow decreasing trends in the number of LFP channels recorded and mean LFP power in different frequency bands when spike signals quality decayed over time. The number of significantly directional tuning LFP channels decreased more slowly than that of tuning SUA and MUA. The variable preferred directions for the same signal features across sessions indicated non-stationarity of neural activity. We also found that LFPs achieved better decoding performance than SUA and MUA in retrained decoder when the quality of spike signals seriously decayed. Especially, when no spike was recorded in one monkey after 671 days post-implantation, LFPs still provided some kinematic information. In addition, LFPs outperformed MUA in long-term decoding stability in a static decoder.<br><br>SIGNIFICANCE: Our results suggested that LFPs were more durable and could provide better decoding performance when spike signals quality seriously decayed. It might be due to their resistance to recording degradation and their high redundancy among channels.},
}
@article {pmid24808859,
year = {2014},
author = {Sohal, HS and Jackson, A and Jackson, R and Clowry, GJ and Vassilevski, K and O'Neill, A and Baker, SN},
title = {The sinusoidal probe: a new approach to improve electrode longevity.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {10},
pmid = {24808859},
issn = {1662-6443},
support = {/WT_/Wellcome Trust/United Kingdom ; 101002/WT_/Wellcome Trust/United Kingdom ; },
abstract = {Micromotion between the brain and implanted electrodes is a major contributor to the failure of invasive brain-machine interfaces. Movements of the electrode tip cause recording instabilities while spike amplitudes decline over the weeks/months post-implantation due to glial cell activation caused by sustained mechanical trauma. We have designed a sinusoidal probe in order to reduce movement of the recording tip relative to the surrounding neural tissue. The probe was microfabricated from flexible materials and incorporated a sinusoidal shaft to minimize tethering forces and a 3D spheroid tip to anchor the recording site within the brain. Compared to standard microwire electrodes, the signal-to-noise ratio and local field potential power of sinusoidal probe recordings from rabbits was more stable across recording periods up to 678 days. Histological quantification of microglia and astrocytes showed reduced neuronal tissue damage especially for the tip region between 6 and 24 months post-implantation. We suggest that the micromotion-reducing measures incorporated into our design, at least partially, decreased the magnitude of gliosis, resulting in enhanced longevity of recording.},
}
@article {pmid24808844,
year = {2014},
author = {Khan, MJ and Hong, MJ and Hong, KS},
title = {Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {244},
pmid = {24808844},
issn = {1662-5161},
abstract = {The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, "forward," "backward," "left," and "right." The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.},
}
@article {pmid24808833,
year = {2014},
author = {Baranauskas, G},
title = {What limits the performance of current invasive brain machine interfaces?.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {68},
pmid = {24808833},
issn = {1662-5137},
abstract = {The concept of a brain-machine interface (BMI) or a computer-brain interface is simple: BMI creates a communication pathway for a direct control by brain of an external device. In reality BMIs are very complex devices and only recently the increase in computing power of microprocessors enabled a boom in BMI research that continues almost unabated to this date, the high point being the insertion of electrode arrays into the brains of 5 human patients in a clinical trial run by Cyberkinetics with few other clinical tests still in progress. Meanwhile several EEG-based BMI devices (non-invasive BMIs) were launched commercially. Modern electronics and dry electrode technology made possible to drive the cost of some of these devices below few hundred dollars. However, the initial excitement of the direct control by brain waves of a computer or other equipment is dampened by large efforts required for learning, high error rates and slow response speed. All these problems are directly related to low information transfer rates typical for such EEG-based BMIs. In invasive BMIs employing multiple electrodes inserted into the brain one may expect much higher information transfer rates than in EEG-based BMIs because, in theory, each electrode provides an independent information channel. However, although invasive BMIs require more expensive equipment and have ethical problems related to the need to insert electrodes in the live brain, such financial and ethical costs are often not offset by a dramatic improvement in the information transfer rate. Thus the main topic of this review is why in invasive BMIs an apparently much larger information content obtained with multiple extracellular electrodes does not translate into much higher rates of information transfer? This paper explores possible answers to this question by concluding that more research on what movement parameters are encoded by neurons in motor cortex is needed before we can enjoy the next generation BMIs.},
}
@article {pmid24808520,
year = {2013},
author = {Zhang, H and Yang, H and Guan, C},
title = {Bayesian learning for spatial filtering in an EEG-based brain-computer interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {24},
number = {7},
pages = {1049-1060},
doi = {10.1109/TNNLS.2013.2249087},
pmid = {24808520},
issn = {2162-2388},
mesh = {Algorithms ; *Bayes Theorem ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Probability Learning ; },
abstract = {Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.},
}
@article {pmid24808413,
year = {2014},
author = {Mainsah, BO and Colwell, KA and Collins, LM and Throckmorton, CS},
title = {Utilizing a language model to improve online dynamic data collection in P300 spellers.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {4},
pages = {837-846},
pmid = {24808413},
issn = {1558-0210},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R33DC010470-03/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Language ; Male ; Models, Theoretical ; *Natural Language Processing ; Online Systems ; Task Performance and Analysis ; Word Processing/*methods ; *Writing ; Young Adult ; },
abstract = {P300 spellers provide a means of communication for individuals with severe physical limitations, especially those with locked-in syndrome, such as amyotrophic lateral sclerosis. However, P300 speller use is still limited by relatively low communication rates due to the multiple data measurements that are required to improve the signal-to-noise ratio of event-related potentials for increased accuracy. Therefore, the amount of data collection has competing effects on accuracy and spelling speed. Adaptively varying the amount of data collection prior to character selection has been shown to improve spelling accuracy and speed. The goal of this study was to optimize a previously developed dynamic stopping algorithm that uses a Bayesian approach to control data collection by incorporating a priori knowledge via a language model. Participants (n = 17) completed online spelling tasks using the dynamic stopping algorithm, with and without a language model. The addition of the language model resulted in improved participant performance from a mean theoretical bit rate of 46.12 bits/min at 88.89% accuracy to 54.42 bits/min () at 90.36% accuracy.},
}
@article {pmid24808381,
year = {2013},
author = {Arvaneh, M and Guan, C and Ang, KK and Quek, C},
title = {Optimizing spatial filters by minimizing within-class dissimilarities in electroencephalogram-based brain-computer interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {24},
number = {4},
pages = {610-619},
doi = {10.1109/TNNLS.2013.2239310},
pmid = {24808381},
issn = {2162-2388},
mesh = {*Algorithms ; Brain-Computer Interfaces/standards/*statistics &amp; numerical data ; Electroencephalography/standards/*statistics &amp; numerical data ; Humans ; },
abstract = {A major challenge in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the inherent nonstationarities in the EEG data. Variations of the signal properties from intra and inter sessions often lead to deteriorated BCI performances, as features extracted by methods such as common spatial patterns (CSP) are not invariant against the changes. To extract features that are robust and invariant, this paper proposes a novel spatial filtering algorithm called Kullback-Leibler (KL) CSP. The CSP algorithm only considers the discrimination between the means of the classes, but does not consider within-class scatters information. In contrast, the proposed KLCSP algorithm simultaneously maximizes the discrimination between the class means, and minimizes the within-class dissimilarities measured by a loss function based on the KL divergence. The performance of the proposed KLCSP algorithm is compared against two existing algorithms, CSP and stationary CSP (sCSP), using the publicly available BCI competition III dataset IVa and a large dataset from stroke patients performing neuro-rehabilitation. The results show that the proposed KLCSP algorithm significantly outperforms both the CSP and the sCSP algorithms, in terms of classification accuracy, by reducing within-class variations. This results in more compact and separable features.},
}
@article {pmid24808078,
year = {2013},
author = {Lécuyer, A and George, L and Marchal, M},
title = {Toward adaptive VR simulators combining visual, haptic, and brain-computer interfaces.},
journal = {IEEE computer graphics and applications},
volume = {33},
number = {5},
pages = {18-23},
doi = {10.1109/MCG.2013.80},
pmid = {24808078},
issn = {1558-1756},
mesh = {*Man-Machine Systems ; *User-Computer Interface ; },
abstract = {The next generation of VR simulators could take into account a novel input: the user's mental state, as measured with electrodes and a brain-computer interface. One illustration of this promising path is a project that adapted a guidance system's force feedback to the user's mental workload in real time. A first application of this approach is a medical training simulator that provides virtual assistance that adapts to the trainee's mental activity. Such results pave the way to VR systems that will automatically reconfigure and adapt to their users' mental states and cognitive processes.},
}
@article {pmid24807028,
year = {2014},
author = {Gandhi, V and Prasad, G and Coyle, D and Behera, L and McGinnity, TM},
title = {Quantum neural network-based EEG filtering for a brain-computer interface.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {25},
number = {2},
pages = {278-288},
doi = {10.1109/TNNLS.2013.2274436},
pmid = {24807028},
issn = {2162-2388},
mesh = {Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.},
}
@article {pmid24805217,
year = {2013},
author = {Shih, MH and Tsai, FS},
title = {Operator control of interneural computing machines.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {24},
number = {12},
pages = {1986-1998},
doi = {10.1109/TNNLS.2013.2271258},
pmid = {24805217},
issn = {2162-2388},
mesh = {Biomimetics/*methods ; *Brain-Computer Interfaces ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; Robotics/methods ; },
abstract = {A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design an interneural computing machine (INCM) made of plastic random interneural connections. We develop a mechanical way to measure collective ensemble firing of neurons in INCM. Two sorts of plasticity operators are derived from the measure of synchronous neural activity and the measure of self-sustaining neural activity, respectively. Such plasticity operators conduct activity-dependent operation to modify the network structure of INCM. The activity-dependent operation meets the neurobiological perspective of Hebbian synaptic plasticity and displays the tendency toward circulation breaking aiming to control neural population dynamics. We call such operation operator control of INCM and develop a population analysis of operator control for measuring how well single neurons of INCM can produce rhythmic, oscillatory activity, but at the level of neural ensembles, generate multiphasic patterns of population responses.},
}
@article {pmid24804474,
year = {2014},
author = {Jia, M and Wang, J and Li, J and Hong, W},
title = {[Application of semi-supervised sparse representation classifier based on help training in EEG classification].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {31},
number = {1},
pages = {1-6},
pmid = {24804474},
issn = {1001-5515},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*classification ; Humans ; },
abstract = {Electroencephalogram (EEG) classification for brain-computer interface (BCI) is a new way of realizing human-computer interreaction. In this paper the application of semi-supervised sparse representation classifier algorithms based on help training to EEG classification for BCI is reported. Firstly, the correlation information of the unlabeled data is obtained by sparse representation classifier and some data with high correlation selected. Secondly, the boundary information of the selected data is produced by discriminative classifier, which is the Fisher linear classifier. The final unlabeled data with high confidence are selected by a criterion containing the information of distance and direction. We applied this novel method to the three benchmark datasets, which were BCI I, BCI II_IV and USPS. The classification rate were 97%, 82% and 84.7%, respectively. Moreover the fastest arithmetic rate was just about 0. 2 s. The classification rate and efficiency results of the novel method are both better than those of S3VM and SVM, proving that the proposed method is effective.},
}
@article {pmid24802700,
year = {2014},
author = {Akcakaya, M and Peters, B and Moghadamfalahi, M and Mooney, AR and Orhan, U and Oken, B and Erdogmus, D and Fried-Oken, M},
title = {Noninvasive brain-computer interfaces for augmentative and alternative communication.},
journal = {IEEE reviews in biomedical engineering},
volume = {7},
number = {},
pages = {31-49},
pmid = {24802700},
issn = {1941-1189},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Self-Help Devices ; },
abstract = {Brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication (AAC) systems, to people with severe speech and physical impairments (SSPI). Research on the subject has been accelerating significantly in the last decade and the research community took great strides toward making BCI-AAC a practical reality to individuals with SSPI. Nevertheless, the end goal has still not been reached and there is much work to be done to produce real-world-worthy systems that can be comfortably, conveniently, and reliably used by individuals with SSPI with help from their families and care givers who will need to maintain, setup, and debug the systems at home. This paper reviews reports in the BCI field that aim at AAC as the application domain with a consideration on both technical and clinical aspects.},
}
@article {pmid24802088,
year = {2015},
author = {McGie, SC and Zariffa, J and Popovic, MR and Nagai, MK},
title = {Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {18},
number = {3},
pages = {233-40; discussion 240},
doi = {10.1111/ner.12185},
pmid = {24802088},
issn = {1525-1403},
mesh = {Adult ; Analysis of Variance ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Electroencephalography ; Electromyography ; Evoked Potentials, Motor/physiology ; Female ; Hand Strength/*physiology ; Humans ; Male ; Muscle, Skeletal/*innervation ; Neuronal Plasticity/*physiology ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {OBJECTIVES: Functional electrical stimulation (FES) has been shown to facilitate the recovery of grasping function in individuals with incomplete spinal cord injury. Neurophysiological theory suggests that this benefit may be further enhanced by a more consistent pairing of the voluntary commands sent from the user's brain down their spinal cord with the electrical stimuli applied to the user's periphery. The objective of the study was to compare brain-machine interfaces (BMIs)-controlled and electromyogram (EMG)-controlled FES therapy to three more well-researched therapies, namely, push button-controlled FES therapy, voluntary grasping (VOL), and BMI-guided voluntary grasping.<br><br>MATERIALS AND METHODS: Ten able-bodied participants underwent one hour of each of five grasping training modalities, including BMI-controlled FES (BMI-FES), EMG-controlled FES (EMG-FES), conventional push button-controlled FES, VOL, and BMI-guided voluntary grasping. Assessments, including motor-evoked potential, grip force, and maximum voluntary contraction, were conducted immediately before and after each training period.<br><br>RESULTS: Motor-evoked potential-based outcome measures were more upregulated following BMI-FES and especially EMG-FES than they were following VOL or FES. No significant changes were found in the more functional outcome measures.<br><br>CONCLUSIONS: These results provide preliminary evidence suggesting the potential of BMI-FES and EMG-FES to induce greater neuroplastic changes than conventional therapies, although the precise mechanism behind these changes remains speculative. Further investigation will be required to elucidate the underlying mechanisms and to conclusively determine whether these effects can translate into better long-term functional outcomes and quality of life for individuals with spinal cord injury.},
}
@article {pmid24801887,
year = {2014},
author = {Zhang, Z and Jung, TP and Makeig, S and Pi, Z and Rao, BD},
title = {Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {6},
pages = {1186-1197},
doi = {10.1109/TNSRE.2014.2319334},
pmid = {24801887},
issn = {1558-0210},
mesh = {*Algorithms ; *Artificial Intelligence ; *Bayes Theorem ; Brain/*physiology ; Brain-Computer Interfaces ; Data Compression/*methods ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; },
abstract = {Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to nonsparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.},
}
@article {pmid24801483,
year = {2015},
author = {Yin, E and Zhou, Z and Jiang, J and Yu, Y and Hu, D},
title = {A Dynamically Optimized SSVEP Brain-Computer Interface (BCI) Speller.},
journal = {IEEE transactions on bio-medical engineering},
volume = {62},
number = {6},
pages = {1447-1456},
doi = {10.1109/TBME.2014.2320948},
pmid = {24801483},
issn = {1558-2531},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.},
}
@article {pmid24800679,
year = {2014},
author = {Barsakcioglu, DY and Liu, Y and Bhunjun, P and Navajas, J and Eftekhar, A and Jackson, A and Quian Quiroga, R and Constandinou, TG},
title = {An analogue front-end model for developing neural spike sorting systems.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {2},
pages = {216-227},
doi = {10.1109/TBCAS.2014.2313087},
pmid = {24800679},
issn = {1940-9990},
mesh = {Action Potentials/*physiology ; Animals ; Basal Ganglia/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Epilepsy/physiopathology ; Haplorhini ; Humans ; *Models, Neurological ; Neocortex/physiology ; Neurons ; *Signal Processing, Computer-Assisted ; },
abstract = {In spike sorting systems, front-end electronics is a crucial pre-processing step that not only has a direct impact on detection and sorting accuracy, but also on power and silicon area. In this work, a behavioural front-end model is proposed to assess the impact of the design parameters (including signal-to-noise ratio, filter type/order, bandwidth, converter resolution/rate) on subsequent spike processing. Initial validation of the model is provided by applying a test stimulus to a hardware platform and comparing the measured circuit response to the expected from the behavioural model. Our model is then used to demonstrate the effect of the Analogue Front-End (AFE) on subsequent spike processing by testing established spike detection and sorting methods on a selection of systems reported in the literature. It is revealed that although these designs have a wide variation in design parameters (and thus also circuit complexity), the ultimate impact on spike processing performance is relatively low (10-15%). This can be used to inform the design of future systems to have an efficient AFE whilst also maintaining good processing performance.},
}
@article {pmid24798264,
year = {2014},
author = {Fabrizio, CS and van Liere, M and Pelto, G},
title = {Identifying determinants of effective complementary feeding behaviour change interventions in developing countries.},
journal = {Maternal &amp; child nutrition},
volume = {10},
number = {4},
pages = {575-592},
pmid = {24798264},
issn = {1740-8709},
mesh = {Child, Preschool ; *Developing Countries ; *Feeding Behavior ; *Health Promotion ; Humans ; Infant ; *Infant Nutritional Physiological Phenomena ; Nutritional Status ; Randomized Controlled Trials as Topic ; },
abstract = {As stunting moves to the forefront of the global agenda, there is substantial evidence that behaviour change interventions (BCI) can improve infant feeding practices and growth. However, this evidence has not been translated into improved outcomes on a national level because we do not know enough about what makes these interventions work, for whom, when, why, at what cost and for how long. Our objective was to examine the design and implementation of complementary feeding BCI, from the peer-reviewed literature, to identify generalisable key determinants. We identified 29 studies that evaluated BCI efficacy or effectiveness, were conducted in developing countries, and reported outcomes on infant and young children aged 6-24 months. Two potential determinants emerged: (1) effective studies used formative research to identify cultural barriers and enablers to optimal feeding practices, to shape the intervention strategy, and to formulate appropriate messages and mediums for delivery; (2) effective studies delineated the programme impact pathway to the target behaviour change and assessed intermediary behaviour changes to learn what worked. We found that BCI that used these developmental and implementation processes could be effective despite heterogeneous approaches and design components. Our analysis was constrained, however, by the limited published data on how design and implementation were carried out, perhaps because of publishing space limits. Information on cost-effectiveness, sustainability and scalability was also very limited. We suggest a more comprehensive reporting process and a more strategic research agenda to enable generalisable evidence to accumulate.},
}
@article {pmid24797649,
year = {2014},
author = {Lobel, DA and Lee, KH},
title = {Brain machine interface and limb reanimation technologies: restoring function after spinal cord injury through development of a bypass system.},
journal = {Mayo Clinic proceedings},
volume = {89},
number = {5},
pages = {708-714},
doi = {10.1016/j.mayocp.2014.02.003},
pmid = {24797649},
issn = {1942-5546},
mesh = {*Brain-Computer Interfaces ; Electric Stimulation Therapy/instrumentation/*methods ; Extremities/*physiology/physiopathology ; Humans ; *Recovery of Function ; Spinal Cord Injuries/*rehabilitation ; },
abstract = {Functional restoration of limb movement after traumatic spinal cord injury (SCI) remains the ultimate goal in SCI treatment and directs the focus of current research strategies. To date, most investigations in the treatment of SCI focus on repairing the injury site. Although offering some promise, these efforts have met with significant roadblocks because treatment measures that are successful in animal trials do not yield similar results in human trials. In contrast to biologic therapies, there are now emerging neural interface technologies, such as brain machine interface (BMI) and limb reanimation through electrical stimulators, to create a bypass around the site of the SCI. The BMI systems analyze brain signals to allow control of devices that are used to assist SCI patients. Such devices may include a computer, robotic arm, or exoskeleton. Limb reanimation technologies, which include functional electrical stimulation, epidural stimulation, and intraspinal microstimulation systems, activate neuronal pathways below the level of the SCI. We present a concise review of recent advances in the BMI and limb reanimation technologies that provides the foundation for the development of a bypass system to improve functional outcome after traumatic SCI. We also discuss challenges to the practical implementation of such a bypass system in both these developing fields.},
}
@article {pmid24797225,
year = {2015},
author = {Koo, B and Lee, HG and Nam, Y and Kang, H and Koh, CS and Shin, HC and Choi, S},
title = {A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {26-32},
doi = {10.1016/j.jneumeth.2014.04.016},
pmid = {24797225},
issn = {1872-678X},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Hemoglobins/*metabolism ; Humans ; Imagination/*physiology ; Male ; *Movement ; Online Systems ; *Self-Control ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {BACKGROUND: For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm.<br><br>NEW METHOD: In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system.<br><br>RESULTS: An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s.<br><br>As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system.<br><br>CONCLUSIONS: From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI.},
}
@article {pmid24797224,
year = {2014},
author = {Colwell, KA and Ryan, DB and Throckmorton, CS and Sellers, EW and Collins, LM},
title = {Channel selection methods for the P300 Speller.},
journal = {Journal of neuroscience methods},
volume = {232},
number = {},
pages = {6-15},
pmid = {24797224},
issn = {1872-678X},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain Mapping ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; ROC Curve ; Students ; Universities ; },
abstract = {The P300 Speller brain-computer interface (BCI) allows a user to communicate without muscle activity by reading electrical signals on the scalp via electroencephalogram. Modern BCI systems use multiple electrodes ("channels") to collect data, which has been shown to improve speller accuracy; however, system cost and setup time can increase substantially with the number of channels in use, so it is in the user's interest to use a channel set of modest size. This constraint increases the importance of using an effective channel set, but current systems typically utilize the same channel montage for each user. We examine the effect of active channel selection for individuals on speller performance, using generalized standard feature-selection methods, and present a new channel selection method, termed jumpwise regression, that extends the Stepwise Linear Discriminant Analysis classifier. Simulating the selections of each method on real P300 Speller data, we obtain results demonstrating that active channel selection can improve speller accuracy for most users relative to a standard channel set, with particular benefit for users who experience low performance using the standard set. Of the methods tested, jumpwise regression offers accuracy gains similar to the best-performing feature-selection methods, and is robust enough for online use.},
}
@article {pmid24795590,
year = {2014},
author = {Lu, J and Mamun, KA and Chau, T},
title = {Online transcranial Doppler ultrasonographic control of an onscreen keyboard.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {199},
pmid = {24795590},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) systems exploit brain activity for generating a control command and may be used by individuals with severe motor disabilities as an alternative means of communication. An emerging brain monitoring modality for BCI development is transcranial Doppler ultrasonography (TCD), which facilitates the tracking of cerebral blood flow velocities associated with mental tasks. However, TCD-BCI studies to date have exclusively been offline. The feasibility of a TCD-based BCI system hinges on its online performance. In this paper, an online TCD-BCI system was implemented, bilaterally tracking blood flow velocities in the middle cerebral arteries for system-paced control of a scanning keyboard. Target letters or words were selected by repetitively rehearsing the spelling while imagining the writing of the intended word, a left-lateralized task. Undesired letters or words were bypassed by performing visual tracking, a non-lateralized task. The keyboard scanning period was 15 s. With 10 able-bodied right-handed young adults, the two mental tasks were differentiated online using a Naïve Bayes classification algorithm and a set of time-domain, user-dependent features. The system achieved an average specificity and sensitivity of 81.44 ± 8.35 and 82.30 ± 7.39%, respectively. The level of agreement between the intended and machine-predicted selections was moderate (κ = 0.60). The average information transfer rate was 0.87 bits/min with an average throughput of 0.31 ± 0.12 character/min. These findings suggest that an online TCD-BCI can achieve reasonable accuracies with an intuitive language task, but with modest throughput. Future interface and signal classification enhancements are required to improve communication rate.},
}
@article {pmid24794517,
year = {2015},
author = {Kober, SE and Witte, M and Stangl, M and Väljamäe, A and Neuper, C and Wood, G},
title = {Shutting down sensorimotor interference unblocks the networks for stimulus processing: an SMR neurofeedback training study.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {126},
number = {1},
pages = {82-95},
doi = {10.1016/j.clinph.2014.03.031},
pmid = {24794517},
issn = {1872-8952},
mesh = {Adolescent ; Adult ; Attention/physiology ; Double-Blind Method ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Memory/physiology ; Nerve Net/*physiology ; Neurofeedback/*methods ; Sensorimotor Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: In the present study, we investigated how the electrical activity in the sensorimotor cortex contributes to improved cognitive processing capabilities and how SMR (sensorimotor rhythm, 12-15Hz) neurofeedback training modulates it. Previous evidence indicates that higher levels of SMR activity reduce sensorimotor interference and thereby promote cognitive processing.<br><br>METHODS: Participants were randomly assigned to two groups, one experimental (N=10) group receiving SMR neurofeedback training, in which they learned to voluntarily increase SMR, and one control group (N=10) receiving sham feedback. Multiple cognitive functions and electrophysiological correlates of cognitive processing were assessed before and after 10 neurofeedback training sessions.<br><br>RESULTS: The experimental group but not the control group showed linear increases in SMR power over training runs, which was associated with behavioural improvements in memory and attentional performance. Additionally, increasing SMR led to a more salient stimulus processing as indicated by increased N1 and P3 event-related potential amplitudes after the training as compared to the pre-test. Finally, functional brain connectivity between motor areas and visual processing areas was reduced after SMR training indicating reduced sensorimotor interference.<br><br>CONCLUSIONS: These results indicate that SMR neurofeedback improves stimulus processing capabilities and consequently leads to improvements in cognitive performance.<br><br>SIGNIFICANCE: The present findings contribute to a better understanding of the mechanisms underlying SMR neurofeedback training and cognitive processing and implicate that SMR neurofeedback might be an effective cognitive training tool.},
}
@article {pmid24794514,
year = {2014},
author = {Tu, Y and Hung, YS and Hu, L and Huang, G and Hu, Y and Zhang, Z},
title = {An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {12},
pages = {2372-2383},
doi = {10.1016/j.clinph.2014.03.028},
pmid = {24794514},
issn = {1872-8952},
mesh = {Adult ; *Automation, Laboratory/methods ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Photic Stimulation/methods ; Signal-To-Noise Ratio ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system.<br><br>METHODS: The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system.<br><br>RESULTS: The offline classification accuracy of the BCI system was significantly improved from 67.6&#xb1;12.5% (raw data) to 97.3&#xb1;2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%.<br><br>CONCLUSIONS: The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems.<br><br>SIGNIFICANCE: This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring.},
}
@article {pmid24789868,
year = {2014},
author = {Hochberg, LR and Cudkowicz, ME},
title = {Locked in, but not out?.},
journal = {Neurology},
volume = {82},
number = {21},
pages = {1852-1853},
doi = {10.1212/WNL.0000000000000460},
pmid = {24789868},
issn = {1526-632X},
support = {R01 DC009899/DC/NIDCD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Communication ; Female ; Humans ; Quadriplegia/*psychology ; },
}
@article {pmid24789862,
year = {2014},
author = {Gallegos-Ayala, G and Furdea, A and Takano, K and Ruf, CA and Flor, H and Birbaumer, N},
title = {Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy.},
journal = {Neurology},
volume = {82},
number = {21},
pages = {1930-1932},
pmid = {24789862},
issn = {1526-632X},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/complications ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication ; Female ; Humans ; Quadriplegia/complications/*psychology ; Spectroscopy, Near-Infrared ; },
abstract = {Amyotrophic lateral sclerosis (ALS) can result in the locked-in state (LIS), characterized by paralysis, and eventual respiratory failure, compensated by artificial ventilation,[1] or the completely LIS (CLIS), with additional total paralysis of eye muscles. Brain–computer interfaces (BCIs) have been used to allow paralyzed people to regain basic communication,[2] although current EEG-based BCIs have not succeeded with CLIS patients.[3] We present Class IV case evidence to establish that communication in the CLIS is possible with a metabolic BCI based on near-infrared spectroscopy (NIRS).},
}
@article {pmid24785329,
year = {2014},
author = {Min, BK and Müller, KR},
title = {Electroencephalography/sonication-mediated human brain-brain interfacing technology.},
journal = {Trends in biotechnology},
volume = {32},
number = {7},
pages = {345-346},
doi = {10.1016/j.tibtech.2014.04.001},
pmid = {24785329},
issn = {1879-3096},
mesh = {Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Signal Processing, Computer-Assisted ; Sonication/*methods ; },
}
@article {pmid24782734,
year = {2014},
author = {Cruz-Garza, JG and Hernandez, ZR and Nepaul, S and Bradley, KK and Contreras-Vidal, JL},
title = {Neural decoding of expressive human movement from scalp electroencephalography (EEG).},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {188},
pmid = {24782734},
issn = {1662-5161},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; },
abstract = {Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.},
}
@article {pmid24782721,
year = {2014},
author = {Alimardani, M and Nishio, S and Ishiguro, H},
title = {Effect of biased feedback on motor imagery learning in BCI-teleoperation system.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {52},
pmid = {24782721},
issn = {1662-5137},
abstract = {Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users' BCI performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects' performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects' BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects' online performance, evaluation of brain activity patterns revealed that subjects' self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects' motor imagery skills.},
}
@article {pmid24782681,
year = {2014},
author = {Atilgan, D and Parlaktas, BS and Uluocak, N and Erdemir, F and Markoc, F and Saylan, O and Erkorkmaz, U},
title = {The effects of trimetazidine and sildenafil on bilateral cavernosal nerve injury induced oxidative damage and cavernosal fibrosis in rats.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {970363},
pmid = {24782681},
issn = {1537-744X},
mesh = {Animals ; Fibrosis/metabolism/physiopathology/*prevention &amp; control ; Male ; Malondialdehyde/metabolism ; Oxidative Stress/*drug effects/physiology ; Penis/*drug effects/innervation/pathology ; Piperazines/*pharmacology ; Purines/pharmacology ; Random Allocation ; Rats, Sprague-Dawley ; Sildenafil Citrate ; Sulfones/*pharmacology ; Superoxide Dismutase/metabolism ; Trauma, Nervous System/metabolism/physiopathology/*prevention &amp; control ; Treatment Outcome ; Trimetazidine/*pharmacology ; Vasodilator Agents/pharmacology ; },
abstract = {AIM: The aim of this study was to compare the effects of sildenafil and trimetazidine on bilateral cavernosal nerve injury-induced oxidative damage and fibrotic changes in cavernosal tissue in rat model.<br><br>MATERIAL AND METHODS: A total of 32 male Sprague-Dawley rats were randomly divided into 4 groups; each group consist 8 rats (control, BCI, BCI + TMZ, and BCI + sildenafil groups). Tissue superoxide dismutase (SOD), malondialdehyde (MDA), and protein carbonyl (PC) levels were determined biochemically and distribution of cavernosal fibrosis density among groups was performed histopathologically.<br><br>RESULTS: Tissue SOD levels in BCI group were significantly lower than the control group (P < 0.05). Tissue MDA and PC levels in BCI group were significantly higher than the control group (P < 0.05). TMZ and sildenafil administration significantly increased tissue SOD levels (P < 0.05) and reduced tissue MDA and PC levels (P < 0.05). Histologically, the degree of cavernosal fibrosis and collagen density was higher in BCI group in comparison to control, TMZ-treated, and sildenafil-treated groups.<br><br>CONCLUSION: BCI caused oxidative damage and increased cavernosal fibrosis in rat penis. TMZ and sildenafil treatment decreased oxidative damage and reduced the degree of fibrosis in penile tissue due to BCI.},
}
@article {pmid24776634,
year = {2014},
author = {Schwarz, DA and Lebedev, MA and Hanson, TL and Dimitrov, DF and Lehew, G and Meloy, J and Rajangam, S and Subramanian, V and Ifft, PJ and Li, Z and Ramakrishnan, A and Tate, A and Zhuang, KZ and Nicolelis, MA},
title = {Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys.},
journal = {Nature methods},
volume = {11},
number = {6},
pages = {670-676},
pmid = {24776634},
issn = {1548-7105},
support = {T32 GM008441/GM/NIGMS NIH HHS/United States ; DP1 MH099903/MH/NIMH NIH HHS/United States ; UL1 TR001117/TR/NCATS NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; DP1MH099903/DP/NCCDPHP CDC HHS/United States ; R01NS073952/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Electrodes, Implanted ; Electronic Data Processing ; Macaca mulatta/*physiology ; Neurophysiology/*instrumentation ; *Wireless Technology ; },
abstract = {Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 neurons (units) per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years) and recording of a broad range of behaviors, such as social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.},
}
@article {pmid24771443,
year = {2014},
author = {Sheikh-Zade, YR and Galenko-Yaroshevskii, PA and Cherednik, IL},
title = {Mathematical description of human body constitution and fatness.},
journal = {Bulletin of experimental biology and medicine},
volume = {156},
number = {4},
pages = {526-529},
doi = {10.1007/s10517-014-2390-7},
pmid = {24771443},
issn = {1573-8221},
mesh = {*Adiposity ; Adolescent ; Algorithms ; Body Mass Index ; Female ; Humans ; Male ; Models, Biological ; Wrist/anatomy &amp; histology ; Young Adult ; },
abstract = {Using mathematical modeling of human body, we demonstrated logical drawbacks of body mass index (BMI1 = M/H(2); A. Quetelet, 1832) and proposed more precise body mass index (BMI2 = M/H(3)) as well as body constitution index (BCI = (M/H(3))(1/2)) and fatness index (FI = M/HC(2)), where M, H, and C are body weight, height, and wrist circumference of the individual.},
}
@article {pmid24768933,
year = {2014},
author = {Potes, C and Brunner, P and Gunduz, A and Knight, RT and Schalk, G},
title = {Spatial and temporal relationships of electrocorticographic alpha and gamma activity during auditory processing.},
journal = {NeuroImage},
volume = {97},
number = {},
pages = {188-195},
pmid = {24768933},
issn = {1095-9572},
support = {R01 EB006356/EB/NIBIB NIH HHS/United States ; NS21135/NS/NINDS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; R56 NS021135/NS/NINDS NIH HHS/United States ; R01 NS021135/NS/NINDS NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Alpha Rhythm/*physiology ; Auditory Perception/*physiology ; Brain Mapping ; Causality ; Electroencephalography/*methods ; Epilepsy/psychology ; Female ; Gamma Rhythm/*physiology ; Humans ; Individuality ; Male ; Middle Aged ; Music/psychology ; Young Adult ; },
abstract = {Neuroimaging approaches have implicated multiple brain sites in musical perception, including the posterior part of the superior temporal gyrus and adjacent perisylvian areas. However, the detailed spatial and temporal relationship of neural signals that support auditory processing is largely unknown. In this study, we applied a novel inter-subject analysis approach to electrophysiological signals recorded from the surface of the brain (electrocorticography (ECoG)) in ten human subjects. This approach allowed us to reliably identify those ECoG features that were related to the processing of a complex auditory stimulus (i.e., continuous piece of music) and to investigate their spatial, temporal, and causal relationships. Our results identified stimulus-related modulations in the alpha (8-12 Hz) and high gamma (70-110 Hz) bands at neuroanatomical locations implicated in auditory processing. Specifically, we identified stimulus-related ECoG modulations in the alpha band in areas adjacent to primary auditory cortex, which are known to receive afferent auditory projections from the thalamus (80 of a total of 15,107 tested sites). In contrast, we identified stimulus-related ECoG modulations in the high gamma band not only in areas close to primary auditory cortex but also in other perisylvian areas known to be involved in higher-order auditory processing, and in superior premotor cortex (412/15,107 sites). Across all implicated areas, modulations in the high gamma band preceded those in the alpha band by 280 ms, and activity in the high gamma band causally predicted alpha activity, but not vice versa (Granger causality, p<1e(-8)). Additionally, detailed analyses using Granger causality identified causal relationships of high gamma activity between distinct locations in early auditory pathways within superior temporal gyrus (STG) and posterior STG, between posterior STG and inferior frontal cortex, and between STG and premotor cortex. Evidence suggests that these relationships reflect direct cortico-cortical connections rather than common driving input from subcortical structures such as the thalamus. In summary, our inter-subject analyses defined the spatial and temporal relationships between music-related brain activity in the alpha and high gamma bands. They provide experimental evidence supporting current theories about the putative mechanisms of alpha and gamma activity, i.e., reflections of thalamo-cortical interactions and local cortical neural activity, respectively, and the results are also in agreement with existing functional models of auditory processing.},
}
@article {pmid24768575,
year = {2015},
author = {Rutkowski, TM and Mori, H},
title = {Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {45-51},
doi = {10.1016/j.jneumeth.2014.04.010},
pmid = {24768575},
issn = {1872-678X},
mesh = {Bone Conduction/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Humans ; Persons with Hearing Disabilities/*rehabilitation ; Psychophysics ; ROC Curve ; Touch/*physiology ; Vision Disorders/*rehabilitation ; },
abstract = {BACKGROUND: The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm.<br><br>NEW METHOD: In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI.<br><br>RESULTS: Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies.<br><br>We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques.<br><br>CONCLUSIONS: The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research.},
}
@article {pmid24763067,
year = {2014},
author = {De Vos, M and Kroesen, M and Emkes, R and Debener, S},
title = {P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036008},
doi = {10.1088/1741-2560/11/3/036008},
pmid = {24763067},
issn = {1741-2552},
mesh = {Adult ; *Amplifiers, Electronic ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Female ; Head Protective Devices ; Humans ; Imagination/*physiology ; Language ; Male ; Middle Aged ; Miniaturization ; Monitoring, Ambulatory/instrumentation ; Psychomotor Performance/physiology ; Telemetry/*instrumentation ; Word Processing/instrumentation ; Young Adult ; },
abstract = {OBJECTIVE: In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et al (2012 Psychophysiology 49 1449-53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system.<br><br>APPROACH: We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain-computer interface (BCI) application. N = 13 individuals repeatedly performed a 6 &#xd7; 6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical.<br><br>MAIN RESULTS: Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77).<br><br>SIGNIFICANCE: We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.},
}
@article {pmid24760941,
year = {2014},
author = {Gowda, S and Orsborn, AL and Overduin, SA and Moorman, HG and Carmena, JM},
title = {Designing dynamical properties of brain-machine interfaces to optimize task-specific performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {5},
pages = {911-920},
doi = {10.1109/TNSRE.2014.2309673},
pmid = {24760941},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Calibration ; Linear Models ; Macaca mulatta ; Male ; Prosthesis Design/*methods ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; },
abstract = {Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance. However, there have been few empirical studies exploring how various dynamical effects of closed-loop BMIs ultimately influence performance. In this work, we utilize experimental data from two macaque monkeys operating a closed-loop BMI to reach to 2-D targets and show that certain dynamical properties correlate with performance loss. We also show that other dynamical properties represent tradeoffs between naturally competing objectives, such as speed versus accuracy. These findings highlight the importance of fine-tuning the dynamical properties of closed-loop BMIs to optimize task-specific performance.},
}
@article {pmid24760930,
year = {2014},
author = {Antuvan, CW and Ison, M and Artemiadis, P},
title = {Embedded human control of robots using myoelectric interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {4},
pages = {820-827},
doi = {10.1109/TNSRE.2014.2302212},
pmid = {24760930},
issn = {1558-0210},
mesh = {Action Potentials/*physiology ; Arm/*physiology ; Electromyography/*methods ; Feedback, Physiological/physiology ; Humans ; *Man-Machine Systems ; Movement/physiology ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; Robotics/*methods ; User-Computer Interface ; },
abstract = {Myoelectric controlled interfaces have become a research interest for use in advanced prostheses, exoskeletons, and robot teleoperation. Current research focuses on improving a user's initial performance, either by training a decoding function for a specific user or implementing "intuitive" mapping functions as decoders. However, both approaches are limiting, with the former being subject specific, and the latter task specific. This paper proposes a paradigm shift on myoelectric interfaces by embedding the human as controller of the system to be operated. Using abstract mapping functions between myoelectric activity and control actions for a task, this study shows that human subjects are able to control an artificial system with increasing efficiency by just learning how to control it. The method efficacy is tested by using two different control tasks and four different abstract mappings relating upper limb muscle activity to control actions for those tasks. The results show that all subjects were able to learn the mappings and improve their performance over time. More interestingly, a chronological evaluation across trials reveals that the learning curves transfer across subsequent trials having the same mapping, independent of the tasks to be executed. This implies that new muscle synergies are developed and refined relative to the mapping used by the control task, suggesting that maximal performance may be achieved by learning a constant, arbitrary mapping function rather than dynamic subject- or task-specific functions. Moreover, the results indicate that the method may extend to the neural control of any device or robot, without limitations for anthropomorphism or human-related counterparts.},
}
@article {pmid24760927,
year = {2014},
author = {Speier, W and Arnold, C and Lu, J and Deshpande, A and Pouratian, N},
title = {Integrating language information with a hidden Markov model to improve communication rate in the P300 speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {3},
pages = {678-684},
pmid = {24760927},
issn = {1558-0210},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T15 LM007356/LM/NLM NIH HHS/United States ; T15-LM007356/LM/NLM NIH HHS/United States ; K23EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Communication Devices for People with Disabilities ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Language ; Male ; Markov Chains ; Online Systems ; Photic Stimulation ; Pilot Projects ; Psychomotor Performance/physiology ; Young Adult ; },
abstract = {The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram (EEG) signal. Information about the structure of natural language can be valuable for BCI communication systems, but few attempts have been made to incorporate this domain knowledge into the classifier. In this study, we treat BCI communication as a hidden Markov model (HMM) where hidden states are target characters and the EEG signal is the visible output. Using the Viterbi algorithm, language information can be incorporated in classification and errors can be corrected automatically. This method was first evaluated offline on a dataset of 15 healthy subjects who had a significant increase in bit rate from a previously published naïve Bayes approach and an average 32% increase from standard classification with dynamic stopping. An online pilot study of five healthy subjects verified these results as the average bit rate achieved using the HMM method was significantly higher than that using the naïve Bayes and standard methods. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance and accuracy.},
}
@article {pmid24760914,
year = {2014},
author = {McMullen, DP and Hotson, G and Katyal, KD and Wester, BA and Fifer, MS and McGee, TG and Harris, A and Johannes, MS and Vogelstein, RJ and Ravitz, AD and Anderson, WS and Thakor, NV and Crone, NE},
title = {Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {4},
pages = {784-796},
pmid = {24760914},
issn = {1558-0210},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; 3R01NS0405956-09S1/NS/NINDS NIH HHS/United States ; 19GM-1088724/GM/NIGMS NIH HHS/United States ; T32 EB003383/EB/NIBIB NIH HHS/United States ; 5T32EB003383-08/EB/NIBIB NIH HHS/United States ; R01 NS088606/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Artificial Intelligence ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Equipment Failure Analysis ; *Eye Movements ; Female ; Humans ; Male ; Man-Machine Systems ; Pilot Projects ; Prosthesis Design ; Robotics/*instrumentation/methods ; Therapy, Computer-Assisted/instrumentation/methods ; },
abstract = {To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.},
}
@article {pmid24760910,
year = {2014},
author = {Spüler, M and Walter, A and Rosenstiel, W and Bogdan, M},
title = {Spatial filtering based on canonical correlation analysis for classification of evoked or event-related potentials in EEG data.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {6},
pages = {1097-1103},
doi = {10.1109/TNSRE.2013.2290870},
pmid = {24760910},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Male ; Middle Aged ; Pattern Recognition, Automated/*methods ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Statistics as Topic ; },
abstract = {Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While canonical correlation analysis (CCA) has previously been used to construct spatial filters that increase classification accuracy for BCIs based on visual evoked potentials, we show in this paper, how CCA can also be used for spatial filtering of event-related potentials like P300. We also evaluate the use of CCA for spatial filtering on other data with evoked and event-related potentials and show that CCA performs consistently better than other standard spatial filtering methods.},
}
@article {pmid24760860,
year = {2014},
author = {Velliste, M and Kennedy, SD and Schwartz, AB and Whitford, AS and Sohn, JW and McMorland, AJ},
title = {Motor cortical correlates of arm resting in the context of a reaching task and implications for prosthetic control.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {34},
number = {17},
pages = {6011-6022},
pmid = {24760860},
issn = {1529-2401},
support = {R01 NS050256/NS/NINDS NIH HHS/United States ; 5R01NS050256/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Artificial Limbs ; Biomechanical Phenomena/physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/physiology ; Psychomotor Performance/*physiology ; },
abstract = {Prosthetic devices are being developed to restore movement for motor-impaired individuals. A robotic arm can be controlled based on models that relate motor-cortical ensemble activity to kinematic parameters. The models are typically built and validated on data from structured trial periods during which a subject actively performs specific movements, but real-world prosthetic devices will need to operate correctly during rest periods as well. To develop a model of motor cortical modulation during rest, we trained monkeys (Macaca mulatta) to perform a reaching task with their own arm while recording motor-cortical single-unit activity. When a monkey spontaneously put its arm down to rest between trials, our traditional movement decoder produced a nonzero velocity prediction, which would cause undesired motion when applied to a prosthetic arm. During these rest periods, a marked shift was found in individual units' tuning functions. The activity pattern of the whole population during rest (Idle state) was highly distinct from that during reaching movements (Active state), allowing us to predict arm resting from instantaneous firing rates with 98% accuracy using a simple classifier. By cascading this state classifier and the movement decoder, we were able to predict zero velocity correctly, which would avoid undesired motion in a prosthetic application. Interestingly, firing rates during hold periods followed the Active pattern even though hold kinematics were similar to those during rest with near-zero velocity. These findings expand our concept of motor-cortical function by showing that population activity reflects behavioral context in addition to the direct parameters of the movement itself.},
}
@article {pmid24759277,
year = {2014},
author = {Gao, S and Wang, Y and Gao, X and Hong, B},
title = {Visual and auditory brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {5},
pages = {1436-1447},
doi = {10.1109/TBME.2014.2300164},
pmid = {24759277},
issn = {1558-2531},
mesh = {*Acoustic Stimulation ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Photic Stimulation ; *Signal Processing, Computer-Assisted ; },
abstract = {Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.},
}
@article {pmid24759276,
year = {2014},
author = {Yuan, H and He, B},
title = {Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {5},
pages = {1425-1435},
pmid = {24759276},
issn = {1558-2531},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain Waves/*physiology ; *Brain-Computer Interfaces ; Electrodes, Implanted ; *Electroencephalography ; Feedback, Physiological ; Humans ; Signal Processing, Computer-Assisted ; Spinal Cord Diseases/rehabilitation ; },
abstract = {Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.},
}
@article {pmid24756025,
year = {2015},
author = {Ang, KK and Chua, KS and Phua, KS and Wang, C and Chin, ZY and Kuah, CW and Low, W and Guan, C},
title = {A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke.},
journal = {Clinical EEG and neuroscience},
volume = {46},
number = {4},
pages = {310-320},
doi = {10.1177/1550059414522229},
pmid = {24756025},
issn = {1550-0594},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Middle Aged ; Physical Therapy Modalities ; Recovery of Function ; *Robotics ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity ; },
abstract = {Electroencephalography (EEG)-based motor imagery (MI) brain-computer interface (BCI) technology has the potential to restore motor function by inducing activity-dependent brain plasticity. The purpose of this study was to investigate the efficacy of an EEG-based MI BCI system coupled with MIT-Manus shoulder-elbow robotic feedback (BCI-Manus) for subjects with chronic stroke with upper-limb hemiparesis. In this single-blind, randomized trial, 26 hemiplegic subjects (Fugl-Meyer Assessment of Motor Recovery After Stroke [FMMA] score, 4-40; 16 men; mean age, 51.4 years; mean stroke duration, 297.4 days), prescreened with the ability to use the MI BCI, were randomly allocated to BCI-Manus or Manus therapy, lasting 18 hours over 4 weeks. Efficacy was measured using upper-extremity FMMA scores at weeks 0, 2, 4 and 12. ElEG data from subjects allocated to BCI-Manus were quantified using the revised brain symmetry index (rBSI) and analyzed for correlation with the improvements in FMMA score. Eleven and 15 subjects underwent BCI-Manus and Manus therapy, respectively. One subject in the Manus group dropped out. Mean total FMMA scores at weeks 0, 2, 4, and 12 weeks improved for both groups: 26.3±10.3, 27.4±12.0, 30.8±13.8, and 31.5±13.5 for BCI-Manus and 26.6±18.9, 29.9±20.6, 32.9±21.4, and 33.9±20.2 for Manus, with no intergroup differences (P=.51). More subjects attained further gains in FMMA scores at week 12 from BCI-Manus (7 of 11 [63.6%]) than Manus (5 of 14 [35.7%]). A negative correlation was found between the rBSI and FMMA score improvement (P=.044). BCI-Manus therapy was well tolerated and not associated with adverse events. In conclusion, BCI-Manus therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. Motor gains were comparable to those attained with intensive robotic therapy (1,040 repetitions/session) despite reduced arm exercise repetitions using EEG-based MI-triggered robotic feedback (136 repetitions/session). The correlation of rBSI with motor improvements suggests that the rBSI can be used as a prognostic measure for BCI-based stroke rehabilitation.},
}
@article {pmid24751733,
year = {2014},
author = {Desmet, J and Wouters, K and De Bodt, M and Van de Heyning, P},
title = {Long-term subjective benefit with a bone conduction implant sound processor in 44 patients with single-sided deafness.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {35},
number = {6},
pages = {1017-1025},
doi = {10.1097/MAO.0000000000000297},
pmid = {24751733},
issn = {1537-4505},
mesh = {Adult ; Aged ; *Bone Conduction ; Deafness/*surgery ; Female ; Follow-Up Studies ; Health Surveys ; Hearing Loss, Unilateral/*surgery ; Humans ; Male ; Middle Aged ; *Patient Satisfaction ; *Prosthesis Design ; Prosthesis Implantation/*methods ; Surveys and Questionnaires ; Time Factors ; Tinnitus/surgery ; Treatment Outcome ; Young Adult ; },
abstract = {INTRODUCTION: Studies that investigate the subjective benefit from a bone conduction implant (BCI) sound processor in patients with single-sided sensorineural deafness (SSD) have been limited to examining short- and mid-term benefit. In the current study, we performed a survey among 44 SSD BCI users with a median follow-up time of 50 months.<br><br>MATERIALS AND METHODS: Forty-four experienced SSD BCI users participated in the survey, which consisted of the Abbreviated Profile of Hearing Aid Benefit, the Single-Sided Deafness Questionnaire, the Short Hearing Handicap Inventory for Adults, and a self-made user questionnaire. For patients with tinnitus, the Tinnitus Questionnaire was also completed. The results of the survey were correlated with contralateral hearing loss, age at implantation, duration of the hearing loss at the time of implantation, duration of BCI use, and the presence and burden of tinnitus.<br><br>RESULTS: In total, 86% of the patients still used their sound processor. The Abbreviated Profile of Hearing Aid Benefit and the Short Hearing Handicap Inventory for Adults show a statistically significant overall improvement with the BCI. The Single-Sided Deafness Questionnaire and the user questionnaire showed that almost 40% of the patients reported daily use of the sound processor. However, the survey of daily use reveals benefit only in certain circumstances. Speech understanding in noisy situations is rated rather low, and 58% of all patients reported that their BCI benefit was less than expected.<br><br>CONCLUSION: The majority of the patients reported an overall improvement from using their BCI. However, the number of users decreases during a longer follow-up time and patients get less enthusiastic about the device after an extended period of use, especially in noisy situations. However, diminished satisfaction because of time-related reductions in processor function could not be ruled out.},
}
@article {pmid24751647,
year = {2014},
author = {Aydemir, O and Kayikcioglu, T},
title = {Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery.},
journal = {Journal of neuroscience methods},
volume = {229},
number = {},
pages = {68-75},
doi = {10.1016/j.jneumeth.2014.04.007},
pmid = {24751647},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; *Decision Trees ; Electroencephalography/*methods ; Electronic Data Processing ; Humans ; Imagination/*physiology ; Male ; Motion Perception/*physiology ; *Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {BACKGROUND: Input signals of an EEG based brain computer interface (BCI) system are naturally non-stationary, have poor signal to noise ratio, depend on physical or mental tasks and are contaminated with various artifacts such as external electromagnetic waves, electromyogram and electrooculogram. All these disadvantages have motivated researchers to substantially improve speed and accuracy of all components of the communication system between brain and a BCI output device.<br><br>NEW METHOD: In this study, a fast and accurate decision tree structure based classification method was proposed for classifying EEG data to up/down/right/left computer cursor movement imagery EEG data. The data sets were acquired from three healthy human subjects in age group of between 24 and 29 years old in two sessions on different days.<br><br>RESULTS: The proposed decision tree structure based method was successfully applied to the present data sets and achieved 55.92%, 57.90% and 82.24% classification accuracy rate on the test data of three subjects.<br><br>The results indicated that the proposed method provided 12.25% improvement over the best results of the most closely related studies although the EEG signals were collected on two different sessions with about 1 week interval.<br><br>CONCLUSIONS: The proposed method required only a training set of the subject and automatically generated specific DTS for each new subject by determining the most appropriate feature set and classifier for each node. Additionally, with further developments of feature extraction and/or classification algorithms, any existing node can be easily replaced with new one without breaking the whole DTS. This attribute makes the proposed method flexible.},
}
@article {pmid24748022,
year = {2014},
author = {Huang, JX and Zhang, J and Shen, Y and Lian, JY and Cao, HL and Ye, WH and Wu, LF and Bin, Y},
title = {Different relationships between temporal phylogenetic turnover and phylogenetic similarity and in two forests were detected by a new null model.},
journal = {PloS one},
volume = {9},
number = {4},
pages = {e95703},
pmid = {24748022},
issn = {1932-6203},
mesh = {Algorithms ; *Ecosystem ; *Forests ; *Models, Theoretical ; Quantitative Trait, Heritable ; *Tropical Climate ; },
abstract = {BACKGROUND: Ecologists have been monitoring community dynamics with the purpose of understanding the rates and causes of community change. However, there is a lack of monitoring of community dynamics from the perspective of phylogeny.<br><br>METHODS/PRINCIPLE FINDINGS: We attempted to understand temporal phylogenetic turnover in a 50 ha tropical forest (Barro Colorado Island, BCI) and a 20 ha subtropical forest (Dinghushan in southern China, DHS). To obtain temporal phylogenetic turnover under random conditions, two null models were used. The first shuffled names of species that are widely used in community phylogenetic analyses. The second simulated demographic processes with careful consideration on the variation in dispersal ability among species and the variations in mortality both among species and among size classes. With the two models, we tested the relationships between temporal phylogenetic turnover and phylogenetic similarity at different spatial scales in the two forests. Results were more consistent with previous findings using the second null model suggesting that the second null model is more appropriate for our purposes. With the second null model, a significantly positive relationship was detected between phylogenetic turnover and phylogenetic similarity in BCI at a 10 m&#xd7;10 m scale, potentially indicating phylogenetic density dependence. This relationship in DHS was significantly negative at three of five spatial scales. This could indicate abiotic filtering processes for community assembly. Using variation partitioning, we found phylogenetic similarity contributed to variation in temporal phylogenetic turnover in the DHS plot but not in BCI plot.<br><br>CONCLUSIONS/SIGNIFICANCE: The mechanisms for community assembly in BCI and DHS vary from phylogenetic perspective. Only the second null model detected this difference indicating the importance of choosing a proper null model.},
}
@article {pmid24746661,
year = {2014},
author = {Aboumohamed, AA and Raza, SJ and Al-Daghmin, A and Tallman, C and Creighton, T and Crossley, H and Dailey, S and Khan, A and Din, R and Mehedint, D and Wang, K and Shi, Y and Sharif, M and Wilding, G and Weizer, A and Guru, KA},
title = {Health-related quality of life outcomes after robot-assisted and open radical cystectomy using a validated bladder-specific instrument: a multi-institutional study.},
journal = {Urology},
volume = {83},
number = {6},
pages = {1300-1308},
doi = {10.1016/j.urology.2014.02.024},
pmid = {24746661},
issn = {1527-9995},
mesh = {Adult ; Aged ; Cohort Studies ; Cystectomy/adverse effects/*instrumentation/*methods/psychology ; Equipment Design ; Female ; Follow-Up Studies ; Humans ; Length of Stay ; Male ; Middle Aged ; Minimally Invasive Surgical Procedures/adverse effects/methods ; Patient Satisfaction/statistics &amp; numerical data ; Postoperative Complications/physiopathology/therapy ; *Quality of Life ; Retrospective Studies ; Risk Assessment ; Robotics/*methods ; Survival Rate ; Treatment Outcome ; Urinary Bladder Neoplasms/mortality/pathology/*psychology/*surgery ; },
abstract = {OBJECTIVE: To evaluate health-related quality of life (HRQL) using validated bladder-specific Bladder Cancer Index (BCI) and European Organization for Research and Treatment of Cancer Body Image scale (BIS) between open radical cystectomy (ORC) and robot-assisted radical cystectomy (RARC).<br><br>METHODS: This was a retrospective case series of all patients who underwent radical cystectomy. Patients were grouped based on surgical approach (open vs robot assisted) and diversion technique (extracorporeal vs intracorporeal). Patients completed BCI and BIS preoperatively and at standardized postoperative intervals (at least 2). The primary exposure variable was surgical approach. The primary outcome measure was difference in interval and baseline BCI and BIS scores in each group. The Fisher exact, Wilcoxon rank-sum, and Kruskal-Wallis tests were used for comparisons.<br><br>RESULTS: Eighty-two and 100 patients underwent RARC and ORC, respectively. Compared with RARC, more patients undergoing ORC had an American Society of Anesthesiology score&#x2265;3 (66% vs 45.1% RARC; P=.007) and shorter median operative time (350 vs 380 minutes; P=.009). Baseline urinary, bowel, sexual function, and body image were not different between both the groups (P=1.0). Longitudinal postoperative analysis revealed better sexual function in ORC group (P=.047), with no significant differences between both the groups in the other 3 domains (P=.11, .58, and .93). Comparisons regarding diversion techniques showed similar findings in baseline and postoperative HRQL data, with no significant differences in the HRQL and body image domains.<br><br>CONCLUSION: RARC has comparable HRQL outcomes to ORC using validated BCI and BIS. The diversion technique used does not seem to affect patients' quality of life.},
}
@article {pmid24744718,
year = {2014},
author = {Lin, YP and Wang, Y and Wei, CS and Jung, TP},
title = {Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {182},
pmid = {24744718},
issn = {1662-5161},
abstract = {Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.},
}
@article {pmid24743234,
year = {2014},
author = {Iturrate, I and Chavarriaga, R and Montesano, L and Minguez, J and Millán, J},
title = {Latency correction of event-related potentials between different experimental protocols.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036005},
doi = {10.1088/1741-2560/11/3/036005},
pmid = {24743234},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Reaction Time/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; },
abstract = {OBJECTIVE: A fundamental issue in EEG event-related potentials (ERPs) studies is the amount of data required to have an accurate ERP model. This also impacts the time required to train a classifier for a brain-computer interface (BCI). This issue is mainly due to the poor signal-to-noise ratio and the large fluctuations of the EEG caused by several sources of variability. One of these sources is directly related to the experimental protocol or application designed, and may affect the amplitude or latency of ERPs. This usually prevents BCI classifiers from generalizing among different experimental protocols. In this paper, we analyze the effect of the amplitude and the latency variations among different experimental protocols based on the same type of ERP.<br><br>APPROACH: We present a method to analyze and compensate for the latency variations in BCI applications. The algorithm has been tested on two widely used ERPs (P300 and observation error potentials), in three experimental protocols in each case. We report the ERP analysis and single-trial classification.<br><br>MAIN RESULTS: The results obtained show that the designed experimental protocols significantly affect the latency of the recorded potentials but not the amplitudes.<br><br>SIGNIFICANCE: These results show how the use of latency-corrected data can be used to generalize the BCIs, reducing the calibration time when facing a new experimental protocol.},
}
@article {pmid24743165,
year = {2014},
author = {Jin, J and Daly, I and Zhang, Y and Wang, X and Cichocki, A},
title = {An optimized ERP brain-computer interface based on facial expression changes.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036004},
doi = {10.1088/1741-2560/11/3/036004},
pmid = {24743165},
issn = {1741-2552},
mesh = {Adult ; Aged ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; *Facial Expression ; Female ; Humans ; Male ; Mental Fatigue/*physiopathology/prevention &amp; control ; Middle Aged ; Pattern Recognition, Visual/*physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.<br><br>APPROACH: Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.<br><br>MAIN RESULTS: The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).<br><br>SIGNIFICANCE: The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.},
}
@article {pmid24739786,
year = {2014},
author = {Bensmaia, SJ and Miller, LE},
title = {Restoring sensorimotor function through intracortical interfaces: progress and looming challenges.},
journal = {Nature reviews. Neuroscience},
volume = {15},
number = {5},
pages = {313-325},
pmid = {24739786},
issn = {1471-0048},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; NS048845/NS/NINDS NIH HHS/United States ; 082865//PHS HHS/United States ; R01 NS082865/NS/NINDS NIH HHS/United States ; NS053603/NS/NINDS NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiology ; Electric Stimulation/*methods ; Humans ; Movement/physiology ; Paralysis/physiopathology/*therapy ; Prostheses and Implants ; Recovery of Function/*physiology ; User-Computer Interface ; },
abstract = {The loss of a limb or paralysis resulting from spinal cord injury has devastating consequences on quality of life. One approach to restoring lost sensory and motor abilities in amputees and patients with tetraplegia is to supply them with implants that provide a direct interface with the CNS. Such brain-machine interfaces might enable a patient to exert voluntary control over a prosthetic or robotic limb or over the electrically induced contractions of paralysed muscles. A parallel interface could convey sensory information about the consequences of these movements back to the patient. Recent developments in the algorithms that decode motor intention from neuronal activity and in approaches to convey sensory feedback by electrically stimulating neurons, using biomimetic and adaptation-based approaches, have shown the promise of invasive interfaces with sensorimotor cortices, although substantial challenges remain.},
}
@article {pmid24737114,
year = {2014},
author = {Perdikis, S and Leeb, R and Williamson, J and Ramsay, A and Tavella, M and Desideri, L and Hoogerwerf, EJ and Al-Khodairy, A and Murray-Smith, R and Millán, JD},
title = {Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036003},
doi = {10.1088/1741-2560/11/3/036003},
pmid = {24737114},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; *Language ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Software ; User-Computer Interface ; },
abstract = {OBJECTIVE: While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively.<br><br>APPROACH: This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design.<br><br>MAIN RESULTS: We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications.<br><br>SIGNIFICANCE: This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.},
}
@article {pmid24737062,
year = {2014},
author = {Tangwiriyasakul, C and Mocioiu, V and van Putten, MJ and Rutten, WL},
title = {Classification of motor imagery performance in acute stroke.},
journal = {Journal of neural engineering},
volume = {11},
number = {3},
pages = {036001},
doi = {10.1088/1741-2560/11/3/036001},
pmid = {24737062},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; Motor Cortex/physiopathology ; *Movement ; Neurofeedback/*methods ; Pattern Recognition, Automated/methods ; *Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/*physiopathology ; *Stroke Rehabilitation ; },
abstract = {OBJECTIVE: Effective motor imagery performance, seen as strong suppression of the sensorimotor rhythm, is the key element in motor imagery therapy. Therefore, optimization of methods to classify whether the subject is performing the imagery task is a prerequisite. An optimal classification method should have high performance accuracy and use a small number of channels. We investigated the additional benefit of the common spatial pattern filtering (CSP) to a linear discriminant analysis (LDA) classifier, for different channel configurations.<br><br>METHODS: Ten hemispheric acute stroke patients and 11 healthy subjects were included. EEGs were recorded using 60 channels. The classifier was trained with a motor execution task. For both healthy controls and patients, analysis of recordings was initially limited to 3 and 11 electrodes recording from the motor cortex area, and later repeated using 45 electrodes.<br><br>RESULTS: No significant improvement on the addition of CSP to LDA was found (in both cases, the area under the receiving operating characteristic (AU-ROC) &#x2248; 0.70 (acceptable)). We then repeated the LDA+CSP method on recordings of 45 electrodes, since the use of imagery neuronal circuits may well extend beyond the motor area. AU-ROC rose to 0.90, but no virtual 'most responsible' electrode was observed. Finally, in mild-to-moderate stroke patients we could successfully use the EEG data recorded from the healthy hemisphere to train the classifier (AU-ROC &#x2248; 0.70).<br><br>SIGNIFICANCE: Including only the channels on the unaffected motor cortex is sufficient to train a classifier.},
}
@article {pmid24731767,
year = {2014},
author = {Ikegami, S and Takano, K and Kondo, K and Saeki, N and Kansaku, K},
title = {A region-based two-step P300-based brain-computer interface for patients with amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {11},
pages = {2305-2312},
doi = {10.1016/j.clinph.2014.03.013},
pmid = {24731767},
issn = {1872-8952},
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; },
abstract = {OBJECTIVE: The P300-based brain-computer interface (BCI) is designed to help patients with motor disabilities to control their environment, and it has been used successfully in patients with amyotrophic lateral sclerosis (ALS). However, some ALS patients were unable to use the visual P300-BCI with the conventional row/column presentation. In this study, we evaluated the effect of a newly developed region-based two-step P300 speller, which has a larger flashing area than the conventional visual array.<br><br>METHODS: Seven ALS patients and seven age- and sex-matched able-bodied control subjects were required to input hiragana characters using our P300 BCI system. We prepared two types of input procedures, the conventional row/column (RC) speller and the two-step speller, and evaluated their online performance.<br><br>RESULTS: The mean online accuracy of the ALS patients was 24% for the RC condition and 55% for the two-step condition. The accuracy of the control subjects was 71% and 83% for the RC and two-step condition, respectively. Accuracy in ALS patients was significantly lower than that in the control subjects, and the new visual stimuli significantly increased accuracy of ALS patients. Using the new speller, two ALS patients showed an initial accuracy sufficient for practical use (>70%). The other two ALS patients, who performed better in the first trial using the new speller, continued to experience the BCI system, and their mean accuracy increased to 92%.<br><br>CONCLUSIONS: The two-step procedure for the visual P300 BCI system provided significantly increased accuracy for ALS patients compared with a conventional RC speller.<br><br>SIGNIFICANCE: The new region-based two-step P300 speller was effective in ALS patients, and the system may be beneficial to expand their range of activities.},
}
@article {pmid24731126,
year = {2014},
author = {Hwang, CS and Weng, HH and Wang, LF and Tsai, CH and Chang, HT},
title = {An eye-tracking assistive device improves the quality of life for ALS patients and reduces the caregivers' burden.},
journal = {Journal of motor behavior},
volume = {46},
number = {4},
pages = {233-238},
doi = {10.1080/00222895.2014.891970},
pmid = {24731126},
issn = {1940-1027},
mesh = {Adult ; Aged ; Aged, 80 and over ; Amyotrophic Lateral Sclerosis/psychology/*rehabilitation ; Brain-Computer Interfaces/*standards ; Caregivers/*psychology ; *Cost of Illness ; Eye Movements/physiology ; Female ; Humans ; Male ; Middle Aged ; Quality of Life/*psychology ; *Self-Help Devices ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease. In some cases, patients with ALS retain a normal level of consciousness but disease progression eventually results in generalized paralysis, which first impedes and then prevents oral communication. This communication obstacle can generate a great deal of stress for the patient, family, and caregiver. Here the authors ask whether the use of an eye-tracking assistive device can improve quality of life for ALS patients and relieves burden of their primary caregivers. Subjects were divided into two groups depending on whether they used (n = 10) or did not use (n = 10) an eye-tracking assistive device. The authors assessed patients' quality of life and severity of depression using the ALS Specific Quality of Life Instrument-Revised and the Taiwanese Depression Questionnaire, respectively. The Caregiver Burden Scale was used to assess the burden on caregivers. Our study shows that the eye-tracking assistive device significantly improved patients' quality of life, as compared with patients in the nonuser group (p <.01). The assistive device also reduced the burden on caregivers (p <.05). This is likely a result of the improvement of patient's autonomy and more effective communication between patient and caregiver.},
}
@article {pmid24728268,
year = {2014},
author = {Clancy, KB and Koralek, AC and Costa, RM and Feldman, DE and Carmena, JM},
title = {Volitional modulation of optically recorded calcium signals during neuroprosthetic learning.},
journal = {Nature neuroscience},
volume = {17},
number = {6},
pages = {807-809},
pmid = {24728268},
issn = {1546-1726},
support = {243393/ERC_/European Research Council/International ; R01 NS072416/NS/NINDS NIH HHS/United States ; 1R01NS072416-01/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; Calcium Signaling/*physiology ; Cerebral Cortex/*physiology ; Learning/*physiology ; Male ; Mice ; Mice, Inbred C57BL ; *Microscopy, Fluorescence, Multiphoton/methods ; Volition/*physiology ; },
abstract = {Brain-machine interfaces are not only promising for neurological applications, but also powerful for investigating neuronal ensemble dynamics during learning. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.},
}
@article {pmid24727834,
year = {2014},
author = {Thorbergsson, PT and Garwicz, M and Schouenborg, J and Johansson, AJ},
title = {Strategies for high-performance resource-efficient compression of neural spike recordings.},
journal = {PloS one},
volume = {9},
number = {4},
pages = {e93779},
pmid = {24727834},
issn = {1932-6203},
mesh = {Action Potentials/physiology ; Brain-Computer Interfaces ; *Data Compression ; Humans ; Neurons/physiology ; },
abstract = {Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.},
}
@article {pmid24727656,
year = {2015},
author = {Zhang, Y and Zhou, G and Jin, J and Wang, X and Cichocki, A},
title = {SSVEP recognition using common feature analysis in brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {8-15},
doi = {10.1016/j.jneumeth.2014.03.012},
pmid = {24727656},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Pattern Recognition, Automated ; Photic Stimulation ; Young Adult ; },
abstract = {BACKGROUND: Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data.<br><br>NEW METHOD: We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition.<br><br>RESULTS: Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA).<br><br>Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s).<br><br>CONCLUSIONS: The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI.},
}
@article {pmid24727498,
year = {2014},
author = {Kampmann, M and Boll, S and Kossuch, J and Bielecki, J and Uhl, S and Kleiner, B and Wichmann, R},
title = {Efficient immobilization of mushroom tyrosinase utilizing whole cells from Agaricus bisporus and its application for degradation of bisphenol A.},
journal = {Water research},
volume = {57},
number = {},
pages = {295-303},
doi = {10.1016/j.watres.2014.03.054},
pmid = {24727498},
issn = {1879-2448},
mesh = {Agaricus/*chemistry/*enzymology ; Alginates/chemistry ; Benzhydryl Compounds/chemistry/*metabolism ; Chitosan/chemistry ; Enzyme Inhibitors/chemistry ; Enzymes, Immobilized/antagonists &amp; inhibitors/chemistry/metabolism ; Fruiting Bodies, Fungal/chemistry/enzymology ; Fungal Proteins/antagonists &amp; inhibitors/chemistry/*metabolism ; Glucuronic Acid/chemistry ; Hexuronic Acids/chemistry ; Monophenol Monooxygenase/antagonists &amp; inhibitors/chemistry/*metabolism ; Phenols/chemistry/*metabolism ; Waste Disposal, Fluid/*methods ; Water Pollutants, Chemical/chemistry/*metabolism ; },
abstract = {A simple and efficient procedure for preparation and immobilization of tyrosinase enzyme was developed utilizing whole cells from the edible mushroom Agaricus bisporus, without the need for enzyme purification. Tyrosinase activity in the cell preparation remained constant during storage at 21 °C for at least six months. The cells were entrapped in chitosan and alginate matrix capsules and characterized with respect to their resulting tyrosinase activity. A modification of the alginate with colloidal silica enhanced the activity due to retention of both cells and tyrosinase from fractured cells, which otherwise leached from matrix capsules. The observed activity was similar to the activity that was obtained with immobilized isolated tyrosinase in the same material. Mushroom cells in water were susceptible to rapid inactivation, whereas the immobilized cells maintained 73% of their initial activity after 30 days of storage in water. Application in repeated batch experiments resulted in almost 100% conversion of endocrine disrupting bisphenol A (BPA) for 11 days, under stirring conditions, and 50-60% conversion after 20 days, without stirring under continuous usage. The results represent the longest yet reported application of immobilized tyrosinase for degradation of BPA in environmental water samples.},
}
@article {pmid24726922,
year = {2014},
author = {Chen, C and Shin, D and Watanabe, H and Nakanishi, Y and Kambara, H and Yoshimura, N and Nambu, A and Isa, T and Nishimura, Y and Koike, Y},
title = {Decoding grasp force profile from electrocorticography signals in non-human primate sensorimotor cortex.},
journal = {Neuroscience research},
volume = {83},
number = {},
pages = {1-7},
doi = {10.1016/j.neures.2014.03.010},
pmid = {24726922},
issn = {1872-8111},
mesh = {*Algorithms ; Animals ; Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Hand Strength/*physiology ; Haplorhini ; Linear Models ; Male ; Sensorimotor Cortex/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {The relatively low invasiveness of electrocorticography (ECoG) has made it a promising candidate for the development of practical, high-performance neural prosthetics. Recent ECoG-based studies have shown success in decoding hand and finger movements and muscle activity in reaching and grasping tasks. However, decoding of force profiles is still lacking. Here, we demonstrate that lateral grasp force profile can be decoded using a sparse linear regression from 15 and 16 channel ECoG signals recorded from sensorimotor cortex in two non-human primates. The best average correlation coefficients of prediction after 10-fold cross validation were 0.82±0.09 and 0.79±0.15 for our monkeys A and B, respectively. These results show that grasp force profile was successfully decoded from ECoG signals in reaching and grasping tasks and may potentially contribute to the development of more natural control methods for grasping in neural prosthetics.},
}
@article {pmid24726625,
year = {2015},
author = {Putrino, D and Wong, YT and Weiss, A and Pesaran, B},
title = {A training platform for many-dimensional prosthetic devices using a virtual reality environment.},
journal = {Journal of neuroscience methods},
volume = {244},
number = {},
pages = {68-77},
pmid = {24726625},
issn = {1872-678X},
support = {/WT_/Wellcome Trust/United Kingdom ; P30 EY013079/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Artificial Limbs ; Attention/physiology ; *Biofeedback, Psychology ; Biomechanical Phenomena ; Macaca mulatta ; Models, Biological ; Motion Perception ; *Prostheses and Implants ; Upper Extremity/*physiology ; *User-Computer Interface ; },
abstract = {Brain machine interfaces (BMIs) have the potential to assist in the rehabilitation of millions of patients worldwide. Despite recent advancements in BMI technology for the restoration of lost motor function, a training environment to restore full control of the anatomical segments of an upper limb extremity has not yet been presented. Here, we develop a virtual upper limb prosthesis with 27 independent dimensions, the anatomical dimensions of the human arm and hand, and deploy the virtual prosthesis as an avatar in a virtual reality environment (VRE) that can be controlled in real-time. The prosthesis avatar accepts kinematic control inputs that can be captured from movements of the arm and hand as well as neural control inputs derived from processed neural signals. We characterize the system performance under kinematic control using a commercially available motion capture system. We also present the performance under kinematic control achieved by two non-human primates (Macaca Mulatta) trained to use the prosthetic avatar to perform reaching and grasping tasks. This is the first virtual prosthetic device that is capable of emulating all the anatomical movements of a healthy upper limb in real-time. Since the system accepts both neural and kinematic inputs for a variety of many-dimensional skeletons, we propose it provides a customizable training platform for the acquisition of many-dimensional neural prosthetic control.},
}
@article {pmid24723632,
year = {2014},
author = {Lu, J and Xie, K and McFarland, DJ},
title = {Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {4},
pages = {847-857},
doi = {10.1109/TNSRE.2014.2315717},
pmid = {24723632},
issn = {1558-0210},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; Spatio-Temporal Analysis ; },
abstract = {Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.},
}
@article {pmid24717350,
year = {2014},
author = {Golub, MD and Yu, BM and Schwartz, AB and Chase, SM},
title = {Motor cortical control of movement speed with implications for brain-machine interface control.},
journal = {Journal of neurophysiology},
volume = {112},
number = {2},
pages = {411-429},
pmid = {24717350},
issn = {1522-1598},
support = {R01 EB005847/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; *Movement ; },
abstract = {Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications.},
}
@article {pmid24713576,
year = {2014},
author = {Cao, L and Li, J and Ji, H and Jiang, C},
title = {A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control.},
journal = {Journal of neuroscience methods},
volume = {229},
number = {},
pages = {33-43},
doi = {10.1016/j.jneumeth.2014.03.011},
pmid = {24713576},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Visual/physiology ; Humans ; Imagination/physiology ; Male ; Motor Activity ; Reproducibility of Results ; *Wheelchairs ; Wireless Technology ; Young Adult ; },
abstract = {BACKGROUND: Brain Computer Interfaces (BCIs) are developed to translate brain waves into machine instructions for external devices control. Recently, hybrid BCI systems are proposed for the multi-degree control of a real wheelchair to improve the systematical efficiency of traditional BCIs. However, it is difficult for existing hybrid BCIs to implement the multi-dimensional control in one command cycle.<br><br>NEW METHOD: This paper proposes a novel hybrid BCI system that combines motor imagery (MI)-based bio-signals and steady-state visual evoked potentials (SSVEPs) to control the speed and direction of a real wheelchair synchronously. Furthermore, a hybrid modalities-based switch is firstly designed to turn on/off the control system of the wheelchair.<br><br>RESULTS: Two experiments were performed to assess the proposed BCI system. One was implemented for training and the other one conducted a wheelchair control task in the real environment. All subjects completed these tasks successfully and no collisions occurred in the real wheelchair control experiment.<br><br>The protocol of our BCI gave much more control commands than those of previous MI and SSVEP-based BCIs. Comparing with other BCI wheelchair systems, the superiority reflected by the index of path length optimality ratio validated the high efficiency of our control strategy.<br><br>CONCLUSIONS: The results validated the efficiency of our hybrid BCI system to control the direction and speed of a real wheelchair as well as the reliability of hybrid signals-based switch control.},
}
@article {pmid24709951,
year = {2014},
author = {Wu, Z},
title = {SSVEP extraction based on the similarity of background EEG.},
journal = {PloS one},
volume = {9},
number = {4},
pages = {e93884},
pmid = {24709951},
issn = {1932-6203},
mesh = {Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation ; Visual Cortex/*physiology ; },
abstract = {Steady-state Visual Evoked Potential (SSVEP) outperforms the other types of ERPs for Brain-computer Interface (BCI), and thus it is widely employed. In order to apply SSVEP-based BCI to real life situations, it is important to improve the accuracy and transfer rate of the system. Aimed at this target, many SSVEP extraction methods have been proposed. All these methods are based directly on the properties of SSVEP, such as power and phase. In this study, we first filtered out the target frequencies from the original EEG to get a new signal and then computed the similarity between the original EEG and the new signal. Based on this similarity, SSVEP in the original EEG can be identified. This method is referred to as SOB (Similarity of Background). The SOB method is used to detect SSVEP in 1s-length and 3s-length EEG segments respectively. The accuracy of detection is compared with its peers computed by the widely-used Power Spectrum (PS) method and the Canonical Coefficient (CC) method. The comparison results illustrate that the SOB method can lead to a higher accuracy than the PS method and CC method when detecting a short period SSVEP signal.},
}
@article {pmid24709603,
year = {2014},
author = {Wander, JD and Rao, RP},
title = {Brain-computer interfaces: a powerful tool for scientific inquiry.},
journal = {Current opinion in neurobiology},
volume = {25},
number = {},
pages = {70-75},
pmid = {24709603},
issn = {1873-6882},
support = {R90 DA033461/DA/NIDA NIH HHS/United States ; T90 DA032436/DA/NIDA NIH HHS/United States ; NS065186-01/NS/NINDS NIH HHS/United States ; T32 656052//PHS HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; *Nervous System Physiological Phenomena ; Neuronal Plasticity/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) are devices that record from the nervous system, provide input directly to the nervous system, or do both. Sensory BCIs such as cochlear implants have already had notable clinical success and motor BCIs have shown great promise for helping patients with severe motor deficits. Clinical and engineering outcomes aside, BCIs can also be tremendously powerful tools for scientific inquiry into the workings of the nervous system. They allow researchers to inject and record information at various stages of the system, permitting investigation of the brain in vivo and facilitating the reverse engineering of brain function. Most notably, BCIs are emerging as a novel experimental tool for investigating the tremendous adaptive capacity of the nervous system.},
}
@article {pmid24708647,
year = {2014},
author = {Alhaddad, MJ and Kamel, MI and Makary, MM and Hargas, H and Kadah, YM},
title = {Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing.},
journal = {Biomedical engineering online},
volume = {13},
number = {},
pages = {36},
pmid = {24708647},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Signal Processing, Computer-Assisted ; *Signal-To-Noise Ratio ; Statistics as Topic/*methods ; *Subtraction Technique ; },
abstract = {BACKGROUND: The signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. This mandated the use of several strategies as preprocessing to allow the extraction of meaningful components of the measured signals to be passed along to further processing steps. In spite of the success present preprocessing methods have to improve the reliability of BCI, there is still room for further improvement to boost the performance even more.<br><br>METHODS: A new preprocessing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks is presented. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing preprocessing and allowing low channel counts to be used.<br><br>RESULTS: The new method is verified using experimental data and compared to the classification results of the same data without denoising and with denoising using present wavelet shrinkage based technique. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed.<br><br>CONCLUSION: The new preprocessing method based on spectral subtraction denoising offer superior performance to existing methods and has potential for practical utility as a new standard preprocessing block in BCI signal processing.},
}
@article {pmid24708603,
year = {2014},
author = {Looned, R and Webb, J and Xiao, ZG and Menon, C},
title = {Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {51},
pmid = {24708603},
issn = {1743-0003},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {*Activities of Daily Living ; Arm/physiology ; *Brain-Computer Interfaces ; Drinking ; Electric Stimulation Therapy/instrumentation/*methods ; Feasibility Studies ; Humans ; Nervous System Diseases/rehabilitation ; *Orthotic Devices ; Robotics/*instrumentation/methods ; },
abstract = {BACKGROUND: The aim of the present study is to demonstrate, through tests with healthy volunteers, the feasibility of potentially assisting individuals with neurological disorders via a portable assistive technology for the upper extremities (UE). For this purpose the task of independently drinking a glass of water was selected, as it is one of the most basic and vital activities of the daily living that is unfortunately not achievable by individuals severely affected by stroke.<br><br>METHODS: To accomplish the aim of this study we introduce a wearable and portable system consisting of a novel lightweight Robotic Arm Orthosis (RAO), a Functional Electrical Stimulation (FES) system, and a simple wireless Brain-Computer Interface (BCI). This system is able to process electroencephalographic (EEG) signals and translate them into motions of the impaired arm. Five healthy volunteers participated in this study and were asked to simulate stroke patient symptoms with no voluntary control of their hand and arm. The setup was designed such as the volitional movements of the healthy volunteers' UE did not interfere with the evaluation of the proposed assistive system. The drinking task was split into eleven phases of which seven were executed by detecting EEG-based signals through the BCI. The user was asked to imagine UE motion related to the specific phase of the task to be assisted. Once detected by the BCI the phase was initiated. Each phase was then terminated when the BCI detected the volunteers clenching their teeth.<br><br>RESULTS: The drinking task was completed by all five participants with an average time of 127&#xa0;seconds with a standard deviation of 23&#xa0;seconds. The incremental motions of elbow extension and elbow flexion were the primary limiting factors for completing this task faster. The BCI control along with the volitional motions also depended upon the users pace, hence the noticeable deviation from the average time.<br><br>CONCLUSION: Through tests conducted with healthy volunteers, this study showed that our proposed system has the potential for successfully assisting individuals with neurological disorders and hemiparetic stroke to independently drink from a glass.},
}
@article {pmid24704394,
year = {2014},
author = {Bonato Felix, L and de Souza Ranaudo, F and D'affonseca Netto, A and Ferreira Leite Miranda de Sá, AM},
title = {A spatial approach of magnitude-squared coherence applied to selective attention detection.},
journal = {Journal of neuroscience methods},
volume = {229},
number = {},
pages = {28-32},
doi = {10.1016/j.jneumeth.2014.03.014},
pmid = {24704394},
issn = {1872-678X},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Auditory Perception/*physiology ; Brain/*physiology ; Electrodes ; Electroencephalography/instrumentation/*methods ; Functional Laterality/physiology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {BACKGROUND: Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface.<br><br>NEW METHOD: In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli.<br><br>RESULTS: The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute.<br><br>Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute).<br><br>CONCLUSIONS: It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention.},
}
@article {pmid24698208,
year = {2014},
author = {Severens, M and Van der Waal, M and Farquhar, J and Desain, P},
title = {Comparing tactile and visual gaze-independent brain-computer interfaces in patients with amyotrophic lateral sclerosis and healthy users.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {11},
pages = {2297-2304},
doi = {10.1016/j.clinph.2014.03.005},
pmid = {24698208},
issn = {1872-8952},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Attention/physiology ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Female ; Humans ; Male ; Middle Aged ; Touch/*physiology ; Vision, Ocular/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCI) tested in patients often are gaze-dependent, while these intended users could possibly lose the ability to focus their gaze. Therefore, a visual and a tactile gaze-independent spelling system were investigated.<br><br>METHODS: Five patients with amyotrophic lateral sclerosis (ALS) tested a visual Hex-o-Spell and a tactile speller. Six healthy participants were also included, mainly to evaluate the tactile stimulators.<br><br>RESULTS: A significant attentional modulation was seen in the P300 for the Hex-o-Spell and in the N2 for the tactile speller. Average on-line classification performance for selecting a step in the speller was above chance level (17%) for both spellers. However, average performance was higher for the Hex-o-Spell (88% and 85% for healthy participants and patients, respectively) than for the tactile speller (56% and 53%, respectively). Likewise, bitrates were higher for the Hex-o-Spell compared with the tactile speller, and in the subjective usability a preference for the Hex-o-Spell was found.<br><br>CONCLUSIONS: The Hex-o-Spell outperformed the tactile speller in classification performance, bit rate and subjective usability.<br><br>SIGNIFICANCE: This is the first study showing the possible use of tactile and visual gaze-independent BCI spelling systems by ALS patients with mild to moderate disabilities.},
}
@article {pmid24695550,
year = {2014},
author = {Onishi, A and Natsume, K},
title = {Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.},
journal = {PloS one},
volume = {9},
number = {4},
pages = {e93045},
pmid = {24695550},
issn = {1932-6203},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; },
abstract = {A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.},
}
@article {pmid24694722,
year = {2014},
author = {Hortal, E and Ubeda, A and Iáñez, E and Azorín, JM},
title = {Control of a 2 DoF robot using a brain-machine interface.},
journal = {Computer methods and programs in biomedicine},
volume = {116},
number = {2},
pages = {169-176},
doi = {10.1016/j.cmpb.2014.02.018},
pmid = {24694722},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces/statistics &amp; numerical data ; Electroencephalography/statistics &amp; numerical data ; Feedback, Sensory/physiology ; Humans ; Imagination/physiology ; Robotics/*instrumentation/statistics &amp; numerical data ; Support Vector Machine ; },
abstract = {In this paper, a non-invasive spontaneous Brain-Machine Interface (BMI) is used to control the movement of a planar robot. To that end, two mental tasks are used to manage the visual interface that controls the robot. The robot used is a PupArm, a force-controlled planar robot designed by the nBio research group at the Miguel Hernández University of Elche (Spain). Two control strategies are compared: hierarchical and directional control. The experimental test (performed by four users) consists of reaching four targets. The errors and time used during the performance of the tests are compared in both control strategies (hierarchical and directional control). The advantages and disadvantages of each method are shown after the analysis of the results. The hierarchical control allows an accurate approaching to the goals but it is slower than using the directional control which, on the contrary, is less precise. The results show both strategies are useful to control this planar robot. In the future, by adding an extra device like a gripper, this BMI could be used in assistive applications such as grasping daily objects in a realistic environment. In order to compare the behavior of the system taking into account the opinion of the users, a NASA Tasks Load Index (TLX) questionnaire is filled out after two sessions are completed.},
}
@article {pmid24694169,
year = {2014},
author = {Li, J and Ji, H and Cao, L and Zang, D and Gu, R and Xia, B and Wu, Q},
title = {Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom.},
journal = {International journal of neural systems},
volume = {24},
number = {4},
pages = {1450014},
doi = {10.1142/S0129065714500142},
pmid = {24694169},
issn = {1793-6462},
mesh = {Acoustic Stimulation ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Electroencephalography Phase Synchronization/*physiology ; Evoked Potentials, Visual/*physiology ; *Feedback, Physiological ; Female ; *Freedom ; Humans ; Imagination ; Intention ; Male ; Man-Machine Systems ; User-Computer Interface ; Wheelchairs/*psychology ; Young Adult ; },
abstract = {There have been many attempts to design brain-computer interfaces (BCIs) for wheelchair control based on steady state visual evoked potential (SSVEP), event-related desynchronization/synchronization (ERD/ERS) during motor imagery (MI) tasks, P300 evoked potential, and some hybrid signals. However, those BCI systems cannot implement the wheelchair navigation flexibly and effectively. In this paper, we propose a hybrid BCI scheme based on two-class MI and four-class SSVEP tasks. It cannot only provide multi-degree control for its user, but also allow the user implement the different types of commands in parallel. In order for the subject to learn the hybrid mental strategies effectively, we design a visual and auditory cues and feedback-based training paradigm. Furthermore, an algorithm based on entropy of classification probabilities is proposed to detect intentional control (IC) state for hybrid tasks, and ensure that multi-degree control commands are accurately and quickly generated. The experiment results attest to the efficiency and flexibility of the hybrid BCI for wheelchair control in the real-world.},
}
@article {pmid24694168,
year = {2014},
author = {Zhang, Y and Zhou, G and Jin, J and Wang, X and Cichocki, A},
title = {Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis.},
journal = {International journal of neural systems},
volume = {24},
number = {4},
pages = {1450013},
doi = {10.1142/S0129065714500130},
pmid = {24694168},
issn = {1793-6462},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated ; Photic Stimulation ; *Recognition, Psychology ; User-Computer Interface ; Young Adult ; },
abstract = {Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in the CCA method often does not result in the optimal recognition accuracy due to their lack of features from the real electro-encephalo-gram (EEG) data. To address this problem, this study proposes a novel method based on multiset canonical correlation analysis (MsetCCA) to optimize the reference signals used in the CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple linear transforms that implement joint spatial filtering to maximize the overall correlation among canonical variates, and hence extracts SSVEP common features from multiple sets of EEG data recorded at the same stimulus frequency. The optimized reference signals are formed by combination of the common features and completely based on training data. Experimental study with EEG data from 10 healthy subjects demonstrates that the MsetCCA method improves the recognition accuracy of SSVEP frequency in comparison with the CCA method and other two competing methods (multiway CCA (MwayCCA) and phase constrained CCA (PCCA)), especially for a small number of channels and a short time window length. The superiority indicates that the proposed MsetCCA method is a new promising candidate for frequency recognition in SSVEP-based BCIs.},
}
@article {pmid24693678,
year = {2013},
author = {Ikeda, T and Akahoshi, T and Kawanaka, H and Uchiyama, H and Yamashita, Y and Morita, M and Oki, E and Saeki, H and Sugimachi, K and Ikegami, T and Yoshizumi, T and Soejima, Y and Shirabe, K and Mimori, K and Watanabe, M and Hashizume, M and Maehara, Y},
title = {[Optimum hepatic parenchymal dissection to prevent bile leak: a comparative study using electrosurgical and stapling devices in swine].},
journal = {Fukuoka igaku zasshi = Hukuoka acta medica},
volume = {104},
number = {12},
pages = {507-514},
pmid = {24693678},
issn = {0016-254X},
mesh = {Anastomotic Leak/*prevention &amp; control ; Animals ; *Bile ; Bile Ducts/*injuries ; Electrosurgery/*adverse effects/*instrumentation ; Female ; Hepatectomy/*adverse effects/*instrumentation ; Intraoperative Complications/*etiology ; Laparoscopy/*adverse effects/*instrumentation ; Liver/*surgery ; Surgical Stapling/*adverse effects/*instrumentation ; Swine ; },
abstract = {BACKGROUND: Bile leakage is a serious complication of liver resection, and its treatment is very time-consuming. In open liver resection, Glisson's sheaths are usually disconnected by ligation to the extent possible during the parenchyma dissection. However, in laparoscopic surgery, the ligation, suture, and hemostasis are more difficult than in open surgery. For this reason, in laparoscopic liver resection, liver parenchyma dissection is generally accomplished using electrosurgical or stapling devices.<br><br>PURPOSE: The purpose of this study was to verify the authenticity of electrosurgical devices attached an automatic irrigation function (AI) and stapling devices for laparoscopic liver parenchymal dissection.<br><br>METHODS: Four devices were used for liver parenchymal dissection in laparoscopic hepatic wedge resection, in pigs: monopolar high-frequency electric cautery attached AI (MCI) (n = 6), bipolar high-frequency electric cautery attached AI (BCI) (n = 6), bipolar tissue sealing system (LigaSure) attached AI (BSI) and an endoscopic stapling device (ECHELON FLEX ENDOPATH) (ES). In each group, burst pressures were tested using an electronic manometer, paying special attention to the location (s) of the first disruption (s). The dissected tissues were examined histologically.<br><br>RESULTS: Pressures used in electrosurgical devices attach AI were significantly higher compared to pressures used in a ES (P < 0.001). While thermal denaturation of the liver parenchyma occurred at approximately 2-3 mm of depth when bipolar high-frequency electric cautery was used for dissection, it reached up to more than 10 mm with monopolar high-frequency electric cautery. All of the first disruption points of stapling were at stapling line.<br><br>CONCLUSIONS: Electrosurgical devices with an automatic irrigation function are useful devices to dissect the liver parenchyma.},
}
@article {pmid24691154,
year = {2014},
author = {McCreadie, KA and Coyle, DH and Prasad, G},
title = {Is sensorimotor BCI performance influenced differently by mono, stereo, or 3-D auditory feedback?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {3},
pages = {431-440},
doi = {10.1109/TNSRE.2014.2312270},
pmid = {24691154},
issn = {1558-0210},
mesh = {*Acoustic Stimulation ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Psychological/*physiology ; Feedback, Sensory ; Female ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Photic Stimulation ; Sensation/*physiology ; Young Adult ; },
abstract = {Imagination of movement can be used as a control method for a brain-computer interface (BCI) allowing communication for the physically impaired. Visual feedback within such a closed loop system excludes those with visual problems and hence there is a need for alternative sensory feedback pathways. In the context of substituting the visual channel for the auditory channel, this study aims to add to the limited evidence that it is possible to substitute visual feedback for its auditory equivalent and assess the impact this has on BCI performance. Secondly, the study aims to determine for the first time if the type of auditory feedback method influences motor imagery performance significantly. Auditory feedback is presented using a stepped approach of single (mono), double (stereo), and multiple (vector base amplitude panning as an audio game) loudspeaker arrangements. Visual feedback involves a ball-basket paradigm and a spaceship game. Each session consists of either auditory or visual feedback only with runs of each type of feedback presentation method applied in each session. Results from seven subjects across five sessions of each feedback type (visual, auditory) (10 sessions in total) show that auditory feedback is a suitable substitute for the visual equivalent and that there are no statistical differences in the type of auditory feedback presented across five sessions.},
}
@article {pmid24686231,
year = {2014},
author = {Xu, R and Jiang, N and Mrachacz-Kersting, N and Lin, C and Asín Prieto, G and Moreno, JC and Pons, JL and Dremstrup, K and Farina, D},
title = {A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {7},
pages = {2092-2101},
doi = {10.1109/TBME.2014.2313867},
pmid = {24686231},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Analysis of Variance ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Electromyography ; Evoked Potentials, Motor/*physiology ; Female ; Foot ; *Foot Orthoses ; Humans ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Neuronal Plasticity/*physiology ; Robotics ; *Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; Transcranial Magnetic Stimulation/methods ; Young Adult ; },
abstract = {In this paper, we present a brain-computer interface (BCI) driven motorized ankle-foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (∼15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ∼15 min (p = 0.009 and , respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size (p = 0.38 and p = 0.15). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke.},
}
@article {pmid24684452,
year = {2014},
author = {Llera, A and Gómez, V and Kappen, HJ},
title = {Adaptive multiclass classification for brain computer interfaces.},
journal = {Neural computation},
volume = {26},
number = {6},
pages = {1108-1127},
doi = {10.1162/NECO_a_00592},
pmid = {24684452},
issn = {1530-888X},
mesh = {Algorithms ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Databases, Factual ; Discriminant Analysis ; Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {We consider the problem of multiclass adaptive classification for brain-computer interfaces and propose the use of multiclass pooled mean linear discriminant analysis (MPMLDA), a multiclass generalization of the adaptation rule introduced by Vidaurre, Kawanabe, von Bünau, Blankertz, and Müller (2010) for the binary class setting. Using publicly available EEG data sets and tangent space mapping (Barachant, Bonnet, Congedo, &amp; Jutten, 2012) as a feature extractor, we demonstrate that MPMLDA can significantly outperform state-of-the-art multiclass static and adaptive methods. Furthermore, efficient learning rates can be achieved using data from different subjects.},
}
@article {pmid24681927,
year = {2014},
author = {Morrison, T and Nagaraju, M and Winslow, B and Bernard, A and Otis, BP},
title = {A 0.5 cm(3) four-channel 1.1 mW wireless biosignal interface with 20 m range.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {1},
pages = {138-147},
doi = {10.1109/TBCAS.2013.2260337},
pmid = {24681927},
issn = {1940-9990},
mesh = {Animals ; Brain-Computer Interfaces ; Electrocardiography ; Electroencephalography ; Equipment Design ; Heart Rate ; Humans ; Mice ; Mice, Inbred C57BL ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; },
abstract = {This paper presents a self-contained, single-chip biosignal monitoring system with wireless programmability and telemetry interface suitable for mainstream healthcare applications. The system consists of low-noise front end amplifiers, ADC, MICS/ISM transmitter and infrared programming capability to configure the state of the chip. An on-chip packetizer ensures easy pairing with standard off-the-shelf receivers. The chip is realized in the IBM 130 nm CMOS process with an area of 2×2 mm(2). The entire system consumes 1.07 mW from a 1.2 V supply. It weighs 0.6 g including a zinc-air battery. The system has been extensively tested in in vivo biological experiments and requires minimal human interaction or calibration.},
}
@article {pmid24681926,
year = {2014},
author = {Hosseini-Nejad, H and Jannesari, A and Sodagar, AM},
title = {Data compression in brain-machine/computer interfaces based on the Walsh-Hadamard transform.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {1},
pages = {129-137},
doi = {10.1109/TBCAS.2013.2258669},
pmid = {24681926},
issn = {1940-9990},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Data Compression/*methods ; Neural Prostheses ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper reports on the application of the Walsh-Hadamard transform (WHT) for data compression in brain-machine/brain-computer interfaces. Using the proposed technique, the amount of the neural data transmitted off the implant is compressed by a factor of at least 63 at the expense of as low as 4.66% RMS error between the signal reconstructed on the external host and the original neural signal on the implant side. Based on the proposed idea, a 128-channel WHT processor was designed in a 0.18- μm CMOS process occupying 1.64 mm(2) of silicon area. The circuit consumes 81 μW (0.63 μW per channel) from a 1.8-V power supply at 250 kHz. A prototype of the proposed processor was implemented and successfully tested using prerecorded neural signals.},
}
@article {pmid24679119,
year = {2014},
author = {Rumisha, SF and Smith, TA and Masanja, H and Abdulla, S and Vounatsou, P},
title = {Relationship between child survival and malaria transmission: an analysis of the malaria transmission intensity and mortality burden across Africa (MTIMBA) project data in Rufiji demographic surveillance system, Tanzania.},
journal = {Malaria journal},
volume = {13},
number = {},
pages = {124},
pmid = {24679119},
issn = {1475-2875},
mesh = {Animals ; Anopheles/*parasitology/physiology ; Bayes Theorem ; Child, Preschool ; Female ; Humans ; Infant ; Infant, Newborn ; Insect Bites and Stings/*epidemiology/etiology ; Malaria/*mortality/*transmission ; Male ; Tanzania/epidemiology ; },
abstract = {BACKGROUND: The precise nature of the relationship between malaria mortality and levels of transmission is unclear. Due to methodological limitations, earlier efforts to assess the linkage have lead to inconclusive results. The malaria transmission intensity and mortality burden across Africa (MTIMBA) project initiated by the INDEPTH Network collected longitudinally entomological data within a number of sites in sub-Saharan Africa to study this relationship. This work linked the MTIMBA entomology database with the routinely collected vital events within the Rufiji Demographic Surveillance System to analyse the transmission-mortality relation in the region.<br><br>METHODS: Bayesian Bernoulli spatio-temporal Cox proportional hazards models with village clustering, adjusted for age and insecticide-treated nets (ITNs), were fitted to assess the relation between mortality and malaria transmission measured by entomology inoculation rate (EIR). EIR was predicted at household locations using transmission models and it was incorporated in the model as a covariate with measure of uncertainty. Effects of covariates estimated by the model are reported as hazard ratios (HR) with 95% Bayesian confidence interval (BCI) and spatial and temporal parameters are presented.<br><br>RESULTS: Separate analysis was carried out for neonates, infants and children 1-4 years of age. No significant relation between all-cause mortality and intensity of malaria transmission was indicated at any age in childhood. However, a strong age effect was shown. Comparing effects of ITN and EIR on mortality at different age categories, a decrease in protective efficacy of ITN was observed (i.e. neonates: HR = 0.65; 95% BCI:0.39-1.05; infants: HR = 0.72; 95% BCI:0.48-1.07; children 1-4 years: HR = 0.88; 95% BCI:0.62-1.23) and reduction on the effect of malaria transmission exposure was detected (i.e. neonates: HR = 1.15; 95% BCI:0.95-1.36; infants: HR = 1.13; 95% BCI:0.98-1.25; children 1-4 years: HR = 1.04; 95% BCI:0.89-1.18). A very strong spatial correlation was also observed.<br><br>CONCLUSION: These results imply that assessing the malaria transmission-mortality relation involves more than the knowledge on the performance of interventions and control measures. This relation depends on the levels of malaria endemicity and transmission intensity, which varies significantly between different settings. Thus, sub-regions analyses are necessary to validate and assess reproducibility of findings.},
}
@article {pmid24678295,
year = {2014},
author = {Stephen, EP and Lepage, KQ and Eden, UT and Brunner, P and Schalk, G and Brumberg, JS and Guenther, FH and Kramer, MA},
title = {Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.},
journal = {Frontiers in computational neuroscience},
volume = {8},
number = {},
pages = {31},
pmid = {24678295},
issn = {1662-5188},
support = {R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R01 DC002852/DC/NIDCD NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; R03 DC011304/DC/NIDCD NIH HHS/United States ; F31 DC011663/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; },
abstract = {The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.},
}
@article {pmid24675760,
year = {2014},
author = {Mora-Cortes, A and Manyakov, NV and Chumerin, N and Van Hulle, MM},
title = {Language model applications to spelling with Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {14},
number = {4},
pages = {5967-5993},
pmid = {24675760},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Language ; *Models, Theoretical ; *Semantics ; },
abstract = {Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.},
}
@article {pmid24672456,
year = {2014},
author = {Soekadar, SR and Witkowski, M and Cossio, EG and Birbaumer, N and Cohen, LG},
title = {Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations.},
journal = {Frontiers in behavioral neuroscience},
volume = {8},
number = {},
pages = {93},
pmid = {24672456},
issn = {1662-5153},
abstract = {OBJECTIVE: Transcranial direct current stimulation (tDCS) improves motor learning and can affect emotional processing and attention. However, it is unclear whether learned electroencephalography (EEG)-based brain-machine interface (BMI) control during tDCS is feasible, how application of transcranial electric currents during BMI control would interfere with feature-extraction of physiological brain signals and how it affects brain control performance. Here we tested this combination and evaluated stimulation-dependent artifacts across different EEG frequencies and stability of motor imagery-based BMI control.<br><br>APPROACH: Ten healthy volunteers were invited to two BMI-sessions, each comprising two 60-trial blocks. During the trials, learned desynchronization of mu-rhythms (8-15 Hz) associated with motor imagery (MI) recorded over C4 was translated into online cursor movements on a computer screen. During block 2, either sham (session A) or anodal tDCS (session B) was applied at 1 mA with the stimulation electrode placed 1 cm anterior of C4.<br><br>MAIN RESULTS: tDCS was associated with a significant signal power increase in the lower frequencies most evident in the signal spectrum of the EEG channel closest to the stimulation electrode. Stimulation-dependent signal power increase exhibited a decay of 12 dB per decade, leaving frequencies above 9 Hz unaffected. Analysis of BMI control performance did not indicate a difference between blocks and tDCS conditions.<br><br>CONCLUSION: Application of tDCS during learned EEG-based self-regulation of brain oscillations above 9 Hz is feasible and safe, and might improve applicability of BMI systems.},
}
@article {pmid24670273,
year = {2014},
author = {Andalib, D and Nabie, R and Abbasi, L},
title = {Silicone intubation for nasolacrimal duct stenosis in adults: monocanalicular or bicanalicular intubation.},
journal = {The Journal of craniofacial surgery},
volume = {25},
number = {3},
pages = {1009-1011},
doi = {10.1097/SCS.0000000000000708},
pmid = {24670273},
issn = {1536-3732},
mesh = {Adult ; Aged ; *Dacryocystorhinostomy ; Device Removal ; Female ; Humans ; Intubation/*instrumentation ; Lacrimal Apparatus Diseases/*surgery ; Male ; Middle Aged ; Nasolacrimal Duct/*surgery ; Postoperative Complications/etiology ; Reoperation ; *Silicones ; },
abstract = {PURPOSE: The purpose of this study was to compare the success rate of monocanalicular versus that of bicanalicular silicone intubations of the nasolacrimal duct for nasolacrimal duct stenosis (NLDS) in adults (patent nasolacrimal duct with resistance to positive-pressure irrigation).<br><br>MATERIALS AND METHODS: In a prospective randomized clinical trial, 52 eyes of 38 patients with NLDS underwent either monocanalicular silicone intubation (MCI) (n = 26 eyes) or bicanalicular silicon intubation (BCI) (n = 26 eyes). All procedures were performed by 1 oculoplastic surgeon. Tube removal was planned for 3 months postoperatively. Treatment success was defined as the complete resolution of epiphora or intermittent epiphora with normal dye disappearance test at 6 months after tube removal.<br><br>RESULTS: The surgical outcome was assessed in 25 eyes with MCI and 21 eyes with BCI. The mean (SD) age of treatment was 52.7 (18.6) years for MCI and 49 (18.8) years for BCI. Treatment success was achieved in 19 of 25 eyes (76%) in the MCI group compared with 16 of 21 eyes (76.2%) in the BCI group. Differences between the 2 groups proved to be not significant (P = 0.9). The only complication was peripunctal pyogenic granuloma in 2 eyes with BCI.<br><br>CONCLUSIONS: Both MCI and BCI were successful in a similar percentage of patients with NLDS. The main advantages of the former technique were simple insertion and easy removal of the tube.},
}
@article {pmid24664526,
year = {2014},
author = {Birbaumer, N and Hummel, FC},
title = {Habit learning and brain-machine interfaces (BMI): a tribute to Valentino Braitenberg's "Vehicles".},
journal = {Biological cybernetics},
volume = {108},
number = {5},
pages = {595-601},
doi = {10.1007/s00422-014-0595-5},
pmid = {24664526},
issn = {1432-0770},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces/history ; *Habits ; History, 20th Century ; Humans ; Learning/*physiology ; Neurofeedback ; Paralysis/physiopathology/psychology ; *User-Computer Interface ; },
abstract = {Brain-Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book "Vehicles," in the concept of a "thought pump" residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish-at least partially-in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.},
}
@article {pmid24661456,
year = {2014},
author = {Shen, G and Zhang, J and Wang, M and Lei, D and Yang, G and Zhang, S and Du, X},
title = {Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.},
journal = {The European journal of neuroscience},
volume = {39},
number = {12},
pages = {2071-2082},
doi = {10.1111/ejn.12547},
pmid = {24661456},
issn = {1460-9568},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain Mapping/*methods ; Female ; Fingers/*physiology ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Motor Activity/*physiology ; Multivariate Analysis ; Neuropsychological Tests ; Photic Stimulation ; Reaction Time ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Visual Perception/physiology ; Young Adult ; },
abstract = {Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement.},
}
@article {pmid24659964,
year = {2014},
author = {Paek, AY and Agashe, HA and Contreras-Vidal, JL},
title = {Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {3},
pmid = {24659964},
issn = {1662-6443},
abstract = {We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8-13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20-30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.},
}
@article {pmid24658406,
year = {2014},
author = {Hill, NJ and Häuser, AK and Schalk, G},
title = {A general method for assessing brain-computer interface performance and its limitations.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {026018},
pmid = {24658406},
issn = {1741-2552},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain-Computer Interfaces/*standards ; Electroencephalography/*methods/*standards ; Female ; Humans ; Male ; Middle Aged ; Pilot Projects ; Young Adult ; },
abstract = {OBJECTIVE: When researchers evaluate brain-computer interface (BCI) systems, we want quantitative answers to questions such as: How good is the system's performance? How good does it need to be? and: Is it capable of reaching the desired level in future? In response to the current lack of objective, quantitative, study-independent approaches, we introduce methods that help to address such questions. We identified three challenges: (I) the need for efficient measurement techniques that adapt rapidly and reliably to capture a wide range of performance levels; (II) the need to express results in a way that allows comparison between similar but non-identical tasks; (III) the need to measure the extent to which certain components of a BCI system (e.g. the signal processing pipeline) not only support BCI performance, but also potentially restrict the maximum level it can reach.<br><br>APPROACH: For challenge (I), we developed an automatic staircase method that adjusted task difficulty adaptively along a single abstract axis. For challenge (II), we used the rate of information gain between two Bernoulli distributions: one reflecting the observed success rate, the other reflecting chance performance estimated by a matched random-walk method. This measure includes Wolpaw's information transfer rate as a special case, but addresses the latter's limitations including its restriction to item-selection tasks. To validate our approach and address challenge (III), we compared four healthy subjects' performance using an EEG-based BCI, a 'Direct Controller' (a high-performance hardware input device), and a 'Pseudo-BCI Controller' (the same input device, but with control signals processed by the BCI signal processing pipeline).<br><br>MAIN RESULTS: Our results confirm the repeatability and validity of our measures, and indicate that our BCI signal processing pipeline reduced attainable performance by about 33% (21 bits min(-1)).<br><br>SIGNIFICANCE: Our approach provides a flexible basis for evaluating BCI performance and its limitations, across a wide range of tasks and task difficulties.},
}
@article {pmid24658251,
year = {2014},
author = {Tomita, Y and Vialatte, FB and Dreyfus, G and Mitsukura, Y and Bakardjian, H and Cichocki, A},
title = {Bimodal BCI using simultaneously NIRS and EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {4},
pages = {1274-1284},
doi = {10.1109/TBME.2014.2300492},
pmid = {24658251},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/physiology ; Head/blood supply/physiology ; Hemodynamics/physiology ; Humans ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {Although noninvasive brain-computer interfaces (BCI) based on electroencephalographic (EEG) signals have been studied increasingly over the recent decades, their performance is still limited in two important aspects. First, the difficulty of performing a reliable detection of BCI commands increases when EEG epoch length decreases, which makes high information transfer rates difficult to achieve. Second, the BCI system often misclassifies the EEG signals as commands, although the subject is not performing any task. In order to circumvent these limitations, the hemodynamic fluctuations in the brain during stimulation with steady-state visual evoked potentials (SSVEP) were measured using near-infrared spectroscopy (NIRS) simultaneously with EEG. BCI commands were estimated based on responses to a flickering checkerboard (ON-period). Furthermore, an "idle" command was generated from the signal recorded by the NIRS system when the checkerboard was not flickering (OFF-period). The joint use of EEG and NIRS was shown to improve the SSVEP classification. For 13 subjects, the relative improvement in error rates obtained by using the NIRS signal, for nine classes including the "idle" mode, ranged from 85% to 53 %, when the epoch length increase from 3 to 12 s. These results were obtained from only one EEG and one NIRS channel. The proposed bimodal NIRS-EEG approach, including detection of the idle mode, may make current BCI systems faster and more reliable.},
}
@article {pmid24658248,
year = {2014},
author = {Kanas, VG and Mporas, I and Benz, HL and Sgarbas, KN and Bezerianos, A and Crone, NE},
title = {Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {4},
pages = {1241-1250},
pmid = {24658248},
issn = {1558-2531},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Cluster Analysis ; Electroencephalography/*methods ; Epilepsy/physiopathology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Speech/*physiology ; },
abstract = {Brain-machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and nonspeech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllables repetition tasks and may contribute to the development of portable ECoG-based communication.},
}
@article {pmid24654268,
year = {2014},
author = {Bundy, DT and Zellmer, E and Gaona, CM and Sharma, M and Szrama, N and Hacker, C and Freudenburg, ZV and Daitch, A and Moran, DW and Leuthardt, EC},
title = {Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings.},
journal = {Journal of neural engineering},
volume = {11},
number = {1},
pages = {016006},
pmid = {24654268},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; TL1 TR000449/TR/NCATS NIH HHS/United States ; UL1 TR000448/TR/NCATS NIH HHS/United States ; 1R0100085606//PHS HHS/United States ; },
mesh = {Algorithms ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Data Interpretation, Statistical ; Dura Mater/*physiology ; Electrodes, Implanted ; *Electroencephalography ; Epidural Space/physiology ; Epilepsy/physiopathology ; Evoked Potentials/physiology ; Head ; Humans ; Microelectrodes ; Models, Anatomic ; Prosthesis Design ; Subdural Space/physiology ; },
abstract = {OBJECTIVE: Electrocorticography (ECoG) electrodes implanted on the surface of the brain have recently emerged as a potential signal platform for brain-computer interface (BCI) systems. While clinical ECoG electrodes are currently implanted beneath the dura, epidural electrodes could reduce the invasiveness and the potential impact of a surgical site infection. Subdural electrodes, on the other hand, while slightly more invasive, may have better signals for BCI application. Because of this balance between risk and benefit between the two electrode positions, the effect of the dura on signal quality must be determined in order to define the optimal implementation for an ECoG BCI system.<br><br>APPROACH: This study utilized simultaneously acquired baseline recordings from epidural and subdural ECoG electrodes while patients rested. Both macro-scale (2 mm diameter electrodes with 1 cm inter-electrode distance, one patient) and micro-scale (75 &#xb5;m diameter electrodes with 1 mm inter-electrode distance, four patients) ECoG electrodes were tested. Signal characteristics were evaluated to determine differences in the spectral amplitude and noise floor. Furthermore, the experimental results were compared to theoretical effects produced by placing epidural and subdural ECoG contacts of different sizes within a finite element model.<br><br>MAIN RESULTS: The analysis demonstrated that for micro-scale electrodes, subdural contacts have significantly higher spectral amplitudes and reach the noise floor at a higher frequency than epidural contacts. For macro-scale electrodes, while there are statistical differences, these differences are small in amplitude and likely do not represent differences relevant to the ability of the signals to be used in a BCI system.<br><br>CONCLUSIONS: Our findings demonstrate an important trade-off that should be considered in developing a chronic BCI system. While implanting electrodes under the dura is more invasive, it is associated with increased signal quality when recording from micro-scale electrodes with very small sizes and spacing. If recording from larger electrodes, such as traditionally used clinically, the signal quality of epidural recordings is similar to that of subdural recordings.},
}
@article {pmid24654266,
year = {2014},
author = {Fan, JM and Nuyujukian, P and Kao, JC and Chestek, CA and Ryu, SI and Shenoy, KV},
title = {Intention estimation in brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {11},
number = {1},
pages = {016004},
pmid = {24654266},
issn = {1741-2552},
support = {R01 NS064318/NS/NINDS NIH HHS/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01-NS066311/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; 1DP1OD006409/OD/NIH HHS/United States ; R01 NS066311/NS/NINDS NIH HHS/United States ; R01-NS064318/NS/NINDS NIH HHS/United States ; T-R01NS076460/NS/NINDS NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/innervation/physiology ; Artificial Intelligence ; Biomechanical Phenomena ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Electronic Data Processing ; Feedback, Physiological ; Hand/innervation/physiology ; Humans ; Macaca mulatta ; Neural Prostheses ; Online Systems ; Psychomotor Performance/physiology ; },
abstract = {OBJECTIVE: The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF).<br><br>APPROACH: This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm.<br><br>MAIN RESULTS: Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied.<br><br>SIGNIFICANCE: These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.},
}
@article {pmid24653694,
year = {2014},
author = {Billinger, M and Brunner, C and Müller-Putz, GR},
title = {SCoT: a Python toolbox for EEG source connectivity.},
journal = {Frontiers in neuroinformatics},
volume = {8},
number = {},
pages = {22},
pmid = {24653694},
issn = {1662-5196},
abstract = {Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.},
}
@article {pmid24642607,
year = {2014},
author = {Ramsey, NF and Aarnoutse, EJ and Vansteensel, MJ},
title = {Brain implants for substituting lost motor function: state of the art and potential impact on the lives of motor-impaired seniors.},
journal = {Gerontology},
volume = {60},
number = {4},
pages = {366-372},
pmid = {24642607},
issn = {1423-0003},
support = {320708/ERC_/European Research Council/International ; },
mesh = {Aged ; Brain/physiopathology/surgery ; *Brain-Computer Interfaces/trends ; Persons with Disabilities ; Humans ; Mobility Limitation ; *Neural Prostheses ; Paralysis/physiopathology/rehabilitation/surgery ; Robotics ; Stroke/physiopathology/surgery ; Stroke Rehabilitation ; },
abstract = {Recent scientific achievements bring the concept of neural prosthetics for reinstating lost motor function closer to medical application. Current research involves severely paralyzed people under the age of 65, but implications for seniors with stroke or trauma-induced impairments are clearly on the horizon. Demographic changes will lead to a shortage of personnel to care for an increasing population of senior citizens, threatening maintenance of an acceptable level of care and urging ways for people to live longer at their home independent from personal assistance. This is particularly challenging when people suffer from disabilities such as partial paralysis after stroke or trauma, where daily personal assistance is required. For some of these people, neural prosthetics can reinstate some lost motor function and/or lost communication, thereby increasing independence and possibly quality of life. In this viewpoint article, we present the state of the art in decoding brain activity in the service of brain-computer interfacing. Although some noninvasive applications produce good results, we focus on brain implants that benefit from better quality brain signals. Fully implantable neural prostheses for home use are not available yet, but clinical trials are being prepared. More sophisticated systems are expected to follow in the years to come, with capabilities of interest for less severe paralysis. Eventually the combination of smart robotics and brain implants is expected to enable people to interact well enough with their environment to live an independent life in spite of motor disabilities.},
}
@article {pmid24639341,
year = {2014},
author = {Steinmetz, C and Mader, I and Arndt, S and Aschendorff, A and Laszig, R and Hassepass, F},
title = {MRI artefacts after Bonebridge implantation.},
journal = {European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery},
volume = {271},
number = {7},
pages = {2079-2082},
pmid = {24639341},
issn = {1434-4726},
mesh = {Adult ; *Artifacts ; Bone Conduction ; *Hearing Aids ; Hearing Loss/etiology/*pathology/*therapy ; Humans ; *Magnetic Resonance Imaging ; Male ; Neuroma, Acoustic/complications/*pathology ; Prosthesis Design ; },
abstract = {The new transcutaneous bone conduction implant (BCI) Bonebridge (BB, MED-EL) allows the skin to remain intact and therefore overcomes some issues related to percutaneous systems, such as skin reaction around the external screw and cosmetic complaints. According to manufacturer, BB is MRI conditional up to 1,5 Tesla (T). The artefact of the neurocranium after BB implantation is extensive as shown in the present report. This has to be taken into account when patients suffering conductive, mixed or single-sided hearing loss with candidacy for a BCI are counselled. In patients with comorbid intracranial tumour or other diseases of the brain that require imaging control scans with MRI percutaneous, BCI should be the implant of choice considering the very small artefact of the percutaneous screw in MRI.},
}
@article {pmid24638879,
year = {2014},
author = {Carotti, ES and Shalchyan, V and Jensen, W and Farina, D},
title = {Denoising and compression of intracortical signals with a modified MDL criterion.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {52},
number = {5},
pages = {429-438},
pmid = {24638879},
issn = {1741-0444},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Data Compression ; Humans ; Male ; Motor Cortex/*physiology ; Rats ; Rats, Sprague-Dawley ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Intracortical signals are usually affected by high levels of noise [0 dB signal-to-noise ratio (SNR) is not uncommon] often due to magnetic or electrical coupling between surrounding sources and the recording system. Apart from hindering effective exploitation of the information content in the signals, noise also influences the bandwidth needed to transmit them, which is a problem especially when a large number of channels are to be recorded. In this paper, we propose a novel technique for joint denoising and compression of intracortical signals based on the minimum description length principle. This method was tested on both simulated and experimental signals, and the results showed that the proposed technique achieves improvements in SNR and compression ratios greater than alternative denoising/compression methods.},
}
@article {pmid24634650,
year = {2014},
author = {Gharabaghi, A and Kraus, D and Leão, MT and Spüler, M and Walter, A and Bogdan, M and Rosenstiel, W and Naros, G and Ziemann, U},
title = {Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {122},
pmid = {24634650},
issn = {1662-5161},
abstract = {Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS) combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation (TMS) of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface (BMI) environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.},
}
@article {pmid24633631,
year = {2014},
author = {Strelnikov, K},
title = {Neuroenergetics at the brain-mind interface: a conceptual approach.},
journal = {Cognitive processing},
volume = {15},
number = {3},
pages = {297-306},
pmid = {24633631},
issn = {1612-4790},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Energy Metabolism/*physiology ; Humans ; Mental Processes/*physiology ; Neuroimaging ; },
abstract = {Modern neuroimaging techniques, such as PET and fMRI, attracted specialists in cognitive processing to the problems of brain energy and its transformations in relation to information processing. Neuroenergetics has experienced explosive progress during the last decade, complex biochemical and biophysical models of energy turnover in the brain necessitate the search of the general principles behind them, which could be linked to the cognitive view of the brain. In our conceptual descriptive generalization, we consider how the basic thermodynamical reasoning can be used to better understand brain energy. We suggest how thermodynamical principles can be applied to the existing data and theories to obtain the holistic framework of energetic processes in the brain coupled with information processing. This novel and purely descriptive framework permits the integration of approaches of different disciplines to cognitive processing: psychology, physics, physiology, mathematics, molecular biology, biochemistry, etc. Thus, the proposed general principled approach would be helpful for specialists from different fields of cognition.},
}
@article {pmid24631910,
year = {2014},
author = {Lee, B and Attenello, FJ and Liu, CY and McLoughlin, MP and Apuzzo, ML},
title = {Recapitulating flesh with silicon and steel: advancements in upper extremity robotic prosthetics.},
journal = {World neurosurgery},
volume = {81},
number = {5-6},
pages = {730-741},
doi = {10.1016/j.wneu.2014.03.012},
pmid = {24631910},
issn = {1878-8769},
mesh = {Amputation, Surgical/*rehabilitation ; Artificial Limbs/*trends ; Brain-Computer Interfaces/*trends ; Humans ; Paralysis/*rehabilitation ; Prosthesis Design/*trends ; Robotics/*trends ; Silicon ; Spinal Cord Injuries/rehabilitation ; Steel ; Stroke Rehabilitation ; },
abstract = {With the loss of function of an upper extremity because of stroke or spinal cord injury or a physical loss from amputation, an individual's life is forever changed, and activities that were once routine become a magnitude more difficult. Much research and effort have been put into developing advanced robotic prostheses to restore upper extremity function. For patients with upper extremity amputations, previously crude prostheses have evolved to become exceptionally functional. Because the upper extremities can perform a wide variety of activities, several types of upper extremity prostheses are available ranging from passive cosmetic limbs to externally powered robotic limbs. In addition, new developments in brain-machine interface are poised to revolutionize how patients can control these advanced prostheses using their thoughts alone. For patients with spinal cord injury or stroke, functional electrical stimulation promises to provide the most sophisticated prosthetic limbs possible by reanimating paralyzed arms of these patients. Advances in technology and robotics continue to help patients recover vital function. This article examines the latest neurorestorative technologies for patients who have either undergone amputation or lost the use of their upper extremities secondary to stroke or spinal cord injury.},
}
@article {pmid24631218,
year = {2014},
author = {Lin, CS and Lai, YC and Lin, JC and Wu, PY and Chang, HC},
title = {A novel method for concentration evaluation of reading behaviors with electrical activity recorded on the scalp.},
journal = {Computer methods and programs in biomedicine},
volume = {114},
number = {2},
pages = {164-171},
doi = {10.1016/j.cmpb.2014.02.005},
pmid = {24631218},
issn = {1872-7565},
mesh = {Acoustic Stimulation ; Adult ; Algorithms ; Attention/*physiology ; Brain-Computer Interfaces/statistics &amp; numerical data ; Electroencephalography/*statistics &amp; numerical data ; Humans ; Male ; Photic Stimulation ; *Reading ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {In this paper, a concentration evaluation of reading behaviors with electrical signal detection on the head is presented. The electrode signal is extracted by brain-computer-interface (BCI) to monitor the user's degree of concentration, where the user is reminded by sound to concentrate, or teaching staffs are reminded to help users improve reading habits, in order to facilitate the user's ability to concentrate. The digital signal processing methods, such as the Kalman Filter, Fast Fourier Transform, the Hamming window, the average value of the total energy of a frame, correlation coefficient, and novel judgment algorithm are used to obtain the corresponding parameters of concentration evaluation. Users can correct their manner of reading with reminders. The repeated test results may be expected to lie with a probability of 95%. Such model training results in better learning effect.},
}
@article {pmid24626608,
year = {2015},
author = {Soekadar, SR and Witkowski, M and Birbaumer, N and Cohen, LG},
title = {Enhancing Hebbian Learning to Control Brain Oscillatory Activity.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {25},
number = {9},
pages = {2409-2415},
pmid = {24626608},
issn = {1460-2199},
mesh = {Adult ; Analysis of Variance ; *Brain Mapping ; Female ; Humans ; Learning/*physiology ; Magnetoencephalography ; Male ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Movement/physiology ; *Periodicity ; Random Allocation ; Retention, Psychology ; Time Factors ; Transcranial Direct Current Stimulation ; Young Adult ; },
abstract = {Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity.},
}
@article {pmid24626393,
year = {2014},
author = {Vato, A and Szymanski, FD and Semprini, M and Mussa-Ivaldi, FA and Panzeri, S},
title = {A bidirectional brain-machine interface algorithm that approximates arbitrary force-fields.},
journal = {PloS one},
volume = {9},
number = {3},
pages = {e91677},
pmid = {24626393},
issn = {1932-6203},
support = {R01 HD072080/HD/NICHD NIH HHS/United States ; 1R01HD072080-01/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Calibration ; Humans ; Models, Statistical ; Motor Cortex/physiology ; Neurons/physiology ; Nonlinear Dynamics ; Normal Distribution ; Somatosensory Cortex/physiology ; },
abstract = {We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.},
}
@article {pmid24621009,
year = {2014},
author = {Cao, T and Wan, F and Wong, CM and da Cruz, JN and Hu, Y},
title = {Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces.},
journal = {Biomedical engineering online},
volume = {13},
number = {1},
pages = {28},
pmid = {24621009},
issn = {1475-925X},
mesh = {Adult ; Analysis of Variance ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Epilepsy/pathology ; *Evoked Potentials, Visual ; Fatigue/*diagnosis ; Humans ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Signal Transduction ; Signal-To-Noise Ratio ; Surveys and Questionnaires ; Time Factors ; Young Adult ; },
abstract = {BACKGROUND: The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users' discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs.<br><br>METHODS: How the EEG indices (amplitudes in &#x3b4;, &#x3b8;, &#x3b1; and &#x3b2; frequency bands), the selected ratio indices (&#x3b8;/&#x3b1; and (&#x3b8;&#x2009;+&#x2009;&#x3b1;)/&#x3b2;), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index.<br><br>RESULTS: Consistent results are obtained in two experiments: the significant increases in &#x3b1; and (&#x3b8;&#x2009;+&#x2009;&#x3b1;)/&#x3b2;, as well as the decrease in &#x3b8;/&#x3b1; are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users' fatigue.<br><br>CONCLUSIONS: The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.},
}
@article {pmid24616644,
year = {2014},
author = {Savić, AM and Malešević, NM and Popović, MB},
title = {Feasibility of a hybrid brain-computer interface for advanced functional electrical therapy.},
journal = {TheScientificWorldJournal},
volume = {2014},
number = {},
pages = {797128},
pmid = {24616644},
issn = {1537-744X},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electric Stimulation Therapy ; Feasibility Studies ; Female ; Humans ; Male ; },
abstract = {We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD). The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64-0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the "will to move" and contingent temporally synchronized sensory feedback.},
}
@article {pmid24611043,
year = {2014},
author = {Wagner, J and Solis-Escalante, T and Scherer, R and Neuper, C and Müller-Putz, G},
title = {It's how you get there: walking down a virtual alley activates premotor and parietal areas.},
journal = {Frontiers in human neuroscience},
volume = {8},
number = {},
pages = {93},
pmid = {24611043},
issn = {1662-5161},
abstract = {Voluntary drive is crucial for motor learning, therefore we are interested in the role that motor planning plays in gait movements. In this study we examined the impact of an interactive Virtual Environment (VE) feedback task on the EEG patterns during robot assisted walking. We compared walking in the VE modality to two control conditions: walking with a visual attention paradigm, in which visual stimuli were unrelated to the motor task; and walking with mirror feedback, in which participants observed their own movements. Eleven healthy participants were considered. Application of independent component analysis to the EEG revealed three independent component clusters in premotor and parietal areas showing increased activity during walking with the adaptive VE training paradigm compared to the control conditions. During the interactive VE walking task spectral power in frequency ranges 8-12, 15-20, and 23-40 Hz was significantly (p ≤ 0.05) decreased. This power decrease is interpreted as a correlate of an active cortical area. Furthermore activity in the premotor cortex revealed gait cycle related modulations significantly different (p ≤ 0.05) from baseline in the frequency range 23-40 Hz during walking. These modulations were significantly (p ≤ 0.05) reduced depending on gait cycle phases in the interactive VE walking task compared to the control conditions. We demonstrate that premotor and parietal areas show increased activity during walking with the adaptive VE training paradigm, when compared to walking with mirror- and movement unrelated feedback. Previous research has related a premotor-parietal network to motor planning and motor intention. We argue that movement related interactive feedback enhances motor planning and motor intention. We hypothesize that this might improve gait recovery during rehabilitation.},
}
@article {pmid24608683,
year = {2014},
author = {Chuang, CH and Ko, LW and Lin, YP and Jung, TP and Lin, CT},
title = {Independent component ensemble of EEG for brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {2},
pages = {230-238},
doi = {10.1109/TNSRE.2013.2293139},
pmid = {24608683},
issn = {1558-0210},
mesh = {Artifacts ; Artificial Intelligence ; Automobile Driving ; *Brain-Computer Interfaces ; Cognition/physiology ; Decision Making ; Decision Making, Computer-Assisted ; Electroencephalography/*statistics &amp; numerical data ; Feasibility Studies ; Humans ; Online Systems ; Principal Component Analysis ; Reaction Time/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Sleep Stages/physiology ; User-Computer Interface ; },
abstract = {Recently, successful applications of independent component analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasibility of using independent processes to elucidate human cognitive states. However, various technical problems arise in the building of an online brain-computer interface (BCI). These include the lack of an automatic procedure for selecting independent components of interest (ICi) and the potential risk of not obtaining a desired ICi. Therefore, this study proposes an ICi-ensemble method that uses multiple classifiers with ICA processing to improve upon existing algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of features of the resultant ICi; 3) the construction of parallel pipelines for effectively training multiple classifiers; and a 4) simple process that combines the multiple decisions. The proposed ICi-ensemble is demonstrated in a typical BCI application, which is the monitoring of participants' cognitive states in a realistic sustained-attention driving task. The results reveal that the proposed ICi-ensemble outperformed the previous method using a single ICi with ∼ 7% (91.6% versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that characterizes the EEG dynamics of multiple brain areas favors the application of BCI in natural environments.},
}
@article {pmid24608682,
year = {2014},
author = {Blokland, Y and Spyrou, L and Thijssen, D and Eijsvogels, T and Colier, W and Floor-Westerdijk, M and Vlek, R and Bruhn, J and Farquhar, J},
title = {Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {2},
pages = {222-229},
doi = {10.1109/TNSRE.2013.2292995},
pmid = {24608682},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Feasibility Studies ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Motor Cortex/physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; Quadriplegia/*rehabilitation ; Somatosensory Cortex/physiology ; Spectroscopy, Near-Infrared/instrumentation/*methods ; User-Computer Interface ; },
abstract = {Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.},
}
@article {pmid24608681,
year = {2014},
author = {Marathe, AR and Ries, AJ and McDowell, K},
title = {Sliding HDCA: single-trial EEG classification to overcome and quantify temporal variability.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {2},
pages = {201-211},
doi = {10.1109/TNSRE.2014.2304884},
pmid = {24608681},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Area Under Curve ; *Brain-Computer Interfaces ; *Discriminant Analysis ; Electroencephalography/*classification/*statistics &amp; numerical data ; Female ; Humans ; Learning ; Male ; Neural Networks, Computer ; Photic Stimulation ; Psychomotor Performance/physiology ; ROC Curve ; Reaction Time/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Patterns of neural data obtained from electroencephalography (EEG) can be classified by machine learning techniques to increase human-system performance. In controlled laboratory settings this classification approach works well; however, transitioning these approaches into more dynamic, unconstrained environments will present several significant challenges. One such challenge is an increase in temporal variability in measured behavioral and neural responses, which often results in suboptimal classification performance. Previously, we reported a novel classification method designed to account for temporal variability in the neural response in order to improve classification performance by using sliding windows in hierarchical discriminant component analysis (HDCA), and demonstrated a decrease in classification error by over 50% when compared to the standard HDCA method (Marathe et al., 2013). Here, we expand upon this approach and show that embedded within this new method is a novel signal transformation that, when applied to EEG signals, significantly improves the signal-to-noise ratio and thereby enables more accurate single-trial analysis. The results presented here have significant implications for both brain-computer interaction technologies and basic science research into neural processes.},
}
@article {pmid24608672,
year = {2014},
author = {Xu, M and Chen, L and Zhang, L and Qi, H and Ma, L and Tang, J and Wan, B and Ming, D},
title = {A visual parallel-BCI speller based on the time-frequency coding strategy.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {026014},
doi = {10.1088/1741-2560/11/2/026014},
pmid = {24608672},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode.<br><br>APPROACH: The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis.<br><br>MAIN RESULTS: Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min(-1), with an average of 54.0 bit min(-1) and 43.0 bit min(-1) in the three rounds and five rounds, respectively.<br><br>SIGNIFICANCE: The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.},
}
@article {pmid24608228,
year = {2014},
author = {Treder, MS and Purwins, H and Miklody, D and Sturm, I and Blankertz, B},
title = {Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {026009},
doi = {10.1088/1741-2560/11/2/026009},
pmid = {24608228},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adult ; Attention/*physiology ; Auditory Perception/*physiology ; Electroencephalography/*classification ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; Middle Aged ; *Music ; Young Adult ; },
abstract = {OBJECTIVE: Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain-computer interface.<br><br>APPROACH: In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern).<br><br>MAIN RESULTS: Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants.<br><br>SIGNIFICANCE: This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain-computer interface and music research.},
}
@article {pmid24608127,
year = {2014},
author = {Widge, AS and Moritz, CT},
title = {Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {024001},
pmid = {24608127},
issn = {1741-2552},
support = {R01 NS066357/NS/NINDS NIH HHS/United States ; },
mesh = {Acoustic Stimulation/methods ; Animals ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; *Electrodes, Implanted ; Electroencephalography/methods ; Female ; Limbic System/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance/*physiology ; Random Allocation ; Rats ; Rats, Long-Evans ; },
abstract = {OBJECTIVE: There is great interest in closed-loop neurostimulators that sense and respond to a patient's brain state. Such systems may have value for neurological and psychiatric illnesses where symptoms have high intraday variability. Animal models of closed-loop stimulators would aid preclinical testing. We therefore sought to demonstrate that rodents can directly control a closed-loop limbic neurostimulator via a brain-computer interface (BCI).<br><br>APPROACH: We trained rats to use an auditory BCI controlled by single units in prefrontal cortex (PFC). The BCI controlled electrical stimulation in the medial forebrain bundle, a limbic structure involved in reward-seeking. Rigorous offline analyses were performed to confirm volitional control of the neurostimulator.<br><br>MAIN RESULTS: All animals successfully learned to use the BCI and neurostimulator, with closed-loop control of this challenging task demonstrated at 80% of PFC recording locations. Analysis across sessions and animals confirmed statistically robust BCI control and specific, rapid modulation of PFC activity.<br><br>SIGNIFICANCE: Our results provide a preliminary demonstration of a method for emotion-regulating closed-loop neurostimulation. They further suggest that activity in PFC can be used to control a BCI without pre-training on a predicate task. This offers the potential for BCI-based treatments in refractory neurological and mental illness.},
}
@article {pmid24604385,
year = {2014},
author = {Postawa, T and Szubert-Kruszyńska, A},
title = {Is parasite load dependent on host aggregation size? The case of the greater mouse-eared bat Myotis myotis (Mammalia: Chiroptera) and its parasitic mite Spinturnix myoti (Acari: Gamasida).},
journal = {Parasitology research},
volume = {113},
number = {5},
pages = {1803-1811},
pmid = {24604385},
issn = {1432-1955},
mesh = {Age Factors ; Animals ; Chiroptera/*parasitology ; *Ecosystem ; Female ; *Host-Parasite Interactions ; Male ; Mites/*physiology ; *Parasite Load ; Poland ; Population Density ; Sex Factors ; },
abstract = {The risk of parasite infection grows with the size of host aggregations, which, in turn, may also depend on host sex and age and the quality of environmental resources. Herein, we studied the relationship between ectoparasitic infections with the wing mite (Spinturnix myoti) and the size of the breeding colonies, sex, age, and body condition index (BCI) of its host, the greater mouse-eared bat (Myotis myotis). The influence of environmental quality in the Carpathian Mountains (Poland) was also examined. We found significant differences in mite abundance and BCI between different breeding aggregations of the greater mouse-eared bat and also between the host sex/age categories. The most heavily infected bats were adult M. myotis females, while young males appeared to be the least infected. The BCI differed significantly between the sexes in young bats (males had a higher BCI than females) and also between colonies. No significant differences in the BCI were found for adult females. We did not find any relationship between the infestation rate of M. myotis, their colony size, the quality of environmental resources (percentage of forest cover around the colony), or the BCI. The prevalence of the various developmental stages of the mites did not differ between the host sex/age categories; however, differences were found in the sex ratios of deutonymphs and adult mites between adult M. myotis females. We predict that parasite load may not be dependent on colony size itself, but mainly on microclimatic factors, which are in turn directly correlated with colony size.},
}
@article {pmid24600359,
year = {2014},
author = {Collinger, JL and Vinjamuri, R and Degenhart, AD and Weber, DJ and Sudre, GP and Boninger, ML and Tyler-Kabara, EC and Wang, W},
title = {Motor-related brain activity during action observation: a neural substrate for electrocorticographic brain-computer interfaces after spinal cord injury.},
journal = {Frontiers in integrative neuroscience},
volume = {8},
number = {},
pages = {17},
pmid = {24600359},
issn = {1662-5145},
support = {R21 NS056136/NS/NINDS NIH HHS/United States ; UL1 TR000005/TR/NCATS NIH HHS/United States ; UL1 RR024153/RR/NCRR NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; },
abstract = {After spinal cord injury (SCI), motor commands from the brain are unable to reach peripheral nerves and muscles below the level of the lesion. Action observation (AO), in which a person observes someone else performing an action, has been used to augment traditional rehabilitation paradigms. Similarly, AO can be used to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface (BCI) even when the user cannot generate overt movements. BCIs use brain signals to control external devices to replace functions that have been lost due to SCI or other motor impairment. Previous studies have reported congruent motor cortical activity during observed and overt movements using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Recent single-unit studies using intracortical microelectrodes also demonstrated that a large number of motor cortical neurons had similar firing rate patterns between overt and observed movements. Given the increasing interest in electrocorticography (ECoG)-based BCIs, our goal was to identify whether action observation-related cortical activity could be recorded using ECoG during grasping tasks. Specifically, we aimed to identify congruent neural activity during observed and executed movements in both the sensorimotor rhythm (10-40 Hz) and the high-gamma band (65-115 Hz) which contains significant movement-related information. We observed significant motor-related high-gamma band activity during AO in both able-bodied individuals and one participant with a complete C4 SCI. Furthermore, in able-bodied participants, both the low and high frequency bands demonstrated congruent activity between action execution and observation. Our results suggest that AO could be an effective and critical procedure for deriving the mapping from ECoG signals to intended movement for an ECoG-based BCI system for individuals with paralysis.},
}
@article {pmid24599891,
year = {2015},
author = {Hsu, WY},
title = {Motor imagery EEG discrimination using the correlation of wavelet features.},
journal = {Clinical EEG and neuroscience},
volume = {46},
number = {2},
pages = {94-99},
doi = {10.1177/1550059413514974},
pmid = {24599891},
issn = {1550-0594},
mesh = {Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; Wavelet Analysis ; },
abstract = {A novel method for motor imagery (MI) electroencephalogram (EEG) data classification is proposed in this study. Time-frequency representation is constructed by means of continuous wavelet transform from EEG signals and then weighted with 2-sample t-statistics, which are also used to automatically select the area of interest in advance. Finally, normalized cross-correlation is used to discriminate the test MI data. Compared with the nonweighted version on MI data, the experimental results indicate that the proposed system achieves satisfactory results in the applications of brain-computer interface (BCI).},
}
@article {pmid24597231,
year = {2013},
author = {Pearse, WD and Jones, FA and Purvis, A},
title = {Barro Colorado Island's phylogenetic assemblage structure across fine spatial scales and among clades of different ages.},
journal = {Ecology},
volume = {94},
number = {12},
pages = {2861-2872},
doi = {10.1890/12-1676.1},
pmid = {24597231},
issn = {0012-9658},
mesh = {*Islands ; Models, Biological ; Panama ; Phylogeny ; Population Dynamics ; Time Factors ; Trees/chemistry/*physiology ; },
abstract = {Phylogenetic analyses of assemblage membership provide insight into how ecological communities are structured. However, despite the scale-dependency of many ecological processes, little is known about how assemblage and source pool size definitions can be altered, either alone or together, to provide insight into how ecological diversity is maintained. Moreover, although studies have acknowledged that different clades within an assemblage may be structured by different forces, there has been no attempt to relate the age of a clade to its community phylogenetic structure. Using assemblage phylogenies and spatially explicit data for trees from Barro Colorado Island (BCI), we show that larger assemblages, and assemblages with larger source pools, are more phylogenetically clustered. We argue that this reflects competition, the influence of pathogens, and chance assembly at smaller spatial scales, all operating within the context of wider-scale habitat filtering. A community phylogenetic measure that is based on a null model derived explicitly from trait evolution theory, D, is better able to detect these differences than commonly used measures such as SES(MPD) and SES(MNTD). We also detect a moderate tendency for stronger phylogenetic clustering in younger clades, which suggests that coarse analyses of diverse assemblages may be missing important variation among clades. Our results emphasize the importance of spatial and phylogenetic scale in community phylogenetics and show how varying these scales can help to untangle complex assembly processes.},
}
@article {pmid24594233,
year = {2014},
author = {Meyer, T and Peters, J and Zander, TO and Schölkopf, B and Grosse-Wentrup, M},
title = {Predicting motor learning performance from Electroencephalographic data.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {24},
pmid = {24594233},
issn = {1743-0003},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Learning/*physiology ; Male ; Motor Activity/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {BACKGROUND: Research on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current state of VMIL independently of motor execution. Here, we present empirical evidence that a subject's performance in a 3D reaching task can be predicted on a trial-to-trial basis from pre-trial electroencephalographic (EEG) data. This evidence provides novel insights into the brain states that support successful VMIL.<br><br>METHODS: Six healthy subjects, attached to a seven degrees-of-freedom (DoF) robot with their right arm, practiced 3D reaching movements in a virtual space, while an EEG recorded their brain's electromagnetic field. A random forest ensemble classifier was used to predict the next trial's performance, as measured by the time needed to reach the goal, from pre-trial data using a leave-one-subject-out cross-validation procedure.<br><br>RESULTS: The learned models successfully generalized to novel subjects. An analysis of the brain regions, on which the models based their predictions, revealed areas matching prevalent motor learning models. In these brain areas, the &#x3b1;/&#x3bc; frequency band (8-14 Hz) was found to be most relevant for performance prediction.<br><br>CONCLUSIONS: VMIL induces changes in cortical processes that extend beyond motor execution, indicating a more complex role of these processes than previously assumed. Our results further suggest that the capability of subjects to modulate their &#x3b1;/&#x3bc; bandpower in brain regions associated with motor learning may be related to performance in VMIL. Accordingly, training subjects in &#x3b1;/&#x3bc;-modulation, e.g., by means of a brain-computer interface (BCI), may have a beneficial impact on VMIL.},
}
@article {pmid24590225,
year = {2014},
author = {Mukaino, M and Ono, T and Shindo, K and Fujiwara, T and Ota, T and Kimura, A and Liu, M and Ushiba, J},
title = {Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke.},
journal = {Journal of rehabilitation medicine},
volume = {46},
number = {4},
pages = {378-382},
doi = {10.2340/16501977-1785},
pmid = {24590225},
issn = {1651-2081},
mesh = {Adult ; *Brain-Computer Interfaces ; Chronic Disease ; *Electric Stimulation ; Electromyography ; Fingers/physiopathology ; Hemiplegia/*etiology/*rehabilitation ; Humans ; Magnetic Resonance Imaging ; Male ; Movement/physiology ; Putaminal Hemorrhage/*complications/diagnosis ; Recovery of Function/physiology ; Stroke/*complications/diagnosis ; Treatment Outcome ; Upper Extremity/physiopathology ; },
abstract = {OBJECTIVE: Brain computer interface technology is of great interest to researchers as a potential therapeutic measure for people with severe neurological disorders. The aim of this study was to examine the efficacy of brain computer interface, by comparing conventional neuromuscular electrical stimulation and brain computer interface-driven neuromuscular electrical stimulation, using an A-B-A-B withdrawal single-subject design.<br><br>METHODS: A 38-year-old male with severe hemiplegia due to a putaminal haemorrhage participated in this study. The design involved 2 epochs. In epoch A, the patient attempted to open his fingers during the application of neuromuscular electrical stimulation, irrespective of his actual brain activity. In epoch B, neuromuscular electrical stimulation was applied only when a significant motor-related cortical potential was observed in the electroencephalogram.<br><br>RESULTS: The subject initially showed diffuse functional magnetic resonance imaging activation and small electro-encephalogram responses while attempting finger movement. Epoch A was associated with few neurological or clinical signs of improvement. Epoch B, with a brain computer interface, was associated with marked lateralization of electroencephalogram (EEG) and blood oxygenation level dependent responses. Voluntary electromyogram (EMG) activity, with significant EEG-EMG coherence, was also prompted. Clinical improvement in upper-extremity function and muscle tone was observed.<br><br>CONCLUSION: These results indicate that self-directed training with a brain computer interface may induce activity- dependent cortical plasticity and promote functional recovery. This preliminary clinical investigation encourages further research using a controlled design.},
}
@article {pmid24589837,
year = {2014},
author = {Ganesan, S and Victoire, TA and Vijayalakshmy, G},
title = {Real-time estimation and detection of non-linearity in bio-signals using wireless brain-computer interface.},
journal = {International journal of bioinformatics research and applications},
volume = {10},
number = {2},
pages = {190-205},
doi = {10.1504/IJBRA.2014.059518},
pmid = {24589837},
issn = {1744-5485},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; Computational Biology/*methods ; Electrocardiography/methods ; Electroencephalography/methods ; Fuzzy Logic ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; },
abstract = {In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal.},
}
@article {pmid24588466,
year = {2014},
author = {Reinfeldt, S and Håkansson, B and Taghavi, H and Eeg-Olofsson, M},
title = {Bone conduction hearing sensitivity in normal-hearing subjects: transcutaneous stimulation at BAHA vs BCI position.},
journal = {International journal of audiology},
volume = {53},
number = {6},
pages = {360-369},
doi = {10.3109/14992027.2014.880813},
pmid = {24588466},
issn = {1708-8186},
mesh = {Acoustic Stimulation ; Adult ; Audiometry, Pure-Tone ; *Auditory Threshold ; *Bone Conduction ; Cochlea/anatomy &amp; histology/*physiology ; Female ; Hearing Tests/instrumentation/*methods ; Humans ; Male ; Predictive Value of Tests ; Pressure ; Reference Values ; Transducers, Pressure ; Young Adult ; },
abstract = {OBJECTIVE: Bone conduction (BC) stimulation closer to the cochlea has previously been shown to give higher cochlear promontory acceleration measured by laser Doppler vibrometry (LDV). This study is investigating whether stimulation closer to the cochlea also gives improved hearing sensitivity. Furthermore, the study compares shifts in hearing sensitivity (BC thresholds) and ear-canal sound pressure (ECSP).<br><br>DESIGN: BC hearing thresholds and ECSP have been measured for stimulation at two positions: the existing bone-anchored hearing aid (BAHA) position, and a new bone conduction implant (BCI) position that is located closer to the cochlea.<br><br>STUDY SAMPLE: The measurements were made on 20 normal-hearing subjects.<br><br>RESULTS: Depending on frequency, the ipsilateral hearing threshold was 3-14 dB better, and the ipsilateral ECSP was 2-12 dB higher for the BCI than for the BAHA position, with no significant differences between threshold and ECSP shifts at group level for most frequencies, and individually only for some subjects.<br><br>CONCLUSIONS: It was found that both the objective ECSP and the subjective hearing threshold measurements gave similar improvement as previous LDV measurements for stimulation closer to the cochlea. One exception was that the LDV measurements did not show the improved sensitivity for frequencies below 500 Hz found here.},
}
@article {pmid24587963,
year = {2014},
author = {Johansson, V and Garwicz, M and Kanje, M and Halldenius, L and Schouenborg, J},
title = {Thinking Ahead on Deep Brain Stimulation: An Analysis of the Ethical Implications of a Developing Technology.},
journal = {AJOB neuroscience},
volume = {5},
number = {1},
pages = {24-33},
pmid = {24587963},
issn = {2150-7740},
abstract = {Deep brain stimulation (DBS) is a developing technology. New generations of DBS technology are already in the pipeline, yet this particular fact has been largely ignored among ethicists interested in DBS. Focusing only on ethical concerns raised by the current DBS technology is, albeit necessary, not sufficient. Since current bioethical concerns raised by a specific technology could be quite different from the concerns it will raise a couple of years ahead, an ethical analysis should be sensitive to such alterations, or it could end up with results that soon become dated. The goal of this analysis is to address these changing bioethical concerns, to think ahead on upcoming and future DBS concerns both in terms of a changing technology and changing moral attitudes. By employing the distinction between inherent and noninherent bioethical concerns we identify and make explicit the particular limits and potentials for change within each category, respectively, including how present and upcoming bioethical concerns regarding DBS emerge and become obsolete. Many of the currently identified ethical problems with DBS, such as stimulation-induced mania, are a result of suboptimal technology. These challenges could be addressed by technical advances, while for instance perceptions of an altered body image caused by the mere awareness of having an implant may not. Other concerns will not emerge until the technology has become sophisticated enough for new uses to be realized, such as concerns on DBS for enhancement purposes. As a part of the present analysis, concerns regarding authenticity are used as an example.},
}
@article {pmid24586440,
year = {2014},
author = {Yang, L and Leung, H and Peterson, DA and Sejnowski, TJ and Poizner, H},
title = {Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.},
journal = {PloS one},
volume = {9},
number = {2},
pages = {e88915},
pmid = {24586440},
issn = {1932-6203},
support = {//Howard Hughes Medical Institute/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography ; Female ; Humans ; *Intention ; Male ; Support Vector Machine ; Time Factors ; Young Adult ; },
abstract = {Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.},
}
@article {pmid24582903,
year = {2014},
author = {Pfurtscheller, G and Walther, M and Bauernfeind, G and Barry, RJ and Witte, H and Müller-Putz, GR},
title = {Entrainment of spontaneous cerebral hemodynamic oscillations to behavioral responses.},
journal = {Neuroscience letters},
volume = {566},
number = {},
pages = {93-97},
doi = {10.1016/j.neulet.2014.02.037},
pmid = {24582903},
issn = {1872-7972},
mesh = {Adult ; Blood Pressure ; Cerebral Cortex/*blood supply ; *Cerebrovascular Circulation ; Female ; Fingers/*physiology ; Heart Rate ; Hemoglobins/*metabolism ; Humans ; Male ; *Movement ; Oxyhemoglobins/metabolism ; Periodicity ; Time Factors ; Young Adult ; },
abstract = {Entrainment in physiological systems can be manifest in cases where phase-coupling (synchronization) between slow intrinsic oscillations and periodic motor responses, or vice versa, takes place. To test whether voluntary movement has something in common with entrainment of slow hemodynamic oscillations to motor responses, we studied blood pressure (BP), heart rate beat-to-beat intervals (RRI) and prefrontal (de)oxyhemoglobin (Hb/HbO2) during 5min of rest, 10min of self-paced, voluntary movements and 10min of stimulus-paced movements at 10s intervals in 9 subjects. Subjects were divided into 2 groups according to the timing of voluntary finger movements. It appeared that these movements occurred at relatively regular intervals of approximately 10s in 5 subjects (group A); while 4 subjects showed random or very short inter-movement intervals (group B). Two remarkable results were obtained: first, the phase coupling (COH(2)) between BP and RRI showed a significant (p=0.0061) interaction between activity (rest vs. movement) and group (A vs. B), with an increased (p=0.0003) coupling in group A. Second, the COH(2) between BP and Hb oscillations showed a significant (p=0.034) interaction between activity and group, with a decreased (p=0.079) coupling in group B. These results suggest that subjects able to initiate self-paced, voluntary movements at relatively regular intervals of ∼10s show an entrainment potential between physiological oscillations and motor responses. This also provides the first evidence that not only physiological oscillations can be entrained to motor responses, but also motor responses (voluntary movements) can be entrained to slow intrinsic oscillations.},
}
@article {pmid24576579,
year = {2014},
author = {Mandal, HS and Knaack, GL and Charkhkar, H and McHail, DG and Kastee, JS and Dumas, TC and Peixoto, N and Rubinson, JF and Pancrazio, JJ},
title = {Improving the performance of poly(3,4-ethylenedioxythiophene) for brain-machine interface applications.},
journal = {Acta biomaterialia},
volume = {10},
number = {6},
pages = {2446-2454},
doi = {10.1016/j.actbio.2014.02.029},
pmid = {24576579},
issn = {1878-7568},
mesh = {*Brain-Computer Interfaces ; Bridged Bicyclo Compounds, Heterocyclic/*chemistry ; Microscopy, Electron, Scanning ; Polymers/*chemistry ; },
abstract = {Conducting polymers, especially poly(3,4-ethylenedioxythiophene) (PEDOT) based materials, are important for developing highly sensitive and microscale neural probes. In the present work, we show that the conductivity and stability of PEDOT can be significantly increased by switching the widely used counter anion poly(styrenesulfonate) (PSS) to the smaller tetrafluoroborate (TFB) anion during the electrodeposition of the polymer. Time-dependent impedance measurements of polymer modified implantable microwires were conducted in physiological buffer solutions under accelerated aging conditions and the relative stability of PEDOT:PSS and PEDOT:TFB modified microwires was compared over time. This study was also extended to carbon nanotube (CNT) incorporated PEDOT:PSS which, according to some reports, is claimed to enhance the stability and electrical performance of the polymer. However, no noticeable difference was observed between PEDOT:PSS and CNT:PEDOT:PSS in our measurements. At the biologically relevant frequency of 1kHz, PEDOT:TFB modified microwires exhibit approximately one order of magnitude higher conductivity and demonstrate enhanced stability over both PEDOT:PSS and CNT:PEDOT:PSS modified microwires. In addition, PEDOT:TFB is not neurotoxic and we show the proof-of-concept for both in vitro and in vivo neuronal recordings using PEDOT:TFB modified microelectrode arrays and chronic electrodes, respectively. Our findings suggest that PEDOT:TFB is a promising conductive polymer coating for the recording of neural activities.},
}
@article {pmid24574101,
year = {2014},
author = {Daneshvar, ED and Smela, E},
title = {Characterization of conjugated polymer actuation under cerebral physiological conditions.},
journal = {Advanced healthcare materials},
volume = {3},
number = {7},
pages = {1026-1035},
pmid = {24574101},
issn = {2192-2659},
support = {P41 EB002030/EB/NIBIB NIH HHS/United States ; },
mesh = {Benzenesulfonates ; Brain Chemistry/*physiology ; Cations/chemistry ; Electric Conductivity ; *Electrodes, Implanted ; Humans ; Materials Testing ; *Models, Biological ; *Neural Prostheses ; Osmolar Concentration ; Polymers/*chemistry ; Prosthesis Design ; Pyrroles/*chemistry ; Temperature ; },
abstract = {Conjugated polymer actuators have potential use in implantable neural interface devices for modulating the position of electrode sites within brain tissue or guiding insertion of neural probes along curved trajectories. The actuation of polypyrrole (PPy) doped with dodecylbenzenesulfonate (DBS) is characterized to ascertain whether it can be employed in the cerebral environment. Microfabricated bilayer beams are electrochemically cycled at either 22 or 37 °C in aqueous NaDBS or in artificial cerebrospinal fluid (aCSF). Nearly all the ions in aCSF are exchanged into the PPy-the cations Na(+) , K(+) , Mg(2+) , Ca(2+) , as well as the anion PO4 (3-) ; Cl(-) is not present. Nevertheless, deflections in aCSF are comparable to those in NaDBS and they are monotonic with oxidation level: strain increases upon reduction, with no reversal of motion despite the mixture of ionic charges and valences being exchanged. Actuation depends on temperature. Upon warming, the cyclic voltammograms show additional peaks and an increase of 70% in the consumed charge. Bending is, however, much less affected: strain increases somewhat (6%-13%) but remains monotonic, and deflections shift (up to 20%). These results show how the actuation environment must be taken into account, and demonstrate proof of concept for actuated implantable neural interfaces.},
}
@article {pmid24572238,
year = {2014},
author = {Roesch, EB and Stahl, F and Gaber, MM},
title = {Bigger data for big data: from Twitter to brain-computer interfaces.},
journal = {The Behavioral and brain sciences},
volume = {37},
number = {1},
pages = {97-98},
doi = {10.1017/S0140525X13001854},
pmid = {24572238},
issn = {1469-1825},
mesh = {*Data Collection ; *Decision Making ; Humans ; *Social Behavior ; *Social Networking ; },
abstract = {We are sympathetic with Bentley et al.'s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain-computer interfaces.},
}
@article {pmid24568276,
year = {2013},
author = {Song, YA and Ibrahim, AM and Rabie, AN and Han, J and Lin, SJ},
title = {Microfabricated nerve-electrode interfaces in neural prosthetics and neural engineering.},
journal = {Biotechnology &amp; genetic engineering reviews},
volume = {29},
number = {},
pages = {113-134},
doi = {10.1080/02648725.2013.801231},
pmid = {24568276},
issn = {0264-8725},
mesh = {*Brain-Computer Interfaces ; Electrochemistry ; Humans ; *Microelectrodes ; Microfluidic Analytical Techniques ; Microtechnology ; *Neural Prostheses ; },
abstract = {Neural interfaces and implants are finding more clinical applications and there are rapid technological advances for more efficient and safe design, fabrication and materials to establish high-fidelity neural interfaces. In this review paper, we highlight new developments of the microfabricated electrodes and substrates with regard to the design, materials, fabrication and their clinical applications. There is a noticeable trend towards integration of microfluidic modules on a single neural platform. In addition to the microelectrodes for neural recording and stimulation, microfluidic channels are integrated into a nerve-electrode interface to explore the rich neurochemistry present at the neural interface and exploit it for enhanced electrochemical stimulation and recording of the central and peripheral nervous system.},
}
@article {pmid24567704,
year = {2014},
author = {Sakurai, Y and Song, K and Tachibana, S and Takahashi, S},
title = {Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface.},
journal = {Frontiers in systems neuroscience},
volume = {8},
number = {},
pages = {11},
pmid = {24567704},
issn = {1662-5137},
abstract = {In this review, we focus on neuronal operant conditioning in which increments in neuronal activities are directly rewarded without behaviors. We discuss the potential of this approach to elucidate neuronal plasticity for enhancing specific brain functions and its interaction with the progress in neurorehabilitation and brain-machine interfaces. The key to-be-conditioned activities that this paper emphasizes are synchronous and oscillatory firings of multiple neurons that reflect activities of cell assemblies. First, we introduce certain well-known studies on neuronal operant conditioning in which conditioned enhancements of neuronal firing were reported in animals and humans. These studies demonstrated the feasibility of volitional control over neuronal activity. Second, we refer to the recent studies on operant conditioning of synchrony and oscillation of neuronal activities. In particular, we introduce a recent study showing volitional enhancement of oscillatory activity in monkey motor cortex and our study showing selective enhancement of firing synchrony of neighboring neurons in rat hippocampus. Third, we discuss the reasons for emphasizing firing synchrony and oscillation in neuronal operant conditioning, the main reason being that they reflect the activities of cell assemblies, which have been suggested to be basic neuronal codes representing information in the brain. Finally, we discuss the interaction of neuronal operant conditioning with neurorehabilitation and brain-machine interface (BMI). We argue that synchrony and oscillation of neuronal firing are the key activities required for developing both reliable neurorehabilitation and high-performance BMI. Further, we conclude that research of neuronal operant conditioning, neurorehabilitation, BMI, and system neuroscience will produce findings applicable to these interrelated fields, and neuronal synchrony and oscillation can be a common important bridge among all of them.},
}
@article {pmid24562322,
year = {2013},
author = {Golub, MD and Chase, SM and Yu, BM},
title = {Learning an Internal Dynamics Model from Control Demonstration.},
journal = {JMLR workshop and conference proceedings},
volume = {},
number = {},
pages = {606-614},
pmid = {24562322},
issn = {1938-7288},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; },
abstract = {Much work in optimal control and inverse control has assumed that the controller has perfect knowledge of plant dynamics. However, if the controller is a human or animal subject, the subject's internal dynamics model may differ from the true plant dynamics. Here, we consider the problem of learning the subject's internal model from demonstrations of control and knowledge of task goals. Due to sensory feedback delay, the subject uses an internal model to generate an internal prediction of the current plant state, which may differ from the actual plant state. We develop a probabilistic framework and exact EM algorithm to jointly estimate the internal model, internal state trajectories, and feedback delay. We applied this framework to demonstrations by a nonhuman primate of brain-machine interface (BMI) control. We discovered that the subject's internal model deviated from the true BMI plant dynamics and provided significantly better explanation of the recorded neural control signals than did the true plant dynamics.},
}
@article {pmid24555843,
year = {2014},
author = {McCane, LM and Sellers, EW and McFarland, DJ and Mak, JN and Carmack, CS and Zeitlin, D and Wolpaw, JR and Vaughan, TM},
title = {Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.},
journal = {Amyotrophic lateral sclerosis &amp; frontotemporal degeneration},
volume = {15},
number = {3-4},
pages = {207-215},
pmid = {24555843},
issn = {2167-9223},
support = {P41 EB018783/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*complications/*diagnosis ; *Biofeedback, Psychology ; *Brain-Computer Interfaces ; Communication Disorders/*etiology/*rehabilitation ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Middle Aged ; Online Systems ; Photic Stimulation ; Psychomotor Performance/physiology ; Reaction Time/physiology ; },
abstract = {Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.},
}
@article {pmid24551050,
year = {2014},
author = {Suk, HI and Fazli, S and Mehnert, J and Müller, KR and Lee, SW},
title = {Predicting BCI subject performance using probabilistic spatio-temporal filters.},
journal = {PloS one},
volume = {9},
number = {2},
pages = {e87056},
pmid = {24551050},
issn = {1932-6203},
mesh = {*Algorithms ; Area Under Curve ; Bayes Theorem ; *Brain-Computer Interfaces ; Cluster Analysis ; Electrodes ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Motor Activity ; Principal Component Analysis ; *Probability ; Rest/physiology ; },
abstract = {Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms-a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects' performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject 'prototypes' (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.},
}
@article {pmid24533888,
year = {2014},
author = {Singla, R and Khosla, A and Jha, R},
title = {Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.},
journal = {Journal of medical engineering &amp; technology},
volume = {38},
number = {3},
pages = {125-134},
doi = {10.3109/03091902.2014.884179},
pmid = {24533888},
issn = {1464-522X},
mesh = {Adult ; *Brain-Computer Interfaces ; Color ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Neural Networks, Computer ; Regression Analysis ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; *Wheelchairs ; Young Adult ; },
abstract = {This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW. Four colours (green, red, blue and violet) were included in the study to investigate the colour influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were first segmented into 1 s windows and features were extracted by using Fast Fourier Transform (FFT). Three different classifiers, two based on Artificial Neural Network (ANN) and one based on Support Vector Machine (SVM), were compared to yield better accuracy. Twenty subjects participated in the experiment and the accuracy was calculated by considering the number of correct detections produced while performing a pre-defined movement sequence. SSVEP with violet colour showed higher performance than green and red. The One-Against-All (OAA) based multi-class SVM classifier showed better accuracy than the ANN classifiers. The classification accuracy over 20 subjects varies between 75-100%, while information transfer rates (ITR) varies from 12.13-27 bpm for BCI wheelchair control with SSVEPs elicited by violet colour stimuli and classified using OAA-SVM.},
}
@article {pmid24531644,
year = {2014},
author = {Danziger, Z},
title = {A reductionist approach to the analysis of learning in brain-computer interfaces.},
journal = {Biological cybernetics},
volume = {108},
number = {2},
pages = {183-201},
doi = {10.1007/s00422-014-0589-3},
pmid = {24531644},
issn = {1432-0770},
support = {1R01NS03581-01A2/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Artificial Intelligence ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Cybernetics ; Female ; Humans ; Male ; Sex Characteristics ; Task Performance and Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {The complexity and scale of brain-computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.},
}
@article {pmid24528900,
year = {2014},
author = {Collinger, JL and Kryger, MA and Barbara, R and Betler, T and Bowsher, K and Brown, EH and Clanton, ST and Degenhart, AD and Foldes, ST and Gaunt, RA and Gyulai, FE and Harchick, EA and Harrington, D and Helder, JB and Hemmes, T and Johannes, MS and Katyal, KD and Ling, GS and McMorland, AJ and Palko, K and Para, MP and Scheuermann, J and Schwartz, AB and Skidmore, ER and Solzbacher, F and Srikameswaran, AV and Swanson, DP and Swetz, S and Tyler-Kabara, EC and Velliste, M and Wang, W and Weber, DJ and Wodlinger, B and Boninger, ML},
title = {Collaborative approach in the development of high-performance brain-computer interfaces for a neuroprosthetic arm: translation from animal models to human control.},
journal = {Clinical and translational science},
volume = {7},
number = {1},
pages = {52-59},
pmid = {24528900},
issn = {1752-8062},
support = {KL2 TR000146/TR/NCATS NIH HHS/United States ; UL1 TR000005/TR/NCATS NIH HHS/United States ; 8KL2TR000146-07/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Animals ; *Artificial Limbs/statistics &amp; numerical data ; *Brain-Computer Interfaces/statistics &amp; numerical data ; Cooperative Behavior ; Electroencephalography ; Humans ; Male ; Models, Animal ; Primates ; Prosthesis Design ; Quadriplegia/rehabilitation ; Robotics/instrumentation/statistics &amp; numerical data ; Software ; Spinal Cord Injuries/rehabilitation ; Translational Research, Biomedical ; User-Computer Interface ; },
abstract = {Our research group recently demonstrated that a person with tetraplegia could use a brain-computer interface (BCI) to control a sophisticated anthropomorphic robotic arm with skill and speed approaching that of an able-bodied person. This multiyear study exemplifies important principles in translating research from foundational theory and animal experiments into a clinical study. We present a roadmap that may serve as an example for other areas of clinical device research as well as an update on study results. Prior to conducting a multiyear clinical trial, years of animal research preceded BCI testing in an epilepsy monitoring unit, and then in a short-term (28 days) clinical investigation. Scientists and engineers developed the necessary robotic and surgical hardware, software environment, data analysis techniques, and training paradigms. Coordination among researchers, funding institutes, and regulatory bodies ensured that the study would provide valuable scientific information in a safe environment for the study participant. Finally, clinicians from neurosurgery, anesthesiology, physiatry, psychology, and occupational therapy all worked in a multidisciplinary team along with the other researchers to conduct a multiyear BCI clinical study. This teamwork and coordination can be used as a model for others attempting to translate basic science into real-world clinical situations.},
}
@article {pmid24528506,
year = {2014},
author = {Schmidt, S and Riel, R and Frances, A and Lorente Garin, JA and Bonfill, X and Martinez-Zapata, MJ and Morales Suarez-Varela, M and dela Cruz, J and Emparanza, JI and Sánchez, MJ and Zamora, J and Goñi, JM and Alonso, J and Ferrer, M and , },
title = {Bladder cancer index: cross-cultural adaptation into Spanish and psychometric evaluation.},
journal = {Health and quality of life outcomes},
volume = {12},
number = {},
pages = {20},
pmid = {24528506},
issn = {1477-7525},
mesh = {Aged ; Analysis of Variance ; *Cross-Cultural Comparison ; European Union ; Female ; Humans ; Language ; Male ; Neoplasm Staging ; Psychometrics/*standards ; Quality of Life/*psychology ; *Surveys and Questionnaires ; Translating ; United States ; Urinary Bladder Neoplasms/diagnosis/*psychology ; },
abstract = {BACKGROUND: The Bladder Cancer Index (BCI) is so far the only instrument applicable across all bladder cancer patients, independent of tumor infiltration or treatment applied. We developed a Spanish version of the BCI, and assessed its acceptability and metric properties.<br><br>METHODS: For the adaptation into Spanish we used the forward and back-translation method, expert panels, and cognitive debriefing patient interviews. For the assessment of metric properties we used data from 197 bladder cancer patients from a multi-center prospective study. The Spanish BCI and the SF-36 Health Survey were self-administered before and 12&#xa0;months after treatment. Reliability was estimated by Cronbach's alpha. Construct validity was assessed through the multi-trait multi-method matrix. The magnitude of change was quantified by effect sizes to assess responsiveness.<br><br>RESULTS: Reliability coefficients ranged 0.75-0.97. The validity analysis confirmed moderate associations between the BCI function and bother subscales for urinary (r&#x2009;=&#x2009;0.61) and bowel (r&#x2009;=&#x2009;0.53) domains; conceptual independence among all BCI domains (r&#x2009;&#x2264;&#x2009;0.3); and low correlation coefficients with the SF-36 scores, ranging 0.14-0.48. Among patients reporting global improvement at follow-up, pre-post treatment changes were statistically significant for the urinary domain and urinary bother subscale, with effect sizes of 0.38 and 0.53.<br><br>CONCLUSIONS: The Spanish BCI is well accepted, reliable, valid, responsive, and similar in performance compared to the original instrument. These findings support its use, both in Spanish and international studies, as a valuable and comprehensive tool for assessing quality of life across a wide range of bladder cancer patients.},
}
@article {pmid24523855,
year = {2014},
author = {Salvaris, M and Haggard, P},
title = {Decoding intention at sensorimotor timescales.},
journal = {PloS one},
volume = {9},
number = {2},
pages = {e85100},
pmid = {24523855},
issn = {1932-6203},
mesh = {Adult ; Area Under Curve ; Brain/physiology ; Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Female ; Humans ; *Intention ; Male ; *Motor Skills ; Movement/physiology ; Psychomotor Performance/*physiology ; ROC Curve ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *Volition ; Young Adult ; },
abstract = {The ability to decode an individual's intentions in real time has long been a 'holy grail' of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered.},
}
@article {pmid24520341,
year = {2014},
author = {Zhang, J and Li, Y and Gu, Z and Yu, ZL},
title = {Recoverability analysis for modified compressive sensing with partially known support.},
journal = {PloS one},
volume = {9},
number = {2},
pages = {e87985},
pmid = {24520341},
issn = {1932-6203},
mesh = {Arrhythmias, Cardiac/diagnosis ; Computer Simulation ; *Data Compression ; Humans ; Models, Theoretical ; Probability ; Signal Processing, Computer-Assisted ; },
abstract = {The recently proposed modified-compressive sensing (modified-CS), which utilizes the partially known support as prior knowledge, significantly improves the performance of recovering sparse signals. However, modified-CS depends heavily on the reliability of the known support. An important problem, which must be studied further, is the recoverability of modified-CS when the known support contains a number of errors. In this letter, we analyze the recoverability of modified-CS in a stochastic framework. A sufficient and necessary condition is established for exact recovery of a sparse signal. Utilizing this condition, the recovery probability that reflects the recoverability of modified-CS can be computed explicitly for a sparse signal with [Formula: see text] nonzero entries. Simulation experiments have been carried out to validate our theoretical results.},
}
@article {pmid24517209,
year = {2014},
author = {Padulo, J and Ardigò, LP},
title = {Evaluating BCI devices: a statistical perspective.},
journal = {Ergonomics},
volume = {57},
number = {2},
pages = {282-283},
doi = {10.1080/00140139.2013.861932},
pmid = {24517209},
issn = {1366-5847},
mesh = {*Communication Devices for People with Disabilities ; *Ergonomics ; Female ; Humans ; Male ; *Research ; *User-Computer Interface ; },
}
@article {pmid24516552,
year = {2014},
author = {Geuze, J and Farquhar, J and Desain, P},
title = {Towards a communication brain computer interface based on semantic relations.},
journal = {PloS one},
volume = {9},
number = {2},
pages = {e87511},
pmid = {24516552},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; *Communication ; Electroencephalography ; Female ; Humans ; Male ; Semantics ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {This article investigates a possible Brain Computer Interface (BCI) based on semantic relations. The BCI determines which prime word a subject has in mind by presenting probe words using an intelligent algorithm. Subjects indicate when a presented probe word is related to the prime word by a single finger tap. The detection of the neural signal associated with this movement is used by the BCI to decode the prime word. The movement detector combined both the evoked (ERP) and induced (ERD) responses elicited with the movement. Single trial movement detection had an average accuracy of 67%. The decoding of the prime word had an average accuracy of 38% when using 100 probes and 150 possible targets, and 41% after applying a dynamic stopping criterium, reducing the average number of probes to 47. The article shows that the intelligent algorithm used to present the probe words has a significantly higher performance than a random selection of probes. Simulations demonstrate that the BCI also works with larger vocabulary sizes, and the performance scales logarithmically with vocabulary size.},
}
@article {pmid24508754,
year = {2014},
author = {Kasabov, NK},
title = {NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {52},
number = {},
pages = {62-76},
doi = {10.1016/j.neunet.2014.01.006},
pmid = {24508754},
issn = {1879-2782},
mesh = {Action Potentials ; Animals ; Brain/*anatomy &amp; histology/*physiology ; Brain Mapping/*methods ; Humans ; *Models, Neurological ; *Neural Networks, Computer ; Neural Pathways/anatomy &amp; histology/physiology ; Neurons/physiology ; Software ; Time ; },
abstract = {The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed.},
}
@article {pmid24507410,
year = {2014},
author = {Dutta, A and Paulus, W and Nitsche, MA},
title = {Facilitating myoelectric-control with transcranial direct current stimulation: a preliminary study in healthy humans.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {13},
pmid = {24507410},
issn = {1743-0003},
mesh = {Adult ; Cerebellum/*physiology ; Electric Stimulation Therapy/*methods ; Electromyography ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Muscle, Skeletal/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Functional Electrical Stimulation (FES) can electrically activate paretic muscles to assist movement for post-stroke neurorehabilitation. Here, sensory-motor integration may be facilitated by triggering FES with residual electromyographic (EMG) activity. However, muscle activity following stroke often suffers from delays in initiation and termination which may be alleviated with an adjuvant treatment at the central nervous system (CNS) level with transcranial direct current stimulation (tDCS) thereby facilitating re-learning and retaining of normative muscle activation patterns.<br><br>METHODS: This study on 12 healthy volunteers was conducted to investigate the effects of anodal tDCS of the primary motor cortex (M1) and cerebellum on latencies during isometric contraction of tibialis anterior (TA) muscle for myoelectric visual pursuit with quick initiation/termination of muscle activation i.e. 'ballistic EMG control' as well as modulation of EMG for 'proportional EMG control'.<br><br>RESULTS: The normalized delay in initiation and termination of muscle activity during post-intervention 'ballistic EMG control' trials showed a significant main effect of the anodal tDCS target: cerebellar, M1, sham (F(2) = 2.33, p < 0.1), and interaction effect between tDCS target and step-response type: initiation/termination of muscle activation (F(2) = 62.75, p < 0.001), but no significant effect for the step-response type (F(1) = 0.03, p = 0.87). The post-intervention population marginal means during 'ballistic EMG control' showed two important findings at 95% confidence interval (critical values from Scheffe's S procedure): 1. Offline cerebellar anodal tDCS increased the delay in initiation of TA contraction while M1 anodal tDCS decreased the same when compared to sham tDCS, 2. Offline M1 anodal tDCS increased the delay in termination of TA contraction when compared to cerebellar anodal tDCS or sham tDCS. Moreover, online cerebellar anodal tDCS decreased the learning rate during 'proportional EMG control' when compared to M1 anodal and sham tDCS.<br><br>CONCLUSIONS: The preliminary results from healthy subjects showed specific, and at least partially antagonistic effects, of M1 and cerebellar anodal tDCS on motor performance during myoelectric control. These results are encouraging, but further studies are necessary to better define how tDCS over particular regions of the cerebellum may facilitate learning of myoelectric control for brain machine interfaces.},
}
@article {pmid24506528,
year = {2015},
author = {McCann, MT and Thompson, DE and Syed, ZH and Huggins, JE},
title = {Electrode subset selection methods for an EEG-based P300 brain-computer interface.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {10},
number = {3},
pages = {216-220},
pmid = {24506528},
issn = {1748-3115},
support = {R21 HD054913/HD/NICHD NIH HHS/United States ; R21HD054913/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electrodes ; Electroencephalography/*instrumentation ; Female ; Humans ; Male ; },
abstract = {PURPOSE: An electroencephalography (EEG)-based P300 speller is a type of brain-computer interface (BCI) that uses EEG to allow a user to select characters without physical movement. In general, using fewer electrodes for such a system makes it easier to set up and less expensive. This study addresses the question of electrode selection for EEG-based P300 systems.<br><br>METHODS: Data from 13 subjects collected with a 16-electrode cap was analyzed. The optimal subsets of electrodes of sizes 1-15 were calculated for each subject and for the group as a whole. The methods of exhaustive search, forward selection, and backward elimination were then compared to each other and to these optimal subsets.<br><br>RESULTS: The results show that, while none of the methods consistently picked the best-performing electrode subsets, all methods were able to find small electrode subsets that provided acceptable accuracy both for individuals and for the whole group. The computationally intensive exhaustive search method provided no statistically significant increase in performance over the much quicker forward and backward selection methods.<br><br>CONCLUSIONS: The forward and backward selection methods are preferred for electrode selection.<br><br>A P300 speller is a type of brain-computer interface that allows a user to select characters without physical movement. Using fewer electrodes reduces setup time and cost for an EEG-based P300 speller. We show that acceptable P300 speller performance can be achieved with as few as four electrodes. We compare methods of selecting electrode sets and identify fast and efficient methods for customizing electrode sets for individuals.},
}
@article {pmid24504613,
year = {2013},
author = {Feng, W and Belagaje, SR},
title = {Recent advances in stroke recovery and rehabilitation.},
journal = {Seminars in neurology},
volume = {33},
number = {5},
pages = {498-506},
doi = {10.1055/s-0033-1364215},
pmid = {24504613},
issn = {1098-9021},
support = {NIH/NCRR UL1 RR 029882//PHS HHS/United States ; },
mesh = {Biological Therapy/methods ; Brain-Computer Interfaces ; Humans ; Recovery of Function/*physiology ; Stroke/*diagnosis/physiopathology ; *Stroke Rehabilitation ; Translational Research, Biomedical/methods ; },
abstract = {Stroke is the fourth leading cause of death in the United States, but remains a leading cause of disability. As more stroke victims survive with advanced acute care, effective strategies and interventions are required to optimize poststroke outcomes. In recent years, knowledge with respect to stroke recovery has expanded greatly through completion of preclinical and clinical trials. Emerging technology may provide further treatment options beyond the standard therapy and practices. In this article, the authors review recent advances in stroke recovery and rehabilitation, including the major determinants of poststroke recovery, challenges in translational stroke recovery research, and several emerging rehabilitation modalities such as noninvasive brain stimulation, brain-computer interface, biotherapeutics, and pharmacologic agents. Potential future directions in research are also addressed.},
}
@article {pmid24503623,
year = {2014},
author = {So, K and Dangi, S and Orsborn, AL and Gastpar, MC and Carmena, JM},
title = {Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {026002},
doi = {10.1088/1741-2560/11/2/026002},
pmid = {24503623},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Primates ; Psychomotor Performance/*physiology ; Random Allocation ; },
abstract = {OBJECTIVE: Intracortical brain-machine interfaces (BMIs) have predominantly utilized spike activity as the control signal. However, an increasing number of studies have shown the utility of local field potentials (LFPs) for decoding motor related signals. Currently, it is unclear how well different LFP frequencies can serve as features for continuous, closed-loop BMI control.<br><br>APPROACH: We demonstrate 2D continuous LFP-based BMI control using closed-loop decoder adaptation, which adapts decoder parameters to subject-specific LFP feature modulations during BMI control. We trained two macaque monkeys to control a 2D cursor in a center-out task by modulating LFP power in the 0-150 Hz range.<br><br>MAIN RESULTS: While both monkeys attained control, they used different strategies involving different frequency bands. One monkey primarily utilized the low-frequency spectrum (0-80 Hz), which was highly correlated between channels, and obtained proficient performance even with a single channel. In contrast, the other monkey relied more on higher frequencies (80-150 Hz), which were less correlated between channels, and had greater difficulty with control as the number of channels decreased. We then restricted the monkeys to use only various sub-ranges (0-40, 40-80, and 80-150 Hz) of the 0-150 Hz band. Interestingly, although both monkeys performed better with some sub-ranges than others, they were able to achieve BMI control with all sub-ranges after decoder adaptation, demonstrating broad flexibility in the frequencies that could potentially be used for LFP-based BMI control.<br><br>SIGNIFICANCE: Overall, our results demonstrate proficient, continuous BMI control using LFPs and provide insight into the subject-specific spectral patterns of LFP activity modulated during control.},
}
@article {pmid24503597,
year = {2014},
author = {Bishop, W and Chestek, CC and Gilja, V and Nuyujukian, P and Foster, JD and Ryu, SI and Shenoy, KV and Yu, BM},
title = {Self-recalibrating classifiers for intracortical brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {11},
number = {2},
pages = {026001},
pmid = {24503597},
issn = {1741-2552},
support = {R90 DA023426/DA/NIDA NIH HHS/United States ; R01 NS054283/NS/NINDS NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; T90 DA022762/DA/NIDA NIH HHS/United States ; 1DP1OD006409/OD/NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R90 DA023426-06/DA/NIDA NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Brain-Computer Interfaces/*classification ; Calibration ; *Electrodes, Implanted ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; },
abstract = {OBJECTIVE: Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers).<br><br>APPROACH: We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis.<br><br>MAIN RESULTS: We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a ~15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier.<br><br>SIGNIFICANCE: We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.},
}
@article {pmid24503051,
year = {2014},
author = {Elwakeel, KZ and Atia, AA and Guibal, E},
title = {Fast removal of uranium from aqueous solutions using tetraethylenepentamine modified magnetic chitosan resin.},
journal = {Bioresource technology},
volume = {160},
number = {},
pages = {107-114},
doi = {10.1016/j.biortech.2014.01.037},
pmid = {24503051},
issn = {1873-2976},
mesh = {Adsorption ; Chitosan/*chemistry ; Ethylenediamines/*chemistry ; Hydrogen-Ion Concentration ; Kinetics ; *Magnetic Phenomena ; Osmolar Concentration ; Resins, Synthetic/*chemistry ; Rheology ; Temperature ; Time Factors ; Uranium/*isolation &amp; purification ; Water Pollutants, Radioactive/*isolation &amp; purification ; },
abstract = {Chitosan was cross-linked using glutaraldehyde in the presence of magnetite. The resin was chemically modified through the reaction with tetraethylenepentamine (TEPA) to produce amine bearing chitosan. The resin showed a higher affinity towards the uptake of UO2(2+) ions from aqueous medium: maximum sorption capacity reached 1.8 mmol g(-1) at pH 4 and 25 °C. The nature of interaction of UO2(2+) ions with the resin was identified. Kinetics were carried out at different temperatures and thermodynamic parameters were evaluated. Breakthrough curves for the removal of UO2(2+) were studied at different flow rates, bed heights and after 3 regeneration cycles. Hydrochloric acid (0.5 M) was used for desorbing UO2(2+) from loaded resin: desorption yield as high as 98% was obtained.},
}
@article {pmid24498055,
year = {2014},
author = {Pohlmeyer, EA and Mahmoudi, B and Geng, S and Prins, NW and Sanchez, JC},
title = {Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.},
journal = {PloS one},
volume = {9},
number = {1},
pages = {e87253},
pmid = {24498055},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Brain/*physiology ; *Brain-Computer Interfaces ; Feedback ; Haplorhini ; Learning/*physiology ; *Man-Machine Systems ; Neurons/*physiology ; Reinforcement, Psychology ; Robotics ; *User-Computer Interface ; },
abstract = {Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.},
}
@article {pmid24489788,
year = {2014},
author = {Corbett, EA and Körding, KP and Perreault, EJ},
title = {Dealing with target uncertainty in a reaching control interface.},
journal = {PloS one},
volume = {9},
number = {1},
pages = {e86811},
pmid = {24489788},
issn = {1932-6203},
mesh = {*Algorithms ; Computer Simulation ; Electromyography ; Humans ; Models, Theoretical ; Muscles/physiology ; *Prostheses and Implants ; *Uncertainty ; },
abstract = {Prosthetic devices need to be controlled by their users, typically using physiological signals. People tend to look at objects before reaching for them and we have shown that combining eye movements with other continuous physiological signal sources enhances control. This approach suffers when subjects also look at non-targets, a problem we addressed with a probabilistic mixture over targets where subject gaze information is used to identify target candidates. However, this approach would be ineffective if a user wanted to move towards targets that have not been foveated. Here we evaluated how the accuracy of prior target information influenced decoding accuracy, as the availability of neural control signals was varied. We also considered a mixture model where we assumed that the target may be foveated or, alternatively, that the target may not be foveated. We tested the accuracy of the models at decoding natural reaching data, and also in a closed-loop robot-assisted reaching task. The mixture model worked well in the face of high target uncertainty. Furthermore, errors due to inaccurate target information were reduced by including a generic model that relied on neural signals only.},
}
@article {pmid24486874,
year = {2014},
author = {Sameni, R and Gouy-Pailler, C},
title = {An iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts.},
journal = {Journal of neuroscience methods},
volume = {225},
number = {},
pages = {97-105},
doi = {10.1016/j.jneumeth.2014.01.024},
pmid = {24486874},
issn = {1872-678X},
mesh = {*Algorithms ; *Artifacts ; *Electroencephalography ; Electrooculography ; Eye Movements ; Humans ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Electroencephalogram (EEG) measurements are always contaminated by non-cerebral signals, which disturb EEG interpretability. Among the different artifacts, ocular artifacts are the most disturbing ones. In previous studies, limited improvement has been obtained using frequency-based methods. Spatial decomposition methods have shown to be more effective for removing ocular artifacts from EEG recordings. Nevertheless, these methods are not able to completely separate cerebral and ocular signals and commonly eliminate important features of the EEG.<br><br>NEW METHOD: In a previous study we have shown the applicability of a deflation algorithm based on generalized eigenvalue decomposition for separating desired and undesired signal subspaces. In this work, we extend this idea for the automatic detection and removal of electrooculogram (EOG) artifacts from multichannel EEG recordings. The notion of effective number of identifiable dimensions, is also used to estimate the number of dominant dimensions of the ocular subspace, which enables the precise and fast convergence of the algorithm.<br><br>RESULTS: The method is applied on real and synthetic data. It is shown that the method enables the separation of cerebral and ocular signals with minimal interference with cerebral signals.<br><br>The proposed approach is compared with two widely used denoising techniques based on independent component analysis (ICA).<br><br>CONCLUSIONS: It is shown that the algorithm outperformed ICA-based approaches. Moreover, the method is computationally efficient and is implemented in real-time. Due to its semi-automatic structure and low computational cost, it has broad applications in real-time EEG monitoring systems and brain-computer interface experiments.},
}
@article {pmid24485002,
year = {2014},
author = {Franco, P and Porta, N and Holliday, JD and Willett, P},
title = {The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation.},
journal = {Journal of cheminformatics},
volume = {6},
number = {1},
pages = {5},
pmid = {24485002},
issn = {1758-2946},
abstract = {BACKGROUND: In the European Union, medicines are authorised for some rare disease only if they are judged to be dissimilar to authorised orphan drugs for that disease. This paper describes the use of 2D fingerprints to show the extent of the relationship between computed levels of structural similarity for pairs of molecules and expert judgments of the similarities of those pairs. The resulting relationship can be used to provide input to the assessment of new active compounds for which orphan drug authorisation is being sought.<br><br>RESULTS: 143 experts provided judgments of the similarity or dissimilarity of 100 pairs of drug-like molecules from the DrugBank 3.0 database. The similarities of these pairs were also computed using BCI, Daylight, ECFC4, ECFP4, MDL and Unity 2D fingerprints. Logistic regression analyses demonstrated a strong relationship between the human and computed similarity assessments, with the resulting regression models having significant predictive power in experiments using data from submissions of orphan drug medicines to the European Medicines Agency. The BCI fingerprints performed best overall on the DrugBank dataset while the BCI, Daylight, ECFP4 and Unity fingerprints performed comparably on the European Medicines Agency dataset.<br><br>CONCLUSIONS: Measures of structural similarity based on 2D fingerprints can provide a useful source of information for the assessment of orphan drug status by regulatory authorities.},
}
@article {pmid24482298,
year = {2014},
author = {Rea, M and Rana, M and Lugato, N and Terekhin, P and Gizzi, L and Brötz, D and Fallgatter, A and Birbaumer, N and Sitaram, R and Caria, A},
title = {Lower Limb Movement Preparation in Chronic Stroke: A Pilot Study Toward an fNIRS-BCI for Gait Rehabilitation.},
journal = {Neurorehabilitation and neural repair},
volume = {28},
number = {6},
pages = {564-575},
doi = {10.1177/1545968313520410},
pmid = {24482298},
issn = {1552-6844},
mesh = {Adult ; Aged ; Brain-Computer Interfaces ; Cerebral Cortex/diagnostic imaging/*physiopathology ; Chronic Disease ; Female ; Functional Neuroimaging/*methods ; Gait Disorders, Neurologic/diagnostic imaging/etiology/*physiopathology ; Hip/*physiopathology ; Humans ; Male ; Middle Aged ; Paresis/diagnostic imaging/etiology/*physiopathology ; Proof of Concept Study ; Spectroscopy, Near-Infrared/*methods ; Stroke/complications/diagnostic imaging/*physiopathology ; Stroke Rehabilitation/methods ; },
abstract = {Background Thus far, most of the brain-computer interfaces (BCIs) developed for motor rehabilitation used electroencephalographic signals to drive prostheses that support upper limb movement. Only few BCIs used hemodynamic signals or were designed to control lower extremity prostheses. Recent technological developments indicate that functional near-infrared spectroscopy (fNIRS)-BCI can be exploited in rehabilitation of lower limb movement due to its great usability and reduced sensitivity to head motion artifacts. Objective The aim of this proof of concept study was to assess whether hemodynamic signals underlying lower limb motor preparation in stroke patients can be reliably measured and classified. Methods fNIRS data were acquired during preparation of left and right hip movement in 7 chronic stroke patients. Results Single-trial analysis indicated that specific hemodynamic changes associated with left and right hip movement preparation can be measured with fNIRS. Linear discriminant analysis classification of totHB signal changes in the premotor cortex and/or posterior parietal cortex indicated above chance accuracy in discriminating paretic from nonparetic movement preparation trials in most of the tested patients. Conclusion The results provide first evidence that fNIRS can detect brain activity associated with single-trial lower limb motor preparation in stroke patients. These findings encourage further investigation of fNIRS suitability for BCI applications in rehabilitation of patients with lower limb motor impairment after stroke.},
}
@article {pmid24480171,
year = {2014},
author = {Akram, F and Han, HS and Kim, TS},
title = {A P300-based brain computer interface system for words typing.},
journal = {Computers in biology and medicine},
volume = {45},
number = {},
pages = {118-125},
doi = {10.1016/j.compbiomed.2013.12.001},
pmid = {24480171},
issn = {1879-0534},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Decision Trees ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Terminology as Topic ; *Writing ; Young Adult ; },
abstract = {P300 is an event related potential of the brain in response to oddball events. Brain Computer Interface (BCI) utilizing P300 is known as a P300 BCI system. A conventional P300 BCI system for character spelling is composed of a paradigm that displays flashing characters and a classification scheme which identifies target characters. To type a word a user has to spell each character of the word: this spelling process is slow and it can take several minutes to type a word. In this study, we propose a new word typing scheme by integrating a word suggestion mechanism with a dictionary search into the conventional P300-based speller. Our new P300-based word typing system consists of an initial character spelling paradigm, a dictionary unit to give suggestions of possible words and the second word selection paradigm to select a word out of the suggestions. Our proposed methodology reduces typing time significantly and makes word typing easy via a P300 BCI system. We have tested our system with ten subjects and our results demonstrate an average word typing time of 1.91 min whereas the conventional took 3.36 min for the same words.},
}
@article {pmid24478695,
year = {2014},
author = {Fischer, J and Milekovic, T and Schneider, G and Mehring, C},
title = {Low-latency multi-threaded processing of neuronal signals for brain-computer interfaces.},
journal = {Frontiers in neuroengineering},
volume = {7},
number = {},
pages = {1},
pmid = {24478695},
issn = {1662-6443},
abstract = {Brain-computer interfaces (BCIs) require demanding numerical computations to transfer brain signals into control signals driving an external actuator. Increasing the computational performance of the BCI algorithms carrying out these calculations enables faster reaction to user inputs and allows using more demanding decoding algorithms. Here we introduce a modular and extensible software architecture with a multi-threaded signal processing pipeline suitable for BCI applications. The computational load and latency (the time that the system needs to react to user input) are measured for different pipeline implementations in typical BCI applications with realistic parameter settings. We show that BCIs can benefit substantially from the proposed parallelization: firstly, by reducing the latency and secondly, by increasing the amount of recording channels and signal features that can be used for decoding beyond the amount which can be handled by a single thread. The proposed software architecture provides a simple, yet flexible solution for BCI applications.},
}
@article {pmid24476906,
year = {2014},
author = {Mattei, TA and Rehman, AA},
title = {Technological developments and future perspectives on graphene-based metamaterials: a primer for neurosurgeons.},
journal = {Neurosurgery},
volume = {74},
number = {5},
pages = {499-516; discussion 516},
doi = {10.1227/NEU.0000000000000302},
pmid = {24476906},
issn = {1524-4040},
mesh = {Arthrodesis/instrumentation ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Carbon ; Fluorescence Resonance Energy Transfer/instrumentation ; Forecasting ; Graphite/chemical synthesis/*chemistry ; Humans ; Materials Testing/*methods ; Nanotechnology/*trends ; Nerve Regeneration ; Neurosurgery/*instrumentation/*trends ; Oxidation-Reduction ; Pathology, Molecular/instrumentation ; Surface Properties ; },
abstract = {Graphene, a monolayer atomic-scale honeycomb lattice of carbon atoms, has been considered the greatest revolution in metamaterials research in the past 5 years. Its developers were awarded the Nobel Prize in Physics in 2010, and massive funding has been directed to graphene-based experimental research in the last years. For instance, an international scientific collaboration has recently received a €1 billion grant from the European Flagship Initiative, the largest amount of financial resources ever granted for a single research project in the history of modern science. Because of graphene's unique optical, thermal, mechanical, electronic, and quantum properties, the incorporation of graphene-based metamaterials to biomedical applications is expected to lead to major technological breakthroughs in the next few decades. Current frontline research in graphene technology includes the development of high-performance, lightweight, and malleable electronic devices, new optical modulators, ultracapacitors, molecular biodevices, organic photovoltaic cells, lithium-ion microbatteries, frequency multipliers, quantum dots, and integrated circuits, just to mention a few. With such advances, graphene technology is expected to significantly impact several areas of neurosurgery, including neuro-oncology, neurointensive care, neuroregeneration research, peripheral nerve surgery, functional neurosurgery, and spine surgery. In this topic review, the authors provide a basic introduction to the main electrophysical properties of graphene. Additionally, future perspectives of ongoing frontline investigations on this new metamaterial are discussed, with special emphasis on those research fields that are expected to most substantially impact experimental and clinical neurosurgery in the near future.},
}
@article {pmid24474913,
year = {2013},
author = {Herff, C and Heger, D and Fortmann, O and Hennrich, J and Putze, F and Schultz, T},
title = {Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {935},
pmid = {24474913},
issn = {1662-5161},
abstract = {When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}
) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.},
}
@article {pmid24468185,
year = {2014},
author = {Leamy, DJ and Kocijan, J and Domijan, K and Duffin, J and Roche, RA and Commins, S and Collins, R and Ward, TE},
title = {An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {9},
pmid = {24468185},
issn = {1743-0003},
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Neurofeedback/*methods ; Paresis/rehabilitation ; Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; *Stroke Rehabilitation ; },
abstract = {BACKGROUND: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke.<br><br>METHODS: 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data.<br><br>RESULTS: Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset.<br><br>CONCLUSIONS: This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.},
}
@article {pmid24467726,
year = {2015},
author = {Guru, KA and Esfahani, ET and Raza, SJ and Bhat, R and Wang, K and Hammond, Y and Wilding, G and Peabody, JO and Chowriappa, AJ},
title = {Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff.},
journal = {BJU international},
volume = {115},
number = {1},
pages = {166-174},
doi = {10.1111/bju.12657},
pmid = {24467726},
issn = {1464-410X},
mesh = {Adult ; Clinical Competence ; Cognition/*physiology ; Educational Measurement/methods ; Electroencephalography ; Humans ; Middle Aged ; Robotic Surgical Procedures/*education/methods ; Surgeons/*education/*standards ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: To investigate the utility of cognitive assessment during robot-assisted surgery (RAS) to define skills in terms of cognitive engagement, mental workload, and mental state; while objectively differentiating between novice and expert surgeons.<br><br>SUBJECTS AND METHODS: In all, 10 surgeons with varying operative experience were assigned to beginner (BG), combined competent and proficient (CPG), and expert (EG) groups based on the Dreyfus model. The participants performed tasks for basic, intermediate and advanced skills on the da Vinci Surgical System. Participant performance was assessed using both tool-based and cognitive metrics.<br><br>RESULTS: Tool-based metrics showed significant differences between the BG vs CPG and the BG vs EG, in basic skills. While performing intermediate skills, there were significant differences only on the instrument-to-instrument collisions between the BG vs CPG (2.0 vs 0.2, P = 0.028), and the BG vs EG (2.0 vs 0.1, P = 0.018). There were no significant differences between the CPG and EG for both basic and intermediate skills. However, using cognitive metrics, there were significant differences between all groups for the basic and intermediate skills. In advanced skills, there were no significant differences between the CPG and the EG except time (1116 vs 599.6&#x2009;s), using tool-based metrics. However, cognitive metrics revealed significant differences between both groups.<br><br>CONCLUSION: Cognitive assessment of surgeons may aid in defining levels of expertise performing complex surgical tasks once competence is achieved. Cognitive assessment may be used as an adjunct to the traditional methods for skill assessment during RAS.},
}
@article {pmid24465376,
year = {2014},
author = {Noonan, MJ and Markham, A and Newman, C and Trigoni, N and Buesching, CD and Ellwood, SA and Macdonald, DW},
title = {Climate and the individual: inter-annual variation in the autumnal activity of the European badger (Meles meles).},
journal = {PloS one},
volume = {9},
number = {1},
pages = {e83156},
pmid = {24465376},
issn = {1932-6203},
mesh = {Animal Nutritional Physiological Phenomena ; Animals ; Europe ; Humidity ; Mustelidae/*physiology ; *Seasons ; Temperature ; },
abstract = {We establish intra-individual and inter-annual variability in European badger (Meles meles) autumnal nightly activity in relation to fine-scale climatic variables, using tri-axial accelerometry. This contributes further to understanding of causality in the established interaction between weather conditions and population dynamics in this species. Modelling found that measures of daylight, rain/humidity, and soil temperature were the most supported predictors of ACTIVITY, in both years studied. In 2010, the drier year, the most supported model included the SOLAR*RH interaction, RAIN, and 30cmTEMP (w = 0.557), while in 2012, a wetter year, the most supported model included the SOLAR*RH interaction, and the RAIN*10cmTEMP (w = 0.999). ACTIVITY also differed significantly between individuals. In the 2012 autumn study period, badgers with the longest per noctem activity subsequently exhibited higher Body Condition Indices (BCI) when recaptured. In contrast, under drier 2010 conditions, badgers in good BCI engaged in less per noctem activity, while badgers with poor BCI were the most active. When compared on the same calendar dates, to control for night length, duration of mean badger nightly activity was longer (9.5 hrs ±3.3 SE) in 2010 than in 2012 (8.3 hrs ±1.9 SE). In the wetter year, increasing nightly activity was associated with net-positive energetic gains (from BCI), likely due to better foraging conditions. In a drier year, with greater potential for net-negative energy returns, individual nutritional state proved crucial in modifying activity regimes; thus we emphasise how a 'one size fits all' approach should not be applied to ecological responses.},
}
@article {pmid24462465,
year = {2014},
author = {van Winssen, FA and Merz, J and Schembecker, G},
title = {Tunable aqueous polymer-phase impregnated resins-technology-a novel approach to aqueous two-phase extraction.},
journal = {Journal of chromatography. A},
volume = {1329},
number = {},
pages = {38-44},
doi = {10.1016/j.chroma.2014.01.001},
pmid = {24462465},
issn = {1873-3778},
mesh = {Biotechnology ; Kinetics ; Liquid-Liquid Extraction/*methods ; Polyethylene Glycols/*chemistry ; Water/chemistry ; },
abstract = {Aqueous Two-Phase Extraction (ATPE) represents a promising unit operation for downstream processing of biotechnological products. The technique provides several advantages such as a biocompatible environment for the extraction of sensitive and biologically active compounds. However, the tendency of some aqueous two-phase systems to form intensive and stable emulsions can lead to long phase separation times causing an increased footprint for the required mixer-settler devices or the need for additional equipment such as centrifuges. In this work, a novel approach to improve ATPE for downstream processing applications called 'Tunable Aqueous Polymer-Phase Impregnated Resins' (TAPPIR(®))-Technology is presented. The technology is based on the immobilization of one aqueous phase inside the pores of a solid support. The second aqueous phase forms the bulk liquid around the impregnated solids. Due to the immobilization of one phase, phase emulsification and phase separation of ATPE are realized in a single step. In this study, a biodegradable and sustainable aqueous two-phase system consisting of aqueous polyethylene glycol/sodiumcitrate solutions was chosen. The impregnation of different macroporous glass and ceramic solids was investigated and could be proven to be stable. Additionally, the separation of the dye Patent blue V was successfully performed with the TAPPIR(®)-Technology. Thus, the "proof of principle" of this technology is presented.},
}
@article {pmid24459879,
year = {2013},
author = {Sudakov, KV and Dzhebrailova, TD and Korobeĭnikova, II and Karatygin, NA},
title = {[Geometrical images of coherent interrelations of biopotentials of EEG different frequency ranges in dynamics of humans goal-directed behavior].},
journal = {Rossiiskii fiziologicheskii zhurnal imeni I.M. Sechenova},
volume = {99},
number = {6},
pages = {706-718},
pmid = {24459879},
issn = {0869-8139},
mesh = {Adolescent ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography ; Goals ; Humans ; Learning/*physiology ; Male ; *Task Performance and Analysis ; Young Adult ; },
abstract = {Spatial organization of teta, alfa, beta-1, beta-2 in EEG potentials in humans who quickly an with a high accuracy performed task to learn and reproduce on PC monitor a definite sequence o accuracy performed task to learn and reproduce on PC monitor a definite sequence of circles wer studied. It was found that geometric images of coherent interrelations of teta, alfa, beta-1, beta-2 in EEG potentials were dynamically changed on the different stages of goal-directed the humans activity. The studies give the grounds to consider dynamically changed geometrical images of coherent links of different EEG potentials as an objective index of external projection of intracerebral processes of system architectonics of goal-directed behavior on brain cortex.},
}
@article {pmid24453113,
year = {2014},
author = {Witte, M and Galán, F and Waldert, S and Braun, C and Mehring, C},
title = {Concurrent stable and unstable cortical correlates of human wrist movements.},
journal = {Human brain mapping},
volume = {35},
number = {8},
pages = {3867-3879},
pmid = {24453113},
issn = {1097-0193},
mesh = {Brain-Computer Interfaces ; Electrooculography ; Feedback, Sensory/physiology ; Female ; Humans ; Magnetoencephalography ; Male ; Motor Activity/*physiology ; Photic Stimulation ; Sensorimotor Cortex/*physiology ; Signal Processing, Computer-Assisted ; Time Factors ; Visual Perception/physiology ; Wrist/*physiology ; Young Adult ; },
abstract = {Cortical activity has been shown to correlate with different parameters of movement. However, the dynamic properties of cortico-motor mappings still remain unexplored in humans. Here, we show that during the repetition of simple stereotyped wrist movements both stable and unstable correlates simultaneously emerge in human sensorimotor cortex. Using visual feedback of wrist movement target inferred online from MEG, we assessed the dynamics of the tuning properties of two neuronal signals: the MEG signal below 1.6 Hz and within the 4 to 6 Hz range. We found that both components are modulated by wrist movement allowing for closed-loop inference of movement targets. Interestingly, while tuning of 4 to 6 Hz signals remained stable over time leading to stable inference of movement target using a static classifier, the tuning of cortical signals below 1.6 Hz significantly changed resulting in steadily decreasing inference accuracy. Our findings demonstrate that non-invasive neuronal population signals in human sensorimotor cortex can reflect a stable correlate of voluntary movements. Hence, we provide first evidence for a stable control signal in non-invasive human brain-machine interface research. However, as not all neuronal signals initially tuned to movement were stable across days, a careful selection of features for real-life applications seems to be mandatory.},
}
@article {pmid24448593,
year = {2014},
author = {Xu, R and Jiang, N and Lin, C and Mrachacz-Kersting, N and Dremstrup, K and Farina, D},
title = {Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {2},
pages = {288-296},
doi = {10.1109/TBME.2013.2294203},
pmid = {24448593},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.},
}
@article {pmid24445482,
year = {2014},
author = {Richner, TJ and Thongpang, S and Brodnick, SK and Schendel, AA and Falk, RW and Krugner-Higby, LA and Pashaie, R and Williams, JC},
title = {Optogenetic micro-electrocorticography for modulating and localizing cerebral cortex activity.},
journal = {Journal of neural engineering},
volume = {11},
number = {1},
pages = {016010},
pmid = {24445482},
issn = {1741-2552},
support = {R01 EB009103/EB/NIBIB NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; 1R01EB009103-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Channelrhodopsins ; Coated Materials, Biocompatible ; Electrodes, Implanted ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Fiber Optic Technology ; Humans ; Imides ; Lasers ; Mice ; Optogenetics/*methods ; Photic Stimulation ; Polymers ; Prosthesis Design ; Signal Processing, Computer-Assisted ; Stereotaxic Techniques ; Xylenes ; },
abstract = {OBJECTIVE: Spatial localization of neural activity from within the brain with electrocorticography (ECoG) and electroencephalography remains a challenge in clinical and research settings, and while microfabricated ECoG (micro-ECoG) array technology continues to improve, complementary methods to simultaneously modulate cortical activity while recording are needed.<br><br>APPROACH: We developed a neural interface utilizing optogenetics, cranial windowing, and micro-ECoG arrays fabricated on a transparent polymer. This approach enabled us to directly modulate neural activity at known locations around micro-ECoG arrays in mice expressing Channelrhodopsin-2. We applied photostimuli varying in time, space and frequency to the cortical surface, and we targeted multiple depths within the cortex using an optical fiber while recording micro-ECoG signals.<br><br>MAIN RESULTS: Negative potentials of up to 1.5 mV were evoked by photostimuli applied to the entire cortical window, while focally applied photostimuli evoked spatially localized micro-ECoG potentials. Two simultaneously applied focal stimuli could be separated, depending on the distance between them. Photostimuli applied within the cortex with an optical fiber evoked more complex micro-ECoG potentials with multiple positive and negative peaks whose relative amplitudes depended on the depth of the fiber.<br><br>SIGNIFICANCE: Optogenetic ECoG has potential applications in the study of epilepsy, cortical dynamics, and neuroprostheses.},
}
@article {pmid24440135,
year = {2014},
author = {Siuly, and Li, Y and Paul Wen, P},
title = {Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.},
journal = {Computer methods and programs in biomedicine},
volume = {113},
number = {3},
pages = {767-780},
doi = {10.1016/j.cmpb.2013.12.020},
pmid = {24440135},
issn = {1872-7565},
mesh = {*Algorithms ; Brain/anatomy &amp; histology/physiology ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Databases, Factual ; Electrodes ; Electroencephalography/instrumentation/*statistics &amp; numerical data ; Epilepsy/physiopathology ; Humans ; Imagination/classification ; Logistic Models ; Models, Neurological ; Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.},
}
@article {pmid24439498,
year = {2014},
author = {Fonollosa, J and Vergara, A and Huerta, R and Marco, S},
title = {Estimation of the limit of detection using information theory measures.},
journal = {Analytica chimica acta},
volume = {810},
number = {},
pages = {1-9},
doi = {10.1016/j.aca.2013.10.030},
pmid = {24439498},
issn = {1873-4324},
mesh = {*Information Theory ; *Limit of Detection ; Probability ; },
abstract = {Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.},
}
@article {pmid24429136,
year = {2015},
author = {O'Sullivan, JA and Power, AJ and Mesgarani, N and Rajaram, S and Foxe, JJ and Shinn-Cunningham, BG and Slaney, M and Shamma, SA and Lalor, EC},
title = {Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {25},
number = {7},
pages = {1697-1706},
pmid = {24429136},
issn = {1460-2199},
support = {R01 DC007657/DC/NIDCD NIH HHS/United States ; R01 DC013825/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Neuropsychological Tests ; *Signal Processing, Computer-Assisted ; Speech Perception/*physiology ; Time Factors ; },
abstract = {How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain-computer interfaces.},
}
@article {pmid24429072,
year = {2014},
author = {Teo, WP and Chew, E},
title = {Is motor-imagery brain-computer interface feasible in stroke rehabilitation?.},
journal = {PM &amp; R : the journal of injury, function, and rehabilitation},
volume = {6},
number = {8},
pages = {723-728},
doi = {10.1016/j.pmrj.2014.01.006},
pmid = {24429072},
issn = {1934-1563},
mesh = {Brain/*physiopathology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Feasibility Studies ; Humans ; Motor Activity/*physiology ; Stroke/diagnosis/physiopathology ; *Stroke Rehabilitation ; },
abstract = {In the past 3 decades, interest has increased in brain-computer interface (BCI) technology as a tool for assisting, augmenting, and rehabilitating sensorimotor functions in clinical populations. Initially designed as an assistive device for partial or total body impairments, BCI systems have since been explored as a possible adjuvant therapy in the rehabilitation of patients who have had a stroke. In particular, BCI systems incorporating a robotic manipulanda to passively manipulate affected limbs have been studied. These systems can use a range of invasive (ie, intracranial implanted electrodes) or noninvasive neurophysiologic recording techniques (ie, electroencephalography [EEG], near-infrared spectroscopy, and magnetoencephalography) to establish communication links between the brain and the BCI system. Trials are most commonly performed on EEG-based BCI in comparison with the other techniques because of its high temporal resolution, relatively low setup costs, portability, and noninvasive nature. EEG-based BCI detects event-related desynchronization/synchronization in sensorimotor oscillatory rhythms associated with motor imagery (MI), which in turn drives the BCI. Previous evidence suggests that the process of MI preferentially activates sensorimotor regions similar to actual task performance and that repeated practice of MI can induce plasticity changes in the brain. It is therefore postulated that the combination of MI and BCI may augment rehabilitation gains in patients who have had a stroke by activating corticomotor networks via MI and providing sensory feedback from the affected limb using end-effector robots. In this review we examine the current literature surrounding the feasibility of EEG-based MI-BCI systems in stroke rehabilitation. We also discuss the limitations of using EEG-based MI-BCI in patients who have had a stroke and suggest possible solutions to overcome these limitations.},
}
@article {pmid24428900,
year = {2014},
author = {Kaufmann, T and Herweg, A and Kübler, A},
title = {Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {11},
number = {},
pages = {7},
pmid = {24428900},
issn = {1743-0003},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Feasibility Studies ; Female ; Humans ; Male ; *Touch ; *Wheelchairs ; Young Adult ; },
abstract = {BACKGROUND: People with severe disabilities, e.g., due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g., recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system.<br><br>METHODS: Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N&#x2009;=&#x2009;15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm.<br><br>RESULTS: Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed.<br><br>CONCLUSION: We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control.},
}
@article {pmid24427224,
year = {2013},
author = {Pan, J and Li, Y and Gu, Z and Yu, Z},
title = {A comparison study of two P300 speller paradigms for brain-computer interface.},
journal = {Cognitive neurodynamics},
volume = {7},
number = {6},
pages = {523-529},
pmid = {24427224},
issn = {1871-4080},
abstract = {In this paper, a comparison of two existing P300 spellers is conducted. In the first speller, the visual stimuli of characters are presented in a single character (SC) paradigm and each button corresponding to a character flashes individually in a random order. The second speller is based on a region-based (RB) paradigm. In the first level, all characters are grouped and each button corresponding to a group flashes individually in a random order. Once a group is selected, the characters in it will appear on the flashing buttons of the second level for the selection of desired character. In a spelling experiment involving 12 subjects, higher online accuracy was obtained on the RB paradigm-based P300 speller than the SC paradigm-based P300 speller. Furthermore, we analyzed P300 detection performance, the P300 waveforms and Fisher ratios using the data collected by the two spellers. It was found that the stimuli display paradigm of the RB speller enhances P300 potential and is more suitable for P300 detection.},
}
@article {pmid24421765,
year = {2013},
author = {Ninaus, M and Kober, SE and Witte, M and Koschutnig, K and Stangl, M and Neuper, C and Wood, G},
title = {Neural substrates of cognitive control under the belief of getting neurofeedback training.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {914},
pmid = {24421765},
issn = {1662-5161},
abstract = {Learning to modulate one's own brain activity is the fundament of neurofeedback (NF) applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI) illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female) performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal areas were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented.},
}
@article {pmid24419067,
year = {2014},
author = {Scott, HM and Vittinghoff, E and Irvin, R and Sachdev, D and Liu, A and Gurwith, M and Buchbinder, SP},
title = {Age, race/ethnicity, and behavioral risk factors associated with per contact risk of HIV infection among men who have sex with men in the United States.},
journal = {Journal of acquired immune deficiency syndromes (1999)},
volume = {65},
number = {1},
pages = {115-121},
pmid = {24419067},
issn = {1944-7884},
support = {P30 MH062246/MH/NIMH NIH HHS/United States ; T32 MH019105/MH/NIMH NIH HHS/United States ; R01-AI083060/AI/NIAID NIH HHS/United States ; UM1 AI069496/AI/NIAID NIH HHS/United States ; R01 AI083060/AI/NIAID NIH HHS/United States ; P30 AI027763/AI/NIAID NIH HHS/United States ; MH-19105-23/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Age Factors ; Ethnicity/psychology/*statistics &amp; numerical data ; HIV Infections/epidemiology/*etiology ; Homosexuality, Male/psychology/*statistics &amp; numerical data ; Humans ; Male ; Racial Groups/psychology/*statistics &amp; numerical data ; Risk Factors ; Sexual Behavior/psychology/statistics &amp; numerical data ; United States/epidemiology ; Unsafe Sex/psychology/statistics &amp; numerical data ; Young Adult ; },
abstract = {OBJECTIVE: Young men who have sex with men (MSM) and MSM of color have the highest HIV incidence in the United States. To explore possible explanations for these disparities and known individual risk factors, we analyzed the per contact risk (PCR) of HIV seroconversion in the early highly active antiretroviral therapy era.<br><br>METHODS: Data from 3 longitudinal studies of MSM (HIV Network for Prevention Trials Vaccine Preparedness Study, EXPLORE behavioral efficacy trial, and VAX004 vaccine efficacy trial) were pooled. The analysis included visits where participants reported unprotected receptive anal intercourse (URA), protected receptive anal intercourse, or unprotected insertive anal intercourse (UIA) with an HIV seropositive, unknown HIV serostatus, or an HIV seronegative partner. We used regression standardization to estimate average PCRs for each type of contact, with bootstrap confidence intervals.<br><br>RESULTS: The estimated PCR was highest for URA with an HIV seropositive partner (0.73%; 95% bootstrap confidence interval [BCI]: 0.45% to 0.98%) followed by URA with a partner of unknown HIV serostatus (0.49%; 95% BCI: 0.32% to 0.62%). The estimated PCR for protected receptive anal intercourse and UIA with an HIV seropositive partner was 0.08% (95% BCI: 0.0% to 0.19%) and 0.22% (95% BCI: 0.05% to 0.39%), respectively. Average PCRs for URA and UIA with HIV seropositive partners were higher by 0.14%-0.34% among younger participants and higher by 0.08% for UIA among Latino participants compared with white participants. Estimated PCRs increased with the increasing number of sexual partners, use of methamphetamines or poppers, and history of sexually transmitted infection.<br><br>CONCLUSIONS: Susceptibility or partner factors may explain the higher HIV conversion risk for younger MSM, some MSM of color, and those reporting individual risk factors.},
}
@article {pmid24416360,
year = {2014},
author = {Liao, K and Xiao, R and Gonzalez, J and Ding, L},
title = {Decoding individual finger movements from one hand using human EEG signals.},
journal = {PloS one},
volume = {9},
number = {1},
pages = {e85192},
pmid = {24416360},
issn = {1932-6203},
mesh = {Adult ; Brain-Computer Interfaces/*psychology ; Electroencephalography/*psychology/statistics &amp; numerical data ; Epilepsy/*physiopathology ; Female ; Fingers/*physiology/physiopathology ; Humans ; Male ; Motor Cortex/*physiology/physiopathology ; Movement/*physiology ; Principal Component Analysis ; Support Vector Machine ; },
abstract = {Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.},
}
@article {pmid24415996,
year = {2013},
author = {Brandmeyer, A and Sadakata, M and Spyrou, L and McQueen, JM and Desain, P},
title = {Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {265},
pmid = {24415996},
issn = {1662-4548},
abstract = {Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). The first part of this paper illustrates how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces.},
}
@article {pmid24415400,
year = {2014},
author = {Lugo, ZR and Rodriguez, J and Lechner, A and Ortner, R and Gantner, IS and Laureys, S and Noirhomme, Q and Guger, C},
title = {A vibrotactile p300-based brain-computer interface for consciousness detection and communication.},
journal = {Clinical EEG and neuroscience},
volume = {45},
number = {1},
pages = {14-21},
doi = {10.1177/1550059413505533},
pmid = {24415400},
issn = {1550-0594},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Consciousness/*physiology ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Physical Stimulation ; Quadriplegia/*physiopathology ; Task Performance and Analysis ; Unconsciousness/*physiopathology ; },
abstract = {Brain-computer interface (BCI) has been used for many years for communication in severely disabled patients. BCI based on electrophysiological signals has enabled communication, using auditory or visual stimuli to elicit event-related potentials (ERPs). The aim of this study was to determine whether patients with locked-in syndrome (LIS) could elicit a P300 wave, using a vibrotactile oddball paradigm for establishing somatosensory BCI-based communication. Six chronic LIS patients performed 2 electroencephalography (EEG)-based vibrotactile P300 oddball tasks. After a simple mental counting task of the target stimuli, participants were instructed to answer 5 questions by counting the vibration on either the right wrist for "yes" or the left wrist for "no." All participants were able to elicit a P300 wave using the vibrotactile oddball paradigm BCI task. In the counting task, 4 patients got accuracies of 100% (average above chance). In the communication task, one patient achieved 100% accuracy (average above chance). We have shown the feasibility of eliciting a P300 response using vibrotactile stimulation in patients with LIS. The present study provides evidence that this approach can be used for EEG-based BCI communications in this patient group. This is the first study to prove the feasibility of a BCI based on somatosensory (vibratory) stimulation in a group of brain-injured patients. Furthermore, this approach could be used for the detection of consciousness in non-communicating patients due to severe brain injuries.},
}
@article {pmid24401829,
year = {2014},
author = {Bai, O and Huang, D and Fei, DY and Kunz, R},
title = {Effect of real-time cortical feedback in motor imagery-based mental practice training.},
journal = {NeuroRehabilitation},
volume = {34},
number = {2},
pages = {355-363},
doi = {10.3233/NRE-131039},
pmid = {24401829},
issn = {1878-6448},
mesh = {Adult ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Electromyography ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Stroke Rehabilitation ; Therapy, Computer-Assisted/*methods ; User-Computer Interface ; Wrist/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Mental practice using motor imagery of limb movement may facilitate motor recovery in persons who have experienced cerebrovascular accident (CVA). However, the lack of a feedback mechanism that can monitor the quality of the motor imagery affects patients' engagement and motivation to participate in the mental practice training program.<br><br>OBJECTIVE: This study investigates the effect of novel real-time motor imagery-associated cortical activity feedback on motor imagery-based mental practice training.<br><br>METHODS: Ten healthy volunteers were randomly assigned into intervention and control groups. Both groups participated in a five-visit motor imagery-based mental practice training program managed over a period of two months. The intervention group received mental practice training with real-time feedback of movement-associated cortical activity-beta band (16-28 Hz) event-related desynchronization (ERD) in electroencephalography (EEG), using a novel custom-made brain-computer interface (BCI) system. The control group received the mental practice training program without EEG cortical feedback. Motor excitability was assessed by measuring the frequency power magnitude of the EEG rhythmic activity associated with physical execution of wrist extension before and after the motor imagery-based mental practice training.<br><br>RESULTS: The EEG frequency power magnitude associated with the physical execution of wrist extension was significantly lower (i.e. more desynchronized) after the mental practice training in the intervention group that received real-time cortical feedback (P < 0.05), whereas no significant difference in EEG frequency power magnitude associated with the physical execution of wrist extension was observed before and after mental practice training in the control group who did not receive feedback.<br><br>CONCLUSIONS: The mental practice training program with motor imagery-associated cortical feedback facilitated motor excitability during the production of voluntary motor control. Motor imagery-based mental practice training with movement-associated cortical activity feedback may provide an effective strategy to facilitate motor recovery in brain injury patients, particularly during the early rehabilitation stage when full participation in physical and occupational therapy programs may not be possible due to excessive motor weakness.},
}
@article {pmid24397404,
year = {2014},
author = {Lang, K and Zierow, J and Buehler, K and Schmid, A},
title = {Metabolic engineering of Pseudomonas sp. strain VLB120 as platform biocatalyst for the production of isobutyric acid and other secondary metabolites.},
journal = {Microbial cell factories},
volume = {13},
number = {},
pages = {2},
pmid = {24397404},
issn = {1475-2859},
mesh = {Acyl Coenzyme A/metabolism ; Aldehydes/chemistry/metabolism ; Biocatalysis ; Biotransformation ; Butanols/chemistry/metabolism ; Carboxy-Lyases/metabolism ; Hemiterpenes ; Isobutyrates/chemistry/*metabolism ; Keto Acids/chemistry/metabolism ; Lactococcus lactis/enzymology ; *Metabolic Engineering ; Oxidation-Reduction ; Pseudomonas/*metabolism ; Valine/metabolism ; },
abstract = {BACKGROUND: Over the recent years the production of Ehrlich pathway derived chemicals was shown in a variety of hosts such as Escherichia coli, Corynebacterium glutamicum, and yeast. Exemplarily the production of isobutyric acid was demonstrated in Escherichia coli with remarkable titers and yields. However, these examples suffer from byproduct formation due to the fermentative growth mode of the respective organism. We aim at establishing a new aerobic, chassis for the synthesis of isobutyric acid and other interesting metabolites using Pseudomonas sp. strain VLB120, an obligate aerobe organism, as host strain.<br><br>RESULTS: The overexpression of kivd, coding for a 2-ketoacid decarboxylase from Lactococcus lactis in Ps. sp. strain VLB120 enabled for the production of isobutyric acid and isobutanol via the valine synthesis route (Ehrlich pathway). This indicates the existence of chromosomally encoded alcohol and aldehyde dehydrogenases catalyzing the reduction and oxidation of isobutyraldehyde. In addition we showed that the strain possesses a complete pathway for isobutyric acid metabolization, channeling the compound via isobutyryl-CoA into valine degradation. Three key issues were addressed to allow and optimize isobutyric acid synthesis: i) minimizing isobutyric acid degradation by host intrinsic enzymes, ii) construction of suitable expression systems and iii) streamlining of central carbon metabolism finally leading to production of up to 26.8&#x2009;&#xb1;&#x2009;1.5 mM isobutyric acid with a carbon yield of 0.12&#x2009;&#xb1;&#x2009;0.01 g g(glc)&#x207b;&#xb9;.<br><br>CONCLUSION: The combination of an increased flux towards isobutyric acid using a tailor-made expression system and the prevention of precursor and product degradation allowed efficient production of isobutyric acid in Ps. sp. strain VLB120. This will be the basis for the development of a continuous reaction process for this bulk chemicals.},
}
@article {pmid24394368,
year = {2014},
author = {Eng, ML and Williams, TD and Letcher, RJ and Elliott, JE},
title = {Assessment of concentrations and effects of organohalogen contaminants in a terrestrial passerine, the European starling.},
journal = {The Science of the total environment},
volume = {473-474},
number = {},
pages = {589-596},
doi = {10.1016/j.scitotenv.2013.12.072},
pmid = {24394368},
issn = {1879-1026},
mesh = {Animals ; Dichlorodiphenyl Dichloroethylene/metabolism ; *Environmental Monitoring ; Environmental Pollutants/*metabolism ; Environmental Pollution/statistics &amp; numerical data ; Halogenated Diphenyl Ethers/metabolism ; Hydrocarbons, Chlorinated/metabolism ; Hydrocarbons, Halogenated/*metabolism ; Ovum/chemistry ; Pesticides/metabolism ; Polychlorinated Biphenyls/metabolism ; Starlings/*metabolism ; },
abstract = {European starlings (Sturnus vulgaris) are a valuable model species for the assessment of concentrations and effects of environmental contaminants in terrestrial birds. Polybrominated diphenyl ethers (PBDEs) are found in birds throughout the world, but relatively little is known of their concentrations or effects in free-living terrestrial passerines. We used a nest box population of European starlings to 1) measure the variation in egg concentrations of persistent organohalogen contaminants at an agricultural site, and 2) assess whether individual variation in PBDE concentrations in eggs was related to reproductive parameters, as well as maternal or nestling characteristics including body condition, thyroid hormones, oxidative stress, and hematocrit. As PBDEs were the main contaminant class of interest, we only assessed a subset of eggs for other organohalogen contaminants to establish background concentrations. Exposure to organohalogen contaminants was extremely variable over this relatively small study area. Geometric mean wet weight concentrations (range in brackets) of the major contaminants were 36.5 (12-174) ng/g ΣDDT (n=6 eggs) and 10.9 (2-307) ng/g ΣPBDEs (n=14). ΣPCBs at 3.58 (1.5-6.4) ng/g (n=6) were lower and less variable. There were low levels of other organochlorine (OC) pesticides such as dieldrin (2.02 ng/g), chlordanes (1.11 ng/g) and chlorobenzenes (0.23 ng/g). The only form of DDT detected was p,p'-DDE. The congener profiles of PBDEs and PCBs reflect those of industrial mixtures (i.e. DE-71, Aroclors 1254, 1260 and 1262). For all of the contaminant classes, concentrations detected in eggs at our study site were below levels previously reported to cause effects. Due to small sample sizes, we did not assess the relationship between ΣPCBs or ΣOCs and adult or chick condition. We observed no correlative relationships between individual variation in PBDE concentrations in starling eggs and reproductive success, maternal condition, or nestling condition in the corresponding nests.},
}
@article {pmid24392125,
year = {2014},
author = {Kheradpisheh, SR and Nowzari-Dalini, A and Ebrahimpour, R and Ganjtabesh, M},
title = {An evidence-based combining classifier for brain signal analysis.},
journal = {PloS one},
volume = {9},
number = {1},
pages = {e84341},
pmid = {24392125},
issn = {1932-6203},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; *Electroencephalography ; Humans ; *Models, Theoretical ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.},
}
@article {pmid24388403,
year = {2014},
author = {Gibson, RM and Chennu, S and Owen, AM and Cruse, D},
title = {Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {8},
pages = {1556-1567},
doi = {10.1016/j.clinph.2013.11.034},
pmid = {24388403},
issn = {1872-8952},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*classification/methods ; Female ; Humans ; Imagery, Psychotherapy/*classification ; Imagination/classification/physiology ; Male ; Movement/physiology ; Music ; Recognition, Psychology/classification/physiology ; Sports/physiology ; Young Adult ; },
abstract = {OBJECTIVE: We sought to determine whether the sensorimotor rhythms (SMR) elicited during motor imagery (MI) of complex and familiar actions could be more reliably detected with electroencephalography (EEG), and subsequently classified on a single-trial basis, than those elicited during relatively simpler imagined actions.<br><br>METHODS: Groups of healthy volunteers, including experienced pianists and ice hockey players, performed MI of varying complexity and familiarity. Their electroencephalograms were recorded and compared using brain-computer interface (BCI) approaches and spectral analyses.<br><br>RESULTS: Relative to simple MI, significantly more participants produced classifiable SMR for complex MI. During MI of performance of a complex musical piece, the EEG of the experienced pianists was classified significantly more accurately than during MI of performance of a simpler musical piece. The accuracy of EEG classification was also significantly more sustained during complex MI.<br><br>CONCLUSION: MI of complex actions results in EEG responses that are more reliably classified for more individuals than MI of relatively simpler actions, and familiarity with actions enhances these responses in some cases.<br><br>SIGNIFICANCE: The accuracy of SMR-based BCIs in non-communicative patients may be improved by employing familiar and complex actions. Increased sensitivity to MI may also improve diagnostic accuracy for severely brain-injured patients in a vegetative state.},
}
@article {pmid24386223,
year = {2013},
author = {Chen, C and Shin, D and Watanabe, H and Nakanishi, Y and Kambara, H and Yoshimura, N and Nambu, A and Isa, T and Nishimura, Y and Koike, Y},
title = {Prediction of hand trajectory from electrocorticography signals in primary motor cortex.},
journal = {PloS one},
volume = {8},
number = {12},
pages = {e83534},
pmid = {24386223},
issn = {1932-6203},
mesh = {Animals ; Brain-Computer Interfaces ; *Electroencephalography ; Female ; Hand/*physiology ; Macaca ; Male ; Motor Cortex/*physiology ; Psychomotor Performance ; },
abstract = {Due to their potential as a control modality in brain-machine interfaces, electrocorticography (ECoG) has received much focus in recent years. Studies using ECoG have come out with success in such endeavors as classification of arm movements and natural grasp types, regression of arm trajectories in two and three dimensions, estimation of muscle activity time series and so on. However, there still remains considerable work to be done before a high performance ECoG-based neural prosthetic can be realized. In this study, we proposed an algorithm to decode hand trajectory from 15 and 32 channel ECoG signals recorded from primary motor cortex (M1) in two primates. To determine the most effective areas for prediction, we applied two electrode selection methods, one based on position relative to the central sulcus (CS) and another based on the electrodes' individual prediction performance. The best coefficients of determination for decoding hand trajectory in the two monkeys were 0.4815 ± 0.0167 and 0.7780 ± 0.0164. Performance results from individual ECoG electrodes showed that those with higher performance were concentrated at the lateral areas and areas close to the CS. The results of prediction according with different numbers of electrodes based on proposed methods were also shown and discussed. These results also suggest that superior decoding performance can be achieved from a group of effective ECoG signals rather than an entire ECoG array.},
}
@article {pmid24374077,
year = {2014},
author = {Morioka, H and Kanemura, A and Morimoto, S and Yoshioka, T and Oba, S and Kawanabe, M and Ishii, S},
title = {Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information.},
journal = {NeuroImage},
volume = {90},
number = {},
pages = {128-139},
doi = {10.1016/j.neuroimage.2013.12.035},
pmid = {24374077},
issn = {1095-9572},
mesh = {Adult ; Attention/*physiology ; Bayes Theorem ; Brain/*physiology ; Brain Mapping/*methods ; Brain-Computer Interfaces ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Humans ; Male ; Signal Processing, Computer-Assisted ; Space Perception/physiology ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments.},
}
@article {pmid24321117,
year = {2013},
author = {Baral, SD and Ketende, S and Mnisi, Z and Mabuza, X and Grosso, A and Sithole, B and Maziya, S and Kerrigan, DL and Green, JL and Kennedy, CE and Adams, D},
title = {A cross-sectional assessment of the burden of HIV and associated individual- and structural-level characteristics among men who have sex with men in Swaziland.},
journal = {Journal of the International AIDS Society},
volume = {16 Suppl 3},
number = {4Suppl 3},
pages = {18768},
pmid = {24321117},
issn = {1758-2652},
support = {//PEPFAR/United States ; },
mesh = {Adolescent ; Adult ; Demography ; Diagnostic Tests, Routine ; Eswatini/epidemiology ; HIV Infections/*epidemiology ; *Homosexuality, Male ; Humans ; Male ; Prevalence ; Risk Factors ; Surveys and Questionnaires ; Young Adult ; },
abstract = {INTRODUCTION: Similar to other Southern African countries, Swaziland has been severely affected by HIV, with over a quarter of its reproductive-age adults estimated to be living with the virus, equating to an estimate of 170,000 people living with HIV. The last several years have witnessed an increase in the understanding of the potential vulnerabilities among men who have sex with men (MSM) in neighbouring countries with similarly widespread HIV epidemics. To date, there are no data characterizing the burden of HIV and the HIV prevention, treatment and care needs of MSM in Swaziland.<br><br>METHODS: In 2011, 324 men who reported sex with another man in the last 12 months were accrued using respondent-driven sampling (RDS). Participants completed HIV testing using Swazi national guidelines as well as structured survey instruments administered by trained staff, including modules on demographics, individual-level behavioural and biological risk factors, social and structural characteristics and uptake of HIV services. Population and individual weights were computed separately for each variable with a data-smoothing algorithm. The weights were used to estimate RDS-adjusted univariate estimates with 95% bootstrapped confidence intervals (BCIs). Crude and RDS-adjusted bivariate and multivariate analyses were completed with HIV as the dependent variable.<br><br>RESULTS: Overall, HIV prevalence was 17.6% (n=50/284), although it was strongly correlated with age in bivariate- [odds ratio (OR) 1.2, 95% BCI 1.15-1.21] and multivariate-adjusted analyses (adjusted OR 1.24, 95% BCI 1.14-1.35) for each additional year of age. Nearly, 70.8% (n=34/48) were unaware of their status of living with HIV. Condom use with all sexual partners and condom-compatible-lubricant use with men were reported by 1.3% (95% CI 0.0-9.7).<br><br>CONCLUSIONS: Although the epidemic in Swaziland is driven by high-risk heterosexual transmission, the burden of HIV and the HIV prevention, treatment and care needs of MSM have been understudied. The data presented here suggest that these men have specific HIV acquisition and transmission risks that differ from those of other reproductive-age adults. The scale-up in HIV services over the past decade has likely had limited benefit for MSM, potentially resulting in a scenario where epidemics of HIV among MSM expand in the context of slowing epidemics in the general population, a reality observed in most of the world.},
}
@article {pmid24370570,
year = {2014},
author = {Oken, BS and Orhan, U and Roark, B and Erdogmus, D and Fowler, A and Mooney, A and Peters, B and Miller, M and Fried-Oken, MB},
title = {Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.},
journal = {Neurorehabilitation and neural repair},
volume = {28},
number = {4},
pages = {387-394},
pmid = {24370570},
issn = {1552-6844},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Artificial Intelligence ; Bayes Theorem ; Brain/*physiopathology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Female ; Humans ; *Language ; Male ; Middle Aged ; Practice, Psychological ; Quadriplegia/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation.<br><br>OBJECTIVE: To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion.<br><br>METHODS: The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally.<br><br>RESULTS: Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5.<br><br>CONCLUSIONS: Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.},
}
@article {pmid24368034,
year = {2014},
author = {Chang, MH and Baek, HJ and Lee, SM and Park, KS},
title = {An amplitude-modulated visual stimulation for reducing eye fatigue in SSVEP-based brain-computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {7},
pages = {1380-1391},
doi = {10.1016/j.clinph.2013.11.016},
pmid = {24368034},
issn = {1872-8952},
mesh = {Adult ; Asthenopia/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Female ; Humans ; Male ; Photic Stimulation/methods ; Young Adult ; },
abstract = {OBJECTIVE: A high-frequency steady-state visual evoked potential (SSVEP) has been suggested for the reduction of eye fatigue for SSVEP-based brain-computer interfaces (BCIs). However, the poor performance of high-frequency SSVEP requires a novel stimulus of better performance even with low eye fatigue. As an alternative to the high-frequency SSVEP, we explore the SSVEP response to an amplitude-modulated stimulus (AM-SSVEP) to verify its availability for brain-computer interfaces (BCIs).<br><br>METHODS: An amplitude-modulated stimulus was generated as the product of two sine waves at a carrier frequency (fc) and a modulating frequency (fm). The carrier frequency was higher than 40 Hz to reduce eye fatigue, and the modulating frequency ranged around the &#x3b1;-band (9-12 Hz) to utilize low-frequency harmonic information. Four targets were used in combinations of three different modulating frequencies and two different carrier frequencies in the offline experiment, and two additional targets were added with one additional modulating and one carrier frequency in online experiments.<br><br>RESULTS: In the AM-SSVEP spectra, seven harmonic components were identified at 2fc, 2fm, fc&#xb1;fm, fc&#xb1;3fm, and 2fc-4fm. Using an optimized combination of the harmonic frequencies, online experiments demonstrated that the accuracy of the AM-SSVEP was equivalent to that of the low-frequency SSVEP. Furthermore, subject evaluation indicated that an AM stimulus caused lower eye fatigue and less sensing of flickering than a low-frequency stimulus, in a manner similar to a high-frequency stimulus.<br><br>CONCLUSIONS: The actual stimulus frequencies of AM-SSVEPs are in the high-frequency band, resulting in reduced eye fatigue. Furthermore, AM-SSVEPs can utilize both fundamental stimulus frequencies and non-integer harmonic frequencies including low frequencies for SSVEP recognition. The feasibility of AM-SSVEP with high BCI performance and low eye fatigue was confirmed through offline and online experiments.<br><br>SIGNIFICANCE: AM-SSVEPs combine the advantages of both low- and high-frequency SSVEPs--high power and low eye fatigue, respectively. AM-SSVEP-based BCI systems exploit these advantages, making them promising for application in practical BCI systems.},
}
@article {pmid24367322,
year = {2013},
author = {Ahn, M and Ahn, S and Hong, JH and Cho, H and Kim, K and Kim, BS and Chang, JW and Jun, SC},
title = {Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {848},
pmid = {24367322},
issn = {1662-5161},
abstract = {While brain computer interface (BCI) can be employed with patients and healthy subjects, there are problems that must be resolved before BCI can be useful to the public. In the most popular motor imagery (MI) BCI system, a significant number of target users (called "BCI-Illiterates") cannot modulate their neuronal signals sufficiently to use the BCI system. This causes performance variability among subjects and even among sessions within a subject. The mechanism of such BCI-Illiteracy and possible solutions still remain to be determined. Gamma oscillation is known to be involved in various fundamental brain functions, and may play a role in MI. In this study, we investigated the association of gamma activity with MI performance among subjects. Ten simultaneous MEG/EEG experiments were conducted; MI performance for each was estimated by EEG data, and the gamma activity associated with BCI performance was investigated with MEG data. Our results showed that gamma activity had a high positive correlation with MI performance in the prefrontal area. This trend was also found across sessions within one subject. In conclusion, gamma rhythms generated in the prefrontal area appear to play a critical role in BCI performance.},
}
@article {pmid24364471,
year = {2013},
author = {Dudzińska, M and Tarach, JS and Zwolak, A and Kurowska, M and Malicka, J and Smoleń, A and Nowakowski, A},
title = {Type 2 diabetes mellitus in relation to place of residence: evaluation of selected aspects of socio-demographic status, course of diabetes and quality of life--a cross-sectional study.},
journal = {Annals of agricultural and environmental medicine : AAEM},
volume = {20},
number = {4},
pages = {869-874},
pmid = {24364471},
issn = {1898-2263},
mesh = {Aged ; Cross-Sectional Studies ; Diabetes Mellitus, Type 2/*epidemiology/pathology ; Female ; Humans ; Male ; Middle Aged ; Poland/epidemiology ; *Quality of Life ; *Rural Population ; Socioeconomic Factors ; *Urban Population ; },
abstract = {INTRODUCTION AND OBJECTIVE: This study aims at answering what are the differences in socio-demographic status of patients with type 2 diabetes living in the city and the countryside and what is the impact of a place of residence on the level of metabolic control, the incidence of complications of diabetes and quality of life (QoL).<br><br>MATERIALS AND METHODS: 274 patients were divided into 2 groups: residents of rural areas-28.2% (n=77) and residents of urban areas-71.9% (n=197). Self-reported questionnaires was used: EQ-5D, DQL-BCI and DSC-R.<br><br>RESULTS: The group of residents of the countryside was characterized by a lower income and education level and a higher number of persons with disability pension. Patients living in the country had a higher body mass index in comparison to town inhabitants (32.6 kg/m(2) vs 30.9 kg/m(2), p=0.008) and shorter diabetes duration (8.4 versus 11.3 years, p=0.008). There were no differences between residents of the countryside and towns in terms of the method of treatment (oral antidiabetic drugs: 70.1% and 65.5%, p=0.3, Insulin: 29.9% and 36.5%, p=0.3, respectively), occurring chronic complications of diabetes (retinopathy: 14.3% and 14.2%, neuropathy: 6.5% and 7.6%, coronary heart disease: 44.45 and 37.1%, respectively) and the availability of diabetologists. Patients living in the countryside did not differ from town inhabitants in metabolic control and QoL assessment (EQ-5D index: 0.80 vs 0.79, p=0.9, EQ-VAS: 56.2 vs 54.3, p=0.2, DQL-BCI: 56.0 vs 53.9, p=0.1, DSC-R: 29.6 vs 29.4, p=0.7).<br><br>CONCLUSIONS: The socio-demographic differences between groups dependent on the place of living did not exert a significant influence on the level of metabolic control of diabetes, the incidence of late complications or QoL assessment in the population studied.},
}
@article {pmid24359452,
year = {2013},
author = {Foldes, ST and Taylor, DM},
title = {Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {116},
pmid = {24359452},
issn = {1743-0003},
support = {NINDS R01-NS058871/NS/NINDS NIH HHS/United States ; NINDS F31-NS065710/NS/NINDS NIH HHS/United States ; NIBIB T32-EB004314/EB/NIBIB NIH HHS/United States ; NINDS N01-NS-5-2365/NS/NINDS NIH HHS/United States ; F31 NS065710/NS/NINDS NIH HHS/United States ; NICHD N01-HD-5-3403//PHS HHS/United States ; },
mesh = {Arm ; Attention/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) systems have been developed to provide paralyzed individuals the ability to command the movements of an assistive device using only their brain activity. BCI systems are typically tested in a controlled laboratory environment were the user is focused solely on the brain-control task. However, for practical use in everyday life people must be able to use their brain-controlled device while mentally engaged with the cognitive responsibilities of daily activities and while compensating for any inherent dynamics of the device itself. BCIs that use electroencephalography (EEG) for movement control are often assumed to require significant mental effort, thus preventing users from thinking about anything else while using their BCI. This study tested the impact of cognitive load as well as speaking on the ability to use an EEG-based BCI.<br><br>FINDINGS: Six participants controlled the two-dimensional (2D) movements of a simulated neuroprosthesis-arm under three different levels of cognitive distraction. The two higher cognitive load conditions also required simultaneously speaking during BCI use. On average, movement performance declined during higher levels of cognitive distraction, but only by a limited amount. Movement completion time increased by 7.2%, the percentage of targets successfully acquired declined by 11%, and path efficiency declined by 8.6%. Only the decline in percentage of targets acquired and path efficiency were statistically significant (p < 0.05).<br><br>CONCLUSION: People who have relatively good movement control of an EEG-based BCI may be able to speak and perform other cognitively engaging activities with only a minor drop in BCI-control performance.},
}
@article {pmid24350578,
year = {2014},
author = {Spataro, R and Ciriacono, M and Manno, C and La Bella, V},
title = {The eye-tracking computer device for communication in amyotrophic lateral sclerosis.},
journal = {Acta neurologica Scandinavica},
volume = {130},
number = {1},
pages = {40-45},
doi = {10.1111/ane.12214},
pmid = {24350578},
issn = {1600-0404},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/complications/*rehabilitation ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Caregivers ; Communication Devices for People with Disabilities/*statistics &amp; numerical data ; Communication Disorders/etiology/*rehabilitation ; Data Collection ; Eye Movements ; Female ; Humans ; Male ; Middle Aged ; },
abstract = {OBJECTIVE: To explore the effectiveness of communication and the variables affecting the eye-tracking computer system (ETCS) utilization in patients with late-stage amyotrophic lateral sclerosis (ALS).<br><br>METHODS: We performed a telephone survey on 30 patients with advanced non-demented ALS that were provisioned an ECTS device. Median age at interview was 55 years (IQR = 48-62), with a relatively high education (13 years, IQR = 8-13). A one-off interview was made and answers were later provided with the help of the caregiver. The interview included items about demographic and clinical variables affecting the daily ETCS utilization.<br><br>RESULTS: The median time of ETCS device possession was 15 months (IQR = 9-20). The actual daily utilization was 300 min (IQR = 100-720), mainly for the communication with relatives/caregiver, internet surfing, e-mailing, and social networking. 23.3% of patients with ALS (n = 7) had a low daily ETCS utilization; most reported causes were eye-gaze tiredness and oculomotor dysfunction.<br><br>CONCLUSIONS: Eye-tracking computer system is a valuable device for AAC in patients with ALS, and it can be operated with a good performance. The development of oculomotor impairment may limit its functional use.},
}
@article {pmid24348740,
year = {2013},
author = {Zhang, R and Xu, P and Liu, T and Zhang, Y and Guo, L and Li, P and Yao, D},
title = {Local temporal correlation common spatial patterns for single trial EEG classification during motor imagery.},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
number = {},
pages = {591216},
pmid = {24348740},
issn = {1748-6718},
mesh = {Adult ; Algorithms ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Models, Theoretical ; Motor Skills/*physiology ; Pattern Recognition, Automated ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application.},
}
@article {pmid24345361,
year = {2013},
author = {Schermer, M},
title = {[A cyborg is only human].},
journal = {Nederlands tijdschrift voor geneeskunde},
volume = {157},
number = {51},
pages = {A6879},
pmid = {24345361},
issn = {1876-8784},
mesh = {Brain-Computer Interfaces ; Humans ; *Prostheses and Implants ; *Robotics ; },
abstract = {New biomedical technologies make it possible to replace parts of the human body or to substitute its functions. Examples include artificial joints, eye lenses and arterial stents. Newer technologies use electronics and software, for example in brain-computer interfaces such as retinal implants and the exoskeleton MindWalker. Gradually we are creating cyborgs: hybrids of man and machine. This raises the question: are cyborgs still humans? It is argued that they are. First, because employing technology is a typically human characteristic. Second, because in western thought the human mind, and not the body, is considered to be the seat of personhood. However, it has been argued by phenomenological philosophers that the body is more than just an object but is also a subject, important for human identity. From this perspective, we can appreciate that a bionic body does not make one less human, but it does influence the experience of being human.},
}
@article {pmid24344691,
year = {2014},
author = {Zhang, Y and Zhou, G and Jin, J and Zhao, Q and Wang, X and Cichocki, A},
title = {Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface.},
journal = {International journal of neural systems},
volume = {24},
number = {1},
pages = {1450003},
doi = {10.1142/S0129065714500038},
pmid = {24344691},
issn = {1793-6462},
mesh = {Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Databases, Factual/statistics &amp; numerical data ; *Discriminant Analysis ; Electroencephalography ; Evoked Potentials/*physiology ; Humans ; ROC Curve ; },
abstract = {Two main issues for event-related potential (ERP) classification in brain-computer interface (BCI) application are curse-of-dimensionality and bias-variance tradeoff, which may deteriorate classification performance, especially with insufficient training samples resulted from limited calibration time. This study introduces an aggregation of sparse linear discriminant analyses (ASLDA) to overcome these problems. In the ASLDA, multiple sparse discriminant vectors are learned from differently l1-regularized least-squares regressions by exploiting the equivalence between LDA and least-squares regression, and are subsequently aggregated to form an ensemble classifier, which could not only implement automatic feature selection for dimensionality reduction to alleviate curse-of-dimensionality, but also decrease the variance to improve generalization capacity for new test samples. Extensive investigation and comparison are carried out among the ASLDA, the ordinary LDA and other competing ERP classification algorithms, based on different three ERP datasets. Experimental results indicate that the ASLDA yields better overall performance for single-trial ERP classification when insufficient training samples are available. This suggests the proposed ASLDA is promising for ERP classification in small sample size scenario to improve the practicability of BCI.},
}
@article {pmid24339929,
year = {2013},
author = {Hachmann, JT and Jeong, JH and Grahn, PJ and Mallory, GW and Evertz, LQ and Bieber, AJ and Lobel, DA and Bennet, KE and Lee, KH and Lujan, JL},
title = {Large animal model for development of functional restoration paradigms using epidural and intraspinal stimulation.},
journal = {PloS one},
volume = {8},
number = {12},
pages = {e81443},
pmid = {24339929},
issn = {1932-6203},
support = {R21 NS087320/NS/NINDS NIH HHS/United States ; K08 NS052232/NS/NINDS NIH HHS/United States ; K08 NS 52232/NS/NINDS NIH HHS/United States ; R01 NS070872/NS/NINDS NIH HHS/United States ; R01 NS 70872/NS/NINDS NIH HHS/United States ; R25 GM055252/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces ; Disease Models, Animal ; Electric Stimulation Therapy/*methods ; Epidural Space ; Female ; Quality of Life ; *Recovery of Function ; Spinal Cord/*physiopathology ; Spinal Cord Injuries/*physiopathology/*therapy ; Swine ; },
abstract = {Restoration of movement following spinal cord injury (SCI) has been achieved using electrical stimulation of peripheral nerves and skeletal muscles. However, practical limitations such as the rapid onset of muscle fatigue hinder clinical application of these technologies. Recently, direct stimulation of alpha motor neurons has shown promise for evoking graded, controlled, and sustained muscle contractions in rodent and feline animal models while overcoming some of these limitations. However, small animal models are not optimal for the development of clinical spinal stimulation techniques for functional restoration of movement. Furthermore, variance in surgical procedure, targeting, and electrode implantation techniques can compromise therapeutic outcomes and impede comparison of results across studies. Herein, we present a protocol and large animal model that allow standardized development, testing, and optimization of novel clinical strategies for restoring motor function following spinal cord injury. We tested this protocol using both epidural and intraspinal stimulation in a porcine model of spinal cord injury, but the protocol is suitable for the development of other novel therapeutic strategies. This protocol will help characterize spinal circuits vital for selective activation of motor neuron pools. In turn, this will expedite the development and validation of high-precision therapeutic targeting strategies and stimulation technologies for optimal restoration of motor function in humans.},
}
@article {pmid24333753,
year = {2014},
author = {Li, T and Hong, J and Zhang, J and Guo, F},
title = {Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.},
journal = {Journal of neuroscience methods},
volume = {224},
number = {},
pages = {26-38},
doi = {10.1016/j.jneumeth.2013.11.015},
pmid = {24333753},
issn = {1872-678X},
mesh = {Adult ; *Artificial Intelligence ; Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Feedback, Physiological ; Female ; Humans ; Male ; Online Systems ; Orientation/physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery.},
}
@article {pmid24328459,
year = {2014},
author = {Kopsky, DJ and Winninghoff, Y and Winninghoff, AC and Stolwijk-Swüste, JM},
title = {A novel spelling system for locked-in syndrome patients using only eye contact.},
journal = {Disability and rehabilitation},
volume = {36},
number = {20},
pages = {1723-1727},
doi = {10.3109/09638288.2013.866700},
pmid = {24328459},
issn = {1464-5165},
mesh = {*Communication Devices for People with Disabilities ; *Eye Movements ; Female ; Humans ; Middle Aged ; Quadriplegia/*physiopathology ; },
abstract = {PURPOSE: We developed and evaluated a novel spelling system for patients with locked-in syndrome: patients with tetraplegia, not able to talk, and only able to blink their eyes.<br><br>METHOD: A new communication grid was compared with existing non-technical communication methods for practical daily use. The means of the number of decision steps to reach a full sentence were compared testing 10 sentences relevant in daily care. These 10 sentences together encompass all letters of the alphabet.<br><br>RESULTS: The new communication grid is organised alphabetically in 4 columns and 2 main rows, with each row subdivided in three rows. The first column contains vowels while the other columns contain consonants. Letters in each column are alphabetically ordered. When spelling a sentence the conversation partner counts the columns, until the patient indicates by an upward eye movement that the column contains the intended letter. Hereafter, the patient indicates by looking straight ahead or by looking down, whether the intended letter is in the upper or in the lower main row, respectively. The conversation partner will then read out the letters until the patient indicates the intended letter. Compared to other spelling systems, this system requires only vertical eye movement, is easier to memorise, and faster in use. The comparison of means of decision steps to reach the 10 full sentences for different communication grids shows that using the new communication grid is approximately one-third to three times faster than the existing spelling systems (p&#x2009;=&#x2009;0.005).<br><br>CONCLUSION: This new grid is a valuable communication tool, especially in situations, such as bathing, getting dressed or out of house activities where no devices are available. Implications for Rehabilitation Communication with patients with locked-in syndrome is a complicated and strenuous task. Communication methods, such as the alphabet board and brain-computer interfaces, are time consuming or too sophisticated to use in daily life tasks. This communication grid is fast, easy to use and memorise and requires only vertical eye movement.},
}
@article {pmid24324155,
year = {2013},
author = {Guggenmos, DJ and Azin, M and Barbay, S and Mahnken, JD and Dunham, C and Mohseni, P and Nudo, RJ},
title = {Restoration of function after brain damage using a neural prosthesis.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {52},
pages = {21177-21182},
pmid = {24324155},
issn = {1091-6490},
mesh = {Action Potentials/*physiology ; Animals ; Brain Injuries/*therapy ; *Brain-Computer Interfaces ; Linear Models ; Male ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Neural Pathways/physiology ; *Neural Prostheses ; Rats ; Rats, Long-Evans ; },
abstract = {Neural interface systems are becoming increasingly more feasible for brain repair strategies. This paper tests the hypothesis that recovery after brain injury can be facilitated by a neural prosthesis serving as a communication link between distant locations in the cerebral cortex. The primary motor area in the cerebral cortex was injured in a rat model of focal brain injury, disrupting communication between motor and somatosensory areas and resulting in impaired reaching and grasping abilities. After implantation of microelectrodes in cerebral cortex, a neural prosthesis discriminated action potentials (spikes) in premotor cortex that triggered electrical stimulation in somatosensory cortex continuously over subsequent weeks. Within 1 wk, while receiving spike-triggered stimulation, rats showed substantially improved reaching and grasping functions that were indistinguishable from prelesion levels by 2 wk. Post hoc analysis of the spikes evoked by the stimulation provides compelling evidence that the neural prosthesis enhanced functional connectivity between the two target areas. This proof-of-concept study demonstrates that neural interface systems can be used effectively to bridge damaged neural pathways functionally and promote recovery after brain injury.},
}
@article {pmid24321081,
year = {2013},
author = {Do, AH and Wang, PT and King, CE and Chun, SN and Nenadic, Z},
title = {Brain-computer interface controlled robotic gait orthosis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {111},
pmid = {24321081},
issn = {1743-0003},
support = {UL1 TR000153/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; *Braces ; *Brain-Computer Interfaces ; Electroencephalography ; Gait/*physiology ; Humans ; Imagination ; Male ; Paraplegia/etiology/rehabilitation ; Robotics/*methods ; Spinal Cord Injuries/complications/*rehabilitation ; Walking/physiology ; },
abstract = {BACKGROUND: Excessive reliance on wheelchairs in individuals with tetraplegia or paraplegia due to spinal cord injury (SCI) leads to many medical co-morbidities, such as cardiovascular disease, metabolic derangements, osteoporosis, and pressure ulcers. Treatment of these conditions contributes to the majority of SCI health care costs. Restoring able-body-like ambulation in this patient population can potentially reduce the incidence of these medical co-morbidities, in addition to increasing independence and quality of life. However, no biomedical solution exists that can reverse this loss of neurological function, and hence novel methods are needed. Brain-computer interface (BCI) controlled lower extremity prostheses may constitute one such novel approach.<br><br>METHODS: One able-bodied subject and one subject with paraplegia due to SCI underwent electroencephalogram (EEG) recordings while engaged in alternating epochs of idling and walking kinesthetic motor imagery (KMI). These data were analyzed to generate an EEG prediction model for online BCI operation. A commercial robotic gait orthosis (RoGO) system (suspended over a treadmill) was interfaced with the BCI computer to allow for computerized control. The subjects were then tasked to perform five, 5-min-long online sessions where they ambulated using the BCI-RoGO system as prompted by computerized cues. The performance of this system was assessed with cross-correlation analysis, and omission and false alarm rates.<br><br>RESULTS: The offline accuracy of the EEG prediction model averaged 86.30% across both subjects (chance: 50%). The cross-correlation between instructional cues and the BCI-RoGO walking epochs averaged across all subjects and all sessions was 0.812 &#xb1; 0.048 (p-value <10(-4)). Also, there were on average 0.8 false alarms per session and no omissions.<br><br>CONCLUSION: These results provide preliminary evidence that restoring brain-controlled ambulation after SCI is feasible. Future work will test the function of this system in a population of subjects with SCI. If successful, this may justify the future development of BCI-controlled lower extremity prostheses for free overground walking for those with complete motor SCI. Finally, this system can also be applied to incomplete motor SCI, where it could lead to improved neurological outcomes beyond those of standard physiotherapy.},
}
@article {pmid24319425,
year = {2013},
author = {Mokienko, OA and Chervyakov, AV and Kulikova, SN and Bobrov, PD and Chernikova, LA and Frolov, AA and Piradov, MA},
title = {Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects.},
journal = {Frontiers in computational neuroscience},
volume = {7},
number = {},
pages = {168},
pmid = {24319425},
issn = {1662-5188},
abstract = {BACKGROUND: Motor imagery (MI) is the mental performance of movement without muscle activity. It is generally accepted that MI and motor performance have similar physiological mechanisms.<br><br>PURPOSE: To investigate the activity and excitability of cortical motor areas during MI in subjects who were previously trained with an MI-based brain-computer interface (BCI).<br><br>SUBJECTS AND METHODS: Eleven healthy volunteers without neurological impairments (mean age, 36 years; range: 24-68 years) were either trained with an MI-based BCI (BCI-trained, n = 5) or received no BCI training (n = 6, controls). Subjects imagined grasping in a blocked paradigm task with alternating rest and task periods. For evaluating the activity and excitability of cortical motor areas we used functional MRI and navigated transcranial magnetic stimulation (nTMS).<br><br>RESULTS: fMRI revealed activation in Brodmann areas 3 and 6, the cerebellum, and the thalamus during MI in all subjects. The primary motor cortex was activated only in BCI-trained subjects. The associative zones of activation were larger in non-trained subjects. During MI, motor evoked potentials recorded from two of the three targeted muscles were significantly higher only in BCI-trained subjects. The motor threshold decreased (median = 17%) during MI, which was also observed only in BCI-trained subjects.<br><br>CONCLUSION: Previous BCI training increased motor cortex excitability during MI. These data may help to improve BCI applications, including rehabilitation of patients with cerebral palsy.},
}
@article {pmid24318283,
year = {2013},
author = {Aono, K and Miyashita, S and Fujiwara, Y and Kodama, M and Hanayama, K and Masakado, Y and Ushiba, J},
title = {Relationship between event-related desynchronization and cortical excitability in healthy subjects and stroke patients.},
journal = {The Tokai journal of experimental and clinical medicine},
volume = {38},
number = {4},
pages = {123-128},
pmid = {24318283},
issn = {2185-2243},
mesh = {Adult ; Aged ; Cortical Synchronization/*physiology ; Electroencephalography ; Evoked Potentials ; Evoked Potentials, Motor ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Stroke/*physiopathology ; Stroke Rehabilitation ; Transcranial Magnetic Stimulation ; Young Adult ; },
abstract = {OBJECTIVE: Relation between cortical excitability and magnitudes of event-related dysynchronizaton (ERD) has not been clarified. This study was investigated that relationshp between cortical excitability and ERD magnitudes in healthy subjects and stroke patients.<br><br>METHODS: Ten healthy subjects and four patients with stroke participated in this study. EEGs were recorded over the sensorimotor cortex (left hemisphere in healthy subjects; damaged hemisphere in stroke subjects) to calculate ERD during motor imagery,. Motor-evoked potential (MEP) induced by single-pulse transcranial magnetic stimulation over the primary motor cortex was recorded from the first dorsal interosseus (FDI) muscle at ERD magnitudes of 10% and 30%.<br><br>RESULTS: MEP significantly increased at 10% and 30% ERD (p<0.01) than that during rest in healthy subjects. The 30% ERD condition showed significantly higher MEP than that at 10% ERD (p<0.05). In stroke patients, MEP increased with ERD induced by motor imagery, but the change of MEP to ERD amplitude was critically different among the subject.<br><br>CONCLUSION: ERD magnitude corresponds to corticospinal excitability increases in healthy subjects and patients with hemiplegic stroke. BCI based on motor imagery-induced ERD may be a potential rehabilitation strategy for patients with hemiplegic stroke.},
}
@article {pmid24317216,
year = {2014},
author = {Eeg-Olofsson, M and Håkansson, B and Reinfeldt, S and Taghavi, H and Lund, H and Jansson, KJ and Håkansson, E and Stalfors, J},
title = {The bone conduction implant--first implantation, surgical and audiologic aspects.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {35},
number = {4},
pages = {679-685},
doi = {10.1097/MAO.0000000000000203},
pmid = {24317216},
issn = {1537-4505},
mesh = {Adult ; Anesthesia, General ; Audiology ; Audiometry/methods ; Bone Conduction/*physiology ; Cochlea/anatomy &amp; histology ; Cochlear Implantation/*methods ; *Cochlear Implants ; Ear Canal/anatomy &amp; histology/surgery ; Female ; Hearing/*physiology ; Hearing Aids ; Hearing Loss, Mixed Conductive-Sensorineural/surgery/therapy ; Humans ; Male ; Middle Aged ; Otologic Surgical Procedures/*methods ; Patient Care Planning ; Signal-To-Noise Ratio ; Skin/anatomy &amp; histology ; Tomography, X-Ray Computed ; Transducers ; Treatment Outcome ; },
abstract = {OBJECTIVE: To report on preoperative assessment, surgery, and audiologic outcome of the first patient implanted with the bone conduction implant (BCI).<br><br>BACKGROUND: The BCI is a bone conduction hearing device with an intact skin solution where the transducer is implanted close to the ear canal opening. By avoiding a percutaneous screw attachment to the skull, the BCI is anticipated to reduce complications associated with the Bone-Anchored Hearing Aid (BAHA) solution.<br><br>METHODS: The first patient to receive a BCI was a 42-year-old woman with a unilateral mixed hearing loss due to tympanosclerosis. Preoperative and postoperative cone beam computed tomography and a virtual planning tool for 3D reconstruction were used to optimize and control the position of the BCI in the mastoid. The transducer was placed in a 5-mm deep seating in the mastoid and secured with a titanium bar. Free field tone and speech audiometry were conducted to evaluate the audiologic outcome at baseline (1 month postoperatively) and 1 month after baseline.<br><br>RESULTS: The BCI was placed in the position according to the preoperative 3D planning. On average, the tone thresholds improved by 30 dB, speech reception thresholds by 25.5 dB and speech signal-to-noise ratio by 9.7 dB. The surgical procedure was considered simple and safe.<br><br>CONCLUSION: The BCI can be implanted by a safe and easy surgical procedure. 3D preoperative planning can be helpful to optimize the BCI position. The BCI is a realistic alternative to the BAHA.},
}
@article {pmid24312569,
year = {2013},
author = {Sato, JR and Basilio, R and Paiva, FF and Garrido, GJ and Bramati, IE and Bado, P and Tovar-Moll, F and Zahn, R and Moll, J},
title = {Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox.},
journal = {PloS one},
volume = {8},
number = {12},
pages = {e81658},
pmid = {24312569},
issn = {1932-6203},
support = {G0902304/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; *Computer Graphics ; Emotions ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; *Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Activity ; Multivariate Analysis ; Neurofeedback/*methods ; Support Vector Machine ; Time Factors ; *User-Computer Interface ; },
abstract = {The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.},
}
@article {pmid24312477,
year = {2013},
author = {Roijendijk, L and Farquhar, J and van Gerven, M and Jensen, O and Gielen, S},
title = {Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing.},
journal = {PloS one},
volume = {8},
number = {12},
pages = {e80489},
pmid = {24312477},
issn = {1932-6203},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Magnetoencephalography/methods ; Male ; Problem Solving/*physiology ; Spatial Behavior/*physiology ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance.<br><br>APPROACH: We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5&#xb0;) or far (7&#xb0;) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block.<br><br>MAIN RESULTS: Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate.<br><br>SIGNIFICANCE: Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.},
}
@article {pmid24312041,
year = {2013},
author = {Risetti, M and Formisano, R and Toppi, J and Quitadamo, LR and Bianchi, L and Astolfi, L and Cincotti, F and Mattia, D},
title = {On ERPs detection in disorders of consciousness rehabilitation.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {775},
pmid = {24312041},
issn = {1662-5161},
abstract = {Disorders of Consciousness (DOC) like Vegetative State (VS), and Minimally Conscious State (MCS) are clinical conditions characterized by the absence or intermittent behavioral responsiveness. A neurophysiological monitoring of parameters like Event-Related Potentials (ERPs) could be a first step to follow-up the clinical evolution of these patients during their rehabilitation phase. Eleven patients diagnosed as VS (n = 8) and MCS (n = 3) by means of the JFK Coma Recovery Scale Revised (CRS-R) underwent scalp EEG recordings during the delivery of a 3-stimuli auditory oddball paradigm, which included standard, deviant tones and the subject own name (SON) presented as a novel stimulus, administered under passive and active conditions. Four patients who showed a change in their clinical status as detected by means of the CRS-R (i.e., moved from VS to MCS), were subjected to a second EEG recording session. All patients, but one (anoxic etiology), showed ERP components such as mismatch negativity (MMN) and novelty P300 (nP3) under passive condition. When patients were asked to count the novel stimuli (active condition), the nP3 component displayed a significant increase in amplitude (p = 0.009) and a wider topographical distribution with respect to the passive listening, only in MCS. In 2 out of the 4 patients who underwent a second recording session consistently with their transition from VS to MCS, the nP3 component elicited by passive listening of SON stimuli revealed a significant amplitude increment (p < 0.05). Most relevant, the amplitude of the nP3 component in the active condition, acquired in each patient and in all recording sessions, displayed a significant positive correlation with the total scores (p = 0.004) and with the auditory sub-scores (p < 0.00001) of the CRS-R administered before each EEG recording. As such, the present findings corroborate the value of ERPs monitoring in DOC patients to investigate residual unconscious and conscious cognitive function.},
}
@article {pmid24312019,
year = {2013},
author = {Opris, I},
title = {Inter-laminar microcircuits across neocortex: repair and augmentation.},
journal = {Frontiers in systems neuroscience},
volume = {7},
number = {},
pages = {80},
pmid = {24312019},
issn = {1662-5137},
}
@article {pmid24311057,
year = {2014},
author = {Schudlo, LC and Chau, T},
title = {Dynamic topographical pattern classification of multichannel prefrontal NIRS signals: II. Online differentiation of mental arithmetic and rest.},
journal = {Journal of neural engineering},
volume = {11},
number = {1},
pages = {016003},
doi = {10.1088/1741-2560/11/1/016003},
pmid = {24311057},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artifacts ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Feedback, Psychological ; Hemoglobins/metabolism ; Humans ; Male ; Mathematics ; Mental Processes/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; Reading ; Rest/*physiology ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {OBJECTIVE: Near-infrared spectroscopy (NIRS) has recently gained attention as a modality for brain-computer interfaces (BCIs), which may serve as an alternative access pathway for individuals with severe motor impairments. For NIRS-BCIs to be used as a real communication pathway, reliable online operation must be achieved. Yet, only a limited number of studies have been conducted online to date. These few studies were carried out under a synchronous paradigm and did not accommodate an unconstrained resting state, precluding their practical clinical implication. Furthermore, the potentially discriminative power of spatiotemporal characteristics of activation has yet to be considered in an online NIRS system.<br><br>APPROACH: In this study, we developed and evaluated an online system-paced NIRS-BCI which was driven by a mental arithmetic activation task and accommodated an unconstrained rest state. With a dual-wavelength, frequency domain near-infrared spectrometer, measurements were acquired over nine sites of the prefrontal cortex, while ten able-bodied participants selected letters from an on-screen scanning keyboard via intentionally controlled brain activity (using mental arithmetic). Participants were provided dynamic NIR topograms as continuous visual feedback of their brain activity as well as binary feedback of the BCI's decision (i.e. if the letter was selected or not). To classify the hemodynamic activity, temporal features extracted from the NIRS signals and spatiotemporal features extracted from the dynamic NIR topograms were used in a majority vote combination of multiple linear classifiers.<br><br>MAIN RESULTS: An overall online classification accuracy of 77.4 &#xb1; 10.5% was achieved across all participants. The binary feedback was found to be very useful during BCI use, while not all participants found value in the continuous feedback provided.<br><br>SIGNIFICANCE: These results demonstrate that mental arithmetic is a potent mental task for driving an online system-paced NIRS-BCI. BCI feedback that reflects the classifier's decision has the potential to improve user performance. The proposed system can provide a framework for future online NIRS-BCI development and testing.},
}
@article {pmid24308848,
year = {2014},
author = {Carmichael, C and Carmichael, P},
title = {BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {9},
number = {1},
pages = {41-47},
doi = {10.3109/17483107.2013.867372},
pmid = {24308848},
issn = {1748-3115},
mesh = {Persons with Disabilities/*rehabilitation ; Equipment Design ; *Ethics, Research ; Humans ; Personhood ; *Research Design ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {PURPOSE: This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants.<br><br>RESULTS: The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of "ideal types" of disabled users may reinforce stereotypes or drown out participant "voices".<br><br>CONCLUSIONS: Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a "duty of care" while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies.},
}
@article {pmid24302977,
year = {2013},
author = {Ganin, IP and Shishkin, SL and Kaplan, AY},
title = {A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.},
journal = {PloS one},
volume = {8},
number = {10},
pages = {e77755},
pmid = {24302977},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; Video Games ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) are tools for controlling computers and other devices without using muscular activity, employing user-controlled variations in signals recorded from the user's brain. One of the most efficient noninvasive BCIs is based on the P300 wave of the brain's response to stimuli and is therefore referred to as the P300 BCI. Many modifications of this BCI have been proposed to further improve the BCI's characteristics or to better adapt the BCI to various applications. However, in the original P300 BCI and in all of its modifications, the spatial positions of stimuli were fixed relative to each other, which can impose constraints on designing applications controlled by this BCI. We designed and tested a P300 BCI with stimuli presented on objects that were freely moving on a screen at a speed of 5.4°/s. Healthy participants practiced a game-like task with this BCI in either single-trial or triple-trial mode within four sessions. At each step, the participants were required to select one of nine moving objects. The mean online accuracy of BCI-based selection was 81% in the triple-trial mode and 65% in the single-trial mode. A relatively high P300 amplitude was observed in response to targets in most participants. Self-rated interest in the task was high and stable over the four sessions (the medians in the 1st/4th sessions were 79/84% and 76/71% in the groups practicing in the single-trial and triple-trial modes, respectively). We conclude that the movement of stimulus positions relative to each other may not prevent the efficient use of the P300 BCI by people controlling their gaze, e.g., in robotic devices and in video games.},
}
@article {pmid24302929,
year = {2013},
author = {Higashi, H and Tanaka, T},
title = {Common spatio-time-frequency patterns for motor imagery-based brain machine interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2013},
number = {},
pages = {537218},
pmid = {24302929},
issn = {1687-5273},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; Movement/physiology ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI.},
}
@article {pmid24298994,
year = {2013},
author = {Walters, MS and Gomi, K and Ashbridge, B and Moore, MA and Arbelaez, V and Heldrich, J and Ding, BS and Rafii, S and Staudt, MR and Crystal, RG},
title = {Generation of a human airway epithelium derived basal cell line with multipotent differentiation capacity.},
journal = {Respiratory research},
volume = {14},
number = {1},
pages = {135},
pmid = {24298994},
issn = {1465-993X},
support = {P30 CA008748/CA/NCI NIH HHS/United States ; P50 HL084936/HL/NHLBI NIH HHS/United States ; R01 HL107882/HL/NHLBI NIH HHS/United States ; R01HL107882/HL/NHLBI NIH HHS/United States ; UL1 RR024143/RR/NCRR NIH HHS/United States ; UL1 TR000457/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Bronchi/*cytology/metabolism ; Cell Culture Techniques/*methods ; *Cell Differentiation ; Cell Line ; Epithelial Cells/*cytology/metabolism ; Humans ; Male ; Multipotent Stem Cells/*cytology/metabolism ; Phenotype ; Retroviridae/genetics ; Telomerase/genetics/metabolism ; Transfection ; },
abstract = {BACKGROUND: As the multipotent progenitor population of the airway epithelium, human airway basal cells (BC) replenish the specialized differentiated cell populations of the mucociliated airway epithelium during physiological turnover and repair. Cultured primary BC divide a limited number of times before entering a state of replicative senescence, preventing the establishment of long-term replicating cultures of airway BC that maintain their original phenotype.<br><br>METHODS: To generate an immortalized human airway BC cell line, primary human airway BC obtained by brushing the airway epithelium of healthy nonsmokers were infected with a retrovirus expressing human telomerase (hTERT). The resulting immortalized cell line was then characterized under non-differentiating and differentiating air-liquid interface (ALI) culture conditions using ELISA, TaqMan quantitative PCR, Western analysis, and immunofluorescent and immunohistochemical staining analysis for cell type specific markers. In addition, the ability of the cell line to respond to environmental stimuli under differentiating ALI culture was assessed.<br><br>RESULTS: We successfully generated an immortalized human airway BC cell line termed BCi-NS1 via expression of hTERT. A single cell derived clone from the parental BCi-NS1 cells, BCi-NS1.1, retains characteristics of the original primary cells for over 40 passages and demonstrates a multipotent differentiation capacity into secretory (MUC5AC, MUC5B), goblet (TFF3), Clara (CC10) and ciliated (DNAI1, FOXJ1) cells on ALI culture. The cells can respond to external stimuli such as IL-13, resulting in alteration of the normal differentiation process.<br><br>CONCLUSION: Development of immortalized human airway BC that retain multipotent differentiation capacity over long-term culture should be useful in understanding the biology of BC, the response of BC to environmental stress, and as a target for assessment of pharmacologic agents.},
}
@article {pmid24298254,
year = {2013},
author = {Hammad, SH and Farina, D and Kamavuako, EN and Jensen, W},
title = {Identification of a self-paced hitting task in freely moving rats based on adaptive spike detection from multi-unit M1 cortical signals.},
journal = {Frontiers in neuroengineering},
volume = {6},
number = {},
pages = {11},
pmid = {24298254},
issn = {1662-6443},
abstract = {Invasive brain-computer interfaces (BCIs) may prove to be a useful rehabilitation tool for severely disabled patients. Although some systems have shown to work well in restricted laboratory settings, their usefulness must be tested in less controlled environments. Our objective was to investigate if a specific motor task could reliably be detected from multi-unit intra-cortical signals from freely moving animals. Four rats were trained to hit a retractable paddle (defined as a "hit"). Intra-cortical signals were obtained from electrodes placed in the primary motor cortex. First, the signal-to-noise ratio was increased by wavelet denoising. Action potentials were then detected using an adaptive threshold, counted in three consecutive time intervals and were used as features to classify either a "hit" or a "no-hit" (defined as an interval between two "hits"). We found that a "hit" could be detected with an accuracy of 75 ± 6% when wavelet denoising was applied whereas the accuracy dropped to 62 ± 5% without prior denoising. We compared our approach with the common daily practice in BCI that consists of using a fixed, manually selected threshold for spike detection without denoising. The results showed the feasibility of detecting a motor task in a less restricted environment than commonly applied within invasive BCI research.},
}
@article {pmid24293161,
year = {2014},
author = {Korostenskaja, M and Wilson, AJ and Rose, DF and Brunner, P and Schalk, G and Leach, J and Mangano, FT and Fujiwara, H and Rozhkov, L and Harris, E and Chen, PC and Seo, JH and Lee, KH},
title = {Real-time functional mapping with electrocorticography in pediatric epilepsy: comparison with fMRI and ESM findings.},
journal = {Clinical EEG and neuroscience},
volume = {45},
number = {3},
pages = {205-211},
pmid = {24293161},
issn = {1550-0594},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiopathology/surgery ; *Computer Simulation ; Diagnosis, Computer-Assisted/*methods ; Dominance, Cerebral/physiology ; Electric Stimulation/methods ; Electroencephalography/*methods ; Epilepsy/*diagnosis/*physiopathology/surgery ; Feasibility Studies ; Frontal Lobe/physiopathology ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Magnetic Resonance Imaging/*methods ; Male ; Multimodal Imaging/methods ; *Signal Processing, Computer-Assisted ; *Software ; Speech Perception/physiology ; Temporal Lobe/physiopathology ; Verbal Behavior/physiology ; Vocabulary ; },
abstract = {SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection) software provides real-time functional mapping (RTFM) of eloquent cortex for epilepsy patients preparing to undergo resective surgery. This study presents the first application of paradigms used in functional magnetic resonance (fMRI) and electrical cortical stimulation mapping (ESM) studies for shared functional cortical mapping in the context of RTFM. Results from the 3 modalities are compared. A left-handed 13-year-old male with intractable epilepsy participated in functional mapping for localization of eloquent language cortex with fMRI, ESM, and RTFM. For RTFM, data were acquired over the frontal and temporal cortex. Several paradigms were sequentially presented: passive (listening to stories) and active (picture naming and verb generation). For verb generation and story processing, fMRI showed atypical right lateralizing language activation within temporal lobe regions of interest and bilateral frontal activation with slight right lateralization. Left hemisphere ESM demonstrated no eloquent language areas. RTFM procedures using story processing and picture naming elicited activity in the right lateral and basal temporal regions. Verb generation elicited strong right lateral temporal lobe activation, as well as left frontal lobe activation. RTFM results confirmed atypical language lateralization evident from fMRI and ESM. We demonstrated the feasibility and usefulness of a new RTFM stimulation paradigm during presurgical evaluation. Block design paradigms used in fMRI may be optimal for this purpose. Further development is needed to create age-appropriate RTFM test batteries.},
}
@article {pmid24292005,
year = {2013},
author = {Yoshimine, T and Yanagisawa, T and Sawada, J and Hazama, T and Mochizuki, H and Hirata, M},
title = {[Communication with ALS patients: neurosurgical approach].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {1405-1407},
doi = {10.5692/clinicalneurol.53.1405},
pmid = {24292005},
issn = {1882-0654},
mesh = {Amyotrophic Lateral Sclerosis/*psychology/*rehabilitation ; *Brain-Computer Interfaces ; *Communication ; *Communication Devices for People with Disabilities ; Electrodes, Implanted ; Electroencephalography ; Equipment Design ; Humans ; Neurosurgery/*instrumentation/*methods ; Thinking/*physiology ; },
abstract = {By progression of the disease, motor neurons degenerate in patients with amyotrophic lateral sclerosis (ALS) eventually lose nearly all voluntary muscles in the body. They are awake and aware but cannot move or communicate (locked-in state). Since the function of the brain is preserved, one possible measure to support their communication is to interpret their motor intention by decoding (deciphering) brain signals and present it with external devices. This technology called "brain-machine interface (BMI)" is now close to clinical use in Japan and USA.In our system, we record electrocorticogram (ECoG) obtained with subudural electrodes during their motor imagery, decode it and determine the movement they intended. So far, one patient of ALS with severe paralysis, implanted with this electrodes, successfully operated the PC communication tool only by thinking.},
}
@article {pmid24292004,
year = {2013},
author = {Hasegawa, RP},
title = {[Development of a cognitive BMI "neurocommunicator" as a communication aid of patients with severe motor deficits].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {1402-1404},
doi = {10.5692/clinicalneurol.53.1402},
pmid = {24292004},
issn = {1882-0654},
mesh = {Amyotrophic Lateral Sclerosis/*psychology/*rehabilitation ; *Brain-Computer Interfaces/trends ; *Communication ; *Communication Devices for People with Disabilities/trends ; Computer Systems ; Electroencephalography ; *Equipment Design/trends ; Humans ; Severity of Illness Index ; },
abstract = {A cognitive brain-machine interface (BMI), "neurocommunicator" has been developed by the author's research group in AIST in order to support communication of patients with severer motor deficits. The system can identify candidate messages (pictograms) in real time from electroencephalography (EEG) data, combining three core technologies; 1) a portable/wireless EEG recorder; 2) a high-speed and high-accuracy decoding algorithm; and 3) a hierarchical message generation system. The accuracy of the model at single predictions of the target was generally over 95%, corresponding to about 32 bits per minute for normal subjects. Monitor experiments have been also started for patients at their home, in which further technical improvements are required.},
}
@article {pmid24292003,
year = {2013},
author = {Oyanagi, K and Mochizuki, Y and Nakayama, Y and Hayashi, K and Shimizu, T and Nagao, M and Hashimoto, T and Yamazaki, M and Matsubara, S and Komori, T},
title = {[Amyotrophic lateral sclerosis in totally locked-in state].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {1399-1401},
doi = {10.5692/clinicalneurol.53.1399},
pmid = {24292003},
issn = {1882-0654},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*pathology/physiopathology/psychology/*rehabilitation ; Brain/pathology/physiopathology ; *Brain-Computer Interfaces ; *Communication ; *Communication Devices for People with Disabilities ; Female ; Humans ; Male ; Middle Aged ; Motor Neurons/pathology ; },
abstract = {Seven autopsy patients with amyotrophic lateral sclerosis (ALS) in totally locked-in state (TLS) were examined neuropathologically. The patients were composed of 4 males and 3 females, and 3 with familial, 1 sporadic but with mutation in SOD1 gene, and 3 sporadic patients with unremarkable gene mutation. The brains weighed 715, 783, 1,019, 1,050, 1,170, 1,190 or 1,233 g. The tegmentum of the brain stem was markedly degenerated in every patient, and the tracts relating to the somatic sensory and auditory were involved in the lesions.},
}
@article {pmid24292002,
year = {2013},
author = {Nakayama, Y and Shimizu, T and Hayashi, K and Mochizuki, Y and Nagao, M and Oyanagi, K},
title = {[Predictors the progression of communication impairment in ALS tracheostomy ventilator users].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {1396-1398},
doi = {10.5692/clinicalneurol.53.1396},
pmid = {24292002},
issn = {1882-0654},
mesh = {Adolescent ; Adult ; Aged ; Amyotrophic Lateral Sclerosis/complications/*rehabilitation ; Communication Disorders/*etiology ; Disease Progression ; Female ; Forecasting ; Humans ; Male ; Middle Aged ; Oculomotor Nerve Diseases/etiology ; Prognosis ; Severity of Illness Index ; Time Factors ; *Tracheostomy ; Ventilators, Mechanical/*adverse effects ; Young Adult ; },
abstract = {We investigated predictive factors associatied with progression of communication impairment in 76 patients with amyotrophic lateral sclerosis (ALS) using tracheostomy ventilation. We classified the patients into the following three groups: patients capable of communication (stage I), patients with difficulties in communication (stage II to IV), and patients incapable of communication (stage V: so-called totally locked-in state) (Hayashi, et al. Clin Neurol 2013). There were no significant difference of disease duration across stages. Statistically significant differences were noted in the time of ventilator use, the time of tube feeding, and the time of complete quadriplegia among the 3 groups (Kruskal-Wallis). Multivariate analyses showed that the durations from onset to the time of ventilator use and complete quadriplegia had significant effecte on the progression from stage I to II, and that the duration from onset to the development of overt oculomotor limitation had signicant effect on the progression from stage IV to V. Faster progression may predict the extent of communication impairment after ventilator use.Accurate prediction of communication impairment after ventilator use may promote medical and social preparation including early application of the brain-machine interface for future communication problems in ALS patients.},
}
@article {pmid24292001,
year = {2013},
author = {Nagao, M},
title = {[Clinical feature of ALS with communication disturbance; the possibility to communicate in TLS].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {1393-1395},
doi = {10.5692/clinicalneurol.53.1393},
pmid = {24292001},
issn = {1882-0654},
mesh = {Amyotrophic Lateral Sclerosis/complications/diagnosis/*psychology/*rehabilitation ; Brain/pathology ; *Brain-Computer Interfaces/trends ; *Communication ; *Communication Devices for People with Disabilities/trends ; Communication Disorders/*etiology/physiopathology/*rehabilitation ; Electroencephalography ; Humans ; Magnetic Resonance Imaging ; Tomography, Emission-Computed, Single-Photon ; Visual Pathways/*physiology ; },
abstract = {In the subsets of amyotrohic lateral sclerosis (ALS), totally-locked in state (TLS) is shown as the result of marked progression of motor neuron degeneration. In TLS, patients are impossible to move any voluntary muscles. As the result, patients with TLS cannot communicate with any augmentative and alternative communication devices(AACD) at present. To find the AACD that enables for TLS to communicate, we examined the clinical character, brain MRI, SPECT and evoked potentials in TLS. Brain MRI showed marked brain atrophy including the brainstem, but the occipital lobe was spared. SPECT and visual evoked potentials (VEP) showed preserved physiological function of the occipital lobe in TLS. The results suggest that neuronal degeneration in TLS is not restricted to motor system, but that the visual pathways are spared. Patients with TLS may be possible to use AACD that utilize the visual pathway.},
}
@article {pmid24291847,
year = {2013},
author = {Yoshimine, T and Yanagisawa, T and Hirata, M},
title = {[Brain-machine interface (BMI) - application to neurological disorders].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {53},
number = {11},
pages = {962-965},
doi = {10.5692/clinicalneurol.53.962},
pmid = {24291847},
issn = {1882-0654},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces/trends ; *Computer Systems ; Humans ; Nervous System Diseases/*rehabilitation ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Brain-machine interface (BMI) is a new technology to receive input from the brain which is translated to operate a computer or other external device in real time. After significant progress during the recent 10 years, this technology is now very close to the clinical use to restore neural functions of patients with severe neurologic impairment. This technology is also a strong tool to investigate the mode of neuro-signal processing in the brain and to understand the mechanism of neural dysfunction which leads to the development of novel neurotechnology for the treatment of various sorts of neurological disorders.},
}
@article {pmid24291237,
year = {2014},
author = {Reiner, M and Gelfeld, TM},
title = {Estimating mental workload through event-related fluctuations of pupil area during a task in a virtual world.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {93},
number = {1},
pages = {38-44},
doi = {10.1016/j.ijpsycho.2013.11.002},
pmid = {24291237},
issn = {1872-7697},
mesh = {Adult ; Evoked Potentials ; Eye Movements/physiology ; Female ; Humans ; Illusions/psychology ; Learning/physiology ; Male ; Psychomotor Performance/*physiology ; Pupil/*physiology ; User-Computer Interface ; Workload/*psychology ; Young Adult ; },
abstract = {Monitoring mental load for optimal performance has become increasingly central with the recently evolving need to cope with exponentially increasing amounts of data. This paper describes a non-intrusive, objective method to estimate mental workload in an immersive virtual reality system, through analysis of frequencies of pupil fluctuations. We tested changes in mental workload with a number of task-repetitions, level of predictability of the task and the effect of prior experience in predictable task performance, on mental workload of unpredictable task performance. Two measures were used to calculate mental workload: the ratio of Low Frequency to High Frequency components of pupil fluctuations, and the High Frequency alone, all extracted from the Power Spectrum Density of pupil fluctuations. Results show that mental workload decreases with a number of repetitions, creating a mode in which the brain acts as an automatic controller. Automaticity during training occurs only after a minimal number of repetitions, which once achieved, resulted in further improvements in the performance of unpredictable motor tasks, following training in a predictable task. These results indicate that automaticity is a central component in the transfer of skills from highly predictable to low predictable motor tasks. Our results suggest a potentially applicable method to brain-computer-interface systems that adapt to human mental workload, and provide intelligent automated support for enhanced performance.},
}
@article {pmid24290502,
year = {2014},
author = {Tangwiriyasakul, C and Verhagen, R and Rutten, WL and van Putten, MJ},
title = {Temporal evolution of event-related desynchronization in acute stroke: a pilot study.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {125},
number = {6},
pages = {1112-1120},
doi = {10.1016/j.clinph.2013.10.047},
pmid = {24290502},
issn = {1872-8952},
mesh = {Brain Mapping ; Calibration ; *Electroencephalography Phase Synchronization ; Evoked Potentials ; Female ; Follow-Up Studies ; Functional Laterality/physiology ; Hand/physiopathology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Movement/physiology ; Nerve Net/physiopathology ; Neural Networks, Computer ; Neuronal Plasticity ; Pilot Projects ; Postsynaptic Potential Summation ; Prognosis ; Recovery of Function/*physiology ; Stroke/diagnosis/*physiopathology ; *Stroke Rehabilitation ; Time Factors ; },
abstract = {OBJECTIVE: Assessment of event-related desynchronization (ERD) may assist in predicting recovery from stroke and rehabilitation, for instance in BCI applications. Here, we explore the temporal evolution of ERD during stroke recovery.<br><br>METHODS: Ten stroke patients and eleven healthy controls were recruited to participate in a hand movement task while EEG was being recorded. Four measurements were conducted in eight patients within four months. We quantified changes of ERD using a modulation strength measure, S(m), which represents an area and amplitude of ERD.<br><br>RESULTS: 7/8 patients showed good recovery. Absence-or-reduction of ipsilesional modulation was initially found in stroke patients but not in the healthy controls. In the patient group, two evolutions were found in 6/8 patients: a significant increase in ipsilesional S(m); and a decreasing trend in contralesional S(m). In the only non-recovery patient, absence of ipsilesional modulation was observed, while his contralesional S(m) increased with time after stroke.<br><br>CONCLUSION: The two evolutions presumably reflect the reorganization of brain networks and functional recovery after acute stroke. The significant increase of ipsilesional S(m) in patients with a good recovery suggests an important role of this hemisphere during recovery.<br><br>SIGNIFICANCE: Improved understanding of ERD in acute stroke may assist in prognostication and rehabilitation.},
}
@article {pmid24288270,
year = {2013},
author = {Giselbrecht, S and Rapp, BE and Niemeyer, CM},
title = {The chemistry of cyborgs--interfacing technical devices with organisms.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {52},
number = {52},
pages = {13942-13957},
doi = {10.1002/anie.201307495},
pmid = {24288270},
issn = {1521-3773},
mesh = {Biocompatible Materials ; Brain-Computer Interfaces ; Electric Stimulation Therapy/instrumentation/*methods ; Electronics ; Humans ; },
abstract = {The term "cyborg" refers to a cybernetic organism, which characterizes the chimera of a living organism and a machine. Owing to the widespread application of intracorporeal medical devices, cyborgs are no longer exclusively a subject of science fiction novels, but technically they already exist in our society. In this review, we briefly summarize the development of modern prosthetics and the evolution of brain-machine interfaces, and discuss the latest technical developments of implantable devices, in particular, biocompatible integrated electronics and microfluidics used for communication and control of living organisms. Recent examples of animal cyborgs and their relevance to fundamental and applied biomedical research and bioethics in this novel and exciting field at the crossroads of chemistry, biomedicine, and the engineering sciences are presented.},
}
@article {pmid24282545,
year = {2013},
author = {Höller, Y and Bergmann, J and Thomschewski, A and Kronbichler, M and Höller, P and Crone, JS and Schmid, EV and Butz, K and Nardone, R and Trinka, E},
title = {Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness.},
journal = {PloS one},
volume = {8},
number = {11},
pages = {e80479},
pmid = {24282545},
issn = {1932-6203},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Consciousness Disorders/*physiopathology ; Discriminant Analysis ; *Electroencephalography ; Humans ; Support Vector Machine ; },
abstract = {Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53-.94) and power spectra (mean = .69; range = .40-.85). The coherence patterns in healthy participants did not match the expectation of central modulated [Formula: see text]-rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p[Formula: see text]0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.},
}
@article {pmid24282396,
year = {2013},
author = {Riccio, A and Simione, L and Schettini, F and Pizzimenti, A and Inghilleri, M and Belardinelli, MO and Mattia, D and Cincotti, F},
title = {Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {732},
pmid = {24282396},
issn = {1662-5161},
abstract = {The purpose of this study was to investigate the support of attentional and memory processes in controlling a P300-based brain-computer interface (BCI) in people with amyotrophic lateral sclerosis (ALS). Eight people with ALS performed two behavioral tasks: (i) a rapid serial visual presentation (RSVP) task, screening the temporal filtering capacity and the speed of the update of the attentive filter, and (ii) a change detection task, screening the memory capacity and the spatial filtering capacity. The participants were also asked to perform a P300-based BCI spelling task. By using correlation and regression analyses, we found that only the temporal filtering capacity in the RSVP task was a predictor of both the P300-based BCI accuracy and of the amplitude of the P300 elicited performing the BCI task. We concluded that the ability to keep the attentional filter active during the selection of a target influences performance in BCI control.},
}
@article {pmid24281580,
year = {2013},
author = {Kung, TA and Bueno, RA and Alkhalefah, GK and Langhals, NB and Urbanchek, MG and Cederna, PS},
title = {Innovations in prosthetic interfaces for the upper extremity.},
journal = {Plastic and reconstructive surgery},
volume = {132},
number = {6},
pages = {1515-1523},
doi = {10.1097/PRS.0b013e3182a97e5f},
pmid = {24281580},
issn = {1529-4242},
mesh = {Afferent Pathways ; Amputation, Surgical ; Arm/*innervation/*surgery ; Artificial Limbs/*trends ; Brain-Computer Interfaces/*trends ; Efferent Pathways ; Humans ; Robotics/*trends ; },
abstract = {Advancements in modern robotic technology have led to the development of highly sophisticated upper extremity prosthetic limbs. High-fidelity volitional control of these devices is dependent on the critical interface between the patient and the mechanical prosthesis. Recent innovations in prosthetic interfaces have focused on several control strategies. Targeted muscle reinnervation is currently the most immediately applicable prosthetic control strategy and is particularly indicated in proximal upper extremity amputations. Investigation into various brain interfaces has allowed acquisition of neuroelectric signals directly or indirectly from the central nervous system for prosthetic control. Peripheral nerve interfaces permit signal transduction from both motor and sensory nerves with a higher degree of selectivity. This article reviews the current developments in each of these interface systems and discusses the potential of these approaches to facilitate motor control and sensory feedback in upper extremity neuroprosthetic devices.},
}
@article {pmid24280623,
year = {2013},
author = {Stoyanova, II and van Wezel, RJ and Rutten, WL},
title = {In vivo testing of a 3D bifurcating microchannel scaffold inducing separation of regenerating axon bundles in peripheral nerves.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066018},
doi = {10.1088/1741-2560/10/6/066018},
pmid = {24280623},
issn = {1741-2552},
mesh = {Animals ; Axons/*physiology ; Female ; *Microelectrodes ; Nerve Regeneration/*physiology ; Organ Culture Techniques ; Peripheral Nerves/growth &amp; development ; Rats ; Rats, Wistar ; Sciatic Nerve/*physiology ; *Tissue Scaffolds ; },
abstract = {Artificial nerve guidance channels enhance the regenerative effectiveness in an injured peripheral nerve but the existing design so far has been limited to basic straight tubes simply guiding the growth to bridge the gap. Hence, one of the goals in development of more effective neuroprostheses is to create bidirectional highly selective neuro-electronic interface between a prosthetic device and the severed nerve. A step towards improving selectivity for both recording and stimulation have been made with some recent in vitro studies which showed that three-dimensional (3D) bifurcating microchannels can separate neurites growing on a planar surface and bring them into contact with individual electrodes. Since the growing axons in vivo have the innate tendency to group in bundles surrounded by connective tissue, one of the big challenges in neuro-prosthetic interface design is how to overcome it. Therefore, we performed experiments with 3D bifurcating guidance scaffolds implanted in the sciatic nerve of rats to test if this new channel architecture could trigger separation pattern of ingrowth also in vivo. Our results showed that this new method enabled the re-growth of neurites into channels with gradually diminished width (80, 40 and 20 µm) and facilitated the separation of the axonal bundles with 91% success. It seems that the 3D bifurcating scaffold might contribute towards conveying detailed neural control and sensory feedback to users of prosthetic devices, and thus could improve the quality of their daily life.},
}
@article {pmid24280591,
year = {2013},
author = {Zhang, Y and Xu, P and Guo, D and Yao, D},
title = {Prediction of SSVEP-based BCI performance by the resting-state EEG network.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066017},
doi = {10.1088/1741-2560/10/6/066017},
pmid = {24280591},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*trends ; Electroencephalography/methods/*trends ; Evoked Potentials, Visual/*physiology ; Female ; Forecasting ; Humans ; Male ; Nerve Net/*physiology ; Photic Stimulation/*methods ; Rest/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: The prediction of brain-computer interface (BCI) performance is a significant topic in the BCI field. Some researches have demonstrated that resting-state data are promising candidates to achieve the goal. However, so far the relationships between the resting-state networks and the steady-state visual evoked potential (SSVEP)-based BCI have not been investigated. In this paper, we investigate the possible relationships between the SSVEP responses, the classification accuracy of five stimulus frequencies and the closed-eye resting-state network topology.<br><br>APPROACH: The resting-state functional connectivity networks of the corresponding five stimulus frequencies were created by coherence, and then three network topology measures--the mean functional connectivity, the clustering coefficient and the characteristic path length of each network--were calculated. In addition, canonical correlation analysis was used to perform frequency recognition with the SSVEP data.<br><br>MAIN RESULTS: Interestingly, we found that SSVEPs of each frequency were negatively correlated with the mean functional connectivity and clustering coefficient, but positively correlated with characteristic path length. Each of the averaged network topology measures across the frequencies showed the same relationship with the SSVEPs averaged across frequencies between the subjects. Furthermore, our results also demonstrated that the classification accuracy can be predicted by three averaged network measures and their combination can further improve the prediction performance.<br><br>SIGNIFICANCE: These findings indicate that the SSVEP responses and performance are predictable using the information at the resting-state, which may be instructive in both SSVEP-aided cognition studies and SSVEP-based BCI applications.},
}
@article {pmid24280103,
year = {2014},
author = {Liu, Y and Li, M and Zhang, H and Wang, H and Li, J and Jia, J and Wu, Y and Zhang, L},
title = {A tensor-based scheme for stroke patients' motor imagery EEG analysis in BCI-FES rehabilitation training.},
journal = {Journal of neuroscience methods},
volume = {222},
number = {},
pages = {238-249},
doi = {10.1016/j.jneumeth.2013.11.009},
pmid = {24280103},
issn = {1872-678X},
mesh = {Aged ; Algorithms ; Brain/*physiopathology ; Computer Simulation ; Electroencephalography/*methods ; Female ; Humans ; *Imagination ; Male ; *Motor Activity ; Motor Cortex/physiopathology ; Psychomotor Performance ; *Signal Processing, Computer-Assisted ; Stroke/physiopathology ; *Stroke Rehabilitation ; Support Vector Machine ; Time Factors ; Treatment Outcome ; Virtual Reality Exposure Therapy ; },
abstract = {BACKGROUND: Stroke is one of the most common disorders among the elderly. A practical problem in stroke rehabilitation systems is that how to separate motor imagery patterns from electroencephalographic (EEG) recordings. There is a sharp decline in performance of these systems when classical algorithms, such as Common Spatial Pattern (CSP), are directly applied on stroke patients.<br><br>NEW METHOD: We propose a tensor-based scheme to detect motor imagery EEG patterns in spatial-spectral-temporal domain directly from multidimensional EEG constructed by wavelet transform method. Discriminative motor imagery EEG patterns are obtained by Fisher score strategy. Furthermore, the most contributed channel groups and frequency bands are selected from these patterns and utilized as prior knowledge for the following motor imagery tasks.<br><br>RESULTS: We evaluate our scheme based on EEG datasets recorded from stroke patients. The results show that our method outperforms five other traditional methods in both online and offline recognition performance.<br><br>Unlike the existing methods, motor imagery EEG patterns in spatial-spectral-temporal domain are simultaneously obtained by our method, preserving the structural information of the multi-channel time-varying EEG.<br><br>CONCLUSIONS: Our scheme is encouraged to be transferred to some other practical rehabilitation applications for its better performance.},
}
@article {pmid24278339,
year = {2013},
author = {Ahn, M and Cho, H and Ahn, S and Jun, SC},
title = {High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery.},
journal = {PloS one},
volume = {8},
number = {11},
pages = {e80886},
pmid = {24278339},
issn = {1932-6203},
mesh = {Adult ; Alpha Rhythm/*physiology ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Educational Status ; Female ; Humans ; *Knowledge ; Male ; Rest/physiology ; Theta Rhythm/*physiology ; },
abstract = {In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called 'BCI-illiterate'. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r(2) = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r(2) = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems.},
}
@article {pmid24275085,
year = {2014},
author = {Tan, LF and Dienes, Z and Jansari, A and Goh, SY},
title = {Effect of mindfulness meditation on brain-computer interface performance.},
journal = {Consciousness and cognition},
volume = {23},
number = {},
pages = {12-21},
doi = {10.1016/j.concog.2013.10.010},
pmid = {24275085},
issn = {1090-2376},
mesh = {Adolescent ; Adult ; Analysis of Variance ; Brain-Computer Interfaces/*psychology ; China ; Electroencephalography/methods ; Female ; Humans ; Male ; Meditation/*psychology ; Mindfulness/*methods ; Music/psychology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Students/psychology ; Surveys and Questionnaires ; Young Adult ; },
abstract = {Electroencephalogram based brain-computer interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and no-treatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects.},
}
@article {pmid24274109,
year = {2013},
author = {Wang, Y and Veluvolu, KC and Lee, M},
title = {Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {109},
pmid = {24274109},
issn = {1743-0003},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Fourier Analysis ; Humans ; *Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {BACKGROUND: Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications.<br><br>METHODS: To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal.<br><br>RESULTS: The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods.<br><br>CONCLUSIONS: Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.},
}
@article {pmid24273623,
year = {2013},
author = {Nijboer, F and Clausen, J and Allison, BZ and Haselager, P},
title = {The Asilomar Survey: Stakeholders' Opinions on Ethical Issues Related to Brain-Computer Interfacing.},
journal = {Neuroethics},
volume = {6},
number = {3},
pages = {541-578},
pmid = {24273623},
issn = {1874-5490},
abstract = {Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4[th] International BCI conference, which took place in May-June 2010 in Asilomar, California. We assessed respondents' opinions about a number of topics. First, we investigated preferences for terminology and definitions relating to BCIs. Second, we assessed respondents' expectations on the marketability of different BCI applications (BCIs for healthy people, BCIs for assistive technology, BCIs-controlled neuroprostheses and BCIs as therapy tools). Third, we investigated opinions about ethical issues related to BCI research for the development of assistive technology: informed consent process with locked-in patients, risk-benefit analyses, team responsibility, consequences of BCI on patients' and families' lives, liability and personal identity and interaction with the media. Finally, we asked respondents which issues are urgent in BCI research.},
}
@article {pmid24273098,
year = {2014},
author = {Milton, K and Giacalone, J},
title = {Differential effects of unusual climatic stress on capuchin (Cebus capucinus) and howler monkey (Alouatta palliata) populations on Barro Colorado Island, Panama.},
journal = {American journal of primatology},
volume = {76},
number = {3},
pages = {249-261},
doi = {10.1002/ajp.22229},
pmid = {24273098},
issn = {1098-2345},
mesh = {Alouatta/*physiology ; Animals ; Arthropods ; Cebus/*physiology ; *Climate ; Diet/veterinary ; Dietary Proteins ; Food Supply ; Fruit ; Monkey Diseases/etiology/mortality ; Mortality ; Panama ; Population Dynamics ; Protein Deficiency/mortality/*veterinary ; *Rain ; Seasons ; Stress, Physiological/*physiology ; },
abstract = {Though the harmful effects anthropogenic disturbances pose to wild primates are well appreciated, comparatively little is known about the effects of natural disturbances. From December 2010 to January 2011, different mortality patterns were observed for two primate species, capuchins and howler monkeys, on Barro Colorado Island (BCI), Panama. Unusually high rainfall in 2010 was associated with census and cadaver data indicating the rapid loss of >70% of the capuchin population in late 2010 to early 2011. In contrast, over this same period, no decline was documented for howler monkeys and cadaver data for howlers was unexceptional. The high mortality experienced by the capuchin population was unexpected and its extent was not fully appreciated until the event was largely over. Explanations proposed for it included effects of hypothermia, disease or a shortage of some essential nutrient(s). Of these, the dietary explanation seems most probable. BCI capuchins depend most heavily on arthropod foods in December, when few higher quality ripe fruits are available. The unprecedented high rainfall in December 2010 is hypothesized to have largely eliminated the arthropod peak expected on BCI each December. A lack of protein-rich arthropods, when coupled with the climatic and nutritional stress capuchins generally experience at this time of year, appears to have precipitated the rapid die-off of most of the island's capuchin population. As howler monkeys obtain dietary protein primarily from leaves, a shortage of edible arthropods would not affect howler numbers. Comparison of our 2010 data with similar data on earlier primate/mammalian mortality events reported for BCI and for Corcovado, Costa Rica indicates that our understanding of the effects of natural disturbances on wild primate populations is not profound. We suggest that more research be devoted to this increasingly timely topic, so important to conservation policy.},
}
@article {pmid24269175,
year = {2014},
author = {Udoekwere, UI and Oza, CS and Giszter, SF},
title = {A pelvic implant orthosis in rodents, for spinal cord injury rehabilitation, and for brain machine interface research: construction, surgical implantation and validation.},
journal = {Journal of neuroscience methods},
volume = {222},
number = {},
pages = {199-206},
pmid = {24269175},
issn = {1872-678X},
support = {R01 NS054894/NS/NINDS NIH HHS/United States ; R01 NS072651/NS/NINDS NIH HHS/United States ; 072651/WT_/Wellcome Trust/United Kingdom ; NS054894/NS/NINDS NIH HHS/United States ; },
mesh = {Analysis of Variance ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Female ; Hindlimb/physiopathology ; Hip Joint/physiopathology ; *Implants, Experimental/adverse effects ; Joints/physiopathology ; Locomotion/physiology ; Orthopedic Procedures ; *Orthotic Devices/adverse effects ; *Pelvis/pathology/surgery ; Rats ; Rats, Sprague-Dawley ; Robotics ; Spinal Cord Injuries/physiopathology/*rehabilitation ; },
abstract = {BACKGROUND: Rodents are important model systems used to explore spinal cord injury (SCI) and rehabilitation, and brain machine interfaces (BMI). We present a new method to provide mechanical interaction for BMI and rehabilitation in rat models of SCI.<br><br>NEW METHOD: We present the design and implantation procedures for a pelvic orthosis that allows direct force application to the skeleton in brain machine interface and robot rehabilitation applications in rodents. We detail the materials, construction, machining, surgery and validation of the device.<br><br>RESULTS: We describe the statistical validation of the implant procedures by comparing stepping parameters of 8 rats prior to and after implantation and surgical recovery. An ANOVA showed no effects of the implantation on stepping. Paired tests in the individual rats also showed no effect in 7/8 rats and minor effects in the last rat, within the group's variance.<br><br>Our method allows interaction with rats at the pelvis without any perturbation of normal stepping in the intact rat. The method bypasses slings, and cuffs, avoiding cuff or slings squeezing the abdomen, or other altered sensory feedback. Our implant osseointegrates, and thus allows an efficient high bandwidth mechanical coupling to a robot. The implants support quadrupedal training and are readily integrated into either treadmill or overground contexts.<br><br>CONCLUSIONS: Our novel device and procedures support a range of novel experimental designs and motor tests for rehabilitative and augmentation devices in intact and SCI model rats, with the advantage of allowing direct force application at the pelvic bones.},
}
@article {pmid24260218,
year = {2013},
author = {Lee, TS and Goh, SJ and Quek, SY and Phillips, R and Guan, C and Cheung, YB and Feng, L and Teng, SS and Wang, CC and Chin, ZY and Zhang, H and Ng, TP and Lee, J and Keefe, R and Krishnan, KR},
title = {A brain-computer interface based cognitive training system for healthy elderly: a randomized control pilot study for usability and preliminary efficacy.},
journal = {PloS one},
volume = {8},
number = {11},
pages = {e79419},
pmid = {24260218},
issn = {1932-6203},
mesh = {Aged ; *Brain-Computer Interfaces ; Cognition/*physiology ; Female ; Humans ; Male ; Middle Aged ; Pilot Projects ; Surveys and Questionnaires ; },
abstract = {UNLABELLED: Cognitive decline in aging is a pressing issue associated with significant healthcare costs and deterioration in quality of life. Previously, we reported the successful use of a novel brain-computer interface (BCI) training system in improving symptoms of attention deficit hyperactivity disorder. Here, we examine the feasibility of the BCI system with a new game that incorporates memory training in improving memory and attention in a pilot sample of healthy elderly. This study investigates the safety, usability and acceptability of our BCI system to elderly, and obtains an efficacy estimate to warrant a phase III trial. Thirty-one healthy elderly were randomized into intervention (n = 15) and waitlist control arms (n = 16). Intervention consisted of an 8-week training comprising 24 half-hour sessions. A usability and acceptability questionnaire was administered at the end of training. Safety was investigated by querying users about adverse events after every session. Efficacy of the system was measured by the change of total score from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) before and after training. Feedback on the usability and acceptability questionnaire was positive. No adverse events were reported for all participants across all sessions. Though the median difference in the RBANS change scores between arms was not statistically significant, an effect size of 0.6SD was obtained, which reflects potential clinical utility according to Simon's randomized phase II trial design. Pooled data from both arms also showed that the median change in total scores pre and post-training was statistically significant (Mdn = 4.0; p<0.001). Specifically, there were significant improvements in immediate memory (p = 0.038), visuospatial/constructional (p = 0.014), attention (p = 0.039), and delayed memory (p<0.001) scores. Our BCI-based system shows promise in improving memory and attention in healthy elderly, and appears to be safe, user-friendly and acceptable to senior users. Given the efficacy signal, a phase III trial is warranted.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT01661894.},
}
@article {pmid24259543,
year = {2014},
author = {Choi, K and Torres, EB},
title = {Intentional signal in prefrontal cortex generalizes across different sensory modalities.},
journal = {Journal of neurophysiology},
volume = {112},
number = {1},
pages = {61-80},
doi = {10.1152/jn.00505.2013},
pmid = {24259543},
issn = {1522-1598},
mesh = {Adult ; Brain Mapping/instrumentation/*methods ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Auditory ; Evoked Potentials, Visual ; *Feedback, Sensory ; Female ; Generalization, Psychological ; Humans ; Male ; Prefrontal Cortex/*physiology ; },
abstract = {Biofeedback-EEG training to learn the mental control of an external device (e.g., a cursor on the screen) has been an important paradigm to attempt to understand the involvements of various areas of the brain in the volitional control and the modulation of intentional thought processes. Often the areas to adapt and to monitor progress are selected a priori. Less explored, however, has been the notion of automatically emerging activation in a particular area or subregions within that area recruited above and beyond the rest of the brain. Likewise, the notion of evoking such a signal as an amodal, abstract one remaining robust across different sensory modalities could afford some exploration. Here we develop a simple binary control task in the context of brain-computer interface (BCI) and use a Bayesian sparse probit classification algorithm to automatically uncover brain regional activity that maximizes task performance. We trained and tested 19 participants using the visual modality for instructions and feedback. Across training blocks we quantified coupling of the frontoparietal nodes and selective involvement of visual and auditory regions as a function of the real-time sensory feedback. The testing phase under both forms of sensory feedback revealed automatic recruitment of the prefrontal cortex with a parcellation of higher strength levels in Brodmann's areas 9, 10, and 11 significantly above those in other brain areas. We propose that the prefrontal signal may be a neural correlate of externally driven intended direction and discuss our results in the context of various aspects involved in the cognitive control of our thoughts.},
}
@article {pmid24258529,
year = {2014},
author = {Naseer, N and Hong, MJ and Hong, KS},
title = {Online binary decision decoding using functional near-infrared spectroscopy for the development of brain-computer interface.},
journal = {Experimental brain research},
volume = {232},
number = {2},
pages = {555-564},
pmid = {24258529},
issn = {1432-1106},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Decision Making/*physiology ; Discriminant Analysis ; Female ; Hemoglobins/metabolism ; Humans ; Male ; *Online Systems ; Oxyhemoglobins/metabolism ; Spectroscopy, Near-Infrared ; Support Vector Machine ; Young Adult ; },
abstract = {In this paper, a functional near-infrared spectroscopy (fNIRS)-based online binary decision decoding framework is developed. Fourteen healthy subjects are asked to mentally make "yes" or "no" decisions in answers to the given questions. For obtaining "yes" decoding, the subjects are asked to perform a mental task that causes a cognitive load on the prefrontal cortex, while for making "no" decoding, they are asked to relax. Signals from the prefrontal cortex are collected using continuous-wave near-infrared spectroscopy. It is observed and verified, using the linear discriminant analysis (LDA) and the support vector machine (SVM) classifications, that the cortical hemodynamic responses for making a "yes" decision are distinguishable from those for making a "no" decision. Using mean values of the changes in the concentration of hemoglobin as features, binary decisions are classified into two classes, "yes" and "no," with an average classification accuracy of 74.28% using LDA and 82.14% using SVM. These results demonstrate and suggest the feasibility of fNIRS for a brain-computer interface.},
}
@article {pmid24240027,
year = {2014},
author = {Samek, W and Kawanabe, M and Müller, KR},
title = {Divergence-based framework for common spatial patterns algorithms.},
journal = {IEEE reviews in biomedical engineering},
volume = {7},
number = {},
pages = {50-72},
doi = {10.1109/RBME.2013.2290621},
pmid = {24240027},
issn = {1941-1189},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Controlling a device with a brain-computer interface requires extraction of relevant and robust features from high-dimensional electroencephalographic recordings. Spatial filtering is a crucial step in this feature extraction process. This paper reviews algorithms for spatial filter computation and introduces a general framework for this task based on divergence maximization. We show that the popular common spatial patterns (CSP) algorithm can be formulated as a divergence maximization problem and computed within our framework. Our approach easily permits enforcing different invariances and utilizing information from other subjects; thus, it unifies many of the recently proposed CSP variants in a principled manner. Furthermore, it allows to design novel spatial filtering algorithms by incorporating regularization schemes into the optimization process or applying other divergences. We evaluate the proposed approach using three regularization schemes, investigate the advantages of beta divergence, and show that subject-independent feature spaces can be extracted by jointly optimizing the divergence problems of multiple users. We discuss the relations to several CSP variants and investigate the advantages and limitations of our approach with simulations. Finally, we provide experimental results on a dataset containing recordings from 80 subjects and interpret the obtained patterns from a neurophysiological perspective.},
}
@article {pmid24240005,
year = {2014},
author = {Aghagolzadeh, M and Mohebi, A and Oweiss, KG},
title = {Sorting and tracking neuronal spikes via simple thresholding.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {4},
pages = {858-869},
doi = {10.1109/TNSRE.2013.2289918},
pmid = {24240005},
issn = {1558-0210},
support = {NS047516/NS/NINDS NIH HHS/United States ; NS062031/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Artificial Intelligence ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Models, Theoretical ; Pattern Recognition, Automated/methods ; Rats ; Reproducibility of Results ; Sensitivity and Specificity ; Sensory Receptor Cells/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {A fundamental goal in systems neuroscience is to assess the individual as well as the synergistic roles of single neurons in a recorded ensemble as they relate to an observed behavior. A mandatory step to achieve this goal is to sort spikes in an extracellularly recorded mixture that belong to individual neurons through feature extraction and clustering techniques. Here, we propose an approach for approximating the often nonlinear and time varying decision boundaries between spike-derived feature classes based on a simple, yet optimal thresholding mechanism. Because thresholding is a binary classifier, we show that the complex nonlinear decision boundaries required for spike class discrimination can be achieved by adequately fusing a set of weak binary classifiers. The thresholds for these binary classifiers are adaptively estimated through a learning algorithm that maximizes the separability between the sparsely represented classes. Based on our previous work, the approach substantially reduces the computational complexity of extracting, aligning and sorting multiple single unit activity early in the data stream. Here, we also show its ability to track changes in spike features over extended periods of time, making it highly suitable for basic neuroscience studies as well as for implementation in miniaturized, fully implantable electronics in brain-machine interface applications.},
}
@article {pmid24239590,
year = {2014},
author = {Haufe, S and Meinecke, F and Görgen, K and Dähne, S and Haynes, JD and Blankertz, B and Bießmann, F},
title = {On the interpretation of weight vectors of linear models in multivariate neuroimaging.},
journal = {NeuroImage},
volume = {87},
number = {},
pages = {96-110},
doi = {10.1016/j.neuroimage.2013.10.067},
pmid = {24239590},
issn = {1095-9572},
mesh = {*Algorithms ; Brain Mapping/*methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Linear Models ; *Models, Neurological ; Models, Theoretical ; Neuroimaging/*methods ; },
abstract = {The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses.},
}
@article {pmid24235291,
year = {2014},
author = {Yao, L and Meng, J and Zhang, D and Sheng, X and Zhu, X},
title = {Combining motor imagery with selective sensation toward a hybrid-modality BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {8},
pages = {2304-2312},
doi = {10.1109/TBME.2013.2287245},
pmid = {24235291},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Sensation/*physiology ; },
abstract = {A hybrid modality brain-computer interface (BCI) is proposed in this paper, which combines motor imagery with selective sensation to enhance the discrimination between left and right mental tasks, e.g., the classification between left/ right stimulation sensation and right/ left motor imagery. In this paradigm, wearable vibrotactile rings are used to stimulate both the skin on both wrists. Subjects are required to perform the mental tasks according to the randomly presented cues (i.e., left hand motor imagery, right hand motor imagery, left stimulation sensation or right stimulation sensation). Two-way ANOVA statistical analysis showed a significant group effect (F (2,20) = 7.17, p = 0.0045), and the Benferroni-corrected multiple comparison test (with α = 0.05) showed that the hybrid modality group is 11.13% higher on average than the motor imagery group, and 10.45% higher than the selective sensation group. The hybrid modality experiment exhibits potentially wider spread usage within ten subjects crossed 70% accuracy, followed by four subjects in motor imagery and five subjects in selective sensation. Six subjects showed statistically significant improvement (Benferroni-corrected) in hybrid modality in comparison with both motor imagery and selective sensation. Furthermore, among subjects having difficulties in both motor imagery and selective sensation, the hybrid modality improves their performance to 90% accuracy. The proposed hybrid modality BCI has demonstrated clear benefits for those poorly performing BCI users. Not only does the requirement of motor and sensory anticipation in this hybrid modality provide basic function of BCI for communication and control, it also has the potential for enhancing the rehabilitation during motor recovery.},
}
@article {pmid24235260,
year = {2013},
author = {Argyriou, V and Kotsia, I and Zafeiriou, S and Petrou, M},
title = {Guest editorial: Introduction to the special issue on modern control for computer games.},
journal = {IEEE transactions on cybernetics},
volume = {43},
number = {6},
pages = {1516-1518},
doi = {10.1109/TCYB.2013.2283551},
pmid = {24235260},
issn = {2168-2275},
mesh = {Biofeedback, Psychology/*methods/*physiology ; Bionics ; *Feedback ; *Game Theory ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; *Video Games ; },
abstract = {A typical gaming scenario, as developed in the past 20 years, involves a player interacting with a game using a specialized input device, such as a joystic, a mouse, a keyboard, etc. Recent technological advances and new sensors (for example, low cost commodity depth cameras) have enabled the introduction of more elaborated approaches in which the player is now able to interact with the game using his body pose, facial expressions, actions, and even his physiological signals. A new era of games has already started, employing computer vision techniques, brain-computer interfaces systems, haptic and wearable devices. The future lies in games that will be intelligent enough not only to extract the player's commands provided by his speech and gestures but also his behavioral cues, as well as his/her emotional states, and adjust their game plot accordingly in order to ensure more realistic and satisfactory gameplay experience. This special issue on modern control for computer games discusses several interdisciplinary factors that influence a user's input to a game, something directly linked to the gaming experience. These include, but are not limited to, the following: behavioral affective gaming, user satisfaction and perception, motion capture and scene modeling, and complete software frameworks that address several challenges risen in such scenarios.},
}
@article {pmid24235153,
year = {2013},
author = {Kamp, SM and Murphy, AR and Donchin, E},
title = {The component structure of event-related potentials in the p300 speller paradigm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {6},
pages = {897-907},
doi = {10.1109/TNSRE.2013.2285398},
pmid = {24235153},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Language ; Male ; Photic Stimulation/*methods ; *Task Performance and Analysis ; Word Processing/*methods ; Young Adult ; },
abstract = {We investigated the componential structure of event-related potentials elicited while participants use the P300 BCI. Six healthy participants "typed" all characters in a 6 × 6 matrix twice in a random sequence. A principal component analysis indicated that in addition to the P300, target flashes elicited an earlier frontal positivity, possibly a Novelty P3. The amplitudes of both P300 and the Novelty P3 varied with the matrix row in which the target character was located. However, the P300 elicited by row flashes was largest for targets in the lower part of the matrix, whereas the Novelty P3 elicited by column flashes was largest in the top part. Classification accuracy using stepwise linear discriminant analysis mirrored the pattern in the Novelty P3 (an accuracy difference of 0.1 between rows 1 and 6). When separate classifiers were generated to rely solely on the P300 or solely on the Novelty P3, the latter function led to higher accuracy (a mean accuracy difference of about 0.2 between classifiers). A possible explanation is that some nontarget flashes elicit a P300, leading to lower selection accuracy of the respective classifier. In an additional set of data from six different participants we replicated the ERP structure of the initial analyses and characterized the spatial distributions more closely by using a dense electrode array. Overall, our findings provide new insights in the componential structure of ERPs elicited in the P300 speller paradigm and have important implications for optimizing the speller's selection accuracy.},
}
@article {pmid24223567,
year = {2013},
author = {Silvoni, S and Cavinato, M and Volpato, C and Cisotto, G and Genna, C and Agostini, M and Turolla, A and Ramos-Murguialday, A and Piccione, F},
title = {Kinematic and neurophysiological consequences of an assisted-force-feedback brain-machine interface training: a case study.},
journal = {Frontiers in neurology},
volume = {4},
number = {},
pages = {173},
pmid = {24223567},
issn = {1664-2295},
abstract = {In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor-training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for 2 weeks coupling somatosensory brain oscillations with force-field control during a robot-mediated center-out motor-task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic, and motor-behavioral measures were recorded in pre- and post-assessments without force assistance. Patient's healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor-training coupling brain oscillations and force feedback during an actual movement.},
}
@article {pmid24223123,
year = {2013},
author = {Zhao, Y and Tang, L and Rennaker, R and Hutchens, C and Ibrahim, TS},
title = {Studies in RF power communication, SAR, and temperature elevation in wireless implantable neural interfaces.},
journal = {PloS one},
volume = {8},
number = {11},
pages = {e77759},
pmid = {24223123},
issn = {1932-6203},
support = {R01 NS062065/NS/NINDS NIH HHS/United States ; R01NS062065/NS/NINDS NIH HHS/United States ; },
mesh = {Absorption ; Algorithms ; Brain/physiopathology ; *Brain-Computer Interfaces ; Humans ; Models, Biological ; *Prostheses and Implants ; Radio Waves ; Temperature ; },
abstract = {Implantable neural interfaces are designed to provide a high spatial and temporal precision control signal implementing high degree of freedom real-time prosthetic systems. The development of a Radio Frequency (RF) wireless neural interface has the potential to expand the number of applications as well as extend the robustness and longevity compared to wired neural interfaces. However, it is well known that RF signal is absorbed by the body and can result in tissue heating. In this work, numerical studies with analytical validations are performed to provide an assessment of power, heating and specific absorption rate (SAR) associated with the wireless RF transmitting within the human head. The receiving antenna on the neural interface is designed with different geometries and modeled at a range of implanted depths within the brain in order to estimate the maximum receiving power without violating SAR and tissue temperature elevation safety regulations. Based on the size of the designed antenna, sets of frequencies between 1 GHz to 4 GHz have been investigated. As expected the simulations demonstrate that longer receiving antennas (dipole) and lower working frequencies result in greater power availability prior to violating SAR regulations. For a 15 mm dipole antenna operating at 1.24 GHz on the surface of the brain, 730 uW of power could be harvested at the Federal Communications Commission (FCC) SAR violation limit. At approximately 5 cm inside the head, this same antenna would receive 190 uW of power prior to violating SAR regulations. Finally, the 3-D bio-heat simulation results show that for all evaluated antennas and frequency combinations we reach FCC SAR limits well before 1 °C. It is clear that powering neural interfaces via RF is possible, but ultra-low power circuit designs combined with advanced simulation will be required to develop a functional antenna that meets all system requirements.},
}
@article {pmid24222678,
year = {2013},
author = {Nair, P},
title = {Brain-machine interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {46},
pages = {18343},
pmid = {24222678},
issn = {1091-6490},
mesh = {Brain-Computer Interfaces/*trends ; Electrodes, Implanted/*trends ; Humans ; Quadriplegia/*rehabilitation ; Robotics/methods/*trends ; },
}
@article {pmid24216627,
year = {2014},
author = {Fuhrmann Alpert, G and Manor, R and Spanier, AB and Deouell, LY and Geva, AB},
title = {Spatiotemporal representations of rapid visual target detection: a single-trial EEG classification algorithm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {8},
pages = {2290-2303},
doi = {10.1109/TBME.2013.2289898},
pmid = {24216627},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated ; Photic Stimulation ; Principal Component Analysis ; *Wavelet Analysis ; Young Adult ; },
abstract = {Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction. As a benchmark algorithm, we consider the hierarchical discriminant component Analysis (HDCA), introduced by Parra, et al. 2007. We also consider a modified version of the HDCA, namely the hierarchical discriminant principal component analysis algorithm (HDPCA). We compare single-trial classification accuracies of all the three algorithms, each applied to detect target images within a rapid serial visual presentation (RSVP, 10 Hz) of images from five different object categories, based on single-trial brain responses. We find a systematic superiority of our classification algorithm in the tested paradigm. Additionally, HDPCA significantly increases classification accuracies compared to the HDCA. Finally, we show that presenting several repetitions of the same image exemplars improve accuracy, and thus may be important in cases where high accuracy is crucial.},
}
@article {pmid24216311,
year = {2013},
author = {Barrese, JC and Rao, N and Paroo, K and Triebwasser, C and Vargas-Irwin, C and Franquemont, L and Donoghue, JP},
title = {Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066014},
pmid = {24216311},
issn = {1741-2552},
support = {R01 NS025074/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Cerebral Cortex/*physiology ; Electrodes, Implanted/*standards ; Equipment Failure Analysis/*instrumentation/methods/*standards ; Female ; Macaca mulatta ; Male ; Microelectrodes/standards ; *Silicon/chemistry ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) using chronically implanted intracortical microelectrode arrays (MEAs) have the potential to restore lost function to people with disabilities if they work reliably for years. Current sensors fail to provide reliably useful signals over extended periods of time for reasons that are not clear. This study reports a comprehensive retrospective analysis from a large set of implants of a single type of intracortical MEA in a single species, with a common set of measures in order to evaluate failure modes.<br><br>APPROACH: Since 1996, 78 silicon MEAs were implanted in 27 monkeys (Macaca mulatta). We used two approaches to find reasons for sensor failure. First, we classified the time course leading up to complete recording failure as acute (abrupt) or chronic (progressive). Second, we evaluated the quality of electrode recordings over time based on signal features and electrode impedance. Failure modes were divided into four categories: biological, material, mechanical, and unknown.<br><br>MAIN RESULTS: Recording duration ranged from 0 to 2104 days (5.75 years), with a mean of 387 days and a median of 182 days (n = 78). Sixty-two arrays failed completely with a mean time to failure of 332 days (median = 133 days) while nine array experiments were electively terminated for experimental reasons (mean = 486 days). Seven remained active at the close of this study (mean = 753 days). Most failures (56%) occurred within a year of implantation, with acute mechanical failures the most common class (48%), largely because of connector issues (83%). Among grossly observable biological failures (24%), a progressive meningeal reaction that separated the array from the parenchyma was most prevalent (14.5%). In the absence of acute interruptions, electrode recordings showed a slow progressive decline in spike amplitude, noise amplitude, and number of viable channels that predicts complete signal loss by about eight years. Impedance measurements showed systematic early increases, which did not appear to affect recording quality, followed by a slow decline over years. The combination of slowly falling impedance and signal quality in these arrays indicates that insulating material failure is the most significant factor.<br><br>SIGNIFICANCE: This is the first long-term failure mode analysis of an emerging BCI technology in a large series of non-human primates. The classification system introduced here may be used to standardize how neuroprosthetic failure modes are evaluated. The results demonstrate the potential for these arrays to record for many years, but achieving reliable sensors will require replacing connectors with implantable wireless systems, controlling the meningeal reaction, and improving insulation materials. These results will focus future research in order to create clinical neuroprosthetic sensors, as well as valuable research tools, that are able to safely provide reliable neural signals for over a decade.},
}
@article {pmid24213959,
year = {2014},
author = {Koenraadt, KL and Duysens, J and Rijken, H and van Nes, IJ and Keijsers, NL},
title = {Preserved foot motor cortex in patients with complete spinal cord injury: a functional near-infrared spectroscopic study.},
journal = {Neurorehabilitation and neural repair},
volume = {28},
number = {2},
pages = {179-187},
doi = {10.1177/1545968313508469},
pmid = {24213959},
issn = {1552-6844},
mesh = {Adult ; Brain-Computer Interfaces ; Foot/*physiopathology ; Humans ; Middle Aged ; Motor Cortex/*physiopathology ; Movement/physiology ; Spectroscopy, Near-Infrared ; Spinal Cord Injuries/*physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Since the brain is intact, persons with a spinal cord injury (SCI) might benefit from a brain-computer interface (BCI) to improve mobility by making use of functional near-infrared spectroscopy (fNIRS).<br><br>OBJECTIVE: We aimed to use fNIRS to detect contralateral primary motor cortex activity during attempted foot movements in participants with complete SCI.<br><br>METHODS: A 6-channel fNIRS, including 2 reference channels, measured relative concentration changes of oxy- (HbO) and deoxy-hemoglobin (HbR) in the contralateral motor cortex for the right foot. Seven subjects, studied within 18 months after injury, performed 12 trials of attempted right foot and real hand movements.<br><br>RESULTS: T tests revealed significant HbO and HbR responses of the left motor cortex for attempted foot movements, but not for right hand movements. A 2-way repeated-measures analysis of variance revealed a larger decrease in HbR for attempted foot movements compared to hand movements. Individual results show major interindividual differences in (number of) channels activated and the sensitive chromophore (HbR or HbO).<br><br>CONCLUSIONS: On group level, activity in the motor cortex of the foot can be measured with fNIRS in patients with complete SCI during attempted foot movements and might in principle be used in future BCI studies and applications.},
}
@article {pmid24212012,
year = {2014},
author = {Jie, X and Cao, R and Li, L},
title = {Emotion recognition based on the sample entropy of EEG.},
journal = {Bio-medical materials and engineering},
volume = {24},
number = {1},
pages = {1185-1192},
doi = {10.3233/BME-130919},
pmid = {24212012},
issn = {1878-3619},
mesh = {Algorithms ; Arousal ; Artifacts ; Brain-Computer Interfaces ; Databases, Factual ; *Electroencephalography ; *Emotions ; Entropy ; Humans ; Music ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {A sample entropy (SampEn)-based emotion recognition approach was presented. The SampEn results of notable EEG channels screened by K-S test were fed to the support vector machine (SVM)-weight classifier for training, after which it was applied to two emotion recognition tasks. One is to distinguish positive and negative emotion with high arousal and the other genitive emotion with different arousal status. Results showed that channels related to emotions were mostly located on the prefrontal region, i.e., F3, CP5, FP2, FZ, and FC2. And they were applied to form the input vectors of SVM-weight classifier. The accuracies of the present algorithm for the two tasks were 80.43% and 79.11%, respectively indicated by the leave-one-person-out validation procedure, demonstrating that the present algorithm had a reasonable generalization capability.},
}
@article {pmid24211817,
year = {2014},
author = {Florin, E and Bock, E and Baillet, S},
title = {Targeted reinforcement of neural oscillatory activity with real-time neuroimaging feedback.},
journal = {NeuroImage},
volume = {88},
number = {},
pages = {54-60},
doi = {10.1016/j.neuroimage.2013.10.028},
pmid = {24211817},
issn = {1095-9572},
mesh = {Adult ; Brain Waves/*physiology ; Cerebral Cortex/diagnostic imaging/*physiology ; Feedback, Sensory/*physiology ; Female ; Functional Neuroimaging/*methods ; Humans ; Magnetic Resonance Imaging ; Magnetoencephalography/*methods ; Male ; },
abstract = {Biofeedback and brain-computer interfacing using EEG has been receiving continuous and increasing interest. However, the limited spatial resolution of low-density scalp recordings is a roadblock to the unequivocal monitoring and targeting of neuroanatomical regions and physiological signaling. This latter aspect is pivotal to the actual efficiency of neurofeedback procedures, which are expected to engage the modulation of well-identified components of neural activity within and between predetermined brain regions. Our group has previously contributed to demonstrate the principles of real-time magnetoencephalography (MEG) source imaging. Here we show how the technique was further developed to provide healthy subjects with region-specific neurofeedback to modulate successfully predetermined components of their brain activity in targeted brain regions. Overall, our results positively indicate that neurofeedback based on time-resolved MEG imaging has the potential to become an innovative therapeutic approach in neurology and neuropsychiatry.},
}
@article {pmid24207064,
year = {2013},
author = {Gramstad, TO and Gjestad, R and Haver, B},
title = {Personality traits predict job stress, depression and anxiety among junior physicians.},
journal = {BMC medical education},
volume = {13},
number = {},
pages = {150},
pmid = {24207064},
issn = {1472-6920},
mesh = {Anxiety/*etiology/psychology ; Burnout, Professional/etiology/psychology ; Depression/*etiology/psychology ; Female ; Humans ; Male ; Medical Staff, Hospital/*psychology ; Occupational Diseases/etiology/psychology ; *Personality ; Personality Assessment ; Personality Inventory ; Psychiatric Status Rating Scales ; Sex Factors ; Stress, Psychological/*etiology/psychology ; Students, Medical/psychology ; Surveys and Questionnaires ; },
abstract = {BACKGROUND: High levels of stress and deteriorating mental health among medical students are commonly reported. In Bergen, Norway, we explored the impact of personality traits measured early in their curriculum on stress reactions and levels of depression and anxiety symptoms as junior physicians following graduation.<br><br>METHODS: Medical students (n = 201) from two classes participated in a study on personality traits and mental health early in the curriculum. A questionnaire measuring personality traits (Basic Character Inventory (BCI)) was used during their third undergraduate year. BCI assesses four personality traits: neuroticism, extroversion, conscientiousness and reality weakness. Questionnaires measuring mental health (Hospital Anxiety and Depression Scale (HADS) and Symptom Checklist 25 (SCL-25)), and stress (Perceived Medical School Stress (PMSS)) were used during their third and sixth undergraduate year. During postgraduate internship, Cooper's Job Stress Questionnaire (CJSQ) was used to measure perceived job stress, while mental health and stress reactions were reassessed using HADS and SCL-25.<br><br>RESULTS: Extroversion had the highest mean value (5.11) among the total group of participants, while reality weakness had the lowest (1.51). Neuroticism and reality weakness were related to high levels of perceived job stress (neuroticism r = .19, reality weakness r = .17) as well as higher levels of anxiety symptoms (neuroticism r = .23, reality weakness r = .33) and symptoms of depression (neuroticism r = .21, reality weakness r = .36) during internship. Neuroticism indirectly predicted stress reactions and levels of depression and anxiety symptoms. These relations were mediated by perceived job stress, while reality weakness predicted these mental health measures directly. Extroversion, on the other hand, protected against symptoms of depression (r = -.20). Furthermore, females reported higher levels of job stress than males (difference = 7.52).<br><br>CONCLUSIONS: Certain personality traits measured early in the course of medical school relates to mental health status as junior physicians during postgraduate internship training. This relation is mediated by high levels of perceived job stress.},
}
@article {pmid24206388,
year = {2014},
author = {Kawanabe, M and Samek, W and Müller, KR and Vidaurre, C},
title = {Robust common spatial filters with a max-min approach.},
journal = {Neural computation},
volume = {26},
number = {2},
pages = {349-376},
doi = {10.1162/NECO_a_00544},
pmid = {24206388},
issn = {1530-888X},
mesh = {Brain-Computer Interfaces/*standards ; Electroencephalography/methods/*standards ; Humans ; *Models, Neurological ; },
abstract = {Electroencephalographic signals are known to be nonstationary and easily affected by artifacts; therefore, their analysis requires methods that can deal with noise. In this work, we present a way to robustify the popular common spatial patterns (CSP) algorithm under a maxmin approach. In contrast to standard CSP that maximizes the variance ratio between two conditions based on a single estimate of the class covariance matrices, we propose to robustly compute spatial filters by maximizing the minimum variance ratio within a prefixed set of covariance matrices called the tolerance set. We show that this kind of maxmin optimization makes CSP robust to outliers and reduces its tendency to overfit. We also present a data-driven approach to construct a tolerance set that captures the variability of the covariance matrices over time and shows its ability to reduce the nonstationarity of the extracted features and significantly improve classification accuracy. We test the spatial filters derived with this approach and compare them to standard CSP and a state-of-the-art method on a real-world brain-computer interface (BCI) data set in which we expect substantial fluctuations caused by environmental differences. Finally we investigate the advantages and limitations of the maxmin approach with simulations.},
}
@article {pmid24204862,
year = {2013},
author = {Kuś, R and Duszyk, A and Milanowski, P and Łabęcki, M and Bierzyńska, M and Radzikowska, Z and Michalska, M and Zygierewicz, J and Suffczyński, P and Durka, PJ},
title = {On the quantification of SSVEP frequency responses in human EEG in realistic BCI conditions.},
journal = {PloS one},
volume = {8},
number = {10},
pages = {e77536},
pmid = {24204862},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; *User-Computer Interface ; Young Adult ; },
abstract = {This article concerns one of the most important problems of brain-computer interfaces (BCI) based on Steady State Visual Evoked Potentials (SSVEP), that is the selection of the a-priori most suitable frequencies for stimulation. Previous works related to this problem were done either with measuring systems that have little in common with actual BCI systems (e.g., single flashing LED) or were presented on a small number of subjects, or the tested frequency range did not cover a broad spectrum. Their results indicate a strong SSVEP response around 10 Hz, in the range 13-25 Hz, and at high frequencies in the band of 40-60 Hz. In the case of BCI interfaces, stimulation with frequencies from various ranges are used. The frequencies are often adapted for each user separately. The selection of these frequencies, however, was not yet justified in quantitative group-level study with proper statistical account for inter-subject variability. The aim of this study is to determine the SSVEP response curve, that is, the magnitude of the evoked signal as a function of frequency. The SSVEP response was induced in conditions as close as possible to the actual BCI system, using a wide range of frequencies (5-30 Hz, in step of 1 Hz). The data were obtained for 10 subjects. SSVEP curves for individual subjects and the population curve was determined. Statistical analysis were conducted both on the level of individual subjects and for the group. The main result of the study is the identification of the optimal range of frequencies, which is 12-18 Hz, for the registration of SSVEP phenomena. The applied criterion of optimality was: to find the largest contiguous range of frequencies yielding the strong and constant-level SSVEP response.},
}
@article {pmid24204705,
year = {2013},
author = {Yu, K and Wang, Y and Shen, K and Li, X},
title = {The synergy between complex channel-specific FIR filter and spatial filter for single-trial EEG classification.},
journal = {PloS one},
volume = {8},
number = {10},
pages = {e76923},
pmid = {24204705},
issn = {1932-6203},
mesh = {*Algorithms ; Brain/*physiology ; Cues ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Reproducibility of Results ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.},
}
@article {pmid24204597,
year = {2013},
author = {Halder, S and Ruf, CA and Furdea, A and Pasqualotto, E and De Massari, D and van der Heiden, L and Bogdan, M and Rosenstiel, W and Birbaumer, N and Kübler, A and Matuz, T},
title = {Prediction of P300 BCI aptitude in severe motor impairment.},
journal = {PloS one},
volume = {8},
number = {10},
pages = {e76148},
pmid = {24204597},
issn = {1932-6203},
mesh = {Acoustic Stimulation ; Adult ; Aged ; Amyotrophic Lateral Sclerosis/physiopathology ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; Motor Activity ; Muscular Dystrophy, Duchenne/physiopathology ; Photic Stimulation ; },
abstract = {Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r = -0.77) and of the N2 (r = -0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.},
}
@article {pmid24204342,
year = {2013},
author = {Orsborn, AL and Carmena, JM},
title = {Creating new functional circuits for action via brain-machine interfaces.},
journal = {Frontiers in computational neuroscience},
volume = {7},
number = {},
pages = {157},
pmid = {24204342},
issn = {1662-5188},
abstract = {Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.},
}
@article {pmid24204231,
year = {2013},
author = {Shanechi, MM and Chemali, JJ and Liberman, M and Solt, K and Brown, EN},
title = {A brain-machine interface for control of medically-induced coma.},
journal = {PLoS computational biology},
volume = {9},
number = {10},
pages = {e1003284},
pmid = {24204231},
issn = {1553-7358},
support = {DP1 OD003646/OD/NIH HHS/United States ; K08-GM094394/GM/NIGMS NIH HHS/United States ; R01 GM104948/GM/NIGMS NIH HHS/United States ; K08 GM094394/GM/NIGMS NIH HHS/United States ; DP1-OD003646/OD/NIH HHS/United States ; },
mesh = {Animals ; Bayes Theorem ; Brain-Computer Interfaces ; Coma/*chemically induced/*physiopathology ; Computer Simulation ; Electroencephalography/*classification/methods ; Feedback ; Hypnotics and Sedatives/*administration &amp; dosage/therapeutic use ; Propofol/*administration &amp; dosage/therapeutic use ; Rats ; Signal Processing, Computer-Assisted ; },
abstract = {Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.},
}
@article {pmid24198757,
year = {2013},
author = {Hammer, J and Fischer, J and Ruescher, J and Schulze-Bonhage, A and Aertsen, A and Ball, T},
title = {The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {200},
pmid = {24198757},
issn = {1662-4548},
abstract = {In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) "copy" of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.},
}
@article {pmid24198380,
year = {2013},
author = {Eyherabide, HG and Samengo, I},
title = {When and why noise correlations are important in neural decoding.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {33},
number = {45},
pages = {17921-17936},
pmid = {24198380},
issn = {1529-2401},
mesh = {Action Potentials/*physiology ; Algorithms ; Brain-Computer Interfaces ; Computer Simulation ; Humans ; *Models, Neurological ; Neurons/*physiology ; Synaptic Transmission/*physiology ; },
abstract = {Information may be encoded both in the individual activity of neurons and in the correlations between their activities. Understanding whether knowledge of noise correlations is required to decode all the encoded information is fundamental for constructing computational models, brain-machine interfaces, and neuroprosthetics. If correlations can be ignored with tolerable losses of information, the readout of neural signals is simplified dramatically. To that end, previous studies have constructed decoders assuming that neurons fire independently and then derived bounds for the information that is lost. However, here we show that previous bounds were not tight and overestimated the importance of noise correlations. In this study, we quantify the exact loss of information induced by ignoring noise correlations and show why previous estimations were not tight. Further, by studying the elementary parts of the decoding process, we determine when and why information is lost on a single-response basis. We introduce the minimum decoding error to assess the distinctive role of noise correlations under natural conditions. We conclude that all of the encoded information can be decoded without knowledge of noise correlations in many more situations than previously thought.},
}
@article {pmid24197735,
year = {2013},
author = {Ifft, PJ and Shokur, S and Li, Z and Lebedev, MA and Nicolelis, MA},
title = {A brain-machine interface enables bimanual arm movements in monkeys.},
journal = {Science translational medicine},
volume = {5},
number = {210},
pages = {210ra154},
pmid = {24197735},
issn = {1946-6242},
support = {F31 NS081931/NS/NINDS NIH HHS/United States ; DP1 MH099903/MH/NIMH NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; DP1MH099903/DP/NCCDPHP CDC HHS/United States ; R01NS073952/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/*physiology ; Behavior, Animal ; *Brain-Computer Interfaces ; Cerebral Cortex/cytology ; Electrodes, Implanted ; Female ; Haplorhini/*physiology ; Humans ; Male ; Movement/*physiology ; Neuronal Plasticity/physiology ; Neurons/physiology ; Task Performance and Analysis ; },
abstract = {Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.},
}
@article {pmid24197734,
year = {2013},
author = {Thakor, NV},
title = {Translating the brain-machine interface.},
journal = {Science translational medicine},
volume = {5},
number = {210},
pages = {210ps17},
doi = {10.1126/scitranslmed.3007303},
pmid = {24197734},
issn = {1946-6242},
mesh = {*Brain-Computer Interfaces/economics ; Clinical Trials as Topic ; Humans ; Neural Prostheses ; *Translational Research, Biomedical/economics ; },
abstract = {Brain-machine and brain-computer interface technologies hold great promise for use in the recovery of sensory and motor functions lost as a result of nervous-system injuries or limb amputations. This Perspective describes the current state of noninvasive and invasive technologies with a view to potential applications. The scientific and technological challenges and barriers to translation are critically analyzed for a variety of approaches.},
}
@article {pmid24196868,
year = {2014},
author = {Homer, ML and Perge, JA and Black, MJ and Harrison, MT and Cash, SS and Hochberg, LR},
title = {Adaptive offset correction for intracortical brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {2},
pages = {239-248},
pmid = {24196868},
issn = {1558-0210},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; N01HD10018/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Arm/innervation/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Cerebral Cortex/*physiology ; Computer Simulation ; Electrodes, Implanted ; Electroencephalography ; Hand/innervation/physiology ; Humans ; Likelihood Functions ; Motor Cortex/physiology ; Neural Pathways/physiology ; Quadriplegia/rehabilitation ; Robotics ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Intracortical brain-computer interfaces (iBCIs) decode intended movement from neural activity for the control of external devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can depart from those observed during calibration, sometimes hindering a user's ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of seconds) and requires no knowledge of the intended movement. The algorithm, called multiple offset correction algorithm (MOCA), was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors (10.6 ± 10.1% ; p < 0.05, pairwise t-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs.},
}
@article {pmid24187291,
year = {2013},
author = {Sulzer, J and Dueñas, J and Stämpili, P and Hepp-Reymond, MC and Kollias, S and Seifritz, E and Gassert, R},
title = {Delineating the whole brain BOLD response to passive movement kinematics.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2013},
number = {},
pages = {6650474},
doi = {10.1109/ICORR.2013.6650474},
pmid = {24187291},
issn = {1945-7901},
mesh = {Adult ; Biomechanical Phenomena/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Fingers/*physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Robotics/*instrumentation ; Young Adult ; },
abstract = {The field of brain-machine interfaces (BMIs) has made great advances in recent years, converting thought to movement, with some of the most successful implementations measuring directly from the motor cortex. However, the ability to record from additional regions of the brain could potentially improve flexibility and robustness of use. In addition, BMIs of the future will benefit from integrating kinesthesia into the control loop. Here, we examine whether changes in passively induced forefinger movement amplitude are represented in different regions than forefinger velocity via a MR compatible robotic manipulandum. Using functional magnetic resonance imaging (fMRI), five healthy participants were exposed to combinations of forefinger movement amplitude and velocity in a factorial design followed by an epoch-based analysis. We found that primary and secondary somatosensory regions were activated, as well as cingulate motor area, putamen and cerebellum, with greater activity from changes in velocity compared to changes in amplitude. This represents the first investigation into whole brain response to parametric changes in passive movement kinematics. In addition to informing BMIs, these results have implications towards neural correlates of robotic rehabilitation.},
}
@article {pmid24187246,
year = {2013},
author = {Lyons, KR and Joshi, SS},
title = {Paralyzed subject controls telepresence mobile robot using novel sEMG brain-computer interface: case study.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2013},
number = {},
pages = {6650428},
doi = {10.1109/ICORR.2013.6650428},
pmid = {24187246},
issn = {1945-7901},
support = {UL1 RR024146/RR/NCRR NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Electromyography ; Humans ; *Man-Machine Systems ; *Paralysis ; *Robotics ; User-Computer Interface ; },
abstract = {Here we demonstrate the use of a new singlesignal surface electromyography (sEMG) brain-computer interface (BCI) to control a mobile robot in a remote location. Previous work on this BCI has shown that users are able to perform cursor-to-target tasks in two-dimensional space using only a single sEMG signal by continuously modulating the signal power in two frequency bands. Using the cursor-to-target paradigm, targets are shown on the screen of a tablet computer so that the user can select them, commanding the robot to move in different directions for a fixed distance/angle. A Wifi-enabled camera transmits video from the robot's perspective, giving the user feedback about robot motion. Current results show a case study with a C3-C4 spinal cord injury (SCI) subject using a single auricularis posterior muscle site to navigate a simple obstacle course. Performance metrics for operation of the BCI as well as completion of the telerobotic command task are developed. It is anticipated that this noninvasive and mobile system will open communication opportunities for the severely paralyzed, possibly using only a single sensor.},
}
@article {pmid24187241,
year = {2013},
author = {Sarac, M and Koyas, E and Erdogan, A and Cetin, M and Patoglu, V},
title = {Brain Computer Interface based robotic rehabilitation with online modification of task speed.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2013},
number = {},
pages = {6650423},
doi = {10.1109/ICORR.2013.6650423},
pmid = {24187241},
issn = {1945-7901},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; *Rehabilitation ; *Robotics ; *Task Performance and Analysis ; },
abstract = {We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile--a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies.},
}
@article {pmid24182379,
year = {2013},
author = {Bruno, MA and Laureys, S and Demertzi, A},
title = {Coma and disorders of consciousness.},
journal = {Handbook of clinical neurology},
volume = {118},
number = {},
pages = {205-213},
doi = {10.1016/B978-0-444-53501-6.00017-2},
pmid = {24182379},
issn = {0072-9752},
mesh = {*Coma ; *Consciousness Disorders ; Humans ; Terminal Care/*ethics ; Withholding Treatment/*ethics ; },
abstract = {Patients in coma, vegetative state/unresponsive wakefulness syndrome, and in minimally conscious states pose medical, scientific, and ethical challenges. As patients with disorders of consciousness are by definition unable to communicate, the assessment of pain, quality of life, and end-of-life preferences in these conditions can only be approached by adopting a third-person perspective. Surveys of healthcare workers' attitudes towards pain and end of life in disorders of consciousness shed light on the background of clinical reality, where no standard medical-legal framework is widely accepted. On the other hand, patients with locked-in syndrome, who are severely paralyzed but fully conscious, can inform about subjective quality of life in serious disability and help us to understand better the underlying factors influencing happiness in disease. In the medico-legal arena, such ethical issues may be resolved by previously drafted advance directives and, when absent, by surrogate representation. Lately, functional medical imaging and electrophysiology provide alternative means to communicate with these challenging patients and will potentially mediate to extract responses of medical-ethical content. Eventually, the clinical translation of these advanced technologies in the medical routine is of paramount importance for the promotion of medical management of these challenging patients.},
}
@article {pmid24167623,
year = {2013},
author = {Speier, W and Arnold, C and Pouratian, N},
title = {Evaluating true BCI communication rate through mutual information and language models.},
journal = {PloS one},
volume = {8},
number = {10},
pages = {e78432},
pmid = {24167623},
issn = {1932-6203},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T15 LM007356/LM/NLM NIH HHS/United States ; T15-LM007356/LM/NLM NIH HHS/United States ; K23EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Humans ; *Models, Theoretical ; *Programming Languages ; *Quadriplegia ; },
abstract = {Brain-computer interface (BCI) systems are a promising means for restoring communication to patients suffering from "locked-in" syndrome. Research to improve system performance primarily focuses on means to overcome the low signal to noise ratio of electroencephalogric (EEG) recordings. However, the literature and methods are difficult to compare due to the array of evaluation metrics and assumptions underlying them, including that: 1) all characters are equally probable, 2) character selection is memoryless, and 3) errors occur completely at random. The standardization of evaluation metrics that more accurately reflect the amount of information contained in BCI language output is critical to make progress. We present a mutual information-based metric that incorporates prior information and a model of systematic errors. The parameters of a system used in one study were re-optimized, showing that the metric used in optimization significantly affects the parameter values chosen and the resulting system performance. The results of 11 BCI communication studies were then evaluated using different metrics, including those previously used in BCI literature and the newly advocated metric. Six studies' results varied based on the metric used for evaluation and the proposed metric produced results that differed from those originally published in two of the studies. Standardizing metrics to accurately reflect the rate of information transmission is critical to properly evaluate and compare BCI communication systems and advance the field in an unbiased manner.},
}
@article {pmid24165805,
year = {2014},
author = {Bhattacharyya, S and Sengupta, A and Chakraborti, T and Konar, A and Tibarewala, DN},
title = {Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {52},
number = {2},
pages = {131-139},
pmid = {24165805},
issn = {1741-0444},
mesh = {Algorithms ; *Artificial Intelligence ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; },
abstract = {Brain-computer interfacing (BCI) has been the most researched technology in neuroprosthesis in the last two decades. Feature extractors and classifiers play an important role in BCI research for the generation of suitable control signals to drive an assistive device. Due to the high dimensionality of feature vectors in practical BCI systems, implantation of efficient feature selection algorithms has been an integral area of research in the past decade. This article proposes an efficient feature selection technique, realized by means of an evolutionary algorithm, which attempts to overcome some of the shortcomings of several state-of-the-art approaches in this field. The outlined scheme produces a subset of salient features which improves the classification accuracy while maintaining a trade-off with the computational speed of the complete scheme. For this purpose, an efficient memetic algorithm has also been proposed for the optimization purpose. Extensive experimental validations have been conducted on two real-world datasets to establish the efficacy of our approach. We have compared our approach to existing algorithms and have established the superiority of our algorithm to the rest.},
}
@article {pmid24156669,
year = {2013},
author = {Hsu, WY},
title = {Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.},
journal = {International journal of neural systems},
volume = {23},
number = {6},
pages = {1350026},
doi = {10.1142/S0129065713500263},
pmid = {24156669},
issn = {1793-6462},
mesh = {*Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Imagination/physiology ; Quantum Theory ; *Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; Support Vector Machine ; },
abstract = {In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.},
}
@article {pmid24152422,
year = {2013},
author = {Khorshidtalab, A and Salami, MJ and Hamedi, M},
title = {Robust classification of motor imagery EEG signals using statistical time-domain features.},
journal = {Physiological measurement},
volume = {34},
number = {11},
pages = {1563-1579},
doi = {10.1088/0967-3334/34/11/1563},
pmid = {24152422},
issn = {1361-6579},
mesh = {Benchmarking ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Motor Activity/*physiology ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Time Factors ; },
abstract = {The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain-machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time-domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time-domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature-classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy.},
}
@article {pmid24151454,
year = {2013},
author = {Rana, M and Gupta, N and Dalboni Da Rocha, JL and Lee, S and Sitaram, R},
title = {A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {170},
pmid = {24151454},
issn = {1662-4548},
abstract = {There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.},
}
@article {pmid24146640,
year = {2013},
author = {Kleih, SC and Kübler, A},
title = {Empathy, motivation, and P300 BCI performance.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {642},
pmid = {24146640},
issn = {1662-5161},
abstract = {Motivation moderately influences brain-computer interface (BCI) performance in healthy subjects when monetary reward is used to manipulate extrinsic motivation. However, the motivation of severely paralyzed patients, who are potentially in need for BCI, could mainly be internal and thus, an intrinsic motivator may be more powerful. Also healthy subjects who participate in BCI studies could be internally motivated as they may wish to contribute to research and thus extrinsic motivation by monetary reward would be less important than the content of the study. In this respect, motivation could be defined as "motivation-to-help." The aim of this study was to investigate, whether subjects with high motivation for helping and who are highly empathic would perform better with a BCI controlled by event-related potentials (P300-BCI). We included N = 20 healthy young participants naïve to BCI and grouped them according to their motivation for participating in a BCI study in a low and highly motivated group. Motivation was further manipulated with interesting or boring presentations about BCI and the possibility to help patients. Motivation for helping did neither influence BCI performance nor the P300 amplitude. Post hoc, subjects were re-grouped according to their ability for perspective taking. We found significantly higher P300 amplitudes on parietal electrodes in participants with a low ability for perspective taking and therefore, lower empathy, as compared to participants with higher empathy. The lack of an effect of motivation on BCI performance contradicts previous findings and thus, requires further investigation. We speculate that subjects with higher empathy who are good perspective takers with regards to patients in potential need of BCI, may be more emotionally involved and therefore, less able to allocate attention on the BCI task at hand.},
}
@article {pmid24144668,
year = {2013},
author = {Kwon, KY and Sirowatka, B and Weber, A and Li, W},
title = {Opto- μECoG array: a hybrid neural interface with transparent μECoG electrode array and integrated LEDs for optogenetics.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {7},
number = {5},
pages = {593-600},
doi = {10.1109/TBCAS.2013.2282318},
pmid = {24144668},
issn = {1940-9990},
mesh = {Animals ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design/*instrumentation ; Neurons/metabolism/*physiology ; Optogenetics/*instrumentation ; Photic Stimulation/instrumentation ; Rats ; Rats, Sprague-Dawley ; Tin Compounds/metabolism ; Visual Cortex/metabolism/*physiology ; },
abstract = {Electrocorticogram (ECoG) recordings, taken from electrodes placed on the surface of the cortex, have been successfully implemented for control of brain machine interfaces (BMIs). Optogenetics, direct optical stimulation of neurons in brain tissue genetically modified to express channelrhodopsin-2 (ChR2), enables targeting of specific types of neurons with sub-millisecond temporal precision. In this work, we developed a BMI device, called an Opto- μECoG array, which combines ECoG recording and optogenetics-based stimulation to enable multichannel, bi-directional interactions with neurons. The Opto- μECoG array comprises two sub-arrays, each containing a 4 × 4 distribution of micro-epidural transparent electrodes (∼ 200 μm diameter) and embedded light-emitting diodes (LEDs) for optical neural stimulation on a 2.5 × 2.5 mm[2] footprint to match the bilateral hemispherical area of the visual cortex in a rat. The transparent electrodes were fabricated with indium tin oxide (ITO). Parylene-C served as the main structural and packaging material for flexibility and biocompatibility. Optical, electrical, and thermal characteristics of the fabricated device were investigated and in vivo experiments were performed to evaluate the efficacy of the device.},
}
@article {pmid24144637,
year = {2014},
author = {Kranczioch, C and Zich, C and Schierholz, I and Sterr, A},
title = {Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {91},
number = {1},
pages = {10-15},
doi = {10.1016/j.ijpsycho.2013.10.004},
pmid = {24144637},
issn = {1872-7697},
mesh = {*Electroencephalography ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy/*instrumentation/*methods ; *Mobile Applications ; Nervous System Diseases/physiopathology/*rehabilitation ; User-Computer Interface ; },
abstract = {Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.},
}
@article {pmid24144634,
year = {2014},
author = {De Vos, M and Debener, S},
title = {Mobile EEG: towards brain activity monitoring during natural action and cognition.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {91},
number = {1},
pages = {1-2},
doi = {10.1016/j.ijpsycho.2013.10.008},
pmid = {24144634},
issn = {1872-7697},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; *Cell Phone/trends ; Cognition/*physiology ; Electroencephalography ; Humans ; Motor Activity/*physiology ; Neurophysiological Monitoring ; },
}
@article {pmid24140740,
year = {2013},
author = {Chen, X and Chen, Z and Gao, S and Gao, X},
title = {Brain-computer interface based on intermodulation frequency.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066009},
doi = {10.1088/1741-2560/10/6/066009},
pmid = {24140740},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Most recent steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have used a single frequency for each target, so that a large number of targets require a large number of stimulus frequencies and therefore a wider frequency band. However, human beings show good SSVEP responses only in a limited range of frequencies. Furthermore, this issue is especially problematic if the SSVEP-based BCI takes a PC monitor as a stimulator, which is only capable of generating a limited range of frequencies. To mitigate this issue, this study presents an innovative coding method for SSVEP-based BCI by means of intermodulation frequencies.<br><br>APPROACH: Simultaneous modulations of stimulus luminance and color at different frequencies were utilized to induce intermodulation frequencies. Luminance flickered at relatively large frequency (10, 12, 15 Hz), while color alternated at low frequency (0.5, 1 Hz). An attractive feature of the proposed method was that it would substantially increase the number of targets at a single flickering frequency by altering color modulated frequencies. Based on this method, the BCI system presented in this study realized eight targets merely using three flickering frequencies.<br><br>MAIN RESULTS: The online results obtained from 15 subjects (14 healthy and 1 with stroke) revealed that an average classification accuracy of 93.83% and information transfer rate (ITR) of 33.80 bit min(-1) were achieved using our proposed SSVEP-based BCI system. Specifically, 5 out of the 15 subjects exhibited an ITR of 40.00 bit min(-1) with a classification accuracy of 100%.<br><br>SIGNIFICANCE: These results suggested that intermodulation frequencies could be adopted as steady responses in BCI, for which our system could be used as a practical BCI system.},
}
@article {pmid24140680,
year = {2013},
author = {Bryan, MJ and Martin, SA and Cheung, W and Rao, RP},
title = {Probabilistic co-adaptive brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066008},
doi = {10.1088/1741-2560/10/6/066008},
pmid = {24140680},
issn = {1741-2552},
mesh = {Adaptation, Physiological/*physiology ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Markov Chains ; Photic Stimulation/methods ; Probability ; Reaction Time/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges.<br><br>APPROACH: We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions ('beliefs') over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP's reward function can be updated over time to allow for co-adaptive behaviour.<br><br>MAIN RESULTS: We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user's control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward.<br><br>SIGNIFICANCE: Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user's brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user's changing circumstances.},
}
@article {pmid24140267,
year = {2014},
author = {Koizumi, A and Nagata, O and Togawa, M and Sazi, T},
title = {The Muscle Sensor for on-site neuroscience lectures to pave the way for a better understanding of brain-machine-interface research.},
journal = {Neuroscience research},
volume = {78},
number = {},
pages = {95-99},
doi = {10.1016/j.neures.2013.10.003},
pmid = {24140267},
issn = {1872-8111},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electromyography/methods ; Humans ; Neurosciences/*education ; Teaching/*methods ; },
abstract = {Neuroscience is an expanding field of science to investigate enigmas of brain and human body function. However, the majority of the public have never had the chance to learn the basics of neuroscience and new knowledge from advanced neuroscience research through hands-on experience. Here, we report that we produced the Muscle Sensor, a simplified electromyography, to promote educational understanding in neuroscience. The Muscle Sensor can detect myoelectric potentials which are filtered and processed as 3-V pulse signals to shine a light bulb and emit beep sounds. With this educational tool, we delivered "On-Site Neuroscience Lectures" in Japanese junior-high schools to facilitate hands-on experience of neuroscientific electrophysiology and to connect their text-book knowledge to advanced neuroscience researches. On-site neuroscience lectures with the Muscle Sensor pave the way for a better understanding of the basics of neuroscience and the latest topics such as how brain-machine-interface technology could help patients with disabilities such as spinal cord injuries.},
}
@article {pmid24136127,
year = {2013},
author = {Modo, M and Ambrosio, F and Friedlander, RM and Badylak, SF and Wechsler, LR},
title = {Bioengineering solutions for neural repair and recovery in stroke.},
journal = {Current opinion in neurology},
volume = {26},
number = {6},
pages = {626-631},
doi = {10.1097/WCO.0000000000000031},
pmid = {24136127},
issn = {1473-6551},
support = {G0900188//Medical Research Council/United Kingdom ; },
mesh = {Animals ; Bioengineering/*methods ; Humans ; Neurons/*physiology ; Recovery of Function/*physiology ; Regeneration/*physiology ; Stroke/*therapy ; },
abstract = {PURPOSE OF REVIEW: This review discusses emerging bioengineering opportunities for the treatment of stroke and their potential to build on current rehabilitation protocols.<br><br>RECENT FINDINGS: Bioengineering is a vast field that ranges from biomaterials to brain-computer interfaces. Biomaterials find application in the delivery of pharmacotherapies, as well as the emerging field of tissue engineering. For the treatment of stroke, these approaches have to be seen in the context of physical therapy in order to maximize functional outcomes. There is also an emergence of rehabilitation that engages engineering solutions, such as robot-assisted training, as well as brain-computer interfaces that can potentially assist in the case of paralysis.<br><br>SUMMARY: Stroke remains the main cause of adult disability with rehabilitation therapy being the focus for chronic impairments. Bioengineering is offering new opportunities to both support and synergize with currently available treatment options, and also promises to potentially dramatically improve available approaches.<br><br>VIDEO ABSTRACT AVAILABLE: See the Video Supplementary Digital Content 1 (http://links.lww.com/CONR/A21).},
}
@article {pmid24135130,
year = {2014},
author = {Borton, D and Bonizzato, M and Beauparlant, J and DiGiovanna, J and Moraud, EM and Wenger, N and Musienko, P and Minev, IR and Lacour, SP and Millán, Jdel R and Micera, S and Courtine, G},
title = {Corticospinal neuroprostheses to restore locomotion after spinal cord injury.},
journal = {Neuroscience research},
volume = {78},
number = {},
pages = {21-29},
doi = {10.1016/j.neures.2013.10.001},
pmid = {24135130},
issn = {1872-8111},
mesh = {Animals ; Brain/physiology ; Brain-Computer Interfaces ; Electric Stimulation Therapy/methods ; Humans ; Locomotion/*physiology ; *Neural Prostheses ; Neuronal Plasticity ; Pyramidal Tracts/*physiopathology ; Rats ; Recovery of Function/*physiology ; Spinal Cord Injuries/*rehabilitation ; Thoracic Vertebrae ; },
abstract = {In this conceptual review, we highlight our strategy for, and progress in the development of corticospinal neuroprostheses for restoring locomotor functions and promoting neural repair after thoracic spinal cord injury in experimental animal models. We specifically focus on recent developments in recording and stimulating neural interfaces, decoding algorithms, extraction of real-time feedback information, and closed-loop control systems. Each of these complex neurotechnologies plays a significant role for the design of corticospinal neuroprostheses. Even more challenging is the coordinated integration of such multifaceted technologies into effective and practical neuroprosthetic systems to improve movement execution, and augment neural plasticity after injury. In this review we address our progress in rodent animal models to explore the viability of a technology-intensive strategy for recovery and repair of the damaged nervous system. The technical, practical, and regulatory hurdles that lie ahead along the path toward clinical applications are enormous - and their resolution is uncertain at this stage. However, it is imperative that the discoveries and technological developments being made across the field of neuroprosthetics do not stay in the lab, but instead reach clinical fruition at the fastest pace possible.},
}
@article {pmid24130549,
year = {2013},
author = {Hu, J and Zheng, Y and Gao, J},
title = {Long-Range Temporal Correlations, Multifractality, and the Causal Relation between Neural Inputs and Movements.},
journal = {Frontiers in neurology},
volume = {4},
number = {},
pages = {158},
pmid = {24130549},
issn = {1664-2295},
abstract = {Understanding the causal relation between neural inputs and movements is very important for the success of brain-machine interfaces (BMIs). In this study, we analyze 104 neurons' firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly non-stationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a "re-setting" effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains' long-range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.},
}
@article {pmid24127595,
year = {2013},
author = {Tabot, GA and Dammann, JF and Berg, JA and Tenore, FV and Boback, JL and Vogelstein, RJ and Bensmaia, SJ},
title = {Restoring the sense of touch with a prosthetic hand through a brain interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {45},
pages = {18279-18284},
pmid = {24127595},
issn = {1091-6490},
support = {R01 NS018787/NS/NINDS NIH HHS/United States ; R01 NS082865/NS/NINDS NIH HHS/United States ; NS082865/NS/NINDS NIH HHS/United States ; R01 NS18787/NS/NINDS NIH HHS/United States ; },
mesh = {Afferent Pathways/physiology ; Animals ; *Artificial Limbs ; Biomimetics/methods ; Brain Mapping ; *Brain-Computer Interfaces ; Electric Stimulation ; *Feedback ; Hand/*physiology ; Humans ; Macaca mulatta ; Pressure ; Somatosensory Cortex/*physiology ; Time Factors ; Touch/*physiology ; },
abstract = {Our ability to manipulate objects dexterously relies fundamentally on sensory signals originating from the hand. To restore motor function with upper-limb neuroprostheses requires that somatosensory feedback be provided to the tetraplegic patient or amputee. Given the complexity of state-of-the-art prosthetic limbs and, thus, the huge state space they can traverse, it is desirable to minimize the need for the patient to learn associations between events impinging on the limb and arbitrary sensations. Accordingly, we have developed approaches to intuitively convey sensory information that is critical for object manipulation--information about contact location, pressure, and timing--through intracortical microstimulation of primary somatosensory cortex. In experiments with nonhuman primates, we show that we can elicit percepts that are projected to a localized patch of skin and that track the pressure exerted on the skin. In a real-time application, we demonstrate that animals can perform a tactile discrimination task equally well whether mechanical stimuli are delivered to their native fingers or to a prosthetic one. Finally, we propose that the timing of contact events can be signaled through phasic intracortical microstimulation at the onset and offset of object contact that mimics the ubiquitous on and off responses observed in primary somatosensory cortex to complement slowly varying pressure-related feedback. We anticipate that the proposed biomimetic feedback will considerably increase the dexterity and embodiment of upper-limb neuroprostheses and will constitute an important step in restoring touch to individuals who have lost it.},
}
@article {pmid24122610,
year = {2014},
author = {Meng, J and Sheng, X and Zhang, D and Zhu, X},
title = {Improved semisupervised adaptation for a small training dataset in the brain-computer interface.},
journal = {IEEE journal of biomedical and health informatics},
volume = {18},
number = {4},
pages = {1461-1472},
doi = {10.1109/JBHI.2013.2285232},
pmid = {24122610},
issn = {2168-2208},
mesh = {Algorithms ; *Artificial Intelligence ; Bayes Theorem ; *Brain-Computer Interfaces ; *Databases, Factual ; Discriminant Analysis ; Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e.g., a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data.},
}
@article {pmid24122565,
year = {2013},
author = {Zhang, Y and Zhou, G and Jin, J and Wang, M and Wang, X and Cichocki, A},
title = {L1-regularized Multiway canonical correlation analysis for SSVEP-based BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {6},
pages = {887-896},
doi = {10.1109/TNSRE.2013.2279680},
pmid = {24122565},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; Young Adult ; },
abstract = {Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.},
}
@article {pmid24122368,
year = {2014},
author = {Bleichner, MG and Jansma, JM and Sellmeijer, J and Raemaekers, M and Ramsey, NF},
title = {Give me a sign: decoding complex coordinated hand movements using high-field fMRI.},
journal = {Brain topography},
volume = {27},
number = {2},
pages = {248-257},
doi = {10.1007/s10548-013-0322-x},
pmid = {24122368},
issn = {1573-6792},
mesh = {Adult ; Brain Mapping ; Female ; *Gestures ; Hand/*physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Young Adult ; },
abstract = {Decoding movements from the human cortex has been a topic of great interest for controlling an artificial limb in non-human primates and severely paralyzed people. Here we investigate feasibility of decoding gestures from the sensorimotor cortex in humans, using 7 T fMRI. Twelve healthy volunteers performed four hand gestures from the American Sign Language Alphabet. These gestures were performed in a rapid event related design used to establish the classifier and a slow event-related design, used to test the classifier. Single trial patterns were classified using a pattern-correlation classifier. The four hand gestures could be classified with an average accuracy of 63 % (range 35–95 %), which was significantly above chance (25 %). The hand region was, as expected, the most active region, and the optimal volume for classification was on average about 200 voxels, although this varied considerably across individuals. Importantly, classification accuracy correlated significantly with consistency of gesture execution. The results of our study demonstrate that decoding gestures from the hand region of the sensorimotor cortex using 7 T fMRI can reach very high accuracy, provided that gestures are executed in a consistent manner. Our results further indicate that the neuronal representation of hand gestures is robust and highly reproducible. Given that the most active foci were located in the hand region, and that 7 T fMRI has been shown to agree with electrocorticography, our results suggest that this confined region could serve to decode sign language gestures for intracranial brain–computer interfacing using surface grids.},
}
@article {pmid24121224,
year = {2014},
author = {Kobayashi, T and Masuda, H and Kitsumoto, C and Haruta, M and Motoyama, M and Ohta, Y and Noda, T and Sasagawa, K and Tokuda, T and Shiosaka, S and Ohta, J},
title = {Functional brain fluorescence plurimetry in rat by implantable concatenated CMOS imaging system.},
journal = {Biosensors &amp; bioelectronics},
volume = {53},
number = {},
pages = {31-36},
doi = {10.1016/j.bios.2013.09.033},
pmid = {24121224},
issn = {1873-4235},
mesh = {Animals ; Biosensing Techniques ; Brain Mapping/*methods ; Fluorescent Dyes/chemistry ; Molecular Imaging ; Potentiometry/*methods ; Rats ; Somatosensory Cortex/anatomy &amp; histology/*physiology ; },
abstract = {Measurement of brain activity in multiple areas simultaneously by minimally invasive methods contributes to the study of neuroscience and development of brain machine interfaces. However, this requires compact wearable instruments that do not inhibit natural movements. Application of optical potentiometry with voltage-sensitive fluorescent dye using an implantable image sensor is also useful. However, the increasing number of leads required for the multiple wired sensors to measure larger domains inhibits natural behavior. For imaging broad areas by numerous sensors without excessive wiring, a web-like sensor that can wrap the brain was developed. Kaleidoscopic potentiometry is possible using the imaging system with concatenated sensors by changing the alignment of the sensors. This paper describes organization of the system, evaluation of the system by a fluorescence imaging, and finally, functional brain fluorescence plurimetry by the sensor. The recorded data in rat somatosensory cortex using the developed multiple-area imaging system compared well with electrophysiology results.},
}
@article {pmid24120558,
year = {2013},
author = {Tehovnik, EJ and Woods, LC and Slocum, WM},
title = {Transfer of information by BMI.},
journal = {Neuroscience},
volume = {255},
number = {},
pages = {134-146},
doi = {10.1016/j.neuroscience.2013.10.003},
pmid = {24120558},
issn = {1873-7544},
mesh = {Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Humans ; Movement/physiology ; Neurons/physiology ; },
abstract = {Brain machine interfaces (BMI) have become important in systems neuroscience with the goal to restore motor function in paralyzed patients. We assess the current ability of BMI devices to move objects. The topics discussed include: (1) the bits of information generated by a BMI signal, (2) the limitations of including more neurons for generating a BMI signal, (3) the superiority of a BMI signal using single cells versus electroencephalography, (4) plasticity and BMI, (5) the selection of a neural code for generating BMI, (6) the suppression of body movements during BMI, and (7) the role of vision in BMI. We conclude that further research on understanding how the brain generates movement is necessary before BMI can become a reasonable option for paralyzed patients.},
}
@article {pmid24119870,
year = {2013},
author = {Leeb, R and Perdikis, S and Tonin, L and Biasiucci, A and Tavella, M and Creatura, M and Molina, A and Al-Khodairy, A and Carlson, T and Millán, JD},
title = {Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled users.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {121-132},
doi = {10.1016/j.artmed.2013.08.004},
pmid = {24119870},
issn = {1873-2860},
mesh = {Adult ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Paralysis/*physiopathology ; },
abstract = {OBJECTIVES: Brain-computer interfaces (BCIs) are no longer only used by healthy participants under controlled conditions in laboratory environments, but also by patients and end-users, controlling applications in their homes or clinics, without the BCI experts around. But are the technology and the field mature enough for this? Especially the successful operation of applications - like text entry systems or assistive mobility devices such as tele-presence robots - requires a good level of BCI control. How much training is needed to achieve such a level? Is it possible to train naïve end-users in 10 days to successfully control such applications?<br><br>MATERIALS AND METHODS: In this work, we report our experiences of training 24 motor-disabled participants at rehabilitation clinics or at the end-users' homes, without BCI experts present. We also share the lessons that we have learned through transferring BCI technologies from the lab to the user's home or clinics.<br><br>RESULTS: The most important outcome is that 50% of the participants achieved good BCI performance and could successfully control the applications (tele-presence robot and text-entry system). In the case of the tele-presence robot the participants achieved an average performance ratio of 0.87 (max. 0.97) and for the text entry application a mean of 0.93 (max. 1.0). The lessons learned and the gathered user feedback range from pure BCI problems (technical and handling), to common communication issues among the different people involved, and issues encountered while controlling the applications.<br><br>CONCLUSION: The points raised in this paper are very widely applicable and we anticipate that they might be faced similarly by other groups, if they move on to bringing the BCI technology to the end-user, to home environments and towards application prototype control.},
}
@article {pmid24119261,
year = {2013},
author = {Yi, W and Qiu, S and Qi, H and Zhang, L and Wan, B and Ming, D},
title = {EEG feature comparison and classification of simple and compound limb motor imagery.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {106},
pmid = {24119261},
issn = {1743-0003},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Electroencephalography ; Female ; *Foot ; *Hand ; Humans ; Imagination/*physiology ; Male ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: Motor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks.<br><br>METHODS: Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM).<br><br>RESULTS: The induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%.<br><br>CONCLUSIONS: The work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.},
}
@article {pmid24115445,
year = {2013},
author = {Bartholdy, S and Musiat, P and Campbell, IC and Schmidt, U},
title = {The potential of neurofeedback in the treatment of eating disorders: a review of the literature.},
journal = {European eating disorders review : the journal of the Eating Disorders Association},
volume = {21},
number = {6},
pages = {456-463},
doi = {10.1002/erv.2250},
pmid = {24115445},
issn = {1099-0968},
support = {RP-PG-0606-1043/DH_/Department of Health/United Kingdom ; },
mesh = {Brain/physiopathology ; Electroencephalography ; Feeding and Eating Disorders/physiopathology/*therapy ; Functional Neuroimaging ; Humans ; Magnetic Resonance Imaging ; *Neurofeedback ; Spectroscopy, Near-Infrared ; },
abstract = {Neurofeedback is defined as the training of voluntary regulation of localised neural activity using real-time feedback through a brain-computer interface. It has shown initial success as a potential clinical treatment tool in proof of concept studies, but has yet to be evaluated with respect to eating disorders. This paper (i) provides a brief overview of the current status of eating disorder treatments; (ii) describes the studies to date that use neurofeedback involving electroencephalography, real-time functional magnetic resonance imaging or near-infrared spectroscopy; and (iii) considers the potential of these technologies as treatments for eating disorders.},
}
@article {pmid24112703,
year = {2014},
author = {Barré, C and Thoulouzan, M and Aillet, G and Nguyen, JM},
title = {Assessing the extirpative quality of a radical prostatectomy technique: categorisation and mapping of technical errors.},
journal = {BJU international},
volume = {114},
number = {4},
pages = {522-531},
doi = {10.1111/bju.12467},
pmid = {24112703},
issn = {1464-410X},
mesh = {Age Factors ; Aged ; Disease-Free Survival ; Humans ; Male ; *Medical Errors ; Middle Aged ; Neoplasm Staging ; Prostate-Specific Antigen/blood ; Prostatectomy/*adverse effects/*methods ; Prostatic Neoplasms/pathology/*surgery ; Retrospective Studies ; Risk Factors ; },
abstract = {OBJECTIVE: To examine the extirpative quality of an open radical prostatectomy (RP) technique by first categorising and mapping all intraprostatic incisions into benign tissue and then determining a cumulative technical error rate given by all intraprostatic incisions into benign and malignant tissue.<br><br>PATIENTS AND METHODS: We performed a retrospective review of prospectively collected data relating to 1065 men with clinically localised prostate cancer who underwent open retropubic RP (70.6% nerve-sparing surgery [NSS]) by a single surgeon (January 2005 to December 2011). We recorded all intraprostatic incisions: (i) iatrogenic positive surgical margins (PSMs), (ii) deep or superficial benign capsular incisions (BCIs), (iii) incisions into benign prostate glands at the prostate apex or bladder neck (benign glandular tissue incisions [BGTIs]), and determined incision location, length and nature (solitary/multiple). We evaluated: (i) associations between benign incisions, NSS and PSMs, (ii) significant predictors for PSM risk by multivariate analysis, (iii) postoperative biochemical recurrence (BCR)-free survival (Kaplan-Meier method).<br><br>RESULTS: Intraprostatic incision rates were 2.3% pT2 PSMs, 6.0% BCIs and 5.4% BGTIs. There were slight variations in rate over time and with NSS technique. Benign incisions were located as follows: 46.8% right posterolateral, 37.5% left posterolateral, and 15.7% bilateral for BCIs; 58.6% bladder neck and 41.4% apical for BGTIs. The median (range) incision length, for solitary and multiple incisions respectively, was 4 (1-13) and 9 (2-25) mm for BCIs and 1 (1-5) and 2 (2-6) mm for BGTIs. BCI rate, but not BGTI rate, was significantly associated with NSS (P = 0.004) and PSM (P = 0.005), and increased PSM risk 3.6-fold. A PSM increased BCR risk two-fold (odds ratio 2.078, 95% confidence interval 1.383-3.122). BCR-free survival decreased significantly even for short PSMs (<1&#x2009;mm; P < 0.001).<br><br>CONCLUSIONS: Although the pT2 PSM rate was low (2.3%), the cumulative technical error rate (patients with at least one pT2 PSM, BCI or BGTI) was five-fold higher (12.5%). Categorising and mapping intraprostatic incisions is a tool surgeons can use in self-audits to identify areas of potential improvement, reduce errors, and improve surgical skills.},
}
@article {pmid24111581,
year = {2014},
author = {Koons, DN and Rockwell, RF and Aubry, LM},
title = {Effects of exploitation on an overabundant species: the lesser snow goose predicament.},
journal = {The Journal of animal ecology},
volume = {83},
number = {2},
pages = {365-374},
doi = {10.1111/1365-2656.12133},
pmid = {24111581},
issn = {1365-2656},
mesh = {Animals ; Arctic Regions ; *Ecosystem ; Geese/growth &amp; development/*physiology ; Manitoba ; Population Dynamics ; Seasons ; },
abstract = {Invasive and overabundant species are an increasing threat to biodiversity and ecosystem functioning world-wide. As such, large amounts of money are spent each year on attempts to control them. These efforts can, however, be thwarted if exploitation is compensated demographically or if populations simply become too numerous for management to elicit an effective and rapid functional response. We examined the influence of these mechanisms on cause-specific mortality in lesser snow geese using multistate capture-reencounter methods. The abundance and destructive foraging behaviours of snow geese have created a trophic cascade that reduces (sub-) Arctic plant, insect and avian biodiversity, bestowing them the status of 'overabundant'. Historically, juvenile snow geese suffered from density-related degradation of their saltmarsh brood-rearing habitat. This allowed harvest mortality to be partially compensated by non-harvest mortality (process correlation between mortality sources: ρ = -0.47; 90% BCI: -0.72 to -0.04). Snow goose family groups eventually responded to their own degradation of habitat by dispersing to non-degraded areas. This relaxed the pressure of density dependence on juvenile birds, but without this mechanism for compensation, harvest began to have an additive effect on overall mortality (ρ = 0.60; 90% BCI: -0.06 to 0.81). In adults, harvest had an additive effect on overall mortality throughout the 42-year study (ρ = 0.24; 90% BCI: -0.59 to 0.67). With the aim of controlling overabundant snow geese, the Conservation Order amendment to the International Migratory Bird Treaty was implemented in February of 1999 to allow for harvest regulations that had not been allowed since the early 1900s (e.g. a spring harvest season, high or unlimited bag limits and use of electronic calls and unplugged shotguns). Although harvest mortality momentarily increased following these actions, the increasing abundance of snow geese has since induced a state of satiation in harvest that has driven harvest rates below the long-term average. More aggressive actions will thus be needed to halt the growth and spread of the devastating trophic cascade that snow geese have triggered. Our approach to investigating the impacts of population control efforts on cause-specific mortality will help guide more effective management of invasive and overabundant species world-wide.},
}
@article {pmid24111460,
year = {2013},
author = {Higashi, H and Tanaka, T},
title = {Regularization using similarities of signals observed in nearby sensors for feature extraction of brain signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7420-7423},
doi = {10.1109/EMBC.2013.6611273},
pmid = {24111460},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; Humans ; Imagery, Psychotherapy ; *Signal Processing, Computer-Assisted ; },
abstract = {In order to solve uncertainty of spatial weights learned with small amount of training samples for feature extraction from brain signals, a regularization using similarity of signals observed in sensors that are located near each other is proposed. Deriving the regularization is begun defining a distance between the sensors. Under the distance, the proposed regularization works so that the spatial weights extracts similar signals in the nearby sensors. The proposed regularization is applied to the well known common spatial pattern (CSP) method that finds spatial weights for EEG based brain machine interface. In the classification experiment using a dataset of EEG signals during motor imagery, the proposed method achieved maximum improvement by 28% in the classification accuracy over the standard CSP in a setting of even when only five samples are used.},
}
@article {pmid24111459,
year = {2013},
author = {Punsawad, Y and Wongsawat, Y},
title = {Hybrid SSVEP-motion visual stimulus based BCI system for intelligent wheelchair.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7416-7419},
doi = {10.1109/EMBC.2013.6611272},
pmid = {24111459},
issn = {2694-0604},
mesh = {Algorithms ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Motion ; Nerve Net/physiology ; *Photic Stimulation ; *Wheelchairs ; },
abstract = {This paper proposes the hybrid BCI modalities for wheelchair control by taking into account weakness of the current BCI systems. The idea is to combine two hybrid BCI systems with the intelligent wheelchair for three states, i.e. normal, fatigue, and emergency states. First system is the hybrid steady state visual evoked potential (SSVEP) and alpha rhythm BCI which is designed to use in the normal state. Second system is the hybrid motion visual stimulus and alpha rhythm which can be employed during the fatigue state (after using the first system). For the experiment, subjects are asked to perform SSVEP system for 30 minutes (until the fatigue states occur). Then, the subjects will be asked to perform the hybrid motion visual stimulus and alpha rhythm testing. The accuracy of the proposed system during fatigue state is approximately 85.62%. With this idea, BCI controlled wheelchair can be efficiently employed in reality.},
}
@article {pmid24111381,
year = {2013},
author = {Zhang, Y and Chase, SM},
title = {A stabilized dual Kalman filter for adaptive tracking of brain-computer interface decoding parameters.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7100-7103},
doi = {10.1109/EMBC.2013.6611194},
pmid = {24111381},
issn = {2694-0604},
mesh = {Algorithms ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; Neural Prostheses ; Neurons/*physiology ; },
abstract = {Neural prosthetics are a promising technology for alleviating paralysis by actuating devices directly from the intention to move. Typical implementations of these devices require a calibration session to define decoding parameters that map recorded neural activity into movement of the device. However, a major factor limiting the clinical deployment of this technology is stability: with fixed decoding parameters, control of the prosthetic device has been shown to degrade over time. Here we apply a dual estimation procedure to adaptively capture changes in decoding parameters. In simulation, we find that our stabilized dual Kalman filter can run autonomously for hundreds of thousands of trials with little change in performance. Further, when we apply our algorithm off-line to estimate arm trajectories from neural data recorded over five consecutive days, we find that it outperforms a static Kalman filter, even when it is re-calibrated at the beginning of each day.},
}
@article {pmid24111373,
year = {2013},
author = {Álvarez-Meza, AM and Velásquez-Martínez, LF and Castellanos-Dominguez, G},
title = {Feature relevance analysis supporting automatic motor imagery discrimination in EEG based BCI systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7068-7071},
doi = {10.1109/EMBC.2013.6611186},
pmid = {24111373},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination ; Motor Activity ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {Recently, there have been many efforts to develop Brain Computer Interface (BCI) systems, allowing identifying and discriminating brain activity, as well as, support the control of external devices, and to understand cognitive behaviors. In this work, a feature relevance analysis approach based on an eigen decomposition method is proposed to support automatic Motor Imagery (MI) discrimination in electroencephalography signals for BCI systems. We select a set of features representing the best as possible the studied process. For such purpose, a variability study is performed based on traditional Principal Component Analysis. EEG signals modelling is carried out by feature estimation of three frequency-based and one time-based. Our approach provides testing over a well-known MI dataset. Attained results show that presented algorithm can be used as tool to support discrimination of MI brain activity, obtaining acceptable results in comparison to state of the art approaches.},
}
@article {pmid24111372,
year = {2013},
author = {Lee, WL and Tan, T and Leung, YH},
title = {An improved P300 extraction using ICA-R for P300-BCI speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7064-7067},
doi = {10.1109/EMBC.2013.6611185},
pmid = {24111372},
issn = {2694-0604},
mesh = {Algorithms ; Electroencephalography/methods ; *Event-Related Potentials, P300 ; Humans ; Photic Stimulation ; Reading ; *Signal Processing, Computer-Assisted ; },
abstract = {In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.},
}
@article {pmid24111371,
year = {2013},
author = {Johnson, EC and Norton, JJ and Jun, D and Bretl, T and Jones, DL},
title = {Sequential selection of window length for improved SSVEP-based BCI classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7060-7063},
doi = {10.1109/EMBC.2013.6611184},
pmid = {24111371},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interfaces (BCI) utilizing steady-state visually evoked potentials (SSVEP) recorded by electroencephalography (EEG) have exciting potential to enable new systems for disabled individuals and novel controls for robotic and computer systems. To interact with SSVEP-based BCIs, users attend to visual stimuli modulated at predetermined frequencies. A key problem for SSVEP-based BCIs is to classify which modulation frequency the user is attending, for which there is an inherent trade-off between speed and accuracy. As SSVEP signals vary with time and stimulation frequency, a fixed-length data window does not necessarily optimize this trade-off. We propose a strategy, developed from sequential analysis, to vary the window-length used for classification. Our proposed technique adapts to the data, continuing to collect data until it is confident enough to make a classification decision. Our strategy was compared to a fixed window-length method using a simple experiment involving five frequencies presented individually to three participants. Using a canonical correlation analysis classifier to compare the proposed variable-length scheme to a standard fixed-length scheme, the variable-length approach improved the classifier information transfer rate by an average of 43%.},
}
@article {pmid24111370,
year = {2013},
author = {Wang, L and Zhang, X and Zhang, Y},
title = {Extending motor imagery by speech imagery for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7056-7059},
doi = {10.1109/EMBC.2013.6611183},
pmid = {24111370},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/physiology ; Male ; Motor Activity ; Motor Cortex/physiology ; Speech/physiology ; Support Vector Machine ; Young Adult ; },
abstract = {An electroencephalogram (EEG)-based brain computer interface (BCI) is a novel tool that translates brain intentions into control signals. As the operational dimensions of motor imagery are limited, we describe in this paper an extension of its capability by including speech imagery. Our new system was tested with the help of subjects, whose native language is Chinese. The tests were divided into two steps. The first step was speech imagery; consequently motor imagery and speech imagery were merged in the second step. Feature vectors of EEG signals were extracted from both common spatial patterns (CSP) and cross-correlation functions; then these vectors were classified by a support vector machine (SVM). The distinguishing accuracies of two intentions were found to be between 79.33% and 88.26%. This result shows that the capability of BCI for motor imagery can be extended by combining motor imagery and speech imagery.},
}
@article {pmid24111369,
year = {2013},
author = {Li, CY and Liu, R and Wang, YY and Wang, YX and Li, X},
title = {Adaptive power projection method for accumulative EEG classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7052-7055},
doi = {10.1109/EMBC.2013.6611182},
pmid = {24111369},
issn = {2694-0604},
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/physiology ; Motor Activity ; Motor Cortex/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {For the dynamic classification of motor imagery mind states in the brain-computer interface (BCI), we propose a power projection based feature extraction method to classify the electroencephalogram (EEG) signals by combining information accumulative posterior Bayesian approach. This method improves the classification accuracy by maximizing the average projection energy difference of the two types of signals. The experimental results on two BCI competition datasets show that the classification accuracy is about 90%. The results of the classification accuracy and mutual information demonstrate the effectiveness of this method.},
}
@article {pmid24111368,
year = {2013},
author = {Samek, W and Binder, A and Muller, KR},
title = {Multiple kernel learning for brain-computer interfacing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {7048-7051},
doi = {10.1109/EMBC.2013.6611181},
pmid = {24111368},
issn = {2694-0604},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Humans ; Motor Activity ; Motor Cortex/physiology ; *Signal Processing, Computer-Assisted ; Software ; },
abstract = {Combining information from different sources is a common way to improve classification accuracy in Brain-Computer Interfacing (BCI). For instance, in small sample settings it is useful to integrate data from other subjects or sessions in order to improve the estimation quality of the spatial filters or the classifier. Since data from different subjects may show large variability, it is crucial to weight the contributions according to importance. Many multi-subject learning algorithms determine the optimal weighting in a separate step by using heuristics, however, without ensuring that the selected weights are optimal with respect to classification. In this work we apply Multiple Kernel Learning (MKL) to this problem. MKL has been widely used for feature fusion in computer vision and allows to simultaneously learn the classifier and the optimal weighting. We compare the MKL method to two baseline approaches and investigate the reasons for performance improvement.},
}
@article {pmid24111334,
year = {2013},
author = {Gheorghe, L and Chavarriaga, R and Millán, Jdel R},
title = {Steering timing prediction in a driving simulator task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6913-6916},
doi = {10.1109/EMBC.2013.6611147},
pmid = {24111334},
issn = {2694-0604},
mesh = {*Automobile Driving ; Brain-Computer Interfaces ; Computer Simulation ; Discriminant Analysis ; Electroencephalography ; Humans ; *Motor Activity ; },
abstract = {In this paper we present the preliminary results of a pioneering attempt to predict the timing of steering actions in a driving task from non-invasive EEG measurements. The experiment took place with the subjects driving a car at a constant speed on a simulated highway in a driving simulator. The EEG activity was analyzed during periods of straight driving and during lane change actions. Classifiers were built on the signals recorded over the motor areas for straight and pre-steering periods. The onset of the steering actions was detected on average 811 ms before the action with a 74.6% true positive rate.},
}
@article {pmid24111332,
year = {2013},
author = {Güneysu, A and Akin, HL},
title = {An SSVEP based BCI to control a humanoid robot by using portable EEG device.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6905-6908},
doi = {10.1109/EMBC.2013.6611145},
pmid = {24111332},
issn = {2694-0604},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation ; Evoked Potentials, Visual ; Humans ; *Robotics ; },
abstract = {Brain Computer Interfaces (BCIs) are systems that allow human subjects to interact with the environment by interpreting brain signals into machine commands. This work provides a design for a BCI to control a humanoid robot by using signals obtained from the Emotiv EPOC, a portable electroencephalogram (EEG) device with 14 electrodes and sampling rate of 128 Hz. The main objective is to process the neuroelectric responses to an externally driven stimulus and generate control signals for the humanoid robot Nao accordingly. We analyze steady-state visually evoked potential (SSVEP) induced by one of four groups of light emitting diodes (LED) by using two distinct signals obtained from the two channels of the EEG device which reside on top of the occipital lobe. An embedded system is designed for generating pulse width modulated square wave signals in order to flicker each group of LEDs with different frequencies. The subject chooses the direction by looking at one of these groups of LEDs that represent four directions. Fast Fourier Transform and a Gaussian model are used to detect the dominant frequency component by utilizing harmonics and neighbor frequencies. Then, a control signal is sent to the robot in order to draw a fixed sized line in that selected direction by BCI. Experimental results display satisfactory performance where the correct target is detected 75% of the time on the average across all test subjects without any training.},
}
@article {pmid24111331,
year = {2013},
author = {Zhang, C and Sun, C and Gao, L and Zheng, N and Chen, W and Zheng, X},
title = {Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6901-6904},
doi = {10.1109/EMBC.2013.6611144},
pmid = {24111331},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Cybernetics ; Electric Stimulation ; Maze Learning ; Rats, Sprague-Dawley ; Reinforcement, Psychology ; Reward ; *Robotics ; },
abstract = {Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.},
}
@article {pmid24111257,
year = {2013},
author = {Roy, RN and Bonnet, S and Charbonnier, S and Campagne, A},
title = {Mental fatigue and working memory load estimation: interaction and implications for EEG-based passive BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6607-6610},
doi = {10.1109/EMBC.2013.6611070},
pmid = {24111257},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Diagnostic Imaging/instrumentation/*methods ; Electroencephalography ; Female ; Humans ; Male ; *Memory ; Mental Fatigue/*pathology/*physiopathology ; },
abstract = {Current mental state monitoring systems, a.k.a. passive brain-computer interfaces (pBCI), allow one to perform a real-time assessment of an operator's cognitive state. In EEG-based systems, typical measurements for workload level assessment are band power estimates in several frequency bands. Mental fatigue, arising from growing time-on-task (TOT), can significantly affect the distribution of these band power features. However, the impact of mental fatigue on workload (WKL) assessment has not yet been evaluated. With this paper we intend to help fill in this lack of knowledge by analyzing the influence of WKL and TOT on EEG band power features, as well as their interaction and its impact on classification performance. Twenty participants underwent an experiment that modulated both their WKL (low/high) and time spent on the task (short/long). Statistical analyses were performed on the EEG signals, behavioral and subjective data. They revealed opposite changes in alpha power distribution between WKL and TOT conditions, as well as a decrease in WKL level discriminability with increasing TOT in both number of statistical differences in band power and classification performance. Implications for pBCI systems and experimental protocol design are discussed.},
}
@article {pmid24111256,
year = {2013},
author = {Ang, KK and Guan, C and Chua, KS and Phua, KS and Wang, C and Chin, ZY and Zhou, L and Tang, KY and Joseph, GJ and Kuah, C},
title = {A clinical study of motor imagery BCI performance in stroke by including calibration data from passive movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6603-6606},
doi = {10.1109/EMBC.2013.6611069},
pmid = {24111256},
issn = {2694-0604},
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Calibration ; Diagnostic Imaging/*instrumentation/*methods/standards ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Male ; Middle Aged ; Paresis/pathology/physiopathology ; Stroke/*pathology/*physiopathology ; },
abstract = {Electroencephalogram (EEG) data from performing motor imagery are usually used to calibrate a subject-specific model in Motor Imagery Brain-Computer Interface (MI-BCI). However, the performance of MI is not directly observable by another person. Studies that attempted to address this issue in order to improve subjects with low MI performance had shown that it is feasible to use calibration data from Passive Movement (PM) to detect MI in healthy subjects. This study investigates the feasibility of using calibration data from PM of stroke patients to detect MI. EEG data from 2 calibration runs of MI and PM by a robotic haptic knob, and 1 evaluation run of MI were collected in one session of recording from 34 hemiparetic stroke patients recruited in the clinical study. In each run, 40 trials of MI or PM and 40 trials of the background rest were collected. The off-line run-to-run transfer kappa values from the calibration runs of MI, PM, and combined MI and PM, to the evaluation run of MI were then evaluated and compared. The results showed that calibration using PM (0.392) yielded significantly lower kappa value than the calibration using MI (0.457, p=4.40e-14). The results may be due to a significant disparity between the EEG data from PM and MI in stroke subjects. Nevertheless, the results showed that the calibration using both MI and PM (0.506) yielded significantly higher kappa value than the calibration using MI (0.457, p=9.54e-14). Hence, the results of this study suggest a promising direction to combine calibration data from PM and MI to improve MI detection on stroke.},
}
@article {pmid24111201,
year = {2013},
author = {Kadambi, P and Lovelace, JA and Beyette, FR},
title = {Changes in behavior of evoked potentials in the brain as a possible indicator of fatigue in people.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6381-6384},
doi = {10.1109/EMBC.2013.6611014},
pmid = {24111201},
issn = {2694-0604},
support = {5U54EB007954/EB/NIBIB NIH HHS/United States ; R21ES019255/ES/NIEHS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain-Computer Interfaces ; Caffeine/pharmacology ; Electroencephalography ; *Evoked Potentials/drug effects ; Eye Movements/physiology ; *Fatigue ; Heart Rate/physiology ; Humans ; Sleep Deprivation ; },
abstract = {Many professions place significant mental and/or physical strain on their workers. Some professionals (such as firefighters, soldiers, and pilots) have an inherent responsibility for the safety of others. Making sure that workers in these remain fit for duty is an important health/safety concern for the workers and those they serve. This paper explores the viability of using EEG as a non-invasive, cost efficient method for assessing fatigue, sleep deprivation, physical exertion and stress. Specifically, P300 evoked potentials are generated in response to certain stimuli. Variations in the response characteristics (magnitude, shape, and peak shift) are explored in relation to sleep deprivation, caffeine usage, and physical exertion. Preliminary data suggests that there are quantifiable changes to the P300 response that may be attributed to fatigue.},
}
@article {pmid24111196,
year = {2013},
author = {Lovelace, JA and Witt, TS and Beyette, FR},
title = {Modular, bluetooth enabled, wireless electroencephalograph (EEG) platform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6361-6364},
doi = {10.1109/EMBC.2013.6611009},
pmid = {24111196},
issn = {2694-0604},
support = {5U54EB007954/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Sleep Wake Disorders/physiopathology ; Wireless Technology ; },
abstract = {A design for a modular, compact, and accurate wireless electroencephalograph (EEG) system is proposed. EEG is the only non-invasive measure for neuronal function of the brain. Using a number of digital signal processing (DSP) techniques, this neuronal function can be acquired and processed into meaningful representations of brain activity. The system described here utilizes Bluetooth to wirelessly transmit the digitized brain signal for an end application use. In this way, the system is portable, and modular in terms of the device to which it can interface. Brain Computer Interface (BCI) has become a popular extension of EEG systems in modern research. This design serves as a platform for applications using BCI capability.},
}
@article {pmid24111192,
year = {2013},
author = {An, J and Jin, SH and Lee, SH and Jang, G and Abibullaev, B and Lee, H and Moon, JI},
title = {Cortical activation pattern for grasping during observation, imagery, execution, FES, and observation-FES integrated BCI: an fNIRS pilot study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6345-6348},
doi = {10.1109/EMBC.2013.6611005},
pmid = {24111192},
issn = {2694-0604},
mesh = {Adult ; Arm ; Brain/physiology ; Brain-Computer Interfaces ; Electric Stimulation ; Electrodes ; Humans ; Male ; Motor Activity ; Motor Cortex/physiology ; Pilot Projects ; Prefrontal Cortex/physiology ; Range of Motion, Articular ; Sensorimotor Cortex/physiology ; *Spectroscopy, Near-Infrared ; },
abstract = {Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.},
}
@article {pmid24111191,
year = {2013},
author = {Bulea, TC and Prasad, S and Kilicarslan, A and Contreras-Vidal, JL},
title = {Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6341-6344},
pmid = {24111191},
issn = {2694-0604},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889-01/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Biomechanical Phenomena ; Brain-Computer Interfaces ; Discriminant Analysis ; *Electroencephalography ; Electromyography ; Humans ; Motor Activity/*physiology ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.},
}
@article {pmid24111119,
year = {2013},
author = {Ma, X and Hu, D and Huang, J and Li, W and He, J},
title = {Selection of cortical neurons for identifying movement transitions in stand and squat.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {6051-6054},
doi = {10.1109/EMBC.2013.6610932},
pmid = {24111119},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Cerebral Cortex ; Haplorhini ; Motor Cortex/*physiology ; Movement/*physiology ; Neurons/metabolism/*physiology ; Signal Processing, Computer-Assisted ; *Support Vector Machine ; },
abstract = {Neural signals collected from motor cortex were quantified for identification of subject's specific movement intentions in a Brain Machine Interface (BMI). Neuron selection serves as an important procedure in this decoding process. In this study, we proposed a neuron selection method for identifying movement transitions in standing and squatting tasks by analyzing cortical neuron spike train patterns. A nonparametric analysis of variation, Kruskal-Wallis test, was introduced to evaluate whether the average discharging rate of each neuron changed significantly among different motion stages, and thereby categorize the neurons according to their active periods. Selection was performed based on neuron categorizing information. Finally, the average firing rates of selected neurons were assembled as feature vectors and a classifier based on support vector machines (SVM) was employed to discriminate different movement stages and identify transitions. The results indicate that our neuron selection method is accurate and efficient for finding neurons correlated with movement transitions in standing and squatting tasks.},
}
@article {pmid24111049,
year = {2013},
author = {Hernandez, R and Yang, Q and Huang, H and Zhang, F and Zhang, X},
title = {Design and implementation of a low power mobile CPU based embedded system for artificial leg control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5769-5772},
doi = {10.1109/EMBC.2013.6610862},
pmid = {24111049},
issn = {2694-0604},
mesh = {Algorithms ; *Artificial Limbs ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electromyography ; Humans ; Leg ; Pattern Recognition, Automated ; Software ; Support Vector Machine ; },
abstract = {This paper presents the design and implementation of a new neural-machine-interface (NMI) for control of artificial legs. The requirements of high accuracy, real-time processing, low power consumption, and mobility of the NMI place great challenges on the computation engine of the system. By utilizing the architectural features of a mobile embedded CPU, we are able to implement our decision-making algorithm, based on neuromuscular phase-dependant support vector machines (SVM), with exceptional accuracy and processing speed. To demonstrate the superiority of our NMI, real-time experiments were performed on an able bodied subject with a 20 ms window increment. The 20 ms testing yielded accuracies of 99.94% while executing our algorithm efficiently with less than 11% processor loads.},
}
@article {pmid24111012,
year = {2013},
author = {Ledochowitsch, P and Koralek, AC and Moses, D and Carmena, JM and Maharbiz, MM},
title = {Sub-mm functional decoupling of electrocortical signals through closed-loop BMI learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5622-5625},
doi = {10.1109/EMBC.2013.6610825},
pmid = {24111012},
issn = {2694-0604},
mesh = {Animals ; *Artificial Intelligence ; Behavior, Animal/physiology ; Brain/physiopathology ; *Brain-Computer Interfaces ; Electric Impedance ; Electrodes, Implanted ; *Electroencephalography ; Feedback ; Male ; Microelectrodes ; Motor Cortex/physiology ; Rats ; Rats, Long-Evans ; Signal Processing, Computer-Assisted/*instrumentation ; Sound ; Subdural Space ; },
abstract = {Volitional control of neural activity lies at the heart of the Brain-Machine Interface (BMI) paradigm. In this work we investigated if subdural field potentials recorded by electrodes < 1mm apart can be decoupled through closed-loop BMI learning. To this end, we fabricated custom, flexible microelectrode arrays with 200 µm electrode pitch and increased the effective electrode area by electrodeposition of platinum black to reduce thermal noise. We have chronically implanted these arrays subdurally over primary motor cortex (M1) of 5 male Long-Evans Rats and monitored the electrochemical electrode impedance in vivo to assess the stability of these neural interfaces. We successfully trained the rodents to perform a one-dimensional center-out task using closed-loop brain control to adjust the pitch of an auditory cursor by differentially modulating high gamma (70-110 Hz) power on pairs of surface microelectrodes that were separated by less than 1 mm.},
}
@article {pmid24111011,
year = {2013},
author = {Do, AH and Wang, PT and King, CE and Schombs, A and Lin, JJ and Sazgar, M and Hsu, FP and Shaw, SJ and Millett, DE and Liu, CY and Szymanska, AA and Chui, LA and Nenadic, Z},
title = {Sensitivity and specificity of upper extremity movements decoded from electrocorticogram.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5618-5621},
doi = {10.1109/EMBC.2013.6610824},
pmid = {24111011},
issn = {2694-0604},
mesh = {Adult ; Artificial Limbs ; Brain-Computer Interfaces ; *Electroencephalography ; Female ; Humans ; *Movement ; *Signal Processing, Computer-Assisted ; Upper Extremity/*physiology ; Young Adult ; },
abstract = {Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-of-freedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the ECoG signals underlying 6 elementary upper extremity movements. Two subjects undergoing ECoG electrode grid implantation for epilepsy surgery evaluation participated in the study. For each task, their data were analyzed to design a decoding model to classify ECoG as idling or movement. The decoding models were found to be highly sensitive in detecting movement, but not specific in distinguishing between different movement types. Since sensitivity and specificity must be traded-off, these results imply that conventional ECoG grids may not provide sufficient resolution for decoding many-DOF upper extremity movements.},
}
@article {pmid24111010,
year = {2013},
author = {Heger, D and Putze, F and Herff, C and Schultz, T},
title = {Subject-to-subject transfer for CSP based BCIs: feature space transformation and decision-level fusion.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5614-5617},
doi = {10.1109/EMBC.2013.6610823},
pmid = {24111010},
issn = {2694-0604},
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Humans ; Imagination ; Movement ; Pattern Recognition, Automated/*methods ; },
abstract = {Modern Brain Computer Interfaces (BCIs) usually require a calibration session to train a machine learning system before each usage. In general, such trained systems are highly specialized to the subject's characteristic activation patterns and cannot be used for other sessions or subjects. This paper presents a feature space transformation that transforms features generated using subject-specific spatial filters into a subject-independent feature space. The transformation can be estimated from little adaptation data of the subject. Furthermore, we combine three different Common Spatial Pattern based feature extraction approaches using decision-level fusion, which enables BCI use when little calibration data is available, but also outperformed the subject-dependent reference approaches for larger amounts of training data.},
}
@article {pmid24111009,
year = {2013},
author = {Davies, SR and James, CJ},
title = {Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5610-5613},
doi = {10.1109/EMBC.2013.6610822},
pmid = {24111009},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Female ; Humans ; *Imagination ; *Movement ; *Psychomotor Performance ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). As frequency is a key component of both IMFs and the μ rhythm (8-13 Hz brain activity generated during motor imagery), IMFs are then grouped by frequency. EMD is applied to the recordings from two electrodes for each trial and the resulting IMFs are grouped according to peak-frequency band via Hierarchical Clustering Analysis (HCA). The cluster containing the frequency band of the μ rhythm (8-13 Hz) is then selected and the sum-total of the IMFs from each electrode are summed together. A simple linear classifier is then sufficient to classify the motor-imagery with 89% sensitivity from a separate test set.},
}
@article {pmid24111008,
year = {2013},
author = {Kilicarslan, A and Prasad, S and Grossman, RG and Contreras-Vidal, JL},
title = {High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5606-5609},
pmid = {24111008},
issn = {2694-0604},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889-01/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Intention ; Male ; Paraplegia/*physiopathology/*psychology ; *Robotics ; Walking ; },
abstract = {Brain-Machine Interface (BMI) systems allow users to control external mechanical systems using their thoughts. Commonly used in literature are invasive techniques to acquire brain signals and decode user's attempted motions to drive these systems (e.g. a robotic manipulator). In this work we use a lower-body exoskeleton and measure the users brain activity using non-invasive electroencephalography (EEG). The main focus of this study is to decode a paraplegic subject's motion intentions and provide him with the ability of walking with a lower-body exoskeleton accordingly. We present our novel method of decoding with high offline evaluation accuracies (around 98%), our closed loop implementation structure with considerably short on-site training time (around 38 sec), and preliminary results from the real-time closed loop implementation (NeuroRex) with a paraplegic test subject.},
}
@article {pmid24111007,
year = {2013},
author = {Paek, AY and Brown, JD and Gillespie, RB and O'Malley, MK and Shewokis, PA and Contreras-Vidal, JL},
title = {Reconstructing surface EMG from scalp EEG during myoelectric control of a closed looped prosthetic device.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5602-5605},
doi = {10.1109/EMBC.2013.6610820},
pmid = {24111007},
issn = {2694-0604},
mesh = {Arm/physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; *Delta Rhythm ; *Electromyography ; Feedback ; Humans ; Male ; *Prostheses and Implants ; Robotics ; *Scalp ; Signal Processing, Computer-Assisted/*instrumentation ; Software ; },
abstract = {In this study, seven able-bodied human subjects controlled a robotic gripper with surface electromyography (sEMG) activity from the biceps. While subjects controlled the gripper, they felt the forces measured by the robotic gripper through an exoskeleton fitted on their non-dominant left arm. Subjects were instructed to identify objects with the force feedback provided by the exoskeleton. While subjects operated the robotic gripper, scalp electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were recorded. We developed neural decoders that used scalp EEG to reconstruct the sEMG used to control the robotic gripper. The neural decoders used a genetic algorithm embedded in a linear model with memory to reconstruct the sEMG from a plurality of EEG channels. The performance of the decoders, measured with Pearson correlation coefficients (median r-value = 0.59, maximum r-value = 0.91) was found to be comparable to previous studies that reconstructed sEMG linear envelopes from neural activity recorded with invasive techniques. These results show the feasibility of developing EEG-based neural interfaces that in turn could be used to control a robotic device.},
}
@article {pmid24110957,
year = {2013},
author = {Bae, J and Sanchez Giraldo, LG and Pohlmeyer, EA and Sanchez, JC and Principe, JC},
title = {A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5402-5405},
doi = {10.1109/EMBC.2013.6610770},
pmid = {24110957},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Behavior, Animal ; *Brain-Computer Interfaces ; Callithrix ; Learning ; Microelectrodes ; *Reinforcement, Psychology ; Software ; },
abstract = {This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.},
}
@article {pmid24110936,
year = {2013},
author = {Gao, L and Sun, C and Zhang, C and Zheng, N and Chen, W and Zheng, X},
title = {Ratbot automatic navigation by electrical reward stimulation based on distance measurement in unknown environments.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5315-5318},
doi = {10.1109/EMBC.2013.6610749},
pmid = {24110936},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Behavior, Animal ; Brain-Computer Interfaces ; Electric Stimulation ; Environment ; *Locomotion ; Rats ; Reward ; Robotics/*methods ; },
abstract = {Traditional automatic navigation methods for bio-robots are constrained to configured environments and thus can't be applied to tasks in unknown environments. With no consideration of bio-robot's own innate living ability and treating bio-robots in the same way as mechanical robots, those methods neglect the intelligence behavior of animals. This paper proposes a novel ratbot automatic navigation method in unknown environments using only reward stimulation and distance measurement. By utilizing rat's habit of thigmotaxis and its reward-seeking behavior, this method is able to incorporate rat's intrinsic intelligence of obstacle avoidance and path searching into navigation. Experiment results show that this method works robustly and can successfully navigate the ratbot to a target in the unknown environment. This work might put a solid base for application of ratbots and also has significant implication of automatic navigation for other bio-robots as well.},
}
@article {pmid24110925,
year = {2013},
author = {Wang, YT and Wang, Y and Cheng, CK and Jung, TP},
title = {Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5271-5274},
doi = {10.1109/EMBC.2013.6610738},
pmid = {24110925},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Cell Phone ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Photic Stimulation ; Signal Processing, Computer-Assisted ; Wavelet Analysis ; },
abstract = {This study integrates visual stimulus presentation and near real-time data processing on a mobile device (e.g. a Tablet or a cell-phone) to implement a steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI). The goal of this study is to increase the practicability, portability and ubiquity of an SSVEP-based BCI for daily use. The accuracy of flickering frequencies on the mobile SSVEP BCI system was tested against that on a laptop/desktop used in our previous studies. This study then analyzed the power spectrum density of the electroencephalogram signals elicited by the visual stimuli rendered on the mobile BCIs. Finally, this study performed an online test with the Tablet-based BCI system and obtained an averaged information transfer rate of 33.87 bits/min in three subjects. The current integration leads to a truly practical and ubiquitous SSVEP BCI on mobile devices for real-life applications.},
}
@article {pmid24110924,
year = {2013},
author = {Yang, L and Leung, H},
title = {An online BCI game based on the decoding of users' attention to color stimulus.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5267-5270},
doi = {10.1109/EMBC.2013.6610737},
pmid = {24110924},
issn = {2694-0604},
mesh = {Adult ; *Attention ; *Brain-Computer Interfaces ; Color ; Electroencephalography/*methods ; Female ; Games, Experimental ; Humans ; Intention ; Male ; Nontherapeutic Human Experimentation ; Photic Stimulation ; Support Vector Machine ; },
abstract = {Studies have shown that statistically there are differences in theta, alpha and beta band powers when people look at blue and red colors. In this paper, a game has been developed to test whether these statistical differences are good enough for online Brain Computer Interface (BCI) application. We implemented a two-choice BCI game in which the subject makes the choice by looking at a color option and our system decodes the subject's intention by analyzing the EEG signal. In our system, band power features of the EEG data were used to train a support vector machine (SVM) classification model. An online mechanism was adopted to update the classification model during the training stage to account for individual differences. Our results showed that an accuracy of 70%-80% could be achieved and it provided evidence for the possibility in applying color stimuli to BCI applications.},
}
@article {pmid24110923,
year = {2013},
author = {Omedes, J and Iturrate, I and Montesano, L and Minguez, J},
title = {Using frequency-domain features for the generalization of EEG error-related potentials among different tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5263-5266},
doi = {10.1109/EMBC.2013.6610736},
pmid = {24110923},
issn = {2694-0604},
mesh = {Adult ; Brain-Computer Interfaces ; Calibration ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Male ; Nontherapeutic Human Experimentation ; },
abstract = {EEG brain-computer interfaces (BCI) require a calibration phase prior to the on-line control of the device, which is a difficulty for the practical development of this technology as it is user-, session- and task-specific. The large body of research in BCIs based on event-related potentials (ERP) use temporal features, which have demonstrated to be stable for each user along time, but do not generalize well among tasks different from the calibration task. This paper explores the use of low frequency features to improve the generalization capabilities of the BCIs using error-potentials. The results show that there exists a stable pattern in the frequency domain that allows a classifier to generalize among the tasks. Furthermore, the study also shows that it is possible to combine temporal and frequency features to obtain the best of both domains.},
}
@article {pmid24110922,
year = {2013},
author = {Iturrate, I and Montesano, L and Minguez, J},
title = {Shared-control brain-computer interface for a two dimensional reaching task using EEG error-related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5258-5262},
doi = {10.1109/EMBC.2013.6610735},
pmid = {24110922},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Computer Systems ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Learning ; Nontherapeutic Human Experimentation ; },
abstract = {One of the main problems of EEG-based brain computer interfaces (BCIs) is their low information rate, thus for complex tasks the user needs large amounts of time to solve the task. In an attempt to reduce this time and improve the application robustness, recent works have explored shared-control strategies where the device does not only execute the decoded commands, but it is also involved in executing the task. This work proposes a shared-control BCI using error potentials for a 2D reaching task with discrete actions and states. The proposed system has several interesting properties: the system is scalable without increasing the complexity of the user's mental task; the interaction is natural for the user, as the mental task is to monitor the device performance to promote its task learning (in this context the reaching task); and the system has the potential to be combined with additional brain signals to recover or learn from interaction errors. Online control experiments were performed with four subjects, showing that it was possible to reach a goal location from any starting point within a 5×5 grid in around 23 actions (about 19 seconds of EEG signal), both with fixed goals and goals freely chosen by the users.},
}
@article {pmid24110921,
year = {2013},
author = {Kapeller, C and Hintermuller, C and Abu-Alqumsan, M and Pruckl, R and Peer, A and Guger, C},
title = {A BCI using VEP for continuous control of a mobile robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5254-5257},
doi = {10.1109/EMBC.2013.6610734},
pmid = {24110921},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Equipment Design ; *Evoked Potentials, Visual ; Feedback ; Humans ; Nontherapeutic Human Experimentation ; Photic Stimulation/methods ; *Robotics ; Signal-To-Noise Ratio ; },
abstract = {A brain-computer interface (BCI) translates brain activity into commands to control devices or software. Common approaches are based on visual evoked potentials (VEP), extracted from the electroencephalogram (EEG) during visual stimulation. High information transfer rates (ITR) can be achieved using (i) steady-state VEP (SSVEP) or (ii) code-modulated VEP (c-VEP). This study investigates how applicable such systems are for continuous control of robotic devices and which method performs best. Eleven healthy subjects steered a robot along a track using four BCI controls on a computer screen in combination with feedback video of the movement. The average time to complete the tasks was (i) 573.43 s and (ii) 222.57 s. In a second non-continuous trial-based validation run the maximum achievable online classification accuracy over all subjects was (i) 91.36 % and (ii) 98.18 %. This results show that the c-VEP fits the needs of a continuous system better than the SSVEP implementation.},
}
@article {pmid24110920,
year = {2013},
author = {Prins, NW and Geng, S and Pohlmeyer, EA and Mahmoudi, B and Sanchez, JC},
title = {Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5250-5253},
doi = {10.1109/EMBC.2013.6610733},
pmid = {24110920},
issn = {2694-0604},
mesh = {Animals ; Biofeedback, Psychology ; *Body Mass Index ; Brain ; *Brain-Computer Interfaces ; Callithrix ; Cluster Analysis ; Nucleus Accumbens/*physiology ; Principal Component Analysis ; *Reinforcement, Psychology ; Reward ; },
abstract = {New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.},
}
@article {pmid24110873,
year = {2013},
author = {Hoang, T and Tran, D and Huang, X},
title = {Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {5061-5064},
doi = {10.1109/EMBC.2013.6610686},
pmid = {24110873},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; *Principal Component Analysis ; Support Vector Machine ; },
abstract = {Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.},
}
@article {pmid24110758,
year = {2013},
author = {Nguyen, JS and Su, SW and Nguyen, HT},
title = {Experimental study on a smart wheelchair system using a combination of stereoscopic and spherical vision.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4597-4600},
doi = {10.1109/EMBC.2013.6610571},
pmid = {24110758},
issn = {2694-0604},
mesh = {Adult ; Artificial Intelligence ; *Brain-Computer Interfaces ; Depth Perception ; Equipment Design ; Female ; Hand ; Humans ; Male ; Middle Aged ; Quadriplegia ; Vision, Ocular ; *Wheelchairs ; Young Adult ; },
abstract = {This paper is concerned with the experimental study performance of a smart wheelchair system named TIM (Thought-controlled Intelligent Machine), which uses a unique camera configuration for vision. Included in this configuration are stereoscopic cameras for 3-Dimensional (3D) depth perception and mapping ahead of the wheelchair, and a spherical camera system for 360-degrees of monocular vision. The camera combination provides obstacle detection and mapping in unknown environments during real-time autonomous navigation of the wheelchair. With the integration of hands-free wheelchair control technology, designed as control methods for people with severe physical disability, the smart wheelchair system can assist the user with automated guidance during navigation. An experimental study on this system was conducted with a total of 10 participants, consisting of 8 able-bodied subjects and 2 tetraplegic (C-6 to C-7) subjects. The hands-free control technologies utilized for this testing were a head-movement controller (HMC) and a brain-computer interface (BCI). The results showed the assistance of TIM's automated guidance system had a statistically significant reduction effect (p-value = 0.000533) on the completion times of the obstacle course presented in the experimental study, as compared to the test runs conducted without the assistance of TIM.},
}
@article {pmid24110684,
year = {2013},
author = {Yeh, WL and Huang, YC and Chiou, JH and Duann, JR and Chiou, JC},
title = {A self produced mother wavelet feature extraction method for motor imagery brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4302-4305},
doi = {10.1109/EMBC.2013.6610497},
pmid = {24110684},
issn = {2694-0604},
mesh = {*Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electrodes ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; *Motor Activity ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {Motor imagery base brain-computer interface (BCI) is an appropriate solution for stroke patient to rehabilitate and communicate with external world. For such applications speculating whether the subjects are doing motor imagery is our primary mission. So the problem turns into how to precisely classify the two tasks, motor imagery and idle state, by using the subjects' electroencephalographic (EEG) signals. Feature extraction is a factor that significantly affects the classification result. Based on the concept of Continuous Wavelet Transform, we proposed a wavelet-liked feature extraction method for motor imagery discrimination. And to compensate the problem that the feature varies between subjects, we use the subjects' own EEG signals as the mother wavelet. After determining the feature vector, we choose Bayes linear discriminant analysis (LDA) as our classifier. The BCI competition III dataset IVa is used to evaluate the classification performance. Comparing with variance and fast Fourier transform (FFT) methods in feature extraction, 2.02% and 16.96% improvement in classification accuracy are obtained in this work respectively.},
}
@article {pmid24110678,
year = {2013},
author = {Bender, T and Kjaer, TW and Thomsen, CE and Sorensen, HB and Puthusserypady, S},
title = {Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4279-4282},
doi = {10.1109/EMBC.2013.6610491},
pmid = {24110678},
issn = {2694-0604},
mesh = {*Adaptation, Ocular ; Adolescent ; Adult ; *Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Young Adult ; },
abstract = {This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.},
}
@article {pmid24110671,
year = {2013},
author = {Shimpo, K and Tanaka, T},
title = {Asynchronous brain-computer interfacing based on intended movement direction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4251-4254},
doi = {10.1109/EMBC.2013.6610484},
pmid = {24110671},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Eye Movements ; Humans ; *Intention ; Male ; *Movement ; Parietal Lobe/physiology ; Pattern Recognition, Automated ; },
abstract = {A brain-computer interface (BCI) is a technique for controlling devices with the measured human brain activities. Especially, an asynchronous BCI is one of the most important topics since practical input interfaces are incomplete without self-paced inputs. In order to construct an asynchronous BCI, it is essential to recognize the standby state, where a user enters no commands. In this paper, we propose a novel method for detecting the standby state and develop an asynchronous BCI based on event-related potentials with the intended movement direction.We conducted online experiments with developed asynchronous BCI. As a result, all three subjects showed considerable recognition accuracies.},
}
@article {pmid24110668,
year = {2013},
author = {Sampanna, R and Mitaim, S},
title = {Noise enhanced array signal detection in P300 speller paradigm using ICA-based subspace projections.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4239-4242},
doi = {10.1109/EMBC.2013.6610481},
pmid = {24110668},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Normal Distribution ; *Signal Processing, Computer-Assisted ; *Signal-To-Noise Ratio ; Stochastic Processes ; },
abstract = {This paper explores how noise can improve prediction accuracy of the Event-Related Potential (ERP) based on P300 signals. We propose an array of ICA-Based P300 processing systems with additive white Gaussian noise. The array system attains maximum accuracy when noise intensity is not zero and thus the system shows the stochastic resonance effect. The prediction accuracy increases as the number of stages of the array increases. Experimental results show that increasing the array size with the proper amount of noise can improve the accuracy of the original P300 signal detection using ICA-based subspace projection technique.},
}
@article {pmid24110667,
year = {2013},
author = {Mumtaz, W and Xia, L and Malik, AS and Mohd Yasin, MA},
title = {EEG classification of physiological conditions in 2D/3D environments using neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4235-4238},
doi = {10.1109/EMBC.2013.6610480},
pmid = {24110667},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; *Electroencephalography ; Entropy ; Environment ; Fractals ; Humans ; Language ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Video Games ; },
abstract = {Higher classification accuracy is more desirable for brain computer interface (BCI) applications. The accuracy can be achieved by appropriate selection of relevant features. In this paper a new scheme is proposed based on six different nonlinear features. These features include Sample entropy (SampEn), Composite permutation entropy index (CPEI), Approximate entropy (ApEn), Fractal dimension (FD), Hurst exponent (H) and Hjorth parameters (complexity and mobility). These features are decision variables for classification of physiological conditions: Eyes Open (EO), Eyes Closed (EC), Game Playing 2D (GP2D), Game playing 3D active (GP3DA) and Game playing 3D passive (GP3DP). Results show that the scheme can successfully classify the conditions with an accuracy of 88.9%.},
}
@article {pmid24110666,
year = {2013},
author = {Onishi, A and Natsume, K},
title = {Ensemble regularized linear discriminant analysis classifier for P300-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {4231-4234},
doi = {10.1109/EMBC.2013.6610479},
pmid = {24110666},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Discriminant Analysis ; *Event-Related Potentials, P300 ; Humans ; Principal Component Analysis ; },
abstract = {This paper demonstrates a better classification performance of an ensemble classifier using a regularized linear discriminant analysis (LDA) for P300-based brain-computer interface (BCI). The ensemble classifier with an LDA is sensitive to the lack of training data because covariance matrices are estimated imprecisely. One of the solution against the lack of training data is to employ a regularized LDA. Thus we employed the regularized LDA for the ensemble classifier of the P300-based BCI. The principal component analysis (PCA) was used for the dimension reduction. As a result, an ensemble regularized LDA classifier showed significantly better classification performance than an ensemble un-regularized LDA classifier. Therefore the proposed ensemble regularized LDA classifier is robust against the lack of training data.},
}
@article {pmid24110584,
year = {2013},
author = {Huang, L and Huang, X and Wang, YT and Wang, Y and Jung, TP and Cheng, CK},
title = {Empirical mode decomposition improves detection of SSVEP.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3901-3904},
doi = {10.1109/EMBC.2013.6610397},
pmid = {24110584},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Normal Distribution ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Software ; },
abstract = {Steady State Visual Evoked Potentials (SSVEPs) have been used to quantify attention-related neural activity to visual targets. This study investigates how empirical mode decomposition (EMD) can improve detection accuracy and rate of SSVEPs. First, the scalp-recorded electroencephalogram (EEG) signals are decomposed into intrinsic mode functions (IMFs) by EMD. Then, IMF components accounting for SSVEPs are selected for target frequency detection. Finally, target frequency is identified by two methods: Gabor transform and Canonical Correlation Analysis (CCA). This study quantitatively explores the impact of EMD on the target frequency detection. Empirical results show that the EMD improves their recognition accuracy when Gabor transform is used, even in a shorter Gaussian window, but has little effects on the performance of the CCA. Further, this study finds that harmonic responses of the target frequency can be used to enhance the SSVEP detection both for the Gabor transform and CCA.},
}
@article {pmid24110507,
year = {2013},
author = {Beuchat, NJ and Chavarriaga, R and Degallier, S and Millán, Jdel R},
title = {Offline decoding of upper limb muscle synergies from EEG slow cortical potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3594-3597},
doi = {10.1109/EMBC.2013.6610320},
pmid = {24110507},
issn = {2694-0604},
mesh = {Adult ; Biomechanical Phenomena ; Case-Control Studies ; *Electroencephalography ; Humans ; *Membrane Potentials ; Middle Aged ; Movement/physiology ; Muscles/*physiology/*physiopathology ; *Signal Processing, Computer-Assisted ; Stroke/physiopathology ; *Upper Extremity ; },
abstract = {Muscle synergies are thought to be the building blocks used by the central nervous system to control the underdetermined problem of muscles activation. Decoding these synergies from EEG could provide useful tools for BCI-controlled orthotic devices. In this paper, we assess the possibility of decoding muscle synergies from EEG slow cortical potentials in two healthy subjects and two stroke patients performing a center-out reaching task. We were able to successfully decode the extracted muscle synergies in both healthy subject and one patient.},
}
@article {pmid24110385,
year = {2013},
author = {Ng, KB and Bradley, AP and Cunnington, R},
title = {Effect of posterized naturalistic stimuli on SSVEP-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3105-3108},
doi = {10.1109/EMBC.2013.6610198},
pmid = {24110385},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Photic Stimulation ; Young Adult ; },
abstract = {Most visual stimuli used in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) are simple and elementary. Examples of such stimuli are checkerboard patterns and sinusoidal gratings. These stimuli exhibit distinct contrasts and edges that conform well to the simple cortical cells behavior first observed by Hubel and Weisel. Hence, they are effective in eliciting VEP. On the other hand, the use of naturalistic stimuli is known to advance our understanding of early visual system. However, naturalistic stimuli are spectrally and spatially complex. They may not elicit the optimal VEP and the results obtained may not easily correlate to the stimulus parameters. Hence, we proposed to posterize natural images to generate naturalistic stimuli suitable for SSVEP-BCI. The posterization process considers both the edge and contrast information of the input image. This study elucidates the effect of posterized naturalistic stimuli on SSVEP amplitudes and phases by exploring the relationship between the number of tones of posterized visual stimuli and their effect on the power spectra and phase synchrony of attended stimuli. Results show that posterized visual stimuli at four tone display a significant effect on the dominant frequency response. Our findings suggest the effectiveness of posterized naturalistic stimuli and should advance the use of naturalistic stimuli in SSVEP-BCI.},
}
@article {pmid24110384,
year = {2013},
author = {Xia, B and An, D and Chen, C and Xie, H and Li, J},
title = {A mental switch-based asynchronous brain-computer interface for 2D cursor control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3101-3104},
doi = {10.1109/EMBC.2013.6610197},
pmid = {24110384},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Humans ; Imagery, Psychotherapy ; Male ; Probability ; Time Factors ; Young Adult ; },
abstract = {In the present study, we developed a mental switch-based asynchronous brain-computer interface for 2D cursor control. Two mental switches were designed: one was to switch from non-intentional to intentional control state, and the other one for conducting the reverse process. 2D control and mental switches are all based on three-class motor imagery. With four subjects participating in the study, the experimental results demonstrated the efficiency of the proposed asynchronous 2D control strategy.},
}
@article {pmid24110383,
year = {2013},
author = {Carlson, T and Tonin, L and Perdikis, S and Leeb, R and del R Millán, J},
title = {A hybrid BCI for enhanced control of a telepresence robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3097-3100},
doi = {10.1109/EMBC.2013.6610196},
pmid = {24110383},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Electromyography ; Humans ; Male ; Robotics/*instrumentation ; Telemedicine/*instrumentation ; },
abstract = {Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work, we propose to exploit the user's residual muscular activity to provide a fast and reliable control channel, which can start/stop the telepresence robot at any moment. Our preliminary results show that not only does this hybrid approach increase the accuracy, but it also helps to reduce the workload and was the preferred control paradigm of all the participants.},
}
@article {pmid24110382,
year = {2013},
author = {Leeb, R and Gwak, K and Kim, DS and del R Millán, J},
title = {Freeing the visual channel by exploiting vibrotactile BCI feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {3093-3096},
doi = {10.1109/EMBC.2013.6610195},
pmid = {24110382},
issn = {2694-0604},
mesh = {Adult ; Attention/physiology ; *Brain-Computer Interfaces ; Electric Stimulation ; Electrodes ; Electroencephalography ; *Feedback, Sensory ; Female ; Humans ; Male ; Touch/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Controlling a brain-actuated device requires the participant to look at and to split his attention between the interaction of the device with its environment and the status information of the Brain-Computer Interface (BCI). Such parallel visual tasks are partly contradictory, with the goal of achieving a good and natural device control. Is there a possibility to free the visual channel from one of these tasks? To address this, a stimulation system based on 6 coin-motors is developed, which provides a spatially continuous tactile illusion as BCI feedback, so that the visual channel can be devoted to the device. Several experiments are conducted in this work, to optimize the tactile illusion patterns and to investigate the influence on the electroencephalogram (EEG). Finally, 6 healthy BCI participants compare visual with tactile feedback in online BCI recordings. The developed stimulator can be used without interfering with the EEG. All subjects are able to perceive this type of tactile feedback well, and no statistical degradation in the online BCI performance could be identified between visual and tactile feedback.},
}
@article {pmid24110330,
year = {2013},
author = {Makkena, G and Bvvsn, PR and Srinivas, MB},
title = {Uniform approximation of Gaussian wavelet for biomedical signal processing in analog domain.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2886-2889},
doi = {10.1109/EMBC.2013.6610143},
pmid = {24110330},
issn = {2694-0604},
mesh = {Adult ; Biomedical Engineering/methods ; *Brain-Computer Interfaces ; Evoked Potentials ; Female ; Humans ; Male ; Normal Distribution ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; Wavelet Analysis ; Young Adult ; },
abstract = {Signal processing in analog domain is favorable when power consumption is a critical design constraint. Continuous Wavelet Transform (CWT), which is increasingly being used in characterizing biomedical signals, when implemented in analog domain consumes less power provided the mother wavelet is properly approximated. This paper presents an approximation of Gaussian wavelet by making use of the Uniform approximation. Simulations of the approximated wavelet and the actual wavelet in MATLAB are performed and the results discussed. Simulations show that (i) approximation obtained closely matches the mother wavelet chosen and (ii) a stable approximation which helps in physical realization using any circuit design methodology.},
}
@article {pmid24110329,
year = {2013},
author = {Tu, Y and Huang, G and Hung, YS and Hu, L and Hu, Y and Zhang, Z},
title = {Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2882-2885},
doi = {10.1109/EMBC.2013.6610142},
pmid = {24110329},
issn = {2694-0604},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Neurologic Examination ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Time Factors ; Wavelet Analysis ; Young Adult ; },
abstract = {Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.},
}
@article {pmid24110311,
year = {2013},
author = {Wronkiewicz, M and Larson, E and Lee, AK},
title = {Towards a next-generation hearing aid through brain state classification and modeling.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2808-2811},
pmid = {24110311},
issn = {2694-0604},
support = {F32 DC012456/DC/NIDCD NIH HHS/United States ; T32 DC000018/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Brain Mapping ; Brain-Computer Interfaces ; Computer Simulation ; *Hearing Aids ; Humans ; *Models, Biological ; Prefrontal Cortex/physiology ; },
abstract = {Traditional brain-state classifications are primarily based on two well-known neural biomarkers: P300 and motor imagery / event-related frequency modulation. Currently, many brain-computer interface (BCI) systems have successfully helped patients with severe neuromuscular disabilities to regain independence. In order to translate this neural engineering success to hearing aid applications, we must be able to capture brain waves across the population reliably in cortical regions that have not previously been incorporated in these systems before, for example, dorsolateral prefrontal cortex (DLPFC) and right temporoparietal junction. Here, we present a brain-state classification framework that incorporates individual anatomical information and accounts for potential anatomical and functional differences across subjects by applying appropriate cortical weighting functions prior to the classification stage. Using an inverse imaging approach, use simulated EEG data to show that our method can outperform the traditional brain-state classification approach that trains only on individual subject's data without considering data available at a population level.},
}
@article {pmid24110309,
year = {2013},
author = {O'Sullivan, JA and Crosse, MJ and Power, AJ and Lalor, EC},
title = {The effects of attention and visual input on the representation of natural speech in EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2800-2803},
doi = {10.1109/EMBC.2013.6610122},
pmid = {24110309},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Behavior ; Electroencephalography/*methods ; Female ; Humans ; Male ; Speech/*physiology ; Visual Perception/*physiology ; },
abstract = {Traditionally, the use of electroencephalography (EEG) to study the neural processing of natural stimuli in humans has been hampered by the need to repeatedly present discrete stimuli. Progress has been made recently by the realization that cortical population activity tracks the amplitude envelope of speech stimuli. This has led to studies using linear regression methods which allow the presentation of continuous speech. One such method, known as stimulus reconstruction, has so far only been utilized in multi-electrode cortical surface recordings and magnetoencephalography (MEG). Here, in two studies, we show that such an approach is also possible with EEG, despite the poorer signal-to-noise ratio of the data. In the first study, we show that it is possible to decode attention in a naturalistic cocktail party scenario on a single trial (≈60 s) basis. In the second, we show that the representation of the envelope of auditory speech in the cortex is more robust when accompanied by visual speech. The sensitivity of this inexpensive, widely-accessible technology for the online monitoring of natural stimuli has implications for the design of future studies of the cocktail party problem and for the implementation of EEG-based brain-computer interfaces.},
}
@article {pmid24110306,
year = {2013},
author = {Jiang, L and Cai, B and Xiao, S and Wang, Y and Chen, W and Zheng, X},
title = {Semantic-based sound retrieval by ERP in rapid serial auditory presentation paradigm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2788-2791},
doi = {10.1109/EMBC.2013.6610119},
pmid = {24110306},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; *Auditory Perception ; Evoked Potentials, Auditory/*physiology ; Humans ; Male ; ROC Curve ; Scalp ; *Semantics ; *Sound ; Time Factors ; Young Adult ; },
abstract = {"Semantic gap" is the major bottleneck of semantic-based multimedia retrieval technique in the field of information retrieval. Studies have shown that robust semantic-based image retrieval can be achieved by single-trial visual evoked event related potential (ERP) detection. However, the question remains whether auditory evoked ERP can be utilized to achieve semantic-based sound retrieval. In this paper, we investigated this question in the rapid serial auditory presentation (RSAP) paradigm. Eight BCI-naïve participants were instructed to perform target detection in RSAP sequences with the vocalizations of 8 familiar animals as sound stimuli, and we compared ERP components and single-trial ERP classification performance between two conditions, the target was a predefined specific one, and the targets were different but belonged to the same semantic category (i.e., semantic-based sound retrieval). Although the amplitudes of ERP components (e.g., N2 and P3) and classification performance decreased a little due to the difficulty of the semantic-based sound retrieval tasks, the best two participants still achieved the area under the receive operating characteristic curve (AUC) of single-trial ERP detection more than 0.77. It suggested that semantic-based sound retrieval by auditory evoked ERP was potentially feasible.},
}
@article {pmid24110305,
year = {2013},
author = {Gonuguntla, V and Wang, Y and Veluvolu, KC},
title = {Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2784-2787},
doi = {10.1109/EMBC.2013.6610118},
pmid = {24110305},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electrodes ; Electroencephalography/*methods ; Humans ; *Imagery, Psychotherapy ; Task Performance and Analysis ; },
abstract = {Recent works on brain functional analysis have highlighted the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is fundamental to relate mechanisms of brain functions and the basis for classifying brain states. This work analyzes the network mechanisms related to motor imagery tasks based on synchronization measure (PLV (phase-locking value)) in EEG alpha-band for the BCI Competition IV Data Set. Based on network dissimilarities between motor imagery and rest tasks, important nodes and important channel pairs corresponding to tasks for all subjects are identified. The identified important channel pairs corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even more higher variation corresponding to tasks. This paper demonstrates the potential of these identified channel pairs in task classification for future BCI applications.},
}
@article {pmid24110303,
year = {2013},
author = {Riechmann, H and Finke, A and Ritter, H},
title = {Hierarchical Codebook Visually Evoked Potentials for fast and flexible BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2776-2779},
doi = {10.1109/EMBC.2013.6610116},
pmid = {24110303},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation ; Task Performance and Analysis ; },
abstract = {Brain-Computer Interfaces provide a direct communication channel from the brain to a technical device. One major problem in state-of-the-art BCIs is their low communication speed. BCIs based on Codebook Visually Evoked Potentials (cVEP) outperform all other non-invasive approaches in terms of information transfer rate. Used only in spelling tasks so far, more flexibility with respect to stimulus structure and properties is needed. We propose using hierarchical codebook vectors together with varying color schemes to increase the stimulus flexibility. An off-line study showed that our novel hcVEP approach is capable of discriminating groups of targets after only 250 ms of stimulus flickering and the final target within the group after 1s. The accuracies are 81% and 67%, respectively. Different color schemes (black/white and green/red) are equally effective.},
}
@article {pmid24110302,
year = {2013},
author = {Yao, L and Sheng, X and Meng, J and Zhang, D and Zhu, X},
title = {Mechanical vibrotactile stimulation effect in motor imagery based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2772-2775},
doi = {10.1109/EMBC.2013.6610115},
pmid = {24110302},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Male ; Motor Activity/*physiology ; Online Systems ; Physical Stimulation ; Time Factors ; Touch/*physiology ; *Vibration ; },
abstract = {Sensory stimulation played a critical role in both motivating subject's anticipation in brain-computer interface but also enhancing the sensory-motor interaction and closing the sensory motor loop. In this paper, mechanical vibrotactile stimulation effect in motor imagery was evaluated on 10 healthy subjects, and preliminary results showed that 5 subjects would achieve a reliable control above 80% with sensory stimulation as comparable with motor imagery without any stimulation. Besides, 3 subjects reached a better control with approximately 70% as compared with a chance level of 50% in motor imagery without sensory stimulation. Further analysis showed subject who was poor in conventional motor imagery condition exhibited enhanced R(2) value distribution in motor imagery with sensory stimulation condition. Meanwhile there was sensorimotor rhythmic enhancement both at upper alpha band and upper beta band in some subjects. But these rhythmic changes resulted performance reduction as incongruence of training and testing sets effect from off-line analysis. This research provided some guidance in integration of the sensory stimulation channel with motor imagery based BCI system.},
}
@article {pmid24110301,
year = {2013},
author = {Dangi, S and Gowda, S and Carmena, JM},
title = {Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2768-2771},
doi = {10.1109/EMBC.2013.6610114},
pmid = {24110301},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; *Algorithms ; Animals ; *Brain-Computer Interfaces ; Computer Simulation ; Humans ; Neurons/physiology ; Primates ; },
abstract = {Closed-loop decoder adaptation (CLDA) is an emerging paradigm for improving or maintaining the online performance of brain-machine interfaces (BMIs). Here, we present Likelihood Gradient Ascent (LGA), a novel CLDA algorithm for a Kalman filter (KF) decoder that uses stochastic, gradient-based corrections to update KF parameters during closed-loop BMI operation. LGA's gradient-based paradigm presents a variety of potential advantages over other "batch" CLDA methods, including the ability to update decoder parameters on any time-scale, even on every decoder iteration. Using a closed-loop BMI simulator, we compare the LGA algorithm to the Adaptive Kalman Filter (AKF), a partially gradient-based CLDA algorithm that has been previously tested in non-human primate experiments. In contrast to the AKF's separate mean-squared error objective functions, LGA's update rules are derived directly from a single log likelihood objective, making it one step towards a potentially optimal continuously adaptive CLDA algorithm for BMIs.},
}
@article {pmid24110295,
year = {2013},
author = {ur Rehman, S and Kamboh, AM},
title = {A new architecture for neural signal amplification in implantable brain machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2744-2747},
doi = {10.1109/EMBC.2013.6610108},
pmid = {24110295},
issn = {2694-0604},
mesh = {Amplifiers, Electronic ; Analog-Digital Conversion ; *Brain-Computer Interfaces ; Humans ; *Neural Prostheses ; Neurons/*physiology ; *Prosthesis Implantation ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {This paper reports a new architecture for variable gain-bandwidth amplification of neural signals to be used in implantable multi-channel recording systems. The two most critical requirements in such a front-end circuit are low power consumption and chip area, especially as number of channels increases. The presented architecture employs a single super-performing amplifier, with tunable gain and bandwidth, combined with several low-key preamplifiers and multiplexors for multi-channel recordings. This is in contrast to using copies of high performing amplifier for each channel as is typically reported in earlier literature. The resulting circuits consume lower power and require smaller area as compared to existing designs. Designed in 0.5 µmCMOS, the 8-channel prototype can simultaneously record Local Field Potentials and neural spikes, with an effective power consumption of 3.5 µW per channel and net core area of 0.407 mm(2).},
}
@article {pmid24110243,
year = {2013},
author = {Dragas, J and Jäckel, D and Franke, F and Hierlemann, A},
title = {An unsupervised method for on-chip neural spike detection in multi-electrode recording systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2535-2538},
pmid = {24110243},
issn = {2694-0604},
support = {267351/ERC_/European Research Council/International ; },
mesh = {Action Potentials ; Algorithms ; Brain-Computer Interfaces ; Electrodes ; Humans ; *Lab-On-A-Chip Devices ; Neurons/*physiology ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Emerging multi-electrode-based brain-machine interfaces (BMIs) and large multi-electrode arrays used in in vitro experiments, enable recording of single neuron's activity on multiple electrodes and allow for an in-depth investigation of neural preparations, even at a sub-cellular level. However, the use of these devices entails stringent area and power consumption constraints for the signal-processing hardware units. In addition, the high autonomy of these units and an ability to automatically adapt to changes in the recorded neural preparations is required. Implementing spike detection in close proximity to recording electrodes offers the advantage of reducing the transmission data bandwidth. By eliminating the need of transmitting the full, redundant recordings of neural activity and by transmitting only the spike waveforms or spike times, significant power savings can be achieved in the majority of cases. Here, we present a low-complexity, unsupervised, adaptable, real-time spike-detection method targeting multi-electrode recording devices and compare this method to other spike-detection methods with regard to complexity and performance.},
}
@article {pmid24110174,
year = {2013},
author = {Ortner, R and Lugo, Z and Prückl, R and Hintermüller, C and Noirhomme, Q and Guger, C},
title = {Performance of a tactile P300 speller for healthy people and severely disabled patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2259-2262},
doi = {10.1109/EMBC.2013.6609987},
pmid = {24110174},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; *Persons with Disabilities ; Event-Related Potentials, P300/*physiology ; Female ; *Healthy Volunteers ; Humans ; Male ; Physical Stimulation ; Quadriplegia/physiopathology ; Touch/*physiology ; *Vocabulary ; Young Adult ; },
abstract = {P300 based Brain-Computer Interfaces (BCIs) for communication are well known since many years. Most of them use visual stimuli to elicit evoked potentials because it is easy to integrate a high number of different classes into the paradigm. Nevertheless, a BCI that depends on visual stimuli is sometimes not feasible due to the presence of visual impairment in patients with severe brain injuries. In this case, it could be possible to use auditory or somatosensory stimulation. In this publication a vibrotactile P300 based BCI is introduced. Two different approaches were tested: a first approach using two stimulators and a second one that utilizes three stimulators for emitting the stimuli. The two paradigms were tested on 16 users: A group of ten healthy users and a second group comprising of 6 patients suffering Locked-In Syndrome. The control accuracy was calculated for both groups and both approaches, proving the feasibility of the device, not only for healthy people but also in severely disabled patients. In a second step we evaluated the influence of the number of stimuli on the accuracy. It was shown that in many cases the maximum accuracy was already reached with a small number of stimuli, this could be used in future tests to speed up the Information transfer rate.},
}
@article {pmid24110173,
year = {2013},
author = {Nam, Y and Cichocki, A and Choi, S},
title = {Common spatial patterns for steady-state somatosensory evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2255-2258},
doi = {10.1109/EMBC.2013.6609986},
pmid = {24110173},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Humans ; Male ; Physical Stimulation ; },
abstract = {Steady-state somatosensory evoked potential (SSSEP) is a recently developing brain-computer interface (BCI) paradigm where the brain response to tactile stimulation of a specific frequency is used. Thus far, spatial information was not examined in depth in SSSEP BCI, because frequency information was regarded as the main concern of SSSEP analysis. However, given that the somatosensory cortex areas, each of which correspond to a different body part, are well clustered, we can assume that the spatial information could be beneficial for SSSEP analysis. Based on this assumption, we apply the common spatial pattern (CSP) method, which is the spatial feature extraction method most widely used for the motor imagery BCI paradigm, to SSSEP BCI. Experimental results show that our approach, where two CSP methods are applied to the signal of each frequency band, has a performance improvement from 70% to 75%.},
}
@article {pmid24110172,
year = {2013},
author = {Akram, F and Han, HS and Jeon, HJ and Park, K and Park, SH and Cho, J and Kim, TS},
title = {An efficient words typing P300-BCI system using a modified T9 interface and random forest classifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2251-2254},
doi = {10.1109/EMBC.2013.6609985},
pmid = {24110172},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Support Vector Machine ; *Task Performance and Analysis ; Time Factors ; *Vocabulary ; },
abstract = {The conventional P300-based character spelling BCI system consists of a character presentation paradigm and a classification system. In this paper, we propose modifications to both in order to increase the word typing speed and accuracy. In the paradigm part, we have modified the T9 (Text on Nine keys) interface which is similar to the keypad of mobile phones being used for text messaging. Then we have integrated a custom-built dictionary to give word suggestions to a user while typing. The user can select one out of the given suggestions to complete word typing. Our proposed paradigms significantly reduce the word typing time and make words typing more convenient by typing complete words with only few initial character spellings. In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects using the proposed BCI system. Our results demonstrate that our system increases typing speed significantly: our proposed system took an average time of 1.83 minutes per word, while typing ten random words, whereas the conventional spelling required 3.35 minutes for the same words under the same conditions, decreasing the typing time by 45.37%.},
}
@article {pmid24110171,
year = {2013},
author = {Anderson, NR and Blakely, T and Brunner, P and Krusienski, DJ and Moran, DW and Leuthardt, EC},
title = {High-frequency spectral changes in Dorsolateral Prefrontal Cortex for potential neuoroprosthetics.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2247-2250},
doi = {10.1109/EMBC.2013.6609984},
pmid = {24110171},
issn = {2694-0604},
support = {EB000856/EB/NIBIB NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Animals ; Child ; Electrodes ; Electroencephalography ; Epilepsy/physiopathology ; Female ; Humans ; Male ; Middle Aged ; *Neural Prostheses ; Prefrontal Cortex/*physiopathology ; Task Performance and Analysis ; Young Adult ; },
abstract = {Dorsolateral Prefrontal Cortex (DLPFC) has been associated with goal encoding in primates. Thus far, the majority of research involving DLPFC, including all electrophysiology studies, has been performed in non-human primates. In this paper, we explore the possibility of utilizing the cortical activity in DLPFC in humans for use in Brain-Computer Interfaces (BCIs). Electrocorticographic signals were recorded from seven patients with intractable epilepsy who had electrode coverage over DLPFC. These subjects performed a visuomotor target-based task to assess DLPFC's involvement in planning, execution, and accomplishment of the simple motor task. These findings demonstrate that there is a distinct high-frequency spectral component in DLPFC associated with accomplishment of the task. It is envisioned that these signals could potentially provide a novel verification of task accomplishment for a BCI.},
}
@article {pmid24110169,
year = {2013},
author = {Lee, JH and Lim, JH and Hwang, HJ and Im, CH},
title = {Development of a hybrid mental speller combining EEG-based brain-computer interface and webcam-based eye-tracking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2240-2242},
doi = {10.1109/EMBC.2013.6609982},
pmid = {24110169},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; *Electroencephalography ; Eye Movements/*physiology ; Female ; Humans ; Male ; Photography/*instrumentation ; *Vocabulary ; Young Adult ; },
abstract = {The main goal of this study was to develop a hybrid mental spelling system combining a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) technology and a webcam-based eye-tracker, which utilizes information from the brain electrical activity and eye gaze direction at the same time. In the hybrid mental spelling system, a character decoded using SSVEP was not typed if the position of the selected character was not matched with the eye direction information ('left' or 'right') obtained from the eye-tracker. Thus, the users did not need to correct a misspelled character using a 'BACKSPACE' key. To verify the feasibility of the developed hybrid mental spelling system, we conducted online experiments with ten healthy participants. Each participant was asked to type 15 English words consisting of 68 characters. As a result, 16.6 typing errors could be prevented on average, demonstrating that the implemented hybrid mental spelling system could enhance the practicality of our mental spelling system.},
}
@article {pmid24110168,
year = {2013},
author = {Han, CH and Hwang, HJ and Lim, JH and Im, CH},
title = {Development of an "eyes-closed" brain-computer interface system for communication of patients with oculomotor impairment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2236-2239},
doi = {10.1109/EMBC.2013.6609981},
pmid = {24110168},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Communication ; *Communication Devices for People with Disabilities ; *Persons with Disabilities ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; *Ocular Physiological Phenomena ; },
abstract = {The goal of this study was to develop a new steady-state visual evoked potential (SSVEP)-based BCI system, which can be applied to disabled individuals with impaired oculomotor function. The developed BCI system allows users to express their binary intentions without needing to open their eyes. To present visual stimuli, we used a pair of glasses with two LEDs flickering at different frequencies. EEG spectral patterns were classified in real time while participants were attending to one of the presented visual stimuli with their eyes closed. Through offline experiments performed with 11 healthy participants, we confirmed that SSVEP responses could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids, and could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants and one locked-in state patient were classified online. The average ITR of the online experiments reached to 10.83 bits/min with an average accuracy of 95.3 %. An online experiment applied to a patient with ALS showed a classification accuracy of 80 % and an ITR of 2.78 bits/min, demonstrating the practical feasibility of our BCI paradigm.},
}
@article {pmid24110167,
year = {2013},
author = {Nishifuji, S},
title = {Toward binary brain computer interface using steady-state visually evoked potential under eyes closed condition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2232-2235},
doi = {10.1109/EMBC.2013.6609980},
pmid = {24110167},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Ocular Physiological Phenomena ; Photic Stimulation ; Young Adult ; },
abstract = {It is highly difficult for severely amyotrophic lateral sclerosis and heavily spinal cord injury patients to use the brain computer interfaces (BCIs) based on the steady-state visual evoked potential (SSVEP) which need to control the direction of their eye gaze. We investigated amplitude change of the SSVEP associated with mental concentration on flicker to develop the SSVEP-based BCI usable under eyes-closed condition. Under the stimulus conditions of the flickering frequency of 10 Hz and the stimulus intensity of 5 lx, significant difference between the SSVEP amplitude in relaxed state and that in concentrated state was observed in the wide region of the scalp except the left frontal region, while such significance was also seen in the bilateral occipital lobes and left parietal region under the conditions of 14 Hz and 5 lx. Such an impact of mental concentration on the SSVEP amplitude was reproducible.},
}
@article {pmid24110166,
year = {2013},
author = {Sakamoto, K},
title = {The potential of multilateral analyses of neuronal activities in future brain-machine interface research.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2228-2231},
doi = {10.1109/EMBC.2013.6609979},
pmid = {24110166},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; Animals ; Behavior ; Brain/*physiology ; *Brain-Computer Interfaces ; Cortical Synchronization/physiology ; Macaca ; Neurons/*physiology ; Prefrontal Cortex/physiology ; *Research ; Time Factors ; },
abstract = {Current brain-machine interfaces are based on the implicit assumption that information encoded by neuronal activities does not change despite some recent physiological studies indicating that information encoded by neuronal activities changes. Here, we highlight the necessity for advanced decoding of neuronal activities. Especially, we discuss the advantages of multilateral analyses of neuronal activities, including synchronization and variability.},
}
@article {pmid24110165,
year = {2013},
author = {Amanpour, B and Erfanian, A},
title = {Classification of brain signals associated with imagination of hand grasping, opening and reaching by means of wavelet-based common spatial pattern and mutual information.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2224-2227},
doi = {10.1109/EMBC.2013.6609978},
pmid = {24110165},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electroencephalography ; Hand/*physiology ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {An important issue in designing a practical brain-computer interface (BCI) is the selection of mental tasks to be imagined. Different types of mental tasks have been used in BCI including left, right, foot, and tongue motor imageries. However, the mental tasks are different from the actions to be controlled by the BCI. It is desirable to select a mental task to be consistent with the desired action to be performed by BCI. In this paper, we investigated the detecting the imagination of the hand grasping, hand opening, and hand reaching in one hand using electroencephalographic (EEG) signals. The results show that the ERD/ERS patterns, associated with the imagination of hand grasping, opening, and reaching are different. For classification of brain signals associated with these mental tasks and feature extraction, a method based on wavelet packet, regularized common spatial pattern (CSP), and mutual information is proposed. The results of an offline analysis on five subjects show that the two-class mental tasks can be classified with an average accuracy of 77.6% using proposed method. In addition, we examine the proposed method on datasets IVa from BCI Competition III and IIa from BCI Competition IV.},
}
@article {pmid24110164,
year = {2013},
author = {Chang, MH and Park, KS},
title = {Frequency recognition methods for dual-frequency SSVEP based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2220-2223},
doi = {10.1109/EMBC.2013.6609977},
pmid = {24110164},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Pattern Recognition, Automated ; },
abstract = {Dual-frequency steady-state visual evoked potential (SSVEP) was suggested to generate more stimuli using a few flickering frequencies for brain-computer interface. Dual--frequency SSVEP peaks at more than two frequencies-both main and harmonic frequencies. However multi-frequency recognition strategy has not been investigated for dual-frequency SSVEP. In this paper, three modified power spectral density analysis (PSDA) methods and two modified canonical correlation analysis (CCA) methods were tested for dual-frequency SSVEP classification. Three methods among the five methods used conventional features or classification techniques, and the other two methods used modified features for harmonic frequencies. As a result, CCA with novel features showed the best BCI performance. Also the use of harmonic frequencies improved BCI performance of dual-frequency SSVEP.},
}
@article {pmid24110163,
year = {2013},
author = {Yilmaz, O and Cho, W and Braun, C and Birbaumer, N and Ramos-Murguialday, A},
title = {Movement related cortical potentials in severe chronic stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2216-2219},
doi = {10.1109/EMBC.2013.6609976},
pmid = {24110163},
issn = {2694-0604},
mesh = {Adult ; Aged ; Chronic Disease ; Electroencephalography ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Paresis/physiopathology ; Recovery of Function ; Stroke/*physiopathology ; },
abstract = {Movement related cortical potentials (MRCPs) have been studied for many years and controlled using brain computer interfaces (BCIs). Furthermore, MRCPs have been proposed as reliable and immediate indicators of cortical reorganizations in motor learning and after stroke. In this study MRCPs preceding and during hand movements in severe chronic stroke were investigated. Eight severely impaired (no residual finger extension) chronic stoke patients underwent EEG and EMG recordings during a cue triggered hand movement paradigm. Four patients presented subcortical lesions only while the other four presented mixed (cortical and subcortical) lesions. MRCPs were measured before (slow cortical potentials SCPs) and at movement onset (motor potentials MPs). SCPs were observed during paretic hand movements only. Latencies were longer and reached their negativity peak earlier during paretic hand movement. When dividing the patients in subcortical only and mixed lesion patients, we observed significantly bigger MP peak amplitudes over the lesioned hemisphere during paretic and healthy hand movements in subcortical stroke patients. Furthermore, we observed a significant difference in MP peak latency between subcortical and mixed stroke patients during paretic hand movements. We demonstrated for the first time significant differences between subcortical only and mixed (cortical and subcortical) stroke patients' MRCPs during motor preparation and execution. Furthermore, we demonstrated how stroke produces a longer MRCP and that lesion location affects MP peak amplitude and latency. Finally, we propose the use MRCP based BCIs to reduce their duration (towards normal) and induce motor function recovery.},
}
@article {pmid24110162,
year = {2013},
author = {Vilic, A and Kjaer, TW and Thomsen, CE and Puthusserypady, S and Sorensen, HB},
title = {DTU BCI speller: an SSVEP-based spelling system with dictionary support.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2212-2215},
doi = {10.1109/EMBC.2013.6609975},
pmid = {24110162},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Denmark ; *Dictionaries as Topic ; Electrodes ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Time Factors ; *Writing ; },
abstract = {In this paper, a new brain computer interface (BCI) speller, named DTU BCI speller, is introduced. It is based on the steady-state visual evoked potential (SSVEP) and features dictionary support. The system focuses on simplicity and user friendliness by using a single electrode for the signal acquisition and displays stimuli on a liquid crystal display (LCD). Nine healthy subjects participated in writing full sentences after a five minutes introduction to the system, and obtained an information transfer rate (ITR) of 21.94 ± 15.63 bits/min. The average amount of characters written per minute (CPM) is 4.90 ± 3.84 with a best case of 8.74 CPM. All subjects reported systematically on different user friendliness measures, and the overall results indicated the potentials of the DTU BCI Speller system. For subjects with high classification accuracies, the introduced dictionary approach greatly reduced the time it took to write full sentences.},
}
@article {pmid24110161,
year = {2013},
author = {Zhang, H and Liu, Y and Liang, J and Cao, J and Zhang, L},
title = {Gaussian mixture modeling in stroke patients' rehabilitation EEG data analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2208-2211},
doi = {10.1109/EMBC.2013.6609974},
pmid = {24110161},
issn = {2694-0604},
mesh = {Aged ; Algorithms ; Brain ; Brain-Computer Interfaces ; Electric Stimulation ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; *Models, Theoretical ; Motor Activity ; Normal Distribution ; *Statistics as Topic ; Stroke/physiopathology ; *Stroke Rehabilitation ; },
abstract = {Traditional 2-class Motor Imagery (MI) Electroencephalography (EEG) classification approaches like Common Spatial Pattern (CSP) and Support Vector Machine (SVM) usually underperform when processing stroke patients' rehabilitation EEG which are flooded with unknown irregular patterns. In this paper, the classical CSP-SVM schema is improved and a feature learning method based on Gaussian Mixture Model (GMM) is utilized for depicting patients' imagery EEG distribution features. We apply the proposed modeling program in two different modules of our online BCI-FES rehabilitation platform and achieve a relatively higher discrimination accuracy. Sufficient observations and test cases on patients' MI data sets have been implemented for validating the GMM model. The results also reveal some working mechanisms and recovery appearances of impaired cortex during the rehabilitation training period.},
}
@article {pmid24110160,
year = {2013},
author = {Liu, Y and Li, M and Zhang, H and Li, J and Jia, J and Wu, Y and Cao, J and Zhang, L},
title = {Single-trial discrimination of EEG signals for stroke patients: a general multi-way analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2204-2207},
doi = {10.1109/EMBC.2013.6609973},
pmid = {24110160},
issn = {2694-0604},
mesh = {Aged ; Algorithms ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; },
abstract = {It has been demonstrated that Brain-Computer Interface (BCI), combined with Functional Electrical Stimulation (FES), is an effective and efficient way for post-stroke patients to restore motor function. However, traditional feature extraction methods, such as Common Spatial Pattern (CSP), do not work well for post-stroke patients' EEG data due to its irregular patterns. In this study, we introduce a novel tensorbased feature extraction algorithm, which takes both spatial-spectral-temporal features of EEG data into consideration. EEG data recorded from post-stroke patients is used for simulation experiments to assess the effectiveness of the proposed algorithm. The results show that the the proposed algorithm outperforms some traditional algorithms.},
}
@article {pmid24110159,
year = {2013},
author = {Li, J and Liu, Y and Lu, Z and Zhang, L},
title = {A competitive brain computer interface: multi-person car racing system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2200-2203},
doi = {10.1109/EMBC.2013.6609972},
pmid = {24110159},
issn = {2694-0604},
mesh = {*Automobile Driving ; Brain/physiology ; *Brain-Computer Interfaces ; Electrodes ; Humans ; Male ; Young Adult ; },
abstract = {Brain computer interface (BCI) technique is successfully utilized to bridge the interruption between brain and peripheral nerves and muscles, and to establish a new pathway making brain directly output information (or command). Up to now, a majority of BCI systems are developed to restore communication ability or movement functionality for people with severe disabilities, especially for paralyzed patients. To our best knowledge, other researchers haven't developed a multi-person BCI with competitive mode. Therefore, in this paper, we introduced a multi-person car racing system, which allows more than one person to play game at the same time and they can compete with each other for the aim of first reaching destination. The reason of development of car racing system has two aspects. At one hand, we introduced BCI to entertainment industry and provided a prototype for entertainment. At the other hand, we proposed a competitive mode for BCI. According to practical evaluation, the results demonstrated that our proposed system achieved a good performance.},
}
@article {pmid24110158,
year = {2013},
author = {Zhang, H and Chavarriaga, R and Gheorghe, L and Millán, Jdel R},
title = {Inferring driver's turning direction through detection of error related brain activity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2196-2199},
doi = {10.1109/EMBC.2013.6609971},
pmid = {24110158},
issn = {2694-0604},
mesh = {Adult ; *Automobile Driving ; Brain/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; ROC Curve ; },
abstract = {This work presents EEG-based Brain-computer interface (BCI) that uses error related brain activity to improve the prediction of driver's intended turning direction. In experiments while subjects drive in a realistic car simulator, we show a directional cue before reaching intersection, and analyze error related EEG potential to infer if the presented direction coincides with the driver's intention. In this protocol, the directional cue provides an initial estimation of the driving direction (based on EEG, environmental or previous driving habits), and we focus on the recognition of error-potentials it may elicit. Experiments with 7 healthy human subjects yield an average classification 0.69 ± 0.16, which confirms the feasibility of decoding these signals to help estimating driver's turning direction. This study can be further exploited by intelligent cars to tune their driving assistant systems to improve their performance and enhance the driving experience.},
}
@article {pmid24110156,
year = {2013},
author = {Bamdadian, A and Guan, C and Ang, KK and Xu, J},
title = {Improving session-to-session transfer performance of motor imagery-based BCI using Adaptive Extreme Learning Machine.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2188-2191},
doi = {10.1109/EMBC.2013.6609969},
pmid = {24110156},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; Humans ; Imagery, Psychotherapy ; Motor Activity ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Non-stationarity of electroencephalograph (EEG) data from session-to-session transfer is one of the challenges for EEG-based brain-computer interface systems, which can inversely affect their performance. Among methods proposed to address non-stationarity, adaptation is a promising method. In this study, an adaptive extreme learning machine (AELM) is proposed to update the initial classifier from the calibration session by using chunks of EEG data from the evaluation session whereby the common spatial pattern (CSP) algorithm is used to extract the most discriminative features. The effectiveness of the proposed algorithm is on motor imagery data collected from 12 healthy subjects during a calibration session and an evaluation session on a separate day. The results from the proposed AELM were compared with non-adaptive ELM and SVM classifiers. The results showed that AELM was significantly better (p=0.03). Moreover, the results also showed that accumulating the evaluation session data and using them for adapting the classifier will significantly improve the performance (p=0.001). Hence, the proposed AELM is effective in addressing the non-stationarity of EEG signal for online BCI systems.},
}
@article {pmid24110155,
year = {2013},
author = {Mullen, T and Kothe, C and Chi, YM and Ojeda, A and Kerth, T and Makeig, S and Cauwenberghs, G and Jung, TP},
title = {Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2184-2187},
pmid = {24110155},
issn = {2694-0604},
support = {R01 MH084819/MH/NIMH NIH HHS/United States ; R01 NS047293/NS/NINDS NIH HHS/United States ; 1R01MH084819-03/MH/NIMH NIH HHS/United States ; },
mesh = {Artifacts ; Brain/physiology ; Brain-Computer Interfaces ; Electroencephalography/instrumentation ; Humans ; Imaging, Three-Dimensional ; Male ; Monitoring, Ambulatory/instrumentation ; Multivariate Analysis ; *Signal Processing, Computer-Assisted ; Software ; Young Adult ; },
abstract = {This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.},
}
@article {pmid24110154,
year = {2013},
author = {Chuang, CH and Lin, YP and Ko, LW and Jung, TP and Lin, CT},
title = {Automatic design for independent component analysis based brain-computer interfacing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2180-2183},
doi = {10.1109/EMBC.2013.6609967},
pmid = {24110154},
issn = {2694-0604},
mesh = {Automobile Driving ; Brain/physiology ; *Brain-Computer Interfaces ; Cognition/physiology ; Computer Simulation ; Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {This study proposes a new framework, independent component ensemble, to leverage the acquired knowledge into a truly automatic and on-line EEG-based brain-computer interfacing (BCI). The envisioned design includes: (1) independent source recover using independent component analysis (ICA) (2) automatic selection of the independent components of interest (ICi) associated with human behaviors; (3) multiple classifiers with a parallel constructing and processing structure; and (4) a simple fusion scheme to combine the decisions from multiple classifiers. Its implications in BCI are demonstrated through a sample application: cognitive-state monitoring of participants performing a realistic sustained-attention driving task. Empirical results showed the proposed ensemble design could provide an improvement of 7% ~ 15% in overall accuracy for the classification of the arousal state and the driving performance. In summary, constructing ICi-ensemble classifiers and combining their outputs demonstrates a practical option for ICA-based BCIs to reduce the risk of not obtaining any desired independent source or selecting an inadequate component. Most importantly, the ensemble design for integrating information across multiple brain areas creates potentials for developing more complicated BCIs for real world applications.},
}
@article {pmid24110153,
year = {2013},
author = {Nakanishi, M and Wang, Y and Wang, YT and Mitsukura, Y and Jung, TP},
title = {An approximation approach for rendering visual flickers in SSVEP-based BCI using monitor refresh rate.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2176-2179},
doi = {10.1109/EMBC.2013.6609966},
pmid = {24110153},
issn = {2694-0604},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Functional Neuroimaging ; Humans ; Photic Stimulation ; Signal-To-Noise Ratio ; User-Computer Interface ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize a direct communication between the human brain and the external environment in practical situations. In the conventional stimulus presentation approach, which requires a constant period of stimulation, the number of frequencies that can be presented on a computer monitor is always limited by the refresh rate of a monitor. Although an alternative approach that uses a variable on/off frame number to approximate a target flickering stimulus has been proposed in our recent study, a direct comparison between SSVEPs elicited by the conventional constant period approach and the approximation approach is still missing. This study aims to compare the amplitude, signal-to-noise ratio (SNR) and target identification accuracy of SSVEPs elicited using these two approaches with a monitor at two refresh rates (75Hz and 120Hz). Results of this study suggest that the SSVEPs elicited by the approximation approach are mostly comparable with those elicited by the constant period approach.},
}
@article {pmid24110152,
year = {2013},
author = {Chen, S and Qu, Y and Guo, S and Shi, Z and Xu, K and Zheng, X},
title = {Encode the "STOP" command by photo-stimulation for precise control of rat-robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2172-2175},
doi = {10.1109/EMBC.2013.6609965},
pmid = {24110152},
issn = {2694-0604},
mesh = {Animals ; Brain-Computer Interfaces ; Male ; Motor Activity ; Neurons/*physiology ; Optogenetics ; Periaqueductal Gray/cytology ; Photic Stimulation ; Rats ; Rats, Sprague-Dawley ; Robotics ; },
abstract = {Studies on behavior control are important for bio-robots designation. For auto or manual navigation of the bio-robots, the accuracy of the command execution is especially critical. In this paper, we reported a precise "STOP" command for the rat-robots by optical stimulation of the central nervous system (CNS). We labeled dorsolateral periaqueductal gray (dlPAG) neurons with light sensitive channelrhodopsin-2 (ChR2) and directly probed the optical fiber to reactivate these neurons. The rats showed freezing behavior only upon the optical stimulation with an appropriate range of laser intensity and stimulation frequency. Neuron spikes and local field potential (LFP) were simultaneously recorded with optical stimulation by optrodes on free moving rat-robots. Together, our findings demonstrated the utility of deep brain optical stimulation for the stopping behavior of rat-robot control and indicated a potential application of optogenetics for precise control of bio-robots in further work.},
}
@article {pmid24110151,
year = {2013},
author = {Chen, X and Xu, K and Ye, S and Guo, S and Zheng, X},
title = {A remote constant current stimulator designed for rat-robot navigation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2168-2171},
doi = {10.1109/EMBC.2013.6609964},
pmid = {24110151},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Deep Brain Stimulation ; Electric Power Supplies ; Implantable Neurostimulators ; Male ; Microelectrodes ; Motor Activity ; Rats ; Rats, Sprague-Dawley ; Robotics ; },
abstract = {In this paper, a remote stimulator is developed for rat-robot navigation based on the technique of Brain-Computer-Interface (BCI). The stimulator can output constant current from 0 to 1000 µA, which overcome several shortages of our previous constant voltage stimulator. The constant current stimulator consists of four major components, including power supply, micro control unit (MCU), constant current source and bluetooth transceiver for downloading stimulation commands. The stimulator has a weight of about 20 g and size of 32 mm*25 mm*6mm. It has five channels of stimulation, which are connected with implanted microelectrodes in rat brain. The electrical parameters were characterized on three rats with different recovery time after brain surgery. Increasing current stimulations were applied on the dorsolateral periaqueductal gray (dlPAG) area to prove the effect of current stimulation on rat behavior.},
}
@article {pmid24110150,
year = {2013},
author = {Yu, T and Li, Y and Long, J and Wang, C},
title = {A brain-computer interface controlled mail client.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2164-2167},
doi = {10.1109/EMBC.2013.6609963},
pmid = {24110150},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Computers ; Electroencephalography/methods ; Electronic Mail ; Event-Related Potentials, P300 ; Humans ; Writing ; Young Adult ; },
abstract = {In this paper, we propose a brain-computer interface (BCI) based mail client. This system is controlled by hybrid features extracted from scalp-recorded electroencephalographic (EEG). We emulate the computer mouse by the motor imagery-based mu rhythm and the P300 potential. Furthermore, an adaptive P300 speller is included to provide text input function. With this BCI mail client, users can receive, read, write mails, as well as attach files in mail writing. The system has been tested on 3 subjects. Experimental results show that mail communication with this system is feasible.},
}
@article {pmid24110149,
year = {2013},
author = {Herff, C and Heger, D and Putze, F and Hennrich, J and Fortmann, O and Schultz, T},
title = {Classification of mental tasks in the prefrontal cortex using fNIRS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2160-2163},
doi = {10.1109/EMBC.2013.6609962},
pmid = {24110149},
issn = {2694-0604},
mesh = {Adult ; Female ; Functional Neuroimaging/methods ; Hemodynamics ; Humans ; Male ; Prefrontal Cortex/*physiology ; Problem Solving/physiology ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared ; },
abstract = {Functional near infrared spectroscopy (fNIRS) is rapidly gaining interest in both the Neuroscience, as well as the Brain-Computer-Interface (BCI) community. Despite these efforts, most single-trial analysis of fNIRS data is focused on motor-imagery, or mental arithmetics. In this study, we investigate the suitability of different mental tasks, namely mental arithmetics, word generation and mental rotation for fNIRS based BCIs. We provide the first systematic comparison of classification accuracies achieved in a sample study. Data was collected from 10 subjects performing these three tasks.},
}
@article {pmid24110148,
year = {2013},
author = {Lapolli, &#xc2;C and Coppa, B and Héliot, R},
title = {Low-power hardware for neural spike compression in BMIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {2156-2159},
doi = {10.1109/EMBC.2013.6609961},
pmid = {24110148},
issn = {2694-0604},
mesh = {*Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Data Compression ; Electrodes, Implanted ; Microelectrodes ; Prostheses and Implants ; Signal Processing, Computer-Assisted ; },
abstract = {Within brain-machine interface systems, cortically implanted microelectrode arrays and associated hardware have a low-power budget for data sampling, processing, and transmission. Recent studies have shown the feasibility of data transmission rate reduction using compressed sensing on detected neural spikes. They provide power savings while maintaining clustering and classification abilities. We propose and analyze here a low-power hardware implementation for spike detection and compression. The resulting integrated circuit, designed in CMOS 65 nm technology, consumes 2.83 µW and provides 97% of data rate reduction.},
}
@article {pmid24110095,
year = {2013},
author = {Huang, KJ and Shih, WY and Chang, JC and Feng, CW and Fang, WC},
title = {A pipeline VLSI design of fast singular value decomposition processor for real-time EEG system based on on-line recursive independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1944-1947},
doi = {10.1109/EMBC.2013.6609908},
pmid = {24110095},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Computer Systems ; Electroencephalography/*methods ; Equipment Design ; Humans ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper presents a pipeline VLSI design of fast singular value decomposition (SVD) processor for real-time electroencephalography (EEG) system based on on-line recursive independent component analysis (ORICA). Since SVD is used frequently in computations of the real-time EEG system, a low-latency and high-accuracy SVD processor is essential. During the EEG system process, the proposed SVD processor aims to solve the diagonal, inverse and inverse square root matrices of the target matrices in real time. Generally, SVD requires a huge amount of computation in hardware implementation. Therefore, this work proposes a novel design concept for data flow updating to assist the pipeline VLSI implementation. The SVD processor can greatly improve the feasibility of real-time EEG system applications such as brain computer interfaces (BCIs). The proposed architecture is implemented using TSMC 90 nm CMOS technology. The sample rate of EEG raw data adopts 128 Hz. The core size of the SVD processor is 580×580 um(2), and the speed of operation frequency is 20MHz. It consumes 0.774mW of power during the 8-channel EEG system per execution time.},
}
@article {pmid24110075,
year = {2013},
author = {Matsushita, K and Hirata, M and Suzuki, T and Ando, H and Ota, Y and Sato, F and Morris, S and Yoshida, T and Matsuki, H and Yoshimine, T},
title = {Development of an implantable wireless ECoG 128ch recording device for clinical brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1867-1870},
doi = {10.1109/EMBC.2013.6609888},
pmid = {24110075},
issn = {2694-0604},
mesh = {Abdomen/pathology ; Amplifiers, Electronic ; Animals ; Brain/*pathology ; *Brain-Computer Interfaces ; Electric Power Supplies ; Electrodes ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Female ; Head/pathology ; Humans ; Magnetic Resonance Imaging ; Male ; Paralysis/*physiopathology ; Prostheses and Implants ; *Signal Processing, Computer-Assisted ; Titanium/chemistry ; Wireless Technology ; },
abstract = {Brain Machine Interface (BMI) is a system that assumes user's intention by analyzing user's brain activities and control devices with the assumed intention. It is considered as one of prospective tools to enhance paralyzed patients' quality of life. In our group, we especially focus on ECoG (electro-corti-gram)-BMI, which requires surgery to place electrodes on the cortex. We try to implant all the devices within the patient's head and abdomen and to transmit the data and power wirelessly. Our device consists of 5 parts: (1) High-density multi-electrodes with a 3D shaped sheet fitting to the individual brain surface to effectively record the ECoG signals; (2) A small circuit board with two integrated circuit chips functioning 128 [ch] analogue amplifiers and A/D converters for ECoG signals; (3) A Wifi data communication &amp; control circuit with the target PC; (4) A non-contact power supply transmitting electrical power minimum 400[mW] to the device 20[mm] away. We developed those devices, integrated them, and, investigated the performance.},
}
@article {pmid24110053,
year = {2013},
author = {Naseer, N and Hong, KS},
title = {Functional near-infrared spectroscopy based discrimination of mental counting and no-control state for development of a brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1780-1783},
doi = {10.1109/EMBC.2013.6609866},
pmid = {24110053},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; Female ; Humans ; Male ; Neurophysiological Monitoring ; Prefrontal Cortex/*physiology ; Rest ; Spectroscopy, Near-Infrared ; Thinking/physiology ; },
abstract = {In this paper we propose to apply functional near-infrared spectroscopy (fNIRS) to measure the brain activity during mental counting and discriminate it from the no-control (rest) state, which could potentially lead to a two-choice brain-computer interface (BCI) application. fNIRS is a relatively new optical brain imaging modality that can be used for BCI. The major advantages using fNIRS are its relatively low cost, safety, portability, wearability and overall ease of use. In the present research, five healthy subjects are asked to perform mental counting during the activity period. Signals from the prefrontal cortex are acquired using a continuous-wave imaging system. The mental counting and no-control states are classified, using linear discriminant analysis (LDA), with an average accuracy of 80.6%. These classified signals can be translated into control commands for a two-choice BCI. These results show fNIRS to be a potential candidate for BCI.},
}
@article {pmid24110004,
year = {2013},
author = {Suminski, AJ and Fagg, AH and Willett, FR and Bodenhamer, M and Hatsopoulos, NG},
title = {Online adaptive decoding of intended movements with a hybrid kinetic and kinematic brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1583-1586},
doi = {10.1109/EMBC.2013.6609817},
pmid = {24110004},
issn = {2694-0604},
support = {NINDS R01 N545853-01//PHS HHS/United States ; },
mesh = {*Algorithms ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Kinetics ; Macaca mulatta ; Male ; Motor Cortex/physiology ; *Movement ; *Online Systems ; },
abstract = {Traditional brain machine interfaces for control of a prosthesis have typically focused on the kinematics of movement, rather than the dynamics. BMI decoders that extract the forces and/or torques to be applied by a prosthesis have the potential for giving the patient a much richer level of control across different dynamic scenarios or even scenarios in which the dynamics of the limb/environment are changing. However, it is a challenge to train a decoder that is able to capture this richness given the small amount of calibration data that is usually feasible to collect a priori. In this work, we propose that kinetic decoders should be continuously calibrated based on how they are used by the subject. Both intended hand position and joint torques are decoded simultaneously as a monkey performs a random target pursuit task. The deviation between intended and actual hand position is used as an estimate of error in the recently decoded joint torques. In turn, these errors are used to drive a gradient descent algorithm for improving the torque decoder parameters. We show that this approach is able to quickly restore the functionality of a torque decoder following substantial corruption with Gaussian noise.},
}
@article {pmid24110003,
year = {2013},
author = {Contreras-Vidal, JL and Grossman, RG},
title = {NeuroRex: a clinical neural interface roadmap for EEG-based brain machine interfaces to a lower body robotic exoskeleton.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1579-1582},
pmid = {24110003},
issn = {2694-0604},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; NIH R01NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Gait ; Haplorhini ; Humans ; Leg/*physiology ; *Robotics ; Software ; },
abstract = {In this communication, a translational clinical brain-machine interface (BMI) roadmap for an EEG-based BMI to a robotic exoskeleton (NeuroRex) is presented. This multi-faceted project addresses important engineering and clinical challenges: It addresses the validation of an intelligent, self-balancing, robotic lower-body and trunk exoskeleton (Rex) augmented with EEG-based BMI capabilities to interpret user intent to assist a mobility-impaired person to walk independently. The goal is to improve the quality of life and health status of wheelchair-bounded persons by enabling standing and sitting, walking and backing, turning, ascending and descending stairs/curbs, and navigating sloping surfaces in a variety of conditions without the need for additional support or crutches.},
}
@article {pmid24110002,
year = {2013},
author = {Shanechi, MM and Chemali, JJ and Liberman, M and Solt, K and Brown, EN},
title = {A brain-machine interface for control of burst suppression in medical coma.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1575-1578},
doi = {10.1109/EMBC.2013.6609815},
pmid = {24110002},
issn = {2694-0604},
mesh = {*Action Potentials ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Coma/*physiopathology ; Electroencephalography ; Probability ; Rats ; },
abstract = {Burst suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of bursts of electrical activity alternating with periods of isoelectricity called suppression. Burst suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of burst suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of burst suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the burst suppression level in real time in rodents. We quantify the burst suppression level using the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of burst suppression without overshoot or undershoot and maintains precise control of time-varying target levels of burst suppression in individual rodents in real time.},
}
@article {pmid24110001,
year = {2013},
author = {Du, L and Zhang, F and He, H and Huang, H},
title = {Improving the performance of a neural-machine interface for prosthetic legs using adaptive pattern classifiers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1571-1574},
doi = {10.1109/EMBC.2013.6609814},
pmid = {24110001},
issn = {2694-0604},
support = {NIH #RHD064968A//PHS HHS/United States ; },
mesh = {*Algorithms ; Amputees ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electromyography ; Femur/surgery ; Humans ; Leg/*physiopathology ; Signal Processing, Computer-Assisted ; Software ; Support Vector Machine ; },
abstract = {Pattern classification has been used for design of neural-machine interface (NMI) that identifies user intent. Our previous NMI based on electromyographic (EMG) signals and intrinsic mechanical feedback has shown great promise for neural control of artificial legs. In order to make this NMI practical, however, it is desired that classification algorithms can adapt to EMG pattern variations over time, caused by various physical and physiological changes. This study aimed to develop an adaptive pattern recognition framework in the NMI to improve the robustness of NMI performance over time. Two adaptive algorithms, i.e. entropy-based adaptation and Learning From Testing Data (LIFT) adaptation, were presented and compared to the NMI with non-adaptive classifiers. Support vector machine (SVM) was selected as the basic classifier. Gradual change of EMG signals was simulated over time on EMG data collected from four transfemoral (TF) amputees. The preliminary results showed that the NMI with adaptive classifiers produced more consistent performance over time than the classifier without adaptation. The results of this preliminary study indicate the potential of using adaptive classifiers to improve the NMI reliability for neural control of powered prosthetic legs.},
}
@article {pmid24110000,
year = {2013},
author = {Darvishi, S and Ridding, MC and Abbott, D and Baumert, M},
title = {Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1567-1570},
doi = {10.1109/EMBC.2013.6609813},
pmid = {24110000},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Time Factors ; Wavelet Analysis ; },
abstract = {Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.},
}
@article {pmid24109946,
year = {2013},
author = {Lin, YP and Wang, Y and Jung, TP},
title = {A mobile SSVEP-based brain-computer interface for freely moving humans: the robustness of canonical correlation analysis to motion artifacts.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1350-1353},
doi = {10.1109/EMBC.2013.6609759},
pmid = {24109946},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Signal-To-Noise Ratio ; Walking ; Young Adult ; },
abstract = {Recently, translating a steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) from laboratory settings to real-life applications has gained increasing attention. This study systematically tests the signal quality of SSVEP acquired by a mobile electroencephalogram (EEG) system, which features dry electrodes and wireless telemetry, under challenging (e.g. walking) recording conditions. Empirical results of this study demonstrated the robustness of canonical correlation analysis (CCA) to movement artifacts for SSVEP detection. This demonstration considerably improves the practicality of real-life applications of mobile and wireless BCI systems for users actively behaving in and interacting with their environments.},
}
@article {pmid24109945,
year = {2013},
author = {Boutani, H and Ohsuga, M},
title = {Applicability of the "Emotiv EEG Neuroheadset" as a user-friendly input interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {1346-1349},
doi = {10.1109/EMBC.2013.6609758},
pmid = {24109945},
issn = {2694-0604},
mesh = {Analog-Digital Conversion ; Artifacts ; Blinking ; Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Evoked Potentials ; Eye Movements/*physiology ; Female ; Humans ; Software ; Young Adult ; },
abstract = {We aimed to develop an input interface by using the P3 component of visual event-related potentials (ERPs). When using electroencephalography (EEG) in daily applications, coping with ocular-motor artifacts and ensuring that the equipment is user-friendly are both important. To address the first issue, we applied a previously proposed method that applies an unmixing matrix to acquire independent components (ICs) obtained from another dataset. For the second issue, we introduced a 14-channel EEG commercial headset called the "Emotiv EEG Neuroheadset". An advantage of the Emotiv headset is that users can put it on by themselves within 1 min without any specific skills. However, only a few studies have investigated whether EEG and ERP signals are accurately measured by Emotiv. Additionally, no electrodes of the Emotiv headset are located over the centroparietal area of the head where P3 components are reported to show large amplitudes. Therefore, we first demonstrated that the P3 components obtained by the headset and by commercial plate electrodes and a multipurpose bioelectric amplifier during an oddball task were comparable. Next, we confirmed that eye-blink and ocular movement components could be decomposed by independent component analysis (ICA) using the 14-channel signals measured by the headset. We also demonstrated that artifacts could be removed with an unmixing matrix, as long as the matrix was obtained from the same person, even if they were measured on different days. Finally, we confirmed that the fluctuation of the sampling frequency of the Emotiv headset was not a major problem.},
}
@article {pmid24109856,
year = {2013},
author = {Chai, R and Ling, SH and Hunter, GP and Tran, Y and Nguyen, HT},
title = {Classification of wheelchair commands using brain computer interface: comparison between able-bodied persons and patients with tetraplegia.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {989-992},
doi = {10.1109/EMBC.2013.6609669},
pmid = {24109856},
issn = {2694-0604},
mesh = {Adult ; Aged ; Aged, 80 and over ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Middle Aged ; Neural Networks, Computer ; Quadriplegia/*physiopathology ; Task Performance and Analysis ; Time Factors ; *Wheelchairs ; },
abstract = {This paper presents a three-class mental task classification for an electroencephalography based brain computer interface. Experiments were conducted with patients with tetraplegia and able bodied controls. In addition, comparisons with different time-windows of data were examined to find the time window with the highest classification accuracy. The three mental tasks used were letter composing, arithmetic and imagery of a Rubik's cube rolling forward; these tasks were associated with three wheelchair commands: left, right and forward, respectively. An eyes closed task was also recorded for the algorithms testing and used as an additional on/off command. The features extraction method was based on the spectrum from a Hilbert-Huang transform and the classification algorithm was based on an artificial neural network with a fuzzy particle swarm optimization with cross-mutated operation. The results show a strong eyes closed detection for both groups with average accuracy at above 90%. The overall result for the combined groups shows an improved average accuracy of 70.6% at 1s, 74.8% at 2s, 77.8% at 3s, 79.6% at 4s and 81.4% at 5s. The accuracy for individual groups were lower for patients with tetraplegia compared to the able-bodied group, however, does improve with increased duration of the time-window.},
}
@article {pmid24109816,
year = {2013},
author = {Foerster, M and Bonnet, S and van Langhenhove, A and Porcherot, J and Charvet, G},
title = {A synchronization method for wireless acquisition systems, application to brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {830-833},
doi = {10.1109/EMBC.2013.6609629},
pmid = {24109816},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Calibration ; Cortical Synchronization/*physiology ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Evoked Potentials, Visual/physiology ; Humans ; Signal Processing, Computer-Assisted ; Wireless Technology/*instrumentation ; },
abstract = {A synchronization method for wireless acquisition systems has been developed and implemented on a wireless ECoG recording implant and on a wireless EEG recording helmet. The presented algorithm and hardware implementation allow the precise synchronization of several data streams from several sensor nodes for applications where timing is critical like in event-related potential (ERP) studies. The proposed method has been successfully applied to obtain visual evoked potentials and compared with a reference biosignal amplifier. The control over the exact sampling frequency allows reducing synchronization errors that will otherwise accumulate during a recording. The method is scalable to several sensor nodes communicating with a shared base station.},
}
@article {pmid24109799,
year = {2013},
author = {Kim, M and Chae, Y and Jo, S},
title = {Hybrid EEG and eye movement interface to multi-directional target selection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {763-766},
doi = {10.1109/EMBC.2013.6609612},
pmid = {24109799},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Eye Movements/*physiology ; Humans ; Task Performance and Analysis ; Time Factors ; },
abstract = {This work addresses the development of a low-cost hybrid interface with eye tracking and brain signals. Eye movement detection is used for search task and EEG-based brain computer interface (BCI) for selection task. Multi-directional target selection experiments with the hybrid interface device were conducted with five subjects to evaluate the proposed hybrid interface scheme. The task asked each user to move a cursor onto a circular target among twelve possible positions and select it. Using the Fitts' law, the interface performance was compared with the computer mouse. With two BCI selection confirmation schemes, the hybrid interface attained 2-2.7 bit/s overall. Based on the results, the potential of the proposed hybrid interface was discussed.},
}
@article {pmid24109745,
year = {2013},
author = {Fiedler, P and Fonseca, C and Pedrosa, P and Martins, A and Vaz, F and Griebel, S and Haueisen, J},
title = {Novel flexible Dry multipin electrodes for EEG: Signal quality and interfacial impedance of Ti and TiN coatings.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {547-550},
doi = {10.1109/EMBC.2013.6609558},
pmid = {24109745},
issn = {2694-0604},
mesh = {Electric Impedance ; Electrodes ; *Electroencephalography/instrumentation ; Humans ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Skin Physiological Phenomena ; Titanium/*chemistry ; },
abstract = {Conventional Silver/Silver-Chloride electrodes are inappropriate for routine high-density EEG and emerging new fields of application like brain computer interfaces. A novel multipin electrode design is proposed. It enables rapid and easy application while maintaining signal quality and patient comfort. The electrode design is described and impedance and EEG tests are performed with Titanium and Titanium Nitride coated electrodes. The results are compared to conventional reference electrodes in a multi-volunteer study. The calculated signal parameters prove the multipin electrode concept to reproducibly acquire EEG signal quality comparable to Ag/AgCl electrodes. The promising results encourage further investigation and can provide a technological base for future preparation-free multichannel EEG systems.},
}
@article {pmid24109744,
year = {2013},
author = {Kidmose, P and Looney, D and Jochumsen, L and Mandic, DP},
title = {Ear-EEG from generic earpieces: a feasibility study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {543-546},
doi = {10.1109/EMBC.2013.6609557},
pmid = {24109744},
issn = {2694-0604},
mesh = {Brain/*physiology ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation/*methods ; Feasibility Studies ; Hearing Aids ; Humans ; Signal-To-Noise Ratio ; },
abstract = {The use of brain monitoring based on EEG, in natural environments and over long time periods, is hindered by the limited portability of current wearable systems, and the invasiveness of implanted systems. To that end, we introduce an ear-EEG recording device based on generic earpieces which meets key patient needs (discreet, unobstrusive, user-friendly, robust) and that is low-cost and suitable for off-the-shelf use; thus promising great advantages for healthcare applications. Its feasibility is validated in a comprehensive comparative study with our established prototype, based on a personalized earpiece, for a key EEG paradigm.},
}
@article {pmid24109716,
year = {2013},
author = {Thomas, KP and Vinod, AP and Guan, C},
title = {Design of an online EEG based neurofeedback game for enhancing attention and memory.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {433-436},
doi = {10.1109/EMBC.2013.6609529},
pmid = {24109716},
issn = {2694-0604},
mesh = {Attention ; Attention Deficit Disorder with Hyperactivity/rehabilitation ; Brain/*physiology ; *Brain-Computer Interfaces ; Cognition ; Computers ; Electroencephalography/instrumentation/*methods ; Healthy Volunteers ; Humans ; *Memory ; *Neurofeedback ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Video Games ; },
abstract = {Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games etc. This paper proposes an Electroencephalogram (EEG) based neurofeedback computer game that allows the player to control the game with the help of attention based brain signals. The proposed game protocol requires the player to memorize a set of numbers in a matrix, and to correctly fill the matrix using his attention. The attention level of the player is quantified using sample entropy features of EEG. The statistically significant performance improvement of five healthy subjects after playing a number of game sessions demonstrates the effectiveness of the proposed game in enhancing their concentration and memory skills.},
}
@article {pmid24109715,
year = {2013},
author = {Yang, H and Guan, C and Ang, KK and Wang, C and Phua, KS and Yin, CT and Zhou, L},
title = {Feature consistency-based model adaptation in session-to-session classification: a study using motor imagery of swallow EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {429-432},
doi = {10.1109/EMBC.2013.6609528},
pmid = {24109715},
issn = {2694-0604},
mesh = {Adaptation, Physiological ; Brain ; Brain-Computer Interfaces ; Calibration ; *Deglutition ; Electrodes ; *Electroencephalography ; Healthy Volunteers ; Humans ; *Imagery, Psychotherapy ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {The performance degradation for session to session classification in brain computer interface is a critical problem. This paper proposes a novel method for model adaptation based on motor imagery of swallow EEG signal for dysphagia rehabilitation. A small amount of calibration testing data is utilized to select the model catering for test data. The features of the training and calibration testing data are firstly clustered and each cluster is labeled by the dominant label of the training data. The cluster with the minimum impurity is selected and the number of features consistent with the cluster label are calculated for both training and calibration testing data. Finally, the training model with the maximum number of consistent features is selected. Experiments conducted on motor imagery of swallow EEG data achieved an average accuracy of 74.29% and 72.64% with model adaptation for Laplacian derivates of power features and wavelet features, respectively. Further, an average accuracy increase of 2.9% is achieved with model adaptation using wavelet features, in comparison with that achieved without model adaptation, which is significant at 5% significance level as demonstrated in the statistical test.},
}
@article {pmid24109685,
year = {2013},
author = {Scheid, MR and Flint, RD and Wright, ZA and Slutzky, MW},
title = {Long-term, stable behavior of local field potentials during brain machine interface use.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {307-310},
doi = {10.1109/EMBC.2013.6609498},
pmid = {24109685},
issn = {2694-0604},
support = {K08NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Motor Cortex/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Local field potentials (LFPs) have the potential to provide robust, long-lasting control signals for brain-machine interfaces (BMIs). Moreover, they have been hypothesized to be a stable signal source. Here we assess the long-term stability of LFPs and multi-unit spikes (MSPs) in two monkeys using both LFP-based and MSP-based, biomimetic BMIs to control a computer cursor. The monkeys demonstrated highly accurate performance using both the LFP- and MSP-based BMIs. This performance remained high for 11 and 6 months, respectively, without adapting or retraining. We evaluated the stability of the LFP features and MSPs themselves by building, in each session, linear decoders of the BMI-controlled cursor velocity using single features or single MSPs. We then used these single-feature decoders to decode BMI-controlled cursor velocity in the last session. Many of the LFP features and MSPs showed stably-high correlations with the cursor velocity over the entire study period. This implies that the monkeys were able to maintain a stable mapping between either motor cortical field potentials or multi-spike potentials and BMI-controlled outputs.},
}
@article {pmid24109684,
year = {2013},
author = {Balasubramanian, K and Southerland, J and Vaidya, M and Qian, K and Eleryan, A and Fagg, AH and Sluzky, M and Oweiss, K and Hatsopoulos, N},
title = {Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {303-306},
doi = {10.1109/EMBC.2013.6609497},
pmid = {24109684},
issn = {2694-0604},
mesh = {Action Potentials ; Amputation, Surgical ; Animals ; Biofeedback, Psychology ; *Brain-Computer Interfaces ; Conditioning, Operant ; Hand/physiology ; Hand Strength ; Humans ; Macaca mulatta ; Movement ; Signal Processing, Computer-Assisted ; },
abstract = {Operant conditioning with biofeedback has been shown to be an effective method to modify neural activity to generate goal-directed actions in a brain-machine interface. It is particularly useful when neural activity cannot be mathematically mapped to motor actions of the actual body such as in the case of amputation. Here, we implement an operant conditioning approach with visual feedback in which an amputated monkey is trained to control a multiple degree-of-freedom robot to perform a reach-to-grasp behavior. A key innovation is that each controlled dimension represents a behaviorally relevant synergy among a set of joint degrees-of-freedom. We present a number of behavioral metrics by which to assess improvements in BMI control with exposure to the system. The use of non-human primates with chronic amputation is arguably the most clinically-relevant model of human amputation that could have direct implications for developing a neural prosthesis to treat humans with missing upper limbs.},
}
@article {pmid24109683,
year = {2013},
author = {Perel, S and Sadtler, PT and Godlove, JM and Ryu, SI and Wang, W and Batista, AP and Chase, SM},
title = {Direction and speed tuning of motor-cortex multi-unit activity and local field potentials during reaching movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {299-302},
pmid = {24109683},
issn = {2694-0604},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01 NS065065/NS/NINDS NIH HHS/United States ; R01NS050256-05S1/NS/NINDS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; 8KL2TR000146-07/TR/NCATS NIH HHS/United States ; P30 NS076405/NS/NINDS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; Motor Cortex/*physiology ; *Movement ; Regression Analysis ; Signal Transduction ; },
abstract = {Primary motor-cortex multi-unit activity (MUA) and local-field potentials (LFPs) have both been suggested as potential control signals for brain-computer interfaces (BCIs) aimed at movement restoration. Some studies report that LFP-based decoding is comparable to spiking-based decoding, while others offer contradicting evidence. Differences in experimental paradigms, tuning models and decoding techniques make it hard to directly compare these results. Here, we use regression and mutual information analyses to study how MUA and LFP encode various kinematic parameters during reaching movements. We find that in addition to previously reported directional tuning, MUA also contains prominent speed tuning. LFP activity in low-frequency bands (15-40Hz, LFPL) is primarily speed tuned, and contains more speed information than both high-frequency LFP (100-300Hz, LFPH) and MUA. LFPH contains more directional information compared to LFPL, but less information when compared with MUA. Our results suggest that a velocity and speed encoding model is most appropriate for both MUA and LFPH, whereas a speed only encoding model is adequate for LFPL.},
}
@article {pmid24109682,
year = {2013},
author = {Kao, JC and Nuyujukian, P and Stavisky, S and Ryu, SI and Ganguli, S and Shenoy, KV},
title = {Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {293-298},
doi = {10.1109/EMBC.2013.6609495},
pmid = {24109682},
issn = {2694-0604},
support = {1DP1OD006409/OD/NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Algorithms ; *Brain-Computer Interfaces ; Humans ; Neurons/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF) we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.},
}
@article {pmid24109681,
year = {2013},
author = {Wong, YT and Putrino, D and Weiss, A and Pesaran, B},
title = {Utilizing movement synergies to improve decoding performance for a brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {289-292},
pmid = {24109681},
issn = {2694-0604},
support = {//Wellcome Trust/United Kingdom ; P30 EY013079/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; Artificial Limbs ; *Brain-Computer Interfaces ; Hand/*physiology ; Humans ; Macaca mulatta ; Movement ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {A major challenge facing the development of high degree of freedom (DOF) brain machine interface (BMI) devices is a limited ability to provide prospective users with independent control of many DOFs when using a complex prosthesis. It has been previously shown that a large range of complex hand postures can be replicated using a relatively low number of movement synergies. Thus, a high DOF joint space, such as the one the hand resides in, may be decomposed via principal component analysis (PCA) into a lower DOF (eigen-reach) space that contains most of the variance of the original movements. By decoding in this eigen-reach space, BMI users need only control a few eigen-reach values to be able to make movements using all DOFs in the arm and hand. In this paper we examine how using PCA before decoding neural activity may lead to improvements in decoding performance.},
}
@article {pmid24109680,
year = {2013},
author = {Dangi, S and So, K and Orsborn, AL and Gastpar, MC and Carmena, JM},
title = {Brain-machine interface control using broadband spectral power from local field potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {285-288},
doi = {10.1109/EMBC.2013.6609493},
pmid = {24109680},
issn = {2694-0604},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Behavior, Animal ; *Brain-Computer Interfaces ; Macaca mulatta/physiology ; Male ; *Spectrum Analysis ; },
abstract = {Recent progress in brain-machine interfaces (BMIs) has shown tremendous improvements in task complexity and degree of control. In particular, closed-loop decoder adaptation (CLDA) has emerged as an effective paradigm for both improving and maintaining the performance of BMI systems. Here, we demonstrate the first reported use of a CLDA algorithm to rapidly achieve high-performance control of a BMI based on local field potentials (LFPs). We trained a non-human primate to control a 2-D computer cursor by modulating LFP activity to perform a center-out reaching task, while applying CLDA to adaptively update the decoder. We show that the subject is quickly able to readily reach and hold at all 8 targets with an average success rate of 74% ± 7% (sustained peak rate of 85%), with rapid convergence in the decoder parameters. Moreover, the subject is able to maintain high performance across 4 days with minimal adaptations to the decoder. Our results indicate that CLDA can be used to facilitate LFP-based BMI systems, allowing for both rapid improvement and maintenance of performance.},
}
@article {pmid24109679,
year = {2013},
author = {Takemi, M and Masakado, Y and Liu, M and Ushiba, J},
title = {Is event-related desynchronization a biomarker representing corticospinal excitability?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {281-284},
doi = {10.1109/EMBC.2013.6609492},
pmid = {24109679},
issn = {2694-0604},
mesh = {Biomarkers ; Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electroencephalography ; Evoked Potentials, Motor/*physiology ; Humans ; Motor Cortex/*physiology ; Motor Neurons/physiology ; Muscles/physiopathology ; Neural Inhibition ; Pyramidal Tracts/*physiology ; Transcranial Magnetic Stimulation ; },
abstract = {Brain computer interfaces (BCIs) using event-related desynchronization (ERD) of the electroencephalogram (EEG), which is believed to represent increased activation of the sensorimotor cortex, have attracted attention as tools for rehabilitation of upper limb motor functions in hemiplegic stroke patients. However, it remains unclear whether the corticospinal excitability is actually correlated with ERD. The purpose of this study was to assess the association between the ERD magnitude and the excitability of primary motor cortex (M1) and spinal motoneurons. M1 excitability was tested by motor evoked potentials (MEPs), short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) using transcranial magnetic stimulation, and spinal motoneuronal excitability was tested by F-waves using peripheral nerve stimulation. Results showed that large ERD during motor imagery was associated with significantly increased F-wave persistence and reduced SICI, but no significant changes in ICF and the response average of F-wave amplitudes. Our findings suggest that ERD magnitude during motor imagery represents the instantaneous excitability of both M1 and spinal motoneurons. This study provides electrophysiological evidence that ERD-based BCI with motor imagery task increases corticospinal excitability as changes accompanying actual movements.},
}
@article {pmid24109677,
year = {2013},
author = {Hashimoto, Y and Ota, T and Mukaino, M and Ushiba, J},
title = {Treatment effectiveness of brain-computer interface training for patients with focal hand dystonia: A double-case study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {273-276},
doi = {10.1109/EMBC.2013.6609490},
pmid = {24109677},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Cortical Synchronization ; Dystonic Disorders/*rehabilitation ; Electroencephalography ; Electromyography ; Handwriting ; Humans ; Middle Aged ; Muscles/physiopathology ; Sensorimotor Cortex/physiopathology ; Task Performance and Analysis ; Treatment Outcome ; Young Adult ; },
abstract = {Neuronal mechanism underlying dystonia is poorly understood. Dystonia can be treated with botulinum toxin injections or deep brain stimulation but these methods are not available for every patient therefore we need to consider other methods Our study aimed to develop a novel rehabilitation training using brain-computer interface system that decreases neural overexcitation in the sensorimotor cortex by bypassing brain and external world without the normal neuromuscular pathway. To achieve this purpose, we recorded electroencephalograms (10 channels) and forearm electromyograms (3 channels) from 2 patients with the diagnosis of writer's cramp and healthy control participants as a preliminary experiment. The patients were trained to control amplitude of their electroencephalographic signal using feedback from the brain-computer interface for 1 hour a day and then continued the training twice a month. After the 5-month training, a patient clearly showed reduction of dystonic movement during writing.},
}
@article {pmid24109676,
year = {2013},
author = {Nam, CS and Lee, J and Bahn, S},
title = {Brain-computer interface supported collaborative work: Implications for rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {269-272},
doi = {10.1109/EMBC.2013.6609489},
pmid = {24109676},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; *Cooperative Behavior ; Electroencephalography/methods ; Evoked Potentials, Visual/physiology ; Female ; Fourier Analysis ; Humans ; Male ; Rehabilitation ; Spectrum Analysis ; Task Performance and Analysis ; Time Factors ; },
abstract = {Working together and collaborating in a group can provide greater benefits for people with severe motor disability. However, it is still not clear how collaboration should be supported by BCI systems. The present study explored BCI-supported collaborative work by investigating differences in performance and brain activity between when a pair of users performs a task jointly with each other and when they do alone only through means of their brain activity. We found differences in performance and brain activity between different work conditions. The results of this research should provide fundamental knowledge of BCI-supported cooperative work.},
}
@article {pmid24109675,
year = {2013},
author = {Ono, T and Mukaino, M and Ushiba, J},
title = {Functional recovery in upper limb function in stroke survivors by using brain-computer interface A single case A-B-A-B design.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {265-268},
doi = {10.1109/EMBC.2013.6609488},
pmid = {24109675},
issn = {2694-0604},
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Cortical Synchronization ; Electric Stimulation Therapy/*instrumentation/*methods ; Electroencephalography ; Equipment Design ; Hand/*physiopathology ; Humans ; Magnetic Resonance Imaging ; Male ; Recovery of Function/*physiology ; Robotics ; Stroke/*physiopathology ; *Survivors ; Time Factors ; },
abstract = {Resent studies suggest that brain-computer interface (BCI) training for chronic stroke patient is useful to improve their motor function of paretic hand. However, these studies does not show the extent of the contribution of the BCI clearly because they prescribed BCI with other rehabilitation systems, e.g. an orthosis itself, a robotic intervention, or electrical stimulation. We therefore compared neurological effects between interventions with neuromuscular electrical stimulation (NMES) with motor imagery and BCI-driven NMES, employing an ABAB experimental design. In epoch A, the subject received NMES on paretic extensor digitorum communis (EDC). The subject was asked to attempt finger extension simultaneously. In epoch B, the subject received NMES when BCI system detected motor-related electroencephalogram change while attempting motor imagery. Both epochs were carried out for 60 min per day, 5 days per week. As a result, EMG activity of EDC was enhanced by BCI-driven NMES and significant cortico-muscular coherence was observed at the final evaluation. These results indicate that the training by BCI-driven NMES is effective even compared to motor imagery combined with NMES, suggesting the superiority of closed-loop training with BCI-driven NMES to open-loop NMES for chronic stroke patients.},
}
@article {pmid24109674,
year = {2013},
author = {Tung, SW and Guan, C and Ang, KK and Phua, KS and Wang, C and Zhao, L and Teo, WP and Chew, E},
title = {Motor imagery BCI for upper limb stroke rehabilitation: An evaluation of the EEG recordings using coherence analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {261-264},
doi = {10.1109/EMBC.2013.6609487},
pmid = {24109674},
issn = {2694-0604},
mesh = {Arm/*physiopathology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Feedback ; Humans ; Imagery, Psychotherapy/*methods ; *Motor Activity ; Robotics/methods ; Stroke/*physiopathology ; *Stroke Rehabilitation ; Time Factors ; },
abstract = {Brain-computer interface (BCI) technology has the potential as a post-stroke rehabilitation tool, and the efficacy of the technology is most often demonstrated through output peripherals such as robots, orthosis and computers. In this study, the EEG signals recorded during the course of upper limb stroke rehabilitaion using motor imagery BCI were analyzed to better understand the effect of BCI therapy for post-stroke rehabilitation. The stroke patients recruited underwent 10 sessions of 1-hour BCI with robotic feedback for 2 weeks, 5 times a week. The analysis was performed by computing the coherences of the EEG in the lesion and contralesion side of the hemisphere from each session, and the coherence index of the lesion hemisphere (0 ≤ CI ≤ 1) was computed. The coherence index represents the rate of activation of the lesion hemisphere, and the correlation with the Fugl-Meyer assessment (FMA) before and after the BCI therapy was investigated. Significant improvement in the FMA scores was reported for five of the six patients (p = 0.01). The analysis showed that the number of sessions with CI ≥ 0.5 correlated with the change in the FMA scores. This suggests that post-stroke motor recovery best results from the activation in the lesion hemisphere, which is in agreement with previous studies performed using multimodal imaging technologies.},
}
@article {pmid24109670,
year = {2013},
author = {Chu, P and Muller, R and Koralek, A and Carmena, JM and Rabaey, JM and Gambini, S},
title = {Equalization for intracortical microstimulation artifact reduction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2013},
number = {},
pages = {245-248},
doi = {10.1109/EMBC.2013.6609483},
pmid = {24109670},
issn = {2694-0604},
mesh = {Animals ; Artifacts ; Brain-Computer Interfaces ; *Deep Brain Stimulation ; Humans ; Microelectrodes ; Rats ; },
abstract = {We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.},
}
@article {pmid24109560,
year = {2013},
author = {O'Rawe, JA and Fang, H and Rynearson, S and Robison, R and Kiruluta, ES and Higgins, G and Eilbeck, K and Reese, MG and Lyon, GJ},
title = {Integrating precision medicine in the study and clinical treatment of a severely mentally ill person.},
journal = {PeerJ},
volume = {1},
number = {},
pages = {e177},
pmid = {24109560},
issn = {2167-8359},
support = {R44 HG006579/HG/NHGRI NIH HHS/United States ; },
abstract = {Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces. Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim(®) Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come. Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care.},
}
@article {pmid24108725,
year = {2013},
author = {van de Laar, B and Plass-Oude Bos, D and Reuderink, B and Poel, M and Nijholt, A},
title = {How much control is enough? Influence of unreliable input on user experience.},
journal = {IEEE transactions on cybernetics},
volume = {43},
number = {6},
pages = {1584-1592},
doi = {10.1109/TCYB.2013.2282279},
pmid = {24108725},
issn = {2168-2275},
mesh = {Adult ; Biofeedback, Psychology/*methods/*physiology ; *Brain-Computer Interfaces ; Decision Making/*physiology ; Female ; Humans ; Male ; Perceptual Masking/*physiology ; Psychomotor Performance/*physiology ; *Video Games ; },
abstract = {Brain–computer interfaces (BCI) provide a valuable new input modality within human–computer interaction systems. However, like other body-based inputs such as gesture or gaze based systems, the system recognition of input commands is still far from perfect. This raises important questions, such as what level of control should such an interface be able to provide. What is the relationship between actual and perceived control? And in the case of applications for entertainment in which fun is an important part of user experience, should we even aim for the highest level of control, or is the optimum elsewhere? In this paper, we evaluate whether we can modulate the amount of control and if a game can be fun with less than perfect control. In the experiment users (n = 158) played a simple game in which a hamster has to be guided to the exit of a maze. The amount of control the user has over the hamster is varied. The variation of control through confusion matrices makes it possible to simulate the experience of using a BCI, while using the traditional keyboard for input. After each session the user completed a short questionnaire on user experience and perceived control. Analysis of the data showed that the perceived control of the user could largely be explained by the amount of control in the respective session. As expected, user frustration decreases with increasing control. Moreover, the results indicate that the relation between fun and control is not linear. Although at lower levels of control fun does increase with improved control, the level of fun drops just before perfect control is reached (with an optimum around 96%). This poses new insights for developers of games who want to incorporate some form of BCI or other modality with unreliable input in their game: for creating a fun game, unreliable input can be used to create a challenge for the user.},
}
@article {pmid24099944,
year = {2013},
author = {Orhan, U and Erdogmus, D and Roark, B and Oken, B and Fried-Oken, M},
title = {Offline analysis of context contribution to ERP-based typing BCI performance.},
journal = {Journal of neural engineering},
volume = {10},
number = {6},
pages = {066003},
pmid = {24099944},
issn = {1741-2552},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; 5R01DC009834/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces/*standards ; Electroencephalography/*standards ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; },
abstract = {OBJECTIVE: We aim to increase the symbol rate of electroencephalography (EEG) based brain-computer interface (BCI) typing systems by utilizing context information.<br><br>APPROACH: Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI typing systems still require improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model (LM) as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with EEG features is proposed, and a specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying LM orders, as well as the number of ERP-inducing repetitions.<br><br>MAIN RESULTS: The results demonstrate that the LMs contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram LM may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words.<br><br>SIGNIFICANCE: Overall, the fusion of evidence from EEG and LMs yields a significant opportunity to increase the symbol rate of a BCI typing system.},
}
@article {pmid24094907,
year = {2013},
author = {Mayaud, L and Congedo, M and Van Laghenhove, A and Orlikowski, D and Figère, M and Azabou, E and Cheliout-Heraut, F},
title = {A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm.},
journal = {Neurophysiologie clinique = Clinical neurophysiology},
volume = {43},
number = {4},
pages = {217-227},
doi = {10.1016/j.neucli.2013.06.002},
pmid = {24094907},
issn = {1769-7131},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Cross-Over Studies ; Electrodes ; Electroencephalography/instrumentation/*methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; },
abstract = {AIMS OF THE STUDY: A brain-computer interface aims at restoring communication and control in severely disabled people by identification and classification of EEG features such as event-related potentials (ERPs). The aim of this study is to compare different modalities of EEG recording for extraction of ERPs. The first comparison evaluates the performance of six disc electrodes with that of the EMOTIV headset, while the second evaluates three different electrode types (disc, needle, and large squared electrode).<br><br>MATERIAL AND METHODS: Ten healthy volunteers gave informed consent and were randomized to try the traditional EEG system (six disc electrodes with gel and skin preparation) or the EMOTIV Headset first. Together with the six disc electrodes, a needle and a square electrode of larger surface were simultaneously recording near lead Cz. Each modality was evaluated over three sessions of auditory P300 separated by one hour.<br><br>RESULTS: No statically significant effect was found for the electrode type, nor was the interaction between electrode type and session number. There was no statistically significant difference of performance between the EMOTIV and the six traditional EEG disc electrodes, although there was a trend showing worse performance of the EMOTIV headset. However, the modality-session interaction was highly significant (P<0.001) showing that, while the performance of the six disc electrodes stay constant over sessions, the performance of the EMOTIV headset drops dramatically between 2 and 3h of use. Finally, the evaluation of comfort by participants revealed an increasing discomfort with the EMOTIV headset starting with the second hour of use.<br><br>CONCLUSION: Our study does not recommend the use of one modality over another based on performance but suggests the choice should be made on more practical considerations such as the expected length of use, the availability of skilled labor for system setup and above all, the patient comfort.},
}
@article {pmid24093606,
year = {2013},
author = {del Valle, J and Navarro, X},
title = {Interfaces with the peripheral nerve for the control of neuroprostheses.},
journal = {International review of neurobiology},
volume = {109},
number = {},
pages = {63-83},
doi = {10.1016/B978-0-12-420045-6.00002-X},
pmid = {24093606},
issn = {2162-5514},
mesh = {Animals ; Bionics/*instrumentation/trends ; *Brain-Computer Interfaces/trends ; *Electrodes, Implanted/trends ; Humans ; Peripheral Nerves/*physiology ; *Prostheses and Implants/trends ; },
abstract = {Nervous system injuries lead to loss of control of sensory, motor, and autonomic functions of the affected areas of the body. Provided the high amount of people worldwide suffering from these injuries and the impact on their everyday life, numerous and different neuroprostheses and hybrid bionic systems have been developed to restore or partially mimic the lost functions. A key point for usable neuroprostheses is the electrode that interfaces the nervous system and translates not only motor orders into electrical outputs that activate the prosthesis but is also able to transform sensory information detected by the machine into signals that are transmitted to the central nervous system. Nerve electrodes have been classified with regard to their invasiveness in extraneural, intraneural, and regenerative. The more invasive is the implant the more selectivity of interfacing can be reached. However, boosting invasiveness and selectivity may also heighten nerve damage. This chapter provides a general overview of nerve electrodes as well as the state-of-the-art of their biomedical applications in neuroprosthetic systems.},
}
@article {pmid24089403,
year = {2014},
author = {Kloosterman, F and Layton, SP and Chen, Z and Wilson, MA},
title = {Bayesian decoding using unsorted spikes in the rat hippocampus.},
journal = {Journal of neurophysiology},
volume = {111},
number = {1},
pages = {217-227},
pmid = {24089403},
issn = {1522-1598},
support = {R01 MH061976/MH/NIMH NIH HHS/United States ; T32 MH074249/MH/NIMH NIH HHS/United States ; MH-061976/MH/NIMH NIH HHS/United States ; },
mesh = {*Action Potentials ; Algorithms ; Animals ; Bayes Theorem ; Hippocampus/*physiology ; *Models, Neurological ; Rats ; },
abstract = {A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.},
}
@article {pmid24088296,
year = {2013},
author = {Ward, S and Scope, A and Rafia, R and Pandor, A and Harnan, S and Evans, P and Wyld, L},
title = {Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis.},
journal = {Health technology assessment (Winchester, England)},
volume = {17},
number = {44},
pages = {1-302},
doi = {10.3310/hta17440},
pmid = {24088296},
issn = {2046-4924},
mesh = {Antineoplastic Agents/economics/*therapeutic use ; Breast Neoplasms/diagnosis/*drug therapy/economics/genetics ; Chemotherapy, Adjuvant/economics/methods ; Cost-Benefit Analysis ; Female ; *Gene Expression Profiling/economics/methods ; Humans ; Immunohistochemistry ; Prognosis ; Treatment Outcome ; },
abstract = {BACKGROUND: Gene expression profiling (GEP) and expanded immunohistochemistry (IHC) tests aim to improve decision-making relating to adjuvant chemotherapy for women with early breast cancer.<br><br>OBJECTIVE: The aim of this report is to assess the clinical effectiveness and cost-effectiveness of nine GEP and expanded IHC tests compared with current prognostic tools in guiding the use of adjuvant chemotherapy in patients with early breast cancer in England and Wales. The nine tests are BluePrint, Breast Cancer Index (BCI), IHC4, MammaPrint, Mammostrat, NPI plus (NPI+), OncotypeDX, PAM50 and Randox Breast Cancer Array.<br><br>DATA SOURCES: Databases searched included MEDLINE, MEDLINE In-Process &amp; Other Non-Indexed Citations, EMBASE and The Cochrane Library. Databases were searched from January 2009 to May 2011 for the OncotypeDX and MammaPrint tests and from January 2002 to May 2011 for the other tests.<br><br>REVIEW METHODS: A systematic review of the evidence on clinical effectiveness (analytical validity, clinical validity and clinical utility) and cost-effectiveness was conducted. An economic model was developed to evaluate the cost-effectiveness of adjuvant chemotherapy treatment guided by four of the nine test (OncotypeDX, IHC4, MammaPrint and Mammostrat) compared with current clinical practice in England and Wales, using clinicopathological parameters, in women with oestrogen receptor-positive (ER+), lymph node-negative (LN-), human epidermal growth factor receptor type 2-negative (HER2-) early breast cancer.<br><br>RESULTS: The literature searches for clinical effectiveness identified 5993 citations, of which 32 full-text papers or abstracts (30 studies) satisfied the criteria for the effectiveness review. A narrative synthesis was performed. Evidence for OncotypeDX supported the prognostic capability of the test. There was some evidence on the impact of the test on decision-making and to support the case that OncotypeDX predicts chemotherapy benefit; however, few studies were UK based and limitations in relation to study design were identified. Evidence for MammaPrint demonstrated that the test score was a strong independent prognostic factor, but the evidence is non-UK based and is based on small sample sizes. Evidence on the Mammostrat test showed that the test was an independent prognostic tool for women with ER+, tamoxifen-treated breast cancer. The three studies appeared to be of reasonable quality and provided data from a UK setting (one study). One large study reported on clinical validity of the IHC4 test, with IHC4 score a highly significant predictor of distant recurrence. This study included data from a UK setting and appeared to be of reasonable quality. Evidence for the remaining five tests (PAM50, NPI+, BCI, BluePrint and Randox) was limited. The economic analysis suggests that treatment guided using IHC4 has the greatest potential to be cost-effective at a &#xa3;20,000 threshold, given the low cost of the test; however, further research is needed on the analytical validity and clinical utility of IHC4, and the exact cost of the test needs to be confirmed. Current limitations in the evidence base produce significant uncertainty in the results. OncotypeDX has a more robust evidence base, but further evidence on its impact on decision-making in the UK and the predictive ability of the test in an ER+, LN-, HER- population receiving current drug regimens is needed. For MammaPrint and Mammostrat there were significant gaps in the available evidence and the estimates of cost-effectiveness produced were not considered to be robust by the External Assessment Group.<br><br>LIMITATIONS: Methodological weaknesses in the clinical evidence base relate to heterogeneity of patient cohorts and issues arising from the retrospective nature of the evidence. Further evidence is required on the clinical utility of all of the tests and on UK-based populations. A key area of uncertainty relates to whether the tests provide prognostic or predictive ability.<br><br>CONCLUSIONS: The clinical evidence base for OncotypeDX is considered to be the most robust. The economic analysis suggested that treatment guided using IHC4 has the most potential to be cost-effective at a threshold of &#xa3;20,000; however, the evidence base to support IHC4 needs significant further research.<br><br>STUDY REGISTRATION: PROSPERO 2011:CRD42011001361, available from www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42011001361.},
}
@article {pmid24086710,
year = {2013},
author = {Friedrich, EV and Neuper, C and Scherer, R},
title = {Whatever works: a systematic user-centered training protocol to optimize brain-computer interfacing individually.},
journal = {PloS one},
volume = {8},
number = {9},
pages = {e76214},
pmid = {24086710},
issn = {1932-6203},
mesh = {Adult ; Analysis of Variance ; *Brain-Computer Interfaces ; Discriminant Analysis ; Education/*methods ; Electroencephalography ; Female ; Humans ; Imagination/physiology ; Male ; },
abstract = {This study implemented a systematic user-centered training protocol for a 4-class brain-computer interface (BCI). The goal was to optimize the BCI individually in order to achieve high performance within few sessions for all users. Eight able-bodied volunteers, who were initially naïve to the use of a BCI, participated in 10 sessions over a period of about 5 weeks. In an initial screening session, users were asked to perform the following seven mental tasks while multi-channel EEG was recorded: mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, motor imagery of the left hand and motor imagery of both feet. Out of these seven mental tasks, the best 4-class combination as well as most reactive frequency band (between 8-30 Hz) was selected individually for online control. Classification was based on common spatial patterns and Fisher's linear discriminant analysis. The number and time of classifier updates varied individually. Selection speed was increased by reducing trial length. To minimize differences in brain activity between sessions with and without feedback, sham feedback was provided in the screening and calibration runs in which usually no real-time feedback is shown. Selected task combinations and frequency ranges differed between users. The tasks that were included in the 4-class combination most often were (1) motor imagery of the left hand (2), one brain-teaser task (word association or mental subtraction) (3), mental rotation task and (4) one more dynamic imagery task (auditory imagery, spatial navigation, imagery of the feet). Participants achieved mean performances over sessions of 44-84% and peak performances in single-sessions of 58-93% in this user-centered 4-class BCI protocol. This protocol is highly adjustable to individual users and thus could increase the percentage of users who can gain and maintain BCI control. A high priority for future work is to examine this protocol with severely disabled users.},
}
@article {pmid24086289,
year = {2013},
author = {Combaz, A and Chatelle, C and Robben, A and Vanhoof, G and Goeleven, A and Thijs, V and Van Hulle, MM and Laureys, S},
title = {A comparison of two spelling Brain-Computer Interfaces based on visual P3 and SSVEP in Locked-In Syndrome.},
journal = {PloS one},
volume = {8},
number = {9},
pages = {e73691},
pmid = {24086289},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Patient Satisfaction ; Quadriplegia/*physiopathology ; Quality of Life ; },
abstract = {OBJECTIVES: We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS).<br><br>METHODS: Seven patients performed repeated sessions with each BCI. We assessed BCI performance, mental workload and overall satisfaction for both systems. We also investigated the effect of the quality of life and level of motor impairment on the performance.<br><br>RESULTS: All seven patients were able to achieve an accuracy of 70% or more with the SSVEP-based BCI, compared to 3 patients with the P3-based BCI, showing a better performance with the SSVEP BCI than with the P3 BCI in the studied cohort. Moreover, the better performance of the SSVEP-based BCI was accompanied by a lower mental workload and a higher overall satisfaction. No relationship was found between BCI performance and level of motor impairment or quality of life.<br><br>CONCLUSION: Our results show a better usability of the SSVEP-based BCI than the P3-based one for the sessions performed by the tested population of locked-in patients with respect to all the criteria considered. The study shows the advantage of developing alternative BCIs with respect to the traditional matrix-based P3 speller using different designs and signal modalities such as SSVEPs to build a faster, more accurate, less mentally demanding and more satisfying BCI by testing both types of BCIs on a convenience sample of LIS patients.},
}
@article {pmid24083136,
year = {2013},
author = {Daliri, MR and Taghizadeh, M and Niksirat, KS},
title = {EEG Signature of Object Categorization from Event-related Potentials.},
journal = {Journal of medical signals and sensors},
volume = {3},
number = {1},
pages = {37-44},
pmid = {24083136},
issn = {2228-7477},
abstract = {Human visual system recognizes objects in a fast manner and the neural activity of the human brain generates signals which provide information about objects categories seen by the subjects. The brain signals can be recorded using different systems like the electroencephalogram (EEG). The EEG signals carry significant information about the stimuli that stimulate the brain. In order to translate information derived from the EEG for the object recognition mechanism, in this study, twelve various categories were selected as visual stimuli and were presented to the subjects in a controlled task and the signals were recorded through 19-channel EEG recording system. Analysis of signals was performed using two different event-related potential (ERP) computations namely the "target/rest" and "target/non-target" tasks. Comparing ERP of target with rest time indicated that the most involved electrodes in our task were F3, F4, C3, C4, Fz, Cz, among others. ERP of "target/non-target" resulted that in target stimuli two positive peaks occurred about 400 ms and 520 ms after stimulus onset; however, in non-target stimuli only one positive peak appeared about 400 ms after stimulus onset. Moreover, reaction times of subjects were computed and the results showed that the category of flower had the lowest reaction time; however, the stationery category had the maximum reaction time among others. The results provide useful information about the channels and the part of the signals that are affected by different object categories in terms of ERP brain signals. This study can be considered as the first step in the context of human-computer interface applications.},
}
@article {pmid24080080,
year = {2013},
author = {Holz, EM and Höhne, J and Staiger-Sälzer, P and Tangermann, M and Kübler, A},
title = {Brain-computer interface controlled gaming: evaluation of usability by severely motor restricted end-users.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {111-120},
doi = {10.1016/j.artmed.2013.08.001},
pmid = {24080080},
issn = {1873-2860},
mesh = {*Brain-Computer Interfaces ; Humans ; Middle Aged ; Paralysis/*physiopathology ; Patient Satisfaction ; Severity of Illness Index ; },
abstract = {OBJECTIVE: Connect-Four, a new sensorimotor rhythm (SMR) based brain-computer interface (BCI) gaming application, was evaluated by four severely motor restricted end-users; two were in the locked-in state and had unreliable eye-movement.<br><br>METHODS: Following the user-centred approach, usability of the BCI prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate (ITR) and subjective workload) and users' satisfaction.<br><br>RESULTS: Online performance varied strongly across users and sessions (median accuracy (%) of end-users: A=.65; B=.60; C=.47; D=.77). Our results thus yielded low to medium effectiveness in three end-users and high effectiveness in one end-user. Consequently, ITR was low (0.05-1.44bits/min). Only two end-users were able to play the game in free-mode. Total workload was moderate but varied strongly across sessions. Main sources of workload were mental and temporal demand. Furthermore, frustration contributed to the subjective workload of two end-users. Nevertheless, most end-users accepted the BCI application well and rated satisfaction medium to high. Sources for dissatisfaction were (1) electrode gel and cap, (2) low effectiveness, (3) time-consuming adjustment and (4) not easy-to-use BCI equipment. All four end-users indicated ease of use as being one of the most important aspect of BCI.<br><br>CONCLUSION: Effectiveness and efficiency are lower as compared to applications using the event-related potential as input channel. Nevertheless, the SMR-BCI application was satisfactorily accepted by the end-users and two of four could imagine using the BCI application in their daily life. Thus, despite moderate effectiveness and efficiency BCIs might be an option when controlling an application for entertainment.},
}
@article {pmid24080078,
year = {2013},
author = {Aloise, F and Aricò, P and Schettini, F and Salinari, S and Mattia, D and Cincotti, F},
title = {Asynchronous gaze-independent event-related potential-based brain-computer interface.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {61-69},
doi = {10.1016/j.artmed.2013.07.006},
pmid = {24080078},
issn = {1873-2860},
mesh = {Adult ; *Brain-Computer Interfaces ; *Evoked Potentials ; Humans ; Young Adult ; },
abstract = {OBJECTIVE: In this study a gaze independent event related potential (ERP)-based brain computer interface (BCI) for communication purpose was combined with an asynchronous classifier endowed with dynamical stopping feature. The aim was to evaluate if and how the performance of such asynchronous system could be negatively affected in terms of communication efficiency and robustness to false positives during the intentional no-control state.<br><br>MATERIAL AND METHODS: The proposed system was validated with the participation of 9 healthy subjects. A comparison was performed between asynchronous and synchronous classification technique outputs while users were controlling the same gaze independent BCI interface. The performance of both classification techniques were assessed both off-line and on-line by means of the efficiency metric introduced by Bianchi et al. (2007). This latter metric allows to set a different misclassification cost for wrong classifications and abstentions. Robustness was evaluated as the rate of false positives occurring during voluntary no-control states.<br><br>RESULTS: The asynchronous classifier did not exhibited significantly higher accuracy or lower error rate with respect to the synchronous classifier (accuracy: 74.66% versus 87.96%, error rate: 7.11% versus 12.04% respectively). However, the on-line and off-line analysis revealed that the communication efficiency was significantly improved (p<.05) with the asynchronous classification modality as compared with the synchronous. Furthermore, the asynchronous classifier proved to be robust to false positives during intentional no-control state which occur during the ongoing visual stimulation (less than 1 false positive every 6min).<br><br>CONCLUSION: As such, the proposed ERP-BCI system which combines an asynchronous classifier with a gaze independent interface is a promising solution to be further explored in order to increase the general usability of ERP-based BCI systems designed for severely disabled people with an impairment of the voluntary control of eye movements. In fact, the asynchronous classifier can improve communication efficiency automatically adapting the number of stimulus repetitions to the current user's state and suspending the control if he/she does not intend to select an item.},
}
@article {pmid24080077,
year = {2013},
author = {Zickler, C and Halder, S and Kleih, SC and Herbert, C and Kübler, A},
title = {Brain Painting: usability testing according to the user-centered design in end users with severe motor paralysis.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {99-110},
doi = {10.1016/j.artmed.2013.08.003},
pmid = {24080077},
issn = {1873-2860},
mesh = {*Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; Paralysis/*physiopathology ; Patient Satisfaction ; },
abstract = {BACKGROUND: For many years the reestablishment of communication for people with severe motor paralysis has been in the focus of brain-computer interface (BCI) research. Recently applications for entertainment have also been developed. Brain Painting allows the user creative expression through painting pictures.<br><br>OBJECTIVE: The second, revised prototype of the BCI Brain Painting application was evaluated in its target function - free painting - and compared to the P300 spelling application by four end users with severe disabilities.<br><br>METHODS: According to the International Organization for Standardization (ISO), usability was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate (ITR)), utility metric, subjective workload (National Aeronautics and Space Administration Task Load Index (NASA TLX)) and user satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) 2.0 and Assistive Technology Device Predisposition Assessment (ATD PA), Device Form).<br><br>RESULTS: The results revealed high performance levels (M&#x2265;80% accuracy) in the free painting and the copy painting conditions, ITRs (4.47-6.65bits/min) comparable to other P300 applications and only low to moderate workload levels (5-49 of 100), thereby proving that the complex task of free painting did neither impair performance nor impose insurmountable workload. Users were satisfied with the BCI Brain Painting application. Main obstacles for use in daily life were the system operability and the EEG cap, particularly the need of extensive support for adjustment.<br><br>CONCLUSION: The P300 Brain Painting application can be operated with high effectiveness and efficiency. End users with severe motor paralysis would like to use the application in daily life. User-friendliness, specifically ease of use, is a mandatory necessity when bringing BCI to end users. Early and active involvement of users and iterative user-centered evaluation enable developers to work toward this goal.},
}
@article {pmid24079396,
year = {2014},
author = {Li, M and Liu, Y and Wu, Y and Liu, S and Jia, J and Zhang, L},
title = {Neurophysiological substrates of stroke patients with motor imagery-based Brain-Computer Interface training.},
journal = {The International journal of neuroscience},
volume = {124},
number = {6},
pages = {403-415},
doi = {10.3109/00207454.2013.850082},
pmid = {24079396},
issn = {1563-5279},
mesh = {Aged ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Electric Stimulation Therapy/*methods ; Electroencephalography ; Female ; Humans ; Imagination/physiology ; Longitudinal Studies ; Male ; Middle Aged ; Motor Activity/physiology ; Neuronal Plasticity/*physiology ; Paralysis/etiology/*rehabilitation ; Sensorimotor Cortex/*physiopathology ; Stroke/complications ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity/*pathology ; },
abstract = {We investigated the efficacy of motor imagery-based Brain Computer Interface (MI-based BCI) training for eight stroke patients with severe upper extremity paralysis using longitudinal clinical assessments. The results were compared with those of a control group (n = 7) that only received FES (Functional Electrical Stimulation) treatment besides conventional therapies. During rehabilitation training, changes in the motor function of the upper extremity and in the neurophysiologic electroencephalographic (EEG) were observed for two groups. After 8 weeks of training, a significant improvement in the motor function of the upper extremity for the BCI group was confirmed (p < 0.05 for ARAT), simultaneously with the activation of bilateral cerebral hemispheres. Additionally, event-related desynchronization (ERD) of the affected sensorimotor cortexes (SMCs) was significantly enhanced when compared to the pretraining course, which was only observed in the BCI group (p < 0.05). Furthermore, the activation of affected SMC and parietal lobe were determined to contribute to motor function recovery (p < 0.05). In brief, our findings demonstrate that MI-based BCI training can enhance the motor function of the upper extremity for stroke patients by inducing the optimal cerebral motor functional reorganization.},
}
@article {pmid24077619,
year = {2013},
author = {Ortiz-Rosario, A and Adeli, H},
title = {Brain-computer interface technologies: from signal to action.},
journal = {Reviews in the neurosciences},
volume = {24},
number = {5},
pages = {537-552},
doi = {10.1515/revneuro-2013-0032},
pmid = {24077619},
issn = {0334-1763},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Fourier Analysis ; Humans ; Neural Networks, Computer ; *Wavelet Analysis ; },
abstract = {Here, we present a state-of-the-art review of the research performed on the brain-computer interface (BCI) technologies with a focus on signal processing approaches. BCI can be divided into three main components: signal acquisition, signal processing, and effector device. The signal acquisition component is generally divided into two categories: noninvasive and invasive. For noninvasive, this review focuses on electroencephalogram. For the invasive, the review includes electrocorticography, local field potentials, multiple-unit activity, and single-unit action potentials. Signal processing techniques reviewed are divided into time-frequency methods such as Fourier transform, autoregressive models, wavelets, and Kalman filter and spatiotemporal techniques such as Laplacian filter and common spatial patterns. Additionally, various signal feature classification algorithms are discussed such as linear discriminant analysis, support vector machines, artificial neural networks, and Bayesian classifiers. The article ends with a discussion of challenges facing BCI and concluding remarks on the future of the technology.},
}
@article {pmid24077618,
year = {2013},
author = {Khurana, S and Li, WK},
title = {Baptisms of fire or death knells for acute-slice physiology in the age of 'omics' and light?.},
journal = {Reviews in the neurosciences},
volume = {24},
number = {5},
pages = {527-536},
doi = {10.1515/revneuro-2013-0028},
pmid = {24077618},
issn = {0334-1763},
mesh = {Action Potentials/physiology ; Animals ; Brain/*cytology ; Cell Death ; *Electrophysiology/instrumentation ; Neurons/*physiology ; },
abstract = {With increasing use of various techniques to record optically and electrophysiologically from awake behaving animals and the growing developments of brain-machine interfaces, one might wonder if the use of acute-slice physiology is on its deathbed. Have we actually arrived at a stage where we can abandon the use of acute slices, with most of the information about brain functions coming from in vivo experiments? We do not believe that this is the case, given that our understanding of the nuts and bolts of the nervous system, such as ion channels and transporters in near-native state, neuronal compartmentalization, and single-neuron computation, is far from complete. We believe that in the foreseeable future, questions in these fields will still be best addressed by acute-slice physiology. We approach this review from the perspective of improving acute-slice physiology so it can continue to provide relevant and valuable contributions to neuroscience. We conclude that the death of acute-slice physiology is an obituary prematurely written, merely due to waxing and waning trends in science and the shortsightedness of investigators. Acute-slice physiology has at least one more life to live after the hype around new techniques has passed, but it needs to reinvent itself in light of current knowledge.},
}
@article {pmid24076343,
year = {2013},
author = {Kübler, A and Mattia, D and Rupp, R and Tangermann, M},
title = {Facing the challenge: bringing brain-computer interfaces to end-users.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {55-60},
doi = {10.1016/j.artmed.2013.08.002},
pmid = {24076343},
issn = {1873-2860},
mesh = {*Brain-Computer Interfaces ; Equipment Design ; Humans ; },
}
@article {pmid24076342,
year = {2013},
author = {Pokorny, C and Klobassa, DS and Pichler, G and Erlbeck, H and Real, RG and Kübler, A and Lesenfants, D and Habbal, D and Noirhomme, Q and Risetti, M and Mattia, D and Müller-Putz, GR},
title = {The auditory P300-based single-switch brain-computer interface: paradigm transition from healthy subjects to minimally conscious patients.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {81-90},
doi = {10.1016/j.artmed.2013.07.003},
pmid = {24076342},
issn = {1873-2860},
mesh = {Acoustic Stimulation ; Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Persistent Vegetative State/*physiopathology ; },
abstract = {OBJECTIVE: Within this work an auditory P300 brain-computer interface based on tone stream segregation, which allows for binary decisions, was developed and evaluated.<br><br>METHODS AND MATERIALS: Two tone streams consisting of short beep tones with infrequently appearing deviant tones at random positions were used as stimuli. This paradigm was evaluated in 10 healthy subjects and applied to 12 patients in a minimally conscious state (MCS) at clinics in Graz, W&#xfc;rzburg, Rome, and Li&#xe8;ge. A stepwise linear discriminant analysis classifier with 10&#xd7;10 cross-validation was used to detect the presence of any P300 and to investigate attentional modulation of the P300 amplitude.<br><br>RESULTS: The results for healthy subjects were promising and most classification results were better than random. In 8 of the 10 subjects, focused attention on at least one of the tone streams could be detected on a single-trial basis. By averaging 10 data segments, classification accuracies up to 90.6% could be reached. However, for MCS patients only a small number of classification results were above chance level and none of the results were sufficient for communication purposes. Nevertheless, signs of consciousness were detected in 9 of the 12 patients, not on a single-trial basis, but after averaging of all corresponding data segments and computing significant differences. These significant results, however, strongly varied across sessions and conditions.<br><br>CONCLUSION: This work shows the transition of a paradigm from healthy subjects to MCS patients. Promising results with healthy subjects are, however, no guarantee of good results with patients. Therefore, more investigations are required before any definite conclusions about the usability of this paradigm for MCS patients can be drawn. Nevertheless, this paradigm might offer an opportunity to support bedside clinical assessment of MCS patients and eventually, to provide them with a means of communication.},
}
@article {pmid24076341,
year = {2013},
author = {Schreuder, M and Riccio, A and Risetti, M and Dähne, S and Ramsay, A and Williamson, J and Mattia, D and Tangermann, M},
title = {User-centered design in brain-computer interfaces-a case study.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {71-80},
doi = {10.1016/j.artmed.2013.07.005},
pmid = {24076341},
issn = {1873-2860},
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Middle Aged ; Neuropsychological Tests ; Stroke/physiopathology ; Stroke Rehabilitation ; Surveys and Questionnaires ; },
abstract = {OBJECTIVE: The array of available brain-computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and - if necessary - adapted to him/her until a suitable BCI system is found.<br><br>METHODS: Described is the case of a 48-year-old woman who suffered from an ischemic brain stem stroke, leading to a severe motor- and communication deficit. She was enrolled in studies with two different BCI systems before a suitable system was found. The first was an auditory event-related potential (ERP) paradigm and the second a visual ERP paradigm, both of which are established in literature.<br><br>RESULTS: The auditory paradigm did not work successfully, despite favorable preconditions. The visual paradigm worked flawlessly, as found over several sessions. This discrepancy in performance can possibly be explained by the user's clinical deficit in several key neuropsychological indicators, such as attention and working memory. While the auditory paradigm relies on both categories, the visual paradigm could be used with lower cognitive workload. Besides attention and working memory, several other neurophysiological and -psychological indicators - and the role they play in the BCIs at hand - are discussed.<br><br>CONCLUSION: The user's performance on the first BCI paradigm would typically have excluded her from further ERP-based BCI studies. However, this study clearly shows that, with the numerous paradigms now at our disposal, the pursuit for a functioning BCI system should not be stopped after an initial failed attempt.},
}
@article {pmid24070543,
year = {2013},
author = {Demirer, RM and Özerdem, MS and Bayrak, C and Mendi, E},
title = {Determination of ECoG information flow activity based on Granger causality and Hilbert transformation.},
journal = {Computer methods and programs in biomedicine},
volume = {112},
number = {3},
pages = {481-489},
doi = {10.1016/j.cmpb.2013.08.011},
pmid = {24070543},
issn = {1872-7565},
mesh = {Cerebral Cortex/physiology ; Electroencephalography/*methods ; Humans ; Tongue/physiology ; },
abstract = {Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm×8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8×8 (0.016-300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.},
}
@article {pmid24070057,
year = {2013},
author = {Ora, H and Takano, K and Kawase, T and Iwaki, S and Parkkonen, L and Kansaku, K},
title = {Implementation of a beam forming technique in real-time magnetoencephalography.},
journal = {Journal of integrative neuroscience},
volume = {12},
number = {3},
pages = {331-341},
doi = {10.1142/S0219635213500192},
pmid = {24070057},
issn = {0219-6352},
mesh = {Brain/physiology ; Humans ; Magnetoencephalography/instrumentation/*methods ; Neurofeedback/instrumentation/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience. In the present study, we implemented voxel-based real-time coherence measurements in a rtMEG system in which we employed a beamformer to localize signal sources in the anatomical space prior to computing imaginary coherence. Our rtMEG experiment showed that a healthy subject could increase coherence between the parietal cortex and visual cortex when attending to a flickering visual stimulus. This finding suggests that our system is suitable for neurofeedback training and can be useful for practical brain-machine interface applications or neurofeedback rehabilitation.},
}
@article {pmid24068982,
year = {2013},
author = {Sakurada, T and Kawase, T and Takano, K and Komatsu, T and Kansaku, K},
title = {A BMI-based occupational therapy assist suit: asynchronous control by SSVEP.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {172},
pmid = {24068982},
issn = {1662-4548},
abstract = {A brain-machine interface (BMI) is an interface technology that uses neurophysiological signals from the brain to control external machines. Recent invasive BMI technologies have succeeded in the asynchronous control of robot arms for a useful series of actions, such as reaching and grasping. In this study, we developed non-invasive BMI technologies aiming to make such useful movements using the subject's own hands by preparing a BMI-based occupational therapy assist suit (BOTAS). We prepared a pre-recorded series of useful actions-a grasping-a-ball movement and a carrying-the-ball movement-and added asynchronous control using steady-state visual evoked potential (SSVEP) signals. A SSVEP signal was used to trigger the grasping-a-ball movement and another SSVEP signal was used to trigger the carrying-the-ball movement. A support vector machine was used to classify EEG signals recorded from the visual cortex (Oz) in real time. Untrained, able-bodied participants (n = 12) operated the system successfully. Classification accuracy and time required for SSVEP detection were ~88% and 3 s, respectively. We further recruited three patients with upper cervical spinal cord injuries (SCIs); they also succeeded in operating the system without training. These data suggest that our BOTAS system is potentially useful in terms of rehabilitation of patients with upper limb disabilities.},
}
@article {pmid24068244,
year = {2013},
author = {Choi, K},
title = {Electroencephalography (EEG)-based neurofeedback training for brain-computer interface (BCI).},
journal = {Experimental brain research},
volume = {231},
number = {3},
pages = {351-365},
pmid = {24068244},
issn = {1432-1106},
mesh = {Adult ; Analysis of Variance ; Brain Mapping ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/blood supply/*physiology ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Image Processing, Computer-Assisted ; Imagination/physiology ; Learning/*physiology ; Magnetic Resonance Imaging ; Male ; Movement/*physiology ; *Neurofeedback ; Oxygen/blood ; Psychomotor Performance ; Young Adult ; },
abstract = {Electroencephalography has become a popular tool in basic brain research, but in recent years, several practical limitations have been highlighted. Some of the drawbacks pertain to the offline analyses of the neural signal that prevent the subjects from engaging in real-time error correction during learning. Other limitations include the complex nature of the visual stimuli, often inducing fatigue and introducing considerable delays, possibly interfering with spontaneous performance. By replacing the complex external visual input with internally driven motor imagery, we can overcome some delay problems, at the expense of losing the ability to precisely parameterize features of the input stimulus. To address these issues, we here introduce a nontrivial modification to brain-computer Interfaces (BCI). We combine the fast signal processing of motor imagery with the ability to parameterize external visual feedback in the context of a very simple control task: attempting to intentionally control the direction of an external cursor on command. By engaging the subject in motor imagery while providing real-time visual feedback on their instantaneous performance, we can take advantage of positive features present in both externally- and internally driven learning. We further use a classifier that automatically selects the cortical activation features that most likely maximize the performance accuracy. Under this closed loop coadaptation system, we saw a progression of the cortical activation that started in sensorymotor areas, when at chance performance motor imagery was explicitly used, migrated to BA6 under deliberate control and ended in the more frontal regions of prefrontal cortex, when at maximal performance accuracy, the subjects reportedly developed spontaneous mental control of the instructed direction. We discuss our results in light of possible applications of this simple BCI paradigm to study various cognitive phenomena involving the deliberate control of a directional signal in decision making tasks performed with intent.},
}
@article {pmid24065906,
year = {2013},
author = {Derosière, G and Mandrick, K and Dray, G and Ward, TE and Perrey, S},
title = {NIRS-measured prefrontal cortex activity in neuroergonomics: strengths and weaknesses.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {583},
pmid = {24065906},
issn = {1662-5161},
}
@article {pmid24064256,
year = {2013},
author = {Rohm, M and Schneiders, M and Müller, C and Kreilinger, A and Kaiser, V and Müller-Putz, GR and Rupp, R},
title = {Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury.},
journal = {Artificial intelligence in medicine},
volume = {59},
number = {2},
pages = {133-142},
doi = {10.1016/j.artmed.2013.07.004},
pmid = {24064256},
issn = {1873-2860},
mesh = {Adult ; Arm/*physiopathology ; *Brain-Computer Interfaces ; Humans ; Male ; *Prostheses and Implants ; Spinal Cord Injuries/*physiopathology ; },
abstract = {BACKGROUND: The bilateral loss of the grasp function associated with a lesion of the cervical spinal cord severely limits the affected individuals' ability to live independently and return to gainful employment after sustaining a spinal cord injury (SCI). Any improvement in lost or limited grasp function is highly desirable. With current neuroprostheses, relevant improvements can be achieved in end users with preserved shoulder and elbow, but missing hand function.<br><br>OBJECTIVE: The aim of this single case study is to show that (1) with the support of hybrid neuroprostheses combining functional electrical stimulation (FES) with orthoses, restoration of hand, finger and elbow function is possible in users with high-level SCI and (2) shared control principles can be effectively used to allow for a brain-computer interface (BCI) control, even if only moderate BCI performance is achieved after extensive training.<br><br>PATIENT AND METHODS: The individual in this study is a right-handed 41-year-old man who sustained a traumatic SCI in 2009 and has a complete motor and sensory lesion at the level of C4. He is unable to generate functionally relevant movements of the elbow, hand and fingers on either side. He underwent extensive FES training (30-45min, 2-3 times per week for 6 months) and motor imagery (MI) BCI training (415 runs in 43 sessions over 12 months). To meet individual needs, the system was designed in a modular fashion including an intelligent control approach encompassing two input modalities, namely an MI-BCI and shoulder movements.<br><br>RESULTS: After one year of training, the end user's MI-BCI performance ranged from 50% to 93% (average: 70.5%). The performance of the hybrid system was evaluated with different functional assessments. The user was able to transfer objects of the grasp-and-release-test and he succeeded in eating a pretzel stick, signing a document and eating an ice cream cone, which he was unable to do without the system.<br><br>CONCLUSION: This proof-of-concept study has demonstrated that with the support of hybrid FES systems consisting of FES and a semiactive orthosis, restoring hand, finger and elbow function is possible in a tetraplegic end-user. Remarkably, even after one year of training and 415 MI-BCI runs, the end user's average BCI performance remained at about 70%. This supports the view that in high-level tetraplegic subjects, an initially moderate BCI performance cannot be improved by extensive training. However, this aspect has to be validated in future studies with a larger population.},
}
@article {pmid24062716,
year = {2013},
author = {Sankar, V and Sanchez, JC and McCumiskey, E and Brown, N and Taylor, CR and Ehlert, GJ and Sodano, HA and Nishida, T},
title = {A highly compliant serpentine shaped polyimide interconnect for front-end strain relief in chronic neural implants.},
journal = {Frontiers in neurology},
volume = {4},
number = {},
pages = {124},
pmid = {24062716},
issn = {1664-2295},
support = {R01 NS053561/NS/NINDS NIH HHS/United States ; },
abstract = {While the signal quality of recording neural electrodes is observed to degrade over time, the degradation mechanisms are complex and less easily observable. Recording microelectrodes failures are attributed to different biological factors such as tissue encapsulation, immune response, and disruption of blood-brain barrier (BBB) and non-biological factors such as strain due to micromotion, insulation delamination, corrosion, and surface roughness on the recording site (1-4). Strain due to brain micromotion is considered to be one of the important abiotic factors contributing to the failure of the neural implants. To reduce the forces exerted by the electrode on the brain, a high compliance 2D serpentine shaped electrode cable was designed, simulated, and measured using polyimide as the substrate material. Serpentine electrode cables were fabricated using MEMS microfabrication techniques, and the prototypes were subjected to load tests to experimentally measure the compliance. The compliance of the serpentine cable was numerically modeled and quantitatively measured to be up to 10 times higher than the compliance of a straight cable of same dimensions and material.},
}
@article {pmid24062669,
year = {2013},
author = {Lotte, F and Larrue, F and Mühl, C},
title = {Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {568},
pmid = {24062669},
issn = {1662-5161},
abstract = {While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.},
}
@article {pmid24059635,
year = {2013},
author = {Gruchattka, E and Hädicke, O and Klamt, S and Schütz, V and Kayser, O},
title = {In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories.},
journal = {Microbial cell factories},
volume = {12},
number = {},
pages = {84},
pmid = {24059635},
issn = {1475-2859},
mesh = {Computer Simulation ; Escherichia coli/enzymology/genetics/*metabolism ; Metabolic Engineering ; Saccharomyces cerevisiae/enzymology/genetics/*metabolism ; Terpenes/*metabolism ; },
abstract = {BACKGROUND: Heterologous microbial production of rare plant terpenoids of medicinal or industrial interest is attracting more and more attention but terpenoid yields are still low. Escherichia coli and Saccharomyces cerevisiae are the most widely used heterologous hosts; a direct comparison of both hosts based on experimental data is difficult though. Hence, the terpenoid pathways of E. coli (via 1-deoxy-D-xylulose 5-phosphate, DXP) and S. cerevisiae (via mevalonate, MVA), the impact of the respective hosts metabolism as well as the impact of different carbon sources were compared in silico by means of elementary mode analysis. The focus was set on the yield of isopentenyl diphosphate (IPP), the general terpenoid precursor, to identify new metabolic engineering strategies for an enhanced terpenoid yield.<br><br>RESULTS: Starting from the respective precursor metabolites of the terpenoid pathways (pyruvate and glyceraldehyde-3-phosphate for the DXP pathway and acetyl-CoA for the MVA pathway) and considering only carbon stoichiometry, the two terpenoid pathways are identical with respect to carbon yield. However, with glucose as substrate, the MVA pathway has a lower potential to supply terpenoids in high yields than the DXP pathway if the formation of the required precursors is taken into account, due to the carbon loss in the formation of acetyl-CoA. This maximum yield is further reduced in both hosts when the required energy and reduction equivalents are considered. Moreover, the choice of carbon source (glucose, xylose, ethanol or glycerol) has an effect on terpenoid yield with non-fermentable carbon sources being more promising. Both hosts have deficiencies in energy and redox equivalents for high yield terpenoid production leading to new overexpression strategies (heterologous enzymes/pathways) for an enhanced terpenoid yield. Finally, several knockout strategies are identified using constrained minimal cut sets enforcing a coupling of growth to a terpenoid yield which is higher than any yield published in scientific literature so far.<br><br>CONCLUSIONS: This study provides for the first time a comprehensive and detailed in silico comparison of the most prominent heterologous hosts E. coli and S. cerevisiae as terpenoid factories giving an overview on several promising metabolic engineering strategies paving the way for an enhanced terpenoid yield.},
}
@article {pmid24059536,
year = {2015},
author = {Fried-Oken, M and Mooney, A and Peters, B and Oken, B},
title = {A clinical screening protocol for the RSVP Keyboard brain-computer interface.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {10},
number = {1},
pages = {11-18},
pmid = {24059536},
issn = {1748-3115},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; 1R01DC009834-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Brain-Computer Interfaces ; Clinical Protocols ; Communication Devices for People with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Physical Therapy Modalities/instrumentation ; Quadriplegia/*rehabilitation ; *Self-Help Devices ; Visual Perception ; },
abstract = {PURPOSE: To propose a screening protocol that identifies requisite sensory, motor, cognitive and communication skills for people with locked-in syndrome (PLIS) to use the RSVP Keyboard™ brain-computer interface (BCI).<br><br>METHOD: A multidisciplinary clinical team of seven individuals representing five disciplines identified requisite skills for the BCI RSVP Keyboard&#x2122;. They chose questions and subtests from existing standardized instruments for auditory comprehension, reading and spelling, modified them to accommodate nonverbal response modalities, and developed novel tasks to screen visual perception, sustained visual attention and working memory. Questions were included about sensory skills, positioning, pain interference and medications. The result is a compilation of questions, adapted subtests and original tasks designed for this new BCI system. It was administered to 12 PLIS and 6 healthy controls.<br><br>RESULTS: Administration required 1&#x2009;h or less. Yes/no choices and eye gaze were adequate response modes for PLIS. Healthy controls and 9 PLIS were 100% accurate on all tasks; 3 PLIS missed single items.<br><br>CONCLUSIONS: The RSVP BCI screening protocol is a brief, repeatable technique for patients with different levels of LIS to identify the presence/absence of skills for BCI use. Widespread adoption of screening methods should be a clinical goal and will help standardize BCI implementation for research and intervention. Implications for Rehabilitation People with locked-in syndrome must have certain sensory, motor, cognitive and communication skills to successfully use a brain-computer interface (BCI) for communication. A screening profile would be useful in identifying potentially suitable candidates for BCI.},
}
@article {pmid24058565,
year = {2013},
author = {Zhang, R and Xu, P and Guo, L and Zhang, Y and Li, P and Yao, D},
title = {Z-score linear discriminant analysis for EEG based brain-computer interfaces.},
journal = {PloS one},
volume = {8},
number = {9},
pages = {e74433},
pmid = {24058565},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Computer Simulation ; Discriminant Analysis ; *Electroencephalography ; Female ; Humans ; Male ; Young Adult ; },
abstract = {Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.},
}
@article {pmid24058009,
year = {2014},
author = {Yin, E and Zhou, Z and Jiang, J and Chen, F and Liu, Y and Hu, D},
title = {A speedy hybrid BCI spelling approach combining P300 and SSVEP.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {2},
pages = {473-483},
doi = {10.1109/TBME.2013.2281976},
pmid = {24058009},
issn = {1558-2531},
mesh = {Adolescent ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers.},
}
@article {pmid24056793,
year = {2013},
author = {Liew, SL and Agashe, H and Bhagat, N and Paek, A and Bulea, TC},
title = {A clinical roadmap for brain--neural machine interfaces: trainees' perspectives on the 2013 International Workshop.},
journal = {IEEE pulse},
volume = {4},
number = {5},
pages = {44-48},
doi = {10.1109/MPUL.2013.2271686},
pmid = {24056793},
issn = {2154-2317},
support = {R13 NS082045/NS/NINDS NIH HHS/United States ; NS082045/NS/NINDS NIH HHS/United States ; },
mesh = {Biomedical Engineering/education ; *Brain-Computer Interfaces ; Humans ; Robotics/instrumentation ; Spinal Cord Injuries/rehabilitation ; },
}
@article {pmid24056432,
year = {2013},
author = {Launer, M and Lyko, S and Fahlenkamp, H and Jagemann, P and Ehrhard, P},
title = {Application of CFD modelling at a full-scale ozonation plant for the removal of micropollutants from secondary effluent.},
journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
volume = {68},
number = {6},
pages = {1336-1344},
doi = {10.2166/wst.2013.378},
pmid = {24056432},
issn = {0273-1223},
mesh = {Diatrizoate/chemistry ; Diclofenac/chemistry ; *Hydrodynamics ; Metoprolol/chemistry ; *Models, Theoretical ; Oxidants/*chemistry ; Ozone/*chemistry ; Waste Disposal, Fluid/*methods ; Water Pollutants, Chemical/*chemistry ; },
abstract = {Since November 2009, Germany's first full-scale ozonation plant for tertiary treatment of secondary effluent is in continuous operation. A kinetic model was developed and combined with the commercial computational fluid dynamics (CFD) software ANSYS(®) CFX(®) to simulate the removal of micropollutants from secondary effluents. Input data like reaction rate constants and initial concentrations of bulk components of the effluent organic matter (EfOM) were derived from experimental batch tests. Additionally, well-known correlations for the mass transfer were implemented into the simulation model. The CFD model was calibrated and validated by full-scale process data and by analytical measurements for micropollutants. The results show a good consistency of simulated values and measured data. Therewith, the validated CFD model described in this study proved to be suited for the application of secondary effluent ozonation. By implementing site-specific ozone exposition and the given reactor geometry the described CFD model can be easily adopted for similar applications.},
}
@article {pmid24054981,
year = {2013},
author = {Abibullaev, B and An, J and Jin, SH and Lee, SH and Moon, JI},
title = {Minimizing inter-subject variability in fNIRS-based brain-computer interfaces via multiple-kernel support vector learning.},
journal = {Medical engineering &amp; physics},
volume = {35},
number = {12},
pages = {1811-1818},
doi = {10.1016/j.medengphy.2013.08.009},
pmid = {24054981},
issn = {1873-4030},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Humans ; Spectrophotometry, Infrared/*methods ; *Support Vector Machine ; },
abstract = {Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain-computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers' feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.},
}
@article {pmid24048242,
year = {2014},
author = {Hsu, WY},
title = {Improving classification accuracy of motor imagery EEG using genetic feature selection.},
journal = {Clinical EEG and neuroscience},
volume = {45},
number = {3},
pages = {163-168},
doi = {10.1177/1550059413491559},
pmid = {24048242},
issn = {1550-0594},
mesh = {*Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; *Neural Networks, Computer ; Neurofeedback ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; Wavelet Analysis ; },
abstract = {In this study, an electroencephalogram (EEG) analysis system combined with feature selection, is proposed to enhance the classification of motor imagery (MI) data. It principally comprises feature extraction, feature selection, and classification. First, several features, including adaptive autoregressive (AAR) parameters, spectral power, asymmetry ratio, coherence and phase-locking value are extracted for subsequent classification. A genetic algorithm is then used to select features from the combination of the aforementioned features. Finally, the selected features are classified by support vector machine (SVM). Compared with "without feature selection" and back-propagation neural network (BPNN) on MI data from 2 data sets, the proposed system achieves better classification accuracy and is suitable for the applications of brain-computer interface (BCI).},
}
@article {pmid24047594,
year = {2013},
author = {Eeg-Olofsson, M and Stenfelt, S and Taghavi, H and Reinfeldt, S and Håkansson, B and Tengstrand, T and Finizia, C},
title = {Transmission of bone conducted sound - correlation between hearing perception and cochlear vibration.},
journal = {Hearing research},
volume = {306},
number = {},
pages = {11-20},
doi = {10.1016/j.heares.2013.08.015},
pmid = {24047594},
issn = {1878-5891},
mesh = {Acoustic Stimulation ; Adult ; Aged ; Auditory Threshold/physiology ; Bone Conduction/*physiology ; Cadaver ; Cochlea/*physiology ; Ear, Inner/physiopathology ; Female ; Hearing ; Hearing Aids ; Humans ; Laser-Doppler Flowmetry ; Male ; Middle Aged ; Semicircular Canals/pathology ; Signal Processing, Computer-Assisted ; Skull/physiology ; *Sound ; Transducers ; *Vibration ; },
abstract = {The vibration velocity of the lateral semicircular canal and the cochlear promontory was measured on 16 subjects with a unilateral middle ear common cavity, using a laser Doppler vibrometer, when the stimulation was by bone conduction (BC). Four stimulation positions were used: three ipsilateral positions and one contralateral position. Masked BC pure tone thresholds were measured with the stimulation at the same four positions. Valid vibration data were obtained at frequencies between 0.3 and 5.0 kHz. Large intersubject variation of the results was found with both methods. The difference in cochlear velocity with BC stimulation at the four positions varied as a function of frequency while the tone thresholds showed a tendency of lower thresholds with stimulation at positions close to the cochlea. The correlation between the vibration velocities of the two measuring sites of the otic capsule was high. Also, relative median data showed similar trends for both vibration and threshold measurements. However, due to the high variability for both vibration and perceptual data, low correlation between the two methods was found at the individual level. The results from this study indicated that human hearing perception from BC sound can be estimated from the measure of cochlear vibrations of the otic capsule. It also showed that vibration measurements of the cochlea in cadaver heads are similar to that measured in live humans.},
}
@article {pmid24046839,
year = {2013},
author = {Normann, RA},
title = {Building on the BRAIN initiative.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {040301},
doi = {10.1088/1741-2560/10/4/040301},
pmid = {24046839},
issn = {1741-2552},
mesh = {Animals ; Biotechnology/*trends ; Brain/*physiology ; Brain-Computer Interfaces/*trends ; *Government Programs ; Humans ; Neurosciences/*trends ; Prostheses and Implants/*trends ; },
}
@article {pmid24046734,
year = {2013},
author = {Micoulaud-Franchi, JA and Fond, G and Dumas, G},
title = {Cyborg psychiatry to ensure agency and autonomy in mental disorders. A proposal for neuromodulation therapeutics.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {463},
pmid = {24046734},
issn = {1662-5161},
abstract = {Neuromodulation therapeutics-as repeated Transcranial Magnetic Stimulation (rTMS) and neurofeedback-are valuable tools for psychiatry. Nevertheless, they currently face some limitations: rTMS has confounding effects on neural activation patterns, and neurofeedback fails to change neural dynamics in some cases. Here we propose how coupling rTMS and neurofeedback can tackle both issues by adapting neural activations during rTMS and actively guiding individuals during neurofeedback. An algorithmic challenge then consists in designing the proper recording, processing, feedback, and control of unwanted effects. But this new neuromodulation technique also poses an ethical challenge: ensuring treatment occurs within a biopsychosocial model of medicine, while considering both the interaction between the patients and the psychiatrist, and the maintenance of individuals' autonomy. Our solution is the concept of Cyborg psychiatry, which embodies the technique and includes a self-engaged interaction between patients and the neuromodulation device.},
}
@article {pmid24045617,
year = {2013},
author = {Edgington, RJ and Thalhammer, A and Welch, JO and Bongrain, A and Bergonzo, P and Scorsone, E and Jackman, RB and Schoepfer, R},
title = {Patterned neuronal networks using nanodiamonds and the effect of varying nanodiamond properties on neuronal adhesion and outgrowth.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056022},
doi = {10.1088/1741-2560/10/5/056022},
pmid = {24045617},
issn = {1741-2552},
mesh = {Animals ; Brain-Computer Interfaces ; Cell Adhesion/*physiology ; *Diamond ; Hippocampus/cytology ; Image Processing, Computer-Assisted ; Immunohistochemistry ; Mice ; Microscopy, Atomic Force ; *Nanoparticles ; Nerve Net/*physiology ; *Neural Networks, Computer ; Neurons/*physiology ; Particle Size ; Primary Cell Culture ; Spectroscopy, Fourier Transform Infrared ; Spectrum Analysis, Raman ; Surface Properties ; },
abstract = {OBJECTIVE: Detonation nanodiamond monolayer coatings are exceptionally biocompatible substrates for in vitro cell culture. However, the ability of nanodiamond coatings of different origin, size, surface chemistry and morphology to promote neuronal adhesion, and the ability to pattern neurons with nanodiamonds have yet to be investigated.<br><br>APPROACH: Various nanodiamond coatings of different type are investigated for their ability to promote neuronal adhesion with respect to surface coating parameters and neurite extension. Nanodiamond tracks are patterned using photolithography and reactive ion etching.<br><br>MAIN RESULTS: Universal promotion of neuronal adhesion is observed on all coatings tested and analysis shows surface roughness to not be a sufficient metric to describe biocompatibility, but instead nanoparticle size and curvature shows a significant correlation with neurite extension. Furthermore, neuronal patterning is achieved with high contrast using patterned nanodiamond coatings down to at least 10 &#xb5;m.<br><br>SIGNIFICANCE: The results of nanoparticle size and curvature being influential upon neuronal adhesion has great implications towards biomaterial design, and the ability to pattern neurons using nanodiamond tracks shows great promise for applications both in vitro and in vivo.},
}
@article {pmid24045504,
year = {2013},
author = {Wissel, T and Pfeiffer, T and Frysch, R and Knight, RT and Chang, EF and Hinrichs, H and Rieger, JW and Rose, G},
title = {Hidden Markov model and support vector machine based decoding of finger movements using electrocorticography.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056020},
pmid = {24045504},
issn = {1741-2552},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; R56 NS021135/NS/NINDS NIH HHS/United States ; NS21135/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Algorithms ; Artificial Intelligence ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electrodes ; Electroencephalography/*instrumentation/methods ; Fingers/*physiology ; Humans ; Male ; *Markov Chains ; *Models, Neurological ; Movement/*physiology ; *Support Vector Machine ; Young Adult ; },
abstract = {OBJECTIVE: Support vector machines (SVM) have developed into a gold standard for accurate classification in brain-computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance.<br><br>APPROACH: We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from electrocorticograms of four subjects performing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features.<br><br>MAIN RESULTS: We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques.<br><br>SIGNIFICANCE: We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online BCIs.},
}
@article {pmid24043767,
year = {2013},
author = {Wang, S and Chen, A and Fang, J and Pacala, SW},
title = {Why abundant tropical tree species are phylogenetically old.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {40},
pages = {16039-16043},
pmid = {24043767},
issn = {1091-6490},
mesh = {Age Factors ; *Biodiversity ; Computer Simulation ; *Models, Biological ; Panama ; *Phylogeny ; Population Dynamics ; Species Specificity ; Trees/*genetics ; Tropical Climate ; },
abstract = {Neutral models of species diversity predict patterns of abundance for communities in which all individuals are ecologically equivalent. These models were originally developed for Panamanian trees and successfully reproduce observed distributions of abundance. Neutral models also make macroevolutionary predictions that have rarely been evaluated or tested. Here we show that neutral models predict a humped or flat relationship between species age and population size. In contrast, ages and abundances of tree species in the Panamanian Canal watershed are found to be positively correlated, which falsifies the models. Speciation rates vary among phylogenetic lineages and are partially heritable from mother to daughter species. Variable speciation rates in an otherwise neutral model lead to a demographic advantage for species with low speciation rate. This demographic advantage results in a positive correlation between species age and abundance, as found in the Panamanian tropical forest community.},
}
@article {pmid24043199,
year = {2013},
author = {Müller-Putz, GR and Schreuder, M and Tangermann, M and Leeb, R and Millán Del, RJ},
title = {The hybrid Brain-Computer Interface: a bridge to assistive technology?.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4435},
pmid = {24043199},
issn = {1862-278X},
}
@article {pmid24043198,
year = {2013},
author = {Rupp, R and Rohm, M and Schneiders, M and Weidner, N and Kaiser, V and Kreilinger, A and Müller-Putz, GR},
title = {Think2grasp - BCI-Controlled Neuroprosthesis for the Upper Extremity.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4440},
pmid = {24043198},
issn = {1862-278X},
}
@article {pmid24043196,
year = {2013},
author = {Pinegger, A and Wriessnegger, S and Müller-Putz, G},
title = {Introduction of a Universal P300 Brain-Computer Interface Communication System.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4445},
pmid = {24043196},
issn = {1862-278X},
}
@article {pmid24043192,
year = {2013},
author = {Kübler, A and Zickler, C and Holz, E and Kaufmann, T and Riccio, A and Mattia, D},
title = {Applying the user-centred design to evaluation of Brain-Computer Interface controlled applications.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4438},
pmid = {24043192},
issn = {1862-278X},
}
@article {pmid24043191,
year = {2013},
author = {Kreilinger, A and Kaiser, V and Rohm, M and Rupp, R and Müller-Putz, GR},
title = {BCI and FES Training of a Spinal Cord Injured End-User to Control a Neuroprosthesis.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4443},
pmid = {24043191},
issn = {1862-278X},
}
@article {pmid24043189,
year = {2013},
author = {Scherer, R and Solis-Escalante, T and Faller, J and Wagner, J and Seeber, M and Müller-Putz, G},
title = {On the use of Non-Invasive Brain-Computer Interface Technology in Neurorehabilitation.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4436},
pmid = {24043189},
issn = {1862-278X},
}
@article {pmid24043184,
year = {2013},
author = {Tangermann, M and Kindermans, PJ and Schreuder, M and Schrauwen, B and Müller, KR},
title = {Zero Training for BCI - Reality for BCI Systems Based on Event-Related Potentials.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4439},
pmid = {24043184},
issn = {1862-278X},
}
@article {pmid24043182,
year = {2013},
author = {Faller, J and Solis-Escalante, T and Scherer, R and Müller-Putz, GR},
title = {Automatic Adaptation to Post-Movement Eventrelated Synchronization in a Brain-Computer Interface.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4444},
pmid = {24043182},
issn = {1862-278X},
}
@article {pmid24043175,
year = {2013},
author = {Ortner, R and Prückl, R and Guger, C},
title = {A tactile P300-based BCI for communication and detection of awareness.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4441},
pmid = {24043175},
issn = {1862-278X},
}
@article {pmid24042609,
year = {2013},
author = {Mahadevappa, M and Shendkar, C and Lenka, P and Biswas, A and Kumar, R},
title = {Modelling a BCI system to estimate FES stimulation intensity for individual stroke survivors in foot drop cases.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {58 Suppl 1},
number = {},
pages = {},
doi = {10.1515/bmt-2013-4031},
pmid = {24042609},
issn = {1862-278X},
}
@article {pmid24039789,
year = {2013},
author = {Zhang, Y and Xu, P and Huang, Y and Cheng, K and Yao, D},
title = {SSVEP response is related to functional brain network topology entrained by the flickering stimulus.},
journal = {PloS one},
volume = {8},
number = {9},
pages = {e72654},
pmid = {24039789},
issn = {1932-6203},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Light ; Male ; Models, Neurological ; Nerve Net/*physiology ; Photic Stimulation ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {Previous studies have shown that the brain network topology correlates with the cognitive function. However, few studies have investigated the relationship between functional brain networks that process sensory inputs and outputs. In this study, we focus on steady-state paradigms using a periodic visual stimulus, which are increasingly being used in both brain-computer interface (BCI) and cognitive neuroscience researches. Using the graph theoretical analysis, we investigated the relationship between the topology of functional networks entrained by periodic stimuli and steady state visually evoked potentials (SSVEP) using two frequencies and eleven subjects. First, the entire functional network (Network 0) of each frequency for each subject was constructed according to the coherence between electrode pairs. Next, Network 0 was divided into three sub-networks, in which the connection strengths were either significantly (positively for Network 1, negatively for Network 3) or non-significantly (Network 2) correlated with the SSVEP responses. Our results revealed that the SSVEP responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while these responses were negatively correlated with the characteristic path length of Networks 0, 1 and 2. Furthermore, the strengths of these connections that significantly correlated with the SSVEP (both positively and negatively) were mainly found to be long-range connections between the parietal-occipital and frontal regions. These results indicate that larger SSVEP responses correspond with better functional network topology structures. This study may provide new insights for understanding brain mechanisms when using SSVEPs as frequency tags.},
}
@article {pmid24035531,
year = {2013},
author = {Sgroi, DC and Sestak, I and Cuzick, J and Zhang, Y and Schnabel, CA and Schroeder, B and Erlander, MG and Dunbier, A and Sidhu, K and Lopez-Knowles, E and Goss, PE and Dowsett, M},
title = {Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population.},
journal = {The Lancet. Oncology},
volume = {14},
number = {11},
pages = {1067-1076},
pmid = {24035531},
issn = {1474-5488},
support = {16591/CRUK_/Cancer Research UK/United Kingdom ; BREAST CANCER NOW RESEARCH CENTRE/BCN_/Breast Cancer Now/United Kingdom ; R01 CA112021/CA/NCI NIH HHS/United States ; },
mesh = {Aged ; Aged, 80 and over ; Anastrozole ; Antineoplastic Agents, Hormonal/*therapeutic use ; Breast Neoplasms/*drug therapy/genetics/metabolism ; Female ; Follow-Up Studies ; *Gene Expression Profiling ; Humans ; Immunoenzyme Techniques ; Middle Aged ; Neoplasm Recurrence, Local/*diagnosis/etiology/metabolism ; Neoplasm Staging ; Nitriles/therapeutic use ; Prognosis ; Prospective Studies ; Receptors, Estrogen/*metabolism ; Tamoxifen/therapeutic use ; Triazoles/therapeutic use ; },
abstract = {BACKGROUND: Biomarkers to improve the risk-benefit of extended adjuvant endocrine therapy for late recurrence in patients with oestrogen-receptor-positive breast cancer would be clinically valuable. We compared the prognostic ability of the breast-cancer index (BCI) assay, 21-gene recurrence score (Oncotype DX), and an immunohistochemical prognostic model (IHC4) for both early and late recurrence in patients with oestrogen-receptor-positive, node-negative (N0) disease who took part in the Arimidex, Tamoxifen, Alone or in Combination (ATAC) clinical trial.<br><br>METHODS: In this prospective comparison study, we obtained archival tumour blocks from the TransATAC tissue bank from all postmenopausal patients with oestrogen-receptor-positive breast cancer from whom the 21-gene recurrence score and IHC4 values had already been derived. We did BCI analysis in matched samples with sufficient residual RNA using two BCI models-cubic (BCI-C) and linear (BCI-L)-using previously validated cutoffs. We assessed prognostic ability of BCI for distant recurrence over 10 years (the primary endpoint) and compared it with that of the 21-gene recurrence score and IHC4. We also tested the ability of the assays to predict early (0-5 years) and late (5-10 years) distant recurrence. To assess the ability of the biomarkers to predict recurrence beyond standard clinicopathological variables, we calculated the change in the likelihood-ratio &#x3c7;(2) (LR-&#x394;&#x3c7;(2)) from Cox proportional hazards models.<br><br>FINDINGS: Suitable tissue was available from 665 patients with oestrogen-receptor-positive, N0 breast cancer for BCI analysis. The primary analysis showed significant differences in risk of distant recurrence over 10 years in the categorical BCI-C risk groups (p<0&#xb7;0001) with 6&#xb7;8% (95% CI 4&#xb7;4-10&#xb7;0) of patients in the low-risk group, 17&#xb7;3% (12&#xb7;0-24&#xb7;7) in the intermediate group, and 22&#xb7;2% (15&#xb7;3-31&#xb7;5) in the high-risk group having distant recurrence. The secondary analysis showed that BCI-L was a much stronger predictor for overall (0-10 year) distant recurrence compared with BCI-C (interquartile HR 2&#xb7;30 [95% CI 1&#xb7;62-3&#xb7;27]; LR-&#x394;&#x3c7;(2)=22&#xb7;69; p<0&#xb7;0001). When compared with BCI-L, the 21-gene recurrence score was less predictive (HR 1&#xb7;48 [95% CI 1&#xb7;22-1&#xb7;78]; LR-&#x394;&#x3c7;(2)=13&#xb7;68; p=0&#xb7;0002) and IHC4 was similar (HR 1&#xb7;69 [95% CI 1&#xb7;51-2&#xb7;56]; LR-&#x394;&#x3c7;(2)=22&#xb7;83; p<0&#xb7;0001). All further analyses were done with the BCI-L model. In a multivariable analysis, all assays had significant prognostic ability for early distant recurrence (BCI-L HR 2&#xb7;77 [95% CI 1&#xb7;63-4&#xb7;70], LR-&#x394;&#x3c7;(2)=15&#xb7;42, p<0&#xb7;0001; 21-gene recurrence score HR 1&#xb7;80 [1&#xb7;42-2&#xb7;29], LR-&#x394;&#x3c7;(2)=18&#xb7;48, p<0&#xb7;0001; IHC4 HR 2&#xb7;90 [2&#xb7;01-4&#xb7;18], LR-&#x394;&#x3c7;(2)=29&#xb7;14, p<0&#xb7;0001); however, only BCI-L was significant for late distant recurrence (BCI-L HR 1&#xb7;95 [95% CI 1&#xb7;22-3&#xb7;14], LR-&#x394;&#x3c7;(2)=7&#xb7;97, p=0&#xb7;0048; 21-gene recurrence score HR 1&#xb7;13 [0&#xb7;82-1&#xb7;56], LR-&#x394;&#x3c7;(2)=0&#xb7;48, p=0&#xb7;47; IHC4 HR 1&#xb7;30 [0&#xb7;88-1&#xb7;94], LR-&#x394;&#x3c7;(2)=1&#xb7;59, p=0&#xb7;20).<br><br>INTERPRETATION: BCI-L was the only significant prognostic test for risk of both early and late distant recurrence and identified two risk populations for each timeframe. It could help to identify patients at high risk for late distant recurrence who might benefit from extended endocrine or other therapy.<br><br>FUNDING: Avon Foundation, National Institutes of Health, Breast Cancer Foundation, US Department of Defense Breast Cancer Research Program, Susan G Komen for the Cure, Breakthrough Breast Cancer through the Mary-Jean Mitchell Green Foundation, AstraZeneca, Cancer Research UK, and the National Institute for Health Research Biomedical Research Centre at the Royal Marsden (London, UK).},
}
@article {pmid24034899,
year = {2013},
author = {Ritaccio, A and Brunner, P and Crone, NE and Gunduz, A and Hirsch, LJ and Kanwisher, N and Litt, B and Miller, K and Moran, D and Parvizi, J and Ramsey, N and Richner, TJ and Tandon, N and Williams, J and Schalk, G},
title = {Proceedings of the Fourth International Workshop on Advances in Electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {29},
number = {2},
pages = {259-268},
pmid = {24034899},
issn = {1525-5069},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; R01-EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01-NS065186/NS/NINDS NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain Mapping ; Brain Waves/*physiology ; *Electroencephalography ; Humans ; *International Cooperation ; },
abstract = {The Fourth International Workshop on Advances in Electrocorticography (ECoG) convened in New Orleans, LA, on October 11-12, 2012. The proceedings of the workshop serves as an accurate record of the most contemporary clinical and experimental work on brain surface recording and represents the insights of a unique multidisciplinary ensemble of expert clinicians and scientists. Presentations covered a broad range of topics, including innovations in passive functional mapping, increased understanding of pathologic high-frequency oscillations, evolving sensor technologies, a human trial of ECoG-driven brain-machine interface, as well as fresh insights into brain electrical stimulation.},
}
@article {pmid24030040,
year = {2013},
author = {Ho, CH and Chen, SJ and Juan, CJ and Lee, HS and Tsai, SH and Fan, HC},
title = {Sudden death due to medulloblastoma: a case report.},
journal = {Acta neurologica Taiwanica},
volume = {22},
number = {2},
pages = {76-80},
pmid = {24030040},
issn = {1028-768X},
mesh = {Brain/pathology ; Brain Neoplasms/*complications/diagnostic imaging ; Child, Preschool ; *Death, Sudden ; Female ; Humans ; Hydrocephalus/etiology ; Medulloblastoma/*complications/diagnostic imaging ; Seizures, Febrile/complications ; Tomography, X-Ray Computed ; },
abstract = {PURPOSE: Medulloblastoma is one of the notorious CNS malignancies for subtle and atypical clinical presentations, causing rapid neurological deterioration and death, especially in pediatric patients. The delay in diagnosis leads to painful remorse, conflicts, and lawsuits for parents and medical staff.<br><br>CASE REPORT: We report a 2 year old girl with initial presentation of febrile pyuria. Soon after admission, a generalized clonic-tonic seizure attacked to her and led to an impression of febrile convulsion. However, an unusual postical slowness of pupils to light stimulation propelled a further investigation. A contrast enhanced brain computer tomography (CT) unexpectedly showed a mass occupied the fourth ventricle resulting in obstructive hydrocephalus and compressed adjacent brain stem and cerebellum. The disease rapidly progressed and she died 18 hours after an emergent decompression with extraventricular drainage (EVD) installation. Cytology of cerebrospinal fluid proved medulloblastoma.<br><br>CONCLUSION: This case report highlights the importance of clinical suspicion, such as a trivial but unusual presentation, a lagged pupil response to light stimulation. A brain CT scan should be done to rule out any possibility of an organic lesion. Close monitor is required in order to catch and treat medulloblastoma early. However, once discovered, the cancer has spread.},
}
@article {pmid24023953,
year = {2013},
author = {Choi, B and Jo, S},
title = {A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.},
journal = {PloS one},
volume = {8},
number = {9},
pages = {e74583},
pmid = {24023953},
issn = {1932-6203},
mesh = {Brain-Computer Interfaces/*economics ; Electroencephalography/*economics ; Illusions ; Reproducibility of Results ; Robotics/*economics ; Time Factors ; },
abstract = {This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.},
}
@article {pmid24012908,
year = {2014},
author = {Huster, RJ and Mokom, ZN and Enriquez-Geppert, S and Herrmann, CS},
title = {Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {91},
number = {1},
pages = {36-45},
doi = {10.1016/j.ijpsycho.2013.08.011},
pmid = {24012908},
issn = {1872-7697},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Neurofeedback/instrumentation/methods ; },
abstract = {Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback.},
}
@article {pmid24012517,
year = {2013},
author = {Al-Omiri, MK and Sghaireen, MG and Alzarea, BK and Lynch, E},
title = {Quantification of incisal tooth wear in upper anterior teeth: conventional vs new method using toolmakers microscope and a three-dimensional measuring technique.},
journal = {Journal of dentistry},
volume = {41},
number = {12},
pages = {1214-1221},
doi = {10.1016/j.jdent.2013.08.022},
pmid = {24012517},
issn = {1879-176X},
mesh = {Adult ; Computer-Aided Design/*instrumentation ; Cuspid/*pathology ; Dental Enamel/pathology ; Dental Pulp Exposure/diagnosis ; Dentin/pathology ; Dentin, Secondary/pathology ; Disease Progression ; Female ; Follow-Up Studies ; Humans ; Imaging, Three-Dimensional/*methods ; Incisor/*pathology ; *Lasers ; Male ; Microscopy/*instrumentation ; Models, Dental ; Tooth Wear/classification/*diagnosis ; Young Adult ; },
abstract = {OBJECTIVES: This study aimed to quantify tooth wear in upper anterior teeth using a new CAD-CAM Laser scanning machine, tool maker microscope and conventional tooth wear index.<br><br>METHODS: Fifty participants (25 males and 25 females, mean age = 25 &#xb1; 4 years) were assessed for incisal tooth wear of upper anterior teeth using Smith and Knight clinical tooth wear index (TWI) on two occasions, the study baseline and 1 year later. Stone dies for each tooth were prepared and scanned using the CAD-CAM Laser Cercon System. Scanned images were printed and examined under a toolmaker microscope to quantify tooth wear and then the dies were directly assessed under the microscope to measure tooth wear. The Wilcoxon Signed Ranks Test was used to analyze the data.<br><br>RESULTS: TWI scores for incisal edges were 0-3 and were similar at both occasions. Score 4 was not detected. Wear values measured by directly assessing the dies under the toolmaker microscope (range = 113 - 150 &#x3bc;m, mean = 130 &#xb1; 20 &#x3bc;m) were significantly more than those measured from Cercon Digital Machine images (range=52-80 &#x3bc;m, mean = 68 &#xb1; 23 &#x3bc;m) and both showed significant differences between the two occasions.<br><br>CONCLUSIONS: Wear progression in upper anterior teeth was effectively detected by directly measuring the dies or the images of dies under toolmaker microscope. Measuring the dies of worn dentition directly under tool maker microscope enabled detection of wear progression more accurately than measuring die images obtained with Cercon Digital Machine. Conventional method was the least sensitive for tooth wear quantification and was unable to identify wear progression in most cases.},
}
@article {pmid24007167,
year = {2013},
author = {Boyce, SJ and Choudhury, KR and Samei, E},
title = {Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise.},
journal = {Medical physics},
volume = {40},
number = {9},
pages = {091916},
doi = {10.1118/1.4818060},
pmid = {24007167},
issn = {2473-4209},
mesh = {Adult ; Humans ; *Phantoms, Imaging ; Radiography, Thoracic/*instrumentation ; Scattering, Radiation ; *Signal-To-Noise Ratio ; Thorax/*anatomy &amp; histology ; },
abstract = {PURPOSE: Stereoscopic chest biplane correlation imaging (stereo∕BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo∕BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes.<br><br>METHODS: Measurements for the eMTF were taken for two phantom sizes with an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI.<br><br>RESULTS: Stereo&#x2215;BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by &#x223c;20%. For both phantoms, at isodose, eDQE(0) for stereo&#x2215;BCI was &#x223c;100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by &#x223c;10 times for stereo&#x2215;BCI. Increasing the dose did not improve eDQE. The detectability index for stereo&#x2215;BCI was &#x223c;100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose.<br><br>CONCLUSIONS: The findings indicate that stereo&#x2215;BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.},
}
@article {pmid24001953,
year = {2014},
author = {Breuer, L and Dammers, J and Roberts, TP and Shah, NJ},
title = {A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography.},
journal = {IEEE transactions on bio-medical engineering},
volume = {61},
number = {2},
pages = {405-414},
doi = {10.1109/TBME.2013.2280143},
pmid = {24001953},
issn = {1558-2531},
mesh = {Adolescent ; Adult ; Algorithms ; Artifacts ; Brain-Computer Interfaces ; Child ; Heart/physiology ; Humans ; Magnetoencephalography/*methods ; Middle Aged ; Principal Component Analysis/*methods ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.},
}
@article {pmid23999175,
year = {2013},
author = {Song, S and Ma, X and Zhan, Y and Zhan, Z and Yao, L and Zhang, J},
title = {Bayesian reconstruction of multiscale local contrast images from brain activity.},
journal = {Journal of neuroscience methods},
volume = {220},
number = {1},
pages = {39-45},
doi = {10.1016/j.jneumeth.2013.08.020},
pmid = {23999175},
issn = {1872-678X},
mesh = {*Algorithms ; Bayes Theorem ; Brain/*physiology ; Brain Mapping/*methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; },
abstract = {BACKGROUND: Recent advances in functional magnetic resonance imaging (fMRI) techniques make it possible to reconstruct contrast-defined visual images from brain activity. In this manner, the stimulus images are represented as the weighted sum of a set of element images with different scales. The contrast weight of local images were decoded using fMRI activity recorded when the subject was viewing the stimulus images. Multivariate methods, such as the sparse multinomial logistic regression model (SMLR), have been proven effective for learning the mapping between fMRI patterns of primary visual cortex voxels and contrast of stimulus images. However, the SMLR method is highly time-consuming in practical application.<br><br>NEW METHOD: The Naive Bayesian classifier based on independent component analysis (NB-ICA) is proposed to efficiently decode the contrast of multi-scale local images. First, temporal independent components of fMRI data which were treated as new features for NB classifier were acquired by ICA decomposition. Second, the contrast for each local element image was computed based on NB estimation theory.<br><br>RESULTS: NB-ICA method can be used to reconstruct novel visual images. The average spatial correlation between the represented and reconstructed images was 0.41 &#xb1; 0.13 (p<0.001).<br><br>At the expense of reconstruction accuracy, NB-ICA is more efficient than SMLR which reduces the computation time from hours to seconds.<br><br>CONCLUSIONS: A new method, termed NB-ICA, is proposed and can efficiently reconstruct contrast-defined visual images from fMRI data. This study provides theoretical support for brain-computer interface research and also provides ideas for the study of real-time fMRI data.},
}
@article {pmid23996579,
year = {2014},
author = {Bunderson, NE},
title = {Real-time control of an interactive impulsive virtual prosthesis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {22},
number = {2},
pages = {363-370},
doi = {10.1109/TNSRE.2013.2274599},
pmid = {23996579},
issn = {1558-0210},
mesh = {Algorithms ; Amputation, Surgical ; Artificial Limbs ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Computer Simulation ; Disarticulation ; Electromyography ; Fingers/physiology ; Hand/physiology ; Humans ; Linear Models ; Male ; Motor Skills ; Movement ; *Prostheses and Implants ; *Prosthesis Design ; Shoulder/physiology ; Torque ; Ulna/physiology ; *User-Computer Interface ; Wrist/physiology ; },
abstract = {An interactive virtual dynamic environment for testing control strategies for neural machine interfacing with artificial limbs offers several advantages. The virtual environment is low-cost, easily configured, and offers a wealth of data for post-hoc analysis compared with real physical prostheses and robots. For use with prosthetics and research involving amputee subjects it allows the valuable time with the subject to be spent in experiments rather than fixing hardware issues. The usefulness of the virtual environment increases as the realism of the environment increases. Most tasks performed with limbs require interactions with objects in the environment. To simulate these tasks the dynamics of frictional contact, in addition to inertial limb dynamics must be modeled. Here, subjects demonstrate real-time control of an eight degree-of-freedom virtual prosthesis while performing an interactive box-and-blocks task. With practice, four nonamputee subjects and one shoulder disarticulation subject were able to successfully transfer blocks in the virtual environment at an average rate of just under two blocks per minute. The virtual environment is configurable in terms of the virtual arm design, control strategy, and task.},
}
@article {pmid23994208,
year = {2014},
author = {De Vos, M and Gandras, K and Debener, S},
title = {Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {91},
number = {1},
pages = {46-53},
doi = {10.1016/j.ijpsycho.2013.08.010},
pmid = {23994208},
issn = {1872-7697},
mesh = {Acoustic Stimulation ; Adult ; Analysis of Variance ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Mobile Applications ; Principal Component Analysis ; Rest/physiology ; Walking/physiology ; Young Adult ; },
abstract = {In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.},
}
@article {pmid23994206,
year = {2014},
author = {Stopczynski, A and Stahlhut, C and Petersen, MK and Larsen, JE and Jensen, CF and Ivanova, MG and Andersen, TS and Hansen, LK},
title = {Smartphones as pocketable labs: visions for mobile brain imaging and neurofeedback.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {91},
number = {1},
pages = {54-66},
doi = {10.1016/j.ijpsycho.2013.08.007},
pmid = {23994206},
issn = {1872-7697},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Brain-Computer Interfaces ; *Cell Phone ; Electroencephalography ; Emotions ; Female ; Fingers ; Functional Laterality ; Humans ; Image Processing, Computer-Assisted ; Male ; Neurofeedback/*instrumentation/*methods ; *Neuroimaging ; Photic Stimulation ; Psychomotor Performance ; Young Adult ; },
abstract = {Mobile brain imaging solutions, such as the Smartphone Brain Scanner, which combines low cost wireless EEG sensors with open source software for real-time neuroimaging, may transform neuroscience experimental paradigms. Normally subject to the physical constraints in labs, neuroscience experimental paradigms can be transformed into dynamic environments allowing for the capturing of brain signals in everyday contexts. Using smartphones or tablets to access text or images may enable experimental design capable of tracing emotional responses when shopping or consuming media, incorporating sensorimotor responses reflecting our actions into brain machine interfaces, and facilitating neurofeedback training over extended periods. Even though the quality of consumer neuroheadsets is still lower than laboratory equipment and susceptible to environmental noise, we show that mobile neuroimaging solutions, like the Smartphone Brain Scanner, complemented by 3D reconstruction or source separation techniques may support a range of neuroimaging applications and thus become a valuable addition to high-end neuroimaging solutions.},
}
@article {pmid25505751,
year = {2013},
author = {Movahedi, MM and Mehdizadeh, A and Alipour, A},
title = {Development of a Brain Computer Interface (BCI) Speller System Based on SSVEP Signals.},
journal = {Journal of biomedical physics &amp; engineering},
volume = {3},
number = {3},
pages = {81-86},
pmid = {25505751},
issn = {2251-7200},
abstract = {BCI is one of the most intriguing technologies among other HCI systems, mostly because of its capability of recording brain activities. Spelling BCIs, which help paralyzed people to maintain communication, are one of the striking topics in the field of BCI. In this scientific a spelling BCI system with high transfer rate and accuracy that uses SSVEP signals is proposed. In addition, we suggested that LED light sources can provide proper signals for speller BCIs and they can be used in future.},
}
@article {pmid23991046,
year = {2013},
author = {Nakanishi, Y and Yanagisawa, T and Shin, D and Fukuma, R and Chen, C and Kambara, H and Yoshimura, N and Hirata, M and Yoshimine, T and Koike, Y},
title = {Prediction of three-dimensional arm trajectories based on ECoG signals recorded from human sensorimotor cortex.},
journal = {PloS one},
volume = {8},
number = {8},
pages = {e72085},
pmid = {23991046},
issn = {1932-6203},
mesh = {Adolescent ; Aged ; Algorithms ; Arm/*physiopathology ; Brain Mapping ; Cerebral Cortex/*physiopathology ; Elbow/physiopathology ; Electroencephalography/*methods ; Humans ; Hypesthesia/physiopathology ; Linear Models ; Male ; Middle Aged ; Motion ; Motor Cortex/*physiopathology ; Movement/physiology ; Muscle Spasticity/physiopathology ; Shoulder/physiopathology ; Wrist Joint/physiopathology ; },
abstract = {Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson's correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44~0.73 and 0.18~0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.},
}
@article {pmid23985960,
year = {2013},
author = {Kothe, CA and Makeig, S},
title = {BCILAB: a platform for brain-computer interface development.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056014},
doi = {10.1088/1741-2560/10/5/056014},
pmid = {23985960},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; Automation ; *Brain-Computer Interfaces ; Calibration ; Computer Graphics ; Computer Systems ; Data Interpretation, Statistical ; Electroencephalography/statistics &amp; numerical data ; Evoked Potentials/physiology ; Humans ; Imagination/physiology ; Models, Neurological ; Neurosciences ; Photic Stimulation ; Prosthesis Design ; Reproducibility of Results ; *Software ; User-Computer Interface ; },
abstract = {OBJECTIVE: The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods.<br><br>APPROACH: Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations.<br><br>MAIN RESULTS: To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature.<br><br>SIGNIFICANCE: The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.},
}
@article {pmid23985904,
year = {2013},
author = {Zaaimi, B and Ruiz-Torres, R and Solla, SA and Miller, LE},
title = {Multi-electrode stimulation in somatosensory cortex increases probability of detection.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056013},
pmid = {23985904},
issn = {1741-2552},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; NS-048845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electric Stimulation/*methods ; *Electrodes, Implanted ; Feedback ; Macaca mulatta ; Motor Cortex/physiology ; Photic Stimulation ; Proprioception/physiology ; Sensory Thresholds/physiology ; Somatosensory Cortex/*physiology ; Touch/physiology ; },
abstract = {OBJECTIVE: Brain machine interfaces (BMIs) that decode control signals from motor cortex have developed tremendously in the past decade, but virtually all rely exclusively on vision to provide feedback. There is now increasing interest in developing an afferent interface to replace natural somatosensation, much as the cochlear implant has done for the sense of hearing. Preliminary experiments toward a somatosensory neuroprosthesis have mostly addressed the sense of touch, but proprioception, the sense of limb position and movement, is also critical for the control of movement. However, proprioceptive areas of cortex lack the precise somatotopy of tactile areas. We showed previously that there is only a weak tendency for neighboring neurons in area 2 to signal similar directions of hand movement. Consequently, stimulation with the relatively large currents used in many studies is likely to activate a rather heterogeneous set of neurons.<br><br>APPROACH: Here, we have compared the effect of single-electrode stimulation at subthreshold levels to the effect of stimulating as many as seven electrodes in combination.<br><br>MAIN RESULTS: We found a mean enhancement in the sensitivity to the stimulus (d') of 0.17 for pairs compared to individual electrodes (an increase of roughly 30%), and an increase of 2.5 for groups of seven electrodes (260%).<br><br>SIGNIFICANCE: We propose that a proprioceptive interface made up of several hundred electrodes may yield safer, more effective sensation than a BMI using fewer electrodes and larger currents.},
}
@article {pmid23978654,
year = {2015},
author = {Li, Y and Long, J and Huang, B and Yu, T and Wu, W and Liu, Y and Liang, C and Sun, P},
title = {Crossmodal integration enhances neural representation of task-relevant features in audiovisual face perception.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {25},
number = {2},
pages = {384-395},
doi = {10.1093/cercor/bht228},
pmid = {23978654},
issn = {1460-2199},
mesh = {Acoustic Stimulation ; Adult ; Attention/physiology ; Auditory Perception/*physiology ; Emotions ; *Face ; Humans ; Judgment/physiology ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neuropsychological Tests ; Pattern Recognition, Physiological/*physiology ; Photic Stimulation ; Sex Characteristics ; Video Recording ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Previous studies have shown that audiovisual integration improves identification performance and enhances neural activity in heteromodal brain areas, for example, the posterior superior temporal sulcus/middle temporal gyrus (pSTS/MTG). Furthermore, it has also been demonstrated that attention plays an important role in crossmodal integration. In this study, we considered crossmodal integration in audiovisual facial perception and explored its effect on the neural representation of features. The audiovisual stimuli in the experiment consisted of facial movie clips that could be classified into 2 gender categories (male vs. female) or 2 emotion categories (crying vs. laughing). The visual/auditory-only stimuli were created from these movie clips by removing the auditory/visual contents. The subjects needed to make a judgment about the gender/emotion category for each movie clip in the audiovisual, visual-only, or auditory-only stimulus condition as functional magnetic resonance imaging (fMRI) signals were recorded. The neural representation of the gender/emotion feature was assessed using the decoding accuracy and the brain pattern-related reproducibility indices, obtained by a multivariate pattern analysis method from the fMRI data. In comparison to the visual-only and auditory-only stimulus conditions, we found that audiovisual integration enhanced the neural representation of task-relevant features and that feature-selective attention might play a role of modulation in the audiovisual integration.},
}
@article {pmid23977196,
year = {2013},
author = {Ušćumlić, M and Chavarriaga, R and Millán, Jdel R},
title = {An iterative framework for EEG-based image search: robust retrieval with weak classifiers.},
journal = {PloS one},
volume = {8},
number = {8},
pages = {e72018},
pmid = {23977196},
issn = {1932-6203},
mesh = {Algorithms ; Brain/*physiology ; Databases, Factual ; Electroencephalography ; Electronic Data Processing ; Event-Related Potentials, P300 ; Humans ; Pattern Recognition, Visual ; Photic Stimulation ; ROC Curve ; Search Engine ; },
abstract = {We revisit the framework for brain-coupled image search, where the Electroencephalography (EEG) channel under rapid serial visual presentation protocol is used to detect user preferences. Extending previous works on the synergy between content-based image labeling and EEG-based brain-computer interface (BCI), we propose a different perspective on iterative coupling. Previously, the iterations were used to improve the set of EEG-based image labels before propagating them to the unseen images for the final retrieval. In our approach we accumulate the evidence of the true labels for each image in the database through iterations. This is done by propagating the EEG-based labels of the presented images at each iteration to the rest of images in the database. Our results demonstrate a continuous improvement of the labeling performance across iterations despite the moderate EEG-based labeling (AUC <75%). The overall analysis is done in terms of the single-trial EEG decoding performance and the image database reorganization quality. Furthermore, we discuss the EEG-based labeling performance with respect to a search task given the same image database.},
}
@article {pmid23973334,
year = {2013},
author = {Naseer, N and Hong, KS},
title = {Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain-computer interface.},
journal = {Neuroscience letters},
volume = {553},
number = {},
pages = {84-89},
doi = {10.1016/j.neulet.2013.08.021},
pmid = {23973334},
issn = {1872-7972},
mesh = {Adult ; *Brain-Computer Interfaces ; Discriminant Analysis ; *Functional Laterality ; Humans ; *Imagination ; Male ; *Movement ; Spectroscopy, Near-Infrared/*statistics &amp; numerical data ; Time Factors ; Wrist/blood supply/*physiology ; Young Adult ; },
abstract = {This paper presents a study on functional near-infrared spectroscopy (fNIRS) indicating that the hemodynamic responses of the right- and left-wrist motor imageries have distinct patterns that can be classified using a linear classifier for the purpose of developing a brain-computer interface (BCI). Ten healthy participants were instructed to imagine kinesthetically the right- or left-wrist flexion indicated on a computer screen. Signals from the right and left primary motor cortices were acquired simultaneously using a multi-channel continuous-wave fNIRS system. Using two distinct features (the mean and the slope of change in the oxygenated hemoglobin concentration), the linear discriminant analysis classifier was used to classify the right- and left-wrist motor imageries resulting in average classification accuracies of 73.35% and 83.0%, respectively, during the 10s task period. Moreover, when the analysis time was confined to the 2-7s span within the overall 10s task period, the average classification accuracies were improved to 77.56% and 87.28%, respectively. These results demonstrate the feasibility of an fNIRS-based BCI and the enhanced performance of the classifier by removing the initial 2s span and/or the time span after the peak value.},
}
@article {pmid23970851,
year = {2013},
author = {Dijksterhuis, C and de Waard, D and Brookhuis, KA and Mulder, BL and de Jong, R},
title = {Classifying visuomotor workload in a driving simulator using subject specific spatial brain patterns.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {149},
pmid = {23970851},
issn = {1662-4548},
abstract = {A passive Brain Computer Interface (BCI) is a system that responds to the spontaneously produced brain activity of its user and could be used to develop interactive task support. A human-machine system that could benefit from brain-based task support is the driver-car interaction system. To investigate the feasibility of such a system to detect changes in visuomotor workload, 34 drivers were exposed to several levels of driving demand in a driving simulator. Driving demand was manipulated by varying driving speed and by asking the drivers to comply to individually set lane keeping performance targets. Differences in the individual driver's workload levels were classified by applying the Common Spatial Pattern (CSP) and Fisher's linear discriminant analysis to frequency filtered electroencephalogram (EEG) data during an off line classification study. Several frequency ranges, EEG cap configurations, and condition pairs were explored. It was found that classifications were most accurate when based on high frequencies, larger electrode sets, and the frontal electrodes. Depending on these factors, classification accuracies across participants reached about 95% on average. The association between high accuracies and high frequencies suggests that part of the underlying information did not originate directly from neuronal activity. Nonetheless, average classification accuracies up to 75-80% were obtained from the lower EEG ranges that are likely to reflect neuronal activity. For a system designer, this implies that a passive BCI system may use several frequency ranges for workload classifications.},
}
@article {pmid23966933,
year = {2013},
author = {Witte, M and Kober, SE and Ninaus, M and Neuper, C and Wood, G},
title = {Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {478},
pmid = {23966933},
issn = {1662-5161},
abstract = {Technological progress in computer science and neuroimaging has resulted in many approaches that aim to detect brain states and translate them to an external output. Studies from the field of brain-computer interfaces (BCI) and neurofeedback (NF) have validated the coupling between brain signals and computer devices; however a cognitive model of the processes involved remains elusive. Psychological parameters usually play a moderate role in predicting the performance of BCI and NF users. The concept of a locus of control, i.e., whether one's own action is determined by internal or external causes, may help to unravel inter-individual performance capacities. Here, we present data from 20 healthy participants who performed a feedback task based on EEG recordings of the sensorimotor rhythm (SMR). One group of 10 participants underwent 10 training sessions where the amplitude of the SMR was coupled to a vertical feedback bar. The other group of ten participants participated in the same task but relied on sham feedback. Our analysis revealed that a locus of control score focusing on control beliefs with regard to technology negatively correlated with the power of SMR. These preliminary results suggest that participants whose confidence in control over technical devices is high might consume additional cognitive resources. This higher effort in turn may interfere with brain states of relaxation as reflected in the SMR. As a consequence, one way to improve control over brain signals in NF paradigms may be to explicitly instruct users not to force mastery but instead to aim at a state of effortless relaxation.},
}
@article {pmid23953939,
year = {2013},
author = {Scherer, R and Pfurtscheller, G},
title = {Thought-based interaction with the physical world.},
journal = {Trends in cognitive sciences},
volume = {17},
number = {10},
pages = {490-492},
doi = {10.1016/j.tics.2013.08.004},
pmid = {23953939},
issn = {1879-307X},
mesh = {Aircraft/*instrumentation ; Biofeedback, Psychology/*instrumentation ; *Brain-Computer Interfaces ; Female ; Humans ; Imagination/*physiology ; Male ; *Man-Machine Systems ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {Operating a brain-computer interface (BCI) is a skill that individuals must learn. A recent study demonstrated that successful skill acquisition enables human individuals to control telepresence robotic devices in three-dimensional physical space using the non-invasive electroencephalogram (EEG). Although the results are very promising, there is room for improvement in the future.},
}
@article {pmid23951715,
year = {2013},
author = {Puerta-Pińero, C and Muller-Landau, HC and Calderón, O and Wright, SJ},
title = {Seed arrival in tropical forest tree fall gaps.},
journal = {Ecology},
volume = {94},
number = {7},
pages = {1552-1562},
doi = {10.1890/12-1012.1},
pmid = {23951715},
issn = {0012-9658},
mesh = {Animals ; Conservation of Natural Resources ; Demography ; *Ecosystem ; Environmental Monitoring ; Seeds/*physiology ; Time Factors ; *Trees ; *Tropical Climate ; },
abstract = {Tree deaths open gaps in closed-canopy forests, which allow light to reach the forest floor and promote seed germination and seedling establishment. Gap dependence of regeneration is an important axis of life history variation among forest plant species, and many studies have evaluated how plant species differ in seedling and sapling performance in gaps. However, relatively little is known about how seed arrival in gaps compares with seed arrival in the understory, even though seed dispersal by wind and animals is expected to be altered in gaps. We documented seed arrival for the first seven years after gap formation in the moist tropical forests of Barro Colorado Island (BCI), Panama, and evaluated how the amount and functional composition of arriving seeds compared with understory sites. On average, in the first three years after gap formation, 72% fewer seeds arrived in gaps than in the understory (207 vs. 740 seeds x m(-2) x yr(-1)). The reduction in number of arriving seeds fell disproportionately on animal-dispersed species, which suffered an 86% reduction in total seed number, while wind-dispersed species experienced only a 47% reduction, and explosively dispersed species showed increased seed numbers arriving. The increase in explosively dispersed seeds consisted entirely of the seeds of several shrub species, a result consistent with greater in situ seed production by explosively dispersed shrubs that survived gap formation or recruited immediately thereafter. Lianas did relatively better in seed arrival into gaps than did trees, suffering less of a reduction in seed arrival compared with understory sites. This result could in large part be explained by the greater predominance of wind dispersal among lianas: there were no significant differences between lianas and trees when controlling for dispersal syndromes. Our results show that seed arrival in gaps is very different from seed arrival in the understory in both total seeds arriving and functional composition. Differential seed arrival in gaps will help to maintain wind-dispersed, explosively dispersed, and possibly other understory species in the community of plants that regenerate in gaps.},
}
@article {pmid23948873,
year = {2013},
author = {Rosas-Cholula, G and Ramirez-Cortes, JM and Alarcon-Aquino, V and Gomez-Gil, P and Rangel-Magdaleno, Jde J and Reyes-Garcia, C},
title = {Gyroscope-driven mouse pointer with an EMOTIV® EEG headset and data analysis based on Empirical Mode Decomposition.},
journal = {Sensors (Basel, Switzerland)},
volume = {13},
number = {8},
pages = {10561-10583},
pmid = {23948873},
issn = {1424-8220},
mesh = {Accelerometry/*instrumentation ; Algorithms ; Blinking/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Computer Peripherals ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Eye Movements/*physiology ; },
abstract = {This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.},
}
@article {pmid23941984,
year = {2014},
author = {Groothuis, J and Ramsey, NF and Ramakers, GM and van der Plasse, G},
title = {Physiological challenges for intracortical electrodes.},
journal = {Brain stimulation},
volume = {7},
number = {1},
pages = {1-6},
doi = {10.1016/j.brs.2013.07.001},
pmid = {23941984},
issn = {1876-4754},
mesh = {Brain-Computer Interfaces/*adverse effects ; Deep Brain Stimulation/*instrumentation ; Electrodes, Implanted/*adverse effects ; Foreign-Body Reaction/*etiology/*prevention &amp; control ; Humans ; },
abstract = {The clinical use of chronic electrode implants for measurement or stimulation of neuronal activity has increased over the past decade with the advent of deep brain stimulation and the use of brain-computer interfaces. However, despite the wide-spread application of electrode implants, their chronic use is still limited by technical difficulties. Many of the reported issues, ranging from short-circuits to loss of signal due to increased electrical impedance, may be traced back to the reaction of the cortical tissue to the implanted devices: the foreign body response (FBR). This response consists of several phases that ultimately result in neuronal loss and the formation of a dense glial sheath that encapsulates the implant. Empirical evidence suggests that reducing the FBR has a positive effect on the electrical properties of implants, which can potentially expand their clinical use by improving their chronic usability. The primary focus of this work is to review the consequences of the FBR and recent developments that can be considered to control and limit its development. We will discuss how the choice of device material and electrode-architecture influences the tissue reaction, as well as modifications that allow for less stiff implants, increase electrode conductivity, or improve the implant-tissue integration. Several promising biological solutions include the local release of anti-inflammatory compounds to weaken the initial inflammatory phase of the FBR, as well as methods to diminish the negative effects of the glial sheath on neuronal regrowth.},
}
@article {pmid23937522,
year = {2013},
author = {Gottipati, MK and Samuelson, JJ and Kalinina, I and Bekyarova, E and Haddon, RC and Parpura, V},
title = {Chemically functionalized single-walled carbon nanotube films modulate the morpho-functional and proliferative characteristics of astrocytes.},
journal = {Nano letters},
volume = {13},
number = {9},
pages = {4387-4392},
doi = {10.1021/nl402226z},
pmid = {23937522},
issn = {1530-6992},
mesh = {Astrocytes/chemistry/*cytology ; Cell Dedifferentiation ; Cell Line ; Cell Proliferation ; Electrodes ; Nanotubes, Carbon/*chemistry ; },
abstract = {We used single-walled carbon nanotube (CNT) films to modulate the morpho-functional and proliferative characteristics of astrocytes. When plated on the CNT films of various thicknesses, astrocytes grow bigger and rounder in shape with a decrease in the immunoreactivity of glial fibrillary acidic protein along with an increase in their proliferation, changes associated with the dedifferentiation of astrocytes in culture. Thus, CNT films, as a coating material for electrodes used in brain machine interface, could reduce astrogliosis around the site of implantation.},
}
@article {pmid23928891,
year = {2013},
author = {Alimardani, M and Nishio, S and Ishiguro, H},
title = {Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators.},
journal = {Scientific reports},
volume = {3},
number = {},
pages = {2396},
pmid = {23928891},
issn = {2045-2322},
mesh = {Arousal/*physiology ; Biofeedback, Psychology/physiology ; Biomimetics/*methods ; Body Image ; Brain/*physiology ; *Brain-Computer Interfaces ; Hand/physiology ; Humans ; Illusions/*physiology ; Imagination/physiology ; *Ownership ; Robotics/*methods ; },
abstract = {Operators of a pair of robotic hands report ownership for those hands when they hold image of a grasp motion and watch the robot perform it. We present a novel body ownership illusion that is induced by merely watching and controlling robot's motions through a brain machine interface. In past studies, body ownership illusions were induced by correlation of such sensory inputs as vision, touch and proprioception. However, in the presented illusion none of the mentioned sensations are integrated except vision. Our results show that during BMI-operation of robotic hands, the interaction between motor commands and visual feedback of the intended motions is adequate to incorporate the non-body limbs into one's own body. Our discussion focuses on the role of proprioceptive information in the mechanism of agency-driven illusions. We believe that our findings will contribute to improvement of tele-presence systems in which operators incorporate BMI-operated robots into their body representations.},
}
@article {pmid23928153,
year = {2014},
author = {Zhang, Y and Xu, P and Cheng, K and Yao, D},
title = {Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {221},
number = {},
pages = {32-40},
doi = {10.1016/j.jneumeth.2013.07.018},
pmid = {23928153},
issn = {1872-678X},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {Multichannel frequency recognition methods are prevalent in SSVEP-BCI systems. These methods increase the convenience of the BCI system for users and require no calibration data. A novel multivariate synchronization index (MSI) for frequency recognition was proposed in this paper. This measure characterized the synchronization between multichannel EEGs and the reference signals, the latter of which were defined according to the stimulus frequency. For the simulation and real data, the proposed method showed better performance than the widely used canonical correlation analysis (CCA) and minimum energy combination (MEC), especially for short data length and a small number of channels. The MSI was also implemented successfully in an online SSVEP-based BCI system, thus further confirming its feasibility for application systems. Because fast and accurate recognition is crucial for practical systems, we recommend MSI as a potential method for frequency recognition in future SSVEP-BCI.},
}
@article {pmid23925374,
year = {2014},
author = {Judy, M and Sodagar, AM and Lotfi, R and Sawan, M},
title = {Nonlinear Signal-Specific ADC for Efficient Neural Recording in Brain-Machine Interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {3},
pages = {371-381},
doi = {10.1109/TBCAS.2013.2270178},
pmid = {23925374},
issn = {1932-4545},
mesh = {*Action Potentials ; Animals ; Auditory Cortex/physiology ; *Brain-Computer Interfaces ; Equipment Design ; Guinea Pigs ; Neurons/physiology ; Neurophysiology/*instrumentation ; *Prostheses and Implants ; },
abstract = {A nonlinear ADC dedicated to the digitization of neural signals in implantable brain-machine interfaces is presented. Benefitting from an exponential quantization function, effective resolution of the proposed ADC in the digitization of action potentials is almost 2 bits more than its physical number of bits. Hence, it is shown in this paper that the choice of a proper nonlinear quantization function helps reduce the outgoing bit rate carrying the recorded neural data. Another major benefit of digitizing neural signals using the proposed signal-specific ADC is the considerable reduction in the background noise of the neural signal. The 8-b exponential ADC reported in this paper digitizes large action potentials with maximum resolution of 10.5 bits , while quantizing the small background noise is performed with a resolution of as low as 3 bits. Fully-integrated version of the circuit was designed and fabricated in a 0.18-μm CMOS process, occupying 0.036 mm(2) silicon area. Designed based on a two-step successive-approximation register ADC architecture, the proposed ADC employs a piecewise-linear approximation of the target exponential function for quantization. Operating at a sampling frequency of 25 kS/s (typical for intra-cortical neural recording) and with a supply voltage of 1.8 V, the entire chip, including the ADC and reference circuits, dissipates 87.2 μW. According to the experiments, Noise-Content-Reduction Ratio (NCRR) of the ADC is 41.1 dB.},
}
@article {pmid23923692,
year = {2013},
author = {Moghimi, S and Kushki, A and Guerguerian, AM and Chau, T},
title = {A review of EEG-based brain-computer interfaces as access pathways for individuals with severe disabilities.},
journal = {Assistive technology : the official journal of RESNA},
volume = {25},
number = {2},
pages = {99-110},
doi = {10.1080/10400435.2012.723298},
pmid = {23923692},
issn = {1040-0435},
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Electroencephalography ; Humans ; },
abstract = {Electroencephalography (EEG) is a non-invasive method for measuring brain activity and is a strong candidate for brain-computer interface (BCI) development. While BCIs can be used as a means of communication for individuals with severe disabilities, the majority of existing studies have reported BCI evaluations by able-bodied individuals. Considering the many differences in body functions and usage scenarios between individuals with disabilities and able-bodied individuals, involvement of the target population in BCI evaluation is necessary. In this review, 39 studies reporting EEG-oriented BCI assessment by individuals with disabilities were identified in the past decade. With respect to participant populations, a need for assessing BCI performance for the pediatric population with severe disabilities was identified as an important future direction. Acquiring a reliable communication pathway during early stages of development is crucial in avoiding learned helplessness in pediatric-onset disabilities. With respect to evaluation, augmenting traditional measures of system performance with those relating to contextual factors was recommended for realizing user-centered designs appropriate for integration in real-life. Considering indicators of user state and developing more effective training paradigms are recommended for future studies of BCI involving individuals with disabilities.},
}
@article {pmid23922873,
year = {2013},
author = {Eliseyev, A and Aksenova, T},
title = {Recursive N-way partial least squares for brain-computer interface.},
journal = {PloS one},
volume = {8},
number = {7},
pages = {e69962},
pmid = {23922873},
issn = {1932-6203},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Calibration ; Haplorhini/physiology ; Humans ; Least-Squares Analysis ; },
abstract = {In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS) regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken. The RNPLS algorithm demonstrates fast and stable convergence of regression coefficients. Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks.},
}
@article {pmid23920867,
year = {2013},
author = {Kurzynski, M},
title = {On a two-level multiclassifier system with error correction applied to the control of bioprosthetic hand.},
journal = {Studies in health technology and informatics},
volume = {192},
number = {},
pages = {1093},
pmid = {23920867},
issn = {1879-8365},
mesh = {Algorithms ; Artificial Intelligence ; *Artificial Limbs ; *Bioprosthesis ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/methods ; Electromyography/instrumentation/methods ; Equipment Failure Analysis ; *Hand ; Humans ; Neurofeedback/instrumentation/*methods ; Pattern Recognition, Automated/*methods ; Prosthesis Design ; Robotics/instrumentation/*methods ; },
abstract = {The paper presents an advanced method of recognition of patient's intention to move of hand prosthesis. The proposed method is based on two-level multiclassifier system (MCS) with homogeneous base classifiers dedicated to EEG, EMG and MMG biosignals and with combining mechanism using a dynamic ensemble selection (DES) scheme and probabilistic competence function. Additionally, the feedback signal derived from the prosthesis sensors is applied to the correction of classification algorithm. The performance of MCS with proposed competence function and combining procedure were experimentally compared against three benchmark MCSs using real data concerning the recognition of six types of grasping movements. The systems developed achieved the highest classification accuracies demonstrating the potential of multiple classifier systems with multimodal biosignals for the control of bioprosthetic hand.},
}
@article {pmid23919646,
year = {2013},
author = {Li, P and Xu, P and Zhang, R and Guo, L and Yao, D},
title = {L1 norm based common spatial patterns decomposition for scalp EEG BCI.},
journal = {Biomedical engineering online},
volume = {12},
number = {},
pages = {77},
pmid = {23919646},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Pattern Recognition, Automated/*methods ; *Scalp ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Brain computer interfaces (BCI) is one of the most popular branches in biomedical engineering. It aims at constructing a communication between the disabled persons and the auxiliary equipments in order to improve the patients' life. In motor imagery (MI) based BCI, one of the popular feature extraction strategies is Common Spatial Patterns (CSP). In practical BCI situation, scalp EEG inevitably has the outlier and artifacts introduced by ocular, head motion or the loose contact of electrodes in scalp EEG recordings. Because outlier and artifacts are usually observed with large amplitude, when CSP is solved in view of L2 norm, the effect of outlier and artifacts will be exaggerated due to the imposing of square to outliers, which will finally influence the MI based BCI performance. While L1 norm will lower the outlier effects as proved in other application fields like EEG inverse problem, face recognition, etc.<br><br>METHODS: In this paper, we present a new CSP implementation using the L1 norm technique, instead of the L2 norm, to solve the eigen problem for spatial filter estimation with aim to improve the robustness of CSP to outliers. To evaluate the performance of our method, we applied our method as well as the standard CSP and the regularized CSP with Tikhonov regularization (TR-CSP), on both the peer BCI dataset with simulated outliers and the dataset from the MI BCI system developed in our group. The McNemar test is used to investigate whether the difference among the three CSPs is of statistical significance.<br><br>RESULTS: The results of both the simulation and real BCI datasets consistently reveal that the proposed method has much higher classification accuracies than the conventional CSP and the TR-CSP.<br><br>CONCLUSIONS: By combining L1 norm based Eigen decomposition into Common Spatial Patterns, the proposed approach can effectively improve the robustness of BCI system to EEG outliers and thus be potential for the actual MI BCI application, where outliers are inevitably introduced into EEG recordings.},
}
@article {pmid23918205,
year = {2013},
author = {Tonin, L and Leeb, R and Sobolewski, A and Millán, Jdel R},
title = {An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056007},
doi = {10.1088/1741-2560/10/5/056007},
pmid = {23918205},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Cues ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Electrophysiological Phenomena/physiology ; Evoked Potentials, Visual/physiology ; Fatigue/physiopathology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Online Systems ; Psychomotor Performance/physiology ; Reproducibility of Results ; Space Perception/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: In this work we present--for the first time--the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery).<br><br>APPROACH: Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability.<br><br>MAIN RESULTS: We report a mean online accuracy across the group of 70.6 &#xb1; 1.5%, and 88.8 &#xb1; 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems.<br><br>SIGNIFICANCE: This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.},
}
@article {pmid23918061,
year = {2013},
author = {Flint, RD and Wright, ZA and Scheid, MR and Slutzky, MW},
title = {Long term, stable brain machine interface performance using local field potentials and multiunit spikes.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056005},
pmid = {23918061},
issn = {1741-2552},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; R25 GM079300/GM/NIGMS NIH HHS/United States ; T32 HD057845/HD/NICHD NIH HHS/United States ; 5K08 NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Evoked Potentials/*physiology ; Female ; Hand/physiology ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex ; Neural Prostheses ; Prosthesis Design ; Psychomotor Performance/physiology ; Reproducibility of Results ; Reward ; },
abstract = {OBJECTIVE: Brain machine interfaces (BMIs) have the potential to restore movement to people with paralysis. However, a clinically-viable BMI must enable consistently accurate control over time spans ranging from years to decades, which has not yet been demonstrated. Most BMIs that use single-unit spikes as inputs will experience degraded performance over time without frequent decoder re-training. Two other signals, local field potentials (LFPs) and multi-unit spikes (MSPs), may offer greater reliability over long periods and better performance stability than single-unit spikes. Here, we demonstrate that LFPs can be used in a biomimetic BMI to control a computer cursor.<br><br>APPROACH: We implanted two rhesus macaques with intracortical microelectrodes in primary motor cortex. We recorded LFP and MSP signals from the monkeys while they performed a continuous reaching task, moving a cursor to randomly-placed targets on a computer screen. We then used the LFP and MSP signals to construct biomimetic decoders for control of the cursor.<br><br>MAIN RESULTS: Both monkeys achieved high-performance, continuous control that remained stable or improved over nearly 12 months using an LFP decoder that was not retrained or adapted. In parallel, the monkeys used MSPs to control a BMI without retraining or adaptation and had similar or better performance, and that predominantly remained stable over more than six months. In contrast to their stable online control, both LFP and MSP signals showed substantial variability when used offline to predict hand movements.<br><br>SIGNIFICANCE: Our results suggest that the monkeys were able to stabilize the relationship between neural activity and cursor movement during online BMI control, despite variability in the relationship between neural activity and hand movements.},
}
@article {pmid23913595,
year = {2014},
author = {Mena, M and Wiafe-Addai, B and Sauvaget, C and Ali, IA and Wiafe, SA and Dabis, F and Anderson, BO and Malvy, D and Sasco, AJ},
title = {Evaluation of the impact of a breast cancer awareness program in rural Ghana: a cross-sectional survey.},
journal = {International journal of cancer},
volume = {134},
number = {4},
pages = {913-924},
doi = {10.1002/ijc.28412},
pmid = {23913595},
issn = {1097-0215},
mesh = {Adult ; *Awareness ; Breast Neoplasms/diagnosis/epidemiology/*prevention &amp; control ; Breast Self-Examination ; Cross-Sectional Studies ; Early Detection of Cancer ; Female ; Follow-Up Studies ; Ghana/epidemiology ; Health Education ; *Health Knowledge, Attitudes, Practice ; Humans ; Middle Aged ; Prognosis ; Rural Population ; Surveys and Questionnaires ; },
abstract = {Community awareness is crucial to early detection of breast cancer in low- and middle-income countries. In Ghana 60% of the cases are detected at late stages. Breast Care International (BCI) is a Ghanaian non-governmental organization dedicated to raising breast cancer awareness. A cross-sectional survey was designed to assess the impact of BCI program on knowledge, attitudes and practices (KAP) toward breast cancer among women from rural communities of Ghana. A total of 232 women were interviewed in June 2011 in the Ashanti region; of these 131 participants were from a community that received the BCI program in August 2010 (intervention group) and 101 from another community that received the program post-survey (referent group). Data analysis was performed using Epi-Info version 3.5.3. Knowledge about breast cancer among participants who received the program was better than among those who did not. Only 53.5% of participants from the referent group knew that breast cancer usually appears as painless breast lump when compared to 82.3% from the intervention group. Participants who attended the program were significantly more likely to obtain higher knowledge scores (odds ratio (OR) = 2.10, 95% confidence interval (CI) = 1.14-3.86) and to state practicing breast self-examination (OR = 12.29, 95% CI = 5.31-28.48). The BCI program improved KAP toward breast cancer. Further research is warranted to provide stronger evidence that the program improves breast cancer early detection.},
}
@article {pmid23912203,
year = {2013},
author = {Bulea, TC and Kilicarslan, A and Ozdemir, R and Paloski, WH and Contreras-Vidal, JL},
title = {Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {77},
pages = {},
pmid = {23912203},
issn = {1940-087X},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889-01/NS/NINDS NIH HHS/United States ; /ImNIH/Intramural NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography/*methods ; Gait/*physiology ; Humans ; Neurons/*physiology ; Walking/*physiology ; },
abstract = {Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.},
}
@article {pmid23908614,
year = {2013},
author = {Pfurtscheller, G and Solis-Escalante, T and Barry, RJ and Klobassa, DS and Neuper, C and Müller-Putz, GR},
title = {Brisk heart rate and EEG changes during execution and withholding of cue-paced foot motor imagery.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {379},
pmid = {23908614},
issn = {1662-5161},
abstract = {Cue-paced motor imagery (MI) is a frequently used mental strategy to realize a Brain-Computer Interface (BCI). Recently it has been reported that two MI tasks can be separated with a high accuracy within the first second after cue presentation onset. To investigate this phenomenon in detail we studied the dynamics of motor cortex beta oscillations in EEG and the changes in heart rate (HR) during visual cue-paced foot MI using a go (execution of imagery) vs. nogo (withholding of imagery) paradigm in 16 healthy subjects. Both execution and withholding of MI resulted in a brisk centrally localized beta event-related desynchronization (ERD) with a maximum at ~400 ms and a concomitant HR deceleration. We found that response patterns within the first second after stimulation differed between conditions. The ERD was significantly larger in go as compared to nogo. In contrast the HR deceleration was somewhat smaller and followed by an acceleration in go as compared to nogo. These findings suggest that the early beta ERD reflects visually induced preparatory activity in motor cortex networks. Both the early beta ERD and the HR deceleration are the result of automatic operating processes that are likely part of the orienting reflex (OR). Of interest, however, is that the preparatory cortical activity is strengthened and the HR modulated already within the first second after stimulation during the execution of MI. The subtraction of the HR time course of the nogo from the go condition revealed a slight HR acceleration in the first seconds most likely due to the increased mental effort associated with the imagery process.},
}
@article {pmid23907472,
year = {2013},
author = {Fujiwara, T and Liu, M},
title = {[Current topics of neurorehabilitation].},
journal = {No shinkei geka. Neurological surgery},
volume = {41},
number = {8},
pages = {663-668},
pmid = {23907472},
issn = {0301-2603},
mesh = {Brain Diseases/rehabilitation/*therapy ; Brain Injuries/rehabilitation/*therapy ; Brain-Computer Interfaces ; *Electric Stimulation Therapy/methods ; *Exercise Therapy/instrumentation/methods ; Humans ; Neuroimaging/methods ; Treatment Outcome ; },
}
@article {pmid23901117,
year = {2013},
author = {Papageorgiou, TD and Lisinski, JM and McHenry, MA and White, JP and LaConte, SM},
title = {Brain-computer interfaces increase whole-brain signal to noise.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {33},
pages = {13630-13635},
pmid = {23901117},
issn = {1091-6490},
mesh = {Adult ; Brain-Computer Interfaces/*psychology ; Computer Systems ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; *Models, Psychological ; Psychomotor Performance ; *Signal-To-Noise Ratio ; Speech/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to measure the quality of covert task performance. We used whole-brain classifier-based real-time functional MRI to address these issues, because the method provides both classifier-based maps to examine the neural requirements of BCI and classification accuracy to quantify the quality of task performance. Subjects performed a covert counting task at fast and slow rates to control a visual interface. Compared with the same task when viewing but not controlling the interface, we observed that being in control of a BCI improved task classification of fast and slow counting states. Additional BCI control increased subjects' whole-brain signal-to-noise ratio compared with the absence of control. The neural pattern for control consisted of a positive network comprised of dorsal parietal and frontal regions and the anterior insula of the right hemisphere as well as an expansive negative network of regions. These findings suggest that real-time functional MRI can serve as a platform for exploring information processing and frontoparietal and insula network-based regulation of whole-brain task signal-to-noise ratio.},
}
@article {pmid23898236,
year = {2013},
author = {Kaufmann, T and Holz, EM and Kübler, A},
title = {Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {129},
pmid = {23898236},
issn = {1662-4548},
abstract = {This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials (ERP) elicited in different modalities for use in brain-computer interface (BCI) systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5) in the visual, auditory and tactile modality were conducted (2 runs per modality). Classifiers were built on one run and tested offline on another run (and vice versa). In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g., partner scanning). Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the study.},
}
@article {pmid23895406,
year = {2013},
author = {Chennu, S and Alsufyani, A and Filetti, M and Owen, AM and Bowman, H},
title = {The cost of space independence in P300-BCI spellers.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {82},
pmid = {23895406},
issn = {1743-0003},
support = {U.1055.01.002.00001.01//Medical Research Council/United Kingdom ; },
mesh = {Adult ; *Brain-Computer Interfaces/economics ; *Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Young Adult ; },
abstract = {BACKGROUND: Though non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user.<br><br>METHODS: In healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller.<br><br>RESULTS: EEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller's performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed.<br><br>CONCLUSIONS: The advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus.},
}
@article {pmid23895049,
year = {2013},
author = {Kumar, G and Kothare, MV},
title = {On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.},
journal = {Neural computation},
volume = {25},
number = {12},
pages = {3183-3206},
doi = {10.1162/NECO_a_00503},
pmid = {23895049},
issn = {1530-888X},
mesh = {Action Potentials/*physiology ; *Brain-Computer Interfaces ; *Neural Networks, Computer ; Neurons/*physiology ; },
abstract = {We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.},
}
@article {pmid23895046,
year = {2013},
author = {Li, X and Zhang, H and Guan, C and Ong, SH and Ang, KK and Pan, Y},
title = {Discriminative learning of propagation and spatial pattern for motor imagery EEG analysis.},
journal = {Neural computation},
volume = {25},
number = {10},
pages = {2709-2733},
doi = {10.1162/NECO_a_00500},
pmid = {23895046},
issn = {1530-888X},
mesh = {Algorithms ; Artificial Intelligence ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Discrimination Learning/*physiology ; Electroencephalography/*statistics &amp; numerical data ; Humans ; Imagination/*physiology ; Learning ; Models, Statistical ; Movement/*physiology ; Neurosciences ; Signal Processing, Computer-Assisted ; },
abstract = {Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG. Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance.},
}
@article {pmid23893789,
year = {2013},
author = {Kamran, MA and Hong, KS},
title = {Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study.},
journal = {Journal of neural engineering},
volume = {10},
number = {5},
pages = {056002},
doi = {10.1088/1741-2560/10/5/056002},
pmid = {23893789},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; Cerebrovascular Circulation/physiology ; Hemodynamics/physiology ; Humans ; Linear Models ; Models, Neurological ; Neuroimaging/*methods ; Neurons/*physiology ; Neurophysiology/*methods ; Normal Distribution ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique that measures brain activities by using near-infrared light of 650-950 nm wavelength. The major advantages of fNIRS are its low cost, portability, and good temporal resolution as a plausible solution to real-time imaging. Recent research has shown the great potential of fNIRS as a tool for brain-computer interfaces.<br><br>APPROACH: This paper presents the first novel technique for fNIRS-based modelling of brain activities using the linear parameter-varying (LPV) method and adaptive signal processing. The output signal of each channel is assumed to be an output of an LPV system with unknown coefficients that are optimally estimated by the affine projection algorithm. The parameter vector is assumed to be Gaussian.<br><br>MAIN RESULTS: The general linear model (GLM) is very popular and is a commonly used method for the analysis of functional MRI data, but it has certain limitations in the case of optical signals. The proposed model is more efficient in the sense that it allows the user to define more states. Moreover, unlike most previous models, it is online. The present results, showing improvement, were verified by random finger-tapping tasks in extensive experiments. We used 24 states, which can be reduced or increased depending on the cost of computation and requirements.<br><br>SIGNIFICANCE: The t-statistics were employed to determine the activation maps and to verify the significance of the results. Comparison of the proposed technique and two existing GLM-based algorithms shows an improvement in the estimation of haemodynamic response. Additionally, the convergence of the proposed algorithm is shown by error reduction in consecutive iterations.},
}
@article {pmid23891081,
year = {2013},
author = {Karumbaiah, L and Saxena, T and Carlson, D and Patil, K and Patkar, R and Gaupp, EA and Betancur, M and Stanley, GB and Carin, L and Bellamkonda, RV},
title = {Relationship between intracortical electrode design and chronic recording function.},
journal = {Biomaterials},
volume = {34},
number = {33},
pages = {8061-8074},
doi = {10.1016/j.biomaterials.2013.07.016},
pmid = {23891081},
issn = {1878-5905},
support = {R25 GM096161/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Brain/immunology/metabolism ; Brain-Computer Interfaces ; Cytokines/metabolism ; *Electrodes, Implanted ; Fluorescent Antibody Technique ; Foreign-Body Reaction ; Immunohistochemistry ; Male ; Rats ; Reverse Transcriptase Polymerase Chain Reaction ; },
abstract = {Intracortical electrodes record neural signals directly from local populations of neurons in the brain, and conduct them to external electronics that control prosthetics. However, the relationship between electrode design, defined by shape, size and tethering; and long-term (chronic) stability of the neuron-electrode interface is poorly understood. Here, we studied the effects of various commercially available intracortical electrode designs that vary in shape (cylindrical, planar), size (15 μm, 50 μm and 75 μm), and tethering [electrode connections to connector with (tethered) and without tethering cable (untethered)] using histological, transcriptomic, and electrophysiological analyses over acute (3 day) and chronic (12 week) timepoints. Quantitative analysis of histological sections indicated that Michigan 50 μm (M50) and Michigan tethered (MT) electrodes induced significantly (p < 0.01) higher glial scarring, and lesser survival of neurons in regions of blood-brain barrier (BBB) breach when compared to microwire (MW) and Michigan 15 μm (M15) electrodes acutely and chronically. Gene expression analysis of the neurotoxic cytokines interleukin (Il)1 (Il1α, Il1β), Il6, Il17 (Il17a, Il17b, Il17f), and tumor necrosis factor alpha (Tnf) indicated that MW electrodes induced significantly (p < 0.05) reduced expression of these transcripts when compared to M15, M50 and FMAA electrodes chronically. Finally, electrophysiological assessment of electrode function indicated that MW electrodes performed significantly (p < 0.05) better than all other electrodes over a period of 12 weeks. These studies reveal that intracortical electrodes with smaller size, cylindrical shape, and without tethering cables produce significantly diminished inflammatory responses when compared to large, planar and tethered electrodes. These studies provide a platform for the rational design and assessment of chronically functional intracortical electrode implants in the future.},
}
@article {pmid23890456,
year = {2013},
author = {Stinson, B and Arthur, D},
title = {A novel EEG for alpha brain state training, neurobiofeedback and behavior change.},
journal = {Complementary therapies in clinical practice},
volume = {19},
number = {3},
pages = {114-118},
doi = {10.1016/j.ctcp.2013.03.003},
pmid = {23890456},
issn = {1873-6947},
mesh = {Algorithms ; *Alpha Rhythm ; Attention ; *Behavior ; *Biofeedback, Psychology ; Brain/*physiology ; Brain Waves ; Humans ; *Meditation ; Pilot Projects ; *Psychophysiology ; *Relaxation ; },
abstract = {Mindfulness meditation, with the resulting alpha brain state, is gaining a strong following as an adjunct to health, so too is applying self-affirmation to stimulate behavior change through subconscious re-programming. Until recently the EEG technology needed to demonstrate this has been cumbersome and required specialist training. This paper reports a pilot study using a remote EEG headband, which through a sophisticated algorithm, provides a real-time EEG readout unencumbered by conventional artifacts. In a convenience sample of 13, the difference in brain waves was examined while the subjects were occupied in an 'attention' and an 'alpha mind state' exercise. There was a significant difference in the mean scores for theta, delta, beta and gamma brain waves. Alpha brain waves remained static suggesting an ability of the headset to discriminate a mindful state and to provide real-time, easy to interpret feedback for the facilitator and subject. The findings provide encouragement for research applications in health care activities providing neurobiofeedback to subjects involved in mindfulness behavior change activities.},
}
@article {pmid23875033,
year = {2013},
author = {Schär, F and Trostdorf, U and Giardina, F and Khieu, V and Muth, S and Marti, H and Vounatsou, P and Odermatt, P},
title = {Strongyloides stercoralis: Global Distribution and Risk Factors.},
journal = {PLoS neglected tropical diseases},
volume = {7},
number = {7},
pages = {e2288},
pmid = {23875033},
issn = {1935-2735},
mesh = {Animals ; Global Health ; Humans ; Neglected Diseases/epidemiology ; Prevalence ; Risk Factors ; Strongyloides stercoralis/*isolation &amp; purification ; Strongyloidiasis/*epidemiology ; *Topography, Medical ; },
abstract = {BACKGROUND: The soil-transmitted threadworm, Strongyloides stercoralis, is one of the most neglected among the so-called neglected tropical diseases (NTDs). We reviewed studies of the last 20 years on S. stercoralis's global prevalence in general populations and risk groups.<br><br>METHODS/PRINCIPAL FINDINGS: A literature search was performed in PubMed for articles published between January 1989 and October 2011. Articles presenting information on infection prevalence were included. A Bayesian meta-analysis was carried out to obtain country-specific prevalence estimates and to compare disease odds ratios in different risk groups taking into account the sensitivities of the diagnostic methods applied. A total of 354 studies from 78 countries were included for the prevalence calculations, 194 (62.4%) were community-based studies, 121 (34.2%) were hospital-based studies and 39 (11.0%) were studies on refugees and immigrants. World maps with country data are provided. In numerous African, Asian and South-American resource-poor countries, information on S. stercoralis is lacking. The meta-analysis showed an association between HIV-infection/alcoholism and S. stercoralis infection (OR: 2.17 BCI: 1.18-4.01; OR: 6.69; BCI: 1.47-33.8), respectively.<br><br>CONCLUSIONS: Our findings show high infection prevalence rates in the general population in selected countries and geographical regions. S. stercoralis infection is prominent in several risk groups. Adequate information on the prevalence is still lacking from many countries. However, current information underscore that S. stercoralis must not be neglected. Further assessments in socio-economic and ecological settings are needed and integration into global helminth control is warranted.},
}
@article {pmid23874567,
year = {2013},
author = {Brandmeyer, A and Farquhar, JD and McQueen, JM and Desain, PW},
title = {Decoding speech perception by native and non-native speakers using single-trial electrophysiological data.},
journal = {PloS one},
volume = {8},
number = {7},
pages = {e68261},
pmid = {23874567},
issn = {1932-6203},
mesh = {Acoustic Stimulation/methods ; Behavior/physiology ; Brain/*physiology ; Brain-Computer Interfaces ; Electroencephalography/methods ; Electrophysiological Phenomena/*physiology ; Humans ; Language ; Learning/physiology ; Male ; Multilingualism ; Phonetics ; Speech Perception/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.},
}
@article {pmid23872857,
year = {2013},
author = {Santoro, M and Mattiucci, S and Work, T and Cimmaruta, R and Nardi, V and Cipriani, P and Bellisario, B and Nascetti, G},
title = {Parasitic infection by larval helminths in Antarctic fishes: pathological changes and impact on the host body condition index.},
journal = {Diseases of aquatic organisms},
volume = {105},
number = {2},
pages = {139-148},
doi = {10.3354/dao02626},
pmid = {23872857},
issn = {0177-5103},
mesh = {Animals ; Antarctic Regions/epidemiology ; Body Composition ; Female ; Fish Diseases/epidemiology/*parasitology ; Fishes ; Helminthiasis, Animal/epidemiology/*parasitology ; Helminths/classification/isolation &amp; purification ; Male ; },
abstract = {We examined pathological changes and relationship between body condition index (BCI) and parasitic infection in 5 species of fish, including 42 icefish Chionodraco hamatus (Channichtyidae), 2 dragonfish Cygnodraco mawsoni (Bathydraconidae), 30 emerald rock cod Trematomus bernacchii, 46 striped rock cod T. hansoni and 9 dusty rock cod T. newnesi (Nototheniidae) from the Ross Sea, Antarctica. All parasites were identified by a combination of morphology and mtDNA cytochrome-oxidase-2 sequence (mtDNA cox2) analysis, except Contracaecum osculatum s.l., for which only the latter was used. Five larval taxa were associated with pathological changes including 2 sibling species (D and E) of the C. osculatum species complex and 3 cestodes including plerocercoids of a diphyllobothridean, and 2 tetraphyllidean forms including cercoids with monolocular and bilocular bothridia. The most heavily infected hosts were C. hamatus and C. mawsoni, with C. hamatus most often infected by C. osculatum sp. D and sp. E and diphyllobothrideans, while C. mawsoni was most often infected with tetraphyllidean forms. Histologically, all fish showed varying severity of chronic inflammation associated with larval forms of helminths. Diphyllobothrideans and C. osculatum spp. were located in gastric muscularis or liver and were associated with necrosis and mild to marked fibrosis. Moderate multifocal rectal mucosal chronic inflammation was associated with attached tetraphyllidean scolices. C. hamatus showed a strong negative correlation between BCI and parasite burden.},
}
@article {pmid23871391,
year = {2013},
author = {Angelov, A and Li, H and Geissler, A and Leis, B and Liebl, W},
title = {Toxicity of indoxyl derivative accumulation in bacteria and its use as a new counterselection principle.},
journal = {Systematic and applied microbiology},
volume = {36},
number = {8},
pages = {585-592},
doi = {10.1016/j.syapm.2013.06.001},
pmid = {23871391},
issn = {1618-0984},
mesh = {Culture Media/chemistry ; Genetics, Microbial/*methods ; Indoles/*metabolism/*toxicity ; Microbial Viability/drug effects ; Micrococcus luteus/drug effects/*genetics/growth &amp; development/metabolism ; Molecular Biology/*methods ; *Selection, Genetic ; Thermus thermophilus/drug effects/*genetics/growth &amp; development/metabolism ; },
abstract = {In this work we describe the conditional toxic effect of the expression of enzymes that cleave 5-bromo-4-chloro-3-indolyl (BCI) substrates and its use as a new counterselection principle useful for the generation of clean and unmarked mutations in the genomes of bacteria. The application of this principle was demonstrated in the thermophile Thermus thermophilus HB27 and in a mesophile for which currently no counterselection markers are available, Micrococcus luteus ATCC 27141. For T. thermophilus, the indigogenic substrate BCI-β-glucoside was used in combination with the T. thermophilus β-glucosidase gene (bgl). For M. luteus, a combination of BCI-β-galactoside and the E. coli lacZ gene was implemented. We observed a strong growth-inhibiting effect when the strains were grown on agar plates containing the appropriate BCI substrates, the inhibition being proportional to the substrate concentration and the level of bgl/lacZ expression. The growth inhibition apparently depends on intracellular BCI substrate cleavage and accumulation of toxic indoxyl precipitates. The bgl and lacZ genes were used as counterselection markers for the rapid generation of scar-less chromosomal deletions in T. thermophilus HB27 (both in a Δbgl and in a wild type background) and in M. luteus ATCC 27141. In addition to Thermus and Micrococcus, sensitivity to BCI substrate cleavage was observed for other Gram-negative and Gram-positive species belonging to various bacterial phyla, including representatives of the genera Staphylococcus, Bacillus, Corynebacterium, Rhodococcus, Paracoccus and Xanthomonas. Thus, the toxicity of indoxyl derivative accumulation upon BCI substrate cleavage can be used for selection purposes in a broad range of microorganisms.},
}
@article {pmid23867792,
year = {2013},
author = {Schudlo, LC and Power, SD and Chau, T},
title = {Dynamic topographical pattern classification of multichannel prefrontal NIRS signals.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046018},
doi = {10.1088/1741-2560/10/4/046018},
pmid = {23867792},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Cognition/*physiology ; Female ; Humans ; Male ; Oxygen/*metabolism ; Pattern Recognition, Automated/*methods ; Prefrontal Cortex/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {OBJECTIVE: Near-infrared spectroscopy (NIRS) is an optical imaging technique that has recently been considered for brain-computer interface (BCI) applications. To date, NIRS-BCI studies have primarily made use of temporal features of brain activity, derived from the time-course of optical signals measured from discrete locations, to differentiate mental states. However, functional brain imaging studies have indicated that the spatial distribution of haemodynamic activity is also rich in information. Thus, the progression of a response over both time and space may be valuable to brain state classification. In this paper, we investigate the implication of including spatiotemporal features in the single-trial classification of haemodynamic events for a two-class problem by exploiting this information from dynamic NIR topograms.<br><br>APPROACH: The value of spatiotemporal information was explored through a comparative analysis of four different classification schemes performed on multichannel NIRS data collected from the prefrontal cortex during a mental arithmetic activation task and rest. Employing a linear discriminant classifier, data were analysed using spatiotemporal features, temporal features, and a collective pool of spatiotemporal and temporal features. We also considered a majority vote combination of three classifiers; each established using one of the above feature sets. Lastly, two separate task durations (20 and 10 s) were considered for feature extraction.<br><br>MAIN RESULTS: With features from the longer task interval, the highest overall classification accuracy was achieved using the majority voting classifier (76.1 &#xb1; 8.4%), which was greater than the accuracy obtained using temporal features alone (73.5 &#xb1; 8.5%) (F3,144 = 7.04, p = 0.0002). While results from the shorter task duration were lower overall, the classifier employing only spatiotemporal features (with an average accuracy of 67.9 &#xb1; 9.3%) achieved a higher average accuracy than the rate obtained using only temporal features (64.4 &#xb1; 8.4%) (F3,144 = 18.58, p < 10(-4)).<br><br>SIGNIFICANCE: Collectively, these results suggest that spatiotemporal information can be of value in the analysis of functional NIRS data, and improved classification rates may be obtained in future NIRS-BCI applications with the inclusion of this information.},
}
@article {pmid23866985,
year = {2013},
author = {King, CE and Wang, PT and Chui, LA and Do, AH and Nenadic, Z},
title = {Operation of a brain-computer interface walking simulator for individuals with spinal cord injury.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {77},
pmid = {23866985},
issn = {1743-0003},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy/methods ; Male ; Middle Aged ; Neurofeedback/*methods ; Spinal Cord Injuries/*rehabilitation ; Virtual Reality Exposure Therapy/*methods ; Walking/*physiology ; Young Adult ; },
abstract = {BACKGROUND: Spinal cord injury (SCI) can leave the affected individuals with paraparesis or paraplegia, thus rendering them unable to ambulate. Since there are currently no restorative treatments for this population, novel approaches such as brain-controlled prostheses have been sought. Our recent studies show that a brain-computer interface (BCI) can be used to control ambulation within a virtual reality environment (VRE), suggesting that a BCI-controlled lower extremity prosthesis for ambulation may be feasible. However, the operability of our BCI has not yet been tested in a SCI population.<br><br>METHODS: Five participants with paraplegia or tetraplegia due to SCI underwent a 10-min training session in which they alternated between kinesthetic motor imagery (KMI) of idling and walking while their electroencephalogram (EEG) were recorded. Participants then performed a goal-oriented online task, where they utilized KMI to control the linear ambulation of an avatar while making 10 sequential stops at designated points within the VRE. Multiple online trials were performed in a single day, and this procedure was repeated across 5 experimental days.<br><br>RESULTS: Classification accuracy of idling and walking was estimated offline and ranged from 60.5% (p&#x2009;=&#x2009;0.0176) to 92.3% (p&#x2009;=&#x2009;1.36&#xd7;10-20) across participants and days. Offline analysis revealed that the activation of mid-frontal areas mostly in the &#x3bc; and low &#x3b2; bands was the most consistent feature for differentiating between idling and walking KMI. In the online task, participants achieved an average performance of 7.4&#xb1;2.3 successful stops in 273&#xb1;51 sec. These performances were purposeful, i.e. significantly different from the random walk Monte Carlo simulations (p<0.01), and all but one participant achieved purposeful control within the first day of the experiments. Finally, all participants were able to maintain purposeful control throughout the study, and their online performances improved over time.<br><br>CONCLUSIONS: The results of this study demonstrate that SCI participants can purposefully operate a self-paced BCI walking simulator to complete a goal-oriented ambulation task. The operation of the proposed BCI system requires short training, is intuitive, and robust against participant-to-participant and day-to-day neurophysiological variations. These findings indicate that BCI-controlled lower extremity prostheses for gait rehabilitation or restoration after SCI may be feasible in the future.},
}
@article {pmid23866606,
year = {2013},
author = {Mokienko, OA and Chernikova, LA and Frolov, AA and Bobrov, PD},
title = {[Motor imagery and its practical application].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {63},
number = {2},
pages = {195-204},
doi = {10.7868/s0044467713020056},
pmid = {23866606},
issn = {0044-4677},
mesh = {Humans ; Imagination/*physiology ; Learning/*physiology ; Movement/physiology ; *Psychomotor Performance ; User-Computer Interface ; },
abstract = {The mechanisms underlying the process of motor imagery are similar to the motor control mechanisms. It can be used for motor learning in patients with movement disorders. Motor imagery may be the only one method for recovery of motor function in patients with severe paresis. It was the prerequisite of increased scientist interest in motor imagery during last decade. Brain-computer interface technology can support the motor imagery trainings.},
}
@article {pmid23865302,
year = {2013},
author = {Wang, L and Wang, S and Kuang, G},
title = {[A study of brain-computer interface paradigm based on mental arithmetic].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {30},
number = {3},
pages = {469-475},
pmid = {23865302},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; *Mathematical Concepts ; Mental Processes/*physiology ; Task Performance and Analysis ; },
abstract = {In the traditional P300 brain-computer interface (BCI) system, the electroencephalogram (EEG) signals can only provide limited information with a low signal-to-noise ratio. A BCI paradigm under visual stimulus was proposed in our study aiming to effectively activate the related brain areas and response signal while dealing with specific cognitive task (mental arithmetic task), so as to enhance the EEG signals. The result was compared with the traditional P300 counting task paradigm. Then the collected EEG data were preprocessed including extracting signal features with coherent averaging method, and analyzing the influences of different experimental paradigms on main components of event related potential (ERP). In the improved paradigm experiments the average increasing rate of P300 amplitude was 6. 83MV (73. 94%). The brain activity from 400ms was more active and lasted longer. Besides, unlike traditional counting task, mental arithmetic task appeared to have apparent activation at 650ms. The results showed that the improved paradigm could activate the related brain areas better and enhance the characteristics of signal. This provides a new system paradigm for BCI.},
}
@article {pmid23865300,
year = {2013},
author = {Yan, S and Liu, C and Wang, H and Zhao, H},
title = {[ECoG classification based on wavelet variance].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {30},
number = {3},
pages = {460-463},
pmid = {23865300},
issn = {1001-5515},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; Signal Processing, Computer-Assisted ; *Wavelet Analysis ; },
abstract = {For a typical electrocorticogram (ECoG)-based brain-computer interface (BCI) system in which the subject's task is to imagine movements of either the left small finger or the tongue, we proposed a feature extraction algorithm using wavelet variance. Firstly the definition and significance of wavelet variance were brought out and taken as feature based on the discussion of wavelet transform. Six channels with most distinctive features were selected from 64 channels for analysis. Consequently the EEG data were decomposed using db4 wavelet. The wavelet coeffi-cient variances containing Mu rhythm and Beta rhythm were taken out as features based on ERD/ERS phenomenon. The features were classified linearly with an algorithm of cross validation. The results of off-line analysis showed that high classification accuracies of 90. 24% and 93. 77% for training and test data set were achieved, the wavelet vari-ance had characteristics of simplicity and effectiveness and it was suitable for feature extraction in BCI research. K},
}
@article {pmid23864261,
year = {2014},
author = {Pokorny, C and Breitwieser, C and Muller-Putz, GR},
title = {A Tactile Stimulation Device for EEG Measurements in Clinical Use.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {8},
number = {3},
pages = {305-312},
doi = {10.1109/TBCAS.2013.2270176},
pmid = {23864261},
issn = {1932-4545},
mesh = {Brain-Computer Interfaces ; Electric Stimulation/*instrumentation ; *Electroencephalography ; *Evoked Potentials, Somatosensory ; Humans ; *Touch ; },
abstract = {A tactile stimulation device for EEG measurements in clinical environments is proposed. The main purpose of the tactile stimulation device is to provide tactile stimulation to different parts of the body. To stimulate all four major types of mechanoreceptors, different stimulation patterns with frequencies in the range of 5-250 Hz have to be generated. The device provides two independent channels, delivers enough power to drive different types of electromagnetic transducers, is small and portable, and no expensive components are required to construct this device. The generated stimulation patterns are very stable, and deterministic control of the device is possible. To meet electrical safety requirements, the device was designed to be fully galvanically isolated. Leakage currents of the entire EEG measurement system including the tactile stimulation device were measured by the European Testing and Certifying Body for Medical Products Graz (Notified Body 0636). All measured currents were far below the maximum allowable currents defined in the safety standard EN 60601-1:2006 for medical electrical equipment. The successful operation of the tactile stimulation device was tested during an EEG experiment. The left and right wrist of one healthy subject were randomly stimulated with seven different frequencies. Steady-state somatosensory evoked potential (SSSEPs) could successfully be evoked and significant tuning curves at electrode positions contralateral to the stimulated wrist could be found. The device is ready to be used in clinical environment in a variety of applications to investigate the somatosensory system, in brain-computer interfaces (BCIs), or to provide tactile feedback.},
}
@article {pmid23862678,
year = {2013},
author = {Homer, ML and Nurmikko, AV and Donoghue, JP and Hochberg, LR},
title = {Sensors and decoding for intracortical brain computer interfaces.},
journal = {Annual review of biomedical engineering},
volume = {15},
number = {},
pages = {383-405},
pmid = {23862678},
issn = {1545-4274},
support = {R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; R01EB007401-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Amputation, Surgical/rehabilitation ; Brain/pathology ; *Brain-Computer Interfaces ; Calibration ; Electrodes, Implanted ; Equipment Design ; Humans ; Paralysis/rehabilitation ; Signal Processing, Computer-Assisted ; },
abstract = {Intracortical brain computer interfaces (iBCIs) are being developed to enable people to drive an output device, such as a computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software that decodes the user's intended movement from those signals. Here, we focus on recent advances in these two areas, placing special attention on contributions that are or may soon be adopted by the iBCI research community. We discuss how these innovations increase the technology's capability, accuracy, and longevity, all important steps that are expanding the range of possible future clinical applications.},
}
@article {pmid23859968,
year = {2013},
author = {Kjaer, TW and Sørensen, HB},
title = {A brain-computer interface to support functional recovery.},
journal = {Frontiers of neurology and neuroscience},
volume = {32},
number = {},
pages = {95-100},
doi = {10.1159/000346430},
pmid = {23859968},
issn = {1662-2804},
mesh = {Animals ; Brain/pathology/*physiology ; Brain-Computer Interfaces/*trends ; Persons with Disabilities/*rehabilitation ; Humans ; Recovery of Function/*physiology ; Stroke/pathology/physiopathology ; *Stroke Rehabilitation ; },
abstract = {Brain-computer interfaces (BCI) register changes in brain activity and utilize this to control computers. The most widely used method is based on registration of electrical signals from the cerebral cortex using extracranially placed electrodes also called electroencephalography (EEG). The features extracted from the EEG may, besides controlling the computer, also be fed back to the patient for instance as visual input. This facilitates a learning process. BCI allow us to utilize brain activity in the rehabilitation of patients after stroke. The activity of the cerebral cortex varies with the type of movement we imagine, and by letting the patient know the type of brain activity best associated with the intended movement the rehabilitation process may be faster and more efficient. The focus of BCI utilization in medicine has changed in recent years. While we previously focused on devices facilitating communication in the rather few patients with locked-in syndrome, much interest is now devoted to the therapeutic use of BCI in rehabilitation. For this latter group of patients, the device is not intended to be a lifelong assistive companion but rather a 'teacher' during the rehabilitation period.},
}
@article {pmid23858742,
year = {2013},
author = {Jin, H and Zhang, Z},
title = {[Research of movement imagery EEG based on Hilbert-Huang transform and BP neural network].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {30},
number = {2},
pages = {249-253},
pmid = {23858742},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Models, Theoretical ; Motor Activity/*physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This paper introduces the characteristics of the Hilbert-Huang transform (HHT), and studies the classification of movement imagery EEG based on the HHT method and BP neural network. After preprocessed, the movement imagery EEG data were descomposed with empirical mode decomposition (EMD) into a series of intrinsic mode functions (IMFs). Then the low frequency IMFs were removed, and the rest of IMFs were conducted by Hilbert transform to get Hilbert marginal spectrum. The marginal spectrum subtracted values between the channal C3 and channal C4 were selected as the original features which were then decreased the dimension by the principal components analysis so as to be jointed with EEG complexity to construct the feature vector. The BP neural network was utilized to classify the EEG pattern of left and right hand motor imagery. The brain computer interface (BCI) competition II data set III was selected to carry out the discrimination, and the classification accuracy rate is up to 87.14%, which is a comparably good result and proves HHT to be a feasible and effective method on EEG analysis.},
}
@article {pmid23858737,
year = {2013},
author = {Wang, J and Yang, C and Hu, B},
title = {[Design and implementation of controlling smart car systems using P300 brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {30},
number = {2},
pages = {223-228},
pmid = {23858737},
issn = {1001-5515},
mesh = {Adult ; Automobiles ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; *Event-Related Potentials, P300 ; Evoked Potentials, Visual ; Female ; Humans ; Male ; *Man-Machine Systems ; Task Performance and Analysis ; },
abstract = {Using human electroencephalogram (EEG) to control external devices in order to achieve a variety of functions has been focus of the field of brain-computer interface (BCI) research. P300 is experiments which stimulate the eye to produce EEG by using letters flashing, and then identify the corresponding letters. In this paper, some improvements based on the P300 experiments were made??. Firstly, the matrix of flashing letters were modified into words which represent a certain sense. Secondly, the BCI2000 procedures were added with the corresponding source code. Thirdly, the smart car systems were designed using the radiofrequency signal. Finally it was realized that the evoked potentials were used to control the state of the smart car.},
}
@article {pmid23852650,
year = {2013},
author = {Little, S and Pogosyan, A and Neal, S and Zavala, B and Zrinzo, L and Hariz, M and Foltynie, T and Limousin, P and Ashkan, K and FitzGerald, J and Green, AL and Aziz, TZ and Brown, P},
title = {Adaptive deep brain stimulation in advanced Parkinson disease.},
journal = {Annals of neurology},
volume = {74},
number = {3},
pages = {449-457},
pmid = {23852650},
issn = {1531-8249},
support = {G0901503/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Aged ; Antiparkinson Agents/therapeutic use ; *Brain-Computer Interfaces ; *Deep Brain Stimulation ; Humans ; Middle Aged ; Parkinson Disease/drug therapy/physiopathology/*therapy ; Subthalamic Nucleus/*physiopathology ; Treatment Outcome ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.<br><br>METHODS: We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.<br><br>RESULTS: Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.<br><br>INTERPRETATION: BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.},
}
@article {pmid23852172,
year = {2013},
author = {Simon, D and Ware, T and Marcotte, R and Lund, BR and Smith, DW and Di Prima, M and Rennaker, RL and Voit, W},
title = {A comparison of polymer substrates for photolithographic processing of flexible bioelectronics.},
journal = {Biomedical microdevices},
volume = {15},
number = {6},
pages = {925-939},
doi = {10.1007/s10544-013-9782-8},
pmid = {23852172},
issn = {1572-8781},
support = {5R01DC008982/DC/NIDCD NIH HHS/United States ; },
mesh = {Absorption ; Acrylates/chemistry ; Animals ; Auditory Cortex ; Brain-Computer Interfaces ; Chromium/chemistry ; *Electrical Equipment and Supplies ; Electrodes ; Gold/chemistry ; Mechanical Phenomena ; *Polymers ; Rats ; Sulfhydryl Compounds/chemistry ; Temperature ; },
abstract = {Flexible bioelectronics encompass a new generation of sensing devices, in which controlled interactions with tissue enhance understanding of biological processes in vivo. However, the fabrication of such thin film electronics with photolithographic processes remains a challenge for many biocompatible polymers. Recently, two shape memory polymer (SMP) systems, based on acrylate and thiol-ene/acrylate networks, were designed as substrates for softening neural interfaces with glass transitions above body temperature (37 °C) such that the materials are stiff for insertion into soft tissue and soften through low moisture absorption in physiological conditions. These two substrates, acrylate and thiol-ene/acrylate SMPs, are compared to polyethylene naphthalate, polycarbonate, polyimide, and polydimethylsiloxane, which have been widely used in flexible electronics research and industry. These six substrates are compared via dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and swelling studies. The integrity of gold and chromium/gold thin films on SMP substrates are evaluated with optical profilometry and electrical measurements as a function of processing temperature above, below and through the glass transition temperature. The effects of crosslink density, adhesion and cure stress are shown to play a critical role in the stability of these thin film materials, and a guide for the future design of responsive polymeric materials suitable for neural interfaces is proposed. Finally, neural interfaces fabricated on thiol-ene/acrylate substrates demonstrate long-term fidelity through both in vitro impedance spectroscopy and the recording of driven local field potentials for 8 weeks in the auditory cortex of laboratory rats.},
}
@article {pmid23844066,
year = {2013},
author = {Petrov, Y and Sridhar, S},
title = {Electric Field Encephalography as a tool for functional brain research: a modeling study.},
journal = {PloS one},
volume = {8},
number = {7},
pages = {e67692},
pmid = {23844066},
issn = {1932-6203},
mesh = {Algorithms ; Brain/physiology ; *Brain Mapping/methods ; Electroencephalography/methods ; Humans ; *Models, Neurological ; },
abstract = {We introduce the notion of Electric Field Encephalography (EFEG) based on measuring electric fields of the brain and demonstrate, using computer modeling, that given the appropriate electric field sensors this technique may have significant advantages over the current EEG technique. Unlike EEG, EFEG can be used to measure brain activity in a contactless and reference-free manner at significant distances from the head surface. Principal component analysis using simulated cortical sources demonstrated that electric field sensors positioned 3 cm away from the scalp and characterized by the same signal-to-noise ratio as EEG sensors provided the same number of uncorrelated signals as scalp EEG. When positioned on the scalp, EFEG sensors provided 2-3 times more uncorrelated signals. This significant increase in the number of uncorrelated signals can be used for more accurate assessment of brain states for non-invasive brain-computer interfaces and neurofeedback applications. It also may lead to major improvements in source localization precision. Source localization simulations for the spherical and Boundary Element Method (BEM) head models demonstrated that the localization errors are reduced two-fold when using electric fields instead of electric potentials. We have identified several techniques that could be adapted for the measurement of the electric field vector required for EFEG and anticipate that this study will stimulate new experimental approaches to utilize this new tool for functional brain research.},
}
@article {pmid23844021,
year = {2013},
author = {Feess, D and Krell, MM and Metzen, JH},
title = {Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.},
journal = {PloS one},
volume = {8},
number = {7},
pages = {e67543},
pmid = {23844021},
issn = {1932-6203},
mesh = {*Algorithms ; Brain/anatomy &amp; histology/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; },
abstract = {A major barrier for a broad applicability of brain-computer interfaces (BCIs) based on electroencephalography (EEG) is the large number of EEG sensor electrodes typically used. The necessity for this results from the fact that the relevant information for the BCI is often spread over the scalp in complex patterns that differ depending on subjects and application scenarios. Recently, a number of methods have been proposed to determine an individual optimal sensor selection. These methods have, however, rarely been compared against each other or against any type of baseline. In this paper, we review several selection approaches and propose one additional selection criterion based on the evaluation of the performance of a BCI system using a reduced set of sensors. We evaluate the methods in the context of a passive BCI system that is designed to detect a P300 event-related potential and compare the performance of the methods against randomly generated sensor constellations. For a realistic estimation of the reduced system's performance we transfer sensor constellations found on one experimental session to a different session for evaluation. We identified notable (and unanticipated) differences among the methods and could demonstrate that the best method in our setup is able to reduce the required number of sensors considerably. Though our application focuses on EEG data, all presented algorithms and evaluation schemes can be transferred to any binary classification task on sensor arrays.},
}
@article {pmid23843600,
year = {2013},
author = {Asensio-Cubero, J and Gan, JQ and Palaniappan, R},
title = {Multiresolution analysis over simple graphs for brain computer interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046014},
doi = {10.1088/1741-2560/10/4/046014},
pmid = {23843600},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Middle Aged ; Motor Cortex/*physiology ; Numerical Analysis, Computer-Assisted ; Reproducibility of Results ; Sensitivity and Specificity ; *Wavelet Analysis ; },
abstract = {OBJECTIVE: Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods may not be the best approach for brain computer interface (BCI) applications. This study aims to develop a new MRA system for extracting tempo-spatial-spectral features for BCI applications based on wavelet lifting over graphs.<br><br>APPROACH: This paper proposes a new graph-based transform for wavelet lifting and a tailored simple graph representation for electroencephalography (EEG) data, which results in an MRA system where temporal, spectral and spatial characteristics are used to extract motor imagery features from EEG data. The transformed data is processed within a simple experimental framework to test the classification performance of the new method.<br><br>MAIN RESULTS: The proposed method can significantly improve the classification results obtained by various wavelet families using the same methodology. Preliminary results using common spatial patterns as feature extraction method show that we can achieve comparable classification accuracy to more sophisticated methodologies. From the analysis of the results we can obtain insights into the pattern development in the EEG data, which provide useful information for feature basis selection and thus for improving classification performance.<br><br>SIGNIFICANCE: Applying wavelet lifting over graphs is a new approach for handling BCI data. The inherent flexibility of the lifting scheme could lead to new approaches based on the hereby proposed method for further classification performance improvement.},
}
@article {pmid23838067,
year = {2013},
author = {Jarosiewicz, B and Masse, NY and Bacher, D and Cash, SS and Eskandar, E and Friehs, G and Donoghue, JP and Hochberg, LR},
title = {Advantages of closed-loop calibration in intracortical brain-computer interfaces for people with tetraplegia.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046012},
pmid = {23838067},
issn = {1741-2552},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; N01HD10018/HD/NICHD NIH HHS/United States ; R37 NS025074/NS/NINDS NIH HHS/United States ; NS25074/NS/NINDS NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Brain Mapping/*standards ; Brain-Computer Interfaces/*standards ; Calibration ; Feedback, Physiological ; Female ; Humans ; Imagination ; Middle Aged ; Motor Cortex/*physiopathology ; Movement ; Quadriplegia/*physiopathology/*rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; United States ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) aim to provide a means for people with severe motor disabilities to control their environment directly with neural activity. In intracortical BCIs for people with tetraplegia, the decoder that maps neural activity to desired movements has typically been calibrated using 'open-loop' (OL) imagination of control while a cursor automatically moves to targets on a computer screen. However, because neural activity can vary across contexts, a decoder calibrated using OL data may not be optimal for 'closed-loop' (CL) neural control. Here, we tested whether CL calibration creates a better decoder than OL calibration even when all other factors that might influence performance are held constant, including the amount of data used for calibration and the amount of elapsed time between calibration and testing.<br><br>APPROACH: Two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial performed a center-out-back task using an intracortical BCI, switching between decoders that had been calibrated on OL versus CL data.<br><br>MAIN RESULTS: Even when all other variables were held constant, CL calibration improved neural control as well as the accuracy and strength of the tuning model. Updating the CL decoder using additional and more recent data resulted in further improvements.<br><br>SIGNIFICANCE: Differences in neural activity between OL and CL contexts contribute to the superiority of CL decoders, even prior to their additional 'adaptive' advantage. In the near future, CL decoder calibration may enable robust neural control without needing to pause ongoing, practical use of BCIs, an important step toward clinical utility.},
}
@article {pmid23822118,
year = {2013},
author = {Pedrocchi, A and Ferrante, S and Ambrosini, E and Gandolla, M and Casellato, C and Schauer, T and Klauer, C and Pascual, J and Vidaurre, C and Gföhler, M and Reichenfelser, W and Karner, J and Micera, S and Crema, A and Molteni, F and Rossini, M and Palumbo, G and Guanziroli, E and Jedlitschka, A and Hack, M and Bulgheroni, M and d'Amico, E and Schenk, P and Zwicker, S and Duschau-Wicke, A and Miseikis, J and Graber, L and Ferrigno, G},
title = {MUNDUS project: MUltimodal neuroprosthesis for daily upper limb support.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {66},
pmid = {23822118},
issn = {1743-0003},
mesh = {Adult ; Aged ; Arm/physiology ; Brain-Computer Interfaces ; Female ; Hand/physiology ; Hand Strength/physiology ; Humans ; Male ; Middle Aged ; *Neural Prostheses ; Neuromuscular Diseases/rehabilitation ; *Prosthesis Design ; Psychomotor Performance/physiology ; Spinal Cord Injuries/rehabilitation ; Treatment Outcome ; Upper Extremity/*physiology ; },
abstract = {BACKGROUND: MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user's direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects.<br><br>METHODS: The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users.<br><br>RESULTS: The functionality of all modules has been successfully demonstrated. User's intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13&#x2009;&#xb1;&#x2009;0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65&#xa0;minutes, scaled on the configuration complexity.<br><br>CONCLUSIONS: The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user's need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising.},
}
@article {pmid23820142,
year = {2013},
author = {Collinger, JL and Foldes, S and Bruns, TM and Wodlinger, B and Gaunt, R and Weber, DJ},
title = {Neuroprosthetic technology for individuals with spinal cord injury.},
journal = {The journal of spinal cord medicine},
volume = {36},
number = {4},
pages = {258-272},
pmid = {23820142},
issn = {1079-0268},
support = {F32 NS074565/NS/NINDS NIH HHS/United States ; F32NS074565/NS/NINDS NIH HHS/United States ; },
mesh = {Brain-Computer Interfaces ; Electric Stimulation Therapy/instrumentation/methods ; Electromyography/instrumentation/methods ; Feedback, Physiological ; Humans ; *Prostheses and Implants ; *Recovery of Function ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Urinary Bladder/physiopathology ; },
abstract = {CONTEXT: Spinal cord injury (SCI) results in a loss of function and sensation below the level of the lesion. Neuroprosthetic technology has been developed to help restore motor and autonomic functions as well as to provide sensory feedback.<br><br>FINDINGS: This paper provides an overview of neuroprosthetic technology that aims to address the priorities for functional restoration as defined by individuals with SCI. We describe neuroprostheses that are in various stages of preclinical development, clinical testing, and commercialization including functional electrical stimulators, epidural and intraspinal microstimulation, bladder neuroprosthesis, and cortical stimulation for restoring sensation. We also discuss neural recording technologies that may provide command or feedback signals for neuroprosthetic devices.<br><br>CONCLUSION/CLINICAL RELEVANCE: Neuroprostheses have begun to address the priorities of individuals with SCI, although there remains room for improvement. In addition to continued technological improvements, closing the loop between the technology and the user may help provide intuitive device control with high levels of performance.},
}
@article {pmid23818322,
year = {2013},
author = {Starr, PA and Ostrem, JL},
title = {Commentary on "Adaptive deep brain stimulation in advanced Parkinson disease".},
journal = {Annals of neurology},
volume = {74},
number = {3},
pages = {447-448},
doi = {10.1002/ana.23966},
pmid = {23818322},
issn = {1531-8249},
mesh = {*Brain-Computer Interfaces ; *Deep Brain Stimulation ; Humans ; Parkinson Disease/*therapy ; Subthalamic Nucleus/*physiopathology ; },
}
@article {pmid23814901,
year = {2013},
author = {Badakva, AM and Bondar', IV and Zobova, LN and Miller, NV and Roshchin, VIu},
title = {[Modeling an invasive brain - computer interface in an experiment with primates].},
journal = {Aviakosmicheskaia i ekologicheskaia meditsina = Aerospace and environmental medicine},
volume = {47},
number = {2},
pages = {61-64},
pmid = {23814901},
issn = {0233-528X},
mesh = {Action Potentials/*physiology ; Animals ; Arm/*physiology ; Brain Mapping/instrumentation/methods ; *Brain-Computer Interfaces ; Humans ; Macaca mulatta ; Male ; Microelectrodes ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Neurons/*physiology ; Stereotaxic Techniques ; },
}
@article {pmid25373024,
year = {2013},
author = {Principe, JC},
title = {The cortical mouse: a piece of forgotten history in noninvasive brain–computer interfaces.},
journal = {IEEE pulse},
volume = {4},
number = {4},
pages = {26-29},
doi = {10.1109/MPUL.2013.2261329},
pmid = {25373024},
issn = {2154-2317},
mesh = {Biomedical Engineering/*history/*instrumentation ; Brain/*physiology ; Brain-Computer Interfaces/*history ; History, 20th Century ; Humans ; },
abstract = {Early research on brain-computer interfaces (BCIs) was fueled by the study of event-related potentials (ERPs) by Farwell and Donchin, who are rightly credited for laying important groundwork for the BCI field. However, many other researchers have made substantial contributions that have escaped the radar screen of the current BCI community. For example, in the late 1980s, I worked with a brilliant multidisciplinary research group in electrical engineering at the University of Florida, Gainesville, headed by Dr. Donald Childers. Childers should be well known to long-time members of the IEEE Engineering in Medicine and Biology Society since he was the editor-in-chief of IEEE Transactions on Biomedical Engineering in the 1970s and the recipient of one of the most prestigious society awards, the William J. Morlock Award, in 1973.},
}
@article {pmid23807515,
year = {2013},
author = {Evers, K and Sigman, M},
title = {Possibilities and limits of mind-reading: a neurophilosophical perspective.},
journal = {Consciousness and cognition},
volume = {22},
number = {3},
pages = {887-897},
doi = {10.1016/j.concog.2013.05.011},
pmid = {23807515},
issn = {1090-2376},
mesh = {Brain/*physiology/physiopathology ; Brain Mapping/*methods ; *Communication ; Consciousness Disorders/*physiopathology ; Functional Neuroimaging/methods ; Humans ; Magnetic Resonance Imaging ; Thinking/*physiology ; },
abstract = {Access to other minds once presupposed other individuals' expressions and narrations. Today, several methods have been developed which can measure brain states relevant for assessments of mental states without 1st person overt external behavior or speech. Functional magnetic resonance imaging and trace conditioning are used clinically to identify patterns of activity in the brain that suggest the presence of consciousness in people suffering from severe consciousness disorders and methods to communicate cerebrally with patients who are motorically unable to communicate. The techniques are also used non-clinically to access subjective awareness in adults and infants. In this article we inspect technical and theoretical limits on brain-machine interface access to other minds. We argue that these techniques hold promises of important medical breakthroughs, open up new vistas of communication, and of understanding the infant mind. Yet they also give rise to ethical concerns, notably misuse as a consequence of hypes and misinterpretations.},
}
@article {pmid23807456,
year = {2013},
author = {Delgado Saa, JF and Çetin, M},
title = {Discriminative methods for classification of asynchronous imaginary motor tasks from EEG data.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {5},
pages = {716-724},
doi = {10.1109/TNSRE.2013.2268194},
pmid = {23807456},
issn = {1558-0210},
mesh = {Algorithms ; Brain-Computer Interfaces/*classification ; Computer Graphics ; Data Interpretation, Statistical ; Electroencephalography/*classification/statistics &amp; numerical data ; Humans ; Imagination/*classification/physiology ; Linear Models ; Models, Neurological ; Motor Skills/*classification/physiology ; Psychomotor Performance/*physiology ; User-Computer Interface ; },
abstract = {In this work, two methods based on statistical models that take into account the temporal changes in the electroencephalographic (EEG) signal are proposed for asynchronous brain-computer interfaces (BCI) based on imaginary motor tasks. Unlike the current approaches to asynchronous BCI systems that make use of windowed versions of the EEG data combined with static classifiers, the methods proposed here are based on discriminative models that allow sequential labeling of data. In particular, the two methods we propose for asynchronous BCI are based on conditional random fields (CRFs) and latent dynamic CRFs (LDCRFs), respectively. We describe how the asynchronous BCI problem can be posed as a classification problem based on CRFs or LDCRFs, by defining appropriate random variables and their relationships. CRF allows modeling the extrinsic dynamics of data, making it possible to model the transitions between classes, which in this context correspond to distinct tasks in an asynchronous BCI system. On the other hand, LDCRF goes beyond this approach by incorporating latent variables that permit modeling the intrinsic structure for each class and at the same time allows modeling extrinsic dynamics. We apply our proposed methods on the publicly available BCI competition III dataset V as well as a data set recorded in our laboratory. Results obtained are compared to the top algorithm in the BCI competition as well as to methods based on hierarchical hidden Markov models (HHMMs), hierarchical hidden CRF (HHCRF), neural networks based on particle swarm optimization (IPSONN) and to a recently proposed approach based on neural networks and fuzzy theory, the S-dFasArt. Our experimental analysis demonstrates the improvements provided by our proposed methods in terms of classification accuracy.},
}
@article {pmid23804587,
year = {2013},
author = {Devereaux, MW and ElMaraghy, AW},
title = {Improving the rapid and reliable diagnosis of complete distal biceps tendon rupture: a nuanced approach to the clinical examination.},
journal = {The American journal of sports medicine},
volume = {41},
number = {9},
pages = {1998-2004},
doi = {10.1177/0363546513493383},
pmid = {23804587},
issn = {1552-3365},
mesh = {Adult ; Aged ; Arm Injuries/*diagnosis ; Female ; Humans ; Male ; Middle Aged ; Physical Examination ; Prospective Studies ; Tendon Injuries/*diagnosis ; },
abstract = {BACKGROUND: Diagnosis of complete distal biceps tendon rupture (DBTR) is frequently missed or delayed on clinical examination. No single clinical test, including MRI, has demonstrated 100% efficacy in assessing the integrity of the distal biceps tendon.<br><br>HYPOTHESIS: Combining 3 validated clinical tests for identifying complete rupture can maximize a true-positive diagnosis for complete DBTR without the need for confirmatory soft tissue imaging when performed in concert with other important factors from the history and clinical examination.<br><br>STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2.<br><br>METHODS: The hook test, the passive forearm pronation (PFP) test, and the biceps crease interval (BCI) test were applied in sequence in conjunction with a standard patient history and physical examination on 48 patients with suspected distal biceps tendon injuries. If results on all 3 special tests were positive for complete rupture, the patient was referred for surgical repair; diagnosis was confirmed intraoperatively. If results on all 3 special tests were negative, diagnosis was confirmed with soft tissue imaging and patients were managed nonoperatively. If results of the 3 tests were not in agreement, soft tissue imaging was used to clarify the disagreement and to confirm the diagnosis.<br><br>RESULTS: Thirty-five patients had unequivocal results based on history, physical examination, and special tests. Thirty-two tested in agreement positive for complete rupture, which were confirmed intraoperatively. Three tested in agreement negative, with subsequent imaging confirming partial rupture. Thirteen patients had equivocal special test results; soft tissue imaging suggested complete rupture in 10 and partial rupture in 3.<br><br>CONCLUSION: Application in sequence of the hook test, the PFP test, and the BCI test results in 100% sensitivity and specificity when the outcomes on all 3 special tests are in agreement.},
}
@article {pmid23800592,
year = {2013},
author = {Sánchez-Chardi, A and García-Pando, M and López-Fuster, MJ},
title = {Chronic exposure to environmental stressors induces fluctuating asymmetry in shrews inhabiting protected Mediterranean sites.},
journal = {Chemosphere},
volume = {93},
number = {6},
pages = {916-923},
doi = {10.1016/j.chemosphere.2013.05.056},
pmid = {23800592},
issn = {1879-1298},
mesh = {Animals ; *Conservation of Natural Resources ; Ecotoxicology ; *Environmental Monitoring ; Mediterranean Region ; Metals/metabolism/toxicity ; Shrews/*physiology ; Soil Pollutants/metabolism/toxicity ; },
abstract = {Many ecotoxicological studies have addressed the effects of contaminant exposure at various levels of biological organization. However, little information exists on the effects of toxicants on wildlife populations. Here we examined exposure of populations of the greater white-toothed shrew Crocidura russula (Soricomorpha, Soricidae) occupying two protected Mediterranean sites (a polluted area, the Ebro Delta, and a control site, Garraf Massif). Bioaccumulation of selected elements (Pb, Hg, Cd, Zn, Cu, Fe, Mn, Cr, Mo, Sr, Ba, and B), a body condition index (BCI) and fluctuating asymmetry (FA) were used to assess the chronic exposure to environmental pollution. BCI was correlated neither to metal concentrations nor to FA, suggesting that this fitness measure only reflects environmental disturbances at a local level. However, shrews from the polluted area showed higher concentrations of metals and metalloids (Pb, Hg, B, and Sr) and greater shape FA than specimens from the reference area. A correlation between FA was found for both first and second principal component vectors suggesting that developmental instability increases as a result of exposure to multiple pollutants. Our results corroborate the suitability of C. russula as a bioindicator of environmental quality and show that FA is an appropriate index to examine impact of developmental stressors in populations inhabiting disturbed areas.},
}
@article {pmid23800158,
year = {2013},
author = {Duvinage, M and Castermans, T and Petieau, M and Hoellinger, T and Cheron, G and Dutoit, T},
title = {Performance of the Emotiv Epoc headset for P300-based applications.},
journal = {Biomedical engineering online},
volume = {12},
number = {},
pages = {56},
pmid = {23800158},
issn = {1475-925X},
mesh = {*Brain-Computer Interfaces/economics ; Capsule Endoscopy ; Electrodes ; Electroencephalography ; *Head ; Humans ; Oxidation-Reduction ; Reproducibility of Results ; Software ; },
abstract = {BACKGROUND: For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer's needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available.<br><br>METHODS: This paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed.<br><br>RESULTS: The Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor.<br><br>CONCLUSIONS: Although this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.},
}
@article {pmid23799679,
year = {2013},
author = {Li, Y and Pan, J and Wang, F and Yu, Z},
title = {A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {11},
pages = {3156-3166},
doi = {10.1109/TBME.2013.2270283},
pmid = {23799679},
issn = {1558-2531},
mesh = {Adult ; Biomedical Engineering/instrumentation ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; ROC Curve ; Reproducibility of Results ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; *Wheelchairs ; Young Adult ; },
abstract = {In this paper, a hybrid brain-computer interface (BCI) system combining P300 and steady-state visual evoked potential (SSVEP) is proposed to improve the performance of asynchronous control. The four groups of flickering buttons were set in the graphical user interface. Each group contained one large button in the center and eight small buttons around it, all of which flashed at a fixed frequency (e.g., 7.5 Hz) to evoke SSVEP. At the same time, the four large buttons of the four groups were intensified through shape and color changes in a random order to produce P300 potential. During the control state, the user focused on a desired group of buttons (target buttons) to evoke P300 potential and SSVEP, simultaneously. Discrimination between the control and idle states was based on the detection of both P300 and SSVEP on the same group of buttons. As an application, this method was used to produce a "go/stop" command in real-time wheelchair control. Several experiments were conducted, and data analysis results showed that combining P300 potential and SSVEP significantly improved the performance of the BCI system in terms of detection accuracy and response time.},
}
@article {pmid23789390,
year = {2013},
author = {Frolov, AA and Biriukova, EV and Bobrov, PD and Mokienko, OA and Platonov, AK and Prianichnikov, VE and Chernikov, LA},
title = {[Principles of neurorehabilitation based on brain-computer interface and biologically plausible control of the exoskeleton].},
journal = {Fiziologiia cheloveka},
volume = {39},
number = {2},
pages = {99-113},
doi = {10.7868/s0131164613020033},
pmid = {23789390},
issn = {0131-1646},
mesh = {*Brain-Computer Interfaces ; Central Nervous System/physiology ; Electroencephalography ; Humans ; Imagination ; Movement/*physiology ; Neurophysiology/*methods ; *Rehabilitation ; User-Computer Interface ; },
abstract = {The paper examines neurophysiological basis for development and performance of brain-computer interface (BCI) that permits cerebral activity alone to control computers or other external technical devices. BCI based on the discrimination of EEG patterns related to an imagery of extremity movements is considered. The problem of BCI application to restoring of motor functions in patients with motor disabilities is discussed.},
}
@article {pmid23791916,
year = {2013},
author = {De Vico Fallani, F and Pichiorri, F and Morone, G and Molinari, M and Babiloni, F and Cincotti, F and Mattia, D},
title = {Multiscale topological properties of functional brain networks during motor imagery after stroke.},
journal = {NeuroImage},
volume = {83},
number = {},
pages = {438-449},
doi = {10.1016/j.neuroimage.2013.06.039},
pmid = {23791916},
issn = {1095-9572},
mesh = {Adult ; Aged ; Brain Mapping ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Movement Disorders/etiology/*physiopathology ; Nerve Net/*physiopathology ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/complications/*physiopathology ; },
abstract = {In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise of regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions with increased connectivity during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results improve on our understanding of stroke-induced alterations in functional brain networks.},
}
@article {pmid23781275,
year = {2013},
author = {Cao, Y and Hao, Y and Liao, Y and Xu, K and Wang, Y and Zhang, S and Zhang, Q and Chen, W and Zheng, X},
title = {Information analysis on neural tuning in dorsal premotor cortex for reaching and grasping.},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
number = {},
pages = {730374},
pmid = {23781275},
issn = {1748-6718},
mesh = {Animals ; Brain-Computer Interfaces/statistics &amp; numerical data ; Computational Biology ; Hand Strength/physiology ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/cytology/*physiology ; Movement/physiology ; Neurons/physiology ; Psychomotor Performance/physiology ; Support Vector Machine ; },
abstract = {Previous studies have shown that the dorsal premotor cortex (PMd) neurons are relevant to reaching as well as grasping. In order to investigate their specific contribution to reaching and grasping, respectively, we design two experimental paradigms to separate these two factors. Two monkeys are instructed to reach in four directions but grasp the same object and grasp four different objects but reach in the same direction. Activities of the neuron ensemble in PMd of the two monkeys are collected while performing the tasks. Mutual information (MI) is carried out to quantitatively evaluate the neurons' tuning property in both tasks. We find that there exist neurons in PMd that are tuned only to reaching, tuned only to grasping, and tuned to both tasks. When applied with a support vector machine (SVM), the movement decoding accuracy by the tuned neuron subset in either task is quite close to the performance by full ensemble. Furthermore, the decoding performance improves significantly by adding the neurons tuned to both tasks into the neurons tuned to one property only. These results quantitatively distinguish the diversity of the neurons tuned to reaching and grasping in the PMd area and verify their corresponding contributions to BMI decoding.},
}
@article {pmid23781166,
year = {2013},
author = {Velu, PD and de Sa, VR},
title = {Single-trial classification of gait and point movement preparation from human EEG.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {84},
pmid = {23781166},
issn = {1662-4548},
support = {T32 GM007198/GM/NIGMS NIH HHS/United States ; },
abstract = {Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).},
}
@article {pmid23780801,
year = {2013},
author = {Graur, S and Siegle, G},
title = {Pupillary motility: bringing neuroscience to the psychiatry clinic of the future.},
journal = {Current neurology and neuroscience reports},
volume = {13},
number = {8},
pages = {365},
pmid = {23780801},
issn = {1534-6293},
support = {K02 MH082998/MH/NIMH NIH HHS/United States ; P50 MH080215/MH/NIMH NIH HHS/United States ; },
mesh = {Brain/physiology/*physiopathology ; Brain-Computer Interfaces/psychology ; Cognition/*physiology ; Emotions/*physiology ; Humans ; Mental Disorders/*physiopathology ; Pupil/*physiology ; },
abstract = {Modern pupillometry has expanded the study and utility of pupil responses in many new domains, including psychiatry, particularly for understanding aspects of cognitive and emotional information processing. Here, we review the applications of pupillometry in psychiatry for understanding patients' information processing styles, predicting treatment, and augmenting function. In the past year pupillometry has been shown to be useful in specifying cognitive/affective occurrences during experimental tasks and informing clinical diagnoses. Such studies demonstrate the potential of pupillary motility to be used in clinical psychiatry much as it has been in neurology for the past century.},
}
@article {pmid23778618,
year = {2013},
author = {LeVan, IK and Fox, KA and Miller, MW},
title = {High elaeophorosis prevalence among harvested Colorado moose.},
journal = {Journal of wildlife diseases},
volume = {49},
number = {3},
pages = {666-669},
doi = {10.7589/2012-12-306},
pmid = {23778618},
issn = {1943-3700},
mesh = {Animals ; Animals, Wild/parasitology ; Colorado ; Deer/*parasitology ; Female ; Filariasis/epidemiology/*veterinary ; Male ; Prevalence ; },
abstract = {Infection with Elaeophora schneideri, a filarial parasite, occurs commonly in mule deer (Odocoileus hemionus) and elk (Cervus elaphus nelsoni), but seemingly less so in moose (Alces alces). Of 109 carotid artery samples from moose harvested throughout Colorado, USA, in 2007, 14 (13%; 95% binomial confidence interval [bCI]=7-21%) showed gross and 91 (83%; 95% bCI=75-90%) showed histologic evidence of elaeophorosis. Although neither blindness nor other clinical signs associated with elaeophorosis were reported among the harvested moose we examined, the pervasiveness of this parasite may motivate further study of the potential effects of elaeophorosis on moose survival and population performance in the southern Rocky Mountains. Our data suggest histopathology may be more sensitive than gross examination in detecting elaeophorosis in harvested moose.},
}
@article {pmid23777523,
year = {2013},
author = {Kowalski, KC and He, BD and Srinivasan, L},
title = {Dynamic analysis of naive adaptive brain-machine interfaces.},
journal = {Neural computation},
volume = {25},
number = {9},
pages = {2373-2420},
doi = {10.1162/NECO_a_00484},
pmid = {23777523},
issn = {1530-888X},
mesh = {*Algorithms ; Animals ; *Artificial Intelligence ; *Brain-Computer Interfaces ; Feedback ; Humans ; },
abstract = {The closed-loop operation of brain-machine interfaces (BMI) provides a context to discover foundational principles behind human-computer interaction, with emerging clinical applications to stroke, neuromuscular diseases, and trauma. In the canonical BMI, a user controls a prosthetic limb through neural signals that are recorded by electrodes and processed by a decoder into limb movements. In laboratory demonstrations with able-bodied test subjects, parameters of the decoder are commonly tuned using training data that include neural signals and corresponding overt arm movements. In the application of BMI to paralysis or amputation, arm movements are not feasible, and imagined movements create weaker, partially unrelated patterns of neural activity. BMI training must begin naive, without access to these prototypical methods for parameter initialization used in most laboratory BMI demonstrations. Naive adaptive BMI refer to a class of methods recently introduced to address this problem. We first identify the basic elements of existing approaches based on adaptive filtering and define a decoder, ReFIT-PPF to represent these existing approaches. We then present Joint RSE, a novel approach that logically extends prior approaches. Using recently developed human- and synthetic-subjects closed-loop BMI simulation platforms, we show that Joint RSE significantly outperforms ReFIT-PPF and nonadaptive (static) decoders. Control experiments demonstrate the critical role of jointly estimating neural parameters and user intent. In addition, we show that nonzero sensorimotor delay in the user significantly degrades ReFIT-PPF but not Joint RSE, owing to differences in the prior on intended velocity. Paradoxically, substantial differences in the nature of sensory feedback between these methods do not contribute to differences in performance between Joint RSE and ReFIT-PPF. Instead, BMI performance improvement is driven by machine learning, which outpaces rates of human learning in the human-subjects simulation platform. In this regime, nuances of error-related feedback to the human user are less relevant to rapid BMI mastery.},
}
@article {pmid23774163,
year = {2013},
author = {Dong, Q and Du, L and Zhuang, L and Li, R and Liu, Q and Wang, P},
title = {A novel bioelectronic nose based on brain-machine interface using implanted electrode recording in vivo in olfactory bulb.},
journal = {Biosensors &amp; bioelectronics},
volume = {49},
number = {},
pages = {263-269},
doi = {10.1016/j.bios.2013.05.035},
pmid = {23774163},
issn = {1873-4235},
mesh = {Animals ; Biosensing Techniques/*instrumentation ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Limit of Detection ; Male ; Neurons/physiology ; Nose/physiology ; Odorants/*analysis ; Olfactory Bulb/cytology/*physiology ; Olfactory Perception ; Rats ; Rats, Sprague-Dawley ; },
abstract = {The mammalian olfactory system has merits of higher sensitivity, selectivity and faster response than current electronic nose system based on chemical sensor array. It is advanced and feasible to detect and discriminate odors by mammalian olfactory system. The purpose of this study is to develop a novel bioelectronic nose based on the brain-machine interface (BMI) technology for odor detection by in vivo electrophysiological measurements of olfactory bulb. In this work, extracellular potentials of mitral/tufted (M/T) cells in olfactory bulb (OB) were recorded by implanted 16-channel microwire electrode arrays. The odor-evoked response signals were analyzed. We found that neural activities of different neurons showed visible different firing patterns both in temporal features and rate features when stimulated by different small molecular odorants. The detection low limit is below 1 ppm for some specific odors. Odors were classified by an algorithm based on population vector similarity and support vector machine (SVM). The results suggested that the novel bioelectonic nose was sensitive to odorant stimuli. The best classifying accuracy was up to 95%. With the development of the BMI and olfactory decoding methods, we believe that this system will represent emerging and promising platforms for wide applications in medical diagnosis and security fields.},
}
@article {pmid23774120,
year = {2014},
author = {Tankus, A and Fried, I and Shoham, S},
title = {Cognitive-motor brain-machine interfaces.},
journal = {Journal of physiology, Paris},
volume = {108},
number = {1},
pages = {38-44},
pmid = {23774120},
issn = {1769-7115},
support = {211055/ERC_/European Research Council/International ; R01 NS084017/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Auditory Perception/physiology ; *Brain-Computer Interfaces/psychology ; Cognition/*physiology ; Humans ; Psychomotor Performance/*physiology ; Speech/physiology ; Visual Perception/physiology ; },
abstract = {Brain-machine interfaces (BMIs) open new horizons for the treatment of paralyzed persons, giving hope for the artificial restoration of lost physiological functions. Whereas BMI development has mainly focused on motor rehabilitation, recent studies have suggested that higher cognitive functions can also be deciphered from brain activity, bypassing low level planning and execution functions, and replacing them by computer-controlled effectors. This review describes the new generation of cognitive-motor BMIs, focusing on three BMI types: By outlining recent progress in developing these BMI types, we aim to provide a unified view of contemporary research towards the replacement of behavioral outputs of cognitive processes by direct interaction with the brain.},
}
@article {pmid23774089,
year = {2013},
author = {Tan, BK and Pothiawala, S and Ong, ME},
title = {Emergency thoracotomy: a review of its role in severe chest trauma.},
journal = {Minerva chirurgica},
volume = {68},
number = {3},
pages = {241-250},
pmid = {23774089},
issn = {0026-4733},
mesh = {*Emergency Treatment ; Humans ; Injury Severity Score ; Thoracic Injuries/*surgery ; *Thoracotomy ; Wounds, Nonpenetrating/*surgery ; Wounds, Penetrating/*surgery ; },
abstract = {AIM: We aim to assess which group of patients with blunt or penetrating chest trauma will benefit from emergency thoracotomy (ET) and have a good functional outcome.<br><br>METHODS: A literature search was conducted using PUBMED, EMBASE, Science Direct and Google Scholar. The search terms used were: emergency thoracotomy; penetrating chest injury; blunt chest injury. The inclusion criteria were human trials, studies and case series on emergency or emergency department thoracotomy in adults and all papers that compared outcomes between patients with penetrating and blunt chest injury. All meta analysis, case reports, thoracotomies in children and the pediatric population, thoracotomies that were not performed in an emergency setting and papers that did not include data on both penetrating and blunt injuries were excluded.<br><br>RESULTS: A total of 20 papers met the above criteria. More ETs were performed in patients with penetrating chest injury (PCI); range 3 to 670, mean 122 compared to blunt chest injury (BCI); range 5 to 319, mean of 51. Survival of the patients who underwent ET seemed to be higher in the PCI group; range 2.7% to 37.5%, mean 17.0% compared to BCI group; range 0.6% to 60%, mean of 4.6%. Mean Survival rate was higher (70.9%) for stab wounds compared to gunshot wounds (29.2%). The mean percentage of neurologically intact survivors among PCI survivors 86% (164) were higher compared to the BCI group 12% (8).<br><br>CONCLUSION: Patients most likely to benefit from ET are those with penetrating chest injury, signs of life at scene or on arrival in the ED or pericardial tamponade. Hospitals should develop specific guidelines for emergency thoracotomy for patients with penetrating trauma, pericardial tamponade and witnessed cardiac arrest, as they are most likely to benefit from ET with improved chances of survival and good neurological outcome.},
}
@article {pmid23773798,
year = {2014},
author = {Poch, MA and Stegemann, AP and Rehman, S and Sharif, MA and Hussain, A and Consiglio, JD and Wilding, GE and Guru, KA},
title = {Short-term patient reported health-related quality of life (HRQL) outcomes after robot-assisted radical cystectomy (RARC).},
journal = {BJU international},
volume = {113},
number = {2},
pages = {260-265},
doi = {10.1111/bju.12162},
pmid = {23773798},
issn = {1464-410X},
mesh = {Aged ; Body Image ; Coitus ; *Cystectomy ; Defecation ; Female ; Health Status ; Humans ; Incidence ; Length of Stay/statistics &amp; numerical data ; Male ; Neoplasm Recurrence, Local/epidemiology ; Postoperative Complications/epidemiology/physiopathology/psychology/*surgery ; Prospective Studies ; Quality Assurance, Health Care ; *Quality of Life ; Recovery of Function ; Risk Factors ; *Robotics ; *Surgery, Computer-Assisted ; Surveys and Questionnaires ; Time Factors ; Treatment Outcome ; Urinary Bladder Neoplasms/epidemiology/physiopathology/psychology/*surgery ; Urination ; },
abstract = {OBJECTIVE: To determine short-term health-related quality of life (HRQL) outcomes after robot-assisted radical cystectomy (RARC) using the Bladder Cancer Index (BCI) and European Organisation for Research and Treatment of Cancer (EORTC) Body Image Scale (BIS).<br><br>PATIENTS AND METHODS: All patients undergoing RARC were enrolled in a quality assurance database. The patients completed two validated questionnaires, BCI and BIS, preoperatively and at standardised postoperative intervals. The primary outcome measure was difference in interval and baseline BCI and BIS scores. Complications were identified and classified by Clavien grade.<br><br>RESULTS: In all, 43 patients completed pre- and postoperative questionnaires There was a decline in the urinary domain at 0-1 month after RARC (P&#x2009;=&#x2009;0.006), but this returned to baseline by 1-2 months. There was a decline in the bowel domain at 0-1 month (P&#x2009;<&#x2009;0.001) and 1-2 months (P&#x2009;=&#x2009;0.024) after RARC, but this returned to baseline by 2-4 months. The decline in BCI scores was greatest for the sexual function domain, but this returned to baseline by 16-24 months after RARC. Body image perception using BIS showed no significant change after RARC except at the 4-10 months period (P&#x2009;=&#x2009;0.018).<br><br>CONCLUSIONS: Based on BCI and BIS scores HRQL outcomes after RARC show recovery of urinary and bowel domains &#x2264;6 months. Longer follow-up with a larger cohort of patients will help refine HRQL outcomes.},
}
@article {pmid23773675,
year = {2013},
author = {Andersson, P and Ramsey, NF and Viergever, MA and Pluim, JP},
title = {7T fMRI reveals feasibility of covert visual attention-based brain-computer interfacing with signals obtained solely from cortical grey matter accessible by subdural surface electrodes.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {11},
pages = {2191-2197},
doi = {10.1016/j.clinph.2013.05.009},
pmid = {23773675},
issn = {1872-8952},
mesh = {Adult ; Attention/*classification/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electrodes, Implanted ; Electroencephalography ; Female ; Healthy Volunteers ; Humans ; *Magnetic Resonance Imaging ; Male ; Multivariate Analysis ; Myelin Sheath ; Signal Processing, Computer-Assisted ; Subdural Space ; Support Vector Machine ; Young Adult ; },
abstract = {OBJECTIVE: There is a growing interest in brain-computer interfaces (BCI) based on invasive technologies. fMRI is exceptionally suited for selecting implant sites since BOLD signals has been shown to correlate well spatially with electric potentials recorded from the brain surface. Previous studies show that it is possible to decode covertly directed visuospatial attention using fMRI. In the present study we increase the relevance of the fMRI analysis for surface-electrodes by only allowing voxels at the surface of the brain.<br><br>METHODS: We classify visuospatial attention directed to four different directions (left, right, up and down) using a support vector machine while enforcing several spatial restrictions on the voxels available for the classifier. All the spatial restrictions applied are based on how accessible the brain areas are for implanted surface electrodes.<br><br>RESULTS: The results show that fMRI signals from only the surface of the brain are sufficient for a good classification. Data also show that the topographical pattern is quite variable across subjects.<br><br>CONCLUSIONS: A good control of BCI systems based on signals from surface electrodes can be achieved using visuospatial attention. Due to the large spatial variations in brain topography, individual mapping with fMRI to locate the optimal electrode implant sites is essential.<br><br>SIGNIFICANCE: Visuospatial attention promises to be an effective target for implanted BCI systems.},
}
@article {pmid23769960,
year = {2013},
author = {Schendel, AA and Thongpang, S and Brodnick, SK and Richner, TJ and Lindevig, BD and Krugner-Higby, L and Williams, JC},
title = {A cranial window imaging method for monitoring vascular growth around chronically implanted micro-ECoG devices.},
journal = {Journal of neuroscience methods},
volume = {218},
number = {1},
pages = {121-130},
pmid = {23769960},
issn = {1872-678X},
support = {1R01EB009103-01/EB/NIBIB NIH HHS/United States ; 1T32EB011434-01A1/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; T32 EB011434/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; 2R01EB000856-06/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Craniotomy/*methods ; Electrodes, Implanted/*adverse effects ; Electroencephalography/instrumentation ; Foreign-Body Reaction/*diagnosis ; Male ; Microelectrodes/*adverse effects ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Implantable neural micro-electrode arrays have the potential to restore lost sensory or motor function to many different areas of the body. However, the invasiveness of these implants often results in scar tissue formation, which can have detrimental effects on recorded signal quality and longevity. Traditional histological techniques can be employed to study the tissue reaction to implanted micro-electrode arrays, but these techniques require removal of the brain from the skull, often causing damage to the meninges and cortical surface. This is especially unfavorable when studying the tissue response to electrode arrays such as the micro-electrocorticography (micro-ECoG) device, which sits on the surface of the cerebral cortex. In order to better understand the biological changes occurring around these types of devices, a cranial window implantation scheme has been developed, through which the tissue response can be studied in vivo over the entire implantation period. Rats were implanted with epidural micro-ECoG arrays, over which glass coverslips were placed and sealed to the skull, creating cranial windows. Vascular growth around the devices was monitored for one month after implantation. It was found that blood vessels grew through holes in the micro-ECoG substrate, spreading over the top of the device. Micro-hematomas were observed at varying time points after device implantation in every animal, and tissue growth between the micro-ECoG array and the window occurred in several cases. Use of the cranial window imaging technique with these devices enabled the observation of tissue changes that would normally go unnoticed with a standard device implantation scheme.},
}
@article {pmid23768011,
year = {2013},
author = {Barbara, M and Perotti, M and Gioia, B and Volpini, L and Monini, S},
title = {Transcutaneous bone-conduction hearing device: audiological and surgical aspects in a first series of patients with mixed hearing loss.},
journal = {Acta oto-laryngologica},
volume = {133},
number = {10},
pages = {1058-1064},
doi = {10.3109/00016489.2013.799293},
pmid = {23768011},
issn = {1651-2251},
mesh = {Audiometry, Pure-Tone ; Auditory Threshold/*physiology ; Bone Conduction/*physiology ; *Electrodes, Implanted ; Female ; *Hearing Aids ; Hearing Loss, Mixed Conductive-Sensorineural/physiopathology/*surgery ; Humans ; Male ; Middle Aged ; Otologic Surgical Procedures/*methods ; Round Window, Ear/surgery ; Speech Perception/*physiology ; Treatment Outcome ; },
abstract = {CONCLUSIONS: The Bonebridge(®) (BB) transcutaneous bone conductive implant (BCI) may overcome some of the issues related to a percutaneous BCI, such as management of the external screw, delayed activation or possible skin complications. Moreover, it has been shown to enable a functional outcome similar to percutaneous BCI in both conductive and mixed types of hearing loss.<br><br>OBJECTIVES: To obtain clinical data from a preliminary series of patients implanted with a new transcutaneous BCI.<br><br>METHODS: Four subjects affected by conductive/mixed hearing loss underwent implantation of the BB by two approaches: the transmastoid, presigmoid approach and the retrosigmoid approach. Soundfield thresholds were assessed with warble tones in a soundproof audiometric booth, and word recognition scores (WRSs) as speech reception thresholds (SRTs) were used to compare the unaided versus the post-implantation condition.<br><br>RESULTS: The surgical procedure was completed in all cases, with only minor intraoperative divergence from the CT-based planning and no postoperative complications. The average improvement of the SRT in quiet with the BB in comparison to the unaided condition was 36.25 dB. All the implanted subjects reached SRT values below 65 dB, indicating a better understanding in quiet, with 100% word recognition.},
}
@article {pmid23762253,
year = {2013},
author = {Yao, L and Meng, J and Zhang, D and Sheng, X and Zhu, X},
title = {Selective sensation based brain-computer interface via mechanical vibrotactile stimulation.},
journal = {PloS one},
volume = {8},
number = {6},
pages = {e64784},
pmid = {23762253},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Male ; Sensation/*physiology ; Touch Perception/*physiology ; Vibration ; Wrist/physiology ; },
abstract = {In this work, mechanical vibrotactile stimulation was applied to subjects' left and right wrist skins with equal intensity, and a selective sensation perception task was performed to achieve two types of selections similar to motor imagery Brain-Computer Interface. The proposed system was based on event-related desynchronization/synchronization (ERD/ERS), which had a correlation with processing of afferent inflow in human somatosensory system, and attentional effect which modulated the ERD/ERS. The experiments were carried out on nine subjects (without experience in selective sensation), and six of them showed a discrimination accuracy above 80%, three of them above 95%. Comparative experiments with motor imagery (with and without presence of stimulation) were also carried out, which further showed the feasibility of selective sensation as an alternative BCI task complementary to motor imagery. Specifically there was significant improvement ([Formula: see text]) from near 65% in motor imagery (with and without presence of stimulation) to above 80% in selective sensation on some subjects. The proposed BCI modality might well cooperate with existing BCI modalities in the literature in enlarging the widespread usage of BCI system.},
}
@article {pmid23761697,
year = {2013},
author = {Takemi, M and Masakado, Y and Liu, M and Ushiba, J},
title = {Event-related desynchronization reflects downregulation of intracortical inhibition in human primary motor cortex.},
journal = {Journal of neurophysiology},
volume = {110},
number = {5},
pages = {1158-1166},
doi = {10.1152/jn.01092.2012},
pmid = {23761697},
issn = {1522-1598},
mesh = {*Cortical Synchronization ; Electroencephalography ; Evoked Potentials, Motor ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; *Movement ; Muscle, Skeletal/innervation/*physiology ; *Neural Inhibition ; Transcranial Magnetic Stimulation ; Wrist/physiology ; Young Adult ; },
abstract = {There is increasing interest in electroencephalogram (EEG)-based brain-computer interface (BCI) as a tool for rehabilitation of upper limb motor functions in hemiplegic stroke patients. This type of BCI often exploits mu and beta oscillations in EEG recorded over the sensorimotor areas, and their event-related desynchronization (ERD) following motor imagery is believed to represent increased sensorimotor cortex excitability. However, it remains unclear whether the sensorimotor cortex excitability is actually correlated with ERD. Thus we assessed the association of ERD with primary motor cortex (M1) excitability during motor imagery of right wrist movement. M1 excitability was tested by motor evoked potentials (MEPs), short-interval intracortical inhibition (SICI), and intracortical facilitation (ICF) with transcranial magnetic stimulation (TMS). Twenty healthy participants were recruited. The participants performed 7 s of rest followed by 5 s of motor imagery and received online visual feedback of the ERD magnitude of the contralateral hand M1 while performing the motor imagery task. TMS was applied to the right hand M1 when ERD exceeded predetermined thresholds during motor imagery. MEP amplitudes, SICI, and ICF were recorded from the agonist muscle of the imagined hand movement. Results showed that the large ERD during wrist motor imagery was associated with significantly increased MEP amplitudes and reduced SICI but no significant changes in ICF. Thus ERD magnitude during wrist motor imagery represents M1 excitability. This study provides electrophysiological evidence that a motor imagery task involving ERD may induce changes in corticospinal excitability similar to changes accompanying actual movements.},
}
@article {pmid23760996,
year = {2013},
author = {Collinger, JL and Boninger, ML and Bruns, TM and Curley, K and Wang, W and Weber, DJ},
title = {Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury.},
journal = {Journal of rehabilitation research and development},
volume = {50},
number = {2},
pages = {145-160},
pmid = {23760996},
issn = {1938-1352},
support = {F32 NS074565/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; UL1 TR000005/TR/NCATS NIH HHS/United States ; },
mesh = {*Activities of Daily Living ; Arm/physiopathology ; *Brain-Computer Interfaces ; Equipment Design ; Fecal Incontinence/etiology/rehabilitation ; Female ; Hand/physiopathology ; Health Knowledge, Attitudes, Practice ; Humans ; Male ; Needs Assessment ; Paraplegia/etiology/rehabilitation ; Quadriplegia/etiology/rehabilitation ; Quality of Life/psychology ; *Self-Help Devices ; Spinal Cord Injuries/complications/*rehabilitation ; Urinary Incontinence/etiology/rehabilitation ; *Veterans/psychology ; Walking ; },
abstract = {Spinal cord injury (SCI) often affects a person's ability to perform critical activities of daily living and can negatively affect his or her quality of life. Assistive technology aims to bridge this gap in order to augment function and increase independence. It is critical to involve consumers in the design and evaluation process as new technologies such as brain-computer interfaces (BCIs) are developed. In a survey study of 57 veterans with SCI participating in the 2010 National Veterans Wheelchair Games, we found that restoration of bladder and bowel control, walking, and arm and hand function (tetraplegia only) were all high priorities for improving quality of life. Many of the participants had not used or heard of some currently available technologies designed to improve function or the ability to interact with their environment. The majority of participants in this study were interested in using a BCI, particularly for controlling functional electrical stimulation to restore lost function. Independent operation was considered to be the most important design criteria. Interestingly, many participants reported that they would consider surgery to implant a BCI even though noninvasiveness was a high-priority design requirement. This survey demonstrates the interest of individuals with SCI in receiving and contributing to the design of BCIs.},
}
@article {pmid23760206,
year = {2013},
author = {Elsen, S and Collin-Faure, V and Gidrol, X and Lemercier, C},
title = {The opportunistic pathogen Pseudomonas aeruginosa activates the DNA double-strand break signaling and repair pathway in infected cells.},
journal = {Cellular and molecular life sciences : CMLS},
volume = {70},
number = {22},
pages = {4385-4397},
pmid = {23760206},
issn = {1420-9071},
mesh = {ADP Ribose Transferases/*metabolism ; Ataxia Telangiectasia Mutated Proteins/metabolism ; Bacterial Proteins/metabolism ; Bacterial Toxins/*metabolism ; Cell Line, Tumor ; Chromosomal Instability ; *DNA Breaks, Double-Stranded ; DNA Repair ; HL-60 Cells ; Histones/metabolism ; Humans ; Intracellular Signaling Peptides and Proteins/metabolism ; JNK Mitogen-Activated Protein Kinases/metabolism ; Phosphorylation ; Pseudomonas aeruginosa/*metabolism ; Signal Transduction ; Tumor Suppressor p53-Binding Protein 1 ; },
abstract = {Highly hazardous DNA double-strand breaks can be induced in eukaryotic cells by a number of agents including pathogenic bacterial strains. We have investigated the genotoxic potential of Pseudomonas aeruginosa, an opportunistic pathogen causing devastating nosocomial infections in cystic fibrosis or immunocompromised patients. Our data revealed that infection of immune or epithelial cells by P. aeruginosa triggered DNA strand breaks and phosphorylation of histone H2AX (γH2AX), a marker of DNA double-strand breaks. Moreover, it induced formation of discrete nuclear repair foci similar to gamma-irradiation-induced foci, and containing γH2AX and 53BP1, an adaptor protein mediating the DNA-damage response pathway. Gene deletion, mutagenesis, and complementation in P. aeruginosa identified ExoS bacterial toxin as the major factor involved in γH2AX induction. Chemical inhibition of several kinases known to phosphorylate H2AX demonstrated that Ataxia Telangiectasia Mutated (ATM) was the principal kinase in P. aeruginosa-induced H2AX phosphorylation. Finally, infection led to ATM kinase activation by an auto-phosphorylation mechanism. Together, these data show for the first time that infection by P. aeruginosa activates the DNA double-strand break repair machinery of the host cells. This novel information sheds new light on the consequences of P. aeruginosa infection in mammalian cells. As pathogenic Escherichia coli or carcinogenic Helicobacter pylori can alter genome integrity through DNA double-strand breaks, leading to chromosomal instability and eventually cancer, our findings highlight possible new routes for further investigations of P. aeruginosa in cancer biology and they identify ATM as a potential target molecule for drug design.},
}
@article {pmid23757379,
year = {2013},
author = {Hashimoto, Y and Ushiba, J},
title = {EEG-based classification of imaginary left and right foot movements using beta rebound.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {11},
pages = {2153-2160},
doi = {10.1016/j.clinph.2013.05.006},
pmid = {23757379},
issn = {1872-8952},
mesh = {Adult ; Beta Rhythm ; Brain/physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Electromyography ; Evoked Potentials/physiology ; Female ; Foot/physiology ; Functional Laterality/*physiology ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Male ; Movement/*physiology ; Neuromuscular Monitoring ; Neurophysiological Monitoring ; ROC Curve ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm.<br><br>METHODS: We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated.<br><br>RESULTS: Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis).<br><br>CONCLUSION: These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery.<br><br>SIGNIFICANCE: By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot.},
}
@article {pmid23757354,
year = {2013},
author = {Zhang, Y and Schnabel, CA and Schroeder, BE and Jerevall, PL and Jankowitz, RC and Fornander, T and Stål, O and Brufsky, AM and Sgroi, D and Erlander, MG},
title = {Breast cancer index identifies early-stage estrogen receptor-positive breast cancer patients at risk for early- and late-distant recurrence.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {19},
number = {15},
pages = {4196-4205},
doi = {10.1158/1078-0432.CCR-13-0804},
pmid = {23757354},
issn = {1557-3265},
support = {P30 CA047904/CA/NCI NIH HHS/United States ; },
mesh = {Adult ; Antineoplastic Agents, Hormonal/administration &amp; dosage ; Breast Neoplasms/*drug therapy/genetics/*pathology ; Female ; Humans ; Middle Aged ; Neoplasm Recurrence, Local/drug therapy/genetics/*pathology ; Neoplasm Staging ; *Prognosis ; Prospective Studies ; Randomized Controlled Trials as Topic ; Receptors, Estrogen/genetics ; Retrospective Studies ; Survival Rate ; Tamoxifen/*administration &amp; dosage ; },
abstract = {PURPOSE: Residual risk of relapse remains a substantial concern for patients with hormone receptor-positive breast cancer, with approximately half of all disease recurrences occurring after five years of adjuvant antiestrogen therapy.<br><br>EXPERIMENTAL DESIGN: The objective of this study was to examine the prognostic performance of an optimized model of Breast Cancer Index (BCI), an algorithmic gene expression-based signature, for prediction of early (0-5 years) and late (>5 years) risk of distant recurrence in patients with estrogen receptor-positive (ER(+)), lymph node-negative (LN(-)) tumors. The BCI model was validated by retrospective analyses of tumor samples from tamoxifen-treated patients from a randomized prospective trial (Stockholm TAM, n = 317) and a multi-institutional cohort (n = 358).<br><br>RESULTS: Within the Stockholm TAM cohort, BCI risk groups stratified the majority (&#x223c;65%) of patients as low risk with less than 3% distant recurrence rate for 0 to 5 years and 5 to 10 years. In the multi-institutional cohort, which had larger tumors, 55% of patients were classified as BCI low risk with less than 5% distant recurrence rate for 0 to 5 years and 5 to 10 years. For both cohorts, continuous BCI was the most significant prognostic factor beyond standard clinicopathologic factors for 0 to 5 years and more than five years.<br><br>CONCLUSIONS: The prognostic sustainability of BCI to assess early- and late-distant recurrence risk at diagnosis has clinical use for decisions of chemotherapy at diagnosis and for decisions for extended adjuvant endocrine therapy beyond five years.},
}
@article {pmid23754426,
year = {2013},
author = {Wander, JD and Blakely, T and Miller, KJ and Weaver, KE and Johnson, LA and Olson, JD and Fetz, EE and Rao, RP and Ojemann, JG},
title = {Distributed cortical adaptation during learning of a brain-computer interface task.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {110},
number = {26},
pages = {10818-10823},
pmid = {23754426},
issn = {1091-6490},
support = {R90 DA033461/DA/NIDA NIH HHS/United States ; 2K12HD001097-16/HD/NICHD NIH HHS/United States ; T90 DA023436-02/DA/NIDA NIH HHS/United States ; K12 HD001097/HD/NICHD NIH HHS/United States ; K01 MH086118/MH/NIMH NIH HHS/United States ; T90 DA032436/DA/NIDA NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; NS065186-01/NS/NINDS NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological ; Adolescent ; Adult ; Brain-Computer Interfaces/*psychology ; Cerebral Cortex/anatomy &amp; histology/*physiology ; Electrophysiological Phenomena ; Female ; Humans ; Learning/*physiology ; Male ; Nerve Net/anatomy &amp; histology/physiology ; Young Adult ; },
abstract = {The majority of subjects who attempt to learn control of a brain-computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were implanted with electrocorticography (ECoG) electrodes and had multiple opportunities to practice a 1D BCI task. As subjects became proficient, strong initial task-related activation was followed by lessening of activation in prefrontal cortex, premotor cortex, and posterior parietal cortex, areas that have previously been implicated in the cognitive phase of motor sequence learning and abstract task learning. These results demonstrate that, although the use of a BCI only requires modulation of a local population of neurons, a distributed network of cortical areas is involved in the acquisition of BCI proficiency.},
}
@article {pmid23751454,
year = {2013},
author = {Billinger, M and Brunner, C and Müller-Putz, GR},
title = {Single-trial connectivity estimation for classification of motor imagery data.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046006},
doi = {10.1088/1741-2560/10/4/046006},
pmid = {23751454},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Many brain-computer interfaces (BCIs) use band power (BP) changes in the electroencephalogram to distinguish between different motor imagery (MI) patterns. Most current approaches do not take connectivity of separated brain areas into account. Our objective is to introduce single-trial connectivity features and apply these features to BCI data.<br><br>APPROACH: We introduce a procedure for extracting single-trial connectivity estimates from vector autoregressive (VAR) models of independent components in a BCI setting.<br><br>MAIN RESULTS: In a simulated BCI, we demonstrate that the directed transfer function (DTF) with full-frequency normalization and the direct DTF give classification results similar to BP, while other measures such as the partial directed coherence perform significantly worse.<br><br>SIGNIFICANCE: We show that single-trial MI classification is possible with connectivity measures extracted from VAR models, and that a BCI could potentially utilize such measures.},
}
@article {pmid23746911,
year = {2013},
author = {Minati, L and Cercignani, M and Chan, D},
title = {Rapid geodesic mapping of brain functional connectivity: implementation of a dedicated co-processor in a field-programmable gate array (FPGA) and application to resting state functional MRI.},
journal = {Medical engineering &amp; physics},
volume = {35},
number = {10},
pages = {1532-1539},
doi = {10.1016/j.medengphy.2013.04.014},
pmid = {23746911},
issn = {1873-4030},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*instrumentation ; *Computers ; Female ; Humans ; *Magnetic Resonance Imaging ; Nerve Net/*physiology ; Rest/*physiology ; *Software ; Time Factors ; },
abstract = {Graph theory-based analyses of brain network topology can be used to model the spatiotemporal correlations in neural activity detected through fMRI, and such approaches have wide-ranging potential, from detection of alterations in preclinical Alzheimer's disease through to command identification in brain-machine interfaces. However, due to prohibitive computational costs, graph-based analyses to date have principally focused on measuring connection density rather than mapping the topological architecture in full by exhaustive shortest-path determination. This paper outlines a solution to this problem through parallel implementation of Dijkstra's algorithm in programmable logic. The processor design is optimized for large, sparse graphs and provided in full as synthesizable VHDL code. An acceleration factor between 15 and 18 is obtained on a representative resting-state fMRI dataset, and maps of Euclidean path length reveal the anticipated heterogeneous cortical involvement in long-range integrative processing. These results enable high-resolution geodesic connectivity mapping for resting-state fMRI in patient populations and real-time geodesic mapping to support identification of imagined actions for fMRI-based brain-machine interfaces.},
}
@article {pmid23746289,
year = {2013},
author = {Lopez-Gordo, MA and Pelayo, F},
title = {A binary phase-shift keying receiver for the detection of attention to human speech.},
journal = {International journal of neural systems},
volume = {23},
number = {4},
pages = {1350016},
doi = {10.1142/S0129065713500160},
pmid = {23746289},
issn = {1793-6462},
mesh = {Acoustic Stimulation ; Attention/*physiology ; Auditory Perception/physiology ; Brain-Computer Interfaces ; Dichotic Listening Tests ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; *Pattern Recognition, Automated ; *Speech ; },
abstract = {Synthetic sounds, tone-beeps, vowels or syllables are typically used in the assessment of attention to auditory stimuli because they evoke a set of well-known event-related potentials, whose characteristics can be statistically contrasted. Such approach rules out the use of stimuli with non-predictable response, such as human speech. In this study we present a procedure based on the robust binary phase-shift keying (BPSK) receiver that permits the real-time detection of selective attention to human speeches in dichotic listening tasks. The goal was achieved by tagging the speeches with two barely-audible tags whose joined EEG response constitutes a reliable BPSK constellation, which can be detected by means of a BPSK receiver. The results confirmed the expected generation of the BPSK constellation by the human auditory system. Also, the bit-error rate and the information transmission rate achieved in the detection of attention fairly followed the expected curves and equations of the standard BPSK receiver. Actually, it was possible to detect attention as well as the estimation a priori of its accuracy based on the signal-to-noise ratio of the BPSK signals. This procedure, which permits the detection of the attention to human speeches, can be of interest for new potential applications, such as brain-computer interfaces, clinical assessment of the attention in real time or for entertainment.},
}
@article {pmid23746288,
year = {2013},
author = {Rodríguez-Bermúdez, G and García-Laencina, PJ and Roca-Dorda, J},
title = {Efficient automatic selection and combination of EEG features in least squares classifiers for motor imagery brain-computer interfaces.},
journal = {International journal of neural systems},
volume = {23},
number = {4},
pages = {1350015},
doi = {10.1142/S0129065713500159},
pmid = {23746288},
issn = {1793-6462},
mesh = {Algorithms ; Automation ; Brain/*physiology ; *Brain-Computer Interfaces ; *Electroencephalography ; Humans ; Image Processing, Computer-Assisted ; Imagination/*physiology ; Least-Squares Analysis ; Regression Analysis ; Time Factors ; *User-Computer Interface ; },
abstract = {Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain-computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with low computational complexity. Features are computed in different time segments using three widely used methods for motor imagery tasks and, then, they are concatenated or averaged in order to take into account the time course variability of the EEG signals. Once EEG features have been extracted, proposed framework comprises two stages. The first stage entails feature ranking and, in this work, two different procedures have been considered, the least angle regression (LARS) and the Wilcoxon rank sum test, to compare the performance of each one. The second stage selects the most relevant features using an efficient leave-one-out (LOO) estimation based on the Allen's PRESS statistic. Experimental comparisons with the state-of-the-art BCI methods shows that this approach gives better results than current state-of-the-art approaches in terms of recognition rates and computational requirements and, also with respect to the first ranking stage, it is confirmed that the LARS algorithm provides better results than the Wilcoxon rank sum test for these experiments.},
}
@article {pmid23744702,
year = {2013},
author = {Shyu, KK and Chiu, YJ and Lee, PL and Liang, JM and Peng, SH},
title = {Adaptive SSVEP-based BCI system with frequency and pulse duty-cycle stimuli tuning design.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {5},
pages = {697-703},
doi = {10.1109/TNSRE.2013.2265308},
pmid = {23744702},
issn = {1558-0210},
mesh = {Analog-Digital Conversion ; *Brain-Computer Interfaces ; Electric Stimulation ; Electrodes ; Electroencephalography ; Equipment Design ; Evoked Potentials, Somatosensory/*physiology ; Humans ; Photic Stimulation ; Signal-To-Noise Ratio ; Somatosensory Cortex/physiology ; },
abstract = {This study aims to design a steady state visual evoked potentials (SSVEP) based brain-computer interface (BCI) system with only three electrodes. It is known that low frequency flickering induces more intensive SSVEP, but might cause users feel uncomfortable and easily tired. Therefore, this paper proposes a novel middle/high frequency flickering stimulus. However, users show different SSVEP responses when gazing at the same stimuli. It is improper to design fixed frequency flickering stimuli for all users. This study firstly proposes a strategy to adjust the stimuli frequency for each user that could cause better SSVEP. Moreover, to further enhance the SSVEP, this study incorporates flickering duty-cycle for stimuli design, which has been discussed less for SSVEP-based BCI systems. The proposed system consists of two modes, flicker frequency/duty-cycle selection mode and application mode. The flicker frequency/duty-cycle selection mode obtains two best frequencies between 24 and 36 Hz with their related optimal duty-cycle. Then the system goes into the application mode to control the devices. A new fact that has been found is that the optimal flicker frequency and duty-cycle do not vary with time. It means once the optical flicker frequency and duty-cycle is determined the first time, flicker frequency/duty-cycle selection mode does not need to operate the next time. Furthermore, the phase coding technology is used to extend the one command/one frequency to multi command/one frequency. Experimental results show the proposed system has good performance with average accuracy 95% and average command transfer interval 4.4925 s per command.},
}
@article {pmid23744624,
year = {2013},
author = {Hwang, EJ and Andersen, RA},
title = {The utility of multichannel local field potentials for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046005},
pmid = {23744624},
issn = {1741-2552},
support = {K99 NS062894/NS/NINDS NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; EY013337/EY/NEI NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Macaca mulatta ; Male ; *Man-Machine Systems ; Microarray Analysis ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Local field potentials (LFPs) that carry information about the subject's motor intention have the potential to serve as a complement or alternative to spike signals for brain-machine interfaces (BMIs). The goal of this study is to assess the utility of LFPs for BMIs by characterizing the largely unknown information coding properties of multichannel LFPs.<br><br>APPROACH: Two monkeys were implanted, each with a 16-channel electrode array, in the parietal reach region where both LFPs and spikes are known to encode the subject's intended reach target. We examined how multichannel LFPs recorded during a reach task jointly carry reach target information, and compared the LFP performance to simultaneously recorded multichannel spikes.<br><br>MAIN RESULTS: LFPs yielded a higher number of channels that were informative about reach targets than spikes. Single channel LFPs provided more accurate target information than single channel spikes. However, LFPs showed significantly larger signal and noise correlations across channels than spikes. Reach target decoders performed worse when using multichannel LFPs than multichannel spikes. The underperformance of multichannel LFPs was mostly due to their larger noise correlation because noise de-correlated multichannel LFPs produced a decoding accuracy comparable to multichannel spikes. Despite the high noise correlation, decoders using LFPs in addition to spikes outperformed decoders using only spikes.<br><br>SIGNIFICANCE: These results demonstrate that multichannel LFPs could effectively complement spikes for BMI applications by yielding more informative channels. The utility of multichannel LFPs may be further augmented if their high noise correlation can be taken into account by decoders.},
}
@article {pmid23739918,
year = {2013},
author = {Kübler, A and Holz, E and Botrel, L},
title = {Addendum.},
journal = {Brain : a journal of neurology},
volume = {136},
number = {Pt 6},
pages = {2005-2006},
doi = {10.1093/brain/awt156},
pmid = {23739918},
issn = {1460-2156},
mesh = {Aged ; Art Therapy/instrumentation/*methods ; *Brain-Computer Interfaces ; Female ; Humans ; Quadriplegia/*diagnosis/*therapy ; },
}
@article {pmid23739780,
year = {2013},
author = {Kimura, Y and Tanaka, T and Higashi, H and Morikawa, N},
title = {SSVEP-based brain-computer interfaces using FSK-modulated visual stimuli.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {10},
pages = {2831-2838},
doi = {10.1109/TBME.2013.2265260},
pmid = {23739780},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; *Signal Processing, Computer-Assisted ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words consisting of binary digits, to which visual stimuli with distinct frequencies are assigned. Frequencies of SSVEP are recognized to detect bits, and a command to be executed is determined from the sequence of detected bits. Experimental results show that the proposed paradigm achieves a reliable BCI with higher accuracies and balanced command executing probabilities.},
}
@article {pmid23739779,
year = {2013},
author = {Xu, Y and Nakajima, Y},
title = {A two-level predictive event-related potential-based brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {10},
pages = {2839-2847},
doi = {10.1109/TBME.2013.2265103},
pmid = {23739779},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; In Vitro Techniques ; Male ; Pattern Recognition, Automated/*methods ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Increasing the freedom of communication using conventional row/column (RC) P300 paradigm by naive way (increasing matrix size) may deteriorate inherent distraction effect and interaction speed. In this paper, we propose a two-level predictive (TLP) paradigm by integrating a 3×3 two-level matrix paradigm with a statistical language model. The TLP paradigm is evaluated using offline and online data from ten healthy subjects. Significantly larger event-related potentials (ERPs) are evoked by the TLP paradigm compared with the classical 6×6 RC. During an online task (correctly spell an English sentence with 57 characters), accuracy and information transfer rate for the TLP are increased by 14.45% and 29.29%, respectively, when compared with the 6×6 RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an 8×8 RC (naive extension of the 6×6 RC) consumed 19.18% more time than the classical 6×6 RC. Furthermore, the statistical language model is also exploited to improve classification accuracy in a Bayesian approach. The proposed Bayesian fusion method is tested offline on data from the online spelling tasks. The results show its potential improvement on single-trial ERP classification.},
}
@article {pmid23735712,
year = {2013},
author = {LaFleur, K and Cassady, K and Doud, A and Shades, K and Rogin, E and He, B},
title = {Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046003},
pmid = {23735712},
issn = {1741-2552},
support = {P30 EY011374/EY/NEI NIH HHS/United States ; R01 EB006433/EB/NIBIB NIH HHS/United States ; EB006433/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aircraft/*instrumentation ; Algorithms ; Biofeedback, Psychology/*instrumentation/physiology ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *Man-Machine Systems ; Motor Cortex/*physiology ; Movement/*physiology ; Task Performance and Analysis ; Young Adult ; },
abstract = {OBJECTIVE: At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task.<br><br>APPROACH: Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone.<br><br>MAIN RESULTS: Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s(-1).<br><br>SIGNIFICANCE: Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.},
}
@article {pmid25206446,
year = {2013},
author = {Rashed-Al-Mahfuz, M and Islam, MR and Hirose, K and Molla, MK},
title = {Artifact suppression and analysis of brain activities with electroencephalography signals.},
journal = {Neural regeneration research},
volume = {8},
number = {16},
pages = {1500-1513},
pmid = {25206446},
issn = {1673-5374},
abstract = {Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.},
}
@article {pmid23730352,
year = {2012},
author = {Claussen, JC and Hofmann, UG},
title = {Sleep, neuroengineering and dynamics.},
journal = {Cognitive neurodynamics},
volume = {6},
number = {3},
pages = {211-214},
pmid = {23730352},
issn = {1871-4080},
abstract = {Modeling of consciousness-related phenomena and neuroengineering are fields that are rapidly growing together. We review recent approaches and developments and point out some promising directions of future research: Understanding the dynamics of consciousness states and associated oscillations, pathological oscillations as well as their treatment by stimulation, neuroprosthetics and brain-computer-interface approaches, and stimulation approaches that probe, influence and strengthen memory consolidation. In all these fields, computational models connect theory, neurophysiology and neuroengineering research and pave a way towards medical applications.},
}
@article {pmid23727415,
year = {2013},
author = {Nitti, VW and Rosenberg, S and Mitcheson, DH and He, W and Fakhoury, A and Martin, NE},
title = {Urodynamics and safety of the β3-adrenoceptor agonist mirabegron in males with lower urinary tract symptoms and bladder outlet obstruction.},
journal = {The Journal of urology},
volume = {190},
number = {4},
pages = {1320-1327},
doi = {10.1016/j.juro.2013.05.062},
pmid = {23727415},
issn = {1527-3792},
mesh = {Acetanilides/adverse effects/*therapeutic use ; Adrenergic beta-3 Receptor Agonists/adverse effects/*therapeutic use ; Double-Blind Method ; Humans ; Lower Urinary Tract Symptoms/*drug therapy/*physiopathology ; Male ; Middle Aged ; Thiazoles/adverse effects/*therapeutic use ; Urinary Bladder Neck Obstruction/*drug therapy/*physiopathology ; *Urodynamics ; },
abstract = {PURPOSE: Bladder outlet obstruction often presents as storage and voiding symptoms. We investigated urodynamic parameters in men with lower urinary tract symptoms and bladder outlet obstruction treated with the β3 agonist mirabegron, a new therapy for overactive bladder symptoms.<br><br>MATERIALS AND METHODS: A total of 200 men 45 years old or older with lower urinary tract symptoms and bladder outlet obstruction were randomized to receive once daily mirabegron 50 mg (70) or 100 mg (65), or placebo (65) for 12 weeks. The primary urodynamic parameters assessed were change from baseline to end of treatment in maximum urinary flow and detrusor pressure at maximum urinary flow (noninferiority margins -3 ml per second and 15 cm H2O, respectively). We evaluated adverse events and vital signs.<br><br>RESULTS: Treatment with mirabegron 50 and 100 mg was noninferior to placebo based on the lower and upper limits of the 95% CI, respectively, for maximum urinary flow and detrusor pressure at maximum urinary flow. The adjusted mean difference vs placebo was 0.40 (95% CI -0.63, 1.42) and 0.62 ml per second (95% CI -0.43, 1.68) for maximum urinary flow, and -5.94 (95% CI -13.98, 2.09) and -1.39 cm H2O (95% CI -9.73, 6.96), respectively, for detrusor pressure at maximum urinary flow. The incidence of adverse events was similar for mirabegron and placebo.<br><br>CONCLUSIONS: Mirabegron did not adversely affect voiding urodynamics (maximum urinary flow and detrusor pressure at maximum urinary flow) compared with placebo after 12 weeks of treatment.},
}
@article {pmid23725723,
year = {2013},
author = {Boninger, M and Mitchell, G and Tyler-Kabara, E and Collinger, J and Schwartz, AB},
title = {Neuroprosthetic control and tetraplegia--authors' reply.},
journal = {Lancet (London, England)},
volume = {381},
number = {9881},
pages = {1900-1901},
doi = {10.1016/S0140-6736(13)61154-X},
pmid = {23725723},
issn = {1474-547X},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; Female ; Humans ; Quadriplegia/*therapy ; },
}
@article {pmid23725722,
year = {2013},
author = {Stone, J and Landau, W},
title = {Neuroprosthetic control and tetraplegia.},
journal = {Lancet (London, England)},
volume = {381},
number = {9881},
pages = {1900},
doi = {10.1016/S0140-6736(13)61153-8},
pmid = {23725722},
issn = {1474-547X},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; Female ; Humans ; Quadriplegia/*therapy ; },
}
@article {pmid23725395,
year = {2013},
author = {Frost, SB and Iliakova, M and Dunham, C and Barbay, S and Arnold, P and Nudo, RJ},
title = {Reliability in the location of hindlimb motor representations in Fischer-344 rats: laboratory investigation.},
journal = {Journal of neurosurgery. Spine},
volume = {19},
number = {2},
pages = {248-255},
pmid = {23725395},
issn = {1547-5646},
support = {R37 NS030853/NS/NINDS NIH HHS/United States ; R37 NS030853-20/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain Mapping/instrumentation/*methods ; Electric Stimulation/instrumentation/methods ; Electrophysiological Phenomena/physiology ; Feasibility Studies ; Hindlimb/*physiology ; Microelectrodes ; Motor Cortex/*physiology ; Rats ; Rats, Inbred F344/*physiology ; Skull/surgery ; },
abstract = {OBJECT: The purpose of the present study was to determine the feasibility of using a common laboratory rat strain for reliably locating cortical motor representations of the hindlimb.<br><br>METHODS: Intracortical microstimulation techniques were used to derive detailed maps of the hindlimb motor representations in 6 adult Fischer-344 rats.<br><br>RESULTS: The organization of the hindlimb movement representation, while variable across individual rats in topographic detail, displayed several commonalities. The hindlimb representation was positioned posterior to the forelimb motor representation and posterolateral to the motor trunk representation. The areal extent of the hindlimb representation across the cortical surface averaged 2.00 &#xb1; 0.50 mm(2). Superimposing individual maps revealed an overlapping area measuring 0.35 mm(2), indicating that the location of the hindlimb representation can be predicted reliably based on stereotactic coordinates. Across the sample of rats, the hindlimb representation was found 1.25-3.75 mm posterior to the bregma, with an average center location approximately 2.6 mm posterior to the bregma. Likewise, the hindlimb representation was found 1-3.25 mm lateral to the midline, with an average center location approximately 2 mm lateral to the midline.<br><br>CONCLUSIONS: The location of the cortical hindlimb motor representation in Fischer-344 rats can be reliably located based on its stereotactic position posterior to the bregma and lateral to the longitudinal skull suture at midline. The ability to accurately predict the cortical localization of functional hindlimb territories in a rodent model is important, as such animal models are being increasingly used in the development of brain-computer interfaces for restoration of function after spinal cord injury.},
}
@article {pmid23724837,
year = {2013},
author = {Vadera, S and Marathe, AR and Gonzalez-Martinez, J and Taylor, DM},
title = {Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface.},
journal = {Neurosurgical focus},
volume = {34},
number = {6},
pages = {E3},
doi = {10.3171/2013.3.FOCUS1373},
pmid = {23724837},
issn = {1092-0684},
support = {R01 NS058871/NS/NINDS NIH HHS/United States ; R01-NS058871/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Epilepsy/*pathology/physiopathology ; Humans ; Neuroimaging ; Stereotaxic Techniques ; },
abstract = {Stereoelectroencephalography (SEEG) is becoming more prevalent as a planning tool for surgical treatment of intractable epilepsy. Stereoelectroencephalography uses long, thin, cylindrical "depth" electrodes containing multiple recording contacts along each electrode's length. Each lead is inserted into the brain percutaneously. The advantage of SEEG is that the electrodes can easily target deeper brain structures that are inaccessible with subdural grid electrodes, and SEEG does not require a craniotomy. Brain-machine interface (BMI) research is also becoming more common in the Epilepsy Monitoring Unit. A brain-machine interface decodes a person's desired movement or action from the recorded brain activity and then uses the decoded brain activity to control an assistive device in real time. Although BMIs are primarily being developed for use by severely paralyzed individuals, epilepsy patients undergoing invasive brain monitoring provide an opportunity to test the effectiveness of different invasive recording electrodes for use in BMI systems. This study investigated the ability to use SEEG electrodes for control of 2D cursor velocity in a BMI. Two patients who were undergoing SEEG for intractable epilepsy participated in this study. Participants were instructed to wiggle or rest the hand contralateral to their SEEG electrodes to control the horizontal velocity of a cursor on a screen. Simultaneously they were instructed to wiggle or rest their feet to control the vertical component of cursor velocity. The BMI system was designed to detect power spectral changes associated with hand and foot activity and translate those spectral changes into horizontal and vertical cursor movements in real time. During testing, participants used their decoded SEEG signals to move the brain-controlled cursor to radial targets that appeared on the screen. Although power spectral information from 28 to 32 electrode contacts were used for cursor control during the experiment, post hoc analysis indicated that better control may have been possible using only a single SEEG depth electrode containing multiple recording contacts in both hand and foot cortical areas. These results suggest that the advantages of using SEEG for epilepsy monitoring may also apply to using SEEG electrodes in BMI systems. Specifically, SEEG electrodes can target deeper brain structures, such as foot motor cortex, and both hand and foot areas can be targeted with a single SEEG electrode implanted percutaneously. Therefore, SEEG electrodes may be an attractive option for simple BMI systems that use power spectral modulation in hand and foot cortex for independent control of 2 degrees of freedom.},
}
@article {pmid23719806,
year = {2013},
author = {Naci, L and Cusack, R and Jia, VZ and Owen, AM},
title = {The brain's silent messenger: using selective attention to decode human thought for brain-based communication.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {33},
number = {22},
pages = {9385-9393},
pmid = {23719806},
issn = {1529-2401},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Auditory Perception/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; *Communication ; Female ; Humans ; Image Processing, Computer-Assisted ; Imagination/physiology ; Magnetic Resonance Imaging ; Male ; Mental Processes/*physiology ; Young Adult ; },
abstract = {The interpretation of human thought from brain activity, without recourse to speech or action, is one of the most provoking and challenging frontiers of modern neuroscience. In particular, patients who are fully conscious and awake, yet, due to brain damage, are unable to show any behavioral responsivity, expose the limits of the neuromuscular system and the necessity for alternate forms of communication. Although it is well established that selective attention can significantly enhance the neural representation of attended sounds, it remains, thus far, untested as a response modality for brain-based communication. We asked whether its effect could be reliably used to decode answers to binary (yes/no) questions. Fifteen healthy volunteers answered questions (e.g., "Do you have brothers or sisters?") in the fMRI scanner, by selectively attending to the appropriate word ("yes" or "no"). Ninety percent of the answers were decoded correctly based on activity changes within the attention network. The majority of volunteers conveyed their answers with less than 3 min of scanning, suggesting that this technique is suited for communication in a reasonable amount of time. Formal comparison with the current best-established fMRI technique for binary communication revealed improved individual success rates and scanning times required to detect responses. This novel fMRI technique is intuitive, easy to use in untrained participants, and reliably robust within brief scanning times. Possible applications include communication with behaviorally nonresponsive patients.},
}
@article {pmid25339986,
year = {2013},
author = {Grimaldi, G and Manto, M and Jdaoudi, Y},
title = {Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs.},
journal = {F1000Research},
volume = {2},
number = {},
pages = {282},
pmid = {25339986},
issn = {2046-1402},
abstract = {Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project "Tremor" (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.},
}
@article {pmid25337331,
year = {2013},
author = {Mirnaziri, M and Rahimi, M and Alavikakhaki, S and Ebrahimpour, R},
title = {Using Combination of µ,β and γ Bands in Classification of EEG Signals.},
journal = {Basic and clinical neuroscience},
volume = {4},
number = {1},
pages = {76-87},
pmid = {25337331},
issn = {2008-126X},
abstract = {INTRODUCTION: In most BCI articles which aim to separate movement imaginations, µ and β frequency bands have been used. In this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well.<br><br>METHODS: In this study we used data set 2a from BCI Competition IV. In this data set, 9 healthy subjects have performed left hand, right hand, foot and tongue movement imaginations. Time and frequency intervals are computed for each subject and then are classified using Common Spatial Pattern (CSP) as a feature extractor. Finally, data is classified by LDA, RBF MLP, SVM and KNN methods. In all experiments, accuracy rate of classification is computed using 4 fold validation method.<br><br>RESULTS: It is seen that most of the time, combination of &#xb5;,&#x3b2; and &#x3b3; bands would have better performance than just using combination of &#xb5; and &#x3b2; bands or &#x3b3; band alone. In general, the improvement rate of the average classification accuracy is computed 2.91%.<br><br>DISCUSSION: In this study, it is shown that using combination of &#xb5;, &#x3b2; and &#x3b3; frequency bands provides more information than only using combination of &#xb5; and &#x3b2; in movement imagination separations.},
}
@article {pmid24961699,
year = {2013},
author = {Castermans, T and Duvinage, M and Cheron, G and Dutoit, T},
title = {Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems.},
journal = {Brain sciences},
volume = {4},
number = {1},
pages = {1-48},
pmid = {24961699},
issn = {2076-3425},
abstract = {In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.},
}
@article {pmid24961621,
year = {2013},
author = {Salari, N and Rose, M},
title = {A brain-computer-interface for the detection and modulation of gamma band activity.},
journal = {Brain sciences},
volume = {3},
number = {4},
pages = {1569-1587},
pmid = {24961621},
issn = {2076-3425},
abstract = {Gamma band oscillations in the human brain (around 40 Hz) play a functional role in information processing, and a real-time assessment of gamma band activity could be used to evaluate the functional relevance more directly. Therefore, we developed a source based Brain-Computer-Interface (BCI) with an online detection of gamma band activity in a selective brain region in the visual cortex. The BCI incorporates modules for online detection of various artifacts (including microsaccades) and the artifacts were continuously fed back to the volunteer. We examined the efficiency of the source-based BCI for Neurofeedback training of gamma- and alpha-band (8-12 Hz) oscillations and compared the specificity for the spatial and frequency domain. Our results demonstrated that volunteers learned to selectively switch between modulating alpha- or gamma-band oscillations and benefited from online artifact information. The analyses revealed a high level of accuracy with respect to frequency and topography for the gamma-band modulations. Thus, the developed BCI can be used to manipulate the fast oscillatory activity with a high level of specificity. These selective modulations can be used to assess the relevance of fast neural oscillations for information processing in a more direct way, i.e., by the adaptive presentation of stimuli within well-described brain states.},
}
@article {pmid24961620,
year = {2013},
author = {Grave de Peralta, R and Gonzalez Andino, S and Perrig, S},
title = {Patient machine interface for the control of mechanical ventilation devices.},
journal = {Brain sciences},
volume = {3},
number = {4},
pages = {1554-1568},
pmid = {24961620},
issn = {2076-3425},
abstract = {The potential of Brain Computer Interfaces (BCIs) to translate brain activity into commands to control external devices during mechanical ventilation (MV) remains largely unexplored. This is surprising since the amount of patients that might benefit from such assistance is considerably larger than the number of patients requiring BCI for motor control. Given the transient nature of MV (i.e., used mainly over night or during acute clinical conditions), precluding the use of invasive methods, and inspired by current research on BCIs, we argue that scalp recorded EEG (electroencephalography) signals can provide a non-invasive direct communication pathway between the brain and the ventilator. In this paper we propose a Patient Ventilator Interface (PVI) to control a ventilator during variable conscious states (i.e., wake, sleep, etc.). After a brief introduction on the neural control of breathing and the clinical conditions requiring the use of MV we discuss the conventional techniques used during MV. The schema of the PVI is presented followed by a description of the neural signals that can be used for the on-line control. To illustrate the full approach, we present data from a healthy subject, where the inspiration and expiration periods during voluntary breathing were discriminated with a 92% accuracy (10-fold cross-validation) from the scalp EEG data. The paper ends with a discussion on the advantages and obstacles that can be forecasted in this novel application of the concept of BCI.},
}
@article {pmid24940658,
year = {2013},
author = {Harris, JP and Tyler, DJ},
title = {Biological, mechanical, and technological considerations affecting the longevity of intracortical electrode recordings.},
journal = {Critical reviews in biomedical engineering},
volume = {41},
number = {6},
pages = {435-456},
pmid = {24940658},
issn = {0278-940X},
mesh = {Animals ; *Biomedical Engineering ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Guinea Pigs ; Haplorhini ; Humans ; Mice ; Rats ; },
abstract = {Intracortical electrodes are important tools, with applications ranging from fundamental laboratory studies to potential solutions to intractable clinical applications. However, the longevity and reliability of the interfaces remain their major limitation to the wider implementation and adoption of this technology, especially in broader translational work. Accordingly, this review summarizes the most significant biological and technical factors influencing the long-term performance of intracortical electrodes. In a laboratory setting, intracortical electrodes have been used to study the normal and abnormal function of the brain. This improved understanding has led to valuable insights regarding many neurological conditions. Likewise, clinical applications of intracortical brain-machine interfaces offer the ability to improve the quality of life of many patients afflicted with high-level paralysis from spinal cord injury, brain stem stroke, amyotrophic lateral sclerosis, or other conditions. It is widely hypothesized that the tissue response to the electrodes, including inflammation, limits their longevity. Many studies have examined and modified the tissue response to intracortical electrodes to improve future intracortical electrode technologies. Overall, the relationship between biological, mechanical, and technological considerations are crucial for the fidelity of chronic electrode recordings and represent a presently active area of investigation in the field of neural engineering.},
}
@article {pmid24887296,
year = {2013},
author = {Blank, A and O'Malley, MK and Francisco, GE and Contreras-Vidal, JL},
title = {A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {},
number = {},
pages = {1159-1162},
pmid = {24887296},
issn = {1948-3546},
support = {P30 AG028747/AG/NIA NIH HHS/United States ; R01 NS081854/NS/NINDS NIH HHS/United States ; },
abstract = {In this paper, we summarize a novel approach to robotic rehabilitation that capitalizes on the benefits of patient intent and real-time assessment of impairment. Specifically, an upper-limb, physical human-robot interface (the MAHI EXO-II robotic exoskeleton) is augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. Robotic measures of motor impairment are derived from real-time sensor data from the MAHI EXO-II and the BMI. These measures can be validated through correlation with widely used clinical measures and used to drive patient-specific therapy sessions adapted to the capabilities of the individual, with the MAHI EXO-II providing assistance or challenging the participant as appropriate to maximize rehabilitation outcomes. This approach to robotic rehabilitation takes a step towards the seamless integration of BMIs and intelligent exoskeletons to create systems that can monitor and interface with brain activity and movement. Such systems will enable more focused study of various issues in development of devices and rehabilitation strategies, including interpretation of measurement data from a variety of sources, exploration of hypotheses regarding large scale brain function during robotic rehabilitation, and optimization of device design and training programs for restoring upper limb function after stroke.},
}
@article {pmid24829821,
year = {2013},
author = {Shaikh, N and Ummunissa, F and Abdel Sattar, M},
title = {Traumatic mitral valve and pericardial injury.},
journal = {Case reports in critical care},
volume = {2013},
number = {},
pages = {385670},
pmid = {24829821},
issn = {2090-6420},
abstract = {Cardiac injury after blunt trauma is common but underreported. Common cardiac trauma after the blunt chest injury (BCI) is cardiac contusion; it is very rare to have cardiac valve injury. The mitral valve injury during chest trauma occurs when extreme pressure is applied at early systole during the isovolumic contraction between the closure of the mitral valve and the opening of the aortic valve. Traumatic mitral valve injury can involve valve leaflet, chordae tendineae, or papillary muscles. For the diagnosis of mitral valve injury, a high index of suspicion is required, as in polytrauma patients, other obvious severe injuries will divert the attention of the treating physician. Clinical picture of patients with mitral valve injury may vary from none to cardiogenic shock. The echocardiogram is the main diagnostic modality of mitral valve injuries. Patient's clinical condition will dictate the timing and type of surgery or medical therapy. We report a case of mitral valve and pericardial injury in a polytrauma patient, successfully treated in our intensive care unit.},
}
@article {pmid24770451,
year = {2013},
author = {Su, K and Robbins, KA},
title = {A Framework for Content-based Retrieval of EEG with Applications to Neuroscience and Beyond.},
journal = {Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks},
volume = {},
number = {},
pages = {1-8},
pmid = {24770451},
issn = {2161-4393},
support = {G12 MD007591/MD/NIMHD NIH HHS/United States ; },
abstract = {This paper introduces a prototype framework for content-based EEG retrieval (CBER). Like content-based image retrieval, the proposed framework retrieves EEG segments similar to the query EEG segment in a large database. Such retrieval of EEG can be used to assist data mining of brain signals by allowing researchers to understand the association between brain patterns, responses, and the environment. Retrieval might also be used to enhance the accuracy of Brain Computer Interface (BCI) systems by providing related samples for training. We present key components of CBER and explain how to handle the distinctive characteristics of EEG. To demonstrate the feasibility of the framework, we implemented a simple EEG database of about 37,000 samples from more than 100 subjects. We ran two retrieval scenarios with a set of EEG features and evaluation metrics. The results of finding similar subjects clearly demonstrate the potential of CBER in many EEG applications.},
}
@article {pmid24707595,
year = {2013},
author = {Purcell-Davis, A},
title = {The representations of novel neurotechnologies in social media: five case studies.},
journal = {The New bioethics : a multidisciplinary journal of biotechnology and the body},
volume = {19},
number = {1},
pages = {30-45},
doi = {10.1179/2050287713z.00000000026},
pmid = {24707595},
issn = {2050-2877},
mesh = {*Biotechnology/ethics/methods/trends ; *Blogging ; *Brain-Computer Interfaces ; China ; *Deep Brain Stimulation ; Humans ; *Medical Tourism ; Neural Stem Cells/transplantation ; *Social Media ; *Stem Cell Transplantation ; United States ; *Video Games ; },
abstract = {The research contained within this article was commissioned by the Nuffield Council on Bioethics, as part of the development of an ethical framework to guide the practice of those involved in novel neurotechnologies. The findings of this study are included in chapter 9 of the report Novel Neurotechnologies: Intervening in the Brain. The purpose of this research was to provide a 'snapshot' of the content found within postings on social media platforms, concerning the technologies of Deep Brain Stimulation, Brain Computer Interface and Neural Stem Cell Therapy. The methodology included an analysis of the postings found on Delicious, Twitter, Facebook, YouTube and blogs and found evidence that social media provided a platform for a variety of voices, including patients, medical personnel and neuroscientists. However, it additionally found evidence of the advertisement and promotion of neurotechnologies as potential medical interventions, the hype of scientific breakthroughs and the hope of cures for neurodegenerative diseases.},
}
@article {pmid24580568,
year = {2013},
author = {Sabut, SK and Bhattacharya, SD and Manjunatha, M},
title = {Functional electrical stimulation on improving foot drop gait in poststroke rehabilitation: a review of its technology and clinical efficacy.},
journal = {Critical reviews in biomedical engineering},
volume = {41},
number = {2},
pages = {149-160},
doi = {10.1615/critrevbiomedeng.2013007621},
pmid = {24580568},
issn = {0278-940X},
mesh = {Electric Stimulation Therapy/*methods ; Gait Disorders, Neurologic/*rehabilitation ; Humans ; Stroke/physiopathology ; *Stroke Rehabilitation ; },
abstract = {This article presents technical developments in and clinical applications of functional electrical stimulation (FES) in the recovery of gait and motor function in poststroke rehabilitation. We review stroke incidence, stimulator design, brain-computer interface-based FES systems, and clinical applications of FES. Developments in different types of foot drop stimulators are reviewed, including hard-wired and microprocessor-based surface stimulator systems. The replacement of the foot switch by using artificial and "natural" sensors as the primary control in foot drop stimulators is reviewed. In addition, this review evaluates the clinical effects of FES applications in gait, motor control, and functional ability compared to conventional therapy alone during poststroke rehabilitation. The literature suggests the combination of FES and a conventional rehabilitation program has a positive therapeutic effect on the recovery of gait, motor function, energy expenditure, and functional ability in stroke patients. On the basis of our review, we recommend using FES therapy along with a conventional rehabilitation program in the poststroke rehabilitation process. In summary, this article describes the need for rigorous technological development, clinical studies, and collaboration between clinicians and engineers for FES systems. Future research would facilitate the design of costeffective FES systems as well as analysis of FES applications in stroke patients to optimize the rehabilitation process.},
}
@article {pmid24579648,
year = {2013},
author = {Sreedharan, S and Sitaram, R and Paul, JS and Kesavadas, C},
title = {Brain-computer interfaces for neurorehabilitation.},
journal = {Critical reviews in biomedical engineering},
volume = {41},
number = {3},
pages = {269-279},
doi = {10.1615/critrevbiomedeng.2014010697},
pmid = {24579648},
issn = {0278-940X},
mesh = {Biomedical Engineering/methods ; Brain/*physiology ; Brain Diseases/*rehabilitation ; *Brain-Computer Interfaces ; Communication ; Communication Devices for People with Disabilities ; Computer Systems ; Electrocardiography/methods ; Electroencephalography/methods ; Equipment Design ; Humans ; Magnetic Resonance Imaging/methods ; Neurofeedback ; Spectroscopy, Near-Infrared/methods ; },
abstract = {Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device. This requires retrieval of signals of interest from the brain, and its use for neuro-rehabilitation by means of interfacing the signals to a computerized device. Brain signals such as action potentials from single neurons or nerve fibers, extracellular local field potentials (LFPs), electrocorticograms, electroencephalogram and its components such as the event-related brain potentials, real-time functional magnetic resonance imaging, near-infrared spectroscopy, and magneto-encephalogram have been used. BCIs are envisaged to be useful for communication, control and self-regulation of brain function. BCIs employ neurofeedback to enable operant conditioning to allow the user to learn using it. Paralytic conditions arising from stroke or other diseases are being targeted for BCI application. Neurofeedback strategies ranging from sensory feedback to direct brain stimulation are being employed. Existing BCIs are limited in their throughput in terms of letters per minute or commands per minute, and need extensive training to use the BCI. Further, they can cause rapid fatigue due to use and have limited adaptability to changes in the patient's brain state. The challenge before BCI technology for neuro-rehabilitation today is to enable effective clinical use of BCIs with minimal effort to set up and operate.},
}
@article {pmid24431926,
year = {2012},
author = {Anand, S and Sutanto, J and Baker, MS and Okandan, M and Muthuswamy, J},
title = {Electrothermal Microactuators With Peg Drive Improve Performance for Brain Implant Applications.},
journal = {Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems},
volume = {21},
number = {5},
pages = {1172-1186},
pmid = {24431926},
issn = {1057-7157},
support = {R01 NS055312/NS/NINDS NIH HHS/United States ; },
abstract = {This paper presents a new actuation scheme for in-plane bidirectional translation of polysilicon microelectrodes. The new Chevron-peg actuation scheme uses microelectromechanical systems (MEMS) based electrothermal microactuators to move microelectrodes for brain implant applications. The design changes were motivated by specific needs identified by the in vivo testing of an earlier generation of MEMS microelectrodes that were actuated by the Chevron-latch type of mechanism. The microelectrodes actuated by the Chevron-peg mechanism discussed here show improved performance in the following key areas: higher force generation capability (111 μN per heat strip compared to 50 μN), reduced power consumption (91 mW compared to 360 mW), and reliable performance with consistent forward and backward movements of microelectrodes. Failure analysis of the Chevron-latch and the Chevron-peg type of actuation schemes showed that the latter is more robust to wear over four million cycles of operation. The parameters for the activation waveforms for Chevron-peg actuators were optimized using statistical analysis. Waveforms with a 1-ms time period and a 1-Hz frequency of operation showed minimal error between the expected and the actual movement of the microelectrodes. The new generation of Chevron-peg actuators and microelectrodes are therefore expected to enhance the longevity and performance of implanted microelectrodes in the brain. [2011-0341].},
}
@article {pmid24028994,
year = {2012},
author = {Bilello, M and Arkuszewski, M and Nasrallah, I and Wu, X and Erus, G and Krejza, J},
title = {Errata Corrige: The Neuroradiology Journal 25: 17-21, 2012. Multiple Sclerosis Lesions in the Brain: Computer-Assisted Assessment of Lesion Load Dynamics on 3D FLAIR MR Images.},
journal = {The neuroradiology journal},
volume = {25},
number = {3},
pages = {379-384},
pmid = {24028994},
issn = {1971-4009},
abstract = {The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Although disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 12 (12.2%) cases, the difference reached statistical significance (p=0.04). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is different from the performance of clinical readers, with a possibly shorter assessment time. The software detected more changes from baseline than clinical readers, suggesting a higher sensitivity, which will have to be confirmed on further analysis.},
}
@article {pmid24028908,
year = {2012},
author = {Aprile, I and Ottaviano, I and Buono, E and Di Renzo, A and Fiaschini, P and Ottaviano, P},
title = {Low-dose brain computer tomography sensitivity: a comparative study with a conventional technique.},
journal = {The neuroradiology journal},
volume = {25},
number = {2},
pages = {151-162},
doi = {10.1177/197140091202500201},
pmid = {24028908},
issn = {1971-4009},
abstract = {A comparative study between brain conventional computed tomography (CT) examinations and low-dose examinations was performed. The aim of the work was to show if a low-dose technique can be used instead of a standard one. Forty patients with 51 brain lesions were studied with both techniques. The low-dose technique was optimized with mAs reduction to obtain a 25% dose reduction compared to standard acquisitions. Even if images have a poor signal-to-noise ratio, the low-dose technique visualized all the lesions disclosed by conventional examination except three chronic vascular lacunar infarcts. In conclusion, the low-dose technique can be adopted instead conventional CT scans in selected cases.},
}
@article {pmid24028871,
year = {2012},
author = {Bilello, M and Arkuszewski, M and Nasrallah, I and Wu, X and Erus, G and Krejza, J},
title = {Multiple Sclerosis Lesions in the Brain: Computer-Assisted Assessment of Lesion Load Dynamics on 3D FLAIR MR Images.},
journal = {The neuroradiology journal},
volume = {25},
number = {1},
pages = {17-21},
doi = {10.1177/197140091202500102},
pmid = {24028871},
issn = {1971-4009},
abstract = {The detection and monitoring of brain lesions caused by multiple sclerosis is commonly performed with the use of magnetic resonance imaging. Analysis of a large number of images is a time-consuming challenge to the neuroradiologist, that can be accelerated with the assistance of computer-detection software. In 98 baseline and follow-up brain magnetic resonance studies from 88 patients with a diagnosis of multiple sclerosis, we employed locally developed lesion-detection software to assess temporal change in the load of brain lesions and compared its results to routine clinical reports. Analyzing the differences between the follow-up study and the baseline study, the software displays the results in the form of a scrollable axial volume, with the changed lesions highlighted in different colors and superimposed on the baseline reference scan. Disagreements between the software and the clinical readers in the detection of changed lesions were observed only in 11 (11.2%) cases, and the difference did not reach statistical significance (p=0.07). The mean interpretation time with assistance of the software was 2.7±2.2 minutes. We conclude that the performance of the software-assisted interpretation in the analysis of change over time in multiple sclerosis brain lesions is comparable to the performance of clinical readers, with a possibly shorter assessment time. Our study demonstrates the potential of including lesion-detection software in the workflow of neuroradiology practice.},
}
@article {pmid24500542,
year = {2012},
author = {Orhan, U and Hild, KE and Erdogmus, D and Roark, B and Oken, B and Fried-Oken, M},
title = {RSVP Keyboard: An EEG Based Typing Interface.},
journal = {Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)},
volume = {},
number = {},
pages = {},
pmid = {24500542},
issn = {1520-6149},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Humans need communication. The desire to communicate remains one of the primary issues for people with locked-in syndrome (LIS). While many assistive and augmentative communication systems that use various physiological signals are available commercially, the need is not satisfactorily met. Brain interfaces, in particular, those that utilize event related potentials (ERP) in electroencephalography (EEG) to detect the intent of a person noninvasively, are emerging as a promising communication interface to meet this need where existing options are insufficient. Existing brain interfaces for typing use many repetitions of the visual stimuli in order to increase accuracy at the cost of speed. However, speed is also crucial and is an integral portion of peer-to-peer communication; a message that is not delivered timely often looses its importance. Consequently, we utilize rapid serial visual presentation (RSVP) in conjunction with language models in order to assist letter selection during the brain-typing process with the final goal of developing a system that achieves high accuracy and speed simultaneously. This paper presents initial results from the RSVP Keyboard system that is under development. These initial results on healthy and locked-in subjects show that single-trial or few-trial accurate letter selection may be possible with the RSVP Keyboard paradigm.},
}
@article {pmid24976741,
year = {2011},
author = {Orhan, U and Erdogmus, D and Hild, KE and Roark, B and Oken, B and Fried-Oken, M},
title = {Context Information Significantly Improves Brain Computer Interface Performance - a Case Study on Text Entry Using a Language Model Assisted BCI.},
journal = {Conference record. Asilomar Conference on Signals, Systems &amp; Computers},
volume = {45},
number = {},
pages = {132-136},
pmid = {24976741},
issn = {1058-6393},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {We present recent results on the design of the RSVP Keyboard - a brain computer interface (BCI) for expressive language generation for functionally locked-in individuals using rapid serial visual presentation of letters or other symbols such as icons. The proposed BCI design tightly incorporates probabilistic contextual information obtained from a language model into the single or multi-trial event related potential (ERP) decision mechanism. This tight fusion of contextual information with instantaneous and independent brain activity is demonstrated to potentially improve accuracy in a dramatic manner. Specifically, a simple regularized discriminant single-trial ERP classifier's performance can be increased from a naive baseline of 75% to 98% in a 28-symbol alphabet operating at 5% false ERP detection rate. We also demonstrate results which show that trained healthy subjects can achieve real-time typing accuracies over 90% mostly relying on single-trial ERP evidence when supplemented with a rudimentary n-gram language model. Further discussion and preliminary results include our initial efforts involving a locked-in individual and our efforts to train him to improve his skill in performing the task.},
}
@article {pmid23851948,
year = {2011},
author = {Keng Hoong Wee, and Turicchia, L and Sarpeshkar, R},
title = {An articulatory silicon vocal tract for speech and hearing prostheses.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {5},
number = {4},
pages = {339-346},
doi = {10.1109/TBCAS.2011.2159858},
pmid = {23851948},
issn = {1940-9990},
abstract = {We describe the concept of a bioinspired feedback loop that combines a cochlear processor with an integrated-circuit vocal tract to create what we call a speech-locked loop. We discuss how the speech-locked loop can be applied in hearing prostheses, such as cochlear implants, to help improve speech recognition in noise. We also investigate speech-coding strategies for brain-machine-interface-based speech prostheses and present an articulatory speech-synthesis system by using an integrated-circuit vocal tract that models the human vocal tract. Our articulatory silicon vocal tract makes the transmission of low bit-rate speech-coding parameters feasible over a bandwidth-constrained body sensor network. To the best of our knowledge, this is the first articulatory speech-prosthesis system reported to date. We also present a speech-prosthesis simulator as a means to generate realistic articulatory parameter sequences.},
}
@article {pmid25309106,
year = {2011},
author = {Dethier, J and Nuyujukian, P and Eliasmith, C and Stewart, T and Elassaad, SA and Shenoy, KV and Boahen, K},
title = {A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.},
journal = {Advances in neural information processing systems},
volume = {2011},
number = {},
pages = {2213-2221},
pmid = {25309106},
issn = {1049-5258},
support = {DP1 OD000965/OD/NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
abstract = {Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.},
}
@article {pmid24352611,
year = {2011},
author = {Dethier, J and Gilja, V and Nuyujukian, P and Elassaad, SA and Shenoy, KV and Boahen, K},
title = {Spiking Neural Network Decoder for Brain-Machine Interfaces.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {},
number = {},
pages = {},
pmid = {24352611},
issn = {1948-3546},
support = {DP1 OD000965/OD/NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; },
abstract = {We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.},
}
@article {pmid24008765,
year = {2011},
author = {Nezamfar, H and Orhan, U and Erdogmus, D and Hild, KE and Purwar, S and Oken, B and Fried-Oken, M},
title = {On Visually Evoked Potentials in EEG Induced by Multiple Pseudorandom Binary Sequences for Brain Computer Interface Design.},
journal = {Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)},
volume = {},
number = {},
pages = {2044-2047},
pmid = {24008765},
issn = {1520-6149},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; },
abstract = {Visually evoked potentials have attracted great attention in the last two decades for the purpose of brain computer interface design. Visually evoked P300 response is a major signal of interest that has been widely studied. Steady state visual evoked potentials that occur in response to periodically flickering visual stimuli have been primarily investigated as an alternative. There also exists some work on the use of an m-sequence and its shifted versions to induce responses that are primarily in the visual cortex but are not periodic. In this paper, we study the use of multiple m-sequences for intent discrimination in the brain interface, as opposed to a single m-sequence whose shifted versions are to be discriminated from each other. Specifically we used four different m-sequences of length 31. Our main goal is to study if the bit presentation rate of the m-sequences have an impact on classification accuracy and speed. In this initial study, where we compared two basic classifier schemes using EEG data acquired with 15Hz and 30Hz bit presentation rates, our results are mixed; while on one subject, we got promising results indicating bit presentation rate could be increased without decrease in classification accuracy; thus leading to a faster decision-rate in the brain interface, on our second subject, this conclusion is not supported. Further detailed experimental studies as well as signal processing methodology design, especially for information fusion across EEG channels, will be conducted to investigate this question further.},
}
@article {pmid23853375,
year = {2010},
author = {Jongwoo Lee, and Johnson, MD and Kipke, DR},
title = {A tunable biquad switched-capacitor amplifier-filter for neural recording.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {4},
number = {5},
pages = {295-300},
doi = {10.1109/TBCAS.2010.2066272},
pmid = {23853375},
issn = {1932-4545},
abstract = {With the emerging interest in local field potentials (LFPs) as input signals for brain-machine interfaces, there is a need for integrated circuits capable of amplifying spikes and LFPs. A two-stage complementary metal-oxide semiconductor (CMOS) amplifier-filter has been implemented with 0.18-μm CMOS for simultaneous, multimodal recording of extracellular unit spikes and LFPs. For the frequency tuning and the reduction of the 1/f noise, it employs a switched-capacitor technique. The filter bandwidth is reconfigurable by using a different sampling clock frequency. The prototype amplifier has gains of 19.1 dB and 37.5 dB for low-pass only filter and cascaded filter, respectively. With a 100-kHz sampling frequency, the equivalent input noise spectral density is 38.8 nV/√Hz while the total power consumption is 69 μW with a 1.6-V supply, including clock generation and biasing occupying an area of 44 × 148 μm(2).},
}
@article {pmid23853372,
year = {2010},
author = {Yuwen Sun, and Shimeng Huang, and Oresko, JJ and Cheng, AC},
title = {Programmable neural processing on a smartdust for brain-computer interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {4},
number = {5},
pages = {265-273},
doi = {10.1109/TBCAS.2010.2049743},
pmid = {23853372},
issn = {1932-4545},
abstract = {Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.},
}
@article {pmid23853367,
year = {2010},
author = {Chin-Teng Lin, and Che-Jui Chang, and Bor-Shyh Lin, and Shao-Hang Hung, and Chih-Feng Chao, and I-Jan Wang, },
title = {A real-time wireless brain-computer interface system for drowsiness detection.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {4},
number = {4},
pages = {214-222},
doi = {10.1109/TBCAS.2010.2046415},
pmid = {23853367},
issn = {1932-4545},
abstract = {A real-time wireless electroencephalogram (EEG)-based brain-computer interface (BCI) system for drowsiness detection has been proposed. Drowsy driving has been implicated as a causal factor in many accidents. Therefore, real-time drowsiness monitoring can prevent traffic accidents effectively. However, current BCI systems are usually large and have to transmit an EEG signal to a back-end personal computer to process the EEG signal. In this study, a novel BCI system was developed to monitor the human cognitive state and provide biofeedback to the driver when drowsy state occurs. The proposed system consists of a wireless physiological signal-acquisition module and an embedded signal-processing module. Here, the physiological signal-acquisition module and embedded signal-processing module were designed for long-term EEG monitoring and real-time drowsiness detection, respectively. The advantages of low owner consumption and small volume of the proposed system are suitable for car applications. Moreover, a real-time drowsiness detection algorithm was also developed and implemented in this system. The experiment results demonstrated the feasibility of our proposed BCI system in a practical driving application.},
}
@article {pmid25205876,
year = {2010},
author = {Janvale, GB and Gawali, BW and Deore, RS and Mehrotra, SC and Deshmukh, SN and Marwale, AV},
title = {Songs induced mood recognition system using EEG signals.},
journal = {Annals of neurosciences},
volume = {17},
number = {2},
pages = {80-84},
pmid = {25205876},
issn = {0972-7531},
abstract = {BACKGROUND: Brain computer interfacing is a system that acquires and analyzes neural signals to create a communication channel directly between the brain and the computer. The EEG records the electrical fields generated by the nerve cells. With the help of Fourier Transformation the EEG signals are classified into four different frequency bands.<br><br>PURPOSE: The main purpose of the present paper is to report results related to classification of EEG signals of different people subjected to different conditions.<br><br>METHODS: The experiment has been done on 10 subjects having activities related to hearing music chosen from categories of patriotic, happy, romantic and sad songs along with relaxation activity. 19 electrodes have been used under (10-20) International Standard. The &#x3b4;, &#x3b8; &#x3b1; and &#x3b2; components of EEG signals to these activities have been determined. Different statistical methods including linear discriminate analysis have been tested for classification.<br><br>RESULTS: Result of the Linear Discriminant Analysis (LDA) made four groups of all modes (Relaxation, Happy, Sad, Patriotic and Romantic Song) labeled group1, Group2, Group3 and Group4 of all ten electrodes for Delta, Theta, alpha and Beta frequencies.<br><br>CONCLUSION: The study may be used for the development of activities induced mood recognition (AIMR) system from the EEG signal.},
}
@article {pmid23853320,
year = {2010},
author = {Kuo-Kai Shyu, and Po-Lei Lee, and Ming-Huan Lee, and Ming-Hong Lin, and Ren-Jie Lai, and Yun-Jen Chiu, },
title = {Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {4},
number = {2},
pages = {125-132},
doi = {10.1109/TBCAS.2010.2042595},
pmid = {23853320},
issn = {1932-4545},
abstract = {This paper proposes a low-cost field-programmable gate-array (FPGA)-based brain-computer interface (BCI) multimedia control system, different from the BCI system, which uses bulky and expensive electroencephalography (EEG) measurement equipment, personal computer, and commercial real-time signal-processing software. The proposed system combines a customized stimulation panel, a brainwave-acquisition circuit, and an FPGA-based real-time signal processor and allows users to use their brainwave to communicate with or control multimedia devices by themselves. This study also designs a light-emitting diode stimulation panel instead of cathode ray tube or liquid-crystal display used in existing studies, to induce a stronger steady-state visual evoked potential (SSVEP), a kind of EEG, used as the input signal of the proposed BCI system. Implementing a prototype of the SSVEP-based BCI multimedia control system verifies the effectiveness of the proposed system. Experimental results show that the subjects' SSVEP can successfully control the multimedia device through the proposed BCI system with high identification accuracy.},
}
@article {pmid23853286,
year = {2009},
author = {Mollazadeh, M and Murari, K and Cauwenberghs, G and Thakor, N},
title = {Wireless micropower instrumentation for multimodal acquisition of electrical and chemical neural activity.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {3},
number = {6},
pages = {388-397},
doi = {10.1109/TBCAS.2009.2031877},
pmid = {23853286},
issn = {1932-4545},
abstract = {The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10(-5). Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented.},
}
@article {pmid23853161,
year = {2009},
author = {Mitra, S and Fusi, S and Indiveri, G},
title = {Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {3},
number = {1},
pages = {32-42},
doi = {10.1109/TBCAS.2008.2005781},
pmid = {23853161},
issn = {1932-4545},
abstract = {Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated.},
}
@article {pmid23852967,
year = {2008},
author = {Sarpeshkar, R and Wattanapanitch, W and Arfin, SK and Rapoport, BI and Mandal, S and Baker, MW and Fee, MS and Musallam, S and Andersen, RA},
title = {Low-power circuits for brain-machine interfaces.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {2},
number = {3},
pages = {173-183},
doi = {10.1109/TBCAS.2008.2003198},
pmid = {23852967},
issn = {1932-4545},
abstract = {This paper presents work on ultra-low-power circuits for brain-machine interfaces with applications for paralysis prosthetics, stroke, Parkinson's disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode-recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.},
}
@article {pmid23852413,
year = {2007},
author = {Uei-Ming Jow, and Ghovanloo, M},
title = {Design and optimization of printed spiral coils for efficient transcutaneous inductive power transmission.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {1},
number = {3},
pages = {193-202},
doi = {10.1109/TBCAS.2007.913130},
pmid = {23852413},
issn = {1932-4545},
abstract = {The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.},
}
@article {pmid24173934,
year = {1995},
author = {Mujeeb-Kazi, A and Cortes, A and Riera-Lizarazu, O},
title = {The cytogenetics of a Triticum turgidum x Psathyrostachys juncea hybrid and its backcross derivatives.},
journal = {TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik},
volume = {90},
number = {3-4},
pages = {430-437},
pmid = {24173934},
issn = {0040-5752},
abstract = {Psathyrostachys juncea (2n = 2x = 14, NN), a source of barley yellow dwarf (BYDV) virus resistance with tolerance to drought and salinity, has been successfully hybridized in its autotetraploid form (2n = 4x = 28, NNNN) as the pollen parent to durum wheat (Triticum turgidum L.). The 2n = 4x = 28 (ABNN) F1 hybrid has a mean meiotic metaphase-I configuration of 20.29 univalents + 0.29 ring bivalents + 3.36 rod bivalents + 0.14 trivalents. Spike length, internode length, glume awn length and lemma awn length, as well as the general spike morphology of the F1 hybrid, are intermediate with those of the two parents. Pollinating the ABNN F1 hybrid has given backcross (BC)-I derivatives of an amphiploid (AABBNN) that expresses limited self-fertility. BC-2 derivatives have been obtained from these plants. Direct transfers of useful genes from Ps. juncea to wheat would require substantial genetic manipulation strategies. Both conventional and novel methodologies, which may complement each other, and so facilitate reaching an agricultural objective end point, are addressed.},
}
@article {pmid24306535,
year = {1985},
author = {Hepburn, AG and White, J},
title = {The effect of right terminal repeat deletion on the oncogenicity of the T-Region of pTiT37.},
journal = {Plant molecular biology},
volume = {5},
number = {1},
pages = {3-11},
pmid = {24306535},
issn = {0167-4412},
abstract = {A modified pTiT37 plasmid was constructed by deleting a 103 base fragment between an AhaIII and a Bc/I site. This fragment, located to the right of the nopaline synthase gene contains the right terminal 25 base pair repeat sequence which defines the right limit of the T-Region. The effect of this deletion was determined on a number of host plants. In contrast to previous reports, the deletion does not destroy tumorigenicity on all plant species. It had no effect on tumorigenicity when Linum usitatissimum was used as the test species and an attenuating effect when Kalanchoë tubiflora was used. Only when Nicotiana tabacum was used did the mutant appear avirulent. We propose from these data and the phenotype of those tumours that form, that a pseudo border located in the 3' untranslated region of the ipt locus has been used to provide the right hand limit of the T-Region in the absence of the normal border.},
}
@article {pmid23717804,
year = {2012},
author = {Perseh, B and Sharafat, AR},
title = {An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection.},
journal = {Journal of medical signals and sensors},
volume = {2},
number = {3},
pages = {128-143},
pmid = {23717804},
issn = {2228-7477},
abstract = {We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain-computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann's dataset for eight subjects, and achieved similar results.},
}
@article {pmid23717256,
year = {2013},
author = {Gu, Y and Farina, D and Murguialday, AR and Dremstrup, K and Birbaumer, N},
title = {Comparison of movement related cortical potential in healthy people and amyotrophic lateral sclerosis patients.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {65},
pmid = {23717256},
issn = {1662-4548},
abstract = {OBJECTIVE: To understand the brain motor functions and neurophysiological changes due to motor disorder by comparing electroencephalographic data between healthy people and amyotrophic lateral sclerosis (ALS) patients.<br><br>METHODS: The movement related cortical potential (MRCP) was recorded from seven healthy subjects and four ALS patients. They were asked to imagine right wrist extension at two speeds (fast and slow). The peak negativity (PN) and rebound rate (RR) were extracted from MRCP for comparison.<br><br>RESULTS: The statistical analysis has showed that there was no significant difference in PN between the healthy and the ALS subjects. However, the healthy subjects presented faster RR than ALS during both fast and slow movement imagination.<br><br>CONCLUSIONS: The weaker RR of ALS patients might reflect the impairment of motor output pathways or the degree of motor degeneration.<br><br>SIGNIFICANCE: The comparison between healthy people and ALS patients provides a way to explain the movement disorder through brain electrical signal. In addition, the characteristics of MRCP could be used to monitor and guide brain plasticity in patients.},
}
@article {pmid23715295,
year = {2013},
author = {Williams, JJ and Rouse, AG and Thongpang, S and Williams, JC and Moran, DW},
title = {Differentiating closed-loop cortical intention from rest: building an asynchronous electrocorticographic BCI.},
journal = {Journal of neural engineering},
volume = {10},
number = {4},
pages = {046001},
doi = {10.1088/1741-2560/10/4/046001},
pmid = {23715295},
issn = {1741-2552},
mesh = {*Algorithms ; Animals ; Attention/*physiology ; Biofeedback, Psychology/instrumentation/*physiology ; *Brain-Computer Interfaces ; Electrocardiography/instrumentation/*methods ; Electrodes, Implanted ; *Intention ; Macaca mulatta ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Recent experiments have shown that electrocorticography (ECoG) can provide robust control signals for a brain-computer interface (BCI). Strategies that attempt to adapt a BCI control algorithm by learning from past trials often assume that the subject is attending to each training trial. Likewise, automatic disabling of movement control would be desirable during resting periods when random brain fluctuations might cause unintended movements of a device. To this end, our goal was to identify ECoG differences that arise between periods of active BCI use and rest.<br><br>APPROACH: We examined spectral differences in multi-channel, epidural micro-ECoG signals recorded from non-human primates when rest periods were interleaved between blocks of an active BCI control task.<br><br>MAIN RESULTS: Post-hoc analyses demonstrated that these states can be decoded accurately on both a trial-by-trial and real-time basis, and this discriminability remains robust over a period of weeks. In addition, high gamma frequencies showed greater modulation with desired movement direction, while lower frequency components demonstrated greater amplitude differences between task and rest periods, suggesting possible specialized BCI roles for these frequencies.<br><br>SIGNIFICANCE: The results presented here provide valuable insight into the neurophysiology of BCI control as well as important considerations toward the design of an asynchronous BCI system.},
}
@article {pmid23714227,
year = {2014},
author = {Kober, SE and Wood, G and Kurzmann, J and Friedrich, EV and Stangl, M and Wippel, T and Väljamäe, A and Neuper, C},
title = {Near-infrared spectroscopy based neurofeedback training increases specific motor imagery related cortical activation compared to sham feedback.},
journal = {Biological psychology},
volume = {95},
number = {},
pages = {21-30},
doi = {10.1016/j.biopsycho.2013.05.005},
pmid = {23714227},
issn = {1873-6246},
mesh = {Adult ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *Neurofeedback ; Psychomotor Performance/physiology ; *Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {In the present study we implemented a real-time feedback system based on multichannel near-infrared spectroscopy (NIRS). Prior studies indicated that NIRS-based neurofeedback can enhance motor imagery related cortical activation. To specify these prior results and to confirm the efficacy of NIRS-based neurofeedback, we examined changes in blood oxygenation level collected in eight training sessions. One group got real feedback about their own brain activity (N=9) and one group saw a playback of another person's feedback recording (N=8). All participants performed motor imagery of a right hand movement. Real neurofeedback induced specific and focused brain activation over left motor areas. This focal brain activation became even more specific over the eight training sessions. In contrast, sham feedback led to diffuse brain activation patterns over the whole cortex. These findings can be useful when training patients with focal brain lesions to increase activity of specific brain areas for rehabilitation purpose.},
}
@article {pmid23710250,
year = {2013},
author = {Xiao, R and Ding, L},
title = {Evaluation of EEG features in decoding individual finger movements from one hand.},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
number = {},
pages = {243257},
pmid = {23710250},
issn = {1748-6718},
mesh = {Adult ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Computational Biology ; Electroencephalography/*statistics &amp; numerical data ; Fingers ; Hand ; Humans ; Models, Neurological ; Movement ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI) is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG) data obtained through a spectral principal component analysis (PCA) and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P < 0.05) and other features investigated (P < 0.05), including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.},
}
@article {pmid23706528,
year = {2013},
author = {Rohani, DA and Henning, WS and Thomsen, CE and Kjaer, TW and Puthusserypady, S and Sorensen, HB},
title = {BCI using imaginary movements: the simulator.},
journal = {Computer methods and programs in biomedicine},
volume = {111},
number = {2},
pages = {300-307},
doi = {10.1016/j.cmpb.2013.04.008},
pmid = {23706528},
issn = {1872-7565},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Computer Simulation ; Feedback ; Humans ; Imagination ; Movement ; Probability ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; User-Computer Interface ; },
abstract = {Over the past two decades, much progress has been made in the rapidly evolving field of Brain Computer Interface (BCI). This paper presents a novel concept: a BCI-simulator, which has been developed for the Hex-O-Spell interface, using the sensory motor rhythms (SMR) paradigm. With the simulator, it is possible to evaluate how the model parameters such as error classifications, delay between classifications and success rate affect the communication rate. Another advantage of the simulator is that it allows us to study for more classes than most online BCI systems which are limited to only two classes. Results show that the BCI simulator is able to give a deeper understanding of the feedback systems. We also find that a 3-class system is more efficient than a 2-class system if it obtains a success rate of at least 55% of the 2-class system.},
}
@article {pmid23702458,
year = {2014},
author = {Ros, T and Munneke, MA and Parkinson, LA and Gruzelier, JH},
title = {Neurofeedback facilitation of implicit motor learning.},
journal = {Biological psychology},
volume = {95},
number = {},
pages = {54-58},
doi = {10.1016/j.biopsycho.2013.04.013},
pmid = {23702458},
issn = {1873-6246},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Humans ; Learning/*physiology ; Male ; Middle Aged ; Neurofeedback/*methods ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; },
abstract = {BACKGROUND: Mu rhythm desynchronisation via EEG-neurofeedback (NFB) has been previously been shown to induce durable motor-cortical disinhibition for at least 20 min. It was hypothesised that the presentation of a novel procedural learning task immediately after this NFB protocol would boost motor performance.<br><br>METHOD: The protocol consisted of firstly activating the right primary motor cortex with a single session of Mu (8-12 Hz) suppression via NFB for a total of 30 min. Shortly after, and with their non-dominant (left) hand, subjects (n=10) performed the serial reaction time task (SRTT), which is used to assess reaction time improvement over multiple trials. During another occasion (1 week before/after), the same subjects were tested on a different sequence without prior NFB, as part of a counterbalanced control condition.<br><br>RESULTS: Compared to a "cross-over" condition without NFB, subjects who received NFB immediately prior to SRTT performance exhibited a significantly faster rate of learning, reflected in a greater reduction of reaction times across blocks (p=0.02). This occurred in the absence of explicit awareness of a repeating sequence. Moreover, no significant differences were observed between conditions in error rate or reaction time variability.<br><br>CONCLUSION: Our results suggest that a single NFB session may be directly used to facilitate the early acquisition of a procedural motor task, and are the first to demonstrate that neurofeedback effects could be exploited immediately after individual training sessions so as to boost behavioural performance and learning.},
}
@article {pmid23701418,
year = {2013},
author = {Botzer, L and Karniel, A},
title = {Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements.},
journal = {The European journal of neuroscience},
volume = {38},
number = {1},
pages = {2108-2123},
doi = {10.1111/ejn.12211},
pmid = {23701418},
issn = {1460-9568},
mesh = {*Adaptation, Physiological ; Biomechanical Phenomena ; Brain/physiology ; *Feedback, Sensory ; Hand/innervation/physiology ; Humans ; Models, Neurological ; *Movement ; Photic Stimulation ; Proprioception/physiology ; Psychomotor Performance/*physiology ; *Reaction Time ; Vision, Ocular/physiology ; },
abstract = {It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces.},
}
@article {pmid23700383,
year = {2013},
author = {Carmena, JM},
title = {Advances in neuroprosthetic learning and control.},
journal = {PLoS biology},
volume = {11},
number = {5},
pages = {e1001561},
pmid = {23700383},
issn = {1545-7885},
mesh = {Animals ; *Artificial Intelligence ; Haplorhini ; Humans ; *Neural Prostheses ; Robotics ; *User-Computer Interface ; },
abstract = {Significant progress has occurred in the field of brain-machine interfaces (BMI) since the first demonstrations with rodents, monkeys, and humans controlling different prosthetic devices directly with neural activity. This technology holds great potential to aid large numbers of people with neurological disorders. However, despite this initial enthusiasm and the plethora of available robotic technologies, existing neural interfaces cannot as yet master the control of prosthetic, paralyzed, or otherwise disabled limbs. Here I briefly discuss recent advances from our laboratory into the neural basis of BMIs that should lead to better prosthetic control and clinically viable solutions, as well as new insights into the neurobiology of action.},
}
@article {pmid23692974,
year = {2013},
author = {Yeh, CL and Lee, PL and Chen, WM and Chang, CY and Wu, YT and Lan, GY},
title = {Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine.},
journal = {Biomedical engineering online},
volume = {12},
number = {},
pages = {46},
pmid = {23692974},
issn = {1475-925X},
mesh = {Adult ; *Brain-Computer Interfaces ; Equipment Design ; *Evoked Potentials, Visual ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Nonlinear Dynamics ; *Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject's physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual's difference in SSVEP is needed but was seldom reported.<br><br>METHODS: This paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user's gaze targets.<br><br>RESULTS: The on-line performance using the proposed approach has achieved high accuracy (89.88&#x2009;&#xb1;&#x2009;4.76%), fast responding time (effective epoch length&#x2009;=&#x2009;1.13&#x2009;&#xb1;&#x2009;0.02 s), and the information transfer rate (ITR) was 50.91&#x2009;&#xb1;&#x2009;8.70 bits/min.<br><br>CONCLUSIONS: The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject's SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.},
}
@article {pmid23690880,
year = {2013},
author = {Yu, T and Li, Y and Long, J and Li, F},
title = {A hybrid brain-computer interface-based mail client.},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
number = {},
pages = {750934},
pmid = {23690880},
issn = {1748-6718},
mesh = {Adult ; *Brain-Computer Interfaces/statistics &amp; numerical data ; Computational Biology ; Electroencephalography/statistics &amp; numerical data ; *Electronic Mail ; Event-Related Potentials, P300/physiology ; Humans ; Young Adult ; },
abstract = {Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.},
}
@article {pmid23690512,
year = {2013},
author = {Krishnaswami, S and Fonnesbeck, C and Penson, D and McPheeters, ML},
title = {Magnetic resonance imaging for locating nonpalpable undescended testicles: a meta-analysis.},
journal = {Pediatrics},
volume = {131},
number = {6},
pages = {e1908-16},
pmid = {23690512},
issn = {1098-4275},
support = {HHSA 290 2007 10065 I//PHS HHS/United States ; },
mesh = {Cryptorchidism/*diagnosis ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Prospective Studies ; Sensitivity and Specificity ; Testis/*pathology ; },
abstract = {BACKGROUND AND OBJECTIVE: Preoperative imaging techniques may guide management of nonpalpable, cryptorchid testicles. We evaluated conventional MRI for identifying and locating nonpalpable testicles in prepubescent boys via meta-analysis.<br><br>METHODS: Databases including Medline were searched from 1980 to February 2012. Eligible studies included &#x2265;10 boys with cryptorchidism/suspected cryptorchidism and reported data on testicular presence/absence and position (abdominal, inguinal, or scrotal) as determined by imaging and surgery. Two investigators independently reviewed studies against inclusion criteria. We captured the number of testicles that were correctly and incorrectly identified and located, relative to surgically verified status, and estimated sensitivity and specificity by using a random-effects model.<br><br>RESULTS: Eight unique prospective case series included 171 boys with 193 nonpalpable testicles (22 with bilateral testicles). Surgery identified 158 testicles (81.9%) present and 35 absent. MRI correctly identified testicles with an estimated median sensitivity of 0.62 (95% Bayesian credible interval [BCI]: 0.47-0.77) and a specificity of 1.0 (95% BCI: 0.99-1.0). MRI located intraabdominal testicles with a sensitivity of 0.55 (95% BCI: 0.09-1.0) and inguino-scrotal testicles with a sensitivity of 0.86 (95% BCI: 0.67-1.0). We were not able to obtain estimates for MRI sensitivity or specificity for locating atrophied testicles. The estimated specificity for location-specific testicles reached almost 100%.<br><br>CONCLUSIONS: Conventional MRI has low sensitivity for estimating the population sensitivity for identifying the presence of nonpalpable cryptorchid testicles. When testicles are identified, MRI is poor at locating both atrophied and intraabdominal testicles but performs modestly well in locating those in the inguino-scrotal regions.},
}
@article {pmid23688143,
year = {2013},
author = {McCleary, N and Duncan, EM and Stewart, F and Francis, JJ},
title = {Active ingredients are reported more often for pharmacologic than non-pharmacologic interventions: an illustrative review of reporting practices in titles and abstracts.},
journal = {Trials},
volume = {14},
number = {},
pages = {146},
pmid = {23688143},
issn = {1745-6215},
support = {HSRU1/CSO_/Chief Scientist Office/United Kingdom ; },
mesh = {*Abstracting and Indexing/standards ; *Behavior Therapy/standards ; Bibliometrics ; Chi-Square Distribution ; Comprehension ; Guidelines as Topic ; Humans ; *Information Dissemination ; *Periodicals as Topic/standards ; *Pharmaceutical Preparations/standards ; *Randomized Controlled Trials as Topic/standards ; *Terminology as Topic ; Treatment Outcome ; },
abstract = {Key components of healthcare interventions include 'active ingredients' (intervention components that can be specifically linked to effects on outcomes such that, were they omitted, the intervention would be ineffective). These should be reported in titles and abstracts of published reports of randomized controlled trials (RCTs). However, reporting of non-pharmacologic interventions (NPIs), particularly behaviour change interventions (BCIs), is difficult, owing to their complexity. This illustrative review compares how pharmacologic interventions (PIs), NPIs and BCIs are specified in titles and abstracts to clarify how reporting of NPIs and BCIs can be improved. MEDLINE and Embase were searched for RCTs published in the British Medical Journal, The Journal of the American Medical Association, The New England Journal of Medicine, The Lancet and Annals of Behavioral Medicine from 2009 to March 2011. All types of intervention, participant and outcome were included. A random sample of 198 studies (sampled proportionally from included journals) stratified by intervention type (PI/NPI) was taken: 98 evaluated PIs, 96 evaluated NPIs and four evaluated both. Studies were coded for the presence or absence of key components. The frequency data were analyzed using the chi-square test. Active ingredients were named in 88% titles and 95% abstracts of PI reports, and in 51% titles and 71% abstracts of NPI reports, with a significant association between intervention type and reporting of active ingredients in titles (χ2(1) = 28.90; P < 0.001) and abstracts (χ2(1) = 16.94; P < 0.001). Active ingredients were named in BCI reports in 37% titles and 56% abstracts, and in other NPI reports in 66% titles and 86% abstracts. There was also a significant association between intervention type and reporting of active ingredients in titles (χ2(1) = 6.68; P = 0.010) and abstracts (χ2(1) = 8.66; P = 0.003). Reporting practices also differed for such components as the trial setting and intervention provider. This review highlights the need for improved reporting of NPIs (particularly BCIs) and indicates that a set of agreed labels and definitions for complex NPIs could facilitate standardized reporting. This would ensure that interventions can be faithfully replicated and that evidence for interventions can be appropriately synthesized.},
}
@article {pmid23685458,
year = {2013},
author = {Schreuder, M and Höhne, J and Blankertz, B and Haufe, S and Dickhaus, T and Tangermann, M},
title = {Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036025},
doi = {10.1088/1741-2560/10/3/036025},
pmid = {23685458},
issn = {1741-2552},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: In brain-computer interface (BCI) research, systems based on event-related potentials (ERP) are considered particularly successful and robust. This stems in part from the repeated stimulation which counteracts the low signal-to-noise ratio in electroencephalograms. Repeated stimulation leads to an optimization problem, as more repetitions also cost more time. The optimal number of repetitions thus represents a data-dependent trade-off between the stimulation time and the obtained accuracy. Several methods for dealing with this have been proposed as 'early stopping', 'dynamic stopping' or 'adaptive stimulation'. Despite their high potential for BCI systems at the patient's bedside, those methods are typically ignored in current BCI literature. The goal of the current study is to assess the benefit of these methods.<br><br>APPROACH: This study assesses for the first time the existing methods on a common benchmark of both artificially generated data and real BCI data of 83 BCI sessions, allowing for a direct comparison between these methods in the context of text entry.<br><br>MAIN RESULTS: The results clearly show the beneficial effect on the online performance of a BCI system, if the trade-off between the number of stimulus repetitions and accuracy is optimized. All assessed methods work very well for data of good subjects, and worse for data of low-performing subjects. Most methods, however, are robust in the sense that they do not reduce the performance below the baseline of a simple no stopping strategy.<br><br>SIGNIFICANCE: Since all methods can be realized as a module between the BCI and an application, minimal changes are needed to include these methods into existing BCI software architectures. Furthermore, the hyperparameters of most methods depend to a large extend on only a single variable-the discriminability of the training data. For the convenience of BCI practitioners, the present study proposes linear regression coefficients for directly estimating the hyperparameters from the data based on this discriminability. The data that were used in this publication are made publicly available to benchmark future methods.},
}
@article {pmid23685269,
year = {2013},
author = {O'Regan, S and Marnane, W},
title = {Multimodal detection of head-movement artefacts in EEG.},
journal = {Journal of neuroscience methods},
volume = {218},
number = {1},
pages = {110-120},
doi = {10.1016/j.jneumeth.2013.04.017},
pmid = {23685269},
issn = {1872-678X},
mesh = {*Algorithms ; *Artifacts ; *Electroencephalography ; Head ; Head Movements ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Artefacts arising from head movements have been a considerable obstacle in the deployment of automatic event detection systems in ambulatory EEG. Recently, gyroscopes have been identified as a useful modality for providing complementary information to the head movement artefact detection task. In this work, a comprehensive data fusion analysis is conducted to investigate how EEG and gyroscope signals can be most effectively combined to provide a more accurate detection of head-movement artefacts in the EEG. To this end, several methods of combining these physiological and physical signals at the feature, decision and score fusion levels are examined. Results show that combination at the feature, score and decision levels is successful in improving classifier performance when compared to individual EEG or gyroscope classifiers, thus confirming that EEG and gyroscope signals carry complementary information regarding the detection of head-movement artefacts in the EEG. Feature fusion and the score fusion using the sum-rule provided the greatest improvement in artefact detection. By extending multimodal head-movement artefact detection to the score and decision fusion domains, it is possible to implement multimodal artefact detection in environments where gyroscope signals are intermittently available.},
}
@article {pmid23684128,
year = {2013},
author = {Daly, I and Billinger, M and Laparra-Hernández, J and Aloise, F and García, ML and Faller, J and Scherer, R and Müller-Putz, G},
title = {On the control of brain-computer interfaces by users with cerebral palsy.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {9},
pages = {1787-1797},
doi = {10.1016/j.clinph.2013.02.118},
pmid = {23684128},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Cerebral Palsy/*physiopathology/*rehabilitation ; *Electroencephalography ; Evoked Potentials, Visual ; *Feedback, Sensory ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; Task Performance and Analysis ; Thinking/physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs.<br><br>METHODS: This study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials.<br><br>RESULTS: Of the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible.<br><br>CONCLUSIONS: The results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved.<br><br>SIGNIFICANCE: This is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean &#xb1; STD, 0.821 &#xb1; 0.116, SSVEP accuracies; 0.422 &#xb1; 0.069).},
}
@article {pmid23680020,
year = {2013},
author = {Thompson, DE and Blain-Moraes, S and Huggins, JE},
title = {Performance assessment in brain-computer interface-based augmentative and alternative communication.},
journal = {Biomedical engineering online},
volume = {12},
number = {},
pages = {43},
pmid = {23680020},
issn = {1475-925X},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Level 1 metrics used to report performance at the output of the BCI Control Module, which translates brain signals into logical control output, and Level 2 metrics at the Selection Enhancement Module, which translates logical control to semantic control. We recommend that: (1) the commensurate metrics Mutual Information or Information Transfer Rate (ITR) be used to report Level 1 BCI performance, as these metrics represent information throughput, which is of interest in BCIs for AAC; 2) the BCI-Utility metric be used to report Level 2 BCI performance, as it is capable of handling all current methods of improving BCI performance; (3) these metrics should be supplemented by information specific to each unique BCI configuration; and (4) studies involving Selection Enhancement Modules should report performance at both Level 1 and Level 2 in the BCI system. Following these recommendations will enable efficient comparison between both BCI Control and Selection Enhancement Modules, accelerating research and development of BCI-based AAC systems.},
}
@article {pmid23674419,
year = {2013},
author = {Kindermans, PJ and Verschore, H and Schrauwen, B},
title = {A unified probabilistic approach to improve spelling in an event-related potential-based brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {10},
pages = {2696-2705},
doi = {10.1109/TBME.2013.2262524},
pmid = {23674419},
issn = {1558-2531},
mesh = {*Algorithms ; *Artificial Intelligence ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Language ; Visual Cortex/*physiology ; *Writing ; },
abstract = {In recent years, in an attempt to maximize performance, machine learning approaches for event-related potential (ERP) spelling have become more and more complex. In this paper, we have taken a step back as we wanted to improve the performance without building an overly complex model, that cannot be used by the community. Our research resulted in a unified probabilistic model for ERP spelling, which is based on only three assumptions and incorporates language information. On top of that, the probabilistic nature of our classifier yields a natural dynamic stopping strategy. Furthermore, our method uses the same parameters across 25 subjects from three different datasets. We show that our classifier, when enhanced with language models and dynamic stopping, improves the spelling speed and accuracy drastically. Additionally, we would like to point out that as our model is entirely probabilistic, it can easily be used as the foundation for complex systems in future work. All our experiments are executed on publicly available datasets to allow for future comparison with similar techniques.},
}
@article {pmid23674410,
year = {2013},
author = {Gu, Z and Yu, Z and Shen, Z and Li, Y},
title = {An online semi-supervised brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {9},
pages = {2614-2623},
doi = {10.1109/TBME.2013.2261994},
pmid = {23674410},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Learning/*physiology ; Least-Squares Analysis ; Pattern Recognition, Automated/methods ; *Support Vector Machine ; *User-Computer Interface ; },
abstract = {Practical brain-computer interface (BCI) systems should require only low training effort for the user, and the algorithms used to classify the intent of the user should be computationally efficient. However, due to inter- and intra-subject variations in EEG signal, intermittent training/calibration is often unavoidable. In this paper, we present an online semi-supervised P300 BCI speller system. After a short initial training (around or less than 1 min in our experiments), the system is switched to a mode where the user can input characters through selective attention. In this mode, a self-training least squares support vector machine (LS-SVM) classifier is gradually enhanced in back end with the unlabeled EEG data collected online after every character input. In this way, the classifier is gradually enhanced. Even though the user may experience some errors in input at the beginning due to the small initial training dataset, the accuracy approaches that of fully supervised method in a few minutes. The algorithm based on LS-SVM and its sequential update has low computational complexity; thus, it is suitable for online applications. The effectiveness of the algorithm has been validated through data analysis on BCI Competition III dataset II (P300 speller BCI data). The performance of the online system was evaluated through experimental results on eight healthy subjects, where all of them achieved the spelling accuracy of 85 % or above within an average online semi-supervised learning time of around 3 min.},
}
@article {pmid23673460,
year = {2013},
author = {Pan, J and Li, Y and Zhang, R and Gu, Z and Li, F},
title = {Discrimination between control and idle states in asynchronous SSVEP-based brain switches: a pseudo-key-based approach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {3},
pages = {435-443},
doi = {10.1109/TNSRE.2013.2253801},
pmid = {23673460},
issn = {1558-0210},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Decision Making/physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Rest/*physiology ; Sensitivity and Specificity ; Visual Perception/*physiology ; },
abstract = {A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) can operate as an asynchronous brain switch. When SSVEP is detected with the "on/off" button flickering at a fixed frequency, the subject is identified as in the control state. Otherwise, he is in the idle state. Generally, the detection of the idle/control state is based on a predefined threshold, which is related to power. However, due to the variability of the electroencephalogram (EEG) signal, it is difficult to find an optimal threshold to achieve a high true-positive rate (TPR) in the control state while maintaining a low false-positive rate (FPR) in the idle state. In this paper, a novel pseudo-key-based approach is presented for better discriminating the control and idle states. A dedicated "on/off" button (target key) and several additional buttons (pseudo-keys) are displayed on the graphical user interface (GUI), and all of these buttons flash at different frequencies. The control state is identified from the EEG signal under two conditions. The first is a common thresholding condition, where the power ratio of the target key frequency component to a certain neighboring frequency band is above a predefined threshold. The second is a comparison condition, where the power of the target key frequency component is higher than any of the pseudo-keys. The effectiveness of the proposed approach is validated by several experiments. Further analysis shows that introducing the pseudo-keys can significantly reduce the probability that the SSVEP will be detected in response to the flickering target key in the idle state without substantially affecting the detection in the control state, providing strong evidence in support of our approach.},
}
@article {pmid23673459,
year = {2013},
author = {Daly, I and Billinger, M and Scherer, R and Muller-Putz, G},
title = {On the automated removal of artifacts related to head movement from the EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {3},
pages = {427-434},
doi = {10.1109/TNSRE.2013.2254724},
pmid = {23673459},
issn = {1558-0210},
mesh = {Accelerometry/methods ; Adult ; Algorithms ; *Artifacts ; Artificial Intelligence ; Brain/*physiopathology ; Cerebral Palsy/*diagnosis/*physiopathology ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Head Movements/*physiology ; Humans ; Middle Aged ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {Contamination of the electroencephalogram (EEG) by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.},
}
@article {pmid23669713,
year = {2013},
author = {Bang, JW and Choi, JS and Park, KR},
title = {Noise reduction in brainwaves by using both EEG signals and frontal viewing camera images.},
journal = {Sensors (Basel, Switzerland)},
volume = {13},
number = {5},
pages = {6272-6294},
pmid = {23669713},
issn = {1424-8220},
mesh = {*Artifacts ; Brain Waves/*physiology ; Discriminant Analysis ; Electrodes ; Electroencephalography/*methods ; Head Movements ; Humans ; Imaging, Three-Dimensional/*methods ; Photography/*instrumentation ; ROC Curve ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; User-Computer Interface ; },
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human-computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.},
}
@article {pmid23666954,
year = {2013},
author = {Daly, I and Nicolaou, N and Nasuto, SJ and Warwick, K},
title = {Automated artifact removal from the electroencephalogram: a comparative study.},
journal = {Clinical EEG and neuroscience},
volume = {44},
number = {4},
pages = {291-306},
doi = {10.1177/1550059413476485},
pmid = {23666954},
issn = {1550-0594},
mesh = {*Algorithms ; *Artifacts ; Brain/*physiopathology ; Data Interpretation, Statistical ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Wavelet Analysis ; },
abstract = {Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.},
}
@article {pmid23665776,
year = {2013},
author = {Johnson, LA and Wander, JD and Sarma, D and Su, DK and Fetz, EE and Ojemann, JG},
title = {Direct electrical stimulation of the somatosensory cortex in humans using electrocorticography electrodes: a qualitative and quantitative report.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036021},
pmid = {23665776},
issn = {1741-2552},
support = {T90 DA023436/DA/NIDA NIH HHS/United States ; R90 DA033461/DA/NIDA NIH HHS/United States ; R25 NS079200/NS/NINDS NIH HHS/United States ; T90 DA032436/DA/NIDA NIH HHS/United States ; R01 NS065186/NS/NINDS NIH HHS/United States ; },
mesh = {Electric Stimulation/*methods ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Humans ; Sensation/*physiology ; Sensory Thresholds/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: Recently, electrocorticography-based brain-computer interfaces have been successfully used to translate cortical activity into control signals for external devices. However, the utility of such devices would be greatly enhanced by somatosensory feedback. Direct stimulation of somatosensory cortex evokes sensory perceptions, and is thus a promising option for closing the loop. Before this can be implemented in humans it is necessary to evaluate how changes in stimulus parameters are perceived and the extent to which they can be discriminated.<br><br>APPROACH: Electrical stimulation was delivered to the somatosensory cortex of human subjects implanted with electrocorticography grids. Subjects were asked to discriminate between stimuli of different frequency and amplitude as well as to report the qualitative sensations elicited by the stimulation.<br><br>MAIN RESULTS: In this study we show that in humans implanted with electrocorticography grids, variations in the amplitude or frequency of cortical electrical stimulation produce graded variations in percepts. Subjects were able to reliably distinguish between different stimuli.<br><br>SIGNIFICANCE: These results indicate that direct cortical stimulation is a feasible option for sensory feedback with brain-computer interface devices.},
}
@article {pmid23663147,
year = {2013},
author = {Arvaneh, M and Guan, C and Ang, KK and Quek, C},
title = {EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface.},
journal = {Neural computation},
volume = {25},
number = {8},
pages = {2146-2171},
doi = {10.1162/NECO_a_00474},
pmid = {23663147},
issn = {1530-888X},
mesh = {Adaptation, Physiological/*physiology ; Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography ; Humans ; Space Perception/*physiology ; },
abstract = {A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).},
}
@article {pmid23659803,
year = {2013},
author = {ElMaraghy, A and Devereaux, M},
title = {The "bicipital aponeurosis flex test": evaluating the integrity of the bicipital aponeurosis and its implications for treatment of distal biceps tendon ruptures.},
journal = {Journal of shoulder and elbow surgery},
volume = {22},
number = {7},
pages = {908-914},
doi = {10.1016/j.jse.2013.02.005},
pmid = {23659803},
issn = {1532-6500},
mesh = {Adult ; Arm Injuries/diagnosis/surgery ; Cohort Studies ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Muscle Contraction/physiology ; Muscle, Skeletal/*injuries/surgery ; Orthopedic Procedures/*methods ; Orthopedics/methods ; Physical Examination/*methods ; Preoperative Care/methods ; Retrospective Studies ; Risk Assessment ; Rupture/diagnosis/surgery ; Tendon Injuries/*diagnosis/surgery ; Tendon Transfer/methods ; Treatment Outcome ; },
abstract = {BACKGROUND: One mitigating factor in the accurate diagnosis of complete distal biceps tendon ruptures (DBTR) is the integrity of the bicipital aponeurosis (BA). Current orthopedic literature lacks a descriptive means of evaluating the integrity of the BA in the presence of distal biceps injury.<br><br>METHODS: A consecutive cohort of 17 patients with suspected DBTR was examined. The hook test, passive forearm pronation test, and the biceps crease interval (BCI) test were performed as part of the overall clinical examination to assess the integrity of the distal tendon. The biceps crease ratio (BCR), a component of the BCI test, was used as an objective measure of distal tendon retraction. Integrity of the BA was assessed using the "BA flex test." The status of the distal tendon and BA were confirmed intraoperatively.<br><br>RESULTS: Sixteen patients had complete rupture of the distal biceps tendon. One had a high-grade partial thickness tear. The BA remained intact in 59%. Application of the BA flex test resulted in 100% sensitivity and 90% specificity, with overall diagnostic accuracy of 94%. Despite complete DBTR, there was a significant difference in the amount of distal tendon retraction (P = .012) between those with the BA intact (median BCR, 1.5, interquartile range, 1.3-1.9) and those where the BA was absent (median BCR, 2.2, interquartile range, 1.7-2.6).<br><br>CONCLUSION: Evaluating the integrity of the BA can help to inform evaluation and treatment of DBTR, especially when visible or palpable alterations in biceps contour and proximal tendon migration are absent or equivocal.},
}
@article {pmid23658903,
year = {2013},
author = {Rezakova, MV and Mazhirina, KG and Pokrovskiy, MA and Savelov, AA and Savelova, OA and Shtark, MB},
title = {Dynamic mapping of brain and cognitive control of virtual gameplay (study by functional magnetic resonance imaging).},
journal = {Bulletin of experimental biology and medicine},
volume = {154},
number = {6},
pages = {706-710},
doi = {10.1007/s10517-013-2035-2},
pmid = {23658903},
issn = {1573-8221},
mesh = {Adult ; Biofeedback, Psychology ; Brain Mapping ; Brain-Computer Interfaces ; Cerebellum/*physiology ; Cerebral Cortex/physiology ; Cognition ; Competitive Behavior/physiology ; Computer Simulation ; Games, Experimental ; Heart Rate ; Humans ; Magnetic Resonance Imaging ; Male ; Video Games ; Young Adult ; },
abstract = {Using functional magnetic resonance imaging technique, we performed online brain mapping of gamers, practiced to voluntary (cognitively) control their heart rate, the parameter that operated a competitive virtual gameplay in the adaptive feedback loop. With the default start picture, the regions of interest during the formation of optimal cognitive strategy were as follows: Brodmann areas 19, 37, 39 and 40, i.e. cerebellar structures (vermis, amygdala, pyramids, clivus). "Localization" concept of the contribution of the cerebellum to cognitive processes is discussed.},
}
@article {pmid23658171,
year = {2013},
author = {Arduin, PJ and Frégnac, Y and Shulz, DE and Ego-Stengel, V},
title = {"Master" neurons induced by operant conditioning in rat motor cortex during a brain-machine interface task.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {33},
number = {19},
pages = {8308-8320},
pmid = {23658171},
issn = {1529-2401},
mesh = {Action Potentials/physiology ; Animals ; *Brain-Computer Interfaces ; Cell Survival ; Conditioning, Operant/*physiology ; Male ; Motor Cortex/*cytology ; Nerve Net/physiology ; Neurons/*physiology ; Rats ; Rats, Wistar ; Reaction Time/physiology ; Reward ; Statistics, Nonparametric ; },
abstract = {Operant control of a prosthesis by neuronal cortical activity is one of the successful strategies for implementing brain-machine interfaces (BMI), by which the subject learns to exert a volitional control of goal-directed movements. However, it remains unknown if the induced brain circuit reorganization affects preferentially the conditioned neurons whose activity controlled the BMI actuator during training. Here, multiple extracellular single-units were recorded simultaneously in the motor cortex of head-fixed behaving rats. The firing rate of a single neuron was used to control the position of a one-dimensional actuator. Each time the firing rate crossed a predefined threshold, a water bottle moved toward the rat, until the cumulative displacement of the bottle allowed the animal to drink. After a learning period, most (88%) conditioned neurons raised their activity during the trials, such that the time to reward decreased across sessions: the conditioned neuron fired strongly, reliably and swiftly after trial onset, although no explicit instruction in the learning rule imposed a fast neuronal response. Moreover, the conditioned neuron fired significantly earlier and more strongly than nonconditioned neighboring neurons. During the first training sessions, an increase in firing rate variability was seen only for the highly conditionable neurons. This variability then decreased while the conditioning effect increased. These findings suggest that modifications during training target preferentially the neuron chosen to control the BMI, which acts then as a "master" neuron, leading in time the reconfiguration of activity in the local cortical network.},
}
@article {pmid23653884,
year = {2013},
author = {Rowland, NC and Breshears, J and Chang, EF},
title = {Neurosurgery and the dawning age of Brain-Machine Interfaces.},
journal = {Surgical neurology international},
volume = {4},
number = {Suppl 1},
pages = {S11-4},
pmid = {23653884},
issn = {2229-5097},
abstract = {Brain-machine interfaces (BMIs) are on the horizon for clinical neurosurgery. Electrocorticography-based platforms are less invasive than implanted microelectrodes, however, the latter are unmatched in their ability to achieve fine motor control of a robotic prosthesis capable of natural human behaviors. These technologies will be crucial to restoring neural function to a large population of patients with severe neurologic impairment - including those with spinal cord injury, stroke, limb amputation, and disabling neuromuscular disorders such as amyotrophic lateral sclerosis. On the opposite end of the spectrum are neural enhancement technologies for specialized applications such as combat. An ongoing ethical dialogue is imminent as we prepare for BMI platforms to enter the neurosurgical realm of clinical management.},
}
@article {pmid23651839,
year = {2014},
author = {Kaiser, V and Bauernfeind, G and Kreilinger, A and Kaufmann, T and Kübler, A and Neuper, C and Müller-Putz, GR},
title = {Cortical effects of user training in a motor imagery based brain-computer interface measured by fNIRS and EEG.},
journal = {NeuroImage},
volume = {85 Pt 1},
number = {},
pages = {432-444},
doi = {10.1016/j.neuroimage.2013.04.097},
pmid = {23651839},
issn = {1095-9572},
mesh = {Adult ; Brain Mapping ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Cortex/physiology ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/physiology ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {The present study aims to gain insights into the effects of training with a motor imagery (MI)-based brain-computer interface (BCI) on activation patterns of the sensorimotor cortex. We used functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to investigate long-term training effects across 10 sessions using a 2-class (right hand and feet) MI-based BCI in fifteen subjects. In the course of the training a significant enhancement of activation pattern emerges, represented by an [oxy-Hb] increase in fNIRS and a stronger event-related desynchronization in the upper β-frequency band in the EEG. These effects were only visible in participants with relatively low BCI performance (mean accuracy ≤ 70%). We found that training with an MI-based BCI affects cortical activation patterns especially in users with low BCI performance. Our results may serve as a valuable contribution to the field of BCI research and provide information about the effects that training with an MI-based BCI has on cortical activation patterns. This might be useful for clinical applications of BCI which aim at promoting and guiding neuroplasticity.},
}
@article {pmid23647099,
year = {2013},
author = {Stacey, WC and Kellis, S and Greger, B and Butson, CR and Patel, PR and Assaf, T and Mihaylova, T and Glynn, S},
title = {Potential for unreliable interpretation of EEG recorded with microelectrodes.},
journal = {Epilepsia},
volume = {54},
number = {8},
pages = {1391-1401},
pmid = {23647099},
issn = {1528-1167},
support = {K08 NS069783/NS/NINDS NIH HHS/United States ; K08NS069783/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Artifacts ; Brain/*physiology ; *Brain Mapping ; Brain Waves/physiology ; Databases, Factual/statistics &amp; numerical data ; Electric Impedance ; Electroencephalography/*methods ; Electronic Data Processing ; Epilepsy/*diagnosis ; Humans ; *Microelectrodes ; },
abstract = {PURPOSE: Recent studies in epilepsy, cognition, and brain machine interfaces have shown the utility of recording intracranial electroencephalography (iEEG) with greater spatial resolution. Many of these studies utilize microelectrodes connected to specialized amplifiers that are optimized for such recordings. We recently measured the impedances of several commercial microelectrodes and demonstrated that they will distort iEEG signals if connected to clinical EEG amplifiers commonly used in most centers. In this study we demonstrate the clinical implications of this effect and identify some of the potential difficulties in using microelectrodes.<br><br>METHODS: Human iEEG data were digitally filtered to simulate the signal recorded by a hybrid grid (two macroelectrodes and eight microelectrodes) connected to a standard EEG amplifier. The filtered iEEG data were read by three trained epileptologists, and high frequency oscillations (HFOs) were detected with a well-known algorithm. The filtering method was verified experimentally by recording an injected EEG signal in a saline bath with the same physical acquisition system used to generate the model. Several electrodes underwent scanning electron microscopy (SEM).<br><br>KEY FINDINGS: Macroelectrode recordings were unaltered compared to the source iEEG signal, but microelectrodes attenuated low frequencies. The attenuated signals were difficult to interpret: all three clinicians changed their clinical scoring of slowing and seizures when presented with the same data recorded on different sized electrodes. The HFO detection algorithm was oversensitive with microelectrodes, classifying many more HFOs than when the same data were recorded with macroelectrodes. In addition, during experimental recordings the microelectrodes produced much greater noise as well as large baseline fluctuations, creating sharply contoured transients, and superimposed "false" HFOs. SEM of these microelectrodes demonstrated marked variability in exposed electrode surface area, lead fractures, and sharp edges.<br><br>SIGNIFICANCE: Microelectrodes should not be used with low impedance (<1 G&#x3a9;) amplifiers due to severe signal attenuation and variability that changes clinical interpretations. The current method of preparing microelectrodes can leave sharp edges and nonuniform amounts of exposed wire. Even when recorded with higher impedance amplifiers, microelectrode data are highly prone to artifacts that are difficult to interpret. Great care must be taken when analyzing iEEG from high impedance microelectrodes.},
}
@article {pmid23643926,
year = {2014},
author = {Ruiz, S and Buyukturkoglu, K and Rana, M and Birbaumer, N and Sitaram, R},
title = {Real-time fMRI brain computer interfaces: self-regulation of single brain regions to networks.},
journal = {Biological psychology},
volume = {95},
number = {},
pages = {4-20},
doi = {10.1016/j.biopsycho.2013.04.010},
pmid = {23643926},
issn = {1873-6246},
mesh = {Brain/*physiology ; Brain Mapping ; *Brain-Computer Interfaces ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/*methods ; Nerve Net/*physiology ; *Neurofeedback ; Neurons/*physiology ; Social Control, Informal ; },
abstract = {With the advent of brain computer interfaces based on real-time fMRI (rtfMRI-BCI), the possibility of performing neurofeedback based on brain hemodynamics has become a reality. In the early stage of the development of this field, studies have focused on the volitional control of activity in circumscribed brain regions. However, based on the understanding that the brain functions by coordinated activity of spatially distributed regions, there have recently been further developments to incorporate real-time feedback of functional connectivity and spatio-temporal patterns of brain activity. The present article reviews the principles of rtfMRI neurofeedback, its applications, benefits and limitations. A special emphasis is given to the discussion of novel developments that have enabled the use of this methodology to achieve self-regulation of the functional connectivity between different brain areas and of distributed brain networks, anticipating new and exciting applications for cognitive neuroscience and for the potential alleviation of neuropsychiatric disorders.},
}
@article {pmid23643578,
year = {2013},
author = {Ono, T and Kimura, A and Ushiba, J},
title = {Daily training with realistic visual feedback improves reproducibility of event-related desynchronisation following hand motor imagery.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {9},
pages = {1779-1786},
doi = {10.1016/j.clinph.2013.03.006},
pmid = {23643578},
issn = {1872-8952},
mesh = {Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; *Electroencephalography ; Feedback, Sensory/*physiology ; Female ; Hand ; Humans ; Imagery, Psychotherapy/*methods ; Learning/*physiology ; Male ; Models, Neurological ; Reference Values ; Reproducibility of Results ; Young Adult ; },
abstract = {OBJECTIVE: Few brain-computer interface (BCI) studies have addressed learning mechanisms by exposure to visual feedback that elicits scalp electroencephalogram. We examined the effect of realistic visual feedback of hand movement associated with sensorimotor rhythm.<br><br>METHODS: Thirty-two healthy participants performed in five daily training in which they were shown motor imagery of their dominant hand. Participants were randomly assigned to 1 of 4 experimental groups receiving different types of visual feedback on event-related desynchronisation (ERD) derived over the contralateral sensorimotor cortex: no feedback as a control, bar feedback with changing bar length, anatomically incongruent feedback in which the hand open/grasp picture on screen was animated at eye level, and anatomically congruent feedback in which the same hand open/grasp picture was animated on the screen overlaying the participant's hand.<br><br>RESULTS: Daily training with all types of visual feedback induced more robust ERD than the no feedback condition (p < 0.05). The anatomically congruent feedback produced the highest reproducibility of ERD with the smallest inter-trial variance (p < 0.05).<br><br>CONCLUSION: Realistic feedback training is a suitable method to acquire the skill to control a BCI system.<br><br>SIGNIFICANCE: This finding highlights the possibility of improvement of reproducibility of ERD and can help to use BCI techniques.},
}
@article {pmid23642833,
year = {2013},
author = {Vidaurre, C and Pascual, J and Ramos-Murguialday, A and Lorenz, R and Blankertz, B and Birbaumer, N and Müller, KR},
title = {Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {9},
pages = {1824-1834},
doi = {10.1016/j.clinph.2013.03.009},
pmid = {23642833},
issn = {1872-8952},
mesh = {Analysis of Variance ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; Efferent Pathways/physiology ; Electric Stimulation/*methods ; *Electroencephalography ; Feedback, Sensory/*physiology ; Humans ; Imagination/*physiology ; Models, Statistical ; Movement/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet.<br><br>METHODS: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI.<br><br>RESULTS: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data.<br><br>CONCLUSION: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI.<br><br>SIGNIFICANCE: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).},
}
@article {pmid23639955,
year = {2013},
author = {Thomas, E and Dyson, M and Clerc, M},
title = {An analysis of performance evaluation for motor-imagery based BCI.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {031001},
doi = {10.1088/1741-2560/10/3/031001},
pmid = {23639955},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; *Task Performance and Analysis ; User-Computer Interface ; },
abstract = {In recent years, numerous brain-computer interfaces (BCIs) based on motor-imagery have been proposed which incorporate features such as adaptive classification, error detection and correction, fusion with auxiliary signals and shared control capabilities. Due to the added complexity of such algorithms, the evaluation strategy and metrics used for analysis must be carefully chosen to accurately represent the performance of the BCI. In this article, metrics are reviewed and contrasted using both simulated examples and experimental data. Furthermore, a review of the recent literature is presented to determine how BCIs are evaluated, in particular, focusing on the relationship between how the data are used relative to the BCI subcomponent under investigation. From the analysis performed in this study, valuable guidelines are presented regarding the choice of metrics and evaluation strategy dependent upon any chosen BCI paradigm.},
}
@article {pmid23632789,
year = {2013},
author = {Taghavi, H and Håkansson, B and Eeg-Olofsson, M and Johansson, CB and Tjellström, A and Reinfeldt, S and Bergqvist, T and Olsson, J},
title = {A vibration investigation of a flat surface contact to skull bone for direct bone conduction transmission in sheep skulls in vivo.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {34},
number = {4},
pages = {690-698},
doi = {10.1097/MAO.0b013e3182877aee},
pmid = {23632789},
issn = {1537-4505},
mesh = {Animals ; Bone Conduction/*physiology ; Female ; Hearing Aids ; Hearing Loss, Conductive/physiopathology/*surgery ; Osseointegration/*physiology ; Prosthesis Implantation/methods ; Sheep ; Skull/physiology/*surgery ; Temporal Bone/physiology/*surgery ; Vibration ; Wound Healing/physiology ; },
abstract = {HYPOTHESIS: Bone conduction implant (BCI) attached with a flat surface contact will offer efficient and linear vibration transmission over time.<br><br>BACKGROUND: Despite that percutaneous bone conduction devices (PBCD) are successful in treating patients with conductive hearing loss, there are some drawbacks related to the need of a permanent skin penetration. The BCI system is designed as an alternative to the PBCD because it leaves the skin intact.<br><br>METHODS: BCI dummy implants were installed in 3 sheep skulls in vivo to study the vibration transmission characteristics over time. Mechanical point impedances and vibration transfer response functions of the BCI implants were measured at the time of surgery and after a healing period of 8 months.<br><br>RESULTS: In 1 sheep both implants healed without complications. In the other 2 sheep, the implants were either partially loose or lost to follow up. In the sheep with stable implants, it was found by the resonance frequency shift of the mechanical point impedance that a firmer integration between the implant and bone tissue as seen in osseointegrated surfaces developed over time. It was also shown that the transcranial vibration transmission remains stable and linear. Providing bone chips in the contact between the implant and the bone did not enhance vibration transmission. The surgical procedure for installing the BCI dummy implants was uneventful.<br><br>CONCLUSION: The mechanical point impedances and vibration transfer response functions indicate that the BCI implants integrate and that transmission conditions remain stable over time.},
}
@article {pmid23629553,
year = {2013},
author = {Khoo, KF and Tan, HJ and R, R and Raymond, AA and M I, N and A, S and W Y, N},
title = {Prevalence of depression in stroke patients with vascular dementia in universiti kebangsaan malaysia medical center.},
journal = {The Medical journal of Malaysia},
volume = {68},
number = {2},
pages = {105-110},
pmid = {23629553},
issn = {0300-5283},
mesh = {Cross-Sectional Studies ; *Dementia, Vascular ; *Depression/diagnosis ; Humans ; Malaysia ; Prevalence ; Stroke/epidemiology ; },
abstract = {OBJECTIVE: Depression among patients with vascular dementia is frequently overlooked and potentially causes significant morbidity. There is limited data in Malaysia on the subject and this study was conducted to determine the prevalence of depression in vascular dementia (VaD) in UKMMC.<br><br>METHODS: This was a cross-sectional study involving diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM IV) criteria and who had a mini mental state examination (MMSE) score of less than 26. All patients were interviewed, examined clinically and their previous brain computer tomography (CT) were reviewed. The prevalence of depression was determined using the Cornell scale of depression.<br><br>RESULTS: A total of 76 patients were recruited with a mean age of 70.5 &#xb1; 9.5 years. The median duration of illness was 2.0 (1.0-4.8) years. The prevalence of depression in the study population was 31.6%. The patients with depression had a significant older mean age (74.5&#xb1;8.7 years old) compared to those without depression (68.6&#xb1;9.4 years old). Patients with large artery stroke of less than 3 years had significant higher frequency of depression (53.6%) compared to patients with small artery stroke (23.8%) and patients with right sided large artery stroke had significantly higher frequency of depression compared to left (70% vs. 44.4%). Median MMSE score (17.0) for depressed patients was significantly lower compared with median MMSE score (22.5) for non depressed patients. Median Barthel Index (30.0) for depressed patients was significantly lower compared with median Barthel score for non depressed patients.<br><br>CONCLUSIONS: Depression was prevalent among post stroke patients with VaD in UKMMC particularly for patients with older age, large artery stroke, right sided large artery stroke, low MMSE score and low Barthel Index. Early recognition of high risk patients is important in the holistic management of patients to prevent significant morbidity arising from depression.},
}
@article {pmid23627660,
year = {2013},
author = {Li, J and Liang, J and Zhao, Q and Li, J and Hong, K and Zhang, L},
title = {Design of assistive wheelchair system directly steered by human thoughts.},
journal = {International journal of neural systems},
volume = {23},
number = {3},
pages = {1350013},
doi = {10.1142/S0129065713500135},
pmid = {23627660},
issn = {1793-6462},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; *Persons with Disabilities ; Electroencephalography ; Functional Laterality ; Humans ; Imagination/physiology ; Movement/*physiology ; Thinking/*physiology ; User-Computer Interface ; *Wheelchairs ; },
abstract = {Integration of brain-computer interface (BCI) technique and assistive device is one of chief and promising applications of BCI system. With BCI technique, people with disabilities do not have to communicate with external environment through traditional and natural pathways like peripheral nerves and muscles, and could achieve it only by their brain activities. In this paper, we designed an electroencephalogram (EEG)-based wheelchair which can be steered by users' own thoughts without any other involvements. We evaluated the feasibility of BCI-based wheelchair in terms of accuracies and real-world testing. The results demonstrate that our BCI wheelchair is of good performance not only in accuracy, but also in practical running testing in a real environment. This fact implies that people can steer wheelchair only by their thoughts, and may have a potential perspective in daily application for disabled people.},
}
@article {pmid23627625,
year = {2012},
author = {Lopez-Gordo, MA and Pelayo, F and Prieto, A and Fernandez, E},
title = {An auditory brain-computer interface with accuracy prediction.},
journal = {International journal of neural systems},
volume = {22},
number = {3},
pages = {1250009},
doi = {10.1142/S0129065712500098},
pmid = {23627625},
issn = {1793-6462},
mesh = {Adult ; Auditory Perception/*physiology ; Bayes Theorem ; Brain/*physiology ; *Brain-Computer Interfaces ; Dichotic Listening Tests ; Evoked Potentials, Auditory/*physiology ; Female ; Fourier Analysis ; Functional Laterality ; Humans ; Male ; Signal-To-Noise Ratio ; Verbal Behavior ; Young Adult ; },
abstract = {Fully auditory Brain-computer interfaces based on the dichotic listening task (DL-BCIs) are suited for users unable to do any muscular movement, which includes gazing, exploration or coordination of their eyes looking for inputs in form of feedback, stimulation or visual support. However, one of their disadvantages, in contrast with the visual BCIs, is their lower performance that makes them not adequate in applications that require a high accuracy. To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy.},
}
@article {pmid23625062,
year = {2013},
author = {De Massari, D and Ruf, CA and Furdea, A and Matuz, T and van der Heiden, L and Halder, S and Silvoni, S and Birbaumer, N},
title = {Brain communication in the locked-in state.},
journal = {Brain : a journal of neurology},
volume = {136},
number = {Pt 6},
pages = {1989-2000},
doi = {10.1093/brain/awt102},
pmid = {23625062},
issn = {1460-2156},
mesh = {Adult ; Aged ; Brain/pathology/*physiology ; Conditioning, Psychological/physiology ; Electroencephalography/methods ; Female ; Humans ; Male ; Quadriplegia/*diagnosis/*physiopathology/psychology ; },
abstract = {Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.},
}
@article {pmid23624244,
year = {2013},
author = {Thomas, E and Fruitet, J and Clerc, M},
title = {Combining ERD and ERS features to create a system-paced BCI.},
journal = {Journal of neuroscience methods},
volume = {216},
number = {2},
pages = {96-103},
doi = {10.1016/j.jneumeth.2013.03.026},
pmid = {23624244},
issn = {1872-678X},
mesh = {*Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Humans ; Imagination/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {An important factor in the usability of a brain-computer interface (BCI) is the setup and calibration time required for the interface to perform accurately. Recently, brain-switches based on the beta rebound following motor imagery of a single limb effector have been investigated as basic BCIs due to their good performance with limited electrodes, and brief training session requirements. Here, a BCI is proposed which expands the methodology of brain-switches to design an interface composed of multiple brain-controlled buttons. The algorithm is designed as a system paced interface which can recognise 2 intentional-control tasks and a no-control state based on the activity during and following motor imagery in only 3 electroencephalogram channels. An online experiment was performed over 6 subjects to validate the algorithm, and the results show that a working BCI can be trained from a single calibration session and that the post motor imagery features are both informative and robust over multiple sessions.},
}
@article {pmid23615167,
year = {2013},
author = {Desmet, JB and Bosman, AJ and Snik, AF and Lambrechts, P and Hol, MK and Mylanus, EA and De Bodt, M and Van de Heyning, P},
title = {Comparison of sound processing strategies for osseointegrated bone conduction implants in mixed hearing loss: multiple-channel nonlinear versus single-channel linear processing.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {34},
number = {4},
pages = {598-603},
doi = {10.1097/MAO.0b013e318287793a},
pmid = {23615167},
issn = {1537-4505},
mesh = {Adult ; Aged ; Aged, 80 and over ; Auditory Threshold/physiology ; *Bone Conduction ; Female ; Hearing/*physiology ; *Hearing Aids ; Hearing Loss, Mixed Conductive-Sensorineural/*physiopathology/rehabilitation ; Humans ; Male ; Middle Aged ; Speech Perception/*physiology ; Surveys and Questionnaires ; },
abstract = {OBJECTIVES: Evaluation of a single-channel linear bone conduction implant sound processor (S-BCI) and a multiple-channel nonlinear bone conduction implant sound processor (M-BCI) with objective and subjective measures in patients with mixed hearing loss.<br><br>STUDY DESIGN: In total, 20 patients with mixed hearing loss were included in the study. For either sound processor aided thresholds and speech perception in quiet with monosyllables were measured. Speech perception in noise was measured with sentences. Two different configurations were used: speech and noise at 0&#xb0; (S0N0) and speech at 0&#xb0; and noise at 180&#xb0; (S0N180). The M-BCI was tested in both omnidirectional and directional mode. Patients were first fitted with the S-BCI and evaluated 3 weeks later. The M-BCI was fitted and, again 3 weeks later, evaluated. Subjectively, patients compared both sound processors with the APHAB questionnaire.<br><br>RESULTS: Aided thresholds were similar for both sound processors in the low- and mid-frequency range. For speech in quiet, no significant differences between both sound processors were observed. For speech in noise in the S0N0 condition, the M-BCI-thresholds were 1.7 dB (SD, 2.2dB; p = 0.002) more favorable than with S-BCI. For the S0N180 configuration, an improvement of 5.8 dB (SD, 2.8 dB; p < 0.001) was seen for the directional mode relative to S-BCI. The APHAB showed statistically significant subjective improvement with the M-BCI on all subscales relative to S-BCI.<br><br>CONCLUSION: Speech intelligibility in noise is better with M-BCI than with S-BCI. This was attributed to better high-frequency gain provided by the M-BCI. Improved signal processing strategies may have contributed to subjective preference for the M-BCI.},
}
@article {pmid23615061,
year = {2013},
author = {Wicks, S},
title = {Identifying tier one key suppliers.},
journal = {Journal of business continuity &amp; emergency planning},
volume = {6},
number = {3},
pages = {210-221},
pmid = {23615061},
issn = {1749-9216},
mesh = {Commerce/*organization &amp; administration ; Communication ; Decision Making ; Disaster Planning/*methods ; Equipment and Supplies/*supply &amp; distribution ; Humans ; London ; Risk Assessment ; },
abstract = {In today's global marketplace, businesses are becoming increasingly reliant on suppliers for the provision of key processes, activities, products and services in support of their strategic business goals. The result is that now, more than ever, the failure of a key supplier has potential to damage reputation, productivity, compliance and financial performance seriously. Yet despite this, there is no recognised standard or guidance for identifying a tier one key supplier base and, up to now, there has been little or no research on how to do so effectively. This paper outlines the key findings of a BCI-sponsored research project to investigate good practice in identifying tier one key suppliers, and suggests a scalable framework process model and risk matrix tool to help businesses effectively identify their tier one key supplier base.},
}
@article {pmid23612883,
year = {2013},
author = {Polprasert, C and Kukieattikool, P and Demeechai, T and Ritcey, JA and Siwamogsatham, S},
title = {New stimulation pattern design to improve P300-based matrix speller performance at high flash rate.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036012},
doi = {10.1088/1741-2560/10/3/036012},
pmid = {23612883},
issn = {1741-2552},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; *Fuzzy Logic ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sample Size ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {OBJECTIVE: We propose a new stimulation pattern design for the P300-based matrix speller aimed at increasing the minimum target-to-target interval (TTI).<br><br>APPROACH: Inspired by the simplicity and strong performance of the conventional row-column (RC) stimulation, the proposed stimulation is obtained by modifying the RC stimulation through alternating row and column flashes which are selected based on the proposed design rules. The second flash of the double-flash components is then delayed for a number of flashing instants to increase the minimum TTI. The trade-off inherited in this approach is the reduced randomness within the stimulation pattern.<br><br>MAIN RESULTS: We test the proposed stimulation pattern and compare its performance in terms of selection accuracy, raw and practical bit rates with the conventional RC flashing paradigm over several flash rates. By increasing the minimum TTI within the stimulation sequence, the proposed stimulation has more event-related potentials that can be identified compared to that of the conventional RC stimulations, as the flash rate increases. This leads to significant performance improvement in terms of the letter selection accuracy, the raw and practical bit rates over the conventional RC stimulation.<br><br>SIGNIFICANCE: These studies demonstrate that significant performance improvement over the RC stimulation is obtained without additional testing or training samples to compensate for low P300 amplitude at high flash rate. We show that our proposed stimulation is more robust to reduced signal strength due to the increased flash rate than the RC stimulation.},
}
@article {pmid23611833,
year = {2013},
author = {Marathe, AR and Taylor, DM},
title = {Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036015},
pmid = {23611833},
issn = {1741-2552},
support = {R01 NS058871/NS/NINDS NIH HHS/United States ; 1R01NS058871/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/*physiology ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor/*physiology ; Haplorhini ; Motor Cortex/*physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys.<br><br>APPROACH: Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis, and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time.<br><br>MAIN RESULTS: Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The 'position-to-velocity' transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys' online performance significantly improved across days of closed-loop training.<br><br>SIGNIFICANCE: Most published BMI studies that use electrocorticographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a substantial improvement in decoding accuracy could be attained by using our new version of the CSP algorithm that extends the traditional CSP method for use with continuous limb movement data.},
}
@article {pmid23611808,
year = {2013},
author = {Garipelli, G and Chavarriaga, R and Millán, Jdel R},
title = {Single trial analysis of slow cortical potentials: a study on anticipation related potentials.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036014},
doi = {10.1088/1741-2560/10/3/036014},
pmid = {23611808},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Anticipation, Psychological/*physiology ; Attention/*physiology ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Choice Behavior/*physiology ; Cues ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering.<br><br>APPROACH: We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days.<br><br>MAIN RESULTS: We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 &#xb1; 0.05 on average with a minimum of 0.81 &#xb1; 0.03 and a maximum of 0.97 &#xb1; 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers.<br><br>SIGNIFICANCE: We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.},
}
@article {pmid23607558,
year = {2013},
author = {Dangi, S and Orsborn, AL and Moorman, HG and Carmena, JM},
title = {Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces.},
journal = {Neural computation},
volume = {25},
number = {7},
pages = {1693-1731},
doi = {10.1162/NECO_a_00460},
pmid = {23607558},
issn = {1530-888X},
mesh = {Action Potentials/physiology ; *Adaptation, Physiological ; *Algorithms ; Animals ; Arm/innervation ; *Brain-Computer Interfaces ; Feedback, Physiological/*physiology ; Likelihood Functions ; Macaca mulatta ; *Models, Neurological ; Motor Cortex/cytology ; Motor Neurons/*physiology ; Movement/physiology ; Neural Pathways ; Time Factors ; Visual Cortex/physiology ; },
abstract = {Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.},
}
@article {pmid23596396,
year = {2013},
author = {Nishimura, Y and Perlmutter, SI and Fetz, EE},
title = {Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury.},
journal = {Frontiers in neural circuits},
volume = {7},
number = {},
pages = {57},
pmid = {23596396},
issn = {1662-5110},
support = {R01 NS012542/NS/NINDS NIH HHS/United States ; R01 NS040867/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; NS 40867/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/methods ; *Electrodes, Implanted ; Macaca nemestrina ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Pyramidal Tracts/*physiology ; Recovery of Function/*physiology ; Spinal Cord Injuries/physiopathology/*therapy ; Upper Extremity/innervation/*physiology ; },
abstract = {Functional loss of limb control in individuals with spinal cord injury or stroke can be caused by interruption of corticospinal pathways, although the neural circuits located above and below the lesion remain functional. An artificial neural connection that bridges the lost pathway and connects cortical to spinal circuits has potential to ameliorate the functional loss. We investigated the effects of introducing novel artificial neural connections in a paretic monkey that had a unilateral spinal cord lesion at the C2 level. The first application bridged the impaired spinal lesion. This allowed the monkey to drive the spinal stimulation through volitionally controlled power of high-gamma activity in either the premotor or motor cortex, and thereby to acquire a force-matching target. The second application created an artificial recurrent connection from a paretic agonist muscle to a spinal site, allowing muscle-controlled spinal stimulation to boost on-going activity in the muscle. These results suggest that artificial neural connections can compensate for interrupted descending pathways and promote volitional control of upper limb movement after damage of descending pathways such as spinal cord injury or stroke.},
}
@article {pmid23594762,
year = {2013},
author = {Manyakov, NV and Chumerin, N and Robben, A and Combaz, A and van Vliet, M and Van Hulle, MM},
title = {Sampled sinusoidal stimulation profile and multichannel fuzzy logic classification for monitor-based phase-coded SSVEP brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036011},
doi = {10.1088/1741-2560/10/3/036011},
pmid = {23594762},
issn = {1741-2552},
mesh = {Adult ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; *Fuzzy Logic ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sample Size ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {OBJECTIVE: The performance and usability of brain-computer interfaces (BCIs) can be improved by new paradigms, stimulation methods, decoding strategies, sensor technology etc. In this study we introduce new stimulation and decoding methods for electroencephalogram (EEG)-based BCIs that have targets flickering at the same frequency but with different phases.<br><br>APPROACH: The phase information is estimated from the EEG data, and used for target command decoding. All visual stimulation is done on a conventional (60-Hz) LCD screen. Instead of the 'on/off' visual stimulation, commonly used in phase-coded BCI, we propose one based on a sampled sinusoidal intensity profile. In order to fully exploit the circular nature of the evoked phase response, we introduce a filter feature selection procedure based on circular statistics and propose a fuzzy logic classifier designed to cope with circular information from multiple channels jointly.<br><br>MAIN RESULTS: We show that the proposed visual stimulation enables us not only to encode more commands under the same conditions, but also to obtain EEG responses with a more stable phase. We also demonstrate that the proposed decoding approach outperforms existing ones, especially for the short time windows used.<br><br>SIGNIFICANCE: The work presented here shows how to overcome some of the limitations of screen-based visual stimulation. The superiority of the proposed decoding approach demonstrates the importance of preserving the circularity of the data during the decoding stage.},
}
@article {pmid23594571,
year = {2013},
author = {McMorland, AJ and Velliste, M},
title = {Baseplate for two-stage cranial mounting of BMI connectors.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {034001},
doi = {10.1088/1741-2560/10/3/034001},
pmid = {23594571},
issn = {1741-2552},
mesh = {Animals ; *Bone Screws ; *Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Haplorhini ; Monitoring, Ambulatory/*instrumentation ; Skull/*surgery ; },
abstract = {OBJECTIVE: Intracortical electrode arrays provide the best spatial and temporal resolution signals for brain-machine interfaces. Wireless technologies are being developed to handle this information capacity, but currently the only means to deliver neural information from the implant to a signal processing unit is by a physical connection starting at a skull-mounted connector. The failure rate of the attachment of these connectors is significant. In this study we report an improvement to the traditional connectors.<br><br>APPROACH: We have designed and applied an intermediary mounting plate that incorporates several features that provide better, more stable fixation to the skull: (1) wide legs allowing distribution of loading forces and distancing the intracranial screws from the skin interface, (2) a thin shelf to allow early osseointegration, (3) a concave interior to accommodate the curvature of the cranium, and (4) two-stage fixation process providing time for osseointegration prior to the application of loading forces from the connector.<br><br>MAIN RESULTS: Six baseplates, over four design iterations, have now been tested in three non-human primates. The baseplates are associated with a substantially lower attachment failure rate.<br><br>SIGNIFICANCE: Our baseplate design improves on the current skull-mounted connectors, leading to better outcomes for subjects and fewer catastrophic failure events that can terminate resource intensive intracortical recording experiments.},
}
@article {pmid23593261,
year = {2013},
author = {Xu, M and Qi, H and Ma, L and Sun, C and Zhang, L and Wan, B and Yin, T and Ming, D},
title = {Channel selection based on phase measurement in P300-based brain-computer interface.},
journal = {PloS one},
volume = {8},
number = {4},
pages = {e60608},
pmid = {23593261},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Male ; Photic Stimulation ; Time Factors ; },
abstract = {Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE) is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE), the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations.},
}
@article {pmid23593130,
year = {2013},
author = {Shanechi, MM and Williams, ZM and Wornell, GW and Hu, RC and Powers, M and Brown, EN},
title = {A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design.},
journal = {PloS one},
volume = {8},
number = {4},
pages = {e59049},
pmid = {23593130},
issn = {1932-6203},
support = {R01 EB006385/EB/NIBIB NIH HHS/United States ; R01 HD059852/HD/NICHD NIH HHS/United States ; R01-EB006385/EB/NIBIB NIH HHS/United States ; DP1-0D003646-01/DP/NCCDPHP CDC HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Feedback ; Female ; Motor Cortex/*physiology ; Observer Variation ; Placenta/diagnostic imaging/*metabolism ; Pregnancy ; Rats ; Rats, Sprague-Dawley ; Ultrasonography ; },
abstract = {Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.},
}
@article {pmid23591720,
year = {2013},
author = {Mucciardi, G and Galì, A and Inferrera, A and Di Benedetto, A and Macchione, L and Mucciardi, M and Magno, C},
title = {Longitudinal observational cohort study about detrusor underactivity as a risk factor for bladder neck contracture after retropubic radical prostatectomy: preliminary results.},
journal = {International urology and nephrology},
volume = {45},
number = {3},
pages = {721-726},
pmid = {23591720},
issn = {1573-2584},
mesh = {Aged ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Neoplasm Staging ; Prognosis ; Prostatectomy/*adverse effects/methods ; Prostatic Neoplasms/pathology/*surgery ; Retrospective Studies ; Risk Factors ; Time Factors ; Urinary Bladder/*physiopathology ; Urinary Bladder Neck Obstruction/*etiology/physiopathology ; Urinary Incontinence/*complications/physiopathology ; Urodynamics/*physiology ; },
abstract = {OBJECTIVES: To evaluate the association between preoperative detrusor underactivity (DU) and symptomatic bladder neck contracture (BNC) in patients undergoing radical retropubic prostatectomy (RRP), in order to identify a possible new risk factor in the etiopathogenic mechanisms of BNC after RRP.<br><br>METHODS: A total of 100 prostate cancer patients underwent RRP after preoperative complete urodynamic examination. Detrusor contractility was evaluated by bladder contractility index (BCI), power at maximum flow (WF-Qmax), and maximum velocity of detrusorial contraction (MVDC). Follow-up included uroflowmetry with bladder post-voiding volume evaluation at 3 and 6 months after surgery and repeated urodynamic examination at 12 months. Statistical evaluation was performed using the Student's t test (P < 0.01).<br><br>RESULTS: The mean patient age was 65.6 &#xb1; 5.4 years, and pathological stage ranged from T2a to T2c. A total of 40 patients (40 %) presented normal detrusor contractility, 47 (47 %) mild DU, and 13 (13 %) severe DU. Detrusor overactivity (DO) was observed in 12 patients (12 %), small cystometric capacity in 10 (10 %), low compliance in 16 (16 %), DO plus DU (mild or severe) in 6 (6 %), and DO plus small cystometric capacity together with low compliance in 5 (5 %). Normal urodynamics were observed in 38 patients (38 %). Overall BNC incidence was 12. All patients with BNC presented preoperative DU; none presented DO or low bladder compliance. DU severity and BNC occurrence were significantly correlated (P < 0.01) for all 3 urodynamic parameters (BCI, WF-Qmax, and MVDC).<br><br>CONCLUSIONS: We identify DU as a possible novel risk factor for BNC formation after radical prostatectomy that may contribute to its development.},
}
@article {pmid23590982,
year = {2013},
author = {Apuzzo, ML},
title = {The reinvention of the human being: new dimensions of functional restoration.},
journal = {World neurosurgery},
volume = {79},
number = {3-4},
pages = {407},
doi = {10.1016/j.wneu.2013.01.086},
pmid = {23590982},
issn = {1878-8769},
mesh = {Biomedical Engineering/*trends ; *Brain-Computer Interfaces ; Humans ; Nervous System Diseases/rehabilitation/*therapy ; Neural Prostheses ; Neurosurgery ; *Recovery of Function ; },
}
@article {pmid23590556,
year = {2014},
author = {Thompson, DE and Gruis, KL and Huggins, JE},
title = {A plug-and-play brain-computer interface to operate commercial assistive technology.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {9},
number = {2},
pages = {144-150},
pmid = {23590556},
issn = {1748-3115},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Amyotrophic Lateral Sclerosis/*rehabilitation ; *Brain-Computer Interfaces ; Persons with Disabilities/*rehabilitation ; Female ; Humans ; Male ; Middle Aged ; *Self-Help Devices ; Young Adult ; },
abstract = {PURPOSE: To determine if a brain-computer interface (BCI) could be used as a plug-and-play input device to operate commercial assistive technology (AT), and to quantify the performance impact of such operation.<br><br>METHOD: Using a hardware device designed in our lab, participants (11 with amyotrophic lateral sclerosis, 22 controls) were asked to operate two devices using a BCI. Results were compared to traditional BCI operation by the same users. Performance was assessed using both accuracy and BCI utility, a throughput metric. 95% confidence bounds on performance differences were developed using a linear mixed model.<br><br>RESULTS: The observed differences in accuracy and throughput were small and not statistically significant. The confidence bounds indicate that if there is a performance impact of using a BCI to control an AT device, the impact could easily be overcome by the benefits of the AT device itself.<br><br>CONCLUSIONS: BCI control of AT devices is possible, and the performance difference appears to be very small. BCI designers are encouraged to incorporate standard outputs into their design to enable future users to interface with familiar AT devices.<br><br>Brain-computer interface (BCI) control of assistive technology (AT) devices is possible. The performance impact of such control is low when BCIs are commercially available, AT providers can use a BCI as an input device to existing AT devices already in use by their clients.},
}
@article {pmid23587933,
year = {2013},
author = {Hwang, HJ and Hwan Kim, D and Han, CH and Im, CH},
title = {A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI).},
journal = {Brain research},
volume = {1515},
number = {},
pages = {66-77},
doi = {10.1016/j.brainres.2013.03.050},
pmid = {23587933},
issn = {1872-6240},
mesh = {Adult ; Brain-Computer Interfaces/*psychology ; *Cell Phone ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {In the present study, we introduce a new dual-frequency stimulation method that can produce more visual stimuli with limited number of stimulation frequencies for use in multiclass steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Methods for increasing the number of visual stimuli are necessary, particularly for the implementation of multi-class SSVEP-based BCI, as available stimulation frequencies are generally limited when visual stimuli are presented through a computer monitor. The new stimulation was based on a conventional black-white checkerboard pattern; however, unlike the conventional approach, ten visual stimuli eliciting distinct SSVEP responses at different frequencies could be generated by combining four different stimulation frequencies. Through the offline experiments conducted with eleven participants, we confirmed that all ten visual stimuli could evoke distinct and discriminable single SSVEP peaks, of which the signal-to-noise ratios were high enough to be used for practical SSVEP-based BCI systems. In order to demonstrate the possibility of the practical use of the proposed method, a mental keypad system was implemented and online experiments were conducted with additional ten participants. We achieved an average information transfer rate of 33.26 bits/min and an average accuracy of 87.23%, and all ten participants succeeded in calling their mobile phones using our online BCI system.},
}
@article {pmid23584550,
year = {2013},
author = {Alvarez, J and Meyer, FL and Granoff, DL and Lundy, A},
title = {The effect of EEG biofeedback on reducing postcancer cognitive impairment.},
journal = {Integrative cancer therapies},
volume = {12},
number = {6},
pages = {475-487},
doi = {10.1177/1534735413477192},
pmid = {23584550},
issn = {1552-695X},
mesh = {Adult ; Aged ; Analysis of Variance ; Antineoplastic Agents/*adverse effects/therapeutic use ; Anxiety/epidemiology/therapy ; Breast Neoplasms/*drug therapy ; Cognition Disorders/chemically induced/*therapy ; Depression/epidemiology/therapy ; Fatigue/epidemiology/therapy ; Feasibility Studies ; Female ; Follow-Up Studies ; Humans ; Middle Aged ; Neurofeedback/*methods ; Prospective Studies ; Psychiatric Status Rating Scales ; Quality of Life ; Sleep Wake Disorders/epidemiology/therapy ; Survivors ; Time Factors ; },
abstract = {UNLABELLED: BACKGROUND AND HYPOTHESES: Postcancer cognitive impairment (PCCI) is observed in a substantial number of breast cancer survivors, persisting for as long as 20 years in some subgroups. Although compensatory strategies are frequently suggested, no restorative interventions have yet been identified. This study examined the feasibility of EEG biofeedback ("neurofeedback") and its potential effectiveness in reducing PCCI as well as the fatigue, sleep disturbance, and psychological symptoms that frequently accompany PCCI.<br><br>STUDY DESIGN: This was a 6-month prospective study with a waitlist control period followed by an active intervention. Participants were female breast cancer survivors (n = 23), 6 to 60 months postchemotherapy, with self-reported cognitive impairment.<br><br>METHODS: Four self-report outcome measures (Functional Assessment of Cancer Therapy-Cognitive Function [FACT-Cog], Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-Fatigue], Pittsburgh Sleep Quality Index [PSQI], and Brief Symptom Inventory [BSI]-18) were administered 3 times during a 10-week waitlist control period, 3 times during a 10-week (20-session) neurofeedback training regimen, and once at 4 weeks postneurofeedback.<br><br>RESULTS: All 23 participants completed the study, demonstrating the feasibility of EEG biofeedback in this population. Initially, the sample demonstrated significant dysfunction on all measures compared with general population norms. Repeated-measures ANOVAs revealed strongly significant improvements (P < .001) on all 4 cognitive measures (perceived cognitive impairment, comments from others, perceived cognitive abilities, and impact on quality of life [QOL]), the fatigue scale, and the 4 psychological scales (somatization, depression, anxiety and global severity index) as well as on 3 of 8 sleep scales (quality, daytime dysfunction, and global). Two of the other sleep scales (latency and disturbance) were significant at P < .01, and 1 (use of medication) at P < .05; 2 were not significant. Improvements were generally linear across the course of training, and were maintained at the follow-up testing. At the follow-up testing, the sample no longer differed significantly from normative populations on 3 of the 4 FACT-Cog measures (impairment, impact on QOL, and comments), FACIT-Fatigue, PSQI sleep quality and habitual efficiency, or any of the BSI-18 measures of psychological disturbance.<br><br>CONCLUSIONS: Data from this limited study suggest that EEG biofeedback has potential for reducing the negative cognitive and emotional sequelae of cancer treatment as well as improving fatigue and sleep patterns.},
}
@article {pmid23579176,
year = {2013},
author = {Ahmadian, P and Cagnoni, S and Ascari, L},
title = {How capable is non-invasive EEG data of predicting the next movement? A mini review.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {124},
pmid = {23579176},
issn = {1662-5161},
abstract = {In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users.},
}
@article {pmid23578057,
year = {2013},
author = {Hsu, WY},
title = {Single-trial motor imagery classification using asymmetry ratio, phase relation, wavelet-based fractal, and their selected combination.},
journal = {International journal of neural systems},
volume = {23},
number = {2},
pages = {1350007},
doi = {10.1142/S012906571350007X},
pmid = {23578057},
issn = {1793-6462},
mesh = {Algorithms ; Brain/*physiology ; *Brain Mapping ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials/*physiology ; *Fractals ; Functional Laterality/physiology ; Humans ; *Imagery, Psychotherapy ; Imagination ; Movement/*physiology ; Spectrum Analysis ; Wavelet Analysis ; },
abstract = {An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with "without enhanced active segment selection," several potential features and linear discriminant analysis (LDA) on MI data from two data sets for 10 subjects, the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.},
}
@article {pmid23578052,
year = {2013},
author = {Müller-Putz, GR and Pokorny, C and Klobassa, DS and Horki, P},
title = {A single-switch BCI based on passive and imagined movements: toward restoring communication in minimally conscious patients.},
journal = {International journal of neural systems},
volume = {23},
number = {2},
pages = {1250037},
doi = {10.1142/S0129065712500372},
pmid = {23578052},
issn = {1793-6462},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication ; Cues ; Electroencephalography ; Electromyography ; Feedback, Physiological ; Female ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Persistent Vegetative State/*physiopathology/*rehabilitation ; Wrist/innervation ; Young Adult ; },
abstract = {We investigate whether an electroencephalography technique could be used for yes/no communication with auditory scanning. To be usable by the target group, i.e., minimally conscious individuals, such a brain-computer interface (BCI) has to be very simple and robust. This leads to the concept of a single-switch BCI (ssBCI). With an ssBCI it is possible to reliably detect one certain, individually trained, brain pattern of the individual, and use it to control all kinds of applications using yes/no responses. A total of 10 healthy volunteers (20-27 years) participated in an initial cue-based session with a motor imagery (MI) task after brisk passive feet/hand movement. Four of them reached MI classification accuracies above 70% and, thus, fulfilled the inclusion criterion for participation in the 2nd session. In the 2nd session, MI was used to communicate yes/no answers to a series of questions in an auditory scanning mode. Two of the three participants of the 2nd session were able to reliably communicate their intent with 90% or above correct and 0% false responses. This work showed, for the 1st time, the use of a ssBCI based on passive and imagined movements for communication in auditory scanning mode.},
}
@article {pmid23576968,
year = {2013},
author = {Choi, I and Rajaram, S and Varghese, LA and Shinn-Cunningham, BG},
title = {Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalography.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {115},
pmid = {23576968},
issn = {1662-5161},
abstract = {Selective auditory attention is essential for human listeners to be able to communicate in multi-source environments. Selective attention is known to modulate the neural representation of the auditory scene, boosting the representation of a target sound relative to the background, but the strength of this modulation, and the mechanisms contributing to it, are not well understood. Here, listeners performed a behavioral experiment demanding sustained, focused spatial auditory attention while we measured cortical responses using electroencephalography (EEG). We presented three concurrent melodic streams; listeners were asked to attend and analyze the melodic contour of one of the streams, randomly selected from trial to trial. In a control task, listeners heard the same sound mixtures, but performed the contour judgment task on a series of visual arrows, ignoring all auditory streams. We found that the cortical responses could be fit as weighted sum of event-related potentials evoked by the stimulus onsets in the competing streams. The weighting to a given stream was roughly 10 dB higher when it was attended compared to when another auditory stream was attended; during the visual task, the auditory gains were intermediate. We then used a template-matching classification scheme to classify single-trial EEG results. We found that in all subjects, we could determine which stream the subject was attending significantly better than by chance. By directly quantifying the effect of selective attention on auditory cortical responses, these results reveal that focused auditory attention both suppresses the response to an unattended stream and enhances the response to an attended stream. The single-trial classification results add to the growing body of literature suggesting that auditory attentional modulation is sufficiently robust that it could be used as a control mechanism in brain-computer interfaces (BCIs).},
}
@article {pmid23574919,
year = {2013},
author = {Dethier, J and Nuyujukian, P and Ryu, SI and Shenoy, KV and Boahen, K},
title = {Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036008},
pmid = {23574919},
issn = {1741-2552},
support = {R01NS076460/NS/NINDS NIH HHS/United States ; DP1 OD000965/OD/NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; DP1-OD006409/OD/NIH HHS/United States ; DPI-OD000965/OD/NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain Mapping/*instrumentation/methods ; *Brain-Computer Interfaces ; Computer Systems ; Computer-Aided Design ; Data Interpretation, Statistical ; Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Haplorhini ; Nerve Net/*physiology ; Neurons/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {OBJECTIVE: Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex.<br><br>APPROACH: One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs).<br><br>MAIN RESULTS: Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques.<br><br>SIGNIFICANCE: These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.},
}
@article {pmid23574821,
year = {2013},
author = {Liyanage, SR and Guan, C and Zhang, H and Ang, KK and Xu, J and Lee, TH},
title = {Dynamically weighted ensemble classification for non-stationary EEG processing.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036007},
doi = {10.1088/1741-2560/10/3/036007},
pmid = {23574821},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: The non-stationary nature of EEG poses a major challenge to robust operation of brain-computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification.<br><br>APPROACH: We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are trained on clustered features. The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified.<br><br>MAIN RESULTS: The clusters of the feature space from the second session spanned a different space compared to the clusters of the feature space from the first session which highlights the processes of session-to-session non-stationarity. The session-to-session performance of the proposed DWEC method was evaluated on two datasets. The results on publicly available BCI Competition IV dataset 2A yielded a significantly higher mean accuracy of 81.48% compared to 75.9% from the baseline support vector machine (SVM) classifier without dynamic weighting. Results on the data collected from our twelve in-house subjects yielded a significantly higher mean accuracy of 73% compared to 69.4% from the baseline SVM classifier without dynamic weighting.<br><br>SIGNIFICANCE: The cluster based analysis provides insight into session-to-session non-stationarity in EEG data. The results demonstrate the effectiveness of the proposed method in addressing non-stationarity in EEG data for the operation of a BCI.},
}
@article {pmid23574741,
year = {2013},
author = {Perge, JA and Homer, ML and Malik, WQ and Cash, S and Eskandar, E and Friehs, G and Donoghue, JP and Hochberg, LR},
title = {Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.},
journal = {Journal of neural engineering},
volume = {10},
number = {3},
pages = {036004},
pmid = {23574741},
issn = {1741-2552},
support = {N01HD10018/HD/NICHD NIH HHS/United States ; HHSN275201100018C/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Circadian Rhythm ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Nerve Net/*physiopathology ; Quadriplegia/*physiopathology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {OBJECTIVE: Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS.<br><br>APPROACH: To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE).<br><br>MAIN RESULTS: 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 &#xb1; 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 &#xb1; 6.5 &#xb5;V or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional 'bias' in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions.<br><br>SIGNIFICANCE: We anticipate that signal acquisition and decoding methods that can adapt to the reported instabilities will further improve the performance of intracortically-based NISs.},
}
@article {pmid23573251,
year = {2013},
author = {Yoo, SS and Kim, H and Filandrianos, E and Taghados, SJ and Park, S},
title = {Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains.},
journal = {PloS one},
volume = {8},
number = {4},
pages = {e60410},
pmid = {23573251},
issn = {1932-6203},
support = {R21 NS074124/NS/NINDS NIH HHS/United States ; 2010-0027294//PHS HHS/United States ; },
mesh = {Acoustic Stimulation ; Animals ; Brain/physiology ; *Brain-Computer Interfaces ; Electrophysiological Phenomena ; Evoked Potentials, Visual ; Humans ; Motor Activity ; Photic Stimulation ; Rats ; Rats, Sprague-Dawley ; Sound ; Tail/physiology ; Transducers ; },
abstract = {Transcranial focused ultrasound (FUS) is capable of modulating the neural activity of specific brain regions, with a potential role as a non-invasive computer-to-brain interface (CBI). In conjunction with the use of brain-to-computer interface (BCI) techniques that translate brain function to generate computer commands, we investigated the feasibility of using the FUS-based CBI to non-invasively establish a functional link between the brains of different species (i.e. human and Sprague-Dawley rat), thus creating a brain-to-brain interface (BBI). The implementation was aimed to non-invasively translate the human volunteer's intention to stimulate a rat's brain motor area that is responsible for the tail movement. The volunteer initiated the intention by looking at a strobe light flicker on a computer display, and the degree of synchronization in the electroencephalographic steady-state-visual-evoked-potentials (SSVEP) with respect to the strobe frequency was analyzed using a computer. Increased signal amplitude in the SSVEP, indicating the volunteer's intention, triggered the delivery of a burst-mode FUS (350 kHz ultrasound frequency, tone burst duration of 0.5 ms, pulse repetition frequency of 1 kHz, given for 300 msec duration) to excite the motor area of an anesthetized rat transcranially. The successful excitation subsequently elicited the tail movement, which was detected by a motion sensor. The interface was achieved at 94.0±3.0% accuracy, with a time delay of 1.59±1.07 sec from the thought-initiation to the creation of the tail movement. Our results demonstrate the feasibility of a computer-mediated BBI that links central neural functions between two biological entities, which may confer unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications.},
}
@article {pmid23567743,
year = {2013},
author = {Blain-Moraes, S and Mashour, GA and Lee, H and Huggins, JE and Lee, U},
title = {Altered cortical communication in amyotrophic lateral sclerosis.},
journal = {Neuroscience letters},
volume = {543},
number = {},
pages = {172-176},
pmid = {23567743},
issn = {1872-7972},
support = {R01 GM098578/GM/NIGMS NIH HHS/United States ; R01GM098578-01/GM/NIGMS NIH HHS/United States ; FRN 123751/CAPMC/CIHR/Canada ; R21 HD054697/HD/NICHD NIH HHS/United States ; R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*physiopathology/psychology ; Brain-Computer Interfaces ; Case-Control Studies ; Cerebral Cortex/*physiopathology ; Cognition ; Electroencephalography ; Female ; Frontal Lobe/physiopathology ; Humans ; Male ; Middle Aged ; Parietal Lobe/physiopathology ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is a disorder associated primarily with the degeneration of the motor system. More recently, functional connectivity studies have demonstrated potentially adaptive changes in ALS brain organization, but disease-related changes in cortical communication remain unknown. We recruited individuals with ALS and age-matched controls to operate a brain-computer interface while electroencephalography was recorded over three sessions. Using normalized symbolic transfer entropy, we measured directed functional connectivity from frontal to parietal (feedback connectivity) and parietal to frontal (feedforward connectivity) regions. Feedback connectivity was not significantly different between groups, but feedforward connectivity was significantly higher in individuals with ALS. This result was consistent across a broad electroencephalographic spectrum (4-35 Hz), and in theta, alpha and beta frequency bands. Feedback connectivity has been associated with conscious state and was found to be independent of ALS symptom severity in this study, which may have significant implications for the detection of consciousness in individuals with advanced ALS. We suggest that increases in feedforward connectivity represent a compensatory response to the ALS-related loss of input such that sensory stimuli have sufficient strength to cross the threshold necessary for conscious processing in the global neuronal workspace.},
}
@article {pmid23565237,
year = {2013},
author = {Geuze, J and van Gerven, MA and Farquhar, J and Desain, P},
title = {Detecting semantic priming at the single-trial level.},
journal = {PloS one},
volume = {8},
number = {4},
pages = {e60377},
pmid = {23565237},
issn = {1932-6203},
mesh = {Adult ; Evoked Potentials/physiology ; Female ; Humans ; Male ; *Semantics ; Young Adult ; },
abstract = {Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N = 12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.},
}
@article {pmid23565083,
year = {2013},
author = {Halder, S and Varkuti, B and Bogdan, M and Kübler, A and Rosenstiel, W and Sitaram, R and Birbaumer, N},
title = {Prediction of brain-computer interface aptitude from individual brain structure.},
journal = {Frontiers in human neuroscience},
volume = {7},
number = {},
pages = {105},
pmid = {23565083},
issn = {1662-5161},
abstract = {OBJECTIVE: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.<br><br>METHODS: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.<br><br>RESULTS: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).<br><br>CONCLUSIONS: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.<br><br>SIGNIFICANCE: This confirms that structural brain traits contribute to individual performance in BCI use.},
}
@article {pmid23562053,
year = {2013},
author = {Saxena, T and Karumbaiah, L and Gaupp, EA and Patkar, R and Patil, K and Betancur, M and Stanley, GB and Bellamkonda, RV},
title = {The impact of chronic blood-brain barrier breach on intracortical electrode function.},
journal = {Biomaterials},
volume = {34},
number = {20},
pages = {4703-4713},
doi = {10.1016/j.biomaterials.2013.03.007},
pmid = {23562053},
issn = {1878-5905},
support = {R25 GM096161/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Blood-Brain Barrier/*pathology/*physiopathology ; Cerebral Cortex/*pathology/*physiopathology ; *Electrodes, Implanted ; Electrophysiological Phenomena ; Immunohistochemistry ; Male ; Models, Neurological ; Myeloid Cells/pathology ; Nerve Degeneration/pathology ; Neuroglia/metabolism/pathology ; Rats ; Rats, Sprague-Dawley ; Reverse Transcriptase Polymerase Chain Reaction ; Wound Healing ; },
abstract = {Brain-computer interfaces (BCIs) have allowed control of prosthetic limbs in paralyzed patients. Unfortunately, the electrodes of the BCI that interface with the brain only function for a short period of time before the signal quality on these electrodes becomes substantially diminished. To truly realize the potential of BCIs, it is imperative to have electrodes that function chronically. In order to elucidate the physiological determinants of a chronically functional neural interface, we studied the role of the blood-brain barrier (BBB) in electrode function, because it is a key mediator of neuronal hemostasis. We monitored the status of the BBB and the consequences of BBB breach on electrode function using non-invasive imaging, electrophysiology, genomic, and histological analyses. Rats implanted with commercially available intracortical electrodes demonstrated an inverse correlation between electrode performance and BBB breach over a period of 16 weeks. Genomic analysis showed that chronically functional electrodes elicit an enhanced wound healing response. Conversely, in poorly functioning electrodes, chronic BBB breach led to local accumulation of neurotoxic factors and an influx of pro-inflammatory myeloid cells, which negatively affect neuronal health. These findings were further verified in a subset of electrodes with graded electrophysiological performance. In this study, we determine the mechanistic link between intracortical electrode function and failure. Our results indicate that BBB status is a critical physiological determinant of intracortical electrode function and can inform future electrode design and biochemical intervention strategies to enhance the functional longevity of BCIs.},
}
@article {pmid23558502,
year = {2013},
author = {McGie, SC and Nagai, MK and Artinian-Shaheen, T},
title = {Clinical ethical concerns in the implantation of brain-machine interfaces.},
journal = {IEEE pulse},
volume = {4},
number = {2},
pages = {32-37},
doi = {10.1109/MPUL.2013.2242014},
pmid = {23558502},
issn = {2154-2317},
mesh = {*Bioethical Issues ; Brain-Computer Interfaces/*ethics/trends ; Humans ; },
}
@article {pmid23558099,
year = {2014},
author = {Obrig, H},
title = {NIRS in clinical neurology - a 'promising' tool?.},
journal = {NeuroImage},
volume = {85 Pt 1},
number = {},
pages = {535-546},
doi = {10.1016/j.neuroimage.2013.03.045},
pmid = {23558099},
issn = {1095-9572},
mesh = {Brain Diseases/diagnosis/psychology ; Cerebrovascular Disorders/diagnosis ; Epilepsy/diagnosis ; Functional Neuroimaging/*methods ; Headache Disorders/diagnosis ; Humans ; Nervous System Diseases/*diagnosis ; Neurology/*instrumentation ; Spectroscopy, Near-Infrared/*methods ; Stroke/diagnosis ; },
abstract = {Near-infrared spectroscopy (NIRS) has become a relevant research tool in neuroscience. In special populations such as infants and for special tasks such as walking, NIRS has asserted itself as a low resolution functional imaging technique which profits from its ease of application, portability and the option to co-register other neurophysiological and behavioral data in a 'near natural' environment. For clinical use in neurology this translates into the option to provide a bed-side oximeter for the brain, broadly available at comparatively low costs. However, while some potential for routine brain monitoring during cardiac and vascular surgery and in neonatology has been established, NIRS is largely unknown to clinical neurologists. The article discusses some of the reasons for this lack of use in clinical neurology. Research using NIRS in three major neurologic diseases (cerebrovascular disease, epilepsy and headache) is reviewed. Additionally the potential to exploit the established position of NIRS as a functional imaging tool with regard to clinical questions such as preoperative functional assessment and neurorehabilitation is discussed.},
}
@article {pmid23554481,
year = {2013},
author = {Rotermund, D and Ernst, UA and Mandon, S and Taylor, K and Smiyukha, Y and Kreiter, AK and Pawelzik, KR},
title = {Toward high performance, weakly invasive brain computer interfaces using selective visual attention.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {33},
number = {14},
pages = {6001-6011},
pmid = {23554481},
issn = {1529-2401},
mesh = {Animals ; Attention/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Discrimination, Psychological/*physiology ; Electrodes, Implanted ; Electroencephalography ; Evoked Potentials ; Macaca mulatta ; Male ; Photic Stimulation ; Reaction Time ; },
abstract = {Brain-computer interfaces have been proposed as a solution for paralyzed persons to communicate and interact with their environment. However, the neural signals used for controlling such prostheses are often noisy and unreliable, resulting in a low performance of real-world applications. Here we propose neural signatures of selective visual attention in epidural recordings as a fast, reliable, and high-performance control signal for brain prostheses. We recorded epidural field potentials with chronically implanted electrode arrays from two macaque monkeys engaged in a shape-tracking task. For single trials, we classified the direction of attention to one of two visual stimuli based on spectral amplitude, coherence, and phase difference in time windows fixed relative to stimulus onset. Classification performances reached up to 99.9%, and the information about attentional states could be transferred at rates exceeding 580 bits/min. Good classification can already be achieved in time windows as short as 200 ms. The classification performance changed dynamically over the trial and modulated with the task's varying demands for attention. For all three signal features, the information about the direction of attention was contained in the γ-band. The most informative feature was spectral amplitude. Together, these findings establish a novel paradigm for constructing brain prostheses as, for example, virtual spelling boards, promising a major gain in performance and robustness for human brain-computer interfaces.},
}
@article {pmid23543872,
year = {2013},
author = {Ejaz, N and Krapp, HG and Tanaka, RJ},
title = {Closed-loop response properties of a visual interneuron involved in fly optomotor control.},
journal = {Frontiers in neural circuits},
volume = {7},
number = {},
pages = {50},
pmid = {23543872},
issn = {1662-5110},
mesh = {Animals ; Diptera ; Female ; Interneurons/*physiology ; Nerve Net/*physiology ; Photic Stimulation/*methods ; Robotics/instrumentation/*methods ; Visual Pathways/*physiology ; Visual Perception/*physiology ; },
abstract = {Due to methodological limitations neural function is mostly studied under open-loop conditions. Normally, however, nervous systems operate in closed-loop where sensory input is processed to generate behavioral outputs, which again change the sensory input. Here, we investigate the closed-loop responses of an identified visual interneuron, the blowfly H1-cell, that is part of a neural circuit involved in optomotor flight and gaze control. Those behaviors may be triggered by attitude changes during flight in turbulent air. The fly analyses the resulting retinal image shifts and performs compensatory body and head rotations to regain its default attitude. We developed a fly robot interface to study H1-cell responses in a 1 degree-of-freedom image stabilization task. Image shifts, induced by externally forced rotations, modulate the cell's spike rate that controls counter rotations of a mobile robot to minimize relative motion between the robot and its visual surroundings. A feedback controller closed the loop between neural activity and the rotation of the robot. Under these conditions we found the following H1-cell response properties: (i) the peak spike rate decreases when the mean image velocity is increased, (ii) the relationship between spike rate and image velocity depends on the standard deviation of the image velocities suggesting adaptive scaling of the cell's signaling range, and (iii) the cell's gain decreases linearly with increasing image accelerations. Our results reveal a remarkable qualitative similarity between the response dynamics of the H1-cell under closed-loop conditions with those obtained in previous open-loop experiments. Finally, we show that the adaptive scaling of the H1-cell's responses, while maximizing information on image velocity, decreases the cell's sensitivity to image accelerations. Understanding such trade-offs in biological vision systems may advance the design of smart vision sensors for autonomous robots.},
}
@article {pmid23541802,
year = {2013},
author = {Faress, A and Chau, T},
title = {Towards a multimodal brain-computer interface: combining fNIRS and fTCD measurements to enable higher classification accuracy.},
journal = {NeuroImage},
volume = {77},
number = {},
pages = {186-194},
doi = {10.1016/j.neuroimage.2013.03.028},
pmid = {23541802},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Multimodal Imaging/*methods ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; Ultrasonography, Doppler, Transcranial/*methods ; Young Adult ; },
abstract = {Previous brain-computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). However, multimodal brain-computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS-TCD systems respectively. In five of nine participants, classification accuracies with the NIRS-TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising method of improving the accuracy of future brain-computer interfaces.},
}
@article {pmid23536714,
year = {2013},
author = {Aggarwal, V and Mollazadeh, M and Davidson, AG and Schieber, MH and Thakor, NV},
title = {State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements.},
journal = {Journal of neurophysiology},
volume = {109},
number = {12},
pages = {3067-3081},
pmid = {23536714},
issn = {1522-1598},
support = {P30 EY001319/EY/NEI NIH HHS/United States ; R01 NS079664/NS/NINDS NIH HHS/United States ; R01 NS-040596-09S1/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; *Brain Waves ; Fingers/innervation/*physiology ; Hand Strength ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/*physiology ; *Movement ; Posture ; Psychomotor Performance ; Reaction Time ; },
abstract = {The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-based state decoder to trigger a spike-based kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-based state decoder combined with a spike-based kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-based kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-based state decoding with spike-based kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation.},
}
@article {pmid23536381,
year = {2013},
author = {Hsu, WY},
title = {Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition.},
journal = {Clinical EEG and neuroscience},
volume = {44},
number = {4},
pages = {257-264},
doi = {10.1177/1550059413477090},
pmid = {23536381},
issn = {1550-0594},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; *Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {In this study, grey-based Hopfield neural network (GHNN), is proposed for the unsupervised analysis of motor imagery (MI) electroencephalogram (EEG) data. Combined with segment selection and feature extraction, GHNN is used for the recognition of left and right MI data. A Gaussian-like filter is proposed to reduce noise, to further enhance performance of active segment selection. Features are extracted by coherence from wavelet data, and then discriminated by GHNN, which is an unsupervised approach suitable for the online classification of nonstationary biomedical signals. Compared to EEG data without segment selection, several usual features, and classifiers, the proposed system is potentially an analytic approach in brain-computer interface (BCI) applications.},
}
@article {pmid23536247,
year = {2014},
author = {Birbaumer, N and Gallegos-Ayala, G and Wildgruber, M and Silvoni, S and Soekadar, SR},
title = {Direct brain control and communication in paralysis.},
journal = {Brain topography},
volume = {27},
number = {1},
pages = {4-11},
doi = {10.1007/s10548-013-0282-1},
pmid = {23536247},
issn = {1573-6792},
mesh = {*Brain-Computer Interfaces/ethics ; *Conditioning, Psychological ; Humans ; Paralysis/*psychology/*therapy ; Quadriplegia/*psychology/*therapy ; },
abstract = {Despite considerable growth in the field of brain-computer or brain-machine interface (BCI/BMI) research reflected in several hundred publications each year, little progress was made to enable patients in complete locked-in state (CLIS) to reliably communicate using their brain activity. Independent of the invasiveness of the BCI systems tested, no sustained direct brain control and communication was demonstrated in a patient in CLIS so far. This suggested a more fundamental theoretical problem of learning and attention in brain communication with BCI/BMI, formulated in the extinction-of-thought hypothesis. While operant conditioning and goal-directed thinking seems impaired in complete paralysis, classical conditioning of brain responses might represent the only alternative. First experimental studies in CLIS using semantic conditioning support this assumption. Evidence that quality-of-life in locked-in-state is not as limited and poor as generally believed draise doubts that "patient wills" or "advanced directives"signed long-before the locked-in-state are useful. On the contrary, they might be used as an excuse to shorten anticipated long periods of care for these patients avoiding associated financial and social burdens. Current state and availability of BCI/BMI systems urge a broader societal discourse on the pressing ethical challenges associated with the advancements in neurotechnology and BCI/BMI research.},
}
@article {pmid23535455,
year = {2013},
author = {Vuckovic, A and Osuagwu, BA},
title = {Using a motor imagery questionnaire to estimate the performance of a Brain-Computer Interface based on object oriented motor imagery.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {8},
pages = {1586-1595},
doi = {10.1016/j.clinph.2013.02.016},
pmid = {23535455},
issn = {1872-8952},
mesh = {Adult ; *Brain-Computer Interfaces ; *Educational Status ; Electroencephalography ; Female ; Humans ; *Imagery, Psychotherapy ; Imagination/physiology ; Male ; *Motor Activity ; *Surveys and Questionnaires ; User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVES: The primary objective was to test whether motor imagery (MI) questionnaires can be used to detect BCI 'illiterate'. The second objective was to test how different MI paradigms, with and without the physical presence of the goal of an action, influence a BCI classifier.<br><br>METHODS: Kinaesthetic (KI) and visual (VI) motor imagery questionnaires were administered to 30 healthy volunteers. Their EEG was recorded during a cue-based, simple imagery (SI) and goal oriented imagery (GOI).<br><br>RESULTS: The strongest correlation (Pearson r(2)=0.53, p=1.6e-5) was found between KI and SI, followed by a moderate correlation between KI and GOI (r(2)=0.33, p=0.001) and a weak correlation between VI and SI (r(2)=0.21, p=0.022) and VI and GOI (r(2)=0.17, p=0.05). Classification accuracy was similar for SI (71.1 &#xb1; 7.8%) and GOI (70.5 &#xb1; 5.9%) though corresponding classification features differed in 70% participants. Compared to SI, GOI improved the classification accuracy in 'poor' imagers while reducing the classification accuracy in 'very good' imagers.<br><br>CONCLUSION: The KI score could potentially be a useful tool to predict the performance of a MI based BCI. The physical presence of the object of an action facilitates motor imagination in 'poor' able-bodied imagers.<br><br>SIGNIFICANCE: Although this study shows results on able-bodied people, its general conclusions should be transferable to BCI based on MI for assisted rehabilitation of the upper extremities in patients.},
}
@article {pmid23532387,
year = {2013},
author = {Velez Edwards, DR and Likis, FE and Andrews, JC and Woodworth, AL and Jerome, RN and Fonnesbeck, CJ and Nikki McKoy, J and Hartmann, KE},
title = {Progestogens for preterm birth prevention: a systematic review and meta-analysis by drug route.},
journal = {Archives of gynecology and obstetrics},
volume = {287},
number = {6},
pages = {1059-1066},
doi = {10.1007/s00404-013-2789-9},
pmid = {23532387},
issn = {1432-0711},
support = {290-2007-10065-I//PHS HHS/United States ; },
mesh = {Administration, Intravaginal ; Administration, Oral ; Bayes Theorem ; Female ; Gestational Age ; Humans ; Infant Mortality ; Infant, Newborn ; Injections, Intramuscular ; MEDLINE ; Pregnancy ; Premature Birth/*prevention &amp; control ; Progestins/*administration &amp; dosage ; Randomized Controlled Trials as Topic ; },
abstract = {PURPOSE: Progestogen has been investigated as a preventive intervention among women with increased preterm birth risk. Our objective was to systematically review the effectiveness of intramuscular (IM), vaginal, and oral progestogens for preterm birth and neonatal death prevention.<br><br>METHODS: We included articles published from January 1966 to January 2013 and found 27 randomized trials with data for Bayesian meta-analysis.<br><br>RESULTS: Across all studies, only vaginal and oral routes were effective at reducing preterm births (IM risk ratio [RR] 0.95, 95 % Bayesian credible interval [BCI]: 0.88-1.03; vaginal RR 0.87, 95 % BCI: 0.80-0.94; oral RR 0.64, 95 % BCI: 0.49-0.85). However, when analyses were limited to only single births all routes were effective at reducing preterm birth (IM RR 0.77, 95 % BCI: 0.69-0.87; vaginal RR 0.80, 95 % BCI: 0.69-0.91; oral RR 0.66, 95 % BCI: 0.47-0.84). Only IM progestogen was effective at reducing neonatal deaths (IM RR 0.78, 95 % BCI: 0.56-0.99; vaginal RR 0.75, 95 % BCI: 0.45-1.09; oral RR 0.72, 95 % BCI: 0.09-1.74). Vaginal progestogen was effective in reducing neonatal deaths when limited to singletons births.<br><br>CONCLUSIONS: All progestogen routes reduce preterm births but not neonatal deaths. Future studies are needed that directly compare progestogen delivery routes.},
}
@article {pmid23531548,
year = {2013},
author = {Jarmolowska, J and Turconi, MM and Busan, P and Mei, J and Battaglini, PP},
title = {A multimenu system based on the P300 component as a time saving procedure for communication with a brain-computer interface.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {39},
pmid = {23531548},
issn = {1662-4548},
abstract = {The present study investigates a Brain-Computer Interface (BCI) spelling procedure based on the P300 evoked potential. It uses a small matrix of words arranged in a tree-shaped organization ("multimenu"), and allows the user to build phrases one word at a time, instead of letter by letter. Experiments were performed in two sessions on a group of seven healthy volunteers. In the former, the "multimenu" was tested with a total of 60 choices: 30 "externally-imposed" selections and 30 "free-choice" selections. In the latter, 3 × 3 matrices were compared with 6 × 6 matrices. Each matrix was composed of letters or words, for a total of four matrices. Differences in classifier accuracy, bit rate and amplitude of the evoked P300 were evaluated. Average accuracy in all subjects was 87% with no differences between the selection methods. The 3 × 3 "multimenu" obtained the same level of classifier accuracy as the 6 × 6 matrices, even with a significantly lower amplitude of the P300. Bit rate was increased when using the 3 × 3 matrices compared to the 6 × 6 ones. The "multimenu" system was equally effective, but faster than conventional, letter-based matrices. By improving the speed of communication, this method can be of help to patients with severe difficulties in communication.},
}
@article {pmid23529202,
year = {2013},
author = {Throckmorton, CS and Colwell, KA and Ryan, DB and Sellers, EW and Collins, LM},
title = {Bayesian approach to dynamically controlling data collection in P300 spellers.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {3},
pages = {508-517},
pmid = {23529202},
issn = {1558-0210},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; 5R21-DC-010470-02/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Bayes Theorem ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Database Management Systems ; *Databases, Factual ; Diagnosis, Computer-Assisted/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Information Storage and Retrieval/methods ; Male ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; Young Adult ; },
abstract = {P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.},
}
@article {pmid23529107,
year = {2013},
author = {Corbett, EA and Körding, KP and Perreault, EJ},
title = {Real-time evaluation of a noninvasive neuroprosthetic interface for control of reach.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {4},
pages = {674-683},
doi = {10.1109/TNSRE.2013.2251664},
pmid = {23529107},
issn = {1558-0210},
mesh = {Algorithms ; Arm/innervation/physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography ; Electromyography ; Eye Movements/physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Muscle, Skeletal/innervation/physiology ; *Neural Prostheses ; Online Systems ; Practice, Psychological ; *Prosthesis Design ; Psychomotor Performance/physiology ; Robotics ; Spinal Cord Injuries/physiopathology ; Young Adult ; },
abstract = {Injuries of the cervical spinal cord can interrupt the neural pathways controlling the muscles of the arm, resulting in complete or partial paralysis. For individuals unable to reach due to high-level injuries, neuroprostheses can restore some of the lost function. Natural, multidimensional control of neuroprosthetic devices for reaching remains a challenge. Electromyograms (EMGs) from muscles that remain under voluntary control can be used to communicate intended reach trajectories, but when the number of available muscles is limited control can be difficult and unintuitive. We combined shoulder EMGs with target estimates obtained from gaze. Natural gaze data were integrated with EMG during closed-loop robotic control of the arm, using a probabilistic mixture model. We tested the approach with two different sets of EMGs, as might be available to subjects with C4- and C5-level spinal cord injuries. Incorporating gaze greatly improved control of reaching, particularly when there were few EMG signals. We found that subjects naturally adapted their eye-movement precision as we varied the set of available EMGs, attaining accurate performance in both tested conditions. The system performs a near-optimal combination of both physiological signals, making control more intuitive and allowing a natural trajectory that reduces the burden on the user.},
}
@article {pmid23529075,
year = {2013},
author = {Samek, W and Meinecke, FC and Muller, KR},
title = {Transferring subspaces between subjects in brain--computer interfacing.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {8},
pages = {2289-2298},
doi = {10.1109/TBME.2013.2253608},
pmid = {23529075},
issn = {1558-2531},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Compensating changes between a subjects' training and testing session in brain-computer interfacing (BCI) is challenging but of great importance for a robust BCI operation. We show that such changes are very similar between subjects, and thus can be reliably estimated using data from other users and utilized to construct an invariant feature space. This novel approach to learning from other subjects aims to reduce the adverse effects of common nonstationarities, but does not transfer discriminative information. This is an important conceptual difference to standard multisubject methods that, e.g., improve the covariance matrix estimation by shrinking it toward the average of other users or construct a global feature space. These methods do not reduces the shift between training and test data and may produce poor results when subjects have very different signal characteristics. In this paper, we compare our approach to two state-of-the-art multisubject methods on toy data and two datasets of EEG recordings from subjects performing motor imagery. We show that it can not only achieve a significant increase in performance, but also that the extracted change patterns allow for a neurophysiologically meaningful interpretation.},
}
@article {pmid23528750,
year = {2013},
author = {Iturrate, I and Montesano, L and Minguez, J},
title = {Task-dependent signal variations in EEG error-related potentials for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026024},
doi = {10.1088/1741-2560/10/2/026024},
pmid = {23528750},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Analysis of Variance ; Brain/physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Discriminant Analysis ; *Electroencephalography ; Electrophysiology ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Principal Component Analysis ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: A major difficulty of brain-computer interface (BCI) technology is dealing with the noise of EEG and its signal variations. Previous works studied time-dependent non-stationarities for BCIs in which the user's mental task was independent of the device operation (e.g., the mental task was motor imagery and the operational task was a speller). However, there are some BCIs, such as those based on error-related potentials, where the mental and operational tasks are dependent (e.g., the mental task is to assess the device action and the operational task is the device action itself). The dependence between the mental task and the device operation could introduce a new source of signal variations when the operational task changes, which has not been studied yet. The aim of this study is to analyse task-dependent signal variations and their effect on EEG error-related potentials.<br><br>APPROACH: The work analyses the EEG variations on the three design steps of BCIs: an electrophysiology study to characterize the existence of these variations, a feature distribution analysis and a single-trial classification analysis to measure the impact on the final BCI performance.<br><br>RESULTS AND SIGNIFICANCE: The results demonstrate that a change in the operational task produces variations in the potentials, even when EEG activity exclusively originated in brain areas related to error processing is considered. Consequently, the extracted features from the signals vary, and a classifier trained with one operational task presents a significant loss of performance for other tasks, requiring calibration or adaptation for each new task. In addition, a new calibration for each of the studied tasks rapidly outperforms adaptive techniques designed in the literature to mitigate the EEG time-dependent non-stationarities.},
}
@article {pmid23528484,
year = {2013},
author = {Lim, JH and Hwang, HJ and Han, CH and Jung, KY and Im, CH},
title = {Classification of binary intentions for individuals with impaired oculomotor function: 'eyes-closed' SSVEP-based brain-computer interface (BCI).},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026021},
doi = {10.1088/1741-2560/10/2/026021},
pmid = {23528484},
issn = {1741-2552},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/physiopathology ; *Brain-Computer Interfaces ; Computer Systems ; Data Interpretation, Statistical ; Persons with Disabilities ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Eye Movements/physiology ; Female ; Functional Laterality/physiology ; Humans ; Male ; Ophthalmoplegia/*physiopathology ; Photic Stimulation ; Young Adult ; },
abstract = {OBJECTIVE: Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function.<br><br>APPROACH: The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS).<br><br>MAIN RESULTS: Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min(-1). A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%.<br><br>SIGNIFICANCE: The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our 'eyes-closed' SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.},
}
@article {pmid23527278,
year = {2013},
author = {Larson, E and Terry, HP and Canevari, MM and Stepp, CE},
title = {Categorical vowel perception enhances the effectiveness and generalization of auditory feedback in human-machine-interfaces.},
journal = {PloS one},
volume = {8},
number = {3},
pages = {e59860},
pmid = {23527278},
issn = {1932-6203},
support = {F32 DC012456/DC/NIDCD NIH HHS/United States ; T32 DC000018/DC/NIDCD NIH HHS/United States ; T32DC000018/DC/NIDCD NIH HHS/United States ; 1F32DC012456/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Analysis of Variance ; *Brain-Computer Interfaces ; Electromyography ; Female ; Humans ; Male ; Neurofeedback/*methods ; Psychomotor Performance ; Reaction Time ; Speech Perception/*physiology ; },
abstract = {Human-machine interface (HMI) designs offer the possibility of improving quality of life for patient populations as well as augmenting normal user function. Despite pragmatic benefits, utilizing auditory feedback for HMI control remains underutilized, in part due to observed limitations in effectiveness. The goal of this study was to determine the extent to which categorical speech perception could be used to improve an auditory HMI. Using surface electromyography, 24 healthy speakers of American English participated in 4 sessions to learn to control an HMI using auditory feedback (provided via vowel synthesis). Participants trained on 3 targets in sessions 1-3 and were tested on 3 novel targets in session 4. An "established categories with text cues" group of eight participants were trained and tested on auditory targets corresponding to standard American English vowels using auditory and text target cues. An "established categories without text cues" group of eight participants were trained and tested on the same targets using only auditory cuing of target vowel identity. A "new categories" group of eight participants were trained and tested on targets that corresponded to vowel-like sounds not part of American English. Analyses of user performance revealed significant effects of session and group (established categories groups and the new categories group), and a trend for an interaction between session and group. Results suggest that auditory feedback can be effectively used for HMI operation when paired with established categorical (native vowel) targets with an unambiguous cue.},
}
@article {pmid23525496,
year = {2013},
author = {Ruiz, S and Birbaumer, N and Sitaram, R},
title = {Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach.},
journal = {Frontiers in psychiatry},
volume = {4},
number = {},
pages = {17},
pmid = {23525496},
issn = {1664-0640},
abstract = {CONSIDERING THAT SINGLE LOCATIONS OF STRUCTURAL AND FUNCTIONAL ABNORMALITIES ARE INSUFFICIENT TO EXPLAIN THE DIVERSE PSYCHOPATHOLOGY OF SCHIZOPHRENIA, NEW MODELS HAVE POSTULATED THAT THE IMPAIRMENTS ASSOCIATED WITH THE DISEASE ARISE FROM A FAILURE TO INTEGRATE THE ACTIVITY OF LOCAL AND DISTRIBUTED NEURAL CIRCUITS: the "abnormal neural connectivity hypothesis." In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI) are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia. The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.},
}
@article {pmid23517673,
year = {2013},
author = {Lin, CC and Ju, MS and Chen, CW and Hwang, JC and Tsai, JJ},
title = {Effects of levetiracetam on μ rhythm in persons with epilepsy.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {20},
number = {5},
pages = {686-691},
doi = {10.1016/j.jocn.2012.04.028},
pmid = {23517673},
issn = {1532-2653},
mesh = {Adult ; Anticonvulsants/*pharmacology ; Brain Waves/*drug effects/physiology ; Electroencephalography/*drug effects/instrumentation/methods ; Epilepsies, Partial/drug therapy/*physiopathology ; Female ; Humans ; Levetiracetam ; Male ; Middle Aged ; Neuropsychological Tests ; Piracetam/*analogs &amp; derivatives/pharmacology ; Reaction Time/drug effects/physiology ; Treatment Outcome ; Visual Perception/drug effects/physiology ; Young Adult ; },
abstract = {Mu rhythm can be suppressed by movements, the so called event-related desynchronization (ERD). Levetiracetam (LEV) is a newer type of antiepileptic drug. A previous study reported that LEV might enhance mu rhythm and caused mu status in one subject. The main purpose of this study was to investigate the effects of LEV on EEG frequency contents and ERD. Seventeen patients with epileptic foci outside the Rolandic area were recruited. The following studies were performed before and after chronically taking LEV. An electroencephalogram (EEG) with 10 minutes of resting state and 5 minutes covering 10 right thumb movements were recorded. Reaction time was evaluated with a simple visual reaction time test. EEG data were analyzed by S-transformation and relative band powers were calculated. The results showed that the relative powers of theta, alpha and beta band in frontal (F3 and F4) and occipital (O1 and O2) leads and mu band in the centro-parietal (C3, C4, P3 and P4) leads were not changed by chronically taking LEV. No mu status was observed in any subject. However, the mean group ERD was enhanced at C3, Cz and P4 leads. Reaction time was similar before and after taking LEV. In conclusion, chronically taking LEV did not change the frequency contents of EEG and did not cause drowsiness, but enhanced ERD. The results suggest that chronically taking medication, such as LEV, is a plausible method to broaden the applicability of ERD-based brain-computer interfaces.},
}
@article {pmid23508245,
year = {2013},
author = {Rombokas, E and Stepp, CE and Chang, C and Malhotra, M and Matsuoka, Y},
title = {Vibrotactile sensory substitution for electromyographic control of object manipulation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {8},
pages = {2226-2232},
doi = {10.1109/TBME.2013.2252174},
pmid = {23508245},
issn = {1558-2531},
support = {5T32HD007424/HD/NICHD NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/instrumentation/*methods/*physiology ; *Brain-Computer Interfaces ; Female ; Hand/*physiopathology ; Humans ; Male ; Movement Disorders/*rehabilitation ; Robotics/instrumentation/*methods ; *Touch ; User-Computer Interface ; Vibration ; Young Adult ; },
abstract = {It has been shown that incorporating augmentative vibrotactile feedback can improve performance of a virtual object manipulation task using finger movement. Vibrotactile sensory substitution for prosthetic applications, however, will necessarily not involve actual finger movement for control. Here we study the utility of such feedback when using myoelectric (EMG) signals for control, and demonstrate task improvement and learning for a force-motion task in a virtual environment. Using vibrotactile feedback, a group of unimpaired participants (N = 10) were able to increase performance in a single session. We go on to study the feasibility of this method for two prosthetic hand users, one of whom had targeted muscle reinnervation allowing the augmentative feedback to be perceived as if it were on the absent hand.},
}
@article {pmid23507390,
year = {2013},
author = {Brodersen, KH and Daunizeau, J and Mathys, C and Chumbley, JR and Buhmann, JM and Stephan, KE},
title = {Variational Bayesian mixed-effects inference for classification studies.},
journal = {NeuroImage},
volume = {76},
number = {},
pages = {345-361},
doi = {10.1016/j.neuroimage.2013.03.008},
pmid = {23507390},
issn = {1095-9572},
support = {091593//Wellcome Trust/United Kingdom ; },
mesh = {*Algorithms ; *Bayes Theorem ; Brain/*physiology ; Brain Mapping/*methods ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Models, Neurological ; },
abstract = {Multivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. Assessing the utility of a classifier in application domains such as cognitive neuroscience, brain-computer interfaces, or clinical diagnostics necessitates inference on classification performance at more than one level, i.e., both in individual subjects and in the population from which these subjects were sampled. Such inference requires models that explicitly account for both fixed-effects (within-subjects) and random-effects (between-subjects) variance components. While models of this sort are standard in mass-univariate analyses of fMRI data, they have not yet received much attention in multivariate classification studies of neuroimaging data, presumably because of the high computational costs they entail. This paper extends a recently developed hierarchical model for mixed-effects inference in multivariate classification studies and introduces an efficient variational Bayes approach to inference. Using both synthetic and empirical fMRI data, we show that this approach is equally simple to use as, yet more powerful than, a conventional t-test on subject-specific sample accuracies, and computationally much more efficient than previous sampling algorithms and permutation tests. Our approach is independent of the type of underlying classifier and thus widely applicable. The present framework may help establish mixed-effects inference as a future standard for classification group analyses.},
}
@article {pmid23503997,
year = {2013},
author = {Bonifazi, P and Difato, F and Massobrio, P and Breschi, GL and Pasquale, V and Levi, T and Goldin, M and Bornat, Y and Tedesco, M and Bisio, M and Kanner, S and Galron, R and Tessadori, J and Taverna, S and Chiappalone, M},
title = {In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.},
journal = {Frontiers in neural circuits},
volume = {7},
number = {},
pages = {40},
pmid = {23503997},
issn = {1662-5110},
mesh = {Action Potentials/*physiology ; Animals ; Brain/cytology/*physiology ; *Brain-Computer Interfaces ; Cells, Cultured ; Guinea Pigs ; Nerve Net/cytology/*physiology ; },
abstract = {Brain-machine interfaces (BMI) were born to control "actions from thoughts" in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI-a neuromorphic chip for brain repair-to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary "bottom-up" approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of "finite size networks" which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.},
}
@article {pmid23502973,
year = {2013},
author = {Blythe, DA and Meinecke, FC and von Bünau, P and Müller, KR},
title = {Explorative data analysis for changes in neural activity.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026018},
doi = {10.1088/1741-2560/10/2/026018},
pmid = {23502973},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; Brain-Computer Interfaces ; *Data Interpretation, Statistical ; Electroencephalography ; Functional Laterality/physiology ; Humans ; Imagination/physiology ; Learning ; Linear Models ; Models, Neurological ; *Nervous System Physiological Phenomena ; Neurons/*physiology ; },
abstract = {Neural recordings are non-stationary time series, i.e. their properties typically change over time. Identifying specific changes, e.g., those induced by a learning task, can shed light on the underlying neural processes. However, such changes of interest are often masked by strong unrelated changes, which can be of physiological origin or due to measurement artifacts. We propose a novel algorithm for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms. A key ingredient is the repeated application of Stationary Subspace Analysis (SSA) using different temporal scales. The usefulness of our explorative approach is demonstrated in simulations, theory and EEG experiments with 80 brain-computer interfacing subjects.},
}
@article {pmid23501172,
year = {2013},
author = {Fabisch, A and Kassahun, Y and Wöhrle, H and Kirchner, F},
title = {Learning in compressed space.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {42},
number = {},
pages = {83-93},
doi = {10.1016/j.neunet.2013.01.020},
pmid = {23501172},
issn = {1879-2782},
mesh = {Algorithms ; *Artificial Intelligence ; Brain-Computer Interfaces ; Computer Simulation ; *Data Compression ; Electroencephalography ; Event-Related Potentials, P300 ; Humans ; *Learning ; Nerve Net/physiology ; *Neural Networks, Computer ; Neurons/physiology ; Perception ; Probability ; },
abstract = {We examine two methods which are used to deal with complex machine learning problems: compressed sensing and model compression. We discuss both methods in the context of feed-forward artificial neural networks and develop the backpropagation method in compressed parameter space. We further show that compressing the weights of a layer of a multilayer perceptron is equivalent to compressing the input of the layer. Based on this theoretical framework, we will use orthogonal functions and especially random projections for compression and perform experiments in supervised and reinforcement learning to demonstrate that the presented methods reduce training time significantly.},
}
@article {pmid23497539,
year = {2013},
author = {Habel, LA and Sakoda, LC and Achacoso, N and Ma, XJ and Erlander, MG and Sgroi, DC and Fehrenbacher, L and Greenberg, D and Quesenberry, CP},
title = {HOXB13:IL17BR and molecular grade index and risk of breast cancer death among patients with lymph node-negative invasive disease.},
journal = {Breast cancer research : BCR},
volume = {15},
number = {2},
pages = {R24},
pmid = {23497539},
issn = {1465-542X},
support = {R01CA112021/CA/NCI NIH HHS/United States ; },
mesh = {Adult ; Aged ; Biomarkers, Tumor/genetics/*metabolism ; Breast Neoplasms/metabolism/*mortality/*pathology ; Case-Control Studies ; Female ; Follow-Up Studies ; Homeodomain Proteins/genetics/*metabolism ; Humans ; Immunoenzyme Techniques ; Lymph Nodes/*pathology ; Middle Aged ; Neoplasm Grading ; Neoplasm Invasiveness ; Prognosis ; RNA, Messenger/genetics ; Real-Time Polymerase Chain Reaction ; Receptors, Interleukin/genetics/*metabolism ; Receptors, Interleukin-17 ; Reverse Transcriptase Polymerase Chain Reaction ; Risk Factors ; },
abstract = {INTRODUCTION: Studies have shown that a two-gene ratio (HOXB13:IL17BR) and a five-gene (BUB1B, CENPA, NEK2, RACGAP1, RRM2) molecular grade index (MGI) are predictive of clinical outcomes among early-stage breast cancer patients. In an independent population of lymph node-negative breast cancer patients from a community hospital setting, we evaluated the performance of two risk classifiers that have been derived from these gene signatures combined, MGI+HOXB13:IL17BR and the Breast Cancer Index (BCI).<br><br>METHODS: A case-control study was conducted among 4,964 Kaiser Permanente patients diagnosed with node-negative invasive breast cancer from 1985 to 1994 who did not receive adjuvant chemotherapy. For 191 cases (breast cancer deaths) and 417 matched controls, archived tumor tissues were available and analyzed for expression levels of the seven genes of interest and four normalization genes by RT-PCR. Logistic regression methods were used to estimate the relative risk (RR) and 10-year absolute risk of breast cancer death associated with prespecified risk categories for MGI+HOXB13:IL17BR and BCI.<br><br>RESULTS: Both MGI+HOXB13:IL17BR and BCI classified over half of all ER-positive patients as low risk. The 10-year absolute risks of breast cancer death for ER-positive, tamoxifen-treated patients classified in the low-, intermediate-, and high-risk groups were 3.7% (95% confidence interval (CI) 1.9% to 5.4%), 5.9% (95% CI 3.0% to 8.6%), and 12.9% (95% CI 7.9% to 17.6%) by MGI+HOXB13:IL17BR and 3.5% (95% CI 1.9% to 5.1%), 7.0% (95% CI 3.8% to 10.1%), and 12.9% (95% CI 7.1% to 18.3%) by BCI. Those for ER-positive, tamoxifen-untreated patients were 5.7% (95% CI 4.0% to 7.4%), 13.8% (95% CI 8.4% to 18.9%), and 15.2% (95% CI 9.4% to 20.5%) by MGI+HOXB13:IL17BR and 5.1% (95% CI 3.6% to 6.6%), 18.6% (95% CI 10.8% to 25.7%), and 17.5% (95% CI 11.1% to 23.5%) by BCI. After adjusting for tumor size and grade, the RRs of breast cancer death comparing high- versus low-risk categories of both classifiers remained elevated but were attenuated for tamoxifen-treated and tamoxifen-untreated patients.<br><br>CONCLUSION: Among ER-positive, lymph node-negative patients not treated with adjuvant chemotherapy, MGI+HOXB13:IL17BR and BCI were associated with risk of breast cancer death. Both risk classifiers appeared to provide risk information beyond standard prognostic factors.},
}
@article {pmid23494615,
year = {2013},
author = {Ramos-Murguialday, A and Broetz, D and Rea, M and Läer, L and Yilmaz, O and Brasil, FL and Liberati, G and Curado, MR and Garcia-Cossio, E and Vyziotis, A and Cho, W and Agostini, M and Soares, E and Soekadar, S and Caria, A and Cohen, LG and Birbaumer, N},
title = {Brain-machine interface in chronic stroke rehabilitation: a controlled study.},
journal = {Annals of neurology},
volume = {74},
number = {1},
pages = {100-108},
pmid = {23494615},
issn = {1531-8249},
support = {ZIA NS003030-06/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Analysis of Variance ; Arm/physiology ; Brain/blood supply/*physiology/physiopathology ; Brain Waves ; *Brain-Computer Interfaces ; Case-Control Studies ; Chronic Disease ; Electroencephalography ; Electromyography ; Female ; Hand/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Activity/physiology ; Outcome Assessment, Health Care ; Physical Therapy Modalities/*instrumentation ; Retrospective Studies ; Stroke/pathology/physiopathology ; *Stroke Rehabilitation ; Young Adult ; },
abstract = {OBJECTIVE: Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study.<br><br>METHODS: Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 &#xb1; 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention.<br><br>RESULTS: A significant group &#xd7; time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 &#xb1; 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups.<br><br>INTERPRETATION: The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.},
}
@article {pmid23490304,
year = {2013},
author = {Shin, S and Livchits, V and Connery, HS and Shields, A and Yanov, S and Yanova, G and Fitzmaurice, GM and Nelson, AK and Greenfield, SF and , },
title = {Effectiveness of alcohol treatment interventions integrated into routine tuberculosis care in Tomsk, Russia.},
journal = {Addiction (Abingdon, England)},
volume = {108},
number = {8},
pages = {1387-1396},
pmid = {23490304},
issn = {1360-0443},
support = {K24 DA019855/DA/NIDA NIH HHS/United States ; R01 AA016318/AA/NIAAA NIH HHS/United States ; K24 DA 019855/DA/NIDA NIH HHS/United States ; },
mesh = {Adult ; Alcohol Abstinence ; Alcohol Deterrents/*therapeutic use ; Alcoholism/complications/*prevention &amp; control ; Behavior Therapy/*methods ; Combined Modality Therapy ; Counseling ; Feasibility Studies ; Female ; Humans ; Male ; Naltrexone/*therapeutic use ; Patient Compliance ; Russia ; Treatment Outcome ; Tuberculosis/complications/*therapy ; },
abstract = {AIMS: To test the feasibility and effectiveness of brief counseling intervention (BCI) and naltrexone integrated into tuberculosis (TB) care in Tomsk, Russia.<br><br>DESIGN: Using a factorial randomized controlled trial design, patients were randomized into: naltrexone (NTX), brief behavioral compliance enhancement therapy (BBCET), treatment as usual (TAU) and BCI.<br><br>SETTING AND PARTICIPANTS: In the Tomsk Oblast, hospitalized TB patients diagnosed with alcohol use disorders (AUDs) by the DSM-IV were referred at the start of TB treatment. Of the 196 participants, the mean age was 41 years and 82% were male. Severe TB (84.7% had cavitary disease) and smoking (92.9%) were common. The majority had a diagnosis of an AUD (63.0%); 27.6% reported nearly daily drinking and consumed a median of 16 standard drinks per day.<br><br>MEASUREMENTS: Primary outcomes were 'favorable' TB outcome (cured, completed treatment) and change in mean number of abstinent days in the last month of study compared with baseline. Change in mean number of heavy drinking days, defined as four drinks per day and five drinks per day for women and men, respectively, and TB adherence, measured as percentage of doses taken as prescribed under direct observation, were secondary outcomes. Analysis based on 'intention-to-treat' was performed for multivariable analysis.<br><br>FINDINGS: Primary TB and alcohol end-points between naltrexone and no-naltrexone or BCI and no-BCI groups did not differ significantly. TB treatment adherence and change in number of heavy drinking days also did not differ significantly among treatment arms. Among individuals with a prior quitting attempt (n&#x2009;=&#x2009;111), naltrexone use was associated with an increased likelihood of favorable TB outcomes (92.3% versus 75.9%, P&#x2009;=&#x2009;0.02).<br><br>CONCLUSIONS: In Tomsk Oblast, Russia, tuberculosis patients with severe alcohol use disorders who were not seeking alcohol treatment did not respond to naltrexone or behavioral counselling integrated into tuberculosis care; however, those patients with past attempts to quit drinking had improved tuberculosis outcomes.},
}
@article {pmid23488130,
year = {2013},
author = {Wang, J and Hu, B},
title = {[Study on the method of feature extraction for brain-computer interface using discriminative common vector].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {30},
number = {1},
pages = {12-5, 27},
pmid = {23488130},
issn = {1001-5515},
mesh = {*Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; *Discriminant Analysis ; Electroencephalography ; Face/*anatomy &amp; histology ; Humans ; Pattern Recognition, Automated/methods ; Principal Component Analysis ; Sample Size ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Discriminative common vector (DCV) is an effective method that was proposed for the small sample size problems of face recognition. There is the same problem in brain-computer interface (BCI). Using directly the linear discriminative analysis (LDA) could result in errors because of the singularity of the within-class matrix of data. In our studies, we used the DCV method from the common vector theory in the within-class scatter matrix of data of all classes, and then applied eigenvalue decomposition to the common vectors to obtain the final projected vectors. Then we used kernel discriminative common vector (KDCV) with different kernel. Three data sets that include BCI Competition I data set, Competition II data set IV, and a data set collected by ourselves were used in the experiments. The experiment results of 93%, 77% and 97% showed that this feature extraction method could be used well in the classification of imagine data in BCI.},
}
@article {pmid23486552,
year = {2013},
author = {Robinson, JT and Jorgolli, M and Park, H},
title = {Nanowire electrodes for high-density stimulation and measurement of neural circuits.},
journal = {Frontiers in neural circuits},
volume = {7},
number = {},
pages = {38},
pmid = {23486552},
issn = {1662-5110},
support = {DP1 OD003893/OD/NIH HHS/United States ; 5DP1OD003893-03/OD/NIH HHS/United States ; },
mesh = {Animals ; Brain/cytology/*physiology ; Electric Stimulation/methods ; Humans ; Intracellular Fluid/physiology ; *Microelectrodes ; *Nanowires ; Nerve Net/cytology/*physiology ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) that can precisely monitor and control neural activity will likely require new hardware with improved resolution and specificity. New nanofabricated electrodes with feature sizes and densities comparable to neural circuits may lead to such improvements. In this perspective, we review the recent development of vertical nanowire (NW) electrodes that could provide highly parallel single-cell recording and stimulation for future BMIs. We compare the advantages of these devices and discuss some of the technical challenges that must be overcome for this technology to become a platform for next-generation closed-loop BMIs.},
}
@article {pmid23486216,
year = {2013},
author = {Choi, JS and Bang, JW and Park, KR and Whang, M},
title = {Enhanced perception of user intention by combining EEG and gaze-tracking for brain-computer interfaces (BCIs).},
journal = {Sensors (Basel, Switzerland)},
volume = {13},
number = {3},
pages = {3454-3472},
pmid = {23486216},
issn = {1424-8220},
mesh = {Brain/*diagnostic imaging ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300 ; Humans ; Perception ; Radiography ; *Signal Processing, Computer-Assisted ; },
abstract = {Speller UI systems tend to be less accurate because of individual variation and the noise of EEG signals. Therefore, we propose a new method to combine the EEG signals and gaze-tracking. This research is novel in the following four aspects. First, two wearable devices are combined to simultaneously measure both the EEG signal and the gaze position. Second, the speller UI system usually has a 6 × 6 matrix of alphanumeric characters, which has disadvantage in that the number of characters is limited to 36. Thus, a 12 × 12 matrix that includes 144 characters is used. Third, in order to reduce the highlighting time of each of the 12 × 12 rows and columns, only the three rows and three columns (which are determined on the basis of the 3 × 3 area centered on the user's gaze position) are highlighted. Fourth, by analyzing the P300 EEG signal that is obtained only when each of the 3 × 3 rows and columns is highlighted, the accuracy of selecting the correct character is enhanced. The experimental results showed that the accuracy of proposed method was higher than the other methods.},
}
@article {pmid23481680,
year = {2013},
author = {Peckham, PH and Kilgore, KL},
title = {Challenges and opportunities in restoring function after paralysis.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {3},
pages = {602-609},
pmid = {23481680},
issn = {1558-2531},
support = {U01 NS069517/NS/NINDS NIH HHS/United States ; R01 EB002091/EB/NIBIB NIH HHS/United States ; R01-EB-001740/EB/NIBIB NIH HHS/United States ; U01-NS-069517/NS/NINDS NIH HHS/United States ; UL1 TR000439/TR/NCATS NIH HHS/United States ; R01-EB-002091/EB/NIBIB NIH HHS/United States ; /ImNIH/Intramural NIH HHS/United States ; R01 EB001740/EB/NIBIB NIH HHS/United States ; UL1TR000439/TR/NCATS NIH HHS/United States ; },
mesh = {*Biomedical Engineering ; Equipment Design ; Humans ; *Neural Prostheses ; *Paralysis/rehabilitation/therapy ; },
abstract = {Neurotechnology has made major advances in development of interfaces to the nervous system that restore function in paralytic disorders. These advances enable both restoration of voluntary function and activation of paralyzed muscles to reanimate movement. The technologies used in each case are different, with external surface stimulation or percutaneous stimulation generally used for restoration of voluntary function, and implanted stimulators generally used for neuroprosthetic restoration. The opportunity to restore function through neuroplasticity has demonstrated significant advances in cases where there are retained neural circuits after the injury, such as spinal cord injury and stroke. In cases where there is a complete loss of voluntary neural control, neural prostheses have demonstrated the capacity to restore movement, control of the bladder and bowel, and respiration and cough. The focus of most clinical studies has been primarily toward activation of paralyzed nerves, but advances in inhibition of neural activity provide additional means of addressing the paralytic complications of pain and spasticity, and these techniques are now reaching the clinic. Future clinical advances necessitate having a better understanding of the underlying mechanisms, and having more precise neural interfaces that will ultimately allow individual nerve fibers or groups of nerve fibers to be controlled with specificity and reliability. While electrical currents have been the primary means of interfacing to the nervous system to date, optical and magnetic techniques under development are beginning to reach the clinic, and provide great opportunity. Ultimately, techniques that combine approaches are likely to be the most effective means for restoring function, for example combining regeneration and neural plasticity to maximize voluntary activity, combined with neural prostheses to augment the voluntary activity to functional levels of performance. It is a substantial challenge to bring any of these techniques through clinical trials, but as each of the individual techniques is sufficiently developed to reach the clinic, these present great opportunities for enabling patients with paralytic disorders to achieve substantial independence and restore their quality of life.},
}
@article {pmid23481383,
year = {2013},
author = {Khodagholy, D and Doublet, T and Quilichini, P and Gurfinkel, M and Leleux, P and Ghestem, A and Ismailova, E and Hervé, T and Sanaur, S and Bernard, C and Malliaras, GG},
title = {In vivo recordings of brain activity using organic transistors.},
journal = {Nature communications},
volume = {4},
number = {},
pages = {1575},
pmid = {23481383},
issn = {2041-1723},
mesh = {Animals ; Brain/*physiology ; Brain Mapping/*instrumentation ; Electroencephalography ; Electrophysiological Phenomena/*physiology ; Epilepsy/physiopathology ; Rats ; Rats, Long-Evans ; Rats, Wistar ; Reproducibility of Results ; Somatosensory Cortex/physiology ; *Transistors, Electronic ; },
abstract = {In vivo electrophysiological recordings of neuronal circuits are necessary for diagnostic purposes and for brain-machine interfaces. Organic electronic devices constitute a promising candidate because of their mechanical flexibility and biocompatibility. Here we demonstrate the engineering of an organic electrochemical transistor embedded in an ultrathin organic film designed to record electrophysiological signals on the surface of the brain. The device, tested in vivo on epileptiform discharges, displayed superior signal-to-noise ratio due to local amplification compared with surface electrodes. The organic transistor was able to record on the surface low-amplitude brain activities, which were poorly resolved with surface electrodes. This study introduces a new class of biocompatible, highly flexible devices for recording brain activity with superior signal-to-noise ratio that hold great promise for medical applications.},
}
@article {pmid23480080,
year = {2013},
author = {Mussa-Ivaldi, FA and Casadio, M and Ranganathan, R},
title = {The body-machine interface: a pathway for rehabilitation and assistance in people with movement disorders.},
journal = {Expert review of medical devices},
volume = {10},
number = {2},
pages = {145-147},
doi = {10.1586/erd.13.3},
pmid = {23480080},
issn = {1745-2422},
support = {1R01HD072080/HD/NICHD NIH HHS/United States ; 1R01NS05358/NS/NINDS NIH HHS/United States ; },
mesh = {Biomechanical Phenomena ; Brain-Computer Interfaces ; Cybernetics/*instrumentation ; Equipment Design ; Humans ; *Man-Machine Systems ; Motor Activity ; Movement Disorders/etiology/physiopathology/psychology/*rehabilitation ; Recovery of Function ; Signal Processing, Computer-Assisted ; Treatment Outcome ; *User-Computer Interface ; },
}
@article {pmid23476005,
year = {2013},
author = {Zhang, Y and Zhou, G and Zhao, Q and Jin, J and Wang, X and Cichocki, A},
title = {Spatial-temporal discriminant analysis for ERP-based brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {2},
pages = {233-243},
doi = {10.1109/TNSRE.2013.2243471},
pmid = {23476005},
issn = {1558-0210},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spatio-Temporal Analysis ; },
abstract = {Linear discriminant analysis (LDA) has been widely adopted to classify event-related potential (ERP) in brain-computer interface (BCI). Good classification performance of the ERP-based BCI usually requires sufficient data recordings for effective training of the LDA classifier, and hence a long system calibration time which however may depress the system practicability and cause the users resistance to the BCI system. In this study, we introduce a spatial-temporal discriminant analysis (STDA) to ERP classification. As a multiway extension of the LDA, the STDA method tries to maximize the discriminant information between target and nontarget classes through finding two projection matrices from spatial and temporal dimensions collaboratively, which reduces effectively the feature dimensionality in the discriminant analysis, and hence decreases significantly the number of required training samples. The proposed STDA method was validated with dataset II of the BCI Competition III and dataset recorded from our own experiments, and compared to the state-of-the-art algorithms for ERP classification. Online experiments were additionally implemented for the validation. The superior classification performance in using few training samples shows that the STDA is effective to reduce the system calibration time and improve the classification accuracy, thereby enhancing the practicability of ERP-based BCI.},
}
@article {pmid23475381,
year = {2013},
author = {Wei, P and He, W and Zhou, Y and Wang, L},
title = {Performance of motor imagery brain-computer interface based on anodal transcranial direct current stimulation modulation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {3},
pages = {404-415},
doi = {10.1109/TNSRE.2013.2249111},
pmid = {23475381},
issn = {1558-0210},
mesh = {Adult ; Biofeedback, Psychology/*methods/physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Healthy Volunteers ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Task Performance and Analysis ; Transcranial Magnetic Stimulation/*methods ; },
abstract = {Voluntarily modulating neural activity plays a key role in brain-computer interface (BCI). In general, the self-regulated neural activation patterns are used in the current BCI systems involving the repetitive trainings with feedback for an attempt to achieve a high-quality control performance. With the limitation posed by the training procedure in most BCI studies, the present work aims to investigate whether directly modulating the neural activity by using an external method could facilitate the BCI control. We designed an experimental paradigm that combines anodal transcranial direct current stimulation (tDCS) with a motor imagery (MI)-based feedback EEG BCI system. Thirty-two young and healthy human subjects were randomly assigned to the real and sham stimulation groups to evaluate the effect of tDCS-induced EEG pattern changes on BCI classification accuracy. Results showed that the anodal tDCS obviously induces sensorimotor rhythm (SMR)-related event-related desynchronization (ERD) pattern changes in the upper-mu (10-14 Hz) and beta (14-26 Hz) rhythm components. Both the online and offline BCI classification results demonstrate that the enhancing ERD patterns could conditionally improve BCI performance. This pilot study suggests that the tDCS is a promising method to help the users to develop reliable BCI control strategy in a relatively short time.},
}
@article {pmid23471568,
year = {2013},
author = {Ruf, CA and De Massari, D and Furdea, A and Matuz, T and Fioravanti, C and van der Heiden, L and Halder, S and Birbaumer, N},
title = {Semantic classical conditioning and brain-computer interface control: encoding of affirmative and negative thinking.},
journal = {Frontiers in neuroscience},
volume = {7},
number = {},
pages = {23},
pmid = {23471568},
issn = {1662-4548},
abstract = {The aim of the study was to investigate conditioned electroencephalography (EEG) responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI). In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs) were paired with two different tones which served as unconditioned stimuli (USs). The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs). After acquisition of the CRs, these CRs to true and false statements were classified offline using a radial basis function kernel support vector machine. A mean single-trial classification accuracy of 50.5% was achieved for differentiating conditioned "yes" versus "no" thinking and mean accuracies of 65.4% for classification of "yes" and 68.8% for "no" thinking (both relative to baseline) were found using the best US. Analysis of the area under the curve of the conditioned EEG responses revealed significant differences between conditioned "yes" and "no" answers. Even though improvements are necessary, these first results indicate that the semantic conditioning paradigm could be a useful basis for further research regarding BCI communication in patients in the complete locked-in state.},
}
@article {pmid23467061,
year = {2013},
author = {Siuly, and Li, Y and Wen, P},
title = {Identification of motor imagery tasks through CC-LR algorithm in brain computer interface.},
journal = {International journal of bioinformatics research and applications},
volume = {9},
number = {2},
pages = {156-172},
doi = {10.1504/IJBRA.2013.052447},
pmid = {23467061},
issn = {1744-5485},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Cluster Analysis ; Databases, Factual ; *Imagination ; Least-Squares Analysis ; Logistic Models ; *Motor Skills ; Pattern Recognition, Automated ; Support Vector Machine ; },
abstract = {This study focuses on the identification of Motor Imagery (MI) tasks for the development of Brain Computer Interface (BCI) technologies combining Cross-Correlation and Logistic Regression (CC-LR) techniques. The proposed method is tested on two benchmark data sets, IVa and IVb of BCI Competition III, and the performance is evaluated through a 3-fold cross-validation procedure. The experimental outcomes are compared with two recently reported algorithms, R-Common Spatial Pattern (CSP) with aggregation and Clustering Technique (CT)-based Least Square Support Vector Machine (LS-SVM) and also other four algorithms using data set IVa. The results demonstrate that our proposed method results in an improvement of at least 3.47% compared with the existing methods tested.},
}
@article {pmid23466266,
year = {2013},
author = {Acqualagna, L and Blankertz, B},
title = {Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP).},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {5},
pages = {901-908},
doi = {10.1016/j.clinph.2012.12.050},
pmid = {23466266},
issn = {1872-8952},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/methods ; Time Factors ; *User-Computer Interface ; *Visual Perception ; Young Adult ; },
abstract = {OBJECTIVE: A Brain Computer Interface (BCI) speller is a communication device, which can be used by patients suffering from neurodegenerative diseases to select symbols in a computer application. For patients unable to overtly fixate the target symbol, it is crucial to develop a speller independent of gaze shifts. In the present online study, we investigated rapid serial visual presentation (RSVP) as a paradigm for mental typewriting.<br><br>METHODS: We investigated the RSVP speller in three conditions, regarding the Stimulus Onset Asynchrony (SOA) and the use of color features. A vocabulary of 30 symbols was presented one-by-one in a pseudo random sequence at the same location of display.<br><br>RESULTS: All twelve participants were able to successfully operate the RSVP speller. The results show a mean online spelling rate of 1.43 symb/min and a mean symbol selection accuracy of 94.8% in the best condition.<br><br>CONCLUSION: We conclude that the RSVP is a promising paradigm for BCI spelling and its performance is competitive with the fastest gaze-independent spellers in literature.<br><br>SIGNIFICANCE: The RSVP speller does not require gaze shifts towards different target locations and can be operated by non-spatial visual attention, therefore it can be considered as a valid paradigm in applications with patients for impaired oculo-motor control.},
}
@article {pmid23465431,
year = {2013},
author = {Brunner, P and Schalk, G},
title = {Toward gaze-independent brain-computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {5},
pages = {831-833},
doi = {10.1016/j.clinph.2013.01.017},
pmid = {23465431},
issn = {1872-8952},
mesh = {Attention/*physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Face/*physiology ; Female ; Humans ; Male ; *User-Computer Interface ; *Visual Perception ; },
}
@article {pmid23465430,
year = {2013},
author = {Speier, W and Fried, I and Pouratian, N},
title = {Improved P300 speller performance using electrocorticography, spectral features, and natural language processing.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {7},
pages = {1321-1328},
pmid = {23465430},
issn = {1872-8952},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T15 LM007356/LM/NLM NIH HHS/United States ; T15-LM007356/LM/NLM NIH HHS/United States ; K23EB014326/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; *Brain Mapping ; Cerebral Cortex/pathology/*physiopathology ; Discriminant Analysis ; Electroencephalography ; Epilepsy/physiopathology ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; *Language ; Male ; Middle Aged ; *Natural Language Processing ; *Spectrum Analysis ; },
abstract = {OBJECTIVE: The P300 speller is a system designed to restore communication to patients with advanced neuromuscular disorders. This study was designed to explore the potential improvement from using electrocorticography (ECoG) compared to the more traditional usage of electroencephalography (EEG).<br><br>METHODS: We tested the P300 speller on two epilepsy patients with temporary subdural electrode arrays over the occipital and temporal lobes respectively. We then performed offline analysis to determine the accuracy and bit rate of the system and integrated spectral features into the classifier and used a natural language processing (NLP) algorithm to further improve the results.<br><br>RESULTS: The subject with the occipital grid achieved an accuracy of 82.77% and a bit rate of 41.02, which improved to 96.31% and 49.47 respectively using a language model and spectral features. The temporal grid patient achieved an accuracy of 59.03% and a bit rate of 18.26 with an improvement to 75.81% and 27.05 respectively using a language model and spectral features. Spatial analysis of the individual electrodes showed best performance using signals generated and recorded near the occipital pole.<br><br>CONCLUSIONS: Using ECoG and integrating language information and spectral features can improve the bit rate of a P300 speller system. This improvement is sensitive to the electrode placement and likely depends on visually evoked potentials.<br><br>SIGNIFICANCE: This study shows that there can be an improvement in BCI performance when using ECoG, but that it is sensitive to the electrode location.},
}
@article {pmid23463412,
year = {2013},
author = {Canis, M and Ihler, F and Blum, J and Matthias, C},
title = {[CT-assisted navigation for retrosigmoidal implantation of the Bonebridge].},
journal = {HNO},
volume = {61},
number = {12},
pages = {1038-1044},
pmid = {23463412},
issn = {1433-0458},
mesh = {*Cochlear Implants ; Cranial Sinuses/diagnostic imaging/*surgery ; *Hearing Aids ; Hearing Loss, Sensorineural/*rehabilitation ; Humans ; Prosthesis Implantation/*methods ; Surgery, Computer-Assisted/*methods ; Tomography, X-Ray Computed/*methods ; Treatment Outcome ; },
abstract = {The Bonebridge is an active bone conduction implant (BCI) that is primarily indicated in patients with conductive and combined hearing loss. However, many of these patients present with a radical cavity as a result of previous surgery. In these cases, the implant should not be introduced into the mastoid region, but rather via a retrosigmoid approach to maintain separation from the pathological alteration. To ensure the best possible acoustic transduction, the Bone Conduction-Floating Mass Transducer (BC-FMT) should be positioned near to the cochlea. This requires precise identification of the sigmoid sinus, which cannot be achieved accurately enough using external anatomical landmarks. We thus report on two patients in whom the Bonebridge was implanted via a retrosigmoid approach using CT-guided navigation.},
}
@article {pmid23457444,
year = {2013},
author = {Halder, S and Hammer, EM and Kleih, SC and Bogdan, M and Rosenstiel, W and Birbaumer, N and Kübler, A},
title = {Prediction of auditory and visual p300 brain-computer interface aptitude.},
journal = {PloS one},
volume = {8},
number = {2},
pages = {e53513},
pmid = {23457444},
issn = {1932-6203},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; *Aptitude ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; Models, Biological ; Photic Stimulation ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) provide a non-muscular communication channel for patients with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)) or otherwise motor impaired people and are also used for motor rehabilitation in chronic stroke. Differences in the ability to use a BCI vary from person to person and from session to session. A reliable predictor of aptitude would allow for the selection of suitable BCI paradigms. For this reason, we investigated whether P300 BCI aptitude could be predicted from a short experiment with a standard auditory oddball.<br><br>METHODS: Forty healthy participants performed an electroencephalography (EEG) based visual and auditory P300-BCI spelling task in a single session. In addition, prior to each session an auditory oddball was presented. Features extracted from the auditory oddball were analyzed with respect to predictive power for BCI aptitude.<br><br>RESULTS: Correlation between auditory oddball response and P300 BCI accuracy revealed a strong relationship between accuracy and N2 amplitude and the amplitude of a late ERP component between 400 and 600 ms. Interestingly, the P3 amplitude of the auditory oddball response was not correlated with accuracy.<br><br>CONCLUSIONS: Event-related potentials recorded during a standard auditory oddball session moderately predict aptitude in an audiory and highly in a visual P300 BCI. The predictor will allow for faster paradigm selection.<br><br>SIGNIFICANCE: Our method will reduce strain on patients because unsuccessful training may be avoided, provided the results can be generalized to the patient population.},
}
@article {pmid23455590,
year = {2013},
author = {Hashimoto, K and Malchow, B and Falkai, P and Schmitt, A},
title = {Glutamate modulators as potential therapeutic drugs in schizophrenia and affective disorders.},
journal = {European archives of psychiatry and clinical neuroscience},
volume = {263},
number = {5},
pages = {367-377},
pmid = {23455590},
issn = {1433-8491},
mesh = {Excitatory Amino Acid Agents/*therapeutic use ; Glutamic Acid/*metabolism ; Humans ; Mood Disorders/*drug therapy ; Schizophrenia/*drug therapy ; },
abstract = {Severe psychiatric disorders such as schizophrenia are related to cognitive and negative symptoms, which often are resistant to current treatment approaches. The glutamatergic system has been implicated in the pathophysiology of schizophrenia and affective disorders. A key component is the dysfunction of the glutamatergic N-methyl-D-aspartate (NMDA) receptor. Substances regulating activation/inhibition of the NMDA receptor have been investigated in schizophrenia and major depression and are promising in therapeutic approaches of negative symptoms, cognition, and mood. In schizophrenia, add-on treatments with glycine, D-serine, D-alanine, D-cycloserine, D-amino acid oxidase inhibitors, glycine transporter-1 (GlyT-1) inhibitors (e.g., sarcosine, bitopertin) and agonists (e.g., LY2140023) or positive allosteric modulator (e.g., ADX71149) of group II metabotropic glutamate receptors (mGluRs) have been studied. In major depression, the NMDA receptor antagonists (e.g., ketamine, AZD6765), GluN2B subtype antagonists (e.g., traxoprodil, MK-0657), and partial agonists (e.g., D-cycloserine, GLYX-13) at the glycine site of the NMDA receptor have been proven to be effective in animal studies and first clinical trials. In addition, clinical studies of mGluR2/3 antagonist BCI-838 (a prodrug of BCI-632 (MGS0039)), mGluR2/3-negative allosteric modulators (NMAs) (e.g., RO499819, RO4432717), and mGluR5 NAMs (e.g., AZD2066, RO4917523) are in progress. Future investigations should include effects on brain structure and activation to elucidate neural mechanisms underlying efficacy of these drugs.},
}
@article {pmid23453295,
year = {2013},
author = {Ubeda, A and Iáñez, E and Azorín, JM and Perez-Vidal, C},
title = {Endogenous brain-machine interface based on the correlation of EEG maps.},
journal = {Computer methods and programs in biomedicine},
volume = {112},
number = {2},
pages = {302-308},
doi = {10.1016/j.cmpb.2013.01.012},
pmid = {23453295},
issn = {1872-7565},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Feedback ; Humans ; },
abstract = {In this paper, a non-invasive endogenous brain-machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life.},
}
@article {pmid23450266,
year = {2013},
author = {Karniel, A},
title = {The minimum transition hypothesis for intermittent hierarchical motor control.},
journal = {Frontiers in computational neuroscience},
volume = {7},
number = {},
pages = {12},
pmid = {23450266},
issn = {1662-5188},
abstract = {In intermittent control, instead of continuously calculating the control signal, the controller occasionally changes this signal at certain sparse points in time. The control law may include feedback, adaptation, optimization, or any other control strategies. When, where, and how does the brain employ intermittency as it controls movement? These are open questions in motor neuroscience. Evidence for intermittency in human motor control has been repeatedly observed in the neural control of movement literature. Moreover, some researchers have provided theoretical models to address intermittency. Even so, the vast majority of current models, and I would dare to say the dogma in most of the current motor neuroscience literature involves continuous control. In this paper, I focus on an area in which intermittent control has not yet been thoroughly considered, the structure of muscle synergies. A synergy in the muscle space is a group of muscles activated together by a single neural command. Under the assumption that the motor control is intermittent, I present the minimum transition hypothesis (MTH) and its predictions with regards to the structure of muscle synergies. The MTH asserts that the purpose of synergies is to minimize the effort of the higher level in the hierarchy by minimizing the number of transitions in an intermittent control signal. The implications of the MTH are not only for the structure of the muscle synergies but also to the intermittent and hierarchical nature of the motor system, with various predictions as to the process of skill learning, and important implications to the design of brain machine interfaces and human robot interaction.},
}
@article {pmid23448963,
year = {2013},
author = {Yuan, P and Gao, X and Allison, B and Wang, Y and Bin, G and Gao, S},
title = {A study of the existing problems of estimating the information transfer rate in online brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026014},
doi = {10.1088/1741-2560/10/2/026014},
pmid = {23448963},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electronic Data Processing ; Equipment Design ; Event-Related Potentials, P300 ; Evoked Potentials, Somatosensory ; Guidelines as Topic ; Humans ; Movement/physiology ; Online Systems ; },
abstract = {OBJECTIVE: Today, the brain-computer interface (BCI) community lacks a standard method to evaluate an online BCI's performance. Even the most commonly used metric, the information transfer rate (ITR), is often reported differently, even incorrectly, in many papers, which is not conducive to BCI research. This paper aims to point out many of the existing problems and give some suggestions and methods to overcome these problems.<br><br>APPROACH: First, the preconditions inherent in ITR calculation based on Wolpaw's definition are summarized and several incorrect ITR calculations, which go against the preconditions, are indicated. Then, the issues affecting ITR estimation during the test of online BCI systems are discussed in detail. Finally, a task-oriented online BCI test platform was proposed, which may help BCI evaluations in real-world applications.<br><br>MAIN RESULTS: The guidelines for ITR calculation in online BCIs testing are proposed. The platform executed in the Beijing BCI Competition 2010 shows that it can be used as a common way to compare the online performances (including the ITR) of existing BCI paradigms.<br><br>SIGNIFICANCE: The proposed guidelines and task-oriented test platform may reduce the uncertainty and artifacts of online BCI performance evaluation; they provide a relatively objective way to compare different BCI's performances in real-world BCI applications, which is a forward step toward developing standards for BCI performance evaluation.},
}
@article {pmid23448913,
year = {2013},
author = {Baek, HJ and Kim, HS and Heo, J and Lim, YG and Park, KS},
title = {Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {024001},
doi = {10.1088/1741-2560/10/2/024001},
pmid = {23448913},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Adult ; Attention/physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Electrodes ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Auditory/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Memory/physiology ; Photic Stimulation ; Psychomotor Performance ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings.<br><br>APPROACH: A capacitive electrode that does not require an electrolytic gel or direct electrode-scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed.<br><br>MAIN RESULTS: Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 &#xb1; 2.08 and 0.7 &#xb1; 0.24 bpm, respectively.<br><br>SIGNIFICANCE: The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.},
}
@article {pmid23446030,
year = {2013},
author = {Wu, SL and Liao, LD and Lu, SW and Jiang, WL and Chen, SA and Lin, CT},
title = {Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {8},
pages = {2133-2141},
doi = {10.1109/TBME.2013.2248154},
pmid = {23446030},
issn = {1558-2531},
mesh = {*Brain-Computer Interfaces ; *Electrodes ; Electrooculography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Eye Movements/*physiology ; Humans ; Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; },
abstract = {Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.},
}
@article {pmid23446029,
year = {2013},
author = {Robinson, N and Vinod, AP and Ang, KK and Tee, KP and Guan, CT},
title = {EEG-based classification of fast and slow hand movements using Wavelet-CSP algorithm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {8},
pages = {2123-2132},
doi = {10.1109/TBME.2013.2248153},
pmid = {23446029},
issn = {1558-2531},
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Wavelet Analysis ; Young Adult ; },
abstract = {A brain-computer interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This paper investigates the application of a noninvasive electroencephalography (EEG)-based BCI to identify brain signal features in regard to actual hand movement speed. This provides a more refined control for a BCI system in terms of movement parameters. An experiment was performed to collect EEG data from subjects while they performed right-hand movement at two different speeds, namely fast and slow, in four different directions. The informative features from the data were obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spatial-spectral resolution. The applicability of these features to classify the two speeds and to reconstruct the speed profile was studied. The results for classifying speed across seven subjects yielded a mean accuracy of 83.71% using a Fisher Linear Discriminant (FLD) classifier. The speed components were reconstructed using multiple linear regression and significant correlation of 0.52 (Pearson's linear correlation coefficient) was obtained between recorded and reconstructed velocities on an average. The spatial patterns of the W-CSP features obtained showed activations in parietal and motor areas of the brain. The results achieved promises to provide a more refined control in BCI by including control of movement speed.},
}
@article {pmid23445258,
year = {2013},
author = {Silvoni, S and Cavinato, M and Volpato, C and Ruf, CA and Birbaumer, N and Piccione, F},
title = {Amyotrophic lateral sclerosis progression and stability of brain-computer interface communication.},
journal = {Amyotrophic lateral sclerosis &amp; frontotemporal degeneration},
volume = {14},
number = {5-6},
pages = {390-396},
doi = {10.3109/21678421.2013.770029},
pmid = {23445258},
issn = {2167-9223},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*physiopathology ; Brain/*physiopathology ; *Brain-Computer Interfaces ; *Communication ; *Communication Devices for People with Disabilities ; Disease Progression ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Female ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; },
abstract = {Our objective was to investigate the relationship between brain-computer interface (BCI) communication skill and disease progression in amyotrophic lateral sclerosis (ALS). We sought also to assess stability of BCI communication performance over time and whether it is related to the progression of neurological impairment before entering the locked-in state. A three years follow-up, BCI evaluation in a group of ALS patients (n = 24) was conducted. For a variety of reasons only three patients completed the three years follow-up. BCI communication skill and disability level, using the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised, were assessed at admission and at each of the three follow-ups. Multiple non-parametric statistical methods were used to ensure reliability of the dependent variables: correlations, paired test and factor analysis of variance. Results demonstrated no significant relationship between BCI communication skill (BCI-CS) and disease evolution. The patients who performed the follow-up evaluations preserved their BCI-CS over time. Patients' age at admission correlated positively with the ability to achieve control over a BCI. In conclusion, disease evolution in ALS does not affect the ability to control a BCI for communication. BCI performance can be maintained in the different stages of the illness.},
}
@article {pmid23443214,
year = {2013},
author = {Fernandez-Vargas, J and Pfaff, HU and Rodríguez, FB and Varona, P},
title = {Assisted closed-loop optimization of SSVEP-BCI efficiency.},
journal = {Frontiers in neural circuits},
volume = {7},
number = {},
pages = {27},
pmid = {23443214},
issn = {1662-5110},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Teach-Back Communication/*methods ; Young Adult ; },
abstract = {We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.},
}
@article {pmid23439659,
year = {2014},
author = {Prinsloo, S and Gabel, S and Lyle, R and Cohen, L},
title = {Neuromodulation of cancer pain.},
journal = {Integrative cancer therapies},
volume = {13},
number = {1},
pages = {30-37},
doi = {10.1177/1534735413477193},
pmid = {23439659},
issn = {1552-695X},
support = {CA016672/CA/NCI NIH HHS/United States ; },
mesh = {Brain/physiology ; Electrophysiological Phenomena ; Humans ; Neoplasms/*complications/*therapy ; Neuronal Plasticity ; Neurotransmitter Agents/*therapeutic use ; Pain/*drug therapy/*etiology ; Pain Management/methods ; },
abstract = {Managing cancer-related chronic pain is challenging to health care professionals as well as cancer patients and survivors. The management of cancer-related pain has largely consisted of pharmacological treatments, which has caused researchers to focus on neurotransmitter activity as a mediator of patients' perception of pain rather than the electrical activity during neurobiological processes of cancer-related pain. Consequently, brain-based pain treatment has focused mainly on neurotransmitters and not electrical neuromodulation. Neuroimaging research has revealed that brain activity is associated with patients' perceptions of symptoms across various diagnoses. The brain modulates internally generated neural activity and adjusts perceptions according to sensory input from the peripheral nervous system. Cancer-related pain may result not only from changes in the peripheral nervous system but also from changes in cortical activity over time. Thus, cortical reorganization by way of the brain's natural, plastic ability (neuroplasticity) may be used to manage pain symptoms. Physical and psychological distress could be modulated by giving patients tools to regulate neural activity in symptom-specific regions of interest. Initial research in nononcology populations suggests that encouraging neuroplasticity through a learning paradigm can be a useful technique to help treat chronic pain. Here we review evidence that indicates a measurable link between brain activity and patient-reported psychological and physical distress. We also summarize findings regarding both the neuroelectrical and neuroanatomical experience of symptoms, review research examining the mechanisms of the brain's ability to modify its own activity, and propose a brain-computer interface as a learning paradigm to augment neuroplasticity for pain management.},
}
@article {pmid23437338,
year = {2013},
author = {Nambu, I and Ebisawa, M and Kogure, M and Yano, S and Hokari, H and Wada, Y},
title = {Estimating the intended sound direction of the user: toward an auditory brain-computer interface using out-of-head sound localization.},
journal = {PloS one},
volume = {8},
number = {2},
pages = {e57174},
pmid = {23437338},
issn = {1932-6203},
mesh = {Acoustic Stimulation ; Brain/*physiology ; *Brain-Computer Interfaces ; Cues ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Sound Localization/*physiology ; *Support Vector Machine ; Young Adult ; },
abstract = {The auditory Brain-Computer Interface (BCI) using electroencephalograms (EEG) is a subject of intensive study. As a cue, auditory BCIs can deal with many of the characteristics of stimuli such as tone, pitch, and voices. Spatial information on auditory stimuli also provides useful information for a BCI. However, in a portable system, virtual auditory stimuli have to be presented spatially through earphones or headphones, instead of loudspeakers. We investigated the possibility of an auditory BCI using the out-of-head sound localization technique, which enables us to present virtual auditory stimuli to users from any direction, through earphones. The feasibility of a BCI using this technique was evaluated in an EEG oddball experiment and offline analysis. A virtual auditory stimulus was presented to the subject from one of six directions. Using a support vector machine, we were able to classify whether the subject attended the direction of a presented stimulus from EEG signals. The mean accuracy across subjects was 70.0% in the single-trial classification. When we used trial-averaged EEG signals as inputs to the classifier, the mean accuracy across seven subjects reached 89.5% (for 10-trial averaging). Further analysis showed that the P300 event-related potential responses from 200 to 500 ms in central and posterior regions of the brain contributed to the classification. In comparison with the results obtained from a loudspeaker experiment, we confirmed that stimulus presentation by out-of-head sound localization achieved similar event-related potential responses and classification performances. These results suggest that out-of-head sound localization enables us to provide a high-performance and loudspeaker-less portable BCI system.},
}
@article {pmid23437188,
year = {2013},
author = {Wu, D and Lance, BJ and Parsons, TD},
title = {Collaborative filtering for brain-computer interaction using transfer learning and active class selection.},
journal = {PloS one},
volume = {8},
number = {2},
pages = {e56624},
pmid = {23437188},
issn = {1932-6203},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Brain-Computer Interfaces ; *Computer Systems ; Cooperative Behavior ; Electroencephalography ; Humans ; Learning/*physiology ; Male ; },
abstract = {Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.},
}
@article {pmid23436927,
year = {2013},
author = {Pathmanathan, N and Balleine, RL},
title = {Ki67 and proliferation in breast cancer.},
journal = {Journal of clinical pathology},
volume = {66},
number = {6},
pages = {512-516},
doi = {10.1136/jclinpath-2012-201085},
pmid = {23436927},
issn = {1472-4146},
mesh = {Breast Neoplasms/*diagnosis/*metabolism/pathology ; *Cell Proliferation ; Female ; Gene Expression ; Humans ; Immunohistochemistry ; Ki-67 Antigen/genetics/*metabolism ; Prognosis ; Receptors, Estrogen/metabolism ; },
abstract = {New approaches to the prognostic assessment of breast cancer have come from molecular profiling studies. A major feature of this work has been to emphasise the importance of cancer cell proliferation as a key discriminative indicator of recurrence risk for oestrogen receptor positive breast cancer in particular. Mitotic count scoring, as a component of histopathological grade, has long formed part of a routine evaluation of breast cancer biology. However, there is an increasingly compelling case to include a specific proliferation score in breast cancer pathology reports based on expression of the cell cycle regulated protein Ki67. Immunohistochemical staining for Ki67 is a widely available and economical test with good tolerance of pre-analytical variations and staining conditions. However, there is currently no evidence based protocol established to derive a reliable and informative Ki67 score for routine clinical use. In this circumstance, pathologists must establish a standardised framework for scoring Ki67 and communicating results to a multidisciplinary team.},
}
@article {pmid23429035,
year = {2013},
author = {Yin, E and Zhou, Z and Jiang, J and Chen, F and Liu, Y and Hu, D},
title = {A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026012},
doi = {10.1088/1741-2560/10/2/026012},
pmid = {23429035},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Data Collection ; Electroencephalography ; Electrophysiology ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Online Systems ; Photic Stimulation ; Prosthesis Design ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {OBJECTIVE: Although extensive studies have shown improvement in spelling accuracy, the conventional P300 speller often exhibits errors, which occur in almost the same row or column relative to the target. To address this issue, we propose a novel hybrid brain-computer interface (BCI) approach by incorporating the steady-state visual evoked potential (SSVEP) into the conventional P300 paradigm.<br><br>APPROACH: We designed a periodic stimuli mechanism and superimposed it onto the P300 stimuli to increase the difference between the symbols in the same row or column. Furthermore, we integrated the random flashings and periodic flickers to simultaneously evoke the P300 and SSVEP, respectively. Finally, we developed a hybrid detection mechanism based on the P300 and SSVEP in which the target symbols are detected by the fusion of three-dimensional, time-frequency features.<br><br>MAIN RESULTS: The results obtained from 12 healthy subjects show that an online classification accuracy of 93.85% and information transfer rate of 56.44 bit/min were achieved using the proposed BCI speller in only a single trial. Specifically, 5 of the 12 subjects exhibited an information transfer rate of 63.56 bit/min with an accuracy of 100%.<br><br>SIGNIFICANCE: The pilot studies suggested that the proposed BCI speller could achieve a better and more stable system performance compared with the conventional P300 speller, and it is promising for achieving quick spelling in stimulus-driven BCI applications.},
}
@article {pmid23428966,
year = {2013},
author = {Willett, FR and Suminski, AJ and Fagg, AH and Hatsopoulos, NG},
title = {Improving brain-machine interface performance by decoding intended future movements.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026011},
pmid = {23428966},
issn = {1741-2552},
support = {KL2 TR000431/TR/NCATS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electrophysiology ; Forecasting ; Intention ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/physiology ; Movement/*physiology ; Online Systems ; Psychomotor Performance/physiology ; },
abstract = {OBJECTIVE: A brain-machine interface (BMI) records neural signals in real time from a subject's brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject's intended movements a short time lead in the future.<br><br>APPROACH: We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user's intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen.<br><br>MAIN RESULTS: Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user's future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads.<br><br>SIGNIFICANCE: This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user's future intent can compensate for the negative effect of control delay on BMI performance.},
}
@article {pmid23428877,
year = {2013},
author = {Xu, K and Wang, Y and Wang, Y and Wang, F and Hao, Y and Zhang, S and Zhang, Q and Chen, W and Zheng, X},
title = {Local-learning-based neuron selection for grasping gesture prediction in motor brain machine interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026008},
doi = {10.1088/1741-2560/10/2/026008},
pmid = {23428877},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Gestures ; Hand Strength/*physiology ; Haplorhini ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/cytology/physiology ; Movement/physiology ; Neurons/*physiology ; Prosthesis Design ; Psychomotor Performance/physiology ; Reproducibility of Results ; Support Vector Machine ; },
abstract = {OBJECTIVE: The high-dimensional neural recordings bring computational challenges to movement decoding in motor brain machine interfaces (mBMI), especially for portable applications. However, not all recorded neural activities relate to the execution of a certain movement task. This paper proposes to use a local-learning-based method to perform neuron selection for the gesture prediction in a reaching and grasping task.<br><br>APPROACH: Nonlinear neural activities are decomposed into a set of linear ones in a weighted feature space. A margin is defined to measure the distance between inter-class and intra-class neural patterns. The weights, reflecting the importance of neurons, are obtained by minimizing a margin-based exponential error function. To find the most dominant neurons in the task, 1-norm regularization is introduced to the objective function for sparse weights, where near-zero weights indicate irrelevant neurons.<br><br>MAIN RESULTS: The signals of only 10 neurons out of 70 selected by the proposed method could achieve over 95% of the full recording's decoding accuracy of gesture predictions, no matter which different decoding methods are used (support vector machine and K-nearest neighbor). The temporal activities of the selected neurons show visually distinguishable patterns associated with various hand states. Compared with other algorithms, the proposed method can better eliminate the irrelevant neurons with near-zero weights and provides the important neuron subset with the best decoding performance in statistics. The weights of important neurons converge usually within 10-20 iterations. In addition, we study the temporal and spatial variation of neuron importance along a period of one and a half months in the same task. A high decoding performance can be maintained by updating the neuron subset.<br><br>SIGNIFICANCE: The proposed algorithm effectively ascertains the neuronal importance without assuming any coding model and provides a high performance with different decoding models. It shows better robustness of identifying the important neurons with noisy signals presented. The low demand of computational resources which, reflected by the fast convergence, indicates the feasibility of the method applied in portable BMI systems. The ascertainment of the important neurons helps to inspect neural patterns visually associated with the movement task. The elimination of irrelevant neurons greatly reduces the computational burden of mBMI systems and maintains the performance with better robustness.},
}
@article {pmid23428826,
year = {2013},
author = {Yeom, HG and Kim, JS and Chung, CK},
title = {Estimation of the velocity and trajectory of three-dimensional reaching movements from non-invasive magnetoencephalography signals.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026006},
doi = {10.1088/1741-2560/10/2/026006},
pmid = {23428826},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Data Interpretation, Statistical ; Female ; Humans ; Linear Models ; *Magnetoencephalography ; Male ; Movement/*physiology ; Photic Stimulation ; Prosthesis Design ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Signal-To-Noise Ratio ; Young Adult ; },
abstract = {OBJECTIVE: Studies on the non-invasive brain-machine interface that controls prosthetic devices via movement intentions are at their very early stages. Here, we aimed to estimate three-dimensional arm movements using magnetoencephalography (MEG) signals with high accuracy.<br><br>APPROACH: Whole-head MEG signals were acquired during three-dimensional reaching movements (center-out paradigm). For movement decoding, we selected 68 MEG channels in motor-related areas, which were band-pass filtered using four subfrequency bands (0.5-8, 9-22, 25-40 and 57-97 Hz). After the filtering, the signals were resampled, and 11 data points preceding the current data point were used as features for estimating velocity. Multiple linear regressions were used to estimate movement velocities. Movement trajectories were calculated by integrating estimated velocities. We evaluated our results by calculating correlation coefficients (r) between real and estimated velocities.<br><br>MAIN RESULTS: Movement velocities could be estimated from the low-frequency MEG signals (0.5-8 Hz) with significant and considerably high accuracy (p <0.001, mean r > 0.7). We also showed that preceding (60-140 ms) MEG signals are important to estimate current movement velocities and the intervals of brain signals of 200-300 ms are sufficient for movement estimation.<br><br>SIGNIFICANCE: These results imply that disabled people will be able to control prosthetic devices without surgery in the near future.},
}
@article {pmid23428648,
year = {2013},
author = {Barthélemy, Q and Gouy-Pailler, C and Isaac, Y and Souloumiac, A and Larue, A and Mars, JI},
title = {Multivariate temporal dictionary learning for EEG.},
journal = {Journal of neuroscience methods},
volume = {215},
number = {1},
pages = {19-28},
doi = {10.1016/j.jneumeth.2013.02.001},
pmid = {23428648},
issn = {1872-678X},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods/statistics &amp; numerical data ; Electroencephalography Phase Synchronization ; Event-Related Potentials, P300 ; Humans ; Learning/*physiology ; Models, Neurological ; Multivariate Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.},
}
@article {pmid23428612,
year = {2013},
author = {Lee, SB and Yin, M and Manns, JR and Ghovanloo, M},
title = {A wideband dual-antenna receiver for wireless recording from animals behaving in large arenas.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {7},
pages = {1993-2004},
pmid = {23428612},
issn = {1558-2531},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; R21 EB009437/EB/NIBIB NIH HHS/United States ; 1R01NS062031-01A1/NS/NINDS NIH HHS/United States ; },
mesh = {Actigraphy/*instrumentation ; Amplifiers, Electronic ; Animals ; Behavior, Animal/*physiology ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Male ; Monitoring, Ambulatory/*instrumentation ; Rats ; Rats, Long-Evans ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {A low-noise wideband receiver (Rx) is presented for a multichannel wireless implantable neural recording (WINeR) system that utilizes time-division multiplexing of pulse width modulated (PWM) samples. The WINeR-6 Rx consists of four parts: 1) RF front end; 2) signal conditioning; 3) analog output (AO); and 4) field-programmable gate array (FPGA) back end. The RF front end receives RF-modulated neural signals in the 403-490 MHz band with a wide bandwidth of 18 MHz. The frequency-shift keying (FSK) PWM demodulator in the FPGA is a time-to-digital converter with 304 ps resolution, which converts the analog pulse width information to 16-bit digital samples. Automated frequency tracking has been implemented in the Rx to lock onto the free-running voltage-controlled oscillator in the transmitter (Tx). Two antennas and two parallel RF paths are used to increase the wireless coverage area. BCI-2000 graphical user interface has been adopted and modified to acquire, visualize, and record the recovered neural signals in real time. The AO module picks three demultiplexed channels and converts them into analog signals for direct observation on an oscilloscope. One of these signals is further amplified to generate an audio output, offering users the ability to listen to ongoing neural activity. Bench-top testing of the Rx performance with a 32-channel WINeR-6 Tx showed that the input referred noise of the entire system at a Tx-Rx distance of 1.5 m was 4.58 μV rms with 8-bit resolution at 640 kSps. In an in vivo experiment, location-specific receptive fields of hippocampal place cells were mapped during a behavioral experiment in which a rat completed 40 laps in a large circular track. Results were compared against those acquired from the same animal and the same set of electrodes by a commercial hardwired recording system to validate the wirelessly recorded signals.},
}
@article {pmid23421021,
year = {2012},
author = {Kopiński, P and Dyczek, A and Chorostowska-Wynimko, J and Marszałek, A and Balicka-Slusarczyk, B and Kubiszewska, I and Szabłowska, K and Półgesek, E and Szpechciński, A},
title = {[Higher incidence of alveolar lymphocytes (AL) apoptosis in smokers depends on BCL-2 expression and specific response to tumor necrosis factor alpha (TNFalpha). Bronchoalveolar lavage (BAL) material analysis from selected interstitial lung diseases (ILD) and healthy controls].},
journal = {Przeglad lekarski},
volume = {69},
number = {10},
pages = {731-736},
pmid = {23421021},
issn = {0033-2240},
mesh = {*Apoptosis ; Bronchoalveolar Lavage Fluid/cytology ; Down-Regulation ; Flow Cytometry ; Humans ; Idiopathic Pulmonary Fibrosis/etiology/*metabolism/pathology ; Lymphocytes/*metabolism/pathology ; Reference Values ; Sarcoidosis, Pulmonary/etiology/*metabolism/pathology ; Smoking/*adverse effects/metabolism/pathology ; Tumor Necrosis Factor-alpha/*metabolism ; bcl-2-Associated X Protein/*metabolism ; },
abstract = {BACKGROUND: We have previously described the increased apoptosis rate in smokers alveolar lymphocytes (AL) that was independent from the FASL/ FAS system activation. Consequently, the role of intrinsic apoptosis pathway and other ligand/death receptor pairs as TNFalpha/TNFR1 and TRAIL/DR4 important for apoptosis regulation should be considered in this phenomenon. The purpose of the study was to evaluate the impact of tobacco consumption on expression of selected BCL-2 family members and ligand/receptors pairs in bronchoalveolar lavage (BAL) harvested from patients with pulmonary sarcoidosis (PS), idiopathic pulmonary fibrosis (IPF) and healthy volunteers. The results were analyzed in the context of AL apoptosis rate.<br><br>METHODS: AL apoptosis from PS (n=36, incl. 22 smokers), IPF (11, incl. 5 smokers) and controls (n=17, incl. 9 smokers) was evaluated by flow cytometry (sub-G1 of cell cycle). AL were stained for BCL-2, BCL-xL, BAK, TNFR1 (CD120A) TNFR2 (CD120B) and DR4. ELISA assay was used to evaluate the BAL supernatant levels of TNFalpha and TRAIL.<br><br>RESULTS: According to previous observations, AL apoptosis rate was significantly higher in smoker subgroups as compared to nonsmoking counterparts. Decreased AL BCI-2+ relative number was observed in smoking PS (80.5 +/- 6.2 vs 91 +/- 9.8% in nonsmokers) and controls (59 +/- 14.1% vs 75 +/- 16.1%, p<0.05). TNFalpha concentration in BAL supernatant was significantly higher only in healthy smokers (2.32 +/- 0.77 vs 0.42 +/- 0.27 pg/ml, p<0.05), whereas TRAIL levels were remarkably enhanced in IPF smokers (44.8 +/- 12.8 vs 13.5 +/- 5.0 pg/ml, p<0.05) only. However, TUNEL. detected AL apoptosis positively correlated with TNFalpha. in smokers (p<0.05) and negatively with AL CD120B:CD120A expression ratio. Paradoxically, TNFalpha levels were positively correlated with AL BCL-2 expression in nonsmokers (Rs +0.58, p<0.01), but not in smokers. No differences were observed in all subgroups in respect to AL expression of DR4, BCL-xL or BAK.<br><br>CONCLUSIONS: 1. AL were not sufficiently protected against apoptosis in smokers. 2. The most likely mechanisms involve down-regulation of BCL-2 expression and altered AL susceptibility to TNFalpha, mediated by imbalance between AL membrane expression of TNF receptor type 1 (death receptor) and type 2 (survival mediator). 3. Mechanisms regulating the increased AL apoptosis in smokers seem to be different in each tested group.},
}
@article {pmid23417326,
year = {2013},
author = {Ceyssens, F and van Kuyck, K and Vande Velde, G and Welkenhuysen, M and Stappers, L and Nuttin, B and Puers, R},
title = {Resorbable scaffold based chronic neural electrode arrays.},
journal = {Biomedical microdevices},
volume = {15},
number = {3},
pages = {481-493},
doi = {10.1007/s10544-013-9748-x},
pmid = {23417326},
issn = {1572-8781},
mesh = {Animals ; Brain/diagnostic imaging/*metabolism ; Brain-Computer Interfaces ; Chitosan/chemistry/*metabolism/pharmacology ; *Electrodes, Implanted/microbiology ; Equipment Design ; Hemostasis/drug effects ; Porosity ; Rats ; Rats, Wistar ; Tomography, X-Ray Computed ; },
abstract = {We have developed a novel type of neural electrode array for future brain-machine interfaces (BMI) and neural implants requiring high resolution recording and stimulation on the surface of brain lesions or on the cortex. The devices differ on two points from commonly used thin film electrode arrays: first, the thin film backbone of the implant is exceptionally thin (down to 5 microns) and finely patterned into spring-like structures. This increases the flexibility of the electrode array and allows stretching and conforming better to a quasi spherical cavity surface. Second, the thin film backbone of the device is reinforced with a porous layer of resorbable chitosan. This design aims at minimal invasiveness and low mechanical irritation during prolonged use, while the chitosan matrix ensures the implant is stiff enough for practical handling during the implantation procedure and dissolves afterwards. Furthermore, the chitosan adds haemostatic and antiseptic properties to the implant and improves adhesion. In the article, the design and fabrication process are presented. In vitro and long term in vivo test results over a 12 month period are shown. By adopting the use of a resorbable scaffold-like material as main constituent of neural implants, the presented work opens up the possibility of applying tissue engineering techniques to further improve neural implant technology.},
}
@article {pmid23416098,
year = {2013},
author = {Hwang, EJ and Bailey, PM and Andersen, RA},
title = {Volitional control of neural activity relies on the natural motor repertoire.},
journal = {Current biology : CB},
volume = {23},
number = {5},
pages = {353-361},
pmid = {23416098},
issn = {1879-0445},
support = {K99 NS062894/NS/NINDS NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; T32 NS007251/NS/NINDS NIH HHS/United States ; EY013337/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Learning/*physiology ; Macaca ; Neurons/*physiology ; Parietal Lobe/*physiology ; Volition ; },
abstract = {BACKGROUND: The results from recent brain-machine interface (BMI) studies suggest that it may be more efficient to use simple arbitrary relationships between individual neuron activity and BMI movements than the complex relationship observed between neuron activity and natural movements. This idea is based on the assumption that individual neurons can be conditioned independently regardless of their natural movement association.<br><br>RESULTS: We tested this assumption in the parietal reach region (PRR), an important candidate area for BMIs in which neurons encode the target location for reaching movements. Monkeys could learn to elicit arbitrarily assigned activity patterns, but the seemingly arbitrary patterns always belonged to the response set for natural reaching movements. Moreover, neurons that are free from conditioning showed correlated responses with the conditioned neurons as if they encoded common reach targets. Thus, learning was accomplished by finding reach targets (intrinsic variable of PRR neurons) for which the natural response of reach planning could approximate the arbitrary patterns.<br><br>CONCLUSIONS: Our results suggest that animals learn to volitionally control single-neuron activity in PRR by preferentially exploring and exploiting their natural movement repertoire. Thus, for optimal performance, BMIs utilizing neural signals in PRR should harness, not disregard, the activity patterns in the natural sensorimotor repertoire.},
}
@article {pmid23411437,
year = {2013},
author = {McGie, S and Nagai, M and Artinian-Shaheen, T},
title = {Clinical ethical concerns in the implantation of brain-machine interfaces: part I: overview, target populations, and alternatives.},
journal = {IEEE pulse},
volume = {4},
number = {1},
pages = {28-32},
doi = {10.1109/MPUL.2012.2228810},
pmid = {23411437},
issn = {2154-2317},
mesh = {Biomedical Engineering/*ethics/*instrumentation ; Biomedical Research/*ethics/*instrumentation ; Brain-Computer Interfaces/*ethics ; Clinical Trials as Topic ; Humans ; Self-Help Devices/*ethics ; },
abstract = {Recently, implantable brain-machine interfaces (BMIs) for the severely disabled have generated a great deal of excitement in the biomedical community, and clinical trials investigating their use as communication aids have already begun in the United States (these trials are discussed in the "Existing Devices and Trials" section). While the hypothetical societal implications of such devices are often discussed, the relative risks and benefits associated with their clinical use, as well as the alternative options available to patients, are not always part of this discussion. This article therefore seeks to outline the associated ethical concerns of the devices, the user populations for which the devices are intended, and existing noninvasive alternatives.},
}
@article {pmid23405137,
year = {2013},
author = {Wang, W and Collinger, JL and Degenhart, AD and Tyler-Kabara, EC and Schwartz, AB and Moran, DW and Weber, DJ and Wodlinger, B and Vinjamuri, RK and Ashmore, RC and Kelly, JW and Boninger, ML},
title = {An electrocorticographic brain interface in an individual with tetraplegia.},
journal = {PloS one},
volume = {8},
number = {2},
pages = {e55344},
pmid = {23405137},
issn = {1932-6203},
support = {1R01EB009103-01/EB/NIBIB NIH HHS/United States ; UL1 TR000005/TR/NCATS NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; 3R01NS050256-05S1/NS/NINDS NIH HHS/United States ; 8KL2TR000146-07/TR/NCATS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; },
mesh = {Adult ; Arm/physiology ; Electroencephalography/*instrumentation/*methods ; Hand/physiology ; Humans ; Male ; Motor Cortex/*physiopathology ; Movement/physiology ; Quadriplegia/physiopathology/*rehabilitation ; Spinal Cord Injuries/physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.},
}
@article {pmid23403106,
year = {2013},
author = {Paraskevopoulou, SE and Barsakcioglu, DY and Saberi, MR and Eftekhar, A and Constandinou, TG},
title = {Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.},
journal = {Journal of neuroscience methods},
volume = {215},
number = {1},
pages = {29-37},
doi = {10.1016/j.jneumeth.2013.01.012},
pmid = {23403106},
issn = {1872-678X},
mesh = {Algorithms ; Brain-Computer Interfaces ; Calibration ; Cluster Analysis ; *Computer Systems ; Computers ; Databases, Factual ; Electrophysiological Phenomena ; Linear Models ; *Neural Prostheses ; Neurons/physiology ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation.},
}
@article {pmid23383315,
year = {2013},
author = {Milekovic, T and Ball, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Detection of error related neuronal responses recorded by electrocorticography in humans during continuous movements.},
journal = {PloS one},
volume = {8},
number = {2},
pages = {e55235},
pmid = {23383315},
issn = {1932-6203},
mesh = {Algorithms ; Brain-Computer Interfaces/*standards ; Cerebral Cortex/*physiology ; Electrodes, Implanted ; Electroencephalography/*methods ; Humans ; Movement/*physiology ; Neurons/*physiology ; *Prostheses and Implants ; Psychomotor Performance/physiology ; },
abstract = {BACKGROUND: Brain-machine interfaces (BMIs) can translate the neuronal activity underlying a user's movement intention into movements of an artificial effector. In spite of continuous improvements, errors in movement decoding are still a major problem of current BMI systems. If the difference between the decoded and intended movements becomes noticeable, it may lead to an execution error. Outcome errors, where subjects fail to reach a certain movement goal, are also present during online BMI operation. Detecting such errors can be beneficial for BMI operation: (i) errors can be corrected online after being detected and (ii) adaptive BMI decoding algorithm can be updated to make fewer errors in the future.<br><br>Here, we show that error events can be detected from human electrocorticography (ECoG) during a continuous task with high precision, given a temporal tolerance of 300-400 milliseconds. We quantified the error detection accuracy and showed that, using only a small subset of 2&#xd7;2 ECoG electrodes, 82% of detection information for outcome error and 74% of detection information for execution error available from all ECoG electrodes could be retained.<br><br>CONCLUSIONS/SIGNIFICANCE: The error detection method presented here could be used to correct errors made during BMI operation or to adapt a BMI algorithm to make fewer errors in the future. Furthermore, our results indicate that smaller ECoG implant could be used for error detection. Reducing the size of an ECoG electrode implant used for BMI decoding and error detection could significantly reduce the medical risk of implantation.},
}
@article {pmid23378199,
year = {2013},
author = {Dickhaus, T and Blankertz, B and Meinecke, FC},
title = {Binary classification with pFDR-pFNR losses.},
journal = {Biometrical journal. Biometrische Zeitschrift},
volume = {55},
number = {3},
pages = {463-477},
doi = {10.1002/bimj.201200054},
pmid = {23378199},
issn = {1521-4036},
mesh = {*Algorithms ; Brain/physiology ; Computer Simulation ; *Data Interpretation, Statistical ; Electroencephalography/methods ; False Positive Reactions ; Humans ; *Models, Statistical ; Signal Processing, Computer-Assisted ; },
abstract = {Connecting multiple testing with binary classification, we derive a false discovery rate-based classification approach for two-class mixture models, where the available data (represented as feature vectors) for each individual comparison take values in Rd for some d≥1 and may exhibit certain forms of autocorrelation. This generalizes previous findings for the independent case in dimension d=1. Two resulting classification procedures are described which allow for incorporating prior knowledge about class probabilities and for user-supplied weighting of the severity of misclassifying a member of the "0"-class as "1" and vice versa. The key mathematical tools to be employed are multivariate estimation methods for probability density functions or density ratios. We compare the two algorithms with respect to their theoretical properties and with respect to their performance in practice. Computer simulations indicate that they can both successfully be applied to autocorrelated time series data with moving average structure. Our approach was inspired and its practicability will be demonstrated by applications from the field of brain-computer interfacing and the processing of electroencephalography data.},
}
@article {pmid23373814,
year = {2013},
author = {Machado, A},
title = {New frontier: the brain machine interface.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {16},
number = {1},
pages = {6-7},
doi = {10.1111/ner.12021},
pmid = {23373814},
issn = {1525-1403},
mesh = {Animals ; Brain-Computer Interfaces/*trends ; Humans ; },
}
@article {pmid23372966,
year = {2012},
author = {Kubben, PL and Pouratian, N},
title = {An open-source and cross-platform framework for Brain Computer Interface-guided robotic arm control.},
journal = {Surgical neurology international},
volume = {3},
number = {},
pages = {149},
pmid = {23372966},
issn = {2152-7806},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain Computer Interfaces (BCIs) have focused on several areas, of which motor substitution has received particular interest. Whereas open-source BCI software is available to facilitate cost-effective collaboration between research groups, it mainly focuses on communication and computer control. We developed an open-source and cross-platform framework, which works with cost-effective equipment that allows researchers to enter the field of BCI-based motor substitution without major investments upfront. It is based on the C++ programming language and the Qt framework, and offers a separate class for custom MATLAB/Simulink scripts. It has been tested using a 14-channel wireless electroencephalography (EEG) device and a low-cost robotic arm that offers 5° of freedom. The software contains four modules to control the robotic arm, one of which receives input from the EEG device. Strengths, current limitations, and future developments will be discussed.},
}
@article {pmid23372028,
year = {2013},
author = {Hsu, WY},
title = {Independent component analysis and multiresolution asymmetry ratio for brain-computer interface.},
journal = {Clinical EEG and neuroscience},
volume = {44},
number = {2},
pages = {105-111},
doi = {10.1177/1550059412463660},
pmid = {23372028},
issn = {1550-0594},
mesh = {Analysis of Variance ; Artifacts ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Electrooculography ; Female ; Humans ; Male ; Models, Statistical ; Signal-To-Noise Ratio ; *Support Vector Machine ; Wavelet Analysis ; Young Adult ; },
abstract = {This study proposes a brain-computer interface (BCI) system for the recognition of single-trial electroencephalogram (EEG) data. With the combination of independent component analysis (ICA) and multiresolution asymmetry ratio, a support vector machine (SVM) is used to classify left and right finger lifting or motor imagery. First, ICA and similarity measures are proposed to eliminate the electrooculography (EOG) artifacts automatically. The features are then extracted from the wavelet data by means of an asymmetry ratio. Finally, the SVM classifier is used to discriminate between the features. Compared to the EEG data without EOG artifact removal, band power, and adoptive autoregressive (AAR) parameter features, the proposed system achieves promising results in BCI applications.},
}
@article {pmid23370146,
year = {2013},
author = {Severens, M and Farquhar, J and Duysens, J and Desain, P},
title = {A multi-signature brain-computer interface: use of transient and steady-state responses.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026005},
doi = {10.1088/1741-2560/10/2/026005},
pmid = {23370146},
issn = {1741-2552},
mesh = {Brain/*physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electrodes ; Electroencephalography ; Electromyography ; Evoked Potentials/physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Humans ; Male ; Online Systems ; Physical Stimulation ; Psychomotor Performance ; Young Adult ; },
abstract = {OBJECTIVE: The aim of this paper was to increase the information transfer in brain-computer interfaces (BCI). Therefore, a multi-signature BCI was developed and investigated. Stimuli were designed to simultaneously evoke transient somatosensory event-related potentials (ERPs) and steady-state somatosensory potentials (SSSEPs) and the ERPs and SSSEPs in isolation.<br><br>APPROACH: Twelve subjects participated in two sessions. In the first session, the single and combined stimulation conditions were compared on these somatosensory responses and on the classification performance. In the second session the on-line performance with the combined stimulation was evaluated while subjects received feedback. Furthermore, in both sessions, the performance based on ERP and SSSEP features was compared.<br><br>MAIN RESULTS: No difference was found in the ERPs and SSSEPs between stimulation conditions. The combination of ERP and SSSEP features did not perform better than with ERP features only. In both sessions, the classification performances based on ERP and combined features were higher than the classification based on SSSEP features.<br><br>SIGNIFICANCE: Although the multi-signature BCI did not increase performance, it also did not negatively impact it. Therefore, such stimuli could be used and the best performing feature set could then be chosen individually.},
}
@article {pmid23369953,
year = {2013},
author = {Chestek, CA and Gilja, V and Blabe, CH and Foster, BL and Shenoy, KV and Parvizi, J and Henderson, JM},
title = {Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026002},
pmid = {23369953},
issn = {1741-2552},
support = {R01 NS066311/NS/NINDS NIH HHS/United States ; R01NS066311-S1/NS/NINDS NIH HHS/United States ; },
mesh = {Arm/physiology ; Computer Systems ; Electrodes ; *Electroencephalography ; Fingers/physiology ; Hand/*physiology ; Humans ; Isometric Contraction/physiology ; Motor Cortex/*physiology ; Movement/physiology ; Online Systems ; Posture/*physiology ; Prostheses and Implants ; Prosthesis Design ; Reproducibility of Results ; Rest ; Seizures/diagnosis ; Somatosensory Cortex/*physiology ; User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.<br><br>APPROACH: We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants.<br><br>MAIN RESULTS: Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1&#xd7;). A similar increase in errors (2.6&#xd7;) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system.<br><br>SIGNIFICANCE: These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.},
}
@article {pmid23369924,
year = {2013},
author = {Xu, M and Qi, H and Wan, B and Yin, T and Liu, Z and Ming, D},
title = {A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature.},
journal = {Journal of neural engineering},
volume = {10},
number = {2},
pages = {026001},
doi = {10.1088/1741-2560/10/2/026001},
pmid = {23369924},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; *Communication Devices for People with Disabilities ; Discriminant Analysis ; Electrodes ; Electroencephalography/statistics &amp; numerical data ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; Photic Stimulation ; Psychomotor Performance ; Reproducibility of Results ; Young Adult ; },
abstract = {OBJECTIVE: Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control by combining another channel of physiologic control signals. Among those studies, little attention has been paid to the combined use of a steady-state visual evoked potential (SSVEP) and P300 potential, both providing the fastest and the most reliable EEG based BCIs. In this paper, a novel hybrid BCI speller is developed to elicit P300 potential and SSVEP blocking (SSVEP-B) distinctly and simultaneously with the same target stimulus.<br><br>APPROACH: Twelve subjects were involved in the study and every one performed offline spelling twice in succession with two different speller paradigms for comparison: hybrid speller and control P300-speller. Feature analysis was adopted from the view of time domain, frequency domain and spatial distribution; the performances were evaluated by character accuracy and information transfer rate (ITR).<br><br>MAIN RESULTS: Signal analysis of the hybrid paradigm shows that SSVEPs are an evident EEG component during the nontarget phase but are dismissed and replaced by P300 potentials after target stimuli. The absence of an SSVEP, called SSVEP-B, mostly appearing in channel Oz, presents a sharp distinction between target responses and nontarget responses. The r(2) value of SSVEP-B in channel Oz is comparable to that of P300 in channel Cz. Compared with the control P300-speller, the hybrid speller achieves significantly higher accuracy and ITR with combined features.<br><br>SIGNIFICANCE: The results indicate that the combination of P300 with an SSVEP-B improves target discrimination greatly; the proposed hybrid paradigm is superior to the control paradigm in spelling performance. Thus, our findings provide a new approach to improve BCI performances.},
}
@article {pmid23369805,
year = {2013},
author = {Ohiorhenuan, I and Zada, G},
title = {Neural prosthesis for recovery of impaired cognitive function: bridging the gap between concept and reality.},
journal = {World neurosurgery},
volume = {79},
number = {3-4},
pages = {409-410},
doi = {10.1016/j.wneu.2013.01.089},
pmid = {23369805},
issn = {1878-8769},
mesh = {Animals ; Brain-Computer Interfaces ; Cognition Disorders/psychology/*rehabilitation ; Computer Simulation ; Electrodes, Implanted ; Haplorhini ; *Neural Prostheses ; Prefrontal Cortex/physiology ; *Recovery of Function ; },
}
@article {pmid23367478,
year = {2012},
author = {Obregon-Henao, G and Babadi, B and Lamus, C and Brown, EN and Purdon, PL},
title = {A fast iterative greedy algorithm for MEG source localization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6748-6751},
doi = {10.1109/EMBC.2012.6347543},
pmid = {23367478},
issn = {2694-0604},
support = {DP1-OD003646/OD/NIH HHS/United States ; DP2-OD006454/OD/NIH HHS/United States ; K25-NS057580/NS/NINDS NIH HHS/United States ; R01-EB006385/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Bayes Theorem ; Brain/pathology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Humans ; Magnetoencephalography/*methods ; Models, Statistical ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Software ; Time Factors ; },
abstract = {Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimum-norm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.},
}
@article {pmid23367476,
year = {2012},
author = {Zhang, H and Chavarriaga, R and Goel, MK and Gheorghe, L and Millán, Jdel R},
title = {Improved recognition of error related potentials through the use of brain connectivity features.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6740-6743},
doi = {10.1109/EMBC.2012.6347541},
pmid = {23367476},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*pathology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Models, Statistical ; ROC Curve ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; User-Computer Interface ; },
abstract = {Brain error processing plays a key role in goal-directed behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in brain-computer interface (BCI) system. We describe the first application of brain connectivity features for recognizing error-related signals in non-invasive BCI. EEG signal were recorded from 16 human subjects when they monitored stimuli moving in either correct or erroneous direction. Classification performance using waveform features, brain connectivity features and their combination were compared. The result of combined features yielded highest classification accuracy, 0:85. In addition, we also show that brain connectivity at theta band around 200 ms after stimuli carry highly discriminant information between error and correct trials. This paper provides evidence that the use of connectivity features improve the performance of an EEG based BCI.},
}
@article {pmid23367475,
year = {2012},
author = {Grandchamp, R and Braboszcz, C and Makeig, S and Delorme, A},
title = {Stability of ICA decomposition across within-subject EEG datasets.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6735-6739},
doi = {10.1109/EMBC.2012.6347540},
pmid = {23367475},
issn = {2694-0604},
mesh = {Algorithms ; Artifacts ; *Blinking ; Brain/pathology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Cluster Analysis ; Electroencephalography/*methods ; *Eye Movements ; Humans ; Models, Statistical ; Scalp/pathology ; *Signal Processing, Computer-Assisted ; Software ; },
abstract = {Independent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research.},
}
@article {pmid23367473,
year = {2012},
author = {Long, J and Li, Y and Wang, H and Yu, T and Pan, J},
title = {Control of a simulated wheelchair based on a hybrid brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6727-6730},
doi = {10.1109/EMBC.2012.6347538},
pmid = {23367473},
issn = {2694-0604},
mesh = {Acceleration ; Algorithms ; *Brain-Computer Interfaces ; Computer Graphics ; Computer Simulation ; Electroencephalography/methods ; Equipment Design ; Event-Related Potentials, P300 ; Humans ; *Man-Machine Systems ; Models, Statistical ; Motor Skills ; Reproducibility of Results ; Software ; User-Computer Interface ; *Wheelchairs ; },
abstract = {In this paper, a hybrid BCI system was described for the control of a simulated wheelchair. This hybrid BCI was based on the motor imagery-based mu rhythm and the P300 potential. With our paradigm, the user may perform left- or right-hand imagery to control the direction (left or right turn) of the simulated wheelchair. Furthermore, a hybrid manner was used for speed control: e.g., foot imagery without button attention for deceleration and a specific button attention without any motor imagery for acceleration. An experiment based on a simulated wheelchair in virtual environment was conducted to assess the BCI control. Subjects effectively steered the simulated wheelchairs by controlling the direction and speed with our hybrid BCI system. Data analysis validated that our hybrid BCI system can be used to control the direction and speed of a simulated wheelchair.},
}
@article {pmid23367472,
year = {2012},
author = {Chavarriaga, R and Perrin, X and Siegwart, R and Millán, Jdel R},
title = {Anticipation- and error-related EEG signals during realistic human-machine interaction: a study on visual and tactile feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6723-6726},
doi = {10.1109/EMBC.2012.6347537},
pmid = {23367472},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Anticipation, Psychological ; Brain/physiology ; Brain Mapping/methods ; Computer Graphics ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials ; *Feedback, Sensory ; Humans ; Male ; *Man-Machine Systems ; Movement ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *Touch ; *Vision, Ocular ; Young Adult ; },
abstract = {The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We focus on cortical potentials linked to the anticipation of future events (i.e. the contingent negative variation, CNV) and error-related potentials elicited by both visual and tactile feedback. Experiments with 6 subjects show brain activity consistent with previous studies using simpler stimuli, both at the level of ERPs and single trial classification. Moreover, we observe comparable signals irrespective of whether the subject was required to perform motor actions. Altogether, these results support the possibility of using these signals for practical brain machine interaction.},
}
@article {pmid23367471,
year = {2012},
author = {Flint, RD and Wright, ZA and Slutzky, MW},
title = {Control of a biomimetic brain machine interface with local field potentials: performance and stability of a static decoder over 200 days.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6719-6722},
doi = {10.1109/EMBC.2012.6347536},
pmid = {23367471},
issn = {2694-0604},
support = {K08NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal ; *Biomimetics ; *Brain-Computer Interfaces ; Calibration ; Computer Simulation ; Electrodes ; Equipment Design ; *Evoked Potentials, Motor ; Macaca mulatta ; Motor Cortex/*pathology ; Movement ; Reproducibility of Results ; Time Factors ; Transducers ; },
abstract = {Brain-machine interfaces (BMIs) have the potential to restore lost function to individuals with severe motor impairments. An important design specification for BMIs to be clinically useful is the ability to achieve high performance over a period of months to years without requiring frequent recalibration. Here, we report the first successful implementation of a biomimetic BMI based on local field potentials (LFPs). A BMI decoder was built from a single recording session of a random-pursuit reaching task for each of two monkeys, and used to control cursor position in real time (online) over a span of 210 days. Performance using this BMI was similar to prior reports using BMIs based on single-unit spikes for 2D cursor control. During this ongoing study, target acquisition rates remained constant (in 1 monkey) or improved slightly (1 monkey) over a 7 month span, and performance metrics of cursor movement (path length and time-to-target) also remained constant or showed mild improvement as the monkeys gained practice. Based on these results, we expect that a stable, high-performance BMI based on LFP signals could serve as a viable alternative to single-unit based BMIs.},
}
@article {pmid23367470,
year = {2012},
author = {Cheung, W and Sarma, D and Scherer, R and Rao, RP},
title = {Simultaneous brain-computer interfacing and motor control: expanding the reach of non-invasive BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6715-6718},
doi = {10.1109/EMBC.2012.6347535},
pmid = {23367470},
issn = {2694-0604},
mesh = {Adult ; Brain/pathology/*physiopathology ; *Brain-Computer Interfaces ; Computer Simulation ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Humans ; Imagery, Psychotherapy ; Learning ; Male ; Motor Skills ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) have traditionally been developed for paralyzed and locked-in individuals with no motor control. However, there is a much larger population of patients with some residual motor function as well as the general population of able-bodied individuals, both of whom could benefit significantly from BCIs. An important question that has yet to be systematically studied is: can subjects use BCIs simultaneously with overt motor activity? We present results from a preliminary study aimed at exploring this question. Three subjects used hand motor imagery in an electroencephalographic (EEG) BCI while simultaneously using a joystick to control a cursor. Particular attention was paid to preventing potential muscle artifacts from influencing imagery-based control. All three subjects were able to use the hybrid "imagery+joystick" mode of control over two days, demonstrating the ability to learn and significantly improve performance. These results suggest that subjects can potentially augment their normal human sensorimotor capability by exercising direct brain control over devices concurrently with overt motor control.},
}
@article {pmid23367469,
year = {2012},
author = {McCreadie, KA and Coyle, DH and Prasad, G},
title = {Learning to modulate sensorimotor rhythms with stereo auditory feedback for a brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6711-6714},
doi = {10.1109/EMBC.2012.6347534},
pmid = {23367469},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; *Feedback, Physiological ; Female ; Hearing ; Humans ; Imagery, Psychotherapy ; Learning ; Male ; Motor Skills ; Regression Analysis ; Reproducibility of Results ; Vision, Ocular ; Young Adult ; },
abstract = {Motor imagery can be used to modulate sensorimotor rhythms (SMR) enabling detection of voltage fluctuations on the surface of the scalp using electroencephalographic (EEG) electrodes. Feedback is essential in learning how to intentionally modulate SMR in non-muscular communication using a brain-computer interface (BCI). A BCI that is not reliant upon the visual modality for feedback is an attractive means of communication for the blind and the vision impaired and to release the visual channel for other purposes during BCI usage. The aim of this study is to demonstrate the feasibility of replacing the traditional visual feedback modality with stereo auditory feedback. Twenty participants split into equal groups took part in ten BCI sessions involving motor imagery. The visual feedback group performed best using two performance measures but did not show improvement over time whilst the auditory group improved as the study progressed. Multiple loudspeaker presentation of audio allows the listener to intuitively assign each of two classes to the corresponding lateral position in a free-field listening environment.},
}
@article {pmid23367468,
year = {2012},
author = {Schreuder, M and Thurlings, ME and Brouwer, AM and Van Erp, JB and Tangermann, M},
title = {Exploring the use of tactile feedback in an ERP-based auditory BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6707-6710},
doi = {10.1109/EMBC.2012.6347533},
pmid = {23367468},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Adult ; Behavior ; Brain/pathology ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography/methods ; *Evoked Potentials ; *Feedback, Sensory ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Middle Aged ; Motor Skills ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Touch ; *User-Computer Interface ; Young Adult ; },
abstract = {Giving direct, continuous feedback on a brain state is common practice in motor imagery based brain-computer interfaces (BCI), but has not been reported for BCIs based on event-related potentials (ERP), where feedback is only given once after a sequence of stimuli. Potentially, direct feedback could allow the user to adjust his strategy during a running trial to obtain the required response. In order to test the usefulness of such feedback, directionally congruent vibrotactile feedback was given during an online auditory BCI experiment. Users received either no feedback, short feedback pulses or continuous feedback. The feedback conditions showed reduced performance both on a behavioral task and in terms of classification accuracy. Several explanations are discussed that give interesting starting points for further research on this topic.},
}
@article {pmid23367419,
year = {2012},
author = {Boutani, H and Ohsuga, M},
title = {Input interface using event-related potential P3.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6504-6507},
doi = {10.1109/EMBC.2012.6347484},
pmid = {23367419},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Artifacts ; Blinking ; Brain/pathology ; Brain-Computer Interfaces ; Cognition ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Electrooculography/*methods ; *Evoked Potentials ; *Eye Movements ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {This paper refers to a basic study toward the goal of developing a simple and easy-to-use input interface based on P3 components of visual, event-related potentials. Because contamination from eye movements and eye blinks is a problem, a method for removing eye movement artifacts from electroencephalogram (EEG) signals by applying an independent component analysis un-mixing matrix was proposed and implemented. Input character decisions were executed using a support vector machine (SVM) for judging the P3 existence of a single stimulus. The performances were compared while varying the number of channels of EEG signals, the types of feature vectors, and the ratio of the number of data used for training the SVM. The results indicated that three EEG signal channels (Fz, Cz, Pz) were enough to remove artifacts related to eye blinks and vertical eye movements and could be used to make a decision about input characters. The number of trials necessary to decide the input characters was ten on average. The best ratio achieved for the number of training data of targets and non-targets was 1∶2. These results should be confirmed using a larger number of data sets.},
}
@article {pmid23367398,
year = {2012},
author = {Wang, D and Hao, Y and Zhang, Q and Zhang, S and Zhao, T and Zheng, X and Chen, W},
title = {Decoding wrist kinematics from local field potentials of the ipsilateral primary motor and dorsal premotor cortices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6418-6421},
doi = {10.1109/EMBC.2012.6347463},
pmid = {23367398},
issn = {2694-0604},
mesh = {Animals ; *Biomechanical Phenomena ; Macaca mulatta ; Male ; Microelectrodes ; Motor Cortex/*physiology ; Wrist/*physiology ; },
abstract = {Local field potentials (LFP) are valuable signals for decoding motor kinematics in brain machine interfaces (BMIs). To take full advantage of LFPs, however, more systematic investigation of the relationship between LFPs and ipsilateral limb movement is required. In this paper we investigated the decoding power of LFPs for the ipsilateral wrist movement from two monkeys performing a 2D center-out task. The results show that LFPs could predict the ipsilateral wrist position and velocity with high accuracy, which is comparable to the accuracy of decoding the contralateral kinematics. Furthermore, similar to contralateral decoding, the low (0.3-5 Hz, 5-15 Hz) and high (100-200 Hz, 200-400 Hz) frequency bands resulted in significantly better decoding performance than the medium frequency bands. These results suggest that ipsilateral LFPs could be used to build better BMIs in similar ways of using contralateral LFPs.},
}
@article {pmid23367397,
year = {2012},
author = {Do, AH and Wang, PT and King, CE and Schombs, A and Cramer, SC and Nenadic, Z},
title = {Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6414-6417},
doi = {10.1109/EMBC.2012.6347462},
pmid = {23367397},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Calibration ; *Electric Stimulation ; Electroencephalography/methods ; Foot/*physiopathology ; Humans ; Models, Theoretical ; Stroke/*physiopathology ; },
abstract = {Gait impairment due to foot drop is a common outcome of stroke, and current physiotherapy provides only limited restoration of gait function. Gait function can also be aided by orthoses, but these devices may be cumbersome and their benefits disappear upon removal. Hence, new neuro-rehabilitative therapies are being sought to generate permanent improvements in motor function beyond those of conventional physiotherapies through positive neural plasticity processes. Here, the authors describe an electroencephalogram (EEG) based brain-computer interface (BCI) controlled functional electrical stimulation (FES) system that enabled a stroke subject with foot drop to re-establish foot dorsiflexion. To this end, a prediction model was generated from EEG data collected as the subject alternated between periods of idling and attempted foot dorsiflexion. This prediction model was then used to classify online EEG data into either "idling" or "dorsiflexion" states, and this information was subsequently used to control an FES device to elicit effective foot dorsiflexion. The performance of the system was assessed in online sessions, where the subject was prompted by a computer to alternate between periods of idling and dorsiflexion. The subject demonstrated purposeful operation of the BCI-FES system, with an average cross-correlation between instructional cues and BCI-FES response of 0.60 over 3 sessions. In addition, analysis of the prediction model indicated that non-classical brain areas were activated in the process, suggesting post-stroke cortical re-organization. In the future, these systems may be explored as a potential therapeutic tool that can help promote positive plasticity and neural repair in chronic stroke patients.},
}
@article {pmid23367395,
year = {2012},
author = {Ofner, P and Müller-Putz, GR},
title = {Decoding of velocities and positions of 3D arm movement from EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6406-6409},
doi = {10.1109/EMBC.2012.6347460},
pmid = {23367395},
issn = {2694-0604},
mesh = {Arm/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Movement ; Reference Values ; },
abstract = {A brain-computer interface (BCI) can be used to control a limb neuroprosthesis with motor imaginations (MI) to restore limb functionality of paralyzed persons. However, existing BCIs lack a natural control and need a considerable amount of training time or use invasively recorded biosignals. We show that it is possible to decode velocities and positions of executed arm movements from electroencephalography signals using a new paradigm without external targets. This is a step towards a non-invasive BCI which uses natural MI. Furthermore, training time will be reduced, because it is not necessary to learn new mental strategies.},
}
@article {pmid23367299,
year = {2012},
author = {Sasagawa, K and Yokota, S and Matsuda, T and Davis, P and Zhang, B and Li, K and Kobayashi, T and Noda, T and Tokuda, T and Ohta, J},
title = {Baseband signal transmission experiment for intra-brain communication with implantable image sensor.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {6011-6014},
doi = {10.1109/EMBC.2012.6347364},
pmid = {23367299},
issn = {2694-0604},
mesh = {*Biosensing Techniques ; Brain/*physiology ; *Brain-Computer Interfaces ; Semiconductors ; *Signal Processing, Computer-Assisted ; },
abstract = {We demonstrate image signal transmission for wireless intra-brain communication. As a preliminary experiment, transmission characteristics of the brain phantom were measured. The baseband output signal from an implantable complementary metal-oxide-semiconductor (CMOS) image sensor is transmitted through the phantom. The image was successfully reproduced from the received signal.},
}
@article {pmid23367283,
year = {2012},
author = {Itai, A and Funase, A},
title = {Spectrum based feature extraction using spectrum intensity ratio for SSVEP detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {5947-5950},
doi = {10.1109/EMBC.2012.6347348},
pmid = {23367283},
issn = {2694-0604},
mesh = {Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Recent years, a Steady-State Visual Evoked Potential (SSVEP) is used as a basis for Brain Computer Interface (BCI)[1]. Various feature extraction and classification techniques are proposed to achieve BCI based on SSVEP. The feature extraction of SSVEP is developed in the frequency domain regardless of the limitation in flickering frequency of visual stimulus caused by hardware architecture. We introduce here the feature extraction using a spectrum intensity ratio. Results show that the detection ratio reaches 84% by using a spectrum intensity ratio with unsupervised classification. It also indicates the SSVEP is enhanced by proposed feature extraction with second harmonic.},
}
@article {pmid23367277,
year = {2012},
author = {Huang, G and Yao, L and Zhang, D and Zhu, X},
title = {Effect of duty cycle in different frequency domains on SSVEP based BCI: a preliminary study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {5923-5926},
doi = {10.1109/EMBC.2012.6347342},
pmid = {23367277},
issn = {2694-0604},
mesh = {Electroencephalography ; *Evoked Potentials, Visual ; Humans ; },
abstract = {Compared with the well learned amplitude-frequency characteristic of Steady State Visual Evoked Potential (SSVEP), the effect of duty cycle is still unclear. In this work, the influence of duty cycle on SSVEP response is investigated in differnt frequency domains. The amplitude surface with the change of both frequencies and duty cycles is plotted. To get a stable response, the experiment arranged in 3 days, and each result is an average of 12 repetitions. It is interesting that the results from power spectral density (PSD) and canonical correlation analysis (CCA) method are not consistent. In addition, the relation between the fundamental component and its second harmonic component with the change of duty cycle is quite different at frequency of 7 Hz, 10 Hz and 13 Hz. Based on the amplitude surface, we try to configure the subject-specific SSVEP based BCI. The frequencies and duty cycles of the stimulus are selected corresponding to the higher SSVEP response in the amplitude surface. Cross validation results show a significant improvement in the performance for the adjustment of duty cycle.},
}
@article {pmid23367123,
year = {2012},
author = {Lee, C and Jung, J and Kwon, G and Kim, L},
title = {Individual optimization of EEG channel and frequency ranges by means of genetic algorithm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {5290-5293},
doi = {10.1109/EMBC.2012.6347188},
pmid = {23367123},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; Electroencephalography/*methods ; Humans ; Male ; },
abstract = {It is well established that motor action/imagery provokes an event-related desynchronization (ERD) response at specific brain areas with specific frequency ranges, typically the sensory motor rhythm and beta bands. However, there are individual differences in both brain areas and frequency ranges which can be used to identify ERD. This often results in low classification accuracy of ERD, which makes it difficult to implement of BCI application such as the control of external devices and motor rehabilitation. To overcome this problem, an individually optimized solution may be desirable for enhancing the accuracy of detecting motor action/imagery with ERD rather than a global solution for all BCI users. This paper presents a method based on a genetic algorithm to find individually optimized brain areas and frequency ranges for ERD classification. To optimize these two components, we designed a chromosome consisting of 64-bit elements represented by a binary number and another 9-bit elements using 512 pre-defined frequency ranges (2^9). The average value of the significant level is set for the properties of the objective function for use in a t-test, (p < 0.01) depending on the random selection from a concurrent population. As a result, contralateral ERD responses in the spatial domain with individually optimized frequency ranges showed a significant difference between resting and motor action. The ERD responses for motor imagery, on the other hand, led to a bilateral pattern with a narrow frequency band compared to motor action. This study provides the possibility of selecting optimized electrode positions and frequency bands which can lead to high levels of ERD classification accuracy.},
}
@article {pmid23367117,
year = {2012},
author = {Chai, R and Ling, SH and Hunter, GP and Nguyen, HT},
title = {Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {5266-5269},
doi = {10.1109/EMBC.2012.6347182},
pmid = {23367117},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; *Cognition ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Wheelchairs ; Young Adult ; },
abstract = {This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.},
}
@article {pmid23367029,
year = {2012},
author = {Fazli, S and Mehnert, J and Steinbrink, J and Blankertz, B},
title = {Using NIRS as a predictor for EEG-based BCI performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4911-4914},
doi = {10.1109/EMBC.2012.6347095},
pmid = {23367029},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; Oxygen Consumption/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {Multimodal recordings of EEG and NIRS of 14 subjects are analyzed in the context of sensory-motor based Brain Computer Interface (BCI). Our findings indicate that performance fluctuations of EEG-based BCI control can be predicted by preceding Near-Infrared Spectroscopy (NIRS) activity. These NIRS-based predictions are then employed to generate new, more robust EEG-based BCI classifiers, which enhance classification significantly, while at the same time minimize performance fluctuations and thus increase the general stability of BCI performance.},
}
@article {pmid23366988,
year = {2012},
author = {Sannelli, C and Vidaurre, C and Müller, KR and Blankertz, B},
title = {Common Spatial Pattern Patches: online evaluation on BCI-naive users.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4744-4747},
doi = {10.1109/EMBC.2012.6347027},
pmid = {23366988},
issn = {2694-0604},
mesh = {Algorithms ; Biofeedback, Psychology/methods/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Brain-Computer Interfaces (BCI) based on the voluntary modulation of sensorimotor rhythms (SMRs) induced by motor imagery are very prominent because allow a continuous control of the external device. Nevertheless, the design of a SMR-based BCI system that provides every user with a reliable BCI control from the first session, i.e., without extensive training, is still a big challenge. Considerable advances in this direction have been made by the machine learning co-adaptive calibration approach, which combines online adaptation techniques with subject learning in order to offer the user a feedback from the beginning of the experiment. Recently, based on offline analyses, we proposed the novel Common Spatial Patterns Patches (CSPP) technique as a good candidate to improve the co-adaptive calibration. CSPP is an ensemble of localized spatial filters, each of them optimized on subject-specific data by CSP analysis. Here, the evaluation of CSPP in online operation is presented for the first time. Results on three BCI-naive participants show indeed promising results. All three users reach the threshold criterion of 70% accuracy within one session, even one candidate for whom the weak SMR at rest predicted deficient BCI control. Concurrent recordings of the SMR during a relax condition as well as the course of BCI performance indicate a clear learning effect.},
}
@article {pmid23366984,
year = {2012},
author = {Schürholz, M and Rana, M and Robinson, N and Ramos-Murguialday, A and Cho, W and Rohm, M and Rupp, R and Birbaumer, N and Sitaram, R},
title = {Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4728-4731},
doi = {10.1109/EMBC.2012.6347023},
pmid = {23366984},
issn = {2694-0604},
mesh = {Cerebrovascular Circulation/*physiology ; Electric Stimulation/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Oxygen Consumption/*physiology ; Spectroscopy, Near-Infrared/*methods ; Young Adult ; },
abstract = {A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES.},
}
@article {pmid23366970,
year = {2012},
author = {Müller-Putz, GR and Klobassa, DS and Pokorny, C and Pichler, G and Erlbeck, H and Real, RG and Kübler, A and Risetti, M and Mattia, D},
title = {The auditory p300-based SSBCI: a door to minimally conscious patients?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4672-4675},
doi = {10.1109/EMBC.2012.6347009},
pmid = {23366970},
issn = {2694-0604},
mesh = {Adult ; Aged ; Biofeedback, Psychology/*methods ; *Brain-Computer Interfaces ; *Event-Related Potentials, P300 ; *Evoked Potentials, Auditory ; Female ; Humans ; Male ; Middle Aged ; Persistent Vegetative State/*physiopathology ; *Pitch Perception ; Young Adult ; },
abstract = {In this study we report on the evaluation of a novel auditory single-switch BCI in nine patients diagnosed with MCS. The task included a simple and a complex oddball paradigm, the latter uses the tone stream segregation phenomenon. In all patients a significant difference between deviant and frequent tones could be observed in EEG. However, in some cases the deviant tones produce a significant negative peak and in some a very late positive peak. These preliminary findings are relevant in order to address future customization of this auditory ssBCI-based paradigm for unresponsive patients.},
}
@article {pmid23366962,
year = {2012},
author = {Sato, T and Muto, Y and Nambu, I and Wada, Y},
title = {Estimation of force direction from functional near-infrared spectroscopy signals using sparse logistic regression.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4639-4642},
doi = {10.1109/EMBC.2012.6347001},
pmid = {23366962},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Functional Neuroimaging/*methods ; Humans ; Isometric Contraction/*physiology ; Logistic Models ; Male ; Motor Cortex/*physiology ; Oxyhemoglobins/*analysis ; Regression Analysis ; Spectroscopy, Near-Infrared/*methods ; *Task Performance and Analysis ; Young Adult ; },
abstract = {The brain-machine interface (BMI) has been used as a communication tool for a person who has lost body function. Extracting functional information from brain signals is important for controlling a BMI in a realistic and natural way. For a BMI, a pattern classification algorithm, such as linear discriminant analysis (LDA) and support vector machine (SVM), has commonly been used. However, the classifier using brain signals tends to suffer from overfitting because there are too many obtained features compared with the number of samples. On the other hand, sparse logistic regression (SLR), which has been proposed as a new pattern classification method for brain signals, can select small number of features to classify and interpret brain functions. Thus, overfitting can be prevented using SLR. In this study, we measured functional near-infrared spectroscopy (fNIRS) signals during isometric arm movements in four directions and performed direction classification. The features to classify force direction were selected from obtained data sets using SLR and were used in a SVM. We compared the types of fNIRS signals (OxyHb and DeoxyHb) and feature selection methods. As a result, the classification accuracy was highest when both OxyHb and DeoxyHb were used as the features and both time and channel were selected. The peak time of the signal, when the task ends, and a few seconds after the task ends, were particularly well selected.},
}
@article {pmid23366961,
year = {2012},
author = {López-Larraz, E and Escolano, C and Minguez, J},
title = {Upper alpha neurofeedback training over the motor cortex increases SMR desynchronization in motor tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4635-4638},
doi = {10.1109/EMBC.2012.6347000},
pmid = {23366961},
issn = {2694-0604},
mesh = {Biofeedback, Psychology/*methods/*physiology ; Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *Task Performance and Analysis ; Young Adult ; },
abstract = {Desynchronization of sensorimotor rhythms (SMR) is a distinctive feature that provides a discriminative pattern for BCI operation. However, individuals such as BCI illiterates can not produce these discriminable patterns with sufficient reliability. Additionally, SMR desynchronization can become deteriorated or extinct in patients with spinal cord injury or a cerebrovascular accident. In all these situations BCI usage is compromised. This paper proposes an intervention based on neurofeedback training of the upper alpha band to improve SMR desynchronization. The feasibility of this intervention is demonstrated in a preliminary study in which five healthy subjects were trained to increase their upper alpha band power. Such increases produced higher SMR desynchronization and better discrimination between rest and execution states of a motor task.},
}
@article {pmid23366958,
year = {2012},
author = {Tadipatri, VA and Tewfik, AH and Ashe, J and Pellizzer, G},
title = {Robust movement direction decoders from local field potentials using spatio-temporal qualitative patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4623-4626},
doi = {10.1109/EMBC.2012.6346997},
pmid = {23366958},
issn = {2694-0604},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spatio-Temporal Analysis ; },
abstract = {A major drawback of using Local Field Potentials (LFP) for Brain Computer Interface (BCI) is their inherent instability and non-stationarity. Specifically, even when a well-trained subject performs the same task over a period of time, the neural data observed are unstable. To overcome this problem in decoding movement direction, this paper proposes the use of qualitative information in the form of spatial patterns of inter-channel ranking of multi-channel LFP recordings. The quality of the decoding was further refined by concentrating on the statistical distributions of the top powered channels. Decoding of movement direction was performed using Support Vector Machines (SVM) to construct decoders, instead of the traditional spatial patterns. Our algorithm provides a decoding power of up to 74% on average over a period of two weeks, compared with the state-of-the-art methods in the literature that yield only 33%. Furthermore, it provides 62.5% direction decoding in novel motor environments, compared with 29.5% with conventional methods. Finally, a comparison with the traditional methods and other surveyed literature is presented.},
}
@article {pmid23366957,
year = {2012},
author = {Takata, Y and Kondo, T and Saeki, M and Izawa, J and Takeda, K and Otaka, Y and It, K},
title = {Analysis of extrinsic and intrinsic factors affecting event related desynchronization production.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4619-4622},
doi = {10.1109/EMBC.2012.6346996},
pmid = {23366957},
issn = {2694-0604},
mesh = {Algorithms ; Attention/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Young Adult ; },
abstract = {Recently there has been an increase in the number of stroke patients with motor paralysis. Appropriate re-afferent sensory feedback synchronized with a voluntary motor intention would be effective for promoting neural plasticity in the stroke rehabilitation. Therefore, BCI technology is considered to be a promising approach in the neuro-rehabilitation. To estimate human motor intention, an event-related desynchronization (ERD), a feature of electroencephalogram (EEG) evoked by motor execution or motor imagery is usually used. However, there exists various factors that affect ERD production, and its neural mechanism is still an open question. As a preliminary stage, we evaluate mutual effects of intrinsic (voluntary motor imagery) and extrinsic (visual and somatosensory stimuli) factors on the ERD production. Experimental results indicate that these three factors are not always additively interacting with each other and affecting the ERD production.},
}
@article {pmid23366944,
year = {2012},
author = {Putrino, D and Wong, YT and Vigeral, M and Pesaran, B},
title = {Development of a closed-loop feedback system for real-time control of a high-dimensional Brain Machine Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4567-4570},
pmid = {23366944},
issn = {2694-0604},
support = {//Wellcome Trust/United Kingdom ; P30 EY013079/EY/NEI NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Arm ; Biofeedback, Psychology/*instrumentation/*methods ; *Brain-Computer Interfaces ; Computer Simulation ; Computer Systems ; Feedback, Physiological ; Macaca mulatta ; *Models, Biological ; *Movement ; *User-Computer Interface ; },
abstract = {As the field of neural prosthetics advances, Brain Machine Interface (BMI) design requires the development of virtual prostheses that allow decoding algorithms to be tested for efficacy in a time- and cost-efficient manner. Using an x-ray and MRI-guided skeletal reconstruction, and a graphic artist's rendering of an anatomically correct macaque upper limb, we created a virtual avatar capable of independent movement across 27 degrees-of-freedom (DOF). Using a custom software interface, we animated the avatar's movements in real-time using kinematic data acquired from awake, behaving macaque subjects using a 16 camera motion capture system. Using this system, we demonstrate real-time, closed-loop control of up to 27 DOFs in a virtual prosthetic device. Thus, we describe a practical method of testing the efficacy of high-complexity BMI decoding algorithms without the expense of fabricating a physical prosthetic.},
}
@article {pmid23366872,
year = {2012},
author = {Kelly, JW and Degenhart, AD and Siewiorek, DP and Smailagic, A and Wang, W},
title = {Sparse linear regression with elastic net regularization for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4275-4278},
doi = {10.1109/EMBC.2012.6346911},
pmid = {23366872},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Computer Simulation ; Electroencephalography/*methods ; Epilepsy/*physiopathology/rehabilitation ; *Evoked Potentials, Motor ; *Hand Strength ; Humans ; Linear Models ; Motor Cortex/*physiopathology ; Nerve Net/*physiopathology ; Neuronal Plasticity ; Pattern Recognition, Automated/methods ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {This paper demonstrates the feasibility of decoding neuronal population signals using a sparse linear regression model with an elastic net penalty. In offline analysis of real electrocorticographic (ECoG) neural data the elastic net achieved a timepoint decoding accuracy of 95% for classifying hand grasps vs. rest, and 82% for moving a cursor in 1-D space towards a target. These results were superior to those obtained using ℓ(2)-penalized and unpenalized linear regression, and marginally better than ℓ(1)-penalized regression. Elastic net and the ℓ(1)-penalty also produced sparse feature sets, but the elastic net did not eliminate correlated features, which could result in a more stable decoder for brain-computer interfaces.},
}
@article {pmid23366871,
year = {2012},
author = {Higashi, H and Tanaka, T},
title = {Time sparsification of EEG signals in motor-imagery based brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4271-4274},
doi = {10.1109/EMBC.2012.6346910},
pmid = {23366871},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {We propose a method of sparsifying EEG signals in the time domain for common spatial patterns (CSP) which are often used for feature extraction in brain computer interfaces (BCI). For accurate classification, it is important to analyze the period of time when a BCI user performs a mental task. We address this problem by optimizing the CSP cost with a time sparsification that removes unnecessary samples from the classification. We design a cost function that has CSP spatial weights and time window as optimization parameters. To find these parameters, we use alternating optimization. In an experiment on classification of motor-imagery EEG signals, the proposed method increased classification accuracy by 6% averaged over five subjects.},
}
@article {pmid23366836,
year = {2012},
author = {Ang, KK and Guan, C and Phua, KS and Wang, C and Teh, I and Chen, CW and Chew, E},
title = {Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4128-4131},
doi = {10.1109/EMBC.2012.6346875},
pmid = {23366836},
issn = {2694-0604},
mesh = {Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor ; Humans ; *Imagination ; Motor Cortex/*physiopathology ; Movement ; Stroke/*physiopathology ; *Stroke Rehabilitation ; Therapy, Computer-Assisted/*methods ; Transcranial Magnetic Stimulation/*methods ; Treatment Outcome ; Upper Extremity/physiopathology ; },
abstract = {Clinical studies had shown that EEG-based motor imagery Brain-Computer Interface (MI-BCI) combined with robotic feedback is effective in upper limb stroke rehabilitation, and transcranial Direct Current Stimulation (tDCS) combined with other rehabilitation techniques further enhanced the facilitating effect of tDCS. This motivated the current clinical study to investigate the effects of combining tDCS with MI-BCI and robotic feedback compared to sham-tDCS for upper limb stroke rehabilitation. The stroke patients recruited were randomized to receive 20 minutes of tDCS or sham-tDCS prior to 10 sessions of 1-hour MI-BCI with robotic feedback for 2 weeks. The online accuracies of detecting motor imagery from idle condition were assessed and offline accuracies of classifying motor imagery from background rest condition were assessed from the EEG of the evaluation and therapy parts of the 10 rehabilitation sessions respectively. The results showed no evident differences between the online accuracies on the evaluation part from both groups, but the offline analysis on the therapy part yielded higher averaged accuracies for subjects who received tDCS (n=3) compared to sham-tDCS (n=2). The results suggest towards tDCS effect in modulating motor imagery in stroke, but a more conclusive result can be drawn when more data are collected in the ongoing study.},
}
@article {pmid23366835,
year = {2012},
author = {Arvaneh, M and Guan, C and Ang, KK and Quek, C},
title = {Omitting the intra-session calibration in EEG-based brain computer interface used for stroke rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4124-4127},
doi = {10.1109/EMBC.2012.6346874},
pmid = {23366835},
issn = {2694-0604},
mesh = {Adult ; Brain Mapping/*methods/standards ; *Brain-Computer Interfaces ; Electroencephalography/*methods/standards ; Humans ; Middle Aged ; Motor Cortex/physiopathology ; Movement ; Paresis/etiology/*physiopathology/*rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; Stroke/complications/*physiopathology ; *Stroke Rehabilitation ; },
abstract = {Brain-computer interface (BCI) as a rehabilitation tool has been used in restoring motor functions in patients with moderate to sever stroke impairments. To achieve the best possible outcome in such an application, it is highly desirable to have a stable and accurate operation of BCI. However, since electroencephalogram (EEG) signals considerably vary between sessions of even the same user, typically a long calibration session is recorded at the beginning of each session. This process is time-consuming and inconvenient for stroke patients who undergo long-term BCI sessions with repeating same mental tasks. This paper investigates the possibility of omitting the intra-session calibration for BCI-based stroke rehabilitation when large data recorded from the same user are available. For this purpose, a large dataset of EEG signals from 11 stroke patients performing 12 BCI-based stroke rehabilitation sessions over one month is used. Our offline results suggest that after recording a number of stroke rehabilitation sessions, the patient does not require calibration any more. The experimental results show that combining 11 sessions, which each session comprises minimum 60 trials per class, yields a model that averagely outperforms the standard calibration model trained by the data recorded directly before the test session.},
}
@article {pmid23366832,
year = {2012},
author = {Cincotti, F and Pichiorri, F and Aricò, P and Aloise, F and Leotta, F and de Vico Fallani, F and Millán, Jdel R and Molinari, M and Mattia, D},
title = {EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4112-4115},
doi = {10.1109/EMBC.2012.6346871},
pmid = {23366832},
issn = {2694-0604},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; Electric Stimulation Therapy/*instrumentation ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Movement Disorders/etiology/*rehabilitation ; Stroke/complications ; *Stroke Rehabilitation ; Therapy, Computer-Assisted/*instrumentation ; Treatment Outcome ; Upper Extremity ; },
abstract = {Brain-Computer Interfaces (BCIs) process brain activity in real time, and mediate non-muscular interaction between and individual and the environment. The subserving algorithms can be used to provide a quantitative measurement of physiological or pathological cognitive processes - such as Motor Imagery (MI) - and feed it back the user. In this paper we propose the clinical application of a BCI-based rehabilitation device, to promote motor recovery after stroke. The BCI-based device and the therapy exploiting its use follow the same principles that drive classical neuromotor rehabilitation, and (i) provides the physical therapist with a monitoring instrument, to assess the patient's participation in the rehabilitative cognitive exercise; (ii) assists the patient in the practice of MI. The device was installed in the ward of a rehabilitation hospital and a group of 29 patients were involved in its testing. Among them, eight have already undergone a one-month training with the device, as an add-on to the regular therapy. An improved system, which includes analysis of Electromyographic (EMG) patterns and Functional Electrical Stimulation (FES) of the arm muscles, is also under clinical evaluation. We found that the rehabilitation exercise based on BCI-mediated neurofeedback mechanisms enables a better engagement of motor areas with respect to motor imagery alone and thus it can promote neuroplasticity in brain regions affected by a cerebrovascular accident. Preliminary results also suggest that the functional outcome of motor rehabilitation may be improved by the use of the proposed device.},
}
@article {pmid23366831,
year = {2012},
author = {Pohlmeyer, EA and Mahmoudi, B and Geng, S and Prins, N and Sanchez, JC},
title = {Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4108-4111},
doi = {10.1109/EMBC.2012.6346870},
pmid = {23366831},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Arm ; Biofeedback, Psychology/methods/*physiology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; Callithrix ; *Expert Systems ; *Man-Machine Systems ; *Reinforcement, Psychology ; Robotics/*methods ; Task Performance and Analysis ; },
abstract = {Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.},
}
@article {pmid23366830,
year = {2012},
author = {Hotson, G and Fifer, MS and Acharya, S and Anderson, WS and Thakor, NV and Crone, NE},
title = {Electrocorticographic decoding of ipsilateral reach in the setting of contralateral arm weakness from a cortical lesion.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4104-4107},
doi = {10.1109/EMBC.2012.6346869},
pmid = {23366830},
issn = {2694-0604},
support = {3R01NS0405956-09S1/NS/NINDS NIH HHS/United States ; 5T32EB003383-08/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Arm/*physiopathology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Epilepsy/complications/*physiopathology ; Evoked Potentials, Motor ; Humans ; Male ; Motor Cortex/*physiopathology ; *Movement ; Paresis/etiology/*physiopathology ; },
abstract = {Brain machine interfaces have the potential for restoring motor function not only in patients with amputations or lesions of efferent pathways in the spinal cord and peripheral nerves, but also patients with acquired brain lesions such as strokes and tumors. In these patients the most efficient components of cortical motor systems are not available for BMI control. Here we had the opportunity to investigate the possibility of utilizing subdural electrocorticographic (ECoG) signals to control natural reaching movements under these circumstances. In a subject with a left arm monoparesis following resection of a recurrent glioma, we found that ECoG signals recorded in remaining cortex were sufficient for decoding kinematics of natural reach movements of the nonparetic arm, ipsilateral to the ECoG recordings. The relationship between the subject's ECoG signals and reach trajectory in three dimensions, two of which were highly correlated, was captured with a computationally simple linear model (mean Pearson's r in depth dimension= 0.68, in height= 0.73, in lateral= 0.24). These results were attained with only a small subset of 7 temporal/spectral neural signal features. The small subset of neural features necessary to attain high decoding results show promise for a restorative BMI controlled solely by ipsilateral ECoG signals.},
}
@article {pmid23366829,
year = {2012},
author = {Antelis, JM and Montesano, L and Ramos-Murguialday, A and Birbaumer, N and Minguez, J},
title = {Continuous decoding of intention to move from contralesional hemisphere brain oscillations in severely affected chronic stroke patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4099-4103},
doi = {10.1109/EMBC.2012.6346868},
pmid = {23366829},
issn = {2694-0604},
mesh = {Aged ; *Algorithms ; *Biological Clocks ; Brain-Computer Interfaces ; Chronic Disease ; Computer Simulation ; Feasibility Studies ; Humans ; *Intention ; Male ; Middle Aged ; Models, Neurological ; Motor Cortex/*physiopathology ; *Movement ; Movement Disorders/etiology/*physiopathology ; Stroke/complications/*physiopathology ; },
abstract = {Decoding motor information directly from brain activity is essential in robot-assisted rehabilitation systems to promote motor relearning. However, patients who suffered a stroke in the motor cortex have lost brain activity in the injured area, and consequently, mobility in contralateral limbs. Such a loss eliminates the possibility of extracting motor information from brain activity while the patient is undergoing therapy for the affected limb. This work proposes to decode motor information from EEG activity of the contralesional hemisphere in patients who suffered a hemiparetic stroke. Four stroke patients participated in this study and the results proved the feasibility of decoding motor information while patients attempted to move the affected limb.},
}
@article {pmid23366828,
year = {2012},
author = {Heger, D and Jäkel, R and Putze, F and Lösch, M and Schultz, T},
title = {Filling a glass of water: continuously decoding the speed of 3D hand movements from EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4095-4098},
doi = {10.1109/EMBC.2012.6346867},
pmid = {23366828},
issn = {2694-0604},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {We present a new system for the continuous decoding of hand movement speed in three-dimensional (3D) space from EEG signals. We recorded experimental data of five subjects during mimicking the natural task of filling a glass of water. The proposed system uses filter bank common spatial patterns and linear regression to estimate the speed of hand movements from artifact cleaned EEG signals. Average Pearson correlations between the speed trajectories predicted from EEG and the speed trajectories measured using a high-precision motion tracking system are r=0.41 for the x-axis, r=0.36 for the y-axis, r=0.48 for the z-axis, and r=0.17 for absolute speed in 3D space.},
}
@article {pmid23366827,
year = {2012},
author = {Kellis, S and Hanrahan, S and Davis, T and House, PA and Brown, R and Greger, B},
title = {Decoding hand trajectories from micro-electrocorticography in human patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4091-4094},
doi = {10.1109/EMBC.2012.6346866},
pmid = {23366827},
issn = {2694-0604},
mesh = {*Algorithms ; Brain Mapping/methods ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Epilepsy/*physiopathology ; *Evoked Potentials, Motor ; Hand/*physiopathology ; Humans ; Male ; Motor Cortex/*physiopathology ; *Movement ; Young Adult ; },
abstract = {A Kalman filter was used to decode hand trajectories from micro-electrocorticography recorded over motor cortex in human patients. In two cases, signals were recorded during stereotyped tasks, and the trajectories were decoded offline, with maximum correlation coefficients between actual and predicted trajectories of 0.51 (x-direction position) and 0.54 (y-direction position). In a third setting, a human patient with full neural control of a computer cursor acquired onscreen targets within 6.24 sec on average, with no algorithmic constraints on the output trajectory. These practical results illustrate the potential utility of signals recorded at the cortical surface with high spatial resolution, demonstrating that surface potentials contain relevant and sufficient information to drive sophisticated brain-computer interface systems.},
}
@article {pmid23366826,
year = {2012},
author = {Willett, FR and Suminski, AJ and Fagg, AH and Hatsopoulos, NG},
title = {Compensating for delays in brain-machine interfaces by decoding intended future movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {4087-4090},
doi = {10.1109/EMBC.2012.6346865},
pmid = {23366826},
issn = {2694-0604},
support = {R01 N545853-01//PHS HHS/United States ; },
mesh = {*Algorithms ; Animals ; Anticipation, Psychological/*physiology ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Computer Simulation ; Feedback, Physiological/physiology ; *Intention ; Macaca mulatta ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Reaction Time/*physiology ; },
abstract = {Typically, brain-machine interfaces that enable the control of a prosthetic arm work by decoding a subjects' intended hand position or velocity and using a controller to move the arm accordingly. Researchers taking this approach often choose to decode the subjects' desired arm state in the present moment, which causes the prosthetic arm to lag behind the state desired by the user, as the dynamics of the arm (and other control delays) constrain how quickly the controller can change the arm's state. We tested the hypothesis that decoding the subjects' intended future movements would mitigate this lag and improve BMI performance. Offline results show that predictions of future movement (≤ 200 ms) can be made with essentially the same accuracy as predictions of present movement. Online results from one monkey show that performance increases as a function of the future prediction time lead, reaching optimum performance at a time lead equal to the delay inherent in the controlled system.},
}
@article {pmid23366796,
year = {2012},
author = {Blokland, Y and Vlek, R and Karaman, B and Özin, F and Thijssen, D and Eijsvogels, T and Colier, W and Floor-Westerdijk, M and Bruhn, J and Farquhar, J},
title = {Detection of event-related desynchronization during attempted and imagined movements in tetraplegics for brain switch control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3967-3969},
doi = {10.1109/EMBC.2012.6346835},
pmid = {23366796},
issn = {2694-0604},
mesh = {Area Under Curve ; Brain/*physiopathology ; Cortical Synchronization/*physiology ; Evoked Potentials/*physiology ; Humans ; *Imagination ; Male ; Middle Aged ; *Movement ; Quadriplegia/*physiopathology ; *User-Computer Interface ; },
abstract = {Motor-impaired individuals such as tetraplegics could benefit from Brain-Computer Interfaces with an intuitive control mechanism, for instance for the control of a neuroprosthesis. Whereas BCI studies in healthy users commonly focus on motor imagery, for the eventual target users, namely patients, attempted movements could potentially be a more promising alternative. In the current study, EEG frequency information was used for classification of both imagined and attempted movements in tetraplegics. Although overall classification rates were considerably lower for tetraplegics than for the control group, both imagined and attempted movement were detectable. Classification rates were significantly higher for the attempted movement condition, with a mean rate of 77%. These results suggest that attempted movement is an appropriate task for BCI control in long-term paralysis patients.},
}
@article {pmid23366795,
year = {2012},
author = {Daly, I and Pichiorri, F and Faller, J and Kaiser, V and Kreilinger, A and Scherer, R and Müller-Putz, G},
title = {What does clean EEG look like?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3963-3966},
doi = {10.1109/EMBC.2012.6346834},
pmid = {23366795},
issn = {2694-0604},
mesh = {*Artifacts ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Lack of a clear analytical metric for identifying artifact free, clean electroencephalographic (EEG) signals inhibits robust comparison of different artifact removal methods and lowers confidence in the results of EEG analysis. An algorithm is presented for identifying clean EEG epochs by thresholding statistical properties of the EEG. Thresholds are trained on EEG datasets from both healthy subjects and stroke / spinal cord injury patient populations via differential evolution (DE).},
}
@article {pmid23366793,
year = {2012},
author = {Mahanta, MS and Aghaei, AS and Plataniotis, KN},
title = {A Bayes optimal matrix-variate LDA for extraction of spatio-spectral features from EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3955-3958},
doi = {10.1109/EMBC.2012.6346832},
pmid = {23366793},
issn = {2694-0604},
mesh = {*Algorithms ; Bayes Theorem ; Computer Simulation ; Discriminant Analysis ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Classification of mental states from electroencephalogram (EEG) signals is used for many applications in areas such as brain-computer interfacing (BCI). When represented in the frequency domain, the multichannel EEG signal can be considered as a two-directional spatio-spectral data of high dimensionality. Extraction of salient features using feature extractors such as the commonly used linear discriminant analysis (LDA) is an essential step for the classification of these signals. However, multichannel EEG is naturally in matrix-variate format, while LDA and other traditional feature extractors are designed for vector-variate input. Consequently, these methods require a prior vectorization of the EEG signals, which ignores the inherent matrix-variate structure in the data and leads to high computational complexity. A matrix-variate formulation of LDA have previously been proposed. However, this heuristic formulation does not provide the Bayes optimality benefits of LDA. The current paper proposes a Bayes optimal matrix-variate formulation of LDA based on a matrix-variate model for the spatio-spectral EEG patterns. The proposed formulation also provides a strategy to select the most significant features among the different rows and columns.},
}
@article {pmid23366777,
year = {2012},
author = {Schuettler, M and Kohler, F and Ordonez, JS and Stieglitz, T},
title = {Hermetic electronic packaging of an implantable brain-machine-interface with transcutaneous optical data communication.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3886-3889},
doi = {10.1109/EMBC.2012.6346816},
pmid = {23366777},
issn = {2694-0604},
mesh = {Adhesiveness ; Brain/*physiology ; *Communication ; Dimethylpolysiloxanes/chemistry ; *Electrodes, Implanted ; *Electronics, Medical ; Humans ; Infrared Rays ; *Product Packaging ; Silicones/chemistry ; Skin/*anatomy &amp; histology ; *User-Computer Interface ; },
abstract = {Future brain-computer-interfaces (BCIs) for severely impaired patients are implanted to electrically contact the brain tissue. Avoiding percutaneous cables requires amplifier and telemetry electronics to be implanted too. We developed a hermetic package that protects the electronic circuitry of a BCI from body moisture while permitting infrared communication through the package wall made from alumina ceramic. The ceramic package is casted in medical grade silicone adhesive, for which we identified MED2-4013 as a promising candidate.},
}
@article {pmid23366768,
year = {2012},
author = {Liu, Y and Ayaz, H and Curtin, A and Shewokis, PA and Onaral, B},
title = {Detection of attention shift for asynchronous P300-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3850-3853},
doi = {10.1109/EMBC.2012.6346807},
pmid = {23366768},
issn = {2694-0604},
mesh = {Area Under Curve ; Attention/*physiology ; Brain/*physiology ; Event-Related Potentials, P300/*physiology ; Humans ; Reproducibility of Results ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) provides patients suffering from severe neuromuscular disorders an alternative way of interacting with the outside world. The P300-based BCI is among the most popular paradigms in the field and most current versions operate in synchronous mode and assume participant engagement throughout operation. In this study, we demonstrate a new approach for assessment of user engagement through a hybrid classification of ERP and band power features of EEG signals that could allow building asynchronous BCIs. EEG signals from nine electrode locations were recorded from nine participants during controlled engagement conditions when subjects were either engaged with the P3speller task or not attending. Statistical analysis of band power showed that there were significant contrasts of attending only for the delta and beta bands as indicators of features for user attendance classification. A hybrid classifier using ERP scores and band power features yielded the best overall performance of 0.98 in terms of the area under the ROC curve (AUC). Results indicate that band powers can provide additional discriminant information to the ERP for user attention detection and this combined approach can be used to assess user engagement for each stimulus sequence during BCI use.},
}
@article {pmid23366767,
year = {2012},
author = {Duvinage, M and Castermans, T and Petieau, M and Seetharaman, K and Hoellinger, T and Cheron, G and Dutoit, T},
title = {A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3845-3849},
doi = {10.1109/EMBC.2012.6346806},
pmid = {23366767},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiopathology ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Lower Extremity/*physiopathology ; Male ; Rehabilitation/*methods ; Surveys and Questionnaires ; *User-Computer Interface ; Young Adult ; },
abstract = {Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes. To assess usability and workload, the System Usability Scale and the NASA Task Load Index questionnaires were administered to five healthy subjects after performing a real-time treadmill speed control. Results show that the P300 BCI approach could suit fitness and rehabilitation applications, whereas prosthesis control, which suffers from a low reactivity, appears too sensitive for risky and crowded areas.},
}
@article {pmid23366766,
year = {2012},
author = {Curtin, A and Ayaz, H and Liu, Y and Shewokis, PA and Onaral, B},
title = {A P300-based EEG-BCI for spatial navigation control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3841-3844},
doi = {10.1109/EMBC.2012.6346805},
pmid = {23366766},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Orientation/*physiology ; Photic Stimulation ; Reproducibility of Results ; *User-Computer Interface ; Young Adult ; },
abstract = {In this study, a Brain Computer Interface (BCI) based on the P300 oddball paradigm has been developed for spatial navigation control in virtual environments. Functionality and efficacy of the system were analyzed with results from nine healthy volunteers. Each participant was asked to gaze at an individual target in a 3×3 P300 matrix containing different symbolic navigational icons while EEG signals were collected. Resulting ERPs were processed online and classification commands were executed to control spatial movements within the MazeSuite virtual environment and presented to the user online during an experiment. Subjects demonstrated on average, ∼89% online accuracy for simple mazes and ∼82% online accuracy in longer more complex mazes. Results suggest that this BCI setup enables guided free-form navigation in virtual 3D environments.},
}
@article {pmid23366765,
year = {2012},
author = {Punsawad, Y and Wongsawat, Y},
title = {Motion visual stimulus for SSVEP-based BCI system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3837-3840},
doi = {10.1109/EMBC.2012.6346804},
pmid = {23366765},
issn = {2694-0604},
mesh = {Attention/physiology ; Brain/*physiology ; Decision Making ; Evoked Potentials, Visual/*physiology ; Humans ; *Motion ; *Photic Stimulation ; User-Computer Interface ; },
abstract = {Steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) system is one of the most accurate assistive technologies for the persons with severe disabilities. However, the existing visual stimulation patterns still lead to the eyes fatigue. Therefore, in this paper, we propose a novel visual stimulator using the idea of the motion visual stimulus to reduce the eyes fatigue while maintaining the merit of the SSVEP phenomena. Two corresponding feature extractions, i.e. 1) attention detection and 2) SSVEP detection, are also proposed to capture the phenomena of the proposed motion visual stimulus. Two-class classification accuracy of both features is approximately 80%, where the maximum accuracy using the attention detection is 90%, and the maximum accuracy using the SSVEP detection is 100%.},
}
@article {pmid23366764,
year = {2012},
author = {Guger, C and Allison, B and Hintermueller, C and Prueckl, R and Grosswindhager, B and Kapeller, C and Edlinger, G},
title = {Poor performance in SSVEP BCIs: are worse subjects just slower?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3833-3836},
doi = {10.1109/EMBC.2012.6346803},
pmid = {23366764},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; Aged ; Brain/*physiology ; Child ; Electrodes ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems translate brain activity into messages or commands. BCI studies that record from a dozen or more subjects typically report substantial variations in performance, as measured by accuracy. Usually, some subjects attain excellent (even perfect) accuracy, while at least one subject performs so poorly that effective communication would not be possible with that BCI. This study aims to further explore the differences between the best and worst performers by studying the changes in estimated accuracy within each trial in an offline simulation of an SSVEP BCI. Results showed that the worst performers not only attained lower accuracies, but needed more time after cue onset before their accuracies improved substantially. This outcome suggests that poor performance may be partly (though not completely) explained by the latency between cue onset and improved accuracy.},
}
@article {pmid23366763,
year = {2012},
author = {Khaliliardali, Z and Chavarriaga, R and Andrei Gheorghe, L and Millán, Jdel R},
title = {Detection of anticipatory brain potentials during car driving.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3829-3832},
doi = {10.1109/EMBC.2012.6346802},
pmid = {23366763},
issn = {2694-0604},
mesh = {Action Potentials/*physiology ; Adult ; *Automobile Driving ; Brain/*physiology ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Time Factors ; Young Adult ; },
abstract = {Recognition of driver's intention from electroencephalogram (EEG) can be helpful in developing an in-car brain computer interface (BCI) systems for intelligent cars. This could be beneficial in enhancing the quality of interaction between the driver and the car to provide the response of the intelligent cars in line with driver's intention. We proposed investigating anticipation as the cognitive state leading to specific actions during car driving. An experimental protocol is designed for recording EEG from 6 subjects while driving the virtual reality driving simulator. The experimental protocol is a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions in driving framework. The results presented in this study support the presence of the slow cortical anticipatory potentials in EEG grand averages and also confirm the discriminability of these potentials in offline single trial classification with the average of 0.76 ± 0.12 in area under the curve (AUC).},
}
@article {pmid23366629,
year = {2012},
author = {Zhang, D and Gong, E and Wu, W and Lin, J and Zhou, W and Hong, B},
title = {Spoken sentences decoding based on intracranial high gamma response using dynamic time warping.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3292-3295},
doi = {10.1109/EMBC.2012.6346668},
pmid = {23366629},
issn = {2694-0604},
mesh = {Adult ; Brain-Computer Interfaces ; Electroencephalography/methods ; Humans ; Male ; *Speech ; },
abstract = {In this study, we explore the discriminability of high gamma activities from speech production cortex during the overt articulation of two sentences. Neural activities were recorded from one intracranial electrode placed approximately over the posterior part of the inferior frontal gyrus. By employing a dynamic time warping (DTW) method to realign single-trial high gamma response during speech productions, averaged temporal activation patterns corresponding to the two spoken sentences were obtained. Single-trial ECoG responses were subsequently classified according to their correlations with these two temporal activation patterns. On average, 77.5% of the trials were correctly classified, which was much higher than the chance-level performance of the SVM classifier without DTW. Our preliminary results shed light on the construction of cortical speech brain-computer interfaces on the sentence level.},
}
@article {pmid23366628,
year = {2012},
author = {Iturrate, I and Chavarriaga, R and Montesano, L and Minguez, J and Millan, Jdel R},
title = {Latency correction of error potentials between different experiments reduces calibration time for single-trial classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3288-3291},
doi = {10.1109/EMBC.2012.6346667},
pmid = {23366628},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; },
abstract = {One fundamental limitation of EEG-based brain-computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of these sources of variability is the application performed: Protocols with different cognitive workloads might yield to different latencies of the ERPs. In this sense, it is still not clear the effect that these latency variations have on the single-trial classification. This work studies the differences in the latencies of error potentials across three experiments with increasing cognitive workloads. A delay-correction algorithm based on the cross-correlation of the averaged signals is presented, and tested with a single-trial classification of the signals. The results showed that latency variations exist between different protocols, and that it is feasible to re-use data from previous experiments to calibrate a classifier able to detect the signals of a new experiment, thus reducing the calibration time.},
}
@article {pmid23366627,
year = {2012},
author = {Tarigoppula, A and Rotella, N and Francis, JT},
title = {Properties of a temporal difference reinforcement learning brain machine interface driven by a simulated motor cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3284-3287},
doi = {10.1109/EMBC.2012.6346666},
pmid = {23366627},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; *Learning ; Motor Cortex/cytology/*physiology ; Neurons/physiology ; Signal-To-Noise Ratio ; },
abstract = {Our overall goal is to develop a reinforcement learning (RL) based decoder for brain machine interfaces. As an important step in this process, we determine the basic stability and convergence properties of a Temporal Difference (TD) RL architecture being driven by a simulated motor cortex.},
}
@article {pmid23366625,
year = {2012},
author = {Yong, X and Fatourechi, M and Ward, RK and Birch, GE},
title = {Adaptive classification in a self-paced hybrid brain-computer interface system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3274-3279},
doi = {10.1109/EMBC.2012.6346664},
pmid = {23366625},
issn = {2694-0604},
mesh = {*Adaptation, Physiological ; Algorithms ; Analysis of Variance ; *Brain-Computer Interfaces ; Electroencephalography ; Eye Movements ; Humans ; ROC Curve ; },
abstract = {As the characteristics of EEG signals change over time, updating the classifier of a brain computer interface, BCI, (over time) would improve the performance of the system. Developing an adaptive classifier for a self-paced BCI however is not easy because the user's intention (and therefore the true labels of the EEG signals) are not known during the operation of the system. For certain applications, it may be possible to predict the labels of some of the EEG segments using some information about the user's state (e.g., the error potentials or gaze information). This study proposes a method that adaptively updates the classifier of a self-paced BCI in a supervised or semi-supervised manner, using those EEG segments whose labels can be predicted. We employ the eye position information obtained from an eye-tracker to predict the EEG labels. This eye-tracker is also used along with a self-paced BCI to form a hybrid BCI system. The results obtained from seven individuals show that the proposed algorithm outperforms the non-adaptive and other unsupervised adaptive classifiers. It achieves a true positive rate of 49.7% and lowers the number of false positives significantly to only 2.2 FPs/minute.},
}
@article {pmid23366624,
year = {2012},
author = {Pascual, J and Velasco-Alvarez, F and Muller, KR and Vidaurre, C},
title = {First study towards linear control of an upper-limb neuroprosthesis with an EEG-based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3269-3273},
doi = {10.1109/EMBC.2012.6346663},
pmid = {23366624},
issn = {2694-0604},
mesh = {Arm/*physiology ; Biofeedback, Psychology ; *Brain-Computer Interfaces ; Electric Stimulation ; Electroencephalography/*instrumentation ; Humans ; *Prostheses and Implants ; },
abstract = {In this study we show how healthy subjects are able to use a non-invasive Motor Imagery (MI)-based Brain Computer Interface (BCI) to achieve linear control of an upper-limb neuromuscular electrical stimulation (NMES) controlled neuroprosthesis in a simple binary target selection task. Linear BCI control can be achieved if two motor imagery classes can be discriminated with a reliability over 80% in single trial. The results presented in this work show that there was no significant loss of performance using the neuroprosthesis in comparison to MI where no stimulation was present. However, it is remarkable how different the experience of the users was in the same experiment. The stimulation either provoked a positive reinforcement feedback, or prevented the user from concentrating in the task.},
}
@article {pmid23366573,
year = {2012},
author = {Nguyen, JS and Nguyen, TN and Tran, Y and Su, SW and Craig, A and Nguyen, HT},
title = {Real-time performance of a hands-free semi-autonomous wheelchair system using a combination of stereoscopic and spherical vision.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {3069-3072},
doi = {10.1109/EMBC.2012.6346612},
pmid = {23366573},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Persons with Disabilities ; Equipment Design ; Humans ; Vision Disparity/physiology ; Vision, Ocular/*physiology ; *Wheelchairs ; },
abstract = {This paper is concerned with the operational performance of a semi-autonomous wheelchair system named TIM (Thought-controlled Intelligent Machine), which uses cameras in a system configuration modeled on the vision system of a horse. This new camera configuration utilizes stereoscopic vision for 3-Dimensional (3D) depth perception and mapping ahead of the wheelchair, combined with a spherical camera system for 360-degrees of monocular vision. The unique combination allows for static components of an unknown environment to be mapped and any surrounding dynamic obstacles to be detected, during real-time autonomous navigation, minimizing blind-spots and preventing accidental collisions with people or obstacles. Combining this vision system with a shared control strategy provides intelligent assistive guidance during wheelchair navigation, and can accompany any hands-free wheelchair control technology for people with severe physical disability. Testing of this system in crowded dynamic environments has displayed the feasibility and real-time performance of this system when assisting hands-free control technologies, in this case being a proof-of-concept brain-computer interface (BCI).},
}
@article {pmid23366538,
year = {2012},
author = {Thomas, E and Fruitet, J and Clerc, M},
title = {Investigating brief motor imagery for an ERD/ERS based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2929-2932},
doi = {10.1109/EMBC.2012.6346577},
pmid = {23366538},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagination/*physiology ; },
abstract = {This study establishes the effectiveness of event related synchronisation (ERS) features for a system paced brain computer interface (BCI). In particular, the relationship between the duration of motor imagery (MI) and the quality of the features extracted from the ERS is investigated. To this end, two groups of users performed brief (2s) or sustained (4s) MI, and offline single trial BCIs were validated on each group based on features extracted from the EEG before, during and after MI. The BCIs were designed to recognise two intentional control tasks and a no-control state. Cross-validated results indicate that brief MI leads to more informative ERS features than sustained MI.},
}
@article {pmid23366525,
year = {2012},
author = {Carreiras, C and Borges de Almeida, L and Sanches, JM},
title = {Phase-locking factor in a motor imagery Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2877-2880},
doi = {10.1109/EMBC.2012.6346564},
pmid = {23366525},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; Support Vector Machine ; },
abstract = {A Brain-Computer Interface (BCI) attempts to create a direct channel of communication between the brain and a computer. This is especially important for patients that are "locked in", as they have limited motor function and thus require an alternative means of communication. In this scope, a BCI can be controlled through the imagination of motor tasks, i.e. Motor Imagery. This thinking of actions produce changes on the ongoing Electroencephalogram (EEG), such as the so called Event-Related Desynchronization (ERD), that can be detected and measured. Traditionally, ERD is measured through the estimation of EEG signal power in specific frequency bands. In this work, a new method based on the phase information from the EEG channels, through the Phase-Locking Factor (PLF), is proposed. Both feature types were tested in real data obtained from 6 voluntary subjects, who performed 7 motor tasks in an EEG session. The features were classified using Support Vector Machine (SVM) classifiers organized in a hierarchical structure. The results show that the PLF features are better, with an average accuracy of ≈ 86%, against an accuracy of ≈ 70% for the band power features. Although more research is still needed, the PLF measure shows promising results for use in a BCI system.},
}
@article {pmid23366524,
year = {2012},
author = {Samek, W and Muller, KR and Kawanabe, M and Vidaurre, C},
title = {Brain-computer interfacing in discriminative and stationary subspaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2873-2876},
doi = {10.1109/EMBC.2012.6346563},
pmid = {23366524},
issn = {2694-0604},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Humans ; Models, Theoretical ; },
abstract = {The non-stationary nature of neurophysiological measurements, e.g. EEG, makes classification of motion intentions a demanding task. Variations in the underlying brain processes often lead to significant and unexpected changes in the feature distribution resulting in decreased classification accuracy in Brain Computer Interfacing (BCI). Several methods were developed to tackle this problem by either adapting to these changes or extracting features that are invariant. Recently, a method called Stationary Subspace Analysis (SSA) was proposed and applied to BCI data. It diminishes the influence of non-stationary changes as learning and classification is performed in a stationary subspace of the data which can be extracted by SSA. In this paper we extend this method in two ways. First we propose a variant of SSA that allows to extract stationary subspaces from labeled data without disregarding class-related variations or treating class-differences as non-stationarities. Second we propose a discriminant variant of SSA that trades-off stationarity and discriminativity, thus it allows to extract stationary subspaces without losing relevant information. We show that learning in a discriminative and stationary subspace is advantageous for BCI application and outperforms the standard SSA method.},
}
@article {pmid23366523,
year = {2012},
author = {Gavett, S and Wygant, Z and Amiri, S and Fazel-Rezai, R},
title = {Reducing human error in P300 speller paradigm for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2869-2872},
doi = {10.1109/EMBC.2012.6346562},
pmid = {23366523},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Models, Theoretical ; },
abstract = {Since the brain-computer interface (BCI) speller paradigm was first introduced by Farwell and Donchin in 1988, there have been many visual modifications to the paradigm. Most of these changes involve the original matrix format such as changes in the number of rows and columns, font size and color, flash time vs. dark time, and flash order. However, recent studies show that there is human error in generating P300 based on this paradigm that none of these changes can help to reduce it. In this study, we analyze this type of error among three paradigms, two based on the matrix structure and one region-based paradigm. It is shown that the human error is reduced significantly in the region-based paradigm.},
}
@article {pmid23366521,
year = {2012},
author = {Tran, Y and Thuraisingham, R and Craig, A and Nguyen, H},
title = {Stationarity and variability in eyes open and eyes closed EEG signals from able-bodied and spinal cord injured persons.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2861-2864},
doi = {10.1109/EMBC.2012.6346560},
pmid = {23366521},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/*methods ; Eye/*physiopathology ; Female ; Humans ; Male ; Signal Transduction/physiology ; Spinal Cord Injuries/*physiopathology ; },
abstract = {This paper examines the assumption of stationarity used in EEG brain activity analyses, despite EEG data often being non-stationary. Transformations necessary to obtain stationary data from measured non-stationary EEG data and methods to assess non-stationarity are illustrated using eyes open (EO) and eyes closed (EC) data. The study shows that even short time EEG records of 10s duration exhibit non-stationary behavior. Examination of the change in variance when going from the EO to the EC state for both able bodied and spinal cord injured participants show that the difference in variance is consistently positive and statistically significant only when stationary data is used. This has implications for brain computer interfaces that utilizes changes in EO and EC EEG signals.},
}
@article {pmid23366498,
year = {2012},
author = {Severens, M and Nienhuis, B and Desain, P and Duysens, J},
title = {Feasibility of measuring event related desynchronization with electroencephalography during walking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2764-2767},
doi = {10.1109/EMBC.2012.6346537},
pmid = {23366498},
issn = {2694-0604},
mesh = {Adult ; Electroencephalography/*methods ; Female ; Humans ; Male ; Walking/*physiology ; Young Adult ; },
abstract = {Brain Computer Interfaces could be useful in rehabilitation of movement, perhaps also for gait. Until recently, research on movement related brain signals has not included measuring electroencephalography (EEG) during walking, because of the potential artifacts. We investigated if it is possible to measure the event Related Desynchronization (ERD) and event related spectral perturbations (ERSP) during walking. Six subjects walked on a treadmill with a slow speed, while EEG, electromyography (EMG) of the neck muscles and step cycle were measured. A Canonical Correlation Analysis (CCA) was used to remove EMG artifacts from the EEG signals. It was shown that this method correctly deleted EMG components. A strong ERD in the mu band and a somewhat less strong ERD in the beta band were found during walking compared to a baseline period. Furthermore, lateralized ERSPs were found, depending on the phase in the step cycle. It is concluded that this is a promising method to use in BCI research on walking. These results therefore pave the way for using brain signals related to walking in a BCI context.},
}
@article {pmid23366494,
year = {2012},
author = {Liao, Y and Wang, Y and Zheng, X and Principe, JC},
title = {Mutual information analysis on non-stationary neuron importance for brain machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2748-2751},
doi = {10.1109/EMBC.2012.6346533},
pmid = {23366494},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; Neurons/*physiology ; },
abstract = {Decoding with the important neuron subset has been widely used in brain machine interfaces (BMIs), as an effective strategy to reduce computational complexity. Previous works usually assume stationary of neuron importance, which may not be true according to recent research. We propose to conduct a mutual information evaluation to track the time-varying neuron importance over time. We found worth noting changes both in information amount and space distribution in our experiment. When the method is applied with a Kalman filter, the decoding performance achieve is better (with higher correlation coefficient) than when a fixed subset, which shows that time-varying neuron importance should be considered in adaptive algorithms.},
}
@article {pmid23366493,
year = {2012},
author = {Yang, Y and Chevallier, S and Wiart, J and Bloch, I},
title = {Time-frequency selection in two bipolar channels for improving the classification of motor imagery EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2744-2747},
doi = {10.1109/EMBC.2012.6346532},
pmid = {23366493},
issn = {2694-0604},
mesh = {Algorithms ; Electroencephalography/*methods ; Humans ; },
abstract = {Time and frequency information is essential to feature extraction in a motor imagery BCI, in particular for systems based on a few channels. In this paper, we propose a novel time-frequency selection method based on a criterion called Time-frequency Discrimination Factor (TFDF) to extract discriminative event-related desynchronization (ERD) features for BCI data classification. Compared to existing methods, the proposed approach generates better classification performances (mean kappa coefficient= 0.62) on experimental data from the BCI competition IV dataset IIb, with only two bipolar channels.},
}
@article {pmid23366492,
year = {2012},
author = {Zhang, Y and Schwartz, AB and Chase, SM and Kass, RE},
title = {Bayesian learning in assisted brain-computer interface tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2740-2743},
doi = {10.1109/EMBC.2012.6346531},
pmid = {23366492},
issn = {2694-0604},
support = {RC1NS07031/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Bayes Theorem ; *Brain-Computer Interfaces ; Humans ; },
abstract = {Successful implementation of a brain-computer interface depends critically on the subject's ability to learn how to modulate the neurons controlling the device. However, the subject's learning process is probably the least understood aspect of the control loop. How should training be adjusted to facilitate dexterous control of a prosthetic device? An effective training schedule should manipulate the difficulty of the task to provide enough information to guide improvement without overwhelming the subject. In this paper, we introduce a bayesian framework for modeling the closed-loop BCI learning process that treats the subject as a bandwidth-limited communication channel. We then develop an adaptive algorithm to find the optimal difficulty-schedule for performance improvement. Simulation results demonstrate that our algorithm yields faster learning rates than several other heuristic training schedules, and provides insight into the factors that might affect the learning process.},
}
@article {pmid23366490,
year = {2012},
author = {Bamdadian, A and Guan, C and Ang, KK and Xu, J},
title = {Online semi-supervised learning with KL distance weighting for motor imagery-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2732-2735},
doi = {10.1109/EMBC.2012.6346529},
pmid = {23366490},
issn = {2694-0604},
mesh = {Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; },
abstract = {Studies had shown that Motor Imagery-based Brain Computer Interface (MI-based BCI) system can be used as a therapeutic tool such as for stroke rehabilitation, but had shown that not all subjects could perform MI well. Studies had also shown that MI and passive movement (PM) could similarly activate the motor system. Although the idea of calibrating MI-based BCI system from PM data is promising, there is an inherent difference between features extracted from MI and PM. Therefore, there is a need for online learning to alleviate the difference and improve the performance. Hence, in this study we propose an online batch mode semi-supervised learning with KL distance weighting to update the model trained from the calibration session by using unlabeled data from the online test session. In this study, the Filter Bank Common Spatial Pattern (FBCSP) algorithm is used to compute the most discriminative features of the EEG data in the calibration session and is updated iteratively on each band after a batch of online data is available for performing semi-supervised learning. The performance of the proposed method was compared with offline FBCSP, and results showed that the proposed method yielded slightly better results in comparison with offline FBCSP. The results also showed that the use of the model trained from PM for online session-to-session transfer compared to the use of the calibration model trained from MI yielded slightly better performance. The results suggest that using PM, due to its better performance and ease of recording is feasible and performance can be improved by using the proposed method to perform online semi-supervised learning while subjects perform MI.},
}
@article {pmid23366489,
year = {2012},
author = {Oliver, G and Sunehag, P and Gedeon, T},
title = {Asynchronous brain computer interface using hidden semi-Markov models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2728-2731},
doi = {10.1109/EMBC.2012.6346528},
pmid = {23366489},
issn = {2694-0604},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; *Markov Chains ; },
abstract = {Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models (HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simple windowed method. Furthermore, due to the computational demands of the algorithm, we adapted the HSMM algorithm to an online setting and demonstrate that this faster version of the algorithm can run in real time.},
}
@article {pmid23366463,
year = {2012},
author = {Coppa, B and Héliot, R and Michel, O and Moisan, E and David, D},
title = {Low-cost intracortical spiking recordings compression with classification abilities for implanted BMI devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2623-2626},
doi = {10.1109/EMBC.2012.6346502},
pmid = {23366463},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; Algorithms ; *Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Humans ; *Microelectrodes ; Principal Component Analysis ; Prostheses and Implants ; },
abstract = {Within Brain-Machine Interface systems, cortically implanted microelectrode arrays and associated hardware have a low power budget for data sampling, processing and transmission. It is already possible to reduce neural data rates by on-site spike detection; we propose a method to further compress spiking data at a low computational cost, with the objective of maintaining clustering and classification abilities. The method relies on random binary vector projections, and simulations show that it is possible to achieve a compression ratio of 5 at virtually no cost in terms of classification errors.},
}
@article {pmid23366444,
year = {2012},
author = {Toppi, J and Petti, M and De Vico Fallani, F and Vecchiato, G and Maglione, AG and Cincotti, F and Salinari, S and Mattia, D and Babiloni, F and Astolfi, L},
title = {Describing relevant indices from the resting state electrophysiological networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2547-2550},
doi = {10.1109/EMBC.2012.6346483},
pmid = {23366444},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Electrophysiology/*methods ; Humans ; Magnetic Resonance Imaging ; },
abstract = {The "Default Mode Network" concept was defined, in fMRI field, as a consistent pattern, involving some regions of the brain, which is active during resting state activity and deactivates during attention demanding or goal-directed tasks. Several fMRI studies described its features also correlating the deactivations with the attentive load required for the task execution. Despite the efforts in EEG field, aiming at correlating the spectral features of EEG signals with DMN, an electrophysiological correlate of the DMN hasn't yet been found. In this study we used advanced techniques for functional connectivity estimation for describing the neuroelectrical properties of DMN. We analyzed the connectivity patterns elicited during the rest condition by 55 healthy subjects by means of Partial Directed Coherence. We extracted some graph indexes in order to describe the properties of the resting network in terms of local and global efficiencies, symmetries and influences between different regions of the scalp. Results highlighted the presence of a consistent network, elicited by more than 70% of analyzed population, involving mainly frontal and parietal regions. The properties of the resting network are uniform among the population and could be used for the construction of a normative database for the identification of pathological conditions.},
}
@article {pmid23366434,
year = {2012},
author = {Brumberg, JS and Lorenz, SD and Galbraith, BV and Guenther, FH},
title = {The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2505-2508},
pmid = {23366434},
issn = {2694-0604},
support = {R03 DC011304/DC/NIDCD NIH HHS/United States ; R03-DC011304/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Programming Languages ; Software ; },
abstract = {In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.},
}
@article {pmid23366433,
year = {2012},
author = {Tangermann, M and Hohne, J and Stecher, H and Schreuder, M},
title = {No surprise--sequence event-related potentials for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2501-2504},
doi = {10.1109/EMBC.2012.6346472},
pmid = {23366433},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Evoked Potentials, Auditory/*physiology ; Humans ; },
abstract = {INTRODUCTION: In the field of Brain-Computer Interfaces (BCI), the original two-class oddball paradigm has been extended to multiple stimuli with balanced probabilities and random presentation sequences. Exploiting the differences between standard and deviant ERP responses, these multi-class paradigms are suitable for communication and control.<br><br>METHODS: The present study investigates the effect of giving up the randomness of stimulation sequences in favor of a repeated, predictable pattern. Data of healthy subjects (n=10) who performed a single session with a 6-class spatial auditory ERP paradigm were analyzed offline. Their auditory evoked potentials (AEP) resulting from the potentially simpler task (using fixed sequences) are compared with the AEP evoked by pseudo-randomized stimulation sequences.<br><br>RESULTS: Class-discriminative EEG responses between target and non-target stimuli were observed for both conditions. The binary classification error estimated for standard epochs of was comparable for both conditions (random: 24%, fixed: 25%). Expanding the standard epochs to include pre-stimulus intervals, we found that the regular structure of the fixed sequence can be exploited. Compared to the standard epoch, the MSE improves by 7%, while in the random condition an improvement could not be observed.},
}
@article {pmid23366432,
year = {2012},
author = {Orhan, U and Erdogmus, D and Roark, B and Oken, B and Purwar, S and Hild, KE and Fowler, A and Fried-Oken, M},
title = {Improved accuracy using recursive bayesian estimation based language model fusion in ERP-based BCI typing systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {2497-2500},
pmid = {23366432},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; 1R01DC009834-01/DC/NIDCD NIH HHS/United States ; },
mesh = {*Bayes Theorem ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Humans ; Language ; },
abstract = {RSVP Keyboard™ is an electroencephalography (EEG) based brain computer interface (BCI) typing system, designed as an assistive technology for the communication needs of people with locked-in syndrome (LIS). It relies on rapid serial visual presentation (RSVP) and does not require precise eye gaze control. Existing BCI typing systems which uses event related potentials (ERP) in EEG suffer from low accuracy due to low signal-to-noise ratio. Henceforth, RSVP Keyboard™ utilizes a context based decision making via incorporating a language model, to improve the accuracy of letter decisions. To further improve the contributions of the language model, we propose recursive bayesian estimation, which relies on non-committing string decisions, and conduct an offline analysis, which compares it with the existing naïve bayesian fusion approach. The results indicate the superiority of the recursive bayesian fusion and in the next generation of RSVP Keyboard™ we plan to incorporate this new approach.},
}
@article {pmid23366278,
year = {2012},
author = {Benz, HL and Collard, M and Tsimpouris, C and Acharya, S and Crone, NE and Thakor, NV and Bezerianos, A},
title = {Directed causality of the human electrocorticogram during dexterous movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1872-1875},
doi = {10.1109/EMBC.2012.6346317},
pmid = {23366278},
issn = {2694-0604},
support = {R0INS40596//PHS HHS/United States ; },
mesh = {Bayes Theorem ; Child ; Electric Stimulation ; Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Movement/*physiology ; Nerve Net/physiopathology ; Time Factors ; Young Adult ; },
abstract = {While significant strides have been made in designing brain-machine interfaces for use in humans, efforts to decode truly dexterous movements in real time have been hindered by difficulty extracting detailed movement-related information from the most practical human neural interface, the electrocorticogram (ECoG). We explore a potentially rich, largely untapped source of movement-related information in the form of cortical connectivity computed with time-varying dynamic Bayesian networks (TV-DBN). We discover that measures of connectivity between ECoG electrodes derived from the local motor potential vary with dexterous movement in 65% of movement-related electrode pairs tested, and measures of connectivity derived from spectral features vary with dexterous movement in 76%. Due to the large number of features generated with connectivity methods, the TV-DBN a promising tool for dexterous decoding.},
}
@article {pmid23366268,
year = {2012},
author = {Chai, R and Ling, SH and Hunter, GP and Nguyen, HT},
title = {Mental task classifications using prefrontal cortex electroencephalograph signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1831-1834},
doi = {10.1109/EMBC.2012.6346307},
pmid = {23366268},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Alpha Rhythm/physiology ; *Electroencephalography ; Female ; Humans ; Male ; Neural Networks, Computer ; Prefrontal Cortex/*physiology ; *Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; },
abstract = {For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%.},
}
@article {pmid23366267,
year = {2012},
author = {Faller, J and Torrellas, S and Miralles, F and Holzner, C and Kapeller, C and Guger, C and Bund, J and Müller-Putz, GR and Scherer, R},
title = {Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1827-1830},
doi = {10.1109/EMBC.2012.6346306},
pmid = {23366267},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Calibration ; Cortical Synchronization ; Evoked Potentials ; Humans ; Male ; Task Performance and Analysis ; },
abstract = {We present the prototype of a context-aware framework that allows users to control smart home devices and to access internet services via a Hybrid BCI system of an auto-calibrating sensorimotor rhythm (SMR) based BCI and another assistive device (Integra Mouse mouth joystick). While there is extensive literature that describes the merit of Hybrid BCIs, auto-calibrating and co-adaptive ERD BCI training paradigms, specialized BCI user interfaces, context-awareness and smart home control, there is up to now, no system that includes all these concepts in one integrated easy-to-use framework that can truly benefit individuals with severe functional disabilities by increasing independence and social inclusion. Here we integrate all these technologies in a prototype framework that does not require expert knowledge or excess time for calibration. In a first pilot-study, 3 healthy volunteers successfully operated the system using input signals from an ERD BCI and an Integra Mouse and reached average positive predictive values (PPV) of 72 and 98% respectively. Based on what we learned here we are planning to improve the system for a test with a larger number of healthy volunteers so we can soon bring the system to benefit individuals with severe functional disability.},
}
@article {pmid23366266,
year = {2012},
author = {Xia, B and Yang, H and Zhang, Q and Xie, H and Yang, W and Li, J and An, D},
title = {Control 2-dimensional movement using a three-class motor imagery based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1823-1826},
doi = {10.1109/EMBC.2012.6346305},
pmid = {23366266},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Computer Simulation ; Humans ; *Imagery, Psychotherapy ; Motor Activity/*physiology ; Movement/*physiology ; Probability ; Time Factors ; Young Adult ; },
abstract = {2-dimensional movement control is an interesting issue in Brain-Computer Interface. In this paper, we present a motor imagery based 2-D cursor control paradigm. To move the cursor to a random position, two-class motor imagery is simultaneously combined to output 2-D command, which directly points to target position. A center-out experiment (8 targets) is set to verify the proposed paradigm. The results of the online experiment (three subjects participated) validate the proposed strategy very well.},
}
@article {pmid23366265,
year = {2012},
author = {Cao, T and Wan, F and Mak, PU and Mak, PI and Vai, MI and Hu, Y},
title = {Flashing color on the performance of SSVEP-based brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1819-1822},
doi = {10.1109/EMBC.2012.6346304},
pmid = {23366265},
issn = {2694-0604},
mesh = {Adult ; *Brain-Computer Interfaces ; Color ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Young Adult ; },
abstract = {A critical problem in using steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) for clinical and commercial use is the visual fatigue the user may suffer when staring at flashing stimuli. Aiming at the design of user-friendly BCIs with satisfactory performance, this work is to preliminarily investigate how different colors influence the SSVEP (i.e. frequency or phase) and system performance. The results show that white stimuli can lead to the highest performance, followed by gray, red, green and blue stimuli.},
}
@article {pmid23366264,
year = {2012},
author = {Schettini, F and Aloise, F and Arico, P and Salinari, S and Mattia, D and Cincotti, F},
title = {Control or no-control? Reducing the gap between brain-computer interface and classical input devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1815-1818},
doi = {10.1109/EMBC.2012.6346303},
pmid = {23366264},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Female ; Humans ; Male ; },
abstract = {In order to improve Brain Computer Interface usability for real life context, they should be able to adapt their speed to the user's current psychophysical state and to understand from the ongoing EEG when he/she intends to suspend the control. In this work we evaluated an asynchronous classifier which provides these feature with 20 healthy subjects, who were engaged in an environmental control task or in a spelling task. We also demonstrated how the proposed classifier can improve communication efficiency with respect to classical synchronous classifiers.},
}
@article {pmid23366263,
year = {2012},
author = {Lin, FC and Zao, JK and Tu, KC and Wang, Y and Huang, YP and Chuang, CW and Kuo, HY and Chien, YY and Chou, CC and Jung, TP},
title = {SNR analysis of high-frequency steady-state visual evoked potentials from the foveal and extrafoveal regions of human retina.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1810-1814},
doi = {10.1109/EMBC.2012.6346302},
pmid = {23366263},
issn = {2694-0604},
mesh = {Adult ; Evoked Potentials, Visual/*physiology ; Female ; Fourier Analysis ; Fovea Centralis/*physiology ; Humans ; Male ; Middle Aged ; Photic Stimulation ; Retinal Cone Photoreceptor Cells/physiology ; Retinal Rod Photoreceptor Cells/physiology ; Signal Processing, Computer-Assisted ; *Signal-To-Noise Ratio ; Visual Perception/physiology ; Young Adult ; },
abstract = {With brain-computer interface (BCI) applications in mind, we analyzed the amplitudes and the signal-to-noise ratios (SNR) of steady-state visual evoked potentials (SSVEP) induced in the foveal and extra-foveal regions of human retina. Eight subjects (age 20-55) have been exposed to 2° circular and 16°-18° annular visual stimulation produced by white LED lights flickering between 5Hz and 65Hz in 5Hz increments. Their EEG signals were recorded using a 64-channel NeuroScan system and analyzed using non-parametric spectral and canonical convolution techniques. Subjects' perception of flickering and their levels of comfort towards the visual stimulation were also noted. Almost all subjects showed distinctively higher SNR in their foveal SSVEP responses between 25Hz and 45Hz. They also noticed less flickering and felt more comfortable with the visual stimulation between 30Hz and 45Hz. These empirical evidences suggest that lights flashing above the critical flicker fusion rates (CFF) of human vision may be used as effective and comfortable stimuli in SSVEP BCI applications.},
}
@article {pmid23366262,
year = {2012},
author = {Wang, YT and Wang, Y and Cheng, CK and Jung, TP},
title = {Measuring steady-state visual evoked potentials from non-hair-bearing areas.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1806-1809},
doi = {10.1109/EMBC.2012.6346301},
pmid = {23366262},
issn = {2694-0604},
mesh = {Electrodes ; Evoked Potentials, Visual/*physiology ; Hair/*anatomy &amp; histology ; Humans ; Male ; Scalp/*anatomy &amp; histology/*physiology ; Signal-To-Noise Ratio ; },
abstract = {Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) applications have been widely applied in laboratories around the world in the recent years. Many studies have shown that the best locations to acquire SSVEPs were from the occipital areas of the scalp. However, for some BCI users such as quadriparetic patients lying face up during ventilation, it is difficult to access the occipital sites. Even for the healthy BCI users, acquiring good-quality EEG signals from the hair-covered occipital sites is inevitably more difficult because it requires skin preparation by a skilled technician and conductive gel usage. Therefore, finding an alternative approach to effectively extract high-quality SSVEPs for BCI practice is highly desirable. Since the non-hair-bearing scalp regions are more accessible by all different types of EEG sensors, this study systematically and quantitatively investigated the feasibility of measuring SSVEPs from non-hair-bearing regions, compared to those measured from the occipital areas. Empirical results showed that the signal quality of the SSVEPs from non-hair-bearing areas was comparable with, if not better than, that measured from hair-covered occipital areas. These results may significantly improve the practicality of a BCI system in real-life applications; especially used in conjunction with newly available dry EEG sensors.},
}
@article {pmid23366261,
year = {2012},
author = {Höhne, J and Tangermann, M},
title = {How stimulation speed affects Event-Related Potentials and BCI performance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1802-1805},
doi = {10.1109/EMBC.2012.6346300},
pmid = {23366261},
issn = {2694-0604},
mesh = {*Acoustic Stimulation ; Area Under Curve ; *Brain-Computer Interfaces ; Electrodes ; Evoked Potentials/*physiology ; Humans ; },
abstract = {In most paradigms for Brain-Computer Interfaces (BCIs) that are based on Event-Related Potentials (ERPs), stimuli are presented with a pre-defined and constant speed. In order to boost BCI performance by optimizing the parameters of stimulation, this offline study investigates the impact of the stimulus onset asynchrony (SOA) on ERPs and the resulting classification accuracy. The SOA is defined as the time between the onsets of two consecutive stimuli, which represents a measure for stimulation speed. A simple auditory oddball paradigm was tested in 14 SOA conditions with a SOA between 50 ms and 1000 ms. Based on an offline ERP analysis, the BCI performance (quantified by the Information Transfer Rate, ITR in bits/min) was simulated. A great variability in the simulated BCI performance was observed within subjects (N=11). This indicates a potential increase in BCI performance (≥ 1.6 bits/min) for ERP-based paradigms, if the stimulation speed is specified for each user individually.},
}
@article {pmid23366260,
year = {2012},
author = {López-Larraz, E and Antelis, JM and Montesano, L and Gil-Agudo, A and Minguez, J},
title = {Continuous decoding of motor attempt and motor imagery from EEG activity in spinal cord injury patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1798-1801},
doi = {10.1109/EMBC.2012.6346299},
pmid = {23366260},
issn = {2694-0604},
mesh = {Adult ; *Electroencephalography ; Humans ; *Imagery, Psychotherapy ; Male ; Motor Activity/*physiology ; Spinal Cord Injuries/*physiopathology ; Time Factors ; },
abstract = {Spinal cord injury (SCI) associates brain reorganization with a loss of cortical representation of paralyzed limbs. This effect is more pronounced in the chronic state, which can be reached approximately 6 months after the lesion. As many of the brain-computer interfaces (BCI) developed to date rely on the user motor activity, loss of this activity hinders the application of BCI technology for rehabilitation or motor compensation in these patients. This work is a preliminary study with three quadriplegic patients close to reaching the chronic state, addressing two questions: (i) whether it is still possible to use BCI technology to detect motor intention of the paralyzed hand at this state of chronicity; and (ii) whether it is better for the BCI decoding to rely on the motor attempt or the motor imagery of the hand as mental paradigm. The results show that one of the three patients had already lost the motor programs related to the hand, so it was not possible to build a motor-related BCI for him. For the other patients it was suitable to design a BCI based on both paradigms, but the results were better using motor attempt as it has broader activation associated patterns that are easier to recognize.},
}
@article {pmid23366259,
year = {2012},
author = {Wu, SL and Liao, LD and Liou, CH and Chen, SA and Ko, LW and Chen, BW and Wang, PS and Chen, SF and Lin, CT},
title = {Design of the multi-channel electroencephalography-based brain-computer interface with novel dry sensors.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1793-1797},
doi = {10.1109/EMBC.2012.6346298},
pmid = {23366259},
issn = {2694-0604},
mesh = {Blinking ; *Brain-Computer Interfaces ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; *Signal Processing, Computer-Assisted ; Wireless Technology ; },
abstract = {The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications.},
}
@article {pmid23366257,
year = {2012},
author = {Venthur, B and Blankertz, B},
title = {Mushu, a free- and open source BCI signal acquisition, written in Python.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1786-1788},
doi = {10.1109/EMBC.2012.6346296},
pmid = {23366257},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Humans ; *Programming Languages ; *Signal Processing, Computer-Assisted ; *Software ; },
abstract = {The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.},
}
@article {pmid23366256,
year = {2012},
author = {Fukami, T and Shimada, T and Forney, E and Anderson, CW},
title = {EEG character identification using stimulus sequences designed to maximize mimimal hamming distance.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1782-1785},
doi = {10.1109/EMBC.2012.6346295},
pmid = {23366256},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; *Electroencephalography ; Humans ; *Photic Stimulation ; *Signal Processing, Computer-Assisted ; },
abstract = {In this study, we have improved upon the P300 speller Brain-Computer Interface paradigm by introducing a new character encoding method. Our concept in detection of the intended character is not based on a classification of target and nontarget responses, but based on an identifaction of the character which maximize the difference between P300 amplitudes in target and nontarget stimuli. Each bit included in the code corresponds to flashing character, '1', and non-flashing, '0'. Here, the codes were constructed in order to maximize the minimum hamming distance between the characters. Electroencephalography was used to identify the characters using a waveform calculated by adding and subtracting the response of the target and non-target stimulus according the codes respectively. This stimulus presentation method was applied to a 3×3 character matrix, and the results were compared with that of a conventional P300 speller of the same size. Our method reduced the time until the correct character was obtained by 24%.},
}
@article {pmid23366255,
year = {2012},
author = {Jia, W and Huang, D and Luo, X and Pu, H and Chen, X and Bai, O},
title = {Electroencephalography(EEG)-based instinctive brain-control of a quadruped locomotion robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1777-1781},
doi = {10.1109/EMBC.2012.6346294},
pmid = {23366255},
issn = {2694-0604},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Feedback, Sensory ; Humans ; Locomotion/*physiology ; Robotics/*methods ; },
abstract = {Artificial intelligence and bionic control have been applied in electroencephalography (EEG)-based robot system, to execute complex brain-control task. Nevertheless, due to technical limitations of the EEG decoding, the brain-computer interface (BCI) protocol is often complex, and the mapping between the EEG signal and the practical instructions lack of logic associated, which restrict the user's actual use. This paper presents a strategy that can be used to control a quadruped locomotion robot by user's instinctive action, based on five kinds of movement related neurophysiological signal. In actual use, the user drives or imagines the limbs/wrists action to generate EEG signal to adjust the real movement of the robot according to his/her own motor reflex of the robot locomotion. This method is easy for real use, as the user generates the brain-control signal through the instinctive reaction. By adopting the behavioral control of learning and evolution based on the proposed strategy, complex movement task may be realized by instinctive brain-control.},
}
@article {pmid23366254,
year = {2012},
author = {Xiao, R and Liao, K and Ding, L},
title = {Discriminating multiple motor imageries of human hands using EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1773-1776},
doi = {10.1109/EMBC.2012.6346293},
pmid = {23366254},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Electroencephalography/*methods ; Hand/*physiology ; Humans ; *Imagery, Psychotherapy ; Male ; Motor Activity/*physiology ; Principal Component Analysis ; },
abstract = {We investigated the feasibility of discriminating four different motor imagery (MI) types from both hands using electroencephalography (EEG) through exploring underlying features related to MIs of thumb and fist from one hand. New spectral and spatial features related to different MIs were extracted using principal component analysis (PCA) and squared cross correlation (R(2)). Extracted features were evaluated using a linear discriminant analysis (LDA) classifier, resulting in an average decoding accuracy about 50%, which is significantly higher than the guess level and the 95% confidence level of guess. The preliminary results demonstrate the great potential of extracting features from different MIs from same hands to generate control signals with more degrees of freedom (DOF) for non-invasive brain-computer interface applications. In addition, for movement related applications, especially for neuroprosthesis, the present study may facilitate the development of a non-invasive BCI, which is highly intuitive and based on users' spontaneous intentions.},
}
@article {pmid23366253,
year = {2012},
author = {Li, J and Wang, Y and Zhang, L and Jung, TP},
title = {Combining ERPs and EEG spectral features for decoding intended movement direction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1769-1772},
doi = {10.1109/EMBC.2012.6346292},
pmid = {23366253},
issn = {2694-0604},
mesh = {Electrodes ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Movement/*physiology ; Parietal Lobe/physiology ; Reproducibility of Results ; Task Performance and Analysis ; Time Factors ; },
abstract = {The posterior parietal cortex (PPC) plays an important role in visuomotor transformations for movement planning and execution. To investigate how noninvasive electroencephalographic (EEG) signals correlate with intended movement directions in the PPC, this study recorded whole-head EEG during a delayed saccade-or-reach task and found direction-related changes in both event-related potentials (ERPs) and the EEG power in the theta and alpha bands in the PPC. Single-trial (left versus right) classification using ERP and EEG spectral features prior to motor execution obtained an average accuracy of 65.4% and 65.6% respectively on 10 subjects. By combining the two types of features, the classification accuracy increased to 69.7%. These results show that ERP and EEG spectral power modulations contribute complementary information to decoding intended movement directions in the PPC. The proposed paradigm might lead to a practical brain-computer interface (BCI) for decoding movement intention of individuals.},
}
@article {pmid23366252,
year = {2012},
author = {Nishifuji, S and Kuroda, T},
title = {Impact of mental focus on steady-state visually evoked potential under eyes closed condition for binary brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1765-1768},
doi = {10.1109/EMBC.2012.6346291},
pmid = {23366252},
issn = {2694-0604},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Electrodes ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; *Ocular Physiological Phenomena ; Photic Stimulation ; Reproducibility of Results ; Young Adult ; },
abstract = {The steady-state visually evoked potential (SSVEP), is found to be affected by mental focusing on the stimuli under eyes closed condition. The amplitude d change of the SSVEP in concentrating on flicker stimuli was investigated for a novel brain computer interface (BCI) based on the SSVEP with eyes closed for severely disabilities who were not able to control their eye movement to use conventional SSVEP-based BCIs. The amplitude of the SSVEP in the posterior region was found to be reduced by more than 20 % in 10 out of 11 healthy adults when the subjects concentrated on the flicker stimuli under the conditions of flicker frequency of 10 Hz and stimulus intensity of 5 lx. Such an effect was observed in the occipital region under the condition of 14Hz and 5 lx. These results suggest the possibility of SSVEP-based binary BCI with eyes closed in terms of the mental focus.},
}
@article {pmid23366251,
year = {2012},
author = {Punsawad, Y and Wongsawat, Y},
title = {On the enhancement of training session performance via attention for single-frequency/multi-commands based steady state auditory evoked potential BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1761-1764},
doi = {10.1109/EMBC.2012.6346290},
pmid = {23366251},
issn = {2694-0604},
mesh = {Acoustic Stimulation ; Attention/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials, Auditory/*physiology ; Humans ; *Task Performance and Analysis ; User-Computer Interface ; },
abstract = {To solve the eye fatigue problem on using the well known steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system, the steady state auditory evoked potential (SSAEP) becomes one of the promising BCI modalities. However, SSAEP-based BCI system still suffers from the low accuracy. To increase the accuracy, in this paper, we propose the new training method to enhance the SSAEP training session. The training process is enhanced by making the users control their attention levels simultaneously with the detected auditory stimulus frequency. Furthermore, with the proposed training method, we also propose the corresponding single-frequency/multi-commands BCI paradigm. With the proposed paradigm, four commands can be detected by using only one auditory stimulus frequency. The proposed training system yields approximately 81% accuracy compared with 66% of the session without performing the proposed training.},
}
@article {pmid23366250,
year = {2012},
author = {Wong, YT and Vigeral, M and Putrino, D and Pfau, D and Merel, J and Paninski, L and Pesaran, B},
title = {Decoding arm and hand movements across layers of the macaque frontal cortices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1757-1760},
pmid = {23366250},
issn = {2694-0604},
support = {//Wellcome Trust/United Kingdom ; P30 EY013079/EY/NEI NIH HHS/United States ; T32 HD007430/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Arm/*physiology ; Electrodes ; Hand/*physiology ; Humans ; Joints/physiology ; Macaca/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {A major goal for brain machine interfaces is to allow patients to control prosthetic devices with high degrees of independent movements. Such devices like robotic arms and hands require this high dimensionality of control to restore the full range of actions exhibited in natural movement. Current BMI strategies fall well short of this goal allowing the control of only a few degrees of freedom at a time. In this paper we present work towards the decoding of 27 joint angles from the shoulder, arm and hand as subjects perform reach and grasp movements. We also extend previous work in examining and optimizing the recording depth of electrodes to maximize the movement information that can be extracted from recorded neural signals.},
}
@article {pmid23366249,
year = {2012},
author = {Oliver, G and Sunehag, P and Gedeon, T},
title = {Recursive channel selection techniques for brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1753-1756},
doi = {10.1109/EMBC.2012.6346288},
pmid = {23366249},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; },
abstract = {Automated channel selection allows the dimension of EEG data to be reduced without expert knowledge. We introduce Recursive Channel Insertion, an extension to Recursive Channel Elimination, which dramatically reduces calculation time with no loss of accuracy. Furthermore we propose Repeated Recursive Channel Insertion, which shows an improvement in accuracy over the previous methods when tested on a standard dataset.},
}
@article {pmid23366248,
year = {2012},
author = {Holmes, CD and Wronkiewicz, M and Somers, T and Liu, J and Russell, E and Kim, D and Rhoades, C and Dunkley, J and Bundy, D and Galboa, E and Leuthardt, E},
title = {IpsiHand Bravo: an improved EEG-based brain-computer interface for hand motor control rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1749-1752},
doi = {10.1109/EMBC.2012.6346287},
pmid = {23366248},
issn = {2694-0604},
mesh = {Artificial Intelligence ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Hand/*physiology ; Humans ; Psychomotor Performance/*physiology ; Rehabilitation/*methods ; Software ; },
abstract = {Stroke and other nervous system injuries can damage or destroy hand motor control and greatly upset daily activities. Brain computer interfaces (BCIs) represent an emerging technology that can bypass damaged nerves to restore basic motor function and provide more effective rehabilitation. A wireless BCI system was implemented to realize these goals using electroencephalographic brain signals, machine learning techniques, and a custom designed orthosis. The IpsiHand Bravo BCI system is designed to reach a large demographic by using non-traditional brain signals and improving on past BCI system pitfalls.},
}
@article {pmid23366247,
year = {2012},
author = {Geronimo, A and Schiff, SJ and Kamrunnahar, M},
title = {Visual evoked potentials for attentional gating in a brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1745-1748},
pmid = {23366247},
issn = {2694-0604},
support = {K25 NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular/physiology ; Humans ; Male ; Task Performance and Analysis ; Young Adult ; },
abstract = {For synchronous brain-computer interface (BCI) paradigms tasks that utilize visual cues to direct the user, the neural signals extracted by the computer are representative of voluntary modulation as well as evoked responses. For these paradigms, the evoked potential is often overlooked as a source of artifact. In this paper, we put forth the hypothesis that cue priming, as a mechanism for attentional gating, is predictive of motor imagery performance, and thus a viable option for self-paced (asynchronous) BCI applications. We approximate attention by the amplitude features of visually evoked potentials (VEP)s found using two methods: trial matching to an average VEP template, and component matching to a VEP template defined using independent component analysis (ICA). Templates were used to rank trials that display high vs. low levels of fixation. Our results show that subject fixation, measured by VEP response, fails as a predictor of successful motor-imagery task completion. The implications for the BCI community and the possibilities for alternative cueing methods are given in the conclusions.},
}
@article {pmid23366246,
year = {2012},
author = {Ashmore, RC and Endler, BM and Smalianchuk, I and Degenhart, AD and Hatsopoulos, NG and Tyler-Kabara, EC and Batista, AP and Wang, W},
title = {Stable online control of an electrocorticographic brain-computer interface using a static decoder.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1740-1744},
doi = {10.1109/EMBC.2012.6346285},
pmid = {23366246},
issn = {2694-0604},
support = {3R01NS050256-05S1/NS/NINDS NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; R01 HD071686/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Humans ; Macaca mulatta ; Male ; Motor Cortex/physiology ; *Online Systems ; Task Performance and Analysis ; },
abstract = {A brain computer interface (BCI) system was implemented by recording electrocorticographic signals (ECoG) from the motor cortex of a Rhesus macaque. These signals were used to control two-dimensional cursor movements in a standard center-out task, utilizing an optimal linear estimation (OLE) method. We examined the time course over which a monkey could acquire accurate control when operating in a co-adaptive training scheme. Accurate and maintained control was achieved after 4-5 days. We then held the decode parameters constant and observed stable control over the next 28 days. We also investigated the underlying neural strategy employed for control, asking whether neural features that were correlated with a given kinematic output (e.g. velocity in a certain direction) were clustered anatomically, and whether the features were coordinated or conflicting in their contributions to the control signal.},
}
@article {pmid23366244,
year = {2012},
author = {Kang, X and Schieber, MH and Thakor, NV},
title = {Decoding of finger, hand and arm kinematics using switching linear dynamical systems with pre-motor cortical ensembles.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1732-1735},
pmid = {23366244},
issn = {2694-0604},
support = {R01 EB010100/EB/NIBIB NIH HHS/United States ; R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS079664/NS/NINDS NIH HHS/United States ; R01 NS040596-09S1/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/*physiology ; Bayes Theorem ; Biomechanical Phenomena/physiology ; Fingers/*physiology ; Hand/*physiology ; Humans ; Image Processing, Computer-Assisted ; Joints/physiology ; Macaca mulatta/*physiology ; Male ; Motor Cortex/*physiology ; Time Factors ; },
abstract = {Previous works in Brain-Machine Interfaces (BMI) have mostly used a single Kalman filter decoder for deriving continuous kinematics in the complete execution of behavioral tasks. A linear dynamical system may not be able to generalize the sequence whose dynamics changes over time. Examples of such data include human motion such as walking, running, and dancing each of which exhibit complex constantly evolving dynamics. Switching linear dynamical systems (S-LDSs) are powerful models capable of describing a physical process governed by state equations that switch from time to time. The present work demonstrates the motion-state-dependent adaptive decoding of hand and arm kinematics in four different behavioral tasks. Single-unit neural activities were recorded from cortical ensembles in the ventral and dorsal premotor (PMv and PMd) areas of a trained rhesus monkey during four different reach-to-grasp tasks. We constructed S-LDSs for decoding of continuous hand and arm kinematics based on different epochs of the experiments, namely, baseline, pre-movement planning, movement, and final fixation. Average decoding accuracies as high as 89.9%, 88.6%, 89.8%, 89.4%, were achieved for motion-state-dependent decoding across four different behavioral tasks, respectively (p<0.05); these results are higher than previous works using a single Kalman filter (accuracy: 0.83). These results demonstrate that the adaptive decoding approach, or motion-state-dependent decoding, may have a larger descriptive capability than the decoding approach using a single decoder. This is a critical step towards the development of a BMI for adaptive neural control of a clinically viable prosthesis.},
}
@article {pmid23366243,
year = {2012},
author = {Skavhaug, IM and Bobell, R and Vernon, B and Joshi, SS},
title = {Pilot study for a Brain-Muscle-Computer Interface using the Extensor Pollicis Longus with preselected frequency bands.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1727-1731},
doi = {10.1109/EMBC.2012.6346282},
pmid = {23366243},
issn = {2694-0604},
mesh = {Adolescent ; *Brain-Computer Interfaces ; Cell Phone ; Electrodes ; Electromyography ; Female ; Humans ; Male ; Muscle, Skeletal/*physiology ; Pilot Projects ; Signal Processing, Computer-Assisted ; Time Factors ; Young Adult ; },
abstract = {We are developing a new class of Brain-Computer Interface that we call a Brain-Muscle-Computer Interface, in which surface electromyography (sEMG) recordings from a single muscle site are used to control the movement of a cursor. Previous work in our laboratory has established that subjects can learn to navigate a cursor to targets by manipulating the sEMG from a head muscle (the Auricularis Superior). Subjects achieved two-dimensional control of the cursor by simultaneously regulating the power in two frequency bands that were chosen to suit the individuals. The purposes of the current pilot study were to investigate (i) subjects' abilities to manipulate power in separate frequency bands in other muscles of the body and (ii) whether subjects can adapt to preselected frequency bands. We report pilot study data suggesting that subjects can learn to perform cursor-to-target tasks on a mobile phone by contracting the Extensor Pollicis Longus (a muscle located on the wrist) using frequency bands that are the same for every individual. After the completion of a short training protocol of less than 30 minutes, three subjects achieved 83%, 60% and 60% accuracies (with mean time-to-targets of 3.4 s, 1.4 s and 2.7 s respectively). All three subjects improved their performance, and two subjects decreased their time-to-targets following training. These results suggest that subjects may be able to use the Extensor Pollicis Longus to control the BMCI and adapt to preselected frequency bands. Further testing will more conclusively investigate these preliminary findings.},
}
@article {pmid23366242,
year = {2012},
author = {Cecotti, H and Eckstein, MP and Giesbrecht, B},
title = {Effects of performing two visual tasks on single-trial detection of event-related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1723-1726},
doi = {10.1109/EMBC.2012.6346281},
pmid = {23366242},
issn = {2694-0604},
mesh = {Area Under Curve ; Behavior ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Photic Stimulation ; ROC Curve ; *Task Performance and Analysis ; Young Adult ; },
abstract = {The detection of event-related potentials (ERPs) in brain-computer interface (BCI) depends on the ability of the subject to pay attention to specific stimuli presented during the BCI task. For healthy users, a BCI shall be used as a complement to other existing devices, which involve the response to other tasks. Those tasks may impair selective attention, particularly if the stimuli have the same modality e.g. visual. It is therefore critical to analyze how single-trial detection of brain evoked response is impaired by the addition of tasks concerning the same modality. We tested 10 healthy participants using an application that has two visual target detection tasks. The first one corresponds to a rapid serial visual presentation paradigm where target detection is achieved by brain-evoked single-trial detection in the recorded electroencephalogram (EEG) signal. The second task is the detection of a visual event on a tactical map by a behavioral response. These tasks were tested individually (single task) and in parallel (dual-task). Whereas the performance of single-trial detection was not impaired between single and dual-task conditions, the behavioral performance decreased during the dual-task condition. These results quantify the performance drop that can occur in a dual-task system using both brain-evoked responses and behavioral responses.},
}
@article {pmid23366241,
year = {2012},
author = {Cecotti, H and Ries, AJ and Eckstein, MP and Giesbrecht, B},
title = {Multiclass classification of single-trial evoked EEG responses.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1719-1722},
doi = {10.1109/EMBC.2012.6346280},
pmid = {23366241},
issn = {2694-0604},
mesh = {Adult ; Area Under Curve ; *Electroencephalography ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/physiology ; Humans ; Male ; Photic Stimulation ; ROC Curve ; *Task Performance and Analysis ; },
abstract = {The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal has several real-world applications, from cognitive state monitoring to brain-computer interfaces. Current systems based on the detection of ERPs only consider a single type of response to detect. Hence, the classification methods that are considered for ERP detection are binary classifiers (target vs. non target). Here we investigated multiclass classification of single-trial evoked responses during a rapid serial visual presentation task in which short video clips were presented to fifteen observers. Each trial contained potential targets that were human or non-human, stationary or moving. The goal of the classification analysis was to discriminate between three classes: moving human targets, moving non-human targets, and non-moving human targets. The analysis revealed that the mean volume under the ROC surface of 0.878. These results suggest that it is possible to efficiently discriminate between more than two types of evoked responses using single-trial detection.},
}
@article {pmid23366240,
year = {2012},
author = {Herff, C and Putze, F and Heger, D and Guan, C and Schultz, T},
title = {Speaking mode recognition from functional Near Infrared Spectroscopy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1715-1718},
doi = {10.1109/EMBC.2012.6346279},
pmid = {23366240},
issn = {2694-0604},
mesh = {Adult ; Corpus Callosum/physiology ; Electrodes ; Hemodynamics/physiology ; Humans ; Male ; Motor Cortex/physiology ; Spectroscopy, Near-Infrared/*methods ; Speech/*physiology ; },
abstract = {Speech is our most natural form of communication and even though functional Near Infrared Spectroscopy (fNIRS) is an increasingly popular modality for Brain Computer Interfaces (BCIs), there are, to the best of our knowledge, no previous studies on speech related tasks in fNIRS-based BCI. We conducted experiments on 5 subjects producing audible, silently uttered and imagined speech or do not produce any speech. For each of these speaking modes, we recorded fNIRS signals from the subjects performing these tasks and distinguish segments containing speech from those not containing speech, solely based on the fNIRS signals. Accuracies between 69% and 88% were achieved using support vector machines and a Mutual Information based Best Individual Feature approach. We are also able to discriminate the three speaking modes with 61% classification accuracy. We thereby demonstrate that speech is a very promising paradigm for fNIRS based BCI, as classification accuracies compare very favorably to those achieved in motor imagery BCIs with fNIRS.},
}
@article {pmid23366239,
year = {2012},
author = {Song, H and Zhang, D and Ling, Z and Zuo, H and Hong, B},
title = {High gamma oscillations enhance the subdural visual speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1711-1714},
doi = {10.1109/EMBC.2012.6346278},
pmid = {23366239},
issn = {2694-0604},
mesh = {Brain Waves/*physiology ; *Brain-Computer Interfaces ; Child ; Electrodes ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation ; Subdural Space/*physiology ; Time Factors ; Young Adult ; },
abstract = {The N200 speller is a non-flashing visual brain-computer interface (BCI) using motion-onset visual evoked potentials (mVEPs). Previous N200 speller was implemented at the scalp EEG level. Compared to scalp EEG, electrocorticography (ECoG) provides a broader frequency band that could be utilized in BCI. In this study, we investigated whether the high gamma brain activities recorded from human intracranial electrodes can enhance the performance of the subdural speller. The ERP and high gamma responses of one most task-related subdural electrode were used together for BCI classification and showed that high gamma responses did enhance the performance for the subdural visual motion speller resulted in an average increase of over 8% (p<0.05, paired t-test).},
}
@article {pmid23366238,
year = {2012},
author = {Fukayama, O and Otsuka, H and Hashimoto, R and Suzuki, T and Mabuchi, K},
title = {Development of exoskeletal robotic limbs for a rat controlled by neural signals based on a vehicular neuro-robotic platform RatCar.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1707-1710},
doi = {10.1109/EMBC.2012.6346277},
pmid = {23366238},
issn = {2694-0604},
mesh = {Animals ; Calibration ; Electrodes ; Extremities/innervation/*physiology ; Locomotion/physiology ; *Nervous System Physiological Phenomena ; Physical Conditioning, Animal ; Rats ; Robotics/*instrumentation ; Sciatic Nerve/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {A pair of exoskeletal limbs for a rat has been developed based on a vehicular Brain-Machine Interface "Rat-Car". The "RatCar" is a whole-body motor prosthesis system for a rat developed by the authors, estimating locomotion velocity according to neural signals pattern to move the rat body by the vehicle instead of its original limbs. In this paper, exoskeletal limbs have displaced the wheels for more natural modality of body control. The system was tested by applying peripheral nerve signals from a behaving rat.},
}
@article {pmid23366237,
year = {2012},
author = {Kanoh, S and Miyamoto, K and Yoshinobu, T},
title = {Generation of spatial filters by ICA for detecting motor-related oscillatory EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1703-1706},
doi = {10.1109/EMBC.2012.6346276},
pmid = {23366237},
issn = {2694-0604},
mesh = {*Algorithms ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Motor Activity/*physiology ; Movement/*physiology ; Task Performance and Analysis ; Time Factors ; },
abstract = {To detect the imagined limb movement from EEG for the use in BCI, the increase (ERS) and decrease (ERD) of the band power of the EEG originated from the sensorimotor cortex are commonly used. A spatial filter using neighboring channels is generally applied to the measured EEG for detecting such brain activity related to the motor imagery. However, the configuration and location of the spatial filter have been selected by the empirical method on trial-and-error basis. In this study, we recorded the EEG during motor imagery of left hand, right hand and feet from five subjects, and the ICA (independent component analysis) was applied to discover the spatial filters for extracting event-related EEG components of the motor imagery. It was suggested that the application of ICA might offer the experimenters appropriate local spatial filters, or at least, the "initial guess" for designing or selecting custom local spatial filters.},
}
@article {pmid23366236,
year = {2012},
author = {Matlack, C and Moritz, C and Chizeck, H},
title = {Applying best practices from digital control systems to BMI implementation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1699-1702},
doi = {10.1109/EMBC.2012.6346275},
pmid = {23366236},
issn = {2694-0604},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; *Brain-Computer Interfaces ; Extremities/physiology ; Macaca/physiology ; Models, Neurological ; Movement/physiology ; Normal Distribution ; *Signal Processing, Computer-Assisted ; },
abstract = {Many brain-machine interface (BMI) algorithms, such as the population vector decoder, must estimate neural spike rates before transforming this information into an external output signal. Often, rate estimation is performed via the selection of a bin width corresponding to the effective sampling rate of the decoding algorithm. Here, we implement real-time rate estimation by extending prior work on the optimization of Gaussian filters for offline rate estimation. We show that higher sampling rates result in improved spike rate estimation. We further show that the choice of sampling rate need not dictate the number of parameters which must be used in an autoregressive decoding algorithm. Multiple studies in other neural signal processing contexts suggest that BMI performance could be improved substantially via careful choice of smoothing filter, discrete-time decoder representation, and sampling rate. Together, these ensure minimal deviation from the behavior of the modeled continuous-time systems.},
}
@article {pmid23366235,
year = {2012},
author = {Shan, H and Yuan, H and Zhu, S and He, B},
title = {EEG-based motor imagery classification accuracy improves with gradually increased channel number.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1695-1698},
doi = {10.1109/EMBC.2012.6346274},
pmid = {23366235},
issn = {2694-0604},
mesh = {Algorithms ; Electroencephalography/*instrumentation ; Humans ; Imagery, Psychotherapy/*classification ; Motor Activity/*physiology ; Task Performance and Analysis ; },
abstract = {The question of how many channels should be sed for classification remains a key issue in the study of Brain-Computer Interface. Several studies have shown that a reduced number of channels can achieve the optimal classification accuracy in the offline analysis of motor imagery paradigm, which does not have real-time feedback as in the online control. However, for the cursor movement control paradigm, it remains unclear as to how many channels should be selected in order to achieve the optimal classification. In the present study, we gradually increased the number of channels, and adopted the time-frequency-spatial synthesized method for left and right motor imagery classification. We compared the effect of increasing channel number in two datasets, an imagery-based cursor movement control dataset and a motor imagery tasks dataset. Our results indicated that for the former dataset, the more channels we used, the higher the accuracy rate was achieved, which is in contrast to the finding in the latter dataset that optimal performance was obtained at a subset number of channels. When gradually increasing the number of channels from 2 to all in the analysis of cursor movement control dataset, the average training and testing accuracies from three subjects improved from 68.7% to 90.4% and 63.7% to 87.7%, respectively.},
}
@article {pmid23366234,
year = {2012},
author = {Li, Y and Hao, Y and Wang, D and Zhang, Q and Liao, Y and Zheng, X and Chen, W},
title = {Decoding grasp types with high frequency of local field potentials from primate primary dorsal premotor cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1691-1694},
doi = {10.1109/EMBC.2012.6346273},
pmid = {23366234},
issn = {2694-0604},
mesh = {Action Potentials/*physiology ; Animals ; Hand Strength/*physiology ; Macaca mulatta/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Recently, local field potentials (LFPs) have been successfully used to extract information of arm and hand movement in some brain-machine interfaces (BMIs) studies, which suggested that LFPs would improve the performance of BMI applications because of its long-term stability. However, the performance of LFPs in different frequency bands has not been investigated in decoding hand grasp types. Here, the LFPs from the monkey's dorsal premotor cortices were collected by microelectrode array when monkey was performing grip-specific grasp task. A K-nearest neighbor classifier performed on the power spectrum of LFPs was used to decode grasping movements. The decoding powers of LFPs in different frequency bands, channels and trials used for training were also studied. The results show that the broad high frequency band (200-400Hz) LFPs achieved the best performance with classification accuracy reaching over 0.9. It infers that high frequency band LFPs in PMd cortex could be a promising source of control signals in developing functional BMIs for hand grasping.},
}
@article {pmid23366233,
year = {2012},
author = {He, W and Wei, P and Zhou, Y and Wang, L},
title = {Combination of amplitude and phase features under a uniform framework with EMD in EEG-based Brain-Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1687-1690},
doi = {10.1109/EMBC.2012.6346272},
pmid = {23366233},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electrodes ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {In a Brain-Computer Interface (BCI) system, the variations of the amplitude and the phase in EEG signal convey subjects' movement intention and underpin the differentiation of the various mental tasks. Combining these two kinds of information under a uniform feature extraction framework can better reflect the brain states and potentially contribute to BCI classification. Here the Common Spatial Pattern (CSP) and the Phase Locking Value (PLV) were used to capture the amplitude and the phase information. To integrate these two feature extraction procedures, the Empirical Mode Decomposition (EMD) is introduced in preprocessing which behaved as filter bank to optimize bands selection automatically for CSP and exactly calculate the instantaneous phase for PLV. The most discriminative features were selected from the feature pool by the sequential floating forward feature selection method (SFFS). The proposed method was applied to both public and recorded datasets (each n=4). Compared with the traditional CSP, the average increment of classification accuracy is 5.4% (2.0% for public and 8.7% for recorded datasets), which both manifests statistically significances (p<0.05). Moreover, we preliminarily investigate the possibility of the online realization of this method and it shows a comparable result with the offline result.},
}
@article {pmid23366228,
year = {2012},
author = {Abualhoul, MY and Svenmarker, P and Wang, Q and Andersson, JY and Johansson, AJ},
title = {Free space optical link for biomedical applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1667-1670},
doi = {10.1109/EMBC.2012.6346267},
pmid = {23366228},
issn = {2694-0604},
mesh = {Animals ; Biomedical Technology/*methods ; Computer Simulation ; In Vitro Techniques ; Mice ; Models, Theoretical ; Monte Carlo Method ; Optics and Photonics/*methods ; Rats ; Signal-To-Noise Ratio ; Skin Physiological Phenomena ; },
abstract = {Free space optics is an interesting alternative for telemetry with medical implants, due to the high data bandwidths available at optical frequencies. Especially implanted brain-computer interfaces gives rise to large data sets that needs to be transmitted transcutaneous. In this paper we show that it is possible to establish such a link at near-IR wavelengths using a modulated reflector in the implant, thus keeping the laser and the detector on the outside. In addition, we show that it will not only work on short, i.e. touch, distances but also at larger distances, in the range of a meter. We have used an electro absorption modulator to modulate the reflection of an external laser source back towards an external detector. The only part of this system that needs to be implanted is the modulator and drive electronics. The study has been done both by Monte-Carlo simulations of a multi-layer model of a rat skull, and with an experiment demonstrating the feasibility of the link when transmitted through biological tissue. The results show that it is possible to establish a transcutaneous link with an external laser source and light detector, and an internal modulated reflector.},
}
@article {pmid23366143,
year = {2012},
author = {Golub, MD and Yu, BM and Chase, SM},
title = {Internal models engaged by brain-computer interface control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1327-1330},
pmid = {23366143},
issn = {2694-0604},
support = {R01 HD071686/HD/NICHD NIH HHS/United States ; R01-HD-071686/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Arm/physiology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Feedback, Sensory/*physiology ; Macaca mulatta ; *Models, Neurological ; Neurons/*physiology ; Task Performance and Analysis ; },
abstract = {Internal models have been proposed to explain the brain's ability to compensate for sensory feedback delays by predicting the sensory consequences of movement commands. Single-neuron studies in the oculomotor and vestibulo-ocular systems have provided evidence of internal models, as have behavioral studies in the skeletomotor system. Here, we present evidence of internal models from simultaneously recorded population activity underlying closed-loop brain-computer interface (BCI) control. We studied cursor-based BCI control by a nonhuman primate implanted with a multi-electrode array in motor cortex. Using a novel BCI task, we measured the visual feedback processing delay to be about 130 milliseconds. By examining the task-based appropriateness of the population activity at different time lags, we found evidence that the subject compensates for the feedback delay by predicting upcoming cursor positions, suggesting the use of an internal forward model. Lastly, we examined the time course of internal model adaptation after altering the mapping between population activity and cursor movements. This study suggests that closed-loop BCI experiments combined with novel statistical analyses can provide insight into the neural substrates of feedback motor control and motor learning.},
}
@article {pmid23366141,
year = {2012},
author = {Gilja, V and Nuyujukian, P and Chestek, CA and Cunningham, JP and Yu, BM and Fan, JM and Ryu, SI and Shenoy, KV},
title = {A brain machine interface control algorithm designed from a feedback control perspective.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1318-1322},
doi = {10.1109/EMBC.2012.6346180},
pmid = {23366141},
issn = {2694-0604},
support = {1DP1OD006409/OD/NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; //Howard Hughes Medical Institute/United States ; },
mesh = {*Algorithms ; Animals ; Arm/physiology ; Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces ; Electrodes, Implanted ; *Feedback ; Macaca ; Male ; },
abstract = {We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the methods by which these algorithms are trained. In online neural control experiments recording from a 96-electrode array implanted in M1 of a macaque monkey, the ReFIT-KF control algorithm demonstrates large performance improvements over the current state of the art velocity Kalman filter, reducing target acquisition time by a factor of two, while maintaining a 500 ms hold period, thereby increasing the clinical viability of BMI systems.},
}
@article {pmid23366140,
year = {2012},
author = {Gowda, S and Orsborn, AL and Carmena, JM},
title = {Parameter estimation for maximizing controllability of linear brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1314-1317},
doi = {10.1109/EMBC.2012.6346179},
pmid = {23366140},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; Biomechanical Phenomena/physiology ; *Brain-Computer Interfaces ; Computer Simulation ; Electrodes, Implanted ; Linear Models ; Macaca mulatta ; Male ; Motor Cortex/physiology ; Neurons/physiology ; *Signal Processing, Computer-Assisted ; Task Performance and Analysis ; },
abstract = {Brain-machine interfaces (BMIs) must be carefully designed for closed-loop control to ensure the best possible performance. The Kalman filter (KF) is a recursive linear BMI algorithm which has been shown to smooth cursor kinematics and improve control over non-recursive linear methods. However, recursive estimators are not without their drawbacks. Here we show that recursive decoders can decrease BMI controllability by coupling kinematic variables that the subject might expect to be unrelated. For instance, a 2D neural cursor where velocity is controlled using a KF can increase the difficulty of straight reaches by linking horizontal and vertical velocity estimates. These effects resemble force fields in arm control. Analysis of experimental data from one non-human primate controlling a position/velocity KF cursor in closed-loop shows that the presence of these force-field effects correlated with decreased performance. We designed a modified KF parameter estimation algorithm to eliminate these effects. Cursor controllability improved significantly when our modifications were used in a closed-loop BMI simulator. Thus, designing highly controllable BMIs requires parameter estimation techniques that carefully craft relationships between decoded variables.},
}
@article {pmid23366066,
year = {2012},
author = {Kim, T and Artan, NS and Viventi, J and Chao, HJ},
title = {Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {1012-1015},
doi = {10.1109/EMBC.2012.6346105},
pmid = {23366066},
issn = {2694-0604},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; *Computer Security ; *Electrodes, Implanted ; *Electroencephalography/instrumentation/methods ; Epilepsy/physiopathology ; Humans ; *Wireless Technology ; },
abstract = {High-resolution Electrocorticography (HR-ECoG) has emerged as a key strategic technology for recording localized neural activity with high temporal and spatial resolution with potential applications in brain-computer interfaces (BCI), and seizure detection for epilepsy. However, HR-ECoG has 400 times the resolution of conventional ECoG, making it a challenge to process, transmit and store the HR-ECoG data. Therefore, simple and efficient compression algorithms are vital for the feasibility of implantable wireless medical devices for HR-ECoG recordings. In this paper, following the observation that HR-ECoG signals have both high spatial and temporal correlations similar to video/image signals, various compression methods suitable for video/image- compression based on motion estimation, discrete cosine transform (DCT) and discrete wavelet transform (DWT)- are investigated for compressing HR-ECoG data. We first simplify these methods to satisfy the low-power requirements for implantable devices. Then, we demonstrate that spatiotemporal compression methods produce up to 46% more data reduction on HR-ECoG data than compression methods using only spatial compression do. We further show that this data reduction can be achieved with low hardware complexity. In particular, among the methods investigated, spatiotemporal compression using DCT-based methods provide the best trade-off between hardware complexity and compression performance, and thus we conclude that DCT-based compression is a promising solution for ultralow-power implantable devices for HR-ECoG.},
}
@article {pmid23366013,
year = {2012},
author = {Semprini, M and Bennicelli, L and Vato, A},
title = {A parametric study of intracortical microstimulation in behaving rats for the development of artificial sensory channels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {799-802},
doi = {10.1109/EMBC.2012.6346052},
pmid = {23366013},
issn = {2694-0604},
mesh = {Animals ; Behavior, Animal/physiology ; *Brain-Computer Interfaces/statistics &amp; numerical data ; Conditioning, Operant ; *Deep Brain Stimulation/statistics &amp; numerical data ; Feedback, Physiological ; *Implantable Neurostimulators/statistics &amp; numerical data ; Male ; Models, Neurological ; Rats ; Rats, Long-Evans ; Somatosensory Cortex/*physiology/surgery ; },
abstract = {In the framework of developing new brain-machine interfaces, many valuable results have been obtained in understanding which features of neural activity can be used in controlling an external device. Somatosensory real-time feedback is crucial for motor planning and for executing "online" errors correction during the movement. In people with sensory motor disabilities cortical microstimulation can be used as sensory feedback to elicit an artificial sensation providing the brain with information about the external environment. Even if intracortical microstimulation (ICMS) is broadly used in several experiments, understanding the psychophysics of such artificial sensory channel is still an open issue. Here we present the results of a parametric study that aims to define which stimulation parameters are needed to create an artificial sensation. Behaving rats were trained to report by pressing a lever the presence of ICMS delivered through microwire electrodes chronically implanted in the barrel cortex. Psychometric curves obtained by varying pulse amplitude, pulse frequency and train duration, demonstrate that in freely moving animals the perception threshold of microstimulation increased with respect to previous studies with head-restrained rats.},
}
@article {pmid23366009,
year = {2012},
author = {Charvet, G and Foerster, M and Chatalic, G and Michea, A and Porcherot, J and Bonnet, S and Filipe, S and Audebert, P and Robinet, S and Josselin, V and Reverdy, J and D'Errico, R and Sauter, F and Mestais, C and Benabid, AL},
title = {A wireless 64-channel ECoG recording electronic for implantable monitoring and BCI applications: WIMAGINE.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {783-786},
doi = {10.1109/EMBC.2012.6346048},
pmid = {23366009},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Brain-Computer Interfaces ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; Monitoring, Physiologic/instrumentation ; Neural Prostheses/statistics &amp; numerical data ; Primates ; Quadriplegia/rehabilitation ; Radio Waves ; Remote Sensing Technology/instrumentation ; Telemetry/instrumentation ; Wireless Technology/*instrumentation ; },
abstract = {A wireless, low power, 64-channel data acquisition system named WIMAGINE has been designed for ElectroCorticoGram (ECoG) recording. This system is based on a custom integrated circuit (ASIC) for amplification and digitization on 64 channels. It allows the RF transmission (in the MICS band) of 32 ECoG recording channels (among 64 channels available) sampled at 1 kHz per channel with a 12-bit resolution. The device is powered wirelessly through an inductive link at 13.56 MHz able to provide 100mW (30mA at 3.3V). This integration is a first step towards an implantable device for brain activity monitoring and Brain-Computer Interface (BCI) applications. The main features of the WIMAGINE platform and its architecture will be presented, as well as its performances and in vivo studies.},
}
@article {pmid23365998,
year = {2012},
author = {Corbett, EA and Kording, KP and Perreault, EJ},
title = {Real-time fusion of gaze and EMG for a reaching neuroprosthesis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {739-742},
doi = {10.1109/EMBC.2012.6346037},
pmid = {23365998},
issn = {2694-0604},
mesh = {Algorithms ; Arm/physiopathology ; Brain-Computer Interfaces ; *Electromyography ; Eye Movements/*physiology ; Female ; Humans ; Male ; Models, Neurological ; Models, Statistical ; Movement/physiology ; *Neural Prostheses ; Robotics ; Spinal Cord Injuries/physiopathology/rehabilitation ; },
abstract = {For rehabilitative devices to restore functional movement to paralyzed individuals, user intent must be determined from signals that remain under voluntary control. Tracking eye movements is a natural way to learn about an intended reach target and, when combined with just a small set of electromyograms (EMGs) in a probabilistic mixture model, can reliably generate accurate trajectories even when the target information is uncertain. To experimentally assess the effectiveness of our algorithm in closed-loop control, we developed a robotic system to simulate a reaching neuroprosthetic. Incorporating target information by tracking subjects' gaze greatly improved performance when the set of EMGs was most limited. In addition we found that online performance was better than predicted by the offline accuracy of the training data. By enhancing the trajectory model with target information the decoder relied less on neural control signals, reducing the burden on the user.},
}
@article {pmid23365901,
year = {2012},
author = {Sun, C and Zhang, X and Zheng, N and Chen, W and Zheng, X},
title = {Bio-robots automatic navigation with electrical reward stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2012},
number = {},
pages = {348-351},
doi = {10.1109/EMBC.2012.6345940},
pmid = {23365901},
issn = {2694-0604},
mesh = {Animals ; *Brain-Computer Interfaces ; Electric Stimulation/methods ; Humans ; *Maze Learning ; Rats ; *Robotics ; },
abstract = {Bio-robots that controlled by outer stimulation through brain computer interface (BCI) suffer from the dependence on realtime guidance of human operators. Current automatic navigation methods for bio-robots focus on the controlling rules to force animals to obey man-made commands, with animals' intelligence ignored. This paper proposes a new method to realize the automatic navigation for bio-robots with electrical micro-stimulation as real-time rewards. Due to the reward-seeking instinct and trial-and-error capability, bio-robot can be steered to keep walking along the right route with rewards and correct its direction spontaneously when rewards are deprived. In navigation experiments, rat-robots learn the controlling methods in short time. The results show that our method simplifies the controlling logic and realizes the automatic navigation for rat-robots successfully. Our work might have significant implication for the further development of bio-robots with hybrid intelligence.},
}
@article {pmid23365562,
year = {2012},
author = {Ehlers, J and Valbuena, D and Stiller, A and Gräser, A},
title = {Age-specific mechanisms in an SSVEP-based BCI scenario: evidences from spontaneous rhythms and neuronal oscillators.},
journal = {Computational intelligence and neuroscience},
volume = {2012},
number = {},
pages = {967305},
pmid = {23365562},
issn = {1687-5273},
mesh = {Adolescent ; Adult ; Age Factors ; Aging/*physiology ; Algorithms ; Analysis of Variance ; Biological Clocks/*physiology ; Child ; Cross-Sectional Studies ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Occipital Lobe/*physiology ; Online Systems ; Photic Stimulation ; *User-Computer Interface ; Young Adult ; },
abstract = {Utilizing changes in steady-state visual evoked potentials (SSVEPs) is an established approach to operate a brain-computer interface (BCI). The present study elucidates to what extent development-specific changes in the background EEG influence the ability to proper handle a stimulus-driven BCI. Therefore we investigated the effects of a wide range of photic driving on children between six and ten years in comparison to an adult control group. The results show differences in the driving profiles apparently in close communication with the specific type of intermittent stimulation. The factor age gains influence with decreasing stimulation frequency, whereby the superior performance of the adults seems to be determined to a great extent by elaborated driving responses at 10 and 11 Hz, matching the dominant resonance frequency of the respective background EEG. This functional interplay was only partially obtained in higher frequency ranges and absent in the induced driving between 30 and 40 Hz, indicating distinctions in the operating principles and developmental changes of the underlying neuronal oscillators.},
}
@article {pmid23359948,
year = {2012},
author = {Dong, J and Liu, YJ and Li, PL and Lin, FJ and Chen, JL and Liu, Y},
title = {[Ecological effects of wheat-oilseed rape intercropping combined with methyl salicylate release on Sitobion avenae and its main natural enemies].},
journal = {Ying yong sheng tai xue bao = The journal of applied ecology},
volume = {23},
number = {10},
pages = {2843-2848},
pmid = {23359948},
issn = {1001-9332},
mesh = {Agriculture/methods ; Animals ; Aphids/drug effects/growth &amp; development/*physiology ; Brassica/growth &amp; development ; China ; Ecosystem ; *Pest Control, Biological ; Pheromones/pharmacology ; *Predatory Behavior ; Salicylates/*chemistry ; Triticum/growth &amp; development/*parasitology ; },
abstract = {In order to explore the effects of wheat-oilseed rape intercropping in combining with methyl salicylate (MeSA) release on Sitobion avenae and its main natural enemies, a field experiment was conducted at the Tai'an Experimental Station of Shandong Agricultural University in East China from October 2008 to June 2010 to study the temporal dynamics of S. avenae and its main natural enemies as well as the ecological control effect on the aphid. In the plots of intercropping combined with MeSA release, the S. avenae apterae population reached a peak about 12 d in advance of the control, but the peak value was significantly lower than that of the control. The average annual number of S. avenae apterae per 100 wheat tillers decreased in the order of wheat monoculture > wheat-oilseed rape intercropping > MeSA release > wheat-oilseed rape intercropping combined with MeSA release. Moreover, the total number of ladybeetles was the highest in the plots of intercropping combined with MeSA release. The population densities of aphid parasitoids reached a peak about 10 d in advance of the control, which could play a significant role in controlling S. avenae at the filling stage of wheat. Taking the biological control index (BCI) as a quantitative indicator, and with the ladybeetles and parasitoids as the dominant control factors in fields, it was observed that wheat-oilseed rape intercropping combined with MeSA release could suppress the population increase of S. avenae apterae effectively from the heading to filling stages of wheat.},
}
@article {pmid23359537,
year = {2013},
author = {Pistohl, T and Schmidt, TS and Ball, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Grasp detection from human ECoG during natural reach-to-grasp movements.},
journal = {PloS one},
volume = {8},
number = {1},
pages = {e54658},
pmid = {23359537},
issn = {1932-6203},
mesh = {Adolescent ; Algorithms ; Cerebral Cortex/physiology ; Discriminant Analysis ; Electroencephalography ; *Hand Strength ; Humans ; *Movement ; Reproducibility of Results ; },
abstract = {Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis.},
}
@article {pmid23359212,
year = {2013},
author = {Chhatbar, PY and Francis, JT},
title = {Towards a naturalistic brain-machine interface: hybrid torque and position control allows generalization to novel dynamics.},
journal = {PloS one},
volume = {8},
number = {1},
pages = {e52286},
pmid = {23359212},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Humans ; },
abstract = {Realization of reaching and grasping movements by a paralytic person or an amputee would greatly facilitate her/his activities of daily living. Towards this goal, control of a computer cursor or robotic arm using neural signals has been demonstrated in rodents, non-human primates and humans. This technology is commonly referred to as a Brain-Machine Interface (BMI) and is achieved by predictions of kinematic parameters, e.g. position or velocity. However, execution of natural movements, such as swinging baseball bats of different weights at the same speed, requires advanced planning for necessary context-specific forces in addition to kinematic control. Here we show, for the first time, the control of a virtual arm with representative inertial parameters using real-time neural control of torques in non-human primates (M. radiata). We found that neural control of torques leads to ballistic, possibly more naturalistic movements than position control alone, and that adding the influence of position in a hybrid torque-position control changes the feedforward behavior of these BMI movements. In addition, this level of control was achievable utilizing the neural recordings from either contralateral or ipsilateral M1. We also observed changed behavior of hybrid torque-position control under novel external dynamic environments that was comparable to natural movements. Our results demonstrate that inclusion of torque control to drive a neuroprosthetic device gives the user a more direct handle on the movement execution, especially when dealing with novel or changing dynamic environments. We anticipate our results to be a starting point of more sophisticated algorithms for sensorimotor neuroprostheses, eliminating the need of fully automatic kinematic-to-dynamic transformations as currently used by traditional kinematic-based decoders. Thus, we propose that direct control of torques, or other force related variables, should allow for more natural neuroprosthetic movements by the user.},
}
@article {pmid23353184,
year = {2013},
author = {Marchetti, M and Piccione, F and Silvoni, S and Gamberini, L and Priftis, K},
title = {Covert visuospatial attention orienting in a brain-computer interface for amyotrophic lateral sclerosis patients.},
journal = {Neurorehabilitation and neural repair},
volume = {27},
number = {5},
pages = {430-438},
doi = {10.1177/1545968312471903},
pmid = {23353184},
issn = {1552-6844},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; Analysis of Variance ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; Orientation/*physiology ; Photic Stimulation ; Visual Perception/*physiology ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) allow people to control devices by translating brain signals into commands. BCIs represent a concrete solution with regard to communication and motor control disabilities of patients with amyotrophic lateral sclerosis (ALS). Most of the BCIs rely on visual interfaces in which patients must move their eyes to achieve efficient BCI control. This fact represents a limitation of BCI use in ALS patients who are in the final stages of the disease.<br><br>OBJECTIVE: We aimed to improve visual interfaces for ALS patients to control the movement of a cursor on a monitor by orienting their covert visuospatial attention (i.e., orienting without eye movements).<br><br>METHODS: A total of 10 ALS patients with different levels of impairment used 2 new visual interfaces in an event-related potential (ERP)-based BCI. In the first interface, they were required to use exogenous visuospatial attention orienting (VAO), whereas in the second interface, they were required to use endogenous VAO.<br><br>RESULTS: . ALS patients were able to use the 2 interfaces for controlling the ERP-based BCI system in real time. Nevertheless, better target classification and information transfer rate were associated with the interface that was based on endogenous VAO.<br><br>CONCLUSIONS: ALS patients can exploit their covert VAO to control a BCI that does not require eye movements. The implementation of endogenous VAO in the design of covert visuospatial attention-based interfaces seems to be suitable for designing more ergonomic and efficient BCIs for ALS patients with impaired eye movements.},
}
@article {pmid23352171,
year = {2013},
author = {Engelhard, B and Ozeri, N and Israel, Z and Bergman, H and Vaadia, E},
title = {Inducing γ oscillations and precise spike synchrony by operant conditioning via brain-machine interface.},
journal = {Neuron},
volume = {77},
number = {2},
pages = {361-375},
doi = {10.1016/j.neuron.2012.11.015},
pmid = {23352171},
issn = {1097-4199},
mesh = {Action Potentials/*physiology ; Animals ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Conditioning, Operant/*physiology ; Electroencephalography Phase Synchronization/*physiology ; Macaca fascicularis ; },
abstract = {Neural oscillations in the low-gamma range (30-50 Hz) have been implicated in neuronal synchrony, computation, behavior, and cognition. Abnormal low-gamma activity, hypothesized to reflect impaired synchronization, has been evidenced in several brain disorders. Thus, understanding the relations between gamma oscillations, neuronal synchrony and behavior is a major research challenge. We used a brain-machine interface (BMI) to train monkeys to specifically increase low-gamma power in selected sites of motor cortex to move a cursor and obtain a reward. The monkeys learned to robustly generate oscillatory gamma waves, which were accompanied by a dramatic increase of spiking synchrony of highly precise spatiotemporal patterns. The findings link volitional control of LFP oscillations, neuronal synchrony, and the behavioral outcome. Subjects' ability to directly modulate specific patterns of neuronal synchrony provides a powerful approach for understanding neuronal processing in relation to behavior and for the use of BMIs in a clinical setting.},
}
@article {pmid23345208,
year = {2013},
author = {Rouse, AG and Williams, JJ and Wheeler, JJ and Moran, DW},
title = {Cortical adaptation to a chronic micro-electrocorticographic brain computer interface.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {33},
number = {4},
pages = {1326-1330},
pmid = {23345208},
issn = {1529-2401},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; R01EB000856/EB/NIBIB NIH HHS/United States ; R01EB009103/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Macaca ; Male ; },
abstract = {Brain-computer interface (BCI) technology decodes neural signals in real time to control external devices. In this study, chronic epidural micro-electrocorticographic recordings were performed over primary motor (M1) and dorsal premotor (PMd) cortex of three macaque monkeys. The differential gamma-band amplitude (75-105 Hz) from two arbitrarily chosen 300 μm electrodes (one located over each cortical area) was used for closed-loop control of a one-dimensional BCI device. Each monkey rapidly learned over a period of days to successfully control the velocity of a computer cursor. While both cortical areas contributed to success on the BCI task, the control signals from M1 were consistently modulated more strongly than those from PMd. Additionally, we observe that gamma-band power during active BCI control is always above resting brain activity. This suggests that purposeful gamma-band modulation is an active process that is obtained through increased cortical activation.},
}
@article {pmid23343902,
year = {2013},
author = {Shaikhouni, A and Donoghue, JP and Hochberg, LR},
title = {Somatosensory responses in a human motor cortex.},
journal = {Journal of neurophysiology},
volume = {109},
number = {8},
pages = {2192-2204},
pmid = {23343902},
issn = {1522-1598},
support = {C06 16549-01A1//PHS HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; RC1 HD-063931/HD/NICHD NIH HHS/United States ; NS-25074/NS/NINDS NIH HHS/United States ; N01 HD-53403/HD/NICHD NIH HHS/United States ; R01 DC-009899/DC/NIDCD NIH HHS/United States ; R01 EB-007401/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain Stem Infarctions/diagnosis/*physiopathology ; Efferent Pathways/physiopathology ; *Evoked Potentials, Somatosensory ; Feedback, Sensory ; Female ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Motor Cortex/*physiopathology ; Movement ; Neurons/physiology ; Quadriplegia/diagnosis/physiopathology ; Upper Extremity/*innervation/physiology ; },
abstract = {Somatic sensory signals provide a major source of feedback to motor cortex. Changes in somatosensory systems after stroke or injury could profoundly influence brain computer interfaces (BCI) being developed to create new output signals from motor cortex activity patterns. We had the unique opportunity to study the responses of hand/arm area neurons in primary motor cortex to passive joint manipulation in a person with a long-standing brain stem stroke but intact sensory pathways. Neurons responded to passive manipulation of the contralateral shoulder, elbow, or wrist as predicted from prior studies of intact primates. Thus fundamental properties and organization were preserved despite arm/hand paralysis and damage to cortical outputs. The same neurons were engaged by attempted arm actions. These results indicate that intact sensory pathways retain the potential to influence primary motor cortex firing rates years after cortical outputs are interrupted and may contribute to online decoding of motor intentions for BCI applications.},
}
@article {pmid23342043,
year = {2013},
author = {Marchetti, M and Onorati, F and Matteucci, M and Mainardi, L and Piccione, F and Silvoni, S and Priftis, K},
title = {Improving the efficacy of ERP-based BCIs using different modalities of covert visuospatial attention and a genetic algorithm-based classifier.},
journal = {PloS one},
volume = {8},
number = {1},
pages = {e53946},
pmid = {23342043},
issn = {1932-6203},
mesh = {Adult ; *Algorithms ; Artifacts ; Attention/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials/*physiology ; Eye Movements/physiology ; Female ; Humans ; Male ; Middle Aged ; *Photic Stimulation ; Young Adult ; },
abstract = {We investigated whether the covert orienting of visuospatial attention can be effectively used in a brain-computer interface guided by event-related potentials. Three visual interfaces were tested: one interface that activated voluntary orienting of visuospatial attention and two interfaces that elicited automatic orienting of visuospatial attention. We used two epoch classification procedures. The online epoch classification was performed via Independent Component Analysis, and then it was followed by fixed features extraction and support vector machines classification. The offline epoch classification was performed by means of a genetic algorithm that permitted us to retrieve the relevant features of the signal, and then to categorise the features with a logistic classifier. The offline classification, but not the online one, allowed us to differentiate between the performances of the interface that required voluntary orienting of visuospatial attention and those that required automatic orienting of visuospatial attention. The offline classification revealed an advantage of the participants in using the "voluntary" interface. This advantage was further supported, for the first time, by neurophysiological data. Moreover, epoch analysis was performed better with the "genetic algorithm classifier" than with the "independent component analysis classifier". We suggest that the combined use of voluntary orienting of visuospatial attention and of a classifier that permits feature extraction ad personam (i.e., genetic algorithm classifier) can lead to a more efficient control of visual BCIs.},
}
@article {pmid23340243,
year = {2013},
author = {Kasabov, N and Dhoble, K and Nuntalid, N and Indiveri, G},
title = {Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {41},
number = {},
pages = {188-201},
doi = {10.1016/j.neunet.2012.11.014},
pmid = {23340243},
issn = {1879-2782},
mesh = {Action Potentials/*physiology ; Artificial Intelligence ; Connectome ; Electroencephalography ; Humans ; *Models, Neurological ; Motion Perception/*physiology ; Nerve Net/physiology ; *Neural Networks, Computer ; Neuronal Plasticity/physiology ; Neurons/physiology ; Pattern Recognition, Visual/*physiology ; Recognition, Psychology/physiology ; Time Perception/physiology ; },
abstract = {On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes (which information is explicitly present in input data streams and would be crucial to consider in some tasks) and of the information contained in the timing of the following spikes that is learned by the dynamic synapses as a whole spatio-temporal pattern.},
}
@article {pmid23339894,
year = {2013},
author = {Diez, PF and Torres Müller, SM and Mut, VA and Laciar, E and Avila, E and Bastos-Filho, TF and Sarcinelli-Filho, M},
title = {Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.},
journal = {Medical engineering &amp; physics},
volume = {35},
number = {8},
pages = {1155-1164},
doi = {10.1016/j.medengphy.2012.12.005},
pmid = {23339894},
issn = {1873-4030},
mesh = {Adult ; Biofeedback, Psychology/instrumentation ; *Brain-Computer Interfaces ; Electroencephalography/instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Man-Machine Systems ; Middle Aged ; Paralysis/physiopathology/*rehabilitation ; Photic Stimulation/instrumentation/methods ; Robotics/*instrumentation ; Therapy, Computer-Assisted/instrumentation/methods ; Visual Cortex/*physiopathology ; *Visual Perception ; *Wheelchairs ; Young Adult ; },
abstract = {This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz). This high frequency stimulation scheme can reduce or even eliminate visual fatigue, allowing the user to achieve a stable performance for long term BCI operation. The BCI system uses power-spectral density analysis associated to three bipolar electroencephalographic channels. As the results show, 2 subjects were reported as SSVEP-BCI illiterates (not able to use the BCI), and, consequently, 13 subjects (12 of them healthy) could navigate the wheelchair in a room with obstacles arranged in four distinct configurations. Volunteers expressed neither discomfort nor fatigue due to flickering stimulation. A transmission rate of up to 72.5 bits/min was obtained, with an average of 44.6 bits/min in four trials. These results show that people could effectively navigate a robotic wheelchair using a SSVEP-based BCI with high frequency flickering stimulation.},
}
@article {pmid23337399,
year = {2013},
author = {Sawyer, AJ and Kyriakides, TR},
title = {Nanoparticle-based evaluation of blood-brain barrier leakage during the foreign body response.},
journal = {Journal of neural engineering},
volume = {10},
number = {1},
pages = {016013},
pmid = {23337399},
issn = {1741-2552},
support = {R01 GM072194/GM/NIGMS NIH HHS/United States ; R01GM072194/GM/NIGMS NIH HHS/United States ; },
mesh = {Animals ; Blood-Brain Barrier/metabolism/*physiopathology/ultrastructure ; Capillary Permeability/*physiology ; *Fluorescent Dyes ; Foreign-Body Reaction/diagnosis/metabolism/*physiopathology ; Mice ; Mice, 129 Strain ; Mice, Inbred C57BL ; *Nanoparticles ; },
abstract = {OBJECTIVE: The brain foreign body response (FBR) is an important process that limits the functionality of electrodes that comprise the brain-machine interface. Associated events in this process include leakage of the blood-brain barrier (BBB), reactive astrogliosis, recruitment and activation of microglia, and neuronal degeneration. Proper BBB function is also integral to maintaining neuronal health and function. Previous attempts to characterize BBB integrity have shown homogeneous leakage of macromolecules up to 10 nm in size. In this study, we describe a new method of measuring BBB permeability during the foreign body response in a mouse model.<br><br>APPROACH: Fluorescent nanoparticles were delivered via the tail vein into implant-bearing mice. Tissue sections were then analyzed using fluorescence microscopy to observe nanoparticles in the tissue. Gold nanoparticles were also used in conjunction with TEM to confirm the presence of nanoparticles in the brain parenchyma.<br><br>MAIN RESULTS: By using polymer nanoparticle tracers, which are significantly larger than conventional macromolecular tracers, we show near-implant BBB gaps of up to 500 nm in size that persist for at least 4 weeks after implantation. Further characterization of the BBB illustrates that leakage during the brain FBR is heterogeneous with gaps between at least 10 and 500 nm. Moreover, electron microscopy was used to confirm that the nanoparticle tracers enter into the brain parenchyma near chronic brain implants.<br><br>SIGNIFICANCE: Taken together, our findings demonstrate that the FBR-induced BBB leakage is characterized by larger gaps and is of longer duration than previously thought. This technique can be applied to examine the BBB in other disease states as well as during induced, transient, BBB opening.},
}
@article {pmid23337361,
year = {2013},
author = {Fruitet, J and Carpentier, A and Munos, R and Clerc, M},
title = {Automatic motor task selection via a bandit algorithm for a brain-controlled button.},
journal = {Journal of neural engineering},
volume = {10},
number = {1},
pages = {016012},
doi = {10.1088/1741-2560/10/1/016012},
pmid = {23337361},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Data Mining/methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on sensorimotor rhythms use a variety of motor tasks, such as imagining moving the right or left hand, the feet or the tongue. Finding the tasks that yield best performance, specifically to each user, is a time-consuming preliminary phase to a BCI experiment. This study presents a new adaptive procedure to automatically select (online) the most promising motor task for an asynchronous brain-controlled button.<br><br>APPROACH: We develop for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and design an EEG experiment to test our method. We compare (offline) the adaptive algorithm to a na&#xef;ve selection strategy which uses uniformly distributed samples from each task. We also run the adaptive algorithm online to fully validate the approach.<br><br>MAIN RESULTS: By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. More precisely, the offline analysis reveals that the use of this algorithm can reduce the time needed to select the most appropriate task by almost half without loss in precision, or alternatively, allow us to investigate twice the number of tasks within a similar time span. Online tests confirm that the method leads to an optimal task selection.<br><br>SIGNIFICANCE: This study is the first one to optimize the task selection phase by an adaptive procedure. By increasing the number of tasks that can be tested in a given time span, the proposed method could contribute to reducing 'BCI illiteracy'.},
}
@article {pmid23336819,
year = {2013},
author = {Zimmermann, R and Marchal-Crespo, L and Edelmann, J and Lambercy, O and Fluet, MC and Riener, R and Wolf, M and Gassert, R},
title = {Detection of motor execution using a hybrid fNIRS-biosignal BCI: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {10},
number = {},
pages = {4},
pmid = {23336819},
issn = {1743-0003},
mesh = {Adult ; Algorithms ; Autonomic Nervous System/physiology ; *Brain-Computer Interfaces ; Cues ; Data Interpretation, Statistical ; Feasibility Studies ; Female ; Fingers/innervation/physiology ; Heart Rate/physiology ; Hemodynamics/physiology ; Hemoglobins/metabolism ; Humans ; Isometric Contraction/physiology ; Male ; Markov Chains ; Models, Statistical ; Motor Cortex/metabolism/physiology ; Movement/physiology ; Respiratory Rate/physiology ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients.<br><br>METHODS: Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined.<br><br>RESULTS: fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062).<br><br>CONCLUSIONS: This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject's hemodynamic and physiological state.},
}
@article {pmid23336389,
year = {2013},
author = {Dupont, S and Rabinstein, AA},
title = {CT evaluation of lateral ventricular dilatation after subarachnoid hemorrhage: baseline bicaudate index values [correction of balues].},
journal = {Neurological research},
volume = {35},
number = {2},
pages = {103-106},
doi = {10.1179/1743132812Y.0000000121},
pmid = {23336389},
issn = {1743-1328},
mesh = {Age Factors ; *Cerebral Ventriculography ; Female ; Humans ; Hydrocephalus/complications/*diagnostic imaging ; Lateral Ventricles/*diagnostic imaging/*pathology ; Male ; Middle Aged ; Reference Values ; Severity of Illness Index ; Subarachnoid Hemorrhage/complications/*diagnostic imaging/pathology ; },
abstract = {OBJECTIVES: To define baseline bicaudate index (BCI) values in patients with subarachnoid hemorrhage (SAH).<br><br>METHODS: We reviewed the clinical and radiological information on consecutive adult patients admitted with acute SAH to our hospital between 1 January 2002 and 1 January 2008. Patients without diagnosis of acute hydrocephalus were entered into this study. Age-stratified BCI values were calculated.<br><br>RESULTS: Our study cohort comprised 108 patients (66 women, 61%). The clinical status at presentation was excellent with a median score of 1 on the World Federation of Neurological Surgeons Scale. Cisternal blood burden was mild to moderate with a median Hijdra score of 17 (out of 30). The upper limits of normal (ninety-fifth percentile) for BCI were 0&#xb7;12 at age 45 years and under, 0&#xb7;14 at 55 years, 0&#xb7;16 at 65 years, and 0&#xb7;17 thereafter.<br><br>CONCLUSION: Albeit not perfect, the BCI is a commonly used linear measure of the lateral ventricular size. We present baseline BCI values in a cohort of patients with acute SAH. A diagnosis of hydrocephalus can be made when the BCI value exceeds the upper limit of normal for age.},
}
@article {pmid23333985,
year = {2013},
author = {Lee, B and Liu, CY and Apuzzo, ML},
title = {A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.},
journal = {World neurosurgery},
volume = {79},
number = {3-4},
pages = {457-471},
doi = {10.1016/j.wneu.2013.01.078},
pmid = {23333985},
issn = {1878-8769},
mesh = {Amyotrophic Lateral Sclerosis/rehabilitation ; Biomedical Engineering/trends ; Brain-Computer Interfaces/*trends ; Cerebral Cortex/physiology ; Electronics ; Humans ; Nanotechnology ; Nervous System Diseases/*rehabilitation ; *Neural Prostheses ; Neurosurgery/trends ; Recovery of Function ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/rehabilitation ; Stroke Rehabilitation ; },
abstract = {Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology.},
}
@article {pmid23332844,
year = {2013},
author = {Dufour, S and Dohoo, IR},
title = {Monitoring herd incidence of intramammary infection in lactating cows using repeated longitudinal somatic cell count measurements.},
journal = {Journal of dairy science},
volume = {96},
number = {3},
pages = {1568-1580},
doi = {10.3168/jds.2012-5902},
pmid = {23332844},
issn = {1525-3198},
mesh = {Animals ; Cattle ; Cell Count/veterinary ; Female ; Incidence ; Lactation/physiology ; Longitudinal Studies ; Mastitis, Bovine/*diagnosis/epidemiology/microbiology/physiopathology ; Milk/*cytology ; },
abstract = {The objective of the study was to evaluate the ability of an estimate of the herd intramammary infection (IMI) incidence rate computed using repeated somatic cell count (SCC) measurements (quarter- and composite-SCC; hereafter, the SCC-derived herd IMI incidence, SCCI)to predict the incidence rate computed using repeated quarter-milk bacteriological culture (hereafter, bacteriological culture incidence, BCI) during the lactating period. A cohort of 91 Canadian dairy herds was followed in 2007 and 2008. In each herd and at each of 4 sampling periods, a series of 3 to 7 quarter-milk samples was collected from a sample of 15 cows. Routine milk bacteriological culture was conducted to identify IMI, SCC was measured on the quarter-milk samples, and composite-SCC of the preceding and following dairy herd improvement (DHI) tests were obtained. Mastitis pathogens were grouped in 3 categories: major, minor, and any pathogens. For each herd and for each period, BCI was computed for each group of organisms. Similarly, SCCI were computed using quarter- and DHI composite-SCC and using a threshold of 200,000 cells/mL to define infected quarters or cows. A linear regression model taking into account the structure of the data was used to compare the SCCI to the BCI. A similar model was used to compare fluctuations (i.e., changes from one sampling period to the next) over time of the SCCI and BCI. Measures of correlation between observed and predicted rates were computed and limits of agreement plots sketched to better explore the predictive ability of the SCCI. The quarter-milk SCC measurements that could be obtained-for instance, using on-line milking system measurements-appeared to be particularly valuable. Quarter-SCCI showed a positive and significant association with the BCI. However, limits of agreement plots indicated important disagreement for the small proportion of observations with very high BCI. Quarter-level SCCI and BCI fluctuations were also significantly associated, and a substantial correlation (Spearman rho ranging from 0.54 to 0.58) could be seen between observed and predicted rates. Conversely, the predictive value of composite-DHI SCC for monitoring IMI incidence during the lactation seemed to be quite limited. Composite SCCI was strictly associated with major IMI BCI, showed a relatively low correlation with the observed rate (Spearman rho: 0.14), and was of little help for longitudinal monitoring of the IMI incidence.},
}
@article {pmid23325145,
year = {2013},
author = {Park, SA and Hwang, HJ and Lim, JH and Choi, JH and Jung, HK and Im, CH},
title = {Evaluation of feature extraction methods for EEG-based brain-computer interfaces in terms of robustness to slight changes in electrode locations.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {51},
number = {5},
pages = {571-579},
pmid = {23325145},
issn = {1741-0444},
mesh = {Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/instrumentation/*methods ; Female ; Hand/physiology ; Humans ; Imagination ; Male ; Movement/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {To date, most EEG-based brain-computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test-retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.},
}
@article {pmid23322211,
year = {2014},
author = {Glannon, W},
title = {Neuromodulation, agency and autonomy.},
journal = {Brain topography},
volume = {27},
number = {1},
pages = {46-54},
doi = {10.1007/s10548-012-0269-3},
pmid = {23322211},
issn = {1573-6792},
mesh = {Brain/physiology ; Brain-Computer Interfaces/*psychology ; Cognition Disorders/psychology/therapy ; Deep Brain Stimulation/*psychology ; Humans ; *Neurofeedback ; Pain, Intractable/psychology/therapy ; *Personal Autonomy ; Quadriplegia/psychology/therapy ; *Self Efficacy ; },
abstract = {Neuromodulation consists in altering brain activity to restore mental and physical functions in individuals with neuropsychiatric disorders and brain and spinal cord injuries. This can be achieved by delivering electrical stimulation that excites or inhibits neural tissue, by using electrical signals in the brain to move computer cursors or robotic arms, or by displaying brain activity to subjects who regulate that activity by their own responses to it. As enabling prostheses, deep-brain stimulation and brain-computer interfaces (BCIs) are forms of extended embodiment that become integrated into the individual's conception of himself as an autonomous agent. In BCIs and neurofeedback, the success or failure of the techniques depends on the interaction between the learner and the trainer. The restoration of agency and autonomy through neuromodulation thus involves neurophysiological, psychological and social factors.},
}
@article {pmid23314027,
year = {2014},
author = {Emery, E and Balossier, A and Mertens, P},
title = {Is the medicolegal issue avoidable in neurosurgery? A retrospective survey of a series of 115 medicolegal cases from public hospitals.},
journal = {World neurosurgery},
volume = {81},
number = {2},
pages = {218-222},
doi = {10.1016/j.wneu.2013.01.029},
pmid = {23314027},
issn = {1878-8769},
mesh = {Central Nervous System Diseases/diagnosis/epidemiology/*surgery ; Data Collection ; Delayed Diagnosis/legislation &amp; jurisprudence ; France/epidemiology ; Hospitals, Public/legislation &amp; jurisprudence ; Humans ; Informed Consent/legislation &amp; jurisprudence ; Malpractice/*legislation &amp; jurisprudence ; Medical Staff, Hospital/legislation &amp; jurisprudence ; Neurosurgery/*legislation &amp; jurisprudence/standards ; Quality of Health Care ; Retrospective Studies ; Risk Factors ; Surgical Wound Infection/epidemiology ; },
abstract = {OBJECTIVE: Since the mid-1950s, neurosurgery has benefited from the remarkable progress due to tremendous advances in neuroimaging techniques, neuroanesthesia, neurostimulation, and brain-computer interfaces, as well as breakthroughs in operating microscopes and surgical instruments. Yet, this specialty has to do with delicate human structures and is hence considered as highly risky by insurance companies. In France, although neurosurgery's casualty rate (6%) is lower than in other specialties, the number of legal prosecutions has increased since 2002 because of easier access to medicolegal procedures. In order to avoid patients' resorting to the law courts, it becomes necessary to clearly identify the risk factors.<br><br>METHODS: From the data bank of the insurer Soci&#xe9;t&#xe9; Hospitali&#xe8;re d'Assurances Mutuelles (SHAM, main insurance company for public hospitals in France), we retrospectively analyzed 115 files (34 cranial and 81 spinal surgeries) covering the period 1997-2007 for the reasons for complaints against French neurosurgeons working in public hospitals.<br><br>RESULTS: Five main causes were identified: surgical site infection (37%), technical error (22%), lack of information (14%), delayed diagnosis (11%), and lack of supervision (9%).<br><br>CONCLUSION: Some causes are definitely avoidable at no cost to the hospital. Besides basic preventive safety procedures, we reiterate the mandatory steps for a good defense when being prosecuted. The evolution of patients' attitudes toward medical institutions observed in most countries has forced surgeons to adapt their practice. In this context, a common action certified by learned societies on sustainable health care quality, patient safety, and respect of good practices appears as the golden path to maintain a favorable legal, insurance, and financial environment.},
}
@article {pmid23313779,
year = {2013},
author = {Zhang, D and Song, H and Xu, R and Zhou, W and Ling, Z and Hong, B},
title = {Toward a minimally invasive brain-computer interface using a single subdural channel: a visual speller study.},
journal = {NeuroImage},
volume = {71},
number = {},
pages = {30-41},
doi = {10.1016/j.neuroimage.2012.12.069},
pmid = {23313779},
issn = {1095-9572},
mesh = {Adolescent ; *Brain-Computer Interfaces ; Child ; Electrodes, Implanted ; Epilepsy/surgery ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Motion Perception/physiology ; Young Adult ; },
abstract = {Electrocorticography (ECoG) has attracted increasing interest for implementing advanced brain-computer interfaces (BCIs) in the past decade. However, real-life application of ECoG BCI demands mitigation of its invasive nature by minimizing both the size of the involved brain regions and the number of implanted electrodes. In this study, we employed a recently proposed BCI paradigm that utilizes the attentional modulation of visual motion response. With ECoG data collected from five epilepsy patients, power increase of the high gamma (60-140Hz) frequency range was found to be associated with the overtly attended moving visual stimuli in the parietal-temporal-occipital junction and the occipital cortex. Event-related potentials (ERPs) were elicited as well but with broader cortical distribution. We achieved significantly higher BCI classification accuracy by employing both high gamma and ERP responses from a single ECoG electrode than by using ERP responses only (84.22±5.54% vs. 75.48±4.18%, p<0.005, paired t-test, 3-trial averaging, binary results of attended vs. unattended). More importantly, the high gamma responses were located within brain regions specialized in visual motion processing as mapped by fMRI, suggesting the spatial location for electrode implantation can be determined prior to surgery using non-invasive imaging. Our findings demonstrate the feasibility of implementing a minimally invasive ECoG BCI.},
}
@article {pmid23313076,
year = {2013},
author = {Keogh, MJ and Atkinson, S and Maniscalco, JM},
title = {Body condition and endocrine profiles of Steller sea lion (Eumetopias jubatus) pups during the early postnatal period.},
journal = {General and comparative endocrinology},
volume = {184},
number = {},
pages = {42-50},
doi = {10.1016/j.ygcen.2012.12.016},
pmid = {23313076},
issn = {1095-6840},
mesh = {Aldosterone/metabolism ; Animals ; Animals, Newborn ; Endocrine System/*metabolism ; Female ; Hydrocortisone/metabolism ; Leptin/metabolism ; Male ; Sea Lions/*metabolism ; Thyroxine/metabolism ; Triiodothyronine/metabolism ; },
abstract = {Body condition indices have been useful in assessing the health of domestic and free ranging populations of terrestrial mammals. Given the high energy demand and rapid growth during the postnatal period of Steller sea lion (Eumetopias jubatus) (SSL) pups, body condition was expected to be related to concentrations of a suite of hormones (cortisol, aldosterone, thyroxine, triiodothyronine, leptin) previously associated with changes in body mass and composition in developing pinnipeds. Blood samples were collected from free ranging SSL pups of known ages and sex. A body condition index (BCI) previously developed for SSL pups based on a mass and length relationship was applied to 61 SSL pups ranging in age from 5 to 38days old. BCI was not related to pup age. Overall, male pups were larger than females and older pups were larger than younger pups. Aldosterone was negatively correlated with BCI only in female pups, whereas no relationship was observed between aldosterone and BCI in males. Further, male pups had higher aldosterone concentrations than females. Concentrations of cortisol, total and free thyroxine (T4), and total triiodothyronine (T3) decreased when regressed against the elapsed time between researchers' arrival on the rookery and removal of pup from the holding corral for blood collection. While the overall variation attributed to the rookery disturbance was low (r(2)<0.293), it may be of significance for future studies on free ranging pinnipeds. This study adds to the current knowledge of the postnatal changes in free ranging SSL pups by providing ranges of the BCI and several hormone concentrations from an apparently stable population.},
}
@article {pmid23312631,
year = {2013},
author = {Wolpaw, JR},
title = {Brain-computer interfaces.},
journal = {Handbook of clinical neurology},
volume = {110},
number = {},
pages = {67-74},
doi = {10.1016/B978-0-444-52901-5.00006-X},
pmid = {23312631},
issn = {0072-9752},
support = {EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Humans ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {Brain-computer interfaces (BCIs) are systems that give their users communication and control capabilities that do not depend on muscles. The user's intentions are determined from activity recorded by electrodes on the scalp, on the cortical surface, or within the brain. BCIs can enable people who are paralyzed by amyotrophic lateral sclerosis (ALS), brainstem stroke, or other disorders to convey their needs and wishes to others, to operate word-processing programs or other software, or possibly to control a wheelchair or a neuroprosthesis. BCI technology might also augment rehabilitation protocols aimed at restoring useful motor function. With continued development and clinical implementation, BCIs could substantially improve the lives of those with severe disabilities.},
}
@article {pmid23304640,
year = {2012},
author = {Iosa, M and Morone, G and Fusco, A and Bragoni, M and Coiro, P and Multari, M and Venturiero, V and De Angelis, D and Pratesi, L and Paolucci, S},
title = {Seven capital devices for the future of stroke rehabilitation.},
journal = {Stroke research and treatment},
volume = {2012},
number = {},
pages = {187965},
pmid = {23304640},
issn = {2042-0056},
abstract = {Stroke is the leading cause of long-term disability for adults in industrialized societies. Rehabilitation's efforts are tended to avoid long-term impairments, but, actually, the rehabilitative outcomes are still poor. Novel tools based on new technologies have been developed to improve the motor recovery. In this paper, we have taken into account seven promising technologies that can improve rehabilitation of patients with stroke in the early future: (1) robotic devices for lower and upper limb recovery, (2) brain computer interfaces, (3) noninvasive brain stimulators, (4) neuroprostheses, (5) wearable devices for quantitative human movement analysis, (6) virtual reality, and (7) tablet-pc used for neurorehabilitation.},
}
@article {pmid23300606,
year = {2012},
author = {Manohar, A and Flint, RD and Knudsen, E and Moxon, KA},
title = {Decoding hindlimb movement for a brain machine interface after a complete spinal transection.},
journal = {PloS one},
volume = {7},
number = {12},
pages = {e52173},
pmid = {23300606},
issn = {1932-6203},
support = {R01 NS057419/NS/NINDS NIH HHS/United States ; NS057419/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Behavior, Animal ; Body Mass Index ; Brain/*physiology ; *Brain-Computer Interfaces ; Electrophysiology ; Forelimb/*physiology ; Gait/physiology ; Hindlimb/*physiology ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Neurons/cytology/*metabolism ; Rats ; Rats, Long-Evans ; Recovery of Function ; Spinal Cord/*physiology/surgery ; },
abstract = {Stereotypical locomotor movements can be made without input from the brain after a complete spinal transection. However, the restoration of functional gait requires descending modulation of spinal circuits to independently control the movement of each limb. To evaluate whether a brain-machine interface (BMI) could be used to regain conscious control over the hindlimb, rats were trained to press a pedal and the encoding of hindlimb movement was assessed using a BMI paradigm. Off-line, information encoded by neurons in the hindlimb sensorimotor cortex was assessed. Next neural population functions, or weighted representations of the neuronal activity, were used to replace the hindlimb movement as a trigger for reward in real-time (on-line decoding) in three conditions: while the animal could still press the pedal, after the pedal was removed and after a complete spinal transection. A novel representation of the motor program was learned when the animals used neural control to achieve water reward (e.g. more information was conveyed faster). After complete spinal transection, the ability of these neurons to convey information was reduced by more than 40%. However, this BMI representation was relearned over time despite a persistent reduction in the neuronal firing rate during the task. Therefore, neural control is a general feature of the motor cortex, not restricted to forelimb movements, and can be regained after spinal injury.},
}
@article {pmid23298784,
year = {2013},
author = {Azizi, SA},
title = {"I think therefore I am": new prospects for neural prostheses.},
journal = {Neuroscience letters},
volume = {538},
number = {},
pages = {1-2},
doi = {10.1016/j.neulet.2012.12.015},
pmid = {23298784},
issn = {1872-7972},
mesh = {*Brain-Computer Interfaces ; Female ; Humans ; Male ; },
abstract = {Three categories of neuro-prosthetics are being developed and used in increasing frequency to ameliorate neurologic disability. They are sensory receivers, motor effectors and brain 'stimulators'. The interfaces necessary to drive these devices are critical for their functioning. The following is a brief commentary on brain machine interface.},
}
@article {pmid23298746,
year = {2013},
author = {van Gerven, MA and Maris, E and Sperling, M and Sharan, A and Litt, B and Anderson, C and Baltuch, G and Jacobs, J},
title = {Decoding the memorization of individual stimuli with direct human brain recordings.},
journal = {NeuroImage},
volume = {70},
number = {},
pages = {223-232},
pmid = {23298746},
issn = {1095-9572},
support = {R21 NS067316/NS/NINDS NIH HHS/United States ; R01 MH055687/MH/NIMH NIH HHS/United States ; R21NS067316/NS/NINDS NIH HHS/United States ; R01MH55687/MH/NIMH NIH HHS/United States ; S10 RR031724/RR/NCRR NIH HHS/United States ; R01 NS048598/NS/NINDS NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiopathology ; Electroencephalography ; Epilepsy/*physiopathology ; Humans ; Memory, Short-Term/*physiology ; },
abstract = {Through decades of research, neuroscientists and clinicians have identified an array of brain areas that each activate when a person views a certain category of stimuli. However, we do not have a detailed understanding of how the brain represents individual stimuli within a category. Here we used direct human brain recordings and machine-learning algorithms to characterize the distributed patterns that distinguish specific cognitive states. Epilepsy patients with surgically implanted electrodes performed a working-memory task and we used machine-learning algorithms to predict the identity of each viewed stimulus. We found that the brain's representation of stimulus-specific information is distributed across neural activity at multiple frequencies, electrodes, and timepoints. Stimulus-specific neuronal activity was most prominent in the high-gamma (65-128 Hz) and theta/alpha (4-16 Hz) bands, but the properties of these signals differed significantly between individuals and for novel stimuli compared to common ones. Our findings are helpful for understanding the neural basis of memory and developing brain-computer interfaces by showing that the brain distinguishes specific cognitive states by diverse spatiotemporal patterns of neuronal.},
}
@article {pmid23290926,
year = {2013},
author = {Friedrich, EV and Scherer, R and Neuper, C},
title = {Long-term evaluation of a 4-class imagery-based brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {5},
pages = {916-927},
doi = {10.1016/j.clinph.2012.11.010},
pmid = {23290926},
issn = {1872-8952},
mesh = {Adult ; Biofeedback, Psychology ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Female ; Follow-Up Studies ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Time Factors ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: The study aimed to improve brain-computer interface (BCI)-usability by using distinct control strategies and evaluating performance, brain activity and psychological variables on a long-term basis over several months.<br><br>METHODS: Fourteen able-bodied users participated in 10 sessions, plus a follow-up session. Users were trained to control an EEG-based 4-class BCI with the mental tasks, word association, mental subtraction, spatial navigation, and motor imagery.<br><br>RESULTS: Eight users reached mean accuracies of 61-72% and managed to control all 4 classes above chance in single-sessions. Performance and brain patterns stayed stable over 10 weeks without training. Motor imagery showed the best performance and most distinct brain patterns. Participants' fear of incompetence decreased while the quality of their imagery and task ease increased over sessions. The evaluation of feedback differed between tasks and correlated with performance.<br><br>CONCLUSION: Users can control a real-time 4-class BCI, driven by distinct mental tasks, with stable performance over months. However, general performance was rather low for effective BCI control in daily life. Possibilities for future optimizations to increase performance are discussed.<br><br>SIGNIFICANCE: The evaluation of alternatives to motor imagery, long-term BCI use, and psychological variables is important to improve usability for mental imagery-based BCIs.},
}
@article {pmid23284889,
year = {2012},
author = {Schnitzer, SA and Mangan, SA and Dalling, JW and Baldeck, CA and Hubbell, SP and Ledo, A and Muller-Landau, H and Tobin, MF and Aguilar, S and Brassfield, D and Hernandez, A and Lao, S and Perez, R and Valdes, O and Yorke, SR},
title = {Liana abundance, diversity, and distribution on Barro Colorado Island, Panama.},
journal = {PloS one},
volume = {7},
number = {12},
pages = {e52114},
pmid = {23284889},
issn = {1932-6203},
mesh = {*Biodiversity ; *Ferns ; Islands ; Panama ; Plant Stems ; Reproduction ; Tropical Climate ; },
abstract = {Lianas are a key component of tropical forests; however, most surveys are too small to accurately quantify liana community composition, diversity, abundance, and spatial distribution - critical components for measuring the contribution of lianas to forest processes. In 2007, we tagged, mapped, measured the diameter, and identified all lianas ≥1 cm rooted in a 50-ha plot on Barro Colorado Island, Panama (BCI). We calculated liana density, basal area, and species richness for both independently rooted lianas and all rooted liana stems (genets plus clones). We compared spatial aggregation patterns of liana and tree species, and among liana species that varied in the amount of clonal reproduction. We also tested whether liana and tree densities have increased on BCI compared to surveys conducted 30-years earlier. This study represents the most comprehensive spatially contiguous sampling of lianas ever conducted and, over the 50 ha area, we found 67,447 rooted liana stems comprising 162 species. Rooted lianas composed nearly 25% of the woody stems (trees and lianas), 35% of woody species richness, and 3% of woody basal area. Lianas were spatially aggregated within the 50-ha plot and the liana species with the highest proportion of clonal stems more spatially aggregated than the least clonal species, possibly indicating clonal stem recruitment following canopy disturbance. Over the past 30 years, liana density increased by 75% for stems ≥1 cm diameter and nearly 140% for stems ≥5 cm diameter, while tree density on BCI decreased 11.5%; a finding consistent with other neotropical forests. Our data confirm that lianas contribute substantially to tropical forest stem density and diversity, they have highly clumped distributions that appear to be driven by clonal stem recruitment into treefall gaps, and they are increasing relative to trees, thus indicating that lianas will play a greater role in the future dynamics of BCI and other neotropical forests.},
}
@article {pmid23284276,
year = {2012},
author = {Canolty, RT and Ganguly, K and Carmena, JM},
title = {Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks.},
journal = {PLoS computational biology},
volume = {8},
number = {12},
pages = {e1002809},
pmid = {23284276},
issn = {1553-7358},
support = {K99 NS070627/NS/NINDS NIH HHS/United States ; 1K99NS070627-01A1/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Brain/cytology/*physiology ; Macaca mulatta ; Male ; Microelectrodes ; Multivariate Analysis ; Neurons/*physiology ; },
abstract = {Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks.},
}
@article {pmid23283643,
year = {2013},
author = {Niazi, IK and Jiang, N and Jochumsen, M and Nielsen, JF and Dremstrup, K and Farina, D},
title = {Detection of movement-related cortical potentials based on subject-independent training.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {51},
number = {5},
pages = {507-512},
pmid = {23283643},
issn = {1741-0444},
mesh = {Adolescent ; Adult ; Aged ; Algorithms ; Ankle Joint/physiology ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/physiology ; Learning ; Male ; Middle Aged ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Stroke/*physiopathology ; Young Adult ; },
abstract = {To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-related cortical potential (MRCP). EEG signals were recorded during ballistic ankle dorsiflexions executed (ME) or imagined (MI) by 20 healthy subjects, and attempted by five stroke subjects. These recordings were used to identify a template (as average over all subjects) for the initial negative phase of the MRCPs, after the application of an optimized spatial filtering used for pre-processing. Using this template, the detection accuracy (mean ± SD) calculated as true positive rate (estimated with leave-one-out procedure) for ME was 69 ± 21 and 58 ± 11 % on single trial basis for healthy and stroke subjects, respectively. This performance was similar to that obtained using an individual template for each subject, which led to accuracies of 71 ± 6 and 55 ± 12 % for healthy and stroke subjects, respectively. The detection accuracy for the MI data was 65 ± 22 % with the average template and 60 ± 13 % with the individual template. These results indicate the possibility of detecting movement intention without an individual training phase and without a significant loss in performance.},
}
@article {pmid23275858,
year = {2012},
author = {Andermann, ML and Kauramäki, J and Palomäki, T and Moore, CI and Hari, R and Jääskeläinen, IP and Sams, M},
title = {Brain state-triggered stimulus delivery: An efficient tool for probing ongoing brain activity.},
journal = {Open journal of neuroscience},
volume = {2},
number = {},
pages = {},
pmid = {23275858},
issn = {2075-9088},
support = {R01 HD040712/HD/NICHD NIH HHS/United States ; },
abstract = {What is the relationship between variability in ongoing brain activity preceding a sensory stimulus and subsequent perception of that stimulus? A challenge in the study of this key topic in systems neuroscience is the relative rarity of certain brain 'states'-left to chance, they may seldom align with sensory presentation. We developed a novel method for studying the influence of targeted brain states on subsequent perceptual performance by online identification of spatiotemporal brain activity patterns of interest, and brain-state triggered presentation of subsequent stimuli. This general method was applied to an electroencephalography study of human auditory selective listening. We obtained online, time-varying estimates of the instantaneous direction of neural bias (towards processing left or right ear sounds). Detection of target sounds was influenced by pre-target fluctuations in neural bias, within and across trials. We propose that brain state-triggered stimulus delivery will enable efficient, statistically tractable studies of rare patterns of ongoing activity in single neurons and distributed neural circuits, and their influence on subsequent behavioral and neural responses.},
}
@article {pmid23274645,
year = {2013},
author = {Philip, BA and Rao, N and Donoghue, JP},
title = {Simultaneous reconstruction of continuous hand movements from primary motor and posterior parietal cortex.},
journal = {Experimental brain research},
volume = {225},
number = {3},
pages = {361-375},
pmid = {23274645},
issn = {1432-1106},
support = {R01 NS025074/NS/NINDS NIH HHS/United States ; T32 MH020068/MH/NIMH NIH HHS/United States ; R01 NS025074-21A1/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Biomechanical Phenomena ; *Brain Mapping ; Feedback, Physiological ; Hand/*physiology ; Macaca mulatta ; Microelectrodes ; Motor Cortex/cytology/*physiology ; Movement/*physiology ; Neurons/physiology ; Parietal Lobe/cytology/*physiology ; Psychomotor Performance/physiology ; Statistics as Topic ; Time Factors ; },
abstract = {Primary motor cortex (MI) and parietal area PE both participate in cortical control of reaching actions, but few studies have been able to directly compare the form of kinematic encoding in the two areas simultaneously during hand tracking movements. To directly compare kinematic coding properties in these two areas under identical behavioral conditions, we recorded simultaneously from two chronically implanted multielectrode arrays in areas MI and PE (or areas 2/5) during performance of a continuous manual tracking task. Monkeys manually pursued a continuously moving target that followed a series of straight-line movement segments, arranged in a sequence where the direction (but not length) of the upcoming segment varied unpredictably as each new segment appeared. Based on recordings from populations of MI (31-143 units) and PE (22-87 units), we compared hand position and velocity reconstructions based on linear filters. We successfully reconstructed hand position and velocity from area PE (mean r (2) = 0.751 for position reconstruction, r (2) = 0.614 for velocity), demonstrating trajectory reconstruction from each area. Combing these populations provided no reconstruction improvements, suggesting that kinematic representations in MI and PE encode overlapping hand movement information, rather than complementary or unique representations. These overlapping representations may reflect the areas' common engagement in a sensorimotor feedback loop for error signals and movement goals, as required by a task with continuous, time-evolving demands and feedback. The similarity of information in both areas suggests that either area might provide a suitable target to obtain control signals for brain computer interface applications.},
}
@article {pmid23272508,
year = {2012},
author = {Manea, P and Ghiuru, R and Gavrilescu, MC and Munteanu, D},
title = {Long-term prognosis of polyvascular patients with associated chronic obstructive pulmonary disease.},
journal = {Revista medico-chirurgicala a Societatii de Medici si Naturalisti din Iasi},
volume = {116},
number = {3},
pages = {669-673},
pmid = {23272508},
issn = {0048-7848},
mesh = {Aged ; Aged, 80 and over ; Body Mass Index ; Cardiovascular Diseases/*complications/diagnosis/mortality/physiopathology/therapy ; Female ; Follow-Up Studies ; Humans ; Incidence ; Male ; Metabolic Syndrome/complications ; Middle Aged ; Patient Compliance ; Peripheral Vascular Diseases/complications ; Practice Guidelines as Topic ; Prognosis ; Pulmonary Disease, Chronic Obstructive/*complications/diagnosis/mortality/physiopathology/therapy ; Quality of Life ; Risk Factors ; Romania/epidemiology ; Smoking/adverse effects ; Spirometry ; Survival Rate ; },
abstract = {OBJECTIVE: To evaluate by various tools the prognosis of the polyvascular patients (defined as the presence of more than one affected vascular bed), who also associate chronic obstructive pulmonary disease.<br><br>MATERIAL AND METHODS: Fifty-eight patients discharged after an episode of acute cardiorespiratory failure were examined at 3 month-intervals for 1, 2 and 3 years (2010-2012). The following were performed: physical examination, biochemical and hematological tests, spirometry, electrocardiography, transthoracic echocardiography, brain computer tomography or magnetic resonance imaging. All patients in our study were smokers with chronic pulmonary obstructive disease. Treatment relied on the European recommendations for cardiac pathology and associated medical conditions.<br><br>RESULTS: A favorable clinical course was noticed in compliant patients. Patients with metabolic syndrome and/or old stroke, and peripheral arterial disease have a poor prognosis. A strong link seems to exist between systolic function of the right ventricle and cardiovascular mortality. The association of this condition to ischemic heart disease modifies the right ventricle hemodynamics.<br><br>CONCLUSIONS: Polyvascular patients in acute cardiorespiratory failure have a mortality of 36% in the first 3 weeks. After 3 years, 86% of the patients survive. The modern methods of diagnosis and treatment allow improving the quality of life and increasing its duration.},
}
@article {pmid23271991,
year = {2012},
author = {Sacchet, MD and Mellinger, J and Sitaram, R and Braun, C and Birbaumer, N and Fetz, E},
title = {Volitional control of neuromagnetic coherence.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {189},
pmid = {23271991},
issn = {1662-453X},
support = {P51 RR000166/RR/NCRR NIH HHS/United States ; },
abstract = {Coherence of neural activity between circumscribed brain regions has been implicated as an indicator of intracerebral communication in various cognitive processes. While neural activity can be volitionally controlled with neurofeedback, the volitional control of coherence has not yet been explored. Learned volitional control of coherence could elucidate mechanisms of associations between cortical areas and its cognitive correlates and may have clinical implications. Neural coherence may also provide a signal for brain-computer interfaces (BCI). In the present study we used the Weighted Overlapping Segment Averaging method to assess coherence between bilateral magnetoencephalograph sensors during voluntary digit movement as a basis for BCI control. Participants controlled an onscreen cursor, with a success rate of 124 of 180 (68.9%, sign-test p < 0.001) and 84 out of 100 (84%, sign-test p < 0.001). The present findings suggest that neural coherence may be volitionally controlled and may have specific behavioral correlates.},
}
@article {pmid23268384,
year = {2013},
author = {Akce, A and Johnson, M and Dantsker, O and Bretl, T},
title = {A brain-machine interface to navigate a mobile robot in a planar workspace: enabling humans to fly simulated aircraft with EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {2},
pages = {306-318},
doi = {10.1109/TNSRE.2012.2233757},
pmid = {23268384},
issn = {1558-0210},
mesh = {*Aircraft ; Algorithms ; Brain Mapping/methods ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; *Man-Machine Systems ; Robotics/*methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper presents an interface for navigating a mobile robot that moves at a fixed speed in a planar workspace, with noisy binary inputs that are obtained asynchronously at low bit-rates from a human user through an electroencephalograph (EEG). The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for a mobile robot. The underlying problem is then to design a communication protocol by which the user can, with vanishing error probability, specify a string in this language using a sequence of inputs. Such a protocol, provided by tools from information theory, relies on a human user's ability to compare smooth curves, just like they can compare strings of text. We demonstrate our interface by performing experiments in which twenty subjects fly a simulated aircraft at a fixed speed and altitude with input only from EEG. Experimental results show that the majority of subjects are able to specify desired paths despite a wide range of errors made in decoding EEG signals.},
}
@article {pmid23114485,
year = {2012},
author = {Clancy, K and Velopulos, C and Bilaniuk, JW and Collier, B and Crowley, W and Kurek, S and Lui, F and Nayduch, D and Sangosanya, A and Tucker, B and Haut, ER and , },
title = {Screening for blunt cardiac injury: an Eastern Association for the Surgery of Trauma practice management guideline.},
journal = {The journal of trauma and acute care surgery},
volume = {73},
number = {5 Suppl 4},
pages = {S301-6},
doi = {10.1097/TA.0b013e318270193a},
pmid = {23114485},
issn = {2163-0763},
support = {K08 1K08HS017952-01/HS/AHRQ HHS/United States ; },
mesh = {Electrocardiography ; Heart Injuries/*diagnosis/diagnostic imaging/physiopathology ; Humans ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Troponin I/blood ; Wounds, Nonpenetrating/*diagnosis/diagnostic imaging/physiopathology ; },
abstract = {BACKGROUND: Diagnosing blunt cardiac injury (BCI) can be difficult. Many patients with mechanism for BCI are admitted to the critical care setting based on associated injuries; however, debate surrounds those patients who are hemodynamically stable and do not otherwise require a higher level of care. To allow safe discharge home or admission to a nonmonitored setting, BCI should be definitively ruled out in those at risk.<br><br>METHODS: This Eastern Association for the Surgery of Trauma (EAST) practice management guideline (PMG) updates the original from 1998. English-language citations were queried for BCI from March 1997 through December 2011, using the PubMed Entrez interface. Of 599 articles identified, prospective or retrospective studies examining BCI were selected. Each article was reviewed by two members of the EAST BCI PMG workgroup. Data were collated, and a consensus was obtained for the recommendations.<br><br>RESULTS: We identified 35 institutional studies evaluating the diagnosis of adult patients with suspected BCI. This PMG has 10 total recommendations, including two Level 2 updates, two upgrades from Level 3 to Level 2, and three new recommendations.<br><br>CONCLUSION: Electrocardiogram (ECG) alone is not sufficient to rule out BCI. Based on four studies showing that the addition of troponin I to ECG improved the negative predictive value to 100%, we recommend obtaining an admission ECG and troponin I from all patients in whom BCI is suspected. BCI can be ruled out only if both ECG result and troponin I level are normal, a significant change from the previous guideline. Patients with new ECG changes and/or elevated troponin I should be admitted for monitoring. Echocardiogram is not beneficial as a screening tool for BCI and should be reserved for patients with hypotension and/or arrhythmias. The presence of a sternal fracture alone does not predict BCI. Cardiac computed tomography or magnetic resonance imaging can be used to differentiate acute myocardial infarction from BCI in trauma patients.},
}
@article {pmid23267312,
year = {2012},
author = {Hill, NJ and Moinuddin, A and Häuser, AK and Kienzle, S and Schalk, G},
title = {Communication and control by listening: toward optimal design of a two-class auditory streaming brain-computer interface.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {181},
pmid = {23267312},
issn = {1662-453X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {Most brain-computer interface (BCI) systems require users to modulate brain signals in response to visual stimuli. Thus, they may not be useful to people with limited vision, such as those with severe paralysis. One important approach for overcoming this issue is auditory streaming, an approach whereby a BCI system is driven by shifts of attention between two simultaneously presented auditory stimulus streams. Motivated by the long-term goal of translating such a system into a reliable, simple yes-no interface for clinical usage, we aim to answer two main questions. First, we asked which of two previously published variants provides superior performance: a fixed-phase (FP) design in which the streams have equal period and opposite phase, or a drifting-phase (DP) design where the periods are unequal. We found FP to be superior to DP (p = 0.002): average performance levels were 80 and 72% correct, respectively. We were also able to show, in a pilot with one subject, that auditory streaming can support continuous control and neurofeedback applications: by shifting attention between ongoing left and right auditory streams, the subject was able to control the position of a paddle in a computer game. Second, we examined whether the system is dependent on eye movements, since it is known that eye movements and auditory attention may influence each other, and any dependence on the ability to move one's eyes would be a barrier to translation to paralyzed users. We discovered that, despite instructions, some subjects did make eye movements that were indicative of the direction of attention. However, there was no correlation, across subjects, between the reliability of the eye movement signal and the reliability of the BCI system, indicating that our system was configured to work independently of eye movement. Together, these findings are an encouraging step forward toward BCIs that provide practical communication and control options for the most severely paralyzed users.},
}
@article {pmid23264217,
year = {2012},
author = {Reszec, J and Hermanowicz, A and Kochanowicz, J and Turek, G and Mariak, Z and Chyczewski, L},
title = {Mast cells evaluation in meningioma of various grades.},
journal = {Folia histochemica et cytobiologica},
volume = {50},
number = {4},
pages = {542-546},
doi = {10.5603/14744},
pmid = {23264217},
issn = {1897-5631},
mesh = {Adult ; Aged ; Aged, 80 and over ; Cell Nucleus/pathology ; Humans ; Mast Cells/enzymology/*pathology ; Meningeal Neoplasms/enzymology/*pathology ; Meningioma/enzymology/*pathology ; Middle Aged ; Neoplasm Grading ; Tryptases/metabolism ; Young Adult ; },
abstract = {Meningioma is a heterogenous group of primary brain tumors. The progression or recurrence is relatively very common; however, prognostic factors which may indicate those events are not known. The aim of the study was to evaluate the presence of mast cells within the low grade and high grade meningiomas. The material included 70 cases of meningomas (63 G1 grade cases) of adult subjects (range 23-84 years). In paraffin sections presence of tryptase, a marker of mast cells, was detected by immunohistochemistry in 10 random fields in each slide under the light microscope. The presence of the peritumoral oedema was estimated by brain computer tomography. The expression of tryptase was observed in 32% of low grade meningiomas and 86% of high grade meningiomas. The immunostained cells were observed close to the blood vessels. We conclude that the number of mast cells might be a significant prognostic factor for the recurrence or bad prognosis of meningiomas.},
}
@article {pmid23263902,
year = {2013},
author = {Jebari, K and Hansson, SO},
title = {European public deliberation on brain machine interface technology: five convergence seminars.},
journal = {Science and engineering ethics},
volume = {19},
number = {3},
pages = {1071-1086},
pmid = {23263902},
issn = {1471-5546},
mesh = {Brain-Computer Interfaces/*ethics ; *Community Participation ; Congresses as Topic ; Europe ; Group Processes ; Humans ; Technology/*ethics ; },
abstract = {We present a novel procedure to engage the public in ethical deliberations on the potential impacts of brain machine interface technology. We call this procedure a convergence seminar, a form of scenario-based group discussion that is founded on the idea of hypothetical retrospection. The theoretical background of this procedure and the results of five seminars are presented.},
}
@article {pmid23258865,
year = {2012},
author = {},
title = {Breakthrough of the year. The runners-up.},
journal = {Science (New York, N.Y.)},
volume = {338},
number = {6114},
pages = {1525-1532},
doi = {10.1126/science.338.6114.1525},
pmid = {23258865},
issn = {1095-9203},
mesh = {Animals ; Brain-Computer Interfaces ; Crystallography, X-Ray ; Elementary Particles ; Embryonic Stem Cells ; Fossils ; Genetic Engineering ; Genome, Human ; Genomics ; Hominidae/genetics ; Humans ; Lasers ; Mars ; Oocytes/cytology ; Protein Conformation ; Protozoan Proteins/chemistry ; *Science ; Sequence Analysis, DNA ; Spacecraft ; Trypanosoma brucei brucei/enzymology ; },
}
@article {pmid23253623,
year = {2013},
author = {Collinger, JL and Wodlinger, B and Downey, JE and Wang, W and Tyler-Kabara, EC and Weber, DJ and McMorland, AJ and Velliste, M and Boninger, ML and Schwartz, AB},
title = {High-performance neuroprosthetic control by an individual with tetraplegia.},
journal = {Lancet (London, England)},
volume = {381},
number = {9866},
pages = {557-564},
pmid = {23253623},
issn = {1474-547X},
support = {UL1 TR000005/TR/NCATS NIH HHS/United States ; RC1 NS070311/NS/NINDS NIH HHS/United States ; 8KL2TR000146-07/TR/NCATS NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; KL2 TR000146/TR/NCATS NIH HHS/United States ; },
mesh = {Arm ; *Artificial Limbs ; *Brain-Computer Interfaces ; Female ; Hand Strength ; Humans ; Microelectrodes ; Middle Aged ; Psychomotor Performance ; Quadriplegia/*therapy ; },
abstract = {BACKGROUND: Paralysis or amputation of an arm results in the loss of the ability to orient the hand and grasp, manipulate, and carry objects, functions that are essential for activities of daily living. Brain-machine interfaces could provide a solution to restoring many of these lost functions. We therefore tested whether an individual with tetraplegia could rapidly achieve neurological control of a high-performance prosthetic limb using this type of an interface.<br><br>METHODS: We implanted two 96-channel intracortical microelectrodes in the motor cortex of a 52-year-old individual with tetraplegia. Brain-machine-interface training was done for 13 weeks with the goal of controlling an anthropomorphic prosthetic limb with seven degrees of freedom (three-dimensional translation, three-dimensional orientation, one-dimensional grasping). The participant's ability to control the prosthetic limb was assessed with clinical measures of upper limb function. This study is registered with ClinicalTrials.gov, NCT01364480.<br><br>FINDINGS: The participant was able to move the prosthetic limb freely in the three-dimensional workspace on the second day of training. After 13 weeks, robust seven-dimensional movements were performed routinely. Mean success rate on target-based reaching tasks was 91&#xb7;6% (SD 4&#xb7;4) versus median chance level 6&#xb7;2% (95% CI 2&#xb7;0-15&#xb7;3). Improvements were seen in completion time (decreased from a mean of 148 s [SD 60] to 112 s [6]) and path efficiency (increased from 0&#xb7;30 [0&#xb7;04] to 0&#xb7;38 [0&#xb7;02]). The participant was also able to use the prosthetic limb to do skilful and coordinated reach and grasp movements that resulted in clinically significant gains in tests of upper limb function. No adverse events were reported.<br><br>INTERPRETATION: With continued development of neuroprosthetic limbs, individuals with long-term paralysis could recover the natural and intuitive command signals for hand placement, orientation, and reaching, allowing them to perform activities of daily living.<br><br>FUNDING: Defense Advanced Research Projects Agency, National Institutes of Health, Department of Veterans Affairs, and UPMC Rehabilitation Institute.},
}
@article {pmid23253622,
year = {2013},
author = {Courtine, G and Micera, S and DiGiovanna, J and Millán, Jdel R},
title = {Brain-machine interface: closer to therapeutic reality?.},
journal = {Lancet (London, England)},
volume = {381},
number = {9866},
pages = {515-517},
doi = {10.1016/S0140-6736(12)62164-3},
pmid = {23253622},
issn = {1474-547X},
mesh = {*Artificial Limbs ; *Brain-Computer Interfaces ; Female ; Humans ; Quadriplegia/*therapy ; },
}
@article {pmid23250668,
year = {2013},
author = {Farquhar, J and Hill, NJ},
title = {Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.},
journal = {Neuroinformatics},
volume = {11},
number = {2},
pages = {175-192},
pmid = {23250668},
issn = {1559-0089},
mesh = {Brain/*physiology ; *Brain Mapping ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/methods/*standards ; Evoked Potentials/*physiology ; Guidelines as Topic/*standards ; Humans ; Signal Processing, Computer-Assisted ; Spectrum Analysis ; },
abstract = {Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.},
}
@article {pmid23248586,
year = {2012},
author = {Tessadori, J and Bisio, M and Martinoia, S and Chiappalone, M},
title = {Modular neuronal assemblies embodied in a closed-loop environment: toward future integration of brains and machines.},
journal = {Frontiers in neural circuits},
volume = {6},
number = {},
pages = {99},
pmid = {23248586},
issn = {1662-5110},
abstract = {Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3-8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: "random" and "modular" populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction.},
}
@article {pmid23248335,
year = {2013},
author = {Hsu, WY},
title = {Embedded prediction in feature extraction: application to single-trial EEG discrimination.},
journal = {Clinical EEG and neuroscience},
volume = {44},
number = {1},
pages = {31-38},
doi = {10.1177/1550059412456094},
pmid = {23248335},
issn = {1550-0594},
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Fractals ; *Fuzzy Logic ; Humans ; *Models, Neurological ; Predictive Value of Tests ; },
abstract = {In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification.},
}
@article {pmid23246631,
year = {2013},
author = {Rehman, T and Rehman, Au and Ali, R and Rehman, A and Bashir, H and Ahmed Bhimani, S and Tran, H and Khan, S},
title = {A radiographic analysis of ventricular trajectories.},
journal = {World neurosurgery},
volume = {80},
number = {1-2},
pages = {173-178},
doi = {10.1016/j.wneu.2012.12.012},
pmid = {23246631},
issn = {1878-8769},
mesh = {Catheterization ; Cerebral Ventriculography/*methods ; Cerebrospinal Fluid/physiology ; Computer Simulation ; Humans ; Hydrocephalus/surgery ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Lateral Ventricles/anatomy &amp; histology/diagnostic imaging/surgery ; Neurosurgical Procedures/*methods ; Skull/*diagnostic imaging ; Software ; Third Ventricle/anatomy &amp; histology/diagnostic imaging/surgery ; Tomography, X-Ray Computed ; User-Computer Interface ; Ventriculostomy/*methods ; },
abstract = {BACKGROUND: The prevalent method of ventriculostomy placement is via freehand insertion to cannulate the ventricle at a 90° angle to the skull to get ideal placement. Our goal was to test the validity of this practice in patients without midline shift and with normal ventricular size.<br><br>METHODS: This study was a virtual radiographic analysis of 3-dimensional data of skull and ventricular anatomy. Data were collected using thin-cut (1-mm) computed tomography scans of 101 randomly selected patients with normal ventricular anatomy. Virtual ventriculostomy trajectories were determined for entry from the right and left sides separately, going in at a 90&#xb0; angle to the skull. Three-dimensional multiplanar reconstructions were performed using Osirix software to see where the catheter would end up within the brain.<br><br>RESULTS: In our patient population, the mean bicaudate index was 0.14. Of the 202 perpendicular lines created from Kocher's point into the brain, 67.8% (137) of the virtual lines passed through the ipsilateral frontal horn of the lateral ventricle, 20.8% (42) passed through the contralateral ventricle, and 10.4% (21) did not pass through a ventricular space. A lower bicaudate index also leads to a greater misplacement even with a perpendicular trajectory. Pushing a catheter beyond an entry length of 6.5 cm if no cerebrospinal fluid flow has been obtained will not result in ipsilateral ventricular catheterization.<br><br>CONCLUSIONS: Our study concludes that not all catheters passed through Kocher's point using a perpendicular trajectory will end up in the ipsilateral frontal horn, and almost 10% of these catheters will be in a nonventricular space. In the instance in which a freehand pass fails to cannulate a ventricle, the safest alternative would be to make only minor adjustments to the perpendicular angle.},
}
@article {pmid23246415,
year = {2013},
author = {Kaufmann, T and Schulz, SM and Köblitz, A and Renner, G and Wessig, C and Kübler, A},
title = {Face stimuli effectively prevent brain-computer interface inefficiency in patients with neurodegenerative disease.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {5},
pages = {893-900},
doi = {10.1016/j.clinph.2012.11.006},
pmid = {23246415},
issn = {1872-8952},
mesh = {Adult ; Brain/physiology ; *Brain-Computer Interfaces/psychology ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Face/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Task Performance and Analysis ; *User-Computer Interface ; Visual Perception/physiology ; Young Adult ; },
abstract = {OBJECTIVES: Recently, we proposed a new stimulation paradigm for brain computer interfaces (BCI) based on event-related potentials (ERP), i.e. flashing characters with superimposed pictures of well-known faces. This new face flashing (FF) paradigm significantly outperformed the commonly used character flashing (CF) approach, i.e. simply highlighting characters.<br><br>METHODS: In the current study we assessed the impact of face stimuli on BCI inefficiency in patients with neurodegenerative disease, i.e. on their inability to communicate by means of a BCI. Healthy participants (N = 16) and those with neurodegenerative disease (N = 9) performed spelling tasks using CF and FF paradigms.<br><br>RESULTS: Online performance with FF was significantly increased as compared to CF in both, healthy and impaired users. Importantly, two patients who were classified "highly inefficient" with the classic CF stimulation were able to spell with high accuracy using FF. Our results particularly emphasize great benefit of the FF paradigm for those users displaying low signal-to-noise ratio of the recorded ERPs in the classic stimulation approach.<br><br>CONCLUSION: In conclusion, we confirm previously reported results now systematically validated in an online setting and display specifically beneficial effects of FF for motor-impaired users.<br><br>SIGNIFICANCE: The FF paradigm thus constitutes a big step forward against the BCI inefficiency phenomenon.},
}
@article {pmid23237418,
year = {2013},
author = {Smits-Bandstra, S and De Nil, LF},
title = {Early-stage chunking of finger tapping sequences by persons who stutter and fluent speakers.},
journal = {Clinical linguistics &amp; phonetics},
volume = {27},
number = {1},
pages = {72-84},
doi = {10.3109/02699206.2012.746397},
pmid = {23237418},
issn = {1464-5076},
mesh = {Efferent Pathways/physiology ; Fingers/*physiology ; Humans ; Learning/physiology ; Male ; Memory/physiology ; Motor Skills/*physiology ; Movement/physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Speech/*physiology ; Speech Therapy ; Stuttering/*physiopathology/therapy ; },
abstract = {This research note explored the hypothesis that chunking differences underlie the slow finger-tap sequencing performance reported in the literature for persons who stutter (PWS) relative to fluent speakers (PNS). Early-stage chunking was defined as an immediate and spontaneous tendency to organize a long sequence into pauses, for motor planning, and chunks of fluent motor performance. A previously published study in which 12 PWS and 12 matched PNS practised a 10-item finger tapping sequence 30 times was examined. Both groups significantly decreased the duration of between-chunk intervals (BCIs) and within-chunk intervals (WCIs) over practice. PNS had significantly shorter WCIs relative to PWS, but minimal differences between groups were found for the number of, or duration of, BCI. Results imply that sequencing differences found between PNS and PWS may be due to differences in automatizing movements within chunks or retrieving chunks from memory rather than chunking per se.},
}
@article {pmid23236433,
year = {2012},
author = {Spüler, M and Rosenstiel, W and Bogdan, M},
title = {Online adaptation of a c-VEP Brain-computer Interface(BCI) based on error-related potentials and unsupervised learning.},
journal = {PloS one},
volume = {7},
number = {12},
pages = {e51077},
pmid = {23236433},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Learning/*physiology ; Male ; *User-Computer Interface ; },
abstract = {The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and increase performance. We compare two different approaches for online adaptation of the system: an unsupervised method and a method that uses the detection of error-related potentials. Both approaches were tested in an online study, in which an average accuracy of 96% was achieved with adaptation based on error-related potentials. This accuracy corresponds to an average information transfer rate of 144 bit/min, which is the highest bitrate reported so far for a non-invasive BCI. In a free-spelling mode, the subjects were able to write with an average of 21.3 error-free letters per minute, which shows the feasibility of the BCI system in a normal-use scenario. In addition we show that a calibration of the BCI system solely based on the detection of error-related potentials is possible, without knowing the true class labels.},
}
@article {pmid23234797,
year = {2013},
author = {Thompson, DE and Warschausky, S and Huggins, JE},
title = {Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy.},
journal = {Journal of neural engineering},
volume = {10},
number = {1},
pages = {016006},
pmid = {23234797},
issn = {1741-2552},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Brain/*physiology ; Brain-Computer Interfaces/*classification/*standards ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Reaction Time/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) that detect event-related potentials (ERPs) rely on classification schemes that are vulnerable to latency jitter, a phenomenon known to occur with ERPs such as the P300 response. The objective of this work was to investigate the role that latency jitter plays in BCI classification.<br><br>APPROACH: We developed a novel method, classifier-based latency estimation (CBLE), based on a generalization of Woody filtering. The technique works by presenting the time-shifted data to the classifier, and using the time shift that corresponds to the maximal classifier score.<br><br>MAIN RESULTS: The variance of CBLE estimates correlates significantly (p < 10(-42)) with BCI accuracy in the Farwell-Donchin BCI paradigm. Additionally, CBLE predicts same-day accuracy, even from small datasets or datasets that have already been used for classifier training, better than the accuracy on the small dataset (p < 0.05). The technique should be relatively classifier-independent, and the results were confirmed on two linear classifiers.<br><br>SIGNIFICANCE: The results suggest that latency jitter may be an important cause of poor BCI performance, and methods that correct for latency jitter may improve that performance. CBLE can also be used to decrease the amount of data needed for accuracy estimation, allowing research on effects with shorter timescales.},
}
@article {pmid23234760,
year = {2013},
author = {Aleem, I and Chau, T},
title = {Towards a hemodynamic BCI using transcranial Doppler without user-specific training data.},
journal = {Journal of neural engineering},
volume = {10},
number = {1},
pages = {016005},
doi = {10.1088/1741-2560/10/1/016005},
pmid = {23234760},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces/statistics &amp; numerical data/trends ; Clinical Competence/standards ; Female ; Hemodynamics/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Ultrasonography, Doppler, Transcranial/*methods/trends ; Young Adult ; },
abstract = {UNLABELLED: Transcranial Doppler (TCD) was recently introduced as a new brain-computer interface (BCI) modality for detecting task-induced hemispheric lateralization. To date, single-trial discrimination between a lateralized mental activity and a rest state has been demonstrated with long (45 s) activation time periods. However, the possibility of detecting successive activations in a user-independent framework (i.e. without training data from the user) remains an open question.<br><br>OBJECTIVE: The objective of this research was to assess TCD-based detection of lateralized mental activity with a user-independent classifier. In so doing, we also investigated the accuracy of detecting successive lateralizations. Approach. TCD data from 18 participants were collected during verbal fluency, mental rotation tasks and baseline counting tasks. Linear discriminant analysis and a set of four time-domain features were used to classify successive left and right brain activations.<br><br>MAIN RESULTS: In a user-independent framework, accuracies up to 74.6 &#xb1; 12.6% were achieved using training data from a single participant, and lateralization task durations of 18 s.<br><br>SIGNIFICANCE: Subject-independent, algorithmic classification of TCD signals corresponding to successive brain lateralization may be a feasible paradigm for TCD-BCI design.},
}
@article {pmid23234494,
year = {2012},
author = {Purmer, IM and van Iperen, EP and Beenen, LF and Kuiper, MJ and Binnekade, JM and Vandertop, PW and Schultz, MJ and Horn, J},
title = {Brain computer tomography in critically ill patients--a prospective cohort study.},
journal = {BMC medical imaging},
volume = {12},
number = {},
pages = {34},
pmid = {23234494},
issn = {1471-2342},
mesh = {Brain/diagnostic imaging/surgery ; Brain Diseases/*diagnostic imaging/*epidemiology/surgery ; Cohort Studies ; Critical Care/methods/*statistics &amp; numerical data ; Critical Illness/*epidemiology ; Female ; Humans ; Male ; Middle Aged ; Netherlands/epidemiology ; Postoperative Complications/*diagnostic imaging/*epidemiology ; Prevalence ; Prospective Studies ; Risk Factors ; Tomography, X-Ray Computed/*statistics &amp; numerical data ; Treatment Outcome ; },
abstract = {BACKGROUND: Brain computer tomography (brain CT) is an important imaging tool in patients with intracranial disorders. In ICU patients, a brain CT implies an intrahospital transport which has inherent risks. The proceeds and consequences of a brain CT in a critically ill patient should outweigh these risks. The aim of this study was to critically evaluate the diagnostic and therapeutic yield of brain CT in ICU patients.<br><br>METHODS: In a prospective observational study data were collected during one year on the reasons to request a brain CT, expected abnormalities, abnormalities found by the radiologist and consequences for treatment. An "expected abnormality" was any finding that had been predicted by the physician requesting the brain CT. A brain CT was "diagnostically positive", if the abnormality found was new or if an already known abnormality was increased. It was "diagnostically negative" if an already known abnormality was unchanged or if an expected abnormality was not found. The treatment consequences of the brain CT, were registered as "treatment as planned", "treatment changed, not as planned", "treatment unchanged".<br><br>RESULTS: Data of 225 brain CT in 175 patients were analyzed. In 115 (51%) brain CT the abnormalities found were new or increased known abnormalities. 115 (51%) brain CT were found to be diagnostically positive. In the medical group 29 (39%) of brain CT were positive, in the surgical group 86 (57%), p 0.01. After a positive brain CT, in which the expected abnormalities were found, treatment was changed as planned in 33%, and in 19% treatment was changed otherwise than planned.<br><br>CONCLUSIONS: The results of this study show that the diagnostic and therapeutic yield of brain CT in critically ill patients is moderate. The development of guidelines regarding the decision rules for performing a brain CT in ICU patients is needed.},
}
@article {pmid23231985,
year = {2012},
author = {Farina, D and Negro, F},
title = {Accessing the neural drive to muscle and translation to neurorehabilitation technologies.},
journal = {IEEE reviews in biomedical engineering},
volume = {5},
number = {},
pages = {3-14},
doi = {10.1109/RBME.2012.2183586},
pmid = {23231985},
issn = {1941-1189},
mesh = {Animals ; Biomedical Engineering/*methods ; Electromyography/*methods ; Humans ; Motor Activity/physiology ; Motor Neurons/*physiology ; Muscle, Skeletal/*physiology ; Neurosciences ; Rehabilitation/*methods ; },
abstract = {This review describes methods for interfacing motor neurons from muscle recordings and their applications in studies on the neural control of movement and in the design of technologies for neurorehabilitation. After describing methods for accessing the neural drive to muscles in vivo in humans, we discuss the mechanisms of transmission of synaptic input into motor neuron output and of force generation. The synaptic input received by a motor neuron population is largely common among motor neurons. This allows linear transmission of the input and a reduced dimensionality of control by the central nervous system. Force is generated by low-pass filtering the neural signal sent to the muscle. These concepts on neural control of movement are used for the development of neurorehabilitation technologies, which are discussed with representative examples on movement replacement, restoration, and neuromodulation. It is concluded that the analysis of the output of spinal motor neurons from muscle signals provides a unique means for understanding the neural coding of movement in vivo in humans and thus for reproducing this code artificially with the aim of restoring lost or impaired motor functions.},
}
@article {pmid23230794,
year = {2012},
author = {Pogue, D},
title = {Remote control in your mind. Forget voice control or gesture recognition. Gadgets may soon link directly to our brain.},
journal = {Scientific American},
volume = {307},
number = {6},
pages = {32},
pmid = {23230794},
issn = {0036-8733},
mesh = {Brain-Computer Interfaces/*trends ; Gestures ; Humans ; Speech Recognition Software ; },
}
@article {pmid23227167,
year = {2012},
author = {Li, Y and Long, J and He, L and Lu, H and Gu, Z and Sun, P},
title = {A sparse representation-based algorithm for pattern localization in brain imaging data analysis.},
journal = {PloS one},
volume = {7},
number = {12},
pages = {e50332},
pmid = {23227167},
issn = {1932-6203},
mesh = {*Algorithms ; Brain/*physiology ; Humans ; Magnetic Resonance Imaging ; },
abstract = {Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., "old people" and "young people"), respectively, are obtained in the human brain.},
}
@article {pmid23225040,
year = {2012},
author = {Wen, P- and Wang, GB and Liu, XH and Chen, ZL and Shang, Y and Cui, D and Song, P and Yuan, Q and Chen, SL and Liao, JX and Li, CR},
title = {[Analysis of clinical features and GCDH gene mutations in four patients with glutaric academia type I].},
journal = {Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics},
volume = {29},
number = {6},
pages = {642-647},
doi = {10.3760/cma.j.issn.1003-9406.2012.06.004},
pmid = {23225040},
issn = {1003-9406},
mesh = {Amino Acid Metabolism, Inborn Errors/*diagnosis/*genetics/metabolism ; Amino Acid Sequence ; Base Sequence ; Brain Diseases, Metabolic/*diagnosis/*genetics/metabolism ; Glutaryl-CoA Dehydrogenase/deficiency/*genetics/metabolism ; Humans ; Infant ; Male ; Molecular Sequence Data ; *Mutation ; Sequence Alignment ; },
abstract = {OBJECTIVE: To review clinical features of four male patients with glutaric academia type I and screen glutaryl-CoA dehydrogenase (GCDH) gene mutations.<br><br>METHODS: The 4 patients underwent brain computer tomography (CT) and magnetic resonance imaging (MRI) analyses. Blood acylcarnitine and urine organic acid were analyzed with tandem mass spectrometry and gas chromatographic mass spectrometry. Genomic DNA was extracted from peripheral blood samples. The 11 exons and flanking sequences of GCDH gene were amplified with PCR and subjected to direct DNA sequencing.<br><br>RESULTS: All patients have manifested macrocephaly, with head circumference measured 50 cm (14 months), 47 cm (9 months), 46 cm (5 months) and 51 cm (14 months), respectively. Imaging analyses also revealed dilation of Sylvian fissure and lateral ventricles, frontotemporal atrophy, subarachnoid space enlargement and cerebellar vermis abnormalities. All patients had elevated glutarylcarnitine (5.8 umol/L, 7.5 umol/L, 8.3 umol/L and 7.9 umol/L, respectively) and high urinary excretion of glutaric acid. Seven mutations were identified among the patients, among which c.146_149del4, IVS6-4_Ex7+4del8, c.508A>G (p.K170E), c.797T>C (p.M266T) and c.420del10 were first discovered.<br><br>CONCLUSION: Macrocephaly and neurological impairment are the most prominent features of glutaric academia type I. Blood tandem mass spectrometry and urine gas chromatographic mass spectrometry analysis can facilitate the diagnosis. The results can be confirmed by analysis of GCDH gene mutations.},
}
@article {pmid23223781,
year = {2013},
author = {Llobera, J and González-Franco, M and Perez-Marcos, D and Valls-Solé, J and Slater, M and Sanchez-Vives, MV},
title = {Virtual reality for assessment of patients suffering chronic pain: a case study.},
journal = {Experimental brain research},
volume = {225},
number = {1},
pages = {105-117},
pmid = {23223781},
issn = {1432-1106},
mesh = {Adult ; Brain-Computer Interfaces ; Calibration ; Chronic Pain/*diagnosis ; Computer Graphics ; Dystonia/diagnosis/physiopathology/rehabilitation ; Electroencephalography ; Electromyography ; Environment ; Hand/physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Muscle, Skeletal/physiopathology ; Nervous System Diseases/diagnosis ; Pain Measurement/*methods ; Psychomotor Performance/physiology ; Recovery of Function ; Self Concept ; *User-Computer Interface ; },
abstract = {The study of body representation and ownership has been a very active research area in recent years. Synchronous multisensory stimulation has been used for the induction of the illusion of ownership over virtual body parts and even full bodies, and it has provided experimental paradigms for the understanding of the brain processing of body representation. However, the illusion of ownership of a virtual body has rarely been used for patient evaluation and diagnosis. Here we propose a method that exploits ownership of a virtual body in combination with a simple brain computer interface (BCI) and basic physiological measures to complement neurological assessment. A male patient presenting a fixed posture dystonia featuring a permanently closed left fist participated in this case study. The patient saw a virtual body that substituted his own after donning a head-mounted display and thereby entering the virtual reality. The left virtual hand had the same posture as his corresponding real hand. After inducing virtual hand ownership by correlated visuo-tactile stimulation and dynamic reflections in a virtual mirror, the virtual hand would open either automatically or through a cognitive task assessed through a BCI that required him to focus attention on the virtual hand. The results reveal that body ownership induced changes on electromyography and BCI performance in the patient that were different from those in five healthy controls. Overall, the case study shows that the induction of virtual body ownership combined with simple electrophysiological measures could be useful for the diagnosis of patients with neurological conditions.},
}
@article {pmid23220879,
year = {2013},
author = {Lu, J and McFarland, DJ and Wolpaw, JR},
title = {Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {10},
number = {1},
pages = {016002},
pmid = {23220879},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; *Filtration ; Humans ; Models, Neurological ; Motor Cortex/*physiology ; Normal Distribution ; *Pressure ; Psychomotor Performance/*physiology ; Reproducibility of Results ; },
abstract = {OBJECTIVE: Sensorimotor rhythms (SMRs) are 8-30 Hz oscillations in the electroencephalogram (EEG) recorded from the scalp over sensorimotor cortex that change with movement and/or movement imagery. Many brain-computer interface (BCI) studies have shown that people can learn to control SMR amplitudes and can use that control to move cursors and other objects in one, two or three dimensions. At the same time, if SMR-based BCIs are to be useful for people with neuromuscular disabilities, their accuracy and reliability must be improved substantially. These BCIs often use spatial filtering methods such as common average reference (CAR), Laplacian (LAP) filter or common spatial pattern (CSP) filter to enhance the signal-to-noise ratio of EEG. Here, we test the hypothesis that a new filter design, called an 'adaptive Laplacian (ALAP) filter', can provide better performance for SMR-based BCIs.<br><br>APPROACH: An ALAP filter employs a Gaussian kernel to construct a smooth spatial gradient of channel weights and then simultaneously seeks the optimal kernel radius of this spatial filter and the regularization parameter of linear ridge regression. This optimization is based on minimizing the leave-one-out cross-validation error through a gradient descent method and is computationally feasible.<br><br>MAIN RESULTS: Using a variety of kinds of BCI data from a total of 22 individuals, we compare the performances of ALAP filter to CAR, small LAP, large LAP and CSP filters. With a large number of channels and limited data, ALAP performs significantly better than CSP, CAR, small LAP and large LAP both in classification accuracy and in mean-squared error. Using fewer channels restricted to motor areas, ALAP is still superior to CAR, small LAP and large LAP, but equally matched to CSP.<br><br>SIGNIFICANCE: Thus, ALAP may help to improve the accuracy and robustness of SMR-based BCIs.},
}
@article {pmid23220700,
year = {2013},
author = {Larsson, KC and Kjäll, P and Richter-Dahlfors, A},
title = {Organic bioelectronics for electronic-to-chemical translation in modulation of neuronal signaling and machine-to-brain interfacing.},
journal = {Biochimica et biophysica acta},
volume = {1830},
number = {9},
pages = {4334-4344},
doi = {10.1016/j.bbagen.2012.11.024},
pmid = {23220700},
issn = {0006-3002},
mesh = {Brain/*drug effects/*physiology ; *Brain-Computer Interfaces ; Electronics, Medical/*methods ; Humans ; Ions/metabolism ; Neurons/*drug effects/*physiology ; Neurotransmitter Agents/metabolism ; Polystyrenes ; Signal Transduction ; Thiophenes ; Transistors, Electronic ; },
abstract = {BACKGROUND: A major challenge when creating interfaces for the nervous system is to translate between the signal carriers of the nervous system (ions and neurotransmitters) and those of conventional electronics (electrons).<br><br>SCOPE OF REVIEW: Organic conjugated polymers represent a unique class of materials that utilizes both electrons and ions as charge carriers. Based on these materials, we have established a series of novel communication interfaces between electronic components and biological systems. The organic electronic ion pump (OEIP) presented in this review is made of the polymer-polyelectrolyte system poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). The OEIP translates electronic signals into electrophoretic migration of ions and neurotransmitters.<br><br>MAJOR CONCLUSIONS: We demonstrate how spatio-temporally controlled delivery of ions and neurotransmitters can be used to modulate intracellular Ca(2+) signaling in neuronal cells in the absence of convective disturbances. The electronic control of delivery enables strict control of dynamic parameters, such as amplitude and frequency of Ca(2+) responses, and can be used to generate temporal patterns mimicking naturally occurring Ca(2+) oscillations. To enable further control of the ionic signals we developed the electrophoretic chemical transistor, an analog of the traditional transistor used to amplify and/or switch electronic signals. Finally, we demonstrate the use of the OEIP in a new "machine-to-brain" interface by modulating brainstem responses in vivo.<br><br>GENERAL SIGNIFICANCE: This review highlights the potential of communication interfaces based on conjugated polymers in generating complex, high-resolution, signal patterns to control cell physiology. We foresee widespread applications for these devices in biomedical research and in future medical devices within multiple therapeutic areas. This article is part of a Special Issue entitled Organic Bioelectronics-Novel Applications in Biomedicine.},
}
@article {pmid23214292,
year = {2012},
author = {Mikołajewska, E and Mikołajewski, D},
title = {Neuroprostheses for increasing disabled patients' mobility and control.},
journal = {Advances in clinical and experimental medicine : official organ Wroclaw Medical University},
volume = {21},
number = {2},
pages = {263-272},
pmid = {23214292},
issn = {1899-5276},
mesh = {Brain-Computer Interfaces ; Computer-Aided Design ; Disability Evaluation ; Persons with Disabilities/*rehabilitation ; Humans ; *Mobility Limitation ; *Motor Activity ; *Movement ; *Neural Prostheses ; Prosthesis Design ; Recovery of Function ; Treatment Outcome ; },
abstract = {Neuroprostheses are electronic devices using electrophysiological signals to stimulate muscles, electronic/ mechanical devices such as substitutes for limbs or parts of limbs, or computers. The development of neuroprostheses was possible thanks to advances in understanding of the physiology of the human brain and in the capabilities of hardware and software. Recent progress in the area of neuroprosthetics may offer important breakthroughs in therapy and rehabilitation. New dedicated solutions for disabled people can lead to their increased participation in social, educational and professional areas. It is worth focussing particular attention on new solutions for people with paralysis, people with communication disorders and amputees. This article aims at investigating the extent to which the available opportunities are being exploited, including current and potential future applications of brain-computer interfaces.},
}
@article {pmid23213431,
year = {2012},
author = {Henson, K and Cooper, MM and Klymkowsky, MW},
title = {Turning randomness into meaning at the molecular level using Muller's morphs.},
journal = {Biology open},
volume = {1},
number = {4},
pages = {405-410},
pmid = {23213431},
issn = {2046-6390},
abstract = {While evolutionary theory follows from observable facts and logical inferences (Mayr, 1985), historically, the origin of novel inheritable variations was a major obstacle to acceptance of natural selection (Bowler, 1992; Bowler, 2005). While molecular mechanisms address this issue (Jablonka and Lamb, 2005), analysis of responses to the Biological Concept Inventory (BCI) (Klymkowsky et al., 2010), revealed that molecular biology majors rarely use molecular level ideas in their discourse, implying that they do not have an accessible framework within which to place evolutionary variation. We developed a "Socratic tutorial" focused on Muller's categorization of mutations' phenotypic effects (Muller, 1932). Using a novel vector-based method to analyzed students' essay responses, we found that a single interaction with this tutorial led to significant changes in thinking toward a clearer articulation of the effects of mutational change. We suggest that Muller's morphs provides an effective framework for facilitating student learning about mutational effects and evolutionary mechanisms.},
}
@article {pmid23206681,
year = {2012},
author = {Lu, CW and Patil, PG and Chestek, CA},
title = {Current challenges to the clinical translation of brain machine interface technology.},
journal = {International review of neurobiology},
volume = {107},
number = {},
pages = {137-160},
doi = {10.1016/B978-0-12-404706-8.00008-5},
pmid = {23206681},
issn = {2162-5514},
mesh = {Animals ; Brain/physiology ; Brain-Computer Interfaces/*trends ; Humans ; Nervous System Diseases/physiopathology/therapy ; Neural Prostheses/*trends ; Paralysis/physiopathology/therapy ; Translational Research, Biomedical/instrumentation/methods/*trends ; },
abstract = {Development of neural prostheses over the past few decades has produced a number of clinically relevant brain-machine interfaces (BMIs), such as the cochlear prostheses and deep brain stimulators. Current research pursues the restoration of communication or motor function to individuals with neurological disorders. Efforts in the field, such as the BrainGate trials, have already demonstrated that such interfaces can enable humans to effectively control external devices with neural signals. However, a number of significant issues regarding BMI performance, device capabilities, and surgery must be resolved before clinical use of BMI technology can become widespread. This chapter reviews challenges to clinical translation and discusses potential solutions that have been reported in recent literature, with focuses on hardware reliability, state-of-the-art decoding algorithms, and surgical considerations during implantation.},
}
@article {pmid23204288,
year = {2013},
author = {Park, C and Looney, D and Naveed ur Rehman, and Ahrabian, A and Mandic, DP},
title = {Classification of motor imagery BCI using multivariate empirical mode decomposition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {1},
pages = {10-22},
doi = {10.1109/TNSRE.2012.2229296},
pmid = {23204288},
issn = {1558-0210},
mesh = {Algorithms ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.},
}
@article {pmid23204287,
year = {2013},
author = {Bermúdez i Badia, S and García Morgade, A and Samaha, H and Verschure, PF},
title = {Using a hybrid brain computer interface and virtual reality system to monitor and promote cortical reorganization through motor activity and motor imagery training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {2},
pages = {174-181},
doi = {10.1109/TNSRE.2012.2229295},
pmid = {23204287},
issn = {1558-0210},
mesh = {Adult ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; *User-Computer Interface ; },
abstract = {Stroke is one of the leading causes of adult disability with high economical and societal costs. In recent years, novel rehabilitation paradigms have been proposed to address the life-long plasticity of the brain to regain motor function. We propose a hybrid brain-computer interface (BCI)-virtual reality (VR) system that combines a personalized motor training in a VR environment, exploiting brain mechanisms for action execution and observation, and a neuro-feedback paradigm using mental imagery as a way to engage secondary or indirect pathways to access undamaged cortico-spinal tracts. Furthermore, we present the development and validation experiments of the proposed system. More specifically, EEG data on nine naïve healthy subjects show that a simultaneous motor activity and motor imagery paradigm is more effective at engaging cortical motor areas and related networks to a larger extent. Additionally, we propose a motor imagery driven BCI-VR version of our system that was evaluated with nine different healthy subjects. Data show that users are capable of controlling a virtual avatar in a motor imagery training task that dynamically adjusts its difficulty to the capabilities of the user. User self-report questionnaires indicate enjoyment and acceptance of the proposed system.},
}
@article {pmid23202227,
year = {2012},
author = {Sung, Y and Cho, K and Um, K},
title = {A development architecture for serious games using BCI (brain computer interface) sensors.},
journal = {Sensors (Basel, Switzerland)},
volume = {12},
number = {11},
pages = {15671-15688},
pmid = {23202227},
issn = {1424-8220},
abstract = {Games that use brainwaves via brain-computer interface (BCI) devices, to improve brain functions are known as BCI serious games. Due to the difficulty of developing BCI serious games, various BCI engines and authoring tools are required, and these reduce the development time and cost. However, it is desirable to reduce the amount of technical knowledge of brain functions and BCI devices needed by game developers. Moreover, a systematic BCI serious game development process is required. In this paper, we present a methodology for the development of BCI serious games. We describe an architecture, authoring tools, and development process of the proposed methodology, and apply it to a game development approach for patients with mild cognitive impairment as an example. This application demonstrates that BCI serious games can be developed on the basis of expert-verified theories.},
}
@article {pmid23197181,
year = {2013},
author = {McCreadie, KA and Coyle, DH and Prasad, G},
title = {Sensorimotor learning with stereo auditory feedback for a brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {51},
number = {3},
pages = {285-293},
pmid = {23197181},
issn = {1741-0444},
mesh = {Adult ; Brain Waves/*physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/methods ; Evoked Potentials, Auditory/*physiology ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Young Adult ; },
abstract = {Motor imagery can be used to modulate sensorimotor rhythms (SMR) enabling detection of voltage fluctuations on the surface of the scalp using electroencephalographic electrodes. Feedback is essential in learning to modulate SMR for non-muscular communication using a brain-computer interface (BCI). A BCI not reliant upon the visual modality not only releases the visual channel for other uses but also offers an attractive means of communication for the physically impaired who are also blind or vision impaired. This study demonstrates the feasibility of replacing the traditional visual feedback modality with stereo auditory feedback. Results from a pilot study were used to select the most appropriate sounds for auditory feedback based on three options: broadband noise and two anechoic instrument samples. Subsequently, an SMR BCI was used to examine the effect on sensorimotor learning with broadband noise utilising a modified stereophonic presentation method. Twenty participants split into equal groups took part in ten sessions. The visual group performed best initially but did not improve over time whilst the auditory group improved as the study progressed. The results demonstrate the feasibility of using stereophonic auditory feedback with broadband noise as opposed to other auditory feedback presentation methods and sounds which are less intuitive.},
}
@article {pmid23197105,
year = {2013},
author = {Fonnesbeck, CJ and McPheeters, ML and Krishnaswami, S and Lindegren, ML and Reimschisel, T},
title = {Estimating the probability of IQ impairment from blood phenylalanine for phenylketonuria patients: a hierarchical meta-analysis.},
journal = {Journal of inherited metabolic disease},
volume = {36},
number = {5},
pages = {757-766},
pmid = {23197105},
issn = {1573-2665},
mesh = {Adolescent ; Adult ; Child ; Humans ; Intellectual Disability/*blood ; Phenylalanine/*blood ; Phenylketonurias/*blood/*psychology ; Young Adult ; },
abstract = {Though the control of blood phenylalanine (Phe) levels is essential for minimizing impairment in individuals with phenylketonuria (PKU), the empirical basis for the selection of specific blood Phe levels as targets has not been evaluated. We evaluated the current evidence that particular Phe levels are optimal for minimizing or avoiding cognitive impairment in individuals with PKU. This work uses meta-estimates of blood Phe-IQ correlation to predict the probability of low IQ for a range of Phe levels. We believe this metric is easily interpretable by clinicians, and hence useful in making recommendations for Phe intake. The median baseline association of Phe with IQ was estimated to be negative, both in the context of historical (median = -0.026, 95 % BCI = [-0.040, -0.013]) and concurrent (-0.007, [-0.014, 0.000]) measurement of Phe relative to IQ. The estimated additive fixed effect of critical period Phe measurement was also nominally negative for historical measurement (-0.010, [-0.022, 0.003]) and positive for concurrent measurement (0.007, [-0.018, 0.035]). Probabilities corresponding to historical measures of blood Phe demonstrated an increasing chance of low IQ with increasing Phe, with a stronger association seen between blood Phe measured during the critical period than later. In contrast, concurrently-measured Phe was more weakly correlated with the probability of low IQ, though the correlation is still positive, irrespective of whether Phe was measured during the critical or non-critical period. This meta-analysis illustrates the utility of a Bayesian hierarchical approach for not only combining information from a set of candidate studies, but also for combining different types of data to estimate parameters of interest.},
}
@article {pmid23196555,
year = {2012},
author = {Liu, M},
title = {[Development of newer rehabilitative measures for hemiparetic upper limb after stroke].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {52},
number = {11},
pages = {1178-1181},
doi = {10.5692/clinicalneurol.52.1178},
pmid = {23196555},
issn = {1882-0654},
mesh = {Brain-Computer Interfaces ; Electric Stimulation/methods ; *Hand ; Humans ; Paralysis/*rehabilitation ; *Stroke Rehabilitation ; },
abstract = {Because recovery of upper extremity (UE) function to a practical level has been difficult in many stroke patients, compensatory approaches have been emphasized. Based on researches indicating greater potential for brain plasticity, newer approaches targeting at functional restoration have been attempted. However, no intervention has been shown to be effective to improve hand function. We therefore devised a therapeutic approach to facilitate the use of the hemiparetic hand in daily life by combining EMG triggered electrical stimulation with a wrist splint, called hybrid assistive neuromuscular dynamic stimulation (HANDS). With HANDS, we demonstrated improved motor function, spasticity, functional scores and neurophysiological parameters in chronic stroke. With a RCT, we also demonstrated its effectiveness in subacute stroke. However, to be its candidates, electromyogram must be recorded from finger extensors, and it cannot be applied to patients with complete paralysis. For them, we recently developed a Brain Machine Interface (BMI) neurorehabilitation system. Based on analysis of volitionally decreased amplitudes of sensory motor rhythm during motor imagery involving extending the affected fingers, real-time visual feedback is provided. In patients with severe hemiparesis, we demonstrated its effectiveness with clinical scales, neuroimaging and electrophysiological studies. These newer interventions might offer useful neurorehabilitative tools for hemiparetic UE.},
}
@article {pmid23196554,
year = {2012},
author = {Miyai, I and Mihara, M and Hattori, N and Hatakenaka, M and Kawano, T and Yagura, H},
title = {[Contribution of brain function analysis to the evolution of neurorehabilitation].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {52},
number = {11},
pages = {1174-1177},
doi = {10.5692/clinicalneurol.52.1174},
pmid = {23196554},
issn = {1882-0654},
mesh = {Brain/*physiology ; Humans ; Neuronal Plasticity/physiology ; *Stroke Rehabilitation ; },
abstract = {Recent studies of functional neuroimaging and clinical neurophysiology have implied that functional recovery after stroke is associated with use-dependent plasticity of the damaged brain. However the property of the reorganized neural network depends on site and size of the lesion, which makes it difficult to assess what the adaptive plasticity is. From clinical point of view there is accumulating randomized controlled trials for the benefit of task-oriented rehabilitative intervention including constraint-induced movement therapy, robotics, and body-weight supported treadmill training. However dose-matched control intervention is usually as effective as a specific intervention. This raises a question regarding the specificity of a task-oriented intervention. Second question is whether such intervention goes beyond the biological destiny of human. Specifically there is no known strategy enhancing recovery of severely impaired hand. To augment functional gain, several methods of neuro-modulation may bring break-through on the assumption that they induce greater adaptive plasticity. Such neuro-modulative methods include neuropharmacological modulation, brain stimulation using transcranial magnetic stimulation and direct current stimulation, peripheral nerve stimulation, neurofeedback using real-time fMRI and real-time fNIRS, and brain-machine interface. A preliminary randomized controlled trial regarding real-time feedback of premotor activities revealed promising results for recovery of paretic hand in patients with stroke.},
}
@article {pmid23193468,
year = {2013},
author = {Thakor, NV},
title = {In the spotlight: Neuroengineering.},
journal = {IEEE reviews in biomedical engineering},
volume = {6},
number = {},
pages = {24-26},
doi = {10.1109/RBME.2012.2228515},
pmid = {23193468},
issn = {1941-1189},
mesh = {Animals ; *Biomedical Engineering ; *Brain-Computer Interfaces ; Humans ; *Neural Prostheses ; *Neurosciences ; Rats ; },
}
@article {pmid23193324,
year = {2013},
author = {Ahmadian, P and Sanei, S and Ascari, L and González-Villanueva, L and Alessandra Umiltà, M},
title = {Constrained blind source extraction of readiness potentials from EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {4},
pages = {567-575},
doi = {10.1109/TNSRE.2012.2227278},
pmid = {23193324},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain-Computer Interfaces ; Computer Simulation ; Contingent Negative Variation/*physiology ; Data Interpretation, Statistical ; Electroencephalography/instrumentation/*statistics &amp; numerical data ; False Negative Reactions ; False Positive Reactions ; Female ; Humans ; Male ; Movement/physiology ; Photic Stimulation ; Principal Component Analysis ; ROC Curve ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {One of the changes seen in electroencephalography (EEG) data preceding human voluntary movement is a cortical potential called readiness potential (RP). Detection of this potential can benefit researchers in clinical neurosciences for rehabilitation of malfunctioning brain and those working on brain-computer interfacing to develop a suitable mechanism to detect the intention of movement. Here, a constrained blind source extraction (CBSE) is attempted for detection of RP. A suitable constraint is defined and applied. The results are also compared with those of the traditional blind source separation in terms of true positive rate, false positive rate, and computation time. The results show that the CBSE approach in overall has superior performance.},
}
@article {pmid23189154,
year = {2012},
author = {Jin, J and Allison, BZ and Kaufmann, T and Kübler, A and Zhang, Y and Wang, X and Cichocki, A},
title = {The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement.},
journal = {PloS one},
volume = {7},
number = {11},
pages = {e49688},
pmid = {23189154},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Emotions ; Event-Related Potentials, P300/*physiology ; *Facial Expression ; Female ; Humans ; Male ; *Motion ; Photic Stimulation ; Reproducibility of Results ; Young Adult ; },
abstract = {BACKGROUND: One of the most common types of brain-computer interfaces (BCIs) is called a P300 BCI, since it relies on the P300 and other event-related potentials (ERPs). In the canonical P300 BCI approach, items on a monitor flash briefly to elicit the necessary ERPs. Very recent work has shown that this approach may yield lower performance than alternate paradigms in which the items do not flash but instead change in other ways, such as moving, changing colour or changing to characters overlaid with faces.<br><br>The present study sought to extend this research direction by parametrically comparing different ways to change items in a P300 BCI. Healthy subjects used a P300 BCI across six different conditions. Three conditions were similar to our prior work, providing the first direct comparison of characters flashing, moving, and changing to faces. Three new conditions also explored facial motion and emotional expression. The six conditions were compared across objective measures such as classification accuracy and bit rate as well as subjective measures such as perceived difficulty. In line with recent studies, our results indicated that the character flash condition resulted in the lowest accuracy and bit rate. All four face conditions (mean accuracy >91%) yielded significantly better performance than the flash condition (mean accuracy&#x200a;=&#x200a;75%).<br><br>CONCLUSIONS/SIGNIFICANCE: Objective results reaffirmed that the face paradigm is superior to the canonical flash approach that has dominated P300 BCIs for over 20 years. The subjective reports indicated that the conditions that yielded better performance were not considered especially burdensome. Therefore, although further work is needed to identify which face paradigm is best, it is clear that the canonical flash approach should be replaced with a face paradigm when aiming at increasing bit rate. However, the face paradigm has to be further explored with practical applications particularly with locked-in patients.},
}
@article {pmid23187009,
year = {2012},
author = {Daly, J and Liu, J and Aghagolzadeh, M and Oweiss, K},
title = {Optimal space-time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {6},
pages = {065004},
pmid = {23187009},
issn = {1741-2552},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; R01 NS093909/NS/NINDS NIH HHS/United States ; NS062031/NS/NINDS NIH HHS/United States ; NS054148/NS/NINDS NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*instrumentation ; *Brain-Computer Interfaces ; Electric Stimulation/*methods ; Extremities/physiology ; Microelectrodes ; Movement ; Nerve Net/physiology ; Neural Pathways/physiology ; Noise ; Sensation/physiology ; Somatosensory Cortex/physiology ; Thalamus/physiology ; },
abstract = {Brain-machine interfaces (BMIs) aim to restore lost sensorimotor and cognitive function in subjects with severe neurological deficits. In particular, lost somatosensory function may be restored by artificially evoking patterns of neural activity through microstimulation to induce perception of tactile and proprioceptive feedback to the brain about the state of the limb. Despite an early proof of concept that subjects could learn to discriminate a limited vocabulary of intracortical microstimulation (ICMS) patterns that instruct the subject about the state of the limb, the dynamics of a moving limb are unlikely to be perceived by an arbitrarily-selected, discrete set of static microstimulation patterns, raising questions about the generalization and the scalability of this approach. In this work, we propose a microstimulation protocol intended to activate optimally the ascending somatosensory pathway. The optimization is achieved through a space-time precoder that maximizes the mutual information between the sensory feedback indicating the limb state and the cortical neural response evoked by thalamic microstimulation. Using a simplified multi-input multi-output model of the thalamocortical pathway, we show that this optimal precoder can deliver information more efficiently in the presence of noise compared to suboptimal precoders that do not account for the afferent pathway structure and/or cortical states. These results are expected to enhance the way microstimulation is used to induce somatosensory perception during sensorimotor control of artificial devices or paralyzed limbs.},
}
@article {pmid23181009,
year = {2012},
author = {Guger, C and Allison, BZ and Großwindhager, B and Prückl, R and Hintermüller, C and Kapeller, C and Bruckner, M and Krausz, G and Edlinger, G},
title = {How Many People Could Use an SSVEP BCI?.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {169},
pmid = {23181009},
issn = {1662-453X},
abstract = {Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects. Also, many articles do not mention data from subjects who performed poorly. These and other factors have made it difficult to estimate how many people could use different BCIs. The present study explored how many subjects could use an SSVEP BCI. We recorded data from 53 subjects while they participated in 1-4 runs that were each 4 min long. During these runs, the subjects focused on one of four LEDs that each flickered at a different frequency. The eight channel EEG data were analyzed with a minimum energy parameter estimation algorithm and classified with linear discriminant analysis into one of the four classes. Online results showed that SSVEP BCIs could provide effective communication for all 53 subjects, resulting in a grand average accuracy of 95.5%. About 96.2% of the subjects reached an accuracy above 80%, and nobody was below 60%. This study showed that SSVEP based BCI systems can reach very high accuracies after only a very short training period. The SSVEP approach worked for all participating subjects, who attained accuracy well above chance level. This is important because it shows that SSVEP BCIs could provide communication for some users when other approaches might not work for them.},
}
@article {pmid23171895,
year = {2012},
author = {Masse, NY and Jarosiewicz, B},
title = {[Neural interface systems: the future is (almost) here].},
journal = {Medecine sciences : M/S},
volume = {28},
number = {11},
pages = {932-934},
doi = {10.1051/medsci/20122811010},
pmid = {23171895},
issn = {0767-0974},
mesh = {Artificial Organs/*trends ; Brain Waves ; Brain-Computer Interfaces/*trends ; Electrodes, Implanted ; Equipment Design ; Forecasting ; Humans ; Man-Machine Systems ; Movement ; Robotics/instrumentation/*trends ; *Self-Help Devices ; User-Computer Interface ; },
}
@article {pmid23165873,
year = {2012},
author = {Lehembre, R and Gosseries, O and Lugo, Z and Jedidi, Z and Chatelle, C and Sadzot, B and Laureys, S and Noirhomme, Q},
title = {Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients.},
journal = {Archives italiennes de biologie},
volume = {150},
number = {2-3},
pages = {122-139},
doi = {10.4449/aib.v150i2.1374},
pmid = {23165873},
issn = {0003-9829},
mesh = {Brain/*physiopathology ; Brain Waves/*physiology ; Brain-Computer Interfaces ; Coma/*pathology ; Electroencephalography/methods/standards ; Evoked Potentials/physiology ; Humans ; Persistent Vegetative State/*pathology ; },
abstract = {Electroencephalographic activity in the context of disorders of consciousness is a swiss knife like tool that can evaluate different aspects of cognitive residual function, detect consciousness and provide a mean to communicate with the outside world without using muscular channels. Standard recordings in the neurological department offer a first global view of the electrogenesis of a patient and can spot abnormal epileptiform activity and therefore guide treatment. Although visual patterns have a prognosis value, they are not sufficient to provide a diagnosis between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) patients. Quantitative electroencephalography (qEEG) processes the data and retrieves features, not visible on the raw traces, which can then be classified. Current results using qEEG show that MCS can be differentiated from VS/UWS patients at the group level. Event Related Potentials (ERP) are triggered by varying stimuli and reflect the time course of information processing related to the stimuli from low-level peripheral receptive structures to high-order associative cortices. It is hence possible to assess auditory, visual, or emotive pathways. Different stimuli elicit positive or negative components with different time signatures. The presence of these components when observed in passive paradigms is usually a sign of good prognosis but it cannot differentiate VS/UWS and MCS patients. Recently, researchers have developed active paradigms showing that the amplitude of the component is modulated when the subject's attention is focused on a task during stimulus presentation. Hence significant differences between ERPs of a patient in a passive compared to an active paradigm can be a proof of consciousness. An EEG-based brain-computer interface (BCI) can then be tested to provide the patient with a communication tool. BCIs have considerably improved the past two decades. However they are not easily adaptable to comatose patients as they can have visual or auditory impairments or different lesions affecting their EEG signal. Future progress will require large databases of resting state-EEG and ERPs experiment of patients of different etiologies. This will allow the identification of specific patterns related to the diagnostic of consciousness. Standardized procedures in the use of BCIs will also be needed to find the most suited technique for each individual patient.},
}
@article {pmid23162504,
year = {2012},
author = {Thinnes-Elker, F and Iljina, O and Apostolides, JK and Kraemer, F and Schulze-Bonhage, A and Aertsen, A and Ball, T},
title = {Intention concepts and brain-machine interfacing.},
journal = {Frontiers in psychology},
volume = {3},
number = {},
pages = {455},
pmid = {23162504},
issn = {1664-1078},
abstract = {Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret ("decode") the agent's intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent's intentions from neural signals in practical BMI applications.},
}
@article {pmid23162436,
year = {2012},
author = {Walter, A and Ramos Murguialday, A and Spüler, M and Naros, G and Leão, MT and Gharabaghi, A and Rosenstiel, W and Birbaumer, N and Bogdan, M},
title = {Coupling BCI and cortical stimulation for brain-state-dependent stimulation: methods for spectral estimation in the presence of stimulation after-effects.},
journal = {Frontiers in neural circuits},
volume = {6},
number = {},
pages = {87},
pmid = {23162436},
issn = {1662-5110},
abstract = {Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and evoked activity present a challenge for a successful implementation of BSDS because they can impair the detection of targeted brain states. Therefore, efficient and robust methods are needed to minimize the influence of the stimulation-induced effects on spectral estimation without violating the real-time constraints of the BCI. In this work, we compared four methods for spectral estimation with autoregressive (AR) models in the presence of pulsed cortical stimulation. Using combined EEG-TMS (electroencephalography-transcranial magnetic stimulation) as well as combined electrocorticography (ECoG) and epidural electrical stimulation, three patients performed a motor task using a sensorimotor-rhythm BCI. Three stimulation paradigms were varied between sessions: (1) no stimulation, (2) single stimulation pulses applied independently (open-loop), or (3) coupled to the BCI output (closed-loop) such that stimulation was given only while an intention to move was detected using neural data. We found that removing the stimulation after-effects by linear interpolation can introduce a bias in the estimation of the spectral power of the sensorimotor rhythm, leading to an overestimation of decoding performance in the closed-loop setting. We propose the use of the Burg algorithm for segmented data to deal with stimulation after-effects. This work shows that the combination of BCIs controlled with spectral features and cortical stimulation in a closed-loop fashion is possible when the influence of stimulation after-effects on spectral estimation is minimized.},
}
@article {pmid23162425,
year = {2012},
author = {Gürel, T and Mehring, C},
title = {Unsupervised adaptation of brain-machine interface decoders.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {164},
pmid = {23162425},
issn = {1662-453X},
abstract = {The performance of neural decoders can degrade over time due to non-stationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI) require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator) instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i) calibration phases interrupt the autonomous operation of the BMI and (ii) between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e., without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories toward targets at random orientations on a 2-dimensional computer screen. For initially unknown and non-stationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error-signals instead or in conjunction with the proposed unsupervised adaptation.},
}
@article {pmid23161257,
year = {2013},
author = {Gongora, M and Peressutti, C and Machado, S and Teixeira, S and Velasques, B and Ribeiro, P},
title = {Progress and prospects in neurorehabilitation: clinical applications of stem cells and brain-computer interface for spinal cord lesions.},
journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology},
volume = {34},
number = {4},
pages = {427-433},
pmid = {23161257},
issn = {1590-3478},
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Motor Activity/physiology ; *Spinal Cord Injuries/pathology/rehabilitation/surgery ; Spinal Cord Regeneration/physiology ; Stem Cell Transplantation/*methods ; Stem Cells/*physiology ; },
abstract = {Spinal cord injury (SCI) is a disease that affects millions of people worldwide, causing a temporary or permanent impairment of neuromotor functions. Mostly associated to traumatic lesions, but also to other forms of disease, the appropriate treatment is still unsure. In this review, several ongoing studies are presented that aim to provide methods of prevention that ensure quality of life, and rehabilitation trends to patients who suffer from this injury. Stem cell research, highlighted in this review, seeks to reduce damage caused to the tissue, as also provide spinal cord regeneration through the application of several types of stem cells. On the other hand, research using brain-computer interface (BCI) technology proposes the development of interfaces based on the interaction of neural networks with artificial tools to restore motor control and full mobility of the injured area. PubMed, MEDLINE and SciELO data basis analyses were performed to identify studies published from 2000 to date, which describe the link between SCI with stem cells and BCI technology.},
}
@article {pmid23158726,
year = {2012},
author = {Gneo, M and Schmid, M and Conforto, S and D'Alessio, T},
title = {A free geometry model-independent neural eye-gaze tracking system.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {9},
number = {},
pages = {82},
pmid = {23158726},
issn = {1743-0003},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Calibration ; Eye Movements/*physiology ; Fixation, Ocular/*physiology ; Functional Laterality/physiology ; Humans ; Models, Neurological ; Neural Networks, Computer ; Neurons/physiology ; Pupil ; Reproducibility of Results ; },
abstract = {BACKGROUND: Eye Gaze Tracking Systems (EGTSs) estimate the Point Of Gaze (POG) of a user. In diagnostic applications EGTSs are used to study oculomotor characteristics and abnormalities, whereas in interactive applications EGTSs are proposed as input devices for human computer interfaces (HCI), e.g. to move a cursor on the screen when mouse control is not possible, such as in the case of assistive devices for people suffering from locked-in syndrome. If the user's head remains still and the cornea rotates around its fixed centre, the pupil follows the eye in the images captured from one or more cameras, whereas the outer corneal reflection generated by an IR light source, i.e. glint, can be assumed as a fixed reference point. According to the so-called pupil centre corneal reflection method (PCCR), the POG can be thus estimated from the pupil-glint vector.<br><br>METHODS: A new model-independent EGTS based on the PCCR is proposed. The mapping function based on artificial neural networks allows to avoid any specific model assumption and approximation either for the user's eye physiology or for the system initial setup admitting a free geometry positioning for the user and the system components. The robustness of the proposed EGTS is proven by assessing its accuracy when tested on real data coming from: i) different healthy users; ii) different geometric settings of the camera and the light sources; iii) different protocols based on the observation of points on a calibration grid and halfway points of a test grid.<br><br>RESULTS: The achieved accuracy is approximately 0.49&#xb0;, 0.41&#xb0;, and 0.62&#xb0; for respectively the horizontal, vertical and radial error of the POG.<br><br>CONCLUSIONS: The results prove the validity of the proposed approach as the proposed system performs better than EGTSs designed for HCI which, even if equipped with superior hardware, show accuracy values in the range 0.6&#xb0;-1&#xb0;.},
}
@article {pmid23155172,
year = {2013},
author = {Oby, ER and Ethier, C and Miller, LE},
title = {Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions.},
journal = {Journal of neurophysiology},
volume = {109},
number = {3},
pages = {666-678},
pmid = {23155172},
issn = {1522-1598},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; NS-053603/NS/NINDS NIH HHS/United States ; F31-NS-071737/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electromyography ; *Generalization, Psychological ; *Locomotion ; Macaca mulatta ; Motor Cortex/cytology/*physiology ; Muscle, Skeletal/innervation/physiology ; Neurons/physiology ; Posture ; Wrist/innervation/physiology ; },
abstract = {It is well known that discharge of neurons in the primary motor cortex (M1) depends on end-point force and limb posture. However, the details of these relations remain unresolved. With the development of brain-machine interfaces (BMIs), these issues have taken on practical as well as theoretical importance. We examined how the M1 encodes movement by comparing single-neuron and electromyographic (EMG) preferred directions (PDs) and by predicting force and EMGs from multiple neurons recorded during an isometric wrist task. Monkeys moved a cursor from a central target to one of eight peripheral targets by exerting force about the wrist while the forearm was held in one of two postures. We fit tuning curves to both EMG and M1 activity measured during the hold period, from which we computed both PDs and the change in PD between forearm postures (ΔPD). We found a unimodal distribution of these ΔPDs, the majority of which were intermediate between the typical muscle response and an unchanging, extrinsic coordinate system. We also discovered that while most neuron-to-EMG predictions generalized well across forearm postures, end-point force measured in extrinsic coordinates did not. The lack of force generalization was due to musculoskeletal changes with posture. Our results show that the dynamics of most of the recorded M1 signals are similar to those of muscle activity and imply that a BMI designed to drive an actuator with dynamics like those of muscles might be more robust and easier to learn than a BMI that commands forces or movements in external coordinates.},
}
@article {pmid23153708,
year = {2013},
author = {De Massari, D and Matuz, T and Furdea, A and Ruf, CA and Halder, S and Birbaumer, N},
title = {Brain-computer interface and semantic classical conditioning of communication in paralysis.},
journal = {Biological psychology},
volume = {92},
number = {2},
pages = {267-274},
doi = {10.1016/j.biopsycho.2012.10.015},
pmid = {23153708},
issn = {1873-6246},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology/*rehabilitation ; Communication Devices for People with Disabilities/psychology ; Conditioning, Classical/*physiology ; Electric Stimulation ; Female ; Humans ; Male ; *Semantics ; Time Factors ; *User-Computer Interface ; Young Adult ; },
abstract = {We propose a classical semantic conditioning procedure to allow basic yes-no communication in the completely locked-in state as an alternative to instrumental-operant learning of brain responses, which is the common approach in brain-computer interface research. More precisely, it was intended to establish cortical responses to the trueness of a statement irrespective of the particular constituent words and letters or sounds of the words. As unconditioned stimulus short aversive stimuli consisting of 1-ms electrical pulses were used. True and false statements were presented acoustically and only the true statements were immediately followed by electrical stimuli. 15 healthy participants and one locked-in ALS patient underwent the experiment. Three different classifiers were employed in order to differentiate between the two cortical responses by means of electroencephalographic recordings. The offline analysis revealed that semantic classical conditioning can be applied successfully to enable basic communication using a non-muscular channel.},
}
@article {pmid23152713,
year = {2012},
author = {Gargiulo, GD and Mohamed, A and McEwan, AL and Bifulco, P and Cesarelli, M and Jin, CT and Ruffo, M and Tapson, J and van Schaik, A},
title = {Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study.},
journal = {Medical devices (Auckland, N.Z.)},
volume = {5},
number = {},
pages = {81-88},
pmid = {23152713},
issn = {1179-1470},
abstract = {Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous μ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone.},
}
@article {pmid23149409,
year = {2012},
author = {Seri, E and Maruvka, YE and Shnerb, NM},
title = {Neutral dynamics and cluster statistics in a tropical forest.},
journal = {The American naturalist},
volume = {180},
number = {6},
pages = {E161-73},
doi = {10.1086/668125},
pmid = {23149409},
issn = {1537-5323},
mesh = {Annonaceae/physiology ; *Biodiversity ; Cluster Analysis ; *Ecosystem ; Models, Biological ; Panama ; *Plant Dispersal ; Population Dynamics ; Rubiaceae/physiology ; Trees/*physiology ; Tropical Climate ; Violaceae/physiology ; },
abstract = {The neutral theory of biodiversity attributes community structure to the effects of chance alone, assuming that all species and individuals are demographically equivalent. Here we present a spatially explicit version of the neutral theory and test it against the Barro Colorado Island (BCI) data. Monitoring the dynamics of clusters, we show that the effect of local heterogeneities (e.g., microtopography) is weak, making a spatially homogenous model plausible. We then compare the cluster statistics of the three most frequent species with the patterns obtained from neutral dynamics, examining two families of recruitment kernels: one that interpolates between a limited distance and panmictic dispersal (local-global) and one that assumes a scale-free Cauchy kernel. The results rule out the local-global dispersal model and show that the spatial patterns fit very nicely those obtained from the fat-tailed kernel. Our work emphasizes the importance of spatiotemporal cluster dynamics as an instrument for detecting the factors that govern community assembly.},
}
@article {pmid23148413,
year = {2013},
author = {Lagang, M and Srinivasan, L},
title = {Stochastic optimal control as a theory of brain-machine interface operation.},
journal = {Neural computation},
volume = {25},
number = {2},
pages = {374-417},
doi = {10.1162/NECO_a_00394},
pmid = {23148413},
issn = {1530-888X},
mesh = {*Algorithms ; Animals ; *Brain-Computer Interfaces ; Humans ; *Models, Theoretical ; },
abstract = {The closed-loop operation of brain-machine interfaces (BMI) provides a framework to study the mechanisms behind neural control through a restricted output channel, with emerging clinical applications to stroke, degenerative disease, and trauma. Despite significant empirically driven improvements in closed-loop BMI systems, a fundamental, experimentally validated theory of closed-loop BMI operation is lacking. Here we propose a compact model based on stochastic optimal control to describe the brain in skillfully operating canonical decoding algorithms. The model produces goal-directed BMI movements with sensory feedback and intrinsically noisy neural output signals. Various experimentally validated phenomena emerge naturally from this model, including performance deterioration with bin width, compensation of biased decoders, and shifts in tuning curves between arm control and BMI control. Analysis of the model provides insight into possible mechanisms underlying these behaviors, with testable predictions. Spike binning may erode performance in part from intrinsic control-dependent constraints, regardless of decoding accuracy. In compensating decoder bias, the brain may incur an energetic cost associated with action potential production. Tuning curve shifts, seen after the mastery of a BMI-based skill, may reflect the brain's implementation of a new closed-loop control policy. The direction and magnitude of tuning curve shifts may be altered by decoder structure, ensemble size, and the costs of closed-loop control. Looking forward, the model provides a framework for the design and simulated testing of an emerging class of BMI algorithms that seek to directly exploit the presence of a human in the loop.},
}
@article {pmid23147846,
year = {2012},
author = {Jackson, A and Zimmermann, JB},
title = {Neural interfaces for the brain and spinal cord--restoring motor function.},
journal = {Nature reviews. Neurology},
volume = {8},
number = {12},
pages = {690-699},
pmid = {23147846},
issn = {1759-4766},
support = {086561//Wellcome Trust/United Kingdom ; 087223//Wellcome Trust/United Kingdom ; },
mesh = {Brain/physiology ; *Brain-Computer Interfaces ; Humans ; *Man-Machine Systems ; *Prostheses and Implants ; Software ; Spinal Cord Injuries/*rehabilitation ; },
abstract = {Regaining motor function is of high priority to patients with spinal cord injury (SCI). A variety of electronic devices that interface with the brain or spinal cord, which have applications in neural prosthetics and neurorehabilitation, are in development. Owing to our advancing understanding of activity-dependent synaptic plasticity, new technologies to monitor, decode and manipulate neural activity are being translated to patient populations, and have demonstrated clinical efficacy. Brain-machine interfaces that decode motor intentions from cortical signals are enabling patient-driven control of assistive devices such as computers and robotic prostheses, whereas electrical stimulation of the spinal cord and muscles can aid in retraining of motor circuits and improve residual capabilities in patients with SCI. Next-generation interfaces that combine recording and stimulating capabilities in so-called closed-loop devices will further extend the potential for neuroelectronic augmentation of injured motor circuits. Emerging evidence suggests that integration of closed-loop interfaces into intentional motor behaviours has therapeutic benefits that outlast the use of these devices as prostheses. In this Review, we summarize this evidence and propose that several known plasticity mechanisms, operating in a complementary manner, might underlie the therapeutic effects that are achieved by closing the loop between electronic devices and the nervous system.},
}
@article {pmid23145699,
year = {2012},
author = {Maddox, RK and Cheung, W and Lee, AK},
title = {Selective attention in an overcrowded auditory scene: implications for auditory-based brain-computer interface design.},
journal = {The Journal of the Acoustical Society of America},
volume = {132},
number = {5},
pages = {EL385-90},
pmid = {23145699},
issn = {1520-8524},
support = {R00 DC010196/DC/NIDCD NIH HHS/United States ; T32 DC005361/DC/NIDCD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Analysis of Variance ; *Attention ; Audiometry, Pure-Tone ; *Auditory Perception ; Auditory Threshold ; *Brain-Computer Interfaces ; Cues ; Equipment Design ; Female ; Humans ; Male ; Middle Aged ; Noise/*adverse effects ; *Perceptual Masking ; Psychoacoustics ; Time Factors ; Young Adult ; },
abstract = {Listeners are good at attending to one auditory stream in a crowded environment. However, is there an upper limit of streams present in an auditory scene at which this selective attention breaks down? Here, participants were asked to attend one stream of spoken letters amidst other letter streams. In half of the trials, an initial primer was played, cueing subjects to the sound configuration. Results indicate that performance increases with token repetitions. Priming provided a performance benefit, suggesting that stream selection, not formation, is the bottleneck associated with attention in an overcrowded scene. Results' implications for brain-computer interfaces are discussed.},
}
@article {pmid23145138,
year = {2012},
author = {Waldert, S and Tüshaus, L and Kaller, CP and Aertsen, A and Mehring, C},
title = {fNIRS exhibits weak tuning to hand movement direction.},
journal = {PloS one},
volume = {7},
number = {11},
pages = {e49266},
pmid = {23145138},
issn = {1932-6203},
mesh = {Adult ; Brain Mapping/methods ; *Brain-Computer Interfaces ; Female ; Hand/*physiology ; Hemodynamics ; Humans ; Male ; Middle Aged ; *Movement ; Spectroscopy, Near-Infrared/*methods ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) has become an established tool to investigate brain function and is, due to its portability and resistance to electromagnetic noise, an interesting modality for brain-machine interfaces (BMIs). BMIs have been successfully realized using the decoding of movement kinematics from intra-cortical recordings in monkey and human. Recently, it has been shown that hemodynamic brain responses as measured by fMRI are modulated by the direction of hand movements. However, quantitative data on the decoding of movement direction from hemodynamic responses is still lacking and it remains unclear whether this can be achieved with fNIRS, which records signals at a lower spatial resolution but with the advantage of being portable. Here, we recorded brain activity with fNIRS above different cortical areas while subjects performed hand movements in two different directions. We found that hemodynamic signals in contralateral sensorimotor areas vary with the direction of movements, though only weakly. Using these signals, movement direction could be inferred on a single-trial basis with an accuracy of ∼65% on average across subjects. The temporal evolution of decoding accuracy resembled that of typical hemodynamic responses observed in motor experiments. Simultaneous recordings with a head tracking system showed that head movements, at least up to some extent, do not influence the decoding of fNIRS signals. Due to the low accuracy, fNIRS is not a viable alternative for BMIs utilizing decoding of movement direction. However, due to its relative resistance to head movements, it is promising for studies investigating brain activity during motor experiments.},
}
@article {pmid23144959,
year = {2012},
author = {Dandekar, S and Ding, J and Privitera, C and Carney, T and Klein, SA},
title = {The fixation and saccade P3.},
journal = {PloS one},
volume = {7},
number = {11},
pages = {e48761},
pmid = {23144959},
issn = {1932-6203},
support = {R01 EY004776/EY/NEI NIH HHS/United States ; R01 EY04776/EY/NEI NIH HHS/United States ; },
mesh = {Eye Movements/physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Photic Stimulation ; Saccades/*physiology ; },
abstract = {Although most instances of object recognition during natural viewing occur in the presence of saccades, the neural correlates of objection recognition have almost exclusively been examined during fixation. Recent studies have indicated that there are post-saccadic modulations of neural activity immediately following eye movement landing; however, whether post-saccadic modulations affect relatively late occurring cognitive components such as the P3 has not been explored. The P3 as conventionally measured at fixation is commonly used in brain computer interfaces, hence characterizing the post-saccadic P3 could aid in the development of improved brain computer interfaces that allow for eye movements. In this study, the P3 observed after saccadic landing was compared to the P3 measured at fixation. No significant differences in P3 start time, temporal persistence, or amplitude were found between fixation and saccade trials. Importantly, sensory neural responses canceled in the target minus distracter comparisons used to identify the P3. Our results indicate that relatively late occurring cognitive neural components such as the P3 are likely less sensitive to post saccadic modulations than sensory neural components and other neural activity occurring shortly after eye movement landing. Furthermore, due to the similarity of the fixation and saccade P3, we conclude that the P3 following saccadic landing could possibly be used as a viable signal in brain computer interfaces allowing for eye movements.},
}
@article {pmid23143511,
year = {2012},
author = {Shanechi, MM and Hu, RC and Powers, M and Wornell, GW and Brown, EN and Williams, ZM},
title = {Neural population partitioning and a concurrent brain-machine interface for sequential motor function.},
journal = {Nature neuroscience},
volume = {15},
number = {12},
pages = {1715-1722},
pmid = {23143511},
issn = {1546-1726},
support = {DP1 OD003646/OD/NIH HHS/United States ; R01 HD059852/HD/NICHD NIH HHS/United States ; 5R01-HD059852/HD/NICHD NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Brain-Computer Interfaces ; Macaca mulatta ; Male ; Motor Cortex/*cytology/*physiology ; Neurons/cytology/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Random Allocation ; Reaction Time/*physiology ; },
abstract = {Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.},
}
@article {pmid23137939,
year = {2012},
author = {Apuzzo, ML},
title = {Game changers: in the realm of ideas.},
journal = {World neurosurgery},
volume = {78},
number = {5},
pages = {377-378},
doi = {10.1016/j.wneu.2012.09.016},
pmid = {23137939},
issn = {1878-8769},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/*history ; Computers/*history/*trends ; Electronics/*history ; Humans ; Neurosurgery/*history/*trends ; Prostheses and Implants/*trends ; *Quantum Theory ; Robotics/*history ; Semiconductors/*history ; Silicon/*history ; Surgery, Computer-Assisted/*history ; },
}
@article {pmid23125829,
year = {2012},
author = {Tikka, P and Väljamäe, A and de Borst, AW and Pugliese, R and Ravaja, N and Kaipainen, M and Takala, T},
title = {Enactive cinema paves way for understanding complex real-time social interaction in neuroimaging experiments.},
journal = {Frontiers in human neuroscience},
volume = {6},
number = {},
pages = {298},
pmid = {23125829},
issn = {1662-5161},
abstract = {We outline general theoretical and practical implications of what we promote as enactive cinema for the neuroscientific study of online socio-emotional interaction. In a real-time functional magnetic resonance imaging (rt-fMRI) setting, participants are immersed in cinematic experiences that simulate social situations. While viewing, their physiological reactions-including brain responses-are tracked, representing implicit and unconscious experiences of the on-going social situations. These reactions, in turn, are analyzed in real-time and fed back to modify the cinematic sequences they are viewing while being scanned. Due to the engaging cinematic content, the proposed setting focuses on living-by in terms of shared psycho-physiological epiphenomena of experience rather than active coping in terms of goal-oriented motor actions. It constitutes a means to parametrically modify stimuli that depict social situations and their broader environmental contexts. As an alternative to studying the variation of brain responses as a function of a priori fixed stimuli, this method can be applied to survey the range of stimuli that evoke similar responses across participants at particular brain regions of interest.},
}
@article {pmid23123181,
year = {2013},
author = {Höller, Y and Bergmann, J and Kronbichler, M and Crone, JS and Schmid, EV and Thomschewski, A and Butz, K and Schütze, V and Höller, P and Trinka, E},
title = {Real movement vs. motor imagery in healthy subjects.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {87},
number = {1},
pages = {35-41},
doi = {10.1016/j.ijpsycho.2012.10.015},
pmid = {23123181},
issn = {1872-7697},
mesh = {Acoustic Stimulation/methods ; Adult ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Male ; Motion Perception/*physiology ; Movement/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Motor imagery tasks are well established procedures in brain computer interfaces, but are also used in the assessment of patients with disorders of consciousness. For testing awareness in unresponsive patients it is necessary to know the natural variance of brain responses to motor imagery in healthy subjects. We examined 22 healthy subjects using EEG in three conditions: movement of both hands, imagery of the same movement, and an instruction to hold both hands still. Single-subject non-parametric statistics were applied to the fast-Fourier transformed data. Most effects were found in the α- and β-frequency ranges over central electrodes, that is, in the μ-rhythm. We found significant power changes in 18 subjects during movement and in 11 subjects during motor imagery. In 8 subjects these changes were consistent over both conditions. The significant power changes during movement were a decrease of μ-rhythm. There were 2 subjects with an increase and 9 subjects with a decrease of μ-rhythm during imagery. α and β are the most responsive frequency ranges, but there is a minor number of subjects who show a synchronization instead of the more common desynchronization during motor imagery. A (de)synchronization of μ-rhythm can be considered to be a normal response.},
}
@article {pmid23118987,
year = {2012},
author = {Normand, JM and Sanchez-Vives, MV and Waechter, C and Giannopoulos, E and Grosswindhager, B and Spanlang, B and Guger, C and Klinker, G and Srinivasan, MA and Slater, M},
title = {Beaming into the rat world: enabling real-time interaction between rat and human each at their own scale.},
journal = {PloS one},
volume = {7},
number = {10},
pages = {e48331},
pmid = {23118987},
issn = {1932-6203},
mesh = {Animals ; Humans ; Interpersonal Relations ; Movement ; Rats ; Robotics ; Time Factors ; Virtual Reality Exposure Therapy/*instrumentation ; },
abstract = {Immersive virtual reality (IVR) typically generates the illusion in participants that they are in the displayed virtual scene where they can experience and interact in events as if they were really happening. Teleoperator (TO) systems place people at a remote physical destination embodied as a robotic device, and where typically participants have the sensation of being at the destination, with the ability to interact with entities there. In this paper, we show how to combine IVR and TO to allow a new class of application. The participant in the IVR is represented in the destination by a physical robot (TO) and simultaneously the remote place and entities within it are represented to the participant in the IVR. Hence, the IVR participant has a normal virtual reality experience, but where his or her actions and behaviour control the remote robot and can therefore have physical consequences. Here, we show how such a system can be deployed to allow a human and a rat to operate together, but the human interacting with the rat on a human scale, and the rat interacting with the human on the rat scale. The human is represented in a rat arena by a small robot that is slaved to the human's movements, whereas the tracked rat is represented to the human in the virtual reality by a humanoid avatar. We describe the system and also a study that was designed to test whether humans can successfully play a game with the rat. The results show that the system functioned well and that the humans were able to interact with the rat to fulfil the tasks of the game. This system opens up the possibility of new applications in the life sciences involving participant observation of and interaction with animals but at human scale.},
}
@article {pmid23117792,
year = {2012},
author = {Rodríguez-Bermúdez, G and García-Laencina, PJ},
title = {Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.},
journal = {Journal of medical systems},
volume = {36 Suppl 1},
number = {},
pages = {S51-63},
pmid = {23117792},
issn = {0148-5598},
mesh = {Algorithms ; Biomedical Engineering ; *Brain-Computer Interfaces ; Electroencephalography/*classification/*instrumentation ; Humans ; Software Design ; },
abstract = {Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.},
}
@article {pmid23115630,
year = {2012},
author = {Lim, CG and Lee, TS and Guan, C and Fung, DS and Zhao, Y and Teng, SS and Zhang, H and Krishnan, KR},
title = {A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder.},
journal = {PloS one},
volume = {7},
number = {10},
pages = {e46692},
pmid = {23115630},
issn = {1932-6203},
mesh = {*Attention ; Attention Deficit Disorder with Hyperactivity/physiopathology/*therapy ; *Brain-Computer Interfaces ; Child ; Female ; Humans ; Male ; Severity of Illness Index ; },
abstract = {UNLABELLED: Attention deficit hyperactivity disorder (ADHD) symptoms can be difficult to treat. We previously reported that a 20-session brain-computer interface (BCI) attention training programme improved ADHD symptoms. Here, we investigated a new more intensive BCI-based attention training game system on 20 unmedicated ADHD children (16 males, 4 females) with significant inattentive symptoms (combined and inattentive ADHD subtypes). This new system monitored attention through a head band with dry EEG sensors, which was used to drive a feed forward game. The system was calibrated for each user by measuring the EEG parameters during a Stroop task. Treatment consisted of an 8-week training comprising 24 sessions followed by 3 once-monthly booster training sessions. Following intervention, both parent-rated inattentive and hyperactive-impulsive symptoms on the ADHD Rating Scale showed significant improvement. At week 8, the mean improvement was -4.6 (5.9) and -4.7 (5.6) respectively for inattentive symptoms and hyperactive-impulsive symptoms (both p<0.01). Cohen's d effect size for inattentive symptoms was large at 0.78 at week 8 and 0.84 at week 24 (post-boosters). Further analysis showed that the change in the EEG based BCI ADHD severity measure correlated with the change ADHD Rating Scale scores. The BCI-based attention training game system is a potential new treatment for ADHD.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT01344044.},
}
@article {pmid23115595,
year = {2011},
author = {Long, J and Gu, Z and Li, Y and Yu, T and Li, F and Fu, M},
title = {Semi-supervised joint spatio-temporal feature selection for P300-based BCI speller.},
journal = {Cognitive neurodynamics},
volume = {5},
number = {4},
pages = {387-398},
pmid = {23115595},
issn = {1871-4099},
abstract = {In this paper, we address the important problem of feature selection for a P300-based brain computer interface (BCI) speller system in several aspects. Firstly, time segment selection and electroencephalogram channel selection are jointly performed for better discriminability of P300 and background signals. Secondly, in view of the situation that training data with labels are insufficient, we propose an iterative semi-supervised support vector machine for joint spatio-temporal feature selection as well as classification, in which both labeled training data and unlabeled test data are utilized. More importantly, the semi-supervised learning enables the adaptivity of the system. The performance of our algorithm has been evaluated through the analysis of a P300 dataset provided by BCI Competition 2005 and another dataset collected from an in-house P300 speller system. The results show that our algorithm for joint feature selection and classification achieves satisfactory performance, meanwhile it can significantly reduce the training effort of the system. Furthermore, this algorithm is implemented online and the corresponding results demonstrate that our algorithm can improve the adaptiveness of the P300-based BCI speller.},
}
@article {pmid23111474,
year = {2012},
author = {Pathmanathan, N and Bilous, AM},
title = {HER2 testing in breast cancer: an overview of current techniques and recent developments.},
journal = {Pathology},
volume = {44},
number = {7},
pages = {587-595},
doi = {10.1097/PAT.0b013e328359cf9a},
pmid = {23111474},
issn = {1465-3931},
mesh = {Biomarkers, Tumor/*genetics/metabolism ; Breast Neoplasms/*diagnosis/genetics ; Chromosomes, Human, Pair 17/*genetics ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Immunohistochemistry/methods ; In Situ Hybridization/methods ; Karyotyping/methods ; Multiplex Polymerase Chain Reaction/methods ; Prognosis ; Receptor, ErbB-2/*genetics/metabolism ; },
abstract = {Testing for HER2 positivity in breast cancer carries implications for prognosis and therapeutic response in patients. In recent times there have been numerous developments and refinements in the available technologies for HER2 testing. In addition to this, guidelines have been developed and modified in an attempt to improve reliability and accuracy of testing. Immunohistochemistry and FISH testing have been the most widely used methodology, and the technique which has the largest knowledge base. Some of the inherent disadvantages have prompted the development of newer brightfield techniques which overcome some of these issues. There is gathering experience with these emerging technologies. Despite efforts to optimise and standardise procedures there remains a small percentage of cases that continue to be unresolved, whether this be due to issues of polysomy of chromosome 17, other complex genetic changes or analytical/interpretative issues. An ideal method for the resolution of these equivocal results should be considered in a specialised testing/referral centre, and this may include karyotyping studies of chromosome 17 or multiple probes for chromosome 17 using fluorescence in situ hybridisation or multiplex ligation-dependent probe amplification.It is timely to review of some of the newer techniques available for routine testing and approaches for cases which prove difficult to resolve using conventional testing methodology.},
}
@article {pmid23110153,
year = {2012},
author = {Shin, D and Watanabe, H and Kambara, H and Nambu, A and Isa, T and Nishimura, Y and Koike, Y},
title = {Prediction of muscle activities from electrocorticograms in primary motor cortex of primates.},
journal = {PloS one},
volume = {7},
number = {10},
pages = {e47992},
pmid = {23110153},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Brain Mapping ; Brain-Computer Interfaces ; Electroencephalography/*methods ; Electromyography ; Feasibility Studies ; Female ; Fingers/physiology ; Linear Models ; Macaca/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Muscles/*physiology ; Reproducibility of Results ; Somatosensory Cortex/physiology ; },
abstract = {Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.},
}
@article {pmid23104010,
year = {2013},
author = {Daly, I and Sweeney-Reed, CM and Nasuto, SJ},
title = {Testing for significance of phase synchronisation dynamics in the EEG.},
journal = {Journal of computational neuroscience},
volume = {34},
number = {3},
pages = {411-432},
pmid = {23104010},
issn = {1573-6873},
mesh = {Brain/*physiology ; Computer Simulation ; Electroencephalography ; Electroencephalography Phase Synchronization/*physiology ; Functional Laterality ; Humans ; Markov Chains ; *Models, Neurological ; *Nonlinear Dynamics ; Psychomotor Performance ; ROC Curve ; Time Factors ; },
abstract = {A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.},
}
@article {pmid23099207,
year = {2012},
author = {Pathmanathan, N and Provan, PJ and Mahajan, H and Hall, G and Byth, K and Bilous, AM and Balleine, RL},
title = {Characteristics of HER2-positive breast cancer diagnosed following the introduction of universal HER2 testing.},
journal = {Breast (Edinburgh, Scotland)},
volume = {21},
number = {6},
pages = {724-729},
doi = {10.1016/j.breast.2012.09.001},
pmid = {23099207},
issn = {1532-3080},
mesh = {Adult ; Age Distribution ; Aged ; Aged, 80 and over ; Biomarkers, Tumor/metabolism ; Breast Neoplasms/*diagnosis/metabolism/pathology ; Early Detection of Cancer/*methods/standards ; Female ; Humans ; Logistic Models ; Lymphatic Metastasis ; Mammography ; Middle Aged ; Neoplasm Grading ; New South Wales ; Receptor, ErbB-2/*metabolism ; Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Tumor Burden ; },
abstract = {The aim of this study was to determine the impact of universal HER2 testing on the clinico-pathologic profile of HER2+ breast cancer. Data were extracted from breast cancer pathology reports spanning two periods: before (2003/4, n = 379), and after (2008/9, n = 560) the introduction of universal testing. In 2003/4, 43.3% of breast cancers were tested for HER2 with 16% of tested cases HER2+. In 2008/9, 98.4% of cases were tested with 14.7% HER2+. In 2008/9, HER2+ status was associated with younger age, higher grade, increased tumour size, lymph node involvement, negative oestrogen and/or progesterone receptor status. HER2+ cases diagnosed in 2003/4 were not significantly different in respect of these features. The rate of HER2+ breast cancer amongst screen detected cases in 2008/9 was 8.3%. The phenotype of HER2+ breast cancer was stable following the introduction of universal testing. The overall rate of HER2+ breast cancer was influenced by screen detection.},
}
@article {pmid23098729,
year = {2012},
author = {Carmena, JM and Cohen, LG},
title = {Brain-machine interfaces and transcranial stimulation: future implications for directing functional movement and improving function after spinal injury in humans.},
journal = {Handbook of clinical neurology},
volume = {109},
number = {},
pages = {435-444},
pmid = {23098729},
issn = {0072-9752},
support = {ZIA NS002978-13/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Humans ; Magnetoencephalography ; Motor Cortex/*physiology ; Movement/*physiology ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Transcranial Magnetic Stimulation/*methods ; },
abstract = {Since its origins, the primary goal of transforming thought into action and sensation into perception has been to improve the quality of life for the physically impaired. Brain-machine interfaces (BMI) aim to improve the quality of life for large numbers of neurological patients. In particular, this novel technology is meant to play a major role in the near future as a serious contribution to spinal cord rehabilitation. During the last decade we have witnessed a dramatic increase in BMI research with impressive demonstrations of rodents, nonhuman primates, and humans controlling robots, wheelchairs, and graphical cursors in real time through signals collected from the brain. In this chapter we first review the different techniques used in the field of BMI, including electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and chronic multielectrode recordings. In addition we review the use of transcranial magnetic stimulation (TMS) for noninvasive modulation of excitability in relatively focal cortical areas. The chapter concludes with a discussion on the future implications of BMIs for directing functional movement and improving function after spinal injury in humans.},
}
@article {pmid23091013,
year = {2012},
author = {Johnson, LA and Blakely, T and Hermes, D and Hakimian, S and Ramsey, NF and Ojemann, JG},
title = {Sleep spindles are locally modulated by training on a brain-computer interface.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {109},
number = {45},
pages = {18583-18588},
pmid = {23091013},
issn = {1091-6490},
support = {R01 NS065186/NS/NINDS NIH HHS/United States ; R01 NS065186-01/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Cluster Analysis ; Electrodes ; Female ; Humans ; Male ; Sleep/*physiology ; Young Adult ; },
abstract = {The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motor-based brain-computer interface device.},
}
@article {pmid23087824,
year = {2012},
author = {Everson, R and Hauptman, JS},
title = {From the bench to the bedside: Brain-machine interfaces in spinal cord injury, the blood-brain barrier, and neurodegeneration, using the hippocampus to improve cognition, metabolism, and epilepsy, and understanding axonal death.},
journal = {Surgical neurology international},
volume = {3},
number = {},
pages = {108},
pmid = {23087824},
issn = {2152-7806},
}
@article {pmid23087607,
year = {2012},
author = {Wang, D and Miao, D and Blohm, G},
title = {Multi-class motor imagery EEG decoding for brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {151},
pmid = {23087607},
issn = {1662-453X},
abstract = {Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.},
}
@article {pmid23077919,
year = {2012},
author = {Aldea, R and Lazăr, AM},
title = {[The possibility of a multiresolution wavelet analysis for detecting the P300 event related potential].},
journal = {Revista medico-chirurgicala a Societatii de Medici si Naturalisti din Iasi},
volume = {116},
number = {1},
pages = {341-346},
pmid = {23077919},
issn = {0048-7848},
mesh = {Artificial Intelligence ; Brain-Computer Interfaces ; *Electroencephalography/methods ; *Event-Related Potentials, P300 ; Humans ; Mathematical Computing ; Nervous System Diseases/diagnosis ; Pattern Recognition, Automated/methods ; *Wavelet Analysis ; },
abstract = {UNLABELLED: The main objective is to high-light the P300 potential on certain electroencephalographic signals. P300 occurs at a relatively well defined time in relation to a stimulus and it represents a signal with a specified band frequency.<br><br>METHOD: The electroencephalographic (EEG) was recorded with 4 wet electrodes by means of g.MOBIlab+ module, a g.tec acquisition system. The multiresolution wavelet transform was chosen to extract the P300 potential from the EEG signal because it provides information on both time and frequency domains.<br><br>RESULTS: The multiresolution wavelet transform decomposes the signal in sub-bands and it helps to highlight the P300 potential. The spectrum of the P300 potential is around 3Hz. For the multiresolution wavelet decomposition this corresponds to coefficients of approximation of order 4 according to 0 to 60 Hz band of the original EEG signal. The representation of these coefficients emphasizes a better detection of P300 potential then in the original signal.<br><br>CONCLUSION: It is shown to be a more appropriate method than the direct analysis of the signal because it works with lower dimensional signals. This method of detection of the P300 potential can be used successfully in the implementation of a Brain Computer Interface (BCI).},
}
@article {pmid23071707,
year = {2012},
author = {Ramos-Murguialday, A and Schürholz, M and Caggiano, V and Wildgruber, M and Caria, A and Hammer, EM and Halder, S and Birbaumer, N},
title = {Proprioceptive feedback and brain computer interface (BCI) based neuroprostheses.},
journal = {PloS one},
volume = {7},
number = {10},
pages = {e47048},
pmid = {23071707},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; *Feedback, Sensory ; Fingers/physiology ; Humans ; Learning ; *Neural Prostheses ; Orthotic Devices ; Robotics/instrumentation/methods ; },
abstract = {Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent "negative" feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks.},
}
@article {pmid23055968,
year = {2012},
author = {Lew, E and Chavarriaga, R and Silvoni, S and Millán, Jdel R},
title = {Detection of self-paced reaching movement intention from EEG signals.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {13},
pmid = {23055968},
issn = {1662-6443},
abstract = {Future neuroprosthetic devices, in particular upper limb, will require decoding and executing not only the user's intended movement type, but also when the user intends to execute the movement. This work investigates the potential use of brain signals recorded non-invasively for detecting the time before a self-paced reaching movement is initiated which could contribute to the design of practical upper limb neuroprosthetics. In particular, we show the detection of self-paced reaching movement intention in single trials using the readiness potential, an electroencephalography (EEG) slow cortical potential (SCP) computed in a narrow frequency range (0.1-1 Hz). Our experiments with 12 human volunteers, two of them stroke subjects, yield high detection rates prior to the movement onset and low detection rates during the non-movement intention period. With the proposed approach, movement intention was detected around 500 ms before actual onset, which clearly matches previous literature on readiness potentials. Interestingly, the result obtained with one of the stroke subjects is coherent with those achieved in healthy subjects, with single-trial performance of up to 92% for the paretic arm. These results suggest that, apart from contributing to our understanding of voluntary motor control for designing more advanced neuroprostheses, our work could also have a direct impact on advancing robot-assisted neurorehabilitation.},
}
@article {pmid23055496,
year = {2012},
author = {Medina, LE and Lebedev, MA and O'Doherty, JE and Nicolelis, MA},
title = {Stochastic facilitation of artificial tactile sensation in primates.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {32},
number = {41},
pages = {14271-14275},
pmid = {23055496},
issn = {1529-2401},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; RC1HD063390/HD/NICHD NIH HHS/United States ; RC1 HD063390/HD/NICHD NIH HHS/United States ; DP1OD006798/OD/NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/methods ; Electrodes, Implanted ; Female ; Macaca mulatta ; Male ; Movement/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; Stochastic Processes ; Touch/*physiology ; },
abstract = {Artificial sensation via electrical or optical stimulation of brain sensory areas offers a promising treatment for sensory deficits. For a brain-machine-brain interface, such artificial sensation conveys feedback signals from a sensorized prosthetic limb. The ways neural tissue can be stimulated to evoke artificial sensation and the parameter space of such stimulation, however, remain largely unexplored. Here we investigated whether stochastic facilitation (SF) could enhance an artificial tactile sensation produced by intracortical microstimulation (ICMS). Two rhesus monkeys learned to use a virtual hand, which they moved with a joystick, to explore virtual objects on a computer screen. They sought an object associated with a particular artificial texture (AT) signaled by a periodic ICMS pattern delivered to the primary somatosensory cortex (S1) through a pair of implanted electrodes. During each behavioral trial, aperiodic ICMS (i.e., noise) of randomly chosen amplitude was delivered to S1 through another electrode pair implanted 1 mm away from the site of AT delivery. Whereas high-amplitude noise worsened AT detection, moderate noise clearly improved the detection of weak signals, significantly raising the proportion of correct trials. These findings suggest that SF could be used to enhance prosthetic sensation.},
}
@article {pmid23050029,
year = {2012},
author = {Zhang, F and Aghagolzadeh, M and Oweiss, K},
title = {A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications.},
journal = {Journal of signal processing systems},
volume = {69},
number = {3},
pages = {351-361},
pmid = {23050029},
issn = {1939-8018},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; },
abstract = {Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.},
}
@article {pmid23047892,
year = {2013},
author = {Shanechi, MM and Wornell, GW and Williams, ZM and Brown, EN},
title = {Feedback-controlled parallel point process filter for estimation of goal-directed movements from neural signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {1},
pages = {129-140},
doi = {10.1109/TNSRE.2012.2221743},
pmid = {23047892},
issn = {1558-0210},
mesh = {Algorithms ; Animals ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Feedback ; *Goals ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; },
abstract = {Real-time brain-machine interfaces have estimated either the target of a movement, or its kinematics. However, both are encoded in the brain. Moreover, movements are often goal-directed and made to reach a target. Hence, modeling the goal-directed nature of movements and incorporating the target information in the kinematic decoder can increase its accuracy. Using an optimal feedback control design, we develop a recursive Bayesian kinematic decoder that models goal-directed movements and combines the target information with the neural spiking activity during movement. To do so, we build a prior goal-directed state-space model for the movement using an optimal feedback control model of the sensorimotor system that aims to emulate the processes underlying actual motor control and takes into account the sensory feedback. Most goal-directed models, however, depend on the movement duration, not known a priori to the decoder. This has prevented their real-time implementation. To resolve this duration uncertainty, the decoder discretizes the duration and consists of a bank of parallel point process filters, each combining the prior model of a discretized duration with the neural activity. The kinematics are computed by optimally combining these filter estimates. Using the feedback-controlled model and even a coarse discretization, the decoder significantly reduces the root mean square error in estimation of reaching movements performed by a monkey.},
}
@article {pmid23041686,
year = {2012},
author = {El-Menyar, A and Al Thani, H and Zarour, A and Latifi, R},
title = {Understanding traumatic blunt cardiac injury.},
journal = {Annals of cardiac anaesthesia},
volume = {15},
number = {4},
pages = {287-295},
doi = {10.4103/0971-9784.101875},
pmid = {23041686},
issn = {0974-5181},
mesh = {Biomarkers ; Echocardiography ; Electrocardiography ; Heart Injuries/diagnosis/*etiology/therapy ; Humans ; Wounds, Nonpenetrating/diagnosis/*etiology/therapy ; },
abstract = {Cardiac injuries are classified as blunt and penetrating injuries. In both the injuries, the major issue is missing the diagnosis and high mortality. Blunt cardiac injuries (BCI) are much more common than penetrating injuries. Aiming at a better understanding of BCI, we searched the literature from January 1847 to January 2012 by using MEDLINE and EMBASE search engines. Using the key word "Blunt Cardiac Injury," we found 1814 articles; out of which 716 articles were relevant. Herein, we review the causes, diagnosis, and management of BCI. In conclusion, traumatic cardiac injury is a major challenge in critical trauma care, but the guidelines are lacking. A high index of suspicion, application of current diagnostic protocols, and prompt and appropriate management is mandatory.},
}
@article {pmid23037775,
year = {2012},
author = {Chung, YG and Kang, JH and Kim, SP},
title = {Correlation of fronto-central phase coupling with sensorimotor rhythm modulation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {36},
number = {},
pages = {46-50},
doi = {10.1016/j.neunet.2012.08.006},
pmid = {23037775},
issn = {1879-2782},
mesh = {Adult ; Alpha Rhythm/*physiology ; Brain Mapping/*methods ; *Communication Devices for People with Disabilities ; Dominance, Cerebral ; Electroencephalography/*methods ; *Electroencephalography Phase Synchronization/physiology ; Foot ; Frontal Lobe/*physiology ; Hand ; Humans ; Imagination/*physiology ; Individuality ; *Man-Machine Systems ; Motor Activity ; Motor Cortex/*physiology ; Signal Processing, Computer-Assisted ; Tongue ; *User-Computer Interface ; },
abstract = {We investigated neurophysiologic correlates of individual differences in the modulation of sensorimotor rhythms (SMRs) in the human electroencephalography (EEG) during motor imagery. The ability of modulating SMRs to different motor imageries was correlated with the strength of alpha phase synchronization across frontal and central sensorimotor areas. The results suggest that fronto-central coupling may elucidate individual variations in SMR modulation that is essential for using SMR-based brain-computer interfaces.},
}
@article {pmid23037360,
year = {2012},
author = {Ke, J and Lam, EY},
title = {Object reconstruction in block-based compressive imaging.},
journal = {Optics express},
volume = {20},
number = {20},
pages = {22102-22117},
doi = {10.1364/OE.20.022102},
pmid = {23037360},
issn = {1094-4087},
mesh = {*Algorithms ; Data Compression/*methods ; Data Interpretation, Statistical ; Image Enhancement/*methods ; Image Interpretation, Computer-Assisted/*methods ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {A block-based compressive imaging (BCI) system using sequential architecture is presented in this paper. Feature measurements are collected using the principal component analysis (PCA) projection. The linear Wiener operator and a nonlinear method based on the Field-of-Expert (FoE) prior model are used for object reconstruction. Experimental results are given to demonstrate the superior reconstruction performance of the FoE-based method over the Wiener operator. In addition, the effects of system parameters, such as the object block size, the number of features per block, and the noise level to the BCI reconstruction performance are discussed with different kinds of objects. Then an optimal block size is defined and studied for BCI.},
}
@article {pmid23034907,
year = {2012},
author = {Naci, L and Monti, MM and Cruse, D and Kübler, A and Sorger, B and Goebel, R and Kotchoubey, B and Owen, AM},
title = {Brain-computer interfaces for communication with nonresponsive patients.},
journal = {Annals of neurology},
volume = {72},
number = {3},
pages = {312-323},
doi = {10.1002/ana.23656},
pmid = {23034907},
issn = {1531-8249},
support = {U.1055.01.002.00001.01//Medical Research Council/United Kingdom ; },
mesh = {Brain/pathology/physiopathology ; *Brain-Computer Interfaces ; Consciousness/physiology ; Electric Stimulation Therapy/*methods ; Electroencephalography ; Humans ; Nervous System Diseases/pathology/*therapy ; Neuroimaging ; *User-Computer Interface ; },
abstract = {A substantial number of patients who survive severe brain injury progress to a nonresponsive state of wakeful unawareness, referred to as a vegetative state (VS). They appear to be awake, but show no signs of awareness of themselves, or of their environment in repeated clinical examinations. However, recent neuroimaging research demonstrates that some VS patients can respond to commands by willfully modulating their brain activity according to instruction. Brain-computer interfaces (BCIs) may allow such patients to circumvent the barriers imposed by their behavioral limitations and communicate with the outside world. However, although such devices would undoubtedly improve the quality of life for some patients and their families, developing BCI systems for behaviorally nonresponsive patients presents substantial technical and clinical challenges. Here we review the state of the art of BCI research across noninvasive neuroimaging technologies, and propose how such systems should be developed further to provide fully fledged communication systems for behaviorally nonresponsive populations.},
}
@article {pmid23033438,
year = {2013},
author = {Clements, IP and Mukhatyar, VJ and Srinivasan, A and Bentley, JT and Andreasen, DS and Bellamkonda, RV},
title = {Regenerative scaffold electrodes for peripheral nerve interfacing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {21},
number = {4},
pages = {554-566},
doi = {10.1109/TNSRE.2012.2217352},
pmid = {23033438},
issn = {1558-0210},
support = {R01 44409//PHS HHS/United States ; R01 NS065109/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Axons/physiology ; *Brain-Computer Interfaces ; Cell Count ; Cell Movement ; Electric Stimulation/*instrumentation ; *Electrodes ; Electrodes, Implanted ; Electrophysiological Phenomena ; Extremities/physiology ; Ganglia, Spinal/physiology ; Immunohistochemistry ; Male ; Nanofibers ; Nerve Regeneration ; Organ Culture Techniques ; Peripheral Nerves/*physiology ; Prostheses and Implants ; Prosthesis Design ; Rats ; Rats, Inbred Lew ; },
abstract = {Advances in neural interfacing technology are required to enable natural, thought-driven control of a prosthetic limb. Here, we describe a regenerative electrode design in which a polymer-based thin-film electrode array is integrated within a thin-film sheet of aligned nanofibers, such that axons regenerating from a transected peripheral nerve are topographically guided across the electrode recording sites. Cultures of dorsal root ganglia were used to explore design parameters leading to cellular migration and neurite extension across the nanofiber/electrode array boundary. Regenerative scaffold electrodes (RSEs) were subsequently fabricated and implanted across rat tibial nerve gaps to evaluate device recording capabilities and influence on nerve regeneration. In 20 of these animals, regeneration was compared between a conventional nerve gap model and an amputation model. Characteristic shaping of regenerated nerve morphology around the embedded electrode array was observed in both groups, and regenerated axon profile counts were similar at the eight week end point. Implanted RSEs recorded evoked neural activity in all of these cases, and also in separate implantations lasting up to five months. These results demonstrate that nanofiber-based topographic cues within a regenerative electrode can influence nerve regeneration, to the potential benefit of a peripheral nerve interface suitable for limb amputees.},
}
@article {pmid23033330,
year = {2012},
author = {Kus, R and Valbuena, D and Zygierewicz, J and Malechka, T and Graeser, A and Durka, P},
title = {Asynchronous BCI based on motor imagery with automated calibration and neurofeedback training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {6},
pages = {823-835},
doi = {10.1109/TNSRE.2012.2214789},
pmid = {23033330},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Calibration ; Computer Graphics ; Cortical Synchronization ; Cues ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Joints/anatomy &amp; histology/physiology ; Logistic Models ; Male ; Movement/*physiology ; Neurofeedback/*instrumentation/methods ; Photic Stimulation ; Psychomotor Performance/physiology ; User-Computer Interface ; Young Adult ; },
abstract = {A new multiclass brain-computer interface (BCI) based on the modulation of sensorimotor oscillations by imagining movements is described. By the application of advanced signal processing tools, statistics and machine learning, this BCI system offers: 1) asynchronous mode of operation, 2) automatic selection of user-dependent parameters based on an initial calibration, 3) incremental update of the classifier parameters from feedback data. The signal classification uses spatially filtered signals and is based on spectral power estimation computed in individualized frequency bands, which are automatically identified by a specially tailored AR-based model. Relevant features are chosen by a criterion based on Mutual Information. Final recognition of motor imagery is effectuated by a multinomial logistic regression classifier. This BCI system was evaluated in two studies. In the first study, five participants trained the ability to imagine movements of the right hand, left hand and feet in response to visual cues. The accuracy of the classifier was evaluated across four training sessions with feedback. The second study assessed the information transfer rate (ITR) of the BCI in an asynchronous application. The subjects' task was to navigate a cursor along a computer rendered 2-D maze. A peak information transfer rate of 8.0 bit/min was achieved. Five subjects performed with a mean ITR of 4.5 bit/min and an accuracy of 74.84%. These results demonstrate that the use of automated interfaces to reduce complexity for the intended operator (outside the laboratory) is indeed possible. The signal processing and classifier source code embedded in BCI2000 is available from https://www.brain-project.org/downloads.html.},
}
@article {pmid23033323,
year = {2012},
author = {Hadjidimitriou, SK and Hadjileontiadis, LJ},
title = {Toward an EEG-based recognition of music liking using time-frequency analysis.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {12},
pages = {3498-3510},
doi = {10.1109/TBME.2012.2217495},
pmid = {23033323},
issn = {1558-2531},
mesh = {*Artificial Intelligence ; Brain/physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Music ; Pleasure/*physiology ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k -NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%}
was achieved using k-NN and HHS-based feature vectors (FVs) representing a bilateral average activity, referred to a resting period, in β (13-30 Hz) and γ (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.},
}
@article {pmid23032116,
year = {2013},
author = {Jin, J and Sellers, EW and Zhang, Y and Daly, I and Wang, X and Cichocki, A},
title = {Whether generic model works for rapid ERP-based BCI calibration.},
journal = {Journal of neuroscience methods},
volume = {212},
number = {1},
pages = {94-99},
pmid = {23032116},
issn = {1872-678X},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; 1 R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; 1 R21 DC010470-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; *Brain-Computer Interfaces ; Calibration ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Models, Theoretical ; Online Systems ; Pattern Recognition, Visual ; Photic Stimulation ; User-Computer Interface ; Young Adult ; },
abstract = {Event-related potential (ERP)-based brain-computer interfacing (BCI) is an effective method of basic communication. However, collecting calibration data, and classifier training, detracts from the amount of time allocated for online communication. Decreasing calibration time can reduce preparation time thereby allowing for additional online use, potentially lower fatigue, and improved performance. Previous studies, using generic online training models which avoid offline calibration, afford more time for online spelling. Such studies have not examined the direct effects of the model on individual performance, and the training sequence exceeded the time reported here. The first goal of this work is to survey whether one generic model works for all subjects and the second goal is to show the performance of a generic model using an online training strategy when participants could use the generic model. The generic model was derived from 10 participant's data. An additional 11 participants were recruited for the current study. Seven of the participants were able to use the generic model during online training. Moreover, the generic model performed as well as models obtained from participant specific offline data with a mean training time of less than 2 min. However, four of the participants could not use this generic model, which shows that one generic mode is not generic for all subjects. More research on ERPs of subjects with different characteristics should be done, which would be helpful to build generic models for subject groups. This result shows a potential valuable direction for improving the BCI system.},
}
@article {pmid23031175,
year = {2012},
author = {Márquez-Chin, C and Popovic, MR and Sanin, E and Chen, R and Lozano, AM},
title = {Real-time two-dimensional asynchronous control of a computer cursor with a single subdural electrode.},
journal = {The journal of spinal cord medicine},
volume = {35},
number = {5},
pages = {382-391},
pmid = {23031175},
issn = {1079-0268},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Aged ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; *Computer Peripherals ; Computer Systems ; Electrodes, Implanted ; Essential Tremor/physiopathology/*rehabilitation ; Female ; Humans ; Models, Neurological ; Motor Cortex/*physiology ; *Neural Prostheses ; Signal Processing, Computer-Assisted ; *Subdural Space ; },
abstract = {OBJECTIVE: To test the feasibility of controlling a computer cursor asynchronously in two dimensions using one subdural electrode.<br><br>DESIGN: Proof of concept study.<br><br>SETTING: Acute care hospital in Toronto, Canada.<br><br>PARTICIPANT: A 68-year-old woman with a subdural electrode implanted for the treatment of essential tremor (ET) using direct brain stimulation of the primary motor cortex (MI).<br><br>INTERVENTIONS: Power changes in the electrocorticography signals were used to implement a "brain switch". To activate the switch the subject had to decrease the power in the 7-13 Hz frequency range using motor imagery of the left hand. The brain switch was connected to a system for asynchronous control of movement in two dimensions. Each time the user reduced the amplitude in the 7-13 Hz frequency band below an experimentally defined threshold the direction of cursor changed randomly. The new direction was always different from those previously rejected ensuring the convergence of the system on the desired direction.<br><br>OUTCOME MEASURES: Training time, time and number of switch activations required to reach specific targets, information transfer rate.<br><br>RESULTS: The user was able to control the cursor to specific targets on the screen after only 15 minutes of training. Each target was reached in 51.7 &#xb1; 40.2 seconds (mean &#xb1; SD) and after 9.4 &#xb1; 6.8 switch activations. Information transfer rate of the system was estimated to be 0.11 bit/second.<br><br>CONCLUSION: A novel brain-machine interface for asynchronous two-dimensional control using one subdural electrode was developed.},
}
@article {pmid23030232,
year = {2013},
author = {Power, SD and Chau, T},
title = {Automatic single-trial classification of prefrontal hemodynamic activity in an individual with Duchenne muscular dystrophy.},
journal = {Developmental neurorehabilitation},
volume = {16},
number = {1},
pages = {67-72},
doi = {10.3109/17518423.2012.718293},
pmid = {23030232},
issn = {1751-8431},
mesh = {*Brain-Computer Interfaces ; Hemodynamics ; Humans ; Male ; Muscular Dystrophy, Duchenne/*physiopathology/*rehabilitation ; Prefrontal Cortex/*physiopathology ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) allow users to control external devices via brain activity alone, circumventing the somatic nervous system and the need for overt movement. Essential to BCI development is the ability to accurately detect and classify patterns of activation associated with different mental tasks. Here, we investigate the ability to automatically distinguish a mental arithmetic (MA) task from a natural baseline state in an individual with Duchenne muscular dystrophy (DMD) using signals acquired via multichannel near-infrared spectroscopy (NIRS). Using dual-wavelength NIRS, we interrogated nine sites around the frontopolar locations while the individual performed MA to answer multiple-choice questions within a system-paced paradigm. An encouraging overall classification accuracy of 71.1% was obtained, which is comparable to the average accuracy we previously reported for healthy individuals performing the same task. This result demonstrates the potential of NIRS-BCI based on task-induced prefrontal activity for use by individuals with DMD.},
}
@article {pmid23027946,
year = {2012},
author = {Hauschild, M and Mulliken, GH and Fineman, I and Loeb, GE and Andersen, RA},
title = {Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {109},
number = {42},
pages = {17075-17080},
pmid = {23027946},
issn = {1091-6490},
mesh = {Animals ; *Brain-Computer Interfaces ; Cognition/*physiology ; Macaca mulatta ; Magnetic Resonance Imaging ; Movement/*physiology ; Neocortex/*physiology ; *Neural Prostheses ; Parietal Lobe/*physiology ; Photic Stimulation ; Synaptic Transmission/*physiology ; },
abstract = {Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.},
}
@article {pmid23024701,
year = {2012},
author = {Kim, HN and Kim, YH and Shin, HC and Aggarwal, V and Schieber, MH and Thakor, NV},
title = {Neuron Selection by Relative Importance for Neural Decoding of Dexterous Finger Prosthesis Control Application.},
journal = {Biomedical signal processing and control},
volume = {7},
number = {6},
pages = {632-639},
pmid = {23024701},
issn = {1746-8094},
support = {P30 EY001319/EY/NEI NIH HHS/United States ; R01 EB010100/EB/NIBIB NIH HHS/United States ; R01 EB010100-01/EB/NIBIB NIH HHS/United States ; R01 NS065902/NS/NINDS NIH HHS/United States ; },
abstract = {Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly-ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain-machine interfaces for direct neural control of hand prostheses.},
}
@article {pmid23023500,
year = {2012},
author = {Li, G and Yu, M and Lee, WW and Tsang, M and Krishnan, E and Weyand, CM and Goronzy, JJ},
title = {Decline in miR-181a expression with age impairs T cell receptor sensitivity by increasing DUSP6 activity.},
journal = {Nature medicine},
volume = {18},
number = {10},
pages = {1518-1524},
pmid = {23023500},
issn = {1546-170X},
support = {R01 AI044142/AI/NIAID NIH HHS/United States ; P01 HL058000/HL/NHLBI NIH HHS/United States ; U19 AI090019/AI/NIAID NIH HHS/United States ; R01 EY011916/EY/NEI NIH HHS/United States ; R01 AG015043/AG/NIA NIH HHS/United States ; U19 AI057266/AI/NIAID NIH HHS/United States ; R01 AR042527/AR/NIAMS NIH HHS/United States ; },
mesh = {Adult ; Aged ; Aged, 80 and over ; Aging/*immunology ; CD4-Positive T-Lymphocytes/*immunology/*metabolism ; Cell Differentiation ; Cell Proliferation ; Cells, Cultured ; Cyclohexylamines/pharmacology ; Dual Specificity Phosphatase 6/*metabolism ; Extracellular Signal-Regulated MAP Kinases/genetics/metabolism ; Female ; Humans ; Indenes/pharmacology ; Lymphocyte Activation ; MAP Kinase Signaling System ; Male ; MicroRNAs/*metabolism ; Middle Aged ; Phosphorylation ; Receptors, Antigen, T-Cell/genetics/*immunology ; ZAP-70 Protein-Tyrosine Kinase/metabolism ; },
abstract = {The ability of the human immune system to respond to vaccination declines with age. We identified an age-associated defect in T cell receptor (TCR)-induced extracellular signal-regulated kinase (ERK) phosphorylation in naive CD4(+) T cells, whereas other signals, such as ζ chain-associated protein kinase 70 (ZAP70) and phospholipase C-γ1 phosphorylation, were not impaired. The defective ERK signaling was caused by the dual specific phosphatase 6 (DUSP6), whose protein expression increased with age due to a decline in repression by miR-181a. Reconstitution of miR-181a lowered DUSP6 expression in naive CD4(+) T cells in elderly individuals. DUSP6 repression using miR-181a or specific siRNA and DUSP6 inhibition by the allosteric inhibitor (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one improved CD4(+) T cell responses, as seen by increased expression of activation markers, improved proliferation and supported preferential T helper type 1 cell differentiation. DUSP6 is a potential intervention target for restoring T cell responses in the elderly, which may augment the effectiveness of vaccination.},
}
@article {pmid23022326,
year = {2013},
author = {Ros, T and Théberge, J and Frewen, PA and Kluetsch, R and Densmore, M and Calhoun, VD and Lanius, RA},
title = {Mind over chatter: plastic up-regulation of the fMRI salience network directly after EEG neurofeedback.},
journal = {NeuroImage},
volume = {65},
number = {},
pages = {324-335},
pmid = {23022326},
issn = {1095-9572},
support = {P20 GM103472/GM/NIGMS NIH HHS/United States ; R01 EB006841/EB/NIBIB NIH HHS/United States ; R01 EB000840/EB/NIBIB NIH HHS/United States ; P20 RR021938/RR/NCRR NIH HHS/United States ; R01 EB020407/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Cortical Synchronization/physiology ; Electroencephalography ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Neural Pathways/*physiology ; Neurofeedback/*physiology ; Neuronal Plasticity/*physiology ; Up-Regulation ; },
abstract = {Neurofeedback (NFB) involves a brain-computer interface that allows users to learn to voluntarily control their cortical oscillations, reflected in the electroencephalogram (EEG). Although NFB is being pioneered as a noninvasive tool for treating brain disorders, there is insufficient evidence on the mechanism of its impact on brain function. Furthermore, the dominant rhythm of the human brain is the alpha oscillation (8-12 Hz), yet its behavioral significance remains multifaceted and largely correlative. In this study with 34 healthy participants, we examined whether during the performance of an attentional task, the functional connectivity of distinct fMRI networks would be plastically altered after a 30-min session of voluntary reduction of alpha rhythm (n=17) versus a sham-feedback condition (n=17). We reveal that compared to sham-feedback, NFB induced an increase of connectivity within regions of the salience network involved in intrinsic alertness (dorsal anterior cingulate), which was detectable 30 min after termination of training. The increase in salience network (default-mode network) connectivity was negatively (positively) correlated with changes in 'on task' mind-wandering as well as resting state alpha rhythm. Crucially, we observed a causal dependence between alpha rhythm synchronization during NFB and its subsequent change at resting state, not exhibited by the SHAM group. Our findings provide neurobehavioral evidence for the brain's exquisite functional plasticity, and for a temporally direct impact of NFB on a key cognitive control network, suggesting a promising basis for its use to treat cognitive disorders under physiological conditions.},
}
@article {pmid23019006,
year = {2012},
author = {de Rugy, A and Loeb, GE and Carroll, TJ},
title = {Virtual biomechanics: a new method for online reconstruction of force from EMG recordings.},
journal = {Journal of neurophysiology},
volume = {108},
number = {12},
pages = {3333-3341},
doi = {10.1152/jn.00714.2012},
pmid = {23019006},
issn = {1522-1598},
mesh = {Adult ; Biomechanical Phenomena/physiology ; Electromyography/*methods ; Humans ; Isometric Contraction/*physiology ; Male ; Muscle, Skeletal/*physiology ; Photic Stimulation/methods ; *User-Computer Interface ; Young Adult ; },
abstract = {Current methods to reconstruct muscle contributions to joint torque usually combine electromyograms (EMGs) with cadaver-based estimates of biomechanics, but both are imperfect representations of reality. Here, we describe a new method that enables online force reconstruction in which we optimize a "virtual" representation of muscle biomechanics. We first obtain tuning curves for the five major wrist muscles from the mean rectified EMG during the hold phase of an isometric aiming task when a cursor is driven by actual force recordings. We then apply a custom, gradient-descent algorithm to determine the set of "virtual pulling vectors" that best reach the target forces when combined with the observed muscle activity. When these pulling vectors are multiplied by the rectified and low-pass-filtered (1.3 Hz) EMG of the five muscles online, the reconstructed force provides a close spatiotemporal match to the true force exerted at the wrist. In three separate experiments, we demonstrate that the technique works equally well for surface and fine-wire recordings and is sensitive to biomechanical changes elicited by a modification of the forearm posture. In all conditions tested, muscle tuning curves obtained when the task was performed with feedback of reconstructed force were similar to those obtained when the task was performed with real force feedback. This online force reconstruction technique provides new avenues to study the relationship between neural control and limb biomechanics since the "virtual biomechanics" can be systematically altered at will.},
}
@article {pmid23016408,
year = {2012},
author = {Zhang, L and Liu, G and Luo, Q and Xu, W},
title = {[Phase synchronization analysis of EEG signal during audio-visual stimulation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {29},
number = {4},
pages = {645-649},
pmid = {23016408},
issn = {1001-5515},
mesh = {Acoustic Stimulation ; Auditory Cortex/*physiology ; Brain/*physiology ; Electroencephalography Phase Synchronization/*physiology ; Entropy ; Humans ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Visual Cortex/*physiology ; },
abstract = {EEG Synchronization is considered the conformity of the brain functional areas. Advanced brain function requires many nervous systems with a specific function in relevant brain regions (areas) to achieve integration and coordination at different levels. In this paper, a new method for phase synchronization analysis-Mutually Approximate Entropy is proposed to process different frequency band of EEG signal during audio-visual stimulation and get Similar results with the method of Synchronization Index and Mutual Information Entropy. This showed that the Mutually Approximate Entropy can lead to a good indication of the phase synchronization between two leads. The paper also explored the brain reaction zone by the results of the phase synchronization analysis. The research work lays the foundation for the brain-computer interface design.},
}
@article {pmid23016406,
year = {2012},
author = {Zhao, Y and Sun, J and Song, Y and Li, F and Ai, H and Wang, M},
title = {[Changes of local field potentials in M1 underlying the specific behavior in rat].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {29},
number = {4},
pages = {634-8, 649},
pmid = {23016406},
issn = {1001-5515},
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Evoked Potentials, Motor/*physiology ; Feeding Behavior/*physiology ; Male ; *Microelectrodes ; Motor Cortex/*physiology ; Rats ; Rats, Wistar ; },
abstract = {The local field potentials (LFPs) underlying specific behavior were recorded and analyzed in this paper from primary motor cortex (M1) with several medium, such as the self-made single channel micro-electrodes, the system of multi-channels physiological signal acquisition and processing and so on. During the experiment, the specific behavior was divided into four periods according to the changes of the recorded LFPs and the changes of the specific behavior recorded simultaneously in rats. The four periods were named prophase of catching period, planning period, catching period and the completion period, respectively. Then several methods were used for the analysis of the LFPs by MATLAB, such as time domain analysis, power spectral distribution analysis and time-frequency analysis. The results suggested that the LFPs which were caused by different behavior from a large number of movement-related neurons of M1 during the specific behavior in the process of catching play an important part in the "code" guiding role in rats. The results demonstrat that the LFPs of M1 may provide a feasibility to discriminate the motor behavior of forelimb.},
}
@article {pmid23013047,
year = {2012},
author = {Debener, S and Minow, F and Emkes, R and Gandras, K and de Vos, M},
title = {How about taking a low-cost, small, and wireless EEG for a walk?.},
journal = {Psychophysiology},
volume = {49},
number = {11},
pages = {1617-1621},
doi = {10.1111/j.1469-8986.2012.01471.x},
pmid = {23013047},
issn = {1469-8986},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/economics/*instrumentation/methods ; Equipment Design/economics/standards ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Monitoring, Ambulatory/*instrumentation/standards ; Walking ; Young Adult ; },
abstract = {To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77%) and outdoor (69%) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.},
}
@article {pmid23010771,
year = {2012},
author = {Wang, PT and King, CE and Chui, LA and Do, AH and Nenadic, Z},
title = {Self-paced brain-computer interface control of ambulation in a virtual reality environment.},
journal = {Journal of neural engineering},
volume = {9},
number = {5},
pages = {056016},
doi = {10.1088/1741-2560/9/5/056016},
pmid = {23010771},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/instrumentation/methods ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; Virtual Reality Exposure Therapy/instrumentation/*methods ; Walking/*physiology/psychology ; Young Adult ; },
abstract = {OBJECTIVE: Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE).<br><br>APPROACH: Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE.<br><br>MAIN RESULTS: The average offline training performance across subjects was 77.2 &#xb1; 11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26 &#xd7; 10(-23)), with chance performance being 50%. The average online performance was 8.5 &#xb1; 1.1 (out of 10) successful stops and 303 &#xb1; 53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p < 0.05) in 44 of the 45 online sessions.<br><br>SIGNIFICANCE: By using a data-driven machine learning approach to decode users' KMI, this BCI-VRE system enabled intuitive and purposeful self-paced control of ambulation after only 10 minutes training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.},
}
@article {pmid23006241,
year = {2012},
author = {Myrden, A and Kushki, A and Sejdić, E and Chau, T},
title = {Towards increased data transmission rate for a three-class metabolic brain-computer interface based on transcranial Doppler ultrasound.},
journal = {Neuroscience letters},
volume = {528},
number = {2},
pages = {99-103},
doi = {10.1016/j.neulet.2012.09.030},
pmid = {23006241},
issn = {1872-7972},
mesh = {Adolescent ; Brain/*blood supply ; *Brain-Computer Interfaces ; Cerebrovascular Circulation ; Female ; Humans ; Imagination ; Male ; Mental Processes ; Middle Cerebral Artery/physiology ; Rest ; Rotation ; *Ultrasonography, Doppler, Transcranial ; Verbal Behavior ; Young Adult ; },
abstract = {In this study, we conducted an offline analysis of transcranial Doppler (TCD) ultrasound recordings to investigate potential methods for increasing data transmission rate in a TCD-based brain-computer interface. Cerebral blood flow velocity was recorded within the left and right middle cerebral arteries while nine able-bodied participants alternated between rest and two different mental activities (word generation and mental rotation). We differentiated these three states using a three-class linear discriminant analysis classifier while the duration of each state was varied between 5 and 30s. Maximum classification accuracies exceeded 70%, and data transmission rate was maximized at 1.2 bits per minute, representing a four-fold increase in data transmission rate over previous two-class analysis of TCD recordings.},
}
@article {pmid23001369,
year = {2012},
author = {Anderson, NR and Blakely, T and Schalk, G and Leuthardt, EC and Moran, DW},
title = {Electrocorticographic (ECoG) correlates of human arm movements.},
journal = {Experimental brain research},
volume = {223},
number = {1},
pages = {1-10},
pmid = {23001369},
issn = {1432-1106},
support = {EB000856/EB/NIBIB NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Algorithms ; Arm/*physiology ; Biomechanical Phenomena ; *Electroencephalography ; Epilepsy/physiopathology ; Female ; Hand/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance/physiology ; },
abstract = {Invasive and non-invasive brain-computer interface (BCI) studies have long focused on the motor cortex for kinematic control of artificial devices. Most of these studies have used single-neuron recordings or electroencephalography (EEG). Electrocorticography (ECoG) is a relatively new recording modality in BCI research that has primarily been built on successes in EEG recordings. We built on prior experiments related to single-neuron recording and quantitatively compare the extent to which different brain regions reflect kinematic tuning parameters of hand speed, direction, and velocity in both a reaching and tracing task in humans. Hand and arm movement experiments using ECoG have shown positive results before, but the tasks were not designed to tease out which kinematics are encoded. In non-human primates, the relationships among these kinematics have been more carefully documented, and we sought to begin elucidating that relationship in humans using ECoG. The largest modulation in ECoG activity for direction, speed, and velocity representation was found in the primary motor cortex. We also found consistent cosine tuning across both tasks, to hand direction and velocity in the high gamma band (70-160 Hz). Thus, the results of this study clarify the neural substrates involved in encoding aspects of motor preparation and execution and confirm the important role of the motor cortex in BCI applications.},
}
@article {pmid23000405,
year = {2012},
author = {Teikari, P and Najjar, RP and Malkki, H and Knoblauch, K and Dumortier, D and Gronfier, C and Cooper, HM},
title = {An inexpensive Arduino-based LED stimulator system for vision research.},
journal = {Journal of neuroscience methods},
volume = {211},
number = {2},
pages = {227-236},
doi = {10.1016/j.jneumeth.2012.09.012},
pmid = {23000405},
issn = {1872-678X},
mesh = {*Computers ; Light ; Photic Stimulation/*instrumentation ; *Software ; },
abstract = {Light emitting diodes (LEDs) are being used increasingly as light sources in life sciences applications such as in vision research, fluorescence microscopy and in brain-computer interfacing. Here we present an inexpensive but effective visual stimulator based on light emitting diodes (LEDs) and open-source Arduino microcontroller prototyping platform. The main design goal of our system was to use off-the-shelf and open-source components as much as possible, and to reduce design complexity allowing use of the system to end-users without advanced electronics skills. The main core of the system is a USB-connected Arduino microcontroller platform designed initially with a specific emphasis on the ease-of-use creating interactive physical computing environments. The pulse-width modulation (PWM) signal of Arduino was used to drive LEDs allowing linear light intensity control. The visual stimulator was demonstrated in applications such as murine pupillometry, rodent models for cognitive research, and heterochromatic flicker photometry in human psychophysics. These examples illustrate some of the possible applications that can be easily implemented and that are advantageous for students, educational purposes and universities with limited resources. The LED stimulator system was developed as an open-source project. Software interface was developed using Python with simplified examples provided for Matlab and LabVIEW. Source code and hardware information are distributed under the GNU General Public Licence (GPL, version 3).},
}
@article {pmid22996896,
year = {2012},
author = {Conti, MA and Ferreira, ME and Amaral, AC and Hearst, N and Cordás, TA and Scagliusi, FB},
title = {[Semantic equivalence of the Brazilian Portuguese version of the "Body Change Inventory"].},
journal = {Ciencia &amp; saude coletiva},
volume = {17},
number = {9},
pages = {2457-2469},
doi = {10.1590/s1413-81232012000900026},
pmid = {22996896},
issn = {1678-4561},
mesh = {Adolescent ; *Body Image ; Brazil ; Cultural Characteristics ; Humans ; Semantics ; *Surveys and Questionnaires ; Translations ; },
abstract = {With the increase in research on the components of Body Image, validated instruments are needed to evaluate its dimensions. The Body Change Inventory (BCI) assesses strategies used to alter body size among adolescents. The scope of this study was to describe the translation and evaluation for semantic equivalence of the BCI in the Portuguese language. The process involved the steps of (1) translation of the questionnaire to the Portuguese language; (2) back-translation to English; (3) evaluation of semantic equivalence; and (4) assessment of comprehension by professional experts and the target population. The six subscales of the instrument were translated into the Portuguese language. Language adaptations were made to render the instrument suitable for the Brazilian reality. The questions were interpreted as easily understandable by both experts and young people. The Body Change Inventory has been translated and adapted into Portuguese. Evaluation of the operational, measurement and functional equivalence are still needed.},
}
@article {pmid22995776,
year = {2013},
author = {Evans, N and Blanke, O},
title = {Shared electrophysiology mechanisms of body ownership and motor imagery.},
journal = {NeuroImage},
volume = {64},
number = {},
pages = {216-228},
doi = {10.1016/j.neuroimage.2012.09.027},
pmid = {22995776},
issn = {1095-9572},
mesh = {Adult ; *Body Image ; Cerebral Cortex/*physiology ; Female ; Hand/*physiology ; Humans ; Illusions/*physiology ; Imagination/*physiology ; Male ; Movement/*physiology ; Nerve Net/*physiology ; },
abstract = {Although we feel, see, and experience our hands as our own (body or hand ownership), recent research has shown that illusory hand ownership can be induced for fake or virtual hands and may be useful for neuroprosthetics and brain-computer interfaces. Despite the vast amount of behavioral data on illusory hand ownership, neuroimaging studies are rare, in particular electrophysiological studies. Thus, while the neural systems underlying hand ownership are relatively well described, the spectral signatures of body ownership as measured by electroencephalography (EEG) remain elusive. Here we induced illusory hand ownership in an automated, computer-controlled manner using virtual reality while recording 64-channel EEG and found that illusory hand ownership is reflected by a body-specific modulation in the mu-band over fronto-parietal cortex. In a second experiment in the same subjects, we then show that mu as well as beta-band activity in highly similar fronto-parietal regions was also modulated during a motor imagery task often used in paradigms employing non-invasive brain-computer interface technology. These data provide insights into the electrophysiological brain mechanisms of illusory hand ownership and their strongly overlapping mechanisms with motor imagery in fronto-parietal cortex. They also highlight the potential of combining high-resolution EEG with virtual reality setups and automatized stimulation protocols for systematic, reproducible stimulus presentation in cognitive neuroscience, and may inform the design of non-invasive brain-computer interfaces.},
}
@article {pmid22995206,
year = {2012},
author = {Tan, Z and Xiang, J and Su, S and Zeng, H and Zhou, C and Sun, L and Hu, S and Qiu, J},
title = {Enhanced capture of elemental mercury by bamboo-based sorbents.},
journal = {Journal of hazardous materials},
volume = {239-240},
number = {},
pages = {160-166},
doi = {10.1016/j.jhazmat.2012.08.053},
pmid = {22995206},
issn = {1873-3336},
mesh = {Adsorption ; Air Pollutants/*chemistry ; Air Pollution/prevention &amp; control ; *Bambusa ; Mercury/*chemistry ; Nitric Oxide/chemistry ; Porosity ; Potassium Iodide/*chemistry ; Sulfur Dioxide/chemistry ; Surface Properties ; },
abstract = {To develop cost-effective sorbent for gas-phase elemental mercury removal, the bamboo charcoal (BC) produced from renewable bamboo and KI modified BC (BC-I) were used for elemental mercury removal. The effect of NO, SO2 on gas-phase Hg0 adsorption by KI modified BC was evaluated on a fixed bed reactor using an online mercury analyzer. BET surface area analysis, temperature programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) were used to determine the pore structure and surface chemistry of the sorbents. The results show that KI impregnation reduced the sorbents' BET surface area and total pore volume compared with that of the original BC. But the BC-I has excellent adsorption capacity for elemental mercury at a relatively higher temperature of 140 °C and 180 °C. The presence of NO or SO2 could inhibit Hg0 capture, but BC-I has strong anti-poisoning ability. The specific reaction mechanism has been further analyzed.},
}
@article {pmid22995178,
year = {2012},
author = {Wang, Y and Veluvolu, KC and Cho, JH and Defoort, M},
title = {Adaptive estimation of EEG for subject-specific reactive band identification and improved ERD detection.},
journal = {Neuroscience letters},
volume = {528},
number = {2},
pages = {137-142},
doi = {10.1016/j.neulet.2012.09.001},
pmid = {22995178},
issn = {1872-7972},
mesh = {Algorithms ; *Electroencephalography ; Electroencephalography Phase Synchronization ; Foot/physiology ; Fourier Analysis ; Hand/physiology ; Humans ; *Imagination ; Models, Neurological ; *Movement ; Tongue/physiology ; },
abstract = {The event-related desynchronization (ERD) is a magnitude decrease phenomenon which can be found in electroencephalogram (EEG) mu-rhythm in a certain narrow frequency band (reactive band) during different sensorimotor tasks and stimuli. The success of ERD detection depends on proper identification of subject specific reactive band. An adaptive algorithm band limited multiple Fourier linear combiner (BMFLC) is employed in this paper for identification of subject specific reactive band for real-time ERD detection. With the time-frequency mapping obtained with BMFLC, a procedure is formulated for reactive band identification. Improved classification is obtained by applying this method to a standard BCI data set compared to traditional ERD detection methods. Study conducted with 8 subjects drawn from BCI Competition IV data set show a 22% increase in ERD and 10% improvement in classification with the proposed method compared to standard ERD based classification.},
}
@article {pmid22985531,
year = {2012},
author = {Shaikhouni, A and Elder, JB},
title = {Computers and neurosurgery.},
journal = {World neurosurgery},
volume = {78},
number = {5},
pages = {392-398},
doi = {10.1016/j.wneu.2012.08.020},
pmid = {22985531},
issn = {1878-8769},
mesh = {Brain-Computer Interfaces/*history ; Computers/*history ; History, 20th Century ; History, 21st Century ; Neurosurgery/*history ; Robotics/*history ; Surgery, Computer-Assisted/*history ; },
abstract = {At the turn of the twentieth century, the only computational device used in neurosurgical procedures was the brain of the surgeon. Today, most neurosurgical procedures rely at least in part on the use of a computer to help perform surgeries accurately and safely. The techniques that revolutionized neurosurgery were mostly developed after the 1950s. Just before that era, the transistor was invented in the late 1940s, and the integrated circuit was invented in the late 1950s. During this time, the first automated, programmable computational machines were introduced. The rapid progress in the field of neurosurgery not only occurred hand in hand with the development of modern computers, but one also can state that modern neurosurgery would not exist without computers. The focus of this article is the impact modern computers have had on the practice of neurosurgery. Neuroimaging, neuronavigation, and neuromodulation are examples of tools in the armamentarium of the modern neurosurgeon that owe each step in their evolution to progress made in computer technology. Advances in computer technology central to innovations in these fields are highlighted, with particular attention to neuroimaging. Developments over the last 10 years in areas of sensors and robotics that promise to transform the practice of neurosurgery further are discussed. Potential impacts of advances in computers related to neurosurgery in developing countries and underserved regions are also discussed. As this article illustrates, the computer, with its underlying and related technologies, is central to advances in neurosurgery over the last half century.},
}
@article {pmid22984404,
year = {2012},
author = {Rapoport, BI and Turicchia, L and Wattanapanitch, W and Davidson, TJ and Sarpeshkar, R},
title = {Efficient universal computing architectures for decoding neural activity.},
journal = {PloS one},
volume = {7},
number = {9},
pages = {e42492},
pmid = {22984404},
issn = {1932-6203},
support = {R01 NS056140/NS/NINDS NIH HHS/United States ; T32 GM007753/GM/NIGMS NIH HHS/United States ; NS056140/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Computer Simulation ; Hippocampus/physiology ; Humans ; Rats ; *User-Computer Interface ; },
abstract = {The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.},
}
@article {pmid22973225,
year = {2012},
author = {Mirabella, G},
title = {Volitional inhibition and brain-machine interfaces: a mandatory wedding.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {20},
pmid = {22973225},
issn = {1662-6443},
}
@article {pmid22973215,
year = {2012},
author = {Derix, J and Iljina, O and Schulze-Bonhage, A and Aertsen, A and Ball, T},
title = {"Doctor" or "darling"? Decoding the communication partner from ECoG of the anterior temporal lobe during non-experimental, real-life social interaction.},
journal = {Frontiers in human neuroscience},
volume = {6},
number = {},
pages = {251},
pmid = {22973215},
issn = {1662-5161},
abstract = {Human brain processes underlying real-life social interaction in everyday situations have been difficult to study and have, until now, remained largely unknown. Here, we investigated whether electrocorticography (ECoG) recorded for pre-neurosurgical diagnostics during the daily hospital life of epilepsy patients could provide a way to elucidate the neural correlates of non-experimental social interaction. We identified time periods in which patients were involved in conversations with either their respective life partners (Condition 1; C1) or attending physicians (Condition 2; C2). These two conditions can be expected to differentially involve subfunctions of social interaction which have been associated with activity in the anterior temporal lobe (ATL), including the temporal pole (TP). Therefore, we specifically focused on ECoG recordings from this brain region and investigated spectral power modulations in the alpha (8-12 Hz) and theta (3-5 Hz) frequency ranges, which have been previously assumed to play an important role in the processing of social interaction. We hypothesized that brain activity in this region might be sensitive to differences in the two interaction situations and tested whether these differences can be detected by single-trial decoding. Condition-specific effects in both theta and alpha bands were observed: the left and right TP exclusively showed increased power in C1 compared to C2, whereas more posterior parts of the ATL exhibited similar (C1 > C2) and also contrary (C2 > C1) effects. Single-trial decoding accuracies for classification of these effects were highly above chance. Our findings demonstrate that it is possible to study the neural correlates of human social interaction in non-experimental conditions. Decoding the identity of the communication partner and adjusting the speech output accordingly may be useful in the emerging field of brain-machine interfacing for restoration of expressive speech.},
}
@article {pmid22970202,
year = {2012},
author = {Blokland, YM and Farquhar, JD and Mourisse, J and Scheffer, GJ and Lerou, JG and Bruhn, J},
title = {Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface.},
journal = {PloS one},
volume = {7},
number = {9},
pages = {e44336},
pmid = {22970202},
issn = {1932-6203},
mesh = {Acoustic Stimulation ; Adult ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography ; Female ; Humans ; Intraoperative Awareness/*physiopathology ; Male ; Monitoring, Intraoperative/*instrumentation ; *Movement ; Reproducibility of Results ; Time Factors ; Young Adult ; },
abstract = {During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.},
}
@article {pmid22965825,
year = {2013},
author = {Andersson, P and Pluim, JP and Viergever, MA and Ramsey, NF},
title = {Navigation of a telepresence robot via covert visuospatial attention and real-time fMRI.},
journal = {Brain topography},
volume = {26},
number = {1},
pages = {177-185},
pmid = {22965825},
issn = {1573-6792},
mesh = {Adult ; Attention/*physiology ; Brain/*blood supply/*physiology ; *Brain-Computer Interfaces ; Female ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Multivariate Analysis ; Oxygen/blood ; Photic Stimulation ; Psychomotor Performance/physiology ; *Robotics ; Space Perception/*physiology ; Support Vector Machine ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) allow people with severe neurological impairment and without ability to control their muscles to regain some control over their environment. The BCI user performs a mental task to regulate brain activity, which is measured and translated into commands controlling some external device. We here show that healthy participants are capable of navigating a robot by covertly shifting their visuospatial attention. Covert Visuospatial Attention (COVISA) constitutes a very intuitive brain function for spatial navigation and does not depend on presented stimuli or on eye movements. Our robot is equipped with motors and a camera that sends visual feedback to the user who can navigate it from a remote location. We used an ultrahigh field MRI scanner (7 Tesla) to obtain fMRI signals that were decoded in real time using a support vector machine. Four healthy subjects with virtually no training succeeded in navigating the robot to at least three of four target locations. Our results thus show that with COVISA BCI, realtime robot navigation can be achieved. Since the magnitude of the fMRI signal has been shown to correlate well with the magnitude of spectral power changes in the gamma frequency band in signals measured by intracranial electrodes, the COVISA concept may in future translate to intracranial application in severely paralyzed people.},
}
@article {pmid22963395,
year = {2012},
author = {Manyakov, NV and Chumerin, N and Van Hulle, MM},
title = {Multichannel decoding for phase-coded SSVEP brain-computer interface.},
journal = {International journal of neural systems},
volume = {22},
number = {5},
pages = {1250022},
doi = {10.1142/S0129065712500220},
pmid = {22963395},
issn = {1793-6462},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods/*statistics &amp; numerical data ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Neural Networks, Computer ; Photic Stimulation ; Visual Cortex ; Young Adult ; },
abstract = {We propose a complex-valued multilayer feedforward neural network classifier for decoding of phase-coded information from steady-state visual evoked potentials. To optimize the performance of the classifier we supply it with two filter-based feature selection strategies. The proposed approaches could be used for a phase-coded brain-computer interface, enabling to encode several targets using only one stimulation frequency. The proposed classifier is a multichannel one, which distinguishes our approach from the existing single-channel ones. We show that the proposed approach outperforms others in terms of accuracy and length of the data segments used for decoding. We show that the decoding based on one optimally selected channel yields an inferior performance compared to the one based on several features, which supports our argument for a multichannel approach.},
}
@article {pmid22961191,
year = {2012},
author = {Ganguly, K and Abrams, GM},
title = {Management of chronic myelopathy symptoms and activities of daily living.},
journal = {Seminars in neurology},
volume = {32},
number = {2},
pages = {161-168},
doi = {10.1055/s-0032-1322582},
pmid = {22961191},
issn = {1098-9021},
mesh = {Activities of Daily Living ; Brain-Computer Interfaces/standards/trends ; Calcium Metabolism Disorders/physiopathology/rehabilitation ; Cardiac Rehabilitation ; Cardiovascular Diseases/etiology/physiopathology ; Chronic Disease ; Humans ; Lung Diseases/etiology/physiopathology/rehabilitation ; Paralysis/complications/physiopathology/rehabilitation ; Physical Therapy Modalities/standards/*trends ; Spinal Cord Diseases/complications/*physiopathology/*rehabilitation ; },
abstract = {Many disorders can injure the spinal cord resulting in long-term chronic myelopathy. Spinal cord dysfunction influences the homeostasis of multiple organ systems ranging from the heart or lung to the integument, thus presenting a wide variety of challenges for medical management. Although most of our knowledge about the consequences of myelopathies derives from the study of traumatic spinal cord injuries, similar complications occur in myelopathies of all etiologies. The authors survey some of the important clinical issues that the general neurologist needs to consider in caring for patients with chronic spinal cord disease.},
}
@article {pmid22960261,
year = {2012},
author = {Townsend, G and Shanahan, J and Ryan, DB and Sellers, EW},
title = {A general P300 brain-computer interface presentation paradigm based on performance guided constraints.},
journal = {Neuroscience letters},
volume = {531},
number = {2},
pages = {63-68},
pmid = {22960261},
issn = {1872-7972},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R21 DC010470/DC/NIDCD NIH HHS/United States ; R21 DC010470-01/DC/NIDCD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Female ; Humans ; Male ; },
abstract = {An electroencephalographic-based brain-computer interface (BCI) can provide a non-muscular method of communication. A general model for P300-based BCI stimulus presentations is introduced--the "m choose n" or C(m (number of flashes per sequence), n (number of flashes per item)) paradigm, which is a universal extension of the previously reported checkerboard paradigm (CBP). C(m,n) captures all possible (unconstrained) ways to flash target items, and then applies constraints to enhance ERP's produced by attended matrix items. We explore a C(36,5) instance of C(m,n) called the "five flash paradigm" (FFP) and compare its performance to the CBP. Eight subjects were tested in each paradigm, counter-balanced. Twelve minutes of calibration data were used as input to a stepwise linear discriminant analysis to derive classification coefficients used for online classification. Accuracy was consistently high for FFP (88%) and CBP (90%); information transfer rate was significantly higher for the FFP (63 bpm) than the CBP (48 bpm). The C(m,n) is a novel and effective general strategy for organizing stimulus groups. Appropriate choices for "m," "n," and specific constraints can improve presentation paradigms by adjusting the parameters in a subject specific manner. This may be especially important for people with neuromuscular disabilities.},
}
@article {pmid22959257,
year = {2013},
author = {Käthner, I and Ruf, CA and Pasqualotto, E and Braun, C and Birbaumer, N and Halder, S},
title = {A portable auditory P300 brain-computer interface with directional cues.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {2},
pages = {327-338},
doi = {10.1016/j.clinph.2012.08.006},
pmid = {22959257},
issn = {1872-8952},
mesh = {*Acoustic Stimulation ; Adolescent ; Adult ; Affect/physiology ; *Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; *Cues ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Feasibility Studies ; Female ; Humans ; Male ; Models, Neurological ; Motivation/physiology ; Photic Stimulation ; *Task Performance and Analysis ; Young Adult ; },
abstract = {OBJECTIVES: The main objective of the current study was to implement and evaluate a P300 based brain-computer interface (BCI) speller that uses directional cues of auditory stimuli, which are presented over headphones. The interstimulus interval (ISI) was successively reduced to determine the optimal combination of speed and accuracy. The study further aimed at quantifying the differences in subjective workload between the auditory and the visual P300 spelling application. The influence of workload, mood and motivation on BCI performance and P300 amplitude was investigated.<br><br>METHODS: Twenty healthy participants performed auditory and visual spelling tasks in an EEG experiment with online feedback.<br><br>RESULTS: Sixteen of twenty participants performed at or above a level necessary for satisfactory communication (&#x2265;70% spelling accuracy) with the auditory BCI. Average bit rates of up to 2.76 bits/min (best subject 7.43 bits/min) were achieved. A significantly higher workload was reported for the auditory speller compared to the visual paradigm. Motivation significantly influenced P300 amplitude at Pz in the auditory condition.<br><br>CONCLUSIONS: The results of the online study suggest that the proposed paradigm offers a means of communication for most healthy users.<br><br>SIGNIFICANCE: The described auditory BCI can serve as a communication channel for completely paralyzed patients.},
}
@article {pmid22956795,
year = {2012},
author = {Bansal, AK and Singer, JM and Anderson, WS and Golby, A and Madsen, JR and Kreiman, G},
title = {Temporal stability of visually selective responses in intracranial field potentials recorded from human occipital and temporal lobes.},
journal = {Journal of neurophysiology},
volume = {108},
number = {11},
pages = {3073-3086},
pmid = {22956795},
issn = {1522-1598},
mesh = {Adolescent ; Adult ; Brain Waves ; Child ; Epilepsy/physiopathology ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Occipital Lobe/*physiopathology ; Photic Stimulation ; Recognition, Psychology/physiology ; Temporal Lobe/*physiopathology ; Time Factors ; Visual Perception/*physiology ; },
abstract = {The cerebral cortex needs to maintain information for long time periods while at the same time being capable of learning and adapting to changes. The degree of stability of physiological signals in the human brain in response to external stimuli over temporal scales spanning hours to days remains unclear. Here, we quantitatively assessed the stability across sessions of visually selective intracranial field potentials (IFPs) elicited by brief flashes of visual stimuli presented to 27 subjects. The interval between sessions ranged from hours to multiple days. We considered electrodes that showed robust visual selectivity to different shapes; these electrodes were typically located in the inferior occipital gyrus, the inferior temporal cortex, and the fusiform gyrus. We found that IFP responses showed a strong degree of stability across sessions. This stability was evident in averaged responses as well as single-trial decoding analyses, at the image exemplar level as well as at the category level, across different parts of visual cortex, and for three different visual recognition tasks. These results establish a quantitative evaluation of the degree of stationarity of visually selective IFP responses within and across sessions and provide a baseline for studies of cortical plasticity and for the development of brain-machine interfaces.},
}
@article {pmid22954906,
year = {2012},
author = {Tankus, A and Fried, I and Shoham, S},
title = {Sparse decoding of multiple spike trains for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {5},
pages = {054001},
pmid = {22954906},
issn = {1741-2552},
support = {211055/ERC_/European Research Council/International ; R01 NS033221/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adult ; Algorithms ; *Brain-Computer Interfaces ; Electrodes, Implanted ; Epilepsy/*physiopathology ; Female ; Humans ; Male ; Middle Aged ; Neurons/physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Speech/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.},
}
@article {pmid22954880,
year = {2012},
author = {Ortner, R and Irimia, DC and Scharinger, J and Guger, C},
title = {A motor imagery based brain-computer interface for stroke rehabilitation.},
journal = {Studies in health technology and informatics},
volume = {181},
number = {},
pages = {319-323},
pmid = {22954880},
issn = {0926-9630},
mesh = {Biofeedback, Psychology ; Electroencephalography ; Humans ; *Imagery, Psychotherapy ; *Man-Machine Systems ; *Stroke Rehabilitation ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {Brain-Computer Interfaces (BCIs) have been used to assist people with impairments since many years. In most of these applications the BCI is intended to substitute functions the user is no longer able to perform without help. For example BCIs could be used for communication and for control of devices like robotic arms, wheelchairs or also orthoses and prostheses. Another approach is not to replace the motor function itself by controlling a BCI, but to utilize a BCI for rehabilitation that enables the user to restore normal or "more normal" motor function. Motor imagery (MI) itself is a common strategy for motor rehabilitation in stroke patients. The idea of this paper is it to assist the MI by presenting online feedback about the imagination to the user. A BCI is presented that classifies MI of the left hand versus the right hand. Feedback is given to the user with two different strategies. One time by an abstract bar feedback, and the second time by a 3-D virtual reality environment: The left and right hand of an avatar in the 1st person's perspective in presented to him/her. If a motor imagery is detected, the according hand of the avatar moves. Preliminary tests were done on three healthy subjects. Offline analysis was then performed to (1) demonstrate the feasibility of the new, immersive, 3-D feedback strategy, (2) to compare it with the quite common bar feedback strategy and (3) to optimize the classification algorithm that detects the MI.},
}
@article {pmid22954879,
year = {2012},
author = {Morris, A and Ulieru, M},
title = {FRIEND: a brain-monitoring agent for adaptive and assistive systems.},
journal = {Studies in health technology and informatics},
volume = {181},
number = {},
pages = {314-318},
pmid = {22954879},
issn = {0926-9630},
mesh = {Biofeedback, Psychology ; Brain/*physiology ; Cell Phone ; Cognition/*physiology ; *Decision Making ; Electroencephalography ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; },
abstract = {This paper presents an architectural design for adaptive-systems agents (FRIEND) that use brain state information to make more effective decisions on behalf of a user; measuring brain context versus situational demands. These systems could be useful for alerting users to cognitive workload levels or fatigue, and could attempt to compensate for higher cognitive activity by filtering noise information. In some cases such systems could also share control of devices, such as pulling over in an automated vehicle. These aim to assist people in everyday systems to perform tasks better and be more aware of internal states. Achieving a functioning system of this sort is a challenge, involving a unification of brain- computer-interfaces, human-computer-interaction, soft-computin deliberative multi-agent systems disciplines. Until recently, these were not able to be combined into a usable platform due largely to technological limitations (e.g., size, cost, and processing speed), insufficient research on extracting behavioral states from EEG signals, and lack of low-cost wireless sensing headsets. We aim to surpass these limitations and develop control architectures for making sense of brain state in applications by realizing an agent architecture for adaptive (human-aware) technology. In this paper we present an early, high-level design towards implementing a multi-purpose brain-monitoring agent system to improve user quality of life through the assistive applications of psycho-physiological monitoring, noise-filtering, and shared system control.},
}
@article {pmid22950051,
year = {2012},
author = {Cipresso, P and Carelli, L and Solca, F and Meazzi, D and Meriggi, P and Poletti, B and Lulé, D and Ludolph, AC and Silani, V and Riva, G},
title = {The use of P300-based BCIs in amyotrophic lateral sclerosis: from augmentative and alternative communication to cognitive assessment.},
journal = {Brain and behavior},
volume = {2},
number = {4},
pages = {479-498},
pmid = {22950051},
issn = {2162-3279},
abstract = {The use of augmentative and alternative communication (AAC) tools in patients with amyotrophic lateral sclerosis (ALS), as effective means to compensate for the progressive loss of verbal and gestural communication, has been deeply investigated in the recent literature. The development of advanced AAC systems, such as eye-tracking (ET) and brain-computer interface (BCI) devices, allowed to bypass the important motor difficulties present in ALS patients. In particular, BCIs could be used in moderate to severe stages of the disease, since they do not require preserved ocular-motor ability, which is necessary for ET applications. Furthermore, some studies have proved the reliability of BCIs, regardless of the severity of the disease and the level of physical decline. However, the use of BCI in ALS patients still shows some limitations, related to both technical and neuropsychological issues. In particular, a range of cognitive deficits in most ALS patients have been observed. At the moment, no effective verbal-motor free measures are available for the evaluation of ALS patients' cognitive integrity; BCIs could offer a new possibility to administer cognitive tasks without the need of verbal or motor responses, as highlighted by preliminary studies in this field. In this review, we outline the essential features of BCIs systems, considering advantages and challenges of these tools with regard to ALS patients and the main applications developed in this field. We then outline the main findings with regard to cognitive deficits observed in ALS and some preliminary attempts to evaluate them by means of BCIs. The definition of specific cognitive profiles could help to draw flexible approaches tailored on patients' needs. It could improve BCIs efficacy and reduce patients' efforts. Finally, we handle the open question, represented by the use of BCIs with totally locked in patients, who seem unable to reliably learn to use such tool.},
}
@article {pmid22949044,
year = {2013},
author = {Higashi, H and Tanaka, T},
title = {Simultaneous design of FIR filter banks and spatial patterns for EEG signal classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {60},
number = {4},
pages = {1100-1110},
doi = {10.1109/TBME.2012.2215960},
pmid = {22949044},
issn = {1558-2531},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Humans ; Imagination/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery-based brain-computer interface (MI-BCI). To achieve accurate classification in CSP, it is necessary to find frequency bands that relate to brain activities associated with BCI tasks. Several methods that determine such a set of frequency bands have been proposed. However, the existing methods cannot find the multiple frequency bands by using only learning data. To address this problem, we propose discriminative filter bank CSP (DFBCSP) that designs finite impulse response filters and the associated spatial weights by optimizing an objective function which is a natural extension of that of CSP. The optimization is conducted by sequentially and alternatively solving subproblems into which the original problem is divided. By experiments, it is shown that DFBCSP can effectively extract discriminative features for MI-BCI. Moreover, experimental results exhibit that DFBCSP can detect and extract the bands related to brain activities of motor imagery.},
}
@article {pmid22941044,
year = {2013},
author = {Brouillet, S and Murthi, P and Hoffmann, P and Salomon, A and Sergent, F and De Mazancourt, P and Dakouane-Giudicelli, M and Dieudonné, MN and Rozenberg, P and Vaiman, D and Barbaux, S and Benharouga, M and Feige, JJ and Alfaidy, N},
title = {EG-VEGF controls placental growth and survival in normal and pathological pregnancies: case of fetal growth restriction (FGR).},
journal = {Cellular and molecular life sciences : CMLS},
volume = {70},
number = {3},
pages = {511-525},
pmid = {22941044},
issn = {1420-9071},
mesh = {Cell Hypoxia ; Cell Proliferation/drug effects ; Cells, Cultured ; Female ; Fetal Growth Retardation/*metabolism/pathology ; Giant Cells/cytology ; Homeodomain Proteins/metabolism ; Humans ; Placenta/cytology/*metabolism ; Placentation ; Pregnancy ; Pregnancy Trimester, First ; RNA, Messenger/metabolism ; Receptors, G-Protein-Coupled/genetics/metabolism ; Receptors, Peptide/genetics/metabolism ; Recombinant Proteins/genetics/metabolism/pharmacology ; Transcription Factors/metabolism ; Trophoblasts/cytology/metabolism ; Up-Regulation/drug effects ; Vascular Endothelial Growth Factor, Endocrine-Gland-Derived/genetics/*metabolism ; },
abstract = {Identifiable causes of fetal growth restriction (FGR) account for 30 % of cases, but the remainders are idiopathic and are frequently associated with placental dysfunction. We have shown that the angiogenic factor endocrine gland-derived VEGF (EG-VEGF) and its receptors, prokineticin receptor 1 (PROKR1) and 2, (1) are abundantly expressed in human placenta, (2) are up-regulated by hypoxia, (3) control trophoblast invasion, and that EG-VEGF circulating levels are the highest during the first trimester of pregnancy, the period of important placental growth. These findings suggest that EG-VEGF/PROKR1 and 2 might be involved in normal and FGR placental development. To test this hypothesis, we used placental explants, primary trophoblast cultures, and placental and serum samples collected from FGR and age-matched control women. Our results show that (1) EG-VEGF increases trophoblast proliferation ([(3)H]-thymidine incorporation and Ki67-staining) via the homeobox-gene, HLX (2) the proliferative effect involves PROKR1 but not PROKR2, (3) EG-VEGF does not affect syncytium formation (measurement of syncytin 1 and 2 and β hCG production) (4) EG-VEGF increases the vascularization of the placental villi and insures their survival, (5) EG-VEGF, PROKR1, and PROKR2 mRNA and protein levels are significantly elevated in FGR placentas, and (6) EG-VEGF circulating levels are significantly higher in FGR patients. Altogether, our results identify EG-VEGF as a new placental growth factor acting during the first trimester of pregnancy, established its mechanism of action, and provide evidence for its deregulation in FGR. We propose that EG-VEGF/PROKR1 and 2 increases occur in FGR as a compensatory mechanism to insure proper pregnancy progress.},
}
@article {pmid22938882,
year = {2012},
author = {Ball, LJ and Fager, S and Fried-Oken, M},
title = {Augmentative and alternative communication for people with progressive neuromuscular disease.},
journal = {Physical medicine and rehabilitation clinics of North America},
volume = {23},
number = {3},
pages = {689-699},
doi = {10.1016/j.pmr.2012.06.003},
pmid = {22938882},
issn = {1558-1381},
mesh = {Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Eye Movements ; Gestures ; Head Movements ; Humans ; Neuromuscular Diseases/*complications/*physiopathology ; Speech Disorders/*etiology ; Speech Recognition Software ; },
abstract = {Individuals with progressive neuromuscular disease often experience complex communication needs and consequently find that interaction using their natural speech may not sufficiently meet their daily needs. Increasingly, assistive technology advances provide accommodations for and/or access to communication. Assistive technology related to communication is referred to as augmentative and alternative communication (AAC). The nature of communication challenges in progressive neuromuscular diseases can be as varied as the AAC options currently available. AAC systems continue to be designed and implemented to provide targeted assistance based on an individual's changing needs.},
}
@article {pmid22937070,
year = {2012},
author = {Pfurtscheller, G and Daly, I and Bauernfeind, G and Müller-Putz, GR},
title = {Coupling between intrinsic prefrontal HbO2 and central EEG beta power oscillations in the resting brain.},
journal = {PloS one},
volume = {7},
number = {8},
pages = {e43640},
pmid = {22937070},
issn = {1932-6203},
mesh = {Adult ; Blood Pressure/physiology ; Brain/*physiology ; Brain Mapping ; Brain Waves/*physiology ; Electroencephalography ; Female ; Heart Rate/physiology ; Hemodynamics/physiology ; Humans ; Male ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared ; },
abstract = {There is increasing interest in the intrinsic activity in the resting brain, especially that of ultraslow and slow oscillations. Using near-infrared spectroscopy (NIRS), electroencephalography (EEG), blood pressure (BP), respiration and heart rate recordings during 5 minutes of rest, combined with cross spectral and sliding cross correlation calculations, we identified a short-lasting coupling (duration [Formula: see text] s) between prefrontal oxyhemoglobin (HbO2) in the frequency band between 0.07 and 0.13 Hz and central EEG alpha and/or beta power oscillations in 8 of the 9 subjects investigated. The HbO2 peaks preceded the EEG band power peaks by 3.7 s in 6 subjects, with moderate or no coupling between BP and HbO2 oscillations. HbO2 and EEG band power oscillations were approximately in phase with BP oscillations in the 2 subjects with an extremely high coupling (squared coherence [Formula: see text]) between BP and HbO2 oscillation. No coupling was identified in one subject. These results indicate that slow precentral (de)oxyhemoglobin concentration oscillations during awake rest can be temporarily coupled with EEG fluctuations in sensorimotor areas and modulate the excitability level in the brains' motor areas, respectively. Therefore, this provides support for the idea that resting state networks fluctuate with frequencies of between 0.01 and 0.1 Hz (Mantini et.al. PNAS 2007).},
}
@article {pmid22928842,
year = {2014},
author = {Liang, X and Kuhlmann, L and Johnston, LA and Grayden, DB and Vogrin, S and Crossley, R and Fuller, K and Lourensz, M and Cook, MJ},
title = {Extending communication for patients with disorders of consciousness.},
journal = {Journal of neuroimaging : official journal of the American Society of Neuroimaging},
volume = {24},
number = {1},
pages = {31-38},
doi = {10.1111/j.1552-6569.2012.00744.x},
pmid = {22928842},
issn = {1552-6569},
mesh = {Adult ; Brain/*physiopathology ; Brain Mapping/*methods ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; Consciousness Disorders/*physiopathology/*rehabilitation ; Female ; Humans ; Imagination ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; *Nonverbal Communication ; Pattern Recognition, Automated/methods ; Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; Surveys and Questionnaires ; },
abstract = {BACKGROUND AND PURPOSE: The difficulty of distinguishing disorders of consciousness from certain disorders of communication leads to the possibility of false diagnosis. Our aim is to communicate with patients with disorders of consciousness through asking them to answer questions with "yes/no" by performing mental imagery tasks using functional magnetic resonance imaging (fMRI).<br><br>METHODS: A 1.5 T fMRI study with 5 patients and a control group is presented. Speech comprehension, mental imagery, and question-answer tests were performed.<br><br>RESULTS: The imagery task of mental calculation produced equally distinct activation patterns when compared to navigation and motor imagery in controls. For controls, we could infer answers to questions based on imagery activations. Two patients produced activations in similar areas to controls for certain imagery tasks, however, no activations were observed for the question-answer task.<br><br>CONCLUSIONS: The results from 2 patients provide independent support of similar work by others with 3 T fMRI, and demonstrate broader clinical utility for these tests at 1.5 T despite lower signal-to-noise ratio. Based on the control results, mental calculation adds a robust imagery task for use in future studies of this kind.},
}
@article {pmid22915261,
year = {2012},
author = {Liberati, G and Birbaumer, N},
title = {Using brain-computer interfaces to overcome the extinction of goal-directed thinking in minimally conscious state patients.},
journal = {Cognitive processing},
volume = {13 Suppl 1},
number = {},
pages = {S239-41},
pmid = {22915261},
issn = {1612-4790},
mesh = {*Brain-Computer Interfaces ; Cognition/physiology ; Extinction, Psychological/*physiology ; *Goals ; Humans ; Imagery, Psychotherapy/methods ; *Persistent Vegetative State/physiopathology/psychology/rehabilitation ; Thinking/*physiology ; },
abstract = {Minimally conscious state (MCS) is a condition of severely altered consciousness, in which patients appear to be wakeful and exhibit fluctuating but reproducible signs of awareness. MCS patients do not respond and are therefore dependent on others. In agreement with the embodied cognition assumption that motor actions influence our cognition, the absence of movement and the decrease in consequences for any type of covert or overt response may cause an extinction of goal-directed thinking. Brain-computer interfaces, which allow a direct output without muscular involvement, may be used to promote goal-directed thinking by allowing the performance of spatial and motor imagery tasks and could facilitate the interaction of MCS patients with their environment, possibly regaining some degree of communication and autonomy.},
}
@article {pmid22920562,
year = {2013},
author = {Lulé, D and Noirhomme, Q and Kleih, SC and Chatelle, C and Halder, S and Demertzi, A and Bruno, MA and Gosseries, O and Vanhaudenhuyse, A and Schnakers, C and Thonnard, M and Soddu, A and Kübler, A and Laureys, S},
title = {Probing command following in patients with disorders of consciousness using a brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {1},
pages = {101-106},
doi = {10.1016/j.clinph.2012.04.030},
pmid = {22920562},
issn = {1872-8952},
mesh = {Adult ; Aged ; Arousal ; *Brain-Computer Interfaces ; Consciousness Disorders/diagnosis/*physiopathology/*psychology ; Data Interpretation, Statistical ; Electroencephalography ; Event-Related Potentials, P300 ; Evoked Potentials, Auditory ; Female ; Humans ; Male ; Middle Aged ; Persistent Vegetative State/physiopathology/psychology ; Quadriplegia/physiopathology/psychology ; },
abstract = {OBJECTIVE: To determine if brain-computer interfaces (BCIs) could serve as supportive tools for detecting consciousness in patients with disorders of consciousness by detecting response to command and communication.<br><br>METHODS: We tested a 4-choice auditory oddball EEG-BCI paradigm on 16 healthy subjects and 18 patients in a vegetative state/unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and in locked-in syndrome (LIS). Subjects were exposed to 4 training trials and 10 -12 questions.<br><br>RESULTS: Thirteen healthy subjects and one LIS patient were able to communicate using the BCI. Four of those did not present with a P3. One MCS patient showed command following with the BCI while no behavioral response could be detected at bedside. All other patients did not show any response to command and could not communicate with the BCI.<br><br>CONCLUSION: The present study provides evidence that EEG based BCI can detect command following in patients with altered states of consciousness and functional communication in patients with locked-in syndrome. However, BCI approaches have to be simplified to increase sensitivity.<br><br>SIGNIFICANCE: For some patients without any clinical sign of consciousness, a BCI might bear the potential to employ a "yes-no" spelling device offering the hope of functional interactive communication.},
}
@article {pmid22919833,
year = {2012},
author = {Pei, NC},
title = {[Identification of plant species based on DNA barcode technology].},
journal = {Ying yong sheng tai xue bao = The journal of applied ecology},
volume = {23},
number = {5},
pages = {1240-1246},
pmid = {22919833},
issn = {1001-9332},
mesh = {DNA Barcoding, Taxonomic/*methods ; DNA, Plant/*analysis/genetics/isolation &amp; purification ; Ecosystem ; Plants/*classification/*genetics ; Species Specificity ; },
abstract = {It is crucial for the studies of taxonomy and biodiversity by using DNA barcode technology to fast and accurately make species identification in the forests across tropics and temperate zones. In this study, the 183 plant species in a 20 hm2 subtropical forest plot in Dinghushan (DHS) National Nature Reserve of South China were sampled and sequenced, and the matK, rbcL, and psbA-trnH were employed to generate multi-locus barcodes. For the plot, the psbA-trnH possessed the highest integral success rate, i. e., the product of sequencing recovery and correct species identification (75%), followed by matK (70%), and rbcL (56%). A combination of three-locus barcode (matK, rbcL and psbA-trnH) could identify greater than 87% of the total species, followed by two-locus barcode (85% for matK+psbA-trnH, 83% for rbcL+psbA-trnH, and 81% for matK+rbcL). A comparison was made with the previously published results from one subtropical forest plot (LFDP in Puerto Rico, 143 species) and two tropical forest plots (BCI in Panama, 296 species; and NRS in French Guiana, 254 species) to evaluate the universality and species identification correctness of the proposed DNA barcodes for these four forest plots. For the plots in tropics and subtropics, the sequencing success rate of rbcL, psbA-trnH and matK were 93% and 95.1%, 91.5% and 94.6%, and 68.5% and 79.7%, respectively. The combination of matK + rbcL showed a high identification capacity in geographically restricted regions in taxonomic groups, whereas the three-locus barcode had a high rate of correct species identification both in tropics (84%) and in subtropics (90%).},
}
@article {pmid22910361,
year = {2012},
author = {Tankus, A and Fried, I and Shoham, S},
title = {Structured neuronal encoding and decoding of human speech features.},
journal = {Nature communications},
volume = {3},
number = {},
pages = {1015},
pmid = {22910361},
issn = {2041-1723},
support = {211055/ERC_/European Research Council/International ; R56 NS033221/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/cytology/*physiology ; Brain-Computer Interfaces ; Epilepsy/*physiopathology/psychology ; Female ; Humans ; Male ; Middle Aged ; Neurons/chemistry/*physiology ; Phonetics ; *Speech ; Speech Perception ; Young Adult ; },
abstract = {Human speech sounds are produced through a coordinated movement of structures along the vocal tract. Here we show highly structured neuronal encoding of vowel articulation. In medial-frontal neurons, we observe highly specific tuning to individual vowels, whereas superior temporal gyrus neurons have nonspecific, sinusoidally modulated tuning (analogous to motor cortical directional tuning). At the neuronal population level, a decoding analysis reveals that the underlying structure of vowel encoding reflects the anatomical basis of articulatory movements. This structured encoding enables accurate decoding of volitional speech segments and could be applied in the development of brain-machine interfaces for restoring speech in paralysed individuals.},
}
@article {pmid22907959,
year = {2012},
author = {Taghavi, H and Håkansson, B and Reinfeldt, S},
title = {Analysis and design of RF power and data link using amplitude modulation of Class-E for a novel bone conduction implant.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {11},
pages = {3050-3059},
doi = {10.1109/TBME.2012.2213252},
pmid = {22907959},
issn = {1558-2531},
mesh = {Biomedical Engineering/*instrumentation ; *Bone Conduction ; Electronics, Medical/*instrumentation ; *Hearing Aids ; Humans ; Models, Theoretical ; *Prostheses and Implants ; Prosthesis Design ; },
abstract = {This paper presents analysis and design of a radio frequency power and data link for a novel Bone Conduction Implant (BCI) system. Patients with conductive and mixed hearing loss and single-sided deafness can be rehabilitated by bone-anchored hearing aids (BAHA). Whereas the conventional hearing aids transmit sound to the tympanic membrane via air conduction, the BAHA transmits sound via vibrations through the skull directly to the cochlea. It uses a titanium screw that penetrates the skin and needs life-long daily care; it may cause skin infection and redness. The BCI is developed as an alternative to the percutaneous BAHA since it leaves the skin intact. The BCI comprises an external audio processor with a transmitter coil and an implanted unit called the bridging bone conductor with a receiver coil. Using amplitude modulation of the Class-E power amplifier that drives the inductive link, the sound signal is transmitted to the implant through the intact skin. It was found that the BCI can generate enough output force level for candidate patients. Maximum power output of the BCI was designed to occur at 5-mm skin thickness and the variability was within 1.5 dB for 1-8-mm skin thickness variations.},
}
@article {pmid22906791,
year = {2012},
author = {Wagner, J and Solis-Escalante, T and Grieshofer, P and Neuper, C and Müller-Putz, G and Scherer, R},
title = {Level of participation in robotic-assisted treadmill walking modulates midline sensorimotor EEG rhythms in able-bodied subjects.},
journal = {NeuroImage},
volume = {63},
number = {3},
pages = {1203-1211},
doi = {10.1016/j.neuroimage.2012.08.019},
pmid = {22906791},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Electroencephalography ; Exercise Therapy/methods ; Female ; Gait/*physiology ; Gait Disorders, Neurologic/rehabilitation ; Humans ; Male ; Robotics/*methods ; Walking/*physiology ; Young Adult ; },
abstract = {In robot assisted gait training, a pattern of human locomotion is executed repetitively with the intention to restore the motor programs associated with walking. Several studies showed that active contribution to the movement is critical for the encoding of motor memory. We propose to use brain monitoring techniques during gait training to encourage active participation in the movement. We investigated the spectral patterns in the electroencephalogram (EEG) that are related to active and passive robot assisted gait. Fourteen healthy participants were considered. Infomax independent component analysis separated the EEG into independent components representing brain, muscle, and eye movement activity, as well as other artifacts. An equivalent current dipole was calculated for each independent component. Independent components were clustered across participants based on their anatomical position and frequency spectra. Four clusters were identified in the sensorimotor cortices that accounted for differences between active and passive walking or showed activity related to the gait cycle. We show that in central midline areas the mu (8-12 Hz) and beta (18-21 Hz) rhythms are suppressed during active compared to passive walking. These changes are statistically significant: mu (F(1, 13)=11.2 p ≤ 0.01) and beta (F(1, 13)=7.7, p ≤ 0.05). We also show that these differences depend on the gait cycle phases. We provide first evidence of modulations of the gamma rhythm in the band 25 to 40 Hz, localized in central midline areas related to the phases of the gait cycle. We observed a trend (F(1, 8)=11.03, p ≤ 0.06) for suppressed low gamma rhythm when comparing active and passive walking. Additionally we found significant suppressions of the mu (F(1, 11)=20.1 p ≤ 0.01), beta (F(1, 11)=11.3 p ≤ 0.05) and gamma (F(1, 11)=4.9 p ≤ 0.05) rhythms near C3 (in the right hand area of the primary motor cortex) during phases of active vs. passive robot assisted walking. To our knowledge this is the first study showing EEG analysis during robot assisted walking. We provide evidence for significant differences in cortical activation between active and passive robot assisted gait. Our findings may help to define appropriate features for single trial detection of active participation in gait training. This work is a further step toward the evaluation of brain monitoring techniques and brain-computer interface technologies for improving gait rehabilitation therapies in a top-down approach.},
}
@article {pmid22905151,
year = {2012},
author = {Frank, S and Lee, S and Preissl, H and Schultes, B and Birbaumer, N and Veit, R},
title = {The obese brain athlete: self-regulation of the anterior insula in adiposity.},
journal = {PloS one},
volume = {7},
number = {8},
pages = {e42570},
pmid = {22905151},
issn = {1932-6203},
mesh = {Adiposity/*physiology ; Adult ; Affect ; Brain/*physiology ; Brain Mapping/methods ; Brain-Computer Interfaces ; Cerebral Cortex/physiology ; Emotions ; Feeding Behavior ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Neural Pathways/physiology ; Obesity/complications ; Time Factors ; },
abstract = {The anterior insular cortex (AIC) is involved in emotional processes and gustatory functions which can be examined by imaging techniques. Such imaging studies showed increased activation in the insula in response to food stimuli as well as a differential activation in lean and obese people. Additionally, studies investigating lean subjects established the voluntary regulation of the insula by a real-time functional magnetic resonance imaging-brain computer interface (rtfMRI-BCI) approach. In this exploratory study, 11 lean and 10 obese healthy, male participants were investigated in a rtfMRI-BCI protocol. During the training sessions, all obese participants were able to regulate the activity of the AIC voluntarily, while four lean participants were not able to regulate at all. In successful regulators, functional connectivity during regulation vs. relaxation between the AIC and all other regions of the brain was determined by a seed voxel approach. Lean in comparison to obese regulators showed stronger connectivity in cingular and temporal cortices during regulation. We conclude, that obese people possess an improved capacity to self-regulate the anterior insula, a brain system tightly related to bodily awareness and gustatory functions.},
}
@article {pmid22902247,
year = {2013},
author = {Sellers, EW},
title = {New horizons in brain-computer interface research.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {1},
pages = {2-4},
pmid = {22902247},
issn = {1872-8952},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Consciousness Disorders/*physiopathology/*psychology ; Female ; Humans ; Male ; },
}
@article {pmid22897888,
year = {2012},
author = {Takahashi, M and Takeda, K and Otaka, Y and Osu, R and Hanakawa, T and Gouko, M and Ito, K},
title = {Event related desynchronization-modulated functional electrical stimulation system for stroke rehabilitation: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {9},
number = {},
pages = {56},
pmid = {22897888},
issn = {1743-0003},
mesh = {Ankle/physiology ; Biomechanical Phenomena ; *Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; Electric Stimulation Therapy/*instrumentation/methods ; Electrodes ; *Electroencephalography ; Electromyography ; Equipment Design ; Feasibility Studies ; Feedback, Physiological ; Female ; Humans ; Intention ; Male ; Middle Aged ; Muscle, Skeletal/physiology ; Paresis/etiology/rehabilitation ; Physical Education and Training ; Range of Motion, Articular ; Stroke/complications ; *Stroke Rehabilitation ; Treatment Outcome ; },
abstract = {BACKGROUND: We developed an electroencephalogram-based brain computer interface system to modulate functional electrical stimulation (FES) to the affected tibialis anterior muscle in a stroke patient. The intensity of FES current increased in a stepwise manner when the event-related desynchronization (ERD) reflecting motor intent was continuously detected from the primary cortical motor area.<br><br>METHODS: We tested the feasibility of the ERD-modulated FES system in comparison with FES without ERD modulation. The stroke patient who presented with severe hemiparesis attempted to perform dorsiflexion of the paralyzed ankle during which FES was applied either with or without ERD modulation.<br><br>RESULTS: After 20&#x2009;minutes of training, the range of movement at the ankle joint and the electromyography amplitude of the affected tibialis anterior muscle were significantly increased following the ERD-modulated FES compared with the FES alone.<br><br>CONCLUSIONS: The proposed rehabilitation technique using ERD-modulated FES for stroke patients was feasible. The system holds potentials to improve the limb function and to benefit stroke patients.},
}
@article {pmid22895995,
year = {2012},
author = {Kaiser, V and Daly, I and Pichiorri, F and Mattia, D and Müller-Putz, GR and Neuper, C},
title = {Relationship between electrical brain responses to motor imagery and motor impairment in stroke.},
journal = {Stroke},
volume = {43},
number = {10},
pages = {2735-2740},
doi = {10.1161/STROKEAHA.112.665489},
pmid = {22895995},
issn = {1524-4628},
mesh = {Adult ; Aged ; Brain/*physiopathology ; Brain-Computer Interfaces ; Cortical Synchronization/*physiology ; *Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Imagery, Psychotherapy ; Male ; Middle Aged ; Motor Skills Disorders/*physiopathology ; Regression Analysis ; Severity of Illness Index ; Stroke/*physiopathology ; Stroke Rehabilitation ; },
abstract = {BACKGROUND AND PURPOSE: New strategies like motor imagery based brain-computer interfaces, which use brain signals such as event-related desynchronization (ERD) or event-related synchronization (ERS) for motor rehabilitation after a stroke, are undergoing investigation. However, little is known about the relationship between ERD and ERS patterns and the degree of stroke impairment. The aim of this work was to clarify this relationship.<br><br>METHODS: EEG during motor imagery and execution were measured in 29 patients with first-ever monolateral stroke causing any degree of motor deficit in the upper limb. The strength and laterality of the ERD or ERS patterns were correlated with the scores of the European Stroke Scale, the Medical Research Council, and the Modified Ashworth Scale.<br><br>RESULTS: Mean age of the patients was 58 &#xb1; 15 years; mean time from the incident was 4 &#xb1; 4 months. Stroke lesions were cortical (n=8), subcortical (n=11), or mixed (n=10), attributable to either an ischemic event (n=26) or a hemorrhage (n=3), affecting the right (n=16) or left (n=13) hemisphere. Higher impairment was related to stronger ERD in the unaffected hemisphere and higher spasticity was related to stronger ERD in the affected hemisphere. Both were related to a relatively stronger ERS in the affected hemisphere.<br><br>CONCLUSIONS: The results of this study may have implications for the design of potential poststroke rehabilitation interventions based on brain-computer interface technologies that use neurophysiological signals like ERD or ERS as neural substrates for the mutual interaction between brain and machine and, ultimately, help stroke patients to regain motor control.},
}
@article {pmid22893447,
year = {2012},
author = {Santaniello, S and Sherman, DL and Thakor, NV and Eskandar, EN and Sarma, SV},
title = {Optimal control-based bayesian detection of clinical and behavioral state transitions.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {5},
pages = {708-719},
doi = {10.1109/TNSRE.2012.2210246},
pmid = {22893447},
issn = {1558-0210},
mesh = {Aged ; *Algorithms ; *Artificial Intelligence ; Bayes Theorem ; Behavior/*physiology ; Brain/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Accurately detecting hidden clinical or behavioral states from sequential measurements is an emerging topic in neuroscience and medicine, which may dramatically impact neural prosthetics, brain-computer interface and drug delivery. For example, early detection of an epileptic seizure from sequential electroencephalographic (EEG) measurements would allow timely administration of anticonvulsant drugs or neurostimulation, thus reducing physical impairment and risks of overtreatment. We develop a Bayesian paradigm for state transition detection that combines optimal control and Markov processes. We define a hidden Markov model of the state evolution and develop a detection policy that minimizes a loss function of both probability of false positives and accuracy (i.e., lag between estimated and actual transition time). Our strategy automatically adapts to each newly acquired measurement based on the state evolution model and the relative loss for false positives and accuracy, thus resulting in a time varying threshold policy. The paradigm was used in two applications: 1) detection of movement onset (behavioral state) from subthalamic single unit recordings in Parkinson's disease patients performing a motor task; 2) early detection of an approaching seizure (clinical state) from multichannel intracranial EEG recordings in rodents treated with pentylenetetrazol chemoconvulsant. Our paradigm performs significantly better than chance and improves over widely used detection algorithms.},
}
@article {pmid22891058,
year = {2012},
author = {Wang, Z and Gunduz, A and Brunner, P and Ritaccio, AL and Ji, Q and Schalk, G},
title = {Decoding onset and direction of movements using Electrocorticographic (ECoG) signals in humans.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {15},
pmid = {22891058},
issn = {1662-6443},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {Communication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.},
}
@article {pmid22890647,
year = {2013},
author = {Wilson, JA and Shutter, LA and Hartings, JA},
title = {COSBID-M3: a platform for multimodal monitoring, data collection, and research in neurocritical care.},
journal = {Acta neurochirurgica. Supplement},
volume = {115},
number = {},
pages = {67-74},
doi = {10.1007/978-3-7091-1192-5_15},
pmid = {22890647},
issn = {0065-1419},
mesh = {*Brain-Computer Interfaces ; Cerebrovascular Circulation/physiology ; Critical Care ; *Data Collection ; Humans ; *Monitoring, Physiologic ; Nervous System Diseases/*physiopathology ; *Signal Processing, Computer-Assisted/instrumentation ; Software ; },
abstract = {Neuromonitoring in patients with severe brain trauma and stroke is often limited to intracranial pressure (ICP); advanced neuroscience intensive care units may also monitor brain oxygenation (partial pressure of brain tissue oxygen, P(bt)O(2)), electroencephalogram (EEG), cerebral blood flow (CBF), or neurochemistry. For example, cortical spreading depolarizations (CSDs) recorded by electrocorticography (ECoG) are associated with delayed cerebral ischemia after subarachnoid hemorrhage and are an attractive target for novel therapeutic approaches. However, to better understand pathophysiologic relations and realize the potential of multimodal monitoring, a common platform for data collection and integration is needed. We have developed a multimodal system that integrates clinical, research, and imaging data into a single research and development (R&amp;D) platform. Our system is adapted from the widely used BCI2000, a brain-computer interface tool which is written in the C++ language and supports over 20 data acquisition systems. It is optimized for real-time analysis of multimodal data using advanced time and frequency domain analyses and is extensible for research development using a combination of C++, MATLAB, and Python languages. Continuous streams of raw and processed data, including BP (blood pressure), ICP, PtiO2, CBF, ECoG, EEG, and patient video are stored in an open binary data format. Selected events identified in raw (e.g., ICP) or processed (e.g., CSD) measures are displayed graphically, can trigger alarms, or can be sent to researchers or clinicians via text message. For instance, algorithms for automated detection of CSD have been incorporated, and processed ECoG signals are projected onto three-dimensional (3D) brain models based on patient magnetic resonance imaging (MRI) and computed tomographic (CT) scans, allowing real-time correlation of pathoanatomy and cortical function. This platform will provide clinicians and researchers with an advanced tool to investigate pathophysiologic relationships and novel measures of cerebral status, as well as implement treatment algorithms based on such multimodal measures.},
}
@article {pmid22877577,
year = {2012},
author = {De Vos, M and Thorne, JD and Yovel, G and Debener, S},
title = {Let's face it, from trial to trial: comparing procedures for N170 single-trial estimation.},
journal = {NeuroImage},
volume = {63},
number = {3},
pages = {1196-1202},
doi = {10.1016/j.neuroimage.2012.07.055},
pmid = {22877577},
issn = {1095-9572},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Visual/physiology ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The estimation of event-related single trial EEG activity is notoriously difficult but is of growing interest in various areas of cognitive neuroscience, such as multimodal neuroimaging and EEG-based brain computer interfaces. However, an objective evaluation of different approaches is lacking. The present study therefore compared four frequently-used single-trial data filtering procedures: raw sensor amplitudes, regression-based estimation, bandpass filtering, and independent component analysis (ICA). High-density EEG data were recorded from 20 healthy participants in a face recognition task and were analyzed with a focus on the face-selective N170 single-trial event-related potential. Linear discriminant analysis revealed significantly better single-trial estimation for ICA compared to raw sensor amplitudes, whereas the other two approaches did not improve classification accuracy. Further analyses suggested that ICA enabled extraction of a face-sensitive independent component in each participant, which led to the superior performance in single trial estimation. Additionally, we show that the face-sensitive component does not directly represent activity from a neuronal population exclusively involved in face-processing, but rather the activity of a network involved in general visual processing. We conclude that ICA effectively facilitates the separation of physiological trial-by-trial fluctuations from measurement noise, in particular when the process of interest is reliably reflected in components representing the neural signature of interest.},
}
@article {pmid22872668,
year = {2012},
author = {Shin, Y and Lee, S and Lee, J and Lee, HN},
title = {Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems.},
journal = {Journal of neural engineering},
volume = {9},
number = {5},
pages = {056002},
doi = {10.1088/1741-2560/9/5/056002},
pmid = {22872668},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*classification ; Databases, Factual ; Electroencephalography/methods ; *Evoked Potentials, Motor/physiology ; Humans ; *Imagination/physiology ; },
abstract = {Motor imagery (MI)-based brain-computer interface systems (BCIs) normally use a powerful spatial filtering and classification method to maximize their performance. The common spatial pattern (CSP) algorithm is a widely used spatial filtering method for MI-based BCIs. In this work, we propose a new sparse representation-based classification (SRC) scheme for MI-based BCI applications. Sensorimotor rhythms are extracted from electroencephalograms and used for classification. The proposed SRC method utilizes the frequency band power and CSP algorithm to extract features for classification. We analyzed the performance of the new method using experimental datasets. The results showed that the SRC scheme provides highly accurate classification results, which were better than those obtained using the well-known linear discriminant analysis classification method. The enhancement of the proposed method in terms of the classification accuracy was verified using cross-validation and a statistical paired t-test (p < 0.001).},
}
@article {pmid22871683,
year = {2012},
author = {Cameirão, MS and Badia, SB and Duarte, E and Frisoli, A and Verschure, PF},
title = {The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke.},
journal = {Stroke},
volume = {43},
number = {10},
pages = {2720-2728},
doi = {10.1161/STROKEAHA.112.653196},
pmid = {22871683},
issn = {1524-4628},
mesh = {Aged ; *Brain-Computer Interfaces ; Chronic Disease ; Feasibility Studies ; Feedback, Sensory/physiology ; Female ; Humans ; Male ; Middle Aged ; Outcome Assessment, Health Care ; Patient Acceptance of Health Care ; Patient Satisfaction ; Recovery of Function/*physiology ; Stroke/physiopathology/*therapy ; *Stroke Rehabilitation ; Treatment Outcome ; Upper Extremity/*physiopathology ; Virtual Reality Exposure Therapy/instrumentation/*methods ; },
abstract = {BACKGROUND AND PURPOSE: Although there is strong evidence on the beneficial effects of virtual reality (VR)-based rehabilitation, it is not yet well understood how the different aspects of these systems affect recovery. Consequently, we do not exactly know what features of VR neurorehabilitation systems are decisive in conveying their beneficial effects.<br><br>METHODS: To specifically address this issue, we developed 3 different configurations of the same VR-based rehabilitation system, the Rehabilitation Gaming System, using 3 different interface technologies: vision-based tracking, haptics, and a passive exoskeleton. Forty-four patients with chronic stroke were randomly allocated to one of the configurations and used the system for 35 minutes a day for 5 days a week during 4 weeks.<br><br>RESULTS: Our results revealed significant within-subject improvements at most of the standard clinical evaluation scales for all groups. Specifically we observe that the beneficial effects of VR-based training are modulated by the use/nonuse of compensatory movement strategies and the specific sensorimotor contingencies presented to the user, that is, visual feedback versus combined visual haptic feedback.<br><br>CONCLUSIONS: Our findings suggest that the beneficial effects of VR-based neurorehabilitation systems such as the Rehabilitation Gaming System for the treatment of chronic stroke depend on the specific interface systems used. These results have strong implications for the design of future VR rehabilitation strategies that aim at maximizing functional outcomes and their retention. Clinical Trial Registration- This trial was not registered because it is a small clinical study that evaluates the feasibility of prototype devices.},
}
@article {pmid22871125,
year = {2012},
author = {Oliynyk, A and Bonifazzi, C and Montani, F and Fadiga, L},
title = {Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.},
journal = {BMC neuroscience},
volume = {13},
number = {},
pages = {96},
pmid = {22871125},
issn = {1471-2202},
mesh = {Action Potentials/*physiology ; Animals ; *Cluster Analysis ; Computer Simulation ; *Fuzzy Logic ; Humans ; Models, Neurological ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; *Software ; },
abstract = {BACKGROUND: Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.<br><br>RESULTS: Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.<br><br>CONCLUSIONS: This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.},
}
@article {pmid22854976,
year = {2012},
author = {Santana, R and Bielza, C and Larrañaga, P},
title = {Regularized logistic regression and multiobjective variable selection for classifying MEG data.},
journal = {Biological cybernetics},
volume = {106},
number = {6-7},
pages = {389-405},
doi = {10.1007/s00422-012-0506-6},
pmid = {22854976},
issn = {1432-0770},
mesh = {Algorithms ; Artificial Intelligence/statistics &amp; numerical data ; Brain-Computer Interfaces/*statistics &amp; numerical data ; Cybernetics ; Data Interpretation, Statistical ; Humans ; Logistic Models ; Magnetoencephalography/*statistics &amp; numerical data ; Models, Statistical ; },
abstract = {This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.},
}
@article {pmid22854211,
year = {2013},
author = {Xu, H and Zhang, D and Ouyang, M and Hong, B},
title = {Employing an active mental task to enhance the performance of auditory attention-based brain-computer interfaces.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {1},
pages = {83-90},
doi = {10.1016/j.clinph.2012.06.004},
pmid = {22854211},
issn = {1872-8952},
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Auditory Perception/*physiology ; *Brain-Computer Interfaces/classification ; Data Interpretation, Statistical ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Female ; Humans ; Male ; Mental Processes/*physiology ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Young Adult ; },
abstract = {OBJECTIVE: A majority of auditory brain-computer interfaces (BCIs) use the attentional modulation of auditory event-related potentials (ERPs) for communication and control. This study investigated whether the performance of an ERP-based auditory BCI can be further improved by increasing the mental efforts associated with the execution of the attention-related task.<br><br>METHODS: Subjects mentally selected a target among a random sequence of spoken digits. Upon the detection of the target digit, the subjects were required to perform an active mental task (AMT) - mentally discriminating the gender property of the target voice. The total number of presented digits was manipulated to investigate possible influences of the number of choices. The subjects also participated in two control experiments, in which they were asked to (1) press a button to report their discrimination results or (2) simply count the appearance of the target digit without performing the AMT.<br><br>RESULTS: Two ERP components, that is, a negative shift around 200 ms (Nd) over the fronto-central area and a positive deflection during 500-600 ms (late positive component, LPC) over the central-parietal area, were modulated by execution of the AMT. Compared to a counting task, the AMT resulted in paradigm-specific enhanced LPC responses. The latency of the LPC was significantly correlated with the behavioural reaction time, indicating that the LPC could originate from a response-related brain network similar to P3b. The AMT paradigm resulted in an increase of 4-6% in BCI classification accuracies, compared to a counting paradigm that was considered to represent the traditional auditory attention BCI paradigms (p < 0.05). In addition, the BCI classification accuracies were not significantly affected by the number of BCI choices in the AMT paradigm.<br><br>CONCLUSIONS: (1) LPC was identified as the AMT-specific ERP component and (2) the performance of auditory BCIs can be improved from the human response side by introducing additional mental efforts when executing attention-related tasks.<br><br>SIGNIFICANCE: The neurophysiological characteristics of the recently proposed auditory BCI paradigm using an AMT were explored. The results suggest the proposed paradigm as a candidate for improving the performance of auditory BCIs.},
}
@article {pmid22851229,
year = {2012},
author = {Ajiboye, AB and Simeral, JD and Donoghue, JP and Hochberg, LR and Kirsch, RF},
title = {Prediction of imagined single-joint movements in a person with high-level tetraplegia.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {10},
pages = {2755-2765},
pmid = {22851229},
issn = {1558-2531},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; R01NS-25074/NS/NINDS NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; C06-16549-01A1//PHS HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; R01EB007401-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Arm/physiology ; *Artificial Limbs ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagery, Psychotherapy ; Imagination/*physiology ; Implants, Experimental ; Microelectrodes ; Middle Aged ; Motor Cortex/physiology ; Movement/*physiology ; *Neural Prostheses ; Photic Stimulation ; Quadriplegia/*rehabilitation ; },
abstract = {Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements, while SUA, multispike trains (MSP), multiunit activity, and local field potential time (LFPrms), and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20-60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints.},
}
@article {pmid22848582,
year = {2012},
author = {Meehan, TD and Werling, BP and Landis, DA and Gratton, C},
title = {Pest-suppression potential of midwestern landscapes under contrasting bioenergy scenarios.},
journal = {PloS one},
volume = {7},
number = {7},
pages = {e41728},
pmid = {22848582},
issn = {1932-6203},
mesh = {Americas ; Biomass ; Crops, Agricultural/*growth &amp; development ; Environmental Policy ; *Models, Statistical ; Pest Control, Biological/*statistics &amp; numerical data ; Renewable Energy/*statistics &amp; numerical data ; },
abstract = {Biomass crops grown on marginal soils are expected to fuel an emerging bioenergy industry in the United States. Bioenergy crop choice and position in the landscape could have important impacts on a range of ecosystem services, including natural pest-suppression (biocontrol services) provided by predatory arthropods. In this study we use predation rates of three sentinel crop pests to develop a biocontrol index (BCI) summarizing pest-suppression potential in corn and perennial grass-based bioenergy crops in southern Wisconsin, lower Michigan, and northern Illinois. We show that BCI is higher in perennial grasslands than in corn, and increases with the amount of perennial grassland in the surrounding landscape. We develop an empirical model for predicting BCI from information on energy crop and landscape characteristics, and use the model in a qualitative assessment of changes in biocontrol services for annual croplands on prime agricultural soils under two contrasting bioenergy scenarios. Our analysis suggests that the expansion of annual energy crops onto 1.2 million ha of existing perennial grasslands on marginal soils could reduce BCI between -10 and -64% for nearly half of the annual cropland in the region. In contrast, replacement of the 1.1 million ha of existing annual crops on marginal land with perennial energy crops could increase BCI by 13 to 205% on over half of the annual cropland in the region. Through comparisons with other independent studies, we find that our biocontrol index is negatively related to insecticide use across the Midwest, suggesting that strategically positioned, perennial bioenergy crops could reduce insect damage and insecticide use on neighboring food and forage crops. We suggest that properly validated environmental indices can be used in decision support systems to facilitate integrated assessments of the environmental and economic impacts of different bioenergy policies.},
}
@article {pmid22848201,
year = {2012},
author = {Wriessnegger, SC and Bauernfeind, G and Schweitzer, K and Kober, S and Neuper, C and Müller-Putz, GR},
title = {The interplay of prefrontal and sensorimotor cortices during inhibitory control of learned motor behavior.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {17},
pmid = {22848201},
issn = {1662-6443},
abstract = {In the present study inhibitory cortical mechanisms have been investigated during execution and inhibition of learned motor programs by means of multi-channel functional near infrared spectroscopy (fNIRS). fNIRS is an emerging non-invasive optical technique for the in vivo assessment of cerebral oxygenation, concretely changes of oxygenated [oxy-Hb], and deoxygenated [deoxy-Hb] hemoglobin. Eleven healthy subjects executed or inhibited previous learned finger and foot movements indicated by a visual cue. The execution of finger/foot movements caused a typical activation pattern namely an increase of [oxy-Hb] and a decrease of [deoxy-Hb] whereas the inhibition of finger/foot movements caused a decrease of [oxy-Hb] and an increase of [deoxy-Hb] in the hand or foot representation area (left or medial somatosensory and primary motor cortex). Additionally an increase of [oxy-Hb] and a decrease of [deoxy-Hb] in the medial area of the anterior prefrontal cortex (APFC) during the inhibition of finger/foot movements were found. The results showed, that inhibition/execution of learned motor programs depends on an interplay of focal increases and decreases of neural activity in prefrontal and sensorimotor areas regardless of the effector. As far as we know, this is the first study investigating inhibitory processes of finger/foot movements by means of multi-channel fNIRS.},
}
@article {pmid22846655,
year = {2012},
author = {Lee, B and Liu, CY and Apuzzo, ML},
title = {Quantum computing: a prime modality in neurosurgery's future.},
journal = {World neurosurgery},
volume = {78},
number = {5},
pages = {404-408},
doi = {10.1016/j.wneu.2012.07.013},
pmid = {22846655},
issn = {1878-8769},
mesh = {Brain/*physiology ; Brain-Computer Interfaces/trends ; Computers/*trends ; Humans ; Neuronavigation/trends ; Neurosurgery/*trends ; Prostheses and Implants/*trends ; *Quantum Theory ; },
abstract = {OBJECTIVE: With each significant development in the field of neurosurgery, our dependence on computers, small and large, has continuously increased. From something as mundane as bipolar cautery to sophisticated intraoperative navigation with real-time magnetic resonance imaging-assisted surgical guidance, both technologies, however simple or complex, require computational processing power to function. The next frontier for neurosurgery involves developing a greater understanding of the brain and furthering our capabilities as surgeons to directly affect brain circuitry and function.<br><br>METHODS: This has come in the form of implantable devices that can electronically and nondestructively influence the cortex and nuclei with the purpose of restoring neuronal function and improving quality of life.<br><br>RESULTS: We are now transitioning from devices that are turned on and left alone, such as vagus nerve stimulators and deep brain stimulators, to "smart" devices that can listen and react to the body as the situation may dictate.<br><br>CONCLUSION: The development of quantum computers and their potential to be thousands, if not millions, of times faster than current "classical" computers, will significantly affect the neurosciences, especially the field of neurorehabilitation and neuromodulation. Quantum computers may advance our understanding of the neural code and, in turn, better develop and program implantable neural devices. When quantum computers reach the point where we can actually implant such devices in patients, the possibilities of what can be done to interface and restore neural function will be limitless.},
}
@article {pmid22845827,
year = {2012},
author = {Llera, A and Gómez, V and Kappen, HJ},
title = {Adaptive classification on brain-computer interfaces using reinforcement signals.},
journal = {Neural computation},
volume = {24},
number = {11},
pages = {2900-2923},
doi = {10.1162/NECO_a_00348},
pmid = {22845827},
issn = {1530-888X},
mesh = {Algorithms ; Artificial Intelligence ; *Brain-Computer Interfaces ; Humans ; *Models, Neurological ; *Models, Statistical ; Pattern Recognition, Automated ; Reinforcement, Psychology ; Signal Processing, Computer-Assisted ; },
abstract = {We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.},
}
@article {pmid22844390,
year = {2012},
author = {Power, SD and Kushki, A and Chau, T},
title = {Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.},
journal = {PloS one},
volume = {7},
number = {7},
pages = {e37791},
pmid = {22844390},
issn = {1932-6203},
mesh = {Brain-Computer Interfaces ; Clinical Trials as Topic/instrumentation/*methods ; Female ; Hemodynamics ; Humans ; Male ; *Mathematics ; Mental Processes/*physiology ; Prefrontal Cortex/*physiology ; Spatio-Temporal Analysis ; Spectroscopy, Near-Infrared/instrumentation/*methods ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) has been recently investigated for use in noninvasive brain-computer interface (BCI) technologies. Previous studies have demonstrated the ability to classify patterns of neural activation associated with different mental tasks (e.g., mental arithmetic) using NIRS signals. Though these studies represent an important step towards the realization of an NIRS-BCI, there is a paucity of literature regarding the consistency of these responses, and the ability to classify them on a single-trial basis, over multiple sessions. This is important when moving out of an experimental context toward a practical system, where performance must be maintained over longer periods. When considering response consistency across sessions, two questions arise: 1) can the hemodynamic response to the activation task be distinguished from a baseline (or other task) condition, consistently across sessions, and if so, 2) are the spatiotemporal characteristics of the response which best distinguish it from the baseline (or other task) condition consistent across sessions. The answers will have implications for the viability of an NIRS-BCI system, and the design strategies (especially in terms of classifier training protocols) adopted. In this study, we investigated the consistency of classification of a mental arithmetic task and a no-control condition over five experimental sessions. Mixed model linear regression on intrasession classification accuracies indicate that the task and baseline states remain differentiable across multiple sessions, with no significant decrease in accuracy (p = 0.67). Intersession analysis, however, revealed inconsistencies in spatiotemporal response characteristics. Based on these results, we investigated several different practical classifier training protocols, including scenarios in which the training and test data come from 1) different sessions, 2) the same session, and 3) a combination of both. Results indicate that when selecting optimal classifier training protocols for NIRS-BCI, a compromise between accuracy and convenience (e.g., in terms of duration/frequency of training data collection) must be considered.},
}
@article {pmid22841698,
year = {2012},
author = {Yue, J and Zhou, Z and Jiang, J and Liu, Y and Hu, D},
title = {Balancing a simulated inverted pendulum through motor imagery: an EEG-based real-time control paradigm.},
journal = {Neuroscience letters},
volume = {524},
number = {2},
pages = {95-100},
doi = {10.1016/j.neulet.2012.07.031},
pmid = {22841698},
issn = {1872-7972},
mesh = {Adult ; Brain/*physiology ; *Electroencephalography ; *Feedback, Sensory ; Female ; Humans ; *Imagination ; Male ; *Motion ; *User-Computer Interface ; Young Adult ; },
abstract = {Most brain-computer interfaces (BCIs) are non-time-restraint systems. However, the method used to design a real-time BCI paradigm for controlling unstable devices is still a challenging problem. This paper presents a real-time feedback BCI paradigm for controlling an inverted pendulum on a cart (IPC). In this paradigm, sensorimotor rhythms (SMRs) were recorded using 15 active electrodes placed on the surface of the subject's scalp. Subsequently, common spatial pattern (CSP) was used as the basic filter to extract spatial patterns. Finally, linear discriminant analysis (LDA) was used to translate the patterns into control commands that could stabilize the simulated inverted pendulum. Offline trainings were employed to teach the subjects to execute corresponding mental tasks, such as left/right hand motor imagery. Five subjects could successfully balance the online inverted pendulum for more than 35s. The results demonstrated that BCIs are able to control nonlinear unstable devices. Furthermore, the demonstration and extension of real-time continuous control might be useful for the real-life application and generalization of BCI.},
}
@article {pmid22838499,
year = {2012},
author = {Yong, X and Fatourechi, M and Ward, RK and Birch, GE},
title = {Automatic artefact removal in a self-paced hybrid brain- computer interface system.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {9},
number = {},
pages = {50},
pmid = {22838499},
issn = {1743-0003},
mesh = {*Algorithms ; *Artifacts ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/instrumentation/methods ; Electromyography ; Electrooculography ; Equipment Design ; Eye Movements/physiology ; Female ; Humans ; Male ; Regression Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Wavelet Analysis ; Young Adult ; },
abstract = {BACKGROUND: A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI's performance.<br><br>METHODS: To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system's performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.<br><br>RESULTS: With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.<br><br>CONCLUSIONS: The proposed artefact removal algorithm greatly improves the BCI's performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.},
}
@article {pmid22833713,
year = {2012},
author = {Kaufmann, T and Völker, S and Gunesch, L and Kübler, A},
title = {Spelling is Just a Click Away - A User-Centered Brain-Computer Interface Including Auto-Calibration and Predictive Text Entry.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {72},
pmid = {22833713},
issn = {1662-453X},
abstract = {Brain-computer interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communication channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP-BCIs can be handled independently by laymen without expert support, which is inevitable for establishing BCIs in end-user's daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character-matrix. N = 19 BCI novices handled a user-centered ERP-BCI application on their own without expert support. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and, importantly, did not reduce accuracy. In sum, this study demonstrates feasibility of auto-calibrating ERP-BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix.},
}
@article {pmid22832242,
year = {2012},
author = {Aloise, F and Schettini, F and Aricò, P and Salinari, S and Babiloni, F and Cincotti, F},
title = {A comparison of classification techniques for a gaze-independent P300-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045012},
doi = {10.1088/1741-2560/9/4/045012},
pmid = {22832242},
issn = {1741-2552},
mesh = {Adult ; Brain-Computer Interfaces/*classification ; Electroencephalography/*classification/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {This off-line study aims to assess the performance of five classifiers commonly used in the brain-computer interface (BCI) community, when applied to a gaze-independent P300-based BCI. In particular, we compared the results of four linear classifiers and one nonlinear: Fisher's linear discriminant analysis (LDA), stepwise linear discriminant analysis (SWLDA), Bayesian linear discriminant analysis (BLDA), linear support vector machine (LSVM) and Gaussian supported vector machine (GSVM). Moreover, different values for the decimation of the training dataset were tested. The results were evaluated both in terms of accuracy and written symbol rate with the data of 19 healthy subjects. No significant differences among the considered classifiers were found. The optimal decimation factor spanned a range from 3 to 24 (12 to 94 ms long bins). Nevertheless, performance on individually optimized classification parameters is not significantly different from a classification with general parameters (i.e. using an LDA classifier, about 48 ms long bins).},
}
@article {pmid22832204,
year = {2012},
author = {Tonin, L and Leeb, R and Del R Millán, J},
title = {Time-dependent approach for single trial classification of covert visuospatial attention.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045011},
doi = {10.1088/1741-2560/9/4/045011},
pmid = {22832204},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Electroencephalography/classification/*methods ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Time Factors ; Visual Perception/*physiology ; },
abstract = {Recently, several studies have started to explore covert visuospatial attention as a control signal for brain-computer interfaces (BCIs). Covert visuospatial attention represents the ability to change the focus of attention from one point in the space without overt eye movements. Nevertheless, the full potential and possible applications of this paradigm remain relatively unexplored. Voluntary covert visuospatial attention might allow a more natural and intuitive interaction with real environments as neither stimulation nor gazing is required. In order to identify brain correlates of covert visuospatial attention, classical approaches usually rely on the whole α-band over long time intervals. In this work, we propose a more detailed analysis in the frequency and time domains to enhance classification performance. In particular, we investigate the contribution of α sub-bands and the role of time intervals in carrying information about visual attention. Previous neurophysiological studies have already highlighted the role of temporal dynamics in attention mechanisms. However, these important aspects are not yet exploited in BCI. In this work, we studied different methods that explicitly cope with the natural brain dynamics during visuospatial attention tasks in order to enhance BCI robustness and classification performances. Results with ten healthy subjects demonstrate that our approach identifies spectro-temporal patterns that outperform the state-of-the-art classification method. On average, our time-dependent classification reaches 0.74 ± 0.03 of the area under the ROC (receiver operating characteristic) curve (AUC) value with an increase of 12.3% with respect to standard methods (0.65 ± 0.4). In addition, the proposed approach allows faster classification (<1 instead of 3 s), without compromising performances. Finally, our analysis highlights the fact that discriminant patterns are not stable for the whole trial period but are changing over short time intervals. These results support the hypothesis that visual attention information is actually indexed by subject-specific α sub-bands and is time dependent.},
}
@article {pmid22832155,
year = {2012},
author = {Eliseyev, A and Moro, C and Faber, J and Wyss, A and Torres, N and Mestais, C and Benabid, AL and Aksenova, T},
title = {L1-penalized N-way PLS for subset of electrodes selection in BCI experiments.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045010},
doi = {10.1088/1741-2560/9/4/045010},
pmid = {22832155},
issn = {1741-2552},
mesh = {Animals ; *Brain-Computer Interfaces ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Haplorhini ; Humans ; *Least-Squares Analysis ; },
abstract = {Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression. In this paper the L1-Penalized NPLS is proposed for sparse BCI system calibration, allowing uniting the projection technique with an effective selection of subset of features. The L1-Penalized NPLS was applied for the binary self-paced BCI system calibration, providing selection of electrodes subset. Our BCI system is designed for animal research, in particular for research in non-human primates.},
}
@article {pmid22832090,
year = {2012},
author = {Falzon, O and Camilleri, KP and Muscat, J},
title = {The analytic common spatial patterns method for EEG-based BCI data.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045009},
doi = {10.1088/1741-2560/9/4/045009},
pmid = {22832090},
issn = {1741-2552},
mesh = {Algorithms ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Statistics as Topic/methods ; },
abstract = {One of the most important stages in a brain-computer interface (BCI) system is that of extracting features that can reliably discriminate data recorded during different user states. A popular technique used for feature extraction in BCIs is the common spatial patterns (CSP) method, which provides a set of spatial filters that optimally discriminate between two classes of data in the least-squares sense. The method also yields a set of spatial patterns that are associated with the most relevant activity for distinguishing between the two classes. The high recognition rates that have been achieved with the method have led to its widespread adoption in the field. Here, a variant of the CSP method that considers EEG data in its complex form is described. By explicitly considering the amplitude and phase information in the data, the analytic CSP (ACSP) technique can provide a more comprehensive picture of the underlying activity, resulting in improved classification accuracies and more informative spatial patterns than the conventional CSP method. In this paper, we elaborate on the theoretical aspects of the ACSP algorithm and demonstrate the advantages of the method through a number of simulations and through tests on EEG data.},
}
@article {pmid22832032,
year = {2012},
author = {Wilson, JA and Walton, LM and Tyler, M and Williams, J},
title = {Lingual electrotactile stimulation as an alternative sensory feedback pathway for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045007},
doi = {10.1088/1741-2560/9/4/045007},
pmid = {22832032},
issn = {1741-2552},
mesh = {*Brain-Computer Interfaces ; Feedback, Sensory/*physiology ; Humans ; Microelectrodes ; Neural Pathways/physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Tongue/*physiology ; Touch/*physiology ; },
abstract = {This article describes a new method of providing feedback during a brain-computer interface movement task using a non-invasive, high-resolution electrotactile vision substitution system. We compared the accuracy and movement times during a center-out cursor movement task, and found that the task performance with tactile feedback was comparable to visual feedback for 11 participants. These subjects were able to modulate the chosen BCI EEG features during both feedback modalities, indicating that the type of feedback chosen does not matter provided that the task information is clearly conveyed through the chosen medium. In addition, we tested a blind subject with the tactile feedback system, and found that the training time, accuracy, and movement times were indistinguishable from results obtained from subjects using visual feedback. We believe that BCI systems with alternative feedback pathways should be explored, allowing individuals with severe motor disabilities and accompanying reduced visual and sensory capabilities to effectively use a BCI.},
}
@article {pmid22832017,
year = {2012},
author = {Schaeff, S and Treder, MS and Venthur, B and Blankertz, B},
title = {Exploring motion VEPs for gaze-independent communication.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045006},
doi = {10.1088/1741-2560/9/4/045006},
pmid = {22832017},
issn = {1741-2552},
mesh = {Adult ; *Communication ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Motion Perception/*physiology ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {Motion visually evoked potentials (mVEPs) have recently been explored as input features for brain-computer interfaces, in particular for the implementation of visual spellers. Due to low contrast and luminance requirements, motion-based intensification is less discomforting to the user than conventional approaches. So far, mVEP spellers were operated in the overt attention mode, wherein eye movements were allowed. However, the dependence on eye movements limits clinical applicability. Hence, the purpose of this study was to evaluate the suitability of mVEPs for gaze-independent communication. Sixteen healthy volunteers participated in an online study. We used a conventional speller layout wherein the possible selections are presented at different spatial locations both in the overt attention mode (fixation of the target) and the covert attention mode (central fixation). Additionally, we tested an alternative speller layout wherein all stimuli are sequentially presented at the same spatial location (foveal stimulation), i.e. eye movements are not required for selection. As can be expected, classification performance breaks down when switching from the overt to the covert operation. Despite reduced performance in the covert setting, conventional mVEP spellers are still potentially useful for users with severely impaired eye movements. In particular, they may offer advantages--such as less visual fatigue--over spellers using flashing stimuli. Importantly, the novel mVEP speller presented here recovers good performance in a gaze-independent setting by resorting to the foveal stimulation.},
}
@article {pmid22831989,
year = {2012},
author = {Thurlings, ME and Brouwer, AM and Van Erp, JB and Blankertz, B and Werkhoven, PJ},
title = {Does bimodal stimulus presentation increase ERP components usable in BCIs?.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045005},
doi = {10.1088/1741-2560/9/4/045005},
pmid = {22831989},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Touch/*physiology ; Young Adult ; },
abstract = {Event-related potential (ERP)-based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. Typically, visual stimuli are used. Tactile stimuli have recently been suggested as a gaze-independent alternative. Bimodal stimuli could evoke additional brain activity due to multisensory integration which may be of use in BCIs. We investigated the effect of visual-tactile stimulus presentation on the chain of ERP components, BCI performance (classification accuracies and bitrates) and participants' task performance (counting of targets). Ten participants were instructed to navigate a visual display by attending (spatially) to targets in sequences of either visual, tactile or visual-tactile stimuli. We observe that attending to visual-tactile (compared to either visual or tactile) stimuli results in an enhanced early ERP component (N1). This bimodal N1 may enhance BCI performance, as suggested by a nonsignificant positive trend in offline classification accuracies. A late ERP component (P300) is reduced when attending to visual-tactile compared to visual stimuli, which is consistent with the nonsignificant negative trend of participants' task performance. We discuss these findings in the light of affected spatial attention at high-level compared to low-level stimulus processing. Furthermore, we evaluate bimodal BCIs from a practical perspective and for future applications.},
}
@article {pmid22831959,
year = {2012},
author = {Andersson, P and Ramsey, NF and Raemaekers, M and Viergever, MA and Pluim, JP},
title = {Real-time decoding of the direction of covert visuospatial attention.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045004},
doi = {10.1088/1741-2560/9/4/045004},
pmid = {22831959},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; *Brain-Computer Interfaces ; *Computer Systems ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) make it possible to translate a person's intentions into actions without depending on the muscular system. Brain activity is measured and classified into commands, thereby creating a direct link between the mind and the environment, enabling, e.g., cursor control or navigation of a wheelchair or robot. Most BCI research is conducted with scalp EEG but recent developments move toward intracranial electrodes for paralyzed people. The vast majority of BCI studies focus on the motor system as the appropriate target for recording and decoding movement intentions. However, properties of the visual system may make the visual system an attractive and intuitive alternative. We report on a study investigating feasibility of decoding covert visuospatial attention in real time, exploiting the full potential of a 7 T MRI scanner to obtain the necessary signal quality, capitalizing on earlier fMRI studies indicating that covert visuospatial attention changes activity in the visual areas that respond to stimuli presented in the attended area of the visual field. Healthy volunteers were instructed to shift their attention from the center of the screen to one of four static targets in the periphery, without moving their eyes from the center. During the first part of the fMRI-run, the relevant brain regions were located using incremental statistical analysis. During the second part, the activity in these regions was extracted and classified, and the subject was given visual feedback of the result. Performance was assessed as the number of trials where the real-time classifier correctly identified the direction of attention. On average, 80% of trials were correctly classified (chance level <25%) based on a single image volume, indicating very high decoding performance. While we restricted the experiment to five attention target regions (four peripheral and one central), the number of directions can be higher provided the brain activity patterns can be distinguished. In summary, the visual system promises to be an effective target for BCI control.},
}
@article {pmid22831919,
year = {2012},
author = {Höhne, J and Krenzlin, K and Dähne, S and Tangermann, M},
title = {Natural stimuli improve auditory BCIs with respect to ergonomics and performance.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045003},
doi = {10.1088/1741-2560/9/4/045003},
pmid = {22831919},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Perception/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Ergonomics/*methods/psychology ; Evoked Potentials, Auditory/physiology ; Humans ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Moving from well-controlled, brisk artificial stimuli to natural and less-controlled stimuli seems counter-intuitive for event-related potential (ERP) studies. As natural stimuli typically contain a richer internal structure, they might introduce higher levels of variance and jitter in the ERP responses. Both characteristics are unfavorable for a good single-trial classification of ERPs in the context of a multi-class brain-computer interface (BCI) system, where the class-discriminant information between target stimuli and non-target stimuli must be maximized. For the application in an auditory BCI system, however, the transition from simple artificial tones to natural syllables can be useful despite the variance introduced. In the presented study, healthy users (N = 9) participated in an offline auditory nine-class BCI experiment with artificial and natural stimuli. It is shown that the use of syllables as natural stimuli does not only improve the users' ergonomic ratings; also the classification performance is increased. Moreover, natural stimuli obtain a better balance in multi-class decisions, such that the number of systematic confusions between the nine classes is reduced. Hopefully, our findings may contribute to make auditory BCI paradigms more user friendly and applicable for patients.},
}
@article {pmid22831906,
year = {2012},
author = {van der Waal, M and Severens, M and Geuze, J and Desain, P},
title = {Introducing the tactile speller: an ERP-based brain-computer interface for communication.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045002},
doi = {10.1088/1741-2560/9/4/045002},
pmid = {22831906},
issn = {1741-2552},
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; *Reading ; Touch/*physiology ; Young Adult ; },
abstract = {In this study, a tactile speller was developed and compared with existing visual speller paradigms in terms of classification performance and elicited event-related potentials (ERPs). The fingertips of healthy participants were stimulated with short mechanical taps while electroencephalographic activity was measured. The letters of the alphabet were allocated to different fingers and subjects could select one of the fingers by silently counting the number of taps on that finger. The offline and online performance of the tactile speller was compared to the overt and covert attention visual matrix speller and the covert attention Hex-o-Spell speller. For the tactile speller, binary target versus non-target classification accuracy was 67% on average. Classification and decoding accuracies of the tactile speller were lower than the overt matrix speller, but higher than the covert matrix speller, and similar to Hex-o-Spell. The average maximum information transfer rate of the tactile speller was 7.8 bits min(-1) (1.51 char min(-1)), with the best subject reaching a bit-rate of 27 bits min(-1) (5.22 char min(-1)). An increased amplitude of the P300 ERP component was found in response to attended stimuli versus unattended stimuli in all speller types. In addition, the tactile and overt matrix spellers also used the N2 component for discriminating between targets and non-targets. Overall, this study shows that it is possible to use a tactile speller for communication. The tactile speller provides a useful alternative to the visual speller, especially for people whose eye gaze is impaired.},
}
@article {pmid22831893,
year = {2012},
author = {Riccio, A and Mattia, D and Simione, L and Olivetti, M and Cincotti, F},
title = {Eye-gaze independent EEG-based brain-computer interfaces for communication.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {045001},
doi = {10.1088/1741-2560/9/4/045001},
pmid = {22831893},
issn = {1741-2552},
mesh = {*Auditory Perception/physiology ; *Brain-Computer Interfaces ; *Communication ; Electroencephalography/*methods ; Fixation, Ocular/physiology ; Humans ; *Touch/physiology ; *Visual Perception/physiology ; },
abstract = {The present review systematically examines the literature reporting gaze independent interaction modalities in non-invasive brain-computer interfaces (BCIs) for communication. BCIs measure signals related to specific brain activity and translate them into device control signals. This technology can be used to provide users with severe motor disability (e.g. late stage amyotrophic lateral sclerosis (ALS); acquired brain injury) with an assistive device that does not rely on muscular contraction. Most of the studies on BCIs explored mental tasks and paradigms using visual modality. Considering that in ALS patients the oculomotor control can deteriorate and also other potential users could have impaired visual function, tactile and auditory modalities have been investigated over the past years to seek alternative BCI systems which are independent from vision. In addition, various attentional mechanisms, such as covert attention and feature-directed attention, have been investigated to develop gaze independent visual-based BCI paradigms. Three areas of research were considered in the present review: (i) auditory BCIs, (ii) tactile BCIs and (iii) independent visual BCIs. Out of a total of 130 search results, 34 articles were selected on the basis of pre-defined exclusion criteria. Thirteen articles dealt with independent visual BCIs, 15 reported on auditory BCIs and the last six on tactile BCIs, respectively. From the review of the available literature, it can be concluded that a crucial point is represented by the trade-off between BCI systems/paradigms with high accuracy and speed, but highly demanding in terms of attention and memory load, and systems requiring lower cognitive effort but with a limited amount of communicable information. These issues should be considered as priorities to be explored in future studies to meet users' requirements in a real-life scenario.},
}
@article {pmid22831863,
year = {2012},
author = {Treder, MS},
title = {Special section on gaze-independent brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {040201},
doi = {10.1088/1741-2560/9/4/040201},
pmid = {22831863},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/psychology/*trends ; Fixation, Ocular/*physiology ; Humans ; },
}
@article {pmid22831853,
year = {2013},
author = {Kaplan, SA and He, W and Koltun, WD and Cummings, J and Schneider, T and Fakhoury, A},
title = {Solifenacin plus tamsulosin combination treatment in men with lower urinary tract symptoms and bladder outlet obstruction: a randomized controlled trial.},
journal = {European urology},
volume = {63},
number = {1},
pages = {158-165},
doi = {10.1016/j.eururo.2012.07.003},
pmid = {22831853},
issn = {1873-7560},
mesh = {Adrenergic alpha-1 Receptor Antagonists/adverse effects/*therapeutic use ; Aged ; Analysis of Variance ; Double-Blind Method ; Drug Therapy, Combination ; Humans ; Lower Urinary Tract Symptoms/diagnosis/*drug therapy/physiopathology ; Male ; Middle Aged ; Muscarinic Antagonists/adverse effects/*therapeutic use ; Pressure ; Quinuclidines/adverse effects/*therapeutic use ; Solifenacin Succinate ; Sulfonamides/adverse effects/*therapeutic use ; Tamsulosin ; Tetrahydroisoquinolines/adverse effects/*therapeutic use ; Time Factors ; Treatment Outcome ; Urinary Bladder/*drug effects/physiopathology ; Urinary Bladder Neck Obstruction/diagnosis/*drug therapy/physiopathology ; Urodynamics/drug effects ; },
abstract = {BACKGROUND: Alpha blockers are prescribed to manage lower urinary tract symptoms (LUTS) associated with benign prostatic hyperplasia (BPH). Antimuscarinics are prescribed to treat overactive bladder (OAB).<br><br>OBJECTIVE: To investigate the safety of a combination of solifenacin (SOLI) and tamsulosin oral controlled absorption system (TOCAS) in men with LUTS and bladder outlet obstruction (BOO).<br><br>Randomized, double-blind, parallel-group, placebo-controlled study in men aged >45 yr with LUTS and BOO for &#x2265;3 mo, total International Prostate Symptom Score (IPSS) &#x2265;8, BOO index &#x2265;20, maximum urinary flow rate (Q(max)) &#x2264;12 ml/s, and voided volume &#x2265;120 ml.<br><br>INTERVENTIONS: Once-daily coadministration of TOCAS 0.4 mg plus SOLI 6 mg, TOCAS 0.4 mg plus SOLI 9 mg, or placebo for 12 wk.<br><br>Primary (safety) measurements: Q(max) and detrusor pressure at Q(max) (P(det)Q(max)). Other safety assessments included postvoid residual (PVR) volume. Secondary end points included bladder contractile index (BCI) score and percent bladder voiding efficiency (BVE). An analysis of covariance model compared each TOCAS plus SOLI combination with placebo.<br><br>RESULTS AND LIMITATIONS: Both active treatment groups were noninferior to placebo at end of treatment (EOT) for P(det)Q(max) and Q(max). Mean change from baseline PVR was significantly higher at all time points for TOCAS 0.4 mg plus SOLI 6 mg, and at weeks 2, 12, and EOT for TOCAS 0.4 mg plus SOLI 9 mg versus placebo. Both treatment groups were similar to placebo for BCI and BVE. Urinary retention was seen in only one patient receiving TOCAS 0.4 mg plus SOLI 6 mg. Limitations of the study were that prostate size and prostate-specific antigen level were not measured.<br><br>CONCLUSIONS: TOCAS 0.4 mg plus SOLI 6 mg or 9 mg was noninferior to placebo at EOT for P(det)Q(max) and Q(max) in men with LUTS and BOO, and there was no clinical or statistical evidence of increased risk of urinary retention.},
}
@article {pmid22829754,
year = {2012},
author = {Vato, A and Semprini, M and Maggiolini, E and Szymanski, FD and Fadiga, L and Panzeri, S and Mussa-Ivaldi, FA},
title = {Shaping the dynamics of a bidirectional neural interface.},
journal = {PLoS computational biology},
volume = {8},
number = {7},
pages = {e1002578},
pmid = {22829754},
issn = {1553-7358},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; R21 HD053608/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Calibration ; Deep Brain Stimulation ; Electron Transport Complex IV/analysis ; Evoked Potentials, Motor/*physiology ; Histocytochemistry ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Neurons/*physiology ; Neurophysiology ; Rats ; Rats, Long-Evans ; Somatosensory Cortex/*physiology ; Staining and Labeling/methods ; },
abstract = {Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.},
}
@article {pmid22826701,
year = {2012},
author = {Toyama, S and Takano, K and Kansaku, K},
title = {A non-adhesive solid-gel electrode for a non-invasive brain-machine interface.},
journal = {Frontiers in neurology},
volume = {3},
number = {},
pages = {114},
pmid = {22826701},
issn = {1664-2295},
abstract = {A non-invasive brain-machine interface (BMI) or brain-computer interface is a technology for helping individuals with disabilities and utilizes neurophysiological signals from the brain to control external machines or computers without requiring surgery. However, when applying electroencephalography (EEG) methodology, users must place EEG electrodes on the scalp each time, and the development of easy-to-use electrodes for clinical use is required. In this study, we developed a conductive non-adhesive solid-gel electrode for practical non-invasive BMIs. We performed basic material testing, including examining the volume resistivity, viscoelasticity, and moisture-retention properties of the solid-gel. Then, we compared the performance of the solid-gel, a conventional paste, and an in-house metal-pin-based electrode using impedance measurements and P300-BMI testing. The solid-gel was observed to be conductive (volume resistivity 13.2 Ωcm) and soft (complex modulus 105.4 kPa), and it remained wet for a prolonged period (>10 h) in a dry environment. Impedance measurements revealed that the impedance of the solid-gel-based and conventional paste-based electrodes was superior to that of the pin-based electrode. The EEG measurement suggested that the signals obtained with the solid-gel electrode were comparable to those with the conventional paste-based electrode. Moreover, the P300-BMI study suggested that systems using the solid-gel or pin-based electrodes were effective. One of the advantages of the solid-gel is that it does not require cleaning after use, whereas the conventional paste adheres to the hair, which requires washing. Furthermore, the solid-gel electrode was not painful compared with a metal-pin electrode. Taken together, the results suggest that the solid-gel electrode worked well for practical BMIs and could be useful for bedridden patients such as those with amyotrophic lateral sclerosis.},
}
@article {pmid22826698,
year = {2012},
author = {Ifft, PJ and Lebedev, MA and Nicolelis, MA},
title = {Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {16},
pmid = {22826698},
issn = {1662-6443},
support = {R01 NS073125/NS/NINDS NIH HHS/United States ; DP1 MH099903/MH/NIMH NIH HHS/United States ; RC1 HD063390/HD/NICHD NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; },
abstract = {The ability to inhibit unwanted movements and change motor plans is essential for behaviors of advanced organisms. The neural mechanisms by which the primate motor system rejects undesired actions have received much attention during the last decade, but it is not well understood how this neural function could be utilized to improve the efficiency of brain-machine interfaces (BMIs). Here we employed linear discriminant analysis (LDA) and a Wiener filter to extract motor plan transitions from the activity of ensembles of sensorimotor cortex neurons. Two rhesus monkeys, chronically implanted with multielectrode arrays in primary motor (M1) and primary sensory (S1) cortices, were overtrained to produce reaching movements with a joystick toward visual targets upon their presentation. Then, the behavioral task was modified to include a distracting target that flashed for 50, 150, or 250 ms (25% of trials each) followed by the true target that appeared at a different screen location. In the remaining 25% of trials, the initial target stayed on the screen and was the target to be approached. M1 and S1 neuronal activity represented both the true and distracting targets, even for the shortest duration of the distracting event. This dual representation persisted both when the monkey initiated movements toward the distracting target and then made corrections and when they moved directly toward the second, true target. The Wiener filter effectively decoded the location of the true target, whereas the LDA classifier extracted the location of both targets from ensembles of 50-250 neurons. Based on these results, we suggest developing real-time BMIs that inhibit unwanted movements represented by brain activity while enacting the desired motor outcome concomitantly.},
}
@article {pmid22822620,
year = {2012},
author = {Aganovic, D and Hadziosmanovic, O and Prcic, A and Kutovac, B},
title = {The significance of the influence of aging and infravesical obstruction caused by benign prostatic enlargement on detrusor impairment.},
journal = {Medical archives (Sarajevo, Bosnia and Herzegovina)},
volume = {66},
number = {3},
pages = {185-189},
doi = {10.5455/medarh.2012.66.185-189},
pmid = {22822620},
issn = {0350-199X},
mesh = {Aged ; Aged, 80 and over ; Aging ; Humans ; Male ; Middle Aged ; Prostatic Hyperplasia/*complications ; Urinary Bladder/physiopathology ; Urinary Bladder Neck Obstruction/*etiology/physiopathology ; Urodynamics ; },
abstract = {OBJECTIVE: to analyze the influence of aging and infravesical obstruction on cystometric characteristics of patients with lower urinary tract symptoms (LUTS) and proven benign prostatic enlargement (BPE).<br><br>METHODOLOGY: A retrospective analysis was performed of basic characteristics of randomly chosen 213 patients with LUTS caused by BPE and urodynamic findings made in period 2005-2009 at the Urology Department of the Sarajevo University Clinical Center. The patients were divided into groups based on their age (<60 years/46 patients, 60-69 years/95 pat., and >70 years/72 pat.), and the degree of bladder compliance loss (<20 ml/cmH2O-76 patients, 20-40 ml/cmH2O-57 pat., and >40 ml/cmH2O-80 pat.). All patients had International Prostate Symptom Score (IPS-S) completed, prostate volume measured transabdominally, free uroflowmetry, as well as complete urodynamic study (UDS) findings--cystometry and pressure/flow studies (PFS). The PFS data were plotted on L-PURR, URA and ICS nomogram, bladder contractility index (BCI) and obstruction coefficient (OCO) were calculated for each patient.<br><br>RESULTS: There was no statistically significant difference of IPS-S, prostate volume and postvoid residual urine among the age groups. Qmax (ml/sec.) declines significantly with age (mean 11.9 vs. 10.3 vs. 7.9, ANOVA p < 0.001), along with statistically significant decrease of cystometric capacity (mean 331 ml vs. 293 mi vs. 264 ml, p = 0.001), bladder compliance (BC-ml/cmH2O) (mean 35.3 vs. 31 vs. 26.5, p = 0.013), with increased incidence of detrusor overactivity (DO) (21.7% vs. 32.6% vs. 45.8%, chi2 test for trend p = 0.006), followed by a higher incidence of obstruction (URA > or = 29 cmH2O) (37% patients vs. 61% patients vs. 72.2% patients Chi2 for trend=13.8; p = 0.0002), along with noticeable reduction of BCI (117 vs. 121 vs. 106; p = 0.02). Patients with severe BC damage (<20 ml/cmH2O) showed a difference with respect to the degree of obstruction and age, along with decreased cystometric capacity and higher incidence of DO, while the difference in IPP-S was insignificant. OCO with cut-off point of 1 showed significant difference with regard to age (66.3 vs. 66.6 years, T test, p = 0.015), prostate volume (45 cc vs. 51.8 cc, p = 0.007) and incidence of DO (26% vs. 43.4%, p = 0.02).<br><br>CONCLUSION: the degree of bladder compliance loss and incidence of obstruction increase with age, as reflected in decreased bladder capacity, decreased urine voided volume and increased incidence of DO, along with noticeably impaired detrusor contractility.},
}
@article {pmid22822397,
year = {2012},
author = {Fazel-Rezai, R and Allison, BZ and Guger, C and Sellers, EW and Kleih, SC and Kübler, A},
title = {P300 brain computer interface: current challenges and emerging trends.},
journal = {Frontiers in neuroengineering},
volume = {5},
number = {},
pages = {14},
pmid = {22822397},
issn = {1662-6443},
abstract = {A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.},
}
@article {pmid22811657,
year = {2012},
author = {Tangermann, M and Müller, KR and Aertsen, A and Birbaumer, N and Braun, C and Brunner, C and Leeb, R and Mehring, C and Miller, KJ and Müller-Putz, GR and Nolte, G and Pfurtscheller, G and Preissl, H and Schalk, G and Schlögl, A and Vidaurre, C and Waldert, S and Blankertz, B},
title = {Review of the BCI Competition IV.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {55},
pmid = {22811657},
issn = {1662-453X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.},
}
@article {pmid22801528,
year = {2012},
author = {Maeder, CL and Sannelli, C and Haufe, S and Blankertz, B},
title = {Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {5},
pages = {653-662},
doi = {10.1109/TNSRE.2012.2205707},
pmid = {22801528},
issn = {1558-0210},
mesh = {Adult ; Biological Clocks/*physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; *Periodicity ; Reproducibility of Results ; Sensitivity and Specificity ; Somatosensory Cortex/*physiology ; },
abstract = {The influence of pre-stimulus ongoing brain activity on post-stimulus task performance has recently been analyzed in several studies. While pre-stimulus activity in the parieto-occipital area has been exhaustively investigated with congruent results, less is known about the sensorimotor areas, for which studies reported inconsistent findings. In this work, the topic is addressed in a brain-computer interface (BCI) setting based on modulations of sensorimotor rhythms (SMR). The goal is to assess whether and how pre-stimulus SMR activity influences the successive task execution quality and consequently the classification performance. Grand average data of 23 participants performing right and left hand motor imagery were analyzed. Trials were separated into two groups depending on the SMR amplitude in the 1000 ms interval preceding the cue, and classification by common spatial patterns (CSPs) preprocessing and linear discriminant analysis (LDA) was carried out in the post-stimulus time interval, i.e., during the task execution. The correlation between trial group and classification performance was assessed by an analysis of variance. As a result of this analysis, trials with higher SMR amplitude in the 1000 ms interval preceding the cue yielded significantly better classification performance than trials with lower amplitude. A further investigation of brain activity patterns revealed that this increase in accuracy is mainly due to the persistence of a higher SMR amplitude over the ipsilateral hemisphere. Our findings support the idea that exploiting information about the ongoing SMR might be the key to boosting performance in future SMR-BCI experiments and motor related tasks in general.},
}
@article {pmid22798957,
year = {2012},
author = {Ikegami, S and Takano, K and Wada, M and Saeki, N and Kansaku, K},
title = {Effect of the Green/Blue Flicker Matrix for P300-Based Brain-Computer Interface: An EEG-fMRI Study.},
journal = {Frontiers in neurology},
volume = {3},
number = {},
pages = {113},
pmid = {22798957},
issn = {1664-2295},
abstract = {The visual P300-brain-computer interface, a popular system for EEG-based BCI, utilizes the P300 event-related potential to select an icon arranged in a flicker matrix. In the conventional P300-BCI speller paradigm, white/gray luminance intensification of each row/column in the matrix is used. In an earlier study, we applied green/blue luminance and chromatic change in the P300-BCI system and reported that this luminance and chromatic flicker matrix was associated with better performance and greater subject comfort compared with the conventional white/gray luminance flicker matrix. In this study, we used simultaneous EEG-functional magnetic resonance imaging (fMRI) recordings to identify brain areas that were more enhanced in the green/blue flicker matrix than in the white/gray flicker matrix, as these may highlight areas devoted to improved P300-BCI performance. The peak of the positive wave in the EEG data was detected under both conditions, and the peak amplitudes were larger at the parietal and occipital electrodes, particularly in the late components, under the green/blue condition than under the white/gray condition. fMRI data showed activation in the bilateral parietal and occipital cortices, and these areas, particularly those in the right hemisphere, were more activated under the green/blue condition than under the white/gray condition. The parietal and occipital regions more involved in the green/blue condition were part of the areas devoted to conventional P300s. These results suggest that the green/blue flicker matrix was useful for enhancing the so-called P300 responses.},
}
@article {pmid22791699,
year = {2012},
author = {Abbott, WW and Faisal, AA},
title = {Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {046016},
doi = {10.1088/1741-2560/9/4/046016},
pmid = {22791699},
issn = {1741-2552},
mesh = {Brain-Computer Interfaces/*economics ; Costs and Cost Analysis ; *Eye Movements/physiology ; *Fixation, Ocular/physiology ; Humans ; Imaging, Three-Dimensional/*economics/*methods ; Video Games/*economics ; Young Adult ; },
abstract = {Eye movements are highly correlated with motor intentions and are often retained by patients with serious motor deficiencies. Despite this, eye tracking is not widely used as control interface for movement in impaired patients due to poor signal interpretation and lack of control flexibility. We propose that tracking the gaze position in 3D rather than 2D provides a considerably richer signal for human machine interfaces by allowing direct interaction with the environment rather than via computer displays. We demonstrate here that by using mass-produced video-game hardware, it is possible to produce an ultra-low-cost binocular eye-tracker with comparable performance to commercial systems, yet 800 times cheaper. Our head-mounted system has 30 USD material costs and operates at over 120 Hz sampling rate with a 0.5-1 degree of visual angle resolution. We perform 2D and 3D gaze estimation, controlling a real-time volumetric cursor essential for driving complex user interfaces. Our approach yields an information throughput of 43 bits s(-1), more than ten times that of invasive and semi-invasive brain-machine interfaces (BMIs) that are vastly more expensive. Unlike many BMIs our system yields effective real-time closed loop control of devices (10 ms latency), after just ten minutes of training, which we demonstrate through a novel BMI benchmark--the control of the video arcade game 'Pong'.},
}
@article {pmid22791228,
year = {2012},
author = {Kasashima, Y and Fujiwara, T and Matsushika, Y and Tsuji, T and Hase, K and Ushiyama, J and Ushiba, J and Liu, M},
title = {Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation (tDCS) in patients with chronic hemiparetic stroke.},
journal = {Experimental brain research},
volume = {221},
number = {3},
pages = {263-268},
pmid = {22791228},
issn = {1432-1106},
mesh = {Adult ; Aged ; Chronic Disease ; Cortical Synchronization/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Paresis/epidemiology/*physiopathology/therapy ; Stroke/epidemiology/*physiopathology/therapy ; Time Factors ; Transcranial Magnetic Stimulation/*methods ; },
abstract = {Electroencephalogram-based brain-computer interface (BCI) has been developed as a new neurorehabilitative tool for patients with severe hemiparesis. However, its application has been limited because of difficulty detecting stable brain signals from the affected hemisphere. It has been reported that transcranial direct current stimulation (tDCS) can modulate event-related desynchronization (ERD) in healthy persons. The objective of this study was to test the hypothesis that anodal tDCS could modulate ERD in patients with severe hemiparetic stroke. The participants were six patients with chronic hemiparetic stroke (mean age, 56.8 ± 9.5 years; mean time from the onset, 70.0 ± 19.6 months; Fugl-Meyer Assessment upper extremity motor score, 30.8 ± 16.5). We applied anodal tDCS (10 min, 1 mA) and sham stimulation over the affected primary motor cortex in a random order. ERD of the mu rhythm (mu ERD) with motor imagery of extension of the affected finger was assessed before and after anodal tDCS and sham stimulation. Mu ERD of the affected hemisphere increased significantly after anodal tDCS, whereas it did not change after sham stimulation. Our results show that anodal tDCS can increase mu ERD in patients with hemiparetic stroke, indicating that anodal tDCS could be used as a conditioning tool for BCI in stroke patients.},
}
@article {pmid22782131,
year = {2012},
author = {Hill, NJ and Gupta, D and Brunner, P and Gunduz, A and Adamo, MA and Ritaccio, A and Schalk, G},
title = {Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {64},
pages = {},
pmid = {22782131},
issn = {1940-087X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology/surgery ; Brain Mapping/instrumentation/*methods ; Electric Stimulation ; Electrodes, Implanted ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; },
abstract = {Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., <1 cm/<1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning, auditory processing and visual-spatial attention. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication and control. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform and the SIGFRIED method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation. PREREQUISITES AND PLANNING: Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure. In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort. At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5(th) Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment. Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator. A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS). Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping. The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.},
}
@article {pmid22778545,
year = {2012},
author = {Wang, CS},
title = {Design of a 32-channel EEG system for brain control interface applications.},
journal = {Journal of biomedicine &amp; biotechnology},
volume = {2012},
number = {},
pages = {274939},
pmid = {22778545},
issn = {1110-7251},
mesh = {Electroencephalography/*instrumentation/*methods ; Equipment Design ; Humans ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This study integrates the hardware circuit design and the development support of the software interface to achieve a 32-channel EEG system for BCI applications. Since the EEG signals of human bodies are generally very weak, in addition to preventing noise interference, it also requires avoiding the waveform distortion as well as waveform offset and so on; therefore, the design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply will generate DC bias which affects the measurement signals. For this reason, this study specially designs an improved single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. For the analog circuit, a frequency band will be taken out through the filtering circuit and then the digital filtering design will be used to adjust the extracted frequency band for the target frequency band, combining with MATLAB to design man-machine interface for displaying brain wave. Finally the measured signals are compared to the traditional 32-channel EEG signals. In addition to meeting the IFCN standards, the system design also conducted measurement verification in the standard EEG isolation room in order to demonstrate the accuracy and reliability of this system design.},
}
@article {pmid22777434,
year = {2012},
author = {Lee, H and Ahn, J and Lee, JM and Park, M and Baek, S},
title = {Clinical effectiveness of monocanalicular and bicanalicular silicone intubation for congenital nasolacrimal duct obstruction.},
journal = {The Journal of craniofacial surgery},
volume = {23},
number = {4},
pages = {1010-1014},
doi = {10.1097/SCS.0b013e31824dfc8a},
pmid = {22777434},
issn = {1536-3732},
mesh = {Anesthesia, General ; Chi-Square Distribution ; *Dacryocystorhinostomy ; Endoscopy ; Female ; Humans ; Infant ; Intubation/instrumentation/*methods ; Lacrimal Duct Obstruction/*congenital ; Male ; Nasolacrimal Duct/*abnormalities/*surgery ; Ophthalmologic Surgical Procedures ; Postoperative Complications ; Prospective Studies ; Republic of Korea ; Silicones ; },
abstract = {BACKGROUND: Numerous surgical techniques of silicone tube intubation in congenital nasolacrimal duct obstruction (CNLDO) have been described; these techniques can be divided into monocanalicular intubation (MCI) and bicanalicular intubation (BCI). The aim of this study was to compare the clinical effectiveness of MCI versus BCI of CNLDO.<br><br>METHODS: In a prospective, nonrandomized, comparative case study, patients with CNLDO underwent probing under endoscopic control and either BCI or MCI under general anesthesia. Demographic data, including age and sex, duration of preoperative symptoms, method of previous treatment, operative time, timing of silicone tube removal, follow-up periods, complications, and outcomes, were analyzed.<br><br>RESULTS: The study included 30 eyes from 22 patients for BCI and 30 eyes from 24 patients for MCI. The mean age in the BCI group was 23.3 months and in the MCI group was 23.1 months. Mean follow-up was 16.4 &#xb1; 5.9 weeks for BCI group and 11.6 &#xb1; 8.2 weeks for MCI group. Operation time was slightly longer in the BCI group. Tubes were most often removed in the operating room under general anesthesia for BCI (66.7%) and in an office setting under topical anesthesia for MCI (100%). Overall, BCI had a 93.3% success rate (28/30), and MCI had a 90.0% success rate (27/30).<br><br>CONCLUSIONS: Although there was no significant difference between the success rates of the 2 groups, MCI allowed technical ease of insertion and tube removal. Moreover, the tubing does not threaten the unprobed part of the lacrimal drainage system. These advantages of MCI should be considered when selecting treatment methods for CNLDO.},
}
@article {pmid22773199,
year = {2012},
author = {Cornwell, AS and Liao, JY and Bryden, AM and Kirsch, RF},
title = {Standard task set for evaluating rehabilitation interventions for individuals with arm paralysis.},
journal = {Journal of rehabilitation research and development},
volume = {49},
number = {3},
pages = {395-403},
pmid = {22773199},
issn = {1938-1352},
support = {N01HD53403/HD/NICHD NIH HHS/United States ; N01-HD-5-340/HD/NICHD NIH HHS/United States ; T32EB004314/EB/NIBIB NIH HHS/United States ; T32 EB004314/EB/NIBIB NIH HHS/United States ; T32 GM007250/GM/NIGMS NIH HHS/United States ; },
mesh = {*Activities of Daily Living ; Arm/*physiopathology ; *Electric Stimulation Therapy/standards ; Electrodes, Implanted ; Humans ; Movement/physiology ; Muscle, Skeletal/physiopathology ; Psychomotor Performance ; Quadriplegia/etiology/physiopathology/*rehabilitation ; Recovery of Function ; Spinal Cord Injuries/complications/physiopathology/*rehabilitation ; },
abstract = {We have developed a set of upper-limb functional tasks to guide the design and test the performance of rehabilitation technologies that restore arm motion in people with high tetraplegia. Our goal was to develop a short set of tasks that would be representative of a much larger set of activities of daily living (ADLs), while also being feasible for a user of a unilateral, implanted functional electrical stimulation (FES) system. To compile this list of tasks, we reviewed existing clinical outcome measures related to arm and hand function and were further informed by surveys of patient desires. We ultimately selected a set of five tasks that captured the most common components of movement seen in ADLs and is therefore highly relevant for assessing FES-restored unilateral arm function in individuals with high cervical spinal cord injury. The tasks are intended to be used when setting design specifications and for evaluating and standardizing rehabilitation technologies under development. While not unique, this set of tasks will provide a common basis for comparing different interventions (e.g., FES, powered orthoses, robotic assistants) and testing different user command interfaces (e.g., sip-and-puff, head joysticks, brain-computer interfaces).},
}
@article {pmid22772975,
year = {2012},
author = {Shalchyan, V and Jensen, W and Farina, D},
title = {Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {9},
pages = {2576-2585},
doi = {10.1109/TBME.2012.2204991},
pmid = {22772975},
issn = {1558-2531},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; Cluster Analysis ; Computer Simulation ; Electrodes, Implanted ; Electroencephalography ; Male ; Motor Cortex/*physiology ; Neurons/*physiology ; Rats ; Rats, Sprague-Dawley ; *Wavelet Analysis ; },
abstract = {Automatic and accurate detection of action potentials of unknown waveforms in noisy extracellular neural recordings is an important requirement for developing brain-computer interfaces. This study introduces a new, wavelet-based manifestation variable that combines the wavelet shrinkage denoising with multiscale edge detection for robustly detecting and finding the occurrence time of action potentials in noisy signals. To further improve the detection performance by eliminating the dependence of the method to the choice of the mother wavelet, we propose an unsupervised optimization for best basis selection. Moreover, another unsupervised criterion based on a correlation similarity measure was defined to update the wavelet selection during the clustering to improve the spike sorting performance. The proposed method was compared to several previously proposed methods by using a wide range of realistic simulated data as well as selected experimental recordings of intracortical signals from freely moving rats. The detection performance of the proposed method substantially surpassed previous methods for all signals tested. Moreover, updating the wavelet selection for the clustering task was shown to improve the classification performance with respect to maintaining the same wavelet as for the detection stage.},
}
@article {pmid22772374,
year = {2012},
author = {Orsborn, AL and Dangi, S and Moorman, HG and Carmena, JM},
title = {Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {4},
pages = {468-477},
doi = {10.1109/TNSRE.2012.2185066},
pmid = {22772374},
issn = {1558-0210},
mesh = {*Algorithms ; Animals ; Biofeedback, Psychology/*methods/physiology ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback ; Humans ; Macaca mulatta ; Male ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Closed-loop decoder adaptation (CLDA) shows great promise to improve closed-loop brain-machine interface (BMI) performance. Developing adaptation algorithms capable of rapidly improving performance, independent of initial performance, may be crucial for clinical applications where patients have limited movement and sensory abilities due to motor deficits. Given the subject-decoder interactions inherent in closed-loop BMIs, the decoder adaptation time-scale may be of particular importance when initial performance is limited. Here, we present SmoothBatch, a CLDA algorithm which updates decoder parameters on a 1-2 min time-scale using an exponentially weighted sliding average. The algorithm was experimentally tested with one nonhuman primate performing a center-out reaching BMI task. SmoothBatch was seeded four ways with varying offline decoding power: 1) visual observation of a cursor (n = 20), 2) ipsilateral arm movements (n = 8), 3) baseline neural activity (n = 17), and 4) arbitrary weights (n = 11). SmoothBatch rapidly improved performance regardless of seeding, with performance improvements from 0.018 ±0.133 successes/min to > 8 successes/min within 13.1 ±5.5 min (n = 56). After decoder adaptation ceased, the subject maintained high performance. Moreover, performance improvements were paralleled by SmoothBatch convergence, suggesting that CLDA involves a co-adaptation process between the subject and the decoder.},
}
@article {pmid22771715,
year = {2012},
author = {Allison, BZ and Brunner, C and Altstätter, C and Wagner, IC and Grissmann, S and Neuper, C},
title = {A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control.},
journal = {Journal of neuroscience methods},
volume = {209},
number = {2},
pages = {299-307},
doi = {10.1016/j.jneumeth.2012.06.022},
pmid = {22771715},
issn = {1872-678X},
mesh = {Brain/*physiology ; *Brain Mapping/instrumentation/methods ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Functional Laterality ; Humans ; Movement/physiology ; *Online Systems ; Photic Stimulation ; Surveys and Questionnaires ; *User-Computer Interface ; },
abstract = {We introduce a new type of BCI for continuous simultaneous two dimensional cursor control. Users tried to control the vertical position of a virtual ball via ERD activity associated with imagined movement while simultaneously controlling horizontal position with SSVEP activity resulting from visual attention. Ten subjects participated in one offline and six online control sessions. The online sessions assessed subjective measures via questionnaires as well as objective measures. Subjects generally reported that the hybrid task combination was not especially difficult or annoying. Two subjects attained very good performance, while the remaining subjects did not. Training did not affect subjective or objective measures. Overall, results show that this new hybrid approach is viable for some users, and that substantial further research is needed to identify and optimize the best BCIs for each user.},
}
@article {pmid22766713,
year = {2012},
author = {Wang, X and Shi, N and Chen, Y and Li, C and Du, X and Jin, W and Chen, Y and Chang, PR},
title = {Improvement in hemocompatibility of chitosan/soy protein composite membranes by heparinization.},
journal = {Bio-medical materials and engineering},
volume = {22},
number = {1-3},
pages = {143-150},
doi = {10.3233/BME-2012-0700},
pmid = {22766713},
issn = {1878-3619},
mesh = {Animals ; Anticoagulants/*chemistry/metabolism ; Blood Coagulation ; Blood Platelets/cytology ; Chitosan/*chemistry/metabolism ; Coated Materials, Biocompatible/*chemistry/metabolism ; Erythrocytes/cytology ; Hemolysis ; Heparin/*chemistry/metabolism ; *Materials Testing ; Membranes, Artificial ; Platelet Adhesiveness ; Rabbits ; Soybean Proteins/*chemistry/metabolism ; Thrombosis/etiology ; },
abstract = {OBJECTIVE: To improve the hemocompatibility of chitosan/soy protein isolate composite membranes by heparinization.<br><br>METHODS: Chitosan/soy protein isolate membranes (ChS-n, n=0, 10 and 30, corresponding to the soy protein isolate content in the membranes) and heparinized ChS-n membranes (HChS-n) were prepared by blending in dilute HAc/NaAc solution. The hemocompatibility of ChS-n and HChS-n membranes were comparatively evaluated by measuring surface heparin density, blood platelet adhesion, plasma recalcification time (PRT), thrombus formation and hemolysis assay.<br><br>RESULTS: The surface heparin density analysis showed that heparinized chitosan/SPI soy protein isolate membranes have been successfully prepared by blending. The density of heparin on the surface of HChS-n membranes was in the range of 0.67-1.29 &#x3bc;g/cm2. The results of platelet adhesion measurement showed that the platelet adhesion numbers of HChS-n membranes were lower than those of the corresponding ChS-n membranes. The PRT of the HChS-0, HChS-10 and HChS-30 membranes were around 292, 306 and 295 s, respectively, which were longer than the corresponding ChS-0 (152 s), ChS-10 (204 s) and ChS-30 (273 s) membranes. The hemolysis rate of HChS-n membranes was lower than 1%.<br><br>CONCLUSION: The hemocompatibility of ChS membranes could be improved by blending with heparin. Compared with ChS membranes, HChS membranes showed lower platelet adhesion, longer PRT, higher BCI, significant thromboresistivity and a lower hemolysis rate due to the heparinization. This widens the application of chitosan and soy protein-based biomaterials that may come in contact with blood.},
}
@article {pmid22759199,
year = {2012},
author = {Chatelle, C and Chennu, S and Noirhomme, Q and Cruse, D and Owen, AM and Laureys, S},
title = {Brain-computer interfacing in disorders of consciousness.},
journal = {Brain injury},
volume = {26},
number = {12},
pages = {1510-1522},
doi = {10.3109/02699052.2012.698362},
pmid = {22759199},
issn = {1362-301X},
mesh = {*Brain-Computer Interfaces ; Consciousness Disorders/*physiopathology ; Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; User-Computer Interface ; },
abstract = {BACKGROUND: Recent neuroimaging research has strikingly demonstrated the existence of covert awareness in some patients with disorders of consciousness (DoC). These findings have highlighted the potential for the development of simple brain-computer interfaces (BCI) as a diagnosis in behaviourally unresponsive patients.<br><br>OBJECTIVES: This study here reviews current EEG-based BCIs that hold potential for assessing and eventually assisting patients with DoC. It highlights key areas for further development that might eventually make their application feasible in this challenging patient group.<br><br>METHODS: The major types of BCIs proposed in the literature are considered, namely those based on the P3 potential, sensorimotor rhythms, steady state oscillations and slow cortical potentials. In each case, a brief overview of the relevant literature is provided and then their relative merits for BCI applications in DoC are considered.<br><br>RESULTS: A range of BCI designs have been proposed and tested for enabling communication in fully conscious, paralysed patients. Although many of these have potential applicability for patients with DoC, they share some key challenges that need to be overcome, including limitations of stimulation modality, feedback, user training and consistency.<br><br>CONCLUSION: Future work will need to address the technical and practical challenges facing reliable implementation at the patient's bedside.},
}
@article {pmid22754496,
year = {2012},
author = {Liang, N and Bougrain, L},
title = {Decoding Finger Flexion from Band-Specific ECoG Signals in Humans.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {91},
pmid = {22754496},
issn = {1662-453X},
abstract = {This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.},
}
@article {pmid22749465,
year = {2013},
author = {Friedrich, EV and Scherer, R and Neuper, C},
title = {Stability of event-related (de-) synchronization during brain-computer interface-relevant mental tasks.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {124},
number = {1},
pages = {61-69},
doi = {10.1016/j.clinph.2012.05.020},
pmid = {22749465},
issn = {1872-8952},
mesh = {Adult ; Algorithms ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography ; Face ; Female ; Hand/physiology ; Humans ; Imagination/physiology ; Mathematics ; Mental Processes/*physiology ; Music/psychology ; Orientation/physiology ; Reproducibility of Results ; Word Association Tests ; Young Adult ; },
abstract = {OBJECTIVE: The aim of this study was to examine the temporal stability of event-related desynchronization/synchronization (ERD/S) patterns over several sessions as a function of mental task, frequency band, brain region and time interval during the imagery period.<br><br>METHODS: Nine volunteers participated in four sessions within 2 weeks of multi-channel EEG recordings. They performed seven mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces, motor imagery) during 7-s imagery periods. Cronbach's alpha coefficients were calculated over sessions to evaluate the stability of ERD/S values.<br><br>RESULTS: The word association, mental subtraction and spatial navigation task showed highest stability. Cronbach's alpha coefficients were highest in the alpha bands (7-10, 10-13 Hz), poorer in the beta bands (13-20, 20-30 Hz) and poorest in the theta band (4-7 Hz). In the majority of tasks, the first time interval and posterior left regions showed highest stability and strongest ERD in the alpha and beta bands.<br><br>CONCLUSION: Stability of ERD/S is strongly dependent on the specific task and differs between time intervals of the imagery period. Furthermore, stability was related to ERD in the alpha and beta bands.<br><br>SIGNIFICANCE: The reliability of brain activation patterns is highly relevant for brain-computer interface developments.},
}
@article {pmid22748322,
year = {2012},
author = {Sorger, B and Reithler, J and Dahmen, B and Goebel, R},
title = {A real-time fMRI-based spelling device immediately enabling robust motor-independent communication.},
journal = {Current biology : CB},
volume = {22},
number = {14},
pages = {1333-1338},
doi = {10.1016/j.cub.2012.05.022},
pmid = {22748322},
issn = {1879-0445},
mesh = {Adult ; Brain/*blood supply/*physiopathology ; Brain Mapping ; *Brain-Computer Interfaces ; Communication ; *Communication Devices for People with Disabilities ; Female ; Humans ; Magnetic Resonance Imaging/*methods ; Male ; Quadriplegia/*physiopathology/*psychology ; Young Adult ; },
abstract = {Human communication entirely depends on the functional integrity of the neuromuscular system. This is devastatingly illustrated in clinical conditions such as the so-called locked-in syndrome (LIS), in which severely motor-disabled patients become incapable to communicate naturally--while being fully conscious and awake. For the last 20 years, research on motor-independent communication has focused on developing brain-computer interfaces (BCIs) implementing neuroelectric signals for communication (e.g., [2-7]), and BCIs based on electroencephalography (EEG) have already been applied successfully to concerned patients. However, not all patients achieve proficiency in EEG-based BCI control. Thus, more recently, hemodynamic brain signals have also been explored for BCI purposes. Here, we introduce the first spelling device based on fMRI. By exploiting spatiotemporal characteristics of hemodynamic responses, evoked by performing differently timed mental imagery tasks, our novel letter encoding technique allows translating any freely chosen answer (letter-by-letter) into reliable and differentiable single-trial fMRI signals. Most importantly, automated letter decoding in real time enables back-and-forth communication within a single scanning session. Because the suggested spelling device requires only little effort and pretraining, it is immediately operational and possesses high potential for clinical applications, both in terms of diagnostics and establishing short-term communication with nonresponsive and severely motor-impaired patients.},
}
@article {pmid22746218,
year = {2012},
author = {Orand, A and Ushiba, J and Tomita, Y and Honda, S},
title = {The comparison of motor learning performance with and without feedback.},
journal = {Somatosensory &amp; motor research},
volume = {29},
number = {3},
pages = {103-110},
doi = {10.3109/08990220.2012.687419},
pmid = {22746218},
issn = {1369-1651},
mesh = {Adult ; Brain-Computer Interfaces/psychology ; Cortical Synchronization/physiology ; Evoked Potentials/physiology ; Feedback, Sensory/*physiology ; Humans ; Imagination/*physiology ; Learning/*physiology ; Models, Neurological ; Movement/*physiology ; Paraplegia/*physiopathology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Ten individuals were divided into two feedback and no-feedback groups. The effect of abstract visual feedback was investigated in these two groups. Using eight electroencephalography (EEG) electrodes, the induced event-related desynchronization/synchronization of the EEG of three motor imagery tasks (left hand, right hand, and right foot) was analyzed by wavelet and spatial filtering methods. Linear discriminant analysis was used to classify the three imagery tasks. Each imagery task's total length was set to 3 s and 1 s of it was used for the classification. The classification result was shown to the subjects of the feedback group in a real-time manner as an abstract visual feedback. While the paired t-test of the first and third sessions of the training days confirmed the improvement of the motor imagery learning in the feedback group (p<0.01), the motor imagery learning of the no-feedback group was not significant.},
}
@article {pmid22736634,
year = {2012},
author = {Wang, H and Xu, D},
title = {Comprehensive common spatial patterns with temporal structure information of EEG data: minimizing nontask related EEG component.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {9},
pages = {2496-2505},
doi = {10.1109/TBME.2012.2205383},
pmid = {22736634},
issn = {1558-2531},
mesh = {Brain-Computer Interfaces ; Databases, Factual ; Electroencephalography/*methods ; Humans ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {In the context of electroencephalogram (EEG)-based brain-computer interfaces (BCI), common spatial patterns (CSP) is widely used for spatially filtering multichannel EEG signals. CSP is a supervised learning technique depending on only labeled trials. Its generalization performance deteriorates due to overfitting occurred when the number of training trials is small. On the other hand, a large number of unlabeled trials are relatively easy to obtain. In this paper, we contribute a comprehensive learning scheme of CSP (cCSP) that learns on both labeled and unlabeled trials. cCSP regularizes the objective function of CSP by preserving the temporal relationship among samples of unlabeled trials in terms of linear representation. The intrinsically temporal structure is characterized by an l(1) graph. As a result, the temporal correlation information of unlabeled trials is incorporated into CSP, yielding enhanced generalization capacity. Interestingly, the regularizer of cCSP can be interpreted as minimizing a nontask related EEG component, which helps cCSP alleviate nonstationarities. Experiment results of single-trial EEG classification on publicly available EEG datasets confirm the effectiveness of the proposed method.},
}
@article {pmid22736630,
year = {2012},
author = {Falzon, O and Camilleri, K and Muscat, J},
title = {Complex-valued spatial filters for SSVEP-based BCIs with phase coding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {9},
pages = {2486-2495},
doi = {10.1109/TBME.2012.2205246},
pmid = {22736630},
issn = {1558-2531},
mesh = {Algorithms ; Brain/physiology ; *Brain-Computer Interfaces ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEPs) have gained considerable popularity because of the robustness and high information transfer rate these can provide. Typical SSVEP setups make use of visual targets flashing at different frequencies, where a user's choice is determined from the SSVEPs elicited by the user gazing at a specific target. The range of stimulus frequencies available for such setups is limited by a variety of factors, including the strength of the evoked potentials as well as user comfort and safety with light stimuli flashing at those frequencies. One way to tackle this limitation is by introducing targets flickering at the same frequency but with different phases. In this paper, we propose the use of the analytic common spatial patterns (ACSPs) method to discriminate between phase coded SSVEP targets, and we demonstrate that the complex-valued spatial filters used for discrimination can exceed the performance of existing techniques. Furthermore, the ACSP method also yields a set of spatial patterns, separable into amplitude and phase components, that provide insight into the underlying brain activity.},
}
@article {pmid22734673,
year = {2012},
author = {Zhang, H and Shih, J and Zhu, J and Kotov, NA},
title = {Layered nanocomposites from gold nanoparticles for neural prosthetic devices.},
journal = {Nano letters},
volume = {12},
number = {7},
pages = {3391-3398},
pmid = {22734673},
issn = {1530-6992},
support = {R21 CA121841/CA/NCI NIH HHS/United States ; 1R21CA121841-01A2/CA/NCI NIH HHS/United States ; },
mesh = {Electrodes ; Gold/*chemistry ; Membranes, Artificial ; Metal Nanoparticles/*chemistry ; Nanocomposites/*chemistry ; *Neural Networks, Computer ; *Neural Prostheses ; Particle Size ; *Prostheses and Implants ; Surface Properties ; },
abstract = {Treatments of neurological diseases, diagnostics of brain malfunctions, and the realization of brain-computer interfaces require ultrasmall electrodes that are "invisible" to resident immune cells. Functional electrodes smaller than 50 μm are impossible to produce with traditional materials due to high interfacial impedance at the characteristic frequency of neural activity and insufficient charge storage capacity. The problem can be resolved by using gold nanoparticle nanocomposites. Careful comparison indicates that layer-by-layer assembled films from Au NPs provide more than 3-fold improvement in interfacial impedance and 1 order of magnitude increase in charge storage capacity. Prototypes of microelectrodes could be made using traditional photolithography. Integration of unique nanocomposite materials with microfabrication techniques opens the door for practical realization of the ultrasmall implantable electrodes. Further improvement of electrical properties is expected when using special shapes of gold nanoparticles.},
}
@article {pmid22733013,
year = {2012},
author = {Flint, RD and Lindberg, EW and Jordan, LR and Miller, LE and Slutzky, MW},
title = {Accurate decoding of reaching movements from field potentials in the absence of spikes.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {046006},
pmid = {22733013},
issn = {1741-2552},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; R01NS048845/NS/NINDS NIH HHS/United States ; K08NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Electrodes, Implanted ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Random Allocation ; },
abstract = {The recent explosion of interest in brain-machine interfaces (BMIs) has spurred research into choosing the optimal input signal source for a desired application. The signals with highest bandwidth--single neuron action potentials or spikes--typically are difficult to record for more than a few years after implantation of intracortical electrodes. Fortunately, field potentials recorded within the cortex (local field potentials, LFPs), at its surface (electrocorticograms, ECoG) and at the dural surface (epidural, EFPs) have also been shown to contain significant information about movement. However, the relative performance of these signals has not yet been directly compared. Furthermore, while it is widely postulated, it has not yet been demonstrated that these field potential signals are more durable than spike recordings. The aim of this study was to address both of these questions. We assessed the offline decoding performance of EFPs, LFPs and spikes, recorded sequentially, in primary motor cortex (M1) in terms of their ability to decode the target of reaching movements, as well as the endpoint trajectory. We also examined the decoding performance of LFPs on electrodes that are not recording spikes, compared with the performance when they did record spikes. Spikes were still present on some of the other electrodes throughout this study. We showed that LFPs performed nearly as well as spikes in decoding velocity, and slightly worse in decoding position and in target classification. EFP performance was slightly inferior to that reported for ECoG in humans. We also provided evidence demonstrating that movement-related information in the LFP remains high regardless of the ability to record spikes concurrently on the same electrodes. This is the first study to provide evidence that LFPs retain information about movement in the absence of spikes on the same electrodes. These results suggest that LFPs may indeed remain informative after spike recordings are lost, thereby providing a robust, accurate signal source for BMIs.},
}
@article {pmid22724028,
year = {2012},
author = {Xie, J and Xu, G and Wang, J and Zhang, F and Zhang, Y},
title = {Steady-state motion visual evoked potentials produced by oscillating Newton's rings: implications for brain-computer interfaces.},
journal = {PloS one},
volume = {7},
number = {6},
pages = {e39707},
pmid = {22724028},
issn = {1932-6203},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Motion Perception/physiology ; Photic Stimulation/*methods ; Young Adult ; },
abstract = {In this study, we utilize a special visual stimulation protocol, called motion reversal, to present a novel steady-state motion visual evoked potential (SSMVEP)-based BCI paradigm that relied on human perception of motions oscillated in two opposite directions. Four Newton's rings with the oscillating expansion and contraction motions served as visual stimulators to elicit subjects' SSMVEPs. And four motion reversal frequencies of 8.1, 9.8, 12.25 and 14 Hz were tested. According to Canonical Correlation Analysis (CCA), the offline accuracy and ITR (mean ± standard deviation) over six healthy subjects were 86.56 ± 9.63% and 15.93 ± 3.83 bits/min, respectively. All subjects except one exceeded the level of 80% mean accuracy. Circular Hotelling's T-Squared test (T2 circ) also demonstrated that most subjects exhibited significantly strong stimulus-locked SSMVEP responses. The results of declining exponential fittings exhibited low-adaptation characteristics over the 100-s stimulation sequences in most experimental conditions. Taken together, these results suggest that the proposed paradigm can provide comparable performance with low-adaptation characteristic and less visual discomfort for BCI applications.},
}
@article {pmid22719888,
year = {2012},
author = {Rapoport, BI and Kedzierski, JT and Sarpeshkar, R},
title = {A glucose fuel cell for implantable brain-machine interfaces.},
journal = {PloS one},
volume = {7},
number = {6},
pages = {e38436},
pmid = {22719888},
issn = {1932-6203},
support = {R01 NS056140/NS/NINDS NIH HHS/United States ; T32 GM007753/GM/NIGMS NIH HHS/United States ; NS-056140/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain-Computer Interfaces ; Catalysis ; Electrodes ; Glucose/*metabolism ; Humans ; Oxygen/metabolism ; },
abstract = {We have developed an implantable fuel cell that generates power through glucose oxidation, producing 3.4 μW cm(-2) steady-state power and up to 180 μW cm(-2) peak power. The fuel cell is manufactured using a novel approach, employing semiconductor fabrication techniques, and is therefore well suited for manufacture together with integrated circuits on a single silicon wafer. Thus, it can help enable implantable microelectronic systems with long-lifetime power sources that harvest energy from their surrounds. The fuel reactions are mediated by robust, solid state catalysts. Glucose is oxidized at the nanostructured surface of an activated platinum anode. Oxygen is reduced to water at the surface of a self-assembled network of single-walled carbon nanotubes, embedded in a Nafion film that forms the cathode and is exposed to the biological environment. The catalytic electrodes are separated by a Nafion membrane. The availability of fuel cell reactants, oxygen and glucose, only as a mixture in the physiologic environment, has traditionally posed a design challenge: Net current production requires oxidation and reduction to occur separately and selectively at the anode and cathode, respectively, to prevent electrochemical short circuits. Our fuel cell is configured in a half-open geometry that shields the anode while exposing the cathode, resulting in an oxygen gradient that strongly favors oxygen reduction at the cathode. Glucose reaches the shielded anode by diffusing through the nanotube mesh, which does not catalyze glucose oxidation, and the Nafion layers, which are permeable to small neutral and cationic species. We demonstrate computationally that the natural recirculation of cerebrospinal fluid around the human brain theoretically permits glucose energy harvesting at a rate on the order of at least 1 mW with no adverse physiologic effects. Low-power brain-machine interfaces can thus potentially benefit from having their implanted units powered or recharged by glucose fuel cells.},
}
@article {pmid22713735,
year = {2012},
author = {Prasad, A and Sahin, M},
title = {Can motor volition be extracted from the spinal cord?.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {9},
number = {},
pages = {41},
pmid = {22713735},
issn = {1743-0003},
support = {R01 NS072385/NS/NINDS NIH HHS/United States ; R21 HD056963-01 A2/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Biomechanical Phenomena ; Efferent Pathways/*physiology ; Electrodes, Implanted ; Forelimb/physiology ; Male ; Microelectrodes ; Rats ; Rats, Long-Evans ; Spinal Cord/*physiology ; Spinal Cord Injuries/physiopathology/rehabilitation ; User-Computer Interface ; Volition/*physiology ; },
abstract = {BACKGROUND: Spinal cord injury (SCI) results in the partial or complete loss of movement and sensation below the level of injury. In individuals with cervical level SCI, there is a great need for voluntary command generation for environmental control, self-mobility, or computer access to improve their independence and quality of life. Brain-computer interfacing is one way of generating these voluntary command signals. As an alternative, this study investigates the feasibility of utilizing descending signals in the dorsolateral spinal cord tracts above the point of injury as a means of generating volitional motor control signals.<br><br>METHODS: In this work, adult male rats were implanted with a 15-channel microelectrode array (MEA) in the dorsolateral funiculus of the cervical spinal cord to record multi-unit activity from the descending pathways while the animals performed a reach-to-grasp task. Mean signal amplitudes and signal-to-noise ratios during the behavior was monitored and quantified for recording periods up to 3&#x2009;months post-implant. One-way analysis of variance (ANOVA) and Tukey's post-hoc analysis was used to investigate signal amplitude stability during the study period. Multiple linear regression was employed to reconstruct the forelimb kinematics, i.e. the hand position, elbow angle, and hand velocity from the spinal cord signals.<br><br>RESULTS: The percentage of electrodes with stable signal amplitudes (p-value&#x2009;<&#x2009;0.05) were 50% in R1, 100% in R2, 72% in R3, and 85% in R4. Forelimb kinematics was reconstructed with correlations of R&#xb2;&#x2009;>&#x2009;0.7 using tap-delayed principal components of the spinal cord signals.<br><br>CONCLUSIONS: This study demonstrated that chronic recordings up to 3-months can be made from the descending tracts of the rat spinal cord with relatively small changes in signal characteristics over time and that the forelimb kinematics can be reconstructed with the recorded signals. Multi-unit recording technique may prove to be a viable alternative to single neuron recording methods for reading the information encoded by neuronal populations in the spinal cord.},
}
@article {pmid22713666,
year = {2012},
author = {Milekovic, T and Fischer, J and Pistohl, T and Ruescher, J and Schulze-Bonhage, A and Aertsen, A and Rickert, J and Ball, T and Mehring, C},
title = {An online brain-machine interface using decoding of movement direction from the human electrocorticogram.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {046003},
doi = {10.1088/1741-2560/9/4/046003},
pmid = {22713666},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; *Brain-Computer Interfaces ; Cerebral Cortex/*physiology ; Electric Stimulation/methods ; Electrodes, Implanted ; Electroencephalography/*methods ; Female ; Humans ; Male ; Movement/*physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.},
}
@article {pmid22713543,
year = {2012},
author = {Grosse-Wentrup, M and Schölkopf, B},
title = {High γ-power predicts performance in sensorimotor-rhythm brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {4},
pages = {046001},
doi = {10.1088/1741-2560/9/4/046001},
pmid = {22713543},
issn = {1741-2552},
mesh = {Brain Waves/*physiology ; *Brain-Computer Interfaces ; Forecasting ; Humans ; Imagination/*physiology ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; },
abstract = {Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.},
}
@article {pmid22695047,
year = {2012},
author = {Torres Valderrama, A and Paclik, P and Vansteensel, MJ and Aarnoutse, EJ and Ramsey, NF},
title = {Error probability of intracranial brain computer interfaces under non-task elicited brain states.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {123},
number = {12},
pages = {2392-2401},
doi = {10.1016/j.clinph.2012.05.006},
pmid = {22695047},
issn = {1872-8952},
mesh = {Brain/*physiopathology ; *Brain-Computer Interfaces ; *Electroencephalography ; Epilepsy ; False Positive Reactions ; Humans ; Probability ; Reproducibility of Results ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Intracranial brain computer interfaces (BCIs) can be connected to the user's cortex permanently. The interfaces response when fed with non-task elicited brain activity becomes important as design criterion: ideally intracranial BCIs should remain silent. We study their error probability in the form of false alarms.<br><br>METHODS: Using electrocorticograms recorded during task and non-task brain states, we compute false alarms, investigate their origin and introduce strategies to reduce them, using signal detection theory, classifier cascading and adaptation concepts.<br><br>RESULTS: We show that the incessant dynamics of the brain is prone to spontaneously produce signals, the spectral and topographical characteristics of which can resemble those associated with common control tasks, generating brain state classification errors.<br><br>CONCLUSIONS: In addition to hit and bit rates, response of BCIs to non-task brain states constitutes an important measure of BCI performance. Static classification cascading reduces considerably false positives during no-task brain states.<br><br>SIGNIFICANCE: False alarms in intracranial BCIs are undesirable and could have dangerous consequences for the users. Designs which effectively incorporate the error correction strategies discussed in this paper, could be more successful when taken from the laboratory or acute care setting and used in the real world.},
}
@article {pmid22692936,
year = {2012},
author = {Long, J and Li, Y and Wang, H and Yu, T and Pan, J and Li, F},
title = {A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {5},
pages = {720-729},
doi = {10.1109/TNSRE.2012.2197221},
pmid = {22692936},
issn = {1558-0210},
mesh = {Biofeedback, Psychology/*instrumentation ; *Brain-Computer Interfaces ; *Computer-Aided Design ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; *Models, Theoretical ; Motion ; Systems Integration ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.},
}
@article {pmid22683716,
year = {2012},
author = {Sugata, H and Goto, T and Hirata, M and Yanagisawa, T and Shayne, M and Matsushita, K and Yoshimine, T and Yorifuji, S},
title = {Neural decoding of unilateral upper limb movements using single trial MEG signals.},
journal = {Brain research},
volume = {1468},
number = {},
pages = {29-37},
doi = {10.1016/j.brainres.2012.05.053},
pmid = {22683716},
issn = {1872-6240},
mesh = {Adult ; Afferent Pathways/physiology ; Analysis of Variance ; *Brain Mapping ; Cerebral Cortex/*physiology ; Elbow/innervation ; Evoked Potentials, Motor/physiology ; Female ; Functional Laterality/*physiology ; Hand Strength/physiology ; Humans ; Magnetic Resonance Imaging ; *Magnetoencephalography ; Male ; Middle Aged ; Movement/*physiology ; Time Factors ; Transcranial Magnetic Stimulation ; Upper Extremity/innervation/*physiology ; Young Adult ; },
abstract = {A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (-200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from -500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas.},
}
@article {pmid22679792,
year = {2012},
author = {Ganin, IP and Shishkin, SL and Kochetova, AG and Kaplan, AIa},
title = {[The P300 based brain-computer interface: effect of stimulus position in a stimulus train].},
journal = {Fiziologiia cheloveka},
volume = {38},
number = {2},
pages = {5-13},
pmid = {22679792},
issn = {0131-1646},
mesh = {Brain/*physiology ; Computers ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; Reaction Time/physiology ; Software ; User-Computer Interface ; Young Adult ; },
abstract = {The P300 brain-computer interface (BCI) is currently the most efficient BCI. This interface is based on detection of the P300 wave of the brain potentials evoked when a symbol related to the intended input is highlighted. To increase operation speed of the P300 BCI, reduction of the number of stimuli repetitions is needed. This reduction leads to increase of the relative contribution to the input symbol detection from the reaction to the first target stimulus. It is known that the event-related potentials (ERP) to the first stimulus presentations can be different from the ERP to stimuli presented latter. In particular, the amplitude of responses to the first stimulus presentations is often increased, which is beneficial for their recognition by the BCI. However, this effect was not studied within the BCI framework. The current study examined the ERP obtained from healthy participants (n = 14) in the standard P300 BCI paradigm using 10 trials, as well as in the modified P300 BCI with stimuli presented on moving objects in triple-trial (n = 6) and single-trial (n = 6) stimulation modes. Increased ERP amplitude was observed in response to the first target stimuli in both conditions, as well as in the single-trial mode comparing to triple-trial. We discuss the prospects of using the specific features of the ERP to first stimuli and the single-trial ERP for optimizing the high-speed modes in the P300 BCIs.},
}
@article {pmid22675291,
year = {2012},
author = {Mattout, J},
title = {Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective.},
journal = {Frontiers in human neuroscience},
volume = {6},
number = {},
pages = {114},
pmid = {22675291},
issn = {1662-5161},
abstract = {A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment.},
}
@article {pmid22666454,
year = {2012},
author = {Salari, N and Büchel, C and Rose, M},
title = {Functional dissociation of ongoing oscillatory brain states.},
journal = {PloS one},
volume = {7},
number = {5},
pages = {e38090},
pmid = {22666454},
issn = {1932-6203},
mesh = {Adaptation, Physiological/physiology ; Adult ; Brain/*physiology ; Electroencephalography ; Female ; Humans ; Male ; Neurofeedback/physiology ; Photic Stimulation ; Visual Cortex/physiology ; Young Adult ; },
abstract = {The state of a neural assembly preceding an incoming stimulus is assumed to modulate the processing of subsequently presented stimuli. The nature of this state can differ with respect to the frequency of ongoing oscillatory activity. Oscillatory brain activity of specific frequency range such as alpha (8-12 Hz) and gamma (above 30 Hz) band oscillations are hypothesized to play a functional role in cognitive processing. Therefore, a selective modulation of this prestimulus activity could clarify the functional role of these prestimulus fluctuations. For this purpose, we adopted a novel non-invasive brain-computer-interface (BCI) strategy to selectively increase alpha or gamma band activity in the occipital cortex combined with an adaptive presentation of visual stimuli within specific brain states. During training, oscillatory brain activity was estimated online and fed back to the participants to enable a deliberate modulation of alpha or gamma band oscillations. Results revealed that volunteers selectively increased alpha and gamma frequency oscillations with a high level of specificity regarding frequency range and localization. At testing, alpha or gamma band activity was classified online and at defined levels of activity, visual objects embedded in noise were presented instantly and had to be detected by the volunteer. In experiment I, the effect of two levels of prestimulus gamma band activity on visual processing was examined. During phases of increased gamma band activity significantly more visual objects were detected. In experiment II, the effect was compared against increased levels of alpha band activity. An improvement of visual processing was only observed for enhanced gamma band activity. Both experiments demonstrate the specific functional role of prestimulus gamma band oscillations for perceptual processing. We propose that the BCI method permits the selective modulation of oscillatory activity and the direct assessment of behavioral consequences to test for functional dissociations of different oscillatory brain states.},
}
@article {pmid22666377,
year = {2012},
author = {Wang, Y and Wang, YT and Jung, TP},
title = {Translation of EEG spatial filters from resting to motor imagery using independent component analysis.},
journal = {PloS one},
volume = {7},
number = {5},
pages = {e37665},
pmid = {22666377},
issn = {1932-6203},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Feasibility Studies ; Hand/physiology ; Humans ; *Mental Processes ; Movement/*physiology ; Rest/*psychology ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; *Statistics as Topic ; },
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters.},
}
@article {pmid22663075,
year = {2012},
author = {Buesing, L and Macke, JH and Sahani, M},
title = {Learning stable, regularised latent models of neural population dynamics.},
journal = {Network (Bristol, England)},
volume = {23},
number = {1-2},
pages = {24-47},
doi = {10.3109/0954898X.2012.677095},
pmid = {22663075},
issn = {1361-6536},
mesh = {Algorithms ; Animals ; *Artificial Intelligence ; Computer Simulation ; Data Interpretation, Statistical ; Electrodes, Implanted ; Likelihood Functions ; Linear Models ; Macaca mulatta ; Models, Neurological ; Motor Cortex/physiology ; Nerve Net/physiology ; *Neural Networks, Computer ; Normal Distribution ; Population Dynamics ; User-Computer Interface ; },
abstract = {Ongoing advances in experimental technique are making commonplace simultaneous recordings of the activity of tens to hundreds of cortical neurons at high temporal resolution. Latent population models, including Gaussian-process factor analysis and hidden linear dynamical system (LDS) models, have proven effective at capturing the statistical structure of such data sets. They can be estimated efficiently, yield useful visualisations of population activity, and are also integral building-blocks of decoding algorithms for brain-machine interfaces (BMI). One practical challenge, particularly to LDS models, is that when parameters are learned using realistic volumes of data the resulting models often fail to reflect the true temporal continuity of the dynamics; and indeed may describe a biologically-implausible unstable population dynamic that is, it may predict neural activity that grows without bound. We propose a method for learning LDS models based on expectation maximisation that constrains parameters to yield stable systems and at the same time promotes capture of temporal structure by appropriate regularisation. We show that when only little training data is available our method yields LDS parameter estimates which provide a substantially better statistical description of the data than alternatives, whilst guaranteeing stable dynamics. We demonstrate our methods using both synthetic data and extracellular multi-electrode recordings from motor cortex.},
}
@article {pmid22645108,
year = {2013},
author = {Várkuti, B and Guan, C and Pan, Y and Phua, KS and Ang, KK and Kuah, CW and Chua, K and Ang, BT and Birbaumer, N and Sitaram, R},
title = {Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke.},
journal = {Neurorehabilitation and neural repair},
volume = {27},
number = {1},
pages = {53-62},
doi = {10.1177/1545968312445910},
pmid = {22645108},
issn = {1552-6844},
mesh = {Adult ; Brain/blood supply/physiopathology ; Brain Mapping ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Image Processing, Computer-Assisted ; Imagery, Psychotherapy/*methods ; Linear Models ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Oxygen ; Principal Component Analysis ; Recovery of Function/*physiology ; *Rest ; Robotics/*methods ; Stroke/pathology ; *Stroke Rehabilitation ; Tomography, X-Ray Computed ; Upper Extremity/*physiopathology ; Young Adult ; },
abstract = {BACKGROUND: Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network.<br><br>OBJECTIVE: The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI.<br><br>METHODS: In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI-based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training.<br><br>RESULTS: . The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement.<br><br>CONCLUSION: FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.},
}
@article {pmid22627008,
year = {2012},
author = {Shimoda, K and Nagasaka, Y and Chao, ZC and Fujii, N},
title = {Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036015},
doi = {10.1088/1741-2560/9/3/036015},
pmid = {22627008},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Artifacts ; Calibration ; Cues ; Electrodes ; Electroencephalography/*methods ; Epidural Space/*physiology ; Food ; Functional Laterality/physiology ; Hand/*physiology ; Macaca ; Magnetic Resonance Imaging ; Mastication ; Prefrontal Cortex/physiology ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Somatosensory Cortex/physiology ; User-Computer Interface ; },
abstract = {Brain–machine interface (BMI) technology captures brain signals to enable control of prosthetic or communication devices with the goal of assisting patients who have limited or no ability to perform voluntary movements. Decoding of inherent information in brain signals to interpret the user’s intention is one of main approaches for developing BMI technology. Subdural electrocorticography (sECoG)-based decoding provides good accuracy, but surgical complications are one of the major concerns for this approach to be applied in BMIs. In contrast, epidural electrocorticography (eECoG) is less invasive, thus it is theoretically more suitable for long-term implementation, although it is unclear whether eECoG signals carry sufficient information for decoding natural movements. We successfully decoded continuous three-dimensional hand trajectories from eECoG signals in Japanese macaques. A steady quantity of information of continuous hand movements could be acquired from the decoding system for at least several months, and a decoding model could be used for ∼10 days without significant degradation in accuracy or recalibration. The correlation coefficients between observed and predicted trajectories were lower than those for sECoG-based decoding experiments we previously reported, owing to a greater degree of chewing artifacts in eECoG-based decoding than is found in sECoG-based decoding. As one of the safest invasive recording methods available, eECoG provides an acceptable level of performance. With the ease of replacement and upgrades, eECoG systems could become the first-choice interface for real-life BMI applications.},
}
@article {pmid22626956,
year = {2012},
author = {Lopez-Gordo, MA and Fernandez, E and Romero, S and Pelayo, F and Prieto, A},
title = {An auditory brain–computer interface evoked by natural speech.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036013},
doi = {10.1088/1741-2560/9/3/036013},
pmid = {22626956},
issn = {1741-2552},
mesh = {Acoustic Stimulation ; Adult ; Algorithms ; Attention/physiology ; Brain/*physiology ; Calibration ; Cognition/physiology ; Communication Devices for People with Disabilities ; Dichotic Listening Tests ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Female ; Humans ; Male ; Psychomotor Performance/physiology ; Speech/*physiology ; *User-Computer Interface ; },
abstract = {Brain–computer interfaces (BCIs) are mainly intended for people unable to perform any muscular movement, such as patients in a complete locked-in state. The majority of BCIs interact visually with the user, either in the form of stimulation or biofeedback. However, visual BCIs challenge their ultimate use because they require the subjects to gaze, explore and shift eye-gaze using their muscles, thus excluding patients in a complete locked-in state or under the condition of the unresponsive wakefulness syndrome. In this study, we present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min[-1] in full-length trials and 2.7 bits min[-1] using the optimal length of trials, recorded with only one channel and without formal training. This novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.},
}
@article {pmid22626911,
year = {2012},
author = {Yu, T and Li, Y and Long, J and Gu, Z},
title = {Surfing the internet with a BCI mouse.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036012},
doi = {10.1088/1741-2560/9/3/036012},
pmid = {22626911},
issn = {1741-2552},
mesh = {Algorithms ; Brain/*physiology ; Computer Graphics ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Frustration ; Humans ; Imagination ; *Internet ; Mental Processes/physiology ; Physical Exertion ; Psychomotor Performance/physiology ; Software ; *User-Computer Interface ; },
abstract = {In this paper, we present a new web browser based on a two-dimensional (2D) brain-computer interface (BCI) mouse, where our major concern is the selection of an intended target in a multi-target web page. A real-world web page may contain tens or even hundreds of targets, including hyperlinks, input elements, buttons, etc. In this case, a target filter designed in our system can be used to exclude most of those targets of no interest. Specifically, the user filters the targets of no interest out by inputting keywords with a P300-based speller, while keeps those containing the keywords. Such filtering largely facilitates the target selection task based on our BCI mouse. When there are only several targets in a web page (either an original sparse page or a target-filtered page), the user moves the mouse toward the target of interest using his/her electroencephalographic signal. The horizontal movement and vertical movement are controlled by motor imagery and P300 potential, respectively. If the mouse encounters a target of no interest, the user rejects it and continues to move the mouse. Otherwise the user selects the target and activates it. With the collaboration of the target filtering and a series of mouse movements and target selections/rejections, the user can select an intended target in a web page. Based on our browser system, common navigation functions, including history rolling forward and backward, hyperlink selection, page scrolling, text input, etc, are available. The system has been tested on seven subjects. Experimental results not only validated the efficacy of the proposed method, but also showed that free internet surfing with a BCI mouse is feasible.},
}
@article {pmid22616161,
year = {2012},
author = {Wang, J and Zhang, L and Hu, B},
title = {[Research on the methods for multi-class kernel CSP-based feature extraction].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {29},
number = {2},
pages = {217-222},
pmid = {22616161},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; *Brain-Computer Interfaces ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted/*instrumentation ; User-Computer Interface ; },
abstract = {To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.},
}
@article {pmid22615548,
year = {2012},
author = {Goossens, HH and van Opstal, AJ},
title = {Optimal control of saccades by spatial-temporal activity patterns in the monkey superior colliculus.},
journal = {PLoS computational biology},
volume = {8},
number = {5},
pages = {e1002508},
pmid = {22615548},
issn = {1553-7358},
mesh = {Action Potentials/*physiology ; Animals ; Computer Simulation ; Feedback, Physiological/physiology ; Macaca mulatta ; *Models, Neurological ; Nerve Net/*physiology ; Neurons/*physiology ; Oculomotor Muscles/*physiology ; Recruitment, Neurophysiological/physiology ; Saccades/*physiology ; Superior Colliculi/*physiology ; },
abstract = {A major challenge in computational neurobiology is to understand how populations of noisy, broadly-tuned neurons produce accurate goal-directed actions such as saccades. Saccades are high-velocity eye movements that have stereotyped, nonlinear kinematics; their duration increases with amplitude, while peak eye-velocity saturates for large saccades. Recent theories suggest that these characteristics reflect a deliberate strategy that optimizes a speed-accuracy tradeoff in the presence of signal-dependent noise in the neural control signals. Here we argue that the midbrain superior colliculus (SC), a key sensorimotor interface that contains a topographically-organized map of saccade vectors, is in an ideal position to implement such an optimization principle. Most models attribute the nonlinear saccade kinematics to saturation in the brainstem pulse generator downstream from the SC. However, there is little data to support this assumption. We now present new neurophysiological evidence for an alternative scheme, which proposes that these properties reside in the spatial-temporal dynamics of SC activity. As predicted by this scheme, we found a remarkably systematic organization in the burst properties of saccade-related neurons along the rostral-to-caudal (i.e., amplitude-coding) dimension of the SC motor map: peak firing-rates systematically decrease for cells encoding larger saccades, while burst durations and skewness increase, suggesting that this spatial gradient underlies the increase in duration and skewness of the eye velocity profiles with amplitude. We also show that all neurons in the recruited population synchronize their burst profiles, indicating that the burst-timing of each cell is determined by the planned saccade vector in which it participates, rather than by its anatomical location. Together with the observation that saccade-related SC cells indeed show signal-dependent noise, this precisely tuned organization of SC burst activity strongly supports the notion of an optimal motor-control principle embedded in the SC motor map as it fully accounts for the straight trajectories and kinematic nonlinearity of saccades.},
}
@article {pmid22614891,
year = {2012},
author = {Rossini, PM and Noris Ferilli, MA and Ferreri, F},
title = {Cortical plasticity and brain computer interface.},
journal = {European journal of physical and rehabilitation medicine},
volume = {48},
number = {2},
pages = {307-312},
pmid = {22614891},
issn = {1973-9095},
mesh = {Cerebral Cortex/*physiology ; Communication Devices for People with Disabilities/*statistics &amp; numerical data ; Persons with Disabilities/*rehabilitation ; Humans ; *Man-Machine Systems ; Movement/*physiology ; Neuronal Plasticity/*physiology ; *User-Computer Interface ; },
abstract = {There is increasing evidence to support the concept that adult brain has the remarkable ability to plastically reorganize itself. Brain plasticity involves distinct functional and structural components and plays a crucial role in reorganizing central nervous system's networks after any lesion in order to partly or totally restore lost and/or compromised functions. The idea that a computer can decode brain electromagnetic signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a reasonable technical possibility. In neurological patients unable to move and to communicate with the external environment, technologies implementing brain-machine interfaces (BMIs) can be of valuable aid and support. The emerging possibility, through neuro-imaging advanced techniques, to clarify some crucial issues underlying brain plasticity will give the possibility to modulate these mechanisms in a BCI-oriented way. This approach may have a tremendous impact in a variety of neuropsychiatric disorders and the clinical advent of this technology will usher in a new era of restorative medicine.},
}
@article {pmid22614631,
year = {2012},
author = {Bundy, DT and Wronkiewicz, M and Sharma, M and Moran, DW and Corbetta, M and Leuthardt, EC},
title = {Using ipsilateral motor signals in the unaffected cerebral hemisphere as a signal platform for brain-computer interfaces in hemiplegic stroke survivors.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036011},
pmid = {22614631},
issn = {1741-2552},
support = {TL1 RR024995/RR/NCRR NIH HHS/United States ; UL1 RR024992/RR/NCRR NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; TL1 TR000449/TR/NCATS NIH HHS/United States ; 1R0100085606//PHS HHS/United States ; R01 HD061117-05A2/HD/NICHD NIH HHS/United States ; R01 MH096482/MH/NIMH NIH HHS/United States ; R01 HD061117/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiology/physiopathology ; Brain Mapping ; Cerebrum/*physiology ; Electroencephalography ; Equipment Design ; Feasibility Studies ; Female ; Functional Laterality/*physiology ; Hand/physiology ; Hemiplegia/etiology/*rehabilitation ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Movement/*physiology ; Online Systems ; Stroke/complications ; *Stroke Rehabilitation ; Survivors ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems have emerged as a method to restore function and enhance communication in motor impaired patients. To date, this has been applied primarily to patients who have a compromised motor outflow due to spinal cord dysfunction, but an intact and functioning cerebral cortex. The cortical physiology associated with movement of the contralateral limb has typically been the signal substrate that has been used as a control signal. While this is an ideal control platform in patients with an intact motor cortex, these signals are lost after a hemispheric stroke. Thus, a different control signal is needed that could provide control capability for a patient with a hemiparetic limb. Previous studies have shown that there is a distinct cortical physiology associated with ipsilateral, or same-sided, limb movements. Thus far, it was unknown whether stroke survivors could intentionally and effectively modulate this ipsilateral motor activity from their unaffected hemisphere. Therefore, this study seeks to evaluate whether stroke survivors could effectively utilize ipsilateral motor activity from their unaffected hemisphere to achieve this BCI control. To investigate this possibility, electroencephalographic (EEG) signals were recorded from four chronic hemispheric stroke patients as they performed (or attempted to perform) real and imagined hand tasks using either their affected or unaffected hand. Following performance of the screening task, the ability of patients to utilize a BCI system was investigated during on-line control of a one-dimensional control task. Significant ipsilateral motor signals (associated with movement intentions of the affected hand) in the unaffected hemisphere, which were found to be distinct from rest and contralateral signals, were identified and subsequently used for a simple online BCI control task. We demonstrate here for the first time that EEG signals from the unaffected hemisphere, associated with overt and imagined movements of the affected hand, can enable stroke survivors to control a one-dimensional computer cursor rapidly and accurately. This ipsilateral motor activity enabled users to achieve final target accuracies between 68% and 91% within 15 min. These findings suggest that ipsilateral motor activity from the unaffected hemisphere in stroke survivors could provide a physiological substrate for BCI operation that can be further developed as a long-term assistive device or potentially provide a novel tool for rehabilitation.},
}
@article {pmid22595090,
year = {2012},
author = {Meurs, MJ and Murphy, C and Morgenstern, I and Butler, G and Powlowski, J and Tsang, A and Witte, R},
title = {Semantic text mining support for lignocellulose research.},
journal = {BMC medical informatics and decision making},
volume = {12 Suppl 1},
number = {Suppl 1},
pages = {S5},
pmid = {22595090},
issn = {1472-6947},
mesh = {Algorithms ; Biomass ; Brain-Computer Interfaces ; Cellulase/biosynthesis ; *Computational Biology ; Data Collection/instrumentation ; Data Mining/*methods ; Humans ; Information Storage and Retrieval/methods ; Internet ; *Lignin ; Natural Language Processing ; *Research Support as Topic ; *Semantics ; Vocabulary, Controlled ; },
abstract = {BACKGROUND: Biofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties.<br><br>RESULTS: Semantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources.<br><br>CONCLUSIONS: Working closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information.},
}
@article {pmid22606672,
year = {2011},
author = {Vahabi, Z and Amirfattahi, R and Mirzaei, A},
title = {Enhancing P300 Wave of BCI Systems Via Negentropy in Adaptive Wavelet Denoising.},
journal = {Journal of medical signals and sensors},
volume = {1},
number = {3},
pages = {165-176},
pmid = {22606672},
issn = {2228-7477},
abstract = {Brian Computer Interface (BCI) is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. EEG separation into target and non-target ones based on presence of P300 signal is of difficult task mainly due to their natural low signal to noise ratio. In this paper a new algorithm is introduced to enhance EEG signals and improve their SNR. Our denoising method is based on multi-resolution analysis via Independent Component Analysis (ICA) Fundamentals. We have suggested combination of negentropy as a feature of signal and subband information from wavelet transform. The proposed method is finally tested with dataset from BCI Competition 2003 and gives results that compare favorably.},
}
@article {pmid22592066,
year = {2012},
author = {Vlek, RJ and Steines, D and Szibbo, D and Kübler, A and Schneider, MJ and Haselager, P and Nijboer, F},
title = {Ethical issues in brain-computer interface research, development, and dissemination.},
journal = {Journal of neurologic physical therapy : JNPT},
volume = {36},
number = {2},
pages = {94-99},
doi = {10.1097/NPT.0b013e31825064cc},
pmid = {22592066},
issn = {1557-0584},
mesh = {*Biomedical Research/ethics/instrumentation/trends ; Brain Diseases/*rehabilitation ; *Communication Devices for People with Disabilities ; Education/*methods ; Humans ; *User-Computer Interface ; },
abstract = {The steadily growing field of brain-computer interfacing (BCI) may develop useful technologies, with a potential impact not only on individuals, but also on society as a whole. At the same time, the development of BCI presents significant ethical and legal challenges. In a workshop during the 4th International BCI meeting (Asilomar, California, 2010), six panel members from various BCI laboratories and companies set out to identify and disentangle ethical issues related to BCI use in four case scenarios, which were inspired by current experiences in BCI laboratories. Results of the discussion are reported in this article, touching on topics such as the representation of persons with communication impairments, dealing with technological complexity and moral responsibility in multidisciplinary teams, and managing expectations, ranging from an individual user to the general public. Furthermore, we illustrate that where treatment and research interests conflict, ethical concerns arise. On the basis of the four case scenarios, we discuss salient, practical ethical issues that may confront any member of a typical multidisciplinary BCI team. We encourage the BCI and rehabilitation communities to engage in a dialogue, and to further identify and address pressing ethical issues as they occur in the practice of BCI research and its commercial applications.},
}
@article {pmid22589242,
year = {2012},
author = {Ng, KB and Bradley, AP and Cunnington, R},
title = {Stimulus specificity of a steady-state visual-evoked potential-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036008},
doi = {10.1088/1741-2560/9/3/036008},
pmid = {22589242},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artifacts ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography ; Electromyography ; Electrooculography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation ; *User-Computer Interface ; Visual Fields ; Young Adult ; },
abstract = {The mechanisms of neural excitation and inhibition when given a visual stimulus are well studied. It has been established that changing stimulus specificity such as luminance contrast or spatial frequency can alter the neuronal activity and thus modulate the visual-evoked response. In this paper, we study the effect that stimulus specificity has on the classification performance of a steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI). For example, we investigate how closely two visual stimuli can be placed before they compete for neural representation in the cortex and thus influence BCI classification accuracy. We characterize stimulus specificity using the four stimulus parameters commonly encountered in SSVEP-BCI design: temporal frequency, spatial size, number of simultaneously displayed stimuli and their spatial proximity. By varying these quantities and measuring the SSVEP-BCI classification accuracy, we are able to determine the parameters that provide optimal performance. Our results show that superior SSVEP-BCI accuracy is attained when stimuli are placed spatially more than 5° apart, with size that subtends at least 2° of visual angle, when using a tagging frequency of between high alpha and beta band. These findings may assist in deciding the stimulus parameters for optimal SSVEP-BCI design.},
}
@article {pmid22586452,
year = {2012},
author = {Storchi, R and Zippo, AG and Caramenti, GC and Valente, M and Biella, GE},
title = {Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.},
journal = {PloS one},
volume = {7},
number = {5},
pages = {e35850},
pmid = {22586452},
issn = {1932-6203},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Brain Mapping ; Electrical Synapses/physiology ; Electroencephalography ; Evoked Potentials, Visual/physiology ; Male ; *Models, Theoretical ; Neurons/*physiology ; Rats ; Rats, Sprague-Dawley ; Somatosensory Cortex/*physiology ; },
abstract = {Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.},
}
@article {pmid22586364,
year = {2012},
author = {Demandt, E and Mehring, C and Vogt, K and Schulze-Bonhage, A and Aertsen, A and Ball, T},
title = {Reaching movement onset- and end-related characteristics of EEG spectral power modulations.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {65},
pmid = {22586364},
issn = {1662-453X},
abstract = {The spectral power of intracranial field potentials shows movement-related modulations during reaching movements to different target positions that in frequencies up to the high-γ range (approximately 50 to above 200 Hz) can be reliably used for single-trial inference of movement parameters. However, identifying spectral power modulations suitable for single-trial analysis for non-invasive approaches remains a challenge. We recorded non-invasive electroencephalography (EEG) during a self-paced center-out and center-in arm movement task, resulting in eight reaching movement classes (four center-out, four center-in). We found distinct slow (≤5 Hz), μ (7.5-10 Hz), β (12.5-25 Hz), low-γ (approximately 27.5-50 Hz), and high-γ (above 50 Hz) movement onset- and end-related responses. Movement class-specific spectral power modulations were restricted to the β band at approximately 1 s after movement end and could be explained by the sensitivity of this response to different static, post-movement electromyography (EMG) levels. Based on the β band, significant single-trial inference of reaching movement endpoints was possible. The findings of the present study support the idea that single-trial decoding of different reaching movements from non-invasive EEG spectral power modulations is possible, but also suggest that the informative time window is after movement end and that the informative frequency range is restricted to the β band.},
}
@article {pmid22586362,
year = {2012},
author = {Guger, C and Krausz, G and Allison, BZ and Edlinger, G},
title = {Comparison of dry and gel based electrodes for p300 brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {60},
pmid = {22586362},
issn = {1662-453X},
abstract = {Most brain-computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word "LUCAS" while receiving real-time, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.},
}
@article {pmid22580222,
year = {2012},
author = {Hwang, HJ and Lim, JH and Jung, YJ and Choi, H and Lee, SW and Im, CH},
title = {Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard.},
journal = {Journal of neuroscience methods},
volume = {208},
number = {1},
pages = {59-65},
doi = {10.1016/j.jneumeth.2012.04.011},
pmid = {22580222},
issn = {1872-678X},
mesh = {Adult ; Communication Devices for People with Disabilities ; *Computer Peripherals ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {In this study, we introduce a new mental spelling system based on steady-state visual evoked potential (SSVEP), adopting a QWERTY style layout keyboard with 30 LEDs flickering with different frequencies. The proposed electroencephalography (EEG)-based mental spelling system allows the users to spell one target character per each target selection, without the need for multiple step selections adopted by conventional SSVEP-based mental spelling systems. Through preliminary offline experiments and online experiments, we confirmed that human SSVEPs elicited by visual flickering stimuli with a frequency resolution of 0.1 Hz could be classified with classification accuracy high enough to be used for a practical brain-computer interface (BCI) system. During the preliminary offline experiments performed with five participants, we optimized various factors influencing the performance of the mental spelling system, such as distances between adjacent keys, light source arrangements, stimulating frequencies, recording electrodes, and visual angles. Additional online experiments were conducted with six participants to verify the feasibility of the optimized mental spelling system. The results of the online experiments were an average typing speed of 9.39 letters per minute (LPM) with an average success rate of 87.58%, corresponding to an average information transfer rate of 40.72 bits per minute, demonstrating the high performance of the developed mental spelling system. Indeed, the average typing speed of 9.39 LPM attained in this study was one of the best LPM results among those reported in previous BCI literatures.},
}
@article {pmid22579858,
year = {2012},
author = {Walter, S and Quigley, C and Andersen, SK and Mueller, MM},
title = {Effects of overt and covert attention on the steady-state visual evoked potential.},
journal = {Neuroscience letters},
volume = {519},
number = {1},
pages = {37-41},
doi = {10.1016/j.neulet.2012.05.011},
pmid = {22579858},
issn = {1872-7972},
mesh = {Adult ; Attention/*physiology ; Biological Clocks/*physiology ; *Deception ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Flickering stimuli evoke an oscillatory brain response with the same frequency as the driving stimulus, the so-called steady-state visual evoked potential (SSVEP). SSVEPs are robust brain signals whose amplitudes are enhanced with attention and thus play a major role in the development and use of non-invasive Brain-Computer Interfaces (BCIs). We compared the modulation of SSVEP amplitudes when subjects directly gazed at a flickering array of static dots (overt attention) to when they covertly shifted attention to the dots keeping their eyes at central fixation. A discrimination task was performed at the attended location to ensure that subjects shifted attention as instructed. Horizontal eye movements (allowed in overt attention but to be avoided in covert attention) were monitored by the horizontal electrooculogram. Subjects' behavioural performance was significantly reduced in covert attention compared to overt attention. Correspondingly, attentional modulation of SSVEP amplitudes by overt attention was larger in magnitude than for covert attention. Overt attention also changed the topographical distribution of SSVEP amplitudes on the scalp. Stimuli elicited the largest amplitudes at central occipital electrodes when they were overtly attended and at contralateral parieto-occipital sites when they were covertly attended. Accordingly, source analysis revealed clear centrally located sources in early visual areas in overt attention, regardless of the attended visual hemifield. Taken together these results affirm that overt and covert attention have qualitatively and quantitatively different effects on SSVEP responses as well as on task performance. Moreover, our results suggest that navigating SSVEP-BCIs with overt attention is more reliable and highlight some of the challenges in developing BCIs for patients who have lost the ability to move their eyes.},
}
@article {pmid22573029,
year = {2012},
author = {Welberg, L},
title = {Brain-machine interfaces: Restoring movement in a paralysed hand.},
journal = {Nature reviews. Neuroscience},
volume = {13},
number = {6},
pages = {360-361},
pmid = {22573029},
issn = {1471-0048},
}
@article {pmid22570195,
year = {2012},
author = {Watanabe, H and Sato, MA and Suzuki, T and Nambu, A and Nishimura, Y and Kawato, M and Isa, T},
title = {Reconstruction of movement-related intracortical activity from micro-electrocorticogram array signals in monkey primary motor cortex.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036006},
doi = {10.1088/1741-2560/9/3/036006},
pmid = {22570195},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Data Interpretation, Statistical ; Electrodes, Implanted ; Electroencephalography/*methods ; Hand/physiology ; Hand Strength/physiology ; Linear Models ; Macaca ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Psychomotor Performance ; User-Computer Interface ; },
abstract = {Subdural electrode arrays provide stable, less invasive electrocorticogram (ECoG) recordings of neural signals than multichannel needle electrodes. Accurate reconstruction of intracortical local field potentials (LFPs) from ECoG signals would provide a critical step for the development of a less invasive, high-performance brain-machine interface; however, neural signals from individual ECoG channels are generally coarse and have limitations in estimating deep layer LFPs. Here, we developed a high-density, 32-channel, micro-ECoG array and applied a sparse linear regression algorithm to reconstruct the LFPs at various depths of primary motor cortex (M1) in a monkey performing a reach-and-grasp task. At 0.2 mm beneath the cortical surface, the real and estimated LFPs were significantly correlated (correlation coefficient (r); 0.66 ± 0.11), and the r at 3.2 mm was still as high as 0.55 ± 0.04. A time-frequency analysis of the reconstructed LFP showed clear transition between resting and movements by the monkey. These methods would be a powerful tool with wide-ranging applicability in neuroscience studies.},
}
@article {pmid22567990,
year = {2012},
author = {Bobrov, PD and Korshakov, AV and Roshchin, VIu and Frolov, AA},
title = {[Bayesian classifier for brain-computer interface based on mental representation of movements].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {62},
number = {1},
pages = {89-99},
pmid = {22567990},
issn = {0044-4677},
mesh = {Adult ; Algorithms ; Bayes Theorem ; Brain/*physiology ; Electroencephalography/*classification ; Eye Movements/*physiology ; Humans ; Imagination/*physiology ; Male ; *User-Computer Interface ; Vision, Ocular/physiology ; Visual Perception/*physiology ; },
abstract = {This paper proposes Bayesian approach to classification of EEG patterns on the basis of imaginary movements of extremities based on analysis ofcovariance matrices of native EEG recordings. An efficacy of a Brain-Computer Interface (BCI) based on the proposed classifier is evaluated. Bayesian classifier is shown to be competitive with the MCSP (Multiclass Common Spatial Patterns) classifier known from the literature as one of the efficient variant for BCI implementation. The influence of eye movement and blinking artifacts on the BCI performance is investigated. It is shown that the presence of such artifacts does not affect the classification accuracy.},
}
@article {pmid22567836,
year = {2012},
author = {Alekseeva, MV and Balioz, NV and Muravleva, KB and Sapina, EV and Bazanova, OM},
title = {[Alpha power voluntary increasing training for cognition enhancement study].},
journal = {Fiziologiia cheloveka},
volume = {38},
number = {1},
pages = {51-60},
pmid = {22567836},
issn = {0131-1646},
mesh = {Adolescent ; Alpha Rhythm/*physiology ; Biofeedback, Psychology/*methods ; Cognition/*physiology ; Electromyography ; Humans ; Male ; Young Adult ; },
abstract = {With the aim simultaneous alpha EEG stimulating and EMG decreasing biofeedback training impact on the alpha-activity and cognitive functions 27 healthy male subjects (18-34 years) were investigated in pre- and post 10 training sessions of the voluntary increasing alpha power in individual upper alpha range. The accuracy of conceptual span task, fluency and flexibility in alternatives use task performance and alpha-activity indices were compared in real (14 participants) and sham (13 participants) biofeedback groups for the discrimination of the feedback role in training. The follow up effect oftrainings was studied through month over the training sessions. Results showed that alpha biofeedback training enhanced the fluency and accuracy in cognitive performance, increased resting frequency, width and power in individual upper alpha range only in participants with low baseline alpha frequency. While mock biofeedback increased resting alpha power only in participants with high baseline resting alpha frequency and did not change the cognitive performance. Biofeedback training eliminated the alpha power decrease in response to arithmetic task in both with high and low alpha frequency participants and this effect was followed up over the month. Mock biofeedback training has no such effect. It could be concluded that alpha-EEG-EMG biofeedback has application not only for cognition enhancement, but also in prognostic aims in clinical practice and brain-computer interface technology.},
}
@article {pmid22547461,
year = {2012},
author = {Niazi, IK and Mrachacz-Kersting, N and Jiang, N and Dremstrup, K and Farina, D},
title = {Peripheral electrical stimulation triggered by self-paced detection of motor intention enhances motor evoked potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {4},
pages = {595-604},
doi = {10.1109/TNSRE.2012.2194309},
pmid = {22547461},
issn = {1558-0210},
mesh = {Ankle Joint/physiology ; Biofeedback, Psychology/methods/physiology ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Intention ; Male ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; Neuronal Plasticity/*physiology ; Pattern Recognition, Automated/*methods ; Pyramidal Tracts/*physiology ; Transcranial Magnetic Stimulation/*methods ; Young Adult ; },
abstract = {This paper proposes the development and experimental tests of a self-paced asynchronous brain-computer interfacing (BCI) system that detects movement related cortical potentials (MRCPs) produced during motor imagination of ankle dorsiflexion and triggers peripheral electrical stimulations timed with the occurrence of MRCPs to induce corticospinal plasticity. MRCPs were detected online from EEG signals in eight healthy subjects with a true positive rate (TPR) of 67.15 ± 7.87% and false positive rate (FPR) of 22.05 ±9.07%. The excitability of the cortical projection to the target muscle (tibialis anterior) was assessed before and after the intervention through motor evoked potentials (MEP) using transcranial magnetic stimulation (TMS). The peak of the evoked potential significantly (P=0.02) increased after the BCI intervention by 53 ± 43% (relative to preintervention measure), although the spinal excitability (tested by stretch reflexes) did not change. These results demonstrate for the first time that it is possible to alter the corticospinal projections to the tibialis anterior muscle by using an asynchronous BCI system based on online motor imagination that triggered peripheral stimulation. This type of repetitive proprioceptive feedback training based on self-generated brain signal decoding may be a requirement for purposeful skill acquisition in intact humans and in the rehabilitation of persons with brain damage.},
}
@article {pmid22543893,
year = {2012},
author = {Fager, S and Bardach, L and Russell, S and Higginbotham, J},
title = {Access to augmentative and alternative communication: new technologies and clinical decision-making.},
journal = {Journal of pediatric rehabilitation medicine},
volume = {5},
number = {1},
pages = {53-61},
doi = {10.3233/PRM-2012-0196},
pmid = {22543893},
issn = {1875-8894},
mesh = {*Access to Information ; Adolescent ; Brain-Computer Interfaces/trends ; Case Management ; Child ; Child, Preschool ; *Communication ; Communication Devices for People with Disabilities/*trends ; Decision Making ; *Children with Disabilities/education/psychology/rehabilitation ; Dysarthria/rehabilitation ; *Education, Special/methods/organization &amp; administration/trends ; Female ; Humans ; Male ; Mobility Limitation ; Patient Care Team/organization &amp; administration ; *Technology/instrumentation/methods/trends ; },
abstract = {Children with severe physical impairments require a variety of access options to augmentative and alternative communication (AAC) and computer technology. Access technologies have continued to develop, allowing children with severe motor control impairments greater independence and access to communication. This article will highlight new advances in access technology, including eye and head tracking, scanning, and access to mainstream technology, as well as discuss future advances. Considerations for clinical decision-making and implementation of these technologies will be presented along with case illustrations.},
}
@article {pmid22537600,
year = {2012},
author = {Potes, C and Gunduz, A and Brunner, P and Schalk, G},
title = {Dynamics of electrocorticographic (ECoG) activity in human temporal and frontal cortical areas during music listening.},
journal = {NeuroImage},
volume = {61},
number = {4},
pages = {841-848},
pmid = {22537600},
issn = {1095-9572},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-10/EB/NIBIB NIH HHS/United States ; R01 EB006356-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Auditory Perception/*physiology ; *Brain Mapping ; Electrodes, Implanted ; Electroencephalography ; Epilepsy/physiopathology ; Female ; Frontal Lobe/*physiology ; Humans ; Male ; Middle Aged ; *Music ; Temporal Lobe/*physiology ; Young Adult ; },
abstract = {Previous studies demonstrated that brain signals encode information about specific features of simple auditory stimuli or of general aspects of natural auditory stimuli. How brain signals represent the time course of specific features in natural auditory stimuli is not well understood. In this study, we show in eight human subjects that signals recorded from the surface of the brain (electrocorticography (ECoG)) encode information about the sound intensity of music. ECoG activity in the high gamma band recorded from the posterior part of the superior temporal gyrus as well as from an isolated area in the precentral gyrus was observed to be highly correlated with the sound intensity of music. These results not only confirm the role of auditory cortices in auditory processing but also point to an important role of premotor and motor cortices. They also encourage the use of ECoG activity to study more complex acoustic features of simple or natural auditory stimuli.},
}
@article {pmid22533845,
year = {2012},
author = {Nam, CS},
title = {Brain-computer interface (BCI) and ergonomics.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {513-515},
doi = {10.1080/00140139.2012.676675},
pmid = {22533845},
issn = {1366-5847},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; *Ergonomics ; Humans ; Neurodegenerative Diseases ; Research ; Self-Help Devices ; *User-Computer Interface ; },
}
@article {pmid22523005,
year = {2012},
author = {Thorbergsson, PT and Garwicz, M and Schouenborg, J and Johansson, AJ},
title = {Minimizing data transfer with sustained performance in wireless brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {9},
number = {3},
pages = {036005},
doi = {10.1088/1741-2560/9/3/036005},
pmid = {22523005},
issn = {1741-2552},
mesh = {Algorithms ; Brain/*physiology ; Computer Simulation ; Data Interpretation, Statistical ; Electronics ; Electrophysiological Phenomena ; Equipment Design ; Fuzzy Logic ; Humans ; Information Systems ; Nonlinear Dynamics ; Principal Component Analysis ; Software ; *User-Computer Interface ; Wavelet Analysis ; *Wireless Technology ; },
abstract = {Brain-machine interfaces (BMIs) may be used to investigate neural mechanisms or to treat the symptoms of neurological disease and are hence powerful tools in research and clinical practice. Wireless BMIs add flexibility to both types of applications by reducing movement restrictions and risks associated with transcutaneous leads. However, since wireless implementations are typically limited in terms of transmission capacity and energy resources, the major challenge faced by their designers is to combine high performance with adaptations to limited resources. Here, we have identified three key steps in dealing with this challenge: (1) the purpose of the BMI should be clearly specified with regard to the type of information to be processed; (2) the amount of raw input data needed to fulfill the purpose should be determined, in order to avoid over- or under-dimensioning of the design; and (3) processing tasks should be allocated among the system parts such that all of them are utilized optimally with respect to computational power, wireless link capacity and raw input data requirements. We have focused on step (2) under the assumption that the purpose of the BMI (step 1) is to assess single- or multi-unit neuronal activity in the central nervous system with single-channel extracellular recordings. The reliability of this assessment depends on performance in detection and sorting of spikes. We have therefore performed absolute threshold spike detection and spike sorting with the principal component analysis and fuzzy c-means on a set of synthetic extracellular recordings, while varying the sampling rate and resolution, noise level and number of target units, and used the known ground truth to quantitatively estimate the performance. From the calculated performance curves, we have identified the sampling rate and resolution breakpoints, beyond which performance is not expected to increase by more than 1-5%. We have then estimated the performance of alternative algorithms for spike detection and spike sorting in order to examine the generalizability of our results to other algorithms. Our findings indicate that the minimization of recording noise is the primary factor to consider in the design process. In most cases, there are breakpoints for sampling rates and resolution that provide guidelines for BMI designers in terms of minimum amount raw input data that guarantees sustained performance. Such guidelines are essential during system dimensioning. Based on these findings we conclude by presenting a quantitative task-allocation scheme that can be followed to achieve optimal utilization of available resources.},
}
@article {pmid22510955,
year = {2012},
author = {Park, J and Kim, KE},
title = {A POMDP approach to optimizing P300 speller BCI paradigm.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {4},
pages = {584-594},
doi = {10.1109/TNSRE.2012.2191979},
pmid = {22510955},
issn = {1558-0210},
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/physiology ; *Language ; Male ; Markov Chains ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {To achieve high performance in brain-computer interfaces (BCIs) using P300, most of the work has been focused on feature extraction and classification algorithms. Although significant progress has been made in such signal processing methods in the lower layer, the issues in the higher layer, specifically determining the stimulus schedule in order to identify the target reliably and efficiently, remain relatively unexplored. In this paper, we propose a systematic approach to compute an optimal stimulus schedule in P300 BCIs. Our approach adopts the partially observable Markov decision process, which is a model for planning in partially observable stochastic environments. We show that the thus obtained stimulus schedule achieves a significant performance improvement in terms of the success rate, bit rate, and practical bit rate through human subject experiments.},
}
@article {pmid22506977,
year = {2012},
author = {Thurlings, ME and van Erp, JB and Brouwer, AM and Blankertz, B and Werkhoven, P},
title = {Control-display mapping in brain-computer interfaces.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {564-580},
doi = {10.1080/00140139.2012.661085},
pmid = {22506977},
issn = {1366-5847},
mesh = {Brain Mapping/*methods ; *Communication Devices for People with Disabilities ; *Data Display ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Male ; *User-Computer Interface ; },
abstract = {UNLABELLED: Event-related potential (ERP) based brain-computer interfaces (BCIs) employ differences in brain responses to attended and ignored stimuli. When using a tactile ERP-BCI for navigation, mapping is required between navigation directions on a visual display and unambiguously corresponding tactile stimuli (tactors) from a tactile control device: control-display mapping (CDM). We investigated the effect of congruent (both display and control horizontal or both vertical) and incongruent (vertical display, horizontal control) CDMs on task performance, the ERP and potential BCI performance. Ten participants attended to a target (determined via CDM), in a stream of sequentially vibrating tactors. We show that congruent CDM yields best task performance, enhanced the P300 and results in increased estimated BCI performance. This suggests a reduced availability of attentional resources when operating an ERP-BCI with incongruent CDM. Additionally, we found an enhanced N2 for incongruent CDM, which indicates a conflict between visual display and tactile control orientations.<br><br>PRACTITIONER SUMMARY: Incongruency in control-display mapping reduces task performance. In this study, brain responses, task and system performance are related to (in)congruent mapping of command options and the corresponding stimuli in a brain-computer interface (BCI). Directional congruency reduces task errors, increases available attentional resources, improves BCI performance and thus facilitates human-computer interaction.},
}
@article {pmid22506831,
year = {2012},
author = {Ekandem, JI and Davis, TA and Alvarez, I and James, MT and Gilbert, JE},
title = {Evaluating the ergonomics of BCI devices for research and experimentation.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {592-598},
doi = {10.1080/00140139.2012.662527},
pmid = {22506831},
issn = {1366-5847},
mesh = {Adult ; *Communication Devices for People with Disabilities ; *Ergonomics ; Female ; Humans ; Male ; *Research ; *User-Computer Interface ; Young Adult ; },
abstract = {The use of brain computer interface (BCI) devices in research and applications has exploded in recent years. Applications such as lie detectors that use functional magnetic resonance imaging (fMRI) to video games controlled using electroencephalography (EEG) are currently in use. These developments, coupled with the emergence of inexpensive commercial BCI headsets, such as the Emotiv EPOC (http://emotiv.com/index.php) and the Neurosky MindWave, have also highlighted the need of performing basic ergonomics research since such devices have usability issues, such as comfort during prolonged use, and reduced performance for individuals with common physical attributes, such as long or coarse hair. This paper examines the feasibility of using consumer BCIs in scientific research. In particular, we compare user comfort, experiment preparation time, signal reliability and ease of use in light of individual differences among subjects for two commercially available hardware devices, the Emotiv EPOC and the Neurosky MindWave. Based on these results, we suggest some basic considerations for selecting a commercial BCI for research and experimentation. STATEMENT OF RELEVANCE: Despite increased usage, few studies have examined the usability of commercial BCI hardware. This study assesses usability and experimentation factors of two commercial BCI models, for the purpose of creating basic guidelines for increased usability. Finding that more sensors can be less comfortable and accurate than devices with fewer sensors.},
}
@article {pmid22506483,
year = {2012},
author = {Felton, EA and Williams, JC and Vanderheiden, GC and Radwin, RG},
title = {Mental workload during brain-computer interface training.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {526-537},
pmid = {22506483},
issn = {1366-5847},
support = {T90 DK070079-05/DK/NIDDK NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; K12 HD049077/HD/NICHD NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; K12 HD049077-01/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain/*physiology ; Child ; *Communication Devices for People with Disabilities ; *Education ; Electroencephalography ; Female ; Humans ; Male ; Mental Fatigue ; Middle Aged ; Neuromuscular Diseases ; *User-Computer Interface ; Workload/*psychology ; Young Adult ; },
abstract = {UNLABELLED: It is not well understood how people perceive the difficulty of performing brain-computer interface (BCI) tasks, which specific aspects of mental workload contribute the most, and whether there is a difference in perceived workload between participants who are able-bodied and disabled. This study evaluated mental workload using the NASA Task Load Index (TLX), a multi-dimensional rating procedure with six subscales: Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, and Frustration. Able-bodied and motor disabled participants completed the survey after performing EEG-based BCI Fitts' law target acquisition and phrase spelling tasks. The NASA-TLX scores were similar for able-bodied and disabled participants. For example, overall workload scores (range 0-100) for 1D horizontal tasks were 48.5 (SD = 17.7) and 46.6 (SD 10.3), respectively. The TLX can be used to inform the design of BCIs that will have greater usability by evaluating subjective workload between BCI tasks, participant groups, and control modalities.<br><br>PRACTITIONER SUMMARY: Mental workload of brain-computer interfaces (BCI) can be evaluated with the NASA Task Load Index (TLX). The TLX is an effective tool for comparing subjective workload between BCI tasks, participant groups (able-bodied and disabled), and control modalities. The data can inform the design of BCIs that will have greater usability.},
}
@article {pmid22503644,
year = {2012},
author = {Yang, J and Singh, H and Hines, EL and Schlaghecken, F and Iliescu, DD and Leeson, MS and Stocks, NG},
title = {Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.},
journal = {Artificial intelligence in medicine},
volume = {55},
number = {2},
pages = {117-126},
doi = {10.1016/j.artmed.2012.02.001},
pmid = {22503644},
issn = {1873-2860},
mesh = {*Algorithms ; Analysis of Variance ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Fingers ; Fourier Analysis ; Hand ; Humans ; Least-Squares Analysis ; Movement/physiology ; *Neural Networks, Computer ; Signal Processing, Computer-Assisted/*instrumentation ; User-Computer Interface ; },
abstract = {OBJECTIVE: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism. In addition, results of ANN learning are usually not easily interpretable. In this paper, we have applied an ANN-based method, the genetic neural mathematic method (GNMM), to two EEG channel selection and classification problems, aiming to address the issues above.<br><br>METHODS AND MATERIALS: Pre-processing steps include: least-square (LS) approximation to determine the overall signal increase/decrease rate; locally weighted polynomial regression (Loess) and fast Fourier transform (FFT) to smooth the signals to determine the signal strength and variations. The GNMM method consists of three successive steps: (1) a genetic algorithm-based (GA-based) input selection process; (2) multi-layer perceptron-based (MLP-based) modelling; and (3) rule extraction based upon successful training. The fitness function used in the GA is the training error when an MLP is trained for a limited number of epochs. By averaging the appearance of a particular channel in the winning chromosome over several runs, we were able to minimize the error due to randomness and to obtain an energy distribution around the scalp. In the second step, a threshold was used to select a subset of channels to be fed into an MLP, which performed modelling with a large number of iterations, thus fine-tuning the input/output relationship. Upon successful training, neurons in the input layer are divided into four sub-spaces to produce if-then rules (step 3). Two datasets were used as case studies to perform three classifications. The first data were electrocorticography (ECoG) recordings that have been used in the BCI competition III. The data belonged to two categories, imagined movements of either a finger or the tongue. The data were recorded using an 8 &#xd7; 8 ECoG platinum electrode grid at a sampling rate of 1000 Hz for a total of 378 trials. The second dataset consisted of a 32-channel, 256 Hz EEG recording of 960 trials where participants had to execute a left- or right-hand button-press in response to left- or right-pointing arrow stimuli. The data were used to classify correct/incorrect responses and left/right hand movements.<br><br>RESULTS: For the first dataset, 100 samples were reserved for testing, and those remaining were for training and validation with a ratio of 90%:10% using K-fold cross-validation. Using the top 10 channels selected by GNMM, we achieved a classification accuracy of 0.80 &#xb1; 0.04 for the testing dataset, which compares favourably with results reported in the literature. For the second case, we performed multi-time-windows pre-processing over a single trial. By selecting 6 channels out of 32, we were able to achieve a classification accuracy of about 0.86 for the response correctness classification and 0.82 for the actual responding hand classification, respectively. Furthermore, 139 regression rules were identified after training was completed.<br><br>CONCLUSIONS: We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.},
}
@article {pmid22498703,
year = {2012},
author = {Huang, D and Qian, K and Fei, DY and Jia, W and Chen, X and Bai, O},
title = {Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {3},
pages = {379-388},
doi = {10.1109/TNSRE.2012.2190299},
pmid = {22498703},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Cues ; Electrodes ; Electroencephalography/*methods ; Electroencephalography Phase Synchronization/*physiology ; Evoked Potentials/physiology ; Female ; Functional Laterality/physiology ; Hand/physiology ; Humans ; Intention ; Male ; Man-Machine Systems ; Movement ; Psychomotor Performance/physiology ; *User-Computer Interface ; Video Games ; *Wheelchairs ; Young Adult ; },
abstract = {This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemoral feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.},
}
@article {pmid22496763,
year = {2012},
author = {Kindermans, PJ and Verstraeten, D and Schrauwen, B},
title = {A bayesian model for exploiting application constraints to enable unsupervised training of a P300-based BCI.},
journal = {PloS one},
volume = {7},
number = {4},
pages = {e33758},
pmid = {22496763},
issn = {1932-6203},
mesh = {Algorithms ; *Artificial Intelligence ; *Bayes Theorem ; Brain/*physiology ; Event-Related Potentials, P300/*physiology ; Humans ; Language ; Models, Theoretical ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datasets for validation of our method and show that our unsupervised classifier performs competitively with supervised state-of-the-art spellers. Finally, we demonstrate the added value of our method in different experimental settings which reflect realistic usage situations of increasing difficulty and which would be difficult or impossible to tackle with existing supervised or adaptive methods.},
}
@article {pmid22496532,
year = {2012},
author = {Chase, SM and Kass, RE and Schwartz, AB},
title = {Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex.},
journal = {Journal of neurophysiology},
volume = {108},
number = {2},
pages = {624-644},
pmid = {22496532},
issn = {1522-1598},
support = {R01EB005847/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adaptation, Physiological/*physiology ; Animals ; Biofeedback, Psychology/methods/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) provide a defined link between neural activity and devices, allowing a detailed study of the neural adaptive responses generating behavioral output. We trained monkeys to perform two-dimensional center-out movements of a computer cursor using a BCI. We then applied a perturbation by randomly selecting a subset of the recorded units and rotating their directional contributions to cursor movement by a consistent angle. Globally, this perturbation mimics a visuomotor transformation, and in the first part of this article we characterize the psychophysical indications of motor adaptation and compare them with known results from adaptation of natural reaching movements. Locally, however, only a subset of the neurons in the population actually contributes to error, allowing us to probe for signatures of neural adaptation that might be specific to the subset of neurons we perturbed. One compensation strategy would be to selectively adapt the subset of cells responsible for the error. An alternate strategy would be to globally adapt the entire population to correct the error. Using a recently developed mathematical technique that allows us to differentiate these two mechanisms, we found evidence of both strategies in the neural responses. The dominant strategy we observed was global, accounting for ∼86% of the total error reduction. The remaining 14% came from local changes in the tuning functions of the perturbed units. Interestingly, these local changes were specific to the details of the applied rotation: in particular, changes in the depth of tuning were only observed when the percentage of perturbed cells was small. These results imply that there may be constraints on the network's adaptive capabilities, at least for perturbations lasting only a few hundreds of trials.},
}
@article {pmid22491973,
year = {2012},
author = {Loram, ID and van de Kamp, C and Gollee, H and Gawthrop, PJ},
title = {Identification of intermittent control in man and machine.},
journal = {Journal of the Royal Society, Interface},
volume = {9},
number = {74},
pages = {2070-2084},
pmid = {22491973},
issn = {1742-5662},
mesh = {*Brain-Computer Interfaces ; Female ; Humans ; Male ; *Models, Biological ; },
abstract = {Regulation by negative feedback is fundamental to engineering and biological processes. Biological regulation is usually explained using continuous feedback models from both classical and modern control theory. An alternative control paradigm, intermittent control, has also been suggested as a model for biological control systems, particularly those involving the central nervous system. However, at present, there is no identification method explicitly formulated to distinguish intermittent from continuous control; here, we present such a method. The identification experiment uses a special paired-step set-point sequence. The corresponding data analysis use a conventional ARMA model to relate a theoretically derived equivalent set-point to control signal; the novelty lies in sequentially and iteratively adjusting the timing of the steps of this equivalent set-point to optimize the linear time-invariant fit. The method was verified using realistic simulation data and was found to robustly distinguish not only between continuous and intermittent control but also between event-driven intermittent and clock-driven intermittent control. When applied to human pursuit tracking, event-driven intermittent control was identified, with an intermittent interval of 260-310 ms (n = 6, p < 0.05). This new identification method is applicable for machine and biological applications.},
}
@article {pmid22485087,
year = {2012},
author = {Hajipour Sardouie, S and Shamsollahi, MB},
title = {Selection of Efficient Features for Discrimination of Hand Movements from MEG Using a BCI Competition IV Data Set.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {42},
pmid = {22485087},
issn = {1662-453X},
abstract = {The aim of a brain-computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The proposed method was validated in the BCI competition IV, where it obtained the best result among BCI competitors: a classification accuracy of 59.5 and 34.3% for subject 1 and subject 2 on the test data respectively.},
}
@article {pmid22481835,
year = {2012},
author = {Faller, J and Vidaurre, C and Solis-Escalante, T and Neuper, C and Scherer, R},
title = {Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {3},
pages = {313-319},
doi = {10.1109/TNSRE.2012.2189584},
pmid = {22481835},
issn = {1558-0210},
mesh = {Acoustic Stimulation ; Adult ; Alpha Rhythm/physiology ; Brain/*physiology ; Calibration ; Cortical Synchronization/physiology ; Cues ; Data Interpretation, Statistical ; Electric Stimulation ; Electroencephalography ; Female ; Humans ; Learning/physiology ; Male ; *Online Systems ; Psychomotor Performance ; *User-Computer Interface ; Young Adult ; },
abstract = {System calibration and user training are essential for operating motor imagery based brain-computer interface (BCI) systems. These steps are often unintuitive and tedious for the user, and do not necessarily lead to a satisfactory level of control. We present an Adaptive BCI framework that provides feedback after only minutes of autocalibration in a two-class BCI setup. During operation, the system recurrently reselects only one out of six predefined logarithmic bandpower features (10-13 and 16-24 Hz from Laplacian derivations over C3, Cz, and C4), specifically, the feature that exhibits maximum discriminability. The system then retrains a linear discriminant analysis classifier on all available data and updates the online paradigm with the new model. Every retraining step is preceded by an online outlier rejection. Operating the system requires no engineering knowledge other than connecting the user and starting the system. In a supporting study, ten out of twelve novice users reached a criterion level of above 70% accuracy in one to three sessions (10-80 min online time) of training, with a median accuracy of 80.2 ± 11.3% in the last session. We consider the presented system a positive first step towards fully autocalibrating motor imagery BCIs.},
}
@article {pmid22479236,
year = {2012},
author = {Ang, KK and Chin, ZY and Wang, C and Guan, C and Zhang, H},
title = {Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {39},
pmid = {22479236},
issn = {1662-453X},
abstract = {The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.},
}
@article {pmid22476215,
year = {2012},
author = {Li, J and Zhang, L},
title = {Active training paradigm for motor imagery BCI.},
journal = {Experimental brain research},
volume = {219},
number = {2},
pages = {245-254},
pmid = {22476215},
issn = {1432-1106},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) allows the use of brain activities for people to directly communicate with the external world or to control external devices without participation of any peripheral nerves and muscles. Motor imagery is one of the most popular modes in the research field of brain-computer interface. Although motor imagery BCI has some advantages compared with other modes of BCI, such as asynchronization, it is necessary to require training sessions before using it. The performance of trained BCI system depends on the quality of training samples or the subject engagement. In order to improve training effect and decrease training time, we proposed a new paradigm where subjects participated in training more actively than in the traditional paradigm. In the traditional paradigm, a cue (to indicate what kind of motor imagery should be imagined during the current trial) is given to the subject at the beginning of a trial or during a trial, and this cue is also used as a label for this trial. It is usually assumed that labels for trials are accurate in the traditional paradigm, although subjects may not have performed the required or correct kind of motor imagery, and trials may thus be mislabeled. And then those mislabeled trials give rise to interference during model training. In our proposed paradigm, the subject is required to reconfirm the label and can correct the label when necessary. This active training paradigm may generate better training samples with fewer inconsistent labels because it overcomes mistakes when subject's motor imagination does not match the given cues. The experiments confirm that our proposed paradigm achieves better performance; the improvement is significant according to statistical analysis.},
}
@article {pmid22464634,
year = {2012},
author = {Cleworth, TW and Horslen, BC and Carpenter, MG},
title = {Influence of real and virtual heights on standing balance.},
journal = {Gait &amp; posture},
volume = {36},
number = {2},
pages = {172-176},
doi = {10.1016/j.gaitpost.2012.02.010},
pmid = {22464634},
issn = {1879-2219},
mesh = {*Accidental Falls ; Adult ; *Anxiety ; *Fear ; Female ; Humans ; Male ; Postural Balance/*physiology ; Psychophysiology ; User-Computer Interface ; Young Adult ; },
abstract = {Fear and anxiety induced by threatening scenarios, such as standing on elevated surfaces, have been shown to influence postural control in young adults. There is also a need to understand how postural threat influences postural control in populations with balance deficits and risk of falls. However, safety and feasibility issues limit opportunities to place such populations in physically threatening scenarios. Virtual reality (VR) has successfully been used to simulate threatening environments, although it is unclear whether the same postural changes can be elicited by changes in virtual and real threat conditions. Therefore, the purpose of this study was to compare the effects of real and virtual heights on changes to standing postural control, electrodermal activity (EDA) and psycho-social state. Seventeen subjects stood at low and high heights in both real and virtual environments matched in scale and visual detail. A repeated measures ANOVA revealed increases with height, independent of visual environment, in EDA, anxiety, fear, and center of pressure (COP) frequency, and decreases with height in perceived stability, balance confidence and COP amplitude. Interaction effects were seen for fear and COP mean position; where real elicited larger changes with height than VR. This study demonstrates the utility of VR, as simulated heights resulted in changes to postural, autonomic and psycho-social measures similar to those seen at real heights. As a result, VR may be a useful tool for studying threat related changes in postural control in populations at risk of falls, and to screen and rehabilitate balance deficits associated with fear and anxiety.},
}
@article {pmid22456363,
year = {2012},
author = {Frost, CM and Wei, B and Baghmanli, Z and Cederna, PS and Urbanchek, MG},
title = {PEDOT electrochemical polymerization improves electrode fidelity and sensitivity.},
journal = {Plastic and reconstructive surgery},
volume = {129},
number = {4},
pages = {933-942},
pmid = {22456363},
issn = {1529-4242},
support = {T32 GM008616/GM/NIGMS NIH HHS/United States ; },
mesh = {Action Potentials ; Amputation Stumps/innervation ; Animals ; Arm ; *Bridged Bicyclo Compounds, Heterocyclic ; Coated Materials, Biocompatible ; *Electric Stimulation Therapy ; *Electrodes, Implanted ; Humans ; Male ; Neural Conduction ; Peroneal Nerve/physiology ; *Polymers ; Rats ; Rats, Inbred F344 ; Sensory Thresholds ; },
abstract = {BACKGROUND: The goal of the authors is to restore fine motor control and sensation for high-arm amputees. They developed a regenerative peripheral nerve interface with the aim of attaining closed loop neural control by integrating directly with the amputee's residual motor and sensory peripheral nerves. PEDOT, poly(3,4-ethylenedioxythiophene), has both electrical and ionic conduction characteristics. This hybrid character could help bridge the salutatory conduction of the nervous system to an electrode. The purpose of this study was to determine whether electrodes polymerized with PEDOT have improved ability to both record and stimulate peripheral nerve action potentials.<br><br>METHODS: Impedance spectroscopy and cyclic voltammetry were performed on electrodes before and after polymerization to measure electrode impedance and charge capacity. Both recording needle and bipolar stimulating electrodes were polymerized with PEDOT. Plain and PEDOT electrodes were tested using rat (n = 18) in situ nerve conduction studies. The peroneal nerve was stimulated using a bipolar electrode at multiple locations along the nerve. Action potentials were measured in the extensor digitorum longus muscle.<br><br>RESULTS: Bench testing showed PEDOT electrodes had a higher charge capacity and lower impedance than plain electrodes, indicating significantly improved electrode fidelity. Nerve conduction testing indicated a significant reduction in the stimulus threshold for both PEDOT recording and PEDOT stimulatory electrodes when compared with plain electrodes, indicating an increase in sensitivity.<br><br>CONCLUSIONS: PEDOT electrochemical polymerization improves electrode fidelity. Electrodes that have been electropolymerized with PEDOT show improved sensitivity when recording or stimulating action potentials at the tissue-electrode interface.},
}
@article {pmid22455595,
year = {2012},
author = {Blain-Moraes, S and Schaff, R and Gruis, KL and Huggins, JE and Wren, PA},
title = {Barriers to and mediators of brain-computer interface user acceptance: focus group findings.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {516-525},
doi = {10.1080/00140139.2012.661082},
pmid = {22455595},
issn = {1366-5847},
mesh = {Amyotrophic Lateral Sclerosis ; *Brain ; *Communication Devices for People with Disabilities ; Female ; Focus Groups ; *Health Knowledge, Attitudes, Practice ; Humans ; Male ; *User-Computer Interface ; },
abstract = {UNLABELLED: Brain-computer interfaces (BCI) are designed to enable individuals with severe motor impairments such as amyotrophic lateral sclerosis (ALS) to communicate and control their environment. A focus group was conducted with individuals with ALS (n=8) and their caregivers (n=9) to determine the barriers to and mediators of BCI acceptance in this population. Two key categories emerged: personal factors and relational factors. Personal factors, which included physical, physiological and psychological concerns, were less important to participants than relational factors, which included corporeal, technological and social relations with the BCI. The importance of these relational factors was analysed with respect to published literature on actor-network theory (ANT) and disability, and concepts of voicelessness and personhood. Future directions for BCI research are recommended based on the emergent focus group themes.<br><br>PRACTITIONER SUMMARY: This manuscript explores human factor issues involved in designing and evaluating brain-computer interface (BCI) systems for users with severe motor disabilities. Using participatory research paradigms and qualitative methods, this work draws attention to personal and relational factors that act as barriers to, or mediators of, user acceptance of this technology.},
}
@article {pmid22455372,
year = {2012},
author = {Aloise, F and Aricò, P and Schettini, F and Riccio, A and Salinari, S and Mattia, D and Babiloni, F and Cincotti, F},
title = {A covert attention P300-based brain-computer interface: Geospell.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {538-551},
doi = {10.1080/00140139.2012.661084},
pmid = {22455372},
issn = {1366-5847},
mesh = {Adult ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Female ; Humans ; Italy ; Male ; *Software ; *User-Computer Interface ; Writing ; Young Adult ; },
abstract = {UNLABELLED: The Farwell and Donchin P300 speller interface is one of the most widely used brain-computer interface (BCI) paradigms for writing text. Recent studies have shown that the recognition accuracy of the P300 speller decreases significantly when eye movement is impaired. This report introduces the GeoSpell interface (Geometric Speller), which implements a stimulation framework for a P300-based BCI that has been optimised for operation in covert visual attention. We compared the Geospell with the P300 speller interface under overt attention conditions with regard to effectiveness, efficiency and user satisfaction. Ten healthy subjects participated in the study. The performance of the GeoSpell interface in covert attention was comparable with that of the P300 speller in overt attention. As expected, the effectiveness of the spelling decreased with the new interface in covert attention. The NASA task load index (TLX) for workload assessment did not differ significantly between the two modalities.<br><br>PRACTITIONER SUMMARY: This study introduces and evaluates a gaze-independent, P300-based brain-computer interface, the efficacy and user satisfaction of which were comparable with those off the classical P300 speller. Despite a decrease in effectiveness due to the use of covert attention, the performance of the GeoSpell far exceeded the threshold of accuracy with regard to effective spelling.},
}
@article {pmid22455346,
year = {2012},
author = {Carabalona, R and Grossi, F and Tessadri, A and Castiglioni, P and Caracciolo, A and de Munari, I},
title = {Light on! Real world evaluation of a P300-based brain-computer interface (BCI) for environment control in a smart home.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {552-563},
doi = {10.1080/00140139.2012.661083},
pmid = {22455346},
issn = {1366-5847},
mesh = {Adult ; Aged ; Brain/*physiology ; *Communication Devices for People with Disabilities ; *Environment, Controlled ; Female ; *Housing ; Humans ; Italy ; Male ; Middle Aged ; Neurodegenerative Diseases ; *Software ; *User-Computer Interface ; },
abstract = {UNLABELLED: Brain-computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use.<br><br>PRACTITIONER SUMMARY: Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.},
}
@article {pmid22451316,
year = {2012},
author = {Liberati, G and Dalboni da Rocha, JL and van der Heiden, L and Raffone, A and Birbaumer, N and Olivetti Belardinelli, M and Sitaram, R},
title = {Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {31 Suppl 3},
number = {},
pages = {S211-20},
doi = {10.3233/JAD-2012-112129},
pmid = {22451316},
issn = {1875-8908},
mesh = {Alzheimer Disease/psychology/*rehabilitation ; Artificial Intelligence ; Brain/*physiopathology ; *Brain-Computer Interfaces ; Communication ; Communication Devices for People with Disabilities ; *Conditioning, Classical ; Electroencephalography ; Emotions ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; },
abstract = {Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis.},
}
@article {pmid22438708,
year = {2012},
author = {Nicolas-Alonso, LF and Gomez-Gil, J},
title = {Brain computer interfaces, a review.},
journal = {Sensors (Basel, Switzerland)},
volume = {12},
number = {2},
pages = {1211-1279},
pmid = {22438708},
issn = {1424-8220},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*instrumentation ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted/instrumentation ; *Transducers ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.},
}
@article {pmid22438336,
year = {2012},
author = {Presacco, A and Forrester, LW and Contreras-Vidal, JL},
title = {Decoding intra-limb and inter-limb kinematics during treadmill walking from scalp electroencephalographic (EEG) signals.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {2},
pages = {212-219},
pmid = {22438336},
issn = {1558-0210},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Algorithms ; Artifacts ; Biomechanical Phenomena ; *Electroencephalography ; Electromyography ; Equipment Design ; Extremities/*physiology ; Female ; Functional Laterality/physiology ; Humans ; Leg/physiology ; Male ; Middle Aged ; Models, Statistical ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Vision, Ocular/physiology ; Walking/*physiology ; Young Adult ; },
abstract = {Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis.},
}
@article {pmid22436666,
year = {2012},
author = {Puthirasigamany, M and van Beijeren, P and Kreis, P},
title = {CHROM[2]--a method to enhance the dynamic binding capacity, yield and productivity of a chromatographic column.},
journal = {Journal of chromatography. A},
volume = {1236},
number = {},
pages = {139-147},
doi = {10.1016/j.chroma.2012.03.015},
pmid = {22436666},
issn = {1873-3778},
mesh = {Adsorption ; Chromatography/*methods ; *Membranes, Artificial ; *Models, Theoretical ; Pressure ; Proteins/isolation &amp; purification ; },
abstract = {Therapeutic proteins are biotechnological products with a fast-growing market. Despite the rapid development of available process technologies, a bottleneck in production capacities is still present due to limitations in the associated downstream process, particularly within chromatographic purification steps. Membrane chromatography has been introduced as a promising alternative for conventional chromatography because it allows for higher throughputs but it does not deliver comparable dynamic binding capacities. To combine the strengths of the two technologies, the so-called "CHROM(2) concepts" are introduced, which merge conventional chromatography with membrane adsorption. The serial connection of a large conventional chromatographic column followed by a small membrane chromatography unit enables to combine the strength of both the individual technologies. The larger column delivers the required high binding capacity, whereas the rapid binding kinetics of membrane chromatography sharpens the breakthrough curve. Furthermore applied higher velocities do not result in poor breakthrough performance since the membrane chromatography is able to compensate for the poor column breakthrough performance. In comparison to column chromatography, the CHROM(2) setup exploits the full column capacity and delivers higher productivities and yields.},
}
@article {pmid22435802,
year = {2012},
author = {Nam, CS and Woo, J and Bahn, S},
title = {Severe motor disability affects functional cortical integration in the context of brain-computer interface (BCI) use.},
journal = {Ergonomics},
volume = {55},
number = {5},
pages = {581-591},
doi = {10.1080/00140139.2011.647095},
pmid = {22435802},
issn = {1366-5847},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Brain/*physiopathology ; Cerebral Palsy/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography ; Female ; Humans ; Male ; Severity of Illness Index ; Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {UNLABELLED: The purpose of this study was to investigate cortical interaction between brain regions in people with and without severe motor disability during brain-computer interface (BCI) operation through coherence analysis. Eighteen subjects, including six patients with cerebral palsy (CP) and three patients with amyotrophic lateral sclerosis (ALS), participated. The results showed (1) the existence of BCI performance difference caused by severe motor disability; (2) different coherence patterns between participants with and without severe motor disability during BCI operation and (3) effects of motor disability on cortical connections varying in the brain regions for the different frequency bands, indicating reduced cortical differentiation and specialisation. Participants with severe neuromuscular impairments, as compared with the able-bodied group, recruited more cortical regions to compensate for the difficulties caused by their motor disability, reflecting a less efficient operating strategy for the BCI task. This study demonstrated that coherence analysis can be applied to examine the ways cortical networks cooperate with each other during BCI tasks.<br><br>PRACTITIONER SUMMARY: Few studies have investigated the electrophysiological underpinnings of differences in BCI performance. This study contributes by assessing neuronal synchrony among brain regions. Our findings revealed that severe motor disability causes more cortical areas to be recruited to perform the BCI task, indicating reduced cortical differentiation and specialisation.},
}
@article {pmid22431526,
year = {2013},
author = {Suk, HI and Lee, SW},
title = {A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {35},
number = {2},
pages = {286-299},
doi = {10.1109/TPAMI.2012.69},
pmid = {22431526},
issn = {1939-3539},
mesh = {Algorithms ; *Artificial Intelligence ; Bayes Theorem ; *Brain-Computer Interfaces ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.},
}
@article {pmid22430510,
year = {2012},
author = {Pouratian, N},
title = {Editorial note on: On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post trial follow-up.},
journal = {Spinal cord},
volume = {50},
number = {9},
pages = {716},
doi = {10.1038/sc.2012.29},
pmid = {22430510},
issn = {1476-5624},
mesh = {*Brain-Computer Interfaces ; Electroencephalography/*methods ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; *Quality of Life ; Spinal Cord Injuries/*physiopathology ; *User-Computer Interface ; },
}
@article {pmid22427488,
year = {2012},
author = {Sussillo, D and Nuyujukian, P and Fan, JM and Kao, JC and Stavisky, SD and Ryu, S and Shenoy, K},
title = {A recurrent neural network for closed-loop intracortical brain-machine interface decoders.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026027},
pmid = {22427488},
issn = {1741-2552},
support = {R01 NS054283/NS/NINDS NIH HHS/United States ; R01-NS054283/NS/NINDS NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; R01 NS076460/NS/NINDS NIH HHS/United States ; 1DP1OD006409/OD/NIH HHS/United States ; DP1 HD075623/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/physiology ; Artificial Intelligence ; Biomechanical Phenomena ; Cerebral Cortex/physiology ; Computer Systems ; Hand/physiology ; Linear Models ; Macaca mulatta ; Male ; *Neural Networks, Computer ; *Neural Prostheses ; Normal Distribution ; Prosthesis Design ; Psychomotor Performance/physiology ; Software ; *User-Computer Interface ; },
abstract = {Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.},
}
@article {pmid22423549,
year = {2012},
author = {Hsu, WY and Li, YC and Hsu, CY and Liu, CT and Chiu, HW},
title = {Application of multiscale amplitude modulation features and fuzzy C-means to brain-computer interface.},
journal = {Clinical EEG and neuroscience},
volume = {43},
number = {1},
pages = {32-38},
doi = {10.1177/1550059411429528},
pmid = {22423549},
issn = {1550-0594},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; *Fuzzy Logic ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This study proposed a recognized system for electroencephalogram (EEG) data classification. In addition to the wavelet-based amplitude modulation (AM) features, the fuzzy c-means (FCM) clustering is used for the discriminant of left finger lifting and resting. The features are extracted from discrete wavelet transform (DWT) data with the AM method. The FCM is then applied to recognize extracted features. Compared with band power features, k-means clustering, and linear discriminant analysis (LDA) classifier, the results indicate that the proposed method is satisfactory in applications of brain-computer interface (BCI).},
}
@article {pmid22422436,
year = {2013},
author = {Boyce, SJ and McAdams, HP and Ravin, CE and Patz, EF and Washington, L and Martinez, S and Koweek, L and Samei, E},
title = {Preliminary evaluation of biplane correlation (BCI) stereographic imaging for lung nodule detection.},
journal = {Journal of digital imaging},
volume = {26},
number = {1},
pages = {109-114},
pmid = {22422436},
issn = {1618-727X},
mesh = {Humans ; Imaging, Three-Dimensional ; Predictive Value of Tests ; ROC Curve ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted/*methods ; Radiography, Thoracic/*methods ; Sensitivity and Specificity ; Solitary Pulmonary Nodule/*diagnostic imaging ; Tomography, X-Ray Computed/*methods ; },
abstract = {A biplane correlation (BCI) imaging system obtains images that can be viewed in stereo, thereby minimizing overlapping structures. This study investigated whether using stereoscopic visualization provides superior lung nodule detection compared to standard postero-anterior (PA) image display. Images were acquired at two oblique views of ±3° as well as at a standard PA position from 60 patients. Images were processed using optimal parameters and displayed on a stereoscopic display. The PA image was viewed in the standard format, while the oblique views were paired to provide a stereoscopic view of the subject. A preliminary observer study was performed with four radiologists who viewed and scored the PA image then viewed and scored the BCI stereoscopic image. The BCI stereoscopic viewing of lung nodules resulted in 71 % sensitivity and 0.31 positive predictive value (PPV) index compared to PA results of 86 % sensitivity and 0.26 PPV index. The sensitivity for lung nodule detection with the BCI stereoscopic system was reduced by 15 %; however, the total number of false positives reported was reduced by 35 % resulting in an improved PPV index of 20 %. The preliminary results indicate observer dependency in terms of relative advantage of either system in the detection of lung nodules, but overall equivalency of the two methods with promising potential for BCI as an adjunct diagnostic technique.},
}
@article {pmid22422212,
year = {2012},
author = {Schaffelhofer, S and Scherberger, H},
title = {A new method of accurate hand- and arm-tracking for small primates.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026025},
doi = {10.1088/1741-2560/9/2/026025},
pmid = {22422212},
issn = {1741-2552},
mesh = {Animals ; Arm/anatomy &amp; histology/innervation/*physiology ; Biomechanical Phenomena ; Conditioning, Operant/physiology ; Electromagnetic Fields ; Electrophysiology ; Female ; Fingers/innervation/physiology ; Hand/anatomy &amp; histology/innervation/*physiology ; Hand Strength/physiology ; Macaca mulatta ; Microelectrodes ; Models, Anatomic ; Neurons/physiology ; Primates/anatomy &amp; histology/*physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Software ; },
abstract = {The investigation of grasping movements in cortical motor areas depends heavily on the measurement of hand kinematics. Currently used methods for small primates need either a large number of sensors or provide insufficient accuracy. Here, we present both a novel glove based on electromagnetic tracking sensors that can operate at a rate of 100 Hz and a new modeling method that allows to monitor 27 degrees of freedom (DOF) of the hand and arm using only seven sensors. A rhesus macaque was trained to wear the glove while performing precision and power grips during a delayed grasping task in the dark without noticeable hindrance. During five recording sessions all 27 joint angles and their positions could be tracked reliably. Furthermore, the field generator did not interfere with electrophysiological recordings below 1 kHz and did not affect single-cell separation. Measurements with the glove proved to be accurate during static and dynamic testing (mean absolute error below 2° and 3°, respectively). This makes the glove a suitable solution for characterizing electrophysiological signals with respect to hand grasping and in particular for brain-machine interface applications.},
}
@article {pmid22416015,
year = {2012},
author = {Clarkson, CE and Erwin, RM and Riscassi, A},
title = {The use of novel biomarkers to determine dietary mercury accumulation in nestling waterbirds.},
journal = {Environmental toxicology and chemistry},
volume = {31},
number = {5},
pages = {1143-1148},
doi = {10.1002/etc.1767},
pmid = {22416015},
issn = {1552-8618},
mesh = {Animals ; Biomarkers/*analysis ; *Birds ; Diet/*veterinary ; Feathers/*chemistry ; Food Chain ; Mercury/analysis/*pharmacokinetics ; New York ; Virginia ; },
abstract = {Mercury (Hg) depuration into growing feathers is a well-studied phenomenon in waterbirds. Although the kinetics of Hg excretion in relation to molt and diet has been studied extensively, the relationship between the individual nutritional condition of nestlings and dietary Hg accumulation has not been investigated. In the present study, a body-condition index (BCI) and nutritional condition index (NCI) for nestlings of two waterbird species occupying different trophic positions on the aquatic food web were determined and used to predict Hg accumulation through diet. Candidate models consisting of these indices and nestling age were compared using Akaike's information criterion corrected for small sample sizes. For both species, the top-performing model contained the sole parameter of nutritional condition index (NCI). The relationship between Hg and NCI was stronger in the species foraging higher on the trophic web, which experienced higher rates of Hg depuration into feathers. Models containing BCI could not be discounted (AICc < 2) for one of the species and the utility of this index is discussed.},
}
@article {pmid22414728,
year = {2012},
author = {Saa, JF and Çetin, M},
title = {A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026020},
doi = {10.1088/1741-2560/9/2/026020},
pmid = {22414728},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; Artificial Intelligence ; Brain Mapping/methods ; Electroencephalography/*classification/*statistics &amp; numerical data ; Humans ; Imagination/*physiology ; Models, Neurological ; Models, Statistical ; Normal Distribution ; Psychomotor Performance/*physiology ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.},
}
@article {pmid22414683,
year = {2012},
author = {Zhang, Y and Zhao, Q and Jin, J and Wang, X and Cichocki, A},
title = {A novel BCI based on ERP components sensitive to configural processing of human faces.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026018},
doi = {10.1088/1741-2560/9/2/026018},
pmid = {22414683},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Analysis of Variance ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography ; Electronic Data Processing ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; *Face ; Humans ; Linear Models ; Male ; Middle Aged ; Online Systems ; Photic Stimulation ; Reproducibility of Results ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {This study introduces a novel brain-computer interface (BCI) based on an oddball paradigm using stimuli of facial images with loss of configural face information (e.g., inversion of face). To the best of our knowledge, till now the configural processing of human faces has not been applied to BCI but widely studied in cognitive neuroscience research. Our experiments confirm that the face-sensitive event-related potential (ERP) components N170 and vertex positive potential (VPP) have reflected early structural encoding of faces and can be modulated by the configural processing of faces. With the proposed novel paradigm, we investigate the effects of ERP components N170, VPP and P300 on target detection for BCI. An eight-class BCI platform is developed to analyze ERPs and evaluate the target detection performance using linear discriminant analysis without complicated feature extraction processing. The online classification accuracy of 88.7% and information transfer rate of 38.7 bits min(-1) using stimuli of inverted faces with only single trial suggest that the proposed paradigm based on the configural processing of faces is very promising for visual stimuli-driven BCI applications.},
}
@article {pmid22414615,
year = {2012},
author = {Zhang, D and Song, H and Xu, H and Wu, W and Gao, S and Hong, B},
title = {An N200 speller integrating the spatial profile for the detection of the non-control state.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026016},
doi = {10.1088/1741-2560/9/2/026016},
pmid = {22414615},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Movement/physiology ; Online Systems ; Photic Stimulation ; ROC Curve ; Reproducibility of Results ; *User-Computer Interface ; Young Adult ; },
abstract = {The N200 speller is a recently developed non-flashing visual brain-computer interface (BCI) paradigm utilizing the overt attention modulation effects on motion-onset visual evoked potentials (mVEP). In this study, a novel algorithm is proposed and applied in an online N200 speller. The proposed algorithm integrates the spatial information of the speller matrix to provide a more precise description of the mVEP response patterns, which is defined as the 'spatial profile'. More importantly, only control state data are used in the algorithm to train a classifier that nonetheless can detect the non-control state effectively. Compared to an algorithm with similar structure but not using the spatial profile information, the proposed algorithm shows significantly higher performance for the recognition of the non-control state while achieving a comparable performance for classifying different control states. Offline and online classification results show that the proposed N200 speller is a promising step toward a practical, online non-flashing BCI system for daily use.},
}
@article {pmid22414111,
year = {2012},
author = {Power, SD and Kushki, A and Chau, T},
title = {Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI.},
journal = {BMC research notes},
volume = {5},
number = {},
pages = {141},
pmid = {22414111},
issn = {1756-0500},
mesh = {Adult ; *Brain-Computer Interfaces ; Discrimination, Psychological/*physiology ; Female ; Humans ; Male ; Mathematics ; Music ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; Singing ; Spectroscopy, Near-Infrared/*methods ; Surveys and Questionnaires ; Young Adult ; },
abstract = {BACKGROUND: Near-infrared spectroscopy (NIRS) is an optical imaging technology that has recently been investigated for use in a safe, non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. To date, most NIRS-BCI studies have attempted to discriminate two mental states (e.g., a mental task and rest), which could potentially lead to a two-choice BCI system. In this study, we attempted to automatically differentiate three mental states - specifically, intentional activity due to 1) a mental arithmetic (MA) task and 2) a mental singing (MS) task, and 3) an unconstrained, "no-control (NC)" state - to investigate the feasibility of a three-choice system-paced NIRS-BCI.<br><br>RESULTS: Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations while 7 able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a ten-dimensional feature set, an overall classification accuracy of 56.2% was achieved for the MA vs. MS vs. NC classification problem and all individual participant accuracies significantly exceeded chance (i.e., 33%). However, as anticipated based on results of previous work, the three-class discrimination was unsuccessful for three participants due to the ineffectiveness of the mental singing task. Excluding these three participants increases the accuracy rate to 62.5%. Even without training, three of the remaining four participants achieved accuracies approaching 70%, the value often cited as being necessary for effective BCI communication.<br><br>CONCLUSIONS: These results are encouraging and demonstrate the potential of a three-state system-paced NIRS-BCI with two intentional control states corresponding to mental arithmetic and mental singing.},
}
@article {pmid22412829,
year = {2012},
author = {Zhang, Y and Xu, P and Liu, T and Hu, J and Zhang, R and Yao, D},
title = {Multiple frequencies sequential coding for SSVEP-based brain-computer interface.},
journal = {PloS one},
volume = {7},
number = {3},
pages = {e29519},
pmid = {22412829},
issn = {1932-6203},
mesh = {Algorithms ; Brain/*physiology ; *Computer Simulation ; Electroencephalography/methods ; *Evoked Potentials, Visual ; Humans ; Photic Stimulation ; Reproducibility of Results ; },
abstract = {BACKGROUND: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has become one of the most promising modalities for a practical noninvasive BCI system. Owing to both the limitation of refresh rate of liquid crystal display (LCD) or cathode ray tube (CRT) monitor, and the specific physiological response property that only a very small number of stimuli at certain frequencies could evoke strong SSVEPs, the available frequencies for SSVEP stimuli are limited. Therefore, it may not be enough to code multiple targets with the traditional frequencies coding protocols, which poses a big challenge for the design of a practical SSVEP-based BCI. This study aimed to provide an innovative coding method to tackle this problem.<br><br>In this study, we present a novel protocol termed multiple frequencies sequential coding (MFSC) for SSVEP-based BCI. In MFSC, multiple frequencies are sequentially used in each cycle to code the targets. To fulfill the sequential coding, each cycle is divided into several coding epochs, and during each epoch, certain frequency is used. Obviously, different frequencies or the same frequency can be presented in the coding epochs, and the different epoch sequence corresponds to the different targets. To show the feasibility of MFSC, we used two frequencies to realize four targets and carried on an offline experiment. The current study shows that: 1) MFSC is feasible and efficient; 2) the performance of SSVEP-based BCI based on MFSC can be comparable to some existed systems.<br><br>CONCLUSIONS/SIGNIFICANCE: The proposed protocol could potentially implement much more targets with the limited available frequencies compared with the traditional frequencies coding protocol. The efficiency of the new protocol was confirmed by real data experiment. We propose that the SSVEP-based BCI under MFSC might be a promising choice in the future.},
}
@article {pmid22410845,
year = {2012},
author = {Onose, G and Grozea, C and Anghelescu, A and Daia, C and Sinescu, CJ and Ciurea, AV and Spircu, T and Mirea, A and Andone, I and Spânu, A and Popescu, C and Mihăescu, AS and Fazli, S and Danóczy, M and Popescu, F},
title = {On the feasibility of using motor imagery EEG-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up.},
journal = {Spinal cord},
volume = {50},
number = {8},
pages = {599-608},
doi = {10.1038/sc.2012.14},
pmid = {22410845},
issn = {1476-5624},
mesh = {Adult ; *Brain-Computer Interfaces ; Calibration ; Chronic Disease ; Electroencephalography/*methods ; Feasibility Studies ; Feedback ; Female ; Follow-Up Studies ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Movement/physiology ; *Quality of Life ; Robotics/instrumentation ; Spinal Cord Injuries/*physiopathology ; *User-Computer Interface ; Young Adult ; },
abstract = {STUDY DESIGN: Survey and long-term clinical post-trial follow-up (interviews/correspondence) on nine chronic, post spinal cord injury (SCI) tetraplegics.<br><br>OBJECTIVE: To assess feasibility of the use of Electroencephalography-based Brain-Computer Interface (EEG-BCI) for reaching/grasping assistance in tetraplegics, through a robotic arm.<br><br>SETTINGS: Physical and (neuromuscular) Rehabilitation Medicine, Cardiology, Neurosurgery Clinic Divisions of TEHBA and UMPCD, in collaboration with 'Brain2Robot' (composed of the European Commission-funded Marie Curie Excellence Team by the same name, hosted by Fraunhofer Institute-FIRST), in the second part of 2008.<br><br>METHODS: Enrolled patients underwent EEG-BCI preliminary training and robot control sessions. Statistics entailed multiple linear regressions and cluster analysis. A follow-up-custom questionnaire based-including patients' perception of their EEG-BCI control capacity was continued up to 14 months after initial experiments.<br><br>RESULTS: EEG-BCI performance/calibration-phase classification accuracy averaged 81.0%; feedback training sessions averaged 70.5% accuracy for 7 subjects who completed at least one feedback training session; 7 (77.7%) of 9 subjects reported having felt control of the cursor; and 3 (33.3%) subjects felt that they were also controlling the robot through their movement imagination. No significant side effects occurred. BCI performance was positively correlated with beta (13-30 Hz) EEG spectral power density (coefficient 0.432, standardized coefficient 0.745, P-value=0.025); another possible influence was sensory AIS score (range: 0 min to 224 max, coefficient -0.177, standardized coefficient -0.512, P=0.089).<br><br>CONCLUSION: Limited but real potential for self-assistance in chronic tetraplegics by EEG-BCI-actuated mechatronic devices was found, which was mainly related to spectral density in the beta range positively (increasing therewith) and to AIS sensory score negatively.},
}
@article {pmid22410731,
year = {2012},
author = {Taghavi, H and Håkansson, B and Reinfeldt, S and Eeg-Olofsson, M and Akhshijan, S},
title = {Feedback analysis in percutaneous bone-conduction device and bone-conduction implant on a dry cranium.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {33},
number = {3},
pages = {413-420},
doi = {10.1097/MAO.0b013e3182487fc8},
pmid = {22410731},
issn = {1537-4505},
mesh = {Acoustic Stimulation ; Algorithms ; Animals ; Bone Conduction/*physiology ; Cadaver ; Ear/anatomy &amp; histology ; Feedback, Physiological/*physiology ; *Hearing Aids ; Humans ; Models, Anatomic ; Skull/*anatomy &amp; histology ; Sound ; },
abstract = {HYPOTHESIS: The bone-conduction implant (BCI) can use a higher gain setting without having feedback problems compared with a percutaneous bone-conduction device (PBCD).<br><br>BACKGROUND: The conventional PBCD, today, is a common treatment for patients with conductive hearing loss and single-sided deafness. However, there are minor drawbacks reported related to the percutaneous implant and specifically poor high-frequency gain. The BCI system is designed as an alternative to the percutaneous system because it leaves the skin intact and is less prone to fall into feedback oscillations, thus allowing more high-frequency gain.<br><br>METHODS: Loop gains of the Baha Classic 300 and the BCI were measured in the frequency range of 100 to 10,000 Hz attached to a Skull simulator and a dry cranium. The Baha and the BCI positions were investigated. The devices were adjusted to full-on gain.<br><br>RESULTS: It was found that the gain headroom using the BCI was generally 0 to 10 dB better at higher frequencies than using the Baha for a given mechanical output. More specifically, if the mechanical output of the devices were normalized at the cochlear level the improvement in gain headroom with the BCI versus the Baha were in the range of 10 to 30 dB.<br><br>CONCLUSION: Using a BCI, significantly higher gain setting can be used without feedback problems as compared with using a PBCD.},
}
@article {pmid22408601,
year = {2012},
author = {Flamary, R and Rakotomamonjy, A},
title = {Decoding Finger Movements from ECoG Signals Using Switching Linear Models.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {29},
pmid = {22408601},
issn = {1662-453X},
abstract = {One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42.},
}
@article {pmid22406226,
year = {2012},
author = {Hasan, BA and Gan, JQ},
title = {Hangman BCI: an unsupervised adaptive self-paced Brain-Computer Interface for playing games.},
journal = {Computers in biology and medicine},
volume = {42},
number = {5},
pages = {598-606},
doi = {10.1016/j.compbiomed.2012.02.004},
pmid = {22406226},
issn = {1879-0534},
mesh = {*Adaptation, Physiological ; Brain/*physiology ; *Game Theory ; Humans ; *Man-Machine Systems ; Models, Theoretical ; User-Computer Interface ; },
abstract = {This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True-False difference for all the classes and a reduction in the number of steps required to solve the problem.},
}
@article {pmid22406183,
year = {2012},
author = {Yu, Z and McKnight, TE and Ericson, MN and Melechko, AV and Simpson, ML and Morrison, B},
title = {Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.},
journal = {Nanomedicine : nanotechnology, biology, and medicine},
volume = {8},
number = {4},
pages = {419-423},
pmid = {22406183},
issn = {1549-9642},
support = {R01 EB006316/EB/NIBIB NIH HHS/United States ; R21 NS052794/NS/NINDS NIH HHS/United States ; 1R21NS052794/NS/NINDS NIH HHS/United States ; 1-R01EB006316/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Cell Culture Techniques ; Cells, Cultured ; Membrane Potentials/*physiology ; *Nanofibers ; *Nanotubes, Carbon ; Neuronal Plasticity/*physiology ; Neurons/cytology/*metabolism ; Rats ; },
abstract = {UNLABELLED: Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell.<br><br>FROM THE CLINICAL EDITOR: The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities.},
}
@article {pmid22405803,
year = {2012},
author = {Minati, L and Nigri, A and Rosazza, C and Bruzzone, MG},
title = {Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals.},
journal = {Medical engineering &amp; physics},
volume = {34},
number = {5},
pages = {650-658},
doi = {10.1016/j.medengphy.2012.02.004},
pmid = {22405803},
issn = {1873-4030},
mesh = {Adult ; Brain/blood supply/physiology ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Oxygen/blood ; Robotics/*methods ; Software ; *Thinking ; Vision, Ocular ; },
abstract = {Previous studies have demonstrated the possibility of using functional MRI to control a robot arm through a brain-machine interface by directly coupling haemodynamic activity in the sensory-motor cortex to the position of two axes. Here, we extend this work by implementing interaction at a more abstract level, whereby imagined actions deliver structured commands to a robot arm guided by a machine vision system. Rather than extracting signals from a small number of pre-selected regions, the proposed system adaptively determines at individual level how to map representative brain areas to the input nodes of a classifier network. In this initial study, a median action recognition accuracy of 90% was attained on five volunteers performing a game consisting of collecting randomly positioned coloured pawns and placing them into cups. The "pawn" and "cup" instructions were imparted through four mental imaginery tasks, linked to robot arm actions by a state machine. With the current implementation in MatLab language the median action recognition time was 24.3s and the robot execution time was 17.7s. We demonstrate the notion of combining haemodynamic brain-machine interfacing with computer vision to implement interaction at the level of high-level commands rather than individual movements, which may find application in future fMRI approaches relevant to brain-lesioned patients, and provide source code supporting further work on larger command sets and real-time processing.},
}
@article {pmid22399162,
year = {2012},
author = {Breitwieser, C and Kaiser, V and Neuper, C and Müller-Putz, GR},
title = {Stability and distribution of steady-state somatosensory evoked potentials elicited by vibro-tactile stimulation.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {50},
number = {4},
pages = {347-357},
pmid = {22399162},
issn = {1741-0444},
mesh = {Adult ; Electroencephalography/methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Fingers/innervation ; Humans ; Male ; Middle Aged ; Physical Stimulation/methods ; Signal Processing, Computer-Assisted ; Touch/*physiology ; User-Computer Interface ; Vibration ; },
abstract = {Steady-state somatosensory evoked potentials (SSSEPs) have been elicited applying vibro-tactile stimulation to all fingertips of the right hand. Nine healthy subjects participated in two sessions within this study. All fingers were stimulated 40 times with a 200-Hz carrier frequency modulated with a rectangular signal. The frequencies of the rectangular signal ranged between 17 and 35 Hz in 2 Hz steps. Relative band power tuning curves were calculated, introducing two different methods. Person-specific resonance-like frequencies were selected based on the data from the first session. The selected resonance-like frequencies were compared with the second session using an ANOVA for repeated measures to investigate the stability of SSSEPs over time. To determine, if SSSEPs can be classified with a classifier based on unseen data, an LDA classifier was trained with data from the first and applied to data from the second session. Person-specific resonance-like frequencies within a range from 19 to 29 Hz were found. The relative band power of the resonance-like frequencies did not differ significantly between the two sessions. Significant differences were found for the two methods and the used channels. SSSEPs were classified with a hit rate from 51 to 96 %.},
}
@article {pmid22389637,
year = {2011},
author = {Zhang, X and Liu, Y and Zhang, F and Ren, J and Sun, YL and Yang, Q and Huang, H},
title = {On Design and Implementation of Neural-Machine Interface for Artificial Legs.},
journal = {IEEE transactions on industrial informatics},
volume = {2011},
number = {99},
pages = {1},
pmid = {22389637},
issn = {1551-3203},
support = {R21 HD064968/HD/NICHD NIH HHS/United States ; R21 HD064968-02/HD/NICHD NIH HHS/United States ; },
abstract = {The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees' intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user's intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user's intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.},
}
@article {pmid22384491,
year = {2012},
author = {Martin, A and Sankar, T and Lipsman, N and Lozano, AM},
title = {Brain-machine interfaces for motor control: a guide for neuroscience clinicians.},
journal = {The Canadian journal of neurological sciences. Le journal canadien des sciences neurologiques},
volume = {39},
number = {1},
pages = {11-22},
doi = {10.1017/s0317167100012622},
pmid = {22384491},
issn = {0317-1671},
mesh = {Animals ; Brain/*physiology ; Humans ; Motor Activity/*physiology ; Nervous System Diseases/therapy ; *Neurosciences ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {With the growing interdependence between medicine and technology, the prospect of connecting machines to the human brain is rapidly being realized. The field of neuroprosthetics is transitioning from the proof of concept stage to the development of advanced clinical treatments. In one area of brain-machine interfaces (BMIs) related to the motor system, also termed 'motor neuroprosthetics', research successes with implanted microelectrodes in animals have demonstrated immense potential for restoring motor deficits. Early human trials have also begun, with some success but also highlighting several technical challenges. Here we review the concepts and anatomy underlying motor BMI designs, review their early use in clinical applications, and offer a framework to evaluate these technologies in order to predict their eventual clinical utility. Ultimately, we hope to help neuroscience clinicians understand and participate in this burgeoning field.},
}
@article {pmid22379493,
year = {2011},
author = {Daly, I and Nasuto, SJ and Warwick, K},
title = {Single tap identification for fast BCI control.},
journal = {Cognitive neurodynamics},
volume = {5},
number = {1},
pages = {21-30},
pmid = {22379493},
issn = {1871-4099},
abstract = {One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.},
}
@article {pmid22366333,
year = {2012},
author = {Gunduz, A and Brunner, P and Daitch, A and Leuthardt, EC and Ritaccio, AL and Pesaran, B and Schalk, G},
title = {Decoding covert spatial attention using electrocorticographic (ECoG) signals in humans.},
journal = {NeuroImage},
volume = {60},
number = {4},
pages = {2285-2293},
pmid = {22366333},
issn = {1095-9572},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856-10/EB/NIBIB NIH HHS/United States ; R01 EB006356-04/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; *Brain Mapping ; Electrodes, Implanted ; Electroencephalography ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Male ; Middle Aged ; Photic Stimulation ; },
abstract = {This study shows that electrocorticographic (ECoG) signals recorded from the surface of the brain provide detailed information about shifting of visual attention and its directional orientation in humans. ECoG allows for the identification of the cortical areas and time periods that hold the most information about covert attentional shifts. Our results suggest a transient distributed fronto-parietal mechanism for orienting of attention that is represented by different physiological processes. This neural mechanism encodes not only whether or not a subject shifts their attention to a location, but also the locus of attention. This work contributes to our understanding of the electrophysiological representation of attention in humans. It may also eventually lead to brain-computer interfaces (BCIs) that optimize user interaction with their surroundings or that allow people to communicate choices simply by shifting attention to them.},
}
@article {pmid22358499,
year = {2012},
author = {Andralojc, K and Srinivas, M and Brom, M and Joosten, L and de Vries, IJ and Eizirik, DL and Boerman, OC and Meda, P and Gotthardt, M},
title = {Obstacles on the way to the clinical visualisation of beta cells: looking for the Aeneas of molecular imaging to navigate between Scylla and Charybdis.},
journal = {Diabetologia},
volume = {55},
number = {5},
pages = {1247-1257},
pmid = {22358499},
issn = {1432-0428},
mesh = {Animals ; Humans ; Insulin-Secreting Cells/*diagnostic imaging/transplantation ; Islets of Langerhans Transplantation/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Mice ; Molecular Imaging/*methods ; Positron-Emission Tomography/methods ; Rats ; Tomography, Emission-Computed, Single-Photon/methods ; },
abstract = {For more than a decade, researchers have been trying to develop non-invasive imaging techniques for the in vivo measurement of viable pancreatic beta cells. However, in spite of intense research efforts, only one tracer for positron emission tomography (PET) imaging is currently under clinical evaluation. To many diabetologists it may remain unclear why the imaging world struggles to develop an effective method for non-invasive beta cell imaging (BCI), which could be useful for both research and clinical purposes. Here, we provide a concise overview of the obstacles and challenges encountered on the way to such BCI, in both native and transplanted islets. We discuss the major difficulties posed by the anatomical and cell biological features of pancreatic islets, as well as the chemical and physical limits of the main imaging modalities, with special focus on PET, SPECT and MRI. We conclude by indicating new avenues for future research in the field, based on several remarkable recent results.},
}
@article {pmid22355264,
year = {2011},
author = {Lee, HJ and Nam, Y and Koh, CS and Im, C and Seo, IS and Choi, S and Shin, HC},
title = {Odor-Dependent Hemodynamic Responses Measured with NIRS in the Main Olfactory Bulb of Anesthetized Rats.},
journal = {Experimental neurobiology},
volume = {20},
number = {4},
pages = {189-196},
pmid = {22355264},
issn = {2093-8144},
abstract = {In this study, we characterize the hemodynamic changes in the main olfactory bulb of anesthetized Sprague-Dawley (SD) rats with near-infrared spectroscopy (NIRS, ISS Imagent) during presentation of two different odorants. Odorants were presented for 10 seconds with clean air via an automatic odor stimulator. Odorants are: (i) plain air as a reference (Blank), (ii) 2-Heptanone (HEP), (iii) Isopropylbenzene (IB). Our results indicated that a plain air did not cause any change in the concentrations of oxygenated (Δ[HbO(2)]) and deoxygenated hemoglobin (Δ[Hbr]), but HEP and IB induced strong changes. Furthermore, these odor-specific changes had regional differences within the MOB. Our results suggest that NIRS technology might be a useful tool to identify of various odorants in a non-invasive manner using animals which has a superb olfactory system.},
}
@article {pmid22350501,
year = {2012},
author = {Mak, JN and McFarland, DJ and Vaughan, TM and McCane, LM and Tsui, PZ and Zeitlin, DJ and Sellers, EW and Wolpaw, JR},
title = {EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026014},
doi = {10.1088/1741-2560/9/2/026014},
pmid = {22350501},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Aged ; Algorithms ; Amyotrophic Lateral Sclerosis/*psychology ; Brain/*physiology ; Brain Mapping ; Data Interpretation, Statistical ; Persons with Disabilities ; Discriminant Analysis ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Forecasting ; Humans ; Least-Squares Analysis ; Linear Models ; Male ; Middle Aged ; Online Systems ; Photic Stimulation ; Reproducibility of Results ; Software ; Theta Rhythm/physiology ; *User-Computer Interface ; },
abstract = {The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for >60% of the variance in copy-spelling performance (p < 0.001, mean R(2) = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.},
}
@article {pmid22350439,
year = {2012},
author = {Samek, W and Vidaurre, C and Müller, KR and Kawanabe, M},
title = {Stationary common spatial patterns for brain-computer interfacing.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026013},
doi = {10.1088/1741-2560/9/2/026013},
pmid = {22350439},
issn = {1741-2552},
mesh = {Algorithms ; Artifacts ; Brain/*physiology ; Calibration ; Data Interpretation, Statistical ; Electrodes ; Electroencephalography/statistics &amp; numerical data ; Electromyography ; Electrooculography ; Foot/physiology ; Hand/physiology ; Humans ; Movement/physiology ; Muscle, Skeletal/physiology ; Reproducibility of Results ; *User-Computer Interface ; },
abstract = {Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.},
}
@article {pmid22350331,
year = {2012},
author = {Jin, J and Sellers, EW and Wang, X},
title = {Targeting an efficient target-to-target interval for P300 speller brain-computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {50},
number = {3},
pages = {289-296},
pmid = {22350331},
issn = {1741-0444},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; 1R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; 1R21 DC010470-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Longer target-to-target intervals (TTI) produce greater P300 event-related potential amplitude, which can increase brain-computer interface (BCI) classification accuracy and decrease the number of flashes needed for accurate character classification. However, longer TTIs requires more time for each trial, which will decrease the information transfer rate of BCI. In this paper, a P300 BCI using a 7 × 12 matrix explored new flash patterns (16-, 18- and 21-flash pattern) with different TTIs to assess the effects of TTI on P300 BCI performance. The new flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one (16-flash pattern), two (18-flash pattern), or three (21-flash pattern) non-target flashes between each target flashes. Online results showed that the 16-flash pattern yielded the lowest classification accuracy among the three patterns. The results also showed that the 18-flash pattern provides a significantly higher information transfer rate (ITR) than the 21-flash pattern; both patterns provide high ITR and high accuracy for all subjects.},
}
@article {pmid22349305,
year = {2012},
author = {Solis-Escalante, T and Müller-Putz, GR and Pfurtscheller, G and Neuper, C},
title = {Cue-induced beta rebound during withholding of overt and covert foot movement.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {123},
number = {6},
pages = {1182-1190},
doi = {10.1016/j.clinph.2012.01.013},
pmid = {22349305},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Cues ; Electroencephalography ; Female ; Foot/physiology ; Functional Laterality/physiology ; Humans ; Male ; Movement/*physiology ; Nerve Net/*physiology ; Physical Stimulation ; Reaction Time/physiology ; },
abstract = {OBJECTIVE: Beta rebound is the term for bursts of EEG activity in the beta band observable after movement or somatosensory stimulation. It is assumed to reflect an active inhibition process. Our aim was to investigate the differences in the beta rebound between movement termination and withholding of movement, and the withholding of overt and covert movement.<br><br>METHODS: Twenty healthy persons completed Go/NoGo experiments with real and imaginary foot movements (dorsiflexion of both feet). Only participants that presented a beta rebound were considered. Event-related (de)synchronization provided the time course of the beta rebound from a participant specific frequency band. Statistical analyses revealed the significant differences between pairs of conditions: motor execution Go vs. motor execution NoGo, and motor execution NoGo vs. motor imagery NoGo.<br><br>RESULTS: The beta rebound is stronger and lasts longer after termination of movement than during withholding of a motor response (9 participants). Withholding of overt movement generates a stronger, longer, and more widespread beta rebound than the withholding of imaginary movement (7 participants). The beta rebound is more common after termination (16/16) and withholding of real movement (12/16) than during withholding of imaginary movements (7/16).<br><br>CONCLUSIONS: These phenomena share a common origin and a common frequency band. Their functional meaning is assumed to be the same, although there are differences in time span and intensity of the beta ERS.<br><br>SIGNIFICANCE: First direct comparison of the beta rebound between motor execution and motor withholding, as well as withholding of overt and covert foot movement. A beta rebound also occurs during withholding of a motor task, and it is more common and strong for overt movement than for covert movement.},
}
@article {pmid22348825,
year = {2012},
author = {Wu, B and Yang, F and Zhang, J and Wang, Y and Zheng, X and Chen, W},
title = {A frequency-temporal-spatial method for motor-related electroencephalography pattern recognition by comprehensive feature optimization.},
journal = {Computers in biology and medicine},
volume = {42},
number = {4},
pages = {353-363},
doi = {10.1016/j.compbiomed.2011.11.014},
pmid = {22348825},
issn = {1879-0534},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/physiology ; Male ; Motor Skills/physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Either imagined or actual movements lead to a combination of electroencephalography signals with distinctive frequency, temporal and spatial characteristics, which correspond to various motor-related neural activities. This frequency-temporal-spatial pattern is the key of motor intention decoding which is the basis of brain-computer interfaces by motor imagery. We present a new method for motor-related electroencephalography recognition which comprehensively optimizes the frequency-time-space features in a user-specific way. The recognition work focuses on three points: proper time and frequency domain segmentation, spatial optimization based on common spatial pattern filters and feature importance evaluation. We show that by combining the advantages of these optimizational methods, the proposed algorithm effectively improves motor task classification, and the recognized signal chanracteristics can be used to visualize the motor related electroencephalography patterns under different conditions.},
}
@article {pmid22347153,
year = {2012},
author = {Zhang, H and Guan, C and Ang, KK and Wang, C},
title = {BCI Competition IV - Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection.},
journal = {Frontiers in neuroscience},
volume = {6},
number = {},
pages = {7},
pmid = {22347153},
issn = {1662-453X},
abstract = {Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set.},
}
@article {pmid22344951,
year = {2012},
author = {Pei, X and Hill, J and Schalk, G},
title = {Silent communication: toward using brain signals.},
journal = {IEEE pulse},
volume = {3},
number = {1},
pages = {43-46},
doi = {10.1109/MPUL.2011.2175637},
pmid = {22344951},
issn = {2154-2317},
support = {EB000856/EB/NIBIB NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Brain Waves/*physiology ; Humans ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {From the 1980s movie Firefox to the more recent Avatar, popular science fiction has speculated about the possibility of a persons thoughts being read directly from his or her brain. Such braincomputer interfaces (BCIs) might allow people who are paralyzed to communicate with and control their environment, and there might also be applications in military situations wherever silent user-to-user communication is desirable. Previous studies have shown that BCI systems can use brain signals related to movements and movement imagery or attention-based character selection. Although these systems have successfully demonstrated the possibility to control devices using brain function, directly inferring which word a person intends to communicate has been elusive. A BCI using imagined speech might provide such a practical, intuitive device. Toward this goal, our studies to date addressed two scientific questions: (1) Can brain signals accurately characterize different aspects of speech? (2) Is it possible to predict spoken or imagined words or their components using brain signals?},
}
@article {pmid22344950,
year = {2012},
author = {Fifer, MS and Acharya, S and Benz, HL and Mollazadeh, M and Crone, NE and Thakor, NV},
title = {Toward electrocorticographic control of a dexterous upper limb prosthesis: building brain-machine interfaces.},
journal = {IEEE pulse},
volume = {3},
number = {1},
pages = {38-42},
pmid = {22344950},
issn = {2154-2317},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS040596-10/NS/NINDS NIH HHS/United States ; 3R01NS040596-09S1/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Artificial Limbs ; Brain Waves/*physiology ; Cerebral Cortex/*physiology ; Electrodes, Implanted ; *Electroencephalography ; Humans ; Movement/*physiology ; Upper Extremity/physiology ; *User-Computer Interface ; },
abstract = {One of the most exciting and compelling areas of research and development is building brain machine interfaces (BMIs) for controlling prosthetic limbs. Prosthetic limb technology is advancing rapidly, and the modular prosthetic limb (MPL) of the Johns Hopkins University/ Applied Physics Laboratory (JHU/APL) permits actuation with 17 degrees of freedom in 26 articulating joints. There are many signals from the brain that can be leveraged, including the spiking rates of neurons in the cortex, electrocorticographic (ECoG) signals from the surface of the cortex, and electroencephalographic (EEG) signals from the scalp. Unlike microelectrodes that record spikes, ECoG does not penetrate the cortex and has a higher spatial specificity, signal-to-noise ratio, and bandwidth than EEG signals. We have implemented an ECoG-based system for controlling the MPL in the Johns Hopkins Hospital Epilepsy Monitoring Unit, where patients are implanted with ECoG electrode grids for clinical seizure mapping and asked to perform various recorded finger or grasp movements. We have shown that low-frequency local motor potentials (LMPs) and ECoG power in the high gamma frequency (70,150 Hz) range correlate well with grasping parameters, and they stand out as good candidate features for closed-loop control of the MPL.},
}
@article {pmid22344949,
year = {2012},
author = {Contreras-Vidal, J and Presacco, A and Agashe, H and Paek, A},
title = {Restoration of whole body movement: toward a noninvasive brain-machine interface system.},
journal = {IEEE pulse},
volume = {3},
number = {1},
pages = {34-37},
pmid = {22344949},
issn = {2154-2317},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01NS075889/NS/NINDS NIH HHS/United States ; },
mesh = {*Artificial Limbs ; Brain/*physiology ; Electroencephalography/methods ; Female ; Humans ; Male ; *User-Computer Interface ; Walking/*physiology ; },
abstract = {This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.},
}
@article {pmid22336293,
year = {2012},
author = {Schmidt, NM and Blankertz, B and Treder, MS},
title = {Online detection of error-related potentials boosts the performance of mental typewriters.},
journal = {BMC neuroscience},
volume = {13},
number = {},
pages = {19},
pmid = {22336293},
issn = {1471-2202},
mesh = {Adult ; Analysis of Variance ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Online Systems ; Psychomotor Performance/*physiology ; ROC Curve ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; *Writing ; Young Adult ; },
abstract = {BACKGROUND: Increasing the communication speed of brain-computer interfaces (BCIs) is a major aim of current BCI-research. The idea to automatically detect error-related potentials (ErrPs) in order to veto erroneous decisions of a BCI has been existing for more than one decade, but this approach was so far little investigated in online mode.<br><br>METHODS: In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller.<br><br>RESULTS: Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection was largest for participants with low spelling accuracy.<br><br>CONCLUSION: The performance of BCIs can be increased by using an automatic error detection mechanism. The benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies compared to healthy people.},
}
@article {pmid22335577,
year = {2012},
author = {Nicolini, A and Ferrari, P and Fallahi, P and Antonelli, A},
title = {An iron regulatory gene signature in breast cancer: more than a prognostic genetic profile?.},
journal = {Future oncology (London, England)},
volume = {8},
number = {2},
pages = {131-134},
doi = {10.2217/fon.11.148},
pmid = {22335577},
issn = {1744-8301},
abstract = {Miller LD, Coffman LG, Chou JW et al. An iron regulatory gene signature predicts outcome in breast cancer. Cancer Res. 71(21), 6728-6737 (2011). In breast cancer, recent progress in technology has enabled us to define different prognostic genetic signatures. Based upon them, breast tumors have been grouped into the four principal categories: basal-like or triple-negative, erbB2-positive, normal-like, and luminal type (A and B); with luminal types sharing the expression of estrogen receptor- and/or progesterone receptor-related genes and, basal-like and erbB2-positive subgroups associated with worse prognosis. So far, Oncotype DX(®) (Genomic Health Inc., Redwood City, CA, USA), Mammaprint(®) (Agendia Inc, Huntington Beach, CA, USA), the Breast Cancer Index(®) (BCI, Biotheranostics, San Diego, CA, USA) and PAM50 (Expression Analysis Inc., Durham, NC, USA) are the only multigene assays that have been marketed in North America and Europe. However, any genetic signature assay still has to gain acceptance as a validated assay before introduction into current clinical practice. This study describes an iron regulatory gene signature (IRGS) in breast cancer associated with clinical outcome. Within the molecular luminal type, the IRGS provides prognostic information similar to Oncotype DX and gene sets selected to assess proliferation. In spite of this, it is relevant that two complementary pathways that are regulatory of iron metabolism - the iron export (Fp/HAMP) and the iron import (TFRC/HFE) gene dyads - were embedded in the IRGS gene set and were associated with clinical outcome as well. Differences in metabolic pathways between cancer and normal cells have been widely described, and potential applications for more refined therapy have been proposed by expanding genetic signature assessment technology to concomitant metabolic pathways investigation. Consistent with this, it is reasonable to imagine that the iron-export and the iron-import gene dyads will be considered potential targets for treatment of breast cancer patients expressing the IRGS genes.},
}
@article {pmid22333135,
year = {2012},
author = {Hill, NJ and Schölkopf, B},
title = {An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026011},
pmid = {22333135},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; Attention/*physiology ; Brain/*physiology ; Cues ; Data Interpretation, Statistical ; Electroencephalography/statistics &amp; numerical data ; Electrooculography ; Evoked Potentials/physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Fixation, Ocular ; Humans ; Male ; Online Systems ; Photic Stimulation ; Psychomotor Performance/physiology ; Software ; *User-Computer Interface ; Young Adult ; },
abstract = {We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users-for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare 'oddball' stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.},
}
@article {pmid22328184,
year = {2012},
author = {Hanson, TL and Ómarsson, B and O'Doherty, JE and Peikon, ID and Lebedev, MA and Nicolelis, MA},
title = {High-side digitally current controlled biphasic bipolar microstimulator.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {3},
pages = {331-340},
pmid = {22328184},
issn = {1558-0210},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; RC1HD063390/HD/NICHD NIH HHS/United States ; RC1 HD063390/HD/NICHD NIH HHS/United States ; DP1OD006798/OD/NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; },
mesh = {Analog-Digital Conversion ; Animals ; Arm/innervation/physiology ; Artifacts ; Cerebral Cortex/*physiology ; Electric Stimulation/adverse effects/*instrumentation ; Electrodes, Implanted/adverse effects ; Electromyography ; Electronics ; Equipment Design ; Internet ; Macaca mulatta ; Movement/physiology ; Nanotechnology ; Nerve Tissue/*physiology ; Software ; },
abstract = {Electrical stimulation of nervous tissue has been extensively used as both a tool in experimental neuroscience research and as a method for restoring of neural functions in patients suffering from sensory and motor disabilities. In the central nervous system, intracortical microstimulation (ICMS) has been shown to be an effective method for inducing or biasing perception, including visual and tactile sensation. ICMS also holds promise for enabling brain-machine-brain interfaces (BMBIs) by directly writing information into the brain. Here we detail the design of a high-side, digitally current-controlled biphasic, bipolar microstimulator, and describe the validation of the device in vivo. As many applications of this technique, including BMBIs, require recording as well as stimulation, we pay careful attention to isolation of the stimulus channels and parasitic current injection. With the realized device and standard recording hardware-without active artifact rejection-we are able to observe stimulus artifacts of less than 2 ms in duration.},
}
@article {pmid22326993,
year = {2012},
author = {Milekovic, T and Ball, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Error-related electrocorticographic activity in humans during continuous movements.},
journal = {Journal of neural engineering},
volume = {9},
number = {2},
pages = {026007},
doi = {10.1088/1741-2560/9/2/026007},
pmid = {22326993},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Data Interpretation, Statistical ; Electrodes ; Electrodes, Implanted ; *Electroencephalography ; Electromyography ; Epilepsy/physiopathology/surgery ; Female ; Fourier Analysis ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Online Systems ; Psychomotor Performance ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Somatosensory Cortex/physiology ; *User-Computer Interface ; Video Games ; },
abstract = {Brain-machine interface (BMI) devices make errors in decoding. Detecting these errors online from neuronal activity can improve BMI performance by modifying the decoding algorithm and by correcting the errors made. Here, we study the neuronal correlates of two different types of errors which can both be employed in BMI: (i) the execution error, due to inaccurate decoding of the subjects' movement intention; (ii) the outcome error, due to not achieving the goal of the movement. We demonstrate that, in electrocorticographic (ECoG) recordings from the surface of the human brain, strong error-related neural responses (ERNRs) for both types of errors can be observed. ERNRs were present in the low and high frequency components of the ECoG signals, with both signal components carrying partially independent information. Moreover, the observed ERNRs can be used to discriminate between error types, with high accuracy (≥83%) obtained already from single electrode signals. We found ERNRs in multiple cortical areas, including motor and somatosensory cortex. As the motor cortex is the primary target area for recording control signals for a BMI, an adaptive motor BMI utilizing these error signals may not require additional electrode implants in other brain areas.},
}
@article {pmid22325364,
year = {2012},
author = {Shih, JJ and Krusienski, DJ and Wolpaw, JR},
title = {Brain-computer interfaces in medicine.},
journal = {Mayo Clinic proceedings},
volume = {87},
number = {3},
pages = {268-279},
pmid = {22325364},
issn = {1942-5546},
mesh = {*Brain/physiology ; Electrodes, Implanted ; Electroencephalography ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.},
}
@article {pmid22323675,
year = {2012},
author = {McCarthy-Jones, S},
title = {Taking back the brain: could neurofeedback training be effective for relieving distressing auditory verbal hallucinations in patients with schizophrenia?.},
journal = {Schizophrenia bulletin},
volume = {38},
number = {4},
pages = {678-682},
pmid = {22323675},
issn = {1745-1701},
mesh = {Brain-Computer Interfaces ; Electroencephalography ; Hallucinations/etiology/*therapy ; Humans ; Neural Pathways ; Neurofeedback/*methods ; Parietal Lobe ; Schizophrenia/complications/*therapy ; Temporal Lobe ; },
abstract = {Progress in identifying the neural correlates of auditory verbal hallucinations (AVHs) experienced by patients with schizophrenia has not fulfilled its promise to lead to new methods of treatments. Given the existence of a large number of such patients who have AVHs that are refractory to traditional treatments, there is the urgent need for the development of new effective interventions. This article proposes that the technique of neurofeedback may be an appropriate method to allow the translation of pure research findings from AVH-research into a clinical intervention. Neurofeedback is a method through which individuals can self-regulate their neural activity in specific neural regions/frequencies, following operant conditioning of their intentional manipulation of visually presented real-time feedback of their neural activity. Four empirically testable hypotheses are proposed as to how neurofeedback may be employed to therapeutic effect in patients with AVHs.},
}
@article {pmid22319464,
year = {2011},
author = {Kreilinger, A and Kaiser, V and Breitwieser, C and Williamson, J and Neuper, C and Müller-Putz, GR},
title = {Switching between Manual Control and Brain-Computer Interface Using Long Term and Short Term Quality Measures.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {147},
pmid = {22319464},
issn = {1662-453X},
abstract = {Assistive devices for persons with limited motor control translate or amplify remaining functions to allow otherwise impossible actions. These assistive devices usually rely on just one type of input signal which can be derived from residual muscle functions or any other kind of biosignal. When only one signal is used, the functionality of the assistive device can be reduced as soon as the quality of the provided signal is impaired. The quality can decrease in case of fatigue, lack of concentration, high noise, spasms, tremors, depending on the type of signal. To overcome this dependency on one input signal, a combination of more inputs should be feasible. This work presents a hybrid Brain-Computer Interface (hBCI) approach where two different input signals (joystick and BCI) were monitored and only one of them was chosen as a control signal at a time. Users could move a car in a game-like feedback application to collect coins and avoid obstacles via either joystick or BCI control. Both control types were constantly monitored with four different long term quality measures to evaluate the current state of the signals. As soon as the quality dropped below a certain threshold, a monitoring system would switch to the other control mode and vice versa. Additionally, short term quality measures were applied to check for strong artifacts that could render voluntary control impossible. These measures were used to prohibit actions carried out during times when highly uncertain signals were recorded. The switching possibility allowed more functionality for the users. Moving the car was still possible even after one control mode was not working any more. The proposed system serves as a basis that shows how BCI can be used as an assistive device, especially in combination with other assistive technology.},
}
@article {pmid22310482,
year = {2012},
author = {Abibullaev, B and An, J},
title = {Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms.},
journal = {Medical engineering &amp; physics},
volume = {34},
number = {10},
pages = {1394-1410},
doi = {10.1016/j.medengphy.2012.01.002},
pmid = {22310482},
issn = {1873-4030},
mesh = {Adult ; *Algorithms ; *Artificial Intelligence ; Cognition/*physiology ; Discriminant Analysis ; Female ; Frontal Lobe/*physiology ; *Hemodynamics ; Humans ; Male ; Neural Networks, Computer ; Spectroscopy, Near-Infrared ; Support Vector Machine ; *Wavelet Analysis ; },
abstract = {Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier.},
}
@article {pmid22306060,
year = {2012},
author = {Mirfeizi, M and Jafarabadi, MA and Toorzani, ZM and Mohammadi, SM and Azad, MD and Mohammadi, AV and Teimori, Z},
title = {Feasibility, reliability and validity of the Iranian version of the Diabetes Quality of Life Brief Clinical Inventory (IDQOL-BCI).},
journal = {Diabetes research and clinical practice},
volume = {96},
number = {2},
pages = {237-247},
doi = {10.1016/j.diabres.2011.12.030},
pmid = {22306060},
issn = {1872-8227},
mesh = {Adult ; Aged ; Diabetes Mellitus/*physiopathology ; Female ; Humans ; Iran ; Male ; Middle Aged ; *Quality of Life ; Surveys and Questionnaires ; },
abstract = {AIMS: To validate and culturally adapt the Diabetes-specific Quality of Life Brief Clinical Inventory (DQOL-BCI) for the Iranian population.<br><br>METHODS: After translation - back translation, content validity was assessed utilizing a panel of six experts. Based on a sample of 180 diabetic patients referred to two Diabetics Clinic Centers from September to May 2011 in Karaj, Iran, construct validity via detecting the factor structure, and convergent and discriminant validity were evaluated by scale-item correlations and known group analyses. Internal consistency and test-retest reliability were assessed in sample of 30 patients by Cronbach's and intraclass correlation coefficient (ICC).<br><br>RESULTS: The IDQOL-BCI showed good content validity (CVI values>0.75 and CVR values>0.99), internal consistency (&#x3b1;=0.75) and test-retest reliability (ICC=0.81). A 3-factor solution was found. In addition, high values of item-scale correlations confirmed the convergence validity, and some subscales and total scores differentiate between groups defined by sex, disease duration, income levels, drug using status and physical activity demonstrated the discriminant validity.<br><br>CONCLUSIONS: Our findings demonstrate the initial feasibility, reliability and validity of the Iranian version of the IDQOL-BCI as a measure of diabetic-specific QOL measure in Iranian patients.},
}
@article {pmid22295687,
year = {2011},
author = {Wang, J and Zhou, L},
title = {[Research on magnetoencephalography-brain computer interface based on the PCA and LDA data reduction].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {28},
number = {6},
pages = {1069-1074},
pmid = {22295687},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography ; Hand/*physiology ; Humans ; Magnetoencephalography/*methods ; Movement/physiology ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The magnetoencephalography (MEG) can be used as a control signal for brain computer interface (BCI). The BCI also includes the pattern information of the direction of hand movement. In the MEG signal classification, the feature extraction based on signal processing and linear classification is usually used. But the recognition rate has been difficult to improve. In the present paper, a principal component analysis (PCA) and linear discriminant analysis (LDA) method has been proposed for the feature extraction, and the non-linear nearest neighbor classification is introduced for the classifier. The confusion matrix is analyzed based on the results. The experimental results show that the PCA + LDA method is effective in the analysis of multi-channel MEG signals, improves the recognition rate to the extent of the average recognition rate 55.7%, which is better than the recognition rate 46.9% in the BCI competition IV.},
}
@article {pmid22289414,
year = {2012},
author = {Friedrich, EV and Scherer, R and Neuper, C},
title = {The effect of distinct mental strategies on classification performance for brain-computer interfaces.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {84},
number = {1},
pages = {86-94},
doi = {10.1016/j.ijpsycho.2012.01.014},
pmid = {22289414},
issn = {1872-7697},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Motor imagery is the task most commonly used to induce changes in electroencephalographic (EEG) signals for mental imagery-based brain computer interfacing (BCI). In this study, we investigated EEG patterns that were induced by seven different mental tasks (i.e. mental rotation, word association, auditory imagery, mental subtraction, spatial navigation, imagery of familiar faces and motor imagery) and evaluated the binary classification performance. The aim was to provide a broad range of reliable and user-appropriate tasks to make individual optimization of BCI control strategies possible. Nine users participated in four sessions of multi-channel EEG recordings. Mental tasks resulting most frequently in good binary classification performance include mental subtraction, word association, motor imagery and mental rotation. Our results indicate that a combination of 'brain-teasers' - tasks that require problem specific mental work (e.g. mental subtraction, word association) - and dynamic imagery tasks (e.g. motor imagery) result in highly distinguishable brain patterns that lead to an increased performance.},
}
@article {pmid22289127,
year = {2012},
author = {Nitiéma, P and Carabin, H and Hounton, S and Praet, N and Cowan, LD and Ganaba, R and Kompaoré, C and Tarnagda, Z and Dorny, P and Millogo, A and Efécab, },
title = {Prevalence case-control study of epilepsy in three Burkina Faso villages.},
journal = {Acta neurologica Scandinavica},
volume = {126},
number = {4},
pages = {270-278},
pmid = {22289127},
issn = {1600-0404},
support = {R01 NS064901/NS/NINDS NIH HHS/United States ; R21 NS055353/NS/NINDS NIH HHS/United States ; R21 NS055353-02/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Age Factors ; Antigens/immunology ; Burkina Faso/epidemiology ; Child ; Cross-Sectional Studies ; Cysticercosis/blood/epidemiology/immunology ; Enzyme-Linked Immunosorbent Assay ; Epilepsy/blood/*epidemiology ; Female ; Humans ; Logistic Models ; Male ; Middle Aged ; Prevalence ; Retrospective Studies ; Risk Factors ; *Rural Population ; Serologic Tests ; Surveys and Questionnaires ; Young Adult ; },
abstract = {PURPOSE: To estimate the association between the prevalence of epilepsy and potential risk factors in three Burkina Faso villages.<br><br>METHODS: Three villages were selected based on local reports of high numbers of epilepsy cases and pig-rearing practices. One person aged 7 or older was selected at random from all households of selected concessions for epilepsy screening and blood sampling. Epilepsy was confirmed by a physician using the ILAE definition. The cross-sectional associations between epilepsy and selected factors and seroresponse to the antigens of Taenia solium were estimated using a Bayesian hierarchical logistic regression. Prevalence odds ratios (POR) and their 95% Bayesian Credible Intervals (95% BCI) were estimated.<br><br>RESULTS: Of 888 individuals interviewed, 39 of 70 screened positive were confirmed to have epilepsy for a lifetime prevalence of 4.5% (95% CI: 3.3; 6.0). The prevalence of epilepsy was associated with a positive reaction to cysticercosis Ag-ELISA serology (POR&#xa0;=&#xa0;3.1, 95% BCI&#xa0;=&#xa0;1.0; 8.3), past pork consumption (POR&#xa0;=&#xa0;9.7, 95% BCI&#xa0;=&#xa0;2.5; 37.9), and being salaried or a trader compared to a farmer or housewife (POR&#xa0;=&#xa0;2.9, 95% BCI&#xa0;=&#xa0;1.2; 6.4).<br><br>DISCUSSION: Several factors were associated with prevalent epilepsy, with Ag-ELISA suggesting the presence of neurocysticercosis. The association between epilepsy and some occupations may reflect differences in local attitudes toward epilepsy and should be further explored.},
}
@article {pmid22287252,
year = {2012},
author = {Siuly, S and Li, Y},
title = {Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {4},
pages = {526-538},
doi = {10.1109/TNSRE.2012.2184838},
pmid = {22287252},
issn = {1558-0210},
mesh = {Biofeedback, Psychology/methods/*physiology ; Computer Simulation ; Data Interpretation, Statistical ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Least-Squares Analysis ; Male ; Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; *Support Vector Machine ; *User-Computer Interface ; Young Adult ; },
abstract = {Although brain-computer interface (BCI) techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as improvement of motor imagery (MI) signal classification. In this paper, we propose a hybrid algorithm to improve the classification success rate of MI-based electroencephalogram (EEG) signals in BCIs. The proposed scheme develops a novel cross-correlation based feature extractor, which is aided with a least square support vector machine (LS-SVM) for two-class MI signals recognition. To verify the effectiveness of the proposed classifier, we replace the LS-SVM classifier by a logistic regression classifier and a kernel logistic regression classifier, separately, with the same features extracted from the cross-correlation technique for the classification. The proposed approach is tested on datasets, IVa and IVb of BCI Competition III. The performances of those methods are evaluated with classification accuracy through a 10-fold cross-validation procedure. We also assess the performance of the proposed method by comparing it with eight recently reported algorithms. Experimental results on the two datasets show that the proposed LS-SVM classifier provides an improvement compared to the logistic regression and kernel logistic regression classifiers. The results also indicate that the proposed approach outperforms the most recently reported eight methods and achieves a 7.40% improvement over the best results of the other eight studies.},
}
@article {pmid22284235,
year = {2012},
author = {Liao, LD and Chen, CY and Wang, IJ and Chen, SF and Li, SY and Chen, BW and Chang, JY and Lin, CT},
title = {Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {9},
number = {},
pages = {5},
pmid = {22284235},
issn = {1743-0003},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electrodes ; Electroencephalography/*instrumentation ; Humans ; *User-Computer Interface ; *Viscoelastic Substances ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.},
}
@article {pmid22277462,
year = {2011},
author = {Nakagawa, M},
title = {[Therapeutic strategies for Charcot-Marie-Tooth disease].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {51},
number = {11},
pages = {1015-1018},
doi = {10.5692/clinicalneurol.51.1015},
pmid = {22277462},
issn = {1882-0654},
mesh = {Charcot-Marie-Tooth Disease/genetics/*therapy ; Humans ; },
abstract = {Recently, causative gene discovery and genetic diagnosis system for Charcot-Marie-Tooth disease (CMT) have been rapidly developed. These genetic information and research progress, however, have not been informed to medical staff and CMT patients. CMT-Japan, which is an association of Japanese CMT patients, has been organized in 2008. Many of CMTJ members have not been diagnosed genetically. Most of medical staff and CMT patients may imagine that there is no hope for the CMT feature. Research on CMT therapy, however, has been progressing such as clinical trial of ascorbic acid, and experimental trial of curcumin and antiprogesterone. The development of robot technology and brain machine interface open a new way of therapy for CMT. Elucidation of molecular mechanisms and finding of effective treatments for CMT using cell culture, iPS cell, animal model, agents to suppress PMP22 expression, and read-through of stop codon methods are expected in the near features. In addition, development of surrogate markers, improvement of clinical trial design, establishment of nationwide diagnostic system, and assessment of natural history with international collaboration study must be done as soon as possible. CMT management manual, review of CMT research, open seminar for CMT, and genetic counseling are essential to improve the medical management for CMT. The collaboration among medical engineers, neurophysiologists, rehabilitation team, orthopedist, neurologists, genetic researchers and CMT patients and their families is of cardinal importance to achieve these studies for CMT.},
}
@article {pmid22277420,
year = {2011},
author = {Ushiba, J},
title = {[Possibility of brain-computer interface in neurorehabilitation].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {51},
number = {11},
pages = {927},
doi = {10.5692/clinicalneurol.51.927},
pmid = {22277420},
issn = {1882-0654},
mesh = {Brain/physiology ; Computers ; Hemiplegia/*rehabilitation ; Humans ; Man-Machine Systems ; *Stroke Rehabilitation ; },
}
@article {pmid22277419,
year = {2011},
author = {Mihara, M},
title = {[Neurorehabilitative intervention with neurofeedback system using functional near-infrared spectroscopy].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {51},
number = {11},
pages = {924-926},
doi = {10.5692/clinicalneurol.51.924},
pmid = {22277419},
issn = {1882-0654},
mesh = {Brain Injuries/rehabilitation ; Humans ; Neurofeedback/*methods/physiology ; *Spectroscopy, Near-Infrared ; },
abstract = {Recent advance in Brain-Machine interface (BMI) technology, including analysis of brain signal, enable a real-time interaction between patients and environment bypassing their damaged neuromuscular systems. Although most of researches have focused on substituting output function, it has been growing interest in applying this technology for restoring their brain. Several studies have proved that feedback of cortical activities (neurofeedback) enable regulating brain activation voluntarily. According to this notion, we have developed a real-time neurofeedback system mediated by near-infrared spectroscopy (NIRS) as a neurofeedback tool in neurorehabilitation. First, we have evaluated whether real-time cortical oxygenated hemoglobin (OxyHb) feedback signals correlated with reference OxyHb signals analyzed off-line during a motor execution task. Our results showed high correlation between results from two analyses. Second, we investigated whether the self-assessment scores for kinesthetic motor imagery and motor imagery related cortical activation was enhanced by neurofeedback. Our experiment with right handed healthy subjects revealed significant improvement of the imagery scale, and enhanced cortical activations including the contralateral premotor area. These results suggest that the neurofeedback technique may improve the efficacy of mental practice with motor imagery.},
}
@article {pmid22275888,
year = {2011},
author = {Ifft, PJ and Lebedev, MA and Nicolelis, MA},
title = {Cortical correlates of fitts' law.},
journal = {Frontiers in integrative neuroscience},
volume = {5},
number = {},
pages = {85},
pmid = {22275888},
issn = {1662-5145},
support = {R01 NS073125/NS/NINDS NIH HHS/United States ; DP1 MH099903/MH/NIMH NIH HHS/United States ; RC1 HD063390/HD/NICHD NIH HHS/United States ; R01 NS073952/NS/NINDS NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; R01 DE011451/DE/NIDCR NIH HHS/United States ; },
abstract = {Fitts' law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts' law has been extensively studied in ergonomics and has guided the design of human-computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts' law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control.},
}
@article {pmid22275718,
year = {2012},
author = {Carlson, T and Demiris, Y},
title = {Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload.},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
volume = {42},
number = {3},
pages = {876-888},
doi = {10.1109/TSMCB.2011.2181833},
pmid = {22275718},
issn = {1941-0492},
mesh = {Algorithms ; *Artificial Intelligence ; Biofeedback, Psychology/*methods/*physiology ; Humans ; Male ; Pattern Recognition, Automated/methods ; Robotics/instrumentation/*methods ; *Task Performance and Analysis ; Therapy, Computer-Assisted/methods ; *Wheelchairs ; *Workload ; },
abstract = {Powered wheelchair users often struggle to drive safely and effectively and, in more critical cases, can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists users as and when they require help. The system uses a multiple-hypothesis method to predict the driver's intentions and, if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance but also, perhaps more importantly, characterize the user performance in an experiment that combines eye tracking with a secondary task. Without assistance, participants experienced multiple collisions while driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely but also they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain-machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input.},
}
@article {pmid22275685,
year = {2011},
author = {Trad, D and Al-ani, T and Monacelli, E and Jemni, M},
title = {Nonlinear and nonstationary framework for feature extraction and classification of motor imagery.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2011},
number = {},
pages = {5975488},
doi = {10.1109/ICORR.2011.5975488},
pmid = {22275685},
issn = {1945-7901},
mesh = {Algorithms ; Discriminant Analysis ; Electroencephalography ; Humans ; Motor Skills/*physiology ; },
abstract = {In this work we investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI). This approach is based on the Empirical Mode Decomposition (EMD) and band power (BP). The EMD method is a data-driven technique to analyze non-stationary and nonlinear signals. It generates a set of stationary time series called Intrinsic Mode Functions (IMF) to represent the original data. These IMFs are analyzed with the power spectral density (PSD) to study the active frequency range correspond to the motor imagery for each subject. Then, the band power is computed within a certain frequency range in the channels. Finally, the data is reconstructed with only the specific IMFs and then the band power is employed on the new database. The classification of motor imagery was performed by using two classifiers, Linear Discriminant Analysis (LDA) and Hidden Markov Models (HMMs). The results obtained show that the EMD method allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using only the direct BP approach.},
}
@article {pmid22275683,
year = {2011},
author = {Ron-Angevin, R and Velasco-Alvarez, F and Sancha-Ros, S and da Silva-Sauer, L},
title = {A two-class self-paced BCI to control a robot in four directions.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2011},
number = {},
pages = {5975486},
doi = {10.1109/ICORR.2011.5975486},
pmid = {22275683},
issn = {1945-7901},
mesh = {Adult ; Brain/*physiopathology ; Electroencephalography ; Female ; Humans ; Male ; Robotics/*instrumentation/*methods ; User-Computer Interface ; Young Adult ; },
abstract = {In this work, an electroencephalographic analysis-based, self-paced (asynchronous) brain-computer interface (BCI) is proposed to control a mobile robot using four different navigation commands: turn right, turn left, move forward and move back. In order to reduce the probability of misclassification, the BCI is to be controlled with only two mental tasks (relaxed state versus imagination of right hand movements), using an audio-cued interface. Four healthy subjects participated in the experiment. After two sessions controlling a simulated robot in a virtual environment (which allowed the user to become familiar with the interface), three subjects successfully moved the robot in a real environment. The obtained results show that the proposed interface enables control over the robot, even for subjects with low BCI performance.},
}
@article {pmid22275589,
year = {2011},
author = {Gomez-Rodriguez, M and Grosse-Wentrup, M and Hill, J and Gharabaghi, A and Scholkopf, B and Peters, J},
title = {Towards brain-robot interfaces in stroke rehabilitation.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2011},
number = {},
pages = {5975385},
doi = {10.1109/ICORR.2011.5975385},
pmid = {22275589},
issn = {1945-7901},
mesh = {Arm/physiology ; Brain/*physiology ; Humans ; Movement/physiology ; Robotics/*instrumentation/*methods ; *Stroke Rehabilitation ; Upper Extremity/physiology ; },
abstract = {A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a robot-based rehabilitation framework that enables artificial support of the sensorimotor feedback loop. The aim is to increase cortical plasticity by means of Hebbian-type learning rules. A BCI-based shared-control strategy is used to drive a Barret WAM 7-degree-of-freedom arm that guides a subject's arm. Experimental validation of our setup is carried out both with healthy subjects and stroke patients. We review the empirical results which we have obtained to date, and argue that they support the feasibility of future rehabilitative treatments employing this novel approach.},
}
@article {pmid22275581,
year = {2011},
author = {Bergamasco, M and Frisoli, A and Fontana, M and Loconsole, C and Leonardis, D and Troncossi, M and Foumashi, MM and Parenti-Castelli, V},
title = {Preliminary results of BRAVO project: brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2011},
number = {},
pages = {5975377},
doi = {10.1109/ICORR.2011.5975377},
pmid = {22275581},
issn = {1945-7901},
mesh = {Brain/*physiology ; Humans ; Robotics/*instrumentation/*methods ; Stroke Rehabilitation ; Upper Extremity/*physiology ; User-Computer Interface ; },
abstract = {This paper presents the preliminary results of the project BRAVO (Brain computer interfaces for Robotic enhanced Action in Visuo-motOr tasks). The objective of this project is to define a new approach to the development of assistive and rehabilitative robots for motor impaired users to perform complex visuomotor tasks that require a sequence of reaches, grasps and manipulations of objects. BRAVO aims at developing new robotic interfaces and HW/SW architectures for rehabilitation and regain/restoration of motor function in patients with upper limb sensorimotor impairment through extensive rehabilitation therapy and active assistance in the execution of Activities of Daily Living. The final system developed within this project will include a robotic arm exoskeleton and a hand orthosis that will be integrated together for providing force assistance. The main novelty that BRAVO introduces is the control of the robotic assistive device through the active prediction of intention/action. The system will actually integrate the information about the movement carried out by the user with a prediction of the performed action through an interpretation of current gaze of the user (measured through eye-tracking), brain activation (measured through BCI) and force sensor measurements.},
}
@article {pmid22273796,
year = {2011},
author = {Schalk, G and Leuthardt, EC},
title = {Brain-computer interfaces using electrocorticographic signals.},
journal = {IEEE reviews in biomedical engineering},
volume = {4},
number = {},
pages = {140-154},
doi = {10.1109/RBME.2011.2172408},
pmid = {22273796},
issn = {1941-1189},
mesh = {Brain/*physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Humans ; User-Computer Interface ; },
abstract = {Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single-neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electrocorticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG-based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.},
}
@article {pmid22272380,
year = {2012},
author = {Cheron, G and Duvinage, M and De Saedeleer, C and Castermans, T and Bengoetxea, A and Petieau, M and Seetharaman, K and Hoellinger, T and Dan, B and Dutoit, T and Sylos Labini, F and Lacquaniti, F and Ivanenko, Y},
title = {From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation.},
journal = {Neural plasticity},
volume = {2012},
number = {},
pages = {375148},
pmid = {22272380},
issn = {1687-5443},
mesh = {Gait Disorders, Neurologic/*physiopathology/*rehabilitation ; Humans ; Models, Neurological ; Nerve Net/physiology ; Prosthesis Design/*methods/trends ; Spinal Cord/cytology/*physiology ; User-Computer Interface ; },
abstract = {Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.},
}
@article {pmid22269596,
year = {2012},
author = {Jin, J and Allison, BZ and Wang, X and Neuper, C},
title = {A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials.},
journal = {Journal of neuroscience methods},
volume = {205},
number = {2},
pages = {265-276},
doi = {10.1016/j.jneumeth.2012.01.004},
pmid = {22269596},
issn = {1872-678X},
mesh = {Brain/*physiology ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Motion ; Motion Perception/physiology ; Photic Stimulation ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) allow users to communicate via brain activity alone. Many BCIs rely on the P300 and other event-related potentials (ERPs) that are elicited when target stimuli flash. Although there have been considerable research exploring ways to improve P300 BCIs, surprisingly little work has focused on new ways to change visual stimuli to elicit more recognizable ERPs. In this paper, we introduce a "combined" BCI based on P300 potentials and motion-onset visual evoked potentials (M-VEPs) and compare it with BCIs based on each simple approach (P300 and M-VEP). Offline data suggested that performance would be best in the combined paradigm. Online tests with adaptive BCIs confirmed that our combined approach is practical in an online BCI, and yielded better performance than the other two approaches (P<0.05) without annoying or overburdening the subject. The highest mean classification accuracy (96%) and practical bit rate (26.7bit/s) were obtained from the combined condition.},
}
@article {pmid22262537,
year = {2011},
author = {Ouanezar, S and Eskiizmirliler, S and Maier, MA},
title = {Asynchronous decoding of finger position and of EMG during precision grip using CM cell activity: application to robot control.},
journal = {Journal of integrative neuroscience},
volume = {10},
number = {4},
pages = {489-511},
doi = {10.1142/S0219635211002853},
pmid = {22262537},
issn = {0219-6352},
mesh = {Analysis of Variance ; Animals ; Biomechanical Phenomena ; Electromyography/instrumentation ; Evoked Potentials, Motor/*physiology ; Fingers/*innervation/physiology ; Hand Strength/*physiology ; Macaca mulatta ; Motion ; Motor Cortex/*cytology ; Motor Neurons/*physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; *Robotics ; Time Factors ; },
abstract = {Recent brain-machine interfaces (BMI) have demonstrated the use of intracortical signals for the kinematic control of robotic arms. However, for potential restoration of manual dexterity, two issues remain to be addressed: (1) Can hand and digit movements for dexterous manipulation be controlled in a similar way to arm movements? (2) Can the potentially large signal space for decoding of the many degrees of freedom (dof) of hand and digit movements be minimized? The first question addresses BMI control of dexterous prosthetic devices, while the second addresses the problem of whether few, but identified, neurons might provide adequate decoding. Asynchronous decoding of precision grip finger movement kinematics from identified corticomotoneuronal (CM) cell activity was performed with an artificial neural network (ANN). After training over a given session, the ANNs successfully decoded trial-by-trial movement kinematics. Average accuracy over sessions was in the order of 80% and 50% for data sets of two monkeys respectively. Decoding accuracy increased as a function of (1) number of simultaneously recorded CM cells used for prediction, and (2) size of the sliding input window. Subsequently, a robot digit actuated by pneumatic artificial muscles, fed with the predicted trajectory, mimicked the recorded movement offline. Furthermore, CM cell signals were used for decoding of time-varying hand muscle EMG activity. The performance of EMG prediction tended to increase if CM cells that facilitated this particular muscle (compared to CM cells that facilitated other muscles) were used. These results provide evidence that an anthropomorphic robot finger can be controlled offline by spike trains recorded from identified corticospinal neurons. This represents a step towards neuroprosthetic devices for dexterous hand movements.},
}
@article {pmid22262524,
year = {2012},
author = {Hsu, WY},
title = {Application of competitive Hopfield neural network to brain-computer interface systems.},
journal = {International journal of neural systems},
volume = {22},
number = {1},
pages = {51-62},
doi = {10.1142/S0129065712002979},
pmid = {22262524},
issn = {1793-6462},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; ROC Curve ; *User-Computer Interface ; },
abstract = {We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.},
}
@article {pmid22256162,
year = {2011},
author = {Alonso-Valerdi, LM and Sepulveda, F},
title = {Programming an offline-analyzer of motor imagery signals via python language.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7861-7864},
doi = {10.1109/IEMBS.2011.6091937},
pmid = {22256162},
issn = {2694-0604},
mesh = {Adult ; Electroencephalography/*instrumentation ; Female ; Humans ; Imagery, Psychotherapy/*instrumentation ; Male ; Motor Activity/*physiology ; *Programming Languages ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Brain Computer Interface (BCI) systems control the user's environment via his/her brain signals. Brain signals related to motor imagery (MI) have become a widespread method employed by the BCI community. Despite the large number of references describing the MI signal treatment, there is not enough information related to the available programming languages that could be suitable to develop a specific-purpose MI-based BCI. The present paper describes the development of an offline-analysis system based on MI-EEG signals via open-source programming languages, and the assessment of the system using electrical activity recorded from three subjects. The analyzer recognized at least 63% of the MI signals corresponding to three classes. The results of the offline analysis showed a promising performance considering that the subjects have never undergone MI trainings.},
}
@article {pmid22256124,
year = {2011},
author = {Giuliana, G and Mario, M and Yassin, J},
title = {A quality parameter for the detection of the intentionality of movement in patients with neurological tremor performing a finger-to-nose test.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7707-7710},
doi = {10.1109/IEMBS.2011.6091899},
pmid = {22256124},
issn = {2694-0604},
mesh = {Electroencephalography ; Female ; Fingers ; Humans ; *Intention ; Male ; Middle Aged ; Movement/*physiology ; Nose ; Sensory Thresholds ; *Task Performance and Analysis ; Tremor/*physiopathology ; },
abstract = {The identification of the intentionality of movement is a key-aspect for the development of brain-computer interfaces (BCIs) applicable to daily life in neurological patients. We present a novel method of processing of electroencephalography (EEG) signals for the extraction of movement intention in neurological patients with upper limb tremor. This method is based on event-related EEG desynchronization, considering α (8-12 Hz), β (13-30 Hz), and γ (30-40 Hz) bands. We have analyzed the EEG signals from the sensorimotor areas of 4 neurological patients presenting an upper limb tremor (grade 1 to 3/4) and executing successive finger-to-nose movements. A Quality Parameter (QP) for the detection of intentionality of movement has been extracted, by considering: (a) the changes in the β[2]/α and β/α ratio (representing bursts of β-γ frequencies) during the pre-movement period; (b) an appropriate threshold predicting the movement; (c) the number of movements executed. This QP allows the prediction of the voluntary movement with a probability between 70% and 90%. This method could be implemented in a wearable BCI to detect the intentionality of movement and could be used, for instance, to trigger the electrical stimulation in selected muscles of upper limbs with the aim of blocking the emergence of tremor.},
}
@article {pmid22256123,
year = {2011},
author = {Corralejo, R and Hornero, R and Álvarez, D},
title = {Feature selection using a genetic algorithm in a motor imagery-based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7703-7706},
doi = {10.1109/IEMBS.2011.6091898},
pmid = {22256123},
issn = {2694-0604},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; Imagery, Psychotherapy/*methods ; Motor Cortex/*physiology ; *User-Computer Interface ; },
abstract = {This study performed an analysis of several feature extraction methods and a genetic algorithm applied to a motor imagery-based Brain Computer Interface (BCI) system. Several features can be extracted from EEG signals to be used for classification in BCIs. However, it is necessary to select a small group of relevant features because the use of irrelevant features deteriorates the performance of the classifier. This study proposes a genetic algorithm (GA) as feature selection method. It was applied to the dataset IIb of the BCI Competition IV achieving a kappa coefficient of 0.613. The use of a GA improves the classification results using extracted features separately (kappa coefficient of 0.336) and the winner competition results (kappa coefficient of 0.600). These preliminary results demonstrated that the proposed methodology could be useful to control motor imagery-based BCI applications.},
}
@article {pmid22256078,
year = {2011},
author = {London, BM and Torres, RR and Slutzky, MW and Miller, LE},
title = {Designing stimulation patterns for an afferent BMI: representation of kinetics in somatosensory cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7521-7524},
doi = {10.1109/IEMBS.2011.6091854},
pmid = {22256078},
issn = {2694-0604},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Afferent Pathways/*physiology ; Animals ; Biomechanical Phenomena ; Electric Stimulation ; Isometric Contraction/physiology ; Kinetics ; Macaca/physiology ; Models, Neurological ; Movement/physiology ; Neurons/physiology ; *Prosthesis Design ; Reproducibility of Results ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {In recent years, much attention has been focused on developing stimulating strategies for somatosensory prostheses. One application of such a somatosensory prosthesis is to supply proprioceptive feedback in a brain machine interface application. One strategy for the development of such a stimulation regime is to mimic the natural representation of limb state variables. In this paper, we demonstrate that end point force is represented in primary somatosensory cortex of the macaque and force, in addition to velocity, can be decoded from S1 neural recordings. Force is represented in S1 in both a movement and isometric tasks; however, models that predict force in one condition do not generalize to the other. Possible interpretations of these apparently contradictory results are discussed.},
}
@article {pmid22256054,
year = {2011},
author = {Finke, A and Knoblauch, A and Koesling, H and Ritter, H},
title = {A hybrid brain interface for a humanoid robot assistant.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7421-7424},
doi = {10.1109/IEMBS.2011.6091728},
pmid = {22256054},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiology ; Computer Simulation ; Evoked Potentials/physiology ; Humans ; Imagery, Psychotherapy ; Male ; Robotics/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {We present an advanced approach towards a semi-autonomous, robotic personal assistant for handicapped people. We developed a multi-functional hybrid brain-robot interface that provides a communication channel between humans and a state-of-the-art humanoid robot, Honda's Humanoid Research Robot. Using cortical signals, recorded, processed and translated by an EEG-based brain-machine interface (BMI), human-robot interaction functions independently of users' motor control deficits. By exploiting two distinct cortical activity patterns, P300 and event-related desynchronization (ERD), the interface provides different dimensions for robot control. An empirical study demonstrated the functionality of the BMI guided humanoid robot. All participants could successfully control the robot that accomplished a shopping task.},
}
@article {pmid22256053,
year = {2011},
author = {Ebisawa, M and Kogure, M and Yano, SH and Matsuzaki, S and Wada, Y},
title = {Estimation of direction of attention using EEG and out-of-head sound localization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7417-7420},
doi = {10.1109/IEMBS.2011.6091727},
pmid = {22256053},
issn = {2694-0604},
mesh = {Attention/*physiology ; Discrimination, Psychological ; Electroencephalography/*methods ; Head ; Humans ; Male ; Sound Localization/*physiology ; Support Vector Machine ; Task Performance and Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {Brain-Machine Interfaces (BMIs) are being researched controlling external devices such as robots and computers by measuring the cranial nerve activity of the operator. The brain activities evoked by visual stimuli have been studied intensively. However, few studies have considered a BMI that uses the brain activities evoked by auditory stimuli. This study investigated whether a person's direction of attention can be estimated using an event-related potential (ERP) generated by selective attention to an auditory stimulus. An auditory stimulus and an out-of-head sound localization system that can create an audio image outside the head that is presented through an earphone were used instead of a loudspeaker system. This system was experimentally evaluated by presenting the subject auditory cues from one of six directions while the subject directed his attention in one direction. An EEG response similar to an ERP was observed. The direction of attention was estimated using support vector machine with an accuracy of 89.2[%] on average for the three subjects. This suggests that a BMI system based on the estimated direction of attention can be developed by using out-of-head sound localization.},
}
@article {pmid22256052,
year = {2011},
author = {Sun, C and Zheng, N and Zhang, X and Chen, W and Zheng, X},
title = {An automatic control model for rat-robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7413-7416},
doi = {10.1109/IEMBS.2011.6091726},
pmid = {22256052},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; *Automation ; Humans ; *Models, Theoretical ; Neural Networks, Computer ; Rats ; Regression Analysis ; Robotics/*instrumentation ; Video Recording ; },
abstract = {In this paper, a control model is developed to automate the process of navigation in rat-robot-a new type of bio-robot based on BCI(Brain-Computer Interface) technique. Because of the particular difficulties in rat-robot control, we design a novel control model to 'learn' and 'imitate' the control behavior of human guidance. General Regression Neural Network (GRNN) model is used to analyze the control commands made by human operators, with the locomotion information of rat-robot recorded and analyzed in a video-based experimental system. The results of the control model shows that the human control process could be well understood and predicted, and expected to generate control commands automatically in future real-time rat-robot navigation experiments.},
}
@article {pmid22256045,
year = {2011},
author = {Liu, R and Xue, KZ and Wang, YX and Yang, L},
title = {A fuzzy-based shared controller for brain-actuated simulated robotic system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7384-7387},
doi = {10.1109/IEMBS.2011.6091719},
pmid = {22256045},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiology ; *Computer Simulation ; Female ; *Fuzzy Logic ; Humans ; Male ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {The primary problems of brain-computer interface (BCI) are the low channel capacity and high error rate. Therefore, an assistive motion control method is important for the brain-actuated robot to realize real-time and reliable control. To make the brain-actuated robot respond to the external environments with more flexibility, a shared control method based on fuzzy logic is proposed. Experimental results obtained with ten healthy voluntary subjects show that the proposed fuzzy-based shared controller has improved performance compared with direct control approach.},
}
@article {pmid22256027,
year = {2011},
author = {Cho, W and Vidaurre, C and Hoffmann, U and Birbaumer, N and Ramos-Murguialday, A},
title = {Afferent and efferent activity control in the design of brain computer interfaces for motor rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7310-7315},
doi = {10.1109/IEMBS.2011.6091705},
pmid = {22256027},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*pathology ; Computers ; Electroencephalography/methods ; Humans ; Man-Machine Systems ; Motor Skills/physiology ; Movement/physiology ; Neurons, Afferent/*physiology ; Neurons, Efferent/*physiology ; Robotics ; *Signal Processing, Computer-Assisted ; Software ; Stroke/physiopathology ; *Stroke Rehabilitation ; Time Factors ; User-Computer Interface ; },
abstract = {Stroke is a cardiovascular accident within the brain resulting in motor and sensory impairment in most of the survivors. A stroke can produce complete paralysis of the limb although sensory abilities are normally preserved. Functional electrical stimulation (FES), robotics and brain computer interfaces (BCIs) have been used to induce motor rehabilitation. In this work we measured the brain activity of healthy volunteers using electroencephalography (EEG) during FES, passive movements, active movements, motor imagery of the hand and resting to compare afferent and efferent brain signals produced during these motor related activities and to define possible features for an online FES-BCI. In the conditions in which the hand was moved we limited the movement range in order to control the afferent flow. Although we observed that there is a subject dependent frequency and spatial distribution of efferent and afferent signals, common patterns between conditions and subjects were present mainly in the low beta frequency range. When averaging all the subjects together the most significant frequency bin comparing each condition versus rest was exactly the same for all conditions but motor imagery. These results suggest that to implement an on-line FES-BCI, afferent brain signals resulting from FES have to be filtered and time-frequency-spatial features need to be used.},
}
@article {pmid22256025,
year = {2011},
author = {Lorrain, T and Niazi, IK and Thibergien, O and Jiang, N and Farina, D},
title = {LivBioSig: development of a toolbox for online bio-signals processing and experimentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7302-7305},
doi = {10.1109/IEMBS.2011.6091703},
pmid = {22256025},
issn = {2694-0604},
mesh = {Algorithms ; Brain/pathology ; Computer Graphics ; Computer Simulation ; Computers ; Humans ; Internet ; Man-Machine Systems ; Models, Neurological ; Programming Languages ; *Signal Processing, Computer-Assisted ; Signal Transduction ; *Software ; User-Computer Interface ; },
abstract = {Various research fields, such as brain computer interface, requires online acquisition and analysis of biological data to validate assumptions or to help obtaining insights into the physiological processes of the human body. In this paper we introduce the LivBioSig toolbox for online bio-signals processing and experimentation. This open source and modularized MATLAB toolbox allows performing various experiment paradigms involving online signal processing. These currently include synchronous and asynchronous BCI experiments, and event related stimulation experiments. The use of Graphic User Interfaces (GUI) makes the system suitable even for beginner Matlab users, and the experiments easily configurable. The modularized structure allows advanced users to develop the toolbox further to adapt it to the needs of the research fields.},
}
@article {pmid22256024,
year = {2011},
author = {Yu, B and Mak, T and Sun, Y and Poon, CS},
title = {Real-time neuronal networks reconstruction using hierarchical systolic arrays.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7298-7301},
doi = {10.1109/IEMBS.2011.6091702},
pmid = {22256024},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*pathology ; Computer Simulation ; Computers ; Electrodes ; Humans ; Man-Machine Systems ; Models, Neurological ; Models, Theoretical ; Neural Networks, Computer ; Neurons/metabolism/*physiology ; *Signal Processing, Computer-Assisted ; Software ; Systole/*physiology ; User-Computer Interface ; },
abstract = {The correlation network of neurons emerges as an important mathematical framework for a spectrum of applications including neural modeling, brain disease prediction and brain-machine interface. However, construction of correlation network is computationally expensive, especially when the number of neurons is large and this prohibits realtime applications. This paper proposes a hardware architecture using hierarchical systolic arrays to reconstruct the correlation network. Through mapping an efficient algorithm for cross-correlation onto a massively parallel structure, the hardware can accomplish the network construction with extremely small delay. The proposed structure is evaluated using Field Programmable Gate Array (FPGA). Results show that our method is three orders of magnitudes faster than the software approach using desktop computer. This new method enables real-time network construction and leads to future novel devices of realtime neuronal network monitoring and rehabilitation.},
}
@article {pmid22255989,
year = {2011},
author = {Hoffmann, U and Cho, W and Ramos-Murguialday, A and Keller, T},
title = {Detection and removal of stimulation artifacts in electroencephalogram recordings.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7159-7162},
doi = {10.1109/IEMBS.2011.6091809},
pmid = {22255989},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Artifacts ; Computer Simulation ; Electrodes ; Electroencephalography/*methods ; Female ; Humans ; Male ; Models, Neurological ; Normal Distribution ; Probability ; *Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Time Factors ; },
abstract = {Stimulation artifacts are short-duration, high-amplitude spikes which can be observed in electroencephalogram (EEG) recordings whenever surface functional electrical stimulation (FES) is applied during recordings. Stimulation artifacts are of non-physiologic origin and hence have to be removed before analysis of the EEG can take place. In this paper, algorithms for the detection and removal of stimulation artifacts are presented. The algorithms require only little computational resources and can be applied online, while signals are recorded. Therefore, the algorithms are suitable for applications such as online control of FES based neuroprostheses by a brain-computer interface. Tests are performed with datasets recorded from two subjects for artifact durations ranging from 0.5 ms to 10 ms. After application of the artifact removal algorithms the signal-to-noise ratio of the reconstructed signals ranges from 15 dB to 45 dB, depending on the duration of artifacts and the type of algorithm.},
}
@article {pmid22255986,
year = {2011},
author = {Eliseyev, A and Faber, J and Aksenova, T},
title = {Classification of multi-modal data in a self-paced binary BCI in freely moving animals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {7147-7150},
doi = {10.1109/IEMBS.2011.6091806},
pmid = {22255986},
issn = {2694-0604},
mesh = {Algorithms ; Animals ; Brain/*pathology ; Electroencephalography/*methods ; False Positive Reactions ; Linear Models ; Man-Machine Systems ; Models, Neurological ; Models, Statistical ; Neurons/physiology ; Predictive Value of Tests ; Rats ; Regression Analysis ; Reproducibility of Results ; *User-Computer Interface ; Wavelet Analysis ; },
abstract = {The goal of the present article is to compare different classifiers using multi-modal data analysis in a binary self-paced BCI. Individual classifiers were applied to multi-modal neuronal data which was projected to a low dimensional space of latent variables using the Iterative N-way Partial Least Squares algorithm. To create a multi-way feature array, electrocorticograms (ECoG) recorded from animal brains were mapped to the spatial-temporal-frequency space using continuous wavelet transformation. To compare the classifiers BCI experiments were simulated. For this purpose we used 9 recordings from behavioral experiments previously recorded in rats free to move in a nature like environment.},
}
@article {pmid22255920,
year = {2011},
author = {Looney, D and Park, C and Kidmose, P and Rank, ML and Ungstrup, M and Rosenkranz, K and Mandic, DP},
title = {An in-the-ear platform for recording electroencephalogram.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6882-6885},
doi = {10.1109/IEMBS.2011.6091733},
pmid = {22255920},
issn = {2694-0604},
mesh = {Brain/pathology ; Ear Protective Devices ; Electrodes ; Electroencephalography/*instrumentation/*methods ; Electrophysiology/methods ; Equipment Design ; Humans ; Man-Machine Systems ; Models, Statistical ; Reproducibility of Results ; Scalp/pathology ; Silver Compounds/chemistry ; Time Factors ; User-Computer Interface ; },
abstract = {We introduce a novel approach to brain monitoring based on electroencephalogram (EEG) recordings from within the ear canal. While existing clinical and wearable systems are limited in terms of portability and ease of use, the proposed in-the-ear (ITE) recording platform promises a number of advantages including ease of implementation, minimally intrusive electrodes and enhanced accuracy (fixed electrode positions). It thus facilitates a crucial step towards the design of brain computer interfaces that integrate naturally with daily life. The feasibility of the ITE concept is demonstrated with recordings made from electrodes embedded on an earplug which are benchmarked against conventional scalp electrodes for a classic EEG paradigm.},
}
@article {pmid22255837,
year = {2011},
author = {Estepp, JR and Christensen, JC},
title = {Physiological cognitive state assessment: applications for designing effective human-machine systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6538-6541},
doi = {10.1109/IEMBS.2011.6091613},
pmid = {22255837},
issn = {2694-0604},
mesh = {Artificial Intelligence ; *Cognition ; Data Collection ; Electroencephalography/methods ; Equipment Design ; Ergonomics ; Humans ; *Man-Machine Systems ; Neurosciences/methods ; Self-Help Devices ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {Significant growth in the field of neuroscience has occurred over the last decade such that new application areas for basic research techniques are opening up to practitioners in many other areas. Of particular interest to many is the principle of neuroergonomics, by which the traditional work in neuroscience and its related topics can be applied to non-traditional areas such as human-machine system design. While work in neuroergonomics certainly predates the use of the term in the literature (previously identified by others as applied neuroscience, operational neuroscience, etc.), there is great promise in the larger framework that is represented by the general context of the terminology. Here, we focus on the very specific concept that principles in brain-computer interfaces, neural prosthetics and the larger realm of machine learning using physiological inputs can be applied directly to the design and implementation of augmented human-machine systems. Indeed, work in this area has been ongoing for more than 25 years with very little cross-talk and collaboration between clinical and applied researchers. We propose that, given increased interest in augmented human-machine systems based on cognitive state, further progress will require research in the same vein as that being done in the aforementioned communities, and that all researchers with a vested interest in physiologically-based machine learning techniques can benefit from increased collaboration. We thereby seek to describe the current state of cognitive state assessment in human-machine systems, the problems and challenges faced, and the tightly-coupled relationship with other research areas. This supports the larger work of the Cognitive State Assessment 2011 Competition by setting the stage for the purpose of the session by showing the need to increase research in the machine learning techniques used by practitioners of augmented human-machine system design.},
}
@article {pmid22255804,
year = {2011},
author = {Kanoh, S and Miyamoto, K and Yoshinobu, T},
title = {A P300-based BCI system for controlling computer cursor movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6405-6408},
doi = {10.1109/IEMBS.2011.6091581},
pmid = {22255804},
issn = {2694-0604},
mesh = {Blinking ; Brain/*physiology ; Computers ; Electroencephalography/methods ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Eye Movements ; Humans ; Internet ; Male ; Pattern Recognition, Automated ; Reproducibility of Results ; Robotics ; Self-Help Devices ; *User-Computer Interface ; Video Recording ; Vision, Ocular ; },
abstract = {A P300-based BCI (brain-computer interface) system for controlling the movement of the cursor displayed on the computer screen was proposed and evaluated. On the LCD computer screen, the cursor was displayed with the surrounded eight small circles, each of which was blinked sequentially in a random order. Five healthy subjects were requested to gaze at one of the circles placed in the preferred direction. The P300 activities elicited by the random blink of the target circle were detected by pattern classifier and they were used to move the cursor to the same direction as the target circle. It was shown that all of the subjects could control the movement of the cursor to their preferred direction by moving their gaze point in a short distance. This system can be applied to the voluntary control of the movement of the computer cursor, and the navigation of robot or video camera, without using users' extremities.},
}
@article {pmid22255803,
year = {2011},
author = {Newman, GI and Aggarwal, V and Schieber, MH and Thakor, NV},
title = {Identifying neuron communities during a reach and grasp task using an unsupervised clustering analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6401-6404},
pmid = {22255803},
issn = {2694-0604},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS040596-10/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; Brain/*physiology ; Cluster Analysis ; Electrodes ; Equipment Design ; Hand Strength/*physiology ; Humans ; Macaca mulatta ; Male ; Models, Statistical ; Motor Cortex/physiology ; Neurons/*physiology ; Reproducibility of Results ; Self-Help Devices ; User-Computer Interface ; },
abstract = {Recent advances in brain-machine interfaces (BMIs) have allowed for high density recordings using microelectrode arrays. However, these large datasets present a challenge in how to practically identify features of interest and discard non-task-related neurons. Thus, we apply a previously reported unsupervised clustering analysis to neural data acquired from a non-human primate as it performed a center-out reach-and-grasp task. Although neurons were recorded from multiple arrays across motor and premotor areas, neurons were found to cluster into only two groups which differ by their mean firing rate. No spatial distribution of neurons was evident in different groups, either across arrays or at different depths. Using a Kalman filter to decode arm, hand, and finger kinematics, we find that using neurons from only one of the groups resulted in higher decoding accuracy (r=0.73) than using randomly selected neurons (r=0.68). This suggests that the proposed method can be used to prune the input space and identify an optimal population of neurons for BMI tasks.},
}
@article {pmid22255799,
year = {2011},
author = {Kamrunnahar, M and Schiff, SJ},
title = {A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6385-6388},
pmid = {22255799},
issn = {2694-0604},
support = {K25 NS061001/NS/NINDS NIH HHS/United States ; K25NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Analysis of Variance ; Brain/*physiology ; Computers ; Electroencephalography/methods ; Equipment Design ; Female ; Hand/physiology ; Humans ; *Imagination ; Male ; Models, Theoretical ; Neural Networks, Computer ; Reproducibility of Results ; Toes/physiology ; Tongue/physiology ; User-Computer Interface ; },
abstract = {We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.},
}
@article {pmid22255798,
year = {2011},
author = {Cecotti, H and Sato-Reinhold, J and Sy, JL and Elliott, JC and Eckstein, MP and Giesbrecht, B},
title = {Impact of target probability on single-trial EEG target detection in a difficult rapid serial visual presentation task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6381-6384},
doi = {10.1109/IEMBS.2011.6091575},
pmid = {22255798},
issn = {2694-0604},
mesh = {Algorithms ; Area Under Curve ; Bayes Theorem ; Electrodes ; Electroencephalography/*methods ; Electronic Data Processing ; Equipment Design ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Probability ; ROC Curve ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; *Vision, Ocular ; },
abstract = {In non-invasive brain-computer interface (BCI), the analysis of event-related potentials (ERP) has typically focused on averaged trials, a current trend is to analyze single-trial evoked response individually with new approaches in pattern recognition and signal processing. Such single trial detection requires a robust response that can be detected in a variety task conditions. Here, we investigated the influence of target probability, a key factor known to influence the amplitude of the evoked response, on single trial target classification in a difficult rapid serial visual presentation (RSVP) task. Our classification approach for detecting target vs. non target responses, considers spatial filters obtained through the maximization of the signal to signal-plus-noise ratio, and then uses the resulting information as inputs to a Bayesian discriminant analysis. The method is evaluated across eight healthy subjects, on four probability conditions (P=0.05, 0.10, 0.25, 0.50). We show that the target probability has a statistically significant effect on both the behavioral performance and the target detection. The best mean area under the ROC curve is achieved with P=0.10, AUC=0.82. These results suggest that optimal performance of ERP detection in RSVP tasks is critically dependent on target probability.},
}
@article {pmid22255797,
year = {2011},
author = {Breitwieser, C and Neuper, C and Müller-Putz, GR},
title = {A concept to standardize raw biosignal transmission for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6377-6380},
doi = {10.1109/IEMBS.2011.6091574},
pmid = {22255797},
issn = {2694-0604},
mesh = {Brain/*physiology ; Computer Communication Networks/instrumentation ; Computer Systems ; Computers ; Equipment Design ; Humans ; Man-Machine Systems ; Programming Languages ; Reference Standards ; Self-Help Devices/*standards ; Signal Processing, Computer-Assisted/*instrumentation ; Software ; Time Factors ; User-Computer Interface ; },
abstract = {With this concept we introduced the attempt of a standardized interface called TiA to transmit raw biosignals. TiA is able to deal with multirate and block-oriented data transmission. Data is distinguished by different signal types (e.g., EEG, EOG, NIRS, …), whereby those signals can be acquired at the same time from different acquisition devices. TiA is built as a client-server model. Multiple clients can connect to one server. Information is exchanged via a control- and a separated data connection. Control commands and meta information are transmitted over the control connection. Raw biosignal data is delivered using the data connection in a unidirectional way. For this purpose a standardized handshaking protocol and raw data packet have been developed. Thus, an abstraction layer between hardware devices and data processing was evolved facilitating standardization.},
}
@article {pmid22255796,
year = {2011},
author = {Breitwieser, C and Pokorny, C and Neuper, C and Müller-Putz, GR},
title = {Somatosensory evoked potentials elicited by stimulating two fingers from one hand--usable for BCI?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6373-6376},
doi = {10.1109/IEMBS.2011.6091573},
pmid = {22255796},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Attention ; Brain/*physiology ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials, Somatosensory/physiology ; Female ; Fingers/*physiology ; Hand/*physiology ; Humans ; Linear Models ; Male ; Man-Machine Systems ; Reproducibility of Results ; Self-Help Devices ; Software ; *User-Computer Interface ; },
abstract = {Steady-state somatosensory evoked potentials (SSSEPs) have been elicited using vibro-tactile stimulation on two fingers of the right hand. Fourteen healthy subjects participated in this study. A screening session, stimulating each participant's thumb, was conducted to determine individual optimal resonance-like frequencies. After this screening session, two stimulation frequencies per subject were selected. Stimulation was then applied simultaneously on the participant's thumbs and middle finger. It was investigated whether it is possible to classify SSSEP changes based on an attention modulation task to determine possible BCI applications. A cue indicated the participants to shift their attention to either the thumb or the middle finger. Offline classification with a lock-in analyzer system (LAS) and a linear discriminant analysis (LDA) classifier was performed. One bipolar channel and no further optimization methods were used. All participants except one reached classification results above chance level classifying a reference period without focused attention against focused attention either to the thumb or the middle finger. Only two subjects reached accuracies above chance, classifying focused attention to the thumb vs. attention to the middle finger.},
}
@article {pmid22255795,
year = {2011},
author = {Iturrate, I and Montesano, L and Chavarriaga, R and del R Millán, J and Minguez, J},
title = {Minimizing calibration time using inter-subject information of single-trial recognition of error potentials in brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6369-6372},
doi = {10.1109/IEMBS.2011.6091572},
pmid = {22255795},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/pathology/*physiology ; Calibration ; Electrodes ; Electroencephalography/methods ; Equipment Design ; Evoked Potentials ; Female ; Humans ; Male ; *Man-Machine Systems ; Neurophysiology/methods ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Time Factors ; User-Computer Interface ; },
abstract = {One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions and users. The calibration process limits the BCI systems to scenarios where the outputs are very controlled, and makes these systems non-friendly and exhausting for the users. Although it has been studied how to reduce calibration time for asynchronous signals, it is still an open issue for event-related potentials. Here, we propose the minimization of the calibration time on single-trial error potentials by using classifiers based on inter-subject information. The results show that it is possible to have a classifier with a high performance from the beginning of the experiment, and which is able to adapt itself making the calibration phase shorter and transparent to the user.},
}
@article {pmid22255794,
year = {2011},
author = {Wang, R and Lou, X and Jiang, B and Cheng, W and Zheng, X and Zhang, S},
title = {Neural decoding using local field potential based on partial least squares regression.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6365-6366},
doi = {10.1109/IEMBS.2011.6091571},
pmid = {22255794},
issn = {2694-0604},
mesh = {Action Potentials ; Algorithms ; Animals ; Biomechanical Phenomena ; Brain/physiology ; Electroencephalography/methods ; Least-Squares Analysis ; Male ; *Man-Machine Systems ; Motor Cortex/pathology ; Neurons/metabolism/pathology/*physiology ; Principal Component Analysis ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Time Factors ; *User-Computer Interface ; },
abstract = {Recent studies have shown that a promising cortical control signal in brain-machine interface is local field potential (LFP), of which low and high frequencies bands contains information about planning or executing dexterous movement. In this paper, we analyzed LFP signals recorded from primary motor cortex of rats as they performed a lever-pressing task. The decoding performance of partial least squares regression (PLSR) in LFP was evaluated by comparing with two traditional decoding algorithms, Wiener filtering (WF) and Kalman filtering (KF). The results demonstrated that PLSR not only had good performance as the other two methods, but also had particular predominance in avoiding over-fitting and computation complexity, due to its capability in dealing with the small sample capacity and high variable dimension that exist in LFP decoding.},
}
@article {pmid22255793,
year = {2011},
author = {Pires, G and Nunes, U and Castelo-Branco, M},
title = {GIBS block speller: toward a gaze-independent P300-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6360-6364},
doi = {10.1109/IEMBS.2011.6091570},
pmid = {22255793},
issn = {2694-0604},
mesh = {Attention ; Brain/*physiology ; Communication ; *Communication Devices for People with Disabilities ; Computers ; Electrodes ; Electroencephalography/methods ; *Event-Related Potentials, P300 ; *Eye Movements ; Humans ; Internet ; Man-Machine Systems ; Motor Skills/*physiology ; Movement ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) opens a new communication channel for individuals with severe motor disorders. In P300-based BCIs, gazing the target event plays an important role in the BCI performance. Individuals who have their eye movements affected may lose the ability to gaze targets that are in the visual periphery. This paper presents a novel P300-based paradigm called gaze independent block speller (GIBS), and compares its performance with that of the standard row-column (RC) speller. GIBS paradigm requires extra selections of blocks of letters. The online experiments made with able-bodied participants show that the users can effectively control GIBS without moving the eyes (covert attention), while this task is not possible with RC speller. Furthermore, with overt attention, the results show that the improved classification accuracy of GIBS over RC speller compensates the extra selections, thereby achieving similar practical bit rates.},
}
@article {pmid22255792,
year = {2011},
author = {Zhang, J and Chen, W and Gu, Y and Wu, B and Qi, Y and Zheng, X},
title = {Classifying real and imaginary finger press tasks on a P300-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6356-6359},
doi = {10.1109/IEMBS.2011.6091569},
pmid = {22255792},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Attention/physiology ; Brain/*physiology ; Electroencephalography/classification/methods ; Event-Related Potentials, P300/physiology ; Female ; Fingers/*physiology ; Hand/physiology ; Humans ; Imagination ; Male ; Models, Statistical ; Motor Skills ; Reproducibility of Results ; Software ; User-Computer Interface ; },
abstract = {Brain computer interfaces based on P300 and sensory-motor rhythms are widely studied and recent advances show some interest in the combination of the two. In this paper, typical P300 paradigm is modified by adding animation guide of the finger press as a stimulus and by using different response strategies (silent counting and actual/imaginary left or right index finger press following the animation). Both P300 potentials and sensory-motor rhythms are directly exploited and discussed. Classification results showed that even under very demanding conditions, which was, 200 ms inter-stimulus interval of the P300 stimuli and actual/imaginary finger press once per 1.6s, the paradigm can evoke both P300 potentials and sensory-motor rhythms simultaneously. Actual finger press increased single trial P300 selection accuracy of different subjects by 5-29.5% compared with silent counting; imaginary finger press did not increase the P300 selection accuracy apparently for most subjects except the two who were very poor at counting task. This showed by using different interface design and adopting certain mental response strategies, the 'BCI illiteracy' may be cured. Also imaginary task had good performance of left versus right classification (with the best subject reached 81.1% of accuracy), which is an additional information that can be used to improve system performance.},
}
@article {pmid22255791,
year = {2011},
author = {Müller, SM and Diez, PF and Bastos-Filho, TF and Sarcinelli-Filho, M and Mut, V and Laciar, E},
title = {SSVEP-BCI implementation for 37-40 Hz frequency range.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6352-6355},
doi = {10.1109/IEMBS.2011.6091568},
pmid = {22255791},
issn = {2694-0604},
mesh = {Algorithms ; Automation ; Brain/*pathology ; Communication ; Communication Devices for People with Disabilities ; Decision Trees ; Electroencephalography/methods ; Equipment Design ; Evoked Potentials, Visual ; Humans ; Man-Machine Systems ; Models, Statistical ; Robotics ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general. Offline results are presented, which corresponds to a correct classification rate of up to 99% and a Information Transfer Rate (ITR) of up to 114.2 bits/min.},
}
@article {pmid22255789,
year = {2011},
author = {Tam, WK and Ke, Z and Tong, KY},
title = {Performance of common spatial pattern under a smaller set of EEG electrodes in brain-computer interface on chronic stroke patients: a multi-session dataset study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6344-6347},
doi = {10.1109/IEMBS.2011.6091566},
pmid = {22255789},
issn = {2694-0604},
mesh = {Adult ; Aged ; Algorithms ; Artificial Intelligence ; Brain/pathology ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Female ; Humans ; Male ; Middle Aged ; Reproducibility of Results ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; *Stroke Rehabilitation ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) uses non-muscular channel of the nervous system for communication. Common Spatial Pattern (CSP) is a popular spatial filtering method used to reduce the effect of volume conduction on EEG signals. It is thought that CSP requires a large number of electrodes to be effective. Using a 20-session dataset of motor imagery BCI usage by 5 stroke patients, we demonstrated that after channel selection, CSP can still maintain a high accuracy with low number of electrodes using a newly proposed channel selection method called CSP-rank (higher than 90% with 8 electrodes). The results showed that using only the first session for channel selection, a high accuracy can be maintained in subsequent sessions. CSP-rank has been compared to the popular support vector machine recursive feature elimination (SVM-RFE). The results showed that the CSP-rank required less electrodes to maintain accuracy higher than 90% (a minimum of 8 compared to 12 of SVM-RFE) and it attained a higher maximum accuracy (91.7% compared with 90.7% of SVM-RFE). This could support clinicians to apply more BCI in routine rehabilitation.},
}
@article {pmid22255788,
year = {2011},
author = {Zimmermann, R and Marchal-Crespo, L and Lambercy, O and Fluet, MC and Riener, R and Wolf, M and Gassert, R},
title = {Towards a BCI for sensorimotor training: initial results from simultaneous fNIRS and biosignal recordings.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6339-6343},
doi = {10.1109/IEMBS.2011.6091565},
pmid = {22255788},
issn = {2694-0604},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Adult ; Blood Pressure ; Brain/*pathology ; Electroencephalography/methods ; Equipment Design ; Female ; Heart Rate ; Hemoglobins/metabolism ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Motor Skills ; Oxyhemoglobins/metabolism ; Respiration ; Robotics ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; Skin Temperature ; Spectroscopy, Near-Infrared/*methods ; Stroke Rehabilitation ; Time Factors ; User-Computer Interface ; },
abstract = {This paper presents the concept and initial results of a novel approach for robot assisted sensorimotor training in stroke rehabilitation. It is based on a brain-body-robot interface (B(2)RI), combining both neural and physiological recordings, that detects the intention to perform a motor task. By directly including the injured brain into the therapy, we ultimately aim at providing a new method for severely impaired patients to engage in active movement therapy. In the present study, seven healthy subjects performed an isometric finger pinching task while functional near-infrared spectroscopy (fNIRS) signals from motor cortical areas and biosignals were recorded simultaneously. Results showed an insignificant increase in the blood pressure during the preparation period prior to motor execution. During the execution period, significant changes in oxy-and deoxyhemoglobin were found in the primary motor cortex, accompanied by an increase in blood pressure, respiration rate and galvanic skin response (GSR). Cortical measurements of premotor areas and heart rate revealed significant changes at the subject level with large inter-subject variability. The results presented here will serve as priors for the design of further studies to test the efficacy of the concept with stroke patients, and the found effects will provide a basis for the development of a classifier for a future B(2)RI.},
}
@article {pmid22255787,
year = {2011},
author = {Liu, J and Perdoni, C and He, B},
title = {Hand movement decoding by phase-locking low frequency EEG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6335-6338},
doi = {10.1109/IEMBS.2011.6091564},
pmid = {22255787},
issn = {2694-0604},
support = {R01 EB006433/EB/NIBIB NIH HHS/United States ; R01EB007920/EB/NIBIB NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Biomechanical Phenomena ; Brain/*pathology ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Extremities/pathology ; Female ; Hand/*physiology ; Humans ; Male ; Models, Statistical ; Movement ; Neurons/pathology ; Regression Analysis ; Self-Help Devices ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {Being noninvasive, low-risk and inexpensive, EEG is a promising methodology in the application of human Brain Computer Interface (BCI) to help those with motor dysfunctions. Here we employed a center-out task paradigm to study the decoding of hand velocity in the EEG recording. We tested the hypothesis using a linear regression model and found a significant correlation between velocity and the low-pass filtered EEG signal (<2 Hz). The low-pass filtered EEG was not only tuned to the direction but also phase-locked to the amplitude of velocity. This suggests an EEG form of the neuronal population vector theory, which is considered to encode limb kinematic information, and provides a new method of BCI implementation.},
}
@article {pmid22255785,
year = {2011},
author = {Ayaz, H and Shewokis, PA and Bunce, S and Onaral, B},
title = {An optical brain computer interface for environmental control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6327-6330},
doi = {10.1109/IEMBS.2011.6091561},
pmid = {22255785},
issn = {2694-0604},
mesh = {Brain/metabolism/*pathology ; Computers ; Environment ; Equipment Design ; Female ; Hemodynamics ; Hemoglobins/chemistry ; Humans ; Imaging, Three-Dimensional/methods ; Male ; Models, Statistical ; Neurons/pathology ; Neurophysiology/methods ; *Optics and Photonics ; Oxygen/chemistry ; Reproducibility of Results ; *Self-Help Devices ; Spectroscopy, Near-Infrared/methods ; User-Computer Interface ; },
abstract = {A brain computer interface (BCI) is a system that translates neurophysiological signals detected from the brain to supply input to a computer or to control a device. Volitional control of neural activity and its real-time detection through neuroimaging modalities are key constituents of BCI systems. The purpose of this study was to develop and test a new BCI design that utilizes intention-related cognitive activity within the dorsolateral prefrontal cortex using functional near infrared (fNIR) spectroscopy. fNIR is a noninvasive, safe, portable and affordable optical technique with which to monitor hemodynamic changes, in the brain's cerebral cortex. Because of its portability and ease of use, fNIR is amenable to deployment in ecologically valid natural working environments. We integrated a control paradigm in a computerized 3D virtual environment to augment interactivity. Ten healthy participants volunteered for a two day study in which they navigated a virtual environment with keyboard inputs, but were required to use the fNIR-BCI for interaction with virtual objects. Results showed that participants consistently utilized the fNIR-BCI with an overall success rate of 84% and volitionally increased their cerebral oxygenation level to trigger actions within the virtual environment.},
}
@article {pmid22255784,
year = {2011},
author = {González-Franco, M and Yuan, P and Zhang, D and Hong, B and Gao, S},
title = {Motor imagery based brain-computer interface: a study of the effect of positive and negative feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6323-6326},
doi = {10.1109/IEMBS.2011.6091560},
pmid = {22255784},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/*pathology ; Computer Graphics ; Electrocardiography/methods ; Electroencephalography/methods ; Feedback ; Female ; Humans ; Imagination ; Learning ; Male ; Motor Skills/physiology ; Patient Satisfaction ; Reproducibility of Results ; Stroke Rehabilitation ; Surveys and Questionnaires ; User-Computer Interface ; Vision, Ocular ; },
abstract = {Co-adaptation between the human brain and computers is an important issue in brain-computer interface (BCI) research. However, most of the research has focused on the computer side of BCI, such as developing powerful machine-learning algorithms, while less research has focused on investigating how BCI users may optimally adapt. This paper assesses the influences of positive and negative visual feedback on motor imagery (MI) skills by evaluating the performance. More precisely, a MI based BCI paradigm was employed with fake visual feedback, regardless of subjects' real performance. Subjects were exposed to two experimental conditions--one positive and one negative, in which 80% or 30% of the trials were associated with positive feedback, respectively. The main EEG feature for MI-BCI classification--the asymmetry of mu-rhythm between hemispheres--was more prominent only after the negative feedback session. In addition, the negative feedback condition was accompanied by larger heart rate variability compared to the positive feedback condition. Our results suggest that visual feedback is an important aspect to take into account when designing BCI skill acquisition sessions.},
}
@article {pmid22255783,
year = {2011},
author = {Filipe, S and Charvet, G and Foerster, M and Porcherot, J and Bêche, JF and Bonnet, S and Audebert, P and Régis, G and Zongo, B and Robinet, S and Condemine, C and Mestais, C and Guillemaud, R},
title = {A wireless multichannel EEG recording platform.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6319-6322},
doi = {10.1109/IEMBS.2011.6091559},
pmid = {22255783},
issn = {2694-0604},
mesh = {Animals ; Brain/*pathology ; Computer Communication Networks ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Humans ; Microcomputers ; Radio Waves ; Rats ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; User-Computer Interface ; Wireless Technology ; },
abstract = {A wireless multichannel data acquisition system is being designed for ElectroEncephaloGraphy (EEG) recording. The system is based on a custom integrated circuit (ASIC) for signal conditioning, amplification and digitization and also on commercial components for RF transmission. It supports the RF transmission of a 32-channel EEG recording sampled at 1 kHz with a 12-bit resolution. The RF communication uses the MICS band (Medical Implant Communication Service) at 402-405 Mhz. This integration is a first step towards a lightweight EEG cap for Brain Computer Interface (BCI) studies. Here, we present the platform architecture and its submodules. In vivo validations are presented with noise characterization and wireless data transfer measurements.},
}
@article {pmid22255782,
year = {2011},
author = {Knudsen, EB and Moxon, KA and Sturgis, EB and Shumsky, JS},
title = {Skilled hindlimb reaching task in rats as a platform for a brain-machine interface to restore motor function after complete spinal cord injury.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6315-6318},
doi = {10.1109/IEMBS.2011.6091558},
pmid = {22255782},
issn = {2694-0604},
mesh = {Animals ; Brain/*pathology ; Hindlimb/*physiopathology ; Locomotion/physiology ; Male ; Motor Activity/physiology ; Motor Skills/physiology ; Movement ; Rats ; Rats, Long-Evans ; Recovery of Function/physiology ; Spinal Cord/physiopathology ; Spinal Cord Injuries/*physiopathology/*rehabilitation ; Time Factors ; User-Computer Interface ; Vision, Ocular ; },
abstract = {Behavioral tasks utilized as models for decoding neural activity for use in brain-machine interfaces are constrained primarily to forelimb tasks or locomotion. We present here our methodology for training adult rats in a novel skilled hindlimb 'reaching' task in which the animal is trained to make different types of hindlimb movements. 6 adult Long-Evans rats were trained to make variable duration (<1 or >1.5 s) hindlimb presses cued by a spatially-independent visual cue. 5 of 6 animals (83.3%) were able to learn the task to proficiency. The training paradigm introduced here serves as a platform to investigate the ability of the animal to transfer motor cortical activity in response to a cue originally generated during normal movments, to a novel context in the absecense of movement and ultimately after complete mid-thoracic spinal cord transection. We also present preliminary results of offline classification of neural activity during trial performance for two trained animals.},
}
@article {pmid22255781,
year = {2011},
author = {Cecotti, H and Kasper, RW and Elliott, JC and Eckstein, MP and Giesbrecht, B},
title = {Multimodal target detection using single trial evoked EEG responses in single and dual-tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6311-6314},
doi = {10.1109/IEMBS.2011.6091557},
pmid = {22255781},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; Algorithms ; Area Under Curve ; Brain/*pathology ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials ; Female ; Humans ; Male ; Models, Statistical ; Normal Distribution ; ROC Curve ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Vision, Ocular ; },
abstract = {The detection of event-related potentials in the electroencephalogram signal is a common way for creating a brain-computer interface (BCI). Successful detection of evoked responses can be enhanced by the user selectively attending to specific stimuli presented in the BCI task. Because BCI users need a system that performs well in a variety of contexts, even ones that may impair selective attention, it is critical to understand how single trial detection is affected by attention. We tested 16 participants using a rapid serial visual/auditory presentation paradigm under three conditions, one in which they detected the presence of a visual target, one in which they detected the presence of an auditory target, and one in which they detected both visual and auditory targets. The behavioral performance indicates that the visual task was more difficult than the auditory task. Consistent with the higher behavioral difficulty of the visual task, single trial performance showed no difference between single and dual-task for the visual target detection (mean=0.76). However, the area under the curve for the auditory target detection was significantly lower than the dual-task (mean=0.81 for single task, 0.75 for dual-task). The results support the conclusion that single-trial target detection is impaired when attention is divided between multiple tasks.},
}
@article {pmid22255780,
year = {2011},
author = {Ng, KB and Bradley, AP and Cunnington, R},
title = {Effect of competing stimuli on SSVEP-based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6307-6310},
doi = {10.1109/IEMBS.2011.6091556},
pmid = {22255780},
issn = {2694-0604},
mesh = {Adult ; Brain/*pathology/physiology ; Electroencephalography/*methods ; Evoked Potentials ; Evoked Potentials, Visual ; Female ; Fovea Centralis ; Humans ; Male ; Models, Neurological ; Models, Statistical ; Reproducibility of Results ; *User-Computer Interface ; Vision, Ocular/physiology ; },
abstract = {Steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) works on the basis that an attended stimulus shows an enhanced visual evoked response. By examining EEG power at the frequency of the dominant evoked response, we are able to determine which stimulus the subject is attending. However, due to the limited processing capability of human visual system, when presented with multiple stimuli in the same visual field, the stimuli will compete for neural representations in the cortices. This study elucidates the effect of competing stimuli on SSVEP amplitudes by exploring the relationship between the number of stimuli and their inter-distance on the power spectra of attended stimuli. Results show that competing stimuli, when placed less than five degrees from the centre of the fovea, create a significant suppressive effect on the dominant frequency response. This result should guide how visual stimuli of SSVEP-based BCIs are spatially designed.},
}
@article {pmid22255779,
year = {2011},
author = {Úbeda, A and Iáñez, E and Azorin, JM},
title = {Mental tasks classification for BCI using image correlation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6303-6306},
doi = {10.1109/IEMBS.2011.6091555},
pmid = {22255779},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*pathology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Humans ; Image Processing, Computer-Assisted ; Imagination ; Models, Statistical ; Models, Theoretical ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Support Vector Machine ; *User-Computer Interface ; },
abstract = {This paper describes a classifier based on image correlation of EEG maps to distinguish between three mental tasks in a Brain-Computer Interface (BCI). The data set V of BCI Competition 2003 has been used to test the classifier. To that end, the EEG maps obtained from this data set have been studied to find the ideal parameters of processing time and frequency. The classifier designed is based on a normalized cross-correlation of images which makes possible to obtain a proper similarity index to perform the classification. The success percentage of the classifier has been shown for different combinations of data. The results obtained are very successful, showing that this kind of techniques may be able to classify between three mental tasks with good results in a future online testing.},
}
@article {pmid22255777,
year = {2011},
author = {Wang, W and Degenhart, AD and Sudre, GP and Pomerleau, DA and Tyler-Kabara, EC},
title = {Decoding semantic information from human electrocorticographic (ECoG) signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6294-6298},
doi = {10.1109/IEMBS.2011.6091553},
pmid = {22255777},
issn = {2694-0604},
support = {3R01NS050256-05S1/NS/NINDS NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Artificial Intelligence ; Bayes Theorem ; Brain/pathology/*physiology ; Brain Mapping/methods ; Child ; Communication ; Communication Devices for People with Disabilities ; Electrodes ; Electrophysiology/*methods ; Epilepsy/physiopathology ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Normal Distribution ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects named pictures of objects belonging to different semantic categories. Robust high-gamma band (60-120 Hz) activation was observed at the left inferior frontal gyrus (LIFG) and the posterior portion of the superior temporal gyrus (pSTG) with a temporal sequence corresponding to speech production and perception. Furthermore, Gaussian Naïve Bayes and Support Vector Machine classifiers, two commonly used machine learning algorithms for pattern recognition, were able to predict the semantic category of an object using cortical activity captured by ECoG electrodes covering the frontal, temporal and parietal cortices. These findings have implications for both basic neuroscience research and development of semantic-based brain-computer interface systems (BCI) that can help individuals with severe motor or communication disorders to express their intention and thoughts.},
}
@article {pmid22255776,
year = {2011},
author = {Mohamed, AK and Marwala, T and John, LR},
title = {Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6289-6293},
doi = {10.1109/IEMBS.2011.6091552},
pmid = {22255776},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Cluster Analysis ; Computer Systems ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Fingers/*physiology ; Humans ; Imagination ; Male ; Movement ; Neural Networks, Computer ; Prostheses and Implants ; Reproducibility of Results ; Self-Help Devices ; User-Computer Interface ; Wrist/*physiology ; },
abstract = {Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.},
}
@article {pmid22255775,
year = {2011},
author = {Rodrigo, M and Montesano, L and Minguez, J},
title = {Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6285-6288},
doi = {10.1109/IEMBS.2011.6091551},
pmid = {22255775},
issn = {2694-0604},
mesh = {Adult ; Algorithms ; Arm/*physiology ; Brain/*pathology/physiology ; Cognition ; Electroencephalography/*methods ; Equipment Design ; Humans ; Image Processing, Computer-Assisted ; Male ; Motion ; Movement/physiology ; Reproducibility of Results ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; *Stroke Rehabilitation ; User-Computer Interface ; },
abstract = {In the last years, there has been an increasing interest in using Brain Computer Interfaces (BCI) within motor rehabilitation therapies that use robotic devices or functional electro stimulation to help or guide the efforts of the patient to move her body. A crucial step of these therapies is to provide help to the user just when she is actually trying to accomplish a certain motion or task One of the most promising applications of BCI systems in this context is its ability to measure the user intentions and actions to trigger the rehabilitation devices accordingly. This paper studies the single-trial classification based on EEG measurements of three basic states during the execution of self-initiated motion: rest, motion preparation (or anticipation) and motion. We conducted an experiment where the participants had to reach at their will eight different locations from a fixed starting position. Results for seven healthy subjects show that it is possible to achieve good classification rates given that features are carefully selected for each subject and for each pair of states.},
}
@article {pmid22255774,
year = {2011},
author = {Kuo, CC and Lin, WS and Dressel, CA and Chiu, AW},
title = {Classification of intended motor movement using surface EEG ensemble empirical mode decomposition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6281-6284},
doi = {10.1109/IEMBS.2011.6091550},
pmid = {22255774},
issn = {2694-0604},
support = {P20RR016456/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Arm/*physiology ; Brain/*pathology/physiology ; Brain Mapping/methods ; Cognition ; Electroencephalography/*methods ; Equipment Design ; Humans ; Image Processing, Computer-Assisted ; Male ; Motion ; Movement/physiology ; Normal Distribution ; Reproducibility of Results ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; User-Computer Interface ; Young Adult ; },
abstract = {Noninvasive electroencephalography (EEG) brain computer interface (BCI) systems are used to investigate intended arm reaching tasks. The main goal of the work is to create a device with a control scheme that allows those with limited motor control to have more command over potential prosthetic devices. Four healthy subjects were recruited to perform various reaching tasks directed by visual cues. Independent component analysis (ICA) was used to identify artifacts. Active post parietal cortex (PPC) activation before arm movement was validated using EEGLAB. Single-trial binary classification strategies using support vector machine (SVM) with radial basis functions (RBF) kernels and Fisher linear discrimination (FLD) were evaluated using signal features from surface electrodes near the PPC regions. No significant improvement can be found by using a nonlinear SVM over a linear FLD classifier (63.65% to 63.41% accuracy). A significant improvement in classification accuracy was found when a normalization factor based on visual cue "signature" was introduced to the raw signal (90.43%) and the intrinsic mode functions (IMF) of the data (93.55%) using Ensemble Empirical Mode Decomposition (EEMD).},
}
@article {pmid22255773,
year = {2011},
author = {Fok, S and Schwartz, R and Wronkiewicz, M and Holmes, C and Zhang, J and Somers, T and Bundy, D and Leuthardt, E},
title = {An EEG-based brain computer interface for rehabilitation and restoration of hand control following stroke using ipsilateral cortical physiology.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6277-6280},
doi = {10.1109/IEMBS.2011.6091549},
pmid = {22255773},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*pathology ; Calibration ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Hand/physiology ; Humans ; Models, Statistical ; Nervous System Diseases/pathology ; Reproducibility of Results ; Self-Help Devices ; Signal Processing, Computer-Assisted ; Stroke/pathology ; *Stroke Rehabilitation ; Time Factors ; User-Computer Interface ; },
abstract = {The loss of motor control severely impedes activities of daily life. Brain computer interfaces (BCIs) offer new possibilities to treat nervous system injuries, but conventional BCIs use signals from primary motor cortex, the same sites most likely damaged in a stroke causing paralysis. Recent studies found distinct cortical physiology associated with contralesional limb movements in regions distinct from primary motor cortex. To capitalize on these findings, we designed and implemented a BCI that localizes and acquires these brain signals to drive a powered, hand orthotic which opens and closes a patient's hand.},
}
@article {pmid22255735,
year = {2011},
author = {Ishikawa, A and Udagawa, H and Masuda, Y and Kohno, S and Amita, T and Inoue, Y},
title = {Development of double density whole brain fNIRS with EEG system for brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6118-6122},
doi = {10.1109/IEMBS.2011.6091511},
pmid = {22255735},
issn = {2694-0604},
mesh = {Absorption ; Brain/*pathology ; Electroencephalography/*methods ; Equipment Design ; Hemoglobins/metabolism ; Humans ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging/methods ; Man-Machine Systems ; Oxygen/chemistry ; Phantoms, Imaging ; Quality of Life ; Robotics ; Self-Help Devices ; Spectroscopy, Near-Infrared/*methods ; User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMI) are expected as new man-machine interfaces. Non-invasive BMI have the potential to improve the quality of life of many disabled individuals with safer operation. The non-invasive BMI using the functional functional near-infrared spectroscopy (fNIRS) with the electroencephalogram (EEG) has potential applicability beyond the restoration of lost movement and rehabilitation in paraplegics and would enable normal individuals to have direct brain control of external devices in their daily lives. To shift stage of the non-invasive BMI from laboratory to clinical, the key factor is to develop high-accuracy signal decoding technology and highly restrictive of the measurement area. In this article, we present the development of a high-accuracy brain activity measurement system by combining fNIRS and EEG. The new fNIRS had high performances with high spatial resolution using double density technique and a large number of measurement channels to cover a whole human brain.},
}
@article {pmid22255731,
year = {2011},
author = {Higashi, H and Tanaka, T},
title = {Optimal design of a bank of spatio-temporal filters for EEG signal classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {6100-6103},
doi = {10.1109/IEMBS.2011.6091507},
pmid = {22255731},
issn = {2694-0604},
mesh = {Algorithms ; Biofeedback, Psychology/methods/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The spatial weights for electrodes called common spatial pattern (CSP) are known to be effective in EEG signal classification for motor imagery based brain computer interfaces (MI-BCI). To achieve accurate classification in CSP, the frequency filter should be properly designed. To this end, several methods for designing the filter have been proposed. However, the existing methods cannot consider plural brain activities described with different frequency bands and different spatial patterns such as activities of mu and beta rhythms. In order to efficiently extract these brain activities, we propose a method to design plural filters and spatial weights which extract desired brain activity. The proposed method designs finite impulse response (FIR) filters and the associated spatial weights by optimization of an objective function which is a natural extension of CSP. Moreover, we show by a classification experiment that the bank of FIR filters which are designed by introducing an orthogonality into the objective function can extract good discriminative features. Moreover, the experiment result suggests that the proposed method can automatically detect and extract brain activities related to motor imagery.},
}
@article {pmid22255673,
year = {2011},
author = {Ghovanloo, M},
title = {An overview of the recent wideband transcutaneous wireless communication techniques.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5864-5867},
pmid = {22255673},
issn = {2694-0604},
support = {R21 EB009437/EB/NIBIB NIH HHS/United States ; NIBIB-5R21EB009437/EB/NIBIB NIH HHS/United States ; },
mesh = {Equipment Design ; Equipment Failure Analysis ; Information Storage and Retrieval/*methods ; *Prostheses and Implants ; Telemetry/*instrumentation ; },
abstract = {Neuroprosthetic devices such as cochlear and retinal implants need to deliver a large volume of data from external sensors into the body, while invasive brain-computer interfaces need to deliver sizeable amounts of data from the central nervous system to target devices outside of the body. Nonetheless, the skin should remain intact. This paper reviews some of the latest techniques to establish wideband wireless communication links across the skin.},
}
@article {pmid22255659,
year = {2011},
author = {Suminski, AJ and Willett, FR and Fagg, AH and Bodenhamer, M and Hatsopoulos, NG},
title = {Continuous decoding of intended movements with a hybrid kinetic and kinematic brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5802-5806},
doi = {10.1109/IEMBS.2011.6091436},
pmid = {22255659},
issn = {2694-0604},
support = {R01 N545853-01//PHS HHS/United States ; },
mesh = {Algorithms ; Animals ; Biofeedback, Psychology/methods/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Extremities/physiology ; *Intention ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion, it is known that motor cortical activity also correlates with kinetic signals, including hand force and joint torque. In this experiment, a monkey used a cortically-controlled BMI to move a visual cursor and hit a sequence of randomly placed targets. By varying the contributions of separate kinetic and kinematic decoders to the movement of a virtual arm, we evaluated the hypothesis that a BMI incorporating both signals (Hybrid BMI) would outperform a BMI decoding kinematic information alone (Position BMI). We show that the trajectories generated by the Hybrid BMI during real-time decoding were straighter and smoother than those of the Position BMI. These results may have important implications for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.},
}
@article {pmid22255655,
year = {2011},
author = {King, CE and Wang, PT and Mizuta, M and Reinkensmeyer, DJ and Do, AH and Moromugi, S and Nenadic, Z},
title = {Noninvasive brain-computer interface driven hand orthosis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5786-5789},
doi = {10.1109/IEMBS.2011.6091432},
pmid = {22255655},
issn = {2694-0604},
support = {RR10-281/RR/NCRR NIH HHS/United States ; },
mesh = {*Algorithms ; Biofeedback, Psychology/instrumentation/methods ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Humans ; Imagination/physiology ; Motor Cortex/*physiology ; *Orthotic Devices ; *User-Computer Interface ; },
abstract = {Neurological conditions, such as stroke, can leave the affected individual with hand motor impairment despite intensive treatments. Novel technologies, such as brain-computer interface (BCI), may be able to restore or augment impaired motor behaviors by engaging relevant cortical areas. Here, we developed and tested an electroencephalogram (EEG) based BCI system for control of hand orthosis. An able-bodied subject performed contralateral hand grasping to achieve continuous online control of the hand orthosis, suggesting that the integration of a noninvasive BCI with a hand orthosis is feasible. The adoption of this technology to stroke survivors may provide a novel neurorehabilitation therapy for hand motor impairment in this population.},
}
@article {pmid22255653,
year = {2011},
author = {Foldes, ST and Vinjamuri, RK and Wang, W and Weber, DJ and Collinger, JL},
title = {Stability of MEG for real-time neurofeedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5778-5781},
doi = {10.1109/IEMBS.2011.6091430},
pmid = {22255653},
issn = {2694-0604},
support = {I01 RX000337/RX/RRD VA/United States ; #KL2RR024154/RR/NCRR NIH HHS/United States ; },
mesh = {Algorithms ; Biofeedback, Psychology/*methods/*physiology ; Computer Systems ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Magnetoencephalography/*methods ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Movement-related field potentials can be extracted and processed in real-time with magnetoencephalography (MEG) and used for brain machine interfacing (BMI). However, due to its immense sensitivity to magnetic fields, MEG is prone to a low signal to noise ratio. It is therefore important to collect enough initial data to appropriately characterize motor-related activity and to ensure that decoders can be built to adequately translate brain activity into BMI-device commands. This is of particular importance for therapeutic BMI applications where less time spent collecting initial open-loop data means more time for performing neurofeedback training which could potentially promote cortical plasticity and rehabilitation. This study evaluated the amount of hand-grasp movement and rest data needed to characterize sensorimotor modulation depth and build classifier functions to decode brain states in real-time. It was determined that with only five minutes of initial open-loop MEG data, decoders can be built to classify brain activity as grasp or rest in real-time with an accuracy of 84 ± 6%.},
}
@article {pmid22255652,
year = {2011},
author = {Orhan, U and Erdogmus, D and Roark, B and Purwar, S and Hild, KE and Oken, B and Nezamfar, H and Fried-Oken, M},
title = {Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5774-5777},
pmid = {22255652},
issn = {2694-0604},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; 1R01DC009834-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Language ; Models, Theoretical ; *Natural Language Processing ; *Task Performance and Analysis ; *User-Computer Interface ; *Writing ; },
abstract = {Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system.},
}
@article {pmid22255651,
year = {2011},
author = {Takahashi, H and Yoshikawa, T and Furuhashi, T},
title = {A novel selective stimulus presentation for P300 speller.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5770-5773},
doi = {10.1109/IEMBS.2011.6091428},
pmid = {22255651},
issn = {2694-0604},
mesh = {Biofeedback, Psychology/*methods/physiology ; *Communication Devices for People with Disabilities ; Computer Graphics ; *Computer Peripherals ; Humans ; Imagination/*physiology ; Photic Stimulation/*methods ; *User-Computer Interface ; *Writing ; },
abstract = {The P300 speller is one of the brain-computer interfaces, allowing users to spell letters just by thoughts. Due to the low signal-to-noise ratio of the P300, however, stimuli are repeatedly presented so that EEG signals can be averaged, which improves the accuracy but degrades the speed. The authors have proposed to discontinue the stimulus presentation adaptively to the P300 response and have shown its superiority in the performance over the standard way that presents a prefixed number of stimuli. In addition to this adaptive stimulus termination, this paper proposes to select stimuli to be presented to avoid presenting redundant stimuli. Both off-line and on-line experiments show that the proposed method is more effective than our conventional method.},
}
@article {pmid22255569,
year = {2011},
author = {Agashe, HA and Contreras-Vidal, JL},
title = {Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5444-5447},
doi = {10.1109/IEMBS.2011.6091389},
pmid = {22255569},
issn = {2694-0604},
support = {P01 HD064653/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; *Hand Strength ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; Task Performance and Analysis ; },
abstract = {With continued research on brain machine interfaces (BMIs), it is now possible to control prosthetic arm position in space to a high degree of accuracy. However, a reliable decoder to infer the dexterous movements of fingers from brain activity during a natural grasping motion is still to be demonstrated. Here, we present a methodology to accurately predict and reconstruct natural hand kinematics from non-invasively recorded scalp electroencephalographic (EEG) signals during object grasping movements. The high performance of our decoder is attributed to a combination of the correct input space (time-domain amplitude modulation of delta-band smoothed EEG signals) and an optimal subset of EEG electrodes selected using a genetic algorithm. Trajectories of the joint angles were reconstructed for metacarpo-phalangeal (MCP) joints of the fingers as well as the carpo-metacarpal (CMC) and MCP joints of the thumb. High decoding accuracy (Pearson's correlation coefficient, r) between the predicted and observed trajectories (r = 0.76 ± 0.01; averaged across joints) indicate that this technique may be suitable for use with a closed-loop real-time BMI to control grasping motion in prosthetics with high degrees of freedom. This demonstrates the first successful decoding of hand pre-shaping kinematics from noninvasive neural signals.},
}
@article {pmid22255568,
year = {2011},
author = {Wong, YT and Hagan, MA and Markowitz, DA and Pesaran, B},
title = {The tracking of reaches in three-dimensions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5440-5443},
pmid = {22255568},
issn = {2694-0604},
support = {R01-MH087882/MH/NIMH NIH HHS/United States ; T32-MH19624/MH/NIMH NIH HHS/United States ; P30 EY013079/EY/NEI NIH HHS/United States ; T32 EY007136/EY/NEI NIH HHS/United States ; R01 MH087882/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Arm/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Macaca mulatta ; Male ; Movement/*physiology ; *Task Performance and Analysis ; },
abstract = {Prosthetic devices to replace upper limb function have made great progress over the last decade. However, current control modalities for these prosthetics still have severe limitations in the degrees of freedom they offer patients. Brain machine interfaces offer the possibility to improve the functionality of prosthetics. Current research on brain machine interfaces is limited by our understanding of the neural representations for various movements. Few electrophysiology studies have examined the encoding of unconstrained multi-joint movements in neural signals. Here we present a system for the high-speed tracking of multiple joints in three dimensions while recording, optimizing and decoding neural signals.},
}
@article {pmid22255567,
year = {2011},
author = {Orsborn, AL and Dangi, S and Moorman, HG and Carmena, JM},
title = {Exploring time-scales of closed-loop decoder adaptation in brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5436-5439},
doi = {10.1109/IEMBS.2011.6091387},
pmid = {22255567},
issn = {2694-0604},
mesh = {*Algorithms ; Animals ; Arm/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback ; Humans ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {Performing closed-loop modifications of a brain-machine interface (BMI) decoder is a technique that shows great promise for improving performance. We compare two algorithms for implementing adaptations that update decoder parameters on different time-scales (discrete batches vs. online), and present experimental results of a non-human primate performing a standard center-out BMI task. To ensure that our experimental training models are representative of a broad range of paralyzed patients, our decoders were initially trained using neural activity recorded during subject observation of cursor movement. We find that both closed-loop adaptation algorithms can be used to boost BMI performance from 20-30% to 80%, yielding movement kinematics similar to natural arm movements. Based on insights derived from the performance of each algorithm, we propose that a hybrid of batch and online decoder adaptation may be the best approach.},
}
@article {pmid22255564,
year = {2011},
author = {Onaran, I and Ince, NF and Cetin, AE},
title = {Classification of multichannel ECoG related to individual finger movements with redundant spatial projections.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5424-5427},
doi = {10.1109/IEMBS.2011.6091341},
pmid = {22255564},
issn = {2694-0604},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We tackle the problem of classifying multichannel electrocorticogram (ECoG) related to individual finger movements for a brain machine interface (BMI). For this particular aim we applied a recently developed hierarchical spatial projection framework of neural activity for feature extraction from ECoG. The algorithm extends the binary common spatial patterns algorithm to multiclass problem by constructing a redundant set of spatial projections that are tuned for paired and group-wise discrimination of finger movements. The groupings were constructed by merging the data of adjacent fingers and contrasting them to the rest, such as the first two fingers (thumb and index) vs. the others (middle, ring and little). We applied this framework to the BCI competition IV ECoG data recorded from three subjects. We observed that the maximum classification accuracy was obtained from the gamma frequency band (65200 Hz). For this particular frequency range the average classification accuracy over three subjects was 86.3%. These results indicate that the redundant spatial projection framework can be used successfully in decoding finger movements from ECoG for BMI.},
}
@article {pmid22255558,
year = {2011},
author = {Bink, H and Lai, Y and Saudari, SR and Helfer, B and Viventi, J and Van der Spiegel, J and Litt, B and Kagan, C},
title = {Flexible organic electronics for use in neural sensing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5400-5403},
pmid = {22255558},
issn = {2694-0604},
support = {U24 NS063930/NS/NINDS NIH HHS/United States ; 2T32HL007954/HL/NHLBI NIH HHS/United States ; R01 NS048598/NS/NINDS NIH HHS/United States ; T32 HL007954/HL/NHLBI NIH HHS/United States ; R01-NS041811/NS/NINDS NIH HHS/United States ; R01 NS 48598/NS/NINDS NIH HHS/United States ; R01 NS041811/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Amplifiers, Electronic ; Elastic Modulus ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Neurons/*physiology ; Organic Chemicals/*chemistry ; Reproducibility of Results ; Sensitivity and Specificity ; *Transistors, Electronic ; },
abstract = {Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. High density, active electrode arrays hold great promise in enabling high-resolution interface with the brain to access and influence this network activity. Integrating flexible electronic devices directly at the neural interface can enable thousands of multiplexed electrodes to be connected using many fewer wires. Active electrode arrays have been demonstrated using flexible, inorganic silicon transistors. However, these approaches may be limited in their ability to be cost-effectively scaled to large array sizes (8 × 8 cm). Here we show amplifiers built using flexible organic transistors with sufficient performance for neural signal recording. We also demonstrate a pathway for a fully integrated, amplified and multiplexed electrode array built from these devices.},
}
@article {pmid22255549,
year = {2011},
author = {Guenther, FH and Brumberg, JS},
title = {Brain-machine interfaces for real-time speech synthesis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5360-5363},
pmid = {22255549},
issn = {2694-0604},
support = {R29 DC002852/DC/NIDCD NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R01 DC002852/DC/NIDCD NIH HHS/United States ; DC002852/DC/NIDCD NIH HHS/United States ; DC007683/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Biofeedback, Psychology/*instrumentation ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Computer Systems ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Imagination ; Pilot Projects ; Quadriplegia/*rehabilitation ; Therapy, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {This paper reports on studies involving brain-machine interfaces (BMIs) that provide near-instantaneous audio feedback from a speech synthesizer to the BMI user. In one study, neural signals recorded by an intracranial electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome were transmitted wirelessly across the scalp and used to drive a formant synthesizer, allowing the user to produce vowels. In a second, pilot study, a neurologically normal user was able to drive the formant synthesizer with imagined movements detected using electroencephalography. Our results support the feasibility of neural prostheses that have the potential to provide near-conversational synthetic speech for individuals with severely impaired speech output.},
}
@article {pmid22255507,
year = {2011},
author = {Vernon, S and Joshi, SS},
title = {Multidimensional control using a mobile-phone based brain-muscle-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5188-5194},
doi = {10.1109/IEMBS.2011.6091284},
pmid = {22255507},
issn = {2694-0604},
support = {UL1 RR024146/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Biofeedback, Psychology/*instrumentation ; Brain/*physiopathology ; *Cell Phone ; Electroencephalography/*instrumentation ; Electromyography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Male ; Muscle Contraction ; Muscle, Skeletal/*physiopathology ; Muscular Atrophy, Spinal/physiopathology/rehabilitation ; Therapy, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {Many well-known brain-computer interfaces measure signals at the brain, and then rely on the brain's ability to learn via operant conditioning in order to control objects in the environment. In our lab, we have been developing brain-muscle-computer interfaces, which measure signals at a single muscle and then rely on the brain's ability to learn neuromuscular skills via operant conditioning. Here, we report a new mobile-phone based brain-muscle-computer interface prototype for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single sEMG signal. Electromyographic activity on the surface of a single face muscle (Auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone. User-modulated power in two separate frequency band serves as two separate and simultaneous control channels for machine control. After signal processing, the Android phone sends commands to external devices via Bluetooth. Users are trained to use the device via biofeedback, with simple cursor-to-target activities on the phone screen.},
}
@article {pmid22255470,
year = {2011},
author = {Liu, M and Kuo, CC and Chiu, AW},
title = {Statistical threshold for nonlinear Granger Causality in motor intention analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {5036-5039},
doi = {10.1109/IEMBS.2011.6091247},
pmid = {22255470},
issn = {2694-0604},
support = {P20RR016456/RR/NCRR NIH HHS/United States ; },
mesh = {*Algorithms ; Cerebral Cortex/*physiology ; Data Interpretation, Statistical ; Differential Threshold/physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; *Intention ; Movement/*physiology ; Nonlinear Dynamics ; Reproducibility of Results ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {Directed influence between multiple channel signal measurements is important for the understanding of large dynamic systems. This research investigates a method to analyze large, complex multi-variable systems using directional flow measure to extract relevant information related to the functional connectivity between different units in the system. The directional flow measure was completed through nonlinear Granger Causality (GC) which is based on the nonlinear predictive models using radial basis functions (RBF). In order to extract relevant information from the causality map, we propose a threshold method that can be set up through a spatial statistical process where only the top 20% of causality pathways is shown. We applied this approach to a brain computer interface (BCI) application to decode the different intended arm reaching movement (left, right and forward) using 128 surface electroencephalography (EEG) electrodes. We also evaluated the importance of selecting the appropriate radius in the region of interest and found that the directions of causal influence of active brain regions were unique with respect to the intended direction.},
}
@article {pmid22255400,
year = {2011},
author = {Bastos, TF and Muller, SM and Benevides, AB and Sarcinelli-Filho, M},
title = {Robotic wheelchair commanded by SSVEP, motor imagery and word generation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4753-4756},
doi = {10.1109/IEMBS.2011.6091177},
pmid = {22255400},
issn = {2694-0604},
mesh = {Decision Trees ; Discriminant Analysis ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Robotics ; *Speech ; *Wheelchairs ; },
abstract = {This work presents a robotic wheelchair that can be commanded by a Brain Computer Interface (BCI) through Steady-State Visual Evoked Potential (SSVEP), Motor Imagery and Word Generation. When using SSVEP, a statistical test is used to extract the evoked response and a decision tree is used to discriminate the stimulus frequency, allowing volunteers to online operate the BCI, with hit rates varying from 60% to 100%, and guide a robotic wheelchair through an indoor environment. When using motor imagery and word generation, three mental task are used: imagination of left or right hand, and imagination of generation of words starting with the same random letter. Linear Discriminant Analysis is used to recognize the mental tasks, and the feature extraction uses Power Spectral Density. The choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifier.},
}
@article {pmid22255369,
year = {2011},
author = {Kato, YX and Yonemura, T and Samejima, K and Maeda, T and Ando, H},
title = {Development of a BCI master switch based on single-trial detection of contingent negative variation related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4629-4632},
doi = {10.1109/IEMBS.2011.6091146},
pmid = {22255369},
issn = {2694-0604},
mesh = {Adult ; Brain/*physiology ; Humans ; Male ; *Man-Machine Systems ; },
abstract = {To control the startup/shutdown of a conventional brain-computer interface (BCI) that is always running for daily use, we proposed and developed a new BCI system called a BCI master switch. We designed it with on/off switching functions by detecting the contingent negative variation (CNV)--related potentials. We chose CNV to improve the single-trial discrimination of user intentions to switch because CNV had a high signal-to-noise ratio and needed high concentration for its elicitation. We also applied a support vector machine (SVM) to improve the single-trial detection of CNV-related potentials. As the best parameters of SVM were estimated and applied, the offline evaluation's best performance achieved a CNV detection rate of 99.3% for the intention to switch and 2.1% for the intention not to switch. Remarkably, this performance was achieved from single-trial detection, imaginary response of user's intention without physical reaction, and the data from only one recording electrode. These results suggest that our proposed BCI system might work as a master switch by single-trial detection.},
}
@article {pmid22255368,
year = {2011},
author = {Wilson, JA},
title = {Using general-purpose graphic processing units for BCI systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4625-4628},
doi = {10.1109/IEMBS.2011.6091145},
pmid = {22255368},
issn = {2694-0604},
mesh = {Action Potentials ; Brain/*physiology ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; },
abstract = {BioMEMS electrode array fabrication techniques are used to develop high-density arrays with hundreds of channels. However, it was previously impossible to process more than a fraction of these channels real-time for online BCI experiments due to computational resource restraints. It is now possible to use graphics processing units (GPUs), which can have several hundred processing cores each, to processes large amounts of data quickly. This paper summarizes advances in using GPUs for BCI processing for EEG, ECoG, and micro-electrode systems, with speedups of more than 30 times that of current state-of-the-art CPU-based BCI implementations.},
}
@article {pmid22255366,
year = {2011},
author = {Mountney, J and Obeid, I and Silage, D},
title = {Modular particle filtering FPGA hardware architecture for brain machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4617-4620},
doi = {10.1109/IEMBS.2011.6091143},
pmid = {22255366},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Computers ; Humans ; *Man-Machine Systems ; },
abstract = {As the computational complexities of neural decoding algorithms for brain machine interfaces (BMI) increase, their implementation through sequential processors becomes prohibitive for real-time applications. This work presents the field programmable gate array (FPGA) as an alternative to sequential processors for BMIs. The reprogrammable hardware architecture of the FPGA provides a near optimal platform for performing parallel computations in real-time. The scalability and reconfigurability of the FPGA accommodates diverse sets of neural ensembles and a variety of decoding algorithms. Throughput is significantly increased by decomposing computations into independent parallel hardware modules on the FPGA. This increase in throughput is demonstrated through a parallel hardware implementation of the auxiliary particle filtering signal processing algorithm.},
}
@article {pmid22255365,
year = {2011},
author = {Wang, D and Hao, Y and Zhu, X and Zhao, T and Wang, Y and Chen, Y and Chen, W and Zheng, X},
title = {FPGA implementation of hardware processing modules as coprocessors in brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4613-4616},
doi = {10.1109/IEMBS.2011.6091142},
pmid = {22255365},
issn = {2694-0604},
mesh = {Action Potentials ; Brain/*physiology ; Humans ; *Man-Machine Systems ; Neural Networks, Computer ; Probability ; },
abstract = {Real-time computation, portability and flexibility are crucial for practical brain-machine interface (BMI) applications. In this work, we proposed Hardware Processing Modules (HPMs) as a method for accelerating BMI computation. Two HPMs have been developed. One is the field-programmable gate array (FPGA) implementation of spike sorting based on probabilistic neural network (PNN), and the other is the FPGA implementation of neural ensemble decoding based on Kalman filter (KF). These two modules were configured under the same framework and tested with real data from motor cortex recording in rats performing a lever-pressing task for water rewards. Due to the parallelism feature of FPGA, the computation time was reduced by several dozen times, while the results are almost the same as those from Matlab implementations. Such HPMs provide a high performance coprocessor for neural signal computation.},
}
@article {pmid22255363,
year = {2011},
author = {Kahn, K and Sheiber, M and Thakor, N and Sarma, SV},
title = {Neuron selection for decoding dexterous finger movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4605-4608},
doi = {10.1109/IEMBS.2011.6091140},
pmid = {22255363},
issn = {2694-0604},
mesh = {Animals ; Biomechanical Phenomena ; Fingers/*physiology ; Macaca mulatta ; Male ; *Movement ; Neurons/*physiology ; },
abstract = {Many brain machine interfaces (BMI) seek to use the activity from hundreds of simultaneously recorded neurons to reconstruct an individual's kinematics. However, many of these neurons are not task related since there is no way to surgically target those neurons. This causes model based decoding to suffer easily from over-fitting on noisy unrelated neurons. Previous methods, such as correlation analysis and sensitivity analysis, seek to select neurons based on which reduced order model best matches the ensemble model and thus does not worry about over fitting. To address this issue, this paper presents a new method, cross model validation, that ranks neuron importance on the neuron model's ability to generalize well to data from correct movements and poorly to data from incorrect movements. This method attempts to highlight the neurons that are able to distinguish between movements the best and decode accurately. Selecting neurons using cross model validation scores as opposed to randomly selecting them can increase decoding accuracy up to 2.5 times or by 44%. These results showcase the importance of neuron selection in decoding and the ability of cross model validation in discerning each neuron's utility in decoding.},
}
@article {pmid22255362,
year = {2011},
author = {Reuderink, B and Farquhar, J and Poel, M and Nijholt, A},
title = {A subject-independent brain-computer interface based on smoothed, second-order baselining.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4600-4604},
doi = {10.1109/IEMBS.2011.6091139},
pmid = {22255362},
issn = {2694-0604},
mesh = {Brain/*physiology ; Calibration ; Humans ; *Man-Machine Systems ; },
abstract = {A brain-computer interface (BCI) enables direct communication from the brain to devices, bypassing the traditional pathway of peripheral nerves and muscles. Traditional approaches to BCIs require the user to train for weeks or even months to learn to control the BCI. In contrast, BCIs based on machine learning only require a calibration session of less than an hour before the system can be used, since the machine adapts to the user's existing brain signals. However, this calibration session has to be repeated before each use of the BCI due to inter-session variability, which makes using a BCI still a time-consuming and an error-prone enterprise. In this work, we present a second-order baselining procedure that reduces these variations, and enables the creation of a BCI that can be applied to new subjects without such a calibration session. The method was validated with a motor-imagery classification task performed by 109 subjects. Results showed that our subject-independent BCI without calibration performs as well as the popular common spatial patterns (CSP)-based BCI that does use a calibration session.},
}
@article {pmid22255361,
year = {2011},
author = {López-Larraz, E and Iterate, I and Escolano, C and García, I and Montesano, L and Minguez, J},
title = {Single-trial classification of feedback potentials within neurofeedback training with an EEG brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4596-4599},
doi = {10.1109/IEMBS.2011.6091138},
pmid = {22255361},
issn = {2694-0604},
mesh = {Brain/*physiology ; Calibration ; Electroencephalography/*methods ; *Feedback, Physiological ; Humans ; *Man-Machine Systems ; },
abstract = {Neurofeedback therapies are an emerging technique used to treat neuropsychological disorders and to enhance cognitive performance. The feedback stimuli presented during the therapy are a key factor, serving as guidance throughout the entire learning process of the brain rhythms. Online decoding of these stimuli could be of great value to measure the compliance and adherence of the subject to the training. This paper describes the modeling and classification of performance feedback potentials with a Brain-Computer Interface (BCI), under a real neurofeedback training with five subjects. LDA and SVM classification techniques are compared and are both able to provide an average performance of approximately 80%.},
}
@article {pmid22255360,
year = {2011},
author = {Szymanski, FD and Semprini, M and Mussa-Ivaldi, FA and Fadiga, L and Panzeri, S and Vato, A},
title = {Dynamic Brain-Machine Interface: a novel paradigm for bidirectional interaction between brains and dynamical systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4592-4595},
doi = {10.1109/IEMBS.2011.6091137},
pmid = {22255360},
issn = {2694-0604},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; },
abstract = {Brain-Machine Interfaces (BMIs) are systems which mediate communication between brains and artificial devices. Their long term goal is to restore motor functions, and this ultimately demands the development of a new generation of bidirectional brain-machine interfaces establishing a two-way brain-world communication channel, by both decoding motor commands from neural activity and providing feedback to the brain by electrical stimulation. Taking inspiration from how the spinal cord of vertebrates mediates communication between the brain and the limbs, here we present a model of a bidirectional brain-machine interface that interacts with a dynamical system by generating a control policy in the form of a force field. In our model, bidirectional communication takes place via two elements: (a) a motor interface decoding activities recorded from a motor cortical area, and (b) a sensory interface encoding the state of the controlled device into electrical stimuli delivered to a somatosensory area. We propose a specific mathematical model of the sensory and motor interfaces guiding a point mass moving in a viscous medium, and we demonstrate its performance by testing it on realistically simulated neural responses.},
}
@article {pmid22255359,
year = {2011},
author = {Presacco, A and Forrester, L and Contreras-Vidal, JL},
title = {Towards a non-invasive brain-machine interface system to restore gait function in humans.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4588-4591},
doi = {10.1109/IEMBS.2011.6091136},
pmid = {22255359},
issn = {2694-0604},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Electroencephalography ; Female ; *Gait ; Humans ; Male ; *Man-Machine Systems ; Middle Aged ; Young Adult ; },
abstract = {Before 2009, the feasibility of applying brain-machine interfaces (BMIs) to control prosthetic devices had been limited to upper limb prosthetics such as the DARPA modular prosthetic limb. Until recently, it was believed that the control of bipedal locomotion involved central pattern generators with little supraspinal control. Analysis of cortical dynamics with electroencephalography (EEG) was also prevented by the lack of analysis tools to deal with excessive signal artifacts associated with walking. Recently, Nicolelis and colleagues paved the way for the decoding of locomotion showing that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to decode bipedal kinematics in rhesus monkeys. However, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This study uses non-invasive EEG signals to decode human walking in six nondisabled adults. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs, to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded concurrently. Our results support the possibility of decoding human bipedal locomotion with EEG. The average of the correlation values (r) between predicted and recorded kinematics for the six subjects was 0.7 (± 0.12) for the right leg and 0.66 (± 0.11) for the left leg. The average signal-to-noise ratio (SNR) values for the predicted parameters were 3.36 (± 1.89) dB for the right leg and 2.79 (± 1.33) dB for the left leg. These results show the feasibility of developing non-invasive neural interfaces for volitional control of devices aimed at restoring human gait function.},
}
@article {pmid22255357,
year = {2011},
author = {Schreuder, M and Hohne, J and Treder, M and Blankertz, B and Tangermann, M},
title = {Performance optimization of ERP-based BCIs using dynamic stopping.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4580-4583},
doi = {10.1109/IEMBS.2011.6091134},
pmid = {22255357},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Evoked Potentials ; Humans ; *Man-Machine Systems ; },
abstract = {Brain-computer interfaces based on event-related potentials face a trade-off between the speed and accuracy of the system, as both depend on the number of iterations. Increasing the number of iterations leads to a higher accuracy but reduces the speed of the system. This trade-off is generally dealt with by finding a fixed number of iterations that give a good result on the calibration data. We show here that this method is sub optimal and increases the performance significantly in only one out of five datasets. Several alternative methods have been described in literature, and we test the generalization of four of them. One method, called rank diff, significantly increased the performance over all datasets. These findings are important, as they show that 1) one should be cautious when reporting the potential performance of a BCI based on post-hoc offline performance curves and 2) simple methods are available that do boost performance.},
}
@article {pmid22255356,
year = {2011},
author = {Higashi, H and Rutkowski, TM and Washizawa, Y and Cichocki, A and Tanaka, T},
title = {EEG auditory steady state responses classification for the novel BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4576-4579},
doi = {10.1109/IEMBS.2011.6091133},
pmid = {22255356},
issn = {2694-0604},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Humans ; *Man-Machine Systems ; },
abstract = {An auditory modality brain computer interface (BCI) is a novel and interesting paradigm in neurotechnology applications. The paper presents a concept of auditory steady state responses (ASSR) utilization for the novel BCI paradigm. Two EEG feature extraction approaches based on a bandpass filtering and an AR spectrum estimation are tested together with two classification schemes in order to validate the proposed auditory BCI paradigm. The resulting good classification scores of users intentional choices, of attending or not to the presented stimuli, support the hypothesis of the ASSR stimuli validity for a solid BCI paradigm.},
}
@article {pmid22255355,
year = {2011},
author = {Iáñez, E and Ùbeda, A and Azorín, JM},
title = {Multimodal human-machine interface based on a brain-computer interface and an electrooculography interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4572-4575},
doi = {10.1109/IEMBS.2011.6091132},
pmid = {22255355},
issn = {2694-0604},
mesh = {Brain/*physiology ; *Electrooculography ; Humans ; *Man-Machine Systems ; },
abstract = {This paper describes a multimodal interface that combines a Brain-Computer Interface (BCI) with an electrooculography (EOG) interface. The non-invasive spontaneous BCI registers the electrical brain activity through surface electrodes. The EOG interface detects the eye movements through electrodes placed on the face around the eyes. Both kind of signals are registered together and processed to obtain the mental task that the user is thinking and the eye movement performed by the user. Both commands (mental task and eye movement) are combined in order to move a dot in a graphic user interface (GUI). Several experimental tests have been made where the users perform a trajectory to get closer to some targets. To perform the trajectory the user moves the dot in a plane with the EOG interface and using the BCI the dot changes its height.},
}
@article {pmid22255354,
year = {2011},
author = {Gao, H and Ouyang, M and Zhang, D and Hong, B},
title = {An auditory brain-computer interface using virtual sound field.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4568-4571},
doi = {10.1109/IEMBS.2011.6091131},
pmid = {22255354},
issn = {2694-0604},
mesh = {Adult ; Auditory Cortex/*physiology ; Electroencephalography ; Female ; Humans ; Male ; *Man-Machine Systems ; },
abstract = {Brain-computer interfaces (BCIs) exploring the auditory communication channel might be preferable for amyotrophic lateral sclerosis (ALS) patients with poor sight or with the visual system being occupied for other uses. Spatial attention was proven to be able to modulate the event-related potentials (ERPs); yet up to now, there is no auditory BCI based on virtual sound field. In this study, auditory spatial attention was introduced by using stimuli in a virtual sound field. Subjects attended selectively to the virtual location of the target sound and discriminated its relevant properties. The concurrently recorded ERP components and the users' performance were compared with those of the paradigm where all sounds were presented in the frontal direction. The early ERP components (100-250 ms) and the simulated online accuracies indicated that spatial attention indeed added effective discriminative information for BCI classification. The proposed auditory paradigm using virtual sound field may lead to a high-performance and portable BCI system.},
}
@article {pmid22255353,
year = {2011},
author = {Zhang, D and Xu, H and Wu, W and Gao, S and Hong, B},
title = {Integrating the spatial profile of the N200 speller for asynchronous brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4564-4567},
doi = {10.1109/IEMBS.2011.6091130},
pmid = {22255353},
issn = {2694-0604},
mesh = {Algorithms ; Brain/*physiology ; Humans ; *Man-Machine Systems ; },
abstract = {The N200 speller is a novel brain-computer interface (BCI) paradigm utilizing the overt attention effects on motion onset visual evoked potentials (mVEP). However, the asynchronous performance of the N200 BCI has not been fully explored. In this paper, a novel algorithm was proposed, integrating the spatial profile of the visual speller to provide a more precise description of the mVEP responses. Most importantly, only control state data were used in the algorithm to train a classifier which can detect the non-control state effectively. Using offline recorded data, the asynchronous performance of the proposed algorithm was shown to be significantly better than that of a similar algorithm without using the spatial information. The proposed algorithm can be used for developing a practical, asynchronous N200 BCI system.},
}
@article {pmid22255352,
year = {2011},
author = {Acqualagna, L and Blankertz, B},
title = {A gaze independent spelling based on rapid serial visual presentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4560-4563},
doi = {10.1109/IEMBS.2011.6091129},
pmid = {22255352},
issn = {2694-0604},
mesh = {Adult ; Electrodes ; Evoked Potentials ; Female ; Humans ; Male ; Middle Aged ; *Visual Acuity ; },
abstract = {An event-related potential (ERP) speller is a brain computer interface (BCI) based on the detection on ERPs that can be used as spelling device for those people deprived of other means of communication. In the present online study we investigated in twelve participants the performance of an ERP speller based on the rapid serial visual presentation paradigm (RSVP). Three variants of the RSVP speller have been investigated regarding chromaticism and speed of stimulus presentation. All the subjects were able to successfully operate the RSVP speller and high mean symbol selection accuracies were reached in all conditions, (93.6% to 94.8%). Offline analysis revealed a possible mean spelling speed of about 2 symb/min for an optimized number of stimulus sequences. The RSVP speller is intuitive to use and it is gaze independent, which makes it suitable for patients with deterioration of oculomotor control.},
}
@article {pmid22255321,
year = {2011},
author = {Ahmadi, A and Jafari, R and Hart, J},
title = {Light-weight single trial EEG signal processing algorithms: computational profiling for low power design.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4426-4430},
doi = {10.1109/IEMBS.2011.6091098},
pmid = {22255321},
issn = {2694-0604},
mesh = {*Algorithms ; Brain/physiology ; Electroencephalography/*methods ; Fourier Analysis ; Humans ; Man-Machine Systems ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {Brain Computer Interface (BCI) systems translate brain rhythms into signals comprehensible by computers. BCI has numerous applications in the clinical domain, the computer gaming, and the military. Real-time analysis of single trial brain signals is a challenging task, due to the low SNR of the incoming signals, added noise due to muscle artifacts, and trial-to-trial variability. In this work we present a computationally lightweight classification method based on several time and frequency domain features. After preprocessing and filtering, wavelet transform and Short Time Fourier Transform (STFT) are used for feature extraction. Feature vectors which are extracted from θ and α frequency bands are classified using a Support Vector Machine (SVM) classifier. EEG data were recorded from 64 electrodes during three different Go/NoGo tasks. We achieved 91% classification accuracy for two-class discrimination. The high recognition rate and low computational complexity makes this approach a promising method for a BCI system running on wearable and mobile devices. Computational profiling shows that this method is suitable for real time signal processing implementation.},
}
@article {pmid22255280,
year = {2011},
author = {Ambrosini, E and Ferrante, S and Tibiletti, M and Schauer, T and Klauer, C and Ferrigno, G and Pedrocchi, A},
title = {An EMG-controlled neuroprosthesis for daily upper limb support: a preliminary study.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4259-4262},
doi = {10.1109/IEMBS.2011.6091057},
pmid = {22255280},
issn = {2694-0604},
mesh = {Arm/*physiology ; Electric Stimulation ; Electromyography/*methods ; Humans ; *Prostheses and Implants ; },
abstract = {MUNDUS is an assistive platform for recovering direct interaction capability of severely impaired people based on upper limb motor functions. Its main concept is to exploit any residual control of the end-user, thus being suitable for long term utilization in daily activities. MUNDUS integrates multimodal information (EMG, eye tracking, brain computer interface) to control different actuators, such as a passive exoskeleton for weight relief, a neuroprosthesis for arm motion and small motors for grasping. Within this project, the present work integreted a commercial passive exoskeleton with an EMG-controlled neuroprosthesis for supporting hand-to-mouth movements. Being the stimulated muscle the same from which the EMG was measured, first it was necessary to develop an appropriate digital filter to separate the volitional EMG and the stimulation response. Then, a control method aimed at exploiting as much as possible the residual motor control of the end-user was designed. The controller provided a stimulation intensity proportional to the volitional EMG. An experimental protocol was defined to validate the filter and the controller operation on one healthy volunteer. The subject was asked to perform a sequence of hand-to-mouth movements holding different loads. The movements were supported by both the exoskeleton and the neuroprosthesis. The filter was able to detect an increase of the volitional EMG as the weight held by the subject increased. Thus, a higher stimulation intensity was provided in order to support a more intense exercise. The study demonstrated the feasibility of an EMG-controlled neuroprosthesis for daily upper limb support on healthy subjects, providing a first step forward towards the development of the final MUNDUS platform.},
}
@article {pmid22255274,
year = {2011},
author = {Royer, AS and Rose, ML and He, B},
title = {Goal selection vs. process control in non-invasive brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4235-4238},
doi = {10.1109/IEMBS.2011.6091051},
pmid = {22255274},
issn = {2694-0604},
support = {R01EB006433/EB/NIBIB NIH HHS/United States ; R01EB007920/EB/NIBIB NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Humans ; *Man-Machine Systems ; },
abstract = {Today's brain-computer interfaces (BCIs) record the electrical signal from the cortex and use that signal to control an external device, such as a computer cursor, wheelchair, or neuroprosthetic. Two control strategies used by BCIs, process control and goal selection, differ in the amount of assistance the BCI system provides the user. This paper looks at non-invasive studies that directly compare goal selection to process control. In these studies, the assistance provided by a BCI using goal selection 1) increased the user's performance with the BCI and 2) resulted in an EEG signal that was more conducive to good performance.},
}
@article {pmid22255273,
year = {2011},
author = {López-Larraz, E and Creatura, M and Iturrate, I and Montesano, L and Minguez, J},
title = {EEG single-trial classification of visual, auditive and vibratory feedback potentials in Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4231-4234},
doi = {10.1109/IEMBS.2011.6091050},
pmid = {22255273},
issn = {2694-0604},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; *Feedback, Physiological ; Hearing ; Humans ; *Man-Machine Systems ; Touch ; Vision, Ocular ; },
abstract = {Feedback stimuli are fundamental components in Brain-Computer Interfaces. It is known that the presentation of feedback stimuli elicits certain brain potentials that can be measured and classified. As stimuli can be given through different sensory modalities, it is important to understand the effects of different types of feedback on brain responses and their impact on classification. This paper presents a protocol used to obtain brain potentials elicited by visual, auditive or vibrotactile feedback stimuli. Experiments were carried out with five different subjects for each modality. Four different single-trial classification strategies were compared, according to the information used to train the classifier, achieving a classification rate of approximately 80% for each modality.},
}
@article {pmid22255272,
year = {2011},
author = {Tonin, L and Carlson, T and Leeb, R and del R Millán, J},
title = {Brain-controlled telepresence robot by motor-disabled people.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4227-4230},
doi = {10.1109/IEMBS.2011.6091049},
pmid = {22255272},
issn = {2694-0604},
mesh = {*Persons with Disabilities ; Female ; Humans ; Male ; *Robotics ; },
abstract = {In this paper we present the first results of users with disabilities in mentally controlling a telepresence robot, a rather complex task as the robot is continuously moving and the user must control it for a long period of time (over 6 minutes) to go along the whole path. These two users drove the telepresence robot from their clinic more than 100 km away. Remarkably, although the patients had never visited the location where the telepresence robot was operating, they achieve similar performances to a group of four healthy users who were familiar with the environment. In particular, the experimental results reported in this paper demonstrate the benefits of shared control for brain-controlled telepresence robots. It allows all subjects (including novel BMI subjects as our users with disabilities) to complete a complex task in similar time and with similar number of commands to those required by manual control.},
}
@article {pmid22255271,
year = {2011},
author = {Contreras-Vidal, JL and Bradberry, TJ},
title = {Design principles for noninvasive brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4223-4226},
doi = {10.1109/IEMBS.2011.6091048},
pmid = {22255271},
issn = {2694-0604},
support = {P01HD064653-01/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Electromyography ; *Equipment Design ; Humans ; *Man-Machine Systems ; Muscles/innervation ; },
abstract = {With the advent of sophisticated prosthetic limbs, the challenge is now to develop and demonstrate optimal closed-loop control of the these limbs using neural measurements from single/multiple unit activity (SUA/MUA), electrocorticography (ECoG), local field potentials (LFP), scalp electroencephalography (EEG) or even electromyography (EMG) after targeted muscle reinnervation (TMR) in subjects with upper limb disarticulation. In this paper we propose design principles for developing a noninvasive EEG-based brain-machine interface (BMI) for dexterous control of a high degree-of-freedom, biologically realistic limb.},
}
@article {pmid22255270,
year = {2011},
author = {Müller-Putz, GR and Ofner, P and Kaiser, V and Clauzel, G and Neuper, C},
title = {Brisk movement imagination for the non-invasive control of neuroprostheses: a first attempt.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4219-4222},
doi = {10.1109/IEMBS.2011.6091047},
pmid = {22255270},
issn = {2694-0604},
mesh = {Adult ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; *Prostheses and Implants ; Spinal Cord Injuries/physiopathology/*therapy ; },
abstract = {The consequences of a spinal cord injury (SCI) are tremendous for the patients. The loss of motor functions, especially of grasping, leads to a dramatic decrease in quality of life. With the help of neuroprostheses, the grasp function can be substantially improved in cervical SCI patients. Nowadays, systems for grasp restoration can only be used by patients with preserved voluntary shoulder and elbow function. In patients with lesions above the 5th vertebra, not only the voluntary movements of the elbow are restricted, but also the overall number of preserved movements available for control purposes decreases. In this work, a new method for the non-invasive use of a Brain-Computer Interface (BCI) for the control of the hand and elbow function is presented.},
}
@article {pmid22255269,
year = {2011},
author = {Liu, R and Newman, GI and Ying, SH and Thakor, NV},
title = {Improved BCI performance with sequential hypothesis testing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4215-4218},
doi = {10.1109/IEMBS.2011.6091046},
pmid = {22255269},
issn = {2694-0604},
mesh = {Brain/*physiology ; Electroencephalography ; Humans ; *Man-Machine Systems ; *Models, Theoretical ; },
abstract = {One of the primary challenges in noninvasive brain-computer interface (BCI) control is low information transfer rate (ITR). An approach that employs a power-based sequential hypothesis testing (SHT) technique is presented for real-time detection of motor commands. Electroencephalogram (EEG) recordings obtained during a BCI task were first analyzed with a hypothesis testing (HT) method. Using serial analysis we minimized the time to determine a cued motor imagery cursor control decision. Experimental results show that the accuracy of the SHT method was above 80% for all the subjects (n = 3). The average decision time was 3.4 s, as compared with 6.0 s for the HT method. Moreover, the proposed SHT method has three times the information transfer rate (ITR) compared with the HT method.},
}
@article {pmid22255267,
year = {2011},
author = {Xu, K and Wang, Y and Zhang, S and Zhao, T and Wang, Y and Chen, W and Zheng, X},
title = {Comparisons between linear and nonlinear methods for decoding motor cortical activities of monkey.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4207-4210},
doi = {10.1109/IEMBS.2011.6091044},
pmid = {22255267},
issn = {2694-0604},
mesh = {Animals ; Macaca mulatta/*physiology ; Motor Cortex/*physiology ; Support Vector Machine ; },
abstract = {Brain Machine Interfaces (BMI) aim at building a direct communication link between the neural system and external devices. The decoding of neuronal signals is one of the important steps in BMI systems. Existing decoding methods commonly fall into two categories, i.e., linear methods and nonlinear methods. This paper compares the performance between the two kinds of methods in the decoding of motor cortical activities of a monkey. Kalman filter (KF) is chosen as an example of linear methods, and General Regression Neural Network (GRNN) and Support Vector Regression (SVR) are two nonlinear approaches evaluated in our work. The experiments are conducted to reconstruct 2D trajectories in a center-out task. The correlation coefficient (CC) and the root mean square error (RMSE) are used to assess the performance. The experimental results show that GRNN and SVR achieve better performance than Kalman filter with average improvements of about 30% in CC and 40% in RMSE. This demonstrates that nonlinear models can better encode the relationship between the neuronal signals and response. In addition, GRNN and SVR are more effective than Kalman filter on noisy data.},
}
@article {pmid22255266,
year = {2011},
author = {Healy, G and Smeaton, AF},
title = {Eye fixation related potentials in a target search task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4203-4206},
doi = {10.1109/IEMBS.2011.6091043},
pmid = {22255266},
issn = {2694-0604},
mesh = {Artificial Intelligence ; Electroencephalography ; *Evoked Potentials ; *Fixation, Ocular ; Humans ; },
abstract = {Typically BCI (Brain Computer Interfaces) are found in rehabilitative or restorative applications, often allowing users a medium of communication that is otherwise unavailable through conventional means. Recently, however, there is growing interest in using BCI to assist users in searching for images. A class of neural signals often leveraged in common BCI paradigms are ERPs (Event Related Potentials), which are present in the EEG (Electroencephalograph) signals from users in response to various sensory events. One such ERP is the P300, and is typically elicited in an oddball experiment where a subject's attention is orientated towards a deviant stimulus among a stream of presented images. It has been shown that these types of neural responses can be used to drive an image search or labeling task, where we can rank images by examining the presence of such ERP signals in response to the display of images. To date, systems like these have been demonstrated when presenting sequences of images containing targets at up to 10 Hz, however, the target images in these tasks do not necessitate any kind of eye movement for their detection because the targets in the images are quite salient. In this paper we analyse the presence of discriminating EEG signals when they are offset to the time of eye fixations in a visual search task where detection of target images does require eye fixations.},
}
@article {pmid22255265,
year = {2011},
author = {Ang, KK and Guan, C and Wang, C and Phua, KS and Tan, AH and Chin, ZY},
title = {Calibrating EEG-based motor imagery brain-computer interface from passive movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4199-4202},
doi = {10.1109/IEMBS.2011.6091042},
pmid = {22255265},
issn = {2694-0604},
mesh = {Brain/*physiology ; Calibration ; Electroencephalography/*methods ; Feasibility Studies ; Humans ; *Man-Machine Systems ; *Movement ; },
abstract = {EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.},
}
@article {pmid22255264,
year = {2011},
author = {Gwin, JT and Ferris, D},
title = {High-density EEG and independent component analysis mixture models distinguish knee contractions from ankle contractions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {4195-4198},
doi = {10.1109/IEMBS.2011.6091041},
pmid = {22255264},
issn = {2694-0604},
mesh = {Ankle/*physiopathology ; Electroencephalography/*methods ; Humans ; Knee/*physiopathology ; *Models, Theoretical ; },
abstract = {Decoding human motor tasks from single trial electroencephalography (EEG) signals can help scientists better understand cortical neurophysiology and may lead to brain computer interfaces (BCI) for motor augmentation. Spatial characteristics of EEG have been used to distinguish left from right hand motor imagery and motor action. We used independent component analysis (ICA) of EEG to distinguish right knee action from right ankle action. We recorded 264-channel EEG while 5 subjects performed a variety of knee and ankle exercises. An adaptive mixture independent component analysis (ICA) algorithm generated two distinct mixture models from a merged set of EEG signals (including both knee and ankle actions) without prior knowledge of the underlying exercise. The ICA mixture models parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach. The fit of the models to the single trial EEG signals distinguished knee exercises from ankle exercise with 90% accuracy. For 3 of 5 subjects, accuracy was 100%. Electrocortical current dipole locations revealed significant differences in the knee and ankle mixture models that were consistent with the somatotopy of the tasks. These data demonstrate that EEG mixture models can distinguish motor tasks that have different somatotopic arrangements, even within the same brain hemisphere.},
}
@article {pmid22255143,
year = {2011},
author = {Jiang, B and Wang, R and Zhang, Q and Zhang, J and Zheng, X and Zhao, T},
title = {A pilot study on two stage decoding strategies.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {3700-3703},
doi = {10.1109/IEMBS.2011.6090627},
pmid = {22255143},
issn = {2694-0604},
mesh = {Animals ; Male ; *Models, Neurological ; *Nerve Net ; Pilot Projects ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Brain-machine interfaces (BMIs) use neural activity related to motion parameters to enable brain directly control external devices. Some linear and nonlinear decoding techniques have been used successfully to infer arm trajectory from neural data. Unfortunately, these One stage decoding techniques can hardly get high accuracy and low computational demands at the same time. Here we introduce a Two Stage Model (TSM) which consists of two linear models, on the basis that different motion states have different neural firing patterns when rats were doing the lever pressing task. The accuracies of the neural firing patterns classification were higher than 90% for all the three datasets. The Correlation coefficients (CC) between the trajectory predicted by TSM and the measured one were up to 0.89, 0.85 and 0.95 for the three datasets respectively higher than those of Kalman Filter (KF) and Partial Least Squares Regression (PLSR). The time consumption of TSM was about only 10% of that of Generalized Regression Neural Network (GRNN). These results show that TSM can simultaneously get both high accuracy and low computational cost.},
}
@article {pmid22255140,
year = {2011},
author = {Artusi, X and Niazi, IK and Lucas, MF and Farina, D},
title = {Accuracy of a BCI based on movement-related and error potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {3688-3691},
doi = {10.1109/IEMBS.2011.6090624},
pmid = {22255140},
issn = {2694-0604},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; },
abstract = {New paradigms for brain computer interfacing (BCI), such as based on imagination of task characteristics, require long training periods, have limited accuracy, and lack adaptation to the changes in the users' conditions. Error potentials generated in response to an error made by the translation algorithm can be used to improve the performance of a BCI, as a feedback extracted from the user and fed into the BCI system. The present study addresses the inclusion of error potentials in a BCI system based on the decoding of movement-related cortical potentials (MRCPs). We theoretically quantify the improvement in accuracy of a BCI system when using error potentials for correcting the output decision, in the general case of multiclass classification. The derived theoretical expressions can be used during the design phase of any BCI system. They were applied to experimentally estimated accuracies in decoding MRCPs and error potentials. The average misclassification rate (n = 6 subjects) of MRCPs associated to the imagination of elbow flexions at two speeds was 26%, with a bit transfer rate of 0.17. The inclusion of error potentials, experimentally recorded and classified with misclassification rate of 20%, led to a theoretical error rate of 14% with a bit transfer rate of 0.30.},
}
@article {pmid22255139,
year = {2011},
author = {Kim, DW and Cho, JH and Hwang, HJ and Lim, JH and Im, CH},
title = {A vision-free brain-computer interface (BCI) paradigm based on auditory selective attention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {3684-3687},
doi = {10.1109/IEMBS.2011.6090623},
pmid = {22255139},
issn = {2694-0604},
mesh = {Adult ; Female ; *Hearing ; Humans ; Male ; *Man-Machine Systems ; *Vision, Ocular ; },
abstract = {Majority of the recently developed brain computer interface (BCI) systems have been using visual stimuli or visual feedbacks. However, the BCI paradigms based on visual perception might not be applicable to severe locked-in patients who have lost their ability to control their eye movement or even their vision. In the present study, we investigated the feasibility of a vision-free BCI paradigm based on auditory selective attention. We used the power difference of auditory steady-state responses (ASSRs) when the participant modulates his/her attention to the target auditory stimulus. The auditory stimuli were constructed as two pure-tone burst trains with different beat frequencies (37 and 43 Hz) which were generated simultaneously from two speakers located at different positions (left and right). Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6 sec), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.},
}
@article {pmid22254991,
year = {2011},
author = {Wodlinger, B and Degenhart, AD and Collinger, JL and Tyler-Kabara, EC and Wang, W},
title = {The impact of electrode characteristics on electrocorticography (ECoG).},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {3083-3086},
doi = {10.1109/IEMBS.2011.6090842},
pmid = {22254991},
issn = {2694-0604},
support = {R01 NS050256/NS/NINDS NIH HHS/United States ; 5 UL1RR024153/RR/NCRR NIH HHS/United States ; 3R01NS050256-05S1/NS/NINDS NIH HHS/United States ; },
mesh = {*Electrodes ; Electroencephalography/*instrumentation/methods ; Humans ; },
abstract = {Used clinically since Penfield and Jasper's pioneering work in the 1950's, electrocorticography (ECoG) has recently been investigated as a promising technology for brain-computer interfacing. Many researchers have attempted to analyze the properties of ECoG recordings, including prediction of optimal electrode spacing and the improved resolution expected with smaller electrodes. This work applies an analytic model of the volume conductor to investigate the sensitivity field of electrodes of various sizes. The benefit to spatial resolution was minimal for electrodes smaller than ~1mm, while smaller electrodes caused a dramatic decrease in signal-to-noise ratio. The temporal correlation between electrode pairs is predicted over a range of spacings and compared to correlation values from a series of recordings in subjects undergoing monitoring for intractable epilepsy. The observed correlations are found to be much higher than predicted by the analytic model and suggest a more detailed model of cortical activity is needed to identify appropriate ECoG grid spacing.},
}
@article {pmid22254974,
year = {2011},
author = {Al-Armaghany, A and Yu, B and Mak, T and Tong, KF and Sun, Y},
title = {Feasibility study for future implantable neural-silicon interface devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {3009-3015},
doi = {10.1109/IEMBS.2011.6090825},
pmid = {22254974},
issn = {2694-0604},
mesh = {Feasibility Studies ; Man-Machine Systems ; *Prostheses and Implants ; *Silicon ; },
abstract = {The emerging neural-silicon interface devices bridge nerve systems with artificial systems and play a key role in neuro-prostheses and neuro-rehabilitation applications. Integrating neural signal collection, processing and transmission on a single device will make clinical applications more practical and feasible. This paper focuses on the wireless antenna part and real-time neural signal analysis part of implantable brain-machine interface (BMI) devices. We propose to use millimeter-wave for wireless connections between different areas of a brain. Various antenna, including microstrip patch, monopole antenna and substrate integrated waveguide antenna are considered for the intra-cortical proximity communication. A Hebbian eigenfilter based method is proposed for multi-channel neuronal spike sorting. Folding and parallel design techniques are employed to explore various structures and make a trade-off between area and power consumption. Field programmable logic arrays (FPGAs) are used to evaluate various structures.},
}
@article {pmid22254961,
year = {2011},
author = {Frewin, CL and Locke, C and Saddow, SE and Weeber, EJ},
title = {Single-crystal cubic silicon carbide: an in vivo biocompatible semiconductor for brain machine interface devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {2957-2960},
doi = {10.1109/IEMBS.2011.6090582},
pmid = {22254961},
issn = {2694-0604},
mesh = {*Biocompatible Materials ; Carbon Compounds, Inorganic/*chemistry ; *Man-Machine Systems ; Microscopy, Fluorescence ; *Semiconductors ; Silicon Compounds/*chemistry ; },
abstract = {Single crystal silicon carbide (SiC) is a wide band-gap semiconductor which has shown both bio- and hemo-compatibility [1-5]. Although single crystalline SiC has appealing bio-sensing potential, the material has not been extensively characterized. Cubic silicon carbide (3C-SiC) has superior in vitro biocompatibility compared to its hexagonal counterparts [3, 5]. Brain machine interface (BMI) systems using implantable neuronal prosthetics offer the possibility of bi-directional signaling, which allow sensory feedback and closed loop control. Existing implantable neural interfaces have limited long-term reliability, and 3C-SiC may be a material that may improve that reliability. In the present study, we investigated in vivo 3C-SiC biocompatibility in the CNS of C56BL/6 mice. 3C-SiC was compared against the known immunoreactive response of silicon (Si) at 5, 10, and 35 days. The material was examined to detect CD45, a protein tyrosine phosphatase (PTP) expressed by activated microglia and macrophages. The 3C-SiC surface revealed limited immunoresponse and significantly reduced microglia compared to Si substrate.},
}
@article {pmid22254868,
year = {2011},
author = {Hamner, B and Leeb, R and Tavella, M and del R Millán, J},
title = {Phase-based features for motor imagery brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {2578-2581},
doi = {10.1109/IEMBS.2011.6090712},
pmid = {22254868},
issn = {2694-0604},
mesh = {Bayes Theorem ; Humans ; *Man-Machine Systems ; Motor Cortex/*physiology ; Probability ; *User-Computer Interface ; },
abstract = {Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject's motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase-locking value (PLV). In this study, we investigated the performance of various phase-based features, including instantaneous phase difference (IPD) and PLV, for control of a MI BCI. Patterns of phase synchrony differentially appear over the motor cortices and between the primary motor cortex (M1) and supplementary motor area (SMA) during MI. Offline results, along with preliminary online sessions, indicate that IPD serves as a robust control signal for differentiating between MI classes, and that the phase relations between channels are relatively stable over several months. Offline and online trial-level classification accuracies based on IPD ranged from 84% to 99%, whereas the performance for the corresponding amplitude features ranged from 70% to 100%.},
}
@article {pmid22254804,
year = {2011},
author = {Yu, B and Mak, T and Smith, L and Sun, Y and Yakovlev, A and Poon, CS},
title = {Memory efficient on-line streaming for multichannel spike train analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {2315-2318},
doi = {10.1109/IEMBS.2011.6090648},
pmid = {22254804},
issn = {2694-0604},
mesh = {Action Potentials/*physiology ; *Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Data Compression/*methods ; Electroencephalography/*methods ; Humans ; },
abstract = {Rapid advances in multichannel neural signal recording technologies in recent years have spawned broad applications in neuro-prostheses and neuro-rehabilitation. The dramatic increase in data bandwidth and volume associated with multichannel recording requires a significant computational effort which presents major design challenges for brain-machine interface (BMI) system in terms of power dissipation and hardware area. In this paper, we present a streaming method for implementing real-time memory efficient neural signal processing hardware. This method exploits the pseudo-stationary property of neural signals and, thus, eliminates the need of temporal storage in batch-based processing. The proposed technique can significantly reduce memory size and dynamic power while effectively maintaining the accuracy of algorithms. The streaming kernel is robust when compared to the batch processing over a range of BMI benchmark algorithms. The advantages of the streaming kernel when implemented on field-programmable gate array (FPGA) devices are also demonstrated.},
}
@article {pmid22254699,
year = {2011},
author = {Jain, A and Kim, I and Gluckman, BJ},
title = {Low cost electroencephalographic acquisition amplifier to serve as teaching and research tool.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {1888-1891},
pmid = {22254699},
issn = {2694-0604},
support = {R01 NS065096/NS/NINDS NIH HHS/United States ; R01NS065096/NS/NINDS NIH HHS/United States ; },
mesh = {*Amplifiers, Electronic ; Biomedical Research/*instrumentation ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We described the development and testing of a low cost, easily constructed electroencephalographic (EEG) acquisition amplifier for noninvasive Brain Computer Interface (BCI) education and research. The acquisition amplifier was constructed from newly available off-the-shelf integrated circuit components, and readily sends a 24-bit data stream via USB (Universal Serial Bus) to a computer platform. We demonstrate here the hardware's use in the analysis of a visually evoked P300 paradigm for a choose one-of-eight task. This clearly shows the applicability of this system as a low cost teaching and research tool.},
}
@article {pmid22254576,
year = {2011},
author = {Faith, A and Chen, Y and Rikakis, T and Iasemidis, L},
title = {Interactive rehabilitation and dynamical analysis of scalp EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {1387-1390},
doi = {10.1109/IEMBS.2011.6090326},
pmid = {22254576},
issn = {2694-0604},
mesh = {Adult ; Aged ; Algorithms ; Biofeedback, Psychology/methods/*physiology ; Brain Mapping/*methods ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Rehabilitation/methods ; Reproducibility of Results ; Scalp/physiology ; Sensitivity and Specificity ; Stroke/physiopathology ; Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).},
}
@article {pmid22254555,
year = {2011},
author = {Nuyujukian, P and Fan, JM and Gilja, V and Kalanithi, PS and Chestek, CA and Shenoy, KV},
title = {Monkey models for brain-machine interfaces: the need for maintaining diversity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {1301-1305},
doi = {10.1109/IEMBS.2011.6090306},
pmid = {22254555},
issn = {2694-0604},
support = {DP1-OD006409/OD/NIH HHS/United States ; },
mesh = {Animals ; Biodiversity ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Macaca mulatta/*classification/*physiology ; *Models, Animal ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move toward next-generation systems, we face the question of which animal models will aid broader patient populations and achieve even higher performance, robustness, and functionality. We review here four general types of rhesus monkey models employed in BMI research, and describe two additional, complementary models. Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease.},
}
@article {pmid22254506,
year = {2011},
author = {Punsawad, Y and Wongsawat, Y},
title = {Multi-command SSVEP-based BCI system via single flickering frequency half-field stimulation pattern.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {1101-1104},
doi = {10.1109/IEMBS.2011.6090257},
pmid = {22254506},
issn = {2694-0604},
mesh = {Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; *Support Vector Machine ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {This paper proposes a half-field steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system to enhance the number of limited commands obtained from the existing SSVEP-based BCI methods. With the theory of vision perception and the concept of the existing half-field SSVEP-based BCI system, we propose the new stimulation pattern that, by using only one frequency, four commands can be generated with the average classification accuracy of approximately 77%. By using only one frequency, eye fatigue can be reduced. Furthermore, this method can be efficiently used to further increase the number of commands for the existing SSVEP-based BCI system.},
}
@article {pmid22254419,
year = {2011},
author = {Srinivasan, L},
title = {Variable-arrival-time reaching with the brain-machine interface: performance comparison on empirically-derived movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {750-752},
doi = {10.1109/IEMBS.2011.6090171},
pmid = {22254419},
issn = {2694-0604},
mesh = {Animals ; Brain/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; *Models, Neurological ; Movement/*physiology ; Primates ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Patients with paralysis will one day rely on clinically-available brain-machine interfaces (BMI) to facilitate activities of daily living. As such, the ability to generate dexterous reaching movements remains a prime target of BMI algorithms research. The Bayesian approach to BMI algorithms requires a statistical model to describe reaching movements. To date, available models have either required fixed targets or fixed arrival times, neither of which can be assumed under natural operating conditions. Recently, we described a generative reach model, GPFD-RSE, that simultaneously breaks both restrictions. This method combines the reach state equation (RSE) with General Purpose Filter Design (GPFD). In the following paper, we further compare GPFD-RSE against standard methods in simulated open-loop decoding using empirically-derived movements, as an adjunct to the idealized movements tested previously. Our results indicate that GPFD-RSE continues to outperform standard methods when reconstructing more realistic arm movements in simulation.},
}
@article {pmid22254249,
year = {2011},
author = {Fiedler, P and Pedrosa, P and Griebel, S and Fonseca, C and Vaz, F and Zanow, F and Haueisen, J},
title = {Novel flexible dry PU/TiN-multipin electrodes: first application in EEG measurements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2011},
number = {},
pages = {55-58},
doi = {10.1109/IEMBS.2011.6089895},
pmid = {22254249},
issn = {2694-0604},
mesh = {Adult ; Biocompatible Materials/chemistry ; Brain/physiology ; Elastic Modulus ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Humans ; Male ; Microarray Analysis/*instrumentation ; *Microelectrodes ; Pilot Projects ; Reproducibility of Results ; Sensitivity and Specificity ; Titanium/*chemistry ; },
abstract = {Dry biosignal electrodes for electro-encephalography (EEG) are an essential step for realization of ubiquitous EEG monitoring and brain computer interface technologies. We propose a novel electrode design with a specific shape for hair layer interfusion and reliable skin contact. An electrically conductive Titanium-Nitride (TiN) thin layer is deposited on a polyurethane substrate using a multiphase DC magnetron sputtering technique. In the current paper we describe the development and manufacturing of the electrode. Furthermore, we perform comparative EEG measurements with conventional Ag/AgCl electrodes in a 6-channel setup. Our results are promising, as the primary shape of the EEG is preserved in the signals of both electrodes sets, according to recordings of spontaneous EEG and visual evoked potentials. The variance of both signals is in the same order of magnitude. The Wilcoxon-Mann-Whitney two-sample rank-sum test revealed no significant differences for 25 of the 28 compared signal episodes. Hence, our novel electrodes show equivalent signal quality compared to conventional Ag/AgCl electrodes.},
}
@article {pmid22252304,
year = {2012},
author = {Birbaumer, N and Piccione, F and Silvoni, S and Wildgruber, M},
title = {Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI).},
journal = {Psychological research},
volume = {76},
number = {2},
pages = {183-191},
pmid = {22252304},
issn = {1430-2772},
mesh = {Amyotrophic Lateral Sclerosis/psychology ; Animals ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Conditioning, Classical/physiology ; Humans ; Paralysis/*psychology ; Rats ; Thinking ; *User-Computer Interface ; },
abstract = {The paper presents some speculations on the loss of voluntary responses and operant learning in long-term paralysis in human patients and curarized rats. Based on a reformulation of the ideomotor thinking hypothesis already described in the 19th century, we present evidence that instrumentally learned responses and intentional cognitive processes extinguish as a consequence of long-term complete paralysis in patients with amyotrophic lateral sclerosis (ALS). Preliminary data collected with ALS patients during extended and complete paralysis suggest semantic classical conditioning of brain activity as the only remaining communication possibility in those states.},
}
@article {pmid22249575,
year = {2012},
author = {Khan, OI and Farooq, F and Akram, F and Choi, MT and Han, SM and Kim, TS},
title = {Robust extraction of P300 using constrained ICA for BCI applications.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {50},
number = {3},
pages = {231-241},
pmid = {22249575},
issn = {1741-0444},
mesh = {Brain/*physiology ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.},
}
@article {pmid22244868,
year = {2012},
author = {Pires, G and Nunes, U and Castelo-Branco, M},
title = {Comparison of a row-column speller vs. a novel lateral single-character speller: assessment of BCI for severe motor disabled patients.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {123},
number = {6},
pages = {1168-1181},
doi = {10.1016/j.clinph.2011.10.040},
pmid = {22244868},
issn = {1872-8952},
mesh = {Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Persons with Disabilities ; Event-Related Potentials, P300/physiology ; Humans ; Neuromuscular Diseases/*rehabilitation ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {OBJECTIVE: Non-invasive brain-computer interface (BCI) based on electroencephalography (EEG) offers a new communication channel for people suffering from severe motor disorders. This paper presents a novel P300-based speller called lateral single-character (LSC). The LSC performance is compared to that of the standard row-column (RC) speller.<br><br>METHODS: We developed LSC, a single-character paradigm comprising all letters of the alphabet following an event strategy that significantly reduces the time for symbol selection, and explores the intrinsic hemispheric asymmetries in visual perception to improve the performance of the BCI. RC and LSC paradigms were tested by 10 able-bodied participants, seven participants with amyotrophic lateral sclerosis (ALS), five participants with cerebral palsy (CP), one participant with Duchenne muscular dystrophy (DMD), and one participant with spinal cord injury (SCI).<br><br>RESULTS: The averaged results, taking into account all participants who were able to control the BCI online, were significantly higher for LSC, 26.11 bit/min and 89.90% accuracy, than for RC, 21.91 bit/min and 88.36% accuracy. The two paradigms produced different waveforms and the signal-to-noise ratio was significantly higher for LSC. Finally, the novel LSC also showed new discriminative features.<br><br>CONCLUSIONS: The results suggest that LSC is an effective alternative to RC, and that LSC still has a margin for potential improvement in bit rate and accuracy.<br><br>SIGNIFICANCE: The high bit rates and accuracy of LSC are a step forward for the effective use of BCI in clinical applications.},
}
@article {pmid22244309,
year = {2012},
author = {Spüler, M and Bensch, M and Kleih, S and Rosenstiel, W and Bogdan, M and Kübler, A},
title = {Online use of error-related potentials in healthy users and people with severe motor impairment increases performance of a P300-BCI.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {123},
number = {7},
pages = {1328-1337},
doi = {10.1016/j.clinph.2011.11.082},
pmid = {22244309},
issn = {1872-8952},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Brain Mapping/methods ; Case-Control Studies ; Communication Devices for People with Disabilities ; Electrodes ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Skills Disorders/*physiopathology ; Online Systems ; Reproducibility of Results ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To investigate whether error-related potentials can be used to increase information transfer rate of a P3 brain-computer interface (BCI) in healthy and motor-impaired individuals.<br><br>METHODS: Extraction and classification of the error-related potential was performed offline on data recorded from six amyotrophic lateral sclerosis (ALS) patients. An online study with 17 healthy and six motor impaired participants followed, using a modified P3 speller to provide explicit feedback of spelled letters. On recognition of error-related potentials, the interface informed users that the incorrect letter was automatically deleted.<br><br>RESULTS: The offline cross-validation estimate of P3 speller data of six ALS patients increased bit rate by 0.44 bit/trial. During online copy spelling, the participants increased their bit rate by 0.52 bit/trial with the error correction system (ECS). Some participants performed free spelling and were able to increase their bit rate. Finally, we demonstrated that healthy participants could increase their bit rate by using a classifier pre-trained on other users' data.<br><br>CONCLUSIONS: Error-related potentials as a secondary source of information can be used to increase overall bit rate in a P3 BCI.<br><br>SIGNIFICANCE: The method should be made available to any patient using the P3 BCI for communication.},
}
@article {pmid22232595,
year = {2012},
author = {Buch, ER and Modir Shanechi, A and Fourkas, AD and Weber, C and Birbaumer, N and Cohen, LG},
title = {Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke.},
journal = {Brain : a journal of neurology},
volume = {135},
number = {Pt 2},
pages = {596-614},
pmid = {22232595},
issn = {1460-2156},
support = {//Intramural NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain Mapping ; Female ; Frontal Lobe/pathology/*physiopathology ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Nerve Fibers, Myelinated/pathology ; Nerve Net/pathology/physiopathology ; Neurons/pathology/*physiology ; Parietal Lobe/pathology/*physiopathology ; Stroke/pathology/*physiopathology ; },
abstract = {Chronic stroke patients with heterogeneous lesions, but no direct damage to the primary sensorimotor cortex, are capable of longitudinally acquiring the ability to modulate sensorimotor rhythms using grasping imagery of the affected hand. Volitional modulation of neural activity can be used to drive grasping functions of the paralyzed hand through a brain-computer interface. The neural substrates underlying this skill are not known. Here, we investigated the impact of individual patient's lesion pathology on functional and structural network integrity related to this volitional skill. Magnetoencephalography data acquired throughout training was used to derive functional networks. Structural network models and local estimates of extralesional white matter microstructure were constructed using T(1)-weighted and diffusion-weighted magnetic resonance imaging data. We employed a graph theoretical approach to characterize emergent properties of distributed interactions between nodal brain regions of these networks. We report that interindividual variability in patients' lesions led to differential impairment of functional and structural network characteristics related to successful post-training sensorimotor rhythm modulation skill. Patients displaying greater magnetoencephalography global cost-efficiency, a measure of information integration within the distributed functional network, achieved greater levels of skill. Analysis of lesion damage to structural network connectivity revealed that the impact on nodal betweenness centrality of the ipsilesional primary motor cortex, a measure that characterizes the importance of a brain region for integrating visuomotor information between frontal and parietal cortical regions and related thalamic nuclei, correlated with skill. Edge betweenness centrality, an analogous measure, which assesses the role of specific white matter fibre pathways in network integration, showed a similar relationship between skill and a portion of the ipsilesional superior longitudinal fascicle connecting premotor and posterior parietal visuomotor regions known to be crucially involved in normal grasping behaviour. Finally, estimated white matter microstructure integrity in regions of the contralesional superior longitudinal fascicle adjacent to primary sensorimotor and posterior parietal cortex, as well as grey matter volume co-localized to these specific regions, positively correlated with sensorimotor rhythm modulation leading to successful brain-computer interface control. Thus, volitional modulation of ipsilesional neural activity leading to control of paralyzed hand grasping function through a brain-computer interface after longitudinal training relies on structural and functional connectivity in both ipsilesional and contralesional parietofrontal pathways involved in visuomotor information processing. Extant integrity of this structural network may serve as a future predictor of response to longitudinal therapeutic interventions geared towards training sensorimotor rhythms in the lesioned brain, secondarily improving grasping function through brain-computer interface applications.},
}
@article {pmid22230230,
year = {2012},
author = {Faugeras, F and Rohaut, B and Weiss, N and Bekinschtein, T and Galanaud, D and Puybasset, L and Bolgert, F and Sergent, C and Cohen, L and Dehaene, S and Naccache, L},
title = {Event related potentials elicited by violations of auditory regularities in patients with impaired consciousness.},
journal = {Neuropsychologia},
volume = {50},
number = {3},
pages = {403-418},
doi = {10.1016/j.neuropsychologia.2011.12.015},
pmid = {22230230},
issn = {1873-3514},
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Aged ; Aged, 80 and over ; Auditory Cortex/*physiology ; Case-Control Studies ; Consciousness/*physiology ; Consciousness Disorders/*physiopathology ; Electroencephalography ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; Middle Aged ; },
abstract = {Improving our ability to detect conscious processing in non communicating patients remains a major goal of clinical cognitive neurosciences. In this perspective, several functional brain imaging tools are currently under development. Bedside cognitive event-related potentials (ERPs) derived from the EEG signal are a good candidate to explore consciousness in these patients because: (1) they have an optimal time resolution within the millisecond range able to monitor the stream of consciousness, (2) they are fully non-invasive and relatively cheap, (3) they can be recorded continuously on dedicated individual systems to monitor consciousness and to communicate with patients, (4) and they can be used to enrich patients' autonomy through brain-computer interfaces. We recently designed an original auditory rule extraction ERP test that evaluates cerebral responses to violations of temporal regularities that are either local in time or global across several seconds. Local violations led to an early response in auditory cortex, independent of attention or the presence of a concurrent visual task, while global violations led to a late and spatially distributed response that was only present when subjects were attentive and aware of the violations. In the present work, we report the results of this test in 65 successive recordings obtained at bedside from 49 non-communicating patients affected with various acute or chronic neurological disorders. At the individual level, we confirm the high specificity of the 'global effect': only conscious patients presented this proposed neural signature of conscious processing. Here, we also describe in details the respective neural responses elicited by violations of local and global auditory regularities, and we report two additional ERP effects related to stimuli expectancy and to task learning, and we discuss their relations to consciousness.},
}
@article {pmid22210463,
year = {2012},
author = {Kreilinger, A and Neuper, C and Müller-Putz, GR},
title = {Error potential detection during continuous movement of an artificial arm controlled by brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {50},
number = {3},
pages = {223-230},
pmid = {22210463},
issn = {1741-0444},
mesh = {Adult ; *Artificial Limbs ; Brain/*physiology ; Electroencephalography/methods ; Female ; Humans ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Patients who benefit from Brain-Computer Interfaces (BCIs) may have difficulties to generate more than one distinct brain pattern which can be used to control applications. Other BCI issues are low performance, accuracy, and, depending on the type of BCI, a long preparation and/or training time. This study aims to show possible solutions. First, we used time-coded motor imagery (MI) with only one pattern. Second, we reduced the training time by recording only 20 trials of active MI to set up a BCI classifier. Third, we investigated a way to record error potentials (ErrPs) during continuous feedback. Ten subjects controlled an artificial arm by performing MI over target time periods between 1 and 4 s. The subsequent movement of this arm served as continuous feedback. Discrete events, which are required to elicit ErrPs, were added by mounting blinking LEDs on top of the continuously moving arm to indicate the future movements. Time epochs after these events were used to evaluate ErrPs offline. The achieved error rate for the arm movement was on average 26.9%. Obtained ErrPs looked similar to results from the previous studies dealing with error detection and the detection rate was above chance level which is a positive outcome and encourages further investigation.},
}
@article {pmid22208124,
year = {2011},
author = {Thongpang, S and Richner, TJ and Brodnick, SK and Schendel, A and Kim, J and Wilson, JA and Hippensteel, J and Krugner-Higby, L and Moran, D and Ahmed, AS and Neimann, D and Sillay, K and Williams, JC},
title = {A micro-electrocorticography platform and deployment strategies for chronic BCI applications.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {259-265},
pmid = {22208124},
issn = {1550-0594},
support = {1R01EB009103-01/EB/NIBIB NIH HHS/United States ; R01 EB000856-10/EB/NIBIB NIH HHS/United States ; R01 EB000856-09/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB009103-04/EB/NIBIB NIH HHS/United States ; R01 EB009103-03/EB/NIBIB NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; 2R01EB000856-06/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Craniotomy ; Deep Brain Stimulation/instrumentation/methods ; Electrodes, Implanted ; Electroencephalography/instrumentation/*methods ; Epilepsy/*physiopathology ; Equipment Design ; Evoked Potentials ; Macaca fascicularis ; Microelectrodes ; Motor Cortex/*physiology/surgery ; *User-Computer Interface ; },
abstract = {Over the past decade, electrocorticography (ECoG) has been used for a wide set of clinical and experimental applications. Recently, there have been efforts in the clinic to adapt traditional ECoG arrays to include smaller recording contacts and spacing. These devices, which may be collectively called "micro-ECoG" arrays, are loosely defined as intercranial devices that record brain electrical activity on the sub-millimeter scale. An extensible 3D-platform of thin film flexible micro-scale ECoG arrays appropriate for Brain-Computer Interface (BCI) application, as well as monitoring epileptic activity, is presented. The designs utilize flexible film electrodes to keep the array in place without applying significant pressure to the brain and to enable radial subcranial deployment of multiple electrodes from a single craniotomy. Deployment techniques were tested in non-human primates, and stimulus-evoked activity and spontaneous epileptic activity were recorded. Further tests in BCI and epilepsy applications will make the electrode platform ready for initial human testing.},
}
@article {pmid22208123,
year = {2011},
author = {Ang, KK and Guan, C and Chua, KS and Ang, BT and Kuah, CW and Wang, C and Phua, KS and Chin, ZY and Zhang, H},
title = {A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {253-258},
doi = {10.1177/155005941104200411},
pmid = {22208123},
issn = {1550-0594},
mesh = {Adolescent ; Adult ; Aged ; Algorithms ; Case-Control Studies ; Electroencephalography/*methods ; Feedback, Sensory/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *Man-Machine Systems ; Middle Aged ; Motor Activity/physiology ; Stroke/physiopathology ; *Stroke Rehabilitation ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (mu=0.74) was significantly lower than finger tapping by 8 patients (mu=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (mu=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (mu=0.76) were not significantly different from the first session (mu=0.72, p=0.16), or from the on-line accuracies of the third independent test session (mu=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.},
}
@article {pmid22208122,
year = {2011},
author = {Silvoni, S and Ramos-Murguialday, A and Cavinato, M and Volpato, C and Cisotto, G and Turolla, A and Piccione, F and Birbaumer, N},
title = {Brain-computer interface in stroke: a review of progress.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {245-252},
doi = {10.1177/155005941104200410},
pmid = {22208122},
issn = {1550-0594},
mesh = {Electroencephalography/*methods ; Feedback, Physiological/physiology ; Humans ; Imagination/physiology ; *Man-Machine Systems ; Neuronal Plasticity ; *Self-Help Devices ; Stroke/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology has been used for rehabilitation after stroke and there are a number of reports involving stroke patients in BCI-feedback training. Most publications have demonstrated the efficacy of BCI technology in post-stroke rehabilitation using output devices such as Functional Electrical Stimulation, robot, and orthosis. The aim of this review is to focus on the progress of BCI-based rehabilitation strategies and to underline future challenges. A brief history of clinical BCI-approaches is presented focusing on stroke motor rehabilitation. A context for three approaches of a BCI-based motor rehabilitation program is outlined: the substitutive strategy, classical conditioning and operant conditioning. Furthermore, we include an overview of a pilot study concerning a new neuro-forcefeedback strategy. This pilot study involved healthy participants. Finally we address some challenges for future BCI-based rehabilitation.},
}
@article {pmid22208121,
year = {2011},
author = {Zickler, C and Riccio, A and Leotta, F and Hillian-Tress, S and Halder, S and Holz, E and Staiger-Sälzer, P and Hoogerwerf, EJ and Desideri, L and Mattia, D and Kübler, A},
title = {A brain-computer interface as input channel for a standard assistive technology software.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {236-244},
doi = {10.1177/155005941104200409},
pmid = {22208121},
issn = {1550-0594},
mesh = {Adult ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; *Man-Machine Systems ; Middle Aged ; Software ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Recently brain-computer interface (BCI) control was integrated into the commercial assistive technology product QualiWORLD (QualiLife Inc., Paradiso-Lugano, CH). Usability of the first prototype was evaluated in terms of effectiveness (accuracy), efficiency (information transfer rate and subjective workload/NASA Task Load Index) and user satisfaction (Quebec User Evaluation of Satisfaction with assistive Technology, QUEST 2.0) by four end-users with severe disabilities. Three assistive technology experts evaluated the device from a third person perspective. The results revealed high performance levels in communication and internet tasks. Users and assistive technology experts were quite satisfied with the device. However, none could imagine using the device in daily life without improvements. Main obstacles were the EEG-cap and low speed.},
}
@article {pmid22208120,
year = {2011},
author = {Thompson, DE and Huggins, JE},
title = {A multi-purpose brain-computer interface output device.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {230-235},
pmid = {22208120},
issn = {1550-0594},
support = {R21 HD054697/HD/NICHD NIH HHS/United States ; R21 HD054697-01A1/HD/NICHD NIH HHS/United States ; R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Persons with Disabilities/*rehabilitation ; Electroencephalography/*methods ; Equipment Design ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {While brain-computer interfaces (BCIs) are a promising alternative access pathway for individuals with severe motor impairments, many BCI systems are designed as stand-alone communication and control systems, rather than as interfaces to existing systems built for these purposes. An individual communication and control system may be powerful or flexible, but no single system can compete with the variety of options available in the commercial assistive technology (AT) market. BCls could instead be used as an interface to these existing AT devices and products, which are designed for improving access and agency of people with disabilities and are highly configurable to individual user needs. However, interfacing with each AT device and program requires significant time and effort on the part of researchers and clinicians. This work presents the Multi-Purpose BCI Output Device (MBOD), a tool to help researchers and clinicians provide BCI control of many forms of AT in a plug-and-play fashion, i.e., without the installation of drivers or software on the AT device, and a proof-of-concept of the practicality of such an approach. The MBOD was designed to meet the goals of target device compatibility, BCI input device compatibility, convenience, and intuitive command structure. The MBOD was successfully used to interface a BCI with multiple AT devices (including two wheelchair seating systems), as well as computers running Windows (XP and 7), Mac and Ubuntu Linux operating systems.},
}
@article {pmid22208119,
year = {2011},
author = {Tsui, CS and Gan, JQ and Hu, H},
title = {A self-paced motor imagery based brain-computer interface for robotic wheelchair control.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {225-229},
doi = {10.1177/155005941104200407},
pmid = {22208119},
issn = {1550-0594},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Feedback ; Humans ; Imagination/*physiology ; *Man-Machine Systems ; *Robotics ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {This paper presents a simple self-paced motor imagery based brain-computer interface (BCI) to control a robotic wheelchair. An innovative control protocol is proposed to enable a 2-class self-paced BCI for wheelchair control, in which the user makes path planning and fully controls the wheelchair except for the automatic obstacle avoidance based on a laser range finder when necessary. In order for the users to train their motor imagery control online safely and easily, simulated robot navigation in a specially designed environment was developed. This allowed the users to practice motor imagery control with the core self-paced BCI system in a simulated scenario before controlling the wheelchair. The self-paced BCI can then be applied to control a real robotic wheelchair using a protocol similar to that controlling the simulated robot. Our emphasis is on allowing more potential users to use the BCI controlled wheelchair with minimal training; a simple 2-class self paced system is adequate with the novel control protocol, resulting in a better transition from offline training to online control. Experimental results have demonstrated the usefulness of the online practice under the simulated scenario, and the effectiveness of the proposed self-paced BCI for robotic wheelchair control.},
}
@article {pmid22208118,
year = {2011},
author = {Aloise, F and Schettini, F and Aricò, P and Salinari, S and Guger, C and Rinsma, J and Aiello, M and Mattia, D and Cincotti, F},
title = {Asynchronous P300-based brain-computer interface to control a virtual environment: initial tests on end users.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {219-224},
doi = {10.1177/155005941104200406},
pmid = {22208118},
issn = {1550-0594},
mesh = {*Activities of Daily Living ; Aged ; Algorithms ; Amyotrophic Lateral Sclerosis/physiopathology/*rehabilitation ; Electroencephalography/*methods ; Environmental Monitoring/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Multiple Sclerosis/physiopathology/*rehabilitation ; *Self-Help Devices ; Stroke/physiopathology ; *Stroke Rehabilitation ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Motor disability and/or ageing can prevent individuals from fully enjoying home facilities, thus worsening their quality of life. Advances in the field of accessible user interfaces for domotic appliances can represent a valuable way to improve the independence of these persons. An asynchronous P300-based Brain-Computer Interface (BCI) system was recently validated with the participation of healthy young volunteers for environmental control. In this study, the asynchronous P300-based BCI for the interaction with a virtual home environment was tested with the participation of potential end-users (clients of a Frisian home care organization) with limited autonomy due to ageing and/or motor disabilities. System testing revealed that the minimum number of stimulation sequences needed to achieve correct classification had a higher intra-subject variability in potential end-users with respect to what was previously observed in young controls. Here we show that the asynchronous modality performed significantly better as compared to the synchronous mode in continuously adapting its speed to the users' state. Furthermore, the asynchronous system modality confirmed its reliability in avoiding misclassifications and false positives, as previously shown in young healthy subjects. The asynchronous modality may contribute to filling the usability gap between BCI systems and traditional input devices, representing an important step towards their use in the activities of daily living.},
}
@article {pmid22208117,
year = {2011},
author = {Ortner, R and Aloise, F and Prückl, R and Schettini, F and Putz, V and Scharinger, J and Opisso, E and Costa, U and Guger, C},
title = {Accuracy of a P300 speller for people with motor impairments: a comparison.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {214-218},
doi = {10.1177/155005941104200405},
pmid = {22208117},
issn = {1550-0594},
mesh = {Algorithms ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Middle Aged ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Stroke/physiopathology ; *Stroke Rehabilitation ; Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {A Brain-Computer Interface (BCI) provides a completely new output pathway that can provide an additional option for a person to express himself/herself if he/she suffers a disorder like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles. For a P300 based BCI a matrix of randomly flashing characters is presented to the participant. To spell a character the person has to attend to it and to count how many times the character flashes. Although most BCIs are designed to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on people suffering motor impairments, such as stroke or ALS, to measurements performed on healthy people. The overall accuracy of the persons with motor impairments reached 70.1% in comparison to 91% obtained for the group of healthy subjects. When looking at single subjects, one interesting example shows that under certain circumstances, when it is difficult for a patient to concentrate on one character for a longer period of time, the accuracy is higher when fewer flashes (i.e., stimuli) are presented. Furthermore, the influence of several tuning parameters is discussed as it shows that for some participants adaptations for achieving valuable spelling results are required. Finally, exclusion criteria for people who are not able to use the device are defined.},
}
@article {pmid22208116,
year = {2011},
author = {Fazel-Rezai, R and Gavett, S and Ahmad, W and Rabbi, A and Schneider, E},
title = {A comparison among several P300 brain-computer interface speller paradigms.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {209-213},
doi = {10.1177/155005941104200404},
pmid = {22208116},
issn = {1550-0594},
mesh = {Adult ; Algorithms ; Analysis of Variance ; *Communication Devices for People with Disabilities ; Discriminant Analysis ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Man-Machine Systems ; Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Since the brain-computer interface (BCI) speller was first proposed by Farwell and Donchin, there have been modifications in the visual aspects of P300 paradigms. Most of the changes are based on the original matrix format such as changes in the number of rows and columns, font size, flash/ blank time, and flash order. The improvement in the resulting accuracy and speed of such systems has always been the ultimate goal. In this study, we have compared several different speller paradigms including row-column, single character flashing, and two region-based paradigms which are not based on the matrix format. In the first region-based paradigm, at the first level, characters and symbols are distributed over seven regions alphabetically, while in the second region-based paradigm they are distributed in the most frequently used order. At the second level, each one of the regions is further subdivided into seven subsets. The experimental results showed that the average accuracy and user acceptability for two region-based paradigms were higher than those for traditional paradigms such as row/column and single character.},
}
@article {pmid22208115,
year = {2011},
author = {Sellers, EW},
title = {Clinical applications of brain-computer interface technology.},
journal = {Clinical EEG and neuroscience},
volume = {42},
number = {4},
pages = {IV-V},
doi = {10.1177/155005941104200403},
pmid = {22208115},
issn = {1550-0594},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R21 DC010470-01/DC/NIDCD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Biofeedback, Psychology ; Brain/*physiology ; Cognition/physiology ; Electroencephalography/*trends ; Humans ; *Man-Machine Systems ; Self-Help Devices/*trends ; Signal Processing, Computer-Assisted ; Stroke Rehabilitation ; *User-Computer Interface ; },
}
@article {pmid23493871,
year = {2012},
author = {Joshi, R and Saraswat, P and Gajendran, R},
title = {A Novel Mu Rhythm-based Brain Computer Interface Design that uses a Programmable System on Chip.},
journal = {Journal of medical signals and sensors},
volume = {2},
number = {1},
pages = {11-16},
pmid = {23493871},
issn = {2228-7477},
abstract = {This paper describes the system design of a portable and economical mu rhythm based Brain Computer Interface which employs Cypress Semiconductors Programmable System on Chip (PSoC). By carrying out essential processing on the PSoC, the use of an extra computer is eliminated, resulting in considerable cost savings. Microsoft Visual Studio 2005 and PSoC Designer 5.01 are employed in developing the software for the system, the hardware being custom designed. In order to test the usability of the BCI, preliminary testing is carried out by training three subjects who were able to demonstrate control over their electroencephalogram by moving a cursor present at the center of the screen towards the indicated direction with an average accuracy greater than 70% and a bit communication rate of up to 7 bits/min.},
}
@article {pmid22206841,
year = {2012},
author = {Pfurtscheller, G and Bauernfeind, G and Neuper, C and Lopes da Silva, FH},
title = {Does conscious intention to perform a motor act depend on slow prefrontal (de)oxyhemoglobin oscillations in the resting brain?.},
journal = {Neuroscience letters},
volume = {508},
number = {2},
pages = {89-94},
doi = {10.1016/j.neulet.2011.12.025},
pmid = {22206841},
issn = {1872-7972},
mesh = {Blood Pressure ; Brain/metabolism/physiology ; Electroencephalography ; Fingers/physiology ; Heart Rate ; Hemoglobins/*metabolism ; Humans ; Intention ; *Motor Activity ; Movement ; Oxyhemoglobins/*metabolism ; Periodicity ; Prefrontal Cortex/*metabolism ; },
abstract = {Characteristically within the resting brain there are slow fluctuations (around 0.1Hz) of EEG and NIRS-(de)oxyhemoglobin ([deoxy-Hb], [oxy-Hb]) signals. An interesting question is whether such slow oscillations can be related to the intention to perform a motor act. To obtain an answer we analyzed continuous blood pressure (BP), heart rate (HR), prefrontal [oxy-Hb], [deoxy-Hb] and EEG signals over sensorimotor areas in 10 healthy subjects during 5min of rest and during 10min of voluntary finger movements. Analyses of prefrontal [oxy-Hb]/[deoxy-Hb] oscillations around 0.1Hz and central EEG band power changes in the beta (alpha) band revealed that the positive [oxy-Hb] peaks preceded the central EEG beta (alpha) power peak by 3.6±0.9s in the majority of subjects. A similar relationship between prefrontal [oxy-Hb] and central EEG beta power was found during voluntary movements whereby the post movement beta power increase (beta rebound) is known to coexist with a decreased excitability of cortico-spinal neurons. Therefore, we speculate that the beta power increase ∼3s after slow fluctuating [oxy-Hb] peaks during rest is indicative for a slow excitability change of central motor cortex neurons. This work provides the first evidence that initiation of finger movements at free will in relatively constant intervals around 10s could be temporally related to slow oscillations of prefrontal [oxy-Hb] and autonomic blood pressure in the resting brain.},
}
@article {pmid22203724,
year = {2012},
author = {Chang, HC and Lee, PL and Lo, MT and Lee, IH and Yeh, TK and Chang, CY},
title = {Independence of amplitude-frequency and phase calibrations in an SSVEP-based BCI using stepping delay flickering sequences.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {3},
pages = {305-312},
doi = {10.1109/TNSRE.2011.2180925},
pmid = {22203724},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Analog-Digital Conversion ; Brain/*physiology ; Calibration ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular ; Fourier Analysis ; Humans ; Male ; Online Systems ; Photic Stimulation/*methods ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {This study proposes a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) independent of amplitude-frequency and phase calibrations. Six stepping delay flickering sequences (SDFSs) at 32-Hz flickering frequency were used to implement a six-command BCI system. EEG signals recorded from Oz position were first filtered within 29-35 Hz, segmented based on trigger events of SDFSs to obtain SDFS epochs, and then stored separately in epoch registers. An epoch-average process suppressed the inter-SDFS interference. For each detection point, the latest six SDFS epochs in each epoch register were averaged and the normalized power of averaged responses was calculated. The visual target that induced the maximum normalized power was identified as the visual target. Eight subjects were recruited in this study. All subjects were requested to produce the "563241" command sequence four times. The averaged accuracy, command transfer interval, and information transfer rate (mean ± std.) values for all eight subjects were 97.38 ± 5.97%, 3.56 ± 0.68 s, and 42.46 ± 11.17 bits/min, respectively. The proposed system requires no calibration in either the amplitude-frequency characteristic or the reference phase of SSVEP which may provide an efficient and reliable channel for the neuromuscular disabled to communicate with external environments.},
}
@article {pmid22203722,
year = {2012},
author = {Ma, R and Aghasadeghi, N and Jarzebowski, J and Bretl, T and Coleman, TP},
title = {A stochastic control approach to optimally designing hierarchical flash sets in P300 communication prostheses.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {1},
pages = {102-112},
doi = {10.1109/TNSRE.2011.2179560},
pmid = {22203722},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Algorithms ; Brain/*physiology ; Data Interpretation, Statistical ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Models, Statistical ; Photic Stimulation ; Reproducibility of Results ; Software ; Stochastic Processes ; *User-Computer Interface ; Young Adult ; },
abstract = {The P300-based speller is a well-established brain-computer interface for communication. It displays a matrix of objects on the computer screen, flashes each object in sequence, and looks for a P300 response induced by flashing the desired object. Most existing P300 spellers uses a fixed set of flash objects. We demonstrate that performance can be significantly improved by sequential selections from a hierarchy of flash sets containing variable number of objects. Theoretically, the optimal hierarchy of flash sets--with respect to a given statistical language model--can be found by solving a stochastic control problem of low computational complexity. Experimentally, statistical analysis demonstrates that the average time per output character at 85% accuracy is reduced by over 50% using our variable-flash-set approach as compared to traditional fixed-flash-set spellers.},
}
@article {pmid22200635,
year = {2011},
author = {Tohyama, T and Fujiwara, T and Matsumoto, J and Honaga, K and Ushiba, J and Tsuji, T and Hase, K and Liu, M},
title = {Modulation of event-related desynchronization during motor imagery with transcranial direct current stimulation in a patient with severe hemiparetic stroke: a case report.},
journal = {The Keio journal of medicine},
volume = {60},
number = {4},
pages = {114-118},
doi = {10.2302/kjm.60.114},
pmid = {22200635},
issn = {1880-1293},
mesh = {Cerebral Infarction/complications/*psychology ; *Deep Brain Stimulation ; *Electroencephalography Phase Synchronization ; Humans ; *Imagination ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/physiopathology ; Motor Skills ; Neuroimaging ; Paresis/etiology/*psychology/therapy ; },
abstract = {Recently, surface electroencephalogram (EEG)-based brain-machine interfaces (BMI) have been used for people with disabilities. As a BMI signal source, event-related desynchronization of alpha-band EEG (8-13 Hz) during motor imagery (mu ERD), which is interpreted as desynchronized activities of the activated neurons, is commonly used. However, it is often difficult for patients with severe hemiparesis to produce mu ERD of sufficient strength to activate BMI. Therefore, whether it is possible to modulate mu ERD during motor imagery with anodal transcranial direct-current stimulation (tDCS) was assessed in a severe left hemiparetic stroke patient. EEG was recorded over the primary motor cortex (M1), and mu ERD during finger flexion imagery was measured before and after a 5-day course of tDCS applied to M1. The ERD recorded over the affected M1 increased significantly after tDCS intervention. Anodal tDCS may increase motor cortex excitability and potentiate ERD during motor imagery in patients with severe hemiparetic stroke.},
}
@article {pmid22183443,
year = {2012},
author = {Tu, T and Xin, Y and Gao, X and Gao, S},
title = {Chirp-modulated visual evoked potential as a generalization of steady state visual evoked potential.},
journal = {Journal of neural engineering},
volume = {9},
number = {1},
pages = {016008},
doi = {10.1088/1741-2560/9/1/016008},
pmid = {22183443},
issn = {1741-2552},
mesh = {Adult ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; Reaction Time/*physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Visual evoked potentials (VEPs) are of great concern in cognitive and clinical neuroscience as well as in the recent research field of brain-computer interfaces (BCIs). In this study, a chirp-modulated stimulation was employed to serve as a novel type of visual stimulus. Based on our empirical study, the chirp stimuli visual evoked potential (Chirp-VEP) preserved frequency features of the chirp stimulus analogous to the steady state evoked potential (SSVEP), and therefore it can be regarded as a generalization of SSVEP. Specifically, we first investigated the characteristics of the Chirp-VEP in the time-frequency domain and the fractional domain via fractional Fourier transform. We also proposed a group delay technique to derive the apparent latency from Chirp-VEP. Results on EEG data showed that our approach outperformed the traditional SSVEP-based method in efficiency and ease of apparent latency estimation. For the recruited six subjects, the average apparent latencies ranged from 100 to 130 ms. Finally, we implemented a BCI system with six targets to validate the feasibility of Chirp-VEP as a potential candidate in the field of BCIs.},
}
@article {pmid22180514,
year = {2012},
author = {Chi, YM and Wang, YT and Wang, Y and Maier, C and Jung, TP and Cauwenberghs, G},
title = {Dry and noncontact EEG sensors for mobile brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {2},
pages = {228-235},
doi = {10.1109/TNSRE.2011.2174652},
pmid = {22180514},
issn = {1558-0210},
mesh = {Algorithms ; Benchmarking ; Electric Impedance ; Electrocardiography ; Electroencephalography/*instrumentation ; Equipment Design ; Evoked Potentials, Somatosensory/physiology ; Evoked Potentials, Visual/physiology ; Humans ; Hydrogels ; Signal Processing, Computer-Assisted ; Telemetry ; *User-Computer Interface ; Wireless Technology ; },
abstract = {Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.},
}
@article {pmid22180513,
year = {2012},
author = {Salvaris, M and Cinel, C and Citi, L and Poli, R},
title = {Novel protocols for P300-based brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {1},
pages = {8-17},
doi = {10.1109/TNSRE.2011.2174463},
pmid = {22180513},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Area Under Curve ; Artifacts ; Brain/*physiology ; Brain Mapping ; Color ; Discrimination, Psychological/physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Photic Stimulation ; Psychomotor Performance ; ROC Curve ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {The oddball protocol is often used in brain-computer interfaces (BCIs) to induce P300 ERPs, although, recently, some issues have been shown to detrimentally effect its performance. In this paper, we study a new periodic protocol and explore whether it can compete with the standard oddball protocol within the context of a BCI mouse. We found that the new protocol consistently and significantly outperforms the standard oddball protocol in relation to information transfer rates (33 bits/min for the former and 22 bits/min for the latter, measured at 90% accuracy) as well as P300 amplitudes. Furthermore, we performed a comparison of two periodic protocols with two less conventional oddball-like protocols that reveals the importance of the interactions between task and sequence in determining the success of a protocol.},
}
@article {pmid22180512,
year = {2012},
author = {Escolano, C and Antelis, JM and Minguez, J},
title = {A telepresence mobile robot controlled with a noninvasive brain-computer interface.},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
volume = {42},
number = {3},
pages = {793-804},
doi = {10.1109/TSMCB.2011.2177968},
pmid = {22180512},
issn = {1941-0492},
mesh = {Algorithms ; *Artificial Intelligence ; Biofeedback, Psychology/*methods ; Computer Simulation ; Decision Support Techniques ; *Event-Related Potentials, P300 ; Humans ; *Man-Machine Systems ; *Models, Theoretical ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system.},
}
@article {pmid22172335,
year = {2012},
author = {Kaufmann, T and Vögele, C and Sütterlin, S and Lukito, S and Kübler, A},
title = {Effects of resting heart rate variability on performance in the P300 brain-computer interface.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {83},
number = {3},
pages = {336-341},
doi = {10.1016/j.ijpsycho.2011.11.018},
pmid = {22172335},
issn = {1872-7697},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Electrocardiography ; Event-Related Potentials, P300/*physiology ; *Feedback ; Female ; Heart Rate/*physiology ; Humans ; Male ; Photic Stimulation ; Regression Analysis ; Rest/*physiology ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; Verbal Behavior/physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain computer interfaces (BCI) can serve as a communication system for people with severe impairment in speech and motor function due to neurodegenerative disease or injury. Reasons for inter-individual differences in capability of BCI usage are not yet fully understood. Paradigms making use of the P300 event-related potential are widely used. Success in a P300 based BCI requires the capability to focus attention and inhibit interference by distracting irrelevant stimuli. Such inhibitory control has been closely linked to peripheral physiological parameters, such as heart rate variability (HRV). The present study investigated the association between resting HRV and performance in the P300-BCI.<br><br>METHODS: Heart rate was recorded from 34 healthy participants under resting conditions, and subsequently a P300-BCI task was performed.<br><br>RESULTS: Frequency domain measures of HRV were significantly associated with BCI-performance, in that higher vagal activation was related to better BCI-performance.<br><br>CONCLUSIONS: Resting HRV accounted for almost 26% of the variance of BCI performance and may, therefore, serve as a predictor for the capacity to control a P300 oddball based BCI.<br><br>SIGNIFICANCE: This is the first study to demonstrate resting vagal-cardiac activation to predict capability of P300-BCI usage.},
}
@article {pmid22165907,
year = {2011},
author = {Belda-Lois, JM and Mena-del Horno, S and Bermejo-Bosch, I and Moreno, JC and Pons, JL and Farina, D and Iosa, M and Molinari, M and Tamburella, F and Ramos, A and Caria, A and Solis-Escalante, T and Brunner, C and Rea, M},
title = {Rehabilitation of gait after stroke: a review towards a top-down approach.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {66},
pmid = {22165907},
issn = {1743-0003},
mesh = {Data Interpretation, Statistical ; Electric Stimulation ; Electroencephalography ; Gait/*physiology ; Humans ; Learning ; Movement ; Neurophysiology ; Robotics ; Spectroscopy, Near-Infrared ; *Stroke Rehabilitation ; User-Computer Interface ; },
abstract = {This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.The methods reviewed comprise classical gait rehabilitation techniques (neurophysiological and motor learning approaches), functional electrical stimulation (FES), robotic devices, and brain-computer interfaces (BCI).From the analysis of these approaches, we can draw the following conclusions. Regarding classical rehabilitation techniques, there is insufficient evidence to state that a particular approach is more effective in promoting gait recovery than other. Combination of different rehabilitation strategies seems to be more effective than over-ground gait training alone. Robotic devices need further research to show their suitability for walking training and their effects on over-ground gait. The use of FES combined with different walking retraining strategies has shown to result in improvements in hemiplegic gait. Reports on non-invasive BCIs for stroke recovery are limited to the rehabilitation of upper limbs; however, some works suggest that there might be a common mechanism which influences upper and lower limb recovery simultaneously, independently of the limb chosen for the rehabilitation therapy. Functional near infrared spectroscopy (fNIRS) enables researchers to detect signals from specific regions of the cortex during performance of motor activities for the development of future BCIs. Future research would make possible to analyze the impact of rehabilitation on brain plasticity, in order to adapt treatment resources to meet the needs of each patient and to optimize the recovery process.},
}
@article {pmid22164006,
year = {2011},
author = {Gomez-Gil, J and San-Jose-Gonzalez, I and Nicolas-Alonso, LF and Alonso-Garcia, S},
title = {Steering a tractor by means of an EMG-based human-machine interface.},
journal = {Sensors (Basel, Switzerland)},
volume = {11},
number = {7},
pages = {7110-7126},
pmid = {22164006},
issn = {1424-8220},
mesh = {Agriculture/*instrumentation ; *Electromyography ; Humans ; *Man-Machine Systems ; *Motor Vehicles ; *User-Computer Interface ; },
abstract = {An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver's scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.},
}
@article {pmid22163863,
year = {2011},
author = {Gosselin, B},
title = {Recent advances in neural recording microsystems.},
journal = {Sensors (Basel, Switzerland)},
volume = {11},
number = {5},
pages = {4572-4597},
pmid = {22163863},
issn = {1424-8220},
mesh = {Biosensing Techniques/instrumentation/*methods ; Equipment Design ; Humans ; Neurons/*metabolism ; Telemetry/instrumentation/*methods ; },
abstract = {The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.},
}
@article {pmid22163825,
year = {2011},
author = {Sánchez-Azofeifa, A and Rivard, B and Wright, J and Feng, JL and Li, P and Chong, MM and Bohlman, SA},
title = {Estimation of the distribution of Tabebuia guayacan (Bignoniaceae) using high-resolution remote sensing imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {11},
number = {4},
pages = {3831-3851},
pmid = {22163825},
issn = {1424-8220},
mesh = {Conservation of Natural Resources ; Ecosystem ; Environmental Monitoring ; Panama ; Photography/*methods ; Population Dynamics ; *Satellite Communications ; Tabebuia/*growth &amp; development ; Trees/*growth &amp; development ; Tropical Climate ; },
abstract = {Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments.},
}
@article {pmid22157115,
year = {2012},
author = {Bansal, AK and Truccolo, W and Vargas-Irwin, CE and Donoghue, JP},
title = {Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.},
journal = {Journal of neurophysiology},
volume = {107},
number = {5},
pages = {1337-1355},
pmid = {22157115},
issn = {1522-1598},
support = {K01 NS057389-05/NS/NINDS NIH HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; NS-25074/NS/NINDS NIH HHS/United States ; K01 NS057389/NS/NINDS NIH HHS/United States ; 5K01-NS-057389/NS/NINDS NIH HHS/United States ; R01-EB-007401-01/EB/NIBIB NIH HHS/United States ; F32-NS-061483/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Hand Strength/*physiology ; Macaca mulatta ; Male ; Motor Activity/physiology ; Motor Cortex/*physiology ; Movement/physiology ; Photic Stimulation/*methods ; Psychomotor Performance/*physiology ; },
abstract = {Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control.},
}
@article {pmid22156918,
year = {2012},
author = {Xuan, P and Zhang, Y and Tzeng, TR and Wan, XF and Luo, F},
title = {A quantitative structure-activity relationship (QSAR) study on glycan array data to determine the specificities of glycan-binding proteins.},
journal = {Glycobiology},
volume = {22},
number = {4},
pages = {552-560},
pmid = {22156918},
issn = {1460-2423},
support = {RC1AI086830/AI/NIAID NIH HHS/United States ; },
mesh = {Algorithms ; Carbohydrate Conformation ; Carbohydrate Sequence ; Models, Molecular ; Molecular Sequence Data ; Online Systems ; Plant Lectins/*chemistry ; Polysaccharides/*chemistry ; Protein Binding ; *Quantitative Structure-Activity Relationship ; Regression Analysis ; Software ; },
abstract = {Advances in glycan array technology have provided opportunities to automatically and systematically characterize the binding specificities of glycan-binding proteins. However, there is still a lack of robust methods for such analyses. In this study, we developed a novel quantitative structure-activity relationship (QSAR) method to analyze glycan array data. We first decomposed glycan chains into mono-, di-, tri- or tetrasaccharide subtrees. The bond information was incorporated into subtrees to help distinguish glycan chain structures. Then, we performed partial least-squares (PLS) regression on glycan array data using the subtrees as features. The application of QSAR to the glycan array data of different glycan-binding proteins demonstrated that PLS regression using subtree features can obtain higher R(2) values and a higher percentage of variance explained in glycan array intensities. Based on the regression coefficients of PLS, we were able to effectively identify subtrees that indicate the binding specificities of a glycan-binding protein. Our approach will facilitate the glycan-binding specificity analysis using the glycan array. A user-friendly web tool of the QSAR method is available at http://bci.clemson.edu/tools/glycan_array.},
}
@article {pmid22156110,
year = {2012},
author = {Speier, W and Arnold, C and Lu, J and Taira, RK and Pouratian, N},
title = {Natural language processing with dynamic classification improves P300 speller accuracy and bit rate.},
journal = {Journal of neural engineering},
volume = {9},
number = {1},
pages = {016004},
pmid = {22156110},
issn = {1741-2552},
support = {K23 EB014326/EB/NIBIB NIH HHS/United States ; T15 LM007356/LM/NLM NIH HHS/United States ; T15-LM007356/LM/NLM NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/methods ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; *Natural Language Processing ; Pattern Recognition, Automated/*methods ; Sensitivity and Specificity ; *User-Computer Interface ; *Writing ; },
abstract = {The P300 speller is an example of a brain-computer interface that can restore functionality to victims of neuromuscular disorders. Although the most common application of this system has been communicating language, the properties and constraints of the linguistic domain have not to date been exploited when decoding brain signals that pertain to language. We hypothesized that combining the standard stepwise linear discriminant analysis with a Naive Bayes classifier and a trigram language model would increase the speed and accuracy of typing with the P300 speller. With integration of natural language processing, we observed significant improvements in accuracy and 40-60% increases in bit rate for all six subjects in a pilot study. This study suggests that integrating information about the linguistic domain can significantly improve signal classification.},
}
@article {pmid22156069,
year = {2012},
author = {Zander, TO and Jatzev, S},
title = {Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment.},
journal = {Journal of neural engineering},
volume = {9},
number = {1},
pages = {016003},
doi = {10.1088/1741-2560/9/1/016003},
pmid = {22156069},
issn = {1741-2552},
mesh = {Attention/physiology ; Awareness/*physiology ; Biofeedback, Psychology/methods/*physiology ; Brain Mapping/*methods ; *Ecosystem ; Evoked Potentials/*physiology ; Humans ; Psychomotor Performance/*physiology ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information-otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org.},
}
@article {pmid22156029,
year = {2012},
author = {Allison, BZ and Leeb, R and Brunner, C and Müller-Putz, GR and Bauernfeind, G and Kelly, JW and Neuper, C},
title = {Toward smarter BCIs: extending BCIs through hybridization and intelligent control.},
journal = {Journal of neural engineering},
volume = {9},
number = {1},
pages = {013001},
doi = {10.1088/1741-2560/9/1/013001},
pmid = {22156029},
issn = {1741-2552},
mesh = {*Artificial Intelligence ; Biofeedback, Psychology/methods/*physiology ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feedback, Physiological/physiology ; Humans ; *User-Computer Interface ; },
abstract = {This paper summarizes two novel ways to extend brain-computer interface (BCI) systems. One way involves hybrid BCIs. A hybrid BCI is a system that combines a BCI with another device to help people send information. Different types of hybrid BCIs are discussed, along with challenges and issues. BCIs are also being extended through intelligent systems. Software that allows high-level control, incorporates context and the environment and/or uses virtual reality can substantially improve BCI systems. Throughout the paper, we critically address the real benefits of these improvements relative to existing technology and practices. We also present new challenges that are likely to emerge as these novel BCI directions become more widespread.},
}
@article {pmid22155383,
year = {2012},
author = {Yu, K and Shen, K and Shao, S and Ng, WC and Kwok, K and Li, X},
title = {A spatio-temporal filtering approach to denoising of single-trial ERP in rapid image triage.},
journal = {Journal of neuroscience methods},
volume = {204},
number = {2},
pages = {288-295},
doi = {10.1016/j.jneumeth.2011.11.023},
pmid = {22155383},
issn = {1872-678X},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Computer Simulation ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; *Imagination ; Male ; Models, Neurological ; Normal Distribution ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Triage/*methods ; Young Adult ; },
abstract = {Conventional search for images containing points of interest (POI) in large-volume imagery is costly and sometimes even infeasible. The rapid image triage (RIT) system which is a human cognition guided computer vision technique is potentially a promising solution to the problem. In the RIT procedure, images are sequentially presented to a subject at a high speed. At the instant of observing a POI image, unique POI event-related potentials (ERP) characterized by P300 will be elicited and measured on the scalp. With accurate single-trial detection of such unique ERP, RIT can differentiate POI images from non-POI images. However, like other brain-computer interface systems relying on single-trial detection, RIT suffers from the low signal-to-noise ratio (SNR) of the single-trial ERP. This paper presents a spatio-temporal filtering approach tailored for the denoising of single-trial ERP for RIT. The proposed approach is essentially a non-uniformly delayed spatial Gaussian filter that attempts to suppress the non-event related background electroencephalogram (EEG) and other noises without significantly attenuating the useful ERP signals. The efficacy of the proposed approach is illustrated by both simulation tests and real RIT experiments. In particular, the real RIT experiments on 20 subjects show a statistically significant and meaningful average decrease of 9.8% in RIT classification error rate, compared to that without the proposed approach.},
}
@article {pmid22155040,
year = {2012},
author = {Quandt, F and Reichert, C and Hinrichs, H and Heinze, HJ and Knight, RT and Rieger, JW},
title = {Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study.},
journal = {NeuroImage},
volume = {59},
number = {4},
pages = {3316-3324},
pmid = {22155040},
issn = {1095-9572},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; R01 NS021135-25/NS/NINDS NIH HHS/United States ; NS21135/NS/NINDS NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; R56 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Electroencephalography ; Female ; Fingers/*physiology ; Hand/physiology ; Humans ; *Magnetoencephalography ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Young Adult ; },
abstract = {It is crucial to understand what brain signals can be decoded from single trials with different recording techniques for the development of Brain-Machine Interfaces. A specific challenge for non-invasive recording methods are activations confined to small spatial areas on the cortex such as the finger representation of one hand. Here we study the information content of single trial brain activity in non-invasive MEG and EEG recordings elicited by finger movements of one hand. We investigate the feasibility of decoding which of four fingers of one hand performed a slight button press. With MEG we demonstrate reliable discrimination of single button presses performed with the thumb, the index, the middle or the little finger (average over all subjects and fingers 57%, best subject 70%, empirical guessing level: 25.1%). EEG decoding performance was less robust (average over all subjects and fingers 43%, best subject 54%, empirical guessing level 25.1%). Spatiotemporal patterns of amplitude variations in the time series provided best information for discriminating finger movements. Non-phase-locked changes of mu and beta oscillations were less predictive. Movement related high gamma oscillations were observed in average induced oscillation amplitudes in the MEG but did not provide sufficient information about the finger's identity in single trials. Importantly, pre-movement neuronal activity provided information about the preparation of the movement of a specific finger. Our study demonstrates the potential of non-invasive MEG to provide informative features for individual finger control in a Brain-Machine Interface neuroprosthesis.},
}
@article {pmid22154873,
year = {2012},
author = {Hoffmann, S and Falkenstein, M},
title = {Predictive information processing in the brain: errors and response monitoring.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {83},
number = {2},
pages = {208-212},
doi = {10.1016/j.ijpsycho.2011.11.015},
pmid = {22154873},
issn = {1872-7697},
mesh = {Animals ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials/*physiology ; Forecasting ; Humans ; Mental Processes/*physiology ; Reaction Time/*physiology ; },
abstract = {The monitoring of one's own actions is essential for adjusting behavior. In particular, response errors are important events that require behavioral adjustments. Correct and incorrect responses, as well as feedback to responses, are followed by brain activity originating mainly in the anterior cingulate, which can be measured with fMRI and event-related potential (ERP) techniques. After each response a small negativity (Nc or CRN) is elicited in the ERP, which is strongly enhanced in incorrect trials (Ne or ERN). Following feedback stimuli that signal a negative outcome of an action, a similar negativity, the feedback-related negativity (FRN) is elicited. Recently it has been shown that these neurophysiological correlates of response monitoring and evaluation can be classified even on the single-trial level in the EEG and thus could be utilized not only to distinguish between correct and erroneous actions, but also can be used online for a wide range of applications such as prediction of clinical outcomes or brain computer interfaces.},
}
@article {pmid22147288,
year = {2012},
author = {Wang, H and Tang, Q and Zheng, W},
title = {L1-norm-based common spatial patterns.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {3},
pages = {653-662},
doi = {10.1109/TBME.2011.2177523},
pmid = {22147288},
issn = {1558-2531},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Models, Theoretical ; Pattern Recognition, Automated ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Common spatial patterns (CSP) is a commonly used method of spatial filtering for multichannel electroencephalogram (EEG) signals. The formulation of the CSP criterion is based on variance using L2-norm, which implies that CSP is sensitive to outliers. In this paper, we propose a robust version of CSP, called CSP-L1, by maximizing the ratio of filtered dispersion of one class to the other class, both of which are formulated by using L1-norm rather than L2-norm. The spatial filters of CSP-L1 are obtained by introducing an iterative algorithm, which is easy to implement and is theoretically justified. CSP-L1 is robust to outliers. Experiment results on a toy example and datasets of BCI competitions demonstrate the efficacy of the proposed method.},
}
@article {pmid22144944,
year = {2011},
author = {Wang, Z and Ji, Q and Miller, KJ and Schalk, G},
title = {Prior knowledge improves decoding of finger flexion from electrocorticographic signals.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {127},
pmid = {22144944},
issn = {1662-453X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.},
}
@article {pmid22132044,
year = {2010},
author = {Zhao, Q and Rutkowski, TM and Zhang, L and Cichocki, A},
title = {Generalized optimal spatial filtering using a kernel approach with application to EEG classification.},
journal = {Cognitive neurodynamics},
volume = {4},
number = {4},
pages = {355-358},
pmid = {22132044},
issn = {1871-4099},
abstract = {Common spatial patterns (CSP) has been widely used for finding the linear spatial filters which are able to extract the discriminative brain activities between two different mental tasks. However, the CSP is difficult to capture the nonlinearly clustered structure from the non-stationary EEG signals. To relax the presumption of strictly linear patterns in the CSP, in this paper, a generalized CSP (GCSP) based on generalized singular value decomposition (GSVD) and kernel method is proposed. Our method is able to find the nonlinear spatial filters which are formulated in the feature space defined by a nonlinear mapping through kernel functions. Furthermore, in order to overcome the overfitting problem, the regularized GCSP is developed by adding the regularized parameters. The experimental results demonstrate that our method is an effective nonlinear spatial filtering method.},
}
@article {pmid22131973,
year = {2011},
author = {Müller-Putz, GR and Breitwieser, C and Cincotti, F and Leeb, R and Schreuder, M and Leotta, F and Tavella, M and Bianchi, L and Kreilinger, A and Ramsay, A and Rohm, M and Sagebaum, M and Tonin, L and Neuper, C and Millán, Jdel R},
title = {Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI.},
journal = {Frontiers in neuroinformatics},
volume = {5},
number = {},
pages = {30},
pmid = {22131973},
issn = {1662-5196},
abstract = {The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.},
}
@article {pmid22131413,
year = {2011},
author = {Yu, S and Yang, H and Nakahara, H and Santos, GS and Nikolić, D and Plenz, D},
title = {Higher-order interactions characterized in cortical activity.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {31},
number = {48},
pages = {17514-17526},
pmid = {22131413},
issn = {1529-2401},
support = {//Intramural NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Cats ; Cerebral Cortex/*physiology ; Macaca mulatta ; Models, Neurological ; Nerve Net/*physiology ; Neurons/*physiology ; },
abstract = {In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain-machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.},
}
@article {pmid22120279,
year = {2012},
author = {Taghva, A and Song, D and Hampson, RE and Deadwyler, SA and Berger, TW},
title = {Determination of relevant neuron-neuron connections for neural prosthetics using time-delayed mutual information: tutorial and preliminary results.},
journal = {World neurosurgery},
volume = {78},
number = {6},
pages = {618-630},
pmid = {22120279},
issn = {1878-8769},
support = {P50 DA006634/DA/NIDA NIH HHS/United States ; R01 DA007625/DA/NIDA NIH HHS/United States ; R01 DA008549/DA/NIDA NIH HHS/United States ; },
mesh = {Animals ; Brain-Computer Interfaces/standards/trends ; Cell Communication/*physiology ; *Information Theory ; Male ; Nerve Net/*physiology ; Neural Prostheses/*standards/trends ; Neurons/cytology/*physiology ; Rats ; Rats, Long-Evans ; Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Identification of functional dependence among neurons is a necessary component in both the rational design of neural prostheses as well as in the characterization of network physiology. The objective of this article is to provide a tutorial for neurosurgeons regarding information theory, specifically time-delayed mutual information, and to compare time-delayed mutual information, an information theoretic quantity based on statistical dependence, with cross-correlation, a commonly used metric for this task in a preliminary analysis of rat hippocampal neurons.<br><br>METHODS: Spike trains were recorded from rats performing delayed nonmatch-to-sample task using an array of electrodes surgically implanted into the hippocampus of each hemisphere of the brain. In addition, spike train simulations of positively correlated neurons, negatively correlated neurons, and neurons correlated by nonlinear functions were generated. These were evaluated by time-delayed mutual information (MI) and cross-correlation.<br><br>RESULTS: Application of time-delayed MI to experimental data indicated the optimal bin size for information capture in the CA3-CA1 system was 40 ms, which may provide some insight into the spatiotemporal nature of encoding in the rat hippocampus. On simulated data, time-delayed MI showed peak values at appropriate time lags in positively correlated, negatively correlated, and complexly correlated data. Cross-correlation showed peak and troughs with positively correlated and negatively correlated data, but failed to capture some higher order correlations.<br><br>CONCLUSIONS: Comparison of time-delayed MI to cross-correlation in identification of functionally dependent neurons indicates that the methods are not equivalent. Time-delayed MI appeared to capture some interactions between CA3-CA1 neurons at physiologically plausible time delays missed by cross-correlation. It should be considered as a method for identification of functional dependence between neurons and may be useful in the development of neural prosthetics.},
}
@article {pmid22120219,
year = {2012},
author = {Langmoen, IA and Berg-Johnsen, J},
title = {The brain-computer interface.},
journal = {World neurosurgery},
volume = {78},
number = {6},
pages = {573-575},
doi = {10.1016/j.wneu.2011.10.021},
pmid = {22120219},
issn = {1878-8769},
mesh = {Animals ; Cell Communication/*physiology ; *Information Theory ; Male ; Nerve Net/*physiology ; Neural Prostheses/*standards ; Neurons/*physiology ; },
}
@article {pmid22119365,
year = {2012},
author = {Vučković, A and Sepulveda, F},
title = {A two-stage four-class BCI based on imaginary movements of the left and the right wrist.},
journal = {Medical engineering &amp; physics},
volume = {34},
number = {7},
pages = {964-971},
doi = {10.1016/j.medengphy.2011.11.001},
pmid = {22119365},
issn = {1873-4030},
mesh = {Adult ; *Brain-Computer Interfaces ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; *Movement ; Signal Processing, Computer-Assisted/*instrumentation ; Wrist/*physiology ; },
abstract = {This paper presents a new concept of a two-modality, four-class brain-computer interface (BCI) classifier based on motor imagination of the left and the right wrist. The noninvasive BCI combines classification of movements of the same limb (wrist flexion and extension) with classification of movements of different limbs, i.e., left and right wrist. Results were obtained from ten right-handed neurologically healthy volunteers. Subjects were not allowed to practice real movements before performing movement imagination. The mean classification accuracy for four different classes was 63±10%. Classification accuracy in four out of ten subjects was ≥70%. A two-stage four-class classifier showed significantly better classification results (p=0.014) than a single four-class classifier. Classifiers were based on Elman's neural networks and features were a selected set of absolute values of Gabor coefficients (GCs), calculated from the Independent Components, rather than the EEG signals' time series. The most representative features for classification between movements of different limbs were in the alpha and the beta range, while for classification between movements of the same limb they were in the delta and the gamma range. There was no statistically significant difference between classification accuracy of movements of the right vs. the left wrist.},
}
@article {pmid22112967,
year = {2012},
author = {Mathieu, MC and Mazouni, C and Kesty, NC and Zhang, Y and Scott, V and Passeron, J and Arnedos, M and Schnabel, CA and Delaloge, S and Erlander, MG and André, F},
title = {Breast Cancer Index predicts pathological complete response and eligibility for breast conserving surgery in breast cancer patients treated with neoadjuvant chemotherapy.},
journal = {Annals of oncology : official journal of the European Society for Medical Oncology},
volume = {23},
number = {8},
pages = {2046-2052},
doi = {10.1093/annonc/mdr550},
pmid = {22112967},
issn = {1569-8041},
mesh = {Adult ; Aged ; Breast Neoplasms/genetics/metabolism/*pathology/*therapy ; Chemotherapy, Adjuvant ; Female ; Humans ; Middle Aged ; Neoadjuvant Therapy ; Neoplasm Staging ; Real-Time Polymerase Chain Reaction ; Receptor, ErbB-2/metabolism ; Receptors, Estrogen/metabolism ; Receptors, Progesterone/metabolism ; Retrospective Studies ; },
abstract = {BACKGROUND: The aim of neoadjuvant chemotherapy is to increase the likelihood of successful breast conservation surgery (BCS). Accurate identification of BCS candidates is a diagnostic challenge. Breast Cancer Index (BCI) predicts recurrence risk in estrogen receptor+lymph node-breast cancer. Performance of BCI to predict chemosensitivity based on pathological complete response (pCR) and BCS was assessed.<br><br>METHODS: Real-time RT-PCR BCI assay was conducted using tumor samples from 150 breast cancer patients treated with neoadjuvant chemotherapy. Logistical regression and c-index were used to assess predictive strength and additive accuracy of BCI beyond clinicopathologic factors.<br><br>RESULTS: BCI classified 42% of patients as low, 35% as intermediate and 23% as high risk. Low BCI risk group had 98.4% negative predictive value (NPV) for pCR and 86% NPV for BCS. High versus low BCI group had a 34 and 5.8 greater likelihood of achieving pCR and BCS, respectively (P=0.0055; P=0.0022). BCI increased c-index for pCR (0.875-0.924; P=0.017) and BCS prediction (0.788-0.843; P=0.027) beyond clinicopathologic factors.<br><br>CONCLUSIONS: BCI significantly predicted pCR and BCS beyond clinicopathologic factors. High NPVs indicate that BCI could be a useful tool to identify breast cancer patients who are not eligible for neoadjuvant chemotherapy. These results suggest that BCI could be used to assess both chemosensitivity and eligibility for BCS.},
}
@article {pmid22112652,
year = {2012},
author = {Bates, KT and Schachner, ER},
title = {Disparity and convergence in bipedal archosaur locomotion.},
journal = {Journal of the Royal Society, Interface},
volume = {9},
number = {71},
pages = {1339-1353},
pmid = {22112652},
issn = {1742-5662},
mesh = {Alligators and Crocodiles/*physiology ; Animals ; Biological Clocks/physiology ; Computer Simulation ; Extremities/*physiology ; Gait/*physiology ; Locomotion/*physiology ; *Models, Biological ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; Species Specificity ; },
abstract = {This study aims to investigate functional disparity in the locomotor apparatus of bipedal archosaurs. We use reconstructions of hindlimb myology of extant and extinct archosaurs to generate musculoskeletal biomechanical models to test hypothesized convergence between bipedal crocodile-line archosaurs and dinosaurs. Quantitative comparison of muscle leverage supports the inference that bipedal crocodile-line archosaurs and non-avian theropods had highly convergent hindlimb myology, suggesting similar muscular mechanics and neuromuscular control of locomotion. While these groups independently evolved similar musculoskeletal solutions to the challenges of parasagittally erect bipedalism, differences also clearly exist, particularly the distinct hip and crurotarsal ankle morphology characteristic of many pseudosuchian archosaurs. Furthermore, comparative analyses of muscle design in extant archosaurs reveal that muscular parameters such as size and architecture are more highly adapted or optimized for habitual locomotion than moment arms. The importance of these aspects of muscle design, which are not directly retrievable from fossils, warns against over-extrapolating the functional significance of anatomical convergences. Nevertheless, links identified between posture, muscle moments and neural control in archosaur locomotion suggest that functional interpretations of osteological changes in limb anatomy traditionally linked to postural evolution in Late Triassic archosaurs could be constrained through musculoskeletal modelling.},
}
@article {pmid22110702,
year = {2011},
author = {Andersson, P and Pluim, JP and Siero, JC and Klein, S and Viergever, MA and Ramsey, NF},
title = {Real-time decoding of brain responses to visuospatial attention using 7T fMRI.},
journal = {PloS one},
volume = {6},
number = {11},
pages = {e27638},
pmid = {22110702},
issn = {1932-6203},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology/physiopathology/surgery ; Electrodes, Implanted ; Epilepsy/physiopathology/surgery ; Eye Movements/physiology ; Feasibility Studies ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Neurosurgery ; Spatial Behavior/*physiology ; Time Factors ; Visual Cortex/physiology/physiopathology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Brain-Computer interface technologies mean to create new communication channels between our mind and our environment, independent of the motor system, by detecting and classifying self regulation of local brain activity. BCIs can provide patients with severe paralysis a means to communicate and to live more independent lives. There has been a growing interest in using invasive recordings for BCI to improve the signal quality. This also potentially gives access to new control strategies previously inaccessible by non-invasive methods. However, before surgery, the best implantation site needs to be determined. The blood-oxygen-level dependent signal changes measured with fMRI have been shown to agree well spatially with those found with invasive electrodes, and are the best option for pre-surgical localization. We show, using real-time fMRI at 7T, that eye movement-independent visuospatial attention can be used as a reliable control strategy for BCIs. At this field strength even subtle signal changes can be detected in single trials thanks to the high contrast-to-noise ratio. A group of healthy subjects were instructed to move their attention between three (two peripheral and one central) spatial target regions while keeping their gaze fixated at the center. The activated regions were first located and thereafter the subjects were given real-time feedback based on the activity in these regions. All subjects managed to regulate local brain areas without training, which suggests that visuospatial attention is a promising new target for intracranial BCI. ECoG data recorded from one epilepsy patient showed that local changes in gamma-power can be used to separate the three classes.},
}
@article {pmid22108142,
year = {2012},
author = {Ahn, M and Hong, JH and Jun, SC},
title = {Feasibility of approaches combining sensor and source features in brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {204},
number = {1},
pages = {168-178},
doi = {10.1016/j.jneumeth.2011.11.002},
pmid = {22108142},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/*methods/*physiology ; Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Feasibility Studies ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Systems Integration ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) provides a new channel for communication between brain and computers through brain signals. Cost-effective EEG provides good temporal resolution, but its spatial resolution is poor and sensor information is blurred by inherent noise. To overcome these issues, spatial filtering and feature extraction techniques have been developed. Source imaging, transformation of sensor signals into the source space through source localizer, has gained attention as a new approach for BCI. It has been reported that the source imaging yields some improvement of BCI performance. However, there exists no thorough investigation on how source imaging information overlaps with, and is complementary to, sensor information. Information (visible information) from the source space may overlap as well as be exclusive to information from the sensor space is hypothesized. Therefore, we can extract more information from the sensor and source spaces if our hypothesis is true, thereby contributing to more accurate BCI systems. In this work, features from each space (sensor or source), and two strategies combining sensor and source features are assessed. The information distribution among the sensor, source, and combined spaces is discussed through a Venn diagram for 18 motor imagery datasets. Additional 5 motor imagery datasets from the BCI Competition III site were examined. The results showed that the addition of source information yielded about 3.8% classification improvement for 18 motor imagery datasets and showed an average accuracy of 75.56% for BCI Competition data. Our proposed approach is promising, and improved performance may be possible with better head model.},
}
@article {pmid22090155,
year = {2012},
author = {Rigby, EL and Aegerter, J and Brash, M and Altringham, JD},
title = {Impact of PIT tagging on recapture rates, body condition and reproductive success of wild Daubenton's bats (Myotis daubentonii).},
journal = {The Veterinary record},
volume = {170},
number = {4},
pages = {101},
doi = {10.1136/vr.100075},
pmid = {22090155},
issn = {2042-7670},
mesh = {Animal Identification Systems/*veterinary ; Animal Welfare ; Animals ; Animals, Wild ; Body Composition/*physiology ; Body Weight/physiology ; Chiroptera/anatomy &amp; histology/*physiology ; Female ; Male ; Reproduction/*physiology ; },
abstract = {A successful and safe methodology for the subcutaneous insertion of passive integrated transponder (PIT) tags in a small- to medium-sized bat (average mass 9 g) under isoflurane-induced anaesthesia is described. Passive integrated transponder (PIT) tagging had no significant impact on the rate of recapture, body condition index (BCI) (bodyweight/forearm length) and reproductive success of tagged individuals, and no visible injuries or health problems were observed in any of the recaptured bats. Tagging success, in terms of retention and function, was 92 per cent (n=61) by the third year of using the method. Sixteen per cent (n=39) of bats tagged during the three-year study period were not producing positive scans with the microchip reader when recaptured after previously successful tag insertion, indicating that the tags were either working their way out of the bats or ceasing to function.},
}
@article {pmid22089232,
year = {2011},
author = {Rachmuth, G and Shouval, HZ and Bear, MF and Poon, CS},
title = {A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {108},
number = {49},
pages = {E1266-74},
pmid = {22089232},
issn = {1091-6490},
support = {R01 HL067966/HL/NHLBI NIH HHS/United States ; HL067966/HL/NHLBI NIH HHS/United States ; EB005460/EB/NIBIB NIH HHS/United States ; RC1 RR028241/RR/NCRR NIH HHS/United States ; R21 EB005460/EB/NIBIB NIH HHS/United States ; RR028241/RR/NCRR NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Biophysical Phenomena ; Calcium/metabolism ; Excitatory Postsynaptic Potentials/physiology ; Humans ; Long-Term Potentiation/physiology ; Long-Term Synaptic Depression/physiology ; Metals/chemistry ; *Models, Neurological ; Nerve Net/metabolism/*physiology ; Neuronal Plasticity/*physiology ; Neurons/metabolism/physiology ; Oxides/chemistry ; Receptors, AMPA/physiology ; Receptors, N-Methyl-D-Aspartate/physiology ; Semiconductors ; Signal Processing, Computer-Assisted/instrumentation ; Synaptic Transmission/physiology ; Time Factors ; },
abstract = {Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.},
}
@article {pmid22084052,
year = {2012},
author = {Benz, HL and Zhang, H and Bezerianos, A and Acharya, S and Crone, NE and Zheng, X and Thakor, NV},
title = {Connectivity analysis as a novel approach to motor decoding for prosthesis control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {2},
pages = {143-152},
pmid = {22084052},
issn = {1558-0210},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS040596-10/NS/NINDS NIH HHS/United States ; R01 NS040596-11/NS/NINDS NIH HHS/United States ; 3R01NS040596-09S1/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Algorithms ; Bayes Theorem ; Brain/physiology ; Cerebral Cortex/physiology ; Electrodes, Implanted ; Electroencephalography/*methods ; Epilepsy/surgery ; Evoked Potentials, Motor/physiology ; Female ; Hand/physiology ; Hand Strength/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/physiology ; Movement/physiology ; *Neural Prostheses ; *Prosthesis Design ; *User-Computer Interface ; Young Adult ; },
abstract = {The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r(2)) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.},
}
@article {pmid22082990,
year = {2012},
author = {Sugata, H and Goto, T and Hirata, M and Yanagisawa, T and Shayne, M and Matsushita, K and Yoshimine, T and Yorifuji, S},
title = {Movement-related neuromagnetic fields and performances of single trial classifications.},
journal = {Neuroreport},
volume = {23},
number = {1},
pages = {16-20},
doi = {10.1097/WNR.0b013e32834d935a},
pmid = {22082990},
issn = {1473-558X},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Female ; Humans ; Magnetoencephalography/methods ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Neurophysiology/methods ; },
abstract = {In order to clarify whether neurophysiological profiles affect the performance of brain machine interfaces (BMI), we examined the relationships between amplitudes of movement-related cortical fields (MRCFs) and decoding performances during movement. Neuromagnetic activities were recorded in nine healthy participants during three types of unilateral upper limb movements. The movement types were inferred by a support vector machine. The amplitude of MRCF components, motor field (MF), movement-evoked field I (MEFI), and movement-evoked field II (MEFII) were compared with the decoding accuracies in all participants. Decoding accuracies at the latencies of MF, MEFI, and MEFII surpassed the chance level in all participants. In particular, accuracies at MEFI and MEFII were significantly higher in comparison with that of MF. The amplitudes and decoding accuracies were strongly correlated (MF, r(s)=0.90; MEFI, r(s)=0.90; and MEFII, r(s)=0.87). Our results show that the variation of MRCF components among participants reflects decoding performance. Neurophysiological profiles may serve as a predictor of individual BMI performance and assist in the improvement of general BMI performance.},
}
@article {pmid22081157,
year = {2011},
author = {Viventi, J and Kim, DH and Vigeland, L and Frechette, ES and Blanco, JA and Kim, YS and Avrin, AE and Tiruvadi, VR and Hwang, SW and Vanleer, AC and Wulsin, DF and Davis, K and Gelber, CE and Palmer, L and Van der Spiegel, J and Wu, J and Xiao, J and Huang, Y and Contreras, D and Rogers, JA and Litt, B},
title = {Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo.},
journal = {Nature neuroscience},
volume = {14},
number = {12},
pages = {1599-1605},
pmid = {22081157},
issn = {1546-1726},
support = {2T32HL007954/HL/NHLBI NIH HHS/United States ; T32 HL007954/HL/NHLBI NIH HHS/United States ; T32 EY007035/EY/NEI NIH HHS/United States ; R01 NS041811/NS/NINDS NIH HHS/United States ; R01 NS048598-04/NS/NINDS NIH HHS/United States ; S10 RR031724/RR/NCRR NIH HHS/United States ; R01 NS048598/NS/NINDS NIH HHS/United States ; R01-NS041811/NS/NINDS NIH HHS/United States ; R01 EY020765/EY/NEI NIH HHS/United States ; R01 NS041811-10/NS/NINDS NIH HHS/United States ; R01-NS48598/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Brain Mapping ; Brain Waves/*physiology ; Cats ; Electric Stimulation/adverse effects/methods ; *Electrodes, Implanted ; Electroencephalography/methods ; Electronics/*instrumentation ; Evoked Potentials, Visual ; Microelectrodes ; Numerical Analysis, Computer-Assisted ; Photic Stimulation ; Seizures/etiology/pathology ; Visual Cortex/*physiology ; },
abstract = {Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.},
}
@article {pmid22078507,
year = {2011},
author = {Hatsopoulos, NG and Suminski, AJ},
title = {Sensing with the motor cortex.},
journal = {Neuron},
volume = {72},
number = {3},
pages = {477-487},
pmid = {22078507},
issn = {1097-4199},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS045853-08/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS048845-07/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Evoked Potentials, Visual/physiology ; Humans ; Learning ; Models, Neurological ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Movement/*physiology ; Photic Stimulation ; Sensation/*physiology ; Time Factors ; },
abstract = {The primary motor cortex is a critical node in the network of brain regions responsible for voluntary motor behavior. It has been less appreciated, however, that the motor cortex exhibits sensory responses in a variety of modalities including vision and somatosensation. We review current work that emphasizes the heterogeneity in sensorimotor responses in the motor cortex and focus on its implications for cortical control of movement as well as for brain-machine interface development.},
}
@article {pmid22047868,
year = {2011},
author = {da Silva Sauer, L and Valero Aguayo, L and Velasco Álvarez, F and Ron Angevin, R},
title = {[Psychological variables in the control of brain-computer interfaces].},
journal = {Psicothema},
volume = {23},
number = {4},
pages = {745-751},
pmid = {22047868},
issn = {1886-144X},
mesh = {Brain/*physiology ; *Electroencephalography ; Female ; Humans ; Male ; *User-Computer Interface ; Young Adult ; },
abstract = {BCI (Brain-Computer Interface) is a system that allows interaction between the human brain and a computer. It is based on analyzing electroencephalographic signals (EEG) and processing them to generate control commands. The study analyzed the possible influence of psychological variables, such as the imaginative kinesthetic capacity and anxiety, in relation to performance in a BCI. All participants (4 male and 19 female students) completed the questionnaires and carried out a session of BCI to control their EEG signals in a virtual setting of a car along a straight road. The group was divided into two subgroups according to their EEG signals or differential responses obtained in the left-right discrimination. Study results showed no significant differences in cognitive variables of imagination or in anxiety. By comparing the degree of participants' BCI control, a new quantitative parameter for comparing performances and making decisions in signal processing was found. The findings, the ongoing research process to refine the control of a BCI, and the interaction of psychological and computer procedures are discussed.},
}
@article {pmid22046549,
year = {2011},
author = {Kaewlai, R and de Moya, MA and Santos, A and Asrani, AV and Avery, LL and Novelline, RA},
title = {Blunt cardiac injury in trauma patients with thoracic aortic injury.},
journal = {Emergency medicine international},
volume = {2011},
number = {},
pages = {848013},
pmid = {22046549},
issn = {2090-2859},
abstract = {Trauma patients with thoracic aortic injury (TAI) suffer blunt cardiac injury (BCI) at variable frequencies. This investigation aimed to determine the frequency of BCI in trauma patients with TAI and compare with those without TAI. All trauma patients with TAI who had admission electrocardiography (ECG) and serum creatine kinase-MB (CK-MB) from January 1999 to May 2009 were included as a study group at a level I trauma center. BCI was diagnosed if there was a positive ECG with either an elevated CK-MB or abnormal echocardiography. There were 26 patients (19 men, mean age 45.1 years, mean ISS 34.4) in the study group; 20 had evidence of BCI. Of 52 patients in the control group (38 men, mean age 46.9 years, mean ISS 38.7), eighteen had evidence of BCI. There was a significantly higher rate of BCI in trauma patients with TAI versus those without TAI (77% versus 35%, P < 0.001).},
}
@article {pmid22046274,
year = {2011},
author = {Doud, AJ and Lucas, JP and Pisansky, MT and He, B},
title = {Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface.},
journal = {PloS one},
volume = {6},
number = {10},
pages = {e26322},
pmid = {22046274},
issn = {1932-6203},
support = {R01EB007920/EB/NIBIB NIH HHS/United States ; R01EB006433/EB/NIBIB NIH HHS/United States ; R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EB007920-04/EB/NIBIB NIH HHS/United States ; R01 EB006433-03/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; },
mesh = {*Aircraft ; Arm ; Brain ; Electroencephalography/*methods ; Electromyography ; Feedback, Sensory ; Hand ; Humans ; Imagery, Psychotherapy/*methods ; Movement ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) allow a user to interact with a computer system using thought. However, only recently have devices capable of providing sophisticated multi-dimensional control been achieved non-invasively. A major goal for non-invasive BCI systems has been to provide continuous, intuitive, and accurate control, while retaining a high level of user autonomy. By employing electroencephalography (EEG) to record and decode sensorimotor rhythms (SMRs) induced from motor imaginations, a consistent, user-specific control signal may be characterized. Utilizing a novel method of interactive and continuous control, we trained three normal subjects to modulate their SMRs to achieve three-dimensional movement of a virtual helicopter that is fast, accurate, and continuous. In this system, the virtual helicopter's forward-backward translation and elevation controls were actuated through the modulation of sensorimotor rhythms that were converted to forces applied to the virtual helicopter at every simulation time step, and the helicopter's angle of left or right rotation was linearly mapped, with higher resolution, from sensorimotor rhythms associated with other motor imaginations. These different resolutions of control allow for interplay between general intent actuation and fine control as is seen in the gross and fine movements of the arm and hand. Subjects controlled the helicopter with the goal of flying through rings (targets) randomly positioned and oriented in a three-dimensional space. The subjects flew through rings continuously, acquiring as many as 11 consecutive rings within a five-minute period. In total, the study group successfully acquired over 85% of presented targets. These results affirm the effective, three-dimensional control of our motor imagery based BCI system, and suggest its potential applications in biological navigation, neuroprosthetics, and other applications.},
}
@article {pmid22046153,
year = {2011},
author = {Gunduz, A and Brunner, P and Daitch, A and Leuthardt, EC and Ritaccio, AL and Pesaran, B and Schalk, G},
title = {Neural correlates of visual-spatial attention in electrocorticographic signals in humans.},
journal = {Frontiers in human neuroscience},
volume = {5},
number = {},
pages = {89},
pmid = {22046153},
issn = {1662-5161},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {Attention is a cognitive selection mechanism that allocates the limited processing resources of the brain to the sensory streams most relevant to our immediate goals, thereby enhancing responsiveness and behavioral performance. The underlying neural mechanisms of orienting attention are distributed across a widespread cortical network. While aspects of this network have been extensively studied, details about the electrophysiological dynamics of this network are scarce. In this study, we investigated attentional networks using electrocorticographic (ECoG) recordings from the surface of the brain, which combine broad spatial coverage with high temporal resolution, in five human subjects. ECoG was recorded when subjects covertly attended to a spatial location and responded to contrast changes in the presence of distractors in a modified Posner cueing task. ECoG amplitudes in the alpha, beta, and gamma bands identified neural changes associated with covert attention and motor preparation/execution in the different stages of the task. The results show that attentional engagement was primarily associated with ECoG activity in the visual, prefrontal, premotor, and parietal cortices. Motor preparation/execution was associated with ECoG activity in premotor/sensorimotor cortices. In summary, our results illustrate rich and distributed cortical dynamics that are associated with orienting attention and the subsequent motor preparation and execution. These findings are largely consistent with and expand on primate studies using intracortical recordings and human functional neuroimaging studies.},
}
@article {pmid22044847,
year = {2012},
author = {Shih, JJ and Krusienski, DJ},
title = {Signals from intraventricular depth electrodes can control a brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {203},
number = {2},
pages = {311-314},
pmid = {22044847},
issn = {1872-678X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Electrodes, Implanted/*standards ; Electroencephalography/*instrumentation/methods ; Electrophysiology/*instrumentation/methods ; Female ; Hippocampus/*physiology/surgery ; Humans ; Lateral Ventricles/*physiology/surgery ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hippocampus can be used to control a BCI P300 Speller paradigm. We report a case in which stereotactic depth electrodes positioned in the ventricle were able to obtain viable signals for a BCI. Our results demonstrate that event-related potentials from intraventricular electrodes can be used to reliably control the P300 Speller BCI paradigm.},
}
@article {pmid22044846,
year = {2012},
author = {Podlipsky, I and Ben-Simon, E and Hendler, T and Intrator, N},
title = {Robust modeling based on optimized EEG bands for functional brain state inference.},
journal = {Journal of neuroscience methods},
volume = {203},
number = {2},
pages = {377-385},
doi = {10.1016/j.jneumeth.2011.10.015},
pmid = {22044846},
issn = {1872-678X},
mesh = {Algorithms ; Brain Mapping/*methods ; Electroencephalography/instrumentation/*methods ; Evoked Potentials/*physiology ; Humans ; *Models, Neurological ; *Signal Processing, Computer-Assisted ; Software/*standards ; },
abstract = {The need to infer brain states in a data driven approach is crucial for BCI applications as well as for neuroscience research. In this work we present a novel classification framework based on Regularized Linear Regression classifier constructed from time-frequency decomposition of an EEG (electro-encephalography) signal. The regression is then used to derive a model of frequency distributions that identifies brain states. The process of classifier construction, preprocessing and selection of optimal regularization parameter by means of cross-validation is presented and discussed. The framework and the feature selection technique are demonstrated on EEG data recorded from 10 healthy subjects while requested to open and close their eyes every 30 s. This paradigm is well known in inducing Alpha power modulations that differ from low power (during eyes opened) to high (during eyes closed). The classifier was trained to infer eyes opened or eyes closed states and achieved higher than 90% classification accuracy. Furthermore, our findings reveal interesting patterns of relations between experimental conditions, EEG frequencies, regularization parameters and classifier choice. This viable tool enables identification of the most contributing frequency bands to any given brain state and their optimal combination in inferring this state. These features allow for much greater detail than the standard Fourier Transform power analysis, making it an essential method for both BCI proposes and neuroimaging research.},
}
@article {pmid22042443,
year = {2012},
author = {So, K and Ganguly, K and Jimenez, J and Gastpar, MC and Carmena, JM},
title = {Redundant information encoding in primary motor cortex during natural and prosthetic motor control.},
journal = {Journal of computational neuroscience},
volume = {32},
number = {3},
pages = {555-561},
pmid = {22042443},
issn = {1573-6873},
mesh = {Action Potentials/physiology ; Animals ; Arm/innervation ; Discriminant Analysis ; Electromyography ; Functional Laterality ; Macaca mulatta ; Male ; Motor Cortex/*cytology/*physiology ; Movement/*physiology ; *Neural Prostheses ; Neurons/*physiology ; Psychomotor Performance/*physiology ; User-Computer Interface ; },
abstract = {Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, 'MC'), and using a brain-machine interface (BMI) via volitional control of neural ensemble activity (brain control, 'BC'). During MC, we found neurons in contralateral M1 to contain higher and more redundant information about target direction than ipsilateral M1 neurons, consistent with the laterality of movement control. During BC, we found that the M1 neurons directly incorporated into the BMI ('direct' neurons) contained the highest and most redundant target information compared to neurons that were not incorporated into the BMI ('indirect' neurons). This effect was even more significant when comparing to M1 neurons of the opposite hemisphere. Interestingly, when we retrained the BMI to use ipsilateral M1 activity, we found that these neurons were more redundant and contained higher information than contralateral M1 neurons, even though ensembles from this hemisphere were previously less redundant during natural arm movement. These results indicate that ensembles most associated to movement contain highest redundancy and information encoding, which suggests a role for redundancy in proficient natural and prosthetic motor control.},
}
@article {pmid22036287,
year = {2011},
author = {Ritaccio, A and Boatman-Reich, D and Brunner, P and Cervenka, MC and Cole, AJ and Crone, N and Duckrow, R and Korzeniewska, A and Litt, B and Miller, KJ and Moran, DW and Parvizi, J and Viventi, J and Williams, J and Schalk, G},
title = {Proceedings of the Second International Workshop on Advances in Electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {22},
number = {4},
pages = {641-650},
pmid = {22036287},
issn = {1525-5069},
support = {U24 NS063930/NS/NINDS NIH HHS/United States ; R01 NS040596/NS/NINDS NIH HHS/United States ; R01-EB006356/EB/NIBIB NIH HHS/United States ; K24 DC010028/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01-EB000856/EB/NIBIB NIH HHS/United States ; R01 NS048598/NS/NINDS NIH HHS/United States ; R01-NS40596/NS/NINDS NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; K24-DC010028/DC/NIDCD NIH HHS/United States ; },
mesh = {Brain/pathology/*physiopathology ; *Brain Mapping ; Brain Waves/*physiology ; Diagnosis, Computer-Assisted ; *Electroencephalography/instrumentation/methods ; Epilepsy/*diagnosis/physiopathology ; Humans ; United States ; User-Computer Interface ; },
abstract = {The Second International Workshop on Advances in Electrocorticography (ECoG) was convened in San Diego, CA, USA, on November 11-12, 2010. Between this meeting and the inaugural 2009 event, a much clearer picture has been emerging of cortical ECoG physiology and its relationship to local field potentials and single-cell recordings. Innovations in material engineering are advancing the goal of a stable long-term recording interface. Continued evolution of ECoG-driven brain-computer interface technology is determining innovation in neuroprosthetics. Improvements in instrumentation and statistical methodologies continue to elucidate ECoG correlates of normal human function as well as the ictal state. This proceedings document summarizes the current status of this rapidly evolving field.},
}
@article {pmid22030246,
year = {2011},
author = {Cardona, GA and Carabin, H and Goñi, P and Arriola, L and Robinson, G and Fernández-Crespo, JC and Clavel, A and Chalmers, RM and Carmena, D},
title = {Identification and molecular characterization of Cryptosporidium and Giardia in children and cattle populations from the province of Álava, North of Spain.},
journal = {The Science of the total environment},
volume = {412-413},
number = {},
pages = {101-108},
doi = {10.1016/j.scitotenv.2011.09.076},
pmid = {22030246},
issn = {1879-1026},
mesh = {Animals ; Cattle ; Cattle Diseases/epidemiology/parasitology/*transmission ; Child ; Child, Preschool ; Chromatography, Affinity/veterinary ; Cross-Sectional Studies ; Cryptosporidiosis/epidemiology/parasitology/transmission/*veterinary ; Cryptosporidium/classification/genetics/*isolation &amp; purification ; DNA, Protozoan/chemistry/genetics ; Enzyme-Linked Immunosorbent Assay/veterinary ; Feces/*parasitology ; Female ; Genotype ; Giardia/classification/genetics/*isolation &amp; purification ; Giardiasis/epidemiology/parasitology/transmission/*veterinary ; Humans ; Infant ; Male ; Microscopy, Fluorescence/veterinary ; Oocysts/classification ; Polymerase Chain Reaction/veterinary ; Prevalence ; Seasons ; Spain/epidemiology ; Species Specificity ; Surveys and Questionnaires ; },
abstract = {The prevalence of and factors associated with the protozoan enteropathogens Cryptosporidium and Giardia have been investigated in selected children and cattle populations from the province of Álava (Northern Spain). The presence of these organisms was detected in fecal samples using commercially available coproantigen-ELISA (CpAg-ELISA) and immunochromatographic (ICT) assays. A total of 327 caregivers of children participants were asked to answer questions on risk factors potentially associated to the prevalence of Cryptosporidium and Giardia, including water-use practices, water sports and contact with domestic or pet animals. Molecular analyses were conducted using a nested-PCR technique to amplify the small-subunit (SSU) rRNA gene of Cryptosporidium and the triosephosphate isomerase (tpi) gene of Giardia. Cryptosporidium oocysts and Giardia cysts were found in 3 and 16 samples using the CpAg-ELISA, and in 5 and 9 samples using the ICT test, respectively. Cryptosporidium and Giardia were also found in 7 and 17 samples by CpAg-ELISA, and 4 and 14 samples by ICT, respectively, of 227 cattle fecal samples. The overall Cryptosporidium and Giardia infection prevalences, based on a Bayesian approach accounting for the imperfect sensitivities and specificities of both diagnostic tests, were estimated to 1.0% (95% BCI: 0.2%-2.8%) and 3.1% (1.5%-5.3%) in children and 3.0% (0.5%-9.2%) and 1.4% (0.0%-6.4%) in cattle, respectively. In humans, a single Cryptosporidium isolate was characterized as C. hominis. Of seven Giardia isolates, four were identified as assemblage B, two as assemblage A-II and one was a mixed assemblage B+A-II infection. No Cryptosporidium or Giardia isolates could be obtained from cattle samples. Although limited, these results seem to suggest that cattle are unlikely to be an important reservoir of zoonotic Cryptosporidium and/or Giardia in the province of Álava.},
}
@article {pmid22027549,
year = {2011},
author = {Niazi, IK and Jiang, N and Tiberghien, O and Nielsen, JF and Dremstrup, K and Farina, D},
title = {Detection of movement intention from single-trial movement-related cortical potentials.},
journal = {Journal of neural engineering},
volume = {8},
number = {6},
pages = {066009},
doi = {10.1088/1741-2560/8/6/066009},
pmid = {22027549},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aged ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; *Intention ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Reaction Time/*physiology ; Stroke/physiopathology ; Young Adult ; },
abstract = {Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of -66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.},
}
@article {pmid22027493,
year = {2012},
author = {Quitadamo, LR and Abbafati, M and Cardarilli, GC and Mattia, D and Cincotti, F and Babiloni, F and Marciani, MG and Bianchi, L},
title = {Evaluation of the performances of different P300 based brain-computer interfaces by means of the efficiency metric.},
journal = {Journal of neuroscience methods},
volume = {203},
number = {2},
pages = {361-368},
doi = {10.1016/j.jneumeth.2011.10.010},
pmid = {22027493},
issn = {1872-678X},
mesh = {Cerebral Cortex/physiology ; Efficiency ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; Software/*standards ; *Software Validation ; *User-Computer Interface ; },
abstract = {The aim of this paper is to show how to use the Efficiency, a brain-computer interface (BCI) performance indicator, to evaluate the performances of a wide range of BCI systems. Unlike the most used metrics in the BCI research field, the Efficiency takes into account the penalties and the strategies to recover errors and this makes it a reliable instrument to describe the behavior of real BCIs. The Efficiency is compared with the accuracy and the information transfer rate, both in the Wolpaw and Nykopp definitions. The comparison covers four widely used classifiers and different stimulation sequences. Results show that the Efficiency is able to predict if the communication will not be possible, because the time spent to correct mistakes is longer than the time needed to generate a correct selection, and therefore it provides a much more realistic evaluation of a system. It can also be easily adapted to evaluate different applications, so it reveals a more general and versatile indicator for BCI systems.},
}
@article {pmid22021914,
year = {2012},
author = {Kilavik, BE and Ponce-Alvarez, A and Trachel, R and Confais, J and Takerkart, S and Riehle, A},
title = {Context-related frequency modulations of macaque motor cortical LFP beta oscillations.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {22},
number = {9},
pages = {2148-2159},
doi = {10.1093/cercor/bhr299},
pmid = {22021914},
issn = {1460-2199},
mesh = {Action Potentials/physiology ; Animals ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {The local field potential (LFP) is a population measure, mainly reflecting local synaptic activity. Beta oscillations (12-40 Hz) occur in motor cortical LFPs, but their functional relevance remains controversial. Power modulation studies have related beta oscillations to a "resting" motor cortex, postural maintenance, attention, sensorimotor binding and planning. Frequency modulations were largely overlooked. We here describe context-related beta frequency modulations in motor cortical LFPs. Two monkeys performed a reaching task with 2 delays. The first delay demanded attention in time in expectation of the visual spatial cue, whereas the second delay involved visuomotor integration and movement preparation. The frequency in 2 beta bands (around 20 and 30 Hz) was systematically 2-5 Hz lower during cue expectancy than during visuomotor integration and preparation. Furthermore, the frequency was directionally selective during preparation, with about 3 Hz difference between preferred and nonpreferred directions. Direction decoding with frequency gave similar accuracy as with beta power, and decoding accuracy improved significantly when combining power and frequency, suggesting that frequency might provide an additional signal for brain-machine interfaces. In conclusion, multiple beta bands coexist in motor cortex, and frequency modulations within each band are as behaviorally meaningful as power modulations, reflecting the changing behavioral context and the movement direction during preparation.},
}
@article {pmid22019933,
year = {2013},
author = {Schur, N and Hürlimann, E and Stensgaard, AS and Chimfwembe, K and Mushinge, G and Simoonga, C and Kabatereine, NB and Kristensen, TK and Utzinger, J and Vounatsou, P},
title = {Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling.},
journal = {Acta tropica},
volume = {128},
number = {2},
pages = {365-377},
doi = {10.1016/j.actatropica.2011.10.006},
pmid = {22019933},
issn = {1873-6254},
mesh = {Adolescent ; Adult ; Africa, Eastern/epidemiology ; Aged ; Aged, 80 and over ; Animals ; Bayes Theorem ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Infant, Newborn ; Male ; Middle Aged ; Prevalence ; Risk Assessment ; Schistosoma haematobium/*isolation &amp; purification ; Schistosoma mansoni/*isolation &amp; purification ; Schistosomiasis mansoni/*epidemiology ; *Topography, Medical ; Young Adult ; },
abstract = {Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities.},
}
@article {pmid22019880,
year = {2012},
author = {Weiskopf, N},
title = {Real-time fMRI and its application to neurofeedback.},
journal = {NeuroImage},
volume = {62},
number = {2},
pages = {682-692},
doi = {10.1016/j.neuroimage.2011.10.009},
pmid = {22019880},
issn = {1095-9572},
support = {091593//Wellcome Trust/United Kingdom ; },
mesh = {Brain/physiology ; Brain Mapping/*history/*methods ; History, 20th Century ; History, 21st Century ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/*history/*methods ; Neurofeedback/*methods ; Oxygen/blood ; },
abstract = {Real-time fMRI (rtfMRI) allows immediate access to experimental results by analyzing data as fast as they are acquired. It was devised soon after the inception of fMRI and has undergone a rapid development since then. The availability of results during the ongoing experiment facilitates a variety of applications such as quality assurance or fast functional localization. RtfMRI can also be used as a brain-computer interface (BCI) with high spatial resolution and whole-brain coverage, overcoming limitations of EEG based BCIs. This review will focus on the application of rtfMRI BCIs to neurofeedback, i.e., the online feedback of the blood oxygen level dependent (BOLD) response. I will motivate its development and place its beginnings into the contemporary scientific context by providing an account of our early work at the University of Tübingen, followed by a review of the accomplishments and the current state of rtfMRI neurofeedback. RtfMRI neurofeedback has been used to train self-regulation of the local BOLD response in various different brain areas and to study consequential behavioral effects. Behavioral effects such as modulation of pain, reaction time, linguistic or emotional processing have been shown in healthy and/or patient populations. RtfMRI neurofeedback presents a new paradigm for studying the relation between brain behavior and physiology, because the latter can be regarded as the independent variable (unlike in conventional neuroimaging studies where behavior is the independent variable). The initial results in patient populations improving pain, tinnitus, depression or modulating perception in schizophrenia are encouraging and merit further controlled clinical studies.},
}
@article {pmid22016719,
year = {2011},
author = {Schreuder, M and Rost, T and Tangermann, M},
title = {Listen, You are Writing! Speeding up Online Spelling with a Dynamic Auditory BCI.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {112},
pmid = {22016719},
issn = {1662-453X},
abstract = {Representing an intuitive spelling interface for brain-computer interfaces (BCI) in the auditory domain is not straight-forward. In consequence, all existing approaches based on event-related potentials (ERP) rely at least partially on a visual representation of the interface. This online study introduces an auditory spelling interface that eliminates the necessity for such a visualization. In up to two sessions, a group of healthy subjects (N = 21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multi-class Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 char/min (7.55 bits/min) could be reached during the second session (average: 0.94 char/min, 5.26 bits/min). For the first time, the presented work shows that an auditory BCI can reach performances similar to state-of-the-art visual BCIs based on covert attention. These results represent an important step toward a purely auditory BCI.},
}
@article {pmid22010143,
year = {2012},
author = {Barachant, A and Bonnet, S and Congedo, M and Jutten, C},
title = {Multiclass brain-computer interface classification by Riemannian geometry.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {4},
pages = {920-928},
doi = {10.1109/TBME.2011.2172210},
pmid = {22010143},
issn = {1558-2531},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of covariance matrices. The main idea is to use spatial covariance matrices as EEG signal descriptors and to rely on Riemannian geometry to directly classify these matrices using the topology of the manifold of symmetric and positive definite (SPD) matrices. This framework allows to extract the spatial information contained in EEG signals without using spatial filtering. Two methods are proposed and compared with a reference method [multiclass Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA)] on the multiclass dataset IIa from the BCI Competition IV. The first method, named minimum distance to Riemannian mean (MDRM), is an implementation of the minimum distance to mean (MDM) classification algorithm using Riemannian distance and Riemannian mean. This simple method shows comparable results with the reference method. The second method, named tangent space LDA (TSLDA), maps the covariance matrices onto the Riemannian tangent space where matrices can be vectorized and treated as Euclidean objects. Then, a variable selection procedure is applied in order to decrease dimensionality and a classification by LDA is performed. This latter method outperforms the reference method increasing the mean classification accuracy from 65.1% to 70.2%.},
}
@article {pmid22008173,
year = {2011},
author = {Wang, W and O'Connell, D and Stuart, K and Boyages, J},
title = {Analysis of 10-year cause-specific mortality of patients with breast cancer treated in New South Wales in 1995.},
journal = {Journal of medical imaging and radiation oncology},
volume = {55},
number = {5},
pages = {516-525},
doi = {10.1111/j.1754-9485.2011.02304.x},
pmid = {22008173},
issn = {1754-9485},
mesh = {Age Distribution ; Aged ; Breast Neoplasms/*mortality/*radiotherapy ; Cause of Death ; Cohort Studies ; Female ; Heart Diseases/*mortality ; Humans ; Kaplan-Meier Estimate ; Middle Aged ; New South Wales/epidemiology ; Radiotherapy, Adjuvant/*statistics &amp; numerical data ; Registries ; },
abstract = {OBJECTIVE: The objective of this study is to assess cause-specific mortality for patients with breast cancer and to determine if excess cardiac death was associated with radiation therapy (RT).<br><br>METHODS: We obtained 10-year cause-specific mortality information from the New South Wales (NSW) Central Cancer Registry and National Death Index on 1242 patients with unilateral stage I-III invasive breast cancer in NSW, Australia, diagnosed over a 6-month period in 1995. We compared actuarial cause-specific mortality (breast cancer, cardiac, other cancers and other causes) for patients who received left-sided, right-sided or no RT.<br><br>RESULTS: Mortality due to breast cancer or due to other cancers was not significantly different (P=0.30 and P=0.11) between the three subgroups. Mortality due to cardiac and other causes was higher in patients who did not have radiotherapy (P=0.001 and P<0.001). A total of 52 cardiac deaths in 1242 patients (4.2%) occurred - six of 274 patients (2.2%) in the left-sided radiotherapy group, four of 245 patients (1.6%) in the right-sided radiotherapy group (P=0.63) and 42 of 723 patients (5.8%) in the no radiotherapy group. Most cardiac deaths (46 of 52 cases) occurred in patients aged 70years or older at the time of diagnosis. There were no differences in cardiac mortality between the three treatment groups for those aged 70years or older (P=0.22, log-rank test), suggesting that the higher overall cardiac mortality rate in the no-RT group is due to a higher percentage of patients aged 70years or older. Of the 10 patients who died from cardiac causes and who had received RT, none had received chemotherapy or irradiation to the internal mammary chain.<br><br>CONCLUSION: There is no excess cardiac mortality due to RT within the first decade in a population series of patients with breast cancer treated with modern radiotherapy.},
}
@article {pmid22007194,
year = {2011},
author = {Devlaminck, D and Wyns, B and Grosse-Wentrup, M and Otte, G and Santens, P},
title = {Multisubject learning for common spatial patterns in motor-imagery BCI.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {217987},
pmid = {22007194},
issn = {1687-5273},
mesh = {*Algorithms ; *Artificial Intelligence ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; *User-Computer Interface ; },
abstract = {Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.},
}
@article {pmid22005673,
year = {2011},
author = {Schieber, MH},
title = {Dissociating motor cortex from the motor.},
journal = {The Journal of physiology},
volume = {589},
number = {Pt 23},
pages = {5613-5624},
pmid = {22005673},
issn = {1469-7793},
support = {R01 NS065902/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Humans ; Motor Cortex/*physiology ; Motor Neurons/*physiology ; Movement/*physiology ; Muscle, Skeletal/physiology ; Psychomotor Performance ; },
abstract = {During closed-loop control of a brain-computer interface, neurons in the primary motor cortex can be intensely active even though the subject may be making no detectable movement or muscle contraction. How can neural activity in the primary motor cortex become dissociated from the movements and muscles of the native limb that it normally controls? Here we examine circumstances in which motor cortex activity is known to dissociate from movement--including mental imagery, visuo-motor dissociation and instructed delay. Many such motor cortex neurons may be related to muscle activity only indirectly. Furthermore, the integration of thousands of synaptic inputs by individual α-motoneurons means that under certain circumstances even cortico-motoneuronal cells, which make monosynaptic connections to α-motoneurons, can become dissociated from muscle activity. The natural ability of motor cortex neurons under voluntarily control to become dissociated from bodily movement may underlie the utility of this cortical area for controlling brain-computer interfaces.},
}
@article {pmid21999244,
year = {2011},
author = {Jankowitz, RC and Cooper, K and Erlander, MG and Ma, XJ and Kesty, NC and Li, H and Chivukula, M and Brufsky, A},
title = {Prognostic utility of the breast cancer index and comparison to Adjuvant! Online in a clinical case series of early breast cancer.},
journal = {Breast cancer research : BCR},
volume = {13},
number = {5},
pages = {R98},
pmid = {21999244},
issn = {1465-542X},
mesh = {Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/drug therapy/genetics/*mortality/*pathology ; Cohort Studies ; Female ; Follow-Up Studies ; GTPase-Activating Proteins/genetics ; Homeodomain Proteins/genetics ; Humans ; Middle Aged ; NIMA-Related Kinases ; Neoplasm Recurrence, Local/drug therapy ; *Online Systems ; Prognosis ; Proportional Hazards Models ; Protein Serine-Threonine Kinases/genetics ; Receptors, Interleukin/genetics ; Receptors, Interleukin-17 ; Survival Rate ; Tamoxifen/therapeutic use ; },
abstract = {INTRODUCTION: Breast Cancer Index (BCI) combines two independent biomarkers, HOXB13:IL17BR (H:I) and the 5-gene molecular grade index (MGI), that assess estrogen-mediated signalling and tumor grade, respectively. BCI stratifies early-stage estrogen-receptor positive (ER+), lymph-node negative (LN-) breast cancer patients into three risk groups and provides a continuous assessment of individual risk of distant recurrence. Objectives of the current study were to validate BCI in a clinical case series and to compare the prognostic utility of BCI and Adjuvant!Online (AO).<br><br>METHODS: Tumor samples from 265 ER+LN- tamoxifen-treated patients were identified from a single academic institution's cancer research registry. The BCI assay was performed and scores were assigned based on a pre-determined risk model. Risk was assessed by BCI and AO and correlated to clinical outcomes in the patient cohort.<br><br>RESULTS: BCI was a significant predictor of outcome in a cohort of 265 ER+LN- patients (median age: 56-y; median follow-up: 10.3-y), treated with adjuvant tamoxifen alone or tamoxifen with chemotherapy (32%). BCI categorized 55%, 21%, and 24% of patients as low, intermediate and high-risk, respectively. The 10-year rates of distant recurrence were 6.6%, 12.1% and 31.9% and of breast cancer-specific mortality were 3.8%, 3.6% and 22.1% in low, intermediate, and high-risk groups, respectively. In a multivariate analysis including clinicopathological factors, BCI was a significant predictor of distant recurrence (HR for 5-unit increase = 5.32 [CI 2.18-13.01; P = 0.0002]) and breast cancer-specific mortality (HR for a 5-unit increase = 9.60 [CI 3.20-28.80; P < 0.0001]). AO was significantly associated with risk of recurrence. In a separate multivariate analysis, both BCI and AO were significantly predictive of outcome. In a time-dependent (10-y) ROC curve accuracy analysis of recurrence risk, the addition of BCI+AO increased predictive accuracy in all patients from 66% (AO only) to 76% (AO+BCI) and in tamoxifen-only treated patients from 65% to 81%.<br><br>CONCLUSIONS: This study validates the prognostic performance of BCI in ER+LN- patients. In this characteristically low-risk cohort, BCI classified high versus low-risk groups with ~5-fold difference in 10-year risk of distant recurrence and breast cancer-specific death. BCI and AO are independent predictors with BCI having additive utility beyond standard of care parameters that are encompassed in AO.},
}
@article {pmid21997321,
year = {2012},
author = {Cano-Izquierdo, JM and Ibarrola, J and Almonacid, M},
title = {Improving motor imagery classification with a new BCI design using neuro-fuzzy S-dFasArt.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {20},
number = {1},
pages = {2-7},
doi = {10.1109/TNSRE.2011.2169991},
pmid = {21997321},
issn = {1558-0210},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Classification ; Electroencephalography/statistics &amp; numerical data ; Functional Laterality/physiology ; *Fuzzy Logic ; Hand/innervation/physiology ; Humans ; Imagination/*physiology ; Memory, Long-Term/physiology ; Memory, Short-Term ; *Models, Neurological ; Movement/*physiology ; Neuronal Plasticity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.},
}
@article {pmid21992570,
year = {2011},
author = {Choi, D and Ryu, Y and Lee, Y and Lee, M},
title = {Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects.},
journal = {Biomedical engineering online},
volume = {10},
number = {},
pages = {91},
pmid = {21992570},
issn = {1475-925X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Cues ; Databases, Factual ; Electroencephalography/*methods ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; Movement ; Support Vector Machine ; Young Adult ; },
abstract = {BACKGROUND: The subjects in EEG-Brain computer interface (BCI) system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment.<br><br>METHODS: The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test.<br><br>RESULTS: In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p < 0.05) but also stable performance in all experiments.<br><br>CONCLUSIONS: We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.},
}
@article {pmid21985984,
year = {2012},
author = {Krusienski, DJ and McFarland, DJ and Wolpaw, JR},
title = {Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain-computer interface.},
journal = {Brain research bulletin},
volume = {87},
number = {1},
pages = {130-134},
pmid = {21985984},
issn = {1873-2747},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*anatomy &amp; histology/*physiology ; Electroencephalography/*methods ; Humans ; Motor Cortex/anatomy &amp; histology/*physiology ; *User-Computer Interface ; },
abstract = {Measures that quantify the relationship between two or more brain signals are drawing attention as neuroscientists explore the mechanisms of large-scale integration that enable coherent behavior and cognition. Traditional Fourier-based measures of coherence have been used to quantify frequency-dependent relationships between two signals. More recently, several off-line studies examined phase-locking value (PLV) as a possible feature for use in brain-computer interface (BCI) systems. However, only a few individuals have been studied and full statistical comparisons among the different classes of features and their combinations have not been conducted. The present study examines the relative BCI performance of spectral power, coherence, and PLV, alone and in combination. The results indicate that spectral power produced classification at least as good as PLV, coherence, or any possible combination of these measures. This may be due to the fact that all three measures reflect mainly the activity of a single signal source (i.e., an area of sensorimotor cortex). This possibility is supported by the finding that EEG signals from different channels generally had near-zero phase differences. Coherence, PLV, and other measures of inter-channel relationships may be more valuable for BCIs that use signals from more than one distinct cortical source.},
}
@article {pmid21984822,
year = {2011},
author = {McFarland, DJ and Wolpaw, JR},
title = {Brain-Computer Interfaces for Communication and Control.},
journal = {Communications of the ACM},
volume = {54},
number = {5},
pages = {60-66},
pmid = {21984822},
issn = {0001-0782},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-10/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; },
}
@article {pmid21984522,
year = {2011},
author = {Stepp, CE and Oyunerdene, N and Matsuoka, Y},
title = {Kinesthetic motor imagery modulates intermuscular coherence.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {6},
pages = {638-643},
pmid = {21984522},
issn = {1558-0210},
support = {T32 HD007424/HD/NICHD NIH HHS/United States ; 5T32HD007424/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Analysis of Variance ; Brain/physiology ; Data Interpretation, Statistical ; Electrodes ; Electroencephalography ; Electromyography ; Equipment Design ; Feedback, Physiological ; Female ; Humans ; Imagination/*physiology ; Kinesthesis/*physiology ; Male ; Motor Cortex/physiology ; Movement/physiology ; Muscle, Skeletal/*physiology ; Psychomotor Performance/physiology ; User-Computer Interface ; Young Adult ; },
abstract = {Intermuscular coherence can identify oscillatory coupling between two electromyographic (EMG) signals, measuring common presynaptic drive to motor neurons. Beta band oscillations (15-30 Hz) are hypothesized to originate largely from primary motor cortex, and are reduced during dynamic relative to static motor tasks. It has yet to be established whether motor imagery modulates beta intermuscular coherence. Using visual feedback, 10 unimpaired participants completed eighteen trials of pinching their right thumb and index finger at a constant force. During the 60-second trials, participants simultaneously engaged in one of three types of kinesthetic imagery: the right thumb and index finger executing a constant force pinch (static), the fingers of the right hand sequentially flexing and extending (dynamic), and the right foot pushing down with constant force (foot). Motor imagery of a dynamic motor task resulted in significantly lower intermuscular beta coherence than imagery of a static motor pinch task, without any difference in task performance or root-mean-square EMG. Thus, motor imagery affects intermuscular coherence in the beta band, even while measures of task performance remain constant. This finding provides insight for incorporation of beta band intermuscular coherence in future motor rehabilitation schemes and brain computer interface design.},
}
@article {pmid21984520,
year = {2011},
author = {Tam, WK and Tong, KY and Meng, F and Gao, S},
title = {A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {6},
pages = {617-627},
doi = {10.1109/TNSRE.2011.2168542},
pmid = {21984520},
issn = {1558-0210},
mesh = {Adult ; Aged ; Algorithms ; Brain/*physiology ; Calibration ; Cerebral Cortex/physiology ; Chronic Disease ; Data Interpretation, Statistical ; Electric Stimulation ; *Electrodes ; Electroencephalography ; Female ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Movement/physiology ; Online Systems ; Psychomotor Performance/physiology ; *Self-Help Devices ; *Stroke Rehabilitation ; Support Vector Machine ; *User-Computer Interface ; },
abstract = {The brain-computer interface (BCI) system has been developed to assist people with motor disability. To make the system more user-friendly, it is a challenge to reduce the electrode preparation time and have a good reliability. This study aims to find a minimal set of electrodes for an individual stroke subject for motor imagery to control an assistive device using functional electrical stimulation for 20 sessions with accuracy higher than 90%. The characteristics of this minimal electrode set were evaluated with two popular algorithms: Fisher's criterion and support-vector machine recursive feature elimination (SVM-RFE). The number of calibration sessions for channel selection required for robust control of these 20 sessions was also investigated. Five chronic stroke patients were recruited for the study. Our results suggested that the number of calibration sessions for channel selection did not have a significant effect on the classification accuracy. A performance index devised in this study showed that one training day with 12 electrodes using the SVM-RFE method achieved the best balance between the number of electrodes and accuracy in the 20-session data. Generally, 8-36 channels were required to maintain accuracy higher than 90% in 20 BCI training sessions for chronic stroke patients.},
}
@article {pmid21984519,
year = {2011},
author = {Soekadar, SR and Witkowski, M and Mellinger, J and Ramos, A and Birbaumer, N and Cohen, LG},
title = {ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {5},
pages = {542-549},
pmid = {21984519},
issn = {1558-0210},
support = {ZIA NS002978-11/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adaptation, Psychological ; Adult ; Algorithms ; Brain/physiology ; Cortical Synchronization/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Feedback, Physiological ; Female ; Functional Laterality/physiology ; Humans ; Imagination/physiology ; Learning/*physiology ; Magnetoencephalography ; Male ; Orthotic Devices ; Proprioception/physiology ; Psychomotor Performance/*physiology ; Reward ; Software ; *Stroke Rehabilitation ; *User-Computer Interface ; Young Adult ; },
abstract = {Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning.Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training,motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p < 0.001) and improved BMI control from S1 to S5 (p = 0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p < 0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.},
}
@article {pmid21984517,
year = {2011},
author = {Reuderink, B and Poel, M and Nijholt, A},
title = {The impact of loss of control on movement BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {6},
pages = {628-637},
doi = {10.1109/TNSRE.2011.2166562},
pmid = {21984517},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Area Under Curve ; Behavior/physiology ; Brain/*physiology ; Contingent Negative Variation/physiology ; Electrodes ; Electroencephalography/*classification ; Electrooculography ; Emotions ; Equipment Failure ; Evoked Potentials/physiology ; Eye Movements/physiology ; Female ; Fingers/physiology ; Humans ; Internal-External Control ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; Video Games ; },
abstract = {Brain-computer interfaces (BCIs) are known to suffer from spontaneous changes in the brain activity. If changes in the mental state of the user are reflected in the brain signals used for control, the behavior of a BCI is directly influenced by these states. We investigate the influence of a state of loss of control in a variant of Pacman on the performance of BCIs based on motor control. To study the effect a temporal loss of control has on the BCI performance, BCI classifiers were trained on electroencephalography (EEG) recorded during the normal control condition, and the classification performance on segments of EEG from the normal and loss of control condition was compared. Classifiers based on event-related desynchronization unexpectedly performed significantly better during the loss of control condition; for the event-related potential classifiers there was no significant difference in performance.},
}
@article {pmid21981809,
year = {2011},
author = {Berthoud, HR},
title = {Metabolic and hedonic drives in the neural control of appetite: who is the boss?.},
journal = {Current opinion in neurobiology},
volume = {21},
number = {6},
pages = {888-896},
pmid = {21981809},
issn = {1873-6882},
support = {R01 DK071082/DK/NIDDK NIH HHS/United States ; R01 DK047348-13/DK/NIDDK NIH HHS/United States ; R01 DK071082-04/DK/NIDDK NIH HHS/United States ; R01 DK047348/DK/NIDDK NIH HHS/United States ; R01 DK071082-04S1/DK/NIDDK NIH HHS/United States ; R01 DK047348-17/DK/NIDDK NIH HHS/United States ; R01 DK071082-04S2/DK/NIDDK NIH HHS/United States ; },
mesh = {Appetite/physiology ; Appetite Regulation/*physiology ; Eating/physiology ; Humans ; Obesity/*etiology/physiopathology ; },
abstract = {Obesity is on the rise in all developed countries, and a large part of this epidemic has been attributed to excess caloric intake, induced by ever present food cues and the easy availability of energy dense foods in an environment of plenty. Clearly, there are strong homeostatic regulatory mechanisms keeping body weight of many individuals exposed to this environment remarkably stable over their adult life. Other individuals, however, seem to eat not only because of metabolic need, but also because of excessive hedonic drive to make them feel better and relieve stress. In the extreme, some individuals exhibit addiction-like behavior toward food, and parallels have been drawn to drug and alcohol addiction. However, there is an important distinction in that, unlike drugs and alcohol, food is a daily necessity. Considerable advances have been made recently in the identification of neural circuits that represent the interface between the metabolic and hedonic drives of eating. We will cover these new findings by focusing first on the capacity of metabolic signals to modulate processing of cognitive and reward functions in cortico-limbic systems (bottom-up) and then on pathways by which the cognitive and emotional brain may override homeostatic regulation (top-down).},
}
@article {pmid21981673,
year = {2012},
author = {Polanía, R and Paulus, W and Nitsche, MA},
title = {Noninvasively decoding the contents of visual working memory in the human prefrontal cortex within high-gamma oscillatory patterns.},
journal = {Journal of cognitive neuroscience},
volume = {24},
number = {2},
pages = {304-314},
doi = {10.1162/jocn_a_00151},
pmid = {21981673},
issn = {1530-8898},
mesh = {Adult ; Brain Mapping ; Electroencephalography ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Photic Stimulation ; Prefrontal Cortex/*physiology ; Reaction Time/physiology ; Visual Perception/*physiology ; },
abstract = {The temporal maintenance and subsequent retrieval of information that no longer exists in the environment is called working memory. It is believed that this type of memory is controlled by the persistent activity of neuronal populations, including the prefrontal, temporal, and parietal cortex. For a long time, it has been controversially discussed whether, in working memory, the PFC stores past sensory events or, instead, its activation is an extramnemonic source of top-down control over posterior regions. Recent animal studies suggest that specific information about the contents of working memory can be decoded from population activity in prefrontal areas. However, it has not been shown whether the contents of working memory during the delay periods can be decoded from EEG recordings in the human brain. We show that by analyzing the nonlinear dynamics of EEG oscillatory patterns it is possible to noninvasively decode with high accuracy, during encoding and maintenance periods, the contents of visual working memory information within high-gamma oscillations in the human PFC. These results are thus in favor of an active storage function of the human PFC in working memory; this, without ruling out the role of PFC in top-down processes. The ability to noninvasively decode the contents of working memory is promising in applications such as brain computer interfaces, together with computation of value function during planning and decision making processes.},
}
@article {pmid21976021,
year = {2011},
author = {O'Doherty, JE and Lebedev, MA and Ifft, PJ and Zhuang, KZ and Shokur, S and Bleuler, H and Nicolelis, MA},
title = {Active tactile exploration using a brain-machine-brain interface.},
journal = {Nature},
volume = {479},
number = {7372},
pages = {228-231},
pmid = {21976021},
issn = {1476-4687},
support = {R01 NS073125-01/NS/NINDS NIH HHS/United States ; RC1 HD063390-01/HD/NICHD NIH HHS/United States ; R01 NS073125/NS/NINDS NIH HHS/United States ; DP1 MH099903/MH/NIMH NIH HHS/United States ; RC1HD063390/HD/NICHD NIH HHS/United States ; DP1 OD006798-01/OD/NIH HHS/United States ; RC1 HD063390/HD/NICHD NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; NS073125/NS/NINDS NIH HHS/United States ; DP1OD006798/OD/NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Artificial Limbs ; Brain/*physiology ; Feedback ; Macaca mulatta/*physiology ; *Man-Machine Systems ; Psychometrics ; Reward ; Somatosensory Cortex/physiology ; Touch/*physiology ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces use neuronal activity recorded from the brain to establish direct communication with external actuators, such as prosthetic arms. It is hoped that brain-machine interfaces can be used to restore the normal sensorimotor functions of the limbs, but so far they have lacked tactile sensation. Here we report the operation of a brain-machine-brain interface (BMBI) that both controls the exploratory reaching movements of an actuator and allows signalling of artificial tactile feedback through intracortical microstimulation (ICMS) of the primary somatosensory cortex. Monkeys performed an active exploration task in which an actuator (a computer cursor or a virtual-reality arm) was moved using a BMBI that derived motor commands from neuronal ensemble activity recorded in the primary motor cortex. ICMS feedback occurred whenever the actuator touched virtual objects. Temporal patterns of ICMS encoded the artificial tactile properties of each object. Neuronal recordings and ICMS epochs were temporally multiplexed to avoid interference. Two monkeys operated this BMBI to search for and distinguish one of three visually identical objects, using the virtual-reality arm to identify the unique artificial texture associated with each. These results suggest that clinical motor neuroprostheses might benefit from the addition of ICMS feedback to generate artificial somatic perceptions associated with mechanical, robotic or even virtual prostheses.},
}
@article {pmid21975364,
year = {2011},
author = {Power, SD and Kushki, A and Chau, T},
title = {Towards a system-paced near-infrared spectroscopy brain-computer interface: differentiating prefrontal activity due to mental arithmetic and mental singing from the no-control state.},
journal = {Journal of neural engineering},
volume = {8},
number = {6},
pages = {066004},
doi = {10.1088/1741-2560/8/6/066004},
pmid = {21975364},
issn = {1741-2552},
mesh = {Adult ; Brain/physiology ; Female ; Humans ; Imagination/physiology ; *Intention ; Male ; *Mathematics/methods ; Photic Stimulation/methods ; Prefrontal Cortex/*physiology ; Psychomotor Performance/physiology ; *Spectroscopy, Near-Infrared/methods ; Thinking/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. For the most part, previous research has investigated the development of NIRS-BCIs operating under synchronous control paradigms, which require the user to exert conscious control over their mental activity whenever the system is vigilant. Though functional, this is mentally demanding and an unnatural way to communicate. An attractive alternative to the synchronous control paradigm is system-paced control, in which users are required to consciously modify their brain activity only when they wish to affect the BCI output, and can remain in a more natural, 'no-control' state at all other times. In this study, we investigated the feasibility of a system-paced NIRS-BCI with one intentional control (IC) state corresponding to the performance of either mental arithmetic or mental singing. In particular, this involved determining if these tasks could be distinguished, individually, from the unconstrained 'no-control' state. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while eight able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a six-dimensional feature set, an overall classification accuracy of 71.2% across participants was achieved for the mental arithmetic versus no-control classification problem. While the mental singing versus no-control classification was less successful across participants (62.7% on average), four participants did attain accuracies well in excess of chance, three of which were above 70%. Analyses were performed offline. Collectively, these results are encouraging, and demonstrate the potential of a system-paced NIRS-BCI with one IC state corresponding to either mental arithmetic or mental singing.},
}
@article {pmid21975312,
year = {2011},
author = {Treder, MS and Schmidt, NM and Blankertz, B},
title = {Gaze-independent brain-computer interfaces based on covert attention and feature attention.},
journal = {Journal of neural engineering},
volume = {8},
number = {6},
pages = {066003},
doi = {10.1088/1741-2560/8/6/066003},
pmid = {21975312},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Attention/*physiology ; Brain/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Eye Movements/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/methods ; *User-Computer Interface ; Young Adult ; },
abstract = {There is evidence that conventional visual brain-computer interfaces (BCIs) based on event-related potentials cannot be operated efficiently when eye movements are not allowed. To overcome this limitation, the aim of this study was to develop a visual speller that does not require eye movements. Three different variants of a two-stage visual speller based on covert spatial attention and non-spatial feature attention (i.e. attention to colour and form) were tested in an online experiment with 13 healthy participants. All participants achieved highly accurate BCI control. They could select one out of thirty symbols (chance level 3.3%) with mean accuracies of 88%-97% for the different spellers. The best results were obtained for a speller that was operated using non-spatial feature attention only. These results show that, using feature attention, it is possible to realize high-accuracy, fast-paced visual spellers that have a large vocabulary and are independent of eye gaze.},
}
@article {pmid21967470,
year = {2012},
author = {Pasqualotto, E and Federici, S and Belardinelli, MO},
title = {Toward functioning and usable brain-computer interfaces (BCIs): a literature review.},
journal = {Disability and rehabilitation. Assistive technology},
volume = {7},
number = {2},
pages = {89-103},
doi = {10.3109/17483107.2011.589486},
pmid = {21967470},
issn = {1748-3115},
mesh = {*Brain ; Cognition/*physiology ; Electroencephalography/instrumentation ; Evoked Potentials, Visual ; Humans ; Man-Machine Systems ; Nervous System Diseases/rehabilitation ; Paralysis/*rehabilitation ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {PURPOSE: The aim of this paper is to provide an exhaustive review of the literature about brain-computer interfaces (BCIs) that could be used with these paralysed patients. The electroencephalography (EEG) is the best candidate for the continuous use in the environment of patients' houses, due to its portability and ease of use. For this reason, the present paper will focus on this kind of BCI. Moreover, it is our aim to focus more on the patients, regarding their active role in the modulation of the brain activity. This leads to a differentiation between studies that use an active regulation and studies that use a non-active regulation.<br><br>METHOD: Relevant articles in the BCIs field were selected using MEDLINE and PsycINFO.<br><br>RESULTS: Research through data banks produced 980 results, which were reduced to 127 after exclusion criteria selection. These references were divided in four categories, based on the use of active or non-active regulation, and on the event related potential used.<br><br>CONCLUSIONS: In most of the examined works, the focus was on the development of systems and algorithms able to recognise and classify brain events. Although this kind of research is fundamental, a user-centred point of view was rarely adopted. [Box: see text].},
}
@article {pmid21964375,
year = {2012},
author = {Hammer, EM and Halder, S and Blankertz, B and Sannelli, C and Dickhaus, T and Kleih, S and Müller, KR and Kübler, A},
title = {Psychological predictors of SMR-BCI performance.},
journal = {Biological psychology},
volume = {89},
number = {1},
pages = {80-86},
doi = {10.1016/j.biopsycho.2011.09.006},
pmid = {21964375},
issn = {1873-6246},
mesh = {Adolescent ; Adult ; Aged ; Algorithms ; Analysis of Variance ; *Biofeedback, Psychology ; Cognition/physiology ; Cortical Spreading Depression/physiology ; Electroencephalography ; Female ; Hand/innervation ; Humans ; Imagery, Psychotherapy ; Male ; *Man-Machine Systems ; Middle Aged ; Movement/*physiology ; Personality Inventory ; Predictive Value of Tests ; Psychological Tests ; Psychomotor Performance/physiology ; Regression Analysis ; Somatosensory Cortex/*physiology ; Statistics, Nonparametric ; Surveys and Questionnaires ; *User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: After about 30 years of research on Brain-Computer Interfaces (BCIs) there is little knowledge about the phenomenon, that some people - healthy as well as individuals with disease - are not able to learn BCI-control. To elucidate this "BCI-inefficiency" phenomenon, the current study investigated whether psychological parameters, such as attention span, personality or motivation, could predict performance in a single session with a BCI controlled by modulation of sensorimotor rhythms (SMR) with motor imagery.<br><br>METHODS: A total of N=83 healthy BCI novices took part in the session. Psychological parameters were measured with an electronic test-battery including clinical, personality and performance tests. Predictors were determined by binary logistic regression analyses.<br><br>RESULTS: The output variable of the Two-Hand Coordination Test (2HAND) "overall mean error duration" which is a measure for the accuracy of fine motor skills accounted for 11% of the variance in BCI-inefficiency. The Attitudes Towards Work (AHA) test variable "performance level" which can be interpreted as a degree of concentration and a neurophysiological SMR predictor were also identified as significant predictors of SMR BCI performance.<br><br>CONCLUSION: Psychological parameters as measured in this study play a moderate role for one-session performance in a BCI controlled by modulation of SMR.},
}
@article {pmid21963400,
year = {2012},
author = {Furdea, A and Ruf, CA and Halder, S and De Massari, D and Bogdan, M and Rosenstiel, W and Matuz, T and Birbaumer, N},
title = {A new (semantic) reflexive brain-computer interface: in search for a suitable classifier.},
journal = {Journal of neuroscience methods},
volume = {203},
number = {1},
pages = {233-240},
doi = {10.1016/j.jneumeth.2011.09.013},
pmid = {21963400},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Conditioning, Classical/*physiology ; Discriminant Analysis ; *Electroencephalography ; Female ; Humans ; Male ; *Semantics ; *Support Vector Machine ; *User-Computer Interface ; Young Adult ; },
abstract = {The goal of the current study is to find a suitable classifier for electroencephalogram (EEG) data derived from a new learning paradigm which aims at communication in paralysis. A reflexive semantic classical (Pavlovian) conditioning paradigm is explored as an alternative to the operant learning paradigms, currently used in most brain-computer interfaces (BCIs). Comparable with a lie-detection experiment, subjects are presented with true and false statements. The EEG activity following true and false statements was classified with the aim to separate covert 'yes' from covert 'no' responses. Four classification algorithms are compared for classifying off-line data collected from a group of 14 healthy participants: (i) stepwise linear discriminant analysis (SWLDA), (ii) shrinkage linear discriminant analysis (SLDA), (iii) linear support vector machine (LIN-SVM) and (iv) radial basis function kernel support vector machine (RBF-SVM). The results indicate that all classifiers perform at chance level when separating conditioned 'yes' from conditioned 'no' responses. However, single conditioned reactions could be successfully classified on a single-trial basis (single conditioned reaction against a baseline interval). All of the four investigated classification methods achieve comparable performance, however results with RBF-SVM show the highest single-trial classification accuracy of 68.8%. The results suggest that the proposed paradigm may allow affirmative and negative (disapproving negative) communication in a BCI experiment.},
}
@article {pmid21960209,
year = {2011},
author = {Behr, A and Reyer, S and Tenhumberg, N},
title = {Selective hydroformylation-hydrogenation tandem reaction of isoprene to 3-methylpentanal.},
journal = {Dalton transactions (Cambridge, England : 2003)},
volume = {40},
number = {44},
pages = {11742-11747},
doi = {10.1039/c1dt11292a},
pmid = {21960209},
issn = {1477-9234},
mesh = {Aldehydes/chemical synthesis/*chemistry ; Butadienes/*chemistry ; Catalysis ; Coordination Complexes/chemistry ; Hemiterpenes/*chemistry ; Hydrogenation ; Kinetics ; Pentanes/*chemistry ; Phosphines/chemistry ; Rhodium/chemistry ; Stereoisomerism ; },
abstract = {The hydroformylation of isoprene catalysed by rhodium phosphine complexes usually yields a broad mixture of the monoaldehydes, the isomeric methylpentenals, as well as the dialdehyde 3-methyl-1,6-hexandial. Under usual reaction conditions the products of a consecutive hydrogenation are only formed as minor by-products. Surprisingly we discovered now a selective auto-tandem reaction consisting of a hydroformylation and a hydrogenation step if a rhodium complex with the chelate ligand bis(diphenylphosphino)ethane is used as catalyst. If branched aromatic solvents like cumene are applied the conversion of isoprene is nearly quantitatively and the yield of the tandem product 3-methylpentanal amounts to 85%.},
}
@article {pmid21949908,
year = {2011},
author = {Francis, JT and Song, W},
title = {Neuroplasticity of the sensorimotor cortex during learning.},
journal = {Neural plasticity},
volume = {2011},
number = {},
pages = {310737},
pmid = {21949908},
issn = {1687-5443},
mesh = {Animals ; Brain/*physiology ; Humans ; Learning/*physiology ; Long-Term Potentiation/physiology ; Motor Cortex/cytology/enzymology/*physiology ; Neuronal Plasticity/*physiology ; Protein Kinase C/antagonists &amp; inhibitors/physiology ; Rats ; Somatosensory Cortex/cytology/enzymology/*physiology ; },
abstract = {We will discuss some of the current issues in understanding plasticity in the sensorimotor (SM) cortices on the behavioral, neurophysiological, and synaptic levels. We will focus our paper on reaching and grasping movements in the rat. In addition, we will discuss our preliminary work utilizing inhibition of protein kinase Mζ (PKMζ), which has recently been shown necessary and sufficient for the maintenance of long-term potentiation (LTP) (Ling et al., 2002). With this new knowledge and inhibitors to this system, as well as the ability to overexpress this system, we can start to directly modulate LTP and determine its influence on behavior as well as network level processing dependent at least in part due to this form of LTP. We will also briefly introduce the use of brain machine interface (BMI) paradigms to ask questions about sensorimotor plasticity and discuss current analysis techniques that may help in our understanding of neuroplasticity.},
}
@article {pmid21947867,
year = {2012},
author = {Abdulghani, AM and Casson, AJ and Rodriguez-Villegas, E},
title = {Compressive sensing scalp EEG signals: implementations and practical performance.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {50},
number = {11},
pages = {1137-1145},
pmid = {21947867},
issn = {1741-0444},
mesh = {Computers ; Data Compression/*methods ; Electroencephalography/*methods ; Equipment Design ; Humans ; Miniaturization ; Scalp ; Signal Processing, Computer-Assisted/*instrumentation ; Signal-To-Noise Ratio ; },
abstract = {Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain-computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.},
}
@article {pmid21947797,
year = {2011},
author = {Brunner, C and Billinger, M and Vidaurre, C and Neuper, C},
title = {A comparison of univariate, vector, bilinear autoregressive, and band power features for brain-computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {49},
number = {11},
pages = {1337-1346},
pmid = {21947797},
issn = {1741-0444},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Humans ; Imagination/physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Selecting suitable feature types is crucial to obtain good overall brain-computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results.},
}
@article {pmid21947530,
year = {2011},
author = {Miller, LE and Weber, DJ},
title = {Brain training: cortical plasticity and afferent feedback in brain-machine interface systems.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {5},
pages = {465-467},
doi = {10.1109/TNSRE.2011.2168989},
pmid = {21947530},
issn = {1558-0210},
mesh = {Cortical Synchronization/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Learning/*physiology ; Male ; Psychomotor Performance/*physiology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
}
@article {pmid21947184,
year = {2011},
author = {Shindo, K and Kawashima, K and Ushiba, J and Ota, N and Ito, M and Ota, T and Kimura, A and Liu, M},
title = {Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study.},
journal = {Journal of rehabilitation medicine},
volume = {43},
number = {10},
pages = {951-957},
doi = {10.2340/16501977-0859},
pmid = {21947184},
issn = {1651-2081},
mesh = {Aged ; Female ; Hand/*physiopathology ; Humans ; Imagery, Psychotherapy ; Male ; Middle Aged ; Neurofeedback/*methods ; Paralysis/physiopathology/*rehabilitation ; Recovery of Function ; Stroke/physiopathology/psychology ; *Stroke Rehabilitation ; Transcranial Magnetic Stimulation ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To explore the effectiveness of neurorehabilitative training using an electroencephalogram-based brain- computer interface for hand paralysis following stroke.<br><br>DESIGN: A case series study.<br><br>SUBJECTS: Eight outpatients with chronic stroke demonstrating moderate to severe hemiparesis.<br><br>METHODS: Based on analysis of volitionally decreased amplitudes of sensory motor rhythm during motor imagery involving extending the affected fingers, real-time visual feedback was provided. After successful motor imagery, a mechanical orthosis partially extended the fingers. Brain-computer interface interventions were carried out once or twice a week for a period of 4-7 months, and clinical and neurophysiological examinations pre- and post-intervention were compared.<br><br>RESULTS: New voluntary electromyographic activity was measured in the affected finger extensors in 4 cases who had little or no muscle activity before the training, and the other participants exhibited improvement in finger function. Significantly greater suppression of the sensory motor rhythm over both hemispheres was observed during motor imagery. Transcranial magnetic stimulation showed increased cortical excitability in the damaged hemisphere. Success rates of brain-computer interface training tended to increase as the session progressed in 4 cases.<br><br>CONCLUSION: Brain-computer interface training appears to have yielded some improvement in motor function and brain plasticity. Further controlled research is needed to clarify the role of the brain-computer interface system.},
}
@article {pmid21945691,
year = {2012},
author = {Yoshimura, N and Dasalla, CS and Hanakawa, T and Sato, MA and Koike, Y},
title = {Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents.},
journal = {NeuroImage},
volume = {59},
number = {2},
pages = {1324-1337},
doi = {10.1016/j.neuroimage.2011.08.029},
pmid = {21945691},
issn = {1095-9572},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Isometric Contraction/*physiology ; Male ; Middle Aged ; Muscle, Skeletal/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Wrist Joint/physiology ; Young Adult ; },
abstract = {The ability to reconstruct muscle activity time series from electroencephalography (EEG) may lead to drastic improvements in brain-machine interfaces (BMIs) by providing a means for realistic continuous reproduction of dexterous movements in human beings. However, it is considered difficult to isolate signals related to individual muscle activities from EEG because EEG sensors record a mixture of signals originating from many cortical regions. Here, we challenge this assumption by reconstructing agonist and antagonist muscle activities (i.e. filtered electromyography (EMG) signals) from EEG cortical currents estimated using a hierarchical Bayesian EEG inverse method. Results of 5 volunteer subjects performing isometric right wrist flexion and extension tasks showed that individual muscle activity time series, as well as muscle activities at different force levels, were well reconstructed using EEG cortical currents and with significantly higher accuracy than when directly reconstructing from EEG sensor signals. Moreover, spatial distribution of weight values for reconstruction models revealed that highly contributing cortical sources to flexion and extension tasks were mutually exclusive, even though they were mapped onto the same cortical region. These results suggest that EEG sensor signals were reasonably isolated into cortical currents using the applied method and provide the first evidence that agonist and antagonist muscle activity time series can be reconstructed using EEG cortical currents.},
}
@article {pmid21941530,
year = {2011},
author = {Manyakov, NV and Chumerin, N and Combaz, A and Van Hulle, MM},
title = {Comparison of classification methods for P300 brain-computer interface on disabled subjects.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {519868},
pmid = {21941530},
issn = {1687-5273},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation ; Communication Disorders/etiology/physiopathology/*rehabilitation ; Persons with Disabilities/rehabilitation ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Infarction, Middle Cerebral Artery/physiopathology/rehabilitation ; Male ; Middle Aged ; Movement Disorders/etiology/physiopathology/*rehabilitation ; Neurofeedback/*methods/physiology ; Subarachnoid Hemorrhage/physiopathology/rehabilitation ; *User-Computer Interface ; },
abstract = {We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.},
}
@article {pmid21934188,
year = {2011},
author = {Kaufmann, T and Schulz, SM and Grünzinger, C and Kübler, A},
title = {Flashing characters with famous faces improves ERP-based brain-computer interface performance.},
journal = {Journal of neural engineering},
volume = {8},
number = {5},
pages = {056016},
doi = {10.1088/1741-2560/8/5/056016},
pmid = {21934188},
issn = {1741-2552},
mesh = {Adult ; Artifacts ; *Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; *Face ; Famous Persons ; Female ; Humans ; Male ; Photic Stimulation/*methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Currently, the event-related potential (ERP)-based spelling device, often referred to as P300-Speller, is the most commonly used brain-computer interface (BCI) for enhancing communication of patients with impaired speech or motor function. Among numerous improvements, a most central feature has received little attention, namely optimizing the stimulus used for eliciting ERPs. Therefore we compared P300-Speller performance with the standard stimulus (flashing characters) against performance with stimuli known for eliciting particularly strong ERPs due to their psychological salience, i.e. flashing familiar faces transparently superimposed on characters. Our results not only indicate remarkably increased ERPs in response to familiar faces but also improved P300-Speller performance due to a significant reduction of stimulus sequences needed for correct character classification. These findings demonstrate a promising new approach for improving the speed and thus fluency of BCI-enhanced communication with the widely used P300-Speller.},
}
@article {pmid21926453,
year = {2011},
author = {Hasan, BA and Gan, JQ},
title = {Temporal modeling of EEG during self-paced hand movement and its application in onset detection.},
journal = {Journal of neural engineering},
volume = {8},
number = {5},
pages = {056015},
doi = {10.1088/1741-2560/8/5/056015},
pmid = {21926453},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/physiology ; Data Interpretation, Statistical ; Electroencephalography/methods/*statistics &amp; numerical data ; Electromyography/methods/statistics &amp; numerical data ; Female ; Hand/*physiology ; Humans ; Linear Models ; Male ; Markov Chains ; Models, Statistical ; Movement/*physiology ; Random Allocation ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.},
}
@article {pmid21926016,
year = {2012},
author = {Long, J and Li, Y and Yu, T and Gu, Z},
title = {Target selection with hybrid feature for BCI-based 2-D cursor control.},
journal = {IEEE transactions on bio-medical engineering},
volume = {59},
number = {1},
pages = {132-140},
doi = {10.1109/TBME.2011.2167718},
pmid = {21926016},
issn = {1558-2531},
mesh = {Adult ; Biofeedback, Psychology/*physiology ; Brain/*physiology ; *Computer Peripherals ; Electrocardiography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination ; Male ; Motion Perception/*physiology ; *User-Computer Interface ; },
abstract = {To control a cursor on a monitor screen, a user generally needs to perform two tasks sequentially. The first task is to move the cursor to a target on the monitor screen (termed a 2-D cursor movement), and the second task is either to select a target of interest by clicking on it or to reject a target that is not of interest by not clicking on it. In a previous study, we implemented the former function in an EEG-based brain-computer interface system using motor imagery and the P300 potential to control the horizontal and vertical cursor movements, respectively. In this study, the target selection or rejection functionality is implemented using a hybrid feature from motor imagery and the P300 potential. Specifically, to select the target of interest, the user must focus his or her attention on a flashing button to evoke the P300 potential, while simultaneously maintaining an idle state of motor imagery. Otherwise, the user performs left-/right-hand motor imagery without paying attention to any buttons to reject the target. Our data analysis and online experimental results validate the effectiveness of our approach. The proposed hybrid feature is shown to be more effective than the use of either the motor imagery feature or the P300 feature alone. Eleven subjects attended our online experiment, in which a trial involved sequential 2-D cursor movement and target selection. The average duration of each trial and average accuracy of target selection were 18.19 s and 93.99% , respectively, and each target selection or rejection event was performed within 2 s.},
}
@article {pmid21922432,
year = {2011},
author = {Lang, Y and Du, P and Shin, HC},
title = {Encoding-based brain-computer interface controlled by non-motor area of rat brain.},
journal = {Science China. Life sciences},
volume = {54},
number = {9},
pages = {841-853},
doi = {10.1007/s11427-011-4214-6},
pmid = {21922432},
issn = {1869-1889},
mesh = {Animals ; Feasibility Studies ; Motor Cortex/*physiology ; Rats ; Rats, Sprague-Dawley ; *User-Computer Interface ; },
abstract = {As the needs of disabled patients are increasingly recognized in society, researchers have begun to use single neuron activity to construct brain-computer interfaces (BCI), designed to facilitate the daily lives of individuals with physical disabilities. BCI systems typically allow users to control computer programs or external devices via signals produced in the motor or pre-motor areas of the brain, rather than producing actual motor movements. However, impairments in these brain areas can hinder the application of BCI. The current paper demonstrates the feasibility of a one-dimensional (1D) machine controlled by rat prefrontal cortex (PFC) neurons using an encoding method. In this novel system, rats are able to quench thirst by varying neuronal firing rate in the PFC to manipulate a water dish that can rotate in 1D. The results revealed that control commands generated by an appropriate firing frequency in rat PFC exhibited performance improvements with practice, indicated by increasing water-drinking duration and frequency. These results demonstrated that it is possible for rats to understand an encoding-based BCI system and control a 1D machine using PFC activity to obtain reward.},
}
@article {pmid21919788,
year = {2011},
author = {Li, Z and O'Doherty, JE and Lebedev, MA and Nicolelis, MA},
title = {Adaptive decoding for brain-machine interfaces through Bayesian parameter updates.},
journal = {Neural computation},
volume = {23},
number = {12},
pages = {3162-3204},
pmid = {21919788},
issn = {1530-888X},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; DP1 OD006798/OD/NIH HHS/United States ; DP1 OD006798-01/OD/NIH HHS/United States ; DP1OD006798/OD/NIH HHS/United States ; },
mesh = {Action Potentials ; Adaptation, Physiological/physiology ; Animals ; *Artificial Intelligence ; *Bayes Theorem ; Macaca mulatta ; Motor Cortex/physiology ; Neural Prostheses/*standards ; Neurons/physiology ; Signal Processing, Computer-Assisted/*instrumentation ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.},
}
@article {pmid21915292,
year = {2011},
author = {Myrden, AJ and Kushki, A and Sejdić, E and Guerguerian, AM and Chau, T},
title = {A brain-computer interface based on bilateral transcranial Doppler ultrasound.},
journal = {PloS one},
volume = {6},
number = {9},
pages = {e24170},
pmid = {21915292},
issn = {1932-6203},
mesh = {Adult ; Brain/physiology/*physiopathology ; Cerebrovascular Circulation/*physiology ; Female ; Humans ; Ultrasonography, Doppler, Transcranial ; Young Adult ; },
abstract = {In this study, we investigate the feasibility of a BCI based on transcranial Doppler ultrasound (TCD), a medical imaging technique used to monitor cerebral blood flow velocity. We classified the cerebral blood flow velocity changes associated with two mental tasks--a word generation task, and a mental rotation task. Cerebral blood flow velocity was measured simultaneously within the left and right middle cerebral arteries while nine able-bodied adults alternated between mental activity (i.e. word generation or mental rotation) and relaxation. Using linear discriminant analysis and a set of time-domain features, word generation and mental rotation were classified with respective average accuracies of 82.9%±10.5 and 85.7%±10.0 across all participants. Accuracies for all participants significantly exceeded chance. These results indicate that TCD is a promising measurement modality for BCI research.},
}
@article {pmid21911058,
year = {2011},
author = {Cecotti, H},
title = {Spelling with non-invasive Brain-Computer Interfaces--current and future trends.},
journal = {Journal of physiology, Paris},
volume = {105},
number = {1-3},
pages = {106-114},
doi = {10.1016/j.jphysparis.2011.08.003},
pmid = {21911058},
issn = {1769-7115},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; *Software ; *User-Computer Interface ; },
abstract = {Brain-Computer Interfaces (BCIs) have become a large research field that include challenges mainly in neuroscience, signal processing, machine learning and user interface. A non-invasive BCI can allow the direct communication between humans and computers by analyzing electrical brain activity, recorded at the surface of the scalp with electroencephalography. The main purpose for BCIs is to enable communication for people with severe disabilities. Spelling is one of the first BCI application, it corresponds to the main communication mean for people who are unable to speak. While spelling can be the most basic application it remains a benchmark for communication applications and one challenge in the BCI community for some patients. This paper proposes a review of the current main strategies, and their limitations, for spelling words. It includes recent BCIs based on P300, steady-state visual evoked potentials and motor imagery. By considering some challenges in BCI spellers and virtual keyboards, some pragmatic issues are pointed out to eliminate false hopes about BCI for both disabled and healthy people.},
}
@article {pmid21911006,
year = {2012},
author = {Veluvolu, KC and Wang, Y and Kavuri, SS},
title = {Adaptive estimation of EEG-rhythms for optimal band identification in BCI.},
journal = {Journal of neuroscience methods},
volume = {203},
number = {1},
pages = {163-172},
doi = {10.1016/j.jneumeth.2011.08.035},
pmid = {21911006},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Electroencephalography ; Female ; Humans ; Male ; *Models, Neurological ; *Models, Theoretical ; *User-Computer Interface ; Young Adult ; },
abstract = {The amplitude of EEG μ-rhythm is large when the subject does not perform or imagine movement and attenuates when the subject either performs or imagines movement. The knowledge of EEG individual frequency components in the time-domain provides useful insight into the classification process. Identification of subject-specific reactive band is crucial for accurate event classification in brain-computer interfaces (BCI). This work develops a simple time-frequency decomposition method for EEG μ rhythm by adaptive modeling. With the time-domain decomposition of the signal, subject-specific reactive band identification method is proposed. Study is conducted on 30 subjects for optimal band selection for four movement classes. Our results show that over 93% the subjects have an optimal band and selection of this band improves the relative power spectral density by 200% with respect to normalized power.},
}
@article {pmid21909321,
year = {2011},
author = {Höhne, J and Schreuder, M and Blankertz, B and Tangermann, M},
title = {A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {99},
pmid = {21909321},
issn = {1662-453X},
abstract = {Brain-computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm - called PASS2D - was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.},
}
@article {pmid21903976,
year = {2012},
author = {Sitaram, R and Veit, R and Stevens, B and Caria, A and Gerloff, C and Birbaumer, N and Hummel, F},
title = {Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study.},
journal = {Neurorehabilitation and neural repair},
volume = {26},
number = {3},
pages = {256-265},
doi = {10.1177/1545968311418345},
pmid = {21903976},
issn = {1552-6844},
mesh = {Biofeedback, Psychology/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/*blood supply/*physiopathology ; Neural Inhibition/physiology ; Oxygen/blood ; Paresis/*pathology/*rehabilitation ; Photic Stimulation ; Psychomotor Performance ; Transcranial Magnetic Stimulation ; *User-Computer Interface ; },
abstract = {BACKGROUND: Despite the availability of various options for movement restoration in stroke patients, there is no effective treatment for patients who show little or no functional recovery of upper limb motor function.<br><br>OBJECTIVE: The present study explored the feasibility of real-time functional magnetic resonance imaging brain-computer interface (fMRI-BCI) as a new tool for rehabilitation of this patient population.<br><br>METHODS: Healthy adults and chronic subcortical stroke patients with residual movement were trained for 3 days to regulate the blood oxygenation level dependent (BOLD) response in the ventral premotor cortex (PMv), a secondary motor area with extensive anatomic connections with the primary motor cortex. Effect of learned modulation of the PMv was evaluated with BOLD signal changes across training sessions, transcranial magnetic stimulation (TMS), and a visuomotor task.<br><br>RESULTS: fMRI-BCI feedback training showed learning with a significantly increasing BOLD signal in the PMv over sessions. Participants' capability to learn self-regulation was found to depend linearly on intracortical facilitation and correlated negatively with intracortical inhibition measured by TMS prior to feedback training. After training, intracortical inhibition decreased significantly with the volitional increase of the BOLD response in the PMv, indicating a beneficial effect of self-regulation training on motor cortical output.<br><br>CONCLUSION: The study provides first evidence for the therapeutic potential of fMRI-BCI in stroke rehabilitation.},
}
@article {pmid21903462,
year = {2012},
author = {Marchetti, M and Piccione, F and Silvoni, S and Priftis, K},
title = {Exogenous and endogenous orienting of visuospatial attention in P300-guided brain computer interfaces: a pilot study on healthy participants.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {123},
number = {4},
pages = {774-779},
doi = {10.1016/j.clinph.2011.07.045},
pmid = {21903462},
issn = {1872-8952},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Eye Movements/physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Learning/physiology ; Male ; Middle Aged ; Orientation/*physiology ; Photic Stimulation ; Pilot Projects ; Psychomotor Performance ; Software ; Space Perception/*physiology ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Studies on brain computer interfaces (BCIs) have been mainly concerned with algorithm improvement for better signal classification. Fewer studies, however, have addressed to date the role of cognitive mechanisms underlying the elicitation of brain-signals in BCIs. We tested the effect of visuospatial attention orienting on a P300-guided BCI, by comparing the effectiveness of three visual interfaces, which elicited different modalities of visuospatial attention orienting (exogenous vs. endogenous).<br><br>METHODS: Twelve healthy participants performed 20 sessions, using the abovementioned P300-guided BCI interfaces to control a cursor. Brain waves were recorded on each trial and were subsequently classified on-line using an ad hoc algorithm. Each time the P300 was correctly classified, the cursor moved towards the target position.<br><br>RESULTS: The "endogenous" interface was associated with significantly higher performance than the other two interfaces during the testing sessions, but not in the follow-up sessions.<br><br>CONCLUSIONS: Endogenous visuospatial attention orienting can be effectively implemented to increase the performance of P300-guided BCIs.<br><br>SIGNIFICANCE: The study of visuospatial attention underlying participants' performance is essential for implementing efficient visual BCIs.},
}
@article {pmid21900804,
year = {2011},
author = {Cheng, JJ and Anderson, WS},
title = {Changing neural networks with brain machine interfaces-neuronal firing adaptations to BMI learning.},
journal = {Neurosurgery},
volume = {69},
number = {4},
pages = {N18-9},
doi = {10.1227/01.neu.0000405595.01331.8b},
pmid = {21900804},
issn = {1524-4040},
mesh = {Animals ; Brain/*physiology ; Haplorhini ; Nerve Net/*physiology ; Neurons/*physiology ; *User-Computer Interface ; },
}
@article {pmid21896285,
year = {2011},
author = {Gierthmuehlen, M and Ball, T and Henle, C and Wang, X and Rickert, J and Raab, M and Freiman, T and Stieglitz, T and Kaminsky, J},
title = {Evaluation of μECoG electrode arrays in the minipig: experimental procedure and neurosurgical approach.},
journal = {Journal of neuroscience methods},
volume = {202},
number = {1},
pages = {77-86},
doi = {10.1016/j.jneumeth.2011.08.021},
pmid = {21896285},
issn = {1872-678X},
mesh = {Animals ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Evoked Potentials, Somatosensory/physiology ; *Microelectrodes ; Neurosurgical Procedures/*instrumentation/methods ; Swine ; Swine, Miniature ; *User-Computer Interface ; },
abstract = {Emerging research on brain-machine interfaces (BMIs) requires the development of animal models for testing implantable BMI electrodes. New models are necessary in order to characterize and test newly constructed electrodes in an acute environment, and their properties and performance need to be evaluated in long-term, chronic implantations. Owing to their availability, small size and neuroanatomical similarity to the human brain, minipigs are frequently used for neurological studies. Despite this fact, there are still no standardized experimental and neurosurgical procedures available for recording of cortical potentials using implantable BMI electrodes in minipigs, and, until now, it was unclear whether these animals could also be used for long-term subdural electrode implantations. We have therefore evaluated the potential use of minipigs for acute and chronic implantation of micro-electrocorticogram (μECoG) electrodes we newly developed for BMI applications and we present a standardized neurosurgical approach to the minipig's cerebral cortex. A neurophysiological setup is described which is suitable to perform recordings of somatosensory evoked potentials (SEPs) with high spatial resolution - down to approx. 1-mm inter-electrode distance. Perioperative management, anesthesia and anatomical landmarks for electrode placement are discussed and common surgical pitfalls are described. While, due to their specific cranial anatomy, minipigs appear not optimally suited for chronic subdural implantations, the findings of the present study indicate that μECoG recording from the minipig cortex is a valuable new approach for acute in vivo characterization of subdural BMI electrode function.},
}
@article {pmid21895328,
year = {2011},
author = {Tanaka, H and Katura, T},
title = {Classification of change detection and change blindness from near-infrared spectroscopy signals.},
journal = {Journal of biomedical optics},
volume = {16},
number = {8},
pages = {087001},
doi = {10.1117/1.3606494},
pmid = {21895328},
issn = {1560-2281},
mesh = {Adult ; Analysis of Variance ; Brain/*blood supply ; Cluster Analysis ; Face ; Female ; Hemodynamics/*physiology ; Humans ; Male ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; *Support Vector Machine ; *Task Performance and Analysis ; },
abstract = {Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.},
}
@article {pmid21886674,
year = {2010},
author = {Huang, G and Liu, G and Meng, J and Zhang, D and Zhu, X},
title = {Model based generalization analysis of common spatial pattern in brain computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {4},
number = {3},
pages = {217-223},
pmid = {21886674},
issn = {1871-4099},
abstract = {In the motor imagery based Brain Computer Interface (BCI) research, Common Spatial Pattern (CSP) algorithm is used widely as a spatial filter on multi-channel electroencephalogram (EEG) recordings. Recently the overfitting effect of CSP has been gradually noticed, but what influence the overfitting is still unclear. In this work, the generalization of CSP is investigated by a simple linear mixing model. Several factors in this model are discussed, and the simulation results indicate that channel numbers and the correlation between signals influence the generalization of CSP significantly. A larger number of training trials and a longer time length of the trial would prevent overfitting. The experiments on real data also verify our conclusion.},
}
@article {pmid21886673,
year = {2010},
author = {Long, J and Li, Y and Yu, Z},
title = {A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces.},
journal = {Cognitive neurodynamics},
volume = {4},
number = {3},
pages = {207-216},
pmid = {21886673},
issn = {1871-4099},
abstract = {Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small.},
}
@article {pmid21879484,
year = {2011},
author = {},
title = {Proceedings of the Fourth International Brain–Computer Interface Meeting. May 31-June 4, 2010, Monterey, California, USA.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {020201-025028},
pmid = {21879484},
issn = {1741-2552},
mesh = {Animals ; *Biofeedback, Psychology ; Humans ; *User-Computer Interface ; },
}
@article {pmid21871681,
year = {2012},
author = {Gruben, KG and Boehm, WL},
title = {Force direction pattern stabilizes sagittal plane mechanics of human walking.},
journal = {Human movement science},
volume = {31},
number = {3},
pages = {649-659},
doi = {10.1016/j.humov.2011.07.006},
pmid = {21871681},
issn = {1872-7646},
mesh = {Adult ; Biomechanical Phenomena ; Exercise Test ; Female ; Humans ; Male ; Models, Theoretical ; Neurons/physiology ; Orientation/*physiology ; Postural Balance/*physiology ; Psychomotor Performance ; Walking/*physiology ; Weight-Bearing/*physiology ; },
abstract = {The neural control and mechanics of human bipedalism are inadequately understood. The variable at the interface of neural control and body mechanics that is key to upright posture during human walking is the force of the ground on the foot (ground reaction force, F). We present a model that predicts sagittal plane F direction as passing through a divergent point (DP) fixed in a reference frame attached to the person. Four reference frames were tested to identify which provided the simplest and most accurate description of F direction. For all reference frames, the DP model predicted nearly all the observed variation in F direction and whole body angular momentum during single leg stance. The reference frame with vertical orientation and with origin on the pelvis provided the best combination of accuracy and simplicity. The DP was located higher than the CM and the predicted F produced a pattern of torque about the CM that caused body pitch oscillations that disrupted upright posture. Despite those oscillations, that torque was evidence of a stability mechanism that may be a critical component enabling humans to remain upright while walking and performing other tasks.},
}
@article {pmid21867810,
year = {2011},
author = {Ouyang, L and Green, R and Feldman, KE and Martin, DC},
title = {Direct local polymerization of poly(3,4-ethylenedioxythiophene) in rat cortex.},
journal = {Progress in brain research},
volume = {194},
number = {},
pages = {263-271},
doi = {10.1016/B978-0-444-53815-4.00001-7},
pmid = {21867810},
issn = {1875-7855},
mesh = {Animals ; Biocompatible Materials/chemistry/metabolism ; Bridged Bicyclo Compounds, Heterocyclic/*chemistry/metabolism ; Cerebral Cortex/*metabolism/*pathology ; Electrochemical Techniques/instrumentation/*methods ; Electrodes ; Polymerization ; Polymers/*chemistry/metabolism ; Rats ; },
abstract = {Glial scar encapsulation is thought to be one of the major reasons for the failure of chronic brain-machine interfaces. Many strategies, including modification of the probe surface chemistry, delivery of anti-inflammatory drugs, and changes of probe geometry, have been employed to reduce glial scar formation. We have proposed that a possible means to establish long-term, reliable communication across the scar is the in situ polymerization of conjugated polymers such as PEDOT in neural tissue. Previously, we exposed entire brain slices to the EDOT monomer. Here, we demonstrate that PEDOT can be polymerized by the direct delivery of EDOT monomer to the reaction site. The monomer was delivered into rat cortex via microcannula and simultaneously electrochemically polymerized within the tissue using a microwire electrode. We found that the resulting PEDOT polymer cloud grew out from the working electrode tip and extended far out into the brain tissue, spanning distances more than 1mm. We also examined the morphology of resulting polymer cloud by optical microscopy.},
}
@article {pmid21867803,
year = {2011},
author = {Linsmeier, CE and Thelin, J and Danielsen, N},
title = {Can histology solve the riddle of the nonfunctioning electrode? Factors influencing the biocompatibility of brain machine interfaces.},
journal = {Progress in brain research},
volume = {194},
number = {},
pages = {181-189},
doi = {10.1016/B978-0-444-53815-4.00008-X},
pmid = {21867803},
issn = {1875-7855},
mesh = {Animals ; Biocompatible Materials/*metabolism ; Cerebral Cortex/physiology ; *Electrodes, Implanted ; Female ; Humans ; Neurons/cytology/physiology ; Rats ; Rats, Sprague-Dawley ; *User-Computer Interface ; },
abstract = {Neural interfaces hold great promise to become invaluable clinical and diagnostic tools in the near future. However, the biocompatibility and the long-term stability of the implanted interfaces are far from optimized. There are several factors that need to be addressed and standardized when improving the long-term success of an implanted electrode. We have chosen to focus on three key factors when evaluating the evoked tissue responses after electrode implantation into the brain: implant size, fixation mode, and evaluation period. Further, we show results from an ultrathin multichannel wire electrode that has been implanted in the rat cerebral cortex for 1 year. To improve biocompatibility of implanted electrodes, we would like to suggest that free-floating, very small, flexible, and, in time, wireless electrodes would elicit a diminished cell encapsulation. We would also like to suggest standardized methods for the electrode design, the electrode implantation method, and the analyses of cell reactions after implantation into the CNS in order to improve the long-term success of implanted neural interfaces.},
}
@article {pmid21867795,
year = {2011},
author = {Benabid, AL and Costecalde, T and Torres, N and Moro, C and Aksenova, T and Eliseyev, A and Charvet, G and Sauter, F and Ratel, D and Mestais, C and Pollak, P and Chabardes, S},
title = {Deep brain stimulation: BCI at large, where are we going to?.},
journal = {Progress in brain research},
volume = {194},
number = {},
pages = {71-82},
doi = {10.1016/B978-0-444-53815-4.00016-9},
pmid = {21867795},
issn = {1875-7855},
mesh = {Algorithms ; Animals ; Deep Brain Stimulation/*instrumentation/*methods ; *Electrodes, Implanted ; Electroencephalography ; Epilepsy/therapy ; Humans ; Mental Disorders/therapy ; Parkinson Disease/therapy ; Software ; *User-Computer Interface ; },
abstract = {UNLABELLED: Brain-computer interfaces (BCIs) include stimulators, infusion devices, and neuroprostheses. They all belong to functional neurosurgery. Deep brain stimulators (DBS) are widely used for therapy and are in need of innovative evolutions. Robotized exoskeletons require BCIs able to drive up to 26 degrees of freedom (DoF). We report the nanomicrotechnology development of prototypes for new 3D DBS and for motor neuroprostheses. For this complex project, all compounds have been designed and are being tested. Experiments were performed in rats and primates for proof of concepts and development of the electroencephalogram (EEG) recognition algorithm.<br><br>METHODS: Various devices have been designed. (A) In human, a programmable multiplexer connecting five tetrapolar (20 contacts) electrodes to one DBS channel has been designed and implanted bilaterally into STN in two Parkinsonian patients. (B) A 50-mm diameter titanium implant, telepowered, including a radioset, emitting ECoG data recorded by a 64-electrode array using an application-specific integrated circuit, is being designed to be implanted in a 50-mm trephine opening. Data received by the radioreceiver are processed through an original wavelet-based Iterative N-way Partial Least Square algorithm (INPLS, CEA patent). Animals, implanted with ECoG recording electrodes, had to press a lever to obtain a reward. The brain signature associated to the lever press (LP) was detected online by ECoG processing using INPLS. This detection allowed triggering the food dispenser.<br><br>RESULTS: (A) The 3D multiplexer allowed tailoring the electrical field to the STN. The multiplication of the contacts affected the battery life and suggested different implantation schemes. (B) The components of the human implantable cortical BCI are being tested for reliability and toxicology to meet criteria for chronicle implantation in 2012. (C) In rats, the algorithm INPLS could detect the cortical signature with an accuracy of about 80% of LPs on the electrodes with the best correlation coefficient (located over the cerebellar cortex), 1% of the algorithm decisions were false positives. We aim to pilot effectors with DoF up to 3 in monkeys.<br><br>CONCLUSION: We have designed multielectrodes wireless implants to open the way for BCI ECoG-driven effectors. These technologies are also used to develop new generations of brain stimulators, either cortical or for deep targets. This chapter is aimed at illustrating that BCIs are actually the daily background of DBS, that the evolution of the method involves a growing multiplicity of targets and indications, that new technologies make possible and simpler than before to design innovative solutions to improve DBS methodology, and that the coming out of BCI-driven neuroprostheses for compensation of motor and sensory deficits is a natural evolution of functional neurosurgery.},
}
@article {pmid21867793,
year = {2011},
author = {Lebedev, MA and Nicolelis, MA},
title = {Toward a whole-body neuroprosthetic.},
journal = {Progress in brain research},
volume = {194},
number = {},
pages = {47-60},
doi = {10.1016/B978-0-444-53815-4.00018-2},
pmid = {21867793},
issn = {1875-7855},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; },
mesh = {Bioengineering/instrumentation/methods ; Brain/anatomy &amp; histology/*physiology ; Clinical Trials as Topic ; Electric Stimulation ; Humans ; Movement/*physiology ; Neurons/physiology ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurological diseases, and limb loss. Considerable progress has been achieved in BMIs that enact arm movements, and initial work has been done on BMIs for lower limb and trunk control. These developments put Duke University Center for Neuroengineering in the position to develop the first BMI for whole-body control. This whole-body BMI will incorporate very large-scale brain recordings, advanced decoding algorithms, artificial sensory feedback based on electrical stimulation of somatosensory areas, virtual environment representations, and a whole-body exoskeleton. This system will be first tested in nonhuman primates and then transferred to clinical trials in humans.},
}
@article {pmid21867792,
year = {2011},
author = {Kleih, SC and Kaufmann, T and Zickler, C and Halder, S and Leotta, F and Cincotti, F and Aloise, F and Riccio, A and Herbert, C and Mattia, D and Kübler, A},
title = {Out of the frying pan into the fire--the P300-based BCI faces real-world challenges.},
journal = {Progress in brain research},
volume = {194},
number = {},
pages = {27-46},
doi = {10.1016/B978-0-444-53815-4.00019-4},
pmid = {21867792},
issn = {1875-7855},
mesh = {*Communication Devices for People with Disabilities ; Event-Related Potentials, P300/*physiology ; Humans ; Internet ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) have been investigated for more than 20 years. Many BCIs use noninvasive electroencephalography as a measurement technique and the P300 event-related potential as an input signal (P300 BCI). Since the first experiment with a P300 BCI system in 1988 by Farwell and Donchin, not only data processing has improved but also stimuli presentation has been varied and a plethora of applications was developed and refined. Nowadays, these applications are facing the challenge of being transferred from the research laboratory into real-life situations to serve motor-impaired people in their homes as assistive technology.},
}
@article {pmid21867567,
year = {2011},
author = {Do, AH and Wang, PT and King, CE and Abiri, A and Nenadic, Z},
title = {Brain-computer interface controlled functional electrical stimulation system for ankle movement.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {49},
pmid = {21867567},
issn = {1743-0003},
support = {UL1 RR031985/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Ankle/*physiology ; Brain/*physiology ; Electric Stimulation Therapy/instrumentation/*methods ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Paralysis/rehabilitation ; *User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: Many neurological conditions, such as stroke, spinal cord injury, and traumatic brain injury, can cause chronic gait function impairment due to foot-drop. Current physiotherapy techniques provide only a limited degree of motor function recovery in these individuals, and therefore novel therapies are needed. Brain-computer interface (BCI) is a relatively novel technology with a potential to restore, substitute, or augment lost motor behaviors in patients with neurological injuries. Here, we describe the first successful integration of a noninvasive electroencephalogram (EEG)-based BCI with a noninvasive functional electrical stimulation (FES) system that enables the direct brain control of foot dorsiflexion in able-bodied individuals.<br><br>METHODS: A noninvasive EEG-based BCI system was integrated with a noninvasive FES system for foot dorsiflexion. Subjects underwent computer-cued epochs of repetitive foot dorsiflexion and idling while their EEG signals were recorded and stored for offline analysis. The analysis generated a prediction model that allowed EEG data to be analyzed and classified in real time during online BCI operation. The real-time online performance of the integrated BCI-FES system was tested in a group of five able-bodied subjects who used repetitive foot dorsiflexion to elicit BCI-FES mediated dorsiflexion of the contralateral foot.<br><br>RESULTS: Five able-bodied subjects performed 10 alternations of idling and repetitive foot dorsifiexion to trigger BCI-FES mediated dorsifiexion of the contralateral foot. The epochs of BCI-FES mediated foot dorsifiexion were highly correlated with the epochs of voluntary foot dorsifiexion (correlation coefficient ranged between 0.59 and 0.77) with latencies ranging from 1.4 sec to 3.1 sec. In addition, all subjects achieved a 100% BCI-FES response (no omissions), and one subject had a single false alarm.<br><br>CONCLUSIONS: This study suggests that the integration of a noninvasive BCI with a lower-extremity FES system is feasible. With additional modifications, the proposed BCI-FES system may offer a novel and effective therapy in the neuro-rehabilitation of individuals with lower extremity paralysis due to neurological injuries.},
}
@article {pmid21859634,
year = {2011},
author = {Liu, J and Khalil, HK and Oweiss, KG},
title = {Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {5},
pages = {521-533},
doi = {10.1109/TNSRE.2011.2162003},
pmid = {21859634},
issn = {1558-0210},
support = {R33 NS054148/NS/NINDS NIH HHS/United States ; R01 NS062031-04/NS/NINDS NIH HHS/United States ; R01 NS062031/NS/NINDS NIH HHS/United States ; R33 NS054148-05/NS/NINDS NIH HHS/United States ; NINDS 054148//PHS HHS/United States ; R33 NS054148-04/NS/NINDS NIH HHS/United States ; },
mesh = {Afferent Pathways/*physiology ; Algorithms ; Brain/*physiology ; Computer Simulation ; Electric Stimulation ; Electronics ; *Feedback, Physiological ; Humans ; Models, Neurological ; Neural Networks, Computer ; Pyramidal Cells/physiology ; Somatosensory Cortex/physiology ; Space Perception/*physiology ; Thalamus/physiology ; Time Perception/*physiology ; *User-Computer Interface ; },
abstract = {In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.},
}
@article {pmid21843639,
year = {2011},
author = {Rivet, B and Cecotti, H and Perrin, M and Maby, E and Mattout, J},
title = {Adaptive training session for a P300 speller brain-computer interface.},
journal = {Journal of physiology, Paris},
volume = {105},
number = {1-3},
pages = {123-129},
doi = {10.1016/j.jphysparis.2011.07.013},
pmid = {21843639},
issn = {1769-7115},
mesh = {Adult ; Algorithms ; Cerebral Cortex/*physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {With a brain-computer interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300 in the oddball paradigm exploited in P300-speller, provides a way to create BCIs by assigning several detected ERP to a command. Due to the noise present in the electroencephalographic signal, the detection of an ERP and its different components requires efficient signal processing and machine learning techniques. As a consequence, a calibration session is needed for training the models, which can be a drawback if its duration is too long. Although the model depends on the subject, the goal is to provide a reliable model for the P300 detection over time. In this study, we propose a new method to evaluate the optimal number of symbols (i.e. the number of ERP that shall be detected given a determined target probability) that should be spelt during the calibration process. The goal is to provide a usable system with a minimum calibration duration and such that it can automatically switch between the training and online sessions. The method allows to adaptively adjust the number of training symbols to each subject. The evaluation has been tested on data recorded on 20 healthy subjects. This procedure lets drastically reduced the calibration session: height symbols during the training session reach an initialized system with an average accuracy of 80% after five epochs.},
}
@article {pmid21840403,
year = {2012},
author = {Christensen, JC and Estepp, JR and Wilson, GF and Russell, CA},
title = {The effects of day-to-day variability of physiological data on operator functional state classification.},
journal = {NeuroImage},
volume = {59},
number = {1},
pages = {57-63},
doi = {10.1016/j.neuroimage.2011.07.091},
pmid = {21840403},
issn = {1095-9572},
mesh = {*Electroencephalography ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; *Task Performance and Analysis ; User-Computer Interface ; Workload/*classification ; Young Adult ; },
abstract = {The application of pattern classification techniques to physiological data has undergone rapid expansion. Tasks as varied as the diagnosis of disease from magnetic resonance images, brain-computer interfaces for the disabled, and the decoding of brain functioning based on electrical activity have been accomplished quite successfully with pattern classification. These classifiers have been further applied in complex cognitive tasks to improve performance, in one example as an input to adaptive automation. In order to produce generalizable results and facilitate the development of practical systems, these techniques should be stable across repeated sessions. This paper describes the application of three popular pattern classification techniques to EEG data obtained from asymptotically trained subjects performing a complex multitask across five days in one month. All three classifiers performed well above chance levels. The performance of all three was significantly negatively impacted by classifying across days; however two modifications are presented that substantially reduce misclassifications. The results demonstrate that with proper methods, pattern classification is stable enough across days and weeks to be a valid, useful approach.},
}
@article {pmid21840399,
year = {2012},
author = {Fazli, S and Mehnert, J and Steinbrink, J and Curio, G and Villringer, A and Müller, KR and Blankertz, B},
title = {Enhanced performance by a hybrid NIRS-EEG brain computer interface.},
journal = {NeuroImage},
volume = {59},
number = {1},
pages = {519-529},
doi = {10.1016/j.neuroimage.2011.07.084},
pmid = {21840399},
issn = {1095-9572},
support = {R42NS050007/NS/NINDS NIH HHS/United States ; R44NS049734/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; Image Interpretation, Computer-Assisted ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI.},
}
@article {pmid21839843,
year = {2012},
author = {Hamamé, CM and Vidal, JR and Ossandón, T and Jerbi, K and Dalal, SS and Minotti, L and Bertrand, O and Kahane, P and Lachaux, JP},
title = {Reading the mind's eye: online detection of visuo-spatial working memory and visual imagery in the inferior temporal lobe.},
journal = {NeuroImage},
volume = {59},
number = {1},
pages = {872-879},
doi = {10.1016/j.neuroimage.2011.07.087},
pmid = {21839843},
issn = {1095-9572},
mesh = {Adolescent ; Brain Mapping ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Memory, Short-Term/*physiology ; Temporal Lobe/*physiology ; Visual Perception/*physiology ; },
abstract = {Several brain regions involved in visual perception have been shown to also participate in non-sensory cognitive processes of visual representations. Here we studied the role of ventral visual pathway areas in visual imagery and working memory. We analyzed intracerebral EEG recordings from the left inferior temporal lobe of an epileptic patient during working memory tasks and mental imagery. We found that high frequency gamma-band activity (50-150 Hz) in the inferior temporal gyrus (ITG) increased with memory load only during visuo-spatial, but not verbal, working memory. Using a real-time set-up to measure and visualize gamma-band activity online--BrainTV--we found a systematic activity increase in ITG when the patient was visualizing a letter (visual imagery), but not during perception of letters. In contrast, only 7 mm more medially, neurons located in the fusiform gyrus exhibited a complete opposite pattern, responding during verbal working memory retention and letter presentation, but not during imagery or visuo-spatial working memory maintenance. Talairach coordinates indicate that the fusiform contact site corresponds to the word form area, suggesting that this region has a role not only in processing letter-strings, but also in working memory retention of verbal information. We conclude that neural networks supporting imagination of a visual element are not necessarily the same as those underlying perception of that element. Additionally, we present evidence that gamma-band activity in the inferior temporal lobe, can be used as a direct measure of the efficiency of top-down attentional control over visual areas with implications for the development of novel brain-computer interfaces. Finally, by just reading gamma-band activity in these two recording sites, it is possible to determine, accurately and in real-time, whether a given memory content is verbal or visuo-spatial.},
}
@article {pmid21828907,
year = {2011},
author = {Zimmermann, JB and Seki, K and Jackson, A},
title = {Reanimating the arm and hand with intraspinal microstimulation.},
journal = {Journal of neural engineering},
volume = {8},
number = {5},
pages = {054001},
pmid = {21828907},
issn = {1741-2552},
support = {/WT_/Wellcome Trust/United Kingdom ; 086561/WT_/Wellcome Trust/United Kingdom ; 087223/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Algorithms ; Animals ; Arm/innervation/*physiology ; Electric Stimulation/*methods ; Electromyography ; Female ; Hand/innervation/*physiology ; Hand Strength/physiology ; Macaca mulatta ; Movement/physiology ; Muscle, Skeletal/innervation/physiology ; Nonlinear Dynamics ; *Prostheses and Implants ; Spinal Cord/*physiology ; Spinal Cord Injuries/therapy ; User-Computer Interface ; },
abstract = {To date, there is no effective therapy for spinal cord injury, and many patients could benefit dramatically from at least partial restoration of arm and hand function. Despite a substantial body of research investigating intraspinal microstimulation (ISMS) in frogs, rodents and cats, little is known about upper-limb responses to cervical stimulation in the primate. Here, we show for the first time that long trains of ISMS delivered to the macaque spinal cord can evoke functional arm and hand movements. Complex movements involving coordinated activation of multiple muscles could be elicited from a single electrode, while just two electrodes were required for independent control of reaching and grasping. We found that the motor responses to ISMS were described by a dual exponential model that depended only on stimulation history. We demonstrate that this model can be inverted to generate stimulus trains capable of eliciting arbitrary, graded motor responses, and could be used to restore volitional movements in a closed-loop brain-machine interface.},
}
@article {pmid21817778,
year = {2011},
author = {Jrad, N and Congedo, M and Phlypo, R and Rousseau, S and Flamary, R and Yger, F and Rakotomamonjy, A},
title = {sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {5},
pages = {056004},
doi = {10.1088/1741-2560/8/5/056004},
pmid = {21817778},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; Brain Mapping ; Electroencephalography/classification/*instrumentation/*methods ; Electronic Data Processing ; Event-Related Potentials, P300 ; Humans ; Linear Models ; Mental Processes ; Nonlinear Dynamics ; Reading ; Reproducibility of Results ; Signal-To-Noise Ratio ; *Support Vector Machine ; *User-Computer Interface ; },
abstract = {In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.},
}
@article {pmid21816172,
year = {2011},
author = {Perego, P and Turconi, AC and Andreoni, G and Maggi, L and Beretta, E and Parini, S and Gagliardi, C},
title = {Cognitive ability assessment by brain-computer interface validation of a new assessment method for cognitive abilities.},
journal = {Journal of neuroscience methods},
volume = {201},
number = {1},
pages = {239-250},
doi = {10.1016/j.jneumeth.2011.06.025},
pmid = {21816172},
issn = {1872-678X},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Cognition/*physiology ; Humans ; Male ; Neuropsychological Tests/*standards ; Psychomotor Performance/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCIs) are systems which can provide communication and environmental control to people with severe neuromuscular diseases. The current study proposes a new BCI-based method for psychometric assessment when traditional or computerized testing cannot be used owing to the subject's output impairment. This administration protocol was based on, and validated against, a widely used clinical test (Raven Colored Progressive Matrix) in order to verify whether BCI affects the brain in terms of cognitive resource with a misstatement result. The operating protocol was structured into two phases: phase 1 was aimed at configuring the BCI system on the subject's features and train him/her to use it; during phase 2 the BCI system was reconfigured and the test performed. A step-by-step checking procedure was adopted to verify progressive inclusion/exclusion criteria and the underpinning variables. The protocol was validated on 19 healthy subjects and the BCI-based administration was compared with a paper-based administration. The results obtained by both methods were correlated as known for traditional assessment of a similarly culture free and reasoning based test. Although our findings need to be validated on pathological participants, in our healthy population the BCI-based administration did not affect performance and added a further control of the response due to the several variables included and analyzed by the computerized task.},
}
@article {pmid21813358,
year = {2011},
author = {Rakotomamonjy, A and Flamary, R and Gasso, G and Canu, S},
title = {lp-lq penalty for sparse linear and sparse multiple kernel multitask learning.},
journal = {IEEE transactions on neural networks},
volume = {22},
number = {8},
pages = {1307-1320},
doi = {10.1109/TNN.2011.2157521},
pmid = {21813358},
issn = {1941-0093},
mesh = {*Artificial Intelligence ; Databases, Factual/classification ; *Linear Models ; Pattern Recognition, Automated/methods ; *Psychomotor Performance ; },
abstract = {Recently, there has been much interest around multitask learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework and focus on l(p)-l(q) (with 0 ≤ p ≤ 1 and 1 ≤ q ≤ 2) mixed norms as sparsity-inducing penalties. Our motivation for addressing such a larger class of penalty is to adapt the penalty to a problem at hand leading thus to better performances and better sparsity pattern. For solving the problem in the general multiple kernel case, we first derive a variational formulation of the l(1)-l(q) penalty which helps us in proposing an alternate optimization algorithm. Although very simple, the latter algorithm provably converges to the global minimum of the l(1)-l(q) penalized problem. For the linear case, we extend existing works considering accelerated proximal gradient to this penalty. Our contribution in this context is to provide an efficient scheme for computing the l(1)-l(q) proximal operator. Then, for the more general case, when , we solve the resulting nonconvex problem through a majorization-minimization approach. The resulting algorithm is an iterative scheme which, at each iteration, solves a weighted l(1)-l(q) sparse MTL problem. Empirical evidences from toy dataset and real-word datasets dealing with brain-computer interface single-trial electroencephalogram classification and protein subcellular localization show the benefit of the proposed approaches and algorithms.},
}
@article {pmid21811433,
year = {2011},
author = {Gonzalez Andino, SL and Herrera-Rincon, C and Panetsos, F and Grave de Peralta, R},
title = {Combining BMI Stimulation and Mathematical Modeling for Acute Stroke Recovery and Neural Repair.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {87},
pmid = {21811433},
issn = {1662-453X},
abstract = {Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - brain machine interfaces (BMIs) can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI-electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.},
}
@article {pmid21810266,
year = {2011},
author = {Winkler, I and Haufe, S and Tangermann, M},
title = {Automatic classification of artifactual ICA-components for artifact removal in EEG signals.},
journal = {Behavioral and brain functions : BBF},
volume = {7},
number = {},
pages = {30},
pmid = {21810266},
issn = {1744-9081},
mesh = {Adult ; Aged ; Artifacts ; Electroencephalography/*classification/methods ; Evoked Potentials, Auditory/*physiology ; Humans ; Male ; Middle Aged ; Reaction Time/*physiology ; *Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts.<br><br>METHODS: We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects.<br><br>RESULTS: Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components.<br><br>CONCLUSIONS: We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.},
}
@article {pmid21779720,
year = {2011},
author = {Lebedev, MA and Tate, AJ and Hanson, TL and Li, Z and O'Doherty, JE and Winans, JA and Ifft, PJ and Zhuang, KZ and Fitzsimmons, NA and Schwarz, DA and Fuller, AM and An, JH and Nicolelis, MA},
title = {Future developments in brain-machine interface research.},
journal = {Clinics (Sao Paulo, Brazil)},
volume = {66 Suppl 1},
number = {Suppl 1},
pages = {25-32},
pmid = {21779720},
issn = {1980-5322},
support = {DP1 MH099903/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Bioengineering/methods/*trends ; Brain/*physiology ; Humans ; *Man-Machine Systems ; Movement/*physiology ; *Prostheses and Implants ; User-Computer Interface ; },
abstract = {Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.},
}
@article {pmid21809479,
year = {2011},
author = {Hsu, WY},
title = {Continuous EEG signal analysis for asynchronous BCI application.},
journal = {International journal of neural systems},
volume = {21},
number = {4},
pages = {335-350},
doi = {10.1142/S0129065711002870},
pmid = {21809479},
issn = {1793-6462},
mesh = {Electroencephalography/*methods ; Electrooculography ; Fractals ; Humans ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Wavelet Analysis ; },
abstract = {In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.},
}
@article {pmid21808603,
year = {2011},
author = {Guger, C and Gener, T and Pennartz, CM and Brotons-Mas, JR and Edlinger, G and Bermúdez I Badia, S and Verschure, P and Schaffelhofer, S and Sanchez-Vives, MV},
title = {Real-time position reconstruction with hippocampal place cells.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {85},
pmid = {21808603},
issn = {1662-453X},
abstract = {Brain-computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rat's trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rat's position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5-6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral-neuronal feedback loops or for implementing neuroprosthetic control.},
}
@article {pmid21803163,
year = {2012},
author = {Berman, BD and Horovitz, SG and Venkataraman, G and Hallett, M},
title = {Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback.},
journal = {NeuroImage},
volume = {59},
number = {2},
pages = {917-925},
pmid = {21803163},
issn = {1095-9572},
support = {Z99 NS999999/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adaptation, Physiological/physiology ; Adult ; Biofeedback, Psychology/*methods/*physiology ; Computer Systems ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/*methods ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Advances in fMRI data acquisition and processing have made it possible to analyze brain activity as rapidly as the images are acquired allowing this information to be fed back to subjects in the scanner. The ability of subjects to learn to volitionally control localized brain activity within motor cortex using such real-time fMRI-based neurofeedback (NF) is actively being investigated as it may have clinical implications for motor rehabilitation after central nervous system injury and brain-computer interfaces. We investigated the ability of fifteen healthy volunteers to use NF to modulate brain activity within the primary motor cortex (M1) during a finger tapping and tapping imagery task. The M1 hand area ROI (ROI(m)) was functionally localized during finger tapping and a visual representation of BOLD signal changes within the ROI(m) fed back to the subject in the scanner. Surface EMG was used to assess motor output during tapping and ensure no motor activity was present during motor imagery task. Subjects quickly learned to modulate brain activity within their ROI(m) during the finger-tapping task, which could be dissociated from the magnitude of the tapping, but did not show a significant increase within the ROI(m) during the hand motor imagery task at the group level despite strongly activating a network consistent with the performance of motor imagery. The inability of subjects to modulate M1 proper with motor imagery may reflect an inherent difficulty in activating synapses in this area, with or without NF, since such activation may lead to M1 neuronal output and obligatory muscle activity. Future real-time fMRI-based NF investigations involving motor cortex may benefit from focusing attention on cortical regions other than M1 for feedback training or alternative feedback strategies such as measures of functional connectivity within the motor system.},
}
@article {pmid21788179,
year = {2011},
author = {Lee, PL and Yeh, CL and Cheng, JY and Yang, CY and Lan, GY},
title = {An SSVEP-based BCI using high duty-cycle visual flicker.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {12},
pages = {3350-3359},
doi = {10.1109/TBME.2011.2162586},
pmid = {21788179},
issn = {1558-2531},
mesh = {Adult ; Communication Devices for People with Disabilities ; Electroencephalography/*methods/psychology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Man-Machine Systems ; Photic Stimulation/*methods ; *Signal Processing, Computer-Assisted ; Surveys and Questionnaires ; *User-Computer Interface ; },
abstract = {Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have generated significant interest due to their high information transfer rate (ITR). Due to the amplitude-frequency characteristic of the SSVEP, the flickering frequency of an SSVEP-based BCI is typically lower than 20 Hz to achieve a high SNR. However, a visual flicker with a flashing frequency below the critical flicker-fusion frequency often makes subjects feel flicker jerky and causes visual discomfort. This study presents a novel technique using high duty-cycle visual flicker to decrease user's visual discomfort. The proposed design uses LEDs flashing at 13.16 Hz, driven by flickering sequences consisting of repetitive stimulus cycles with a duration T (T = 76 ms). Each stimulus cycle included an ON state with a duration T(ON) and an OFF state with a duration T(OFF) (T = T(ON) + T(OFF)), and the duty cycle, defined as T(ON)/T, varied from 10.5% to 89.5%. This study also includes a questionnaire survey and analyzes the SSVEPs induced by different duty-cycle flickers. An 89.5% duty-cycle flicker, reported as a comfortable flicker, was adopted in a phase-tagged SSVEP system. Six subjects were asked to sequentially input a sequence of cursor commands with the 25.08-bits/min ITR.},
}
@article {pmid21779234,
year = {2011},
author = {Kaiser, V and Kreilinger, A and Müller-Putz, GR and Neuper, C},
title = {First Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {86},
pmid = {21779234},
issn = {1662-453X},
abstract = {A new approach in motor rehabilitation after stroke is to use motor imagery (MI). To give feedback on MI performance brain-computer interface (BCIs) can be used. This requires a fast and easy acquisition of a reliable classifier. Usually, for training a classifier, electroencephalogram (EEG) data of MI without feedback is used, but it would be advantageous if we could give feedback right from the beginning. The sensorimotor EEG changes of the motor cortex during active and passive movement (PM) and MI are similar. The aim of this study is to explore, whether it is possible to use EEG data from active or PM to set up a classifier for the detection of MI in a group of elderly persons. In addition, the activation patterns of the motor cortical areas of elderly persons were analyzed during different motor tasks. EEG was recorded from three Laplacian channels over the sensorimotor cortex in a sample of 19 healthy elderly volunteers. Participants performed three different tasks in consecutive order, passive, active hand movement, and hand MI. Classifiers were calculated with data of every task. These classifiers were then used to detect event-related desynchronization (ERD) in the MI data. ERD values, related to the different tasks, were calculated and analyzed statistically. The performance of classifiers calculated from passive and active hand movement data did not differ significantly regarding the classification accuracy for detecting MI. The EEG patterns of the motor cortical areas during the different tasks was similar to the patterns normally found in younger persons but more widespread regarding localization and frequency range of the ERD. In this study, we have shown that it is possible to use classifiers calculated with data from passive and active hand movement to detect MI. Hence, for working with stroke patients, a physiotherapy session could be used to obtain data for classifier set up and the BCI-rehabilitation training could start immediately.},
}
@article {pmid21774234,
year = {2011},
author = {Li, P and Ding, H and Wan, B and Ming, D},
title = {[Research progress on application of brain-computer-interface in mobile peripheral control].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {28},
number = {3},
pages = {613-617},
pmid = {21774234},
issn = {1001-5515},
mesh = {Algorithms ; Brain Diseases/*rehabilitation ; *Communication Devices for People with Disabilities ; *Computer Systems ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Neuromuscular Diseases/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted/instrumentation ; User-Computer Interface ; },
abstract = {Brain computer interface (BCI) is an information channel independent of routine brain output ways such as peripheral nerves and muscle organization. As a special human-computer interface mode, it provides a direct communication pathway between the brain and external devices so as to exert control over those devices by ways other than primitive human communication. Controlling over mobile peripheral devices such as intelligent wheelchairs or nursing robots is a very important application of BCI technology in the future. This paper describes the newest progress of the above mentioned technology, analyzes and compares key techniques involved, and forecasts future development in this field.},
}
@article {pmid21772075,
year = {2011},
author = {Zhang, H and Liyanage, SR and Wang, C and Guan, C},
title = {Learning from feedback training data at a self-paced brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046035},
doi = {10.1088/1741-2560/8/4/046035},
pmid = {21772075},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Calibration ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography ; Feasibility Studies ; *Feedback ; Humans ; Online Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Inherent changes that appear in brain signals when transferring from calibration to feedback sessions are a challenging but critical issue in brain-computer interface (BCI) applications. While previous studies have mostly focused on the adaptation of classifiers, in this paper we study the feasibility and the importance of the adaptation of feature extraction in a self-paced BCI paradigm. First, we conduct calibration and feedback training on able-bodied naïve subjects using a new self-paced motor imagery BCI including the idle state. The online results suggest that the feature space constructed from calibration data may become ineffective during feedback sessions. Hence, we propose a new supervised method that learns from a feedback session to construct a more appropriate feature space, on the basis of the maximum mutual information principle between feedback signal, target signal and EEG. Specifically, we formulate the learning objective as maximizing a kernel-based mutual information estimate with respect to the spatial-spectral filtering parameters. We then derive a gradient-based optimization algorithm for the learning task. An experimental study is conducted using offline simulation. The results show that the proposed method is able to construct effective feature spaces to capture the discriminative information in feedback training data and, consequently, the prediction error can be significantly reduced using the new features.},
}
@article {pmid21768121,
year = {2011},
author = {Presacco, A and Goodman, R and Forrester, L and Contreras-Vidal, JL},
title = {Neural decoding of treadmill walking from noninvasive electroencephalographic signals.},
journal = {Journal of neurophysiology},
volume = {106},
number = {4},
pages = {1875-1887},
pmid = {21768121},
issn = {1522-1598},
support = {R01 NS075889/NS/NINDS NIH HHS/United States ; R01 NS075889-01/NS/NINDS NIH HHS/United States ; R01-NS075889-01/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Ankle Joint/physiology ; Artifacts ; Biomechanical Phenomena ; *Brain Mapping ; Computer Systems ; *Electroencephalography/methods ; Eye Movements/physiology ; Feedback, Sensory ; Female ; Gait ; Hip Joint/physiology ; Humans ; Knee Joint/physiology ; Leg/*physiology ; Male ; Motor Cortex/*physiology ; Scalp ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; Walking/*physiology ; Young Adult ; },
abstract = {Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.},
}
@article {pmid21768042,
year = {2011},
author = {Huang, H and Zhang, F and Hargrove, LJ and Dou, Z and Rogers, DR and Englehart, KB},
title = {Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {10},
pages = {2867-2875},
pmid = {21768042},
issn = {1558-2531},
support = {R21 HD064968/HD/NICHD NIH HHS/United States ; R21 HD064968-01/HD/NICHD NIH HHS/United States ; R21 HD064968-02/HD/NICHD NIH HHS/United States ; 5R21HD064968-02/HD/NICHD NIH HHS/United States ; },
mesh = {Amputees/rehabilitation ; *Artificial Limbs ; Electromyography/*methods ; Humans ; Locomotion/*physiology ; Muscle, Skeletal/physiology ; *Signal Processing, Computer-Assisted ; *Support Vector Machine ; Thigh ; },
abstract = {In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.},
}
@article {pmid21763528,
year = {2011},
author = {Renaud, P and Joyal, C and Stoleru, S and Goyette, M and Weiskopf, N and Birbaumer, N},
title = {Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia.},
journal = {Progress in brain research},
volume = {192},
number = {},
pages = {263-272},
doi = {10.1016/B978-0-444-53355-5.00014-2},
pmid = {21763528},
issn = {1875-7855},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Brain Mapping/*methods ; Humans ; Magnetic Resonance Imaging/*methods ; Neurofeedback/physiology ; Pedophilia/physiopathology/*therapy ; Plethysmography/methods ; ROC Curve ; Time Factors ; *User-Computer Interface ; },
abstract = {This chapter proposes a prospective view on using a real-time functional magnetic imaging (rt-fMRI) brain-computer interface (BCI) application as a new treatment for pedophilia. Neurofeedback mediated by interactive virtual stimuli is presented as the key process in this new BCI application. Results on the diagnostic discriminant power of virtual characters depicting sexual stimuli relevant to pedophilia are given. Finally, practical and ethical implications are briefly addressed.},
}
@article {pmid21763521,
year = {2011},
author = {Chase, SM and Schwartz, AB},
title = {Inference from populations: going beyond models.},
journal = {Progress in brain research},
volume = {192},
number = {},
pages = {103-112},
doi = {10.1016/B978-0-444-53355-5.00007-5},
pmid = {21763521},
issn = {1875-7855},
mesh = {Arm/physiology ; Behavior/*physiology ; Brain/*physiology ; Humans ; Models, Neurological ; Movement/physiology ; Prostheses and Implants ; *User-Computer Interface ; },
abstract = {How are abstract signals, like intent, represented in neural populations? By creating a direct link between neural activity and behavior, brain-computer interfaces (BCIs) can help answer this question. Early instantiations of these devices sought mainly to mimic arm movements: by building models of arm tuning for the neurons, desired arm movements could be read out and used to control various prosthetic devices. However, as the functionality of these devices increases, a more general approach that relies less on endogenous control signals may be required. Here we review some of the current, model-based approaches for finding volitional control signals for spiking-based BCIs, and present some new approaches for finding control signals without resorting to parametric models of neural activity.},
}
@article {pmid21763520,
year = {2011},
author = {Rebesco, JM and Miller, LE},
title = {Stimulus-driven changes in sensorimotor behavior and neuronal functional connectivity application to brain-machine interfaces and neurorehabilitation.},
journal = {Progress in brain research},
volume = {192},
number = {},
pages = {83-102},
doi = {10.1016/B978-0-444-53355-5.00006-3},
pmid = {21763520},
issn = {1875-7855},
support = {F31NS062552/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological ; Afferent Pathways/physiology ; Animals ; Brain Injuries/physiopathology/*rehabilitation ; Electric Stimulation ; Humans ; Models, Animal ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/anatomy &amp; histology/physiology ; Neuronal Plasticity/physiology ; Neurons/*cytology/*physiology ; Rats ; Somatosensory Cortex/*physiology ; Synapses/physiology ; *User-Computer Interface ; },
abstract = {Normal brain function requires constant adaptation as an organism interacts with the environment and learns to associate important sensory stimuli with appropriate motor actions. Neurological disorders may disrupt these learned associations, potentially requiring new functional pathways to be formed to replace the lost function. As a consequence, neural plasticity is a critical aspect of both normal brain function as well as the response to neurological injury. A brain-machine interface (BMI) represents a unique adaptive challenge to the nervous system. Efferent BMIs have been developed, which harness signals recorded from a tiny proportion of the motor cortex (M1) to effect control of an external device. There is also interest in the development of an afferent BMI that would supply information directly to the brain (e.g., the somatosensory cortex-S1) via electrical stimulation. If a bidirectional BMI that combined these interfaces were to be successful, new functional pathways would be necessary between the artificial inputs and outputs. Indeed, stimulation of S1 that is contingent upon the consequences of motor command signals recorded from M1 might form the basis for artificial Hebbian associations not unlike those driving learning in the normal brain. In this chapter, we review recent developments in both efferent and afferent BMIs, as well as experimental attempts to understand and mimic the Hebbian processes that give rise to plastic changes within the cortex. We have used a rat model to develop the computational and experimental tools necessary to describe changes in the way small networks of sensorimotor neurons interact and process information. We show that by repetitively pairing the recorded spikes of one neuron with electrical stimulation of another or by repetitively pairing electrical stimulation of two neurons, we can strengthen the inferred functional connection between the pair of neurons. We have also used the dual-stimulation protocol to enhance the ability of a trained rat to detect intracortical microstimulation behavioral cues. These results provide an important proof of concept, demonstrating the feasibility of Hebbian conditioning protocols to alter information flow in the brain. In addition to their possible application to BMI research, techniques like this may improve the efficacy of traditional rehabilitation for patients with neurologic injury.},
}
@article {pmid21763518,
year = {2011},
author = {Hogan, N and Krebs, HI},
title = {Physically interactive robotic technology for neuromotor rehabilitation.},
journal = {Progress in brain research},
volume = {192},
number = {},
pages = {59-68},
doi = {10.1016/B978-0-444-53355-5.00004-X},
pmid = {21763518},
issn = {1875-7855},
support = {R01-HD-045343/HD/NICHD NIH HHS/United States ; },
mesh = {Cost-Benefit Analysis ; Humans ; Neuronal Plasticity/*physiology ; Prostheses and Implants ; Randomized Controlled Trials as Topic ; Recovery of Function/*physiology ; Robotics/*methods ; Stroke/physiopathology ; *Stroke Rehabilitation ; Trauma, Nervous System/physiopathology/*rehabilitation ; User-Computer Interface ; },
abstract = {Robotic technology can provide innovative responses to the severe challenges of providing cost-effective care to restore sensory-motor function following neurological and biomechanical injury. It may be deployed at several points on a continuum of care, to provide precisely controlled sensory-motor therapy to ameliorate disability and promote recovery of function, or to provide assistance to compensate for functions that cannot be recovered, or to replace limbs lost irretrievably. This chapter reviews recent progress using robotic technology to capitalize on neural plasticity and promote recovery after neurological injury such as stroke (cerebral vascular accident), research on brain-computer interfaces as a source of control signals for assistive technologies, and research on high-performance multiple-degree-of-freedom upper-extremity prosthetic limbs.},
}
@article {pmid21763434,
year = {2012},
author = {Pistohl, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C and Ball, T},
title = {Decoding natural grasp types from human ECoG.},
journal = {NeuroImage},
volume = {59},
number = {1},
pages = {248-260},
doi = {10.1016/j.neuroimage.2011.06.084},
pmid = {21763434},
issn = {1095-9572},
mesh = {Adolescent ; *Algorithms ; Arm/physiology ; Brain Mapping/*methods ; Electrodes, Implanted ; Electroencephalography ; Female ; Hand/physiology ; Hand Strength/*physiology ; Humans ; Motor Cortex/*physiology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Electrocorticographic (ECoG) signals have been successfully used to provide information about arm movement direction, individual finger movements and even continuous arm movement trajectories. Thus, ECoG has been proposed as a potential control signal for implantable brain-machine interfaces (BMIs) in paralyzed patients. For the neuronal control of a prosthesis with versatile hand/arm functions, it is also necessary to successfully decode different types of grasping movements, such as precision grip and whole-hand grip. Although grasping is one of the most frequent and important hand movements performed in everyday life, until now, the decoding of ECoG activity related to different grasp types has not been systematically investigated. Here, we show that two different grasp types (precision vs. whole-hand grip) can be reliably distinguished in natural reach-to-grasp movements in single-trial ECoG recordings from the human motor cortex. Self-paced movement execution in a paradigm accounting for variability in grasped object position and weight was chosen to create a situation similar to everyday settings. We identified three informative signal components (low-pass-filtered component, low-frequency and high-frequency amplitude modulations), which allowed for accurate decoding of precision and whole-hand grips. Importantly, grasp type decoding generalized over different object positions and weights. Within the frontal lobe, informative signals predominated in the precentral motor cortex and could also be found in the right hemisphere's homologue of Broca's area. We conclude that ECoG signals are promising candidates for BMIs that include the restoration of grasping movements.},
}
@article {pmid21756342,
year = {2011},
author = {Diez, PF and Mut, VA and Avila Perona, EM and Laciar Leber, E},
title = {Asynchronous BCI control using high-frequency SSVEP.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {39},
pmid = {21756342},
issn = {1743-0003},
mesh = {Adult ; *Algorithms ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Neurofeedback/*instrumentation/*methods ; Photic Stimulation ; *Software ; *User-Computer Interface ; },
abstract = {BACKGROUND: Steady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP.<br><br>METHODS: This research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels.<br><br>RESULTS: The research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min.<br><br>CONCLUSIONS: Our proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.},
}
@article {pmid21750692,
year = {2011},
author = {Li, Y and Wang, G and Long, J and Yu, Z and Huang, B and Li, X and Yu, T and Liang, C and Li, Z and Sun, P},
title = {Reproducibility and discriminability of brain patterns of semantic categories enhanced by congruent audiovisual stimuli.},
journal = {PloS one},
volume = {6},
number = {6},
pages = {e20801},
pmid = {21750692},
issn = {1932-6203},
mesh = {*Acoustic Stimulation ; Adult ; Aging/physiology ; Brain/*physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Pattern Recognition, Physiological ; *Photic Stimulation ; Reproducibility of Results ; *Semantics ; Temporal Lobe/physiology ; },
abstract = {One of the central questions in cognitive neuroscience is the precise neural representation, or brain pattern, associated with a semantic category. In this study, we explored the influence of audiovisual stimuli on the brain patterns of concepts or semantic categories through a functional magnetic resonance imaging (fMRI) experiment. We used a pattern search method to extract brain patterns corresponding to two semantic categories: "old people" and "young people." These brain patterns were elicited by semantically congruent audiovisual, semantically incongruent audiovisual, unimodal visual, and unimodal auditory stimuli belonging to the two semantic categories. We calculated the reproducibility index, which measures the similarity of the patterns within the same category. We also decoded the semantic categories from these brain patterns. The decoding accuracy reflects the discriminability of the brain patterns between two categories. The results showed that both the reproducibility index of brain patterns and the decoding accuracy were significantly higher for semantically congruent audiovisual stimuli than for unimodal visual and unimodal auditory stimuli, while the semantically incongruent stimuli did not elicit brain patterns with significantly higher reproducibility index or decoding accuracy. Thus, the semantically congruent audiovisual stimuli enhanced the within-class reproducibility of brain patterns and the between-class discriminability of brain patterns, and facilitate neural representations of semantic categories or concepts. Furthermore, we analyzed the brain activity in superior temporal sulcus and middle temporal gyrus (STS/MTG). The strength of the fMRI signal and the reproducibility index were enhanced by the semantically congruent audiovisual stimuli. Our results support the use of the reproducibility index as a potential tool to supplement the fMRI signal amplitude for evaluating multimodal integration.},
}
@article {pmid21750644,
year = {2011},
author = {Zhou, JY and Pu, JL and Chen, S and Hong, Y and Ling, CH and Zhang, JM},
title = {Mirror-image arachnoid cysts in a pair of monozygotic twins: a case report and review of the literature.},
journal = {International journal of medical sciences},
volume = {8},
number = {5},
pages = {402-405},
pmid = {21750644},
issn = {1449-1907},
mesh = {Arachnoid Cysts/*diagnosis/pathology ; Brain/diagnostic imaging/pathology ; Cerebrospinal Fluid ; Humans ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; *Twins, Monozygotic ; },
abstract = {Mirror-imaging of arachnoid cysts (ACs) in monozygotic twins (MZ) is extremely rare. We describe a pair of MZ who developed mirror-imaging of ACs in the temporal fossas, and we also review the literature. Brain computer tomography (CT) and Magnetic Resonance Imaging (MRI) of the MZ revealed mirror-imaging of vast lesions of cerebrospinal fluid intensity in their temporal fossas. This is the second ever report of such a case according to the available literature. Unlike the prior case, our patients were 14 months, which is a much younger age than the patients of the previous report. Consequently, our case is better in supporting a genetic origin in the pathogenesis of AC. The findings in our case indicate that early neuroimaging is mandatory in the counterpart of the symptomatic patient with AC, irrespective of the absence of symptoms.},
}
@article {pmid21750369,
year = {2011},
author = {Pei, X and Barbour, DL and Leuthardt, EC and Schalk, G},
title = {Decoding vowels and consonants in spoken and imagined words using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046028},
pmid = {21750369},
issn = {1741-2552},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 DC009215/DC/NIDCD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain Mapping ; Cerebral Cortex/physiology ; Communication Devices for People with Disabilities ; Data Interpretation, Statistical ; Discrimination, Psychological/physiology ; Electrodes, Implanted ; Electroencephalography/*methods ; Epilepsy/surgery ; Female ; Functional Laterality/physiology ; Humans ; Male ; Middle Aged ; Movement ; Speech Perception/*physiology ; *User-Computer Interface ; },
abstract = {Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to 'read the mind', i.e. to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brain-based communication using imagined speech.},
}
@article {pmid21748032,
year = {2011},
author = {Pouratian, N},
title = {The brain and computer: The neurosurgical interface.},
journal = {Surgical neurology international},
volume = {2},
number = {},
pages = {79},
pmid = {21748032},
issn = {2152-7806},
abstract = {Neurosurgery has always had a strong interest in innovating new technologies to improve neurological function and quality of life. Now, novel interventions that modulate central nervous system activity at the nanoparticle, molecular, genetic, cellular, and network level all seem to be on the horizon. Advances in biomedical engineering, including imaging techniques, sensor technologies, bio-signal analyses and classification, and prosthetics, have particularly accelerated the development brain-computer interfaces (BCI). Clinical translation of BCI technology will require multidisciplinary collaboration and effort to develop all necessary components, including advanced sensor technologies, sophisticated and real-time signal analyses and classifications, and complex effector technologies. Although the field has primarily been driven by basic scientists, neurosurgeons need to play a critical role in the further development of each component of these technologies because of our unique access to the awake and behaving human brain, our perspective with respect to the practicalities of technology implementation in the clinical setting, and because of our historical commitment to improving neurological function and quality-of-life. The current state of BCI research, the challenges, and the critical role that neurosurgeons must play in BCI development are briefly reviewed to advocate for increased neurosurgical involvement and commitment to this emerging translational field.},
}
@article {pmid21747754,
year = {2011},
author = {Indiveri, G and Linares-Barranco, B and Hamilton, TJ and van Schaik, A and Etienne-Cummings, R and Delbruck, T and Liu, SC and Dudek, P and Häfliger, P and Renaud, S and Schemmel, J and Cauwenberghs, G and Arthur, J and Hynna, K and Folowosele, F and Saighi, S and Serrano-Gotarredona, T and Wijekoon, J and Wang, Y and Boahen, K},
title = {Neuromorphic silicon neuron circuits.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {73},
pmid = {21747754},
issn = {1662-453X},
abstract = {Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.},
}
@article {pmid21744296,
year = {2012},
author = {Rivet, B and Cecotti, H and Maby, E and Mattout, J},
title = {Impact of spatial filters during sensor selection in a visual P300 brain-computer interface.},
journal = {Brain topography},
volume = {25},
number = {1},
pages = {55-63},
doi = {10.1007/s10548-011-0193-y},
pmid = {21744296},
issn = {1573-6792},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Brain Waves/physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; *Signal Detection, Psychological ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {A challenge in designing a Brain-Computer Interface (BCI) is the choice of the channels, e.g. the most relevant sensors. Although a setup with many sensors can be more efficient for the detection of Event-Related Potential (ERP) like the P300, it is relevant to consider only a low number of sensors for a commercial or clinical BCI application. Indeed, a reduced number of sensors can naturally increase the user comfort by reducing the time required for the installation of the EEG (electroencephalogram) cap and can decrease the price of the device. In this study, the influence of spatial filtering during the process of sensor selection is addressed. Two of them maximize the Signal to Signal-plus-Noise Ratio (SSNR) for the different sensor subsets while the third one maximizes the differences between the averaged P300 waveform and the non P300 waveform. We show that the locations of the most relevant sensors subsets for the detection of the P300 are highly dependent on the use of spatial filtering. Applied on data from 20 healthy subjects, this study proves that subsets obtained where sensors are suppressed in relation to their individual SSNR are less efficient than when sensors are suppressed in relation to their contribution once the different selected sensors are combined for enhancing the signal. In other words, it highlights the difference between estimating the P300 projection on the scalp and evaluating the more efficient sensor subsets for a P300-BCI. Finally, this study explores the issue of channel commonality across subjects. The results support the conclusion that spatial filters during the sensor selection procedure allow selecting better sensors for a visual P300 Brain-Computer Interface.},
}
@article {pmid21741290,
year = {2011},
author = {Pérez-Marcos, D and Buitrago, JA and Velásquez, FD},
title = {Writing through a robot: a proof of concept for a brain-machine interface.},
journal = {Medical engineering &amp; physics},
volume = {33},
number = {10},
pages = {1314-1317},
doi = {10.1016/j.medengphy.2011.06.005},
pmid = {21741290},
issn = {1873-4030},
mesh = {*Brain/physiology ; Communication ; Feasibility Studies ; Female ; Humans ; Robotics/*instrumentation ; *User-Computer Interface ; *Writing ; },
abstract = {This paper describes a non-invasive human brain-actuated robotic arm experiment, which allows remote writing. In the local environment, the participant decides on an arbitrary word to transmit. A mental speller interface is then used to select the letters. A robot arm placed in the remote environment writes the word on a whiteboard in real time. A multidisciplinary framework such as the one presented here exemplifies a class of interactive applications with possible relevance in a variety of fields, such as entertainment and clinical environments.},
}
@article {pmid21736522,
year = {2011},
author = {Pachariyanon, P and Barth, E and Agar, DW},
title = {Enzyme immobilisation in permselective microcapsules.},
journal = {Journal of microencapsulation},
volume = {28},
number = {5},
pages = {370-383},
doi = {10.3109/02652048.2011.576781},
pmid = {21736522},
issn = {1464-5246},
mesh = {Alginates ; Biocatalysis ; Capsules/*chemistry/metabolism ; Enzymes, Immobilized/*chemistry/metabolism ; Glucuronic Acid ; Hexuronic Acids ; Magnetite Nanoparticles ; Membranes, Artificial ; Permeability ; Silicon Dioxide ; },
abstract = {The objective of this investigation was to study the permselective behaviour of calcium alginate membranes, including the modifying effects of silica additives, which were subsequently used as microcapsule shells. Diffusion experiments and HPLC were carried out to ascertain the size-exclusion property of the membranes for a mixed molecular-weight dextran solution. Hollow microcapsules containing the enzyme dextranase were prepared using double concentric nozzles and the encapsulation performance was evaluated based on an analysis of the enzyme reactivity and stability. To improve mass transport within the microcapsules, magnetic nanoparticles were introduced into the liquid core and agitated using an alternating external magnetic field. The modified membranes exhibited better size-exclusion behaviour than the unmodified membranes. The magnetic nanoparticles slightly improved mass transport inside the microcapsule. The encapsulated enzyme yielded nearly 80% of the free enzyme activity and retained about 80% of the initial catalytic activity even after being used for eight reaction cycles.},
}
@article {pmid21731647,
year = {2011},
author = {Jonasson, KA and Willis, CK},
title = {Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome.},
journal = {PloS one},
volume = {6},
number = {6},
pages = {e21061},
pmid = {21731647},
issn = {1932-6203},
mesh = {Animals ; Chiroptera/*microbiology/*physiology ; Female ; Hibernation/*physiology ; Male ; *Models, Biological ; Mycoses/*veterinary ; Population Dynamics ; *Sex Characteristics ; Syndrome ; },
abstract = {White-nose syndrome (WNS) is a new disease of bats that has devastated populations in eastern North America. Infection with the fungus, Geomyces destructans, is thought to increase the time bats spend out of torpor during hibernation, leading to starvation. Little is known about hibernation in healthy, free-ranging bats and more data are needed to help predict consequences of WNS. Trade-offs presumably exist between the energetic benefits and physiological/ecological costs of torpor, leading to the prediction that the relative importance of spring energy reserves should affect an individual's use of torpor and depletion of energy reserves during winter. Myotis lucifugus mate during fall and winter but females do not become pregnant until after spring emergence. Thus, female reproductive success depends on spring fat reserves while male reproductive success does not. Consequently, females should be "thrifty" in their use of fat compared to males. We measured body condition index (BCI; mass/forearm length) of 432 M. lucifugus in Manitoba, Canada during the winter of 2009/2010. Bats were captured during the fall mating period (n = 200), early hibernation (n = 125), and late hibernation (n = 128). Adult females entered hibernation with greater fat reserves and consumed those reserves more slowly than adult males and young of the year. Consequently, adult females may be more likely than males or young of the year to survive the disruption of energy balance associated with WNS, although surviving females may not have sufficient reserves to support reproduction.},
}
@article {pmid21719743,
year = {2011},
author = {Lee, VJ and Chen, MI and Yap, J and Ong, J and Lim, WY and Lin, RT and Barr, I and Ong, JB and Mak, TM and Goh, LG and Leo, YS and Kelly, PM and Cook, AR},
title = {Comparability of different methods for estimating influenza infection rates over a single epidemic wave.},
journal = {American journal of epidemiology},
volume = {174},
number = {4},
pages = {468-478},
pmid = {21719743},
issn = {1476-6256},
mesh = {Adult ; Bayes Theorem ; Cross-Sectional Studies ; Female ; Humans ; Incidence ; *Influenza A Virus, H1N1 Subtype ; Influenza, Human/*epidemiology/virology ; Male ; Middle Aged ; Population Surveillance/*methods ; Sensitivity and Specificity ; Sentinel Surveillance ; Seroepidemiologic Studies ; Severity of Illness Index ; Singapore/epidemiology ; },
abstract = {Estimation of influenza infection rates is important for determination of the extent of epidemic spread and for calculation of severity indicators. The authors compared estimated infection rates from paired and cross-sectional serologic surveys, rates of influenza like illness (ILI) obtained from sentinel general practitioners (GPs), and ILI samples that tested positive for influenza using data from similar periods collected during the 2009 H1N1 epidemic in Singapore. The authors performed sensitivity analyses to assess the robustness of estimates to input parameter uncertainties, and they determined sample sizes required for differing levels of precision. Estimates from paired seroconversion were 17% (95% Bayesian credible interval (BCI): 14, 20), higher than those from cross-sectional serology (12%, 95% BCI: 9, 17). Adjusted ILI estimates were 15% (95% BCI: 10, 25), and estimates computed from ILI and laboratory data were 12% (95% BCI: 8, 18). Serologic estimates were least sensitive to the risk of input parameter misspecification. ILI-based estimates were more sensitive to parameter misspecification, though this was lessened by incorporation of laboratory data. Obtaining a 5-percentage-point spread for the 95% confidence interval in infection rates would require more than 1,000 participants per serologic study, a sentinel network of 90 GPs, or 50 GPs when combined with laboratory samples. The various types of estimates will provide comparable findings if accurate input parameters can be obtained.},
}
@article {pmid21708506,
year = {2011},
author = {Vinjamuri, R and Weber, DJ and Mao, ZH and Collinger, JL and Degenhart, AD and Kelly, JW and Boninger, ML and Tyler-Kabara, EC and Wang, W},
title = {Toward synergy-based brain-machine interfaces.},
journal = {IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {15},
number = {5},
pages = {726-736},
doi = {10.1109/TITB.2011.2160272},
pmid = {21708506},
issn = {1558-0032},
support = {R01 EB007749/EB/NIBIB NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; 3R01NS050256-05S1/NS/NINDS NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Humans ; Male ; *Man-Machine Systems ; Models, Theoretical ; },
abstract = {This paper demonstrates a synergy-based brain-machine interface that uses low-dimensional command signals to control a high dimensional virtual hand. First, temporal postural synergies were extracted from the angular velocities of finger joints of five healthy subjects when they performed hand movements that were similar to activities of daily living. Two synergies inspired from the extracted synergies, namely, two-finger pinch and whole-hand grasp, were used in real-time brain control, where a virtual hand with 10 degrees of freedom was controlled to grasp or pinch virtual objects. These two synergies were controlled by electrocorticographic (ECoG) signals recorded from two electrodes of an electrode array that spanned motor and speech areas of an individual with intractable epilepsy, thus demonstrating closed loop control of a synergy-based brain-machine interface.},
}
@article {pmid21701852,
year = {2011},
author = {Bauernfeind, G and Scherer, R and Pfurtscheller, G and Neuper, C},
title = {Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {49},
number = {9},
pages = {979-984},
pmid = {21701852},
issn = {1741-0444},
mesh = {Adult ; Brain Mapping/methods ; Female ; Humans ; Male ; Mathematical Concepts ; Mental Processes/*physiology ; Oxyhemoglobins/*metabolism ; Prefrontal Cortex/*metabolism ; Problem Solving/physiology ; Spectroscopy, Near-Infrared/methods ; User-Computer Interface ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that can be used for brain-computer interfaces (BCIs) systems. A common challenge for BCIs is a stable and reliable classification of single-trial data, especially for cognitive (mental) tasks. With antagonistic activation pattern, recently found for mental arithmetic (MA) tasks, an improved online classification for optical BCIs using MA should become possible. For this investigation, we used the data of a previous study where we found antagonistic activation patterns (focal bilateral increase of [oxy-Hb] in the dorsolateral prefrontal cortex in parallel with a [oxy-Hb] decrease in the medial area of the anterior prefrontal cortex) in eight subjects. We used the [oxy-Hb] responses to search for the best antagonistic feature combination and compared it to individual features from the same regions. In addition, we investigated the use of antagonistic [deoxy-Hb], total hemoglobin [Hbtot] and pairs of [oxy-Hb] and [deoxy-Hb] features as well as the existence of a group-related feature set. Our results indicate that the use of the antagonistic [oxy-Hb] features significantly increases the classification accuracy from 63.3 to 79.7%. These results support the hypothesis that antagonistic hemodynamic response patterns are a suitable control strategy for optical BCI, and that only two prefrontal NIRS channels are needed for good performance.},
}
@article {pmid21696919,
year = {2011},
author = {Llera, A and van Gerven, MA and Gómez, V and Jensen, O and Kappen, HJ},
title = {On the use of interaction error potentials for adaptive brain computer interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {24},
number = {10},
pages = {1120-1127},
doi = {10.1016/j.neunet.2011.05.006},
pmid = {21696919},
issn = {1879-2782},
mesh = {Adaptation, Physiological/physiology ; Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Humans ; Magnetoencephalography/methods ; Neurofeedback/methods/*physiology ; Pattern Recognition, Automated/methods ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition we compare static versus adaptive classification performance using artificial and MEG data. We show that the proposed adaptive framework significantly improves the static classification methods.},
}
@article {pmid21695206,
year = {2011},
author = {Bobrov, P and Frolov, A and Cantor, C and Fedulova, I and Bakhnyan, M and Zhavoronkov, A},
title = {Brain-computer interface based on generation of visual images.},
journal = {PloS one},
volume = {6},
number = {6},
pages = {e20674},
pmid = {21695206},
issn = {1932-6203},
mesh = {Adult ; Artifacts ; Bayes Theorem ; Blinking/physiology ; Brain ; Electrodes ; Electroencephalography ; Electrooculography ; Eye Movements/physiology ; Humans ; Imagination/*physiology ; Male ; *User-Computer Interface ; Vision, Ocular/*physiology ; Young Adult ; },
abstract = {This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.},
}
@article {pmid21693389,
year = {2011},
author = {Pfurtscheller, G and Klobassa, DS and Altstätter, C and Bauernfeind, G and Neuper, C},
title = {About the stability of phase shifts between slow oscillations around 0.1 Hz in cardiovascular and cerebral systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {7},
pages = {2064-2071},
doi = {10.1109/TBME.2011.2134851},
pmid = {21693389},
issn = {1558-2531},
mesh = {Adult ; Baroreflex ; Blood Pressure/*physiology ; Female ; Fingers/physiology ; Heart Rate/*physiology ; Hemodynamics/physiology ; Humans ; Male ; Man-Machine Systems ; Motor Activity/physiology ; Oxyhemoglobins/*physiology ; Prefrontal Cortex/blood supply/*physiology ; Rest ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared ; },
abstract = {One important feature of the baroreflex loop is its strong preference for oscillations around 0.1 Hz. In this study, we investigated heart rate intervals, arterial blood pressure (BP), and prefrontal oxyhemoglobin changes during 5 min rest and during brisk finger movements in 19 healthy subjects. We analyzed the phase coupling around 0.1 Hz between cardiovascular and (de)oxyhemoglobin oscillations, using the cross-spectral method. The analyses revealed phase shifts for slow oscillations in BP and heart rate intervals between -10° and -118° (BP always leading). These phase shifts increased significantly (p<0.01) in the movement session. The coupling between cardiovascular and oxyhemoglobin oscillations was less clear. Only 12 subjects demonstrated a phase coupling (COH(2) ≥ 0.5) between oxyhemoglobin and BP oscillations. This may be explained by an overwhelming proportion of nonlinearity in cardiovascular and hemodynamic systems. The phase shifts between slow cardiovascular and hemodynamic oscillations are relatively stable subject-specific biometric features and could be of interest for person identification in addition to other biometric data. Slow BP-coupled oscillations in prefrontal oxyhemoglobin changes can seriously impair the detection of mentally induced hemodynamic changes in an optical brain-computer interface, a novel nonmuscular communication system.},
}
@article {pmid21690116,
year = {2011},
author = {Breshears, JD and Gaona, CM and Roland, JL and Sharma, M and Anderson, NR and Bundy, DT and Freudenburg, ZV and Smyth, MD and Zempel, J and Limbrick, DD and Smart, WD and Leuthardt, EC},
title = {Decoding motor signals from the pediatric cortex: implications for brain-computer interfaces in children.},
journal = {Pediatrics},
volume = {128},
number = {1},
pages = {e160-8},
doi = {10.1542/peds.2010-1519},
pmid = {21690116},
issn = {1098-4275},
mesh = {Adolescent ; Adult ; Child ; Electrodes, Implanted ; Epilepsy/*physiopathology ; Feasibility Studies ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To demonstrate the decodable nature of pediatric brain signals for the purpose of neuroprosthetic control. We hypothesized that children would achieve levels of brain-derived computer control comparable to performance previously reported for adults.<br><br>PATIENTS AND METHODS: Six pediatric patients with intractable epilepsy who were invasively monitored underwent screening for electrocortical control signals associated with specific motor or phoneme articulation tasks. Subsequently, patients received visual feedback as they used these associated electrocortical signals to direct one dimensional cursor movement to a target on a screen.<br><br>RESULTS: All patients achieved accuracies between 70% and 99% within 9 minutes of training using the same screened motor and articulation tasks. Two subjects went on to achieve maximum accuracies of 73% and 100% using imagined actions alone. Average mean and maximum performance for the 6 pediatric patients was comparable to that of 5 adults. The mean accuracy of the pediatric group was 81% (95% confidence interval [CI]: 71.5-90.5) over a mean training time of 11.6 minutes, whereas the adult group had a mean accuracy of 72% (95% CI: 61.2-84.3) over a mean training time of 12.5 minutes. Maximum performance was also similar between the pediatric and adult groups (89.6% [95% CI: 83-96.3] and 88.5% [95% CI: 77.1-99.8], respectively).<br><br>CONCLUSIONS: Similarly to adult brain signals, pediatric brain signals can be decoded and used for BCI operation. Therefore, BCI systems developed for adults likely hold similar promise for children with motor disabilities.},
}
@article {pmid21687590,
year = {2011},
author = {Delorme, A and Mullen, T and Kothe, C and Akalin Acar, Z and Bigdely-Shamlo, N and Vankov, A and Makeig, S},
title = {EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {130714},
pmid = {21687590},
issn = {1687-5273},
support = {R01 NS047293/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; *Brain Mapping ; *Brain Waves ; *Electroencephalography/instrumentation/methods ; Humans ; Models, Biological ; *Signal Processing, Computer-Assisted ; *Software ; User-Computer Interface ; },
abstract = {We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.},
}
@article {pmid21687575,
year = {2011},
author = {Degenhart, AD and Kelly, JW and Ashmore, RC and Collinger, JL and Tyler-Kabara, EC and Weber, DJ and Wang, W},
title = {Craniux: a LabVIEW-based modular software framework for brain-machine interface research.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {363565},
pmid = {21687575},
issn = {1687-5273},
support = {R21 NS056136/NS/NINDS NIH HHS/United States ; R01 EB007749/EB/NIBIB NIH HHS/United States ; UL1 RR024153/RR/NCRR NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; Brain Waves/*physiology ; Electroencephalography ; Humans ; Information Storage and Retrieval ; Numerical Analysis, Computer-Assisted ; *Software ; Software Design ; Time Factors ; *User-Computer Interface ; },
abstract = {This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.},
}
@article {pmid21687573,
year = {2011},
author = {Sudre, G and Parkkonen, L and Bock, E and Baillet, S and Wang, W and Weber, DJ},
title = {rtMEG: a real-time software interface for magnetoencephalography.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {327953},
pmid = {21687573},
issn = {1687-5273},
support = {R21 NS056136/NS/NINDS NIH HHS/United States ; R90 DA023426/DA/NIDA NIH HHS/United States ; R01 EB007749/EB/NIBIB NIH HHS/United States ; UL1 RR024153/RR/NCRR NIH HHS/United States ; KL2RR024154/RR/NCRR NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; T90 DA022762/DA/NIDA NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Brain Mapping ; Brain Waves/*physiology ; Humans ; *Magnetoencephalography ; Neurofeedback ; Signal Processing, Computer-Assisted ; *Software ; Time Factors ; User-Computer Interface ; },
abstract = {To date, the majority of studies using magnetoencephalography (MEG) rely on off-line analysis of the spatiotemporal properties of brain activity. Real-time MEG feedback could potentially benefit multiple areas of basic and clinical research: brain-machine interfaces, neurofeedback rehabilitation of stroke and spinal cord injury, and new adaptive paradigm designs, among others. We have developed a software interface to stream MEG signals in real time from the 306-channel Elekta Neuromag MEG system to an external workstation. The signals can be accessed with a minimal delay (≤45 ms) when data are sampled at 1000 Hz, which is sufficient for most real-time studies. We also show here that real-time source imaging is possible by demonstrating real-time monitoring and feedback of alpha-band power fluctuations over parieto-occipital and frontal areas. The interface is made available to the academic community as an open-source resource.},
}
@article {pmid21687463,
year = {2011},
author = {Jensen, O and Bahramisharif, A and Oostenveld, R and Klanke, S and Hadjipapas, A and Okazaki, YO and van Gerven, MA},
title = {Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience.},
journal = {Frontiers in psychology},
volume = {2},
number = {},
pages = {100},
pmid = {21687463},
issn = {1664-1078},
abstract = {Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.},
}
@article {pmid21685664,
year = {2011},
author = {Krausz, G and Ortner, R and Opisso, E},
title = {Accuracy of a Brain Computer Interface (P300 spelling device) used by people with motor impairments.},
journal = {Studies in health technology and informatics},
volume = {167},
number = {},
pages = {182-186},
pmid = {21685664},
issn = {0926-9630},
mesh = {Adult ; Brain/*physiopathology ; Central Nervous System Diseases/physiopathology/*rehabilitation ; *Computer Simulation ; Evoked Potentials, Visual ; Female ; Humans ; Male ; Middle Aged ; *User-Computer Interface ; },
abstract = {A Brain-Computer Interface (BCI) provides a completely new output pathway and so, an additional possible way a person can express himself if he/she suffers from disorders like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, or other diseases which impair the function of the common output pathways which are responsible for the control of muscles or impair the muscles. Although most BCIs are thought to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on 10 physically disabled people, to the results of a previous study, taken using 100 healthy participants. We prove that, under certain constraints, most patients are able to control a P300-based spelling device with almost the same accuracy as the healthy ones. Tuning parameters are discussed, as well as criteria for people who are not able to use this device.},
}
@article {pmid21683346,
year = {2011},
author = {Hsu, WY},
title = {EEG-based motor imagery classification using enhanced active segment selection and adaptive classifier.},
journal = {Computers in biology and medicine},
volume = {41},
number = {8},
pages = {633-639},
doi = {10.1016/j.compbiomed.2011.05.014},
pmid = {21683346},
issn = {1879-0534},
mesh = {*Algorithms ; Analysis of Variance ; Discriminant Analysis ; Electroencephalography/*methods ; Hand ; Humans ; Imagination/*physiology ; Movement ; User-Computer Interface ; *Wavelet Analysis ; },
abstract = {In this study, an adaptive electroencephalogram (EEG) analysis system is proposed for a two-session, single-trial classification of motor imagery (MI) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the adaptive linear discriminant analysis (LDA) is used for classification of left- and right-hand MI data and for simultaneous and continuous update of its parameters. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. The classification in session 2 is performed by adaptive LDA, which is trial-by-trial updated using the Kalman filter after the trial is classified. Compared with original active segment selection and non-adaptive LDA on six subjects from two data sets, the results indicate that the proposed method is helpful to realize adaptive BCI systems.},
}
@article {pmid21682906,
year = {2011},
author = {Holper, L and Wolf, M},
title = {Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {34},
pmid = {21682906},
issn = {1743-0003},
mesh = {Adult ; Brain/*physiology ; Data Interpretation, Statistical ; Discriminant Analysis ; Electromyography ; Female ; Fingers/physiology ; Functional Laterality ; Hemodynamics/physiology ; Hemoglobins/metabolism ; Humans ; Imagination/*physiology ; Kinesthesis ; Linear Models ; Male ; Movement/*physiology ; Nervous System Diseases/rehabilitation ; Psychomotor Performance/physiology ; Rehabilitation/*instrumentation ; Reproducibility of Results ; *Spectroscopy, Near-Infrared ; *User-Computer Interface ; },
abstract = {BACKGROUND: For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks.<br><br>METHODS: 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four &#x394;[O2Hb] signal features (&#x394;[O2Hb] mean signal amplitudes, variance, skewness and kurtosis).<br><br>RESULTS: The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four &#x394;[O2Hb] signal features comprising &#x394;[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p &#x2264; 0.001), over secondary motor areas with an average classification accuracy of 81%.<br><br>CONCLUSIONS: Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation.},
}
@article {pmid21681514,
year = {2011},
author = {Chen, M and Guan, J and Liu, H},
title = {Enabling fast brain-computer interaction by single-trial extraction of visual evoked potentials.},
journal = {Journal of medical systems},
volume = {35},
number = {5},
pages = {1323-1331},
pmid = {21681514},
issn = {0148-5598},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; *Evoked Potentials, Visual ; Humans ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; Wireless Technology ; },
abstract = {This paper investigates the challenging issue of enabling fast brain-computer interaction to construct a mental speller. Exploiting visual evoked potentials as communication carriers, an online paradigm called "imitating-human-natural-reading" is realized. In this online paradigm, single-trial estimation with the intrinsically real-time feature should be used instead of grand average that is traditionally used in the cognitive or clinical experiments. By the use of several montages of component features from four channels with parameter optimization, we explored the support vector machines-based single-trial estimation of evoked potentials. The results on a human-subject show the advantages of the inducing paradigm used in our mental speller with a high classification rate.},
}
@article {pmid21681436,
year = {2011},
author = {Komínek, P and Cervenka, S and Pniak, T and Zeleník, K and Tomášková, H and Matoušek, P},
title = {Monocanalicular versus bicanalicular intubation in the treatment of congenital nasolacrimal duct obstruction.},
journal = {Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie},
volume = {249},
number = {11},
pages = {1729-1733},
pmid = {21681436},
issn = {1435-702X},
mesh = {Anesthesia, General ; Child, Preschool ; Device Removal ; Female ; Fluorescein/metabolism ; Humans ; Infant ; Intubation/*methods ; Lacrimal Duct Obstruction/*congenital/physiopathology/*therapy ; Male ; Nasolacrimal Duct/*abnormalities/physiopathology ; Postoperative Complications ; Prospective Studies ; *Silicone Elastomers ; Treatment Outcome ; },
abstract = {BACKGROUND: To compare the success rate of monocanalicular intubation (MCI) compared with bicanalicular silicone intubation (BCI) in congenital nasolacrimal duct obstruction (CNLDO) in infants and toddlers.<br><br>METHODS: In a prospective, nonrandomized, comparative study, MCI (n = 35 eyes) through the inferior canaliculus or BCI (n = 35 eyes) were performed under general anaesthesia in children aged 10 to 36 months with CNLDO. The tubes were removed 3-4 months after tube placement, and the children were followed up for 6 months after the removal of tubes. Therapeutic success was defined as the fluorescein dye disappearance test grade 0-1, corresponding with a complete resolution of previous symptoms. Partial success was defined as improvement with some residual symptoms.<br><br>RESULTS: Complete and partial improvement was achieved in 31/35 (88.57%) in the BCI group and 34/35 (97.14%) in the MCI group. The difference between the two groups was not significant (p = 0.584). Complications occurred in both groups. Dislodgement of the tube and premature removal was observed in four BCI cases, and loss of the tube was observed twice in the MCI group. Canalicular slitting was observed in five eyes in the BCI group. Granuloma pyogenicum observed in 2 cases with MCI revealed a few weeks after the tube removal. Corneal erosion in the inferior medial quadrant was observed in one MCI eye and revealed in a few days after the local treatment without tube removal.<br><br>CONCLUSIONS: Both MCI and the BCI are effective methods for treating CNLDO. MCI has the advantage of a lower incidence of canalicular slit and easy placement.},
}
@article {pmid21672270,
year = {2011},
author = {Treder, MS and Bahramisharif, A and Schmidt, NM and van Gerven, MA and Blankertz, B},
title = {Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {24},
pmid = {21672270},
issn = {1743-0003},
mesh = {Adolescent ; Adult ; Alpha Rhythm/*physiology ; Attention/*physiology ; Cues ; Electrodes ; Electroencephalography ; Electrooculography ; Female ; Fixation, Ocular/physiology ; Functional Laterality/physiology ; Humans ; Logistic Models ; Male ; Neurophysiology ; Photic Stimulation ; *User-Computer Interface ; Young Adult ; },
abstract = {BACKGROUND: Visual brain-computer interfaces (BCIs) often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural processes underlying visual attention. Covert shifts of visual attention induce changes in oscillatory alpha activity in posterior cortex, even in the absence of visual stimulation. The aim was to investigate whether different pairs of directions of attention shifts can be reliably differentiated based on the electroencephalogram. To this end, healthy participants (N = 8) had to strictly fixate a central dot and covertly shift visual attention to one out of six cued directions.<br><br>RESULTS: Covert attention shifts induced a prolonged alpha synchronization over posterior electrode sites (PO and O electrodes). Spectral changes had specific topographies so that different pairs of directions could be differentiated. There was substantial variation across participants with respect to the direction pairs that could be reliably classified. Mean accuracy for the best-classifiable pair amounted to 74.6%. Furthermore, an alpha power index obtained during a relaxation measurement showed to be predictive of peak BCI performance (r = .66).<br><br>CONCLUSIONS: Results confirm posterior alpha power modulations as a viable input modality for gaze-independent EEG-based BCIs. The pair of directions yielding optimal performance varies across participants. Consequently, participants with low control for standard directions such as left-right might resort to other pairs of directions including top and bottom. Additionally, a simple alpha index was shown to predict prospective BCI performance.},
}
@article {pmid21667185,
year = {2012},
author = {Choi, K},
title = {Control of a vehicle with EEG signals in real-time and system evaluation.},
journal = {European journal of applied physiology},
volume = {112},
number = {2},
pages = {755-766},
pmid = {21667185},
issn = {1439-6327},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/*instrumentation ; Brain/*physiology ; Computer Systems ; Electroencephalography/*methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; *Man-Machine Systems ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted/*instrumentation ; *Wheelchairs ; },
abstract = {To construct and evaluate a novel wheelchair system that can be freely controlled via electroencephalogram signals in order to allow people paralyzed from the neck down to interact with society more freely. A brain-machine interface (BMI) wheelchair control system was constructed by effective signal processing methods, and subjects were trained by a feedback method to decrease the training time and improve accuracy. The implemented system was evaluated through experiments on controlling bars and avoiding obstacles using three subjects. Furthermore, the effectiveness of the feedback training method was evaluated by comparison with an imaginary movement experiment without any visual feedback for two additional subjects. In the bar-controlling experiment, two subjects achieved a 95.00% success rate, and the third had a 91.66% success rate. In the obstacle avoidance experiment, all three achieved success rate over 90% success rate, and required almost the same amount of time to reach as that when driving with a joystick. In the experiment on imaginary movement without visual feedback, the two additional subjects adapted to the experiment far slower than they did with visual feedback. In this study, the feedback training method allowed subjects to easily and rapidly gain accurate control over the implemented wheelchair system. These results show the importance of the feedback training method using neuroplasticity in BMI systems.},
}
@article {pmid21666308,
year = {2011},
author = {Faradji, F and Ward, RK and Birch, GE},
title = {Toward development of a two-state brain-computer interface based on mental tasks.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046014},
doi = {10.1088/1741-2560/8/4/046014},
pmid = {21666308},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Discriminant Analysis ; Electroencephalography ; False Negative Reactions ; False Positive Reactions ; Humans ; Imagination/physiology ; Male ; Mathematics ; Mental Processes/*physiology ; Models, Neurological ; Models, Statistical ; Prosthesis Design ; Regression Analysis ; Reproducibility of Results ; *User-Computer Interface ; Young Adult ; },
abstract = {A recently collected EEG dataset is analyzed and processed in order to evaluate the performance of a previously designed brain-computer interface (BCI) system. The EEG signals are collected from 29 channels distributed over the scalp. Four subjects completed three sessions each by performing four different mental tasks during each session. The BCI is designed in such a way that only one of the mental tasks can activate it. One important advantage of this BCI is its simplicity, since autoregressive modeling and quadratic discriminant analysis are used for feature extraction and classification, respectively. The autoregressive order which yields the best overall performance is obtained during a fivefold nested cross-validation process. The results are promising as the false positive rates are zero while the true positive rates are sufficiently high (67.26% average).},
}
@article {pmid21666287,
year = {2011},
author = {Roland, J and Miller, K and Freudenburg, Z and Sharma, M and Smyth, M and Gaona, C and Breshears, J and Corbetta, M and Leuthardt, EC},
title = {The effect of age on human motor electrocorticographic signals and implications for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046013},
doi = {10.1088/1741-2560/8/4/046013},
pmid = {21666287},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Aging/*physiology ; Algorithms ; Alpha Rhythm/physiology ; Beta Rhythm/physiology ; Brain/*growth &amp; development/*physiology ; Child ; Cues ; Delta Rhythm/physiology ; Electrodes, Implanted ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/growth &amp; development/*physiology ; Neural Pathways/growth &amp; development/physiology ; Photic Stimulation ; Prosthesis Design ; Psychomotor Performance/physiology ; Thalamus/growth &amp; development/physiology ; Theta Rhythm/physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have emerged as a new signal platform for neuroprosthetic application. ECoG-based platforms have shown significant promise for clinical application due to the high level of information that can be derived from the ECoG signal, the signal's stability, and its intermediate nature of surgical invasiveness. However, before long-term BCI applications can be realized it will be important to also understand how the cortical physiology alters with age. Such understanding may provide an appreciation for how this may affect the control signals utilized by a chronic implant. In this study, we report on a large population of adult and pediatric invasively monitored subjects to determine the impact that age will have on surface cortical physiology. We evaluated six frequency bands--delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma band (30-50 Hz), and high gamma band (76-100 Hz)--to evaluate the effect of age on the magnitude of power change, cortical area of activation, and cortical networks. When significant trends are evaluated as a whole, it appears that the aging process appears to more substantively alter thalamocortical interactions leading to an increase in cortical inefficiency. Despite this, we find that higher gamma rhythms appear to be more anatomically constrained with age, while lower frequency rhythms appear to broaden in cortical involvement as time progresses. From an independent signal standpoint, this would favor high gamma rhythms' utilization as a separable signal that could be maintained chronically.},
}
@article {pmid21661550,
year = {2011},
author = {Feeley, KJ and Davies, SJ and Perez, R and Hubbell, SP and Foster, RB},
title = {Directional changes in the species composition of a tropical forest.},
journal = {Ecology},
volume = {92},
number = {4},
pages = {871-882},
doi = {10.1890/10-0724.1},
pmid = {21661550},
issn = {0012-9658},
mesh = {*Ecosystem ; El Nino-Southern Oscillation ; Geography ; Models, Biological ; Panama ; Species Specificity ; Time Factors ; *Trees ; *Tropical Climate ; },
abstract = {Long-term studies have revealed that the structure and dynamics of many tropical forests are changing, but the causes and consequences of these changes remain debated. To learn more about the forces driving changes within tropical forests, we investigated shifts in tree species composition over the past 25 years within the 50-ha Forest Dynamics Plot on Barro Colorado Island (BCI), Panama, and examined how observed patterns relate to predictions of (1) random population fluctuations, (2) carbon fertilization, (3) succession from past disturbance, (4) recovery from an extreme El Niño drought at the start of the study period, and (5) long-term climate change. We found that there have been consistent and directional changes in the tree species composition. These shifts have led to increased relative representations of drought-tolerant species as determined by the species' occurrence both across a gradient of soil moisture within BCI and across a wider precipitation gradient from a dry forest near the Pacific coast of Panama to a wet forest near its Caribbean coast. These nonrandom changes cannot be explained by stochastic fluctuations or carbon fertilization. They may be the legacy of the El Niño drought, or alternatively, potentially reflect increased aridity due to long-term climate change. By investigating compositional changes, we increased not only our understanding of the ecology of tropical forests and their responses to large-scale disturbances, but also our ability to predict how future global change will impact some of the critical services provided by these important ecosystems.},
}
@article {pmid21659695,
year = {2011},
author = {Eliseyev, A and Moro, C and Costecalde, T and Torres, N and Gharbi, S and Mestais, C and Benabid, AL and Aksenova, T},
title = {Iterative N-way partial least squares for a binary self-paced brain-computer interface in freely moving animals.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046012},
doi = {10.1088/1741-2560/8/4/046012},
pmid = {21659695},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Behavior, Animal/physiology ; Calibration ; Electroencephalography ; Electrophysiological Phenomena ; *Least-Squares Analysis ; Models, Neurological ; Models, Statistical ; *Prosthesis Design ; Rats ; Reproducibility of Results ; *User-Computer Interface ; Wavelet Analysis ; },
abstract = {In this paper a tensor-based approach is developed for calibration of binary self-paced brain-computer interface (BCI) systems. In order to form the feature tensor, electrocorticograms, recorded during behavioral experiments in freely moving animals (rats), were mapped to the spatial-temporal-frequency space using the continuous wavelet transformation. An N-way partial least squares (NPLS) method is applied for tensor factorization and the prediction of a movement intention depending on neuronal activity. To cope with the huge feature tensor dimension, an iterative NPLS (INPLS) algorithm is proposed. Computational experiments demonstrated the good accuracy and robustness of INPLS. The algorithm does not depend on any prior neurophysiological knowledge and allows fully automatic system calibration and extraction of the BCI-related features. Based on the analysis of time intervals preceding the BCI events, the calibration procedure constructs a predictive model of control. The BCI system was validated by experiments in freely moving animals under conditions close to those in a natural environment.},
}
@article {pmid21659037,
year = {2011},
author = {Jackson, A and Fetz, EE},
title = {Interfacing with the computational brain.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {5},
pages = {534-541},
pmid = {21659037},
issn = {1558-0210},
support = {G0802195/MRC_/Medical Research Council/United Kingdom ; R37 NS012542/NS/NINDS NIH HHS/United States ; 086561/WT_/Wellcome Trust/United Kingdom ; R37 NS012542-38/NS/NINDS NIH HHS/United States ; /WT_/Wellcome Trust/United Kingdom ; },
mesh = {Arm/physiology ; Biomimetics ; Brain/*physiology ; Electromyography ; Feedback, Sensory ; Humans ; Mental Processes/physiology ; Movement/physiology ; Neurofeedback ; Neuronal Plasticity/physiology ; Prosthesis Design ; Sensation ; *User-Computer Interface ; },
abstract = {Neuroscience is just beginning to understand the neural computations that underlie our remarkable capacity to learn new motor tasks. Studies of natural movements have emphasized the importance of concepts such as dimensionality reduction within hierarchical levels of redundancy, optimization of behavior in the presence of sensorimotor noise and internal models for predictive control. These concepts also provide a framework for understanding the improvements in performance seen in myoelectric-controlled interface and brain-machine interface paradigms. Recent experiments reveal how volitional activity in the motor system combines with sensory feedback to shape neural representations and drives adaptation of behavior. By elucidating these mechanisms, a new generation of intelligent interfaces can be designed to exploit neural plasticity and restore function after neurological injury.},
}
@article {pmid21655253,
year = {2011},
author = {Wang, Y and Jung, TP},
title = {A collaborative brain-computer interface for improving human performance.},
journal = {PloS one},
volume = {6},
number = {5},
pages = {e20422},
pmid = {21655253},
issn = {1932-6203},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Motor Activity/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Electroencephalogram (EEG) based brain-computer interfaces (BCI) have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1) Event-related potentials (ERP) averaging, (2) Feature concatenating, and (3) Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right) was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC), which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.},
}
@article {pmid21654039,
year = {2011},
author = {Bleichner, MG and Vansteensel, MJ and Huiskamp, GM and Hermes, D and Aarnoutse, EJ and Ferrier, CH and Ramsey, NF},
title = {The effects of blood vessels on electrocorticography.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {044002},
doi = {10.1088/1741-2560/8/4/044002},
pmid = {21654039},
issn = {1741-2552},
mesh = {Algorithms ; Analysis of Variance ; Blood Vessels/anatomy &amp; histology/*physiology ; Cerebral Cortex/anatomy &amp; histology/blood supply/physiology ; Cerebrovascular Circulation/*physiology ; Craniotomy ; Data Interpretation, Statistical ; Electrodes, Implanted ; Electroencephalography/*methods/statistics &amp; numerical data ; Epilepsy/pathology/surgery ; Humans ; User-Computer Interface ; },
abstract = {Electrocorticography, primarily used in a clinical context, is becoming increasingly important for fundamental neuroscientific research, as well as for brain-computer interfaces. Recordings from these implanted electrodes have a number of advantages over non-invasive recordings in terms of band width, spatial resolution, smaller vulnerability to artifacts and overall signal quality. However, an unresolved issue is that signals vary greatly across electrodes. Here, we examine the effect of blood vessels lying between an electrode and the cortex on signals recorded from subdural grid electrodes. Blood vessels of different sizes cover extensive parts of the cortex causing variations in the electrode-cortex connection across grids. The power spectral density of electrodes located on the cortex and electrodes located on blood vessels obtained from eight epilepsy patients is compared. We find that blood vessels affect the power spectral density of the recorded signal in a frequency-band-specific way, in that frequencies between 30 and 70 Hz are attenuated the most. Here, the signal is attenuated on average by 30-40% compared to electrodes directly on the cortex. For lower frequencies this attenuation effect is less pronounced. We conclude that blood vessels influence the signal properties in a non-uniform manner.},
}
@article {pmid21654038,
year = {2011},
author = {Ludwig, KA and Miriani, RM and Langhals, NB and Marzullo, TC and Kipke, DR},
title = {Use of a Bayesian maximum-likelihood classifier to generate training data for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {4},
pages = {046009},
pmid = {21654038},
issn = {1741-2552},
support = {P41 EB002030/EB/NIBIB NIH HHS/United States ; P41 EB002030-15/EB/NIBIB NIH HHS/United States ; P41EB002030/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Artificial Intelligence ; *Bayes Theorem ; Behavior, Animal/physiology ; Cerebral Cortex/physiology ; Conditioning, Operant/physiology ; Electric Stimulation ; Electrodes, Implanted ; Electroencephalography ; Electrophysiological Phenomena ; Evoked Potentials, Auditory/physiology ; *Likelihood Functions ; Neurons/physiology ; Psychomotor Performance/physiology ; Rats ; Rats, Sprague-Dawley ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.},
}
@article {pmid21653209,
year = {2012},
author = {Chen, D and Li, N and Wan, P and Xiao, J and Liu, Y and Wang, X and Wang, Z},
title = {A novel procedure for treating canalicular obstruction by re-canaliculisation and bicanalicular intubation.},
journal = {The British journal of ophthalmology},
volume = {96},
number = {3},
pages = {366-369},
doi = {10.1136/bjo.2011.202200},
pmid = {21653209},
issn = {1468-2079},
mesh = {Adult ; Female ; Humans ; Intubation/*methods ; Lacrimal Duct Obstruction/*therapy ; Male ; Middle Aged ; *Nasolacrimal Duct ; Patient Satisfaction ; Reoperation ; Silicones ; Tears/metabolism ; Young Adult ; },
abstract = {AIM: The aim of this study was to evaluate a new procedure for treating canalicular obstruction by re-canaliculisation and bicanalicular intubation (RC-BCI).<br><br>METHODS: Thirty adult patients (32 eyes) with canalicular obstruction were treated with RC-BCI from September 2005 to December 2007 at Zhongshan Ophthalmic Centre (Guangzhou, China). Silicone tubes were left in place for 2-3 months and were removed when patients had relief by tearing. Patients were evaluated postoperatively by symptoms, lacrimal irrigation and satisfaction rate.<br><br>RESULTS: Mean follow-up time after tube removal was 21.5 (range 6-26) months. Twenty-six eyes (81.25%) had complete epiphora relief, two eyes (6.25%) had partial relief and four eyes (12.5%) had no improvement after the removal of the tubes. One eye (3.13%) had lower punctum splitting 2 months after the surgery. The overall satisfaction rate was 93.3% in 30 patients. No other complications occurred.<br><br>CONCLUSION: Our findings demonstrated that the RC-BCI was an effective procedure for treating canalicular obstruction with few complications.},
}
@article {pmid21647345,
year = {2011},
author = {Zander, TO and Lehne, M and Ihme, K and Jatzev, S and Correia, J and Kothe, C and Picht, B and Nijboer, F},
title = {A Dry EEG-System for Scientific Research and Brain-Computer Interfaces.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {53},
pmid = {21647345},
issn = {1662-453X},
abstract = {Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.},
}
@article {pmid21642032,
year = {2011},
author = {Cerutti, S and Baselli, G and Bianchi, A and Caiani, E and Contini, D and Cubeddu, R and Dercole, F and Rienzo, L and Liberati, D and Mainardi, L and Ravazzani, P and Rinaldi, S and Signorini, M and Torricelli, A},
title = {Biomedical signal and image processing.},
journal = {IEEE pulse},
volume = {2},
number = {3},
pages = {41-54},
doi = {10.1109/MPUL.2011.941522},
pmid = {21642032},
issn = {2154-2317},
mesh = {Algorithms ; *Biomedical Engineering ; *Diagnostic Imaging ; Electrodiagnosis ; Humans ; *Image Interpretation, Computer-Assisted ; *Image Processing, Computer-Assisted ; Models, Biological ; *Signal Processing, Computer-Assisted ; },
abstract = {Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.},
}
@article {pmid21629191,
year = {2011},
author = {Manckoundia, P and Mazen, E and Coste, AS and Somana, S and Marilier, S and Duez, JM and Camus, A and Popitean, L and Bador, J and Pfitzenmeyer, P},
title = {A case of meningitis due to Achromobacter xylosoxidans denitrificans 60 years after a cranial trauma.},
journal = {Medical science monitor : international medical journal of experimental and clinical research},
volume = {17},
number = {6},
pages = {CS63-5},
pmid = {21629191},
issn = {1643-3750},
mesh = {Achromobacter denitrificans/*physiology ; Aged, 80 and over ; Humans ; Male ; Meningitis/*microbiology ; Skull/*pathology ; Wounds and Injuries/*pathology ; },
abstract = {BACKGROUND: Achromobacter xylosoxidans (AX) is a non-fermentative aerobic gram-negative bacillus. It is an opportunistic pathogen and the causative agent of various infections. We report an original case of late posttraumatic meningitis due to AX denitrificans.<br><br>CASE REPORT: An 83-year-old man was hospitalized for acute headache, nausea and vomiting. The emergency brain computer tomography (CT) scan did not reveal any anomaly. In his medical history, there was an auditory injury due to a cranial trauma incurred in a skiing accident 60 years earlier. Cytobiochemical analysis of the cerebrospinal fluid (CSF) revealed increased levels of neutrophils and proteins. The CSF bacterial culture was positive: the Gram stain showed a gram-negative bacillus, oxidase + and catalase +, and the biochemical pattern using the API 20 NE strip revealed AX dentrificans. Late posttraumatic meningitis on a possible osteomeningeal breach was diagnosed even though the breach was not confirmed because the patient declined a second brain CT scan. The patient was successfully treated with meropenem.<br><br>CONCLUSIONS: This report demonstrates the importance of searching for unusual or atypical organisms when the clinician encounters meningitis in a particular context, as well as the importance of adequate follow-up of craniofacial traumas.},
}
@article {pmid21618054,
year = {2012},
author = {Samei, E and Majdi-Nasab, N and Dobbins, JT and McAdams, HP},
title = {Biplane correlation imaging: a feasibility study based on phantom and human data.},
journal = {Journal of digital imaging},
volume = {25},
number = {1},
pages = {137-147},
pmid = {21618054},
issn = {1618-727X},
support = {R01 CA080490/CA/NCI NIH HHS/United States ; R01 CA109074/CA/NCI NIH HHS/United States ; R01CA80490/CA/NCI NIH HHS/United States ; R21CA91806/CA/NCI NIH HHS/United States ; R01CA109074/CA/NCI NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Algorithms ; Artifacts ; False Positive Reactions ; Feasibility Studies ; Humans ; Lung Neoplasms/diagnostic imaging ; Middle Aged ; Pattern Recognition, Automated/methods ; *Phantoms, Imaging ; ROC Curve ; Radiographic Image Enhancement/*methods ; Radiographic Image Interpretation, Computer-Assisted/*methods ; Radiography, Thoracic/*methods ; Sampling Studies ; Solitary Pulmonary Nodule/*diagnostic imaging ; },
abstract = {The objective of this study was to implement and evaluate the performance of a biplane correlation imaging (BCI) technique aimed to reduce the effect of anatomic noise and improve the detection of lung nodules in chest radiographs. Seventy-one low-dose posterior-anterior images were acquired from an anthropomorphic chest phantom with 0.28° angular separations over a range of ±10° along the vertical axis within an 11 s interval. Similar data were acquired from 19 human subjects with institutional review board approval and informed consent. The data were incorporated into a computer-aided detection (CAD) algorithm in which suspect lesions were identified by examining the geometrical correlation of the detected signals that remained relatively constant against variable anatomic backgrounds. The data were analyzed to determine the effect of angular separation, and the overall sensitivity and false-positives for lung nodule detection. The best performance was achieved for angular separations of the projection pairs greater than 5°. Within that range, the technique provided an order of magnitude decrease in the number of false-positive reports when compared with CAD analysis of single-view images. Overall, the technique yielded ~1.1 false-positive per patient with an average sensitivity of 75%. The results indicated that the incorporation of angular information can offer a reduction in the number of false-positives without a notable reduction in sensitivity. The findings suggest that the BCI technique has the potential for clinical implementation as a cost-effective technique to improve the detection of subtle lung nodules with lowered rate of false-positives.},
}
@article {pmid21613587,
year = {2011},
author = {Witte, M},
title = {Role of local field potentials in encoding hand movement kinematics.},
journal = {Journal of neurophysiology},
volume = {106},
number = {4},
pages = {1601-1603},
doi = {10.1152/jn.00269.2011},
pmid = {21613587},
issn = {1522-1598},
mesh = {Action Potentials/*physiology ; Animals ; Evoked Potentials/*physiology ; Hand Strength/*physiology ; Motor Cortex/*physiology ; },
abstract = {How the brain orchestrates the musculoskeletal system to produce complex three-dimensional movements is still poorly understood. Despite first promising results in brain-machine interfaces that translate cortical activity to control output, there is an ongoing debate about which brain signals provide richest information related to movement planning and execution. Novel results by Bansal and colleagues (2011) now suggest that neuronal spiking and local field potentials jointly encode kinematics during skilled reach and grasp movements.},
}
@article {pmid21575185,
year = {2011},
author = {Merians, AS and Fluet, GG and Qiu, Q and Saleh, S and Lafond, I and Davidow, A and Adamovich, SV},
title = {Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {8},
number = {},
pages = {27},
pmid = {21575185},
issn = {1743-0003},
support = {R01 HD058301/HD/NICHD NIH HHS/United States ; HD58301/HD/NICHD NIH HHS/United States ; H133E050011//PHS HHS/United States ; },
mesh = {Adult ; Aged ; Arm/*physiology ; Biomechanical Phenomena ; Computer Simulation ; Data Interpretation, Statistical ; Female ; Fingers/*physiology ; Hand/physiology ; Humans ; Male ; Middle Aged ; Movement/*physiology ; Paresis/etiology/*rehabilitation ; *Robotics ; Stroke/complications ; Stroke Rehabilitation ; Treatment Outcome ; User-Computer Interface ; Video Games ; },
abstract = {BACKGROUND: Recovery of upper extremity function is particularly recalcitrant to successful rehabilitation. Robotic-assisted arm training devices integrated with virtual targets or complex virtual reality gaming simulations are being developed to deal with this problem. Neural control mechanisms indicate that reaching and hand-object manipulation are interdependent, suggesting that training on tasks requiring coordinated effort of both the upper arm and hand may be a more effective method for improving recovery of real world function. However, most robotic therapies have focused on training the proximal, rather than distal effectors of the upper extremity. This paper describes the effects of robotically-assisted, integrated upper extremity training.<br><br>METHODS: Twelve subjects post-stroke were trained for eight days on four upper extremity gaming simulations using adaptive robots during 2-3 hour sessions.<br><br>RESULTS: The subjects demonstrated improved proximal stability, smoothness and efficiency of the movement path. This was in concert with improvement in the distal kinematic measures of finger individuation and improved speed. Importantly, these changes were accompanied by a robust 16-second decrease in overall time in the Wolf Motor Function Test and a 24-second decrease in the Jebsen Test of Hand Function.<br><br>CONCLUSIONS: Complex gaming simulations interfaced with adaptive robots requiring integrated control of shoulder, elbow, forearm, wrist and finger movements appear to have a substantial effect on improving hemiparetic hand function. We believe that the magnitude of the changes and the stability of the patient's function prior to training, along with maintenance of several aspects of the gains demonstrated at retention make a compelling argument for this approach to training.},
}
@article {pmid21571004,
year = {2011},
author = {McFarland, DJ and Sarnacki, WA and Wolpaw, JR},
title = {Should the parameters of a BCI translation algorithm be continually adapted?.},
journal = {Journal of neuroscience methods},
volume = {199},
number = {1},
pages = {103-107},
pmid = {21571004},
issn = {1872-678X},
support = {R01 EB000856-09/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; *Analysis of Variance ; *Artificial Intelligence ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Discriminant Analysis ; Electroencephalography/instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Learning Curve ; Least-Squares Analysis ; Male ; *Man-Machine Systems ; Middle Aged ; Scalp/physiology ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.},
}
@article {pmid21566275,
year = {2011},
author = {Pan, J and Gao, X and Duan, F and Yan, Z and Gao, S},
title = {Enhancing the classification accuracy of steady-state visual evoked potential-based brain-computer interfaces using phase constrained canonical correlation analysis.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036027},
doi = {10.1088/1741-2560/8/3/036027},
pmid = {21566275},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Data Interpretation, Statistical ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Sensitivity and Specificity ; Statistics as Topic ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {In this study, a novel method of phase constrained canonical correlation analysis (p-CCA) is presented for classifying steady-state visual evoked potentials (SSVEPs) using multichannel electroencephalography (EEG) signals. p-CCA is employed to improve the performance of the SSVEP-based brain-computer interface (BCI) system using standard CCA. SSVEP response phases are estimated based on the physiologically meaningful apparent latency and are added as a reliable constraint into standard CCA. The results of EEG experiments involving 10 subjects demonstrate that p-CCA consistently outperforms standard CCA in classification accuracy. The improvement is up to 6.8% using 1-4 s data segments. The results indicate that the reliable measurement of phase information is of importance in SSVEP-based BCIs.},
}
@article {pmid21562364,
year = {2011},
author = {Pohlmeyer, EA and Wang, J and Jangraw, DC and Lou, B and Chang, SF and Sajda, P},
title = {Closing the loop in cortically-coupled computer vision: a brain-computer interface for searching image databases.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036025},
doi = {10.1088/1741-2560/8/3/036025},
pmid = {21562364},
issn = {1741-2552},
mesh = {*Artificial Intelligence ; *Database Management Systems ; *Databases, Factual ; Image Interpretation, Computer-Assisted/*methods ; Pattern Recognition, Automated/*methods ; *Radiology Information Systems ; *User-Computer Interface ; },
abstract = {We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.},
}
@article {pmid21562185,
year = {2011},
author = {Cherian, A and Krucoff, MO and Miller, LE},
title = {Motor cortical prediction of EMG: evidence that a kinetic brain-machine interface may be robust across altered movement dynamics.},
journal = {Journal of neurophysiology},
volume = {106},
number = {2},
pages = {564-575},
pmid = {21562185},
issn = {1522-1598},
support = {NS-048845/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Electromyography/*methods ; Haplorhini ; Kinetics ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Predictive Value of Tests ; Random Allocation ; },
abstract = {During typical movements, signals related to both the kinematics and kinetics of movement are mutually correlated, and each is correlated to some extent with the discharge of neurons in the primary motor cortex (M1). However, it is well known, if not always appreciated, that causality cannot be inferred from correlations. Although these mutual correlations persist, their nature changes with changing postural or dynamical conditions. Under changing conditions, only signals directly controlled by M1 can be expected to maintain a stable relationship with its discharge. If one were to rely on noncausal correlations for a brain-machine interface, its generalization across conditions would likely suffer. We examined this effect, using multielectrode recordings in M1 as input to linear decoders of both end point kinematics (position and velocity) and proximal limb myoelectric signals (EMG) during reaching. We tested these decoders across tasks that altered either the posture of the limb or the end point forces encountered during movement. Within any given task, the accuracy of the kinematic predictions tended to be somewhat better than the EMG predictions. However, when we used the decoders developed under one task condition to predict the signals recorded under different postural or dynamical conditions, only the EMG decoders consistently generalized well. Our results support the view that M1 discharge is more closely related to kinetic variables like EMG than it is to limb kinematics. These results suggest that brain-machine interface applications using M1 to control kinetic variables may prove to be more successful than the more standard kinematic approach.},
}
@article {pmid21561040,
year = {2010},
author = {Jain, N},
title = {Brain-machine interface: the future is now.},
journal = {The National medical journal of India},
volume = {23},
number = {6},
pages = {321-323},
pmid = {21561040},
issn = {0970-258X},
mesh = {Brain/*physiology ; Electroencephalography/ethics/*trends ; Forecasting ; Humans ; *Man-Machine Systems ; Robotics/ethics/trends ; Self-Help Devices/ethics/trends ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
}
@article {pmid21560336,
year = {2011},
author = {Zhou, L and Zhang, HY},
title = {[The study on local field potentials in rat's primary motor cortex during pressing paddle behavior].},
journal = {Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology},
volume = {27},
number = {1},
pages = {37-40},
pmid = {21560336},
issn = {1000-6834},
mesh = {Animals ; Brain-Computer Interfaces ; Evoked Potentials, Motor/*physiology ; Male ; Microelectrodes ; Motor Cortex/*physiology ; Movement/*physiology ; Rats ; Rats, Sprague-Dawley ; },
abstract = {OBJECTIVE: The local field potential (LFP) is a summation of dendritic potentials. The main objective of the present work is to view the features of LFP in M1 during the experimental rat pushed a paddle with the right forelimb.<br><br>METHODS: Four rats were trained to press a paddle with the right forelimb for water. Then Two bundle micro-electrode with four channels were implanted into the rat's left and right primary motor cortex (M1)(AP + 3.0 mm, ML +/- 1.6 mm, H-1.6 mm) with stereotaxic apparatus. After three days recovery, 8-channel Deep-EEG and the pulse signal of paddle pressed were recorded during the rats were in operant chamber, and at the same time, the behavior were also recorded with video recorder.<br><br>RESULTS: The LFP in left M1 were defined as the substance between two channel deep-EEG. It is found that low frequency, high amplitude signal appear aligned with the paddle pressed pulse signals. With threshold detect method, about 80% press-paddle behavior could be detected.<br><br>CONCLUSION: The result indicates that LFPs in this position in M1 are relative to forelimb's movement, and a powerful brain-computer interface system maybe developed with the LFPs.},
}
@article {pmid21559019,
year = {2011},
author = {Jerevall, PL and Ma, XJ and Li, H and Salunga, R and Kesty, NC and Erlander, MG and Sgroi, DC and Holmlund, B and Skoog, L and Fornander, T and Nordenskjöld, B and Stål, O},
title = {Prognostic utility of HOXB13:IL17BR and molecular grade index in early-stage breast cancer patients from the Stockholm trial.},
journal = {British journal of cancer},
volume = {104},
number = {11},
pages = {1762-1769},
pmid = {21559019},
issn = {1532-1827},
support = {R01 CA112021/CA/NCI NIH HHS/United States ; },
mesh = {Biomarkers, Tumor/analysis ; Breast Neoplasms/*diagnosis/drug therapy/mortality/pathology ; Early Detection of Cancer ; Female ; Homeodomain Proteins/*analysis ; Humans ; Neoplasm Metastasis ; Neoplasms, Hormone-Dependent/diagnosis ; Postmenopause ; Prognosis ; Randomized Controlled Trials as Topic ; Receptors, Interleukin/*analysis ; Receptors, Interleukin-17 ; Reverse Transcriptase Polymerase Chain Reaction ; Risk Assessment ; Sweden ; Tamoxifen/therapeutic use ; },
abstract = {BACKGROUND: A dichotomous index combining two gene expression assays, HOXB13:IL17BR (H:I) and molecular grade index (MGI), was developed to assess risk of recurrence in breast cancer patients. The study objective was to demonstrate the prognostic utility of the combined index in early-stage breast cancer.<br><br>METHODS: In a blinded retrospective analysis of 588 ER-positive tamoxifen-treated and untreated breast cancer patients from the randomised prospective Stockholm trial, H:I and MGI were measured using real-time RT-PCR. Association with patient outcome was evaluated by Kaplan-Meier analysis and Cox proportional hazard regression. A continuous risk index was developed using Cox modelling.<br><br>RESULTS: The dichotomous H:I+MGI was significantly associated with distant recurrence and breast cancer death. The >50% of tamoxifen-treated patients categorised as low-risk had <3% 10-year distant recurrence risk. A continuous risk model (Breast Cancer Index (BCI)) was developed with the tamoxifen-treated group and the prognostic performance tested in the untreated group was 53% of patients categorised as low risk with an 8.3% 10-year distant recurrence risk.<br><br>CONCLUSION: Retrospective analysis of this randomised, prospective trial cohort validated the prognostic utility of H:I+MGI and was used to develop and test a continuous risk model that enables prediction of distant recurrence risk at the patient level.},
}
@article {pmid21555849,
year = {2011},
author = {Fins, JJ and Schlaepfer, TE and Nuttin, B and Kubu, CS and Galert, T and Sturm, V and Merkel, R and Mayberg, HS},
title = {Ethical guidance for the management of conflicts of interest for researchers, engineers and clinicians engaged in the development of therapeutic deep brain stimulation.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {033001},
doi = {10.1088/1741-2560/8/3/033001},
pmid = {21555849},
issn = {1741-2552},
mesh = {Biomedical Engineering/*ethics ; Biomedical Research/*ethics ; *Conflict of Interest ; Deep Brain Stimulation/*ethics ; Humans ; Internationality ; Medical Staff/*ethics ; },
abstract = {The clinical promise of deep brain stimulation (DBS) for neuropsychiatric conditions is coupled with the potential for ethical conflicts of interest because the work is so heavily reliant upon collaborations between academia, industry and the clinic. To foster transparency and public trust, we offer ethical guidance for the management of conflicts of interest in the conduct of DBS research and practice so that this nascent field can better balance competing goods and engineer new and better strategies for the amelioration of human suffering. We also hope that our ethical analysis will be of relevance to those working with other related neuroprosthetic devices, such brain-computer interfaces and neural arrays, which naturally share many of the same concerns.},
}
@article {pmid21555847,
year = {2011},
author = {Volosyak, I},
title = {SSVEP-based Bremen-BCI interface--boosting information transfer rates.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036020},
doi = {10.1088/1741-2560/8/3/036020},
pmid = {21555847},
issn = {1741-2552},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP-based Bremen BCI system, which allowed one of the subjects in an online experiment to reach a peak information transfer rate (ITR) of 124 bit min(-1). It is worth mentioning that this ITR value is higher than all values previously published in the literature for any kind of BCI paradigm.},
}
@article {pmid21543839,
year = {2011},
author = {Rouse, AG and Stanslaski, SR and Cong, P and Jensen, RM and Afshar, P and Ullestad, D and Gupta, R and Molnar, GF and Moran, DW and Denison, TJ},
title = {A chronic generalized bi-directional brain-machine interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036018},
pmid = {21543839},
issn = {1741-2552},
support = {R01 EB009103/EB/NIBIB NIH HHS/United States ; R01 EB009103-03/EB/NIBIB NIH HHS/United States ; R01EB009103/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; Electric Stimulation Therapy/*instrumentation ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Parkinson Disease/diagnosis/*physiopathology/*rehabilitation ; *Prostheses and Implants ; Therapy, Computer-Assisted/*instrumentation ; },
abstract = {A bi-directional neural interface (NI) system was designed and prototyped by incorporating a novel neural recording and processing subsystem into a commercial neural stimulator architecture. The NI system prototype leverages the system infrastructure from an existing neurostimulator to ensure reliable operation in a chronic implantation environment. In addition to providing predicate therapy capabilities, the device adds key elements to facilitate chronic research, such as four channels of electrocortigram/local field potential amplification and spectral analysis, a three-axis accelerometer, algorithm processing, event-based data logging, and wireless telemetry for data uploads and algorithm/configuration updates. The custom-integrated micropower sensor and interface circuits facilitate extended operation in a power-limited device. The prototype underwent significant verification testing to ensure reliability, and meets the requirements for a class CF instrument per IEC-60601 protocols. The ability of the device system to process and aid in classifying brain states was preclinically validated using an in vivo non-human primate model for brain control of a computer cursor (i.e. brain-machine interface or BMI). The primate BMI model was chosen for its ability to quantitatively measure signal decoding performance from brain activity that is similar in both amplitude and spectral content to other biomarkers used to detect disease states (e.g. Parkinson's disease). A key goal of this research prototype is to help broaden the clinical scope and acceptance of NI techniques, particularly real-time brain state detection. These techniques have the potential to be generalized beyond motor prosthesis, and are being explored for unmet needs in other neurological conditions such as movement disorders, stroke and epilepsy.},
}
@article {pmid21541307,
year = {2011},
author = {Takano, K and Hata, N and Kansaku, K},
title = {Towards intelligent environments: an augmented reality-brain-machine interface operated with a see-through head-mount display.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {60},
pmid = {21541307},
issn = {1662-453X},
abstract = {The brain-machine interface (BMI) or brain-computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR-BMI with both the HMD and LCD monitor. Our results indicate that AR-BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments.},
}
@article {pmid21541256,
year = {2011},
author = {Meacham, KW and Guo, L and Deweerth, SP and Hochman, S},
title = {Selective stimulation of the spinal cord surface using a stretchable microelectrode array.},
journal = {Frontiers in neuroengineering},
volume = {4},
number = {},
pages = {5},
pmid = {21541256},
issn = {1662-6443},
support = {R01 EB000786/EB/NIBIB NIH HHS/United States ; T32 GM008169/GM/NIGMS NIH HHS/United States ; },
abstract = {By electrically stimulating the spinal cord, it is possible to activate functional populations of neurons that modulate motor and sensory function. One method for accessing these neurons is via their associated axons, which project as functionally segregated longitudinal columns of white-matter funiculi (i.e., spinal tracts). To stimulate spinal tracts without penetrating the cord, we have recently developed technology that enables close-proximity, multi-electrode contact with the spinal cord surface. Our stretchable microelectrode arrays (sMEAs) are fabricated using an elastomer polydimethylsiloxane substrate and can be wrapped circumferentially around the spinal cord to optimize electrode contact. Here, sMEAs were used to stimulate the surfaces of rat spinal cords maintained in vitro, and their ability to selectively activate axonal surface tracts was compared to rigid bipolar tungsten microelectrodes pressed firmly onto the cord surface. Along dorsal column tracts, the axonal response to sMEA stimulation was compared to that evoked by rigid microelectrodes through measurement of their evoked axonal compound action potentials (CAPs). Paired t-tests failed to reveal significant differences between the sMEA's and the rigid microelectrode's stimulus resolution, or in their ranges of evoked CAP conduction velocities. Additionally, dual-site stimulation using sMEA electrodes recruited spatially distinct populations of spinal axons. Site-specific stimulation of the ventrolateral funiculus - a tract capable of evoking locomotor-like activity - recruited ventral root efferent activity that spanned several spinal segments. These findings indicate that the sMEA stimulates the spinal cord surface with selectivity similar to that of rigid microelectrodes, while possessing potential advantages concerning circumferential contact and mechanical compatibility with the cord surface.},
}
@article {pmid21534845,
year = {2011},
author = {Huggins, JE and Wren, PA and Gruis, KL},
title = {What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis.},
journal = {Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases},
volume = {12},
number = {5},
pages = {318-324},
pmid = {21534845},
issn = {1471-180X},
support = {R21 HD054913/HD/NICHD NIH HHS/United States ; R21 HD054913-01A2/HD/NICHD NIH HHS/United States ; R21 HD054913-02/HD/NICHD NIH HHS/United States ; },
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*psychology/*rehabilitation ; Communication Devices for People with Disabilities/psychology/*statistics &amp; numerical data ; Data Collection/methods ; Female ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; *Patient Satisfaction ; Reaction Time/physiology ; *User-Computer Interface ; },
abstract = {Universal design principles advocate inclusion of end users in every design stage, including research and development. Brain-computer interfaces (BCIs) have long been described as potential tools to enable people with amyotrophic lateral sclerosis (ALS) to operate technology without moving. Therefore the objective of the current study is to determine the opinions and priorities of people with ALS regarding BCI design. This information will guide BCIs in development to meet end-user needs. A telephone survey was undertaken of 61 people with ALS from the University of Michigan's Motor Neuron Disease Clinic. With regard to BCI design, participants prioritized accuracy of command identification of at least 90% (satisfying 84% of respondents), speed of operation comparable to at least 15-19 letters per minute (satisfying 72%), and accidental exits from a standby mode not more than once every 2-4 h (satisfying 84%). While 84% of respondents would accept using an electrode cap, 72% were willing to undergo outpatient surgery and 41% to undergo surgery with a short hospital stay in order to obtain a BCI. In conclusion, people with ALS expressed a strong interest in obtaining BCIs, but current BCIs do not yet provide desired BCI performance.},
}
@article {pmid21511526,
year = {2011},
author = {Friedrich, EV and Scherer, R and Sonnleitner, K and Neuper, C},
title = {Impact of auditory distraction on user performance in a brain-computer interface driven by different mental tasks.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {10},
pages = {2003-2009},
doi = {10.1016/j.clinph.2011.03.019},
pmid = {21511526},
issn = {1872-8952},
mesh = {Acoustic Stimulation/*methods ; Adult ; Attention/*physiology ; Brain/*physiology ; Female ; Humans ; Male ; Psychomotor Performance/*physiology ; Thinking/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: Mental imagery-based brain-computer interface (BCI)-protocols mostly allow users to focus on the task without external interferences. Environmental stimuli, however, may hamper users' ability to generate proper brain activity patterns. The aim of this study was to investigate whether users are able to retain satisfactory BCI control during auditory distraction, and whether distinct mental tasks are affected differently from auditory distraction.<br><br>METHODS: Twelve participants controlled a 4-class BCI with the mental tasks word association, mental subtraction, spatial navigation and motor imagery by modulation of EEG frequency bands in 10 sessions. Simultaneously to the imagery task, users had to either ignore all tones (passive distraction) that were presented according to an oddball paradigm or react upon the target tone (active distraction).<br><br>RESULTS: Passive distraction led to an increased user performance compared to active distraction and no distraction condition. Differences between motor imagery and the other three mental tasks in performance were reflected in the P300 amplitude, latency and reaction time and thus might indicate differences in workload.<br><br>CONCLUSION: Auditory distraction had no adverse effect on the BCI performance of the examined mental task.<br><br>SIGNIFICANCE: Our results are encouraging for real-world application as participants succeeded in operating the 4-class BCI during auditory distraction.},
}
@article {pmid21511006,
year = {2011},
author = {Shishkin, SL and Ganin, IP and Kaplan, AY},
title = {Event-related potentials in a moving matrix modification of the P300 brain-computer interface paradigm.},
journal = {Neuroscience letters},
volume = {496},
number = {2},
pages = {95-99},
doi = {10.1016/j.neulet.2011.03.089},
pmid = {21511006},
issn = {1872-7972},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Motion Perception/*physiology ; Photic Stimulation/*methods ; Reaction Time/physiology ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In the standard design of the brain-computer interfaces (BCI) based on the P300 component of the event-related potentials (ERP), target and non-target stimuli are presented at fixed positions in a motionless matrix. Can we let this matrix be moving (e.g., if attached to a robot) without loosing the efficiency of BCI? We assessed changes of the positive peak at Pz in the time interval 300-500 ms after the stimulus onset (P300) and the negative peak at the occipital electrodes in the range 140-240 ms (N1), both important for the operation of the P300 BCI, during fixating a target cell of a moving matrix in healthy participants (n=12). N1 amplitude in the difference (target-non-target) waveforms decreased with the velocity, although remained high (M=-4.3, SD=2.1) even at highest velocity (20°/s). In general, the amplitudes and latencies of these ERP components were remarkably stable in studied types of matrix movement and all velocities of horizontal movement (5, 10 and 20°/s) comparing to matrix in fixed position. These data suggest that, for the users controlling their gaze, the P300 BCI design can be extended to modifications requiring stimuli matrix motion.},
}
@article {pmid21508492,
year = {2011},
author = {Royer, AS and Rose, ML and He, B},
title = {Goal selection versus process control while learning to use a brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036012},
pmid = {21508492},
issn = {1741-2552},
support = {T32 EB008389-02/EB/NIBIB NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; R01 EB007920-03/EB/NIBIB NIH HHS/United States ; R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EB006433-03/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; R01 EB006433-02/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Decision Making/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Female ; *Goals ; Humans ; Learning/*physiology ; Male ; *Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {A brain-computer interface (BCI) can be used to accomplish a task without requiring motor output. Two major control strategies used by BCIs during task completion are process control and goal selection. In process control, the user exerts continuous control and independently executes the given task. In goal selection, the user communicates their goal to the BCI and then receives assistance executing the task. A previous study has shown that goal selection is more accurate and faster in use. An unanswered question is, which control strategy is easier to learn? This study directly compares goal selection and process control while learning to use a sensorimotor rhythm-based BCI. Twenty young healthy human subjects were randomly assigned either to a goal selection or a process control-based paradigm for eight sessions. At the end of the study, the best user from each paradigm completed two additional sessions using all paradigms randomly mixed. The results of this study were that goal selection required a shorter training period for increased speed, accuracy, and information transfer over process control. These results held for the best subjects as well as in the general subject population. The demonstrated characteristics of goal selection make it a promising option to increase the utility of BCIs intended for both disabled and able-bodied users.},
}
@article {pmid21508491,
year = {2011},
author = {Slutzky, MW and Jordan, LR and Lindberg, EW and Lindsay, KE and Miller, LE},
title = {Decoding the rat forelimb movement direction from epidural and intracortical field potentials.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036013},
pmid = {21508491},
issn = {1741-2552},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; K08 NS060223-05/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Dura Mater/physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Somatosensory/physiology ; Forelimb/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Rats ; Somatosensory Cortex/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) use signals from the brain to control a device such as a computer cursor. Various types of signals have been used as BMI inputs, from single-unit action potentials to scalp potentials. Recently, intermediate-level signals such as subdural field potentials have also shown promise. These different signal types are likely to provide different amounts of information, but we do not yet know what signal types are necessary to enable a particular BMI function, such as identification of reach target location, control of a two-dimensional cursor or the dynamics of limb movement. Here we evaluated the performance of field potentials, measured either intracortically (local field potentials, LFPs) or epidurally (epidural field potential, EFPs), in terms of the ability to decode reach direction. We trained rats to move a joystick with their forepaw to control the motion of a sipper tube to one of the four targets in two dimensions. We decoded the forelimb reach direction from the field potentials using linear discriminant analysis. We achieved a mean accuracy of 69 ± 3% with EFPs and 57 ± 2% with LFPs, both much better than chance. Signal quality remained good up to 13 months after implantation. This suggests that using epidural signals could provide BMI inputs of high quality with less risk to the patient than using intracortical recordings.},
}
@article {pmid21501353,
year = {2011},
author = {Jin, H and Pan, N and Mou, Y and Wang, B and Liu, P},
title = {Long-term effect of interferon treatment on the progression of chronic hepatitis B: Bayesian meta-analysis and meta-regression.},
journal = {Hepatology research : the official journal of the Japan Society of Hepatology},
volume = {41},
number = {6},
pages = {512-523},
doi = {10.1111/j.1872-034X.2011.00801.x},
pmid = {21501353},
issn = {1872-034X},
abstract = {AIM: The long-term effects of interferon treatment on the progression of chronic hepatitis B (CHB) have been studied extensively, but its true clinical benefits and the predictors of its efficacy remain unclear.<br><br>METHODS: &#x2002; A systematic published work search was undertaken. Eligible studies included those with interferon treatment and control groups, and with liver cirrhosis (LC), hepatocellular carcinoma (HCC) or death as main outcomes. Bayesian meta-analysis and meta-regression were performed to assess associations between interferon treatment and disease progression, and the impacts of potential covariates.<br><br>RESULTS: &#x2002; Eleven articles met the inclusion criteria. LC, HCC and death were end-points in four, nine and six studies, respectively. In all studies, interferon was associated with significant preventive effects on HCC according to the DerSimonian-Laird method (relative risk [RR]&#x2003;=&#x2003;0.470, 95% confidence interval [CI]&#x2003;=&#x2003;0.260-0.850) and Bayesian method adjusting underlying risk (RR&#x2003;=&#x2003;0.249, 95% Bayesian credible intervals [BCI]&#x2003;=&#x2003;0.049-0.961), but not according to Bayesian meta-analysis (RR&#x2003;=&#x2003;0.274, 95% BCI&#x2003;=&#x2003;0.059-1.031); and it showed similar effects in death but not in LC. However, most of the high-quality studies never revealed protective benefits in these end-points. Bayesian meta-regression identified Asian ethnicity in death, higher hepatitis B e-antigen (HBeAg) seroconversion rate or positivity rate, and length of follow up (&#x2264;5&#x2003;years) in HCC as potentially protective against disease progression. Subgroup analysis confirmed similar effects from these factors in HCC and death.<br><br>CONCLUSION: &#x2002; Additional evidence is needed to support the role of interferon in delaying CHB progression.},
}
@article {pmid21499255,
year = {2011},
author = {Ganguly, K and Dimitrov, DF and Wallis, JD and Carmena, JM},
title = {Reversible large-scale modification of cortical networks during neuroprosthetic control.},
journal = {Nature neuroscience},
volume = {14},
number = {5},
pages = {662-667},
pmid = {21499255},
issn = {1546-1726},
support = {R01 NS021135/NS/NINDS NIH HHS/United States ; P01 NS040813/NS/NINDS NIH HHS/United States ; NS21135/NS/NINDS NIH HHS/United States ; R01 DA019028/DA/NIDA NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; R56 NS021135/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Behavior, Animal ; Cerebral Cortex/cytology/*physiology ; Electromyography/methods ; Macaca mulatta ; Male ; Movement/*physiology ; Nerve Net/*physiology ; Neurons/physiology ; Online Systems ; Orientation/physiology ; *Prostheses and Implants ; Psychomotor Performance/*physiology ; Reward ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.},
}
@article {pmid21497931,
year = {2011},
author = {Wang, W and Wainstein, R and Freixa, X and Dzavik, V and Fyles, A},
title = {Quantitative coronary angiography findings of patients who received previous breast radiotherapy.},
journal = {Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology},
volume = {100},
number = {2},
pages = {184-188},
doi = {10.1016/j.radonc.2011.03.009},
pmid = {21497931},
issn = {1879-0887},
mesh = {Breast Neoplasms/*radiotherapy ; *Coronary Angiography ; Coronary Artery Disease/*etiology ; Female ; Humans ; Middle Aged ; Radiation Injuries/*etiology ; Radiotherapy/adverse effects ; },
abstract = {PURPOSE: To compare the coronary angiographic findings between patients who had previous left sided versus right sided breast cancer radiotherapy (RT).<br><br>MATERIALS AND METHODS: Between 1995 and 2009, 12,696 patients who underwent curative RT for breast cancer at Princess Margaret Hospital were screened to assess if they had been investigated with a post-RT coronary angiogram. Two cardiologists, blinded to the laterality of radiation treatment, assessed all angiograms and measured the percentage of stenotic lesions, the mean diameters of each segment of the left anterior descending artery (LAD) and the right coronary artery (RCA) using quantitative coronary angiography (QCA).<br><br>RESULTS: Ninety-one patients were included, 49 patients with left sided RT and 42 with right sided RT. The median time from RT to coronary angiogram was 4.2 years (range: 22 days-16.9 years). Seventeen patients (35%) in the left sided RT group and 17 (40%) in the right sided RT group needed coronary revascularization (percutaneous coronary intervention or by-pass surgery). The LAD territory was revascularized in 12 (24%) and 11 (26%) patients, respectively. The proportion of clinically significant stenoses, degree of stenoses and mean vessel diameter were not significantly different between the two groups. In 33 patients who had coronary angiograms >5 years after breast RT (17 left-sided and 16 right-sided), the only statistically significant finding was marginally narrower mid RCA segments among those who had right sided RT: 2.52 mm versus 2.92 mm (P=0.039).<br><br>CONCLUSIONS: In our patients, left sided breast cancer RT did not increase the risk of coronary artery disease within the first few years, when compared to right sided RT. However, with the limitation of short duration between radiotherapy and coronary angiogram, late development of coronary artery stenoses 10-15 years after left sided RT could not be excluded.},
}
@article {pmid21493978,
year = {2011},
author = {Bradberry, TJ and Gentili, RJ and Contreras-Vidal, JL},
title = {Fast attainment of computer cursor control with noninvasively acquired brain signals.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036010},
doi = {10.1088/1741-2560/8/3/036010},
pmid = {21493978},
issn = {1741-2552},
support = {P01 HD064653-01/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; *Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {Brain-computer interface (BCI) systems are allowing humans and non-human primates to drive prosthetic devices such as computer cursors and artificial arms with just their thoughts. Invasive BCI systems acquire neural signals with intracranial or subdural electrodes, while noninvasive BCI systems typically acquire neural signals with scalp electroencephalography (EEG). Some drawbacks of invasive BCI systems are the inherent risks of surgery and gradual degradation of signal integrity. A limitation of noninvasive BCI systems for two-dimensional control of a cursor, in particular those based on sensorimotor rhythms, is the lengthy training time required by users to achieve satisfactory performance. Here we describe a novel approach to continuously decoding imagined movements from EEG signals in a BCI experiment with reduced training time. We demonstrate that, using our noninvasive BCI system and observational learning, subjects were able to accomplish two-dimensional control of a cursor with performance levels comparable to those of invasive BCI systems. Compared to other studies of noninvasive BCI systems, training time was substantially reduced, requiring only a single session of decoder calibration (∼ 20 min) and subject practice (∼ 20 min). In addition, we used standardized low-resolution brain electromagnetic tomography to reveal that the neural sources that encoded observed cursor movement may implicate a human mirror neuron system. These findings offer the potential to continuously control complex devices such as robotic arms with one's mind without lengthy training or surgery.},
}
@article {pmid21493037,
year = {2011},
author = {Yoon, JW and Roberts, SJ and Dyson, M and Gan, JQ},
title = {Bayesian inference for an adaptive Ordered Probit model: an application to Brain Computer Interfacing.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {24},
number = {7},
pages = {726-734},
doi = {10.1016/j.neunet.2011.03.019},
pmid = {21493037},
issn = {1879-2782},
mesh = {Algorithms ; *Bayes Theorem ; *Brain ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper proposes an algorithm for adaptive, sequential classification in systems with unknown labeling errors, focusing on the biomedical application of Brain Computer Interfacing (BCI). The method is shown to be robust in the presence of label and sensor noise. We focus on the inference and prediction of target labels under a nonlinear and non-Gaussian model. In order to handle missing or erroneous labeling, we model observed labels as a noisy observation of a latent label set with multiple classes (≥ 2). Whilst this paper focuses on the method's application to BCI systems, the algorithm has the potential to be applied to many application domains in which sequential missing labels are to be imputed in the presence of uncertainty. This dynamic classification algorithm combines an Ordered Probit model and an Extended Kalman Filter (EKF). The EKF estimates the parameters of the Ordered Probit model sequentially with time. We test the performance of the classification approach by processing synthetic datasets and real experimental EEG signals with multiple classes (2, 3 and 4 labels) for a Brain Computer Interfacing (BCI) experiment.},
}
@article {pmid21488814,
year = {2011},
author = {Nam, Y and Wheeler, BC},
title = {In vitro microelectrode array technology and neural recordings.},
journal = {Critical reviews in biomedical engineering},
volume = {39},
number = {1},
pages = {45-61},
doi = {10.1615/critrevbiomedeng.v39.i1.40},
pmid = {21488814},
issn = {0278-940X},
mesh = {Animals ; Cells, Cultured ; Cytological Techniques/*instrumentation ; Electrophysiological Phenomena ; Mice ; *Microelectrodes ; Neurons/*physiology ; Rats ; Signal Processing, Computer-Assisted ; Tissue Array Analysis/*instrumentation ; },
abstract = {In vitro microelectrode array (MEA) technology has evolved into a widely used and effective methodology to study cultured neural networks. An MEA forms a unique electrical interface with the cultured neurons in that neurons are directly grown on top of the electrode (neuron-on-electrode configuration). Theoretical models and experimental results suggest that physical configuration and biological environments of the cell-electrode interface play a key role in the outcome of neural recordings, such as yield of recordings, signal shape, and signal-to-noise ratio. Recent interdisciplinary approaches have shown that MEA performance can be enhanced through novel nanomaterials, structures, surface chemistry, and biotechnology. In vitro and in vivo neural interfaces share some common factors, and in vitro neural interface issues can be extended to solve in vivo neural interface problems of brain-machine interface or neuromodulation techniques.},
}
@article {pmid21488812,
year = {2011},
author = {Lega, BC and Serruya, MD and Zaghloul, KA},
title = {Brain-machine interfaces: electrophysiological challenges and limitations.},
journal = {Critical reviews in biomedical engineering},
volume = {39},
number = {1},
pages = {5-28},
doi = {10.1615/critrevbiomedeng.v39.i1.20},
pmid = {21488812},
issn = {0278-940X},
mesh = {Cerebral Cortex/physiology ; *Deep Brain Stimulation ; Electroencephalography ; Humans ; *Man-Machine Systems ; *Neural Prostheses ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-machine interfaces (BMI) seek to directly communicate with the human nervous system in order to diagnose and treat intrinsic neurological disorders. While the first generation of these devices has realized significant clinical successes, they often rely on gross electrical stimulation using empirically derived parameters through open-loop mechanisms of action that are not yet fully understood. Their limitations reflect the inherent challenge in developing the next generation of these devices. This review identifies lessons learned from the first generation of BMI devices (chiefly deep brain stimulation), identifying key problems for which the solutions will aid the development of the next generation of technologies. Our analysis examines four hypotheses for the mechanism by which brain stimulation alters surrounding neurophysiologic activity. We then focus on motor prosthetics, describing various approaches to overcoming the problems of decoding neural signals. We next turn to visual prosthetics, an area for which the challenges of signal coding to match neural architecture has been partially overcome. Finally, we close with a review of cortical stimulation, examining basic principles that will be incorporated into the design of future devices. Throughout the review, we relate the issues of each specific topic to the common thread of BMI research: translating new knowledge of network neuroscience into improved devices for neuromodulation.},
}
@article {pmid21485182,
year = {2011},
author = {Wang, M and Song, Y and Suen, J and Zhao, Y and Jia, A and Zhu, J},
title = {[A telemetery system for neural signal acquiring and processing].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {28},
number = {1},
pages = {49-53},
pmid = {21485182},
issn = {1001-5515},
mesh = {Animals ; Behavior, Animal/*physiology ; Brain/*physiology ; Electroencephalography/*methods ; Microelectrodes ; Rats ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; User-Computer Interface ; },
abstract = {Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.},
}
@article {pmid21478575,
year = {2011},
author = {Thomas, KP and Guan, C and Lau, CT and Vinod, AP and Ang, KK},
title = {Adaptive tracking of discriminative frequency components in electroencephalograms for a robust brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036007},
doi = {10.1088/1741-2560/8/3/036007},
pmid = {21478575},
issn = {1741-2552},
mesh = {*Algorithms ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Wavelet Analysis ; },
abstract = {In an electroencephalogram (EEG)-based brain-computer interface (BCI), motor imagery has been successfully used as a communication strategy. Motor imagery causes detectable amplitude changes in certain frequency bands of EEGs, which are dubbed event-related desynchronization\synchronization. The frequency components that give effective discrimination between different types of motor imagery are subject specific and identification of these subject-specific discriminative frequency components (DFCs) is important for the accurate classification of motor imagery activities. In this paper, we propose a new method to estimate the DFC using the Fisher criterion and investigate the variability of these DFCs over multiple sessions of EEG recording. Observing the variability of DFC over sessions in the analysis, a new BCI approach called the Adaptively Weighted Spectral-Spatial Patterns (AWSSP) algorithm is proposed. AWSSP tracks the variation in DFC over time adaptively based on the deviation of discriminative weight values of frequency components. The classification performance of the proposed AWSSP is compared with a static BCI approach that employs fixed DFCs. In the offline and online experiments, AWSSP offers better classification performance than the static approach, emphasizing the significance of tracking the variability of DFCs in EEGs for developing robust motor imagery-based BCI systems. A study of the effect of feedback on the variation in DFCs is also performed in online experiments and it is found that the presence of visual feedback results in increased variation in DFCs.},
}
@article {pmid21474878,
year = {2011},
author = {Gomez-Rodriguez, M and Peters, J and Hill, J and Schölkopf, B and Gharabaghi, A and Grosse-Wentrup, M},
title = {Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036005},
doi = {10.1088/1741-2560/8/3/036005},
pmid = {21474878},
issn = {1741-2552},
mesh = {Brain/*physiology ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Feedback, Physiological/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Robotics/methods ; Touch/*physiology ; User-Computer Interface ; },
abstract = {The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.},
}
@article {pmid21474877,
year = {2011},
author = {Jin, J and Allison, BZ and Sellers, EW and Brunner, C and Horki, P and Wang, X and Neuper, C},
title = {An adaptive P300-based control system.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036006},
pmid = {21474877},
issn = {1741-2552},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; 1 R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; 1 R21 DC010470-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Adaptation, Physiological/*physiology ; Adult ; Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Feedback, Physiological/*physiology ; Female ; Humans ; Male ; Task Performance and Analysis ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {An adaptive P300 brain-computer interface (BCI) using a 12 × 7 matrix explored new paradigms to improve bit rate and accuracy. During online use, the system adaptively selects the number of flashes to average. Five different flash patterns were tested. The 19-flash paradigm represents the typical row/column presentation (i.e. 12 columns and 7 rows). The 9- and 14-flash A and B paradigms present all items of the 12 × 7 matrix three times using either 9 or 14 flashes (instead of 19), decreasing the amount of time to present stimuli. Compared to 9-flash A, 9-flash B decreased the likelihood that neighboring items would flash when the target was not flashing, thereby reducing the interference from items adjacent to targets. 14-flash A also reduced the adjacent item interference and 14-flash B additionally eliminated successive (double) flashes of the same item. Results showed that the accuracy and bit rate of the adaptive system were higher than those of the non-adaptive system. In addition, 9- and 14-flash B produced significantly higher performance than their respective A conditions. The results also show the trend that the 14-flash B paradigm was better than the 19-flash pattern for naive users.},
}
@article {pmid21472032,
year = {2011},
author = {Muralidharan, A and Chae, J and Taylor, DM},
title = {Extracting Attempted Hand Movements from EEGs in People with Complete Hand Paralysis Following Stroke.},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {39},
pmid = {21472032},
issn = {1662-453X},
support = {K24 HD054600/HD/NICHD NIH HHS/United States ; R01 HD049777/HD/NICHD NIH HHS/United States ; },
abstract = {This study examines the feasibility of using electroencephalograms (EEGs) to rapidly detect the intent to open one's hand in individuals with complete hand paralysis following a subcortical ischemic stroke. If detectable, this motor-planning activity could be used in real time to trigger a motorized hand exoskeleton or an electrical stimulation device that opens/closes the hand. While EEG-triggered movement-assist devices could restore function, they may also promote recovery by reinforcing the use of remaining cortical circuits. EEGs were recorded while participants were cued to either relax or attempt to extend their fingers. Linear-discriminant analysis was used to detect onset of finger-extension from the EEGs in a leave-one-trial-out cross-validation process. In each testing trial, the classifier was applied in pseudo-real-time starting from an initial hand-relaxed phase, through movement planning, and into the initial attempted-finger-extension phase (finger-extension phase estimated from typical time-to-movement-onset measured in the unaffected hand). The classifiers detected attempted-finger-extension at a significantly higher rate during both motor-planning and early attempted execution compared to rest. To reduce inappropriate triggering of a movement-assist device during rest, the classification threshold could be adjusted to require more certainty about one's intent to move before triggering a device. Additionally, a device could be set to activate only after multiple time samples in a row were classified as finger-extension events. These options resulted in some sessions with no false triggers while the person was resting, but moderate-to-high true trigger rates during attempted-movements.},
}
@article {pmid21471638,
year = {2011},
author = {Leuthardt, EC and Gaona, C and Sharma, M and Szrama, N and Roland, J and Freudenberg, Z and Solis, J and Breshears, J and Schalk, G},
title = {Using the electrocorticographic speech network to control a brain-computer interface in humans.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036004},
pmid = {21471638},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Feedback, Physiological/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Nerve Net/*physiology ; Speech Production Measurement/*methods ; *User-Computer Interface ; },
abstract = {Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.},
}
@article {pmid21470084,
year = {2011},
author = {Stalgis-Bilinski, KL and Boyages, J and Salisbury, EL and Dunstan, CR and Henderson, SI and Talbot, PL},
title = {Burning daylight: balancing vitamin D requirements with sensible sun exposure.},
journal = {The Medical journal of Australia},
volume = {194},
number = {7},
pages = {345-348},
doi = {10.5694/j.1326-5377.2011.tb03003.x},
pmid = {21470084},
issn = {1326-5377},
mesh = {Australia ; Dose-Response Relationship, Radiation ; Guideline Adherence ; *Health Policy ; Heliotherapy/adverse effects/*methods ; Humans ; Seasons ; Skin Pigmentation ; Sunlight/*adverse effects ; Time Factors ; Vitamin D/*biosynthesis ; Vitamin D Deficiency/*prevention &amp; control ; },
abstract = {OBJECTIVE: To examine the feasibility of balancing sunlight exposure to meet vitamin D requirements with sun protection guidelines.<br><br>DESIGN AND SETTING: We used standard erythemal dose and Ultraviolet Index (UVI) data for 1 June 1996 to 30 December 2005 for seven Australian cities to estimate duration of sun exposure required for fair-skinned individuals to synthesise 1000 IU (25 &#xb5;g) of vitamin D, with 11% and 17% body exposure, for each season and hour of the day. Periods were classified according to whether the UVI was < 3 or &#x2265; 3 (when sun protection measures are recommended), and whether required duration of exposure was &#x2264; 30 min, 31-60 min, or > 60 min.<br><br>MAIN OUTCOME MEASURE: Duration of sunlight exposure required to achieve 1000 IU of vitamin D synthesis.<br><br>RESULTS: Duration of sunlight exposure required to synthesise 1000 IU of vitamin D varied by time of day, season and city. Although peak UVI periods are typically promoted as between 10 am and 3 pm, UVI was often &#x2265; 3 before 10 am or after 3 pm. When the UVI was < 3, there were few opportunities to synthesise 1000 IU of vitamin D within 30 min, with either 11% or 17% body exposure.<br><br>CONCLUSION: There is a delicate line between balancing the beneficial effects of sunlight exposure while avoiding its damaging effects. Physiological and geographical factors may reduce vitamin D synthesis, and supplementation may be necessary to achieve adequate vitamin D status for individuals at risk of deficiency.},
}
@article {pmid21468317,
year = {2011},
author = {Tarafder, MR and Carabin, H and McGarvey, ST and Joseph, L and Balolong, E and Olveda, R},
title = {Assessing the impact of misclassification error on an epidemiological association between two helminthic infections.},
journal = {PLoS neglected tropical diseases},
volume = {5},
number = {3},
pages = {e995},
pmid = {21468317},
issn = {1935-2735},
support = {R01 TW001582/TW/FIC NIH HHS/United States ; R01 TW01582/TW/FIC NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Animals ; Anthelmintics/administration &amp; dosage ; Ascaris lumbricoides/isolation &amp; purification ; Child ; Child, Preschool ; Comorbidity ; Diagnostic Errors ; Feces/parasitology ; Female ; Helminthiasis/drug therapy/*epidemiology ; Helminths/*classification/*isolation &amp; purification ; Humans ; Incidence ; Longitudinal Studies ; Male ; Middle Aged ; Parasitology/methods ; Philippines/epidemiology ; Schistosoma japonicum/isolation &amp; purification ; Trichuris/isolation &amp; purification ; Young Adult ; },
abstract = {BACKGROUND: Polyparasitism can lead to severe disability in endemic populations. Yet, the association between soil-transmitted helminth (STH) and the cumulative incidence of Schistosoma japonicum infection has not been described. The aim of this work was to quantify the effect of misclassification error, which occurs when less than 100% accurate tests are used, in STH and S. japonicum infection status on the estimation of this association.<br><br>Longitudinal data from 2276 participants in 50 villages in Samar province, Philippines treated at baseline for S. japonicum infection and followed for one year, served as the basis for this analysis. Participants provided 1-3 stool samples at baseline and 12 months later (2004-2005) to detect infections with STH and S. japonicum using the Kato-Katz technique. Variation from day-to-day in the excretion of eggs in feces introduces individual variations in the sensitivity and specificity of the Kato-Katz to detect infection. Bayesian logit models were used to take this variation into account and to investigate the impact of misclassification error on the association between these infections. Uniform priors for sensitivity and specificity of the diagnostic test to detect the three STH and S. japonicum were used. All results were adjusted for age, sex, occupation, and village-level clustering. Without correction for misclassification error, the odds ratios (ORs) between hookworm, Ascaris lumbricoides, and Trichuris trichiura, and S. japonicum infections were 1.28 (95% Bayesian credible intervals: 0.93, 1.76), 0.91 (95% BCI: 0.66, 1.26), and 1.11 (95% BCI: 0.80, 1.55), respectively, and 2.13 (95% BCI: 1.16, 4.08), 0.74 (95% BCI: 0.43, 1.25), and 1.32 (95% BCI: 0.80, 2.27), respectively, after correction for misclassification error for both exposure and outcome.<br><br>CONCLUSIONS/SIGNIFICANCE: The misclassification bias increased with decreasing test accuracy. Hookworm infection was found to be associated with increased 12-month cumulative incidence of S. japonicum infection after correction for misclassification error. Such important associations might be missed in analyses which do not adjust for misclassification errors.},
}
@article {pmid21464522,
year = {2011},
author = {Vlek, RJ and Schaefer, RS and Gielen, CC and Farquhar, JD and Desain, P},
title = {Sequenced subjective accents for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {3},
pages = {036002},
doi = {10.1088/1741-2560/8/3/036002},
pmid = {21464522},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adult ; Auditory Perception/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Music ; Pattern Recognition, Physiological/*physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Subjective accenting is a cognitive process in which identical auditory pulses at an isochronous rate turn into the percept of an accenting pattern. This process can be voluntarily controlled, making it a candidate for communication from human user to machine in a brain-computer interface (BCI) system. In this study we investigated whether subjective accenting is a feasible paradigm for BCI and how its time-structured nature can be exploited for optimal decoding from non-invasive EEG data. Ten subjects perceived and imagined different metric patterns (two-, three- and four-beat) superimposed on a steady metronome. With an offline classification paradigm, we classified imagined accented from non-accented beats on a single trial (0.5 s) level with an average accuracy of 60.4% over all subjects. We show that decoding of imagined accents is also possible with a classifier trained on perception data. Cyclic patterns of accents and non-accents were successfully decoded with a sequence classification algorithm. Classification performances were compared by means of bit rate. Performance in the best scenario translates into an average bit rate of 4.4 bits min(-1) over subjects, which makes subjective accenting a promising paradigm for an online auditory BCI.},
}
@article {pmid21463695,
year = {2011},
author = {Fazli, S and Danóczy, M and Schelldorfer, J and Müller, KR},
title = {ℓ(1)-penalized linear mixed-effects models for high dimensional data with application to BCI.},
journal = {NeuroImage},
volume = {56},
number = {4},
pages = {2100-2108},
doi = {10.1016/j.neuroimage.2011.03.061},
pmid = {21463695},
issn = {1095-9572},
mesh = {Biometry/*methods ; Brain/*physiology ; Electroencephalography ; Humans ; *Linear Models ; *Signal Processing, Computer-Assisted ; },
abstract = {Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ(1)-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.},
}
@article {pmid21445031,
year = {2011},
author = {Ejaz, N and Peterson, KD and Krapp, HG},
title = {An experimental platform to study the closed-loop performance of brain-machine interfaces.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {49},
pages = {},
pmid = {21445031},
issn = {1940-087X},
mesh = {Animals ; Diptera ; *Man-Machine Systems ; Neurons/physiology ; Photic Stimulation/methods ; Robotics/methods ; *User-Computer Interface ; },
abstract = {The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.},
}
@article {pmid21437227,
year = {2011},
author = {Vidaurre, C and Sander, TH and Schlögl, A},
title = {BioSig: the free and open source software library for biomedical signal processing.},
journal = {Computational intelligence and neuroscience},
volume = {2011},
number = {},
pages = {935364},
pmid = {21437227},
issn = {1687-5273},
mesh = {Algorithms ; Artifacts ; Brain/*physiology ; Computer Simulation ; Electrocardiography ; Electroencephalography ; Electromyography ; Electrooculography ; Heart Rate/physiology ; Humans ; *Models, Neurological ; *Neurophysiology ; *Signal Processing, Computer-Assisted ; *Software ; },
abstract = {BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.},
}
@article {pmid21436539,
year = {2011},
author = {Sannelli, C and Vidaurre, C and Müller, KR and Blankertz, B},
title = {CSP patches: an ensemble of optimized spatial filters. An evaluation study.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025012},
doi = {10.1088/1741-2560/8/2/025012},
pmid = {21436539},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Laplacian filters are widely used in neuroscience. In the context of brain-computer interfacing, they might be preferred to data-driven approaches such as common spatial patterns (CSP) in a variety of scenarios such as, e.g., when no or few user data are available or a calibration session with a multi-channel recording is not possible, which is the case in various applications. In this paper we propose the use of an ensemble of local CSP patches (CSPP) which can be considered as a compromise between Laplacian filters and CSP. Our CSPP only needs a very small number of trials to be optimized and significantly outperforms Laplacian filters in all settings studied. Additionally, CSPP also outperforms multi-channel CSP and a regularized version of CSP even when only very few calibration data are available, acting as a CSP regularizer without the need of additional hyperparameters and at a very low cost: 2-5 min of data recording, i.e. ten times less than CSP.},
}
@article {pmid21436538,
year = {2011},
author = {Brunner, C and Allison, BZ and Altstätter, C and Neuper, C},
title = {A comparison of three brain-computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025010},
doi = {10.1088/1741-2560/8/2/025010},
pmid = {21436538},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography Phase Synchronization/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Man-Machine Systems ; *User-Computer Interface ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems rely on the direct measurement of brain signals, such as event-related desynchronization (ERD), steady state visual evoked potentials (SSVEPs), P300s, or slow cortical potentials. Unfortunately, none of these BCI approaches work for all users. This study compares two conventional BCI approaches (ERD and SSVEP) within subjects, and also evaluates a novel hybrid BCI based on a combination of these signals. We recorded EEG data from 12 subjects across three conditions. In the first condition, subjects imagined moving both hands or both feet (ERD). In the second condition, subjects focused on one of the two oscillating visual stimuli (SSVEP). In the third condition, subjects simultaneously performed both tasks. We used logarithmic band power features at sites and frequencies consistent with ERD and SSVEP activity, and subjects received real-time feedback based on their performance. Subjects also completed brief questionnaires. All subjects could simultaneously perform the movement and visual task in the hybrid condition even though most subjects had little or no training. All subjects showed both SSVEP and ERD activity during the hybrid task, consistent with the activity in both single tasks. Subjects generally considered the hybrid condition moderately more difficult, but all of them were able to complete the hybrid task. Results support the hypothesis that subjects who do not have strong ERD activity might be more effective with an SSVEP BCI, and suggest that SSVEP BCIs work for more subjects. A simultaneous hybrid BCI is feasible, although the current hybrid approach, which involves combining ERD and SSVEP in a two-choice task to improve accuracy, is not significantly better than a comparable SSVEP BCI. Switching to an SSVEP BCI could increase reliability in subjects who have trouble producing the EEG activity necessary to use an ERD BCI. Subjects who are proficient in both BCI approaches might be able to combine these approaches in different ways and for different goals.},
}
@article {pmid21436537,
year = {2011},
author = {Anderson, C and Forney, E and Hains, D and Natarajan, A},
title = {Reliable identification of mental tasks using time-embedded EEG and sequential evidence accumulation.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025023},
doi = {10.1088/1741-2560/8/2/025023},
pmid = {21436537},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/physiology ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Eleven channels of EEG were recorded from a subject performing four mental tasks. A time-embedded representation of the untransformed EEG samples was constructed. Classification of the time-embedded samples was performed by linear and quadratic discriminant analysis and by an artificial neural network. A classifier's output for consecutive samples is combined to increase reliability. A new performance measure is defined as the number of correct selections that would be made by a brain-computer interface (BCI) user of the system, accounting for the need for an incorrect selection to be followed by a correct one to 'delete' the previous selection. A best result of 0.32 correct selections s(-1) (about 3 s per BCI decision) was obtained with a neural network using a time-embedding dimension of 50.},
}
@article {pmid21436536,
year = {2011},
author = {Brunner, P and Bianchi, L and Guger, C and Cincotti, F and Schalk, G},
title = {Current trends in hardware and software for brain-computer interfaces (BCIs).},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025001},
doi = {10.1088/1741-2560/8/2/025001},
pmid = {21436536},
issn = {1741-2552},
mesh = {Biofeedback, Psychology/*instrumentation ; Brain/*physiology ; Brain Mapping/*instrumentation/trends ; Electroencephalography/*instrumentation/trends ; Equipment Design/trends ; Equipment Failure Analysis ; Humans ; *Man-Machine Systems ; Software/*trends ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.},
}
@article {pmid21436535,
year = {2011},
author = {Hermes, D and Vansteensel, MJ and Albers, AM and Bleichner, MG and Benedictus, MR and Mendez Orellana, C and Aarnoutse, EJ and Ramsey, NF},
title = {Functional MRI-based identification of brain areas involved in motor imagery for implantable brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025007},
doi = {10.1088/1741-2560/8/2/025007},
pmid = {21436535},
issn = {1741-2552},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*methods ; Electrodes, Implanted ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/*methods ; Male ; *User-Computer Interface ; Young Adult ; },
abstract = {For the development of minimally invasive brain-computer interfaces (BCIs), it is important to accurately localize the area of implantation. Using fMRI, we investigated which brain areas are involved in motor imagery. Twelve healthy subjects performed a motor execution and imagery task during separate fMRI and EEG measurements. fMRI results showed that during imagery, premotor and parietal areas were most robustly activated in individual subjects, but surprisingly, no activation was found in the primary motor cortex. EEG results showed that spectral power decreases in contralateral sensorimotor rhythms (8-24 Hz) during both movement and imagery. To further verify the involvement of the motor imagery areas found with fMRI, one epilepsy patient performed the same task during both fMRI and ECoG recordings. Significant ECoG low (8-24 Hz) and high (65-95 Hz) frequency power changes were observed selectively on premotor cortex and these co-localized with fMRI. During a subsequent BCI task, excellent performance (91%) was obtained based on ECoG power changes from the localized premotor area. These results indicate that other areas than the primary motor area may be more reliably activated during motor imagery. Specifically, the premotor cortex may be a better area to implant an invasive BCI.},
}
@article {pmid21436534,
year = {2011},
author = {Grosse-Wentrup, M and Mattia, D and Oweiss, K},
title = {Using brain-computer interfaces to induce neural plasticity and restore function.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025004},
pmid = {21436534},
issn = {1741-2552},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; R01 NS062031-04/NS/NINDS NIH HHS/United States ; R33 NS054148/NS/NINDS NIH HHS/United States ; R33 NS054148-05/NS/NINDS NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/methods ; Brain/*physiopathology ; Brain Mapping/*trends ; Forecasting ; Humans ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; *Neuronal Plasticity ; *Recovery of Function ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles of and challenges in inducing neural plasticity by means of a BCI, and delineate the problems of study design and outcome evaluation arising in this context. We conclude with a list of open questions and recommendations for future research in this field.},
}
@article {pmid21436533,
year = {2011},
author = {Zander, TO and Ihme, K and Gärtner, M and Rötting, M},
title = {A public data hub for benchmarking common brain-computer interface algorithms.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025021},
doi = {10.1088/1741-2560/8/2/025021},
pmid = {21436533},
issn = {1741-2552},
mesh = {*Algorithms ; Benchmarking ; Brain Mapping/methods ; *Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/physiology ; Information Dissemination/methods ; *Internet ; Motor Cortex/*physiology ; Pattern Recognition, Automated/methods ; *Software ; Software Validation ; User-Computer Interface ; },
abstract = {Methods of statistical machine learning have recently proven to be very useful in contemporary brain-computer interface (BCI) research based on the discrimination of electroencephalogram (EEG) patterns. Because of this, many research groups develop new algorithms for both feature extraction and classification. However, until now, no large-scale comparison of these algorithms has been accomplished due to the fact that little EEG data is publicly available. Therefore, we at Team PhyPA recorded 32-channel EEGs, electromyograms and electrooculograms of 36 participants during a simple finger movement task. The data are published on our website www.phypa.org and are freely available for downloading. We encourage BCI researchers to test their algorithms on these data and share their results. This work also presents exemplary benchmarking procedures of common feature extraction methods for slow cortical potentials and event-related desynchronization as well as for classification algorithms based on these features.},
}
@article {pmid21436532,
year = {2011},
author = {Wilson, JJ and Palaniappan, R},
title = {Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025026},
doi = {10.1088/1741-2560/8/2/025026},
pmid = {21436532},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Algorithms ; *Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; *Internet ; Male ; Middle Aged ; Online Systems ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {The steady state visual evoked protocol has recently become a popular paradigm in brain-computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control.},
}
@article {pmid21436531,
year = {2011},
author = {Moritz, CT and Fetz, EE},
title = {Volitional control of single cortical neurons in a brain-machine interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025017},
pmid = {21436531},
issn = {1741-2552},
support = {F32 NS051013/NS/NINDS NIH HHS/United States ; P51RR0016/RR/NCRR NIH HHS/United States ; F32 NS051013-03/NS/NINDS NIH HHS/United States ; NS12542/NS/NINDS NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; F32NS5101/NS/NINDS NIH HHS/United States ; R37 NS012542-38/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Biofeedback, Psychology/methods/*physiology ; Brain Mapping/methods ; Cerebral Cortex/*physiology ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Humans ; Macaca nemestrina ; Male ; Neurons/*physiology ; Photic Stimulation/*methods ; *User-Computer Interface ; Volition/*physiology ; },
abstract = {Volitional control of cortical activity is relevant for optimizing control signals for neuroprosthetic devices. We explored the control of firing rates of single cortical cells in two Macaca nemestrina monkeys by providing visual feedback of neural activity and rewarding changes in cell rates. During 'brain-control' sessions, the monkeys modulated the activity of each of 246 cells to acquire targets requiring high or low discharge rates. Cell control improved more than two-fold from the beginning of practice to peak performance. Cell activity was modulated substantially more during brain control than during wrist movements. When recording stability permitted, the monkeys practiced controlling activity of the same cells across multiple days. The performance improved substantially for 27 of 36 cells when practicing brain control across days. The monkeys maintained discharge rates within each target for 1 s, but could maintain rates for up to 3 s for some cells, and performed the brain-control task equally well using cells recorded from the pre-central cortex compared to cells in the post-central cortex, and independently of any directional tuning. These findings demonstrate that arbitrary single cortical neurons, regardless of the strength of directional tuning, are capable of controlling cursor movements in a one-dimensional brain-machine interface. It is possible that direct conversion of activity from single cortical cells to control signals for neuroprosthetic devices may be a useful complementary strategy to population decoding of the intended movement direction.},
}
@article {pmid21436530,
year = {2011},
author = {Shahid, S and Prasad, G},
title = {Bispectrum-based feature extraction technique for devising a practical brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025014},
doi = {10.1088/1741-2560/8/2/025014},
pmid = {21436530},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {The extraction of distinctly separable features from electroencephalogram (EEG) is one of the main challenges in designing a brain-computer interface (BCI). Existing feature extraction techniques for a BCI are mostly developed based on traditional signal processing techniques assuming that the signal is Gaussian and has linear characteristics. But the motor imagery (MI)-related EEG signals are highly non-Gaussian, non-stationary and have nonlinear dynamic characteristics. This paper proposes an advanced, robust but simple feature extraction technique for a MI-related BCI. The technique uses one of the higher order statistics methods, the bispectrum, and extracts the features of nonlinear interactions over several frequency components in MI-related EEG signals. Along with a linear discriminant analysis classifier, the proposed technique has been used to design an MI-based BCI. Three performance measures, classification accuracy, mutual information and Cohen's kappa have been evaluated and compared with a BCI using a contemporary power spectral density-based feature extraction technique. It is observed that the proposed technique extracts nearly recording-session-independent distinct features resulting in significantly much higher and consistent MI task detection accuracy and Cohen's kappa. It is therefore concluded that the bispectrum-based feature extraction is a promising technique for detecting different brain states.},
}
@article {pmid21436529,
year = {2011},
author = {Marathe, AR and Taylor, DM},
title = {Decoding position, velocity, or goal: does it matter for brain-machine interfaces?.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025016},
pmid = {21436529},
issn = {1741-2552},
support = {R01 NS058871/NS/NINDS NIH HHS/United States ; R01 NS058871-03/NS/NINDS NIH HHS/United States ; 1R01NS058871/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; *Task Performance and Analysis ; User-Computer Interface ; },
abstract = {Arm end-point position, end-point velocity, and the intended final location or 'goal' of a reach have all been decoded from cortical signals for use in brain-machine interface (BMI) applications. These different aspects of arm movement can be decoded from the brain and used directly to control the position, velocity, or movement goal of a device. However, these decoded parameters can also be remapped to control different aspects of movement, such as using the decoded position of the hand to control the velocity of a device. People easily learn to use the position of a joystick to control the velocity of an object in a videogame. Similarly, in BMI systems, the position, velocity, or goal of a movement could be decoded from the brain and remapped to control some other aspect of device movement. This study evaluates how easily people make transformations between position, velocity, and reach goal in BMI systems. It also evaluates how different amounts of decoding error impact on device control with and without these transformations. Results suggest some remapping options can significantly improve BMI control. This study provides guidance on what remapping options to use when various amounts of decoding error are present.},
}
@article {pmid21436528,
year = {2011},
author = {Frye, GE and Hauser, CK and Townsend, G and Sellers, EW},
title = {Suppressing flashes of items surrounding targets during calibration of a P300-based brain-computer interface improves performance.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025024},
pmid = {21436528},
issn = {1741-2552},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R21 DC010470-01/DC/NIDCD NIH HHS/United States ; 1 R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R21 DC010470-02/DC/NIDCD NIH HHS/United States ; R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-05/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain Mapping/*methods ; Calibration ; Cues ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Perceptual Masking/*physiology ; Photic Stimulation/*methods ; User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Since the introduction of the P300 brain-computer interface (BCI) speller by Farwell and Donchin in 1988, the speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. New presentation paradigms have also led to improvements in bit rate and accuracy (e.g. Townsend et al (2010 Clin. Neurophysiol. 121 1109-20)). In particular, the checkerboard paradigm for online P300 BCI-based spelling performs well, has started to document what makes for a successful paradigm, and is a good platform for further experimentation. The current paper further examines the checkerboard paradigm by suppressing items which surround the target from flashing during calibration (i.e. the suppression condition). In the online feedback mode the standard checkerboard paradigm is used with a stepwise linear discriminant classifier derived from the suppression condition and one classifier derived from the standard checkerboard condition, counter-balanced. The results of this research demonstrate that using suppression during calibration produces significantly more character selections/min ((6.46) time between selections included) than the standard checkerboard condition (5.55), and significantly fewer target flashes are needed per selection in the SUP condition (5.28) as compared to the RCP condition (6.17). Moreover, accuracy in the SUP and RCP conditions remained equivalent (∼90%). Mean theoretical bit rate was 53.62 bits/min in the suppression condition and 46.36 bits/min in the standard checkerboard condition (ns). Waveform morphology also showed significant differences in amplitude and latency.},
}
@article {pmid21436527,
year = {2011},
author = {Bin, G and Gao, X and Wang, Y and Li, Y and Hong, B and Gao, S},
title = {A high-speed BCI based on code modulation VEP.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025015},
doi = {10.1088/1741-2560/8/2/025015},
pmid = {21436527},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation/*methods ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Recently, electroencephalogram-based brain-computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by a time-shifted binary pseudorandom sequence. A multichannel identification method based on canonical correlation analysis (CCA) was used for target identification. The online system achieved an average information transfer rate (ITR) of 108 ± 12 bits min(-1) on five subjects with a maximum ITR of 123 bits min(-1) for a single subject.},
}
@article {pmid21436526,
year = {2011},
author = {Grozea, C and Voinescu, CD and Fazli, S},
title = {Bristle-sensors--low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025008},
doi = {10.1088/1741-2560/8/2/025008},
pmid = {21436526},
issn = {1741-2552},
mesh = {Biofeedback, Psychology/*instrumentation ; Brain/*physiology ; Brain Mapping/*instrumentation ; Communication Devices for People with Disabilities ; Elasticity ; *Electrodes ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Transducers ; *User-Computer Interface ; },
abstract = {In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.},
}
@article {pmid21436525,
year = {2011},
author = {Mak, JN and Arbel, Y and Minett, JW and McCane, LM and Yuksel, B and Ryan, D and Thompson, D and Bianchi, L and Erdogmus, D},
title = {Optimizing the P300-based brain-computer interface: current status, limitations and future directions.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025003},
doi = {10.1088/1741-2560/8/2/025003},
pmid = {21436525},
issn = {1741-2552},
support = {R21HD054697/HD/NICHD NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*methods ; Brain/*physiology ; Brain Mapping/*trends ; Electroencephalography/*trends ; Event-Related Potentials, P300/*physiology ; Forecasting ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper summarizes the presentations and discussions at a workshop held during the Fourth International BCI Meeting charged with reviewing and evaluating the current state, limitations and future development of P300-based brain-computer interface (P300-BCI) systems. We reviewed such issues as potential users, recording methods, stimulus presentation paradigms, feature extraction and classification algorithms, and applications. A summary of the discussions and the panel's recommendations for each of these aspects are presented.},
}
@article {pmid21436524,
year = {2011},
author = {Leeb, R and Sagha, H and Chavarriaga, R and Millán, Jdel R},
title = {A hybrid brain-computer interface based on the fusion of electroencephalographic and electromyographic activities.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025011},
doi = {10.1088/1741-2560/8/2/025011},
pmid = {21436524},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Electroencephalography/*methods ; Electromyography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Muscle Contraction/physiology ; Muscle, Skeletal/*physiology ; Systems Integration ; *User-Computer Interface ; },
abstract = {Hybrid brain-computer interfaces (BCIs) are representing a recent approach to develop practical BCIs. In such a system disabled users are able to use all their remaining functionalities as control possibilities in parallel with the BCI. Sometimes these people have residual activity of their muscles. Therefore, in the presented hybrid BCI framework we want to explore the parallel usage of electroencephalographic (EEG) and electromyographic (EMG) activity, whereby the control abilities of both channels are fused. Results showed that the participants could achieve a good control of their hybrid BCI independently of their level of muscular fatigue. Thereby the multimodal fusion approach of muscular and brain activity yielded better and more stable performance compared to the single conditions. Even in the case of an increasing muscular fatigue a good control (moderate and graceful degradation of the performance compared to the non-fatigued case) and a smooth handover could be achieved. Therefore, such systems allow the users a very reliable hybrid BCI control although they are getting more and more exhausted or fatigued during the day.},
}
@article {pmid21436523,
year = {2011},
author = {Belitski, A and Farquhar, J and Desain, P},
title = {P300 audio-visual speller.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025022},
doi = {10.1088/1741-2560/8/2/025022},
pmid = {21436523},
issn = {1741-2552},
mesh = {Acoustic Stimulation/*methods ; Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; *User-Computer Interface ; *Writing ; },
abstract = {The Farwell and Donchin matrix speller is well known as one of the highest performing brain-computer interfaces (BCIs) currently available. However, its use of visual stimulation limits its applicability to users with normal eyesight. Alternative BCI spelling systems which rely on non-visual stimulation, e.g. auditory or tactile, tend to perform much more poorly and/or can be very difficult to use. In this paper we present a novel extension of the matrix speller, based on flipping the letter matrix, which allows us to use the same interface for visual, auditory or simultaneous visual and auditory stimuli. In this way we aim to allow users to utilize the best available input modality for their situation, that is use visual + auditory for best performance and move smoothly to purely auditory when necessary, e.g. when disease causes the user's eyesight to deteriorate. Our results on seven healthy subjects demonstrate the effectiveness of this approach, with our modified visual + auditory stimulation slightly out-performing the classic matrix speller. The purely auditory system performance was lower than for visual stimulation, but comparable to other auditory BCI systems.},
}
@article {pmid21436522,
year = {2011},
author = {Vaughan, TM and Wolpaw, JR},
title = {Special issue containing contributions from the Fourth International Brain-Computer Interface Meeting.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {020201},
doi = {10.1088/1741-2560/8/2/020201},
pmid = {21436522},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R13 DC010536/DC/NIDCD NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*methods ; Brain/*physiology ; Brain Mapping/*trends ; Communication Devices for People with Disabilities/*trends ; Electroencephalography/*trends ; Humans ; Man-Machine Systems ; Neuromuscular Diseases/rehabilitation ; *User-Computer Interface ; },
}
@article {pmid21436521,
year = {2011},
author = {Krusienski, DJ and Shih, JJ},
title = {Control of a brain-computer interface using stereotactic depth electrodes in and adjacent to the hippocampus.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025006},
pmid = {21436521},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/instrumentation ; Biotechnology ; Brain Mapping/*instrumentation/methods ; Communication Devices for People with Disabilities ; *Computer Peripherals ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; *Evoked Potentials ; Hippocampus/*physiopathology/surgery ; Humans ; Imagination ; Male ; Man-Machine Systems ; Middle Aged ; Neuromuscular Diseases/rehabilitation ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this study, event-related potentials (ERPs) recorded from bilateral stereotactic depth electrodes implanted in and adjacent to the hippocampus were used to control a P300 Speller paradigm. The ERPs were preprocessed and used to train a linear classifier to subsequently predict the intended target letters. The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in the two subjects tested. Our results demonstrate that ERPs from hippocampal and hippocampal adjacent depth electrodes can be used to reliably control the P300 Speller BCI paradigm.},
}
@article {pmid21436520,
year = {2011},
author = {Aloise, F and Schettini, F and Aricò, P and Leotta, F and Salinari, S and Mattia, D and Babiloni, F and Cincotti, F},
title = {P300-based brain-computer interface for environmental control: an asynchronous approach.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025025},
doi = {10.1088/1741-2560/8/2/025025},
pmid = {21436520},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Environmental Monitoring/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.},
}
@article {pmid21436519,
year = {2011},
author = {Krusienski, DJ and Grosse-Wentrup, M and Galán, F and Coyle, D and Miller, KJ and Forney, E and Anderson, CW},
title = {Critical issues in state-of-the-art brain-computer interface signal processing.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025002},
pmid = {21436519},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*methods ; Brain/*physiology ; Brain Mapping/*trends ; Electroencephalography/*trends ; Forecasting ; Humans ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper reviews several critical issues facing signal processing for brain-computer interfaces (BCIs) and suggests several recent approaches that should be further examined. The topics were selected based on discussions held during the 4th International BCI Meeting at a workshop organized to review and evaluate the current state of, and issues relevant to, feature extraction and translation of field potentials for BCIs. The topics presented in this paper include the relationship between electroencephalography and electrocorticography, novel features for performance prediction, time-embedded signal representations, phase information, signal non-stationarity, and unsupervised adaptation.},
}
@article {pmid21436518,
year = {2011},
author = {Hasan, BA and Gan, JQ},
title = {Conditional random fields as classifiers for three-class motor-imagery brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025013},
doi = {10.1088/1741-2560/8/2/025013},
pmid = {21436518},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/methods ; Computer Simulation ; Data Interpretation, Statistical ; Effect Modifier, Epidemiologic ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Models, Neurological ; Models, Statistical ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {Conditional random fields (CRFs) are demonstrated to be a discriminative model able to exploit the temporal properties of EEG data obtained during synchronous three-class motor-imagery-based brain-computer interface experiments. The advantages of CRFs over the hidden Markov model (HMM) are both theoretical and practical. Theoretically, CRFs focus on modeling latent variables (labels) rather than both observation and latent variables. Furthermore, CRFs' loss function is convex, guaranteeing convergence to the global optimum. Practically, CRFs are much less prone to singularity problems. This property allows for the use of both time- and frequency-based features, such as band power. The HMM, on the other hand, requires temporal features such as autoregressive coefficients. A CRF-based classifier is tested on 13 subjects. Significant improvement is found when applying CRFs over HMM- and LDA-based classifiers.},
}
@article {pmid21436517,
year = {2011},
author = {Wang, YT and Wang, Y and Jung, TP},
title = {A cell-phone-based brain-computer interface for communication in daily life.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025018},
doi = {10.1088/1741-2560/8/2/025018},
pmid = {21436517},
issn = {1741-2552},
mesh = {*Activities of Daily Living ; Algorithms ; Brain Mapping/instrumentation ; *Cell Phone ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.},
}
@article {pmid21436516,
year = {2011},
author = {Lakey, CE and Berry, DR and Sellers, EW},
title = {Manipulating attention via mindfulness induction improves P300-based brain-computer interface performance.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025019},
pmid = {21436516},
issn = {1741-2552},
support = {R21 DC010470/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R21 DC010470-01/DC/NIDCD NIH HHS/United States ; R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Algorithms ; Attention/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.},
}
@article {pmid21436515,
year = {2011},
author = {Vidaurre, C and Sannelli, C and Müller, KR and Blankertz, B},
title = {Co-adaptive calibration to improve BCI efficiency.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025009},
doi = {10.1088/1741-2560/8/2/025009},
pmid = {21436515},
issn = {1741-2552},
mesh = {*Algorithms ; Brain Mapping/*instrumentation ; Calibration ; Electroencephalography/*instrumentation ; Evoked Potentials, Somatosensory/*physiology ; Humans ; Motor Cortex/*physiology ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {All brain-computer interface (BCI) groups that have published results of studies involving a large number of users performing BCI control based on the voluntary modulation of sensorimotor rhythms (SMR) report that BCI control could not be achieved by a non-negligible number of subjects (estimated 20% to 25%). This failure of the BCI system to read the intention of the user is one of the greatest problems and challenges in BCI research. There are two main causes for this problem in SMR-based BCI systems: either no idle SMR is observed over motor areas of the user, or this idle rhythm is not modulated during motor imagery, resulting in a classification performance lower than 70% (criterion level) that renders the control of a BCI application (like a speller) difficult or impossible. Previously, we introduced the concept of machine learning based co-adaptive calibration, which provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adaptive learning enables significant BCI control for completely novice users, as well as for those who could not achieve control with a conventional SMR-based BCI.},
}
@article {pmid21436514,
year = {2011},
author = {Pichiorri, F and De Vico Fallani, F and Cincotti, F and Babiloni, F and Molinari, M and Kleih, SC and Neuper, C and Kübler, A and Mattia, D},
title = {Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025020},
doi = {10.1088/1741-2560/8/2/025020},
pmid = {21436514},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Biological Clocks/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Evoked Potentials, Somatosensory/*physiology ; Humans ; Imagination/*physiology ; Learning/physiology ; Male ; Motor Cortex/*physiology ; User-Computer Interface ; },
abstract = {The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.},
}
@article {pmid21436513,
year = {2011},
author = {Simeral, JD and Kim, SP and Black, MJ and Donoghue, JP and Hochberg, LR},
title = {Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025027},
pmid = {21436513},
issn = {1741-2552},
support = {R01DC009899/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; C06-16549-01A1//PHS HHS/United States ; R37 NS025074/NS/NINDS NIH HHS/United States ; R01EB007401/EB/NIBIB NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899-01/DC/NIDCD NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; RC1 HD063931-02/HD/NICHD NIH HHS/United States ; R01 NS025074-22/NS/NINDS NIH HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; NS25074/NS/NINDS NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; R01 EB007401-05/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; *Evoked Potentials ; Female ; Humans ; Imagination ; *Microelectrodes ; Middle Aged ; Quadriplegia/diagnosis/*physiopathology/rehabilitation ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.},
}
@article {pmid21436512,
year = {2011},
author = {Zander, TO and Kothe, C},
title = {Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025005},
doi = {10.1088/1741-2560/8/2/025005},
pmid = {21436512},
issn = {1741-2552},
mesh = {Biofeedback, Psychology/*methods ; Brain/*physiology ; Brain Mapping/*trends ; Cognition/*physiology ; Electroencephalography/*trends ; Forecasting ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.},
}
@article {pmid21436511,
year = {2011},
author = {Riccio, A and Leotta, F and Bianchi, L and Aloise, F and Zickler, C and Hoogerwerf, EJ and Kübler, A and Mattia, D and Cincotti, F},
title = {Workload measurement in a communication application operated through a P300-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {8},
number = {2},
pages = {025028},
doi = {10.1088/1741-2560/8/2/025028},
pmid = {21436511},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Consumer Behavior ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *User-Computer Interface ; *Workload ; },
abstract = {Advancing the brain-computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the user's subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions--visual stimuli needed to evoke the P300 response were either overlaid onto the application's graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.},
}
@article {pmid21431597,
year = {2011},
author = {Hema, CR and Paulraj, MP and Yaacob, S and Adom, AH and Nagarajan, R},
title = {Asynchronous brain machine interface-based control of a wheelchair.},
journal = {Advances in experimental medicine and biology},
volume = {696},
number = {},
pages = {565-572},
doi = {10.1007/978-1-4419-7046-6_57},
pmid = {21431597},
issn = {0065-2598},
mesh = {Brain/physiology ; Computational Biology ; Computer Systems ; Electroencephalography ; Equipment Design ; Humans ; *Man-Machine Systems ; Nervous System Diseases/rehabilitation ; Neural Networks, Computer ; User-Computer Interface ; *Wheelchairs ; },
abstract = {A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.},
}
@article {pmid21427553,
year = {2011},
author = {Galanina, N and Bossuyt, V and Harris, LN},
title = {Molecular predictors of response to therapy for breast cancer.},
journal = {Cancer journal (Sudbury, Mass.)},
volume = {17},
number = {2},
pages = {96-103},
doi = {10.1097/PPO.0b013e318212dee3},
pmid = {21427553},
issn = {1540-336X},
mesh = {Antineoplastic Agents/therapeutic use ; Biomarkers, Tumor/*genetics ; Breast Neoplasms/*drug therapy/*genetics ; Gene Expression Profiling ; Humans ; Receptor, ErbB-2/genetics/metabolism ; Receptors, Estrogen/genetics/metabolism ; Receptors, Progesterone/genetics/metabolism ; },
abstract = {For several decades, measurements from tumor tissue biomarkers have been used to identify subsets of breast cancer patients that may benefit from specific therapies. Since the 1980s, estrogen receptor testing has been routinely performed on breast carcinoma samples to determine whether hormonal therapy is indicated. Today, estrogen receptor, progesterone receptor, and human epidermal growth factor receptor type 2 testing to guide treatment decisions are standard of care. In recent years, multigene assays have been introduced to predict breast tumor behavior. In particular, the OncotypeDx and MammaPrint assays have been commercialized and are used in North America and Europe to guide clinical decisions. Others, including the Breast Cancer Index (BCI; bioTheranostics) and PAM50 (Expression Analysis, Inc), are gaining acceptance as validated assays with associated clinical outcomes. In addition, certain germ line genetic tests are now reported to predict response to specific treatments (e.g., BRCA1, 2, CYP2D6). The optimal use of these novel molecular assays is a challenge to the practicing oncologist. In this review, we will focus on the role of biomarkers that predict response to treatment of breast cancer patients and provide a framework for oncologists to understand and evaluate these tools for use in clinical practice.},
}
@article {pmid21427014,
year = {2011},
author = {Arvaneh, M and Guan, C and Ang, KK and Quek, C},
title = {Optimizing the channel selection and classification accuracy in EEG-based BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {6},
pages = {1865-1873},
doi = {10.1109/TBME.2011.2131142},
pmid = {21427014},
issn = {1558-2531},
mesh = {*Algorithms ; Artificial Intelligence ; Databases, Factual ; Electrocardiography/*methods ; Humans ; Imagination ; Motor Activity ; *Neural Prostheses ; *Signal Processing, Computer-Assisted ; },
abstract = {Multichannel EEG is generally used in brain-computer interfaces (BCIs), whereby performing EEG channel selection 1) improves BCI performance by removing irrelevant or noisy channels and 2) enhances user convenience from the use of lesser channels. This paper proposes a novel sparse common spatial pattern (SCSP) algorithm for EEG channel selection. The proposed SCSP algorithm is formulated as an optimization problem to select the least number of channels within a constraint of classification accuracy. As such, the proposed approach can be customized to yield the best classification accuracy by removing the noisy and irrelevant channels, or retain the least number of channels without compromising the classification accuracy obtained by using all the channels. The proposed SCSP algorithm is evaluated using two motor imagery datasets, one with a moderate number of channels and another with a large number of channels. In both datasets, the proposed SCSP channel selection significantly reduced the number of channels, and outperformed existing channel selection methods based on Fisher criterion, mutual information, support vector machine, common spatial pattern, and regularized common spatial pattern in classification accuracy. The proposed SCSP algorithm also yielded an average improvement of 10% in classification accuracy compared to the use of three channels (C3, C4, and Cz).},
}
@article {pmid21423797,
year = {2011},
author = {Mahmoudi, B and Sanchez, JC},
title = {A symbiotic brain-machine interface through value-based decision making.},
journal = {PloS one},
volume = {6},
number = {3},
pages = {e14760},
pmid = {21423797},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Behavior, Animal ; Computer Simulation ; *Decision Making ; *Man-Machine Systems ; Nucleus Accumbens/physiology ; Rats ; Stereotaxic Techniques ; *User-Computer Interface ; },
abstract = {BACKGROUND: In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC).<br><br>METHODOLOGY: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc.<br><br>CONCLUSIONS: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain.},
}
@article {pmid21422928,
year = {2011},
author = {Zheng, Y and Koehnke, J and Besing, J and Spitzer, J},
title = {Effects of noise and reverberation on virtual sound localization for listeners with bilateral cochlear implants.},
journal = {Ear and hearing},
volume = {32},
number = {5},
pages = {569-572},
doi = {10.1097/AUD.0b013e318216eba6},
pmid = {21422928},
issn = {1538-4667},
mesh = {Adult ; *Cochlear Implantation ; Diagnosis, Computer-Assisted/*methods ; Feasibility Studies ; Female ; Hearing Loss, Bilateral/*diagnosis/*rehabilitation ; Hearing Tests/methods ; Humans ; Male ; Middle Aged ; Noise ; Signal-To-Noise Ratio ; *Sound Localization ; User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: This study investigated the effects of both noise and reverberation on the ability of listeners with bilateral cochlear implants (BCIs) to localize and the feasibility of using a virtual localization test to evaluate BCI users.<br><br>DESIGN: Seven adults with normal hearing (NH) and two adults with BCIs participated. All subjects completed the virtual localization test in quiet and at 0, -4, -8 dB signal-to-noise ratio in simulated anechoic and reverberant environments. BCI users were also tested at +4 dB signal-to-noise ratio. The noise source was at 0&#xb0;. A three-word phrase was presented at 70 dB SPL from nine simulated locations in the frontal horizontal plane (&#xb1;90&#xb0;).<br><br>RESULTS: Results revealed significantly poorer localization accuracy for BCI users than NH listeners in all conditions. Significant reverberation effects were observed for BCI users but not listeners with NH.<br><br>CONCLUSION: Noise and reverberation have a significant effect on BCI users, and their localization ability can be evaluated using these virtual tests.},
}
@article {pmid21421448,
year = {2011},
author = {Volosyak, I and Valbuena, D and Lüth, T and Malechka, T and Gräser, A},
title = {BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {3},
pages = {232-239},
doi = {10.1109/TNSRE.2011.2121919},
pmid = {21421448},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Age Factors ; Algorithms ; Brain/*physiology ; Computers ; Demography ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Fatigue/psychology ; Female ; Humans ; Information Theory ; Male ; Middle Aged ; Photic Stimulation ; Psychomotor Performance/physiology ; Robotics ; Sex Factors ; Signal Processing, Computer-Assisted ; Software ; Surveys and Questionnaires ; *User-Computer Interface ; Video Games ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.},
}
@article {pmid21420499,
year = {2011},
author = {Wu, W and Chen, Z and Gao, S and Brown, EN},
title = {A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG.},
journal = {NeuroImage},
volume = {56},
number = {4},
pages = {1929-1945},
pmid = {21420499},
issn = {1095-9572},
support = {DP1 OD003646/OD/NIH HHS/United States ; DP1 OD003646-01/OD/NIH HHS/United States ; R01 EB006385/EB/NIBIB NIH HHS/United States ; R01 EB006385-01A1/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Bayes Theorem ; Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Models, Statistical ; *Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatio-temporal patterns and the dynamics of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data.},
}
@article {pmid21414932,
year = {2011},
author = {Song, W and Giszter, SF},
title = {Adaptation to a cortex-controlled robot attached at the pelvis and engaged during locomotion in rats.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {31},
number = {8},
pages = {3110-3128},
pmid = {21414932},
issn = {1529-2401},
support = {R01 NS054894-05/NS/NINDS NIH HHS/United States ; R01 NS072651/NS/NINDS NIH HHS/United States ; R01 NS054894-04/NS/NINDS NIH HHS/United States ; R01 NS054894/NS/NINDS NIH HHS/United States ; R01 NS072651-01/NS/NINDS NIH HHS/United States ; R01 NS054894-03/NS/NINDS NIH HHS/United States ; R01 NS054894-02/NS/NINDS NIH HHS/United States ; R01 NS044564/NS/NINDS NIH HHS/United States ; R01 NS054894-01A2/NS/NINDS NIH HHS/United States ; NS44564/NS/NINDS NIH HHS/United States ; NS54894/NS/NINDS NIH HHS/United States ; },
mesh = {Adaptation, Physiological/*physiology ; Animals ; Female ; Learning/*physiology ; Locomotion/*physiology ; Models, Animal ; Motor Cortex/*anatomy &amp; histology/physiology/surgery ; Prostheses and Implants/*trends ; Rats ; Rats, Sprague-Dawley ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) should ideally show robust adaptation of the BMI across different tasks and daily activities. Most BMIs have used overpracticed tasks. Little is known about BMIs in dynamic environments. How are mechanically body-coupled BMIs integrated into ongoing rhythmic dynamics, for example, in locomotion? To examine this, we designed a novel BMI using neural discharge in the hindlimb/trunk motor cortex in rats during locomotion to control a robot attached at the pelvis. We tested neural adaptation when rats experienced (1) control locomotion, (2) "simple elastic load" (a robot load on locomotion without any BMI neural control), and (3) "BMI with elastic load" (in which the robot loaded locomotion and a BMI neural control could counter this load). Rats significantly offset applied loads with the BMI while preserving more normal pelvic height compared with load alone. Adaptation occurred over ∼100-200 step cycles in a trial. Firing rates increased in both the loaded conditions compared with baseline. Mean phases of the discharge of cells in the step cycle shifted significantly between BMI and the simple load condition. Over time, more BMI cells became positively correlated with the external force and modulated more deeply, and the network correlations of neurons on a 100 ms timescale increased. Loading alone showed none of these effects. The BMI neural changes of rate and force correlations persisted or increased over repeated trials. Our results show that rats have the capacity to use motor adaptation and motor learning to fairly rapidly engage hindlimb/trunk-coupled BMIs in their locomotion.},
}
@article {pmid21411366,
year = {2011},
author = {Boulay, CB and Sarnacki, WA and Wolpaw, JR and McFarland, DJ},
title = {Trained modulation of sensorimotor rhythms can affect reaction time.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {9},
pages = {1820-1826},
pmid = {21411366},
issn = {1872-8952},
support = {R01 EB000856-09/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Motor Activity/*physiology ; Reaction Time/*physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects.<br><br>METHODS: Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task.<br><br>RESULTS: Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms.<br><br>CONCLUSIONS: Voluntary modulation of EEG SMR can affect motor behavior.<br><br>SIGNIFICANCE: These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.},
}
@article {pmid21394652,
year = {2011},
author = {Horki, P and Solis-Escalante, T and Neuper, C and Müller-Putz, G},
title = {Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {49},
number = {5},
pages = {567-577},
pmid = {21394652},
issn = {1741-0444},
mesh = {Adult ; *Artificial Limbs ; Brain Mapping/methods ; Elbow Joint/physiology ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Female ; Hand Strength/physiology ; Humans ; Imagination/*physiology ; Male ; *User-Computer Interface ; Young Adult ; },
abstract = {A Brain-Computer Interface (BCI) is a device that transforms brain signals, which are intentionally modulated by a user, into control commands. BCIs based on motor imagery (MI) and steady-state visual evoked potentials (SSVEP) can partially restore motor control in spinal cord injured patients. To determine whether these BCIs can be combined for grasp and elbow function control independently, we investigated a control method where the beta rebound after brisk feet MI is used to control the grasp function, and a two-class SSVEP-BCI the elbow function of a 2 degrees-of-freedom artificial upper limb. Subjective preferences for the BCI control were assessed with a questionnaire. The results of the initial evaluation of the system suggests that this is feasible.},
}
@article {pmid21389714,
year = {2011},
author = {Håkansson, B},
title = {The future of bone conduction hearing devices.},
journal = {Advances in oto-rhino-laryngology},
volume = {71},
number = {},
pages = {140-152},
doi = {10.1159/000323715},
pmid = {21389714},
issn = {0065-3071},
mesh = {Correction of Hearing Impairment/*instrumentation ; Forecasting ; Hearing Aids/adverse effects/*trends ; Hearing Loss, Conductive/*rehabilitation ; Hearing Loss, Mixed Conductive-Sensorineural/*therapy ; Humans ; Prosthesis Design ; Transducers ; },
abstract = {The bone-anchored hearing aid (Baha) is today an important rehabilitation alternative for patients with mixed and conductive hearing loss and where air conduction devices should not or cannot be used. Some patients with single-sided deafness are also successfully treated with a Baha. Despite successful treatment of these patient groups, there is always a need for future improvements. First, it is well known that Baha are associated with some drawbacks related to skin infections, accidental or spontaneous loss of the bone implant, and patient refusal for treatment due to stigma. Therefore, in this chapter some alternatives to the Baha which have the potential to reduce these drawbacks are generally discussed. They all have the common feature that they do not need a permanent skin penetration. The alternatives to the Baha are: (1) improved conventional bone conduction (BC) devices, (2) devices with an implanted transducer referred to as BC implants (BCI), (3) dental-attached devices. Disregarding skin complication issues, direct BC devices like the Baha, have a superior advantage of better sound quality in the high-frequency range. How these devices might be improved in the future is also discussed. Finally, some recent advances in the development of a new BCI system will be presented, where the implanted transducer uses a non-screw attachment to a hollow recess of the temporal bone. Some preclinical studies have been performed showing that a BCI system can provide similar or higher output as compared with a Baha.},
}
@article {pmid21386125,
year = {2011},
author = {Majima, K and Kamitani, Y},
title = {[An outlook on the present and future of brain-machine interface research].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {63},
number = {3},
pages = {241-246},
pmid = {21386125},
issn = {1881-6096},
mesh = {Brain/*physiology ; Forecasting ; Humans ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The goal of brain-machine interface (BMI) research is to interpret brain signals in order to control an external device. Substantial progress toward this goal has been achieved over the last decade. Currently, BMI algorithms can translate neural signals into motor commands that reproduce arm-reaching and hand-grasping movements in artificial actuators, thereby promising the restoration of limb mobility in paralyzed people. In one study, a tetraplegic human subject used a clinical neuromotor prosthesis to restore his communication and mobility. Furthermore, a recently developed neural decoding technology provides an effective means to read out mental states from human brain activity. Decoding of mental states could be used for direct human-human communication outside the brain's normal pathways. However, for BMI practical, long-term stability of signal interpretation is required. Unfortunately, the classical invasive BMI methods suffer from poor long-term stability because of deterioration in signal quality. Two new approaches to long-term BMI applications are showing promising results in maintaining signal quality. One is the use of newly developed electrodes that are less harmful to neural tissues, and the other is the use of electrocorticograms (ECoGs), which measure population activity of neurons with electrodes placed on the surface of the brain. Both these new technologies facilitate clearer signals from the brain and greater stability of brain signals over time. In this review, we summarize the previous BMI approaches and shed light upon the new advances that may enable long-term BMI use.},
}
@article {pmid21374997,
year = {2010},
author = {Li, X},
title = {[EEG feature extraction based on ICA and CSP algorithms].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {27},
number = {6},
pages = {1370-1374},
pmid = {21374997},
issn = {1001-5515},
mesh = {*Algorithms ; *Artifacts ; Brain/physiology ; Electroencephalography/*methods ; Humans ; Principal Component Analysis/*methods ; *Signal Processing, Computer-Assisted ; Support Vector Machine ; },
abstract = {This research was aimed at the feature extraction problem in brain computer interface (BCI). The combination algorithm based on independent component analysis (ICA) and common spatial pattern (CSP) was introduced into this work for exploring frequency domain characteristics from Electroencephalography (EEG). Firstly, a pre-processing step with ICA was applied to remove artifacts, and EEG was filtered through an 8-30 Hz bandpass filter. Secondly, EEG was decomposed into spatial patterns with CSP, which were extracted from two most discriminative populations, and event related desynchronization (ERD)/event related synchronization (ERS) characteristic was extracted with power spectrum analysis. Finally, support vector machine (SVM) was used to classify motor imagery tasks, and good results were obtained. For validation, the motor imagery EEG data provided by BCI Competition 2008-Graz data set B were used, and the results showed that the combination algorithm enhanced the signal-to-noise ratio and extracted discriminative characteristics. It was an effective method for classification recognition.},
}
@article {pmid21369351,
year = {2011},
author = {Brunner, P and Ritaccio, AL and Emrich, JF and Bischof, H and Schalk, G},
title = {Rapid Communication with a "P300" Matrix Speller Using Electrocorticographic Signals (ECoG).},
journal = {Frontiers in neuroscience},
volume = {5},
number = {},
pages = {5},
pmid = {21369351},
issn = {1662-453X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {A brain-computer interface (BCI) can provide a non-muscular communication channel to severely disabled people. One particular realization of a BCI is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17 characters/min (i.e., 69 bits/min), and achieved a peak rate of 22 characters/min (i.e., 113 bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller BCI system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.},
}
@article {pmid21353631,
year = {2011},
author = {Vlek, RJ and Schaefer, RS and Gielen, CC and Farquhar, JD and Desain, P},
title = {Shared mechanisms in perception and imagery of auditory accents.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {8},
pages = {1526-1532},
doi = {10.1016/j.clinph.2011.01.042},
pmid = {21353631},
issn = {1872-8952},
mesh = {Acoustic Stimulation ; Adult ; Artificial Intelligence ; Auditory Perception/*physiology ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Principal Component Analysis ; Psychoacoustics ; Reaction Time ; Young Adult ; },
abstract = {OBJECTIVE: An auditory rhythm can be perceived as a sequence of accented (loud) and non-accented (soft) beats or it can be imagined. Subjective rhythmization refers to the induction of accenting patterns during the presentation of identical auditory pulses at an isochronous rate. It can be an automatic process, but it can also be voluntarily controlled. We investigated whether imagined accents can be decoded from brain signals on a single-trial basis, and if there is information shared between perception and imagery in the contrast of accents and non-accents.<br><br>METHODS: Ten subjects perceived and imagined three different metric patterns (two-, three-, and four-beat) superimposed on a steady metronome while electroencephalography (EEG) measurements were made. Shared information between perception and imagery EEG is investigated by means of principal component analysis and by means of single-trial classification.<br><br>RESULTS: Classification of accented from non-accented beats was possible with an average accuracy of 70% for perception and 61% for imagery data. Cross-condition classification yielded significant performance above chance level for a classifier trained on perception and tested on imagery data (up to 66%), and vice versa (up to 60%).<br><br>CONCLUSIONS: Results show that detection of imagined accents is possible and reveal similarity in brain signatures relevant to distinction of accents from non-accents in perception and imagery.<br><br>SIGNIFICANCE: Our results support the idea of shared mechanisms in perception and imagery for auditory processing. This is relevant for a number of clinical settings, most notably by elucidating the basic mechanisms of rhythmic auditory cuing paradigms, e.g. as used in motor rehabilitation or therapy for Parkinson's disease. As a novel Brain-Computer Interface (BCI) paradigm, our results imply a reduction of the necessary BCI training in healthy subjects and in patients.},
}
@article {pmid22432626,
year = {2011},
author = {Górak, A and Stankiewicz, A},
title = {Intensified reaction and separation systems.},
journal = {Annual review of chemical and biomolecular engineering},
volume = {2},
number = {},
pages = {431-451},
doi = {10.1146/annurev-chembioeng-061010-114159},
pmid = {22432626},
issn = {1947-5438},
mesh = {Bioreactors ; Chemical Industry/instrumentation/*methods ; *Chemical Phenomena ; Molecular Structure ; Pharmaceutical Preparations/*chemistry/*isolation &amp; purification ; Thermodynamics ; },
abstract = {Process intensification follows four main goals: to maximize the effectiveness of intra- and intermolecular events, to give each molecule the same processing experience, to optimize the driving forces/maximize specific interfacial areas, and to maximize the synergistic effects of partial processes. This paper shows how these goals can be reached in reaction and separation systems at all relevant time and length scales and is focused on the structuring of reactors and separation units, on the use of different energy forms to improve the reaction and separation, on combining and superimposing of different phenomena in one integrated unit or reactor, and on the application of oscillations for intensification of reaction and separation processes.},
}
@article {pmid22541905,
year = {2010},
author = {Gaunez, N and Larré, S and Pirès, C and Doré, B and Wei, J and Pfister, C and Irani, J},
title = {[French translation and linguistic validation of the questionnaire Bladder Cancer Index (BCI)].},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {22},
number = {6},
pages = {350-353},
doi = {10.1016/j.purol.2011.12.004},
pmid = {22541905},
issn = {1166-7087},
mesh = {Adult ; Aged ; Female ; Humans ; Language ; Male ; Middle Aged ; *Surveys and Questionnaires ; Translating ; *Urinary Bladder Neoplasms/diagnosis ; },
abstract = {OBJECTIVE: Translation and linguistic validation of the French version of Bladder Cancer Index (BCI).<br><br>MATERIAL AND METHODS: A double-back translation of the original Bladder Cancer Index was performed. First, two urologists translated the English version in French. Then, a first consensus meeting between the translators and a group composed of urologists and nurses was achieved. Back-translation of this version was then done by professional translators (Nagpal, Paris) to ensure that no distortion was detected between the two questionnaires. Finally, a pilot study followed by an interview was carried out among one woman and five men having bladder cancer.<br><br>RESULTS: The consensus version is attached to the article. No difficulties were reported by the pilot population to comprehend or to complete this BCI French version.<br><br>CONCLUSION: This BCI French version-attached to the article-makes it possible for researchers among a French population to use this validated and internationally recognized tool among a French population. The impact of various bladder cancer treatment on quality of life could hence be assessed and compared.},
}
@article {pmid22767558,
year = {2010},
author = {Wijeyaratne, SM and Weerasinghe, C and Cassim, MR},
title = {Blunt carotid injury from a penetrating stick: an unexpected injury.},
journal = {BMJ case reports},
volume = {2010},
number = {},
pages = {},
pmid = {22767558},
issn = {1757-790X},
mesh = {Accidental Falls ; Angiography/methods ; Carotid Artery Injuries/complications/diagnostic imaging/*surgery ; Carotid Artery, Common/diagnostic imaging ; Carotid Stenosis/diagnostic imaging/etiology/*surgery ; Endovascular Procedures/methods ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; Neck Injuries/*complications/diagnosis ; Risk Assessment ; *Stents ; Tomography, X-Ray Computed/methods ; Treatment Outcome ; Wounds, Nonpenetrating/*complications/diagnosis ; Wounds, Penetrating/*complications/diagnosis ; },
abstract = {Unattended blunt carotid injury (BCI) has stroke high risk of stroke and screening based on injury probability is recommended. Penetrating forces are not considered high risk and concomitant BCI would go unattended. The authors report a case of a 48-year-old man who fell out of a tree on to an upright stick that penetrated his lateral neck. He presented with impalement, which was removed after surgically laying open the entire wound. The carotid sheath had been breached and the internal jugular vein was bleeding. The adjacent common carotid artery was intact and pulsating with no external evidence of injury. However, injury proximity led to vascular imaging that demonstrated intimal disruption without thrombus or stenosis. Although he remained asymptomatic on heparin, the injury progressed to cause significant lumen stenosis. Endovascular stenting re-established the vessel lumen and he remains well on aspirin 9 months later. Awareness that penetrating neck trauma may cause BCIs is important.},
}
@article {pmid22275198,
year = {2010},
author = {Thakor, N},
title = {In the spotlight: neuroengineering.},
journal = {IEEE reviews in biomedical engineering},
volume = {3},
number = {},
pages = {19-22},
doi = {10.1109/RBME.2010.2086872},
pmid = {22275198},
issn = {1941-1189},
mesh = {Artificial Limbs ; Brain/*physiology ; *Functional Neuroimaging ; Humans ; Memory/physiology ; Neurosciences/*methods ; },
abstract = {Neuroengineering and neuroscience frontiers are being discussed in this report. The forum for grand challenges in neuroengineering that took place in Washington, DC, on May 7-8, 2010. The major themes covered were: brain-machine interface, decoding brain activity, brain as computer-information processor, reverse engineering the brain and functional neuroimaging.},
}
@article {pmid22205874,
year = {2010},
author = {Grimaldi, G and Manto, M},
title = {Neurological tremor: sensors, signal processing and emerging applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {10},
number = {2},
pages = {1399-1422},
pmid = {22205874},
issn = {1424-8220},
mesh = {Biofeedback, Psychology ; Electrodes ; Humans ; Man-Machine Systems ; Movement ; *Signal Processing, Computer-Assisted ; Therapy, Computer-Assisted ; Tremor/*diagnosis/physiopathology/rehabilitation ; User-Computer Interface ; },
abstract = {Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions.},
}
@article {pmid21629761,
year = {2010},
author = {Sato, H and Maharbiz, MM},
title = {Recent developments in the remote radio control of insect flight.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {199},
pmid = {21629761},
issn = {1662-453X},
abstract = {The continuing miniaturization of digital circuits and the development of low power radio systems coupled with continuing studies into the neurophysiology and dynamics of insect flight are enabling a new class of implantable interfaces capable of controlling insects in free flight for extended periods. We provide context for these developments, review the state-of-the-art and discuss future directions in this field.},
}
@article {pmid21438193,
year = {2010},
author = {Machado, S and Araújo, F and Paes, F and Velasques, B and Cunha, M and Budde, H and Basile, LF and Anghinah, R and Arias-Carrión, O and Cagy, M and Piedade, R and de Graaf, TA and Sack, AT and Ribeiro, P},
title = {EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation.},
journal = {Reviews in the neurosciences},
volume = {21},
number = {6},
pages = {451-468},
doi = {10.1515/revneuro.2010.21.6.451},
pmid = {21438193},
issn = {0334-1763},
mesh = {Brain/*physiology ; Central Nervous System Diseases/*pathology/*rehabilitation ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Humans ; *User-Computer Interface ; },
abstract = {Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally 'locked in' their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BCI, the main advances in communication, motor control restoration and the downregulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to downregulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BCI) related to clinical neurorehabilitation of severely limited patients will generate viable clinical applications in the near future.},
}
@article {pmid22444813,
year = {2009},
author = {Clarke, AM and Drennan, MJ and McGee, M and Kenny, DA and Evans, RD and Berry, DP},
title = {Live animal measurements, carcass composition and plasma hormone and metabolite concentrations in male progeny of sires differing in genetic merit for beef production.},
journal = {Animal : an international journal of animal bioscience},
volume = {3},
number = {7},
pages = {933-945},
doi = {10.1017/S1751731109004327},
pmid = {22444813},
issn = {1751-7311},
abstract = {In genetic improvement programmes for beef cattle, the effect of selecting for a given trait or index on other economically important traits, or their predictors, must be quantified to ensure no deleterious consequential effects go unnoticed. The objective was to compare live animal measurements, carcass composition and plasma hormone and metabolite concentrations of male progeny of sires selected on an economic index in Ireland. This beef carcass index (BCI) is expressed in euros and based on weaning weight, feed intake, carcass weight and carcass conformation and fat scores. The index is used to aid in the genetic comparison of animals for the expected profitability of their progeny at slaughter. A total of 107 progeny from beef sires of high (n = 11) or low (n = 11) genetic merit for the BCI were compared in either a bull (slaughtered at 16 months of age) or steer (slaughtered at 24 months of age) production system, following purchase after weaning (8 months of age) from commercial beef herds. Data were analysed as a 2 × 2 factorial design (two levels of genetic merit by two production systems). Progeny of high BCI sires had heavier carcasses, greater (P < 0.01) muscularity scores after weaning, greater (P < 0.05) skeletal scores and scanned muscle depth pre-slaughter, higher (P < 0.05) plasma insulin concentrations and greater (P < 0.01) animal value (obtained by multiplying carcass weight by carcass value, which was based on the weight of meat in each cut by its commercial value) than progeny of low BCI sires. Regression of progeny performance on sire genetic merit was also undertaken across the entire data set. In steers, the effect of BCI on carcass meat proportion, calculated carcass value (c/kg) and animal value was positive (P < 0.01), while a negative association was observed for scanned fat depth pre-slaughter and carcass fat proportion (P < 0.01), but there was no effect in bulls. The effect of sire expected progeny difference (EPD) for carcass weight followed the same trends as BCI. Muscularity scores, carcass meat proportion and calculated carcass value increased, whereas scanned fat depth, carcass fat and bone proportions decreased with increasing sire EPD for conformation score. The opposite association was observed for sire EPD for fat score. Results from this study show that selection using the BCI had positive effects on live animal muscularity, carcass meat proportion, proportions of high-value cuts and carcass value in steer progeny, which are desirable traits in beef production.},
}
@article {pmid21572940,
year = {2009},
author = {Watanabe, H and Takahashi, H and Nakao, M and Walton, K and Llinás, RR},
title = {Intravascular Neural Interface with Nanowire Electrode.},
journal = {Electronics and communications in Japan = Denki Gakkai ronbunshi},
volume = {92},
number = {7},
pages = {29-37},
pmid = {21572940},
issn = {1942-9533},
support = {P01 NS013742-28/NS/NINDS NIH HHS/United States ; P01 NS013742-25/NS/NINDS NIH HHS/United States ; P01 NS013742-31/NS/NINDS NIH HHS/United States ; P01 NS013742-26/NS/NINDS NIH HHS/United States ; P01 NS013742-28S1/NS/NINDS NIH HHS/United States ; P01 NS013742-31S19001/NS/NINDS NIH HHS/United States ; P01 NS013742-22/NS/NINDS NIH HHS/United States ; P01 NS013742-30/NS/NINDS NIH HHS/United States ; P01 NS013742-29A1/NS/NINDS NIH HHS/United States ; P01 NS013742-27S1/NS/NINDS NIH HHS/United States ; P01 NS013742-27/NS/NINDS NIH HHS/United States ; P01 NS013742-31S1/NS/NINDS NIH HHS/United States ; P01 NS013742-24A1/NS/NINDS NIH HHS/United States ; P01 NS013742-23/NS/NINDS NIH HHS/United States ; P01 NS013742-23S1/NS/NINDS NIH HHS/United States ; },
abstract = {A minimally invasive electrical recording and stimulating technique capable of simultaneously monitoring the activity of a significant number (e.g., 10(3) to 10(4)) of neurons is an absolute prerequisite in developing an effective brain-machine interface. Although there are many excellent methodologies for recording single or multiple neurons, there has been no methodology for accessing large numbers of cells in a behaving experimental animal or human individual. Brain vascular parenchyma is a promising candidate for addressing this problem. It has been proposed [1, 2] that a multitude of nanowire electrodes introduced into the central nervous system through the vascular system to address any brain area may be a possible solution. In this study we implement a design for such microcatheter for ex vivo experiments. Using Wollaston platinum wire, we design a submicron-scale electrode and develop a fabrication method. We then evaluate the mechanical properties of the electrode in a flow when passing through the intricacies of the capillary bed in ex vivo Xenopus laevis experiments. Furthermore, we demonstrate the feasibility of intravascular recording in the spinal cord of Xenopus laevis.},
}
@article {pmid22444765,
year = {2009},
author = {Clarke, AM and Drennan, MJ and McGee, M and Kenny, DA and Evans, RD and Berry, DP},
title = {Intake, growth and carcass traits in male progeny of sires differing in genetic merit for beef production.},
journal = {Animal : an international journal of animal bioscience},
volume = {3},
number = {6},
pages = {791-801},
doi = {10.1017/S1751731109004200},
pmid = {22444765},
issn = {1751-7311},
abstract = {Validation of economic indexes under a controlled experimental environment, can aid in their acceptance and use as breeding tools to increase herd profitability. The objective of this study was to compare intake, growth and carcass traits in bull and steer progeny of high and low ranking sires, for genetic merit in an economic index. The Beef Carcass Index (BCI; expressed in euro (€) and based on weaning weight, feed intake, carcass weight, carcass conformation and fat scores) was generated by the Irish Cattle Breeding Federation as a tool to compare animals on genetic merit for the expected profitability of their progeny at slaughter. A total of 107 male suckler herd progeny, from 22 late-maturing 'continental' beef sires of high (n = 11) or low (n = 11) BCI were compared under either a bull or steer production system, and slaughtered at approximately 16 and 24 months of age, respectively. All progeny were purchased after weaning at approximately 6 to 8 months of age. Dry matter (DM) intake and live-weight gain in steer progeny offered grazed grass or grass silage alone, did not differ between the two genetic groups. Similarly, DM intake and feed efficiency did not differ between genetic groups during an ad libitum concentrate-finishing period on either production system. Carcasses of progeny of high BCI sires were 14 kg heavier (P < 0.05) than those of low BCI sires. In a series of regression analyses, increasing sire BCI resulted in increases in carcass weight (P < 0.01) and carcass conformation (P = 0.051) scores, and decreases in carcass fat (P < 0.001) scores, but had no effect on weaning weight or DM intake of the progeny. Each unit increase in sire expected progeny difference led to an increase in progeny weaning weight, DM intake, carcass weight, carcass conformation score and carcass fat score of 1.0 (s.e. = 0.53) kg, 1.1 (s.e. = 0.32) kg, 1.3 (s.e. = 0.31) kg, 0.9 (s.e. = 0.32; scale 1 to 15) and 1.0 (s.e. = 0.25; scale 1 to 15), respectively, none of which differed from the theoretical expectation of unity. The expected difference in profitability at slaughter between progeny of the high and low BCI sires was €42, whereas the observed phenotypic profit differential of the progeny was €53 in favour of the high BCI sires. Results from this study indicate that the BCI is a useful tool in the selection of genetically superior sires, and that actual progeny performance under the conditions of this study is within expectations for both bull and steer beef production systems.},
}
@article {pmid22275040,
year = {2009},
author = {Thakor, N},
title = {In the spotlight: neuroengineering.},
journal = {IEEE reviews in biomedical engineering},
volume = {2},
number = {},
pages = {18-20},
doi = {10.1109/RBME.2009.2034697},
pmid = {22275040},
issn = {1941-1189},
mesh = {Biomedical Engineering/*methods ; Brain/*physiology ; Deep Brain Stimulation/methods ; Humans ; Neuroimaging/*methods ; Neurons/physiology ; *User-Computer Interface ; },
abstract = {This article reviews some of the highlights of what has been covered and explored at various conferences and published in major journals about neuroengineering. The "hot" areas, at least as measured by popularity and visibility, continue to be the fields of brain-computer interface (BCI) or brain-machine interface (BMI), the application of BMI to neural prosthesis, deep brain stimulation (DBS), neural interface technologies, and brain imaging.},
}
@article {pmid22275038,
year = {2009},
author = {Cerutti, S},
title = {In the spotlight: biomedical signal processing--a well established discipline or a paradigm to promising integrated visions?.},
journal = {IEEE reviews in biomedical engineering},
volume = {2},
number = {},
pages = {9-11},
doi = {10.1109/RBME.2009.2034698},
pmid = {22275038},
issn = {1941-1189},
mesh = {Algorithms ; Biomedical Engineering/instrumentation/methods ; *Cardiovascular Physiological Phenomena ; Heart Rate/physiology ; Humans ; *Image Interpretation, Computer-Assisted ; *Image Processing, Computer-Assisted ; Models, Biological ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Biomedical signals carry fundamental information about the nature and the status of the living systems under study. A proper processing of these signals obtains useful physiological and clinical information. A closer integration between signal processing and modeling of the relevant biological systems is capable to directly attribute pathophysiological meaning to the parameters obtained from the processing and vice versa. Another issue of great interest in which BSP plays an important role is the Brain-Computer Interface (BCI) or Brain-Machine Interface (BMI) where fast and reliable signal processing approaches are fundamental for a practical implementation. The physiological mechanisms underlying these heart rate variability findings are supposed to be related to stochastic processes at the cellular level, to influence of respiration on the heart rate, and to the interactions of the multiple feedback loops regulating the cardiovascular system. Another important area in which BSP plays a pivotal role is the "computational genomics and proteomics." It is true that "traditional" biomedical engineering studies biomedical signals which are obtained at the level of the major physiological systems.},
}
@article {pmid22151145,
year = {2008},
author = {Obuchi, T and Katayama, Y and Kobayashi, K and Oshima, H and Fukaya, C and Yamamoto, T},
title = {Direction and predictive factors for the shift of brain structure during deep brain stimulation electrode implantation for advanced Parkinson's disease.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {11},
number = {4},
pages = {302-310},
doi = {10.1111/j.1525-1403.2008.00180.x},
pmid = {22151145},
issn = {1094-7159},
abstract = {Objectives. The aims of this study were to clarify the direction and degree of brain shift, and to determine the predictive factors for a brain shift during deep brain stimulation (DBS) of the subthalamic nucleus (STN). Materials and Methods. To evaluate the brain shift during bilateral STN-DBS, the position of the anterior commissure (AC), posterior commissure (PC), midcommissure point (MC), and tip of the frontal lobe and anterior horn of the lateral ventricle were calculated pre- and poststereotactic operations in the three-dimensional direction employing special software (Leksell SurgiPlan). To determine the predictive factors for a brain shift, patient's age, operation hours, width of the third ventricle, bicaudate index (BCI), and cella media index (CMI) were compared with the shift of MC. Results. In 50 patients, the MC shifted mainly in the posterior direction (y-axis: 1.27 ± 0.7 mm), and the shifts in the inferior direction (z-axis: 0.11 ± 0.43 mm) and lateral direction (x-axis: 0.02 ± 0.39 mm) were small. The shift of the MC in the posterior direction correlated well with the shift of the tip of the anterior lobe and anterior horn. Among the predictive factors examined, namely, the patient's age, operation hours, width of the third ventricle, BCI, and CMI, only the CMI showed a correlation with the shift of the MC (r = 0.42, p < 0.01, Pearson's correlation coefficient; and p < 0.05, logistic regression analysis). Conclusions. In bilateral STN-DBS, brain shift occurred mainly in the posterior direction, and the CMI is useful for the prediction of a brain shift. Enlargement of the body part of the lateral ventricle is the most reliable factor for predicting a brain shift.},
}
@article {pmid22274896,
year = {2008},
author = {Thakor, N},
title = {In the Spotlight: Neuroengineering.},
journal = {IEEE reviews in biomedical engineering},
volume = {1},
number = {},
pages = {18-20},
doi = {10.1109/RBME.2008.2008231},
pmid = {22274896},
issn = {1941-1189},
mesh = {Animals ; Biomedical Engineering/*methods/*trends ; Biomedical Technology/*methods/*trends ; *Models, Neurological ; Portraits as Topic ; },
abstract = {This article reviews current researches in the field of neuroengineering. Special focus is given to neural prosthesis, neuroprosthetic control and brain-computer interfaces (BCIs) for anthropomorphic and sensory prosthetic control.},
}
@article {pmid23217761,
year = {2007},
author = {Wolpe, PR},
title = {Ethical and social challenges of brain-computer interfaces.},
journal = {The virtual mentor : VM},
volume = {9},
number = {2},
pages = {128-131},
doi = {10.1001/virtualmentor.2007.9.2.msoc1-0702},
pmid = {23217761},
issn = {1937-7010},
}
@article {pmid22151336,
year = {2004},
author = {Boord, P and Barriskill, A and Craig, A and Nguyen, H},
title = {Brain-Computer Interface-FES Integration: Towards a Hands-free Neuroprosthesis Command System.},
journal = {Neuromodulation : journal of the International Neuromodulation Society},
volume = {7},
number = {4},
pages = {267-276},
doi = {10.1111/j.1094-7159.2004.04212.x},
pmid = {22151336},
issn = {1094-7159},
abstract = {This paper presents a critical review of brain-computer interfaces (BCIs) and their potential for neuroprosthetic applications. Summaries are provided for the command interface requirements of hand grasp, multijoint, and lower extremity neuroprostheses, and the characteristics of various BCIs are discussed in relation to these requirements. The review highlights the current limitations of BCIs and areas of research that need to be addressed to enhance BCI-FES integration.},
}
@article {pmid21323821,
year = {2011},
author = {Feibelmann, S and Yang, TS and Uzogara, EE and Sepucha, K},
title = {What does it take to have sustained use of decision aids? A programme evaluation for the Breast Cancer Initiative.},
journal = {Health expectations : an international journal of public participation in health care and health policy},
volume = {14 Suppl 1},
number = {Suppl 1},
pages = {85-95},
pmid = {21323821},
issn = {1369-7625},
mesh = {Breast Neoplasms/*therapy ; *Decision Support Techniques ; Female ; Humans ; *Information Dissemination ; Patient Education as Topic/*methods ; },
abstract = {BACKGROUND: The Breast Cancer Initiative (BCI) was started in 2002 to disseminate breast cancer decision aids (PtDAs) to providers.<br><br>METHODS: We analysed BCI programme data for 195 sites and determined the proportion of sites involved in each of five stages of dissemination and implementation of PtDAs. We conducted cross-sectional mail and telephone surveys of 79 sites with the most interest in implementation. We examined barriers associated with sustained use of the PtDAs.<br><br>RESULTS: Since 2002 we attempted contact with 195 sites to join the BCI. The majority indicated interest in using PtDAs 172 of 195 (88%), 93 of 195 signed up for the BCI (48%), 57 of 195 reported distributing PtDAs to at least one patient (57%), and 46 of 195 reported sustained use (24%). We analysed data from interviews with 59 of 79 active sites (75% response rate). The majority of providers 49 of 59 (83%) had watched the PtDAs, and 46 of 59 (78%) distributed them to patients. The most common barriers were lack of a reliable way to identify patients before decisions are made (37%), a lack of time to distribute the PtDAs (22%) and having too many educational materials (15%). Sites that indicated a lack of clinician support as a barrier were significantly less likely to have sustained use compared to sites that didn't (33% vs. 74%, P = 0.02).<br><br>CONCLUSIONS: Community breast cancer providers, both physicians and non-physicians, express a high interest in using PtDAs with their patients. About a quarter of sites report sustained use of the PtDAs in routine care.},
}
@article {pmid21346200,
year = {2011},
author = {Darling, RD and Takatsuki, K and Griffin, AL and Berry, SD},
title = {Eyeblink conditioning contingent on hippocampal theta enhances hippocampal and medial prefrontal responses.},
journal = {Journal of neurophysiology},
volume = {105},
number = {5},
pages = {2213-2224},
doi = {10.1152/jn.00801.2010},
pmid = {21346200},
issn = {1522-1598},
mesh = {Action Potentials/physiology ; Animals ; Blinking/*physiology ; Conditioning, Eyelid/*physiology ; Hippocampus/*physiology ; Prefrontal Cortex/*physiology ; Rabbits ; Random Allocation ; Theta Rhythm/*physiology ; },
abstract = {Trace eyeblink classical conditioning (tEBCC) can be accelerated by making training trials contingent on the naturally generated hippocampal 3- to 7-Hz theta rhythm. However, it is not well-understood how the presence (or absence) of theta affects stimulus-driven changes within the hippocampus and how it correlates with patterns of neural activity in other essential trace conditioning structures, such as the medial prefrontal cortex (mPFC). In the present study, a brain-computer interface delivered paired or unpaired conditioning trials to rabbits during the explicit presence (T(+)) or absence (T(-)) of theta, yielding significantly faster behavioral learning in the T(+)-paired group. The stimulus-elicited hippocampal unit responses were larger and more rhythmic in the T(+)-paired group. This facilitation of unit responses was complemented by differences in the hippocampal local field potentials (LFP), with the T(+)-paired group demonstrating more coherent stimulus-evoked theta than T(-)-paired animals and both unpaired groups. mPFC unit responses in the rapid learning T(+)-paired group displayed a clear inhibitory/excitatory sequential pattern of response to the tone that was not seen in any other group. Furthermore, sustained mPFC unit excitation continued through the trace interval in T(+) animals but not in T(-) animals. Thus theta-contingent training is accompanied by 1) acceleration in behavioral learning, 2) enhancement of the hippocampal unit and LFP responses, and 3) enhancement of mPFC unit responses. Together, these data provide evidence that pretrial hippocampal state is related to enhanced neural activity in critical structures of the distributed network supporting the acquisition of tEBCC.},
}
@article {pmid21342856,
year = {2011},
author = {Zhang, J and Sudre, G and Li, X and Wang, W and Weber, DJ and Bagic, A},
title = {Clustering linear discriminant analysis for MEG-based brain computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {3},
pages = {221-231},
doi = {10.1109/TNSRE.2011.2116125},
pmid = {21342856},
issn = {1558-0210},
support = {1R01EB007749/EB/NIBIB NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; KL2 RR024154/RR/NCRR NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; Cluster Analysis ; Data Interpretation, Statistical ; Discriminant Analysis ; Electroencephalography ; Humans ; Linear Models ; Magnetoencephalography/methods/*statistics &amp; numerical data ; Movement/physiology ; Psychomotor Performance/physiology ; *User-Computer Interface ; },
abstract = {In this paper, we propose a clustering linear discriminant analysis algorithm (CLDA) to accurately decode hand movement directions from a small number of training trials for magnetoencephalography-based brain computer interfaces (BCIs). CLDA first applies a spectral clustering algorithm to automatically partition the BCI features into several groups where the within-group correlation is maximized and the between-group correlation is minimized. As such, the covariance matrix of all features can be approximated as a block diagonal matrix, thereby facilitating us to accurately extract the correlation information required by movement decoding from a small set of training data. The efficiency of the proposed CLDA algorithm is theoretically studied and an error bound is derived. Our experiment on movement decoding of five human subjects demonstrates that CLDA achieves superior decoding accuracy over other traditional approaches. The average accuracy of CLDA is 87% for single-trial movement decoding of four directions (i.e., up, down, left, and right).},
}
@article {pmid21342855,
year = {2011},
author = {Park, C and Looney, D and Kidmose, P and Ungstrup, M and Mandic, DP},
title = {Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {4},
pages = {366-373},
doi = {10.1109/TNSRE.2011.2116805},
pmid = {21342855},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; Cognition/physiology ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*methods/statistics &amp; numerical data ; Functional Laterality/*physiology ; Humans ; Imagination/physiology ; Nonlinear Dynamics ; Psychomotor Performance/physiology ; Rotation ; Signal-To-Noise Ratio ; User-Computer Interface ; },
abstract = {A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG). It is shown how this property can be used to obtain a more accurate estimate of the marginalized spectrum, critical for the localized calculation of amplitude asymmetry in frequency. Simulations on synthetic data sets and feature estimation for a brain-computer interface (BCI) application are used to validate the proposed asymmetry estimation methodology.},
}
@article {pmid21335304,
year = {2011},
author = {Panicker, RC and Puthusserypady, S and Sun, Y},
title = {An asynchronous P300 BCI with SSVEP-based control state detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {6},
pages = {1781-1788},
doi = {10.1109/TBME.2011.2116018},
pmid = {21335304},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Area Under Curve ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/physiology ; Female ; Humans ; Male ; *Neural Prostheses ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {In this paper, an asynchronous brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEPs) paradigms is proposed. The information transfer is accomplished using P300 event-related potential paradigm and the control state (CS) detection is achieved using SSVEP, overlaid on the P300 base system. Offline and online experiments have been performed with ten subjects to validate the proposed system. It is shown to achieve fast and accurate CS detection without significantly compromising the performance. In online experiments, the system is found to be capable of achieving an average data transfer rate of 19.05 bits/min, with CS detection accuracy of about 88%.},
}
@article {pmid21335094,
year = {2011},
author = {Xiang, XL and Xi, YL and Wen, XL and Zhang, G and Wang, JX and Hu, K},
title = {Patterns and processes in the genetic differentiation of the Brachionus calyciflorus complex, a passively dispersing freshwater zooplankton.},
journal = {Molecular phylogenetics and evolution},
volume = {59},
number = {2},
pages = {386-398},
doi = {10.1016/j.ympev.2011.02.011},
pmid = {21335094},
issn = {1095-9513},
mesh = {Animals ; Base Sequence ; China ; Crosses, Genetic ; DNA Primers/genetics ; DNA, Ribosomal/genetics ; *Demography ; Evolution, Molecular ; *Genetic Speciation ; *Genetic Variation ; Likelihood Functions ; Models, Genetic ; Molecular Sequence Data ; *Phylogeny ; Rotifera/*genetics ; Sequence Analysis ; Sequence Analysis, DNA ; Species Specificity ; },
abstract = {Elucidating the evolutionary patterns and processes of extant species is an important objective of any research program that seeks to understand population divergence and, ultimately, speciation. The island-like nature and temporal fluctuation of limnetic habitats create opportunities for genetic differentiation in rotifers through space and time. To gain further understanding of spatio-temporal patterns of genetic differentiation in rotifers other than the well-studied Brachionus plicatilis complex in brackish water, a total of 318 nrDNA ITS sequences from the B. calyciflorus complex in freshwater were analysed using phylogenetic and phylogeographic methods. DNA taxonomy conducted by both the sequence divergence and the GMYC model suggested the occurrence of six potential cryptic species, supported also by reproductive isolation among the tested lineages. The significant genetic differentiation and non-significant correlation between geographic and genetic distances existed in the most abundant cryptic species, BcI-W and Bc-SW. The large proportion of genetic variability for cryptic species Bc-SW was due to differences between sampling localities within seasons, rather than between different seasons. Nested Clade Analysis suggested allopatric or past fragmentation, contiguous range expansion and long-distance colonization possibly coupled with subsequent fragmentation as the probable main forces shaping the present-day phylogeographic structure of the B. calyciflorus species complex.},
}
@article {pmid21335029,
year = {2011},
author = {Kim, DW and Hwang, HJ and Lim, JH and Lee, YH and Jung, KY and Im, CH},
title = {Classification of selective attention to auditory stimuli: toward vision-free brain-computer interfacing.},
journal = {Journal of neuroscience methods},
volume = {197},
number = {1},
pages = {180-185},
doi = {10.1016/j.jneumeth.2011.02.007},
pmid = {21335029},
issn = {1872-678X},
mesh = {Acoustic Stimulation/methods ; Adult ; Attention/*physiology ; Auditory Perception/*physiology ; Brain/*physiology ; Electroencephalography/*methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Male ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) is a developing, novel mode of communication for individuals with severe motor impairments or those who have no other options for communication aside from their brain signals. However, the majority of current BCI systems are based on visual stimuli or visual feedback, which may not be applicable for severe locked-in patients that have lost their eyesight or the ability to control their eye movements. In the present study, we investigated the feasibility of using auditory steady-state responses (ASSRs), elicited by selective attention to a specific sound source, as an electroencephalography (EEG)-based BCI paradigm. In our experiment, two pure tone burst trains with different beat frequencies (37 and 43 Hz) were generated simultaneously from two speakers located at different positions (left and right). Six participants were instructed to close their eyes and concentrate their attention on either auditory stimulus according to the instructions provided randomly through the speakers during the inter-stimulus interval. EEG signals were recorded at multiple electrodes mounted over the temporal, occipital, and parietal cortices. We then extracted feature vectors by combining spectral power densities evaluated at the two beat frequencies. Our experimental results showed high classification accuracies (64.67%, 30 commands/min, information transfer rate (ITR) = 1.89 bits/min; 74.00%, 12 commands/min, ITR = 2.08 bits/min; 82.00%, 6 commands/min, ITR = 1.92 bits/min; 84.33%, 3 commands/min, ITR = 1.12 bits/min; without any artifact rejection, inter-trial interval = 6s), enough to be used for a binary decision. Based on the suggested paradigm, we implemented a first online ASSR-based BCI system that demonstrated the possibility of materializing a totally vision-free BCI system.},
}
@article {pmid21314273,
year = {2011},
author = {Yanagisawa, T and Hirata, M and Saitoh, Y and Goto, T and Kishima, H and Fukuma, R and Yokoi, H and Kamitani, Y and Yoshimine, T},
title = {Real-time control of a prosthetic hand using human electrocorticography signals.},
journal = {Journal of neurosurgery},
volume = {114},
number = {6},
pages = {1715-1722},
doi = {10.3171/2011.1.JNS101421},
pmid = {21314273},
issn = {1933-0693},
mesh = {*Artificial Limbs ; Electroencephalography/*methods ; Hand ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; Movement/*physiology ; Stroke/*physiopathology ; *User-Computer Interface ; },
abstract = {OBJECT: A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements.<br><br>METHODS: A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analysis was performed with the ECoG signals to select 3 frequency bands (1-8, 25-40, and 80-150 Hz) that revealed characteristic power modulation during the movements. Using these selected features, 2 classifiers (decoders) were trained to predict the movement state--that is, whether the patient was moving his hand or not--and the movement type based on a linear support vector machine. The decoding accuracy was compared among the 3 frequency bands to identify the most informative features. With the trained decoders, novel ECoG signals were decoded online while the patient performed the same task without cues (free-run period). According to the results of the real-time decoding, the prosthetic hand mimicked the patient's hand movements.<br><br>RESULTS: Offline cross-validation analysis of the ECoG data measured during the calibration period revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively. Using the trained decoders, the onset of the hand movement was detected within 0.37 &#xb1; 0.29 seconds of the actual movement. At the detected onset timing, the type of movement was inferred with an accuracy of 69.2%. In the free-run period, the patient's hand movements were faithfully mimicked by the prosthetic hand in real time.<br><br>CONCLUSIONS: The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.},
}
@article {pmid21300495,
year = {2012},
author = {Duan, Y and Li, G and Yang, Y and Li, J and Huang, H and Wang, H and Xu, F and Chen, W},
title = {Changes in cerebral hemodynamics after carotid stenting of symptomatic carotid artery.},
journal = {European journal of radiology},
volume = {81},
number = {4},
pages = {744-748},
doi = {10.1016/j.ejrad.2011.01.042},
pmid = {21300495},
issn = {1872-7727},
mesh = {Aged ; Blood Flow Velocity ; Carotid Stenosis/diagnostic imaging/*physiopathology/*surgery ; Cerebral Angiography/*methods ; *Cerebrovascular Circulation ; Female ; Humans ; Male ; Middle Aged ; Perfusion Imaging/*methods ; *Stents ; Tomography, X-Ray Computed/*methods ; Treatment Outcome ; },
abstract = {PURPOSE: To evaluate changes in cerebral hemodynamics after carotid stenting of symptomatic carotid artery in the patients who underwent ischemic stroke caused by carotid artery stenosis.<br><br>METHODS: Twenty patients with unilateral symptomatic carotid artery stenosis received brain computer tomography perfusion (CTP) scan a week before and a week after carotid artery stenting. Three absolute values including mean transit time (MTT), cerebral blood volume (CBV), and cerebral blood flow (CBF) were acquired and analyzed by use of the post-processing software. Six vascular territories such as ACA territory, MCA territory, PCA territory, basal ganglia, watershed between ACA and MCA territory (frontal watershed), watershed between MCA and PCA territory (posterior watershed) were chosen for comparison. Relative parameter values were defined as rCBF (relative CBF), rCBV (relative CBV), rMTT (relative MTT) through comparing absolute values in symptomatic hemispheres to absolute values in asymptomatic hemispheres. The relative perfusion parameter values before treatment were compared with post-treatment values. These analyses were performed by using the paired t test.<br><br>RESULTS: The mean rMTT decreased significantly in ACA territory, MCA territory and two watershed after treatment, while the mean rCBF increased significantly in those areas after treatment. But the mean rCBV had no significant changes in all six vascular territories. In PCA territory, all the parameters had no significant changes.<br><br>CONCLUSION: Carotid artery stenting yields satisfactory cerebral perfusion in ACA territory, MCA territory, basal ganglia and two watersheds.},
}
@article {pmid21300442,
year = {2011},
author = {Zhu, H and Sun, Y and Zeng, J and Sun, H},
title = {Mirror neural training induced by virtual reality in brain-computer interfaces may provide a promising approach for the autism therapy.},
journal = {Medical hypotheses},
volume = {76},
number = {5},
pages = {646-647},
doi = {10.1016/j.mehy.2011.01.022},
pmid = {21300442},
issn = {1532-2777},
mesh = {Autistic Disorder/*therapy ; Brain/*pathology/physiopathology ; Communication ; Electrodes ; Electroencephalography/methods ; Humans ; Imitative Behavior/*physiology ; Language ; Man-Machine Systems ; Models, Theoretical ; Neurons/pathology/physiology ; Psychomotor Performance/*physiology ; Research Design ; Software ; },
abstract = {Previous studies have suggested that the dysfunction of the human mirror neuron system (hMNS) plays an important role in the autism spectrum disorder (ASD). In this work, we propose a novel training program from our interdisciplinary research to improve mirror neuron functions of autistic individuals by using a BCI system with virtual reality technology. It is a promising approach for the autism to learn and develop social communications in a VR environment. A test method for this hypothesis is also provided.},
}
@article {pmid21298109,
year = {2011},
author = {Thelin, J and Jörntell, H and Psouni, E and Garwicz, M and Schouenborg, J and Danielsen, N and Linsmeier, CE},
title = {Implant size and fixation mode strongly influence tissue reactions in the CNS.},
journal = {PloS one},
volume = {6},
number = {1},
pages = {e16267},
pmid = {21298109},
issn = {1932-6203},
mesh = {Animals ; Astrocytes ; *Brain ; Cell Shape ; Cell Survival ; Cerebral Cortex/cytology/metabolism ; Electrodes ; *Electrodes, Implanted/adverse effects ; Equipment Design ; *Implants, Experimental/adverse effects ; Microglia ; Microscopy, Fluorescence ; *Neurons/metabolism ; Rats ; },
abstract = {The function of chronic brain machine interfaces depends on stable electrical contact between neurons and electrodes. A key step in the development of interfaces is therefore to identify implant configurations that minimize adverse long-term tissue reactions. To this end, we here characterized the separate and combined effects of implant size and fixation mode at 6 and 12 weeks post implantation in rat (n = 24) cerebral cortex. Neurons and activated microglia and astrocytes were visualized using NeuN, ED1 and GFAP immunofluorescence microscopy, respectively. The contributions of individual experimental variables to the tissue response were quantified. Implants tethered to the skull caused larger tissue reactions than un-tethered implants. Small diameter (50 µm) implants elicited smaller tissue reactions and resulted in the survival of larger numbers of neurons than did large diameter (200 µm) implants. In addition, tethering resulted in an oval-shaped cavity, with a cross-section area larger than that of the implant itself, and in marked changes in morphology and organization of neurons in the region closest to the tissue interface. Most importantly, for implants that were both large diameter and tethered, glia activation was still ongoing 12 weeks after implantation, as indicated by an increase in GFAP staining between week 6 and 12, while this pattern was not observed for un-tethered, small diameter implants. Our findings therefore clearly indicate that the combined small diameter, un-tethered implants cause the smallest tissue reactions.},
}
@article {pmid21297944,
year = {2011},
author = {Xu, P and Yang, P and Lei, X and Yao, D},
title = {An enhanced probabilistic LDA for multi-class brain computer interface.},
journal = {PloS one},
volume = {6},
number = {1},
pages = {e14634},
pmid = {21297944},
issn = {1932-6203},
mesh = {*Artificial Intelligence ; Bayes Theorem ; Brain/*physiology ; Computer Systems ; Humans ; *Probability ; *User-Computer Interface ; },
abstract = {BACKGROUND: There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results.<br><br>In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA) for multi-class motor imagery BCI, which utilized Bayesian linear discriminant analysis (BLDA) with probabilistic output to improve the classification performance. EBLDA builds a new classifier that enlarges training dataset by adding test samples with high probability. EBLDA is based on the hypothesis that unlabeled samples with high probability provide valuable information to enhance learning process and generate a classifier with refined decision boundaries. To investigate the performance of EBLDA, we first used carefully designed simulated datasets to study how EBLDA works. Then, we adopted a real BCI dataset for further evaluation. The current study shows that: 1) Probabilistic information can improve the performance of BCI for subjects with high kappa coefficient; 2) With supplementary training samples from the test samples of high probability, EBLDA is significantly better than BLDA in classification, especially for small training datasets, in which EBLDA can obtain a refined decision boundary by a shift of BLDA decision boundary with the support of the information from test samples.<br><br>CONCLUSIONS/SIGNIFICANCE: The proposed EBLDA could potentially reduce training effort. Therefore, it is valuable for us to realize an effective online BCI system, especially for multi-class BCI systems.},
}
@article {pmid21295863,
year = {2011},
author = {Murphy, J and Summerfield, AQ and O'Donoghue, GM and Moore, DR},
title = {Spatial hearing of normally hearing and cochlear implanted children.},
journal = {International journal of pediatric otorhinolaryngology},
volume = {75},
number = {4},
pages = {489-494},
pmid = {21295863},
issn = {1872-8464},
support = {MC_U135097130/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adolescent ; Age Factors ; Ambulatory Care ; Auditory Perception/*physiology ; Auditory Threshold/physiology ; Case-Control Studies ; Child ; Cochlear Implantation/methods ; *Cochlear Implants ; Deafness/congenital/diagnosis/*surgery ; Female ; Hearing/*physiology ; Hearing Tests/methods ; Humans ; Male ; Prognosis ; Reference Values ; Risk Assessment ; Sex Factors ; Treatment Outcome ; },
abstract = {OBJECTIVE: Spatial hearing uses both monaural and binaural mechanisms that require sensitive hearing for normal function. Deaf children using either bilateral (BCI) or unilateral (UCI) cochlear implants would thus be expected to have poorer spatial hearing than normally hearing (NH) children. However, the relationship between spatial hearing in these various listener groups has not previously been extensively tested under ecologically valid conditions using a homogeneous group of children who are UCI users. We predicted that NH listeners would outperform BCI listeners who would, in turn, outperform UCI listeners.<br><br>METHODS: We tested two methods of spatial hearing to provide norms for NH and UCI using children and preliminary data for BCI users. NH children (n=40) were age matched (6-15 years) to UCI (n=12) and BCI (n=6) listeners. Testing used a horizontal ring of loudspeakers within a booth in a hospital outpatient clinic. In a 'lateral release' task, single nouns were presented frontally, and masking noises were presented frontally, or 90&#xb0; left or right. In a 'localization' task, allowing head movements, nouns were presented from loudspeakers separated by 30&#xb0;, 60&#xb0; or 120&#xb0; about the midline.<br><br>RESULTS: Normally hearing children improved with age in speech detection in noise, but not in quiet or in lateral release. Implant users performed more poorly on all tasks. For frontal signals and noise, UCI and BCI listeners did not differ. For lateral noise, BCI listeners performed better on both sides (within ~2 dB of NH), whereas UCI listeners benefited only when the noise was opposite the unimplanted ear. Both the BCI and, surprisingly, the UCI listeners performed better than chance at all loudspeaker separations on the ecologically valid, localization task. However, the BCI listeners performed about twice as well and, in two cases, approached the performance of NH children.<br><br>CONCLUSION: Children using either UCI or BCI have useful spatial hearing. BCI listeners gain benefits on both sides, and localize better, but not as well as NH listeners.},
}
@article {pmid21278858,
year = {2011},
author = {Ryan, DB and Frye, GE and Townsend, G and Berry, DR and Mesa-G, S and Gates, NA and Sellers, EW},
title = {Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication.},
journal = {International journal of human-computer interaction},
volume = {27},
number = {1},
pages = {69-84},
pmid = {21278858},
issn = {1044-7318},
support = {R21 DC010470-01/DC/NIDCD NIH HHS/United States ; R21 DC010470/DC/NIDCD NIH HHS/United States ; R33 DC010470/DC/NIDCD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R15 DC011002-01/DC/NIDCD NIH HHS/United States ; R15 DC011002/DC/NIDCD NIH HHS/United States ; },
abstract = {This study compared a conventional P300 speller brain-computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8×9 matrix of letters, numbers, and other keyboard commands was used. Participants (n = 24) were required to correctly complete the same 58 character sentence (i.e., correcting for errors) using the predictive speller (PS) and the non-predictive speller (NS), counterbalanced. The PS produced significantly higher OCMs than the NS. Time to complete the task in the PS condition was 12min 43sec as compared to 20min 20sec in the NS condition. Despite the marked improvement in overall output, accuracy was significantly higher in the NS paradigm. P300 amplitudes were significantly larger in the NS than in the PS paradigm; which is attributed to increased workload and task demands. These results demonstrate the potential efficacy of predictive spelling in the context of BCI.},
}
@article {pmid21278024,
year = {2011},
author = {Kim, SP and Simeral, JD and Hochberg, LR and Donoghue, JP and Friehs, GM and Black, MJ},
title = {Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {2},
pages = {193-203},
pmid = {21278024},
issn = {1558-0210},
support = {R37 NS025074/NS/NINDS NIH HHS/United States ; R01DC009899/DC/NIDCD NIH HHS/United States ; RC1HD063931/HD/NICHD NIH HHS/United States ; R01 DC009899-02/DC/NIDCD NIH HHS/United States ; N01 HD053403/HD/NICHD NIH HHS/United States ; R56 NS025074-23/NS/NINDS NIH HHS/United States ; RC1 HD063931/HD/NICHD NIH HHS/United States ; N01HD53403/HD/NICHD NIH HHS/United States ; R01 DC009899-01/DC/NIDCD NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 NS50867-01/NS/NINDS NIH HHS/United States ; R01 DC009899/DC/NIDCD NIH HHS/United States ; RC1 HD063931-02/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/complications ; Feedback, Psychological ; Female ; Humans ; Intention ; Learning ; Male ; Middle Aged ; Models, Neurological ; Models, Statistical ; Motor Cortex/cytology/*physiology ; Neurons/physiology ; Psychomotor Performance/physiology ; *Quadriplegia/etiology ; Stroke/complications ; *User-Computer Interface ; },
abstract = {We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (< 3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (∼40) can be used for natural point-and-click 2-D cursor control of a personal computer.},
}
@article {pmid21276859,
year = {2011},
author = {Besserve, M and Martinerie, J and Garnero, L},
title = {Improving quantification of functional networks with EEG inverse problem: evidence from a decoding point of view.},
journal = {NeuroImage},
volume = {55},
number = {4},
pages = {1536-1547},
doi = {10.1016/j.neuroimage.2011.01.056},
pmid = {21276859},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Cognition/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Models, Neurological ; Movement/*physiology ; Nerve Net/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Decoding experimental conditions from single trial Electroencephalographic (EEG) signals is becoming a major challenge for the study of brain function and real-time applications such as Brain Computer Interface. EEG source reconstruction offers principled ways to estimate the cortical activities from EEG signals. But to what extent it can enhance informative brain signals in single trial has not been addressed in a general setting. We tested this using the minimum norm estimate solution (MNE) to estimate spectral power and coherence features at the cortical level. With a fast implementation, we computed a support vector machine (SVM) classifier output from these quantities in real-time, without prior on the relevant functional networks. We applied this approach to single trial decoding of ongoing mental imagery tasks using EEG data recorded in 5 subjects. Our results show that reconstructing the underlying cortical network dynamics significantly outperforms a usual electrode level approach in terms of information transfer and also reduces redundancy between coherence and power features, supporting a decrease of volume conduction effects. Additionally, the classifier coefficients reflect the most informative features of network activity, showing an important contribution of localized motor and sensory brain areas, and of coherence between areas up to 6cm distance. This study provides a computationally efficient and interpretable strategy to extract information from functional networks at the cortical level in single trial. Moreover, this sets a general framework to evaluate the performance of EEG source reconstruction methods by their decoding abilities.},
}
@article {pmid21267657,
year = {2011},
author = {Kamrunnahar, M and Dias, NS and Schiff, SJ},
title = {Toward a model-based predictive controller design in brain-computer interfaces.},
journal = {Annals of biomedical engineering},
volume = {39},
number = {5},
pages = {1482-1492},
pmid = {21267657},
issn = {1573-9686},
support = {K02 MH001493/MH/NIMH NIH HHS/United States ; K25 NS061001/NS/NINDS NIH HHS/United States ; K02MH01493/MH/NIMH NIH HHS/United States ; K25NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Computers ; Humans ; *Models, Biological ; *User-Computer Interface ; },
abstract = {A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.},
}
@article {pmid21257387,
year = {2011},
author = {Gowreesunker, BV and Tewfik, AH and Tadipatri, VA and Ashe, J and Pellize, G and Gupta, R},
title = {A subspace approach to learning recurrent features from brain activity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {3},
pages = {240-248},
doi = {10.1109/TNSRE.2011.2106802},
pmid = {21257387},
issn = {1558-0210},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Computer Simulation ; Electroencephalography/methods ; Evoked Potentials, Motor/physiology ; Humans ; Movement/physiology ; Neurons/physiology ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {This paper introduces a novel technique to address the instability and time variability challenges associated with brain activity recorded on different days. A critical challenge when working with brain signal activity is the variability in their characteristics when the signals are collected in different sessions separated by a day or more. Such variability is due to the acute and chronic responses of the brain tissue after implantation, variations as the subject learns to optimize performance, physiological changes in a subject due to prior activity or rest periods and environmental conditions. We propose a novel approach to tackle signal variability by focusing on learning subspaces which are recurrent over time. Furthermore, we illustrate how we can use projections on those subspaces to improve classification for an application such as brain-machine interface (BMI). In this paper, we illustrate the merits of finding recurrent subspaces in the context of movement direction decoding using local field potential (LFP). We introduce two methods for using the learned subspaces in movement direction decoding and show a decoding power improvement from 76% to 88% for a particularly unstable subject and consistent decoding across subjects.},
}
@article {pmid21256567,
year = {2011},
author = {Song, K and Choo, MS and Lee, KS and Han, JY and Lee, YS and Kim, JC and Cho, JS},
title = {The long-term effect of alfuzosin in patients with lower urinary tract symptoms suggestive of benign prostate hyperplasia: evaluation of voiding and storage function with respect to bladder outlet obstruction grade and contractility.},
journal = {Urology},
volume = {77},
number = {5},
pages = {1177-1182},
doi = {10.1016/j.urology.2010.10.012},
pmid = {21256567},
issn = {1527-9995},
mesh = {Adrenergic alpha-1 Receptor Antagonists/*therapeutic use ; Aged ; Aged, 80 and over ; Humans ; Male ; Middle Aged ; Muscle Contraction ; Prospective Studies ; Prostatic Hyperplasia/complications/*drug therapy/*physiopathology ; Prostatism/*drug therapy/etiology/*physiopathology ; Quinazolines/*therapeutic use ; Time Factors ; Urinary Bladder Neck Obstruction/*drug therapy/etiology/*physiopathology ; },
abstract = {OBJECTIVES: To evaluate the efficacy of alfuzosin treatment on voiding and storage in patients with lower urinary tract symptoms (LUTS)/benign prostatic hyperplasia (BPH) with respect to bladder outlet obstruction and contractility.<br><br>METHODS: A 12-month, multicenter, observational, prospective study was conducted at four university hospitals in Korea. Patients were divided into four groups: group 1 (bladder outlet obstruction index (BOOI) &#x2265;20, bladder contractility index (BCI) &#x2265;100), group 2 (BOOI &#x2265;20, BCI <100), group 3 (BOOI <20, BCI &#x2265;100), and group 4 (BOOI <20, BCI <100), with respect to BOOI and BCI evaluated by pressure-flow study. Treatment efficacy was analyzed by validated symptom scores.<br><br>RESULTS: Two-hundred thirty-two men with LUTS/BPH were enrolled, and 165 (41, 50, 30, and 44 in groups 1-4, respectively) were followed to the end of the study. After 12 months of alfuzosin treatment, all International Prostate Symptom Score (IPSS) parameters improved in all four groups. Mean improvement in IPSS subscore for voiding was 4.0 points in group 1, 5.5 points in group 2, 5.5 points in group 3, and 3.0 points in group 4. Change in IPSS subscore for storage was 2.5 points in group 1, 3.6 points in group 2, 2.9 points in group 3, and 1.8 points in group 4. There was no difference among four groups in improvements seen in storage or voiding IPSS subscore. International Continence Society male questionnaire scores significantly improved in all four groups with no between-group differences.<br><br>CONCLUSIONS: Alfuzosin treatment in men with LUTS indicative of BPH effectively improved voiding and storage symptoms regardless of BOOI or BCI.},
}
@article {pmid21256234,
year = {2011},
author = {Halder, S and Agorastos, D and Veit, R and Hammer, EM and Lee, S and Varkuti, B and Bogdan, M and Rosenstiel, W and Birbaumer, N and Kübler, A},
title = {Neural mechanisms of brain-computer interface control.},
journal = {NeuroImage},
volume = {55},
number = {4},
pages = {1779-1790},
doi = {10.1016/j.neuroimage.2011.01.021},
pmid = {21256234},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/methods/*physiology ; Brain/*physiology ; Brain Mapping/*methods ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging/*methods ; Male ; *Man-Machine Systems ; Nerve Net/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) enable people with paralysis to communicate with their environment. Motor imagery can be used to generate distinct patterns of cortical activation in the electroencephalogram (EEG) and thus control a BCI. To elucidate the cortical correlates of BCI control, users of a sensory motor rhythm (SMR)-BCI were classified according to their BCI control performance. In a second session these participants performed a motor imagery, motor observation and motor execution task in a functional magnetic resonance imaging (fMRI) scanner. Group difference analysis between high and low aptitude BCI users revealed significantly higher activation of the supplementary motor areas (SMA) for the motor imagery and the motor observation tasks in high aptitude users. Low aptitude users showed no activation when observing movement. The number of activated voxels during motor observation was significantly correlated with accuracy in the EEG-BCI task (r=0.53). Furthermore, the number of activated voxels in the right middle frontal gyrus, an area responsible for processing of movement observation, correlated (r=0.72) with BCI-performance. This strong correlation highlights the importance of these areas for task monitoring and working memory as task goals have to be activated throughout the BCI session. The ability to regulate behavior and the brain through learning mechanisms involving imagery such as required to control a BCI constitutes the consequence of ideo-motor co-activation of motor brain systems during observation of movements. The results demonstrate that acquisition of a sensorimotor program reflected in SMR-BCI-control is tightly related to the recall of such sensorimotor programs during observation of movements and unrelated to the actual execution of these movement sequences.},
}
@article {pmid21252415,
year = {2011},
author = {Rebesco, JM and Miller, LE},
title = {Enhanced detection threshold for in vivo cortical stimulation produced by Hebbian conditioning.},
journal = {Journal of neural engineering},
volume = {8},
number = {1},
pages = {016011},
pmid = {21252415},
issn = {1741-2552},
support = {R01 NS048845-06/NS/NINDS NIH HHS/United States ; F31NS062552/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; F31 NS062552/NS/NINDS NIH HHS/United States ; F31 NS062552-03/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Cerebral Cortex/*physiology ; Conditioning, Psychological/*physiology ; Electric Stimulation/instrumentation/methods ; Electrodes, Implanted ; Random Allocation ; Rats ; Reaction Time/*physiology ; },
abstract = {Normal brain function requires constant adaptation, as an organism learns to associate important sensory stimuli with the appropriate motor actions. Neurological disorders may disrupt these learned associations and require the nervous system to reorganize itself. As a consequence, neural plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. Associative, or Hebbian, pairing of pre- and post-synaptic activity has been shown to alter stimulus-evoked responses in vivo; however, to date, such protocols have not been shown to affect the animal's subsequent behavior. We paired stimulus trains separated by a brief time delay to two electrodes in rat sensorimotor cortex, which changed the statistical pattern of spikes during subsequent behavior. These changes were consistent with strengthened functional connections from the leading electrode to the lagging electrode. We then trained rats to respond to a microstimulation cue, and repeated the paradigm using the cue electrode as the leading electrode. This pairing lowered the rat's ICMS-detection threshold, with the same dependence on intra-electrode time lag that we found for the functional connectivity changes. The timecourse of the behavioral effects was very similar to that of the connectivity changes. We propose that the behavioral changes were a consequence of strengthened functional connections from the cue electrode to other regions of sensorimotor cortex. Such paradigms might be used to augment recovery from a stroke, or to promote adaptation in a bidirectional brain-machine interface.},
}
@article {pmid21245524,
year = {2011},
author = {Cecotti, H and Rivet, B and Congedo, M and Jutten, C and Bertrand, O and Maby, E and Mattout, J},
title = {A robust sensor-selection method for P300 brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {8},
number = {1},
pages = {016001},
doi = {10.1088/1741-2560/8/1/016001},
pmid = {21245524},
issn = {1741-2552},
mesh = {Adult ; Brain ; Brain Mapping/*instrumentation/*methods ; Electroencephalography/*instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; *User-Computer Interface ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a specific type of human-computer interface that enables direct communication between human and computer through decoding of brain activity. As such, event-related potentials like the P300 can be obtained with an oddball paradigm whose targets are selected by the user. This paper deals with methods to reduce the needed set of EEG sensors in the P300 speller application. A reduced number of sensors yields more comfort for the user, decreases installation time duration, may substantially reduce the financial cost of the BCI setup and may reduce the power consumption for wireless EEG caps. Our new approach to select relevant sensors is based on backward elimination using a cost function based on the signal to signal-plus-noise ratio, after some spatial filtering. We show that this cost function selects sensors' subsets that provide a better accuracy in the speller recognition rate during the test sessions than selected subsets based on classification accuracy. We validate our selection strategy on data from 20 healthy subjects.},
}
@article {pmid21243015,
year = {2011},
author = {Dimyan, MA and Cohen, LG},
title = {Neuroplasticity in the context of motor rehabilitation after stroke.},
journal = {Nature reviews. Neurology},
volume = {7},
number = {2},
pages = {76-85},
pmid = {21243015},
issn = {1759-4766},
support = {ZIA NS002978-11/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Persons with Disabilities/rehabilitation ; Exercise Therapy/*methods ; Humans ; Motor Activity/*physiology ; Neuronal Plasticity/*physiology ; Recovery of Function/*physiology ; Stroke/*physiopathology ; *Stroke Rehabilitation ; },
abstract = {Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain-computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.},
}
@article {pmid21233042,
year = {2011},
author = {Wang, H},
title = {Multiclass filters by a weighted pairwise criterion for EEG single-trial classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {5},
pages = {1412-1420},
doi = {10.1109/TBME.2011.2105869},
pmid = {21233042},
issn = {1558-2531},
mesh = {Algorithms ; Bayes Theorem ; Databases, Factual ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {The filtering technique for dimensionality reduction of multichannel electroencephalogram (EEG) recordings, modeled using common spatial patterns and its variants, is commonly used in two-class brain-computer interfaces (BCI). For a multiclass problem, the optimization of certain separability criteria in the output space is not directly related to the classification error of EEG single-trial segments . In this paper, we derive a new discriminant criterion, termed weighted pairwise criterion (WPC), for optimizing multiclass filters by minimizing the upper bound of the Bayesian error that is intentionally formulated for classifying EEG single-trial segments. The WPC approach pays more attention to close class pairs that are more likely to be misclassified than far away class pairs that are already well separated. Moreover, we extend WPC by integrating temporal information of EEG series. Computationally, we employ the rank-one update and power iteration technique to optimize the proposed discriminant criterion. The experiments of multiclass classification on the datasets of BCI competitions demonstrate the efficacy of the proposed method.},
}
@article {pmid21216696,
year = {2011},
author = {Zhang, H and Chin, ZY and Ang, KK and Guan, C and Wang, C},
title = {Optimum spatio-spectral filtering network for brain-computer interface.},
journal = {IEEE transactions on neural networks},
volume = {22},
number = {1},
pages = {52-63},
doi = {10.1109/TNN.2010.2084099},
pmid = {21216696},
issn = {1941-0093},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; *Neural Networks, Computer ; Pattern Recognition, Automated/*standards ; *User-Computer Interface ; },
abstract = {This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance (≥ 95% confidence level) in most cases.},
}
@article {pmid21215296,
year = {2011},
author = {Bahramisharif, A and Heskes, T and Jensen, O and van Gerven, MA},
title = {Lateralized responses during covert attention are modulated by target eccentricity.},
journal = {Neuroscience letters},
volume = {491},
number = {1},
pages = {35-39},
doi = {10.1016/j.neulet.2011.01.003},
pmid = {21215296},
issn = {1872-7972},
mesh = {Attention/*physiology ; Eye Movements/*physiology ; Female ; Functional Laterality/*physiology ; Humans ; Male ; Space Perception/*physiology ; Visual Fields/*physiology ; Visual Perception/*physiology ; },
abstract = {Various studies have demonstrated that covert attention to different locations in the visual field can be used as a control signal for brain computer interfacing. It is well known that when covert attention is directed to the left visual hemifield, posterior alpha activity decreases in the right hemisphere while simultaneously increasing in the left hemisphere and vice versa. However, it remains unknown if and how the classical lateralization pattern depends on the eccentricity of the locations to which one attends. In this paper we study the effect of target eccentricity on the performance of a brain computer interface system that is driven by covert attention. Results show that the lateralization pattern becomes more pronounced as target eccentricity increases and suggest that in the current design the minimum eccentricity for having an acceptable classification performance for two targets at equal distance from fixation in opposite hemifields is about 6° of visual angle.},
}
@article {pmid21209937,
year = {2010},
author = {Simanova, I and van Gerven, M and Oostenveld, R and Hagoort, P},
title = {Identifying object categories from event-related EEG: toward decoding of conceptual representations.},
journal = {PloS one},
volume = {5},
number = {12},
pages = {e14465},
pmid = {21209937},
issn = {1932-6203},
mesh = {Adolescent ; Adult ; Bayes Theorem ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Man-Machine Systems ; Multivariate Analysis ; Regression Analysis ; Reproducibility of Results ; Time Factors ; User-Computer Interface ; },
abstract = {Multivariate pattern analysis is a technique that allows the decoding of conceptual information such as the semantic category of a perceived object from neuroimaging data. Impressive single-trial classification results have been reported in studies that used fMRI. Here, we investigate the possibility to identify conceptual representations from event-related EEG based on the presentation of an object in different modalities: its spoken name, its visual representation and its written name. We used Bayesian logistic regression with a multivariate Laplace prior for classification. Marked differences in classification performance were observed for the tested modalities. Highest accuracies (89% correctly classified trials) were attained when classifying object drawings. In auditory and orthographical modalities, results were lower though still significant for some subjects. The employed classification method allowed for a precise temporal localization of the features that contributed to the performance of the classifier for three modalities. These findings could help to further understand the mechanisms underlying conceptual representations. The study also provides a first step towards the use of concept decoding in the context of real-time brain-computer interface applications.},
}
@article {pmid21200434,
year = {2010},
author = {Ince, NF and Gupta, R and Arica, S and Tewfik, AH and Ashe, J and Pellizzer, G},
title = {High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials.},
journal = {PloS one},
volume = {5},
number = {12},
pages = {e14384},
pmid = {21200434},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; Brain Mapping/methods ; Electrodes ; Macaca mulatta ; Man-Machine Systems ; Models, Statistical ; Motor Cortex/*physiology ; *Movement ; Primates ; Prosthesis Design ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {BACKGROUND: The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages.<br><br>We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3-4 Hz) and high (48-200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week.<br><br>CONCLUSIONS/SIGNIFICANCE: The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.},
}
@article {pmid21194825,
year = {2011},
author = {Ney, L and Körner, M and Leibig, M and Heindl, B},
title = {Traumatic dissection of a coronary artery: detection by multislice computed tomography and use of tirofiban as a reversible platelet inhibitor.},
journal = {Resuscitation},
volume = {82},
number = {3},
pages = {358-360},
doi = {10.1016/j.resuscitation.2010.10.021},
pmid = {21194825},
issn = {1873-1570},
mesh = {Accidents, Traffic ; Aortic Dissection/*diagnostic imaging/etiology ; Coronary Aneurysm/*diagnostic imaging/etiology ; Coronary Angiography/*methods ; Humans ; Male ; Myocardial Ischemia/drug therapy/etiology ; Platelet Aggregation Inhibitors/*therapeutic use ; Thoracic Injuries/complications ; Tirofiban ; Tomography, X-Ray Computed/*methods ; Tyrosine/*analogs &amp; derivatives/therapeutic use ; Young Adult ; },
abstract = {We report on a trauma victim without history of or risk factors for cardiac disease, who suffered coronary artery dissection caused by blunt chest injury (BCI). Myocardial ischaemia was detected by multislice computed tomography (MSCT) promptly after trauma centre admission and managed by immediate revascularisation. Thoracic trauma may cause myocardial ischaemia in the absence of a specific risk profile. MSCT, as part of initial work-up in severely injured patients, may support differential diagnosis after BCI. Tirofiban and unfractionated heparin as short-acting anticoagulants warrant stent patency and concurrently offer the possibility of quick recovery of haemostasis in case of haemorrhage.},
}
@article {pmid21194547,
year = {2011},
author = {Wu, CH and Chang, HC and Lee, PL and Li, KS and Sie, JJ and Sun, CW and Yang, CY and Li, PH and Deng, HT and Shyu, KK},
title = {Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing.},
journal = {Journal of neuroscience methods},
volume = {196},
number = {1},
pages = {170-181},
doi = {10.1016/j.jneumeth.2010.12.014},
pmid = {21194547},
issn = {1872-678X},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular/physiology ; Humans ; Male ; Models, Neurological ; Photic Stimulation ; Recognition, Psychology/*physiology ; *User-Computer Interface ; Visual Perception/physiology ; Wavelet Analysis ; Young Adult ; },
abstract = {This paper presents an empirical mode decomposition (EMD) and refined generalized zero crossing (rGZC) approach to achieve frequency recognition in steady-stated visual evoked potential (SSVEP)-based brain computer interfaces (BCIs). Six light emitting diode (LED) flickers with high flickering rates (30, 31, 32, 33, 34, and 35 Hz) functioned as visual stimulators to induce the subjects' SSVEPs. EEG signals recorded in the Oz channel were segmented into data epochs (0.75 s). Each epoch was then decomposed into a series of oscillation components, representing fine-to-coarse information of the signal, called intrinsic mode functions (IMFs). The instantaneous frequencies in each IMF were calculated by refined generalized zero-crossing (rGZC). IMFs with mean instantaneous frequencies (f(GZC)) within 29.5 Hz and 35.5 Hz (i.e., 29.5≤f(GZC)≤35.5 Hz) were designated as SSVEP-related IMFs. Due to the time-locked and phase-locked characteristics of SSVEP, the induced SSVEPs had the same frequency as the gazing visual stimulator. The LED flicker that contributed the majority of the frequency content in SSVEP-related IMFs was chosen as the gaze target. This study tests the proposed system in five male subjects (mean age=25.4±2.07 y/o). Each subject attempted to activate four virtual commands by inputting a sequence of cursor commands on an LCD screen. The average information transfer rate (ITR) and accuracy were 36.99 bits/min and 84.63%. This study demonstrates that EMD is capable of extracting SSVEP data in SSVEP-based BCI system.},
}
@article {pmid21187338,
year = {2011},
author = {Pullan, RL and Kabatereine, NB and Bukirwa, H and Staedke, SG and Brooker, S},
title = {Heterogeneities and consequences of Plasmodium species and hookworm coinfection: a population based study in Uganda.},
journal = {The Journal of infectious diseases},
volume = {203},
number = {3},
pages = {406-417},
pmid = {21187338},
issn = {1537-6613},
support = {081673//Wellcome Trust/United Kingdom ; /MRC_/Medical Research Council/United Kingdom ; },
mesh = {Adolescent ; Adult ; Age Distribution ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; Cross-Sectional Studies ; Female ; Hookworm Infections/*complications/epidemiology ; Humans ; Infant ; Malaria/*complications/epidemiology ; Male ; Middle Aged ; Odds Ratio ; Plasmodium ; Prevalence ; Risk Factors ; Uganda/epidemiology ; Young Adult ; },
abstract = {BACKGROUND: Previous studies have suggested that helminth infection exacerbates malaria, but few existing epidemiological studies adequately control for infection heterogeneities and confounding factors. In this study, we investigate spatial and household heterogeneities, predictors, and consequences of Plasmodium species and hookworm coinfection in rural communities in Uganda.<br><br>METHODS: A cross-sectional study was conducted among 1770 individuals aged 0-88 years in 4 villages. We recorded demographic, socioeconomic, and microgeographic factors during household surveys. We determined malaria parasitemia and hemoglobin concentration and collected stool samples on 2 consecutive days. For data analysis, we used a hierarchical, spatially explicit Bayesian framework.<br><br>RESULTS: Prevalence of Plasmodium-hookworm coinfection was 15.5% overall and highest among school-aged children. We found strong evidence of spatial and household clustering of coinfection and an enduring positive association between Plasmodium-species and hookworm infection among preschool-aged children (odds ratio [OR], 2.36; 95% Bayesian credible interval [BCI], 1.26-4.30) and adults (OR, 2.09; 95% BCI, 1.35-3.16) but not among school-aged children. Coinfection was associated with lower hemoglobin level only among school-aged children.<br><br>CONCLUSIONS: Plasmodium-hookworm coinfection exhibits marked age dependency and significant spatial and household heterogeneity, and among preschool-aged children and adults, occurs more than would be expected by chance. Such heterogeneities provide insight into factors underlying observed patterns and the design of integrated control strategies.},
}
@article {pmid21184353,
year = {2010},
author = {Ganguly, K and Carmena, JM},
title = {Neural correlates of skill acquisition with a cortical brain-machine interface.},
journal = {Journal of motor behavior},
volume = {42},
number = {6},
pages = {355-360},
doi = {10.1080/00222895.2010.526457},
pmid = {21184353},
issn = {1940-1027},
mesh = {Animals ; Computer Simulation ; Macaca ; Microelectrodes ; Models, Neurological ; Motor Cortex/*physiology ; Motor Skills/*physiology ; Movement/*physiology ; Muscles/innervation/physiology ; *Neural Prostheses ; Neurofeedback ; Systems Theory ; *User-Computer Interface ; },
abstract = {Research into the development of brain-machine interfaces (BMIs) has led to demonstrations of rodents, nonhuman primates, and humans controlling prosthetic devices in real time through modulation of neural signals. In particular, cortical BMI studies have shown that improvements in performance require learning and are associated with changes in neuronal tuning properties. These studies have further shown evidence of long-term improvements in performance with practice. The authors conducted experiments to understand long-term skill acquisition with BMIs and to characterize the neural correlates of improvements in task performance. They specifically assessed long-term acquisition of neuroprosthetic skill (i.e., accurate task performance readily recalled across days). In 2 monkeys performing a center-out task using a brain-controlled (BC) computer cursor, they closely monitored daily performance trends and the neural correlates under different conditions. Importantly, they assessed BC performance using a continuous-control multistep task. The authors first conducted experiments that mimicked experimental conditions commonly used. Specifically, a large set of neurons was incorporated with daily recalibration of the transform of neural activity to BC. Under such conditions, they found evidence of variable daily performance. In contrast, when a fixed transform was applied to stable recordings from an ensemble of neurons across days, there was consistent evidence of long-term skill acquisition. Such skill acquisition was associated with the crystallization of a cortical map for prosthetic control. Taken together, the results suggest that the primate motor cortex can achieve skilled control of a neuroprosthetic device through consolidation of a cortical representation.},
}
@article {pmid21184352,
year = {2010},
author = {Wolpaw, JR},
title = {Brain-computer interface research comes of age: traditional assumptions meet emerging realities.},
journal = {Journal of motor behavior},
volume = {42},
number = {6},
pages = {351-353},
doi = {10.1080/00222895.2010.526471},
pmid = {21184352},
issn = {1940-1027},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Cerebral Cortex/physiology ; Computer Simulation ; Humans ; *Man-Machine Systems ; *Models, Neurological ; Motor Activity/physiology ; Movement/*physiology ; Muscles/innervation/physiology ; *Neural Prostheses ; Neurofeedback ; Neurosciences/methods ; Research Design ; Systems Theory ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions.},
}
@article {pmid21183404,
year = {2011},
author = {Brunner, P and Schalk, G},
title = {Toward a gaze-independent matrix speller brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {6},
pages = {1063-1064},
doi = {10.1016/j.clinph.2010.11.014},
pmid = {21183404},
issn = {1872-8952},
mesh = {Attention/*physiology ; Brain/*physiology ; Fixation, Ocular/*physiology ; Humans ; *User-Computer Interface ; },
}
@article {pmid21178643,
year = {2011},
author = {Bakardjian, H and Tanaka, T and Cichocki, A},
title = {Emotional faces boost up steady-state visual responses for brain-computer interface.},
journal = {Neuroreport},
volume = {22},
number = {3},
pages = {121-125},
doi = {10.1097/WNR.0b013e32834308b0},
pmid = {21178643},
issn = {1473-558X},
mesh = {Adult ; Brain/*physiology ; Computer Systems/standards ; Emotions/*physiology ; Evoked Potentials, Visual/*physiology ; Face/*physiology ; Fatigue/prevention &amp; control ; Feedback ; Female ; Humans ; Male ; Recognition, Psychology/*physiology ; Robotics/instrumentation/methods ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) can be used successfully for brain-computer interfaces (BCI) with multiple commands and high information transfer rates. In this study, we investigated a novel affective SSVEP paradigm using flickering video clips of emotional human faces, and evaluated their performance in an 8-command BCI controlling a robotic arm in near real-time. Single-trial affective SSVEP responses, estimated using a new phase-locking value variability and a wavelet energy variability measures, were significantly enhanced compared with blurred-face flicker and standard checkerboards. For multicommand SSVEP-based BCI, affective face-flicker boosted up the information transfer rates from 50 to 64 bits/min, while reducing user fatigue and enhancing visual attention and reliability. In the 5-12 Hz flicker frequency range, the strongest affective SSVEP responses were obtained at 10 Hz. These findings suggest new directions for SSVEP-based neural applications, including affective BCI and enhanced steady-state clinical probes.},
}
@article {pmid21176975,
year = {2011},
author = {Green, AM and Kalaska, JF},
title = {Learning to move machines with the mind.},
journal = {Trends in neurosciences},
volume = {34},
number = {2},
pages = {61-75},
doi = {10.1016/j.tins.2010.11.003},
pmid = {21176975},
issn = {1878-108X},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Adaptation, Psychological ; Animals ; Arm ; Brain/anatomy &amp; histology/*physiology ; *Computer Systems ; Humans ; *Learning ; Movement/physiology ; Psychomotor Performance/*physiology ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) extract signals from neural activity to control remote devices ranging from computer cursors to limb-like robots. They show great potential to help patients with severe motor deficits perform everyday tasks without the constant assistance of caregivers. Understanding the neural mechanisms by which subjects use BCI systems could lead to improved designs and provide unique insights into normal motor control and skill acquisition. However, reports vary considerably about how much training is required to use a BCI system, the degree to which performance improves with practice and the underlying neural mechanisms. This review examines these diverse findings, their potential relationship with motor learning during overt arm movements, and other outstanding questions concerning the volitional control of BCI systems.},
}
@article {pmid21167911,
year = {2011},
author = {Pfurtscheller, G and Klobassa, DS and Bauernfeind, G and Neuper, C},
title = {Cardiovascular responses after brisk finger movement and their dependency on the "eigenfrequency" of the baroreflex loop.},
journal = {Neuroscience letters},
volume = {490},
number = {1},
pages = {31-35},
doi = {10.1016/j.neulet.2010.12.020},
pmid = {21167911},
issn = {1872-7972},
mesh = {Adult ; Analysis of Variance ; Baroreflex/*physiology ; Blood Pressure/*physiology ; Cerebrovascular Circulation/physiology ; Electrocardiography/methods ; Electroencephalography ; Female ; Fingers/*physiology ; Heart Rate/*physiology ; Humans ; Male ; Movement/*physiology ; Statistics as Topic ; Ultrasonography, Doppler ; User-Computer Interface ; Young Adult ; },
abstract = {The baroreflex is mainly involved in short-term blood pressure regulation and strongly influenced by activations of medullary circulation centres in the brain stem and higher brain centres. One important feature of the baroreflex is its strong preference for oscillations around 0.1Hz, which can be seen as resonance or "eigenfrequency" (EF) of the control loop (so-called Mayer waves). In the present study we investigated beat-to-beat heart rate intervals (RRI) and arterial blood pressure (BP) changes after brisk finger movement and their relationship to the "eigenfrequency" determined by cross spectral analysis between RRI and arterial blood pressure time series of 17 healthy subjects. The analyses revealed significant correlations between BP response magnitude (r=0.63, p<0.01) respectively RRI response magnitude (r=0.59, p<0.05) and EF. This can be interpreted in such a way that subjects with a "high" EF (> 0.10 Hz) elicit larger BP responses as well as larger RRI responses when compared to subjects with a "low" EF (< 0.10 Hz).},
}
@article {pmid21165175,
year = {2010},
author = {Blankertz, B and Tangermann, M and Vidaurre, C and Fazli, S and Sannelli, C and Haufe, S and Maeder, C and Ramsey, L and Sturm, I and Curio, G and Müller, KR},
title = {The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {198},
pmid = {21165175},
issn = {1662-453X},
abstract = {Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.},
}
@article {pmid21163695,
year = {2011},
author = {Liu, Y and Zhou, Z and Hu, D},
title = {Gaze independent brain-computer speller with covert visual search tasks.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {6},
pages = {1127-1136},
doi = {10.1016/j.clinph.2010.10.049},
pmid = {21163695},
issn = {1872-8952},
mesh = {Adult ; *Attention ; Brain/*physiology ; Brain Mapping ; Electroencephalography/methods ; Electroretinography/methods ; Evoked Potentials, Visual/physiology ; Female ; *Fixation, Ocular ; Humans ; *Language ; Male ; Online Systems ; Photic Stimulation/methods ; Predictive Value of Tests ; Reaction Time/physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) provides a mean of communication for the patients that are severely disabled by neuromuscular diseases. The performance of the classical P300 speller, however, declines noticeably in the gaze fixation condition. The speller paradigm presented in this paper aims to release the gaze dependency at the cost of an extra covert visual search task.<br><br>METHODS: Clusters of characters were presented sequentially in the near-central vision as stimulation. Participants fixed their gaze on the center, searched and recognized the target character with covert shift of attention. Random position (RP) and fixed position (FP) presentation modes designed with different searching set size (6 for RP, and &#x2264; 2 for FP) were examined.<br><br>RESULTS: Online sessions using 10 stimulus sequences achieved character accuracies of 94.4% and 96.3% for RP and FP mode, respectively. For offline overall evaluation, the peak written symbol rate (WSR) of 1.38 symbols/min was obtained, with corresponding accuracies of 87.8% (RP) and 84.1% (FP). The P300 waveform of RP mode has evident longer latency and larger amplitude. Electrooculogram (EOG) analysis indicated that the performance was independent of gaze shift.<br><br>CONCLUSIONS: The proposed speller could be operated effectively and gaze independently by healthy participants.<br><br>SIGNIFICANCE: The proposed gaze independent BCI approach promises reasonable communication capability for the profoundly paralyzed patients with head or ocular motor impairments.},
}
@article {pmid21162666,
year = {2011},
author = {Vidaurre, C and Sannelli, C and Müller, KR and Blankertz, B},
title = {Machine-learning-based coadaptive calibration for brain-computer interfaces.},
journal = {Neural computation},
volume = {23},
number = {3},
pages = {791-816},
doi = {10.1162/NECO_a_00089},
pmid = {21162666},
issn = {1530-888X},
mesh = {Adaptation, Physiological ; Adaptation, Psychological ; Algorithms ; *Artificial Intelligence ; Brain/physiology ; *Brain-Computer Interfaces ; *Calibration ; Electroencephalography/methods ; Feedback, Psychological ; Humans ; Neuronal Plasticity ; },
abstract = {Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%-30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features' drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.},
}
@article {pmid21160550,
year = {2010},
author = {Venthur, B and Scholler, S and Williamson, J and Dähne, S and Treder, MS and Kramarek, MT and Müller, KR and Blankertz, B},
title = {Pyff - a pythonic framework for feedback applications and stimulus presentation in neuroscience.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {179},
pmid = {21160550},
issn = {1662-453X},
abstract = {This paper introduces Pyff, the Pythonic feedback framework for feedback applications and stimulus presentation. Pyff provides a platform-independent framework that allows users to develop and run neuroscientific experiments in the programming language Python. Existing solutions have mostly been implemented in C++, which makes for a rather tedious programming task for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual or auditory applications. Pyff was designed to make experimental paradigms (i.e., feedback and stimulus applications) easily programmable. It includes base classes for various types of common feedbacks and stimuli as well as useful libraries for external hardware such as eyetrackers. Pyff is also equipped with a steadily growing set of ready-to-use feedbacks and stimuli. It can be used as a standalone application, for instance providing stimulus presentation in psychophysics experiments, or within a closed loop such as in biofeedback or brain-computer interfacing experiments. Pyff communicates with other systems via a standardized communication protocol and is therefore suitable to be used with any system that may be adapted to send its data in the specified format. Having such a general, open-source framework will help foster a fruitful exchange of experimental paradigms between research groups. In particular, it will decrease the need of reprogramming standard paradigms, ease the reproducibility of published results, and naturally entail some standardization of stimulus presentation.},
}
@article {pmid21159949,
year = {2010},
author = {Suminski, AJ and Tkach, DC and Fagg, AH and Hatsopoulos, NG},
title = {Incorporating feedback from multiple sensory modalities enhances brain-machine interface control.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {50},
pages = {16777-16787},
pmid = {21159949},
issn = {1529-2401},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS048845-06/NS/NINDS NIH HHS/United States ; R01 N545853-01//PHS HHS/United States ; },
mesh = {Animals ; Arm/physiology ; Feedback, Sensory/*physiology ; Kinesthesis/physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Neurons/physiology ; Reaction Time/physiology ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain-machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey's arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback.},
}
@article {pmid21156054,
year = {2010},
author = {Prasad, G and Herman, P and Coyle, D and McDonough, S and Crosbie, J},
title = {Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {7},
number = {},
pages = {60},
pmid = {21156054},
issn = {1743-0003},
mesh = {Aged ; Electroencephalography/*methods ; Feedback, Physiological/*physiology ; Female ; Humans ; Imagery, Psychotherapy/instrumentation/*methods ; Male ; Middle Aged ; Paresis/etiology/physiopathology/*rehabilitation ; Recovery of Function/physiology ; Stroke/complications/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {BACKGROUND: There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to confirm patient engagement during an MI in the absence of any on-line measure. Fortunately an EEG-based brain-computer interface (BCI) can provide an on-line measure of MI activity as a neurofeedback for the BCI user to help him/her focus better on the MI task. However initial performance of novice BCI users may be quite moderate and may cause frustration. This paper reports a pilot study in which a BCI system is used to provide a computer game-based neurofeedback to stroke participants during the MI part of a protocol.<br><br>METHODS: The participants included five chronic hemiplegic stroke sufferers. Participants received up to twelve 30-minute MI practice sessions (in conjunction with PP sessions of the same duration) on 2 days a week for 6 weeks. The BCI neurofeedback performance was evaluated based on the MI task classification accuracy (CA) rate. A set of outcome measures including action research arm test (ARAT) and grip strength (GS), was made use of in assessing the upper limb functional recovery. In addition, since stroke sufferers often experience physical tiredness, which may influence the protocol effectiveness, their fatigue and mood levels were assessed regularly.<br><br>RESULTS: Positive improvement in at least one of the outcome measures was observed in all the participants, while improvements approached a minimal clinically important difference (MCID) for the ARAT. The on-line CA of MI induced sensorimotor rhythm (SMR) modulation patterns in the form of lateralized event-related desynchronization (ERD) and event-related synchronization (ERS) effects, for novice participants was in a moderate range of 60-75% within the limited 12 training sessions. The ERD/ERS change from the first to the last session was statistically significant for only two participants.<br><br>CONCLUSIONS: Overall the crucial observation is that the moderate BCI classification performance did not impede the positive rehabilitation trends as quantified with the rehabilitation outcome measures adopted in this study. Therefore it can be concluded that the BCI supported MI is a feasible intervention as part of a post-stroke rehabilitation protocol combining both PP and MI practice of rehabilitation tasks. Although these findings are promising, the scope of the final conclusions is limited by the small sample size and the lack of a control group.},
}
@article {pmid21151375,
year = {2010},
author = {Münßinger, JI and Halder, S and Kleih, SC and Furdea, A and Raco, V and Hösle, A and Kübler, A},
title = {Brain Painting: First Evaluation of a New Brain-Computer Interface Application with ALS-Patients and Healthy Volunteers.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {182},
pmid = {21151375},
issn = {1662-453X},
abstract = {Brain-computer interfaces (BCIs) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user-friendliness of P300-Brain Painting, a new BCI application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A colored matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black and white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (colored matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black and white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS-patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.},
}
@article {pmid21133839,
year = {2010},
author = {Jiang, N and Falla, D and d'Avella, A and Graimann, B and Farina, D},
title = {Myoelectric control in neurorehabilitation.},
journal = {Critical reviews in biomedical engineering},
volume = {38},
number = {4},
pages = {381-391},
doi = {10.1615/critrevbiomedeng.v38.i4.30},
pmid = {21133839},
issn = {0278-940X},
mesh = {Biofeedback, Psychology/*methods ; Electromyography/*methods ; Humans ; Nervous System Diseases/*rehabilitation ; Rehabilitation/*methods ; Therapy, Computer-Assisted/*methods ; },
abstract = {A myoelectric signal, or electromyogram (EMG), is the electrical manifestation of a muscle contraction. Through advanced signal processing techniques, information on the neural control of muscles can be extracted from the EMG, and the state of the neuromuscular system can be inferred. Because of its easy accessibility and relatively high signal-to-noise ratio, EMG has been applied as a control signal in several neurorehabilitation devices and applications, such as multi-function prostheses and orthoses, rehabilitation robots, and functional electrical stimulation/therapy. These EMG-based neurorehabilitation modules, which constitute muscle-machine interfaces, are applied for replacement, restoration, or modulation of lost or impaired function in research and clinical settings. The purpose of this review is to discuss the assumptions of EMG-based control and its applications in neurorehabilitation.},
}
@article {pmid21132872,
year = {2010},
author = {Welberg, L},
title = {Brain-machine interfaces: See what you want to see.},
journal = {Nature reviews. Neuroscience},
volume = {11},
number = {12},
pages = {785},
doi = {10.1038/nrn2958},
pmid = {21132872},
issn = {1471-0048},
}
@article {pmid21129404,
year = {2011},
author = {Pires, G and Nunes, U and Castelo-Branco, M},
title = {Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis.},
journal = {Journal of neuroscience methods},
volume = {195},
number = {2},
pages = {270-281},
doi = {10.1016/j.jneumeth.2010.11.016},
pmid = {21129404},
issn = {1872-678X},
mesh = {Adolescent ; Adult ; Aged ; Amyotrophic Lateral Sclerosis/pathology/*physiopathology ; Brain/physiopathology ; *Brain Mapping ; Cerebral Palsy/pathology/*physiopathology ; Discrimination, Psychological ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Visual/physiology ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Spectrum Analysis ; },
abstract = {The effective use of brain-computer interfaces (BCIs) in real-world environments depends on a satisfactory throughput. In a P300-based BCI, this can be attained by reducing the number of trials needed to detect the P300 signal. However, this task is hampered by the very low signal-to-noise-ratio (SNR) of P300 event related potentials. This paper proposes an efficient methodology that achieves high classification accuracy and high transfer rates for both disabled and able-bodied subjects in a standard P300-based speller system. The system was tested by three subjects with cerebral palsy (CP), two subjects with amyotrophic lateral sclerosis (ALS), and nineteen able-bodied subjects. The paper proposes the application of three statistical spatial filters. The first is a beamformer that maximizes the ratio of signal power and noise power (Max-SNR). The second is a beamformer based on the Fisher criterion (FC). The third approach cascades the FC beamformer with the Max-SNR beamformer satisfying simultaneously sub-optimally both criteria (C-FMS). The calibration process of the BCI system takes about 5 min to collect data and a couple of minutes to obtain spatial filters and classification models. Online results showed that subjects with disabilities have achieved, on average, an accuracy and transfer rate only slightly lower than able-bodied subjects. Taking 23 of the 24 participants, the averaged results achieved a transfer rate of 4.33 symbols per minute with a 91.80% accuracy, corresponding to a bandwidth of 19.18 bits per minute. This study shows the feasibility of the proposed methodology and that effective communication rates are achievable.},
}
@article {pmid21124927,
year = {2010},
author = {Caillaud, D and Crofoot, MC and Scarpino, SV and Jansen, PA and Garzon-Lopez, CX and Winkelhagen, AJ and Bohlman, SA and Walsh, PD},
title = {Modeling the spatial distribution and fruiting pattern of a key tree species in a neotropical forest: methodology and potential applications.},
journal = {PloS one},
volume = {5},
number = {11},
pages = {e15002},
pmid = {21124927},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; *Biodiversity ; Computer Simulation ; Dipteryx/*growth &amp; development ; Ecology/methods ; Fruit/*growth &amp; development ; Geography ; *Models, Biological ; Monte Carlo Method ; Panama ; Population Dynamics ; Trees/growth &amp; development ; Tropical Climate ; },
abstract = {BACKGROUND: The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we demonstrate how advances in statistics permit the combination of sparse ground sampling with remote sensing imagery to generate biological relevant, spatially and temporally explicit distributions of food resources. We illustrate our procedure by creating a detailed simulation model of fruit production patterns for Dipteryx oleifera, a keystone tree species, on Barro Colorado Island (BCI), Panama.<br><br>Aerial photographs providing GPS positions for large, canopy trees, the complete census of a 50-ha and 25-ha area, diameter at breast height data from haphazardly sampled trees and long-term phenology data from six trees were used to fit 1) a point process model of tree spatial distribution and 2) a generalized linear mixed-effect model of temporal variation of fruit production. The fitted parameters from these models are then used to create a stochastic simulation model which incorporates spatio-temporal variations of D. oleifera fruit availability on BCI.<br><br>CONCLUSIONS AND SIGNIFICANCE: We present a framework that can provide a statistical characterization of the habitat that can be included in agent-based models of animal movements. When environmental heterogeneity cannot be exhaustively mapped, this approach can be a powerful alternative. The results of our model on the spatio-temporal variation in D. oleifera fruit availability will be used to understand behavioral and movement patterns of several species on BCI.},
}
@article {pmid21112747,
year = {2011},
author = {Logar, V and Belič, A},
title = {Brain-computer interface analysis of a dynamic visuo-motor task.},
journal = {Artificial intelligence in medicine},
volume = {51},
number = {1},
pages = {43-51},
doi = {10.1016/j.artmed.2010.10.004},
pmid = {21112747},
issn = {1873-2860},
mesh = {Adult ; *Artificial Intelligence ; Brain/*physiology ; Brain Waves ; *Electroencephalography ; Fuzzy Logic ; Humans ; Male ; *Models, Biological ; Motor Activity ; Principal Component Analysis ; *Psychomotor Performance ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; *User-Computer Interface ; Wrist/*innervation ; Young Adult ; },
abstract = {BACKGROUND: The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data.<br><br>OBJECTIVE: In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface.<br><br>MATERIALS AND METHODS: For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used.<br><br>RESULTS: The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable.<br><br>CONCLUSION: The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface.},
}
@article {pmid21097374,
year = {2011},
author = {Kelly, JW and Siewiorek, DP and Smailagic, A and Collinger, JL and Weber, DJ and Wang, W},
title = {Fully automated reduction of ocular artifacts in high-dimensional neural data.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {3},
pages = {598-606},
doi = {10.1109/TBME.2010.2093932},
pmid = {21097374},
issn = {1558-2531},
support = {R21 NS056136/NS/NINDS NIH HHS/United States ; R01 EB007749/EB/NIBIB NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; 5UL1RR024153/RR/NCRR NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; },
mesh = {*Artifacts ; Electrooculography/*methods ; Humans ; Magnetoencephalography ; Man-Machine Systems ; Principal Component Analysis ; Regression Analysis ; *Wavelet Analysis ; },
abstract = {The reduction of artifacts in neural data is a key element in improving analysis of brain recordings and the development of effective brain-computer interfaces. This complex problem becomes even more difficult as the number of channels in the neural recording is increased. Here, new techniques based on wavelet thresholding and independent component analysis (ICA) are developed for use in high-dimensional neural data. The wavelet technique uses a discrete wavelet transform with a Haar basis function to localize artifacts in both time and frequency before removing them with thresholding. Wavelet decomposition level is automatically selected based on the smoothness of artifactual wavelet approximation coefficients. The ICA method separates the signal into independent components, detects artifactual components by measuring the offset between the mean and median of each component, and then removing the correct number of components based on the aforementioned offset and the power of the reconstructed signal. A quantitative method for evaluating these techniques is also presented. Through this evaluation, the novel adaptation of wavelet thresholding is shown to produce superior reduction of ocular artifacts when compared to regression, principal component analysis, and ICA.},
}
@article {pmid21097301,
year = {2010},
author = {Hebert, P and Burdick, J},
title = {The minimum interval for confident spike sorting: A sequential decision method.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4838-4841},
doi = {10.1109/IEMBS.2010.5628016},
pmid = {21097301},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Confidence Intervals ; *Data Interpretation, Statistical ; *Decision Support Techniques ; Electroencephalography/*methods ; Haplorhini ; Neurons/*physiology ; Parietal Lobe/*physiology ; },
abstract = {This paper develops a method to determine the minimum duration interval which ensures that the process of "sorting" the extracellular action potentials recorded during that interval achieves a desired confidence level of accuracy. During the recording process, a sequential decision theory approach continually evaluates a variant of the likelihood ratio test using the model evidence of the sorting/clustering hypotheses. The test is compared against a threshold which encodes a desired confidence level on the accuracy of the subsequent clustering procedure. When the threshold is exceeded, the clustering model with the highest model evidence is accepted. We first develop a testing procedure for a single recording interval, and then extend the method to multi-interval recording by using both Bayesian priors from previous recording intervals and recently developed cluster tracking procedure. Lastly, a more advanced tracker is implemented and initials results are presented. This later procedure is useful for real time applications such as brain machine interfaces and autonomous recording electrodes. We test our theory on recordings from Macaque parietal cortex, showing that the method does reach the desired confidence level.},
}
@article {pmid21097299,
year = {2010},
author = {Tadipatri, VA and Tewfik, AH and Gowreesunker, B and Ashe, J and Pellizzer, G and Gupta, R},
title = {Time robust movement direction decoding in Local Field Potentials using channel ranking.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4825-4828},
doi = {10.1109/IEMBS.2010.5627909},
pmid = {21097299},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than the absolute power of the recorded signals. In this novel method, we use a power based inter-channel ranking system to define the quality of a channel in multi-channel LFP. This representation enables us to bypass the problems associated with the dynamic ranges of absolute power. We also introduce a parameter based ranking system that provides the same rank to channels that have comparable powers. We show that using our algorithms, we can develop models that provide stable decoding of eight movement directions with an average efficiency of above 56% over a period of two weeks. Moreover, the decoding power using this method is 46% at the end of two weeks versus the 13% using the traditional approaches. We also applied these models to decoding movements performed in a force field and again achieved significantly higher decoding power than the existing methods.},
}
@article {pmid21097270,
year = {2010},
author = {Valbuena, D and Volosyak, I and Graser, A},
title = {sBCI: fast detection of steady-state visual evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3966-3969},
doi = {10.1109/IEMBS.2010.5627990},
pmid = {21097270},
issn = {2375-7477},
mesh = {*Evoked Potentials, Visual ; Humans ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems enable communication and control without movement. Although advanced signal processing methods are used in BCI research, the output of a BCI is still unreliable, and the information transfer rates are very low compared with conventional human interaction interfaces such as keyboard and mouse. Therefore, improvements in signal classification methods and the exploitation of the learning skills of the user are required to compensate the unreliability of the BCI system. This work analyzes the response time of the Bremen-BCI based on steady-state visual evoked potentials (SSVEP) previously tested on 27 subjects, and presents an enhanced method for faster detection of SSVEP responses. The aim is toward the development of a swift BCI (sBCI) that robustly detects the exact time point where the user starts modulating his brain signals.},
}
@article {pmid21097232,
year = {2010},
author = {Kamrunnahar, M and Geronimo, A},
title = {Motor imagery task discrimination using wide-band frequency spectra with Slepian tapers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3349-3352},
doi = {10.1109/IEMBS.2010.5627899},
pmid = {21097232},
issn = {2375-7477},
support = {K25 NS061001/NS/NINDS NIH HHS/United States ; K25NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Algorithms ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {We here studied the efficacy of wide-band frequency spectra (WBFS) features using multi-taper (MT) spectral analysis in application to motor imagery based Brain Computer Interfaces. We acquired motor imagery task related human scalp electroencephalography (EEG) signals for left vs. right hand movements using 3 different pairs of visual arrow cues. Left vs. right movement imagery discrimination was conducted using a Naïve Bayesian classifier using WBFS features and commonly used Mu-Beta spectral features for EEG signals from central+parietal and central only electrode positions. Task discrimination accuracy results showed that WBFS features using MT spectral analysis provided significantly better performance (with a 95% confidence level) than that of using Mu-Beta spectral features commonly used. The use of central+parietal electrode signals improved discrimination accuracy significantly when compared to the accuracy using the central only signals, implying that sensory information enhanced task discrimination significantly.},
}
@article {pmid21097231,
year = {2010},
author = {Chin, ZY and Ang, KK and Wang, C and Guan, C},
title = {Online performance evaluation of motor imagery BCI with augmented-reality virtual hand feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3341-3344},
doi = {10.1109/IEMBS.2010.5627911},
pmid = {21097231},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/methods/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Hand/*physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {The online performance of a motor imagery-based Brain-Computer Interface (MI-BCI) influences its effectiveness and usability in real-world clinical applications such as the restoration of motor control. The online performance depends on factors such as the different feedback techniques and motivation of the subject. This paper investigates the online performance of the MI-BCI with an augmented-reality (AR) 3D virtual hand feedback. The subject experiences the interaction with 3D virtual hands, which have been superimposed onto his real hands and displayed on the computer monitor from a first person point-of-view. While performing motor imagery, he receives continuous visual feedback from the MI-BCI in the form of different degrees of reaching and grasping actions of the 3D virtual hands with other virtual objects. The AR feedback is compared with the conventional horizontal bar feedback on 8 subjects, of whom 7 are BCI-naïve. The subjects found the AR feedback to be more engaging and motivating. Despite the higher mental workload involved in the AR feedback, their online MI-BCI performance compared to the conventional horizontal bar feedback was not affected. The results provide motivation to further develop and refine the AR feedback protocol for MI-BCI.},
}
@article {pmid21097230,
year = {2010},
author = {Rocon, E and Gallego, JA and Barrios, L and Victoria, AR and Ibanez, J and Farina, D and Negro, F and Dideriksen, JL and Conforto, S and D'Alessio, T and Severini, G and Belda-Lois, JM and Popovic, LZ and Grimaldi, G and Manto, M and Pons, JL},
title = {Multimodal BCI-mediated FES suppression of pathological tremor.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3337-3340},
doi = {10.1109/IEMBS.2010.5627914},
pmid = {21097230},
issn = {2375-7477},
mesh = {Algorithms ; Biofeedback, Psychology/*methods ; Electric Stimulation Therapy/*methods ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Humans ; Man-Machine Systems ; *Movement ; Therapy, Computer-Assisted/methods ; Tremor/*diagnosis/physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.},
}
@article {pmid21097229,
year = {2010},
author = {Torres Müller, SM and Freire Bastos-Filho, T and Sarcinelli-Filho, M},
title = {Incremental SSVEP analysis for BCI implementation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3333-3336},
doi = {10.1109/IEMBS.2010.5627913},
pmid = {21097229},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {This work presents an incremental analysis of EEG records containing Steady-State Visual Evoked Potential (SSVEP). This analysis consists of two steps: feature extraction, performed using a statistic test, and classification, performed by a decision tree. The result is a system with high classification rate (a test with six volunteers resulted in an average classification rate of 91.2%), high Information Transfer Rate (ITR) (a test with the same six volunteers resulted in an average value of 100.2 bits/min) and processing time, for each incremental analysis, of approximately 120 ms. These are very good features for an efficient Brain-Computer Interface (BCI) implementation.},
}
@article {pmid21097171,
year = {2010},
author = {Lan, T and Erdogmus, D and Black, L and Van Santen, J},
title = {A comparison of different dimensionality reduction and feature selection methods for single trial ERP detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {6329-6332},
pmid = {21097171},
issn = {2375-7477},
support = {R01 DC009834/DC/NIDCD NIH HHS/United States ; 1R01-DC009834-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Algorithms ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier was designed as the ERP (Event Related Potential) detector for its simplicity. Different dimensionality reduction and feature selection methods were applied and compared in a greedy wrapper framework. Experimental results showed that PCA with the first 10 principal components for each channel performed best and could be used in both online and offline systems.},
}
@article {pmid21097168,
year = {2010},
author = {Fruitet, J and Clerc, M},
title = {Reconstruction of cortical sources activities for online classification of electroencephalographic signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {6317-6320},
doi = {10.1109/IEMBS.2010.5627713},
pmid = {21097168},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; Electroencephalography/*methods ; Humans ; Male ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {We compare the results given by different methods to reconstruct cortical sources activity in order to classify EEG in real time. Two motor imagery experiments were performed. The aim was to retrieve from 1-second windows of signal which motor imagery task the subjects were performing. The use of cortical activity reconstruction was compared to Laplacian filtering, which is often used in BCI. A recursive algorithm using Student's t-test was used to select relevant cortical sources. The Beamformer method led to an improvement of the classification for the first experiment, which included six motor imagery tasks. The weighted Minimum-Norm method required the use of a specific head model, extracted from the subject's MRI, to improve the classification. It then gave the best results on the second experiment, achieving a classification rate of 77% compared to 71% for direct use of electrode data and 75% for Laplacian filtering and Beamformer.},
}
@article {pmid21097115,
year = {2010},
author = {Brockmeier, AJ and Park, I and Mahmoudi, B and Sanchez, JC and Principe, JC},
title = {Spatio-temporal clustering of firing rates for neural state estimation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {6023-6026},
doi = {10.1109/IEMBS.2010.5627600},
pmid = {21097115},
issn = {2375-7477},
support = {N66001-10C-2008//PHS HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Cluster Analysis ; Computer Simulation ; Neural Pathways/*physiology ; Nucleus Accumbens/*physiology ; Poisson Distribution ; Rats ; Time Factors ; },
abstract = {Characterizing the dynamics of neural data by a discrete state variable is desirable in experimental analysis and brain-machine interfaces. Previous successes have used dynamical modeling including Hidden Markov Models, but the methods do not always produce meaningful results without being carefully trained or initialized. We propose unsupervised clustering in the spatio-temporal space of neural data using time embedding and a corresponding distance measure. By defining performance measures, the method parameters are investigated for a set of neural and simulated data with promising results. Our investigations demonstrate a different view of how to extract information to maximize the utility of state estimation.},
}
@article {pmid21097114,
year = {2010},
author = {Krusienski, DJ and Shih, JJ},
title = {A case study on the relation between electroencephalographic and electrocorticographic event-related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {6019-6022},
doi = {10.1109/IEMBS.2010.5627603},
pmid = {21097114},
issn = {2375-7477},
support = {EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Cerebral Cortex/*physiology ; Electrodes ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Feedback, Sensory/physiology ; Humans ; Male ; Task Performance and Analysis ; User-Computer Interface ; },
abstract = {This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci. The patient performed one experimental session using the BCI spelling paradigm controlled by scalp-recorded EEG prior to the ECoG grid implantation, and one identical session controlled by ECoG after the grid implantation. The patient was able to achieve near perfect spelling accuracy using EEG and ECoG. An offline analysis of the average ERPs was performed to assess how accurately the average EEG ERPs could be predicted from the ECoG data. The preliminary results indicate that EEG ERPs can be accurately estimated from proximal asynchronous ECoG data using simple linear spatial models.},
}
@article {pmid21097061,
year = {2010},
author = {Viventi, J and Blanco, J and Litt, B},
title = {Mining terabytes of submillimeter-resolution ECoG datasets for neurophysiologic biomarkers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3825-3826},
pmid = {21097061},
issn = {2375-7477},
support = {R01 NS048598/NS/NINDS NIH HHS/United States ; R01-NS041811-04/NS/NINDS NIH HHS/United States ; R01-NS48598-01/NS/NINDS NIH HHS/United States ; R01 NS041811/NS/NINDS NIH HHS/United States ; R01 NS048598-04/NS/NINDS NIH HHS/United States ; R01 NS041811-09/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; *Biomarkers ; Brain/*physiology ; Humans ; Software ; },
abstract = {Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. We present innovations in both hardware and software that allow sampling and interpretation of data from brain networks from hundreds or thousands of sensors at submillimeter resolution. These innovations consist of novel flexible, active electrode arrays and unsupervised algorithms for detecting and classifying neurophysiologic biomarkers, specifically high frequency oscillations. We propose these innovations as the foundation for a new generation of closed loop diagnostic and therapeutic medical devices, and brain-machine interfaces.},
}
@article {pmid21096910,
year = {2010},
author = {Diez, PF and Mut, V and Laciar, E and Avila, E},
title = {A comparison of monopolar and bipolar EEG recordings for SSVEP detection.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5803-5806},
doi = {10.1109/IEMBS.2010.5627451},
pmid = {21096910},
issn = {2375-7477},
mesh = {Adult ; Brain/physiology ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {This paper presents a comparative study over the detection of Steady-State Visual Evoked Potential (SSVEP) with monopolar or bipolar electroencephalographic (EEG) recordings in a Brain-Computer Interface experiment. Five subjects participated in this study. They were stimulated with four flickering lights at 13, 14, 15 and 16 Hz and the EEG was measured simultaneously with two bipolar channels (O(1)-P(3) and O(2)-P(4)) and with six monopolar channels at O(1), O(2), P(3), P(4), T(5) and T(6) referenced to F(Z). The EEG was processed by means of spectral analysis and the estimation of power at each stimulation frequency and its harmonics. In average, the monopolar recordings present accuracy in classification of 74.5% against an 80.1% for bipolar recordings. It was found that bipolar recording are better than monopolar recordings for detection of SSVEP.},
}
@article {pmid21096903,
year = {2010},
author = {Barbosa, AF and Souza, BC and Ferro, D and Pantoja, AL and Doria, AD and Pereira, A and Guerreiro, AM},
title = {Exploring preprocessing techniques in a three-class brain-machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4242-4245},
doi = {10.1109/IEMBS.2010.5627382},
pmid = {21096903},
issn = {2375-7477},
mesh = {Brain/*physiology ; Electrodes ; Functional Laterality ; Humans ; Male ; *Man-Machine Systems ; },
abstract = {In this work, we implemented a brain-machine interface (BMI) based on electroencephalographic (EEG) signals and used it to classify and separate three types of mental tasks: motor imagery with the right and left hands and simple arithmetic sums. In order to reduce dimension of variables and increase classification power, we used both PCA and ICA based algorithms for spectral analysis. Our results show that we were no able to reduce dimension without reducing classification performance.},
}
@article {pmid21096902,
year = {2010},
author = {Lee, Y and Kim, J and Lee, S and Lee, M},
title = {Characteristics of motor imagery based EEG-brain computer interface using combined cue and neuro-feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4238-4241},
doi = {10.1109/IEMBS.2010.5627378},
pmid = {21096902},
issn = {2375-7477},
mesh = {Algorithms ; *Electroencephalography ; *Feedback, Physiological ; Humans ; *Man-Machine Systems ; },
abstract = {In this paper, we evaluated BCI algorithm using CSP for finding out about realistic possibility of BCI based on CSP. BCI algorithm that was comprised of CSP and least square linear classifier was evaluated in 10 persons. According to the result of the experiment, the effect of combined cue and neurofeedback is evaluated. In case of combined cue, the correlation of combined cue and visual cue is higher than other conditions. And in case of neurofeedback, some subject is exceptional but general trend shows the performance improvement by neurofeedback.},
}
@article {pmid21096901,
year = {2010},
author = {Tan, HG and Kong, KH and Shee, CY and Wang, CC and Guan, CT and Ang, WT},
title = {Post-acute stroke patients use brain-computer interface to activate electrical stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4234-4237},
doi = {10.1109/IEMBS.2010.5627381},
pmid = {21096901},
issn = {2375-7477},
mesh = {Adult ; Aged ; Aged, 80 and over ; Brain/*physiopathology ; *Electric Stimulation ; Humans ; *Man-Machine Systems ; Middle Aged ; Stroke/*physiopathology ; },
abstract = {Through certain mental actions, our electroencephalogram (EEG) can be regulated to operate a brain-computer interface (BCI), which translates the EEG patterns into commands that can be used to operate devices such as prostheses. This allows paralyzed persons to gain direct brain control of the paretic limb, which could open up many possibilities for rehabilitative and assistive applications. When using a BCI neuroprosthesis in stroke, one question that has surfaced is whether stroke patients are able to produce a sufficient change in EEG that can be used as a control signal to operate a prosthesis.},
}
@article {pmid21096900,
year = {2010},
author = {Leamy, DJ and Ward, TE},
title = {A novel co-locational and concurrent fNIRS/EEG measurement system: design and initial results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4230-4233},
doi = {10.1109/IEMBS.2010.5627377},
pmid = {21096900},
issn = {2375-7477},
mesh = {Adult ; Discriminant Analysis ; *Electroencephalography ; Hemodynamics ; Humans ; Male ; *Spectroscopy, Near-Infrared ; },
abstract = {We describe here the design, set-up and first time classification results of a novel co-locational functional Near-Infrared Spectroscopy/Electroencephalography (fNIRS/EEG) recording device suitable for brain computer interfacing applications using neural-hemodynamic signals. Our dual-modality system recorded both hemodynamic and electrical activity at seven sites over the motor cortex during an overt finger-tapping task. Data was collected from two subjects and classified offline using Linear Discriminant Analysis (LDA) and Leave-One-Out Cross-Validation (LOOCV). Classification of fNIRS features, EEG features and a combination of fNIRS/EEG features were performed separately. Results illustrate that classification of the combined fNIRS/EEG feature space offered average improved performance over classification of either feature space alone. The complementary nature of the physiological origin of the dual measurements offer a unique and information rich signal for a small measurement area of cortex. We feel this technology may be particularly useful in the design of BCI devices for the augmentation of neurorehabilitation therapy.},
}
@article {pmid21096899,
year = {2010},
author = {Chavarriaga, R and Biasiucci, A and Forster, K and Roggen, D and Troster, G and Millan, Jdel R},
title = {Adaptation of hybrid human-computer interaction systems using EEG error-related potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4226-4229},
doi = {10.1109/IEMBS.2010.5627376},
pmid = {21096899},
issn = {2375-7477},
mesh = {*Adaptation, Physiological ; Bayes Theorem ; Calibration ; *Electroencephalography ; *Evoked Potentials ; Humans ; *Man-Machine Systems ; },
abstract = {Performance improvement in both humans and artificial systems strongly relies in the ability of recognizing erroneous behavior or decisions. This paper, that builds upon previous studies on EEG error-related signals, presents a hybrid approach for human computer interaction that uses human gestures to send commands to a computer and exploits brain activity to provide implicit feedback about the recognition of such commands. Using a simple computer game as a case study, we show that EEG activity evoked by erroneous gesture recognition can be classified in single trials above random levels. Automatic artifact rejection techniques are used, taking into account that subjects are allowed to move during the experiment. Moreover, we present a simple adaptation mechanism that uses the EEG signal to label newly acquired samples and can be used to re-calibrate the gesture recognition system in a supervised manner. Offline analysis show that, although the achieved EEG decoding accuracy is far from being perfect, these signals convey sufficient information to significantly improve the overall system performance.},
}
@article {pmid21096898,
year = {2010},
author = {Andersson, P and Ramsey, NF and Pluim, JP and Viergever, MA},
title = {BCI control using 4 direction spatial visual attention and real-time fMRI at 7T.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4221-4225},
doi = {10.1109/IEMBS.2010.5627372},
pmid = {21096898},
issn = {2375-7477},
mesh = {Adult ; *Attention ; Brain/*physiology ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; *Man-Machine Systems ; *Vision, Ocular ; },
abstract = {The goal of Brain-Computer-Interface (BCI) technologies is to "outsource" the muscular control to a computer and create new communication channels, e.g. to people with severe paralysis, by measuring cortical activation changes and linking these changes to commands. Using real-time fMRI at 7T we show that visuospatial attention can be used to reliably regulate cortical activity and that it is possible to separate the cortical responses to multiple attention target regions in real time. The activated regions were first located on the fly using an incremental statistical analysis and the subjects were then given feedback based on the activity in these regions. Visuospatial attention is an attractive addition to the existing BCI control strategies, and the fact that it leaves the motor system still available makes it suitable also for applications aimed for healthy people.},
}
@article {pmid21096897,
year = {2010},
author = {Touyama, H},
title = {A study on EEG quality in physical movements with Steady-State Visual Evoked Potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4217-4220},
doi = {10.1109/IEMBS.2010.5627375},
pmid = {21096897},
issn = {2375-7477},
mesh = {Brain/physiology ; Electrodes ; Electroencephalography ; *Evoked Potentials, Visual ; Humans ; Male ; Man-Machine Systems ; *Movement ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; },
abstract = {In this paper, we investigated the quality of ElectroEncephaloGraphic (EEG) signals during performing physical movements. By using a portable EEG device, the Steady-State Visual Evoked Potential (SSVEP) was recorded on parietal and occipital locations. The SSVEP induced by flickering stimuli was successfully observed in the self-paced mimic walking conditions as well as in the sitting conditions. To see the dependence of temporal and spatial filters on the potential performance of Brain-Computer Interfaces (BCI) we applied the signal processing of Principal Component Analysis and Linear Discriminant Analysis. The pattern recognition performances in inferring the subject's eye gaze directions from the EEG signals could be perfect even in the self-paced mimic walking conditions. It was found that three electrodes on parieto-occipital and occipital locations were essential in order to have perfect performances. From these results, we conclude that the applications using SSVEP-based BCI can be realized even in the physically moving context.},
}
@article {pmid21096895,
year = {2010},
author = {Lage-Castellanos, A and Nieto, JI and Quiñones, I and Martinez-Montes, E},
title = {A zero-training algorithm for EEG single-trial classification applied to a face recognition ERP experiment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4209-4212},
doi = {10.1109/IEMBS.2010.5627395},
pmid = {21096895},
issn = {2375-7477},
mesh = {*Algorithms ; *Electroencephalography ; *Evoked Potentials ; *Face ; Humans ; *Visual Perception ; },
abstract = {This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface (BCI) systems are areas with a significant amount of potential.},
}
@article {pmid21096894,
year = {2010},
author = {Li, Y and Long, J and Yu, T and Yu, Z and Wang, C and Zhang, H and Guan, C},
title = {A hybrid BCI system for 2-D asynchronous cursor control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4205-4208},
doi = {10.1109/IEMBS.2010.5627394},
pmid = {21096894},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Computers ; Electroencephalography ; Evoked Potentials ; Female ; Humans ; Male ; *Man-Machine Systems ; },
abstract = {In this paper, a hybrid EEG-based brain computer interface (BCI) is designed for two-dimensional cursor control. In our approach, two brain activity patterns, i.e., motor imagery and P300 potential, are used for controlling the horizontal and the vertical movements of the cursor respectively. A real-time BCI system based on this approach is implemented and evaluated through an online experiment. Six subjects attending this experiment can perform 2-D cursor control effectively. Our experimental results show that the system has the following merits compared with prior systems: 1) it does not rely on intensive user training; 2) it allows cursor movement between arbitrary positions.},
}
@article {pmid21096893,
year = {2010},
author = {Volosyak, I and Guger, C and Graser, A},
title = {Toward BCI Wizard - best BCI approach for each user.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4201-4204},
doi = {10.1109/IEMBS.2010.5627390},
pmid = {21096893},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; *Computers ; Female ; Humans ; Male ; *Man-Machine Systems ; Reference Values ; },
abstract = {Modern brain-computer interface (BCI) systems use different types of neural activity for control. Most BCI systems only allow the customization of very few parameters and focus only on one type of BCI approach. Many articles reported that a certain BCI did not work for some users (so called BCI illiteracy). We are introducing the BCI wizard as a system that automatically identifies key parameters to customize the best BCI paradigm for each user. With a BCI wizard it is possible to develop an interface that relies on the best mental strategy for each user and therefore makes the difference between an ineffective system and a working BCI. This work presents a preliminary study that aims to develop a BCI wizard exploring the two most effective BCI approaches (SSVEP and P300). These types of non-invasive BCIs were tested and evaluated in a group of 14 healthy subjects. During online tests all subjects were asked to spell three words with two spelling applications and at the end of the experiment they chose their preferred approach. Results showed that all subjects could communicate with the P300-based BCI with an accuracy above 69% (5 reached 100% accuracy), 10 out of 14 subjects could effectively use the SSVEP-based BCI (2 reached 100% accuracy). These promising results confirm that BCI wizard will enable BCIs customized to each user with considerably greater flexibility and independence than present systems allow.},
}
@article {pmid21096891,
year = {2010},
author = {Garcia, PA and Haberman, M and Spinelli, EM},
title = {A versatile hardware platform for brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4193-4196},
doi = {10.1109/IEMBS.2010.5627389},
pmid = {21096891},
issn = {2375-7477},
mesh = {Brain/*physiology ; *Computers ; Electroencephalography ; Humans ; *User-Computer Interface ; },
abstract = {This article presents the development of a versatile hardware platform for brain computer interfaces (BCI). The aim of this work is to produce a small, autonomous and configurable BCI platform adaptable to the user's needs.},
}
@article {pmid21096890,
year = {2010},
author = {Salvaris, M and Cinel, C and Poli, R and Citi, L and Sepulveda, F},
title = {Exploring multiple protocols for a brain-computer interface mouse.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4189-4192},
doi = {10.1109/IEMBS.2010.5627388},
pmid = {21096890},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; Humans ; *User-Computer Interface ; },
abstract = {In recent years, various visual protocols have been explored for P300-based BCI. In stimulus-driven BCI paradigms such as P300 BCIs it is vital to optimise the stimulation protocol as much as possible in order to achieve the best performance. Due to the inherent variability between subjects and the complex nature of the brain it is unlikely that an optimal protocol will be identified through a single iteration of random exploration. That is why in this paper we explore 8 different visual protocol configurations based on recent literature, in the hope of identifying key features that can later be used to create further improved protocols. Results indicate that luminosity changes, the standard method of stimulation used in visual P300 BCI protocols, do provide the best performance of the variations presented here.},
}
@article {pmid21096889,
year = {2010},
author = {Hohne, J and Schreuder, M and Blankertz, B and Tangermann, M},
title = {Two-dimensional auditory p300 speller with predictive text system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4185-4188},
doi = {10.1109/IEMBS.2010.5627379},
pmid = {21096889},
issn = {2375-7477},
mesh = {*Acoustic Stimulation ; Adult ; Algorithms ; Female ; Humans ; Male ; *User-Computer Interface ; },
abstract = {P300-based Brain Computer Interfaces offer communication pathways which are independent of muscle activity. Mostly visual stimuli, e.g. blinking of different letters are used as a paradigm of interaction. Neural degenerative diseases like amyotrophic lateral sclerosis (ALS) also cause a decrease in sight, but the ability of hearing is usually unaffected. Therefore, the use of the auditory modality might be preferable. This work presents a multiclass BCI paradigm using two-dimensional auditory stimuli: cues are varying in pitch (high/medium/low) and location (left/middle/right). The resulting nine different classes are embedded in a predictive text system, enabling to spell a letter with a 9-class decision. Moreover, an unbalanced subtrial selection is investigated and compared to the well-established sequence-wise paradigm. Twelve healthy subjects participated in an online study to investigate these approaches.},
}
@article {pmid21096887,
year = {2010},
author = {Kaneswaran, K and Arshak, K and Burke, E and Condron, J},
title = {Towards a brain controlled assistive technology for powered mobility.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4176-4180},
doi = {10.1109/IEMBS.2010.5627385},
pmid = {21096887},
issn = {2375-7477},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology ; Brain/*physiopathology ; Brain Injuries/physiopathology ; Female ; Humans ; Male ; *Movement ; Multiple Sclerosis/physiopathology ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {For individuals with mobility limitations, powered wheelchair systems provide improved functionality, increased access to healthcare, education and social activities. Input devices such as joystick and switches can provide the necessary input required for efficient control of the powered wheelchair. For persons with limited dexterity, or fine control of the fingers, access to mechanical hardware such as buttons and joysticks can be quite difficult and sometimes painful. For individuals with conditions such as Traumatic Brain Injury (TBI), Multiple Sclerosis (MS) or Amyotrophic lateral sclerosis (ALS) voluntary control of limb movement maybe substantially limited or completely absent. Brain Computer Interfaces (BCI) are emerging as a possible method to replace the brains normal output pathways of peripheral nerves and muscles, allowing individuals with paralysis a method of communication and computer control. This study involves the analysis of non-invasive electroencephalograms (EEG) arising from the use of a newly developed Human Machine Interface (HMI) for powered wheelchair control. Using a delayed response task, binary classification of left and right movement intentions were classified with a best classification rate of 81.63% from single trial EEG. Results suggest that this method may be used to enhance control of HMI's for individuals with severe mobility limitations.},
}
@article {pmid21096852,
year = {2010},
author = {Zanotelli, T and Santos Filho, SA and Tierra-Criollo, CJ},
title = {Optimum principal components for spatial filtering of EEG to detect imaginary movement by coherence.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3646-3649},
doi = {10.1109/IEMBS.2010.5627418},
pmid = {21096852},
issn = {2375-7477},
mesh = {*Algorithms ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Several techniques have been used to improve the signal-to-noise ratio to increase the detection rate of Event Related Potentials (ERPs). This work investigates the application of spatial filtering based on principal component analysis (PCA) to detect ERP due to left-hand index finger movement imagination. The EEG signals were recorded of central derivations (C4, C2, Cz, C1 and C3), positioned according to 10-10 International System. The optimal spatial filter was found by using the first principal component and the ERP detection was obtained by magnitude squared coherence technique. The best detection rate, by using original signal (without filtering), was obtained at C2 derivation, with 54.73% for significance level of 5%. For the same significance level, the detection rate of the filtered signal was drastically improved to 96.84%. Results suggest that spatial filter by using PCA might be a very useful tool in assisting the ERP detection for movement imagination for applications on brain machine interface.},
}
@article {pmid21096744,
year = {2010},
author = {Leeb, R and Gubler, M and Tavella, M and Miller, H and Del Millan, JR},
title = {On the road to a neuroprosthetic hand: a novel hand grasp orthosis based on functional electrical stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {146-149},
doi = {10.1109/IEMBS.2010.5627412},
pmid = {21096744},
issn = {2375-7477},
mesh = {*Artificial Limbs ; Electric Stimulation Therapy/*instrumentation ; Hand/*physiology ; Hand Strength/*physiology ; Humans ; Man-Machine Systems ; *Neural Prostheses ; *Orthotic Devices ; Paralysis/rehabilitation ; },
abstract = {To patients who have lost the functionality of their hands as a result of a severe spinal cord injury or brain stroke, the development of new techniques for grasping is indispensable for reintegration and independency in daily life. Functional Electrical Stimulation (FES) of residual muscles can reproduce the most dominant grasping tasks and can be initialized by brain signals. However, due to the very complex hand anatomy and current limitations in FES-technology with surface electrodes, these grasp patterns cannot be smoothly executed. In this paper, we present an adaptable passive hand orthosis which is capable of producing natural and smooth movements when coupled with FES. It evenly synchronizes the grasping movements and applied forces on all fingers, allowing for naturalistic gestures and functional grasps of everyday objects. The orthosis is also equipped with a lock, which allows it to remain in the desired position without the need for long-term stimulation. Furthermore, we quantify improvements offered by the orthosis compare them with natural grasps on healthy subjects.},
}
@article {pmid21096742,
year = {2010},
author = {Pohlmeyer, EA and Jangraw, DC and Wang, J and Chang, SF and Sajda, P},
title = {Combining computer and human vision into a BCI: can the whole be greater than the sum of its parts?.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {138-141},
doi = {10.1109/IEMBS.2010.5627403},
pmid = {21096742},
issn = {2375-7477},
mesh = {Algorithms ; Databases, Factual ; *Electroencephalography ; Humans ; Image Processing, Computer-Assisted/*methods ; *Information Storage and Retrieval ; *Man-Machine Systems ; ROC Curve ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; Visual Perception ; },
abstract = {Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.},
}
@article {pmid21096741,
year = {2010},
author = {Rossini, L and Rossini, PM},
title = {Combining ENG and EEG integrated analysis for better sensitivity and specificity of neuroprosthesis operations.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {134-137},
doi = {10.1109/IEMBS.2010.5627402},
pmid = {21096741},
issn = {2375-7477},
mesh = {Adult ; Arm ; Electroencephalography/*methods ; Humans ; Male ; *Man-Machine Systems ; *Neural Prostheses ; Neurofeedback/instrumentation/*methods ; Scalp ; *Self-Help Devices ; *Signal Processing, Computer-Assisted ; },
abstract = {Combining non-invasive monitoring of action-related brain signals with the invasive recordings of the nerve motor output could provide robust natural and bidirectional multimodal Brain-Machine interfaces. One 26 years old, right-handed male who had suffered traumatic trans-radial amputation of the left arm was connected in a bidirectional way with a robotic hand prostheses. Cortical signals related with movement programming, execution, and feed-back were recorded by non-invasive scalp electrodes to detect high-level information (i.e. onset of movement intention), while the efferent neural activity containing the low-level commands towards the missing limb was recorded from the amputated nerves by multipolar intra-neural electrodes. The aim of this article is to report advanced experiences aiming to investigate whether information on "hand-related" activities can be decoded by the combined analysis of motor-related signals simultaneously gathered via intraneural electrodes implanted into the peripheral nervous system and scalp recorded electroencephalography signals to govern a dexterous hand prosthesis using the natural neural "pathway".},
}
@article {pmid21096685,
year = {2010},
author = {Beier, BL and Brandner, EM and Musick, KM and Matsumoto, A and Panitch, A and Nauman, EA and Irazoqui, PP},
title = {Preliminary characterization of a glucose-sensitive hydrogel.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5014-5017},
doi = {10.1109/IEMBS.2010.5627210},
pmid = {21096685},
issn = {2375-7477},
mesh = {Biological Transport ; Chemistry Techniques, Analytical/*methods ; Fluorescence ; Fluorescence Recovery After Photobleaching ; Glucose/*analysis ; Hydrogels/*chemistry ; Time Factors ; },
abstract = {We present proof-of-concept studies that display the potential for using a glucose-sensitive hydrogel as a continuous glucose sensor. A study to characterize the swelling ratio of the hydrogel at normal physiological and pathological hyperglycemic glucose levels was performed. The hydrogel exposed to the hyperglycemic glucose solution had a higher equilibrium swelling ratio than the hydrogel exposed to the normal glucose concentration solution. The diffusivity of a small molecule, fluorescein isothiocyanate (FITC), through a hydrogel exposed to a hyperglycemic solution was determined using fluorescence recovery after photobleaching (FRAP). The diffusivity was found to be 4.2 × 10(-14) m(2)/s, a value approximately four orders of magnitude smaller than the diffusivity of FITC in glucose solution. The permeability of the hydrogel after equilibration in a hyperglycemic solution was found to be 5.1 × 10(-17) m(2), in the range of 2-4% agarose gels.},
}
@article {pmid21096670,
year = {2010},
author = {Liang, SF and Shaw, FZ and Young, CP and Chang, DW and Liao, YC},
title = {A closed-loop brain computer interface for real-time seizure detection and control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4950-4953},
doi = {10.1109/IEMBS.2010.5627243},
pmid = {21096670},
issn = {2375-7477},
mesh = {Animals ; Biofeedback, Psychology/*instrumentation ; Computer Systems ; Deep Brain Stimulation/*instrumentation ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Rats ; Seizures/*diagnosis/*prevention &amp; control ; Signal Processing, Computer-Assisted/instrumentation ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {The worldwide prevalence of epilepsy is approximately 1%, and 25% of epilepsy patients cannot be treated sufficiently by available therapies. Brain stimulation with closed-loop seizure control has recently been proposed as an innovative and effective alternative. In this paper, a portable closed-loop brain computer interface for seizure control was developed and shown with several aspects of advantages, including high seizure detection rate (92-99% during wake-sleep states), low false detection rate (1.2-2.5%), and small size. The seizure detection and electrical stimulation latency was not greater than 0.6 s after seizure onset. A wireless communication feature also provided flexibility for subjects freeing from the hassle of wires. Experimental data from freely moving rats supported the functional possibility of a real-time closed-loop seizure controller.},
}
@article {pmid21096640,
year = {2010},
author = {Thorbergsson, PT and Garwicz, M and Schouenborg, J and Johansson, A},
title = {Statistical modelling of spike libraries for simulation of extracellular recordings in the cerebellum.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4250-4253},
doi = {10.1109/IEMBS.2010.5627177},
pmid = {21096640},
issn = {2375-7477},
mesh = {*Action Potentials ; Animals ; Cats ; Cerebellum/*physiology ; *Models, Statistical ; Principal Component Analysis ; },
abstract = {Brain machine interfaces with chronically implanted microelectrode arrays for signal acquisition require algorithms for successful detection and classification of neural spikes. During the design of such algorithms, signals with a priori known characteristics need to be present. A common way to establish such signals is to model the recording environment, simulate the recordings and store ground truth about spiking activity for later comparison. In this paper, we present a statistical method to expand the spike libraries that are used in a previously presented simulation tool for the purpose described above. The method has been implemented and shown to successfully provide quick access to a large assembly of synthetic extracellular spikes with realistic characteristics. Simulations of extracellular recordings using synthesized spikes have shown to possess characteristics similar to those of in-vivo recordings in the cat cerebellum.},
}
@article {pmid21096633,
year = {2010},
author = {Zhang, H and Ma, C and He, J},
title = {Predicting lower limb muscular activity during standing and squatting using spikes of primary motor cortical neurons in monkeys.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4124-4127},
doi = {10.1109/IEMBS.2010.5627320},
pmid = {21096633},
issn = {2375-7477},
mesh = {*Action Potentials ; Animals ; Electromyography ; Haplorhini ; Hindlimb/*physiology ; Models, Biological ; Motor Cortex/cytology/*physiology ; Neurons/*physiology ; },
abstract = {In this study, we investigated predicting lower limb muscular activities of monkeys during standing and squatting motions using neuronal spikes in primary motor cortex M1. Finite impulse response models were built for prediction. Acute electrode arrays were used to collect neuronal spikes in the lower limb representation area of M1 in the left hemisphere, and electrodes were implanted to the right leg muscles to collect EMG signals. Multiple regions of the lower limb representation area of M1 were explored. The neurons from two common regions demonstrated high predictive power on all 6 investigated right leg EMG signals. This study shows that the cortical neuronal spikes can be used to predict lower limb muscular activities with high accuracy, and identifies regions of high predictive power, where chronic electrodes can be implanted for future brain machine interface applications.},
}
@article {pmid21096620,
year = {2010},
author = {Hwang, EJ and Andersen, RA},
title = {Cognitively driven brain machine control using neural signals in the parietal reach region.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3329-3332},
doi = {10.1109/IEMBS.2010.5627277},
pmid = {21096620},
issn = {2375-7477},
support = {EY 013337/EY/NEI NIH HHS/United States ; T32 NS007251/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Arm/physiology ; Biofeedback, Psychology/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feasibility Studies ; Macaca mulatta ; Male ; *Man-Machine Systems ; Movement/*physiology ; Parietal Lobe/*physiology ; },
abstract = {This study demonstrates that the spiking and local field potential (LFP) activity in the parietal reach region (PRR) of the macaque monkey can be jointly used to control the location of the computer cursor when the correct target location must be inferred symbolically, e.g., leftward arrow for the leftward target, etc. The average correct target acquisition rate during this brain machine control task without actual movements was 86% for the six discrete target locations when using spikes and LFPs from 16 electrodes. This performance was significantly better than using spikes or LFPs alone. These results, together with our previous findings, suggest that a single decoder based on both spikes and LFPs in PRR can robustly provide the subjects' motor intent under varying contexts for neural prosthetic applications.},
}
@article {pmid21096616,
year = {2010},
author = {Atum, Y and Gareis, I and Gentiletti, G and Acevedo, R and Rufiner, L},
title = {Genetic feature selection to optimally detect P300 in brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {3289-3292},
doi = {10.1109/IEMBS.2010.5627254},
pmid = {21096616},
issn = {2375-7477},
mesh = {*Algorithms ; Artificial Intelligence ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Man-Machine Systems ; Models, Genetic ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.},
}
@article {pmid21096524,
year = {2010},
author = {Zhang, H and Benz, HL and Bezerianos, A and Acharya, S and Crone, NE and Maybhate, A and Zheng, X and Thakor, NV},
title = {Connectivity mapping of the human ECoG during a motor task with a time-varying dynamic Bayesian network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {130-133},
pmid = {21096524},
issn = {2375-7477},
support = {R01 EB010100/EB/NIBIB NIH HHS/United States ; R01 NS040596/NS/NINDS NIH HHS/United States ; R01 NS040596-09A2/NS/NINDS NIH HHS/United States ; 1R01EB010100-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Bayes Theorem ; Brain Mapping/*methods ; Cerebral Cortex/physiology ; Electrodes, Implanted ; Electroencephalography/*methods ; Epilepsy ; Hand/physiology ; Humans ; Image Processing, Computer-Assisted ; Motor Skills/*physiology ; Nerve Net ; Regression Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {As a partially invasive and clinically obtained neural signal, the electrocorticogram (ECoG) provides a unique opportunity to study cortical processing in humans in vivo. Functional connectivity mapping based on the ECoG signal can provide insight into epileptogenic zones and putative cortical circuits. We describe the first application of time-varying dynamic Bayesian networks (TVDBN) to the ECoG signal for the identification and study of cortical circuits. Connectivity between motor areas as well as between sensory and motor areas preceding and during movement is described. We further apply the connectivity results of the TVDBN to a movement decoder, which achieves a correlation between actual and predicted hand movements of 0.68. This paper presents evidence that the connectivity information discovered with TVDBN is applicable to the design of an ECoG-based brain-machine interface.},
}
@article {pmid21096523,
year = {2010},
author = {Tavella, M and Leeb, R and Rupp, R and Millan, Jdel R},
title = {Towards natural non-invasive hand neuroprostheses for daily living.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {126-129},
doi = {10.1109/IEMBS.2010.5627178},
pmid = {21096523},
issn = {2375-7477},
mesh = {Activities of Daily Living ; Adult ; Electroencephalography/*methods ; Female ; Hand Strength/physiology ; Humans ; Male ; *Man-Machine Systems ; *Neural Prostheses ; *Self-Help Devices ; *Signal Processing, Computer-Assisted ; },
abstract = {In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to deliver mental commands with high accuracy and speed. Our neuroprosthetic approach relies on a natural interaction paradigm, where subjects delivers congruent MI commands (i.e., they imagining a movement of the same hand they control through FES). Furthermore, we have tested our approach in a common daily task such as handwriting, which requires the user to split his/her attention to multitask between BCI control, reaching, and the primary handwriting task itself. Interestingly, the very low number of erroneous trials illustrates how during the experiments subjects were able to deliver commands just when they intended to do so. Similarly, the subjects could perform actions while delivering, or preparing to deliver, mental commands.},
}
@article {pmid21096475,
year = {2010},
author = {Ang, KK and Guan, C and Chua, KS and Ang, BT and Kuah, C and Wang, C and Phua, KS and Chin, ZY and Zhang, H},
title = {Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5549-5552},
doi = {10.1109/IEMBS.2010.5626782},
pmid = {21096475},
issn = {2375-7477},
mesh = {Adult ; Aged ; Brain/physiopathology ; Calibration ; Demography ; Electroencephalography/*methods ; Feedback, Sensory/*physiology ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Motor Activity/*physiology ; Robotics/*methods ; Stroke/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; Young Adult ; },
abstract = {This clinical study investigates the ability of hemiparetic stroke patients in operating EEG-based motor imagery brain-computer interface (MI-BCI). It also assesses the efficacy in motor improvements on the stroke-affected upper limb using EEG-based MI-BCI with robotic feedback neurorehabilitation compared to robotic rehabilitation that delivers movement therapy. 54 hemiparetic stroke patients with mean age of 51.8 and baseline Fugl-Meyer Assessment (FMA) 14.9 (out of 66, higher = better) were recruited. Results showed that 48 subjects (89%) operated EEG-based MI-BCI better than at chance level, and their ability to operate EEG-based MI-BCI is not correlated to their baseline FMA (r=0.358). Those subjects who gave consent are randomly assigned to each group (N=11 and 14) for 12 1-hour rehabilitation sessions for 4 weeks. Significant gains in FMA scores were observed in both groups at post-rehabilitation (4.5, 6.2; p=0.032, 0.003) and 2-month post-rehabilitation (5.3, 7.3; p=0.020, 0.013), but no significant differences were observed between groups (p=0.512, 0.550). Hence, this study showed evidences that a majority of hemiparetic stroke patients can operate EEG-based MI-BCI, and that EEG-based MI-BCI with robotic feedback neurorehabilitation is effective in restoring upper extremities motor function in stroke.},
}
@article {pmid21096397,
year = {2010},
author = {Chhatbar, PY and Francis, JT},
title = {Comparison of force and power generation patterns and their predictions under different external dynamic environments.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1686-1690},
doi = {10.1109/IEMBS.2010.5626832},
pmid = {21096397},
issn = {2375-7477},
mesh = {*Algorithms ; Animals ; Electroencephalography/*methods ; Energy Transfer/*physiology ; Evoked Potentials, Motor/*physiology ; Female ; Macaca radiata ; Movement/*physiology ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Use of neural activity to predict kinematic variables such as position, velocity and direction etc of movements has been implemented in real-time control of robotic systems and computer cursors. In everyday life, however, we generate variable amounts of force to manipulate objects of different inertial properties or to follow the same trajectory under different external dynamic environments like air or water. The resultant work during such movements, and its time derivative power, should depend on the dynamics of the movement. In order to give the users of a brain-machine interface (BMI) comprehensive control of a prosthetic limb under different dynamic conditions, it is imperative to consider the dynamics-related parameters like end-effector forces, joint torques or power. In this paper, we show distribution patterns of two such dynamics parameters - force and power - and their predictive efficiency under different dynamic environmental conditions. We intend to find the force-related parameter, which has optimal predictive efficiency across different dynamic environments that is generalization. Our ultimate goal is to materialize a force-based brain-machine interface (fBMI).},
}
@article {pmid21096396,
year = {2010},
author = {Mahmoudi, B and Principe, JC and Sanchez, JC},
title = {Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1682-1685},
doi = {10.1109/IEMBS.2010.5626827},
pmid = {21096396},
issn = {2375-7477},
support = {N66001-10-C-2008//PHS HHS/United States ; },
mesh = {*Algorithms ; Animals ; Biofeedback, Psychology/*physiology ; Brain/*physiology ; Computer-Aided Design ; Electroencephalography/*methods ; Equipment Design ; Equipment Failure Analysis ; Nervous System Diseases/rehabilitation ; Neuronal Plasticity/*physiology ; *Prostheses and Implants ; Rats ; Rats, Sprague-Dawley ; *Reward ; },
abstract = {The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.},
}
@article {pmid21096394,
year = {2010},
author = {Rebesco, JM and Miller, LE},
title = {Altering function in cortical networks by short-latency, paired stimulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1674-1677},
doi = {10.1109/IEMBS.2010.5626822},
pmid = {21096394},
issn = {2375-7477},
support = {F31NS062552/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adaptation, Physiological/physiology ; Animals ; Biological Clocks/*physiology ; Brain/*physiology ; Electric Stimulation/*methods ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; Rats ; Reaction Time/*physiology ; },
abstract = {Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. Numerous experimental studies have attempted to elucidate its underlying mechanisms under both in vitro and in vivo conditions. Short latency, associative pairing of presynaptic "trigger" spiking with stimulus-induced postsynaptic depolarization of a target neuron has been shown to lead to changes in the effectiveness of a stimulus applied to the presynaptic neuron. We have used similar methods to demonstrate changes in the statistically inferred functional connections among small groups of recorded neurons in rat sensorimotor cortex. These induced changes transcended simple changes in stimulus-evoked activity. Rather, they reflected a robust reorganization of network connectivity revealed by changes in the patterns of spikes in the cells' spontaneous discharge. We hypothesized that by strengthening the functional connections from trigger to target neurons, we might demonstrate a related behavioral change. To test this hypothesis, we trained rats to respond to a near-threshold, intracortical stimulus cue. Following 1-2 days of paired, short latency stimulation, the sensitivity of these rats to the cue was increased. The latency dependence and the timecourse of this effect were very similar to the corresponding parameters of the inferred connectivity changes in the first experiment. Such targeted connectivity changes may provide a tool for rerouting the flow of information through a cortical network, with profound implications for both rehabilitation and brain-machine interface applications.},
}
@article {pmid21096393,
year = {2010},
author = {Heliot, R and Venkatraman, S and Carmena, JM},
title = {Decoder remapping to counteract neuron loss in brain-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1670-1673},
doi = {10.1109/IEMBS.2010.5626694},
pmid = {21096393},
issn = {2375-7477},
mesh = {*Algorithms ; Animals ; *Artifacts ; Brain Mapping/*methods ; Macaca ; Man-Machine Systems ; Neurons/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Variability of single-unit neural recordings can significantly affect the overall performance achieved by brain machine interfaces (BMI). In this paper, we present a novel technique to adapt a linear filter commonly used in BMI to compensate for loss of neurons from the recorded neural ensemble, thus minimizing loss in performance. We simulate the gains achieved by this technique using a model of the learning process during closed-loop BMI operation. This simulation suggests that we can adapt to the loss of 24% of the neurons controlling a BMI with only 13% drop in performance.},
}
@article {pmid21096383,
year = {2010},
author = {Aghagolzadeh, M and Zhang, F and Oweiss, K},
title = {An implantable VLSI architecture for real time spike sorting in cortically controlled Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1569-1572},
doi = {10.1109/IEMBS.2010.5626691},
pmid = {21096383},
issn = {2375-7477},
support = {R01 NS062031/NS/NINDS NIH HHS/United States ; R01 NS062031-04/NS/NINDS NIH HHS/United States ; NS062031/NS/NINDS NIH HHS/United States ; NS054148/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Biofeedback, Psychology/*instrumentation ; Brain/*physiology ; Electroencephalography/*instrumentation ; Pattern Recognition, Automated/methods ; Rats ; Semiconductors ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications.},
}
@article {pmid21096374,
year = {2010},
author = {Gojo, R and Saito, H and Suzuki, T and Mabuchi, K},
title = {Optimizing the diameter of holes for flexible regeneration microelectrode.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1531-1534},
doi = {10.1109/IEMBS.2010.5626829},
pmid = {21096374},
issn = {2375-7477},
mesh = {Animals ; Elastic Modulus ; Electric Stimulation Therapy/*instrumentation ; *Electrodes, Implanted ; Equipment Design ; Equipment Failure Analysis ; Nerve Regeneration/*physiology ; Porosity ; Rats ; Sciatic Neuropathy/*therapy ; Treatment Outcome ; },
abstract = {In this study, we suggest a new guideline for regeneration microelectrode to be implanted between the severed stumps of peripheral nerves, the microelectrode designed particularly for connecting the signal line of an artificial hand directly to the nerve system. The nerve regeneration microelectrode is an interface device expected to realize a BMI (brain-machine interface).},
}
@article {pmid21096368,
year = {2010},
author = {Talebinejad, M and Musallam, S},
title = {Effects of TMS coil geometry on stimulation specificity.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1507-1510},
doi = {10.1109/IEMBS.2010.5626840},
pmid = {21096368},
issn = {2375-7477},
support = {//Canadian Institutes of Health Research/Canada ; },
mesh = {Computer-Aided Design ; Equipment Design ; Equipment Failure Analysis ; Humans ; Magnetics/*instrumentation ; Pattern Recognition, Automated/*methods ; Transcranial Magnetic Stimulation/*instrumentation ; },
abstract = {Transcranial magnetic stimulation has become an established tool in experimental cognitive neuroscience and has more recently been applied clinically. The current spatial extent of neural activation is several millimeters but with greater specificity, transcranial magnetic stimulation can potentially deliver real time feedback to reinforce or extinguish behavior by exciting or inhibiting localized neural circuits. The specificity of transcranial magnetic stimulation is a function of the stimulation coil geometry. In this paper, a practical multilayer framework for the design of miniaturized stimulation coils is presented. This framework is based on a magnet wire fabricated from 2500 braided ultrafine wires. Effects of coil bending angle on stimulation specificity are examined using realistic finite element method simulations. A novel stimulation coil with one degree of freedom is also proposed that shows improved specificity over the conventional fixed coils. This type of coil could be potentially used as a feedback system for a bidirectional brain machine interface.},
}
@article {pmid21096331,
year = {2010},
author = {Punsawad, Y and Wongsawat, Y and Parnichkun, M},
title = {Hybrid EEG-EOG brain-computer interface system for practical machine control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1360-1363},
doi = {10.1109/IEMBS.2010.5626745},
pmid = {21096331},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Electrooculography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.},
}
@article {pmid21096265,
year = {2010},
author = {Bartels, G and Shi, LC and Lu, BL},
title = {Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5383-5386},
doi = {10.1109/IEMBS.2010.5626481},
pmid = {21096265},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; *Artifacts ; Automation/*methods ; Electroencephalography/*methods ; Humans ; Male ; Movement/physiology ; Muscles/physiology ; Young Adult ; },
abstract = {Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.},
}
@article {pmid21096264,
year = {2010},
author = {Rivet, B and Cecotti, H and Phlypo, R and Bertrand, O and Maby, E and Mattout, J},
title = {EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5379-5382},
doi = {10.1109/IEMBS.2010.5626485},
pmid = {21096264},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/*instrumentation/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Photic Stimulation ; *Software ; *User-Computer Interface ; },
abstract = {A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory. We propose a new algorithm to select a relevant subset of electrodes by estimating sparse spatial filters. A l(1)-norm penalization term, as an approximation of the l(0)-norm, is introduced in the xDAWN algorithm, which maximizes the signal to signal-plus-noise ratio. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 10 sensors, the loss in classification accuracy is less than 5%.},
}
@article {pmid21096242,
year = {2010},
author = {Wang, B and Wong, C and Wan, F and Mak, PU and Mak, PI and Vai, MI},
title = {Trial pruning for classification of single-trial EEG data during motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4666-4669},
doi = {10.1109/IEMBS.2010.5626453},
pmid = {21096242},
issn = {2375-7477},
mesh = {*Algorithms ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After trial pruning, a subset of the EEG data are selected, on which common spatial pattern (CSP) and Gaussian classifier are trained. The performance of the proposed method is tested on Data set IIa of BCI Competition IV, and the simulation result demonstrates a significant improvement for six out of nine subjects.},
}
@article {pmid21096218,
year = {2010},
author = {von Bunau, P and Meinecke, FC and Scholler, S and Muller, KR},
title = {Finding stationary brain sources in EEG data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2810-2813},
doi = {10.1109/IEMBS.2010.5626537},
pmid = {21096218},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*pathology ; Brain Mapping/methods ; Calibration ; Electroencephalography/*methods ; Equipment Design ; Humans ; Magnetic Resonance Imaging/methods ; Models, Statistical ; Motor Skills ; Multivariate Analysis ; Normal Distribution ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Neurophysiological measurements obtained from e.g. EEG or fMRI are inherently non-stationary because the properties of the underlying brain processes vary over time. For example, in Brain-Computer-Interfacing (BCI), deteriorating performance (bitrate) is a common phenomenon since the parameters determined during the calibration phase can be suboptimal under the application regime, where the brain state is different, e.g. due to increased tiredness or changes in the experimental paradigm. We show that Stationary Subspace Analysis (SSA), a time series analysis method, can be used to identify the underlying stationary and non-stationary brain sources from high-dimensional EEG measurements. Restricting the BCI to the stationary sources found by SSA can significantly increase the performance. Moreover, SSA yields topographic maps corresponding to stationary- and non-stationary brain sources which reveal their spatial characteristics.},
}
@article {pmid21096199,
year = {2010},
author = {Acqualagna, L and Treder, MS and Schreuder, M and Blankertz, B},
title = {A novel brain-computer interface based on the rapid serial visual presentation paradigm.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2686-2689},
doi = {10.1109/IEMBS.2010.5626548},
pmid = {21096199},
issn = {2375-7477},
mesh = {Adult ; Biomedical Engineering/methods ; Brain/*pathology ; Computers ; Electroencephalography/methods ; Event-Related Potentials, P300 ; Evoked Potentials ; Female ; Humans ; Male ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; Time Factors ; User-Computer Interface ; },
abstract = {Most present-day visual brain computer interfaces (BCIs) suffer from the fact that they rely on eye movements, are slow-paced, or feature a small vocabulary. As a potential remedy, we explored a novel BCI paradigm consisting of a central rapid serial visual presentation (RSVP) of the stimuli. It has a large vocabulary and realizes a BCI system based on covert non-spatial selective visual attention. In an offline study, eight participants were presented sequences of rapid bursts of symbols. Two different speeds and two different color conditions were investigated. Robust early visual and P300 components were elicited time-locked to the presentation of the target. Offline classification revealed a mean accuracy of up to 90% for selecting the correct symbol out of 30 possibilities. The results suggest that RSVP-BCI is a promising new paradigm, also for patients with oculomotor impairments.},
}
@article {pmid21096198,
year = {2010},
author = {Koralek, AC and Long, JD and Costa, RM and Carmena, JM},
title = {Corticostriatal dynamics during learning and performance of a neuroprosthetic task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2682-2685},
doi = {10.1109/IEMBS.2010.5626632},
pmid = {21096198},
issn = {2375-7477},
mesh = {Animals ; Biomedical Engineering/methods ; Brain/pathology ; Corpus Striatum/pathology ; Equipment Design ; Learning ; Male ; Man-Machine Systems ; Models, Neurological ; Motor Cortex/*pathology ; Neuronal Plasticity/physiology ; Neurons/*pathology ; Rats ; Rats, Long-Evans ; Time Factors ; },
abstract = {Corticostriatal dynamics exhibit gross alterations over the course of natural motor learning, yet little is known about the role they play in neuroprosthetic tasks. We therefore investigated interactions between the striatum and primary motor cortex while rats learned to control a brain-machine interface. Striatal firing rates increased greatly from early to late in learning, suggesting that the striatum underlies similar functions in both natural and neuroprosthetic motor learning. In addition, spike-field coherence between neurons in primary motor cortex and local field potentials in the striatum increased greatly in the alpha band in late learning relative to early learning, suggesting the development of functional interactions in corticostriatal networks over the course of learning.},
}
@article {pmid21096196,
year = {2010},
author = {Mountney, J and Silage, D and Obeid, I},
title = {Parallel field programmable gate array particle filtering architecture for real-time neural signal processing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2674-2677},
doi = {10.1109/IEMBS.2010.5626626},
pmid = {21096196},
issn = {2375-7477},
mesh = {Algorithms ; Bayes Theorem ; Brain/physiology ; Computer Simulation ; Computers ; Equipment Design ; Humans ; Likelihood Functions ; Models, Neurological ; Neurons/metabolism/*pathology ; *Signal Processing, Computer-Assisted ; Software ; Time Factors ; },
abstract = {Both linear and nonlinear estimation algorithms have been successfully applied as neural decoding techniques in brain machine interfaces. Nonlinear approaches such as Bayesian auxiliary particle filters offer improved estimates over other methodologies seemingly at the expense of computational complexity. Real-time implementation of particle filtering algorithms for neural signal processing may become prohibitive when the number of neurons in the observed ensemble becomes large. By implementing a parallel hardware architecture, filter performance can be improved in terms of throughput over conventional sequential processing. Such an architecture is presented here and its FPGA resource utilization is reported.},
}
@article {pmid21096139,
year = {2010},
author = {Yazdani, A and Vesin, JM and Izzo, D and Ampatzis, C and Ebrahimi, T},
title = {The impact of expertise on brain computer interface based salient image retrieval.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1646-1649},
doi = {10.1109/IEMBS.2010.5626655},
pmid = {21096139},
issn = {2375-7477},
mesh = {Algorithms ; Brain Mapping/*methods ; Event-Related Potentials, P300/*physiology ; *Expert Systems ; Female ; Humans ; Male ; Memory, Short-Term/*physiology ; Pattern Recognition, Visual/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient images using electroencephalogram (EEG) and brain computer interface (BCI) and rapid image presentation. To this end, two groups of subjects, namely, expert and novice subjects were asked to participate in our experiments. We show that a relatively high retrieval accuracy can be achieved for most of the subjects. Furthermore, to assess the impact of expertise on the retrieval process, we study their EEG signals separately and show that there is a clear difference in their brainwaves while observing salient images.},
}
@article {pmid21096028,
year = {2010},
author = {Herrera-Rincon, C and Torets, C and Sanchez-Jimenez, A and Avendaño, C and Guillen, P and Panetsos, F},
title = {Structural preservation of deafferented cortex induced by electrical stimulation of a sensory peripheral nerve.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5066-5069},
doi = {10.1109/IEMBS.2010.5626229},
pmid = {21096028},
issn = {2375-7477},
mesh = {Afferent Pathways/pathology/*surgery ; Amputation, Surgical ; Animals ; Calbindins ; Electric Stimulation ; Electron Transport Complex IV/metabolism ; Female ; Implants, Experimental ; Parvalbumins/metabolism ; Peripheral Nerves/*surgery ; Rats ; Rats, Wistar ; S100 Calcium Binding Protein G/metabolism ; Somatosensory Cortex/*pathology/*surgery ; },
abstract = {Any manipulation to natural sensory input has direct effects on the morphology and physiology of the Central Nervous System. In the particular case of amputations, sensory areas of the brain undergo degenerative processes with a marked reduction in neuronal activity and global disinhibition. This is probably due to a deregulation of the circuits devoted to the control of the cortical activity. These changes are detected in the organization of the representational maps, the metabolic labeling by 2-deoxyglucose or cytochrome oxidase, the density of afferent and efferent axonal connections and the reduced expression of inhibitory neurotransmitters. In the present study, performed in animals, we have evaluated the therapeutic potential of Brain Machine Interfaces in reversing or limiting the degenerative/deregulation processes of amputations. Applying electrical stimulation on amputated peripheral nerves, we have achieved to maintain in approximately normal values 1) the cortical activity and 2) the expression of GABA-associated molecules of the inhibitory interneurons of the primary somatosensory cortex.},
}
@article {pmid21096003,
year = {2010},
author = {Sannelli, C and Vidaurre, C and Muller, KR and Blankertz, B},
title = {Common spatial pattern patches - an optimized filter ensemble for adaptive brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4351-4354},
doi = {10.1109/IEMBS.2010.5626227},
pmid = {21096003},
issn = {2375-7477},
mesh = {Brain/*physiology ; *Computers ; Humans ; *Man-Machine Systems ; },
abstract = {Laplacian filters are commonly used in Brain Computer Interfacing (BCI). When only data from few channels are available, or when, like at the beginning of an experiment, no previous data from the same user is available complex features cannot be used. In this case band power features calculated from Laplacian filtered channels represents an easy, robust and general feature to control a BCI, since its calculation does not involve any class information. For the same reason, the performance obtained with Laplacian features is poor in comparison to subject-specific optimized spatial filters, such as Common Spatial Patterns (CSP) analysis, which, on the other hand, can be used just in a later phase of the experiment, since they require a considerable amount of training data in order to enroll a stable and good performance. This drawback is particularly evident in case of poor performing BCI users, whose data is highly non-stationary and contains little class relevant information. Therefore, Laplacian filtering is preferred to CSP, e.g., in the initial period of co-adaptive calibration, a novel BCI paradigm designed to alleviate the problem of BCI illiteracy. In fact, in the co-adaptive calibration design the experiment starts with a subject-independent classifier and simple features are needed in order to obtain a fast adaptation of the classifier to the newly acquired user's data. Here, the use of an ensemble of local CSP patches (CSPP) is proposed, which can be considered as a compromise between Laplacians and CSP: CSPP needs less data and channels than CSP, while being superior to Laplacian filtering. This property is shown to be particularly useful for the co-adaptive calibration design and is demonstrated on off-line data from a previous co-adaptive BCI study.},
}
@article {pmid21096001,
year = {2010},
author = {Leeb, R and Sagha, H and Chavarriaga, R and Del R Millan, J},
title = {Multimodal fusion of muscle and brain signals for a hybrid-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4343-4346},
doi = {10.1109/IEMBS.2010.5626233},
pmid = {21096001},
issn = {2375-7477},
mesh = {Brain/*physiology ; Electroencephalography ; Electromyography ; Humans ; *Man-Machine Systems ; Muscles/*physiology ; Task Performance and Analysis ; },
abstract = {Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in the framework of a so called "Hybrid-BCI" (hBCI) approach. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yields good performance, it is outperformed by the hybrid fusing of EEG and EMG. Two different fusion techniques are explored showing graceful performance degradation in the case of signal attenuation. Such a system allows a very reliable control and a smooth handover if the subjects get exhausted or fatigued during the day.},
}
@article {pmid21096000,
year = {2010},
author = {Rattanatamrong, P and Matsunaga, A and Raiturkar, P and Mesa, D and Zhao, M and Mahmoudi, B and Digiovanna, J and Principe, J and Figueiredo, R and Sanchez, J and Fortes, J},
title = {Model development, testing and experimentation in a CyberWorkstation for Brain-Machine Interface research.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4339-4342},
doi = {10.1109/IEMBS.2010.5626234},
pmid = {21096000},
issn = {2375-7477},
support = {N66001-10-C-2008//PHS HHS/United States ; },
mesh = {Brain/*physiology ; *Computer Simulation ; *Computers ; Humans ; *Man-Machine Systems ; Software ; },
abstract = {The CyberWorkstation (CW) is an advanced cyber-infrastructure for Brain-Machine Interface (BMI) research. It allows the development, configuration and execution of BMI computational models using high-performance computing resources. The CW's concept is implemented using a software structure in which an "experiment engine" is used to coordinate all software modules needed to capture, communicate and process brain signals and motor-control commands. A generic BMI-model template, which specifies a common interface to the CW's experiment engine, and a common communication protocol enable easy addition, removal or replacement of models without disrupting system operation. This paper reviews the essential components of the CW and shows how templates can facilitate the processes of BMI model development, testing and incorporation into the CW. It also discusses the ongoing work towards making this process infrastructure independent.},
}
@article {pmid21095940,
year = {2010},
author = {Patrick, E and Sankar, V and Rowe, W and Sanchez, JC and Nishida, T},
title = {An implantable integrated low-power amplifier-microelectrode array for Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1816-1819},
doi = {10.1109/IEMBS.2010.5626419},
pmid = {21095940},
issn = {2375-7477},
support = {NS053561/NS/NINDS NIH HHS/United States ; },
mesh = {*Amplifiers, Electronic ; Animals ; Electric Power Supplies ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Male ; *Man-Machine Systems ; Motor Cortex/*physiology ; Rats ; Rats, Sprague-Dawley ; Systems Integration ; *User-Computer Interface ; },
abstract = {One of the important challenges in designing Brain-Machine Interfaces (BMI) is to build implantable systems that have the ability to reliably process the activity of large ensembles of cortical neurons. In this paper, we report the design, fabrication, and testing of a polyimide-based microelectrode array integrated with a low-power amplifier as part of the Florida Wireless Integrated Recording Electrode (FWIRE) project at the University of Florida developing a fully implantable neural recording system for BMI applications. The electrode array was fabricated using planar micromachining MEMS processes and hybrid packaged with the amplifier die using a flip-chip bonding technique. The system was tested both on bench and in-vivo. Acute and chronic neural recordings were obtained from a rodent for a period of 42 days. The electrode-amplifier performance was analyzed over the chronic recording period with the observation of a noise floor of 4.5 microVrms, and an average signal-to-noise ratio of 3.8.},
}
@article {pmid21095937,
year = {2010},
author = {Charvet, G and Billoint, O and Gharbi, S and Heuschkel, M and Georges, C and Kauffmann, T and Pellissier, A and Yvert, B and Guillemaud, R},
title = {A modular 256-channel micro electrode array platform for in vitro and in vivo neural stimulation and recording: BioMEA.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {1804-1807},
doi = {10.1109/IEMBS.2010.5626403},
pmid = {21095937},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; Animals ; Electric Stimulation/*instrumentation ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Hippocampus/*physiology ; Microarray Analysis/*instrumentation ; *Microelectrodes ; Neurons/*physiology ; Rats ; Rats, Wistar ; },
abstract = {In order to understand the dynamics of large neural networks, where information is widely distributed over thousands of cells, one of today's challenges is to successfully monitor the simultaneous activity of as many neurons as possible. This is made possible by using the Micro-Electrode Array (MEA) technology allowing neural cell culture and/or tissue slice experimentation in vitro. Thanks to development of microelectronics' technologies, a novel data acquisition system based on MEA technology has been developed, the BioMEA™. It combines the most advanced MEA biochips with integrated electronics, and a novel user-friendly software interface. To move from prototype (result of the RMNT research project NEUROCOM) to manufactured product, a number of changes have been made. Here, we present a 256-channel MEA data acquisition system with integrated electronics (BioMEA™) allowing simultaneous recording and stimulation of neural networks for in vitro and in vivo applications. This integration is a first step towards an implantable device for BCI (Brain Computer Interface) studies and neural prosthesis.},
}
@article {pmid21095885,
year = {2010},
author = {Lopes, AC and Nunes, U and Vaz, L and Vaz, L},
title = {Assisted navigation based on shared-control, using discrete and sparse human-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {471-474},
doi = {10.1109/IEMBS.2010.5626221},
pmid = {21095885},
issn = {2375-7477},
mesh = {Algorithms ; Computer Simulation ; Fuzzy Logic ; Humans ; Locomotion ; *Man-Machine Systems ; Robotics/*instrumentation ; *Self-Help Devices ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {This paper presents a shared-control approach for Assistive Mobile Robots (AMR), which depends on the user's ability to navigate a semi-autonomous powered wheelchair, using a sparse and discrete human-machine interface (HMI). This system is primarily intended to help users with severe motor disabilities that prevent them to use standard human-machine interfaces. Scanning interfaces and Brain Computer Interfaces (BCI), characterized to provide a small set of commands issued sparsely, are possible HMIs. This shared-control approach is intended to be applied in an Assisted Navigation Training Framework (ANTF) that is used to train users' ability in steering a powered wheelchair in an appropriate manner, given the restrictions imposed by their limited motor capabilities. A shared-controller based on user characterization, is proposed. This controller is able to share the information provided by the local motion planning level with the commands issued sparsely by the user. Simulation results of the proposed shared-control method, are presented.},
}
@article {pmid21095857,
year = {2011},
author = {Vidaurre, C and Kawanabe, M and von Bünau, P and Blankertz, B and Müller, KR},
title = {Toward unsupervised adaptation of LDA for brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {3},
pages = {587-597},
doi = {10.1109/TBME.2010.2093133},
pmid = {21095857},
issn = {1558-2531},
mesh = {Adult ; Artificial Intelligence ; Brain/*physiology ; Calibration ; Discriminant Analysis ; Electroencephalography ; Feedback, Physiological/physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.},
}
@article {pmid21095811,
year = {2010},
author = {Haufe, S and Tomioka, R and Dickhaus, T and Sannelli, C and Blankertz, B and Nolte, G and Muller, KR},
title = {Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {5137-5140},
doi = {10.1109/IEMBS.2010.5626177},
pmid = {21095811},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; Electroencephalography Phase Synchronization/*physiology ; Female ; Fourier Analysis ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {We localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX). The analysis reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal. As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized.},
}
@article {pmid21095775,
year = {2010},
author = {Escolano, C and Ramos Murguialday, A and Matuz, T and Birbaumer, N and Minguez, J},
title = {A telepresence robotic system operated with a P300-based brain-computer interface: initial tests with ALS patients.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {4476-4480},
doi = {10.1109/IEMBS.2010.5626045},
pmid = {21095775},
issn = {2375-7477},
mesh = {Amyotrophic Lateral Sclerosis/*rehabilitation ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Electroencephalography/*methods ; Equipment Design ; Equipment Failure Analysis ; *Event-Related Potentials, P300 ; Humans ; Middle Aged ; Pilot Projects ; Robotics/*instrumentation ; Therapy, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) open a new valuable communication channel for people with severe neurological or motor degenerative diseases, such as ALS patients. On the other hand, the ability to teleoperate robots in a remote scenario provides a physical entity embodied in a real environment ready to perceive, explore, and interact. The combination of both functionalities provides a system with benefits for ALS patients in the context of neurorehabilitation or maintainment of the neural activity. This paper reports a BCI telepresence system which offers navigation, exploration and bidirectional communication, only controlled by brain activity; and an initial study of applicability with ALS patients. The results show the feasibility of this technology in real patients.},
}
@article {pmid21095703,
year = {2010},
author = {Contreras-Vidal, JL and Bradberry, TJ and Agashe, H},
title = {Movement decoding from noninvasive neural signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2825-2828},
doi = {10.1109/IEMBS.2010.5626081},
pmid = {21095703},
issn = {2375-7477},
mesh = {Aging ; Animals ; Biomechanical Phenomena ; Brain/*pathology ; Brain Mapping/*methods ; Haplorhini ; Humans ; Magnetoencephalography/methods ; Maryland ; Models, Neurological ; Motion Perception ; Nerve Net ; Neurons/*metabolism ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {It is generally assumed that noninvasively-acquired neural signals contain an insufficient level of information for decoding or reconstructing detailed kinematics of natural, multi-joint limb movements and hand gestures. Here, we review recent findings from our laboratory at the University of Maryland showing that noninvasive scalp electroencephalography (EEG) or magnetoencephalography (MEG) can be used to continuously decode the kinematics of 2D 'center-out' drawing, unconstrained 3D 'center-out' reaching and 3D finger gesturing. These findings suggest that these 'far-field', extra-cranial neural signals contain rich information about the neural representation of movement at the macroscale, and thus these neural representations provide alternative methods for developing noninvasive brain-machine interfaces with wide-ranging clinical relevance and for understanding functional and pathological brain states at various stages of development and aging.},
}
@article {pmid21095698,
year = {2010},
author = {Yu, H and Lu, H and Ouyang, T and Liu, H and Lu, BL},
title = {Vigilance detection based on sparse representation of EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2010},
number = {},
pages = {2439-2442},
doi = {10.1109/IEMBS.2010.5626084},
pmid = {21095698},
issn = {2375-7477},
mesh = {Adolescent ; Adult ; Algorithms ; Brain/pathology ; Computer Simulation ; Electroencephalography/*instrumentation/*methods ; Humans ; Male ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; Time Factors ; User-Computer Interface ; },
abstract = {Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become successful tools in the fields of signal reconstruction and machine learning. In this paper, we propose to use the sparse representation of EEG to the vigilance detection problem. We first use continuous wavelet transform to extract the rhythm features of EEG data, and then employ the sparse representation method to the wavelet transform coefficients. We collect five subjects' EEG recordings in a simulation driving environment and apply the proposed method to detect the vigilance of the subjects. The experimental results show that the algorithm framework proposed in this paper can successfully estimate driver's vigilance with the average accuracy about 94.22 %. We also compare our algorithm framework with other vigilance estimation methods using different feature extraction and classifier selection approaches, the result shows that the proposed method has obvious advantages in the classification accuracy.},
}
@article {pmid21085607,
year = {2010},
author = {Cui, X and Bray, S and Reiss, AL},
title = {Speeded near infrared spectroscopy (NIRS) response detection.},
journal = {PloS one},
volume = {5},
number = {11},
pages = {e15474},
pmid = {21085607},
issn = {1932-6203},
support = {S10 RR024657/RR/NCRR NIH HHS/United States ; S10RR024657/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping ; Female ; Fingers/innervation/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement ; Neurofeedback/physiology ; Psychomotor Performance/*physiology ; Spectroscopy, Near-Infrared/*methods ; Task Performance and Analysis ; Young Adult ; },
abstract = {The hemodynamic response measured by Near Infrared Spectroscopy (NIRS) is temporally delayed from the onset of the underlying neural activity. As a consequence, NIRS based brain-computer-interfaces (BCIs) and neurofeedback learning systems, may have a latency of several seconds in responding to a change in participants' behavioral or mental states, severely limiting the practical use of such systems. To explore the possibility of reducing this delay, we used a multivariate pattern classification technique (linear support vector machine, SVM) to decode the true behavioral state from the measured neural signal and systematically evaluated the performance of different feature spaces (signal history, history gradient, oxygenated or deoxygenated hemoglobin signal and spatial pattern). We found that the latency to decode a change in behavioral state can be reduced by 50% (from 4.8 s to 2.4 s), which will enhance the feasibility of NIRS for real-time applications.},
}
@article {pmid21068460,
year = {2010},
author = {Yokoi, H and Yamamura, O and Kobayashi, Y and Kato, R and Nakamura, T and Morishita, S},
title = {[Development of a reflex electrical stimulation device to assist walking].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {62},
number = {11},
pages = {1227-1238},
pmid = {21068460},
issn = {1881-6096},
mesh = {Biofeedback, Psychology/*instrumentation ; Brain/physiology ; Electric Stimulation Therapy/*instrumentation ; Humans ; Magnetic Resonance Imaging ; Man-Machine Systems ; *Walking ; },
abstract = {This paper is a summary of the biofeedback technology for the reflex electrical stimulation device to assist walking. The experiments showed that electrical stimulation resulted in prominent stimulation with less habituation. The research elements were an input-type brain machine interface (BMI), functional magnetic resonance imaging (f-MRI) analysis to detect brain activity, multi-channel electrical stimulation, reflex stimulation for muscle contraction, and an adaptive rehabilitation fitting to the walking gate. The results showed that neuro rehabilitation may be attained by the integration of these research elements.},
}
@article {pmid21067970,
year = {2011},
author = {McFarland, DJ and Sarnacki, WA and Townsend, G and Vaughan, T and Wolpaw, JR},
title = {The P300-based brain-computer interface (BCI): effects of stimulus rate.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {4},
pages = {731-737},
pmid = {21067970},
issn = {1872-8952},
support = {R01 EB000856-09/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R01 HD030146-09/HD/NICHD NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Analysis of Variance ; *Communication Devices for People with Disabilities ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Feedback, Psychological/physiology ; Female ; Humans ; Male ; Middle Aged ; Online Systems ; Photic Stimulation ; Psychomotor Performance/physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate.<br><br>METHODS: In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time.<br><br>RESULTS: For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results.<br><br>CONCLUSIONS: The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance.<br><br>SIGNIFICANCE: These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.},
}
@article {pmid21060801,
year = {2010},
author = {Rothschild, RM},
title = {Neuroengineering tools/applications for bidirectional interfaces, brain-computer interfaces, and neuroprosthetic implants - a review of recent progress.},
journal = {Frontiers in neuroengineering},
volume = {3},
number = {},
pages = {112},
pmid = {21060801},
issn = {1662-6443},
abstract = {The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain-computer interfaces (BCIs), bidirectional interfaces, and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability, and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography and near-infrared spectroscopy. Then the problem of gliosis and solutions for (semi-) permanent implant biocompatibility such as innovative implant coatings, materials, and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques.},
}
@article {pmid21060686,
year = {2010},
author = {Schreeg, LA and Kress, WJ and Erickson, DL and Swenson, NG},
title = {Phylogenetic analysis of local-scale tree soil associations in a lowland moist tropical forest.},
journal = {PloS one},
volume = {5},
number = {10},
pages = {e13685},
pmid = {21060686},
issn = {1932-6203},
mesh = {*Phylogeny ; Principal Component Analysis ; *Soil ; Species Specificity ; *Trees ; *Tropical Climate ; },
abstract = {BACKGROUND: Local plant-soil associations are commonly studied at the species-level, while associations at the level of nodes within a phylogeny have been less well explored. Understanding associations within a phylogenetic context, however, can improve our ability to make predictions across systems and can advance our understanding of the role of evolutionary history in structuring communities.<br><br>Here we quantified evolutionary signal in plant-soil associations using a DNA sequence-based community phylogeny and several soil variables (e.g., extractable phosphorus, aluminum and manganese, pH, and slope as a proxy for soil water). We used published plant distributional data from the 50-ha plot on Barro Colorado Island (BCI), Republic of Panam&#xe1;. Our results suggest some groups of closely related species do share similar soil associations. Most notably, the node shared by Myrtaceae and Vochysiaceae was associated with high levels of aluminum, a potentially toxic element. The node shared by Apocynaceae was associated with high extractable phosphorus, a nutrient that could be limiting on a taxon specific level. The node shared by the large group of Laurales and Magnoliales was associated with both low extractable phosphorus and with steeper slope. Despite significant node-specific associations, this study detected little to no phylogeny-wide signal. We consider the majority of the 'traits' (i.e., soil variables) evaluated to fall within the category of ecological traits. We suggest that, given this category of traits, phylogeny-wide signal might not be expected while node-specific signals can still indicate phylogenetic structure with respect to the variable of interest.<br><br>CONCLUSIONS: Within the BCI forest dynamics plot, distributions of some plant taxa are associated with local-scale differences in soil variables when evaluated at individual nodes within the phylogenetic tree, but they are not detectable by phylogeny-wide signal. Trends highlighted in this analysis suggest how plant-soil associations may drive plant distributions and diversity at the local-scale.},
}
@article {pmid21054290,
year = {2010},
author = {McMurdo, ME and Sugden, J and Argo, I and Boyle, P and Johnston, DW and Sniehotta, FF and Donnan, PT},
title = {Do pedometers increase physical activity in sedentary older women? A randomized controlled trial.},
journal = {Journal of the American Geriatrics Society},
volume = {58},
number = {11},
pages = {2099-2106},
doi = {10.1111/j.1532-5415.2010.03127.x},
pmid = {21054290},
issn = {1532-5415},
support = {CZH/4/463/CSO_/Chief Scientist Office/United Kingdom ; G0900686/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Aged ; Female ; Humans ; Monitoring, Ambulatory/instrumentation ; *Motor Activity ; Prospective Studies ; *Sedentary Behavior ; Single-Blind Method ; },
abstract = {OBJECTIVES: To determine the effectiveness of a behavior change intervention (BCI) with or without a pedometer in increasing physical activity in sedentary older women.<br><br>DESIGN: Prospective randomized controlled trial.<br><br>SETTING: Primary care, City of Dundee, Scotland.<br><br>PARTICIPANTS: Two hundred four sedentary women aged 70 and older.<br><br>INTERVENTIONS: Six months of BCI, BCI plus pedometer (pedometer plus), or usual care.<br><br>PRIMARY OUTCOME: change in daily activity counts measured by accelerometry.<br><br>SECONDARY OUTCOMES: Short Physical Performance Battery, health-related quality of life, depression and anxiety, falls, and National Health Service resource use.<br><br>RESULTS: One hundred seventy-nine of 204 (88%) women completed the 6-month trial. Withdrawals were highest from the BCI group (15/68) followed by the pedometer plus group (8/68) and then the control group (2/64). After adjustment for baseline differences, accelerometry counts increased significantly more in the BCI group at 3 months than in the control group (P = .002) and the pedometer plus group (P = .04). By 6 months, accelerometry counts in both intervention groups had fallen to levels that were no longer statistically significantly different from baseline. There were no significant changes in the secondary outcomes.<br><br>CONCLUSION: The BCI was effective in objectively increasing physical activity in sedentary older women. Provision of a pedometer yielded no additional benefit in physical activity, but may have motivated participants to remain in the trial.},
}
@article {pmid21048286,
year = {2010},
author = {Volosyak, I and Valbuena, D and Malechka, T and Peuscher, J and Gräser, A},
title = {Brain-computer interface using water-based electrodes.},
journal = {Journal of neural engineering},
volume = {7},
number = {6},
pages = {066007},
doi = {10.1088/1741-2560/7/6/066007},
pmid = {21048286},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Communication ; *Electrodes ; Electroencephalography/*instrumentation ; Female ; Gels ; Humans ; Male ; Psychomotor Performance/physiology ; *User-Computer Interface ; Water/*chemistry ; },
abstract = {Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min(-1) and of 26.56 ± 9.224 bit min(-1), respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.},
}
@article {pmid21043578,
year = {2010},
author = {Horki, P and Neuper, C and Pfurtscheller, G and Müller-Putz, G},
title = {Asynchronous steady-state visual evoked potential based BCI control of a 2-DoF artificial upper limb.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {55},
number = {6},
pages = {367-374},
doi = {10.1515/BMT.2010.044},
pmid = {21043578},
issn = {1862-278X},
mesh = {Adult ; *Algorithms ; *Artificial Limbs ; Brain/*physiology ; Electroencephalography/*instrumentation ; Equipment Failure Analysis ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Prosthesis Design ; Robotics/*instrumentation ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) provides a direct connection between the human brain and a computer. One type of BCI can be realized using steady-state visual evoked potentials (SSVEPs), resulting from repetitive stimulation. The aim of this study was the realization of an asynchronous SSVEP-BCI, based on canonical correlation analysis, suitable for the control of a 2-degrees of freedom (DoF) hand and elbow neuroprosthesis. To determine whether this BCI is suitable for the control of 2-DoF neuroprosthetic devices, online experiments with a virtual and a robotic limb feedback were conducted with eight healthy subjects and one tetraplegic patient. All participants were able to control the artificial limbs with the BCI. In the online experiments, the positive predictive value (PPV) varied between 69% and 83% and the false negative rate (FNR) varied between 1% and 17%. The spinal cord injured patient achieved PPV and FNR values within one standard deviation of the mean for all healthy subjects.},
}
@article {pmid21035069,
year = {2010},
author = {Andalib, D and Gharabaghi, D and Nabai, R and Abbaszadeh, M},
title = {Monocanalicular versus bicanalicular silicone intubation for congenital nasolacrimal duct obstruction.},
journal = {Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus},
volume = {14},
number = {5},
pages = {421-424},
doi = {10.1016/j.jaapos.2010.08.003},
pmid = {21035069},
issn = {1528-3933},
mesh = {Anesthesia ; Child ; Child, Preschool ; Device Removal ; Humans ; Infant ; Intubation/*instrumentation/*methods ; Lacrimal Duct Obstruction/congenital/*therapy ; *Nasolacrimal Duct ; Outpatients ; *Silicones ; Stents ; Treatment Outcome ; },
abstract = {PURPOSE: To compare the success rate of monocanalicular versus bicanalicular silicone intubation of the nasolacrimal duct for congenital nasolacrimal duct obstruction (CNLDO).<br><br>METHODS: In a prospective randomized clinical trial, 70 eyes of 57 children with CNLDO underwent either monocanalicular silicone intubation (MCI) (n = 35 eyes) or bicanalicular silicone intubation (BCI) (n = 35 eyes). All procedures were performed by 1 oculoplastic surgeon. Tube removal was planned for 3 months postoperatively. The results were assessed using a Munk score. Treatment success was defined as Munk score 0-1 at 3 months after tube removal.<br><br>RESULTS: The surgical outcome was assessed in 29 eyes with MCI and 27 eyes with BCI. The mean age of treatment was 34.9 &#xb1; 12.7 months for MCI and 38.7 &#xb1; 18.6 months for BCI. Treatment success was achieved in 25 of 29 eyes (86.2%; 95% CI, 79%-96%) in the MCI group compared with 24 of 27 eyes (89%; 95% CI, 84%-94%) in the BCI group (RR = 0.96; 95% CI, 0.79-1.18). There were no corneal or canalicular complications in either group.<br><br>CONCLUSIONS: MCI and BCI were successful in a similar percentage of children with CNLDO. The mainadvantage of the former technique was easy tube removal without sedation in the office.},
}
@article {pmid21031089,
year = {2010},
author = {Lee, SW and Cho, JM and Kang, JY and Yoo, TK},
title = {Clinical and urodynamic significance of morphological differences in intravesical prostatic protrusion.},
journal = {Korean journal of urology},
volume = {51},
number = {10},
pages = {694-699},
pmid = {21031089},
issn = {2005-6745},
abstract = {PURPOSE: The objectives of this study were to evaluate whether morphologic differences correlated with urodynamic and clinical characteristics in patients with benign prostatic hyperplasia (BPH) with intravesical prostatic protrusion (IPP) of trilobar or bilobar adenoma.<br><br>MATERIALS AND METHODS: Between January 2008 and June 2009, 72 male patients who had undergone transurethral resection (TUR) owing to BPH with IPP were included in this study. They underwent preoperative urodynamic studies, the International Prostate Symptom Score (IPSS)/quality of life (QoL), maximal flow rate (Qmax), post-voiding residual urine volume (PVR), transrectal ultrasonography (TRUS), and serum prostate-specific antigen (PSA) measurement. The patients were classified into 2 groups (the trilobar and bilobar adenoma groups) on the basis of video findings during the TUR operation.<br><br>RESULTS: The trilobar and bilobar adenoma groups consisted of 37 patients and 35 patients, respectively. The Mean&#xb1;SD IPP, prostate volume (PV), and transition zone volume of the trilobar and bilobar adenoma groups were 11.8&#xb1;5.2 mm and 9.0&#xb1;3.8 mm (p=0.014), 81.1&#xb1;25.8 g and 59.3&#xb1;22.5 g (p<0.001), and 49.6&#xb1;20.6 g and 34.8&#xb1;19.4 g (p=0.003), respectively. The Mean&#xb1;SD PSA, bladder contractility index (BCI), and bladder outlet obstruction index (BOOI) were 4.6&#xb1;2.5 ng/ml and 3.5&#xb1;1.7 ng/ml (p=0.042), 119.8&#xb1;33.4 and 87.7&#xb1;24.4 (p<0.001), and 62.6&#xb1;29.5 and 44.6&#xb1;20.4 (p=0.005), respectively. There were no significant differences in IPSS/QoL, Qmax, PVR, acute urinary retention, or detrusor overactivity in the 2 groups.<br><br>CONCLUSIONS: IPP has two morphologic types of trilobar or bilobar enlargement. The PV, BOOI, and BCI were significantly smaller in the bilobar adenoma group than in the trilobar adenoma group.},
}
@article {pmid21031032,
year = {2010},
author = {Sigman, M and Etchemendy, P and Slezak, DF and Cecchi, GA},
title = {Response time distributions in rapid chess: a large-scale decision making experiment.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {60},
pmid = {21031032},
issn = {1662-453X},
abstract = {Rapid chess provides an unparalleled laboratory to understand decision making in a natural environment. In a chess game, players choose consecutively around 40 moves in a finite time budget. The goodness of each choice can be determined quantitatively since current chess algorithms estimate precisely the value of a position. Web-based chess produces vast amounts of data, millions of decisions per day, incommensurable with traditional psychological experiments. We generated a database of response times (RTs) and position value in rapid chess games. We measured robust emergent statistical observables: (1) RT distributions are long-tailed and show qualitatively distinct forms at different stages of the game, (2) RT of successive moves are highly correlated both for intra- and inter-player moves. These findings have theoretical implications since they deny two basic assumptions of sequential decision making algorithms: RTs are not stationary and can not be generated by a state-function. Our results also have practical implications. First, we characterized the capacity of blunders and score fluctuations to predict a player strength, which is yet an open problem in chess softwares. Second, we show that the winning likelihood can be reliably estimated from a weighted combination of remaining times and position evaluation.},
}
@article {pmid21029784,
year = {2011},
author = {Pei, X and Leuthardt, EC and Gaona, CM and Brunner, P and Wolpaw, JR and Schalk, G},
title = {Spatiotemporal dynamics of electrocorticographic high gamma activity during overt and covert word repetition.},
journal = {NeuroImage},
volume = {54},
number = {4},
pages = {2960-2972},
pmid = {21029784},
issn = {1095-9572},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-09/EB/NIBIB NIH HHS/United States ; R01 EB006356-04/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain/physiology ; *Brain Mapping ; Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Signal Processing, Computer-Assisted ; Verbal Behavior/*physiology ; },
abstract = {Language is one of the defining abilities of humans. Many studies have characterized the neural correlates of different aspects of language processing. However, the imaging techniques typically used in these studies were limited in either their temporal or spatial resolution. Electrocorticographic (ECoG) recordings from the surface of the brain combine high spatial with high temporal resolution and thus could be a valuable tool for the study of neural correlates of language function. In this study, we defined the spatiotemporal dynamics of ECoG activity during a word repetition task in nine human subjects. ECoG was recorded while each subject overtly or covertly repeated words that were presented either visually or auditorily. ECoG amplitudes in the high gamma (HG) band confidently tracked neural changes associated with stimulus presentation and with the subject's verbal response. Overt word production was primarily associated with HG changes in the superior and middle parts of temporal lobe, Wernicke's area, the supramarginal gyrus, Broca's area, premotor cortex (PMC), primary motor cortex. Covert word production was primarily associated with HG changes in superior temporal lobe and the supramarginal gyrus. Acoustic processing from both auditory stimuli as well as the subject's own voice resulted in HG power changes in superior temporal lobe and Wernicke's area. In summary, this study represents a comprehensive characterization of overt and covert speech using electrophysiological imaging with high spatial and temporal resolution. It thereby complements the findings of previous neuroimaging studies of language and thus further adds to current understanding of word processing in humans.},
}
@article {pmid20979665,
year = {2010},
author = {Lv, J and Li, Y and Gu, Z},
title = {Decoding hand movement velocity from electroencephalogram signals during a drawing task.},
journal = {Biomedical engineering online},
volume = {9},
number = {},
pages = {64},
pmid = {20979665},
issn = {1475-925X},
mesh = {Algorithms ; Art ; Discriminant Analysis ; Electroencephalography/*methods ; Hand/*physiology ; Humans ; Male ; Motor Activity/*physiology ; Scalp ; *Signal Processing, Computer-Assisted ; },
abstract = {BACKGROUND: Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.<br><br>METHODS: Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.<br><br>RESULTS: The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.<br><br>CONCLUSIONS: These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.},
}
@article {pmid20964544,
year = {2011},
author = {Yang, C and Olson, B and Si, J},
title = {A multiscale correlation of wavelet coefficients approach to spike detection.},
journal = {Neural computation},
volume = {23},
number = {1},
pages = {215-250},
doi = {10.1162/NECO_a_00063},
pmid = {20964544},
issn = {1530-888X},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain/physiology ; Computer Simulation/standards ; Electrophysiology/*methods ; Nerve Net/*physiology ; Neurons/physiology ; Rats ; *Signal Processing, Computer-Assisted ; },
abstract = {Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.},
}
@article {pmid20964540,
year = {2011},
author = {Martens, SM and Mooij, JM and Hill, NJ and Farquhar, J and Schölkopf, B},
title = {A graphical model framework for decoding in the visual ERP-based BCI speller.},
journal = {Neural computation},
volume = {23},
number = {1},
pages = {160-182},
doi = {10.1162/NECO_a_00066},
pmid = {20964540},
issn = {1530-888X},
mesh = {Artificial Intelligence ; Computer User Training/standards ; Discrimination Learning/physiology ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/*physiology ; Humans ; Language ; *Models, Neurological ; *Models, Theoretical ; Reading ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/physiology ; },
abstract = {We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between brain signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the brain signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.},
}
@article {pmid20957974,
year = {2010},
author = {Anderson, NR and DeVries, EM},
title = {Brain computer interface (BCI) tools developed in a clinical environment.},
journal = {American journal of electroneurodiagnostic technology},
volume = {50},
number = {3},
pages = {187-198},
pmid = {20957974},
issn = {1086-508X},
mesh = {Biofeedback, Psychology/*instrumentation/*methods ; Brain/*physiology ; Brain Mapping/*instrumentation/*methods ; *Communication Devices for People with Disabilities ; Equipment Design ; Humans ; *User-Computer Interface ; },
abstract = {Brain computer interfaces are devices that collect signals from a subject's cortical surface and interpret these signals to control a computer Recently much development has been done on these devices with the help of epilepsy patients and the clinical staff who treat these patients. The types of data collected from epilepsy patients, particularly the invasive data give a unique opportunity to researchers in this area. The clinical staff has a unique opportunity to use the treatment of one patient population to help another},
}
@article {pmid20952183,
year = {2010},
author = {Moran, D},
title = {Evolution of brain-computer interface: action potentials, local field potentials and electrocorticograms.},
journal = {Current opinion in neurobiology},
volume = {20},
number = {6},
pages = {741-745},
pmid = {20952183},
issn = {1873-6882},
support = {R01 EB009103/EB/NIBIB NIH HHS/United States ; R01 EB009103-01/EB/NIBIB NIH HHS/United States ; 1R01EB009103/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Brain/physiology ; Cerebral Cortex/*physiology ; Humans ; Microelectrodes ; Movement/*physiology ; Neuronal Plasticity/physiology ; *User-Computer Interface ; },
abstract = {Brain computer interfaces (BCIs) were originally developed to give severely motor impaired patients a method to communicate and interact with their environment. Initially most BCI systems were based on non-invasive electroencephalographic recordings from the surface of the scalp. To increase control speed, accuracy and complexity, researchers began utilizing invasive recording modalities. BCIs using multi-single unit action potentials have provided elegant multi-dimensional control of both computer cursors and robotic limbs in the last few years. However, long-term stability issues with single-unit arrays has lead researchers to investigate other invasive recording modalities such as high-frequency local field potentials and electrocorticography (ECoG). Although ECoG originally evolved as a replacement for single-unit BCIs, it has come full circle to become an effective tool for studying cortical neurophysiology.},
}
@article {pmid20943945,
year = {2011},
author = {Cunningham, JP and Nuyujukian, P and Gilja, V and Chestek, CA and Ryu, SI and Shenoy, KV},
title = {A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces.},
journal = {Journal of neurophysiology},
volume = {105},
number = {4},
pages = {1932-1949},
pmid = {20943945},
issn = {1522-1598},
support = {R01 NS054283/NS/NINDS NIH HHS/United States ; DP1 OD006409/OD/NIH HHS/United States ; 1DP1OD006409/OD/NIH HHS/United States ; R01 NS054283-05/NS/NINDS NIH HHS/United States ; /HHMI/Howard Hughes Medical Institute/United States ; },
mesh = {Adult ; Algorithms ; Animals ; Computers ; *Electric Stimulation ; *Feedback ; Humans ; Macaca mulatta ; Male ; Models, Animal ; *Neural Prostheses ; Software ; *User-Computer Interface ; },
abstract = {Neural prosthetic systems seek to improve the lives of severely disabled people by decoding neural activity into useful behavioral commands. These systems and their decoding algorithms are typically developed "offline," using neural activity previously gathered from a healthy animal, and the decoded movement is then compared with the true movement that accompanied the recorded neural activity. However, this offline design and testing may neglect important features of a real prosthesis, most notably the critical role of feedback control, which enables the user to adjust neural activity while using the prosthesis. We hypothesize that understanding and optimally designing high-performance decoders require an experimental platform where humans are in closed-loop with the various candidate decode systems and algorithms. It remains unexplored the extent to which the subject can, for a particular decode system, algorithm, or parameter, engage feedback and other strategies to improve decode performance. Closed-loop testing may suggest different choices than offline analyses. Here we ask if a healthy human subject, using a closed-loop neural prosthesis driven by synthetic neural activity, can inform system design. We use this online prosthesis simulator (OPS) to optimize "online" decode performance based on a key parameter of a current state-of-the-art decode algorithm, the bin width of a Kalman filter. First, we show that offline and online analyses indeed suggest different parameter choices. Previous literature and our offline analyses agree that neural activity should be analyzed in bins of 100- to 300-ms width. OPS analysis, which incorporates feedback control, suggests that much shorter bin widths (25-50 ms) yield higher decode performance. Second, we confirm this surprising finding using a closed-loop rhesus monkey prosthetic system. These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients.},
}
@article {pmid20943928,
year = {2010},
author = {Chase, SM and Schwartz, AB and Kass, RE},
title = {Latent inputs improve estimates of neural encoding in motor cortex.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {41},
pages = {13873-13882},
pmid = {20943928},
issn = {1529-2401},
support = {R01 EB005847/EB/NIBIB NIH HHS/United States ; R01 EB005847-04/EB/NIBIB NIH HHS/United States ; R01EB005847/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Electrodes, Implanted ; Electrophysiology ; Macaca mulatta ; Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Neurons/*physiology ; User-Computer Interface ; },
abstract = {Typically, tuning curves in motor cortex are constructed by fitting the firing rate of a neuron as a function of some observed action, such as arm direction or movement speed. These tuning curves are then often interpreted causally as representing the firing rate as a function of the desired movement, or intent. This interpretation implicitly assumes that the motor command and the motor act are equivalent. However, any kind of perturbation, be it external, such as a visuomotor rotation, or internal, such as muscle fatigue, can create a difference between the motor intent and the action. How do we estimate the tuning curve under these conditions? Furthermore, it is well known that, during learning or adaptation, the relationship between neural firing and the observed movement can change. Does this change indicate a change in the inputs to the population, or a change in the way those inputs are processed? In this work, we present a method to infer the latent, unobserved inputs into the population of recorded neurons. Using data from nonhuman primates performing brain-computer interface experiments, we show that tuning curves based on these latent directions fit better than tuning curves based on actual movements. Finally, using data from a brain-computer interface learning experiment in which half of the units were decoded incorrectly, we demonstrate how this method might differentiate various aspects of motor adaptation.},
}
@article {pmid20940505,
year = {2010},
author = {Sakurai, Y},
title = {[Complete Brain-machine Interfaces and Plastic Changes in the Brain].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {62},
number = {10},
pages = {1059-1065},
pmid = {20940505},
issn = {1881-6096},
mesh = {Animals ; Brain/*physiology ; Haplorhini ; *Man-Machine Systems ; Motor Cortex/physiology ; Neuronal Plasticity/*physiology ; Rats ; },
abstract = {Brain-machine interfaces (BMIs) are artificial systems that control external devices or body muscles with signals generated by the neural activities of working brains. The BMIs currently under development can be divided into 2 types,i.e.,conventional (noninvasive) BMIs and complete (invasive) BMIs. Only the latter type of BMI can ultimately be used in the future. This paper describes some recent studies on invasive BMI using monkeys as subjects and discusses the progress of and problems revealed in these studies. The focus then shifts to plastic changes in neuronal activities caused by the BMIs. When a BMI is in use,the brain inevitably changes its own functions and structures in order to operate external devices more efficiently. Therefore,basic research on BMIs inevitably involves study on neural plasticity; such research is essential for further development of neurorehabilitation and for high performance of BMIs. This paper describes 2 recent pioneering BMI studies-one involving the rat motor cortex and the other involving the monkey primary motor cortex. Both studies revealed rapid and plastic changes in neuronal function during the period the animals were learning to operate external devices with the BMIs. The fact that the neuronal changes were caused by the contingency of neuronal activity and rewards emphasizes the significance of the neural-operant paradigm for research on neuronal plasticity in BMIs. The present paper describes a neural-operant experiment involving a recently developed high-performance BMI system and reports rapid and plastic changes in firing frequency and synchrony of the hippocampal neurons in both adult and aged rats. Finally,the paper suggests that complete BMIs can be developed by neuroscience research and should be able to unmask the enigmas of the neural code,brain-body interaction,and ongoing activity in the working brain.},
}
@article {pmid20926674,
year = {2010},
author = {Fabbri, S and Caramazza, A and Lingnau, A},
title = {Tuning curves for movement direction in the human visuomotor system.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {40},
pages = {13488-13498},
pmid = {20926674},
issn = {1529-2401},
mesh = {Adult ; Brain Mapping/methods ; Cerebral Cortex/anatomy &amp; histology/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Motion Perception/*physiology ; Nerve Net/anatomy &amp; histology/physiology ; Neuropsychological Tests ; Orientation/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Space Perception/*physiology ; Visual Pathways/physiology ; Young Adult ; },
abstract = {Neurons in macaque primary motor cortex (M1) are broadly tuned to arm movement direction. Recent evidence suggests that human M1 contains directionally tuned neurons, but it is unclear which other areas are part of the network coding movement direction and what characterizes the responses of neuronal populations in those areas. Such information would be highly relevant for the implementation of brain-computer interfaces (BCIs) in paralyzed patients. We used functional magnetic resonance imaging adaptation to identify which areas of the human brain show directional selectivity and the degree to which these areas are affected by the type of motor act (to press vs to grasp). After adapting participants to one particular hand movement direction, we measured the release from adaptation during occasional test trials, parametrically varying the angular difference between adaptation and test direction. We identified multiple areas broadly tuned to movement direction, including M1, dorsal premotor cortex, intraparietal sulcus, and the parietal reach region. Within these areas, we observed a gradient of directional selectivity, with highest directional selectivity in the right parietal reach region, for both right- and left-hand movements. Moreover, directional selectivity was modulated by the type of motor act to varying degrees, with the largest effect in M1 and the smallest modulation in the parietal reach region. These data provide an important extension of our knowledge about directional tuning in the human brain. Furthermore, our results suggest that the parietal reach region might be an ideal candidate for the implementation of BCI in paralyzed patients.},
}
@article {pmid20921326,
year = {2011},
author = {Krusienski, DJ and Shih, JJ},
title = {Control of a visual keyboard using an electrocorticographic brain-computer interface.},
journal = {Neurorehabilitation and neural repair},
volume = {25},
number = {4},
pages = {323-331},
pmid = {20921326},
issn = {1552-6844},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiology ; Computers/standards ; Electrodes, Implanted/psychology/standards ; Electrophysiology/instrumentation/*methods ; Epilepsy/*rehabilitation ; Event-Related Potentials, P300/*physiology ; Humans ; Software/standards ; Teaching/*methods ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) are devices that enable severely disabled people to communicate and interact with their environments using their brain waves. Most studies investigating BCI in humans have used scalp EEG as the source of electrical signals and focused on motor control of prostheses or computer cursors on a screen. The authors hypothesize that the use of brain signals obtained directly from the cortical surface will more effectively control a communication/spelling task compared to scalp EEG.<br><br>METHODS: A total of 6 patients with medically intractable epilepsy were tested for the ability to control a visual keyboard using electrocorticographic (ECOG) signals. ECOG data collected during a P300 visual task paradigm were preprocessed and used to train a linear classifier to subsequently predict the intended target letters.<br><br>RESULTS: The classifier was able to predict the intended target character at or near 100% accuracy using fewer than 15 stimulation sequences in 5 of the 6 people tested. ECOG data from electrodes outside the language cortex contributed to the classifier and enabled participants to write words on a visual keyboard.<br><br>CONCLUSIONS: This is a novel finding because previous invasive BCI research in humans used signals exclusively from the motor cortex to control a computer cursor or prosthetic device. These results demonstrate that ECOG signals from electrodes both overlying and outside the language cortex can reliably control a visual keyboard to generate language output without voice or limb movements.},
}
@article {pmid20890671,
year = {2011},
author = {Jin, J and Allison, BZ and Sellers, EW and Brunner, C and Horki, P and Wang, X and Neuper, C},
title = {Optimized stimulus presentation patterns for an event-related potential EEG-based brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {49},
number = {2},
pages = {181-191},
pmid = {20890671},
issn = {1741-0444},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {P300 brain-computer interface (BCI) systems typically use a row/column (RC) approach. This article presents a P300 BCI based on a 12 x 7 matrix and new paradigmatic approaches to flashing characters designed to decrease the number of flashes needed to identify a target character. Using an RC presentation, a 12 x 7 matrix requires 19 flashes to present all items twice (12 columns and seven rows) per trial. A 12 x 7 matrix contains 84 elements (characters). To identify a target character in 12 x 7 matrix using the RC pattern, 19 flashes (sub-trials) are necessary. In each flash, the selected characters (one column or one row in the RC pattern) are flashing. We present four new paradigms and compare the performance to the RC paradigm. These paradigms present quasi-random groups of characters using 9, 12, 14 and 16 flashes per trial to identify a target character. The 12-, 14- and 16-flash patterns were optimized so that the same character never flashed twice in succession. We assessed the practical bit rate and classification accuracy of the 9-, 12-, 14-, 16- and RC (19-flash) pattern conditions in an online experiment and with offline simulations. The results indicate that 16-flash pattern is better than other patterns and performance of an online P300 BCI can be significantly improved by selecting the best presentation paradigm for each subject.},
}
@article {pmid20889426,
year = {2011},
author = {Lotte, F and Guan, C},
title = {Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {2},
pages = {355-362},
doi = {10.1109/TBME.2010.2082539},
pmid = {20889426},
issn = {1558-2531},
mesh = {*Algorithms ; Electroencephalography/*methods ; Humans ; *Man-Machine Systems ; *Models, Neurological ; Pattern Recognition, Automated/*methods ; Regression Analysis ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {One of the most popular feature extraction algorithms for brain-computer interfaces (BCI) is common spatial patterns (CSPs). Despite its known efficiency and widespread use, CSP is also known to be very sensitive to noise and prone to overfitting. To address this issue, it has been recently proposed to regularize CSP. In this paper, we present a simple and unifying theoretical framework to design such a regularized CSP (RCSP). We then present a review of existing RCSP algorithms and describe how to cast them in this framework. We also propose four new RCSP algorithms. Finally, we compare the performances of 11 different RCSP (including the four new ones and the original CSP), on electroencephalography data from 17 subjects, from BCI competition datasets. Results showed that the best RCSP methods can outperform CSP by nearly 10% in median classification accuracy and lead to more neurophysiologically relevant spatial filters. They also enable us to perform efficient subject-to-subject transfer. Overall, the best RCSP algorithms were CSP with Tikhonov regularization and weighted Tikhonov regularization, both proposed in this paper.},
}
@article {pmid20889425,
year = {2010},
author = {Lu, H and Eng, HL and Guan, C and Plataniotis, KN and Venetsanopoulos, AN},
title = {Regularized common spatial pattern with aggregation for EEG classification in small-sample setting.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {12},
pages = {2936-2946},
doi = {10.1109/TBME.2010.2082540},
pmid = {20889425},
issn = {1558-2531},
mesh = {*Algorithms ; Electroencephalography/*methods ; Humans ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {Common spatial pattern (CSP) is a popular algorithm for classifying electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). This paper presents a regularization and aggregation technique for CSP in a small-sample setting (SSS). Conventional CSP is based on a sample-based covariance-matrix estimation. Hence, its performance in EEG classification deteriorates if the number of training samples is small. To address this concern, a regularized CSP (R-CSP) algorithm is proposed, where the covariance-matrix estimation is regularized by two parameters to lower the estimation variance while reducing the estimation bias. To tackle the problem of regularization parameter determination, R-CSP with aggregation (R-CSP-A) is further proposed, where a number of R-CSPs are aggregated to give an ensemble-based solution. The proposed algorithm is evaluated on data set IVa of BCI Competition III against four other competing algorithms. Experiments show that R-CSP-A significantly outperforms the other methods in average classification performance in three sets of experiments across various testing scenarios, with particular superiority in SSS.},
}
@article {pmid20889384,
year = {2010},
author = {Ritaccio, A and Brunner, P and Cervenka, MC and Crone, N and Guger, C and Leuthardt, E and Oostenveld, R and Stacey, W and Schalk, G},
title = {Proceedings of the first international workshop on advances in electrocorticography.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {19},
number = {3},
pages = {204-215},
doi = {10.1016/j.yebeh.2010.08.028},
pmid = {20889384},
issn = {1525-5069},
mesh = {Brain/*physiopathology ; *Brain Mapping ; Diagnosis, Computer-Assisted ; *Electroencephalography ; Humans ; *International Cooperation ; Seizures/diagnosis ; Signal Detection, Psychological ; },
abstract = {In October 2009, a group of neurologists, neurosurgeons, computational neuroscientists, and engineers congregated to present novel developments transforming human electrocorticography (ECoG) beyond its established relevance in clinical epileptology. The contents of the proceedings advanced the role of ECoG in seizure detection and prediction, neurobehavioral research, functional mapping, and brain-computer interface technology. The meeting established the foundation for future work on the methodology and application of surface brain recordings.},
}
@article {pmid20888628,
year = {2011},
author = {Rota, G and Handjaras, G and Sitaram, R and Birbaumer, N and Dogil, G},
title = {Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing.},
journal = {Brain and language},
volume = {117},
number = {3},
pages = {123-132},
doi = {10.1016/j.bandl.2010.07.008},
pmid = {20888628},
issn = {1090-2155},
mesh = {Adult ; Brain Mapping/methods ; Emotions/*physiology ; Feedback, Physiological ; Frontal Lobe/*physiology ; Functional Laterality/physiology ; Humans ; Image Processing, Computer-Assisted ; *Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; *Signal Processing, Computer-Assisted ; Speech Perception/*physiology ; Temporal Lobe/*physiology ; *User-Computer Interface ; },
abstract = {Mechanisms of cortical reorganization underlying the enhancement of speech processing have been poorly investigated. In the present study, we addressed changes in functional and effective connectivity induced in subjects who learned to deliberately increase activation in the right inferior frontal gyrus (rIFG), and improved their ability to identify emotional intonations by using a real-time fMRI Brain-Computer Interface. At the beginning of their training process, we observed a massive connectivity of the rIFG to a widespread network of frontal and temporal areas, which decreased and lateralized to the right hemisphere with practice. Volitional control of activation strengthened connectivity of this brain region to the right prefrontal cortex, whereas training increased its connectivity to bilateral precentral gyri. These findings suggest that changes of connectivity in a functionally specific manner play an important role in the enhancement of speech processing. Also, these findings support previous accounts suggesting that motor circuits play a role in the comprehension of speech.},
}
@article {pmid20888292,
year = {2011},
author = {Murguialday, AR and Hill, J and Bensch, M and Martens, S and Halder, S and Nijboer, F and Schoelkopf, B and Birbaumer, N and Gharabaghi, A},
title = {Transition from the locked in to the completely locked-in state: a physiological analysis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {5},
pages = {925-933},
doi = {10.1016/j.clinph.2010.08.019},
pmid = {20888292},
issn = {1872-8952},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Area Under Curve ; Brain/*physiopathology ; Communication Devices for People with Disabilities ; *Disease Progression ; Electroencephalography ; Electromyography ; Humans ; Male ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {OBJECTIVE: To clarify the physiological and behavioral boundaries between locked-in (LIS) and the completely locked-in state (CLIS) (no voluntary eye movements, no communication possible) through electrophysiological data and to secure brain-computer-interface (BCI) communication.<br><br>METHODS: Electromyography from facial muscles, external anal sphincter (EAS), electrooculography and electrocorticographic data during different psychophysiological tests were acquired to define electrophysiological differences in an amyotrophic lateral sclerosis (ALS) patient with an intracranially implanted grid of 112 electrodes for nine months while the patient passed from the LIS to the CLIS.<br><br>RESULTS: At the very end of the LIS there was no facial muscle activity, nor external anal sphincter but eye control. Eye movements were slow and lasted for short periods only. During CLIS event related brain potentials (ERP) to passive limb movements and auditory stimuli were recorded, vibrotactile stimulation of different body parts resulted in no ERP response.<br><br>CONCLUSIONS: The results presented contradict the commonly accepted assumption that the EAS is the last remaining muscle under voluntary control and demonstrate complete loss of eye movements in CLIS. The eye muscle was shown to be the last muscle group under voluntary control. The findings suggest ALS as a multisystem disorder, even affecting afferent sensory pathways.<br><br>SIGNIFICANCE: Auditory and proprioceptive brain-computer-interface (BCI) systems are the only remaining communication channels in CLIS.},
}
@article {pmid20884361,
year = {2011},
author = {Toda, A and Imamizu, H and Kawato, M and Sato, MA},
title = {Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods.},
journal = {NeuroImage},
volume = {54},
number = {2},
pages = {892-905},
doi = {10.1016/j.neuroimage.2010.09.057},
pmid = {20884361},
issn = {1095-9572},
mesh = {Adult ; Bayes Theorem ; Brain/*physiology ; Brain Mapping/*methods ; Humans ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging ; *Magnetoencephalography ; Male ; Middle Aged ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Reconstruction of movements from non-invasively recorded brain activity is a key technology for brain-machine interfaces (BMIs). However, electroencephalography (EEG) or magnetoencephalography (MEG) inevitably records a mixture of signals originating from many cortical regions, and thus it is not only less effective than invasive methods but also poses more difficulty for incorporating neuroscience knowledge. We combined two sparse Bayesian methods to overcome this difficulty. First, thousands of cortical currents were estimated on the order of millimeters and milliseconds by a hierarchical Bayesian MEG inverse method, and then a sparse regression method automatically selected only relevant cortical currents in accurate reconstruction of movements by a linear weighted sum of their time series. Using the combined methods, we reconstructed two-dimensional trajectories of the index fingertip during pointing movements to various directions by moving the wrist joint. A good generalization (reconstruction) performance was observed for test datasets: mean error between the predicted and actual positions was 15 mm, which was 7% of the path length of the required movement. The reconstruction accuracy of the proposed method was significantly higher than directly using MEG sensor signals. Moreover, spatial distribution and temporal characteristics of weight values revealed that the primary sensorimotor, higher motor, and parietal regions mainly contributed to the reconstruction with expected time courses. These results suggest that the combined sparse Bayesian methods provide effective means to predict movement trajectory from non-invasive brain activity directly related to sensorimotor control.},
}
@article {pmid20880741,
year = {2011},
author = {Ikegami, S and Takano, K and Saeki, N and Kansaku, K},
title = {Operation of a P300-based brain-computer interface by individuals with cervical spinal cord injury.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {5},
pages = {991-996},
doi = {10.1016/j.clinph.2010.08.021},
pmid = {20880741},
issn = {1872-8952},
mesh = {Adult ; Analysis of Variance ; Cerebral Cortex/*physiopathology ; Cervical Vertebrae ; *Communication Devices for People with Disabilities ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; Male ; Middle Aged ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Spinal Cord Injuries/*physiopathology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This study evaluates the efficacy of a P300-based brain-computer interface (BCI) with green/blue flicker matrices for individuals with cervical spinal cord injury (SCI).<br><br>METHODS: Ten individuals with cervical SCI (age 26-53, all male) and 10 age- and sex-matched able-bodied controls (age 27-52, all male) with no prior BCI experience were asked to input hiragana (Japanese alphabet) characters using the P300 BCI with two distinct types of visual stimuli, white/gray and green/blue, in an 8&#xd7;10 flicker matrix. Both online and offline performance were evaluated.<br><br>RESULTS: The mean online accuracy of the SCI subjects was 88.0% for the white/gray and 90.7% for the green/blue flicker matrices. The accuracy of the control subjects was 77.3% and 86.0% for the white/gray and green/blue, respectively. There was a significant difference in online accuracy between the two types of flicker matrix. SCI subjects performed with greater accuracy than controls, but the main effect was not significant.<br><br>CONCLUSIONS: Individuals with cervical SCI successfully controlled the P300 BCI, and the green/blue flicker matrices were associated with significantly higher accuracy than the white/gray matrices.<br><br>SIGNIFICANCE: The P300 BCI with the green/blue flicker matrices is effective for use not only in able-bodied subjects, but also in individuals with cervical SCI.},
}
@article {pmid20880005,
year = {2011},
author = {Liu, P and Xiao, S and Shi, ZX and Bi, XX and Yang, HT and Jin, H},
title = {Bayesian evaluation of the human immunodeficiency virus antibody screening strategy of duplicate enzyme-linked immunosorbent assay in Xuzhou Blood Center, China.},
journal = {Transfusion},
volume = {51},
number = {4},
pages = {793-798},
doi = {10.1111/j.1537-2995.2010.02890.x},
pmid = {20880005},
issn = {1537-2995},
mesh = {Bayes Theorem ; Blood Donors ; China ; Donor Selection/*methods ; Enzyme-Linked Immunosorbent Assay/*methods ; HIV Antibodies/*analysis/immunology ; Humans ; },
abstract = {BACKGROUND: Accurate estimation of the risk of human immunodeficiency virus (HIV) infection through transfusion is essential for monitoring blood safety. The risk, however, is so low that it can only be estimated by mathematical modeling. With the Bayesian dependence model, this study evaluates the HIV antibody screening strategy of duplicate enzyme-linked immunosorbent assay (ELISA) in Xuzhou Blood Center and therefore estimates part of the total risks of transfusion-transmitted HIV infection.<br><br>STUDY DESIGN AND METHODS: Data from Xuzhou Blood Center between 2004 and 2008 were used. Information was obtained on donor profiles and screening and confirmatory test results. The portion of the risks of HIV infection through transfusion concerned was estimated by evaluating the screening algorithm in terms of its accuracy and predictive power with the Bayesian dependence model.<br><br>RESULTS: A total of 234,602 donations from voluntary blood donors in Xuzhou Blood Center were screened for HIV antibody. For the study screening algorithm, its sensitivity, specificity, false-positive predictive value (FPPV), and false-negative predictive value (FNPV) were 0.9951 (95% Bayesian credible interval [BCI], 0.9763-0.9997), 0.9991 (95% BCI, 0.9990-0.9992), 0.9647 (95% BCI, 0.9018-0.9923), and 1.52 &#xd7; 10(-7) (95% BCI, 7.31 &#xd7; 10(-9) -1.15 &#xd7; 10(-6)), respectively. For the positive detection rate (9.60 &#xd7; 10(-4)) and FPPV (0.9647), the differences between their own Bayesian median estimates and real values were 2.70 &#xd7; 10(-5) and -0.0033, respectively.<br><br>CONCLUSIONS: The HIV antibody screening algorithm of duplicate ELISA is well evaluated in its accuracy and predictive power with the Bayesian dependence model. The FNPV measures the part of the risks of transfusion-associated HIV transmission concerned.},
}
@article {pmid20877434,
year = {2010},
author = {Millán, JD and Rupp, R and Müller-Putz, GR and Murray-Smith, R and Giugliemma, C and Tangermann, M and Vidaurre, C and Cincotti, F and Kübler, A and Leeb, R and Neuper, C and Müller, KR and Mattia, D},
title = {Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {},
pmid = {20877434},
issn = {1662-453X},
abstract = {In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, "Communication and Control", "Motor Substitution", "Entertainment", and "Motor Recovery". We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users' mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.},
}
@article {pmid20876032,
year = {2010},
author = {Royer, AS and Doud, AJ and Rose, ML and He, B},
title = {EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {6},
pages = {581-589},
pmid = {20876032},
issn = {1558-0210},
support = {T32 EB008389-02/EB/NIBIB NIH HHS/United States ; T32 EB008389/EB/NIBIB NIH HHS/United States ; R01 EB007920-03/EB/NIBIB NIH HHS/United States ; R01EB007920/EB/NIBIB NIH HHS/United States ; T32 EB008389-03/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; T32EB008389/EB/NIBIB NIH HHS/United States ; R01 EB007920-04/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Aircraft ; Algorithms ; Brain/*physiology ; Computer Graphics ; *Computer Simulation ; Data Interpretation, Statistical ; *Electroencephalography ; Humans ; Information Theory ; Psychomotor Performance/physiology ; Space Perception/physiology ; *User-Computer Interface ; },
abstract = {Films like Firefox, Surrogates, and Avatar have explored the possibilities of using brain-computer interfaces (BCIs) to control machines and replacement bodies with only thought. Real world BCIs have made great progress toward that end. Invasive BCIs have enabled monkeys to fully explore 3-D space using neuroprosthetics. However, noninvasive BCIs have not been able to demonstrate such mastery of 3-D space. Here, we report our work, which demonstrates that human subjects can use a noninvasive BCI to fly a virtual helicopter to any point in a 3-D world. Through use of intelligent control strategies, we have facilitated the realization of controlled flight in 3-D space. We accomplished this through a reductionist approach that assigns subject-specific control signals to the crucial components of 3-D flight. Subject control of the helicopter was comparable when using either the BCI or a keyboard. By using intelligent control strategies, the strengths of both the user and the BCI system were leveraged and accentuated. Intelligent control strategies in BCI systems such as those presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined.},
}
@article {pmid20876031,
year = {2011},
author = {Falk, TH and Guirgis, M and Power, S and Chau, TT},
title = {Taking NIRS-BCIs outside the lab: towards achieving robustness against environment noise.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {2},
pages = {136-146},
doi = {10.1109/TNSRE.2010.2078516},
pmid = {20876031},
issn = {1558-0210},
mesh = {Acoustic Stimulation ; Adult ; Autonomic Nervous System/physiology ; Cerebrovascular Circulation/physiology ; Environment ; Female ; Functional Laterality/physiology ; Galvanic Skin Response/physiology ; Heart Rate/physiology ; Humans ; Male ; Markov Chains ; Mental Processes/physiology ; Prefrontal Cortex/blood supply/*physiology ; Prosthesis Design ; Reflex, Startle/physiology ; Respiratory Mechanics/physiology ; Skin Temperature/physiology ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; },
abstract = {This paper reported initial findings on the effects of environmental noise and auditory distractions on the performance of mental state classification based on near-infrared spectroscopy (NIRS) signals recorded from the prefrontal cortex. Characterization of the performance losses due to environmental factors could provide useful information for the future development of NIRS-based brain-computer interfaces that can be taken beyond controlled laboratory settings and into everyday environments. Experiments with a hidden Markov model-based classifier showed that while significant performance could be attained in silent conditions, only chance levels of sensitivity and specificity were obtained in noisy environments. In order to achieve robustness against environment noise, two strategies were proposed and evaluated. First, physiological responses harnessed from the autonomic nervous system were used as complementary information to NIRS signals. More specifically, four physiological signals (electrodermal activity, skin temperature, blood volume pulse, and respiration effort) were collected in synchrony with the NIRS signals as the user sat at rest and/or performed music imagery tasks. Second, an acoustic monitoring technique was proposed and used to detect startle noise events, as both the prefrontal cortex and ANS are known to involuntarily respond to auditory startle stimuli. Experiments with eight participants showed that with a startle noise compensation strategy in place, performance comparable to that observed in silent conditions could be recovered with the hybrid ANS-NIRS system.},
}
@article {pmid20875978,
year = {2011},
author = {Ortner, R and Allison, BZ and Korisek, G and Gaggl, H and Pfurtscheller, G},
title = {An SSVEP BCI to control a hand orthosis for persons with tetraplegia.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {19},
number = {1},
pages = {1-5},
doi = {10.1109/TNSRE.2010.2076364},
pmid = {20875978},
issn = {1558-0210},
mesh = {Biofeedback, Psychology/methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Hand/*physiopathology ; Humans ; *Orthotic Devices ; Quadriplegia/*physiopathology/*rehabilitation ; Robotics/methods ; *User-Computer Interface ; Visual Cortex/physiology ; },
abstract = {Brain-computer interface (BCI) systems allow people to send messages or commands without moving, and hence can provide an alternative communication and control channel for people with limited motor function. In this study, we demonstrate a BCI system for orthosis control. Our BCI was asynchronous, meaning that subjects could move the orthosis whenever they wanted, instead of pacing themselves to external cues. Seven subjects each performed two tasks with a BCI that relied on steady state visual evoked potentials (SSVEPs). Although none of the subjects had any training, six subjects showed good control with a positive predictive value (PPV) higher than 60%. The overall PPV for all subjects reached 78% ±10%. However, the false positive rate was high, and some subjects dislike the flickering lights required in SSVEP BCIs. In follow-up work, we hope to reduce both the false positive rate and the annoyance produced by flickering lights by hybridizing this BCI with a "brain switch," which could allow people to turn the SSVEP system on or off using a second type of brain activity when they do not wish to control the orthosis. We also hope to validate this approach with people with tetraplegia.},
}
@article {pmid20875456,
year = {2010},
author = {Li, J and Zhang, L},
title = {Bilateral adaptation and neurofeedback for brain computer interface system.},
journal = {Journal of neuroscience methods},
volume = {193},
number = {2},
pages = {373-379},
doi = {10.1016/j.jneumeth.2010.09.010},
pmid = {20875456},
issn = {1872-678X},
mesh = {Adaptation, Physiological/*physiology ; Algorithms ; Brain/*physiology ; Brain Mapping ; Electroencephalography/methods ; Functional Laterality/*physiology ; Humans ; Learning ; Neurofeedback/*methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain computer interface (BCI) provides an alternative communication pathway between human brain and external devices without the participation of peripheral nerves and muscles. Although the BCI techniques have been developing quickly in recent decades, there still exist a number of unsolved problems, such as instability, unreliability and low transmission rate in real time applications of BCI. In the present study, we design a bilateral training framework for both human and the BCI system to improve recognition accuracy and to reduce the impact caused by non-stationary EEG signal. The statistical analysis is used to test whether there is an obvious improvement in recognition performance after using the bilateral adaptation strategy. The statistical analysis indicates that our algorithm is significantly different from the existing method in both conditions of trials (p=0.0073) and sliding time windows (p=0.00077). The results of statistical analysis reconfirm that performance using our algorithm is distinctly improved. The online experiments also demonstrate that the proposed algorithm achieves higher prediction accuracy and reliability compared with the existing method. The objective of our research is to transfer this strategy to some practical applications (e.g., electrical wheelchair control) for the better performance.},
}
@article {pmid20859445,
year = {2010},
author = {Müller-Putz, GR and Scherer, R and Pfurtscheller, G and Neuper, C},
title = {Temporal coding of brain patterns for direct limb control in humans.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {},
pmid = {20859445},
issn = {1662-453X},
abstract = {For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A brain-computer interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom - grasp and elbow function - of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating online feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.},
}
@article {pmid20858924,
year = {2010},
author = {Brunner, P and Joshi, S and Briskin, S and Wolpaw, JR and Bischof, H and Schalk, G},
title = {Does the 'P300' speller depend on eye gaze?.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056013},
pmid = {20858924},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Event-Related Potentials, P300/*physiology ; Eye Movements/*physiology ; Female ; Humans ; Male ; Middle Aged ; Models, Neurological ; Photic Stimulation/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.},
}
@article {pmid20846418,
year = {2010},
author = {Hashimoto, Y and Ushiba, J and Kimura, A and Liu, M and Tomita, Y},
title = {Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy.},
journal = {BMC neuroscience},
volume = {11},
number = {},
pages = {117},
pmid = {20846418},
issn = {1471-2202},
mesh = {Adult ; Brain/*physiopathology ; Chronic Disease ; *Communication ; *Computer Graphics ; Cues ; Electroencephalography ; Feasibility Studies ; Functional Laterality/physiology ; Humans ; Intention ; Internet ; Male ; Motor Cortex/physiology ; Muscular Dystrophies/*physiopathology/psychology ; Psychomotor Performance/physiology ; Quadriplegia/*physiopathology/psychology ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {BACKGROUND: For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.<br><br>RESULTS: After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).<br><br>CONCLUSIONS: These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.},
}
@article {pmid20844441,
year = {2010},
author = {Wei, Q and Lu, Z and Chen, K and Ma, Y},
title = {Channel selection for optimizing feature extraction in an electrocorticogram-based brain-computer interface.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {27},
number = {5},
pages = {321-327},
doi = {10.1097/WNP.0b013e3181f52f2d},
pmid = {20844441},
issn = {1537-1603},
mesh = {Algorithms ; *Artificial Intelligence ; Cerebral Cortex/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Epilepsies, Partial/physiopathology/surgery ; Humans ; Pattern Recognition, Automated/methods ; *User-Computer Interface ; },
abstract = {Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related data recording channels. In this article, a machine-learning algorithm is proposed for optimizing feature extraction in an electrocorticogram-based brain-computer interface. Common spatial pattern was used for feature extraction, and channel selection was performed by genetic algorithm for optimizing the feature extraction. Fisher discriminant analysis was used as classifier, and the channel subset chosen at each generation was evaluated by classification accuracy. The algorithm was applied to three electrocorticogram datasets that were recorded during two kinds of motor imagery tasks. The results suggest that the channel number used for building a brain-computer interface system could be significantly decreased without losing classification accuracy, and the accuracy rate could be noticeably improved by using the optimal channel subsets chosen by genetic algorithm.},
}
@article {pmid20841700,
year = {2010},
author = {McCullagh, P and Ware, M and Mulvenna, M and Lightbody, G and Nugent, C and McAllister, G and Thomson, E and Martin, S and Mathews, S and Todd, D and Cruz Medina, V and Carro, S},
title = {Can brain computer interfaces become practical assistive devices in the community?.},
journal = {Studies in health technology and informatics},
volume = {160},
number = {Pt 1},
pages = {314-318},
pmid = {20841700},
issn = {0926-9630},
mesh = {Brain/*physiology ; *Computer Graphics ; Electroencephalography/*methods ; *Needs Assessment ; *Self-Help Devices ; United Kingdom ; *User-Computer Interface ; },
abstract = {A Brain Computer Interface (BCI) provides direct communication from the brain to a computer or electronic device. In order for BCIs to become practical assistive devices it is necessary to develop robust systems, which can be used outside of the laboratory. This paper appraises the technical challenges, and outlines the design of an intuitive user interface, which can be used for smart device control and entertainment applications, of specific interest to users. We adopted a user-centred approach, surveying two groups of participants: fifteen volunteers who could use BCI as an additional technology and six users with complex communication and assistive technology needs. Interaction is based on a four way choice, parsing a hierarchical menu structure which allows selection of room location and then device (e.g. light, television) within a smart home. The interface promotes ease of use which aim to improve the BCI communication rate.},
}
@article {pmid20841636,
year = {2010},
author = {Zhang, H and Guan, C},
title = {A maximum mutual information approach for constructing a 1D continuous control signal at a self-paced brain-computer interface.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056009},
doi = {10.1088/1741-2560/7/5/056009},
pmid = {20841636},
issn = {1741-2552},
mesh = {Brain/physiology ; Humans ; Motor Cortex/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper addresses an important issue in a self-paced brain-computer interface (BCI): constructing subject-specific continuous control signal. To this end, we propose an alternative to the conventional regression/classification-based mechanism for building the transformation from EEG features into a univariate control signal. Based on information theory, the mechanism formulates the optimum transformation as maximizing the mutual information between the control signal and the mental state. We introduce a non-parametric mutual information estimate for general output distribution, and then develop a gradient-based algorithm to optimize the transformation using training data. We conduct an offline simulation study using motor imagery data from the BCI Competition IV Data Set I. The results show that the learning algorithm converged quickly, and the proposed method yielded significantly higher BCI performance than the conventional mechanism.},
}
@article {pmid20841635,
year = {2010},
author = {Wang, Y and Principe, JC},
title = {Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056010},
doi = {10.1088/1741-2560/7/5/056010},
pmid = {20841635},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; Animals ; Brain/physiology ; Female ; Macaca mulatta ; Motor Cortex/*physiology ; Nerve Net/*physiology ; Neurons/*physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to relate instantaneous kinematics to every neural spike activity. This requirement is unlike the tuning methods commonly used in computational neuroscience, which are based on time windows of neural and kinematic data. This paper develops a novel, online, encoding model that uses the instantaneous kinematic variables (position, velocity and acceleration in 2D or 3D space) to estimate the mean value of an inhomogeneous Poisson model. During BMI decoding the mapping from neural spikes to kinematics is one to one and easy to implement by simply reading the spike times directly. Due to the high temporal resolution of the encoding, the delay between motor cortex neurons and kinematics needs to be estimated in the encoding stage. Mutual information is employed to select the optimal time index defined as the lag for which the spike event is maximally informative with respect to the kinematics. We extensively compare the windowed tuning models with the proposed method. The big difference between them resides in the high firing rate portion of the tuning curve, which is rather important for BMI-decoding performance. This paper shows that implementing such an instantaneous tuning model in sequential Monte Carlo point process estimation based on spike timing provides statistically better kinematic reconstructions than the linear and exponential spike-tuning models.},
}
@article {pmid20838900,
year = {2011},
author = {Henle, C and Raab, M and Cordeiro, JG and Doostkam, S and Schulze-Bonhage, A and Stieglitz, T and Rickert, J},
title = {First long term in vivo study on subdurally implanted micro-ECoG electrodes, manufactured with a novel laser technology.},
journal = {Biomedical microdevices},
volume = {13},
number = {1},
pages = {59-68},
doi = {10.1007/s10544-010-9471-9},
pmid = {20838900},
issn = {1572-8781},
mesh = {Animals ; Cerebral Cortex/cytology/*physiology ; Dielectric Spectroscopy ; Dimethylpolysiloxanes/chemistry ; *Electrodes, Implanted ; *Electrophysiological Phenomena ; Female ; *Lasers ; Microelectrodes ; Models, Biological ; Platinum/chemistry ; Rats ; Rats, Wistar ; Subdural Space ; Time Factors ; },
abstract = {A novel computer aided manufacturing (CAM) method for electrocorticography (ECoG) microelectrodes was developed to be able to manufacture small, high density microelectrode arrays based on laser-structuring medical grade silicone rubber and high purity platinum. With this manufacturing process, we plan to target clinical applications, such as presurgical epilepsy monitoring, functional imaging during cerebral tumor resections and brain-computer interface control in paralysed patients, in the near future. This paper describes the manufacturing, implantation and long-term behaviour of such an electrode array. In detail, we implanted 8-channel electrode arrays subdurally over rat cerebral cortex over a period of up to 25 weeks. Our primary objective was to ascertain the electrode's stability over time, and to analyse the host response in vivo. For this purpose, impedance measurements were carried out at regular intervals over the first 18 weeks of the implantation period. The impedances changed between day 4 and day 7 after implantation, and then remained stable until the end of the implantation period, in accordance with typical behaviour of chronically implanted microelectrodes. A post-mortem histological examination was made to assess the tissue reaction due to the implantation. A mild, chronically granulated inflammation was found in the area of the implant, which was essentially restricted to the leptomeninges. Overall, these findings suggest that the concept of the presented ECoG-electrodes is promising for use in long-term implantations.},
}
@article {pmid20838477,
year = {2010},
author = {Rebesco, JM and Stevenson, IH and Körding, KP and Solla, SA and Miller, LE},
title = {Rewiring neural interactions by micro-stimulation.},
journal = {Frontiers in systems neuroscience},
volume = {4},
number = {},
pages = {},
pmid = {20838477},
issn = {1662-5137},
support = {F31 NS062552/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
abstract = {Plasticity is a crucial component of normal brain function and a critical mechanism for recovery from injury. In vitro, associative pairing of presynaptic spiking and stimulus-induced postsynaptic depolarization causes changes in the synaptic efficacy of the presynaptic neuron, when activated by extrinsic stimulation. In vivo, such paradigms can alter the responses of whole groups of neurons to stimulation. Here, we used in vivo spike-triggered stimulation to drive plastic changes in rat forelimb sensorimotor cortex, which we monitored using a statistical measure of functional connectivity inferred from the spiking statistics of the neurons during normal, spontaneous behavior. These induced plastic changes in inferred functional connectivity depended on the latency between trigger spike and stimulation, and appear to reflect a robust reorganization of the network. Such targeted connectivity changes might provide a tool for rerouting the flow of information through a network, with implications for both rehabilitation and brain-machine interface applications.},
}
@article {pmid20833204,
year = {2010},
author = {Liu, P and Yang, HT and Qiang, LY and Jin, H and Xiao, S and Shi, ZX},
title = {Evaluation of 30 commercial assays for the detection of antibodies to HIV in China using classical and Bayesian statistics.},
journal = {Journal of virological methods},
volume = {170},
number = {1-2},
pages = {73-79},
doi = {10.1016/j.jviromet.2010.09.001},
pmid = {20833204},
issn = {1879-0984},
mesh = {*AIDS Serodiagnosis ; Bayes Theorem ; China ; Confidence Intervals ; Data Interpretation, Statistical ; Enzyme-Linked Immunosorbent Assay/*standards ; HIV/immunology ; HIV Antibodies/*blood ; HIV Seropositivity/blood/diagnosis ; Humans ; Immunoassay/*standards ; Predictive Value of Tests ; Reagent Kits, Diagnostic ; Sensitivity and Specificity ; },
abstract = {The purpose of this study was to evaluate the 30 commercial HIV-antibody (HIV-Ab) assays in the nationwide assessment program of China using classical and Bayesian statistical methods. The classical estimates of sensitivity and specificity varied from 95.9% to 100% and from 94.6% to 100%, respectively. The proportions of assays with 100% sensitivity and with 100% specificity reached 63.3% (19/30) and 3.3% (1/30), respectively. Using the Bayesian logit hierarchical model, the overall estimates of sensitivity and specificity were 99.8% (95% Bayesian credible interval [BCI]: 99.4-100%) and 98.1% (95% BCI: 97.4-98.7%), respectively, for the 17 ELISAs under evaluation. For the 13 rapid assays, the corresponding overall estimates were reported to be 99.2% (95% BCI: 98.5-99.8%) and 98.4% (95% BCI: 97.8-98.9%), respectively. In addition, given the prevalences of HIV infection among the general population of China and the intravenous drug user group in China, the positive predictive values were estimated for each individual assay in the framework of the two schools of statistical thought. Furthermore, by comparing the two types of estimates, it is concluded that the two types of statistical methods were complementary for the evaluation of very accurate HIV-Ab assays.},
}
@article {pmid20832477,
year = {2011},
author = {Haufe, S and Tomioka, R and Dickhaus, T and Sannelli, C and Blankertz, B and Nolte, G and Müller, KR},
title = {Large-scale EEG/MEG source localization with spatial flexibility.},
journal = {NeuroImage},
volume = {54},
number = {2},
pages = {851-859},
doi = {10.1016/j.neuroimage.2010.09.003},
pmid = {20832477},
issn = {1095-9572},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; *Electroencephalography ; Humans ; *Magnetoencephalography ; *Signal Processing, Computer-Assisted ; },
abstract = {We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain-Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices.},
}
@article {pmid20822389,
year = {2010},
author = {Brumberg, JS and Guenther, FH},
title = {Development of speech prostheses: current status and recent advances.},
journal = {Expert review of medical devices},
volume = {7},
number = {5},
pages = {667-679},
pmid = {20822389},
issn = {1745-2422},
support = {R01 DC002852-15/DC/NIDCD NIH HHS/United States ; R01DC007683/DC/NIDCD NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R01 DC002852/DC/NIDCD NIH HHS/United States ; R01DC002852/DC/NIDCD NIH HHS/United States ; R01 DC007683-05/DC/NIDCD NIH HHS/United States ; },
mesh = {Humans ; Prostheses and Implants/*trends ; Speech/*physiology ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) have been developed over the past decade to restore communication to persons with severe paralysis. In the most severe cases of paralysis, known as locked-in syndrome, patients retain cognition and sensation, but are capable of only slight voluntary eye movements. For these patients, no standard communication method is available, although some can use BCIs to communicate by selecting letters or words on a computer. Recent research has sought to improve on existing techniques by using BCIs to create a direct prediction of speech utterances rather than to simply control a spelling device. Such methods are the first steps towards speech prostheses as they are intended to entirely replace the vocal apparatus of paralyzed users. This article outlines many well known methods for restoration of communication by BCI and illustrates the difference between spelling devices and direct speech prediction or speech prosthesis.},
}
@article {pmid20811093,
year = {2010},
author = {Kellis, S and Miller, K and Thomson, K and Brown, R and House, P and Greger, B},
title = {Decoding spoken words using local field potentials recorded from the cortical surface.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056007},
pmid = {20811093},
issn = {1741-2552},
support = {P30 EY014800/EY/NEI NIH HHS/United States ; P30 EY014800-07/EY/NEI NIH HHS/United States ; R01 EY019363/EY/NEI NIH HHS/United States ; R01EY019363/EY/NEI NIH HHS/United States ; },
mesh = {Acoustic Stimulation/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; Motor Cortex/*physiology ; Speech/*physiology ; Temporal Lobe/*physiology ; },
abstract = {Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as 'locked-in syndrome'. Communication in this state is often reduced to selecting individual letters or words by arduous residual movements. More intuitive and rapid communication may be restored by directly interfacing with language areas of the cerebral cortex. We used a grid of closely spaced, nonpenetrating micro-electrodes to record local field potentials (LFPs) from the surface of face motor cortex and Wernicke's area. From these LFPs we were successful in classifying a small set of words on a trial-by-trial basis at levels well above chance. We found that the pattern of electrodes with the highest accuracy changed for each word, which supports the idea that closely spaced micro-electrodes are capable of capturing neural signals from independent neural processing assemblies. These results further support using cortical surface potentials (electrocorticography) in brain-computer interfaces. These results also show that LFPs recorded from the cortical surface (micro-electrocorticography) of language areas can be used to classify speech-related cortical rhythms and potentially restore communication to locked-in patients.},
}
@article {pmid20811092,
year = {2010},
author = {Citi, L and Poli, R and Cinel, C},
title = {Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056006},
doi = {10.1088/1741-2560/7/5/056006},
pmid = {20811092},
issn = {1741-2552},
mesh = {Algorithms ; Brain/physiology ; *Event-Related Potentials, P300 ; Humans ; *Models, Neurological ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top-performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.},
}
@article {pmid20811091,
year = {2010},
author = {Huang, H and Zhang, F and Sun, YL and He, H},
title = {Design of a robust EMG sensing interface for pattern classification.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056005},
pmid = {20811091},
issn = {1741-2552},
support = {R21 HD064968/HD/NICHD NIH HHS/United States ; #RHD064968A//PHS HHS/United States ; },
mesh = {Adult ; Aged ; *Artificial Limbs ; Electromyography/*classification/*instrumentation/methods ; Equipment Design/*instrumentation/methods ; Female ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/*physiology ; },
abstract = {Electromyographic (EMG) pattern classification has been widely investigated for neural control of external devices in order to assist with movements of patients with motor deficits. Classification performance deteriorates due to inevitable disturbances to the sensor interface, which significantly challenges the clinical value of this technique. This study aimed to design a sensor fault detection (SFD) module in the sensor interface to provide reliable EMG pattern classification. This module monitored the recorded signals from individual EMG electrodes and performed a self-recovery strategy to recover the classification performance when one or more sensors were disturbed. To evaluate this design, we applied synthetic disturbances to EMG signals collected from leg muscles of able-bodied subjects and a subject with a transfemoral amputation and compared the accuracies for classifying transitions between different locomotion modes with and without the SFD module. The results showed that the SFD module maintained classification performance when one signal was distorted and recovered about 20% of classification accuracy when four signals were distorted simultaneously. The method was simple to implement. Additionally, these outcomes were observed for all subjects, including the leg amputee, which implies the promise of the designed sensor interface for providing a reliable neural-machine interface for artificial legs.},
}
@article {pmid20811088,
year = {2010},
author = {Salvaris, M and Sepulveda, F},
title = {Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {7},
number = {5},
pages = {056004},
doi = {10.1088/1741-2560/7/5/056004},
pmid = {20811088},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Electroencephalography/*classification/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Psychomotor Performance/physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).},
}
@article {pmid20810180,
year = {2010},
author = {Min, BK and Marzelli, MJ and Yoo, SS},
title = {Neuroimaging-based approaches in the brain-computer interface.},
journal = {Trends in biotechnology},
volume = {28},
number = {11},
pages = {552-560},
doi = {10.1016/j.tibtech.2010.08.002},
pmid = {20810180},
issn = {1879-3096},
mesh = {*Artificial Intelligence ; Automation/*methods ; *Computer Systems ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Techniques to enable direct communication between the brain and computers/machines, such as the brain-computer interface (BCI) or the brain-machine interface (BMI), are gaining momentum in the neuroscientific realm, with potential applications ranging from medicine to general consumer electronics. Noninvasive BCI techniques based on neuroimaging modalities are reviewed in terms of their methodological approaches as well as their similarities and differences. Trends in automated data interpretation through machine learning algorithms are also introduced. Applications of functional neuromodulation techniques to BCI systems would allow for bidirectional communication between the brain and the computer. Such bidirectional interfaces can relay information directly from one brain to another using a computer as a medium, ultimately leading to the concept of a brain-to-brain interface (BBI).},
}
@article {pmid20805058,
year = {2010},
author = {Mugler, EM and Ruf, CA and Halder, S and Bensch, M and Kubler, A},
title = {Design and implementation of a P300-based brain-computer interface for controlling an internet browser.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {6},
pages = {599-609},
doi = {10.1109/TNSRE.2010.2068059},
pmid = {20805058},
issn = {1558-0210},
mesh = {Adult ; Affect/physiology ; Algorithms ; Amyotrophic Lateral Sclerosis/psychology ; Brain/*physiology ; Data Interpretation, Statistical ; Depression/psychology ; Disease Progression ; *Electroencephalography ; Equipment Design ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Information Systems ; *Internet ; Male ; Middle Aged ; Motivation ; Paralysis/*psychology/*rehabilitation ; Quality of Life ; *User-Computer Interface ; Young Adult ; },
abstract = {An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.},
}
@article {pmid20800538,
year = {2011},
author = {Nam, CS and Jeon, Y and Kim, YJ and Lee, I and Park, K},
title = {Movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS): motor-imagery duration effects.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {3},
pages = {567-577},
doi = {10.1016/j.clinph.2010.08.002},
pmid = {20800538},
issn = {1872-8952},
mesh = {Algorithms ; Beta Rhythm/physiology ; *Cortical Synchronization ; Data Interpretation, Statistical ; Dominance, Cerebral/physiology ; Electroencephalography ; Female ; Functional Laterality/*physiology ; Hand/innervation/physiology ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Photic Stimulation ; Psychomotor Performance/physiology ; Somatosensory Cortex/physiology ; Young Adult ; },
abstract = {OBJECTIVE: To investigate movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS) during two motor-imagery tasks with varying movement duration (brief versus continuous).<br><br>METHODS: Twelve subjects performed or kinesthetically imagined the indicated movement (left or right hand movement) for 1 s (brief) or 5 s (continuous) while electroencephalograms (EEGs) were recorded using 16 electrodes covering the sensorimotor cortex of the brain according to the modified 10-20 system.<br><br>RESULTS: Significant hemispheric differences were found between contralateral and ipsilateral area in mu ERD, mu ERS and beta ERD during both brief and continuous conditions, showing contralateral dominance of mu and beta ERD and ipsilateral dominance of mu ERS. Beta ERS showed a significant ipsilateral dominance only in the brief condition. Movement imagery duration influenced the lateralization of mu ERD, beta ERD, and beta ERS, but not mu ERS.<br><br>CONCLUSIONS: The results of this study will aid in clarifying movement-related lateralization in association with imagery tasks under varying movement duration.<br><br>SIGNIFICANCE: For designing an EEG-based brain-computer interface (BCI) for people with severe neuromuscular impairments, movement imagery-related lateralization can play a key role in utilizing motor-imagery tasks as a control or communication strategy.},
}
@article {pmid20739599,
year = {2010},
author = {Wang, W and Sudre, GP and Xu, Y and Kass, RE and Collinger, JL and Degenhart, AD and Bagic, AI and Weber, DJ},
title = {Decoding and cortical source localization for intended movement direction with MEG.},
journal = {Journal of neurophysiology},
volume = {104},
number = {5},
pages = {2451-2461},
pmid = {20739599},
issn = {1522-1598},
support = {R90 DA023426/DA/NIDA NIH HHS/United States ; 1R21 NS-056136/NS/NINDS NIH HHS/United States ; R01 MH064537/MH/NIMH NIH HHS/United States ; R01 MH064537-09/MH/NIMH NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; T90 DA022762/DA/NIDA NIH HHS/United States ; 1R01 EB-007749/EB/NIBIB NIH HHS/United States ; R01 EB-005847/EB/NIBIB NIH HHS/United States ; R01 MH-064537/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Analysis of Variance ; Brain Mapping ; Female ; Functional Laterality/physiology ; Humans ; Imagination/physiology ; Magnetoencephalography ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; },
abstract = {Magnetoencephalography (MEG) enables a noninvasive interface with the brain that is potentially capable of providing movement-related information similar to that obtained using more invasive neural recording techniques. Previous studies have shown that movement direction can be decoded from multichannel MEG signals recorded in humans performing wrist movements. We studied whether this information can be extracted without overt movement of the subject, because the targeted users of brain-controlled interface (BCI) technology are those with severe motor disabilities. The objectives of this study were twofold: 1) to decode intended movement direction from MEG signals recorded during the planning period before movement onset and during imagined movement and 2) to localize cortical sources modulated by intended movement direction. Ten able-bodied subjects performed both overt and imagined wrist movement while their cortical activities were recorded using a whole head MEG system. The intended movement direction was decoded using linear discriminant analysis and a Bayesian classifier. Minimum current estimation (MCE) in combination with a bootstrapping procedure enabled source-space statistical analysis, which showed that the contralateral motor cortical area was significantly modulated by intended movement direction, and this modulation was the strongest ∼100 ms before the onset of overt movement. These results suggest that it is possible to study cortical representation of specific movement information using MEG, and such studies may aid in presurgical localization of optimal sites for implanting electrodes for BCI systems.},
}
@article {pmid20739570,
year = {2010},
author = {Nokia, MS and Penttonen, M and Wikgren, J},
title = {Hippocampal ripple-contingent training accelerates trace eyeblink conditioning and retards extinction in rabbits.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {34},
pages = {11486-11492},
pmid = {20739570},
issn = {1529-2401},
mesh = {Animals ; Association Learning/*physiology ; Conditioning, Eyelid/*physiology ; Electromyography ; Extinction, Psychological/*physiology ; Hippocampus/*physiology ; Male ; Rabbits ; Random Allocation ; Time Factors ; },
abstract = {There are at least two distinct oscillatory states of the hippocampus that are related to distinct behavioral patterns. Theta (4-12 Hz) oscillation has been suggested to indicate selective attention during which the animal concentrates on some features of the environment while suppressing reactivity to others. In contrast, sharp-wave ripples (approximately 200 Hz) can be seen in a state in which the hippocampus is at its most responsive to any kind of afferent stimulation. In addition, external stimulation tends to evoke and reset theta oscillation, the phase of which has been shown to modulate synaptic plasticity in the hippocampus. Theoretically, training on a hippocampus-dependent learning task contingent upon ripples could enhance learning rate due to elevated responsiveness and enhanced phase locking of the theta oscillation. We used a brain-computer interface to detect hippocampal ripples in rabbits to deliver trace eyeblink conditioning and extinction trials selectively contingent upon them. A yoked control group was trained regardless of their ongoing neural state. Ripple-contingent training expedited acquisition of the conditioned response early in training and evoked stronger theta-band phase locking to the conditioned stimulus. Surprisingly, ripple-contingent training also resulted in slower extinction in well trained animals. We suggest that the ongoing oscillatory activity in the hippocampus determines the extent to which a stimulus can induce a phase reset of the theta oscillation, which in turn is the determining factor of learning rate in trace eyeblink conditioning.},
}
@article {pmid20729160,
year = {2011},
author = {Jia, C and Gao, X and Hong, B and Gao, S},
title = {Frequency and phase mixed coding in SSVEP-based brain--computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {58},
number = {1},
pages = {200-206},
doi = {10.1109/TBME.2010.2068571},
pmid = {20729160},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Fourier Analysis ; Humans ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; },
abstract = {Frequency coding has been the traditional method implemented in steady-state visual evoked potential (SSVEP)-based brain--computer interfaces (BCI). However, it is limited in terms of possible target numbers and, consequently, inappropriate for certain applications involving liquid crystal display (LCD) with multiple stimuli. This paper proposes an innovative coding method for SSVEP that, through a combination of frequency and phase, increases the number of targets, thus it improves the information transfer rate (ITR). With this method, a BCI system with 15 targets was developed using three stimulus frequencies, which is five times as many targets as the traditional method. Additionally, this paper defines the concept of reference phase, and decodes the EEG by means of Fourier coefficient projections onto the reference phase directions. Through the optimization of lead position, reference phase, data segment length, and harmonic components, the average ITR exceeded 60 bits/min in a simulated online test with ten subjects.},
}
@article {pmid20718931,
year = {2011},
author = {Caria, A and Weber, C and Brötz, D and Ramos, A and Ticini, LF and Gharabaghi, A and Braun, C and Birbaumer, N},
title = {Chronic stroke recovery after combined BCI training and physiotherapy: a case report.},
journal = {Psychophysiology},
volume = {48},
number = {4},
pages = {578-582},
doi = {10.1111/j.1469-8986.2010.01117.x},
pmid = {20718931},
issn = {1469-8986},
mesh = {Aged ; Data Interpretation, Statistical ; Diffusion Tensor Imaging ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Neural Pathways/physiology ; Paralysis/etiology/rehabilitation ; *Physical Therapy Modalities ; Pyramidal Tracts/physiology ; *Recovery of Function ; Stroke/psychology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {A case of partial recovery after stroke and its associated brain reorganization in a chronic patient after combined brain computer interface (BCI) training and physiotherapy is presented. A multimodal neuroimaging approach based on fMRI and diffusion tensor imaging was used to investigate plasticity of the brain motor system in parallel with longitudinal clinical assessments. A convergent association between functional and structural data in the ipsilesional premotor areas was observed. As a proof of concept investigation, these results encourage further research on a specific role of BCI on brain plasticity and recovery after stroke.},
}
@article {pmid20709284,
year = {2010},
author = {Demetriades, AK and Demetriades, CK and Watts, C and Ashkan, K},
title = {Brain-machine interface: the challenge of neuroethics.},
journal = {The surgeon : journal of the Royal Colleges of Surgeons of Edinburgh and Ireland},
volume = {8},
number = {5},
pages = {267-269},
doi = {10.1016/j.surge.2010.05.006},
pmid = {20709284},
issn = {1479-666X},
mesh = {*Bioethical Issues ; Humans ; Medical Laboratory Science/*ethics ; Neurosciences/*ethics ; Prostheses and Implants/ethics ; User-Computer Interface ; },
abstract = {The burning question surrounding the use of Brain-Machine Interface (BMI) devices is not merely whether they should be used, but how widely they should be used, especially in view of some ethical implications that arise concerning the social and legal aspects of human life. As technology advances, it can be exploited to affect the quality of life. Since the effects of BMIs can be both positive and negative, it is imperative to address the issue of the ethics surrounding them. This paper presents the ways in which BMIs can be used and focuses on the ethical concerns to which neuroscience is thus exposed. The argument put forward supports the use of BMIs solely for purposes of medical treatment, and invites the legal framing of this.},
}
@article {pmid20703739,
year = {2012},
author = {Luo, G and Min, W},
title = {Distance-constrained orthogonal Latin squares for brain-computer interface.},
journal = {Journal of medical systems},
volume = {36},
number = {1},
pages = {159-166},
pmid = {20703739},
issn = {0148-5598},
mesh = {Amyotrophic Lateral Sclerosis/*rehabilitation ; Brain/physiology ; Electroencephalography/*statistics &amp; numerical data ; *Event-Related Potentials, P300 ; Humans ; Mathematics ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The P300 brain-computer interface (BCI) using electroencephalogram (EEG) signals can allow amyotrophic lateral sclerosis (ALS) patients to instruct computers to perform tasks. To strengthen the P300 response and increase classification accuracy, we proposed an experimental design where characters are intensified according to orthogonal Latin square pairs. These orthogonal Latin square pairs satisfy certain distance constraint so that neighboring characters are not intensified simultaneously. However, it is unknown whether such distance-constrained, orthogonal Latin square pairs actually exist. In this paper, we show that for every matrix size commonly used in P300 BCI, thousands to millions of such distance-constrained, orthogonal Latin square pairs can be systematically and efficiently constructed and are sufficient for the purpose of being used in P300 BCI.},
}
@article {pmid20700777,
year = {2010},
author = {Stieglitz, T},
title = {[Neural prostheses and neuromodulation : Research and clinical practice in therapy and rehabilitation].},
journal = {Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz},
volume = {53},
number = {8},
pages = {783-790},
doi = {10.1007/s00103-010-1093-0},
pmid = {20700777},
issn = {1437-1588},
mesh = {Bioengineering/instrumentation ; Biomedical Technology/*instrumentation/trends ; Blindness/rehabilitation ; Communication Devices for People with Disabilities/trends ; Deafness/rehabilitation ; Deep Brain Stimulation/*instrumentation/trends ; Persons with Disabilities/*rehabilitation ; Electrodes, Implanted/trends ; Forecasting ; Germany ; Humans ; *Neural Prostheses/trends ; Paraplegia/rehabilitation ; Prosthesis Design ; Software Design ; Spinal Cord Injuries/rehabilitation ; User-Computer Interface ; },
abstract = {Stimulation of the nervous system with the aid of electrical active implants has changed the therapy of neurological diseases and rehabilitation of lost functions and has expanded clinical practice within the last few years. Alleviation of effects of neurodegenerative diseases, therapy of psychiatric diseases, the functional restoration of hearing as well as other applications have been transferred successfully into clinical practice. Other approaches are still under development in preclinical and clinical trials. The restoration of sight by implantable electronic systems that interface with the retina in the eye is an example how technological progress promotes novel medical devices. The idea of using the electrical signal of the brain to control technical devices and (neural) prostheses is driving current research in the field of brain-computer interfaces. The benefit for the patient always has to be balanced with the risks and side effects of those implants in comparison to medicinal and surgical treatments. How these and other developments become established in practice depends finally on their acceptance by the patients and the reimbursement of their costs.},
}
@article {pmid20700521,
year = {2010},
author = {Nijboer, F and Birbaumer, N and Kübler, A},
title = {The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {},
pmid = {20700521},
issn = {1662-453X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; },
abstract = {The current study investigated the effects of psychological well-being measured as quality of life (QoL), depression, current mood and motivation on brain-computer interface (BCI) performance in amyotrophic lateral sclerosis (ALS). Six participants with most advanced ALS were trained either for a block of 20 sessions with a BCI based on sensorimotor rhythms (SMR) or a block of 10 sessions with a BCI based on event-related potentials, or both. Questionnaires assessed QoL and severity of depressive symptoms before each training block and mood and motivation before each training session. The SMR-BCI required more training than the P300-BCI. The information transfer rate was higher with the P300-BCI (3.25 bits/min) than with the SMR-BCI (1.16 bits/min). Mood and motivation were related to the number of BCI sessions. Motivational factors, specifically challenge and mastery confidence, were positively related to BCI performance (controlled for the number of sessions) in tow participants, while incompetence fear was negatively related with performance in one participant. BCI performance was not related to motivational factors in three other participants nor to mood in any of the six participants. We conclude that motivational factors may be related to BCI performance in individual subjects and suggest that motivational factors and well-being should be assessed in standard BCI protocols. We also recommend using P300-based BCI as first choice in severely paralyzed patients who present with a P300 evoked potential.},
}
@article {pmid20700080,
year = {2010},
author = {Egeland, BM and Urbanchek, MG and Peramo, A and Richardson-Burns, SM and Martin, DC and Kipke, DR and Kuzon, WM and Cederna, PS},
title = {In vivo electrical conductivity across critical nerve gaps using poly(3,4-ethylenedioxythiophene)-coated neural interfaces.},
journal = {Plastic and reconstructive surgery},
volume = {126},
number = {6},
pages = {1865-1873},
doi = {10.1097/PRS.0b013e3181f61848},
pmid = {20700080},
issn = {1529-4242},
mesh = {Algorithms ; Animals ; *Artificial Limbs ; Bionics/*methods ; *Bridged Bicyclo Compounds, Heterocyclic ; *Coated Materials, Biocompatible ; *Electric Conductivity ; Electromyography ; Male ; Microsurgery/methods ; Muscle, Skeletal ; Neural Conduction/*physiology ; Peripheral Nerves/*physiology/*surgery ; Peroneal Nerve/physiology/surgery ; *Polymers ; Prosthesis Design ; Rats ; Rats, Inbred F344 ; Reaction Time/physiology ; Sural Nerve/physiology/surgery ; Tissue Scaffolds ; *User-Computer Interface ; },
abstract = {BACKGROUND: Bionic limbs require sensitive, durable, and physiologically relevant bidirectional control interfaces. Modern central nervous system interfacing is high risk, low fidelity, and failure prone. Peripheral nervous system interfaces will mitigate this risk and increase fidelity by greatly simplifying signal interpretation and delivery. This study evaluates in vivo relevance of a hybrid peripheral nervous system interface consisting of biological acellular muscle scaffolds made electrically conductive using poly(3,4-ethylenedioxythiophene).<br><br>METHODS: Peripheral nervous system interfaces were tested in vivo using the rat hind-limb conduction-gap model for motor (peroneal) and sensory (sural) nerves. Experimental groups included acellular muscle, iron(III) chloride-treated acellular muscle, and poly(3,4-ethylenedioxythiophene) polymerized on acellular muscle, each compared with intact nerve, autogenous nerve graft, and empty (nonreconstructed) nerve gap controls (n=5 for each). Interface lengths tested included 0, 5, 10, and 20 mm. Immediately following implantation, the interface underwent electrophysiologic characterization in vivo using nerve conduction studies, compound muscle action potentials, and antidromic sensory nerve action potentials.<br><br>RESULTS: Both efferent and afferent electrophysiology demonstrates acellular muscle-poly(3,4-ethylenedioxythiophene) interfaces conduct physiologic action potentials across nerve conduction gaps of at least 20 mm with amplitude and latency not differing from intact nerve or nerve grafts, with the exception of increased velocity in the acellular muscle-poly(3,4-ethylenedioxythiophene) interfaces.<br><br>CONCLUSIONS: Nonmetallic, biosynthetic acellular muscle-poly(3,4-ethylenedioxythiophene) peripheral nervous system interfaces both sense and stimulate physiologically relevant efferent and afferent action potentials in vivo. This demonstrates their relevance not only as a nerve-electronic coupling device capable of reaching the long-sought goal of closed-loop neural control of a prosthetic limb, but also in a multitude of other bioelectrical applications.},
}
@article {pmid20699201,
year = {2010},
author = {Grychtol, B and Lakany, H and Valsan, G and Conway, BA},
title = {Human behavior integration improves classification rates in real-time BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {4},
pages = {362-368},
doi = {10.1109/TNSRE.2010.2053218},
pmid = {20699201},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; Computer Simulation ; Cues ; Data Interpretation, Statistical ; Electroencephalography ; Electromyography ; Feedback, Physiological/physiology ; Humans ; Psychomotor Performance/physiology ; Reaction Time/physiology ; *User-Computer Interface ; Wheelchairs ; },
abstract = {Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to interact with their environment, communicate and control mobility aids. Two key factors which affect the performance of a BCI and its usability are the feedback given to the participant and the subject's motivation. This paper presents the results from a study investigating the effects of feedback and motivation on the performance of the Strathclyde Brain Computer Interface. The paper discusses how the performance of the system can be improved by behavior integration and human-in-the-loop design.},
}
@article {pmid20696254,
year = {2011},
author = {Toda, H and Suzuki, T and Sawahata, H and Majima, K and Kamitani, Y and Hasegawa, I},
title = {Simultaneous recording of ECoG and intracortical neuronal activity using a flexible multichannel electrode-mesh in visual cortex.},
journal = {NeuroImage},
volume = {54},
number = {1},
pages = {203-212},
doi = {10.1016/j.neuroimage.2010.08.003},
pmid = {20696254},
issn = {1095-9572},
mesh = {Animals ; Dominance, Ocular/physiology ; Electrocardiography/*methods ; Electrodes ; Monitoring, Physiologic/instrumentation/methods ; Neurons/*physiology ; Photic Stimulation ; Rats ; Scalp/physiology ; Signal Transduction ; Visual Cortex/*physiology ; Visual Fields ; },
abstract = {Electrocorticogram (ECoG) is a well-balanced methodology for stably mapping brain surface local field potentials (LFPs) over a wide cortical region with high signal fidelity and minimal invasiveness to the brain tissue. To directly compare surface ECoG signals with intracortical neuronal activity immediately underneath, we fabricated a flexible multichannel electrode array with mesh-form structure using micro-electro-mechanical systems. A Parylene-C-based "electrode-mesh" for rats contained a 6×6 gold electrode array with 1-mm interval. Specifically, the probe had 800×800 μm(2) fenestrae in interelectrode spaces, through which simultaneous penetration of microelectrode was capable. This electrode-mesh was placed acutely or chronically on the dural/pial surface of the visual cortex of Long-Evans rats for up to 2 weeks. We obtained reliable trial-wise profiles of visually evoked ECoG signals through individual eye stimulation. Visually evoked ECoG signals from the electrode-mesh exhibited as well or larger signal amplitudes as intracortical LFPs and less across-trial variability than conventional silver-ball ECoG. Ocular selectivity of ECoG responses was correlated with that of intracortical spike/LFP activities. Moreover, single-trial ECoG signals carried sufficient information for predicting the stimulated eye with a correct performance approaching 90%, and the decoding was significantly generalized across sessions over 6 hours. Electrode impedance or signal quality did not obviously deteriorate for 2 weeks following implantation. These findings open up a methodology to directly explore ECoG signals with reference to intracortical neuronal sources and would provide a key to developing minimally invasive next-generation brain-machine interfaces.},
}
@article {pmid20692351,
year = {2011},
author = {Sitaram, R and Lee, S and Ruiz, S and Rana, M and Veit, R and Birbaumer, N},
title = {Real-time support vector classification and feedback of multiple emotional brain states.},
journal = {NeuroImage},
volume = {56},
number = {2},
pages = {753-765},
doi = {10.1016/j.neuroimage.2010.08.007},
pmid = {20692351},
issn = {1095-9572},
mesh = {Adult ; *Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; Emotions/*physiology ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Young Adult ; },
abstract = {An important question that confronts current research in affective neuroscience as well as in the treatment of emotional disorders is whether it is possible to determine the emotional state of a person based on the measurement of brain activity alone. Here, we first show that an online support vector machine (SVM) can be built to recognize two discrete emotional states, such as happiness and disgust from fMRI signals, in healthy individuals instructed to recall emotionally salient episodes from their lives. We report the first application of real-time head motion correction, spatial smoothing and feature selection based on a new method called Effect mapping. The classifier also showed robust prediction rates in decoding three discrete emotional states (happiness, disgust and sadness) in an extended group of participants. Subjective reports ascertained that participants performed emotion imagery and that the online classifier decoded emotions and not arbitrary states of the brain. Offline whole brain classification as well as region-of-interest classification in 24 brain areas previously implicated in emotion processing revealed that the frontal cortex was critically involved in emotion induction by imagery. We also demonstrate an fMRI-BCI based on real-time classification of BOLD signals from multiple brain regions, for each repetition time (TR) of scanning, providing visual feedback of emotional states to the participant for potential applications in the clinical treatment of dysfunctional affect.},
}
@article {pmid20682350,
year = {2011},
author = {Hinds, O and Ghosh, S and Thompson, TW and Yoo, JJ and Whitfield-Gabrieli, S and Triantafyllou, C and Gabrieli, JD},
title = {Computing moment-to-moment BOLD activation for real-time neurofeedback.},
journal = {NeuroImage},
volume = {54},
number = {1},
pages = {361-368},
pmid = {20682350},
issn = {1095-9572},
support = {T32 MH082718/MH/NIMH NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/*physiology ; Computing Methodologies ; Feedback, Physiological ; Feedback, Psychological ; Humans ; Image Processing, Computer-Assisted/*methods ; Kinetics ; Magnetic Resonance Imaging/*methods ; Oxygen/*blood ; Reproducibility of Results ; Signal Transduction ; },
abstract = {Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.},
}
@article {pmid20675187,
year = {2011},
author = {Bai, O and Rathi, V and Lin, P and Huang, D and Battapady, H and Fei, DY and Schneider, L and Houdayer, E and Chen, X and Hallett, M},
title = {Prediction of human voluntary movement before it occurs.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {2},
pages = {364-372},
pmid = {20675187},
issn = {1872-8952},
support = {Z99 NS999999/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adult ; Anticipation, Psychological/*physiology ; Electroencephalography/methods ; Female ; Forecasting ; Humans ; Male ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Time Factors ; Young Adult ; },
abstract = {OBJECTIVE: Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME.<br><br>METHODS: We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace.<br><br>RESULTS: The average online prediction time was 0.62&#xb1;0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement.<br><br>CONCLUSION: Human voluntary movement can be predicted before movement occurs.<br><br>SIGNIFICANCE: The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.},
}
@article {pmid20659857,
year = {2010},
author = {Casson, A and Yates, D and Smith, S and Duncan, J and Rodriguez-Villegas, E},
title = {Wearable electroencephalography. What is it, why is it needed, and what does it entail?.},
journal = {IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine &amp; Biology Society},
volume = {29},
number = {3},
pages = {44-56},
doi = {10.1109/MEMB.2010.936545},
pmid = {20659857},
issn = {1937-4186},
mesh = {*Clothing ; Diagnosis, Computer-Assisted/*instrumentation ; Electroencephalography/*instrumentation ; Equipment Design ; Monitoring, Ambulatory/*instrumentation/trends ; Signal Processing, Computer-Assisted/*instrumentation ; Telemedicine/*instrumentation/trends ; },
abstract = {The electroencephalogram (EEG) is a classic noninvasive method for measuring a person's brain waves and is used in a large number of fields: from epilepsy and sleep disorder diagnosis to brain-computer interfaces (BCIs). Electrodes are placed on the scalp to detect the microvolt-sized signals that result from synchronized neuronal activity within the brain. Current long-term EEG monitoring is generally either carried out as an inpatient in combination with video recording and long cables to an amplifier and recording unit or is ambulatory. In the latter, the EEG recorder is portable but bulky, and in principle, the subject can go about their normal daily life during the recording. In practice, however, this is rarely the case. It is quite common for people undergoing ambulatory EEG monitoring to take time off work and stay at home rather than be seen in public with such a device. Wearable EEG is envisioned as the evolution of ambulatory EEG units from the bulky, limited lifetime devices available today to small devices present only on the head that can record EEG for days, weeks, or months at a time. Such miniaturized units could enable prolonged monitoring of chronic conditions such as epilepsy and greatly improve the end-user acceptance of BCI systems. In this article, we aim to provide a review and overview of wearable EEG technology, answering the questions: What is it, why is it needed, and what does it entail? We first investigate the requirements of portable EEG systems and then link these to the core applications of wearable EEG technology: epilepsy diagnosis, sleep disorder diagnosis, and BCIs. As a part of our review, we asked 21 neurologists (as a key user group) for their views on wearable EEG. This group highlighted that wearable EEG will be an essential future tool. Our descriptions here will focus mainly on epilepsy and the medical applications of wearable EEG, as this is the historical background of the EEG, our area of expertise, and a core motivating area in itself, but we will also discuss the other application areas. We continue by considering the forthcoming research challenges, principally new electrode technology and lower power electronics, and we outline our approach for dealing with the electronic power issues. We believe that the optimal approach to realizing wearable EEG technology is not to optimize any one part but to find the best set of tradeoffs at both the system and implementation level. In this article, we discuss two of these tradeoffs in detail: investigating the online compression of EEG data to reduce the system power consumption and the optimal method for providing this data compression.},
}
@article {pmid20659350,
year = {2010},
author = {Barbero, A and Grosse-Wentrup, M},
title = {Biased feedback in brain-computer interfaces.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {7},
number = {},
pages = {34},
pmid = {20659350},
issn = {1743-0003},
mesh = {Adult ; Biofeedback, Psychology/methods ; Brain/*physiology ; *Feedback, Psychological ; Female ; Humans ; Imagination/physiology ; Male ; Mental Processes ; Motor Activity/physiology ; Photic Stimulation ; *User-Computer Interface ; Visual Perception ; },
abstract = {Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.},
}
@article {pmid20655362,
year = {2010},
author = {Shyu, KK and Lee, PL and Liu, YJ and Sie, JJ},
title = {Dual-frequency steady-state visual evoked potential for brain computer interface.},
journal = {Neuroscience letters},
volume = {483},
number = {1},
pages = {28-31},
doi = {10.1016/j.neulet.2010.07.043},
pmid = {20655362},
issn = {1872-7972},
mesh = {Adult ; Brain/physiology ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *User-Computer Interface ; },
abstract = {This study presents a new steady-state visual evoked potential (SSVEP) for brain computer interface (BCI) systems. The goal of this study is to increase the number of selections using fewer stimulation frequencies. This study analyzes the SSVEPs induced by six groups of light-emitting diodes (LEDs). The proposed method produces more selections than the number of stimulation frequencies through a suitable combination of dual frequencies for stimulation. Further, the six groups of LEDs are generated by four frequencies. The symmetric harmonic phenomena in this study helps increase recognition efficiency. This study tests seven subjects to verify the feasibility of the proposed method.},
}
@article {pmid20649048,
year = {2010},
author = {Lin, H and He, Q and Yan, Q and Feng, Z and Wu, B},
title = {[Development of practicality of EEG-based brain-computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {27},
number = {3},
pages = {702-706},
pmid = {20649048},
issn = {1001-5515},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Humans ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a system that can create direct connection between brain activity and external devices. In the past 20 years, important' achievements of research on BCI have been made. Now there are lots of research methods based on electroencephalic signals, and researchers are trying to make the BCI system possess the characteristics of real-time and become more natural and practical. This paper presents an overview of real-time and stimulating way to EEG-based BCI research. Through the discussions on the applications of DSP in BCI system, in signal preprocessing and in algorithm optimization, the high lights in real-time research are pointed out. In the discussions about the way to produce EEG signals in BCI, the researchers suggested that the imaging movement be the most ideal way in that it will reduce the discomfort in stimulation by application of the virtual reality technology in BCI system, thus it will be conducive to improvement in the performance of BCI system.},
}
@article {pmid20644245,
year = {2010},
author = {Herzfeld, DJ and Beardsley, SA},
title = {Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering.},
journal = {Journal of neural engineering},
volume = {7},
number = {4},
pages = {046012},
doi = {10.1088/1741-2560/7/4/046012},
pmid = {20644245},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Humans ; Linear Models ; Models, Neurological ; Motor Neurons/*physiology ; Nerve Net/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces.},
}
@article {pmid20639171,
year = {2010},
author = {Panicker, RC and Puthusserypady, S and Sun, Y},
title = {Adaptation in P300 brain-computer interfaces: a two-classifier cotraining approach.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {12},
pages = {2927-2935},
doi = {10.1109/TBME.2010.2058804},
pmid = {20639171},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Bayes Theorem ; Discriminant Analysis ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Man-Machine Systems ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based brain-computer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fisher's linear discriminant analysis and Bayesian linear discriminant analysis progressively teaching each other to build a final classifier, which is robust and able to learn effectively from unlabeled data. Detailed analysis of the performance is carried out through extensive cross-validations, and it is shown that the proposed approach is able to build high-performance classifiers from just a few minutes of labeled data and by making efficient use of unlabeled data. An average bit rate of more than 37 bits/min was achieved with just one and a half minutes of training, achieving an increase of about 17 bits/min compared to the fully supervised classification in one of the configurations. This performance improvement is shown to be even more significant in cases where the training data as well as the number of trials that are averaged for detection of a character is low, both of which are desired operational characteristics of a practical BCI system. Moreover, the proposed method outperforms the self-training-based approaches where the confident predictions of a classifier is used to retrain itself.},
}
@article {pmid20631853,
year = {2010},
author = {Schalk, G},
title = {Can Electrocorticography (ECoG) Support Robust and Powerful Brain-Computer Interfaces?.},
journal = {Frontiers in neuroengineering},
volume = {3},
number = {},
pages = {9},
pmid = {20631853},
issn = {1662-6443},
}
@article {pmid20627514,
year = {2010},
author = {Fernandes, MS and Dias, NS and Silva, AF and Nunes, JS and Lanceros-Méndez, S and Correia, JH and Mendes, PM},
title = {Hydrogel-based photonic sensor for a biopotential wearable recording system.},
journal = {Biosensors &amp; bioelectronics},
volume = {26},
number = {1},
pages = {80-86},
doi = {10.1016/j.bios.2010.05.013},
pmid = {20627514},
issn = {1873-4235},
mesh = {Biosensing Techniques/*instrumentation ; *Electrodes ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Head Protective Devices ; Humans ; Hydrogels/*chemistry ; Monitoring, Ambulatory/*instrumentation ; *Optical Devices ; Photometry/instrumentation ; },
abstract = {Wearable devices are used to record several physiological signals, providing unobtrusive and continuous monitoring. These systems are of particular interest for applications such as ambient-assisted living (AAL), which deals with the use of technologies, like brain-computer interface (BCI). The main challenge in these applications is to develop new wearable solutions for acquisition of electroenchephalogram (EEG) signals. Conventional solutions based on brain caps, are difficult and uncomfortable to wear. This work presents a new optical fiber biosensor based on electro-active gel - polyacrylamide (PAAM) hydrogel - with the ability to measure the required EEG signals and whose technology principle leads to contactless electrodes. Experiments were performed in order to evaluate the electro-active properties of the hydrogel and its frequency response, using an electric and optical setup. A sinusoidal electric field was applied to the hydrogel while the light passes through the sample. An optical detector was used to collect the resultant modulated light. The results have shown an adequate sensitivity in the range of μV, as well as a good frequency response, pointing the PAAM hydrogel sensor as an eligible sensing component for wearable biopotential recording applications.},
}
@article {pmid20621291,
year = {2010},
author = {Lima, CA and Coelho, AL and Eisencraft, M},
title = {Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study.},
journal = {Computers in biology and medicine},
volume = {40},
number = {8},
pages = {705-714},
doi = {10.1016/j.compbiomed.2010.06.005},
pmid = {20621291},
issn = {1879-0534},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/physiology/physiopathology ; Electroencephalography/*methods ; Epilepsy/physiopathology ; Humans ; Least-Squares Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset.},
}
@article {pmid20617188,
year = {2010},
author = {Jackson, N and Sridharan, A and Anand, S and Baker, M and Okandan, M and Muthuswamy, J},
title = {Long-Term Neural Recordings Using MEMS Based Movable Microelectrodes in the Brain.},
journal = {Frontiers in neuroengineering},
volume = {3},
number = {},
pages = {10},
pmid = {20617188},
issn = {1662-6443},
support = {F32 NS073422/NS/NINDS NIH HHS/United States ; R01 NS055312/NS/NINDS NIH HHS/United States ; },
abstract = {One of the critical requirements of the emerging class of neural prosthetic devices is to maintain good quality neural recordings over long time periods. We report here a novel MEMS (Micro Electro Mechanical Systems) based technology that can move microelectrodes in the event of deterioration in neural signal to sample a new set of neurons. Microscale electro-thermal actuators are used to controllably move microelectrodes post-implantation in steps of approximately 9 mum. In this study, a total of 12 movable microelectrode chips were individually implanted in adult rats. Two of the twelve movable microelectrode chips were not moved over a period of 3 weeks and were treated as control experiments. During the first 3 weeks of implantation, moving the microelectrodes led to an improvement in the average signal to noise ratio (SNR) from 14.61 +/- 5.21 dB before movement to 18.13 +/- 4.99 dB after movement across all microelectrodes and all days. However, the average root-mean-square values of noise amplitudes were similar at 2.98 +/- 1.22 muV and 3.01 +/- 1.16 muV before and after microelectrode movement. Beyond 3 weeks, the primary observed failure mode was biological rejection of the PMMA (dental cement) based skull mount resulting in the device loosening and eventually falling from the skull. Additionally, the average SNR for functioning devices beyond 3 weeks was 11.88 +/- 2.02 dB before microelectrode movement and was significantly different (p < 0.01) from the average SNR of 13.34 +/- 0.919 dB after movement. The results of this study demonstrate that MEMS based technologies can move microelectrodes in rodent brains in long-term experiments resulting in improvements in signal quality. Further improvements in packaging and surgical techniques will potentially enable movable microelectrodes to record cortical neuronal activity in chronic experiments.},
}
@article {pmid20615806,
year = {2010},
author = {Li, Y and Long, J and Yu, T and Yu, Z and Wang, C and Zhang, H and Guan, C},
title = {An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {10},
pages = {2495-2505},
doi = {10.1109/TBME.2010.2055564},
pmid = {20615806},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Electrodes ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Male ; *Man-Machine Systems ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Two-dimensional cursor control is an important and challenging issue in EEG-based brain-computer interfaces (BCIs). To address this issue, here we propose a new approach by combining two brain signals including Mu/Beta rhythm during motor imagery and P300 potential. In particular, a motor imagery detection mechanism and a P300 potential detection mechanism are devised and integrated such that the user is able to use the two signals to control, respectively, simultaneously, and independently, the horizontal and the vertical movements of the cursor in a specially designed graphic user interface. A real-time BCI system based on this approach is implemented and evaluated through an online experiment involving six subjects performing 2-D control tasks. The results attest to the efficacy of obtaining two independent control signals by the proposed approach. Furthermore, the results show that the system has merit compared with prior systems: it allows cursor movement between arbitrary positions.},
}
@article {pmid20613708,
year = {2010},
author = {Vogt, A and Codore, H and Day, BW and Hukriede, NA and Tsang, M},
title = {Development of automated imaging and analysis for zebrafish chemical screens.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {40},
pages = {},
pmid = {20613708},
issn = {1940-087X},
support = {P01 CA078039/CA/NCI NIH HHS/United States ; P01 CA78039/CA/NCI NIH HHS/United States ; R01 DK069403/DK/NIDDK NIH HHS/United States ; 1R01HL088016/HL/NHLBI NIH HHS/United States ; 1R01HD053287/HD/NICHD NIH HHS/United States ; R01 HL088016/HL/NHLBI NIH HHS/United States ; R01 HD053287/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Automation/methods ; Embryo, Nonmammalian/chemistry ; Female ; Green Fluorescent Proteins/analysis/biosynthesis/genetics ; Image Processing, Computer-Assisted/*methods ; MAP Kinase Signaling System ; Male ; Zebrafish/genetics/*metabolism ; },
abstract = {We demonstrate the application of image-based high-content screening (HCS) methodology to identify small molecules that can modulate the FGF/RAS/MAPK pathway in zebrafish embryos. The zebrafish embryo is an ideal system for in vivo high-content chemical screens. The 1-day old embryo is approximately 1mm in diameter and can be easily arrayed into 96-well plates, a standard format for high throughput screening. During the first day of development, embryos are transparent with most of the major organs present, thus enabling visualization of tissue formation during embryogenesis. The complete automation of zebrafish chemical screens is still a challenge, however, particularly in the development of automated image acquisition and analysis. We previously generated a transgenic reporter line that expresses green fluorescent protein (GFP) under the control of FGF activity and demonstrated their utility in chemical screens. To establish methodology for high throughput whole organism screens, we developed a system for automated imaging and analysis of zebrafish embryos at 24-48 hours post fertilization (hpf) in 96-well plates. In this video we highlight the procedures for arraying transgenic embryos into multiwell plates at 24 hpf and the addition of a small molecule (BCI) that hyperactivates FGF signaling. The plates are incubated for 6 hours followed by the addition of tricaine to anesthetize larvae prior to automated imaging on a Molecular Devices ImageXpress Ultra laser scanning confocal HCS reader. Images are processed by Definiens Developer software using a Cognition Network Technology algorithm that we developed to detect and quantify expression of GFP in the heads of transgenic embryos. In this example we highlight the ability of the algorithm to measure dose-dependent effects of BCI on GFP reporter gene expression in treated embryos.},
}
@article {pmid20600972,
year = {2011},
author = {LaConte, SM},
title = {Decoding fMRI brain states in real-time.},
journal = {NeuroImage},
volume = {56},
number = {2},
pages = {440-454},
doi = {10.1016/j.neuroimage.2010.06.052},
pmid = {20600972},
issn = {1095-9572},
support = {R21DA026086/DA/NIDA NIH HHS/United States ; },
mesh = {*Artificial Intelligence ; Brain/*physiology ; Brain Mapping/methods ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging ; Neurofeedback ; },
abstract = {This article reviews a technological advance that originates from two areas of ongoing neuroimaging innovation-(1) the use of multivariate supervised learning to decode brain states and (2) real-time functional magnetic resonance imaging (rtfMRI). The approach uses multivariate methods to train a model capable of decoding a subject's brain state from fMRI images. The decoded brain states can be used as a control signal for a brain computer interface (BCI) or to provide neurofeedback to the subject. The ability to adapt the stimulus during the fMRI experiment adds a new level of flexibility for task paradigms and has potential applications in a number of areas, including performance enhancement, rehabilitation, and therapy. Multivariate approaches to real-time fMRI are complementary to region-of-interest (ROI)-based methods and provide a principled method for dealing with distributed patterns of brain responses. Specifically, a multivariate approach is advantageous when network activity is expected, when mental strategies could vary from individual to individual, or when one or a few ROIs are not unequivocally the most appropriate for the investigation. Beyond highlighting important developments in rtfMRI and supervised learning, the article discusses important practical issues, including implementation considerations, existing resources, and future challenges and opportunities. Some possible future directions are described, calling for advances arising from increased experimental flexibility, improvements in predictive modeling, better comparisons across rtfMRI and other BCI implementations, and further investigation of the types of feedback and degree to which interface modulation is obtainable for various tasks.},
}
@article {pmid20595034,
year = {2010},
author = {Ruiz, Y and Pockett, S and Freeman, WJ and Gonzalez, E and Li, G},
title = {A method to study global spatial patterns related to sensory perception in scalp EEG.},
journal = {Journal of neuroscience methods},
volume = {191},
number = {1},
pages = {110-118},
doi = {10.1016/j.jneumeth.2010.05.021},
pmid = {20595034},
issn = {1872-678X},
mesh = {Acoustic Stimulation/classification/methods ; Adult ; Biological Clocks/physiology ; Brain Mapping/classification/*methods ; Cerebral Cortex/*physiology ; Cognition/classification/physiology ; Cortical Synchronization ; Discrimination Learning/classification/physiology ; Electroencephalography/*classification/*methods ; Evoked Potentials/physiology ; Humans ; Male ; Pattern Recognition, Automated ; Perception/*physiology ; Photic Stimulation/methods ; Sensation/*physiology ; *Signal Processing, Computer-Assisted ; Software/classification/standards ; Young Adult ; },
abstract = {Prior studies of multichannel ECoG from animals showed that beta and gamma oscillations carried perceptual information in both local and global spatial patterns of amplitude modulation, when the subjects were trained to discriminate conditioned stimuli (CS). Here the hypothesis was tested that similar patterns could be found in the scalp EEG human subjects trained to discriminate simultaneous visual-auditory CS. Signals were continuously recorded from 64 equispaced scalp electrodes and band-pass filtered. The Hilbert transform gave the analytic phase, which segmented the EEG into temporal frames, and the analytic amplitude, which expressed the pattern in each frame as a feature vector. Methods applied to the ECoG were adapted to the EEG for systematic search of the beta-gamma spectrum, the time period after CS onset, and the scalp surface to locate patterns that could be classified with respect to type of CS. Spatial patterns of EEG amplitude modulation were found from all subjects that could be classified with respect to stimulus combination type significantly above chance levels. The patterns were found in the beta range (15-22 Hz) but not in the gamma range. They occurred in three short bursts following CS onset. They were non-local, occupying the entire array. Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function, so that future studies in brain-computer interfacing can be better focused. Our methods may be most valuable for analyzing data from dense arrays with very high spatial and temporal sampling rates.},
}
@article {pmid20592100,
year = {2011},
author = {Koh, KH and Wong, HS and Go, KW and Morad, Z},
title = {Normalized bioimpedance indices are better predictors of outcome in peritoneal dialysis patients.},
journal = {Peritoneal dialysis international : journal of the International Society for Peritoneal Dialysis},
volume = {31},
number = {5},
pages = {574-582},
doi = {10.3747/pdi.2009.00140},
pmid = {20592100},
issn = {1718-4304},
mesh = {Adolescent ; Adult ; Aged ; Electric Capacitance ; *Electric Impedance ; Female ; Humans ; Kidney Failure, Chronic/therapy ; Logistic Models ; Male ; Middle Aged ; Nutritional Status ; *Peritoneal Dialysis/adverse effects/methods ; Prospective Studies ; Survival Analysis ; Treatment Outcome ; Young Adult ; },
abstract = {BACKGROUND: While phase angle of bioimpedance analysis (BIA) has great survival-predicting value in dialysis populations, it is known to be higher in male than in female subjects. In this study, we aimed to explore the factors influencing the predictive value of phase angle and to identify the appropriate physics terms for normalizing capacitance (C) and resistance (R).<br><br>METHODS: We formulated body capacitive index (BCI), C(BMI) (capacitance &#xd7; height(2)/weight), body resistive index (BRI), R(BMI) (resistance &#xd7; weight/height(2)), and CH(2) (capacitance &#xd7; height(2)). We also studied H(2)/R, R/H, and reactance of a capacitor/height (X(C) /H). There are 3 components in this study design: (1) establishment of normal values in a control Malaysian population, (2) comparison of these with a CAPD population, and (3) prediction of survival within a CAPD population. We initially performed a BIA study in 206 female and 116 male healthy volunteers, followed by a prospective study in a cohort of 128 CAPD patients [47 with diabetes mellitus (DM), 81 non-DM; 59 males, 69 females] for at least 2 years. All the parameters during enrolment, including BIA, serum albumin, peritoneal equilibrium test, age, and DM status, were analyzed. Outcome measurement was survival.<br><br>RESULTS: In healthy volunteers, both genders had the same BCI (2.0 nF kg/m(2)). On the contrary, female normal subjects had higher BRI than male normal subjects (median 15 642 vs 13242 &#x3a9; kg/m(2), p < 0.001) due to higher fat percentage (35.4% &#xb1; 0.4% vs 28.0% &#xb1; 0.6%, p < 0.001), resulting in a lower phase angle (mean 5.82 &#xb1; 0.04 vs 6.86 &#xb1; 0.07 degrees, p < 0.001). Logistic regression showed that BCI was the best risk indicator in 128 CAPD patients versus 322 normal subjects. In age- and body mass index (BMI)-matched head-to-head comparison, BCI had the highest &#x3c7;(2) value (&#x3c7;(2) = 102.63), followed by CH(2) (or H(2)/X(C); &#x3c7;(2) = 81.00), BRI (&#x3c7;(2) = 20.54), and X(C)/H (&#x3c7;(2) = 20.48), with p value < 0.001 for these parameters. In comparison, phase angle (&#x3c7;(2) = 11.42), R/H (&#x3c7;(2) = 7.19), and H(2)/R (&#x3c7;(2) = 5.69) had lower &#x3c7;(2) values. 35 (27.3%) patients died during the study period. Univariate analysis adjusted for DM status and serum albumin level demonstrated that non-surviving patients had significantly higher CH(2) (245 vs 169 nF m(2), p < 0.001) and BCI (4.0 vs 2.9 nF m(2)/kg, p = 0.005) than patients that survived. CH(2) was the best predictor for all-cause mortality in Cox regression analysis, followed by BCI, phase angle, and X(C)/H.<br><br>CONCLUSION: Measures that normalize, such as BCI and CH(2), have higher risk discrimination and survival prediction ability than measures that do not normalize, such as phase angle. Unlike phase angle, measurement of BCI overcomes the gender effect. In this study, the best risk indicator for CAPD patients versus the general population is BCI, reflecting deficit in nutritional concentration, while CH(2) reflects total nutritional deficit and thus is the major risk indicator for survival of CAPD patients.},
}
@article {pmid20589094,
year = {2010},
author = {Mussa-Ivaldi, FA and Alford, ST and Chiappalone, M and Fadiga, L and Karniel, A and Kositsky, M and Maggiolini, E and Panzeri, S and Sanguineti, V and Semprini, M and Vato, A},
title = {New Perspectives on the Dialogue between Brains and Machines.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {44},
pmid = {20589094},
issn = {1662-453X},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; R21 HD053608/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.},
}
@article {pmid20583947,
year = {2010},
author = {Sellers, EW and Vaughan, TM and Wolpaw, JR},
title = {A brain-computer interface for long-term independent home use.},
journal = {Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases},
volume = {11},
number = {5},
pages = {449-455},
doi = {10.3109/17482961003777470},
pmid = {20583947},
issn = {1471-180X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Amyotrophic Lateral Sclerosis/physiopathology ; Brain/*physiology ; *Communication Devices for People with Disabilities ; *Persons with Disabilities ; Electroencephalography/*instrumentation/methods ; Event-Related Potentials, P300 ; *Homebound Persons ; Humans ; Male ; Middle Aged ; *User-Computer Interface ; },
abstract = {Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.},
}
@article {pmid20582271,
year = {2010},
author = {Pfurtscheller, G and Allison, BZ and Brunner, C and Bauernfeind, G and Solis-Escalante, T and Scherer, R and Zander, TO and Mueller-Putz, G and Neuper, C and Birbaumer, N},
title = {The hybrid BCI.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {30},
pmid = {20582271},
issn = {1662-453X},
abstract = {Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a "brain switch". For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.},
}
@article {pmid20582261,
year = {2010},
author = {Brouwer, AM and van Erp, JB},
title = {A tactile P300 brain-computer interface.},
journal = {Frontiers in neuroscience},
volume = {4},
number = {},
pages = {19},
pmid = {20582261},
issn = {1662-453X},
abstract = {In this study, we investigated a Brain-Computer Interface (BCI) based on EEG responses to vibro-tactile stimuli around the waist. P300 BCIs based on tactile stimuli have the advantage of not taxing the visual or auditory system and of being potentially unnoticeable to other people. A tactile BCI could be especially suitable for patients whose vision or eye movements are impaired. In Experiment 1, we investigated its feasibility and the effect of the number of equally spaced tactors. Whereas a large number of tactors is expected to enhance the P300 amplitude since the target will be less frequent, it could also negatively affect the P300 since it will be difficult to identify the target when tactor density increases. Participants were asked to attend to the vibrations of a target tactor, embedded within a stream of distracters. The number of tactors was two, four or six. We demonstrated the feasibility of a tactile P300 BCI. We did not find a difference in SWLDA classification performance between the different numbers of tactors. In a second set of experiments we reduced the stimulus onset asynchrony (SOA) by shortening the on- and/or off-time of the tactors. The SOA for an optimum performance as measured in our experiments turned out to be close to conventional SOAs of visual P300 BCIs.},
}
@article {pmid20581801,
year = {2010},
author = {Lim, CG and Lee, TS and Guan, C and Sheng Fung, DS and Cheung, YB and Teng, SS and Zhang, H and Krishnan, KR},
title = {Effectiveness of a brain-computer interface based programme for the treatment of ADHD: a pilot study.},
journal = {Psychopharmacology bulletin},
volume = {43},
number = {1},
pages = {73-82},
pmid = {20581801},
issn = {0048-5764},
mesh = {*Attention ; Attention Deficit Disorder with Hyperactivity/diagnosis/physiopathology/psychology/*therapy ; Brain/*physiopathology ; Child ; Feasibility Studies ; Female ; Humans ; Male ; Pilot Projects ; Singapore ; Time Factors ; Treatment Outcome ; *User-Computer Interface ; *Video Games ; },
abstract = {Majority of children with attention deficit hyperactivity disorder (ADHD) have significant inattentive symptoms. We developed a progressive series of activities involving brain-computer interface-based games which could train users to improve their concentration. This pilot study investigated if the intervention could be utilized in children and if it could improve inattentive symptoms of ADHD. Ten medication-naive children aged 7 to 12 diagnosed with ADHD (combined or inattentive subtypes) received 20 sessions of therapy over a 10-week period. They were compared with age- and gendermatched controls. Both parent and teacher-rated inattentive score on the ADHD Rating Scale-IV improved more in the intervention group. A larger scale trial is warranted to further investigate the efficacy of our treatment programme in treating ADHD.},
}
@article {pmid20580646,
year = {2010},
author = {Lee, EC and Woo, JC and Kim, JH and Whang, M and Park, KR},
title = {A brain-computer interface method combined with eye tracking for 3D interaction.},
journal = {Journal of neuroscience methods},
volume = {190},
number = {2},
pages = {289-298},
doi = {10.1016/j.jneumeth.2010.05.008},
pmid = {20580646},
issn = {1872-678X},
mesh = {Algorithms ; Alpha Rhythm ; Arm/physiology ; Beta Rhythm ; Brain/*physiology ; Cortical Synchronization ; Electroencephalography/instrumentation/*methods ; Equipment Design ; *Eye Movement Measurements/instrumentation ; Eye Movements/physiology ; Feasibility Studies ; Humans ; Imagination/physiology ; Motor Activity/physiology ; Neuropsychological Tests ; Pupil ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {With the recent increase in the number of three-dimensional (3D) applications, the need for interfaces to these applications has increased. Although the eye tracking method has been widely used as an interaction interface for hand-disabled persons, this approach cannot be used for depth directional navigation. To solve this problem, we propose a new brain computer interface (BCI) method in which the BCI and eye tracking are combined to analyze depth navigation, including selection and two-dimensional (2D) gaze direction, respectively. The proposed method is novel in the following five ways compared to previous works. First, a device to measure both the gaze direction and an electroencephalogram (EEG) pattern is proposed with the sensors needed to measure the EEG attached to a head-mounted eye tracking device. Second, the reliability of the BCI interface is verified by demonstrating that there is no difference between the real and the imaginary movements for the same work in terms of the EEG power spectrum. Third, depth control for the 3D interaction interface is implemented by an imaginary arm reaching movement. Fourth, a selection method is implemented by an imaginary hand grabbing movement. Finally, for the independent operation of gazing and the BCI, a mode selection method is proposed that measures a user's concentration by analyzing the pupil accommodation speed, which is not affected by the operation of gazing and the BCI. According to experimental results, we confirmed the feasibility of the proposed 3D interaction method using eye tracking and a BCI.},
}
@article {pmid20577634,
year = {2010},
author = {Marin, C and Fernández, E},
title = {Biocompatibility of intracortical microelectrodes: current status and future prospects.},
journal = {Frontiers in neuroengineering},
volume = {3},
number = {},
pages = {8},
pmid = {20577634},
issn = {1662-6443},
abstract = {Rehabilitation of sensory and/or motor functions in patients with neurological diseases is more and more dealing with artificial electrical stimulation and recording from populations of neurons using biocompatible chronic implants. As more and more patients have benefited from these approaches, the interest in neural interfaces has grown significantly. However an important problem reported with all available microelectrodes to date is long-term viability and biocompatibility. Therefore it is essential to understand the signals that lead to neuroglial activation and create a targeted intervention to control the response, reduce the adverse nature of the reactions and maintain an ideal environment for the brain-electrode interface. We discuss some of the exciting opportunities and challenges that lie in this intersection of neuroscience research, bioengineering, neurology and biomaterials.},
}
@article {pmid20573887,
year = {2010},
author = {Legenstein, R and Chase, SM and Schwartz, AB and Maass, W},
title = {A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {25},
pages = {8400-8410},
pmid = {20573887},
issn = {1529-2401},
support = {R01-NS050256/NS/NINDS NIH HHS/United States ; R01 EB005847/EB/NIBIB NIH HHS/United States ; R01 NS050256-05/NS/NINDS NIH HHS/United States ; R01 EB005847-01/EB/NIBIB NIH HHS/United States ; R01 NS050256/NS/NINDS NIH HHS/United States ; EB005847/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Cerebral Cortex/physiology ; Computer Simulation ; Learning/*physiology ; Models, Neurological ; Motor Neurons/physiology ; Nerve Net/*physiology ; *Neural Networks, Computer ; Neuronal Plasticity/*physiology ; *Reward ; Synapses/physiology ; Synaptic Transmission ; },
abstract = {It has recently been shown in a brain-computer interface experiment that motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters. In particular, it was shown that the three-dimensional tuning curves of neurons whose decoding parameters were reassigned changed more than those of neurons whose decoding parameters had not been reassigned. In this article, we propose a simple learning rule that can reproduce this effect. Our learning rule uses Hebbian weight updates driven by a global reward signal and neuronal noise. In contrast to most previously proposed learning rules, this approach does not require extrinsic information to separate noise from signal. The learning rule is able to optimize the performance of a model system within biologically realistic periods of time under high noise levels. Furthermore, when the model parameters are matched to data recorded during the brain-computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments.},
}
@article {pmid20573542,
year = {2011},
author = {Lopez-Gordo, MA and Prieto, A and Pelayo, F and Morillas, C},
title = {Customized stimulation enhances performance of independent binary SSVEP-BCIs.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {122},
number = {1},
pages = {128-133},
doi = {10.1016/j.clinph.2010.05.021},
pmid = {20573542},
issn = {1872-8952},
mesh = {Adult ; Brain Mapping/*methods ; *Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Neuropsychological Tests/standards ; Photic Stimulation/*methods ; Signal Processing, Computer-Assisted ; Time Factors ; Visual Perception/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces based on steady-state visual evoked potentials (SSVEP-BCIs) achieve the highest performance, due to their multiclass nature, in paradigms in which different visual stimuli are shown. Studies of independent binary SSVEP-BCIs have been previously presented in which it was not necessary to gaze at the stimuli at the cost of performance. Despite that, the energy of the SSVEPs is largely affected by the temporal and spatial frequencies of the stimulus, there are no studies in the BCI literature about its combined impact on the final performance of SSVEP-BCIs. The objective of this study is to present an experiment that evaluates the best configuration of the visual stimulus for each subject, thus minimizing the decline in performance of independent binary SSVEP-BCIs.<br><br>METHODS: The participants attended and ignored a single structured stimulus configured with a combination of spatial and temporal frequencies at a time. They were instructed to gaze at a central point during the whole experiment. The best combination of spatial and temporal frequencies achieved for each subject, in terms of signal-to-noise ratio (SNR), was subsequently determined.<br><br>RESULTS: The SNR showed a significant dependency on the combination of frequencies, in such a way that only a reduced set of these combinations was applicable for obtaining an optimum SNR. The selection of an inappropriate stimulus configuration may cause a degradation of the information transmission rate (ITR) as it does the SNR.<br><br>CONCLUSIONS: The appropriate selection of the optimal spatial and temporal frequencies determines the performance of independent binary SSVEP-BCIs. This fact is critical to enhance its low performance; hence, they should be adjusted independently for each subject.<br><br>SIGNIFICANCE: Independent binary SSVEP-BCIs can be used in patients who are unable to control their gaze sufficiently. The correct selection of the spatial and temporal frequencies has a considerable benefit on their low ITR that must be taken into account. In order to find the most suitable frequencies, a test similar to the presented in this study should be performed beforehand for each SSVEP-BCI user. This regard is not documented in studies conducted in the BCI literature.},
}
@article {pmid20571185,
year = {2010},
author = {Zhou, ZX and Wan, BK and Ming, D and Qi, HZ},
title = {A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action.},
journal = {Journal of neural engineering},
volume = {7},
number = {4},
pages = {046008},
doi = {10.1088/1741-2560/7/4/046008},
pmid = {20571185},
issn = {1741-2552},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Extremities/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique combined with phase synchronization analysis to investigate the human brain synchrony of the supplementary motor area (SMA) and primary motor area (M1) during complex motor imagination of combined body and limb action. We separated the EEG data of the SMA and M1 into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the instantaneous phases of the characteristic IMFs were obtained by the Hilbert transformation, and the single-trial phase-locking value (PLV) features for brain synchrony measurement between the SMA and M1 were investigated separately. The classification performance suggests that the proposed approach is effective for phase synchronization analysis and is promising for the application of a brain-computer interface in motor nerve reconstruction of the lower limbs.},
}
@article {pmid20570777,
year = {2010},
author = {Chavarriaga, R and Millan, Jdel R},
title = {Learning from EEG error-related potentials in noninvasive brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {4},
pages = {381-388},
doi = {10.1109/TNSRE.2010.2053387},
pmid = {20570777},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/*statistics &amp; numerical data ; Electrooculography/statistics &amp; numerical data ; Evoked Potentials/*physiology ; Humans ; Learning/physiology ; *User-Computer Interface ; },
abstract = {We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of brain-computer interaction. In this approach, single trial detection of error-related electroencephalography (EEG) potentials is used to infer the optimal agent behavior by decreasing the probability of agent decisions that elicited such potentials. Contrasting with traditional approaches, the user acts as a critic of an external autonomous system instead of continuously generating control commands. This sets a cognitive monitoring loop where the human directly provides information about the overall system performance that, in turn, can be used for its improvement. We show that it is possible to recognize erroneous and correct agent decisions from EEG (average recognition rates of 75.8% and 63.2%, respectively), and that the elicited signals are stable over long periods of time (from 50 to > 600 days). Moreover, these performances allow to infer the optimal behavior of a simple agent in a brain-computer interaction paradigm after a few trials.},
}
@article {pmid20568942,
year = {2010},
author = {Kotchetkov, IS and Hwang, BY and Appelboom, G and Kellner, CP and Connolly, ES},
title = {Brain-computer interfaces: military, neurosurgical, and ethical perspective.},
journal = {Neurosurgical focus},
volume = {28},
number = {5},
pages = {E25},
doi = {10.3171/2010.2.FOCUS1027},
pmid = {20568942},
issn = {1092-0684},
mesh = {Adult ; Animals ; Brain/*physiology ; Communication Devices for People with Disabilities/trends ; Electrodes, Implanted ; Electroencephalography/instrumentation/methods ; Ethics, Professional ; Forecasting ; Humans ; Macaca mulatta ; Man-Machine Systems ; Military Medicine/instrumentation/*methods ; Neurosurgery/ethics/*instrumentation/methods ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are devices that acquire and transform neural signals into actions intended by the user. These devices have been a rapidly developing area of research over the past 2 decades, and the military has made significant contributions to these efforts. Presently, BCIs can provide humans with rudimentary control over computer systems and robotic devices. Continued advances in BCI technology are especially pertinent in the military setting, given the potential for therapeutic applications to restore function after combat injury, and for the evolving use of BCI devices in military operations and performance enhancement. Neurosurgeons will play a central role in the further development and implementation of BCIs, but they will also have to navigate important ethical questions in the translation of this highly promising technology. In the following commentary the authors discuss realistic expectations for BCI use in the military and underscore the intersection of the neurosurgeon's civic and clinical duty to care for those who serve their country.},
}
@article {pmid20567055,
year = {2011},
author = {Cecotti, H and Gräser, A},
title = {Convolutional neural networks for P300 detection with application to brain-computer interfaces.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {33},
number = {3},
pages = {433-445},
doi = {10.1109/TPAMI.2010.125},
pmid = {20567055},
issn = {1939-3539},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials ; Humans ; *Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted/*instrumentation ; Software ; *User-Computer Interface ; },
abstract = {A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.},
}
@article {pmid20561975,
year = {2010},
author = {Horvát, S and Derzsi, A and Néda, Z and Balog, A},
title = {A spatially explicit model for tropical tree diversity patterns.},
journal = {Journal of theoretical biology},
volume = {265},
number = {4},
pages = {517-523},
doi = {10.1016/j.jtbi.2010.05.032},
pmid = {20561975},
issn = {1095-8541},
mesh = {*Biodiversity ; Computer Simulation ; Geography ; *Models, Biological ; Monte Carlo Method ; Panama ; Species Specificity ; Time Factors ; Trees/*growth &amp; development ; *Tropical Climate ; },
abstract = {A simple two-parameter model resembling the classical voter model is introduced to describe macroecological properties of tropical tree communities. The parameters of the model characterize the speciation- and global-dispersion rates. Monte Carlo type computer simulations are performed on the model, investigating species abundances and the spatial distribution of individuals and species. Simulation results are critically compared with the experimental data obtained from a tree census on a 50 hectare area of the Barro Colorado Island (BCI), Panama. Fitting to only two observable quantities from the BCI data (total species number and the slope of the log-log species-area curve at the maximal area), it is possible to reproduce the full species-area curve, the relative species abundance distribution, and a more realistic spatial distribution of species.},
}
@article {pmid20542711,
year = {2010},
author = {Lynn, MT and Berger, CC and Riddle, TA and Morsella, E},
title = {Mind control? Creating illusory intentions through a phony brain-computer interface.},
journal = {Consciousness and cognition},
volume = {19},
number = {4},
pages = {1007-1012},
doi = {10.1016/j.concog.2010.05.007},
pmid = {20542711},
issn = {1090-2376},
mesh = {Awareness ; *Feedback, Psychological ; Humans ; *Illusions ; *Intention ; *Perceptual Distortion ; Proprioception ; *Psychomotor Performance ; *User-Computer Interface ; *Visual Perception ; *Volition ; },
abstract = {Can one be fooled into believing that one intended an action that one in fact did not intend? Past experimental paradigms have demonstrated that participants, when provided with false perceptual feedback about their actions, can be fooled into misperceiving the nature of their intended motor act. However, because veridical proprioceptive/perceptual feedback limits the extent to which participants can be fooled, few studies have been able to answer our question and induce the illusion to intend. In a novel paradigm addressing this question, participants were instructed to move a line on the computer screen by use of a phony brain-computer interface. Line movements were actually controlled by computer program. Demonstrating the illusion to intend, participants reported more intentions to move the line when it moved frequently than when it moved infrequently. Consistent with ideomotor theory, the finding illuminates the intimate liaisons among ideomotor processing, the sense of agency, and action production.},
}
@article {pmid20541462,
year = {2010},
author = {Gourab, K and Schmit, BD},
title = {Changes in movement-related β-band EEG signals in human spinal cord injury.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {12},
pages = {2017-2023},
doi = {10.1016/j.clinph.2010.05.012},
pmid = {20541462},
issn = {1872-8952},
mesh = {Acoustic Stimulation/methods ; Analysis of Variance ; *Beta Rhythm ; Brain Mapping ; Electromyography/methods ; Evoked Potentials/*physiology ; Humans ; Linear Models ; Movement/*physiology ; Spinal Cord Injuries/diagnosis/*physiopathology ; Time Factors ; },
abstract = {OBJECTIVE: The purpose of this study was to characterize differences in movement-related β-band signals of the brain between people with chronic spinal cord injury (SCI) and neurologically intact volunteers.<br><br>METHODS: A 64 channel EEG system was used to record EEG while subjects attempted brisk toe plantar flexion in response to auditory cues. Change in amplitude in &#x3b2;-band frequencies during times of event-related desynchronization and synchronization (ERD and ERS) and topography of ERD and ERS were compared across groups and correlated to ASIA motor and sensory impairment scores for SCI subjects.<br><br>RESULTS: ERS amplitude immediately following the movement attempt was significantly smaller (t-test; p<0.001) in SCI subjects as compared to controls. The ERD change tended to be greater and the topography was more widespread in SCI subjects. Incomplete SCI subjects with more severe neurological injury (lesser ASIA motor score) had lower peak ERS amplitude and a significant correlation was observed between sensorimotor impairments and ERS amplitude (r(2)=0.79; p=0.02).<br><br>CONCLUSIONS: Our results suggest that motor processing in the brain is altered after SCI, and that it occurs in proportion to the severity of neurological injury.<br><br>SIGNIFICANCE: These results are important for brain computer interface applications that rely on ERD and ERS pattern recognition.},
}
@article {pmid20525062,
year = {2010},
author = {Bahramisharif, A and van Gerven, M and Heskes, T and Jensen, O},
title = {Covert attention allows for continuous control of brain-computer interfaces.},
journal = {The European journal of neuroscience},
volume = {31},
number = {8},
pages = {1501-1508},
doi = {10.1111/j.1460-9568.2010.07174.x},
pmid = {20525062},
issn = {1460-9568},
mesh = {Algorithms ; Alpha Rhythm ; Attention/*physiology ; Brain/*physiology ; Cues ; Fixation, Ocular ; Humans ; Magnetoencephalography ; Motion Perception/physiology ; Neuropsychological Tests ; Photic Stimulation ; Regression Analysis ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {While brain-computer interfaces (BCIs) can be used for controlling external devices, they also hold the promise of providing a new tool for studying the working brain. In this study we investigated whether modulations of brain activity by changes in covert attention can be used as a continuous control signal for BCI. Covert attention is the act of mentally focusing on a peripheral sensory stimulus without changing gaze direction. The ongoing brain activity was recorded using magnetoencephalography in subjects as they covertly attended to a moving cue while maintaining fixation. Based on posterior alpha power alone, the direction to which subjects were attending could be recovered using circular regression. Results show that the angle of attention could be predicted with a mean absolute deviation of 51 degrees in our best subject. Averaged over subjects, the mean deviation was approximately 70 degrees. In terms of information transfer rate, the optimal data length used for recovering the direction of attention was found to be 1700 ms; this resulted in a mean absolute deviation of 60 degrees for the best subject. The results were obtained without any subject-specific feature selection and did not require prior subject training. Our findings demonstrate that modulations of posterior alpha activity due to the direction of covert attention has potential as a control signal for continuous control in a BCI setting. Our approach will have several applications, including a brain-controlled computer mouse and improved methods for neuro-feedback that allow direct training of subjects' ability to modulate posterior alpha activity.},
}
@article {pmid20525534,
year = {2010},
author = {Gibson, S and Judy, JW and Marković, D},
title = {Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {5},
pages = {469-478},
doi = {10.1109/TNSRE.2010.2051683},
pmid = {20525534},
issn = {1558-0210},
mesh = {Action Potentials/*physiology ; *Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Nerve Net/*physiology ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Applications such as brain-machine interfaces require hardware spike sorting in order to 1) obtain single-unit activity and 2) perform data reduction for wireless data transmission. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection, feature-extraction, and dimensionality-reduction algorithms for spike sorting are described and evaluated in terms of accuracy versus complexity. The nonlinear energy operator is chosen as the optimal spike-detection algorithm, being most robust over noise and relatively simple. Discrete derivatives is chosen as the optimal feature-extraction method, maintaining high accuracy across signal-to-noise ratios with a complexity orders of magnitude less than that of traditional methods such as principal-component analysis. We introduce the maximum-difference algorithm, which is shown to be the best dimensionality-reduction method for hardware spike sorting.},
}
@article {pmid20519741,
year = {2010},
author = {Broetz, D and Braun, C and Weber, C and Soekadar, SR and Caria, A and Birbaumer, N},
title = {Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report.},
journal = {Neurorehabilitation and neural repair},
volume = {24},
number = {7},
pages = {674-679},
doi = {10.1177/1545968310368683},
pmid = {20519741},
issn = {1552-6844},
mesh = {Aged ; Chronic Disease ; Computers/*trends ; Hemiplegia/physiopathology/*rehabilitation ; Humans ; Male ; Physical Therapy Modalities/instrumentation/*trends ; Robotics/instrumentation/*methods/trends ; Stroke/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {BACKGROUND: There is no accepted and efficient rehabilitation strategy to reduce focal impairments for patients with chronic stroke who lack residual movements.<br><br>METHODS: A 67-year-old hemiplegic patient with no active finger extension was trained with a brain-computer interface (BCI) combined with a specific daily life-oriented physiotherapy. The BCI used electrical brain activity (EEG) and magnetic brain activity (MEG) to drive an orthosis and a robot affixed to the patient's affected upper extremity, which enabled him to move the paralyzed arm and hand driven by voluntary modulation of micro-rhythm activity. In addition, the patient practiced goal-directed physiotherapy training. Over 1 year, he completed 3 training blocks. Arm motor function, gait capacities (using Fugl-Meyer Assessment, Wolf Motor Function Test, Modified Ashworth Scale, 10-m walk speed, and goal attainment score), and brain reorganization (functional MRI, MEG) were repeatedly assessed.<br><br>RESULTS: The ability of hand and arm movements as well as speed and safety of gait improved significantly (mean 46.6%). Improvement of motor function was associated with increased micro-oscillations in the ipsilesional motor cortex.<br><br>CONCLUSION: This proof-of-principle study suggests that the combination of BCI training with goal-directed, active physical therapy may improve the motor abilities of chronic stroke patients despite apparent initial paralysis.},
}
@article {pmid20517943,
year = {2010},
author = {Vansteensel, MJ and Hermes, D and Aarnoutse, EJ and Bleichner, MG and Schalk, G and van Rijen, PC and Leijten, FS and Ramsey, NF},
title = {Brain-computer interfacing based on cognitive control.},
journal = {Annals of neurology},
volume = {67},
number = {6},
pages = {809-816},
doi = {10.1002/ana.21985},
pmid = {20517943},
issn = {1531-8249},
mesh = {Cognition/*physiology ; *Computers ; Electrodes ; Electroencephalography/methods ; Epilepsy/*physiopathology/rehabilitation ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Neuropsychological Tests ; Oxygen/blood ; Prefrontal Cortex/blood supply/*physiopathology ; Psychomotor Performance/physiology ; Spectrum Analysis ; Time Factors ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network.<br><br>METHODS: Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC).<br><br>RESULTS: All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks.<br><br>INTERPRETATION: The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications.},
}
@article {pmid20511483,
year = {2010},
author = {Pistoia, F and Conson, M and Sarà, M},
title = {Opsoclonus-myoclonus syndrome in patients with locked-in syndrome: a therapeutic porthole with gabapentin.},
journal = {Mayo Clinic proceedings},
volume = {85},
number = {6},
pages = {527-531},
pmid = {20511483},
issn = {1942-5546},
mesh = {Aged ; Amines/administration &amp; dosage/*therapeutic use ; Cyclohexanecarboxylic Acids/administration &amp; dosage/*therapeutic use ; Eye Movements/drug effects ; Female ; Gabapentin ; Humans ; Male ; Middle Aged ; Nonverbal Communication ; Opsoclonus-Myoclonus Syndrome/*complications/*drug therapy ; Quadriplegia/*complications ; gamma-Aminobutyric Acid/administration &amp; dosage/*therapeutic use ; },
abstract = {Patients with locked-in syndrome, although fully conscious, have quadriplegia, mutism, and lower cranial nerve paralysis. The preservation of vertical gaze and upper eyelid movements usually enables them to interact with the environment through an eye-coded communication. However, locked-in syndrome may be complicated by the development of an opsoclonus-myoclonus syndrome that may represent an additional impediment to communication. We evaluated whether off-label treatment with gabapentin could help patients with locked-in syndrome and opsoclonus-myoclonus symptoms regain voluntary control of full eye movements. A mechanism responsible for gabapentin-induced improvement has been also hypothesized. In this study, 4 patients presenting with locked-in syndrome complicated by opsoclonus-myoclonus syndrome were continuously treated with gabapentin up to 1200 mg/d. The treatment resulted in a rapid and long-lasting resolution of opsoclonus-myoclonus symptoms without adverse effects. After 2 weeks, patients showed voluntary attempts to communicate through eye blinking and thereafter regained voluntary control of full eye movements. This event enabled them to regain a communication channel with relatives and physicians and to start using eye-controlled brain-computer interfaces. Because of its effectiveness in restoring eye movement control, gabapentin opened a communicative porthole in the patients' lives. Since opsoclonus may be related to disorders of the inhibitory control of saccadic burst neurons by pontine pause cells, we hypothesize that gabapentin acts as a regulator of saccadic circuits.},
}
@article {pmid20510641,
year = {2010},
author = {Hazrati, MKh and Erfanian, A},
title = {An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network.},
journal = {Medical engineering &amp; physics},
volume = {32},
number = {7},
pages = {730-739},
doi = {10.1016/j.medengphy.2010.04.016},
pmid = {20510641},
issn = {1873-4030},
mesh = {Adult ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Female ; Hand Strength/*physiology ; Humans ; Male ; *Neural Networks, Computer ; *Online Systems ; Probability ; Time Factors ; *User-Computer Interface ; Young Adult ; },
abstract = {This paper presents a new online single-trial EEG-based brain-computer interface (BCI) for controlling hand holding and sequence of hand grasping and opening in an interactive virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. One of the major challenges in the BCI research is the subject training. Currently, in most online BCI systems, the classifier was trained offline using the data obtained during the experiments without feedback, and used in the next sessions in which the subjects receive feedback. We investigated whether the subject could achieve satisfactory online performance without offline training while the subjects receive feedback from the beginning of the experiments during hand movement imagination. Another important issue in designing an online BCI system is the machine learning to classify the brain signal which is characterized by significant day-to-day and subject-to-subject variations and time-varying probability distributions. Due to these variabilities, we introduce the use of an adaptive probabilistic neural network (APNN) working in a time-varying environment for classification of EEG signals. The experimental evaluation on ten naïve subjects demonstrated that an average classification accuracy of 75.4% was obtained during the first experiment session (day) after about 3 min of online training without offline training, and 81.4% during the second session (day). The average rates during third and eighth sessions are 79.0% and 84.0%, respectively, using previously calculated classifier during the first sessions, without online training and without the need to calibrate. The results obtained from more than 5000 trials on ten subjects showed that the method could provide a robust performance over different experiment sessions and different subjects.},
}
@article {pmid20509913,
year = {2010},
author = {Treder, MS and Blankertz, B},
title = {(C)overt attention and visual speller design in an ERP-based brain-computer interface.},
journal = {Behavioral and brain functions : BBF},
volume = {6},
number = {},
pages = {28},
pmid = {20509913},
issn = {1744-9081},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; Electroencephalography/methods ; *Evoked Potentials ; Eye Movement Measurements ; Eye Movements ; Female ; Fixation, Ocular ; Humans ; Male ; Photic Stimulation ; Scalp ; Time Factors ; *User-Computer Interface ; Visual Perception/*physiology ; *Writing ; Young Adult ; },
abstract = {BACKGROUND: In a visual oddball paradigm, attention to an event usually modulates the event-related potential (ERP). An ERP-based brain-computer interface (BCI) exploits this neural mechanism for communication. Hitherto, it was unclear to what extent the accuracy of such a BCI requires eye movements (overt attention) or whether it is also feasible for targets in the visual periphery (covert attention). Also unclear was how the visual design of the BCI can be improved to meet peculiarities of peripheral vision such as low spatial acuity and crowding.<br><br>METHOD: Healthy participants (N = 13) performed a copy-spelling task wherein they had to count target intensifications. EEG and eye movements were recorded concurrently. First, (c)overt attention was investigated by way of a target fixation condition and a central fixation condition. In the latter, participants had to fixate a dot in the center of the screen and allocate their attention to a target in the visual periphery. Second, the effect of visual speller layout was investigated by comparing the symbol Matrix to an ERP-based Hex-o-Spell, a two-levels speller consisting of six discs arranged on an invisible hexagon.<br><br>RESULTS: We assessed counting errors, ERP amplitudes, and offline classification performance. There is an advantage (i.e., less errors, larger ERP amplitude modulation, better classification) of overt attention over covert attention, and there is also an advantage of the Hex-o-Spell over the Matrix. Using overt attention, P1, N1, P2, N2, and P3 components are enhanced by attention. Using covert attention, only N2 and P3 are enhanced for both spellers, and N1 and P2 are modulated when using the Hex-o-Spell but not when using the Matrix. Consequently, classifiers rely mainly on early evoked potentials in overt attention and on later cognitive components in covert attention.<br><br>CONCLUSIONS: Both overt and covert attention can be used to drive an ERP-based BCI, but performance is markedly lower for covert attention. The Hex-o-Spell outperforms the Matrix, especially when eye movements are not permitted, illustrating that performance can be increased if one accounts for peculiarities of peripheral vision.},
}
@article {pmid20509581,
year = {2010},
author = {Skoczeń, S and Gozdzik, J and Krasowska-Kwiecień, A and Wiecha, O and Czogała, W and Wedrychowicz, A and Zygadło, D},
title = {[Can brain-machine interface improve quality of life of patients with chronic motor dysfunction?].},
journal = {Przeglad lekarski},
volume = {67},
number = {1},
pages = {80-82},
pmid = {20509581},
issn = {0033-2240},
mesh = {Brain Damage, Chronic/*rehabilitation ; Equipment Design ; Humans ; *Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; *Quality of Life ; Stroke Rehabilitation ; *User-Computer Interface ; Wheelchairs ; },
abstract = {In departments of neurology, neurosurgery and hospice care there is a group of patients with compete motor function impairment having normal central nervous system function. Victims of spinal cord injury, cerebral palsy, cerebral stroke, loss of extremities, neuromuscular diseases, between others belong to them. Since two decades an intensive studies of use of brain waves to steer peripheral equipments has been performed. Brain Computer Interface and Brain-Machine Interface will allow in the near future for even partial restore of skills in permanently disabled patients. Recently new sets composed of games steered by brain waves have been introduced to the market. Exercises with such equipment will help to control an ability to concentrate and precise steer of the peripheral electronic equipments. The next phase will be use of the new skills to steer the wheelchairs and other computer programs with the brain signals to control own healthy organs or artificial machines.},
}
@article {pmid20470224,
year = {2010},
author = {Devlaminck, D and Waegeman, W and Wyns, B and Otte, G and Santens, P},
title = {On the role of cost-sensitive learning in multi-class brain-computer interfaces.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {55},
number = {3},
pages = {163-172},
doi = {10.1515/BMT.2010.015},
pmid = {20470224},
issn = {1862-278X},
mesh = {Adult ; *Algorithms ; Animals ; *Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.},
}
@article {pmid20464895,
year = {2010},
author = {Vincent, C and Bisson, J and Langlois, E and Cantin, JF},
title = {[Use of a brain-computer interface by a patient with a craniocerebral injury].},
journal = {Canadian journal of occupational therapy. Revue canadienne d'ergotherapie},
volume = {77},
number = {2},
pages = {101-112},
doi = {10.2182/cjot.2010.77.2.6},
pmid = {20464895},
issn = {0008-4174},
mesh = {Activities of Daily Living ; Adolescent ; Adult ; Brain Injuries/*rehabilitation ; Child ; *Cybernetics ; Follow-Up Studies ; Humans ; Interpersonal Relations ; Male ; *Occupational Therapy ; *Software ; Time Factors ; *User-Computer Interface ; Writing ; },
abstract = {BACKGROUND: There is limited knowledge concerning the effectiveness of computer access modes. This article presents a case report of a client with a serious traumatic brain injury who, four years after his accident, tried a brain-computer interface (Cyberlink software).<br><br>PURPOSE: To examine the client's computer performance (keyboard and mouse tasks) and the degree of realisation of three occupations (written communication, interpersonal relations and leisure).<br><br>METHODS: A training over 16 weeks (2 x per week) and a follow-up at 3 months were completed. The activation of the computer with Cyberlink was tested with lateral movements of the eyes, relaxation waves, waves of activation of the brain and facial musculature.<br><br>FINDINGS: After 8 weeks of training with Cyberlink, no improvements were noted in the use of the keyboard and the mouse. The trial of another interface (tactile screen) finally made the optimization of mouse functions possible.<br><br>IMPLICATIONS: The endurance and memory problems were circumvented with a long, repetitive and flexible training of the computer use.},
}
@article {pmid20464504,
year = {2010},
author = {Belov, DP and Eram, SY and Kolodyazhnyi, SF and Kanunikov, IE and Getmanenko, OV},
title = {Electrooculogram detection of eye movements on gaze displacement.},
journal = {Neuroscience and behavioral physiology},
volume = {40},
number = {5},
pages = {583-591},
pmid = {20464504},
issn = {1573-899X},
mesh = {Eye Movements/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Psychomotor Performance/physiology ; Saccades/physiology ; Sex Factors ; },
abstract = {Changes in potential are known to occur in the orbital area during saccades. The sign of these changes depends on the position of the electrode and the direction of eye rotation, while their amplitude depends on the rotation angle. The patterns of potentials can be used to resolve the reverse task, i.e., that of describing the gaze trajectory, such that the eye can be used to control a computer in an on-line regime. This requires a screen cursor to be placed at the calculated gaze fixation point, i.e., the point at which the observer is looking. Electrodes beneath the eyes were used to assess the vertical component of gaze displacement, while side electrodes at the corners of the orbit were used to assess the horizontal component. Sharp unipolar changes in potential occurring on saccades were apparent as steps which could be detected and measured. The signal was digitally filtered using a complex filter constructed by ourselves. The ratio of the amplitudes at the four points was then used to calculate the direction and angle of eye rotation. Characteristic changes in potential during spontaneous blinking were identified and ignored. Voluntary blinks of one eye were used to simulate mouse clicks. Subjects were initially told to make changes in gaze through specified angles in eight directions. This allowed calibration of standard saccades (in microV). Calibration curves were used to resolve the reverse task - changes in potential (in microV) were used to calculate the point on the screen (the pixel) to which the cursor was to be moved. Subjects were then trained to control the cursor using their eyes, and control of the computer was then completely handed over to the subject. The apparatus described here provides a brain-computer interface. Some interesting data on eye coordination were obtained during these studies: saccades were preceded by short negative electrooculogram (EOG) potentials lasting 10-15 msec. With age, the amplitude of saccade-related EOG potentials decreased. When gaze was shifted to the left, deviation of the eyes was more significant than when gaze was shifted to the right, while on shifting of gaze to the right, the lateral deviations of the eyes were similar. On diagonal right-down and left-up movements, right eye skew was greater than left eye skew, while on right-up and left-down movements, left eye skew was greater than right eye skew. Differences in eye coordination between genders were minor.},
}
@article {pmid20460690,
year = {2010},
author = {McFarland, DJ and Sarnacki, WA and Wolpaw, JR},
title = {Electroencephalographic (EEG) control of three-dimensional movement.},
journal = {Journal of neural engineering},
volume = {7},
number = {3},
pages = {036007},
pmid = {20460690},
issn = {1741-2552},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-07/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; *Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination ; Male ; Motion ; Spinal Cord Injuries/*physiopathology/rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.},
}
@article {pmid20460212,
year = {2010},
author = {Rebsamen, B and Guan, C and Zhang, H and Wang, C and Teo, C and Ang, MH and Burdet, E},
title = {A brain controlled wheelchair to navigate in familiar environments.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {6},
pages = {590-598},
doi = {10.1109/TNSRE.2010.2049862},
pmid = {20460212},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; Computer Graphics ; Data Interpretation, Statistical ; Electrodes ; Environment ; Equipment Design ; Event-Related Potentials, P300/physiology ; Humans ; Motion ; Psychomotor Performance/physiology ; Space Perception ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.},
}
@article {pmid20451626,
year = {2011},
author = {Grosse-Wentrup, M and Schölkopf, B and Hill, J},
title = {Causal influence of gamma oscillations on the sensorimotor rhythm.},
journal = {NeuroImage},
volume = {56},
number = {2},
pages = {837-842},
doi = {10.1016/j.neuroimage.2010.04.265},
pmid = {20451626},
issn = {1095-9572},
mesh = {Adult ; Cerebral Cortex/*physiology ; Electroencephalography ; Female ; Humans ; Imagination/*physiology ; Male ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain-computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.},
}
@article {pmid20449765,
year = {2010},
author = {Uşakli, AB},
title = {Modeling of movement-related potentials using a fractal approach.},
journal = {Journal of computational neuroscience},
volume = {28},
number = {3},
pages = {595-603},
pmid = {20449765},
issn = {1573-6873},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/therapy ; Cerebral Cortex/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; *Fractals ; Humans ; Movement/*physiology ; Nerve Net/physiology ; Psychomotor Performance/physiology ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {In bio-signal applications, classification performance depends greatly on feature extraction, which is also the case for electroencephalogram (EEG) based applications. Feature extraction, and consequently classification of EEG signals is not an easy task due to their inherent low signal-to-noise ratios and artifacts. EEG signals can be treated as the output of a non-linear dynamical (chaotic) system in the human brain and therefore they can be modeled by their dimension values. In this study, the variance fractal dimension technique is suggested for the modeling of movement-related potentials (MRPs). Experimental data sets consist of EEG signals recorded during the movements of right foot up, lip pursing and a simultaneous execution of these two tasks. The experimental results and performance tests show that the proposed modeling method can efficiently be applied to MRPs especially in the binary approached brain computer interface applications aiming to assist severely disabled people such as amyotrophic lateral sclerosis patients in communication and/or controlling devices.},
}
@article {pmid20449119,
year = {2009},
author = {Behr, A and Beckmann, T and Nachtrodt, H},
title = {Multiphase telomerisation of butadiene with phenol: optimisation and scale-up in different reactor types.},
journal = {Dalton transactions (Cambridge, England : 2003)},
volume = {},
number = {31},
pages = {6214-6219},
doi = {10.1039/b902588j},
pmid = {20449119},
issn = {1477-9234},
mesh = {Butadienes/*chemistry ; Ethers/*chemical synthesis/chemistry ; Molecular Structure ; Phenols/*chemistry ; },
abstract = {The telomerisation with phenol is an efficient way to convert the well accessible 1,3-butadiene into products of higher value. This article describes the optimisation of this reaction both on a laboratory scale using a novel multiphase semi-batch mode and in a loop reactor as an alternative concept for a continuous operation mode. The optimised parameters are applied in a miniplant offering an interesting salt-free route to octadienylphenols.},
}
@article {pmid20435514,
year = {2010},
author = {Dyson, M and Sepulveda, F and Gan, JQ},
title = {Localisation of cognitive tasks used in EEG-based BCIs.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {9},
pages = {1481-1493},
doi = {10.1016/j.clinph.2010.03.011},
pmid = {20435514},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Cognition/*physiology ; Discrimination, Psychological/physiology ; Electrodes ; Electroencephalography ; Functional Laterality/physiology ; Humans ; Imagination/physiology ; Male ; Mathematics ; Mental Recall/physiology ; Neuropsychological Tests ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: To provide candidate electrode sites and neurophysiological reference information for cognitive tasks used in brain-computer interfacing research.<br><br>METHODS: Six cognitive tasks were tested against the idle state. Data representing the idle state were collected with active cognitive task data during each recording session. Cross subject candidate electrode sites were obtained via a wrapper method based upon a sequential forward floating search algorithm. Source localisation results were obtained using sLORETA software.<br><br>RESULTS: Spatial feature distributions and localisation results are presented. Primary centres of activity for motor imagery tasks are localised to the pre- and postcentral gyrus. Auditory-based tasks show activity in the middle temporal gyrus. Calculation activity was localised to the left inferior frontal gyrus and right supramarginal gyrus. Navigation imagery produced activity in the precuneus and anterior cingulate cortex.<br><br>CONCLUSIONS: Spatial areas of activation suggest that arithmetic and auditory tasks show promise for pairwise discrimination based on single recording sites. sLORETA significance levels suggest that motor imagery tasks will show greatest discrimination from baseline EEG activity.<br><br>SIGNIFICANCE: This is the first study to provide candidate electrode sites for multiple tasks used in brain-computer interfacing.},
}
@article {pmid20427620,
year = {2010},
author = {Hai, A and Shappir, J and Spira, ME},
title = {Long-term, multisite, parallel, in-cell recording and stimulation by an array of extracellular microelectrodes.},
journal = {Journal of neurophysiology},
volume = {104},
number = {1},
pages = {559-568},
doi = {10.1152/jn.00265.2010},
pmid = {20427620},
issn = {1522-1598},
mesh = {Amino Acid Sequence ; Animals ; Aplysia ; Calibration ; Cell Membrane/physiology ; Cells, Cultured ; Computer Simulation ; Cytosol/physiology ; Electric Stimulation/*methods ; *Microelectrodes ; Molecular Sequence Data ; Nanotechnology ; Neuromuscular Junction/physiology ; Neurons/*physiology ; Surface Properties ; Synapses/physiology ; },
abstract = {Here we report on the development of a novel neuroelectronic interface consisting of an array of noninvasive gold-mushroom-shaped microelectrodes (gMmicroEs) that practically provide intracellular recordings and stimulation of many individual neurons, while the electrodes maintain an extracellular position. The development of this interface allows simultaneous, multisite, long-term recordings of action potentials and subthreshold potentials with matching quality and signal-to-noise ratio of conventional intracellular sharp glass microelectrodes or patch electrodes. We refer to the novel approach as "in-cell recording and stimulation by extracellular electrodes" to differentiate it from the classical intracellular recording and stimulation methods. This novel technique is expected to revolutionize the analysis of neuronal networks in relations to learning, information storage and can be used to develop novel drugs as well as high fidelity neural prosthetics and brain-machine systems.},
}
@article {pmid20422715,
year = {2010},
author = {Peters, BR and Wyss, J and Manrique, M},
title = {Worldwide trends in bilateral cochlear implantation.},
journal = {The Laryngoscope},
volume = {120 Suppl 2},
number = {},
pages = {S17-44},
doi = {10.1002/lary.20859},
pmid = {20422715},
issn = {1531-4995},
mesh = {Adolescent ; Adult ; Child ; Child, Preschool ; Cochlear Implantation/statistics &amp; numerical data/*trends ; Contraindications ; Data Collection ; Humans ; Motivation ; Prospective Studies ; Retrospective Studies ; },
abstract = {OBJECTIVES/HYPOTHESIS: The goal of this study is to ascertain worldwide experience with bilateral cochlear implantation (BCI) with regard to patient demographics, trends in provision of BCI to adult and child patient populations, differences and similarities in BCI candidacy criteria, diagnostic requirements, and treatment approaches among clinicians in high-volume cochlear implant centers.<br><br>STUDY DESIGN: Retrospective/prospective.<br><br>METHODS: : An electronic survey consisting of 59 mainly multiple-choice questions was developed for online completion. It examined the implant experience and clinical opinion of expert cochlear implant (CI) centers worldwide on the indications, motivations, and contraindications for adult and pediatric, simultaneous and sequential BCI candidacy. Centers were chosen to complete the survey based on their known reputation as a center of excellence. Patient demographics were queried for two time periods to elucidate trends: 2006 and prior, and for the year 2007.<br><br>RESULTS: Seventy-one percent (25/35) of the CI clinics approached completed the survey. Collectively, these 25 clinics represent experience with approximately 23,200 CI users globally, representing 15% of the total estimated CI population worldwide. The total number of BCI surgeries reflected in their experience (2,880) represents 36% of the estimated number worldwide as of December 2007. Cumulatively to the end of 2007, 70% of all BCI surgeries have occurred in children, with the 3- to 10-year-old age group having the highest representation (33% of all BCIs), followed in order by adults (30%), children under 3 years of age (26%), and children between 11 and 18 years of age (11%). Seventy-two percent of all BCI surgeries were performed sequentially (70% of children, 76% of adults). Children <3 years of age represent the only age group of all patients in which simultaneous surgeries predominate (58% simultaneous). For all other age groups, sequential surgeries far outnumber simultaneous (3-10 years, 84% sequential; 11-18 years, 94% sequential; adults, 76% sequential). Prior to January 2007, 68% of BCIs were performed in children. This increased to 79% for the year 2007 (P < .001). With regard to children only, a change is apparent over time in terms of the age group making up the majority of pediatric BCI surgeries performed. Prior to 2007, children 3 to 10 years of age made up 50% of the children undergoing BCI, whereas those <3 years made up only 33%. In 2007 this shifted more toward the younger age group (47% for those <3 years and 40% for 3-10-year-olds; P < .001). United States clinics had a higher proportion of adult BCI patients (59% children, 41% adults) than the non-United States clinics (78% children, 22% adults; P < .001). The majority of responders do not hold to a minimum or maximum age by which they limit BCI.<br><br>CONCLUSIONS: Worldwide experience with BCI is now quite extensive and provides a useful base for evaluating clinical outcomes across patient categories and for providing further support during the patient/parent counseling process.},
}
@article {pmid20415628,
year = {2010},
author = {Molina, GG and Mihajlovic, V},
title = {Spatial filters to detect steady-state visual evoked potentials elicited by high frequency stimulation: BCI application.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {55},
number = {3},
pages = {173-182},
doi = {10.1515/BMT.2010.013},
pmid = {20415628},
issn = {1862-278X},
mesh = {*Algorithms ; Animals ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Models, Neurological ; *User-Computer Interface ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) require minimal user training and can offer higher information throughput compared to other BCI modalities. We focused on SSVEPs elicited by high-frequency stimuli (>30 Hz) because they cause minimal fatigue/annoyance and reduce the risk of inducing photoepileptic seizures. This paper presents an approach that analyzes electroencephalographic activity to automatically obtain the optimum spatial filter for detecting the SSVEP at a given stimulation frequency from a short signal where the stimulation is presented at intermittent periods interspersed with breaks. A vector space generated by sinusoidal signals at the stimulation frequency and harmonics is defined. The spatial filter coefficients result from maximizing the ratio between the energy of the spatially filtered signal and that of its orthogonal component with regard to the vector space. The spatial filters are customized for each BCI user through a short calibration procedure taking into account individual specificity. Our experiments on six subjects applying the spatial filters resulted in an average transfer rate ranging from 20.9 to 22.7 bits/min.},
}
@article {pmid20411593,
year = {2010},
author = {Manyakov, NV and Van Hulle, MM},
title = {Decoding grating orientation from microelectrode array recordings in monkey cortical area V4.},
journal = {International journal of neural systems},
volume = {20},
number = {2},
pages = {95-108},
doi = {10.1142/S0129065710002280},
pmid = {20411593},
issn = {1793-6462},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Brain Mapping ; Discriminant Analysis ; Macaca mulatta ; *Microelectrodes ; Models, Neurological ; Orientation/*physiology ; Pattern Recognition, Automated/methods ; Photic Stimulation/methods ; Principal Component Analysis ; Pyramidal Cells/*physiology ; User-Computer Interface ; Visual Cortex/cytology/*physiology ; },
abstract = {We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating's spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the required information for the decoder. Two types of feature selection procedures are compared, filter and wrapper. The wrapper is combined with a linear discriminant analysis classifier, and the filter is followed by a radial-basis function support vector machine classifier. In addition, since we have a multiclass classification problen, different methods for combining pairwise classifiers are compared.},
}
@article {pmid20407639,
year = {2010},
author = {Chao, ZC and Nagasaka, Y and Fujii, N},
title = {Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys.},
journal = {Frontiers in neuroengineering},
volume = {3},
number = {},
pages = {3},
pmid = {20407639},
issn = {1662-6443},
abstract = {Brain-machine interfaces (BMIs) employ the electrical activity generated by cortical neurons directly for controlling external devices and have been conceived as a means for restoring human cognitive or sensory-motor functions. The dominant approach in BMI research has been to decode motor variables based on single-unit activity (SUA). Unfortunately, this approach suffers from poor long-term stability and daily recalibration is normally required to maintain reliable performance. A possible alternative is BMIs based on electrocorticograms (ECoGs), which measure population activity and may provide more durable and stable recording. However, the level of long-term stability that ECoG-based decoding can offer remains unclear. Here we propose a novel ECoG-based decoding paradigm and show that we have successfully decoded hand positions and arm joint angles during an asynchronous food-reaching task in monkeys when explicit cues prompting the onset of movement were not required. Performance using our ECoG-based decoder was comparable to existing SUA-based systems while evincing far superior stability and durability. In addition, the same decoder could be used for months without any drift in accuracy or recalibration. These results were achieved by incorporating the spatio-spectro-temporal integration of activity across multiple cortical areas to compensate for the lower fidelity of ECoG signals. These results show the feasibility of high-performance, chronic and versatile ECoG-based neuroprosthetic devices for real-life applications. This new method provides a stable platform for investigating cortical correlates for understanding motor control, sensory perception, and high-level cognitive processes.},
}
@article {pmid20405196,
year = {2010},
author = {Bianchi, L and Sami, S and Hillebrand, A and Fawcett, IP and Quitadamo, LR and Seri, S},
title = {Which physiological components are more suitable for visual ERP based brain-computer interface? A preliminary MEG/EEG study.},
journal = {Brain topography},
volume = {23},
number = {2},
pages = {180-185},
doi = {10.1007/s10548-010-0143-0},
pmid = {20405196},
issn = {1573-6792},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; Discriminant Analysis ; Electroencephalography ; Event-Related Potentials, P300 ; *Evoked Potentials ; Female ; Humans ; Linear Models ; Magnetoencephalography ; Male ; Middle Aged ; Models, Neurological ; Occipital Lobe/physiology ; Scalp/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; *Writing ; },
abstract = {We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain-computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (<400 ms) trial length traditionally used in this class of BCIs. To investigate which scalp regions conveyed information suitable for BCI, a stepwise linear discriminant analysis classifier was used. The method iteratively analyzed each individual sensor and determined its performance indicators. These were then plotted on a 2-D topographic head map. Preliminary results for both EEG and MEG data suggest that components other than the P300 maximally represented in the occipital region, could be successfully used to improve classification accuracy and finally drive this class of BCIs.},
}
@article {pmid20404396,
year = {2010},
author = {Liu, T and Goldberg, L and Gao, S and Hong, B},
title = {An online brain-computer interface using non-flashing visual evoked potentials.},
journal = {Journal of neural engineering},
volume = {7},
number = {3},
pages = {036003},
doi = {10.1088/1741-2560/7/3/036003},
pmid = {20404396},
issn = {1741-2552},
mesh = {Adult ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Lighting ; Male ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Not until recently have motion-onset visual evoked potentials (mVEPs) been explored as a modality for brain-computer interface (BCI) applications. In this study, the first online BCI system based on mVEPs is presented, in which selection is discerned by subjects' focused attention to the moving cursor at a target virtual button. An adaptive approach was used to adjust the number of trial presentations according to the participants' online performance. With the EEG signal acquired from only a single channel, an acceptable information transfer rate of 42.1 bits min(-1) was achieved, averaged by 12 subjects. Furthermore, an online application for the Google search system was developed based on this paradigm. The promising results, that all of 12 participants were able to operate the system freely, validate the feasibility of a practical motion-onset VEP-based BCI which could be embedded into computer screen elements, such as menu, button and icon, for various applications.},
}
@article {pmid20403781,
year = {2010},
author = {Wilson, JA and Mellinger, J and Schalk, G and Williams, J},
title = {A procedure for measuring latencies in brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {7},
pages = {1785-1797},
pmid = {20403781},
issn = {1558-2531},
support = {KL2 RR025012/RR/NCRR NIH HHS/United States ; R01 EB006356-01/EB/NIBIB NIH HHS/United States ; R01-EB006356/EB/NIBIB NIH HHS/United States ; UL1 RR025011/RR/NCRR NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; 1-T90-DK070079-01/DK/NIDDK NIH HHS/United States ; 1KL2RR025012-01/RR/NCRR NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Computer Systems ; Electroencephalography ; Evoked Potentials ; Humans ; Models, Neurological ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.},
}
@article {pmid20400953,
year = {2010},
author = {Kim, DH and Viventi, J and Amsden, JJ and Xiao, J and Vigeland, L and Kim, YS and Blanco, JA and Panilaitis, B and Frechette, ES and Contreras, D and Kaplan, DL and Omenetto, FG and Huang, Y and Hwang, KC and Zakin, MR and Litt, B and Rogers, JA},
title = {Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics.},
journal = {Nature materials},
volume = {9},
number = {6},
pages = {511-517},
pmid = {20400953},
issn = {1476-4660},
support = {R01 NS048598/NS/NINDS NIH HHS/United States ; R01-NS041811-04/NS/NINDS NIH HHS/United States ; T32 EY007035/EY/NEI NIH HHS/United States ; R01 EY020765/EY/NEI NIH HHS/United States ; R01 NS 48598-04/NS/NINDS NIH HHS/United States ; R01 NS041811/NS/NINDS NIH HHS/United States ; P41 EB002520/EB/NIBIB NIH HHS/United States ; R01 NS048598-04/NS/NINDS NIH HHS/United States ; R01 NS041811-09/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Capillary Action ; Cats ; Electrodes ; Electronics/instrumentation/*methods ; *Fibroins ; Microscopy, Confocal/methods ; Models, Animal ; Polymethyl Methacrylate ; Prostheses and Implants ; *Silk ; Solubility ; Stress, Mechanical ; Surgical Instruments ; },
abstract = {Electronics that are capable of intimate, non-invasive integration with the soft, curvilinear surfaces of biological tissues offer important opportunities for diagnosing and treating disease and for improving brain/machine interfaces. This article describes a material strategy for a type of bio-interfaced system that relies on ultrathin electronics supported by bioresorbable substrates of silk fibroin. Mounting such devices on tissue and then allowing the silk to dissolve and resorb initiates a spontaneous, conformal wrapping process driven by capillary forces at the biotic/abiotic interface. Specialized mesh designs and ultrathin forms for the electronics ensure minimal stresses on the tissue and highly conformal coverage, even for complex curvilinear surfaces, as confirmed by experimental and theoretical studies. In vivo, neural mapping experiments on feline animal models illustrate one mode of use for this class of technology. These concepts provide new capabilities for implantable and surgical devices.},
}
@article {pmid20395590,
year = {2010},
author = {Cerdá, M and Messner, SF and Tracy, M and Vlahov, D and Goldmann, E and Tardiff, KJ and Galea, S},
title = {Investigating the effect of social changes on age-specific gun-related homicide rates in New York City during the 1990s.},
journal = {American journal of public health},
volume = {100},
number = {6},
pages = {1107-1115},
pmid = {20395590},
issn = {1541-0048},
support = {R01 DA022720-02/DA/NIDA NIH HHS/United States ; R01 DA022720/DA/NIDA NIH HHS/United States ; R01 DA017642-05/DA/NIDA NIH HHS/United States ; DA 017642/DA/NIDA NIH HHS/United States ; R01 DA017642/DA/NIDA NIH HHS/United States ; R24 HD044943/HD/NICHD NIH HHS/United States ; R24 HD044943-08/HD/NICHD NIH HHS/United States ; DA 06354/DA/NIDA NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Age Factors ; Alcohol Drinking/epidemiology ; Bayes Theorem ; Cocaine-Related Disorders/epidemiology ; Crime/statistics &amp; numerical data ; Female ; Homicide/*statistics &amp; numerical data ; Humans ; Male ; Markov Chains ; Monte Carlo Method ; New York City/epidemiology ; Public Assistance/statistics &amp; numerical data ; *Social Change ; Wounds, Gunshot/*mortality ; Young Adult ; },
abstract = {OBJECTIVES: We assessed whether New York City's gun-related homicide rates in the 1990s were associated with a range of social determinants of homicide rates.<br><br>METHODS: We used cross-sectional time-series data for 74 New York City police precincts from 1990 through 1999, and we estimated Bayesian hierarchical models with a spatial error term. Homicide rates were estimated separately for victims aged 15-24 years (youths), 25-34 years (young adults), and 35 years or older (adults).<br><br>RESULTS: Decreased cocaine consumption was associated with declining homicide rates in youths (posterior median [PM] = 0.25; 95% Bayesian confidence interval [BCI] = 0.07, 0.45) and adults (PM = 0.07; 95% BCI = 0.02, 0.12), and declining alcohol consumption was associated with fewer homicides in young adults (PM = 0.14; 95% BCI = 0.02, 0.25). Receipt of public assistance was associated with fewer homicides for young adults (PM = -104.20; 95% BCI = -182.0, -26.14) and adults (PM = -28.76; 95% BCI = -52.65, -5.01). Misdemeanor policing was associated with fewer homicides in adults (PM = -0.01; 95% BCI = -0.02, -0.001).<br><br>CONCLUSIONS: Substance use prevention policies and expansion of the social safety net may be able to cause major reductions in homicide among age groups that drive city homicide trends.},
}
@article {pmid20388606,
year = {2010},
author = {Guo, J and Gao, S and Hong, B},
title = {An auditory brain-computer interface using active mental response.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {3},
pages = {230-235},
doi = {10.1109/TNSRE.2010.2047604},
pmid = {20388606},
issn = {1558-0210},
mesh = {Acoustic Stimulation ; Adult ; Artificial Intelligence ; Brain/*physiology ; Data Interpretation, Statistical ; Electrodes ; *Electroencephalography ; Event-Related Potentials, P300/physiology ; Evoked Potentials/physiology ; Functional Laterality/physiology ; Humans ; *User-Computer Interface ; Young Adult ; },
abstract = {This study proposes a novel auditory brain-computer interface paradigm, which allows the subject to mentally select a target among a random sequence of spoken digits. The subject's voluntary recognition of the property of the target digits enhances the discriminability between brain responses to target and nontarget digits. EEG data from 14 subjects has shown that the amplitude of N2 and the late positive component (LPC) elicited by target digits was significantly higher than that of nontarget ones. Three classification methods, i.e., N2/LPC area comparison, Fisher discriminant analysis and support vector machine (SVM), were adopted to assess the target detection accuracy using EEG data from a single electrode. For SVM classification, a mean accuracy of 85% was achieved with five trials averaged. This new paradigm could be useful for locked-in patients with vision impairment.},
}
@article {pmid20388587,
year = {2010},
author = {Osterhagen, L and Breteler, M and van Luijtelaar, G},
title = {Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?.},
journal = {Epilepsy research},
volume = {90},
number = {1-2},
pages = {75-82},
doi = {10.1016/j.eplepsyres.2010.03.010},
pmid = {20388587},
issn = {1872-6844},
mesh = {Analysis of Variance ; Animals ; *Arousal ; Biofeedback, Psychology/*methods ; Conditioning, Operant/*physiology ; Disease Models, Animal ; Electrodes, Implanted ; Epilepsy, Absence/genetics/*physiopathology/*rehabilitation ; Male ; Rats ; Rats, Inbred Strains ; *Reinforcement, Psychology ; },
abstract = {One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques.},
}
@article {pmid20385484,
year = {2010},
author = {Boyages, J and Jayasinghe, UW and Coombs, N},
title = {Multifocal breast cancer and survival: each focus does matter particularly for larger tumours.},
journal = {European journal of cancer (Oxford, England : 1990)},
volume = {46},
number = {11},
pages = {1990-1996},
doi = {10.1016/j.ejca.2010.03.003},
pmid = {20385484},
issn = {1879-0852},
mesh = {Adult ; Aged ; Breast Neoplasms/*mortality/*pathology ; Female ; Follow-Up Studies ; Humans ; Lymphatic Metastasis ; Middle Aged ; New South Wales/epidemiology ; Survival Analysis ; *Tumor Burden ; },
abstract = {PURPOSE: The objective of this study is to determine whether the aggregate tumour size of every focus in multifocal breast cancer more accurately predicts 10-year survival than current staging systems which use the largest or dominant tumour size.<br><br>PATIENTS AND METHODS: This study examined the original histological reports of 848 consecutive patients with invasive breast cancer treated in New South Wales (NSW), Australia between 1 April 1995 and 30 September 1995. Multifocal tumours were assessed using two estimates of pathologic tumour size: largest tumour focus diameter and the aggregate diameter of every tumour focus. The 10-year survival of patients with multifocal tumours measured in both ways was compared to that with unifocal tumours.<br><br>RESULTS: At a median follow-up of 10.4 years, 27 of 94 patients (28.7%) with multifocal breast cancer have died of breast cancer compared to 141 of 754 (18.7%) with unifocal breast cancer (P=.022). Ten-year survival was not affected by size for tumours measuring 20mm or less, whether or not dominant tumour size (87.9%) or aggregate tumour size (87.0%) was used for multifocal tumours, compared to unifocal tumours (88.1%). For tumours larger than 20mm, 10-year survival was 72.1% for unifocal tumours compared to 54.7% (P=.008) for multifocal tumours using dominant tumour size, but this was 69.5% and not significant when multifocal tumours were classified using aggregate tumour size (P=.49). Multivariate analysis also confirmed the above-mentioned results after adjustment for important prognostic factors.<br><br>CONCLUSION: Aggregate size of every focus should be considered along with other prognostic factors for metastasis when treatment is planned. The current convention of using the largest (dominant) lesion as a measure of stage and associated breast cancer survival needs further validation.},
}
@article {pmid20384819,
year = {2010},
author = {Ros, T and Munneke, MA and Ruge, D and Gruzelier, JH and Rothwell, JC},
title = {Endogenous control of waking brain rhythms induces neuroplasticity in humans.},
journal = {The European journal of neuroscience},
volume = {31},
number = {4},
pages = {770-778},
doi = {10.1111/j.1460-9568.2010.07100.x},
pmid = {20384819},
issn = {1460-9568},
support = {//Medical Research Council/United Kingdom ; },
mesh = {Adult ; Alpha Rhythm/*psychology ; Beta Rhythm/*psychology ; Feedback, Physiological/*physiology ; Feedback, Sensory ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Neuronal Plasticity/*physiology ; Random Allocation ; Transcranial Magnetic Stimulation ; *User-Computer Interface ; Wakefulness ; },
abstract = {This study explores the possibility of noninvasively inducing long-term changes in human corticomotor excitability by means of a brain-computer interface, which enables users to exert internal control over the cortical rhythms recorded from the scalp. We demonstrate that self-regulation of electroencephalogram rhythms in quietly sitting, naive humans significantly affects the subsequent corticomotor response to transcranial magnetic stimulation, producing durable and correlated changes in neurotransmission. Specifically, we show that the intrinsic suppression of alpha cortical rhythms can in itself produce robust increases in corticospinal excitability and decreases in intracortical inhibition of up to 150%, which last for at least 20 min. Our observations may have important implications for therapies of brain disorders associated with abnormal cortical rhythms, and support the use of electroencephalogram-based neurofeedback as a noninvasive tool for establishing a causal link between rhythmic cortical activities and their functions.},
}
@article {pmid20381546,
year = {2010},
author = {Pfurtscheller, G and Bauernfeind, G and Wriessnegger, SC and Neuper, C},
title = {Focal frontal (de)oxyhemoglobin responses during simple arithmetic.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {76},
number = {3},
pages = {186-192},
doi = {10.1016/j.ijpsycho.2010.03.013},
pmid = {20381546},
issn = {1872-7697},
mesh = {Adult ; Brain Mapping/*methods ; Female ; Functional Laterality/physiology ; Hemoglobins/metabolism ; Humans ; Male ; *Mathematical Concepts ; Mental Processes/physiology ; Oxyhemoglobins/*metabolism ; Prefrontal Cortex/*metabolism ; Problem Solving/*physiology ; Reference Values ; Spectroscopy, Near-Infrared ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) is a functional brain imaging method able to study hemodynamic changes during cortical activation. We studied the changes of oxy- and deoxyhemoglobin ([oxy-Hb], [deoxy-Hb]) with a 52-channel NIRS system during simple mental arithmetic in ten healthy volunteers over the prefrontal cortex. We found that eight of the ten subjects showed a relative focal bilateral increase of [oxy-Hb] in the dorsolateral prefrontal cortex (DLPFC) in parallel with a decrease in the medial area of the anterior prefrontal cortex (APFC). The [oxy-Hb] response in left DLPFC and APFC was significant, while the [deoxy-Hb] response was clearly smaller and not significant. These observations were discussed within the context of "focal activation/surround deactivation".},
}
@article {pmid20381529,
year = {2010},
author = {Hsu, WY},
title = {EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features.},
journal = {Journal of neuroscience methods},
volume = {189},
number = {2},
pages = {295-302},
doi = {10.1016/j.jneumeth.2010.03.030},
pmid = {20381529},
issn = {1872-678X},
mesh = {Algorithms ; Area Under Curve ; Brain/*physiology ; Databases as Topic ; Discriminant Analysis ; Electroencephalography/*methods ; *Fractals ; Functional Laterality ; *Fuzzy Logic ; Humans ; Imagination/physiology ; Linear Models ; Motor Activity/physiology ; Neural Networks, Computer ; ROC Curve ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {In this paper, a feature extraction method through the time-series prediction based on the adaptive neuro-fuzzy inference system (ANFIS) is proposed for brain-computer interface (BCI) applications. The ANFIS time-series prediction together with multiresolution fractal feature vectors (MFFVs) is applied for feature extraction in motor imagery (MI) classification. The features are extracted from the electroencephalography (EEG) signals recorded from subjects performing left and right MI. Two ANFISs are trained to perform time-series predictions for respective left and right MI data. Features obtained from the difference of MFFVs between the predicted and actual signals are then calculated through a window of EEG signals. Finally, a simple linear classifier, namely linear discriminant analysis (LDA), is used for classification. The proposed method is estimated with classification accuracy and the area under the receiver operating characteristics curve (AUC) on six subjects from two data sets. I also assess the performance of proposed method by comparing it with well-known linear adaptive autoregressive (AAR) model, AAR time-series prediction, and neural network (NN) time-series prediction. The results indicate that ANFIS time-series prediction together with MFFV features is a promising method in MI classification.},
}
@article {pmid20368976,
year = {2010},
author = {Schreuder, M and Blankertz, B and Tangermann, M},
title = {A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue.},
journal = {PloS one},
volume = {5},
number = {4},
pages = {e9813},
pmid = {20368976},
issn = {1932-6203},
mesh = {Acoustic Stimulation/*methods ; Adult ; Amyotrophic Lateral Sclerosis/physiopathology ; Brain/*physiology ; *Cues ; Humans ; Time Factors ; *User-Computer Interface ; },
abstract = {Most P300-based brain-computer interface (BCI) approaches use the visual modality for stimulation. For use with patients suffering from amyotrophic lateral sclerosis (ALS) this might not be the preferable choice because of sight deterioration. Moreover, using a modality different from the visual one minimizes interference with possible visual feedback. Therefore, a multi-class BCI paradigm is proposed that uses spatially distributed, auditory cues. Ten healthy subjects participated in an offline oddball task with the spatial location of the stimuli being a discriminating cue. Experiments were done in free field, with an individual speaker for each location. Different inter-stimulus intervals of 1000 ms, 300 ms and 175 ms were tested. With averaging over multiple repetitions, selection scores went over 90% for most conditions, i.e., in over 90% of the trials the correct location was selected. One subject reached a 100% correct score. Corresponding information transfer rates were high, up to an average score of 17.39 bits/minute for the 175 ms condition (best subject 25.20 bits/minute). When presenting the stimuli through a single speaker, thus effectively canceling the spatial properties of the cue, selection scores went down below 70% for most subjects. We conclude that the proposed spatial auditory paradigm is successful for healthy subjects and shows promising results that may lead to a fast BCI that solely relies on the auditory sense.},
}
@article {pmid20364340,
year = {2010},
author = {Wieser, M and Haefeli, J and Bütler, L and Jäncke, L and Riener, R and Koeneke, S},
title = {Temporal and spatial patterns of cortical activation during assisted lower limb movement.},
journal = {Experimental brain research},
volume = {203},
number = {1},
pages = {181-191},
pmid = {20364340},
issn = {1432-1106},
mesh = {Adult ; Alpha Rhythm ; Beta Rhythm ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Electromyography ; Female ; Humans ; Leg/*physiology ; Male ; Movement/*physiology ; Muscle, Skeletal/physiology ; Self-Help Devices ; Time Factors ; Walking/*physiology ; },
abstract = {Human gait is a complex process in the central nervous system that results from the integrity of various mechanisms, including different cortical and subcortical structures. In the present study, we investigated cortical activity during lower limb movement using EEG. Assisted by a dynamic tilt table, all subjects performed standardized stepping movements in an upright position. Source localization of the movement-related potential in relation to spontaneous EEG showed activity in brain regions classically associated with human gait such as the primary motor cortex, the premotor cortex, the supplementary motor cortex, the cingulate cortex, the primary somatosensory cortex and the somatosensory association cortex. Further, we observed a task-related power decrease in the alpha and beta frequency band at electrodes overlying the leg motor area. A temporal activation and deactivation of the involved brain regions as well as the chronological sequence of the movement-related potential could be mapped to specific phases of the gait-like leg movement. We showed that most cortical capacity is needed for changing the direction between the flexion and extension phase. An enhanced understanding of the human gait will provide a basis to improve applications in the field of neurorehabilitation and brain-computer interfaces.},
}
@article {pmid20349527,
year = {2010},
author = {Yu, Y and Zhang, SM and Zhang, HJ and Liu, XC and Zhang, QS and Zheng, XX and Dai, JH},
title = {Neural decoding based on probabilistic neural network.},
journal = {Journal of Zhejiang University. Science. B},
volume = {11},
number = {4},
pages = {298-306},
pmid = {20349527},
issn = {1862-1783},
mesh = {Algorithms ; Animals ; Electrodes, Implanted ; *Man-Machine Systems ; Models, Neurological ; Models, Statistical ; Motor Cortex ; Movement/physiology ; *Neural Networks, Computer ; Pressure ; Probability ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Water ; },
abstract = {Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper, two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced, the PNN decoder and the modified PNN (MPNN) decoder. In the experiment, rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity, and pressure was recorded by a pressure sensor synchronously. After training, the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their performances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder, with a CC of 0.8657 and an MSE of 0.2563, outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance, indicating that the MPNN decoder can handle different tasks in BMI system, including the detection of movement states and estimation of continuous kinematic parameters.},
}
@article {pmid20347612,
year = {2010},
author = {Bai, O and Lin, P and Huang, D and Fei, DY and Floeter, MK},
title = {Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {8},
pages = {1293-1303},
pmid = {20347612},
issn = {1872-8952},
support = {ZIA NS002976-11/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Amyotrophic Lateral Sclerosis ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Electromyography ; Female ; Humans ; Imagination/*physiology ; Male ; Middle Aged ; Motor Activity/physiology ; Movement/physiology ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load.<br><br>METHODS: Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit.<br><br>RESULTS: Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery.<br><br>CONCLUSION: Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior.<br><br>SIGNIFICANCE: The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients.},
}
@article {pmid20347387,
year = {2010},
author = {Townsend, G and LaPallo, BK and Boulay, CB and Krusienski, DJ and Frye, GE and Hauser, CK and Schwartz, NE and Vaughan, TM and Wolpaw, JR and Sellers, EW},
title = {A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {7},
pages = {1109-1120},
pmid = {20347387},
issn = {1872-8952},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-07/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Nervous System Diseases/physiopathology/rehabilitation ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; *User-Computer Interface ; },
abstract = {OBJECTIVE: An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).<br><br>METHODS: Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.<br><br>RESULTS: Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.<br><br>CONCLUSIONS: These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.<br><br>SIGNIFICANCE: The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.},
}
@article {pmid20347386,
year = {2010},
author = {Qian, K and Nikolov, P and Huang, D and Fei, DY and Chen, X and Bai, O},
title = {A motor imagery-based online interactive brain-controlled switch: paradigm development and preliminary test.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {8},
pages = {1304-1313},
doi = {10.1016/j.clinph.2010.03.001},
pmid = {20347386},
issn = {1872-8952},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Brain Mapping ; Electroencephalography ; Electromyography ; Female ; Functional Laterality ; Humans ; Imagination/*physiology ; Male ; Man-Machine Systems ; Motor Activity/physiology ; Movement/physiology ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {OBJECTIVE: To develop a practical motor imagery-based brain-controlled switch as functional as a real-world switch that is reliable with a minimal false positive operation rate and convenient for users without the need of attention to the switch during a 'No Control' state (when not to activate the switch).<br><br>METHODS: Four healthy volunteers were instructed to perform an intended motor imagery task following an external sync signal in order to turn on a virtual switch provided on a computer screen. No specific mental task was required during the 'No Control' state. The beta band event-related frequency power (event-related desynchronization or ERD) from a single EEG Laplacian channel was monitored online in real-time. The computer continuously monitored the relative ERD power level until it exceeded a pre-set threshold and turned on the virtual switch.<br><br>RESULTS: Subject 1 achieved lowest average false positive rate of 0.4+/-0.9% in a five-session online study during the entire 'No Control' state, whereby the subject required 6.8+/-0.6 s of active urging time or total response time of 20.5+/-1.9 s to perform repeated attempts in order to turn on the switch in the online interactive switch operation. The average false positive rate among four subjects was 0.8+/-0.4% with average active urging time of 12.3+/-4.4 s or average response time of 36.9+/-13.0 s. Offline analysis from subject 2 shows that the overall performance from 10-fold cross-validation was 96.2% with 3 consecutive epoch averaging, which was further improved to 99.0% by computationally intensive methods.<br><br>CONCLUSIONS: The novel design of the brain-controlled switch using the ERD feature associated with motor imagery achieved minimal false positive rate with a reasonable active urging time or response time to activate the switch.<br><br>SIGNIFICANCE: The reliability and convenience of the developed brain-controlled switch may extend current brain-computer interface capacities in practical communication and control applications.},
}
@article {pmid20336561,
year = {2010},
author = {Jeyabalan, V and Samraj, A and Loo, CK},
title = {Motor imaginary-based brain-machine interface design using programmable logic controllers for the disabled.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {13},
number = {5},
pages = {617-623},
doi = {10.1080/10255840903405678},
pmid = {20336561},
issn = {1476-8259},
mesh = {*Persons with Disabilities ; Electroencephalography ; Fuzzy Logic ; Humans ; *Man-Machine Systems ; User-Computer Interface ; },
abstract = {Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.},
}
@article {pmid20332551,
year = {2010},
author = {Luo, A and Sullivan, TJ},
title = {A user-friendly SSVEP-based brain-computer interface using a time-domain classifier.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26010},
doi = {10.1088/1741-2560/7/2/026010},
pmid = {20332551},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Computers ; Electrodes/economics ; Electroencephalography/economics/instrumentation/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {We introduce a user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. Single-channel EEG is recorded using a low-noise dry electrode. Compared to traditional gel-based multi-sensor EEG systems, a dry sensor proves to be more convenient, comfortable and cost effective. A hardware system was built that displays four LED light panels flashing at different frequencies and synchronizes with EEG acquisition. The visual stimuli have been carefully designed such that potential risk to photosensitive people is minimized. We describe a novel stimulus-locked inter-trace correlation (SLIC) method for SSVEP classification using EEG time-locked to stimulus onsets. We studied how the performance of the algorithm is affected by different selection of parameters. Using the SLIC method, the average light detection rate is 75.8% with very low error rates (an 8.4% false positive rate and a 1.3% misclassification rate). Compared to a traditional frequency-domain-based method, the SLIC method is more robust (resulting in less annoyance to the users) and is also suitable for irregular stimulus patterns.},
}
@article {pmid20332550,
year = {2010},
author = {Allison, BZ and Brunner, C and Kaiser, V and Müller-Putz, GR and Neuper, C and Pfurtscheller, G},
title = {Toward a hybrid brain-computer interface based on imagined movement and visual attention.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26007},
doi = {10.1088/1741-2560/7/2/026007},
pmid = {20332550},
issn = {1741-2552},
mesh = {Adolescent ; Adult ; Attention/*physiology ; Brain/*physiology ; Electroencephalography/methods ; Evoked Potentials, Visual ; Female ; Functional Laterality ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Neuropsychological Tests ; Periodicity ; Photic Stimulation ; Pilot Projects ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems do not work for all users. This article introduces a novel combination of tasks that could inspire BCI systems that are more accurate than conventional BCIs, especially for users who cannot attain accuracy adequate for effective communication. Subjects performed tasks typically used in two BCI approaches, namely event-related desynchronization (ERD) and steady state visual evoked potential (SSVEP), both individually and in a 'hybrid' condition that combines both tasks. Electroencephalographic (EEG) data were recorded across three conditions. Subjects imagined moving the left or right hand (ERD), focused on one of the two oscillating visual stimuli (SSVEP), and then simultaneously performed both tasks. Accuracy and subjective measures were assessed. Offline analyses suggested that half of the subjects did not produce brain patterns that could be accurately discriminated in response to at least one of the two tasks. If these subjects produced comparable EEG patterns when trying to use a BCI, these subjects would not be able to communicate effectively because the BCI would make too many errors. Results also showed that switching to a different task used in BCIs could improve accuracy in some of these users. Switching to a hybrid approach eliminated this problem completely, and subjects generally did not consider the hybrid condition more difficult. Results validate this hybrid approach and suggest that subjects who cannot use a BCI should consider switching to a different BCI approach, especially a hybrid BCI. Subjects proficient with both approaches might combine them to increase information throughput by improving accuracy, reducing selection time, and/or increasing the number of possible commands.},
}
@article {pmid20303409,
year = {2010},
author = {Blankertz, B and Sannelli, C and Halder, S and Hammer, EM and Kübler, A and Müller, KR and Curio, G and Dickhaus, T},
title = {Neurophysiological predictor of SMR-based BCI performance.},
journal = {NeuroImage},
volume = {51},
number = {4},
pages = {1303-1309},
doi = {10.1016/j.neuroimage.2010.03.022},
pmid = {20303409},
issn = {1095-9572},
mesh = {Adult ; Algorithms ; Artifacts ; Biofeedback, Psychology ; Calibration ; Computer Literacy ; Cues ; Data Interpretation, Statistical ; *Electroencephalography ; Female ; Functional Laterality/physiology ; Hand/innervation/physiology ; Humans ; Male ; Motor Cortex/*physiology ; Photic Stimulation ; Psychomotor Performance/physiology ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).},
}
@article {pmid20299704,
year = {2010},
author = {Bruzzone, L and Marconcini, M},
title = {Domain adaptation problems: a DASVM classification technique and a circular validation strategy.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {32},
number = {5},
pages = {770-787},
doi = {10.1109/TPAMI.2009.57},
pmid = {20299704},
issn = {1939-3539},
mesh = {*Algorithms ; *Artificial Intelligence ; Computer Simulation ; *Models, Theoretical ; Pattern Recognition, Automated/*methods ; },
abstract = {This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.},
}
@article {pmid20234874,
year = {2009},
author = {Schermer, M},
title = {The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction.},
journal = {Nanoethics},
volume = {3},
number = {3},
pages = {217-230},
pmid = {20234874},
issn = {1871-4757},
abstract = {Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our 'symbolic order'-on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility.},
}
@article {pmid20232063,
year = {2010},
author = {Blank, LM and Kuepfer, L},
title = {Metabolic flux distributions: genetic information, computational predictions, and experimental validation.},
journal = {Applied microbiology and biotechnology},
volume = {86},
number = {5},
pages = {1243-1255},
doi = {10.1007/s00253-010-2506-6},
pmid = {20232063},
issn = {1432-0614},
mesh = {Computational Biology/methods ; Genetic Code ; Genetic Engineering ; *Metabolic Networks and Pathways/genetics ; Models, Biological ; Saccharomyces cerevisiae/metabolism ; },
abstract = {Flux distributions in intracellular metabolic networks are of immense interest to fundamental and applied research, since they are quantitative descriptors of the phenotype and the operational mode of metabolism in the face of external growth conditions. In particular, fluxes are of relevance because they do not belong to the cellular inventory (e.g., transcriptome, proteome, metabolome), but are rather quantitative moieties, which link the phenotype of a cell to the specific metabolic mode of operation. A frequent application of measuring and redirecting intracellular fluxes is strain engineering, which ultimately aims at shifting metabolic activity toward a desired product to achieve a high yield and/or rate. In this article, we first review the assessment of intracellular flux distributions by either qualitative or rather quantitative computational methods and also discuss methods for experimental measurements. The tools at hand will then be exemplified on strain engineering projects from the literature. Finally, the achievements are discussed in the context of future developments in Metabolic Engineering and Synthetic Biology.},
}
@article {pmid20227914,
year = {2010},
author = {Nikulin, VV and Kegeles, J and Curio, G},
title = {Miniaturized electroencephalographic scalp electrode for optimal wearing comfort.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {7},
pages = {1007-1014},
doi = {10.1016/j.clinph.2010.02.008},
pmid = {20227914},
issn = {1872-8952},
mesh = {Electric Stimulation/methods ; Electroencephalography/*instrumentation/methods/*standards ; Equipment Design/*standards ; Evoked Potentials, Somatosensory/physiology ; Humans ; Microelectrodes/*standards ; Movement/physiology ; Rest/physiology ; *Scalp/physiology ; },
abstract = {OBJECTIVE: Current mainstream EEG electrode setups permit efficient recordings, but are often bulky and uncomfortable for subjects. Here we introduce a novel type of EEG electrode, which is designed for an optimal wearing comfort. The electrode is referred to as C-electrode where "C" stands for comfort.<br><br>METHODS: The C-electrode does not require any holder/cap for fixation on the head nor does it use traditional pads/lining of disposable electrodes - thus, it does not disturb subjects. Fixation of the C-electrode on the scalp is based entirely on the adhesive interaction between the very light C-electrode/wire construction (<35 mg) and a droplet of EEG paste/gel. Moreover, because of its miniaturization, both C-electrode (diameter 2-3mm) and a wire (diameter approximately 50 microm) are minimally (or not at all) visible to an external observer. EEG recordings with standard and C-electrodes were performed during rest condition, self-paced movements and median nerve stimulation.<br><br>RESULTS: The quality of EEG recordings for all three types of experimental conditions was similar for standard and C-electrodes, i.e., for near-DC recordings (Bereitschaftspotential), standard rest EEG spectra (1-45 Hz) and very fast oscillations approximately 600 Hz (somatosensory evoked potentials). The tests showed also that once being placed on a subject's head, C-electrodes can be used for 9h without any loss in EEG recording quality. Furthermore, we showed that C-electrodes can be effectively utilized for Brain-Computer Interfacing. C-electrodes proved to posses a high stability of mechanical fixation (stayed attached with 2.5 g accelerations). Subjects also reported not having any tactile sensations associated with wearing of C-electrodes.<br><br>CONCLUSION: C-electrodes provide optimal wearing comfort without any loss in the quality of EEG recordings.<br><br>SIGNIFICANCE: We anticipate that C-electrodes can be used in a wide range of clinical, research and emerging neuro-technological environments.},
}
@article {pmid20224799,
year = {2010},
author = {Zhu, D and Bieger, J and Garcia Molina, G and Aarts, RM},
title = {A survey of stimulation methods used in SSVEP-based BCIs.},
journal = {Computational intelligence and neuroscience},
volume = {2010},
number = {},
pages = {702357},
pmid = {20224799},
issn = {1687-5273},
mesh = {Brain/*physiology ; Computers/standards/*trends ; Electroencephalography/*methods ; Equipment Design/methods ; Evoked Potentials, Visual/*physiology ; Humans ; Mental Processes/physiology ; Photic Stimulation/*methods ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.},
}
@article {pmid20209672,
year = {2010},
author = {Millán, Jdel R and Carmena, JM},
title = {Invasive or noninvasive: understanding brain-machine interface technology.},
journal = {IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine &amp; Biology Society},
volume = {29},
number = {1},
pages = {16-22},
doi = {10.1109/memb.2009.935475},
pmid = {20209672},
issn = {1937-4186},
mesh = {Biotechnology/*instrumentation/methods ; Brain/*physiology ; Brain Mapping/*instrumentation/methods ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; *Prostheses and Implants ; Technology Assessment, Biomedical ; *User-Computer Interface ; },
}
@article {pmid20209151,
year = {2010},
author = {White, JR and Levy, T and Bishop, W and Beaty, JD},
title = {Real-time decision fusion for multimodal neural prosthetic devices.},
journal = {PloS one},
volume = {5},
number = {3},
pages = {e9493},
pmid = {20209151},
issn = {1932-6203},
mesh = {Algorithms ; Extremities ; Humans ; Man-Machine Systems ; Models, Neurological ; Models, Statistical ; Motor Cortex ; Movement ; Neural Networks, Computer ; Normal Distribution ; *Prostheses and Implants ; *Prosthesis Design ; Reproducibility of Results ; Software ; User-Computer Interface ; },
abstract = {BACKGROUND: The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device.<br><br>Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.<br><br>CONCLUSIONS: Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.},
}
@article {pmid20208465,
year = {2009},
author = {Daly, JJ and Cheng, R and Rogers, J and Litinas, K and Hrovat, K and Dohring, M},
title = {Feasibility of a new application of noninvasive Brain Computer Interface (BCI): a case study of training for recovery of volitional motor control after stroke.},
journal = {Journal of neurologic physical therapy : JNPT},
volume = {33},
number = {4},
pages = {203-211},
doi = {10.1097/NPT.0b013e3181c1fc0b},
pmid = {20208465},
issn = {1557-0584},
support = {R01 NS-063275/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiopathology ; Brain Mapping ; Disability Evaluation ; *Electric Stimulation Therapy ; Electroencephalography ; Female ; Hand/*physiopathology ; Humans ; Magnetic Resonance Imaging ; Mental Processes ; Stroke/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {BACKGROUND/PURPOSE: A large proportion of individuals with stroke have persistent deficits for which current interventions have not restored normal motor behavior. Noninvasive brain computer interfaces (BCIs) have potential advantages for restoration of function. There are also potential advantages for combining BCI with functional electrical stimulation (FES). We tested the feasibility of combined BCI + FES for motor learning after stroke.<br><br>CASE DESCRIPTION: The participant was a 43-year-old woman who was 10 months post-stroke. She was unable to produce isolated movement of any of the digits of her involved hand. With effort she exhibited simultaneous mass hyperextension of metacarpal phalangeal joints of all four fingers and thumb with simultaneous flexion of proximal interphalangeal and distal interphalangeal joints of all fingers.<br><br>INTERVENTION: Brain signals from the lesioned hemisphere were used to trigger FES for movement practice. The BCI + FES intervention consisted of trials of either attempted finger movement and relax conditions or imagined finger movement and relax conditions. The training was performed three times per week for three weeks (nine sessions total).<br><br>OUTCOME: : The participant exhibited highly accurate control of brain signal in the first session for attempted movement (97%), imagined movement (83%), and some difficulties with attempted relaxation (65%). By session 6, control of relaxation (deactivation of brain signal) improved to >80%. After nine sessions (three per week) of BCI + FES intervention, the participant demonstrated recovery of volitional isolated index finger extension.<br><br>DISCUSSION: BCI + FES training for motor learning after stroke was feasible. A highly accurate brain signal control was achieved, and this signal could be reliably used to trigger the FES device for isolated index finger extension. With training, volitional control of isolated finger extension was attained in a small number of sessions. The source of motor recovery could be attributable to BCI, FES, combined BCI + FES, or whole arm or hand motor task practice.},
}
@article {pmid20206804,
year = {2010},
author = {Li, W and D'Ayala, M and Hirshberg, A and Briggs, W and Wise, L and Tortolani, A},
title = {Comparison of conservative and operative treatment for blunt carotid injuries: analysis of the National Trauma Data Bank.},
journal = {Journal of vascular surgery},
volume = {51},
number = {3},
pages = {593-9, 599.e1-2},
doi = {10.1016/j.jvs.2009.10.108},
pmid = {20206804},
issn = {1097-6809},
mesh = {Adult ; Carotid Artery Injuries/diagnosis/mortality/surgery/*therapy ; Critical Care ; Databases as Topic ; Disability Evaluation ; Female ; Glasgow Coma Scale ; Hospital Mortality ; Humans ; Length of Stay ; Linear Models ; Logistic Models ; Male ; Middle Aged ; Patient Discharge ; Registries ; Respiration, Artificial ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Severity of Illness Index ; Time Factors ; Tomography, X-Ray Computed ; Treatment Outcome ; United States/epidemiology ; *Vascular Surgical Procedures/adverse effects/mortality ; Wounds, Nonpenetrating/diagnosis/mortality/surgery/*therapy ; Young Adult ; },
abstract = {OBJECTIVES: Blunt carotid injury (BCI) is uncommon but potentially devastating. The best treatment modality for this injury remains undetermined. We conducted this study to better understand the hospital course and treatment outcomes for patients with BCI who received different interventions.<br><br>METHODS: BCI and related vascular procedures were identified by ICD-9-CM codes from the National Trauma Data Bank(1) using data gathered from 2002 to 2006. Conservative and operative treatment groups were compared by variables of patient demographics, initial assessment in the emergency department (ED), hospital course, and treatment outcomes. Open surgical and endovascular interventions were further compared.<br><br>RESULTS: A total of 842 BCI were identified from 1,633,126 discharged blunt trauma patients (0.05%). Of these, 762 (90.5%) were treated conservatively and 80 (9.5%) received operative intervention. No differences in demographics were observed between these treatment groups. On initial assessment, no differences between conservative and operative treatment groups were noted with regard to vital signs, Glasgow coma scale, presence of drugs or alcohol in blood, or Trauma Related Injury Severity Score survival probability. Significant differences were seen in terms of the presence of a base deficit (-3.1 +/- 6.8 vs -7.6 +/- 8.3; P = .01), likelihood of a positive head computed tomography (CT) scan (58.6% vs 26.1%; P = .003), and total Injury Severity Score (29.8 +/- 13.3 vs 26.1 +/- 14.1; P = .02). Hospital course and treatment outcomes were comparable, with no differences in hospital length of stay (13.4 +/- 15.3 days vs 13.7 +/- 13.6 days; P = .86), total Functional Independence Measure (8.8 +/- 3.3 vs 9.3 +/- 3.1; P = .38), progression of original neurologic insult (7.5% vs 4.6%; P = .61) or mortality (28.1% vs 19%; P = .08). When comparing open surgical to endovascular interventions (46 open, 34 endovascular, including 3 combined), the only significant differences were in the total Injury Severity Score (22.4 +/- 12.2 vs 31.4 +/- 15.4; P = .01) and length of intensive care unit (ICU) and hospital stay (5.0 +/- 6.0 days vs 10.7 +/- 10.4 days; P = .01, and 10.3 +/- 9.2 days vs 19.3 +/- 17.7 days; P = .01). Multivariate regression analysis confirmed that neither Functional Independence Measure (FIM) nor mortality was associated with conservative or operative treatment.<br><br>CONCLUSION: BCI is rare and carries a poor prognosis. Operative intervention is not associated with functional improvement or a survival advantage. This study was unable to support that less invasive endovascular treatment improves treatment outcome when compared to open surgery.},
}
@article {pmid20205299,
year = {2009},
author = {Shishkin, SL and Ganin, IP and Basyul, IA and Zhigalov, AY and Kaplan, AY},
title = {N1 wave in the P300 BCI is not sensitive to the physical characteristics of stimuli.},
journal = {Journal of integrative neuroscience},
volume = {8},
number = {4},
pages = {471-485},
doi = {10.1142/s0219635209002320},
pmid = {20205299},
issn = {0219-6352},
mesh = {Algorithms ; Attention/physiology ; Biofeedback, Psychology/physiology ; Brain Mapping/methods ; Cognition/physiology ; Communication Devices for People with Disabilities ; Conditioning, Psychological/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Neuropsychological Tests ; Photic Stimulation/*methods ; Psychomotor Performance/physiology ; Psychophysiology/instrumentation/*methods ; Reaction Time/physiology ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; Teaching/methods ; Therapy, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {One of the widely used paradigms for the brain-computer interface (BCI), the P300 BCI, was proposed by Farwell and Donchin as a variation of the classical visual oddball paradigm, known to elicit the P300 component of the brain event-related potentials (ERP). We show that this paradigm, unlike the standard oddball paradigm, elicit not only the P300 wave but also a strong posterior N1 wave. Moreover, we present evidence that the sensitivity of this ERP component to targets cannot be explained by the variations of the perceived stimuli energy. This evidence is based on comparing the ERP obtained for usual P300 BCI stimuli and for the "inverted" stimulation scheme with low stimulus related variations of light energy (gray letters on the light gray background, "highlighted" by very light darkening). Despite the dramatic difference between the stimuli in the standard and "inverted" schemes, no difference between N1 amplitudes were found, supporting the view that this component's sensitivity to targets cannot be based simply on "foveating" the target, but may be related to spatial attention mechanisms, which involvement is natural for the P300 BCI. Efforts to optimize the P300 BCI should address better use of both P300 and N1 waves.},
}
@article {pmid20204836,
year = {2010},
author = {Nagaoka, T and Sakatani, K and Awano, T and Yokose, N and Hoshino, T and Murata, Y and Katayama, Y and Ishikawa, A and Eda, H},
title = {Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy.},
journal = {Advances in experimental medicine and biology},
volume = {662},
number = {},
pages = {497-503},
doi = {10.1007/978-1-4419-1241-1_72},
pmid = {20204836},
issn = {0065-2598},
mesh = {Adult ; Brain/*physiology ; Hand Strength/physiology ; Humans ; Male ; Motor Activity/physiology ; Rehabilitation/*methods ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; },
abstract = {We describe the set-up for an electrical muscle stimulation device based on near-infrared spectroscopy (NIRS), designed for use as a brain-computer interface (BCI). Employing multi-channel NIRS, we measured evoked cerebral blood oxygenation (CBO) responses during real motor tasks and motor-imagery tasks. When a supra-threshold increase in oxyhemoglobin concentration was detected, electrical stimulation (50 Hz) of the biceps brachii muscle was applied to the side contralateral to the hand grasping task or ipsilateral to the motor-imagery task. We observed relatively stable and reproducible CBO responses during real motor tasks with an average accuracy of 100%, and during motor imagery tasks with an average accuracy of 61.5%. Flexion movement of the arm was evoked in all volunteers in association with electrical muscle stimulation and no adverse effects were noted. These findings suggest that application of the electrical muscle stimulation system based on a NIRS-BCI is non-invasive and safe, and may be useful for the physical training of disabled patients.},
}
@article {pmid20204164,
year = {2010},
author = {Brumberg, JS and Nieto-Castanon, A and Kennedy, PR and Guenther, FH},
title = {Brain-Computer Interfaces for Speech Communication.},
journal = {Speech communication},
volume = {52},
number = {4},
pages = {367-379},
pmid = {20204164},
issn = {0167-6393},
support = {R01 DC002852-14/DC/NIDCD NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R44 DC007050/DC/NIDCD NIH HHS/United States ; R01 DC007683-01A1/DC/NIDCD NIH HHS/United States ; R44 DC007050-02/DC/NIDCD NIH HHS/United States ; R01 DC002852/DC/NIDCD NIH HHS/United States ; },
abstract = {This paper briefly reviews current silent speech methodologies for normal and disabled individuals. Current techniques utilizing electromyographic (EMG) recordings of vocal tract movements are useful for physically healthy individuals but fail for tetraplegic individuals who do not have accurate voluntary control over the speech articulators. Alternative methods utilizing EMG from other body parts (e.g., hand, arm, or facial muscles) or electroencephalography (EEG) can provide capable silent communication to severely paralyzed users, though current interfaces are extremely slow relative to normal conversation rates and require constant attention to a computer screen that provides visual feedback and/or cueing. We present a novel approach to the problem of silent speech via an intracortical microelectrode brain computer interface (BCI) to predict intended speech information directly from the activity of neurons involved in speech production. The predicted speech is synthesized and acoustically fed back to the user with a delay under 50 ms. We demonstrate that the Neurotrophic Electrode used in the BCI is capable of providing useful neural recordings for over 4 years, a necessary property for BCIs that need to remain viable over the lifespan of the user. Other design considerations include neural decoding techniques based on previous research involving BCIs for computer cursor or robotic arm control via prediction of intended movement kinematics from motor cortical signals in monkeys and humans. Initial results from a study of continuous speech production with instantaneous acoustic feedback show the BCI user was able to improve his control over an artificial speech synthesizer both within and across recording sessions. The success of this initial trial validates the potential of the intracortical microelectrode-based approach for providing a speech prosthesis that can allow much more rapid communication rates.},
}
@article {pmid20203202,
year = {2010},
author = {Bradberry, TJ and Gentili, RJ and Contreras-Vidal, JL},
title = {Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {30},
number = {9},
pages = {3432-3437},
pmid = {20203202},
issn = {1529-2401},
mesh = {Biomechanical Phenomena/physiology ; Brain Mapping ; Cerebral Cortex/anatomy &amp; histology/physiology ; Cues ; *Electroencephalography ; Evoked Potentials, Motor/*physiology ; Hand/innervation/*physiology ; Humans ; Magnetoencephalography ; Movement/*physiology ; Neuropsychological Tests ; Photic Stimulation ; Prostheses and Implants ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between measured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially. We subsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a finding to consider during the development of brain-computer interface systems. Overall, the ability to continuously decode 3D hand velocity from EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.},
}
@article {pmid20197598,
year = {2010},
author = {Slutzky, MW and Jordan, LR and Krieg, T and Chen, M and Mogul, DJ and Miller, LE},
title = {Optimal spacing of surface electrode arrays for brain-machine interface applications.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26004},
pmid = {20197598},
issn = {1741-2552},
support = {R01 NS046375/NS/NINDS NIH HHS/United States ; R01 NS048845-02/NS/NINDS NIH HHS/United States ; K08 NS060223/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS046375-03/NS/NINDS NIH HHS/United States ; K08 NS060223-01A1/NS/NINDS NIH HHS/United States ; R01NS046375/NS/NINDS NIH HHS/United States ; R01NS048845/NS/NINDS NIH HHS/United States ; K08NS060223/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Electrodes ; Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Finite Element Analysis ; Head/physiology ; Humans ; Male ; Models, Biological ; Rats ; Rats, Long-Evans ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) use signals recorded directly from the brain to control an external device, such as a computer cursor or a prosthetic limb. These control signals have been recorded from different levels of the brain, from field potentials at the scalp or cortical surface to single neuron action potentials. At present, the more invasive recordings have better signal quality, but also lower stability over time. Recently, subdural field potentials have been proposed as a stable, good quality source of control signals, with the potential for higher spatial and temporal bandwidth than EEG. Here we used finite element modeling in rats and humans and spatial spectral analysis in rats to compare the spatial resolution of signals recorded epidurally (outside the dura), with those recorded from subdural and scalp locations. Resolution of epidural and subdural signals was very similar in rats and somewhat less so in human models. Both were substantially better than signals recorded at the scalp. Resolution of epidural and subdural signals in humans was much more similar when the cerebrospinal fluid layer thickness was reduced. This suggests that the less invasive epidural recordings may yield signals of similar quality to subdural recordings, and hence may be more attractive as a source of control signals for BMIs.},
}
@article {pmid20192055,
year = {2010},
author = {Zhang, L and He, W and He, C and Wang, P},
title = {Improving mental task classification by adding high frequency band information.},
journal = {Journal of medical systems},
volume = {34},
number = {1},
pages = {51-60},
pmid = {20192055},
issn = {0148-5598},
mesh = {Artifacts ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; Mental Processes/*physiology ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Features extracted from delta, theta, alpha, beta and gamma bands spanning low frequency range are commonly used to classify scalp-recorded electroencephalogram (EEG) for designing brain-computer interface (BCI) and higher frequencies are often neglected as noise. In this paper, we implemented an experimental validation to demonstrate that high frequency components could provide helpful information for improving the performance of the mental task based BCI. Electromyography (EMG) and electrooculography (EOG) artifacts were removed by using blind source separation (BSS) techniques. Frequency band powers and asymmetry ratios from the high frequency band (40-100 Hz) together with those from the lower frequency bands were used to represent EEG features. Finally, Fisher discriminant analysis (FDA) combining with Mahalanobis distance were used as the classifier. In this study, four types of classifications were performed using EEG signals recorded from four subjects during five mental tasks. We obtained significantly higher classification accuracy by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.},
}
@article {pmid20192030,
year = {2010},
author = {Ushiba, J},
title = {[Brain-machine interface--current status and future prospects].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {62},
number = {2},
pages = {101-111},
pmid = {20192030},
issn = {1881-6096},
mesh = {Brain/*physiology ; Electroencephalography/instrumentation ; Humans ; Nervous System Diseases/*rehabilitation ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Recent advances in brain activity analysis and computational algorithms have enabled people with severe motor disorders to control external devices via brain activity. Brain-machine interface (BMI)/brain-computer interface has gained importance as the ultimate strategy for functional compensation because it improves impaired neuromuscular systems. Invasive BMI performed using needle arrays can best control robotic arms or computer cursors because it records neural activity in the primary motor cortex in detail. The extensive and validated physiological background of recorded signals enables researchers to develop highly accurate BMI systems with needle electrodes. Less invasive neural recording with an electrocorticogram (ECoG)-electrode array provides good temporal and spatial information for use in prosthetic control. ECoG electrodes have wide clinical applications in pain control and epilepsy; therefore, techniques for electrode implantation are well established compared to those for needle arrays. These electrodes may find wide clinical applications if their accuracy level reaches that suitable for practical use. Noninvasive BMI involving neural recording by electroencephalography (EEG) is the most widely used technique because of a convenient experimental setup, although it provides a limited range of decodable motor outputs. In EEG, arc-shaped mu rhythms of 8-12 Hz appear around the sensorimotor area in the resting state and diminish in amplitude during motor imagery. Thus, the mu rhythm amplitude may correlate with cortical excitability of the sensorimotor area, and EEG-BMI may be useful in the neurorehabilitation of patients with stroke-induced hemiplegia. Research on BMI as a therapeutic tool though emergent, may widen the scope of conventional BMI.},
}
@article {pmid20188627,
year = {2010},
author = {Kleih, SC and Nijboer, F and Halder, S and Kübler, A},
title = {Motivation modulates the P300 amplitude during brain-computer interface use.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {7},
pages = {1023-1031},
doi = {10.1016/j.clinph.2010.01.034},
pmid = {20188627},
issn = {1872-8952},
mesh = {Adult ; Brain/*physiology ; Event-Related Potentials, P300/*physiology ; Humans ; Motivation/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Reaction Time/physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This study examined the effect of motivation as a possible psychological influencing variable on P300 amplitude and performance in a brain-computer interface (BCI) controlled by event-related potentials (ERP).<br><br>METHODS: Participants were instructed to copy spell a sentence by attending to cells of a randomly flashing 7*7 matrix. Motivation was manipulated by monetary reward. In two experimental groups participants received 25 (N=11) or 50 (N=11) Euro cent for each correctly selected character; the control group (N=11) was not rewarded. BCI performance was defined as the overall percentage of correctly selected characters (correct response rate=CRR).<br><br>RESULTS: Participants performed at an average of 99%. At electrode location Cz the P300 amplitude was positively correlated to self-rated motivation. The P300 amplitude of the most motivated participants was significantly higher than that of the least motivated participants. Highly motivated participants were able to communicate correctly faster with the ERP-BCI than less motivated participants.<br><br>CONCLUSIONS: Motivation modulates the P300 amplitude in an ERP-BCI.<br><br>SIGNIFICANCE: Motivation may contribute to variance in BCI performance and should be monitored in BCI settings.},
}
@article {pmid20184908,
year = {2010},
author = {Anand, M and Langille, A},
title = {A model-based method for estimating effective dispersal distance in tropical plant populations.},
journal = {Theoretical population biology},
volume = {77},
number = {4},
pages = {219-226},
doi = {10.1016/j.tpb.2010.02.004},
pmid = {20184908},
issn = {1096-0325},
mesh = {*Geography ; Models, Theoretical ; Panama ; *Plant Development ; Population Dynamics ; Trees ; *Tropical Climate ; },
abstract = {Dispersal is a key mechanism to help populations propagate across space and thus is important in helping to understand spatial patterns. However, it is often difficult to quantify empirically as it requires intensive and detailed field study. Here we describe a method for estimating the effective dispersal distance of tropical plant populations. The method integrates a simple spatially explicit, individual-based dynamic model and spatial statistical analysis. The model is partly parameterized from spatial point pattern data as well as time series data from a 50 ha tropical forest plot in Barro Colorado Island (BCI) in Panama. Correlation between our estimated dispersal distances and those from inverse modeling based on field studies to date on BCI raises some questions about the match between our methods and those previously used. The method we propose can be generalized to any population for which spatial point pattern data are available. Additional field studies would be useful to further validate our method.},
}
@article {pmid20177780,
year = {2010},
author = {Lee, PL and Sie, JJ and Liu, YJ and Wu, CH and Lee, MH and Shu, CH and Li, PH and Sun, CW and Shyu, KK},
title = {An SSVEP-actuated brain computer interface using phase-tagged flickering sequences: a cursor system.},
journal = {Annals of biomedical engineering},
volume = {38},
number = {7},
pages = {2383-2397},
doi = {10.1007/s10439-010-9964-y},
pmid = {20177780},
issn = {1573-9686},
mesh = {Adult ; Artifacts ; Base Sequence ; Brain/*physiology ; *Computer Systems ; Computers ; Electrodes ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Eye Movements ; Female ; Flicker Fusion ; Humans ; Male ; },
abstract = {This study presents a new steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). SSVEPs, induced by phase-tagged flashes in eight light emitting diodes (LEDs), were used to control four cursor movements (up, right, down, and left) and four button functions (on, off, right-, and left-clicks) on a screen menu. EEG signals were measured by one EEG electrode placed at Oz position, referring to the international EEG 10-20 system. Since SSVEPs are time-locked and phase-locked to the onsets of SSVEP flashes, EEG signals were bandpass-filtered and segmented into epochs, and then averaged across a number of epochs to sharpen the recorded SSVEPs. Phase lags between the measured SSVEPs and a reference SSVEP were measured, and targets were recognized based on these phase lags. The current design used eight LEDs to flicker at 31.25 Hz with 45 degrees phase margin between any two adjacent SSVEP flickers. The SSVEP responses were filtered within 29.25-33.25 Hz and then averaged over 60 epochs. Owing to the utilization of high-frequency flickers, the induced SSVEPs were away from low-frequency noises, 60 Hz electricity noise, and eye movement artifacts. As a consequence, we achieved a simple architecture that did not require eye movement monitoring or other artifact detection and removal. The high-frequency design also achieved a flicker fusion effect for better visualization. Seven subjects were recruited in this study to sequentially input a command sequence, consisting of a sequence of eight cursor functions, repeated three times. The accuracy and information transfer rate (mean +/- SD) over the seven subjects were 93.14 +/- 5.73% and 28.29 +/- 12.19 bits/min, respectively. The proposed system can provide a reliable channel for severely disabled patients to communicate with external environments.},
}
@article {pmid20176528,
year = {2010},
author = {Gollee, H and Volosyak, I and McLachlan, AJ and Hunt, KJ and Gräser, A},
title = {An SSVEP-based brain-computer interface for the control of functional electrical stimulation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {8},
pages = {1847-1855},
doi = {10.1109/TBME.2010.2043432},
pmid = {20176528},
issn = {1558-2531},
mesh = {Abdominal Muscles ; Adult ; Algorithms ; Brain/physiology ; *Communication Devices for People with Disabilities ; Electric Stimulation/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Man-Machine Systems ; Prostheses and Implants ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEPs) is combined with a functional electrical stimulation (FES) system to allow the user to control stimulation settings and parameters. The system requires four flickering lights of distinct frequencies that are used to form a menu-based interface, enabling the user to interact with the FES system. The approach was evaluated in 12 neurologically intact subjects to change the parameters and operating mode of an abdominal stimulation system for respiratory assistance. No major influence of the FES on the raw EEG signal could be observed. In tests with a self-paced task, a mean accuracy of more than 90% was achieved, with detection times of approximately 7.7 s and an average information transfer rate of 12.5 bits/min. There was no significant dependency of the accuracy or time of detection on the FES stimulation intensity. The results indicate that the system could be used to control FES-based neuroprostheses with a high degree of accuracy and robustness.},
}
@article {pmid20172795,
year = {2010},
author = {Li, Y and Kambara, H and Koike, Y and Sugiyama, M},
title = {Application of covariate shift adaptation techniques in brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {6},
pages = {1318-1324},
doi = {10.1109/TBME.2009.2039997},
pmid = {20172795},
issn = {1558-2531},
mesh = {*Algorithms ; Brain Mapping/*methods ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; *User-Computer Interface ; },
abstract = {A phenomenon often found in session-to-session transfers of brain-computer interfaces (BCIs) is nonstationarity. It can be caused by fatigue and changing attention level of the user, differing electrode placements, varying impedances, among other reasons. Covariate shift adaptation is an effective method that can adapt to the testing sessions without the need for labeling the testing session data. The method was applied on a BCI Competition III dataset. Results showed that covariate shift adaptation compares favorably with methods used in the BCI competition in coping with nonstationarities. Specifically, bagging combined with covariate shift helped to increase stability, when applied to the competition dataset. An online experiment also proved the effectiveness of bagged-covariate shift method. Thus, it can be summarized that covariate shift adaptation is helpful to realize adaptive BCI systems.},
}
@article {pmid20172781,
year = {2010},
author = {Chow, EY and Chlebowski, AL and Chakraborty, S and Chappell, WJ and Irazoqui, PP},
title = {Fully wireless implantable cardiovascular pressure monitor integrated with a medical stent.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {6},
pages = {1487-1496},
doi = {10.1109/TBME.2010.2041058},
pmid = {20172781},
issn = {1558-2531},
mesh = {Blood Pressure Determination/*instrumentation ; *Blood Vessel Prosthesis ; Equipment Design ; Equipment Failure Analysis ; *Prostheses and Implants ; Reproducibility of Results ; Sensitivity and Specificity ; *Stents ; Telemetry/*instrumentation ; },
abstract = {This paper presents a fully wireless cardiac pressure sensing system. Food and Drug Administration (FDA) approved medical stents are explored as radiating structures to support simultaneous transcutaneous wireless telemetry and powering. An application-specific integrated circuit (ASIC), designed and fabricated using the Texas Instruments 130-nm CMOS process, enables wireless telemetry, remote powering, voltage regulation, and processing of pressure measurements from a microelectromechanical systems (MEMS) capacitive sensor. This paper demonstrates fully wireless-pressure-sensing functionality with an external 35-dB.m RF powering source across a distance of 10 cm. Measurements in a regulated pressure chamber demonstrate the ability of the cardiac system to achieve pressure resolutions of 0.5 mmHg over a range of 0-50 mmHg using a channel data-rate of 42.2 kb/s.},
}
@article {pmid20169142,
year = {2010},
author = {Dal Seno, B and Matteucci, M and Mainardi, L},
title = {Online detection of P300 and error potentials in a BCI speller.},
journal = {Computational intelligence and neuroscience},
volume = {2010},
number = {},
pages = {307254},
pmid = {20169142},
issn = {1687-5273},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Humans ; Mental Processes/physiology ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; *Writing ; },
abstract = {Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances.},
}
@article {pmid20168003,
year = {2010},
author = {Martens, SM and Leiva, JM},
title = {A generative model approach for decoding in the visual event-related potential-based brain-computer interface speller.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26003},
doi = {10.1088/1741-2560/7/2/026003},
pmid = {20168003},
issn = {1741-2552},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; *Evoked Potentials, Visual ; Humans ; *Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; *Writing ; },
abstract = {There is a strong tendency towards discriminative approaches in brain-computer interface (BCI) research. We argue that generative model-based approaches are worth pursuing and propose a simple generative model for the visual ERP-based BCI speller which incorporates prior knowledge about the brain signals. We show that the proposed generative method needs less training data to reach a given letter prediction performance than the state of the art discriminative approaches.},
}
@article {pmid20168002,
year = {2010},
author = {Yuan, H and Perdoni, C and He, B},
title = {Relationship between speed and EEG activity during imagined and executed hand movements.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26001},
pmid = {20168002},
issn = {1741-2552},
support = {NIH RO1EB006433/EB/NIBIB NIH HHS/United States ; R01 EB006433/EB/NIBIB NIH HHS/United States ; R01 EB007920-04/EB/NIBIB NIH HHS/United States ; R01 EB006433-03/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; T90 DK070106/DK/NIDDK NIH HHS/United States ; R01 EB006433-02/EB/NIBIB NIH HHS/United States ; R01EB007920/EB/NIBIB NIH HHS/United States ; R90 DK070106/DK/NIDDK NIH HHS/United States ; },
mesh = {Adult ; Alpha Rhythm ; Beta Rhythm ; Biomechanical Phenomena ; Brain/*physiology ; Electroencephalography ; Electromyography ; Female ; Functional Laterality ; Hand/*physiology ; Humans ; Imagination/*physiology ; Linear Models ; Male ; Motor Activity/*physiology ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {The relationship between primary motor cortex and movement kinematics has been shown in nonhuman primate studies of hand reaching or drawing tasks. Studies have demonstrated that the neural activities accompanying or immediately preceding the movement encode the direction, speed and other information. Here we investigated the relationship between the kinematics of imagined and actual hand movement, i.e. the clenching speed, and the EEG activity in ten human subjects. Study participants were asked to perform and imagine clenching of the left hand and right hand at various speeds. The EEG activity in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were found to be linearly correlated with the speed of imagery clenching. Similar parametric modulation was also found during the execution of hand movements. A single equation relating the EEG activity to the speed and the hand (left versus right) was developed. This equation, which contained a linear independent combination of the two parameters, described the time-varying neural activity during the tasks. Based on the model, a regression approach was developed to decode the two parameters from the multiple-channel EEG signals. We demonstrated the continuous decoding of dynamic hand and speed information of the imagined clenching. In particular, the time-varying clenching speed was reconstructed in a bell-shaped profile. Our findings suggest an application to providing continuous and complex control of noninvasive brain-computer interface for movement-impaired paralytics.},
}
@article {pmid20168001,
year = {2010},
author = {Power, SD and Falk, TH and Chau, T},
title = {Classification of prefrontal activity due to mental arithmetic and music imagery using hidden Markov models and frequency domain near-infrared spectroscopy.},
journal = {Journal of neural engineering},
volume = {7},
number = {2},
pages = {26002},
doi = {10.1088/1741-2560/7/2/026002},
pmid = {20168001},
issn = {1741-2552},
mesh = {Adult ; Cognition/*physiology ; Feasibility Studies ; Female ; Humans ; Imagination/physiology ; Likelihood Functions ; Male ; *Markov Chains ; Mathematical Concepts ; Music ; Neuropsychological Tests ; Prefrontal Cortex/*physiology ; *Signal Processing, Computer-Assisted ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; },
abstract = {Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% +/- 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.},
}
@article {pmid20167469,
year = {2010},
author = {Schmid, A and Kortmann, H and Dittrich, PS and Blank, LM},
title = {Chemical and biological single cell analysis.},
journal = {Current opinion in biotechnology},
volume = {21},
number = {1},
pages = {12-20},
doi = {10.1016/j.copbio.2010.01.007},
pmid = {20167469},
issn = {1879-0429},
mesh = {Animals ; Biopolymers/*metabolism ; *Bioreactors ; Cell Culture Techniques/*methods ; *Cell Physiological Phenomena ; Flow Injection Analysis/*methods ; Humans ; Microfluidic Analytical Techniques/*methods ; },
abstract = {Single cells represent the minimal functional unit of life. A major goal of biology is to understand the mechanisms operating in this minimal unit. Nowadays, analysis of the single cell can be performed at unprecedented resolution using new lab-on-a-chip devices and advanced analytical methods. While cell handling and cultivation devices can be classified into finite volume reactors and flow systems, the analytical approaches differ in respect to invasive (i.e. chemical) and noninvasive (i.e. biological/living cell) analysis. Using these new and exciting technologies cell-to-cell differences, originating from regulatory circuits and distinct microenvironments, can now be explored. For example, it could be shown that the rates of transcription and translation are stochastic. Chemical and biological single cell analyses provide an unprecedented access to the understanding of cell-to-cell differences and basic biological concepts.},
}
@article {pmid20162347,
year = {2010},
author = {Sannelli, C and Dickhaus, T and Halder, S and Hammer, EM and Müller, KR and Blankertz, B},
title = {On optimal channel configurations for SMR-based brain-computer interfaces.},
journal = {Brain topography},
volume = {23},
number = {2},
pages = {186-193},
doi = {10.1007/s10548-010-0135-0},
pmid = {20162347},
issn = {1573-6792},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electroencephalography/*instrumentation/*methods ; Feedback ; Female ; Humans ; Imagination/physiology ; Male ; Motor Activity/physiology ; Motor Cortex/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {One crucial question in the design of electroencephalogram (EEG)-based brain-computer interface (BCI) experiments is the selection of EEG channels. While a setup with few channels is more convenient and requires less preparation time, a dense placement of electrodes provides more detailed information and henceforth could lead to a better classification performance. Here, we investigate this question for a specific setting: a BCI that uses the popular CSP algorithm in order to classify voluntary modulations of sensorimotor rhythms (SMR). In a first approach 13 different fixed channel configurations are compared to the full one consisting of 119 channels. The configuration with 48 channels results to be the best one, while configurations with less channels, from 32 to 8, performed not significantly worse than the best configuration in cases where only few training trials are available. In a second approach an optimal channel configuration is obtained by an iterative procedure in the spirit of stepwise variable selection with nonparametric multiple comparisons. As a surprising result, in the second approach a setting with 22 channels centered over the motor areas was selected. Thanks to the acquisition of a large data set recorded from 80 novice participants using 119 EEG channels, the results of this study can be expected to have a high degree of generalizability.},
}
@article {pmid20161808,
year = {2009},
author = {Sato, H and Berry, CW and Peeri, Y and Baghoomian, E and Casey, BE and Lavella, G and Vandenbrooks, JM and Harrison, JF and Maharbiz, MM},
title = {Remote radio control of insect flight.},
journal = {Frontiers in integrative neuroscience},
volume = {3},
number = {},
pages = {24},
pmid = {20161808},
issn = {1662-5145},
abstract = {We demonstrated the remote control of insects in free flight via an implantable radio-equipped miniature neural stimulating system. The pronotum mounted system consisted of neural stimulators, muscular stimulators, a radio transceiver-equipped microcontroller and a microbattery. Flight initiation, cessation and elevation control were accomplished through neural stimulus of the brain which elicited, suppressed or modulated wing oscillation. Turns were triggered through the direct muscular stimulus of either of the basalar muscles. We characterized the response times, success rates, and free-flight trajectories elicited by our neural control systems in remotely controlled beetles. We believe this type of technology will open the door to in-flight perturbation and recording of insect flight responses.},
}
@article {pmid20161510,
year = {2009},
author = {Zacksenhouse, M and Nemets, S and Lebedev, MA and Nicolelis, MA},
title = {Robust Satisficing Linear Regression: performance/robustness trade-off and consistency criterion.},
journal = {Mechanical systems and signal processing},
volume = {23},
number = {6},
pages = {1954-1964},
pmid = {20161510},
issn = {0888-3270},
support = {R13 NS059245/NS/NINDS NIH HHS/United States ; R13 NS059245-01/NS/NINDS NIH HHS/United States ; },
abstract = {Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new criterion, which assesses the consistency between the observations and the linear model. The proposed criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.},
}
@article {pmid20153371,
year = {2010},
author = {Brunner, C and Allison, BZ and Krusienski, DJ and Kaiser, V and Müller-Putz, GR and Pfurtscheller, G and Neuper, C},
title = {Improved signal processing approaches in an offline simulation of a hybrid brain-computer interface.},
journal = {Journal of neuroscience methods},
volume = {188},
number = {1},
pages = {165-173},
pmid = {20153371},
issn = {1872-678X},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Algorithms ; Analysis of Variance ; Brain/*physiology ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {In a conventional brain-computer interface (BCI) system, users perform mental tasks that yield specific patterns of brain activity. A pattern recognition system determines which brain activity pattern a user is producing and thereby infers the user's mental task, allowing users to send messages or commands through brain activity alone. Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective communication is impossible. We recently introduced a new idea to improve accuracy, especially for users with poor performance. In an offline simulation of a "hybrid" BCI, subjects performed two mental tasks independently and then simultaneously. This hybrid BCI could use two different types of brain signals common in BCIs - event-related desynchronization (ERD) and steady-state evoked potentials (SSEPs). This study suggested that such a hybrid BCI is feasible. Here, we re-analyzed the data from our initial study. We explored eight different signal processing methods that aimed to improve classification and further assess both the causes and the extent of the benefits of the hybrid condition. Most analyses showed that the improved methods described here yielded a statistically significant improvement over our initial study. Some of these improvements could be relevant to conventional BCIs as well. Moreover, the number of illiterates could be reduced with the hybrid condition. Results are also discussed in terms of dual task interference and relevance to protocol design in hybrid BCIs.},
}
@article {pmid20144923,
year = {2010},
author = {Pfurtscheller, G and Solis-Escalante, T and Ortner, R and Linortner, P and Müller-Putz, GR},
title = {Self-paced operation of an SSVEP-Based orthosis with and without an imagery-based "brain switch:" a feasibility study towards a hybrid BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {4},
pages = {409-414},
doi = {10.1109/TNSRE.2010.2040837},
pmid = {20144923},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; *Artificial Limbs ; Brain/*physiology ; Cues ; Data Interpretation, Statistical ; Electroencephalography ; Equipment Design ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Female ; Hand/physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/physiology ; *Orthotic Devices ; Psychomotor Performance/physiology ; *User-Computer Interface ; Visual Cortex/physiology ; Young Adult ; },
abstract = {This work introduces a hybrid brain-computer interface (BCI) composed of an imagery-based brain switch and a steady-state visual evoked potential (SSVEP)-based BCI. The brain switch (event related synchronization (ERS)-based BCI) was used to activate the four-step SSVEP-based orthosis (via gazing at a 8 Hz LED to open and gazing at a 13 Hz LED to close) only when needed for control, and to deactivate the LEDs during resting periods. Only two EEG channels were required, one over the motor cortex and one over the visual cortex. As a basis for comparison, the orthosis was also operated without using the brain switch. Six subjects participated in this study. This combination of two BCIs operated with different mental strategies is one example of a "hybrid" BCI and revealed a much lower rate of FPs per minute during resting periods or breaks compared to the SSVEP BCI alone (FP=1.46+/-1.18 versus 5.40 +/- 0.90). Four out of the six subjects succeeded in operating the self-paced hybrid BCI with a good performance (positive prediction value PPVb > 0.70).},
}
@article {pmid20143173,
year = {2010},
author = {Boord, P and Craig, A and Tran, Y and Nguyen, H},
title = {Discrimination of left and right leg motor imagery for brain-computer interfaces.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {48},
number = {4},
pages = {343-350},
pmid = {20143173},
issn = {1741-0444},
mesh = {Adult ; Brain/*physiology ; Discrimination, Psychological/physiology ; Electroencephalography/methods ; Female ; Humans ; Imagination/*physiology ; Leg/physiology ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {This article reports on a study to identify electroencephalography (EEG) signals with potential to provide new BCI channels through mental motor imagery (MMI). Leg motion was assessed to see if left and right leg MMI could be discriminated in the EEG. The study also explored simultaneous observation of leg movement as a means to enhance MMI evoked EEG signals. The results demonstrate that MMI of the left and right leg produce a contralateral preponderance of EEG alpha band desynchronization, which can be spatially discriminated. This suggests that lower extremity MMI could provide signals for additional BCI channels. The study also shows that movement imitation enhances alpha band desynchronization during MMI, and might provide a useful aid in the identification and training of BCI signals.},
}
@article {pmid20128741,
year = {2010},
author = {Jin, J and Allison, BZ and Brunner, C and Wang, B and Wang, X and Zhang, J and Neuper, C and Pfurtscheller, G},
title = {P300 Chinese input system based on Bayesian LDA.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {55},
number = {1},
pages = {5-18},
doi = {10.1515/BMT.2010.003},
pmid = {20128741},
issn = {0013-5585},
mesh = {*Algorithms ; Artificial Intelligence ; Bayes Theorem ; Brain/*physiology ; China ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Discriminant Analysis ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a new communication channel between humans and computers that translates brain activity into recognizable command and control signals. Attended events can evoke P300 potentials in the electroencephalogram. Hence, the P300 has been used in BCI systems to spell, control cursors or robotic devices, and other tasks. This paper introduces a novel P300 BCI to communicate Chinese characters. To improve classification accuracy, an optimization algorithm (particle swarm optimization, PSO) is used for channel selection (i.e., identifying the best electrode configuration). The effects of different electrode configurations on classification accuracy were tested by Bayesian linear discriminant analysis offline. The offline results from 11 subjects show that this new P300 BCI can effectively communicate Chinese characters and that the features extracted from the electrodes obtained by PSO yield good performance.},
}
@article {pmid20126436,
year = {2010},
author = {Digiovanna, J and Rattanatamrong, P and Zhao, M and Mahmoudi, B and Hermer, L and Figueiredo, R and Principe, JC and Fortes, J and Sanchez, JC},
title = {Cyber-workstation for computational neuroscience.},
journal = {Frontiers in neuroengineering},
volume = {2},
number = {},
pages = {17},
pmid = {20126436},
issn = {1662-6443},
abstract = {A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.},
}
@article {pmid20112135,
year = {2010},
author = {Dias, NS and Kamrunnahar, M and Mendes, PM and Schiff, SJ and Correia, JH},
title = {Feature selection on movement imagery discrimination and attention detection.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {48},
number = {4},
pages = {331-341},
pmid = {20112135},
issn = {1741-0444},
support = {K02 MH001493/MH/NIMH NIH HHS/United States ; K25 NS061001/NS/NINDS NIH HHS/United States ; K02MH01493/MH/NIMH NIH HHS/United States ; K25 NS061001-03/NS/NINDS NIH HHS/United States ; K25NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Attention/*physiology ; Brain/physiology ; Discrimination, Psychological/physiology ; Electroencephalography/methods ; Evoked Potentials/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.},
}
@article {pmid20109492,
year = {2010},
author = {Torres Valderrama, A and Oostenveld, R and Vansteensel, MJ and Huiskamp, GM and Ramsey, NF},
title = {Gain of the human dura in vivo and its effects on invasive brain signal feature detection.},
journal = {Journal of neuroscience methods},
volume = {187},
number = {2},
pages = {270-279},
doi = {10.1016/j.jneumeth.2010.01.019},
pmid = {20109492},
issn = {1872-678X},
mesh = {Algorithms ; Brain/*physiology ; Cerebral Cortex/physiology ; Data Interpretation, Statistical ; Dura Mater/*physiology ; Electrocardiography ; Electrodes, Implanted ; Electroencephalography ; Electrophysiology ; Epidural Space/physiology ; Epilepsy/surgery ; Equipment Design ; Humans ; Image Processing, Computer-Assisted ; Individuality ; Mastoid/physiology ; Motor Cortex/physiology ; Signal Detection, Psychological/*physiology ; Subdural Space/physiology ; *User-Computer Interface ; },
abstract = {Invasive brain signal recordings generally rely on bioelectrodes implanted on the cortex underneath the dura. Subdural recordings have strong advantages in terms of bandwidth, spatial resolution and signal quality. However, subdural electrodes also have the drawback of compromising the long-term stability of such implants and heighten the risk of infection. Epidurally implanted electrodes might provide a viable alternative to subdural electrodes, offering a compromise between signal quality and invasiveness. Determining the feasibility of epidural electrode implantation for e.g., clinical research, brain-computer interfacing (BCI) and cognitive experiments, requires the characterization of the electrical properties of the dura, and its effect on signal feature detection. In this paper we report measurements of brain signal attenuation by the human dura in vivo. In addition, we use signal detection theory to study how the presence of the dura between the sources and the recording electrodes affects signal power features in motor BCI experiments. For noise levels typical of clinical brain signal recording equipment, we observed no detrimental effects on signal feature detection due to the dura. Subdural recordings were found to be more robust with respect to increased instrumentation noise level as compared to their epidural counterpart nonetheless. Our findings suggest that epidural electrode implantation is a viable alternative to subdural implants from the feature detection viewpoint.},
}
@article {pmid20105095,
year = {2010},
author = {Håkansson, B and Reinfeldt, S and Eeg-Olofsson, M and Ostli, P and Taghavi, H and Adler, J and Gabrielsson, J and Stenfelt, S and Granström, G},
title = {A novel bone conduction implant (BCI): engineering aspects and pre-clinical studies.},
journal = {International journal of audiology},
volume = {49},
number = {3},
pages = {203-215},
doi = {10.3109/14992020903264462},
pmid = {20105095},
issn = {1708-8186},
mesh = {Aged ; Aged, 80 and over ; *Bone Conduction ; Female ; *Hearing Aids ; Humans ; Male ; Prosthesis Design ; Prosthesis Implantation/methods ; },
abstract = {Percutaneous bone anchored hearing aids (BAHA) are today an important rehabilitation alternative for patients suffering from conductive or mixed hearing loss. Despite their success they are associated with drawbacks such as skin infections, accidental or spontaneous loss of the bone implant, and patient refusal for treatment due to stigma. A novel bone conduction implant (BCI) system has been proposed as an alternative to the BAHA system because it leaves the skin intact. Such a BCI system has now been developed and the encapsulated transducer uses a non-screw attachment to a hollow recess of the lateral portion of the temporal bone. The aim of this study is to describe the basic engineering principals and some preclinical results obtained with the new BCI system. Laser Doppler vibrometer measurements on three cadaver heads show that the new BCI system produces 0-10 dB higher maximum output acceleration level at the ipsilateral promontory relative to conventional ear-level BAHA at speech frequencies. At the contralateral promontory the maximum output acceleration level was considerably lower for the BCI than for the BAHA.},
}
@article {pmid20096528,
year = {2010},
author = {Kuncheva, LI and Rodríguez, JJ},
title = {Classifier ensembles for fMRI data analysis: an experiment.},
journal = {Magnetic resonance imaging},
volume = {28},
number = {4},
pages = {583-593},
doi = {10.1016/j.mri.2009.12.021},
pmid = {20096528},
issn = {1873-5894},
mesh = {Algorithms ; Artificial Intelligence ; Bayes Theorem ; Brain/*pathology ; Brain Mapping ; Computer Simulation ; Computers ; Humans ; Image Enhancement ; Magnetic Resonance Imaging/*methods ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Statistics as Topic ; },
abstract = {Functional magnetic resonance imaging (fMRI) is becoming a forefront brain-computer interface tool. To decipher brain patterns, fast, accurate and reliable classifier methods are needed. The support vector machine (SVM) classifier has been traditionally used. Here we argue that state-of-the-art methods from pattern recognition and machine learning, such as classifier ensembles, offer more accurate classification. This study compares 18 classification methods on a publicly available real data set due to Haxby et al. [Science 293 (2001) 2425-2430]. The data comes from a single-subject experiment, organized in 10 runs where eight classes of stimuli were presented in each run. The comparisons were carried out on voxel subsets of different sizes, selected through seven popular voxel selection methods. We found that, while SVM was robust, accurate and scalable, some classifier ensemble methods demonstrated significantly better performance. The best classifiers were found to be the random subspace ensemble of SVM classifiers, rotation forest and ensembles with random linear and random spherical oracle.},
}
@article {pmid20093075,
year = {2010},
author = {Halder, S and Rea, M and Andreoni, R and Nijboer, F and Hammer, EM and Kleih, SC and Birbaumer, N and Kübler, A},
title = {An auditory oddball brain-computer interface for binary choices.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {121},
number = {4},
pages = {516-523},
doi = {10.1016/j.clinph.2009.11.087},
pmid = {20093075},
issn = {1872-8952},
mesh = {Acoustic Stimulation/methods ; Adult ; Brain/*physiology ; *Brain Mapping ; Choice Behavior/*physiology ; Discrimination, Psychological/*physiology ; Electroencephalography ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Neuropsychological Tests ; Reaction Time/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) provide non-muscular communication for individuals diagnosed with late-stage motoneuron disease (e.g., amyotrophic lateral sclerosis (ALS)). In the final stages of the disease, a BCI cannot rely on the visual modality. This study examined a method to achieve high accuracies using auditory stimuli only.<br><br>METHODS: We propose an auditory BCI based on a three-stimulus paradigm. This paradigm is similar to the standard oddball but includes an additional target (i.e. two target stimuli, one frequent stimulus). Three versions of the task were evaluated in which the target stimuli differed in loudness, pitch or direction.<br><br>RESULTS: Twenty healthy participants achieved an average information transfer rate (ITR) of up to 2.46 bits/min and accuracies of 78.5%. Most subjects (14 of 20) achieved their best performance with targets differing in pitch.<br><br>CONCLUSIONS: With this study, the viability of the paradigm was shown for healthy participants and will next be evaluated with individuals diagnosed with ALS or locked-in syndrome (LIS) after stroke.<br><br>SIGNIFICANCE: The here presented BCI offers communication with binary choices (yes/no) independent of vision. As it requires only little time per selection, it may constitute a reliable means of communication for patients who lost all motor function and have a short attention span.},
}
@article {pmid20083864,
year = {2010},
author = {Zhang, D and Maye, A and Gao, X and Hong, B and Engel, AK and Gao, S},
title = {An independent brain-computer interface using covert non-spatial visual selective attention.},
journal = {Journal of neural engineering},
volume = {7},
number = {1},
pages = {16010},
doi = {10.1088/1741-2560/7/1/016010},
pmid = {20083864},
issn = {1741-2552},
mesh = {Adult ; Attention/*physiology ; Brain/*physiology ; China ; Cohort Studies ; Electroencephalography/*methods ; Evoked Potentials, Visual ; Female ; Germany ; Humans ; Learning/physiology ; Male ; Motion Perception/physiology ; Photic Stimulation ; Practice, Psychological ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; Visual Perception/*physiology ; Young Adult ; },
abstract = {In this paper, a novel independent brain-computer interface (BCI) system based on covert non-spatial visual selective attention of two superimposed illusory surfaces is described. Perception of two superimposed surfaces was induced by two sets of dots with different colors rotating in opposite directions. The surfaces flickered at different frequencies and elicited distinguishable steady-state visual evoked potentials (SSVEPs) over parietal and occipital areas of the brain. By selectively attending to one of the two surfaces, the SSVEP amplitude at the corresponding frequency was enhanced. An online BCI system utilizing the attentional modulation of SSVEP was implemented and a 3-day online training program with healthy subjects was carried out. The study was conducted with Chinese subjects at Tsinghua University, and German subjects at University Medical Center Hamburg-Eppendorf (UKE) using identical stimulation software and equivalent technical setup. A general improvement of control accuracy with training was observed in 8 out of 18 subjects. An averaged online classification accuracy of 72.6 +/- 16.1% was achieved on the last training day. The system renders SSVEP-based BCI paradigms possible for paralyzed patients with substantial head or ocular motor impairments by employing covert attention shifts instead of changing gaze direction.},
}
@article {pmid20083463,
year = {2010},
author = {Allison, B and Luth, T and Valbuena, D and Teymourian, A and Volosyak, I and Graser, A},
title = {BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {2},
pages = {107-116},
doi = {10.1109/TNSRE.2009.2039495},
pmid = {20083463},
issn = {1558-0210},
mesh = {Adolescent ; Adult ; Aged ; Aging/physiology ; Brain/*physiology ; Communication ; Communication Devices for People with Disabilities ; *Computer Literacy ; Demography ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Humans ; Individuality ; Male ; Middle Aged ; Photic Stimulation ; Sex Characteristics ; Software ; *User-Computer Interface ; Young Adult ; },
abstract = {Brain-computer interface (BCI) systems enable communication without movement. It is unclear why some BCI approaches or parameters are less effective with some users. This study elucidates BCI demographics by exploring correlations among BCI performance, personal preferences, and different subject factors such as age or gender. Results showed that most people, despite having no prior BCI experience, could use the Bremen SSVEP BCI system in a very noisy field setting. Performance tended to be better in both young and female subjects. Most subjects stated that they did not consider the flickering stimuli annoying and would use or recommend this BCI system. These and other demographic analyses may help identify the best BCI for each user.},
}
@article {pmid20075503,
year = {2010},
author = {Fruitet, J and McFarland, DJ and Wolpaw, JR},
title = {A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {7},
number = {1},
pages = {16003},
pmid = {20075503},
issn = {1741-2552},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Computer Simulation ; Electroencephalography/*methods ; Female ; Humans ; Linear Models ; Male ; Mental Processes/*physiology ; Regression Analysis ; *Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; Time Factors ; *User-Computer Interface ; Young Adult ; },
abstract = {People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.},
}
@article {pmid20071274,
year = {2010},
author = {Cecotti, H},
title = {A self-paced and calibration-less SSVEP-based brain-computer interface speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {2},
pages = {127-133},
doi = {10.1109/TNSRE.2009.2039594},
pmid = {20071274},
issn = {1558-0210},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Communication ; Decision Trees ; Electrodes ; Electroencephalography/instrumentation ; Evoked Potentials, Visual/*physiology ; Eye Movements/physiology ; Female ; Humans ; Male ; Photic Stimulation/adverse effects ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a communication system based on neural activity. Its goal is to provide a new output channel for the brain that requires voluntary control. We propose a new self-paced BCI speller based on the detection of steady-state visual evoked potential (SSVEP). The speller does not require any training from the user or from the signal processing part. The system is ready once the subject is prepared. The speller introduces a selection based on a decision tree and an undo command for correcting eventual errors. It was tested on eight healthy subjects who had no prior experience with the application. The average accuracy and information transfer rate are 92.25% and 37.62 bits per minute, which is translated in the speller with an average speed of 5.51 letters per minute.},
}
@article {pmid20070576,
year = {2010},
author = {Poli, R and Cinel, C and Citi, L and Sepulveda, F},
title = {Reaction-time binning: a simple method for increasing the resolving power of ERP averages.},
journal = {Psychophysiology},
volume = {47},
number = {3},
pages = {467-485},
doi = {10.1111/j.1469-8986.2009.00959.x},
pmid = {20070576},
issn = {1469-8986},
mesh = {Adult ; Algorithms ; Artifacts ; *Data Interpretation, Statistical ; Electroencephalography/*statistics &amp; numerical data ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Models, Statistical ; Photic Stimulation ; Psychomotor Performance/physiology ; Reaction Time/*physiology ; Sample Size ; Young Adult ; },
abstract = {Stimulus-locked, response-locked, and ERP-locked averaging are effective methods for reducing artifacts in ERP analysis. However, they suffer from a magnifying-glass effect: they increase the resolution of specific ERPs at the cost of blurring other ERPs. Here we propose an extremely simple technique-binning trials based on response times and then averaging-which can significantly alleviate the problems of other averaging methods. We have empirically evaluated the technique in an experiment where the task requires detecting a target in the presence of distractors. We have also studied the signal-to-noise ratio and the resolving power of averages with and without binning. Results indicate that the method produces clearer representations of ERPs than either stimulus-locked and response-locked averaging, revealing finer details of ERPs and helping in the evaluation of the amplitude and latency of ERP waves. The method is applicable to within-subject and between-subject averages.},
}
@article {pmid20070491,
year = {2010},
author = {Vanhaesebrouck, AE and Bhatti, SF and Bavegems, V and Gielen, IM and Van Soens, I and Vercauteren, G and Polis, I and Van Ham, LM},
title = {Inspiratory stridor secondary to palatolingual myokymia in a Maltese dog.},
journal = {The Journal of small animal practice},
volume = {51},
number = {3},
pages = {173-175},
doi = {10.1111/j.1748-5827.2009.00865.x},
pmid = {20070491},
issn = {1748-5827},
mesh = {Animals ; Anticonvulsants/therapeutic use ; Dog Diseases/*diagnosis/drug therapy ; Dogs ; Electromyography/veterinary ; Facial Muscles/*innervation/*physiopathology ; Fatal Outcome ; Male ; Myokymia/diagnosis/drug therapy/*veterinary ; Phenytoin/therapeutic use ; },
abstract = {A nine-year-old male Maltese dog was presented with an eight-month history of inspiratory stridor leading to exertional dyspnoea and cyanosis. Myokymic contractions in the palatolingual muscles were noticed and confirmed by electromyography. Brain computer tomography-scan showed ventricular dilatation. Cerebrospinal fluid analysis revealed a slightly elevated protein level. Treatment with slow-release phenytoin was unsuccessful and symptoms gradually worsened over the next nine months. At post-mortem examination a small pituitary adenoma was found. Apart from a single canine report of facial myokymia, this is the only other description of spontaneous focal myokymia in animals. Palatolingual myokymia has only been reported in one human being. Although the co-occurrence with a pituitary adenoma might be incidental, a paraneoplastic pathogenetic mechanism is proposed. Its unique clinical presentation adds a new, albeit uncommon, syndrome to the differential diagnosis of upper airway complaints in dogs.},
}
@article {pmid20065851,
year = {2010},
author = {Do, L and Syed, N and Puthawala, A and Azawi, S and Williams, R and Vora, N},
title = {Prognostic significance of bone or cartilage invasion of locally advanced head and neck cancers.},
journal = {American journal of clinical oncology},
volume = {33},
number = {6},
pages = {591-594},
doi = {10.1097/COC.0b013e3181bead63},
pmid = {20065851},
issn = {1537-453X},
mesh = {Aged ; Bone Neoplasms/mortality/*secondary/therapy ; Carcinoma, Squamous Cell/mortality/pathology/*secondary/*therapy ; Cartilage/pathology ; Chemotherapy, Adjuvant ; Cohort Studies ; Combined Modality Therapy ; Disease-Free Survival ; Female ; Head and Neck Neoplasms/mortality/*pathology/*therapy ; Humans ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Multivariate Analysis ; Neck Dissection/methods ; Neoplasm Invasiveness/pathology ; Neoplasm Staging ; Neoplasms, Connective Tissue/mortality/*secondary/therapy ; Prognosis ; Radiotherapy, Adjuvant ; Retrospective Studies ; Risk Assessment ; Survival Analysis ; Treatment Outcome ; },
abstract = {PURPOSE/OBJECTIVE(S): Locally advanced squamous cell cancers of the head and neck with bone and cartilage invasion (BCI) traditionally have been treated with resection followed up with radiotherapy or less commonly definitive chemoradiotherapy (CRT). However, it is unclear whether bone or cartilage invasion confers a worse prognosis in comparison with each other.<br><br>MATERIALS/METHODS: T4N0-3M0 squamous cell cancers of the head and neck patients underwent CRT or radical resection followed up with postoperative CRT. Oral cavity, oropharynx, laryngeal and hypopharyngeal squamous cell cancers were included. Radiotherapy ranged from 59.4 to 72 Gy. Concurrent chemotherapy was platinum based.<br><br>RESULTS: Forty-six patients with BCI were treated. When treated with CRT, 5-year local control was 55% and 43% for BCI, respectively (P = 0.23). Five-year overall survival for these patients was 54% and 29% for BCI, respectively (P = 0.99). When treated with upfront resection, 5-year local control was not significantly different (P = 0.60) nor was 5-year overall survival (P = 0.15).<br><br>CONCLUSIONS: This study suggests similar outcomes between patients with bone or cartilage invasion treated with upfront CRT or resection followed by CRT. Concurrent CRT may be viable alternative to resection in patients with either bone or cartilage invasion.},
}
@article {pmid20064766,
year = {2010},
author = {Dal Seno, B and Matteucci, M and Mainardi, LT},
title = {The utility metric: a novel method to assess the overall performance of discrete brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {18},
number = {1},
pages = {20-28},
doi = {10.1109/TNSRE.2009.2032642},
pmid = {20064766},
issn = {1558-0210},
mesh = {*Algorithms ; Brain/*physiology ; Brain Mapping/*classification/*methods ; Evoked Potentials/*physiology ; Humans ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {A relevant issue in a brain-computer interface (BCI) is the capability to efficiently convert user intentions into correct actions, and how to properly measure this efficiency. Usually, the evaluation of a BCI system is approached through the quantification of the classifier performance, which is often measured by means of the information transfer rate (ITR). A shortcoming of this approach is that the control interface design is neglected, and hence a poor description of the overall performance is obtained for real systems. To overcome this limitation, we propose a novel metric based on the computation of BCI Utility. The new metric can accurately predict the overall performance of a BCI system, as it takes into account both the classifier and the control interface characteristics. It is therefore suitable for design purposes, where we have to select the best options among different components and different parameters setup. In the paper, we compute Utility in two scenarios, a P300 speller and a P300 speller with an error correction system (ECS), for different values of accuracy of the classifier and recall of the ECS. Monte Carlo simulations confirm that Utility predicts the performance of a BCI better than ITR.},
}
@article {pmid20059399,
year = {2010},
author = {Blank, LM and Ebert, BE and Buehler, K and Bühler, B},
title = {Redox biocatalysis and metabolism: molecular mechanisms and metabolic network analysis.},
journal = {Antioxidants &amp; redox signaling},
volume = {13},
number = {3},
pages = {349-394},
doi = {10.1089/ars.2009.2931},
pmid = {20059399},
issn = {1557-7716},
mesh = {Animals ; *Biocatalysis ; *Energy Metabolism ; *Metabolic Networks and Pathways ; Molecular Structure ; *Oxidation-Reduction ; Oxidoreductases/metabolism ; Oxygenases/metabolism ; Peroxidases/metabolism ; Substrate Specificity ; },
abstract = {Whole-cell biocatalysis utilizes native or recombinant enzymes produced by cellular metabolism to perform synthetically interesting reactions. Besides hydrolases, oxidoreductases represent the most applied enzyme class in industry. Oxidoreductases are attributed a high future potential, especially for applications in the chemical and pharmaceutical industries, as they enable highly interesting chemistry (e.g., the selective oxyfunctionalization of unactivated C-H bonds). Redox reactions are characterized by electron transfer steps that often depend on redox cofactors as additional substrates. Their regeneration typically is accomplished via the metabolism of whole-cell catalysts. Traditionally, studies towards productive redox biocatalysis focused on the biocatalytic enzyme, its activity, selectivity, and specificity, and several successful examples of such processes are running commercially. However, redox cofactor regeneration by host metabolism was hardly considered for the optimization of biocatalytic rate, yield, and/or titer. This article reviews molecular mechanisms of oxidoreductases with synthetic potential and the host redox metabolism that fuels biocatalytic reactions with redox equivalents. The tools discussed in this review for investigating redox metabolism provide the basis for studies aiming at a deeper understanding of the interplay between synthetically active enzymes and metabolic networks. The ultimate goal of rational whole-cell biocatalyst engineering and use for fine chemical production is discussed.},
}
@article {pmid20058543,
year = {2009},
author = {Yoshimura, N and Itakura, N},
title = {A transient VEP-based real-time brain-computer interface using non-direct gazed visual stimuli.},
journal = {Electromyography and clinical neurophysiology},
volume = {49},
number = {8},
pages = {323-335},
pmid = {20058543},
issn = {0301-150X},
mesh = {Adult ; Blinking ; *Communication Devices for People with Disabilities ; Electrodes ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual ; Photic Stimulation/adverse effects/*methods ; *User-Computer Interface ; },
abstract = {It is necessary for brain-computer interfaces (BCIs) to be non-offensive devices for daily use to improve the quality of life of users, especially for the motor disabled. Some BCIs which are based on steady-state visual evoked potentials (SSVEPs), however, are unpleasant because users have to gaze at high-speed blinking light as visual stimuli. Furthermore, these kinds of BCIs may not be used as universal devices because SSVEPs are not detectable by some users. Considering these facts, we previously proposed a BCI using a non-direct gazing method based on transient VEPs. This interface used a low-speed blinking lattice pattern as visual stimuli and visual targets displayed on the right and the left sides of the stimuli. The gazing direction was determined by the waveform difference of transient VEPs detected when users gazed at either target. Compared with SSVEP-based BCLs, this BCI was less annoying because it used low-speed blinking visual stimuli, and it was not necessary for users to gaze at the stimuli directly. In this study, we propose an improved version of the BCI. Specifically, the gazing direction is determined in real time, and another gazing direction in which users gaze at a visual target displayed on the center of the screen is introduced while maintaining the annoyance-free advantage of the BCI. Experiments with 6 volunteer subjects showed an 84.2% accuracy rate in gazing direction judgments. The result suggests that the proposed BCI is more practical than the previous one because it can adapt to the change of gazing direction in real time.},
}
@article {pmid20052556,
year = {2010},
author = {Müller-Putz, GR and Kaiser, V and Solis-Escalante, T and Pfurtscheller, G},
title = {Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {48},
number = {3},
pages = {229-233},
pmid = {20052556},
issn = {1741-0444},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/methods ; Female ; Foot/*physiology ; Humans ; Imagination/*physiology ; Male ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {Bringing a Brain-Computer Interface (BCI) out of the lab one of the main problems has to be solved: to shorten the training time. Finding a solution for this problem, the use of a BCI will be open not only for people who have no choice, e.g., persons in a locked-in state, or suffering from a degenerating nerve disease. By reducing the training time to a minimum, also healthy persons will make use of the system, e.g., for using this kind of control for games. For realizing such a control, the post-movement beta rebound occurring after brisk feet movement was used to set up a classifier. This classifier was then used in a cue-based motor imagery system. After classifier adaptation, a self-paced brain-switch based on brisk foot motor imagery (MI) was evaluated. Four out of six subjects showed that a post-movement beta rebound after feet MI and succeeded with a true positive rate between 69 and 89%, while the positive predictive value was between 75 and 93%.},
}
@article {pmid20041311,
year = {2010},
author = {Cabrera, AF and Farina, D and Dremstrup, K},
title = {Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {48},
number = {2},
pages = {123-132},
pmid = {20041311},
issn = {1741-0444},
mesh = {Adult ; Brain/*physiology ; Communication Devices for People with Disabilities ; *Eidetic Imagery ; Electroencephalography/methods ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r (2)). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r(2) for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.},
}
@article {pmid20035870,
year = {2010},
author = {Konrad, P and Shanks, T},
title = {Implantable brain computer interface: challenges to neurotechnology translation.},
journal = {Neurobiology of disease},
volume = {38},
number = {3},
pages = {369-375},
doi = {10.1016/j.nbd.2009.12.007},
pmid = {20035870},
issn = {1095-953X},
mesh = {Animals ; Brain/*physiopathology ; Electrodes, Implanted ; Humans ; *User-Computer Interface ; },
abstract = {This article reviews three concepts related to implantable brain computer interface (BCI) devices being designed for human use: neural signal extraction primarily for motor commands, signal insertion to restore sensation, and technological challenges that remain. A significant body of literature has occurred over the past four decades regarding motor cortex signal extraction for upper extremity movement or computer interface. However, little is discussed regarding postural or ambulation command signaling. Auditory prosthesis research continues to represent the majority of literature on BCI signal insertion. Significant hurdles continue in the technological translation of BCI implants. These include developing a stable neural interface, significantly increasing signal processing capabilities, and methods of data transfer throughout the human body. The past few years, however, have provided extraordinary human examples of BCI implant potential. Despite technological hurdles, proof-of-concept animal and human studies provide significant encouragement that BCI implants may well find their way into mainstream medical practice in the foreseeable future.},
}
@article {pmid20028222,
year = {2010},
author = {Ting, JA and D'Souza, A and Vijayakumar, S and Schaal, S},
title = {Efficient learning and feature selection in high-dimensional regression.},
journal = {Neural computation},
volume = {22},
number = {4},
pages = {831-886},
doi = {10.1162/neco.2009.02-08-702},
pmid = {20028222},
issn = {1530-888X},
mesh = {*Algorithms ; Humans ; Learning/*physiology ; *Linear Models ; *Neural Networks, Computer ; Pattern Recognition, Automated ; },
abstract = {We present a novel algorithm for efficient learning and feature selection in high-dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the expectation-maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This variational Bayesian least squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust black-box approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, the relevance vector machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. The iterative nature of VBLS makes it most suitable for real-time incremental learning, which is crucial especially in the application domain of robotics, brain-machine interfaces, and neural prosthetics, where real-time learning of models for control is needed. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques.},
}
@article {pmid20021239,
year = {2010},
author = {Poole-Warren, L and Lovell, N and Baek, S and Green, R},
title = {Development of bioactive conducting polymers for neural interfaces.},
journal = {Expert review of medical devices},
volume = {7},
number = {1},
pages = {35-49},
doi = {10.1586/erd.09.58},
pmid = {20021239},
issn = {1745-2422},
mesh = {Animals ; *Brain ; Electrodes, Implanted ; Humans ; *Nerve Tissue ; *Polymers ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {Bioelectrodes for neural recording and neurostimulation are an integral component of a number of neuroprosthetic devices, including the commercially available cochlear implant, and developmental devices, such as the bionic eye and brain-machine interfaces. Current electrode designs limit the application of such devices owing to suboptimal material properties that lead to minimal interaction with the target neural tissue and the formation of fibrotic capsules. In designing an ideal bioelectrode, a number of design criteria must be considered with respect to physical, mechanical, electrical and biological properties. Conducting polymers have the potential to address the synergistic interaction of these properties and show promise as superior coatings for next-generation electrodes in implant devices.},
}
@article {pmid20020346,
year = {2010},
author = {Bassani, T and Nievola, JC},
title = {Brain-computer interface using wavelet transformation and naïve bayes classifier.},
journal = {Advances in experimental medicine and biology},
volume = {657},
number = {},
pages = {147-165},
doi = {10.1007/978-0-387-79100-5_8},
pmid = {20020346},
issn = {0065-2598},
mesh = {Algorithms ; Bayes Theorem ; Brain/*physiology ; *Brain Mapping ; Electroencephalography/methods ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The main purpose of this work is to establish an exploratory approach using electroencephalographic (EEG) signal, analyzing the patterns in the time-frequency plane. This work also aims to optimize the EEG signal analysis through the improvement of classifiers and, eventually, of the BCI performance. In this paper a novel exploratory approach for data mining of EEG signal based on continuous wavelet transformation (CWT) and wavelet coherence (WC) statistical analysis is introduced and applied. The CWT allows the representation of time-frequency patterns of the signal's information content by WC qualiatative analysis. Results suggest that the proposed methodology is capable of identifying regions in time-frequency spectrum during the specified task of BCI. Furthermore, an example of a region is identified, and the patterns are classified using a Naïve Bayes Classifier (NBC). This innovative characteristic of the process justifies the feasibility of the proposed approach to other data mining applications. It can open new physiologic researches in this field and on non stationary time series analysis.},
}
@article {pmid20013989,
year = {2009},
author = {Behr, A and Johnen, L},
title = {Myrcene as a natural base chemical in sustainable chemistry: a critical review.},
journal = {ChemSusChem},
volume = {2},
number = {12},
pages = {1072-1095},
doi = {10.1002/cssc.200900186},
pmid = {20013989},
issn = {1864-564X},
mesh = {Acyclic Monoterpenes ; Alkenes/*chemistry ; Biological Products/chemistry ; Green Chemistry Technology/*methods ; Monoterpenes/*chemistry ; Terpenes/chemistry ; },
abstract = {Currently, a shift towards chemical products derived from renewable, biological feedstocks is observed more and more. However, substantial differences with traditional feedstocks, such as their "hyperfunctionalization," ethical problems caused by competition with foods, and problems with a constant qualitative/quantitative availability of the natural products, occasionally complicate the large-scale market entry of renewable resources. In this context the vast family of terpenes is often not taken into consideration, although the terpenes have been known for hundreds of years as components of essential oils obtained from leaves, flowers, and fruits of many plants. The simple acyclic monoterpenes, particularly the industrially available myrcene, provide a classical chemistry similar to unsaturated hydrocarbons already known from oil and gas. Hence, this Review is aimed at reviving myrcene as a renewable compound suitable for sustainable chemistry in the area of fine chemicals. The versatility of the unsaturated C(10)-hydrocarbon myrcene, leading to products with several different areas of application, is pointed out.},
}
@article {pmid20011144,
year = {2009},
author = {Slater, M and Perez-Marcos, D and Ehrsson, HH and Sanchez-Vives, MV},
title = {Inducing illusory ownership of a virtual body.},
journal = {Frontiers in neuroscience},
volume = {3},
number = {2},
pages = {214-220},
pmid = {20011144},
issn = {1662-453X},
abstract = {We discuss three experiments that investigate how virtual limbs and bodies can come to feel like real limbs and bodies. The first experiment shows that an illusion of ownership of a virtual arm appearing to project out of a person's shoulder can be produced by tactile stimulation on a person's hidden real hand and synchronous stimulation on the seen virtual hand. The second shows that the illusion can be produced by synchronous movement of the person's hidden real hand and a virtual hand. The third shows that a weaker form of the illusion can be produced when a brain-computer interface is employed to move the virtual hand by means of motor imagery without any tactile stimulation. We discuss related studies that indicate that the ownership illusion may be generated for an entire body. This has important implications for the scientific understanding of body ownership and several practical applications.},
}
@article {pmid20011034,
year = {2009},
author = {Guenther, FH and Brumberg, JS and Wright, EJ and Nieto-Castanon, A and Tourville, JA and Panko, M and Law, R and Siebert, SA and Bartels, JL and Andreasen, DS and Ehirim, P and Mao, H and Kennedy, PR},
title = {A wireless brain-machine interface for real-time speech synthesis.},
journal = {PloS one},
volume = {4},
number = {12},
pages = {e8218},
pmid = {20011034},
issn = {1932-6203},
support = {R01-DC007683/DC/NIDCD NIH HHS/United States ; R44-DC007050/DC/NIDCD NIH HHS/United States ; R01 DC002852/DC/NIDCD NIH HHS/United States ; R01-DC002852/DC/NIDCD NIH HHS/United States ; R01 DC007683/DC/NIDCD NIH HHS/United States ; R44 DC007050/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Electrodes, Implanted ; Humans ; Male ; Time Factors ; },
abstract = {BACKGROUND: Brain-machine interfaces (BMIs) involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for restoring communication can provide important capabilities via a typing process, but unfortunately they are only capable of slow communication rates. In the current study we use a novel approach to speech restoration in which we decode continuous auditory parameters for a real-time speech synthesizer from neuronal activity in motor cortex during attempted speech.<br><br>Neural signals recorded by a Neurotrophic Electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome, characterized by near-total paralysis with spared cognition, were transmitted wirelessly across the scalp and used to drive a speech synthesizer. A Kalman filter-based decoder translated the neural signals generated during attempted speech into continuous parameters for controlling a synthesizer that provided immediate (within 50 ms) auditory feedback of the decoded sound. Accuracy of the volunteer's vowel productions with the synthesizer improved quickly with practice, with a 25% improvement in average hit rate (from 45% to 70%) and 46% decrease in average endpoint error from the first to the last block of a three-vowel task.<br><br>CONCLUSIONS/SIGNIFICANCE: Our results support the feasibility of neural prostheses that may have the potential to provide near-conversational synthetic speech output for individuals with severely impaired speech motor control. They also provide an initial glimpse into the functional properties of neurons in speech motor cortical areas.},
}
@article {pmid20009767,
year = {2010},
author = {Kelley, GA and Kelley, KS},
title = {Isometric handgrip exercise and resting blood pressure: a meta-analysis of randomized controlled trials.},
journal = {Journal of hypertension},
volume = {28},
number = {3},
pages = {411-418},
doi = {10.1097/HJH.0b013e3283357d16},
pmid = {20009767},
issn = {1473-5598},
mesh = {Adult ; Aged ; Aged, 80 and over ; *Blood Pressure ; Exercise ; Female ; *Hand Strength ; Humans ; Male ; Middle Aged ; *Randomized Controlled Trials as Topic ; },
abstract = {OBJECTIVE: To examine the efficacy of isometric handgrip exercise for reducing resting SBP and DBP in adult humans.<br><br>METHODS: Meta-analysis of studies retrieved from five electronic databases as well as cross-referencing from identified articles. The criteria for inclusion were randomized controlled trials published in any language over an approximate 38-year period (1 January 1971 to 1 February 2009), isometric handgrip training of at least 4 weeks performed by adults of at least 18 years of age, and data for changes in resting SBP and DBP available. Dual coding of studies was performed by both investigators. Data were analyzed a priori using random-effects models and nonparametric 95% bootstrap percentile confidence intervals (BCIs, 5000 iterations). Because of the small sample size, analyses were also performed using fixed-effects models post hoc.<br><br>RESULTS: Eighty-one men and women (42 exercise and 39 control) from three of 287 reviewed studies were pooled for analysis. Using random-effects models, statistically significant exercise minus control group reductions of approximately 10% were observed for both resting SBP and DBP (SBP: Xd , -13.4 mmHg; 95% BCI, -15.3 to -11.0 mmHg and DBP: X , -7.8 mmHg; 95% BCI, -16.5 to -3.0 mmHg). Results were also statistically significant when fixed-effects models were used (SBP: X , -13.8 mmHg; 95% BCI, -15.3 to -11.0 mmHg and DBP: X , -6.1 mmHg; 95% BCI, -16.5 to -3.2 mmHg).<br><br>CONCLUSION: Isometric handgrip exercise is efficacious for reducing resting SBP and DBP in adult humans. However, the generalizability of these findings is limited given the small number of studies included.},
}
@article {pmid20007050,
year = {2010},
author = {Héliot, R and Ganguly, K and Jimenez, J and Carmena, JM},
title = {Learning in closed-loop brain-machine interfaces: modeling and experimental validation.},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
volume = {40},
number = {5},
pages = {1387-1397},
doi = {10.1109/TSMCB.2009.2036931},
pmid = {20007050},
issn = {1941-0492},
mesh = {Biofeedback, Psychology/methods/physiology ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; Learning/*physiology ; *Models, Neurological ; *User-Computer Interface ; },
abstract = {Closed-loop operation of a brain-machine interface (BMI) relies on the subject's ability to learn an inverse transformation of the plant to be controlled. In this paper, we propose a model of the learning process that undergoes closed-loop BMI operation. We first explore the properties of the model and show that it is able to learn an inverse model of the controlled plant. Then, we compare the model predictions to actual experimental neural and behavioral data from nonhuman primates operating a BMI, which demonstrate high accordance of the model with the experimental data. Applying tools from control theory to this learning model will help in the design of a new generation of neural information decoders which will maximize learning speed for BMI users.},
}
@article {pmid19965253,
year = {2009},
author = {Ang, KK and Guan, C and Chua, KS and Ang, BT and Kuah, C and Wang, C and Phua, KS and Chin, ZY and Zhang, H},
title = {A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5981-5984},
doi = {10.1109/IEMBS.2009.5335381},
pmid = {19965253},
issn = {2375-7477},
mesh = {Arm ; Biofeedback, Psychology/instrumentation ; Electroencephalography/*instrumentation ; Evoked Potentials, Motor ; Female ; Humans ; *Imagination ; Male ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; Paresis/*rehabilitation ; Robotics/*methods ; Therapy, Computer-Assisted/methods ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {Non-invasive EEG-based motor imagery brain-computer interface (MI-BCI) holds promise to effectively restore motor control to stroke survivors. This clinical study investigates the effects of MI-BCI for upper limb robotic rehabilitation compared to standard robotic rehabilitation. The subjects are hemiparetic stroke patients with mean age of 50.2 and baseline Fugl-Meyer (FM) score 29.7 (out of 66, higher = better) randomly assigned to each group respectively (N = 8 and 10). Each subject underwent 12 sessions of 1-hour rehabilitation for 4 weeks. Significant gains in FM scores were observed in both groups at post-rehabilitation (4.9, p = 0.001) and 2-month post-rehabilitation (4.9, p = 0.002). The experimental group yielded higher 2-month post-rehabilitation gain than the control (6.0 versus 4.0) but no significance was found (p = 0.475). However, among subjects with positive gain (N = 6 and 7), the initial difference of 2.8 between the two groups was increased to a significant 6.5 (p = 0.019) after adjustment for age and gender. Hence this study provides evidence that BCI-driven robotic rehabilitation is effective in restoring motor control for stroke.},
}
@article {pmid19965221,
year = {2009},
author = {Oskoei, MA and Gan, JQ and Hu, H},
title = {Adaptive schemes applied to online SVM for BCI data classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2600-2603},
doi = {10.1109/IEMBS.2009.5335328},
pmid = {19965221},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain/pathology ; Brain Mapping/*instrumentation/methods ; Computational Biology/methods ; Equipment Design ; Fuzzy Logic ; Humans ; Internet ; Models, Statistical ; *Pattern Recognition, Automated ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Software ; User-Computer Interface ; },
abstract = {This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate.},
}
@article {pmid19965216,
year = {2009},
author = {Diez, PF and Mut, V and Laciar, E and Torres, A and Avila, E},
title = {Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2579-2582},
doi = {10.1109/IEMBS.2009.5335278},
pmid = {19965216},
issn = {2375-7477},
mesh = {Algorithms ; Cognition/*physiology ; Electroencephalography/*methods ; Fourier Analysis ; Humans ; Mental Processes/*physiology ; Models, Statistical ; Models, Theoretical ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Psychomotor Performance/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Vision, Ocular ; },
abstract = {In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91+/-5% and 87+/-5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.},
}
@article {pmid19965154,
year = {2009},
author = {Wan, B and Zhou, Z and Xu, L and Ming, D and Qi, H and Cheng, L},
title = {Mu rhythm desynchronization detection based on empirical mode decomposition.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2232-2235},
doi = {10.1109/IEMBS.2009.5335012},
pmid = {19965154},
issn = {2375-7477},
mesh = {Algorithms ; Biomedical Engineering/methods ; *Cortical Synchronization ; Electroencephalography/*methods ; Fourier Analysis ; Hand/physiology ; Humans ; Models, Statistical ; Motor Skills Disorders/physiopathology ; Movement/physiology ; *Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {The aim of this paper is to investigate the possibility of using empirical mode decomposition (EMD) method in detecting the desynchronized mu rhythm of motor imagery EEG signal. A number of EEG studies have identified the mu rhythm desynchronization a reliable EEG pattern for brain-computer interface. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to decompose the EEG signal into intrinsic mode functions (IMFs). By analyzing the power spectral density (PSD) of the IMFs, the characteristics one representing mu rhythm oscillations can be detected. Then by Hilbert transformation, the event-related desynchronization phenomenon can be found by the envelope of the characteristics IMF. Results demonstrate that the EMD method is an effective time-frequency analysis tool for non-stationary EEG signal.},
}
@article {pmid19965103,
year = {2009},
author = {Ince, NF and Tadipatri, VA and Göksu, F and Tewfik, AH},
title = {Denoising of multiscale/multiresolution structural feature dictionaries for rapid training of a brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {21-24},
doi = {10.1109/IEMBS.2009.5334902},
pmid = {19965103},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Information Storage and Retrieval/*methods ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Multichannel neural activities such as EEG or ECoG in a brain computer interface can be classified with subset selection algorithms running on large feature dictionaries describing subject specific features in spectral, temporal and spatial domain. While providing high accuracies in classification, the subset selection techniques are associated with long training times due to the large feature set constructed from multichannel neural recordings. In this paper we study a novel denoising technique for reducing the dimensionality of the feature space which decreases the computational complexity of the subset selection step radically without causing any degradation in the final classification accuracy. The denoising procedure was based on the comparison of the energy in a particular time segment and in a given scale/level to the energy of the raw data. By setting denoising threshold a priori the algorithm removes those nodes which fail to capture the energy in the raw data in a given scale. We provide experimental studies towards the classification of motor imagery related multichannel ECoG recordings for a brain computer interface. The denoising procedure was able to reach the same classification accuracy without denoising and a computational complexity around 5 times smaller. We also note that in some cases the denoised procedure performed better classification.},
}
@article {pmid19965060,
year = {2009},
author = {Gentili, RJ and Bradberry, TJ and Hatfield, BD and Contreras-Vidal, JL},
title = {Brain biomarkers of motor adaptation using phase synchronization.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5930-5933},
doi = {10.1109/IEMBS.2009.5334743},
pmid = {19965060},
issn = {2375-7477},
mesh = {Adaptation, Physiological/*physiology ; Adult ; Brain Mapping/*methods ; Cortical Synchronization ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; *Task Performance and Analysis ; },
abstract = {A growing number of brain monitoring tools for medical and biomedical applications such as surgery have been developed. Although many assistive technologies (e.g., brain computer interface (BCI) systems) aiming to restore cognitive-motor deficits are under development, no functional neural indicator or brain biomarker able to track the cortical dynamics of the brain when interacting with new tools and/or novel environments in ecological situations are available. Therefore this study aimed to investigate potential biomarkers reflecting the dynamic cognitive-motor states of subjects who had to learn a new tool. These biomarkers were derived from phase synchronization measures of electroencephalographic (EEG) signals (coherence, phase locking value (PLV)). The findings indicate a linear decrease of phase synchronization for both movement planning and execution as subjects adapt during tool learning. These changes were correlated with enhanced kinematics as the task progressed. These non-invasive biomarkers may play a role in bioengineering applications and particularly in BCI systems, allowing the establishment of co-adaptation/cooperation between the user's brain and the decoding algorithm to design adaptive neuroprostheses.},
}
@article {pmid19965051,
year = {2009},
author = {Sharma, M and Gaona, C and Roland, J and Anderson, N and Freudenberg, Z and Leuthardt, EC},
title = {Ipsilateral directional encoding of joystick movements in human cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5502-5505},
pmid = {19965051},
issn = {2375-7477},
support = {L30 NS063404/NS/NINDS NIH HHS/United States ; L30 NS063404-01/NS/NINDS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB 000856-06/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Computer Peripherals ; Electrocardiography/*methods ; Epilepsy/*physiopathology ; *Evoked Potentials, Motor ; Humans ; Middle Aged ; Motor Cortex/*physiopathology ; *Movement ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The majority of Brain Computer Interfaces have relied on signals related to primary motor cortex and the operation of the contralateral limb. Recently, the physiology associated with same-sided (ipsilateral) motor movements has been found to have a unique cortical physiology. This study sets out to assess whether more complex motor movements can be discerned utilizing ipsilateral cortical signals. In this study, three invasively monitored human subjects were recorded while performing a center out joystick task with the hand ipsilateral to the hemispheric subdural grid array. It was found that directional tuning was present in ipsilateral cortex. This information was encoded in both distinct anatomic populations and spectral distributions. These findings support the notion that ipsilateral signals may provide added information for BCI operation in the future.},
}
@article {pmid19965050,
year = {2009},
author = {Schalk, G},
title = {Effective brain-computer interfacing using BCI2000.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5498-5501},
doi = {10.1109/IEMBS.2009.5334558},
pmid = {19965050},
issn = {2375-7477},
support = {R01 EB 006356/EB/NIBIB NIH HHS/United States ; R01 EB 00856/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; Electrocardiography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Rehabilitation/*instrumentation ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {To facilitate research and development in Brain-Computer Interface (BCI) research, we have been developing a general-purpose BCI system, called BCI2000, over the past nine years. This system has enjoyed a growing adoption in BCI and related areas and has been the basis for some of the most impressive studies reported to date. This paper gives an update on the status of this project by describing the principles of the BCI2000 system, its benefits, and impact on the field to date.},
}
@article {pmid19965049,
year = {2009},
author = {Stanslaski, S and Cong, P and Carlson, D and Santa, W and Jensen, R and Molnar, G and Marks, WJ and Shafquat, A and Denison, T},
title = {An implantable bi-directional brain-machine interface system for chronic neuroprosthesis research.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5494-5497},
doi = {10.1109/IEMBS.2009.5334562},
pmid = {19965049},
issn = {2375-7477},
mesh = {Biomedical Research/instrumentation ; Brain/*physiopathology ; Chronic Disease ; Electric Stimulation Therapy/*instrumentation ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Nervous System Diseases/diagnosis/*rehabilitation ; Prostheses and Implants ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; Therapy, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {An implantable bi-directional brain-machine interface (BMI) prototype is presented. With sensing, algorithm, wireless telemetry, and stimulation therapy capabilities, the system is designed for chronic studies exploring closed-loop and diagnostic opportunities for neuroprosthetics. In particular, we hope to enable fundamental chronic research into the physiology of neurological disorders, define key electrical biomarkers related to disease, and apply this learning to patient-specific algorithms for therapeutic stimulation and diagnostics. The ultimate goal is to provide practical neuroprosthetics with adaptive therapy for improved efficiency and efficacy.},
}
@article {pmid19964967,
year = {2009},
author = {Teli, MN and Anderson, C},
title = {Nonlinear dimensionality reduction of electroencephalogram (EEG) for Brain Computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2486-2489},
doi = {10.1109/IEMBS.2009.5334802},
pmid = {19964967},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*physiology ; Computers ; Discriminant Analysis ; Electroencephalography/*methods ; Humans ; Man-Machine Systems ; Models, Statistical ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Patterns in electroencephalogram (EEG) signals are analyzed for a Brain Computer Interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a Neural Network (NN) in an auto-encoder with bottleneck configuration can find such a transformation. We implemented two approximate second-order methods to optimize the weights of these networks, because the more common first-order methods are very slow to converge for networks like these with more than three layers of computational units. The resulting non-linear projections of time embedded EEG signals show interesting separations that are related to tasks. The bottleneck networks do indeed discover nonlinear transformations to low-dimensional spaces that capture much of the information present in EEG signals. However, the resulting low-dimensional representations do not improve classification rates beyond what is possible using Quadratic Discriminant Analysis (QDA) on the original time-lagged EEG.},
}
@article {pmid19964966,
year = {2009},
author = {Higashi, H and Tanaka, T and Funase, A},
title = {Classification of single trial EEG during imagined hand movement by rhythmic component extraction.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2482-2485},
doi = {10.1109/IEMBS.2009.5334806},
pmid = {19964966},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Electrodes ; Electroencephalography/*methods ; Hand/*physiology ; Hand Strength ; Humans ; Male ; Models, Statistical ; *Movement ; Pattern Recognition, Automated ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {An electroencephalograph (EEG)-based brain computer interface (BCI) requires rapid and reliable extraction of features in EEG signal. Recently, the rhythmic component extraction (RCE) method has been proposed to extract features of multi-channel EEG. RCE can extract a signal component with a certain frequency from multi-sensor signals. In this paper, we applied RCE to extract a feature corresponding to hand movement imagery tasks from signals measured by EEG. This feature from a single trial EEG signal is classified between imaginary left/right hand movement EEG using machine learning. On two subjects, our experiment shows that the combination of RCE and fisher discriminant analysis outperforms common spatial patterns (CSP) in classification accuracy. It is also reported that other major classifiers together with RCE give better performance than CSP. Additionally, we consider the relationship between data length and classification accuracy. It is shown that the accuracy tends to decrease as the data length becomes small.},
}
@article {pmid19964963,
year = {2009},
author = {Kawanabe, M and Vidaurre, C and Scholler, S and Müller, KR},
title = {Robust common spatial filters with a maxmin approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2470-2473},
doi = {10.1109/IEMBS.2009.5334786},
pmid = {19964963},
issn = {2375-7477},
mesh = {Algorithms ; Artifacts ; Artificial Intelligence ; Biomedical Engineering/*methods ; Computer Simulation ; Electroencephalography/*methods ; Humans ; Models, Statistical ; Models, Theoretical ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalographic signals are known to be non-stationary and easily affected by artifacts, therefore their analysis requires methods that can deal with noise. In this work we present two ways of calculating robust common spatial patterns under a maxmin approach. The worst-case objective function is optimized within prefixed sets of the covariance matrices that are defined either very simply as identity matrices or in a data driven way using PCA. We test common spatial filters derived with these two approaches with real world brain-computer interface (BCI) data sets in which we expect substantial "day-to-day" fluctuations (session transfer problem). We compare our results with the classical common spatial filters and show that both can improve the performance of the latter.},
}
@article {pmid19964939,
year = {2009},
author = {Tyler, ME and Braun, JG and Danilov, YP},
title = {Spatial mapping of electrotactile sensation threshold and intensity range on the human tongue: initial results.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {559-562},
doi = {10.1109/IEMBS.2009.5334556},
pmid = {19964939},
issn = {2375-7477},
mesh = {Differential Threshold/*physiology ; Female ; Humans ; Male ; Pilot Projects ; Tongue/innervation/*physiology ; Touch/*physiology ; Young Adult ; },
abstract = {We have developed a novel, tongue-based electrotactile brain-machine interface. Variability of the tactile sensation intensity across the stimulated area, however, limits the amount of reliable information transmission. We have conducted an experiment to characterize local sensitivity across the region stimulated by the array. From this data we have constructed an isointensity algorithm to compensate for the variability in electrotactile sensation levels across the stimulated area of the tongue.},
}
@article {pmid19964841,
year = {2009},
author = {Qi, H and Zhu, Y and Ming, D and Wan, B},
title = {Independent Component Analysis using clustering on motor imagery EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4735-4738},
doi = {10.1109/IEMBS.2009.5334189},
pmid = {19964841},
issn = {2375-7477},
mesh = {Algorithms ; *Cluster Analysis ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; },
abstract = {Motor imagery is a popular paradigm in electrophysiology research and brain computer interface but the evoked EEG signals always contaminated significantly. In this paper we use the Independent Component Analysis to enhance the signal-to-noise ratio of multi trail EEG signals evoked by imaginary hand movement. Infomax algorithm was used to decompose multi channel EEG signals into independent components trail by trail, and then an automatic clustering method was used to group these components into several clusters. For the higher similarity between task relevant components, they can be assembled into one cluster that occupies the highest mean mutual information of pairwise components intra cluster. Furthermore, the reconstructed signals of task relevant cluster showed a high discrepancy features to left versus right hand task, which evaluated by Fisher criterion scores and served as the signal-to-noise ratio measurement.},
}
@article {pmid19964789,
year = {2009},
author = {Tsiaras, V and Andreou, D and Tollis, IG},
title = {BrainNetVis: analysis and visualization of brain functional networks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2911-2914},
doi = {10.1109/IEMBS.2009.5334489},
pmid = {19964789},
issn = {2375-7477},
mesh = {Algorithms ; Biomedical Engineering/*methods ; Brain/physiology ; Brain Mapping/*methods ; Computers ; Electroencephalography/*methods ; Foot/pathology ; Hand/pathology ; Humans ; Models, Statistical ; Programming Languages ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {BrainNetVis is an application, written in Java, that displays and analyzes synchronization networks from brain signals. The program implements a number of network indices and visualization techniques. We demonstrate its use through a case study of left hand and foot motor imagery. The data sets were provided by the Berlin BCI group. Using this program we managed to find differences between the average left hand and foot synchronization networks by comparing them with the average idle state synchronization network.},
}
@article {pmid19964778,
year = {2009},
author = {McCarthy, PT and Madangopal, R and Otto, KJ and Rao, MP},
title = {Titanium-based multi-channel, micro-electrode array for recording neural signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2062-2065},
doi = {10.1109/IEMBS.2009.5334429},
pmid = {19964778},
issn = {2375-7477},
mesh = {Animals ; Auditory Cortex/*physiology ; Auditory Perception/physiology ; Biomechanical Phenomena ; Brain/*physiology ; Elasticity ; Equipment Design ; Microelectrodes ; Neurons/*physiology ; Rats ; Signal Transduction ; Thalamus/*physiology ; Titanium ; },
abstract = {Micro-scale brain-machine interface (BMI) devices have provided an opportunity for direct probing of neural function and have also shown significant promise for restoring neurological functions lost to stroke, injury, or disease. However, the eventual clinical translation of such devices may be hampered by limitations associated with the materials commonly used for their fabrication, e.g. brittleness of silicon, insufficient rigidity of polymeric devices, and unproven chronic biocompatibility of both. Herein, we report, for the first time, the development of titanium-based "Michigan" type multi-channel, microelectrode arrays that seek to address these limitations. Titanium provides unique properties of immediate relevance to microelectrode arrays, such as high toughness, moderate modulus, and excellent biocompatibility, which may enhance structural reliability, safety, and chronic recording reliability. Realization of these devices is enabled by recently developed techniques which provide the opportunity for fabrication of high aspect ratio micromechanical structures in bulk titanium substrates. Details regarding the design, fabrication, and characterization of these devices for eventual use in rat auditory cortex and thalamus recordings are presented, as are preliminary results.},
}
@article {pmid19964759,
year = {2009},
author = {Chernyy, N and Schiff, SJ and Gluckman, BJ},
title = {Time dependence of stimulation/recording-artifact transfer function estimates for neural interface systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {1380-1383},
pmid = {19964759},
issn = {2375-7477},
support = {K02 MH001493/MH/NIMH NIH HHS/United States ; R01EB001507/EB/NIBIB NIH HHS/United States ; R01 MH050006/MH/NIMH NIH HHS/United States ; R01MH50006/MH/NIMH NIH HHS/United States ; K02MH01493/MH/NIMH NIH HHS/United States ; R01 EB001507/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Artifacts ; Biomedical Engineering ; Electric Impedance ; Electric Stimulation/*methods ; Electric Stimulation Therapy/statistics &amp; numerical data ; Electrodes, Implanted ; Electroencephalography/statistics &amp; numerical data ; Feedback, Physiological ; Fourier Analysis ; Linear Models ; Male ; Models, Neurological ; Rats ; Rats, Sprague-Dawley ; Signal Processing, Computer-Assisted ; },
abstract = {A continuous feedback-enabled control system requires simultaneous measurements of the system states and generation of a control output. In neural systems, electric stimulation used to interact with neural activity also creates additional electrical potential variations at measurement points used to monitor neural activity. This stimulus artifact confounds recording of underlying neural activity through the addition of both common mode and differential potentials. We model this artifact as a linearly filtered version of the applied electrical current. We demonstrate a method to determine the properties of this filter using multi-taper techniques for chronically implanted animals stimulated with polarizing low-frequency electric fields (PLEF). When measured repeatedly in chronic experiments with continuous recordings, we observe slow changes of up to 50% transfer function magnitude (figure 1). Such changes reflect a combination bulk impedance changes of the tissue and changes in electrode interface properties. These variations need to be tracked and accommodated for successful chronic continuous feedback neural control systems.},
}
@article {pmid19964746,
year = {2009},
author = {Montazeri, N and Shamsollahi, MB and Hajipour, S},
title = {MEG based classification of wrist movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {986-989},
doi = {10.1109/IEMBS.2009.5334472},
pmid = {19964746},
issn = {2375-7477},
mesh = {Algorithms ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Magnetoencephalography/*methods ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; User-Computer Interface ; Wrist Joint/*physiology ; },
abstract = {Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1 and 36%-40% for subject 2) prove that the suggested method shows better performance compared with other methods.},
}
@article {pmid19964726,
year = {2009},
author = {Thorbergsson, PT and Jorntell, H and Bengtsson, F and Garwicz, M and Schouenborg, J and Johansson, A},
title = {Spike library based simulator for extracellular single unit neuronal signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6998-7001},
doi = {10.1109/IEMBS.2009.5333847},
pmid = {19964726},
issn = {2375-7477},
mesh = {*Action Potentials ; Algorithms ; Animals ; Artificial Intelligence ; Biomedical Engineering ; Cats ; Cerebellum/physiology ; Extracellular Space/physiology ; *Models, Neurological ; Neurons/*physiology ; User-Computer Interface ; },
abstract = {A well defined set of design criteria is of great importance in the process of designing brain machine interfaces (BMI) based on extracellular recordings with chronically implanted micro-electrode arrays in the central nervous system (CNS). In order to compare algorithms and evaluate their performance under various circumstances, ground truth about their input needs to be present. Obtaining ground truth from real data would require optimal algorithms to be used, given that those exist. This is not possible since it relies on the very algorithms that are to be evaluated. Using realistic models of the recording situation facilitates the simulation of extracellular recordings. The simulation gives access to a priori known signal characteristics such as spike times and identities. In this paper, we describe a simulator based on a library of spikes obtained from recordings in the cat cerebellum and observed statistics of neuronal behavior during spontaneous activity. The simulator has proved to be useful in the task of generating extracellular recordings with realistic background noise and known ground truth to use in the evaluation of algorithms for spike detection and sorting.},
}
@article {pmid19964656,
year = {2009},
author = {Zhang, H and Ang, KK and Guan, C and Wang, C},
title = {Spatio-spectral feature selection based on robust mutual information estimate for Brain Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4978-4981},
doi = {10.1109/IEMBS.2009.5334093},
pmid = {19964656},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography ; Humans ; *User-Computer Interface ; },
abstract = {This paper addresses the issue of selecting optimal spatio-spectral features, which is key to high performance motor imagery (MI) classification that is in turn one of the central topics in EEG-based brain computer interfaces. In particular, this work proposes a novel method which first formulates the selection of features as maximizing mutual information between class labels and features. It then uses a robust estimate of mutual information, within a filter-bank and common spatial pattern feature extraction framework, to select an effective feature set. We have assessed the proposed method on both BCI Competition IV Set I and a separate data set collected in our lab from 7 healthy subjects. The results indicate the method is effective in selecting optimal spatial-spectral features for classification.},
}
@article {pmid19964647,
year = {2009},
author = {Lotte, F and Guan, C and Ang, KK},
title = {Comparison of designs towards a subject-independent brain-computer interface based on motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4543-4546},
doi = {10.1109/IEMBS.2009.5334126},
pmid = {19964647},
issn = {2375-7477},
mesh = {Databases, Factual ; Electroencephalography/*methods ; Humans ; *Man-Machine Systems ; Motor Activity/*physiology ; Regression Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {A major limitation of current Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) is that they are subject-specific BCI, which require data recording and system training for each new user. This process is time consuming and inconvenient, especially for casual users or portable BCI with limited computational resources. In this paper, we explore the design of a Subject-Independent (SI) MI-based BCI, i.e., a BCI that can be used immediately by any new user without training the BCI with the user's data. This is achieved by training the BCI on data acquired from several other subjects. In order to assess the possibility to build such a BCI, we compared several designs based on different features and classifiers, on data from 9 subjects. Our results suggested that linear classifiers were the most appropriate for the design of MI-based SI-BCI. We also proposed a filter bank common spatial patterns feature extraction method based on a multi-resolution frequency decomposition which achieved the highest accuracy.},
}
@article {pmid19964646,
year = {2009},
author = {Yuan, H and He, B},
title = {Cortical imaging of sensorimotor rhythms for BCI applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4539-4542},
doi = {10.1109/IEMBS.2009.5334130},
pmid = {19964646},
issn = {2375-7477},
support = {R01EB007920/EB/NIBIB NIH HHS/United States ; T90 DK070106/DK/NIDDK NIH HHS/United States ; },
mesh = {Brain Mapping/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback, Sensory/physiology ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging ; Motor Cortex/physiology ; Random Allocation ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {Rhythmic electroencephalographic (EEG) activities associated with movement imaginations are widely used in developing noninvasive Brain-Computer Interface (BCI) towards replacing or restoring the lost motor function in the paralytics. And it is of great importance to develop imaging techniques to enhance the spatial resolution and specificity of the EEG modality. In our work, we developed an innovative approach of imaging the distributed rhythmic brain activity in the spectral domain. In the present study, we evaluated the proposed technique in experimental data of offline and online imaginations in naive and well-trained BCI subjects. Our results identified the cortical origins of sensorimotor rhythms. We also applied the source imaging approach to classifying mental states for BCI applications and demonstrated its feasibility and superior performance.},
}
@article {pmid19964644,
year = {2009},
author = {Hong, B and Lou, B and Guo, J and Gao, S},
title = {Adaptive active auditory brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4531-4534},
doi = {10.1109/IEMBS.2009.5334133},
pmid = {19964644},
issn = {2375-7477},
mesh = {Algorithms ; *Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Auditory/*physiology ; Female ; Humans ; Male ; *Self-Help Devices ; *User-Computer Interface ; Voice/physiology ; },
abstract = {An active paradigm was employed to produce reliable and prominent target response in an auditory brain computer interface (BCI), in which subject's voluntary recognition of the property of a target human voice enhances the discriminability between target and non-target EEG response. Furthermore, to adaptively decide the optimal number of trials being averaged for SVM classification, a statistical approach was proposed to convert each sample's margin in support vector space into probabilities of each voice choice being the target. In a testing of 8 subjects' EEG data from the active auditory BCI experiment, the proposed adaptive approach needs only about 4-6 trials to reach the equivalent accuracy of 15-trial averaging. The improved information transfer rate suggests the advantage of adaptive strategy in an active auditory BCI.},
}
@article {pmid19964602,
year = {2009},
author = {Li, L and Seth, S and Park, I and Sanchez, JC and Principe, JC},
title = {Estimation and visualization of neuronal functional connectivity in motor tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2926-2929},
doi = {10.1109/IEMBS.2009.5333991},
pmid = {19964602},
issn = {2375-7477},
mesh = {Algorithms ; Animals ; Aotidae ; Biomedical Engineering/methods ; Brain/physiology ; Brain Mapping ; Models, Neurological ; Motor Cortex/physiology ; Motor Neurons/*pathology ; Movement/physiology ; Nerve Net/physiology ; Neurons/metabolism/*pathology ; Signal Processing, Computer-Assisted ; Software ; },
abstract = {In brain-machine interface (BMI) modeling, the firing patterns of hundreds of neurons are used to reconstruct a variety of kinematic variables. The large number of neurons produces an explosion in the number of free parameters, which affects model generalization. This paper proposes a model-free measure of pairwise neural dependence to rank the importance of neurons in neural to motor mapping. Compared to a model-dependent approach such as sensitivity analysis, sixty percent of the neurons with the strongest dependence coincide with the top 10 most sensitive neurons trained through the model. Using this data-driven approach that operates on the input data alone, it is possible to perform neuron selection in a more efficient way that is not subject to assumptions about decoding models. To further understand the functional dependencies that influence neural to motor mapping, we use an open source available graph visualization toolkit called Prefuse to visualize the neural dependency graph and quantify the functional connectivity in motor cortex. This tool when adapted to the analysis of neuronal recordings has the potential to easily display the relationships in data of large dimension.},
}
@article {pmid19964572,
year = {2009},
author = {Langhals, NB and Kipke, DR},
title = {Validation of a novel three-dimensional electrode array within auditory cortex.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {2066-2069},
pmid = {19964572},
issn = {2375-7477},
support = {P41 EB002030/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Auditory Cortex/anatomy &amp; histology/*physiology ; Bone Screws ; *Electrodes, Implanted ; Electrophysiology/methods ; Equipment Design ; Image Processing, Computer-Assisted ; Male ; Neurophysiology/*methods ; Organ Size ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Software ; Steel ; Stereotaxic Techniques ; },
abstract = {Three-dimensional electrode arrays have a variety of potential applications in the fields of both intracortical mapping as well as basic research studies designed to characterize and understand the physiology of the brain. While higher channels counts are desired in brain-machine interface applications, the ability to analyze synchronous data from multiple cortical locations, including various depths is pivotal to fully mapping the underlying neurophysiology of sensory cortices. Within this study, we present a proof of concept validation of a 3D probe technology consisting of 16 silicon shanks in a 4x4 grid arrangement with four electrode sites per shank. This 3D array has been implanted in a rat primary auditory cortex and electrophysiological data are presented showing the utility of electrode sites spanning multilateral cortical space as well as cortical depth.},
}
@article {pmid19964479,
year = {2009},
author = {Sakamoto, Y and Aono, M},
title = {Supervised adaptive downsampling for P300-based brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {567-570},
doi = {10.1109/IEMBS.2009.5334054},
pmid = {19964479},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Data Compression/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Sample Size ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {To realize Brain Computer Interface, a recording electroencephalogram (EEG) and determining whether or not P300 is evoked by the presented stimulus have become increasingly important. Using the machine learning method for this classification is effective, but constructing feature vectors with all data points might result in very high-dimensional data. Because such redundant features are undesirable from the viewpoint of computation and classification performance, EEG has been downsampled in several studies. In the present study, we propose a new downsampling method aiming at the improvement of P300 classification accuracy. In particular, each single trial EEG is segmented at non-uniform intervals and then averaged in each segment. The segmentation is decided in such a way that the degree of separating two classes from training data is increased by applying a time series segmentation algorithm. Our experiment using the BCI Competition III P300 Speller paradigm data set demonstrated that our method resulted in higher accuracy than traditional downsampling methods.},
}
@article {pmid19964437,
year = {2009},
author = {Kamrunnahar, M and Dias, NS and Schiff, SJ},
title = {Optimization of electrode channels in Brain Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6477-6480},
pmid = {19964437},
issn = {2375-7477},
support = {K25 NS061001/NS/NINDS NIH HHS/United States ; K25 NS061001-01A2/NS/NINDS NIH HHS/United States ; K02MH01493/MH/NIMH NIH HHS/United States ; K25NS061001/NS/NINDS NIH HHS/United States ; },
mesh = {*Electrodes ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.},
}
@article {pmid19964436,
year = {2009},
author = {Yao, J and Dewald, JP},
title = {Impact of time-frequency representation to the generalization ability of synthesized time-frequency spatial patterns algorithm in Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6473-6476},
pmid = {19964436},
issn = {2375-7477},
support = {R01 HD039343/HD/NICHD NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; R01 50R1HD39343-02/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Humans ; Motor Cortex/*physiopathology ; Movement Disorders/*physiopathology/rehabilitation ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper focuses on the problem of how time-frequency representation influences the generalization ability of the 'synthesized time-frequency spatial pattern (TFSP)' algorithm in Brain Computer Interface (BCI) for classification. TFSP methods use time-frequency analysis to extract features in both time and frequency domains. Different time-frequency analysis methods have been used before. However, it is still unknown how these different approaches influence the generalization ability. We compared the performance of three different TFSP methods in classifying 3 stroke survivors' intention in hand opening and closing. Each of these TFSP methods uses different time-frequency analysis approaches with different time-frequency resolutions. Our results show that a high resolution in time-frequency resolution doesn't guarantee better generalization ability. It seems that although large redundancy in feature reduces the generalization ability of TFSP method, certain redundancy is necessary for achieving high generalization ability.},
}
@article {pmid19964435,
year = {2009},
author = {Uejima, T and Kita, K and Fujii, T and Kato, R and Takita, M and Yokoi, H},
title = {Motion classification using epidural electrodes for low-invasive brain-machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6469-6472},
doi = {10.1109/IEMBS.2009.5333547},
pmid = {19964435},
issn = {2375-7477},
mesh = {Algorithms ; Animals ; Dura Mater/*surgery ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Minimally Invasive Surgical Procedures/methods ; Motor Cortex/*physiology ; Movement/*physiology ; Rats ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons' communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat's neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.},
}
@article {pmid19964434,
year = {2009},
author = {Miller, KJ and Hermes, D and Schalk, G and Ramsey, NF and Jagadeesh, B and den Nijs, M and Ojemann, JG and Rao, RP},
title = {Detection of spontaneous class-specific visual stimuli with high temporal accuracy in human electrocorticography.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6465-6468},
doi = {10.1109/IEMBS.2009.5333546},
pmid = {19964434},
issn = {2375-7477},
mesh = {*Algorithms ; Electrocardiography/*methods ; *Evoked Potentials, Visual ; Humans ; Male ; Pattern Recognition, Automated ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiopathology ; },
abstract = {Most brain-computer interface classification experiments from electrical potential recordings have been focused on the identification of classes of stimuli or behavior where the timing of experimental parameters is known or pre-designated. Real world experience, however, is spontaneous, and to this end we describe an experiment predicting the occurrence, timing, and types of visual stimuli perceived by a human subject from electrocorticographic recordings. All 300 of 300 presented stimuli were correctly detected, with a temporal precision of order 20 ms. The type of stimulus (face/house) was correctly identified in 95% of these cases. There were approximately 20 false alarm events, corresponding to a late 2nd neuronal response to a previously identified event.},
}
@article {pmid19964433,
year = {2009},
author = {Yan, Z and Gao, X and Bin, G and Hong, B and Gao, S},
title = {A half-field stimulation pattern for SSVEP-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6461-6464},
doi = {10.1109/IEMBS.2009.5333544},
pmid = {19964433},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; *Artificial Intelligence ; Electrocardiography/*methods ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiopathology ; Young Adult ; },
abstract = {A novel stimulation pattern has been designed for brain-computer interface (BCI) using steady-state visual evoked potential (SSVEP) signals. Each target is composed of two flickers placed on right-and-left visual fields. The user is expected to concentrate his or her sight on the fixation point which is located in the middle of the two flickers modulated at specific frequencies respectively. Considering the role of optic chiasm, the two frequency components could be extracted from contralateral occipital regions. Canonical correlation analysis (CCA) was applied to distinguish the electroencephalography (EEG) frequency components from right-and-left visual cortex. The attractive feature of this method is that it would substantially increase the number of targets by a combination of frequencies. Based on this technique a nine-target SSVEP-based BCI system was designed using only three different frequencies. The test results with 8 subjects showed a classification accuracy between 40.0% and 96.3%.},
}
@article {pmid19964432,
year = {2009},
author = {Takahashi, H and Yoshikawa, T and Furuhashi, T},
title = {Application of support vector machines to reliability-based automatic repeat request for Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6457-6460},
doi = {10.1109/IEMBS.2009.5333543},
pmid = {19964432},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Electrocardiography/*methods ; *Evoked Potentials, Motor ; Humans ; Imagination ; Motor Cortex/*physiopathology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A Brain-Computer Interface (BCI) is a system that could enable patients like those with Amyotrophic Lateral Sclerosis to control some equipment and to communicate with other people, and has been anticipated to be achieved. One of the problems in BCI research is a trade-off between speed and accuracy, and it is practically important to adjust those two performance measures effectively. So far the authors have considered BCIs as communications between users and computers, and have proposed an error control method, Reliability-Based Automatic Repeat reQuest (RB-ARQ). It has been shown that, with Linear Discriminant Analysis (LDA) as a classifier, RB-ARQ is more effective than other error control methods. In this paper, Support Vector Machines (SVMs), one of the most popular classifiers, are applied to RB-ARQ. A quantitative comparison showed that there was no significant difference between LDA and SVM. Also, it was demonstrated that RB-ARQ improved the accuracy from the one acquired by the top ranked methods in the BCI competition to 100 percents, with less loss of the speed.},
}
@article {pmid19964345,
year = {2009},
author = {Rapoport, BI and Wattanapanitch, W and Penagos, HL and Musallam, S and Andersen, RA and Sarpeshkar, R},
title = {A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4214-4217},
pmid = {19964345},
issn = {2375-7477},
support = {R01-EY15545/EY/NEI NIH HHS/United States ; R01-NS056140/NS/NINDS NIH HHS/United States ; R01 NS056140/NS/NINDS NIH HHS/United States ; R01 EY013337-06A1/EY/NEI NIH HHS/United States ; R01 EY015545-01A1/EY/NEI NIH HHS/United States ; T32 GM007753/GM/NIGMS NIH HHS/United States ; R01-EY13337/EY/NEI NIH HHS/United States ; R01 NS056140-02/NS/NINDS NIH HHS/United States ; R01 EY013337/EY/NEI NIH HHS/United States ; R01 EY015545/EY/NEI NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Bayes Theorem ; *Biomimetics ; Brain/pathology ; Equipment Design ; Models, Neurological ; Models, Statistical ; Nerve Net ; Neurons/pathology ; Rats ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/instrumentation ; Time Factors ; User-Computer Interface ; },
abstract = {Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.},
}
@article {pmid19964338,
year = {2009},
author = {Li, K and Sankar, R and Arbel, Y and Donchin, E},
title = {Single trial independent component analysis for P300 BCI system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4035-4038},
doi = {10.1109/IEMBS.2009.5333745},
pmid = {19964338},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain/physiology ; *Data Interpretation, Statistical ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Man-Machine Systems ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {A Brain Computer Interface (BCI) is a device that allows the user to communicate with the world without utilizing voluntary muscle activity (i.e., using only the electrical activity of the brain). It makes use of the well-studied observation that the brain reacts differently to different stimuli, as a function of the level of attention allotted to the stimulus stream and the specific processing triggered by the stimulus. In this article we present a single trial independent component analysis (ICA) method that is working with a BCI system proposed by Farwell and Donchin. It can dramatically reduce the signal processing time and improve the data communicating rate. This ICA method achieved 76.67% accuracy on single trial P300 response identification.},
}
@article {pmid19964337,
year = {2009},
author = {Khan, OI and Kim, SH and Rasheed, T and Khan, A and Kim, TS},
title = {Extraction of P300 using constrained independent component analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4031-4034},
doi = {10.1109/IEMBS.2009.5333727},
pmid = {19964337},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain/physiology ; *Data Interpretation, Statistical ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {A brain computer interface (BCI) uses electrophysiological activities of the brain such as natural rhythms and evoked potentials to communicate with some external devices. P300 is a positive evoked potential (EP), elicited approximately 300 ms after an attended external stimulus. A P300-based BCI uses this evoked potential as a means of communication with the external devices. Until now this P300-based BCI has been rather slow, as it is difficult to detect a P300 response without averaging over a number of trials. Previously, independent component analysis (ICA) has been used in the extraction of P300. However, the drawback of ICA is that it extracts not only P300 but also non-P300 related components requiring a proper selection of P300 ICs by the system. In this study we propose an algorithm based on constrained independent component analysis (cICA) for P300 extraction which can extract only the relevant component by incorporating a priori information. A reference signal is generated as this a priori information of P300 and cICA is applied to extract the P300 related component. Then the extracted P300 IC is segmented, averaged, and classified into target and non-target events by means of a linear classifier. The method is fast, reliable, computationally inexpensive as compared to ICA and achieves an accuracy of 98.3% in the detection of P300.},
}
@article {pmid19964232,
year = {2009},
author = {Arboleda, C and Garcia, E and Posada, A and Torres, R},
title = {P300-based brain computer interface experimental setup.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {598-601},
doi = {10.1109/IEMBS.2009.5333794},
pmid = {19964232},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Pattern Recognition, Visual/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {A Brain-Computer interface (BCI) is a communication system that enables the generation of a control signal from brain signals such as sensorymotor rhythms and evoked potentials; therefore, it constitutes a novel communication option for people with severe motor disabilities (such as Amyotrophic Lateral Sclerosis patients). This paper presents the development of a P300-based BCI. This prototype uses a homemade six-channel electroencephalograph for the acquisition of the signals, and a visual stimulation matrix; since this matrix contains letters of the alphabet as well as images associated to them, it permits word-writing and the elaboration of messages with the images. To process the signals the software BCI2000 and MATLAB 7.0 were used. The latter was used to program three linear translation algorithms (Stepwise Linear Discriminant Analysis, Lineal Discriminant Analysis and Least Squares) to convert the brain signals into communication signals. These algorithms had a classification accuracy of 90.73 %, 95.75 % and 89.45 % respectively, when using raw data; and of 90.78%, 49.48 % and 53.9 %, when data was previously common-average filtered. The experimental setup was tested in ten healthy volunteers; 5 of them got a 100% success, 1 a 90% success, 2 an around 70% success and 2 a 50% success, in the online free-spelling tests.},
}
@article {pmid19964231,
year = {2009},
author = {Kanoh, S and Murayama, YM and Miyamoto, K and Yoshinobu, T and Kawashima, R},
title = {A NIRS-based brain-computer interface system during motor imagery: system development and online feedback training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {594-597},
doi = {10.1109/IEMBS.2009.5333710},
pmid = {19964231},
issn = {2375-7477},
mesh = {Algorithms ; Biofeedback, Psychology/instrumentation/*physiology ; Brain/*physiology ; Brain Mapping/methods ; Evoked Potentials, Motor/*physiology ; Hemoglobins/*analysis ; Humans ; Imagination/*physiology ; Online Systems ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {A brain-computer interface (BCI) to detect motor imagery from cerebrum hemodynamic data measured by NIRS (near-infrared spectroscopy) was constructed and the effect of the online feedback training for subjects was evaluated. Concentrations of Oxy- and deOxy-hemoglobin in the motor cortex during motor imagery of subject's right hand was measured by 52-channel NIRS system, and the mean magnitude of measured signal near C3 in the International 10-20 System was visually fed back online to the subject. On two out of three subjects, it was shown that the ratio between the averaged value and the standard deviation over trials (S/N ratio) of Oxy-hemoglobin signal elicited by the imagery of subject's right hand was increased by the 5-day online feedback training. Detailed investigation of the effect of the online feedback training on brain activities was left for further study.},
}
@article {pmid19964229,
year = {2009},
author = {Wang, W and Degenhart, AD and Collinger, JL and Vinjamuri, R and Sudre, GP and Adelson, PD and Holder, DL and Leuthardt, EC and Moran, DW and Boninger, ML and Schwartz, AB and Crammond, DJ and Tyler-Kabara, EC and Weber, DJ},
title = {Human motor cortical activity recorded with Micro-ECoG electrodes, during individual finger movements.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {586-589},
pmid = {19964229},
issn = {2375-7477},
support = {R01 EB007749-01/EB/NIBIB NIH HHS/United States ; R90 DA023426/DA/NIDA NIH HHS/United States ; R01 EB007749/EB/NIBIB NIH HHS/United States ; UL1 RR024153/RR/NCRR NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; R01 EB007749-04/EB/NIBIB NIH HHS/United States ; R21 NS056136-01A1/NS/NINDS NIH HHS/United States ; T90 DA022762/DA/NIDA NIH HHS/United States ; R21 NS056136-02/NS/NINDS NIH HHS/United States ; R01 EB007749-02/EB/NIBIB NIH HHS/United States ; R01 EB007749-03/EB/NIBIB NIH HHS/United States ; R21 NS056136/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Brain Mapping/instrumentation ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Female ; Fingers/*physiology ; Humans ; *Microelectrodes ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {In this study human motor cortical activity was recorded with a customized micro-ECoG grid during individual finger movements. The quality of the recorded neural signals was characterized in the frequency domain from three different perspectives: (1) coherence between neural signals recorded from different electrodes, (2) modulation of neural signals by finger movement, and (3) accuracy of finger movement decoding. It was found that, for the high frequency band (60-120 Hz), coherence between neighboring micro-ECoG electrodes was 0.3. In addition, the high frequency band showed significant modulation by finger movement both temporally and spatially, and a classification accuracy of 73% (chance level: 20%) was achieved for individual finger movement using neural signals recorded from the micro-ECoG grid. These results suggest that the micro-ECoG grid presented here offers sufficient spatial and temporal resolution for the development of minimally-invasive brain-computer interface applications.},
}
@article {pmid19964228,
year = {2009},
author = {Usakli, AB and Gurkan, S and Aloise, F and Vecchiato, G and Babiloni, F},
title = {A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {543-546},
doi = {10.1109/IEMBS.2009.5333742},
pmid = {19964228},
issn = {2375-7477},
mesh = {*Communication Devices for People with Disabilities ; Communication Disorders/etiology/*rehabilitation ; Electroencephalography/*instrumentation ; Electrooculography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Motor Neuron Disease/complications/*rehabilitation ; Signal Processing, Computer-Assisted/*instrumentation ; Systems Integration ; Therapy, Computer-Assisted/instrumentation ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {An efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amiotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. Often, such diseases leave the ocular movements preserved for a relatively long time. The aim of this study is to present a new approach for the hybrid system which is based on the recognition of electrooculogram (EOG) and electroencephalogram (EEG) measurements for efficient communication and control. As a first step we show that the EOG-based side of the system for communication and controls is useful for patients. The EOG side of the system has been equipped with an interface including a speller to notify of messages. A comparison of the performance of the EOG-based system has been made with a BCI system that uses P300 waveforms. As a next step, we plan to integrate EOG and EEG sides. The final goal of the project is to realize a unique noninvasive device able to offer the patient the partial restoration of communication and control abilities with EOG and EEG signals.},
}
@article {pmid19964227,
year = {2009},
author = {Zhang, D and Gao, X and Gao, S and Engel, AK and Maye, A},
title = {An independent brain-computer interface based on covert shifts of non-spatial visual attention.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {539-542},
doi = {10.1109/IEMBS.2009.5333740},
pmid = {19964227},
issn = {2375-7477},
mesh = {Adult ; Attention/*physiology ; Brain Mapping/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Motion Perception/*physiology ; Perceptual Masking/*physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Modulation of steady-state visual evoked potential (SSVEP) by directing gaze to targets flickering at different frequencies has been utilized in many brain-computer interface (BCI) studies. However, this paradigm may not work with patients suffering from complete locked-in syndrome or other severe motor disabilities that do not allow conscious control of gaze direction. In this paper, we present a novel, independent BCI paradigm based on covert shift of non-spatial visual selective attention. Subjects viewed a display consisting of two spatially overlapping sets of randomly positioned dots. The two dot sets differed in color, motion and flickering frequency. Two types of motion, rotation and linear motion, were investigated. Both, the SSVEP amplitude and phase response were modulated by selectively attending to one of the two dot sets. Offline analysis revealed a predicted online classification accuracy of 69.3+/-10.2% for the rotating dots, and 80.7+/-10.4% for the linearly moving dots.},
}
@article {pmid19964225,
year = {2009},
author = {Battapady, H and Lin, P and Fei, DY and Huang, D and Bai, O},
title = {Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {524-527},
doi = {10.1109/IEMBS.2009.5333632},
pmid = {19964225},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; Brain Mapping/methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Magnetoencephalography/*methods ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Volition/*physiology ; },
abstract = {The objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Seven naïve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed. The multi-class classification for four-directional control was evaluated offline from 10-fold cross-validation using direct-decision tree classifier and genetic algorithm based Mahalanobis linear distance. Beta band (15-30Hz) event-related desynchronization and event related synchronization were observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed four-direction classification from SAM- filtered MEG signal was as high as 95-97% for physical movements and 86-87% for motor imagery. The high classification accuracy suggests that a reliable high performance two-dimensional BCI can be achieved from single trial detection of human natural movement intentions from SAM-filtered MEG signals, where user may not need extensive training.},
}
@article {pmid19964224,
year = {2009},
author = {Rossini, L and Izzo, D and Summerer, L},
title = {Brain-machine interfaces for space applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {520-523},
doi = {10.1109/IEMBS.2009.5333678},
pmid = {19964224},
issn = {2375-7477},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Man-Machine Systems ; Space Flight/*methods ; *User-Computer Interface ; },
abstract = {In human space flight, astronauts are the most precious "payload" and astronaut time is extremely valuable. Astronauts operate under unusual and difficult conditions since the absence of gravity makes some of simple tasks tedious and cumbersome. Therefore, computer interfaces for astronauts are generally designed first for safety and then for functionality. In addition to general constraints like mass, volume, robustness, technological solutions need to enhance their functionality and efficiency while not compromising safety. Brain-machine interfaces show promising properties in this respect. It is however not obvious that devices developed for functioning on-ground can be used as hands-free interfaces for astronauts. This paper intends to address the potential of brain-machine interfaces for space applications, to review expected issues related with microgravity effects on brain activities, to highlight those research directions on brain-machine interfaces with the perceived highest potential impact on future space applications, and to embed these into long-term plans with respect to human space flight. We conclude by suggesting research and development steps considered necessary to include brain-machine interface technology in future architectures for human space flight.},
}
@article {pmid19964124,
year = {2009},
author = {Rouse, AG and Moran, DW},
title = {Neural adaptation of epidural electrocorticographic (EECoG) signals during closed-loop brain computer interface (BCI) tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {5514-5517},
doi = {10.1109/IEMBS.2009.5333180},
pmid = {19964124},
issn = {2375-7477},
support = {1R01 EB 009103-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Adaptation, Physiological/physiology ; Animals ; Biofeedback, Psychology/methods/*physiology ; Dura Mater/physiopathology ; Electroencephalography/*methods ; Haplorhini ; Motor Cortex/*physiology ; Movement/*physiology ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; *Task Performance and Analysis ; User-Computer Interface ; },
abstract = {Invasive BCI studies have classically relied on actual or imagined movements to train their neural decoding algorithms. In this study, non-human primates were required to perform a 2D BCI task using epidural microECoG recordings. The decoding weights and cortical locations of the electrodes used for control were randomly chosen and fixed for a series of daily recording sessions for five days. Over a period of one week, the subjects learned to accurately control a 2D computer cursor through neural adaptation of microECoG signals over "cortical control columns" having diameters on a the order of a few mm. These results suggest that the spatial resolution of microECoG recordings can be increased via neural plasticity.},
}
@article {pmid19964077,
year = {2009},
author = {Muller-Putz, GR and Scherer, R and Pfurtscheller, G and Neuper, C and Rupp, R},
title = {Non-invasive control of neuroprostheses for the upper extremity: temporal coding of brain patterns.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {3353-3356},
doi = {10.1109/IEMBS.2009.5333185},
pmid = {19964077},
issn = {2375-7477},
mesh = {Brain/*pathology ; Electric Stimulation Therapy ; *Electrodes, Implanted ; Electroencephalography/methods ; Electrophysiology/methods ; Equipment Design ; Hand/physiology ; Hand Strength ; Humans ; Man-Machine Systems ; Motor Skills ; Movement ; Spinal Cord Injuries/*physiopathology ; *User-Computer Interface ; },
abstract = {Spinal cord injury (SCI) results in deficits of sensory, motor and autonomous functions, with tremendous consequences for the patients. The loss of motor functions, especially grasping, leads to a dramatic decrease in quality of life. With the help of neuroprostheses, the grasp function can be substantially improved in cervical SCI patients. Nowadays, systems for grasp restoration can only be used by patients with preserved voluntary shoulder and elbow function. In patients with lesions above the 5th vertebra, not only the voluntary movements of the elbow are restricted, but also the overall number of preserved movements available for control purposes decreases. A Brain-Computer Interface (BCI) offers a method to overcome this problem. This work gives an overview of the Graz BCI used for the control of grasp neuroprostheses as well as a new control method for combining grasp and elbow function is introduced.},
}
@article {pmid19963973,
year = {2009},
author = {Valsan, G and Grychtol, B and Lakany, H and Conway, BA},
title = {The Strathclyde brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {606-609},
doi = {10.1109/IEMBS.2009.5333506},
pmid = {19963973},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to control their environment, communicate, and control mobility aids. However, the key to BCI usability rests in being able to extract relevant time varying signals that can be classified into usable commands in real time. This paper reports the first success of the Strathclyde BCI controlling a wheelchair on-line in Virtual Reality. Surface EEG recorded during wrist movement in two different directions were classified and used to control a wheelchair within a virtual reality environment. While Principal Component Analysis was used for feature vector quantiser distances were used for classification. Classification success rates between 68% and 77% were obtained using these relatively simple methods.},
}
@article {pmid19963970,
year = {2009},
author = {Kohler, P and Linsmeier, CE and Thelin, J and Bengtsson, M and Jorntell, H and Garwicz, M and Schouenborg, J and Wallman, L},
title = {Flexible multi electrode brain-machine interface for recording in the cerebellum.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {536-538},
doi = {10.1109/IEMBS.2009.5333498},
pmid = {19963970},
issn = {2375-7477},
mesh = {Animals ; Cats ; Cerebellum/*physiology ; Computer-Aided Design ; Elastic Modulus ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; *Microelectrodes ; Rats ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A new type of chip based microelectrode for acute electrophysiological recordings in the CNS has been developed. It's designed to be adaptable to a multitude of specific neuronal environments, in this study the cerebellar cortex of rat and cat. Photolithographically patternened SU-8 is used to yield flexible and biocompatible penetrating shanks with gold leads. Electrodes with an impedance of about 300 kOmega at 1kHz have excellent signal to noise ratio in acute recordings in cat cerebellum.},
}
@article {pmid19963969,
year = {2009},
author = {Dias, NS and Jacinto, LR and Mendes, PM and Correia, JH},
title = {Visual gate for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {532-535},
doi = {10.1109/IEMBS.2009.5333496},
pmid = {19963969},
issn = {2375-7477},
mesh = {Adult ; Attention/*physiology ; *Cues ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Brain-Computer Interfaces (BCI) based on event related potentials (ERP) have been successfully developed for applications like virtual spellers and navigation systems. This study tests the use of visual stimuli unbalanced in the subject's field of view to simultaneously cue mental imagery tasks (left vs. right hand movement) and detect subject attention. The responses to unbalanced cues were compared with the responses to balanced cues in terms of classification accuracy. Subject specific ERP spatial filters were calculated for optimal group separation. The unbalanced cues appear to enhance early ERPs related to cue visuospatial processing that improved the classification accuracy (as low as 6%) of ERPs in response to left vs. right cues soon (150-200 ms) after the cue presentation. This work suggests that such visual interface may be of interest in BCI applications as a gate mechanism for attention estimation and validation of control decisions.},
}
@article {pmid19963849,
year = {2009},
author = {Lopes, AC and Nunes, U},
title = {An assisted navigation training framework based on judgment theory using sparse and discrete human-machine interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4603-4606},
doi = {10.1109/IEMBS.2009.5332770},
pmid = {19963849},
issn = {2375-7477},
mesh = {Algorithms ; Computer Simulation ; Persons with Disabilities/rehabilitation ; Humans ; Judgment/*physiology ; Learning ; *Man-Machine Systems ; *Models, Theoretical ; *Robotics ; *Wheelchairs ; },
abstract = {This paper aims to present a new framework to train people with severe motor disabilities steering an assisted mobile robot (AMR), such as a powered wheelchair. Users with high level of motor disabilities are not able to use standard HMIs, which provide a continuous command signal (e. g. standard joystick). For this reason HMIs providing a small set of simple commands, which are sparse and discrete in time must be used (e. g. scanning interface, or brain computer interface), making very difficult to steer the AMR. In this sense, the assisted navigation training framework (ANTF) is designed to train users driving the AMR, in indoor structured environments, using this type of HMIs. Additionally it provides user characterization on steering the robot, which will later be used to adapt the AMR navigation system to human competence steering the AMR. A rule-based lens (RBL) model is used to characterize users on driving the AMR. Individual judgment performance choosing the best manoeuvres is modeled using a genetic-based policy capturing (GBPC) technique characterized to infer non-compensatory judgment strategies from human decision data. Three user models, at three different learning stages, using the RBL paradigm, are presented.},
}
@article {pmid19963835,
year = {2009},
author = {Jimenez, J and Heliot, R and Carmena, JM},
title = {Learning to use a brain-machine interface: model, simulation and analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4551-4554},
doi = {10.1109/IEMBS.2009.5332718},
pmid = {19963835},
issn = {2375-7477},
mesh = {Algorithms ; *Computer Simulation ; Feedback ; Learning/*physiology ; *Man-Machine Systems ; *Models, Biological ; Monte Carlo Method ; Sensitivity and Specificity ; },
abstract = {This paper presents a model of the learning process occurring during operation of a closed-loop brain-machine interface. The model consists of a population of simulated cortical neurons, a decoder that transforms neural activity into motor output, a feedback controller whose role is to reduce the error based on an error-descent algorithm, and an open-loop controller whose parameters are updated based on the corrections made by the feedback controller. We present evidence of the convergence of the internal model to the decoder's inverse model and use global sensitivity analysis to study the convergence's dependence on the parameters of the overall learning model. This model can be used as a simulation tool that predicts the outcome of closed-loop BMI experiments.},
}
@article {pmid19963834,
year = {2009},
author = {Huang, D and Lin, P and Fei, DY and Chen, X and Bai, O},
title = {EEG-based online two-dimensional cursor control.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {4547-4550},
doi = {10.1109/IEMBS.2009.5332722},
pmid = {19963834},
issn = {2375-7477},
mesh = {Cortical Synchronization/*methods ; Electromyography/*methods ; Evoked Potentials/*physiology ; Hand ; Humans ; Imagination/*physiology ; Man-Machine Systems ; Movement ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {This study aims to explore whether human intentions to move or cease to move right and left hands can provide four spatiotemporal patterns in single-trial non-invasive EEG signals to achieve a two-dimensional cursor control. Subjects performed motor tasks by either physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored to support accurate computer pattern recognition. The performance was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the moving hand for both physical movement and motor imagery. The offline classification of four motor tasks provided 10-fold cross-validation accuracy as high as 88% for physical movement and 73% for motor imagery. Subjects participating in experiments with physical movement were able to complete the online game with the average accuracy of 85.5 + or - 4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed brain-computer interface (BCI) provided a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.},
}
@article {pmid19963797,
year = {2009},
author = {Fagg, AH and Hatsopoulos, NG and London, BM and Reimer, J and Solla, SA and Wang, D and Miller, LE},
title = {Toward a biomimetic, bidirectional, brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {3376-3380},
doi = {10.1109/IEMBS.2009.5332819},
pmid = {19963797},
issn = {2375-7477},
support = {NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Biomechanical Phenomena ; *Biomimetics ; Brain/pathology/physiology ; Equipment Design ; Humans ; Kinetics ; *Man-Machine Systems ; Models, Statistical ; Motor Cortex/pathology ; Movement ; Robotics ; Torque ; *User-Computer Interface ; Vision, Ocular ; },
abstract = {The interest in Brain Machine Interface (BMI) systems has increased tremendously in recent times; many groups have become involved in this type of research, and progress has been quite encouraging. However, two fundamental limitations remain: 1) With a few notable exceptions, BMIs extract only kinematic information from the brain, ignoring the wealth of force or kinetic information also present in the primary motor cortex, and 2) most existing BMIs depend exclusively on natural vision to guide movement, lacking the rapid proprioceptive feedback that is critical for normal movement. The work reported here describes our efforts to address both of these limitations.},
}
@article {pmid19963795,
year = {2009},
author = {Mahmoudi, B and Principe, JC and Sanchez, JC},
title = {An Actor-Critic architecture and simulator for goal-directed Brain-Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {3365-3368},
doi = {10.1109/IEMBS.2009.5332825},
pmid = {19963795},
issn = {2375-7477},
mesh = {Animals ; Artificial Intelligence ; Biomedical Engineering ; Brain/pathology ; Computer Simulation ; Computers ; Equipment Design ; Humans ; *Man-Machine Systems ; Neural Networks, Computer ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; User-Computer Interface ; },
abstract = {The Perception-Action Cycle (PAC) is a central component of goal-directed behavior because it links internal percepts with external outcomes in the environment. Using inspiration from the PAC, we are developing a Brain-Machine Interface control architecture that utilizes both motor commands and goal information directly from the brain to navigate to novel targets in an environment. An Actor-Critic algorithm was selected for decoding the neural motor commands because it is a PAC-based computational framework where the perception component is implemented in the critic structure and the actor is responsible for taking actions. We develop in this work a biologically realistic simulator to analyze the performance of the decoder in terms of convergence and target acquisition. Experience from the simulator will guide parameter selection and assist in understanding the architecture before animal experiments. By varying the signal to noise ratio of the neural input and error signal, we were able to demonstrate how the learning rate and initial conditions affect a motor control target selection task. In this framework, the naïve decoder was able to reach targets in the presence of noise in the error signal and neural motor command with 98% accuracy.},
}
@article {pmid19963737,
year = {2009},
author = {Faradji, F and Ward, RK and Birch, GE},
title = {A self-paced BCI using stationary wavelet packets.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {962-965},
doi = {10.1109/IEMBS.2009.5332802},
pmid = {19963737},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Cognition/*physiology ; Electrocardiography/*methods ; Evoked Potentials/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets decompose the signal into eight components. The features used are the autoregressive coefficients obtained by applying autoregressive modeling on the resultant wavelet components. Classification is a two-stage process. The first stage is based on quadratic discriminant analysis which is extremely fast. The second stage is a simple majority voting classifier. During model selection, which is performed via 5-folded cross-validation, the combination of decomposed components and the autoregressive model order that yield the best performance are selected. Results show enhancements in the overall performance for three subjects comparing to our previously designed BCI.},
}
@article {pmid19963717,
year = {2009},
author = {Sagara, K and Kido, K and Ozawa, K},
title = {Portable single-channel NIRS-based BMI system for motor disabilities' communication tools.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {602-605},
doi = {10.1109/IEMBS.2009.5333071},
pmid = {19963717},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/*instrumentation ; *Communication Devices for People with Disabilities ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Hemoglobins/analysis ; Humans ; Male ; Middle Aged ; Miniaturization ; Movement Disorders/*rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/instrumentation ; Spectroscopy, Near-Infrared/*instrumentation ; *User-Computer Interface ; },
abstract = {A portable near-infrared spectroscopy (NIRS) -based brain-machine Interface (BMI) system featuring single-channel probe, BMI controller and Infrared-emission apparatus was developed. As a switching technology for external devices, the threshold logic was proposed, which detects the blood volume change in the operator's frontal lobe. Experiments showed that the operator was able to change the TV programs or get forward the toy robot within 16 s (the mean is 11.77 s and the standard deviation is 2.35 s) after the mental calculation. In addition, the menu selection program was proposed for motor disabilities and the preliminary test showed that he could successively select the sentence from several candidates. It was shown that this system would provide the external device's control capabilities for motor disabilities.},
}
@article {pmid19963715,
year = {2009},
author = {Ang, KK and Chin, ZY and Zhang, H and Guan, C},
title = {Robust filter bank common spatial pattern (RFBCSP) in motor-imagery-based brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {578-581},
doi = {10.1109/IEMBS.2009.5332817},
pmid = {19963715},
issn = {2375-7477},
mesh = {*Algorithms ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The Filter Bank Common Spatial Pattern (FBCSP) algorithm performs autonomous selection of key temporal-spatial discriminative EEG characteristics in motor imagery-based Brain Computer Interfaces (MI-BCI). However, FBCSP is sensitive to outliers because it involves multiple estimations of covariance matrices from EEG measurements. This paper proposes a Robust FBCSP (RFBCSP) algorithm whereby the estimates of the covariance matrices are replaced with the robust Minimum Covariance Determinant (MCD) estimator. The performance of RFBCSP is investigated on a publicly available dataset and compared against FBCSP using 10x10-fold cross-validation accuracies on training data, and session-to-session transfer kappa values on independent test data. The results showed that RFBCSP yielded improvements in certain subjects and slight improvement in overall performance across subjects. Analysis on one subject who improved suggested that outliers were excluded from the robust covariance matrices estimation. These results revealed a promising direction of RFBCSP for robust classifications of EEG measurements in MI-BCI.},
}
@article {pmid19963675,
year = {2009},
author = {Lu, H and Plataniotis, KN and Venetsanopoulos, AN},
title = {Regularized common spatial patterns with generic learning for EEG signal classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {6599-6602},
doi = {10.1109/IEMBS.2009.5332554},
pmid = {19963675},
issn = {2375-7477},
mesh = {Algorithms ; Brain Mapping ; Electroencephalography/*classification/*methods ; Humans ; *Learning ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; },
abstract = {The common spatial patterns (CSP) algorithm is commonly used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals in the context of brain-computer interfaces (BCIs). However, CSP is based on a sample-based covariance matrix estimation. Therefore, its performance is limited when the number of available training samples is small. In this paper, the CSP method is considered in such a small-sample setting. We propose a regularized common spatial patterns (R-CSP) algorithm by incorporating the principle of generic learning. The covariance matrix estimation in R-CSP is regularized through two regularization parameters to increase the estimation stability while reducing the estimation bias due to limited number of training samples. The proposed method is tested on data set IVa of the third BCI competition and the results show that R-CSP can outperform the classical CSP algorithm by 8.5% on average. Moreover, the regularization introduced is particularly effective in the small-sample setting.},
}
@article {pmid19963573,
year = {2009},
author = {Besio, WG and Kay, SM and Liu, X},
title = {An optimal spatial filtering electrode for brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {3138-3141},
doi = {10.1109/IEMBS.2009.5332575},
pmid = {19963573},
issn = {2375-7477},
mesh = {Algorithms ; Biomedical Engineering/methods ; Brain/*pathology ; Brain Mapping/instrumentation/methods ; Computer Simulation ; Electrodes ; Electroencephalography/*methods ; Electrophysiology/methods ; Equipment Design ; Humans ; Man-Machine Systems ; Models, Neurological ; Models, Statistical ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {There are millions of people in the U.S. and many more worldwide who could benefit from a noninvasive-based electroencephalography (EEG) brain computer interface (BCI). A BCI is an alternative or augmentative communication method for people with severe motor disabilities. However, EEG suffers from poor spatial resolution and signal-to-noise ratio (SNR). To improve the spatial resolution and SNR many researchers have turned to implantable electrodes. We have previously reported on significant improvements in BCI recognition rates using tripolar concentric ring electrodes compared to disc electrodes. We now report on a optimal method for combining the outputs from the independent elements of the tripolar concentric ring electrodes to improve the spatial resolution further. We used minimum variance distortionless look (MVDL), a beamformer, on simulated data to compare the spatial sensitivity of the optimal combination to disc electrodes and the tripolar concentric ring electrode surface Laplacian. The optimal combination shows the highest spatial sensitivity with the Laplacian a close second and disc electrodes resulting in a distant third. Further analysis is necessary with a more realistic computer model and then real signals. however it appears that the optimal combination may improve the spatial resolution of EEG further which in turn can be utilized to improve noninvasive EEG-based BCIs.},
}
@article {pmid19963572,
year = {2009},
author = {Gowreesunker, BV and Tewfik, AH and Tadipatri, VA and Ince, NF and Ashe, J and Pellizzer, G},
title = {Overcoming measurement time variability in brain machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {3134-3137},
doi = {10.1109/IEMBS.2009.5332568},
pmid = {19963572},
issn = {2375-7477},
mesh = {Algorithms ; Biomedical Engineering/*methods ; Brain/*pathology ; Equipment Design ; Humans ; Learning ; Least-Squares Analysis ; Man-Machine Systems ; Models, Neurological ; Movement ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {We introduce a subspace learning approach for multi-channel Local Field Potentials (LFP), and demonstrate its application in movement direction decoding for 8 directions movement. We show that the subspace learning method can effectively address the issue of signal instability across recording sessions by extracting recurrent features from the data. We present results for movement direction decoding, where we trained on two recording sessions, and evaluated decoding performance on a third session. We combine our method with a classifier based on Error-Correcting Output Codes (ECOC) and Common Spatial Patterns (CSP) and found improvement in Decoding Power (DP) from 76% to 88% for a subject known to have strong inter-session variability. Furthermore, we saw an increase from 86% to 90% DP with another subject which exhibited significantly less variability.},
}
@article {pmid19963467,
year = {2009},
author = {Royer, AS and McCullough, A and He, B},
title = {A sensorimotor rhythm based goal selection brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {575-577},
pmid = {19963467},
issn = {2375-7477},
support = {T32 EB008389/EB/NIBIB NIH HHS/United States ; 5 T90 DK70106/DK/NIDDK NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; 1 T32 EB008389-01/EB/NIBIB NIH HHS/United States ; T90 DK070106/DK/NIDDK NIH HHS/United States ; R01EB007920-01/EB/NIBIB NIH HHS/United States ; T32 GM008471/GM/NIGMS NIH HHS/United States ; },
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Feedback, Sensory/*physiology ; Humans ; *Periodicity ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Different control strategies exist for use in a brain-computer interface (BCI). Although process control is the prevailing control strategy for most sensorimotor rhythm based BCIs, the goal selection strategy more closely resembles normal motor control and may be more accurate, faster to use, and easier to learn. We describe here a sensorimotor rhythm based goal selection BCI and a pilot study to compare it with process control strategy in terms of accuracy and speed of use. In both trained and naïve subjects studied, goal selection outperformed process control.},
}
@article {pmid19963466,
year = {2009},
author = {Chin, ZY and Ang, KK and Wang, C and Guan, C and Zhang, H},
title = {Multi-class filter bank common spatial pattern for four-class motor imagery BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2009},
number = {},
pages = {571-574},
doi = {10.1109/IEMBS.2009.5332383},
pmid = {19963466},
issn = {2375-7477},
mesh = {*Algorithms ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence, this paper proposes 3 approaches of multi-class extension to the FBCSP algorithm: One-versus-Rest, Pair-Wise and Divide-and-Conquer. These approaches decompose the multi-class problem into several binary-class problems. The study is conducted on the BCI Competition IV dataset IIa, which comprises single-trial EEG data from 9 subjects performing 4-class motor imagery of left-hand, right-hand, foot and tongue actions. The results showed that the multi-class FBCSP algorithm could extract features that matched neurophysiological knowledge, and yielded the best performance on the evaluation data compared to other international submissions.},
}
@article {pmid19963032,
year = {2010},
author = {Vialatte, FB and Maurice, M and Dauwels, J and Cichocki, A},
title = {Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.},
journal = {Progress in neurobiology},
volume = {90},
number = {4},
pages = {418-438},
doi = {10.1016/j.pneurobio.2009.11.005},
pmid = {19963032},
issn = {1873-5118},
mesh = {Animals ; Brain Diseases/physiopathology ; Brain Mapping/methods ; Cognition/physiology ; *Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Vision, Binocular/physiology ; Visual Cortex/anatomy &amp; histology/*physiology ; Visual Pathways/anatomy &amp; histology/*physiology ; Visual Perception/physiology ; },
abstract = {After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain-computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.},
}
@article {pmid19812974,
year = {2010},
author = {Malarkey, EB and Parpura, V},
title = {Carbon nanotubes in neuroscience.},
journal = {Acta neurochirurgica. Supplement},
volume = {106},
number = {},
pages = {337-341},
pmid = {19812974},
issn = {0065-1419},
support = {R01 MH069791/MH/NIMH NIH HHS/United States ; R01 MH069791-05/MH/NIMH NIH HHS/United States ; MH 069791/MH/NIMH NIH HHS/United States ; },
mesh = {Animals ; Humans ; Nanotechnology/*methods ; Nanotubes, Carbon/chemistry/*statistics &amp; numerical data/ultrastructure ; *Neurosciences ; },
abstract = {Carbon nanotubes have electrical, mechanical and chemical properties that make them one of the most promising materials for applications in neuroscience. Single-walled and multi-walled carbon nanotubes have been increasingly used as scaffolds for neuronal growth and more recently for neural stem cell growth and differentiation. They are also used in interfaces with neurons, where they can detect neuronal electrical activity and also deliver electrical stimulation to these cells. The emerging picture is that carbon nanotubes do not have obvious adverse effects on mammalian health. Thus in the near future they could be used in brain-machine interfaces.},
}
@article {pmid19951784,
year = {2010},
author = {Wang, W and Collinger, JL and Perez, MA and Tyler-Kabara, EC and Cohen, LG and Birbaumer, N and Brose, SW and Schwartz, AB and Boninger, ML and Weber, DJ},
title = {Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity.},
journal = {Physical medicine and rehabilitation clinics of North America},
volume = {21},
number = {1},
pages = {157-178},
pmid = {19951784},
issn = {1558-1381},
support = {R01 EB007749-01/EB/NIBIB NIH HHS/United States ; R21 NS056136/NS/NINDS NIH HHS/United States ; R01 EB007749/EB/NIBIB NIH HHS/United States ; UL1 RR024153/RR/NCRR NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; 5 UL1 RR024153/RR/NCRR NIH HHS/United States ; R21 NS056136-02/NS/NINDS NIH HHS/United States ; },
mesh = {Activities of Daily Living ; Biofeedback, Psychology ; Persons with Disabilities/*rehabilitation ; Electric Stimulation Therapy/*instrumentation ; Electrodes, Implanted ; Humans ; Neuronal Plasticity/*physiology ; *Prostheses and Implants ; Psychomotor Performance/*physiology ; Quality of Life ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This article reviews neural interface technology and its relationship with neuroplasticity. Two types of neural interface technology are reviewed, highlighting specific technologies that the authors directly work with: (1) neural interface technology for neural recording, such as the micro-ECoG BCI system for hand prosthesis control, and the comprehensive rehabilitation paradigm combining MEG-BCI, action observation, and motor imagery training; (2) neural interface technology for functional neural stimulation, such as somatosensory neural stimulation for restoring somatosensation, and non-invasive cortical stimulation using rTMS and tDCS for modulating cortical excitability and stroke rehabilitation. The close interaction between neural interface devices and neuroplasticity leads to increased efficacy of neural interface devices and improved functional recovery of the nervous system. This symbiotic relationship between neural interface technology and the nervous system is expected to maximize functional gain for individuals with various sensory, motor, and cognitive impairments, eventually leading to better quality of life.},
}
@article {pmid19946737,
year = {2010},
author = {Vidaurre, C and Blankertz, B},
title = {Towards a cure for BCI illiteracy.},
journal = {Brain topography},
volume = {23},
number = {2},
pages = {194-198},
pmid = {19946737},
issn = {1573-6792},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; *Feedback, Psychological ; Humans ; Imagination/physiology ; Learning/physiology ; Motor Activity/physiology ; Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of "BCI Illiteracy", which is that BCI control does not work for a non-negligible portion of users (estimated 15 to 30%). Here, we investigate the illiteracy problem in BCI systems which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user from an initial subject-independent classifier that operates on simple features to a subject-optimized state-of-the-art classifier within one session while the user interacts the whole time with the same feedback application. While initial runs use supervised adaptation methods for robust co-adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an unbiased measure of BCI performance. Using this approach, which does not involve any offline calibration measurement, good performance was obtained by good BCI participants (also one novice) after 3-6 min of adaptation. More importantly, the use of machine learning techniques allowed users who were unable to achieve successful feedback before to gain significant control over the BCI system. In particular, one participant had no peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop such peak during the course of the session (and use voluntary modulation of its amplitude to control the feedback application).},
}
@article {pmid19940240,
year = {2009},
author = {Hoffmann, LC and Berry, SD},
title = {Cerebellar theta oscillations are synchronized during hippocampal theta-contingent trace conditioning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {106},
number = {50},
pages = {21371-21376},
pmid = {19940240},
issn = {1091-6490},
mesh = {Animals ; Behavior, Animal ; Cerebellar Cortex ; Cerebellum/*physiology ; Conditioning, Classical/*physiology ; Conditioning, Eyelid ; Hippocampus/*physiology ; Learning ; Rabbits ; *Theta Rhythm ; },
abstract = {The hippocampus and cerebellum are critically involved in trace eyeblink classical conditioning (EBCC). The mechanisms underlying the hippocampal-cerebellar interaction during this task are not well-understood, although hippocampal theta (3-7 Hz) oscillations are known to reflect a favorable state for EBCC. Two groups of rabbits received trace EBCC in which a brain-computer interface administered trials in either the explicit presence or absence of naturally occurring hippocampal theta. A high percentage of robust theta led to a striking enhancement of learning accompanied by rhythmic theta-band (6-7 Hz) oscillations in the interpositus nucleus (IPN) and cerebellar cortex that were time-locked both to hippocampal rhythms and sensory stimuli during training. Rhythmic oscillations were absent in the cerebellum of the non-theta group. These data strongly suggest a beneficial impact of theta-based coordination of hippocampus and cerebellum and, importantly, demonstrate that hippocampal theta oscillations can be used to index, and perhaps modulate, the functional properties of the cerebellum.},
}
@article {pmid19938302,
year = {2009},
author = {Perez-Marcos, D and Slater, M and Sanchez-Vives, MV},
title = {Inducing a virtual hand ownership illusion through a brain-computer interface.},
journal = {Neuroreport},
volume = {20},
number = {6},
pages = {589-594},
doi = {10.1097/wnr.0b013e32832a0a2a},
pmid = {19938302},
issn = {1473-558X},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Electroencephalography ; Electromyography ; *Hand ; Humans ; *Illusions ; Male ; Motor Skills ; Muscle, Skeletal/physiology ; Touch ; *User-Computer Interface ; Young Adult ; },
abstract = {The apparently stable brain representation of our bodies is easily challenged. We have recently shown that the illusion of ownership of a three-dimensional virtual hand can be evoked through synchronous tactile stimulation of a person's hidden real hand and that of the virtual hand. This reproduces the well-known rubber-hand illusion, but in virtual reality. Here we show that some aspects of the illusion can also occur through motor imagery used to control movements of a virtual hand. When movements of the virtual hand followed motor imagery, the illusion of ownership of the virtual hand was evoked and muscle activity measured through electromyogram correlated with movements of the virtual arm. Using virtual bodies has a great potential in the fields of physical and neural rehabilitation, making the understanding of ownership of a virtual body highly relevant.},
}
@article {pmid19938210,
year = {2009},
author = {Poznanski, RR},
title = {Model-based neuroimaging for cognitive computing.},
journal = {Journal of integrative neuroscience},
volume = {8},
number = {3},
pages = {345-369},
doi = {10.1142/s021963520900223x},
pmid = {19938210},
issn = {0219-6352},
mesh = {Brain/*physiology ; Brain Mapping/*methods ; Cognition/*physiology ; *Models, Neurological ; *Neural Networks, Computer ; },
abstract = {The continuity of the mind is suggested to mean the continuous spatiotemporal dynamics arising from the electrochemical signature of the neocortex: (i) globally through volume transmission in the gray matter as fields of neural activity, and (ii) locally through extrasynaptic signaling between fine distal dendrites of cortical neurons. If the continuity of dynamical systems across spatiotemporal scales defines a stream of consciousness then intentional metarepresentations as templates of dynamic continuity allow qualia to be semantically mapped during neuroimaging of specific cognitive tasks. When interfaced with a computer, such model-based neuroimaging requiring new mathematics of the brain will begin to decipher higher cognitive operations not possible with existing brain-machine interfaces.},
}
@article {pmid19931592,
year = {2010},
author = {Bakardjian, H and Tanaka, T and Cichocki, A},
title = {Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface.},
journal = {Neuroscience letters},
volume = {469},
number = {1},
pages = {34-38},
doi = {10.1016/j.neulet.2009.11.039},
pmid = {19931592},
issn = {1872-7972},
mesh = {*Biofeedback, Psychology ; Brain/*physiology ; Electric Stimulation ; *Evoked Potentials, Visual ; Humans ; *User-Computer Interface ; },
abstract = {This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8, 14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on.},
}
@article {pmid19930235,
year = {2010},
author = {Greenfield, SF and Shields, A and Connery, HS and Livchits, V and Yanov, SA and Lastimoso, CS and Strelis, AK and Mishustin, SP and Fitzmaurice, G and Mathew, TA and Shin, S},
title = {Integrated Management of Physician-delivered Alcohol Care for Tuberculosis Patients: Design and Implementation.},
journal = {Alcoholism, clinical and experimental research},
volume = {34},
number = {2},
pages = {317-330},
pmid = {19930235},
issn = {1530-0277},
support = {R01 AA016318-01/AA/NIAAA NIH HHS/United States ; K24 K24DA019855/DA/NIDA NIH HHS/United States ; K24 DA019855/DA/NIDA NIH HHS/United States ; R01 AA016318/AA/NIAAA NIH HHS/United States ; K24 DA019855-01/DA/NIDA NIH HHS/United States ; R01AA016318/AA/NIAAA NIH HHS/United States ; },
mesh = {Alcoholism/*complications/psychology/*therapy ; Behavior Therapy ; Combined Modality Therapy ; Counseling ; Delivery of Health Care, Integrated ; Humans ; Monitoring, Physiologic ; Naltrexone/therapeutic use ; Narcotic Antagonists/therapeutic use ; *Patient Care Management ; Patient Compliance ; Patient Selection ; *Physicians ; Psychiatric Status Rating Scales ; Russia ; Treatment Outcome ; Tuberculosis/*complications/psychology/*therapy ; United States ; },
abstract = {BACKGROUND: While the integration of alcohol screening, treatment, and referral in primary care and other medical settings in the U.S. and worldwide has been recognized as a key health care priority, it is not routinely done. In spite of the high co-occurrence and excess mortality associated with alcohol use disorders (AUDs) among individuals with tuberculosis (TB), there are no studies evaluating effectiveness of integrating alcohol care into routine treatment for this disorder.<br><br>METHODS: We designed and implemented a randomized controlled trial (RCT) to determine the effectiveness of integrating pharmacotherapy and behavioral treatments for AUDs into routine medical care for TB in the Tomsk Oblast Tuberculosis Service (TOTBS) in Tomsk, Russia. Eligible patients are diagnosed with alcohol abuse or dependence, are newly diagnosed with TB, and initiating treatment in the TOTBS with Directly Observed Therapy-Short Course (DOTS) for TB. Utilizing a factorial design, the Integrated Management of Physician-delivered Alcohol Care for Tuberculosis Patients (IMPACT) study randomizes eligible patients who sign informed consent into 1 of 4 study arms: (1) Oral Naltrexone + Brief Behavioral Compliance Enhancement Therapy (BBCET) + treatment as usual (TAU), (2) Brief Counseling Intervention (BCI) + TAU, (3) Naltrexone + BBCET + BCI + TAU, or (4) TAU alone.<br><br>RESULTS: Utilizing an iterative, collaborative approach, a multi-disciplinary U.S. and Russian team has implemented a model of alcohol management that is culturally appropriate to the patient and TB physician community in Russia. Implementation to date has achieved the integration of routine alcohol screening into TB care in Tomsk; an ethnographic assessment of knowledge, attitudes, and practices of AUD management among TB physicians in Tomsk; translation and cultural adaptation of the BCI to Russia and the TB setting; and training and certification of TB physicians to deliver oral naltrexone and brief counseling interventions for alcohol abuse and dependence as part of routine TB care. The study is successfully enrolling eligible subjects in the RCT to evaluate the relationship of integrating effective pharmacotherapy and brief behavioral intervention on TB and alcohol outcomes, as well as reduction in HIV risk behaviors.<br><br>CONCLUSIONS: The IMPACT study utilizes an innovative approach to adapt 2 effective therapies for treatment of alcohol use disorders to the TB clinical services setting in the Tomsk Oblast, Siberia, Russia, and to train TB physicians to deliver state of the art alcohol pharmacotherapy and behavioral treatments as an integrated part of routine TB care. The proposed treatment strategy could be applied elsewhere in Russia and in other settings where TB control is jeopardized by AUDs. If demonstrated to be effective, this model of integrating alcohol interventions into routine TB care has the potential for expanded applicability to other chronic co-occurring infectious and other medical conditions seen in medical care settings.},
}
@article {pmid19928392,
year = {2009},
author = {Murthy, JN and van Jaarsveld, J and Fei, J and Pavlidis, I and Harrykissoon, RI and Lucke, JF and Faiz, S and Castriotta, RJ},
title = {Thermal infrared imaging: a novel method to monitor airflow during polysomnography.},
journal = {Sleep},
volume = {32},
number = {11},
pages = {1521-1527},
pmid = {19928392},
issn = {0161-8105},
support = {UL1 RR024148/RR/NCRR NIH HHS/United States ; UL1RR024148/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Aged ; Airway Resistance/physiology ; Body Mass Index ; Feasibility Studies ; Female ; Humans ; *Image Processing, Computer-Assisted ; Male ; Middle Aged ; Nasal Cavity ; *Polysomnography ; Pulmonary Ventilation/*physiology ; Reproducibility of Results ; Sleep Apnea, Obstructive/*diagnosis/*physiopathology ; Thermography/*methods ; Young Adult ; },
abstract = {STUDY OBJECTIVES: This is a feasibility study designed to evaluate the accuracy of thermal infrared imaging (TIRI) as a noncontact method to monitor airflow during polysomnography and to ascertain the chance-corrected agreement (K) between TIRI and conventional airflow channels (nasal pressure [Pn], oronasal thermistor and expired CO2 [P(E)CO2]) in the detection of apnea and hypopnea.<br><br>DESIGN: Subjects were recruited to undergo polysomnography for 1 to 2 hours, during which simultaneous recordings from electroencephalography, electrooculography, electromyography, respiratory impedance plethysmography, conventional airflow channels, and TIRI were obtained.<br><br>SETTING: University-affiliated, American Academy of Sleep Medicine-accredited sleep disorders center.<br><br>PATIENTS OR PARTICIPANTS: Fourteen volunteers without a history of sleep disordered breathing and 13 patients with a history of obstructive sleep apnea were recruited.<br><br>MEASUREMENTS AND RESULTS: In the detection of apnea and hypopnea, excellent agreement was noted between TIRI and thermistor (kappa = 0.92, Bayesian Credible Interval [BCI] 0.86, 0.96; pkappa = 0.99). Good agreement was noted between TIRI and Pn (kappa = 0.83, BCI 0.70, 0.90; pkappa = 0.98) and between TIRI and P(E)CO2 (kappa = 0.80, BCI 0.66, 0.89; pkappa = 0.94).<br><br>CONCLUSIONS: TIRI is a feasible noncontact technology to monitor airflow during polysomnography. In its current methodologic incarnation, it demonstrates a high degree of chance-corrected agreement with the oronasal thermistor in the detection of apnea and hypopneas but demonstrates a lesser degree of chance-corrected agreement with Pn. Further overnight validation studies must be performed to evaluate its potential in clinical sleep medicine.},
}
@article {pmid19918858,
year = {2009},
author = {Beaver, K and Hollingworth, W and McDonald, R and Dunn, G and Tysver-Robinson, D and Thomson, L and Hindley, AC and Susnerwala, SS and Luker, K},
title = {Economic evaluation of a randomized clinical trial of hospital versus telephone follow-up after treatment for breast cancer.},
journal = {The British journal of surgery},
volume = {96},
number = {12},
pages = {1406-1415},
doi = {10.1002/bjs.6753},
pmid = {19918858},
issn = {1365-2168},
support = {G0800800/MRC_/Medical Research Council/United Kingdom ; PB-PG-0610-22123/DH_/Department of Health/United Kingdom ; },
mesh = {Breast Neoplasms/*economics/nursing ; Cancer Care Facilities/economics ; Cost of Illness ; Cost-Benefit Analysis ; England ; Female ; Follow-Up Studies ; Hospitalization/*economics ; Hospitals, District/economics ; Humans ; Neoplasm Metastasis ; Neoplasm Recurrence, Local/economics/nursing ; Nurse Clinicians/*economics ; Prospective Studies ; Referral and Consultation ; Telephone/*economics ; Travel ; },
abstract = {BACKGROUND: This was an economic evaluation of hospital versus telephone follow-up by specialist nurses after treatment for breast cancer.<br><br>METHODS: A cost minimization analysis was carried out from a National Health Service (NHS) perspective using data from a trial in which 374 women were randomized to telephone or hospital follow-up. Primary analysis compared NHS resource use for routine follow-up over a mean of 24 months. Secondary analyses included patient and carer travel and productivity costs, and NHS and personal social services costs of care in patients with recurrent breast cancer.<br><br>RESULTS: Patients who had telephone follow-up had approximately 20 per cent more consultations (634 versus 524). The longer duration of telephone consultations and the frequent use of junior medical staff in hospital clinics resulted in higher routine costs for telephone follow-up (mean difference pound 55 (bias-corrected 95 per cent confidence interval (b.c.i.) pound 29 to pound 77)). There were no significant differences in the costs of treating recurrence, but patients who had hospital-based follow-up had significantly higher travel and productivity costs (mean difference pound 47 (95 per cent b.c.i. pound 40 to pound 55)).<br><br>CONCLUSION: Telephone follow-up for breast cancer may reduce the burden on busy hospital clinics but will not necessarily lead to cost or salary savings.},
}
@article {pmid19916776,
year = {2009},
author = {Tometzki, T and Engell, S},
title = {Hybrid evolutionary optimization of two-stage stochastic integer programming problems: an empirical investigation.},
journal = {Evolutionary computation},
volume = {17},
number = {4},
pages = {511-526},
doi = {10.1162/evco.2009.17.4.17404},
pmid = {19916776},
issn = {1530-9304},
mesh = {*Algorithms ; *Computing Methodologies ; *Decision Making ; Stochastic Processes ; },
abstract = {In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.},
}
@article {pmid19914046,
year = {2009},
author = {Paquette, V and Beauregard, M and Beaulieu-Prévost, D},
title = {Effect of a psychoneurotherapy on brain electromagnetic tomography in individuals with major depressive disorder.},
journal = {Psychiatry research},
volume = {174},
number = {3},
pages = {231-239},
doi = {10.1016/j.pscychresns.2009.06.002},
pmid = {19914046},
issn = {0165-1781},
mesh = {Adult ; Brain/pathology/*physiopathology ; *Brain Mapping ; Cognitive Behavioral Therapy/*methods ; Depressive Disorder, Major/*pathology/*physiopathology/therapy ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Image Processing, Computer-Assisted ; Magnetoencephalography/*methods ; Male ; Middle Aged ; Multivariate Analysis ; Neuropsychological Tests ; Self Concept ; Spectrum Analysis ; Surveys and Questionnaires ; },
abstract = {Recent advances in power spectral analysis of electroencephalography (EEG) signals and brain-computer interface (BCI) technology may significantly contribute to the development of psychoneurotherapies. The goal of this study was to measure the effect of a psychoneurotherapy on brain source generators of abnormal EEG activity in individuals with major depressive disorder (MDD). Thirty participants with unipolar MDD were recruited in the community. The proposed psychoneurotherapy was developed based on the relationship between the localization of abnormal EEG activity and depressive symptomatology. Brain electromagnetic abnormalities in MDD were identified with low resolution brain electromagnetic tomography (LORETA) and a normative EEG database. Localization of brain changes after treatment was assessed through the standardized version of LORETA (sLORETA). Before treatment, excessive high-beta (18-30 Hz) activity was noted in several brain regions located in the fronto-temporal regions. After treatment, only participants who successfully normalized EEG activity in cortico-limbic/paralimbic regions could be considered in clinical remission. In these regions, significant correlations were found between the percentage of change of depressive symptoms and the percentage of reduction in high-beta activity. These results suggest that the normalization of high-beta activity in cortico-limbic/paralimbic regions can be associated with a significant reduction of depressive symptoms.},
}
@article {pmid19908635,
year = {2009},
author = {Zender, HO and Olivier, P and Genné, D},
title = {[Acute community-acquired bacterial meningitis in adults].},
journal = {Revue medicale suisse},
volume = {5},
number = {220},
pages = {1968-70, 1972-4},
pmid = {19908635},
issn = {1660-9379},
mesh = {Acute Disease ; Adult ; Community-Acquired Infections/diagnosis/drug therapy/prevention &amp; control ; Humans ; *Meningitis, Bacterial/diagnosis/drug therapy/prevention &amp; control ; },
abstract = {Bacterial meningitis in adults is fatal in 20% of patients and leads to sequels in 30%. The clinical presentation includes two of the following four symptoms and signs: fever, headache, stiff neck, altered mental status. The essential ancillary test is the analysis of the cerebrospinal fluid. Sometimes, the lumbar puncture is not feasible or deferred (brain computer tomography), requiring antibiotics and corticosteroids early. 80% of bacterial meningitis are secondary to pneumococcus or meningococcus. Empirical antibiotics must be given as soon as possible and provide coverage for these both bacteria. Corticosteroids are also recommended for some meningitis. A score can predict the evolution. Preventive measure must be taken for close contacts of a patient with a meningococcal meningitis.},
}
@article {pmid19908295,
year = {2010},
author = {Yang, S and Zhang, L and Jia, C and Ma, H and Henter, JI and Shen, K},
title = {Frequency and development of CNS involvement in Chinese children with hemophagocytic lymphohistiocytosis.},
journal = {Pediatric blood &amp; cancer},
volume = {54},
number = {3},
pages = {408-415},
doi = {10.1002/pbc.22239},
pmid = {19908295},
issn = {1545-5017},
mesh = {Central Nervous System Diseases/cerebrospinal fluid/diagnosis/diagnostic imaging/*etiology ; Child ; Child, Preschool ; China ; Female ; Follow-Up Studies ; Humans ; Infant ; Lymphohistiocytosis, Hemophagocytic/cerebrospinal fluid/*complications/diagnosis/diagnostic imaging ; Male ; Prospective Studies ; Radiography ; },
abstract = {BACKGROUND: We investigated the characteristics, frequency, and prognosis of central nervous system (CNS) involvement in patients with hemophagocytic lymphohistiocytosis (HLH).<br><br>PROCEDURE: Neurological manifestations were prospectively assessed in 92 children with HLH treated from January 2004 to August 2008 at our center; 82 (89%) had associated viral infections (69 Epstein-Barr virus), one empyema, while no associated disease was identified in the remaining nine. Prior to treatment, all underwent cerebrospinal fluid (CSF) evaluation, brain computer tomography (CT) and/or magnetic resonance imaging (MRI).<br><br>RESULTS: At diagnosis, 43 (47%) children had CNS involvement. Twelve patients (13%) had neurological symptoms, including seizures, ataxia, coma, cranial nerve palsy, and hemiplegia. All patients improved after 8 weeks of therapy, but one later developed progressive neurological symptoms and six discontinued therapy due to progressive systemic symptoms and/or other reasons. Fifteen patients had CSF abnormalities that all normalized completely after 6 weeks of treatment. Thirty-six patients (39%) had neuroradiological abnormalities; with 5 still under treatment, 15 lost to follow-up, and 16 followed after completion of therapy. Of these 16, 12 improved, 3 were unchanged, and 1 progressed. Among all 21 children with CNS involvement followed after completion of therapy, 10 recovered completely, 10 improved (3 had remaining neuroradiological abnormalities), and 1 progressed clinically and neuroradiologically.<br><br>CONCLUSION: Most patients reported here suffered from secondary HLH and since CNS involvement is frequent in HLH, brain MRI at diagnosis is recommended in all HLH patients. Clinical and CSF abnormalities often improved within 8 weeks of therapy, but CT/MRI abnormalities normalized more slowly and less frequently.},
}
@article {pmid19904595,
year = {2010},
author = {Koyama, S and Chase, SM and Whitford, AS and Velliste, M and Schwartz, AB and Kass, RE},
title = {Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control.},
journal = {Journal of computational neuroscience},
volume = {29},
number = {1-2},
pages = {73-87},
pmid = {19904595},
issn = {1573-6873},
mesh = {Action Potentials/physiology ; *Algorithms ; Animals ; Computer Simulation ; Haplorhini ; *Models, Neurological ; Motor Cortex/*physiology ; Neurons/*physiology ; *User-Computer Interface ; },
abstract = {Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.},
}
@article {pmid19900285,
year = {2009},
author = {Tai, K and Chau, T},
title = {Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {6},
number = {},
pages = {39},
pmid = {19900285},
issn = {1743-0003},
mesh = {Adult ; Artifacts ; Brain Waves/physiology ; *Brain-Computer Interfaces ; Communication Devices for People with Disabilities ; Electroencephalography/*methods/standards ; Emotions/*physiology ; Female ; Humans ; Male ; *Models, Neurological ; Psychomotor Performance/physiology ; Reaction Time/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted/*instrumentation ; Spectroscopy, Near-Infrared/*methods/standards ; Young Adult ; },
abstract = {BACKGROUND: Corporeal machine interfaces (CMIs) are one of a few available options for restoring communication and environmental control to those with severe motor impairments. Cognitive processes detectable solely with functional imaging technologies such as near-infrared spectroscopy (NIRS) can potentially provide interfaces requiring less user training than conventional electroencephalography-based CMIs. We hypothesized that visually-cued emotional induction tasks can elicit forehead hemodynamic activity that can be harnessed for a CMI.<br><br>METHODS: Data were collected from ten able-bodied participants as they performed trials of positively and negatively-emotional induction tasks. A genetic algorithm was employed to select the optimal signal features, classifier, task valence (positive or negative emotional value of the stimulus), recording site, and signal analysis interval length for each participant. We compared the performance of Linear Discriminant Analysis and Support Vector Machine classifiers. The latency of the NIRS hemodynamic response was estimated as the time required for classification accuracy to stabilize.<br><br>RESULTS: Baseline and activation sequences were classified offline with accuracies upwards of 75.0%. Feature selection identified common time-domain discriminatory features across participants. Classification performance varied with the length of the input signal, and optimal signal length was found to be feature-dependent. Statistically significant increases in classification accuracy from baseline rates were observed as early as 2.5 s from initial stimulus presentation.<br><br>CONCLUSION: NIRS signals during affective states were shown to be distinguishable from baseline states with classification accuracies significantly above chance levels. Further research with NIRS for corporeal machine interfaces is warranted.},
}
@article {pmid19896364,
year = {2009},
author = {Scherberger, H},
title = {Neural control of motor prostheses.},
journal = {Current opinion in neurobiology},
volume = {19},
number = {6},
pages = {629-633},
doi = {10.1016/j.conb.2009.10.008},
pmid = {19896364},
issn = {1873-6882},
mesh = {Animals ; Brain/*cytology/physiology ; Communication Devices for People with Disabilities ; Feedback, Sensory/*physiology ; Fingers/physiology ; Humans ; Movement/*physiology ; Muscle, Skeletal/physiology ; Neuronal Plasticity/physiology ; Neurons/*physiology ; *Prostheses and Implants ; },
abstract = {Neural interfaces (NIs) for motor control have recently become increasingly advanced. This has been possible owing to substantial progress in our understanding of the cortical motor system as well as the development of appropriate decoding methods in both non-human primates and paralyzed patients. So far, neural interfaces have controlled mainly computer screens and robotic arms. An important advancement has been the demonstration of neural interfaces that can directly control the subject's muscles. Furthermore, it has been shown that cortical plasticity alone can optimize neural interface performance in the absence of machine learning, which emphasizes the role of the brain for neural interface adaptation. Future motor prostheses may use also sensory feedback to enhance their control capabilities.},
}
@article {pmid19888613,
year = {2010},
author = {Awwad Shiekh Hasan, B and Gan, JQ},
title = {Unsupervised movement onset detection from EEG recorded during self-paced real hand movement.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {48},
number = {3},
pages = {245-253},
pmid = {19888613},
issn = {1741-0444},
mesh = {Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/methods ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; Psychomotor Performance ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This article presents an unsupervised method for movement onset detection from electroencephalography (EEG) signals recorded during self-paced real hand movement. A Gaussian Mixture Model (GMM) is used to model the movement and idle-related EEG data. The GMM built along with appropriate classification and post processing methods are used to detect movement onsets using self-paced EEG signals recorded from five subjects, achieving True-False rate difference between 63 and 98%. The results show significant performance enhancement using the proposed unsupervised method, both in the sample-by-sample classification accuracy and the event-by-event performance, in comparison with the state-of-the-art supervised methods. The effectiveness of the proposed method suggests its potential application in self-paced Brain-Computer Interfaces (BCI).},
}
@article {pmid19886026,
year = {2009},
author = {Bauchet, L and Lonjon, N and Perrin, FE and Gilbert, C and Privat, A and Fattal, C},
title = {Strategies for spinal cord repair after injury: a review of the literature and information.},
journal = {Annals of physical and rehabilitation medicine},
volume = {52},
number = {4},
pages = {330-351},
doi = {10.1016/j.annrmp.2008.10.004},
pmid = {19886026},
issn = {1877-0665},
mesh = {Humans ; Nerve Regeneration/drug effects/physiology ; Orthopedic Procedures/*methods ; Spinal Cord/anatomy &amp; histology/surgery ; Spinal Cord Injuries/physiopathology/prevention &amp; control/*therapy ; },
abstract = {INTRODUCTION: Thanks to the Internet, we can now have access to more information about spinal cord repair. Spinal cord injured (SCI) patients request more information and hospitals offer specific spinal cord repair medical consultations.<br><br>OBJECTIVE: Provide practical and relevant elements to physicians and other healthcare professionals involved in the care of SCI patients in order to provide adequate answers to their questions.<br><br>METHOD: Our literature review was based on English and French publications indexed in PubMed and the main Internet websites dedicated to spinal cord repair.<br><br>RESULTS: A wide array of research possibilities including notions of anatomy, physiology, biology, anatomopathology and spinal cord imaging is available for the global care of the SCI patient. Prevention and repair strategies (regeneration, transplant, stem cells, gene therapy, biomaterials, using sublesional uninjured spinal tissue, electrical stimulation, brain/computer interface, etc.) for the injured spinal cord are under development. It is necessary to detail the studies conducted and define the limits of these new strategies and benchmark them to the realistic medical and rehabilitation care available to these patients.<br><br>CONCLUSION: Research is quickly progressing and clinical trials will be developed in the near future. They will have to answer to strict methodological and ethical guidelines. They will first be designed for a small number of patients. The results will probably be fragmented and progress will be made through different successive steps.},
}
@article {pmid19879294,
year = {2010},
author = {Zhou, F and Liu, J and Yu, Y and Tian, X and Liu, H and Hao, Y and Zhang, S and Chen, W and Dai, J and Zheng, X},
title = {Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats.},
journal = {Journal of neuroscience methods},
volume = {185},
number = {2},
pages = {299-306},
doi = {10.1016/j.jneumeth.2009.10.001},
pmid = {19879294},
issn = {1872-678X},
mesh = {Algorithms ; Animals ; Brain Mapping ; Computer Simulation ; Conditioning, Operant/physiology ; Electric Stimulation ; Male ; Models, Neurological ; *Models, Statistical ; Motor Cortex/*physiology ; Motor Skills/physiology ; *Neural Networks, Computer ; Rats ; Rats, Sprague-Dawley ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A practical brain-machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply-add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications.},
}
@article {pmid19861147,
year = {2010},
author = {Pfurtscheller, G and Ortner, R and Bauernfeind, G and Linortner, P and Neuper, C},
title = {Does conscious intention to perform a motor act depend on slow cardiovascular rhythms?.},
journal = {Neuroscience letters},
volume = {468},
number = {1},
pages = {46-50},
doi = {10.1016/j.neulet.2009.10.060},
pmid = {19861147},
issn = {1872-7972},
mesh = {Adult ; Baroreflex ; Blood Pressure/*physiology ; *Consciousness ; Female ; Fingers/physiology ; Heart Rate/physiology ; Humans ; Male ; *Movement ; Oxyhemoglobins/metabolism ; Periodicity ; Prefrontal Cortex/metabolism ; Respiration ; Young Adult ; },
abstract = {Slow oscillations around 0.1 Hz are characteristic features of both the cardiovascular and central nervous systems. Such oscillation have been reported, e.g. in blood pressure, heart rate, EEG and brain oxygenation. Hence, conscious intention of a motor act may occur only as a result of brain activity changes in frontal and related brain areas, or might be entrained by slow oscillations in the blood pressure. Twenty-six subjects were asked to perform voluntary, self-paced (at free will) brisk finger movements. Some subjects performed self-paced movements in relatively periodic intervals of around 10s at the decreasing slope of the slow 0.1-Hz blood pressure oscillation. Our study reveals the first time that self-paced movements, at least in some subjects, do not stem from "free will" based on brain activity alone, but are influenced by slow blood pressure oscillations.},
}
@article {pmid19860924,
year = {2009},
author = {Lee, Y and Lee, H and Kim, J and Shin, HC and Lee, M},
title = {Classification of BMI control commands from rat's neural signals using extreme learning machine.},
journal = {Biomedical engineering online},
volume = {8},
number = {},
pages = {29},
pmid = {19860924},
issn = {1475-925X},
mesh = {Algorithms ; Animals ; Artificial Intelligence ; Brain/physiology ; Electrodes ; Female ; Hippocampus/pathology ; Male ; Models, Neurological ; Models, Statistical ; Neural Networks, Computer ; Neurons/*physiology ; Pattern Recognition, Automated/methods ; Rats ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.},
}
@article {pmid19853630,
year = {2010},
author = {Kansaku, K and Hata, N and Takano, K},
title = {My thoughts through a robot's eyes: an augmented reality-brain-machine interface.},
journal = {Neuroscience research},
volume = {66},
number = {2},
pages = {219-222},
doi = {10.1016/j.neures.2009.10.006},
pmid = {19853630},
issn = {1872-8111},
mesh = {Adult ; Analysis of Variance ; Electroencephalography ; Environment ; *Equipment Design ; Evoked Potentials/physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Movement ; Robotics/*instrumentation ; *User-Computer Interface ; },
abstract = {A brain-machine interface (BMI) uses neurophysiological signals from the brain to control external devices, such as robot arms or computer cursors. Combining augmented reality with a BMI, we show that the user's brain signals successfully controlled an agent robot and operated devices in the robot's environment. The user's thoughts became reality through the robot's eyes, enabling the augmentation of real environments outside the anatomy of the human body.},
}
@article {pmid19841687,
year = {2010},
author = {Usakli, AB and Gurkan, S and Aloise, F and Vecchiato, G and Babiloni, F},
title = {On the use of electrooculogram for efficient human computer interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2010},
number = {},
pages = {135629},
pmid = {19841687},
issn = {1687-5273},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/psychology ; Brain/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography ; Electrooculography/*instrumentation/methods ; Event-Related Potentials, P300 ; Humans ; *User-Computer Interface ; },
abstract = {The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as "clean-up" could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.},
}
@article {pmid19841276,
year = {2009},
author = {Kress, WJ and Erickson, DL and Jones, FA and Swenson, NG and Perez, R and Sanjur, O and Bermingham, E},
title = {Plant DNA barcodes and a community phylogeny of a tropical forest dynamics plot in Panama.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {106},
number = {44},
pages = {18621-18626},
pmid = {19841276},
issn = {1091-6490},
mesh = {Base Sequence ; DNA, Plant/*genetics ; Molecular Sequence Data ; Panama ; *Phylogeny ; Sequence Analysis, DNA ; Species Specificity ; Trees/*genetics ; *Tropical Climate ; },
abstract = {The assembly of DNA barcode libraries is particularly relevant within species-rich natural communities for which accurate species identifications will enable detailed ecological forensic studies. In addition, well-resolved molecular phylogenies derived from these DNA barcode sequences have the potential to improve investigations of the mechanisms underlying community assembly and functional trait evolution. To date, no studies have effectively applied DNA barcodes sensu strictu in this manner. In this report, we demonstrate that a three-locus DNA barcode when applied to 296 species of woody trees, shrubs, and palms found within the 50-ha Forest Dynamics Plot on Barro Colorado Island (BCI), Panama, resulted in >98% correct identifications. These DNA barcode sequences are also used to reconstruct a robust community phylogeny employing a supermatrix method for 281 of the 296 plant species in the plot. The three-locus barcode data were sufficient to reliably reconstruct evolutionary relationships among the plant taxa in the plot that are congruent with the broadly accepted phylogeny of flowering plants (APG II). Earlier work on the phylogenetic structure of the BCI forest dynamics plot employing less resolved phylogenies reveals significant differences in evolutionary and ecological inferences compared with our data and suggests that unresolved community phylogenies may have increased type I and type II errors. These results illustrate how highly resolved phylogenies based on DNA barcode sequence data will enhance research focused on the interface between community ecology and evolution.},
}
@article {pmid19840893,
year = {2009},
author = {Isa, T and Fetz, EE and Müller, KR},
title = {Recent advances in brain-machine interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1201-1202},
pmid = {19840893},
issn = {1879-2782},
support = {UL1 RR025014/RR/NCRR NIH HHS/United States ; UL1 RR025014-02/RR/NCRR NIH HHS/United States ; UL1 TR000423/TR/NCATS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Bioethics ; Brain/*physiology ; Humans ; Neural Networks, Computer ; Neurons/physiology ; Prostheses and Implants/trends ; *User-Computer Interface ; },
}
@article {pmid19834713,
year = {2010},
author = {Yokoyama, O and Ito, H and Aoki, Y and Oyama, N and Miwa, Y and Akino, H},
title = {Selective α1A-blocker improves bladder storage function in rats via suppression of C-fiber afferent activity.},
journal = {World journal of urology},
volume = {28},
number = {5},
pages = {609-614},
pmid = {19834713},
issn = {1433-8726},
mesh = {Adrenergic alpha-1 Receptor Antagonists/pharmacology/*therapeutic use ; Afferent Pathways/drug effects/*physiology ; Animals ; Cerebral Infarction/complications ; Dinoprostone/pharmacology ; Dose-Response Relationship, Drug ; Female ; Indoles/pharmacology/*therapeutic use ; Models, Animal ; Nerve Fibers, Unmyelinated/drug effects/*physiology ; Rats ; Rats, Sprague-Dawley ; Receptors, Adrenergic, alpha-1/drug effects/physiology ; Urinary Bladder/drug effects/*physiology ; Urinary Bladder, Overactive/*drug therapy/etiology/physiopathology ; Urination/drug effects/physiology ; },
abstract = {PURPOSE: In the present study, we used animal models to investigate whether the selective α(1A)-blocker silodosin exerts inhibitory effects on detrusor overactivity by modulating C-fiber afferent activity.<br><br>METHODS: To desensitize C-fiber afferents, 0.3 mg/kg of resiniferatoxin (RTX) was subcutaneously injected into some female Sprague-Dawley rats 2 days before creation of each model. (1) Left middle cerebral artery occlusion was performed to create a cerebral infarction (CI) model (CI rats). The effects of intravenous (i.v.) and intrathecal (i.t.) administrations of silodosin on cystometrography parameters were evaluated in conscious rats. (2) Rhythmic bladder pressure was recorded in rats under urethane anesthesia. Prostaglandin (PG) E(2) (0.4 mg/mL) was continuously administered intraurethrally, and the effects of intra-arterial (i.a.) silodosin on the micturition reflex (MR) were investigated.<br><br>RESULTS: (1) Silodosin (i.v.) dose-dependently increased bladder capacity (BC) in CI rats without decreasing bladder contraction pressure, but had no effects on BC in RTX-CI rats. Silodosin (i.t.) markedly increased BC in CI rats, but not in RTX-CI rats. (2) After intraurethral administration of PGE(2), the bladder contraction interval (BCI) was markedly reduced in non-RTX rats, but unchanged in RTX rats. Silodosin (i.a.) significantly prolonged BCI in non-RTX rats receiving intraurethral PGE(2).<br><br>CONCLUSIONS: These results suggest that the &#x3b1;(1A)-AR subtype activates C-fiber afferents, and that consequently &#x3b1;(1A)-blockade can improve bladder storage function.},
}
@article {pmid19828809,
year = {2009},
author = {Ganguly, K and Secundo, L and Ranade, G and Orsborn, A and Chang, EF and Dimitrov, DF and Wallis, JD and Barbaro, NM and Knight, RT and Carmena, JM},
title = {Cortical representation of ipsilateral arm movements in monkey and man.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {29},
number = {41},
pages = {12948-12956},
pmid = {19828809},
issn = {1529-2401},
support = {P01 NS040813/NS/NINDS NIH HHS/United States ; L30 NS060463/NS/NINDS NIH HHS/United States ; P01NS040813/NS/NINDS NIH HHS/United States ; NS21135/NS/NINDS NIH HHS/United States ; K99 NS065120/NS/NINDS NIH HHS/United States ; K99 NS065120-01A1/NS/NINDS NIH HHS/United States ; R01 DA019028/DA/NIDA NIH HHS/United States ; R01 DA019028-01A1/DA/NIDA NIH HHS/United States ; R37 NS021135/NS/NINDS NIH HHS/United States ; R56 NS021135/NS/NINDS NIH HHS/United States ; R01 NS021135/NS/NINDS NIH HHS/United States ; R00 NS065120-03/NS/NINDS NIH HHS/United States ; F32 NS061552/NS/NINDS NIH HHS/United States ; R00 NS065120/NS/NINDS NIH HHS/United States ; R01 NS021135-23/NS/NINDS NIH HHS/United States ; L30 NS060463-01/NS/NINDS NIH HHS/United States ; L30 NS060463-02/NS/NINDS NIH HHS/United States ; R00 NS065120-02/NS/NINDS NIH HHS/United States ; F32 NS061552-01/NS/NINDS NIH HHS/United States ; R01DA19028/DA/NIDA NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Adolescent ; Adult ; Analysis of Variance ; Animals ; *Arm ; *Brain Mapping ; Electroencephalography/methods ; Electromyography/methods ; Evoked Potentials, Motor/physiology ; Functional Laterality/*physiology ; Humans ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Predictive Value of Tests ; Task Performance and Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {A fundamental organizational principle of the primate motor system is cortical control of contralateral limb movements. Motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. Interestingly, this representation was more correlated with joint angles than hand position. Using bilateral electromyography recordings, we excluded the possibility that postural or mirror movements could exclusively account for these findings. In addition, linear methods could decode limb position from cortical field potentials in both monkeys. We also found that M1 spiking activity could control a biomimetic brain-machine interface reflecting ipsilateral kinematics. Finally, we recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.},
}
@article {pmid19818908,
year = {2009},
author = {Sorger, B and Dahmen, B and Reithler, J and Gosseries, O and Maudoux, A and Laureys, S and Goebel, R},
title = {Another kind of 'BOLD Response': answering multiple-choice questions via online decoded single-trial brain signals.},
journal = {Progress in brain research},
volume = {177},
number = {},
pages = {275-292},
doi = {10.1016/S0079-6123(09)17719-1},
pmid = {19818908},
issn = {1875-7855},
mesh = {Adult ; Brain/*blood supply/*physiopathology ; Brain Mapping ; Choice Behavior/*physiology ; Communication ; Consciousness/*physiology ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Male ; Neuropsychological Tests ; *Online Systems ; Oxygen/blood ; Signal Processing, Computer-Assisted ; Statistics as Topic ; *User-Computer Interface ; Young Adult ; },
abstract = {The term 'locked-in'syndrome (LIS) describes a medical condition in which persons concerned are severely paralyzed and at the same time fully conscious and awake. The resulting anarthria makes it impossible for these patients to naturally communicate, which results in diagnostic as well as serious practical and ethical problems. Therefore, developing alternative, muscle-independent communication means is of prime importance. Such communication means can be realized via brain-computer interfaces (BCIs) circumventing the muscular system by using brain signals associated with preserved cognitive, sensory, and emotional brain functions. Primarily, BCIs based on electrophysiological measures have been developed and applied with remarkable success. Recently, also blood flow-based neuroimaging methods, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), have been explored in this context. After reviewing recent literature on the development of especially hemodynamically based BCIs, we introduce a highly reliable and easy-to-apply communication procedure that enables untrained participants to motor-independently and relatively effortlessly answer multiple-choice questions based on intentionally generated single-trial fMRI signals that can be decoded online. Our technique takes advantage of the participants' capability to voluntarily influence certain spatio-temporal aspects of the blood oxygenation level-dependent (BOLD) signal: source location (by using different mental tasks), signal onset and offset. We show that healthy participants are capable of hemodynamically encoding at least four distinct information units on a single-trial level without extensive pretraining and with little effort. Moreover, real-time data analysis based on simple multi-filter correlations allows for automated answer decoding with a high accuracy (94.9%) demonstrating the robustness of the presented method. Following our 'proof of concept', the next step will involve clinical trials with LIS patients, undertaken in close collaboration with their relatives and caretakers in order to elaborate individually tailored communication protocols. As our procedure can be easily transferred to MRI-equipped clinical sites, it may constitute a simple and effective possibility for online detection of residual consciousness and for LIS patients to communicate basic thoughts and needs in case no other alternative communication means are available (yet)--especially in the acute phase of the LIS. Future research may focus on further increasing the efficiency and accuracy of fMRI-based BCIs by implementing sophisticated data analysis methods (e.g., multivariate and independent component analysis) and neurofeedback training techniques. Finally, the presented BCI approach could be transferred to portable fNIRS systems as only this would enable hemodynamically based communication in daily life situations.},
}
@article {pmid19794237,
year = {2009},
author = {Kubánek, J and Miller, KJ and Ojemann, JG and Wolpaw, JR and Schalk, G},
title = {Decoding flexion of individual fingers using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {6},
number = {6},
pages = {066001},
pmid = {19794237},
issn = {1741-2552},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; T32 NS007144/NS/NINDS NIH HHS/United States ; NS07144/NS/NINDS NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB000856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Biomechanical Phenomena ; Brain/*physiology ; Electrodiagnosis ; Epilepsy ; Female ; Fingers/*physiology ; Humans ; Male ; Microelectrodes ; Middle Aged ; Motor Activity/*physiology ; Rest/physiology ; Thumb/physiology ; Time Factors ; Young Adult ; },
abstract = {Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.},
}
@article {pmid19789106,
year = {2010},
author = {Gouy-Pailler, C and Congedo, M and Brunner, C and Jutten, C and Pfurtscheller, G},
title = {Nonstationary brain source separation for multiclass motor imagery.},
journal = {IEEE transactions on bio-medical engineering},
volume = {57},
number = {2},
pages = {469-478},
doi = {10.1109/TBME.2009.2032162},
pmid = {19789106},
issn = {1558-2531},
mesh = {Algorithms ; Analysis of Variance ; Brain/physiology ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; *Man-Machine Systems ; Motor Activity ; Psychomotor Performance ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; },
abstract = {This paper describes a method to recover task-related brain sources in the context of multiclass brain--computer interfaces (BCIs) based on noninvasive EEG. We extend the method joint approximate diagonalization (JAD) for spatial filtering using a maximum likelihood framework. This generic formulation: 1) bridges the gap between the common spatial patterns (CSPs) and blind source separation of nonstationary sources; and 2) leads to a neurophysiologically adapted version of JAD, accounting for the successive activations/deactivations of brain sources during motor imagery (MI) trials. Using dataset 2a of BCI Competition IV (2008) in which nine subjects were involved in a four-class two-session MI-based BCI experiment, a quantitative evaluation of our extension is provided by comparing its performance against JAD and CSP in the case of cross-validation, as well as session-to-session transfer. While JAD, as already proposed in other works, does not prove to be significantly better than classical one-versus-rest CSP, our extension is shown to perform significantly better than CSP for cross-validated and session-to-session performance. The extension of JAD introduced in this paper yields among the best session-to-session transfer results presented so far for this particular dataset; thus, it appears to be of great interest for real-life BCIs.},
}
@article {pmid19781986,
year = {2009},
author = {Battapady, H and Lin, P and Holroyd, T and Hallett, M and Chen, X and Fei, DY and Bai, O},
title = {Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {11},
pages = {1978-1987},
pmid = {19781986},
issn = {1872-8952},
support = {Z99 NS999999/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adult ; Evoked Potentials/physiology ; Female ; Humans ; Magnetics/*methods ; Magnetoencephalography/*methods ; Male ; Motor Activity/physiology ; Movement/*physiology ; Photic Stimulation/methods ; Young Adult ; },
abstract = {OBJECTIVE: To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signals for motor execution and motor imagery.<br><br>METHODS: Seven healthy volunteers, na&#xef;ve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG, and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification was performed offline. Genetic algorithm based Mahalanobis linear distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation.<br><br>RESULTS: Through SAM imaging, strong and distinct event-related desynchronization (ERD) associated with sustaining, and event-related synchronization (ERS) patterns associated with ceasing of right and left hand movements were observed in the beta band (15-30Hz) on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these areas of high activity for the corresponding events as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single-trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51+/-2.43%) as well as motor imagery sessions (GA-MLD: 89.69+/-3.34%).<br><br>CONCLUSION: Multiple movement intentions can be reliably detected from SAM-based spatially filtered single-trial MEG signals.<br><br>SIGNIFICANCE: MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control.},
}
@article {pmid19772859,
year = {2010},
author = {Tarafder, MR and Carabin, H and Joseph, L and Balolong, E and Olveda, R and McGarvey, ST},
title = {Estimating the sensitivity and specificity of Kato-Katz stool examination technique for detection of hookworms, Ascaris lumbricoides and Trichuris trichiura infections in humans in the absence of a 'gold standard'.},
journal = {International journal for parasitology},
volume = {40},
number = {4},
pages = {399-404},
pmid = {19772859},
issn = {1879-0135},
support = {R01 TW001582/TW/FIC NIH HHS/United States ; R01 TW001582-01/TW/FIC NIH HHS/United States ; R01 TW01582/TW/FIC NIH HHS/United States ; },
mesh = {Adult ; Ancylostomatoidea/isolation &amp; purification ; Animals ; Ascariasis/*diagnosis ; Ascaris lumbricoides/isolation &amp; purification ; Child, Preschool ; Feces/*parasitology ; Female ; Hookworm Infections/*diagnosis ; Humans ; Male ; Middle Aged ; Parasitology/*methods ; Philippines ; Reference Standards ; Sensitivity and Specificity ; Trichuriasis/*diagnosis ; Trichuris/isolation &amp; purification ; Young Adult ; },
abstract = {The accuracy of the Kato-Katz technique in identifying individuals with soil-transmitted helminth (STH) infections is limited by day-to-day variation in helminth egg excretion, confusion with other parasites and the laboratory technicians' experience. We aimed to estimate the sensitivity and specificity of the Kato-Katz technique to detect infection with Ascaris lumbricoides, hookworm and Trichuris trichiura using a Bayesian approach in the absence of a 'gold standard'. Data were obtained from a longitudinal study conducted between January 2004 and December 2005 in Samar Province, the Philippines. Each participant provided between one and three stool samples over consecutive days. Stool samples were examined using the Kato-Katz technique and reported as positive or negative for STHs. In the presence of measurement error, the true status of each individual is considered as latent data. Using a Bayesian method, we calculated marginal posterior densities of sensitivity and specificity parameters from the product of the likelihood function of observed and latent data. A uniform prior distribution was used (beta distribution: alpha=1, beta=1). A total of 5624 individuals provided at least one stool sample. One, two and three stool samples were provided by 1582, 1893 and 2149 individuals, respectively. All STHs showed variation in test results from day to day. Sensitivity estimates of the Kato-Katz technique for one stool sample were 96.9% (95% Bayesian Credible Interval [BCI]: 96.1%, 97.6%), 65.2% (60.0%, 69.8%) and 91.4% (90.5%, 92.3%), for A. lumbricoides, hookworm and T. trichiura, respectively. Specificity estimates for one stool sample were 96.1% (95.5%, 96.7%), 93.8% (92.4%, 95.4%) and 94.4% (93.2%, 95.5%), for A. lumbricoides, hookworm and T. trichiura, respectively. Our results show that the Kato-Katz technique can perform with reasonable accuracy with one day's stool collection for A. lumbricoides and T. trichiura. Low sensitivity of the Kato-Katz for detection of hookworm infection may be related to rapid degeneration of delicate hookworm eggs with time.},
}
@article {pmid19762208,
year = {2009},
author = {Sannelli, C and Braun, M and Müller, KR},
title = {Improving BCI performance by task-related trial pruning.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1295-1304},
doi = {10.1016/j.neunet.2009.08.006},
pmid = {19762208},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; Artifacts ; *Artificial Intelligence ; Brain/*physiology ; Calibration ; Electroencephalography/*methods ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Noise in electroencephalography data (EEG) is an ubiquitous problem that limits the performance of brain computer interfaces (BCI). While typical EEG artifacts are usually removed by trial rejection or by filtering, noise induced in the data by the subject's failure to produce the required mental state is very harmful. Such "noise" effects are rather common, especially for naive subjects in their training phase and, thus, standard artifact removal methods would inevitably fail. In this paper, we present a novel method which aims to detect such defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space. In this manner, our method effectively "cleans" the training data and thus allows better BCI classification. Preliminary results conducted on a data set of 43 naive subjects show a significant improvement for 74% of the subjects.},
}
@article {pmid19750199,
year = {2009},
author = {O'Doherty, JE and Lebedev, MA and Hanson, TL and Fitzsimmons, NA and Nicolelis, MA},
title = {A brain-machine interface instructed by direct intracortical microstimulation.},
journal = {Frontiers in integrative neuroscience},
volume = {3},
number = {},
pages = {20},
pmid = {19750199},
issn = {1662-5145},
abstract = {Brain-machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine-brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.},
}
@article {pmid19733128,
year = {2009},
author = {Do, L and Puthawala, A and Syed, N},
title = {Interstitial brachytherapy as boost for locally advanced T4 head and neck cancer.},
journal = {Brachytherapy},
volume = {8},
number = {4},
pages = {385-391},
doi = {10.1016/j.brachy.2009.03.191},
pmid = {19733128},
issn = {1538-4721},
mesh = {*Brachytherapy ; Carcinoma, Squamous Cell/drug therapy/*radiotherapy ; Combined Modality Therapy ; Disease-Free Survival ; Female ; Humans ; Kaplan-Meier Estimate ; Male ; Mouth Neoplasms/drug therapy/*radiotherapy ; Neoplasm Recurrence, Local/*prevention &amp; control ; Neoplasm Staging ; Oropharyngeal Neoplasms/drug therapy/*radiotherapy ; Radiotherapy Dosage ; Radiotherapy, Adjuvant ; Retrospective Studies ; },
abstract = {PURPOSE: Locally advanced squamous cell cancers of the head and neck (SCCHN) with bone and cartilage invasion (BCI) or those with soft-tissue invasion (STI) have been treated with resection followedup with chemoradiotherapy (CRT) or definitive CRT. However, locoregional recurrence remained a large component of treatment failure. High-dose-rate interstitial brachytherapy (BT) has been used for dose escalation to further prevent local relapse. This is a review of our experience.<br><br>METHODS AND MATERIALS: T4N0-3M0 locally advanced oral cavity and oropharyngeal squamous cell carcinoma (SCCA) patients underwent definitive CRT or radiotherapy (RT) followedup with brachytherapy (BT). RT doses ranged from 45 to 50.4Gy. The patients were reassessed at this dose and if response was inadequate, patients underwent BT. BT doses ranged from 24 to 30Gy at 3-4Gy per fraction BID with 6h in between fractions. Concurrent chemotherapy was platinum based.<br><br>RESULTS: Twenty patients were treated with CRT or RT alone followed by BT. Thirteen patients had STI and 7 had BCI; 14 patients were treated with CRT followed by BT; and 6 patients were treated with RT alone followed by BT. Five-year locoregional control was 61%. Five-year overall survival was 29%. When we excluded the patients treated with RT alone, 5-year overall survival was 36%. Nodal status was the only prognostic factor.<br><br>CONCLUSIONS: This study suggests CRT followedup with BT for patients with T4 locally advanced SCCHN of the oral cavity, and oropharynx is a feasible treatment option. In patients with poor response to CRT, BT may be used for dose escalation to increase locoregional control.},
}
@article {pmid19721186,
year = {2009},
author = {Fraser, GW and Chase, SM and Whitford, A and Schwartz, AB},
title = {Control of a brain-computer interface without spike sorting.},
journal = {Journal of neural engineering},
volume = {6},
number = {5},
pages = {055004},
doi = {10.1088/1741-2560/6/5/055004},
pmid = {19721186},
issn = {1741-2552},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback/*physiology ; Macaca mulatta ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {Two rhesus monkeys were trained to move a cursor using neural activity recorded with silicon arrays of 96 microelectrodes implanted in the primary motor cortex. We have developed a method to extract movement information from the recorded single and multi-unit activity in the absence of spike sorting. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter extraction algorithm. The animals achieved high-quality control comparable to the performance of decoding schemes based on sorted spikes. Our results suggest that even the simplest signal processing is sufficient for high-quality neuroprosthetic control.},
}
@article {pmid19721181,
year = {2009},
author = {Gaunt, RA and Hokanson, JA and Weber, DJ},
title = {Microstimulation of primary afferent neurons in the L7 dorsal root ganglia using multielectrode arrays in anesthetized cats: thresholds and recruitment properties.},
journal = {Journal of neural engineering},
volume = {6},
number = {5},
pages = {055009},
pmid = {19721181},
issn = {1741-2552},
support = {R01 EB007749/EB/NIBIB NIH HHS/United States ; 1R21NS056136/NS/NINDS NIH HHS/United States ; 1R01EB007749/EB/NIBIB NIH HHS/United States ; R21 NS056136-01A1/NS/NINDS NIH HHS/United States ; R01 EB007749-03/EB/NIBIB NIH HHS/United States ; R21 NS056136/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; Cats ; Differential Threshold/physiology ; Electric Stimulation/*instrumentation ; *Electrodes, Implanted ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/physiology ; Ganglia, Spinal/*physiology ; *Microelectrodes ; Neurons, Afferent/*physiology ; Recruitment, Neurophysiological/*physiology ; },
abstract = {Current research in motor neural prosthetics has focused primarily on issues related to the extraction of motor command signals from the brain (e.g. brain-machine interfaces) to direct the motion of prosthetic limbs. Patients using these types of systems could benefit from a somatosensory neural interface that conveys natural tactile and kinesthetic sensations for the prosthesis. Electrical microstimulation within the dorsal root ganglia (DRG) has been proposed as one method to accomplish this, yet little is known about the recruitment properties of electrical microstimulation in activating nerve fibers in this structure. Current-controlled microstimulation pulses in the range of 1-15 microA (200 micros, leading cathodic pulse) were delivered to the L7 DRG in four anesthetized cats using penetrating microelectrode arrays. Evoked responses and their corresponding conduction velocities (CVs) were measured in the sciatic nerve with a 5-pole nerve cuff electrode arranged as two adjacent tripoles. It was found that in 76% of the 69 electrodes tested, the stimulus threshold was less than or equal to 3 microA, with the lowest recorded threshold being 1.1 microA. The CVs of afferents recruited at threshold had a bimodal distribution with peaks at 70 m s(-1) and 85 m s(-1). In 53% of cases, the CV of the response at threshold was slower (i.e. smaller diameter fiber) than the CVs of responses observed at increasing stimulation amplitudes. In summary, we found that microstimulation applied through penetrating microelectrodes in the DRG provides selective recruitment of afferent fibers from a range of sensory modalities (as identified by CVs) at very low stimulation intensities. We conclude that the DRG may serve as an attractive location from which to introduce surrogate somatosensory feedback into the nervous system.},
}
@article {pmid19715762,
year = {2010},
author = {Darvas, F and Scherer, R and Ojemann, JG and Rao, RP and Miller, KJ and Sorensen, LB},
title = {High gamma mapping using EEG.},
journal = {NeuroImage},
volume = {49},
number = {1},
pages = {930-938},
pmid = {19715762},
issn = {1095-9572},
support = {K01 EB007362/EB/NIBIB NIH HHS/United States ; K01 EB007362-03/EB/NIBIB NIH HHS/United States ; EB007362/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Electromyography ; Female ; Functional Laterality/physiology ; Humans ; Male ; Middle Aged ; Models, Anatomic ; Motor Cortex/physiology ; Young Adult ; },
abstract = {High gamma (HG) power changes during motor activity, especially at frequencies above 70 Hz, play an important role in functional cortical mapping and as control signals for BCI (brain-computer interface) applications. Most studies of HG activity have used ECoG (electrocorticography) which provides high-quality spatially localized signals, but is an invasive method. Recent studies have shown that non-invasive modalities such as EEG and MEG can also detect task-related HG power changes. We show here that a 27 channel EEG (electroencephalography) montage provides high-quality spatially localized signals non-invasively for HG frequencies ranging from 83 to 101 Hz. We used a generic head model, a weighted minimum norm least squares (MNLS) inverse method, and a self-paced finger movement paradigm. The use of an inverse method enables us to map the EEG onto a generic cortex model. We find the HG activity during the task to be well localized in the contralateral motor area. We find HG power increases prior to finger movement, with average latencies of 462 ms and 82 ms before EMG (electromyogram) onset. We also find significant phase-locking between contra- and ipsilateral motor areas over a similar HG frequency range; here the synchronization onset precedes the EMG by 400 ms. We also compare our results to ECoG data from a similar paradigm and find EEG mapping and ECoG in good agreement. Our findings demonstrate that mapped EEG provides information on two important parameters for functional mapping and BCI which are usually only found in HG of ECoG signals: spatially localized power increases and bihemispheric phase-locking.},
}
@article {pmid19709960,
year = {2009},
author = {Peng, CC and Xiao, Z and Bashirullah, R},
title = {Toward energy efficient neural interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {11 Pt 2},
pages = {2697-2700},
doi = {10.1109/TBME.2009.2029704},
pmid = {19709960},
issn = {1558-2531},
support = {R01 NS053561-01A2/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Analog-Digital Conversion ; *Communication Devices for People with Disabilities ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; },
abstract = {This letter presents progress toward an energy efficient neural data acquisition transponder for brain-computer interfaces. The transponder utilizes a four-channel time-multiplexed analog front-end and an energy efficient short-range backscattering RF link to transmit digitized wireless data. In addition, a low-complexity autonomous and adaptive digital neural signal processor is proposed to minimize wireless bandwidth and overall power dissipation.},
}
@article {pmid19700814,
year = {2009},
author = {Felton, EA and Radwin, RG and Wilson, JA and Williams, JC},
title = {Evaluation of a modified Fitts law brain-computer interface target acquisition task in able and motor disabled individuals.},
journal = {Journal of neural engineering},
volume = {6},
number = {5},
pages = {056002},
pmid = {19700814},
issn = {1741-2552},
support = {K12 HD049112/HD/NICHD NIH HHS/United States ; KL2 RR025012/RR/NCRR NIH HHS/United States ; T32 GM008692/GM/NIGMS NIH HHS/United States ; K12 RR023268/RR/NCRR NIH HHS/United States ; K12 HD049077/HD/NICHD NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; },
mesh = {Adult ; Aged ; *Algorithms ; Brain/*physiopathology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; *Movement ; Movement Disorders/diagnosis/*physiopathology ; *Psychomotor Performance ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {A brain-computer interface (BCI) is a communication system that takes recorded brain signals and translates them into real-time actions, in this case movement of a cursor on a computer screen. This work applied Fitts' law to the evaluation of performance on a target acquisition task during sensorimotor rhythm-based BCI training. Fitts' law, which has been used as a predictor of movement time in studies of human movement, was used here to determine the information transfer rate, which was based on target acquisition time and target difficulty. The information transfer rate was used to make comparisons between control modalities and subject groups on the same task. Data were analyzed from eight able-bodied and five motor disabled participants who wore an electrode cap that recorded and translated their electroencephalogram (EEG) signals into computer cursor movements. Direct comparisons were made between able-bodied and disabled subjects, and between EEG and joystick cursor control in able-bodied subjects. Fitts' law aptly described the relationship between movement time and index of difficulty for each task movement direction when evaluated separately and averaged together. This study showed that Fitts' law can be successfully applied to computer cursor movement controlled by neural signals.},
}
@article {pmid19668698,
year = {2009},
author = {Rolston, JD and Gross, RE and Potter, SM},
title = {A low-cost multielectrode system for data acquisition enabling real-time closed-loop processing with rapid recovery from stimulation artifacts.},
journal = {Frontiers in neuroengineering},
volume = {2},
number = {},
pages = {12},
pmid = {19668698},
issn = {1662-6443},
support = {F30 NS060392/NS/NINDS NIH HHS/United States ; K08 NS046322/NS/NINDS NIH HHS/United States ; R21 NS054809/NS/NINDS NIH HHS/United States ; T32 NS007480/NS/NINDS NIH HHS/United States ; },
abstract = {Commercially available data acquisition systems for multielectrode recording from freely moving animals are expensive, often rely on proprietary software, and do not provide detailed, modifiable circuit schematics. When used in conjunction with electrical stimulation, they are prone to prolonged, saturating stimulation artifacts that prevent the recording of short-latency evoked responses. Yet electrical stimulation is integral to many experimental designs, and critical for emerging brain-computer interfacing and neuroprosthetic applications. To address these issues, we developed an easy-to-use, modifiable, and inexpensive system for multielectrode neural recording and stimulation. Setup costs are less than US$10,000 for 64 channels, an order of magnitude lower than comparable commercial systems. Unlike commercial equipment, the system recovers rapidly from stimulation and allows short-latency action potentials (<1 ms post-stimulus) to be detected, facilitating closed-loop applications and exposing neural activity that would otherwise remain hidden. To illustrate this capability, evoked activity from microstimulation of the rodent hippocampus is presented. System noise levels are similar to existing platforms, and extracellular action potentials and local field potentials can be recorded simultaneously. The system is modular, in banks of 16 channels, and flexible in usage: while primarily designed for in vivo use, it can be combined with commercial preamplifiers to record from in vitro multielectrode arrays. The system's open-source control software, NeuroRighter, is implemented in C#, with an easy-to-use graphical interface. As C# functions in a managed code environment, which may impact performance, analysis was conducted to ensure comparable speed to C++ for this application. Hardware schematics, layout files, and software are freely available. Since maintaining wired headstage connections with freely moving animals is difficult, we describe a new method of electrode-headstage coupling using neodymium magnets.},
}
@article {pmid19668422,
year = {2008},
author = {Jirásková, N and Rozsíval, P},
title = {Idiopathic intracranial hypertension in pediatric patients.},
journal = {Clinical ophthalmology (Auckland, N.Z.)},
volume = {2},
number = {4},
pages = {723-726},
pmid = {19668422},
issn = {1177-5467},
abstract = {PURPOSE: To evaluate retrospectively the features, treatment, and outcome of idiopathic intracranial hypertension (IIH) in children.<br><br>METHODS: Nine patients, 15 years and younger, diagnosed with IIH. Inclusion criteria were papilledema, normal brain computer tomography or magnetic resonance imaging, cerebrospinal fluid pressure greater than 250 mm H(2)O, normal cerebrospinal fluid content, and a nonfocal neurologic examination except for sixth nerve palsy.<br><br>RESULTS: Of the nine patients, eight were girls. Five girls were overweight and one boy was obese. The most common presenting symptom was headache (5 patients). Diplopia or strabismus did not occur in our group. Visual field abnormalities were present in all eyes, and severe visual loss resulting in light perception vision occurred in both eyes of one patient. Eight patients were treated medically with acetazolamide alone, and one girl needed a combination of acetazolamide and corticosteroids. This girl also required optic nerve sheath decompression surgery. Resolution of papilledema and recovery of visual function occurred in all patients.<br><br>CONCLUSIONS: Idiopathic intracranial hypertension in prepubertal children is rather uncommon. Prompt diagnosis and management are important to prevent permanent visual loss.},
}
@article {pmid19667458,
year = {2009},
author = {Tankus, A and Yeshurun, Y and Fried, I},
title = {An automatic measure for classifying clusters of suspected spikes into single cells versus multiunits.},
journal = {Journal of neural engineering},
volume = {6},
number = {5},
pages = {056001},
pmid = {19667458},
issn = {1741-2552},
support = {R01 NS033221/NS/NINDS NIH HHS/United States ; R01 NS033221-11/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Adolescent ; Adult ; Algorithms ; Artificial Intelligence ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Epilepsy/*diagnosis/*physiopathology ; Female ; Humans ; Male ; Nerve Net/*physiopathology ; Neurons ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {While automatic spike sorting has been investigated for decades, little attention has been allotted to consistent evaluation criteria that will automatically determine whether a cluster of spikes represents the activity of a single cell or a multiunit. Consequently, the main tool for evaluation has remained visual inspection by a human. This paper quantifies the visual inspection process. The results are well-defined criteria for evaluation, which are mainly based on visual features of the spike waveform, and an automatic adaptive algorithm that learns the classification by a given human and can apply similar visual characteristics for classification of new data. To evaluate the suggested criteria, we recorded the activity of 1652 units (single cells and multiunits) from the cerebrum of 12 human patients undergoing evaluation for epilepsy surgery requiring implantation of chronic intracranial depth electrodes. The proposed method performed similar to human classifiers and obtained significantly higher accuracy than two existing methods (three variants of each). Evaluation on two synthetic datasets is also provided. The criteria are suggested as a standard for evaluation of the quality of separation that will allow comparison between different studies. The proposed algorithm is suitable for real-time operation and as such may allow brain-computer interfaces to treat single cells differently than multiunits.},
}
@article {pmid19666343,
year = {2009},
author = {Fagg, AH and Ojakangas, GW and Miller, LE and Hatsopoulos, NG},
title = {Kinetic trajectory decoding using motor cortical ensembles.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {5},
pages = {487-496},
doi = {10.1109/TNSRE.2009.2029313},
pmid = {19666343},
issn = {1558-0210},
support = {NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Motor Cortex/*physiology ; Movement/*physiology ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion such as hand position and velocity, it is known that motor cortical activity also correlates with kinetic signals, including active hand force and joint torque. Here, we attempted to reconstruct torque trajectories of the shoulder and elbow joints from the activity of simultaneously recorded units in primary motor cortex (MI) as monkeys (Macaca Mulatta) made reaching movements in the horizontal plane. Using a linear filter decoding approach that considers the history of neuronal activity up to one second in the past, we found torque reconstruction performance nearly equal to that of Cartesian hand position and velocity, despite the considerably greater bandwidth of the torque signals. Moreover, the addition of delayed position and velocity feedback to the torque decoder substantially improved the torque reconstructions, suggesting that simple limb-state feedback may be useful to optimize BMI performance. These results may be relevant for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.},
}
@article {pmid19665554,
year = {2009},
author = {Waldert, S and Pistohl, T and Braun, C and Ball, T and Aertsen, A and Mehring, C},
title = {A review on directional information in neural signals for brain-machine interfaces.},
journal = {Journal of physiology, Paris},
volume = {103},
number = {3-5},
pages = {244-254},
doi = {10.1016/j.jphysparis.2009.08.007},
pmid = {19665554},
issn = {1769-7115},
mesh = {Action Potentials/physiology ; Animals ; Brain/*physiology ; Electrodiagnosis/methods ; Humans ; *Man-Machine Systems ; Movement/physiology ; Neurons/*physiology ; Psychomotor Performance/physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.},
}
@article {pmid19664900,
year = {2009},
author = {Hasegawa, RP and Hasegawa, YT and Segraves, MA},
title = {Neural mind reading of multi-dimensional decisions by monkey mid-brain activity.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1247-1256},
doi = {10.1016/j.neunet.2009.07.028},
pmid = {19664900},
issn = {1879-2782},
support = {R01 EY008212/EY/NEI NIH HHS/United States ; EY08212/EY/NEI NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Cues ; Decision Making/*physiology ; Female ; Functional Laterality ; Macaca mulatta ; Mesencephalon/physiology ; Microelectrodes ; Motor Activity/physiology ; Neurons/*physiology ; Neuropsychological Tests ; Psychomotor Performance/*physiology ; Reaction Time ; Regression Analysis ; Superior Colliculi/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) have the potential to improve the quality of life for individuals with disabilities. We engaged in the development of neural mind-reading techniques for cognitive BMIs to provide a readout of decision processes. We trained 2 monkeys on go/no-go tasks, and monitored the activity of groups of neurons in their mid-brain superior colliculus (SC). We designed a virtual decision function (VDF) reflecting the continuous progress of binary decisions on a single-trial basis, and applied it to the ensemble activity of SC neurons. Post hoc analyses using the VDF predicted the cue location as well as the monkey's motor choice (go or no-go) soon after the presentation of the cue. These results suggest that our neural mind-reading techniques have the potential to provide rapid real-time control of communication support devices.},
}
@article {pmid19660908,
year = {2009},
author = {Vidaurre, C and Krämer, N and Blankertz, B and Schlögl, A},
title = {Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1313-1319},
doi = {10.1016/j.neunet.2009.07.020},
pmid = {19660908},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Calibration ; Electroencephalography/*methods ; Feedback, Psychological/physiology ; Female ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {Several feature types have been used with EEG-based Brain-Computer Interfaces. Among the most popular are logarithmic band power estimates with more or less subject-specific optimization of the frequency bands. In this paper we introduce a feature called Time Domain Parameter that is defined by the generalization of the Hjorth parameters. Time Domain Parameters are studied under two different conditions. The first setting is defined when no data from a subject is available. In this condition our results show that Time Domain Parameters outperform all band power features tested with all spatial filters applied. The second setting is the transition from calibration (no feedback) to feedback, in which the frequency content of the signals can change for some subjects. We compare Time Domain Parameters with logarithmic band power in subject-specific bands and show that these features are advantageous in this situation as well.},
}
@article {pmid19660668,
year = {2009},
author = {Benabid, AL and Chabardes, S and Torres, N and Piallat, B and Krack, P and Fraix, V and Pollak, P},
title = {Functional neurosurgery for movement disorders: a historical perspective.},
journal = {Progress in brain research},
volume = {175},
number = {},
pages = {379-391},
doi = {10.1016/S0079-6123(09)17525-8},
pmid = {19660668},
issn = {1875-7855},
mesh = {Animals ; Brain/*physiology/*surgery ; Deep Brain Stimulation/history/methods ; History, 20th Century ; History, 21st Century ; Humans ; Movement Disorders/*surgery ; Neurosurgical Procedures/*history/*methods ; },
abstract = {Since the 1960s, deep brain stimulation and spinal cord stimulation at low frequency (30 Hz) have been used to treat intractable pain of various origins. For this purpose, specific hardware have been designed, including deep brain electrodes, extensions, and implantable programmable generators (IPGs). In the meantime, movement disorders, and particularly parkinsonian and essential tremors, were treated by electrolytic or mechanic lesions in various targets of the basal ganglia, particularly in the thalamus and in the internal pallidum. The advent in the 1960s of levodopa, as well as the side effects and complications of ablative surgery (e.g., thalamotomy and pallidotomy), has sent functional neurosurgery of movement disorders to oblivion. In 1987, the serendipitous discovery of the effect of high-frequency stimulation (HFS), mimicking lesions, allowed the revival of the surgery of movement disorders by stimulation of the thalamus, which treated tremors with limited morbidity, and adaptable and reversible results. The stability along time of these effects allowed extending it to new targets suggested by basic research in monkeys. The HFS of the subthalamic nucleus (STN) has profoundly challenged the practice of functional surgery as the effect on the triad of dopaminergic symptoms was very significant, allowing to decrease the drug dosage and therefore a decrease of their complications, the levodopa-induced dyskinesias. In the meantime, based on the results of previous basic research in various fields, HFS has been progressively extended to potentially treat epilepsy and, more recently, psychiatric disorders, such as obsessive-compulsive disorders, Gilles de la Tourette tics, and severe depression. Similarly, suggested by the observation of changes in PET scan, applications have been extended to cluster headaches by stimulation of the posterior hypothalamus and even more recently, to obesity and drug addiction. In the field of movement disorders, it has become clear that STN stimulation is not efficient on the nondopaminergic symptoms such as freezing of gait. Based on experimental data obtained in MPTP-treated parkinsonian monkeys, the pedunculopontine nucleus has been used as a new target, and as suggested by the animal research results, its use indeed improves walking and stability when stimulation is performed at low frequency (25 Hz). The concept of simultaneous stimulation of multiple targets eventually at low or high frequency, and that of several electrodes in one target, is being accepted to increase the efficiency. This leads to and is being facilitated by the development of new hardware (multiple-channel IPGs, specific electrodes, rechargeable batteries). Still additional efforts are needed at the level of the stimulation paradigm and in the waveform. The recent development of nanotechnologies allows the design of totally new systems expanding the field of deep brain stimulation. These new techniques will make it possible to not only inhibit or excite deep brain structures to alleviate abnormal symptoms but also open the field for the use of recording cortical activities to drive neuroprostheses through brain-computer interfaces. The new field of compensation of deficits will then become part of the field of functional neurosurgery.},
}
@article {pmid19660664,
year = {2009},
author = {Stieglitz, T and Rubehn, B and Henle, C and Kisban, S and Herwik, S and Ruther, P and Schuettler, M},
title = {Brain-computer interfaces: an overview of the hardware to record neural signals from the cortex.},
journal = {Progress in brain research},
volume = {175},
number = {},
pages = {297-315},
doi = {10.1016/S0079-6123(09)17521-0},
pmid = {19660664},
issn = {1875-7855},
mesh = {Animals ; Brain/*physiology ; Electric Stimulation Therapy/*instrumentation/*methods ; Electrodes, Implanted ; Humans ; Nervous System Diseases/*rehabilitation ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) record neural signals from cortical origin with the objective to control a user interface for communication purposes, a robotic artifact or artificial limb as actuator. One of the key components of such a neuroprosthetic system is the neuro-technical interface itself, the electrode array. In this chapter, different designs and manufacturing techniques will be compared and assessed with respect to scaling and assembling limitations. The overview includes electroencephalogram (EEG) electrodes and epicortical brain-machine interfaces to record local field potentials (LFPs) from the surface of the cortex as well as intracortical needle electrodes that are intended to record single-unit activity. Two exemplary complementary technologies for micromachining of polyimide-based arrays and laser manufacturing of silicone rubber are presented and discussed with respect to spatial resolution, scaling limitations, and system properties. Advanced silicon micromachining technologies have led to highly sophisticated intracortical electrode arrays for fundamental neuroscientific applications. In this chapter, major approaches from the USA and Europe will be introduced and compared concerning complexity, modularity, and reliability. An assessment of the different technological solutions comparable to a strength weaknesses opportunities, and threats (SWOT) analysis might serve as guidance to select the adequate electrode array configuration for each control paradigm and strategy to realize robust, fast, and reliable BCIs.},
}
@article {pmid19651550,
year = {2009},
author = {Vautrin, D and Artusi, X and Lucas, MF and Farina, D},
title = {A novel criterion of wavelet packet best basis selection for signal classification with application to brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {11 Pt 2},
pages = {2734-2738},
doi = {10.1109/TBME.2009.2028014},
pmid = {19651550},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Male ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This study proposes a method to select a wavelet basis for classification. It uses a strategy defined by Wickerhauser and Coifman and proposes a new additive criterion describing the contrast between classes. Its performance is compared with other approaches on simulated signals and on experimental EEG signals for brain-computer interface applications.},
}
@article {pmid19647981,
year = {2009},
author = {Gunduz, A and Sanchez, JC and Carney, PR and Principe, JC},
title = {Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1257-1270},
doi = {10.1016/j.neunet.2009.06.036},
pmid = {19647981},
issn = {1879-2782},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Electrodes, Implanted ; Electrodiagnosis/*methods ; Epilepsy ; Feasibility Studies ; *Hand ; Humans ; Linear Models ; Motor Activity/*physiology ; Neural Networks, Computer ; Nonlinear Dynamics ; *Signal Processing, Computer-Assisted ; },
abstract = {Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.},
}
@article {pmid19646534,
year = {2010},
author = {Tomioka, R and Müller, KR},
title = {A regularized discriminative framework for EEG analysis with application to brain-computer interface.},
journal = {NeuroImage},
volume = {49},
number = {1},
pages = {415-432},
doi = {10.1016/j.neuroimage.2009.07.045},
pmid = {19646534},
issn = {1095-9572},
mesh = {Algorithms ; Brain/*physiology ; Discrimination Learning/physiology ; Electroencephalography/*statistics &amp; numerical data ; Event-Related Potentials, P300/physiology ; Fingers/innervation/physiology ; Humans ; Logistic Models ; Models, Statistical ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {We propose a framework for signal analysis of electroencephalography (EEG) that unifies tasks such as feature extraction, feature selection, feature combination, and classification, which are often independently tackled conventionally, under a regularized empirical risk minimization problem. The features are automatically learned, selected and combined through a convex optimization problem. Moreover we propose regularizers that induce novel types of sparsity providing a new technique for visualizing EEG of subjects during tasks from a discriminative point of view. The proposed framework is applied to two typical BCI problems, namely the P300 speller system and the prediction of self-paced finger tapping. In both datasets the proposed approach shows competitive performance against conventional methods, while at the same time the results are easier accessible to neurophysiological interpretation. Note that our novel approach is not only applicable to Brain imaging beyond EEG but also to general discriminative modeling of experimental paradigms beyond BCI.},
}
@article {pmid19645535,
year = {2010},
author = {Ringer, AJ and Matern, E and Parikh, S and Levine, NB},
title = {Screening for blunt cerebrovascular injury: selection criteria for use of angiography.},
journal = {Journal of neurosurgery},
volume = {112},
number = {5},
pages = {1146-1149},
doi = {10.3171/2009.6.JNS08416},
pmid = {19645535},
issn = {1933-0693},
mesh = {Brain Injuries/*diagnostic imaging/*epidemiology/pathology ; Carotid Artery, Internal, Dissection/diagnostic imaging/epidemiology/pathology ; Cerebral Angiography/*methods ; Humans ; Mass Screening/*methods ; Wounds, Nonpenetrating/*diagnostic imaging/*epidemiology/pathology ; },
abstract = {OBJECT: Blunt cerebrovascular injury (BCI) to the carotid and vertebral arteries is being recognized with increasing frequency in trauma victims. Yet, only broadly defined criteria exist for the use of screening angiography. In this study, the authors systematically identified the associated injuries that predict BCI and provide guidelines for the types of injuries best evaluated by angiography.<br><br>METHODS: Criteria for screening angiography were developed with intentionally broad inclusion to maximize sensitivity. Screening criteria for each patient and angiographic results (5-point scale of BCI) were recorded prospectively. Injuries most often associated with a positive angiogram were identified. Dissection grades of 0-1 were classified as minor.<br><br>RESULTS: Of 365 patients evaluated for trauma by angiography between January 2000 and December 2005, 40 patients with penetrating trauma were excluded. Of the 325 patients included in the study, 100 (30.8%) had positive angiographic findings, including 79 (24.3%) with major injuries. Fractures of the cervical spine and midface (or mandibular ramus) were associated with major BCI (identified in 30.7% of patients with cervical fractures and 30.8% of patients with midface fractures). However, thoracic trauma and soft tissue injury of the neck were rarely associated with a significant BCI (0 and 3 cases, respectively). Horner syndrome and cervical bruit were associated with arterial dissection in 9 of 10 patients. Skull base fractures and unexplained neurological findings were associated with major BCI in 13 (18.3%) of 71 and 11 (16.9%) of 65 patients, respectively.<br><br>CONCLUSIONS: Cervical and facial fractures resulting from blunt trauma were highly associated with BCI. After significant thoracic trauma or soft tissue injury to the neck, angiography should be reserved for patients with unexplained neurological findings or expanding hematomas of the neck.},
}
@article {pmid19641479,
year = {2009},
author = {Wilson, JA and Schalk, G and Walton, LM and Williams, JC},
title = {Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {29},
pages = {},
pmid = {19641479},
issn = {1940-087X},
support = {1R01EB009103-01/EB/NIBIB NIH HHS/United States ; KL2 RR025012/RR/NCRR NIH HHS/United States ; 1KL2RR025012-01/RR/NCRR NIH HHS/United States ; R01 EB009103-01/EB/NIBIB NIH HHS/United States ; KL2 RR025012-01/RR/NCRR NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; T90 DK070079-01/DK/NIDDK NIH HHS/United States ; R01 EB009103/EB/NIBIB NIH HHS/United States ; R01 EB000856-06/EB/NIBIB NIH HHS/United States ; 1 T90 DK070079-01/DK/NIDDK NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; },
mesh = {Brain/*physiology ; Calibration ; Electrodes ; Electroencephalography/*instrumentation/methods ; Humans ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].},
}
@article {pmid19641287,
year = {2010},
author = {Lin, CT and Ko, LW and Chang, MH and Duann, JR and Chen, JY and Su, TP and Jung, TP},
title = {Review of wireless and wearable electroencephalogram systems and brain-computer interfaces--a mini-review.},
journal = {Gerontology},
volume = {56},
number = {1},
pages = {112-119},
doi = {10.1159/000230807},
pmid = {19641287},
issn = {1423-0003},
mesh = {Aged ; Aging ; Brain Diseases/*rehabilitation ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation/methods ; Humans ; Telemetry/*instrumentation/methods ; *User-Computer Interface ; },
abstract = {Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems.},
}
@article {pmid19640783,
year = {2009},
author = {Hong, B and Guo, F and Liu, T and Gao, X and Gao, S},
title = {N200-speller using motion-onset visual response.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {9},
pages = {1658-1666},
doi = {10.1016/j.clinph.2009.06.026},
pmid = {19640783},
issn = {1872-8952},
mesh = {Adult ; Algorithms ; Attention/physiology ; Color Perception/physiology ; Data Interpretation, Statistical ; *Electroencephalography ; Event-Related Potentials, P300/physiology ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Microcomputers ; *Motion ; Photic Stimulation ; Psychomotor Performance/physiology ; Recognition, Psychology/physiology ; *User-Computer Interface ; Visual Perception/physiology ; Young Adult ; },
abstract = {OBJECTIVE: This study presents a brain-computer interface (BCI) named N200-speller. A matrix of motion stimuli are displayed for inducing the motion-onset visual response that allows the subject to spell out a message by scalp EEG.<br><br>METHODS: The brief motion of chromatic visual objects embedded in a 36 virtual button onscreen interface is employed to evoke a motion-onset specific N200 component. The user focuses attention on the button labeled with the letter to be communicated and performs color recognition task. The computer determines the target letter by identifying the attended row and column respectively. A support vector machine (SVM) is used in the target detection procedures of the BCI system.<br><br>RESULTS: Ten subjects participated in this study. The neurophysiological characteristics of the N200-speller were compared with the classical P300-speller. The two paradigms elicit components with distinct spatio-temporal patterns. Classification of the data registered from all subjects reveals that the N200-speller achieves a comparable target detection accuracy with that of the P300-speller, given the same number of trials considered.<br><br>CONCLUSIONS: With the advantages of low contrast and luminance tolerance, the proposed motion-onset stimulus presentation paradigm can be applied to brain-computer interface.<br><br>SIGNIFICANCE: A novel motion-onset paradigm N200-speller is proposed and assessed for BCI spelling application.},
}
@article {pmid19638781,
year = {2009},
author = {Newman, LR and Lown, BA and Jones, RN and Johansson, A and Schwartzstein, RM},
title = {Developing a peer assessment of lecturing instrument: lessons learned.},
journal = {Academic medicine : journal of the Association of American Medical Colleges},
volume = {84},
number = {8},
pages = {1104-1110},
doi = {10.1097/ACM.0b013e3181ad18f9},
pmid = {19638781},
issn = {1938-808X},
support = {AG008812/AG/NIA NIH HHS/United States ; },
mesh = {*Academic Medical Centers ; Delphi Technique ; Faculty, Medical/*standards ; Humans ; *Peer Review ; Pilot Projects ; Program Development ; Program Evaluation ; Quality Control ; Reproducibility of Results ; Staff Development ; Teaching/*standards ; },
abstract = {Peer assessment of teaching can improve the quality of instruction and contribute to summative evaluation of teaching effectiveness integral to high-stakes decision making. There is, however, a paucity of validated, criterion-based peer assessment instruments. The authors describe development and pilot testing of one such instrument and share lessons learned. The report provides a description of how a task force of the Shapiro Institute for Education and Research at Harvard Medical School and Beth Israel Deaconess Medical Center used the Delphi method to engage academic faculty leaders to develop a new instrument for peer assessment of medical lecturing. The authors describe how they used consensus building to determine the criteria, scoring rubric, and behavioral anchors for the rating scale. To pilot test the instrument, participants assessed a series of medical school lectures. Statistical analysis revealed high internal consistency of the instrument's scores (alpha = 0.87, 95% bootstrap confidence interval [BCI] = 0.80 to 0.91), yet low interrater agreement across all criteria and the global measure (intraclass correlation coefficient = 0.27, 95% BCI = -0.08 to 0.44).The authors describe the importance of faculty involvement in determining a cohesive set of criteria to assess lectures. They discuss how providing evidence that a peer assessment instrument is credible and reliable increases the faculty's trust in feedback. The authors point to the need for proper peer rater training to obtain high interrater agreement measures, and posit that once such measures are obtained, reliable and accurate peer assessment of teaching could be used to inform the academic promotion process.},
}
@article {pmid19636394,
year = {2009},
author = {Wilson, JA and Williams, JC},
title = {Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.},
journal = {Frontiers in neuroengineering},
volume = {2},
number = {},
pages = {11},
pmid = {19636394},
issn = {1662-6443},
abstract = {The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.},
}
@article {pmid19635654,
year = {2009},
author = {Finke, A and Lenhardt, A and Ritter, H},
title = {The MindGame: a P300-based brain-computer interface game.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1329-1333},
doi = {10.1016/j.neunet.2009.07.003},
pmid = {19635654},
issn = {1879-2782},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/methods ; *Event-Related Potentials, P300 ; Feedback, Psychological ; Female ; Humans ; Linear Models ; Male ; *Play and Playthings ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; Young Adult ; },
abstract = {We present a Brain-Computer Interface (BCI) game, the MindGame, based on the P300 event-related potential. In the MindGame interface P300 events are translated into movements of a character on a three-dimensional game board. A linear feature selection and classification scheme is applied to identify P300 events and calculate gradual feedback features from a scalp electrode array. The classification during the online run of the game is computed on a single-trial basis without averaging over subtrials. We achieve classification rates of 0.65 on single-trials during the online operation of the system while providing gradual feedback to the player.},
}
@article {pmid19622847,
year = {2009},
author = {van Gerven, M and Farquhar, J and Schaefer, R and Vlek, R and Geuze, J and Nijholt, A and Ramsey, N and Haselager, P and Vuurpijl, L and Gielen, S and Desain, P},
title = {The brain-computer interface cycle.},
journal = {Journal of neural engineering},
volume = {6},
number = {4},
pages = {041001},
doi = {10.1088/1741-2560/6/4/041001},
pmid = {19622847},
issn = {1741-2552},
mesh = {Artificial Intelligence ; Biofeedback, Psychology ; Brain/*physiology ; Computers ; Diagnostic Imaging ; Humans ; Neuropsychological Tests ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.},
}
@article {pmid19622442,
year = {2009},
author = {Lei, X and Yang, P and Yao, D},
title = {An empirical bayesian framework for brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {6},
pages = {521-529},
doi = {10.1109/TNSRE.2009.2027705},
pmid = {19622442},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Bayes Theorem ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; Young Adult ; },
abstract = {Current brain-computer interface (BCI) systems suffer from high complex feature selectors in comparison to simple classifiers. Meanwhile, neurophysiological and experimental information are hard to be included in these two separate phases. In this paper, based on the hierarchical observation model, we proposed an empirical Bayesian linear discriminant analysis (BLDA), in which the neurophysiological and experimental priors are considered simultaneously; the feature selection, weighted differently, and classification are performed jointly, thus it provides a novel systematic algorithm framework which can utilize priors related to feature and trial in the classifier design in a BCI. BLDA was comparatively evaluated by two simulations of a two-class and a four-class problem, and then it was applied to two real four-class motor imagery BCI datasets. The results confirmed that BLDA is superior in accuracy and robustness to LDA, regularized LDA, and SVM.},
}
@article {pmid19619982,
year = {2009},
author = {Farquhar, J},
title = {A linear feature space for simultaneous learning of spatio-spectral filters in BCI.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1278-1285},
doi = {10.1016/j.neunet.2009.06.035},
pmid = {19619982},
issn = {1879-2782},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/methods ; Computer Simulation ; Electroencephalography/methods ; Humans ; *Learning ; Linear Models ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {It is shown how two of the most common types of feature mapping used for classification of single trial Electroencephalography (EEG), i.e. spatial and frequency filtering, can be equivalently performed as linear operations in the space of frequency-specific detector covariance tensors. Thus by first mapping the data to this space, a simple linear classifier can directly learn optimal spatial + frequency filters. Significantly, if the classifier's loss function is convex, learning these filters is a convex minimisation problem. It is also shown how to pre-process the data such that the resulting decision function is robust to the biases inherent in EEG data. Further, based upon ideas from Max Margin Matrix Factorisation, it is shown how the trace norm can be used to select solutions which have low rank. Low rank solutions are preferred as they reflect prior information about the types of EEG signals we expect to see, i.e. that the classifiable information is contained in only a few spatio/spectral pairs. They are also easier to interpret. This feature-space transformation is compared with the Common-Spatial-Patterns on simulated and real Imagined Movement Brain Computer Interface (BCI) data and shown to give state-of-the-art performance.},
}
@article {pmid19616995,
year = {2009},
author = {Vuckovic, A},
title = {Non-invasive BCI: how far can we get with motor imagination?.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {8},
pages = {1422-1423},
doi = {10.1016/j.clinph.2009.06.007},
pmid = {19616995},
issn = {1872-8952},
mesh = {Brain/*physiology ; Electroencephalography/methods ; Humans ; Imagination/*physiology ; Motor Activity/*physiology ; *User-Computer Interface ; },
}
@article {pmid19616405,
year = {2009},
author = {Haselager, P and Vlek, R and Hill, J and Nijboer, F},
title = {A note on ethical aspects of BCI.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1352-1357},
doi = {10.1016/j.neunet.2009.06.046},
pmid = {19616405},
issn = {1879-2782},
mesh = {*Bioethics ; Brain/*physiology ; Communication ; Communications Media/ethics ; Cooperative Behavior ; Humans ; Informed Consent/ethics ; Professional-Patient Relations/ethics ; Quadriplegia/therapy ; *User-Computer Interface ; },
abstract = {This paper focuses on ethical aspects of BCI, as a research and a clinical tool, that are challenging for practitioners currently working in the field. Specifically, the difficulties involved in acquiring informed consent from locked-in patients are investigated, in combination with an analysis of the shared moral responsibility in BCI teams, and the complications encountered in establishing effective communication with media.},
}
@article {pmid19615852,
year = {2009},
author = {Wang, Y and Principe, JC and Sanchez, JC},
title = {Ascertaining neuron importance by information theoretical analysis in motor Brain-Machine Interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {5-6},
pages = {781-790},
doi = {10.1016/j.neunet.2009.06.007},
pmid = {19615852},
issn = {1879-2782},
mesh = {Action Potentials ; Algorithms ; Animals ; Arm ; Biomechanical Phenomena ; Computer Simulation ; Electrodes, Implanted ; Haplorhini ; Information Theory ; *Models, Neurological ; Monte Carlo Method ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Neurons/*physiology ; Poisson Distribution ; Psychomotor Performance/physiology ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {Point process modeling of neural spike recordings has the potential to capture with high specificity the information contained in spike time occurrence. In Brain-Machine Interfaces (BMIs) the neural tuning characteristic assessed from neural spike recordings can distinguish neuron importance in terms of its modulation with the movement task. Consequently, it improves generalization and reduces significantly computation in previous decoding algorithms, where models reconstruct the kinematics from recorded activities of hundreds of neurons. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the important neuron subsets for point process decoding on BMI. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performance using subset selection is studied with respect to different number of neurons and compared to the one by the full neuron ensemble. With much less computation, the extracted importance neurons provide comparable kinematic reconstructions compared to the full neuron ensemble. The performance of the extracted subset is compared to the random selected subset with same number of neurons to further validate the effectiveness of the subset-extraction approach.},
}
@article {pmid19608382,
year = {2009},
author = {Yoon, JW and Roberts, SJ and Dyson, M and Gan, JQ},
title = {Adaptive classification for Brain Computer Interface systems using Sequential Monte Carlo sampling.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1286-1294},
doi = {10.1016/j.neunet.2009.06.005},
pmid = {19608382},
issn = {1879-2782},
mesh = {*Algorithms ; Area Under Curve ; Bayes Theorem ; Brain/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Electromyography ; Humans ; Models, Theoretical ; *Monte Carlo Method ; Nonlinear Dynamics ; ROC Curve ; *Signal Processing, Computer-Assisted ; Uncertainty ; *User-Computer Interface ; },
abstract = {Adaptive classification is a key function of Brain Computer Interfacing (BCI) systems. This paper proposes robust mathematical frameworks and their implementation for the on-line sequential classification of EEG signals in BCI systems. The proposed algorithms are extensions to the basic method of Andrieu et al. [Andrieu, C., de Freitas, N., and Doucet, A. (2001). Sequential bayesian semi-parametric binary classification. In Proc. NIPS], modified to be suitable for BCI use. We focus on the inference and prediction of target labels under a non-linear and non-Gaussian model. In this paper we introduce two new algorithms to handle missing or erroneous labeling in BCI data. One algorithm introduces auxiliary labels to process the uncertainty of the labels and the other modifies the optimal proposal functions to allow for uncertain labels. Although we focus on BCI problems in this paper, the algorithms can be generalized and applied to other application domains in which sequential missing labels are to be imputed under the presence of uncertainty.},
}
@article {pmid19608002,
year = {2009},
author = {Summerer, L and Izzo, D and Rossini, L},
title = {Brain-machine interfaces for space applications-research, technological development, and opportunities.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {213-223},
doi = {10.1016/S0074-7742(09)86016-9},
pmid = {19608002},
issn = {0074-7742},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; *Research/trends ; Research Design ; Space Flight/*instrumentation ; United States ; United States National Aeronautics and Space Administration ; *User-Computer Interface ; },
abstract = {Recent advances in brain research and brain-machine interfaces suggest these devices could play a central role in future generation computer interfaces. Successes in the use of brain machine interfaces for patients affected by motor paralysis, as well as first developments of games and gadgets based on this technology have matured the field and brought brain-machine interfaces to the brink of more general usability and eventually of opening new markets. In human space flight, astronauts are the most precious "payload" and astronaut time is extremely valuable. Astronauts operate under difficult and unusual conditions since the absence of gravity renders some of the very simple tasks tedious and cumbersome. Therefore, computer interfaces are generally designed for safety and functionality. All improvements and technical aids to enhance their functionality and efficiency, while not compromising safety or overall mass requirements, are therefore of great interest. Brain machine interfaces show some interesting properties in this respect. It is however not obvious that devices developed for functioning on-ground can be used as hands-free interfaces for astronauts. This chapter intends to highlight the research directions of brain machine interfaces with the perceived highest potential impact on future space applications, and to present an overview of the long-term plans with respect to human space flight. We conclude by suggesting research and development steps considered necessary to include brain-machine interface technology in future architectures for human space flight.},
}
@article {pmid19608001,
year = {2009},
author = {Citi, L and Tonet, O and Marinelli, M},
title = {Matching brain-machine interface performance to space applications.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {199-212},
doi = {10.1016/S0074-7742(09)86015-7},
pmid = {19608001},
issn = {0074-7742},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; Humans ; *Man-Machine Systems ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; *Space Flight ; *User-Computer Interface ; },
abstract = {A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied users, such as astronauts, a BMI would only be practical if conceived as an augmenting interface. A method is presented for pointing out effective combinations of HMIs and applications of robotics and automation to space. Latency and throughput are selected as performance measures for a hybrid bionic system (HBS), that is, the combination of a user, a device, and a HMI. We classify and briefly describe HMIs and space applications and then compare the performance of classes of interfaces with the requirements of classes of applications, both in terms of latency and throughput. Regions of overlap correspond to effective combinations. Devices requiring simpler control, such as a rover, a robotic camera, or environmental controls are suitable to be driven by means of BMI technology. Free flyers and other devices with six degrees of freedom can be controlled, but only at low-interactivity levels. More demanding applications require conventional interfaces, although they could be controlled by BMIs once the same levels of performance as currently recorded in animal experiments are attained. Robotic arms and manipulators could be the next frontier for noninvasive BMIs. Integrating smart controllers in HBSs could improve interactivity and boost the use of BMI technology in space applications.},
}
@article {pmid19608000,
year = {2009},
author = {Millàn, Jdel R and Ferrez, PW and Seidl, T},
title = {Validation of brain-machine interfaces during parabolic flight.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {189-197},
doi = {10.1016/S0074-7742(09)86014-5},
pmid = {19608000},
issn = {0074-7742},
mesh = {Brain/*physiology ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; *Man-Machine Systems ; Mental Processes/physiology ; Neuropsychological Tests ; Psychomotor Performance/physiology ; *Space Flight ; *User-Computer Interface ; *Weightlessness ; },
abstract = {Here we report on a validation study on brain-machine interfaces (BMIs) performed during the December 2007 ESA parabolic flight campaign. We investigated the feasibility of using BMIs for space applications by performing tests in microgravity. Brain signals were recorded with noninvasive electroencephalography before (calibration sessions) and during the parabolic flights on two subjects with prior BMI experience. The results of our experiments show that an experienced BMI user can achieve stable performance in all gravity conditions examined and, hence, demonstrate the feasibility of operating noninvasive BMIs in space.},
}
@article {pmid19607999,
year = {2009},
author = {Cheron, G and Cebolla, AM and Petieau, M and Bengoetxea, A and Palmero-Soler, E and Leroy, A and Dan, B},
title = {Adaptive changes of rhythmic EEG oscillations in space implications for brain-machine interface applications.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {171-187},
doi = {10.1016/S0074-7742(09)86013-3},
pmid = {19607999},
issn = {0074-7742},
mesh = {Adaptation, Physiological/*physiology ; Brain/*physiology ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; *Weightlessness ; },
abstract = {The dramatic development of brain machine interfaces has enhanced the use of human brain signals conveying mental action for controlling external actuators. This chapter will outline current evidences that the rhythmic electroencephalographic activity of the brain is sensitive to microgravity environment. Experiments performed in the International Space Station have shown significant changes in the power of the astronauts' alpha and mu oscillations in resting condition, and other adaptive modifications in the beta and gamma frequency range during the immersion in virtual navigation. In this context, the dynamic aspects of the resting or default condition of the awaken brain, the influence of the "top-down" dynamics, and the possibility to use a more constrained configuration by a new somatosensory-evoked potential (gating approach) are discussed in the sense of future uses of brain computing interface in space mission. Although, the state of the art of the noninvasive BCI approach clearly demonstrates their ability and the great expectance in the field of rehabilitation for the restoration of defective communication between the brain and external world, their future application in space mission urgently needs a better understanding of brain neurophysiology, in particular in aspects related to neural network rhythmicity in microgravity.},
}
@article {pmid19607998,
year = {2009},
author = {Jerbi, K and Freyermuth, S and Minotti, L and Kahane, P and Berthoz, A and Lachaux, JP},
title = {Watching brain TV and playing brain ball exploring novel BCI strategies using real-time analysis of human intracranial data.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {159-168},
doi = {10.1016/S0074-7742(09)86012-1},
pmid = {19607998},
issn = {0074-7742},
mesh = {Attention/*physiology ; Biofeedback, Psychology ; Brain/*physiology ; Brain Mapping ; Cognition/physiology ; *Electroencephalography ; Humans ; *Man-Machine Systems ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; *Television ; *User-Computer Interface ; },
abstract = {A large body of evidence from animal studies indicates that motor intention can be decoded via multiple single-unit recordings or from local field potentials (LFPs) recorded not only in primary motor cortex, but also in premotor or parietal areas. In humans, reports of invasive data acquisition for the purpose of BCI developments are less numerous and signal selection for optimal control still remains poorly investigated. Here we report on our recent implementation of a real-time analysis platform for the investigation of ongoing oscillations in human intracerebral recordings and review various results illustrating its utility for the development of novel brain-computer and brain-robot interfaces. Our findings show that the insight gained both from off-line experiments and from online functional exploration can be used to guide future selection of the sites and frequency bands to be used in a translation algorithm such as the one needed for a BCI-driven cursor control. Overall, the findings reported with our online spectral analysis platforms (Brain TV and Brain Ball) indicate the feasibility of online functional exploration via intracranial recordings in humans and outline the direct benefits of this approach for the improvement of invasive BCI strategies in humans. In particular, our findings suggest that current BCI performance may be improved by using signals recorded from various systems previously unexplored in the context of BCI research such as the oscillatory activity recorded in the oculomotor networks as well as higher cognitive processes including working memory, attention, and mental calculation networks. Finally, we discuss current limitations of the methodology and outline future paths for innovative BCI research.},
}
@article {pmid19607997,
year = {2009},
author = {Krusienski, DJ and Wolpaw, JR},
title = {Brain-computer interface research at the wadsworth center developments in noninvasive communication and control.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {147-157},
doi = {10.1016/S0074-7742(09)86011-X},
pmid = {19607997},
issn = {0074-7742},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Event-Related Potentials, P300 ; Humans ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) research at the Wadsworth Center focuses on noninvasive, electroencephalography (EEG)-based BCI methods for helping severely disabled individuals communicate and interact with their environment. We have demonstrated that these individuals, as well as able-bodied individuals, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one and two dimensions. We have also developed a practical P300-based BCI that enables users to access and control the full functionality of their personal computer. We are currently translating this laboratory-proved BCI technology into a system that can be used by severely disabled individuals in their homes with minimal ongoing technical oversight. Our comprehensive approach to BCI design has led to several innovations that are applicable in other BCI contexts, such as space missions.},
}
@article {pmid19607996,
year = {2009},
author = {Babiloni, F and Cincotti, F and Marciani, M and Salinari, S and Astolfi, L and Aloise, F and De Vico Fallani, F and Mattia, D},
title = {On the use of brain-computer interfaces outside scientific laboratories toward an application in domotic environments.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {133-146},
doi = {10.1016/S0074-7742(09)86010-8},
pmid = {19607996},
issn = {0074-7742},
support = {EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/methods ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography/methods ; Humans ; *Man-Machine Systems ; Models, Neurological ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) applications were initially designed to provide final users with special capabilities, like writing letters on a screen, to communicate with others without muscular effort. In these last few years, the BCI scientific community has been interested in bringing BCI applications outside the scientific laboratories, initially to provide useful applications in everyday life and in future in more complex environments, such as space. Recently, we implemented a control of a domestic environment realized with BCI applications. In the present chapter, we analyze the methodological approach employed to allow the interaction between subjects and domestic devices by use of noninvasive EEG recordings. In particular, we analyze whether the cortical activity estimated from noninvasive EEG recordings could be useful in detecting mental states related to imagined limb movements. We estimate cortical activity from high-resolution EEG recordings in a group of healthy subjects by using realistic head models. Such cortical activity was estimated in a region of interest associated with the subjects' Brodmann areas by use of depth-weighted minimum norm solutions. Results show that the use of the estimated cortical activity instead of unprocessed EEG improves the recognition of the mental states associated with limb-movement imagination in a group of healthy subjects. The BCI methodology here presented has been used in a group of disabled patients to give them suitable control of several electronic devices disposed in a three-room environment devoted to neurorehabilitation. Four of six patients were able to control several electronic devices in the domotic context with the BCI system, with a percentage of correct responses averaging over 63%.},
}
@article {pmid19607995,
year = {2009},
author = {Scherer, R and Müller-Putz, GR and Pfurtscheller, G},
title = {Flexibility and practicality graz brain-computer interface approach.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {119-131},
doi = {10.1016/S0074-7742(09)86009-1},
pmid = {19607995},
issn = {0074-7742},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography ; Feedback/*physiology ; *Man-Machine Systems ; Signal Processing, Computer-Assisted ; Therapy, Computer-Assisted ; *User-Computer Interface ; },
abstract = {"Graz brain-computer interface (BCI)" transforms changes in oscillatory electroencephalogram (EEG) activity into control signals for external devices and feedback. Steady-state evoked potentials (SSEPs) and event-related desynchronization (ERD) are employed to encode user messages. User-specific setup and training are important issues for robust and reliable classification. Furthermore, in order to implement small and thus affordable systems, focus is put on the minimization of the number of EEG sensors. The system also supports the self-paced operation mode, that is, users have on-demand access to the system at any time and can autonomously initiate communication. Flexibility, usability, and practicality are essential to increase user acceptance. Here, we illustrate the possibilities offered by now from EEG-based communication. Results of several studies with able-bodied and disabled individuals performed inside the laboratory and in real-world environments are presented; their characteristics are shown and open issues are mentioned. The applications include the control of neuroprostheses and spelling devices, the interaction with Virtual Reality, and the operation of off-the-shelf software such as Google Earth.},
}
@article {pmid19607994,
year = {2009},
author = {Birbaumer, N and Ramos Murguialday, A and Weber, C and Montoya, P},
title = {Neurofeedback and brain-computer interface clinical applications.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {107-117},
doi = {10.1016/S0074-7742(09)86008-X},
pmid = {19607994},
issn = {0074-7742},
mesh = {*Biofeedback, Psychology ; Brain/blood supply/*physiology ; *Communication Devices for People with Disabilities ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; *Man-Machine Systems ; Oxygen/blood ; *User-Computer Interface ; },
abstract = {Most of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)-rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies. The clinical target populations for BMI-treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG-measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients with psychopathological conditions.},
}
@article {pmid19607993,
year = {2009},
author = {Sepulveda, F},
title = {An overview of BMIs.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {93-106},
doi = {10.1016/S0074-7742(09)86007-8},
pmid = {19607993},
issn = {0074-7742},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; *User-Computer Interface ; },
abstract = {Research in BMIs has grown rapidly in the last few years. However, little attention has been paid to the overall system behavior, most published work being focused on the signal classification (i.e., translation) stage. More recently an increasing amount of work has been centred around the feature selection stage that precedes translation. The emphasis in feature selection and translation has stemmed from the large number of researchers with a machine learning or pattern recognition background who have recently joined the field. While there is an important contribution to BMIs, two crucial elements have not been sufficiently explored: the selection of suitable mental tasks and feedback protocols. This review presents an overview of BMIs and its main elements, with a focus on why each stage is important for the overall performance of such systems.},
}
@article {pmid19607992,
year = {2009},
author = {Rossini, PM},
title = {Implications of brain plasticity to brain-machine interfaces operation a potential paradox?.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {81-90},
doi = {10.1016/S0074-7742(09)86006-6},
pmid = {19607992},
issn = {0074-7742},
mesh = {Animals ; Brain/*physiology ; Humans ; *Man-Machine Systems ; Neuronal Plasticity/*physiology ; *User-Computer Interface ; },
abstract = {The adult brain has the remarkable ability to plastically reorganize itself in order to record memories (experiences), to add abilities, and to learn skills, significantly expanding the carnet of resources useful for facing and solving the unpredictability of any daily life activity, that is, artistic and cultural activities. Brain plasticity also plays a crucial role in reorganizing central nervous system's networks after any lesion, being it sudden and localized, or progressive and diffuse, in order to partly or totally restore lost and/or compromised functions. In severely affected neurological patients unable to move and to communicate with the external environment, technologies implementing brain-machine interfaces (BMIs) can be of valuable help and support. Subjects operating within a BMI frame must learn how to produce a meaningful signal for an external reader; how to increase the signal-to-noise ratio at a level which makes it suitable for rapid communication with the machine; and how to improve the speed and specificity (bit rate) of signal production as a new language for governing and controlling a machine. Since it is of absolute importance for the patient to be able to maintain such a skill for a prolonged lapse of time (i.e., until his/her lost abilities are restored by a therapy and/or a different technology), neurophysiological phenomena at the base of plastic changes are obviously of remarkable importance within any BMI and are the content of the present chapter.},
}
@article {pmid19607987,
year = {2009},
author = {Carpi, F and Rossi, DD},
title = {EMG-based and gaze-tracking-based man-machine interfaces.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {3-21},
doi = {10.1016/S0074-7742(09)86001-7},
pmid = {19607987},
issn = {0074-7742},
mesh = {Electromyography/*methods ; Electrooculography ; *Ergonomics ; Eye Movements/*physiology ; Humans ; Oculomotor Muscles/*physiology ; *User-Computer Interface ; },
abstract = {A great demand for brain-machine and, more generally, man-machine interfaces is arising nowadays, pushed by several promising scientific and technological results, which are encouraging the concentration of efforts in this field. The possibility of measuring, processing and decoding brain activity, so as to interpret neural signals, is often looked at as a possibility to bypass lost or damaged neural and/or motor structures. Beyond that, such interfaces currently show a potential for applications in other fields, space science being certainly one of them. At present, the concept of "reading" the brain to detect intended actions and use these to control external devices is being studied with several technical and methodological approaches; among these, interfaces based on electroencephalographic signals play today a prominent role. Within such a context, the aim of this section is to present a brief survey on two types of noninvasive man-machine interfaces based on a different approach. In particular, they rely on the extraction of control signals from the user with techniques that adopt electromyography and gaze tracking. Working principles, implementations, typical features, and applications of these two types of interfaces are reported.},
}
@article {pmid19607985,
year = {2009},
author = {De Winne, F},
title = {Brain machine interfaces for space applications: enhancing astronaut capabilities. Foreword.},
journal = {International review of neurobiology},
volume = {86},
number = {},
pages = {xv-xvi},
doi = {10.1016/S0074-7742(09)86021-2},
pmid = {19607985},
issn = {0074-7742},
mesh = {*Astronauts ; Brain/*physiology ; Humans ; *Space Flight/instrumentation/methods ; *User-Computer Interface ; },
}
@article {pmid19605314,
year = {2009},
author = {Thomas, KP and Guan, C and Lau, CT and Vinod, AP and Ang, KK},
title = {A new discriminative common spatial pattern method for motor imagery brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {11 Pt 2},
pages = {2730-2733},
doi = {10.1109/TBME.2009.2026181},
pmid = {19605314},
issn = {1558-2531},
mesh = {*Algorithms ; Brain Mapping/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.},
}
@article {pmid19605313,
year = {2009},
author = {Song, W and Ramakrishnan, A and Udoekwere, UI and Giszter, SF},
title = {Multiple types of movement-related information encoded in hindlimb/trunk cortex in rats and potentially available for brain-machine interface controls.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {11 Pt 2},
pages = {2712-2716},
pmid = {19605313},
issn = {1558-2531},
support = {R01 NS054894/NS/NINDS NIH HHS/United States ; R01 NS054894-02/NS/NINDS NIH HHS/United States ; R01 NS044564/NS/NINDS NIH HHS/United States ; R01 NS054894-01A2/NS/NINDS NIH HHS/United States ; R01 NS044564-05/NS/NINDS NIH HHS/United States ; NS44564/NS/NINDS NIH HHS/United States ; NS54894/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Hindlimb/*physiology ; Locomotion/*physiology ; Motor Cortex/*physiology ; Rats ; Rats, Sprague-Dawley ; *User-Computer Interface ; },
abstract = {Brain-machine interface (BMI) systems hold the potential to return lost functions to patients with motor disorders. To date, most efforts in BMI have concentrated on decoding neural activity from forearm areas of cortex to operate a robotic arm or perform other manipulation tasks. Efforts have neglected the locomotion functions of hindlimb/trunk cortex. However, the role of cortex in hindlimb locomotion of intact rats, which are often model systems for BMI testing, is usually considered to be small. Thus, the quality of representations of locomotion available in this area was uncertain. We designed a new rodent BMI system, and tested decoding of the kinematics of trunk and hindlimbs during locomotion using linear regression. Recordings were made from the motor cortex of the hindlimb/trunk area in rats using arrays of six tetrodes (24 channels total). We found that multiple movement-related variables could be decoded simultaneously during locomotion, ranging from the proximal robot/pelvis attachment point, and the distal toe position, through hindlimb joint angles and limb endpoint in a polar coordinate system. Remarkably, the best reconstructed motion parameters were the more proximal kinematics, which might relate to global task variables. The pelvis motion was significantly better reconstructed than any other motion features.},
}
@article {pmid19603074,
year = {2009},
author = {Li, Z and O'Doherty, JE and Hanson, TL and Lebedev, MA and Henriquez, CS and Nicolelis, MA},
title = {Unscented Kalman filter for brain-machine interfaces.},
journal = {PloS one},
volume = {4},
number = {7},
pages = {e6243},
pmid = {19603074},
issn = {1932-6203},
mesh = {Algorithms ; Animals ; *Artificial Limbs ; Behavior, Animal ; Brain/*physiology ; Macaca mulatta/physiology ; Models, Biological ; },
abstract = {Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.},
}
@article {pmid19602731,
year = {2009},
author = {Salvaris, M and Sepulveda, F},
title = {Visual modifications on the P300 speller BCI paradigm.},
journal = {Journal of neural engineering},
volume = {6},
number = {4},
pages = {046011},
doi = {10.1088/1741-2560/6/4/046011},
pmid = {19602731},
issn = {1741-2552},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Cues ; Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; *Software ; *User-Computer Interface ; Young Adult ; },
abstract = {The best known P300 speller brain-computer interface (BCI) paradigm is the Farwell and Donchin paradigm. In this paper, various changes to the visual aspects of this protocol are explored as well as their effects on classification. Changes to the dimensions of the symbols, the distance between the symbols and the colours used were tested. The purpose of the present work was not to achieve the highest possible accuracy results, but to ascertain whether these simple modifications to the visual protocol will provide classification differences between them and what these differences will be. Eight subjects were used, with each subject carrying out a total of six different experiments. In each experiment, the user spelt a total of 39 characters. Two types of classifiers were trained and tested to determine whether the results were classifier dependant. These were a support vector machine (SVM) with a radial basis function (RBF) kernel and Fisher's linear discriminant (FLD). The single-trial classification results and multiple-trial classification results were recorded and compared. Although no visual protocol was the best for all subjects, the best performances, across both classifiers, were obtained with the white background (WB) visual protocol. The worst performance was obtained with the small symbol size (SSS) visual protocol.},
}
@article {pmid19592793,
year = {2009},
author = {Carabalona, R and Castiglioni, P and Gramatica, F},
title = {Brain-computer interfaces and neurorehabilitation.},
journal = {Studies in health technology and informatics},
volume = {145},
number = {},
pages = {160-176},
pmid = {19592793},
issn = {0926-9630},
mesh = {Brain/*physiology ; Electrodes ; Humans ; Nervous System Diseases/*rehabilitation ; Self-Help Devices ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) directly uses brain-activity signals to allow users to operate the environment without any muscular activation. Thanks to this feature, BCI systems can be employed not only as assistive devices, but also as neurorehabilitation tools in clinical settings. However, several critical issues need to be addressed before using BCI in neurorehabilitation, issues ranging from signal acquisition and selection of the proper BCI paradigm to the evaluation of the affective state, cognitive load and system acceptability of the users. Here we discuss these issues, illustrating how a rehabilitation program can benefit from BCI sessions, and summarize the results obtained so far in this field. Also provided are experimental data concerning two important topics related to BCI usability in rehabilitation: the possibility of using dry electrodes for EEG acquisition, and the monitoring of psychophysiological effects during BCI tasks.},
}
@article {pmid19592760,
year = {2009},
author = {Ron-Angevin, R},
title = {Changes in EEG Behavior through Feedback Presentation.},
journal = {Studies in health technology and informatics},
volume = {144},
number = {},
pages = {184-188},
pmid = {19592760},
issn = {0926-9630},
mesh = {Brain-Computer Interfaces ; Computer Simulation ; *Electroencephalography ; Feedback ; Humans ; *User-Computer Interface ; },
abstract = {Performance of brain-computer interface (BCI) will depend, to a great extent, on the ability of subjects to control their own electroencephalographic signals (EEG). To this end, it is necessary to follow a suitable training and to provide some type of visual feedback. The objective of this study is to explore the possibility of improving the EEG control via feedback presentation. Eighteen untrained subjects, divided in two groups, were trained using a BCI system based on virtual reality techniques, which submits subjects to a more familiar environment, such as controlling a car to avoid different obstacles. Different types of obstacles were introduced for each group. Significant differences in classification error rates between both groups were obtained during the last second of the feedback period.},
}
@article {pmid19585542,
year = {2009},
author = {Frewin, CL and Jaroszeski, M and Weeber, E and Muffly, KE and Kumar, A and Peters, M and Oliveros, A and Saddow, SE},
title = {Atomic force microscopy analysis of central nervous system cell morphology on silicon carbide and diamond substrates.},
journal = {Journal of molecular recognition : JMR},
volume = {22},
number = {5},
pages = {380-388},
doi = {10.1002/jmr.966},
pmid = {19585542},
issn = {1099-1352},
mesh = {Animals ; Carbon Compounds, Inorganic/adverse effects/*pharmacology ; Cell Line, Tumor ; Cell Survival/*drug effects ; Central Nervous System/*drug effects/*pathology ; Diamond/adverse effects/*pharmacology ; Humans ; Microscopy, Atomic Force ; Neurons/*drug effects/pathology ; PC12 Cells ; Rats ; Silicon Compounds/adverse effects/*pharmacology ; },
abstract = {Brain machine interface (BMI) devices offer a platform that can be used to assist people with extreme disabilities, such as amyotrophic lateral sclerosis (ALS) and Parkinson's disease. Silicon (Si) has been the material of choice used for the manufacture of BMI devices due to its mechanical strength, its electrical properties and multiple fabrication techniques; however, chronically implanted BMI devices have usually failed within months of implantation due to biocompatibility issues and the fact that Si does not withstand the harsh environment of the body. Single crystal cubic silicon carbide (3C-SiC) and nanocrystalline diamond (NCD) are semiconductor materials that have previously shown good biocompatibility with skin and bone cells. Like Si, these materials have excellent physical characteristics, good electrical properties, but unlike Si, they are chemically inert. We have performed a study to evaluate the general biocompatibility levels of all of these materials through the use of in vitro techniques. H4 human neuroglioma and PC12 rat pheochromocytoma cell lines were used for the study, and polystyrene (PSt) and amorphous glass were used as controls or for morphological comparison. MTT [3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyltetrazolium Bromide] assays were performed to determine general cell viability with each substrate and atomic force microscopy (AFM) was used to quantify the general cell morphology on the substrate surface along with the substrate permissiveness to lamellipodia extension. 3C-SiC was the only substrate tested to have good viability and superior lamellipodia permissiveness with both cell lines, while NCD showed a good level of viability with the neural H4 line but a poor viability with the PC12 line and lower permissiveness than 3C-SiC. Explanations pertaining to the performance of each substrate with both cell lines are presented and discussed along with future work that must be performed to further evaluate specific cell reactions on these substrates.},
}
@article {pmid19578332,
year = {2009},
author = {Molina, G and Vogt, A and Bakan, A and Dai, W and Queiroz de Oliveira, P and Znosko, W and Smithgall, TE and Bahar, I and Lazo, JS and Day, BW and Tsang, M},
title = {Zebrafish chemical screening reveals an inhibitor of Dusp6 that expands cardiac cell lineages.},
journal = {Nature chemical biology},
volume = {5},
number = {9},
pages = {680-687},
pmid = {19578332},
issn = {1552-4469},
support = {U01 CA052995/CA/NCI NIH HHS/United States ; R01 HL 088016/HL/NHLBI NIH HHS/United States ; R01 LM007994-06/LM/NLM NIH HHS/United States ; U54 MH074411/MH/NIMH NIH HHS/United States ; U19 CA052995/CA/NCI NIH HHS/United States ; HD053287/HD/NICHD NIH HHS/United States ; MH074411/MH/NIMH NIH HHS/United States ; R01 GM086238-01/GM/NIGMS NIH HHS/United States ; CA78039/CA/NCI NIH HHS/United States ; R01 HL088016/HL/NHLBI NIH HHS/United States ; CA52995/CA/NCI NIH HHS/United States ; R01 HD053287/HD/NICHD NIH HHS/United States ; R01 LM007994/LM/NLM NIH HHS/United States ; R01 GM086238/GM/NIGMS NIH HHS/United States ; P01 CA078039/CA/NCI NIH HHS/United States ; },
mesh = {Allosteric Site ; Animals ; Animals, Genetically Modified/*metabolism ; *Cell Lineage/genetics ; Cyclohexylamines/chemical synthesis/chemistry/*pharmacology ; Dual Specificity Phosphatase 6/*antagonists &amp; inhibitors/genetics ; Enzyme Inhibitors/chemical synthesis/chemistry/*pharmacology ; Fibroblast Growth Factors/metabolism ; Gene Expression Regulation, Developmental/drug effects ; *Heart/embryology ; Indenes/chemical synthesis/chemistry/*pharmacology ; Mitogen-Activated Protein Kinase 1/metabolism ; Protein Binding ; Small Molecule Libraries ; Substrate Specificity ; Zebrafish/embryology/*genetics/metabolism ; },
abstract = {The dual-specificity phosphatase 6 (Dusp6) functions as a feedback regulator of fibroblast growth factor (FGF) signaling to limit the activity of extracellular signal-regulated kinases (ERKs) 1 and 2. We have identified a small-molecule inhibitor of Dusp6-(E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI)-using a transgenic zebrafish chemical screen. BCI treatment blocked Dusp6 activity and enhanced FGF target gene expression in zebrafish embryos. Docking simulations predicted an allosteric binding site for BCI within the phosphatase domain. In vitro studies supported a model in which BCI inhibits Dusp6 catalytic activation by ERK2 substrate binding. We used BCI treatment at varying developmental stages to uncover a temporal role for Dusp6 in restricting cardiac progenitors and controlling heart organ size. This study highlights the power of in vivo zebrafish chemical screens to identify new compounds targeting Dusp6, a component of the FGF signaling pathway that has eluded traditional high-throughput in vitro screens.},
}
@article {pmid19577900,
year = {2009},
author = {van Gerven, M and Bahramisharif, A and Heskes, T and Jensen, O},
title = {Selecting features for BCI control based on a covert spatial attention paradigm.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1271-1277},
doi = {10.1016/j.neunet.2009.06.004},
pmid = {19577900},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; Alpha Rhythm ; *Attention ; Brain/*physiology ; Brain Mapping/methods ; Female ; Humans ; Information Theory ; Logistic Models ; Magnetoencephalography/methods ; Male ; *Signal Processing, Computer-Assisted ; *Space Perception ; Time Factors ; *User-Computer Interface ; },
abstract = {Covert attention to spatial locations in the visual field is a relatively new control signal for brain-computer interfaces. Previous EEG research has shown that trials can be classified by thresholding based on left and right hemisphere alpha power in covert spatial attention paradigms. We reexamine the covert attention paradigm based on MEG measurements for fifteen subjects. It is shown that classification performance can be improved by applying sparse logistic regression in order to select a subset of the sensors specific to each subject as the basis for classification. Furthermore, insight is gained into how classification performance changes as a function of the length of the attention period and as a function of the number of trials. Classification performance steadily increases as the length of the attention period over which is averaged is increased, although this does not necessarily translate into higher bit rates. Good classification performance using early components of the attention period may be related to evoked response. With regard to the number of used trials, classification performance became maximal after 150 samples had been obtained, requiring a training time of approximately eleven minutes under the current experimental paradigm.},
}
@article {pmid19574091,
year = {2009},
author = {Klobassa, DS and Vaughan, TM and Brunner, P and Schwartz, NE and Wolpaw, JR and Neuper, C and Sellers, EW},
title = {Toward a high-throughput auditory P300-based brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {7},
pages = {1252-1261},
pmid = {19574091},
issn = {1872-8952},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Acoustic Stimulation/*methods ; Adult ; Aged ; Auditory Cortex/physiology ; Brain/*physiology ; Communication Devices for People with Disabilities/*trends ; *Persons with Disabilities ; *Electroencephalography ; Event-Related Potentials, P300/physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation ; *User-Computer Interface ; Visual Cortex/physiology ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller.<br><br>METHODS: A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice.<br><br>RESULTS: Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50% or more, and four of these achieved 75% or more, online accuracy (2.8% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min.<br><br>CONCLUSIONS: This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility.<br><br>SIGNIFICANCE: With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.},
}
@article {pmid19569896,
year = {2009},
author = {Rohatgi, P and Langhals, NB and Kipke, DR and Patil, PG},
title = {In vivo performance of a microelectrode neural probe with integrated drug delivery.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E8},
pmid = {19569896},
issn = {1092-0684},
support = {P41 EB002030/EB/NIBIB NIH HHS/United States ; P41 EB002030-14/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; Brain/*drug effects/physiology ; Drug Delivery Systems/*instrumentation/methods ; Electrodes, Implanted ; Electrophysiology/*instrumentation/statistics &amp; numerical data ; Equipment Design ; Humans ; Infusion Pumps ; Injections/instrumentation ; *Microelectrodes ; Microfluidics/*instrumentation ; Rats ; Rats, Sprague-Dawley ; *User-Computer Interface ; },
abstract = {OBJECT: The availability of sophisticated neural probes is a key prerequisite in the development of future brain-machine interfaces (BMIs). In this study, the authors developed and validated a neural probe design capable of simultaneous drug delivery and electrophysiology recordings in vivo. Focal drug delivery promises to extend dramatically the recording lives of neural probes, a limiting factor to clinical adoption of BMI technology.<br><br>METHODS: To form the multifunctional neural probe, the authors affixed a 16-channel microfabricated silicon electrode array to a fused silica catheter. Three experiments were conducted in rats to characterize the performance of the device. Experiment 1 examined cellular damage from probe insertion and the drug distribution in tissue. Experiment 2 measured the effects of saline infusions delivered through the probe on concurrent electrophysiological measurements. Experiment 3 demonstrated that a physiologically relevant amount of drug can be delivered in a controlled fashion. For these experiments, Hoechst and propidium iodide stains were used to assess insertion trauma and the tissue distribution of the infusate. Artificial CSF (aCSF) and tetrodotoxin (TTX) were injected to determine the efficacy of drug delivery.<br><br>RESULTS: The newly developed multifunctional neural probes were successfully inserted into rat cortex and were able to deliver fluids and drugs that resulted in the expected electrophysiological and histological responses. The damage from insertion of the device into brain tissue was substantially less than the volume of drug dispersion in tissue. Electrophysiological activity, including both individual spikes as well as local field potentials, was successfully recorded with this device during real-time drug delivery. No significant changes were seen in response to delivery of aCSF as a control experiment, whereas delivery of TTX produced the expected result of suppressing all spiking activity in the vicinity of the catheter outlet.<br><br>CONCLUSIONS: Multifunctional neural probes such as the ones developed and validated within this study have great potential to help further understand the design space and criteria for the next generation of neural probe technology. By incorporating integrated drug delivery functionality into the probes, new treatment options for neurological disorders and regenerative neural interfaces using localized and feedback-controlled delivery of drugs can be realized in the near future.},
}
@article {pmid19569892,
year = {2009},
author = {Leuthardt, EC and Schalk, G and Roland, J and Rouse, A and Moran, DW},
title = {Evolution of brain-computer interfaces: going beyond classic motor physiology.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E4},
pmid = {19569892},
issn = {1092-0684},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-06/EB/NIBIB NIH HHS/United States ; R01-EB000856-06/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Cerebral Cortex/physiology ; Humans ; Man-Machine Systems ; Motor Cortex/*physiology ; Movement/physiology ; Movement Disorders/rehabilitation ; Neuronal Plasticity/physiology ; *Prostheses and Implants ; Research/trends ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.},
}
@article {pmid19569889,
year = {2009},
author = {Pancrazio, JJ},
title = {National Institute of Neurological Disorders and Stroke support for brain-machine interface technology.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E14},
pmid = {19569889},
issn = {1092-0684},
support = {NIH0011642045/ImNIH/Intramural NIH HHS/United States ; NIH0011642045//PHS HHS/United States ; },
mesh = {Brain/*physiology ; Humans ; Man-Machine Systems ; *National Institute of Neurological Disorders and Stroke (U.S.) ; Neurosurgery/instrumentation/methods ; Paralysis/rehabilitation ; *Prostheses and Implants ; *Research Support as Topic ; United States ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) offer the promise of restoring communication, enabling control of assistive devices, and allowing volitional control of extremities in paralyzed individuals. Working in multidisciplinary teams, neurosurgeons can play an invaluable role in the design, development, and demonstration of novel BMI technology. At the National Institutes of Health, the National Institute of Neurological Disorders and Stroke has a long history of supporting neural engineering and prosthetics efforts including BMI, and these research opportunities continue today. The author provides a brief overview of the opportunities and programs currently available to support BMI projects.},
}
@article {pmid19569888,
year = {2009},
author = {Blakely, T and Miller, KJ and Zanos, SP and Rao, RP and Ojemann, JG},
title = {Robust, long-term control of an electrocorticographic brain-computer interface with fixed parameters.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E13},
doi = {10.3171/2009.4.FOCUS0977},
pmid = {19569888},
issn = {1092-0684},
mesh = {Adult ; Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Epilepsy/diagnosis/rehabilitation ; Evoked Potentials, Motor/physiology ; Feedback ; Humans ; Imagination/physiology ; Male ; Movement/physiology ; Neocortex/physiology ; Pattern Recognition, Automated/statistics &amp; numerical data ; Prostheses and Implants ; Somatosensory Cortex/physiology ; Subdural Space/physiology ; *User-Computer Interface ; },
abstract = {All previous multiple-day brain-computer interface (BCI) experiments have dynamically adjusted the parameterization between the signals measured from the brain and the features used to control the interface. The authors present the results of a multiple-day electrocorticographic (ECoG) BCI experiment. A patient with a subdural electrode array implanted for seizure localization performed tongue motor tasks. After an initial screening and feature selection on the 1st day, 5 consecutive days of cursor-based feedback were performed with a fixed parameterization. Control of the interface was robust throughout all days, with performance increasing to a stable state in which high-frequency ECoG signal could immediately be translated into cursor control. These findings demonstrate that ECoG-based BCIs can be implemented for multiple-day control without the necessity for sophisticated retraining and adaptation.},
}
@article {pmid19569887,
year = {2009},
author = {Scherer, R and Zanos, SP and Miller, KJ and Rao, RP and Ojemann, JG},
title = {Classification of contralateral and ipsilateral finger movements for electrocorticographic brain-computer interfaces.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E12},
doi = {10.3171/2009.4.FOCUS0981},
pmid = {19569887},
issn = {1092-0684},
mesh = {Adult ; Brain Mapping/methods ; Cerebral Cortex/*physiology ; Electrodes, Implanted ; Electroencephalography/methods/*statistics &amp; numerical data ; Electromyography ; Epilepsy/diagnosis/rehabilitation ; Evoked Potentials, Motor/physiology ; Fingers/*physiology ; Functional Laterality/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Prostheses and Implants ; Prosthesis Design/methods ; Reaction Time/physiology ; Somatosensory Cortex/physiology ; Subdural Space/physiology ; *User-Computer Interface ; },
abstract = {Electrocorticography (ECoG) offers a powerful and versatile platform for developing brain-computer interfaces; it avoids the risks of brain-invasive methods such as intracortical implants while providing significantly higher signal-to-noise ratio than noninvasive techniques such as electroencephalography. The authors demonstrate that both contra- and ipsilateral finger movements can be discriminated from ECoG signals recorded from a single brain hemisphere. The ECoG activation patterns over sensorimotor areas for contra- and ipsilateral movements were found to overlap to a large degree in the recorded hemisphere. Ipsilateral movements, however, produced less pronounced activity compared with contralateral movements. The authors also found that single-trial classification of movements could be improved by selecting patient-specific frequency components in high-frequency bands (> 50 Hz). Their discovery that ipsilateral hand movements can be discriminated from ECoG signals from a single hemisphere has important implications for neurorehabilitation, suggesting in particular the possibility of regaining ipsilateral movement control using signals from an intact hemisphere after damage to the other hemisphere.},
}
@article {pmid19569886,
year = {2009},
author = {Reddy, CG and Reddy, GG and Kawasaki, H and Oya, H and Miller, LE and Howard, MA},
title = {Decoding movement-related cortical potentials from electrocorticography.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E11},
pmid = {19569886},
issn = {1092-0684},
support = {R01 DC004290/DC/NIDCD NIH HHS/United States ; M01-RR-59/RR/NCRR NIH HHS/United States ; R01-DC04290/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Cerebral Cortex/*physiology ; Electrodes, Implanted ; Electroencephalography/methods/*statistics &amp; numerical data ; Epilepsy/diagnosis ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Man-Machine Systems ; Motor Activity/physiology ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; Subdural Space/physiology ; *User-Computer Interface ; },
abstract = {OBJECT: Control signals for brain-machine interfaces may be obtained from a variety of sources, each with their own relative merits. Electrocorticography (ECoG) provides better spatial and spectral resolution than scalp electroencephalography and does not include the risks attendant upon penetration of the brain parenchyma associated with single and multiunit recordings. For these reasons, subdural electrode recordings have been proposed as useful primary or adjunctive control signals for brain-machine interfaces. The goal of the present study was to determine if 2D control signals could be decoded from ECoG.<br><br>METHODS: Six patients undergoing invasive monitoring for medically intractable epilepsy using subdural grid electrodes were asked to perform a motor task involving moving a joystick in 1 of 4 cardinal directions (up, down, left, or right) and a fifth condition ("trigger"). Evoked activity was synchronized to joystick movement and analyzed in the theta, alpha, beta, gamma, and high-gamma frequency bands.<br><br>RESULTS: Movement-related cortical potentials could be accurately differentiated from rest with very high accuracy (83-96%). Further distinguishing the movement direction (up, down, left, or right) could also be resolved with high accuracy (58-86%) using information only from the high-gamma range, whereas distinguishing the trigger condition from the remaining directions provided better accuracy.<br><br>CONCLUSIONS: Two-dimensional control signals can be derived from ECoG. Local field potentials as measured by ECoG from subdural grids will be useful as control signals for a brain-machine interface.},
}
@article {pmid19569885,
year = {2009},
author = {Leuthardt, EC and Freudenberg, Z and Bundy, D and Roland, J},
title = {Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E10},
pmid = {19569885},
issn = {1092-0684},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; R01-EB000856-06/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/physiology ; Brain Mapping ; Cerebral Cortex/physiology ; *Electrodes, Implanted ; Electroencephalography/*methods/statistics &amp; numerical data ; Electromyography/methods/statistics &amp; numerical data ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Man-Machine Systems ; Microelectrodes/*statistics &amp; numerical data ; Middle Aged ; Motor Cortex/*physiology ; Movement/physiology ; Seizures/diagnosis ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {OBJECT: There is a growing interest in the use of recording from the surface of the brain, known as electrocorticography (ECoG), as a practical signal platform for brain-computer interface application. The signal has a combination of high signal quality and long-term stability that may be the ideal intermediate modality for future application. The research paradigm for studying ECoG signals uses patients requiring invasive monitoring for seizure localization. The implanted arrays span cortex areas on the order of centimeters. Currently, it is unknown what level of motor information can be discerned from small regions of human cortex with microscale ECoG recording.<br><br>METHODS: In this study, a patient requiring invasive monitoring for seizure localization underwent concurrent implantation with a 16-microwire array (1-mm electrode spacing) placed over primary motor cortex. Microscale activity was recorded while the patient performed simple contra- and ipsilateral wrist movements that were monitored in parallel with electromyography. Using various statistical methods, linear and nonlinear relationships between these microcortical changes and recorded electromyography activity were defined.<br><br>RESULTS: Small regions of primary motor cortex (< 5 mm) carry sufficient information to separate multiple aspects of motor movements (that is, wrist flexion/extension and ipsilateral/contralateral movements).<br><br>CONCLUSIONS: These findings support the conclusion that small regions of cortex investigated by ECoG recording may provide sufficient information about motor intentions to support brain-computer interface operations in the future. Given the small scale of the cortical region required, the requisite implanted array would be minimally invasive in terms of surgical placement of the electrode array.},
}
@article {pmid19569884,
year = {2009},
author = {Patil, PG},
title = {Introduction: advances in brain-machine interfaces.},
journal = {Neurosurgical focus},
volume = {27},
number = {1},
pages = {E1},
doi = {10.3171/2009.5.FOCUS.JULY09.INTRO},
pmid = {19569884},
issn = {1092-0684},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; Neural Networks, Computer ; Prostheses and Implants/trends ; *User-Computer Interface ; },
}
@article {pmid19560965,
year = {2009},
author = {Takano, K and Komatsu, T and Hata, N and Nakajima, Y and Kansaku, K},
title = {Visual stimuli for the P300 brain-computer interface: a comparison of white/gray and green/blue flicker matrices.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {8},
pages = {1562-1566},
doi = {10.1016/j.clinph.2009.06.002},
pmid = {19560965},
issn = {1872-8952},
mesh = {Adult ; Brain Mapping ; Color Perception/*physiology ; Contrast Sensitivity/*physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Functional Laterality ; Humans ; Male ; Middle Aged ; Online Systems ; Photic Stimulation/*methods ; Reaction Time/physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {OBJECTIVE: The white/gray flicker matrix has been used as a visual stimulus for the so-called P300 brain-computer interface (BCI), but the white/gray flash stimuli might induce discomfort. In this study, we investigated the effectiveness of green/blue flicker matrices as visual stimuli.<br><br>METHODS: Ten able-bodied, non-trained subjects performed Alphabet Spelling (Japanese Alphabet: Hiragana) using an 8 x 10 matrix with three types of intensification/rest flicker combinations (L, luminance; C, chromatic; LC, luminance and chromatic); both online and offline performances were evaluated.<br><br>RESULTS: The accuracy rate under the online LC condition was 80.6%. Offline analysis showed that the LC condition was associated with significantly higher accuracy than was the L or C condition (Tukey-Kramer, p < 0.05). No significant difference was observed between L and C conditions.<br><br>CONCLUSIONS: The LC condition, which used the green/blue flicker matrix was associated with better performances in the P300 BCI.<br><br>SIGNIFICANCE: The green/blue chromatic flicker matrix can be an efficient tool for practical BCI application.},
}
@article {pmid19560898,
year = {2009},
author = {Fazli, S and Popescu, F and Danóczy, M and Blankertz, B and Müller, KR and Grozea, C},
title = {Subject-independent mental state classification in single trials.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1305-1312},
doi = {10.1016/j.neunet.2009.06.003},
pmid = {19560898},
issn = {1879-2782},
mesh = {Algorithms ; Brain/*physiology ; Calibration ; Databases as Topic ; Electroencephalography/*methods ; Humans ; Mental Processes/*physiology ; Regression Analysis ; Reproducibility of Results ; Time Factors ; *User-Computer Interface ; },
abstract = {Current state-of-the-art in Brain Computer Interfacing (BCI) involves tuning classifiers to subject-specific training data acquired from calibration sessions prior to functional BCI use. Using a large database of EEG recordings from 45 subjects, who took part in movement imagination task experiments, we construct an ensemble of classifiers derived from subject-specific temporal and spatial filters. The ensemble is then sparsified using quadratic regression with l(1) regularization such that the final classifier generalizes reliably to data of subjects not included in the ensemble. Our offline results indicate that BCI-naïve users could start real-time BCI use without any prior calibration at only very limited loss of performance.},
}
@article {pmid19556679,
year = {2009},
author = {Huang, D and Lin, P and Fei, DY and Chen, X and Bai, O},
title = {Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control.},
journal = {Journal of neural engineering},
volume = {6},
number = {4},
pages = {046005},
doi = {10.1088/1741-2560/6/4/046005},
pmid = {19556679},
issn = {1741-2552},
mesh = {Adult ; Beta Rhythm ; Brain/*physiology ; Calibration ; *Electroencephalography ; Electromyography ; Female ; Functional Laterality ; Humans ; Imagination/physiology ; Male ; Mental Processes/physiology ; Motor Activity/*physiology ; Motor Cortex/physiology ; Psychomotor Performance/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Young Adult ; },
abstract = {This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive EEG in order to control a discrete two-dimensional cursor movement for a potential multidimensional brain-computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at an average accuracy of 85.5 +/- 4.65%; the subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multidimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.},
}
@article {pmid19548797,
year = {2009},
author = {Wang, Y and Paiva, AR and Príncipe, JC and Sanchez, JC},
title = {Sequential Monte Carlo point-process estimation of kinematics from neural spiking activity for brain-machine interfaces.},
journal = {Neural computation},
volume = {21},
number = {10},
pages = {2894-2930},
doi = {10.1162/neco.2009.01-08-699},
pmid = {19548797},
issn = {0899-7667},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Arm/physiology ; Artificial Limbs ; Biomechanical Phenomena/*physiology ; Brain/*physiology ; Computer Simulation ; Computers ; Humans ; Linear Models ; *Monte Carlo Method ; Motor Cortex/physiology ; Movement/physiology ; Neurons/*physiology ; Normal Distribution ; Psychomotor Performance/physiology ; Robotics/instrumentation/methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. We have also proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. This letter presents a systematic testing of this algorithm in a simulated neural spike train decoding experiment and then in BMI data. Compared to a point-process adaptive filtering algorithm with a linear observation model and a gaussian approximation (the counterpart for point processes of the Kalman filter), our sequential Monte Carlo estimation methodology exploits a detailed encoding model (tuning function) derived for each neuron from training data. However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.},
}
@article {pmid19545601,
year = {2009},
author = {Guger, C and Daban, S and Sellers, E and Holzner, C and Krausz, G and Carabalona, R and Gramatica, F and Edlinger, G},
title = {How many people are able to control a P300-based brain-computer interface (BCI)?.},
journal = {Neuroscience letters},
volume = {462},
number = {1},
pages = {94-98},
doi = {10.1016/j.neulet.2009.06.045},
pmid = {19545601},
issn = {1872-7972},
mesh = {Adult ; *Biofeedback, Psychology ; Brain/*physiology ; *Electroencephalography/methods ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Neuropsychological Tests ; Practice, Psychological ; Signal Processing, Computer-Assisted ; Surveys and Questionnaires ; *User-Computer Interface ; Writing ; },
abstract = {An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.},
}
@article {pmid19543222,
year = {2009},
author = {Nicolelis, MA and Lebedev, MA},
title = {Principles of neural ensemble physiology underlying the operation of brain-machine interfaces.},
journal = {Nature reviews. Neuroscience},
volume = {10},
number = {7},
pages = {530-540},
doi = {10.1038/nrn2653},
pmid = {19543222},
issn = {1471-0048},
mesh = {Animals ; Behavior, Animal/physiology ; *Brain/cytology/physiology ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Motor Activity/physiology ; Neuronal Plasticity/physiology ; Neurons/cytology/*physiology ; Paralysis/therapy ; Prostheses and Implants ; *User-Computer Interface ; },
abstract = {Research on brain-machine interfaces has been ongoing for at least a decade. During this period, simultaneous recordings of the extracellular electrical activity of hundreds of individual neurons have been used for direct, real-time control of various artificial devices. Brain-machine interfaces have also added greatly to our knowledge of the fundamental physiological principles governing the operation of large neural ensembles. Further understanding of these principles is likely to have a key role in the future development of neuroprosthetics for restoring mobility in severely paralysed patients.},
}
@article {pmid19536346,
year = {2009},
author = {Naeem, M and Brunner, C and Pfurtscheller, G},
title = {Dimensionality reduction and channel selection of motor imagery electroencephalographic data.},
journal = {Computational intelligence and neuroscience},
volume = {2009},
number = {},
pages = {537504},
pmid = {19536346},
issn = {1687-5273},
abstract = {The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.},
}
@article {pmid19535480,
year = {2009},
author = {Dickey, AS and Suminski, A and Amit, Y and Hatsopoulos, NG},
title = {Single-unit stability using chronically implanted multielectrode arrays.},
journal = {Journal of neurophysiology},
volume = {102},
number = {2},
pages = {1331-1339},
pmid = {19535480},
issn = {0022-3077},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS-045853/NS/NINDS NIH HHS/United States ; R01 NS-48845/NS/NINDS NIH HHS/United States ; 5 T32 GM-07281/GM/NIGMS NIH HHS/United States ; },
mesh = {Action Potentials ; Adaptation, Psychological/physiology ; Algorithms ; Animals ; *Electrodes, Implanted ; Electrophysiology/*instrumentation/*methods ; Frontal Lobe/physiology ; Macaca mulatta ; Motor Activity ; Motor Cortex/physiology ; Neurons/*physiology ; Time Factors ; },
abstract = {The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.},
}
@article {pmid19535289,
year = {2009},
author = {Gu, Y and Dremstrup, K and Farina, D},
title = {Single-trial discrimination of type and speed of wrist movements from EEG recordings.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {8},
pages = {1596-1600},
doi = {10.1016/j.clinph.2009.05.006},
pmid = {19535289},
issn = {1872-8952},
mesh = {Adult ; Analysis of Variance ; Biophysical Phenomena/physiology ; *Brain Mapping ; *Electroencephalography ; Electromyography ; Electrooculography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted ; Wrist/*innervation ; Young Adult ; },
abstract = {OBJECTIVE: The study explored the possibility of identifying movement type and speed from EEG recordings.<br><br>METHODS: EEG signals were acquired from 9 healthy volunteers during imagination of four tasks of the right wrist that involved two speeds (fast and slow) and two types of movement (wrist extension and rotation), each repeated 60 times in random order. Average movement-related cortical potentials (MRCPs) were compared among the four tasks. Moreover, single-trial classification was performed using the rebound rate of MRCP and the power in the mu and beta bands as features.<br><br>RESULTS: The rebound rate of the average MRCPs was greater for faster than for slower movements but did not depend on the type of movement. Accordingly, pairs of tasks executed at different speeds led to lower misclassification rate than pairs of tasks executed at the same speed. The average misclassification rate between task pairs was 21+/-2% for the best channel and task pair.<br><br>CONCLUSION: The task parameter speed can be discriminated in single-trial EEG traces with greater accuracy than the type of movement when tasks are executed at the same joint.<br><br>SIGNIFICANCE: The speed of movement execution may be included among the variables that characterize imagined tasks for brain-computer interface applications.},
}
@article {pmid19525091,
year = {2009},
author = {Suminski, AJ and Tkach, DC and Hatsopoulos, NG},
title = {Exploiting multiple sensory modalities in brain-machine interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1224-1234},
pmid = {19525091},
issn = {1879-2782},
support = {R01 NS048845-04/NS/NINDS NIH HHS/United States ; R01 NS545853-01/NS/NINDS NIH HHS/United States ; R01 NS048845-02/NS/NINDS NIH HHS/United States ; R01 NS048845-01A1/NS/NINDS NIH HHS/United States ; R01 NS048845-03/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Biomechanical Phenomena ; Brain/physiology ; Electrodes, Implanted ; Evoked Potentials ; Feedback, Physiological/physiology ; Information Theory ; Macaca mulatta ; Male ; Microelectrodes ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Neurons/*physiology ; Proprioception/*physiology ; Time Factors ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {Recent improvements in cortically-controlled brain-machine interfaces (BMIs) have raised hopes that such technologies may improve the quality of life of severely motor-disabled patients. However, current generation BMIs do not perform up to their potential due to the neglect of the full range of sensory feedback in their strategies for training and control. Here we confirm that neurons in the primary motor cortex (MI) encode sensory information and demonstrate a significant heterogeneity in their responses with respect to the type of sensory modality available to the subject about a reaching task. We further show using mutual information and directional tuning analyses that the presence of multi-sensory feedback (i.e. vision and proprioception) during replay of movements evokes neural responses in MI that are almost indistinguishable from those responses measured during overt movement. Finally, we suggest how these playback-evoked responses may be used to improve BMI performance.},
}
@article {pmid19524399,
year = {2009},
author = {Sitaram, R and Caria, A and Birbaumer, N},
title = {Hemodynamic brain-computer interfaces for communication and rehabilitation.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1320-1328},
doi = {10.1016/j.neunet.2009.05.009},
pmid = {19524399},
issn = {1879-2782},
mesh = {Brain/blood supply/*physiology ; Cerebrovascular Circulation ; Humans ; Magnetic Resonance Imaging/*methods ; Oxygen/*blood ; Spectroscopy, Near-Infrared/*methods ; *User-Computer Interface ; },
abstract = {Functional near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) are non-invasive methods for acquiring hemodynamic signals from the brain with the primary benefit of anatomical specificity of signals. Recently, there has been a surge of studies with NIRS and fMRI for the implementation of a brain-computer interface (BCI), for the acquisition, decoding and regulation of hemodynamic signals in the brain, and to investigate their behavioural consequences. Both NIRS and fMRI rely on the measurement of the task-induced blood oxygen level-dependent response. In this review, we consider fundamental principles, recent developments, applications and future directions and challenges of NIRS-based and fMRI-based BCIs.},
}
@article {pmid19523710,
year = {2010},
author = {Gutiérrez, D and Escalona-Vargas, DI},
title = {EEG data classification through signal spatial redistribution and optimized linear discriminants.},
journal = {Computer methods and programs in biomedicine},
volume = {97},
number = {1},
pages = {39-47},
doi = {10.1016/j.cmpb.2009.05.005},
pmid = {19523710},
issn = {1872-7565},
mesh = {Brain Mapping/methods ; *Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*classification ; Humans ; *Linear Models ; Pattern Recognition, Automated/*methods ; ROC Curve ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This paper presents a preprocessing technique for improving the classification of electroencephalographic (EEG) data in brain-computer interfaces (BCI) for the case of realistic measuring conditions, such as low signal-to-noise ratio (SNR), reduced number of measuring electrodes, and reduced amount of data used to train the classifier. The proposed method is based on a linear minimum mean squared error (LMMSE) spatial filter specifically designed to improve the SNR of the signals before being classified. The design parameters of the spatial filter are obtained through an optimized version of Fisher's linear discriminant (FLD) whose area under the receiver operating characteristics (ROC) curve is maximized. The combination of the spatial filter and the optimized FLD increases the SNR and changes the spatial distribution of the measured signals. As a result, the signals can be more easily discriminated by means of a simple sign detector or threshold-based classifier. A series of experiments on simulated EEG data compare the performance of the proposed classification scheme to the performance of the Mahalanobis distance-based classifier, which is widely used in BCI systems. Numerical results show that the proposed preprocessing technique enhances the classifier's performance even for low SNR conditions and few measurements, while the Mahalanobis classifier is not reliable under such realistic operating conditions. Furthermore, real EEG data from a self-paced key typing experiment is used to demonstrate the applicability of the preprocessing technique. The proposed method has the potential of improving the efficiency of real-life BCI systems, as well as reducing the computational complexity associated with their implementation.},
}
@article {pmid19506704,
year = {2009},
author = {Garde, K and Keefer, E and Botterman, B and Galvan, P and Romero, MI},
title = {Early interfaced neural activity from chronic amputated nerves.},
journal = {Frontiers in neuroengineering},
volume = {2},
number = {},
pages = {5},
pmid = {19506704},
issn = {1662-6443},
abstract = {Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive inflammation currently limit their long-term use. Here we demonstrate that enticement of peripheral nerve regeneration through a non-obstructive multi-electrode array, after either acute or chronic nerve amputation, offers a viable alternative to obtain early neural recordings and to enhance long-term interfacing of nerve activity. Non-restrictive electrode arrays placed in the path of regenerating nerve fibers allowed the recording of action potentials as early as 8 days post-implantation with high signal-to-noise ratio, as long as 3 months in some animals, and with minimal inflammation at the nerve tissue-metal electrode interface. Our findings suggest that regenerative multi-electrode arrays of open design allow early and stable interfacing of neural activity from amputated peripheral nerves and might contribute towards conveying full neural control and sensory feedback to users of robotic prosthetic devices.},
}
@article {pmid19506456,
year = {2009},
author = {Do, L and Puthawala, A and Syed, N and Azawi, S and Williams, R and Vora, N},
title = {Treatment outcomes of T4 locally advanced head and neck cancers with soft tissue invasion or bone and cartilage invasion.},
journal = {American journal of clinical oncology},
volume = {32},
number = {5},
pages = {477-482},
doi = {10.1097/COC.0b013e31819380a8},
pmid = {19506456},
issn = {1537-453X},
mesh = {Antineoplastic Agents/administration &amp; dosage ; Bone Neoplasms/*secondary/therapy ; Carcinoma, Squamous Cell/mortality/secondary/*therapy ; Cartilage/pathology ; Cisplatin/administration &amp; dosage ; Combined Modality Therapy ; Female ; Follow-Up Studies ; Head and Neck Neoplasms/mortality/pathology/*therapy ; Humans ; Male ; Middle Aged ; Neoplasm Invasiveness ; Neoplasm Recurrence, Local ; Neoplasm Staging ; Prognosis ; Retrospective Studies ; Soft Tissue Neoplasms/*secondary/therapy ; Survival Analysis ; Treatment Outcome ; },
abstract = {PURPOSE/OBJECTIVE(S): T4 locally advanced squamous cell cancers of the head and neck (SCCHN) with bone and cartilage invasion (BCI) traditionally have been treated with resection followed up with chemoradiotherapy (CRT). Because the organ preservation trials, more patients with BCI, as well as those with soft tissue invasion (STI), have been treated with definitive CRT. This is a review of our experience.<br><br>MATERIALS/METHODS: We performed a retrospective review of patients who underwent definitive CRT or radical resection followed up with postoperative CRT for T4N0-3M0 locally advanced SCCHN. We analyzed outcomes based on STI/BCI and types of treatment. Radiotherapy doses ranged from 59.4 to 72 Gy. Concurrent chemotherapy was platinum based in all CRT patients.<br><br>RESULTS: From 1995 to 2006, 101 patients with locally advanced SCCHN were treated definitively. Of these, 51 had STI and 50 had BCI. Of the 51 patients with STI, 42 were treated with CRT, 5 patients were treated with resection followed by CRT, and 4 patients were treated with radiotherapy alone. Of the 50 patients with BCI, 26 patients were treated with CRT, 20 patients were treated with radical resection followed by radiotherapy or CRT, and 4 patients were treated with radiotherapy alone. Five-year local-regional control was 51% and 43% for STI and BCI patients treated with CRT, respectively, and 44% for BCI treated with radical resection. Five-year overall survival was 23%, 51%, and 28% for STI treated with CRT, BCI treated with CRT, and BCI treated with radical resection. Outcomes were not statistically different between these groups.<br><br>CONCLUSIONS: This study suggests similar outcomes for CRT or resection followed up with chemoradiotherapy for patients with locally advanced SCCHN with BCI. Concurrent CRT may be viable alternative to upfront resection in these patients. Further studies should be performed to validate these provocative findings.},
}
@article {pmid19505037,
year = {2009},
author = {Belov, DR and Eram, SIu and Kolodiazhnyĭ, SF and Kanunikov, IE and Getmanenko, OV},
title = {[Eye movement detection with the aid of the oculogram in shifting the gaze].},
journal = {Rossiiskii fiziologicheskii zhurnal imeni I.M. Sechenova},
volume = {95},
number = {4},
pages = {347-358},
pmid = {19505037},
issn = {0869-8139},
mesh = {Eye Movements/*physiology ; Female ; Fixation, Ocular/physiology ; Humans ; Male ; Psychomotor Performance/physiology ; Saccades/physiology ; Sex Factors ; },
abstract = {Eye saccades are accompanied by changes of ocular electric potential. The sign of these changes involves a function of electrode location and eye movement direction while an indicator of the rotation angle is the amplitude. Based on the spatial distribution of the ocular potential one can solve an inverse problem and recover eye movement trajectory to be used for on-line computer control. To achieve this a system has to be able to place a cursor to a point on a screen corresponding to the current gaze direction of the user. We used four electrodes, two inferior and two lateral around the ocular depths. Inferior electrodes were used for estimation of the vertical gaze shift component and the lateral electrodes for estimation of the horizontal component. We detected and processed saccadic unipolar potential changes whose morphology resembles that of the step function. Detection and processing was performed using our proprietary multistage filter applied to the 4-channel data. The output of this filter was used to compute eye rotation parameters. Characteristic potential changes during the spontaneous blinks were identified and excluded from processing. Voluntary winks were used to mimic mouse clicks. In the beginning, our subjects went through a calibration stage during which they had to follow the cursor in eight basic directions. Using the calibration results the inverse problem was solved, i. e. based on the spatial distribution of ocular potential we computed screen position coordinates corresponding to the gaze direction. The presented technique belongs to the class of brain-computer interfaces. In addition, this work led us to a set of interesting observations regarding the characteristic patterns of eye movements. For instance, we found that just prior to a saccade EOG demonstrates short negative potential of 10-15 ms duration. We have also observed that with age the saccade amplitude decreases. Interestingly, when the gaze is shifted to the left, the left eye deviation significantly exceeds that of the right eye but the right shift does not exhibit such an asymmetry. During the diagonal shifts (bottom-right, top-left) the right eye skew exceeds that of the left one and the situation reverses for the two complimentary directions. We have observed no significant differences in eye coordination due to the subject gender.},
}
@article {pmid19503802,
year = {2009},
author = {Klonowski, W and Duch, W and Perovic, A and Jovanovic, A},
title = {Some computational aspects of the brain computer interfaces based on inner music.},
journal = {Computational intelligence and neuroscience},
volume = {2009},
number = {},
pages = {950403},
pmid = {19503802},
issn = {1687-5273},
abstract = {We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the natural language. Moreover, this approach could be used to test musical abilities. Such BCI interface could be superior when there is a need for a broader command language. Some computational estimates and unresolved difficulties are presented.},
}
@article {pmid19502132,
year = {2009},
author = {Song, YK and Borton, DA and Park, S and Patterson, WR and Bull, CW and Laiwalla, F and Mislow, J and Simeral, JD and Donoghue, JP and Nurmikko, AV},
title = {Active microelectronic neurosensor arrays for implantable brain communication interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {4},
pages = {339-345},
pmid = {19502132},
issn = {1558-0210},
support = {R01 EB007401/EB/NIBIB NIH HHS/United States ; R01 EB007401-01/EB/NIBIB NIH HHS/United States ; 1R01 EB 007401-01/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Amplifiers, Electronic ; Animals ; Brain/*physiology ; Communication Devices for People with Disabilities ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Male ; Miniaturization ; Nerve Net/physiology ; Pattern Recognition, Automated/*methods ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; Transducers ; *User-Computer Interface ; },
abstract = {We have built a wireless implantable microelectronic device for transmitting cortical signals transcutaneously. The device is aimed at interfacing a cortical microelectrode array to an external computer for neural control applications. Our implantable microsystem enables 16-channel broadband neural recording in a nonhuman primate brain by converting these signals to a digital stream of infrared light pulses for transmission through the skin. The implantable unit employs a flexible polymer substrate onto which we have integrated ultra-low power amplification with analog multiplexing, an analog-to-digital converter, a low power digital controller chip, and infrared telemetry. The scalable 16-channel microsystem can employ any of several modalities of power supply, including radio frequency by induction, or infrared light via photovoltaic conversion. As of the time of this report, the implant has been tested as a subchronic unit in nonhuman primates (approximately 1 month), yielding robust spike and broadband neural data on all available channels.},
}
@article {pmid19502004,
year = {2009},
author = {Chase, SM and Schwartz, AB and Kass, RE},
title = {Bias, optimal linear estimation, and the differences between open-loop simulation and closed-loop performance of spiking-based brain-computer interface algorithms.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1203-1213},
pmid = {19502004},
issn = {1879-2782},
support = {R01 EB005847/EB/NIBIB NIH HHS/United States ; R01 EB005847-04/EB/NIBIB NIH HHS/United States ; R01-EB005847/EB/NIBIB NIH HHS/United States ; },
mesh = {*Action Potentials ; *Algorithms ; Animals ; Brain/*physiology ; *Computer Simulation ; Electrodes, Implanted ; Linear Models ; Macaca mulatta ; Male ; Microelectrodes ; Time Factors ; *User-Computer Interface ; },
abstract = {The activity of dozens of simultaneously recorded neurons can be used to control the movement of a robotic arm or a cursor on a computer screen. This motor neural prosthetic technology has spurred an increased interest in the algorithms by which motor intention can be inferred. The simplest of these algorithms is the population vector algorithm (PVA), where the activity of each cell is used to weight a vector pointing in that neuron's preferred direction. Off-line, it is possible to show that more complicated algorithms, such as the optimal linear estimator (OLE), can yield substantial improvements in the accuracy of reconstructed hand movements over the PVA. We call this open-loop performance. In contrast, this performance difference may not be present in closed-loop, on-line control. The obvious difference between open and closed-loop control is the ability to adapt to the specifics of the decoder in use at the time. In order to predict performance gains that an algorithm may yield in closed-loop control, it is necessary to build a model that captures aspects of this adaptation process. Here we present a framework for modeling the closed-loop performance of the PVA and the OLE. Using both simulations and experiments, we show that (1) the performance gain with certain decoders can be far less extreme than predicted by off-line results, (2) that subjects are able to compensate for certain types of bias in decoders, and (3) that care must be taken to ensure that estimation error does not degrade the performance of theoretically optimal decoders.},
}
@article {pmid19497832,
year = {2009},
author = {Gupta, R and Ashe, J},
title = {Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {3},
pages = {254-262},
doi = {10.1109/TNSRE.2009.2023290},
pmid = {19497832},
issn = {1558-0210},
support = {NS42278/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; Motor Cortex/*physiology ; Muscle Contraction/*physiology ; Muscle Strength/*physiology ; Muscle, Skeletal/*physiology ; Stress, Mechanical ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories--those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.},
}
@article {pmid19497829,
year = {2009},
author = {Chestek, CA and Gilja, V and Nuyujukian, P and Kier, RJ and Solzbacher, F and Ryu, SI and Harrison, RR and Shenoy, KV},
title = {HermesC: low-power wireless neural recording system for freely moving primates.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {4},
pages = {330-338},
doi = {10.1109/TNSRE.2009.2023293},
pmid = {19497829},
issn = {1558-0210},
support = {N01-NS-42362/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Electrocardiography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; *Information Storage and Retrieval ; Macaca mulatta ; Monitoring, Ambulatory/*instrumentation ; Motor Cortex/*physiology ; Nerve Net/physiology ; *Prostheses and Implants ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation ; },
abstract = {Neural prosthetic systems have the potential to restore lost functionality to amputees or patients suffering from neurological injury or disease. Current systems have primarily been designed for immobile patients, such as tetraplegics functioning in a rather static, carefully tailored environment. However, an active patient such as amputee in a normal dynamic, everyday environment may be quite different in terms of the neural control of movement. In order to study motor control in a more unconstrained natural setting, we seek to develop an animal model of freely moving humans. Therefore, we have developed and tested HermesC-INI3, a system for recording and wirelessly transmitting neural data from electrode arrays implanted in rhesus macaques who are freely moving. This system is based on the integrated neural interface (INI3) microchip which amplifies, digitizes, and transmits neural data across a approximately 900 MHz wireless channel. The wireless transmission has a range of approximately 4 m in free space. All together this device consumes 15.8 mA and 63.2 mW. On a single 2 A-hr battery pack, this device runs contiguously for approximately six days. The smaller size and power consumption of the custom IC allows for a smaller package (51 x 38 x 38 mm (3)) than previous primate systems. The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage.},
}
@article {pmid19497710,
year = {2009},
author = {DaSalla, CS and Kambara, H and Sato, M and Koike, Y},
title = {Single-trial classification of vowel speech imagery using common spatial patterns.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1334-1339},
doi = {10.1016/j.neunet.2009.05.008},
pmid = {19497710},
issn = {1879-2782},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Language ; Male ; Nonlinear Dynamics ; *Phonetics ; *Signal Processing, Computer-Assisted ; Speech/physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {With the goal of providing a speech prosthesis for individuals with severe communication impairments, we propose a control scheme for brain-computer interfaces using vowel speech imagery. Electroencephalography was recorded in three healthy subjects for three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Trial averages revealed readiness potentials at 200 ms after stimulus and speech related potentials peaking after 350 ms. Spatial filters optimized for task discrimination were designed using the common spatial patterns method, and the resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68% to 78%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller.},
}
@article {pmid19494422,
year = {2009},
author = {Bin, G and Gao, X and Yan, Z and Hong, B and Gao, S},
title = {An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method.},
journal = {Journal of neural engineering},
volume = {6},
number = {4},
pages = {046002},
doi = {10.1088/1741-2560/6/4/046002},
pmid = {19494422},
issn = {1741-2552},
mesh = {Algorithms ; Brain/*physiology ; Databases, Factual ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Occipital Lobe/physiology ; Parietal Lobe/physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 +/- 9.6 bit min(-1). The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.},
}
@article {pmid19493851,
year = {2009},
author = {Coyle, D and Prasad, G and McGinnity, TM},
title = {Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface.},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
volume = {39},
number = {6},
pages = {1458-1471},
doi = {10.1109/TSMCB.2009.2018469},
pmid = {19493851},
issn = {1941-0492},
mesh = {Algorithms ; Artificial Intelligence ; Brain/physiology ; Electroencephalography/methods ; *Fuzzy Logic ; Humans ; *Man-Machine Systems ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper introduces a number of modifications to the learning algorithm of the self-organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown that the modified SOFNN favorably compares to other evolving fuzzy systems in terms of accuracy and structural complexity. An analysis of the SOFNN's effectiveness when applied in an electroencephalogram (EEG)-based brain-computer interface (BCI) involving the neural-time-series-prediction-preprocessing (NTSPP) framework is also presented, where a sensitivity analysis (SA) of the SOFNN hyperparameters was performed using EEG data recorded from three subjects during left/right-motor-imagery-based BCI experiments. The aim of this one-time SA was to eliminate the need to choose subject- and signal-specific hyperparameters for the SOFNN and thus apply the SOFNN in the NTSPP framework as a parameterless self-organizing framework for EEG preprocessing. The results indicate that a general set of NTSPP parameters chosen via the SA provide the best results when tested in a BCI system. Therefore, with this general set of SOFNN parameters and its self-organizing structure, in conjunction with parameterless feature extraction and linear discriminant classification, a fully parameterless BCI that lends itself well to autonomous adaptation is realizable.},
}
@article {pmid19487151,
year = {2009},
author = {Xu, Q and Zhou, H and Wang, Y and Huang, J},
title = {Fuzzy support vector machine for classification of EEG signals using wavelet-based features.},
journal = {Medical engineering &amp; physics},
volume = {31},
number = {7},
pages = {858-865},
doi = {10.1016/j.medengphy.2009.04.005},
pmid = {19487151},
issn = {1873-4030},
mesh = {*Artificial Intelligence ; Brain/physiology ; Databases, Factual ; Electroencephalography/*classification ; Female ; *Fuzzy Logic ; Humans ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; },
abstract = {Translation of electroencephalographic (EEG) recordings into control signals for brain-computer interface (BCI) systems needs to be based on a robust classification of the various types of information. EEG-based BCI features are often noisy and likely to contain outliers. This contribution describes the application of a fuzzy support vector machine (FSVM) with a radial basis function kernel for classifying motor imagery tasks, while the statistical features over the set of the wavelet coefficients were extracted to characterize the time-frequency distribution of EEG signals. In the proposed FSVM classifier, a low fraction of support vectors was used as a criterion for choosing the kernel parameter and the trade-off parameter, together with the membership parameter based solely on training data. FSVM and support vector machine (SVM) classifiers outperformed the winner of the BCI Competition 2003 and other similar studies on the same Graz dataset, in terms of the competition criterion of the mutual information (MI), while the FSVM classifier yielded a better performance than the SVM approach. FSVM and SVM classifiers perform much better than the winner of the BCI Competition 2005 on the same Graz dataset for the subject O3 according to the competition criterion of the maximal MI steepness, while the FSVM classifier outperforms the SVM method. The proposed FSVM model has potential in reducing the effects of noise or outliers in the online classification of EEG signals in BCIs.},
}
@article {pmid19477348,
year = {2009},
author = {Zentgraf, K and Green, N and Munzert, J and Schack, T and Tenenbaum, G and Vickers, JN and Weigelt, M and Wolfensteller, U and Heekeren, HR},
title = {How are actions physically implemented?.},
journal = {Progress in brain research},
volume = {174},
number = {},
pages = {303-318},
doi = {10.1016/S0079-6123(09)01324-7},
pmid = {19477348},
issn = {1875-7855},
mesh = {Brain/anatomy &amp; histology/*physiology ; *Brain Mapping ; Electronic Data Processing/methods ; Humans ; Imagery, Psychotherapy/methods ; Mental Processes/*physiology ; Neurosciences ; Psychology ; Psychomotor Performance/*physiology ; },
abstract = {This chapter focuses on the interdisciplinary discussion between cognitive psychologists and neuroscientists on how actions, the results of decision processes, are implemented. After surveying the approaches used in action implementation research, we analyze the contributions of these different approaches in more detail. Topics covered include expertise research in sports science, knowledge structures, neuroscientific research on motor imagery and decision making, computational models in motor control, robotics, and brain-machine interfaces. This forms the basis for discussing central issues for interdisciplinary research on action implementation from different viewpoints. In essence, most findings show the need to abandon serial frameworks of information processing suggesting a step-by-step pattern from perception, evaluation, and selection to execution. Instead, an outlook on new approaches is given, opening a route for future research in this field.},
}
@article {pmid19469662,
year = {2009},
author = {Ron-Angevin, R and Díaz-Estrella, A and Velasco-Alvarez, F},
title = {A two-class brain computer interface to freely navigate through virtual worlds.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {54},
number = {3},
pages = {126-133},
doi = {10.1515/BMT.2009.014},
pmid = {19469662},
issn = {0013-5585},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Ecosystem ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *User-Computer Interface ; },
abstract = {A brain computer interface that enables navigation through a virtual environment (VE) using four different navigation commands (turn right, turn left, move forward and move back) is presented. A graphical interface allows subjects to select a specific command. In this interface, the different navigation commands are surrounding a circle. A bar in the center of the circle is continuously rotating. The subject controls, by only two mental tasks, the bar extension to reach the chosen command. In this study, after an initial training based on three sessions, 8 out of 15 naive subjects were able to navigate through the VE discriminating between imagination of right-hand movements and relaxed state. All subjects (except one) improved their performance in each run and a mean error rate of 23.75% was obtained.},
}
@article {pmid19468773,
year = {2009},
author = {Dolan, K and Martens, HC and Schuurman, PR and Bour, LJ},
title = {Automatic noise-level detection for extra-cellular micro-electrode recordings.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {47},
number = {7},
pages = {791-800},
pmid = {19468773},
issn = {1741-0444},
mesh = {Artifacts ; Brain Mapping/*methods ; Deep Brain Stimulation/*methods ; Humans ; Intraoperative Care/methods ; Microelectrodes ; Parkinson Disease/physiopathology/surgery ; *Signal Processing, Computer-Assisted ; },
abstract = {Extra-cellular neuro-recording signals used for functional mapping in deep brain stimulation (DBS) surgery and invasive brain computer interfaces, may suffer from poor signal to noise ratio. Therefore, a reliable automatic noise estimate is essential to extract spikes from recordings. We show that current methods are biased toward overestimation of noise-levels with increasing neuronal activity or artifacts. An improved and novel method is proposed that is based on an estimate of the mode of the distribution of the signal envelope. Our method makes use of the inherent characteristics of the noise distribution. For band-limited Gaussian noise the envelope of the signal is known to follow the Rayleigh distribution. The location of the peak of this distribution provides a reliable noise-level estimate. It is demonstrated that this new 'envelope' method gives superior performance both on simulated data, and on actual micro-electrode recordings made during the implantation surgery of DBS electrodes for the treatment of Parkinson's disease.},
}
@article {pmid19467460,
year = {2009},
author = {Mondet, F and Oddou, JH and Boyer, C and Corsois, L and Collomb, D},
title = {[Length of needle and size of prostatic biopsies influence identification of capsular and extracapsular elements].},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {19},
number = {6},
pages = {414-418},
doi = {10.1016/j.purol.2009.02.005},
pmid = {19467460},
issn = {1166-7087},
mesh = {Biopsy, Fine-Needle/*instrumentation ; Humans ; Male ; Prospective Studies ; Prostate/*pathology ; Prostatic Neoplasms/diagnosis ; },
abstract = {OBJECTIVE: To evaluate the influence of the length of prostate biopsies (PB) on identification of prostatic capsule and periprostatic tissue.<br><br>MATERIALS AND METHOD: A prospective study was carried out in one center by two urologists during 22 months on 339 consecutive protocols of standardized ten-needle PB (PSA<10ng/ml regardless of digital rectal examination). Pathologic reports were standardized. The conclusion of the pathologic report included the average length of the ten-needle PB (Lm) and the number of prostatic core biopsies on which pathologist identified prostatic capsule and periprostatic tissue (BCI). Protocols of PB were spread in 16 groups depending on the value of Lm in millimeter: [0-1], [1-2], [2-3]... [15-16]. Relationship between Lm's and BCI's was evaluated using the linear regression and the correlation coefficient (r).<br><br>RESULTS: Average Lm=10.7 (2.1-15.7; s=2.3) (n=339). Average BCI=6.6 (0-10; s=2.3) (n=339). The value of IGap increased when the value of Lm increased (r=0.89).<br><br>CONCLUSIONS: The pathologists better identify the capsule of the prostate and the periprostatic tissue when the PB's are of large size. PB's of small size are of poor quality either for samplings of the prostatic gland or samplings of the capsule and the periprostatic tissues.},
}
@article {pmid19464514,
year = {2009},
author = {Peikon, ID and Fitzsimmons, NA and Lebedev, MA and Nicolelis, MA},
title = {Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.},
journal = {Journal of neuroscience methods},
volume = {180},
number = {2},
pages = {224-233},
doi = {10.1016/j.jneumeth.2009.03.010},
pmid = {19464514},
issn = {1872-678X},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; Biomechanical Phenomena/*physiology ; Brain/physiology ; Computer Simulation ; Extremities/*physiology ; Image Processing, Computer-Assisted/instrumentation/methods ; Macaca mulatta ; Motor Cortex/physiology ; Movement/*physiology ; Neurons/physiology ; Neurophysiology/instrumentation/*methods ; Pattern Recognition, Automated/*methods ; Range of Motion, Articular/physiology ; Signal Processing, Computer-Assisted ; Time Factors ; User-Computer Interface ; Video Recording/instrumentation/*methods ; },
abstract = {Collection and analysis of limb kinematic data are essential components of the study of biological motion, including research into biomechanics, kinesiology, neurophysiology and brain-machine interfaces (BMIs). In particular, BMI research requires advanced, real-time systems capable of sampling limb kinematics with minimal contact to the subject's body. To answer this demand, we have developed an automated video tracking system for real-time tracking of multiple body parts in freely behaving primates. The system employs high-contrast markers painted on the animal's joints to continuously track the three-dimensional positions of their limbs during activity. Two-dimensional coordinates captured by each video camera are combined and converted to three-dimensional coordinates using a quadratic fitting algorithm. Real-time operation of the system is accomplished using direct memory access (DMA). The system tracks the markers at a rate of 52 frames per second (fps) in real-time and up to 100fps if video recordings are captured to be later analyzed off-line. The system has been tested in several BMI primate experiments, in which limb position was sampled simultaneously with chronic recordings of the extracellular activity of hundreds of cortical cells. During these recordings, multiple computational models were employed to extract a series of kinematic parameters from neuronal ensemble activity in real-time. The system operated reliably under these experimental conditions and was able to compensate for marker occlusions that occurred during natural movements. We propose that this system could also be extended to applications that include other classes of biological motion.},
}
@article {pmid19457738,
year = {2009},
author = {Das, K and Rizzuto, DS and Nenadic, Z},
title = {Mental state estimation for brain--computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {8},
pages = {2114-2122},
doi = {10.1109/TBME.2009.2022948},
pmid = {19457738},
issn = {1558-2531},
mesh = {Algorithms ; Arm/physiology ; Brain/*physiology ; Electroencephalography/*methods ; Epilepsy/physiopathology ; Humans ; Mental Recall/physiology ; Movement/physiology ; Pattern Recognition, Automated/methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Mental state estimation is potentially useful for the development of asynchronous brain--computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis technique has been applied to high-dimensional, statistically sparse ECoGs recorded by a large number of electrodes. The strength of the proposed technique lies in its ability to jointly extract spatial and temporal patterns, responsible for encoding mental state differences. As such, the technique offers a systematic way of analyzing the spatiotemporal aspects of brain information processing and may be applicable to a wide range of spatiotemporal neurophysiological signals.},
}
@article {pmid19455433,
year = {2010},
author = {Schimel, KA and Boone, DR},
title = {Biogas plasticization coupled anaerobic digestion: continuous flow anaerobic pump test results.},
journal = {Applied biochemistry and biotechnology},
volume = {160},
number = {3},
pages = {912-926},
doi = {10.1007/s12010-009-8652-6},
pmid = {19455433},
issn = {1559-0291},
mesh = {Anaerobiosis ; *Biofuels ; Biomass ; *Bioreactors ; *Hot Temperature ; Kinetics ; Nitrogen/chemistry ; Reproducibility of Results ; Sewage ; },
abstract = {In this investigation, the Anaerobic Pump (TAP) and a conventional continuous flow stirred tank reactor (CFSTR) were tested side by side to compare performance. TAP integrates anaerobic digestion (AD) with biogas plasticization-disruption cycle to improve mass conversion to methane. Both prototypes were fed a "real world" 50:50 mixture of waste-activated sludge (WAS) and primary sludge and operated at room temperature (20 degrees Celsius). The quantitative results from three steady states show TAP peaked at 97% conversion of the particulate COD in a system hydraulic residence time (HRT) of only 6 days. It achieved a methane production of 0.32 STP cubic meter CH(4) per kilogram COD fed and specific methane yield of 0.78 m(3) CH(4) per cubic meter per day. This was more than three times the CFSTR specific methane yield (0.22 m(3) CH(4) per cubic meter per day) and more than double the CFSTR methane production (0.15 m(3) CH(4) per kilogram COD fed). A comparative kinetics analysis showed the TAP peak substrate COD removal rate (R (o)) was 2.24 kg COD per cubic meter per day, more than three times the CFSTR substrate removal rate of 0.67 kg COD per cubic meter per day. The three important factors contributing to the superior TAP performance were (1) effective solids capture (96%) with (2) mass recycle and (3) stage II plasticization-disruption during active AD. The Anaerobic Pump (TAP) is a high rate, high efficiency-low temperature microbial energy engine that could be used to improve renewable energy yields from classic AD waste substrates like refuse-derived fuels, treatment plant sludges, food wastes, livestock residues, green wastes and crop residuals.},
}
@article {pmid19449735,
year = {2009},
author = {Kharitonova, MA and Vershinina, VI},
title = {[Biosynthesis of secreted ribonucleases by Bacillus intermedius and Bacillus circulans during nitrogen starvation].},
journal = {Mikrobiologiia},
volume = {78},
number = {2},
pages = {220-225},
pmid = {19449735},
issn = {0026-3656},
mesh = {Ammonium Sulfate/metabolism ; Bacillus/*enzymology/*genetics ; Bacillus subtilis/metabolism ; Base Sequence ; Culture Media ; Endoribonucleases/biosynthesis/genetics ; *Gene Expression Regulation, Bacterial ; Genes, Bacterial ; Molecular Sequence Data ; Nitrogen/*metabolism ; Recombinant Proteins/biosynthesis/genetics ; Ribonucleases/*biosynthesis/genetics ; Sequence Alignment ; },
}
@article {pmid19449706,
year = {2009},
author = {Chao, A and Colwell, RK and Lin, CW and Gotelli, NJ},
title = {Sufficient sampling for asymptotic minimum species richness estimators.},
journal = {Ecology},
volume = {90},
number = {4},
pages = {1125-1133},
doi = {10.1890/07-2147.1},
pmid = {19449706},
issn = {0012-9658},
mesh = {Animals ; *Biodiversity ; Computer Simulation ; Lepidoptera/physiology ; *Models, Biological ; Panama ; Plants ; Sample Size ; United Kingdom ; },
abstract = {Biodiversity sampling is labor intensive, and a substantial fraction of a biota is often represented by species of very low abundance, which typically remain undetected by biodiversity surveys. Statistical methods are widely used to estimate the asymptotic number of species present, including species not yet detected. Additional sampling is required to detect and identify these species, but richness estimators do not indicate how much sampling effort (additional individuals or samples) would be necessary to reach the asymptote of the species accumulation curve. Here we develop the first statistically rigorous nonparametric method for estimating the minimum number of additional individuals, samples, or sampling area required to detect any arbitrary proportion (including 100%) of the estimated asymptotic species richness. The method uses the Chao1 and Chao2 nonparametric estimators of asymptotic richness, which are based on the frequencies of rare species in the original sampling data. To evaluate the performance of the proposed method, we randomly subsampled individuals or quadrats from two large biodiversity inventories (light trap captures of Lepidoptera in Great Britain and censuses of woody plants on Barro Colorado Island [BCI], Panama). The simulation results suggest that the method performs well but is slightly conservative for small sample sizes. Analyses of the BCI results suggest that the method is robust to nonindependence arising from small-scale spatial aggregation of species occurrences. When the method was applied to seven published biodiversity data sets, the additional sampling effort necessary to capture all the estimated species ranged from 1.05 to 10.67 times the original sample (median approximately equal to 2.23). Substantially less effort is needed to detect 90% of the species (0.33-1.10 times the original effort; median approximately equal to 0.80). An Excel spreadsheet tool is provided for calculating necessary sampling effort for either abundance data or replicated incidence data.},
}
@article {pmid19443448,
year = {2009},
author = {Pons, JL and Labesse, G},
title = {@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.},
journal = {Nucleic acids research},
volume = {37},
number = {Web Server issue},
pages = {W485-91},
pmid = {19443448},
issn = {1362-4962},
mesh = {Ligands ; *Models, Molecular ; Phosphoprotein Phosphatases/chemistry ; Protein Folding ; Protein Kinases/chemistry ; Sequence Analysis, Protein ; *Software ; *Structural Homology, Protein ; Transcription Factors/chemistry ; User-Computer Interface ; },
abstract = {@TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/},
}
@article {pmid19439361,
year = {2009},
author = {Faradji, F and Ward, RK and Birch, GE},
title = {Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.},
journal = {Journal of neuroscience methods},
volume = {180},
number = {2},
pages = {330-339},
doi = {10.1016/j.jneumeth.2009.03.011},
pmid = {19439361},
issn = {1872-678X},
mesh = {Algorithms ; Artificial Intelligence ; Cognition ; Communication Devices for People with Disabilities ; *Computer Simulation ; Computers ; Discriminant Analysis ; Electroencephalography/*methods ; Fuzzy Logic ; Humans ; Imagination ; *Man-Machine Systems ; Mental Processes/*physiology ; Neural Networks, Computer ; Pattern Recognition, Automated/methods ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted ; Software ; Software Validation ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.},
}
@article {pmid19430593,
year = {2009},
author = {Kositsky, M and Chiappalone, M and Alford, ST and Mussa-Ivaldi, FA},
title = {Brain-machine interactions for assessing the dynamics of neural systems.},
journal = {Frontiers in neurorobotics},
volume = {3},
number = {},
pages = {1},
pmid = {19430593},
issn = {1662-5218},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; },
abstract = {A critical advance for brain-machine interfaces is the establishment of bi-directional communications between the nervous system and external devices. However, the signals generated by a population of neurons are expected to depend in a complex way upon poorly understood neural dynamics. We report a new technique for the identification of the dynamics of a neural population engaged in a bi-directional interaction with an external device. We placed in vitro preparations from the lamprey brainstem in a closed-loop interaction with simulated dynamical devices having different numbers of degrees of freedom. We used the observed behaviors of this composite system to assess how many independent parameters - or state variables - determine at each instant the output of the neural system. This information, known as the dynamical dimension of a system, allows predicting future behaviors based on the present state and the future inputs. A relevant novelty in this approach is the possibility to assess a computational property - the dynamical dimension of a neuronal population - through a simple experimental technique based on the bi-directional interaction with simulated dynamical devices. We present a set of results that demonstrate the possibility of obtaining stable and reliable measures of the dynamical dimension of a neural preparation.},
}
@article {pmid19428521,
year = {2009},
author = {Hwang, HJ and Kwon, K and Im, CH},
title = {Neurofeedback-based motor imagery training for brain-computer interface (BCI).},
journal = {Journal of neuroscience methods},
volume = {179},
number = {1},
pages = {150-156},
doi = {10.1016/j.jneumeth.2009.01.015},
pmid = {19428521},
issn = {1872-678X},
mesh = {Adult ; Arm/physiology ; Biofeedback, Psychology/*methods ; Brain/*physiology ; Electroencephalography/*methods ; Electromyography ; Humans ; Imagination/*physiology ; Learning/physiology ; Male ; Man-Machine Systems ; Psychomotor Performance/*physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.},
}
@article {pmid19428515,
year = {2009},
author = {van Gerven, M and Jensen, O},
title = {Attention modulations of posterior alpha as a control signal for two-dimensional brain-computer interfaces.},
journal = {Journal of neuroscience methods},
volume = {179},
number = {1},
pages = {78-84},
doi = {10.1016/j.jneumeth.2009.01.016},
pmid = {19428515},
issn = {1872-678X},
mesh = {Adult ; *Alpha Rhythm ; Attention/*physiology ; Brain/*physiology ; Female ; Humans ; Magnetoencephalography ; Male ; Man-Machine Systems ; Space Perception/*physiology ; Spatial Behavior/physiology ; *User-Computer Interface ; },
abstract = {Research on brain-computer interfaces (BCIs) is gaining strong interest. This is motivated by BCIs being applicable for helping disabled, for gaming, and as a tool in cognitive neuroscience. Often, motor imagery is used to produce (binary) control signals. However, finding other types of control signals that allow the discrimination of multiple classes would help to increase the applicability of BCIs. We have investigated if modulation of posterior alpha activity by means of covert spatial attention in two dimensions can be reliably classified in single trials. Magnetoencephalography (MEG) data were collected for 15 subjects who were engaged in a task where they covertly had to visually attend left, right, up or down during a period of 2500 ms. We then classified the trials using support vector machines. The four orientations of covert attention could be reliably classified up to a maximum of 69% correctly classified trials (25% chance level) without the need for lengthy and burdensome subject training. Low classification performance in some subjects was explained by a low alpha signal. These findings support the case that modulation of alpha activity by means of covert spatial attention is promising as a control signal for a two-dimensional BCI.},
}
@article {pmid19423426,
year = {2009},
author = {Grosse-Wentrup, M and Liefhold, C and Gramann, K and Buss, M},
title = {Beamforming in noninvasive brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {4},
pages = {1209-1219},
doi = {10.1109/TBME.2008.2009768},
pmid = {19423426},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; Artifacts ; Brain Mapping/*methods ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Cortex/physiology ; Pattern Recognition, Automated/methods ; Reference Values ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.},
}
@article {pmid19421416,
year = {2009},
author = {Parini, S and Maggi, L and Turconi, AC and Andreoni, G},
title = {A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication.},
journal = {Computational intelligence and neuroscience},
volume = {2009},
number = {},
pages = {864564},
pmid = {19421416},
issn = {1687-5273},
abstract = {In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.},
}
@article {pmid19421415,
year = {2009},
author = {Pfurtscheller, G and Linortner, P and Winkler, R and Korisek, G and Müller-Putz, G},
title = {Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury.},
journal = {Computational intelligence and neuroscience},
volume = {2009},
number = {},
pages = {104180},
pmid = {19421415},
issn = {1687-5273},
abstract = {EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean +/- SD) were obtained: left versus right hand 65.03% +/- 8.52, left hand versus feet 68.19% +/- 11.08, and right hand versus feet 65.05% +/- 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.},
}
@article {pmid19419576,
year = {2009},
author = {Kayagil, TA and Bai, O and Henriquez, CS and Lin, P and Furlani, SJ and Vorbach, S and Hallett, M},
title = {A binary method for simple and accurate two-dimensional cursor control from EEG with minimal subject training.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {6},
number = {},
pages = {14},
pmid = {19419576},
issn = {1743-0003},
support = {//Intramural NIH HHS/United States ; },
mesh = {Adult ; *Brain-Computer Interfaces ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Computer Systems ; Electroencephalography/*methods ; Female ; Hand ; Humans ; Imagination ; Male ; Middle Aged ; *Models, Theoretical ; Movement ; Psychomotor Performance ; Reference Values ; Signal Processing, Computer-Assisted ; Software ; Young Adult ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use a signal from five EEG channels (comprising one primary channel with four additional channels used to calculate its Laplacian derivation) to provide two-dimensional (2-D) control of a cursor on a computer screen, with simple threshold-based binary classification of band power readings taken over pre-defined time windows during subject hand movement.<br><br>METHODS: We tested the paradigm with four healthy subjects, none of whom had prior BCI experience. Each subject played a game wherein he or she attempted to move a cursor to a target within a grid while avoiding a trap. We also present supplementary results including one healthy subject using motor imagery, one primary lateral sclerosis (PLS) patient, and one healthy subject using a single EEG channel without Laplacian derivation.<br><br>RESULTS: For the four healthy subjects using real hand movement, the system provided accurate cursor control with little or no required user training. The average accuracy of the cursor movement was 86.1% (SD 9.8%), which is significantly better than chance (p = 0.0015). The best subject achieved a control accuracy of 96%, with only one incorrect bit classification out of 47. The supplementary results showed that control can be achieved under the respective experimental conditions, but with reduced accuracy.<br><br>CONCLUSION: The binary method provides na&#xef;ve subjects with real-time control of a cursor in 2-D using dichotomous classification of synchronous EEG band power readings from a small number of channels during hand movement. The primary strengths of our method are simplicity of hardware and software, and high accuracy when used by untrained subjects.},
}
@article {pmid19412982,
year = {2009},
author = {Phothisonothai, M and Nakagawa, M},
title = {A classification method of different motor imagery tasks based on fractal features for brain-machine interface.},
journal = {Journal of integrative neuroscience},
volume = {8},
number = {1},
pages = {95-122},
doi = {10.1142/s0219635209002071},
pmid = {19412982},
issn = {0219-6352},
mesh = {Brain/*physiology ; Brain Mapping ; Electroencephalography ; *Fractals ; Humans ; Imagery, Psychotherapy/*methods ; Motor Activity/*physiology ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; Time Factors ; *User-Computer Interface ; },
abstract = {The objective of this study is to classify spontaneous electroencephalogram (EEG) signal on the basis of fractal concepts. Four motor imagery tasks (left hand movement, right hand movement, feet movement, and tongue movement) were investigated for each EEG recording session. Ten subjects volunteered to participate in this study. As we known, fractal geometry is a mathematical tool for dealing with complex systems like EEG signal. Therefore, we used the fractal dimension (FD) as feature for the application of brain-machine interface (BMI). Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded FD values between relaxing and imaging states of the recorded EEG signal. To show the pattern of FDs, we propose a windowing-based method or also called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. The K-L divergence and different expected values are employed as the input parameters of classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results show that the proposed method is more effective than the conventional methods.},
}
@article {pmid19404469,
year = {2008},
author = {Rachmuth, G and Poon, CS},
title = {Transistor analogs of emergent iono-neuronal dynamics.},
journal = {HFSP journal},
volume = {2},
number = {3},
pages = {156-166},
pmid = {19404469},
issn = {1955-2068},
support = {R01 HL072849/HL/NHLBI NIH HHS/United States ; R01 HL079503/HL/NHLBI NIH HHS/United States ; R21 EB005460/EB/NIBIB NIH HHS/United States ; },
abstract = {Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.},
}
@article {pmid19404467,
year = {2008},
author = {Kawato, M},
title = {Brain controlled robots.},
journal = {HFSP journal},
volume = {2},
number = {3},
pages = {136-142},
pmid = {19404467},
issn = {1955-2068},
abstract = {In January 2008, Duke University and the Japan Science and Technology Agency (JST) publicized their successful control of a brain-machine interface for a humanoid robot by a monkey brain across the Pacific Ocean. The activities of a few hundred neurons were recorded from a monkey's motor cortex in Miguel Nicolelis's lab at Duke University, and the kinematic features of monkey locomotion on a treadmill were decoded from neural firing rates in real time. The decoded information was sent to a humanoid robot, CB-i, in ATR Computational Neuroscience Laboratories located in Kyoto, Japan. This robot was developed by the JST International Collaborative Research Project (ICORP) as the "Computational Brain Project." CB-i's locomotion-like movement was video-recorded and projected on a screen in front of the monkey. Although the bidirectional communication used a conventional Internet connection, its delay was suppressed below one over several seconds, partly due to a video-streaming technique, and this encouraged the monkey's voluntary locomotion and influenced its brain activity. This commentary introduces the background and future directions of the brain-controlled robot.},
}
@article {pmid19404411,
year = {2009},
author = {Fitzsimmons, NA and Lebedev, MA and Peikon, ID and Nicolelis, MA},
title = {Extracting kinematic parameters for monkey bipedal walking from cortical neuronal ensemble activity.},
journal = {Frontiers in integrative neuroscience},
volume = {3},
number = {},
pages = {3},
pmid = {19404411},
issn = {1662-5145},
abstract = {The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain-machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques - both offline and in real time. Linear decoders extracted 3D coordinates of leg joints and leg muscle electromyograms from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again.},
}
@article {pmid19403356,
year = {2009},
author = {Nazarpour, K and Praamstra, P and Miall, RC and Sanei, S},
title = {Steady-state movement related potentials for brain-computer interfacing.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {8},
pages = {2104-2113},
pmid = {19403356},
issn = {1558-2531},
support = {/WT_/Wellcome Trust/United Kingdom ; 069439/WT_/Wellcome Trust/United Kingdom ; },
mesh = {Brain Mapping ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Fingers/*physiology ; Humans ; Male ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible.},
}
@article {pmid19403263,
year = {2009},
author = {Sanchez, JC and Mahmoudi, B and DiGiovanna, J and Principe, JC},
title = {Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {3},
pages = {305-315},
doi = {10.1016/j.neunet.2009.03.015},
pmid = {19403263},
issn = {1879-2782},
mesh = {Adaptation, Psychological/*physiology ; Artificial Intelligence ; Persons with Disabilities/*rehabilitation ; Equipment Design/methods/trends ; *Goals ; Learning/physiology ; Prostheses and Implants/*trends ; Psychomotor Performance/physiology ; Reinforcement, Psychology ; *User-Computer Interface ; Volition/*physiology ; },
abstract = {The success of brain-machine interfaces (BMI) is enabled by the remarkable ability of the brain to incorporate the artificial neuroprosthetic 'tool' into its own cognitive space and use it as an extension of the user's body. Unlike other tools, neuroprosthetics create a shared space that seamlessly spans the user's internal goal representation of the world and the external physical environment enabling a much deeper human-tool symbiosis. A key factor in the transformation of 'simple tools' into 'intelligent tools' is the concept of co-adaptation where the tool becomes functionally involved in the extraction and definition of the user's goals. Recent advancements in the neuroscience and engineering of neuroprosthetics are providing a blueprint for how new co-adaptive designs based on reinforcement learning change the nature of a user's ability to accomplish tasks that were not possible using conventional methodologies. By designing adaptive controls and artificial intelligence into the neural interface, tools can become active assistants in goal-directed behavior and further enhance human performance in particular for the disabled population. This paper presents recent advances in computational and neural systems supporting the development of symbiotic neuroprosthetic assistants.},
}
@article {pmid19394363,
year = {2009},
author = {Paralikar, KJ and Rao, CR and Clement, RS},
title = {New approaches to eliminating common-noise artifacts in recordings from intracortical microelectrode arrays: inter-electrode correlation and virtual referencing.},
journal = {Journal of neuroscience methods},
volume = {181},
number = {1},
pages = {27-35},
pmid = {19394363},
issn = {1872-678X},
support = {R21 DC007227-01A2/DC/NIDCD NIH HHS/United States ; 1R21DC007227-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Animals ; *Artifacts ; Cerebral Cortex/*cytology ; *Microelectrodes ; Neurons/*physiology ; Rats ; Reference Values ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Statistics as Topic ; *User-Computer Interface ; },
abstract = {Intracortical microelectrode arrays record multi-unit extracellular activity for neurophysiology studies and for brain-machine interface applications. The common first step is neural spike-detection; a process complicated by common-noise signals from motion artifacts, electromyographic activity, and electric field pickup, especially in awake/behaving subjects. Often common-noise spikes are very similar to neural spikes in their magnitude, spectral, and temporal features. Provided sufficient spacing exists between electrodes of the array, a local neural spike is rarely recorded on multiple electrodes simultaneously. This is not true for distant common-noise sources. Two new techniques compatible with standard spike-detection schemes are introduced and evaluated. The first method, virtual referencing (VR), takes the average recording from all functional electrodes in the array (represents the signal from a virtual-electrode at the array's center) and subtracts it from the test electrode signal. The second method, inter-electrode correlation (IEC), computes a correlation coefficient between threshold exceeding candidate spike segments on the test electrode and concurrent segments from remaining electrodes. When sufficient correlation is detected, the candidate spike is rejected as originating from a distant common-noise source. The performance of these algorithms was compared with standard thresholding and differential referencing approaches using neural recordings from un-anaesthetized rats. By evaluating characteristics of mean-spike waveforms generated by each method under different levels of common-noise, it was found that IEC consistently offered the most robust means of neural spike-detection. Furthermore, IEC's rejection of supra-threshold events not likely originating from local neurons significantly reduces data handling for downstream spike sorting and processing operations.},
}
@article {pmid19392717,
year = {2009},
author = {Ernest, SK and White, EP and Brown, JH},
title = {Changes in a tropical forest support metabolic zero-sum dynamics.},
journal = {Ecology letters},
volume = {12},
number = {6},
pages = {507-515},
pmid = {19392717},
issn = {1461-0248},
support = {P20 RR018754/RR/NCRR NIH HHS/United States ; P20 RR018754-06A1/RR/NCRR NIH HHS/United States ; },
mesh = {Biochemical Phenomena ; Carbon Dioxide/metabolism ; Ecosystem ; Energy Metabolism ; Environmental Monitoring ; Geography ; Greenhouse Effect ; *Models, Biological ; Nitrogen/metabolism ; Panama Canal Zone ; Photosynthesis ; Population Dynamics ; Trees/*growth &amp; development/*metabolism ; Tropical Climate ; },
abstract = {Major shifts in many ecosystem-level properties of tropical forests have been observed, but the processes driving these changes are poorly understood. The forest on Barro Colorado Island (BCI) exhibited a 20% decrease in the number of trees and a 10% increase in average diameter. Using a metabolism-based zero-sum framework, we show that increases in per capita resource use at BCI, caused by increased tree size and increased temperature, compensated for the observed declines in abundance. This trade-off between abundance and average resource use resulted in no net change in the rate resources are fluxed by the forest. Observed changes in the forest are not consistent with other hypotheses, including changes in overall resource availability and existing self-thinning models. The framework successfully predicts interrelated changes in size, abundance and temperature, indicating its utility for understanding changes in the structure and dynamics of ecosystems.},
}
@article {pmid19389689,
year = {2009},
author = {Anderson, NR and Wisneski, K and Eisenman, L and Moran, DW and Leuthardt, EC and Krusienski, DJ},
title = {An offline evaluation of the autoregressive spectrum for electrocorticography.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {3},
pages = {913-916},
doi = {10.1109/TBME.2009.2009767},
pmid = {19389689},
issn = {1558-2531},
mesh = {Analysis of Variance ; Cerebral Cortex/*physiology ; *Diagnostic Techniques, Neurological ; Electrodes, Implanted ; Electrodiagnosis/*methods ; Epilepsy ; Evoked Potentials, Visual/physiology ; Humans ; *Models, Neurological ; *Signal Processing, Computer-Assisted ; },
abstract = {Electrical signals acquired from the cortical surface, or electrocorticography (ECoG), exhibit high spatial and temporal resolution and are valuable for mapping brain activity, detecting irregularities, and controlling a brain-computer interface. As with scalp-recorded EEG, much of the identified information content in ECoG is manifested as amplitude modulations of specific frequency bands. Autoregressive (AR) spectral estimation has proven successful for modeling the well-defined and comparatively limited EEG spectrum. However, because the ECoG spectrum is significantly more extensive with yet undefined dynamics, it cannot be assumed that the ECoG spectrum can be accurately estimated using the same AR model parameters that are valid for analogous EEG studies. This study provides an offline evaluation of AR modeling of ECoG signals for detecting tongue movements. The resulting model parameters can serve as a reference for related AR spectral analysis of ECoG signals.},
}
@article {pmid19380125,
year = {2009},
author = {Zhou, J and Yao, J and Deng, J and Dewald, JP},
title = {EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects.},
journal = {Computers in biology and medicine},
volume = {39},
number = {5},
pages = {443-452},
pmid = {19380125},
issn = {1879-0534},
support = {R01 HD039343/HD/NICHD NIH HHS/United States ; R01 5R01HD047569-04/HD/NICHD NIH HHS/United States ; R03 HD39804-01A1/HD/NICHD NIH HHS/United States ; R03 HD039804/HD/NICHD NIH HHS/United States ; R01 5R01HD39343-02/HD/NICHD NIH HHS/United States ; R01 HD047569/HD/NICHD NIH HHS/United States ; UL1 RR025741/RR/NCRR NIH HHS/United States ; R01 HD047569-05/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Diagnosis, Computer-Assisted/methods ; Elbow/innervation/physiology/*physiopathology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/physiology/physiopathology ; Movement/physiology ; Movement Disorders/etiology/physiopathology ; *Pattern Recognition, Automated ; Reproducibility of Results ; Shoulder/innervation/physiology/*physiopathology ; Signal Processing, Computer-Assisted ; Stroke/complications/*physiopathology ; *Torque ; },
abstract = {The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (approximately 100% in healthy subjects and >80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.},
}
@article {pmid19377164,
year = {2009},
author = {Rastjoo, A and Arabalibeik, H},
title = {Evaluation of Hidden Markov Model for p300 detection in EEG signal.},
journal = {Studies in health technology and informatics},
volume = {142},
number = {},
pages = {265-267},
pmid = {19377164},
issn = {0926-9630},
mesh = {*Electroencephalography ; Event-Related Potentials, P300/*physiology ; Humans ; *Markov Chains ; Nerve Net ; },
abstract = {Hidden Markov Model (HMM) was evaluated for P300 detection in electroencephalogram (EEG) signal. In some applications like the brain-computer interface (BCI), where real time detection is a concern, HMM could be a useful tool. Wavelet enhanced independent component analysis (wICA) was used for electrooculogram (EOG) artifact removal and B-spline wavelet transform for background EEG noise cancellation. HMM results are enhanced by a multilayer perceptron (MLP) neural network. Accuracy of the proposed HMM classifier is 81.6% on the validation dataset.},
}
@article {pmid19372650,
year = {2009},
author = {Veekmans, K and Ressel, L and Mueller, J and Vischer, M and Brockmeier, SJ},
title = {Comparison of music perception in bilateral and unilateral cochlear implant users and normal-hearing subjects.},
journal = {Audiology &amp; neuro-otology},
volume = {14},
number = {5},
pages = {315-326},
doi = {10.1159/000212111},
pmid = {19372650},
issn = {1421-9700},
mesh = {Acoustic Stimulation/psychology ; Adult ; Aged ; Auditory Perceptual Disorders/*psychology/therapy ; *Cochlear Implants ; *Hearing ; Hearing Loss, Bilateral/*psychology/therapy ; Humans ; Middle Aged ; Music/*psychology ; Patient Satisfaction ; Pitch Perception ; Surveys and Questionnaires ; Time Perception ; Young Adult ; },
abstract = {Music plays an important role in the daily life of cochlear implant (CI) users, but electrical hearing and speech processing pose challenges for enjoying music. Studies of unilateral CI (UCI) users' music perception have found that these subjects have little difficulty recognizing tempo and rhythm but great difficulty with pitch, interval and melody. The present study is an initial step towards understanding music perception in bilateral CI (BCI) users. The Munich Music Questionnaire was used to investigate music listening habits and enjoyment in 23 BCI users compared to 2 control groups: 23 UCI users and 23 normal-hearing (NH) listeners. Bilateral users appeared to have a number of advantages over unilateral users, though their enjoyment of music did not reach the level of NH listeners.},
}
@article {pmid19366638,
year = {2009},
author = {Brunner, P and Ritaccio, AL and Lynch, TM and Emrich, JF and Wilson, JA and Williams, JC and Aarnoutse, EJ and Ramsey, NF and Leuthardt, EC and Bischof, H and Schalk, G},
title = {A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {15},
number = {3},
pages = {278-286},
pmid = {19366638},
issn = {1525-5069},
support = {EB006356/EB/NIBIB NIH HHS/United States ; KL2 RR025012/RR/NCRR NIH HHS/United States ; K12 HD049077/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; 1T90DK070079-01/DK/NIDDK NIH HHS/United States ; R01 EB006356-04/EB/NIBIB NIH HHS/United States ; K12-HD049077/HD/NICHD NIH HHS/United States ; 1KL2RR025012-01/RR/NCRR NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; T90 DK070079/DK/NIDDK NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; *Brain Mapping ; Cerebral Cortex/pathology/*physiopathology ; Electric Stimulation ; Electrodes, Implanted ; Electroencephalography/*methods ; Epilepsy/pathology/physiopathology ; Female ; Humans ; Male ; Middle Aged ; *Practice Guidelines as Topic ; Signal Processing, Computer-Assisted ; Young Adult ; },
abstract = {Functional mapping of eloquent cortex is often necessary prior to invasive brain surgery, but current techniques that derive this mapping have important limitations. In this article, we demonstrate the first comprehensive evaluation of a rapid, robust, and practical mapping system that uses passive recordings of electrocorticographic signals. This mapping procedure is based on the BCI2000 and SIGFRIED technologies that we have been developing over the past several years. In our study, we evaluated 10 patients with epilepsy from four different institutions and compared the results of our procedure with the results derived using electrical cortical stimulation (ECS) mapping. The results show that our procedure derives a functional motor cortical map in only a few minutes. They also show a substantial concurrence with the results derived using ECS mapping. Specifically, compared with ECS maps, a next-neighbor evaluation showed no false negatives, and only 0.46 and 1.10% false positives for hand and tongue maps, respectively. In summary, we demonstrate the first comprehensive evaluation of a practical and robust mapping procedure that could become a new tool for planning of invasive brain surgeries.},
}
@article {pmid19359512,
year = {2009},
author = {Odhiambo, JF and Rhinehart, JD and Helmondollar, R and Pritchard, JY and Osborne, PI and Felton, EE and Dailey, RA},
title = {Effect of weaning regimen on energy profiles and reproductive performance of beef cows.},
journal = {Journal of animal science},
volume = {87},
number = {7},
pages = {2428-2436},
doi = {10.2527/jas.2008-1138},
pmid = {19359512},
issn = {1525-3163},
mesh = {Aging/physiology ; Animal Husbandry/*methods ; Animals ; Body Composition ; Body Weight/physiology ; *Cattle ; Energy Metabolism/*physiology ; Female ; Lactation ; Pregnancy ; Reproduction/*physiology ; Time Factors ; *Weaning ; },
abstract = {The effect of shifting calf-weaning age on profiles of energy status (BW, BCS, and rib and rump fat) and reproductive performance of beef cows was evaluated in a 3-yr study. Pregnant and lactating crossbred beef cows (n = 408), mainly of Angus and Hereford breeding, were stratified by age and by sex and BW of their calves and assigned randomly into 2 treatments: weaning at approximately 180 d (early weaning) and normal weaning 45 d later (control). Cows were managed together on native range pastures and supplemented with harvested forage during the winter months. Cow BW, BCS, rib fat, and rump fat were measured periodically from early weaning through the next breeding. Reproductive performance was evaluated by calving intervals (CI), days from initiation of breeding to calving (BCI), retention in the herd, and adjusted 205-d weaning BW of the subsequent calf. Early weaned cows had greater (P < 0.001) BW at normal weaning than control cows, but the overall pattern of cow BW did not differ (P > 0.05) among treatments. Peak and nadir BCS occurred at precalving and postcalving periods, respectively and were greater (P < 0.001) at each period in early weaned than in control cows and in cows > or =5-yr-old than in younger cows. Patterns for rib fat and rump fat were nearly identical to those of BCS except for the 3-way interaction (P < 0.001) of treatment, age, and period on rump fat. Mean CI (372.4 +/- 2.1 d) and BCI (299.7 +/- 1.9 d) were not affected (P = 0.42) by treatment but varied (P < 0.001) with age of the cow. Age of cow accounted for 16% of total variation in CI and 12% of total variation in gestation length (P < 0.001). The intervals were longer (P < 0.001) in primiparous cows than in older cows. Early weaning decreased risk of culling in cows and thereby increased (P < 0.05) overall persistence by 11% over control cows. Earlier weaning of cows in the previous year increased (P < 0.001) weaning weight of the subsequent calf by 8.6 kg per cow per yr. Shifting weaning time increased storage of consumed energy as evidenced by increased rump fat, for use later during high-energy demand, ultimately improving overall productivity of the cow-calf system.},
}
@article {pmid19357940,
year = {2009},
author = {Chen, CW and Ju, MS and Sun, YN and Lin, CC},
title = {Model analyses of visual biofeedback training for EEG-based brain-computer interface.},
journal = {Journal of computational neuroscience},
volume = {27},
number = {3},
pages = {357-368},
pmid = {19357940},
issn = {1573-6873},
mesh = {Adult ; Biofeedback, Psychology/*methods ; Brain/*physiology ; Computer Simulation ; Electroencephalography ; Evoked Potentials, Visual/physiology ; Humans ; Male ; Man-Machine Systems ; *Models, Theoretical ; Photic Stimulation/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Teaching/*methods ; *User-Computer Interface ; Vision, Ocular/*physiology ; Visual Pathways/physiology ; Young Adult ; },
abstract = {The primary goal of this study was to construct a simulation model of a biofeedback brain-computer interface (BCI) system to analyze the effect of biofeedback training on BCI users. A mathematical model of a man-machine visual-biofeedback BCI system was constructed to simulate a subject using a BCI system to control cursor movements. The model consisted of a visual tracking system, a thalamo-cortical model for EEG generation, and a BCI system. The BCI system in the model was realized for real experiments of visual biofeedback training. Ten sessions of visual biofeedback training were performed in eight normal subjects during a 3-week period. The task was to move a cursor horizontally across a screen, or to hold it at the screen's center. Experimental conditions and EEG data obtained from real experiments were then simulated with the model. Three model parameters, representing the adaptation rate of gain in the visual tracking system and the relative synaptic strength between the thalamic reticular and thalamo-cortical cells in the Rolandic areas, were estimated by optimization techniques so that the performance of the model best fitted the experimental results. The serial changes of these parameters over the ten sessions, reflecting the effects of biofeedback training, were analyzed. The model simulation could reproduce results similar to the experimental data. The group mean success rate and information transfer rate improved significantly after training (56.6 to 81.1% and 0.19 to 0.76 bits/trial, respectively). All three model parameters displayed similar and statistically significant increasing trends with time. Extensive simulation with systematic changes of these parameters also demonstrated that assigning larger values to the parameters improved the BCI performance. We constructed a model of a biofeedback BCI system that could simulate experimental data and the effect of training. The simulation results implied that the improvement was achieved through a quicker adaptation rate in visual tracking gain and a larger synaptic gain from the visual tracking system to the thalamic reticular cells. In addition to the purpose of this study, the constructed biofeedback BCI model can also be used both to investigate the effects of different biofeedback paradigms and to test, estimate, or predict the performances of other newly developed BCI signal processing algorithms.},
}
@article {pmid19351359,
year = {2009},
author = {Kübler, A and Furdea, A and Halder, S and Hammer, EM and Nijboer, F and Kotchoubey, B},
title = {A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients.},
journal = {Annals of the New York Academy of Sciences},
volume = {1157},
number = {},
pages = {90-100},
doi = {10.1111/j.1749-6632.2008.04122.x},
pmid = {19351359},
issn = {1749-6632},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/physiopathology/therapy ; Brain/*physiopathology ; *Communication Devices for People with Disabilities/statistics &amp; numerical data ; Discriminant Analysis ; Electroencephalography ; *Event-Related Potentials, P300 ; *Evoked Potentials, Auditory ; Female ; Humans ; Male ; Middle Aged ; Quadriplegia/physiopathology/*therapy ; Software Design ; *User-Computer Interface ; },
abstract = {Using brain-computer interfaces (BCI) humans can select letters or other targets on a computer screen without any muscular involvement. An intensively investigated kind of BCI is based on the recording of visual event-related brain potentials (ERP). However, some severely paralyzed patients who need a BCI for communication have impaired vision or lack control of gaze movement, thus making a BCI depending on visual input no longer feasible. In an effort to render the ERP-BCI usable for this group of patients, the ERP-BCI was adapted to auditory stimulation. Letters of the alphabet were assigned to cells in a 5 x 5 matrix. Rows of the matrix were coded with numbers 1 to 5, and columns with numbers 6 to 10, and the numbers were presented auditorily. To select a letter, users had to first select the row and then the column containing the desired letter. Four severely paralyzed patients in the end-stage of a neurodegenerative disease were examined. All patients performed above chance level. Spelling accuracy was significantly lower with the auditory system as compared with a similar visual system. Patients reported difficulties in concentrating on the task when presented with the auditory system. In future studies, the auditory ERP-BCI should be adjusted by taking into consideration specific features of severely paralyzed patients, such as reduced attention span. This adjustment in combination with more intensive training will show whether an auditory ERP-BCI can become an option for visually impaired patients.},
}
@article {pmid19349227,
year = {2009},
author = {Yanagisawa, T and Hirata, M and Saitoh, Y and Kato, A and Shibuya, D and Kamitani, Y and Yoshimine, T},
title = {Neural decoding using gyral and intrasulcal electrocorticograms.},
journal = {NeuroImage},
volume = {45},
number = {4},
pages = {1099-1106},
doi = {10.1016/j.neuroimage.2008.12.069},
pmid = {19349227},
issn = {1095-9572},
mesh = {Adult ; Aged ; Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {Electrocorticography of the primary motor cortex (M1) is a promising tool for controlling a brain-computer interface (BCI). Electrocorticograms (ECoG) of the human M1 within the central sulcus (intrasulcal ECoG) have been rarely examined. In order to evaluate the usefulness of intrasulcal ECoG for BCI, we examined patients with subdural electrodes placed temporarily inside the central sulcus and over the sensorimotor cortex (gyral ECoG). Five patients were asked to perform or imagine two or three classes of simple upper limb movements. Univariate statistical analysis of the results revealed that the intrasulcal ECoG on M1 showed significant variability across movement classes. A support vector machine was used for classification of single-trial ECoG signals to infer movement class (neural decoding). The movement classes were predicted with 80-90% accuracy (chance level: 33% or 50%). To reveal the relative importance of anatomical areas for neural decoding, the decoding performance was compared between gyral and intrasulcal ECoGs. The intrasulcal ECoG on the motor bank showed higher performance than the equally-sized gyral ECoG or the intrasulcal ECoG on the sensory bank. Analysis using a short time window revealed that movement class could be decoded even before movement onset. These results suggest the usefulness of intrasulcal ECoG on M1 to infer upper limb movements and present a promising application for a practical BCI system.},
}
@article {pmid19349143,
year = {2009},
author = {Cunningham, JP and Gilja, V and Ryu, SI and Shenoy, KV},
title = {Methods for estimating neural firing rates, and their application to brain-machine interfaces.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {22},
number = {9},
pages = {1235-1246},
pmid = {19349143},
issn = {1879-2782},
support = {R01 NS054283/NS/NINDS NIH HHS/United States ; R01 NS054283-04/NS/NINDS NIH HHS/United States ; },
mesh = {*Action Potentials ; Algorithms ; Animals ; Artificial Limbs ; Bayes Theorem ; Biomechanical Phenomena ; Brain/*physiology ; Electrodes, Implanted ; Hand ; Macaca mulatta ; Male ; Microelectrodes ; Motor Activity/*physiology ; Neurons/*physiology ; Normal Distribution ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.},
}
@article {pmid19308425,
year = {2009},
author = {Zhao, Y and Jin, HM and Sun, LP and Jiang, F and Zhang, T and Ma, J},
title = {Organized intrasylvian subarachnoid hematoma in post-traumatic child with dyskinesia for 8 years: a case report and review of the literature.},
journal = {Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery},
volume = {25},
number = {7},
pages = {881-887},
pmid = {19308425},
issn = {1433-0350},
mesh = {Brain/blood supply/pathology/surgery ; Cerebral Angiography ; Child ; Craniotomy ; Dyskinesias/*etiology ; Follow-Up Studies ; Gliosis/pathology ; Head Injuries, Closed/*complications ; Hematoma/*complications/pathology/surgery ; Humans ; Intracranial Hemorrhages/pathology ; Magnetic Resonance Imaging ; Male ; *Subarachnoid Space ; Tomography, X-Ray Computed ; Treatment Outcome ; },
abstract = {OBJECT: The authors present their experience with an organized intrasylvian subarachnoid hematoma (OISH) in a post-traumatic pediatric patient with dyskinesia for nearly 8 years.<br><br>METHODS: An 11-year-old Chinese boy was admitted to the authors' hospital because of dyskinesia in his right upper and lower extremities. When he was 18 months old, he fell down from a trolley and then his mouth drooped to a right angle. The brain computer tomography (CT) revealed a space-occupying lesion in his left temporoparietal region. The symptom improved after 20 days of acupuncture therapy in local hospital. Two years later when he was 4 years old, his right lower limb became lame gradually with sensorial deficit. A concealed arteriovenous malformation was suggested by the brain magnetic resonance imaging and magnetic resonance angiography at that time. The child had been treated with ginkgo biloba leaf extract from 2001 to 2007 and the symptom improved gradually during that period. However, the symptom of his right upper and lower extremities deteriorated continually since January 2007. He fell down again when he was walking 1 month before he was admitted to the authors' department in July 2007. An enlarged left pterional craniotomy was performed to remove the lesion. Histopathology diagnosis was compatible with an organized hematoma with remote hemorrhage and gliosis. The child is presently healthy after 1 year's follow-up.<br><br>CONCLUSION: The rarity of an OISH in a post-traumatic pediatric patient with dyskinesia for nearly 8 years makes this case very peculiar. This is the first reported pediatric case of OISH found in the literature.},
}
@article {pmid19302127,
year = {2009},
author = {Grøtan, V and Saether, BE and Engen, S and van Balen, JH and Perdeck, AC and Visser, ME},
title = {Spatial and temporal variation in the relative contribution of density dependence, climate variation and migration to fluctuations in the size of great tit populations.},
journal = {The Journal of animal ecology},
volume = {78},
number = {2},
pages = {447-459},
doi = {10.1111/j.1365-2656.2008.01488.x},
pmid = {19302127},
issn = {1365-2656},
mesh = {Animal Migration/*physiology ; Animals ; *Climate ; Female ; Male ; Models, Biological ; Population Dynamics ; Sparrows/*physiology ; Time Factors ; },
abstract = {1. The aim of the present study is to model the stochastic variation in the size of five populations of great tit Parus major in the Netherlands, using a combination of individual-based demographic data and time series of population fluctuations. We will examine relative contribution of density-dependent effects, and variation in climate and winter food on local dynamics as well as on number of immigrants. 2. Annual changes in population size were strongly affected by temporal variation in number of recruits produced locally as well as by the number of immigrants. The number of individuals recruited from one breeding season to the next was mainly determined by the population size in year t, the beech crop index (BCI) in year t and the temperature during March-April in year t. The number of immigrating females in year t + 1 was also explained by the number of females present in the population in year t, the BCI in autumn year t and the temperature during April-May in year t. 3. By comparing predictions of the population model with the recorded number of females, the simultaneous modelling of local recruitment and immigration explained a large proportion of the annual variation in recorded population growth rates. 4. Environmental stochasticity especially caused by spring temperature and BCI did in general contribute more to annual fluctuations in population size than density-dependent effects. Similar effects of climate on local recruitment and immigration also caused covariation in temporal fluctuations of immigration and local production of recruits. 5. The effects of various variables in explaining fluctuations in population size were not independent, and the combined effect of the variables were generally non-additive. Thus, the effects of variables causing fluctuations in population size should not be considered separately because the total effect will be influenced by covariances among the explanatory variables. 6. Our results show that fluctuations in the environment affect local recruitment as well as annual fluctuations in the number of immigrants. This effect of environment on the interchange of individuals among populations is important for predicting effects of global climate change on the pattern of population fluctuations.},
}
@article {pmid19289859,
year = {2009},
author = {Cottaris, NP and Elfar, SD},
title = {Assessing the efficacy of visual prostheses by decoding ms-LFPs: application to retinal implants.},
journal = {Journal of neural engineering},
volume = {6},
number = {2},
pages = {026007},
doi = {10.1088/1741-2560/6/2/026007},
pmid = {19289859},
issn = {1741-2552},
mesh = {Algorithms ; Animals ; Cats ; Electric Stimulation ; Electrodes, Implanted ; *Models, Neurological ; Principal Component Analysis ; Probability ; *Prostheses and Implants ; Retina/*physiology ; Time ; *User-Computer Interface ; Vision, Ocular/*physiology ; Visual Cortex/physiology ; Visual Perception/physiology ; },
abstract = {Visual prostheses are brain-computer interfaces that are implanted in early processing stages of the visual system of blind patients. In an effort to induce light sensations, visual prostheses inject, via arrays of stimulating electrodes, spatiotemporal trains of current pulses which excite the adjacent neural tissue. Human experiments with current state-of-the art retinal prostheses have revealed that, although visual percepts can be elicited by electrical stimulation, these percepts are not closely related to the spatial patterns of stimulation. One of the main reasons for this failure is that present methods of prosthetic stimulation result in non-specific activation of multiple retinal pathways. Recent evidence, however, suggests that the specificity of neural activation can be increased by manipulations of the spatiotemporal parameters of stimulation. Before these notions are evaluated in human experiments, which are subjective and prone to patient fatigue and frustration, it is imperative that they are assessed in animal models using cortical recordings. Toward this end, we have developed a computational method for analyzing the cortical multi-site local field potential (ms-LFP) evoked in response to electrical stimulation of a site presynaptic to where LFPs are recorded. This method applies a nonlinear decoding technique on the recorded ms-LFP signal to quantify the information transmitted downstream from the stimulation site. Validation of this method using an implant attached to the epiretinal surface of cats and ms-LFP recordings from layer 4 of cat primary visual cortex, demonstrates that the spatial origin, the duration and the amplitude of injected current pulses can all be decoded simultaneously from single-trial ms-LFP responses. Our findings indicate that the developed method is a highly sensitive probe for characterizing the efficacy of visual prosthetic stimulation.},
}
@article {pmid19273039,
year = {2009},
author = {Li, J and Zhang, L and Tao, D and Sun, H and Zhao, Q},
title = {A prior neurophysiologic knowledge free tensor-based scheme for single trial EEG classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {2},
pages = {107-115},
doi = {10.1109/TNSRE.2008.2008394},
pmid = {19273039},
issn = {1558-0210},
mesh = {Algorithms ; Artificial Intelligence ; Bayes Theorem ; Electroencephalography/*classification/statistics &amp; numerical data ; Fourier Analysis ; Humans ; *Models, Statistical ; Reproducibility of Results ; *User-Computer Interface ; },
abstract = {Single trial electroencephalogram (EEG) classification is essential in developing brain-computer interfaces (BCIs). However, popular classification algorithms, e.g., common spatial patterns (CSP), usually highly depend on the prior neurophysiologic knowledge for noise removing, although this knowledge is not always known in practical applications. In this paper, a novel tensor-based scheme is proposed for single trial EEG classification, which performs well without the prior neurophysiologic knowledge. In this scheme, EEG signals are represented in the spatial-spectral-temporal domain by the wavelet transform, the multilinear discriminative subspace is reserved by the general tensor discriminant analysis (GTDA), redundant indiscriminative patterns are removed by Fisher score, and the classification is conducted by the support vector machine (SVM). Applications to three datasets confirm the effectiveness and the robustness of the proposed tensor scheme in analyzing EEG signals, especially in the case of lacking prior neurophysiologic knowledge.},
}
@article {pmid19272934,
year = {2009},
author = {Gangadhar, G and Chavarriaga, R and Millán, Jdel R},
title = {Fast recognition of anticipation-related potentials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {4},
pages = {1257-1260},
doi = {10.1109/TBME.2008.2005486},
pmid = {19272934},
issn = {1558-2531},
mesh = {Attention/physiology ; Contingent Negative Variation/*physiology ; *Electroencephalography ; Humans ; Least-Squares Analysis ; User-Computer Interface ; },
abstract = {Anticipation increases the efficiency of daily tasks by partial advance activation of neural substrates involved in it. Here, we develop a method for the recognition of EEG correlates of this activation as early as possible on single trials, which is essential for brain--computer interaction. We explore various features from the EEG recorded in a contingent negative variation (CNV) paradigm. We also develop a novel technique called time aggregation of classification (TAC) for fast and reliable decisions that combines the posterior probabilities of several classifiers trained with features computed from temporal blocks of EEG until a certainty threshold is reached. Experiments with nine naive subjects performing the CNV experiment with GO (anticipation) and NOGO (control) conditions with an interstimulus interval of 4 s show that the performance of the TAC method is above 70% for four subjects, around 60% for two other subjects, and random for the remaining subjects. On average over all subjects, more than 50% of the correct decisions are made at 2 s, without needing to wait until 4 s.},
}
@article {pmid19264143,
year = {2009},
author = {Ball, T and Kern, M and Mutschler, I and Aertsen, A and Schulze-Bonhage, A},
title = {Signal quality of simultaneously recorded invasive and non-invasive EEG.},
journal = {NeuroImage},
volume = {46},
number = {3},
pages = {708-716},
doi = {10.1016/j.neuroimage.2009.02.028},
pmid = {19264143},
issn = {1095-9572},
mesh = {Adolescent ; *Artifacts ; Brain/*physiopathology ; Diagnosis, Computer-Assisted/*methods ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Epilepsy/*diagnosis/*physiopathology ; Female ; Humans ; Male ; Reproducibility of Results ; Sensitivity and Specificity ; Young Adult ; },
abstract = {Both invasive and non-invasive electroencephalographic (EEG) recordings from the human brain have an increasingly important role in neuroscience research and are candidate modalities for medical brain-machine interfacing. It is often assumed that the major artifacts that compromise non-invasive EEG, such as caused by blinks and eye movement, are absent in invasive EEG recordings. Quantitative investigations on the signal quality of simultaneously recorded invasive and non-invasive EEG in terms of artifact contamination are, however, lacking. Here we compared blink related artifacts in non-invasive and invasive EEG, simultaneously recorded from prefrontal and motor cortical regions using an approach suitable for detection of small artifact contamination. As expected, we find blinks to cause pronounced artifacts in non-invasive EEG both above prefrontal and motor cortical regions. Unexpectedly, significant blink related artifacts were also found in the invasive recordings, in particular in the prefrontal region. Computing a ratio of artifact amplitude to the amplitude of ongoing brain activity, we find that the signal quality of invasive EEG is 20 to above 100 times better than that of simultaneously obtained non-invasive EEG. Thus, while our findings indicate that ocular artifacts do exist in invasive recordings, they also highlight the much better signal quality of invasive compared to non-invasive EEG data. Our findings suggest that blinks should be taken into account in the experimental design of ECoG studies, particularly when event related potentials in fronto-anterior brain regions are analyzed. Moreover, our results encourage the application of techniques for reducing ocular artifacts to further optimize the signal quality of invasive EEG.},
}
@article {pmid19259996,
year = {2009},
author = {Behr, A and Becker, M and Beckmann, T and Johnen, L and Leschinski, J and Reyer, S},
title = {Telomerization: advances and applications of a versatile reaction.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {48},
number = {20},
pages = {3598-3614},
doi = {10.1002/anie.200804599},
pmid = {19259996},
issn = {1521-3773},
abstract = {The transition-metal catalyzed telomerization of 1,3-dienes with different nucleophiles leads to the synthesis of numerous products, such as sugar ethers, substituted lactones, or terpene derivatives, which can be applied in the cosmetic and pharmaceutical industry as well as in polymers and flavors. The reaction can be controlled by the choice of the catalytic system, the feedstock, and the reaction conditions. Since telomerization was developed in 1967, there have been many efforts to utilize this reaction. Herein we give an overview of the versatility of telomerization based on examples from research and industry, particular emphasis is placed on catalyst and process development as well as mechanistic aspects.},
}
@article {pmid19255462,
year = {2009},
author = {Martens, SM and Hill, NJ and Farquhar, J and Schölkopf, B},
title = {Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.},
journal = {Journal of neural engineering},
volume = {6},
number = {2},
pages = {026003},
doi = {10.1088/1741-2560/6/2/026003},
pmid = {19255462},
issn = {1741-2552},
mesh = {Algorithms ; Brain/*physiology ; Cognition/*physiology ; Computer Simulation ; Electroencephalography ; *Event-Related Potentials, P300 ; Humans ; Models, Neurological ; Pattern Recognition, Automated/methods ; Photic Stimulation ; Semantics ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; *User-Computer Interface ; *Writing ; },
abstract = {We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.},
}
@article {pmid19255459,
year = {2009},
author = {Rizk, M and Bossetti, CA and Jochum, TA and Callender, SH and Nicolelis, MA and Turner, DA and Wolf, PD},
title = {A fully implantable 96-channel neural data acquisition system.},
journal = {Journal of neural engineering},
volume = {6},
number = {2},
pages = {026002},
pmid = {19255459},
issn = {1741-2552},
support = {F31 EB007897/EB/NIBIB NIH HHS/United States ; F31 EB007897-02/EB/NIBIB NIH HHS/United States ; F31EB007897/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials ; Animals ; Brain/*physiology ; Computers ; Electrodes, Implanted ; *Equipment Design ; Haplorhini ; *Prostheses and Implants ; Sheep ; *Signal Processing, Computer-Assisted ; Telemetry ; Temperature ; User-Computer Interface ; },
abstract = {A fully implantable neural data acquisition system is a key component of a clinically viable brain-machine interface. This type of system must communicate with the outside world and obtain power without the use of wires that cross through the skin. We present a 96-channel fully implantable neural data acquisition system. This system performs spike detection and extraction within the body and wirelessly transmits data to an external unit. Power is supplied wirelessly through the use of inductively coupled coils. The system was implanted acutely in sheep and successfully recorded, processed and transmitted neural data. Bidirectional communication between the implanted system and an external unit was successful over a range of 2 m. The system is also shown to integrate well into a brain-machine interface. This demonstration of a high channel-count fully implanted neural data acquisition system is a critical step in the development of a clinically viable brain-machine interface.},
}
@article {pmid19248075,
year = {2009},
author = {Ghosh, SK and Azhakar, R and Kitagawa, S},
title = {Control of structure dimensionality and functional studies of flexible Cu(II) coordination polymers.},
journal = {Chemistry, an Asian journal},
volume = {4},
number = {6},
pages = {870-875},
doi = {10.1002/asia.200800458},
pmid = {19248075},
issn = {1861-471X},
abstract = {Four Cu(II) coordination polymers [Cu(2)(bci)(2)(H(2)O)(2)] x 3 H(2)O (1), [Cu(tciH)(H(2)O)] (2), {[Cu(tci)](2) [Cu(5)(tci)(2) (OH)(2) (H(2)O)(8)]}
x 22 H(2)O (3), and [Cu(3)(tci)(2)(py)(4)(H(2)O)(2)] (4) (bciH(2): bis(2-carboxyethyl) isocyanurate; tciH(2): tris(2-carboxyethyl) isocyanurate) were synthesized by using two flexible organic ligands at room temperature. Control synthesis of the compounds showed a variety of structural motifs, namely, one-dimensional (1D) chains (1 and 2), 2D layers with 0D units (3), and 3D frameworks (4). The 1D chain structure of 1 is formed by the bipodal ligand bciH(2) with Cu(II) ions linked by the Cu(2)(CO(2))(4) "paddlewheel" secondary building units (SBUs). The structure of 2 is very similar to 1, where two carboxylic acid groups of the similar tripodal ligand tciH(3) are used to make a 1D chain structure and one carboxylic acid group of the ligand remains protonated. Use of an excess amount of base (NaOH) deprotonated all three carboxylic acid groups to form 3, which contains an anionic 2D sheet structure neutralized by the 0D cationic Cu(5) units. When pyridine was used as base, it also functioned as a co-ligand to make 3D frameworks of 4. Compound 3 showed reversible structural transformations between crystalline and amorphous phases upon dehydration and rehydration. The dehydrated phase showed size and affinity based selective sorption, where MeOH molecules were adsorbed but MeCN and EtOH molecules with similar and larger sizes, respectively, were not adsorbed. The sorption profile of MeOH showed gate-opening phenomenon with a hysteresis profile, which indicates dynamic structural transformations.},
}
@article {pmid19230997,
year = {2009},
author = {Kim, HK and Park, S and Srinivasan, MA},
title = {Developments in brain-machine interfaces from the perspective of robotics.},
journal = {Human movement science},
volume = {28},
number = {2},
pages = {191-203},
doi = {10.1016/j.humov.2008.12.001},
pmid = {19230997},
issn = {1872-7646},
mesh = {Brain/*physiopathology ; *Computers ; Forecasting ; Humans ; *Man-Machine Systems ; Movement Disorders/*physiopathology/*therapy ; Neurobiology/*instrumentation ; Neurons/*physiology ; *Prosthesis Implantation ; Recovery of Function ; *Robotics/trends ; *User-Computer Interface ; },
abstract = {Many patients suffer from the loss of motor skills, resulting from traumatic brain and spinal cord injuries, stroke, and many other disabling conditions. Thanks to technological advances in measuring and decoding the electrical activity of cortical neurons, brain-machine interfaces (BMI) have become a promising technology that can aid paralyzed individuals. In recent studies on BMI, robotic manipulators have demonstrated their potential as neuroprostheses. Restoring motor skills through robot manipulators controlled by brain signals may improve the quality of life of people with disability. This article reviews current robotic technologies that are relevant to BMI and suggests strategies that could improve the effectiveness of a brain-operated neuroprosthesis through robotics.},
}
@article {pmid19228561,
year = {2009},
author = {Lu, S and Guan, C and Zhang, H},
title = {Unsupervised brain computer interface based on intersubject information and online adaptation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {17},
number = {2},
pages = {135-145},
doi = {10.1109/TNSRE.2009.2015197},
pmid = {19228561},
issn = {1558-0210},
mesh = {Algorithms ; Brain/*physiology ; Calibration ; Data Collection ; Electroencephalography ; Event-Related Potentials, P300/physiology ; Humans ; Models, Statistical ; Online Systems ; Reference Values ; Reproducibility of Results ; *User-Computer Interface ; },
abstract = {Conventional brain computer interfaces rely on a guided calibration procedure to address the problem of considerable variations in electroencephalography (EEG) across human subjects. This calibration, however, implies inconvenience to the end users. In this paper, we propose an online-adaptive-learning method to address this problem for P300-based brain computer interfaces. By automatically capturing subject-specific EEG characteristics during online operation, this method allows a new user to start operating a P300-based brain-computer interface without guided (supervised) calibration. The basic principle is to first learn a generic model termed subject-independent model offline from EEG of a pool of subjects to capture common P300 characteristics. For a new user, a new model termed subject-specific model is then adapted online based on EEG recorded from the new subject and the corresponding labels predicted by either the subject-independent model or the adapted subject-specific model, depending on a confidence score. To verify the proposed method, a study involving 10 healthy subjects is carried out and positive results are obtained. For instance, after 2-4 min online adaptation (spelling of 10-20 characters), the accuracy of the adapted model converges to that of a fully trained supervised subject-specific model.},
}
@article {pmid19225819,
year = {2009},
author = {Tsui, CS and Gan, JQ and Roberts, SJ},
title = {A self-paced brain-computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {47},
number = {3},
pages = {257-265},
pmid = {19225819},
issn = {1741-0444},
mesh = {Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Humans ; Imagination/physiology ; Online Systems ; Practice, Psychological ; Robotics ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain-computer interface (BCI) systems. Self-paced BCIs offer more natural human-machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.},
}
@article {pmid19224719,
year = {2009},
author = {DiGiovanna, J and Mahmoudi, B and Fortes, J and Principe, JC and Sanchez, JC},
title = {Coadaptive brain-machine interface via reinforcement learning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {1},
pages = {54-64},
doi = {10.1109/TBME.2008.926699},
pmid = {19224719},
issn = {1558-2531},
mesh = {Algorithms ; Animals ; Brain/*physiology ; Electrodes, Implanted ; Learning/*physiology ; Male ; *Man-Machine Systems ; Microelectrodes ; Models, Neurological ; Rats ; Rats, Sprague-Dawley ; *Reinforcement, Psychology ; Reward ; Robotics/*instrumentation ; },
abstract = {This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the user's neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.},
}
@article {pmid19205769,
year = {2009},
author = {Rizk, M and Wolf, PD},
title = {Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {47},
number = {9},
pages = {955-966},
pmid = {19205769},
issn = {1741-0444},
support = {F31 EB007897/EB/NIBIB NIH HHS/United States ; F31 EB007897-02/EB/NIBIB NIH HHS/United States ; F31EB007897/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Electricity ; Electrodes, Implanted ; Humans ; Man-Machine Systems ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Thresholding is an often-used method of spike detection for implantable neural signal processors due to its computational simplicity. A means for automatically selecting the threshold is desirable, especially for high channel count data acquisition systems. Estimating the noise level and setting the threshold to a multiple of this level is a computationally simple means of automatically selecting a threshold. We present an analysis of this method as it is commonly applied to neural waveforms. Four different operators were used to estimate the noise level in neural waveforms and set thresholds for spike detection. An optimal multiplier was identified for each noise measure using a metric appropriate for a brain-machine interface application. The commonly used root-mean-square operator was found to be least advantageous for setting the threshold. Investigators using this form of automatic threshold selection or developing new unsupervised methods can benefit from the optimization framework presented here.},
}
@article {pmid19177803,
year = {2009},
author = {Mima, T},
title = {[Social impact of recent advances in neuroscience].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {61},
number = {1},
pages = {18-26},
pmid = {19177803},
issn = {1881-6096},
mesh = {Bioethics/*trends ; Humans ; Neurosciences/*ethics/*trends ; *Social Change ; },
abstract = {Recent advances in neuroscience opened up new technical possibilities, such as enabling possible human mindreading, neuroenhancement, and application of brain-machine-interface into everyday life, as well as the advent of new powerful psychotropic drugs. In addition to the conventional problems in bioethics, such as obtaining informed consent, neuroscience technology has generated new array of ethical questions. The social impact of advanced brain science or neuroscience and its technological applications is a major topic in bioethics, which is frequently termed as "Neuroethics." Here, we summarize the ethical, legal, and social issues of cutting-edge brain science by analyzing a classic science fiction novel entitled "Flowers for Algernon" authored by Daniel Keyes (1966). Three aspects of social problems faced by brain science are apparent: biomedical risk assessment, issues related to human subjectivity and identity, and socio-cultural value of brain science technology. To understand this last aspect, enhancement-achievement and/or enhancement-treatment dichotomy can prove useful. In addition, we introduced the first national poll results in Japan (n=2,500) on the social impact of brain science. Although half the respondents believed that the advancement of brain science can aid individuals in the future, 56% of respondents suggested the necessity for guidelines or regulation policies mediating brain science. Technological application of brain science in treatment is generally accepted; however, not just for the personal purpose or enhancement of the normal function. In this regard, it is important to hold further discussions including the general public.},
}
@article {pmid19174332,
year = {2009},
author = {Rivet, B and Souloumiac, A and Attina, V and Gibert, G},
title = {xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {56},
number = {8},
pages = {2035-2043},
doi = {10.1109/TBME.2009.2012869},
pmid = {19174332},
issn = {1558-2531},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Male ; *Man-Machine Systems ; *Signal Processing, Computer-Assisted ; },
abstract = {A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate.},
}
@article {pmid19170946,
year = {2009},
author = {Furdea, A and Halder, S and Krusienski, DJ and Bross, D and Nijboer, F and Birbaumer, N and Kübler, A},
title = {An auditory oddball (P300) spelling system for brain-computer interfaces.},
journal = {Psychophysiology},
volume = {46},
number = {3},
pages = {617-625},
doi = {10.1111/j.1469-8986.2008.00783.x},
pmid = {19170946},
issn = {1469-8986},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adolescent ; Adult ; Brain/*physiology ; Electroencephalography ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Language ; Male ; Photic Stimulation ; Psychomotor Performance/physiology ; *User-Computer Interface ; Young Adult ; },
abstract = {This study was designed to develop and test an auditory event-related potential (ERP) based spelling system for a brain-computer interface (BCI) and to compare user's performance between the auditory and visual modality. The spelling system, where letters in a matrix were coded with acoustically presented numbers, was tested on a group of healthy volunteers. The results were compared with a visual spelling system. Nine of the 13 participants presented with the auditory ERP spelling system scored above a predefined criterion level control for communication. Compared to the visual spelling system, users' performance was lower and the peak latencies of the auditorily evoked ERPs were delayed. It was concluded that auditorily evoked ERPs from the majority of the users could be reliably classified. High accuracies were achieved in these users, rendering item selection with a BCI based on auditory stimulation feasible for communication.},
}
@article {pmid19168013,
year = {2009},
author = {Mondet, F and Oddou, JH and Boyer, C and Corsois, L and Collomb, D},
title = {[Development of a pathological quality score of prostate biopsies].},
journal = {Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie},
volume = {19},
number = {2},
pages = {107-111},
doi = {10.1016/j.purol.2008.07.007},
pmid = {19168013},
issn = {1166-7087},
mesh = {Adult ; Aged ; Aged, 80 and over ; Biopsy/standards ; Humans ; Male ; Middle Aged ; Prospective Studies ; Prostate/*pathology ; },
abstract = {OBJECTIVES: Develop a score allowing the pathologist to objectively report on the overall quality of extended standardized prostatic biopsy (EPB).<br><br>METHODS: A prospective study was carried out on 339 consecutive protocols of 10 core EPB (PSA<10 ng/ml). Reports are standardized and computerized. The conclusion of the reports includes an estimate of the overall quality of the EPB based on three items to classify the protocols in three groups: protocol of "good" quality (group 1), "medium" quality (group 2) and "poor" quality (group 3). The score (IGap) is automatically computed from three objective criteria also shown on the conclusion of the report: the average length of the 10 biopsies (LM), the number of biopsies on which capsular elements can be identified (BCI) and the average number of fragment per biopsy (Fm). The IGap index rank from 0 to 1. The average IGap of the three groups is computed using the t-test.<br><br>RESULTS: The average IGaps of the groups 1, 2 and 3 are respectively of 0,65 (0,37-0,89 ; n=250), 0,52 (0,36-0,71 ; n=69) and 0,43 (0,22-0,6 ; n=20), (p<0,001).<br><br>CONCLUSION: IGap is a pertinent score reporting objectively of the overall quality of EPB. An IGap close to one indicates a good quality of EPB. An IGap close to zero indicate a poor quality of EPB.},
}
@article {pmid19164072,
year = {2008},
author = {Zanos, S and Miller, KJ and Ojemann, JG},
title = {Electrocorticographic spectral changes associated with ipsilateral individual finger and whole hand movement.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5939-5942},
doi = {10.1109/IEMBS.2008.4650569},
pmid = {19164072},
issn = {2375-7477},
support = {NS12542/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Brain Mapping/methods ; Electroencephalography/*methods ; Epilepsy/*physiopathology ; *Evoked Potentials, Motor ; Fingers/*physiopathology ; Hand/physiopathology ; Humans ; Male ; Motor Cortex/*physiopathology ; *Movement ; Signal Processing, Computer-Assisted ; },
abstract = {The study of the human sensorimotor (SM) cortex activations associated with hand motor movement is central to the design of efficient and clinically useful brain-computer interfaces. Whereas the electrocorticographic (ECoG) signatures of contralateral hand movement have been studied in detail, those of ipsilateral hand and individual finger movements have not been characterized. We studied the low (8-32 Hz) and high-frequency (76-100 Hz) SM cortical ECoG spectral changes associated with contralateral and ipsilateral whole hand and individual finger movement and assessed their discriminability. We find that ipsilateral movement is associated with widespread decreases in the low-frequency band (LFB) and more focal increases in the high-frequency band (HFB). The HFB component discriminates between ipsilateral and contralateral movement-associated activations, in contrast to the LFB. The HFB also discriminates between thumb and index finger movement-associated activations, for both the contralateral and the ipsilateral case, whereas the LFB does not. This is the first published report of a discriminable ipsilateral motor signal, with important implications in the use of brain-computer interfaces in hemiplegic patients.},
}
@article {pmid19164015,
year = {2008},
author = {Wijesuriya, N and Tran, Y and Thuraisingham, RA and Nguyen, HT and Craig, A},
title = {Effects of mental fatigue on 8-13Hz brain activity in people with spinal cord injury.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5716-5719},
doi = {10.1109/IEMBS.2008.4650512},
pmid = {19164015},
issn = {2375-7477},
mesh = {Brain/*physiopathology ; Brain Mapping/*methods ; *Cognition ; *Evoked Potentials ; Female ; Humans ; Male ; Mental Fatigue/*complications/*physiopathology ; Middle Aged ; Spinal Cord Injuries/*complications/*physiopathology ; },
abstract = {Brain computer interfaces (BCIs) can be implemented into assistive technologies to provide 'hands-free' control for the severely disabled. BCIs utilise voluntary changes in one's brain activity as a control mechanism to control devices in the person's immediate environment. Performance of BCIs could be adversely affected by negative physiological conditions such as fatigue and altered electrophysiology commonly seen in spinal cord injury (SCI). This study examined the effects of mental fatigue from an increase in cognitive demand on the brain activity of those with SCI. Results show a trend of increased alpha (8-13Hz) activity in able-bodied controls after completing a set of cognitive tasks. Conversely, the SCI group showed a decrease in alpha activity due to mental fatigue. Results suggest that the brain activity of SCI persons are altered in its mechanism to adjust to mental fatigue. These altered brain conditions need to be addressed when using BCIs in clinical populations such as SCI. The findings have implications for the improvement of BCI technology.},
}
@article {pmid19163998,
year = {2008},
author = {Soraghan, C and Matthews, F and Markham, C and Pearlmutter, BA and O'Neill, R and Ward, TE},
title = {A 12-channel, real-time near-infrared spectroscopy instrument for brain-computer interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5648-5651},
doi = {10.1109/IEMBS.2008.4650495},
pmid = {19163998},
issn = {2375-7477},
mesh = {Adult ; Brain Mapping/*instrumentation/methods ; Computer Systems ; Equipment Design ; Equipment Failure Analysis ; Fiber Optic Technology/*instrumentation ; Humans ; Oximetry/*instrumentation ; Oxygen/*analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*instrumentation/methods ; *User-Computer Interface ; },
abstract = {A continuous wave near-infrared spectroscopy (NIRS) instrument for brain-computer interface (BCI) applications is presented. In the literature, experiments have been carried out on subjects with such motor degenerative diseases as amyotrophic lateral sclerosis, which have demonstrated the suitability of NIRS to access intentional functional activity, which could be used in a BCI as a communication aid. Specifically, a real-time, multiple channel NIRS tool is needed to realise access to even a few different mental states, for reasonable baud rates. The 12-channel instrument described here has a spatial resolution of 30 mm, employing a flexible software demodulation scheme. Temporal resolution of approximately 100 ms is maintained since typical topographic imaging is not needed, since we are only interested in exploiting the vascular response for BCI control. A simple experiment demonstrates the ability of the system to report on haemodynamics during single trial mental arithmetic tasks. Multiple trial averaging is not required.},
}
@article {pmid19163919,
year = {2008},
author = {Fukayama, O and Taniguchi, N and Suzuki, T and Mabuchi, K},
title = {RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5322-5325},
doi = {10.1109/IEMBS.2008.4650416},
pmid = {19163919},
issn = {2375-7477},
mesh = {*Algorithms ; Animals ; Brain/*physiology ; Electrocardiography/*methods ; Evoked Potentials/*physiology ; Locomotion/*physiology ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Rats ; *User-Computer Interface ; },
abstract = {An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.},
}
@article {pmid19163918,
year = {2008},
author = {Miller, KJ and Blakely, T and Schalk, G and den Nijs, M and Rao, RP and Ojemann, JG},
title = {Three cases of feature correlation in an electrocorticographic BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5318-5321},
doi = {10.1109/IEMBS.2008.4650415},
pmid = {19163918},
issn = {2375-7477},
support = {EB006356/EB/NIBIB NIH HHS/United States ; T32-NS07144/NS/NINDS NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Algorithms ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Statistics as Topic ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.},
}
@article {pmid19163916,
year = {2008},
author = {Nazarpour, K and Praamstra, P and Miall, R and Sanei, S},
title = {Steady-state movement related potentials for brain computer interfacing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5310-5313},
doi = {10.1109/IEMBS.2008.4650413},
pmid = {19163916},
issn = {2375-7477},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {An approach for brain computer interfacing (BCI) by analysis of the steady-state movement related potentials (ssMRP) is proposed in this paper. The neurological background of the ssMRPs which are primarily studied by means of the averaged electroencephalogram (EEG) signals are briefly reviewed. A simple feature extraction method is suggested for single trial ssMRP processing. The proposed BCI paradigm is tested by using the Fishers linear discriminant (FLD) classifier. The novelty of this approach is mainly in the application of rhythmic cues for BCI, simple recording setup, and straightforward computations which make the real-time implementations plausible.},
}
@article {pmid19163914,
year = {2008},
author = {Aghagolzadeh, M and Shetliffe, M and Oweiss, KG},
title = {Impact of compressed sensing of motor cortical activity on spike train decoding in Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5302-5305},
doi = {10.1109/IEMBS.2008.4650411},
pmid = {19163914},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; *Algorithms ; Data Compression/*methods ; Electroencephalography/*methods ; Humans ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Decoding spike trains is an essential step to translate multiple single unit activity to useful control commands in cortically controlled Brain Machine Interface (BMI) systems. Extracting the spike trains of individual neurons from the recorded mixtures requires spike sorting, a computationally prohibitive step that precludes the development of fully implantable, small size and low power electronics. Previously, we reported on the ability to extract the critical information in these spike trains such as precise spike timing and firing rate of individual neurons using a compressed sensing strategy that overcomes the computational burden of the spike sorting step. Herein, we assess the decoding performance using this method and compare it to the case where classical spike sorting takes place prior to decoding. We use the local average of the sparsely represented data as discriminative features to 'informally' detect and classify spikes in the data stream. We demonstrate that there is a substantial gain in performance assessed under different decoding strategies, while much less computations are needed compared to spike sorting in the traditional sense.},
}
@article {pmid19163849,
year = {2008},
author = {Noureddin, B and Lawrence, PD and Birch, GE},
title = {Quantitative evaluation of ocular artifact removal methods based on real and estimated EOG signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5041-5044},
doi = {10.1109/IEMBS.2008.4650346},
pmid = {19163849},
issn = {2375-7477},
mesh = {*Algorithms ; *Artifacts ; Brain Mapping/*methods ; Computer Simulation ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Electrooculography/*methods ; Humans ; *Models, Neurological ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {We propose a novel metric for quantitatively evaluating ocular artifact (OA) removal methods on real electroencephalogram (EEG) data. For real EEG, existing metrics measure the amount of artifact removed. Our metric measures how much a given method is likely to distort the underlying EEG. The new metric was used to evaluate two existing OA removal algorithms that use the electro-oculogram (EOG) as a reference signal. The combination of a previous metric and our new metric showed there is a trade-off between how well an algorithm removes OAs and how likely it is to distort the underlying EEG. These algorithms require a reference EOG signal, yet for certain applications (e.g., a brain computer interface or BCI) it is preferable or necessary to avoid attaching electrodes around the eyes. We thus also used various combinations of up to 55 channels of EEG to estimate the EOG reference. The metric was again used to compare the use of estimated vs. measured EOG. Our initial results showed that using EOG estimated from as few as 4 EEG electrodes increased the likelihood of distorting the EEG from 14% to 19% and from 21% to 23% for the two algorithms. For some applications (e.g., BCI), the slight reduction in performance may be acceptable in order to avoid using EOG electrodes.},
}
@article {pmid19163848,
year = {2008},
author = {Krusienski, DJ and Allison, BZ},
title = {Harmonic coupling of steady-state visual evoked potentials.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5037-5040},
doi = {10.1109/IEMBS.2008.4650345},
pmid = {19163848},
issn = {2375-7477},
mesh = {Adolescent ; Adult ; *Algorithms ; Biological Clocks/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Visual Cortex/*physiology ; Young Adult ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are oscillating components of the electroencephalogram (EEG) that are detected over the occipital areas, having frequencies corresponding to visual stimulus frequencies. SSVEPs have been demonstrated to be reliable control signals for operating a brain-computer interface (BCI). This study uses offline analyses to investigate the characteristics of SSVEP harmonic amplitude and phase coupling and the impact of using this information to construct a matched filter for continuously tracking the signal.},
}
@article {pmid19163843,
year = {2008},
author = {Gibson, S and Judy, JW and Markovic, D},
title = {Comparison of spike-sorting algorithms for future hardware implementation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {5015-5020},
doi = {10.1109/IEMBS.2008.4650340},
pmid = {19163843},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; *Algorithms ; Computer Simulation ; Data Interpretation, Statistical ; Humans ; *Models, Neurological ; Models, Statistical ; Neurons/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; },
abstract = {Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.},
}
@article {pmid19163800,
year = {2008},
author = {Matthews, F and Soraghan, C and Ward, TE and Markham, C and Pearlmutter, BA},
title = {Software platform for rapid prototyping of NIRS brain computer interfacing techniques.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4840-4843},
doi = {10.1109/IEMBS.2008.4650297},
pmid = {19163800},
issn = {2375-7477},
mesh = {Brain/*physiology ; Brain Mapping/*instrumentation/*methods ; Electroencephalography/*instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/instrumentation ; *Software ; Software Design ; *User-Computer Interface ; },
abstract = {This paper describes the control system of a next-generation optical brain-computer interface (BCI). Using functional near-infrared spectroscopy (fNIRS) as a BCI modality is a relatively new concept, and research has only begun to explore approaches for its implementation. It is necessary to have a system by which it is possible to investigate the signal processing and classification techniques available in the BCI community. Most importantly, these techniques must be easily testable in real-time applications. The system we describe was built using LABVIEW, a graphical programming language designed for interaction with National Instruments hardware. This platform allows complete configurability from hardware control and regulation, testing and filtering in a graphical interface environment.},
}
@article {pmid19163755,
year = {2008},
author = {Bentley, AS and Andrew, CM and John, LR},
title = {An offline auditory P300 brain-computer interface using principal and independent component analysis techniques for functional electrical stimulation application.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4660-4663},
doi = {10.1109/IEMBS.2008.4650252},
pmid = {19163755},
issn = {2375-7477},
support = {//Medical Research Council/United Kingdom ; },
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping ; Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electric Stimulation Therapy/instrumentation/methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Models, Theoretical ; *Principal Component Analysis ; Reproducibility of Results ; User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) provides technology that allows communication and control for people who are unable to interact with their environment. A P300 BCI exploits the fact that external or internal stimuli may provide a recognition response in the brain's electrical activity which may be recorded by an electroencephalogram (EEG) to act as a control signal. Additionally an auditory BCI does not require the user to avert their visual attention away from the task at hand and is thus more practical in a real environment than other visual stimulus BCIs.},
}
@article {pmid19163714,
year = {2008},
author = {Oehler, M and Neumann, P and Becker, M and Curio, G and Schilling, M},
title = {Extraction of SSVEP signals of a capacitive EEG helmet for human machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4495-4498},
doi = {10.1109/IEMBS.2008.4650211},
pmid = {19163714},
issn = {2375-7477},
mesh = {Algorithms ; Brain Mapping/*methods ; Computer Simulation ; Diagnosis, Computer-Assisted/*methods ; Electrodes ; Electroencephalography/*methods ; Equipment Design ; Evoked Potentials, Visual/*physiology ; Humans ; Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/methods ; Sensitivity and Specificity ; User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {The use of capacitive electrodes for measuring EEG eliminates the preparation procedure known from classical noninvasive EEG measurements. The insulated interface to the brain signals in combination with steady-state visual evoked potentials (SSVEP) enables a zero prep human machine interface triggered by brain signals. This paper presents a 28-channel EEG helmet system based on our capacitive electrodes measuring and analyzing SSVEPs even through scalp hair. Correlation analysis is employed to extract the stimulation frequency of the EEG signal. The system is characterized corresponding to the available detection time with different subjects. As demonstration of the use of capacitive electrodes for SSVEP measurements, preliminary online Brain-Computer Interface (BCI) results of the system are presented. Detection times lie about a factor of 3 higher than in galvanic EEG SSVEP measurements, but are low enough to establish a proper communication channel for Human Machine Interface (HMI).},
}
@article {pmid19163713,
year = {2008},
author = {Mahmoudi, B and Digiovanna, J and Principe, JC and Sanchez, JC},
title = {Neuronal tuning in a brain-machine interface during Reinforcement Learning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4491-4494},
doi = {10.1109/IEMBS.2008.4650210},
pmid = {19163713},
issn = {2375-7477},
mesh = {Algorithms ; Animals ; Artificial Intelligence ; Computers ; Equipment Design ; Feedback ; *Learning ; Male ; Neural Networks, Computer ; Rats ; Rats, Sprague-Dawley ; Reinforcement, Psychology ; Robotics ; Time Factors ; *User-Computer Interface ; },
abstract = {In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.},
}
@article {pmid19163711,
year = {2008},
author = {Kamrunnahar, M and Dias, NS and Schiff, SJ and Gluckman, BJ},
title = {Model-based responses and features in Brain Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4482-4485},
doi = {10.1109/IEMBS.2008.4650208},
pmid = {19163711},
issn = {2375-7477},
support = {K25 NS061001/NS/NINDS NIH HHS/United States ; K25 NS061001-01A2/NS/NINDS NIH HHS/United States ; K02MH01493/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Electroencephalography/classification/instrumentation/*methods ; Female ; Humans ; Linear Models ; Male ; Models, Theoretical ; Movement/*physiology ; Regression Analysis ; Reproducibility of Results ; Software ; User-Computer Interface ; },
abstract = {Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%-30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.},
}
@article {pmid19163710,
year = {2008},
author = {Guo, F and Hong, B and Gao, X and Gao, S},
title = {A brain computer interface based on motion-onset VEPs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4478-4481},
doi = {10.1109/IEMBS.2008.4650207},
pmid = {19163710},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Attention/physiology ; Brain/*physiology ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Motion ; Photic Stimulation ; Psychomotor Performance/physiology ; Regression Analysis ; User-Computer Interface ; Young Adult ; },
abstract = {In this article, a novel brain-computer interface (BCI) based on motion-onset visual evoked potentials (mVEP) is proposed. Examination on the spatio-temporal pattern of motion-onset VEPs showed that the amplitude of N2 and P2 components of mVEP, evoked by attended target, was significantly higher than that by unattended ones. The area of N2 and P2 component was used as features for classifying the offline data of a five-class BCI, achieving an average accuracy of 98.33% in five subjects.},
}
@article {pmid19163709,
year = {2008},
author = {Dyson, M and Sepulveda, F and Gan, JQ},
title = {Mental task classification against the idle state: a preliminary investigation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4473-4477},
doi = {10.1109/IEMBS.2008.4650206},
pmid = {19163709},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; *Attention ; Brain/*physiology ; Electrodes ; Electroencephalography/methods ; Equipment Design ; Humans ; Imagery, Psychotherapy ; Male ; Models, Theoretical ; Psychomotor Performance/physiology ; Reproducibility of Results ; Software ; User-Computer Interface ; },
abstract = {The motivation for this study was to obtain candidate electrode sites for use in online self-paced brain-computer interfaces and preliminary classification results for comparison to online tests. Six mental tasks were tested for classification against an idle state. Data representing the idle state was collected in association with active mental task data during each recording session. Features were extracted in two representations, band power and reflection coefficients. A sequential forward floating search algorithm was used to obtain prevailing electrode-feature pairs for each subject-task combination under two conditions: maximising classification accuracy and maximising mean trial accuracy. Methods used to select electrode-feature combinations are found to lead to differing electrode sites in a number of task-feature combinations. An across task prevalence towards electrodes positioned in the left frontal hemisphere is observed when maximising classification accuracy.},
}
@article {pmid19163649,
year = {2008},
author = {Herman, P and Prasad, G and McGinnity, TM},
title = {Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4242-4245},
doi = {10.1109/IEMBS.2008.4650146},
pmid = {19163649},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*pathology ; Computer Systems ; Computers ; Electroencephalography/*methods ; Equipment Design/instrumentation ; Feedback ; Fuzzy Logic ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; *Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {The practical applicability of brain-computer interface (BCI) technology is limited due to its insufficient reliability and robustness. One of the major problems in this regard is the extensive variability and inconsistency of brain signal patterns, observed especially in electroencephalogram (EEG). This paper presents a fuzzy logic (FL) approach to the problem of handling of the resultant uncertainty effects. In particular, it outlines the design of a novel type-2 FL system (T2FLS) classifier within the framework of an EEG-based BCI, and examines its on-line applicability in the presence of shortand long-term nonstationarities of spectral EEG correlates of motor imagery (imagination of left vs. right hand movement). The developed system is shown to effectively cope with real-time constraints. In addition, a comparative post hoc analysis has revealed that the proposed T2FLS classifier outperforms conventional BCI methods, like LDA and SVM, in terms of the maximum classification accuracy (CA) rates by a relatively small, yet statistically significant, margin.},
}
@article {pmid19163633,
year = {2008},
author = {Ang, KK and Guan, C and Chua, KS and Ang, BT and Kuah, CW and Wang, C and Phua, KS and Chin, ZY and Zhang, H},
title = {A clinical evaluation of non-invasive motor imagery-based brain-computer interface in stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4178-4181},
doi = {10.1109/IEMBS.2008.4650130},
pmid = {19163633},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Case-Control Studies ; Evoked Potentials/physiology ; Humans ; Imagination/physiology ; Movement/*physiology ; Nervous System Diseases/rehabilitation ; Paresis/rehabilitation ; Reproducibility of Results ; *Stroke Rehabilitation ; User-Computer Interface ; },
abstract = {This clinical study investigates whether the performance of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain-Computer Interface (MI-BCI) is comparable to healthy subjects. The study is performed on 8 healthy subjects and 35 BCI-naïve hemiparetic stroke patients. This study also investigates whether the performance of the stroke patients in operating MI-BCI correlates with the extent of neurological disability. The performance is objectively computed from the 10 x 10-fold cross-validation accuracy of employing the Filter Bank Common Spatial Pattern (FBCSP) algorithm on their EEG measurements. The neurological disability is subjectively estimated using the Fugl-Meyer Assessment (FMA) of the upper extremity. The results show that the performance of BCI-naïve hemiparetic stroke patients is comparable to healthy subjects, and no correlation is found between the accuracy of their performance and their motor impairment in terms of FMA.},
}
@article {pmid19163632,
year = {2008},
author = {Ang, KK and Guan, C and Chua, KS and Ang, BT and Kuah, C and Wang, C and Phua, KS and Chin, ZY and Zhang, H},
title = {A clinical evaluation on the spatial patterns of non-invasive motor imagery-based brain-computer interface in stroke.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4174-4177},
doi = {10.1109/IEMBS.2008.4650129},
pmid = {19163632},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*pathology ; Computers ; Electrodes ; Electroencephalography/methods ; Humans ; Image Processing, Computer-Assisted ; Imagery, Psychotherapy ; Motor Skills ; Neurons/pathology ; Neurophysiology/methods ; Software ; Stroke/*physiopathology ; *Stroke Rehabilitation ; User-Computer Interface ; },
abstract = {This clinical study investigates whether the spatial patterns of hemiparetic stroke patients operating a non-invasive Motor Imagery-based Brain Computer Interface (MI-BCI) is comparable to healthy subjects. The spatial patterns for a specific frequency range are generated using the common spatial pattern (CSP) algorithm, of which is highly successful for discriminating two classes of EEG measurements in MI-BCI. The spatial patterns illustrate how the presumed sources project on the scalp and are effective in verifying the neurophysiological plausibility of the computed solution. The spatial patterns show focused activity in ipsilateral as well as contralateral hemisphere with respect to the hand by tapping or motor imagery in 2 BCI-artful healthy subjects and 12 BCI-naïve hemiparetic stroke patients. The results also show that neurophysiologically interpretable spatial patterns is more common in performing motor imagery compared to finger tapping by hemiparetic stroke patients. Hence, this shows that hemiparetic stroke patients are capable of operating MI-BCI.},
}
@article {pmid19163618,
year = {2008},
author = {Touyama, H and Hirose, M},
title = {Non-target photo images in oddball paradigm improve EEG-based personal identification rates.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {4118-4121},
doi = {10.1109/IEMBS.2008.4650115},
pmid = {19163618},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; *Attention ; Brain/pathology ; Electroencephalography/*methods ; Humans ; Male ; *Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Software ; Surveys and Questionnaires ; Time Factors ; User-Computer Interface ; },
abstract = {A research on biometry based on human brain activities has lately been emerging. In this study, we investigate the feasibility of personal identification using one-channel electroencephalogram during photo retrieval in oddball paradigm. The use of non-target photo images was examined to improve the identification performances. Nine photo images were randomly presented one after another to five subjects. The Principal Component Analysis and the Linear Discriminant Analysis were applied for the signal processing. With EEG activities both during target and non-target photo retrieval, the algorithm successfully improved the identification rates. The rates were 87.2, 95.0, and 97.6% using 5, 10, and 20-time averaging, respectively. The performances with EEG only during target photo retrieval were lower by 5-13%. This study reveals a future possibility of photo retrieval tasks to realize the personal identification using human brain activities, which will yield rich controls of machine for the users of brain computer-interface.},
}
@article {pmid19163560,
year = {2008},
author = {Phothisonothai, M and Nakagawa, M},
title = {EEG signal classification method based on fractal features and neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3880-3883},
doi = {10.1109/IEMBS.2008.4650057},
pmid = {19163560},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Brain Mapping/methods ; Electroencephalography/*methods ; Fractals ; Humans ; Imagery, Psychotherapy ; Models, Statistical ; Neural Networks, Computer ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {In this paper, we propose a method to classify electroencephalogram (EEG) signal recorded from left- and right-hand movement imaginations. Three subjects (two males and one female) are volunteered to participate in the experiment. We use a technique of complexity measure based on fractal analysis to reveal feature patterns in the EEG signal. Effective algorithm, namely, detrended fluctuation analysis (DFA) has been selected to estimate embedded fractal dimension (FD) values between relaxing and imaging states of the recorded EEG signal. To show the waveform of FDs, we use a windowing-based method or called time-dependent fractal dimension (TDFD) and the Kullback-Leibler (K-L) divergence. Two feature parameters; K-L divergence and different expected values are proposed to be input variables of the classifier. Finally, featured data are classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Experimental results can be considerably applied in a brain-computer interface (BCI) application and show that the proposed method is more effective than the conventional method by improving average classification rates of 87.5% and 88.3% for left- and right-hand movement imagery tasks, respectively.},
}
@article {pmid19163552,
year = {2008},
author = {Wang, L and Xu, G and Wang, J and Yang, S and Yan, W},
title = {Application of Hilbert-Huang transform for the study of motor imagery tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3848-3851},
doi = {10.1109/IEMBS.2008.4650049},
pmid = {19163552},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electrodes ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Imagination/*physiology ; Male ; Models, Statistical ; Motor Activity/*physiology ; Neural Networks, Computer ; Oscillometry ; *User-Computer Interface ; },
abstract = {A motor based Brain-Computer Interface (BCI) translates the subject's motor intention into a control signal by means of the method which extracts characteristic feature from EEG recorded from the scalp. In this paper, the EEG signal recorded during three motor imagery tasks, which were imagination of left hand, right hand and foot movements, was investigated. A novel method named Hilbert-Huang transform (HHT) is introduced to extract the feature from signal. Firstly, raw signal is decomposed using Empirical Mode Decomposition (EMD). And then, several Intrinsic Mode Functions (IMF) are gained. For further study, the IMFs whose main frequency is higher than 5 Hz are selected. Secondly, based on the IMFs selected above, Hilbert spectrum is calculated. In each motor imagery task, local instantaneous energies, within specific frequency band of electrode C3 and C4, are selected as the features. A three-layer BP Neural Network classifier is structured for pattern classification. The classification results show that HHT can be used in EEG-based BCI research as a method to analysis non-linear and non-stationary signal.},
}
@article {pmid19163548,
year = {2008},
author = {Panoulas, KI and Hadjileontiadis, LJ and Panas, SM},
title = {Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3832-3835},
doi = {10.1109/IEMBS.2008.4650045},
pmid = {19163548},
issn = {2375-7477},
mesh = {Brain/*physiology ; Brain Mapping ; Communication Devices for People with Disabilities ; Databases, Factual ; Electrodes ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory ; Feedback ; Humans ; Models, Statistical ; Online Systems ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain Computer Interfaces (BCI) usually utilize the suppression of mu-rhythm during actual or imagined motor activity. In order to create a BCI system, a signal processing method is required to extract features upon which the discrimination is based. In this article, the Empirical Mode Decomposition along with the Hilbert-Huang Spectrum (HHS) is found to contain the necessary information to be considered as an input to a discriminator. Also, since the HHS defines amplitude and instantaneous frequency for each sample, it can be used for an online BCI system. Experimental results when the HHS applied to EEG signals from an on-line database (BCI Competition III) show the potentiality of the proposed analysis to capture the imagined motor activity, contributing to a more enhanced BCI performance.},
}
@article {pmid19163532,
year = {2008},
author = {Slutzky, MW and Jordan, LR and Miller, LE},
title = {Optimal spatial resolution of epidural and subdural electrode arrays for brain-machine interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3771-3774},
doi = {10.1109/IEMBS.2008.4650029},
pmid = {19163532},
issn = {2375-7477},
support = {K08 NS060223/NS/NINDS NIH HHS/United States ; K08 NS060223-03/NS/NINDS NIH HHS/United States ; 5K08 NS 060223-02/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials ; Biofeedback, Psychology ; Brain/*physiology ; Brain Mapping ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory/physiology ; Humans ; Man-Machine Systems ; Models, Theoretical ; Neurons/metabolism ; Online Systems ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Brain-machine interfaces (BMIs) have the potential to improve quality of life for thousands of motor-impaired individuals. Many different signal sources have been investigated for use in controlling a BMI, including scalp EEG, field potentials from inside and the surface of the cerebral cortex, and single-neuron action potentials. A relatively unexplored region for recording signals is the epidural space. This study attempts to help determine the optimal spatial resolution of epidural and subdural electrode arrays using both a mathematical model and spatial spectral analysis. For rats, optimal spacing for both epidural and subdural electrodes was approximately 0.7 mm.},
}
@article {pmid19163529,
year = {2008},
author = {Bin, G and Lin, Z and Gao, X and Hong, B and Gao, S},
title = {The SSVEP topographic scalp maps by canonical correlation analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3759-3762},
doi = {10.1109/IEMBS.2008.4650026},
pmid = {19163529},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Cerebral Cortex/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Statistics as Topic ; User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {As the number of electrodes increases, topographic scalp mapping methods for electroencephalogram (EEG) data analysis are becoming important. Canonical correlation analysis (CCA) is a method of extracting similarity between two data sets. This paper presents an EEG topographic scalp mapping -based CCA for the steady-state visual evoked potentials (SSVEP) analysis. Multi-channel EEG data and the sinusoidal reference signal were used as the inputs of CCA. The output linear combination was then employed for mapping. Our experimental results prove the topographic scalp mapping-based CCA can instruct for the improvement of SSVEP-based brain computer interface (BCI) system.},
}
@article {pmid19163426,
year = {2008},
author = {Durand, DM and Park, HJ and Wodlinger, B},
title = {Localization and control of activity in peripheral nerves.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3352-3354},
doi = {10.1109/IEMBS.2008.4649923},
pmid = {19163426},
issn = {2375-7477},
support = {R01NS032845-10/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Algorithms ; Computer Simulation ; Electrodes ; Electrodiagnosis/*methods ; Equipment Design ; Humans ; Models, Neurological ; Models, Statistical ; Paralysis/pathology ; Pattern Recognition, Automated/*methods ; Peripheral Nerves/*physiology ; Reproducibility of Results ; Time Factors ; },
abstract = {Interest in the field of the natural control of human limb using physiological signals has risen dramatically in the past 20 years due to the success of the brain machine interface. Cortical signals carry significant information but are difficult to access. The peripheral nerves of the body carry both command and sensory signals and are far more accessible. While numerous studies have documented the selective stimulation properties of, conventionally round, nerve cuff electrodes (i.e., transverse geometry) and even self-sizing electrodes, recording the activity levels from individual fascicles using these electrodes is still an unsolved problem. Moreover, the control algorithms for the control of joint movement with multiple contact electrodes such as the flat interface nerve electrode (FINE) have been difficult to implement. We propose solutions to both these problems by using beam forming techniques to detect the location and the activity in various fascicles. We also developed a control algorithm that separates the dynamic from the passive properties to solve the redundancy problem in multiple joint problems. This techniques could find application in the natural control of artificial limbs from peripheral nerve signals for patients with amputated limbs or to restore function in patients with stroke or paralyzed limbs.},
}
@article {pmid19163377,
year = {2008},
author = {Patrick, E and Sankar, V and Rowe, W and Yen, SF and Sanchez, JC and Nishida, T},
title = {Flexible polymer substrate and tungsten microelectrode array for an implantable neural recording system.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {3158-3161},
doi = {10.1109/IEMBS.2008.4649874},
pmid = {19163377},
issn = {2375-7477},
support = {NS053561/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Biocompatible Materials ; Electrodes ; *Electrodes, Implanted ; Electrophysiology/*instrumentation/*methods ; Equipment Design ; Humans ; Male ; Miniaturization ; Models, Statistical ; Polymers/*chemistry ; Rats ; Rats, Sprague-Dawley ; Tungsten/chemistry ; },
abstract = {This paper describes the process flow and testing of a substrate for a fully implantable neural recording system. Tungsten microwires are hybrid-packaged on a micromachined flexible polymer substrate forming an intracortical microelectrode array for brain machine interfaces. The microelectrode array is characterized on the bench top and tested in vivo. The microelectrode noise floor is less than 2 microV and acute recording results show a signal to noise ratio of 9.9-17.3 dB. The technique of hybrid fabrication of the electrodes on a flexible substrate provides a general platform for the development of an implantable neural recording system.},
}
@article {pmid19163244,
year = {2008},
author = {Renfrew, M and Cheng, R and Daly, JJ and Cavusoglu, M},
title = {Comparison of filtering and classification techniques of electroencephalography for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {2634-2637},
doi = {10.1109/IEMBS.2008.4649741},
pmid = {19163244},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Models, Theoretical ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Psychomotor Performance/physiology ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {In this paper several methods are investigated for feature extraction and classification of mu features from electroencephalographic (EEG) readings of subjects engaged in motor tasks. EEG features are extracted by autoregressive (AR) filtering, mu-matched filtering, and wavelet decomposition (WD) methods, and the resulting features are classified by a linear classifier whose weights are set by an expert using a-priori knowledge, as well as support vector machines (SVM) using various kernels. The classification accuracies are compared to each other. SVMs are shown to offer a potential improvement over the simple linear classifier, and wavelets and mu-matched filtering are shown to offer potential improvement over AR filtering.},
}
@article {pmid19163242,
year = {2008},
author = {Coyle, D and Satti, A and Prasad, G and McGinnity, TM},
title = {Neural time-series prediction preprocessing meets common spatial patterns in a brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {2626-2629},
doi = {10.1109/IEMBS.2008.4649739},
pmid = {19163242},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain Mapping/methods ; Electrodes ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Image Processing, Computer-Assisted ; Motor Cortex/*physiology ; Movement/physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; User-Computer Interface ; },
abstract = {This paper shows for the first time how a popular and successful filtering approach, known as the common spatial patterns (CSP) approach, compares to the neural time series prediction preprocessing (NTSPP) approach when applied in a 2-class EEG-based brain-computer interface (BCI), either using 2 or 60 EEG channels. Additionally, a novel NTSPP-CSP combination is developed to produce a 2-channel BCI system which significantly outperforms either approach operating independently and has the potential to outperform a 60 channel BCI involving the CSP approach with no NTSPP. The advantages of reducing the number of EEG channels being a reduction in the time used to mount electrodes and reducing the obtrusiveness of the electrode montage for the user. It is also shown that NTSPP can improve the potential of employing existing BCI methods with no subject-specific parameter tuning. Non subject-specific spectral filters are also employed with both approaches and tested with four different classifiers.},
}
@article {pmid19163177,
year = {2008},
author = {Figueiredo, CP and Dias, NS and Hoffmann, KP and Mendes, PM},
title = {3D electrode localization on wireless sensor networks for wearable BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {2365-2368},
doi = {10.1109/IEMBS.2008.4649674},
pmid = {19163177},
issn = {2375-7477},
mesh = {Algorithms ; Computer Communication Networks/*instrumentation ; Electrodes ; Equipment Design ; Humans ; Monitoring, Ambulatory/*instrumentation ; Signal Processing, Computer-Assisted ; Telemetry/*instrumentation ; },
abstract = {This paper presents a solution for electrode localization on wearable BCI radio-enabled electrodes. Electrode positioning is a common issue in any electrical physiological recording. Although wireless node localization is a very active research topic, a precise method with few centimeters of range and a resolution in the order of millimeters is still to be found, since far-field measurements are very prone to error. The calculation of 3D coordinates for each electrode is based on anchorless range-based localization algorithms such as Multidimensional Scaling and Self-Positioning Algorithm. The implemented solution relies on the association of a small antenna to measure the magnetic field and a microcontroller to each electrode, which will be part of the wireless sensor network module. The implemented solution is suitable for EEG applications, namely the wearable BCI, with expected range of 20 cm and resolution of 5 mm.},
}
@article {pmid19163109,
year = {2008},
author = {Faradji, F and Ward, RK and Birch, GE},
title = {Self-paced BCI using multiple SWT-based classifiers.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {2095-2098},
doi = {10.1109/IEMBS.2008.4649606},
pmid = {19163109},
issn = {2375-7477},
mesh = {Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Visual ; Humans ; *Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The presence of false activations inhibits the use of existing self-paced BCIs in real life applications. We present a new design method for a self-paced BCI that yielded 0% false activations using the data of two subjects. This system obtains templates/shapes of the movement related finger flexion patterns. To obtain the templates, the intentional control data are decomposed into 5 levels using the stationary wavelet transform. Then, ensemble averaging is done. These templates are used to train 5 radial basis function neural networks. This is followed by a majority voting classifier.},
}
@article {pmid19163087,
year = {2008},
author = {Digiovanna, J and Citi, L and Yoshida, K and Carpaneto, J and Principe, JC and Sanchez, JC and Micera, S},
title = {Inferring the stability of LIFE through Brain Machine Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {2008-2011},
doi = {10.1109/IEMBS.2008.4649584},
pmid = {19163087},
issn = {2375-7477},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Electrodes, Implanted ; Electrophysiology/methods ; Female ; Models, Neurological ; Muscle, Skeletal/*innervation ; Rabbits ; Signal Processing, Computer-Assisted/*instrumentation ; User-Computer Interface ; },
abstract = {We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting is applied to discriminate single units. We investigate the effect of discriminating between identified units on Brain Machine Interface (BMI) decoding performance. We infer the stability of LIFE based on decoding performance with and without current BMI methods to counter-act electrode neural signal degradation.},
}
@article {pmid19163011,
year = {2008},
author = {Wang, Y and Principe, JC},
title = {Tracking the non-stationary neuron tuning by dual Kalman filter for brain machine interfaces decoding.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1720-1723},
doi = {10.1109/IEMBS.2008.4649508},
pmid = {19163011},
issn = {2375-7477},
mesh = {Algorithms ; Animals ; Biomechanical Phenomena ; Biomedical Engineering ; Brain/*physiology ; Female ; Macaca mulatta ; Models, Neurological ; Motor Neurons/*physiology ; User-Computer Interface ; },
abstract = {Previous decoding approaches assume stationarity of the functional relationship between the neural activity and animal's movement in brain machine interfaces (BMIs). Studies show that the activity of individual neurons changes considerably from day to day. We propose to implement a dual Kalman structure to track neural tuning during the decoding process. While the kinematics are inferred as the state from the observation of neuron firing rates, the preferred direction of neuron tuning is also optimized by dual Kalman filtering on the linear coefficients of the observation model. When compared with the fixed tuning Kalman filter, the decoding performance of the adaptive dual Kalman filter is better (less Normalized Mean Square Error), which means that the evolving tuning of motor neuron is being tracked.},
}
@article {pmid19163007,
year = {2008},
author = {Aggarwal, V and Singhal, G and He, J and Schieber, MH and Thakor, NV},
title = {Towards closed-loop decoding of dexterous hand movements using a virtual integration environment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1703-1706},
doi = {10.1109/IEMBS.2008.4649504},
pmid = {19163007},
issn = {2375-7477},
support = {R01/R37 NS27686/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Biofeedback, Psychology ; Biomedical Engineering ; Brain/physiology ; Databases, Factual ; Hand/*physiology ; Macaca mulatta ; Male ; Movement/*physiology ; Neural Networks, Computer ; Software ; *User-Computer Interface ; },
abstract = {It has been shown that Brain-Computer Interfaces (BCIs) involving closed-loop control of an external device, while receiving visual feedback, allows subjects to adaptively correct errors and improve the accuracy of control. Although closed-loop cortical control of gross arm movements has been demonstrated, closed-loop decoding of more dexterous movements such as individual fingers has not been shown. Neural recordings were obtained from rhesus monkeys in three different experiments involving individuated flexion/extension of each finger, wrist rotation, and dexterous grasps. Separate decoding filters were implemented in Matlab's Simulink environment to independently decode this suite of dexterous movements in real-time. Average real-time decoding accuracies of 80% was achieved for all dexterous tasks with as few as 15 neurons for individual finger flexion/extension, 41 neurons for wrist rotation, and 79 neurons for grasps. In lieu of the availability of advanced multi-fingered prosthetic hands, real-time visual feedback of the decoded output was provided through actuation of a virtual prosthetic hand in a Virtual Integration Environment. This work lays the foundation for future closed-loop experiments with monkeys in the loop and dexterous control of an actual prosthetic limb.},
}
@article {pmid19162981,
year = {2008},
author = {Thakor, NV},
title = {Neuroengineering: building interfaces from neurons to brain.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1602-1603},
doi = {10.1109/IEMBS.2008.4649478},
pmid = {19162981},
issn = {2375-7477},
mesh = {Biomedical Engineering/*methods ; Brain/*physiology ; Humans ; Neurons/*physiology ; *User-Computer Interface ; },
abstract = {Neuroengineering is emerging as an exciting new field with wide ranging research opportunities to contribute to both basic and clinical neurosciences. Armed with training and diverse research tools, engineers are now contributing to technologies to interface to neurons and to the whole brain. The research on modern neural and brain interface technologies has culminated in very recent, exciting programs to develop brain-machine interfaces such as neutrally controlled prostheses.},
}
@article {pmid19162921,
year = {2008},
author = {Quitadamo, LR and Abbafati, M and Saggio, G and Marciani, MG and Cardarilli, GC and Bianchi, L},
title = {A UML model for the description of different brain-computer interface systems.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1363-1366},
doi = {10.1109/IEMBS.2008.4649418},
pmid = {19162921},
issn = {2375-7477},
mesh = {*Brain ; Computer Systems ; *Man-Machine Systems ; *User-Computer Interface ; },
abstract = {BCI research lacks a universal descriptive language among labs and a unique standard model for the description of BCI systems. This results in a serious problem in comparing performances of different BCI processes and in unifying tools and resources. In such a view we implemented a Unified Modeling Language (UML) model for the description virtually of any BCI protocol and we demonstrated that it can be successfully applied to the most common ones such as P300, mu-rhythms, SCP, SSVEP, fMRI. Finally we illustrated the advantages in utilizing a standard terminology for BCIs and how the same basic structure can be successfully adopted for the implementation of new systems.},
}
@article {pmid19162911,
year = {2008},
author = {Bianchi, L and Pronesti, D and Abbafati, M and Quitadamo, LR and Marciani, MG and Saggio, G},
title = {A new visual feed-back modality for the reduction of artifacts in mu-rhythm based brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1323-1326},
doi = {10.1109/IEMBS.2008.4649408},
pmid = {19162911},
issn = {2375-7477},
mesh = {Adult ; *Artifacts ; Electroencephalography/*methods ; *Eye Movements ; Feedback ; Humans ; Periodicity ; User-Computer Interface ; Young Adult ; },
abstract = {A common problem in EEG recording sessions is that results can be heavily contaminated by artifacts. One of the main reasons is that eyes movements generate a noise signal that superimpose to the data. In some BCI protocols the user has generally to control the movement of a cursor on a PC screen by self-regulating his/her mu-rhythm. In general this requires the user to move the eyes to follow the same cursor, thus intrinsically generating a huge amount of noise. To overcome this problem a new feedback modality has been developed, which is able to dramatically reduce the artifacts as it does not require subjects to move their eyes.},
}
@article {pmid19162894,
year = {2008},
author = {Thorbergsson, PT and Garwicz, M and Schouenborg, J and Johansson, AJ},
title = {Implementation of a telemetry system for neurophysiological signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1254-1257},
doi = {10.1109/IEMBS.2008.4649391},
pmid = {19162894},
issn = {2375-7477},
mesh = {Animals ; Brain/*physiology ; *Signal Processing, Computer-Assisted ; Telemetry/instrumentation/*methods ; User-Computer Interface ; },
abstract = {With an ever increasing need for assessment of neurophysiological activity in connection with injury and basic research, the demand for an efficient and reliable data acquisition system rises. Brain-machine interfaces is one class of such systems that targets the central nervous system. A necessary step in the development of a brain-machine interface is to design and implement a reliable and efficient measurement system for neurophysiological signals. The use of telemetric devices increases the flexibility of the devices in terms of subject mobility and unobtrusiveness of the equipment. In this paper, we present a complete system architecture for a wearable telemetry system for the acquisition of neurophysiological data. The system has been miniaturized and implemented using surface-mount technology. System performance has been successfully verified and bottlenecks in the architecture have been identified.},
}
@article {pmid19162867,
year = {2008},
author = {Fazel-Rezai, R and Abhari, K},
title = {A comparison between a matrix-based and a region-based P300 speller paradigms for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1147-1150},
doi = {10.1109/IEMBS.2008.4649364},
pmid = {19162867},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; *Writing ; Young Adult ; },
abstract = {A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on P300 wave. The P300 is a positive peak of an event-related potential (ERP) that happens 300 ms after a stimulus. One of the most well-known and widely-used P300 applications is P300 speller designed by Farwell-Donchin in 1988. The Farwell-Donchin paradigm has been a benchmark in P300 BCI. In this paradigm, a 6x6 matrix of letters and numbers is displayed and subject focuses on a target character while rows and columns of characters flash. By detecting P300 for one row and one column, the target character can be identified. In this paper, it is shown that there is a human perceptual error in Farwell-Donchin paradigm. To remove this error, a new region-based paradigm is presented. Using experimental results, it is shown that the new paradigm has several advantages and it achieves a better accuracy compared to the Farwell-Donchin paradigm.},
}
@article {pmid19162856,
year = {2008},
author = {Thomas, KP and Guan, C and Tong, LC and Prasad, VA},
title = {An adaptive filter bank for motor imagery based Brain Computer Interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1104-1107},
doi = {10.1109/IEMBS.2008.4649353},
pmid = {19162856},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system.},
}
@article {pmid19162832,
year = {2008},
author = {Lou, B and Hong, B and Gao, S},
title = {Task-irrelevant alpha component analysis in motor imagery based brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1021-1024},
doi = {10.1109/IEMBS.2008.4649329},
pmid = {19162832},
issn = {2375-7477},
mesh = {Algorithms ; Alpha Rhythm/methods ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {In motor imagery based BCI, the alpha rhythm shares the same frequency band with sensorimotor rhythm (SMR), and does not correlate with mental task, which contaminates the SMR recording. Independent component analysis (ICA) was applied to decompose original EEG signal into source components, and a comprehensive method was proposed to discriminate those source components by combining temporal, frequency, spatial, and class label information. Task-irrelevant alpha components were sorted out and their projections were reduced by proper bipolar electrode placement for improving the BCI performance.},
}
@article {pmid19162829,
year = {2008},
author = {Hazrati, MKh and Erfanian, A},
title = {An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1009-1012},
doi = {10.1109/IEMBS.2008.4649326},
pmid = {19162829},
issn = {2375-7477},
mesh = {*Algorithms ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Hand Strength/*physiology ; Humans ; *Neural Networks, Computer ; Online Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.},
}
@article {pmid19162828,
year = {2008},
author = {Chin, ZY and Ang, KK and Guan, C},
title = {Multiclass voluntary facial expression classification based on Filter Bank Common Spatial Pattern.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1005-1008},
doi = {10.1109/IEMBS.2008.4649325},
pmid = {19162828},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Biometry/methods ; Electroencephalography/*methods ; Electromyography/*methods ; *Facial Expression ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper investigates the classification of voluntary facial expressions from electroencephalogram (EEG) and electromyogram (EMG) signals using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm is an autonomous and effective machine learning approach for classifying two classes of EEG measurements in motor imagery-based Brain Computer Interface (BCI). However, the problem of facial expression recognition typically involves more than just two classes of measurements. Hence, this paper proposes an extension of FBCSP to the multiclass paradigm using a decision threshold-based classifier for classifying facial expressions from EEG and EMG measurements. A study is conducted using the proposed Multiclass FBCSP on 4 subjects who performed 6 different facial expressions. The results show that the Multiclass FBCSP is effective in classifying multiple facial expressions from the EEG and EMG measurements.},
}
@article {pmid19162827,
year = {2008},
author = {Göksu, F and Ince, NF and Tadipatri, VA and Tewfik, AH},
title = {Classification of EEG with structural feature dictionaries in a brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {1001-1004},
doi = {10.1109/IEMBS.2008.4649324},
pmid = {19162827},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Electroencephalography/classification/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We present a new method for the classification of EEG in a brain computer interface by adapting subject specific features in spectral, temporal and spatial domain. For this particular purpose we extend our previous work on ECoG classification based on structural feature dictionary and apply it to extract the spectro-temporal patterns of multichannel EEG recordings related to a motor imagery task. The construction of the feature dictionary based on undecimated wavelet packet transform is extended to block FFT. We evaluate several subset selection algorithms to select a small number of features for final classification. We tested our proposed approach on five subjects of BCI Competition 2005 dataset- IVa. By adapting the wavelet filter for each subject, the algorithm achieved an average classification accuracy of 91.4% The classification results and characteristic of selected features indicate that the proposed algorithm can jointly adapt to EEG patterns in spectro-spatio-temporal domain and provide classification accuracies as good as existing methods used in the literature.},
}
@article {pmid19162743,
year = {2008},
author = {Momose, K},
title = {Simultaneous detection method of P300 event-related potentials and eye gaze point using multi-pseudorandom and flash stimulation for brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {666-669},
doi = {10.1109/IEMBS.2008.4649240},
pmid = {19162743},
issn = {2375-7477},
mesh = {Adult ; Electrocardiography/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Fixation, Ocular/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {A method for simultaneous detecting of P300 response and eye gaze point using visual evoked potentials (VEPs) elicited by multi-pseudorandom and flash stimuli was examined. Prototype system which would be a practical brain computer interface is established and evaluated. Visual stimuli consisted of six small squares (visual angle of 0.7x0.7 deg) surrounded by frames/a frame (2x2 deg). Squares were flashed with an interval of 180 ms to elicit event-related potential of P300, and luminance of each frame was modulated, based on pseudorandom binary sequences (PRBS) of 10.23 seconds. Six visual stimuli were simultaneously presented on the monitor and subjects were instructed to focus attention successively on an appointed square and EEG was recorded during this task. The cross correlation functions (kernels) of EEGs and each PRBS were calculated and used to determine the subject gazed target. Clear P300 and kernel response for target were simultaneously detected, indicating that this technique could be useful as a practical brain computer interface system.},
}
@article {pmid19162742,
year = {2008},
author = {Eskandari, P and Erfanian, A},
title = {Improving the performance of brain-computer interface through meditation practicing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {662-665},
doi = {10.1109/IEMBS.2008.4649239},
pmid = {19162742},
issn = {2375-7477},
mesh = {Algorithms ; Attention/physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Meditation/*methods ; Motor Cortex/*physiology ; Movement/*physiology ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Cognitive tasks using motor imagery have been used for generating and controlling EEG activity in most brain-computer interface (BCI). Nevertheless, during the performance of a particular mental task, different factors such as concentration, attention, level of consciousness and the difficulty of the task, may be affecting the changes in the EEG activity. Accordingly, training the subject to consistently and reliably produce and control the changes in the EEG signals is a critical issue in developing a BCI system. In this work, we used meditation practice to enhance the mind controllability during the performance of a mental task in a BCI system. The mental states to be discriminated are the imaginative hand movement and the idle state. The experiments were conducted on two groups of subject, meditation group and control group. The time-frequency analysis of EEG signals for meditation practitioners showed an event-related desynchronization (ERD) of beta rhythm before imagination during resting state. In addition, a strong event-related synchronization (ERS) of beta rhythm was induced in frequency around 25 Hz during hand motor imagery. The results demonstrated that the meditation practice can improve the classification accuracy of EEG patterns. The average classification accuracy was 88.73% in the meditation group, while it was 70.28% in the control group. An accuracy as high as 98.0% was achieved in the meditation group.},
}
@article {pmid19162741,
year = {2008},
author = {Pires, G and Castelo-Branco, M and Nunes, U},
title = {Visual P300-based BCI to steer a wheelchair: a Bayesian approach.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {658-661},
doi = {10.1109/IEMBS.2008.4649238},
pmid = {19162741},
issn = {2375-7477},
mesh = {Artificial Intelligence ; Bayes Theorem ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Robotics/*methods ; Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; *Wheelchairs ; },
abstract = {This paper presents a new P300 paradigm for brain computer interface. Visual stimuli consisting of 8 arrows randomly intensified are used for direction target selection for wheelchair steering. The classification is based on a Bayesian approach that uses prior statistical knowledge of target and non-target components. Recorded brain activity from several channels is combined with a Bayesian sensor fusion and then events are grouped to improve event detection. The system has an adaptive performance that adapts to user and P300 pattern quality. The classification algorithms were obtained offline from training and then validated offline and online. The system achieved a transfer rate of 7 commands/min with 95% false positive classification accuracy.},
}
@article {pmid19162739,
year = {2008},
author = {Fatourechi, M and Ward, RK and Birch, GE},
title = {Evaluating the performance of a self-paced BCI with a new movement and using a more engaging environment.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {650-653},
doi = {10.1109/IEMBS.2008.4649236},
pmid = {19162739},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; Electroencephalography/*methods ; Environment ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Software ; Software Validation ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {In previous studies, we proposed a self-paced brain computer interface (SBCI) system that employed three neurological phenomena to identify intentional control (IC) commands from the no control (NC) states of EEG signals. We showed that this SBCI system achieved a good performance that was better than those of other EEG-based SBCI systems. In this paper, we carry out a new study to show that this system can be generalized. Specifically, we show that it can also achieve good performance when 1) a new type of movement is used (hand extension vs. the finger flexion this system was designed for), and 2) NC data are recorded in an engaging environment. A more reliable artifact monitoring system is also added to the system to rule out not only the effects of eye blinks but also the frontalis muscles when controlling the system. Using the data from five participants it is shown that the system obtains good performance compared to other EEG-based SBCI systems.},
}
@article {pmid19162738,
year = {2008},
author = {Zhao, M and Rattanatamrong, P and DiGiovanna, J and Mahmoudi, B and Figueiredo, RJ and Sanchez, JC and Príncipe, JC and Fortes, JA},
title = {BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {646-649},
doi = {10.1109/IEMBS.2008.4649235},
pmid = {19162738},
issn = {2375-7477},
mesh = {Artificial Intelligence ; Brain/*physiology ; *Computers ; Cybernetics/*instrumentation/methods ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/physiology ; Humans ; Information Storage and Retrieval/*methods ; *Man-Machine Systems ; *Software ; *User-Computer Interface ; },
abstract = {Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.},
}
@article {pmid19162737,
year = {2008},
author = {Kanoh, S and Miyamoto, K and Yoshinobu, T},
title = {A brain-computer interface (BCI) system based on auditory stream segregation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {642-645},
doi = {10.1109/IEMBS.2008.4649234},
pmid = {19162737},
issn = {2375-7477},
mesh = {Acoustic Stimulation/*methods ; Algorithms ; Auditory Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Auditory/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Pitch Perception/*physiology ; *User-Computer Interface ; },
abstract = {An auditory brain-computer interface (BCI) which detected event-related potential (ERP) elicited by selective attention to one of the tone streams was proposed. Each tone in two kinds of frequency oddball tone sequences with different tone frequency range was presented alternatively to subjects, and they were perceived by subjects as two kinds of segregated streams. Event-related potentials elicited by two kinds of deviant tones were classified by linear discriminant analysis (LDA) to find which streams subjects paid selective attention. By the experiments to six subjects, it was shown that this system could realize binary selection from two kinds of segregated tone streams.},
}
@article {pmid19162736,
year = {2008},
author = {Lu, S and Guan, C and Zhang, H},
title = {Unsupervised brain computer interface based on inter-subject information.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {638-641},
doi = {10.1109/IEMBS.2008.4649233},
pmid = {19162736},
issn = {2375-7477},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper presents an unsupervised subject modeling technique and its application to a P300-based word speller. Due to EEG variations across subjects, a special training procedure is required to learn a subject-specific classification model (SSCM). To deal with the inter-subject variation, we first study a subject independent classification model (SICM) that is learned from EEG of a pool of subjects. Next we further adapt the SICM by learning from a subset of the pooled EEG that is automatically selected based on its similarity to the EEG of a new subject. Experiments over ten healthy subjects show that the SICM learned from all pooled EEG outperforms the cross-subject models greatly. More importantly, the adapted SICM achieves virtually the same performance as the SSCM, hence removing the complicated and tedious training procedure.},
}
@article {pmid19162735,
year = {2008},
author = {Geng, T and Gan, JQ},
title = {Motor prediction in Brain-Computer Interfaces for controlling mobile robots.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {634-637},
doi = {10.1109/IEMBS.2008.4649232},
pmid = {19162735},
issn = {2375-7477},
mesh = {Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Man-Machine Systems ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {EEG-based Brain-Computer Interface (BCI) can be regarded as a new channel for motor control except that it does not involve muscles. Normal neuromuscular motor control has two fundamental components: (1) to control the body, and (2) to predict the consequences of the control command, which is called motor prediction. In this study, after training with a specially designed BCI paradigm based on motor imagery, two subjects learnt to predict the time course of some features of the EEG signals. It is shown that, with this newly-obtained motor prediction skill, subjects can use motor imagery of feet to directly control a mobile robot to avoid obstacles and reach a small target in a time-critical scenario.},
}
@article {pmid19162733,
year = {2008},
author = {Wei, Q and Tu, W},
title = {Channel selection by genetic algorithms for classifying single-trial ECoG during motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {624-627},
doi = {10.1109/IEMBS.2008.4649230},
pmid = {19162733},
issn = {2375-7477},
mesh = {*Algorithms ; Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Models, Genetic ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {The classification performance of a brain-computer interface (BCI) depends largely on the methods of data recording and feature extraction. The electrocorticogram (ECoG)-based BCIs are a BCI modality that has the potential to achieve high classification accuracy. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The optimal channel subsets are first selected by genetic algorithms from multi-channel ECoG recordings, then the power features are extracted by common spatial pattern (CSP), and finally Fisher discriminant analysis (FDA) is used for classification. The algorithm is applied to Data set I of BCI Competition III and the classification accuracy of 90% is achieved on test set by using only seven channels.},
}
@article {pmid19162732,
year = {2008},
author = {Fujisawa, J and Touyama, H and Hirose, M},
title = {Extracting alpha band modulation during visual spatial attention without flickering stimuli using common spatial pattern.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {620-623},
doi = {10.1109/IEMBS.2008.4649229},
pmid = {19162732},
issn = {2375-7477},
mesh = {Adult ; *Algorithms ; Attention/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Photic Stimulation/*methods ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {In this paper, alpha band modulation during visual spatial attention without visual stimuli was focused. Visual spatial attention has been expected to provide a new channel of non-invasive independent brain computer interface (BCI), but little work has been done on the new interfacing method. The flickering stimuli used in previous work cause a decline of independency and have difficulties in a practical use. Therefore we investigated whether visual spatial attention could be detected without such stimuli. Further, the common spatial patterns (CSP) were for the first time applied to the brain states during visual spatial attention. The performance evaluation was based on three brain states of left, right and center direction attention. The 30-channel scalp electroencephalographic (EEG) signals over occipital cortex were recorded for five subjects. Without CSP, the analyses made 66.44 (range 55.42 to 72.27) % of average classification performance in discriminating left and right attention classes. With CSP, the averaged classification accuracy was 75.39 (range 63.75 to 86.13) %. It is suggested that CSP is useful in the context of visual spatial attention, and the alpha band modulation during visual spatial attention without flickering stimuli has the possibility of a new channel for independent BCI as well as motor imagery.},
}
@article {pmid19162621,
year = {2008},
author = {Vidaurre, C and Schlögl, A},
title = {Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {173-176},
doi = {10.1109/IEMBS.2008.4649118},
pmid = {19162621},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Computer Simulation ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Linear Models ; Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known.},
}
@article {pmid19162590,
year = {2008},
author = {Paralikar, K and Rao, C and Clement, RS},
title = {Automated reduction of non-neuronal signals from intra-cortical microwire array recordings by use of correlation technique.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2008},
number = {},
pages = {46-49},
doi = {10.1109/IEMBS.2008.4649087},
pmid = {19162590},
issn = {2375-7477},
mesh = {*Algorithms ; Animals ; *Artifacts ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography/*instrumentation/*methods ; Evoked Potentials/*physiology ; *Microelectrodes ; Neurons/*physiology ; Pattern Recognition, Automated/methods ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; },
abstract = {Implanted intra-cortical micro-electrode arrays record multi-unit extracellular spike activity that is used in deciphering the neural basis for adaptation, learning, plasticity and as command signal for brain-machine interfaces (BMI). Detection of spike activity is the first step in successful implementation of all the aforementioned applications. However, with awake and behaving animals, micro-electrode arrays typically also record non-neuronal signals induced by the animal's movement, feeding and grooming actions. The spectral and temporal nature of these artifacts is similar to neural spikes, which complicates accurate detection. The distal source and higher strength of non-neuronal signals result in their near simultaneous registration on most electrodes, while neural spiking event is rarely recorded on more than one electrode of an array. This difference is utilized in identifying non-neuronal content from acquired data by performing a correlation analysis. The efficacy of the method is evaluated by comparing outcomes from algorithms that use absolute threshold and Principal Component Analysis (PCA) as a means of identifying neural spikes with the same methods incorporating correlation analysis.},
}
@article {pmid19155552,
year = {2009},
author = {Royer, AS and He, B},
title = {Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms.},
journal = {Journal of neural engineering},
volume = {6},
number = {1},
pages = {016005},
pmid = {19155552},
issn = {1741-2560},
support = {T32 EB008389/EB/NIBIB NIH HHS/United States ; 5 T90 DK70106/DK/NIDDK NIH HHS/United States ; R01 EB007920-01A1/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; T90 DK070106-03/DK/NIDDK NIH HHS/United States ; R01EB007920-01/EB/NIBIB NIH HHS/United States ; T32 EB008389-01/EB/NIBIB NIH HHS/United States ; 1 T32 EB008389-01/EB/NIBIB NIH HHS/United States ; T90 DK070106/DK/NIDDK NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; Computers ; Electroencephalography ; Female ; *Goals ; Humans ; Information Theory ; Male ; Psychomotor Performance/*physiology ; Reaction Time ; *User-Computer Interface ; },
abstract = {In a brain-computer interface (BCI) utilizing a process control strategy, the signal from the cortex is used to control the fine motor details normally handled by other parts of the brain. In a BCI utilizing a goal selection strategy, the signal from the cortex is used to determine the overall end goal of the user, and the BCI controls the fine motor details. A BCI based on goal selection may be an easier and more natural system than one based on process control. Although goal selection in theory may surpass process control, the two have never been directly compared, as we are reporting here. Eight young healthy human subjects participated in the present study, three trained and five naïve in BCI usage. Scalp-recorded electroencephalograms (EEG) were used to control a computer cursor during five different paradigms. The paradigms were similar in their underlying signal processing and used the same control signal. However, three were based on goal selection, and two on process control. For both the trained and naïve populations, goal selection had more hits per run, was faster, more accurate (for seven out of eight subjects) and had a higher information transfer rate than process control. Goal selection outperformed process control in every measure studied in the present investigation.},
}
@article {pmid19155551,
year = {2009},
author = {Ball, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Differential representation of arm movement direction in relation to cortical anatomy and function.},
journal = {Journal of neural engineering},
volume = {6},
number = {1},
pages = {016006},
doi = {10.1088/1741-2560/6/1/016006},
pmid = {19155551},
issn = {1741-2560},
mesh = {Action Potentials ; Adult ; Arm/*physiology ; Electric Stimulation ; Electrodes, Implanted ; Female ; Frontal Lobe/anatomy &amp; histology/*physiology ; Humans ; Magnetic Resonance Imaging ; Male ; Movement ; Neurons/physiology ; Parietal Lobe/anatomy &amp; histology/*physiology ; Psychomotor Performance/*physiology ; Young Adult ; },
abstract = {Information about arm movement direction in neuronal activity of the cerebral cortex can be used for movement control mediated by a brain-machine interface (BMI). Here we provide a topographic analysis of the information related to arm movement direction that can be extracted from single trials of electrocorticographic (ECoG) signals recorded from the human frontal and parietal cortex based on a precise assignment of ECoG recording channels to the subjects' individual cortical anatomy and function. To this aim, each electrode contact was identified on structural MRI scans acquired while the electrodes were implanted and was thus related to the brain anatomy of each patient. Cortical function was assessed by direct cortical electrical stimulation. We show that activity from the primary motor cortex, in particular from the region showing hand and arm motor responses upon electrical stimulation, carries most directional information. The premotor, posterior parietal and lateral prefrontal cortex contributed gradually less, but still significant information. This gradient was observed for decoding from movement-related potentials, and from spectral amplitude modulations in low frequencies and in the high gamma band. Our findings thus demonstrate a close topographic correlation between cortical functional anatomy and direction-related information in humans that might be used for brain-machine interfacing.},
}
@article {pmid19121977,
year = {2009},
author = {Neuper, C and Scherer, R and Wriessnegger, S and Pfurtscheller, G},
title = {Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {2},
pages = {239-247},
doi = {10.1016/j.clinph.2008.11.015},
pmid = {19121977},
issn = {1872-8952},
mesh = {Adult ; Analysis of Variance ; Brain Mapping ; *Electroencephalography/methods ; Electromyography/methods ; Feedback/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Man-Machine Systems ; Mental Processes/*physiology ; Periodicity ; Photic Stimulation/methods ; Psychometrics/methods ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; Surveys and Questionnaires ; Time Factors ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This study investigates the impact of a continuously presented visual feedback in the form of a grasping hand on the modulation of sensorimotor EEG rhythms during online control of a brain-computer interface (BCI).<br><br>METHODS: Two groups of participants were trained to use left or right hand motor imagery to control a specific output signal on a computer monitor: the experimental group controlled a moving hand performing an object-related grasp ('realistic feedback'), whereas the control group controlled a moving bar ('abstract feedback'). Continuous feedback was realized by using the outcome of a real-time classifier which was based on EEG signals recorded from left and right central sites.<br><br>RESULTS: The classification results show no difference between the two feedback groups. For both groups, ERD/ERS analysis revealed a significant larger ERD during feedback presentation compared to an initial motor imagery screening session without feedback. Increased ERD during online BCI control was particularly found for the lower alpha (8-10 Hz) and for the beta bands (16-20, 20-24 Hz).<br><br>CONCLUSIONS: The present study demonstrates that visual BCI feedback clearly modulates sensorimotor EEG rhythms. When the feedback provides equivalent information on both the continuous and final outcomes of mental actions, the presentation form (abstract versus realistic) does not influence the performance in a BCI, at least in initial training sessions.<br><br>SIGNIFICANCE: The present results are of practical interest for classifier development and BCI use in the field of motor restoration.},
}
@article {pmid20582286,
year = {2009},
author = {Gu, Y and Farina, D and Murguialday, AR and Dremstrup, K and Montoya, P and Birbaumer, N},
title = {Offline Identification of Imagined Speed of Wrist Movements in Paralyzed ALS Patients from Single-Trial EEG.},
journal = {Frontiers in neuroscience},
volume = {3},
number = {},
pages = {62},
pmid = {20582286},
issn = {1662-453X},
abstract = {The study investigated the possibility of identifying the speed of an imagined movement from EEG recordings in amyotrophic lateral sclerosis (ALS) patients. EEG signals were acquired from four ALS patients during imagination of wrist extensions at two speeds (fast and slow), each repeated up to 100 times in random order. The movement-related cortical potentials (MRCPs) and averaged sensorimotor rhythm associated with the two tasks were obtained from the EEG recordings. Moreover, offline single-trial EEG classification was performed with discrete wavelet transform for feature extraction and support vector machine for classification. The speed of the task was encoded in the time delay of peak negativity in the MRCPs, which was shorter for faster than for slower movements. The average single-trial misclassification rate between speeds was 30.4 +/- 3.5% when the best scalp location and time interval were selected for each individual. The scalp location and time interval leading to the lowest misclassification rate varied among patients. The results indicate that the imagination of movements at different speeds is a viable strategy for controlling a brain-computer interface system by ALS patients.},
}
@article {pmid20582284,
year = {2009},
author = {Silvoni, S and Volpato, C and Cavinato, M and Marchetti, M and Priftis, K and Merico, A and Tonin, P and Koutsikos, K and Beverina, F and Piccione, F},
title = {P300-Based Brain-Computer Interface Communication: Evaluation and Follow-up in Amyotrophic Lateral Sclerosis.},
journal = {Frontiers in neuroscience},
volume = {3},
number = {},
pages = {60},
pmid = {20582284},
issn = {1662-453X},
abstract = {To describe results of training and 1-year follow-up of brain-communication in a larger group of early and middle stage amyotrophic lateral sclerosis (ALS) patients using a P300-based brain-computer interface (BCI), and to investigate the relationship between clinical status, age and BCI performance. A group of 21 ALS patients were tested with a BCI-system using two-dimensional cursor movements. A four choice visual paradigm was employed to training and test the brain-communication abilities. The task consisted of reaching with the cursor one out of four icons representing four basic needs. Five patients performed a follow-up test 1 year later. The clinical severity in all patients were assessed with a battery of clinical tests. A comparable control group of nine healthy subjects was employed to investigate performance differences. Nineteen patients and nine healthy subjects were able to achieve good and excellent cursor movements' control, acquiring at least communication abilities above chance level; during follow-up the patients maintained their BCI-skill. We found mild cognitive impairments in the ALS group which may be attributed to motor deficiencies, while no relevant correlation has been found between clinical data and BCI performance. A positive correlation between age and the BCI-skill in patients was found. Time since training acquisition and clinical status did not affect the patients brain-communication skill at early and middle stage of the disease. A brain-communication tool can be used in most ALS patients at early and middle stage of the disease before entering the locked-in stage.},
}
@article {pmid20442804,
year = {2009},
author = {Mak, JN and Wolpaw, JR},
title = {Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.},
journal = {IEEE reviews in biomedical engineering},
volume = {2},
number = {},
pages = {187-199},
pmid = {20442804},
issn = {1941-1189},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; },
abstract = {Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.},
}
@article {pmid20228862,
year = {2009},
author = {Vaadia, E and Birbaumer, N},
title = {Grand challenges of brain computer interfaces in the years to come.},
journal = {Frontiers in neuroscience},
volume = {3},
number = {2},
pages = {151-154},
pmid = {20228862},
issn = {1662-453X},
}
@article {pmid19104138,
year = {2009},
author = {Luu, S and Chau, T},
title = {Decoding subjective preference from single-trial near-infrared spectroscopy signals.},
journal = {Journal of neural engineering},
volume = {6},
number = {1},
pages = {016003},
doi = {10.1088/1741-2560/6/1/016003},
pmid = {19104138},
issn = {1741-2560},
mesh = {Computers ; Decision Making/*physiology ; Discriminant Analysis ; Female ; Food Preferences/physiology ; Humans ; Male ; Prefrontal Cortex/physiology ; Spectroscopy, Near-Infrared/*methods ; User-Computer Interface ; Young Adult ; },
abstract = {Near-infrared spectroscopy (NIRS) has recently been identified as a safe, portable and relatively low-cost signal acquisition tool for non-invasive brain-computer interface (BCI) development. The ultimate goal of BCI research is for the user to be able to communicate functional intent directly through thoughts. In this paper we propose an NIRS-BCI paradigm based on directly decoding neural correlates of decision making, specifically subjective preference evaluation. Nine subjects were asked to mentally evaluate two possible drinks and decide which they preferred. Frequency domain near-infrared spectroscopy was used to image each subject's prefrontal cortex during the task. Using mean signal amplitudes as features and linear discriminant analysis, we were able to decode which drink was preferred on a single-trial basis with an average accuracy of 80%.},
}
@article {pmid19097130,
year = {2009},
author = {Behr, A and Leschinski, J and Awungacha, C and Simic, S and Knoth, T},
title = {Telomerization of butadiene with glycerol: reaction control through process engineering, solvents, and additives.},
journal = {ChemSusChem},
volume = {2},
number = {1},
pages = {71-76},
doi = {10.1002/cssc.200800197},
pmid = {19097130},
issn = {1864-564X},
mesh = {Butadienes/*chemistry ; Cyclodextrins/chemistry ; Glycerol/*chemistry ; Glycols/chemistry ; Hydrogen-Ion Concentration ; Polymers/*chemistry ; Salts/chemistry ; Solvents/*chemistry ; Substrate Specificity ; },
abstract = {Owing to the large amount of glycerol that is formed as a by-product during biodiesel production, there have been great efforts to develop new reactions and processes based on glycerol as a renewable feedstock. One example is the telomerization of butadiene with glycerol which provides an atom-economic route to amphiphilic molecules. The reaction is catalyzed by homogeneous palladium catalysts which necessitates efficient catalyst recycling. By employing an aqueous biphasic system, an increased selectivity towards the desired mono-ethers was observed in the telomerization reaction. The performance of the reaction and separation and recycling of the catalyst were optimized by the addition of organic solvents as well as cyclodextrins. By adding cyclodextrins, the conversion of glycerol could be increased and the leaching of palladium could be reduced. Leaching of palladium into the organic phase could be lowered also by using 2-octanol or 2-propanol as additional solvents. Furthermore, the catalyst system could be stabilized by adding nitriles or phosphonium salts, and radical polymerization, which leads to fouling, could be inhibited successfully.},
}
@article {pmid19084556,
year = {2009},
author = {Demirer, RM and Ozerdem, MS and Bayrak, C},
title = {Classification of imaginary movements in ECoG with a hybrid approach based on multi-dimensional Hilbert-SVM solution.},
journal = {Journal of neuroscience methods},
volume = {178},
number = {1},
pages = {214-218},
doi = {10.1016/j.jneumeth.2008.11.011},
pmid = {19084556},
issn = {0165-0270},
mesh = {*Artificial Intelligence ; Brain/*physiology ; Electrocardiography/*methods ; Entropy ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {The study presented in this paper shows that electrocorticographic (ECoG) signals can be classified for making use of a human brain-computer interface (BCI) field. The results show that certain invariant phase transition features can be reliably used to classify two types of imagined movements accurately. Those are the left small-finger and tongue movements. Our approach consists of two main parts: channel selection based on Tsallis entropy in Hilbert domain and the nonlinear classification of motor imagery with support vector machines (SVMs). The new approach, based on Hilbert and statistical/entropy measurements, were combined with SVMs based on admissible kernels for classification purposes. The classification accuracy rates were 95% (264/278) and 73% (73/100) for training and testing sets, respectively. The results support the use of classification methods for ECoG-based BCIs.},
}
@article {pmid19072907,
year = {2008},
author = {Synofzik, M and Schlaepfer, TE},
title = {Stimulating personality: ethical criteria for deep brain stimulation in psychiatric patients and for enhancement purposes.},
journal = {Biotechnology journal},
volume = {3},
number = {12},
pages = {1511-1520},
doi = {10.1002/biot.200800187},
pmid = {19072907},
issn = {1860-7314},
mesh = {*Bioethical Issues ; Biomedical Research/*ethics ; Deep Brain Stimulation/*ethics ; Germany ; Humans ; Mental Disorders/*rehabilitation ; Neurosciences/*ethics ; *Personality ; },
abstract = {Within the recent development of brain-machine-interfaces deep brain stimulation (DBS) has become one of the most promising approaches for neuromodulation. After its introduction more than 20 years ago, it has in clinical routine become a successful tool for treating neurological disorders like Parkinson's disease, essential tremor and dystonia. Recent evidence also demonstrates efficacy in improving emotional and cognitive processing in obsessive-compulsive disorder and major depression, thus allowing new treatment options for treatment refractory psychiatric diseases, and even indicating future potential to enhance functioning in healthy subjects. We demonstrate here that DBS is neither intrinsically unethical for psychiatric indications nor for enhancement purposes. To gain normative orientation, the concept of "personality" is not useful--even if a naturalistic notion is employed. As an alternative, the common and widely accepted bioethical criteria of beneficence, non-maleficence, and autonomy allow a clinically applicable, highly differentiated context- and case-sensitive approach. Based on these criteria, an ethical analysis of empirical evidence from both DBS in movement disorders and DBS in psychiatric disease reveals that wide-spread use of DBS for psychiatric indications is currently not legitimated and that the basis for enhancement purposes is even more questionable. Nevertheless, both applications might serve as ethically legitimate, promising purposes in the future.},
}
@article {pmid19072905,
year = {2008},
author = {Clausen, J},
title = {Moving minds: ethical aspects of neural motor prostheses.},
journal = {Biotechnology journal},
volume = {3},
number = {12},
pages = {1493-1501},
doi = {10.1002/biot.200800244},
pmid = {19072905},
issn = {1860-7314},
mesh = {*Bioethical Issues ; Biomedical Research/*ethics ; Germany ; Movement Disorders/*rehabilitation ; Neurosciences/*ethics ; Prostheses and Implants/*ethics ; *User-Computer Interface ; },
abstract = {Modern brain technology is a highly dynamic and innovative field of research with great potential for medical applications. Recent advances in recording neural signals from the brain by brain-machine interfacing presage new therapeutic options for paralyzed people by means of neural motor prostheses. This paper examines foreseeable ethical questions related to the research on brainmachine interfaces and their possible future applications. It identifies four major topics that need to be considered: first, the questions of personality and its possible alterations; second, responsibility and its possible constraints; third, therapeutic applications and their possible exceedance; and fourth, questions of research ethics that arise when progressing from animal experimentation to application to human subjects. This paper, in identifying and addressing the ethical questions raised by brain-machine interfaces, presents concerns that need to be considered if possible prosthetics based on modern brain technology are to be used cautiously and responsibly.},
}
@article {pmid19064183,
year = {2009},
author = {Ecklund, JM and Ling, GS},
title = {From the battlefront: peripheral nerve surgery in modern day warfare.},
journal = {Neurosurgery clinics of North America},
volume = {20},
number = {1},
pages = {107-10, vii},
doi = {10.1016/j.nec.2008.07.022},
pmid = {19064183},
issn = {1558-1349},
mesh = {Blast Injuries/physiopathology/*surgery ; Humans ; Military Medicine/methods/statistics &amp; numerical data/trends ; Neurosurgical Procedures/methods/statistics &amp; numerical data/trends ; Peripheral Nervous System Diseases/etiology/physiopathology/*surgery ; Prostheses and Implants/trends ; Robotics/trends ; Trauma, Nervous System/etiology/physiopathology/*surgery ; *Warfare ; Wounds, Gunshot/physiopathology/*surgery ; },
abstract = {Warfare historically causes a large number of peripheral nerve injuries. During the current global war on terror, an increased use of advanced regional anesthesia techniques appears to have significantly reduced pain syndromes that have been previously reported with missile-induced nerve injuries. Additionally, a new program has been established to develop advanced prosthetic devises that can interface with neural tissue to obtain direct neural control. As this technology matures, the functional restoration gained from these new generation prosthetic devices may exceed that which can be obtained by standard nerve repair techniques.},
}
@article {pmid19047633,
year = {2008},
author = {Jarosiewicz, B and Chase, SM and Fraser, GW and Velliste, M and Kass, RE and Schwartz, AB},
title = {Functional network reorganization during learning in a brain-computer interface paradigm.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {105},
number = {49},
pages = {19486-19491},
pmid = {19047633},
issn = {1091-6490},
support = {R01 EB005847/EB/NIBIB NIH HHS/United States ; EB005847/EB/NIBIB NIH HHS/United States ; NS-2-2346/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Association Learning/*physiology ; *Electrodes, Implanted ; Macaca mulatta ; Male ; Models, Neurological ; Motor Cortex/*physiology ; *Neural Networks, Computer ; Neuronal Plasticity/*physiology ; Prostheses and Implants ; Psychomotor Performance ; *User-Computer Interface ; },
abstract = {Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.},
}
@article {pmid19028138,
year = {2009},
author = {Pfurtscheller, G and Solis-Escalante, T},
title = {Could the beta rebound in the EEG be suitable to realize a "brain switch"?.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {120},
number = {1},
pages = {24-29},
doi = {10.1016/j.clinph.2008.09.027},
pmid = {19028138},
issn = {1872-8952},
mesh = {Adult ; *Beta Rhythm ; Brain/*physiology ; *Brain Mapping ; Electroencephalography/*methods ; Feedback/*physiology ; Functional Laterality ; Humans ; Imagination ; Male ; Movement ; Time Factors ; User-Computer Interface ; Young Adult ; },
abstract = {OBJECTIVE: Performing foot motor imagery is accompanied by a peri-imagery ERD and a post-imagery beta ERS (beta rebound). Our aim was to study whether the post-imagery beta rebound is a suitable feature for a simple "brain switch". Such a brain switch is a specifically designed brain-computer interface (BCI) with the aim to detect only one predefined brain state (e.g. EEG pattern) in ongoing brain activity.<br><br>METHOD: One EEG (Laplacian) recorded at the vertex during cue-based brisk foot motor imagery was analysed in 5 healthy subjects. The peri-imagery ERD and the post-imagery beta rebound (ERS) were analysed in detail between 6 and 40Hz and classified with two support vector machines.<br><br>RESULTS: The ERD was detected in ongoing EEG (simulation of asynchronous BCI) with a true positive rate (TPR) of 28.4%+/-13.5 and the beta rebound with a TPR of 59.2%+/-20.3. In single runs with 30 cues each, the TPR for beta rebound detection was 78.6%+/-12.8. The false positive rate was always kept below 10%.<br><br>CONCLUSION: The findings suggest that the beta rebound at Cz during foot motor imagery is a relatively stable and reproducible phenomenon detectable in single EEG trials.<br><br>SIGNIFICANCE: Our results indicate that the beta rebound is a suitable feature to realize a "brain switch" with one single EEG (Laplacian) channel only.},
}
@article {pmid19026717,
year = {2009},
author = {Lee, JH and Ryu, J and Jolesz, FA and Cho, ZH and Yoo, SS},
title = {Brain-machine interface via real-time fMRI: preliminary study on thought-controlled robotic arm.},
journal = {Neuroscience letters},
volume = {450},
number = {1},
pages = {1-6},
pmid = {19026717},
issn = {0304-3940},
support = {R01 NS048242/NS/NINDS NIH HHS/United States ; U41 RR019703/RR/NCRR NIH HHS/United States ; U41 RR019703-01A2/RR/NCRR NIH HHS/United States ; R01 NS048242-01A1/NS/NINDS NIH HHS/United States ; U41RR019703/RR/NCRR NIH HHS/United States ; R01-NS048242/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Arm ; Feedback ; Female ; Hand ; Humans ; Imagination/physiology ; Magnetic Resonance Imaging/*instrumentation ; Male ; Monte Carlo Method ; Motor Cortex/*physiology ; Movement ; Robotics/*methods ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.},
}
@article {pmid19025641,
year = {2008},
author = {Iversen, I and Ghanayim, N and Kübler, A and Neumann, N and Birbaumer, N and Kaiser, J},
title = {Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology.},
journal = {Behavioral and brain functions : BBF},
volume = {4},
number = {},
pages = {53},
pmid = {19025641},
issn = {1744-9081},
abstract = {BACKGROUND: Brain-computer interface methodology based on self-regulation of slow-cortical potentials (SCPs) of the EEG (electroencephalogram) was used to assess conditional associative learning in one severely paralyzed, late-stage ALS patient. After having been taught arbitrary stimulus relations, he was evaluated for formation of equivalence classes among the trained stimuli.<br><br>METHODS: A monitor presented visual information in two targets. The method of teaching was matching to sample. Three types of stimuli were presented: signs (A), colored disks (B), and geometrical shapes (C). The sample was one type, and the choice was between two stimuli from another type. The patient used his SCP to steer a cursor to one of the targets. A smiley was presented as a reward when he hit the correct target. The patient was taught A-B and B-C (sample - comparison) matching with three stimuli of each type. Tests for stimulus equivalence involved the untaught B-A, C-B, A-C, and C-A relations. An additional test was discrimination between all three stimuli of one equivalence class presented together versus three unrelated stimuli. The patient also had sessions with identity matching using the same stimuli.<br><br>RESULTS: The patient showed high accuracy, close to 100%, on identity matching and could therefore discriminate the stimuli and control the cursor correctly. Acquisition of A-B matching took 11 sessions (of 70 trials each) and had to be broken into simpler units before he could learn it. Acquisition of B-C matching took two sessions. The patient passed all equivalence class tests at 90% or higher.<br><br>CONCLUSION: The patient may have had a deficit in acquisition of the first conditional association of signs and colored disks. In contrast, the patient showed clear evidence that A-B and B-C training had resulted in formation of equivalence classes. The brain-computer interface technology combined with the matching to sample method is a useful way to assess various cognitive abilities of severely paralyzed patients, who are without reliable motor control.},
}
@article {pmid19024431,
year = {2008},
author = {Wu, Y and He, Q and Huang, H and Zhang, L and Zhuo, Y and Xie, Q and Wu, B},
title = {[Analysis and research of brain-computer interface experiments for imaging left-right hands movement].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {25},
number = {5},
pages = {983-988},
pmid = {19024431},
issn = {1001-5515},
mesh = {Algorithms ; Brain/*physiology ; *Electroencephalography/methods ; Evoked Potentials, Motor/physiology ; Hand/*physiology ; Humans ; Movement/physiology ; Neural Networks, Computer ; Pattern Recognition, Physiological ; *Signal Processing, Computer-Assisted ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {This is a research carried out to explore a pragmatic way of BCI based imaging movement, i. e. to extract the feature of EEG for reflecting different thinking by searching suitable methods of signal extraction and recognition algorithm processing, to boost the recognition rate of communication for BCI system, and finally to establish a substantial theory and experimental support for BCI application. In this paper, different mental tasks for imaging left-right hands movement from 6 subjects were studied in three different time sections (hint keying at 2s, 1s and 0s after appearance of arrow). Then we used wavelet analysis and Feed-forward Back-propagation Neural Network (BP-NN) method for processing and analyzing the experimental data of off-line. Delay time delta t2, delta t1 and delta t0 for all subjects in the three different time sections were analyzed. There was significant difference between delta to and delta t2 or delta t1 (P<0.05), but no significant difference was noted between delta t2 and delta t1 (P>0.05). The average results of recognition rate were 65%, 86.67% and 72%, respectively. There were obviously different features for imaging left-right hands movement about 0.5-1s before actual movement; these features displayed significant difference. We got higher recognition rate of communication under the hint keying at about 1s after the appearance of arrow. These showed the feasibility of using the feature signals extracted from the project as the external control signals for BCI system, and demon strated that the project provided new ideas and methods for feature extraction and classification of mental tasks for BCI.},
}
@article {pmid19015583,
year = {2008},
author = {Kim, SP and Simeral, JD and Hochberg, LR and Donoghue, JP and Black, MJ},
title = {Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia.},
journal = {Journal of neural engineering},
volume = {5},
number = {4},
pages = {455-476},
pmid = {19015583},
issn = {1741-2560},
support = {R37 NS025074/NS/NINDS NIH HHS/United States ; R01 NS025074/NS/NINDS NIH HHS/United States ; R01 NS 50867-01/NS/NINDS NIH HHS/United States ; R01 NS025074-22/NS/NINDS NIH HHS/United States ; R01 EB007401/EB/NIBIB NIH HHS/United States ; NS25074/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/physiopathology ; Artificial Intelligence ; Biomechanical Phenomena ; Electrodes, Implanted ; *Electroencephalography ; Electrophysiology ; Humans ; Male ; Motor Cortex/*physiology ; Photic Stimulation ; Psychomotor Performance/physiology ; Quadriplegia/*physiopathology ; Spinal Cord Injuries/physiopathology ; *User-Computer Interface ; },
abstract = {Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding.},
}
@article {pmid19015582,
year = {2008},
author = {Guo, F and Hong, B and Gao, X and Gao, S},
title = {A brain-computer interface using motion-onset visual evoked potential.},
journal = {Journal of neural engineering},
volume = {5},
number = {4},
pages = {477-485},
doi = {10.1088/1741-2560/5/4/011},
pmid = {19015582},
issn = {1741-2560},
mesh = {Adult ; Algorithms ; Attention/physiology ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Linear Models ; Male ; *Motion ; Photic Stimulation ; Psychomotor Performance/physiology ; Regression Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {This paper presents a novel brain-computer interface (BCI) based on motion-onset visual evoked potentials (mVEPs). mVEP has never been used in BCI research, but has been widely studied in basic research. For the BCI application, the brief motion of objects embedded into onscreen virtual buttons is used to evoke mVEP that is time locked to the onset of motion. EEG data registered from 15 subjects are used to investigate the spatio-temporal pattern of mVEP in this paradigm. N2 and P2 components, with distinct temporo-occipital and parietal topography, respectively, are selected as the salient features of the brain response to the attended target that the subject selects by gazing at it. The computer determines the attended target by finding which button elicited prominent N2/P2 components. Besides a simple feature extraction of N2/P2 area calculation, the stepwise linear discriminant analysis is adopted to assess the target detection accuracy of a five-class BCI. A mean accuracy of 98% is achieved when ten trials data are averaged. Even with only three trials, the accuracy remains above 90%, suggesting that the proposed mVEP-based BCI could achieve a high information transfer rate in online implementation.},
}
@article {pmid19003505,
year = {2007},
author = {Freeman, WJ},
title = {Definitions of state variables and state space for brain-computer interface : Part 2. Extraction and classification of feature vectors.},
journal = {Cognitive neurodynamics},
volume = {1},
number = {2},
pages = {85-96},
pmid = {19003505},
issn = {1871-4080},
abstract = {The hypothesis is proposed that the central dynamics of the action-perception cycle has five steps: emergence from an existing macroscopic brain state of a pattern that predicts a future goal state; selection of a mesoscopic frame for action control; execution of a limb trajectory by microscopic spike activity; modification of microscopic cortical spike activity by sensory inputs; construction of mesoscopic perceptual patterns; and integration of a new macroscopic brain state. The basis is the circular causality between microscopic entities (neurons) and the mesoscopic and macroscopic entities (populations) self-organized by axosynaptic interactions. Self-organization of neural activity is bidirectional in all cortices. Upwardly the organization of mesoscopic percepts from microscopic spike input predominates in primary sensory areas. Downwardly the organization of spike outputs that direct specific limb movements is by mesoscopic fields constituting plans to achieve predicted goals. The mesoscopic fields in sensory and motor cortices emerge as frames within macroscopic activity. Part 1 describes the action-perception cycle and its derivative reflex arc qualitatively. Part 2 describes the perceptual limb of the arc from microscopic MSA to mesoscopic wave packets, and from these to macroscopic EEG and global ECoG fields that express experience-dependent knowledge in successive states. These macroscopic states are conceived to embed and control mesoscopic frames in premotor and motor cortices that are observed in local ECoG and LFP of frontoparietal areas. The fields sampled by ECoG and LFP are conceived as local patterns of neural activity in which trajectories of multiple spike activities (MSA) emerge that control limb movements. Mesoscopic frames are located by use of the analytic signal from the Hilbert transform after band pass filtering. The state variables in frames are measured to construct feature vectors by which to describe and classify frame patterns. Evidence is cited to justify use of linear analysis. The aim of the review is to enable researchers to conceive and identify goal-oriented states in brain activity for use as commands, in order to relegate the details of execution to adaptive control devices outside the brain.},
}
@article {pmid19003492,
year = {2007},
author = {Freeman, WJ},
title = {Definitions of state variables and state space for brain-computer interface : Part 1. Multiple hierarchical levels of brain function.},
journal = {Cognitive neurodynamics},
volume = {1},
number = {1},
pages = {3-14},
pmid = {19003492},
issn = {1871-4080},
abstract = {Neocortical state variables are defined and evaluated at three levels: microscopic using multiple spike activity (MSA), mesoscopic using local field potentials (LFP) and electrocorticograms (ECoG), and macroscopic using electroencephalograms (EEG) and brain imaging. Transactions between levels occur in all areas of cortex, upwardly by integration (abstraction, generalization) and downwardly by differentiation (speciation). The levels are joined by circular causality: microscopic activity upwardly creates mesoscopic order parameters, which downwardly constrain the microscopic activity that creates them. Integration dominates in sensory cortices. Microscopic activity evoked by receptor input in sensation induces emergence of mesoscopic activity in perception, followed by integration of perceptual activity into macroscopic activity in concept formation. The reverse process dominates in motor cortices, where the macroscopic activity embodying the concepts supports predictions of future states as goals. These macroscopic states are conceived to order mesoscopic activity in patterns that constitute plans for actions to achieve the goals. These planning patterns are conceived to provide frames in which the microscopic activity evolves in trajectories that adapted to the immediate environmental conditions detected by new stimuli. This circular sequence forms the action-perception cycle. Its upward limb is understood through correlation of sensory cortical activity with behavior. Now brain-machine interfaces (BMI) offer a means to understand the downward sequence through correlation of behavior with motor cortical activity, beginning with macroscopic goal states and concluding with recording of microscopic MSA trajectories that operate neuroprostheses. Part 1 develops a hypothesis that describes qualitatively the neurodynamics that supports the action-perception cycle and derivative reflex arc. Part 2 describes episodic, "cinematographic" spatial pattern formation and predicts some properties of the macroscopic and mesoscopic frames by which the embedded trajectories of the microscopic activity of cortical sensorimotor neurons might be organized and controlled.},
}
@article {pmid19000739,
year = {2009},
author = {Ron-Angevin, R and Díaz-Estrella, A},
title = {Brain-computer interface: changes in performance using virtual reality techniques.},
journal = {Neuroscience letters},
volume = {449},
number = {2},
pages = {123-127},
doi = {10.1016/j.neulet.2008.10.099},
pmid = {19000739},
issn = {0304-3940},
mesh = {Adult ; Automobile Driving/psychology ; Brain/*physiology ; Electroencephalography/methods ; Evoked Potentials/*physiology ; Feedback/physiology ; Female ; Humans ; Male ; Paralysis/rehabilitation ; Psychomotor Performance/*physiology ; Rehabilitation/methods ; Robotics/instrumentation/methods ; *User-Computer Interface ; Visual Perception/physiology ; Volition/*physiology ; Young Adult ; },
abstract = {The ability to control electroencephalographic (EEG) signals when different mental tasks are carried out would provide a method of communication for people with serious motor function problems. This system is known as a brain-computer interface (BCI). Due to the difficulty of controlling one's own EEG signals, a suitable training protocol is required to motivate subjects, as it is necessary to provide some type of visual feedback allowing subjects to see their progress. Conventional systems of feedback are based on simple visual presentations, such as a horizontal bar extension. However, virtual reality is a powerful tool with graphical possibilities to improve BCI-feedback presentation. The objective of the study is to explore the advantages of the use of feedback based on virtual reality techniques compared to conventional systems of feedback. Sixteen untrained subjects, divided into two groups, participated in the experiment. A group of subjects was trained using a BCI system, which uses conventional feedback (bar extension), and another group was trained using a BCI system, which submits subjects to a more familiar environment, such as controlling a car to avoid obstacles. The obtained results suggest that EEG behaviour can be modified via feedback presentation. Significant differences in classification error rates between both interfaces were obtained during the feedback period, confirming that an interface based on virtual reality techniques can improve the feedback control, specifically for untrained subjects.},
}
@article {pmid18995827,
year = {2008},
author = {Donoghue, JP},
title = {Bridging the brain to the world: a perspective on neural interface systems.},
journal = {Neuron},
volume = {60},
number = {3},
pages = {511-521},
doi = {10.1016/j.neuron.2008.10.037},
pmid = {18995827},
issn = {1097-4199},
mesh = {Animals ; Bionics ; Biosensing Techniques ; Brain/anatomy &amp; histology/*physiology ; Communication Devices for People with Disabilities ; Humans ; *Man-Machine Systems ; Paralysis ; Robotics ; *User-Computer Interface ; },
abstract = {Neural interface (NI) systems hold the potential to return lost functions to persons with paralysis. Impressive progress has been made, including evaluation of neural control signals, sensor testing in humans, signal decoding advances, and proof-of-concept validation. Most importantly, the field has demonstrated that persons with paralysis can use prototype systems for spelling, "point and click," and robot control. Human and animal NI research is advancing knowledge about neural information processing and plasticity in healthy, diseased, and injured nervous systems. This emerging field promises a range of neurotechnologies able to return communication, independence, and control to people with movement limitations.},
}
@article {pmid18990647,
year = {2008},
author = {Bigdely-Shamlo, N and Vankov, A and Ramirez, RR and Makeig, S},
title = {Brain activity-based image classification from rapid serial visual presentation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {5},
pages = {432-441},
doi = {10.1109/TNSRE.2008.2003381},
pmid = {18990647},
issn = {1558-0210},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {We report the design and performance of a brain-computer interface (BCI) system for real-time single-trial binary classification of viewed images based on participant-specific dynamic brain response signatures in high-density (128-channel) electroencephalographic (EEG) data acquired during a rapid serial visual presentation (RSVP) task. Image clips were selected from a broad area image and presented in rapid succession (12/s) in 4.1-s bursts. Participants indicated by subsequent button press whether or not each burst of images included a target airplane feature. Image clip creation and search path selection were designed to maximize user comfort and maintain user awareness of spatial context. Independent component analysis (ICA) was used to extract a set of independent source time-courses and their minimally-redundant low-dimensional informative features in the time and time-frequency amplitude domains from 128-channel EEG data recorded during clip burst presentations in a training session. The naive Bayes fusion of two Fisher discriminant classifiers, computed from the 100 most discriminative time and time-frequency features, respectively, was used to estimate the likelihood that each clip contained a target feature. This estimator was applied online in a subsequent test session. Across eight training/test session pairs from seven participants, median area under the receiver operator characteristic curve, by tenfold cross validation, was 0.97 for within-session and 0.87 for between-session estimates, and was nearly as high (0.83) for targets presented in bursts that participants mistakenly reported to include no target features.},
}
@article {pmid18990646,
year = {2008},
author = {Yuan, H and Doud, A and Gururajan, A and He, B},
title = {Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {5},
pages = {425-431},
pmid = {18990646},
issn = {1558-0210},
support = {R01 EB000178-05/EB/NIBIB NIH HHS/United States ; R01EB007920/EB/NIBIB NIH HHS/United States ; R01 EB007920-01A1/EB/NIBIB NIH HHS/United States ; R01 EB007920/EB/NIBIB NIH HHS/United States ; T90 DK070106/DK/NIDDK NIH HHS/United States ; R01 EB000178/EB/NIBIB NIH HHS/United States ; R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Young Adult ; },
abstract = {It is of wide interest to study the brain activity that correlates to the control of brain-computer interface (BCI). In the present study, we have developed an approach to image the cortical rhythmic modulation associated with motor imagery using minimum-norm estimates in the frequency domain (MNEFD). The distribution of cortical sources of mu activity during online control of BCI was obtained with the MNEFD. Contralateral decrease (event-related desynchronization) and ipsilateral increase (event-related synchronization) are localized in the sensorimotor cortex during online control of BCI in a group of human subjects. Statistical source analysis revealed that maximum correlation with movement imagination is localized in sensorimotor cortex.},
}
@article {pmid18990639,
year = {2008},
author = {do Nascimento, OF and Farina, D},
title = {Movement-related cortical potentials allow discrimination of rate of torque development in imaginary isometric plantar flexion.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {11},
pages = {2675-2678},
doi = {10.1109/TBME.2008.2001139},
pmid = {18990639},
issn = {1558-2531},
mesh = {Adult ; Analysis of Variance ; Cerebral Cortex/*physiology ; Electroencephalography ; Electromyography ; Electrooculography ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination ; *Isometric Contraction ; Male ; *Models, Neurological ; Motor Activity/physiology ; Movement/*physiology ; Torque ; User-Computer Interface ; Young Adult ; },
abstract = {The aim of this study was to discriminate on a single-trial basis the cortical activity associated to two rates of torque development (RTDs) in imaginary isometric plantar flexions. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) signals were recorded while ten healthy subjects imagined right-sided isometric ankle plantar-flexion tasks at moderate [from 0% to 60% of the maximal voluntary contraction (MVC) in 4 s] and ballistic (from 0% to 60% MVC as fast as possible) RTDs. The EEG signals were classified using feature extraction based on the marginal distribution of a discrete wavelet transform with optimization of the mother wavelet. The classifier was based on support vector machines (SVMs). Minimum misclassification rate for the best case was 8.3%. Average minimum misclassification rate over the ten subjects was (17.4 +/- 8.4)%. The two RTDs could be best differentiated from channel C4 on average. In conclusion, different RTDs could be differentiated in imaginary isometric plantar-flexion by only using cortical potentials recorded with surface EEG. This result constitutes the first step for the development of a new type of brain-computer interfaces that rely on kinetic parameters of a single limb rather than movements of opposite limbs.},
}
@article {pmid18990156,
year = {2009},
author = {Sahai, A and Sangster, P and Kalsi, V and Khan, MS and Fowler, CJ and Dasgupta, P},
title = {Assessment of urodynamic and detrusor contractility variables in patients with overactive bladder syndrome treated with botulinum toxin-A: is incomplete bladder emptying predictable?.},
journal = {BJU international},
volume = {103},
number = {5},
pages = {630-634},
doi = {10.1111/j.1464-410X.2008.08076.x},
pmid = {18990156},
issn = {1464-410X},
mesh = {Adolescent ; Adult ; Aged ; Botulinum Toxins, Type A/adverse effects/*therapeutic use ; Female ; Humans ; Male ; Middle Aged ; Muscle Contraction/drug effects ; Muscle, Smooth/drug effects ; Neuromuscular Agents/adverse effects/*therapeutic use ; Sensitivity and Specificity ; Treatment Outcome ; Urinary Bladder/drug effects/*physiopathology ; Urinary Bladder, Overactive/*drug therapy/physiopathology ; Urinary Catheterization ; Urodynamics/*physiology ; Young Adult ; },
abstract = {OBJECTIVE: To assess whether incomplete bladder emptying and the need for clean intermittent self-catheterization (CISC) is predictable, by analysing urodynamic and detrusor contractility variables in patients treated with botulinum toxin-A (BTX-A) for refractory idiopathic detrusor overactivity (IDO).<br><br>PATIENTS AND METHODS: Sixty-seven patients (mean age 50.3) with IDO, from two centres, had bladder injections of 200 U BTX-A. Patients with difficulty in emptying their bladder and/or persistent overactive bladder symptoms, with postvoid residual volumes (PVR) of >150 mL after treatment were started on CISC. Urodynamics were conducted at baseline, 4 and 12-16 weeks after injection with BTX-A. Detrusor contractility was assessed using the projected isovolumetric pressure (PIP1) in women and bladder contractility index (BCI) in men.<br><br>RESULTS: There were improvements in the mean maximum cystometric capacity, bladder compliance and maximum detrusor pressures during filling cystometry after BTX-A injections. The PVR was significantly increased at 4 but not at 12 weeks. Nineteen patients required CISC and when compared with those not needing CISC their pretreatment maximum flow rate (15 vs 22 mL/s, P = 0.003), PIP1 (43 vs 58, P = 0.02) and BCI (113 vs 180, P = 0.001) were lower. Receiver operator characteristic curve analysis suggested that a PIP1 of < or =50 in women (sensitivity 0.83; specificity 0.70; area under the curve 0.822) and BCI < or =120 (sensitivity 0.7; specificity 0.79; area 0.879) might predict the need for CISC.<br><br>CONCLUSION: The maximum flow rate, PIP1 and BCI were significantly lower in patients who required CISC after BTX-A treatment than in those who did not. A PIP1 of < or =50 in women and a BCI of < or =120 might be predictive of a need for CISC in this setting, and might help when counselling patients.},
}
@article {pmid18989104,
year = {2008},
author = {Birbaumer, N and Murguialday, AR and Cohen, L},
title = {Brain-computer interface in paralysis.},
journal = {Current opinion in neurology},
volume = {21},
number = {6},
pages = {634-638},
doi = {10.1097/WCO.0b013e328315ee2d},
pmid = {18989104},
issn = {1350-7540},
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; Humans ; Paralysis/psychology/*rehabilitation ; *User-Computer Interface ; },
abstract = {PURPOSE OF REVIEW: Communication with patients suffering from locked-in syndrome and other forms of paralysis is an unsolved challenge. Movement restoration for patients with chronic stroke or other brain damage also remains a therapeutic problem and available treatments do not offer significant improvements. This review considers recent research in brain-computer interfaces (BCIs) as promising solutions to these challenges.<br><br>RECENT FINDINGS: Experimentation with nonhuman primates suggests that intentional goal directed movements of the upper limbs can be reconstructed and transmitted to external manipulandum or robotic devices controlled from a relatively small number of microelectrodes implanted into movement-relevant brain areas after some training, opening the door for the development of BCI or brain-machine interfaces in humans. Although noninvasive BCIs using electroencephalographic recordings or event-related-brain-potentials in healthy individuals and patients with amyotrophic lateral sclerosis or stroke can transmit up to 80 bits/min of information, the use of BCIs - invasive or noninvasive - in severely or totally paralyzed patients has met some unforeseen difficulties.<br><br>SUMMARY: Invasive and noninvasive BCIs using recordings from nerve cells, large neuronal pools such as electrocorticogram and electroencephalography, or blood flow based measures such as functional magnetic resonance imaging and near-infrared spectroscopy show potential for communication in locked-in syndrome and movement restoration in chronic stroke, but controlled phase III clinical trials with larger populations of severely disturbed patients are urgently needed.},
}
@article {pmid18981872,
year = {2008},
author = {Mislow, JM and Friedlander, RM},
title = {Neuromotor prosthetics: brain-computer interfaces, a step closer to benefitting paralyzed patients.},
journal = {Neurosurgery},
volume = {63},
number = {4},
pages = {N8-9},
doi = {10.1227/01.NEU.0000339451.71215.3E},
pmid = {18981872},
issn = {1524-4040},
mesh = {Animals ; *Brain/physiology ; Haplorhini ; Humans ; *Man-Machine Systems ; Neurodegenerative Diseases/complications/physiopathology/*rehabilitation ; Paralysis/etiology/*rehabilitation ; Prostheses and Implants/*trends ; *User-Computer Interface ; },
}
@article {pmid18975019,
year = {2009},
author = {Pop-Jordanov, J and Pop-Jordanova, N},
title = {Neurophysical substrates of arousal and attention.},
journal = {Cognitive processing},
volume = {10 Suppl 1},
number = {},
pages = {S71-9},
pmid = {18975019},
issn = {1612-4790},
mesh = {Alpha Rhythm ; Animals ; Arousal/*physiology ; Attention/*physiology ; Beta Rhythm ; Brain/cytology/*physiology ; Consciousness/*physiology ; *Electroencephalography ; Entropy ; Humans ; Neurons/*physiology ; },
abstract = {The study of arousal and attention could be of prominent importance for elucidating both fundamental and practical aspects of the mind-brain puzzle. Defined as "general activation of mind" (Kahnemann in Attention and effort. Prentice-Hall, New Jersey, 1973), or "general operation of consciousness" (Thacher and John in Functional neuroscience: foundations of cognitive processing. Erlbaum, Hillsdale, 1977), arousal can be considered as a starting point of fundamental research on consciousness. Similar role could be assigned to attention, which can be defined by substituting the attributes "general" with "focused". Concerning the practical applications, the empirically established correlation between neuronal oscillations and arousal/attention levels is widely used in research and clinics, including neurofeedback, brain-computer communication, etc. However, the neurophysical mechanism underlying this correlation is still not clear enough. In this paper, after reviewing some present classical and quantum approaches, a transition probability concept of arousal based on field-dipole quantum interactions and information entropy is elaborated. The obtained analytical expressions and numerical values correspond to classical empirical results for arousal and attention, including the characteristic frequency dependence and intervals. Simultaneously, the fundamental (substrate) role of EEG spectrum has been enlightened, whereby the attention appears to be a bridge between arousal and the content of consciousness. Finally, some clinical implications, including the brain-rate parameter as an indicator of arousal and attention levels, are provided.},
}
@article {pmid18973568,
year = {2008},
author = {Pfurtscheller, G and Scherer, R and Müller-Putz, GR and Lopes da Silva, FH},
title = {Short-lived brain state after cued motor imagery in naive subjects.},
journal = {The European journal of neuroscience},
volume = {28},
number = {7},
pages = {1419-1426},
doi = {10.1111/j.1460-9568.2008.06441.x},
pmid = {18973568},
issn = {1460-9568},
mesh = {Adult ; Brain Mapping ; *Cues ; Electroencephalography ; Evoked Potentials/*physiology ; Foot/physiology ; Functional Laterality/physiology ; Hand/physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Neuropsychological Tests ; Photic Stimulation ; Psychomotor Performance/*physiology ; Time Factors ; Visual Perception/*physiology ; Young Adult ; },
abstract = {Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects) were asked to imagine the indicated limb movement for some seconds. Common spatial filtering and linear single-trial classification was applied to discriminate between two conditions (two brain states: right hand vs. left hand, left hand vs. foot and right hand vs. foot). The corresponding classification accuracies (mean +/- SD) were 80.0 +/- 10.6%, 83.3 +/- 10.2% and 83.6 +/- 8.8%, respectively. Inspection of central mu and beta rhythms revealed a short-lasting somatotopically specific event-related desynchronization (ERD) in the upper mu and/or beta bands starting approximately 300 ms after the cue onset and lasting for less than 1 s.},
}
@article {pmid18972888,
year = {2008},
author = {Hsieh, C and Knudson, D},
title = {Student factors related to learning in biomechanics.},
journal = {Sports biomechanics},
volume = {7},
number = {3},
pages = {398-402},
doi = {10.1080/14763140802233207},
pmid = {18972888},
issn = {1476-3141},
mesh = {Adult ; Biology/*education ; Biomechanical Phenomena ; California ; *Educational Measurement ; Female ; *Health Knowledge, Attitudes, Practice ; Humans ; *Learning ; Male ; Physics/*education ; Sex Distribution ; Students/*statistics &amp; numerical data ; *Task Performance and Analysis ; },
abstract = {The aim of this study was to identify the student behaviours and characteristics that are related to learning biomechanical concepts. The Biomechanics Concept Inventory (BCI) was given to 53 kinesiology majors before and after an introductory biomechanics class together with a survey of student behaviours to determine factors that assisted in learning. Analysis of scores from 49 students showed significant (P < 0.001) improvement following instruction. Variables that significantly (P < 0.05) and uniquely correlated with improvement were grade point average (r = 0.46) and student interest in biomechanics (r = 0.41). Thirty-one percent of the variance in learning could be accounted for by these two variables, with no distinctive associations with student behaviours like course reading, hours studying, and credits earned in maths and physics. However, grade point average was significantly correlated with several student behaviour variables. Consequently, student learning of biomechanical concepts is likely a complex phenomenon with individual learning related to variables that interact with student interest and overall academic ability.},
}
@article {pmid18971518,
year = {2008},
author = {Wang, Z and Logothetis, NK and Liang, H},
title = {Decoding a bistable percept with integrated time-frequency representation of single-trial local field potential.},
journal = {Journal of neural engineering},
volume = {5},
number = {4},
pages = {433-442},
doi = {10.1088/1741-2560/5/4/008},
pmid = {18971518},
issn = {1741-2560},
support = {R01 MH072034/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Artificial Intelligence ; Data Interpretation, Statistical ; Electrophysiology ; Evoked Potentials, Visual/*physiology ; Fixation, Ocular ; Linear Models ; Macaca mulatta ; Male ; Models, Statistical ; Visual Cortex/physiology ; Visual Perception/*physiology ; },
abstract = {Bistable perception emerges when a stimulus under continuous view is perceived as the alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper we analyze the dynamic activity of local field potential (LFP), simultaneously collected from multiple channels in the middle temporal (MT) visual cortex of a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. Based on the observation that the discriminative information of neuronal population activity evolves and accumulates over time, we propose to select features from the integrated time-frequency representation of LFP using a relaxation (RELAX) algorithm and a sequential forward selection (SFS) algorithm with maximizing the Mahalanobis distance as the criterion function. The integrated-spectrogram based feature selection is much more robust and can achieve significantly better features than the instantaneous-spectrogram based feature selection. We exploit the support vector machines (SVM) classifier and the linear discriminant analysis (LDA) classifier based on the selected features to decode the reported perception on a single trial basis. Our results demonstrate the excellent performance of the integrated-spectrogram based feature selection and suggest that the features in the gamma frequency band (30-100 Hz) of LFP within specific temporal windows carry the most discriminative information for decoding bistable perception. The proposed integrated-spectrogram based feature selection approach may have potential for a myriad of applications involving multivariable time series such as brain-computer interfaces (BCI).},
}
@article {pmid18927456,
year = {2008},
author = {Wisneski, KJ and Anderson, N and Schalk, G and Smyth, M and Moran, D and Leuthardt, EC},
title = {Unique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications.},
journal = {Stroke},
volume = {39},
number = {12},
pages = {3351-3359},
doi = {10.1161/STROKEAHA.108.518175},
pmid = {18927456},
issn = {1524-4628},
mesh = {Adolescent ; Adult ; *Artificial Limbs ; *Bionics/instrumentation ; *Brain Mapping ; Child ; *Dominance, Cerebral ; Electroencephalography ; Female ; Hand/*physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Paresis/etiology/*rehabilitation ; *Prosthesis Design ; Psychomotor Performance/*physiology ; Stroke/*complications ; *User-Computer Interface ; Volition ; },
abstract = {BACKGROUND AND PURPOSE: Brain computer interfaces (BCIs) offer little direct benefit to patients with hemispheric stroke because current platforms rely on signals derived from the contralateral motor cortex (the same region injured by the stroke). For BCIs to assist hemiparetic patients, the implant must use unaffected cortex ipsilateral to the affected limb. This requires the identification of distinct electrophysiological features from the motor cortex associated with ipsilateral hand movements.<br><br>METHODS: In this study we studied 6 patients undergoing temporary placement of intracranial electrode arrays. Electrocorticographic (ECoG) signals were recorded while the subjects engaged in specific ipsilateral or contralateral hand motor tasks. Spectral changes were identified with regards to frequency, location, and timing.<br><br>RESULTS: Ipsilateral hand movements were associated with electrophysiological changes that occur in lower frequency spectra, at distinct anatomic locations, and earlier than changes associated with contralateral hand movements. In a subset of 3 patients, features specific to ipsilateral and contralateral hand movements were used to control a cursor on a screen in real time. In ipsilateral derived control this was optimal with lower frequency spectra.<br><br>CONCLUSIONS: There are distinctive cortical electrophysiological features associated with ipsilateral movements which can be used for device control. These findings have implications for patients with hemispheric stroke because they offer a potential methodology for which a single hemisphere can be used to enhance the function of a stroke induced hemiparesis.},
}
@article {pmid18923392,
year = {2008},
author = {Moritz, CT and Perlmutter, SI and Fetz, EE},
title = {Direct control of paralysed muscles by cortical neurons.},
journal = {Nature},
volume = {456},
number = {7222},
pages = {639-642},
pmid = {18923392},
issn = {1476-4687},
support = {R37 NS012542-33S1/NS/NINDS NIH HHS/United States ; F32 NS051013/NS/NINDS NIH HHS/United States ; R01 NS040867-08/NS/NINDS NIH HHS/United States ; F32 NS051013-03/NS/NINDS NIH HHS/United States ; R37 NS012542-33/NS/NINDS NIH HHS/United States ; P51 RR000166/RR/NCRR NIH HHS/United States ; P51 RR000166-476525/RR/NCRR NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; F32 NS051013-01A1/NS/NINDS NIH HHS/United States ; R37 NS012542-35/NS/NINDS NIH HHS/United States ; R01 NS040867-09/NS/NINDS NIH HHS/United States ; R37 NS012542-34/NS/NINDS NIH HHS/United States ; P51 RR000166-476519/RR/NCRR NIH HHS/United States ; R01 NS040867/NS/NINDS NIH HHS/United States ; F32 NS051013-02/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation ; Macaca nemestrina/*physiology ; Motor Cortex/*cytology/physiology ; Movement ; Muscles/*innervation/*physiology/physiopathology ; Nerve Block ; Neurons/*physiology ; Paralysis/*physiopathology ; Psychomotor Performance ; Torque ; },
abstract = {A potential treatment for paralysis resulting from spinal cord injury is to route control signals from the brain around the injury by artificial connections. Such signals could then control electrical stimulation of muscles, thereby restoring volitional movement to paralysed limbs. In previously separate experiments, activity of motor cortex neurons related to actual or imagined movements has been used to control computer cursors and robotic arms, and paralysed muscles have been activated by functional electrical stimulation. Here we show that Macaca nemestrina monkeys can directly control stimulation of muscles using the activity of neurons in the motor cortex, thereby restoring goal-directed movements to a transiently paralysed arm. Moreover, neurons could control functional stimulation equally well regardless of any previous association to movement, a finding that considerably expands the source of control signals for brain-machine interfaces. Monkeys learned to use these artificial connections from cortical cells to muscles to generate bidirectional wrist torques, and controlled multiple neuron-muscle pairs simultaneously. Such direct transforms from cortical activity to muscle stimulation could be implemented by autonomous electronic circuitry, creating a relatively natural neuroprosthesis. These results are the first demonstration that direct artificial connections between cortical cells and muscles can compensate for interrupted physiological pathways and restore volitional control of movement to paralysed limbs.},
}
@article {pmid18848844,
year = {2009},
author = {Hsu, WY and Sun, YN},
title = {EEG-based motor imagery analysis using weighted wavelet transform features.},
journal = {Journal of neuroscience methods},
volume = {176},
number = {2},
pages = {310-318},
doi = {10.1016/j.jneumeth.2008.09.014},
pmid = {18848844},
issn = {0165-0270},
mesh = {Area Under Curve ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; Imagination/*physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {In this study, an electroencephalogram (EEG) analysis system for single-trial classification of motor imagery (MI) data is proposed. Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. The continuous wavelet transform (CWT) is applied together with Student's two-sample t-statistics for 2D time-scale feature extraction, where features are extracted from EEG signals recorded from subjects performing left and right MI. First, we utilize the CWT to construct a 2D time-scale feature, which yields a highly redundant representation of EEG signals in the time-frequency domain, from which we can obtain precise localization of event-related brain desynchronization and synchronization (ERD and ERS) components. We then weight the 2D time-scale feature with Student's two-sample t-statistics, representing a time-scale plot of discriminant information between left and right MI. These important characteristics, including precise localization and significant discriminative ability, substantially enhance the classification of mental tasks. Finally, a correlation coefficient is used to classify the MI data. Due to its simplicity, it will enable the performance of our proposed method to be clearly demonstrated. Compared to a conventional 2D time-frequency feature and three well-known time-frequency approaches, the experimental results show that the proposed method provides reliable 2D time-scale features for BCI classification.},
}
@article {pmid18845473,
year = {2008},
author = {Morash, V and Bai, O and Furlani, S and Lin, P and Hallett, M},
title = {Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {11},
pages = {2570-2578},
pmid = {18845473},
issn = {1388-2457},
support = {Z01 NS002669-23//Intramural NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping ; Cerebral Cortex/*physiology ; *Cortical Synchronization ; Electroencephalography ; Female ; *Foot ; Functional Laterality/*physiology ; Hand/*innervation ; Humans ; *Imagination ; Male ; Middle Aged ; Movement/*physiology ; Photic Stimulation ; Predictive Value of Tests ; Time Factors ; Tongue/*innervation ; Young Adult ; },
abstract = {OBJECTIVE: To use the neural signals preceding movement and motor imagery to predict which of the four movements/motor imageries is about to occur, and to access this utility for brain-computer interface (BCI) applications.<br><br>METHODS: Eight na&#xef;ve subjects performed or kinesthetically imagined four movements while electroencephalogram (EEG) was recorded from 29 channels over sensorimotor areas. The task was instructed with a specific stimulus (S1) and performed at a second stimulus (S2). A classifier was trained and tested offline at differentiating the EEG signals from movement/imagery preparation (the 1.5-s preceding movement/imagery execution).<br><br>RESULTS: Accuracy of movement/imagery preparation classification varied between subjects. The system preferentially selected event-related (de)synchronization (ERD/ERS) signals for classification, and high accuracies were associated with classifications that relied heavily on the ERD/ERS to discriminate movement/imagery planning.<br><br>CONCLUSIONS: The ERD/ERS preceding movement and motor imagery can be used to predict which of the four movements/imageries is about to occur. Prediction accuracy depends on this signal's accessibility.<br><br>SIGNIFICANCE: The ERD/ERS is the most specific pre-movement/imagery signal to the movement/imagery about to be performed.},
}
@article {pmid18838371,
year = {2008},
author = {Blankertz, B and Losch, F and Krauledat, M and Dornhege, G and Curio, G and Müller, KR},
title = {The Berlin Brain--Computer Interface: accurate performance from first-session in BCI-naïve subjects.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {10},
pages = {2452-2462},
doi = {10.1109/TBME.2008.923152},
pmid = {18838371},
issn = {1558-2531},
mesh = {Adult ; Artificial Intelligence ; Biofeedback, Psychology ; Brain/physiology ; Brain Mapping ; Electroencephalography ; Electromyography ; Electrooculography ; Evoked Potentials, Visual ; Female ; Foot/physiology ; Functional Laterality ; Hand/physiology ; Humans ; Imagination/physiology ; Learning/physiology ; Male ; *Man-Machine Systems ; Movement/physiology ; Pattern Recognition, Automated ; *Psychomotor Performance/physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The Berlin Brain--Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. MUller, and G. Curio. (2007) The non-invasive Berlin brain--computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage. [Online]. 37(2), pp. 539--550. Available: http://dx.doi.org/10.1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naIve subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.},
}
@article {pmid18838120,
year = {2008},
author = {Tkach, D and Reimer, J and Hatsopoulos, NG},
title = {Observation-based learning for brain-machine interfaces.},
journal = {Current opinion in neurobiology},
volume = {18},
number = {6},
pages = {589-594},
pmid = {18838120},
issn = {1873-6882},
support = {R01 NS048845-04/NS/NINDS NIH HHS/United States ; R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS048845-02/NS/NINDS NIH HHS/United States ; R01 NS048845-01A1/NS/NINDS NIH HHS/United States ; R01 NS048845-03/NS/NINDS NIH HHS/United States ; R01 NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Humans ; Learning/*physiology ; Motor Cortex/cytology/*physiology ; Neurons/*physiology ; },
abstract = {Canonically, 'mirror neurons' are cells in area F5 of the ventral premotor cortex that are active during both observation and execution of goal-directed movements. Recently, cells with similar properties have been observed in a number of other areas in the motor system, including the primary motor cortex. Mirror neurons are a part of a system whose function is thought to involve the prediction and interpretation of the sensory consequences of our own actions as well as the actions of others. Mirror-like responses are relevant to the development of brain-machine interfaces (BMIs) because they provide a robust way to map neural activity to behavior, and because they represent high-level information about goals and intentions that may have utility in future BMI applications.},
}
@article {pmid18836265,
year = {2008},
author = {Hong, JM and Bang, OY and Chung, CS and Joo, IS and Huh, K},
title = {Frequency and clinical significance of acute bilateral cerebellar infarcts.},
journal = {Cerebrovascular diseases (Basel, Switzerland)},
volume = {26},
number = {5},
pages = {541-548},
doi = {10.1159/000160211},
pmid = {18836265},
issn = {1421-9786},
mesh = {Acute Disease ; Aged ; Angiography, Digital Subtraction ; Cerebellum/*blood supply ; Cerebral Angiography ; *Cerebral Infarction/etiology/mortality/pathology/therapy ; Diffusion Magnetic Resonance Imaging ; Female ; Humans ; Length of Stay ; Magnetic Resonance Angiography ; Male ; Middle Aged ; Odds Ratio ; Risk Assessment ; Risk Factors ; Severity of Illness Index ; Time Factors ; Treatment Outcome ; },
abstract = {BACKGROUND: Unlike acute unilateral cerebellar infarct (UCI), acute bilateral cerebellar infarcts (BCI) have attracted little attention. To evaluate the clinical significance of BCI, we compared UCI and BCI and analyzed potentially prognostic factors.<br><br>METHODS: Patients who were consecutively admitted at a university hospital over a 4-year period with acute cerebellar infarcts, proven by diffusion-weighted imaging, were studied. Cerebellar infarcts were topographically classified, and divided into 2 groups: UCI and BCI. The demographics, involved territories, concomitant lesions outside the cerebellum (CLOC), bilateral involvement, infarct volume, hospital courses, and mechanisms were analyzed. We performed multiple regression analysis to predict the poor outcome at discharge [> or =3 on the modified Rankin Scale (mRS)].<br><br>RESULTS: Among 162 patients with acute cerebellar infarcts, 31% (n = 50) were BCI. Territorial infarcts were 74% (n = 120) and non-territorial infarcts 26% (n = 42) of the total. Posterior inferior cerebellar artery infarcts were the most common, and combined-territorial infarcts were the rarest. Baseline demographics were not significantly different between UCI and BCI, except for initial stroke severity (modified NIH Stroke Scale and infarct volume) and diabetes. Large-artery atherosclerosis was significantly higher in BCI, whereas undetermined causes were higher in UCI (p = 0.028). By multiple regression analysis, BCI was the only independent radiological factor for poor prognosis (odds ratio, 6.96; 95% CI, 1.80-26.92), and represented a significantly more unstable hospital course, longer hospital stay, worse mRS at discharge, and higher mortality.<br><br>CONCLUSIONS: In acute cerebellar infarcts, bilateral involvement is common and appears to be a superior determinant for early prognosis rather than territories involved or CLOC.},
}
@article {pmid18835541,
year = {2008},
author = {Daly, JJ and Wolpaw, JR},
title = {Brain-computer interfaces in neurological rehabilitation.},
journal = {The Lancet. Neurology},
volume = {7},
number = {11},
pages = {1032-1043},
doi = {10.1016/S1474-4422(08)70223-0},
pmid = {18835541},
issn = {1474-4422},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01NS063275/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Communication Devices for People with Disabilities/trends ; Computers/*trends ; Electroencephalography/instrumentation/methods/*trends ; Humans ; *Man-Machine Systems ; Movement Disorders/physiopathology/*rehabilitation ; Paralysis/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted/instrumentation ; Teaching/methods/trends ; *User-Computer Interface ; },
abstract = {Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient.},
}
@article {pmid18827310,
year = {2008},
author = {Retterer, ST and Smith, KL and Bjornsson, CS and Turner, JN and Isaacson, MS and Shain, W},
title = {Constant pressure fluid infusion into rat neocortex from implantable microfluidic devices.},
journal = {Journal of neural engineering},
volume = {5},
number = {4},
pages = {385-391},
doi = {10.1088/1741-2560/5/4/003},
pmid = {18827310},
issn = {1741-2560},
mesh = {Algorithms ; Animals ; Benzimidazoles ; Coloring Agents ; Equipment Design ; Evans Blue ; Fluorescent Dyes ; Image Processing, Computer-Assisted ; *Infusion Pumps, Implantable ; Male ; Microscopy, Confocal ; Microscopy, Fluorescence ; Nanotechnology ; Neocortex/*physiology ; Pressure ; Propidium ; Rats ; Rats, Sprague-Dawley ; },
abstract = {Implantable electrode arrays capable of recording and stimulating neural activity with high spatial and temporal resolution will provide a foundation for future brain computer interface technology. Currently, their clinical impact has been curtailed by a general lack of functional stability, which can be attributed to the acute and chronic reactive tissue responses to devices implanted in the brain. Control of the tissue environment surrounding implanted devices through local drug delivery could significantly alter both the acute and chronic reactive responses, and thus enhance device stability. Here, we characterize pressure-mediated release of test compounds into rat cortex using an implantable microfluidic platform. A fixed volume of fluorescent cell marker cocktail was delivered using constant pressure infusion at reservoir backpressures of 0, 5 and 10 psi. Affected tissue volumes were imaged and analyzed using epifluorescence and confocal microscropies and quantitative image analysis techniques. The addressable tissue volume for the 5 and 10 psi infusions, defined by fluorescent staining with Hoescht 33342 dye, was significantly larger than the tissue volume addressed by simple diffusion (0 psi) and the tissue volume exhibiting insertion-related cell damage (stained by propidium iodide). The results demonstrate the potential for using constant pressure infusion to address relevant tissue volumes with appropriate pharmacologies to alleviate reactive biological responses around inserted neuroprosthetic devices.},
}
@article {pmid18824406,
year = {2008},
author = {Kübler, A and Birbaumer, N},
title = {Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {11},
pages = {2658-2666},
pmid = {18824406},
issn = {1388-2457},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain Diseases/complications ; *Communication Devices for People with Disabilities ; *Computer Systems ; Electroencephalography/methods ; Event-Related Potentials, P300/physiology ; Female ; *Goals ; Humans ; Male ; Meta-Analysis as Topic ; Middle Aged ; Paralysis/etiology/*rehabilitation ; Periodicity ; Thinking/*physiology ; User-Computer Interface ; },
abstract = {OBJECTIVE: To investigate the relationship between physical impairment and brain-computer interface (BCI) performance.<br><br>METHOD: We present a meta-analysis of 29 patients with amyotrophic lateral sclerosis and six patients with other severe neurological diseases in different stages of physical impairment who were trained with a BCI. In most cases voluntary regulation of slow cortical potentials has been used as input signal for BCI-control. More recently sensorimotor rhythms and the P300 event-related brain potential were recorded.<br><br>RESULTS: A strong correlation has been found between physical impairment and BCI performance, indicating that performance worsens as impairment increases. Seven patients were in the complete locked-in state (CLIS) with no communication possible. After removal of these patients from the analysis, the relationship between physical impairment and BCI performance disappeared. The lack of a relation between physical impairment and BCI performance was confirmed when adding BCI data of patients from other BCI research groups.<br><br>CONCLUSIONS: Basic communication (yes/no) was not restored in any of the CLIS patients with a BCI. Whether locked-in patients can transfer learned brain control to the CLIS remains an open empirical question.<br><br>SIGNIFICANCE: Voluntary brain regulation for communication is possible in all stages of paralysis except the CLIS.},
}
@article {pmid18799392,
year = {2008},
author = {Wang, Y and Gao, X and Hong, B and Jia, C and Gao, S},
title = {Brain-computer interfaces based on visual evoked potentials.},
journal = {IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine &amp; Biology Society},
volume = {27},
number = {5},
pages = {64-71},
doi = {10.1109/MEMB.2008.923958},
pmid = {18799392},
issn = {1937-4186},
mesh = {Brain Mapping/*methods ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Imagination/*physiology ; *Man-Machine Systems ; *User-Computer Interface ; Visual Cortex/*physiology ; },
}
@article {pmid18786603,
year = {2009},
author = {Friedrich, EV and McFarland, DJ and Neuper, C and Vaughan, TM and Brunner, P and Wolpaw, JR},
title = {A scanning protocol for a sensorimotor rhythm-based brain-computer interface.},
journal = {Biological psychology},
volume = {80},
number = {2},
pages = {169-175},
pmid = {18786603},
issn = {1873-6246},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R01 HD030146-08/HD/NICHD NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 EB000856-08/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain/*physiology ; *Brain Mapping ; Choice Behavior/physiology ; Electroencephalography/methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Learning ; Male ; Middle Aged ; Photic Stimulation ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p<0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.},
}
@article {pmid18784852,
year = {2008},
author = {Wang, Z and Maier, A and Logothetis, NK and Liang, H},
title = {Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine.},
journal = {EURASIP journal on advances in signal processing},
volume = {2008},
number = {},
pages = {592742},
pmid = {18784852},
issn = {1687-6172},
support = {R01 MH072034/MH/NIMH NIH HHS/United States ; R01 MH072034-02/MH/NIMH NIH HHS/United States ; },
abstract = {We propose an empirical mode decomposition (EMD-) based method to extract features from the multichannel recordings of local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs) with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP) approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI).},
}
@article {pmid18773922,
year = {2008},
author = {Hollmann, M and Mönch, T and Mulla-Osman, S and Tempelmann, C and Stadler, J and Bernarding, J},
title = {A new concept of a unified parameter management, experiment control, and data analysis in fMRI: application to real-time fMRI at 3T and 7T.},
journal = {Journal of neuroscience methods},
volume = {175},
number = {1},
pages = {154-162},
doi = {10.1016/j.jneumeth.2008.08.013},
pmid = {18773922},
issn = {0165-0270},
mesh = {Adult ; Brain/*blood supply/physiology ; *Data Interpretation, Statistical ; Female ; Fingers/physiology ; Functional Laterality ; Humans ; Image Processing, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*instrumentation/*methods ; Male ; Mental Processes/physiology ; Oxygen/blood ; Psychomotor Performance/physiology ; Research Design ; Software ; Time Factors ; },
abstract = {In functional MRI (fMRI) complex experiments and applications require increasingly complex parameter handling as the experimental setup usually consists of separated soft- and hardware systems. Advanced real-time applications such as neurofeedback-based training or brain computer interfaces (BCIs) may even require adaptive changes of the paradigms and experimental setup during the measurement. This would be facilitated by an automated management of the overall workflow and a control of the communication between all experimental components. We realized a concept based on an XML software framework called Experiment Description Language (EDL). All parameters relevant for real-time data acquisition, real-time fMRI (rtfMRI) statistical data analysis, stimulus presentation, and activation processing are stored in one central EDL file, and processed during the experiment. A usability study comparing the central EDL parameter management with traditional approaches showed an improvement of the complete experimental handling. Based on this concept, a feasibility study realizing a dynamic rtfMRI-based brain computer interface showed that the developed system in combination with EDL was able to reliably detect and evaluate activation patterns in real-time. The implementation of a centrally controlled communication between the subsystems involved in the rtfMRI experiments reduced potential inconsistencies, and will open new applications for adaptive BCIs.},
}
@article {pmid18769364,
year = {2008},
author = {Håkansson, B and Eeg-Olofsson, M and Reinfeldt, S and Stenfelt, S and Granström, G},
title = {Percutaneous versus transcutaneous bone conduction implant system: a feasibility study on a cadaver head.},
journal = {Otology &amp; neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology},
volume = {29},
number = {8},
pages = {1132-1139},
doi = {10.1097/MAO.0b013e31816fdc90},
pmid = {18769364},
issn = {1537-4505},
mesh = {Bone Conduction/*physiology ; *Cadaver ; Feasibility Studies ; Head ; *Hearing Aids ; Humans ; *Parietal Bone ; Prostheses and Implants ; Prosthesis Implantation/*methods ; Skin Physiological Phenomena ; *Temporal Bone ; Vibration ; },
abstract = {OBJECTIVE: Percutaneous bone-anchored hearing aid (BAHA) is an important rehabilitation alternative for patients who have conductive or mixed hearing loss. However, these devices use a percutaneous and bone-anchored implant that has some drawbacks reported. A transcutaneous bone conduction implant system (BCI) is proposed as an alternative to the percutaneous system because it leaves the skin intact. The BCI transmits the signal to a permanently implanted transducer with an induction loop system through the intact skin. The aim of this study was to compare the electroacoustic performance of the BAHA Classic-300 with a full-scale BCI on a cadaver head in a sound field. The BCI comprised the audio processor of the vibrant sound bridge connected to a balanced vibration transducer (balanced electromagnetic separation transducer).<br><br>METHODS: Implants with snap abutments were placed in the parietal bone (Classic-300) and 15-mm deep in the temporal bone (BCI). The vibration responses at the ipsilateral and contralateral cochlear promontories were measured with a laser Doppler vibrometer, with the beam aimed through the ear canal.<br><br>RESULTS: Results show that the BCI produces approximately 5 dB higher maximum output level and has a slightly lower distortion than the Classic-300 at the ipsilateral promontorium at speech frequencies. At the contralateral promontorium, the maximum output level was considerably lower for the BCI than for the Classic-300 except in the 1-2 kHz range, where it was similar.<br><br>CONCLUSION: Present results support the proposal that a BCI system can be a realistic alternative to a BAHA.},
}
@article {pmid18766491,
year = {2008},
author = {Hautamäki, MP and Aho, AJ and Alander, P and Rekola, J and Gunn, J and Strandberg, N and Vallittu, PK},
title = {Repair of bone segment defects with surface porous fiber-reinforced polymethyl methacrylate (PMMA) composite prosthesis: histomorphometric incorporation model and characterization by SEM.},
journal = {Acta orthopaedica},
volume = {79},
number = {4},
pages = {555-564},
doi = {10.1080/17453670710015571},
pmid = {18766491},
issn = {1745-3682},
mesh = {Animals ; Biocompatible Materials ; Biomechanical Phenomena ; *Bone Substitutes ; Female ; Glass ; Materials Testing ; Microscopy, Electron, Scanning ; Models, Biological ; Osteogenesis/physiology ; *Polymethyl Methacrylate ; *Prostheses and Implants ; Rabbits ; Surface Properties ; Tibia/surgery ; Wound Healing ; },
abstract = {BACKGROUND AND PURPOSE: Polymer technology has provided solutions for filling of bone defects in situations where there may be technical or biological complications with autografts, allografts, and metal prostheses. We present an experimental study on segmental bone defect reconstruction using a polymethylmethacrylate-(PMMA-) based bulk polymer implant prosthesis. We concentrated on osteoconductivity and surface characteristics.<br><br>MATERIAL AND METHODS: A critical size segment defect of the rabbit tibia in 19 animals aged 18-24 weeks was reconstructed with a surface porous glass fiber-reinforced (SPF) prosthesis made of polymethylmethacrylate (PMMA). The biomechanical properties of SPF implant material were previously adjusted technically to mimic the properties of normal cortical bone. A plain PMMA implant with no porosity or fiber reinforcement was used as a control. Radiology, histomorphometry, and scanning electron microscopy (SEM) were used for analysis of bone growth into the prosthesis during incorporation.<br><br>RESULTS: The radiographic and histological incorporation model showed good host bone contact, and strong formation of new bone as double cortex. Histomorphometric evaluation showed that the bone contact index (BCI) at the posterior surface interface was higher with the SPF implant than for the control. The total appositional bone growth over the posterior surface (area %) was also stronger for the SPF implant than for controls. Both bone growth into the porous surface and the BCI results were related to the quality, coverage, and regularity of the microstructure of the porous surface.<br><br>INTERPRETATION: Porous surface structure enhanced appositional bone growth onto the SPF implant. Under load-bearing conditions the implant appears to function like an osteoconductive prosthesis, which enables direct mobilization and rapid return to full weight bearing.},
}
@article {pmid18762448,
year = {2008},
author = {Iversen, IH and Ghanayim, N and Kübler, A and Neumann, N and Birbaumer, N and Kaiser, J},
title = {A brain-computer interface tool to assess cognitive functions in completely paralyzed patients with amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {10},
pages = {2214-2223},
doi = {10.1016/j.clinph.2008.07.001},
pmid = {18762448},
issn = {1388-2457},
mesh = {Amyotrophic Lateral Sclerosis/*complications ; Biofeedback, Psychology/methods ; Brain/*physiopathology ; Cognition/*physiology ; Electroencephalography ; Humans ; Male ; Middle Aged ; Paralysis/*etiology ; Reaction Time/physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface methodology based on self-regulation of slow-cortical potentials (SCPs) of the EEG was used to assess cognitive abilities of two late-stage ALS patients.<br><br>METHODS: A monitor presented visual information in two targets. Patients used their SCPs to steer a cursor to one of the targets. Within-subject methodology tested the ability to differentiate odd/even numbers, consonants/vowels, nouns/verbs, large/small numbers, and the ability to perform simple computations. One patient had a short-term memory task with delays up to 15s.<br><br>RESULTS: Both patients reached accuracy near 90% correct on simple tasks showing that they understood the instructions, discriminated the visual stimuli, and could use the SCP to control the cursor. Both patients showed some deficit on the task that involved computations. The patient with the short-term memory task showed a large reduction in accuracy on delay trials but retained high accuracy on non-delay trials.<br><br>CONCLUSION: The fully computerized method is a useful tool for presenting a variety of two-choice tasks to assess certain cognitive functions in the severely paralyzed patient.<br><br>SIGNIFICANCE: The task can potentially be used to examine maintenance or decline of cognitive abilities in individual ALS patients.},
}
@article {pmid18761037,
year = {2008},
author = {Solis-Escalante, T and Müller-Putz, G and Pfurtscheller, G},
title = {Overt foot movement detection in one single Laplacian EEG derivation.},
journal = {Journal of neuroscience methods},
volume = {175},
number = {1},
pages = {148-153},
doi = {10.1016/j.jneumeth.2008.07.019},
pmid = {18761037},
issn = {0165-0270},
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; *Cortical Synchronization ; Cues ; Evoked Potentials/*physiology ; Female ; *Foot ; Humans ; Male ; Models, Neurological ; Movement/*physiology ; Probability ; ROC Curve ; Time Factors ; User-Computer Interface ; Young Adult ; },
abstract = {In this work one single Laplacian derivation and a full description of band power values in a broad frequency band are used to detect brisk foot movement execution in the ongoing EEG. Two support vector machines (SVM) are trained to detect the event-related desynchronization (ERD) during motor execution and the following beta rebound (event-related synchronization, ERS) independently. Their performance is measured through the simulation of an asynchronous brain switch. ERS (true positive rate=0.74+/-0.21) after motor execution is shown to be more stable than ERD (true positive rate=0.21+/-0.12). A novel combination of ERD and post-movement ERS is introduced. The SVM outputs are combined with a product rule to merge ERD and ERS detection. For this novel approach the average information transfer rate obtained was 11.19+/-3.61bits/min.},
}
@article {pmid18756030,
year = {2008},
author = {Lou, B and Hong, B and Gao, X and Gao, S},
title = {Bipolar electrode selection for a motor imagery based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {5},
number = {3},
pages = {342-349},
doi = {10.1088/1741-2560/5/3/007},
pmid = {18756030},
issn = {1741-2560},
mesh = {*Algorithms ; Brain Mapping/*instrumentation/methods ; *Electrodes ; Electroencephalography/*instrumentation/methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/physiology ; Principal Component Analysis ; *User-Computer Interface ; },
abstract = {A motor imagery based brain-computer interface (BCI) provides a non-muscular communication channel that enables people with paralysis to control external devices using their motor imagination. Reducing the number of electrodes is critical to improving the portability and practicability of the BCI system. A novel method is proposed to reduce the number of electrodes to a total of four by finding the optimal positions of two bipolar electrodes. Independent component analysis (ICA) is applied to find the source components of mu and alpha rhythms, and optimal electrodes are chosen by comparing the projection weights of sources on each channel. The results of eight subjects demonstrate the better classification performance of the optimal layout compared with traditional layouts, and the stability of this optimal layout over a one week interval was further verified.},
}
@article {pmid18718544,
year = {2008},
author = {Schalk, G and Leuthardt, EC and Brunner, P and Ojemann, JG and Gerhardt, LA and Wolpaw, JR},
title = {Real-time detection of event-related brain activity.},
journal = {NeuroImage},
volume = {43},
number = {2},
pages = {245-249},
pmid = {18718544},
issn = {1095-9572},
support = {EB006356/EB/NIBIB NIH HHS/United States ; R01 EB006356-01/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R01 EB006356-02/EB/NIBIB NIH HHS/United States ; R01 EB006356-03/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Computer Systems ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Epilepsy/*diagnosis/*physiopathology ; *Evoked Potentials ; Female ; Humans ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.},
}
@article {pmid18714840,
year = {2008},
author = {Zhang, H and Guan, C and Wang, C},
title = {Asynchronous P300-based brain-computer interfaces: a computational approach with statistical models.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {6},
pages = {1754-1763},
doi = {10.1109/tbme.2008.919128},
pmid = {18714840},
issn = {0018-9294},
mesh = {Artificial Intelligence ; Brain/*physiology ; Brain Mapping/*methods ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {Asynchronous control is an important issue for brain-computer interfaces (BCIs) working in real-life settings, where the machine should determine from brain signals not only the desired command but also when the user wants to input it. In this paper, we propose a novel computational approach for robust asynchronous control using electroencephalogram (EEG) and a P300-based oddball paradigm. In this approach, we first address the mathematical modeling of target P300, nontarget P300, and noncontrol signals, by using Gaussian distribution models in a support vector margin space. Furthermore, we derive a method to compute the likelihood of control state in a time window of EEG. Finally, we devise a recursive algorithm to detect control states in ongoing EEG for online application. We conducted experiments with four subjects to study both the asynchronous BCI's receiver operating characteristics and its performance in actual online tests. The results show that the BCI is able to achieve an averaged information transfer rate of approximately 20 b/min at a low false positive rate (one event per minute).},
}
@article {pmid18714838,
year = {2008},
author = {Wu, W and Gao, X and Hong, B and Gao, S},
title = {Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL).},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {6},
pages = {1733-1743},
doi = {10.1109/tbme.2008.919125},
pmid = {18714838},
issn = {0018-9294},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {In most current motor-imagery-based brain-computer interfaces (BCIs), machine learning is carried out in two consecutive stages: feature extraction and feature classification. Feature extraction has focused on automatic learning of spatial filters, with little or no attention being paid to optimization of parameters for temporal filters that still require time-consuming, ad hoc manual tuning. In this paper, we present a new algorithm termed iterative spatio-spectral patterns learning (ISSPL) that employs statistical learning theory to perform automatic learning of spatio-spectral filters. In ISSPL, spectral filters and the classifier are simultaneously parameterized for optimization to achieve good generalization performance. A detailed derivation and theoretical analysis of ISSPL are given. Experimental results on two datasets show that the proposed algorithm can correctly identify the discriminative frequency bands, demonstrating the algorithm's superiority over contemporary approaches in classification performance.},
}
@article {pmid18695575,
year = {2008},
author = {Elder, JB and Hoh, DJ and Oh, BC and Heller, AC and Liu, CY and Apuzzo, ML},
title = {The future of cerebral surgery: a kaleidoscope of opportunities.},
journal = {Neurosurgery},
volume = {62},
number = {6 Suppl 3},
pages = {1555-79; discussion 1579-82},
doi = {10.1227/01.neu.0000333820.33143.0d},
pmid = {18695575},
issn = {1524-4040},
mesh = {Biocompatible Materials ; Brain Diseases/surgery ; Cerebrum/*surgery ; Computers ; Humans ; Miniaturization ; Nanotechnology ; Neurosurgical Procedures/*trends ; Radiosurgery ; },
abstract = {The emerging future of cerebral surgery will witness the refined evolution of current techniques, as well as the introduction of numerous novel concepts. Clinical practice and basic science research will benefit greatly from their application. The sum of these efforts will result in continued minimalism and improved accuracy and efficiency of neurosurgical diagnostic and therapeutic methodologies.Initially, the refinement of current technologies will further enhance various aspects of cerebral surgery. Advances in computing power and information technology will speed data acquisition, storage, and transfer. Miniaturization of current devices will impact diverse areas, such as modulation of endoscopy and endovascular techniques. The increased penetrance of surgical technologies such as stereotactic radiosurgery, neuronavigation, intraoperative imaging, and implantable electrodes for neurodegenerative disorders and epilepsy will enhance the knowledge and experience in these areas and facilitate refinements and advances in these technologies. Further into the future, technologies that are currently relatively remote to surgical events will fundamentally alter the complexity and scale at which a neurological disease may be treated or investigated. Seemingly futuristic concepts will become ubiquitous in the daily experience of the neurosurgeon. These include diverse fields such as nanotechnology, virtual reality, and robotics. Ultimately, combining advances in multiple fields will yield progress in diverse realms such as brain tumor therapy, neuromodulation for psychiatric diseases, and neuroprosthetics. Operating room equipment and design will benefit from each of the aforementioned advances. In this work, we discuss new developments in three parts. In Part I, concepts in minimalism important for future cerebral surgery are discussed. These include concrete and abstract ideas in miniaturization, as well as recent and future work in microelectromechanical systems and nanotechnology. Part II presents advances in computational sciences and technological fields dependent on these developments. Future breakthroughs in the components of the "computer," including data storage, electrical circuitry, and computing hardware and techniques, are discussed. Additionally, important concepts in the refinement of virtual environments and the brain-machine interface are presented, as their incorporation into cerebral surgery is closely linked to advances in computing and electronics. Finally, Part III offers insights into the future evolution of surgical and nonsurgical diagnostic and therapeutic modalities that are important for the future cerebral surgeon. A number of topics relevant to cerebral surgery are discussed, including the operative environment, imaging technologies, endoscopy, robotics, neuromodulation, stem cell therapy, radiosurgery, and technical methods of restoration of neural function. Cerebral surgery in the near and distant future will reflect the application of these emerging technologies. As this article indicates, the key to maximizing the impact of these advancements in the clinical arena is continued collaboration between scientists and neurosurgeons, as well as the emergence of a neurosurgeon whose scientific grounding and technical focus are far removed from those of his predecessors.},
}
@article {pmid18714327,
year = {2008},
author = {deCharms, RC},
title = {Applications of real-time fMRI.},
journal = {Nature reviews. Neuroscience},
volume = {9},
number = {9},
pages = {720-729},
doi = {10.1038/nrn2414},
pmid = {18714327},
issn = {1471-003X},
support = {DA021877/DA/NIDA NIH HHS/United States ; MH067290/MH/NIMH NIH HHS/United States ; N43DA-4-7748/DA/NIDA NIH HHS/United States ; N43DA-7-4408/DA/NIDA NIH HHS/United States ; NS049673/NS/NINDS NIH HHS/United States ; NS050642/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*blood supply/*physiology ; Brain Mapping ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/*methods ; Oxygen/blood ; *Psychophysiology ; Time Factors ; },
abstract = {For centuries people have aspired to understand and control the functions of the mind and brain. It has now become possible to image the functioning of the human brain in real time using functional MRI (fMRI), and thereby to access both sides of the mind-brain interface--subjective experience (that is, one's mind) and objective observations (that is, external, quantitative measurements of one's brain activity)--simultaneously. Developments in neuroimaging are now being translated into many new potential practical applications, including the reading of brain states, brain-computer interfaces, communicating with locked-in patients, lie detection, and learning control over brain activation to modulate cognition or even treat disease.},
}
@article {pmid18703323,
year = {2008},
author = {Salimi-Khorshidi, G and Nasrabadi, AM and Golpayegani, MH},
title = {Fusion of classic P300 detection methods' inferences in a framework of fuzzy labels.},
journal = {Artificial intelligence in medicine},
volume = {44},
number = {3},
pages = {247-259},
doi = {10.1016/j.artmed.2008.06.002},
pmid = {18703323},
issn = {1873-2860},
mesh = {Brain/*physiology ; Electrodes ; Electroencephalography ; *Fuzzy Logic ; Humans ; Magnetic Resonance Imaging ; },
abstract = {OBJECTIVE: Designing a reliable and accurate brain-computer interface (BCI) is one of the most challenging fields in biomedical signal processing. To achieve this goal, different methods have been adopted in different blocks of a typical BCI system (i.e., in preprocessing, feature extraction, feature classification and feature selection blocks). Since BCI's speed plays a crucial role in its success in real-life applications, using mathematically simple techniques with accurate and reliable performance can improve this aspect of BCI systems' design.<br><br>METHODS AND MATERIALS: In this paper, a new method is introduced, which combines information from different classic time series similarity measures, using a simple fuzzy fusion framework. This method is accurate and reliable in P300 (a positive event-related component occurring 300 ms after stimulus onset) detection. This framework is used to combine two computationally simple signal detection methods: "peak picking" and "template matching". Fusion takes place in the last step (decision-making step) by means of a fuzzy rule-base.<br><br>RESULTS AND CONCLUSIONS: Compared to similar works on electroencephalogram-based (EEG-based) BCI datasets, in spite of being computationally simple, this new technique's performance is comparable to very complicated methods, like support vector machines. This research indicates that, using both spatial and temporal information content of EEG trials (from all electrodes or a subset of them), even under a non-complicated mathematical framework can yield an accurate and powerful classification.},
}
@article {pmid18701380,
year = {2008},
author = {Herman, P and Prasad, G and McGinnity, TM and Coyle, D},
title = {Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {4},
pages = {317-326},
doi = {10.1109/TNSRE.2008.926694},
pmid = {18701380},
issn = {1558-0210},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain-computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper.},
}
@article {pmid18698427,
year = {2008},
author = {Krauledat, M and Tangermann, M and Blankertz, B and Müller, KR},
title = {Towards zero training for brain-computer interfacing.},
journal = {PloS one},
volume = {3},
number = {8},
pages = {e2967},
pmid = {18698427},
issn = {1932-6203},
mesh = {Artificial Intelligence ; Brain/*physiology ; Brain Mapping ; Cortical Synchronization/methods ; Electroencephalography/methods ; Evoked Potentials/physiology ; Humans ; Learning ; Neurophysiology/methods ; Pattern Recognition, Automated/methods ; *User-Computer Interface ; Wakefulness ; },
abstract = {Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these 'experienced' BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed.},
}
@article {pmid18693884,
year = {2007},
author = {Luo, G and Min, W},
title = {Subject-adaptive real-time sleep stage classification based on conditional random field.},
journal = {AMIA ... Annual Symposium proceedings. AMIA Symposium},
volume = {2007},
number = {},
pages = {488-492},
pmid = {18693884},
issn = {1942-597X},
mesh = {Classification/methods ; Electroencephalography/methods ; Humans ; Natural Language Processing ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; *Sleep Stages ; },
abstract = {Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject S(new) with limited training data D(new), we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from D(new) a regulated estimate of CRF's parameters. Using sleep recordings from human subjects, we show that even without any D(new), our sleep stager can achieve an average classification accuracy of 70% on S(new). This accuracy increases with the size of D(new) and eventually becomes close to the theoretical limit.},
}
@article {pmid18689241,
year = {2008},
author = {Mikhaĭlova, ES and Chicherov, VA and Ptushenko, EA and Shevelev, IA},
title = {[Spatial gradient of P300 area of the brain visual evoked potential in the brain-computer interface paradigm].},
journal = {Zhurnal vysshei nervnoi deiatelnosti imeni I P Pavlova},
volume = {58},
number = {3},
pages = {302-308},
pmid = {18689241},
issn = {0044-4677},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/*physiology ; *User-Computer Interface ; },
abstract = {In 12 adult healthy subjects we studied Brain-Computer-Interface recognition of different intended letters by the square of P300 wave in the averaged VEP. Horizontal and vertical spatial gradient of this square was studied as well as tuning acuteness of visual attention to a significant letter. High acuteness of this tuning was found (width of the tuning curve at its half height was equal to 1.6 grad) independent of the letter position on the letter matrix. Horizontal and vertical gradient of P300 were found to be very similar, but in the half of cases the first one revealed some kind of the "lateral inhibition": decrease of P300 square for the columns neighboring to the meaningful one. Tuning acuteness was found to be reliable and directly interrelated with P300 square. The data are discussed in relation to selectivity of the local visual attention.},
}
@article {pmid18689055,
year = {2008},
author = {Parasuraman, R and Wilson, GF},
title = {Putting the brain to work: neuroergonomics past, present, and future.},
journal = {Human factors},
volume = {50},
number = {3},
pages = {468-474},
doi = {10.1518/001872008X288349},
pmid = {18689055},
issn = {0018-7208},
mesh = {*Cognition ; *Ergonomics ; Humans ; Molecular Biology ; Research ; Workload/*psychology ; },
abstract = {OBJECTIVE: The authors describe research and applications in prominent areas of neuroergonomics.<br><br>BACKGROUND: Because human factors/ergonomics examines behavior and mind at work, it should include the study of brain mechanisms underlying human performance.<br><br>METHODS: Neuroergonomic studies are reviewed in four areas: workload and vigilance, adaptive automation, neuroengineering, and molecular genetics and individual differences.<br><br>RESULTS: Neuroimaging studies have helped identify the components of mental workload, workload assessment in complex tasks, and resource depletion in vigilance. Furthermore, real-time neurocognitive assessment of workload can trigger adaptive automation. Neural measures can also drive brain-computer interfaces to provide disabled users new communication channels. Finally, variants of particular genes can be associated with individual differences in specific cognitive functions.<br><br>CONCLUSIONS: Neuroergonomics shows that considering what makes work possible - the human brain - can enrich understanding of the use of technology by humans and can inform technological design.<br><br>APPLICATION: Applications of neuroergonomics include the assessment of operator workload and vigilance, implementation of real-time adaptive automation, neuroengineering for people with disabilities, and design of selection and training methods.},
}
@article {pmid18672345,
year = {2008},
author = {Ting, JA and D'Souza, A and Yamamoto, K and Yoshioka, T and Hoffman, D and Kakei, S and Sergio, L and Kalaska, J and Kawato, M and Strick, P and Schaal, S},
title = {Variational Bayesian least squares: an application to brain-machine interface data.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {21},
number = {8},
pages = {1112-1131},
pmid = {18672345},
issn = {0893-6080},
support = {P01 NS044393/NS/NINDS NIH HHS/United States ; P01 NS044393-01A10003/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Bayes Theorem ; Brain/*cytology/physiology ; Electromyography ; Haplorhini ; *Least-Squares Analysis ; Linear Models ; Models, Biological ; *Neural Networks, Computer ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {An increasing number of projects in neuroscience require statistical analysis of high-dimensional data, as, for instance, in the prediction of behavior from neural firing or in the operation of artificial devices from brain recordings in brain-machine interfaces. Although prevalent, classical linear analysis techniques are often numerically fragile in high dimensions due to irrelevant, redundant, and noisy information. We developed a robust Bayesian linear regression algorithm that automatically detects relevant features and excludes irrelevant ones, all in a computationally efficient manner. In comparison with standard linear methods, the new Bayesian method regularizes against overfitting, is computationally efficient (unlike previously proposed variational linear regression methods, is suitable for data sets with large numbers of samples and a very high number of input dimensions) and is easy to use, thus demonstrating its potential as a drop-in replacement for other linear regression techniques. We evaluate our technique on synthetic data sets and on several neurophysiological data sets. For these neurophysiological data sets we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed from neural activity in motor cortices. Results demonstrate the success of our newly developed method, in comparison with other approaches in the literature, and, from the neurophysiological point of view, confirms recent findings on the organization of the motor cortex. Finally, an incremental, real-time version of our algorithm demonstrates the suitability of our approach for real-time interfaces between brains and machines.},
}
@article {pmid18671288,
year = {2009},
author = {Ronchi, P and Gravina, GL and Galatioto, GP and Costa, AM and Martella, O and Vicentini, C},
title = {Urodynamic parameters after solifenacin treatment in men with overactive bladder symptoms and detrusor underactivity.},
journal = {Neurourology and urodynamics},
volume = {28},
number = {1},
pages = {52-57},
doi = {10.1002/nau.20586},
pmid = {18671288},
issn = {1520-6777},
mesh = {Aged ; Humans ; Male ; Middle Aged ; Muscarinic Antagonists/*therapeutic use ; Muscle Contraction/*drug effects ; Perception ; Prospective Studies ; Quality of Life ; Quinuclidines/*therapeutic use ; Solifenacin Succinate ; Tetrahydroisoquinolines/*therapeutic use ; Time Factors ; Treatment Outcome ; Urinary Bladder/*drug effects/physiopathology ; Urinary Bladder, Overactive/*drug therapy/physiopathology ; Urodynamics/*drug effects ; },
abstract = {AIMS: To describe the changes in urodynamic parameters and to assess patients' perceptions of voiding difficulties and improvements in symptom bother after solifenacin treatment in men with overactive bladder (OAB) and detrusor underactivity (DUA).<br><br>METHODS: In this prospective study, 49 neurologically intact men were enrolled. DUA was defined as a bladder contractility index (BCI) <100. All subjects received 5 mg of solifenacin once a day for 120 days. A complete urodynamic study was carried out on the day before to the first dose of solifenacin and at day 120.<br><br>RESULTS: Solifenacin treatment resulted in a decrease in Q(max) during UDS (-0.6 ml/sec; P = 0.007), P(det)Q(max) (-6.4 cmH(2)O; P < 0.001), BOOI (-7.5; P < 0.001), BCI (-3.8; P = 0.001), BVE (-4.4%; P = 0.006), and voided volume (-7.5 ml; P = 0.09). On the contrary, PVR (+6 ml; P = 0.152), and maximum cystometric capacity (+22.9 ml; P = 0.001) increased. The regression analysis suggested that changes in urodynamic parameters after solifenacin treatment were limited for BOOI (9.4%), P(det)Q(max) (8.4%), and BCI (6.5%), with no significant impact on Q(max) during UDS, BVE, volume voided and PVR. No significant change in subjective perception of voiding difficulties was found. The incidence of AUR was 2.2% and improvement in patient's experience of OAB symptoms bother after solifenacin treatment was observed.<br><br>CONCLUSIONS: Solifenacin treatment results in changes of urodynamic parameters. These changes, however, seem not to be of clinical significance as suggested by the lack of subjective deterioration in voiding difficulties and by the low incidence of AUR.},
}
@article {pmid18671209,
year = {2008},
author = {Ron-Angevin, R and Díaz-Estrella, A},
title = {[Training protocol evaluation of a brain-computer interface: mental tasks proposal].},
journal = {Revista de neurologia},
volume = {47},
number = {4},
pages = {197-203},
pmid = {18671209},
issn = {1576-6578},
mesh = {Adult ; Electroencephalography ; Female ; Humans ; *Learning ; Male ; *Mental Processes ; Motor Skills Disorders/*rehabilitation ; Surveys and Questionnaires ; *Task Performance and Analysis ; *User-Computer Interface ; Young Adult ; },
abstract = {INTRODUCTION: A brain-computer interface (BCI) is based on the analysis of the electroencephalographic (EEG) signals recorded during certain mental activities, to control an external device. Main users are people with severe neuromuscular disorders, like amyotrophic lateral sclerosis. One of the most important problems to control a BCI is the need of providing suitable training, helping subjects to get some control of the EEG signals.<br><br>AIM: To carry out a study of possible effects of the use of specific mental tasks during the first phase of the training period.<br><br>SUBJECTS AND METHODS: Eighteen healthy untrained subjects took part in the experiment. A group of subjects were trained to discriminate between two motor imagery tasks (imagination of right and left hand movements). Another group were trained to discriminate between a motor imagery task (imagination of right hand movements) and mental relaxation. Objective and subjective measures based on questionnaires were taken.<br><br>RESULTS: Some subjects do not achieved EEG control, but subjects at the second group showed a greater facility to control a BCI.<br><br>CONCLUSION: Training protocols should not be randomly chosen; they must be adapted to the subject to be effective. Sometimes it is necessary to increase the number of sessions without feedback before submitting a subject to a session with feedback, and a correct choice of the mental tasks is very important. Mental tasks which are easy to discriminate improve classification result and produce better satisfaction to the subject.},
}
@article {pmid18667540,
year = {2008},
author = {Radhakrishnan, SM and Baker, SN and Jackson, A},
title = {Learning a novel myoelectric-controlled interface task.},
journal = {Journal of neurophysiology},
volume = {100},
number = {4},
pages = {2397-2408},
pmid = {18667540},
issn = {0022-3077},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {Adult ; Algorithms ; Arm/innervation/physiology ; Biofeedback, Psychology/physiology ; Brain/*physiology ; Brain Mapping ; Data Interpretation, Statistical ; Electromyography ; Hand/innervation/physiology ; Humans ; Learning ; Muscle, Skeletal/innervation/metabolism/*physiology ; *Prostheses and Implants ; *User-Computer Interface ; Vibration ; },
abstract = {Control of myoelectric prostheses and brain-machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.},
}
@article {pmid18660878,
year = {2008},
author = {Müller, H},
title = {Sensors, signals, and images in medical informatics: progress and evaluation. Findings from the Yearbook 2008 Section on Sensors, Signals, and Imaging Informatics.},
journal = {Yearbook of medical informatics},
volume = {},
number = {},
pages = {64-66},
pmid = {18660878},
issn = {0943-4747},
mesh = {*Diagnostic Imaging ; Humans ; *Medical Informatics ; },
abstract = {OBJECTIVES: To summarize current research in the field of sensors, signals, and imaging in medicine and the impact of it in the medical informatics field through the selection of important and representative papers.<br><br>METHODS: Survey of the 2007 biomedical literature in the area of sensors, signals, and imaging informatics.<br><br>RESULTS: The review process of many candidate papers reflects the large variety of this research field. Four articles were finally selected with the help of the reviewers representing the important domains of brain-computer interfaces, brain shift correction, computer-aided interventions, and wearable sensors.<br><br>CONCLUSIONS: The four selected papers show the wide variety in medical informatics research concerning sensors, signals, and images. Imaging and signal research becomes increasingly broad and the number of techniques available and used in clinical practice is enormous and constantly increasing. The selected articles can only present a few highlights and many important topics had to be left out of this overview.},
}
@article {pmid18657956,
year = {2008},
author = {Logar, V and Skrjanc, I and Belic, A and Brezan, S and Koritnik, B and Zidar, J},
title = {Identification of the phase code in an EEG during gripping-force tasks: a possible alternative approach to the development of the brain-computer interfaces.},
journal = {Artificial intelligence in medicine},
volume = {44},
number = {1},
pages = {41-49},
doi = {10.1016/j.artmed.2008.06.003},
pmid = {18657956},
issn = {0933-3657},
mesh = {Adult ; Brain/*physiology ; *Electroencephalography ; Female ; Fuzzy Logic ; Hand Strength/*physiology ; Humans ; Male ; Mental Processes/*physiology ; Models, Neurological ; Predictive Value of Tests ; Principal Component Analysis ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {BACKGROUND: The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information.<br><br>OBJECTIVE: The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs.<br><br>MATERIALS AND METHODS: In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data.<br><br>RESULTS: The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subject's performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome.<br><br>CONCLUSION: The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.},
}
@article {pmid18656500,
year = {2008},
author = {Cabrera, AF and Dremstrup, K},
title = {Auditory and spatial navigation imagery in Brain-Computer Interface using optimized wavelets.},
journal = {Journal of neuroscience methods},
volume = {174},
number = {1},
pages = {135-146},
doi = {10.1016/j.jneumeth.2008.06.026},
pmid = {18656500},
issn = {0165-0270},
mesh = {Acoustic Stimulation ; Action Potentials/physiology ; Adult ; Algorithms ; Artifacts ; Auditory Perception/physiology ; Brain/*physiology ; Brain Mapping/methods ; Computer Simulation ; Electrodes/standards ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Mental Processes/physiology ; Neuropsychological Tests ; Reaction Time/physiology ; *Signal Processing, Computer-Assisted ; Space Perception/physiology ; *User-Computer Interface ; },
abstract = {Features extracted with optimized wavelets were compared with standard methods for a Brain-Computer Interface driven by non-motor imagery tasks. Two non-motor imagery tasks were used, Auditory Imagery of a familiar tune and Spatial Navigation Imagery through a familiar environment. The aims of this study were to evaluate which method extracts features that could be best differentiated and determine which channels are best suited for classification. EEG activity from 18 electrodes over the temporal and parietal lobes of nineteen healthy subjects was recorded. The features used were autoregressive and reflection coefficients extracted using autoregressive modeling with several model orders and marginals of the wavelet spaces generated by the Discrete Wavelet Transform (DWT). An optimization algorithm with 4 and 6 taps filters and mother wavelets from the Daubechies family were used. The classification was performed for each single channel and for all possible combination of two channels using a Bayesian Classifier. The best classification results were found using the marginals of the Optimized DWT spaces for filters with 6 taps in a 2 channels classification basis. Classification using 2 channels was found to be significantly better than using 1 channel (p<<0.01). The marginals of the optimized DWT using 6 taps filters showed to be significantly better than the marginals of the Daubechies family and autoregressive coefficients. The influence of the combination of number of channels and feature extraction method over the classification results was not significant (p=0.97).},
}
@article {pmid18654569,
year = {2008},
author = {Keefer, EW and Botterman, BR and Romero, MI and Rossi, AF and Gross, GW},
title = {Carbon nanotube coating improves neuronal recordings.},
journal = {Nature nanotechnology},
volume = {3},
number = {7},
pages = {434-439},
doi = {10.1038/nnano.2008.174},
pmid = {18654569},
issn = {1748-3395},
mesh = {Brain/*physiology ; Cells, Cultured ; Coated Materials, Biocompatible/*chemistry ; Electric Stimulation/*instrumentation/methods ; Electrocardiography/*instrumentation ; *Electrodes, Implanted ; Equipment Design ; Equipment Failure Analysis ; Humans ; *Microelectrodes ; Nanotechnology/instrumentation/methods ; Nanotubes, Carbon/*chemistry/ultrastructure ; },
abstract = {Implanting electrical devices in the nervous system to treat neural diseases is becoming very common. The success of these brain-machine interfaces depends on the electrodes that come into contact with the neural tissue. Here we show that conventional tungsten and stainless steel wire electrodes can be coated with carbon nanotubes using electrochemical techniques under ambient conditions. The carbon nanotube coating enhanced both recording and electrical stimulation of neurons in culture, rats and monkeys by decreasing the electrode impedance and increasing charge transfer. Carbon nanotube-coated electrodes are expected to improve current electrophysiological techniques and to facilitate the development of long-lasting brain-machine interface devices.},
}
@article {pmid18632362,
year = {2008},
author = {Grosse-Wentrup, M and Buss, M},
title = {Multiclass common spatial patterns and information theoretic feature extraction.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {8},
pages = {1991-2000},
doi = {10.1109/TBME.2008.921154},
pmid = {18632362},
issn = {1558-2531},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Humans ; Magnetoencephalography/*methods ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain--computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.},
}
@article {pmid18621580,
year = {2008},
author = {Galán, F and Nuttin, M and Lew, E and Ferrez, PW and Vanacker, G and Philips, J and Millán, Jdel R},
title = {A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {9},
pages = {2159-2169},
doi = {10.1016/j.clinph.2008.06.001},
pmid = {18621580},
issn = {1388-2457},
mesh = {Brain/*physiology ; Brain Mapping ; Electroencephalography/methods ; Humans ; *Robotics ; *User-Computer Interface ; *Wheelchairs ; },
abstract = {OBJECTIVE: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair.<br><br>METHODS: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1h to 2months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before.<br><br>RESULTS: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials.<br><br>CONCLUSIONS: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks.<br><br>SIGNIFICANCE: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and non-invasive BCI.},
}
@article {pmid18607780,
year = {2008},
author = {Quitadamo, LR and Marciani, MG and Cardarilli, GC and Bianchi, L},
title = {Describing different brain computer interface systems through a unique model: a UML implementation.},
journal = {Neuroinformatics},
volume = {6},
number = {2},
pages = {81-96},
pmid = {18607780},
issn = {1559-0089},
mesh = {Animals ; Brain/*physiology ; Cerebral Cortex/physiology ; Communication Devices for People with Disabilities ; Computer Peripherals ; *Computer Simulation ; Computers/*trends ; Persons with Disabilities/rehabilitation ; Equipment Design ; Event-Related Potentials, P300/physiology ; Feedback/physiology ; Humans ; Man-Machine Systems ; *Programming Languages ; Signal Processing, Computer-Assisted ; Software/*trends ; Software Design ; Software Validation ; *User-Computer Interface ; },
abstract = {All the protocols currently implemented in brain computer interface (BCI) experiments are characterized by different structural and temporal entities. Moreover, due to the lack of a unique descriptive model for BCI systems, there is not a standard way to define the structure and the timing of a BCI experimental session among different research groups and there is also great discordance on the meaning of the most common terms dealing with BCI, such as trial, run and session. The aim of this paper is to provide a unified modeling language (UML) implementation of BCI systems through a unique dynamic model which is able to describe the main protocols defined in the literature (P300, mu-rhythms, SCP, SSVEP, fMRI) and demonstrates to be reasonable and adjustable according to different requirements. This model includes a set of definitions of the typical entities encountered in a BCI, diagrams which explain the structural correlations among them and a detailed description of the timing of a trial. This last represents an innovation with respect to the models already proposed in the literature. The UML documentation and the possibility of adapting this model to the different BCI systems built to date, make it a basis for the implementation of new systems and a mean for the unification and dissemination of resources. The model with all the diagrams and definitions reported in the paper are the core of the body language framework, a free set of routines and tools for the implementation, optimization and delivery of cross-platform BCI systems.},
}
@article {pmid18593098,
year = {2008},
author = {Alpert, S},
title = {Brain-Computer Interface devices: risks and Canadian regulations.},
journal = {Accountability in research},
volume = {15},
number = {2},
pages = {63-86},
doi = {10.1080/08989620701783774},
pmid = {18593098},
issn = {0898-9621},
mesh = {Canada ; *Consumer Product Safety/legislation &amp; jurisprudence/standards ; Device Approval/legislation &amp; jurisprudence/standards ; Equipment Design ; Equipment Safety ; Humans ; Prostheses and Implants/adverse effects/*standards ; Risk Assessment ; Self-Help Devices/adverse effects/*standards ; *User-Computer Interface ; },
abstract = {Implantable Brain-Computer Interface (BCI) devices are currently in clinical trials in the U.S., and their introduction into the Canada could follow in the next few years. This article provides an overview of the research, developments, design issues, and risks in BCIs and an analysis of the adequacy of the regulatory framework in place for the approval of medical devices in Canada, emphasizing device investigational testing. The article concludes that until better safeguards are in place, to best protect potential research subjects, BCIs should not be approved for investigational testing in Canada.},
}
@article {pmid18592230,
year = {2008},
author = {Enzinger, C and Ropele, S and Fazekas, F and Loitfelder, M and Gorani, F and Seifert, T and Reiter, G and Neuper, C and Pfurtscheller, G and Müller-Putz, G},
title = {Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training.},
journal = {Experimental brain research},
volume = {190},
number = {2},
pages = {215-223},
pmid = {18592230},
issn = {1432-1106},
mesh = {Adaptation, Physiological/physiology ; Adult ; Brain/*physiology ; Efferent Pathways/physiology ; Evoked Potentials/physiology ; Extremities/innervation/physiology ; Humans ; *Imagery, Psychotherapy ; Imagination/physiology ; Magnetic Resonance Imaging ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Muscle, Skeletal/innervation/physiology ; Neuronal Plasticity/physiology ; Physical Therapy Modalities ; Quadriplegia/physiopathology/rehabilitation ; Recovery of Function/*physiology ; Spinal Cord Injuries/physiopathology/*rehabilitation ; Teaching ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {Although several features of brain motor function appear to be preserved even in chronic complete SCI, previous functional MRI (fMRI) studies have also identified significant derangements such as a strongly reduced volume of activation, a poor modulation of function and abnormal activation patterns. It might be speculated that extensive motor imagery training may serve to prevent such abnormalities. We here report on a unique patient with a complete traumatic SCI below C5 who learned to elicit electroencephalographic signals beta-bursts in the midline region upon imagination of foot movements. This enabled him to use a neuroprosthesis and to "walk from thought" in a virtual environment via a brain-computer interface (BCI). We here used fMRI at 3T during imagined hand and foot movements to investigate the effects of motor imagery via persistent BCI training over 8 years on brain motor function and compared these findings to a group of five untrained healthy age-matched volunteers during executed and imagined movements. We observed robust primary sensorimotor cortex (SMC) activity in expected somatotopy in the tetraplegic patient upon movement imagination while such activation was absent in healthy untrained controls. Sensorimotor network activation with motor imagery in the patient (including SMC contralateral to and the cerebellum ipsilateral to the imagined side of movement as well as supplementary motor areas) was very similar to the pattern observed with actual movement in the controls. We interpret our findings as evidence that BCI training as a conduit of motor imagery training may assist in maintaining access to SMC in largely preserved somatopy despite complete deafferentation.},
}
@article {pmid18590711,
year = {2008},
author = {Carrillo-de-la-Peña, MT and Galdo-Alvarez, S and Lastra-Barreira, C},
title = {Equivalent is not equal: primary motor cortex (MI) activation during motor imagery and execution of sequential movements.},
journal = {Brain research},
volume = {1226},
number = {},
pages = {134-143},
doi = {10.1016/j.brainres.2008.05.089},
pmid = {18590711},
issn = {0006-8993},
mesh = {Adult ; Brain Mapping ; Electroencephalography/methods ; Evoked Potentials, Motor/*physiology ; Female ; Functional Laterality ; Humans ; *Imagination ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Statistics, Nonparametric ; Time Factors ; },
abstract = {The motor hierarchy hypothesis and the related debate about the role of the primary motor cortex (MI) in motor preparation are major topics in cognitive neuroscience today. The present study combines the two strategies that have been followed to clarify the role of MI in motor preparation independently from execution: motor imagery and the use of precueing tasks. Event-related potentials (ERPs) were recorded while subjects either performed or just imagined sequential finger movements in response to a central target (numbers 1, 2 or 3) which was precued by arrows (at both sides of the screen) that provided information about response side. Both motor imagery and execution elicited Lateralized Readiness Potentials (LRPs) with similar morphology and latency. Given that the LRP is generated in MI, the results show that the primary motor cortex is also active during imagery and give support for the hypothesis of a functional equivalence between motor imagery and execution. Nevertheless, the analysis of the different moments of motor preparation (precue vs. target-induced activity) revealed important differences between both conditions: whereas there were no differences in LRPs nor in brain areas estimated by standardized low resolution tomographies (sLORETA) related to precue presentation, larger LRP amplitudes and higher activation of MI were found during motor execution than imagery in the target-related activity. These results have important implications for the development of brain-computer devices and for the use of motor imagery in neurorehabilitation.},
}
@article {pmid18590571,
year = {2008},
author = {Khoa, TQ and Nakagawa, M},
title = {Recognizing brain activities by functional near-infrared spectroscope signal analysis.},
journal = {Nonlinear biomedical physics},
volume = {2},
number = {1},
pages = {3},
pmid = {18590571},
issn = {1753-4631},
abstract = {BACKGROUND: Functional Near-Infrared Spectroscope (fNIRs) is one of the latest technologies which utilize light in the near-infrared range to determine brain activities. Near-infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems. This indicates that fNIRs signal monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis to show that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a Brain-Computer interface.<br><br>RESULTS: We applied Higuchi's fractal dimension algorithms to analyse irregular and complex characteristics of fNIRs signals, and then Wavelets transform is used to analysis for preprocessing as signal filters and feature extractions and Neural networks is a module for cognition brain tasks.<br><br>CONCLUSION: Throughout two experiments, we have demonstrated the feasibility of fNIRs analysis to recognize human brain activities.},
}
@article {pmid18586600,
year = {2008},
author = {Wu, W and Hatsopoulos, NG},
title = {Real-time decoding of nonstationary neural activity in motor cortex.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {3},
pages = {213-222},
pmid = {18586600},
issn = {1558-0210},
support = {R01 NS045853/NS/NINDS NIH HHS/United States ; R01 NS45853/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Brain Mapping/*methods ; Computer Systems ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Male ; *Man-Machine Systems ; Motor Cortex/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Neural decoding has played a key role in recent advances in brain-machine interfaces (BMIs) by converting brain signals into control commands to drive external devices such as robotic limbs or computer cursors. A number of practical algorithms including the well-known linear regression and Kalman filter models have been used to predict continuous movement in a real-time online context using recordings from a chronically implanted multielectrode microarray in the motor cortex. Though effective, those models were often based on a strong assumption that the neural signal sequence is a stationary process. Recent work, however, indicates that the motor system significantly varies over time. To characterize the dynamic relationship between neural signals and hand kinematics, here we develop an adaptive approach for each of the linear regression and Kalman filter methods. Experimental results show that the new adaptive algorithms generate more accurate decoding than the nonadaptive algorithms. To make the new algorithms feasible in an online situation, we further develop a recursive update approach and theoretically demonstrate its superior efficiency. In particular, the adaptive Kalman filter is shown to be more accurate and efficient. We also test the new methods in a simulated BMI experiment where the true hand motion is not known. The successful performance suggests these methods could be useful decoding algorithms for practical applications.},
}
@article {pmid18584040,
year = {2008},
author = {Geng, T and Gan, JQ and Dyson, M and Tsui, CS and Sepulveda, F},
title = {A novel design of 4-class BCI using two binary classifiers and parallel mental tasks.},
journal = {Computational intelligence and neuroscience},
volume = {2008},
number = {},
pages = {437306},
pmid = {18584040},
issn = {1687-5265},
abstract = {A novel 4-class single-trial brain computer interface (BCI) based on two (rather than four or more) binary linear discriminant analysis (LDA) classifiers is proposed, which is called a "parallel BCI." Unlike other BCIs where mental tasks are executed and classified in a serial way one after another, the parallel BCI uses properly designed parallel mental tasks that are executed on both sides of the subject body simultaneously, which is the main novelty of the BCI paradigm used in our experiments. Each of the two binary classifiers only classifies the mental tasks executed on one side of the subject body, and the results of the two binary classifiers are combined to give the result of the 4-class BCI. Data was recorded in experiments with both real movement and motor imagery in 3 able-bodied subjects. Artifacts were not detected or removed. Offline analysis has shown that, in some subjects, the parallel BCI can generate a higher accuracy than a conventional 4-class BCI, although both of them have used the same feature selection and classification algorithms.},
}
@article {pmid18575676,
year = {2007},
author = {Besserve, M and Jerbi, K and Laurent, F and Baillet, S and Martinerie, J and Garnero, L},
title = {Classification methods for ongoing EEG and MEG signals.},
journal = {Biological research},
volume = {40},
number = {4},
pages = {415-437},
pmid = {18575676},
issn = {0716-9760},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography/*classification ; Humans ; Linear Models ; Magnetoencephalography/*classification ; Motor Activity/*physiology ; Pattern Recognition, Visual/*physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; },
abstract = {Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.},
}
@article {pmid18571984,
year = {2008},
author = {Nijboer, F and Sellers, EW and Mellinger, J and Jordan, MA and Matuz, T and Furdea, A and Halder, S and Mochty, U and Krusienski, DJ and Vaughan, TM and Wolpaw, JR and Birbaumer, N and Kübler, A},
title = {A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {8},
pages = {1909-1916},
pmid = {18571984},
issn = {1388-2457},
support = {R01 EB000856-02/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-07/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; R01 EB000856-03/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; R01 HD030146-09/HD/NICHD NIH HHS/United States ; R01 HD030146-08/HD/NICHD NIH HHS/United States ; R01 EB000856-04/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; R01 HD030146-10/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-05/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*pathology/*physiopathology ; Brain/*physiopathology ; Discriminant Analysis ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Feedback, Psychological ; Female ; Humans ; Male ; Middle Aged ; Pattern Recognition, Visual/physiology ; Photic Stimulation ; Reaction Time ; *User-Computer Interface ; },
abstract = {OBJECTIVE: The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS.<br><br>METHODS: Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback.<br><br>RESULTS: In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks.<br><br>CONCLUSIONS: Participants could communicate with the P300-based BCI and performance was stable over many months.<br><br>SIGNIFICANCE: BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.},
}
@article {pmid18553399,
year = {2008},
author = {Blank, LM and Ebert, BE and Bühler, B and Schmid, A},
title = {Metabolic capacity estimation of Escherichia coli as a platform for redox biocatalysis: constraint-based modeling and experimental verification.},
journal = {Biotechnology and bioengineering},
volume = {100},
number = {6},
pages = {1050-1065},
doi = {10.1002/bit.21837},
pmid = {18553399},
issn = {1097-0290},
mesh = {Bacterial Proteins/genetics/metabolism ; Catalysis ; Computer Simulation ; Escherichia coli/*metabolism ; Feedback, Physiological/physiology ; Gene Expression Regulation, Bacterial ; Glucose/analysis/metabolism ; Glycolysis ; Kinetics ; *Models, Biological ; NAD/analysis/metabolism ; *Oxidation-Reduction ; Pentose Phosphate Pathway ; Protein Engineering ; },
abstract = {Whole-cell redox biocatalysis relies on redox cofactor regeneration by the microbial host. Here, we applied flux balance analysis based on the Escherichia coli metabolic network to estimate maximal NADH regeneration rates. With this optimization criterion, simulations showed exclusive use of the pentose phosphate pathway at high rates of glucose catabolism, a flux distribution usually not found in wild-type cells. In silico, genetic perturbations indicated a strong dependency of NADH yield and formation rate on the underlying metabolic network structure. The linear dependency of measured epoxidation activities of recombinant central carbon metabolism mutants on glucose uptake rates and the linear correlation between measured activities and simulated NADH regeneration rates imply intracellular NADH shortage. Quantitative comparison of computationally predicted NADH regeneration and experimental epoxidation rates indicated that the achievable biocatalytic activity is determined by metabolic and enzymatic limitations including non-optimal flux distributions, high maintenance energy demands, energy spilling, byproduct formation, and uncoupling. The results are discussed in the context of cellular optimization of biotransformation processes and may guide a priori design of microbial cells as redox biocatalysts.},
}
@article {pmid18551332,
year = {2008},
author = {Blank, LM and Hugenholtz, P and Nielsen, LK},
title = {Evolution of the hyaluronic acid synthesis (has) operon in Streptococcus zooepidemicus and other pathogenic streptococci.},
journal = {Journal of molecular evolution},
volume = {67},
number = {1},
pages = {13-22},
pmid = {18551332},
issn = {0022-2844},
mesh = {Base Sequence ; *Evolution, Molecular ; Gene Duplication ; Gene Transfer, Horizontal ; Genes, Bacterial ; Hyaluronic Acid/*biosynthesis ; Molecular Sequence Data ; *Operon ; Phylogeny ; Sequence Alignment ; Streptococcus/classification/genetics ; Streptococcus equi/classification/enzymology/*genetics ; },
abstract = {Hyaluronic acid (HA) is a ubiquitous linear polysaccharide in vertebrates and also is the capsule material of some pathogenic bacteria including group A and C streptococci. In bacteria, the HA synthase occurs in an operon (has) coding for enzymes involved in the production of HA precursors. We report two new members of the has operon family from Streptococcus equi subsp. zooepidemicus (S. zooepidemicus) and Streptococcus equi subsp. equi (S. equi). The has operon of S. zooepidemicus contains, in order, hasA, hasB, hasC, glum, and pgi, whereas these genes are separated on two operons in S. equi (hasA, hasB, hasC and hasC, glmU, pgi). The transcription start site and a sigma(70) promoter were experimentally identified 50 bp upstream of hasA in S. zooepidemicus. We performed a phylogenetic analysis of each of the has operon genes to determine the evolutionary origin(s) of the streptococcal has operon. In contrast to other capsular and exopolysaccharide operons, has operons have undergone no detectable interspecies lateral gene transfers in their construction, instead relying on intragenome gene duplication for their assembly. Specifically, hasC and glmU appear to have been duplicated into the S. zooepidemicus has operon from remotely located but near-identical paralogues most likely to improve HA productivity by gene dosage in this streptococcus. The intragene rearrangements appear to be ongoing events and the two has operons of the S. equi subspecies represent two alternatives of the same gene arrangement. A scenario for the evolution of streptococcal has operons is proposed.},
}
@article {pmid18547575,
year = {2008},
author = {Williams, CT and Kitaysky, AS and Kettle, AB and Buck, CL},
title = {Corticosterone levels of tufted puffins vary with breeding stage, body condition index, and reproductive performance.},
journal = {General and comparative endocrinology},
volume = {158},
number = {1},
pages = {29-35},
doi = {10.1016/j.ygcen.2008.04.018},
pmid = {18547575},
issn = {1095-6840},
mesh = {Animals ; Body Constitution/*physiology ; Body Weight/physiology ; Breeding ; Charadriiformes/blood/growth &amp; development/*physiology ; Corticosterone/*blood ; Efficiency ; Female ; Male ; Nesting Behavior/physiology ; Reproduction/*physiology ; Sexual Behavior, Animal/physiology ; Sexual Maturation/*physiology ; Transcortin/analysis ; },
abstract = {Corticosterone (CORT) levels in free-living animals are seasonally modulated and vary with environmental conditions. Although most studies measure total CORT concentrations, levels of corticosteroid binding globulin (CBG) may also be modulated, thus altering the concentration of CORT available for diffusion into tissues (free CORT). We investigated the seasonal dynamics of CBG, total CORT, and free CORT in breeding tufted puffins (Fratercula cirrhata) during 2 years characterized by high rates of nestling growth and survival. We then compared concentrations of total CORT in this population to levels in chick-rearing puffins at another colony during 2 years with low productivity. At the high productivity colony, levels of CBG, total baseline CORT, free baseline CORT, and total maximum CORT were all higher prior to egg-laying than during late incubation and late chick-rearing. Levels of CBG were positively correlated with body condition index (BCI) and free baseline CORT was negatively correlated with BCI. Total baseline levels of CORT during chick-rearing were two to four times higher at the colony with low rates of nestling growth and survival. Our results demonstrate the need for long-term datasets to disentangle seasonal trends in CORT levels from trends driven by changes in environmental conditions. Given the negative effects associated with chronic elevation of CORT, our results indicate the cost of reproduction may be higher during years characterized by low productivity.},
}
@article {pmid18539345,
year = {2008},
author = {Serruya, MD and Kahana, MJ},
title = {Techniques and devices to restore cognition.},
journal = {Behavioural brain research},
volume = {192},
number = {2},
pages = {149-165},
pmid = {18539345},
issn = {0166-4328},
support = {R25 NS065745/NS/NINDS NIH HHS/United States ; R25 NS065745-01/NS/NINDS NIH HHS/United States ; },
mesh = {Attention/physiology ; Cognition/*physiology ; Cognition Disorders/physiopathology/psychology/*therapy ; Electric Stimulation/instrumentation/methods ; Humans ; Memory/physiology ; Psychological Techniques/*instrumentation ; Recovery of Function/*physiology ; Transcranial Magnetic Stimulation/instrumentation/methods ; },
abstract = {Executive planning, the ability to direct and sustain attention, language and several types of memory may be compromised by conditions such as stroke, traumatic brain injury, cancer, autism, cerebral palsy and Alzheimer's disease. No medical devices are currently available to help restore these cognitive functions. Recent findings about the neurophysiology of these conditions in humans coupled with progress in engineering devices to treat refractory neurological conditions imply that the time has arrived to consider the design and evaluation of a new class of devices. Like their neuromotor counterparts, neurocognitive prostheses might sense or modulate neural function in a non-invasive manner or by means of implanted electrodes. In order to paint a vision for future device development, it is essential to first review what can be achieved using behavioral and external modulatory techniques. While non-invasive approaches might strengthen a patient's remaining intact cognitive abilities, neurocognitive prosthetics comprised of direct brain-computer interfaces could in theory physically reconstitute and augment the substrate of cognition itself.},
}
@article {pmid18519614,
year = {2008},
author = {Garvin-Doxas, K and Klymkowsky, MW},
title = {Understanding randomness and its impact on student learning: lessons learned from building the Biology Concept Inventory (BCI).},
journal = {CBE life sciences education},
volume = {7},
number = {2},
pages = {227-233},
pmid = {18519614},
issn = {1931-7913},
mesh = {Biological Evolution ; Biology/*education ; Curriculum ; Learning ; Selection, Genetic ; Students ; Teaching ; Universities ; },
abstract = {While researching student assumptions for the development of the Biology Concept Inventory (BCI; http://bioliteracy.net), we found that a wide class of student difficulties in molecular and evolutionary biology appears to be based on deep-seated, and often unaddressed, misconceptions about random processes. Data were based on more than 500 open-ended (primarily) college student responses, submitted online and analyzed through our Ed's Tools system, together with 28 thematic and think-aloud interviews with students, and the responses of students in introductory and advanced courses to questions on the BCI. Students believe that random processes are inefficient, whereas biological systems are very efficient. They are therefore quick to propose their own rational explanations for various processes, from diffusion to evolution. These rational explanations almost always make recourse to a driver, e.g., natural selection in evolution or concentration gradients in molecular biology, with the process taking place only when the driver is present, and ceasing when the driver is absent. For example, most students believe that diffusion only takes place when there is a concentration gradient, and that the mutational processes that change organisms occur only in response to natural selection pressures. An understanding that random processes take place all the time and can give rise to complex and often counterintuitive behaviors is almost totally absent. Even students who have had advanced or college physics, and can discuss diffusion correctly in that context, cannot make the transfer to biological processes, and passing through multiple conventional biology courses appears to have little effect on their underlying beliefs.},
}
@article {pmid18509337,
year = {2008},
author = {Velliste, M and Perel, S and Spalding, MC and Whitford, AS and Schwartz, AB},
title = {Cortical control of a prosthetic arm for self-feeding.},
journal = {Nature},
volume = {453},
number = {7198},
pages = {1098-1101},
doi = {10.1038/nature06996},
pmid = {18509337},
issn = {1476-4687},
support = {N01-2-2346//PHS HHS/United States ; NS050256/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Arm ; Biomechanical Phenomena ; *Eating ; Feeding Behavior ; Food ; Macaca mulatta/*physiology ; *Man-Machine Systems ; Motion ; Motor Cortex/*physiology ; Robotics/*instrumentation/*methods ; },
abstract = {Arm movement is well represented in populations of neurons recorded from the motor cortex. Cortical activity patterns have been used in the new field of brain-machine interfaces to show how cursors on computer displays can be moved in two- and three-dimensional space. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment ('embodiment') has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects' cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control, previous experiments have lacked physical interaction even in cases where a robotic arm or hand was included in the control loop, because the subjects did not use it to interact with physical objects-an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.},
}
@article {pmid18508127,
year = {2008},
author = {Chan, HL and Lin, MA and Wu, T and Lee, ST and Tsai, YT and Chao, PK},
title = {Detection of neuronal spikes using an adaptive threshold based on the max-min spread sorting method.},
journal = {Journal of neuroscience methods},
volume = {172},
number = {1},
pages = {112-121},
doi = {10.1016/j.jneumeth.2008.04.014},
pmid = {18508127},
issn = {0165-0270},
mesh = {Action Potentials/*physiology ; Adaptation, Physiological/*physiology ; Algorithms ; Animals ; Computer Simulation ; Differential Threshold/*physiology ; Microelectrodes ; *Models, Neurological ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Neuronal spike information can be used to correlate neuronal activity to various stimuli, to find target neural areas for deep brain stimulation, and to decode intended motor command for brain-machine interface. Typically, spike detection is performed based on the adaptive thresholds determined by running root-mean-square (RMS) value of the signal. Yet conventional detection methods are susceptible to threshold fluctuations caused by neuronal spike intensity. In the present study we propose a novel adaptive threshold based on the max-min spread sorting method. On the basis of microelectrode recording signals and simulated signals with Gaussian noises and colored noises, the novel method had the smallest threshold variations, and similar or better spike detection performance than either the RMS-based method or other improved methods. Moreover, the detection method described in this paper uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data (as multi-electrode recordings).},
}
@article {pmid18504910,
year = {2007},
author = {Ojemann, JG and Leuthardt, EC and Miller, KJ},
title = {Brain-machine interface: restoring neurological function through bioengineering.},
journal = {Clinical neurosurgery},
volume = {54},
number = {},
pages = {134-136},
pmid = {18504910},
issn = {0069-4827},
mesh = {Artificial Limbs ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; Mobility Limitation ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; Wheelchairs ; },
}
@article {pmid18503151,
year = {2008},
author = {Velcheva, I and Nikolova, G},
title = {Hemorheological disturbances and cognitive function in patients with cerebrovascular disease.},
journal = {Clinical hemorheology and microcirculation},
volume = {39},
number = {1-4},
pages = {397-402},
pmid = {18503151},
issn = {1386-0291},
mesh = {Adult ; Aged ; Blood Viscosity ; Cerebral Infarction/complications/diagnosis ; Cerebrovascular Disorders/*complications/*diagnosis ; Cognition ; Female ; Fibrinogen/metabolism ; Hematocrit ; Hemorheology/*methods ; Humans ; Ischemic Attack, Transient/complications/diagnosis ; Male ; Middle Aged ; Stroke ; },
abstract = {The aim of the study was to follow the relationship of the hemorheological variables with the cognitive functions in patients with ischemic cerebrovascular disease (CVD). The patient material comprised 117 patients with CVD, distributed in two main groups: 44 with transient ischemic attacks (TIAs) and 73 with chronic cerebral infarctions (CCI), 48 of them being unilateral (UCI) and 25 bilateral (BCI). Additional relative distribution according to the mean arterial blood pressure (MABP) values or to the presence of pathological asymmetries of the hemispheric cerebral blood flow (CBF) was made. The main hemorheological variables: hematocrit (Ht), fibrinogen (Fib) and plasma viscosity (PV) were examined. The cognitive functions were assessed with a psychological test battery for evaluation of the general cognitive state, the nonverbal intellect, the episodic memory, the selective attention and the executive functions. The hemorheological investigation revealed predominant increase of PV. The results of all neuropsychological tests showed significant impairment in the patients with CCI in comparison to TIAs. Fibrinogen correlated best with the psychological parameters. Its increase was associated with disturbance of the nonverbal intellect and the general cognitive capacity in the patients with CCI and BCI. In the presence of lower MABP or lack of pathological asymmetries the correlations of Fib and PV with the psychological scores predominated. The results of our study reveal distinct association between the blood rheological properties and the cognitive functions in the patients with ischemic CVD, which is probably based not only on vascular but also on other nonvascular mechanisms.},
}
@article {pmid18500413,
year = {2008},
author = {Robaina Padrón, FJ},
title = {[Surgical neuromodulation: new frontiers in neurosurgery].},
journal = {Neurocirugia (Asturias, Spain)},
volume = {19},
number = {2},
pages = {143-155},
doi = {10.4321/s1130-14732008000200006},
pmid = {18500413},
issn = {1130-1473},
mesh = {Humans ; Neurosurgery/*trends ; Neurosurgical Procedures/*methods ; Prosthesis Implantation ; },
abstract = {OBJECTIVES: Surgical neuromodulation refers to all those techniques that use implantable devices that discharge electricity or chemical substances that modify nerve signal transmission in order to achieve inhibition, excitation or modulation of the activity of neuronal groups and networks, and to achieve a therapeutic effect. Neuromodulation encompasses different scientific aspects and technologies which need to be defined.<br><br>MATERIAL AND METHOD: From the surgical point of view, neuromodulation is defined as: those intervention techniques that alter the transmission of neuronal signals using implantable electrical or chemical devices with the objective of stimulating, inhibiting or modulating the activity of neurones or neuronal networks to achieve therapeutic effects. A clinical definition makes reference to the use of reversible electrical or chemical stimulation of the nervous system to manipulate its activity in order to treat some specific types of chronic pain and conditions such as spasticity, epilepsy, cardiac ischemia, alterations in the motility of the intestine and of the bladder, lesions of the nervous system, and alterations in mobility, visual, auditory or psychiatric status. Neurosurgeons have been well trained to perform a great number of surgical techniques of neuromodulation, even including helping to significantly increase biomedical activities and the application of high technology to the central and peripheral nervous system.<br><br>CONCLUSIONS: Surgical neuromodulation encourages the neurosurgeon to go also away from the classical techniques of surgical resection and neuroablative procedures, and to enter into the new field of neuroengineering to re-establish lost neurological functions. The inter-relationship between the brain and the computer (brain-machine interface) has already occurred and has been applied in the field of neuroprosthetics and deep brain stimulation. For neurosurgery in general and for Spain in particular, this represents a new opportunity to embark on a high technology path that would involve years of research but, applying these new, non-invasive surgical techniques would help resolve the neurological problems of many of our patients.},
}
@article {pmid18497872,
year = {2008},
author = {Fatourechi, M and Ward, RK and Birch, GE},
title = {Performance of a self-paced brain computer interface on data contaminated with eye-movement artifacts and on data recorded in a subsequent session.},
journal = {Computational intelligence and neuroscience},
volume = {2008},
number = {},
pages = {749204},
pmid = {18497872},
issn = {1687-5265},
abstract = {The performance of a specific self-paced BCI (SBCI) is investigated using two different datasets to determine its suitability for using online: (1) data contaminated with large-amplitude eye movements, and (2) data recorded in a session subsequent to the original sessions used to design the system. No part of the data was rejected in the subsequent session. Therefore, this dataset can be regarded as a "pseudo-online" test set. The SBCI under investigation uses features extracted from three specific neurological phenomena. Each of these neurological phenomena belongs to a different frequency band. Since many prominent artifacts are either of mostly low-frequency (e.g., eye movements) or mostly high-frequency nature (e.g., muscle movements), it is expected that the system shows a fairly robust performance over artifact-contaminated data. Analysis of the data of four participants using epochs contaminated with large-amplitude eye-movement artifacts shows that the system's performance deteriorates only slightly. Furthermore, the system's performance during the session subsequent to the original sessions remained largely the same as in the original sessions for three out of the four participants. This moderate drop in performance can be considered tolerable, since allowing artifact-contaminated data to be used as inputs makes the system available for users at ALL times.},
}
@article {pmid18495541,
year = {2008},
author = {Hinterberger, T and Widman, G and Lal, TN and Hill, J and Tangermann, M and Rosenstiel, W and Schölkopf, B and Elger, C and Birbaumer, N},
title = {Voluntary brain regulation and communication with electrocorticogram signals.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {13},
number = {2},
pages = {300-306},
doi = {10.1016/j.yebeh.2008.03.014},
pmid = {18495541},
issn = {1525-5069},
mesh = {Adult ; Biofeedback, Psychology/physiology ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Dominance, Cerebral/physiology ; *Electroencephalography ; Epilepsies, Partial/physiopathology/*rehabilitation ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; Motor Activity/physiology ; Motor Cortex/physiopathology ; *Signal Processing, Computer-Assisted ; Software ; Somatosensory Cortex/physiopathology ; Theta Rhythm ; *User-Computer Interface ; *Writing ; },
abstract = {Brain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.},
}
@article {pmid18494541,
year = {2008},
author = {Little, MP and Hoel, DG and Molitor, J and Boice, JD and Wakeford, R and Muirhead, CR},
title = {New models for evaluation of radiation-induced lifetime cancer risk and its uncertainty employed in the UNSCEAR 2006 report.},
journal = {Radiation research},
volume = {169},
number = {6},
pages = {660-676},
doi = {10.1667/RR1091.1},
pmid = {18494541},
issn = {0033-7587},
mesh = {Bayes Theorem ; Calibration ; Humans ; Japan ; Likelihood Functions ; Models, Statistical ; Models, Theoretical ; Monte Carlo Method ; Neoplasms, Radiation-Induced/*diagnosis/*epidemiology ; Nuclear Warfare ; Radiation Dosage ; Radioactive Fallout ; Regression Analysis ; Risk ; Risk Assessment/*methods ; },
abstract = {Generalized relative and absolute risk models are fitted to the latest Japanese atomic bomb survivor solid cancer and leukemia mortality data (through 2000), with the latest (DS02) dosimetry, by classical (regression calibration) and Bayesian techniques, taking account of errors in dose estimates and other uncertainties. Linear-quadratic and linear-quadratic-exponential models are fitted and used to assess risks for contemporary populations of China, Japan, Puerto Rico, the U.S. and the UK. Many of these models are the same as or very similar to models used in the UNSCEAR 2006 report. For a test dose of 0.1 Sv, the solid cancer mortality for a UK population using the generalized linear-quadratic relative risk model is estimated as 5.4% Sv(-1) [90% Bayesian credible interval (BCI) 3.1, 8.0]. At 0.1 Sv, leukemia mortality for a UK population using the generalized linear-quadratic relative risk model is estimated as 0.50% Sv(-1) (90% BCI 0.11, 0.97). Risk estimates varied little between populations; at 0.1 Sv the central estimates ranged from 3.7 to 5.4% Sv(-1) for solid cancers and from 0.4 to 0.6% Sv(-1) for leukemia. Analyses using regression calibration techniques yield central estimates of risk very similar to those for the Bayesian approach. The central estimates of population risk were similar for the generalized absolute risk model and the relative risk model. Linear-quadratic-exponential models predict lower risks (at least at low test doses) and appear to fit as well, although for other (theoretical) reasons we favor the simpler linear-quadratic models.},
}
@article {pmid18483450,
year = {2008},
author = {Bell, CJ and Shenoy, P and Chalodhorn, R and Rao, RP},
title = {Control of a humanoid robot by a noninvasive brain-computer interface in humans.},
journal = {Journal of neural engineering},
volume = {5},
number = {2},
pages = {214-220},
doi = {10.1088/1741-2560/5/2/012},
pmid = {18483450},
issn = {1741-2560},
mesh = {Adolescent ; Adult ; *Algorithms ; Biomimetics/*methods ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.},
}
@article {pmid18472250,
year = {2008},
author = {Aziz-Zadeh, L and Damasio, A},
title = {Embodied semantics for actions: findings from functional brain imaging.},
journal = {Journal of physiology, Paris},
volume = {102},
number = {1-3},
pages = {35-39},
doi = {10.1016/j.jphysparis.2008.03.012},
pmid = {18472250},
issn = {0928-4257},
mesh = {Brain/anatomy &amp; histology/*physiology ; *Brain Mapping ; Diagnostic Imaging/*methods ; Humans ; Image Processing, Computer-Assisted ; Movement/*physiology ; Neuropsychological Tests ; Psycholinguistics ; *Semantics ; },
abstract = {The theory of embodied semantics for actions specifies that the sensory-motor areas used for producing an action are also used for the conceptual representation of the same action. Here we review the functional imaging literature that has explored this theory and consider both supporting as well as challenging fMRI findings. In particular we address the representation of actions and concepts as well as literal and metaphorical phrases in the premotor cortex.},
}
@article {pmid18440905,
year = {2008},
author = {Noirhomme, Q and Kitney, RI and Macq, B},
title = {Single-trial EEG source reconstruction for brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {5},
pages = {1592-1601},
doi = {10.1109/TBME.2007.913986},
pmid = {18440905},
issn = {0018-9294},
mesh = {Brain/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Man-Machine Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {A new way to improve the classification rate of an EEG-based brain-computer interface (BCI) could be to reconstruct the brain sources of EEG and to apply BCI methods to these derived sources instead of raw measured electrode potentials. EEG source reconstruction methods are based on electrophysiological information that could improve the discrimination between BCI tasks. In this paper, we present an EEG source reconstruction method for BCI. The results are compared with results from raw electrode potentials to enable direct evaluation of the method. Features are based on frequency power change and Bereitschaft potential. The features are ranked with mutual information before being fed to a proximal support vector machine. The dataset IV of the BCI competition II and data from four subjects serve as test data. Results show that the EEG inverse solution improves the classification rate and can lead to results comparable to the best currently known methods.},
}
@article {pmid18440904,
year = {2008},
author = {Lin, CT and Chen, YC and Huang, TY and Chiu, TT and Ko, LW and Liang, SF and Hsieh, HY and Hsu, SH and Duann, JR},
title = {Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver's drowsiness detection and warning.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {5},
pages = {1582-1591},
doi = {10.1109/TBME.2008.918566},
pmid = {18440904},
issn = {0018-9294},
mesh = {Accidents, Traffic/prevention &amp; control ; *Automobile Driving ; Brain/*physiology ; Computer Systems ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Monitoring, Ambulatory/*instrumentation ; Sleep Stages/*physiology ; Telemetry/*instrumentation/methods ; *User-Computer Interface ; },
abstract = {Biomedical signal monitoring systems have been rapidly advanced with electronic and information technologies in recent years. However, most of the existing physiological signal monitoring systems can only record the signals without the capability of automatic analysis. In this paper, we proposed a novel brain-computer interface (BCI) system that can acquire and analyze electroencephalogram (EEG) signals in real-time to monitor human physiological as well as cognitive states, and, in turn, provide warning signals to the users when needed. The BCI system consists of a four-channel biosignal acquisition/amplification module, a wireless transmission module, a dual-core signal processing unit, and a host system for display and storage. The embedded dual-core processing system with multitask scheduling capability was proposed to acquire and process the input EEG signals in real time. In addition, the wireless transmission module, which eliminates the inconvenience of wiring, can be switched between radio frequency (RF) and Bluetooth according to the transmission distance. Finally, the real-time EEG-based drowsiness monitoring and warning algorithms were implemented and integrated into the system to close the loop of the BCI system. The practical online testing demonstrates the feasibility of using the proposed system with the ability of real-time processing, automatic analysis, and online warning feedback in real-world operation and living environments.},
}
@article {pmid18440897,
year = {2008},
author = {Siu, KL and Ahn, JM and Ju, K and Lee, M and Shin, K and Chon, KH},
title = {Statistical approach to quantify the presence of phase coupling using the bispectrum.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {5},
pages = {1512-1520},
doi = {10.1109/TBME.2007.913418},
pmid = {18440897},
issn = {0018-9294},
support = {HL069629/HL/NHLBI NIH HHS/United States ; },
mesh = {Animals ; *Artifacts ; Blood Pressure Determination/*methods ; *Data Interpretation, Statistical ; Diagnosis, Computer-Assisted/*methods ; Hypertension, Renal/*diagnosis/*physiopathology ; Manometry/*methods ; Rats ; Rats, Inbred SHR ; Rats, Wistar ; },
abstract = {The bispectrum is a method to detect the presence of phase coupling between different components in a signal. The traditional way to quantify phase coupling is by means of the bicoherence index, which is essentially a normalized bispectrum. The major drawback of the bicoherence index (BCI) is that determination of significant phase coupling becomes compromised with noise and low coupling strength. To overcome this limitation, a statistical approach that combines the bispectrum with a surrogate data method to determine the statistical significance of the phase coupling is introduced. Our method does not rely on the use of the BCI, where the normalization procedure of the BCI is the major culprit in its poor specificity. We demonstrate the accuracy of the proposed approach using simulation examples that are designed to test its robustness against noise contamination as well as varying levels of phase coupling. Our results show that the proposed approach outperforms the bicoherence index in both sensitivity and specificity and provides an unbiased and statistical approach to determining the presence of quadratic phase coupling. Application of this new method to renal hemodynamic data was applied to renal stop flow pressure data obtained from normotensive (N = 7) and hypertensive (N = 7) rats. We found significant nonlinear interactions in both strains of rats with a greater magnitude of coupling and smaller number of interaction peaks in normotensive rats than hypertensive rats.},
}
@article {pmid18435249,
year = {2008},
author = {Yang, L and Li, J and Yao, Y and Wu, X},
title = {[A P300 detection algorithm based on F-score feature selection and support vector machines].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {25},
number = {1},
pages = {23-6, 52},
pmid = {18435249},
issn = {1001-5515},
mesh = {*Algorithms ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/methods ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; },
abstract = {How to detect the P300 component in EEG accurately and instantly is a hot problem in the research field of Brain-Computer Interface. In this paper, an algorithm based on F-score feature selection and support vector machines was introduced for P300 detection. Using F-score feature selection method, we reduced input features to overcome the shortcoming of support vector machines in terms of low detection speed, and then implemented the detection of P300 component with support vector machines, which have good classification performance. The algorithm was tested with a P300 dataset from the BCI competition 2003. The results showed that the algorithm achieved an accuracy of 100% in P300 detection within five repetitions, and the detection speed of this algorithm was 2 times higher than that of the traditional support vector machines algorithm without F-score feature selection.},
}
@article {pmid18430974,
year = {2008},
author = {McFarland, DJ and Wolpaw, JR},
title = {Sensorimotor rhythm-based brain-computer interface (BCI): model order selection for autoregressive spectral analysis.},
journal = {Journal of neural engineering},
volume = {5},
number = {2},
pages = {155-162},
pmid = {18430974},
issn = {1741-2560},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Biological Clocks/*physiology ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Periodicity ; Regression Analysis ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {People can learn to control EEG features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. Cursor movement depends on the estimate of the amplitudes of sensorimotor rhythms. Autoregressive models are often used to provide these estimates. The order of the autoregressive model has varied widely among studies. Through analyses of both simulated and actual EEG data, the present study examines the effects of model order on sensorimotor rhythm measurements and BCI performance. The results show that resolution of lower frequency signals requires higher model orders and that this requirement reflects the temporal span of the model coefficients. This is true for both simulated EEG data and actual EEG data during brain-computer interface (BCI) operation. Increasing model order, and decimating the signal were similarly effective in increasing spectral resolution. Furthermore, for BCI control of two-dimensional cursor movement, higher model orders produced better performance in each dimension and greater independence between horizontal and vertical movements. In sum, these results show that autoregressive model order selection is an important determinant of BCI performance and should be based on criteria that reflect system performance.},
}
@article {pmid18403288,
year = {2008},
author = {Besio, WG and Cao, H and Zhou, P},
title = {Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {2},
pages = {191-194},
doi = {10.1109/TNSRE.2007.916303},
pmid = {18403288},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain Mapping/*instrumentation/*methods ; *Electrodes ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/physiology ; Psychomotor Performance/physiology ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.},
}
@article {pmid18403280,
year = {2008},
author = {Lenhardt, A and Kaper, M and Ritter, HJ},
title = {An adaptive P300-based online brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {2},
pages = {121-130},
doi = {10.1109/TNSRE.2007.912816},
pmid = {18403280},
issn = {1534-4320},
mesh = {*Algorithms ; Artificial Intelligence ; Brain Mapping/*methods ; Cognition/*physiology ; Event-Related Potentials, P300/*physiology ; Online Systems ; Pattern Recognition, Automated/*methods ; Sensitivity and Specificity ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The P300 component of an event related potential is widely used in conjunction with brain-computer interfaces (BCIs) to translate the subjects intent by mere thoughts into commands to control artificial devices. A well known application is the spelling of words while selection of the letters is carried out by focusing attention to the target letter. In this paper, we present a P300-based online BCI which reaches very competitive performance in terms of information transfer rates. In addition, we propose an online method that optimizes information transfer rates and/or accuracies. This is achieved by an algorithm which dynamically limits the number of subtrial presentations, according to the subject's current online performance in real-time. We present results of two studies based on 19 different healthy subjects in total who participated in our experiments (seven subjects in the first and 12 subjects in the second one). In the first, study peak information transfer rates up to 92 bits/min with an accuracy of 100% were achieved by one subject with a mean of 32 bits/min at about 80% accuracy. The second experiment employed a dynamic classifier which enables the user to optimize bitrates and/or accuracies by limiting the number of subtrial presentations according to the current online performance of the subject. At the fastest setting, mean information transfer rates could be improved to 50.61 bits/min (i.e., 13.13 symbols/min). The most accurate results with 87.5% accuracy showed a transfer rate of 29.35 bits/min.},
}
@article {pmid18394526,
year = {2008},
author = {Cincotti, F and Mattia, D and Aloise, F and Bufalari, S and Schalk, G and Oriolo, G and Cherubini, A and Marciani, MG and Babiloni, F},
title = {Non-invasive brain-computer interface system: towards its application as assistive technology.},
journal = {Brain research bulletin},
volume = {75},
number = {6},
pages = {796-803},
pmid = {18394526},
issn = {0361-9230},
support = {R01 EB006356-01/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856-01/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; GUP03562/TI_/Telethon/Italy ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Activities of Daily Living/psychology ; Adolescent ; Adult ; *Brain/physiology ; Child ; Electroencephalography/methods ; Evoked Potentials, Motor/physiology ; Female ; Humans ; Learning/physiology ; Male ; Middle Aged ; Motor Skills/physiology ; Muscular Dystrophy, Duchenne/*rehabilitation ; Pilot Projects ; Prostheses and Implants/trends ; Robotics/*instrumentation/methods/trends ; Self-Help Devices/*trends ; Software/trends ; Spinal Muscular Atrophies of Childhood/*rehabilitation ; *User-Computer Interface ; Volition/physiology ; },
abstract = {The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user's residual motor abilities. Brain-computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.},
}
@article {pmid18387214,
year = {2009},
author = {Gradel, KO and Søgaard, M and Dethlefsen, C and Nielsen, H and Schønheyder, HC},
title = {Magnitude of bacteraemia is a predictor of mortality during 1 year of follow-up.},
journal = {Epidemiology and infection},
volume = {137},
number = {1},
pages = {94-101},
doi = {10.1017/S0950268808000575},
pmid = {18387214},
issn = {0950-2688},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Bacteremia/*mortality ; Blood/*microbiology ; Colony Count, Microbial/*methods ; Female ; Humans ; Male ; Middle Aged ; Models, Statistical ; Prognosis ; Risk Factors ; },
abstract = {We evaluated magnitude of bacteraemia as a predictor of mortality, comprising all adult patients with a first-time mono-microbial bacteraemia. The number of positive bottles [1 (reference), 2, or 3] in the first positive blood culture (BC) was an index of magnitude of bacteraemia. We used Cox's regression analysis to determine age and comorbidity adjusted risk of mortality at days 0-7, 8-30, and 31-365. Of 6406 patients, 31.1% had BC index 1 (BCI 1), 18.3% BCI 2, and 50.6% BCI 3. BCI 3 patients had increased risk of mortality for days 0-7 (1.30, 95% CI 1.10-1.55) and days 8-30 (1.37, 95% CI 1.12-1.68), but not thereafter. However, in surgical patients mortality increased only beyond day 7 (8-30 days: 2.04, 95% CI 1.25-3.33; 31-365 days: 1.27, 95% CI 0.98-1.65). Thus, high magnitude of bacteraemia predicted mortality during the first month with a shift towards long-term mortality in surgical patients.},
}
@article {pmid18368142,
year = {2007},
author = {Leeb, R and Friedman, D and Müller-Putz, GR and Scherer, R and Slater, M and Pfurtscheller, G},
title = {Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {79642},
pmid = {18368142},
issn = {1687-5265},
abstract = {The aim of the present study was to demonstrate for the first time that brain waves can be used by a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram (EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for a self-paced (asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording. The subject was placed inside a virtual street populated with avatars. The task was to "go" from avatar to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the participant was able to successfully perform this asynchronous experiment with a performance of 90%, single runs up to 100%.},
}
@article {pmid18368141,
year = {2007},
author = {Qin, J and Li, Y and Sun, W},
title = {A semisupervised support vector machines algorithm for BCI systems.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {94397},
pmid = {18368141},
issn = {1687-5265},
abstract = {As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.},
}
@article {pmid18367779,
year = {2008},
author = {McFarland, DJ and Krusienski, DJ and Sarnacki, WA and Wolpaw, JR},
title = {Emulation of computer mouse control with a noninvasive brain-computer interface.},
journal = {Journal of neural engineering},
volume = {5},
number = {2},
pages = {101-110},
pmid = {18367779},
issn = {1741-2560},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; R01 HD030146-06/HD/NICHD NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Middle Aged ; Spinal Cord Injuries/*rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user's commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user's task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.},
}
@article {pmid18364991,
year = {2007},
author = {Woon, WL and Cichocki, A},
title = {Novel features for brain-computer interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {82827},
pmid = {18364991},
issn = {1687-5265},
abstract = {While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques.},
}
@article {pmid18364986,
year = {2007},
author = {Song, L and Epps, J},
title = {Classifying EEG for brain-computer interface: learning optimal filters for dynamical system features.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {57180},
pmid = {18364986},
issn = {1687-5265},
abstract = {Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.},
}
@article {pmid18354735,
year = {2007},
author = {Zhao, Q and Zhang, L},
title = {Temporal and spatial features of single-trial EEG for brain-computer interface.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {37695},
pmid = {18354735},
issn = {1687-5265},
abstract = {Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.},
}
@article {pmid18354734,
year = {2007},
author = {Cincotti, F and Kauhanen, L and Aloise, F and Palomäki, T and Caporusso, N and Jylänki, P and Mattia, D and Babiloni, F and Vanacker, G and Nuttin, M and Marciani, MG and Del R Millán, J},
title = {Vibrotactile feedback for brain-computer interface operation.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {48937},
pmid = {18354734},
issn = {1687-5265},
abstract = {To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow of feedback to the user. This feedback is usually delivered through the visual channel. Our aim was to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified. In three studies with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms using tactile feedback; (II) the compatibility of this form of feedback in presence of a visual distracter; (III) the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The stimulation protocol we developed supports a general usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, more natural for both controls and SCI users.},
}
@article {pmid18354730,
year = {2007},
author = {Wang, S and James, CJ},
title = {Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {41468},
pmid = {18354730},
issn = {1687-5265},
abstract = {We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system.},
}
@article {pmid18354725,
year = {2007},
author = {Martinez, P and Bakardjian, H and Cichocki, A},
title = {Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {94561},
pmid = {18354725},
issn = {1687-5265},
abstract = {We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.},
}
@article {pmid18354722,
year = {2007},
author = {Gupta, CN and Palaniappan, R},
title = {Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {28692},
pmid = {18354722},
issn = {1687-5265},
abstract = {We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.},
}
@article {pmid18352986,
year = {2008},
author = {Neilands, TB and Silvera, DH and Perry, JA and Richardsen, A and Holte, A},
title = {A validation and short form of the Basic Character Inventory.},
journal = {Scandinavian journal of psychology},
volume = {49},
number = {2},
pages = {161-168},
doi = {10.1111/j.1467-9450.2008.00630.x},
pmid = {18352986},
issn = {0036-5564},
mesh = {Adolescent ; Adult ; *Character ; Factor Analysis, Statistical ; Female ; Humans ; Male ; Middle Aged ; Norway ; Personality/physiology ; Personality Disorders/diagnosis/psychology ; Personality Inventory/*standards/*statistics &amp; numerical data ; Psychometrics/methods/statistics &amp; numerical data ; Reproducibility of Results ; Students/psychology ; },
abstract = {The Basic Character Inventory (BCI) contains 136 items, 17 lower-order personality factors and three higher-order personality factors derived from psychoanalytic theory: Oral, Obsessive Compulsion, and Hysteria. Previous research that investigated the BCI's psychometric properties examined small, special populations and did not use modern statistical methods to validate the BCI. The present study validates the BCI via confirmatory factor analyses using a large sample of 6,285 Norwegian nursing and teaching students. Reliability, convergent validity, and divergent validity of the BCI were also assessed. Results indicated general support for the original BCI factor structure in a reduced form of the BCI that possesses strong reliability and validity, and is suitable for use in time-limited measurement settings.},
}
@article {pmid18350134,
year = {2007},
author = {Babiloni, F and Cincotti, F and Marciani, M and Salinari, S and Astolfi, L and Tocci, A and Aloise, F and De Vico Fallani, F and Bufalari, S and Mattia, D},
title = {The estimation of cortical activity for brain-computer interface: applications in a domotic context.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {91651},
pmid = {18350134},
issn = {1687-5265},
support = {R01 EB006356/EB/NIBIB NIH HHS/United States ; },
abstract = {In order to analyze whether the use of the cortical activity, estimated from noninvasive EEG recordings, could be useful to detect mental states related to the imagination of limb movements, we estimate cortical activity from high-resolution EEG recordings in a group of healthy subjects by using realistic head models. Such cortical activity was estimated in region of interest associated with the subject's Brodmann areas by using a depth-weighted minimum norm technique. Results showed that the use of the cortical-estimated activity instead of the unprocessed EEG improves the recognition of the mental states associated to the limb movement imagination in the group of normal subjects. The BCI methodology presented here has been used in a group of disabled patients in order to give them a suitable control of several electronic devices disposed in a three-room environment devoted to the neurorehabilitation. Four of six patients were able to control several electronic devices in this domotic context with the BCI system.},
}
@article {pmid18350133,
year = {2007},
author = {Scherer, R and Schloegl, A and Lee, F and Bischof, H and Jansa, J and Pfurtscheller, G},
title = {The self-paced graz brain-computer interface: methods and applications.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {79826},
pmid = {18350133},
issn = {1687-5265},
abstract = {We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.},
}
@article {pmid18350130,
year = {2007},
author = {Zhdanov, A and Hendler, T and Ungerleider, L and Intrator, N},
title = {Inferring functional brain states using temporal evolution of regularized classifiers.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {52609},
pmid = {18350130},
issn = {1687-5265},
abstract = {We present a framework for inferring functional brain state from electrophysiological (MEG or EEG) brain signals. Our approach is adapted to the needs of functional brain imaging rather than EEG-based brain-computer interface (BCI). This choice leads to a different set of requirements, in particular to the demand for more robust inference methods and more sophisticated model validation techniques. We approach the problem from a machine learning perspective, by constructing a classifier from a set of labeled signal examples. We propose a framework that focuses on temporal evolution of regularized classifiers, with cross-validation for optimal regularization parameter at each time frame. We demonstrate the inference obtained by this method on MEG data recorded from 10 subjects in a simple visual classification experiment, and provide comparison to the classical nonregularized approach.},
}
@article {pmid18338534,
year = {2008},
author = {Yoshimura, N and Itakura, N},
title = {Study on transient VEP-based brain-computer interface using non-direct gazed visual stimuli.},
journal = {Electromyography and clinical neurophysiology},
volume = {48},
number = {1},
pages = {43-51},
pmid = {18338534},
issn = {0301-150X},
mesh = {Adult ; Brain/*physiology ; Communication Devices for People with Disabilities ; *Computer Systems ; Electrodes ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Photic Stimulation/*methods ; *User-Computer Interface ; Vision, Ocular/physiology ; Visual Perception/physiology ; },
abstract = {It is necessary for brain-computer interfaces (BCIs) to be non-offensive devices for daily use to improve the quality of life of users, especially for the motor disabled. Some BCIs which are based on steady-state visual evoked potentials (SSVEPs), however, are unpleasant because users have to gaze at high-speed blinking light as visual stimuli. Furthermore, these kinds of BCIs may not be used as universal devices because SSVEPs are not detectable by some users. Considering these facts, we propose a novel BCI using a non-direct gazing method based on transient VEPs. This interface uses a low-speed blinking lattice pattern as visual stimuli, and users gaze at other visual targets displayed on the right and the left sides of the stimuli. The gazing direction is determined by the waveform difference of transient VEPs detected when users gaze at either target. Compared with SSVEP-based BCIs, the proposed BCI is less annoying because it uses a low-speed blinking pattern as visual stimuli and users do not have to gaze at the stimuli directly. In addition, bipolar derivation could reduce unnecessary signals and the number of responses used for signal averaging to detect transient VEPs, which leads to shorter detection time of the VEPs providing this interface with acceptable speed as a BCI in terms of determining gazing direction. Experiments with 7 volunteer subjects showed more than an 85% accuracy rate in gaze direction judgments. The result suggests that the proposed BCI can be used as a substitute for SSVEP-based BCIs, especially for users in which SSVEPs are not detected.},
}
@article {pmid18334407,
year = {2008},
author = {Rakotomamonjy, A and Guigue, V},
title = {BCI competition III: dataset II- ensemble of SVMs for BCI P300 speller.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {3},
pages = {1147-1154},
doi = {10.1109/TBME.2008.915728},
pmid = {18334407},
issn = {0018-9294},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Databases, Factual ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Word Processing/methods ; },
abstract = {Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer interface. We propose a method that copes with such variabilities through an ensemble of classifiers approach. Each classifier is composed of a linear support vector machine trained on a small part of the available data and for which a channel selection procedure has been performed. Performances of our algorithm have been evaluated on dataset II of the BCI Competition III and has yielded the best performance of the competition.},
}
@article {pmid18334383,
year = {2008},
author = {Ferrez, PW and del R Millan, J},
title = {Error-related EEG potentials generated during simulated brain-computer interaction.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {3},
pages = {923-929},
doi = {10.1109/TBME.2007.908083},
pmid = {18334383},
issn = {0018-9294},
mesh = {Algorithms ; Artifacts ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feedback/physiology ; Gyrus Cinguli/*physiology ; Humans ; Imagination/*physiology ; Intention ; Man-Machine Systems ; Motor Cortex/*physiology ; Reproducibility of Results ; Robotics/*methods ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: "Are ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?"; We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.},
}
@article {pmid18332811,
year = {2008},
author = {Zanini, MA and de Lima Resende, LA and de Souza Faleiros, AT and Gabarra, RC},
title = {Traumatic subdural hygromas: proposed pathogenesis based classification.},
journal = {The Journal of trauma},
volume = {64},
number = {3},
pages = {705-713},
doi = {10.1097/TA.0b013e3180485cfc},
pmid = {18332811},
issn = {1529-8809},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Craniocerebral Trauma/complications/diagnostic imaging/physiopathology ; Female ; Glasgow Coma Scale ; Humans ; Male ; Middle Aged ; Subdural Effusion/*classification/diagnostic imaging/etiology/physiopathology ; Tomography, X-Ray Computed ; },
abstract = {BACKGROUND: Traumatic subdural hygroma (TSHy) is an accumulation of cerebrospinal fluid (CSF) in the subdural space after head injury. It appears to be relatively common, but its onset time and natural history are not well defined. Considered a benign epiphenomenon of trauma, the pathogenesis of TSHy is still unclear and many questions remain unanswered. This study adds to the information on TSHy, and proposes a classification based on pathogenesis.<br><br>METHODS: Thirty-four consecutive adult patients with TSHy were analyzed for clinical evolution and serial CT scan, during a period of several months. TSHy diagnosis was based on published CT scan criteria of hypodense subdural collection after trauma, without enhancement and neomembrane, with a minimum distance of 3 mm between the skull and brain. Ventricle size was analyzed by calculating the bicaudate index (BCI). For comparison, the BCI was measured from CT scan at three moments: admission, at time of TSHy diagnosis, and from last CT scan.<br><br>RESULTS: There were 34 patients, aged between 16 and 85 years (mean 40), half of them were below 40 years. Road traffic crashes were the main cause of head injury. The mean time for hygroma diagnosis was 9 days. Twenty-one patients (61.8%) underwent conservative treatment for TSHy and 13 (38.2%), surgical treatment. TSHy are early lesions and can be detected in the first 24 hours after trauma, usually as small subdural effusion (SSEff). Based on clinical and CT scan findings, we divided the 34 patients into 3 groups, (Ia and Ib) without evident mass effect and (II) with evident mass effect. Group Ia includes patients without ventricle dilation; Ib, patients with associated ventricle dilations.<br><br>CONCLUSIONS: SSEff detected in the first 24 hours posttrauma in our series evolved into TSHy suggesting that this is an early lesion; all THSy were divided in three groups according to the pathophysiologic mechanism. These three groups probably represent a continuum of CSF absorption impairment. Group Ia represents what most authors consider a simple hygroma, with no impairment on CSF absorption. Group Ib represent the external hydrocephalus form with various degrees of CSF imbalance, and group II were the cases presenting marked mass effect.},
}
@article {pmid18316226,
year = {2008},
author = {Wu, Z and Lai, Y and Xia, Y and Wu, D and Yao, D},
title = {Stimulator selection in SSVEP-based BCI.},
journal = {Medical engineering &amp; physics},
volume = {30},
number = {8},
pages = {1079-1088},
doi = {10.1016/j.medengphy.2008.01.004},
pmid = {18316226},
issn = {1350-4533},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Electrocardiography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation/*methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Steady-state visual evoked potentials (SSVEP) are increasingly used in the development of brain-computer interface techniques (BCI). We investigated the spectrum differences of three kinds of flickers and the differences in SSVEPs evoked by three different stimulators, i.e. the light-emitting diode, the cathode ray tube of a desktop monitor and the liquid crystal display of a laptop screen. The results showed that the SSVEP differences were strongly related to the frequency spectrum differences of the flickers. According to these differences, the stimulator was selected based on the complexity of the BCI system.},
}
@article {pmid18310813,
year = {2008},
author = {Schalk, G and Miller, KJ and Anderson, NR and Wilson, JA and Smyth, MD and Ojemann, JG and Moran, DW and Wolpaw, JR and Leuthardt, EC},
title = {Two-dimensional movement control using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {5},
number = {1},
pages = {75-84},
pmid = {18310813},
issn = {1741-2560},
support = {EB006356/EB/NIBIB NIH HHS/United States ; K23 NS041272/NS/NINDS NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; K23 NS041272-06/NS/NINDS NIH HHS/United States ; R01 EB000856-06/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; NS41272/NS/NINDS NIH HHS/United States ; R01 EB006356-03/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain Mapping ; Data Interpretation, Statistical ; Drug Resistance ; Electrocardiography ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Epilepsy/physiopathology/surgery ; Female ; Humans ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.},
}
@article {pmid18310809,
year = {2008},
author = {Wu, Z and Yao, D},
title = {Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs.},
journal = {Journal of neural engineering},
volume = {5},
number = {1},
pages = {36-43},
doi = {10.1088/1741-2560/5/1/004},
pmid = {18310809},
issn = {1741-2560},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; Photic Stimulation ; *User-Computer Interface ; },
abstract = {Due to the relative noise and artifact insensitivity, steady-state visual evoked potential (SSVEP) has been used increasingly in the study of a brain-computer interface (BCI). However, SSVEP is still influenced by the same frequency component in the spontaneous EEG, and it is meaningful to find a parameter that can avoid or decrease this influence to improve the transfer rate and the accuracy of the SSVEP-based BCI. In this work, with wavelet analysis, a new parameter named stability coefficient (SC) was defined to measure the stability of a frequency, and then the electrode with the highest stability was selected as the signal electrode for further analysis. After that, the SC method and the traditional power spectrum (PS) method were used comparatively to recognize the stimulus frequency from an analogous BCI data constructed from a real SSVEP data, and the results showed that the SC method is better for a short time window data.},
}
@article {pmid18310808,
year = {2008},
author = {Bai, O and Lin, P and Vorbach, S and Floeter, MK and Hattori, N and Hallett, M},
title = {A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior.},
journal = {Journal of neural engineering},
volume = {5},
number = {1},
pages = {24-35},
doi = {10.1088/1741-2560/5/1/003},
pmid = {18310808},
issn = {1741-2560},
support = {//Intramural NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Amyotrophic Lateral Sclerosis/physiopathology/psychology ; *Beta Rhythm ; Brain/*physiology ; Calibration ; Cortical Synchronization ; Data Interpretation, Statistical ; Electromyography ; Evoked Potentials/physiology ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; Movement/*physiology ; Paresis/physiopathology/psychology ; ROC Curve ; Stroke/psychology ; *User-Computer Interface ; Video Games ; },
abstract = {UNLABELLED: To explore the reliability of a high performance brain-computer interface (BCI) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/ approximately 80% for six healthy volunteers, >80%/ approximately 80% for the stroke patient and approximately 90%/ approximately 80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders.<br><br>SIGNIFICANCE: The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training.},
}
@article {pmid18310807,
year = {2008},
author = {Fatourechi, M and Ward, RK and Birch, GE},
title = {A self-paced brain-computer interface system with a low false positive rate.},
journal = {Journal of neural engineering},
volume = {5},
number = {1},
pages = {9-23},
doi = {10.1088/1741-2560/5/1/002},
pmid = {18310807},
issn = {1741-2560},
mesh = {Algorithms ; Brain/*physiology ; Chromosomes/genetics ; Electroencephalography ; False Positive Reactions ; Genetics/statistics &amp; numerical data ; Humans ; ROC Curve ; *User-Computer Interface ; },
abstract = {The performance of current EEG-based self-paced brain-computer interface (SBCI) systems is not suitable for most practical applications. In this paper, an improved SBCI that uses features extracted from three neurological phenomena (movement-related potentials, changes in the power of Mu rhythms and changes in the power of Beta rhythms) to detect an intentional control command in noisy EEG signals is proposed. The proposed system achieves a high true positive (TP) to false positive (FP) ratio. To extract features for each neurological phenomenon in every EEG signal, a method that consists of a stationary wavelet transform followed by matched filtering is developed. For each neurological phenomenon in every EEG channel, features are classified using a support vector machine classifier (SVM). For each neurological phenomenon, a multiple classifier system (MCS) then combines the outputs of the SVMs. Another MCS combines the outputs of MCSs designed for the three neurological phenomena. Various configurations for combining the outputs of these MCSs are considered. A hybrid genetic algorithm (HGA) is proposed to simultaneously select the features, the values of the classifiers' parameters and the configuration for combining MCSs that yield the near optimal performance. Analysis of the data recorded from four able-bodied subjects shows a significant performance improvement over previous SBCIs.},
}
@article {pmid18310804,
year = {2008},
author = {Schalk, G},
title = {Brain-computer symbiosis.},
journal = {Journal of neural engineering},
volume = {5},
number = {1},
pages = {P1-P15},
pmid = {18310804},
issn = {1741-2560},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB006356-03/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Computers ; Humans ; *User-Computer Interface ; },
abstract = {The theoretical groundwork of the 1930s and 1940s and the technical advance of computers in the following decades provided the basis for dramatic increases in human efficiency. While computers continue to evolve, and we can still expect increasing benefits from their use, the interface between humans and computers has begun to present a serious impediment to full realization of the potential payoff. This paper is about the theoretical and practical possibility that direct communication between the brain and the computer can be used to overcome this impediment by improving or augmenting conventional forms of human communication. It is about the opportunity that the limitations of our body's input and output capacities can be overcome using direct interaction with the brain, and it discusses the assumptions, possible limitations and implications of a technology that I anticipate will be a major source of pervasive changes in the coming decades.},
}
@article {pmid18304747,
year = {2008},
author = {Yousif, N and Bayford, R and Wang, S and Liu, X},
title = {Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation.},
journal = {Neuroscience},
volume = {152},
number = {3},
pages = {683-691},
pmid = {18304747},
issn = {0306-4522},
support = {78512/MRC_/Medical Research Council/United Kingdom ; G0600168(78512)/MRC_/Medical Research Council/United Kingdom ; G0600168/MRC_/Medical Research Council/United Kingdom ; 71766/MRC_/Medical Research Council/United Kingdom ; G0400794/MRC_/Medical Research Council/United Kingdom ; },
mesh = {Action Potentials/physiology ; Biological Clocks/physiology ; Brain/anatomy &amp; histology/*physiology ; Computer Simulation ; Deep Brain Stimulation/instrumentation/methods/standards ; Electrodes, Implanted/standards ; Electrodiagnosis/instrumentation/*methods ; Electrophysiology/instrumentation/*methods ; Evoked Potentials/*physiology ; Finite Element Analysis ; Humans ; Microelectrodes/standards ; Models, Neurological ; Neurons/physiology ; Neurophysiology/instrumentation/*methods ; Signal Processing, Computer-Assisted/instrumentation ; },
abstract = {A depth electrode-brain interface (EBI) is formed once electrodes are implanted into the human brain. We investigated the impact of the EBI on the crossing electric currents during both deep brain recording (DBR) and deep brain stimulation (DBS) over the acute, chronic and transitional stages post-implantation, in order to investigate and quantify the effect which changes at the EBI have on both DBR and DBS. We combined two complementary methods: (1) physiological recording of local field potentials via the implanted electrode in patients; and (2) computational simulations of an EBI model. Our depth recordings revealed that the physiological modulation of the EBI in the acute stage via brain pulsation selectively affected the crossing neural signals in a frequency-dependent manner, as the amplitude of the electrode potential was inversely correlated with that of the tremor-related oscillation, but not the beta oscillation. Computational simulations of DBS during the transitional period showed that the shielding effect of partial giant cell growth on the injected current could shape the field in an unpredictable manner. These results quantitatively demonstrated that physiological modulation of the EBI significantly affected the crossing currents in both DBR and DBS. Studying the microenvironment of the EBI may be a key step in investigating the mechanisms of DBR and DBS, as well as brain-computer interactions in general.},
}
@article {pmid18303806,
year = {2008},
author = {Citi, L and Poli, R and Cinel, C and Sepulveda, F},
title = {P300-based BCI mouse with genetically-optimized analogue control.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {1},
pages = {51-61},
doi = {10.1109/TNSRE.2007.913184},
pmid = {18303806},
issn = {1534-4320},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Computer Graphics ; Electrodes ; Electroencephalography ; Event-Related Potentials, P300/*genetics/*physiology ; Female ; Humans ; Male ; Psychomotor Performance/physiology ; *User-Computer Interface ; },
abstract = {In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2-D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.},
}
@article {pmid18303804,
year = {2008},
author = {Huang, H and Zhou, P and Li, G and Kuiken, TA},
title = {An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {1},
pages = {37-45},
pmid = {18303804},
issn = {1534-4320},
support = {N01-HD-5-3402/HD/NICHD NIH HHS/United States ; R01 HD044798/HD/NICHD NIH HHS/United States ; R01 HD043137-01/HD/NICHD NIH HHS/United States ; N01 HD053402/HD/NICHD NIH HHS/United States ; R01 HD043137/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artificial Limbs ; *Electrodes, Implanted ; Electromyography/*instrumentation ; Electrophysiology ; Female ; Hand/innervation/physiology ; Humans ; Linear Models ; Male ; Movement/physiology ; Muscle, Skeletal/*innervation ; Pattern Recognition, Automated ; Shoulder/physiology/surgery ; },
abstract = {Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of four TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0 (+/-3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7+/-4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses.},
}
@article {pmid18303803,
year = {2008},
author = {London, BM and Jordan, LR and Jackson, CR and Miller, LE},
title = {Electrical stimulation of the proprioceptive cortex (area 3a) used to instruct a behaving monkey.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {1},
pages = {32-36},
pmid = {18303803},
issn = {1534-4320},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS048845-01A1/NS/NINDS NIH HHS/United States ; NS048845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Behavior, Animal/*physiology ; Electric Stimulation ; Electrodes, Implanted ; Extremities/innervation/physiology ; Macaca mulatta ; Magnetic Resonance Imaging ; Male ; Motor Cortex/*physiology/surgery ; Movement/physiology ; Proprioception/*physiology ; Reaction Time/physiology ; },
abstract = {A growing number of brain-machine interfaces have now been developed that allow movements of an external device to be controlled using recordings from the brain. This work has been undertaken with a number of different animal models, as well as several human patients with quadriplegia. The resulting movements, whether of computer cursors or robotic limbs, remain quite slow and unstable compared to normal limb movements. It is an open question, how much of this instability is the result of the limited forward control path, and how much has to do with the total lack of normal proprioceptive feedback. We have begun preliminary studies of the effectiveness of electrical stimulation in the proprioceptive area of the primary somatosensory cortex (area 3a) as a potential means to deliver an artificial sense of proprioception to a monkey. We have demonstrated that it is possible for the monkey to detect brief stimulus trains at relatively low current levels, and to discriminate between trains of different frequencies. These observations need to be expanded to include more complex, time-varying waveforms that could potentially convey information about the state of the limb.},
}
@article {pmid18303801,
year = {2008},
author = {Acharya, S and Tenore, F and Aggarwal, V and Etienne-Cummings, R and Schieber, MH and Thakor, NV},
title = {Decoding individuated finger movements using volume-constrained neuronal ensembles in the M1 hand area.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {1},
pages = {15-23},
pmid = {18303801},
issn = {1534-4320},
support = {R01 NS027686/NS/NINDS NIH HHS/United States ; R37 NS027686/NS/NINDS NIH HHS/United States ; R37 NS027686-19/NS/NINDS NIH HHS/United States ; R01/R37 NS27686/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Computer Simulation ; Conditioning, Operant/physiology ; Efferent Pathways/cytology/*physiology ; Fingers/*innervation/*physiology ; Hand/innervation/*physiology ; Macaca mulatta ; Male ; Microelectrodes ; Models, Statistical ; Motor Cortex/*physiology ; Motor Neurons/*physiology ; Movement/*physiology ; Nonlinear Dynamics ; Prosthesis Design ; Wrist/innervation/physiology ; },
abstract = {Individuated finger and wrist movements can be decoded using random subpopulations of neurons that are widely distributed in the primary motor (M1) hand area. This work investigates 1) whether it is possible to decode dexterous finger movements using spatially-constrained volumes of neurons as typically recorded from a microelectrode array; and 2) whether decoding accuracy differs due to the configuration or location of the array within the M1 hand area. Single-unit activities were sequentially recorded from task-related neurons in two rhesus monkeys as they performed individuated movements of the fingers and the wrist. Simultaneous neuronal ensembles were simulated by constraining these activities to the recording field dimensions of conventional microelectrode array architectures. Artificial neural network (ANN) based filters were able to decode individuated finger movements with greater than 90% accuracy for the majority of movement types, using as few as 20 neurons from these ensemble activities. Furthermore, for the large majority of cases there were no significant differences (p < 0.01) in decoding accuracy as a function of the location of the recording volume. The results suggest that a brain-machine interface (BMI) for dexterous control of individuated fingers and the wrist can be implemented using microelectrode arrays placed broadly in the M1 hand area.},
}
@article {pmid18303800,
year = {2008},
author = {Aggarwal, V and Acharya, S and Tenore, F and Shin, HC and Etienne-Cummings, R and Schieber, MH and Thakor, NV},
title = {Asynchronous decoding of dexterous finger movements using M1 neurons.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {16},
number = {1},
pages = {3-14},
pmid = {18303800},
issn = {1534-4320},
support = {R01 NS027686/NS/NINDS NIH HHS/United States ; R37 NS027686/NS/NINDS NIH HHS/United States ; R37 NS027686-19/NS/NINDS NIH HHS/United States ; R01/R37 NS27686/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Artificial Limbs ; Electrophysiology ; Fingers/*innervation/*physiology ; Hand/innervation/*physiology ; Macaca mulatta ; Male ; Models, Statistical ; Motor Neurons/*physiology ; Motor Skills ; Movement/physiology ; Neural Networks, Computer ; *Prosthesis Design ; Robotics ; Wrist/innervation/physiology ; },
abstract = {Previous efforts in brain-machine interfaces (BMI) have looked at decoding movement intent or hand and arm trajectory, but current cortical control strategies have not focused on the decoding of dexterous [corrected] actions such as finger movements. The present work demonstrates the asynchronous decoding (i.e., where cues indicating the onset of movement are not known) of individual and combined finger movements. Single-unit activities were recorded sequentially from a population of neurons in the M1 hand area of trained rhesus monkeys during flexion and extension movements of each finger and the wrist. Nonlinear filters were designed to detect the onset of movement and decode the movement type from randomly selected neuronal ensembles (assembled from individually recorded single-unit activities). Average asynchronous decoding accuracies as high as 99.8%, 96.2%, and 90.5%, were achieved for individuated finger and wrist movements with three monkeys. Average decoding accuracy was still 92.5% when combined movements of two fingers were included. These results demonstrate that it is possible to asynchronously decode dexterous finger movements from a neuronal ensemble with high accuracy. This work takes an important step towards the development of a BMI for direct neural control of a state-of-the-art, multifingered hand prosthesis.},
}
@article {pmid18301717,
year = {2007},
author = {Babiloni, F and Cichocki, A and Gao, S},
title = {Brain-computer interfaces: towards practical implementations and potential applications.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {62637},
doi = {10.1155/2007/62637},
pmid = {18301717},
issn = {1687-5265},
}
@article {pmid18301205,
year = {2008},
author = {Berne, JD and Reuland, KR and Villarreal, DH and McGovern, TM and Rowe, SA and Norwood, SH},
title = {Internal carotid artery stenting for blunt carotid artery injuries with an associated pseudoaneurysm.},
journal = {The Journal of trauma},
volume = {64},
number = {2},
pages = {398-405},
doi = {10.1097/TA.0b013e31815eb788},
pmid = {18301205},
issn = {1529-8809},
mesh = {Accidents, Traffic ; Adolescent ; Adult ; Aneurysm, False/diagnostic imaging/etiology/*therapy ; Aspirin/therapeutic use ; Carotid Artery Injuries/complications/diagnostic imaging/*therapy ; Carotid Artery, Internal/*diagnostic imaging ; Clopidogrel ; Female ; Humans ; Male ; Middle Aged ; Platelet Aggregation Inhibitors/therapeutic use ; Prospective Studies ; *Stents ; Ticlopidine/analogs &amp; derivatives/therapeutic use ; Tomography, X-Ray Computed ; Wounds, Nonpenetrating/complications/*therapy ; },
abstract = {BACKGROUND: Blunt carotid artery injuries (BCI) are being recognized and treated with increasing frequency because of improved screening protocols. Recent advances in endovascular techniques using microcoils, angioplasty, and stenting offer a new treatment strategy for those patients with traumatic pseudoaneurysms (PA) (BCI and PA). Experience with these techniques is limited because of the rarity of these injuries.<br><br>HYPOTHESIS: Early anticoagulation (AC) or antiplatelet (AP) therapy combined with carotid artery stenting is a safe alternative to AC alone for the treatment of grade III carotid artery injuries (BCI and PA).<br><br>DESIGN: Prospective cohort study.<br><br>SETTING: A rural, community Level I trauma center.<br><br>PATIENTS AND METHODS: All patients with a nonocclusive BCI and PA during a 5.5 year period from June 23, 2000 to December 31, 2005 were included in the study.<br><br>RESULTS: : Eleven patients with grade BCI and PA underwent endovascular repair. Nine patients (81%) had associated traumatic intracranial hemorrhage. AC (heparin drip) or AP therapy (clopidogrel or aspirin or both) was initiated in all patients within 48 hours of diagnosis of BCI. Time from admission to AC or AP was 21 +/- 9.5 hours (mean +/- SD). Mortality rate was 18% (2 of 11). One death was attributed to severe brain injury. The other was attributed to a stroke from the carotid injury. No patient had radiologic progression of traumatic intracranial hemorrhage on head computed tomography despite AP or AC. One patient sustained a mild embolic cerebrovascular ischemic event before stenting. No other survivors developed a stroke or any other evidence of cerebral ischemic symptoms. Two recurrent PAs developed during hospitalization and were successfully managed with an additional stent. All survivors were discharged with a good neurologic outcome. Seven patients had follow-up from 6 months to 4 years: one developed asymptomatic 50% stenosis at 6 months requiring successful angioplasty. All others showed complete healing without stenosis.<br><br>CONCLUSIONS: Carotid artery stenting is safe and effective initial therapy for patients with nonocclusive BCI and PA. Initial intermediate-term follow-up also fails to demonstrate significant morbidity for up to 4 years.},
}
@article {pmid18288260,
year = {2007},
author = {Bashashati, A and Ward, RK and Birch, GE},
title = {Towards development of a 3-state self-paced brain-computer interface.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {84386},
pmid = {18288260},
issn = {1687-5265},
abstract = {Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI.},
}
@article {pmid18288259,
year = {2007},
author = {Halder, S and Bensch, M and Mellinger, J and Bogdan, M and Kübler, A and Birbaumer, N and Rosenstiel, W},
title = {Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {82069},
pmid = {18288259},
issn = {1687-5265},
abstract = {We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.},
}
@article {pmid18288256,
year = {2007},
author = {Menendez, RG and Noirhomme, Q and Cincotti, F and Mattia, D and Aloise, F and González Andino, S},
title = {Modern electrophysiological methods for brain-computer interfaces.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {56986},
pmid = {18288256},
issn = {1687-5265},
abstract = {Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far.},
}
@article {pmid18288247,
year = {2007},
author = {Kauhanen, L and Jylänki, P and Lehtonen, J and Rantanen, P and Alaranta, H and Sams, M},
title = {EEG-based brain-computer interface for tetraplegics.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {23864},
pmid = {18288247},
issn = {1687-5265},
abstract = {Movement-disabled persons typically require a long practice time to learn how to use a brain-computer interface (BCI). Our aim was to develop a BCI which tetraplegic subjects could control only in 30 minutes. Six such subjects (level of injury C4-C5) operated a 6-channel EEG BCI. The task was to move a circle from the centre of the computer screen to its right or left side by attempting visually triggered right- or left-hand movements. During the training periods, the classifier was adapted to the user's EEG activity after each movement attempt in a supervised manner. Feedback of the performance was given immediately after starting the BCI use. Within the time limit, three subjects learned to control the BCI. We believe that fast initial learning is an important factor that increases motivation and willingness to use BCIs. We have previously tested a similar single-trial classification approach in healthy subjects. Our new results show that methods developed and tested with healthy subjects do not necessarily work as well as with motor-disabled patients. Therefore, it is important to use motor-disabled persons as subjects in BCI development.},
}
@article {pmid18277385,
year = {2008},
author = {Mani, KM and Lefebvre, C and Wang, K and Lim, WK and Basso, K and Dalla-Favera, R and Califano, A},
title = {A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas.},
journal = {Molecular systems biology},
volume = {4},
number = {},
pages = {169},
pmid = {18277385},
issn = {1744-4292},
support = {T15 LM007079/LM/NLM NIH HHS/United States ; U54CA121852/CA/NCI NIH HHS/United States ; U54 CA121852/CA/NCI NIH HHS/United States ; R01AI066116/AI/NIAID NIH HHS/United States ; R01 AI066116/AI/NIAID NIH HHS/United States ; R01CA109755/CA/NCI NIH HHS/United States ; R01 CA109755/CA/NCI NIH HHS/United States ; 5 T15 LM007079-15/LM/NLM NIH HHS/United States ; },
mesh = {Algorithms ; Benchmarking ; Computational Biology/methods ; Gene Expression Profiling ; Gene Regulatory Networks/*genetics ; Genome, Human ; Humans ; Lymphoma, B-Cell/classification/*genetics ; Metabolic Networks and Pathways/*genetics ; Models, Genetic ; Oligonucleotide Array Sequence Analysis ; *Oncogenes ; Reproducibility of Results ; *Systems Biology ; },
abstract = {The computational identification of oncogenic lesions is still a key open problem in cancer biology. Although several methods have been proposed, they fail to model how such events are mediated by the network of molecular interactions in the cell. In this paper, we introduce a systems biology approach, based on the analysis of molecular interactions that become dysregulated in specific tumor phenotypes. Such a strategy provides important insights into tumorigenesis, effectively extending and complementing existing methods. Furthermore, we show that the same approach is highly effective in identifying the targets of molecular perturbations in a human cellular context, a task virtually unaddressed by existing computational methods. To identify interactions that are dysregulated in three distinct non-Hodgkin's lymphomas and in samples perturbed with CD40 ligand, we use the B-cell interactome (BCI), a genome-wide compendium of human B-cell molecular interactions, in combination with a large set of microarray expression profiles. The method consistently ranked the known gene in the top 20 (0.3%), outperforming conventional approaches in 3 of 4 cases.},
}
@article {pmid18274615,
year = {2007},
author = {Sitaram, R and Caria, A and Veit, R and Gaber, T and Rota, G and Kuebler, A and Birbaumer, N},
title = {FMRI brain-computer interface: a tool for neuroscientific research and treatment.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {25487},
pmid = {18274615},
issn = {1687-5265},
abstract = {Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.},
}
@article {pmid18274604,
year = {2007},
author = {Zhang, D and Wang, Y and Gao, X and Hong, B and Gao, S},
title = {An algorithm for idle-state detection in motor-imagery-based brain-computer interface.},
journal = {Computational intelligence and neuroscience},
volume = {2007},
number = {},
pages = {39714},
pmid = {18274604},
issn = {1687-5265},
abstract = {For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the "idle state") so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of "idle-state detection without training samples." The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including "idle" task.},
}
@article {pmid18270008,
year = {2008},
author = {Lehtonen, J and Jylänki, P and Kauhanen, L and Sams, M},
title = {Online classification of single EEG trials during finger movements.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {2 Pt 1},
pages = {713-720},
doi = {10.1109/TBME.2007.912653},
pmid = {18270008},
issn = {1558-2531},
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Fingers/*physiology ; Humans ; Male ; Movement/*physiology ; Online Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {Many offline studies have explored the feasibility of EEG potentials related to single limb movements for a brain-computer interface (BCI) control signal. However, only few functional online single-trial BCI systems have been reported. We investigated whether inexperienced subjects could control a BCI accurately by means of visually-cued left versus right index finger movements, performed every 2 s, after only a 20-min training period. Ten subjects tried to move a circle from the center to a target location at the left or right side of the computer screen by moving their left or right index finger. The classifier was updated after each trial using the correct class labels, enabling up-to-date feedback to the subjects throughout the training. Therefore, a separate data collection session for optimizing the classification algorithm was not needed. When the performance of the BCI was tested, the classifier was not updated. Seven of the ten subjects were able to control the BCI well. They could choose the correct target in 84%-100% of the cases, 3.5-7.7 times a minute. Their mean single trial classification rate was 80% and bit rate 10 bits/min. These results encourage the development of BCIs for paralyzed persons based on detection of single-trial movement attempts.},
}
@article {pmid18270004,
year = {2008},
author = {Scherer, R and Lee, F and Schlogl, A and Leeb, R and Bischof, H and Pfurtscheller, G},
title = {Toward self-paced brain-computer communication: navigation through virtual worlds.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {2 Pt 1},
pages = {675-682},
doi = {10.1109/TBME.2007.903709},
pmid = {18270004},
issn = {1558-2531},
mesh = {Biofeedback, Psychology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {The self-paced control paradigm enables users to operate brain-computer interfaces (BCI) in a more natural way: no longer is the machine in control of the timing and speed of communication, but rather the user is. This is important to enhance the usability, flexibility, and response time of a BCI. In this work, we show how subjects, after performing cue-based feedback training (smiley paradigm), learned to navigate self-paced through the "freeSpace" virtual environment (VE). Similar to computer games, subjects had the task of picking up items by using the following navigation commands: rotate left, rotate right, and move forward (three classes). Since the self-paced control paradigm allows subjects to make voluntary decisions on time, type, and duration of mental activity, no cues or routing directives were presented. The BCI was based only on three bipolar electroencephalogram channels and operated by motor imagery. Eye movements (electrooculogram) and electromyographic artifacts were reduced and detected online. The results of three able-bodied subjects are reported and problems emerging from self-paced control are discussed.},
}
@article {pmid18258825,
year = {2008},
author = {Buch, E and Weber, C and Cohen, LG and Braun, C and Dimyan, MA and Ard, T and Mellinger, J and Caria, A and Soekadar, S and Fourkas, A and Birbaumer, N},
title = {Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke.},
journal = {Stroke},
volume = {39},
number = {3},
pages = {910-917},
pmid = {18258825},
issn = {1524-4628},
support = {Z99 NS999999/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Aged ; Brain/*physiopathology ; Chronic Disease ; *Hand/physiopathology ; Hand Strength ; Humans ; Magnetic Resonance Imaging ; *Magnetoencephalography ; Middle Aged ; *Orthotic Devices ; Paralysis/*etiology ; Stroke/complications/diagnosis/physiopathology ; *Stroke Rehabilitation ; *User-Computer Interface ; Volition ; },
abstract = {BACKGROUND AND PURPOSE: Stroke is a leading cause of long-term motor disability among adults. Present rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment, particularly in relation to hand function. Here we tested the hypothesis that patients experiencing hand plegia as a result of a single, unilateral subcortical, cortical or mixed stroke occurring at least 1 year previously, could be trained to operate a mechanical hand orthosis through a brain-computer interface (BCI).<br><br>METHODS: Eight patients with chronic hand plegia resulting from stroke (residual finger extension function rated on the Medical Research Council scale=0/5) were recruited from the Stroke Neurorehabilitation Clinic, Human Cortical Physiology Section of the National Institute for Neurological Disorders and Stroke (NINDS) (n=5) and the Clinic of Neurology of the University of T&#xfc;bingen (n=3). Diagnostic MRIs revealed single, unilateral subcortical, cortical or mixed lesions in all patients. A magnetoencephalography-based BCI system was used for this study. Patients participated in between 13 to 22 training sessions geared to volitionally modulate micro rhythm amplitude originating in sensorimotor areas of the cortex, which in turn raised or lowered a screen cursor in the direction of a target displayed on the screen through the BCI interface. Performance feedback was provided visually in real-time. Successful trials (in which the cursor made contact with the target) resulted in opening/closing of an orthosis attached to the paralyzed hand.<br><br>RESULTS: Training resulted in successful BCI control in 6 of 8 patients. This control was associated with increased range and specificity of mu rhythm modulation as recorded from sensors overlying central ipsilesional (4 patients) or contralesional (2 patients) regions of the array. Clinical scales used to rate hand function showed no significant improvement after training.<br><br>CONCLUSIONS: These results suggest that volitional control of neuromagnetic activity features recorded over central scalp regions can be achieved with BCI training after stroke, and used to control grasping actions through a mechanical hand orthosis.},
}
@article {pmid18251709,
year = {2008},
author = {Bauernfeind, G and Leeb, R and Wriessnegger, SC and Pfurtscheller, G},
title = {Development, set-up and first results for a one-channel near-infrared spectroscopy system.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {53},
number = {1},
pages = {36-43},
doi = {10.1515/BMT.2008.005},
pmid = {18251709},
issn = {0013-5585},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Brain Mapping/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/physiology ; Female ; Humans ; Lighting/instrumentation ; Male ; Oxyhemoglobins/*analysis ; Pilot Projects ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted/*instrumentation ; Spectroscopy, Near-Infrared/*instrumentation/methods ; },
abstract = {Abstract Near-infrared spectroscopy (NIRS) is a non-invasive optical technique that can be used to assess functional activity in the human brain. This work describes the set-up of a one-channel NIRS system designed for use as an optical brain-computer interface (BCI) and reports on first measurements of deoxyhemoglobin (Hb) and oxyhemoglobin (HbO(2)) changes during mental arithmetic tasks. We found relatively stable and reproducible hemodynamic responses in a group of 13 healthy subjects. Unexpected observations of a decrease in HbO(2) and increase in Hb concentrations measured over the prefrontal cortex were in contrast to the typical hemodynamic responses (increase in HbO(2), decrease in Hb) during cortical activation previously reported.},
}
@article {pmid18232506,
year = {2007},
author = {Luo, P and Xie, G and Jiang, Z},
title = {[The progress in researches on biocompatibility for direct brain-machine interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {24},
number = {6},
pages = {1416-1418},
pmid = {18232506},
issn = {1001-5515},
mesh = {*Biocompatible Materials ; Biofeedback, Psychology ; Brain/*physiology ; Brain Diseases/*rehabilitation ; *Electrodes, Implanted ; Humans ; Microelectrodes ; Paralysis/rehabilitation ; Prostheses and Implants ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {An important application of the direct brain-machine interfaces are providing an outlet for severely paralyzed individuals to communicate with the world. According to different type of microelectrodes, brain-machine interfaces are divided into indirect-BMI and direct-BMI. Direct-BMI are intracortical recording devices designed to capture the action potentials of many individual neurons, especially those that code for movement or its intent. A key problem in research of BMI is how to enhance biocompatibility for direct-BMI. This review introduces some new microelectrodes of direct brain-machine interface which all have higher biocompatibility.},
}
@article {pmid18232384,
year = {2008},
author = {Müller-Putz, GR and Pfurtscheller, G},
title = {Control of an electrical prosthesis with an SSVEP-based BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {1},
pages = {361-364},
doi = {10.1109/TBME.2007.897815},
pmid = {18232384},
issn = {0018-9294},
mesh = {Adolescent ; Adult ; *Artificial Intelligence ; Electronics/instrumentation ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Female ; Hand/*physiology ; Hand Strength/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Pattern Recognition, Automated/methods ; Prosthesis Design ; Task Performance and Analysis ; Therapy, Computer-Assisted/instrumentation/methods ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are systems that establish a direct connection between the human brain and a computer, thus providing an additional communication channel. They are used in a broad field of applications nowadays. One important issue is the control of neuroprosthetic devices for the restoration of the grasp function in spinal-cord-injured people. In this communication, an asynchronous (self-paced) four-class BCI based on steady-state visual evoked potentials (SSVEPs) was used to control a two-axes electrical hand prosthesis. During training, four healthy participants reached an online classification accuracy between 44% and 88%. Controlling the prosthetic hand asynchronously, the participants reached a performance of 75.5 to 217.5 s to copy a series of movements, whereas the fastest possible duration determined by the setup was 64 s. The number of false negative (FN) decisions varied from 0 to 10 (the maximal possible decisions were 34). It can be stated that the SSVEP-based BCI, operating in an asynchronous mode, is feasible for the control of neuroprosthetic devices with the flickering lights mounted on its surface.},
}
@article {pmid18232371,
year = {2008},
author = {Shenoy, P and Miller, KJ and Ojemann, JG and Rao, RP},
title = {Generalized features for electrocorticographic BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {55},
number = {1},
pages = {273-280},
doi = {10.1109/TBME.2007.903528},
pmid = {18232371},
issn = {0018-9294},
mesh = {Algorithms ; Artificial Intelligence ; Brain Mapping/*methods ; Electrocardiography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; *User-Computer Interface ; },
abstract = {This paper studies classifiability of electrocorticographic signals (ECoG) for use in a human brain-computer interface (BCI). The results show that certain spectral features can be reliably used across several subjects to accurately classify different types of movements. Sparse and nonsparse versions of the support vector machine and regularized linear discriminant analysis linear classifiers are assessed and contrasted for the classification problem. In conjunction with a careful choice of features, the classification process automatically and consistently identifies neurophysiological areas known to be involved in the movements. An average two-class classification accuracy of 95% for real movement and around 80% for imagined movement is shown. The high accuracy and generalizability of these results, obtained with as few as 30 data samples per class, support the use of classification methods for ECoG-based BCIs.},
}
@article {pmid18216207,
year = {2008},
author = {Waldert, S and Preissl, H and Demandt, E and Braun, C and Birbaumer, N and Aertsen, A and Mehring, C},
title = {Hand movement direction decoded from MEG and EEG.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {28},
number = {4},
pages = {1000-1008},
pmid = {18216207},
issn = {1529-2401},
mesh = {Electroencephalography/*methods ; Evoked Potentials, Motor/physiology ; Hand/*physiology ; Humans ; Magnetoencephalography/*methods ; Movement/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; },
abstract = {Brain activity can be used as a control signal for brain-machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for < or = 7 Hz (low-frequency band) and 62-87 Hz (high-gamma band) and a decrease for 10-30 Hz (beta band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the beta and high-gamma bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.},
}
@article {pmid18210784,
year = {2007},
author = {Ikeda, A},
title = {[Human supplementary motor area: a role in voluntary movements and its clinical significance].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {47},
number = {11},
pages = {723-726},
pmid = {18210784},
issn = {0009-918X},
mesh = {Epilepsy/physiopathology ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {Clinically, seizures from supplementary motor area (SMA) are characterized by asymmetric bilateral tonic posturing without loss of awareness, and its dysfunction is also strongly related to the clinical cardinal features in patients with Parkinson's disease and dystonia. By investigating Bereitschaftspotentials (BPs) from SMA, the following normal functions are elucidated. 1) SMA proper, a caudal part of SMA showed a somatotopy of BP generators in accordance with each part of the voluntary movements in the body, 2) bilateral SMAs were involved in each side of the body movements equally, and the amplitude did not differ from one in the contralateral primary motor area (MI), 3) pre-SMA was strongly related sensorimotor integration, decision making, repetitive rate of voluntary movements, voluntary motor inhibition and negative motor response. We look forward to clinical application of brain potentials from SMA in the field of brain-computer interface such as assessment and restorative approach in patients with spinal cord injury, paraplegia or motor neuron disease.},
}
@article {pmid18198704,
year = {2007},
author = {Leeb, R and Lee, F and Keinrath, C and Scherer, R and Bischof, H and Pfurtscheller, G},
title = {Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {15},
number = {4},
pages = {473-482},
doi = {10.1109/TNSRE.2007.906956},
pmid = {18198704},
issn = {1534-4320},
mesh = {Adult ; Artifacts ; Brain/*physiology ; Electrodes ; Electroencephalography ; Electromyography ; Eye Movements/physiology ; Feedback ; Female ; Functional Laterality ; Humans ; Male ; *User-Computer Interface ; },
abstract = {The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.},
}
@article {pmid18198132,
year = {2008},
author = {Egeth, M},
title = {A "Turing Test" and BCI for locked-in children and adults.},
journal = {Medical hypotheses},
volume = {70},
number = {5},
pages = {1067},
doi = {10.1016/j.mehy.2007.12.001},
pmid = {18198132},
issn = {0306-9877},
mesh = {Adult ; Brain/*pathology ; Child ; *Communication ; *Communication Devices for People with Disabilities ; *Computer Systems ; Electroencephalography ; Humans ; Nerve Net ; Neurons/metabolism ; Paralysis/rehabilitation ; *User-Computer Interface ; },
}
@article {pmid20454553,
year = {2008},
author = {Yao, J and Sheaff, C and Dewald, JP},
title = {Usage of the ACT Robot in a Brain Machine Interface for Hand Opening and Closing in Stroke Survivors.},
journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
volume = {2007},
number = {},
pages = {938-942},
pmid = {20454553},
issn = {1945-7901},
support = {R01 HD039343/HD/NICHD NIH HHS/United States ; R01 HD047569-04/HD/NICHD NIH HHS/United States ; T32 EB009406/EB/NIBIB NIH HHS/United States ; T32 EB005170/EB/NIBIB NIH HHS/United States ; T32 EB005170-02/EB/NIBIB NIH HHS/United States ; T32 EB009406-01/EB/NIBIB NIH HHS/United States ; R01 HD047569/HD/NICHD NIH HHS/United States ; },
abstract = {At six months after brain injury, about 65% of stroke survivors have been shown to be unable to incorporate the affected hand into activities of daily living (ADL). Using a reliable Brain-Machine-Interface (BMI) together with Neural Electronic Stimulation (NES) is a possible solution for the restoration of hand function in severely impaired hemiparetic stroke survivors. However, discoordination, i.e. the abnormal coupling between adjacent joints, causes an expected reduction in the performance of BMI algorithms. In this study, we test whether the active support of an ACT(3D) robot can increase the performance of two brain-machine-interface (BMI) algorithms in separating the subject's intention to open or close the impaired hand during reach. Improvement in recognition rate was obtained in 4 chronic hemiparetic stroke subjects when support from the robot was available. Further analysis on one subject suggests that such an improvement is related to quantitative changes in cortical activity. This result suggests that the ACT(3D) robot can be used to train severely impaired stroke subjects to use a BMI-controlled NES device.},
}
@article {pmid18184778,
year = {2008},
author = {Talmi, D and Seymour, B and Dayan, P and Dolan, RJ},
title = {Human pavlovian-instrumental transfer.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {28},
number = {2},
pages = {360-368},
pmid = {18184778},
issn = {1529-2401},
support = {//Wellcome Trust/United Kingdom ; 078865//Wellcome Trust/United Kingdom ; },
mesh = {Adult ; Analysis of Variance ; Brain/blood supply/*physiology ; Brain Mapping ; Conditioning, Classical/*physiology ; Conditioning, Operant/*physiology ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Male ; *Motivation ; Oxygen/blood ; Transfer, Psychology/*physiology ; },
abstract = {The vigor with which a participant performs actions that produce valuable outcomes is subject to a complex set of motivational influences. Many of these are believed to involve the amygdala and the nucleus accumbens, which act as an interface between limbic and motor systems. One prominent class of influences is called pavlovian-instrumental transfer (PIT), in which the motivational characteristics of a predictor influence the vigor of an action with respect to which it is formally completely independent. We provide a demonstration of behavioral PIT in humans, with an audiovisual predictor of the noncontingent delivery of money inducing participants to perform more avidly an action involving squeezing a handgrip to earn money. Furthermore, using functional magnetic resonance imaging, we show that this enhanced motivation was associated with a trial-by-trial correlation with the blood oxygenation level-dependent (BOLD) signal in the nucleus accumbens and a subject-by-subject correlation with the BOLD signal in the amygdala. Our data dovetails well with the animal literature and sheds light on the neural control of vigor.},
}
@article {pmid19520633,
year = {2010},
author = {Fripp, J and Crozier, S and Warfield, SK and Ourselin, S},
title = {Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee.},
journal = {IEEE transactions on medical imaging},
volume = {29},
number = {1},
pages = {55-64},
pmid = {19520633},
issn = {1558-254X},
support = {R01 RR021885-04/RR/NCRR NIH HHS/United States ; R03 CA126466-02/CA/NCI NIH HHS/United States ; R01 RR021885/RR/NCRR NIH HHS/United States ; R03 CA126466/CA/NCI NIH HHS/United States ; R01 EB008015-03/EB/NIBIB NIH HHS/United States ; R01 EB008015/EB/NIBIB NIH HHS/United States ; R01 RR021885-04S1/RR/NCRR NIH HHS/United States ; R01 GM074068/GM/NIGMS NIH HHS/United States ; R01 GM074068-04/GM/NIGMS NIH HHS/United States ; },
mesh = {Algorithms ; Cartilage, Articular/*anatomy &amp; histology ; Humans ; Image Processing, Computer-Assisted/*methods ; Knee Joint/*anatomy &amp; histology ; Magnetic Resonance Imaging/*methods ; Models, Biological ; Reproducibility of Results ; },
abstract = {In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.},
}
@article {pmid19317181,
year = {2008},
author = {Sakurai, Y and Takahashi, S},
title = {Dynamic synchrony of local cell assembly.},
journal = {Reviews in the neurosciences},
volume = {19},
number = {6},
pages = {425-440},
doi = {10.1515/revneuro.2008.19.6.425},
pmid = {19317181},
issn = {0334-1763},
mesh = {Animals ; Cortical Synchronization/*methods ; Haplorhini ; Memory/*physiology ; Neurons/*physiology ; Prefrontal Cortex/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {In the present paper, we focus on the problem of the dynamic size of a cell assembly and discuss how we can detect synchronized firing of a local cell assembly consisting of closely neighboring neurons in the working brain. A local cell assembly is difficult to detect because of the problem of spike overlapping of neighboring neurons, which cannot be overcome by ordinary spike-sorting techniques. We introduce a unique technique of spike-sorting that combines independent component analysis (ICA) and an ordinary sorting method to separate individual neighboring neurons and analyze their firing synchrony in behaving animals. One of our experiments employing this method showed that some closely neighboring neurons in the monkey prefrontal cortex have dynamic and sharp synchrony of firing reflecting local cell assemblies during working-memory processes. Another experiment showed that our other method (ICSort) of novel spike-sorting by ICA using special electrodes (dodecatrodes) can distinguish firing signals from the soma and those from the dendrites of individual neurons in behaving rats and suggests that the somatic and dendritic signals have different roles in information processing. This indicates that functional connectivity among neurons may be more dynamic and complex and spikes from the soma and dendrites of individual neurons should be considered in the investigation of the activity of local cell assemblies. We finally propose that detailed and real features of a local cell assembly consisting of closely neighboring neurons should be examined further and detection of local cell assemblies could be applied to the development of neuronal prosthetic devices, that is, brain-machine interfaces (BMIs).},
}
@article {pmid18164655,
year = {2008},
author = {Lee, PL and Hsieh, JC and Wu, CH and Shyu, KK and Wu, YT},
title = {Brain computer interface using flash onset and offset visual evoked potentials.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {3},
pages = {605-616},
doi = {10.1016/j.clinph.2007.11.013},
pmid = {18164655},
issn = {1388-2457},
mesh = {Adult ; Attention/*physiology ; Brain Mapping ; *Computer Systems ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Eye Movements/physiology ; Female ; *Field Dependence-Independence ; Humans ; Male ; Pattern Recognition, Visual/physiology ; Photic Stimulation/*methods ; Reaction Time/physiology ; Time Factors ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This paper presents a brain computer interface (BCI) actuated by flash onset and offset visual evoked potentials (FVEPs). Flashing stimuli, such as digits or letters, are displayed on a LCD screen for inducing onset and offset FVEPs when one stares at one of them. Subjects can shift their gaze at target flashing digits or letters to generate a string for communication purposes.<br><br>METHODS: By designing the flickering sequences with mutually independent flash onsets (or offsets) and employing the inherent property that onset (or offset) FVEPs are time-locked and phase-locked to flash onsets (or offsets) of gazed stimuli, segmented epochs based on the flash onsets (or offsets) of gazed stimuli will be enhanced after averaging whereas those based on the onsets (or offsets) of non-gazed stimuli will be suppressed after averaging. The amplitude difference between the N2 and P2 peaks of averaged onset FVEPs, denoted by Amp(onset), and that between the N1 and P1 peaks of averaged offset FVEPs, denoted by Amp(offset), are detected during experiments. The stimulus inducing the largest value of the sum Amp(onset)+Amp(offset) is identified as the gazed target and the representative digit or letter is sent out.<br><br>RESULTS: Five subjects participated in two experiments. In the first experiment, subjects were asked to gaze at 25 flickering stimuli one by one with each for a duration of 1min. The mean accuracy with 10-epoch averages was 97.4%. In the second task, subjects were instructed to generate a string '0287513694E' by staring at stimuli on a pseudo keypad comprising ten digits '0-9' and two letters 'B' and 'E'. The mean accuracy and information transfer rates were 92.18% and 33.65bits/min, respectively.<br><br>CONCLUSIONS: The onset and offset FVEP-based BCI has shown that high information transfer rate has been achieved.<br><br>SIGNIFICANCE: A novel FVEP-based BCI system is proposed as an efficient and reliable tool for disabled people to communicate with external environments.},
}
@article {pmid18164493,
year = {2008},
author = {Patil, PG and Turner, DA},
title = {The development of brain-machine interface neuroprosthetic devices.},
journal = {Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics},
volume = {5},
number = {1},
pages = {137-146},
pmid = {18164493},
issn = {1933-7213},
mesh = {Biofeedback, Psychology ; Brain Diseases/*rehabilitation ; *Communication Devices for People with Disabilities ; Electroencephalography ; Equipment Design ; Humans ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {The development of brain-machine interface technology is a logical next step in the overall direction of neuroprosthetics. Many of the required technological advances that will be required for clinical translation of brain-machine interfaces are already under development, including a new generation of recording electrodes, the decoding and interpretation of signals underlying intention and planning, actuators for implementation of mental plans in virtual or real contexts, direct somatosensory feedback to the nervous system to refine actions, and training to encourage plasticity in neural circuits. Although pre-clinical studies in nonhuman primates demonstrate high efficacy in a realistic motor task with motor cortical recordings, there are many challenges in the clinical translation of even simple tasks and devices. Foremost among these challenges is the development of biocompatible electrodes capable of long-term, stable recording of brain activity and implantable amplifiers and signal processors that are sufficiently resistant to noise and artifact to faithfully transmit recorded signals to the external environment. Whether there is a suitable market for such new technology depends on its efficacy in restoring and enhancing neural function, its risks of implantation, and its long-term efficacy and usefulness. Now is a critical time in brain-machine interface development because most ongoing studies are science-based and noncommercial, allowing new approaches to be included in commercial schemes under development.},
}
@article {pmid18085990,
year = {2008},
author = {Ventura, V},
title = {Spike train decoding without spike sorting.},
journal = {Neural computation},
volume = {20},
number = {4},
pages = {923-963},
pmid = {18085990},
issn = {0899-7667},
support = {1R01EB005847/EB/NIBIB NIH HHS/United States ; R01 EB005847/EB/NIBIB NIH HHS/United States ; 2RP01MH064537/MH/NIMH NIH HHS/United States ; R01 MH064537-03/MH/NIMH NIH HHS/United States ; R01 MH064537/MH/NIMH NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Artifacts ; Artificial Intelligence ; Electrophysiology/*methods ; Humans ; Motor Cortex/*physiology ; Nerve Net/physiology ; Neurons/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used with any of the current decoding algorithms, such as population vector or likelihood-based algorithms. Based on analytical results and an extensive simulation study, we show that our paradigm is comparable to, and sometimes more efficient than, the traditional approach based on well-isolated neurons and that it remains efficient even when all electrodes are severely corrupted by noise, a situation that would render spike sorting particularly difficult. Our paradigm will also save time and computational effort, both of which are crucially important for successful operation of real-time brain-machine interfaces. Indeed, in place of the lengthy spike-sorting task of the traditional approach, it involves an exact expectation EM algorithm that is fast enough that it could also be left to run during decoding to capture potential slow changes in the states of the neurons.},
}
@article {pmid18077208,
year = {2008},
author = {Allison, BZ and McFarland, DJ and Schalk, G and Zheng, SD and Jackson, MM and Wolpaw, JR},
title = {Towards an independent brain-computer interface using steady state visual evoked potentials.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {2},
pages = {399-408},
pmid = {18077208},
issn = {1388-2457},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 EB000856-05/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Attention/*physiology ; Brain/*physiology ; Brain Mapping ; Dose-Response Relationship, Radiation ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual ; Photic Stimulation ; Spectrum Analysis ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.<br><br>METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.<br><br>RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.<br><br>CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.<br><br>SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.},
}
@article {pmid18065266,
year = {2008},
author = {Vuckovic, A and Sepulveda, F},
title = {Quantification and visualisation of differences between two motor tasks based on energy density maps for brain-computer interface applications.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {119},
number = {2},
pages = {446-458},
doi = {10.1016/j.clinph.2007.10.015},
pmid = {18065266},
issn = {1388-2457},
mesh = {Adult ; Brain/*physiology ; *Brain Mapping ; Electrodes ; Electroencephalography/methods ; Electrooculography ; Female ; Humans ; Imagination/physiology ; Male ; Movement/*physiology ; Principal Component Analysis ; Spectrum Analysis/methods ; Time Factors ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To determine the most discriminative features for a brain-computer interface (BCI) system based on statistically significant differences between two energy density maps calculated from EEG signals during two different motor tasks.<br><br>METHODS: EEG was recorded in ten healthy volunteers while performing different cue based, 3s sustained, real and imaginary right hand movements. Energy density maps were calculated over fixed 240 ms and 2 Hz time-frequency windows (called resels) for each movement and statistically significant resels were determined. After that, normalised energy values of the statistically significant resels were compared between two real as well as between two imaginary movements using a parametric test.<br><br>RESULTS: The largest differences between energy density maps between two motor tasks were noticed on electrode location Cp3 in the higher alpha and the beta bands (i.e., 12-30 Hz), for both real and imaginary movements. The method reduced a total number of discriminative features between two motor tasks to fewer than 2% for the imaginary and fewer than 3% for the real movements on the electrode location Cp3.<br><br>CONCLUSIONS: The method can be used for visualisation and feature extraction for BCI and other applications where event related desynchronisation/synchronisation (ERD/ERS) maps should be compared.<br><br>SIGNIFICANCE: If a reliable on-line classification of imaginary movements of the same limb would be achieved it could be combined with classification of movements of different parts of the body. That would increase a number of separable classes of a BCI system, thereby providing a larger number of command signals to control the external devises such as computers and robotic devices.},
}
@article {pmid18058664,
year = {2007},
author = {Synofzik, M},
title = {[Intervening in the neural basis of one's personality: a practice-oriented ethical analysis of neuropharmacology and deep-brain stimulation].},
journal = {Deutsche medizinische Wochenschrift (1946)},
volume = {132},
number = {50},
pages = {2711-2713},
doi = {10.1055/s-2007-993124},
pmid = {18058664},
issn = {1439-4413},
mesh = {Deep Brain Stimulation/*ethics ; Evidence-Based Medicine ; Humans ; Mental Disorders/drug therapy/*therapy ; Nervous System Diseases/drug therapy/*therapy ; Neuropharmacology/*ethics ; *Personality ; },
abstract = {Through the rapid progress in neuropharmacology it seems to become possible to effectively improve our cognitive capacities and emotional states by easily applicable means. Moreover, deep-brain stimulation may allow an effective therapeutic option for those neurological and psychiatric diseases which still can not be sufficiently treated by pharmacological measures. So far, however, both the benefit and the harm of these techniques are only insufficiently understood by neuroscience and detailed ethical analyses are still missing. In this article ethical criteria and most recent empirical evidence are systematically brought together for the first time. This analysis shows that it is irrelevant for an ethical evaluation whether a drug or a brain-machine interface is categorized as "enhancement" or "treatment" or whether it changes "human nature". The only decisive criteria are whether the intervention (1.) benefits the patient, (2.) does not harm the patient and (3.) is desired by the patient. However, current empirical data in both fields, neuropharmacology and deep-brain stimulation are still too sparse to adequately evaluate these criteria. Moreover, the focus in both fields has been strongly misled by neglecting the distinction between "benefit" and "efficacy": In past years research and clinical practice have only focused on physiological effects, but not on the actual benefit to the patient.},
}
@article {pmid18057504,
year = {2007},
author = {Pohlmeyer, EA and Solla, SA and Perreault, EJ and Miller, LE},
title = {Prediction of upper limb muscle activity from motor cortical discharge during reaching.},
journal = {Journal of neural engineering},
volume = {4},
number = {4},
pages = {369-379},
pmid = {18057504},
issn = {1741-2560},
support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS053603-01/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Animals ; Computer Simulation ; Electroencephalography/*methods ; Electromyography/methods ; Evoked Potentials, Motor/*physiology ; Macaca mulatta ; Models, Biological ; Motor Activity/*physiology ; Motor Cortex/*physiology ; Movement/physiology ; Muscle Contraction/physiology ; Muscle, Skeletal/*physiology ; Upper Extremity/*physiology ; },
abstract = {Movement representation by the motor cortex (M1) has been a theoretical interest for many years, but in the past several years it has become a more practical question, with the advent of the brain-machine interface. An increasing number of groups have demonstrated the ability to predict a variety of kinematic signals on the basis of M1 recordings and to use these predictions to control the movement of a cursor or robotic limb. We, on the other hand, have undertaken the prediction of myoelectric (EMG) signals recorded from various muscles of the arm and hand during button pressing and prehension movements. We have shown that these signals can be predicted with accuracy that is similar to that of kinematic signals, despite their stochastic nature and greater bandwidth. The predictions were made using a subset of 12 or 16 neural signals selected in the order of each signal's unique, output-related information content. The accuracy of the resultant predictions remained stable through a typical experimental session. Accuracy remained above 80% of its initial level for most muscles even across periods as long as two weeks. We are exploring the use of these predictions as control signals for neuromuscular electrical stimulation in quadriplegic patients.},
}
@article {pmid18057501,
year = {2007},
author = {Scherer, R and Müller-Putz, GR and Pfurtscheller, G},
title = {Self-initiation of EEG-based brain-computer communication using the heart rate response.},
journal = {Journal of neural engineering},
volume = {4},
number = {4},
pages = {L23-9},
doi = {10.1088/1741-2560/4/4/L01},
pmid = {18057501},
issn = {1741-2560},
mesh = {Adult ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electrocardiography/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feedback ; Female ; Heart Rate/*physiology ; Humans ; Male ; Man-Machine Systems ; Respiratory Mechanics/*physiology ; Volition/physiology ; },
abstract = {Self-initiation, that is the ability of a brain-computer interface (BCI) user to autonomously switch on and off the system, is a very important issue. In this work we analyze whether the respiratory heart rate response, induced by brisk inspiration, can be used as an additional communication channel. After only 20 min of feedback training, ten healthy subjects were able to self-initiate and operate a 4-class steady-state visual evoked potential-based (SSVEP) BCI by using one bipolar ECG and one bipolar EEG channel only. Threshold detection was used to measure a beat-to-beat heart rate increase. Despite this simple method, during a 30 min evaluation period on average only 2.9 non-intentional switches (heart rate changes) were detected.},
}
@article {pmid17959494,
year = {2006},
author = {Krauledat, M and Blankertz, B and Dornhege, G and Schröder, M and Curio, G and Müller, K-},
title = {On-line differentiation of neuroelectric activities: algorithms and applications.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {Suppl},
number = {},
pages = {6715-6719},
doi = {10.1109/IEMBS.2006.260929},
pmid = {17959494},
issn = {1557-170X},
mesh = {*Algorithms ; Animals ; *Artificial Intelligence ; Brain Mapping/methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated ; *Software ; *User-Computer Interface ; },
abstract = {This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview of the Berlin-Brain Computer Interface (BBCI).},
}
@article {pmid17959455,
year = {2006},
author = {Olson, B and Si, J and Silver, J},
title = {Decoding high level signals for asynchronous brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {Suppl},
number = {},
pages = {6569-6572},
doi = {10.1109/IEMBS.2006.260876},
pmid = {17959455},
issn = {1557-170X},
mesh = {*Artificial Intelligence ; Brain/*physiology ; Humans ; *Man-Machine Systems ; User-Computer Interface ; },
abstract = {While many brain machine interface (BMI) systems have been presented in the literature, most of these systems present the user with an always on interface with no way to shut the interface down when not needed. This paper proposes two extensions of previous BMI work to create an asynchronous BMI in which the system only produces outputs when needed. The first classifies incoming signals into not only task related states, but also an idle state. A refinement of this system utilizes a Markov Model (MM) of the task to impose order on the sequence of states produced by the system. This MM filter improves the accuracy of the system an average of 16%.},
}
@article {pmid17959448,
year = {2006},
author = {James, CJ and Wang, S},
title = {Blind source separation in single-channel EEG analysis: an application to BCI.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {Suppl},
number = {},
pages = {6544-6547},
doi = {10.1109/IEMBS.2006.260887},
pmid = {17959448},
issn = {1557-170X},
mesh = {*Algorithms ; Electroencephalography/*methods ; Humans ; *Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {In this work we present a technique for applying Blind Source Separation (BSS) to single channel recordings of Electromagnetic (EM) brain signals. Single channel recordings of brain signals are preprocessed through the method of delays, and the delay matrix processed with the BSS technique described here called LSDIAGTD which uses temporal decorrelation to implement the now popular Independent Component Analysis (ICA) algorithm. This allows the identification and extraction of statistically independent sources underlying these single channel recordings. In particular we depict the analysis of single channel recordings from a Brain-Computer Interfacing paradigm. We show that BSS technique applied in this way extracts a series of codebook vectors representing the spectral content underlying the recorded signal. It then becomes possible to identify and extract particular rhythmic activity underlying the recordings. We show that rhythmic activity in the 8 to 12Hz band can be extracted in the case of imagined hand movements for a particular BCI paradigm.},
}
@article {pmid17959429,
year = {2006},
author = {Gilmour, TP and Krishnan, L and Gaumond, RP and Clement, RS},
title = {A comparison of neural feature extraction methods for brain-machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {Suppl},
number = {},
pages = {6473-6477},
doi = {10.1109/IEMBS.2006.260863},
pmid = {17959429},
issn = {1557-170X},
mesh = {Animals ; Behavior, Animal/*physiology ; *Models, Neurological ; Rats ; Rats, Sprague-Dawley ; *User-Computer Interface ; },
abstract = {Brain-machine interferes (BMIs) have shown promise in augmenting people's control of their surroundings, especially for those suffering from paralysis due to neurological disorders. This paper describes an experiment using the rodent model to explore information available in neural signals recorded from chronically implanted intracortical microelectrode arrays. In offline experiments, a number of neural feature extraction methods were utilized to obtain neural activity vectors (NAVs) describing the activity of the underlying neural population while rats performed a discrimination task. The methods evaluated included standard techniques such as binned spike rates and local field potential spectra as well as more novel approaches including matched-filter energy, raw signal spectra, and an autocorrelation energy measure (AEM) approach. Support vector machines (SVMs) were trained offline to classify left from right going movements by utilizing features contained in the NAVs obtained by the different methods. Each method was evaluated for accuracy and robustness. Results show that most algorithms worked well for decoding neural signals both during and prior to movement, with spectral methods providing the best stability.},
}
@article {pmid17700907,
year = {2007},
author = {Westphal, R},
title = {Sensors, medical image and signal processing. Findings from the section on sensor, signal and imaging informatics.},
journal = {Yearbook of medical informatics},
volume = {},
number = {},
pages = {70-73},
pmid = {17700907},
issn = {0943-4747},
mesh = {Medical Informatics ; Monitoring, Physiologic/*instrumentation/trends ; Publishing/standards ; Telemedicine/instrumentation/trends ; },
abstract = {OBJECTIVES: To summarize current excellent research in the field of sensor, signal and imaging informatics.<br><br>METHOD: Synopsis of the articles selected for the IMIA Yearbook 2007.<br><br>RESULTS: The selection process for this yearbook section "Sensor, signal and imaging informatics" results in five excellent articles, representing research in four different nations. Papers from the fields of brain machine interfaces, sound surveillance in telemonitoring, soft tissue modeling, and body sensors have been selected.<br><br>CONCLUSION: The selection for this yearbook section can only reflect a small portion of the worldwide copious work in the field of sensors, signal and image processing with applications in medical informatics. However, the selected papers demonstrate, how advances in this field may positively affect future patient care.},
}
@article {pmid18044568,
year = {2007},
author = {Fripp, J and Crozier, S and Warfield, SK and Ourselin, S},
title = {Automatic segmentation of articular cartilage in magnetic resonance images of the knee.},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
volume = {10},
number = {Pt 2},
pages = {186-194},
doi = {10.1007/978-3-540-75759-7_23},
pmid = {18044568},
support = {R01 RR021885/RR/NCRR NIH HHS/United States ; R21 MH067054/MH/NIMH NIH HHS/United States ; },
mesh = {*Algorithms ; *Artificial Intelligence ; Cartilage, Articular/*anatomy &amp; histology ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/*methods ; Imaging, Three-Dimensional/*methods ; Knee Joint/*anatomy &amp; histology ; Magnetic Resonance Imaging/*methods ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {To perform cartilage quantitative analysis requires the accurate segmentation of each individual cartilage. In this paper we present a model based scheme that can automatically and accurately segment each individual cartilage in healthy knees from a clinical MR sequence (fat suppressed spoiled gradient recall). This scheme consists of three stages; the automatic segmentation of the bones, the extraction of the bone-cartilage interfaces (BCI) and segmentation of the cartilages. The bone segmentation is performed using three-dimensional active shape models. The BCI is extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. A cartilage thickness model then provides constraints and regularizes the cartilage segmentation performed from the BCI. The accuracy and robustness of the approach was experimentally validated, with (patellar, tibial and femoral) cartilage segmentations having a median DSC of (0.870, 0.855, 0.870), performing significantly better than non-rigid registration (0.787, 0.814, 0.795). The total cartilage segmentation had an average DSC of (0.891), close to the (0.896) obtained using a semi-automatic watershed algorithm. The error in quantitative volume and thickness measures was (8.29, 4.94, 5.56)% and (0.19, 0.33, 0.10) mm respectively.},
}
@article {pmid18035381,
year = {2008},
author = {Nikulin, VV and Hohlefeld, FU and Jacobs, AM and Curio, G},
title = {Quasi-movements: a novel motor-cognitive phenomenon.},
journal = {Neuropsychologia},
volume = {46},
number = {2},
pages = {727-742},
doi = {10.1016/j.neuropsychologia.2007.10.008},
pmid = {18035381},
issn = {0028-3932},
mesh = {Adult ; *Alpha Rhythm ; Analysis of Variance ; Electromyography ; Evoked Potentials, Motor/physiology ; Female ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; Inhibins/*physiology ; Intention ; Kinesthesis/physiology ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Reference Values ; Statistics, Nonparametric ; },
abstract = {We introduce quasi-movements and define them as volitional movements which are minimized by the subject to such an extent that finally they become undetectable by objective measures. They are intended as overt movements, but the absence of the measurable motor responses and the subjective experience make quasi-movements similar to motor imagery. We used the amplitude dynamics of electroencephalographic alpha oscillations as a marker of the regional involvement of cortical areas in three experimental tasks: movement execution, kinesthetic motor imagery, and quasi-movements. All three conditions were associated with a significant suppression of alpha oscillations over the sensorimotor hand area of the contralateral hemisphere. This suppression was strongest for executed movements, and stronger for quasi-movements than for motor imagery. The topography of alpha suppression was similar in all three conditions. Proprioceptive sensations related to quasi-movements contribute to the assumption that the "sense of movement" can originate from central efferent processes. Quasi-movements are also congruent with the postulated continuity between motor imagery and movement preparation/execution. We also show that in healthy subjects quasi-movements can be effectively used in brain-computer interface research leading to a significantly smaller classification error (approximately 47% of relative decrease) in comparison to the errors obtained with conventionally used motor imagery strategies.},
}
@article {pmid18031824,
year = {2008},
author = {Müller, KR and Tangermann, M and Dornhege, G and Krauledat, M and Curio, G and Blankertz, B},
title = {Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {82-90},
doi = {10.1016/j.jneumeth.2007.09.022},
pmid = {18031824},
issn = {0165-0270},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping ; Communication Devices for People with Disabilities ; *Electroencephalography ; Electromyography ; Feedback ; Functional Laterality ; Humans ; *Man-Machine Systems ; Mental Processes/*physiology ; *Signal Processing, Computer-Assisted ; Spectrum Analysis ; *User-Computer Interface ; },
abstract = {Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.},
}
@article {pmid18022247,
year = {2008},
author = {Pistohl, T and Ball, T and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Prediction of arm movement trajectories from ECoG-recordings in humans.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {105-114},
doi = {10.1016/j.jneumeth.2007.10.001},
pmid = {18022247},
issn = {0165-0270},
mesh = {Adolescent ; Adult ; Arm/*physiopathology ; Brain Mapping ; Electric Stimulation/methods ; *Electroencephalography ; Epilepsy/physiopathology/therapy ; Humans ; Motor Cortex/*physiopathology ; Movement/*physiology ; Numerical Analysis, Computer-Assisted ; Predictive Value of Tests ; Spectrum Analysis ; *User-Computer Interface ; },
abstract = {Electrocorticographic (ECoG) signals have been shown to contain reliable information about the direction of arm movements and can be used for on-line cursor control. These findings indicate that the ECoG is a potential basis for a brain-machine interface (BMI) for application in paralyzed patients. However, previous approaches to ECoG-BMIs were either based on classification of different movement patterns or on a voluntary modulation of spectral features. For a continuous multi-dimensional BMI control, the prediction of complete movement trajectories, as it has already been shown for spike data and local field potentials (LFPs), would be a desirable addition for the ECoG, too. Here, we examined ECoG signals from six subjects with subdurally implanted ECoG-electrodes during continuous two-dimensional arm movements between random target positions. Our results show that continuous trajectories of 2D hand position can be approximately predicted from the ECoG recorded from hand/arm motor cortex. This indicates that ECoG signals, related to body movements, can directly be transferred to equivalent controls of an external effector for continuous BMI control.},
}
@article {pmid18018686,
year = {2007},
author = {Davoodi, R and Urata, C and Hauschild, M and Khachani, M and Loeb, GE},
title = {Model-based development of neural prostheses for movement.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {11},
pages = {1909-1918},
doi = {10.1109/TBME.2007.902252},
pmid = {18018686},
issn = {0018-9294},
mesh = {Computer Simulation ; Computer-Aided Design ; Electric Stimulation Therapy/*instrumentation/methods ; Humans ; *Models, Biological ; *Movement ; Movement Disorders/physiopathology/*rehabilitation ; *Prostheses and Implants ; Prosthesis Design/methods ; Software ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {Neural prostheses for restoration of limb movement in paralyzed and amputee patients tend to be complex systems. Subjective intuition and trial-and-error approaches have been applied to the design and clinical fitting of simple systems with limited functionality. These approaches are time consuming, difficult to apply in larger scale, and not applicable to limbs under development with more anthropomorphic motion and actuation. The field of neural prosthetics is in need of more systematic methods, including tools that will allow users to develop accurate models of neural prostheses and simulate their behavior under various conditions before actual manufacturing or clinical application. Such virtual prototyping would provide an efficient and safe test-bed for narrowing the design choices and tuning the control parameters before actual clinical application. We describe a software environment that we have developed to facilitate the construction and modification of accurate mathematical models of paralyzed and prosthetic limbs and simulate their movement under various neural control strategies. These simulations can be run in real time with a stereoscopic display to enable design engineers and prospective users to evaluate a candidate neural prosthetic system and learn to operate it before actually receiving it.},
}
@article {pmid18006069,
year = {2008},
author = {Marathe, AR and Carey, HL and Taylor, DM},
title = {Virtual reality hardware and graphic display options for brain-machine interfaces.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {2-14},
pmid = {18006069},
issn = {0165-0270},
support = {N01 HD53403/HD/NICHD NIH HHS/United States ; N01 NS52365/NS/NINDS NIH HHS/United States ; N01-NS-5-2365/NS/NINDS NIH HHS/United States ; N01-HD-5-3403/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiopathology ; Computer Graphics ; *Computer Simulation ; Depth Perception/*physiology ; Discrimination, Psychological/physiology ; *Hand ; Humans ; Movement/*physiology ; Psychomotor Performance ; Spinal Cord Injuries/physiopathology/therapy ; *User-Computer Interface ; },
abstract = {Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target-matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing.},
}
@article {pmid18003533,
year = {2007},
author = {Chow, EY and Kahn, A and Irazoqui, PP},
title = {High data-rate 6.7 GHz wireless ASIC transmitter for neural prostheses.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {6581-6584},
doi = {10.1109/IEMBS.2007.4353867},
pmid = {18003533},
issn = {2375-7477},
mesh = {Animals ; Brain/physiology ; In Vitro Techniques ; Prostheses and Implants ; Skin ; Swine ; Telemetry/*methods ; },
abstract = {A high-frequency transmitter has been designed for high data-rate biomedical telemetry. Although high frequencies face greater attenuation, transcutaneous transmission was successfully tested and verified using a 3.76 mm thick sample of porcine skin. The structure transmits over 440 microW of power, consumes about 4.9 mA of current from a 1.8 V supply, and achieves a phase noise of -72 dBc/Hz at 100 KHz. The transmitter operates at around 6.7 GHz with a 50 MHz tuning range and is fully integrated on the CMOS IBM7RF 0.18 microm process.},
}
@article {pmid18003519,
year = {2007},
author = {Das, K and Osechinskiy, S and Nenadic, Z},
title = {A classwise PCA-based recognition of neural data for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {6520-6523},
doi = {10.1109/IEMBS.2007.4353853},
pmid = {18003519},
issn = {2375-7477},
mesh = {Algorithms ; Brain/*physiology ; *Electroencephalography ; Humans ; *Principal Component Analysis ; User-Computer Interface ; },
abstract = {We present a simple, computationally efficient recognition algorithm that can systematically extract useful information from any large-dimensional neural datasets. The technique is based on classwise Principal Component Analysis, which employs the distribution characteristics of each class to discard non-informative subspace. We propose a two-step procedure, comprising of removal of sparse non-informative subspace of the large-dimensional data, followed by a linear combination of the data in the remaining subspace to extract meaningful features for efficient classification. Our method produces significant improvement over the standard discriminant analysis based methods. The classification results are given for iEEG and EEG signals recorded from the human brain.},
}
@article {pmid18003244,
year = {2007},
author = {Washizawa, Y and Yamashita, Y and Tanaka, T and Cichocki, A},
title = {Extraction of steady state visually evoked potential signal and estimation of distribution map from EEG data.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5449-5452},
doi = {10.1109/IEMBS.2007.4353578},
pmid = {18003244},
issn = {2375-7477},
mesh = {*Algorithms ; Brain Mapping/*methods ; Computer Simulation ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; *Models, Neurological ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Visual Cortex/*physiology ; },
abstract = {We propose a signal extraction method from multi-channel EEG signals and apply to extract Steady State Visually Evoked Potential (SSVEP) signal. SSVEP is a response to visual stimuli presented in the form of flushing patterns. By using several flushing patterns with different frequency, brain machine (computer) interface (BMI/BCI) can be realized. Therefore it is important to extract SSVEP signals from multi-channel EEG signals. At first, we estimate the power of the objective signal in each electrode. Estimation of the power is helpful in not only extraction of the signal but also drawing a distribution map of the signal, finding electrodes which have large SNR, and ranking electrodes in sort of information with respect to the power of the signal. Experimental results show that the proposed method 1) estimates more accurate power than existing methods, 2) estimates the global signal which has larger SNR than existing methods, and 3) allows us to draw a distribution map of the signal, and it conforms the biological theory.},
}
@article {pmid18003240,
year = {2007},
author = {Wang, S and James, CJ},
title = {On the independent component analysis of evoked potentials through single or few recording channels.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5433-5436},
doi = {10.1109/IEMBS.2007.4353574},
pmid = {18003240},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; Reproducibility of Results ; Sample Size ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In this work we propose a technique based on Independent Component Analysis (ICA), applied to single or two channel(s) recordings of electroencephalogram (EEG) brain signals. Standard (ensemble) ICA requires multiple channel recordings to work, however when single of few channels are required ensemble ICA cannot be readily applied. Single channel ICA (temporal ICA) can be performed by preprocessed the data using the method of delays. Few channels (space-time ICA) can be analysed in an extension to this method. These techniques are demonstrated on the P300 evoked potentials (EPs) of a brain-computer interfacing (BCI) word speller dataset. We furthermore show how it is possible to extract single trial evoked EPs (i.e. non-stimulus locked) within a little as 3 epochs and even on channels not over the event focus. Due to the poor SNR, as well as the presence of other artifacts, it is difficult to detect the P300 pattern on raw signal data. The results show that proposed algorithms are able to accurately and repeatedly extract the relevant information buried within noisy signals and to do so without the requirement of stimulus locked averages. These advantages are paramount for building a more reliable and robust system for use in real-world BCI--i.e. for use outside of the clinical laboratory.},
}
@article {pmid18003214,
year = {2007},
author = {Tsubone, T and Muroga, T and Wada, Y},
title = {Application to robot control using brain function measurement by near-infrared spectroscopy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5342-5345},
doi = {10.1109/IEMBS.2007.4353548},
pmid = {18003214},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Hemoglobins/*analysis ; Humans ; Male ; Man-Machine Systems ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Robotics/*methods ; Sensitivity and Specificity ; Spectroscopy, Near-Infrared/*methods ; User-Computer Interface ; },
abstract = {In recent years, study of brain computer interface (BCI) is conducted actively and many researches of implementation using electro encephalic gram (EEG) are reported. On the other hand, some realization of BCI based on near-infrared spectroscopy (NIRS) also had been reported. Since a measurement instrument for NIRS is comparatively small-scale and it can perform noninvasive measurements, NIRS is expected as one of useful tool in order to realize versatile BCIs. In this paper, the estimation method is shown the possibility of applications to the ON/OFF control of BCI by NIRS. We measured regional cerebral blood flow during tapping movement of the right hand by NIRS and we propose a method to quantitatively estimate start and end timing of movement by using a neural network.},
}
@article {pmid18003213,
year = {2007},
author = {Utsugi, K and Obata, A and Sato, H and Katsura, T and Sagara, K and Maki, A and Koizumi, H},
title = {Development of an optical brain-machine interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5338-5341},
doi = {10.1109/IEMBS.2007.4353547},
pmid = {18003213},
issn = {2375-7477},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Optics and Photonics/instrumentation ; Signal Processing, Computer-Assisted/*instrumentation ; Spectroscopy, Near-Infrared/*instrumentation/methods ; *User-Computer Interface ; },
abstract = {We have developed a brain-machine interface (BMI) by using a method based on near-infrared spectroscopy. We call our interface "Optical-BMI". It functions as a practical, unrestrictive, non-invasive brain-switch without the need for large equipment. During an experiment with the prototype system, an operator manipulated external electrically controlled equipment while we measured the corresponding spatiotemporal changes in the hemoglobin concentration in the blood flowing through his or her pre-frontal cortex by using a probe cap with 22 measurement points.},
}
@article {pmid18003147,
year = {2007},
author = {Oveisi, F and Erfanian, A},
title = {A tree-structure mutual information-based feature extraction and its application to EEG-based brain-computer interfacing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5075-5078},
doi = {10.1109/IEMBS.2007.4353481},
pmid = {18003147},
issn = {2375-7477},
mesh = {*Algorithms ; Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Information Storage and Retrieval/methods ; Motor Cortex/*physiology ; Movement/physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper presents a novel algorithm for efficient feature extraction using mutual information (MI). In terms of mutual information, the optimal feature extraction is creating a new feature set from the data which jointly have largest dependency on the target class. However, it is not always easy to get an accurate estimation for high-dimensional MI. In this paper, we propose an efficient method for feature extraction using two-dimensional MI estimates. A new feature is created such that the MI between the new feature and the target class is maximized and the redundancy is minimized. The effectiveness of the proposed algorithm is evaluated by using the classification of EEG signals. The tasks to be discriminated are the imaginative hand movement and the resting state. The results demonstrate that the proposed mutual information-based feature extraction (MIFX) algorithm performed well in several experiments on different subjects and can improve the classification accuracy of the EEG patterns. The results show that the classification accuracy obtained by MIFX is higher than that achieved by full feature set.},
}
@article {pmid18003146,
year = {2007},
author = {Rivet, B and Souloumiac, A},
title = {Subspace estimation approach to P300 detection and application to brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5071-5074},
doi = {10.1109/IEMBS.2007.4353480},
pmid = {18003146},
issn = {2375-7477},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a system for direct communication between brain and computer. In this work, a new unsupervised algorithm is introduced for P300 subspace estimation: the raw EEG are thus enhanced by projection on the estimated subspace. Moreover a simple scheme to detect the P300 potentials in the human EEG by dimension reduction and linear support vector machine (SVM) is proposed to build a BCI based on the P300 speller. The proposed algorithm is finally tested with dataset from the BCI Competition 2003 and gives results that compare favourably to the state of the art.},
}
@article {pmid18003145,
year = {2007},
author = {Zhang, H and Wang, C and Guan, C},
title = {Towards asynchronous brain-computer interfaces: a P300-based approach with statistical models.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5067-5070},
doi = {10.1109/IEMBS.2007.4353479},
pmid = {18003145},
issn = {2375-7477},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; *Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Asynchronous control is a critical issue in developing brain-computer interfaces for real-life applications, where the machine should be able to detect the occurrence of a mental command. In this paper we propose a computational approach for robust asynchronous control using the P300 signal, in a variant of oddball paradigm. First, we use Gaussian models in the support vector margin space to describe various types of EEG signals that are present in an asynchronous P300-based BCI. This allows us to derive a probability measure of control state given EEG observations. Second, we devise a recursive algorithm to detect and locate control states in ongoing EEG. Experimental results indicate that our system allows information transfer at approx. 20bit/min at low false alarm rate (1/min).},
}
@article {pmid18003144,
year = {2007},
author = {Momose, K},
title = {Evaluation of an eye gaze point detection method using VEP elicited by multi-pseudorandom stimulation for brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5063-5066},
doi = {10.1109/IEMBS.2007.4353478},
pmid = {18003144},
issn = {2375-7477},
mesh = {Brain/*physiology ; Evoked Potentials, Visual/*physiology ; *Fixation, Ocular ; Humans ; Photic Stimulation ; Reproducibility of Results ; *User-Computer Interface ; Vision, Ocular ; Visual Cortex/physiology ; },
abstract = {A method for detecting eye gaze point using visual evoked potentials (VEPs) elicited by pseudorandom stimuli was examined. Prototype system which would be a practical brain computer interface is established and evaluated. Four luminance modulated red characters based on four different pseudorandom binary sequences (PRBS) of 5.11 seconds were simultaneously presented on a monitor, and the cross correlation functions (kernels) of VEPs and each PRBS were calculated and used to determine the subject's gazed target. In an experiment with subjects with normal vision, their gazed target was obtained from VEPs within 7 seconds, and the mean error rate of detection was 22%. Results indicated that this technique could be useful as a practical brain computer interface system.},
}
@article {pmid18003143,
year = {2007},
author = {Wang, Y and Hong, B and Gao, X and Gao, S},
title = {Implementation of a brain-computer interface based on three states of motor imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5059-5062},
doi = {10.1109/IEMBS.2007.4353477},
pmid = {18003143},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Functional Laterality ; Humans ; Imagination/*physiology ; Male ; Motor Activity ; *User-Computer Interface ; },
abstract = {A motor imagery based brain-computer interface (BCI) translates the subject's motor intention into a control signal through real-time detection of characteristic EEG spatial distributions corresponding to motor imagination of different body parts. In this paper, we implemented a three-class BCI manipulated through imagination of left hand, right hand and foot movements, inducing different spatial patterns of event-related desynchronization (ERD) on mu rhythms over the sensory-motor cortex. A two-step training approach was proposed including consecutive steps of online adaptive training and offline training. Then, the optimized parameters and classifiers were utilized for online control. This paradigm facilitated three directional movement controls which could be easily applied to help the motion-disabled to operate a wheelchair. The average online and offline classification accuracy on five subjects was 79.48% and 85.00% respectively, promoting the three-class motor imagery based BCI a promising means to realize brain control of a mobile device.},
}
@article {pmid18003142,
year = {2007},
author = {Sepulveda, F and Dyson, M and Gan, JQ and Tsui, CL},
title = {A Comparison of Mental Task Combinations for Asynchronous EEG-Based BCIs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {5055-5058},
doi = {10.1109/IEMBS.2007.4353476},
pmid = {18003142},
issn = {2375-7477},
mesh = {Attention ; Brain/*physiology ; Electrodes ; Electroencephalography ; Hearing ; Humans ; Male ; Mathematics ; Memory ; Mental Processes/*physiology ; Motor Activity ; Music ; *User-Computer Interface ; },
abstract = {Aiming at developing asynchronous BCIs, we tested 21 2-class combinations of 7 mental tasks to determine whether any pair of tasks may be more suitable. The tasks under consideration were: auditory recall, mental navigation, sensorimotor attention (left hand), sensorimotor attention (right hand), mental calculation, imaginary movement (left hand), imaginary movement (right hand). Sensorimotor attention is novel in this application domain. All possible pairs were tried in 5 subjects using data from 10s periods in which subjects were free to execute the required mental task at their own pace. Recordings were done whilst the subject controlled a robot navigation simulator on a computer monitor, with the robot direction being related to the mental task. Classification of the data was done using LDA. Class-separation was estimated using the Davies-Bouldin index. Best classification results were obtained when auditory recall was followed or preceded by mental calculation. Of the possible 21 task combinations, this task pair was in the top 5 (performance-wise) for 4 of the 5 subjects. This was also the case when class-separation was used as a criterion.},
}
@article {pmid18003064,
year = {2007},
author = {Cincotti, F and Kauhanen, L and Aloise, F and Palomaki, T and Caporusso, N and Jylänki, P and Babiloni, F and Vanacker, G and Nuttin, M and Marciani, MG and Del R Millan, J and Mattia, D},
title = {Preliminary experimentation on vibrotactile feedback in the context of mu-rhythm based BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {4739-4742},
doi = {10.1109/IEMBS.2007.4353398},
pmid = {18003064},
issn = {2375-7477},
mesh = {Brain/*physiology ; *Electroencephalography ; Feedback ; Humans ; Magnetics ; Shoulder ; Touch ; User-Computer Interface ; Vibration ; },
abstract = {Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users' training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control. in which all parts reflected the computational and actuation process of an actual BCI, except the souce, which was simulated using a "noisy" PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task.},
}
@article {pmid18003062,
year = {2007},
author = {Ogata, H and Mukai, T and Yagi, T},
title = {A study on the frontal cortex in cognitive tasks using near-infrared spectroscopy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {4731-4734},
doi = {10.1109/IEMBS.2007.4353396},
pmid = {18003062},
issn = {2375-7477},
mesh = {Cerebrovascular Circulation ; Cognition/*physiology ; Frontal Lobe/*physiology ; Humans ; Image Processing, Computer-Assisted ; Language ; Mathematics ; Nerve Fibers/physiology ; Oxyhemoglobins/analysis ; Spectrophotometry, Infrared/methods ; User-Computer Interface ; },
abstract = {The frontal cortex is the part of the brain that relates to higher brain functions, such as logical thinking and emotion. As part of the development of a brain-computer interface, we tested whether the frontal cortex reacts differently in people as they performed three different cognitive tasks. The reaction of the cortex was tested using near-infrared spectroscopy. Our preliminary research results showed a difference in blood flow volume occurred before and after two assigned tasks: a math task and a word task. However, after a naming task there was no particular reaction. We believe that mental stress might be the cause of the difference in frontal cortex activity.},
}
@article {pmid18003060,
year = {2007},
author = {Funase, A and Hashimoto, T and Yagi, T and Barros, AK and Cichocki, A and Takumi, I},
title = {Research for estimating direction of saccadic eye movements by single trial processing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {4723-4726},
doi = {10.1109/IEMBS.2007.4353394},
pmid = {18003060},
issn = {2375-7477},
mesh = {Acoustic Stimulation ; Persons with Disabilities ; *Electroencephalography ; Functional Laterality ; Humans ; Saccades/*physiology ; Vision, Ocular/physiology ; },
abstract = {Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface(BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional Fast ICA with Reference signal(FICAR). Visually guided saccade tasks and auditorily guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. Saccade-related EEG signals and saccade-related ICs in visually and Auditorily guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging. In next step, we tried to estimate direction of saccade from raw EEG signals by FICAR.},
}
@article {pmid18003058,
year = {2007},
author = {Quitadamo, LR and Abbafati, M and Saggio, G and Marciani, MG and Bianchi, L},
title = {Brain computer interface research at the neuroscience department of the "Tor Vergata" University of Rome, Italy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {4715-4718},
doi = {10.1109/IEMBS.2007.4353392},
pmid = {18003058},
issn = {2375-7477},
mesh = {Brain/*physiology ; Equipment Design ; Evoked Potentials ; Humans ; Neurosciences/*methods ; Rome ; Software ; Universities ; *User-Computer Interface ; },
abstract = {Brain Computer Interface (BCI) systems have gained great visibility in the last years as they represent a quite innovative way of communication and a new instrument aimed at exploring brain functions. A lot of research labs are developing their own BCI system, everyone being involved in some particular aspects of them. At the "Tor Vergata" University our purpose is to develop tools for the evaluation and the optimization of the performances of BCI systems and to delineate some criteria for the analysis and implementation of different BCI systems; also we have defined file formats for BCI data in order to allow the sharing of tools among groups and to create models for the generalization and therefore the unification of the resources. All the tools and routines mentioned are part of the Body Language Framework++ 2.0.},
}
@article {pmid18003055,
year = {2007},
author = {Wang, C and Zhang, H and Phua, KS and Dat, TH and Guan, C},
title = {Introduction to NeuroComm: a platform for developing real-time EEG-based brain-computer interface applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {4703-4706},
doi = {10.1109/IEMBS.2007.4353389},
pmid = {18003055},
issn = {2375-7477},
mesh = {Brain/*physiology ; *Electroencephalography ; Equipment Design ; Humans ; Image Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {NeuroComm is a platform to develop real time Brain Computer Interface (BCI) applications. This paper introduces the basic modules of this platform and discusses some implementation issues. With a user management module, our system is user friendly and suitable for multiple users. Also, with flexible configuration files and signal processing algorithm libraries, it is easier to integrate multiple BCI applications into one system. The NeuroComm platform also acts as a flexible tool for BCI research.},
}
@article {pmid18002686,
year = {2007},
author = {Sadeghian, EB and Moradi, MH},
title = {Continuous detection of motor imagery in a four-class asynchronous BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {3241-3244},
doi = {10.1109/IEMBS.2007.4353020},
pmid = {18002686},
issn = {2375-7477},
mesh = {Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Asynchronous Brain Computer Interface (BCI) is an important class of BCI systems that has not received enough attention from the BCI community. In this work we introduce for the first time a system for classification of four different motor imageries in the context of an asynchronous BCI system which distinguishes between periods of movement imagination occurrence and idling or resting periods of ongoing EEG signal as well as classifying the 4 class motor imageries. We used two multi class extensions of the method of Common Spatial Patterns (CSP) for feature extraction and LDA, SVM, and MDA well known classifiers for combination purposes. We have applied our procedure to data set IIIa from BCI Competition III [2]. Offline evaluation of a prototype system demonstrated true positive rates in the range of 56%-88% with corresponding false positive rates in the range of 18%-9%.},
}
@article {pmid18002681,
year = {2007},
author = {Darvishi, S and Al-Ani, A},
title = {Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {3220-3223},
doi = {10.1109/IEMBS.2007.4353015},
pmid = {18002681},
issn = {2375-7477},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; *Fuzzy Logic ; Humans ; *Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier.},
}
@article {pmid18002512,
year = {2007},
author = {Dias, NS and Kamrunnahar, M and Mendes, PM and Schiff, SJ and Correia, JH},
title = {Comparison of EEG pattern classification methods for brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2540-2543},
doi = {10.1109/IEMBS.2007.4352846},
pmid = {18002512},
issn = {2375-7477},
support = {K02MH01493/MH/NIMH NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; *Algorithms ; Brain/*physiology ; Electroencephalography/classification/instrumentation/*methods ; Humans ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three runs per subject took place. The classification methods were tested in 240 trials per subject after merging all runs for the same subject. The mental tasks performed were feet, tongue, left hand and right hand movement imagery. In order to avoid previous assumptions on preferable channel locations and frequency ranges, 105 (21 electrodesx5 frequency ranges) electroencephalogram (EEG) features were extracted from the data. The best performance for each classification method was taken into account. The discriminant stepwise method showed better performance than the PCA based method. The classification error by the stepwise method varied between 31.73% and 38.5% for all subjects whereas the error range using the PCA based method was 39.42% to 54%.},
}
@article {pmid18002511,
year = {2007},
author = {Blumberg, J and Rickert, J and Waldert, S and Schulze-Bonhage, A and Aertsen, A and Mehring, C},
title = {Adaptive classification for brain computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2536-2539},
doi = {10.1109/IEMBS.2007.4352845},
pmid = {18002511},
issn = {2375-7477},
mesh = {*Algorithms ; *Brain ; Communication Devices for People with Disabilities/*classification ; Humans ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {In this paper we evaluate the performance of a new adaptive classifier for the use within a Brain Computer-Interface (BCI). The classifier can either be adaptive in a completely unsupervised manner or using unsupervised adaptation in conjunction with a neuronal evaluation signal to improve adaptation. The first variant, termed Adaptive Linear Discriminant Analysis (ALDA), updates mean values as well as covariances of the class distributions continuously in time. In simulated as well as experimental data ALDA substantially outperforms the non-adaptive LDA. The second variant, termed Adaptive Linear Discriminant Analysis with Error Correction (ALDEC), extends the unsupervised algorithm with an additional independent neuronal evaluation signal. Such a signal could be an error related potential which indicates when the decoder did not classify correctly. When the mean values of the class distributions circle around each other or even cross their way, ALDEC can yield a substantially better adaptation than ALDA depending on the reliability of the error signal. Given the non-stationarity of EEG signals during BCI control our approach might strongly improve the precision and the time needed to gain accurate control in future BCI applications.},
}
@article {pmid18002510,
year = {2007},
author = {Cincotti, F and Aloise, F and Bufalari, S and Schalk, G and Oriolo, G and Cherubini, A and Davide, F and Babiloni, F and Marciani, MG and Mattia, D},
title = {Non-invasive brain-computer interface system to operate assistive devices.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2532-2535},
doi = {10.1109/IEMBS.2007.4352844},
pmid = {18002510},
issn = {2375-7477},
support = {EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {*Brain ; *Communication Devices for People with Disabilities ; Computer Systems ; Humans ; Neurodegenerative Diseases/psychology/rehabilitation ; Quality of Life ; *Self-Help Devices ; *Software ; User-Computer Interface ; },
abstract = {In this pilot study, a system that allows disabled persons to improve or recover their mobility and communication within the surrounding environment was implemented and validated. The system is based on a software controller that offers to the user a communication interface that is matched with the individual's residual motor abilities. Fourteen patients with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program. All users utilized regular assistive control options (e.g., microswitches or head trackers) while four patients learned to operate the system by means of a non-invasive EEG-based Brain-Computer Interface, based on the subjects' voluntary modulations of EEG sensorimotor rhythms recorded on the scalp.},
}
@article {pmid18002509,
year = {2007},
author = {Salvaris, MS and Sepulveda, F},
title = {Robustness of the Farwell &amp; Donchin BCI protocol to visual stimulus parameter changes.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2528-2531},
doi = {10.1109/IEMBS.2007.4352843},
pmid = {18002509},
issn = {2375-7477},
mesh = {*Acoustics ; Adult ; Algorithms ; Brain/*pathology ; Computer Simulation ; Electrophysiology/*instrumentation/methods ; Equipment Design ; Event-Related Potentials, P300 ; Humans ; Man-Machine Systems ; *Neural Networks, Computer ; Nonlinear Dynamics ; Robotics ; Software ; User-Computer Interface ; },
abstract = {In this paper a number of visual modifications were carried out upon the Farwell &amp; Donchin protocol. The effects of these modifications were studied both in the classification accuracy of the classifiers and the electrophysiological morphology of the P3 potential. The classifiers used were a Support Vector Machine with a gaussian kernel and a Fisher Linear Discriminant. The electrophysiological aspects of the P3 potential studied were the amplitude and latency. The results indicate that although small fluctuations in the classifier accuracy were observed between the differing visual protocols, the relative changes were not statistically significant. This means that in this set of experiments the Farwell &amp; Donchin has proved to be robust to visual stimulus parameter changes. The experiments also demonstrate the difficulties of using Brain Computer Interfaces and the inconsistent results they often provide across subjects. Furthermore, the experiments have introduced some interesting changes to the visual layout of the Farwell &amp; Donchin protocol.},
}
@article {pmid18002508,
year = {2007},
author = {Geng, T and Dyson, M and Tsui, CS and Gan, JQ},
title = {A 3-class asynchronous BCI controlling a simulated mobile robot.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2524-2527},
doi = {10.1109/IEMBS.2007.4352842},
pmid = {18002508},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Biomimetics ; Computer Simulation ; Computers ; Electronics, Medical ; Equipment Design ; False Positive Reactions ; Humans ; Man-Machine Systems ; *Pattern Recognition, Automated ; *Robotics ; Therapy, Computer-Assisted/*instrumentation ; Wheelchairs ; },
abstract = {We present our design and online experiments of a 3-class asynchronous BCI controlling a simulated robot in an indoor environment. Two characteristics of our design have efficiently decreased the false positive rate during the NC (No Control) mode. First, three one-vs-rest LDA classifiers are combined to control the switching between NC and IC (In Control) mode. Second, the hierarchical structure of our controller allows the most reliable class (mental task) in a specific subject to play a dominant role in the robot control. A group of simple rules triggered by local sensor signals are designed for safety and obstacle avoidance in the NC mode. In online experiments, subjects successfully controlled the robot to circumnavigate obstacles and reach small targets in separate rooms.},
}
@article {pmid18002507,
year = {2007},
author = {Gouy-Pailler, C and Achard, S and Rivet, B and Jutten, C and Maby, E and Souloumiac, A and Congedo, M},
title = {Topographical dynamics of brain connections for the design of asynchronous brain-computer interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2520-2523},
doi = {10.1109/IEMBS.2007.4352841},
pmid = {18002507},
issn = {2375-7477},
mesh = {Brain/*pathology ; Brain Mapping ; Cognition ; Computers ; Equipment Design ; Evoked Potentials ; Humans ; Man-Machine Systems ; Models, Neurological ; Models, Statistical ; Nerve Net ; *Neural Networks, Computer ; Nonlinear Dynamics ; *User-Computer Interface ; },
abstract = {This article presents a new processing method to design brain-computer interfaces (BCIs). It shows how to use the perturbations of the communication between different cortical areas due to a cognitive task. For this, the network of the cerebral connections is built from correlations between cortical areas at specific frequencies and is analyzed using graph theory. This allows us to describe the topological organisation of the networks using quantitative measures. This method is applied to an auditive steady-state evoked potentials experiment (dichotic binaural listening) and compared to a more classical method based on spectral filtering.},
}
@article {pmid18002506,
year = {2007},
author = {Fazel-Rezai, R},
title = {Human error in P300 speller paradigm for brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2516-2519},
doi = {10.1109/IEMBS.2007.4352840},
pmid = {18002506},
issn = {2375-7477},
mesh = {Algorithms ; Attention ; Brain/*pathology ; Brain Mapping ; Cognition ; Equipment Design ; *Event-Related Potentials, P300 ; Evoked Potentials ; Humans ; *Language ; *Nerve Net ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on P300 speller BCI paradigm designed by Farwell and Donchin in 1988. It has been the most widely used and a benchmark in P300 BCI. In this paradigm, a 6 x 6 matrix of letters and numbers is displayed and subject focuses on a character while different rows and columns flash. The work presented in this paper is an attempt to improve the accuracy of P300 BCI systems by understanding a source of error in this paradigm. It is shown that adjacent rows and columns to the target ones play major role in the error. This can be attributed to human error that when the adjacent row or column to the target one flashes, it attracts subject's attention and creates the P300.},
}
@article {pmid18002505,
year = {2007},
author = {Parini, S and Maggi, L and Andreoni, G},
title = {An automated method for relevant frequency bands identification based on genetic algorithms and dedicated to the Motor Imagery BCI protocol.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {2512-2515},
doi = {10.1109/IEMBS.2007.4352839},
pmid = {18002505},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; *Automation ; Brain/*pathology ; Electroencephalography/instrumentation/methods ; Equipment Design ; Evoked Potentials, Motor ; Humans ; Models, Statistical ; Models, Theoretical ; Movement ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper presents an automated method for relevant frequency bands identification to be used in a left/right hand motor imagery based Brain Computer Interface system. The adopted optimization method aimed at maximizing the ratio between the mutual information and the error rate obtained using a Regularized Linear Discriminant Analysis (RLDA) based classifier and band-specific amplitude modulated envelopes as features. The search problem was handled by a genetic algorithm starting from an initial population determined on the basis of a-priori mu and beta relevant frequency bands identified by means of a standard power spectral density analysis between the idle and the left/right imagery data subset.},
}
@article {pmid18002363,
year = {2007},
author = {Wang, L and Xu, G and Wang, J and Yang, S and Yan, W},
title = {Feature extraction of mental task in BCI based on the method of approximate entropy.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {1941-1944},
doi = {10.1109/IEMBS.2007.4352697},
pmid = {18002363},
issn = {2375-7477},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*pathology ; Brain Mapping ; Cognition ; Data Interpretation, Statistical ; Electroencephalography/instrumentation/methods ; Entropy ; Equipment Design ; Humans ; *Nerve Net ; Pattern Recognition, Automated ; Perception ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Brain computer interface (BCI) is based on processing brain signals recorded from the scalp or the surface of the cortex in order to identify the different brain states and covert to corresponded control command. The key problems in BCI research are feature extraction and classification. In this paper, two experiments were performed, and the EEG data were recording during each experiment. One experiment contains five mental tasks, including "baseline", "rotation", "multiplication", "counting" and "letter-composing", the other contains two mental tasks which are left hand imagery movement and right hand imagery movement. EEG data recorded from both experiments are analyzed by approximate entropy (Apen), which is used to extract the characteristic feature of different mental tasks. A three-layer BP Neural Network classifier was structured for pattern classification. Different results were gained from the mental task experiment and imagery movement experiment. The results show that Apen is an effective method to extract the feature of different brain states.},
}
@article {pmid18002347,
year = {2007},
author = {Raiesdana, S and Shamsollahi, MB and Hashemi, MR and Rezazadeh, I},
title = {Wavelet packet decomposition of a new filter -- based on underlying neural activity -- for ERP classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {1876-1879},
doi = {10.1109/IEMBS.2007.4352681},
pmid = {18002347},
issn = {2375-7477},
mesh = {Algorithms ; Computer Simulation ; Data Interpretation, Statistical ; Electric Impedance ; Equipment Design ; *Evoked Potentials ; Humans ; Models, Theoretical ; Neural Networks, Computer ; Regression Analysis ; Reproducibility of Results ; *Signal Processing, Computer-Assisted ; *Software ; },
abstract = {This paper introduces a wavelet packet algorithm based on a new wavelet like filter created by a neural mass model in place of wavelet. The hypothesis is that the performance of an ERP based BCI system can be improved by choosing an optimal wavelet derived from underlying mechanism of ERPs. The wavelet packet transform has been chosen for its generalization in comparison to wavelet. We compared the performance of proposed algorithm with existing standard wavelets as Db4, Bior4.4 and Coif3 in wavelet packet platform. The results showed a lowest cross validation error for the new filter in classification of two different kinds of ERP datasets via a SVM classifier.},
}
@article {pmid18002238,
year = {2007},
author = {El Dawlatly, S and Oweiss, KG},
title = {Clustering synaptically-coupled neuronal populations under systematic variations in temporal dependence.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {1445-1448},
doi = {10.1109/IEMBS.2007.4352572},
pmid = {18002238},
issn = {2375-7477},
support = {NS054148-01A1/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; *Cluster Analysis ; Computer Simulation ; *Models, Neurological ; Nerve Net/*physiology ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; Synaptic Transmission/*physiology ; },
abstract = {Identifying clusters of neurons that exhibit functional interdependency in a recorded population has recently emerged as a direct result of the ability to simultaneously record multiple single unit activity with high-density microelectrode arrays. We demonstrated in a previous study that a graph theoretic approach can identify functional interdependency over multiple time scales between models of neuronal firing in response to a common input or synaptically-coupled in a multi-cluster population. In this paper, we investigate the performance of the technique in the case of neuronal interaction arising at various latencies and interval lengths. We report the capability of the approach to track these variable degrees of interactions. This feature can be very useful in decoding variable motor cortical response patterns during sensorimotor integration in Brain Machine Interface applications.},
}
@article {pmid18002053,
year = {2007},
author = {Coyle, D and McGinnity, TM and Prasad, G},
title = {Identifying local ultrametricity of EEG time series for feature extraction in a brain-computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {701-704},
doi = {10.1109/IEMBS.2007.4352387},
pmid = {18002053},
issn = {2375-7477},
mesh = {Adult ; Brain/*physiology ; *Electroencephalography ; Electronic Data Processing/*methods ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; *User-Computer Interface ; },
abstract = {The accurate discrimination of EEG times-series is a challenging problem and has become a topic of prominent research interest, given the extent of the research activity in the area of brain-computer interface (BCI) technology. Many signal processing algorithms involving preprocessing, feature extraction/selection, and classification have been deployed and yet, the most appropriate and robust solutions are still being sought. This paper presents an analysis of a new methodology for feature extraction in a BCI which is based on identifying the extent of ultrametricity from EEG time-series. This work is inspired by the idea that there are natural, not necessarily unique, tree or hierarchy structures defined by the ultrametric topology of EEG time-series. The objective is to determine if coefficients which reflect the extent of ultrametricity can be used as distinct features of different EEG time series, recorded whilst subjects imagine left/right hand movements (motor imagery(MI)). The results show that MI based EEG time-series can be separated using a local ultrametricity quantifier and a linear discriminant classifier or Bayes classifier. Also, it is shown that neural-time-series-prediction-preprocessing (NTSPP) produces a higher dimensional space in which local ultrametricity is more separable for two classes of EEG signals.},
}
@article {pmid18002047,
year = {2007},
author = {de Kruif, BJ and Schaefer, R and Desain, P},
title = {Classification of imagined beats for use in a brain computer interface.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {678-681},
doi = {10.1109/IEMBS.2007.4352381},
pmid = {18002047},
issn = {2375-7477},
mesh = {Brain/*physiology ; *Electrocardiography ; Female ; Humans ; Imagination/*physiology ; Male ; *User-Computer Interface ; },
abstract = {The power spectrum of an EEG signal shows differences with respect to its baseline the moment a subject is hearing, or expecting, a tone. As this difference also occurs when one is not actually hearing it, a Brain Computer Interface can be developed in which imagined rhythms are used to transfer information. Four healthy subjects participated in this study in which they had to imagine a simple rhythm. A metronome was kept ticking so that the subjects would not drift in their tempo. Solely based on the EEG signals, the classifier had to distinguish between imagined accented and non-accented tones. The features for the classification were automatically selected out of a set of possible features that focussed on phase and power differences of independent components. The classification rate found is about 0.6 for two of the four subjects, and several classifications can be combined to increase this classification rate to values larger than 0.7 with 2 s worth of data for the best performing subject. Chance level for our classification task is 0.5.},
}
@article {pmid18001984,
year = {2007},
author = {Borghi, T and Bonfanti, A and Zambra, G and Gusmeroli, R and Spinelli, AS and Baranauskas, G},
title = {A compact multichannel system for acquisition and processing of neural signals.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2007},
number = {},
pages = {441-444},
doi = {10.1109/IEMBS.2007.4352318},
pmid = {18001984},
issn = {2375-7477},
mesh = {Amplifiers, Electronic ; Animals ; Cells, Cultured ; *Electronic Data Processing ; Humans ; *Microcomputers ; Microelectrodes ; *Neurons ; Neurophysiology/*instrumentation/methods ; *Signal Transduction ; },
abstract = {An increasing popularity of multichannel recordings from freely behaving animals and the need to develop a practical brain-machine interface has fuelled the development of miniature multichannel recording systems. Here we describe our prototype miniature 64-channel acquisition system that could be used for multichannel recordings in freely behaving monkeys or other large animals. The system's features include an high impedance input for neurophysiology electrodes, an integrated battery fed circuitry with a 64 low-noise multiplexed amplifiers array that permits the parallel recording of all channels, a 10-bit resolution ADC, an FPGA digital core for online processing and data transmission, a USB 2.0 link and a custom software for data visualization and whole system control.},
}
@article {pmid17992083,
year = {2007},
author = {Kübler, A and Kotchoubey, B},
title = {Brain-computer interfaces in the continuum of consciousness.},
journal = {Current opinion in neurology},
volume = {20},
number = {6},
pages = {643-649},
doi = {10.1097/WCO.0b013e3282f14782},
pmid = {17992083},
issn = {1350-7540},
mesh = {Biofeedback, Psychology/methods/physiology ; Brain/anatomy &amp; histology/*physiopathology ; Communication Devices for People with Disabilities/trends ; Computers/*trends ; Consciousness/physiology ; Consciousness Disorders/*diagnosis/*physiopathology/therapy ; Disability Evaluation ; Humans ; Man-Machine Systems ; *User-Computer Interface ; },
abstract = {PURPOSE OF REVIEW: To summarize recent developments and look at important future aspects of brain-computer interfaces.<br><br>RECENT FINDINGS: Recent brain-computer interface studies are largely targeted at helping severely or even completely paralysed patients. The former are only able to communicate yes or no via a single muscle twitch, and the latter are totally nonresponsive. Such patients can control brain-computer interfaces and use them to select letters, words or items on a computer screen, for neuroprosthesis control or for surfing the Internet. This condition of motor paralysis, in which cognition and consciousness appear to be unaffected, is traditionally opposed to nonresponsiveness due to disorders of consciousness. Although these groups of patients may appear to be very alike, numerous transition states between them are demonstrated by recent studies.<br><br>SUMMARY: All nonresponsive patients can be regarded on a continuum of consciousness which may vary even within short time periods. As overt behaviour is lacking, cognitive functions in such patients can only be investigated using neurophysiological methods. We suggest that brain-computer interfaces may provide a new tool to investigate cognition in disorders of consciousness, and propose a hierarchical procedure entailing passive stimulation, active instructions, volitional paradigms, and brain-computer interface operation.},
}
@article {pmid17980917,
year = {2008},
author = {Müller-Putz, GR and Eder, E and Wriessnegger, SC and Pfurtscheller, G},
title = {Comparison of DFT and lock-in amplifier features and search for optimal electrode positions in SSVEP-based BCI.},
journal = {Journal of neuroscience methods},
volume = {168},
number = {1},
pages = {174-181},
doi = {10.1016/j.jneumeth.2007.09.024},
pmid = {17980917},
issn = {0165-0270},
mesh = {Adult ; Amplifiers, Electronic ; Brain/*physiology ; *Brain Mapping ; *Electrodes ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Female ; *Fourier Analysis ; Humans ; Male ; Photic Stimulation/methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) have been investigated increasingly in the last years. This type of brain signals resulting from repetitive flicker stimulation has the same fundamental frequency as the stimulation including higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by localizing individual electroencephalogram (EEG) recording positions. In the current work, a 4-class SSVEP-based BCI system was set up. Ten subjects participated and EEG was recorded from 21 channels overlying occipital areas. Features were extracted by applying Discrete Fourier transformation and a lock-in analyzer system. A simple one versus the rest classifier was applied to compare methods and localize individual electrode positions. It was shown that the use of three SSVEP-harmonics recorded from individual channels yielded significantly higher classification accuracy compared to one harmonic and to the standard positions O1 and O2. Furthermore, the application of a simple one versus the rest classifier and the use of a lock-in analyzer system lead to a higher classification accuracy (mean+/-S.D., about 74+/-16%) in a 4-class BCI compared to the commonly used Discrete Fourier transformation (DFT, 62+/-14%). By applying a screening procedure, the optimal electrode positions for bipolar derivations can be detected. Furthermore, information about subject's specific 'resonance-like' frequency regions can be obtained by observing higher harmonics of the SSVEPs.},
}
@article {pmid17978021,
year = {2007},
author = {Fagg, AH and Hatsopoulos, NG and de Lafuente, V and Moxon, KA and Nemati, S and Rebesco, JM and Romo, R and Solla, SA and Reimer, J and Tkach, D and Pohlmeyer, EA and Miller, LE},
title = {Biomimetic brain machine interfaces for the control of movement.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {27},
number = {44},
pages = {11842-11846},
pmid = {17978021},
issn = {1529-2401},
support = {R01 NS048845/NS/NINDS NIH HHS/United States ; R01 NS048845-01A1/NS/NINDS NIH HHS/United States ; R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS053603-02/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Artificial Intelligence ; *Biomimetics ; Brain/*physiology ; Humans ; *Man-Machine Systems ; Models, Neurological ; Movement/*physiology ; Nonlinear Dynamics ; *User-Computer Interface ; },
abstract = {Quite recently, it has become possible to use signals recorded simultaneously from large numbers of cortical neurons for real-time control. Such brain machine interfaces (BMIs) have allowed animal subjects and human patients to control the position of a computer cursor or robotic limb under the guidance of visual feedback. Although impressive, such approaches essentially ignore the dynamics of the musculoskeletal system, and they lack potentially critical somatosensory feedback. In this mini-symposium, we will initiate a discussion of systems that more nearly mimic the control of natural limb movement. The work that we will describe is based on fundamental observations of sensorimotor physiology that have inspired novel BMI approaches. We will focus on what we consider to be three of the most important new directions for BMI development related to the control of movement. (1) We will present alternative methods for building decoders, including structured, nonlinear models, the explicit incorporation of limb state information, and novel approaches to the development of decoders for paralyzed subjects unable to generate an output signal. (2) We will describe the real-time prediction of dynamical signals, including joint torque, force, and EMG, and the real-time control of physical plants with dynamics like that of the real limb. (3) We will discuss critical factors that must be considered to incorporate somatosensory feedback to the BMI user, including its potential benefits, the differing representations of sensation and perception across cortical areas, and the changes in the cortical representation of tactile events after spinal injury.},
}
@article {pmid17971857,
year = {2007},
author = {Lachaux, JP and Jerbi, K and Bertrand, O and Minotti, L and Hoffmann, D and Schoendorff, B and Kahane, P},
title = {A blueprint for real-time functional mapping via human intracranial recordings.},
journal = {PloS one},
volume = {2},
number = {10},
pages = {e1094},
pmid = {17971857},
issn = {1932-6203},
mesh = {Brain/*pathology ; Brain Mapping/*methods ; Cerebral Cortex/*pathology ; Electric Stimulation ; Electroencephalography/*methods ; Epilepsy/*diagnosis/pathology/*therapy ; Evoked Potentials, Auditory ; Humans ; *Language ; Models, Biological ; Nerve Net ; Oscillometry ; Software ; Temporal Lobe/pathology ; Video Recording ; },
abstract = {BACKGROUND: The surgical treatment of patients with intractable epilepsy is preceded by a pre-surgical evaluation period during which intracranial EEG recordings are performed to identify the epileptogenic network and provide a functional map of eloquent cerebral areas that need to be spared to minimize the risk of post-operative deficits. A growing body of research based on such invasive recordings indicates that cortical oscillations at various frequencies, especially in the gamma range (40 to 150 Hz), can provide efficient markers of task-related neural network activity.<br><br>PRINCIPAL FINDINGS: Here we introduce a novel real-time investigation framework for mapping human brain functions based on online visualization of the spectral power of the ongoing intracranial activity. The results obtained with the first two implanted epilepsy patients who used the proposed online system illustrate its feasibility and utility both for clinical applications, as a complementary tool to electrical stimulation for presurgical mapping purposes, and for basic research, as an exploratory tool used to detect correlations between behavior and oscillatory power modulations. Furthermore, our findings suggest a putative role for high gamma oscillations in higher-order auditory processing involved in speech and music perception.<br><br>CONCLUSION/SIGNIFICANCE: The proposed real-time setup is a promising tool for presurgical mapping, the investigation of functional brain dynamics, and possibly for neurofeedback training and brain computer interfaces.},
}
@article {pmid17967559,
year = {2007},
author = {Bai, O and Lin, P and Vorbach, S and Li, J and Furlani, S and Hallett, M},
title = {Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {118},
number = {12},
pages = {2637-2655},
pmid = {17967559},
issn = {1388-2457},
support = {NIH0011402999/ImNIH/Intramural NIH HHS/United States ; Z01 NS002669-23/ImNIH/Intramural NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Analysis of Variance ; Bayes Theorem ; Brain Mapping/methods ; Data Collection ; Data Interpretation, Statistical ; Dominance, Cerebral/physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Hand/innervation/physiology ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; Volition/classification/*physiology ; },
abstract = {OBJECTIVE: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).<br><br>METHODS: Twelve na&#xef;ve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed.<br><br>RESULTS: The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention.<br><br>CONCLUSIONS: Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training.<br><br>SIGNIFICANCE: Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.},
}
@article {pmid17947145,
year = {2006},
author = {Kohlenberg, J and Chau, T},
title = {Detecting controlled signals in the human brain by near infrared spectroscopy.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {5480-5482},
doi = {10.1109/IEMBS.2006.259877},
pmid = {17947145},
issn = {1557-170X},
mesh = {Absorption ; Adult ; Biomedical Engineering/methods ; Brain/metabolism/*pathology ; Brain Mapping/methods ; Cerebrovascular Circulation ; Female ; Humans ; Models, Theoretical ; Motor Cortex ; Software ; Spectrophotometry ; Spectroscopy, Near-Infrared/*instrumentation/*methods ; Time Factors ; User-Computer Interface ; },
abstract = {We present here results from a preliminary trial of brain activation data collection by near infrared spectroscopy (NIRS). Light in the NIR region was incident upon the human motor cortex in anticipation of observing a detectable change during periods of motor activation with respect to periods of rest. Frequency domain near infrared spectroscopy (NIRS) was used to obtain the amplitude (AC) and intensity (DC) of the NIR signal after it passed through the brain tissue. Analysis of the DC component indicates that the absorptive properties of the tissue are altered during periods of activation. Spectral estimation reveals some frequency components in both amplitude and intensity signals that may serve to discriminate between the periods of activation and the periods of rest. These characteristic differences may be harnessed to control a brain computer interface (BCI).},
}
@article {pmid17947135,
year = {2006},
author = {Funase, A and Yagi, T and Barros, AK and Cichocki, A and Takumi, I},
title = {Single trial method for brain-computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {5277-5281},
doi = {10.1109/IEMBS.2006.259741},
pmid = {17947135},
issn = {1557-170X},
mesh = {Algorithms ; Brain/*pathology ; Electroencephalography/*instrumentation/*methods ; Equipment Design ; Evoked Potentials ; *Eye Movements ; Humans ; Models, Theoretical ; Movement ; Reference Values ; *Saccades ; Signal Processing, Computer-Assisted ; Statistics as Topic ; User-Computer Interface ; },
abstract = {Electroencephalogram (EEG) related to fast eye movement (saccade), has been the subject of application oriented research by our group toward developing a brain-computer interface (BCI). Our goal is to develop novel BCI based on eye movements system employing EEG signals online. Most of the analysis of the saccade-related EEG data has been performed using ensemble averaging approaches. However, ensemble averaging is not suitable for BCI. In order to process raw EEG data in real time, we performed saccade-related EEG experiments and processed data by using the non-conventional fast ICA with reference signal (FICAR). The FICAR algorithm can extract desired independent components (IC) which have strong correlation against a reference signal. Visually guided saccade tasks and auditory guided saccade tasks were performed and the EEG signal generated in the saccade was recorded. The EEG processing was performed in three stages: PCA preprocessing and noise reduction, extraction of the desired IC using Wiener filter with reference signal, and post-processing using higher order statistics fast ICA based on maximization of kurtosis. Form the experimental results and analysis we found that using FICAR it is possible to extract form raw EEG data the saccade-related ICs and to predict saccade in advance by about 10 [ms] before real movements of eyes occurs. For single trail EEG data we have successfully extracted the desire ICs with recognition rate about 70%. In next steps, saccade-related EEG signals and saccade-related ICs in visually and auditory guided saccade task are compared in the point of the latency between starting time of a saccade and time when a saccade-related EEG signal or an IC has maximum value and in the point of the peak scale where a saccade-related EEG signal or an IC has maximum value. As results, peak time when saccade-related ICs have maximum amplitude is earlier than peak time when saccade-related EEG signals have maximum amplitude. This is very important advantage for developing our BCI. However, S/N ratio in being processed by FICAR is not improved comparing S/N ratio in being processed by ensemble averaging.},
}
@article {pmid17946993,
year = {2006},
author = {Teo, E and Huang, A and Lian, Y and Guan, C and Li, Y and Zhang, H},
title = {Media communication center using brain computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2954-2957},
doi = {10.1109/IEMBS.2006.260092},
pmid = {17946993},
issn = {1557-170X},
mesh = {Biomedical Engineering ; Bionics/instrumentation/*methods ; Brain/*physiology ; *Communication ; Cybernetics ; Event-Related Potentials, P300 ; Humans ; *Man-Machine Systems ; Paralysis/physiopathology/rehabilitation/therapy ; Software ; *User-Computer Interface ; },
abstract = {This paper attempts to make use of brain computer interface (BCI) in implementing an application called the media communication center for the paralyzed people. The application is based on the event-related potential called P300 to perform button selections on media and communication programs such as the mp3 player, video player, photo gallery and e-book. One of the key issues in such system is the usability. We study how various tasks affect the application operation, in particular, how typical mental activities cause false trigger during the operation of the application. We study the false acceptance rate under the conditions of closing eyes, reading a book, listening to music and watching a video. Data from 5 subjects is used to obtain the false rejection rate and false acceptance rate of the BCI system. Our study shows that different mental activities show different impacts on the false acceptance performances.},
}
@article {pmid17946962,
year = {2006},
author = {Darmanjian, S and Cieslewski, G and Morrison, S and Dang, B and Gugel, K and Principe, J},
title = {A reconfigurable neural signal processor (NSP) for brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2502-2505},
doi = {10.1109/IEMBS.2006.260423},
pmid = {17946962},
issn = {1557-170X},
mesh = {Brain/*physiology ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Humans ; Man-Machine Systems ; Miniaturization ; Robotics/*instrumentation ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/*instrumentation/methods ; Therapy, Computer-Assisted/instrumentation/methods ; *User-Computer Interface ; },
abstract = {In this paper, we present a design for a wearable computational DSP system that alleviates the issues of a previous design and provides a much smaller and lower power solution for the overall BMI goals. The system first acquires the neural data through a high speed data bus in order to train and evaluate prediction models. Then it wirelessly transmits the predicted results to a simulated robot arm. This system has been built and successfully tested with real and simulated data.},
}
@article {pmid17946886,
year = {2006},
author = {Gilmour, TP and Krishnan, L and Gaumond, RP and Clement, RS},
title = {A comparison of neural feature extraction methods for brain-machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1268-1272},
doi = {10.1109/IEMBS.2006.260518},
pmid = {17946886},
issn = {1557-170X},
mesh = {Animals ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Pattern Recognition, Automated/*methods ; Photic Stimulation/methods ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Brain-machine interfaces (BMIs) have shown promise in augmenting people's control of their surroundings, especially for those suffering from paralysis due to neurological disorders. This paper describes an experiment using the rodent model to explore information available in neural signals recorded from chronically implanted intracortical microelectrode arrays. In offline experiments, a number of neural feature extraction methods were utilized to obtain neural activity vectors (NAVs) describing the activity of the underlying neural population while rats performed a discrimination task. The methods evaluated included standard techniques such as binned spike rates and local field potential spectra as well as more novel approaches including matched-filter energy, raw signal spectra, and an autocorrelation energy measure (AEM) approach. Support vector machines (SVMs) were trained offline to classify left from right going movements by utilizing features contained in the NAVs obtained by the different methods. Each method was evaluated for accuracy and robustness. Results show that most algorithms worked well for decoding neural signals both during and prior to movement, with spectral methods providing the best stability.},
}
@article {pmid17946885,
year = {2006},
author = {Doyle, TE and Kucerovsky, Z and Ieta, A},
title = {Affective state control for neuroprostheses.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1248-1251},
doi = {10.1109/IEMBS.2006.260531},
pmid = {17946885},
issn = {1557-170X},
mesh = {Affect/*physiology ; *Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Evoked Potentials/*physiology ; Humans ; Nervous System Diseases/physiopathology/rehabilitation ; Pattern Recognition, Automated/*methods ; Prostheses and Implants ; Therapy, Computer-Assisted/methods ; *User-Computer Interface ; },
abstract = {The control and communication in man and the machine has been an active area of research since the early 1940's and since then the usage of the computing machine for the enhancement, augmentation, and rehabilitation of mankind has been broadly investigated. One active area of such research is the interface of the human brain to the computer; brain-computer-interfacing (BCI) or neuroprostheses. Current examples of functional BCI typically control the computer screen cursor movement, but require extensive subject training and significant, if not full, cognitive focus. Our model proposed an alternative approach to implementing the BCI for the application of controlling a digital hearing aid by autonomously modifying the speech signal based on the identification of electrophysiological response, or an affective state. Using a support vector machine binary classifier our model successfully demonstrated the efficacy of single-trial identification of affective states as an enhanced method of hearing neuroprosthetic control at a communication transfer rate of 240 bits/minute.},
}
@article {pmid17946876,
year = {2006},
author = {Fukayama, O and Taniguchi, N and Suzuki, T and Mabuchi, K},
title = {Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1138-1141},
doi = {10.1109/IEMBS.2006.260297},
pmid = {17946876},
issn = {1557-170X},
mesh = {*Algorithms ; Animals ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Locomotion/*physiology ; *Models, Neurological ; Motor Cortex/*physiology ; *Motor Vehicles ; Pattern Recognition, Automated/*methods ; Rats ; Robotics/*methods ; },
abstract = {We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.},
}
@article {pmid17946815,
year = {2006},
author = {Acharya, S and Mollazadeh, M and Murari, K and Thakor, N},
title = {Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {327-330},
doi = {10.1109/IEMBS.2006.259436},
pmid = {17946815},
issn = {1557-170X},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of principle component analysis, independent component analysis and dipole source localization to design a spatiotemporal multiple source tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the event-related spectral perturbation (ERSP) were measured and used to train a linear support vector machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (large Laplacian and common average reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.},
}
@article {pmid17946749,
year = {2006},
author = {Mirghasemi, H and Fazel-Rezai, R and Shamsollahi, MB},
title = {Analysis of p300 classifiers in brain computer interface speller.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {6205-6208},
doi = {10.1109/IEMBS.2006.259521},
pmid = {17946749},
issn = {1557-170X},
mesh = {Artificial Intelligence ; Brain/*pathology ; Brain Mapping ; Diagnosis, Computer-Assisted ; Electrodes ; Electroencephalography/*instrumentation/methods ; *Event-Related Potentials, P300 ; Humans ; Models, Statistical ; Neural Networks, Computer ; *Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (GSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any optimization similar to other methods. In addition, in this paper, it is shown that the efficiency of using Principal Component Analysis (PCA) for feature reduction results in decreasing the time for the classification and increasing the accuracy.},
}
@article {pmid17946745,
year = {2006},
author = {Wang, Y and Sanchez, JC and Principe, JC and Mitzelfelt, JD and Gunduz, A},
title = {Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {6185-6188},
doi = {10.1109/IEMBS.2006.260516},
pmid = {17946745},
issn = {1557-170X},
mesh = {Action Potentials ; Animals ; Brain/*pathology ; Brain Mapping ; Electric Stimulation ; Electrodes ; *Evoked Potentials, Motor ; Male ; Models, Statistical ; Motor Cortex/*anatomy &amp; histology/*pathology ; Neurons/metabolism/*pathology ; Rats ; Rats, Sprague-Dawley ; Signal Processing, Computer-Assisted ; *Synaptic Transmission ; },
abstract = {Neuronal firing rate has been the signal of choice for invasive motor brain machine interfaces (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.},
}
@article {pmid17946689,
year = {2006},
author = {Shayegh, F and Erfanian, A},
title = {Real-time ocular artifacts suppression from EEG signals using an unsupervised adaptive blind source separation.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {5269-5272},
doi = {10.1109/IEMBS.2006.259611},
pmid = {17946689},
issn = {1557-170X},
mesh = {Algorithms ; Artifacts ; Blinking ; Brain Mapping ; Computer Simulation ; Computers ; Electroencephalography/*instrumentation/*methods ; Electrooculography/*instrumentation/*methods ; Equipment Design ; *Eye Movements ; Humans ; Signal Processing, Computer-Assisted ; Statistics as Topic ; Time Factors ; Vision, Ocular ; },
abstract = {Independent component analysis (ICA) has been shown to be a powerful tool for artifactual suppression from electroencephalogram (EEG) recordings. However, the real-time application of this method for artifact rejection has not been considered so far. This article presents a method based on an unsupervised, self-normalizing, adaptive learning algorithm for on-line blind source separation. Simulation results are provided to show the validity and effectiveness of the technique with different distributions. The results from real-data demonstrate that the proposed scheme removes perfectly eye blink and eye movement artifacts from the EEG signals and is suitable for use during on-line EEG monitoring such as EEG-based brain computer interface.},
}
@article {pmid17946616,
year = {2006},
author = {Paiva, AC and Príncipe, JC and Sanchez, JC},
title = {Gravity transform for input conditioning in brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {4261-4264},
doi = {10.1109/IEMBS.2006.260507},
pmid = {17946616},
issn = {1557-170X},
mesh = {Animals ; Brain/*physiology ; Cerebral Cortex/*physiology ; Gravitation ; Male ; Models, Neurological ; Neurons/*physiology ; Prosthesis Design ; Rats ; Rats, Sprague-Dawley ; Sensitivity and Specificity ; Synchrotrons ; *User-Computer Interface ; },
abstract = {Gravity transform measures cooperative neural activity being utilized for the analysis of neural assemblies. In this paper we verify the applicability of the gravity transform to specify components of neural assemblies, which could be combined, leading ultimately to a reduction of the input dimensionality in brain-machine interface models. Our analysis was performed on data collected from rats performing a lever pressing task. We compare the results from the gravity transform analysis with the assignment obtained through a sensitivity analysis applied on a linear optimal filter.},
}
@article {pmid17946524,
year = {2006},
author = {Ince, NF and Tewfik, AH and Arica, S},
title = {A space-time-frequency analysis approach for the classification motor imagery EEG recordings in a brain computer interface task.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2581-2584},
doi = {10.1109/IEMBS.2006.260052},
pmid = {17946524},
issn = {1557-170X},
mesh = {*Algorithms ; Brain Mapping/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; User-Computer Interface ; },
abstract = {We introduce an adaptive space time frequency analysis to extract and classify subject specific brain oscillations induced by motor imagery in a brain computer interface task. The introduced method requires no prior knowledge of the reactive frequency bands, their temporal behavior or cortical locations. The algorithm implements an arbitrary time-frequency segmentation procedure by using a flexible local discriminant base algorithm for given multichannel brain activity recordings to extract subject specific ERD and ERS patterns. Extracted time-frequency features are processed by principal component analysis to reduce the feature set which is highly correlated due to volume conduction and the neighbor cortical regions. The reduced feature set is then fed to a linear discriminant analysis for classification. We give experimental results for 9 subjects to show the superior performance of the proposed method where the classification accuracy varied between 76.4% and 96.8% and the average classification accuracy was 84.9%},
}
@article {pmid17946502,
year = {2006},
author = {Coyle, D and McGinnity, TM and Prasad, G},
title = {Creating a nonparametric brain-computer interface with neural time-series prediction preprocessing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2183-2186},
doi = {10.1109/IEMBS.2006.260626},
pmid = {17946502},
issn = {1557-170X},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/physiology ; Pattern Recognition, Automated/*methods ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more complex FEPs. Neural time-series prediction preprocessing (NTSPP) has been shown to enhance the separability of both time- and frequency-based features and is used in this work to improve the applicability of these FEPs. NTSPP uses a number of prediction modules (PMs) to perform m-step ahead prediction of EEG time-series recorded whilst subjects perform motor imagery-based mental tasks. Depending on the PMs, the NTSPP framework normally requires subject-specific parameters to be predefined. In this work each PM is a self-organizing fuzzy neural network (SOFNN). The SOFNN has a self-organizing structure and good nonlinear approximation capabilities however; a number of parameters must be defined prior to training. This is problematic therefore the practicality of a general set of parameters, previously selected via a sensitivity analysis (SA), is analyzed. The results indicate that a general set of NTSPP parameters may provide the best results and therefore a fully nonparametric BCI may be realizable.},
}
@article {pmid17946456,
year = {2006},
author = {Mirghasemi, H and Shamsollahi, MB and Fazel-Rezai, R},
title = {Assessment of preprocessing on classifiers used in the p300 speller paradigm.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1319-1322},
doi = {10.1109/IEMBS.2006.259520},
pmid = {17946456},
issn = {1557-170X},
mesh = {*Algorithms ; *Artificial Intelligence ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Pattern Recognition, Visual/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve the 96% accuracy i.e. 31 of 32 characters were predicted correctly. These two approaches have different classifier and different preprocessing method. It means that the performance of each classifier can be enhanced with a specific preprocessing method. In our approach, we used only three electrodes of 64 applied electrodes. Therefore it can noticeably reduce the time and cost of EEG measurement.},
}
@article {pmid17946448,
year = {2006},
author = {Choi, SH and Lee, M and Wang, Y and Hong, B},
title = {Estimation of optimal location of EEG reference electrode for motor imagery based BCI using fMRI.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1193-1196},
doi = {10.1109/IEMBS.2006.260270},
pmid = {17946448},
issn = {1557-170X},
mesh = {Brain Mapping/instrumentation/*methods ; *Electrodes ; Electroencephalography/instrumentation/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Magnetic Resonance Imaging/*methods ; Motor Cortex/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain computer interface (BCI) is based on brain activity from voluntary will, and controls a computer system through only the imagination or other mental activity. In order to improve the performance of the BCI system based on the scalp EEG, it is important to determine suitable locations for the EEG electrodes according to brain activity as well as the location of reference electrode of the EEG, while most of conventional studies do not much consider about the location of the reference electrode. In this paper, we estimate the proper reference electrode location of the BCI system whose mental tasks are left and right finger movement imagination. The suggested location of the reference electrode is obtained by analyzing the fMRI imaging results. Further online EEG experiment confirmed that choosing supplementary motor area (SMA) as the reference is effective in enhancing the performance of the BCI system.},
}
@article {pmid17946326,
year = {2006},
author = {Das, K and Meyer, J and Nenadic, Z},
title = {Analysis of large-scale brain data for brain-computer interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {5731-5734},
doi = {10.1109/IEMBS.2006.259678},
pmid = {17946326},
issn = {1557-170X},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*pathology ; Brain Mapping ; Computers ; Data Interpretation, Statistical ; Discriminant Analysis ; Electroencephalography/instrumentation/*methods ; Equipment Design ; Humans ; Image Interpretation, Computer-Assisted ; Models, Theoretical ; Pattern Recognition, Automated ; Time Factors ; User-Computer Interface ; },
abstract = {We present a systematic technique for extraction of useful information from large-scale neural data in the context of brain-computer interfaces. The technique is based on a direct linear discriminant analysis, recently developed for face recognition problems. We show that this technique is capable of extracting useful information from brain data in a systematic fashion and can serve as a general analytical tool for other types of biomedical data, such as images and collections of images (movies). The performance of the method is tested on intracranial electroencephalographic data recorded from the human brain.},
}
@article {pmid17946151,
year = {2006},
author = {Patrick, E and Ordonez, M and Alba, N and Sanchez, JC and Nishida, T},
title = {Design and fabrication of a flexible substrate microelectrode array for brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2966-2969},
doi = {10.1109/IEMBS.2006.260581},
pmid = {17946151},
issn = {1557-170X},
mesh = {Action Potentials ; Animals ; Biomedical Engineering ; Bionics/instrumentation ; Brain/*physiology/*surgery ; Equipment Design ; Humans ; Male ; *Man-Machine Systems ; *Microelectrodes ; Rats ; Rats, Sprague-Dawley ; },
abstract = {We report a neural microelectrode array design that leverages the recording properties of conventional microwire electrode arrays with the additional features of precise control of the electrode geometries. Using microfabrication techniques, a neural probe array is fabricated that possesses a flexible polyimide-based cable. The performance of the design was tested with electrochemical impedance spectroscopy and in vivo studies. The gold-plated electrode site has an impedance value of 0.9 M Omega at 1 kHz. Acute neural recording provided high neuronal yields, peak-to-peak amplitudes (as high as 100 microV), and signal-to-noise ratios (27 dB).},
}
@article {pmid17946093,
year = {2006},
author = {Al-Ani, A and Al-Sukker, A},
title = {Effect of feature and channel selection on EEG classification.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2171-2174},
doi = {10.1109/IEMBS.2006.259833},
pmid = {17946093},
issn = {1557-170X},
mesh = {Adult ; Algorithms ; Brain Mapping/*methods ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {In this paper, we evaluate the significance of feature and channel selection on EEG classification. The selection process is performed by searching the feature/channel space using genetic algorithm, and evaluating the importance of subsets using a linear support vector machine classifier. Three approaches have been considered: (i) selecting a subset of features that will be used to represent a specified set of channels, (ii) selecting channels that are each represented by a specified set of features, and (iii) selecting individual features from different channels. When applied to a brain-computer interface (BCI) problem, results indicate that improvement in classification accuracy can be achieved by considering the correct combination of channels and features.},
}
@article {pmid17946051,
year = {2006},
author = {Wei, Q and Gao, X and Gao, S},
title = {Feature extraction and subset selection for classifying single-trial ECoG during motor imagery.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1589-1592},
doi = {10.1109/IEMBS.2006.260561},
pmid = {17946051},
issn = {1557-170X},
mesh = {*Algorithms ; Artificial Intelligence ; Brain Mapping/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {The electrocorticogram (ECoG) recorded from subdural electrodes is a kind of BCI signal source that has the potential to achieve good classification results. The feature extraction and its subset selection are crucial for increasing classification accuracy rate. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The nonlinear regressive coefficients between signals on 10 leads are extracted in two frequency bands 0-3 Hz and 8-30 Hz as classification features. A genetic algorithm is used for the selection of the optimal feature subset and a support vector machine for their evaluation. The generalization error of 7% is achieved on data set I of BCI Competition III.},
}
@article {pmid17946038,
year = {2006},
author = {Krusienski, DJ and McFarland, DJ and Wolpaw, JR},
title = {An evaluation of autoregressive spectral estimation model order for brain-computer interface applications.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1323-1326},
doi = {10.1109/IEMBS.2006.259822},
pmid = {17946038},
issn = {1557-170X},
support = {EB 00856/EB/NIBIB NIH HHS/United States ; HD 30146/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Humans ; *Models, Neurological ; Models, Statistical ; Pattern Recognition, Automated/*methods ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Autoregressive (AR) spectral estimation is a popular method for modeling the electroencephalogram (EEG), and therefore the frequency domain EEG phenomena that are used for control of a brain-computer interface (BCI). Several studies have been conducted to evaluate the optimal AR model order for EEG, but the criteria used in these studies does not necessarily equate to the optimal AR model order for sensorimotor rhythm (SMR)-based BCI control applications. The present study confirms this by evaluating the EEG spectra of data obtained during control of SMR-BCI using different AR model orders and model evaluation criteria. The results indicate that the AR model order that optimizes SMR-BCI control performance is generally higher than the model orders that are frequently used in SMR-BCI studies.},
}
@article {pmid17946034,
year = {2006},
author = {Maggi, L and Parini, S and Piccini, L and Panfili, G and Andreoni, G},
title = {A four command BCI system based on the SSVEP protocol.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1264-1267},
doi = {10.1109/IEMBS.2006.260353},
pmid = {17946034},
issn = {1557-170X},
mesh = {Adult ; *Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {This paper discusses the development of a four command BCI system. This system is composed of a wearable electroencephalogram acquisition unit interfaced to a computer by a wireless Bluetooth (BT) connection. The implemented system relies on the steady-state visual evoked potential (SSVEP) protocol applied to a four selection system. In order to achieve the maximum reliability against false positives a five class classifier was used considering the idle state as an independent class. In order to maximize the usability of the system a two channel solution was tested and adopted. The BCI algorithm was based on a supervised multi-class classifier implemented by combining different binary regularized linear discriminant analysis (RLDA) classifiers. The biofeedback was evaluated by combining the resultant time signed distance with quality index related to the number of coherent identification obtained with the one-vs-all approach.},
}
@article {pmid17946028,
year = {2006},
author = {Li, H and Li, Y and Guan, C},
title = {An effective BCI speller based on semi-supervised learning.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1161-1164},
doi = {10.1109/IEMBS.2006.260694},
pmid = {17946028},
issn = {1557-170X},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Language ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Brain-computer interfaces (BCIs) aim to provide an alternative channel for paralyzed patients to communicate with external world. Reducing the time needed for the initial calibration is one important objective in P300 based BCI research. In this paper, the training time is reduced by a semi-supervised learning approach. A model is trained by small training set first. The on-line test data with predicted labels are then added to the initial training data to extend the training data. And the model is updated online using the extended training set. The method is tested by a data set of P300 based word speller. The experimental results show that 93.4% of the training time for this data set can be reduced by the proposed method while keeping satisfactory accuracy rate. This semi-supervised learning approach is applied on-line to obtain robust and adaptive model for P300 based speller with small training set, which is believed to be very essential to improve the feasibility of the P300 based BCI.},
}
@article {pmid17945793,
year = {2006},
author = {Bufalari, S and Mattia, D and Babiloni, F and Mattiocco, M and Marciani, MG and Cincotti, F},
title = {Autoregressive spectral analysis in Brain Computer Interface context.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {3736-3739},
doi = {10.1109/IEMBS.2006.260238},
pmid = {17945793},
issn = {1557-170X},
mesh = {Adult ; Algorithms ; Automation ; Brain/*physiology ; Brain Mapping ; Cortical Synchronization/methods ; *Electroencephalography ; Evoked Potentials ; Humans ; Learning ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Over the past decade, a number of studies have evaluated the possibility that scalp-recorded electroencephalogram (EEG) activity might be the basis for a brain-computer interface (BCI), a system able to determine the intent of the user from a variety of different electrophysiological signals. With our current EEG-based communication system, users learn over a series of training sessions to use EEG to move a cursor on a video screen: to make this possible users must learn to control the EEG features that determines cursor movement and we must improve signal processing methods to extract from background noise the EEG features that the system translates into cursor movement. Non-invasive data acquisition, makes automated feature extraction challenging, since the signals of interest are "hidden" in a highly noisy environment. It was demonstrated that the spatial filtering operations improve the signal-to-noise ratio. On the contrary, autoregressive modeling has been successfully used by many investigators for EEG signals analysis in BCI context, but to our knowledge no clear guidelines exist on how to choose the parameters of the spectral estimation. Here we present an analysis of the dependence of BCI performance on the parameters of the feature extraction algorithm. In order to optimize user performances, we observed that a different model order value had to be chosen correspondently to different EEG features used to control the system, according to the differences in the spectral power content of alpha and/or beta bands.},
}
@article {pmid17945723,
year = {2006},
author = {Li, Y and Guan, C},
title = {A semi-supervised SVM learning algorithm for joint feature extraction and classification in brain computer interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2570-2573},
doi = {10.1109/IEMBS.2006.260327},
pmid = {17945723},
issn = {1557-170X},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {In machine learning based Brain Computer Interfaces (BCIs), it is a challenge to use only a small amount of labelled data to build a classifier for a specific subject. This challenge was specifically addressed in BCI Competition 2005. Moreover, an effective BCI system should be adaptive to tackle the dynamic variations in brain signal. One of the solutions is to have its parameters adjustable while the system is used online. In this paper we introduce a new semi-supervised support vector machine (SVM) learning algorithm. In this method, the feature extraction and classification are jointly performed in iterations. This method allows us to use a small training set to train the classifier while maintaining high performance. Therefore, the tedious initial calibration process is shortened. This algorithm can be used online to make the BCI system robust to possible signal changes. We analyze two important issues of the proposed algorithm, the robustness of the features to noise and the convergence of algorithm. We applied our method to data from BCI competition 2005, and the results demonstrated the validity of the proposed algorithm.},
}
@article {pmid17945704,
year = {2006},
author = {Dat, TH and Shue, L and Guan, C},
title = {Electrocorticographic signal classification based on time-frequency decomposition and nonparametric statistical modeling.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {2292-2295},
doi = {10.1109/IEMBS.2006.259906},
pmid = {17945704},
issn = {1557-170X},
mesh = {Algorithms ; *Artificial Intelligence ; Computer Simulation ; Data Interpretation, Statistical ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Models, Neurological ; Models, Statistical ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In this paper, we propose a novel statistical framework based on time-frequency decomposition and nonparametric modelling of electrocortical (ECoG) signals in the context of a Brain Computer Interface. The proposed method decomposes the ECoG signals into subbands (with no down-sampling) using Gabor filters. The subband signals are then encoded using a nonparametric statistical modeling and the distance between the resulting empirical distributions is as used as the classification criterion. Cross-validation experiments were carried out to pre-select the channel (from the multi-channel sources) and subbands which can archive the best classification scores. The proposed framework has been evaluated using Data Set I from the BCI Competition III and results indicate a superiority over conventional vector quantization method particularly when the number of training samples is small. It was found that the proposed nonparametric distribution modeling based on empirical inverse cumulative distribution distance is fast, robust and applicable to the mobile systems.},
}
@article {pmid17945625,
year = {2006},
author = {Abdollahi, F and Motie-Nasrabadi, A},
title = {Combination of frequency bands in EEG for feature reduction in mental task classification.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1146-1149},
doi = {10.1109/IEMBS.2006.260229},
pmid = {17945625},
issn = {1557-170X},
mesh = {*Algorithms ; Cognition/*physiology ; *Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Fourier Analysis ; Humans ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; },
abstract = {Brain-computer interfaces require online processing of electroencephalogram (EEG) measurements. Therefore, speed of signal processing is of great importance in BCI systems. We present a method of feature reduction by combining frequency band powers of EEG, in order to speed up processing and meanwhile avoid classifier overfitting. As a result a linear combination of power spectrum of EEG frequency bands (alpha, beta, gamma, delta &amp; theta) was found that reduces the dimension of feature vector by a factor of 5. This method gives a total correct classification rate of 91.71% comparing to 87.96% achieved from direct use of frequency band powers and 85.54% achieved from PCA feature reduction method applied to the same feature vector with 14 components.},
}
@article {pmid17945624,
year = {2006},
author = {Cososchi, S and Strungaru, R and Ungureanu, A and Ungureanu, M},
title = {EEG features extraction for motor imagery.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {1142-1145},
doi = {10.1109/IEMBS.2006.260004},
pmid = {17945624},
issn = {1557-170X},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; *Fuzzy Logic ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; User-Computer Interface ; },
abstract = {Motor imagery is the mental simulation of a motor act that includes preparation for movement, passive observations of action and mental operations of motor representations implicitly or explicitly. Motor imagery as preparation for immediate movement likely involves the motor executive brain regions. Implicit mental operations of motor representations are considered to underlie cognitive functions. Another problem concerning neuro-imaging studies on motor imagery is that the performance of imagination is very difficult to control. The ability of an individual to control its EEG may enable him to communicate without being able to control their voluntary muscles. Communication based on EEG signals does not require neuromuscular control and the individuals who have neuromuscular disorders and who may have no more control over any of their conventional communication abilities may still be able to communicate through a direct brain-computer interface. A brain-computer interface replaces the use of nerves and muscles and the movements they produce with electrophysiological signals and is coupled with the hardware and software that translate those signals into physical actions. One of the most important components of a brain-computer interface is the EEG feature extraction procedure. This paper presents an approach that uses self-organizing fuzzy neural network based time series prediction that performs EEG feature extraction in the time domain only. EEG is recorded from two electrodes placed on the scalp over the motor cortex. EEG signals from each electrode are predicted by a single fuzzy neural network. Features derived from the mean squared error of the predictions and from the mean squared of the predicted signals are extracted from EEG data by means of a sliding window. The architecture of the two auto-organizing fuzzy neural networks is a network with multi inputs and single output.},
}
@article {pmid17945612,
year = {2006},
author = {Mattiocco, M and Babiloni, F and Mattia, D and Bufalari, S and Sergio, S and Salinari, S and Marciani, MG and Cincotti, F},
title = {Neuroelectrical source imaging of mu rhythm control for BCI applications.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {980-983},
doi = {10.1109/IEMBS.2006.260128},
pmid = {17945612},
issn = {1557-170X},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; *User-Computer Interface ; },
abstract = {In the last decade, the possibility to noninvasively estimate cortical activity has been highlighted by the application of the techniques known as high resolution EEG. These techniques include a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multi-dipole source model, and regularized linear inverse source estimates of cortical current density. The aim of this paper is to demonstrate that the use of cortical activity estimated from noninvasive EEG recordings of motor imagery is useful in the context of a brain computer interface as compared with others scalp spatial filters usually used on-line.},
}
@article {pmid17945570,
year = {2006},
author = {Wang, Y and Hong, B and Gao, X and Gao, S},
title = {Phase synchrony measurement in motor cortex for classifying single-trial EEG during motor imagery.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {75-78},
doi = {10.1109/IEMBS.2006.259673},
pmid = {17945570},
issn = {1557-170X},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Cortical Synchronization/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; },
abstract = {A motor imagery based brain-computer interface (BCI) translates the subject's motor intention into a control signal. For this BCI system, most algorithms are based on power changes of mu and beta rhythms. In this paper, we employ the measurement of phase synchrony to investigate the activities of the supplementary motor area (SMA) and primary motor area (M1) during left/right hand movement imagery. The single-trial phase locking value (PLV) features were derived from intrinsic large-scale and local-scale phase synchronies between and within SMA and M1. The classification performance suggests that phase synchrony is an additional robust feature for differentiating motor imagery states.},
}
@article {pmid17941986,
year = {2007},
author = {Chatterjee, A and Aggarwal, V and Ramos, A and Acharya, S and Thakor, NV},
title = {A brain-computer interface with vibrotactile biofeedback for haptic information.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {4},
number = {},
pages = {40},
pmid = {17941986},
issn = {1743-0003},
mesh = {Adult ; Biofeedback, Psychology/*methods ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *Task Performance and Analysis ; Touch/*physiology ; *User-Computer Interface ; Vibration ; },
abstract = {BACKGROUND: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.<br><br>METHODS: A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.<br><br>RESULTS AND CONCLUSION: Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.},
}
@article {pmid17939885,
year = {2007},
author = {Moxon, KA and Hallman, S and Aslani, A and Kalkhoran, NM and Lelkes, PI},
title = {Bioactive properties of nanostructured porous silicon for enhancing electrode to neuron interfaces.},
journal = {Journal of biomaterials science. Polymer edition},
volume = {18},
number = {10},
pages = {1263-1281},
doi = {10.1163/156856207782177882},
pmid = {17939885},
issn = {0920-5063},
mesh = {Action Potentials ; Animals ; Biocompatible Materials/*chemistry ; Brain/metabolism ; Cell Proliferation ; Drug Delivery Systems ; Electrodes ; Electrophysiology ; Immunohistochemistry/methods ; Microscopy, Electron, Scanning ; Nanostructures/*chemistry ; Neurites/metabolism ; Neuroglia/metabolism ; Neurons/*metabolism ; PC12 Cells ; Rats ; Silicon/*chemistry ; Surface Properties ; },
abstract = {Many different types of microelectrodes have been developed for use as a direct brain-machine interface (BMI) to chronically recording single-neuron action potentials from ensembles of neurons. Unfortunately, the recordings from these microelectrode devices are not consistent and often last for only on the order of months. For most microelectrode types, the loss of these recordings is not due to failure of the electrodes, but most likely due to damage to surrounding tissue that results in the formation of non-conductive glial scar. Since the extracellular matrix consists of nanostructured fibrous protein assemblies, we have postulated that neurons may prefer a more complex surface structure than the smooth surface typical of thin-film microelectrodes. This porous structure could then act as a drug-delivery reservoir to deliver bioactive agents to aid in the repair or survival of cells around the microelectrode, further reducing the glial scar. We, therefore, investigated the suitability of a nanoporous silicon surface layer to increase the biocompatibility of our thin film ceramic-insulated multisite electrodes. In vitro testing demonstrated increased extension of neurites from PC12 pheochromocytoma cells on porous silicon surfaces compared to smooth silicon surfaces. Moreover, the size of the pores and the pore coverage did not interfere with this bioactive surface property, suggesting that large highly porous nanostructured surfaces can be used for drug delivery. The most porous nanoporous surfaces were then tested in vivo and found to be more biocompatible than smooth surface, producing less glial activation and allowing more neurons to remain close to the device. Collectively, these results support our hypothesis that nanoporous silicon may be an ideal material to improve biocompatibility of chronically implanted microelectrodes. The next step in this work will be to apply these surfaces to active microelectrodes, use them to deliver bioactive agents, and test that they do improve neural recordings.},
}
@article {pmid17925242,
year = {2007},
author = {Tsien, JZ},
title = {Real-time neural coding of memory.},
journal = {Progress in brain research},
volume = {165},
number = {},
pages = {105-122},
doi = {10.1016/S0079-6123(06)65007-3},
pmid = {17925242},
issn = {0079-6123},
mesh = {Animals ; Computer Simulation ; Humans ; Memory/*physiology ; *Models, Neurological ; Nerve Net/*physiology ; *Neural Networks, Computer ; Neurons/*physiology ; },
abstract = {Recent identification of network-level functional coding units, termed neural cliques, in the hippocampus has allowed real-time patterns of memory traces to be mathematically described, intuitively visualized, and dynamically deciphered. Any given episodic event is represented and encoded by the activation of a set of neural clique assemblies that are organized in a categorical and hierarchical manner. This hierarchical feature-encoding pyramid is invariantly composed of the general feature-encoding clique at the bottom, sub-general feature-encoding cliques in the middle, and highly specific feature-encoding cliques at the top. This hierarchical and categorical organization of neural clique assemblies provides the network-level mechanism the capability of not only achieving vast storage capacity, but also generating commonalities from the individual behavioral episodes and converting them to the abstract concepts and generalized knowledge that are essential for intelligence and adaptive behaviors. Furthermore, activation patterns of the neural clique assemblies can be mathematically converted to strings of binary codes that would permit universal categorizations of the brain's internal representations across individuals and species. Such universal brain codes can also potentially facilitate the unprecedented brain-machine interface communications.},
}
@article {pmid17920134,
year = {2008},
author = {Schalk, G and Brunner, P and Gerhardt, LA and Bischof, H and Wolpaw, JR},
title = {Brain-computer interfaces (BCIs): detection instead of classification.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {51-62},
doi = {10.1016/j.jneumeth.2007.08.010},
pmid = {17920134},
issn = {0165-0270},
support = {EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Brain Mapping ; Electrocardiography/methods ; Electroencephalography/methods ; Humans ; Male ; Man-Machine Systems ; Normal Distribution ; Online Systems ; Signal Detection, Psychological/*physiology ; *Signal Processing, Computer-Assisted ; Software Validation ; *User-Computer Interface ; },
abstract = {Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer through brain-computer interfaces (BCIs). These devices operate by recording signals from the brain and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into clinical applications is the current requirement for preliminary analyses to identify the brain signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting brain activity so that any change in relevant brain signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording brain signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.},
}
@article {pmid17919734,
year = {2008},
author = {Ward, BD and Mazaheri, Y},
title = {Information transfer rate in fMRI experiments measured using mutual information theory.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {22-30},
doi = {10.1016/j.jneumeth.2007.06.027},
pmid = {17919734},
issn = {0165-0270},
mesh = {Adult ; Brain/*blood supply ; Female ; Humans ; *Information Theory ; *Magnetic Resonance Imaging ; Male ; Monte Carlo Method ; *Numerical Analysis, Computer-Assisted ; Oxygen/blood ; *User-Computer Interface ; },
abstract = {Information theory provides a mathematical framework for analysis of fMRI experiments. By modeling the fMRI experiment as a communication system, various results from information theory can be applied to measure information transfer rate in fMRI experiments. The information transfer rate has important implications for design and analysis of brain-computer interface (BCI) experiments. A key factor in the effective implementation of BCI techniques is to achieve maximum information transfer rate. In this report, mutual information rate (MIR) was used to evaluate the efficiency of alternative experimental designs. The channel capacity, a fundamental physical limit on the rate at which information can be extracted from an fMRI experiment, was estimated and compared with the theoretical limit specified by the Hartley-Shannon Theorem. We present an information theory framework for the analysis of fMRI time-series assuming a known hemodynamic response function. Using MIR to evaluate fMRI experimental designs, we show that block lengths of 3-5s have maximum information transfer rates. For designs with shorter block lengths, the MIR is limited by the channel capacity. For experimental designs with longer block lengths, the MIR is limited by the low source information transmission rate.},
}
@article {pmid17898441,
year = {2007},
author = {Lin, Z and Radcliffe, DE and Beck, MB and Risse, LM},
title = {Modeling phosphorus in the upper Etowah River basin: identifying sources under uncertainty.},
journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
volume = {56},
number = {6},
pages = {29-37},
doi = {10.2166/wst.2007.584},
pmid = {17898441},
issn = {0273-1223},
mesh = {Georgia ; *Models, Theoretical ; Phosphorus/*analysis ; *Rivers ; Uncertainty ; Water Movements ; Water Pollution/analysis/statistics &amp; numerical data ; },
abstract = {The Uniform Covering by Probabilistic Rejection (UCPR) algorithm was used, in conjunction with the Soil and Water Assessment Tool (SWAT) model, to identify P loads from point source and nonpoint source polluters in the upper Etowah River basin (UERB) in Georgia. The key findings of the research are as follows. The mean absolute error was preferred over the root mean square error as a search criterion for the UCPR algorithm when water quality observations are scarce. The undocumented P load from point sources in the UERB was consistently estimated as about 43 kg/d by the proposed method; but the method was not able to identify the broiler litter application rate to the poultry/beef operation pastures. Point sources (both documented and undocumented), poultry/beef operation pastures, and forests are the three major contributors of P. During 1992-1996, on average they accounted for 36.4, 31.7, and 17.2% of P load from the UERB, respectively.},
}
@article {pmid17890008,
year = {2007},
author = {Romeiro, RR and Romano-Silva, MA and De Marco, L and Teixeira, AL and Correa, H},
title = {Can variation in aquaporin 4 gene be associated with different outcomes in traumatic brain edema?.},
journal = {Neuroscience letters},
volume = {426},
number = {2},
pages = {133-134},
doi = {10.1016/j.neulet.2007.09.004},
pmid = {17890008},
issn = {0304-3940},
mesh = {Adolescent ; Adult ; Aquaporin 4/*genetics ; Brain Edema/etiology/*genetics/pathology ; Brain Injuries/complications ; Exons/genetics ; Genetic Variation/*genetics ; Glasgow Coma Scale ; Humans ; Male ; Middle Aged ; Tomography, X-Ray Computed ; },
abstract = {In traumatic brain injury (TBI), cerebral edema and hemorrhage are factors involved in the determination of the clinical presentation and outcome. The aquaporin 4 (AQP4) water channel is abundant in mammalian brain and there is a growing body of evidence suggesting that this protein plays a major role in the control of water flow within the central nervous system. Previous studies examined the influence of genetic variants in cerebral edema of TBI. However, to our knowledge, there are no previous studies of molecular variations of the AQP4 gene and its association with TBI. Thus, we sought to investigate if the clinical presentation and outcome of TBI could be influenced by the presence of mutations on exon 4 of the AQP4 gene. One hundred and two patients were enrolled in this study. A neurologist assessed the clinical severity at admission according to the GCS followed by a brain computer tomography (CT) scan. Then, DNA was extracted from blood cells and exon 4 of the AQP4 gene amplified by the polymerase chain reaction and directly sequenced. On discharge, GOS was assigned by a neurologist blind to the CGS on admission. We did not find any variation in exon 4 of the AQP4 gene in our considerable large sample. Despite this negative result, there is a strong biological rationale for the involvement of AQP4 gene in brain edema regulation and, as consequence, in TBI. Therefore, further studies should be performed, including the assessment of the other three exons of the AQP4 gene.},
}
@article {pmid17873435,
year = {2007},
author = {Achtman, N and Afshar, A and Santhanam, G and Yu, BM and Ryu, SI and Shenoy, KV},
title = {Free-paced high-performance brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {336-347},
doi = {10.1088/1741-2560/4/3/018},
pmid = {17873435},
issn = {1741-2560},
mesh = {*Algorithms ; Animals ; Cerebral Cortex/*physiology ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Models, Neurological ; *User-Computer Interface ; },
abstract = {Neural prostheses aim to improve the quality of life of severely disabled patients by translating neural activity into control signals for guiding prosthetic devices or computer cursors. We recently demonstrated that plan activity from premotor cortex, which specifies the endpoint of the upcoming arm movement, can be used to swiftly and accurately guide computer cursors to the desired target locations. However, these systems currently require additional, non-neural information to specify when plan activity is present. We report here the design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone. Prosthesis performance was nearly as good when state estimation was used as when perfect plan timing information was provided separately (approximately 5 percentage points lower, when using 200 ms of plan activity). These results strongly suggest that a completely neurally-driven high-performance brain-computer interface is possible.},
}
@article {pmid17873434,
year = {2007},
author = {Won, DS and Tiesinga, PH and Henriquez, CS and Wolf, PD},
title = {An analytical comparison of the information in sorted and non-sorted cosine-tuned spike activity.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {322-335},
doi = {10.1088/1741-2560/4/3/017},
pmid = {17873434},
issn = {1741-2560},
mesh = {Action Potentials/*physiology ; Brain/*physiology ; Computer Simulation ; Electroencephalography/*methods ; *Models, Neurological ; Nerve Net/*physiology ; Neurons/*physiology ; },
abstract = {Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a neuromotor prosthesis. No systematic analysis of the value of spike sorting has been carried out, and little is known about the effects of spike sorting error on the ability of a brain-machine interface (BMI) to decode intended motor commands. We developed a theoretical framework to examine the effects of spike processing on the information available to a BMI decoder. We computed the mutual information in neural activity in a simplified model of directional cosine tuning to compare the effects of pooling activity from up to four neurons to the effects of sorting with varying amounts of spike error. The results showed that information in a small population of cosine-tuned neurons is maximized when the responses are sorted and there is diverse tuning of units, but information was affected little when pooling units with similar preferred directions. Spike error had adverse effects on information, such that non-sorted population activity had 79-92% of the information in its sorted counterpart for reasonable amounts of detection and sorting error and for units with moderate differences in preferred direction. This quantification of information loss associated with pooling units and with spike detection and sorting error will help to guide the engineering decisions in designing a BMI spike processing system.},
}
@article {pmid17873433,
year = {2007},
author = {Rizk, M and Obeid, I and Callender, SH and Wolf, PD},
title = {A single-chip signal processing and telemetry engine for an implantable 96-channel neural data acquisition system.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {309-321},
doi = {10.1088/1741-2560/4/3/016},
pmid = {17873433},
issn = {1741-2560},
mesh = {Animals ; Brain/*physiology ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; Systems Integration ; Telemetry/*instrumentation ; },
abstract = {A fully implantable neural data acquisition system is a key component of a clinically viable cortical brain-machine interface. We present the design and implementation of a single-chip device that serves the processing needs of such a system. Our device processes 96 channels of multi-unit neural data and performs all digital processing necessary for bidirectional wireless communication. The implementation utilizes a single programmable logic device that is responsible for performing data reduction on the 96 channels of neural data, providing a bidirectional telemetry interface to a transceiver and performing command interpretation and system supervision. The device takes as input neural data sampled at 31.25 kHz and outputs a line-encoded serial bitstream containing the information to be transmitted by the transceiver. Data can be output in one of the following four modes: (1) streaming uncompressed data from a single channel, (2) extracted spike waveforms from any subset of the 96 channels, (3) 1 ms bincounts for each channel or (4) streaming data along with extracted spikes from a single channel. The device can output up to 2000 extracted spikes per second with latencies suitable for a brain-machine interface application. This device provides all of the digital processing components required by a fully implantable system.},
}
@article {pmid17873429,
year = {2007},
author = {Schalk, G and Kubánek, J and Miller, KJ and Anderson, NR and Leuthardt, EC and Ojemann, JG and Limbrick, D and Moran, D and Gerhardt, LA and Wolpaw, JR},
title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {264-275},
doi = {10.1088/1741-2560/4/3/012},
pmid = {17873429},
issn = {1741-2560},
support = {EB006356/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; NS41272/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Arm/*physiology ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Movement/*physiology ; },
abstract = {Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.},
}
@article {pmid17873427,
year = {2007},
author = {Liao, X and Yao, D and Li, C},
title = {Transductive SVM for reducing the training effort in BCI.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {246-254},
doi = {10.1088/1741-2560/4/3/010},
pmid = {17873427},
issn = {1741-2560},
mesh = {Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) provides a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In this work, the main concern is to reduce the training effort for BCI, which is often tedious and time consuming. Here we introduce a transductive support vector machines (TSVM) algorithm for the classification of EEG signals associated with mental tasks. TSVM possess the property of using both labeled and unlabeled data for reducing the calibration time in BCI and achieving good performance in classification accuracy. The advantages of the proposed method over the traditional supervised support vector machines (SVM) method are confirmed by about 2%-9% higher classification accuracies on a set of EEG recordings of three subjects from three-tasks-based mental imagery experiments.},
}
@article {pmid17873424,
year = {2007},
author = {Coyle, SM and Ward, TE and Markham, CM},
title = {Brain-computer interface using a simplified functional near-infrared spectroscopy system.},
journal = {Journal of neural engineering},
volume = {4},
number = {3},
pages = {219-226},
doi = {10.1088/1741-2560/4/3/007},
pmid = {17873424},
issn = {1741-2560},
mesh = {Adult ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Electroencephalography/*instrumentation ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials, Motor/*physiology ; Female ; Fiber Optic Technology/*instrumentation ; Humans ; Imagination/physiology ; Male ; Movement/physiology ; Signal Processing, Computer-Assisted/*instrumentation ; Spectrophotometry, Infrared/*instrumentation/methods ; User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a device that allows a user to communicate with external devices through thought processes alone. A novel signal acquisition tool for BCIs is near-infrared spectroscopy (NIRS), an optical technique to measure localized cortical brain activity. The benefits of using this non-invasive modality are safety, portability and accessibility. A number of commercial multi-channel NIRS system are available; however we have developed a straightforward custom-built system to investigate the functionality of a fNIRS-BCI system. This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, 'Mindswitch' that harnesses motor imagery for control. Analysis is performed online and feedback of performance is presented to the user. Mindswitch presents a basic 'on/off' switching option to the user, where selection of either state takes 1 min. Initial results show that fNIRS can support simple BCI functionality and shows much potential. Although performance may be currently inferior to many EEG systems, there is much scope for development particularly with more sophisticated signal processing and classification techniques. We hope that by presenting fNIRS as an accessible and affordable option, a new avenue of exploration will open within the BCI research community and stimulate further research in fNIRS-BCIs.},
}
@article {pmid17828570,
year = {2008},
author = {Parker, MA},
title = {Symbiotic relationships of legumes and nodule bacteria on Barro Colorado Island, Panama: a review.},
journal = {Microbial ecology},
volume = {55},
number = {4},
pages = {662-672},
pmid = {17828570},
issn = {0095-3628},
mesh = {Bacterial Typing Techniques ; Biodiversity ; Bradyrhizobium/*classification/genetics/isolation &amp; purification ; Burkholderia/*classification/genetics/isolation &amp; purification ; DNA, Bacterial/genetics ; Fabaceae/*microbiology ; Genes, rRNA ; Genetic Variation ; Panama ; Phylogeny ; RNA, Ribosomal, 16S/genetics ; RNA, Ribosomal, 23S/genetics ; Root Nodules, Plant/*microbiology ; Sequence Alignment ; Sequence Analysis, DNA ; Soil Microbiology ; Species Specificity ; *Symbiosis ; },
abstract = {Abstract New data on 129 bacterial isolates were analyzed together with prior samples to characterize community-level patterns of legume-rhizobial symbiosis on Barro Colorado Island (BCI), Panama. Nodules have been sampled from 24 BCI legume species in 18 genera, representing about one quarter of the legume species and one half of the genera on the island. Most BCI legumes associated exclusively with nodule symbionts in the genus Bradyrhizobium, which comprised 86.3% of all isolates (315 of 365). Most of the remaining isolates (44 of 365) belonged to the beta-proteobacterial genus Burkholderia; these were restricted to two genera in the legume subfamily Mimosoideae. Multilocus sequence analysis indicated that BCI Bradyrhizobium strains were differentiated into at least eight lineages with deoxyribonucleic acid divergence of the same magnitude as found among currently recognized species in this bacterial genus. Two of these lineages were widely distributed across BCI legumes. One lineage was utilized by 15 host species of diverse life form (herbs, lianas, and trees) in 12 genera spanning two legume subfamilies. A second common lineage closely related to the taxon B. elkanii was associated with at least five legume genera in four separate tribes. Thus, BCI legume species from diverse clades within the family frequently share interaction with a few common lineages of nodule symbionts. However, certain host species were associated with unique symbiont lineages that have not been found on other coexisting BCI legumes. More comprehensive sampling of host taxa will be needed to characterize the overall diversity of nodule bacteria and the patterns of symbiont sharing among legumes in this community.},
}
@article {pmid17785266,
year = {2007},
author = {Feeley, KJ and Davies, SJ and Ashton, PS and Bunyavejchewin, S and Nur Supardi, MN and Kassim, AR and Tan, S and Chave, J},
title = {The role of gap phase processes in the biomass dynamics of tropical forests.},
journal = {Proceedings. Biological sciences},
volume = {274},
number = {1627},
pages = {2857-2864},
pmid = {17785266},
issn = {0962-8452},
mesh = {Biomass ; Carbon Dioxide/metabolism ; Greenhouse Effect ; Malaysia ; Panama ; Regression Analysis ; Thailand ; Trees/*growth &amp; development/metabolism ; *Tropical Climate ; },
abstract = {The responses of tropical forests to global anthropogenic disturbances remain poorly understood. Above-ground woody biomass in some tropical forest plots has increased over the past several decades, potentially reflecting a widespread response to increased resource availability, for example, due to elevated atmospheric CO2 and/or nutrient deposition. However, previous studies of biomass dynamics have not accounted for natural patterns of disturbance and gap phase regeneration, making it difficult to quantify the importance of environmental changes. Using spatially explicit census data from large (50 ha) inventory plots, we investigated the influence of gap phase processes on the biomass dynamics of four 'old-growth' tropical forests (Barro Colorado Island (BCI), Panama; Pasoh and Lambir, Malaysia; and Huai Kha Khaeng (HKK), Thailand). We show that biomass increases were gradual and concentrated in earlier-phase forest patches, while biomass losses were generally of greater magnitude but concentrated in rarer later-phase patches. We then estimate the rate of biomass change at each site independent of gap phase dynamics using reduced major axis regressions and ANCOVA tests. Above-ground woody biomass increased significantly at Pasoh (+0.72% yr(-1)) and decreased at HKK (-0.56% yr(-1)) independent of changes in gap phase but remained stable at both BCI and Lambir. We conclude that gap phase processes play an important role in the biomass dynamics of tropical forests, and that quantifying the role of gap phase processes will help improve our understanding of the factors driving changes in forest biomass as well as their place in the global carbon budget.},
}
@article {pmid19516995,
year = {2007},
author = {Glinwood, R and Gradin, T and Karpinska, B and Ahmed, E and Jonsson, L and Ninkovic, V},
title = {Aphid acceptance of barley exposed to volatile phytochemicals differs between plants exposed in daylight and darkness.},
journal = {Plant signaling &amp; behavior},
volume = {2},
number = {5},
pages = {321-326},
pmid = {19516995},
issn = {1559-2316},
abstract = {It is well known that volatile cues from damaged plants may induce resistance in neighboring plants. Much less is known about the effects of volatile interaction between undamaged plants. In this study, barley plants, Hordeum vulgare cv. Kara, were exposed to volatiles from undamaged plants of barley cv. Alva or thistle Cirsium vulgare, and to the volatile phytochemicals, methyl salicylate or methyl jasmonate. Exposures were made either during natural daylight or darkness. Acceptance of exposed plants by the aphid Rhopalosiphum padi was assessed, as well as the expression of putative marker genes for the different treatments. Aphid acceptance of plants exposed to either barley or C. vulgare was significantly reduced, and an effect of the volatiles from undamaged plants was confirmed by the induction of pathogenesis-related protein, PR1a in exposed plants. However the effect on aphid acceptance was seen only when plants were exposed during darkness, whereas PR1a was induced only after treatment during daylight. Aphid acceptance of plants exposed to either methyl salicylate or methyl jasmonate was significantly reduced, but only when plants were exposed to the chemicals during daylight. AOS2 (allene oxide synthase) was induced by methyl jasmonate and BCI-4 (barley chemical inducible gene-4) by methyl salicylate in both daylight and darkness. It is concluded that (a) the effects on aphids of exposing barley to volatile phytochemicals was influenced by the presence or absence of light and (b) the response of barley to methyl salicylate/methyl jasmonate and to volatiles from undamaged plants differed at the gene and herbivore level.},
}
@article {pmid17716370,
year = {2007},
author = {Aberg, MC and Wessberg, J},
title = {Evolutionary optimization of classifiers and features for single-trial EEG discrimination.},
journal = {Biomedical engineering online},
volume = {6},
number = {},
pages = {32},
pmid = {17716370},
issn = {1475-925X},
mesh = {Adult ; *Algorithms ; *Artificial Intelligence ; Brain Mapping/*methods ; Diagnosis, Computer-Assisted/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Male ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; },
abstract = {BACKGROUND: State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were implemented as well by limiting the degree of optimization.<br><br>RESULTS: Using only 10 features and 100 patterns, the non-linear wrapper approach achieved the highest validation classification accuracy (subject mean 75%), closely followed by the linear wrapper method (73.5%). The optimal features differed much between subjects, yet some physiologically plausible patterns were observed.<br><br>CONCLUSION: High degrees of classifier parameter, structure and feature subset tailoring on individual levels substantially increase single-trial EEG classification rates, an important consideration in areas where highly accurate detection rates are essential. Also, the presented method provides insight into the spatial characteristics of finger movement EEG patterns.},
}
@article {pmid17712218,
year = {2007},
author = {Kaas, JH},
title = {Introduction: The use of animal research in developing treatments for human motor disorders: brain-computer interfaces and the regeneration of damaged brain circuits.},
journal = {ILAR journal},
volume = {48},
number = {4},
pages = {313-316},
doi = {10.1093/ilar.48.4.313},
pmid = {17712218},
issn = {1084-2020},
support = {R01 NS016446/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Brain Injuries/therapy ; Central Nervous System/injuries/physiology ; *Computers ; *Disease Models, Animal ; Humans ; Motor Neuron Disease/therapy ; Movement Disorders/*therapy ; *Nerve Regeneration ; Spinal Cord Injuries/therapy ; },
}
@article {pmid17706292,
year = {2008},
author = {Cincotti, F and Mattia, D and Aloise, F and Bufalari, S and Astolfi, L and De Vico Fallani, F and Tocci, A and Bianchi, L and Marciani, MG and Gao, S and Millan, J and Babiloni, F},
title = {High-resolution EEG techniques for brain-computer interface applications.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {31-42},
doi = {10.1016/j.jneumeth.2007.06.031},
pmid = {17706292},
issn = {0165-0270},
support = {GUP03562/TI_/Telethon/Italy ; EB006356/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Biofeedback, Psychology ; Brain/*physiology ; *Brain Mapping ; Communication Devices for People with Disabilities ; Electrodes ; *Electroencephalography ; Evoked Potentials, Motor/physiology ; Evoked Potentials, Somatosensory ; Female ; Humans ; Male ; Online Systems ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.},
}
@article {pmid17696294,
year = {2007},
author = {Lee, H and Kim, YD and Cichocki, A and Choi, S},
title = {Nonnegative tensor factorization for continuous EEG classification.},
journal = {International journal of neural systems},
volume = {17},
number = {4},
pages = {305-317},
doi = {10.1142/S0129065707001159},
pmid = {17696294},
issn = {0129-0657},
mesh = {Algorithms ; Biometry/*methods ; Brain/*physiology ; *Brain Mapping ; Computer Simulation ; Electroencephalography/*classification ; Humans ; User-Computer Interface ; },
abstract = {In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.},
}
@article {pmid17694874,
year = {2007},
author = {Kim, HK and Carmena, JM and Biggs, SJ and Hanson, TL and Nicolelis, MA and Srinivasan, MA},
title = {The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {8},
pages = {1520-1529},
doi = {10.1109/TBME.2007.900818},
pmid = {17694874},
issn = {0018-9294},
mesh = {Arm/*physiology ; Bone and Bones/*physiology ; Brain/*physiology ; Computer Simulation ; Electric Impedance ; Evoked Potentials/physiology ; Feedback ; Humans ; *Man-Machine Systems ; *Models, Biological ; Muscle Contraction/*physiology ; Muscle, Skeletal/*physiology ; User-Computer Interface ; },
abstract = {Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.},
}
@article {pmid17691404,
year = {2007},
author = {Stieglitz, T},
title = {Neural prostheses in clinical practice: biomedical microsystems in neurological rehabilitation.},
journal = {Acta neurochirurgica. Supplement},
volume = {97},
number = {Pt 1},
pages = {411-418},
doi = {10.1007/978-3-211-33079-1_54},
pmid = {17691404},
issn = {0065-1419},
mesh = {Animals ; Bionics/instrumentation/methods ; Electric Stimulation/*instrumentation/*methods ; Electrodes, Implanted ; Humans ; Nervous System Diseases/*rehabilitation ; *Prostheses and Implants ; User-Computer Interface ; },
abstract = {Technical devices have supported physicians in diagnosis, therapy, and rehabilitation since ancient times. Neural prostheses interface parts of the nervous system with technical (micro-) systems to partially restore sensory and motor functions that have been lost due to trauma or diseases. Electrodes act as transducers to record neural signals or to excite neural cells by means of electrical stimulation. The field of neural prostheses has grown over the last decades. An overview of neural prostheses illustrates the opportunities and limitations of the implants and performance in their current size and complexity. The implementation of microsystem technology with integrated microelectronics in neural implants 20 years ago opened new fields of application, but also new design paradigms and approaches with respect to the biostability of passivation and housing concepts and electrode interfaces. Microsystem specific applications in the peripheral nervous system, vision prostheses and brain-machine interfaces show the variety of applications and the challenges in biomedical microsystems for chronic nerve interfaces in new and emerging research fields that bridge neuroscientific disciplines with material science and engineering. Different scenarios are discussed where system complexity strongly depends on the rehabilitation objective and the amount of information that is necessary for the chosen neuro-technical interface.},
}
@article {pmid17691347,
year = {2007},
author = {Angelakis, E and Hatzis, A and Panourias, IG and Sakas, DE},
title = {Brain-computer interface: a reciprocal self-regulated neuromodulation.},
journal = {Acta neurochirurgica. Supplement},
volume = {97},
number = {Pt 2},
pages = {555-559},
doi = {10.1007/978-3-211-33081-4_64},
pmid = {17691347},
issn = {0065-1419},
mesh = {Animals ; Biofeedback, Psychology ; Brain/*physiology ; *Computer Simulation ; Humans ; *Models, Neurological ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is a system that records brain activity and process it through a computer, allowing the individual whose activity is recorded to monitor this activity at the same time. Applications of BCIs include assistive modules for severely paralyzed patients to help them control external devices or to communicate, as well as brain biofeedback to self regulate brain activity for treating epilepsy, attention-deficit hyperactivity disorder (ADHD), anxiety, and other psychiatric conditions, or to enhance cognitive performance in healthy individuals. The vast majority of BCIs utilizes non-invasive scalp recorded electroencephalographic (EEG) signals, but other techniques like invasive intracortical EEG, or near-infrared spectroscopy measuring brain blood oxygenation are tried experimentally.},
}
@article {pmid17691344,
year = {2007},
author = {Warwick, K and Gasson, MN and Spiers, AJ},
title = {Therapeutic potential of computer to cerebral cortex implantable devices.},
journal = {Acta neurochirurgica. Supplement},
volume = {97},
number = {Pt 2},
pages = {529-535},
doi = {10.1007/978-3-211-33081-4_61},
pmid = {17691344},
issn = {0065-1419},
mesh = {Animals ; Cerebral Cortex/*physiology ; Feedback ; Humans ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {In this article, an overview of some of the latest developments in the field of cerebral cortex to computer interfacing (CCCI) is given. This is posed in the more general context of Brain-Computer Interfaces in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system. Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bidirectionally with technology and the internet. A view is taken as to the prospects for the future for CCCI, in terms of its broad therapeutic role.},
}
@article {pmid17691284,
year = {2007},
author = {Sakas, DE and Panourias, IG and Simpson, BA},
title = {An introduction to neural networks surgery, a field of neuromodulation which is based on advances in neural networks science and digitised brain imaging.},
journal = {Acta neurochirurgica. Supplement},
volume = {97},
number = {Pt 2},
pages = {3-13},
doi = {10.1007/978-3-211-33081-4_1},
pmid = {17691284},
issn = {0065-1419},
mesh = {*Brain/anatomy &amp; histology/physiology/surgery ; Brain Mapping ; Diagnostic Imaging/*methods ; Electric Stimulation Therapy/instrumentation/*methods ; Humans ; *Nerve Net/anatomy &amp; histology/physiology/surgery ; *Signal Processing, Computer-Assisted ; Synaptic Transmission/physiology ; },
abstract = {Operative Neuromodulation is the field of altering electrically or chemically the signal transmission in the nervous system by implanted devices in order to excite, inhibit or tune the activities of neurons or neural networks and produce therapeutic effects. The present article reviews relevant literature on procedures or devices applied either in contact with the cerebral cortex or cranial nerves or in deep sites inside the brain in order to treat various refractory neurological conditions such as: a) chronic pain (facial, somatic, deafferentation, phantom limb), b) movement disorders (Parkinson's disease, dystonia, Tourette syndrome), c) epilepsy, d) psychiatric disease, e) hearing deficits, and f) visual loss. These data indicate that in operative neuromodulation, a new field emerges that is based on neural networks research and on advances in digitised stereometric brain imaging which allow precise localisation of cerebral neural networks and their relay stations; this field can be described as Neural networks surgery because it aims to act extrinsically or intrinsically on neural networks and to alter therapeutically the neural signal transmission with the use of implantable electrical or electronic devices. The authors also review neurotechnology literature relevant to neuroengineering, nanotechnologies, brain computer interfaces, hybrid cultured probes, neuromimetics, neuroinformatics, neurocomputation, and computational neuromodulation; the latter field is dedicated to the study of the biophysical and mathematical characteristics of electrochemical neuromodulation. The article also brings forward particularly interesting lines of research such as the carbon nanofibers electrode arrays for simultaneous electrochemical recording and stimulation, closed-loop systems for responsive neuromodulation, and the intracortical electrodes for restoring hearing or vision. The present review of cerebral neuromodulatory procedures highlights the transition from the conventional neurosurgery of resective or ablative techniques to a highly selective "surgery of networks". The dynamics of the convergence of the above biomedical and technological fields with biological restorative approaches have important implications for patients with severe neurological disorders.},
}
@article {pmid17690078,
year = {2007},
author = {Perniola, T and Dicuonzo, F and Margari, L and Presicci, A and Ventura, P and Palma, M and Carella, A},
title = {Costello syndrome: cognitive and proton magnetic resonance spectroscopy findings--a case report.},
journal = {Journal of child neurology},
volume = {22},
number = {5},
pages = {650-654},
doi = {10.1177/0883073807302615},
pmid = {17690078},
issn = {0883-0738},
mesh = {Aspartic Acid/analogs &amp; derivatives/metabolism ; Child ; Choline/metabolism ; Cognition/*physiology ; Creatine/metabolism ; Female ; Humans ; *Magnetic Resonance Spectroscopy ; Neuropsychological Tests ; Temporomandibular Joint Dysfunction Syndrome/*diagnosis/*physiopathology ; },
abstract = {The authors describe a girl with Costello syndrome who showed cerebral palsy and neurosensorial deafness. Brain computer tomography and magnetic resonance findings were normal. Multivoxel proton magnetic resonance spectroscopy showed a lowering of the peak of choline with a reduced choline/creatine ratio at the level of the centrum semiovale. These findings might be due to a congenital dysmyelinating or hypomyelinating condition. A complete neuroimaging study can play a relevant role to better clarify the pathogenesis of brain involvement in Costello syndrome.},
}
@article {pmid17674861,
year = {2007},
author = {Nöthe, T and Hartmann, D and von Sonntag, J and von Sonntag, C and Fahlenkamp, H},
title = {Elimination of the musk fragrances galaxolide and tonalide from wastewater by ozonation and concomitant stripping.},
journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
volume = {55},
number = {12},
pages = {287-292},
doi = {10.2166/wst.2007.422},
pmid = {17674861},
issn = {0273-1223},
mesh = {Animals ; Benzopyrans/analysis/*chemistry ; Ions/chemistry ; Molecular Structure ; Ozone/*chemistry ; Perfume/*chemistry ; Tetrahydronaphthalenes/analysis/*chemistry ; Waste Products ; Water Pollutants, Chemical/*chemistry ; Water Purification/*methods ; },
abstract = {Ozone reacts with the musk fragrances tonalide and galaxolide with rate constants of 8 M(-1)s(-1) and 140 M(-1)s(-1), respectively. In wastewater, ozone eliminates only the more reactive compound, galaxolide, in competition with its reaction with the wastewater matrix. As both compounds are also stripped in a bubble column, tonalide is also eliminated to some extent.},
}
@article {pmid17671106,
year = {2007},
author = {Quiroga, RQ and Reddy, L and Koch, C and Fried, I},
title = {Decoding visual inputs from multiple neurons in the human temporal lobe.},
journal = {Journal of neurophysiology},
volume = {98},
number = {4},
pages = {1997-2007},
doi = {10.1152/jn.00125.2007},
pmid = {17671106},
issn = {0022-3077},
mesh = {Adolescent ; Adult ; Algorithms ; Data Interpretation, Statistical ; Electrophysiology ; Epilepsy/physiopathology/surgery ; Female ; Humans ; Image Processing, Computer-Assisted ; Linear Models ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neurons/*physiology ; Photic Stimulation ; Temporal Lobe/cytology/*physiology ; },
abstract = {We investigated the representation of visual inputs by multiple simultaneously recorded single neurons in the human medial temporal lobe, using their firing rates to infer which images were shown to subjects. The selectivity of these neurons was quantified with a novel measure. About four spikes per neuron, triggered between 300 and 600 ms after image onset in a handful of units (7.8 on average), predicted the identity of images far above chance. Decoding performance increased linearly with the number of units considered, peaked between 400 and 500 ms, did not improve when considering correlations among simultaneously recorded units, and generalized to very different images. The feasibility of decoding sensory information from human extracellular recordings has implications for the development of brain-machine interfaces.},
}
@article {pmid17658263,
year = {2007},
author = {Sciabola, S and Carosati, E and Cucurull-Sanchez, L and Baroni, M and Mannhold, R},
title = {Novel TOPP descriptors in 3D-QSAR analysis of apoptosis inducing 4-aryl-4H-chromenes: comparison versus other 2D- and 3D-descriptors.},
journal = {Bioorganic &amp; medicinal chemistry},
volume = {15},
number = {19},
pages = {6450-6462},
doi = {10.1016/j.bmc.2007.06.051},
pmid = {17658263},
issn = {0968-0896},
mesh = {*Algorithms ; Apoptosis/*drug effects ; Benzopyrans/chemistry/*pharmacology ; Cell Line, Tumor/drug effects/pathology ; Data Interpretation, Statistical ; *Drug Design ; Humans ; Models, Molecular ; Predictive Value of Tests ; *Quantitative Structure-Activity Relationship ; Software ; Stereoisomerism ; },
abstract = {Novel 3D-descriptors using Triplets Of Pharmacophoric Points (TOPP) were evaluated in QSAR-studies on 80 apoptosis-inducing 4-aryl-4H-chromenes. A predictive QSAR model was obtained using PLS, confirmed by means of internal and external validations. Performance of the TOPP approach was compared with that of other 2D- and 3D-descriptors; statistical analysis indicates that TOPP descriptors perform best. A ranking of TOPP>GRIND>BCI 4096=ECFP>FCFP>GRID-GOLPE>>DRAGON>>>MDL 166 was achieved. Finally, in a 'consensus' analysis predictions obtained using the single methods were compared with an average approach using six out of eight methods. The use of the average is statistically superior to the single methods. Beyond it, the use of several methods can help to easily investigate the presence/absence of outliers according to the 'consensus' of the predicted values: agreement among all the methods indicates a precise prediction, whereas large differences between predicted values (for the same compounds by different methods) would demand caution when using such predictions.},
}
@article {pmid17653264,
year = {2007},
author = {Popescu, F and Fazli, S and Badower, Y and Blankertz, B and Müller, KR},
title = {Single trial classification of motor imagination using 6 dry EEG electrodes.},
journal = {PloS one},
volume = {2},
number = {7},
pages = {e637},
pmid = {17653264},
issn = {1932-6203},
mesh = {Brain/*physiology ; Cues ; *Electrodes ; Electroencephalography/*methods ; Female ; Humans ; Imagination/*physiology ; Male ; Mental Competency ; Mental Processes/*physiology ; Motor Activity/*physiology ; Patient Selection ; Signal Transduction ; *User-Computer Interface ; },
abstract = {BACKGROUND: Brain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity.<br><br>A novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex.<br><br>CONCLUSIONS/SIGNIFICANCE: Our study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring.},
}
@article {pmid17637835,
year = {2007},
author = {Zacksenhouse, M and Lebedev, MA and Carmena, JM and O'Doherty, JE and Henriquez, C and Nicolelis, MA},
title = {Cortical modulations increase in early sessions with brain-machine interface.},
journal = {PloS one},
volume = {2},
number = {7},
pages = {e619},
pmid = {17637835},
issn = {1932-6203},
mesh = {Animals ; Brain/physiology ; Brain Mapping/methods ; Cerebral Cortex/*physiology ; Female ; Learning/*physiology ; Macaca mulatta ; Models, Neurological ; Motor Activity/physiology ; Motor Cortex/physiology ; Movement/physiology ; Neurons/*physiology ; Parietal Lobe/physiology ; Psychomotor Performance/*physiology ; Regression Analysis ; Robotics ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {BACKGROUND: During planning and execution of reaching movements, the activity of cortical motor neurons is modulated by a diversity of motor, sensory, and cognitive signals. Brain-machine interfaces (BMIs) extract part of these modulations to directly control artificial actuators. However, cortical modulations that emerge in the novel context of operating the BMI are poorly understood.<br><br>Here we analyzed the changes in neuronal modulations that occurred in different cortical motor areas as monkeys learned to use a BMI to control reaching movements. Using spike-train analysis methods we demonstrate that the modulations of the firing-rates of cortical neurons increased abruptly after the monkeys started operating the BMI. Regression analysis revealed that these enhanced modulations were not correlated with the kinematics of the movement. The initial enhancement in firing rate modulations declined gradually with subsequent training in parallel with the improvement in behavioral performance.<br><br>CONCLUSIONS/SIGNIFICANCE: We conclude that the enhanced modulations are related to computational tasks that are significant especially in novel motor contexts. Although the function and neuronal mechanism of the enhanced cortical modulations are open for further inquiries, we discuss their potential role in processing execution errors and representing corrective or explorative activity. These representations are expected to contribute to the formation of internal models of the external actuator and their decoding may facilitate BMI improvement.},
}
@article {pmid17619669,
year = {2007},
author = {Brennan, M and French, J},
title = {Thyroid lumps and bumps.},
journal = {Australian family physician},
volume = {36},
number = {7},
pages = {531-536},
pmid = {17619669},
issn = {0300-8495},
mesh = {Adult ; Diagnosis, Differential ; Female ; Humans ; Male ; Thyroid Neoplasms/diagnostic imaging/*surgery ; Thyroid Nodule/*classification/diagnosis/pathology ; Ultrasonography ; },
abstract = {BACKGROUND: Thyroid nodules are extremely common, with 7% of adults having palpable nodules and up to 50% having nodules visible on ultrasound. About 5% of thyroid nodules are malignant. Thyroid nodules may occur as isolated, often incidental findings, or may be associated with systemic features of thyrotoxicosis or hypothyroidism. They may be solitary or may present as a dominant nodule in a multinodular goitre.<br><br>OBJECTIVE: This article presents an outline of the common causes of lumps in the thyroid (solitary and multiple) and provides a simple approach to diagnosis and management in the general practice setting. The focus is on the patient presenting with a lump in the thyroid rather than the patient presenting with hyper- or hypothyroidism.<br><br>DISCUSSION: The challenge for the general practitioner is to assess the nodule and determine which patients require referral for further investigation and management. Referral may be required to exclude or confirm malignancy and is also indicated for patients who are symptomatic from benign thyroid nodules.},
}
@article {pmid17613705,
year = {2007},
author = {Clark, A},
title = {Re-inventing ourselves: the plasticity of embodiment, sensing, and mind.},
journal = {The Journal of medicine and philosophy},
volume = {32},
number = {3},
pages = {263-282},
doi = {10.1080/03605310701397024},
pmid = {17613705},
issn = {0360-5310},
mesh = {Biotechnology/*trends ; Cognition/*physiology ; Cognitive Science/*trends ; Humans ; Perception/*physiology ; },
abstract = {Recent advances in cognitive science and cognitive neuroscience open up new vistas for human enhancement. Central to much of this work is the idea of new human-machine interfaces (in general) and new brain-machine interfaces (in particular). But despite the increasing prominence of such ideas, the very idea of such an interface remains surprisingly under-explored. In particular, the notion of human enhancement suggests an image of the embodied and reasoning agent as literally extended or augmented, rather than the more conservative image of a standard (non-enhanced) agent using a tool via some new interface. In this essay, I explore this difference, and attempt to lay out some of the conditions under which the more radical reading (positing brand new integrated agents or systemic wholes) becomes justified. I adduce some empirical evidence suggesting that the radical result is well within our scientific reach. The main reason why this is so has less to do with the advancement of our science (though that certainly helps) than with our native biological plasticity. We humans, I shall try to show, are biologically disposed towards literal (and repeated) episodes of sensory re-calibration, of bodily re-configuration and of mental extension. Such potential for literal and repeated re-configuration is the mark of what I shall call "profoundly embodied agency," contrasting it with a variety of weaker (less philosophically and scientifically interesting) understandings of the nature and importance of embodiment for minds and persons. The article ends by relating the image of profound embodiment to some questions (and fears) concerning converging technologies for improving human performance.},
}
@article {pmid17608800,
year = {2007},
author = {Pfurtscheller, G and Grabner, RH and Brunner, C and Neuper, C},
title = {Phasic heart rate changes during word translation of different difficulties.},
journal = {Psychophysiology},
volume = {44},
number = {5},
pages = {807-813},
doi = {10.1111/j.1469-8986.2007.00553.x},
pmid = {17608800},
issn = {0048-5772},
mesh = {Adult ; Electrocardiography ; Electroencephalography ; Electrooculography ; Female ; Heart Rate/*physiology ; Humans ; Language ; Male ; Photic Stimulation ; Psychomotor Performance/*physiology ; Stress, Psychological/*physiopathology/psychology ; },
abstract = {The heart rate (HR) can be modulated by diverse mental activities ranging from stimulus anticipation to higher order cognitive information processing. In the present study we report on HR changes during word translation and examine how the HR is influenced by the difficulty of the translation task. Twelve students of translation and interpreting were presented English high- and low-frequency words as well as familiar and unfamiliar technical terms that had to be translated into German. Analyses revealed that words of higher translation difficulty were accompanied by a more pronounced HR deceleration than words that were easier to translate. We additionally show that anticipatory HR deceleration and HR changes induced by motor preparation and activity due to typing the translation do not depend on task difficulty. These results provide first evidence of a link between task difficulty in language translation and event-related HR changes.},
}
@article {pmid17605682,
year = {2007},
author = {Allison, BZ and Wolpaw, EW and Wolpaw, JR},
title = {Brain-computer interface systems: progress and prospects.},
journal = {Expert review of medical devices},
volume = {4},
number = {4},
pages = {463-474},
doi = {10.1586/17434440.4.4.463},
pmid = {17605682},
issn = {1743-4440},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Body Mass Index ; Brain/*physiology ; *Computers ; Persons with Disabilities ; Electroencephalography/instrumentation/methods ; Equipment Design ; Evoked Potentials, Visual ; Humans ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.},
}
@article {pmid17601202,
year = {2007},
author = {Fabien, L and Anatole, L and Fabrice, L and Bruno, A},
title = {Studying the use of fuzzy inference systems for motor imagery classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {15},
number = {2},
pages = {322-324},
doi = {10.1109/TNSRE.2007.897032},
pmid = {17601202},
issn = {1534-4320},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; *Fuzzy Logic ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper studies the use of fuzzy inference systems (FISs) for motor imagery classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The results of the four studies achieved are promising as, on the analysed data, the used FIS was efficient, interpretable, showed good capabilities of rejecting outliers and offered the possibility of using a priori knowledge.},
}
@article {pmid17601190,
year = {2007},
author = {Bianchi, L and Quitadamo, LR and Garreffa, G and Cardarilli, GC and Marciani, MG},
title = {Performances evaluation and optimization of brain computer interface systems in a copy spelling task.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {15},
number = {2},
pages = {207-216},
doi = {10.1109/TNSRE.2007.897024},
pmid = {17601190},
issn = {1534-4320},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Cognition/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Humans ; Man-Machine Systems ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The evaluation of the performances of brain-computer interface (BCI) systems could be difficult as a standard procedure does not exist. In fact, every research team creates its own experimental protocol (different input signals, different trial structure, different output devices, etc.) and this makes systems comparison difficult. Moreover, the great question is whether these experiments can be extrapolated to real world applications or not. To overcome some intrinsic limitations of the most used criteria a new efficiency indicator will be described and used. Its main advantages are that it can predict with a high accuracy the performances of a whole system, a fact that can be used to successfully improve its behavior. Finally, simulations were performed to illustrate that the best system is built by tuning the transducer (TR) and the control interface (CI), which are the two main components of a BCI system, so that the best TR and the best CI do not exist but just the best combination of them.},
}
@article {pmid17587169,
year = {2007},
author = {Stavrinou, ML and Moraru, L and Cimponeriu, L and Della Penna, S and Bezerianos, A},
title = {Evaluation of cortical connectivity during real and imagined rhythmic finger tapping.},
journal = {Brain topography},
volume = {19},
number = {3},
pages = {137-145},
doi = {10.1007/s10548-007-0020-7},
pmid = {17587169},
issn = {0896-0267},
mesh = {Adult ; *Brain Mapping ; Cortical Synchronization ; Female ; Fingers/innervation/physiology ; Humans ; Imagination/*physiology ; Male ; *Models, Neurological ; Motor Cortex/*physiology ; Movement/*physiology ; Neural Pathways/physiology ; },
abstract = {Accumulating evidence suggests the existence of a shared neural substrate between imagined and executed movements. However, a better understanding of the mechanisms involved in the motor execution and motor imagery requires knowledge of the way the co-activated brain regions interact to each other during the particular (real or imagined) motor task. Within this general framework, the aim of the present study is to investigate the cortical activation and connectivity sub-serving real and imaginary rhythmic finger tapping, from the analysis of multi-channel electroencephalogram (EEG) scalp recordings. A sequence of 250 auditory pacing stimuli has been used for both the real and imagined right finger tapping task, with a constant inter-stimulus interval of 1.5 s length. During the motor execution, healthy subjects were asked to tap in synchrony with the regular sequence of stimulus events, whereas in the imagery condition subjects imagined themselves tapping in time with the auditory cue. To improve the spatial resolution of the scalp fields and suppress unwanted interferences, the EEG data have been spatially filtered. Further, event related synchronization and desynchronization phenomena and phase synchronization analysis have been employed for the study of functionally active brain areas and their connectivity during real and imagery finger tapping. Our results show a fronto-parietal co-activation during both real and imagined movements and similar connectivity patterns among contralateral brain areas. The results support the hypothesis that functional connectivity over the contralateral hemisphere during finger tapping is preserved in imagery. The approach and results can be regarded as indicative evidences of a new strategy for recognizing imagined movements in EEG-based brain computer interface research.},
}
@article {pmid17582507,
year = {2008},
author = {Sanchez, JC and Gunduz, A and Carney, PR and Principe, JC},
title = {Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {63-81},
doi = {10.1016/j.jneumeth.2007.04.019},
pmid = {17582507},
issn = {0165-0270},
mesh = {Adolescent ; Biofeedback, Psychology ; *Brain Mapping ; Cerebral Cortex/*physiopathology ; *Electroencephalography ; Epilepsies, Partial/pathology/physiopathology/rehabilitation ; Female ; Hand/physiopathology ; Humans ; Magnetic Resonance Imaging ; Physical Therapy Modalities ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; Spectrum Analysis ; *User-Computer Interface ; },
abstract = {Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.},
}
@article {pmid17557568,
year = {2007},
author = {Yoo, NJ and Soung, YH and Lee, SH and Jeong, EG and Lee, SH},
title = {Mutational analysis of the BH3 domains of proapoptotic Bcl-2 family genes Bad, Bmf and Bcl-G in laryngeal squamous cell carcinomas.},
journal = {Tumori},
volume = {93},
number = {2},
pages = {195-197},
doi = {10.1177/030089160709300214},
pmid = {17557568},
issn = {0300-8916},
mesh = {Adaptor Proteins, Signal Transducing/genetics ; Adult ; Aged ; Apoptosis/genetics ; Carcinoma, Squamous Cell/*genetics/pathology ; *DNA Mutational Analysis ; Female ; Genes, bcl-2/*genetics ; Humans ; Laryngeal Neoplasms/*genetics/pathology ; Male ; Middle Aged ; Proto-Oncogene Proteins c-bcl-2/genetics ; bcl-Associated Death Protein/genetics ; },
abstract = {AIMS: There is mounting evidence that deregulation of apoptosis is involved in the mechanisms of cancer development. Somatic mutations of apoptosis-related genes have been reported in many human cancers. The aim of this study was to explore the possibility that mutation of the BH3 domains of the proapoptotic Bcl-2 genes Bad, Bmf and Bcl-G might be involved in the development of laryngeal cancer.<br><br>METHODS: We analyzed the BH3 domains of Bad, Bmf and Bcl-G for the detection of somatic mutations in 33 squamous cell carcinomas of the larynx by a polymerase chain reaction-based single-strand conformation polymorphism assay.<br><br>RESULTS: There were no somatic mutations of the BH3 domains of Bad, Bmfand BcI-G in the laryngeal squamous cell carcinoma samples.<br><br>CONCLUSIONS: The data presented here indicate that BH3 domain mutation of the proapoptotic genes Bad, Bmf and Bcl-G is rare in laryngeal squamous cell carcinoma and may not contribute to the apoptosis-resistance mechanisms of laryngeal squamous cell carcinoma.},
}
@article {pmid17549911,
year = {2007},
author = {Lin, Z and Zhang, C and Wu, W and Gao, X},
title = {Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {6 Pt 2},
pages = {1172-1176},
doi = {10.1109/tbme.2006.889197},
pmid = {17549911},
issn = {0018-9294},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Fourier Analysis ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; Photic Stimulation/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Statistics as Topic ; *User-Computer Interface ; Visual Cortex/*physiology ; Visual Perception/*physiology ; },
abstract = {Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method.},
}
@article {pmid17542939,
year = {2007},
author = {Etienne, RS},
title = {A neutral sampling formula for multiple samples and an 'exact' test of neutrality.},
journal = {Ecology letters},
volume = {10},
number = {7},
pages = {608-618},
doi = {10.1111/j.1461-0248.2007.01052.x},
pmid = {17542939},
issn = {1461-0248},
mesh = {*Algorithms ; *Biodiversity ; Computer Simulation ; Data Collection/*methods ; Ecology/*methods ; Likelihood Functions ; *Models, Biological ; Panama ; Species Specificity ; Trees/*growth &amp; development ; },
abstract = {As the utility of the neutral theory of biodiversity is increasingly being recognized, there is also an increasing need for proper tools to evaluate the relative importance of neutral processes (dispersal limitation and stochasticity). One of the key features of neutral theory is its close link to data: sampling formulas, giving the probability of a data set conditional on a set of model parameters, have been developed for parameter estimation and model comparison. However, only single local samples can be handled with the currently available sampling formulas, whereas data are often available for many small spatially separated plots. Here, I present a sampling formula for multiple, spatially separated samples from the same metacommunity, which is a generalization of earlier sampling formulas. I also provide an algorithm to generate data sets with the model and I introduce a general test of neutrality that does not require an alternative model; this test compares the probability of the observed data (calculated using the new sampling formula) with the probability of model-generated data sets. I illustrate this with tree abundance data from three large Panamanian neotropical forest plots. When the test is performed with model parameters estimated from the three plots, the model cannot be rejected; however, when parameter estimates previously reported for BCI are used, the model is strongly rejected. This suggests that neutrality cannot explain the structure of the three Panamanian tree communities on the local (BCI) and regional (Panama Canal Zone) scale simultaneously. One should be aware, however, that aspects of the model other than neutrality may be responsible for its failure. I argue that the spatially implicit character of the model is a potential candidate.},
}
@article {pmid17541665,
year = {2007},
author = {Galán, F and Oliva, F and Guàrdia, J},
title = {Using mental tasks transitions detection to improve spontaneous mental activity classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {45},
number = {6},
pages = {603-609},
pmid = {17541665},
issn = {0140-0118},
mesh = {Algorithms ; Brain/physiology ; Discriminant Analysis ; Electroencephalography/methods ; Hand/physiology ; Humans ; Imagination ; Mental Processes/classification/*physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III--Data Set V: Multiclass Problem, Continuous EEG--achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem.},
}
@article {pmid17518278,
year = {2007},
author = {Liao, X and Yao, D and Wu, D and Li, C},
title = {Combining spatial filters for the classification of single-trial EEG in a finger movement task.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {5},
pages = {821-831},
doi = {10.1109/TBME.2006.889206},
pmid = {17518278},
issn = {0018-9294},
mesh = {Algorithms ; Brain Mapping ; Electroencephalography/*methods ; Evoked Potentials, Motor ; Fingers/*physiology ; Humans ; Microelectrodes ; Movement/*physiology ; Pattern Recognition, Physiological ; Time Factors ; User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.},
}
@article {pmid17513884,
year = {2007},
author = {Kelly, L and White, S and Stone, PC},
title = {The B12/CRP index as a simple prognostic indicator in patients with advanced cancer: a confirmatory study.},
journal = {Annals of oncology : official journal of the European Society for Medical Oncology},
volume = {18},
number = {8},
pages = {1395-1399},
doi = {10.1093/annonc/mdm138},
pmid = {17513884},
issn = {0923-7534},
mesh = {Adult ; Aged ; Aged, 80 and over ; C-Reactive Protein/*analysis ; Female ; Humans ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Neoplasms/*blood/*mortality ; Palliative Care ; Predictive Value of Tests ; Prognosis ; Vitamin B 12/*blood ; },
abstract = {BACKGROUND: The vitamin B(12)/C-reactive protein Index (BCI) has been proposed as a prognostic indicator in patients with advanced cancer. The purpose of this study was to confirm the utility of the BCI in palliative care patients.<br><br>PATIENTS AND METHODS: Patients with advanced cancer provided a blood specimen for analysis. Demographic and disease-related variables were recorded. Patients were followed up for at least 90 days or until death.<br><br>RESULTS: Patients (n = 329) were divided into three groups according to their BCI score. Patients in group 3 (BCI >40,000; median survival 29 days) had a significantly (P < 0.01) worse survival than patients in group 2 (BCI 10,001-40,000; median survival 43 days) and patients in group 1 (BCI < or =10,000; median survival 71 days). However, patients in group 1 did not have a significantly better prognosis than those in group 2 (P = 0.091). The point estimates for 90-day mortality for each of the three risk groups were different from the figures previously reported during the development phase of the BCI (group 1, 58.9% versus 47.2%; group 2, 64.0 versus 72.5%; group 3, 78.9% versus 90.6%).<br><br>CONCLUSIONS: An elevated BCI (>40,000) predicts poor survival in patients with advanced cancer.},
}
@article {pmid17499364,
year = {2008},
author = {Tonet, O and Marinelli, M and Citi, L and Rossini, PM and Rossini, L and Megali, G and Dario, P},
title = {Defining brain-machine interface applications by matching interface performance with device requirements.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {91-104},
doi = {10.1016/j.jneumeth.2007.03.015},
pmid = {17499364},
issn = {0165-0270},
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; Computer Simulation ; Electroencephalography ; Feedback ; Humans ; *Man-Machine Systems ; Numerical Analysis, Computer-Assisted ; Reaction Time ; *User-Computer Interface ; },
abstract = {Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.},
}
@article {pmid17498145,
year = {2007},
author = {Feeley, KJ and Joseph Wright, S and Nur Supardi, MN and Kassim, AR and Davies, SJ},
title = {Decelerating growth in tropical forest trees.},
journal = {Ecology letters},
volume = {10},
number = {6},
pages = {461-469},
doi = {10.1111/j.1461-0248.2007.01033.x},
pmid = {17498145},
issn = {1461-0248},
mesh = {Trees/*growth &amp; development ; *Tropical Climate ; },
abstract = {The impacts of global change on tropical forests remain poorly understood. We examined changes in tree growth rates over the past two decades for all species occurring in large (50-ha) forest dynamics plots in Panama and Malaysia. Stem growth rates declined significantly at both forests regardless of initial size or organizational level (species, community or stand). Decreasing growth rates were widespread, occurring in 24-71% of species at Barro Colorado Island, Panama (BCI) and in 58-95% of species at Pasoh, Malaysia (depending on the sizes of stems included). Changes in growth were not consistently associated with initial growth rate, adult stature, or wood density. Changes in growth were significantly associated with regional climate changes: at both sites growth was negatively correlated with annual mean daily minimum temperatures, and at BCI growth was positively correlated with annual precipitation and number of rainfree days (a measure of relative insolation). While the underlying cause(s) of decelerating growth is still unresolved, these patterns strongly contradict the hypothesized pantropical increase in tree growth rates caused by carbon fertilization. Decelerating tree growth will have important economic and environmental implications.},
}
@article {pmid17491331,
year = {2007},
author = {Ikeda, A},
title = {[Human supplementary motor area: a role in voluntary movements and its clinical significance].},
journal = {Rinsho shinkeigaku = Clinical neurology},
volume = {47},
number = {1},
pages = {8-20},
pmid = {17491331},
issn = {0009-918X},
mesh = {Basal Ganglia/physiology ; *Brain Mapping ; Dystonia/physiopathology ; Electric Stimulation ; *Evoked Potentials ; Evoked Potentials, Somatosensory ; Fingers/physiology ; Hand/physiology ; Humans ; Motor Cortex/*physiology ; Movement/*physiology ; Muscle, Skeletal/*physiology ; Parkinson Disease/*physiopathology ; },
abstract = {There were two hypotheses of functions of supplementary motor area (SMA): supplementary vs. supramotor, in 1980s. Clinically, SMA can develop a very intractable seizure focus characterized by unique ictal motor symptoms, and its dysfunction is also strongly related to the cardinal clinical features in patients with Parkinson's disease and dystonia. In patients with intractable partial seizures arising from the mesial frontal area who needed clinically chronic implantation of the subdural electrode grids for 1-2 weeks prior to the focus resection, we recorded movement-related cortical potentials or Bereitschaftspotentials (BPs) prior to the voluntary movements. As the results, 1) SMA proper, a caudal part of SMA showed a somatotopy of BP generators in accordance with each part of the voluntary movements in the body, 2) bilateral SMAs were involved in each side of the body movements equally, and the amplitude did not differ from one in the contralateral primary motor area (MI), and thus it proved that SMA proper played as a significant role in preparation for voluntary movements as MI. Furthermore, we clarified the functional significance of pre-SMA with regard to sensorimotor integration, decision making, repetitive rate of voluntary movements, voluntary motor inhibition and negative motor response. Clinically we also clarified the pathophysiology of SMA seizures, and impairment of SMA function in Parkinson's disease and dystonia. We look forward to clinical application of brain potentials from SMA in the field of brain-computer interface such as assessment and restorative approach in patients with spinal cord injury, paraplegia or motor neuron disease.},
}
@article {pmid17482922,
year = {2007},
author = {Paick, JS and Um, JM and Kwak, C and Kim, SW and Ku, JH},
title = {Influence of bladder contractility on short-term outcomes of high-power potassium-titanyl-phosphate photoselective vaporization of the prostate.},
journal = {Urology},
volume = {69},
number = {5},
pages = {859-863},
doi = {10.1016/j.urology.2007.01.042},
pmid = {17482922},
issn = {1527-9995},
mesh = {Aged ; Aged, 80 and over ; Follow-Up Studies ; Humans ; Laser Therapy/instrumentation/*methods ; Male ; Middle Aged ; Muscle Contraction/physiology ; Muscle, Smooth/*physiology ; Patient Satisfaction ; Phosphates ; Probability ; Prospective Studies ; Prostatic Hyperplasia/complications/diagnosis/*surgery ; Risk Assessment ; Severity of Illness Index ; Statistics, Nonparametric ; Titanium ; Treatment Outcome ; Urinary Bladder/physiology ; Urinary Retention/etiology/*surgery ; Urodynamics ; Volatilization ; },
abstract = {OBJECTIVES: To determine the effect of bladder contractility on the outcomes of high-power (80 W) potassium-titanyl-phosphate laser vaporization of the prostate in men with lower urinary tract symptoms.<br><br>METHODS: A total of 68 men with a median age of 68.5 years (range 53 to 86) were included in the study. The median follow-up was 9 months (range 6 to 21).<br><br>RESULTS: The median International Prostate Symptom Score and quality-of-life index decreased from 18 to 8.5 (P <0.001) and from 4 to 2 (P <0.001), respectively. The median maximal flow rate increased from 10 to 16.1 mL/s (P <0.001) and the median postvoid residual urine volume decreased from 28 to 10 mL (P <0.001). No differences were found in the change in the International Prostate Symptom Score or maximal flow rate according to age, prostate volume, or bladder outlet obstruction index. The weak bladder contractility index (BCI) group (BCI less than 100) had a smaller decrease in the median International Prostate Symptom Score and a smaller increase in the maximal flow rate than did those in the higher BCI group (BCI of 100 or more; P = 0.047 and P = 0.035, respectively). The baseline clinical parameters, including age, prostate volume, serum prostate-specific antigen, and bladder outlet obstruction index, were not significantly different between the low and greater BCI groups.<br><br>CONCLUSIONS: The results of the present study have shown that after high-power potassium-titanyl-phosphate laser vaporization, patients with weak bladder contractility had less subjective and objective improvement than did those patients with normal or strong bladder contractility.},
}
@article {pmid17481530,
year = {2007},
author = {Edwards, NM and Fabian, TC and Claridge, JA and Timmons, SD and Fischer, PE and Croce, MA},
title = {Antithrombotic therapy and endovascular stents are effective treatment for blunt carotid injuries: results from longterm followup.},
journal = {Journal of the American College of Surgeons},
volume = {204},
number = {5},
pages = {1007-13; discussion 1014-5},
doi = {10.1016/j.jamcollsurg.2006.12.041},
pmid = {17481530},
issn = {1072-7515},
mesh = {Adolescent ; Adult ; Aged ; Angiography ; Carotid Artery Injuries/*drug therapy/*surgery ; Combined Modality Therapy ; Female ; Follow-Up Studies ; Humans ; Longitudinal Studies ; Male ; Middle Aged ; *Stents ; Thrombolytic Therapy/*methods ; Treatment Outcome ; Wounds, Nonpenetrating/*drug therapy/*surgery ; },
abstract = {BACKGROUND: Significant confusion exists about management of blunt carotid injuries (BCI). Currently, three common treatments are being used without significant longterm followup data to demonstrate efficacy. Although heparin has been shown to reduce in-hospital stroke rates, antiplatelet therapy (aspirin and clopidogrel) has emerged as an alternate therapy without proved efficacy; carotid stenting has also been implemented for pseudoaneurysms (13% BCI), but its utility has recently been challenged. This is the first study to assess longterm efficacy of various therapeutic approaches.<br><br>STUDY DESIGN: Consecutive patients treated and followed at a single regional trauma center over 10 years (1996 to 2005) were reviewed. Outcomes evaluated included cerebral infarction, functional status, and angiographic evolution.<br><br>RESULTS: One hundred ten patients (11/year) were diagnosed with 133 injuries (23 bilateral). Overall mortality was 26%, with 6% directly attributable to BCI. Angiographic followup was available on 67 injuries (in 50 patients) at a mean of 6 months (range 0.25 to 67 months); 75% remained the same or improved. Clinical followup was available in 55 of 81 patients (68%) who survived to discharge (mean, 34.4 months [range 1 to 109 months]). Of surviving patients receiving antithrombotic therapy, 44% were treated with antiplatelet therapy, 49% with anticoagulation, and 7% with both. No patients experienced cerebral infarction after discharge, and there was no difference in functional outcomes based on the therapy received. Twenty-two endovascular stents were placed (18 for pseudoaneurysms, 4 for extensive dissection). Mean followup on these patients was 29.7 months (range 3 to 94 months). No patients receiving stents experienced periprocedural complications, and one patient with an associated brain injury had a cerebral infarction.<br><br>CONCLUSIONS: Longterm followup of BCI demonstrates that antithrombotic therapy prevents cerebral infarction; antiplatelet therapy and anticoagulation are equally effective; and carotid stents appear to be safe and effective for lesions that develop pseudoaneurysms or extensive dissections.},
}
@article {pmid17476000,
year = {2007},
author = {Lulé, D and Diekmann, V and Kassubek, J and Kurt, A and Birbaumer, N and Ludolph, AC and Kraft, E},
title = {Cortical plasticity in amyotrophic lateral sclerosis: motor imagery and function.},
journal = {Neurorehabilitation and neural repair},
volume = {21},
number = {6},
pages = {518-526},
doi = {10.1177/1545968307300698},
pmid = {17476000},
issn = {1545-9683},
mesh = {Adult ; Aged ; Amyotrophic Lateral Sclerosis/*physiopathology ; Communication Devices for People with Disabilities ; Female ; Humans ; Imagination/*physiology ; *Magnetic Resonance Imaging ; Male ; Man-Machine Systems ; Middle Aged ; Motor Cortex/*physiology ; Neuronal Plasticity/*physiology ; Parietal Lobe/physiology ; Severity of Illness Index ; User-Computer Interface ; },
abstract = {BACKGROUND: Cortical networks underlying motor imagery are functionally close to motor performance networks and can be activated by patients with severe motor disabilities.<br><br>OBJECTIVE: The aim of the study was to examine the longitudinal effect of progressive motoneuron degeneration on cortical representation of motor imagery and function in amyotrophic lateral sclerosis.<br><br>METHODS: The authors studied 14 amyotrophic lateral sclerosis patients and 15 healthy controls and a subgroup of 11 patients and 14 controls after 6 months with a grip force paradigm comprising imagery and execution tasks using functional magnetic resonance imaging.<br><br>RESULTS: Motor imagery activated similar neural networks as motor execution in amyotrophic lateral sclerosis patients and healthy subjects in the primary motor (BA 4), premotor, and supplementary motor (BA 6) cortex. Amyotrophic lateral sclerosis patients presented a stronger response within premotor and primary motor areas for imagery and execution compared to controls. After 6 months, these differences persisted with additional activity in the precentral gyrus in patients as well as in a frontoparietal network for motor imagery, in which activity increased with impairment.<br><br>CONCLUSION: The findings suggest an ongoing compensatory process within the higher order motor-processing system of amyotrophic lateral sclerosis patients, probably to overcome loss of function in primary motor and motor imagery-specific networks. The increased activity in precentral and frontoparietal networks in motor imagery might be used to control brain-computer interfaces to drive communication and limb prosthetic devices in patients with loss of motor control such as severely disabled amyotrophic lateral sclerosis patients in a locked-in-like state.},
}
@article {pmid17475513,
year = {2007},
author = {Blankertz, B and Dornhege, G and Krauledat, M and Müller, KR and Curio, G},
title = {The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects.},
journal = {NeuroImage},
volume = {37},
number = {2},
pages = {539-550},
doi = {10.1016/j.neuroimage.2007.01.051},
pmid = {17475513},
issn = {1053-8119},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Computer User Training/methods ; Electroencephalography ; Humans ; Learning/physiology ; Male ; *Man-Machine Systems ; Middle Aged ; Psychomotor Performance/*physiology ; *User-Computer Interface ; },
abstract = {Brain-Computer Interface (BCI) systems establish a direct communication channel from the brain to an output device. These systems use brain signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications. BCI systems that bypass conventional motor output pathways of nerves and muscles can provide novel control options for paralyzed patients. One classical approach to establish EEG-based control is to set up a system that is controlled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature. In contrast, the Berlin Brain-Computer Interface (BBCI) uses well established motor competencies of its users and a machine learning approach to extract subject-specific patterns from high-dimensional features optimized for detecting the user's intent. Thus the long subject training is replaced by a short calibration measurement (20 min) and machine learning (1 min). We report results from a study in which 10 subjects, who had no or little experience with BCI feedback, controlled computer applications by voluntary imagination of limb movements: these intentions led to modulations of spontaneous brain activity specifically, somatotopically matched sensorimotor 7-30 Hz rhythms were diminished over pericentral cortices. The peak information transfer rate was above 35 bits per minute (bpm) for 3 subjects, above 23 bpm for two, and above 12 bpm for 3 subjects, while one subject could achieve no BCI control. Compared to other BCI systems which need longer subject training to achieve comparable results, we propose that the key to quick efficiency in the BBCI system is its flexibility due to complex but physiologically meaningful features and its adaptivity which respects the enormous inter-subject variability.},
}
@article {pmid17475511,
year = {2007},
author = {Mellinger, J and Schalk, G and Braun, C and Preissl, H and Rosenstiel, W and Birbaumer, N and Kübler, A},
title = {An MEG-based brain-computer interface (BCI).},
journal = {NeuroImage},
volume = {36},
number = {3},
pages = {581-593},
pmid = {17475511},
issn = {1053-8119},
support = {R01 EB006356-01/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; R01 EB006356/EB/NIBIB NIH HHS/United States ; R01 EB000856/EB/NIBIB NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artifacts ; Brain/*physiology ; Electroencephalography ; Electromagnetic Fields ; Electromyography ; Feedback ; Female ; Foot/physiology ; Hand/physiology ; Head Movements/physiology ; Humans ; Magnetic Resonance Imaging ; Magnetoencephalography/*instrumentation ; Male ; Movement/physiology ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.},
}
@article {pmid17461017,
year = {2007},
author = {Konyshev, VA and Karlovskiĭ, DV and Mikhaĭlova, ES and Slavutskaia, AV and Avdeĭchik, VG and Shmelev, AS and Shevelev, IA},
title = {[Study of the letter and word recognition by the brain-computer-interface with P300 wave of human visual evoked potential].},
journal = {Rossiiskii fiziologicheskii zhurnal imeni I.M. Sechenova},
volume = {93},
number = {2},
pages = {141-149},
pmid = {17461017},
issn = {0869-8139},
mesh = {Adult ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; *Pattern Recognition, Visual ; *Reading ; *User-Computer Interface ; *Word Processing ; },
abstract = {In four adult healthy subjects in 18 experiments, we studied Brain-Computer-Interface recognition of different intended words by P300 wave in the VEP. The set of optimal characteristics of visual stimulation which rise reliability of recognition up to 100 %, as well as effective registration locus (Pz) were determined. It was found that the best processing criteria for letter recognition were: P300 square and superposition of all three criteria (P300 amplitude, square and covariation coefficient).},
}
@article {pmid17451904,
year = {2007},
author = {Weiskopf, N and Sitaram, R and Josephs, O and Veit, R and Scharnowski, F and Goebel, R and Birbaumer, N and Deichmann, R and Mathiak, K},
title = {Real-time functional magnetic resonance imaging: methods and applications.},
journal = {Magnetic resonance imaging},
volume = {25},
number = {6},
pages = {989-1003},
doi = {10.1016/j.mri.2007.02.007},
pmid = {17451904},
issn = {0730-725X},
support = {//Wellcome Trust/United Kingdom ; },
mesh = {Brain/pathology ; Brain Mapping/methods ; Cognition ; Data Interpretation, Statistical ; Diagnostic Imaging/*methods/trends ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/*instrumentation/*methods ; Quality Control ; Research Design ; Software ; Time Factors ; },
abstract = {Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.},
}
@article {pmid17447521,
year = {2007},
author = {Miyai, I},
title = {[Neuroscience based strategies for neurorehabilitation].},
journal = {Brain and nerve = Shinkei kenkyu no shinpo},
volume = {59},
number = {4},
pages = {347-355},
pmid = {17447521},
issn = {1881-6096},
mesh = {Animals ; Brain/physiology ; Humans ; Nerve Net/physiology ; *Stroke Rehabilitation ; },
abstract = {Recent advances in basic neuroscience revealed that functional recovery after brain damages is attributed to vicarious function of neural networks. From clinical point of view, functional neuroimaging and neurophysiological testing also have shown functional reorganization of the damaged neural networks after stroke. Understanding of such neural mechanisms has induced an evolutional progress in strategies for neurorehabilitation. Use-dependent plasticity of the central nervous system is attributed to both dose-dependent and context dependent effects of rehabilitative intervention referred as enriched environment and enriched rehabilitation. For instance constraint-induced movement therapy emphasizes not only forced use of the paretic hand but also "shaping" by which patients are always rewarded in structural and progressive approaches. Principals of motor learning such as task-oriented repetitive and rhythmical approaches, feedback of knowledge of results and mental practice using motor imagery has been also applied to rehabilitative strategies. Robot-assisted rehabilitation also provides useful information about the context of neurorehabilitation. There is accumulative evidence that plasticity of the damaged brain is modified by neuropharmacological intervention and noninvasive and invasive brain stimulation coupled with rehabilitation. Furthermore development of brain-machine interfaces might enable to produce new connections among brain regions, muscles, computer and prosthesis bypassing the damaged area. Efficacy of these strategies is based on the assumption that the damaged areas are stable. However if these strategies results in dramatic enhancement and acceleration of functional recovery, patients with neurological diseases of recurrent or degenerative nature might also have real-world benefit, which is trade-off between gains and progression, from neurorehabilitation.},
}
@article {pmid17445904,
year = {2008},
author = {Hoffmann, U and Vesin, JM and Ebrahimi, T and Diserens, K},
title = {An efficient P300-based brain-computer interface for disabled subjects.},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {115-125},
doi = {10.1016/j.jneumeth.2007.03.005},
pmid = {17445904},
issn = {0165-0270},
mesh = {Adult ; Brain/*physiopathology ; Brain Diseases/*physiopathology ; Brain Mapping ; *Persons with Disabilities ; Electroencephalography ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; *Numerical Analysis, Computer-Assisted ; Photic Stimulation/methods ; Reaction Time ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.},
}
@article {pmid17442753,
year = {2007},
author = {Jerbi, K and Lachaux, JP and N'Diaye, K and Pantazis, D and Leahy, RM and Garnero, L and Baillet, S},
title = {Coherent neural representation of hand speed in humans revealed by MEG imaging.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {104},
number = {18},
pages = {7676-7681},
pmid = {17442753},
issn = {0027-8424},
support = {R01 EB002010/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain ; Hand/*physiology ; Humans ; Magnetoencephalography ; Male ; Movement/*physiology ; Neurons/*physiology ; Time Factors ; },
abstract = {The spiking activity of single neurons in the primate motor cortex is correlated with various limb movement parameters, including velocity. Recent findings obtained using local field potentials suggest that hand speed may also be encoded in the summed activity of neuronal populations. At this macroscopic level, the motor cortex has also been shown to display synchronized rhythmic activity modulated by motor behavior. Yet whether and how neural oscillations might be related to limb speed control is still poorly understood. Here, we applied magnetoencephalography (MEG) source imaging to the ongoing brain activity in subjects performing a continuous visuomotor (VM) task. We used coherence and phase synchronization to investigate the coupling between the estimated activity throughout the brain and the simultaneously recorded instantaneous hand speed. We found significant phase locking between slow (2- to 5-Hz) oscillatory activity in the contralateral primary motor cortex and time-varying hand speed. In addition, we report long-range task-related coupling between primary motor cortex and multiple brain regions in the same frequency band. The detected large-scale VM network spans several cortical and subcortical areas, including structures of the frontoparietal circuit and the cerebello-thalamo-cortical pathway. These findings suggest a role for slow coherent oscillations in mediating neural representations of hand kinematics in humans and provide further support for the putative role of long-range neural synchronization in large-scale VM integration. Our findings are discussed in the context of corticomotor communication, distributed motor encoding, and possible implications for brain-machine interfaces.},
}
@article {pmid17436876,
year = {2007},
author = {Kronegg, J and Chanel, G and Voloshynovskiy, S and Pun, T},
title = {EEG-based synchronized brain-computer interfaces: a model for optimizing the number of mental tasks.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {15},
number = {1},
pages = {50-58},
doi = {10.1109/TNSRE.2007.891389},
pmid = {17436876},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/*physiology ; Male ; *Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The information-transfer rate (ITR) is commonly used to assess the performance of brain-computer interfaces (BCIs). Various studies have shown that the optimal number of mental tasks to be used is fairly low, around 3 or 4. We propose an experimental validation as well as a formal approach to demonstrate and confirm that this optimum is user and BCI design dependent. Even if increasing the number of mental tasks to the optimum indeed leads to an increase of the ITR, the gain remains small. This might not justify the added complexity in terms of protocol design.},
}
@article {pmid17433370,
year = {2007},
author = {Pagnutti, C and Azzouz, M and Anand, M},
title = {Propagation of local interactions create global gap structure and dynamics in a tropical rainforest.},
journal = {Journal of theoretical biology},
volume = {247},
number = {1},
pages = {168-181},
doi = {10.1016/j.jtbi.2007.02.012},
pmid = {17433370},
issn = {0022-5193},
mesh = {Biodiversity ; Cluster Analysis ; *Models, Biological ; Trees/*growth &amp; development ; *Tropical Climate ; },
abstract = {Gap dynamics in tropical forests are of interest because an understanding of them can help to predict canopy structure and biodiversity. We present a simple cellular automaton model that is capable of capturing many of the trends seen in the canopy gap pattern of a complex tropical rainforest on the Barro Colorado Island (BCI) using a single set of model parameters. We fit the global and local densities, the cluster size distributions, and two correlation functions, for gaps, gap formations, and gap closures determined from a spatial map of the forest (1983-1984). To the best of our knowledge, this is the first report that the cluster size distributions of gap formations and closures in the BCI are both power laws. An important element in the model is that when a transition from gap to non-gap (closure), or vice versa (formation), occurs, this transition is allowed to expand into adjacent cells in order to make different cluster sizes of transitions. Model results are in excellent agreement with reported field data. The propagation of local interactions is necessary in order to obtain the complex dynamics of the gap pattern. We also establish a connection between the global and local densities via the neighborhood-dependent transition rates and the effective global transition rates.},
}
@article {pmid17419343,
year = {2007},
author = {Karlovskiĭ, DV and Konyshev, VA and Selishchev, SV},
title = {[A P300-based brain-computer interface].},
journal = {Meditsinskaia tekhnika},
volume = {},
number = {1},
pages = {28-32},
pmid = {17419343},
issn = {0025-8075},
mesh = {Algorithms ; *Computers ; Electroencephalography/*methods ; *Event-Related Potentials, P300 ; Humans ; *Software ; },
abstract = {The goal of this work was to describe a system for real-time typing controlled by brain biopotential signals. A 6 (6 matrix containing Russian alphabet letters and auxiliary symbols was shown on PC screen. Electroencephalogram was taken, and the P300 component was extracted (this component appeared only upon presentation of a significant stimulus). A combination of several detection methods was used to identify the P300 component, which made it possible to increase the probability of correct identification to 91.6 (5.2%. It was shown that the developed interface could be implemented on the basis of a single active electrode in Pz (Cz) position.},
}
@article {pmid17410804,
year = {2007},
author = {Nowotny, N and Epp, B and von Sonntag, C and Fahlenkamp, H},
title = {Quantification and modeling of the elimination behavior of ecologically problematic wastewater micropollutants by adsorption on powdered and granulated activated carbon.},
journal = {Environmental science &amp; technology},
volume = {41},
number = {6},
pages = {2050-2055},
doi = {10.1021/es0618595},
pmid = {17410804},
issn = {0013-936X},
mesh = {Adsorption ; Charcoal/*chemistry ; Contrast Media/analysis/chemistry ; Inorganic Chemicals/analysis/chemistry ; *Models, Theoretical ; Pharmaceutical Preparations/analysis/chemistry ; Waste Disposal, Fluid/*methods ; Water Pollutants, Chemical/*analysis ; Water Purification/*methods ; },
abstract = {The adsorption on powdered activated carbon (PAC) of ecotoxic or potentially ecotoxic micropollutants (ten pharmaceuticals, four X-ray contrast media, and eight industrial chemicals) present in a biologically treated municipal wastewater is studied. All but the X-ray contrast media are eliminated with high efficiency at an economically feasible PAC dosage of 10 mg/L. Based on the experimental data, the competition between the background organic matter and the micropollutant for the active sites of the adsorbent is modeled with the help of the adsorption and tracer analysis supported by the Ideal Adsorption Solution Theory. With granulated activated carbon, adsorption isotherms are determined by spiking. Based on these experimental data and modeled parameters, a lay-out of fixed-bed adsorbers may be simulated.},
}
@article {pmid17409486,
year = {2007},
author = {Wei, Q and Wang, Y and Gao, X and Gao, S},
title = {Amplitude and phase coupling measures for feature extraction in an EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {120-129},
doi = {10.1088/1741-2560/4/2/012},
pmid = {17409486},
issn = {1741-2560},
mesh = {Adult ; Algorithms ; *Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Most of the feature extraction methods in existing brain-computer interfaces (BCIs) are based on the dynamic behavior of separate signals, without using the coupling information between different brain regions. In this paper, amplitude and phase coupling measures, quantified by a nonlinear regressive coefficient and phase locking value respectively, were used for feature extraction. The two measures were based on three different coupling methods determined by neurophysiological a priori knowledge, and applied to a small number of electrodes of interest, leading to six feature vectors for classification. Five subjects participated in an online BCI experiment during which they were asked to imagine a movement of either the left or right hand. The electroencephalographic (EEG) recordings from all subjects were analyzed offline. The averaged classification accuracies of the five subjects ranged from 87.4% to 92.9% for the six feature vectors and the best classification accuracies of the six feature vectors ranged between 84.4% and 99.6% for the five subjects. The performance of coupling features was compared with that of the autoregressive (AR) feature. Results indicated that coupling measures are appropriate methods for feature extraction in BCIs. Furthermore, the combination of coupling and AR feature can effectively improve the classification accuracy due to their complementarities.},
}
@article {pmid17409484,
year = {2007},
author = {Rossi, L and Foffani, G and Marceglia, S and Bracchi, F and Barbieri, S and Priori, A},
title = {An electronic device for artefact suppression in human local field potential recordings during deep brain stimulation.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {96-106},
doi = {10.1088/1741-2560/4/2/010},
pmid = {17409484},
issn = {1741-2560},
mesh = {*Artifacts ; Brain Mapping/*instrumentation/methods ; Deep Brain Stimulation/*methods ; Diagnosis, Computer-Assisted/*methods ; Female ; Humans ; Male ; Parkinson Disease/*diagnosis/*therapy ; Reproducibility of Results ; Sensitivity and Specificity ; Therapy, Computer-Assisted/*methods ; Treatment Outcome ; },
abstract = {The clinical efficacy of high-frequency deep brain stimulation (DBS) for Parkinson's disease and other neuropsychiatric disorders likely depends on the modulation of neuronal rhythms in the target nuclei. This modulation could be effectively measured with local field potential (LFP) recordings during DBS. However, a technical drawback that prevents LFPs from being recorded from the DBS target nuclei during stimulation is the stimulus artefact. To solve this problem, we designed and developed 'FilterDBS', an electronic amplification system for artefact-free LFP recordings (in the frequency range 2-40 Hz) during DBS. After defining the estimated system requirements for LFP amplification and DBS artefact suppression, we tested the FilterDBS system by conducting experiments in vitro and in vivo in patients with advanced Parkinson's disease undergoing DBS of the subthalamic nucleus (STN). Under both experimental conditions, in vitro and in vivo, the FilterDBS system completely suppressed the DBS artefact without inducing significant spectral distortion. The FilterDBS device pioneers the development of an adaptive DBS system retroacted by LFPs and can be used in novel closed-loop brain-machine interface applications in patients with neurological disorders.},
}
@article {pmid17409476,
year = {2007},
author = {Kamousi, B and Amini, AN and He, B},
title = {Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {17-25},
doi = {10.1088/1741-2560/4/2/002},
pmid = {17409476},
issn = {1741-2560},
support = {R01 EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; *Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {The goal of the present study is to employ the source imaging methods such as cortical current density estimation for the classification of left- and right-hand motor imagery tasks, which may be used for brain-computer interface (BCI) applications. The scalp recorded EEG was first preprocessed by surface Laplacian filtering, time-frequency filtering, noise normalization and independent component analysis. Then the cortical imaging technique was used to solve the EEG inverse problem. Cortical current density distributions of left and right trials were classified from each other by exploiting the concept of Von Neumann entropy. The proposed method was tested on three human subjects (180 trials each) and a maximum accuracy of 91.5% and an average accuracy of 88% were obtained. The present results confirm the hypothesis that source analysis methods may improve accuracy for classification of motor imagery tasks. The present promising results using source analysis for classification of motor imagery enhances our ability of performing source analysis from single trial EEG data recorded on the scalp, and may have applications to improved BCI systems.},
}
@article {pmid17409474,
year = {2007},
author = {Bashashati, A and Fatourechi, M and Ward, RK and Birch, GE},
title = {A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {R32-57},
doi = {10.1088/1741-2560/4/2/R03},
pmid = {17409474},
issn = {1741-2560},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?},
}
@article {pmid17409472,
year = {2007},
author = {Lotte, F and Congedo, M and Lécuyer, A and Lamarche, F and Arnaldi, B},
title = {A review of classification algorithms for EEG-based brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {R1-R13},
doi = {10.1088/1741-2560/4/2/R01},
pmid = {17409472},
issn = {1741-2560},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.},
}
@article {pmid17409469,
year = {2007},
author = {Rennaker, RL and Miller, J and Tang, H and Wilson, DA},
title = {Minocycline increases quality and longevity of chronic neural recordings.},
journal = {Journal of neural engineering},
volume = {4},
number = {2},
pages = {L1-5},
pmid = {17409469},
issn = {1741-2560},
support = {R21 DC007112/DC/NIDCD NIH HHS/United States ; R21 DC007112-01A1/DC/NIDCD NIH HHS/United States ; },
mesh = {Action Potentials/drug effects/*physiology ; Animals ; Auditory Cortex/drug effects/*physiology ; Evoked Potentials, Auditory/drug effects/*physiology ; Male ; Minocycline/*administration &amp; dosage ; Neurons, Afferent/drug effects/*physiology ; Neuroprotective Agents/*administration &amp; dosage ; Rats ; Rats, Long-Evans ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Brain/machine interfaces could potentially be used in the treatment of a host of neurological disorders ranging from paralysis to sensory deficits. Insertion of chronic micro-electrode arrays into neural tissue initiates a host of immunological responses, which typically leads to the formation of a cellular sheath around the implant, resulting in the loss of useful signals. Minocycline has been shown to have neuroprotective and neurorestorative effects in certain neural injury and neurodegenerative disease models. This study examined the effects of minocycline administration on the quality and longevity of chronic multi-channel microwire neural implants 1 week and 1 month post-implantation in auditory cortex. The mean signal-to-noise ratio for the minocycline group stabilized at the end of week 1 and remained above 4.6 throughout the following 3 weeks. The control group signal-to-noise ratio dropped throughout the duration of the study and at the end of 4 weeks was 2.6. Furthermore, 68% of electrodes from the minocycline group showed significant stimulus-driven activity at week 4 compared to 12.5% of electrodes in the control group. There was a significant reduction in the number of activated astrocytes around the implant in minocycline subjects, as well as a reduction in total area occupied by activated astrocytes at 1 and 4 weeks.},
}
@article {pmid17405382,
year = {2007},
author = {Friman, O and Volosyak, I and Gräser, A},
title = {Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {4},
pages = {742-750},
doi = {10.1109/TBME.2006.889160},
pmid = {17405382},
issn = {0018-9294},
mesh = {Adult ; *Artificial Intelligence ; Brain Mapping/*methods ; Electrocardiography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed.},
}
@article {pmid17399930,
year = {2007},
author = {McKinney, A and Ott, F and Short, J and McKinney, Z and Truwit, C},
title = {Angiographic frequency of blunt cerebrovascular injury in patients with carotid canal or vertebral foramen fractures on multidetector CT.},
journal = {European journal of radiology},
volume = {62},
number = {3},
pages = {385-393},
doi = {10.1016/j.ejrad.2007.01.008},
pmid = {17399930},
issn = {0720-048X},
mesh = {Adult ; Carotid Arteries/diagnostic imaging ; Carotid Artery Injuries/*diagnosis/epidemiology ; Cerebral Angiography/*methods ; Cerebrovascular Trauma/*diagnosis/epidemiology ; Cervical Vertebrae/diagnostic imaging/injuries ; Contrast Media/administration &amp; dosage ; Female ; Humans ; Incidence ; Male ; Middle Aged ; Observer Variation ; Predictive Value of Tests ; Radiographic Image Enhancement/methods ; Retrospective Studies ; Risk Factors ; Spinal Fractures/*diagnostic imaging/epidemiology ; Tomography, X-Ray Computed/*methods ; Triiodobenzoic Acids ; Vertebral Artery/diagnostic imaging/injuries ; Wounds, Nonpenetrating/*diagnosis/epidemiology ; },
abstract = {PURPOSE: Blunt carotid injuries (BCI's) and blunt vertebral artery injuries (BVI's), known jointly as BCVI's, are common in "high risk" patients. The purpose is to evaluate the rate of occurrence of BCI/BVI in patients screened purely by the radiologic criteria of fracture through the carotid canal or vertebral transverse foramina, or significant cervical subluxation, noted by multidetector CT.<br><br>METHODS: Seventy-one patients with 108 catheterized vessels were included over a 13-month interval. The angiographic examinations were prompted by current hospital protocol, solely by the presence of fractures involving/adjacent to the carotid canal, cervical fractures involving/adjacent to the foramen transversarium, or cervical fractures with significant subluxation. The incidence of each grade of blunt injury was calculated after review of the CT scans and catheter angiograms by two neuroradiologists.<br><br>RESULTS: Two thousand and seventy-three total blunt trauma admissions occurred during the time period, with a BCVI rate of 0.92-1.0% (depending on the reviewer), similar to previous studies. Mean time to catheter angiography was 16.6 h. Of the 71 included patients, there were 11-12 BCI's and 10-12 BVI's, an overall rate of 27-30% of BCVI in the patients with foraminal fractures. Interobserver agreement in reviewing the catheter angiograms was excellent (Kappa 0.795). Of note, three internal carotid pseudoaneurysms resolved spontaneously after anticoagulation or aspirin.<br><br>CONCLUSION: This study confirms that there is a high rate of BCVI in the presence of carotid canal or vertebral foramen fractures that are noted by multidetector CT. Utilization of purely radiologic criteria of foraminal involvement may be a significant screening tool in the decision of whether to evaluate these patients acutely by catheter or CT angiography, and for early detection of patients at risk for symptomatology, to initiate prompt, prophylactic treatment.},
}
@article {pmid17399797,
year = {2008},
author = {Nijboer, F and Furdea, A and Gunst, I and Mellinger, J and McFarland, DJ and Birbaumer, N and Kübler, A},
title = {An auditory brain-computer interface (BCI).},
journal = {Journal of neuroscience methods},
volume = {167},
number = {1},
pages = {43-50},
pmid = {17399797},
issn = {0165-0270},
support = {R01 HD030146/HD/NICHD NIH HHS/United States ; },
mesh = {Acoustic Stimulation ; Adult ; *Biofeedback, Psychology ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/methods ; Emotions/physiology ; Evoked Potentials, Auditory/*physiology ; Evoked Potentials, Visual ; Feasibility Studies ; Female ; Humans ; Male ; Naphthalenes ; Oxepins ; Photic Stimulation ; Reaction Time ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) translate brain activity into signals controlling external devices. BCIs based on visual stimuli can maintain communication in severely paralyzed patients, but only if intact vision is available. Debilitating neurological disorders however, may lead to loss of intact vision. The current study explores the feasibility of an auditory BCI. Sixteen healthy volunteers participated in three training sessions consisting of 30 2-3 min runs in which they learned to increase or decrease the amplitude of sensorimotor rhythms (SMR) of the EEG. Half of the participants were presented with visual and half with auditory feedback. Mood and motivation were assessed prior to each session. Although BCI performance in the visual feedback group was superior to the auditory feedback group there was no difference in performance at the end of the third session. Participants in the auditory feedback group learned slower, but four out of eight reached an accuracy of over 70% correct in the last session comparable to the visual feedback group. Decreasing performance of some participants in the visual feedback group is related to mood and motivation. We conclude that with sufficient training time an auditory BCI may be as efficient as a visual BCI. Mood and motivation play a role in learning to use a BCI.},
}
@article {pmid17379316,
year = {2007},
author = {Hsu, WY and Lin, CC and Ju, MS and Sun, YN},
title = {Wavelet-based fractal features with active segment selection: application to single-trial EEG data.},
journal = {Journal of neuroscience methods},
volume = {163},
number = {1},
pages = {145-160},
doi = {10.1016/j.jneumeth.2007.02.004},
pmid = {17379316},
issn = {0165-0270},
mesh = {Artificial Intelligence ; Brain/*physiology ; Brain Mapping ; Electroencephalography/*methods ; Fractals ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; Time Factors ; *User-Computer Interface ; },
abstract = {Feature extraction in brain-computer interface (BCI) work is one of the most important issues that significantly affect the success of brain signal classification. A new electroencephalogram (EEG) analysis system utilizing active segment selection and multiresolution fractal features is designed and tested for single-trial EEG classification. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the proposed system consists of three main procedures including active segment selection, feature extraction, and classification. The active segment selection is based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, and is used to obtain the optimal active time segment in the time-frequency domain. We then utilize a modified fractal dimension to extract multiresolution fractal feature vectors from the discrete wavelet transform (DWT) data for movement classification. By using a simple linear classifier, we find significant improvements in the rate of correct classification over the conventional approaches in all of our single-trial experiments for real finger movement. These results can be extended to see the good adaptability of the proposed method to imaginary movement data acquired from the public databases.},
}
@article {pmid17370340,
year = {2008},
author = {Lee, JH and O'Leary, HM and Park, H and Jolesz, FA and Yoo, SS},
title = {Atlas-based multichannel monitoring of functional MRI signals in real-time: automated approach.},
journal = {Human brain mapping},
volume = {29},
number = {2},
pages = {157-166},
pmid = {17370340},
issn = {1065-9471},
support = {U41 RR019703/RR/NCRR NIH HHS/United States ; R01 NS 048242/NS/NINDS NIH HHS/United States ; U41 RR 019703/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Brain/anatomy &amp; histology/*physiology ; Brain Mapping/*methods ; Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; *Magnetic Resonance Imaging ; Male ; Psychomotor Performance/physiology ; Time ; User-Computer Interface ; },
abstract = {We report an automated method to simultaneously monitor blood-oxygenation-level-dependent (BOLD) MR signals from multiple cortical areas in real-time. Individual brain anatomy was normalized and registered to a pre-segmented atlas in standardized anatomical space. Subsequently, using real-time fMRI (rtfMRI) data acquisition, localized BOLD signals were measured and displayed from user-selected areas labeled with anatomical and Brodmann's Area (BA) nomenclature. The method was tested on healthy volunteers during the performance of hand motor and internal speech generation tasks employing a trial-based design. Our data normalization and registration algorithm, along with image reconstruction, movement correction and a data display routine were executed with enough processing and communication bandwidth necessary for real-time operation. Task-specific BOLD signals were observed from the hand motor and language areas. One of the study participants was allowed to freely engage in hand clenching tasks, and associated brain activities were detected from the motor-related neural substrates without prior knowledge of the task onset time. The proposed method may be applied to various applications such as neurofeedback, brain-computer-interface, and functional mapping for surgical planning where real-time monitoring of region-specific brain activity is needed.},
}
@article {pmid17367076,
year = {2007},
author = {Felton, EA and Wilson, JA and Williams, JC and Garell, PC},
title = {Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases.},
journal = {Journal of neurosurgery},
volume = {106},
number = {3},
pages = {495-500},
doi = {10.3171/jns.2007.106.3.495},
pmid = {17367076},
issn = {0022-3085},
mesh = {Adolescent ; Adult ; Brain Diseases/*rehabilitation ; Communication Devices for People with Disabilities ; Electrodes, Implanted ; Electroencephalography/*methods ; Female ; Humans ; Imagery, Psychotherapy/*methods ; Male ; Middle Aged ; Subdural Space ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.},
}
@article {pmid17366596,
year = {2007},
author = {Gilbert, SM and Wood, DP and Dunn, RL and Weizer, AZ and Lee, CT and Montie, JE and Wei, JT},
title = {Measuring health-related quality of life outcomes in bladder cancer patients using the Bladder Cancer Index (BCI).},
journal = {Cancer},
volume = {109},
number = {9},
pages = {1756-1762},
doi = {10.1002/cncr.22556},
pmid = {17366596},
issn = {0008-543X},
support = {2 T32 DK007782-06/DK/NIDDK NIH HHS/United States ; },
mesh = {Administration, Intravesical ; Adult ; Aged ; Aged, 80 and over ; Antineoplastic Agents/administration &amp; dosage ; Cystectomy/adverse effects ; Cystoscopy/adverse effects ; Female ; Humans ; Male ; Middle Aged ; Quality of Life/*psychology ; Surveys and Questionnaires ; Urinary Bladder Neoplasms/*psychology/*therapy ; Urinary Diversion/adverse effects ; Urinary Incontinence/etiology ; },
abstract = {BACKGROUND: Health-related quality of life (HRQOL) has not been adequately measured in bladder cancer. A recently developed reliable and disease-specific quality of life instrument (Bladder Cancer Index, BCI) was used to measure urinary, sexual, and bowel function and bother domains in patients with bladder cancer managed with several different interventions, including cystectomy and endoscopic-based procedures.<br><br>METHODS: Patients with bladder cancer were identified from a prospective bladder cancer outcomes database and contacted as part of an Institutional Review Board-approved study to assess treatment impact on HRQOL. HRQOL was measured using the BCI across stratified treatment groups. Bivariate and multivariable analyses adjusted for age, gender, income, education, relationship status, and follow-up time were performed to compare urinary, bowel, and sexual domains between treatment groups.<br><br>RESULTS: In all, 315 bladder cancer patients treated at the University of Michigan completed the BCI in 2004. Significant differences were seen in mean BCI function and bother scores between cystectomy and native bladder treatment groups. In addition, urinary function scores were significantly lower among cystectomy patients treated with continent neobladder compared with those treated with ileal conduit (all pairwise P<.05).<br><br>CONCLUSIONS: The BCI is responsive to functional and bother differences in patients with bladder cancer treated with different surgical approaches. Significant differences between therapy groups in each of the urinary, bowel, and sexual domains exist. Among patients treated with orthotopic continent urinary diversion, functional impairments related to urinary incontinence and lack of urinary control account for the low observed urinary function scores.},
}
@article {pmid17361054,
year = {2007},
author = {Nakajima, T and Mushiake, H and Inui, T and Tanji, J},
title = {Decoding higher-order motor information from primate non-primary motor cortices.},
journal = {Technology and health care : official journal of the European Society for Engineering and Medicine},
volume = {15},
number = {2},
pages = {103-110},
pmid = {17361054},
issn = {0928-7329},
mesh = {Animals ; Biomedical Engineering ; Electromyography ; Forearm/innervation/physiology ; Frontal Lobe/*physiology ; Macaca/physiology ; Male ; Memory/physiology ; Models, Animal ; Motor Skills/*physiology ; Muscle, Skeletal/innervation/physiology ; Neurons/*physiology ; Psychomotor Performance ; Random Allocation ; },
abstract = {To investigate the involvement of primate non-primary motor cortices in bimanual sequential movements, we recorded neuronal activity in the supplementary motor area (SMA) and presupplementary motor area (pre-SMA) while an animal was performing bimanual motor tasks that required two sequential arm movements consisting of either pronation or supination of the right or left arms with delay periods. We also recorded electromyograms (EMGs) from the arm while the animal performed the bimanual task to compare muscle and neuronal activity. This paper focuses on the neuronal activity before the onset of sequential movements. We found that the prime-mover forelimb muscles were selectively active when an impending arm movement involved recorded muscles, but was not dependent on whether the arm movements were bimanual or unimanual. In contrast, we found that neurons in the non-primary motor cortices showed different activity depending on whether the forthcoming sequential arm movements were unimanual or bimanual. Our results suggest that neuronal activity in the SMA and pre-SMA reflects higher-order information about arm use before motor execution. By extracting this type of information, we can use it to control prosthetic arms in a more intelligent manner through a brain-machine interface.},
}
@article {pmid17355071,
year = {2007},
author = {Vidaurre, C and Schlögl, A and Cabeza, R and Scherer, R and Pfurtscheller, G},
title = {Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {3},
pages = {550-556},
doi = {10.1109/TBME.2006.888836},
pmid = {17355071},
issn = {0018-9294},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Man-Machine Systems ; Online Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {A study of different on-line adaptive classifiers, using various feature types is presented. Motor imagery brain computer interface (BCI) experiments were carried out with 18 naive able-bodied subjects. Experiments were done with three two-class, cue-based, electroencephalogram (EEG)-based systems. Two continuously adaptive classifiers were tested: adaptive quadratic and linear discriminant analysis. Three feature types were analyzed, adaptive autoregressive parameters, logarithmic band power estimates and the concatenation of both. Results show that all systems are stable and that the concatenation of features with continuously adaptive linear discriminant analysis classifier is the best choice of all. Also, a comparison of the latter with a discontinuously updated linear discriminant analysis, carried out in on-line experiments with six subjects, showed that on-line adaptation performed significantly better than a discontinuous update. Finally a static subject-specific baseline was also provided and used to compare performance measurements of both types of adaptation.},
}
@article {pmid17355065,
year = {2007},
author = {Hammon, PS and de Sa, VR},
title = {Preprocessing and meta-classification for brain-computer interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {3},
pages = {518-525},
doi = {10.1109/TBME.2006.888833},
pmid = {17355065},
issn = {0018-9294},
mesh = {Action Potentials/physiology ; Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Man-Machine Systems ; Neurons ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.},
}
@article {pmid17347748,
year = {2007},
author = {Gysels, E and Renevey, P and Celka, P},
title = {Fast feature selection to compare broadband with narrowband phase synchronization in brain-computer interfaces.},
journal = {Methods of information in medicine},
volume = {46},
number = {2},
pages = {160-163},
pmid = {17347748},
issn = {0026-1270},
mesh = {Algorithms ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Female ; Hand/*physiology ; Humans ; Male ; Motor Cortex ; Movement/*physiology ; Neurophysiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {OBJECTIVE: Brain-computer interface (BCI) research aims at developing communication devices for the motor disabled. Such devices are not driven by muscle activity, but by brain activity recorded during different mental tasks. We present here the comparison of phase synchronization and power spectral density (PSD) features, computed from broadband and narrowband filtered EEG signals and their ability to discriminate three mental tasks.<br><br>METHODS: EEG signals were recorded from five subjects while performing left and right hand movement imagination and word generation. We applied a modified Fast Correlation Based Filter (FCBF) [9] for the purpose of feature selection.<br><br>RESULTS: We found that the features were selected from electrode signals corresponding to neurophysiological evidence, i.e. electrodes lying over the motor cortex. PSD and phase locking value (PLV) features were more discriminative when computed from narrowband (8-12 Hz) and broadband (8-30 Hz) filtered signals respectively.<br><br>CONCLUSIONS: The generalization performance is as good as the one obtained with SVM-rfe, but this algorithm is faster and selects fewer features. These properties may make FCBF a valuable tool for further improvement of BCIs.},
}
@article {pmid17347747,
year = {2007},
author = {Yamawaki, N and Wilke, C and Hue, L and Liu, Z and He, B},
title = {Enhancement of classification accuracy of a time-frequency approach for an EEG-based brain-computer interface.},
journal = {Methods of information in medicine},
volume = {46},
number = {2},
pages = {155-159},
pmid = {17347747},
issn = {0026-1270},
support = {R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Hand/*physiology ; Humans ; Movement/*physiology ; Pilot Projects ; *Signal Processing, Computer-Assisted ; Time ; *User-Computer Interface ; },
abstract = {OBJECTIVES: The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI).<br><br>METHODS: We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns.<br><br>RESULTS AND CONCLUSIONS: Through off-line analysis of data collected during a "cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.},
}
@article {pmid17347744,
year = {2007},
author = {Durand, DM},
title = {Neural engineering--a new discipline for analyzing and interacting with the nervous system.},
journal = {Methods of information in medicine},
volume = {46},
number = {2},
pages = {142-146},
pmid = {17347744},
issn = {0026-1270},
support = {2R01-NS-32845/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; *Central Nervous System ; Dogs ; *Electrodes, Implanted ; Hypoglossal Nerve/physiology ; Models, Animal ; Models, Biological ; Nerve Tissue/*physiology ; Peripheral Nerves/*physiology ; *Prostheses and Implants ; Sciatic Nerve/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVES: The field of neural engineering focuses on an area of research at the interface between neuroscience and engineering. The area of neural engineering was first associated with the brain machine interface but is much broader and encompasses experimental, computational, and theoretical aspects of neural interfacing, neuroelectronics, neuromechanical systems, neuroinformatics, neuroimaging, neural prostheses, artificial and biological neural circuits, neural control, neural tissue regeneration, neural signal processing, neural modelling and neuro-computation. One of the goals of neural engineering is to develop a selective interface for the peripheral nervous system.<br><br>METHODS: Nerve cuffs electrodes have been developed to either reshape or maintain the nerve into an elongated shape in order to increase the circumference to cross sectional ratio. It is then possible to place many electrodes around the nerve to achieve selectivity. This new cuff (flat interface nerve electrode: FINE) was applied to the hypoglossal nerve and the sciatic nerve in dogs and cats to estimate the selectivity of the interface.<br><br>RESULTS: By placing many contacts close to the axons, three different types of selectivity were achieved: 1) The FINE could generate a high degree of stimulation selectivity as estimated by the individual fascicle recording. 2) Similarly, recording selectivity was also demonstrated and blind source algorithms were applied to recover the signals. 3) Finally, by placing arrays of electrodes along the nerve, small fiber diameters could be excited before large fibers thereby reversing the recruitment order.<br><br>CONCLUSION: Taking advantage of the fact that nerves are not round but oblong or flat allows a novel design for selective nerve interface with the peripheral nervous system. This new design has found applications in many disorders of the nervous system such as bladder incontinence, obstructive sleep apnea and stroke.},
}
@article {pmid17344984,
year = {2005},
author = {Costa Filho, HA and Berezovsky, A},
title = {[Critical analysis of the progressive performance of low vision in Benjamin Constant Institute].},
journal = {Arquivos brasileiros de oftalmologia},
volume = {68},
number = {6},
pages = {815-820},
doi = {10.1590/s0004-27492005000600018},
pmid = {17344984},
issn = {0004-2749},
mesh = {Academies and Institutes/history/*standards/statistics &amp; numerical data ; Adolescent ; Adult ; Aged ; Attitude of Health Personnel ; Brazil ; Child ; Child, Preschool ; Delivery of Health Care, Integrated/history/*standards/statistics &amp; numerical data ; *Education of Persons with Visual Disabilities ; Health Personnel/education ; *Health Policy ; Health Services Accessibility/statistics &amp; numerical data ; Health Services Research ; History, 20th Century ; Humans ; Infant ; Infant, Newborn ; Middle Aged ; National Health Programs/standards ; Social Justice/standards ; Vision, Low/rehabilitation/*therapy ; },
abstract = {PURPOSE: To evaluate effectiveness of the Low Vision the Benjamin Constant Institute (BCI) and confirm the real necessity of an Institute like BCI in the present inclusion policy.<br><br>METHODS: Ecological study, analyzing 3 periods of Low Vision Assistance at the Benjamin Constant Institute from October 1, 1990 to December 20, 2002: a) 1991--starting assistance; b) 1995--medical pedagogic integration; c) 2002--present-day situation. We considered in this analysis as indicators: I--Low Vision Assistance, II--Low Vision sector in the Benjamin Constant Institute, III--Associates.<br><br>RESULTS: This study demonstrated an increase in assistance, reaching a wider spectrum of patients after medical-pedagogic integration. Other indicators, such as physician capacitation, participation in Benjamin Constant Capacitation Courses, increase in orientation to institutions, schools and others and referrals to the Benjamin Constant Institute, and Rehabilitation also attest the effectiveness of the Low Vision sector of the Benjamin Constant Institute.<br><br>CONCLUSIONS: The Low Vision sector proved to be the interface between the Medical and Pedagogic Departments, and later on the Rehabilitation and Physical Education Coordination sectors. This has implied alterations in the way to manage the low-vision patient, not only regarding the regular Benjamin Constant Institute student as well as any other patient in the community. The Benjamin Constant Institute proved its importance as regards inclusion policy.},
}
@article {pmid17334681,
year = {2007},
author = {Ibrahim, TS and Abraham, D and Rennaker, RL},
title = {Electromagnetic power absorption and temperature changes due to brain machine interface operation.},
journal = {Annals of biomedical engineering},
volume = {35},
number = {5},
pages = {825-834},
doi = {10.1007/s10439-007-9264-3},
pmid = {17334681},
issn = {0090-6964},
mesh = {Body Temperature/*physiology/*radiation effects ; Brain/*physiology/*radiation effects ; Computer Simulation ; Energy Transfer/physiology/radiation effects ; Humans ; *Microwaves ; Models, Biological ; Radiation ; *Radio Waves ; Relative Biological Effectiveness ; *User-Computer Interface ; },
abstract = {To fully understand neural function, chronic neural recordings must be made simultaneously from 10s or 100s of neurons. To accomplish this goal, several groups are developing brain machine interfaces. For these devices to be viable for chronic human use, it is likely that they will need to be operated and powered externally via a radiofrequency (RF) source. However, RF exposure can result in tissue heating and is regulated by the FDA/FCC. This paper provides an initial estimate of the amount of tissue heating and specific absorption rate (SAR) associated with the operation of a brain-machine interface (BMI). The operation of a brain machine interface was evaluated in an 18-tissue anatomically detailed human head mesh using simulations of electromagnetics and bio-heat phenomena. The simulations were conducted with a single chip, as well as with eight chips, placed on the surface of the human brain and each powered at four frequencies (13.6 MHz, 1.0 GHz, 2.4 GHz, and 5.8 GHz). The simulated chips consist of a wire antenna on a silicon chip covered by a Teflon dura patch. SAR values were calculated using the finite-difference time-domain method and used to predict peak temperature changes caused by electromagnetic absorption in the head using two-dimensional bio-heat equation. Results due to SAR alone show increased heating at higher frequencies, with a peak temperature change at 5.8 GHz of approximately 0.018 degrees C in the single-chip configuration and 0.06 degrees C in the eight-chip configuration with 10 mW of power absorption (in the human head) per chip. In addition, temperature elevations due to power dissipation in the chip(s) were studied. Results show that for the neural tissue, maximum temperature rises of 3.34 degrees C in the single-chip configuration and 7.72 degrees C in the eight-chip configuration were observed for 10 mW dissipation in each chip. Finally, the maximum power dissipation allowable in each chip before a 1.0 degrees C temperature increase (most stringent standards as denoted in the FDA guidelines) is exceeded in the head was simulated and found to be 2.92 mW in the single-chip configuration and 1.25 mW in the eight-chip configuration. As thermal heating due to SAR was insignificant, this study suggests that wireless electromagnetics, i.e., RF may be a viable option for powering, and communicating with brain machine interfaces for clinical applications.},
}
@article {pmid17331989,
year = {2007},
author = {Tecchio, F and Porcaro, C and Barbati, G and Zappasodi, F},
title = {Functional source separation and hand cortical representation for a brain-computer interface feature extraction.},
journal = {The Journal of physiology},
volume = {580},
number = {Pt.3},
pages = {703-721},
pmid = {17331989},
issn = {0022-3751},
mesh = {*Brain Mapping ; Cerebral Cortex/*physiology/physiopathology ; Communication Devices for People with Disabilities ; Hand/*physiology/physiopathology ; Humans ; Magnetoencephalography ; *Signal Processing, Computer-Assisted ; Stroke/physiopathology ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features - spatial and time-frequency properties - are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients.},
}
@article {pmid17327652,
year = {2007},
author = {Fripp, J and Crozier, S and Warfield, SK and Ourselin, S},
title = {Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee.},
journal = {Physics in medicine and biology},
volume = {52},
number = {6},
pages = {1617-1631},
doi = {10.1088/0031-9155/52/6/005},
pmid = {17327652},
issn = {0031-9155},
support = {R01 RR021885/RR/NCRR NIH HHS/United States ; R21 MH067054/MH/NIMH NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Automation ; Bone and Bones/diagnostic imaging/*pathology ; Cartilage, Articular/diagnostic imaging/metabolism/*pathology ; Connective Tissue/pathology ; Humans ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Knee/*anatomy &amp; histology/*pathology ; Magnetic Resonance Imaging/*methods ; Models, Anatomic ; Models, Statistical ; Osteoarthritis/pathology ; Radiography ; },
abstract = {The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.},
}
@article {pmid17318660,
year = {2007},
author = {Boostani, R and Graimann, B and Moradi, MH and Pfurtscheller, G},
title = {A comparison approach toward finding the best feature and classifier in cue-based BCI.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {45},
number = {4},
pages = {403-412},
pmid = {17318660},
issn = {0140-0118},
mesh = {Algorithms ; Brain/*physiology ; Communication Devices for People with Disabilities ; Cues ; Discriminant Analysis ; Fractals ; Hand/physiology ; Humans ; Imagination/physiology ; *Man-Machine Systems ; Movement/physiology ; Neural Networks, Computer ; User-Computer Interface ; },
abstract = {In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain-Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.},
}
@article {pmid17317306,
year = {2007},
author = {Chen, CW and Lin, CC and Ju, MS},
title = {Detecting movement-related EEG change by wavelet decomposition-based neural networks trained with single thumb movement.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {118},
number = {4},
pages = {802-814},
doi = {10.1016/j.clinph.2006.12.008},
pmid = {17317306},
issn = {1388-2457},
mesh = {Algorithms ; Analysis of Variance ; Electric Stimulation ; *Electroencephalography ; Electromyography/methods ; Evoked Potentials/physiology/radiation effects ; Fingers/*physiology ; Humans ; Movement/*physiology ; *Neural Networks, Computer ; ROC Curve ; Sensitivity and Specificity ; Sensory Thresholds/physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVE: The main goal of this study was to develop a real-time detection algorithm of movement-related EEG changes for the naïve subjects with a very small amount of training data. Such an algorithm is vital for the realization of brain-computer interface.<br><br>METHODS: The target algorithm developed in this study was based on the wavelet decomposition neural network (WDNN). Surface Laplacian EEG was recorded at central cortical areas and processed with wavelet decomposition (WD) for feature extraction and neural network for pattern recognition. The new algorithm was compared with nother three methods, namely, threshold-based WD and short-time Fourier transform (STFT), and Fourier transform neural network (FTNN), for performance. The trainings of all algorithms were based, respectively, on the changes of mu and beta rhythms before and after voluntary movements. In order to investigate whether WDNN could adapt to the nonstationarity of EEG or not, we also compared two training modes, namely, fixed and updated weight. The significances of the success rates were tested by ANOVA (analysis of variance) and verified by ROC (receiver operating characteristic) analysis.<br><br>RESULTS: The experimental data showed that (1) success rates of movement detection were acceptable even when the training set was reduced to a single trial data, (2) WDNN performed better than WD or STFT without optimized thresholds and (3) when weights were updated and thresholds were optimized, WDNN still performed better than WD, while FTNN had a marginal advantage over STFT.<br><br>CONCLUSIONS: We developed a detection algorithm based on WDNN with the training set being reduced to a single trial data. The overall performance of this algorithm was better than the conventional methods as such.<br><br>SIGNIFICANCE: mu wave suppression could be detected more precisely by the wavelet decomposition with neural network than the conventional algorithms such as STFT and WD. The size of training data could be reduced to a single trial and the success rates were up to 75-80%.},
}
@article {pmid17312228,
year = {2007},
author = {Sia, AT and Lim, Y and Ocampo, C},
title = {A comparison of a basal infusion with automated mandatory boluses in parturient-controlled epidural analgesia during labor.},
journal = {Anesthesia and analgesia},
volume = {104},
number = {3},
pages = {673-678},
doi = {10.1213/01.ane.0000253236.89376.60},
pmid = {17312228},
issn = {1526-7598},
mesh = {Adult ; Amides/administration &amp; dosage ; Analgesia, Epidural/*methods ; Analgesia, Obstetrical/*methods ; Analgesia, Patient-Controlled/*methods ; Anesthetics, Intravenous/administration &amp; dosage ; Anesthetics, Local/administration &amp; dosage ; Female ; Fentanyl/administration &amp; dosage ; Humans ; Labor Pain/*drug therapy ; *Labor, Obstetric ; Pregnancy ; Ropivacaine ; Time Factors ; },
abstract = {BACKGROUND: The use of parturient-controlled epidural analgesia (PCEA) with a basal infusion is commonly used in laboring women. We compared a novel approach of providing basal intermittent boluses concurrently with PCEA: PCEA plus automated mandatory boluses (PCEA+AMB) versus PCEA plus basal continuous infusion (PCEA+BCI). We hypothesized that epidural local anesthetic consumption would be lower if basal intermittent boluses were used instead of a basal infusion.<br><br>METHODS: We randomized 42 healthy parturients in early labor to receive 0.1% ropivacaine + fentanyl 2 microg/mL either via PCEA+BCI (n = 21,bolus 5 mL, lockout 10 min, basal infusion 5 mL/h) or via PCEA+AMB (n = 21, patient-activated bolus of 5 mL, lockout 10 min, basal automated boluses of 5 mL/h [omitted if a patient-activated bolus was successfully administered in the last 1 h]) after successful induction of combined spinal epidural analgesia.<br><br>RESULTS: We found a reduction in the hourly consumption of ropivacaine with PCEA+AMB, i.e., the primary outcome measure (mean = 6.5 mL, sd = 3.4 in the PCEA+AMB group vs 7.5 mL, sd = 2.0 PCEA+BCI group, P = 0.011). A larger proportion of parturients in the PCEA+AMB group did not self-bolus (6/21 vs 1/21 in PCEA+BCI, P = 0.03). The time to the first self-bolus after combined spinal epidural was longer in the PCEA+AMB group (mean survival time 315 min vs 190 min in PCEA+BCI group, P = 0.04 by log rank test). There was no difference in pain scores or side effects.<br><br>CONCLUSION: Our study showed that PCEA+AMB reduced analgesic consumption and could be useful as the mode of maintenance for epidural analgesia.},
}
@article {pmid17293025,
year = {2007},
author = {Hashim, H and Elhilali, M and Bjerklund Johansen, TE and Abrams, P and , },
title = {The immediate and 6-mo reproducibility of pressure-flow studies in men with benign prostatic enlargement.},
journal = {European urology},
volume = {52},
number = {4},
pages = {1186-1193},
doi = {10.1016/j.eururo.2007.01.075},
pmid = {17293025},
issn = {0302-2838},
mesh = {Adult ; Aged ; Aged, 80 and over ; Diuresis/*physiology ; Double-Blind Method ; Humans ; Male ; Middle Aged ; Placebos ; Pressure ; Prostatic Hyperplasia/*physiopathology ; Reproducibility of Results ; Urination Disorders/etiology/physiopathology ; Urodynamics/*physiology ; },
abstract = {OBJECTIVES: Urodynamics is an objective method of diagnosing bladder outlet obstruction (BOO) in men. This study examined the immediate and 6-mo reproducibility of this investigation.<br><br>METHODS: Urodynamics was performed in men as part of a multinational, multicentre, double-blind, placebo-controlled drug trial. Each patient had two fill/void cycles both at baseline and 6 mo. The BOO index (BOOI) and bladder contractility index (BCI) were calculated for each cycle and data analysed to look for changes in immediate and 6-mo reproducibility between the two fill/void cycles.<br><br>RESULTS: A total of 114 patients had urodynamics at baseline. In the immediate term, although there was a small but statistical fall in both the BOOI and BCI, with cycle one figures greater than those in cycle two, 81% and 79% of patients remained in the same BOOI and BCI category, respectively. At 6 mo, the differences were not statistically different with 70% of patients remaining unchanged in their BOOI category in cycle one and 71% in cycle two; 65% remained unchanged in their BCI category in cycle one and 74% in cycle two. No patient with a BOOI > 65 changed category in the second investigation, and only 5 of 103 first cycles with a BOOI > or = 50 changed category to equivocal obstruction.<br><br>CONCLUSIONS: Urodynamics has good reproducibility when looking at the BOOI and BCI, indicating that a second study is not necessary in most patients and one investigation is sufficient for an accurate diagnosis on which treatment options can be based.},
}
@article {pmid17282857,
year = {2005},
author = {Liu, B and Wang, M and Yu, H and Yu, L and Liu, Z},
title = {Study of Feature Classification Methods in BCI Based on Neural Networks.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2932-2935},
doi = {10.1109/IEMBS.2005.1617088},
pmid = {17282857},
issn = {1557-170X},
abstract = {Feature classification is one of the important aspects in Brain-computer interfaces (BCI) system. It has been known that a higher precision can be achieved if use neutral networks in a proper way for feature classification. In this paper, three feature identification ways were introduced and discussed. In the experiment of left-right hand classification, the arithmetic of the small mean square difference is proposed and studied, so as to get a good converging in the task classification. The design method of input and output layer for the BP neural network was discussed. Experiment results show that it is a feasible processing algorithm to classify the different events.},
}
@article {pmid17282716,
year = {2005},
author = {Wu, W and Gao, X and Gao, S},
title = {One-Versus-the-Rest(OVR) Algorithm: An Extension of Common Spatial Patterns(CSP) Algorithm to Multi-class Case.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2387-2390},
doi = {10.1109/IEMBS.2005.1616947},
pmid = {17282716},
issn = {1557-170X},
abstract = {Extraction of relevant features that capture the invariant characteristics specific to each brain state is very important in order to implement a suitable Brain-Computer Interface (BCI) system. This paper presents an algorithm called One-Versus-the-Rest (OVR), which is an extension of a well-known method called Common Spatial Patterns (CSP) to multi-class case, to extract signal components specific to one condition from electroencephalography (EEG) dataa sets of multiple conditions. The alagorithm was previously mentioned in [7], yet without an elaborate description. In this paper, detailed mathematicaal derivation of the algorithm is given, followed by a computer simulation. The computer simulation suggests that the algorithm is capable of reconstructing the actual specific part of each condition with high quality, even when the data are contaminated with considerable noise. We also hint future possible applications of the algorithm in the context of BCI at the end of the paper.},
}
@article {pmid17282648,
year = {2005},
author = {Parikh, H and Gage, G and Marzullo, T and Kipke, D},
title = {Real-time Detection of Unitary Events For Cortical Control.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2122-2125},
doi = {10.1109/IEMBS.2005.1616879},
pmid = {17282648},
issn = {1557-170X},
abstract = {Traditional brain-machine interfaces have typically focused on methods that use rate-based codes as a source for control signals. Opposed to rate, timing of firing across different neurons and within each neuron could also provide information that can be used for controlling brain-machine interfaces or neuroprosthetic devices. Findings have indicated that synchronization of individual spike discharges may help serve the organization of cortical motor processes. We are investigating neural firing synchrony in the context of using it for real-time control for neuroprostheses systems. Our results with rats suggest that subjects can be trained to synchronize neural firing and increase unitary events i.e. spike coincidence patterns that are significantly above chance. Temporal coding methods could be used as additional or alternative cortical control signals for neuroprostheses and brain machine interfaces.},
}
@article {pmid17282646,
year = {2005},
author = {Cai, X and Shimansky, Y and He, J},
title = {Learning-induced Dependence of Neuronal Activity in Primary Motor Cortex on Motor Task Condition.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2114-2117},
doi = {10.1109/IEMBS.2005.1616877},
pmid = {17282646},
issn = {1557-170X},
abstract = {A brain-computer interface (BCI) system such as a cortically controlled robotic arm must have a capacity of adjusting its function to a specific environmental condition. We studied this capacity in non-human primates based on chronic multi-electrode recording from the primary motor cortex of a monkey during the animal's performance of a center-out 3D reaching task and adaptation to external force perturbations. The main condition-related feature of motor cortical activity observed before the onset of force perturbation was a phasic raise of activity immediately before the perturbation onset. This feature was observed during a series of perturbation trials, but were absent under no perturbations. After adaptation has been completed, it usually was taking the subject only one trial to recognize a change in the condition to switch the neuronal activity accordingly. These condition-dependent features of neuronal activity can be used by a BCI for recognizing a change in the environmental condition and making corresponding adjustments, which requires that the BCI-based control system possess such advanced properties of the neural motor control system as capacity to learn and adapt.},
}
@article {pmid17282645,
year = {2005},
author = {Gage, G and Ionides, E and Kipke, D},
title = {Information capacity of brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2110-2113},
doi = {10.1109/IEMBS.2005.1616876},
pmid = {17282645},
issn = {1557-170X},
abstract = {Brain Machine Interfaces (BMIs) are emerging as an important research area in clinical therapy. A large range of potential BMI control signals can be found in the brain. In increasing order of volume of brain tissue being sampled, these signal includes recordings of electric discharges from multi unit activity (MUA), summed population activity of thousands of neurons via local field potentials (LFPs), and electrical activity recorded from either the surface of the brain via electrocorticograms (ECoGs) or the surface of the scalp via electroencephalograms (EEGs). While each of these signals have been studied separately, it has been difficult to compare the potential that each signal has for general prosthetic control across studies. Information theory has been proposed as an abstract measurement to bridge this gap, however the maximum information rates of any experiment is limited by the parameters defined by that experiment (e.g. inter-trial interval length, number of targets). Here we propose a different measure of information, which we call information capacity, which measures the maximum possible information rate that a signal can provide. An advantage of measuring information capacity is that it can readily be compared between different signals and different tasks. We show how to calculate information capacity making linear Gaussian assumptions, and we discuss more general possibilities. We present a case study involving a rat BMI task involving either MUA or LFP signals.},
}
@article {pmid17282640,
year = {2005},
author = {Song, L},
title = {Desynchronization network analysis for the recognition of imagined movement.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2091-2094},
doi = {10.1109/IEMBS.2005.1616871},
pmid = {17282640},
issn = {1557-170X},
abstract = {This paper reports on the use of electroencephalogram (EEG)-based phase desynchronization networks for the recognition of imagined movements. Features derived solely from these networks are classified using linear support vector machine. An average accuracy of 73% is achieved for the single-trial imagined hand versus foot movements. The results demonstrate that phase desynchronizations provide relevant information for the discrimination of mental tasks. This novel approach will potentially benefit the development of brain-computer interfaces.},
}
@article {pmid17282636,
year = {2005},
author = {Oweiss, K},
title = {Latency Reduction during Telemetry Transmission in Brain-Machine Interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2075-2078},
doi = {10.1109/IEMBS.2005.1616867},
pmid = {17282636},
issn = {1557-170X},
abstract = {Advanced array processing techniques are becoming an indispensable requirement for integrating the rapid developments in wireless high-density electronic interfaces to the central nervous system with computational neuroscience. This work aims at describing a systems approach for latency reduction in telemetry-linked brain machine interfaces to enable real-time transmission of high volumes of neural data. We show that the tradeoff between transmission bit rate and processing complexity requires a smart processing mechanism to strip the redundancy and extract the useful information early in the data stream. The results presented demonstrate that space-time processing offers tremendous savings in communication costs compared to on-chip spike detection followed by off-chip classification. They also demonstrate that the performance asymptotically approaches that of on-chip spike detection and sorting. Detailed performance evaluation is described.},
}
@article {pmid17282630,
year = {2005},
author = {Guan, JA and Chen, Y and Lin, J},
title = {Single-trial estimation of imitating-natural-reading evoked potentials in single-channel.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2052-2055},
doi = {10.1109/IEMBS.2005.1616861},
pmid = {17282630},
issn = {1557-170X},
abstract = {Using Imitating-Natural-Reading Induced Potentials as communication carriers, we are constructing a Brain-computer interface based mental speller which enable users to interaction with computers. The potentials were induced in this way: In a trial, strings consisted of target and non-target symbols were moving smoothly from right to left through a little visual window at the center of computer screen. Subject was instructed to stare at the visual window to count the target, and thus potentials were evoked. In practical applications, fewer electroencephalograph recording channels are preferred. We explored the single-trial estimating of event-related potentials recorded in single-channel using support vector machines in three subjects. With carefully feature selections, we obtained satisfying results of correct classification rate, which is 92.1%, 94.1% and 91.5%, respectively. The results demonstrated the advantages of the inducing paradigm used in our experiments.},
}
@article {pmid17282621,
year = {2005},
author = {Jiang, Z and Ning, Y and An, B and Li, A and Feng, H},
title = {Detecting mental EEG properties using detrended fluctuation analysis.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {2017-2020},
doi = {10.1109/IEMBS.2005.1616852},
pmid = {17282621},
issn = {1557-170X},
abstract = {Based on detrended fluctuation analysis (DFA), we explore the characteristics of multichannel electroencephalogram (EEG), which is recorded from many subjects performing different mental tasks. The results show that mental EEG exhibits long-range power-law correlations by calculating its scaling exponents (alpha), which can reflect the kinds of mental tasks. The scaling exponent of letter-composing is different from that of multiplication especially at positions C3 and C4, and at positions O1 and O2 the scaling exponent of rotation is also different distinctively from that of multiplication. Detrended fluctuation analysis exhibits its robustness against noises in our works. We could benefit more from the results of this paper in designing mental tasks and selecting brain areas in brain-computer interface systems.},
}
@article {pmid17282206,
year = {2005},
author = {Marzullo, TC and Dudley, JR and Miller, CR and Trejo, L and Kipke, DR},
title = {Spikes, Local Field Potentials, and Electrocorticogram Characterization during Motor Learning in Rats for Brain Machine Interface Tasks.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {429-431},
doi = {10.1109/IEMBS.2005.1616437},
pmid = {17282206},
issn = {1557-170X},
abstract = {Brain machine interface development typically falls into two arenas, invasive extracellular recording and non-invasive electroencephalogram recording methods. The relationship between action potentials and field potentials is not well understood, and investigation of interrelationships may improve design of neuroprosthetic control systems. Rats were trained on a motor learning task whereby they had to insert their noses into an aperture while simultaneously pressing down on levers with their forepaws; spikes, local field potentials (LFPs), and electrocorticograms (ECoGs) over the motor cortex were recorded and characterized. Preliminary results suggest that the LFP activity in lower cortical layers oscillates with the ECoG.},
}
@article {pmid17282055,
year = {2005},
author = {Hatsopoulos, N and Mukand, J and Polykoff, G and Friehs, G and Donoghue, J},
title = {Cortically controlled brain-machine interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7660-7663},
doi = {10.1109/IEMBS.2005.1616286},
pmid = {17282055},
issn = {1557-170X},
abstract = {Over the past ten years, we have tested and helped develop a multi-electrode array for chronic cortical recordings in behaving non-human primates. We have found that it is feasible to record from dozens of single units in the motor cortex for extended periods of time and that these signals can be decoded in a closedloop, real-time system to generate goal-directed behavior of external devices. This work has culminated in a FDA clinical trial that has demonstrated that a tetraplegic patient can voluntarily modulate motor cortical activity in order to move a computer cursor to visual targets. Further advances in BMI technology using non-human primates have focused on using multiple modes of control from signals in different cortical areas. We demonstrate that primary motor cortical activity may be optimized for continuous movement control whereas signals from the premotor cortex may be better suited for discrete target selection. We propose a hybrid BMI whereby decoding can be voluntarily switched from discrete to continuous control modes.},
}
@article {pmid17282042,
year = {2005},
author = {Jianhui, L and Xiaoming, W and Pengsheng, H and Tianling, R and Litian, L},
title = {Impedance spectroscopy analysis of cell-electrode interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7608-7611},
doi = {10.1109/IEMBS.2005.1616273},
pmid = {17282042},
issn = {1557-170X},
abstract = {Many chronically implanted electrodes suffer sensitivity loss in their applications in brain computer interface systems. It is hard to diagnose the cause of the problem because few measures are available to analyze directly what happened on the cell-electrode interface. In this paper, the impedance characterization of the cell-electrode interface was discussed in detail using equivalent circuit approach, which was used to evaluate the cause of the electrode sensitivity loss. The impedance spectroscopy of the cell-electrode interface acts as a function of several parameters, such as the sealing resistance and the shunt capacitance between the microelectrode and the electrolyte. Changes of the impedance spectroscopy can be traced to the parameter changes of the equivalent circuit, which reflect the status of the cell-electrode interface, such as the cell-electrode gap change, the erosion of microelectrodes, and so on. The circuit impedance simulation results give an important reference for the monitor of the cell-electrode connection, and are also helpful for the improvement of the microelectrode design.},
}
@article {pmid17281976,
year = {2005},
author = {Vetter, RJ and Miriani, RM and Casey, BE and Kong, K and Hetke, JF and Kipke, DR},
title = {Development of a Microscale Implantable Neural Interface (MINI) Probe System.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7341-7344},
doi = {10.1109/IEMBS.2005.1616207},
pmid = {17281976},
issn = {1557-170X},
abstract = {Cortical recording devices hold promise for providing augmented control of neuroprostheses and brain-computer interfaces in patients with severe loss of motor function due to injury or disease. This paper reports on the preliminary in vitro and in vivo results of our microscale implantable neural interface (MINI) probe system. The MINI is designed to use proven components and materials with a modular structure to facilitate ongoing improvements as new technologies become available. This device takes advantage of existing, well-characterized Michigan probe technologies and combines them to form a multichannel, multiprobe cortical assembly. To date, rat, rabbit, and non-human primate models have been implanted to test surgical techniques and in vivo functionality of the MINI. Results demonstrate the ability to form a contained hydrostatic environment surrounding the implanted probes for extended periods and the ability of this device to record electrophysiological signals with high SNRs. This is the first step in the realization of a cortically-controlled neuroprosthesis designed for human applications.},
}
@article {pmid17281902,
year = {2005},
author = {Lan, T and Erdogmus, D and Adami, A and Pavel, M and Mathan, S},
title = {Salient EEG channel selection in brain computer interfaces by mutual information maximization.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7064-7067},
doi = {10.1109/IEMBS.2005.1616133},
pmid = {17281902},
issn = {1557-170X},
abstract = {Modern brain computer interface (BCI) applications use information obtained from the user's electroencephalogram (EEG) to estimate the mental states. Selecting an optimal subset of the EEG channels instead of using all of them is especially important for ambulatory EEG where the user is mobile due to reduced data communication and computational load requirements. In addition, elimination of irrelevant sensors improves the robustness of the classification system by reducing dimensionality. In this paper, we propose a filter approach for EEG channel selection using mutual information (MI) maximization. This method ranks the EEG channels, such that the MI between the selected sensors and class labels is maximized. This selection criterion is known to reduce classification error. We employ a computationally efficient approach for MI estimation and EEG channel ranking. This approach is illustrated on EEG data recorded from three subjects performing two mental tasks. Experiment results show that the proposed approach works well and the position of the selected channels using the proposed method is consistent with the expected cortical areas for the mental tasks.},
}
@article {pmid17281900,
year = {2005},
author = {Kota, P and Sundaresan, K and Jansen, B},
title = {Artifacts, habituation and p300-based brain machine interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7056-7059},
doi = {10.1109/IEMBS.2005.1616131},
pmid = {17281900},
issn = {1557-170X},
abstract = {We investigated to what degree the detection rate of the P300 in single trial event-related potentials is affected by short-term and long-term habituation effects, and we present an algorithm to eliminate eye-movement artifacts. Data from 26 subjects were collected using a visual oddball paradigm. P300 components were detected using a threshold algorithm operating on the delta band (0-4 Hz). Using data from four subjects, collected over a 7 to 12 week period, it was observed that the P300 amplitude tended to decrease within a session, and also between successive sessions. However, this decrease did not affect the detection rate. The eye-movement removal algorithm was tested on simulated and actual data, and resulted in a significant increase in detection rate.},
}
@article {pmid17281899,
year = {2005},
author = {Zhu, X and Guan, C and Wu, J and Cheng, Y and Wang, Y},
title = {Bayesian Method for Continuous Cursor Control in EEG-Based Brain-Computer Interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {7052-7055},
doi = {10.1109/IEMBS.2005.1616130},
pmid = {17281899},
issn = {1557-170X},
abstract = {To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in Brain-Computer Interface (BCI). To train a classifier for continuous prediction, trials in training dataset are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. In this paper, we propose a novel statistical approach under Bayesian learning framework to learn the parameters of a classifier. To make use of all the training dataset, we iteratively estimate probability of the unknown label, and use this probability to assist the training process. Experimental results have shown that the performance of the proposed method is equal to or better than the best results so far.},
}
@article {pmid17281626,
year = {2005},
author = {Nicolaou, N and Nasuto, SJ},
title = {Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5991-5994},
doi = {10.1109/IEMBS.2005.1615856},
pmid = {17281626},
issn = {1557-170X},
abstract = {The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain- Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.},
}
@article {pmid17281479,
year = {2005},
author = {Chen, GS and Lu, CC and Chen, CW and Ju, MS and Lin, CC},
title = {Portable active surface laplacian EEG sensor for real-time mu rhythms detection.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5424-5426},
doi = {10.1109/IEMBS.2005.1615709},
pmid = {17281479},
issn = {1557-170X},
abstract = {A novel active surface Laplacian electroencephalogram (LEEG) sensor for the real-time mu rhythms detection has been developed in our study. Analog LEEG signals with high signal to noise ratio obtained directly by using the active sensor can reduce the duration and quantum error of digital signal processing and computation for the quick and precise control of brain-computer interface (BCI) systems. The portable active surface LEEG sensor comprises five gold electrodes integrated to a cross-shaped structure and a battery-powered, low-noise amplifier with the following specification: gain of 10,000, band-pass filtering from 2.5 Hz to 55 Hz, input impedance of 10 GΩ, common mode rejection ratio (CMRR) of 110 dB. The clinical experiments showed that the amplitude suppression of mu waves detected directly by active surface LEEG sensors was obvious as the normal subject was asked to imagine grasping something with his right hand. Furthermore, the distribution of mu waves captured real time in the un-shielded room by active surface LEEG sensor was similar to that acquired in the shielded room of hospital through EEG data collection by an EEG instrument, and then offline analyses. Real-time mu rhythms obtained by the active surface LEEG sensor will be utilized to control a device or system via the BCI in real time.},
}
@article {pmid17281478,
year = {2005},
author = {Shoker, L and Sanei, S and Sumich, A},
title = {Distinguishing Between Left and Right Finger Movement from EEG using SVM.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5420-5423},
doi = {10.1109/IEMBS.2005.1615708},
pmid = {17281478},
issn = {1557-170X},
abstract = {A hybrid BSS-SVM method for distinguishing between left and right finger movements from the electroencephalogram (EEG) has been developed. Support vector machines (SVM) is used to effectively classify the extracted features incorporating blind source separation (BSS) and directed transfer functions (DTF). This is the basis for a brain computer interface (BCI). We analyzed 200 trials of 64 electrode EEG data from which we trained the classifier and tested our system. We demonstrated that by classification of such appropriate features we can reliably distinguish between left and right finger movements.},
}
@article {pmid17281476,
year = {2005},
author = {Coyle, D and Prasad, G and McGinnity, T},
title = {Improving signal separability and inter-session stability for a brain-computer interface by time-series-prediction-preprocessing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5412-5415},
doi = {10.1109/IEMBS.2005.1615706},
pmid = {17281476},
issn = {1557-170X},
abstract = {This paper presents a preprocessing procedure for improving the separability of electroencephalogram (EEG) signals recorded from subjects for a right/left motor imagery based brain-computer interface (BCI). The EEG data is preprocessed utilizing a recently proposed time-series-prediction (TSP) technique. Two neural networks (NNs) are trained to perform one-step-ahead predictions of the EEG time-series data where one NN is trained to predict right motor imagery signals and the other left motor imagery signals. The NNs are used in a procedure referred to as neural-time-series-prediction-preprocessing (NTSPP) where signals are fed into both NNs and two new signal types are produced i.e. the predicted signals (Ys) or the prediction error signals (Es). In this investigation the well known adaptive autoregressive modeling (AAR) technique is used to extract features from the Es and Ys signals. Classification is performed using linear discriminant analysis (LDA). This NTSPP procedure is tested offline on three subjects and classification accuracy (CA) rates approach 98%. The approach shows significant potential for improving robustness and feature stability across sessions and a clearly distinguishable improvement in performance is observed when features are extracted from the NTSPP signals compared to those extracted from the original signals (Os).},
}
@article {pmid17281475,
year = {2005},
author = {Liu, B and Liu, Z and Wang, M and Li, T},
title = {Identification and Classification for finger movement based on EEG.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5408-5411},
doi = {10.1109/IEMBS.2005.1615705},
pmid = {17281475},
issn = {1557-170X},
abstract = {Identification and classification technology plays an important part in study of the BCI system. There are many algorithms to classify the event of different task related. Here, finger movement was used as the basic and typical tasks to be identified in the BCI experiments. The ideas of BP and ERD were introduced and discussed. The CSSD (common spatial subspace decomposition) algorithm was used for classifying single-trial EEG during the preparation of left-right finger movements after the two kinds of phenomena were expounded in detail in this paper. Experiment and simulating results show that the averaged classification accuracy can be up to the 75.6%.},
}
@article {pmid17281474,
year = {2005},
author = {Lakany, H and Conway, BA},
title = {Classification of Wrist Movements using EEG-based Wavelets Features.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5404-5407},
doi = {10.1109/IEMBS.2005.1615704},
pmid = {17281474},
issn = {1557-170X},
abstract = {Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially useful in building brain-computer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals. Our method is based on extracting salient spatio-temporal features from the EEG signals using continuous wavelet transform. We perform principal component analysis on these features as means to assess their usefulness for classification and to reduce the dimensionality of the problem. We use the results from the PCA as means to represent the different directions. We use a simple technique based on Euclidean distance to classify the data. The classification results show that we are able to discriminate between different directions using the selected features.},
}
@article {pmid17281473,
year = {2005},
author = {Wang, C and Guan, C and Zhang, H},
title = {P300 brain-computer interface design for communication and control applications.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5400-5403},
doi = {10.1109/IEMBS.2005.1615703},
pmid = {17281473},
issn = {1557-170X},
abstract = {This paper introduces the design of a P300-based brain-computer interface (BCI) system. Based on this system, two applications are implemented: a word speller and a remote control device, which are to assist physically disabled people to communicate and control. A number of specific implementation techniques are proposed to achieve good performance in terms of accuracy and reliability. The word speller can achieve a spelling rate of up to 4-6 letters per minute, while both applications achieve 99% accuracy in our experiments with healthy subjects.},
}
@article {pmid17281472,
year = {2005},
author = {Thulasidas, M and Guan, C},
title = {Optimization of BCI Speller Based on P300 Potential.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5396-5399},
doi = {10.1109/IEMBS.2005.1615702},
pmid = {17281472},
issn = {1557-170X},
abstract = {We report our studies on a Brain Computer Interface (BCI) speller application with an aim to optimize its performance and usability. We study the dependence of the spelling accuracy as a function of (a) the number of visual stimuli (repetitions) presented to the user, (b) the P300 segment length used, (c) the number of channels used, and (d) the amount of data used in training, in terms of the number of characters and repetitions. Reducing the number of repetitions results in a direct reduction of the time needed to spell a character, while minimizing the number of channels translates to shorter subject preparation time and thus improves the usability of the system. The usability is further enhanced by decreasing the training required, while maintaining the accuarcy. We show that very high accuracies of the order of 99% can be achieved with a short training session of less than 10 minutes using only about 10 channels. The high accuracies, short training and preparation time requirements along with real-time performance make this BCI speller a viable communication tool for severely disabled individuals, who have no other means to communicate with the external world.},
}
@article {pmid17281471,
year = {2005},
author = {Wang, Y and Gao, S and Gao, X},
title = {Common Spatial Pattern Method for Channel Selelction in Motor Imagery Based Brain-computer Interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5392-5395},
doi = {10.1109/IEMBS.2005.1615701},
pmid = {17281471},
issn = {1557-170X},
abstract = {A brain-computer interface(BCI) based on motor imagery (MI) translates the subject's motor intention into a control signal through classifying the electroencephalogram (EEG) patterns of different imagination tasks, e.g. hand and foot movements. Characteristic EEG spatial patterns make MI tasks substantially discriminable. Multi-channel EEGs are usually necessary for spatial pattern identification and therefore MI-based BCI is still in the stage of laboratory demonstration, to some extent, due to the need for constanly troublesome recording preparation. This paper presents a method for channel reduction in MI-based BCI. Common spatial pattern (CSP) method was employed to analyze spatial patterns of imagined hand and foot movements. Significant channels were selelcted by searching the maximunms of spatial pattern vectors in scalp mappings. A classification algorithm was developed by means of combining linear discriminat analysis towards even-related desynchronization (ERD) and readiness potential (RP). The classification accuracies with four optimal channels were 93.45% and 91.88% for two subjects.},
}
@article {pmid17281470,
year = {2005},
author = {Zhang, H and Guan, C and Wang, C},
title = {A statistical model of brain signals with application to brain-computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5388-5391},
doi = {10.1109/IEMBS.2005.1615700},
pmid = {17281470},
issn = {1557-170X},
abstract = {This paper presents a novel approach to improving the robustness of brain-computer interfaces by using a statistical model of brain signals especially P300. We study the distributions of support vector machine scores for the signals and derive a posteriori probability model of P300/non-P300. We further derive a statistical model for multi-trial brain signals, and apply it to the rejection of undesired signals. Six subjects have been involved in an experimental study. The results demonstrate that the P300 model and the rejection method are appropriate and can help improve the robustness of the system significantly.},
}
@article {pmid17281469,
year = {2005},
author = {Piccini, L and Parini, S and Maggi, L and Andreoni, G},
title = {A Wearable Home BCI system: preliminary results with SSVEP protocol.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5384-5387},
doi = {10.1109/IEMBS.2005.1615699},
pmid = {17281469},
issn = {1557-170X},
abstract = {This paper presents and discusses the realization and the performances of a wearable system for EEG-based BCI applications. The system (called Kimera) consists of a two-layer hardware architecture (the wireless acquisition and transmission board based on a Bluetooth ® ARM chip, and a low power miniaturized biosignal acquisition analog front end) together with a software suite (called Bellerophonte) for the Graphic User Interface management, protocol execution, data recording, transmission and processing. The implemented BCI system was based on the SSVEP protocol, applied to a two state selection by using standards display/monitor with a couple of high efficiency LEDs. The frequency features of the signal were computed and used in the intention detection. The BCI algorithm is based on a supervised classifier implemented through a multi-class Canonical Discriminant Analysis (CDA) with a continuous realtime feedback based on the mahalanobis distance parameter. Five healthy subjects participated in the first phase for a preliminary device validation. The obtained results are very interesting and promising, being lined out to the most recent performance reported in literature with a significant improvement both in system and in classification capabilities. The user-friendliness and low cost of the Kimera&amp; Bellerophonte platform make it suitable for the development of home BCI applications.},
}
@article {pmid17281468,
year = {2005},
author = {Jaganathan, V and Srihari Mukesh, TM and Ramasubba Reddy, M},
title = {Design and implementation of High Performance Visual Stimulator for Brain Computer Interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5381-5383},
doi = {10.1109/IEMBS.2005.1615698},
pmid = {17281468},
issn = {1557-170X},
abstract = {An algorithm for implementing visual stimulators on generic computers has been developed for Brain Computer Interfaces (BCIs). It uses the hardware counter present in these systems to derive accurate timing. Simultaneous display of 20 patterns (e.g. 3×3 checkerboards) modulated at different frequencies is possible. The pattern used for stimulating the Steady State Visual Evoked Potential (SSVEP) can be changed with ease. The stimulators are evaluated using software counters. High accuracy (less than 0.73% error) and precision (0.1% coefficient of variation) is recorded for 20 patterns set with frequencies between 6 Hz and 15 Hz.},
}
@article {pmid17281466,
year = {2005},
author = {Aihua, Z and Yuhan, Z},
title = {Phase synchronization analysis and support vector machine for recognition of mental tasks.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5373-5376},
doi = {10.1109/IEMBS.2005.1615696},
pmid = {17281466},
issn = {1557-170X},
abstract = {A measuring method of phase synchronization is presented for analyzing event-related electroencephalogram (EEG) recordings. The mean phase coherence (MPC) of EEG recordings during imagination of hand movement is calculated. Furthermore, the support vector machine is used for classification of the mental tasks. The preliminary results suggest that MPC could be promising method for brain-computer interfaces. Dynamic MPC measurement provides a new idea for recognition of mental tasks.},
}
@article {pmid17281465,
year = {2005},
author = {Liu, H and Wang, J and Zheng, C and He, P},
title = {Study on the effect of different frequency bands of EEG signals on mental tasks classification.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5369-5372},
doi = {10.1109/IEMBS.2005.1615695},
pmid = {17281465},
issn = {1557-170X},
abstract = {Currently, frequency bands not more than 40 Hz are usually used to perform mental tasks classification in brain-computer interface systems. In this study, by using Keirn's EEG data, we studied the effects of ten 10 Hz-wide subbands between 0 and 100 Hz on mental tasks classification. Features were computed in frequency domain as the sum of weighted power spectral value in each subband at each channel (C3, C4, P3, P4, O1, and O2). Fisher's linear discriminant was used to perform task-pair classification. Our results indicated that subbands ranging from 30 to 100 Hz resulted in relatively greater classification accuracy at many scalp sites. The average classification accuracy of 98.3% across 130 task pairs was achieved by using features including those obtained on gamma bands (30-100 Hz), which is much greater than that of 89.3% by using the frequency band 0-30 Hz only.},
}
@article {pmid17281464,
year = {2005},
author = {Arbabi, E and Shamsollahi, M and Sameni, R},
title = {Comparison between Effective Features Used for the Bayesian and the SVM Classifiers in BCI.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5365-5368},
doi = {10.1109/IEMBS.2005.1615694},
pmid = {17281464},
issn = {1557-170X},
support = {R01 EB001659/EB/NIBIB NIH HHS/United States ; R01 GM104987/GM/NIGMS NIH HHS/United States ; U01 EB008577/EB/NIBIB NIH HHS/United States ; },
abstract = {Brain-computer interface (BCI) is based on processing signals recorded from the scalp, the surface of the cortex or from the inside of the brain in order to identify desired actions or behaviors. In BCI we are interested in extracting the most effective features from rare data in order to have the desired classification results. In this paper besides proposing two discrimination algorithms for classifying imagined movements of the left small finger and the tongue, a comparison has been done between the effective features applied by the Bayesian and the SVM classifiers for the BCI task. In fact the comparison was done on the most effective features found from a pool of extracted features for each classifier, separately. Finally using the most effective features of each classifier, the classification accuracy of 89.21% and 91.01% were achieved for the Bayesian and the SVM classifiers, respectively.},
}
@article {pmid17281462,
year = {2005},
author = {Dharwarkar, G and Basir, O},
title = {Enhancing Temporal Classification of AAR Parameters in EEG single-trial analysis for Brain-Computer Interfacing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5358-5361},
doi = {10.1109/IEMBS.2005.1615692},
pmid = {17281462},
issn = {1557-170X},
abstract = {Adaptive autoregressive (AAR) coefficients provide dynamic spectral information in EEG single-trial analysis. In this paper we propose a temporal evidence accumulation framework to enhance classification of AAR features. The results for a single subject, using 280 trials, indicate distinct improvements over a conventional method of temporal classification. We illustrate how the framework is applicable to AAR features, as well as to wavelet features as reported in [13]. These findings put the two time-frequency features on equal footing for comparison in this context.},
}
@article {pmid17281461,
year = {2005},
author = {Herman, P and Prasad, G and McGinnity, TM},
title = {Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5354-5357},
doi = {10.1109/IEMBS.2005.1615691},
pmid = {17281461},
issn = {1557-170X},
abstract = {Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noise. Recently, the type-2 fuzzy logic systems (T2 FLSs) have been found effective in modeling uncertain data. This paper examines the potential of the T2 FLS methodology in devising an EEG-based brain-computer interface (BCI). In particular, a T2 FLS has been designed to classify imaginary left and right hand movements based on time-frequency information extracted from the EEG with the short time Fourier transform (STFT). Robustness of the method has also been verified in the presence of additive noise. The performance of the classifier is quantified with the classification accuracy (CA). The T2 fuzzy classifier has been proven to outperform its type-1 (T1) counterpart on all data sets recorded from three subjects examined. It has also compared favorably to the well known classifier based on linear discriminant analysis (LDA).},
}
@article {pmid17281460,
year = {2005},
author = {Jun, J and Mengsun, Y and Yubin, Z and Zhangrui, J},
title = {A wireless EEG sensors system for computer assisted detection of alpha wave in sleep.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5351-5353},
doi = {10.1109/IEMBS.2005.1615690},
pmid = {17281460},
issn = {1557-170X},
abstract = {To gain good alpha wave for diagnosing insomnia in the situation which patient feeling comfort, an EEG sensor was designed with shorter electrode wires, battery supplied and wireless transmitter, whose another usage is brain-computer interface (BCI). The result of test represents the system can get good alpha wave and reject power-line interference at all.},
}
@article {pmid17281459,
year = {2005},
author = {Edlinger, G and Guger, C},
title = {Laboratory PC and Mobile Pocket PC Brain-Computer Interface Architectures.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5347-5350},
doi = {10.1109/IEMBS.2005.1615689},
pmid = {17281459},
issn = {1557-170X},
abstract = {EEG-based brain-computer interface (BCI) systems convert brain activity into control signals and have been developed for people with severe disabilities to improve their quality of life. A BCI system has to satisfy different demands depending on the application area. A laboratory PC based system allows the flexible design of multiple/single channel feature extraction, classification methods and experimental paradigms. The key advantage of a Pocket PC based BCI approach is its small dimension and battery supply. Hence a mobile BCI system e.g. mounted on a wheelchair can be realized. This study compares and discusses thoroughly the two mentioned approaches.},
}
@article {pmid17281456,
year = {2005},
author = {Li, Y and Guan, C and Qin, J},
title = {Enhancing feature extraction with sparse component analysis for brain-computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {5335-5338},
doi = {10.1109/IEMBS.2005.1615686},
pmid = {17281456},
issn = {1557-170X},
abstract = {Feature extraction is very important to EEG-based brain computer interfaces (BCI) in helping achieve high classification accuracy. Preprocessing of EEG signals plays an important role, because an effective preprocessing method will help enhance the efficiency of the feature extraction. In this paper, sparse component analysis (SCA) is employed as a preprocessing method for EEG based BCI. A combined feature vector is constructed. This feature vector consists of a dynamical power feature and a dynamical common spatial pattern (CSP) feature. The dynamical power feature is extracted from selected SCA components, while the dynamical CSP feature is extracted from raw EEG data. Using the presented preprocessing and feature extraction method, we analyze the data for a cursor control BCI carried out at Wadsworth Center. Our results show that SCA preprocessing is the most effective in extracting a component which reflects the subject's intention, and demonstrate the validity of SCA preprocessing for the enhancement of feature extraction.},
}
@article {pmid17281271,
year = {2005},
author = {Akrami, A and Solhjoo, S and Motie-Nasrabadi, A and Hashemi-Golpayegani, MR},
title = {EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {4626-4629},
doi = {10.1109/IEMBS.2005.1615501},
pmid = {17281271},
issn = {1557-170X},
abstract = {Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a "Brain-Computer Interface," is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject's EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.},
}
@article {pmid17281157,
year = {2005},
author = {Chen, Y and Gao, K},
title = {An Improved Algorithm to Extract ERP Component for Brain-Computer Interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {4187-4190},
doi = {10.1109/IEMBS.2005.1615387},
pmid = {17281157},
issn = {1557-170X},
abstract = {In order to improve the communication rates of brain-computer interface(BCI's), scientists are developing appropriate signal processing methods to extract the user's messages and commands from electroencephalograph (EEG). A fast fixed-point algorithm for independent component analysis(FastICA), possesses the advantages of simply structure and fast computation. However, in some cases, many signals are not completely independent, the stability of the algorithm won't be as ideal as people have expected. In fact, the reason that system does not converge steadily is the fixed step size in FastICA algorithm, that is, The negentropy J(wn+1TZ) of random vectors no longer monotonic increasing in the iterative process of separated vectors. We define a cost function δJ=J(w<inf>n+1</inf>[T]Z)-J(w<inf>n</inf>[T]Z) and a time-variant step size μ(t), and put forward a algorithm of adjusting step size by the variety of the cost function in iterative process. Results from a series of simulation and experiments show that, the stability and convergence of algorithm is improved.},
}
@article {pmid17281143,
year = {2005},
author = {Zhou, J and Yao, J and Deng, J and Dewald, J},
title = {EEG-based Discrimination of Elbow/Shoulder Torques using Brain Computer Interface Algorithms: Implications for Rehabilitation.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {4134-4137},
doi = {10.1109/IEMBS.2005.1615373},
pmid = {17281143},
issn = {1557-170X},
abstract = {Brain computer interface (BCI) algorithms are used to predict the torque generation in the direction of shoulder abduction or elbow flexion using scalp EEG signals from 163 electrodes. Based on features extracted from both frequency and time domains, three classifiers are employed including support vector classifier, classification trees and K nearest neighbor. Support vector classifier achieves the highest recognition rate of 92.9% on two able-bodied subjects in average. The recognition rates we obtained on the able-bodied subjects are among the highest compared with previous reports on predicting motor intent using scalp EEG. This demonstrates the feasibility of separating the shoulder/elbow torques using scalp EEG as well as the potential of support vector classifier in applications of BCI. Preliminary experiments on two hemiparetic stroke subjects using support vector classifier reports an accuracy of 84.1% in average, which shows an increased difficulty in predicting intent presumably due to cortical reorganization resulting from the stroke.},
}
@article {pmid17281008,
year = {2005},
author = {Ivanova, G and Perez, D and Both, R},
title = {Threshold adaptation for mean value based operant conditioning.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2005},
number = {},
pages = {3612-3615},
doi = {10.1109/IEMBS.2005.1617263},
pmid = {17281008},
issn = {1557-170X},
abstract = {Biofeedback and a variety of brain-computer-interface methods imply as very first stages the obtainment of control of selected signals e.g. the related generating psycho-physiological processes. The basic mechanism in the learning phase is the operant conditioning, which represent a complex behavioral method consisting of several components. One of the most important components is the setting and adjustment of thresholds for the triggering of corresponding rewarding options. An adaptive threshold optimization method, for the training based on average values is presented. The procedure is derivated from the sequential test from Wald. The application of the sequential tests in the learning/training process allows a threshold adaptation corresponding to the abilities of the particular person and to the learning success.},
}
@article {pmid17278584,
year = {2007},
author = {Krusienski, DJ and Schalk, G and McFarland, DJ and Wolpaw, JR},
title = {A mu-rhythm matched filter for continuous control of a brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {54},
number = {2},
pages = {273-280},
doi = {10.1109/TBME.2006.886661},
pmid = {17278584},
issn = {0018-9294},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Cerebral Cortex/*physiology ; Cortical Synchronization/methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the continuous amplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.},
}
@article {pmid17271724,
year = {2004},
author = {Nai-Jen, H and Palaniappan, R},
title = {Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {507-510},
doi = {10.1109/IEMBS.2004.1403205},
pmid = {17271724},
issn = {1557-170X},
abstract = {Classification of EEG signals extracted during mental tasks is a technique for designing brain computer interfaces (BCI). We classify EEG signals that were extracted during mental tasks using fixed autoregressive (FAR) and adaptive AR (AAR) models. Five different mental tasks from 4 subjects were used in the experimental study and combinations of 2 different mental tasks are studied for each subject. Four different feature extraction methods were used to extract features from these EEG signals: FAR coefficients computed with Burg's algorithm using 125 data points, without segmentation and with segmentation of 25 data points, AAR coefficients computed with least-mean-square (LMS) algorithm using 125 data points, without segmentation and with segmentation of 25 data points. Multilayer perception (MLP) neural network (NN) trained by the backpropagation (BP) algorithm is used to classify these features into the different categories representing the mental tasks. The best results for FAR was 92.70% while for AAR was only 81.80%. The results obtained here indicated that FAR using 125 data points without segmentation gave better classification performance as compared to AAR, with all other parameters constant.},
}
@article {pmid17271709,
year = {2004},
author = {Hoffmann, U and Garcia, G and Vesin, JM and Ebrahimi, T},
title = {Application of the evidence framework to brain-computer interfaces.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {446-449},
doi = {10.1109/IEMBS.2004.1403190},
pmid = {17271709},
issn = {1557-170X},
abstract = {A brain-computer interface (BCI) is a communication system, that implements the principle of "think and make it happen without any physical effort". This means a BCI allows a user to act on his environment only by using his thoughts, without using peripheral nerves and muscles. Nearly all BCIs contain as a core part a machine learning algorithm, which learns from training data a function, that can be used to discriminate different brain activities. In the present work we use a Bayesian framework for machine learning, the evidence framework [1], [2] to develop a variant of linear discriminant analysis for the use in a BCI based on electroencephalographic measurements (EEG). Properties of the resulting algorithm are: a) a continuous probabilistic output is given, b) fast estimation of regularization constants, and c) the possibility to select among different feature sets, the one which is most promising for classification. The algorithm has been tested on one dataset from the BCI competition 2002 and two datasets from the BCI competition 2003 and provides a classification accuracy of 95%, 81%, and 79% respectively.},
}
@article {pmid20572974,
year = {2000},
author = {Beßer, K and Jarosch, B and Langen, G and Kogel, KH},
title = {Expression analysis of genes induced in barley after chemical activation reveals distinct disease resistance pathways.},
journal = {Molecular plant pathology},
volume = {1},
number = {5},
pages = {277-286},
doi = {10.1046/j.1364-3703.2000.00031.x},
pmid = {20572974},
issn = {1364-3703},
abstract = {Abstract Salicylic acid (SA) and its synthetic mimics 2,6-dichloroisonicotinic acid (DCINA) and benzo(1,2,3)thiadiazole-7-carbothioic acid S-methyl ester (BTH), protect barley systemically against powdery mildew (Blumeria graminis f.sp. hordei, Bgh) infection by strengthening plant defence mechanisms that result in effective papillae and host cell death. Here, we describe the differential expression of a number of newly identified barley chemically induced (BCI) genes encoding a lipoxygenase (BCI-1), a thionin (BCI-2), an acid phosphatase (BCI-3), a Ca(2+)-binding EF-hand protein (BCI-4), a serine proteinase inhibitor (BCI-7), a fatty acid desaturase (BCI-8) and several further proteins with as yet unknown function. Compared with SA, the chemicals DCINA and BTH were more potent inducers of both gene expression and resistance. Homologues of four BCI genes were detected in wheat and were also differentially regulated upon chemical activation of disease resistance. Except for BCI-4 and BCI-5 (unknown function), the genes were also induced by exogenous application of jasmonates, whereas treatments that raise endogenous jasmonates as well as wounding were less effective. The fact that BCI genes were not expressed during incompatible barley-Bgh interactions governed by gene-for-gene relationships suggests the presence of separate pathways leading to powdery mildew resistance.},
}
@article {pmid18222826,
year = {1991},
author = {Trebossen, R and Mazoyer, B},
title = {Count rate performances of TTVO3: the CEA-LETI time-of-flight positron emission tomograph.},
journal = {IEEE transactions on medical imaging},
volume = {10},
number = {3},
pages = {261-266},
doi = {10.1109/42.97574},
pmid = {18222826},
issn = {0278-0062},
abstract = {The authors present the count rate performance of the CEA-LETI TTVO3 time-of-flight positron emission tomography (PET) system using both physical measurements and H(2)(15)O bolus human myocardial studies. They also present a comparison between the counting statistics of H(2)(15)O brain studies performed on this machine and on the latest available high-resolution brain bismuth germanate (BGO) tomograph, the ECAT 953B/31. During the 80 mCi cerebral blood flow study, the count rate reached 100 K events/s, and the same experiment performed on a high-resolution BGO brain machine gave only a 30% increase in signal. These results demonstrate that TTVO3 is particularly suitable for H(2)(15)O flow studies.},
}
@article {pmid20787589,
year = {1949},
author = {},
title = {Mind, Machine, and Man.},
journal = {British medical journal},
volume = {1},
number = {4616},
pages = {1129-1130},
pmid = {20787589},
issn = {0007-1447},
}
@article {pmid17271678,
year = {2004},
author = {Kawada, M},
title = {Analysis on synchronous time-frequency components of human movement related cortical potential using wavelet transform.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {333-336},
doi = {10.1109/IEMBS.2004.1403160},
pmid = {17271678},
issn = {1557-170X},
abstract = {Electroencephalogram (EEG) has been generally known as a non-destructive method to examine the function of human brain. In recent years, brain-computer interface (BCI) based on EEG has been a growing field of research and development. This work presents to identify time frequency components of movement-related cortical potential (MRCP) associated with the human voluntary muscle behaviors using the wavelet transform. The EEG was recorded with the 5-channel referential derivations, Fz, Cz, C3, C4, Pz, referenced to the right earlobe based on the international 10-20 system. The voluntary activity was recorded with the electrode placed on the palm side of the metacarpophalangeal joint when the subjects flexed the right - or left - hand index finger respectively and eyes closed. The synchronous time-frequency components of MRCP were shown in this study.},
}
@article {pmid17271653,
year = {2004},
author = {Erfanian, A and Erfani, A},
title = {ICA-based classification scheme for EEG-based brain-computer interface: the role of mental practice and concentration skills.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {235-238},
doi = {10.1109/IEMBS.2004.1403135},
pmid = {17271653},
issn = {1557-170X},
abstract = {This article explores the use of independent component analysis (ICA) approach to design a new EEG-based brain-computer interface (BCI) for natural control of prosthetic hand grasp. ICA is a useful technique that allows blind separation of sources, linearly mixed, assuming only the statistical independence of these sources. This suggests the possibility of using ICA to separate different independent brain activities during motor imagery into separate components. This work provides a natural basis for developing an efficient BCI based on single-source data obtained by independent component analysis of multi-channel EEG. The tasks to be discriminated are the imagination of hand grasping and opening and the resting state. The results indicate that the proposed scheme can improve the classification accuracy of the EEG patterns. Imagery is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of EEG-based BCI. We are going to examine the role of mental practice and concentration skills on the performance of BCI. The surprising results indicate that mental training has a significant effect on the performance of BCI over the primary motor cortex, temporal, and frontal areas. This supports the hypothesis that mental practice is an effective method for performance enhancement and motor skill learning.},
}
@article {pmid17271589,
year = {2004},
author = {Jia, W and Zhao, X and Liu, H and Gao, X and Gao, S and Yang, F},
title = {Classification of single trial EEG during motor imagery based on ERD.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2006},
number = {},
pages = {5-8},
doi = {10.1109/IEMBS.2004.1403076},
pmid = {17271589},
issn = {1557-170X},
abstract = {EEG-based brain computer interface (BCI) provides a completely new communication channel between human brain and computer. Classification of EEG signals is a difficult task, especially when the classification has to be preformed on a single-trial EEG to continuously control a device. Event related desynchronization (ERD) has proven to be induced on the contralateral sensorimotor area during imagination of a left or right hand movement. In this paper, we introduced a quantification of ERD, with which a lower classification error rate and a higher information transfer rate can be obtained. The performance was tested by the Graz dataset for BCI competition 2003.},
}
@article {pmid17271549,
year = {2004},
author = {Kipke, DR},
title = {Implantable neural probe systems for cortical neuroprostheses.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {5344-5347},
doi = {10.1109/IEMBS.2004.1404492},
pmid = {17271549},
issn = {1557-170X},
abstract = {Advanced microfabrication processes, biomaterials, and systems technologies are enabling progressively more sophisticated devices to interface with the brain. In particular, microscale implantable neural probe systems have been developed to reliably stimulate and/or record populations of neurons for long periods of time. Our group has developed a silicon-based probe technology is effective for recording neural activity from neuronal populations for sustained time periods. In a recent study in rats, these probes consistently and reliably provided high-quality spike recordings over extended periods of time. These probes are being used to investigate and develop cortical neuroprostheses and brain-machine interface systems. This neural probe technology is currently being extended to include polymer substrates, chemical interfaces for drug delivery, advanced coatings for improved biocompatibility, and integrated electronics for wireless communication to the outside world.},
}
@article {pmid17271544,
year = {2004},
author = {Sarje, A and Thakor, N},
title = {Neural interfacing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {5325-5328},
doi = {10.1109/IEMBS.2004.1404487},
pmid = {17271544},
issn = {1557-170X},
abstract = {The problem of interfacing microsystems to neurons or brain has led to exciting developments in the fields of micro/nanotechnologies and integrated circuitry and systems. Neurons have been patterned using micro/nanotechnologies to form structural and functional networks. Micro-electrodes and integrated circuitry have been developed for large scale, multichannel measurements from brain tissue. Driving force for this technology comes from research and clinical interest in the emerging fields of neural prosthesis, deep brain stimulations and brain-machine interface. This review presents some examples of the work done in the field of neural patterning, tissue interfacing, electrodes, recording and system integration.},
}
@article {pmid17271543,
year = {2004},
author = {Sanchez, JC and Principe, JC and Carmena, JM and Lebedev, MA and Nicolelis, MA},
title = {Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {5321-5324},
doi = {10.1109/IEMBS.2004.1404486},
pmid = {17271543},
issn = {1557-170X},
abstract = {Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.},
}
@article {pmid17271364,
year = {2004},
author = {Kelly, SP and Lalor, E and Finucane, C and Reilly, RB},
title = {A comparison of covert and overt attention as a control option in a steady-state visual evoked potential-based brain computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4725-4728},
doi = {10.1109/IEMBS.2004.1404308},
pmid = {17271364},
issn = {1557-170X},
abstract = {EEG data were recorded from occipital scalp regions of subjects who attended to an alternating checkerboard stimulus in one visual field while ignoring a similar stimulus of a different frequency in the opposite visual field. Classification of left/right spatial attention is attempted by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli to assess the potential use of such a spatial selective attention paradigm in a brain computer interface (BCI). Experimental setup and analysis procedure in a previous study in which eye movement is permitted are replicated in order to quantify differences in classification performance using overt and covert attention. Four variations of the basic paradigm, involving both feedback and addition of extra mental load, are studied for comparison. The average accuracy is found to be reduced by approximately 20% in the switch from overt to covert attention when no other specifications of the task are changed.},
}
@article {pmid17271316,
year = {2004},
author = {Coyle, S and Ward, T and Markham, C},
title = {Physiological noise in near-infrared spectroscopy: implications for optical brain computer interfacing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4540-4543},
doi = {10.1109/IEMBS.2004.1404260},
pmid = {17271316},
issn = {1557-170X},
abstract = {Near-infrared spectroscopy is a non-invasive optical method used to detect functional activation of the cerebral cortex. Cognitive, visual, auditory and motor tasks are among the functions that have been investigated by this technique in the context of optical brain computer interfacing. In order to determine whether the optical response is due to a stimulus, it is essential to identify and reduce the effects of physiological noise. This paper characterizes noise typically present in optical responses and reports signal processing approaches used to overcome such noise.},
}
@article {pmid17271309,
year = {2004},
author = {Krauledat, M and Dornhege, G and Blankertz, B and Losch, F and Curio, G and Müller, KR},
title = {Improving speed and accuracy of brain-computer interfaces using readiness potential features.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4511-4515},
doi = {10.1109/IEMBS.2004.1404253},
pmid = {17271309},
issn = {1557-170X},
abstract = {To enhance human interaction with machines, research interest is growing to develop a 'brain-computer interface', which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which brain-computer interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.},
}
@article {pmid17271308,
year = {2004},
author = {Wang, Y and Zhang, Z and Gao, X and Gao, S},
title = {Lead selection for SSVEP-based brain-computer interface.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4507-4510},
doi = {10.1109/IEMBS.2004.1404252},
pmid = {17271308},
issn = {1557-170X},
abstract = {SSVEP-based brain-computer interface (BCI) has potential advantage of high information transfer rate. However, individual difference greatly affects its practical applications. This paper presents a method of lead selection to improve the applicability of SSVEP-based BCI system. Independent component analysis (ICA) is employed to decompose EEGs over visual cortex into SSVEP signal and background noise. Optimal bipolar lead is selected by comparing signal correlation and noise correlation between different channels. The system with one optimal bipolar lead has reached an average transfer rate about 42 bits/min for normal subjects. It has also been successfully applied to an environmental controller for the motion-disabled.},
}
@article {pmid17271307,
year = {2004},
author = {Leeb, R and Pfurtscheller, G},
title = {Walking through a virtual city by thought.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4503-4506},
doi = {10.1109/IEMBS.2004.1404251},
pmid = {17271307},
issn = {1557-170X},
abstract = {This paper gives a short overview of the feasibility of walking through a virtual city by using motor imagery. Therefore an electroencephalogram-based brain-computer interface (BCI) is combined with virtual reality technology. A BCI transforms bioelectrical brain signals, modulated by mental activity (e.g. imagination of foot or right hand movements), into a control signal. This signal is used to walk forward / backward or to remain stationary inside a virtual city. Results of the first experimental sessions are presented.},
}
@article {pmid17271301,
year = {2004},
author = {Poon, CS},
title = {Sensorimotor learning and information processing by Bayesian internal models.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4481-4482},
doi = {10.1109/IEMBS.2004.1404245},
pmid = {17271301},
issn = {1557-170X},
abstract = {Fundamental to effective brain-machine interface and neuroprosthesis designs is an understanding of how sensory and motor information are encoded, integrated and adapted by the nervous system. Special session "Neural Information Processing by Bayesian and Internal Models" expounds two current theories of sensorimotor integration which posit that neural information may be encoded centrally as an "internal model" of the environment or as a stochastic state-space model that modulates the activity of spiking neurons. Underlying both theories is a possible role for Bayes' rule--as suggested by the recent findings that the brain may employ Bayesian internal models during certain types of sensorimotor learning in order to optimize task-specific performance and that the emergent activity of certain neural ensembles may be modeled as joint Bayesian point processes. These emerging concepts of neural signal processing have far-reaching implications in applications from rehabilitation engineering to artificial intelligence.},
}
@article {pmid17271277,
year = {2004},
author = {Thulasidas, M and Guan, C and Ranganatha, S and Wu, JK and Zhu, X and Xu, W},
title = {Effect of ocular artifact removal in brain computer interface accuracy.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4385-4388},
doi = {10.1109/IEMBS.2004.1404220},
pmid = {17271277},
issn = {1557-170X},
abstract = {We report the effect of removing ocular artifacts on the performance of a word-processing application based on the event related potential P300. Various methods of removing artifacts have been reported. The efficiency of these algorithms are usually done by subjective visual comparisons. Noting that there is a direct correlation of artifact rectifying algorithms to the accuracy in a brain computer interface system's accuracy, we present this work as a means to compare different algorithms.},
}
@article {pmid17271274,
year = {2004},
author = {Babiloni, F and Cincotti, F and Mattiocco, M and Timperi, A and Salinari, S and Marciani, MG and Donatella, M},
title = {Brain computer interface: estimation of cortical activity from non invasive high resolution EEG recordings.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4375-4376},
doi = {10.1109/IEMBS.2004.1404217},
pmid = {17271274},
issn = {1557-170X},
abstract = {The aim of this paper is to analyze whether the use of the cortical activity estimated from non invasive EEG recordings could be useful to detect mental states related to the imagination of limb movements. Estimation of cortical activity was performed on high resolution EEG data related to the imagination of limb movements gathered in five normal healthy subjects by using realistic head models. Cortical activity was estimated in region of interest associated with the subject's Brodmann areas by using depth-weighted minimum norm solutions. Comparisons between surface recorded EEG and the estimated cortical activity were performed. The estimated cortical activity related to the mental imagery of limbs in the five subjects is located mainly over the contralateral primary motor area. The unbalance between brain activity estimated in contralateral and ipsilateral motor cortical areas relative to the finger movement imagination is greater than those obtained in the scalp EEG recordings. Results suggest that the use of the estimated cortical activity for the motor imagery of upper limbs could be potentially superior with respect to the use of surface EEG recordings. This is due to a greater statistically significant unbalance between the activity estimated in the contralateral and ipsilateral hemisphere with respect to those observed with surface EEG. These results are useful in the context of the development of a non invasive brain computer interface.},
}
@article {pmid17271273,
year = {2004},
author = {Coyle, D and Prasad, G and McGinnity, TM},
title = {Extracting features for a brain-computer interface by self-organising fuzzy neural network-based time series prediction.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4371-4374},
doi = {10.1109/IEMBS.2004.1404216},
pmid = {17271273},
issn = {1557-170X},
abstract = {This paper presents a novel feature extraction procedure (FEP) for extracting features from the electroencephalogram (EEG) recorded from subjects producing right and left motor imagery. Four self-organizing fuzzy neural networks (SOFNNs) are coalesced to perform one-step-ahead predictions for the EEG time series data. Features are derived from the mean squared error (MSE) in prediction or the mean squared of the predicted signals (MSY). Classification is performed using linear discriminant analysis (LDA). This novel FEP is tested on three subjects offline and classification accuracy (CA) rates approach 94% with information transfer (IT) rates >10 bits/min. Minimum subject specific data analysis is required and the approach shows good potential for online feature extraction and autonomous system adaptation.},
}
@article {pmid17271271,
year = {2004},
author = {Kaper, M and Ritter, H},
title = {Generalizing to new subjects in brain-computer interfacing.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4363-4366},
doi = {10.1109/IEMBS.2004.1404214},
pmid = {17271271},
issn = {1557-170X},
abstract = {This paper evaluates an algorithm based on support vector machines to analyze EEG data from the P300 speller brain-computer interface paradigm. We evaluated the performance of this technique on own experimental data from 8 subjects and achieved high transfer rates of up to 97.57 bits/min (mean 47.26 bits/min) within subjects. We then investigated how well the classifier generalizes when it is trained on data from a set of several subjects and then applied on data from a new subject to use this BCI in a pretrained fashion. Transfer rates up to 61.04 bits/min were achieved (mean 17.64 bits/min) for this situation indicating an encouraging generalization performance.},
}
@article {pmid17271270,
year = {2004},
author = {Wang, T and Deng, J and He, B},
title = {Classification of motor imagery EEG patterns and their topographic representation.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4359-4362},
doi = {10.1109/IEMBS.2004.1404213},
pmid = {17271270},
issn = {1557-170X},
abstract = {We have developed a single trial motor imagery (MI) classification strategy for the brain computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from EEG rhythmic components as the feature description. The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which formed the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process were used to synthesize the contributions at the time-frequency domains. The overall classification accuracies for three selected human subjects performing left or right hand movement imagery tasks, were about 87 percent in the ten-fold cross validation without rejecting trials. The loci of motor imagery activity were shown in the spatial topography of differential mode patterns over the sensorimotor area. The present method promises to provide a useful alternative as a general purpose classification procedure for motor imagery classification.},
}
@article {pmid17271181,
year = {2004},
author = {Hu, J and Si, J and Olson, BP and He, J},
title = {Principle component feature detector for motor cortical control.},
journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference},
volume = {2004},
number = {},
pages = {4021-4024},
doi = {10.1109/IEMBS.2004.1404123},
pmid = {17271181},
issn = {1557-170X},
abstract = {Principle component analysis (PCA) was performed on recorded neuronal action potentials from neural ensembles in rat's motor cortex when the rat was involved in a closed-loop real-time brain machine interface (BCI). The implanted rat was placed in a conditioning chamber, but freely moving, to decide which one of the two paddles should be activated to shift the light to the center. It is found that the principle component feature vectors revealed the importance of individual neurons and their temporal dynamics in relation to the intention of activating either left or right paddle. In addition, the first principle component feature has much higher discriminative capability than others although it represents only a few percentage of the total variance. Using the first principle component with the Bayes classifier achieved 90% classification accuracy, which is comparable with the accuracy obtained by a more sophisticated high performance support vector classifiers.},
}
@article {pmid17270445,
year = {2007},
author = {Houssami, N and Boyages, J and Stuart, K and Brennan, M},
title = {Quality of breast imaging reports falls short of recommended standards.},
journal = {Breast (Edinburgh, Scotland)},
volume = {16},
number = {3},
pages = {271-279},
doi = {10.1016/j.breast.2006.11.006},
pmid = {17270445},
issn = {0960-9776},
mesh = {Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/*diagnostic imaging ; Communication ; Community Health Services ; Female ; Humans ; *Mammography ; Medical Audit ; Medical Records/*standards ; Middle Aged ; Radiology Department, Hospital ; *Ultrasonography, Mammary ; },
abstract = {Initial diagnosis and treatment of women with breast cancer is based on the imaging findings. Anecdotal experience suggests that the quality of breast imaging reports is variable; however, systematic evaluation of the content of reports has not been documented to date. We present an audit of the breast imaging reports of all new breast cancer cases referred to a multidisciplinary breast centre during 2004, based on 244 imaging reports from 253 cases. We focus on the quality of imaging reports from the perspective of completeness, concordance with standards, and provision of information considered relevant to clinical decision-making. The audit shows that many reports do not provide key information, and that there are substantial variations in the quality of reports between breast screening services (as part of a coordinated national programme) and community-based radiology services. About one-quarter of all reports do not provide an imaging diagnosis, and only half of all imaging reports are concordant with standards for structured reporting. The least reported variables were breast density category (reported in 24%), lesion depth (37%), lesion shape (55% for mammography, 39% for ultrasound), and location (59%). The most frequently provided information was mammography lesion type (99.6%), sonographic lesion size (90.4%), and recommendation for further investigation (89%). The vast majority of reports from screening services used structured reporting, and these were more likely to provide the information recommended in standards than were reports from community-based radiologists. This work indicates that the quality (content and completeness) of breast imaging reports, particularly community-based radiology reports, is not in line with standards. The clinical implications of these findings warrant further study.},
}
@article {pmid17255164,
year = {2007},
author = {Wolpaw, JR},
title = {Brain-computer interfaces as new brain output pathways.},
journal = {The Journal of physiology},
volume = {579},
number = {Pt 3},
pages = {613-619},
pmid = {17255164},
issn = {0022-3751},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Ataxia/*rehabilitation ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Efferent Pathways/*physiology ; Humans ; Movement/physiology ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control.},
}
@article {pmid17240328,
year = {2007},
author = {Lemon, R},
title = {The neurochip: promoting plasticity with a neural implant.},
journal = {Current biology : CB},
volume = {17},
number = {2},
pages = {R54-5},
doi = {10.1016/j.cub.2006.12.017},
pmid = {17240328},
issn = {0960-9822},
mesh = {Animals ; *Electrodes, Implanted ; Motor Cortex/*physiology ; Neuronal Plasticity/*physiology ; Primates ; },
abstract = {A new discovery suggests that converting the brain's own natural activity into electrical stimuli that are delivered back into another brain region can induce long-term plastic change. This discovery could provide a powerful and useful addition to therapeutic uses of brain-machine interfaces.},
}
@article {pmid17234696,
year = {2007},
author = {Birbaumer, N and Cohen, LG},
title = {Brain-computer interfaces: communication and restoration of movement in paralysis.},
journal = {The Journal of physiology},
volume = {579},
number = {Pt 3},
pages = {621-636},
pmid = {17234696},
issn = {0022-3751},
support = {//Intramural NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Humans ; Movement/*physiology ; Paralysis/*rehabilitation ; *User-Computer Interface ; },
abstract = {The review describes the status of brain-computer or brain-machine interface research. We focus on non-invasive brain-computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.},
}
@article {pmid17234689,
year = {2007},
author = {Fetz, EE},
title = {Volitional control of neural activity: implications for brain-computer interfaces.},
journal = {The Journal of physiology},
volume = {579},
number = {Pt 3},
pages = {571-579},
pmid = {17234689},
issn = {0022-3751},
support = {NS12542/NS/NINDS NIH HHS/United States ; P51 RR000166/RR/NCRR NIH HHS/United States ; R37 NS012542/NS/NINDS NIH HHS/United States ; RR00166/RR/NCRR NIH HHS/United States ; R01 NS012542/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Biofeedback, Psychology/*instrumentation ; Bionics/instrumentation ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Humans ; *User-Computer Interface ; Volition/*physiology ; },
abstract = {Successful operation of brain-computer interfaces (BCI) and brain-machine interfaces (BMI) depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviours, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized.},
}
@article {pmid17234384,
year = {2007},
author = {Cho, J and Paiva, AR and Kim, SP and Sanchez, JC and Príncipe, JC},
title = {Self-organizing maps with dynamic learning for signal reconstruction.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {20},
number = {2},
pages = {274-284},
doi = {10.1016/j.neunet.2006.12.002},
pmid = {17234384},
issn = {0893-6080},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; Brain/cytology/*physiology ; Humans ; Information Storage and Retrieval ; *Learning ; Models, Neurological ; Neural Networks, Computer ; Neurons/physiology ; *Nonlinear Dynamics ; *Numerical Analysis, Computer-Assisted ; Pattern Recognition, Automated/*methods ; Rats ; },
abstract = {Wireless Brain Machine Interface (BMI) communication protocols are faced with the challenge of transmitting the activity of hundreds of neurons which requires large bandwidth. Previously a data compression scheme for neural activity was introduced based on Self Organizing Maps (SOM). In this paper we propose a dynamic learning rule for improved training of the SOM on signals with sparse events which allows for more representative prototype vectors to be found, and consequently better signal reconstruction. This work was developed with BMI applications in mind and therefore our examples are geared towards this type of signals. The simulation results show that the proposed strategy outperforms conventional vector quantization methods for spike reconstruction.},
}
@article {pmid17229403,
year = {2007},
author = {Müller-Putz, GR and Zimmermann, D and Graimann, B and Nestinger, K and Korisek, G and Pfurtscheller, G},
title = {Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients.},
journal = {Brain research},
volume = {1137},
number = {1},
pages = {84-91},
doi = {10.1016/j.brainres.2006.12.052},
pmid = {17229403},
issn = {0006-8993},
mesh = {Adolescent ; Adult ; *Beta Rhythm ; Brain Mapping ; Evoked Potentials, Motor/*physiology ; Female ; *Foot ; Humans ; *Intention ; Male ; Middle Aged ; Movement/*physiology ; Paraplegia/*physiopathology/*psychology ; },
abstract = {A number of electroencephalographic (EEG) studies report on motor event-related desynchronization and synchronization (ERD/ERS) in the beta band, i.e. a decrease and increase of spectral amplitudes of central beta rhythms in the range from 13 to 35 Hz. Following an ERD that occurs shortly before and during the movement, bursts of beta oscillations (beta ERS) appear within a 1-s interval after movement offset. Such a post-movement beta ERS has been reported after voluntary hand movements, passive movements, movement imagination, and also after movements induced by functional electrical stimulation. The present study compares ERD/ERS patterns in paraplegic patients (suffering from a complete spinal cord injury) and healthy subjects during attempted (active) and passive foot movements. The aim of this work is to address the question, whether patients do have the same focal beta ERD/ERS pattern during attempted foot movement as healthy subjects do. The results showed midcentral-focused beta ERD/ERS patterns during passive, active, and imagined foot movements in healthy subjects. This is in contrast to a diffuse and broad distributed ERD/ERS pattern during attempted foot movements in patients. Only one patient showed a similar ERD/ERS pattern. Furthermore, no significant ERD/ERS patterns during passive foot movement in the group of the paraplegics could be found.},
}
@article {pmid17196832,
year = {2007},
author = {Sitaram, R and Zhang, H and Guan, C and Thulasidas, M and Hoshi, Y and Ishikawa, A and Shimizu, K and Birbaumer, N},
title = {Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface.},
journal = {NeuroImage},
volume = {34},
number = {4},
pages = {1416-1427},
doi = {10.1016/j.neuroimage.2006.11.005},
pmid = {17196832},
issn = {1053-8119},
mesh = {Adult ; Brain/anatomy &amp; histology/*physiology/physiopathology ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Motor Activity ; Motor Cortex/*physiology ; Motor Neuron Disease/physiopathology ; Reference Values ; Spectrophotometry, Infrared/methods ; *User-Computer Interface ; },
abstract = {There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.},
}
@article {pmid17187470,
year = {2007},
author = {Ohnishi, K and Weir, RF and Kuiken, TA},
title = {Neural machine interfaces for controlling multifunctional powered upper-limb prostheses.},
journal = {Expert review of medical devices},
volume = {4},
number = {1},
pages = {43-53},
doi = {10.1586/17434440.4.1.43},
pmid = {17187470},
issn = {1743-4440},
support = {R01 EB001672/EB/NIBIB NIH HHS/United States ; },
mesh = {*Amputation, Surgical ; Arm/surgery ; Artificial Limbs/*trends ; Brain/physiology ; Electromyography ; Electrophysiology ; Humans ; Neural Pathways ; *Pattern Recognition, Automated ; Peripheral Nerves/physiology ; Prosthesis Design/*trends ; Psychomotor Performance ; *Signal Processing, Computer-Assisted ; Technology Assessment, Biomedical ; },
abstract = {This article investigates various neural machine interfaces for voluntary control of externally powered upper-limb prostheses. Epidemiology of upper limb amputation, as well as prescription and follow-up studies of externally powered upper-limb prostheses are discussed. The use of electromyographic interfaces and peripheral nerve interfaces for prosthetic control, as well as brain machine interfaces suitable for prosthetic control, are examined in detail along with available clinical results. In addition, studies on interfaces using muscle acoustic and mechanical properties and the problem of interfacing sensory information to the nervous system are discussed.},
}
@article {pmid17169606,
year = {2007},
author = {Fatourechi, M and Bashashati, A and Ward, RK and Birch, GE},
title = {EMG and EOG artifacts in brain computer interface systems: A survey.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {118},
number = {3},
pages = {480-494},
doi = {10.1016/j.clinph.2006.10.019},
pmid = {17169606},
issn = {1388-2457},
mesh = {Algorithms ; *Artifacts ; Brain/*physiology ; Computer Simulation ; *Electromyography ; *Electrooculography ; Humans ; *User-Computer Interface ; },
abstract = {It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts.},
}
@article {pmid17153207,
year = {2006},
author = {Brunner, C and Scherer, R and Graimann, B and Supp, G and Pfurtscheller, G},
title = {Online control of a brain-computer interface using phase synchronization.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {12 Pt 1},
pages = {2501-2506},
doi = {10.1109/TBME.2006.881775},
pmid = {17153207},
issn = {0018-9294},
mesh = {Adolescent ; Adult ; *Algorithms ; Artificial Intelligence ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; *Man-Machine Systems ; Online Systems ; Pattern Recognition, Automated/methods ; *User-Computer Interface ; },
abstract = {Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.},
}
@article {pmid17152442,
year = {2006},
author = {Lin, Z and Zhang, C and Wu, W and Gao, X},
title = {Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {12 Pt 2},
pages = {2610-2614},
doi = {10.1109/tbme.2006.886577},
pmid = {17152442},
issn = {0018-9294},
mesh = {Algorithms ; *Artificial Intelligence ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Statistics as Topic ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.},
}
@article {pmid17145129,
year = {2007},
author = {Valentine, H and Chen, Y and Guo, H and McCormick, J and Wu, Y and Sezen, SF and Hoke, A and Burnett, AL and Steiner, JP},
title = {Neuroimmunophilin ligands protect cavernous nerves after crush injury in the rat: new experimental paradigms.},
journal = {European urology},
volume = {51},
number = {6},
pages = {1724-1731},
pmid = {17145129},
issn = {0302-2838},
support = {P01 MH070056/MH/NIMH NIH HHS/United States ; P01 MH070056-010003/MH/NIMH NIH HHS/United States ; R01 DK064679/DK/NIDDK NIH HHS/United States ; DK064679/DK/NIDDK NIH HHS/United States ; },
mesh = {Administration, Oral ; Analysis of Variance ; Animals ; Injections, Intraperitoneal ; Ligands ; Male ; *Nerve Crush ; Nerve Regeneration/*drug effects ; Penis/*injuries/*innervation ; *Peripheral Nerve Injuries ; Prostatectomy/adverse effects ; Pyrrolidines/administration &amp; dosage/*pharmacology ; Rats ; Rats, Sprague-Dawley ; Recovery of Function ; Tacrolimus/administration &amp; dosage/*pharmacology ; },
abstract = {OBJECTIVES: We investigated the effects of the orally bioavailable non-immunosuppressive immunophilin ligand GPI 1046 (GPI) on erectile function and cavernous nerve (CN) histology following unilateral or bilateral crush injury (UCI, BCI, respectively) of the CNs.<br><br>METHODS: Adult male Sprague-Dawley rats were administered GPI 15 mg/kg intraperitoneally (ip) or 30 mg/kg orally (po), FK506 1 mg/kg, ip, or vehicle controls for each route of administration just prior to UCI or BCI and daily up to 7 d following injury. At day 1 or 7 of treatment, erectile function induced by CN electrical stimulation was measured, and electron microscopic analysis of the injured CN was performed.<br><br>RESULTS: Intraperitoneal administration of GPI to rats with injured CN protected erectile function, in a fashion similar to the prototypic immunophilin ligand FK506, compared with vehicle-treated animals (93%+/-9% vs. 70%+/-5% vs. 45%+/-1%, p<0.01, respectively). Oral administration of GPI elicited the same level of significant protection from CN injury. GPI administered po at 30 mg/kg/d, dosing either once daily or four times daily with 7.5 mg/kg, provided nearly complete protection of erectile function. In a more severe BCI model, po administration of GPI maintained erectile function at 24 h after CN injury. Ultrastructural analysis of injured CNs indicated that GPI administered at the time of CN injury prevents degeneration of about 83% of the unmyelinated axons at 7 d after CN injury.<br><br>CONCLUSIONS: The orally administered immunophilin ligand GPI neuroprotects CNs and maintains erectile function in rats under various conditions of CN crush injury.},
}
@article {pmid17140048,
year = {2006},
author = {Martínez-Consuegra, N and Baquera-Heredia, J and de León-Bojorge, B and Padilla-Rodríguez, A and Hidalgo, CO},
title = {[Expression of p53 and BCI-2 as prognostic markers and for anatomical location in gastrointestinal stromal tumors (GIST). Clinico-pathological and immunohistochemistry study of 19 cases].},
journal = {Revista de gastroenterologia de Mexico},
volume = {71},
number = {3},
pages = {269-278},
pmid = {17140048},
issn = {0375-0906},
mesh = {Adult ; Aged ; Aged, 80 and over ; Female ; Gastrointestinal Stromal Tumors/*metabolism/*pathology ; Guanine Nucleotide Exchange Factors/*biosynthesis ; Humans ; Immunohistochemistry ; Intestinal Neoplasms/*metabolism/*pathology ; Male ; Middle Aged ; Prognosis ; Proto-Oncogene Proteins c-bcl-2/*biosynthesis ; Stomach Neoplasms/*metabolism/*pathology ; Ubiquitin-Protein Ligases ; },
abstract = {OBJECTIVE: To correlate the expression of p53 and BCl-2 with the clinical outcome and anatomic location of the gastrointestinal stromal tumours (GIST).<br><br>BACKGROUND DATA: The GIST are the most common nonepithelial neoplasm of the gastrointestinal tract. In spite of the existence of a wide range of predictive factors, their clinical outcome is unpredictable. There are several studies that correlate the expression of p53 and Bcl-2 with the clinical outcome and anatomic location of the GIST.<br><br>METHODS: We obtained 19 cases from the archives of the Department of Pathology of the ABC Medical Center, in Mexico City. GIST were classified into risk groups according to the Fletcher et al. classification. We performed an immunohistochemestry panel including CD117, CD34, actin, desmin, P-S100, p53 and BCl-2 and correlated their expression to the risk group and anatomical site of the tumors.<br><br>RESULTS: There was less expression of p53 in the gastric tumors (27%) than in small bowel tumors (100%). There was greater expression of p53 in the high-risk tumors than in the very low-risk ones, regardless of the anatomical site. Bcl-2 expression was more expressed in the small intestine tumors (100%) than in those located in the duodenum (50%) The high risk tumors showed slightly more expression of Bcl-2 than the low risk ones (89% vs. 100%), despite the anatomical location.<br><br>CONCLUSIONS: Both, p53 and Bcl-2 are important markers to establish the anatomical site of GIST and are also helpful to predict the clinical behavior of these tumors.},
}
@article {pmid17139517,
year = {2007},
author = {Vidaurre, C and Scherer, R and Cabeza, R and Schlögl, A and Pfurtscheller, G},
title = {Study of discriminant analysis applied to motor imagery bipolar data.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {45},
number = {1},
pages = {61-68},
pmid = {17139517},
issn = {0140-0118},
mesh = {Brain/physiology ; *Discriminant Analysis ; Humans ; *Imagination ; Movement ; *User-Computer Interface ; },
abstract = {We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain-computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.},
}
@article {pmid17137212,
year = {2006},
author = {Kimura, N and Yonemoto, S and Machiguchi, T and Li, X and Kimura, H and Yoshida, H},
title = {Synthetic/secreting and apoptotic phenotypes in renal biopsy tissues from hypertensive nephrosclerosis patients.},
journal = {Hypertension research : official journal of the Japanese Society of Hypertension},
volume = {29},
number = {8},
pages = {573-580},
doi = {10.1291/hypres.29.573},
pmid = {17137212},
issn = {0916-9636},
mesh = {Actins/metabolism ; Adult ; Aged ; Apoptosis ; Biomarkers/metabolism ; Collagen Type III/metabolism ; Female ; Humans ; Hypertension/*pathology/physiopathology ; Kidney/metabolism/*pathology/physiopathology ; Kidney Glomerulus/metabolism ; Male ; Middle Aged ; Nephrosclerosis/*pathology/physiopathology ; Phenotype ; Proto-Oncogene Proteins c-bcl-2/metabolism ; bcl-2-Associated X Protein/metabolism ; },
abstract = {The major glomerular abnormalities in hypertensive nephrosclerosis are described as glomerular obsolescence (GO), glomerulosclerosis (GS), and glomerular collapse (GC). However, glomerular cellular changes caused by hypertensive insults have not been well analyzed. Using an immunoenzyme method, we examined eleven biopsy samples from patients with hypertensive nephrosclerosis for two synthetic and secreting phenotypes, a-smooth muscle actin (alpha-SMA) and collagen type III (Col. III), and two apoptotic phenotypes, pro-apoptotic molecule Bax and anti-apoptotic molecule BcI-2. Together with the glomerular and vascular changes and interstitial fibrosis (IF) area, the results were scored quantitatively and semi-quantitatively and compared to the clinical findings, which included systolic blood pressure (SBP), mean arterial pressure (MAP), serum creatinine levels (sCr) and creatinine clearance (Ccr), using univariate and multivariate analyses. As a result, GS was frequently observed in the mild-to-moderate hypertensive group (140 < or = SBP<180 mmHg), whereas GC was positively correlated with SBP. Furthermore, there was a positive correlation of GS with mesangial alpha-SMA and Col. III, suggesting that GS was the reflection of these synthetic and secreting phenotypic changes in mesangial cells. Endothelial Bax was positively correlated with Ccr (p<0.01); in contrast, podocytic Bax was positively correlated with sCr (p<0.05) and showed a tendency to correlate with MAP (p=0.054). In conclusion, these findings support the view that mesangial synthetic and secreting phenotypic changes may be a reflection of cellular activation caused by mild-to-moderate hypertension and that apoptotic phenotypic expression in podocytes, rather than endothelial cells, may be related to the development of a severe form of hypertensive nephrosclerosis.},
}
@article {pmid17133383,
year = {2007},
author = {LaConte, SM and Peltier, SJ and Hu, XP},
title = {Real-time fMRI using brain-state classification.},
journal = {Human brain mapping},
volume = {28},
number = {10},
pages = {1033-1044},
pmid = {17133383},
issn = {1065-9471},
support = {R01EB002009/EB/NIBIB NIH HHS/United States ; R21NS050183-01/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Arousal/physiology ; Biofeedback, Psychology/*physiology ; Brain/anatomy &amp; histology/*physiology ; Brain Mapping/*methods ; Cerebrovascular Circulation/physiology ; Cognition/*physiology ; Evoked Potentials/physiology ; Feedback/physiology ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/*methods ; Male ; Middle Aged ; Motor Activity/physiology ; Neuropsychological Tests ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Time Factors ; User-Computer Interface ; },
abstract = {We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed approximately 80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness.},
}
@article {pmid17133325,
year = {2006},
author = {Santana-Vargas, AD and Pérez, ML and Ostrosky-Solís, F},
title = {[Communication based on the P300 component of event-related potentials: a proposal for a matrix with images].},
journal = {Revista de neurologia},
volume = {43},
number = {11},
pages = {653-658},
pmid = {17133325},
issn = {0210-0010},
mesh = {Adult ; Brain/physiology ; *Communication Devices for People with Disabilities ; *Data Display ; Electroencephalography/*instrumentation ; Equipment Design ; *Event-Related Potentials, P300 ; Female ; Humans ; Image Processing, Computer-Assisted/*methods ; Male ; *User-Computer Interface ; },
abstract = {INTRODUCTION: For more than two decades, several research groups have tried to build a device called "brain computer interface" (BCI) to make it available for people having several disabilities such as the locked in syndrome through the use of the recording of electroencephalography activity while the patients are being visually stimulated. AIM. To obtain a P300 component elicited by intensifications of images arranged in a matrix in an oddball paradigm in two selection modes: assigned and free.<br><br>SUBJECTS AND METHODS: A 5 x 5 matrix for communication purposes was used to visually stimulate 12 volunteers while their event related potentials were recorded in three leads (Fz, Cz and Pz). Off-line analyses were performed to obtain the P300 component which was elicited by targets images intensified randomly by rows or columns.<br><br>RESULTS: In both modalities assigned and free, all volunteers generated a reliable P300 component. Confirmation of the selected images was made through a comparison of the P300 when each target, row and column matched. In the free selection mode, higher amplitude and a broader activation including frontal leads was observed. No significant differences in the P300 latency were found.<br><br>CONCLUSION: In all volunteers the elicited P300 allows the identification of the selected images in the 5 x 5 matrix. In the present study the use of drawings representing ideas instead of letters might increase the communication rate in a P300-based BCI.},
}
@article {pmid17124332,
year = {2006},
author = {Prasad, A and Sahin, M},
title = {Extraction of motor activity from the cervical spinal cord of behaving rats.},
journal = {Journal of neural engineering},
volume = {3},
number = {4},
pages = {287-292},
pmid = {17124332},
issn = {1741-2560},
support = {R21 HD056963/HD/NICHD NIH HHS/United States ; R21 HD056963-01A2/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Behavior, Animal/*physiology ; Data Interpretation, Statistical ; Electrodes, Implanted ; Electrophysiology ; Forelimb/physiology ; Joints/physiology ; Linear Models ; Male ; Microelectrodes ; Motor Activity/*physiology ; Rats ; Rats, Long-Evans ; Spinal Cord/anatomy &amp; histology/*physiology ; },
abstract = {Injury at the cervical region of the spinal cord results in the loss of the skeletal muscle control from below the shoulders and hence causes quadriplegia. The brain-computer interface technique is one way of generating a substitute for the lost command signals in these severely paralyzed individuals using the neural signals from the brain. In this study, we are investigating the feasibility of an alternative method where the volitional signals are extracted from the cervical spinal cord above the point of injury. A microelectrode array assembly was implanted chronically at the C5-C6 level of the spinal cord in rats. Neural recordings were made during the face cleaning behavior with forelimbs as this task involves cyclic forelimb movements and does not require any training. The correlation between the volitional motor signals and the elbow movements was studied. Linear regression technique was used to reconstruct the arm movement from the rectified-integrated version of the principal neural components. The results of this study demonstrate the feasibility of extracting the motor signals from the cervical spinal cord and using them for reconstruction of the elbow movements.},
}
@article {pmid17124328,
year = {2006},
author = {Yang, BH and Yan, GZ and Yan, RG and Wu, T},
title = {Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition.},
journal = {Journal of neural engineering},
volume = {3},
number = {4},
pages = {251-256},
doi = {10.1088/1741-2560/3/4/001},
pmid = {17124328},
issn = {1741-2560},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Computers ; Electroencephalography/*instrumentation/statistics &amp; numerical data ; Entropy ; Female ; Humans ; Imagination/physiology ; Male ; Motor Cortex/physiology ; Motor Skills ; Movement/physiology ; *User-Computer Interface ; },
abstract = {A method based on wavelet packet best basis decomposition (WPBBD) is investigated for the purpose of extracting features of electroencephalogram signals produced during motor imagery tasks in brain-computer interfaces. The method includes the following three steps. (1) Original signals are decomposed by wavelet packet transform (WPT) and a wavelet packet library can be formed. (2) The best basis for classification is selected from the library. (3) Subband energies included in the best basis are used as effective features. Three different motor imagery tasks are discriminated using the features. The WPBBD produces a 70.3% classification accuracy, which is 4.2% higher than that of the existing wavelet packet method.},
}
@article {pmid17099508,
year = {2006},
author = {Netto, FA and Tien, H and Hamilton, P and Rizoli, SB and Chu, P and Maggisano, R and Brenneman, F and Tremblay, LN},
title = {Diagnosis and outcome of blunt caval injuries in the modern trauma center.},
journal = {The Journal of trauma},
volume = {61},
number = {5},
pages = {1053-1057},
doi = {10.1097/01.ta.0000241148.50832.87},
pmid = {17099508},
issn = {0022-5282},
mesh = {Abdomen/diagnostic imaging ; Adult ; Aged ; Aorta/injuries ; Female ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Retrospective Studies ; Tomography, X-Ray Computed ; Trauma Centers ; Treatment Outcome ; Ultrasonography ; Vena Cava, Inferior/*injuries ; Wounds, Nonpenetrating/*diagnosis/mortality/therapy ; },
abstract = {BACKGROUND: Blunt vena caval injury (BCI) is uncommon with only a few published reports in the literature. Recently, with high resolution computed tomography (CT) scan imaging signs of caval injury are sometimes found in hemodynamically stable patients. The purpose of this study was to assess the current course of patients with BCI.<br><br>METHODS: Retrospective review of all patients with BCI treated at a Regional Trauma Center from April 1999 to May 2005. Data collected included demographics, mechanism of injury, associated injuries, diagnostic investigations, surgical findings, and outcomes.<br><br>RESULTS: During the 6-year study period, 10 patients presented with BCI (age 42 +/- 19 years; 70% mortality; Injury Severity Score 39 +/- 15). The spectrum of vena cava injury ranged from an intimal flap to extensive destruction. Six of the seven deaths were secondary to exsanguination and one secondary to severe brain injury. Four patients presented with refractory shock and were taken emergently to surgery (all died). Six patients responded to fluid resuscitation and underwent CT imaging (three out of six survived). Although active venous contrast extravasation was not seen in any patient, all six had indirect signs on CT suggestive of BCI. Overall, the diagnosis of BCI was confirmed at surgery in nine patients. The remaining patient had an intimal flap and contained pericaval hematoma confirmed by ultrasound, and was successfully managed nonoperatively.<br><br>CONCLUSIONS: The spectrum of BCI ranges from intimal flaps to extensive destruction. CT imaging may not diagnose or may underestimate the severity of BCI. Stable patients with intimal flaps and contained hematoma may be successfully managed nonoperatively.},
}
@article {pmid17096429,
year = {2006},
author = {Campbell, CJ},
title = {Lethal intragroup aggression by adult male spider monkeys (Ateles geoffroyi).},
journal = {American journal of primatology},
volume = {68},
number = {12},
pages = {1197-1201},
doi = {10.1002/ajp.20305},
pmid = {17096429},
issn = {0275-2565},
mesh = {Age Factors ; *Aggression ; Animals ; Atelinae/*psychology ; Cooperative Behavior ; Female ; Male ; Wounds and Injuries/etiology ; },
abstract = {I report three cases of coalitionary aggression by adult male black-handed spider monkeys (Ateles geoffroyi) against subadult males within their community on Barro Colorado Island (BCI), Panama. Two of these cases were followed by the disappearance and presumed death of the victim. Similar behavior was recently reported by Valero et al. [in press], who suggested that this behavior may be the result of intense male reproductive competition. Like the single instance they reported, the cases I report all occurred when the operational sex ratio was approximately 1:1, which suggests that intense competition among males for access to reproductively viable females may be a contributing factor. Additionally the very low density of spider monkeys on BCI may play a significant role in the occurrence of this lethal aggression. Large numbers of adult males are not necessary to protect a territorial boundary against neighboring groups, and additional males may act merely as mating competition.},
}
@article {pmid17095557,
year = {2007},
author = {Dobkin, BH},
title = {Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation.},
journal = {The Journal of physiology},
volume = {579},
number = {Pt 3},
pages = {637-642},
pmid = {17095557},
issn = {0022-3751},
mesh = {Brain/physiology ; *Communication Devices for People with Disabilities ; Humans ; Movement/*physiology ; Neuronal Plasticity/*physiology ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.},
}
@article {pmid17082507,
year = {2006},
author = {Karim, AA and Hinterberger, T and Richter, J and Mellinger, J and Neumann, N and Flor, H and Kübler, A and Birbaumer, N},
title = {Neural internet: Web surfing with brain potentials for the completely paralyzed.},
journal = {Neurorehabilitation and neural repair},
volume = {20},
number = {4},
pages = {508-515},
doi = {10.1177/1545968306290661},
pmid = {17082507},
issn = {1545-9683},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation ; Brain/*physiology ; Cognition/physiology ; Computer User Training/*methods/trends ; Electroencephalography/instrumentation/methods/trends ; Evoked Potentials/*physiology ; Feedback/physiology ; Humans ; Internet/instrumentation/*trends ; Learning/physiology ; Male ; Quadriplegia/physiopathology/*rehabilitation ; Software/trends ; *User-Computer Interface ; },
abstract = {Neural Internet is a new technological advancement in brain-computer interface research, which enables locked-in patients to operate a Web browser directly with their brain potentials. Neural Internet was successfully tested with a locked-in patient diagnosed with amyotrophic lateral sclerosis rendering him the first paralyzed person to surf the Internet solely by regulating his electrical brain activity. The functioning of Neural Internet and its clinical implications for motor-impaired patients are highlighted.},
}
@article {pmid17076808,
year = {2006},
author = {Birbaumer, N},
title = {Breaking the silence: brain-computer interfaces (BCI) for communication and motor control.},
journal = {Psychophysiology},
volume = {43},
number = {6},
pages = {517-532},
doi = {10.1111/j.1469-8986.2006.00456.x},
pmid = {17076808},
issn = {0048-5772},
mesh = {Action Potentials/physiology ; Amyotrophic Lateral Sclerosis/psychology/rehabilitation ; Brain/*physiology ; *Communication ; Conditioning, Operant/physiology ; Electroencephalography ; Humans ; Movement/*physiology ; Paralysis/physiopathology/psychology/rehabilitation ; Quality of Life ; Seizures/therapy ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.},
}
@article {pmid17073333,
year = {2006},
author = {Dornhege, G and Blankertz, B and Krauledat, M and Losch, F and Curio, G and Müller, KR},
title = {Combined optimization of spatial and temporal filters for improving brain-computer interfacing.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {11},
pages = {2274-2281},
doi = {10.1109/TBME.2006.883649},
pmid = {17073333},
issn = {0018-9294},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Man-Machine Systems ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.},
}
@article {pmid17071247,
year = {2006},
author = {Pfurtscheller, G and Neuper, C},
title = {Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {433-437},
doi = {10.1016/S0079-6123(06)59028-4},
pmid = {17071247},
issn = {0079-6123},
mesh = {Brain/*physiology ; *Cortical Synchronization ; Electroencephalography ; Humans ; Stroke Rehabilitation ; *User-Computer Interface ; },
abstract = {ERD/ERS patterns characterize the dynamics of brain oscillations time-locked but not phase-locked to an externally or internally triggered event. Recent studies have shown that ERD/ERS phenomena in narrow frequency bands are remarkably stable over time and across different testing situations. The high reproducibility of ERD/ERS promotes the usefulness of this biometric measure in assessing individual characteristics. In addition to the spatio-temporal patterns of (de)synchronization processes the most reactive frequency components are especially highly subject-specific and, therefore, open up new possibilities for user authentication and person identification. In contrast, ERD/ERS research will continue to be useful in clinical brain-computer interface (BCI) implementation. Promising novel applications of an ERD/ERS based BCI may contribute to enhanced functional recovery and rehabilitation in patients suffering from chronic stroke. According to current therapeutic strategies, feedback-regulated motor imagery could be used to enhance antagonistic ERD/ERS patterns and therewith, support activation of the stroke affected and inhibition of the non-affected, contralesional hemisphere.},
}
@article {pmid17071245,
year = {2006},
author = {McFarland, DJ and Krusienski, DJ and Wolpaw, JR},
title = {Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {411-419},
doi = {10.1016/S0079-6123(06)59026-0},
pmid = {17071245},
issn = {0079-6123},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adaptation, Physiological/physiology ; *Beta Rhythm ; Brain/*physiology ; *Electroencephalography ; Humans ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.},
}
@article {pmid17071244,
year = {2006},
author = {Neuper, C and Müller-Putz, GR and Scherer, R and Pfurtscheller, G},
title = {Motor imagery and EEG-based control of spelling devices and neuroprostheses.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {393-409},
doi = {10.1016/S0079-6123(06)59025-9},
pmid = {17071244},
issn = {0079-6123},
mesh = {*Communication Devices for People with Disabilities ; Cortical Synchronization ; *Electroencephalography ; Feedback/physiology ; Humans ; Imagination/*physiology ; Prostheses and Implants ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. With this, a BCI provides a new non-muscular communication channel, which can be used to assist patients who have highly compromised motor functions. The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex for device control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in single EEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm controlled BCI. The major frequency bands of cortical oscillations considered here are the 8-13 and 15-30 Hz bands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinical applications.},
}
@article {pmid17071243,
year = {2006},
author = {Birbaumer, N and Weber, C and Neuper, C and Buch, E and Haapen, K and Cohen, L},
title = {Physiological regulation of thinking: brain-computer interface (BCI) research.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {369-391},
doi = {10.1016/S0079-6123(06)59024-7},
pmid = {17071243},
issn = {0079-6123},
mesh = {Amyotrophic Lateral Sclerosis/psychology/therapy ; Animals ; Brain/*physiology ; Brain Chemistry/physiology ; Communication ; *Computers ; Humans ; Paralysis/rehabilitation ; Seizures/therapy ; *User-Computer Interface ; },
abstract = {The discovery of event-related desynchronization (ERD) and event-related synchronization (ERS) by Pfurtscheller paved the way for the development of brain-computer interfaces (BCIs). BCIs allow control of computers or external devices with the regulation of brain activity only. Two different research traditions produced two different types of BCIs: invasive BCIs, realized with implanted electrodes in brain tissue and noninvasive BCIs using electrophysiological recordings in humans such as electroencephalography (EEG) and magnetoencephalography (MEG) and metabolic changes such as functional magnetic resonance imaging (fMRI) and near infrared spectroscopy (NIRS). Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials (SCPs), sensorimotor rhythms (SMRs), and P300 and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients. Movement restoration was achieved with noninvasive BCIs based on SMRs control in single cases with spinal cord lesions and chronic stroke. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations from spike patterns and extracellular field potentials. Whether invasive approaches allow superior brain control of motor responses compared to noninvasive BCI with intelligent peripheral devices and electrical muscle stimulation and EMG feedback remains to be demonstrated. The newly developed fMRI-BCIs and NIRS-BCIs offer promise for the learned regulation of emotional disorders and also disorders of small children (in the case of NIRS).},
}
@article {pmid17071238,
year = {2006},
author = {Crone, NE and Sinai, A and Korzeniewska, A},
title = {High-frequency gamma oscillations and human brain mapping with electrocorticography.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {275-295},
doi = {10.1016/S0079-6123(06)59019-3},
pmid = {17071238},
issn = {0079-6123},
support = {R01 NS040596/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/anatomy &amp; histology/diagnostic imaging/*physiology ; *Brain Mapping ; Data Interpretation, Statistical ; *Electroencephalography ; Humans ; Language ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; },
abstract = {Invasive EEG recordings with depth and/or subdural electrodes are occasionally necessary for the surgical management of patients with epilepsy refractory to medications. In addition to their vital clinical utility, electrocorticographic (ECoG) recordings provide an unprecedented opportunity to study the electrophysiological correlates of functional brain activation in greater detail than non-invasive recordings. The proximity of ECoG electrodes to the cortical sources of EEG activity enhances their spatial resolution, as well as their sensitivity and signal-to-noise ratio, particularly for high-frequency EEG activity. ECoG recordings have, therefore, been used to study the event-related dynamics of brain oscillations in a variety of frequency ranges, and in a variety of functional-neuroanatomic systems, including somatosensory and somatomotor systems, visual and auditory perceptual systems, and cortical networks responsible for language. These ECoG studies have confirmed and extended the original non-invasive observations of ERD/ERS phenomena in lower frequencies, and have discovered novel event-related responses in gamma frequencies higher than those previously observed in non-invasive recordings. In particular, broadband event-related gamma responses greater than 60 Hz, extending up to approximately 200 Hz, have been observed in a variety of functional brain systems. The observation of these "high gamma" responses requires a recording system with an adequate sampling rate and dynamic range (we use 1000 Hz at 16-bit A/D resolution) and is facilitated by event-related time-frequency analyses of the recorded signals. The functional response properties of high-gamma activity are distinct from those of ERD/ERS phenomena in lower frequencies. In particular, the timing and spatial localization of high-gamma ERS often appear to be more specific to the putative timing and localization of functional brain activation than alpha or beta ERD/ERS. These findings are consistent with the proposed role of synchronized gamma oscillations in models of neural computation, which have in turn been inspired by observations of gamma activity in animal preparations, albeit at somewhat lower frequencies. Although ECoG recordings cannot directly measure the synchronization of action potentials among assemblies of neurons, they may demonstrate event-related interactions between gamma oscillations in macroscopic local field potentials (LFP) generated by different large-scale populations of neurons engaged by the same functional task. Indeed, preliminary studies suggest that such interactions do occur in gamma frequencies, including high-gamma frequencies, at latencies consistent with the timing of task performance. The neuronal mechanisms underlying high-gamma activity and its unique response properties in humans are still largely unknown, but their investigation through invasive methods is expected to facilitate and expand their potential clinical and research applications, including functional brain mapping, brain-computer interfaces, and neurophysiological studies of human cognition.},
}
@article {pmid17071225,
year = {2006},
author = {Graimann, B and Pfurtscheller, G},
title = {Quantification and visualization of event-related changes in oscillatory brain activity in the time-frequency domain.},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {79-97},
doi = {10.1016/S0079-6123(06)59006-5},
pmid = {17071225},
issn = {0079-6123},
mesh = {Algorithms ; Brain/*physiology ; Cortical Synchronization ; Data Interpretation, Statistical ; Electroencephalography/*statistics &amp; numerical data ; Evoked Potentials/*physiology ; Humans ; Principal Component Analysis ; },
abstract = {In this chapter we review the traditional approach for ERD/ERS quantification and a more recent approach based on wavelet transform. In particular, we address the visualization of these phenomena and the validation of the results through statistical significance testing. Furthermore, we report on preprocessing using independent component analysis (ICA) and introduce a novel ERD/ERS maximization method.},
}
@article {pmid17071221,
year = {2006},
author = {Pfurtscheller, G},
title = {The cortical activation model (CAM).},
journal = {Progress in brain research},
volume = {159},
number = {},
pages = {19-27},
doi = {10.1016/S0079-6123(06)59002-8},
pmid = {17071221},
issn = {0079-6123},
mesh = {Animals ; Cerebral Cortex/*physiology ; Consciousness ; Cortical Synchronization ; Electroencephalography ; Evoked Potentials/physiology ; Humans ; },
abstract = {The Cortical Activation Model (CAM) is an attempt to explain whether an internally or externally paced event reveals an event-related desynchronization (ERD) or event-related synchronization (ERS) in a specific frequency band. It is assumed that the amplitude of network-specific oscillations depends on, in addition to other factors, the number of neurons available for synchronization and the excitability level of neurons and forms a bell-shaped curve with a maximum of oscillatory activity at a certain balance of both factors. Depending on the baseline level of cortical activation (CA) and the location of the "working point" (WP), a sudden change of activation can induce either ERD or ERS in a given area.},
}
@article {pmid17054122,
year = {2007},
author = {Schimel, KA},
title = {Biogas plasticization coupled anaerobic digestion: batch test results.},
journal = {Biotechnology and bioengineering},
volume = {97},
number = {2},
pages = {297-307},
doi = {10.1002/bit.21227},
pmid = {17054122},
issn = {0006-3592},
mesh = {*Anaerobiosis ; Bacteria, Anaerobic/*metabolism ; *Bioreactors ; Diffusion ; *Facility Design and Construction ; Industrial Microbiology/*methods ; Methane/biosynthesis ; Water Purification/*methods ; },
abstract = {Biogas has unique properties for improving the biodegradability of biomass solids during anaerobic digestion (AD). This report presents batch test results of the first investigation into utilizing biogas plasticization to "condition" organic polymers during active digestion of waste activated sludge (WAS). Preliminary design calculations based on polymer diffusion rate limitation are presented. Analysis of the 20 degrees C batch test data determined the first order (k(1)) COD conversion coefficient to be 0.167 day(-1) with a maximum COD utilization rate of 11.25 g L(-1) day(-1). Comparison of these batch test results to typical conventional AD performance parameters showed orders of magnitude improvement. These results show that biogas plasticization during active AD could greatly improve renewable energy yields from biomass waste materials such as MSW RDF, STP sludges, food wastes, animal manure, green wastes, and agricultural crop residuals.},
}
@article {pmid17052970,
year = {2006},
author = {Opala, T and Rzymski, P and Pischel, I and Wilczak, M and Wozniak, J},
title = {Efficacy of 12 weeks supplementation of a botanical extract-based weight loss formula on body weight, body composition and blood chemistry in healthy, overweight subjects--a randomised double-blind placebo-controlled clinical trial.},
journal = {European journal of medical research},
volume = {11},
number = {8},
pages = {343-350},
pmid = {17052970},
issn = {0949-2321},
mesh = {Adult ; Apolipoproteins A/blood ; Blood Glucose/analysis ; Body Composition/*drug effects ; Body Weight ; Cholesterol/blood ; *Dietary Supplements ; Double-Blind Method ; Fasting ; Female ; Humans ; Insulin/blood ; Lipoproteins, HDL/blood ; Lipoproteins, LDL/blood ; Male ; Middle Aged ; Overweight/*drug effects ; Plant Extracts/adverse effects/chemistry/*therapeutic use ; Time Factors ; Treatment Outcome ; Triglycerides/blood ; Weight Loss/*drug effects ; },
abstract = {OBJECTIVE: The aim of this study was to evaluate the efficacy and safety of composite extracts in reducing weight, as the main outcome measure. Secondary measures of the study were body composition change.<br><br>DESIGN: Randomised, double blind, placebo-controlled clinical trial.<br><br>SETTING: Tertiary university clinic.<br><br>SUBJECTS: hundred and five subjects, 5 of them withdrawn consent, 2 drop-outs not related to study preparation.<br><br>INTERVENTION: two tablet per meal concept supposed to generate a "psychological" therapy-like approach during 12 weeks supported by measured physical activity. The tablets 1 (one hour before meals, comprises extracts of Asparagus, Green tea, Black tea, Guarana, Mate and Kidney beans) and 2 (taken half an hour after meals, comprises extracts of Kidney bean pods, Garcinia cambogia, and Chromium yeast) are taken twice daily with two main meals.<br><br>RESULTS: A significant change of the Body Composition Improvement Index (BCI) was observed in the active extract group compared to placebo (p = 0.012). Weight, BMI, waist-to-hip ratio was not statistically different between groups. Body fat loss was greater in active group (p = 0.011) compared to placebo. A weight loss parameter corrected for exercise was introduced and found to be higher in active group (p = 0.046) than in placebo, meaning that the formula was more efficacious, due to a concurrently performed exercise program--a recommended strategy for life style modification.<br><br>CONCLUSIONS: A significant change of the Body Composition Improvement Index and the decrease in body fat was statistically significant in active extract subjects compared to placebo. A change in some outcome measures like: weight, BMI failed to produce significant difference between groups.},
}
@article {pmid17051297,
year = {2006},
author = {Westphal, R and Winkelmann, S},
title = {Sensors, medical image and signal processing. Findings from the Section on Sensor, Signal and Imaging Informatics.},
journal = {Yearbook of medical informatics},
volume = {},
number = {},
pages = {68-71},
pmid = {17051297},
issn = {0943-4747},
mesh = {*Awards and Prizes ; *Diagnostic Imaging ; History, 21st Century ; Humans ; Image Processing, Computer-Assisted ; Man-Machine Systems ; *Medical Informatics/history ; *Medical Informatics Applications ; Societies, Medical ; },
abstract = {OBJECTIVES: To summarize current excellent research in the field of sensor, signal and imaging informatics.<br><br>METHODS: Synopsis of the articles selected for the IMIA Yearbook of Medical Informatics 2006.<br><br>RESULTS: The selection process for this yearbook's section 'Sensor, signal and imaging informatics' results in six excellent articles, representing research in five different nations. We selected a cross section of the wide range of application, ranging from model based image segmentation, image retrieval and data mining, image based diagnosis assistance, bio-impedance based skin cancer screening, brain computer interfaces to MRI based computational models for fluid-structure-interactions.<br><br>CONCLUSIONS: The selected articles indicate a small but meaningful extract from the research field of sensors, signal and image processing, which has a wide range of applications in medical informatics. The articles present excellent research with a possibility of having high relevance for the future in patient care.},
}
@article {pmid17051296,
year = {2006},
author = {Lehmann, TM and Aach, T and Witte, H},
title = {Sensor, signal, and image informatics - state of the art and current topics.},
journal = {Yearbook of medical informatics},
volume = {},
number = {},
pages = {57-67},
pmid = {17051296},
issn = {0943-4747},
mesh = {*Diagnostic Imaging ; Humans ; *Image Processing, Computer-Assisted ; Man-Machine Systems ; *Medical Informatics Applications ; },
abstract = {OBJECTIVES: The number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.<br><br>METHODS: Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CT-based diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.<br><br>RESULTS: The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.<br><br>CONCLUSIONS: Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.},
}
@article {pmid17049927,
year = {2006},
author = {Martinović, Z and Simonović, P and Djokić, R},
title = {Preventing depression in adolescents with epilepsy.},
journal = {Epilepsy &amp; behavior : E&amp;B},
volume = {9},
number = {4},
pages = {619-624},
doi = {10.1016/j.yebeh.2006.08.017},
pmid = {17049927},
issn = {1525-5050},
mesh = {Adaptation, Psychological ; Adolescent ; Adult ; *Cognitive Behavioral Therapy ; *Counseling ; Depression/diagnosis/etiology/*prevention &amp; control ; Epilepsy/complications/*psychology ; Female ; Humans ; Male ; Risk Factors ; },
abstract = {PURPOSE: The goal of the work described in this article was to test the possibility of preventing depression among adolescents with epilepsy.<br><br>METHODS: Adolescents with newly diagnosed epilepsy (104 patients) were screened for depression. The risk for depression was increased in 30 (28.8%) patients (mean age 17.4, 60% females) who were randomized into two equal treatment groups: (1) cognitive-behavioral intervention (CBI) group and (2) treatment with counseling as usual (TAU) group. The Beck Depression Inventory (BDI), Center for Epidemiological Study on Depression (CES-D) scale, Hamilton Depression Scale (HAMD), and Quality of Life in Epilepsy Inventory (QOLIE-31) were administered at baseline and during the 9-month follow-up.<br><br>RESULTS: Initial BDI and HAMD scores for the two groups were comparable. Depression was diagnosed during follow-up in three patients in the TAU group. Subthreshold depressive disorder significantly improved at follow-up in the BCI group compared with the TAU group (P<0.05). QOLIE-31 Total scores significantly correlated with both mood improvement and seizure-free state.},
}
@article {pmid17049865,
year = {2006},
author = {Duffau, H},
title = {Brain plasticity: from pathophysiological mechanisms to therapeutic applications.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {13},
number = {9},
pages = {885-897},
doi = {10.1016/j.jocn.2005.11.045},
pmid = {17049865},
issn = {0967-5868},
mesh = {Animals ; Brain/anatomy &amp; histology/*physiology ; Brain Injuries/physiopathology/therapy ; Electric Stimulation Therapy/trends ; Humans ; Nerve Regeneration/*physiology ; Neural Pathways/anatomy &amp; histology/injuries/physiology ; Neuronal Plasticity/*physiology ; Postoperative Complications/etiology/physiopathology/prevention &amp; control ; Recovery of Function/*physiology ; Synaptic Transmission/physiology ; },
abstract = {Cerebral plasticity, which is the dynamic potential of the brain to reorganize itself during ontogeny, learning, or following damage, has been widely studied in the last decade, in vitro, in animals, and also in humans since the development of functional neuroimaging. In the first part of this review, the main hypotheses about the pathophysiological mechanisms underlying plasticity are presented. At a microscopic level, modulations of synaptic efficacy, unmasking of latent connections, phenotypic modifications and neurogenesis have been identified. At a macroscopic level, diaschisis, functional redundancies, sensory substitution and morphological changes have been described. In the second part, the behavioral consequences of such cerebral phenomena in physiology, namely the "natural" plasticity, are analyzed in humans. The review concludes on the therapeutic implications provided by a better understanding of these mechanisms of brain reshaping. Indeed, this plastic potential might be 'guided' in neurological diseases, using rehabilitation, pharmacological drugs, transcranial magnetic stimulation, neurosurgical methods, and even new techniques of brain-computer interface - in order to improve the quality of life of patients with damaged nervous systems.},
}
@article {pmid17031007,
year = {2006},
author = {Kaats, GR and Michalek, JE and Preuss, HG},
title = {Evaluating efficacy of a chitosan product using a double-blinded, placebo-controlled protocol.},
journal = {Journal of the American College of Nutrition},
volume = {25},
number = {5},
pages = {389-394},
doi = {10.1080/07315724.2006.10719550},
pmid = {17031007},
issn = {0731-5724},
mesh = {Absorptiometry, Photon ; Adult ; Anti-Obesity Agents/*therapeutic use ; Blood Chemical Analysis ; Body Composition/*drug effects ; Chitosan/*therapeutic use ; Double-Blind Method ; Energy Intake/physiology ; Exercise/physiology ; Female ; Humans ; Male ; Middle Aged ; Obesity/*drug therapy ; Treatment Outcome ; Weight Loss/*drug effects ; },
abstract = {OBJECTIVE: To examine the safety and efficacy of a chitosan dietary supplement on body composition under free-living conditions.<br><br>DESIGN: In a randomized, double-blinded, placebo-controlled dietary intervention protocol, subjects were assigned to a treatment group (TRT), a placebo group (PLA) and a control group (CTL).<br><br>SUBJECTS: A total of 150 overweight adults enrolled; 134 (89.3%) completed the study; 111 (82.8%) were women who were similarly distributed in the three groups.<br><br>INTERVENTION: The TRT group took six 500 mg chitosan capsules per day and both TRT and PLA groups wore pedometers during their waking hours and recorded daily step totals. The CTL group followed weight loss programs of their choice, and took the same baseline and ending tests.<br><br>MEASURES OF OUTCOME: Outcome measures were Dual Energy X-ray Absorptiometry tests, fasting blood chemistries, and self-reported daily activity levels and caloric intakes.<br><br>RESULTS: Compared to CTL, the TRT group lost more weight (-2.8 lbs vs. +0.8 lbs, p < 0.001) and fat mass (-2.6 lbs vs. +0.1 lbs, p = 0.006). Compared to PLA, the TRT group lost more weight (-2.8 lbs. vs. -0.6 lbs, p = 0.03), % fat (-0.8% vs. +0.4%, p = 0.003), fat mass (-2.6 lbs vs. +0.6 lbs, p = 0.001) and had a greater body composition improvement index (BCI) (+2.4 lbs vs. -1.9 lbs, p = 0.002).<br><br>CONCLUSIONS: These data provide evidence for the efficacy of a chitosan compound to facilitate the depletion of excess body fat under free-living conditions with minimal loss of fat-free or lean body mass.},
}
@article {pmid17029033,
year = {2006},
author = {Lee, PL and Hsieh, JC and Wu, CH and Shyu, KK and Chen, SS and Yeh, TC and Wu, YT},
title = {The brain computer interface using flash visual evoked potential and independent component analysis.},
journal = {Annals of biomedical engineering},
volume = {34},
number = {10},
pages = {1641-1654},
doi = {10.1007/s10439-006-9175-8},
pmid = {17029033},
issn = {0090-6964},
mesh = {Adult ; Biomedical Engineering ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography ; *Evoked Potentials, Visual ; Female ; Humans ; Male ; Photic Stimulation ; Principal Component Analysis ; Software Design ; *User-Computer Interface ; },
abstract = {In this study flashing stimuli, such as digits or letters, are displayed on a LCD screen to induce flash visual evoked potentials (FVEPs). The aim of the proposed interface is to generate desired strings while one stares at target stimulus one after one. To effectively extract visually-induced neural activities with superior signal-to-noise ratio, independent component analysis (ICA) is employed to decompose the measured EEG and task-related components are subsequently selected for data reconstruction. In addition, all the flickering sequences are designed to be mutually independent in order to remove the contamination induced by surrounding non-target stimuli from the ICA-recovered signals. Since FVEPs are time-locked and phase-locked to flash onsets of gazed stimulus, segmented epochs from ICA-recovered signals based on flash onsets of gazed stimulus will be sharpen after averaging whereas those based on flash onsets of non-gazed stimuli will be suppressed after averaging. The stimulus inducing the largest averaged FVEPs is identified as the gazed target and corresponding digit or letter is sent out. Five subjects were asked to gaze at each stimulus. The mean detection accuracy resulted from averaging 15 epochs was 99.7%. Another experiment was to generate a specified string '0287513694E'. The mean accuracy and information transfer rates were 83% and 23.06 bits/min, respectively.},
}
@article {pmid17028907,
year = {2006},
author = {Mahmoudi, B and Erfanian, A},
title = {Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {44},
number = {11},
pages = {959-969},
pmid = {17028907},
issn = {0140-0118},
mesh = {Attention ; Brain/*physiology ; Case-Control Studies ; *Electroencephalography ; Humans ; *Imagination ; Learning ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.},
}
@article {pmid17020197,
year = {2006},
author = {Hochberg, LR and Donoghue, JP},
title = {Sensors for brain-computer interfaces.},
journal = {IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine &amp; Biology Society},
volume = {25},
number = {5},
pages = {32-38},
doi = {10.1109/memb.2006.1705745},
pmid = {17020197},
issn = {0739-5175},
mesh = {Biosensing Techniques/*instrumentation/methods ; Brain/*physiopathology ; Brain Mapping/*instrumentation/methods ; Equipment Design ; Evoked Potentials ; Humans ; Neuromuscular Diseases/physiopathology/*rehabilitation ; Therapy, Computer-Assisted/*instrumentation/methods ; *Transducers ; *User-Computer Interface ; },
}
@article {pmid17016572,
year = {2006},
author = {Behr, A and Becker, M},
title = {The telomerisation of 1,3-butadiene and carbon dioxide: process development and optimisation in a continuous miniplant.},
journal = {Dalton transactions (Cambridge, England : 2003)},
volume = {},
number = {38},
pages = {4607-4613},
doi = {10.1039/b608552k},
pmid = {17016572},
issn = {1477-9226},
abstract = {The telomerisation of 1,3-butadiene and carbon dioxide is one of the first homogeneously catalyzed reactions using carbon dioxide as a C1-building block. In this article we describe the process development for a miniplant applying this telomerisation in a continuous scale. Through repeated optimisation of the plant setup combined with parallel laboratory batch experiments the overall space-time-yield of the plant was enhanced significantly.},
}
@article {pmid17010962,
year = {2007},
author = {Ince, NF and Tewfik, AH and Arica, S},
title = {Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification.},
journal = {Computers in biology and medicine},
volume = {37},
number = {4},
pages = {499-508},
doi = {10.1016/j.compbiomed.2006.08.014},
pmid = {17010962},
issn = {0010-4825},
mesh = {*Algorithms ; Cortical Synchronization/classification ; Dominance, Cerebral/physiology ; Electroencephalography/*classification ; Evoked Potentials/*physiology ; Fourier Analysis ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; Linear Models ; Motor Cortex/*physiology ; Psychomotor Performance/*physiology ; *Signal Processing, Computer-Assisted ; Software ; Time Perception/physiology ; *User-Computer Interface ; },
abstract = {We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.},
}
@article {pmid17009489,
year = {2006},
author = {Palaniappan, R},
title = {Utilizing gamma band to improve mental task based brain-computer interface design.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {3},
pages = {299-303},
doi = {10.1109/TNSRE.2006.881539},
pmid = {17009489},
issn = {1534-4320},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Therapy, Computer-Assisted/instrumentation/methods ; *User-Computer Interface ; },
abstract = {A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.},
}
@article {pmid16999577,
year = {2006},
author = {Li, Y and Guan, C},
title = {An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces.},
journal = {Neural computation},
volume = {18},
number = {11},
pages = {2730-2761},
doi = {10.1162/neco.2006.18.11.2730},
pmid = {16999577},
issn = {0899-7667},
mesh = {*Algorithms ; *Brain ; *Electroencephalography ; Humans ; Learning/*physiology ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {For many electroencephalogram (EEG)-based brain-computer interfaces (BCIs), a tedious and time-consuming training process is needed to set parameters. In BCI Competition 2005, reducing the training process was explicitly proposed as a task. Furthermore, an effective BCI system needs to be adaptive to dynamic variations of brain signals; that is, its parameters need to be adjusted online. In this article, we introduce an extended expectation maximization (EM) algorithm, where the extraction and classification of common spatial pattern (CSP) features are performed jointly and iteratively. In each iteration, the training data set is updated using all or part of the test data and the labels predicted in the previous iteration. Based on the updated training data set, the CSP features are reextracted and classified using a standard EM algorithm. Since the training data set is updated frequently, the initial training data set can be small (semi-supervised case) or null (unsupervised case). During the above iterations, the parameters of the Bayes classifier and the CSP transformation matrix are also updated concurrently. In online situations, we can still run the training process to adjust the system parameters using unlabeled data while a subject is using the BCI system. The effectiveness of the algorithm depends on the robustness of CSP feature to noise and iteration convergence, which are discussed in this article. Our proposed approach has been applied to data set IVa of BCI Competition 2005. The data analysis results show that we can obtain satisfying prediction accuracy using our algorithm in the semisupervised and unsupervised cases. The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis.},
}
@article {pmid16967290,
year = {2006},
author = {Sun, S and Zhang, C},
title = {Adaptive feature extraction for EEG signal classification.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {44},
number = {10},
pages = {931-935},
pmid = {16967290},
issn = {0140-0118},
mesh = {Algorithms ; Artificial Intelligence ; Brain/physiology ; Electroencephalography/instrumentation/*methods ; Humans ; Imagination ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted ; },
abstract = {One challenge in the current research of brain-computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.},
}
@article {pmid16921207,
year = {2006},
author = {Ince, NF and Arica, S and Tewfik, A},
title = {Classification of single trial motor imagery EEG recordings with subject adapted non-dyadic arbitrary time-frequency tilings.},
journal = {Journal of neural engineering},
volume = {3},
number = {3},
pages = {235-244},
doi = {10.1088/1741-2560/3/3/006},
pmid = {16921207},
issn = {1741-2560},
mesh = {*Algorithms ; Artificial Intelligence ; Diagnosis, Computer-Assisted/*methods ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) electrode locations of the standard 10/20 system is adaptively segmented in time, over a non-dyadic grid by maximizing the probabilistic distances between expansion coefficients corresponding to left and right hand movement imagery. This is followed by a frequency domain clustering procedure in each adapted time segment to maximize the discrimination power of the resulting time-frequency features. Then, the most discriminant features from the resulting arbitrarily segmented time-frequency plane are sorted. A principal component analysis (PCA) step is applied to reduce the dimensionality of the feature space. This reduced feature set is finally fed to a linear discriminant for classification. The online step simply computes the reduced dimensionality features determined by the offline step and feeds them to the linear discriminant. We provide experimental data to show that the method can adapt to physio-anatomical differences, subject-specific and hemisphere-specific motor imagery patterns. The algorithm was applied to all nine subjects of the BCI Competition 2002. The classification performance of the proposed algorithm varied between 70% and 92.6% across subjects using just two electrodes. The average classification accuracy was 80.6%. For comparison, we also implemented an adaptive autoregressive model based classification procedure that achieved an average error rate of 76.3% on the same subjects, and higher error rates than the proposed approach on each individual subject.},
}
@article {pmid16921204,
year = {2006},
author = {Naeem, M and Brunner, C and Leeb, R and Graimann, B and Pfurtscheller, G},
title = {Seperability of four-class motor imagery data using independent components analysis.},
journal = {Journal of neural engineering},
volume = {3},
number = {3},
pages = {208-216},
doi = {10.1088/1741-2560/3/3/003},
pmid = {16921204},
issn = {1741-2560},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.},
}
@article {pmid16915766,
year = {2006},
author = {Müller-Putz, GR and Scherer, R and Pfurtscheller, G and Rupp, R},
title = {Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {51},
number = {2},
pages = {57-63},
doi = {10.1515/BMT.2006.011},
pmid = {16915766},
issn = {0013-5585},
mesh = {Adult ; Brain/*physiopathology ; Communication ; Electroencephalography/*methods ; Evoked Potentials ; Feedback ; Humans ; Male ; Man-Machine Systems ; *Prostheses and Implants ; Quadriplegia/complications/physiopathology/*rehabilitation ; Spinal Cord Injuries/complications/physiopathology/*rehabilitation ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design. Based on the results of two clinical applications, where 'thought' control of neuroprostheses based on movement imagery in tetraplegic patients with a high spinal cord injury has been established, the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed. The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined.},
}
@article {pmid16904786,
year = {2006},
author = {Keinrath, C and Wriessnegger, S and Müller-Putz, GR and Pfurtscheller, G},
title = {Post-movement beta synchronization after kinesthetic illusion, active and passive movements.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {62},
number = {2},
pages = {321-327},
doi = {10.1016/j.ijpsycho.2006.06.001},
pmid = {16904786},
issn = {0167-8760},
mesh = {Adult ; *Beta Rhythm ; Female ; Humans ; Illusions/*physiology ; Kinesthesis/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Perception/physiology ; Psychomotor Performance/physiology ; Somatosensory Cortex/*physiology ; Vibration ; Volition/physiology ; },
abstract = {After the completion of a voluntary movement or in response to somatosensory stimulation, a short-lasting burst of beta oscillations (post movement beta ERS, beta rebound) can be observed. In the present study, we investigated if this is also true for the illusion of movements, induced by a vibration at 80 Hz on the biceps tendon. We compared the post-movement synchronization of EEG beta rhythms induced by active and passive movements and illusion in eight right-handed healthy subjects. As a result, a short-lasting post-movement beta ERS was present over motor areas after both active and passive and also after illusion of movement in all subjects. These results suggested a possible role of MI and the somatosensory cortex in the somatic perception of limb movement in humans.},
}
@article {pmid16893090,
year = {2006},
author = {Neshige, R and Murayama, N and Tanoue, K and Kurokawa, H and Igasaki, T},
title = {Optimal methods of stimulus presentation and frequency analysis in P300-based brain-computer interfaces for patients with severe motor impairment.},
journal = {Supplements to Clinical neurophysiology},
volume = {59},
number = {},
pages = {35-42},
doi = {10.1016/s1567-424x(09)70009-1},
pmid = {16893090},
issn = {1567-424X},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/rehabilitation/*therapy ; *Communication Devices for People with Disabilities ; Communication Barriers ; Persons with Disabilities ; Discrimination, Psychological ; *Event-Related Potentials, P300 ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/*methods ; *User-Computer Interface ; },
}
@article {pmid16869413,
year = {2006},
author = {Hubbell, SP},
title = {Neutral theory and the evolution of ecological equivalence.},
journal = {Ecology},
volume = {87},
number = {6},
pages = {1387-1398},
doi = {10.1890/0012-9658(2006)87[1387:ntateo]2.0.co;2},
pmid = {16869413},
issn = {0012-9658},
mesh = {*Biodiversity ; *Biological Evolution ; Ecology/methods ; *Models, Biological ; Seeds/physiology ; Trees/*physiology ; },
abstract = {Since the publication of the unified neutral theory in 2001, there has been much discussion of the theory, pro and con. The hypothesis of ecological equivalence is the fundamental yet controversial idea behind neutral theory. Assuming trophically similar species are demographically alike (symmetric) on a per capita basis is only an approximation, but it is equivalent to asking: How many of the patterns of ecological communities are the result of species similarities, rather than of species differences? The strategy behind neutral theory is to see how far one can get with the simplification of assuming ecological equivalence before introducing more complexity. In another paper, I review the empirical evidence that led me to hypothesize ecological equivalence among many of the tree species in the species-rich tropical forest on Barro Colorado Island (BCI). In this paper, I develop a simple model for the evolution of ecological equivalence or niche convergence, using as an example evolution of the suite of life history traits characteristic of shade tolerant tropical tree species. Although the model is simple, the conclusions from it seem likely to be robust. I conclude that ecological equivalence for resource use are likely to evolve easily and often, especially in species-rich communities that are dispersal and recruitment limited. In the case of the BCI forest, tree species are strongly dispersal- and recruitment-limited, not only because of restricted seed dispersal, but also because of low recruitment success due to heavy losses of the seedling stages to predators and pathogens and other abiotic stresses such as drought. These factors and the high species richness of the community strongly reduce the potential for competitive exclusion of functionally equivalent or nearly equivalent species.},
}
@article {pmid16860920,
year = {2006},
author = {Sellers, EW and Krusienski, DJ and McFarland, DJ and Vaughan, TM and Wolpaw, JR},
title = {A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.},
journal = {Biological psychology},
volume = {73},
number = {3},
pages = {242-252},
doi = {10.1016/j.biopsycho.2006.04.007},
pmid = {16860920},
issn = {0301-0511},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Arousal/*physiology ; Attention/*physiology ; Brain Mapping ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; *Electroencephalography ; Event-Related Potentials, P300/*physiology ; Feedback, Psychological ; Female ; Humans ; Male ; Middle Aged ; Motor Neuron Disease/physiopathology/rehabilitation ; Pattern Recognition, Visual/*physiology ; Reaction Time/*physiology ; Signal Processing, Computer-Assisted ; Size Perception/*physiology ; *User-Computer Interface ; },
abstract = {We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.},
}
@article {pmid16859758,
year = {2006},
author = {Lebedev, MA and Nicolelis, MA},
title = {Brain-machine interfaces: past, present and future.},
journal = {Trends in neurosciences},
volume = {29},
number = {9},
pages = {536-546},
doi = {10.1016/j.tins.2006.07.004},
pmid = {16859758},
issn = {0166-2236},
mesh = {Animals ; *Biofeedback, Psychology ; Brain/*physiology ; Brain Diseases/*rehabilitation ; *Computer Systems ; Electrodes, Implanted ; Electroencephalography ; Humans ; Microelectrodes ; Prostheses and Implants ; *Self-Help Devices ; User-Computer Interface ; },
abstract = {Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.},
}
@article {pmid16859333,
year = {2001},
author = {Phillips, CS},
title = {Culture, social minds, and governance in evolution.},
journal = {Politics and the life sciences : the journal of the Association for Politics and the Life Sciences},
volume = {20},
number = {2},
pages = {189-202},
doi = {10.1017/s0730938400005475},
pmid = {16859333},
issn = {0730-9384},
abstract = {In the past quarter century, the concept of culture has undergone change as evolutionary scientists have come to include social behavior in their purview. Evolutionary psychology is the newest field to concern itself with culture by claiming that most specific human behaviors are generated by minds specifically designed for these behaviors -- and not from a general-purpose mind -- as a result of adaptations made during the Pleistocene. Thus, mental behaviors are explained as having formed independently of cultural learning. In defending the concept, however, the leading proponents practically slough off culture as significant in human affairs. I argue that they have neglected the powerful explanatory statement of Darwin regarding at least one general-purpose adaptation of social animals, namely, the instinct for sociability, a position supported by recent neurological studies. Expanding the Darwinian concept, modern research shows that (1) the human brain was selected for sociability, which explains the origin and strength of culture, as well as its variability; (2) the development of complex culture in a pre-human primate initiated the two-and-one-half million-year evolution to modern humans; and (3) there are political contributions to cultural evolution that rest on the nature of groups (competitive and cooperative).},
}
@article {pmid16859293,
year = {2006},
author = {Gregori-Puigjané, E and Mestres, J},
title = {SHED: Shannon entropy descriptors from topological feature distributions.},
journal = {Journal of chemical information and modeling},
volume = {46},
number = {4},
pages = {1615-1622},
doi = {10.1021/ci0600509},
pmid = {16859293},
issn = {1549-9596},
mesh = {*Combinatorial Chemistry Techniques ; *Entropy ; },
abstract = {A novel set of molecular descriptors called SHED (SHannon Entropy Descriptors) is presented. They are derived from distributions of atom-centered feature pairs extracted directly from the topology of molecules. The value of a SHED is then obtained by applying the information-theoretical concept of Shannon entropy to quantify the variability in a feature-pair distribution. The collection of SHED values reflecting the overall distribution of pharmacophoric features in a molecule constitutes its SHED profile. Similarity between pairs of molecules is then assessed by calculating the Euclidean distance of their SHED profiles. Under the assumption that molecules having similar pharmacological profiles should contain similar features distributed in a similar manner, examples are given to show the ability of SHED for scaffold hopping in virtual chemical screening and pharmacological profiling compared to that of substructural BCI fingerprints and three-dimensional GRIND descriptors.},
}
@article {pmid16856372,
year = {2006},
author = {Chen, Q and Peng, H and Jiang, C and Feng, H},
title = {[Off-line experiments and analysis of independent brain--computer interface].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {23},
number = {3},
pages = {478-482},
pmid = {16856372},
issn = {1001-5515},
mesh = {Brain/*physiology ; Cortical Synchronization ; *Electroencephalography ; Humans ; *Man-Machine Systems ; *Task Performance and Analysis ; User-Computer Interface ; },
abstract = {In order to study event-related desynchronization (ERD) related to voluntary movement, we designed two experiments. In the first experiment, untrained subjects were required to imagine the action of typing with left or right index finger for about 1 second before real action, whereas they were required to type instantly after instruction in the second experiment. By analyzing spontaneous EEG signals between the instruction and the action, we predicted which finger was used. The prediction accuracy in the first experiment fell from 85% to 71% with the progress of experiment, the average accuracy being 78%, whereas the prediction result was almost random guess in the second experiment. The results demonstrate that (1) ERD patterns are significantly affected by the effective duration of motion imagination, (2) unconscious reduction of this duration can decrease the prediction accuracy. Therefore, when designing subsequent BCI experiments, we should devote our attention to the question of how to keep the effective duration of motion imagination.},
}
@article {pmid16838020,
year = {2006},
author = {Santhanam, G and Ryu, SI and Yu, BM and Afshar, A and Shenoy, KV},
title = {A high-performance brain-computer interface.},
journal = {Nature},
volume = {442},
number = {7099},
pages = {195-198},
doi = {10.1038/nature04968},
pmid = {16838020},
issn = {1476-4687},
mesh = {Animals ; Bionics/*methods ; Brain/*physiology ; Brain Injuries/physiopathology/rehabilitation ; Electrodes ; Humans ; Macaca mulatta/*physiology ; *Prostheses and Implants ; Psychomotor Performance/physiology ; *User-Computer Interface ; },
abstract = {Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.},
}
@article {pmid16838014,
year = {2006},
author = {Hochberg, LR and Serruya, MD and Friehs, GM and Mukand, JA and Saleh, M and Caplan, AH and Branner, A and Chen, D and Penn, RD and Donoghue, JP},
title = {Neuronal ensemble control of prosthetic devices by a human with tetraplegia.},
journal = {Nature},
volume = {442},
number = {7099},
pages = {164-171},
doi = {10.1038/nature04970},
pmid = {16838014},
issn = {1476-4687},
mesh = {Adult ; Bionics/*methods ; Electrodes ; Humans ; Male ; Middle Aged ; Movement ; *Prostheses and Implants ; Quadriplegia/*physiopathology/*rehabilitation ; Robotics/methods ; User-Computer Interface ; },
abstract = {Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.},
}
@article {pmid16830940,
year = {2006},
author = {Oweiss, KG},
title = {A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {7},
pages = {1364-1377},
doi = {10.1109/TBME.2006.873749},
pmid = {16830940},
issn = {0018-9294},
support = {NS047516/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Data Compression/*methods ; Electrocardiography/methods ; Evoked Potentials/*physiology ; Guinea Pigs ; *Man-Machine Systems ; Reaction Time/*physiology ; *User-Computer Interface ; },
abstract = {This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.},
}
@article {pmid16830794,
year = {2006},
author = {Gao, SK and Zhang, ZG and Gao, XR and Hong, B and Yang, FS},
title = {[Neural engineering and neural prostheses].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {30},
number = {2},
pages = {79-82},
pmid = {16830794},
issn = {1671-7104},
mesh = {Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities/trends ; Electroencephalography/instrumentation/methods ; Evoked Potentials/physiology ; Humans ; Neuromuscular Diseases/rehabilitation ; Rehabilitation/instrumentation ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The motivation of the brain-computer interface (BCI) research and its potential applications are introduced in this paper. Some of the problems in BCI-based medical device developments are also discussed.},
}
@article {pmid16823294,
year = {2006},
author = {Leuthardt, EC and Schalk, G and Moran, D and Ojemann, JG},
title = {The emerging world of motor neuroprosthetics: a neurosurgical perspective.},
journal = {Neurosurgery},
volume = {59},
number = {1},
pages = {1-14; discussion 1-14},
doi = {10.1227/01.NEU.0000221506.06947.AC},
pmid = {16823294},
issn = {1524-4040},
support = {NS007144/NS/NINDS NIH HHS/United States ; NS41272/NS/NINDS NIH HHS/United States ; },
mesh = {*Brain ; Humans ; *Man-Machine Systems ; *Movement ; *Neurosurgery/methods/trends ; *Prostheses and Implants ; *User-Computer Interface ; },
abstract = {A MOTOR NEUROPROSTHETIC device, or brain computer interface, is a machine that can take some type of signal from the brain and convert that information into overt device control such that it reflects the intentions of the user's brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these brain computer interfaces that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a brain computer interface is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.},
}
@article {pmid16792306,
year = {2006},
author = {Yamawaki, N and Wilke, C and Liu, Z and He, B},
title = {An enhanced time-frequency-spatial approach for motor imagery classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {250-254},
pmid = {16792306},
issn = {1534-4320},
support = {R01 EB000178/EB/NIBIB NIH HHS/United States ; R01 EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Man-Machine Systems ; Systems Integration ; *User-Computer Interface ; Volition/physiology ; },
abstract = {Human motor imagery (MI) tasks evoke electroencephalogram (EEG) signal changes. The features of these changes appear as subject-specific temporal traces of EEG rhythmic components at specific channels located over the scalp. Accurate classification of MI tasks based upon EEG may lead to a noninvasive brain-computer interface (BCI) to decode and convey intention of human subjects. We have previously proposed two novel methods on time-frequency feature extraction, expression and classification for high-density EEG recordings (Wang and He 2004; Wang, Deng, and He, 2004). In the present study, we refined the above time-frequency-spatial approach and applied it to a one-dimensional "cursor control" BCI experiment with online feedback. Through offline analysis of the collected data, we evaluated the capability of the present refined method in comparison with the original time-frequency-spatial methods. The enhanced performance in terms of classification accuracy was found for the proposed approach, with a mean accuracy rate of 91.1% for two subjects studied.},
}
@article {pmid16792305,
year = {2006},
author = {Wilson, JA and Felton, EA and Garell, PC and Schalk, G and Williams, JC},
title = {ECoG factors underlying multimodal control of a brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {246-250},
doi = {10.1109/TNSRE.2006.875570},
pmid = {16792305},
issn = {1534-4320},
support = {K23 DC006415-01/DC/NIDCD NIH HHS/United States ; },
mesh = {Adult ; Brain Mapping/*methods ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Computer Peripherals ; *Evoked Potentials ; Female ; Humans ; Imagination ; Male ; Man-Machine Systems ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; Systems Integration ; *User-Computer Interface ; Volition/physiology ; },
abstract = {Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.},
}
@article {pmid16792304,
year = {2006},
author = {Wills, SA and MacKay, DJ},
title = {DASHER--an efficient writing system for brain-computer interfaces?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {244-246},
doi = {10.1109/TNSRE.2006.875573},
pmid = {16792304},
issn = {1534-4320},
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Electroencephalography/*methods ; Humans ; Man-Machine Systems ; *Software ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; *Writing ; },
abstract = {DASHER is a human-computer interface for entering text using continuous or discrete gestures. Through its use of an internal language model, DASHER efficiently converts bits received from the user into text, and has been shown to be a competitive alternative to existing text-entry methods in situations where an ordinary keyboard cannot be used. We propose that DASHER would be well-matched to the low bit-rate, noisy output obtained from brain-computer interfaces (BCIs), and discuss the issues surrounding the use of DASHER with BCI systems.},
}
@article {pmid16792302,
year = {2006},
author = {Wang, Y and Wang, R and Gao, X and Hong, B and Gao, S},
title = {A practical VEP-based brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {234-239},
doi = {10.1109/TNSRE.2006.875576},
pmid = {16792302},
issn = {1534-4320},
mesh = {Adult ; *Artificial Intelligence ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Imagination/physiology ; Male ; Neuromuscular Diseases/physiopathology/*rehabilitation ; Pattern Recognition, Automated/methods ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.},
}
@article {pmid16792301,
year = {2006},
author = {Vaughan, TM and McFarland, DJ and Schalk, G and Sarnacki, WA and Krusienski, DJ and Sellers, EW and Wolpaw, JR},
title = {The Wadsworth BCI Research and Development Program: at home with BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {229-233},
doi = {10.1109/TNSRE.2006.875577},
pmid = {16792301},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiopathology ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; New York ; Research Design ; Switzerland ; Therapy, Computer-Assisted/*methods ; Universities ; *User-Computer Interface ; },
abstract = {The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.},
}
@article {pmid16792300,
year = {2006},
author = {Trejo, LJ and Rosipal, R and Matthews, B},
title = {Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {225-229},
doi = {10.1109/TNSRE.2006.875578},
pmid = {16792300},
issn = {1534-4320},
mesh = {*Algorithms ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Data Display ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Feedback/physiology ; Humans ; Man-Machine Systems ; Task Performance and Analysis ; *User-Computer Interface ; Visual Cortex/*physiology ; Volition ; },
abstract = {We have developed and tested two electroencephalogram (EEG)-based brain-computer interfaces (BCI) for users to control a cursor on a computer display. Our system uses an adaptive algorithm, based on kernel partial least squares classification (KPLS), to associate patterns in multichannel EEG frequency spectra with cursor controls. Our first BCI, Target Practice, is a system for one-dimensional device control, in which participants use biofeedback to learn voluntary control of their EEG spectra. Target Practice uses a KPLS classifier to map power spectra of 62-electrode EEG signals to rightward or leftward position of a moving cursor on a computer display. Three subjects learned to control motion of a cursor on a video display in multiple blocks of 60 trials over periods of up to six weeks. The best subject's average skill in correct selection of the cursor direction grew from 58% to 88% after 13 training sessions. Target Practice also implements online control of two artifact sources: 1) removal of ocular artifact by linear subtraction of wavelet-smoothed vertical and horizontal electrooculograms (EOG) signals, 2) control of muscle artifact by inhibition of BCI training during periods of relatively high power in the 40-64 Hz band. The second BCI, Think Pointer, is a system for two-dimensional cursor control. Steady-state visual evoked potentials (SSVEP) are triggered by four flickering checkerboard stimuli located in narrow strips at each edge of the display. The user attends to one of the four beacons to initiate motion in the desired direction. The SSVEP signals are recorded from 12 electrodes located over the occipital region. A KPLS classifier is individually calibrated to map multichannel frequency bands of the SSVEP signals to right-left or up-down motion of a cursor on a computer display. The display stops moving when the user attends to a central fixation point. As for Target Practice, Think Pointer also implements wavelet-based online removal of ocular artifact; however, in Think Pointer muscle artifact is controlled via adaptive normalization of the SSVEP. Training of the classifier requires about 3 min. We have tested our system in real-time operation in three human subjects. Across subjects and sessions, control accuracy ranged from 80% to 100% correct with lags of 1-5 s for movement initiation and turning. We have also developed a realistic demonstration of our system for control of a moving map display (http://ti.arc.nasa.gov/).},
}
@article {pmid16792299,
year = {2006},
author = {Sellers, EW and Kübler, A and Donchin, E},
title = {Brain-computer interface research at the University of South Florida Cognitive Psychophysiology Laboratory: the P300 Speller.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {221-224},
doi = {10.1109/TNSRE.2006.875580},
pmid = {16792299},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiopathology ; Cognition/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials ; Florida ; Humans ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; Psychophysiology/methods ; Research Design ; Therapy, Computer-Assisted/methods ; Universities ; *User-Computer Interface ; },
abstract = {We describe current efforts to implement and improve P300-BCI communication tools. The P300 Speller first described by Farwell and Donchin (in 1988) adapted the so-called oddball paradigm (OP) as the operating principle of the brain-computer interface (BCI) and was the first P300-BCI. The system operated by briefly intensifying each row and column of a matrix and the attended row and column elicited a P300 response. This paradigm has been the benchmark in P300-BCI systems, and in the past few years the P300 Speller paradigm has been solidified as a promising communication tool. While promising, we have found that some people who have amyotrophic lateral sclerosis (ALS) would be better suited with a system that has a limited number of choices, particularly if the 6 x 6 matrix is difficult to use. Therefore, we used the OP to implement a four-choice system using the commands: Yes, No, Pass, and End; we also used three presentation modes: auditory, visual, and auditory and visual. We summarize results from both paradigms and also discuss obstacles we have identified while working with the ALS population outside of the laboratory environment.},
}
@article {pmid16792297,
year = {2006},
author = {Ramsey, NF and van de Heuvel, MP and Kho, KH and Leijten, FS},
title = {Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {214-217},
doi = {10.1109/TNSRE.2006.875582},
pmid = {16792297},
issn = {1534-4320},
mesh = {Attention/physiology ; Cognition/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Memory, Short-Term/*physiology ; Prefrontal Cortex/*physiology ; *User-Computer Interface ; },
abstract = {One of the critical issues in brain-computer interface (BCI) research is how to translate a person's intention into brain signals for controlling computer programs. The motor system is currently the primary focus, where signals are obtained during imagined motor responses. However, cognitive brain systems are also attractive candidates, in that they may be more amenable to conscious control, yielding better regulation of magnitude and duration of localized brain activity. We report on a proof of principle study for the potential use of a higher cognitive system for BCI, namely the working memory (WM) system. We show that mental calculation reliably activates the WM network as measured with functional magnetic resonance imaging (fMRI). Moreover, activity in the dorsolateral prefrontal cortex (DLPFC) indicates that this region is active for the duration of mental processing. This supports the notion that DLPFC can be activated, and remains active, at will. Further confirmation is obtained from a patient with an implanted electrode grid for diagnostic purposes, in that gamma power within DLPFC increases during mental calculation and remains elevated for the duration thereof. These results indicate that cortical regions involved in higher cognitive functions may serve as a readily self-controllable input for BCI applications. It also shows that fMRI is an effective tool for identifying function-specific foci in individual subjects for subsequent placement of cortical electrodes. The fact that electrocorticographic (ECoG) signal confirmed the functional localization of fMRI provides a strong argument for incorporating fMRI in BCI research.},
}
@article {pmid16792296,
year = {2006},
author = {Pun, T and Alecu, TI and Chanel, G and Kronegg, J and Voloshynovskiy, S},
title = {Brain-computer interaction research at the Computer Vision and Multimedia Laboratory, University of Geneva.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {210-213},
doi = {10.1109/TNSRE.2006.875544},
pmid = {16792296},
issn = {1534-4320},
mesh = {Animals ; *Artificial Intelligence ; Brain/*physiopathology ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; *Multimedia ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; Research Design ; Switzerland ; Therapy, Computer-Assisted/methods ; Universities ; *User-Computer Interface ; },
abstract = {This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.},
}
@article {pmid16792295,
year = {2006},
author = {Pfurtscheller, G and Müller-Putz, GR and Schlögl, A and Graimann, B and Scherer, R and Leeb, R and Brunner, C and Keinrath, C and Lee, F and Townsend, G and Vidaurre, C and Neuper, C},
title = {15 years of BCI research at Graz University of Technology: current projects.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {205-210},
doi = {10.1109/TNSRE.2006.875528},
pmid = {16792295},
issn = {1534-4320},
mesh = {Animals ; Austria ; Brain/*physiopathology ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; *Research Design ; Therapy, Computer-Assisted/*methods ; Universities ; *User-Computer Interface ; },
abstract = {Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.},
}
@article {pmid16792294,
year = {2006},
author = {Nielsen, KD and Cabrera, AF and do Nascimento, OF},
title = {EEG based BCI-towards a better control. Brain-computer interface research at Aalborg University.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {202-204},
doi = {10.1109/TNSRE.2006.875529},
pmid = {16792294},
issn = {1534-4320},
mesh = {Animals ; Brain/*physiopathology ; Denmark ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Neuromuscular Diseases/*physiopathology/*rehabilitation ; *Research Design ; Therapy, Computer-Assisted/methods ; Universities ; *User-Computer Interface ; },
abstract = {This paper summarizes the brain-computer interface (BCI)-related research being conducted at Aalborg University. Namely, an online synchronized BCI system using steady-state visual evoked potentials, and investigations on cortical modulation of movement-related parameters are presented.},
}
@article {pmid16792293,
year = {2006},
author = {Mushahwar, VK and Guevremont, L and Saigal, R},
title = {Could cortical signals control intraspinal stimulators? A theoretical evaluation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {198-201},
doi = {10.1109/TNSRE.2006.875532},
pmid = {16792293},
issn = {1534-4320},
mesh = {Animals ; Cats ; Cerebral Cortex/*physiopathology ; Electric Stimulation Therapy/*methods ; Feasibility Studies ; Feedback ; Gait Disorders, Neurologic/etiology/*physiopathology/*rehabilitation ; Male ; Spinal Cord/*physiopathology ; Spinal Cord Injuries/complications/physiopathology/rehabilitation ; Therapy, Computer-Assisted/methods ; *User-Computer Interface ; },
abstract = {In this paper, we examine the control signals that are required to generate stepping using two different intraspinal microstimulation (ISMS) paradigms and discuss the theoretical feasibility of controlling ISMS-evoked stepping using a brain computer interface. Tonic (constant amplitude) and phasic (modulated amplitude) ISMS protocols were used to produce stepping in the hind limbs of paralyzed cats. Low-amplitude tonic ISMS activated a spinal locomotor-like network that resulted in bilateral stepping of the hind limbs. Phasic ISMS generated coordinated stepping by simultaneously activating flexor synergies in one limb coupled with extensor synergies in the other. Using these ISMS paradigms, we propose that one or two independent cortical signals will be adequate for controlling ISMS-induced stepping after SCI.},
}
@article {pmid16792292,
year = {2006},
author = {Leuthardt, EC and Miller, KJ and Schalk, G and Rao, RP and Ojemann, JG},
title = {Electrocorticography-based brain computer interface--the Seattle experience.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {194-198},
doi = {10.1109/TNSRE.2006.875536},
pmid = {16792292},
issn = {1534-4320},
support = {JGO-NS41272/NS/NINDS NIH HHS/United States ; NS007144/NS/NINDS NIH HHS/United States ; },
mesh = {Cerebral Cortex/*physiopathology ; Electroencephalography/*methods ; Epilepsy/*physiopathology/*rehabilitation ; Evoked Potentials ; Humans ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; Washington ; },
abstract = {Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain-computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%-100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints.},
}
@article {pmid16792291,
year = {2006},
author = {Kauhanen, L and Nykopp, T and Lehtonen, J and Jylänki, P and Heikkonen, J and Rantanen, P and Alaranta, H and Sams, M},
title = {EEG and MEG brain-computer interface for tetraplegic patients.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {190-193},
doi = {10.1109/TNSRE.2006.875546},
pmid = {16792291},
issn = {1534-4320},
mesh = {Artificial Intelligence ; Brain/*physiopathology ; Cluster Analysis ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials ; Humans ; Magnetoencephalography/*methods ; Male ; Pattern Recognition, Automated/methods ; Quadriplegia/*physiopathology/*rehabilitation ; Reproducibility of Results ; Sensitivity and Specificity ; Software ; Therapy, Computer-Assisted/*methods ; },
abstract = {We characterized features of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated in the sensorimotor cortex of three tetraplegics attempting index finger movements. Single MEG and EEG trials were classified offline into two classes using two different classifiers, a batch trained classifier and a dynamic classifier. Classification accuracies obtained with dynamic classifier were better, at 75%, 89%, and 91% in different subjects, when features were in the 0.5-3.0-Hz frequency band. Classification accuracies of EEG and MEG did not differ.},
}
@article {pmid16792290,
year = {2006},
author = {Jackson, A and Moritz, CT and Mavoori, J and Lucas, TH and Fetz, EE},
title = {The Neurochip BCI: towards a neural prosthesis for upper limb function.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {187-190},
doi = {10.1109/TNSRE.2006.875547},
pmid = {16792290},
issn = {1534-4320},
support = {NS12542/NS/NINDS NIH HHS/United States ; RR00166/RR/NCRR NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiopathology ; Electric Stimulation Therapy/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; Evoked Potentials ; Haplorhini ; Humans ; Motor Cortex/physiopathology ; Movement Disorders/*physiopathology/*rehabilitation ; Muscle, Skeletal/innervation/physiopathology ; Pyramidal Tracts/physiopathology ; Therapy, Computer-Assisted/*instrumentation/methods ; Upper Extremity/innervation/*physiopathology ; *User-Computer Interface ; },
abstract = {The Neurochip BCI is an autonomously operating interface between an implanted computer chip and recording and stimulating electrodes in the nervous system. By converting neural activity recorded in one brain area into electrical stimuli delivered to another site, the Neurochip BCI could form the basis for a simple, direct neural prosthetic. In tests with normal, unrestrained monkeys, the Neurochip continuously recorded activity of single neurons in primary motor cortex for several weeks at a time. Cortical activity was correlated with simultaneously-recorded electromyogram (EMG) activity from arm muscles during free behavior. In separate experiments with anesthetized monkeys, we found that microstimulation of the cervical spinal cord evoked movements of the arm and hand, often involving multiple muscles synergies. These observations suggest that spinal microstimulation controlled by cortical neurons could help compensate for damaged corticospinal projections.},
}
@article {pmid16792289,
year = {2006},
author = {Hill, NJ and Lal, TN and Schröder, M and Hinterberger, T and Wilhelm, B and Nijboer, F and Mochty, U and Widman, G and Elger, C and Schölkopf, B and Kübler, A and Birbaumer, N},
title = {Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {183-186},
doi = {10.1109/TNSRE.2006.875548},
pmid = {16792289},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; *Artificial Intelligence ; Cluster Analysis ; Computer User Training/methods ; Electroencephalography/*methods ; *Evoked Potentials ; Female ; Humans ; Imagination ; Male ; Middle Aged ; Paralysis/*physiopathology/rehabilitation ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.},
}
@article {pmid16792288,
year = {2006},
author = {Heldman, DA and Wang, W and Chan, SS and Moran, DW},
title = {Local field potential spectral tuning in motor cortex during reaching.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {180-183},
doi = {10.1109/TNSRE.2006.875549},
pmid = {16792288},
issn = {1534-4320},
support = {C06 RR015502/RR/NCRR NIH HHS/United States ; },
mesh = {Animals ; Arm/physiology ; Brain/*physiology ; Brain Mapping/*methods ; Evoked Potentials, Motor/*physiology ; Haplorhini ; Macaca ; Motor Cortex/*physiology ; Movement/*physiology ; *Task Performance and Analysis ; User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {In this paper, intracortical local field potentials (LFPs) and single units were recorded from the motor cortices of monkeys (Macaca fascicularis) while they preformed a standard three-dimensional (3-D) center-out reaching task. During the center-out task, the subjects held their hands at the location of a central target and then reached to one of eight peripheral targets forming the corners of a virtual cube. The spectral amplitudes of the recorded LFPs were calculated, with the high-frequency LFP (HF-LFP) defined as the average spectral amplitude change from baseline from 60 to 200 Hz. A 3-D linear regression across the eight center-out targets revealed that approximately 6% of the beta LFPs (18-26 Hz) and 18% of the HF-LFPs were tuned for velocity (p-value < 0.05), while 10% of the beta LFPs and 15% of the HF-LFPs were tuned for position. These results suggest that a multidegree-of-freedom brain-machine interface is possible using high-frequency LFP recordings in motor cortex.},
}
@article {pmid16792287,
year = {2006},
author = {Gerson, AD and Parra, LC and Sajda, P},
title = {Cortically coupled computer vision for rapid image search.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {174-179},
doi = {10.1109/TNSRE.2006.875550},
pmid = {16792287},
issn = {1534-4320},
support = {EB004730/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Artificial Intelligence ; Biomedical Research/methods ; Biomimetics/methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Pattern Recognition, Visual/*physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.},
}
@article {pmid16792284,
year = {2006},
author = {Buttfield, A and Ferrez, PW and Millán, Jdel R},
title = {Towards a robust BCI: error potentials and online learning.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {164-168},
doi = {10.1109/TNSRE.2006.875555},
pmid = {16792284},
issn = {1534-4320},
mesh = {Algorithms ; Artifacts ; *Artificial Intelligence ; Brain/*physiology ; Cognition/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Learning/*physiology ; Neuromuscular Diseases/rehabilitation ; Online Systems ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Recent advances in the field of brain-computer interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful laboratory demonstrations, there are still issues that need to be addressed before BCIs can be used by nonexperts outside the laboratory. At IDIAP Research Institute, we have been investigating several areas that we believe will allow us to improve the robustness, flexibility, and reliability of BCIs. One area is recognition of cognitive error states, that is, identifying errors through the brain's reaction to mistakes. The production of these error potentials (ErrP) in reaction to an error made by the user is well established. We have extended this work by identifying a similar but distinct ErrP that is generated in response to an error made by the interface, (a misinterpretation of a command that the user has given). This ErrP can be satisfactorily identified in single trials and can be demonstrated to improve the theoretical performance of a BCI. A second area of research is online adaptation of the classifier. BCI signals change over time, both between sessions and within a single session, due to a number of factors. This means that a classifier trained on data from a previous session will probably not be optimal for a new session. In this paper, we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase. We also discuss the future direction of this research, including the combination of these two currently separate issues to create a potentially very powerful BCI.},
}
@article {pmid16792282,
year = {2006},
author = {Blankertz, B and Müller, KR and Krusienski, DJ and Schalk, G and Wolpaw, JR and Schlögl, A and Pfurtscheller, G and Millán, Jdel R and Schröder, M and Birbaumer, N},
title = {The BCI competition. III: Validating alternative approaches to actual BCI problems.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {153-159},
doi = {10.1109/TNSRE.2006.875642},
pmid = {16792282},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Neuromuscular Diseases/rehabilitation ; *Software Validation ; Technology Assessment, Biomedical/*methods ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.},
}
@article {pmid16792281,
year = {2006},
author = {Blankertz, B and Dornhege, G and Krauledat, M and Müller, KR and Kunzmann, V and Losch, F and Curio, G},
title = {The Berlin Brain-Computer Interface: EEG-based communication without subject training.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {147-152},
doi = {10.1109/TNSRE.2006.875557},
pmid = {16792281},
issn = {1534-4320},
mesh = {*Algorithms ; *Communication Devices for People with Disabilities ; Computer User Training/methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Germany ; Humans ; Imagination/physiology ; Learning/physiology ; Man-Machine Systems ; Movement/*physiology ; Neuromuscular Diseases/rehabilitation ; Psychomotor Performance/*physiology ; },
abstract = {The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.},
}
@article {pmid16792279,
year = {2006},
author = {Wolpaw, JR and Loeb, GE and Allison, BZ and Donchin, E and do Nascimento, OF and Heetderks, WJ and Nijboer, F and Shain, WG and Turner, JN},
title = {BCI Meeting 2005--workshop on signals and recording methods.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {138-141},
doi = {10.1109/TNSRE.2006.875583},
pmid = {16792279},
issn = {1534-4320},
support = {R13EB00511401/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Biotechnology/methods ; Brain/physiology ; Communication Devices for People with Disabilities/*ethics ; Electroencephalography/*methods ; Humans ; Information Storage and Retrieval/ethics/*methods ; Internationality ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper describes the highlights of presentations and discussions during the Third International BCI Meeting in a workshop that evaluated potential brain-computer interface (BCI) signals and currently available recording methods. It defined the main potential user populations and their needs, addressed the relative advantages and disadvantages of noninvasive and implanted (i.e., invasive) methodologies, considered ethical issues, and focused on the challenges involved in translating BCI systems from the laboratory to widespread clinical use. The workshop stressed the critical importance of developing useful applications that establish the practical value of BCI technology.},
}
@article {pmid16792278,
year = {2006},
author = {McFarland, DJ and Anderson, CW and Müller, KR and Schlögl, A and Krusienski, DJ},
title = {BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {135-138},
doi = {10.1109/TNSRE.2006.875637},
pmid = {16792278},
issn = {1534-4320},
support = {R13EB00511401/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Artificial Intelligence ; Biotechnology/*methods ; Brain/*physiology ; Communication Devices for People with Disabilities/*trends ; Electroencephalography/*methods ; Humans ; Internationality ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; Pattern Recognition, Automated/methods ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.},
}
@article {pmid16792277,
year = {2006},
author = {Kübler, A and Mushahwar, VK and Hochberg, LR and Donoghue, JP},
title = {BCI Meeting 2005--workshop on clinical issues and applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {131-134},
doi = {10.1109/tnsre.2006.875585},
pmid = {16792277},
issn = {1534-4320},
support = {R13EB00511401/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Biotechnology/*ethics/*trends ; Brain/physiology ; Communication Devices for People with Disabilities/*ethics/*trends ; Electroencephalography/ethics/*methods ; Humans ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; Patient Selection/ethics ; *User-Computer Interface ; },
abstract = {This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) clinical applications. These include potential BCI users, applications, validation, getting BCIs to users, role of government and industry, plasticity, and ethics.},
}
@article {pmid16792276,
year = {2006},
author = {Cincotti, F and Bianchi, L and Birch, G and Guger, C and Mellinger, J and Scherer, R and Schmidt, RN and Yáñez Suárez, O and Schalk, G},
title = {BCI meeting 2005--workshop on technology: hardware and software.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {128-131},
doi = {10.1109/TNSRE.2006.875584},
pmid = {16792276},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Algorithms ; Biotechnology/*instrumentation/*trends ; Brain/physiology ; Communication Devices for People with Disabilities/*trends ; Computers/trends ; Electroencephalography/*methods ; Equipment Design ; Humans ; Internationality ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; Software/*trends ; *User-Computer Interface ; },
abstract = {This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.},
}
@article {pmid16792275,
year = {2006},
author = {Vaughan, TM and Wolpaw, JR},
title = {The Third International Meeting on Brain-Computer Interface Technology: making a difference.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {2},
pages = {126-127},
pmid = {16792275},
issn = {1534-4320},
support = {R13EB00511401/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Biotechnology/*trends ; Brain/*physiology ; Communication Devices for People with Disabilities/*trends ; Electroencephalography/*methods ; Humans ; Internationality ; Man-Machine Systems ; Neuromuscular Diseases/*rehabilitation ; *User-Computer Interface ; },
abstract = {This special issue of the IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING provides a representative and comprehensive bird's-eye view of the most recent developments in brain-computer interface (BCI) technology from laboratories around the world. The 30 research communications and papers are the direct outcome of the Third International Meeting on Brain-Computer Interface Technology held at the Rensselaerville Institute, Rensselaerville, NY, in June 2005. Fifty-three research groups from North and South America, Europe, and Asia, representing the majority of all the existing BCI laboratories around the world, participated in this highly focused meeting sponsored by the National Institutes of Health and organized by the BCI Laboratory of the Wadsworth Center of the New York State Department of Health. As demonstrated by the papers in this special issue, the rapid advances in BCI research and development make this technology capable of providing communication and control to people severely disabled by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and other neuromuscular disorders. Future work is expected to improve the performance and utility of BCIs, and to focus increasingly on making them a viable, practical, and affordable communication alternative for many thousands of severely disabled people worldwide.},
}
@article {pmid16781118,
year = {2006},
author = {Dominich, S and Góth, J and Kiezer, T},
title = {NeuRadIR: web-based neuroradiological information retrieval system using three methods to satisfy different user aspects.},
journal = {Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society},
volume = {30},
number = {4},
pages = {263-272},
doi = {10.1016/j.compmedimag.2006.04.001},
pmid = {16781118},
issn = {0895-6111},
mesh = {Brain/*diagnostic imaging ; Humans ; *Information Storage and Retrieval ; *Internet ; Tomography, X-Ray Computed ; User-Computer Interface ; },
abstract = {This paper presents our research results obtained within the Cost Effective Health Preservation Consortium Project to apply three different information retrieval techniques to brain computer tomography (CT) image and report retrieval in order to satisfy different information needs. The results are materialised in the NeuRadIR (neuroradiological information retrieval) system, which enables physicians to use medical text and image databases, related to human brain CT, over the Web in order to support diagnosis and patient care. After describing briefly the approaches in image retrieval, the applied retrieval methods are presented. This is followed by the description of the NeuRadIR system. Evaluation results are also reported and discussed.},
}
@article {pmid16766962,
year = {2006},
author = {Berne, JD and Reuland, KS and Villarreal, DH and McGovern, TM and Rowe, SA and Norwood, SH},
title = {Sixteen-slice multi-detector computed tomographic angiography improves the accuracy of screening for blunt cerebrovascular injury.},
journal = {The Journal of trauma},
volume = {60},
number = {6},
pages = {1204-9; discussion 1209-10},
doi = {10.1097/01.ta.0000220435.55791.ce},
pmid = {16766962},
issn = {0022-5282},
mesh = {Angiography/*instrumentation ; Carotid Artery Injuries/*diagnostic imaging ; Cerebrovascular Trauma/*diagnostic imaging ; Humans ; Sensitivity and Specificity ; Tomography, Spiral Computed/*instrumentation ; Vertebral Artery/*injuries ; Wounds, Nonpenetrating/*diagnostic imaging ; },
abstract = {BACKGROUND: Blunt cerebrovascular injuries (BCVI) are rare but potentially devastating injuries, particularly if the diagnosis is delayed. Only four-vessel cerebral angiography (FVCA) has been shown to be adequately sensitive and specific as a screening tool for BCVI but is resource-intensive and invasive. Computed tomography (CT) angiography has emerged as a possible alternative, but its accuracy has been poor, particularly for low-grade injuries. Recent advances in CT technology, particularly the use of a multi-detector array for image acquisition should improve the accuracy of this technique. This study is the first reported experience of the role of the 16-slice multi- detector CT scanner in screening for BCVI.<br><br>METHODS: From January 2, 2003 to October 31, 2004, all patients who met predefined screening criteria were screened for blunt injury to the carotid (BCI) and vertebral (BVI) arteries with a 16-slice multi-detector CT scanner with angiographic reconstruction (CTA). If CTA was positive or equivocal for BCVI, FVCA was performed as a confirmatory test. If CTA was negative, no further diagnostic studies were performed.<br><br>RESULTS: There were 435 patients who met criteria and were screened with CTA. Of these, 25 injuries were identified in 24 patients for an incidence of BCVI of 1.2% (24/2023) among all blunt admissions (BTA) and 5.5% (24/435) among screened patients (SP). This was increased compared with the four-slice era (0.38% BTA, 2.4% SP, p<0.01). No patient with a negative CTA was subsequently identified as having, or developed neurologic symptoms attributable to a missed BCVI.<br><br>CONCLUSION: Sixteen-slice multi-detector CT angiography is an excellent tool to screen for BCVI and detects all clinically significant injuries. The detected incidence of BCVI increased more than threefold with the 16-slice scanner when compared with the four-slice scanner. This demonstrates a clear technological improvement in our ability to screen for these injuries.},
}
@article {pmid16761852,
year = {2006},
author = {Vidaurre, C and Schlögl, A and Cabeza, R and Scherer, R and Pfurtscheller, G},
title = {A fully on-line adaptive BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {6},
pages = {1214-1219},
doi = {10.1109/TBME.2006.873542},
pmid = {16761852},
issn = {0018-9294},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback/physiology ; Humans ; Imagination/*physiology ; Online Systems ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.},
}
@article {pmid16761843,
year = {2006},
author = {Kim, HK and Biggs, SJ and Schloerb, DW and Carmena, JM and Lebedev, MA and Nicolelis, MA and Srinivasan, MA},
title = {Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {6},
pages = {1164-1173},
doi = {10.1109/TBME.2006.870235},
pmid = {16761843},
issn = {0018-9294},
mesh = {Arm/*physiopathology ; Brain/*physiology ; Electroencephalography/methods ; Evoked Potentials/physiology ; Feedback ; Hand Strength/physiology ; Humans ; Imagination/physiology ; *Movement ; Movement Disorders/*rehabilitation ; Robotics/instrumentation/*methods ; Systems Integration ; Telemedicine/instrumentation/methods ; Therapy, Computer-Assisted/instrumentation/*methods ; *User-Computer Interface ; },
abstract = {Research on brain-machine interfaces (BMI's) is directed toward enabling paralyzed individuals to manipulate their environment through slave robots. Even for able-bodied individuals, using a robot to reach and grasp objects in unstructured environments can be a difficult telemanipulation task. Controlling the slave directly with neural signals instead of a hand-master adds further challenges, such as uncertainty about the intended trajectory coupled with a low update rate for the command signal. To address these challenges, a continuous shared control (CSC) paradigm is introduced for BMI where robot sensors produce reflex-like reactions to augment brain-controlled trajectories. To test the merits of this approach, CSC was implemented on a 3-degree-of-freedom robot with a gripper bearing three co-located range sensors. The robot was commanded to follow eighty-three reach-and-grasp trajectories estimated previously from the outputs of a population of neurons recorded from the brain of a monkey. Five different levels of sensor-based reflexes were tested. Weighting brain commands 70% and sensor commands 30% produced the best task performance, better than brain signals alone by more than seven-fold. Such a marked performance improvement in this test case suggests that some level of machine autonomy will be an important component of successful BMI systems in general.},
}
@article {pmid16761606,
year = {2006},
author = {Gilbert, B and Wright, SJ and Muller-Landau, HC and Kitajima, K and Hernandéz, A},
title = {Life history trade-offs in tropical trees and lianas.},
journal = {Ecology},
volume = {87},
number = {5},
pages = {1281-1288},
doi = {10.1890/0012-9658(2006)87[1281:lhtitt]2.0.co;2},
pmid = {16761606},
issn = {0012-9658},
mesh = {*Adaptation, Physiological ; Analysis of Variance ; Biomass ; Borneo ; *Ecosystem ; Species Specificity ; Sunlight ; Trees/growth &amp; development/*physiology ; *Tropical Climate ; },
abstract = {It has been hypothesized that tropical trees partition forest light environments through a life history trade-off between juvenile growth and survival; however, the generality of this trade-off across life stages and functional groups has been questioned. We quantified trade-offs between growth and survival for trees and lianas on Barro Colorado Island (BCI), Panama using first-year seedlings of 22 liana and 31 tree species and saplings (10 mm < dbh < 39 mm) of 30 tree species. Lianas showed trade-offs similar to those of trees, with both groups exhibiting broadly overlapping ranges in survival and relative growth rates as seedlings. Life history strategies at the seedling stage were highly correlated with those at the sapling stage among tree species, with all species showing an increase in survival with size. Only one of 30 tree species demonstrated a statistically significant ontogenetic shift, having a relatively lower survival rate at the sapling stage than expected. Our results indicate that similar life history trade-offs apply across two functional groups (lianas and trees), and that life history strategies are largely conserved across seedling and sapling life-stages for most tropical tree species.},
}
@article {pmid16750480,
year = {2006},
author = {Matei, VA and Feng, F and Pauley, S and Beisel, KW and Nichols, MG and Fritzsch, B},
title = {Near-infrared laser illumination transforms the fluorescence absorbing X-Gal reaction product BCI into a transparent, yet brightly fluorescent substance.},
journal = {Brain research bulletin},
volume = {70},
number = {1},
pages = {33-43},
pmid = {16750480},
issn = {0361-9230},
support = {1 C06 RR17417-01/RR/NCRR NIH HHS/United States ; DC005590/DC/NIDCD NIH HHS/United States ; R01 DC005590-05/DC/NIDCD NIH HHS/United States ; C06 RR017417/RR/NCRR NIH HHS/United States ; R01 DC005590/DC/NIDCD NIH HHS/United States ; },
mesh = {Animals ; Brain/cytology/metabolism ; Diagnostic Imaging/methods ; Ear/anatomy &amp; histology ; *Fluorescence ; Galactosides/*metabolism ; Indoles/*metabolism ; Lac Operon/genetics ; *Lasers ; *Lighting ; Mice ; Mice, Transgenic ; Microscopy, Confocal ; Photic Stimulation/methods ; Photochemistry ; beta-Galactosidase/genetics/metabolism ; },
abstract = {The beta-galactosidase protein generated by the bacterial LacZ gene is widely used to map gene expression patterns. The ease of its use is only rivaled by green fluorescent protein, which can be used in combination with various other procedures such as immunocytochemistry, flow cytometry, or tract tracing. The beta-galactosidase enzymatic reaction potentially provides a more sensitive assay of gene expression than green fluorescent protein. However, the virtual impermeability and tendency to absorb light over a wide range limit the use of the most frequently used beta-galactosidase substrate, X-Gal, in combination with other fluorescent labeling procedures. Here, we provide details on a simple photoactivation procedure that transforms the light-absorbing X-Gal product, 5-bromo-4-chloro-3-indolyl (BCI) precipitate, into an intensely fluorescent product excited by 488 and 633 nm light. Photoactivation is achieved through exposure to 730 nm near-infrared light emitted from a femtosecond titanium-doped Sapphire laser. Photoactivation of BCI occurs in tissue sections suspended in buffered saline, glycerol, or even embedded in epoxy resin. A protocol for the use of BCI photoactivation is here provided. Importantly, the BCI photoactivated product is photoswitchable, displaying bistable photochromism. This permits the use of the fluorescent product in a variety of co-localization studies in conjunction with other imaging modalities. As with other bistable and photoswitchable products, the BCI reaction product shows concentration quenching at high density and can be degraded by continuous exposure to intense 730 nm illumination. Therefore, care must be taken in developing imaging strategies. Our findings have implications for the use of X-Gal in gene and protein detection and provide a novel substrate for high density digital information storage.},
}
@article {pmid16711663,
year = {2006},
author = {Bakay, RA},
title = {Limits of brain-computer interface. Case report.},
journal = {Neurosurgical focus},
volume = {20},
number = {5},
pages = {E6},
doi = {10.3171/foc.2006.20.5.7},
pmid = {16711663},
issn = {1092-0684},
mesh = {Adult ; Atrophy ; Brain/*physiopathology ; Cerebral Cortex/pathology ; Humans ; Magnetic Resonance Imaging ; Male ; Quadriplegia/diagnosis/*physiopathology/*rehabilitation ; Treatment Failure ; *User-Computer Interface ; },
abstract = {Most patients who are candidates for brain-computer interface studies have an injury to their central nervous system and therefore may not be ideal for rigorous testing of the full abilities and limits of the interface. This is a report on a quadriplegic patient who appeared to be a reasonable candidate for intracranial implantation of neurotrophic electrodes. He had significant cortical atrophy in both the motor and parietal cortical areas but was able to generate signal changes on functional magnetic resonance images by thinking about hand movements. Only a few low-amplitude action potentials were obtained, however, and he was unable to achieve single-unit control. Despite this failure, the use of field potentials offered an alternative method of control and allowed him some limited computer interactions. There are clearly limits to what can be achieved with brain-computer interfaces, and the presence of cortical atrophy should serve as a warning for future investigators that less invasive techniques may be a more prudent approach for this type of patient.},
}
@article {pmid16711662,
year = {2006},
author = {Sclabassi, RJ and Liu, Q and Hackworth, SA and Justin, GA and Sun, M},
title = {Platform technologies to support brain-computer interfaces.},
journal = {Neurosurgical focus},
volume = {20},
number = {5},
pages = {E5},
doi = {10.3171/foc.2006.20.5.6},
pmid = {16711662},
issn = {1092-0684},
support = {R01EB002099/EB/NIBIB NIH HHS/United States ; },
mesh = {Animals ; *Biomedical Technology ; *Brain/surgery ; Humans ; Prostheses and Implants ; *User-Computer Interface ; },
abstract = {There is a lack of adequate and cost-effective treatment options for many neurodegenerative diseases. The number of affected patients is in the millions, and this number will only increase as the population ages. The developing areas of neuromimetics and stimulative implants provide hope for treatment, as evidenced by the currently available, but limited, implants. New technologies are emerging that are leading to the development of highly intelligent, implantable sensors, activators, and mobile robots that will provide in vivo diagnosis, therapeutic interventions, and functional replacement. Two key platform technologies that are required to facilitate the development of these neuromimetic and stimulative implants are data communication channels and the devices' power supplies. In the research reported in this paper, investigators have examined the use of novel concepts that address these two needs. These concepts are based on ionic volume conduction (VC) to provide a natural communication channel to support the functioning of these devices, and on biofuel cells to provide a continuously rechargeable power supply that obtains electrons from the natural metabolic pathways. The fundamental principles of the VC communication channels, including novel antenna design, are demonstrated. These principles include the basic mechanisms, device sensitivity, bidirectionality of communication, and signal recovery. The demonstrations are conducted using mathematical and finite element analysis, physical experiments, and animal experiments. The fundamental concepts of the biofuel cells are presented, and three versions of the cells that have been studied are discussed, including bacteria-based cells and two white cell-based experiments. In this paper the authors summarize the proof or principal experiments for both a biomimetic data channel communication method and a biofuel cell approach, which promise to provide innovative platform technologies to support complex devices that will be ready for implantation in the human nervous system in the next decade.},
}
@article {pmid16711660,
year = {2006},
author = {Matsuoka, Y and Afshar, P and Oh, M},
title = {On the design of robotic hands for brain-machine interface.},
journal = {Neurosurgical focus},
volume = {20},
number = {5},
pages = {E3},
doi = {10.3171/foc.2006.20.5.4},
pmid = {16711660},
issn = {1092-0684},
mesh = {Brain/*physiopathology ; *Hand ; Humans ; *Man-Machine Systems ; *Prosthesis Design ; *Robotics ; },
abstract = {Brain-machine interface (BMI) is the latest solution to a lack of control for paralyzed or prosthetic limbs. In this paper the authors focus on the design of anatomical robotic hands that use BMI as a critical intervention in restorative neurosurgery and they justify the requirement for lower-level neuromusculoskeletal details (relating to biomechanics, muscles, peripheral nerves, and some aspects of the spinal cord) in both mechanical and control systems. A person uses his or her hands for intimate contact and dexterous interactions with objects that require the user to control not only the finger endpoint locations but also the forces and the stiffness of the fingers. To recreate all of these human properties in a robotic hand, the most direct and perhaps the optimal approach is to duplicate the anatomical musculoskeletal structure. When a prosthetic hand is anatomically correct, the input to the device can come from the same neural signals that used to arrive at the muscles in the original hand. The more similar the mechanical structure of a prosthetic hand is to a human hand, the less learning time is required for the user to recreate dexterous behavior. In addition, removing some of the nonlinearity from the relationship between the cortical signals and the finger movements into the peripheral controls and hardware vastly simplifies the needed BMI algorithms. (Nonlinearity refers to a system of equations in which effects are not proportional to their causes. Such a system could be difficult or impossible to model.) Finally, if a prosthetic hand can be built so that it is anatomically correct, subcomponents could be integrated back into remaining portions of the user's hand at any transitional locations. In the near future, anatomically correct prosthetic hands could be used in restorative neurosurgery to satisfy the user's needs for both aesthetics and ease of control while also providing the highest possible degree of dexterity.},
}
@article {pmid16706341,
year = {2006},
author = {Guan, J and Chen, Y and Huang, M},
title = {[Single-trial estimation of visual evoked potentials in single channel single-trial estimation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {23},
number = {2},
pages = {252-256},
pmid = {16706341},
issn = {1001-5515},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Electroencephalography ; Evoked Potentials, Visual/*physiology ; Humans ; Male ; *Signal Processing, Computer-Assisted ; },
abstract = {We constructed a Brain-computer interface-based mental speller which realizes user-computer interaction. The feature signals of user's intention are embedded in spontaneous EEG background. Single-trial feature estimation should be used on this online occasion instead of the grand average usually used in cognitive or clinical experiments. To demonstrate this technique beyond laboratories, fewer EEG recording channels are preferred. A unique paradigm, which is called imitating-natural-reading, was exploited to induce visual evoked potentials. We explored the single-trial estimation of VEP recorded in single channel using support vector machine on three subjects, and obtained satisfactory data, the classification accuracy being 92.1%, 94.1% and 91.5%, respectively. These results put forward a significant step fowards the ultimate realization of our mental speller.},
}
@article {pmid16705272,
year = {2006},
author = {Wahnoun, R and He, J and Helms Tillery, SI},
title = {Selection and parameterization of cortical neurons for neuroprosthetic control.},
journal = {Journal of neural engineering},
volume = {3},
number = {2},
pages = {162-171},
doi = {10.1088/1741-2560/3/2/010},
pmid = {16705272},
issn = {1741-2560},
mesh = {Action Potentials/*physiology ; Algorithms ; Animals ; Artificial Intelligence ; Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback ; Humans ; Macaca mulatta ; Motor Cortex/*physiology ; Nervous System Diseases/physiopathology/*rehabilitation ; Neurons/*physiology ; Pattern Recognition, Automated/methods ; Prosthesis Design ; *User-Computer Interface ; },
abstract = {When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.},
}
@article {pmid16705271,
year = {2006},
author = {Kim, SP and Sanchez, JC and Rao, YN and Erdogmus, D and Carmena, JM and Lebedev, MA and Nicolelis, MA and Principe, JC},
title = {A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.},
journal = {Journal of neural engineering},
volume = {3},
number = {2},
pages = {145-161},
doi = {10.1088/1741-2560/3/2/009},
pmid = {16705271},
issn = {1741-2560},
mesh = {Action Potentials/physiology ; *Algorithms ; Animals ; Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Haplorhini ; Humans ; Linear Models ; *Models, Neurological ; Nonlinear Dynamics ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.},
}
@article {pmid16705270,
year = {2006},
author = {Rezaei, S and Tavakolian, K and Nasrabadi, AM and Setarehdan, SK},
title = {Different classification techniques considering brain computer interface applications.},
journal = {Journal of neural engineering},
volume = {3},
number = {2},
pages = {139-144},
doi = {10.1088/1741-2560/3/2/008},
pmid = {16705270},
issn = {1741-2560},
mesh = {*Algorithms ; Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; *User-Computer Interface ; },
abstract = {In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.},
}
@article {pmid16689957,
year = {2006},
author = {Gray, JS and Bjørgesaeter, A and Ugland, KI},
title = {On plotting species abundance distributions.},
journal = {The Journal of animal ecology},
volume = {75},
number = {3},
pages = {752-756},
doi = {10.1111/j.1365-2656.2006.01095.x},
pmid = {16689957},
issn = {0021-8790},
mesh = {Animals ; *Biodiversity ; *Ecosystem ; *Models, Biological ; Population Density ; Population Dynamics ; Species Specificity ; Statistics as Topic ; *Trees ; },
abstract = {1. There has been a revival of interest in species abundance distribution (SAD) models, stimulated by the claim that the log-normal distribution gave an underestimate of the observed numbers of rare species in species-rich assemblages. This led to the development of the neutral Zero Sum Multinomial distribution (ZSM) to better fit the observed data. 2. Yet plots of SADs, purportedly of the same data, showed differences in frequencies of species and of statistical fits to the ZSM and log-normal models due to the use of different binning methods. 3. We plot six different binning methods for the Barro Colorado Island (BCI) tropical tree data. The appearances of the curves are very different for the different binning methods. Consequently, the fits to different models may vary depending on the binning system used. 4. There is no agreed binning method for SAD plots. Our analysis suggests that a simple doubling of the number of individuals per species in each bin is perhaps the most practical one for illustrative purposes. Alternatively rank-abundance plots should be used. 5. For fitting and testing models exact methods have been developed and application of these does not require binning of data. Errors are introduced unnecessarily if data are binned before testing goodness-of-fit to models.},
}
@article {pmid16647153,
year = {2006},
author = {Pfurtscheller, G and Leeb, R and Slater, M},
title = {Cardiac responses induced during thought-based control of a virtual environment.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {62},
number = {1},
pages = {134-140},
doi = {10.1016/j.ijpsycho.2006.03.001},
pmid = {16647153},
issn = {0167-8760},
mesh = {Adult ; Brain/*physiology ; Electrocardiography/methods ; Electroencephalography/methods ; *Environment ; Heart Rate/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Time Factors ; User-Computer Interface ; },
abstract = {Cardiac responses induced by motor imagery were investigated in 3 subjects in a series of experiments with a synchronous (cue-based) Brain-Computer Interface (BCI). The cue specified right hand vs. leg/foot motor imagery. After a number of BCI training sessions reaching a classification accuracy of at least 80%, the BCI experiments were carried out in an immersive virtual environment (VE), commonly referred as a "CAVE". In this VE, the subjects were able to move along a virtual street by motor imagery alone. The thought-based control of VE resulted in an acceleration of the heart rate in 2 subjects and a heart rate deceleration in the other subject. In control experiments in front of a PC, all 3 subjects displayed a significant heart rate deceleration of the order of about 3-5%. This heart rate decrease during motor imagery in a normal environment is similar to that observed during preparation for a voluntary movement. The heart rate acceleration in the VE is interpreted as effect of an increased mental effort to walk as far as possible in VE.},
}
@article {pmid16607692,
year = {2006},
author = {Boyages, J and Taylor, R and Chua, B and Ung, O and Bilous, M and Salisbury, E and Wilcken, N},
title = {A risk index for early node-negative breast cancer.},
journal = {The British journal of surgery},
volume = {93},
number = {5},
pages = {564-571},
doi = {10.1002/bjs.5207},
pmid = {16607692},
issn = {0007-1323},
mesh = {Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/*diagnosis/radiotherapy/surgery ; Female ; Humans ; Lymph Node Excision ; Middle Aged ; Neoplasm Recurrence, Local/prevention &amp; control ; Risk Assessment/methods/standards ; },
abstract = {BACKGROUND: This study compared the application of the St Gallen 2001 classification with a risk index developed at the New South Wales Breast Cancer Institute (BCI Index) for women with node-negative breast cancer treated without adjuvant systemic therapy.<br><br>METHODS: The BCI risk categories were constructed by identifying combinations of prognostic indicators that produced homogeneous low-, intermediate- and high-risk groups using the same variables as in the St Gallen classification.<br><br>RESULTS: The BCI low-risk category consisted of women aged 35 years or more with a grade 1 oestrogen receptor (ER)-positive tumour 20 mm or less in diameter, or with a grade 2 ER-positive tumour of 15 mm or less. This category constituted 40.1 per cent of patients, with a 10-year distant relapse-free survival (DRFS) rate of 97.2 per cent. The BCI intermediate-risk category included women aged 35 years or more with a grade 2 ER-positive tumour of diameter 16-20 mm, or a grade 1 or 2 ER-negative tumour measuring 15 mm or less, and comprised 12.1 per cent of the women, with a 10-year DRFS rate of 88 per cent. The high-risk category comprised 47.7 per cent of women, with a 10-year DRFS rate of 68.4 per cent.<br><br>CONCLUSION: If confirmed in other data sets, the BCI Index may be used to identify women at low risk of distant relapse (2.8 per cent at 10 years) who are unlikely to benefit from adjuvant systemic therapy, and women at intermediate risk of distant relapse (12 per cent at 10 years) in whom the benefit of adjuvant systemic therapy is small.},
}
@article {pmid16602595,
year = {2006},
author = {Kitamura, T and Nishino, D},
title = {Training of a leaning agent for navigation--inspired by brain-machine interface.},
journal = {IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
volume = {36},
number = {2},
pages = {353-365},
doi = {10.1109/tsmcb.2005.857291},
pmid = {16602595},
issn = {1083-4419},
mesh = {*Algorithms ; Animals ; Biomimetics/methods ; Brain/*physiology ; Cybernetics/*methods ; *Expert Systems ; Humans ; Image Interpretation, Computer-Assisted/methods ; *Man-Machine Systems ; Rats ; *Robotics ; *User-Computer Interface ; },
abstract = {The design clue for the remote control of a mobile robot is inspired by the Talwar's brain-machine interface technology for remotely training and controlling rats. Our biologically inspired autonomous robot control consciousness-based architecture (CBA) is used for the remote control of a robot as a substitute for a rat. CBA is a developmental hierarchy model of the relationship between consciousness and behavior, including a training algorithm. This training algorithm computes a shortcut path to a goal using a cognitive map created based on behavior obstructions during a single successful trial. However, failures in reaching the goal due to errors of the vision and dead reckoning sensors require human intervention to improve autonomous navigation. A human operator remotely intervenes in autonomous behaviors in two ways: low-level intervention in reflexive actions and high-level ones in the cognitive map. Experiments are conducted to test CBA functions for intervention with a joystick for a Khepera robot navigating from the center of a square obstacle with an open side toward a goal. Their statistical results show that both human interventions, especially high-level ones, are effective in drastically improving the success rate of autonomous detours.},
}
@article {pmid16602570,
year = {2006},
author = {Townsend, G and Graimann, B and Pfurtscheller, G},
title = {A comparison of common spatial patterns with complex band power features in a four-class BCI experiment.},
journal = {IEEE transactions on bio-medical engineering},
volume = {53},
number = {4},
pages = {642-651},
doi = {10.1109/TBME.2006.870237},
pmid = {16602570},
issn = {0018-9294},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We report on the offline analysis of four-class brain-computer interface (BCI) data recordings. Although the analysis is done within defined time windows (cue-based BCI), our goal is to work toward an approach which classifies on-going electroencephalogram (EEG) signals without the use of such windows (un-cued BCI). To that end, we provide some elements of that analysis related to timing issues that will become important as we pursue this goal in the future. A new set of features called complex band power (CBP) features which make explicit use of phase are introduced and are shown to produce good results. As reference methods we used traditional band power features and the method of common spatial patterns. We consider also for the first time in the context of a four-class problem the issue of variability of the features over time and how much data is required to give good classification results. We do this in a practical way where training data precedes testing data in time.},
}
@article {pmid16585840,
year = {2006},
author = {Congedo, M and Lotte, F and Lécuyer, A},
title = {Classification of movement intention by spatially filtered electromagnetic inverse solutions.},
journal = {Physics in medicine and biology},
volume = {51},
number = {8},
pages = {1971-1989},
doi = {10.1088/0031-9155/51/8/002},
pmid = {16585840},
issn = {0031-9155},
mesh = {Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Computer Simulation ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Electromagnetic Fields ; Evoked Potentials, Motor/*physiology ; Fingers/physiology ; Humans ; Imagination/physiology ; *Intention ; *Models, Neurological ; Movement/*physiology ; User-Computer Interface ; },
abstract = {We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.},
}
@article {pmid16562629,
year = {2006},
author = {Müller-Putz, GR and Scherer, R and Neuper, C and Pfurtscheller, G},
title = {Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {1},
pages = {30-37},
doi = {10.1109/TNSRE.2005.863842},
pmid = {16562629},
issn = {1534-4320},
mesh = {Adult ; Biofeedback, Psychology ; Brain/*physiology ; Electric Stimulation ; Electroencephalography ; Evoked Potentials, Somatosensory/*physiology ; Female ; Fingers/physiology ; Humans ; Learning/physiology ; Male ; Movement/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {One of the main issues in designing a brain-computer interface (BCI) is to find brain patterns, which could easily be detected. One of these pattern is the steady-state evoked potential (SSEP). SSEPs induced through the visual sense have already been used for brain-computer communication. In this work, a BCI system is introduced based on steady-state somatosensory evoked potentials (SSSEPs). Transducers have been used for the stimulation of both index fingers using tactile stimulation in the "resonance"-like frequency range of the somatosensory system. Four subjects participated in the experiments and were trained to modulate induced SSSEPs. Two of them learned to modify the patterns in order to set up a BCI with an accuracy of between 70% and 80%. Results presented in this work give evidence that it is possible to set up a BCI which is based on SSSEPs.},
}
@article {pmid16562628,
year = {2006},
author = {Thulasidas, M and Guan, C and Wu, J},
title = {Robust classification of EEG signal for brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {14},
number = {1},
pages = {24-29},
doi = {10.1109/TNSRE.2005.862695},
pmid = {16562628},
issn = {1534-4320},
mesh = {Adult ; Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; *Computers ; Data Collection ; Electroencephalography/*classification ; Electrophysiology ; Event-Related Potentials, P300/physiology ; Humans ; Individuality ; Learning ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.},
}
@article {pmid16533491,
year = {2006},
author = {Baker, WE and Servais, EL and Burke, PA and Agarwal, SK},
title = {Blunt carotid injury.},
journal = {Current treatment options in cardiovascular medicine},
volume = {8},
number = {2},
pages = {167-173},
pmid = {16533491},
issn = {1092-8464},
abstract = {Blunt carotid injury (BCI) is an uncommon disorder, occurring in trauma patients as a result of cervical hyperextension, hyperflexion, or direct blow. BCI is commonly present in initially asymptomatic patients who subsequently develop devastating thromboembolic complications of their injury. Although clinical predictors of injury have been developed, they are of limited accuracy. Nevertheless, employment of clinical screening criteria is of value in identifying at-risk patients in need of diagnostic testing. Liberalized screening of these trauma patients with angiography or the latest generation (64-multidetector) CT angiography facilitates early diagnosis and provides opportunity for timely intervention in asymptomatic victims. Anticoagulation and/or antithrombotic therapy in specific categories of these patients reduces neurologic morbidity and mortality. Endovascular stenting shows promise as a treatment modality for specific subsets of individuals with BCI. Surgery remains a therapeutic option for some surgically accessible lesions.},
}
@article {pmid16526960,
year = {2006},
author = {Tarafder, MR and Balolong, E and Carabin, H and Bélisle, P and Tallo, V and Joseph, L and Alday, P and Gonzales, RO and Riley, S and Olveda, R and McGarvey, ST},
title = {A cross-sectional study of the prevalence of intensity of infection with Schistosoma japonicum in 50 irrigated and rain-fed villages in Samar Province, the Philippines.},
journal = {BMC public health},
volume = {6},
number = {},
pages = {61},
pmid = {16526960},
issn = {1471-2458},
support = {R01 TW001582/TW/FIC NIH HHS/United States ; R01 TW01582/TW/FIC NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Age Distribution ; Animals ; Bayes Theorem ; Child ; Cross-Sectional Studies ; Family Characteristics ; Feces/parasitology ; Female ; Humans ; Male ; Philippines/epidemiology ; Prevalence ; Rain/parasitology ; Rural Health/*statistics &amp; numerical data ; Schistosoma japonicum/*isolation &amp; purification/parasitology ; Schistosomiasis japonica/*epidemiology/transmission ; Water/parasitology ; },
abstract = {BACKGROUND: Few studies have described heterogeneity in Schistosoma japonicum infection intensity, and none were done in Philippines. The purpose of this report is to describe the village-to-village variation in the prevalence of two levels of infection intensity across 50 villages of Samar Province, the Philippines.<br><br>METHODS: This cross-sectional study was conducted in 25 rain-fed and 25 irrigated villages endemic for S. japonicum between August 2003 and November 2004. Villages were selected based on irrigation and farming criteria. A maximum of 35 eligible households were selected per village. Each participant was asked to provide stool samples on three consecutive days. All those who provided at least one stool sample were included in the analysis. A Bayesian three category outcome hierarchical cumulative logit regression model with adjustment for age, sex, occupation and measurement error of the Kato-Katz technique was used for analysis.<br><br>RESULTS: A total of 1427 households and 6917 individuals agreed to participate in the study. A total of 5624 (81.3%) participants provided at least one stool sample. The prevalences of those lightly and at least moderately infected varied from 0% (95% Bayesian credible interval (BCI): 0%-3.1%) to 45.2% (95% BCI: 36.5%-53.9%) and 0% to 23.0% (95% BCI: 16.4%-31.2%) from village-to-village, respectively. Using the 0-7 year old group as a reference category, the highest odds ratio (OR) among males and females were that of being aged 17-40-year old (OR = 8.76; 95% BCI: 6.03-12.47) and 11-16-year old (OR = 8.59; 95% BCI: 4.74-14.28), respectively. People who did not work on a rice farm had a lower prevalence of infection than those working full time on a rice farm. The OR for irrigated villages compared to rain-fed villages was 1.41 (95% BCI: 0.50-3.21).<br><br>DISCUSSION: We found very important village-to-village variation in prevalence of infection intensity. This variation is probably due to village-level variables other than that explained by a crude classification of villages into the irrigated and non-irrigated categories. We are planning to capture this spatial heterogeneity by updating our initial transmission dynamics model with the data reported here combined with 1-year post-treatment follow-up of study participants.},
}
@article {pmid16525523,
year = {2006},
author = {Brennan, M and Black, E and French, J and Boyages, J},
title = {Breast cancer--guiding your patient through treatment.},
journal = {Australian family physician},
volume = {35},
number = {3},
pages = {117-120},
pmid = {16525523},
issn = {0300-8495},
mesh = {Access to Information ; Breast Neoplasms/*diagnosis/*therapy ; Combined Modality Therapy/adverse effects ; Female ; Humans ; Nurse Clinicians ; Patient Care Team ; *Physician-Patient Relations ; *Physicians, Family ; Social Support ; Truth Disclosure ; },
}
@article {pmid16517206,
year = {2007},
author = {Yang, BH and Yan, GZ and Yan, RG and Wu, T},
title = {Adaptive subject-based feature extraction in brain-computer interfaces using wavelet packet best basis decomposition.},
journal = {Medical engineering &amp; physics},
volume = {29},
number = {1},
pages = {48-53},
doi = {10.1016/j.medengphy.2006.01.009},
pmid = {16517206},
issn = {1350-4533},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electrocardiography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In this paper we discuss a subject-based feature extraction method using wavelet packet best basis decomposition (WPBBD) in brain-computer interfaces (BCIs). The idea is to employ the wavelet packet best basis algorithm to adapt to each subject separately. Firstly, original electroencephalogram (EEG) signals are decomposed to a given level by wavelet packet transform. Secondly, for each subject, the best basis algorithm is used to find the best-adapted basis for that particular subject. Finally, subband energies contained in the best basis are used as effective features. Adaptive and specific features of a subject are so obtained. Three different motor imagery tasks of six subjects are discriminated using the above features. Experiment results show that the subject-based adaptation method yields significantly higher classification performance than the non-subject-based adaptation and non-adaptive approaches.},
}
@article {pmid16514537,
year = {2006},
author = {Engelbrecht, BM and Dalling, JW and Pearson, TR and Wolf, RL and Gálvez, DA and Koehler, T and Tyree, MT and Kursar, TA},
title = {Short dry spells in the wet season increase mortality of tropical pioneer seedlings.},
journal = {Oecologia},
volume = {148},
number = {2},
pages = {258-269},
pmid = {16514537},
issn = {0029-8549},
mesh = {Bombacaceae/physiology ; Cecropia Plant/physiology ; Melastomataceae/physiology ; Panama ; Piper/physiology ; Seedlings/*physiology ; Soil ; Tiliaceae/physiology ; *Tropical Climate ; Water/*physiology ; Weather ; },
abstract = {Variation in plant species performance in response to water availability offers a potential axis for temporal and spatial habitat partitioning and may therefore affect community composition in tropical forests. We hypothesized that short dry spells during the wet season are a significant source of mortality for the newly emerging seedlings of pioneer species that recruit in treefall gaps in tropical forests. An analysis of a 49-year rainfall record for three forests across a rainfall gradient in central Panama confirmed that dry spells of > or = 10 days during the wet season occur on average once a year in a deciduous forest, and once every other year in a semi-deciduous moist and an evergreen wet forest. The effect of wet season dry spells on the recruitment of pioneers was investigated by comparing seedling survival in rain-protected dry plots and irrigated control plots in four large artificially created treefall gaps in a semi-deciduous tropical forest. In rain-protected plots surface soil layers dried rapidly, leading to a strong gradient in water potential within the upper 10 cm of soil. Seedling survival for six pioneer species was significantly lower in rain-protected than in irrigated control plots after only 4 days. The strength of the irrigation effect differed among species, and first became apparent 3-10 days after treatments started. Root allocation patterns were significantly, or marginally significantly, different between species and between two groups of larger and smaller seeded species. However, they were not correlated with seedling drought sensitivity, suggesting allocation is not a key trait for drought sensitivity in pioneer seedlings. Our data provide strong evidence that short dry spells in the wet season differentially affect seedling survivorship of pioneer species, and may therefore have important implications to seedling demography and community dynamics.},
}
@article {pmid16510942,
year = {2006},
author = {Pei, XM and Zheng, CX and Xu, J and Bin, GY and Wang, HW},
title = {Multi-channel linear descriptors for event-related EEG collected in brain computer interface.},
journal = {Journal of neural engineering},
volume = {3},
number = {1},
pages = {52-58},
doi = {10.1088/1741-2560/3/1/006},
pmid = {16510942},
issn = {1741-2560},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.},
}
@article {pmid16510936,
year = {2006},
author = {Shenoy, P and Krauledat, M and Blankertz, B and Rao, RP and Müller, KR},
title = {Towards adaptive classification for BCI.},
journal = {Journal of neural engineering},
volume = {3},
number = {1},
pages = {R13-23},
doi = {10.1088/1741-2560/3/1/R02},
pmid = {16510936},
issn = {1741-2560},
mesh = {Adaptation, Physiological/physiology ; Algorithms ; Artificial Intelligence ; Brain/*physiology ; Electrocardiography/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; Retrospective Studies ; *User-Computer Interface ; },
abstract = {Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.},
}
@article {pmid16461003,
year = {2006},
author = {Sellers, EW and Donchin, E},
title = {A P300-based brain-computer interface: initial tests by ALS patients.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {117},
number = {3},
pages = {538-548},
doi = {10.1016/j.clinph.2005.06.027},
pmid = {16461003},
issn = {1388-2457},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*diagnosis/*physiopathology ; Brain/*physiopathology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Photic Stimulation/methods ; Reaction Time ; Time Factors ; *User-Computer Interface ; },
abstract = {OBJECTIVE: The current study evaluates the effectiveness of a brain-computer interface (BCI) system that operates by detecting a P300 elicited by one of four randomly presented stimuli (i.e. YES, NO, PASS, END).<br><br>METHODS: Two groups of participants were tested. The first group included three amyotrophic lateral sclerosis (ALS) patients that varied in degree of disability, but all retained the ability to communicate; the second group included three non-ALS controls. Each participant participated in ten experimental sessions during a period of approximately 6 weeks. During each run the participant's task was to attend to one stimulus and disregard the other three. Stimuli were presented auditorily, visually, or in both modes.<br><br>RESULTS: Two of the 3 ALS patient's classification rates were equal to those achieved by the non-ALS participants. Waveform morphology varied as a function of the presentation mode, but not in a similar pattern for each participant.<br><br>CONCLUSIONS: The event-related potentials elicited by the target stimuli could be discriminated from the non-target stimuli for the non-ALS and the ALS groups. Future studies will begin to examine online classification.<br><br>SIGNIFICANCE: The results of offline classification suggest that a P300-based BCI can serve as a non-muscular communication device in both ALS, and non-ALS control groups.},
}
@article {pmid16458595,
year = {2006},
author = {Birbaumer, N},
title = {Brain-computer-interface research: coming of age.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {117},
number = {3},
pages = {479-483},
doi = {10.1016/j.clinph.2005.11.002},
pmid = {16458595},
issn = {1388-2457},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Humans ; Research/*trends ; Research Design ; *User-Computer Interface ; },
}
@article {pmid16458069,
year = {2006},
author = {Piccione, F and Giorgi, F and Tonin, P and Priftis, K and Giove, S and Silvoni, S and Palmas, G and Beverina, F},
title = {P300-based brain computer interface: reliability and performance in healthy and paralysed participants.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {117},
number = {3},
pages = {531-537},
doi = {10.1016/j.clinph.2005.07.024},
pmid = {16458069},
issn = {1388-2457},
mesh = {Adult ; Aged ; Biofeedback, Psychology ; Brain/*physiopathology ; Electroencephalography/methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Middle Aged ; Paralysis/*physiopathology ; Pattern Recognition, Visual/physiology ; Photic Stimulation/methods ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This study aimed to describe the use of the P300 event-related potential as a control signal in a brain computer interface (BCI) for healthy and paralysed participants.<br><br>METHODS: The experimental device used the P300 wave to control the movement of an object on a graphical interface. Visual stimuli, consisting of four arrows (up, right, down, left) were randomly presented in peripheral positions on the screen. Participants were instructed to recognize only the arrow indicating a specific direction for an object to move. P300 epochs, synchronized with the stimulus, were analyzed on-line via Independent Component Analysis (ICA) with subsequent feature extraction and classification by using a neural network.<br><br>RESULTS: We tested the reliability and the performance of the system in real-time. The system needed a short training period to allow task completion and reached good performance. Nonetheless, severely impaired patients had lower performance than healthy participants.<br><br>CONCLUSIONS: The proposed system is effective for use with healthy participants, whereas further research is needed before it can be used with locked-in syndrome patients.<br><br>SIGNIFICANCE: The P300-based BCI described can reliably control, in 'real time', the motion of a cursor on a graphical interface, and no time-consuming training is needed in order to test possible applications for motor-impaired patients.},
}
@article {pmid16448814,
year = {2006},
author = {Brennan, ME and Houssami, N},
title = {Image-detected 'probably benign' breast lesions: a significant reason for referral from primary care.},
journal = {Breast (Edinburgh, Scotland)},
volume = {15},
number = {5},
pages = {683-686},
doi = {10.1016/j.breast.2005.12.002},
pmid = {16448814},
issn = {0960-9776},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Australia/epidemiology ; Breast Neoplasms/*diagnosis/diagnostic imaging/*epidemiology/pathology ; Child ; Family Practice/*standards ; Female ; Gatekeeping ; Humans ; *Medical Audit ; Medical Records ; Middle Aged ; Outcome Assessment, Health Care ; *Practice Patterns, Physicians' ; Primary Health Care/*standards ; Radiography ; Referral and Consultation/*statistics &amp; numerical data ; Retrospective Studies ; Risk Assessment ; },
abstract = {In Australia, and many health care provider systems, primary care physicians are the first to see women with breast symptoms and are responsible for making decisions on whether to investigate and when to refer to specialist teams. We present an audit of new patient referrals from primary care triaged to a 'low-risk' (low likelihood of cancer) clinic on the basis of benign findings. The most common reason for referral was 'breast lump' (38%) followed by 'image-detected' abnormality (26%.) We have identified that (outside of population screening services) many women are being referred from primary care to specialist clinics for management of screen-detected lesions considered benign on imaging. Further research is needed to identify the reasons for such referrals and to develop appropriate educational strategies and clinical policy, both for the primary care and the specialist breast practitioner.},
}
@article {pmid16443377,
year = {2006},
author = {Pfurtscheller, G and Brunner, C and Schlögl, A and Lopes da Silva, FH},
title = {Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.},
journal = {NeuroImage},
volume = {31},
number = {1},
pages = {153-159},
doi = {10.1016/j.neuroimage.2005.12.003},
pmid = {16443377},
issn = {1053-8119},
mesh = {Adult ; Brain Mapping ; Cerebral Cortex/*physiology ; Cortical Synchronization/*psychology ; Dominance, Cerebral/*physiology ; Electroencephalography/*classification ; Evoked Potentials/physiology ; Female ; Foot/innervation ; Hand/innervation ; Humans ; Imagination/*physiology ; Male ; Motor Activity/*physiology ; Reference Values ; *Signal Processing, Computer-Assisted ; Tongue/innervation ; },
abstract = {We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.},
}
@article {pmid16425827,
year = {2005},
author = {Coyle, D and Prasad, G and McGinnity, TM},
title = {A time-series prediction approach for feature extraction in a brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {4},
pages = {461-467},
doi = {10.1109/TNSRE.2005.857690},
pmid = {16425827},
issn = {1534-4320},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; Time Factors ; *User-Computer Interface ; },
abstract = {This paper presents a feature extraction procedure (FEP) for a brain-computer interface (BCI) application where features are extracted from the electroencephalogram (EEG) recorded from subjects performing right and left motor imagery. Two neural networks (NNs) are trained to perform one-step-ahead predictions for the EEG time-series data, where one NN is trained on right motor imagery and the other on left motor imagery. Features are derived from the power (mean squared) of the prediction error or the power of the predicted signals. All features are calculated from a window through which all predicted signals pass. Separability of features is achieved due to the morphological differences of the EEG signals and each NNs specialization to the type of data on which it is trained. Linear discriminant analysis (LDA) is used for classification. This FEP is tested on three subjects off-line and classification accuracy (CA) rates range between 88% and 98%. The approach compares favorably to a well-known adaptive autoregressive (AAR) FEP and also a linear AAR model based prediction approach.},
}
@article {pmid16422115,
year = {2005},
author = {Chen, X and Yang, J and Ye, Z and Liang, Z and He, W and Feng, H},
title = {[Application of high frequency component in classification of different mental tasks].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {22},
number = {6},
pages = {1259-1263},
pmid = {16422115},
issn = {1001-5515},
mesh = {Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Principal Component Analysis ; *Signal Processing, Computer-Assisted ; Thinking/*physiology ; },
abstract = {Electroencephalogram (EEG) signals of different mental tasks were preprocessed using Independent Component Analysis (ICA). Auto-Regressive (AR) model was used to extract the feature, and Back-Propagation (BP) network as the classifier. When features were extracted from 20-100 Hz high frequency range, the classification accuracy was the same as that taken from the whole frequency range and was more higher than the result of 2-35 Hz normal EEG rhythm. The explanation of this phenomenon is: brain displays different rhythm assimilation during different mental task under the effect of 60 Hz power frequency, so the high frequency components of EEG include more mental modulated information which is useful for improving the classification accuracy. The result presents a new evidence for the brain rhythm assimilation phenomenon and gives a novel feature extraction method for realizing high accuracy real-time BCI based on mental task.},
}
@article {pmid16419943,
year = {2005},
author = {Yang, BH and Yan, GZ and Yan, RG},
title = {[A review of brain-computer interfaces (BCIs)].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {29},
number = {5},
pages = {353-357},
pmid = {16419943},
issn = {1671-7104},
mesh = {Brain/*physiology ; *Computers ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Software ; *User-Computer Interface ; },
abstract = {This paper introduces the general constitutions and principle of BCI systems. In addition, some characteristics and limitations of different research methods are discussed and compared. Finally, this paper points out the existing problems and future trends of BCIs. brain-computer interface (BCI), human-computer Interface (HCI), electroencephalography (EEG).},
}
@article {pmid16413826,
year = {2006},
author = {Kauhanen, L and Nykopp, T and Sams, M},
title = {Classification of single MEG trials related to left and right index finger movements.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {117},
number = {2},
pages = {430-439},
doi = {10.1016/j.clinph.2005.10.024},
pmid = {16413826},
issn = {1388-2457},
mesh = {Adult ; *Brain Mapping ; Electroencephalography ; Evoked Potentials, Motor/physiology ; Female ; Fingers/*physiology/radiation effects ; Functional Laterality/*physiology ; Humans ; Magnetoencephalography/*classification/*methods ; Male ; Motor Cortex/physiology ; Movement/*physiology/radiation effects ; Reaction Time/physiology/radiation effects ; Time Factors ; },
abstract = {OBJECTIVE: Most non-invasive brain-computer interfaces (BCIs) classify EEG signals. Here, we measured brain activity with magnetoencephalography (MEG) with an aim to characterize and classify single MEG trials during finger movements. We also examined whether averaging consecutive trials, or averaging signals from neighboring sensors, would improve classification accuracy.<br><br>METHODS: MEG was recorded in five subjects during lifting the left, right or both index fingers. Trials were classified using features, defined by an expert, from averaged spectra and time-frequency representations.<br><br>RESULTS: Classification accuracy of left vs. right finger movements was 80-94%. In the three-category classification (left, right, both), accuracy was 57-67%. Averaging three consecutive trials improved classification significantly in three subjects. Instead, spatial averaging across neighboring sensors decreased accuracy.<br><br>CONCLUSIONS: The use of averaged signals to find appropriate features for single-trial classification proved useful for the two-class classification. The classification accuracy was comparable to that in previous EEG studies.<br><br>SIGNIFICANCE: MEG provides another useful method to measure brain signals to be used in BCIs. Good performance was obtained when the classified signals were generated by two distinct sources in the left and right hemisphere. The present findings should be extended to multi-task cases involving additional brain areas.},
}
@article {pmid16405926,
year = {2006},
author = {Pfurtscheller, G and Leeb, R and Keinrath, C and Friedman, D and Neuper, C and Guger, C and Slater, M},
title = {Walking from thought.},
journal = {Brain research},
volume = {1071},
number = {1},
pages = {145-152},
doi = {10.1016/j.brainres.2005.11.083},
pmid = {16405926},
issn = {0006-8993},
mesh = {Adult ; Brain/*physiology ; Computer Graphics ; Electroencephalography/methods ; Humans ; Imagination/*physiology ; Movement/physiology ; Online Systems ; Signal Processing, Computer-Assisted/instrumentation ; Thinking/*physiology ; User-Computer Interface ; Walking/*physiology ; },
abstract = {Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements. This is achieved by exploiting a brain-computer interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a "Cave" (Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Proceedings of the 20th annual conference on Computer graphics and interactive techniques, ACM Press, 1993, pp. 135-142) where participants were able to move through a virtual street by foot imagery only. Prior to the final experiments in the Cave, the participants underwent an extensive BCI training.},
}
@article {pmid16398815,
year = {2005},
author = {Houssami, N and Irwig, L and Ung, O},
title = {Review of complex breast cysts: implications for cancer detection and clinical practice.},
journal = {ANZ journal of surgery},
volume = {75},
number = {12},
pages = {1080-1085},
doi = {10.1111/j.1445-2197.2005.03608.x},
pmid = {16398815},
issn = {1445-1433},
mesh = {Algorithms ; Australia/epidemiology ; Breast Cyst/*diagnostic imaging/epidemiology/physiopathology ; Female ; Humans ; *Ultrasonography, Mammary ; },
abstract = {The use of ultrasound in breast diagnosis has resulted in the increasing identification of incidental benign-appearing lesions, of which complex (or atypical) breast cysts are frequently reported. Complex breast cysts were estimated to be reported in approximately 5% of breast ultrasound examinations. A systematic review of the literature on sonographically detected complex breast cysts was carried out. The quality of primary studies and extracted data on cancer detection was assessed. Very few studies have examined complex breast cysts and quantified the associated cancer detection rate. In most of these studies, subjects have been selected on the basis of progress to intervention, which would overestimate the likelihood of malignancy. The only study to examine complex cysts from all consecutive ultrasounds reported one case of non-invasive cancer from 308 lesions--0.3% (95% confidence interval, 0.01-1.84). Ultrasound features associated with a higher risk of the lesion being a cancer are: thickened walls, thick internal septations, a mix of cystic and solid components, and an imaging classification of indeterminate. Using the information from the present review, complex breast cysts were categorized on the basis of associated risk of malignancy, and an approach to the management of these lesions to assist clinical decision-making was suggested. Provided adequate information is given to the patient, complex breast cysts with a very low risk of malignancy do not always require image-guided biopsy.},
}
@article {pmid16385646,
year = {2005},
author = {Danilov, Y and Tyler, M},
title = {Brainport: an alternative input to the brain.},
journal = {Journal of integrative neuroscience},
volume = {4},
number = {4},
pages = {537-550},
doi = {10.1142/s0219635205000914},
pmid = {16385646},
issn = {0219-6352},
mesh = {Brain/*physiology ; *Computer Systems ; Humans ; Tongue/physiology ; *User-Computer Interface ; },
abstract = {Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.},
}
@article {pmid16370147,
year = {2005},
author = {Vidaurre, C and Schlögl, A and Cabeza, R and Scherer, R and Pfurtscheller, G},
title = {Adaptive on-line classification for EEG-based brain computer interfaces with AAR parameters and band power estimates.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {50},
number = {11},
pages = {350-354},
doi = {10.1515/BMT.2005.049},
pmid = {16370147},
issn = {0013-5585},
mesh = {Algorithms ; *Artificial Intelligence ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {We present the result of on-line feedback Brain Computer Interface experiments using adaptive and non-adaptive feature extraction methods with an on-line adaptive classifier based on Quadratic Discriminant Analysis. Experiments were performed with 12 naïve subjects, feedback was provided from the first moment and no training sessions were needed. Experiments run in three different days with each subject. Six of them received feedback with Adaptive Autoregressive parameters and the rest with logarithmic Band Power estimates. The study was done using single trial analysis of each of the sessions and the value of the Error Rate and the Mutual Information of the classification were used to discuss the results. Finally, it was shown that even subjects starting with a low performance were able to control the system in a few hours: and contrary to previous results no differences between AAR and BP estimates were found.},
}
@article {pmid16365511,
year = {2006},
author = {Srihari Mukesh, TM and Jaganathan, V and Reddy, MR},
title = {A novel multiple frequency stimulation method for steady state VEP based brain computer interfaces.},
journal = {Physiological measurement},
volume = {27},
number = {1},
pages = {61-71},
doi = {10.1088/0967-3334/27/1/006},
pmid = {16365511},
issn = {0967-3334},
mesh = {Adult ; Brain Mapping/*methods ; Electroencephalography/methods ; Evoked Potentials, Visual/*physiology ; Factor Analysis, Statistical ; Feasibility Studies ; Humans ; Middle Aged ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {The objective is to increase the number of selections in brain computer interfaces (BCI) by recording and analyzing the steady state visual evoked potential response to dual stimulation. A BCI translates the VEP signals into user commands. The frequency band from which stimulation frequency can be selected is limited for SSVEP. This paper discusses a method to increase the number of commands by using a suitable combination of frequencies for stimulation. A biopotential amplifier based on the driven right leg circuit (DRL) is used to record 60 s epochs of the SSVEP (O(z)-A(1)) on 15 subjects using simultaneous overlapped stimulation (6, 7, 12, 13 and 14 Hzs and corresponding half frequencies). The power spectrum of each recording is obtained by frequency domain averaging of 400 ms SSVEPs and the spectral peaks were normalized. The spectral peaks of the combination frequencies of stimulation are predominant compared to individual stimulating frequencies. This method increases the number of selections by using a limited number of stimulating frequencies in BCI. For example, six selections are possible by generating only three frequencies.},
}
@article {pmid16317237,
year = {2005},
author = {Deng, J and Yao, J and Dewald, JP},
title = {Classification of the intention to generate a shoulder versus elbow torque by means of a time-frequency synthesized spatial patterns BCI algorithm.},
journal = {Journal of neural engineering},
volume = {2},
number = {4},
pages = {131-138},
doi = {10.1088/1741-2560/2/4/009},
pmid = {16317237},
issn = {1741-2560},
support = {5R01HD 39343-02/HD/NICHD NIH HHS/United States ; R03 HD39804-01A1/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; Brain Mapping/methods ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Movement/physiology ; Muscle Contraction ; Muscle, Skeletal/innervation/physiology ; Pattern Recognition, Automated/*methods ; Shoulder/*physiology ; Torque ; *User-Computer Interface ; },
abstract = {In this paper, we attempt to determine a subject's intention of generating torque at the shoulder or elbow, two neighboring joints, using scalp electroencephalogram signals from 163 electrodes for a brain-computer interface (BCI) application. To achieve this goal, we have applied a time-frequency synthesized spatial patterns (TFSP) BCI algorithm with a presorting procedure. Using this method, we were able to achieve an average recognition rate of 89% in four healthy subjects, which is comparable to the highest rates reported in the literature but now for tasks with much closer spatial representations on the motor cortex. This result demonstrates, for the first time, that the TFSP BCI method can be applied to separate intentions between generating static shoulder versus elbow torque. Furthermore, in this study, the potential application of this BCI algorithm for brain-injured patients was tested in one chronic hemiparetic stroke subject. A recognition rate of 76% was obtained, suggesting that this BCI method can provide a potential control signal for neural prostheses or other movement coordination improving devices for patients following brain injury.},
}
@article {pmid16317236,
year = {2005},
author = {Müller-Putz, GR and Scherer, R and Brauneis, C and Pfurtscheller, G},
title = {Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.},
journal = {Journal of neural engineering},
volume = {2},
number = {4},
pages = {123-130},
doi = {10.1088/1741-2560/2/4/008},
pmid = {16317236},
issn = {1741-2560},
mesh = {Adult ; *Algorithms ; Artificial Intelligence ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Female ; Fourier Analysis ; Homeostasis/physiology ; Humans ; Male ; Pattern Recognition, Automated/methods ; Photic Stimulation/methods ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.},
}
@article {pmid16317229,
year = {2005},
author = {Qin, L and He, B},
title = {A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {2},
number = {4},
pages = {65-72},
doi = {10.1088/1741-2560/2/4/001},
pmid = {16317229},
issn = {1741-2560},
support = {R01 EB000178/EB/NIBIB NIH HHS/United States ; R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.},
}
@article {pmid16317224,
year = {2005},
author = {Schlögl, A and Lee, F and Bischof, H and Pfurtscheller, G},
title = {Characterization of four-class motor imagery EEG data for the BCI-competition 2005.},
journal = {Journal of neural engineering},
volume = {2},
number = {4},
pages = {L14-22},
doi = {10.1088/1741-2560/2/4/L02},
pmid = {16317224},
issn = {1741-2560},
mesh = {Algorithms ; Artificial Intelligence ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.},
}
@article {pmid16309237,
year = {2005},
author = {Wang, YR and Wigington, DP and Strugnell, SA and Knutson, JC},
title = {Growth inhibition of cancer cells by an active metabolite of a novel vitamin D prodrug.},
journal = {Anticancer research},
volume = {25},
number = {6B},
pages = {4333-4339},
pmid = {16309237},
issn = {0250-7005},
mesh = {Antineoplastic Combined Chemotherapy Protocols/*pharmacology ; Breast Neoplasms/*drug therapy/pathology ; Calcitriol/pharmacology ; Carcinoma, Hepatocellular/metabolism ; Cell Growth Processes/drug effects ; Cell Line, Tumor ; Doxorubicin/administration &amp; dosage ; Ergocalciferols/pharmacology ; Female ; Genistein/administration &amp; dosage ; Humans ; Liver Neoplasms/metabolism ; Male ; Prodrugs/metabolism/pharmacology ; Prostatic Neoplasms/*drug therapy/pathology ; Vitamin D/administration &amp; dosage/*analogs &amp; derivatives/metabolism/pharmacology ; },
abstract = {Active vitamin D compounds have been developed that maintain antiproliferative properties with low calcemic activity. BCI-210, a novel vitamin D pro-drug developed in our laboratory, is activated through side chain hydroxylation and possesses lower calcemic activity than calcitriol. The human hepatoma cell line (HepG2) was used to produce an active metabolite, which was characterized and identified as 27-hydroxy-BCI-210. We compared the ability of 27-OH-BCI-210 with calcitriol to inhibit proliferation of prostate (LNCaP), and breast (MCF-7) cancer cells. Cells were plated in multi-well plates and incubated with vehicle or vitamin D compounds for 6 days, after which the cell numbers were determined by a colorimetric assay. 27-OH-BCI-210 produced a dose-dependent growth inhibition, although a concentration five-fold greater than calcitriol was required to produce equivalent inhibition. We also examined the antiproliferative activity of 27-OH-BCI-210 in combination with chemotherapeutic drugs. With genistein and doxorubicin, 27-OH-BCI-210 produced synergistic inhibition of proliferation of LNCaP and MCF-7 cells. These synergistic interactions suggest the potential clinical utility of 27-OH-BCI-210 in the treatment of prostate and breast tumors.},
}
@article {pmid16291944,
year = {2005},
author = {Carmena, JM and Lebedev, MA and Henriquez, CS and Nicolelis, MA},
title = {Stable ensemble performance with single-neuron variability during reaching movements in primates.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {25},
number = {46},
pages = {10712-10716},
pmid = {16291944},
issn = {1529-2401},
mesh = {Action Potentials/*physiology ; Animals ; Female ; Macaca mulatta ; Motor Cortex/physiology ; Movement/*physiology ; Neurons/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Significant variability in firing properties of individual neurons was observed while two monkeys, chronically implanted with multielectrode arrays in frontal and parietal cortical areas, performed a continuous arm movement task. Although the degree of correlation between the firing of single neurons and movement parameters was nonstationary, stable predictions of arm movements could be obtained from the activity of neuronal ensembles. This result adds support to the idea that movement parameters are redundantly encoded in the motor cortex, such that brain networks can achieve the same behavioral goals through different patterns and relative contribution of individual neuron activity. This has important implications for neural prosthetics, suggesting that accurate operation of a brain-machine interface requires recording from large neuronal ensembles to minimize the effect of variability and ensuring stable performance over long periods of time.},
}
@article {pmid16289548,
year = {2004},
author = {Weiskopf, N and Scharnowski, F and Veit, R and Goebel, R and Birbaumer, N and Mathiak, K},
title = {Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI).},
journal = {Journal of physiology, Paris},
volume = {98},
number = {4-6},
pages = {357-373},
doi = {10.1016/j.jphysparis.2005.09.019},
pmid = {16289548},
issn = {0928-4257},
mesh = {Algorithms ; Behavior/physiology ; Biofeedback, Psychology/*physiology ; Brain/*physiology ; Brain Mapping ; Cognition/physiology ; Humans ; *Magnetic Resonance Imaging ; Neuronal Plasticity/physiology ; Neurons/physiology ; Oxygen/blood ; Signal Processing, Computer-Assisted ; Time Factors ; },
abstract = {Functional magnetic resonance imaging (fMRI) measures the blood oxygen level-dependent (BOLD) signal related to neuronal activity. So far, this technique has been limited by time-consuming data analysis impeding on-line analysis. In particular, no brain-computer interface (BCI) was available which provided on-line feedback to learn physiological self-regulation of the BOLD signal. Recently, studies have shown that fMRI feedback is feasible and facilitates voluntary control of brain activity. Here we review these studies to make the fMRI feedback methodology accessible to a broader scientific community such as researchers concerned with functional brain imaging and the neurobiology of learning. Methodological and conceptual limitations were substantially reduced by artefact control, sensitivity improvements, real-time algorithms, and adapted experimental designs. Physiological self-regulation of the local BOLD response is a new paradigm for cognitive neuroscience to study brain plasticity and the functional relevance of regulated brain areas by modification of behaviour. Voluntary control of abnormal activity in circumscribed brain areas may even be applied as psychophysiological treatment.},
}
@article {pmid16285389,
year = {2005},
author = {Glassman, EL},
title = {A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {11},
pages = {1851-1862},
doi = {10.1109/TBME.2005.856277},
pmid = {16285389},
issn = {0018-9294},
mesh = {Action Potentials/*physiology ; Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Computer Simulation ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Models, Neurological ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; Scalp/physiology ; Signal Processing, Computer-Assisted ; Therapy, Computer-Assisted/methods ; User-Computer Interface ; },
abstract = {This paper describes the development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of a wavelet, in a wavelet transform for improving EEG wavelet analysis, intended for brain-computer interfaces. The hypothesis is that an optimal wavelet can be approximated by deriving it from underlying components of the EEG. The SNAP was compared to standard wavelets by measuring Support Vector Machine-based EEG classification accuracy when using different wavelets/filters for EEG analysis. When classifying P300 evoked potentials, the error, as a function of the wavelet/filter used, ranged from 6.92% to 11.99%, almost twofold. Classification using the SNAP was more accurate than that with any of the six standard wavelets tested. Similarly, when differentiating between preparation for left- or right-hand movements, classification using the SNAP was more accurate (10.03% error) than for four out of five of the standard wavelets (9.54% to 12.00% error) and internationally competitive (7% error) on the 2001 NIPS competition test set. Phenomena shown only in maps of discriminatory EEG activity may explain why the SNAP appears to have promise for improving EEG wavelet analysis. It represents the initial exploration of a potential family of EEG-specific wavelets.},
}
@article {pmid16256373,
year = {2006},
author = {Tang, A and Sutherland, M and Wang, Y},
title = {Contrasting single-trial ERPs between experimental manipulations: improving differentiability by blind source separation.},
journal = {NeuroImage},
volume = {29},
number = {1},
pages = {335-346},
doi = {10.1016/j.neuroimage.2005.07.058},
pmid = {16256373},
issn = {1053-8119},
mesh = {Adult ; Algorithms ; Data Interpretation, Statistical ; Electroencephalography/*statistics &amp; numerical data ; Evoked Potentials/*physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Functional Laterality/physiology ; Humans ; Male ; Median Nerve/physiology ; Nerve Net/physiology ; Physical Stimulation ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/physiology ; },
abstract = {Contrasting event-related potentials (ERPs) generated under different experimental conditions and inferring differential brain responses is widely practiced in cognitive neuroscience research. Traditionally, these contrasts and subsequent inferences have proceeded directly from ERPs measured at the scalp. For certain tasks, it is not unusual that ERPs from a subset of channels are given particular emphasis in data analysis, such as the channels displaying the maximum peak amplitude in regions of interest ("best sensors") or channels showing the largest averaged ERP waveform differences. With the aid of a blind source separation (BSS) algorithm, second-order blind identification (SOBI), which has been recently validated for its ability to recover correlated neuronal sources, we show that single-trial ERPs from previously validated neuronal sources were more distinguishable among different experimental manipulations than the single-trial ERPs measured at the comparable "best sensors". This suggests that by using validated SOBI-recovered neuronal sources, ERP researchers can improve the ability to detect differences in neuronal responses induced by experimental manipulations. Critically, our observations were made at the level of single trials, as opposed to the averaged ERP. Therefore, our conclusions are particularly relevant to phenomena involving trial-to-trial changes in brain activation, for example, rapid induction of brain plasticity and perceptual learning, as well as to the development of brain-computer interfaces. Similar advantages would also apply to analogous situations with magnetoencephalography (MEG).},
}
@article {pmid16252118,
year = {2006},
author = {Wang, YH and Augspurger, C},
title = {Comparison of seedling recruitment under arborescent palms in two Neotropical forests.},
journal = {Oecologia},
volume = {147},
number = {3},
pages = {533-545},
pmid = {16252118},
issn = {0029-8549},
mesh = {Light ; Seedlings/*growth &amp; development ; Trees/*growth &amp; development ; },
abstract = {Certain overlying strata in forests may disproportionately reduce seedling density and species richness. For eight arborescent palm species, we quantified the relative restriction of seedling recruitment under individual palms versus non-palm sites and extended to the landscape scale by quantifying the total area covered by arborescent palms at Barro Colorado Island (BCI), Panama and La Selva Biological Station, Costa Rica. We also examined whether differences among palm species in restricting seedling recruitment were associated with differences in crown architecture, litter depth, and light availability. Woody seedlings had lower mean density/m2 and mean number of species/m2 under individual palms than at non-palm sites for all four palm species at BCI, but for none at La Selva. Estimated species richness for woody seedlings, derived via rarefaction, was lower under palm than non-palm microsites at both BCI and La Selva, but not for non-woody seedlings. Differences in seedling density corresponded to some key architectural characters that differed among the palm species. Light availability was lower under palm than non-palm microsites at both BCI and La Selva, but only estimated species richness of woody seedlings at BCI was strongly correlated with % canopy openness. The coverage of arborescent palms was much lower at BCI than La Selva. Therefore, at BCI, the relative restriction of woody seedling recruitment under individual palms does not accumulate greatly at the landscape scale. At La Selva, for woody seedlings, only estimated species richness was relatively limited under palms, and non-woody seedlings had relatively lower mean density/m2 and mean number of species/m2 under only one palm species. Therefore, the relative restriction of seedling recruitment by arborescent palms at La Selva is limited at both individual and landscape scales.},
}
@article {pmid16243104,
year = {2005},
author = {Ismailov, RM and Ness, RB and Weiss, HB and Lawrence, BA and Miller, TR},
title = {Trauma associated with acute myocardial infarction in a multi-state hospitalized population.},
journal = {International journal of cardiology},
volume = {105},
number = {2},
pages = {141-146},
doi = {10.1016/j.ijcard.2004.11.025},
pmid = {16243104},
issn = {0167-5273},
mesh = {Age Distribution ; Aged ; Confounding Factors, Epidemiologic ; Coronary Angiography ; Female ; Hospitalization/*statistics &amp; numerical data ; Humans ; Incidence ; Male ; Middle Aged ; Myocardial Infarction/diagnostic imaging/epidemiology/*etiology ; *Population Surveillance ; Retrospective Studies ; Risk Factors ; Sex Distribution ; Survival Rate ; United States ; Wounds and Injuries/complications/*epidemiology ; },
abstract = {INTRODUCTION: Trauma has been suggested, in case series, as one of the nonatherosclerotic mechanisms leading to acute myocardial infarction (AMI), the leading cause of death in the US. AMI following non-penetrating injury has been shown to carry significant morbidity and mortality.<br><br>OBJECTIVE: To determine whether hospitalized injuries in a large multi state population are associated with increased risk of AMI during the initial hospital stay.<br><br>METHODS: Statewide injury hospital discharge data were collected from 19 states in 1997. Affected body regions of interest included thoracic, abdominal or pelvic, spine or back and blunt cardiac injury (BCI). The outcome of interest was AMI which was identified based on ICD-9-CM discharge diagnoses for the same visit. Unadjusted and adjusted multivariate logistic regression analyses were performed.<br><br>RESULTS: Independent of confounding factors and coronary arteriography (CA) status, BCI was associated with 2.6-fold increased risk for AMI in persons 46 years or older. When the diagnosis of AMI was confirmed by CA, BCI was associated with 8-fold risk elevation among patients 46 years and older and a 31-fold elevation among patients 45 years and younger. Abdominal or pelvic trauma, irrespective of confounding factors and CA status, was associated with a 65% increase in the risk of AMI among patients 45 years and younger and 93% increase in the risk of among patients 46 years and older. When the diagnosis of AMI was confirmed by CA, abdominal or pelvic trauma was associated with 6-fold risk elevation among patients 46 years and older.<br><br>CONCLUSION: Direct trauma to the heart, as characterized by a diagnosis of BCI, was observed to carry the greatest risk for AMI. Abdominal or pelvic trauma also increased the risk for AMI. Longitudinal studies are warranted to better understand the relationship between trauma and AMI.},
}
@article {pmid16236487,
year = {2005},
author = {Neuper, C and Scherer, R and Reiner, M and Pfurtscheller, G},
title = {Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG.},
journal = {Brain research. Cognitive brain research},
volume = {25},
number = {3},
pages = {668-677},
doi = {10.1016/j.cogbrainres.2005.08.014},
pmid = {16236487},
issn = {0926-6410},
mesh = {Adolescent ; Adult ; Brain Mapping ; Classification ; *Electroencephalography ; Female ; Hand/physiology ; Humans ; Imagination/*physiology ; Individuality ; Learning/physiology ; Male ; Middle Aged ; Movement/*physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; Visual Perception/*physiology ; },
abstract = {Single-trial motor imagery classification is an integral part of a number of brain-computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual-motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual-motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.},
}
@article {pmid16221703,
year = {2006},
author = {Brown, AJ and Ritter, CS and Knutson, JC and Strugnell, SA},
title = {The vitamin D prodrugs 1alpha(OH)D2, 1alpha(OH)D3 and BCI-210 suppress PTH secretion by bovine parathyroid cells.},
journal = {Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association},
volume = {21},
number = {3},
pages = {644-650},
doi = {10.1093/ndt/gfi186},
pmid = {16221703},
issn = {0931-0509},
mesh = {Animals ; Bone Density Conservation Agents/pharmacology ; Cattle ; Cells, Cultured ; Chromatography, High Pressure Liquid ; Cytochrome P-450 Enzyme Inhibitors ; Cytochrome P-450 Enzyme System/metabolism ; Enzyme-Linked Immunosorbent Assay ; Ergocalciferols/*pharmacology ; Gene Expression/genetics ; Hydroxycholecalciferols/*pharmacology ; In Vitro Techniques ; Ketoconazole/pharmacology ; Parathyroid Glands/cytology/drug effects/*metabolism ; Parathyroid Hormone/*antagonists &amp; inhibitors/genetics/*metabolism ; RNA, Messenger/genetics ; Vitamin D/*analogs &amp; derivatives/pharmacology ; },
abstract = {BACKGROUND: Active vitamin D compounds are widely used in the treatment of secondary hyperparathyroidism associated with renal failure. These compounds reduce PTH secretion through vitamin D receptor (VDR)-dependent repression of PTH gene transcription. In previous studies, 1alpha(OH)D3, a vitamin D prodrug, inhibited PTH secretion in cultured bovine parathyroid cells, but it was unclear whether 1alpha(OH)D3 itself or an active metabolite produced this inhibition.<br><br>METHODS: We determined the effectiveness of the vitamin D prodrugs 1alpha(OH)D3, 1alpha(OH)D2 and 1alpha(OH)-24(R)-methyl-25-ene-D2 (BCI-210) at inhibiting PTH secretion in bovine parathyroid cell cultures, and examined the metabolism of [3H]1alpha(OH)D2 in these cells.<br><br>RESULTS: All three prodrugs suppressed PTH secretion with approximately 10% of the activity of 1,25(OH)2D3; much higher activity than expected based on the VDR affinities of these prodrugs (0.25% of 1,25(OH)2D3). Parathyroid cells activated [3H]1alpha(OH)D2 to both 1,25(OH)2D2 and 1,24(OH)2D2. 1,24(OH)2D2 was detectable at 4 h, increased to a maximum at 8 h, and then decreased. In contrast, 1,25(OH)2D2 levels increased linearly with time, suggesting the presence of constitutively active vitamin D-25-hydroxylase not previously reported in parathyroid cells. The cytochrome P-450 inhibitor ketoconazole (50 microM) reduced 1alpha(OH)D2 metabolism to below detectable levels, but did not significantly affect suppression of PTH by 1alpha(OH)D2.<br><br>CONCLUSIONS: The vitamin D prodrugs 1alpha(OH)D3, 1alpha(OH)D2 and BCI-210 suppressed PTH production by cultured parathyroid cells. The ability of 1alpha(OH)D2 to reduce PTH despite inhibition of its metabolism suggests a direct action of this 'prodrug' on the parathyroid gland, but the mechanism underlying this activity is not yet known.},
}
@article {pmid16200760,
year = {2005},
author = {McFarland, DJ and Wolpaw, JR},
title = {Sensorimotor rhythm-based brain-computer interface (BCI): feature selection by regression improves performance.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {3},
pages = {372-379},
doi = {10.1109/TNSRE.2005.848627},
pmid = {16200760},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Artificial Intelligence ; Biological Clocks ; Brain/*physiopathology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Cortex/*physiopathology ; Pattern Recognition, Automated/*methods ; Regression Analysis ; Somatosensory Cortex/*physiopathology ; Spinal Cord Injuries/*physiopathology/rehabilitation ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {People can learn to control electroencephalogram (EEG) features consisting of sensorimotor rhythm amplitudes and can use this control to move a cursor in one or two dimensions to a target on a screen. In the standard one-dimensional application, the cursor moves horizontally from left to right at a fixed rate while vertical cursor movement is continuously controlled by sensorimotor rhythm amplitude. The right edge of the screen is divided among 2-6 targets, and the user's goal is to control vertical cursor movement so that the cursor hits the correct target when it reaches the right edge. Up to the present, vertical cursor movement has been a linear function of amplitude in a specific frequency band [i.e., 8-12 Hz (mu) or 18-26 Hz (beta)] over left and/or right sensorimotor cortex. The present study evaluated the effect of controlling cursor movement with a weighted combination of these amplitudes in which the weights were determined by an regression algorithm on the basis of the user's past performance. Analyses of data obtained from a representative set of trained users indicated that weighted combinations of sensorimotor rhythm amplitudes could support cursor control significantly superior to that provided by a single feature. Inclusion of an interaction term further improved performance. Subsequent online testing of the regression algorithm confirmed the improved performance predicted by the offline analyses. The results demonstrate the substantial value for brain-computer interface applications of simple multivariate linear algorithms. In contrast to many classification algorithms, such linear algorithms can easily incorporate multiple signal features, can readily adapt to changes in the user's control of these features, and can accommodate additional targets without major modifications.},
}
@article {pmid16200749,
year = {2005},
author = {Hu, J and Si, J and Olson, BP and He, J},
title = {Feature detection in motor cortical spikes by principal component analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {3},
pages = {256-262},
doi = {10.1109/TNSRE.2005.847389},
pmid = {16200749},
issn = {1534-4320},
mesh = {Action Potentials/physiology ; Animals ; *Artificial Intelligence ; Behavior, Animal/*physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Male ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; Rats ; Rats, Sprague-Dawley ; Statistics as Topic ; Therapy, Computer-Assisted/methods ; *User-Computer Interface ; },
abstract = {Principal component analysis was performed on recorded neural spike trains in rats' motor cortices when rats were involved in real-time control tasks using brain-machine interfaces. The rat with implanted microelectrode array was placed in a conditioning chamber, but freely moving, to decide which one of the two paddles should be activated to shift the cue light to the center. It is found that the principal component feature vectors revealed the importance of individual neurons and windows of time in the decision making process. In addition, one of the first principal components has much higher discriminative capability than others, although it represents only a small percentage of the total variance in the data. Using one to six principal components with a Bayes classifier achieved classification accuracy comparable to that obtained by a more sophisticated high performance support vector classifier.},
}
@article {pmid16189972,
year = {2005},
author = {Kelly, SP and Lalor, EC and Finucane, C and McDarby, G and Reilly, RB},
title = {Visual spatial attention control in an independent brain-computer interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {9},
pages = {1588-1596},
doi = {10.1109/TBME.2005.851510},
pmid = {16189972},
issn = {0018-9294},
mesh = {Adult ; Algorithms ; Attention/*physiology ; *Communication Devices for People with Disabilities ; Computer Systems ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Humans ; Middle Aged ; Space Perception/*physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.},
}
@article {pmid16189967,
year = {2005},
author = {Lemm, S and Blankertz, B and Curio, G and Müller, KR},
title = {Spatio-spectral filters for improving the classification of single trial EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {9},
pages = {1541-1548},
doi = {10.1109/TBME.2005.851521},
pmid = {16189967},
issn = {0018-9294},
mesh = {Brain/*physiology ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; User-Computer Interface ; },
abstract = {Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.},
}
@article {pmid16186045,
year = {2005},
author = {Kübler, A and Neumann, N},
title = {Brain-computer interfaces--the key for the conscious brain locked into a paralyzed body.},
journal = {Progress in brain research},
volume = {150},
number = {},
pages = {513-525},
doi = {10.1016/S0079-6123(05)50035-9},
pmid = {16186045},
issn = {0079-6123},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiopathology ; *Consciousness ; Electroencephalography ; Humans ; Quadriplegia/*physiopathology/*psychology ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are systems that allow us to translate in real-time the electrical activity of the brain in commands to control devices. They do not rely on muscular activity and can therefore provide communication and control for those who are severely paralyzed (locked-in) due to injury or disease. It has been shown that locked-in patients are able to achieve EEG-controlled cursor or limb movement and patients have successfully communicated by means of a BCI. Current BCIs differ in how the neural activity of the brain is recorded, how subjects (humans and animals) are trained to produce a specific EEG response, how the signals are translated into device commands, and which application is provided to the user. The present review focuses on approaches to BCIs that process the EEG on-line and provide EEG feedback or feedback of results to the user. We regard online processing and feedback cornerstones for routine application of BCIs in the field. Because training patients in their home environment is effortful and personal and financial resources are limited, only few studies on BCI long-term use for communication with paralyzed patients are available. The need for multidisciplinary research, comprising computer science, engineering, neuroscience, and psychology is now being acknowledged by the BCI community. A standard BCI platform, referred to as BCI2000, has been developed, which allows us to better combine and compare the different BCI approaches of different laboratories. As BCI laboratories now also join to unify their expertise and collaborations are funded, we consider it realistic that within few years we will be able to offer a BCI, which will be easy to operate for patients and caregivers.},
}
@article {pmid16184208,
year = {2005},
author = {Brennan, M and Wilcken, N and French, J and Ung, O and Boyages, J},
title = {Management of early breast cancer--the current approach.},
journal = {Australian family physician},
volume = {34},
number = {9},
pages = {755-760},
pmid = {16184208},
issn = {0300-8495},
mesh = {Aromatase Inhibitors/therapeutic use ; Axilla ; Breast Neoplasms/pathology/*therapy ; Chemotherapy, Adjuvant/methods ; Female ; Holistic Health ; Humans ; Lymph Node Excision/methods ; Mastectomy/methods ; Neoplasm Recurrence, Local ; Patient Care Team/organization &amp; administration ; Radiotherapy, Adjuvant/methods ; Risk Factors ; Selective Estrogen Receptor Modulators/therapeutic use ; Tamoxifen/therapeutic use ; },
abstract = {This seventh article in our series on breast disease will focus on what is new in the management of invasive primary breast cancer. Up-to-date information on the key aspects of breast cancer management is presented, including descriptions of the new technique of sentinel lymph node biopsy and the new hormone treatment, aromatase inhibitors. Current trends in surgery for breast cancer and the adjuvant treatments of chemotherapy and radiotherapy are also discussed.},
}
@article {pmid16143385,
year = {2006},
author = {Johansson, F and Carlberg, P and Danielsen, N and Montelius, L and Kanje, M},
title = {Axonal outgrowth on nano-imprinted patterns.},
journal = {Biomaterials},
volume = {27},
number = {8},
pages = {1251-1258},
doi = {10.1016/j.biomaterials.2005.07.047},
pmid = {16143385},
issn = {0142-9612},
mesh = {Animals ; Axons/*physiology ; Cell Culture Techniques ; Cells, Cultured ; Female ; Growth Cones/physiology ; Mice ; Microscopy, Electron, Scanning ; *Nanotechnology ; Polymethyl Methacrylate ; Silicon ; },
abstract = {Nanotechnology has provided methods to fabricate surface patterns with features down to a few nm. If cells or cell processes exhibit contact guidance in response to such small patterns is an interesting question and could be pertinent for many applications. In the present study we investigated if axonal outgrowth was affected by nano-printed patterns in polymethylmethacrylate (PMMA)-covered silicon chips. To this end adult mouse sympathetic and sensory ganglia were mounted in Matrigel on the chips close to the nano-patterns. The patterns consisted of parallel grooves with depths of 300 nm and varying widths of 100-400 nm. The distance between two adjacent grooves was 100-1600 nm. The chips were cultured in medium containing 25 ng/ml of nerve growth factor to stimulate axonal outgrowth. After 1 week of incubation, axonal outgrowth was investigated by immunocytochemistry or scanning electron microscopy. Axons displayed contact guidance on all patterns. Furthermore, we found that the nerve cell processes preferred to grow on ridge edges and elevations in the patterns rather than in grooves, a seemingly claustrophobic behavior. We conclude that axons of peripheral neurons might be guided by nanopatterns on PMMA when the lateral features are 100 nm or larger. The present results can be utilized for nerve regenerating scaffolds or the construction of a stable, high-resolution electronic interface to neurons, which is required for future brain machine interfaces.},
}
@article {pmid16135888,
year = {2005},
author = {Karniel, A and Kositsky, M and Fleming, KM and Chiappalone, M and Sanguineti, V and Alford, ST and Mussa-Ivaldi, FA},
title = {Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system.},
journal = {Journal of neural engineering},
volume = {2},
number = {3},
pages = {S250-65},
doi = {10.1088/1741-2560/2/3/S08},
pmid = {16135888},
issn = {1741-2560},
mesh = {Animals ; Brain/*physiology ; Cybernetics/*methods ; Humans ; Lampreys ; *Man-Machine Systems ; *Models, Neurological ; Nerve Net/*physiology ; Neuronal Plasticity/*physiology ; Robotics/*methods ; *User-Computer Interface ; },
abstract = {When the brain interacts with the environment it constantly adapts by representing the environment in a form that is called an internal model. The neurobiological basis for internal models is provided by the connectivity and the dynamical properties of neurons. Thus, the interactions between neural tissues and external devices provide a fundamental means for investigating the connectivity and dynamical properties of neural populations. We developed this idea, suggested in the 1980s by Valentino Braitenberg, for investigating and representing the dynamical behavior of neuronal populations in the brainstem of the lamprey. The brainstem was maintained in vitro and connected in a closed loop with two types of artificial device: (a) a simulated dynamical system and (b) a small mobile robot. In both cases, the device was controlled by recorded extracellular signals and its output was translated into electrical stimuli delivered to the neural system. The goal of the first study was to estimate the dynamical dimension of neural preparation in a single-input/single-output configuration. The dynamical dimension is the number of state variables that together with the applied input determine the output of a system. The results indicate that while this neural system has significant dynamical properties, its effective complexity, as established by the dynamical dimension, is rather moderate. In the second study, we considered a more specific situation, in which the same portion of the nervous system controls a robotic device in a two-input/two-output configuration. We fitted the input-output data from the neuro-robotic preparation to neural network models having different internal dynamics and we observed the generalization error of each model. Consistent with the first study, this second experiment showed that a simple recurrent dynamical model was able to capture the behavior of the hybrid system. This experimental and computational framework provides the means for investigating neural plasticity and internal representations in the context of brain-machine interfaces.},
}
@article {pmid16133914,
year = {2005},
author = {Hung, CI and Lee, PL and Wu, YT and Chen, LF and Yeh, TC and Hsieh, JC},
title = {Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers.},
journal = {Annals of biomedical engineering},
volume = {33},
number = {8},
pages = {1053-1070},
doi = {10.1007/s10439-005-5772-1},
pmid = {16133914},
issn = {0090-6964},
mesh = {Adult ; Brain/*physiology ; Electroencephalography ; Female ; Humans ; Male ; Mental Processes/*physiology ; Perception/*physiology ; Recognition, Psychology/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used to provide neural input signals to activate a brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable components: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract two reliable neural features, termed contralateral and ipsilateral rebound maps, by removing artifacts from motor imagery EEG based on independent component analysis (ICA), and to employ four classifiers to investigate the efficacy of rebound maps. Results demonstrate that, with the use of ICA, recognition rates for four classifiers (fisher linear discriminant (FLD), back-propagation neural network (BP-NN), radial-basis function neural network (RBF-NN), and support vector machine (SVM)) improved significantly, from 54%, 54%, 57% and 55% to 70.5%, 75.5%, 76.5% and 77.3%, respectively. In addition, the areas under the receiver operating characteristics (ROC) curve, which assess the quality of classification over a wide range of misclassification costs, also improved from .65, .60, .62, and .64 to .74, .76, .80 and .81, respectively.},
}
@article {pmid16106662,
year = {2004},
author = {Wolpaw, JR},
title = {Brain-computer interfaces (BCIs) for communication and control: a mini-review.},
journal = {Supplements to Clinical neurophysiology},
volume = {57},
number = {},
pages = {607-613},
doi = {10.1016/s1567-424x(09)70400-3},
pmid = {16106662},
issn = {1567-424X},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Brain/*physiology ; Brain Mapping ; *Communication ; Evoked Potentials/physiology ; Humans ; Models, Neurological ; Signal Detection, Psychological/*physiology ; *User-Computer Interface ; },
}
@article {pmid16106660,
year = {2004},
author = {Pfurtscheller, G and Graimann, B and Huggins, JE and Levine, SP},
title = {Brain-computer communication based on the dynamics of brain oscillations.},
journal = {Supplements to Clinical neurophysiology},
volume = {57},
number = {},
pages = {583-591},
doi = {10.1016/s1567-424x(09)70398-8},
pmid = {16106660},
issn = {1567-424X},
mesh = {Brain/*physiology ; Brain Mapping ; *Cortical Synchronization ; Hand Strength/physiology ; Humans ; Imagination/physiology ; Movement/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This chapter presents a review of brain-computer communication based on motor imagery and the dynamics of brain oscillations. The concept of motor imagery as experimental strategy and the two different modes of operation a brain-computer interface can have are explained. An EEG based brain switch that can control a FES-induced hand grasp of a tetraplegic and an approach towards an ECoG based brain switch are presented.},
}
@article {pmid16099513,
year = {2006},
author = {Kim, KH and Kim, SS and Kim, SJ},
title = {Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study.},
journal = {Journal of neuroscience methods},
volume = {150},
number = {2},
pages = {202-211},
doi = {10.1016/j.jneumeth.2005.06.015},
pmid = {16099513},
issn = {0165-0270},
mesh = {Action Potentials/*physiology ; *Algorithms ; Brain/physiology ; *Models, Neurological ; Neurons/*physiology ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {One of the most important building blocks of the brain-machine interface (BMI) based on neuronal spike trains is the decoding algorithm, a computational method for the reconstruction of desired information from spike trains. Previous studies have reported that a simple linear filter is effective for this purpose and that no noteworthy gain is achieved from the use of nonlinear algorithms. In order to test this premise, we designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR). Their performances were assessed using multiple neuronal spike trains generated by a biophysical neuron model and by a directional tuning model of the primary motor cortex. The performances of the nonlinear algorithms, in general, were superior. The advantages of using nonlinear algorithms were more profound for cases where false-positive/negative errors occurred in spike trains. When the MLPs were trained using trial-and-error, they often showed disappointing performance comparable to that of the linear filter. The nonlinear SVR showed the highest performance, and this may be due to the superiority of SVR in training and generalization.},
}
@article {pmid16098328,
year = {2005},
author = {Ismailov, RM and Weiss, HB and Ness, RB and Lawrence, BA and Miller, TR},
title = {Blunt cardiac injury associated with cardiac valve insufficiency: trauma links to chronic disease?.},
journal = {Injury},
volume = {36},
number = {9},
pages = {1022-1028},
doi = {10.1016/j.injury.2005.05.028},
pmid = {16098328},
issn = {0020-1383},
mesh = {Age Factors ; Aged ; Aged, 80 and over ; Chronic Disease ; Female ; Heart Injuries/*complications ; Heart Valve Diseases/*etiology ; Humans ; Male ; Middle Aged ; Risk Factors ; Wounds, Nonpenetrating/*complications ; },
abstract = {CONTEXT: Cardiac injury has been well recognised as a complication of blunt chest trauma. Its clinical spectrum ranges from blunt cardiac injury (BCI) to complete rupture of cardiac tissues, with cardiac valvular injury often being overlooked.<br><br>OBJECTIVE: To determine whether hospitalised BCI is associated with increased risk of cardiac valve insufficiency in a large multi-state hospitalised population.<br><br>METHODS: Cases with BCI and cardiac valve insufficiency were identified based on discharge diagnoses in 1997 statewide hospital discharge data from 19 states. Four valvular outcomes were studied: (1) mitral valve insufficiency, incompetence, regurgitation (MVIIR); (2) aortic valve insufficiency, incompetence, regurgitation, stenosis (AVIIRS); (3) tricuspid valve insufficiency, incompetence, regurgitation, stenosis (TVIIRS); and (4) pulmonary valve insufficiency, incompetence, regurgitation, stenosis (PVIIRS).<br><br>RESULTS: Among 1,051,081 injury discharges, 2709 (0.26%) people had BCI; 13,087 (1.25%) had MVIIR; 9811 (0.93%) had AVIIRS; 1338 (0.13%) had TVIIRS; 178 (0.02%) had PVIIRS. Independent of potential confounding factors, discharge for BCI was associated with a 12-fold increased risk for TVIIRS and a 3.4-fold increased risk for AVIIRS.<br><br>CONCLUSION: Cardiac valve insufficiency has been well recognised as an important risk factor for congestive heart failure. With the findings that BCI is associated with an increased risk of specific valvular disorders, it is possible that trauma may play an important and heretofore largely unrecognised role in a portion of the burden of cardiovascular morbidity and mortality.},
}
@article {pmid16093411,
year = {2005},
author = {Pham, M and Hinterberger, T and Neumann, N and Kübler, A and Hofmayer, N and Grether, A and Wilhelm, B and Vatine, JJ and Birbaumer, N},
title = {An auditory brain-computer interface based on the self-regulation of slow cortical potentials.},
journal = {Neurorehabilitation and neural repair},
volume = {19},
number = {3},
pages = {206-218},
doi = {10.1177/1545968305277628},
pmid = {16093411},
issn = {1545-9683},
mesh = {Adult ; Cerebral Cortex/*physiology ; Communication Devices for People with Disabilities ; Conditioning, Operant ; Electroencephalography ; Evoked Potentials, Auditory/*physiology ; Evoked Potentials, Visual/*physiology ; Feasibility Studies ; Feedback ; Female ; Humans ; Male ; Reference Values ; *User-Computer Interface ; },
abstract = {OBJECTIVES: Communication support for severely paralyzed patients with visual impairment is needed. Therefore, the feasibility of a brain-computer interface (BCI) using auditory stimuli alone, based on the self-regulation of slow cortical potentials (SCPs), was investigated.<br><br>METHODS: Auditory stimuli were used for task and feedback presentation in an SCP self-regulation paradigm. Voluntarily produced SCP responses and measures of communication performance were compared between 3 groups (total of N = 59) of visual, auditory, and cross-modal visual-auditory modality. Electroencephalogram recordings and training from Cz-mastoids were carried out on 3 consecutive sessions. Data of 1500 trials per subject were collected.<br><br>RESULTS: Best performance was achieved for the visual, followed by the auditory condition. The performance deficit of the auditory condition was partly due to decreased self-produced positivity. Larger SCP response variability also accounted for lower performance of the auditory condition. Cross-modally presented stimuli did not lead to significant learning and control of SCP.<br><br>CONCLUSIONS: Brain-computer communication using auditory stimuli only is possible. Smaller cortical positivity achieved in the auditory condition, as compared to the visual condition, may be a consequence of increased selective attention to simultaneously presented auditory stimuli. To optimize performance, auditory stimuli characteristics may have to be adapted. Other suggestions for enhancement of communication performance with auditory stimuli are discussed.},
}
@article {pmid16055377,
year = {2005},
author = {Moffitt, MA and McIntyre, CC},
title = {Model-based analysis of cortical recording with silicon microelectrodes.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {116},
number = {9},
pages = {2240-2250},
doi = {10.1016/j.clinph.2005.05.018},
pmid = {16055377},
issn = {1388-2457},
mesh = {Algorithms ; Animals ; Brain Edema/physiopathology ; Cerebral Cortex/cytology/*physiology ; Computer Simulation ; Data Collection ; Extracellular Space/physiology ; Finite Element Analysis ; Microelectrodes ; Models, Neurological ; Models, Statistical ; Nonlinear Dynamics ; Pyramidal Cells/physiology ; Rats ; Silicon ; },
abstract = {OBJECTIVE: The purpose of this study was to use computational modeling to better understand factors that impact neural recordings with silicon microelectrodes used in brain-machine interfaces.<br><br>METHODS: A non-linear cable model of a layer V pyramidal cell was coupled with a finite-element electric field model with explicit representation of the microelectrode. The model system enabled analysis of extracellular neural recordings as a function of the electrode contact size, neuron position, edema, and chronic encapsulation.<br><br>RESULTS: The model predicted spike waveforms and amplitudes that were consistent with experimental recordings. Small (< 1000 microm2) and large (10 k microm2) electrode contacts had similar volumes of recording sensitivity, but small contacts exhibited higher signal amplitudes (approximately 50%) when neurons were in close proximity (50 microm) to the electrode. The model results support the notion that acute edema causes a signal decrease (approximately 24%), and certain encapsulation conditions can result in a signal increase (approximately 17%), a mechanism that may contribute to signal increases observed experimentally in chronic recordings.<br><br>CONCLUSIONS: Optimal electrode design is application-dependent. Small and large contact sizes have contrasting recording properties that can be exploited in the design process. In addition, the presence of local electrical inhomogeneities (encapsulation, edema, coatings) around the electrode shank can substantially influence neural recordings and requires further theoretical and experimental investigation.<br><br>SIGNIFICANCE: Thought-controlled devices using cortical command signals have exciting therapeutic potential for persons with neurological deficit. The results of this study provide the foundation for refining and optimizing microelectrode design for human brain-machine interfaces.},
}
@article {pmid16054256,
year = {2006},
author = {Allison, BZ and Pineda, JA},
title = {Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system.},
journal = {International journal of psychophysiology : official journal of the International Organization of Psychophysiology},
volume = {59},
number = {2},
pages = {127-140},
doi = {10.1016/j.ijpsycho.2005.02.007},
pmid = {16054256},
issn = {0167-8760},
mesh = {Adolescent ; Adult ; Analysis of Variance ; Attention/*physiology ; Brain/*physiology ; Communication Devices for People with Disabilities ; Computer Systems ; Evoked Potentials/physiology ; Female ; *Field Dependence-Independence ; Frontal Lobe/physiology ; Humans ; Male ; Parietal Lobe/physiology ; Pattern Recognition, Visual/*physiology ; Perceptual Masking/*physiology ; Photic Stimulation ; Reference Values ; Statistics, Nonparametric ; *User-Computer Interface ; },
abstract = {P3 brain-computer interfaces (BCIs) are synchronous communication systems that allow users to communicate interest in a target event by choosing to attend to it while ignoring other events. In such a system, a cogneme refers to the user's response to: "/attend to the event/" or "/ignore the event/". The present study examined subjects' ability to generate more cognemes per minute (by varying stimulus onset asynchrony or SOA), or requiring fewer cognemes to convey a message (by varying the pattern of stimulus presentation). Both of these have implications for improved information throughput in a P3 BCI. SOAs of 125, 250, and 500 ms were used. Additionally, the conventional "single flash" approach was compared to a new "multiple flash" condition in which half of the stimuli in an 8 x 8 grid were flashed simultaneously. In both conditions, P3-like component amplitudes decreased with faster SOAs at low target probabilities, but the trend did not hold for higher probabilities. The multiple flash condition produced more robust ERPs at the faster speeds. The results also indicate that attend/ignore differences were more apparent following multiple flashes for low target probabilities, but less apparent for high target probabilities. Although information throughput alone does not support the superiority of one approach over the other, only six cognemes are needed in the multiple flash conditions to identify a character, compared to sixteen cognemes in the single flash condition. This suggests that the former approach could operate more rapidly. Thus, the present results suggest that the multiple flash approach may be a more efficient and faster basis for a P3 BCI system.},
}
@article {pmid16044305,
year = {2005},
author = {Georgopoulos, AP and Langheim, FJ and Leuthold, AC and Merkle, AN},
title = {Magnetoencephalographic signals predict movement trajectory in space.},
journal = {Experimental brain research},
volume = {167},
number = {1},
pages = {132-135},
pmid = {16044305},
issn = {0014-4819},
mesh = {Brain/*physiology ; *Brain Mapping ; Electroencephalography ; Humans ; *Magnetoencephalography ; Movement/*physiology ; Predictive Value of Tests ; Psychomotor Performance ; Reproducibility of Results ; Time Factors ; },
abstract = {Brain-machine interface (BMI) efforts have been focused on using either invasive implanted electrodes or training-extensive conscious manipulation of brain rhythms to control prosthetic devices. Here we demonstrate an excellent prediction of movement trajectory by real-time magnetoencephalography (MEG). Ten human subjects copied a pentagon for 45 s using an X-Y joystick while MEG signals were being recorded from 248 sensors. A linear summation of weighted contributions of the MEG signals yielded a predicted movement trajectory of high congruence to the actual trajectory (median correlation coefficient: r=0.91 and 0.97 for unsmoothed and smoothed predictions, respectively). This congruence was robust since it remained high in cross-validation analyses (based on the first half of data to predict the second half; median correlation coefficient: r=0.76 and 0.85 for unsmoothed and smoothed predictions, respectively).},
}
@article {pmid16041995,
year = {2005},
author = {Shoham, S and Paninski, LM and Fellows, MR and Hatsopoulos, NG and Donoghue, JP and Normann, RA},
title = {Statistical encoding model for a primary motor cortical brain-machine interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {7},
pages = {1312-1322},
doi = {10.1109/TBME.2005.847542},
pmid = {16041995},
issn = {0018-9294},
support = {R01NS25074/NS/NINDS NIH HHS/United States ; },
mesh = {Algorithms ; Animals ; Brain Mapping/methods ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Macaca ; *Models, Neurological ; Models, Statistical ; Monte Carlo Method ; Motor Cortex/*physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.},
}
@article {pmid16025358,
year = {2005},
author = {Pfurtscheller, J and Rupp, R and Müller, GR and Fabsits, E and Korisek, G and Gerner, HJ and Pfurtscheller, G},
title = {[Functional electrical stimulation instead of surgery? Improvement of grasping function with FES in a patient with C5 tetraplegia].},
journal = {Der Unfallchirurg},
volume = {108},
number = {7},
pages = {587-590},
pmid = {16025358},
issn = {0177-5537},
mesh = {*Activities of Daily Living ; Adult ; Cervical Vertebrae/injuries/surgery ; Drinking ; Electric Stimulation Therapy/*methods ; *Hand Strength ; Humans ; Male ; Quadriplegia/etiology/*rehabilitation/surgery ; Spinal Cord Injuries/complications/*rehabilitation/surgery ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {The aim of this study was to restore the grasp function of a tetraplegic patient with a C5 spinal cord injury (SCI) by means of functional electrical stimulation (FES). Using three pairs of surface electrodes and orthotic wrist stabilisation a simple palmar grasp was realised. The FES was controlled with a switch mounted on a wheelchair or-for the first time-with an EEG-based brain-computer interface (BCI). Application of this stimulation system enabled the patient to drink for the first time after the accident from a glass without any additional help.},
}
@article {pmid16019574,
year = {2005},
author = {Kaplan, AY and Lim, JJ and Jin, KS and Park, BW and Byeon, JG and Tarasova, SU},
title = {Unconscious operant conditioning in the paradigm of brain-computer interface based on color perception.},
journal = {The International journal of neuroscience},
volume = {115},
number = {6},
pages = {781-802},
doi = {10.1080/00207450590881975},
pmid = {16019574},
issn = {0020-7454},
mesh = {Adult ; Biofeedback, Psychology ; Brain/*physiology ; *Color Perception ; *Conditioning, Operant ; Electroencephalography ; Humans ; Male ; *Unconscious, Psychology ; *User-Computer Interface ; },
abstract = {This study investigate the mutual fine-tuning of ongoing EEG rhythmic features with RGB values controlling color shades of computer screen during neuro-feedback training. Fifteen participants had not been informed about the existence of neurofeedback loop (NF), but were guided only to look at the computer screen. It was found that during such unconscious NF training, a variety of color shades on the screen gradually changed from rather various types to the main one within the framework of color palette specified for each individual. This phenomenon was not observed in control experiments with simulated neuro-feedback. Individual color patterns induced on the screen during NF did not depend on the schema of connection between of EEG rhythms and RGB controller. It is suggested that the basic neurophysiological mechanism of described NF training consists of the directed selection of EEG patterns reinforced by comfortable color shades without conscious control.},
}
@article {pmid16003902,
year = {2005},
author = {Sanchez, JC and Erdogmus, D and Nicolelis, MA and Wessberg, J and Principe, JC},
title = {Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {2},
pages = {213-219},
doi = {10.1109/TNSRE.2005.847382},
pmid = {16003902},
issn = {1534-4320},
mesh = {*Algorithms ; Animals ; Aotidae ; Artificial Intelligence ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Feedback ; Hand/innervation/physiology ; Movement/*physiology ; *Neural Networks, Computer ; Neurons/*physiology ; Pattern Recognition, Automated/*methods ; Time Factors ; *User-Computer Interface ; },
abstract = {We propose the use of optimized brain-machine interface (BMI) models for interpreting the spatial and temporal neural activity generated in motor tasks. In this study, a nonlinear dynamical neural network is trained to predict the hand position of primates from neural recordings in a reaching task paradigm. We first develop a method to reveal the role attributed by the model to the sampled motor, premotor, and parietal cortices in generating hand movements. Next, using the trained model weights, we derive a temporal sensitivity measure to asses how the model utilized the sampled cortices and neurons in real-time during BMI testing.},
}
@article {pmid16003896,
year = {2005},
author = {Kelly, SP and Lalor, EC and Reilly, RB and Foxe, JJ},
title = {Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {2},
pages = {172-178},
doi = {10.1109/TNSRE.2005.847369},
pmid = {16003896},
issn = {1534-4320},
support = {MH65350/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Attention/*physiology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Humans ; Photic Stimulation/methods ; Space Perception/*physiology ; *User-Computer Interface ; Visual Cortex/*physiology ; },
abstract = {The steady-state visual evoked potential (SSVEP) has been employed successfully in brain-computer interface (BCI) research, but its use in a design entirely independent of eye movement has until recently not been reported. This paper presents strong evidence suggesting that the SSVEP can be used as an electrophysiological correlate of visual spatial attention that may be harnessed on its own or in conjunction with other correlates to achieve control in an independent BCI. In this study, 64-channel electroencephalography data were recorded from subjects who covertly attended to one of two bilateral flicker stimuli with superimposed letter sequences. Offline classification of left/right spatial attention was attempted by extracting SSVEPs at optimal channels selected for each subject on the basis of the scalp distribution of SSVEP magnitudes. This yielded an average accuracy of approximately 71% across ten subjects (highest 86%) comparable across two separate cases in which flicker frequencies were set within and outside the alpha range respectively. Further, combining SSVEP features with attention-dependent parieto-occipital alpha band modulations resulted in an average accuracy of 79% (highest 87%).},
}
@article {pmid16003895,
year = {2005},
author = {Kamousi, B and Liu, Z and He, B},
title = {Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {2},
pages = {166-171},
doi = {10.1109/TNSRE.2005.847386},
pmid = {16003895},
issn = {1534-4320},
support = {R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials ; Algorithms ; Brain Mapping/*methods ; Computer Simulation ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Imagination/*physiology ; *Models, Neurological ; Movement/*physiology ; Pattern Recognition, Automated/*methods ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.},
}
@article {pmid15992412,
year = {2005},
author = {Carless, PA and Moxey, AJ and Stokes, BJ and Henry, DA},
title = {Are antifibrinolytic drugs equivalent in reducing blood loss and transfusion in cardiac surgery? A meta-analysis of randomized head-to-head trials.},
journal = {BMC cardiovascular disorders},
volume = {5},
number = {},
pages = {19},
pmid = {15992412},
issn = {1471-2261},
mesh = {Aminocaproic Acid/economics/therapeutic use ; Antifibrinolytic Agents/economics/*therapeutic use ; Aprotinin/economics/therapeutic use ; Bayes Theorem ; Blood Loss, Surgical/*prevention &amp; control ; *Blood Transfusion ; *Cardiac Surgical Procedures ; Erythrocyte Transfusion ; Humans ; Randomized Controlled Trials as Topic ; Tranexamic Acid/economics/therapeutic use ; },
abstract = {BACKGROUND: Aprotinin has been shown to be effective in reducing peri-operative blood loss and the need for re-operation due to continued bleeding in cardiac surgery. The lysine analogues tranexamic acid (TXA) and epsilon aminocaproic acid (EACA) are cheaper, but it is not known if they are as effective as aprotinin.<br><br>METHODS: Studies were identified by searching electronic databases and bibliographies of published articles. Data from head-to-head trials were pooled using a conventional (Cochrane) meta-analytic approach and a Bayesian approach which estimated the posterior probability of TXA and EACA being equivalent to aprotinin; we used as a non-inferiority boundary a 20% increase in the rates of transfusion or re-operation because of bleeding.<br><br>RESULTS: Peri-operative blood loss was significantly greater with TXA and EACA than with aprotinin: weighted mean differences were 106 mls (95% CI 37 to 227 mls) and 185 mls (95% CI 134 to 235 mls) respectively. The pooled relative risks (RR) of receiving an allogeneic red blood cell (RBC) transfusion with TXA and EACA, compared with aprotinin, were 1.08 (95% CI 0.88 to 1.32) and 1.14 (95% CI 0.84 to 1.55) respectively. The equivalent Bayesian posterior mean relative risks were 1.15 (95% Bayesian Credible Interval [BCI] 0.90 to 1.68) and 1.21 (95% BCI 0.79 to 1.82) respectively. For transfusion, using a 20% non-inferiority boundary, the posterior probabilities of TXA and EACA being non-inferior to aprotinin were 0.82 and 0.76 respectively. For re-operation the Cochrane RR for TXA vs. aprotinin was 0.98 (95% CI 0.51 to 1.88), compared with a posterior mean Bayesian RR of 0.63 (95% BCI 0.16 to 1.46). The posterior probability of TXA being non-inferior to aprotinin was 0.92, but this was sensitive to the inclusion of one small trial.<br><br>CONCLUSION: The available data are conflicting regarding the equivalence of lysine analogues and aprotinin in reducing peri-operative bleeding, transfusion and the need for re-operation. Decisions are sensitive to the choice of clinical outcome and non-inferiority boundary. The data are an uncertain basis for replacing aprotinin with the cheaper lysine analogues in clinical practice. Progress has been hampered by small trials and failure to study clinically relevant outcomes.},
}
@article {pmid15978025,
year = {2005},
author = {Hinterberger, T and Veit, R and Wilhelm, B and Weiskopf, N and Vatine, JJ and Birbaumer, N},
title = {Neuronal mechanisms underlying control of a brain-computer interface.},
journal = {The European journal of neuroscience},
volume = {21},
number = {11},
pages = {3169-3181},
doi = {10.1111/j.1460-9568.2005.04092.x},
pmid = {15978025},
issn = {0953-816X},
mesh = {Adult ; Biofeedback, Psychology/*physiology ; Brain/anatomy &amp; histology/*physiology ; Brain Mapping ; Cerebral Cortex/anatomy &amp; histology/physiology ; Cerebrovascular Circulation/physiology ; Cognition/*physiology ; Corpus Striatum/anatomy &amp; histology/physiology ; Electroencephalography ; Evoked Potentials/physiology ; Female ; Functional Laterality/physiology ; Humans ; Learning/*physiology ; Magnetic Resonance Imaging ; Male ; Neuropsychological Tests ; Reinforcement, Psychology ; Signal Processing, Computer-Assisted ; Thalamus/anatomy &amp; histology/physiology ; *User-Computer Interface ; Volition/*physiology ; },
abstract = {Brain-computer interfaces (BCIs) enable humans or animals to communicate or control external devices without muscle activity using electric brain signals. The BCI used here is based on self-regulation of slow cortical potentials (SCPs), a skill that most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain potentials have been described but the neural mechanisms underlying the self-regulation skill for the use of a BCI are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the BCI was combined with functional magnetic resonance imaging. The electroencephalogram was recorded inside the magnetic resonance imaging scanner in 12 healthy participants who learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill allowing a person to activate an external device; a successful positive SCP shift compared with a negative shift was closely related to an increase of the blood oxygen level-dependent response in the basal ganglia. Successful negativity was related to an increased blood oxygen level-dependent response in the thalamus compared with successful positivity. These results may indicate learned regulation of a cortico-striatal-thalamic loop modulating local excitation thresholds of cortical assemblies. The data support the assumption that human subjects learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite for direct brain communication using an SCP-driven BCI. This skill depends critically on an intact and flexible interaction between the cortico-basal ganglia-thalamic circuits.},
}
@article {pmid15960801,
year = {2005},
author = {Blank, LM and Kuepfer, L and Sauer, U},
title = {Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast.},
journal = {Genome biology},
volume = {6},
number = {6},
pages = {R49},
pmid = {15960801},
issn = {1474-760X},
mesh = {Carbon/*metabolism ; Carbon Isotopes ; *Gene Deletion ; Genes, Duplicate/genetics ; Genome, Fungal/genetics ; Glucose/metabolism ; Saccharomyces cerevisiae/*genetics/*metabolism ; },
abstract = {BACKGROUND: Quantification of intracellular metabolite fluxes by 13C-tracer experiments is maturing into a routine higher-throughput analysis. The question now arises as to which mutants should be analyzed. Here we identify key experiments in a systems biology approach with a genome-scale model of Saccharomyces cerevisiae metabolism, thereby reducing the workload for experimental network analyses and functional genomics.<br><br>RESULTS: Genome-scale 13C flux analysis revealed that about half of the 745 biochemical reactions were active during growth on glucose, but that alternative pathways exist for only 51 gene-encoded reactions with significant flux. These flexible reactions identified in silico are key targets for experimental flux analysis, and we present the first large-scale metabolic flux data for yeast, covering half of these mutants during growth on glucose. The metabolic lesions were often counteracted by flux rerouting, but knockout of cofactor-dependent reactions, as in the adh1, ald6, cox5A, fum1, mdh1, pda1, and zwf1 mutations, caused flux responses in more distant parts of the network. By integrating computational analyses, flux data, and physiological phenotypes of all mutants in active reactions, we quantified the relative importance of 'genetic buffering' through alternative pathways and network redundancy through duplicate genes for genetic robustness of the network.<br><br>CONCLUSIONS: The apparent dispensability of knockout mutants with metabolic function is explained by gene inactivity under a particular condition in about half of the cases. For the remaining 207 viable mutants of active reactions, network redundancy through duplicate genes was the major (75%) and alternative pathways the minor (25%) molecular mechanism of genetic network robustness in S. cerevisiae.},
}
@article {pmid15928412,
year = {2005},
author = {Gage, GJ and Ludwig, KA and Otto, KJ and Ionides, EL and Kipke, DR},
title = {Naive coadaptive cortical control.},
journal = {Journal of neural engineering},
volume = {2},
number = {2},
pages = {52-63},
doi = {10.1088/1741-2560/2/2/006},
pmid = {15928412},
issn = {1741-2560},
support = {P41 EB 002030-11/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adaptation, Physiological/physiology ; Algorithms ; Animals ; Auditory Cortex/*physiology ; Computer Peripherals ; Discrimination Learning/physiology ; Electroencephalography/*methods ; Evoked Potentials, Auditory/*physiology ; Feedback/*physiology ; Neuronal Plasticity/*physiology ; Pitch Perception/*physiology ; Prosthesis Design/methods ; Rats ; Rats, Long-Evans ; *User-Computer Interface ; },
abstract = {The ability to control a prosthetic device directly from the neocortex has been demonstrated in rats, monkeys and humans. Here we investigate whether neural control can be accomplished in situations where (1) subjects have not received prior motor training to control the device (naive user) and (2) the neural encoding of movement parameters in the cortex is unknown to the prosthetic device (naive controller). By adopting a decoding strategy that identifies and focuses on units whose firing rate properties are best suited for control, we show that naive subjects mutually adapt to learn control of a neural prosthetic system. Six untrained Long-Evans rats, implanted with silicon micro-electrodes in the motor cortex, learned cortical control of an auditory device without prior motor characterization of the recorded neural ensemble. Single- and multi-unit activities were decoded using a Kalman filter to represent an audio "cursor" (90 ms tone pips ranging from 250 Hz to 16 kHz) which subjects controlled to match a given target frequency. After each trial, a novel adaptive algorithm trained the decoding filter based on correlations of the firing patterns with expected cursor movement. Each behavioral session consisted of 100 trials and began with randomized decoding weights. Within 7 +/- 1.4 (mean +/- SD) sessions, all subjects were able to significantly score above chance (P < 0.05, randomization method) in a fixed target paradigm. Training lasted 24 sessions in which both the behavioral performance and signal to noise ratio of the peri-event histograms increased significantly (P < 0.01, ANOVA). Two rats continued training on a more complex task using a bilateral, two-target control paradigm. Both subjects were able to significantly discriminate the target tones (P < 0.05, Z-test), while one subject demonstrated control above chance (P < 0.05, Z-test) after 12 sessions and continued improvement with many sessions achieving over 90% correct targets. Dynamic analysis of binary trial responses indicated that early learning for this subject occurred during session 6. This study demonstrates that subjects can learn to generate neural control signals that are well suited for use with external devices without prior experience or training.},
}
@article {pmid15911809,
year = {2005},
author = {Kübler, A and Nijboer, F and Mellinger, J and Vaughan, TM and Pawelzik, H and Schalk, G and McFarland, DJ and Birbaumer, N and Wolpaw, JR},
title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.},
journal = {Neurology},
volume = {64},
number = {10},
pages = {1775-1777},
doi = {10.1212/01.WNL.0000158616.43002.6D},
pmid = {15911809},
issn = {1526-632X},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Aged ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Electroencephalography/methods/trends ; Evoked Potentials, Motor/*physiology ; Evoked Potentials, Somatosensory/physiology ; Female ; Humans ; Imagination/physiology ; Male ; Middle Aged ; Motor Cortex/*physiology ; Movement/physiology ; Paralysis/etiology/*rehabilitation ; Photic Stimulation/methods ; *Prostheses and Implants ; Somatosensory Cortex/physiology ; Treatment Outcome ; *User-Computer Interface ; },
abstract = {People with severe motor disabilities can maintain an acceptable quality of life if they can communicate. Brain-computer interfaces (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based BCI could help maintain quality of life for people with ALS.},
}
@article {pmid15911143,
year = {2005},
author = {Müller-Putz, GR and Scherer, R and Pfurtscheller, G and Rupp, R},
title = {EEG-based neuroprosthesis control: a step towards clinical practice.},
journal = {Neuroscience letters},
volume = {382},
number = {1-2},
pages = {169-174},
doi = {10.1016/j.neulet.2005.03.021},
pmid = {15911143},
issn = {0304-3940},
mesh = {Adult ; Brain/physiology ; Electric Stimulation ; Electrodes, Implanted ; Electroencephalography/*instrumentation ; Foot/physiology ; Hand/physiology ; Hand Strength/physiology ; Humans ; Imagination/physiology ; Male ; Motor Skills ; Movement/physiology ; *Prostheses and Implants ; Quadriplegia/physiopathology ; Refractory Period, Electrophysiological ; Spinal Cord Injuries/*therapy ; },
abstract = {This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain-Computer Interface (BCI) with an implanted neuroprosthesis (Freehand system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain-Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.},
}
@article {pmid15907854,
year = {2006},
author = {Kelley, GA and Kelley, KS},
title = {Aerobic exercise and HDL2-C: a meta-analysis of randomized controlled trials.},
journal = {Atherosclerosis},
volume = {184},
number = {1},
pages = {207-215},
pmid = {15907854},
issn = {0021-9150},
support = {R01 HL069802/HL/NHLBI NIH HHS/United States ; R01 HL069802-03/HL/NHLBI NIH HHS/United States ; R01-HL069802/HL/NHLBI NIH HHS/United States ; },
mesh = {Adult ; Biomarkers/blood ; Coronary Disease/blood/rehabilitation ; Exercise/*physiology ; Female ; Humans ; Lipoproteins, HDL/*blood ; Lipoproteins, HDL2 ; Male ; Randomized Controlled Trials as Topic ; Risk Factors ; },
abstract = {PURPOSE: Use the meta-analytic approach to examine the effects of aerobic exercise on high-density lipoprotein two cholesterol (HDL2-C) in adults. STUDY SOURCES: (1) Computerized literature searches; (2) cross-referencing from retrieved articles; (3) hand-searching; and (4) expert review of our reference list.<br><br>STUDY SELECTION: (1) Randomized controlled trials; (2) aerobic exercise > or = 8 weeks; (3) adults > or = 18 years of age; (4) studies published in journal, dissertation, or master's thesis format; (5) studies published in the English-language between January 1, 1955 and January 1, 2003; and (6) assessment of HDL2-C in the fasting state.<br><br>DATA ABSTRACTION: All coding conducted by both authors, independent of each other. Discrepancies were resolved by consensus.<br><br>RESULTS: Nineteen randomized controlled trials representing 20 HDL2-C outcomes from 984 males and females (516 exercise, 468 control) were pooled for analysis. Using random-effects modeling and bootstrap confidence intervals (BCI), a statistically significant increase of approximately 11% was observed for HDL2-C (X +/- S.E.M., 2.6 +/- 0.9 mg/dl, 95% BCI, 1.0-4.4 mg/dl). With each study deleted from the model once, results remained statistically significant. Increases in HDL2-C were independent of decreases in body weight, body mass index (kg/m2), and percent body fat.<br><br>CONCLUSION: Aerobic exercise increases HDL2-C in adults.},
}
@article {pmid15888644,
year = {2005},
author = {Lebedev, MA and Carmena, JM and O'Doherty, JE and Zacksenhouse, M and Henriquez, CS and Principe, JC and Nicolelis, MA},
title = {Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {25},
number = {19},
pages = {4681-4693},
pmid = {15888644},
issn = {1529-2401},
mesh = {Adaptation, Physiological/*physiology ; Animals ; Behavior, Animal ; Brain Mapping ; Female ; Hand/physiology ; Learning/*physiology ; Macaca mulatta ; Motor Cortex/cytology/*physiology ; Movement/*physiology ; Neurons/*physiology ; Predictive Value of Tests ; Psychomotor Performance/physiology ; Time Perception/physiology ; *User-Computer Interface ; },
abstract = {Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.},
}
@article {pmid15887938,
year = {2005},
author = {Brennan, M and Houssami, N and French, J},
title = {Management of benign breast conditions. Part 3--Other breast problems.},
journal = {Australian family physician},
volume = {34},
number = {5},
pages = {353-355},
pmid = {15887938},
issn = {0300-8495},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Breast Diseases/*diagnosis/*therapy ; Breast Neoplasms/diagnosis ; Family Practice/*methods ; Female ; Gynecomastia/diagnosis/therapy ; Humans ; Infant ; Male ; Mastitis/diagnosis/therapy ; Nipples/metabolism ; },
abstract = {This is the third article in a series of breast disorders with an emphasis on diagnosis and management in the general practice setting. This article discusses conditions that, although less frequently seen in general practice, pose challenges in diagnosis and management.},
}
@article {pmid15884704,
year = {2005},
author = {Leeb, R and Scherer, R and Keinrath, C and Guger, C and Pfurtscheller, G},
title = {Exploring virtual environments with an EEG-based BCI through motor imagery.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {50},
number = {4},
pages = {86-91},
doi = {10.1515/BMT.2005.012},
pmid = {15884704},
issn = {0013-5585},
mesh = {Adult ; Algorithms ; Brain/*physiology ; Computer Graphics ; Computer Simulation ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Environment ; Evoked Potentials, Motor/*physiology ; Feasibility Studies ; Humans ; Imagination/*physiology ; Online Systems ; Pattern Recognition, Automated/*methods ; Psychomotor Performance/physiology ; *User-Computer Interface ; },
abstract = {In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subject's learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45 degrees during one trial.},
}
@article {pmid15876641,
year = {2004},
author = {Boostani, R and Moradi, MH},
title = {A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier.},
journal = {Journal of neural engineering},
volume = {1},
number = {4},
pages = {212-217},
doi = {10.1088/1741-2560/1/4/004},
pmid = {15876641},
issn = {1741-2560},
mesh = {*Algorithms ; *Artificial Intelligence ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Fractals ; Humans ; Imagination/physiology ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {High rate classification of imagery tasks is still one of the hot topics among the brain computer interface (BCI) groups. In order to improve this rate, a new approach based on fractal dimension as feature and Adaboost as classifier is presented for five subjects in this paper. To have a comparison, features such as band power, Hjorth parameters along with LDA classifier have been taken into account. Fractal dimension as a feature with Adaboost and LDA can be considered as alternative combinations for BCI applications.},
}
@article {pmid15876633,
year = {2004},
author = {Huan, NJ and Palaniappan, R},
title = {Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design.},
journal = {Journal of neural engineering},
volume = {1},
number = {3},
pages = {142-150},
doi = {10.1088/1741-2560/1/3/003},
pmid = {15876633},
issn = {1741-2560},
mesh = {*Algorithms ; Brain/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; *Neural Networks, Computer ; Pattern Recognition, Automated/*methods ; Psychomotor Performance/physiology ; Regression Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.},
}
@article {pmid15876632,
year = {2004},
author = {Qin, L and Ding, L and He, B},
title = {Motor imagery classification by means of source analysis for brain-computer interface applications.},
journal = {Journal of neural engineering},
volume = {1},
number = {3},
pages = {135-141},
pmid = {15876632},
issn = {1741-2560},
support = {R01 EB000178/EB/NIBIB NIH HHS/United States ; R01 EB000178-03/EB/NIBIB NIH HHS/United States ; R01 EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; Brain Mapping/methods ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; *Evoked Potentials, Motor ; Humans ; *Imagination ; Models, Neurological ; Motor Cortex/*physiopathology ; Pattern Recognition, Automated/*methods ; Principal Component Analysis ; *User-Computer Interface ; },
abstract = {We report a pilot study of performing classification of motor imagery for brain-computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs.},
}
@article {pmid15876624,
year = {2004},
author = {Leuthardt, EC and Schalk, G and Wolpaw, JR and Ojemann, JG and Moran, DW},
title = {A brain-computer interface using electrocorticographic signals in humans.},
journal = {Journal of neural engineering},
volume = {1},
number = {2},
pages = {63-71},
doi = {10.1088/1741-2560/1/2/001},
pmid = {15876624},
issn = {1741-2560},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; NS41272/NS/NINDS NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Diagnosis, Computer-Assisted/*methods ; Electrodes, Implanted ; Electroencephalography/*methods ; *Evoked Potentials ; Female ; Humans ; Imagination ; Male ; Movement Disorders/physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.},
}
@article {pmid15876616,
year = {2004},
author = {Wang, T and He, B},
title = {An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain-computer interface.},
journal = {Journal of neural engineering},
volume = {1},
number = {1},
pages = {1-7},
doi = {10.1088/1741-2560/1/1/001},
pmid = {15876616},
issn = {1741-2560},
support = {R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Algorithms ; *Artificial Intelligence ; Biological Clocks/physiology ; Brain/*physiology ; Cluster Analysis ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Imagination/*physiology ; Information Storage and Retrieval/methods ; Pattern Recognition, Automated/*methods ; Periodicity ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The recognition of mental states during motor imagery tasks is crucial for EEG-based brain-computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.},
}
@article {pmid15865142,
year = {2005},
author = {Erfanian, A and Mahmoudi, B},
title = {Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {43},
number = {2},
pages = {296-305},
pmid = {15865142},
issn = {0140-0118},
mesh = {*Artifacts ; Blinking/physiology ; Electroencephalography/*methods ; Electrooculography ; Humans ; Neural Networks, Computer ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.},
}
@article {pmid15861746,
year = {2005},
author = {Brennan, M and Houssami, N and French, J},
title = {Management of benign breast conditions. Part 2--breast lumps and lesions.},
journal = {Australian family physician},
volume = {34},
number = {4},
pages = {253-255},
pmid = {15861746},
issn = {0300-8495},
mesh = {Adult ; Breast Cyst/diagnosis/therapy ; Breast Diseases/*diagnosis/*therapy ; Diagnosis, Differential ; Family Practice/*methods ; Female ; Fibroadenoma/diagnosis/therapy ; Fibrocystic Breast Disease/diagnosis/therapy ; Humans ; Middle Aged ; },
abstract = {This is the second article in a series on breast disorders with an emphasis on diagnosis and management in the general practice setting. This article provides an overview of the investigation of patients with a breast symptom and discusses the assessment and management of benign breast lesions including localised nodularity, fibroadenomas and breast cysts.},
}
@article {pmid15860078,
year = {2005},
author = {Carabin, H and Cowan, LD and Beebe, LA and Skaggs, VJ and Thompson, D and Agbangla, C},
title = {Does participation in a nurse visitation programme reduce the frequency of adverse perinatal outcomes in first-time mothers?.},
journal = {Paediatric and perinatal epidemiology},
volume = {19},
number = {3},
pages = {194-205},
doi = {10.1111/j.1365-3016.2005.00651.x},
pmid = {15860078},
issn = {0269-5022},
mesh = {Adult ; Female ; *Home Care Services/statistics &amp; numerical data ; Humans ; Infant Mortality ; Infant, Low Birth Weight ; Infant, Newborn ; Infant, Very Low Birth Weight ; Nurse's Role ; Oklahoma/epidemiology ; *Patient Acceptance of Health Care ; Pregnancy ; Pregnancy Outcome/*epidemiology ; Premature Birth/epidemiology ; Regression Analysis ; Risk Factors ; },
abstract = {Children First (C1), a nurse home visitation programme for first-time mothers, was implemented statewide in Oklahoma in mid-1997. The objective of this study was to compare the risks of low (< 2500 g) and very low birthweight (< 1500 g), preterm (< 37 weeks) and very preterm (< 30 weeks) deliveries and infant mortality between mothers participating and not participating in C1. All 239,466 Oklahoma birth certificates were reviewed. The C1 and birth certificate databases were matched to identify C1 participants. Mother's age at delivery, education level, race, marital status, prior pregnancy loss or pregnancy risk factors, birthweight and gestational age at delivery were measured from the birth certificates. Death certificates were matched to the birth certificates to identify infant deaths. A Bayesian multivariable logistic regression was used to analyse the data. Among single mothers without pregnancy risk factors, the risks of all study outcomes were lower for participants in C1: adjusted odds ratio (aOR) 0.89, [95% Bayesian Credible Interval (BCI) 0.79, 1.00] for preterm delivery; aOR 0.71, [95% BCI 0.50, 0.98] for very preterm delivery; aOR 0.86, [95% BCI 0.75, 0.98] for low birthweight; aOR 0.77, [95% BCI 0.56, 1.02] for very low birthweight and aOR 0.36, [95% BCI 0.17, 0.63] for infant mortality. These risk reductions were not observed among married mothers. In both single and married mothers, the presence of pregnancy risk factors reduced the impact of C1 on lowering the risk of low birthweight and preterm deliveries. The C1 programme targets young, pregnant women of low socio-economic level. We found that among single mothers, the risks of perinatal adverse outcomes are reduced or similar to those found in non-participating mothers. A reduced effect of C1 in the presence of pregnancy risk factors may be because mothers with pregnancy risk factors who did not participate in C1 received better prenatal care, or that C1 interventions do not impact these particular factors. C1 shows promise in reducing infant mortality in single mothers. Lower incidence of preterm and very preterm deliveries is especially interesting and future analyses should focus on isolating programme components specifically associated with influencing these outcomes.},
}
@article {pmid15848811,
year = {2005},
author = {Scherberger, H and Jarvis, MR and Andersen, RA},
title = {Cortical local field potential encodes movement intentions in the posterior parietal cortex.},
journal = {Neuron},
volume = {46},
number = {2},
pages = {347-354},
doi = {10.1016/j.neuron.2005.03.004},
pmid = {15848811},
issn = {0896-6273},
mesh = {Animals ; *Intention ; Macaca mulatta ; Male ; Membrane Potentials/physiology ; Parietal Lobe/*physiology ; Photic Stimulation ; Psychomotor Performance/*physiology ; Saccades/*physiology ; },
abstract = {The cortical local field potential (LFP) is a summation signal of excitatory and inhibitory dendritic potentials that has recently become of increasing interest. We report that LFP signals in the parietal reach region (PRR) of the posterior parietal cortex of macaque monkeys have temporal structure that varies with the type of planned or executed motor behavior. LFP signals from PRR provide better decode performance for reaches compared to saccades and have stronger coherency with simultaneously recorded spiking activity during the planning of reach movements than during saccade planning. LFP signals predict the animal's behavioral state (e.g., planning a reach or saccade) and the direction of the currently planned movement from single-trial information. This new evidence provides further support for a role of the parietal cortex in movement planning and the potential application of LFP signals for a brain-machine interface.},
}
@article {pmid15813410,
year = {2005},
author = {Serby, H and Yom-Tov, E and Inbar, GF},
title = {An improved P300-based brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {1},
pages = {89-98},
doi = {10.1109/TNSRE.2004.841878},
pmid = {15813410},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Principal Component Analysis ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 x 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5 % as apposed to the 4.8 symbols/min with an accuracy of 56 % in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.},
}
@article {pmid15813408,
year = {2005},
author = {Olson, BP and Si, J and Hu, J and He, J},
title = {Closed-loop cortical control of direction using support vector machines.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {1},
pages = {72-80},
doi = {10.1109/TNSRE.2004.843174},
pmid = {15813408},
issn = {1534-4320},
mesh = {*Algorithms ; Animals ; *Artificial Intelligence ; Diagnosis, Computer-Assisted/*methods ; Electrodes, Implanted ; Electroencephalography/*methods ; Feedback/physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Rats ; Rats, Sprague-Dawley ; Therapy, Computer-Assisted/methods ; *User-Computer Interface ; },
abstract = {Motor neuroprosthetics research has focused on reproducing natural limb motions by correlating firing rates of cortical neurons to continuous movement parameters. We propose an alternative system where specific spatial-temporal spike patterns, emerging in tasks, allow detection of classes of behavior with the aid of sophisticated nonlinear classification algorithms. Specifically, we attempt to examine ensemble activity from motor cortical neurons, not to reproduce the action this neural activity normally precedes, but rather to predict an output supervisory command to potentially control a vehicle. To demonstrate the principle, this design approach was implemented in a discrete directional task taking a small number of motor cortical signals (8-10 single units) fed into a support vector machine (SVM) to produce the commands Left and Right. In this study, rats were placed in a conditioning chamber performing a binary paddle pressing task mimicking the control of a wheelchair turning left or right. Four animal subjects (male Sprague-Dawley rats) were able to use such a brain-machine interface (BMI) with an average accuracy of 78% on their first day of exposure. Additionally, one animal continued to use the interface for three consecutive days with an average accuracy over 90%.},
}
@article {pmid15813401,
year = {2005},
author = {Burke, DP and Kelly, SP and de Chazal, P and Reilly, RB and Finucane, C},
title = {A parametric feature extraction and classification strategy for brain-computer interfacing.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {13},
number = {1},
pages = {12-17},
doi = {10.1109/TNSRE.2004.841881},
pmid = {15813401},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Male ; Models, Neurological ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Parametric modeling strategies are explored in conjunction with linear discriminant analysis for use in an electroencephalogram (EEG)-based brain-computer interface (BCI). A left/right self-paced typing exercise is analyzed by extending the usual autoregressive (AR) model for EEG feature extraction with an AR with exogenous input (ARX) model for combined filtering and feature extraction. The ensemble averaged Bereitschafts potential (an event related potential preceding the onset of movement) forms the exogenous signal input to the ARX model. Based on trials with six subjects, the ARX case of modeling both the signal and noise was found to be considerably more effective than modeling the noise alone (common in BCI systems) with the AR method yielding a classification accuracy of 52.8+/-4.8% and the ARX method an accuracy of 79.1+/-3.9 % across subjects. The results suggest a role for ARX-based feature extraction in BCIs based on evoked and event-related potentials.},
}
@article {pmid15809315,
year = {2005},
author = {Arimoto, R and Prasad, MA and Gifford, EM},
title = {Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors.},
journal = {Journal of biomolecular screening},
volume = {10},
number = {3},
pages = {197-205},
doi = {10.1177/1087057104274091},
pmid = {15809315},
issn = {1087-0571},
mesh = {*Artificial Intelligence ; Computer Simulation ; Cytochrome P-450 CYP3A ; *Cytochrome P-450 Enzyme Inhibitors ; Drug Evaluation, Preclinical/*methods ; Enzyme Inhibitors/*chemistry/pharmacology ; Humans ; *Models, Chemical ; Models, Molecular ; Molecular Structure ; },
abstract = {Computational models of cytochrome P450 3A4 inhibition were developed based on high-throughput screening data for 4470 proprietary compounds. Multiple models differentiating inhibitors (IC(50) <3 microM) and noninhibitors were generated using various machine-learning algorithms (recursive partitioning [RP], Bayesian classifier, logistic regression, k-nearest-neighbor, and support vector machine [SVM]) with structural fingerprints and topological indices. Nineteen models were evaluated by internal 10-fold cross-validation and also by an independent test set. Three most predictive models, Barnard Chemical Information (BCI)-fingerprint/SVM, MDL-keyset/SVM, and topological indices/RP, correctly classified 249, 248, and 236 compounds of 291 noninhibitors and 135, 137, and 147 compounds of 179 inhibitors in the validation set. Their overall accuracies were 82%, 82%, and 81%, respectively. Investigating applicability of the BCI/SVM model found a strong correlation between the predictive performance and the structural similarity to the training set. Using Tanimoto similarity index as a confidence measurement for the predictions, the limitation of the extrapolation was 0.7 in the case of the BCI/SVM model. Taking consensus of the 3 best models yielded a further improvement in predictive capability, kappa = 0.65 and accuracy = 83%. The consensus model could also be tuned to minimize either false positives or false negatives depending on the emphasis of the screening.},
}
@article {pmid15799661,
year = {2005},
author = {Brennan, M and Houssami, N and French, J},
title = {Management of benign breast conditions. Part 1--Painful breasts.},
journal = {Australian family physician},
volume = {34},
number = {3},
pages = {143-144},
pmid = {15799661},
issn = {0300-8495},
mesh = {Analgesia/methods ; Breast Diseases/*complications/diagnosis/*therapy ; Complementary Therapies/methods ; Contraceptives, Oral/therapeutic use ; Family Practice/*methods ; Female ; Humans ; Pain/*etiology ; *Pain Management ; },
abstract = {This is the first article in a series on breast disorders with an emphasis on diagnosis and management in the general practice setting. This article provides an overview of the investigation of patients with a breast symptom and discusses the assessment and management of mastalgia.},
}
@article {pmid15792197,
year = {2005},
author = {Dax, JF and Müller-Putz, GR and Pfurtscheller, K and Urlesberger, B and Müller, W and Pfurtscheller, G},
title = {[Semiautomatic procedure for the investigation of synchronized activity of EEG and heart rate--examination of preterm births].},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {50},
number = {1-2},
pages = {19-24},
doi = {10.1515/BMT.2005.004},
pmid = {15792197},
issn = {0013-5585},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Diagnosis, Computer-Assisted/*methods ; Electrocardiography/*methods ; Electroencephalography/*methods ; Female ; Heart Rate/*physiology ; Humans ; Male ; Pattern Recognition, Automated/methods ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Recordings of the electroencephalogram (EEG) and of the heart rate variability (HRV) of preterm neonates can give important information on the actual state of the nervous system. Both signals, EEG and HRV, are affected by parameters such as gestational age, stage of maturation and behavioral state. This work describes a method for automatic detection of slow wave EEG-bursts and a tool to average changes in the EEG and the corresponding heart rate. The detection is based on the hjorth activity (HA), calculated from the EEG. HA spikes (HAS) are identified by the determination of the beginning and end of existing spikes. HAS maxima and the time between two consecutive HAS are the basis for the triggering of the bursts. EEG power and time synchronized HR changes are averaged with a time window length of 20 s. Resultant, HR increase and duration are determined. These parameters, obtained by the automatic detection, proved to be comparable to the results of an expert.},
}
@article {pmid15791939,
year = {2005},
author = {Wichmann, R and Vasic-Racki, D},
title = {Cofactor regeneration at the lab scale.},
journal = {Advances in biochemical engineering/biotechnology},
volume = {92},
number = {},
pages = {225-260},
doi = {10.1007/b98911},
pmid = {15791939},
issn = {0724-6145},
mesh = {*Bioreactors ; Biotechnology/instrumentation/*methods ; Coenzymes/*chemistry/*metabolism ; Electrochemistry/instrumentation/*methods ; Enzyme Reactivators/chemistry ; Photochemistry/instrumentation/*methods ; Pilot Projects ; },
abstract = {Progress made in lab-scale applications of various coenzyme regeneration systems over the last two decades has mainly focused on the applications of NAD+/NADH- and NADP+/NADPH-dependent oxidoreductase reactions. In situ regeneration systems for these reactions, as well as whole cell, enzymatic, electro-enzymatic, chemical, and photochemical reactions are presented, including details about their efficiency and novelty. The progress of enzyme reaction engineering is also reported.},
}
@article {pmid15778670,
year = {2005},
author = {Kashkouli, MB and Kempster, RC and Galloway, GD and Beigi, B},
title = {Monocanalicular versus bicanalicular silicone intubation for nasolacrimal duct stenosis in adults.},
journal = {Ophthalmic plastic and reconstructive surgery},
volume = {21},
number = {2},
pages = {142-147},
doi = {10.1097/01.iop.0000155524.04390.7b},
pmid = {15778670},
issn = {0740-9303},
mesh = {Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Intubation/instrumentation/*methods ; Lacrimal Duct Obstruction/*therapy ; Male ; Middle Aged ; *Nasolacrimal Duct ; Ophthalmologic Surgical Procedures ; Retrospective Studies ; *Silicone Elastomers ; Treatment Outcome ; },
abstract = {PURPOSE: To compare the success rate of monocanalicular versus bicanalicular silicone intubation of incomplete nasolacrimal duct obstruction (nasolacrimal duct stenosis) in adults.<br><br>METHODS: In a retrospective, nonrandomized comparative case series, 48 eyes of 44 adult patients with nasolacrimal duct stenosis underwent endoscopic probing and either bicanalicular (BCI; n=22 eyes) or monocanalicular (MCI; n=26 eyes) nasolacrimal duct intubation under general anesthesia. "Complete success" was defined as complete disappearance of the symptoms, "partial success" as improvement with some residual symptoms, and "failure" as absence of improvement or worsening of symptoms at last follow-up. The last follow-up examination included diagnostic probing and irrigation if there was not complete success.<br><br>RESULTS: Patient ages ranged from 31 to 90 years (mean, 69; SD, 11.5). Forty-five tubes were removed 6 to 17 weeks (mean, 9.1; SD, 3) after surgery. Premature tube dislocation and removal occurred in one eye with BCI and in two eyes with MCI. Follow-up ranged from 6 to 52 months (mean, 14.9; SD, 8.4). The complete success rate was nearly the same in eyes with MCI (16/26, 61.53%) and BCI (13/22, 59.09%). Partial success (MCI: 8/26, 30.76%; BCI: 1/22, 4.54%) and failure (MCI: 2/26, 7.69%; BCI: 8/22, 36.36%) were, however, significantly different (p=0.010). Complications included 3 slit puncta with BCI and 4 temporary superficial punctuate keratopathy after MCI.<br><br>CONCLUSIONS: MCI had virtually the same complete success rate as BCI, a higher partial success rate than BCI, and a lower failure rate than BCI in treatment of nasolacrimal duct stenosis in adults.},
}
@article {pmid15778651,
year = {2005},
author = {Harnirattisai, T and Johnson, RA},
title = {Effectiveness of a behavioral change intervention in Thai elders after knee replacement.},
journal = {Nursing research},
volume = {54},
number = {2},
pages = {97-107},
doi = {10.1097/00006199-200503000-00004},
pmid = {15778651},
issn = {0029-6562},
mesh = {Aged ; Arthroplasty, Replacement, Knee/*rehabilitation ; *Exercise ; Female ; Humans ; Longitudinal Studies ; Male ; Medical Records ; *Motor Activity ; Outcome Assessment, Health Care ; Patients/psychology ; Self Efficacy ; Thailand ; Walking ; },
abstract = {BACKGROUND: After total knee replacement, elders need an effective intervention to change exercise and physical activity behavior.<br><br>OBJECTIVES: This study examined the effects of a behavioral change intervention (BCI) on self-efficacy and outcome expectations for exercise and functional activity, physical activity participation, and physical performance of older adults.<br><br>METHODS: The study was based on the social cognitive theory (SCT), with a longitudinal quasi-experimental, pretest-posttest control group design. Sixty-three Thai elders undergoing knee replacement surgery were studied. The experimental group received a BCI based on SCT given by the investigator. Outcome measures were the Self-Efficacy for Exercise Scale (SEES) and Self-Efficacy for Functional Activity Scale (SEFAS), the Outcome Expectations for Exercise Scale (OEES) and Outcome Expectations for Functional Activity Scale (OEFAS), the Physical Performance Test (PPT), and the Physical Activity Diary (PAD).<br><br>RESULTS: The experimental group had significantly greater improvements in self-efficacy for exercise, outcome expectations for exercise, and functional activity, significantly more participation in exercise and walking, and significantly greater improvement in physical performance than did the control group at postoperative weeks 2 and 6.<br><br>DISCUSSION: The BCI based on SCT was effective in changing the outcomes in the expected direction. This BCI may be applicable, with modifications, to elders in other situations in which changing behavior is the key to recovery.},
}
@article {pmid15709667,
year = {2005},
author = {Tregoubov, M and Birbaumer, N},
title = {On the building of binary spelling interfaces for augmentative communication.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {2},
pages = {300-305},
doi = {10.1109/TBME.2004.836505},
pmid = {15709667},
issn = {0018-9294},
mesh = {*Algorithms ; *Communication Devices for People with Disabilities ; Humans ; Information Storage and Retrieval/*methods ; *Natural Language Processing ; Pattern Recognition, Automated/*methods ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; Vocabulary, Controlled ; Word Processing/*methods ; },
abstract = {A criterion for design of optimal binary spelling interfaces (SI)--the average expectation of number of writing steps (trials) required to write one letter--is presented and discussed. This criterion is relevant for practical usage of any menu-oriented alternative communication system, mechanical device or brain-computer-interface, when a user (typically, a patient with devastating neuromuscular handicap) can not create an intended single binary response with an absolute reliability. An algorithm for building of a corresponding binary tree is developed and evaluated. This algorithm is efficient, when selection probabilities have essentially different values (the worst case).},
}
@article {pmid15709658,
year = {2005},
author = {Hinterberger, T and Wilhelm, B and Mellinger, J and Kotchoubey, B and Birbaumer, N},
title = {A device for the detection of cognitive brain functions in completely paralyzed or unresponsive patients.},
journal = {IEEE transactions on bio-medical engineering},
volume = {52},
number = {2},
pages = {211-220},
doi = {10.1109/TBME.2004.840190},
pmid = {15709658},
issn = {0018-9294},
mesh = {Adult ; Aged ; *Algorithms ; Brain/*physiopathology ; *Cognition ; Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; *Evoked Potentials ; Humans ; Middle Aged ; Persistent Vegetative State/*physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {Unresponsive patients with remaining cognitive abilities may be able to communicate with a brain-computer interface (BCI) such as the Thought Translation Device (TTD). Before initiating TTD learning, which may imply considerable effort, it is important to classify the patients' state of awareness and their remaining cognitive abilities. A tool for detection of cognitive activity (DCA) in the completely paralyzed was developed and integrated into the TTD which is a psychophysiological system for direct brain communication. In the present version, DCA entails five event-related brain-potential (ERP) experiments and investigates the capability of a patient to discriminate, e.g., between semantically related and unrelated concepts and categories. ERPs serve as an indicator of the patients' cortical information processing. Data from five severely brain-injured patients in persistent vegetative state diagnosed as unresponsive and five healthy controls are presented to illustrate the methodology. Two patients showing the highest responsiveness were selected for TTD training. The DCA integrated in the TTD allows screening of cognitive abilities and direct brain communication in the patients' home.},
}
@article {pmid15689936,
year = {2005},
author = {Brower, V},
title = {When mind meets machine.},
journal = {EMBO reports},
volume = {6},
number = {2},
pages = {108-110},
pmid = {15689936},
issn = {1469-221X},
mesh = {Animals ; Aotidae ; Brain/*physiology ; Computers/*trends ; Electroencephalography/trends ; Forecasting ; Humans ; Robotics/*trends ; },
abstract = {A new wave of brain–machine interfaces helps disabled people connect with the outside world},
}
@article {pmid15687805,
year = {2004},
author = {Bayliss, JD and Inverso, SA and Tentler, A},
title = {Changing the P300 brain computer interface.},
journal = {Cyberpsychology &amp; behavior : the impact of the Internet, multimedia and virtual reality on behavior and society},
volume = {7},
number = {6},
pages = {694-704},
doi = {10.1089/cpb.2004.7.694},
pmid = {15687805},
issn = {1094-9313},
support = {1-P41-RR09283/RR/NCRR NIH HHS/United States ; },
mesh = {Brain/*physiology ; *Computers ; Event-Related Potentials, P300/*physiology ; Humans ; Models, Theoretical ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) are now feasible for use as an alternative control option for those with severe motor impairments. The P300 component of the evoked potential has proven useful as a control signal. Individuals do not need to be trained to produce the signal, and it is fairly stable and has a large evoked potential. Even with recent signal classification advances, on-line experiments with P300-based BCIs remain far from perfect. We present two potential methods for improving control accuracy. Experimental results in an evoked potential BCI, used to control items in a virtual apartment, show a reduced response exists when items are accidentally controlled. The presence of a P300-like signal in response to goal items means that it can be used for automatic error correction. Preliminary results from an interface experiment using three different button configurations for a yes/no BCI task show that the configuration of buttons may affect on-line signal classification. These results will be discussed in light of the special considerations needed when working with an amyotrophic lateral sclerosis (ALS) patient.},
}
@article {pmid15670644,
year = {2005},
author = {Brunner, C and Graimann, B and Huggins, JE and Levine, SP and Pfurtscheller, G},
title = {Phase relationships between different subdural electrode recordings in man.},
journal = {Neuroscience letters},
volume = {375},
number = {2},
pages = {69-74},
doi = {10.1016/j.neulet.2004.11.052},
pmid = {15670644},
issn = {0304-3940},
support = {5 R01 EB002093-04/EB/NIBIB NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; Adult ; Beta Rhythm ; Biological Clocks/physiology ; Brain Mapping/instrumentation/methods ; Cerebral Cortex/anatomy &amp; histology/*physiology ; Cortical Synchronization ; Electrodes/standards ; Electroencephalography/*methods ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted/*instrumentation ; Subdural Space ; Time Factors ; },
abstract = {Almost all brain-computer interfaces (BCIs) ignore information related to the phase coupling between electroencephalogram (EEG) or electrocorticogram (ECoG) recordings from different electrodes. This paper investigates whether additional information can be found when calculating the amount of synchronization between two electrode channels by using a phase locking measurement called the phase locking value (PLV). Special emphasis is put on the beta band (around 20 Hz) as well as the gamma band (high frequencies up to 95 Hz), which can only be used when subdural electrode recordings are available.},
}
@article {pmid15646357,
year = {2004},
author = {Yang, K and Tian, M and Zhang, H and Zhao, Y},
title = {[Advance in brain-computer interface technology].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {21},
number = {6},
pages = {1024-1027},
pmid = {15646357},
issn = {1001-5515},
mesh = {Brain/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Humans ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {This paper introduces one of the young, energetic and rapidly growing research fields in biomedical engineering-Brain-computer interface (BCI) technology, which can provide augmentative communication and control capabilities to patients with severe motor disabilities. We summarize the first two international meetings for BCI, and present the most typical research fruits. The problems in current studies and the direction for future investigation are analyzed.},
}
@article {pmid15622126,
year = {2004},
author = {Mason, SG and Bohringer, R and Borisoff, JF and Birch, GE},
title = {Real-time control of a video game with a direct brain--computer interface.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {21},
number = {6},
pages = {404-408},
doi = {10.1097/01.wnp.0000146840.78749.79},
pmid = {15622126},
issn = {0736-0258},
mesh = {Adult ; Brain/physiology ; *Communication Devices for People with Disabilities ; Computer Systems ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Spinal Cord Injuries/*rehabilitation ; *User-Computer Interface ; Video Games ; },
abstract = {Mason and Birch have developed a direct brain-computer interface for intermittent control of devices such as environmental control systems and neuroprotheses. This EEG-based brain switch, named the LF-ASD, has been used in several off-line studies, but little is known about its usability with real-world devices and computer applications. In this study, able-bodied individuals and people with high-level spinal injury used the LF-ASD brain switch to control a video game in real time. Both subject groups demonstrated switch activations varying from 30% to 78% and false-positive rates in the range of 0.5% to 2.2% over three 1-hour test sessions. These levels correspond to switch classification accuracies greater than 94% for all subjects. The results suggest that subjects with spinal cord injuries can operate the brain switch to the same ability as able-bodied subjects in a real-time control environment. These results support the findings of previous studies.},
}
@article {pmid15614996,
year = {2004},
author = {Gysels, E and Celka, P},
title = {Phase synchronization for the recognition of mental tasks in a brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {12},
number = {4},
pages = {406-415},
doi = {10.1109/TNSRE.2004.838443},
pmid = {15614996},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain/*physiology ; Brain Mapping/*methods ; Cognition ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Pattern Recognition, Automated/*methods ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) may be a future communication channel for motor-disabled people. In surface electroencephalogram (EEG)-based BCIs, the extracted features are often derived from spectral estimates and autoregressive models. We examined the usefulness of synchronization between EEG signals for classifying mental tasks. To this end, we investigated the performance of features derived from the phase locking value (PLV) and from the spectral coherence and compared them to the classification rates resulting from the power densities in alpha, beta1, beta2, and 8-30-Hz frequency bands. Five recordings of 60 min, acquired from three subjects while performing three different mental tasks, were analyzed offline. No artifacts were removed or rejected. We noticed significant differences between PLV and mean spectral coherence. For sole use of synchronization measures, classification accuracies up to 62% were achieved. In general, the best result was obtained combining phase synchronization measures with alpha power spectral density estimates. The results demonstrate that phase synchronization provides relevant information for the classification of spontaneous EEG during mental tasks.},
}
@article {pmid15614994,
year = {2004},
author = {Congedo, M and Lubar, JF and Joffe, D},
title = {Low-resolution electromagnetic tomography neurofeedback.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {12},
number = {4},
pages = {387-397},
doi = {10.1109/TNSRE.2004.840492},
pmid = {15614994},
issn = {1534-4320},
mesh = {Adult ; Biofeedback, Psychology/*methods ; Brain Mapping/*methods ; Cognition/*physiology ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials ; Feedback/*physiology ; Female ; Gyrus Cinguli/*physiology ; Humans ; Male ; Models, Neurological ; Reproducibility of Results ; Sensitivity and Specificity ; Tomography/methods ; },
abstract = {Through continuous feedback of the electroencephalogram (EEG) humans can learn how to shape their brain electrical activity in a desired direction. The technique is known as EEG biofeedback, or neurofeedback, and has been used since the late 1960s in research and clinical applications. A major limitation of neurofeedback relates to the limited information provided by a single or small number of electrodes placed on the scalp. We establish a method for extracting and feeding back intracranial current density and we carry out an experimental study to ascertain the ability of the participants to drive their own EEG power in a desired direction. To derive current density within the brain volume, we used the low-resolution electromagnetic tomography (LORETA). Six undergraduate students (three males, three females) underwent tomographic neurofeedback (based on 19 electrodes placed according to the 10-20 system) to enhance the current density power ratio between the frequency bands beta (16-20 Hz) and alpha (8-10 Hz). According to LORETA modeling, the region of interest corresponded to the Anterior Cingulate (cognitive division). The protocol was designed to improve the performance of the subjects on the dimension of sustained attention. Two hypotheses were tested: 1) that the beta/alpha current density power ratio increased over sessions and 2) that by the end of the training subjects acquired the ability of increasing that ratio at will. Both hypotheses received substantial experimental support in this study. This is the first application of an EEG inverse solution to neurofeedback. Possible applications of the technique include the treatment of epileptic foci, the rehabilitation of specific brain regions damaged as a consequence of traumatic brain injury and, in general, the training of any spatial specific cortical electrical activity. These findings may also have relevant consequences for the development of brain-computer interfaces.},
}
@article {pmid15591175,
year = {2004},
author = {Wickelgren, I},
title = {Neuroprosthetics. Brain-computer interface adds a new dimension.},
journal = {Science (New York, N.Y.)},
volume = {306},
number = {5703},
pages = {1878-1879},
doi = {10.1126/science.306.5703.1878a},
pmid = {15591175},
issn = {1095-9203},
mesh = {Algorithms ; Animals ; Artificial Intelligence ; *Beta Rhythm ; Brain/*physiology ; Electrodes ; *Electroencephalography ; Humans ; Software ; Spinal Cord Injuries/rehabilitation ; *User-Computer Interface ; },
}
@article {pmid15589184,
year = {2005},
author = {McFarland, DJ and Sarnacki, WA and Vaughan, TM and Wolpaw, JR},
title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {116},
number = {1},
pages = {56-62},
doi = {10.1016/j.clinph.2004.07.004},
pmid = {15589184},
issn = {1388-2457},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Biofeedback, Psychology ; Brain/*physiology ; Brain Mapping ; Electroencephalography/methods ; Electromyography/methods ; Female ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/physiology ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted ; *Teaching ; *User-Computer Interface ; },
abstract = {OBJECTIVE: People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.<br><br>METHODS: In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users.<br><br>RESULTS: Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10.<br><br>CONCLUSIONS: EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination.<br><br>SIGNIFICANCE: Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.},
}
@article {pmid15585584,
year = {2004},
author = {Wolpaw, JR and McFarland, DJ},
title = {Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {101},
number = {51},
pages = {17849-17854},
pmid = {15585584},
issn = {0027-8424},
support = {R01 EB000856/EB/NIBIB NIH HHS/United States ; R01 HD030146/HD/NICHD NIH HHS/United States ; EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Brain/*physiology ; Female ; Humans ; Male ; *Man-Machine Systems ; Movement/*physiology ; Robotics/instrumentation/methods ; *Signal Processing, Computer-Assisted ; User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.},
}
@article {pmid15582374,
year = {2004},
author = {Andersen, RA and Musallam, S and Pesaran, B},
title = {Selecting the signals for a brain-machine interface.},
journal = {Current opinion in neurobiology},
volume = {14},
number = {6},
pages = {720-726},
doi = {10.1016/j.conb.2004.10.005},
pmid = {15582374},
issn = {0959-4388},
mesh = {Action Potentials/physiology ; Animals ; Brain/*physiology ; Humans ; Neurons/physiology ; Paralysis/*therapy ; Prostheses and Implants/*trends ; Robotics/*trends ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.},
}
@article {pmid15546783,
year = {2004},
author = {Wang, T and Deng, J and He, B},
title = {Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {115},
number = {12},
pages = {2744-2753},
doi = {10.1016/j.clinph.2004.06.022},
pmid = {15546783},
issn = {1388-2457},
support = {R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain Mapping ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Hand ; Humans ; Imagination/*physiology ; *Models, Neurological ; Movement ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To develop a single trial motor imagery (MI) classification strategy for the brain-computer interface (BCI) applications by using time-frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description.<br><br>METHODS: The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time-frequency grid. Time-frequency weights determined by training process are used to synthesize the contributions from the time-frequency domains.<br><br>RESULTS: The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area.<br><br>CONCLUSIONS: The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact.<br><br>SIGNIFICANCE: The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification.},
}
@article {pmid15545204,
year = {2004},
author = {Al-Hayek, S and Thomas, A and Abrams, P},
title = {Natural history of detrusor contractility--minimum ten-year urodynamic follow-up in men with bladder outlet obstruction and those with detrusor.},
journal = {Scandinavian journal of urology and nephrology. Supplementum},
volume = {},
number = {215},
pages = {101-108},
doi = {10.1080/03008880410015453},
pmid = {15545204},
issn = {0300-8886},
mesh = {Adult ; Aged ; Follow-Up Studies ; Humans ; Male ; Middle Aged ; *Muscle Contraction ; Muscle, Smooth/*physiopathology ; Time Factors ; Urinary Bladder Neck Obstruction/*physiopathology ; *Urodynamics ; },
abstract = {OBJECTIVE: To check the long-term effect, in male patients, of treated and untreated bladder outlet obstruction (BOO) on detrusor contractility and to explore the relationship between ageing and detrusor underactivity (DUA).<br><br>MATERIAL AND METHODS: Men investigated at the urodynamic department of Southmead Hospital in Bristol between 1972 and 1986 were traced and three groups were invited for repeat pressure-flow urodynamic studies (PFS). The first two groups included patients over 40 years old, with untreated or surgically treated BOO, and the third group had patients with DUA from all age groups.<br><br>RESULTS: 196 patients (with a minimum 10 year gap from the first assessment) agreed to have repeat PFS. There was no statistically significant change in bladder contractility index (BCI) in patients with BOO treated by transurethral resection of the prostate (TURP) (mean difference in BCI was 0.01, 95% confidence interval -0.07 to 0.09, n=114). There was also no significant difference in BCI in untreated patients with BOO (p=0.10, n=53). The follow-up BCI was higher in untreated patients than in the surgically treated group. The BCI in patients with DUA did not change significantly after a minimum of 10 years' follow-up.<br><br>CONCLUSIONS: There is no evidence to suggest that detrusor contractility declines with long-term BOO. Relieving the obstruction surgically does not improve the contractility. This is important when considering and counselling for TURP. Underactive detrusors remain underactive, but do not get worse with time, which could indicate that this is not an ageing process per se and may even have a congenital basis.},
}
@article {pmid15516880,
year = {2004},
author = {Aho, AJ and Tirri, T and Kukkonen, J and Strandberg, N and Rich, J and Seppälä, J and Yli-Urpo, A},
title = {Injectable bioactive glass/biodegradable polymer composite for bone and cartilage reconstruction: concept and experimental outcome with thermoplastic composites of poly(epsilon-caprolactone-co-D,L-lactide) and bioactive glass S53P4.},
journal = {Journal of materials science. Materials in medicine},
volume = {15},
number = {10},
pages = {1165-1173},
pmid = {15516880},
issn = {0957-4530},
mesh = {Animals ; *Biocompatible Materials ; Biomechanical Phenomena ; Bone Substitutes ; Bone and Bones/metabolism/pathology ; Cartilage/pathology ; Chondrocytes/cytology ; Female ; Glass/*chemistry ; Materials Testing ; Microscopy, Electron, Scanning ; Polymers/chemistry ; Rabbits ; Temperature ; Time Factors ; },
abstract = {Injectable composites (Glepron) of particulate bioactive glass S53P4 (BAG) and Poly(epsilon-caprolactone-co-D,L-lactide) as thermoplastic carrier matrix were investigated as bone fillers in cancellous and cartilagineous subchondral bone defects in rabbits. Composites were injected as viscous liquid or mouldable paste. The glass granules of the composites resulted in good osteoconductivity and bone bonding that occurred initially at the interface between the glass and the host bone. The bone bioactivity index (BBI) indicating bone contacts between BAG and bone, as well as the bone coverage index (BCI) indicating bone ongrowth, correlated with the amount of glass in the composites. The indices were highest with 70 wt % of BAG, granule size 90-315 microm and did not improve by the addition of sucrose as in situ porosity creating agent in the composite or by using smaller (<45 microm) glass granules. The percentage of new bone ingrowth into the composite with 70 wt % of BAG was 6-8% at 23 weeks. At the articular surface cartilage regeneration with chondroblasts and mature chondrocytes was often evident. The composites were osteoconductive and easy to handle with short setting time. They were biocompatible with low foreign body cellular reaction. Results indicate a suitable working concept as a filler bone substitute for subchondral cancellous bone defects.},
}
@article {pmid15486335,
year = {2004},
author = {Friehs, GM and Zerris, VA and Ojakangas, CL and Fellows, MR and Donoghue, JP},
title = {Brain-machine and brain-computer interfaces.},
journal = {Stroke},
volume = {35},
number = {11 Suppl 1},
pages = {2702-2705},
doi = {10.1161/01.STR.0000143235.93497.03},
pmid = {15486335},
issn = {1524-4628},
mesh = {Animals ; Brain/*physiology ; Electrodes, Implanted ; Electroencephalography ; Humans ; *Neural Networks, Computer ; *Prostheses and Implants ; *Stroke Rehabilitation ; },
abstract = {The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.},
}
@article {pmid15480856,
year = {2005},
author = {De Mitri, MS and Morsica, G and Cassini, R and Bagaglio, S and Andreone, P and Bianchi, G and Margotti, M and Bernardi, M},
title = {Genetic variability of hepatitis C virus in HBV/HCV co-infection and HCV single-infection.},
journal = {Archives of virology},
volume = {150},
number = {2},
pages = {261-271},
doi = {10.1007/s00705-004-0415-7},
pmid = {15480856},
issn = {0304-8608},
mesh = {Adult ; Aged ; Amino Acid Sequence ; Chronic Disease ; DNA, Viral/blood ; Female ; Genetic Variation ; Hepacivirus/*genetics/isolation &amp; purification ; Hepatitis B/blood/complications/*virology ; Hepatitis B virus/isolation &amp; purification ; Hepatitis C/blood/complications/*virology ; Humans ; Liver Cirrhosis/virology ; Male ; Middle Aged ; Molecular Sequence Data ; Protein Structure, Tertiary ; RNA, Viral/blood ; Sequence Alignment ; Viral Nonstructural Proteins/genetics ; Viral Proteins/genetics ; },
abstract = {To describe the virological profile of HCV in HBV/HCV co-infection, we investigated the variability of HVR-1 and NS5A domains, which may be involved in viral persistence and replication efficiency. We studied 95 patients: 37 with serological markers of HBV/HCV co-infection, 33 with single HBV and 25 with single HCV infection. HVR-1 complexity and NS5A gene variability were respectively explored by means of PCR-SSCP and direct sequencing. Serum HBV genomes were detected in all coinfected patients: 19 also had circulating HCV particles (group BC-I), whereas HCV were undetectable in the other 18 (group BC-II). Group BC-I was characterised by a significantly lower HBV replication capacity, that reflects the replicative dominance of HCV, although the dominant virus had the same degree of variability as the HCV in single infection. HBV viral load was higher in group BC-II, but not significantly different from that observed in the single infection. Our data indicate an alternation in replicative dominance in co-infection: HBV can suppress HCV replication to undetectable levels, whereas HCV may reduce but does not abrogate the replication capacity of HBV. Furthermore, in the cases of HCV dominance, circulating HBV genomes did not have a significant effect on the viral heterogeneity of HCV.},
}
@article {pmid15473195,
year = {2004},
author = {Fabiani, GE and McFarland, DJ and Wolpaw, JR and Pfurtscheller, G},
title = {Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {12},
number = {3},
pages = {331-338},
doi = {10.1109/TNSRE.2004.834627},
pmid = {15473195},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; Artificial Intelligence ; Brain/*physiopathology ; Cerebral Palsy/physiopathology/*rehabilitation ; *Communication Devices for People with Disabilities ; Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials, Somatosensory/*physiology ; Female ; Humans ; Male ; Middle Aged ; Online Systems ; Pattern Recognition, Automated ; Task Performance and Analysis ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; Word Processing/methods ; },
abstract = {The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.},
}
@article {pmid15446844,
year = {2004},
author = {Whittle, M and Gillet, VJ and Willett, P and Alex, A and Loesel, J},
title = {Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients.},
journal = {Journal of chemical information and computer sciences},
volume = {44},
number = {5},
pages = {1840-1848},
doi = {10.1021/ci049867x},
pmid = {15446844},
issn = {0095-2338},
mesh = {Pharmaceutical Preparations/*chemistry ; },
abstract = {This paper evaluates the effectiveness of various similarity coefficients for 2D similarity searching when multiple bioactive target structures are available. Similarity searches using several different activity classes within the MDL Drug Data Report and the Dictionary of Natural Products databases are performed using BCI 2D fingerprints. Using data fusion techniques to combine the resulting nearest neighbor lists we obtain group recall results which, in many cases, are a considerable improvement on standard average recall values obtained for individual structures. It is shown that the degree of improvement can be related to the structural diversity of the activity class that is searched for, the best results being found for the most diverse groups. The group recall of active compounds using subsets of the class is also investigated: for highly self-similar activity classes, the group recall improvement saturates well before the full activity class size is reached. A rough correlation is found between the relative improvement using the group recall and the square of the number of unique compounds available in all of the merged lists. The Tanimoto coefficient is found unambiguously to be the best coefficient to use for the recovery of active compounds using multiple targets. Furthermore, when using the Tanimoto coefficient, the "MAX" fusion rule is found to be more effective than the "SUM" rule for the combination of similarity searches from multiple targets. The use of group recall can lead to improved enrichment in database searches and virtual screening.},
}
@article {pmid15382823,
year = {2004},
author = {Coyle, S and Ward, T and Markham, C and McDarby, G},
title = {On the suitability of near-infrared (NIR) systems for next-generation brain-computer interfaces.},
journal = {Physiological measurement},
volume = {25},
number = {4},
pages = {815-822},
doi = {10.1088/0967-3334/25/4/003},
pmid = {15382823},
issn = {0967-3334},
mesh = {*Algorithms ; Cerebral Cortex/blood supply/*physiology ; Electroencephalography ; Hemodynamics ; Humans ; Learning ; Optics and Photonics ; *Spectroscopy, Near-Infrared ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user. Ultimately this can lead to abandonment of the device. The primary reason for this is that relatively indirect measures of cognitive function, as can be gleaned from the electroencephalogram (EEG), are harnessed. A more suitable and usable interface would need to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. In this paper, we propose a novel approach, using non-invasive near-infrared imaging technology to develop a user-friendly optical BCI. As an alternative to the traditional EEG-based devices, we have used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training.},
}
@article {pmid15378459,
year = {2004},
author = {Santana, D and Ramírez, M and Ostrosky-Solís, F},
title = {[Recent advances in rehabilitation technology: a review of the brain-computer interface].},
journal = {Revista de neurologia},
volume = {39},
number = {5},
pages = {447-450},
pmid = {15378459},
issn = {0210-0010},
mesh = {Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography ; Electrophysiology ; Humans ; Neuromuscular Diseases/*rehabilitation ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {INTRODUCTION AND AIMS: In this work we review some of the options available in rehabilitation technology that are used to aid people with severe neuromuscular disorders, and which take electrophysiological activity as a source of biological signals with which to design interfaces.<br><br>DEVELOPMENT: A number of different researchers have generated a novel communication and control system that utilises the electrical activity of the brain as a signal that represents the messages or commands an individual sends to the outside world, without using the normal output pathways of the brain, such as peripheral nerves and muscles; instead, this is achieved through an artificial system that extracts, encodes and applies them, called a brain-computer interface (BCI). The electrophysiological activity for a BCI can be obtained by means of superficial or implanted electrodes, and may therefore be classified as invasive or non-invasive. Five types of brain signals have been explored for use with a BCI: visual evoked potentials, slow cortical potentials, cortical neuronal activity, beta and mu rhythms, and event-related potentials.<br><br>CONCLUSIONS: Thanks to recent improvements and developments in prototypes, this technology is sure to open up new possibilities of communication and control for the affected population; it also represents a valuable field of multidisciplinary research with numerous interesting applications in areas beyond the sphere of health care.},
}
@article {pmid15375723,
year = {2004},
author = {Wiese, J and Kranz, T and Schubert, S},
title = {Induction of pathogen resistance in barley by abiotic stress.},
journal = {Plant biology (Stuttgart, Germany)},
volume = {6},
number = {5},
pages = {529-536},
doi = {10.1055/s-2004-821176},
pmid = {15375723},
issn = {1435-8603},
mesh = {Ascomycota/pathogenicity ; Hordeum/drug effects/*metabolism/*microbiology ; Osmotic Pressure ; Plant Diseases/microbiology ; Protons ; Sodium Chloride ; Thiadiazoles/pharmacology ; },
abstract = {Enhanced resistance of barley (Hordeum vulgare L. cv. Ingrid) against barley powdery mildew (Blumeria graminis f. sp. hordei race A6) was induced by abiotic stress in a concentration-dependent manner. The papilla-mediated resistance was not only induced by osmotic stress, but also by proton stress. Resistance was directly correlated with increasing concentrations of various salts in the nutrient solution. Resistance induced by proton stress also depended on the stress intensity. Resistance induction occurred even at low stress intensities. Any specific ion toxicity affecting the fungal growth directly, and therefore leading to enhanced pathogen resistance, can be excluded because of the independence of resistance induction of the ion used and of the time course of sodium accumulation in the leaves. BCI-4, a marker for benzo[1,2,3]thiadiazolecarbothioic acid S-methyl ester (BTH)-induced resistance was not induced by these abiotic stresses. However, resistance was induced in the same concentration-dependent manner by the application of the stress hormone ABA to the root medium. During the relief of water stress, resistance did not decrease constantly. On the contrary, after a phase of decreasing resistance for 24 h the pathogen resistance increased again for 48 h before decreasing finally to control levels.},
}
@article {pmid15337130,
year = {2004},
author = {Kuo, JR and Chou, TJ and Chio, CC},
title = {Coagulopathy as a parameter to predict the outcome in head injury patients--analysis of 61 cases.},
journal = {Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia},
volume = {11},
number = {7},
pages = {710-714},
doi = {10.1016/j.jocn.2003.10.011},
pmid = {15337130},
issn = {0967-5868},
mesh = {Adult ; Blood Coagulation Disorders/blood/*etiology ; Craniocerebral Trauma/blood/*complications/*diagnosis ; Female ; Fibrin Fibrinogen Degradation Products/metabolism ; Glasgow Coma Scale ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Prothrombin/metabolism ; Reflex, Pupillary/physiology ; *Statistics as Topic ; Time Factors ; Tomography, X-Ray Computed/methods ; },
abstract = {The correlation of coagulopathy and pupillary light reflex, the degree of midline shift in brain computer tomography and Glasgow outcome scale (GOS) after head injury were prospectively evaluated. From September 2002 to March 2003, 61 patients (45 males and 16 females; mean age: 41.9 years) after head injury were enrolled in the study. A modified coagulopathy score (CS) defined by prothrombin time, partial thromboplastin time, platelet count, D-dimer and fibrinogen was calculated for each patient within 24 h after injury. The CS was 2.3+/-2.7 (mean+/-SD). The incidence of abnormal coagulation following head injury in non-survival cases was 100% and in survival cases 66%. The mortality rate was significantly increased to 75% in CS above 4 and 100% if CS was 6 or greater. The increase of D-dimer concentration appears to be common yet abnormal platelet counts are relatively uncommon among head trauma patients. Within 4 h after head injury, there is an initial hypercoagulable stage followed by hypocoagulable stage 6 h after head injury. Our results showed pupillary light reflex has the most significant correlation to GOS (rho = 0.727, p < 0.0001). It also reveals that coagulopathy score > or 4 (positive predictive value 90%) may have higher degree of accuracy to predict mortality comparing to both pupils being fixed or brain CT midline shift > or = 15 mm. We conclude that: (1) Coagulation state in head injury patients within 24 h after injury is of value in determining the outcome. (2) Coagulopathy score > or = 4 is a good predictor to evaluate mortality rate of head injury patients.},
}
@article {pmid15295155,
year = {2004},
author = {Riva, G and Morganti, F and Villamira, M},
title = {Immersive Virtual Telepresence: virtual reality meets eHealth.},
journal = {Studies in health technology and informatics},
volume = {99},
number = {},
pages = {255-262},
pmid = {15295155},
issn = {0926-9630},
mesh = {Biosensing Techniques ; Italy ; *Telemedicine ; *User-Computer Interface ; },
abstract = {Immersive Virtual Telepresence (IVT) tools are virtual reality environments combined with wireless multimedia facilities--real-time video and audio--and advanced input devices--tracking sensors, biosensors, brain-computer interfaces. For its features IVT can be considered an innovative communication interface based on interactive 3D visualization, able to collect and integrate different inputs and data sets in a single real-like experience. In this paper we try to outline the current state of research and technology that is relevant to the development of IVT in medicine. Moreover, we discuss the clinical principles and possible advantages associated with the use of IVT in this field.},
}
@article {pmid15285050,
year = {2004},
author = {Ichikawa, M and Matsumoto, G},
title = {The brain-computer: origin of the idea and progress in its realization.},
journal = {Journal of integrative neuroscience},
volume = {3},
number = {2},
pages = {125-132},
doi = {10.1142/s0219635204000476},
pmid = {15285050},
issn = {0219-6352},
mesh = {Algorithms ; Animals ; Artificial Intelligence ; Brain/*physiology ; Brain Mapping ; *Computers ; Humans ; *Models, Neurological ; Neural Networks, Computer ; Robotics ; },
abstract = {The Brain-Computer is a physical analogue of a real organism which uses both a brain-inspired memory-based architecture and an output-driven learning algorithm. This system can be realized by creating a scaled-down model car that learns how to drive by heuristically connecting image processing with behavior control. This study proves that learning efficiency progresses rapidly when the acquired behaviors are prioritized. We develop a small real-world device that moves about purposefully in an artificial environment. The robot uses imaging information acquired through its random actions to make a mental map. This map, then, provides the cognitive structure for acquiring necessary information for autonomous behavior.},
}
@article {pmid15277601,
year = {2004},
author = {Hatsopoulos, N and Joshi, J and O'Leary, JG},
title = {Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles.},
journal = {Journal of neurophysiology},
volume = {92},
number = {2},
pages = {1165-1174},
doi = {10.1152/jn.01245.2003},
pmid = {15277601},
issn = {0022-3077},
support = {N01 NS 22345/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Arm/physiology ; Behavior, Animal/*physiology ; Electrophysiology ; Hand/physiology ; Joints/physiology ; Macaca mulatta ; Models, Biological ; Models, Statistical ; Motor Cortex/*physiology ; Psychomotor Performance/*physiology ; },
abstract = {Decoding motor behavior from neuronal signals has important implications for the development of a brain-machine interface (BMI) but also provides insights into the nature of different movement representations within cortical ensembles. Motor control can be hierarchically characterized as the selection and planning of discrete movement classes and/or postures followed by the execution of continuous limb trajectories. Based on simultaneous recordings in primary motor (MI) and dorsal premotor (PMd) cortices in behaving monkeys, we demonstrate that an MI ensemble can reconstruct hand or joint trajectory more accurately than an equally sized PMd ensemble. In contrast, PMd can more precisely predict the future occurrence of one of several discrete targets to be reached. This double dissociation suggests that a general-purpose BMI could take advantage of multiple cortical areas to control a wider variety of motor actions. These results also support the hierarchical view that MI ensembles are involved in lower-level movement execution, whereas PMd populations represent the early intention to move to visually presented targets.},
}
@article {pmid15249083,
year = {2004},
author = {Dhillon, GS and Lawrence, SM and Hutchinson, DT and Horch, KW},
title = {Residual function in peripheral nerve stumps of amputees: implications for neural control of artificial limbs.},
journal = {The Journal of hand surgery},
volume = {29},
number = {4},
pages = {605-15; discussion 616-8},
doi = {10.1016/j.jhsa.2004.02.006},
pmid = {15249083},
issn = {0363-5023},
mesh = {Action Potentials ; Amputation Stumps/*innervation/physiopathology ; *Amputees ; *Artificial Limbs ; Axons/physiology ; *Biofeedback, Psychology ; Electric Stimulation ; Electrodes, Implanted ; Humans ; Movement/physiology ; Proprioception ; *Sensation ; Touch ; },
abstract = {PURPOSE: It is not known whether motor and sensory pathways associated with a missing or denervated limb remain functionally intact over periods of many months or years after amputation or chronic peripheral nerve transection injury. We examined the extent to which activity on chronically severed motor nerve fibers could be controlled by human amputees and whether distally referred tactile and proprioceptive sensations could be induced by stimulation of sensory axons in the nerve stumps.<br><br>METHODS: Amputees undergoing elective stump procedures were invited to participate in this study. Longitudinal intrafascicular electrodes were threaded percutaneously and implanted in severed nerves of human amputees. The electrodes were interfaced to an amplifier and stimulator system controlled by a laptop computer. Electrophysiologic tests were conducted for 2 consecutive days after recovery from the surgery.<br><br>RESULTS: It was possible to record volitional motor nerve activity uniquely associated with missing limb movements. Electrical stimulation through the implanted electrodes elicited discrete, unitary, graded sensations of touch, joint movement, and position, referring to the missing limb.<br><br>CONCLUSIONS: These findings indicate that both central and peripheral motor and somatosensory pathways retain significant residual connectivity and function for many years after limb amputation. This implies that peripheral nerve interfaces could be used to provide amputees with prosthetic limbs that have more natural feel and control than is possible with current myoelectric and body-powered control systems.},
}
@article {pmid15232289,
year = {2004},
author = {Yoo, SS and Fairneny, T and Chen, NK and Choo, SE and Panych, LP and Park, H and Lee, SY and Jolesz, FA},
title = {Brain-computer interface using fMRI: spatial navigation by thoughts.},
journal = {Neuroreport},
volume = {15},
number = {10},
pages = {1591-1595},
doi = {10.1097/01.wnr.0000133296.39160.fe},
pmid = {15232289},
issn = {0959-4965},
mesh = {Adult ; Brain/blood supply/*physiology ; Brain Mapping ; Humans ; Image Enhancement/methods ; Image Processing, Computer-Assisted/methods ; *Magnetic Resonance Imaging ; Male ; *Neuronavigation ; Oxygen/blood ; Signal Processing, Computer-Assisted ; Thinking/*physiology ; User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a way of conveying an individual's thoughts to control computer or electromechanical hardware. Capitalizing on the ability to characterize brain activity in a reproducible manner, we explored the possibility of using real-time fMRI to interpret the spatial distribution of brain function as BCI commands. Using a high-field (3T) MRI scanner, brain activities associated with four distinct covert functional tasks were detected and subsequently translated into predetermined computer commands for moving four directional cursors. The proposed fMRI-BCI method allowed volunteer subjects to navigate through a simple 2D maze solely through their thought processes.},
}
@article {pmid15218939,
year = {2004},
author = {Townsend, G and Graimann, B and Pfurtscheller, G},
title = {Continuous EEG classification during motor imagery--simulation of an asynchronous BCI.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {12},
number = {2},
pages = {258-265},
doi = {10.1109/TNSRE.2004.827220},
pmid = {15218939},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; *Communication Devices for People with Disabilities ; Computer Simulation ; Data Display ; Electroencephalography/classification/*methods ; Environment ; Evoked Potentials, Motor/physiology ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Online Systems ; Pattern Recognition, Automated/methods ; *User-Computer Interface ; },
abstract = {Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.},
}
@article {pmid15214971,
year = {2004},
author = {Patil, PG and Carmena, JM and Nicolelis, MA and Turner, DA},
title = {Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface.},
journal = {Neurosurgery},
volume = {55},
number = {1},
pages = {27-35; discussion 35-8},
pmid = {15214971},
issn = {0148-396X},
mesh = {Electrodes, Implanted ; Electroencephalography ; Essential Tremor/*physiopathology ; Feasibility Studies ; Hand Strength/physiology ; Humans ; Microelectrodes ; Motor Activity/*physiology ; Neurons/*physiology ; Parkinson Disease/*physiopathology ; Predictive Value of Tests ; Subthalamic Nucleus/*physiopathology ; Task Performance and Analysis ; },
abstract = {OBJECTIVE: Patients with severe neurological injury, such as quadriplegics, might benefit greatly from a brain-machine interface that uses neuronal activity from motor centers to control a neuroprosthetic device. Here, we report an implementation of this strategy in the human intraoperative setting to assess the feasibility of using neurons in subcortical motor areas to drive a human brain-machine interface.<br><br>METHODS: Acute ensemble recordings from subthalamic nucleus and thalamic motor areas (ventralis oralis posterior [VOP]/ventralis intermediate nucleus [VIM]) were obtained in 11 awake patients during deep brain stimulator surgery by use of a 32-microwire array. During extracellular neuronal recordings, patients simultaneously performed a visual feedback hand-gripping force task. Offline analysis was then used to explore the relationship between neuronal modulation and gripping force.<br><br>RESULTS: Individual neurons (n = 28 VOP/VIM, n = 119 subthalamic nucleus) demonstrated a variety of modulation responses both before and after onset of changes in gripping force of the contralateral hand. Overall, 61% of subthalamic nucleus neurons and 81% of VOP/VIM neurons modulated with gripping force. Remarkably, ensembles of 3 to 55 simultaneously recorded neurons were sufficiently information-rich to predict gripping force during 30-second test periods with considerable accuracy (up to R = 0.82, R(2) = 0.68) after short training periods. Longer training periods and larger neuronal ensembles were associated with improved predictive accuracy.<br><br>CONCLUSION: This initial feasibility study bridges the gap between the nonhuman primate laboratory and the human intraoperative setting to suggest that neuronal ensembles from human subcortical motor regions may be able to provide informative control signals to a future brain-machine interface.},
}
@article {pmid15197086,
year = {2004},
author = {Mayberry, JC and Brown, CV and Mullins, RJ and Velmahos, GC},
title = {Blunt carotid artery injury: the futility of aggressive screening and diagnosis.},
journal = {Archives of surgery (Chicago, Ill. : 1960)},
volume = {139},
number = {6},
pages = {609-12; discussion 612-3},
doi = {10.1001/archsurg.139.6.609},
pmid = {15197086},
issn = {0004-0010},
mesh = {Adolescent ; Adult ; Angiography/*methods ; Carotid Artery Injuries/diagnosis/*diagnostic imaging/etiology ; Humans ; Mass Screening/methods ; Middle Aged ; Retrospective Studies ; Stroke/etiology ; Wounds, Nonpenetrating/complications/diagnosis/*diagnostic imaging ; },
abstract = {BACKGROUND: Blunt carotid artery injury (BCI) remains a rare but potentially lethal condition. Recent studies recommend that aggressive screening based on broad criteria (hyperextension-hyperflexion mechanism of injury, basilar skull fracture, cervical spine injury, midface fracture, mandibular fracture, diffuse axonal brain injury, and neck seat-belt sign) increases the rate of diagnosis of BCI by 9-fold. If this recommendation becomes a standard of care, it will require a major consumption of resources and may give rise to liability claims. The benefits of aggressive screening are unclear because the natural history of asymptomatic BCI is unknown and the existing treatments are controversial.<br><br>HYPOTHESIS: The lack of an aggressive angiographic screening protocol does not result in delayed BCI diagnosis or BCI-related neurologic deficits.<br><br>METHODS: A 10-year medical record review of patients with BCI was undertaken in 2 level I academic trauma centers. In both centers, urgent screening for BCI was performed in patients with focal neurologic signs or neurologic symptoms unexplainable by results of computed tomography of the brain as well as in selected patients undergoing angiography for another reason.<br><br>RESULTS: Of 35 212 blunt trauma admissions, 17 patients (0.05%) were diagnosed as having BCI. Six showed no evidence of BCI-related neurologic symptoms during hospitalization or prior to death as a result of associated injuries. Eleven sustained a BCI-related stroke, 9 of whom had it within 2 hours of injury. The remaining 2 had a delayed diagnosis (9 and 12 hours after injury) and received only anticoagulation because the lesions were surgically inaccessible. Just 1 of these 2 patients met the criteria for BCI screening and could have been offered earlier treatment, of uncertain benefit, if we had adopted an aggressive screening policy.<br><br>CONCLUSIONS: Of the few patients with BCI, most remain asymptomatic or develop neurologic deficits shortly after injury. Although a widely applied, resource-consuming screening program may increase the rate of early diagnosis of BCI, an improvement in outcome is uncertain. A cost-effectiveness analysis should be done before trauma surgeons accept an aggressive screening protocol as the standard of care.},
}
@article {pmid15194608,
year = {2004},
author = {Bach-y-Rita, P},
title = {Tactile sensory substitution studies.},
journal = {Annals of the New York Academy of Sciences},
volume = {1013},
number = {},
pages = {83-91},
doi = {10.1196/annals.1305.006},
pmid = {15194608},
issn = {0077-8923},
mesh = {Biomimetics/methods/trends ; Blindness/*physiopathology/*rehabilitation ; Discrimination Learning ; Humans ; *Man-Machine Systems ; Physical Stimulation/methods ; Sensory Aids/*trends ; *Touch ; *User-Computer Interface ; Vestibular Diseases/*physiopathology/*rehabilitation ; },
abstract = {Forty years ago a project to explore late brain plasticity was initiated that was to lead into a broad area of sensory substitution studies. The questions at that time were: Can a person who has never seen learn to see as an adult? Is the brain sufficiently plastic to develop an entirely new sensory system? The short answer to both questions is yes, first clearly demonstrated in 1969 ((Bach-y-Rita et al., 1969)). To reach that conclusion, it was first necessary to find a way to get visual information to the brain. That took many years and is still the most challenging aspect of the research and the development of practical sensory substitution and augmentation systems. The sensor array is not a problem: a TV camera for blind persons; an accelerometer for persons with vestibular loss; a microphone for deaf persons. These are common and fully developed devices. The problem is the brain-machine interface (BMI). In this short report, only two substitution systems are discussed, vision and vestibular substitution.},
}
@article {pmid15188883,
year = {2004},
author = {Wang, Y and Zhang, Z and Li, Y and Gao, X and Gao, S and Yang, F},
title = {BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1081-1086},
doi = {10.1109/TBME.2004.826697},
pmid = {15188883},
issn = {0018-9294},
mesh = {*Algorithms ; Artificial Intelligence ; Cerebral Cortex/*physiology ; Cognition/physiology ; Computer Peripherals ; Databases, Factual ; Discriminant Analysis ; Electroencephalography/classification/*methods ; Evoked Potentials, Motor/*physiology ; Fingers/*physiology ; Humans ; Imagination/physiology ; Models, Neurological ; Motor Cortex/physiology ; Movement/*physiology ; Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.},
}
@article {pmid15188882,
year = {2004},
author = {Lemm, S and Schäfer, C and Curio, G},
title = {BCI Competition 2003--Data set III: probabilistic modeling of sensorimotor mu rhythms for classification of imaginary hand movements.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1077-1080},
doi = {10.1109/TBME.2004.827076},
pmid = {15188882},
issn = {0018-9294},
mesh = {*Algorithms ; Artificial Intelligence ; Cerebral Cortex/*physiology ; Cognition/physiology ; Databases, Factual ; Electroencephalography/classification/*methods ; Evoked Potentials, Motor/*physiology ; Hand/*physiology ; Humans ; Imagination/physiology ; Models, Neurological ; Motor Cortex/physiology ; Movement/*physiology ; Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces require effective online processing of electroencephalogram (EEG) measurements, e.g., as a part of feedback systems. We present an algorithm for single-trial online classification of imaginary left and right hand movements, based on time-frequency information derived from filtering EEG wideband raw data with causal Morlet wavelets, which are adapted to individual EEG spectra. Since imaginary hand movements lead to perturbations of the ongoing pericentral mu rhythm, we estimate probabilistic models for amplitude modulation in lower (10 Hz) and upper (20 Hz) frequency bands over the sensorimotor hand cortices both contra- and ipsilaterally to the imagined movements (i.e., at EEG channels C3 and C4). We use an integrative approach to accumulate over time evidence for the subject's unknown motor intention. Disclosure of test data labels after the competition showed this approach to succeed with an error rate as low as 10.7%.},
}
@article {pmid15188881,
year = {2004},
author = {Kaper, M and Meinicke, P and Grossekathoefer, U and Lingner, T and Ritter, H},
title = {BCI Competition 2003--Data set IIb: support vector machines for the P300 speller paradigm.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1073-1076},
doi = {10.1109/TBME.2004.826698},
pmid = {15188881},
issn = {0018-9294},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Databases, Factual ; Electroencephalography/classification/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Word Processing ; },
abstract = {We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.},
}
@article {pmid15188880,
year = {2004},
author = {Xu, N and Gao, X and Hong, B and Miao, X and Gao, S and Yang, F},
title = {BCI Competition 2003--Data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1067-1072},
doi = {10.1109/TBME.2004.826699},
pmid = {15188880},
issn = {0018-9294},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Computer Peripherals ; Databases, Factual ; Electroencephalography/classification/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated ; Principal Component Analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; Word Processing ; },
abstract = {An algorithm based on independent component analysis (ICA) is introduced for P300 detection. After ICA decomposition, P300-related independent components are selected according to the a priori knowledge of P300 spatio-temporal pattern, and clear P300 peak is reconstructed by back projection of ICA. Applied to the dataset IIb of BCI Competition 2003, the algorithm achieved an accuracy of 100% in P300 detection within five repetitions.},
}
@article {pmid15188879,
year = {2004},
author = {Blanchard, G and Blankertz, B},
title = {BCI Competition 2003--Data set IIa: spatial patterns of self-controlled brain rhythm modulations.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1062-1066},
doi = {10.1109/TBME.2004.826691},
pmid = {15188879},
issn = {0018-9294},
mesh = {*Algorithms ; Artificial Intelligence ; Biofeedback, Psychology/*physiology ; Brain/*physiology ; Cognition/*physiology ; Databases, Factual ; Electroencephalography/classification/*methods ; Event-Related Potentials, P300/*physiology ; Humans ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) is a system that should in its ultimate form translate a subject's intent into a technical control signal without resorting to the classical neuromuscular communication channels. By using that signal to, e.g., control a wheelchair or a neuroprosthesis, a BCI could become a valuable tool for paralyzed patients. One approach to implement a BCI is to let users learn to self-control the amplitude of some of their brain rhythms as extracted from multichannel electroencephalogram. We present a method that estimates subject-specific spatial filters which allow for a robust extraction of the rhythm modulations. The effectiveness of the method was proved by achieving the minimum prediction error on data set IIa in the BCI Competition 2003, which consisted of data from three subjects recorded in ten sessions.},
}
@article {pmid15188878,
year = {2004},
author = {Bostanov, V},
title = {BCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1057-1061},
doi = {10.1109/TBME.2004.826702},
pmid = {15188878},
issn = {0018-9294},
mesh = {*Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology ; *Artificial Intelligence ; *Brain ; Cognition ; Databases, Factual ; Electroencephalography/classification/*methods ; *Evoked Potentials ; Humans ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.},
}
@article {pmid15188877,
year = {2004},
author = {Mensh, BD and Werfel, J and Seung, HS},
title = {BCI Competition 2003--Data set Ia: combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1052-1056},
doi = {10.1109/TBME.2004.827081},
pmid = {15188877},
issn = {0018-9294},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Databases, Factual ; Electroencephalography/classification/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {In one type of brain-computer interface (BCI), users self-modulate brain activity as detected by electroencephalography (EEG). To infer user intent, EEG signals are classified by algorithms which typically use only one of the several types of information available in these signals. One such BCI uses slow cortical potential (SCP) measures to classify single trials. We complemented these measures with estimates of high-frequency (gamma-band) activity, which has been associated with attentional and intentional states. Using a simple linear classifier, we obtained significantly greater classification accuracy using both types of information from the same recording epochs compared to using SCPs alone.},
}
@article {pmid15188876,
year = {2004},
author = {Blankertz, B and Müller, KR and Curio, G and Vaughan, TM and Schalk, G and Wolpaw, JR and Schlögl, A and Neuper, C and Pfurtscheller, G and Hinterberger, T and Schröder, M and Birbaumer, N},
title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1044-1051},
doi = {10.1109/TBME.2004.826692},
pmid = {15188876},
issn = {0018-9294},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Amyotrophic Lateral Sclerosis/*physiopathology ; *Artificial Intelligence ; *Brain ; Cognition ; Databases, Factual ; Electroencephalography/classification/*methods ; *Evoked Potentials ; Humans ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.},
}
@article {pmid15188875,
year = {2004},
author = {Schalk, G and McFarland, DJ and Hinterberger, T and Birbaumer, N and Wolpaw, JR},
title = {BCI2000: a general-purpose brain-computer interface (BCI) system.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1034-1043},
doi = {10.1109/TBME.2004.827072},
pmid = {15188875},
issn = {0018-9294},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Brain/*physiology ; Cognition ; Communication Devices for People with Disabilities ; Computer Peripherals ; Electroencephalography/*instrumentation/*methods ; Equipment Design ; Equipment Failure Analysis/*methods ; Evoked Potentials/*physiology ; Humans ; Systems Integration ; *User-Computer Interface ; },
abstract = {Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.},
}
@article {pmid15188874,
year = {2004},
author = {Millán, Jdel R and Renkens, F and Mouriño, J and Gerstner, W},
title = {Noninvasive brain-actuated control of a mobile robot by human EEG.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1026-1033},
doi = {10.1109/TBME.2004.827086},
pmid = {15188874},
issn = {0018-9294},
mesh = {*Algorithms ; Cerebral Cortex/*physiology ; Cognition/*physiology ; Cybernetics/methods ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; Pattern Recognition, Automated ; Reproducibility of Results ; Robotics/*methods ; Sensitivity and Specificity ; Task Performance and Analysis ; },
abstract = {Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74.},
}
@article {pmid15188873,
year = {2004},
author = {Li, Y and Gao, X and Liu, H and Gao, S},
title = {Classification of single-trial electroencephalogram during finger movement.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1019-1025},
doi = {10.1109/TBME.2004.826688},
pmid = {15188873},
issn = {0018-9294},
mesh = {Adult ; *Algorithms ; Cerebral Cortex/physiology ; Electroencephalography/*classification/*methods ; Evoked Potentials, Motor/*physiology ; Fingers/*physiology ; Humans ; Male ; Motor Cortex/physiology ; Movement/*physiology ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; Somatosensory Cortex/physiology ; Task Performance and Analysis ; },
abstract = {We present an algorithm to discriminate between the single-trial electroencephalograms (EEG) of two different finger movement tasks. The method uses a spatio-temporal analysis to classify the EEG recorded during voluntary left versus right finger movement tasks. This algorithm produced a classification accuracy of 92.1% on the data from five subjects, without requiring subject training or data selection. This technique can be employed in an EEG-based brain-computer interface due to its high recognition rate, insensitivity to noise, and simplicity in computation.},
}
@article {pmid15188872,
year = {2004},
author = {Hinterberger, T and Schmidt, S and Neumann, N and Mellinger, J and Blankertz, B and Curio, G and Birbaumer, N},
title = {Brain-computer communication and slow cortical potentials.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1011-1018},
doi = {10.1109/TBME.2004.827067},
pmid = {15188872},
issn = {0018-9294},
mesh = {Adult ; Algorithms ; Biofeedback, Psychology/*physiology ; Cerebral Cortex/*physiology ; Cognition/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A thought translation device (TTD) has been designed to enable direct brain-computer communication using self-regulation of slow cortical potentials (SCPs). However, accuracy of SCP control reveals high intersubject variability. To guarantee the highest possible communication speed, some important aspects of training SCPs are discussed. A baseline correction of SCPs can increase performance. Multichannel recordings show that SCPs are of highest amplitude around the vertex electrode used for feedback, but in some subjects more global distributions were observed. A new method for control of eye movement is presented. Sequential effects of trial-to-trial interaction may also cause difficulties for the user. Finally, psychophysiological factors determining SCP communication are discussed.},
}
@article {pmid15188871,
year = {2004},
author = {Lal, TN and Schröder, M and Hinterberger, T and Weston, J and Bogdan, M and Birbaumer, N and Schölkopf, B},
title = {Support vector channel selection in BCI.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {1003-1010},
doi = {10.1109/TBME.2004.827827},
pmid = {15188871},
issn = {0018-9294},
mesh = {*Algorithms ; *Artificial Intelligence ; Cerebral Cortex/*physiology ; Cluster Analysis ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Hand/physiology ; Humans ; Male ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM). These algorithms can provide more accurate solutions than standard filter methods for feature selection. We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.},
}
@article {pmid15188870,
year = {2004},
author = {Dornhege, G and Blankertz, B and Curio, G and Müller, KR},
title = {Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {993-1002},
doi = {10.1109/TBME.2004.827088},
pmid = {15188870},
issn = {0018-9294},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials, Motor/*physiology ; Humans ; Information Storage and Retrieval/*methods ; Motor Cortex/*physiology ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the pre-movement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.},
}
@article {pmid15188869,
year = {2004},
author = {Borisoff, JF and Mason, SG and Bashashati, A and Birch, GE},
title = {Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {985-992},
doi = {10.1109/TBME.2004.827078},
pmid = {15188869},
issn = {0018-9294},
mesh = {Adult ; *Algorithms ; Brain/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feedback/physiology ; Female ; Humans ; Information Storage and Retrieval/methods ; Male ; Middle Aged ; Motor Cortex/*physiology ; Online Systems ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects.},
}
@article {pmid15188868,
year = {2004},
author = {Scherer, R and Müller, GR and Neuper, C and Graimann, B and Pfurtscheller, G},
title = {An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {979-984},
doi = {10.1109/TBME.2004.827062},
pmid = {15188868},
issn = {0018-9294},
mesh = {*Algorithms ; Brain/physiology ; Cerebral Cortex/*physiology ; *Communication Devices for People with Disabilities ; *Computer Peripherals ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Information Storage and Retrieval/methods ; Online Systems ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; *Word Processing ; },
abstract = {An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate sigma, the number of correctly spelled letters/min, up to sigma = 3.38 (average sigma = 1.99).},
}
@article {pmid15188867,
year = {2004},
author = {Jansen, BH and Allam, A and Kota, P and Lachance, K and Osho, A and Sundaresan, K},
title = {An exploratory study of factors affecting single trial P300 detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {975-978},
doi = {10.1109/TBME.2004.826684},
pmid = {15188867},
issn = {0018-9294},
support = {1 R01 MH58784/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; *Algorithms ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Feasibility Studies ; Female ; Humans ; Male ; Pilot Projects ; Reproducibility of Results ; Sensitivity and Specificity ; },
abstract = {A threshold detector for single-trial P300 detection has been evaluated. The detector operates on the 0-4 Hz band, isolated from the raw electroencephalogram using low-pass filtering, wavelet transforms, or the piecewise prony method (PPM). A detection rate around 70% was found, irregardless of stimulus type, interstimulus interval (ISI), probability of occurrence (Pr) of the target stimuli, intrasession and intersession effects, or filtering method. This suggests that P300-based brain-machine interfaces can use an ISI as short as 1 s and a Pr of 45%, to increase throughput.},
}
@article {pmid15188866,
year = {2004},
author = {Hinterberger, T and Weiskopf, N and Veit, R and Wilhelm, B and Betta, E and Birbaumer, N},
title = {An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI).},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {971-974},
doi = {10.1109/TBME.2004.827069},
pmid = {15188866},
issn = {0018-9294},
mesh = {Biofeedback, Psychology/*methods/physiology ; Cerebral Cortex/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Feasibility Studies ; Feedback/physiology ; Hippocampus/physiology ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Motor Cortex/physiology ; Online Systems ; Pilot Projects ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {Self-regulation of slow cortical potentials (SCPs) has been successfully used to prevent epileptic seizures as well as to communicate with completely paralyzed patients. The thought translation device (TTD) is a brain-computer interface (BCI) that was developed for training and application of SCP self-regulation. To investigate the neurophysiological mechanisms of SCP regulation the TTD was combined with functional magnetic resonance imaging (fMRI). The technical aspects and pitfalls of combined fMRI data acquisition and EEG neurofeedback are discussed. First data of SCP feedback during fMRI are presented.},
}
@article {pmid15188865,
year = {2004},
author = {Weiskopf, N and Mathiak, K and Bock, SW and Scharnowski, F and Veit, R and Grodd, W and Goebel, R and Birbaumer, N},
title = {Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI).},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {966-970},
doi = {10.1109/TBME.2004.827063},
pmid = {15188865},
issn = {0018-9294},
mesh = {Adult ; Biofeedback, Psychology/*methods/physiology ; Brain/*physiology ; Brain Mapping/*methods ; Feasibility Studies ; Feedback/*physiology ; Female ; Hippocampus/physiology ; Humans ; Image Interpretation, Computer-Assisted/*methods ; Magnetic Resonance Imaging/*methods ; Male ; Motor Cortex/physiology ; Online Systems ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {A brain-computer interface (BCI) based on functional magnetic resonance imaging (fMRI) records noninvasively activity of the entire brain with a high spatial resolution. We present a fMRI-based BCI which performs data processing and feedback of the hemodynamic brain activity within 1.3 s. Using this technique, differential feedback and self-regulation is feasible as exemplified by the supplementary motor area (SMA) and parahippocampal place area (PPA). Technical and experimental aspects are discussed with respect to neurofeedback. The methodology now allows for studying behavioral effects and strategies of local self-regulation in healthy and diseased subjects.},
}
@article {pmid15188864,
year = {2004},
author = {Francis, JT and Chapin, JK},
title = {Force field apparatus for investigating movement control in small animals.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {963-965},
doi = {10.1109/TBME.2004.827463},
pmid = {15188864},
issn = {0018-9294},
support = {N524707//PHS HHS/United States ; N52672213//PHS HHS/United States ; N54054303//PHS HHS/United States ; },
mesh = {Animals ; Equipment Design ; Equipment Failure Analysis ; Feedback/*physiology ; Homeostasis/*physiology ; Movement/*physiology ; Physical Stimulation/*instrumentation/methods ; Postural Balance/*physiology ; Rats ; Reproducibility of Results ; Sensitivity and Specificity ; Stress, Mechanical ; Torque ; *Transducers ; },
abstract = {As part of our overall effort to build a closed loop brain-machine interface (BMI), we have developed a simple, low weight, and low inertial torque manipulandum that is ideal for use in motor system investigations with small animals such as rats. It is inexpensive and small but emulates features of large and very expensive systems currently used in monkey and human research. Our device consists of a small programmable torque-motor system that is attached to a manipulandum. Rats are trained to grasp this manipulandum and move it to one or more targets against programmed force field perturbations. Here we report several paradigms that may be used with this device and results from rat's making reaching movements in a variety of force fields. These and other available experimental manipulations allow one to experimentally separate several key variables that are critical for understanding and ultimately emulating the feedforward and feedback mechanisms of motor control.},
}
@article {pmid15188862,
year = {2004},
author = {Sanchez, JC and Carmena, JM and Lebedev, MA and Nicolelis, MA and Harris, JG and Principe, JC},
title = {Ascertaining the importance of neurons to develop better brain-machine interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {943-953},
doi = {10.1109/TBME.2004.827061},
pmid = {15188862},
issn = {0018-9294},
mesh = {Action Potentials/physiology ; Algorithms ; Animals ; Cerebral Cortex/physiology ; Computer Simulation ; Electroencephalography/*methods ; Female ; Hand/*physiology ; Likelihood Functions ; Macaca ; *Models, Neurological ; Models, Statistical ; Movement/*physiology ; Nerve Net/*physiology ; Neurons/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.},
}
@article {pmid15188859,
year = {2004},
author = {Bossetti, CA and Carmena, JM and Nicolelis, MA and Wolf, PD},
title = {Transmission latencies in a telemetry-linked brain-machine interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {919-924},
doi = {10.1109/TBME.2004.827090},
pmid = {15188859},
issn = {0018-9294},
mesh = {Action Potentials/*physiology ; Animals ; Cerebral Cortex/*physiology ; Data Compression/*methods ; Electroencephalography/*methods ; Macaca mulatta ; Nerve Net/physiology ; Neurons/*physiology ; Radio Waves ; Reproducibility of Results ; Sensitivity and Specificity ; Telemetry/*methods ; *User-Computer Interface ; },
abstract = {To be clinically viable, a brain-machine interface (BMI) requires transcutaneous telemetry. Spike-based compression algorithms can be used to reduce the amount of telemetered data, but this type of system is subject to queuing-based transmission delays. This paper examines the relationships between the ratio of output to average input bandwidth of an implanted device and transmission latency and required queue depth. The examination was performed with a computer model designed to simulate the telemetry link. The input to the model was presorted spike data taken from a macaque monkey performing a motor task. The model shows that when the output bandwidth/average input bandwidth is in unity, significant transmission latencies occur. For a 32-neuron system, transmitting 50 bytes of data per spike and with an average neuron firing rate of 8.93 spikes/s, the average maximum delay was approximately 3.2 s. It is not until the output bandwidth is four times the average input bandwidth that average maximum delays are reduced to less than 10 ms. A comparison of neuron firing rate and resulting latencies shows that high latencies result from neuron bursting. These results will impact the design of transcutaneous telemetry in a BMI.},
}
@article {pmid15188857,
year = {2004},
author = {Obeid, I and Wolf, PD},
title = {Evaluation of spike-detection algorithms for a brain-machine interface application.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {905-911},
doi = {10.1109/TBME.2004.826683},
pmid = {15188857},
issn = {0018-9294},
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Brain/*physiology ; Diagnosis, Computer-Assisted/methods ; Electroencephalography/*methods ; Neurons/*physiology ; Pattern Recognition, Automated ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. A design-specific cost function was developed to score the relative merits of each detector; correct detections increased the score, while false detections and computational burden reduced it. Test signals both with and without overlapping action potentials were considered. We also investigated the utility of rejecting spikes that violate a minimum refractory period by occurring within a fixed time window after the preceding threshold crossing. Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system.},
}
@article {pmid15188854,
year = {2004},
author = {Moxon, KA and Kalkhoran, NM and Markert, M and Sambito, MA and McKenzie, JL and Webster, JT},
title = {Nanostructured surface modification of ceramic-based microelectrodes to enhance biocompatibility for a direct brain-machine interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {6},
pages = {881-889},
doi = {10.1109/TBME.2004.827465},
pmid = {15188854},
issn = {0018-9294},
support = {1R43 NS41690-01A1/NS/NINDS NIH HHS/United States ; },
mesh = {Action Potentials/physiology ; Animals ; Astrocytes/*cytology/physiology ; Brain/physiology ; Cell Adhesion/physiology ; Cell Division/physiology ; Cells, Cultured ; Ceramics/chemistry ; *Electrodes, Implanted ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Equipment Failure Analysis ; *Microelectrodes ; Nanotechnology/*instrumentation/methods ; Neurons/*physiology ; PC12 Cells ; Rats ; Rats, Long-Evans ; Reproducibility of Results ; Sensitivity and Specificity ; Silicon/chemistry ; Surface Properties ; *User-Computer Interface ; },
abstract = {Many different types of microelectrodes have been developed for use as a direct Brain-Machine Interface (BMI) to chronically recording single neuron action potentials from ensembles of neurons. Unfortunately, the recordings from these microelectrode devices are not consistent and often last for only a few weeks. For most microelectrode types, the loss of these recordings is not due to failure of the electrodes but most likely due to damage to surrounding tissue that results in the formation of nonconductive glial-scar. Since the extracellular matrix consists of nanostructured microtubules, we have postulated that neurons may prefer a more complex surface structure than the smooth surface typical of thin-film microelectrodes. We, therefore, investigated the suitability of a nano-porous silicon surface layer to increase the biocompatibility of our thin film ceramic-insulated multisite electrodes. In-vitro testing demonstrated, for the first time, decreased adhesion of astrocytes and increased extension of neurites from pheochromocytoma cells on porous silicon surfaces compared to smooth silicon sufaces. Moreover, nano-porous surfaces were more biocompatible than macroporous surfaces. Collectively, these results support our hypothesis that nano-porous silicon may be an ideal material to improve biocompatibility of chronically implanted microelectrodes. We next developed a method to apply nano-porous surfaces to ceramic insulated, thin-film, microelectrodes and tested them in vivo. Chronic testing demonstrated that the nano-porous surface modification did not alter the electrical properties of the recording sites and did not interfere with proper functioning of the microelectrodes in vivo.},
}
@article {pmid15176409,
year = {2004},
author = {Morris, K},
title = {Mind moves onscreen: brain-computer interface comes to trial.},
journal = {The Lancet. Neurology},
volume = {3},
number = {6},
pages = {329},
doi = {10.1016/s1474-4422(04)00787-2},
pmid = {15176409},
issn = {1474-4422},
mesh = {Clinical Trials as Topic/trends ; Electrodes, Implanted/adverse effects/*trends ; Humans ; Motor Cortex/physiology ; Movement/physiology ; *Psychophysiology ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
}
@article {pmid15173696,
year = {2004},
author = {Parr, MJ},
title = {Blunt cardiac injury.},
journal = {Minerva anestesiologica},
volume = {70},
number = {4},
pages = {201-205},
pmid = {15173696},
issn = {0375-9393},
mesh = {Critical Care ; Heart Injuries/*therapy ; Humans ; Prognosis ; Risk Assessment ; Wounds, Nonpenetrating/*therapy ; },
abstract = {Blunt cardiac injury (BCI) is a common complication of chest trauma. With improvements in pre-hospital care and rapid regional transport, more patients with severe BCI may arrive at the hospital with signs of life. Prompt recognition and expeditious surgical and critical care treatment may increase the number of survivors. This paper reviews current clinical considerations in dealing with patients suffering BCI.},
}
@article {pmid15142321,
year = {2004},
author = {Vallabhaneni, A and He, B},
title = {Motor imagery task classification for brain computer interface applications using spatiotemporal principle component analysis.},
journal = {Neurological research},
volume = {26},
number = {3},
pages = {282-287},
doi = {10.1179/016164104225013950},
pmid = {15142321},
issn = {0161-6412},
support = {R01EB00178/EB/NIBIB NIH HHS/United States ; },
mesh = {Brain/*physiology ; Brain Mapping ; Electrodes ; Electroencephalography/methods ; Evoked Potentials ; Functional Laterality ; Humans ; Imagery, Psychotherapy/methods ; Imagination ; Movement/*physiology ; Perception/*physiology ; Principal Component Analysis/*methods ; User-Computer Interface ; },
abstract = {Classification of single-trial imagined left- and right-hand movements recorded through scalp EEG are explored in this study. Classical event-related desynchronization/synchronization (ERD/ERS) calculation approach was utilized to extract ERD features from the raw scalp EEG signal. Principle Component Analysis (PCA) was used for feature extraction and applied on spatial, as well as temporal dimensions in two consecutive steps. A Support Vector Machine (SVM) classifier using a linear decision function was used to classify each trial as either left or right. The present approach has yielded good classification results and promises to have potential for further refinement for increased accuracy as well as application in online brain computer interface (BCI).},
}
@article {pmid15132497,
year = {2004},
author = {Sykacek, P and Roberts, SJ and Stokes, M},
title = {Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {51},
number = {5},
pages = {719-727},
doi = {10.1109/TBME.2004.824128},
pmid = {15132497},
issn = {0018-9294},
mesh = {*Algorithms ; Bayes Theorem ; Brain/*physiology ; Cognition/*physiology ; *Communication Devices for People with Disabilities ; Diagnosis, Computer-Assisted/*methods ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Feedback ; Humans ; Models, Anatomic ; Models, Statistical ; *Signal Processing, Computer-Assisted ; Stochastic Processes ; Systems Theory ; *User-Computer Interface ; },
abstract = {This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.},
}
@article {pmid15123189,
year = {2004},
author = {Guinet, C and Servera, N and Mangin, S and Georges, JY and Lacroix, A},
title = {Change in plasma cortisol and metabolites during the attendance period ashore in fasting lactating subantarctic fur seals.},
journal = {Comparative biochemistry and physiology. Part A, Molecular &amp; integrative physiology},
volume = {137},
number = {3},
pages = {523-531},
doi = {10.1016/j.cbpb.2003.11.006},
pmid = {15123189},
issn = {1095-6433},
mesh = {3-Hydroxybutyric Acid/blood ; Animals ; Animals, Newborn ; Antarctic Regions ; Body Weight ; Fasting ; Female ; Fur Seals/*physiology ; Hydrocortisone/*blood/metabolism ; Lactation/*physiology ; Lipids/analysis/chemistry ; Milk/chemistry ; Urea/blood ; },
abstract = {Lactating fur seals (Arctocephalus tropicalis) alternate foraging trips at sea and pup attendance periods ashore. During the onshore nursing periods, lactating females do not have access to food and meet both their own metabolic requirements and milk production from their body reserve. Blood and milk samples were collected from females captured soon after their arrival ashore from a foraging trip and before their departure. Milk lipid but not milk protein content was positively related to the body condition index (BCI) of the female. During the 4-day attendance period ashore, females lost body mass, and plasma cortisol levels increased, whereas plasma urea concentration decreased and beta-hydroxybutyrate (beta-OHB) remained unchanged. The increase in cortisol level took place while blood urea concentration decreased and beta-OHB remained at a low level suggesting that it was independent from the transition from phase II to phase III that is indicative of the depletion of lipid body store as described in penguins. Thus, our results suggest that the increase in cortisol level in relation to decreasing BCI may either contribute to the mobilization of protein stores to ensure milk production when easily mobilized stores are used and/or could act as a re-feeding signal which is triggered well before females have depleted their body store.},
}
@article {pmid15121106,
year = {2004},
author = {Singh, RR and Barry, MC and Ireland, A and Bouchier Hayes, D},
title = {Current diagnosis and management of blunt internal carotid artery injury.},
journal = {European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery},
volume = {27},
number = {6},
pages = {577-584},
doi = {10.1016/j.ejvs.2004.01.005},
pmid = {15121106},
issn = {1078-5884},
mesh = {Anticoagulants/therapeutic use ; Balloon Occlusion ; Carotid Artery Injuries/*diagnosis/*therapy ; *Carotid Artery, Internal/surgery ; Cerebral Angiography ; Humans ; Ligation ; Magnetic Resonance Angiography ; Magnetic Resonance Imaging ; Platelet Aggregation Inhibitors/therapeutic use ; Stents ; Tomography, X-Ray Computed ; Ultrasonography, Doppler, Transcranial ; Wounds, Nonpenetrating/*diagnosis/*therapy ; },
abstract = {BACKGROUND: Blunt carotid artery injury (BCI) is a rare but potentially devastating injury. When undiagnosed it can result in severe disability or death.<br><br>METHODS: A Medline-based literature search was performed using key words 'blunt carotid injury' and cross-referenced with further original papers obtained from the references from this search.<br><br>RESULTS AND CONCLUSIONS: The incidence of BCI is very low. However, given the serious consequences of a missed injury, recent efforts have focussed on targeted screening for this injury in trauma patients. Conventional angiography remains the investigation of choice but may be superceded in the future by non-invasive methods such as magnetic resonance angiography or CT angiography. Operative intervention is rarely required and anti-coagulation remains the treatment of choice where dissection or pseudoaneurysm is diagnosed. The role of anti-platelet therapy is currently being investigated. Endovascular management using stents has been described but medium to long term results are not yet available.},
}
@article {pmid15113267,
year = {2004},
author = {Griffin, AL and Asaka, Y and Darling, RD and Berry, SD},
title = {Theta-contingent trial presentation accelerates learning rate and enhances hippocampal plasticity during trace eyeblink conditioning.},
journal = {Behavioral neuroscience},
volume = {118},
number = {2},
pages = {403-411},
doi = {10.1037/0735-7044.118.2.403},
pmid = {15113267},
issn = {0735-7044},
mesh = {Animals ; Behavior, Animal ; Blinking/*physiology ; *Conditioning, Psychological ; Hippocampus/*physiology ; *Learning ; Neuronal Plasticity/*physiology ; Rabbits ; *Theta Rhythm ; Time Factors ; },
abstract = {Hippocampal theta activity has been established as a key predictor of acquisition rate in rabbit (Orcytolagus cuniculus) classical conditioning. The current study used an online brain--computer interface to administer conditioning trials only in the explicit presence or absence of spontaneous theta activity in the hippocampus-dependent task of trace conditioning. The findings indicate that animals given theta-contingent training learned significantly faster than those given nontheta-contingent training. In parallel with the behavioral results, the theta-triggered group, and not the nontheta-triggered group, exhibited profound increases in hippocampal conditioned unit responses early in training. The results not only suggest that theta-contingent training has a dramatic facilitory effect on trace conditioning but also implicate theta activity in enhancing the plasticity of hippocampal neurons.},
}
@article {pmid15098221,
year = {2004},
author = {Vandoninck, V and van Balken, MR and Finazzi Agrò, E and Heesakkers, JP and Debruyne, FM and Kiemeney, LA and Bemelmans, BL},
title = {Posterior tibial nerve stimulation in the treatment of voiding dysfunction: urodynamic data.},
journal = {Neurourology and urodynamics},
volume = {23},
number = {3},
pages = {246-251},
doi = {10.1002/nau.10158},
pmid = {15098221},
issn = {0733-2467},
mesh = {Adult ; Aged ; *Electric Stimulation Therapy/adverse effects ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Prospective Studies ; Quality of Life ; Tibial Nerve/*physiology ; Treatment Outcome ; Urinary Bladder/physiopathology ; Urination Disorders/*physiopathology/psychology/*therapy ; Urodynamics/*physiology ; },
abstract = {OBJECTIVES: To determine urodynamic changes and predictive factors in patients with voiding dysfunction who underwent 12 percutaneous tibial nerve stimulations.<br><br>METHODS: Thirty nine patients with chronic voiding dysfunction were enrolled in a prospective multicenter trial in the Netherlands (n = 19) and in Italy (n = 20). A 50% reduction in total catheterised volume per 24 hr was taken as a primary objective outcome measure. Patients' request for continuation of treatment was regarded as subjective success. Objective urodynamic parameters and bladder indices were determined. Odds ratios and their 95% confidence interval were computed as a measure for predictive power in order to reveal predictive factors (Pdet at Qmax, Qmax, BVE, and BCI).<br><br>RESULTS: Primary outcome measure was obtained in 41%, an additional 26% reduced their 24 hr residuals with more than 25%. Fifty nine percent of patients chose to continue treatment. Detrusor pressure at maximal flow, cystometric residuals, and bladder indices improved significantly for all patients (P < 0.05). Patients with minor voiding dysfunction were more prone to notice success (Odds ratio: 0.73; 95% CI: 0.51-0.94).<br><br>CONCLUSIONS: PTNS is a young treatment modality, minimally invasive, and easily accessible. It might be an attractive first line option for patients with (minor) voiding dysfunction.},
}
@article {pmid15098212,
year = {2004},
author = {Tan, TL and Bergmann, MA and Griffiths, D and Resnick, NM},
title = {Stop test or pressure-flow study? Measuring detrusor contractility in older females.},
journal = {Neurourology and urodynamics},
volume = {23},
number = {3},
pages = {184-189},
doi = {10.1002/nau.20020},
pmid = {15098212},
issn = {0733-2467},
support = {P01 AG 08812/AG/NIA NIH HHS/United States ; R01 DK 49482/DK/NIDDK NIH HHS/United States ; },
mesh = {Aged ; Algorithms ; Female ; Humans ; Middle Aged ; Muscle Contraction/physiology ; Muscle, Smooth, Vascular/*physiology ; Pressure ; Retrospective Studies ; Urinary Bladder/*physiology ; Urodynamics/*physiology ; },
abstract = {AIMS: Impaired detrusor contractility is common in older adults. One aspect, detrusor contraction strength during voiding, can be measured by the isovolumetric detrusor pressure attained if flow is interrupted mechanically (a stop test). Because interruption is awkward in practice, however, simple indices or nomograms based on measurements made during uninterrupted voiding are an appealing alternative. We investigated whether such methods, originally developed for males, might be applicable in female subjects, and attempted to identify a single best method.<br><br>METHODS: We compared stop-test isovolumetric pressures with estimates based on pressure-flow studies in a group of elderly women suffering from urge incontinence. Measurements were made pre- and post-treatment with placebo or oxybutynin, allowing investigation of test-retest reliability and responsiveness to small changes of contractility.<br><br>RESULTS: Existing methods of estimating detrusor contraction strength from pressure-flow studies, including the Sch&#xe4;fer contractility nomogram and the projected isovolumetric pressure PIP, greatly overestimate the isovolumetric pressure in these female patients. A simple modification provides a more reliable estimate, PIP(1), equal to p(det.Qmax) + Q(max) (with pressure in cmH(2)O and Q(max) in ml/sec). Typically PIP(1) ranges from 30 to 75 cmH(2)O in this population of elderly urge-incontinent women. PIP(1), however, is less responsive to a small change in contraction strength than the isovolumetric pressure measured by mechanical interruption.<br><br>CONCLUSIONS: The parameter PIP(1) is simple to calculate from a standard pressure-flow study and may be useful for clinical assessment of detrusor contraction strength in older females. For research, however, a mechanical stop test still remains the most reliable and responsive method. The Sch&#xe4;fer contractility nomogram and related parameters such as DECO and BCI are not suitable for use in older women.},
}
@article {pmid15092974,
year = {2000},
author = {Anderson, DW and Newman, SH and Kelly, PR and Herzog, SK and Lewis, KP},
title = {An experimental soft-release of oil-spill rehabilitated American coots (Fulica americana): I. Lingering effects on survival, condition and behavior.},
journal = {Environmental pollution (Barking, Essex : 1987)},
volume = {107},
number = {3},
pages = {285-294},
doi = {10.1016/s0269-7491(99)00180-3},
pmid = {15092974},
issn = {0269-7491},
abstract = {In spring 1995, we studied survival, condition and behavior of 37 oiled/rehabilitated (OR) American coots (Fulica americana) (RHB) and compared them to 38 wild-caught, non-oiled and non-rehabilitated coots (REF). All coots were wing-clipped to prevent long-range dispersal, mixed equally and randomly and soft-released into two fenced marshes. Twenty RHB+20 REF coots were subjected to handling and sampling four times during the 4-month study and the remainder were left undisturbed. The study ended before any coots dispersed following remige regrowth. Overall survival was significantly lower for RHB coots, regardless of the way survival was viewed (four Chi 2 tests varied between p<0.045 and p<0.006). Mortality was 2.1 times higher in RHB coots: 51% mortality in RHB coots and 24% in REF coots (4 months total). RHB coots began the experiment 9% lighter in mean body condition indices (BCI=a standardization that corrected for different-sized birds) than REF coots, but REF coots also needed a period of adjustment to captivity. BCIs then varied (p<0.02) similarly among both groups throughout the experiment. Initially, RHB coots lost more weight in comparison to REF coots (although RHB coots fed more), but those RHB coots that did survive recovered to REF-comparable BCIs after about 6 weeks: both higher and equivalent at the beginning of moult and then both equivalent but lower through the moulting period. Long-term RHB coot and REF coot survivors both had significant (p<0.001) positive correlations between their initial and ending body weights. A similar relationship was also suggested for the non-surviving REF coots, but could not be tested for statistical significance. In contrast to all other groups, however, non-surviving RHB coots failed to show any relationship between their initial and ending body weights (p>0.10), indicating that non-surviving RHB coots were unable to gain or maintain body condition for about 2-3 months following their oiling/rehabilitation experience. Throughout the experiment, RHB coots preened more on water and on land, bathed more, slept less during the day, and exhibited feeding and drinking behaviors more frequently or of greater duration than REF coots (all statistical tests with Bonferroni-corrected p<0.05).},
}
@article {pmid15085504,
year = {2004},
author = {Ampil, FL and Ghali, GE and Caldito, G and Hardin, JC},
title = {Treatment of head and neck cancer with bone or cartilage invasion by surgery and postoperative radiotherapy.},
journal = {Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons},
volume = {62},
number = {4},
pages = {408-411},
doi = {10.1016/j.joms.2003.05.014},
pmid = {15085504},
issn = {0278-2391},
mesh = {Adult ; Aged ; Aged, 80 and over ; Bone Neoplasms/*radiotherapy ; Carcinoma, Squamous Cell/*pathology/*radiotherapy/surgery ; Cartilage Diseases/*radiotherapy ; Disease-Free Survival ; Female ; Head and Neck Neoplasms/*pathology/*radiotherapy/surgery ; Humans ; Male ; Middle Aged ; Neoplasm Invasiveness ; Neoplasm Recurrence, Local ; Postoperative Care ; Radiotherapy, Adjuvant ; Soft Tissue Neoplasms/*radiotherapy ; },
abstract = {PURPOSE: The study goal was to review our experience with patients with bone or cartilage invasion (BCI) by adjacent head and neck cancer (HNC) who were treated with curative surgery and postoperative radiotherapy (SPR).<br><br>PATIENTS AND METHODS: Thirty-eight individuals treated with SPR for HNC with BCI were identified after review of the radiation oncology charts and pathology reports for the period 1981 through 2000.<br><br>RESULTS: The thyroid cartilage and mandible were predominantly invaded by HNC. The follow-up time for the surviving patients was 65.5 months (range, 17 to 106 months). The local, regional, and distant relapse rates were 5%, 11%, and 13%, respectively. The overall disease-free survival rates at 3 and 5 years were 54% and 41%, respectively.<br><br>CONCLUSION: Although these results were not obtained from a randomized trial, the present observations may be beneficial in clinical decision making concerning patients with HNC and contiguous invasion of bone or cartilage.},
}
@article {pmid15068187,
year = {2004},
author = {Curran, E and Sykacek, P and Stokes, M and Roberts, SJ and Penny, W and Johnsrude, I and Owen, AM},
title = {Cognitive tasks for driving a brain-computer interfacing system: a pilot study.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {12},
number = {1},
pages = {48-54},
doi = {10.1109/TNSRE.2003.821372},
pmid = {15068187},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Cognition/*physiology ; *Communication ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Female ; Humans ; Male ; Middle Aged ; *Pattern Recognition, Automated ; Pilot Projects ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p < 0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p << 0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.},
}
@article {pmid15063938,
year = {2004},
author = {Ngowi, HA and Kassuku, AA and Maeda, GE and Boa, ME and Carabin, H and Willingham, AL},
title = {Risk factors for the prevalence of porcine cysticercosis in Mbulu District, Tanzania.},
journal = {Veterinary parasitology},
volume = {120},
number = {4},
pages = {275-283},
doi = {10.1016/j.vetpar.2004.01.015},
pmid = {15063938},
issn = {0304-4017},
mesh = {Animal Husbandry/methods ; Animals ; Bayes Theorem ; Cross-Sectional Studies ; Cysticercosis/epidemiology/parasitology/*veterinary ; Female ; Male ; Prevalence ; Risk Factors ; Seasons ; Surveys and Questionnaires ; Swine ; Swine Diseases/epidemiology/*parasitology ; Taenia solium/*growth &amp; development ; Taeniasis/epidemiology/parasitology/*veterinary ; Tanzania/epidemiology ; Toilet Facilities ; Tongue/parasitology ; },
abstract = {To estimate prevalence of and risk factors for the prevalence of porcine cysticercosis in Mbulu District, Tanzania, 770 live pigs were examined by lingual examination in 21 villages. Structured observations and questionnaire interviews were used to assess pig rearing practices and household use of latrines. Associations between factors were analyzed using a Bayesian hierarchical model to obtain prevalence odds ratio (OR) and 95% Bayesian Credible Intervals (95% BCI). Prevalence was 17.4% (village-specific range 3.2-46.7%). Prevalence of porcine cysticercosis was considerably higher in pigs reared in households lacking latrines than in those reared in households that were using latrines (OR = 2.04; 95% BCI = 1.25, 3.45). About 96% of the pigs were kept under free-range conditions. This study suggests the need for further studies in order to design and implement effective prevention and control measures for porcine cysticercosis in Mbulu District, Tanzania.},
}
@article {pmid15036059,
year = {2004},
author = {Neumann, N and Hinterberger, T and Kaiser, J and Leins, U and Birbaumer, N and Kübler, A},
title = {Automatic processing of self-regulation of slow cortical potentials: evidence from brain-computer communication in paralysed patients.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {115},
number = {3},
pages = {628-635},
doi = {10.1016/j.clinph.2003.10.030},
pmid = {15036059},
issn = {1388-2457},
mesh = {Adaptation, Psychological ; Adult ; Amyotrophic Lateral Sclerosis/complications ; Brain/*physiopathology ; Cerebral Cortex/*physiopathology ; *Communication ; Communication Devices for People with Disabilities ; *Computers ; Electrooculography ; Electrophysiology ; Equipment Design ; Humans ; Male ; Paralysis/etiology/*physiopathology/*psychology ; Reaction Time ; *Social Control, Informal ; },
abstract = {OBJECTIVE: Direct brain-computer communication utilizes self-regulation of brain potentials to select letters, words or symbols from a computer menu. Selection of letters or words with brain potentials requires simultaneous processing of several tasks such as production of certain brain potentials at predefined time points simultaneously with processing of presented letter strings. This study addresses the question of whether the self-regulation of slow cortical potentials (SCP) automatizes with practice and can thus be considered as a skill comparable to motor or cognitive skills.<br><br>METHODS: Two nearly completely paralysed patients learned over several months to produce electrocortically negative and positive SCP by means of visual feedback. Improved performance and a reduction in performance variability were regarded as behavioural indicators for automaticity, while the topographic focalization of cortical activation was considered as a neurophysiological indicator for automaticity. Different indicators of automaticity were expected to covary along with practice.<br><br>RESULTS: In patient 1, performance measured as the percentage of correct SCP shifts increased simultaneously with the topographic focalization of cortical activation. His performance became more stable with practice. For this patient the criteria for automaticity were all met. In patient 2, performance also improved, but his cortical activity became topographically less focal. His performance was less stable than that of patient 1.<br><br>CONCLUSIONS: The present findings, albeit on only two subjects, provide preliminary evidence that SCP self-regulation may automatize with long-term practice and can therefore be considered a skill.},
}
@article {pmid15022474,
year = {2004},
author = {He, Q and Peng, C and Wu, B and Wang, H},
title = {[Experimental study on brain-computer interface based on visual evoked potentials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {21},
number = {1},
pages = {93-96},
pmid = {15022474},
issn = {1001-5515},
mesh = {Brain/physiology ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; Wavelet Analysis ; },
abstract = {Brain-computer interface is a novel EEG-based communication and control system between human brain and computer or some other electric device, which has found important applications in many fields such as rehabilitation engineering. Study has been done in brain-computer interface using visual evoked potentials(VEP). Multiple stimulation patterns were produced on the computer screen through programming. Several flickering blocks were adopted for representing a number of possible selections, and when the subject was fixing his(her) eyes on an object on the screen, the very object could be distinguished by the analysis of the VEPs. Surface electrodes were placed at Oz of the inion and Cz to collect VEP. The wavelet filter and averaging method was used to extract VEP signal. Off-line experimental data analysis indicated that the proposed method may be valuable for developing real brain-computer interface with relatively high accuracy and speed, and the information transfer rates could be higher than 30 bit/min when 12 selections were on the screen.},
}
@article {pmid15011268,
year = {2003},
author = {Tyler, M and Danilov, Y and Bach-Y-Rita, P},
title = {Closing an open-loop control system: vestibular substitution through the tongue.},
journal = {Journal of integrative neuroscience},
volume = {2},
number = {2},
pages = {159-164},
doi = {10.1142/s0219635203000263},
pmid = {15011268},
issn = {0219-6352},
support = {1 R43 DC04738/DC/NIDCD NIH HHS/United States ; },
mesh = {Acceleration ; Adult ; Brain/physiopathology ; Electric Stimulation ; Female ; Head/*physiopathology ; Humans ; Male ; Middle Aged ; Neuronal Plasticity ; *Posture ; *Sensory Aids ; Tongue/*physiopathology ; *Touch ; Vestibular Diseases/*physiopathology ; },
abstract = {The human postural coordination mechanism is an example of a complex closed-loop control system based on multisensory integration [9,10,13,14]. In models of this process, sensory data from vestibular, visual, tactile and proprioceptive systems are integrated as linearly additive inputs that drive multiple sensory-motor loops to provide effective coordination of body movement, posture and alignment [5-8, 10, 11]. In the absence of normal vestibular (such as from a toxic drug reaction) and other inputs, unstable posture occurs. This instability may be the result of noise in a functionally open-loop control system [9]. Nonetheless, after sensory loss the brain can utilize tactile information from a sensory substitution system for functional compensation [1-4, 12]. Here we have demonstrated that head-body postural coordination can be restored by means of vestibular substitution using a head-mounted accelerometer and a brain-machine interface that employs a unique pattern of electrotactile stimulation on the tongue. Moreover, postural stability persists for a period of time after removing the vestibular substitution, after which the open-loop instability reappears.},
}
@article {pmid15006097,
year = {2004},
author = {Barbieri, R and Frank, LM and Nguyen, DP and Quirk, MC and Solo, V and Wilson, MA and Brown, EN},
title = {Dynamic analyses of information encoding in neural ensembles.},
journal = {Neural computation},
volume = {16},
number = {2},
pages = {277-307},
doi = {10.1162/089976604322742038},
pmid = {15006097},
issn = {0899-7667},
support = {DA015644/DA/NIDA NIH HHS/United States ; MH59733/MH/NIMH NIH HHS/United States ; MH61637/MH/NIMH NIH HHS/United States ; MH65018/MH/NIMH NIH HHS/United States ; },
mesh = {Action Potentials/*physiology ; *Algorithms ; Animals ; Exploratory Behavior/physiology ; Hippocampus/*physiology ; Nerve Net/*physiology ; Neural Networks, Computer ; Neurons/*physiology ; Rats ; Rats, Long-Evans ; Reaction Time/physiology ; Reproducibility of Results ; Signal Processing, Computer-Assisted ; Stochastic Processes ; Synaptic Transmission/physiology ; },
abstract = {Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.},
}
@article {pmid15003752,
year = {2004},
author = {Cheng, M and Jia, W and Gao, X and Gao, S and Yang, F},
title = {Mu rhythm-based cursor control: an offline analysis.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {115},
number = {4},
pages = {745-751},
doi = {10.1016/j.clinph.2003.11.038},
pmid = {15003752},
issn = {1388-2457},
mesh = {*Algorithms ; Electroencephalography/*methods ; Humans ; *Models, Neurological ; Motor Cortex/*physiology ; Somatosensory Cortex/*physiology ; },
abstract = {OBJECTIVE: To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices.<br><br>METHODS: The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification.<br><br>RESULTS: Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%.<br><br>CONCLUSIONS: Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification.<br><br>SIGNIFICANCE: The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.},
}
@article {pmid14979329,
year = {2004},
author = {Schultz, JM and Trunkey, DD},
title = {Blunt cardiac injury.},
journal = {Critical care clinics},
volume = {20},
number = {1},
pages = {57-70},
doi = {10.1016/s0749-0704(03)00092-7},
pmid = {14979329},
issn = {0749-0704},
mesh = {Algorithms ; Echocardiography ; Electrocardiography ; Heart Function Tests ; *Heart Injuries/classification/diagnosis/epidemiology ; Humans ; Incidence ; *Wounds, Nonpenetrating/classification/diagnosis/epidemiology ; },
abstract = {In summary, the incidence of BCI following blunt thoracic trauma patients has been reported between 20% and 76%, and no gold standard exists to diagnose BCI. Diagnostic tests should be limited to identify those patients who are at risk of developing cardiac complications as a result of BCI. Therapeutic interventions should be directed to treat the complications of BCI. Finally, the prognosis and outcome of BCI patients is encouraging},
}
@article {pmid14966539,
year = {2004},
author = {Paz, R and Vaadia, E},
title = {Learning-induced improvement in encoding and decoding of specific movement directions by neurons in the primary motor cortex.},
journal = {PLoS biology},
volume = {2},
number = {2},
pages = {E45},
pmid = {14966539},
issn = {1545-7885},
mesh = {Animals ; Brain Mapping ; Female ; Functional Laterality ; Learning/*physiology ; Macaca mulatta ; Motion Perception/*physiology ; Motor Cortex/*physiology ; Neurons/physiology ; },
abstract = {Many recent studies describe learning-related changes in sensory and motor areas, but few have directly probed for improvement in neuronal coding after learning. We used information theory to analyze single-cell activity from the primary motor cortex of monkeys, before and after learning a local rotational visuomotor task. We show that after learning, neurons in the primary motor cortex conveyed more information about the direction of movement and did so with relation to their directional sensitivity. Similar to recent findings in sensory systems, this specific improvement in encoding is correlated with an increase in the slope of the neurons' tuning curve. We further demonstrate that the improved information after learning enables a more accurate reconstruction of movement direction from neuronal populations. Our results suggest that similar mechanisms govern learning in sensory and motor areas and provide further evidence for a tight relationship between the locality of learning and the properties of neurons; namely, cells only show plasticity if their preferred direction is near the training one. The results also suggest that simple learning tasks can enhance the performance of brain-machine interfaces.},
}
@article {pmid14960111,
year = {2003},
author = {Felzer, T and Freisleben, B},
title = {Analyzing EEG signals using the probability estimating guarded neural classifier.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {4},
pages = {361-371},
doi = {10.1109/TNSRE.2003.819785},
pmid = {14960111},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; *Artificial Intelligence ; Brain/*physiology ; Cognition/*physiology ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Humans ; Male ; Models, Neurological ; Models, Statistical ; *Neural Networks, Computer ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {This paper introduces a neural network architecture for classifying feature vectors symbolizing portions (or segments) of an electroencephalogram (EEG) trace of a human subject. This classification task is the one that is typically required when developing a so-called brain-computer interface (BCI), which analyzes the EEG signals of a subject in order to "understand" the subject's thoughts. However, instead of merely saying which "category of thoughts" (i.e., which class) the respective input feature vector belongs to, the network described here estimates the probabilities of an EEG segment being associated with each individual class. The network, which is called PeGNC (for probability estimating guarded neural classifier), is tested with two kinds of experiments. In the first experiment, the alpha-rhythm associated with a human subject closing the eyes is detected online with the help of a frequency-based representation. Since the EEG signal is, in general, always a mixture of numerous action potentials generated simultaneously and it is, thus, very likely that mental activities result in overlapping classes, it is reasonable to believe that the PeGNC network--which does not select any one single class, but determines probability values for each mental category--is particularly suitable for this kind of EEG analysis. The second experiment deals with this issue on the basis of an offline analysis of simulated data.},
}
@article {pmid14749567,
year = {2004},
author = {Davis, RP and McGwin, G and Melton, SM and Reiff, DA and Whitley, D and Rue, LW},
title = {Specific occupant and collision characteristics are associated with motor vehicle collision-related blunt cerebrovascular artery injury.},
journal = {The Journal of trauma},
volume = {56},
number = {1},
pages = {64-67},
doi = {10.1097/01.TA.0000094428.99903.12},
pmid = {14749567},
issn = {0022-5282},
mesh = {Accidents, Traffic/*statistics &amp; numerical data ; Adult ; Cerebral Arteries/*injuries ; Female ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Wounds, Nonpenetrating/classification/diagnosis/*etiology ; },
abstract = {BACKGROUND: Blunt cerebrovascular artery injury (BCI) remains difficult to diagnose but is recognized with increasing frequency after motor vehicle collisions (MVCs). Failure to detect this injury in a timely fashion can be devastating. Criteria that can be used to heighten the suspicion of this injury have been suggested; however, more encompassing screening has been recommended. To address this need, we sought to describe occupant, vehicle, and collision characteristics among MVC occupants who sustained a BCI.<br><br>METHODS: All cases of BCI identified in the National Automotive Sampling System Crashworthiness Data System, a national probability sample of passenger vehicles involved in police-reported tow-away MVCs, between 1993 and 2001 were selected. Information on occupant (e.g., demographics, seating position, and restraint use), collision (e.g., collision type and severity), and vehicle characteristics were obtained and summarized using descriptive statistics.<br><br>RESULTS: Nine-hundred forty individuals with BCI were identified in the Crashworthiness Data System data files. Over half were belted (57.4%) and 82.3% had airbag deployment; 16.2% were partially or completely ejected from the vehicle. Head and thoracic injuries were common (44.4% and 40.8%, respectively); 27.8% sustained a cervical spine fracture and 21.0% sustained a soft-tissue injury to the neck. The mean Injury Severity Score was 33.6. The case fatality rate was 44.5%. The majority of BCI occupants were drivers (76.0%). Among belted occupants, the lap/shoulder was the most commonly attributed as the injury source (61.4%). Among unbelted occupants, frequent injury sources included air bags (15.0%), windshield (13.7%), and other interior objects. With respect to collision characteristics, the average change in velocity (Delta V) was 43.3 km/h. The majority of collisions were frontal (76.2%).<br><br>CONCLUSION: This study indicates that BCI is both a rare and lethal injury typified by specific occupant and collision characteristics. These characteristics provide insight as to the cause of this injury that may aid in the evaluation and management of the blunt trauma patient at risk for BCI.},
}
@article {pmid14737933,
year = {2003},
author = {Knudson, D and Noffal, G and Bauer, J and McGinnis, P and Bird, M and Chow, J and Bahamonde, R and Blackwell, J and Strohmeyer, S and Abendroth-Smith, J},
title = {Development and evaluation of a biomechanics concept inventory.},
journal = {Sports biomechanics},
volume = {2},
number = {2},
pages = {267-277},
doi = {10.1080/14763140308522823},
pmid = {14737933},
issn = {1476-3141},
mesh = {Biomechanical Phenomena ; Humans ; Teaching/*methods ; *Teaching Materials/standards ; United States ; },
abstract = {To help instructors in evaluating innovations in biomechanics instruction, a standardised test of the key concepts taught in the introductory biomechanics course was developed. The Biomechanics Concept Inventory (BCI) consists of 24 questions that test four prerequisite competencies and eight biomechanics competencies. Three hundred and sixty seven students from ten universities throughout the United States took the test at the beginning and the end of the introductory biomechanics course. Analysis of a sub-sample of the students showed that the BCI was reliable with typical errors in internal consistency and test-retest conditions of 1.4 and 2.0 questions, respectively. Mean pre-test scores (8.5 +/- 2.0) significantly (p < 0.0001) improved to 10.5 +/- 3.2 in the posttest (n = 305). Typical biomechanics students could correctly answer half of the prerequisite questions on the pre-test. Instruction resulted in a mean normalised gain (g) of 13.0% of maximum possible improvement that was consistent with research on traditional instruction in introductory physics courses. It was concluded that the BCI could be an effective tool to evaluate the overall effect of pedagogical strategies on student learning of key biomechanical concepts in the introductory biomechanics course.},
}
@article {pmid14720420,
year = {2003},
author = {Yang, ZJ and Yang, WY and Li, GW and , },
title = {[The distributive characteristics of impaired glucose metabolism subcategories in Chinese adult population].},
journal = {Zhonghua yi xue za zhi},
volume = {83},
number = {24},
pages = {2128-2131},
pmid = {14720420},
issn = {0376-2491},
mesh = {Adult ; Age Factors ; Blood Glucose/metabolism ; China ; Diabetes Mellitus/*classification/genetics/metabolism ; Female ; Glucose Tolerance Test ; Humans ; Male ; },
abstract = {OBJECTIVE: To clarify the frequencies and clinical features of different impaired glucose metabolism subcategories in Chinese adults.<br><br>METHODS: A cross-sectional analysis of the data of 15,637 Chinese adults (aged >or= 25 years) who underwent standard 75 g oral glucose tolerance test from the National Diabetes Mellitus Survey (1994) was conducted. According to the 1999 WHO criteria for diabetes, the subjects were divided into 7 groups: normal glucose tolerance (NGT, FPG < 6.1 mmol/L and PG 2 h < 7.8 mmol/L), isolated impaired fasting glucose (i-IFG, 6.1 <or= FPG < 7.0 mmol/L and PG 2 h < 7.8 mmol/L), isolated impaired glucose tolerance (i-IGT, FPG < 6.1 mmol/L and 7.8 <or= PG 2 h < 11.1 mmol/L), combined i-IFG and i-IGT (IFG/IGT, 6.1 <or= FPG < 7.0 mmol/L and 7.8 <or= PG 2 h < 11.1 mmol/L), isolated fasting hyperglycemia (IFG, FPG >or= 7.0 mmol/L and PG 2 h < 11.1 mmol/L), isolated postload hyperglycemia (IPH, FPG < 7.0 mmol/L and PG 2 h >or= 11.1 mmol/L), and combined IFH and IPH (IFH/IPH, FPG >or= 7.0 mmol/L and PG 2 h >or= 11.1 mmol/L). The frequencies of the above subcategories were calculated and the clinical characteristics were compared.<br><br>RESULTS: (1) The frequencies of NGT, i-IFG, i-IGT, IFG/IGT, IFG, IPH, and IFH/IPH were 50.8%, 8.8%, 12.3%, 6.1%, 6.4%, 5.2%, and 10.4% respectively. (2) The frequencies of i-IGT, IFG/IGT, IPH, and IFH/IPH increased with age, whereas the frequencies of i-IFG and IFH tended to plateau in the age groups of 25 - 34 years and 55 - 64 years. (3) The mean age and blood pressure were significantly lower in the i-IFG group (vs the i-IGT or IFG/IGT group) and the IFH group (vs IPH or IFH/IPH group). Compared with the IPH group, the IFH group had higher homeostasis model assessment (HOMA) insulin resistance index (HOMA-IR) and lower beta cell function index (BCI).<br><br>CONCLUSIONS: i-IGT is the most common impaired glucose regulation (IGR) subcategory, and IFH/IPH is the most common diabetes subcategory in Chinese adults. The frequencies of i-IGT and IFH/IPH increase with age. The clinical features of i-IFG (IFG) are greatly different from those of i-IGT (IPH), suggesting that the determinants of FPG and PG 2 h differ.},
}
@article {pmid14678180,
year = {2004},
author = {Hirayama, A and Samma, S and Yamaguchi, A and Fukui, Y and Matsumoto, Y and Fujimoto, K and Hirao, Y},
title = {Alpha-blocker test: alternative to pressure-flow study of bladder outlet obstruction and detrusor contractility in patients without an enlarged prostate.},
journal = {International journal of urology : official journal of the Japanese Urological Association},
volume = {11},
number = {1},
pages = {20-25},
doi = {10.1111/j.1442-2042.2004.00737.x},
pmid = {14678180},
issn = {0919-8172},
mesh = {*Adrenergic alpha-Antagonists/therapeutic use ; Aged ; Aged, 80 and over ; Humans ; Male ; Middle Aged ; Muscle Contraction ; Muscle, Smooth/physiopathology ; *Sulfonamides/therapeutic use ; Tamsulosin ; Urinary Bladder Neck Obstruction/*diagnosis/drug therapy/physiopathology ; Urodynamics ; },
abstract = {OBJECTIVES: We investigated whether the cause of urinary disturbance in men with a prostate volume < or =20 mL can be determined by analyzing the efficacy of alpha1-adrenoceptor antagonist (alpha-blocker) treatment.<br><br>METHODS: Thirty-five men who were >50 years of age, with an International Prostate Symptom Score (IPSS) > or =8 points, a quality of life (QOL) index > or =2 points and a prostate volume </=20 mL were enrolled in the non-enlarged prostate group. Twenty-six men who met the above conditions but who had a prostate volume >20 mL served as controls. The alpha1-adrenoceptor antagonist tamsulosin was administered at a dose of 0.2 mg/day for 4 weeks. Results for the IPSS, QOL index, free flowmetry and pressure-flow studies were obtained before and after tamsulosin administration.<br><br>RESULTS: In both groups, tamsulosin improved the IPSS and QOL index and the bladder outlet obstruction index (BOOI) was lowered without reducing the bladder contractility index (BCI). No parameter showed a significant difference in treatment efficacy between the two groups. In the non-enlarged prostate group, both the pretreatment BOOI and BCI correlated with the efficacy of treatment in improving maximum flow rate (Qmax). In the enlarged prostate group, BOOI and BCI did not correlate with Qmax. When Qmax was improved by > or =3.5 mL/s, the positive predictive value for both pretreatment BOOI >40 and BCI >100 was 100% in the non-enlarged prostate group.<br><br>CONCLUSIONS: The alpha-blocker test is one method to assess the presence of bladder outlet obstruction and the state of detrusor contractility in men without an enlarged prostate.},
}
@article {pmid14677109,
year = {2003},
author = {Neuper, C and Müller, GR and Staiger-Sälzer, P and Skliris, D and Kübler, A and Birbaumer, N and Pfurtscheller, G},
title = {[EEG-based communication--a new concept for rehabilitative support in patients with severe motor impairment].},
journal = {Die Rehabilitation},
volume = {42},
number = {6},
pages = {371-377},
doi = {10.1055/s-2003-812543},
pmid = {14677109},
issn = {0034-3536},
mesh = {Action Potentials/physiology ; Biofeedback, Psychology/physiology ; Cerebral Cortex/physiopathology ; Cerebral Palsy/physiopathology/*rehabilitation ; *Communication Devices for People with Disabilities ; Computer Systems ; Computer Terminals ; Electroencephalography/*instrumentation ; Electromyography/instrumentation ; Electrooculography/instrumentation ; Humans ; Imagination/*physiology ; Microcomputers ; Nonverbal Communication/*physiology ; Remote Consultation/instrumentation ; Signal Processing, Computer-Assisted/*instrumentation ; Telemetry/instrumentation ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {This paper describes a paralyzed patient diagnosed with severe infantile cerebral palsy, trained over a period of several months to use an EEG-based brain-computer interface (BCI) for verbal communication. The patient learned to "produce" two distinct EEG patterns by mental imagery and to use this skill for BCI-controlled spelling. The EEG feedback training was conducted at a clinic for Assisted Communications, supervised from a distant laboratory with the help of a telemonitoring system. As a function of training sessions significant learning progress was found, resulting in an average accuracy level of 70% correct responses for letter selection. At present, "copy spelling" can be performed with a rate of approximately one letter per minute. The proposed communication device, the "Virtual Keyboard", may improve actual levels of communication ability in completely paralyzed patients. "Telemonitoring-assisted" training facilitates clinical application in a larger number of patients.},
}
@article {pmid14655228,
year = {2003},
author = {Biedler, AE and Wilhelm, W and Kreuer, S and Soltesz, S and Bach, F and Mertzlufft, FO and Molter, GP},
title = {Accuracy of portable quantitative capnometers and capnographs under prehospital conditions.},
journal = {The American journal of emergency medicine},
volume = {21},
number = {7},
pages = {520-524},
doi = {10.1016/j.ajem.2003.08.005},
pmid = {14655228},
issn = {0735-6757},
mesh = {Capnography/*instrumentation ; Emergency Treatment/*instrumentation ; Equipment Design ; Humans ; Statistics, Nonparametric ; },
abstract = {This study was designed to assess the pCO(2) accuracy of portable mainstream (Tidal Wave, Novametrix; Propaq 106, Protocol) and sidestream capnometers (Capnocheck 8200, BCI; Capnocount mini, Weinmann; NPB-75, Nellcor Puritan Bennett; SC-210, Pryon) with respect to international standards and preclinical emergency conditions. Measurements were performed under temperature conditions of +22 degrees C and -20 degrees C using dry gas mixtures with different CO(2) concentrations (STPD) and in patients ventilated with pure oxygen (BTPS). Accuracy presented to be between +1% (Capnocheck) and +12% (Propaq) (STPD) and between -0.4% (Capnocheck) and +11% (Tidal Wave) (BTPS). The measurements were affected by low ambient temperature only in the NPB-75 (+15%). Our results indicate that portable quantitative capnometers are able to fulfill accuracy requirements as requested by international standards but can be affected by changing ambient temperatures.},
}
@article {pmid14643192,
year = {2004},
author = {Satake, A and Iwasa, Y and Hakoyama, H and Hubbell, SP},
title = {Estimating local interaction from spatiotemporal forest data, and Monte Carlo bias correction.},
journal = {Journal of theoretical biology},
volume = {226},
number = {2},
pages = {225-235},
doi = {10.1016/j.jtbi.2003.09.003},
pmid = {14643192},
issn = {0022-5193},
mesh = {*Data Interpretation, Statistical ; *Ecosystem ; Environmental Monitoring/*methods ; Likelihood Functions ; Monte Carlo Method ; Observer Variation ; Telecommunications ; *Trees ; },
abstract = {We point out a general problem in fitting continuous time spatially explicit models to a temporal sequence of spatial data observed at discrete times. To illustrate the problem, we examined the continuous time Markov model for forest gap dynamics. A forest is assumed to be apportioned into discrete cells (or sites) arranged in a regular square lattice. Each site is characterized as either a gap or a non-gap site according to the vegetation height of trees. The model incorporates the influence of neighboring sites on transition rate: transition rate from a non-gap to a gap site increases linearly with the number of neighbors that are currently in the gap state, and vice versa. We fitted the model to the spatiotemporal data of canopy height observed at the permanent plot in Barro Colorado Island (BCI). When we used the approximate maximum likelihood method to estimate the parameters of the model, the estimated transition rates included a large bias-in particular, the strength of interaction between nearby sites was underestimated. This bias originated from the assumption that each transition between two observation times is independent. The interaction between sites at local scale creates a long chain of transitions within a single census interval, which violates the independence of each transition. We show that a computer-intensive method, called Monte Carlo bias correction (MCBC), is very effective in removing the bias included in the estimate. The global and local gap densities measuring spatial aggregation of gap sites were computed from simulated and real gap dynamics to assess the model. When the approximate likelihood estimates were applied to the model, the predicted local gap density was clearly lower than the observed one. The use of MCBC estimates, suggesting a strong interaction between sites, improved this discrepancy.},
}
@article {pmid14642401,
year = {2003},
author = {Chajara, A and Raoudi, M and Delpech, B and Levesque, H},
title = {Inhibition of arterial cells proliferation in vivo in injured arteries by hyaluronan fragments.},
journal = {Atherosclerosis},
volume = {171},
number = {1},
pages = {15-19},
doi = {10.1016/s0021-9150(03)00303-4},
pmid = {14642401},
issn = {0021-9150},
mesh = {Adjuvants, Immunologic/*administration &amp; dosage/blood ; Animals ; Aorta, Thoracic/*cytology/drug effects/*injuries ; Catheterization/adverse effects ; Cell Division/drug effects ; DNA/drug effects ; Disease Models, Animal ; Dose-Response Relationship, Drug ; Enzyme-Linked Immunosorbent Assay ; Hyaluronic Acid/*administration &amp; dosage/blood ; Hyaluronoglucosaminidase/administration &amp; dosage/blood ; Injections, Intravenous ; Male ; Models, Cardiovascular ; Muscle, Smooth, Vascular/cytology ; Rats ; Rats, Wistar ; Subclavian Artery/cytology/drug effects/injuries ; Tunica Intima/cytology/drug effects/injuries ; },
abstract = {It has been demonstrated previously that administration of high levels of high molecular mass hyaluronan (hyaluronic acid, HA) to rats was able to reduce in a significant way neointima formation in the injured arteries. In the present study, our aim was to verify whether small forms of HA (4-16 saccharides) are still able to reduce the proliferative response of ASMC to aortic injury. Treated rats received a total of 8 injections of a fixed dose of HA fragments (27 mg/kg rat contained in a volume of 550 microl). Two injections were given on the day of balloon catheter injury (BCI): one, intravenous, 10 min before BCI and one, subcutaneous, immediately after the BCI. The others injections (subcutaneous) were at 2, 4, 6, 8, 10 and 12 days after BCI. Control rats received an equivalent volume of the dissolving buffer containing only hyaluronidase, which has been destroyed before injection to rats. Neointima formation was analysed 14 days after the BCI. Intima-media wet weight and DNA content were significantly reduced in rats receiving HA fragments in comparison to controls (2P=0.01 for wet weight and 0.03 for DNA). This finding was confirmed by the histomorphometric study which showed that both neointima area and the ratio neointima/neointima+media were significantly decreased in treated rats (2P=0.03 for intima area and 0.049 for the ratio). Our data showed thus and for the first time that administration of HA fragments with a very low molecular mass (4-16 saccharides) reduces the proliferative reaction of aorta to injury in vivo. In conclusion, HA fragments, which are components with an excellent safety profile, may offer hope for the prevention of restenosis after angioplasty.},
}
@article {pmid14648013,
year = {2004},
author = {Hinterberger, T and Neumann, N and Pham, M and Kübler, A and Grether, A and Hofmayer, N and Wilhelm, B and Flor, H and Birbaumer, N},
title = {A multimodal brain-based feedback and communication system.},
journal = {Experimental brain research},
volume = {154},
number = {4},
pages = {521-526},
pmid = {14648013},
issn = {0014-4819},
mesh = {Acoustic Stimulation/methods ; Adolescent ; Adult ; Aged ; Analysis of Variance ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; Feedback/*physiology ; Female ; Humans ; Male ; Middle Aged ; Photic Stimulation/methods ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {The Thought Translation Device (TTD) is a brain-computer interface based on the self-regulation of slow cortical potentials (SCPs) and enables completely paralyzed patients to communicate using their brain potentials. Here, an extended version of the TTD is presented that has an auditory and a combined visual and auditory feedback modality added to the standard visual feedback. This feature is necessary for locked-in patients who are no longer able to focus their gaze. In order to test performance of physiological regulation with auditory feedback 54 healthy participants were randomly assigned to visual, auditory or combined visual-auditory feedback of slow cortical potentials. The training consisted of three sessions with 500 trials per session with random assignment of required cortical positivity or negativity in half of the trials. The data show that physiological regulation of SCPs can be learned with auditory and combined auditory and visual feedback although the performance of auditory feedback alone was significantly worse than with visual feedback alone.},
}
@article {pmid14624244,
year = {2003},
author = {Carmena, JM and Lebedev, MA and Crist, RE and O'Doherty, JE and Santucci, DM and Dimitrov, DF and Patil, PG and Henriquez, CS and Nicolelis, MA},
title = {Learning to control a brain-machine interface for reaching and grasping by primates.},
journal = {PLoS biology},
volume = {1},
number = {2},
pages = {E42},
pmid = {14624244},
issn = {1545-7885},
mesh = {Animals ; Arm ; Artificial Intelligence ; Behavior, Animal ; *Biomechanical Phenomena ; Biophysical Phenomena ; *Biophysics ; Brain/*pathology ; Brain Mapping ; Electromyography/methods ; Electrophysiology ; Female ; Hand ; *Hand Strength ; Learning ; Macaca ; Models, Neurological ; Models, Statistical ; Models, Theoretical ; Motor Activity ; Motor Cortex/pathology ; Movement ; Neurons/metabolism ; Primates ; Psychomotor Performance/*physiology ; Robotics ; Somatosensory Cortex/pathology ; Space Perception ; Time Factors ; },
abstract = {Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.},
}
@article {pmid14550907,
year = {2003},
author = {Pfurtscheller, G and Müller, GR and Pfurtscheller, J and Gerner, HJ and Rupp, R},
title = {'Thought'--control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia.},
journal = {Neuroscience letters},
volume = {351},
number = {1},
pages = {33-36},
doi = {10.1016/s0304-3940(03)00947-9},
pmid = {14550907},
issn = {0304-3940},
mesh = {Adult ; Electric Stimulation Therapy/*methods ; Electrodes ; Electroencephalography ; *Hand Strength ; Humans ; Male ; Movement ; Quadriplegia/physiopathology/*therapy ; },
abstract = {The aim of the present study was to demonstrate the first time the non-invasive restoration of hand grasp function in a tetraplegic patient by electroencephalogram (EEG)-recording and functional electrical stimulation (FES) using surface electrodes. The patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movement. These beta bursts were analyzed and classified by a brain-computer interface (BCI) and the output signal used to control a FES device. The patient was able to grasp a cylinder with the paralyzed hand.},
}
@article {pmid14506504,
year = {2003},
author = {Klymkowsky, MW and Garvin-Doxas, K and Zeilik, M},
title = {Bioliteracy and teaching efficacy: what biologists can learn from physicists.},
journal = {Cell biology education},
volume = {2},
number = {3},
pages = {155-161},
doi = {10.1187/cbe.03-03-0014},
pmid = {14506504},
issn = {1536-7509},
mesh = {Biology/*education ; Educational Measurement/methods ; Humans ; Physics/education ; Teaching/*methods ; },
abstract = {The introduction of the Force Concept Inventory (FCI) by David Hestenes and colleagues in 1992 produced a remarkable impact within the community of physics teachers. An instrument to measure student comprehension of the Newtonian concept of force, the FCI demonstrates that active learning leads to far superior student conceptual learning than didactic lectures. Compared to a working knowledge of physics, biological literacy and illiteracy have an even more direct, dramatic, and personal impact. They shape public research and reproductive health policies, the acceptance or rejection of technological advances, such as vaccinations, genetically modified foods and gene therapies, and, on the personal front, the reasoned evaluation of product claims and lifestyle choices. While many students take biology courses at both the secondary and the college levels, there is little in the way of reliable and valid assessment of the effectiveness of biological education. This lack has important consequences in terms of general bioliteracy and, in turn, for our society. Here we describe the beginning of a community effort to define what a bioliterate person needs to know and to develop, validate, and disseminate a tiered series of instruments collectively known as the Biology Concept Inventory (BCI), which accurately measures student comprehension of concepts in introductory, genetic, molecular, cell, and developmental biology. The BCI should serve as a lever for moving our current educational system in a direction that delivers a deeper conceptual understanding of the fundamental ideas upon which biology and biomedical sciences are based.},
}
@article {pmid12964454,
year = {2003},
author = {Krausz, G and Scherer, R and Korisek, G and Pfurtscheller, G},
title = {Critical decision-speed and information transfer in the "Graz Brain-Computer Interface".},
journal = {Applied psychophysiology and biofeedback},
volume = {28},
number = {3},
pages = {233-240},
doi = {10.1023/a:1024637331493},
pmid = {12964454},
issn = {1090-0586},
mesh = {Adult ; Cerebral Cortex/physiology ; *Decision Making ; *Electroencephalography ; *Feedback, Psychological ; Humans ; Imagery, Psychotherapy ; Male ; *Mental Processes ; Motor Skills ; Paraplegia/*rehabilitation ; Task Performance and Analysis ; *User-Computer Interface ; },
abstract = {The "Graz Brain-Computer Interface (BCI)" transforms changes in oscillatory EEG activity into control signals for external devices and feedback. These changes are induced by various motor imageries performed by the user. For this study, 2 different types of motor imagery (movement of the right vs. left hand or both feet) were classified by processing 2 bipolar EEG-channels (derived at electrode positions C3 and C4). After a few sessions, within some weeks, 4 young paraplegic patients learned to control the BCI. In accordance with the participants, decision-speed (trial length) was varied and the information transfer rate (ITR) was calculated for each run. All experimental runs have been feedback-runs employing a simple computer-game-like paradigm. A falling ball had to be led into a randomly marked target halfway down the screen. The horizontal position was controlled by the BCI-output signal and the trial length was varied by the investigator across runs. The goal was to find values for trial length enabling a maximum ITR. Three out of 4 participants had good results after a few runs. Analysis of their last 2 experimental sessions, each containing between 10 and 16 runs, showed that the trial length can be reduced to values around 2 s to obtain the highest possible information transfer. Attainable ITRs were between 5 and 17 bit/min depending on the participant's performance and condition.},
}
@article {pmid12964453,
year = {2003},
author = {McFarland, DJ and Wolpaw, JR},
title = {EEG-based communication and control: speed-accuracy relationships.},
journal = {Applied psychophysiology and biofeedback},
volume = {28},
number = {3},
pages = {217-231},
doi = {10.1023/a:1024685214655},
pmid = {12964453},
issn = {1090-0586},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Algorithms ; *Biofeedback, Psychology ; *Communication ; Persons with Disabilities ; *Electroencephalography ; Female ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Motor Skills Disorders/*rehabilitation ; Reproducibility of Results ; *Robotics ; Task Performance and Analysis ; *User-Computer Interface ; Video Recording ; },
abstract = {People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In our current EEG-based brain-computer interface (BCI) system, cursor movement is a linear function of mu or beta rhythm amplitude. In order to maximize the participant's control over the direction of cursor movement, the intercept in this equation is kept equal to the mean amplitude of recent performance. Selection of the optimal slope, or gain, which determines the magnitude of the individual cursor movements, is a more difficult problem. This study examined the relationship between gain and accuracy in a 1-dimensional EEG-based cursor movement task in which individuals select among 2 or more choices by holding the cursor at the desired choice for a fixed period of time (i.e., the dwell time). With 4 targets arranged in a vertical column on the screen, large gains favored the end targets whereas smaller gains favored the central targets. In addition, manipulating gain and dwell time within participants produces results that are in agreement with simulations based on a simple theoretical model of performance. Optimal performance occurs when correct selection of targets is uniform across position. Thus, it is desirable to remove any trend in the function relating accuracy to target position. We evaluated a controller that is designed to minimize the linear and quadratic trends in the accuracy with which participants hit the 4 targets. These results indicate that gain should be adjusted to the individual participants, and suggest that continual online gain adaptation could increase the speed and accuracy of EEG-based cursor control.},
}
@article {pmid12952624,
year = {2003},
author = {DeGusta, D and Everett, MA and Milton, K},
title = {Natural selection on molar size in a wild population of howler monkeys (Alouatta palliata).},
journal = {Proceedings. Biological sciences},
volume = {270 Suppl 1},
number = {Suppl 1},
pages = {S15-7},
pmid = {12952624},
issn = {0962-8452},
mesh = {Alouatta/*genetics ; Animals ; Animals, Wild ; Molar/*anatomy &amp; histology/growth &amp; development ; Panama ; Selection, Genetic ; },
abstract = {Dental traits have long been assumed to be under selection in mammals, based on the macroevolutionary correlation between dental morphology and feeding behaviour. However, natural selection acting on dental morphology has rarely, if ever, been documented in wild populations. We investigated the possibility of microevolutionary selection on dental traits by measuring molar breadth in a sample of Alouatta palliata (mantled howler monkey) crania from Barro Colorado Island (BCI), Panama. The age at death of the monkeys is an indicator of their fitness, since they were all found dead of natural causes. Howlers with small molars have significantly decreased fitness as they die, on average, at an earlier age (well before sexual maturity) than those with larger molars. This documents the existence of phenotypic viability selection on molar tooth size in the BCI howlers, regardless of causality or heritability. The selection is further shown to occur during the weaning phase of A. palliata life history, establishing a link between this period of increased mortality and selection on a specific morphological feature. These results provide initial empirical support for the long-held assumption that primate molar size is under natural selection.},
}
@article {pmid12948787,
year = {2003},
author = {Goncharova, II and McFarland, DJ and Vaughan, TM and Wolpaw, JR},
title = {EMG contamination of EEG: spectral and topographical characteristics.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {114},
number = {9},
pages = {1580-1593},
doi = {10.1016/s1388-2457(03)00093-2},
pmid = {12948787},
issn = {1388-2457},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adolescent ; Adult ; Brain/*physiology ; *Brain Mapping ; Electrodes, Implanted ; *Electroencephalography ; *Electromyography ; *Electronic Data Processing ; Female ; Humans ; Individuality ; Male ; Middle Aged ; Muscle Contraction/physiology ; Muscles/*physiology ; Relaxation/physiology ; },
abstract = {OBJECTIVE: Electromyogram (EMG) contamination is often a problem in electroencephalogram (EEG) recording, particularly, for those applications such as EEG-based brain-computer interfaces that rely on automated measurements of EEG features. As an essential prelude to developing methods for recognizing and eliminating EMG contamination of EEG, this study defines the spectral and topographical characteristics of frontalis and temporalis muscle EMG over the entire scalp. It describes both average data and the range of individual differences.<br><br>METHODS: In 25 healthy adults, signals from 64 scalp and 4 facial locations were recorded during relaxation and during defined (15, 30, or 70% of maximum) contractions of frontalis or temporalis muscles.<br><br>RESULTS: In the average data, EMG had a broad frequency distribution from 0 to >200 Hz. Amplitude was greatest at 20-30 Hz frontally and 40-80 Hz temporally. Temporalis spectra also showed a smaller peak around 20 Hz. These spectral components attenuated and broadened centrally. Even with weak (15%) contraction, EMG was detectable (P<0.001) near the vertex at frequencies >12 Hz in the average data and >8 Hz in some individuals.<br><br>CONCLUSIONS: Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals. EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks.<br><br>SIGNIFICANCE: While EMG contamination is greatest at the periphery of the scalp near the active muscles, even weak contractions can produce EMG that obscures or mimics EEG alpha, mu, or beta rhythms over the entire scalp. Recognition and elimination of this contamination is likely to require recording from an appropriate set of peripheral scalp locations.},
}
@article {pmid12939222,
year = {2003},
author = {Köhler, R and Wulff, H and Eichler, I and Kneifel, M and Neumann, D and Knorr, A and Grgic, I and Kämpfe, D and Si, H and Wibawa, J and Real, R and Borner, K and Brakemeier, S and Orzechowski, HD and Reusch, HP and Paul, M and Chandy, KG and Hoyer, J},
title = {Blockade of the intermediate-conductance calcium-activated potassium channel as a new therapeutic strategy for restenosis.},
journal = {Circulation},
volume = {108},
number = {9},
pages = {1119-1125},
doi = {10.1161/01.CIR.0000086464.04719.DD},
pmid = {12939222},
issn = {1524-4539},
support = {MH59222/MH/NIMH NIH HHS/United States ; },
mesh = {Angioplasty, Balloon/adverse effects ; Animals ; Cell Line ; Cells, Cultured ; Clotrimazole/therapeutic use ; Epidermal Growth Factor/pharmacology ; Graft Occlusion, Vascular/*drug therapy/etiology/pathology/physiopathology ; Hyperplasia ; Intermediate-Conductance Calcium-Activated Potassium Channels ; Large-Conductance Calcium-Activated Potassium Channels ; Muscle, Smooth, Vascular/drug effects/metabolism/physiopathology ; Patch-Clamp Techniques ; Potassium Channel Blockers/therapeutic use ; Potassium Channels/genetics/*metabolism ; Potassium Channels, Calcium-Activated/genetics/metabolism ; Pyrazoles/therapeutic use ; RNA, Messenger/biosynthesis ; Rats ; Rats, Sprague-Dawley ; Tunica Intima/cytology/pathology ; },
abstract = {BACKGROUND: Angioplasty stimulates proliferation and migration of vascular smooth muscle cells (VSMC), leading to neointimal thickening and vascular restenosis. In a rat model of balloon catheter injury (BCI), we investigated whether alterations in expression of Ca2+-activated K+ channels (KCa) contribute to intimal hyperplasia and vascular restenosis.<br><br>METHODS AND RESULTS: Function and expression of KCa in mature medial and neointimal VSMC were characterized in situ by combined single-cell RT-PCR and patch-clamp analysis. Mature medial VSMC exclusively expressed large-conductance KCa (BKCa) channels. Two weeks after BCI, expression of BKCa was significantly reduced in neointimal VSMC, whereas expression of intermediate-conductance KCa (IKCa1) channels was upregulated. In the aortic VSMC cell line, A7r5 epidermal growth factor (EGF) induced IKCa1 upregulation and EGF-stimulated proliferation was suppressed by the selective IKCa1 blocker TRAM-34. Daily in vivo administration of TRAM-34 to rats significantly reduced intimal hyperplasia by approximately 40% at 1, 2, and 6 weeks after BCI. Two weeks of treatment with the related compound clotrimazole was equally effective. Reduction of intimal hyperplasia was accompanied by decreased neointimal cell content, with no change in the rate of apoptosis or collagen content.<br><br>CONCLUSIONS: The switch toward IKCa1 expression may promote excessive neointimal VSMC proliferation. Blockade of IKCa1 could therefore represent a new therapeutic strategy to prevent restenosis after angioplasty.},
}
@article {pmid12899275,
year = {2003},
author = {Wolpaw, JR and McFarland, DJ and Vaughan, TM and Schalk, G},
title = {The Wadsworth Center brain-computer interface (BCI) research and development program.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {204-207},
doi = {10.1109/TNSRE.2003.814442},
pmid = {12899275},
issn = {1534-4320},
support = {EB00856/EB/NIBIB NIH HHS/United States ; HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Academic Medical Centers ; Adult ; Algorithms ; Artifacts ; Brain/*physiology/physiopathology ; Brain Mapping/*methods ; Electroencephalography/*methods ; Evoked Potentials, Visual/*physiology ; Feedback ; Humans ; Middle Aged ; Nervous System Diseases/rehabilitation ; Research Design ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {Brain-computer interface (BCI) research at the Wadsworth Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.},
}
@article {pmid12899272,
year = {2003},
author = {Sykacek, P and Roberts, S and Stokes, M and Curran, E and Gibbs, M and Pickup, L},
title = {Probabilistic methods in BCI research.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {192-195},
doi = {10.1109/TNSRE.2003.814447},
pmid = {12899272},
issn = {1534-4320},
mesh = {*Algorithms ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Humans ; *Models, Neurological ; Models, Statistical ; Retrospective Studies ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {This paper suggests a probabilistic treatment of the signal processing part of a brain-computer interface (BCI). We suggest two improvements for BCIs that cannot be obtained easily with other data driven approaches. Simply by using one large joint distribution as a model of the entire signal processing part of the BCI, we can obtain predictions that implicitly weight information according to its certainty. Offline experiments reveal that this results in statistically significant higher bit rates. Probabilistic methods are also very useful to obtain adaptive learning algorithms that can cope with nonstationary problems. An experimental evaluation shows that an adaptive BCI outperforms the equivalent static implementations, even when using only a moderate number of trials. This suggests that adaptive translation algorithms might help in cases where brain dynamics change due to learning effects or fatigue.},
}
@article {pmid12899269,
year = {2003},
author = {Sajda, P and Gerson, A and Müller, KR and Blankertz, B and Parra, L},
title = {A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {184-185},
doi = {10.1109/TNSRE.2003.814453},
pmid = {12899269},
issn = {1534-4320},
mesh = {*Algorithms ; *Artificial Intelligence ; Brain/physiology ; Databases, Factual ; Electroencephalography/*methods ; Evoked Potentials, Visual/physiology ; Feedback ; Fingers/physiology ; Humans ; Movement/physiology ; Patient Compliance ; Photic Stimulation/methods ; Thinking/physiology ; },
abstract = {We present three datasets that were used to conduct an open competition for evaluating the performance of various machine-learning algorithms used in brain-computer interfaces. The datasets were collected for tasks that included: 1) detecting explicit left/right (L/R) button press; 2) predicting imagined L/R button press; and 3) vertical cursor control. A total of ten entries were submitted to the competition, with winning results reported for two of the three datasets.},
}
@article {pmid12899268,
year = {2003},
author = {Pineda, JA and Silverman, DS and Vankov, A and Hestenes, J},
title = {Learning to control brain rhythms: making a brain-computer interface possible.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {181-184},
doi = {10.1109/TNSRE.2003.814445},
pmid = {12899268},
issn = {1534-4320},
mesh = {Adaptation, Psychological ; Adolescent ; Adult ; Biofeedback, Psychology/*methods ; Cognition/physiology ; Electroencephalography/*methods ; Evoked Potentials, Visual/physiology ; Humans ; Learning/*physiology ; Male ; Motor Cortex/*physiology ; Photic Stimulation ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time.},
}
@article {pmid12899267,
year = {2003},
author = {Pfurtscheller, G and Neuper, C and Müller, GR and Obermaier, B and Krausz, G and Schlögl, A and Scherer, R and Graimann, B and Keinrath, C and Skliris, D and Wörtz, M and Supp, G and Schrank, C},
title = {Graz-BCI: state of the art and clinical applications.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {177-180},
doi = {10.1109/TNSRE.2003.814454},
pmid = {12899267},
issn = {1534-4320},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/*rehabilitation ; *Artificial Limbs ; Brain/physiopathology ; Cognition ; *Communication Devices for People with Disabilities ; Electroencephalography/instrumentation/*methods ; Evoked Potentials ; Hand ; Humans ; Internet ; Patient Education as Topic/methods ; Pilot Projects ; Quadriplegia/*rehabilitation ; Telemedicine/methods ; *User-Computer Interface ; },
abstract = {The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.},
}
@article {pmid12899266,
year = {2003},
author = {Parra, LC and Spence, CD and Gerson, AD and Sajda, P},
title = {Response error correction--a demonstration of improved human-machine performance using real-time EEG monitoring.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {173-177},
doi = {10.1109/TNSRE.2003.814446},
pmid = {12899266},
issn = {1534-4320},
mesh = {*Algorithms ; Blinking/*physiology ; Brain/*physiology ; Discriminant Analysis ; Electroencephalography/*methods ; Evoked Potentials/physiology ; Feedback ; Humans ; Markov Chains ; Principal Component Analysis ; Stochastic Processes ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.},
}
@article {pmid12899265,
year = {2003},
author = {Neumann, N and Kübler, A},
title = {Training locked-in patients: a challenge for the use of brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {169-172},
doi = {10.1109/TNSRE.2003.814431},
pmid = {12899265},
issn = {1534-4320},
mesh = {Adaptation, Physiological ; Amyotrophic Lateral Sclerosis/rehabilitation ; Biofeedback, Psychology/methods ; Brain/physiopathology ; Cerebral Cortex/physiopathology ; Education, Special/*methods ; Humans ; *Learning ; Patient Education as Topic/*methods ; Patient Selection ; Professional-Patient Relations ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
abstract = {Training severely paralyzed patients to use a brain-computer interface (BCI) for communication poses a number of issues and problems. Over the past six years, we have trained 11 patients to self-regulate their slow cortical brain potentials and to use this skill to move a cursor on a computer screen. This paper describes our experiences with this patient group including the problems of accepting and rejecting patients, communicating and interacting with patients, how training may be affected by social, familial, and institutional circumstances, and the importance of motivation and available reinforcers.},
}
@article {pmid12899264,
year = {2003},
author = {Müller, KR and Anderson, CW and Birch, GE},
title = {Linear and nonlinear methods for brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {165-169},
doi = {10.1109/TNSRE.2003.814484},
pmid = {12899264},
issn = {1534-4320},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/classification/*methods ; Humans ; Linear Models ; *Models, Neurological ; Neural Networks, Computer ; Nonlinear Dynamics ; Practice Patterns, Physicians' ; Reproducibility of Results ; Sensitivity and Specificity ; *User-Computer Interface ; },
abstract = {At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets.},
}
@article {pmid12899263,
year = {2003},
author = {Moore, MM},
title = {Real-world applications for brain-computer interface technology.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {162-165},
doi = {10.1109/TNSRE.2003.814433},
pmid = {12899263},
issn = {1534-4320},
support = {BLF34//PHS HHS/United States ; },
mesh = {Activities of Daily Living ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Computer Graphics ; Environment ; Feedback ; Georgia ; Humans ; Quadriplegia/*rehabilitation ; Research Design ; Robotics/*methods ; Universities ; *User-Computer Interface ; Wheelchairs ; },
abstract = {The mission of the Georgia State University BrainLab is to create and adapt methods of human-computer interaction that will allow brain-computer interface (BCI) technologies to effectively control real-world applications. Most of the existing BCI applications were designed largely for training and demonstration purposes. Our goal is to research ways of transitioning BCI control skills learned in training to real-world scenarios. Our research explores some of the problems and challenges of combining BCI outputs with human-computer interface paradigms in order to achieve optimal interaction. We utilize a variety of application domains to compare and validate BCI interactions, including communication, environmental control, neural prosthetics, and creative expression. The goal of this research is to improve quality of life for those with severe disabilities.},
}
@article {pmid12899262,
year = {2003},
author = {Millán, Jdel R and Mouriño, J},
title = {Asynchronous BCI and local neural classifiers: an overview of the Adaptive Brain Interface project.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {159-161},
doi = {10.1109/TNSRE.2003.814435},
pmid = {12899262},
issn = {1534-4320},
mesh = {Adaptation, Physiological/*physiology ; Algorithms ; Brain/*physiology ; Electroencephalography/classification/*methods ; Evoked Potentials/physiology ; Humans ; Pattern Recognition, Automated ; Thinking/classification/*physiology ; *User-Computer Interface ; },
abstract = {In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair).},
}
@article {pmid12899259,
year = {2003},
author = {Kennedy, PR and Adams, KD},
title = {A decision tree for brain-computer interface devices.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {148-150},
doi = {10.1109/TNSRE.2003.814420},
pmid = {12899259},
issn = {1534-4320},
support = {2R44NS36913-02/NS/NINDS NIH HHS/United States ; },
mesh = {*Algorithms ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Brain/*physiopathology ; Decision Making, Computer-Assisted ; *Decision Trees ; Electroencephalography/instrumentation/methods ; Electromyography/instrumentation/methods ; Evoked Potentials ; Humans ; Nervous System Diseases/rehabilitation ; Quadriplegia/rehabilitation ; Therapy, Computer-Assisted/*methods ; *User-Computer Interface ; },
abstract = {This paper is a first attempt to present a "decision tree" to assist in choosing a brain-computer interface device for patients who are nearly or completely "locked-in" (cognitively intact but unable to move or communicate.) The first step is to assess any remaining function. There are six inflexion points in the decision-making process. These depend on the functional status of the patient: 1) some residual movement; 2) no movement, but some residual electromyographic (EMG) activity; 3) fully locked-in with no EMG activity or movements but with conjugate eye movements; 4) same as 3 but with disconjugate eye movements; 5) same as 4 but with inadequate assistance from the available EEG-based systems; 6) same as 5 and accepting of an invasive system.},
}
@article {pmid12899258,
year = {2003},
author = {Guger, C and Edlinger, G and Harkam, W and Niedermayer, I and Pfurtscheller, G},
title = {How many people are able to operate an EEG-based brain-computer interface (BCI)?.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {145-147},
doi = {10.1109/TNSRE.2003.814481},
pmid = {12899258},
issn = {1534-4320},
mesh = {Adaptation, Physiological/physiology ; Adult ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Evoked Potentials, Visual/physiology ; Feedback ; Humans ; Photic Stimulation/methods ; Psychomotor Performance/*physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Task Performance and Analysis ; Thinking/*physiology ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session--40 trials in which the subject had to control a horizontal bar on a computer screen--was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.},
}
@article {pmid12899257,
year = {2003},
author = {Garrett, D and Peterson, DA and Anderson, CW and Thaut, MH},
title = {Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {141-144},
doi = {10.1109/TNSRE.2003.814441},
pmid = {12899257},
issn = {1534-4320},
mesh = {*Algorithms ; Brain/*physiology ; Computer Simulation ; Discriminant Analysis ; Electroencephalography/classification/*methods ; Evoked Potentials/*physiology ; Fingers/physiology ; Humans ; Linear Models ; Models, Neurological ; Movement/physiology ; Neural Networks, Computer ; Nonlinear Dynamics ; Pattern Recognition, Automated ; Signal Processing, Computer-Assisted ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.},
}
@article {pmid12899256,
year = {2003},
author = {Gao, X and Xu, D and Cheng, M and Gao, S},
title = {A BCI-based environmental controller for the motion-disabled.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {137-140},
doi = {10.1109/TNSRE.2003.814449},
pmid = {12899256},
issn = {1534-4320},
mesh = {Algorithms ; Electroencephalography/instrumentation/methods ; Electronics ; Equipment Design ; Evoked Potentials, Visual/*physiology ; Feedback ; Humans ; Movement Disorders/*rehabilitation ; Photic Stimulation/methods ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {With the development of brain-computer interface (BCI) technology, researchers are now attempting to put current BCI techniques into practical application. This paper presents an environmental controller using a BCI technique based on steady-state visual evoked potential. The system is composed of a stimulator, a digital signal processor, and a trainable infrared remote-controller. The attractive features of this system include noninvasive signal recording, little training requirement, and a high information transfer rate. Our test results have shown that this system can distinguish at least 48 targets and provide a transfer rate up to 68 b/min. The system has been applied to the control of an electric apparatus successfully.},
}
@article {pmid12899255,
year = {2003},
author = {Delorme, A and Makeig, S},
title = {EEG changes accompanying learned regulation of 12-Hz EEG activity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {133-137},
doi = {10.1109/TNSRE.2003.814428},
pmid = {12899255},
issn = {1534-4320},
mesh = {Adaptation, Physiological/physiology ; *Algorithms ; Biofeedback, Psychology/methods ; Brain/*physiology ; Electroencephalography/*methods ; Feedback ; Humans ; Learning/*physiology ; Male ; Photic Stimulation/methods ; Principal Component Analysis ; Signal Processing, Computer-Assisted ; Task Performance and Analysis ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {We analyzed 15 sessions of 64-channel electroencephalographic (EEG) data recorded from a highly trained subject during sessions in which he attempted to regulate power at 12 Hz over his left- and right-central scalp to control the altitude of a cursor moving toward target boxes placed at the top-, middle-, or bottom-right of a computer screen. We used infomax independent component analysis (ICA) to decompose 64-channel EEG data from trials in which the subject successfully up- or down-regulated the measured EEG signals. Applying time-frequency analysis to the time courses of activity of several of the resulting 64 independent EEG components revealed that successful regulation of the measured activity was accompanied by extensive, asymmetrical changes in power and coherence, at both nearby and distant frequencies, in several parts of cortex. A more complete understanding of these phenomena could help to explain the nature and locus of learned regulation of EEG rhythms and might also suggest ways to further optimize the performance of brain-computer interfaces.},
}
@article {pmid12899254,
year = {2003},
author = {Cincotti, F and Mattia, D and Babiloni, C and Carducci, F and Salinari, S and Bianchi, L and Marciani, MG and Babiloni, F},
title = {The use of EEG modifications due to motor imagery for brain-computer interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {131-133},
doi = {10.1109/TNSRE.2003.814455},
pmid = {12899254},
issn = {1534-4320},
mesh = {Algorithms ; Brain/*physiology ; Electroencephalography/*methods ; Evoked Potentials/*physiology ; Evoked Potentials, Motor/physiology ; Fingers/physiology ; Humans ; Imagination/*physiology ; Movement/physiology ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {The opening of a communication channel between brain and computer [brain-computer interface (BCI)] is possible by using changes in electroencephalogram (EEG) power spectra related to the imagination of movements. In this paper, we present results obtained by recording EEG during an upper limb motor imagery task in a total of 18 subjects by using low-resolution surface Laplacian, different linear and quadratic classifiers, as well as a variable number of scalp electrodes, from 2 to 26. The results (variable correct classification rate of mental imagery between 75% and 95%) suggest that it is possible to recognize quite reliably ongoing mental movement imagery for BCI applications.},
}
@article {pmid12899253,
year = {2003},
author = {Blankertz, B and Dornhege, G and Schäfer, C and Krepki, R and Kohlmorgen, J and Müller, KR and Kunzmann, V and Losch, F and Curio, G},
title = {Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {127-131},
doi = {10.1109/TNSRE.2003.814456},
pmid = {12899253},
issn = {1534-4320},
mesh = {*Algorithms ; Brain/*physiology ; Electroencephalography/classification/*methods ; Evoked Potentials/*physiology ; Evoked Potentials, Motor/physiology ; Fingers/physiology ; Humans ; Movement/*physiology ; Pattern Recognition, Automated ; Quality Control ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces (BCIs) involve two coupled adapting systems--the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.},
}
@article {pmid12899252,
year = {2003},
author = {Birch, GE and Mason, SG and Borisoff, JF},
title = {Current trends in brain-computer interface research at the Neil Squire Foundation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {123-126},
doi = {10.1109/TNSRE.2003.814450},
pmid = {12899252},
issn = {1534-4320},
mesh = {Adult ; Algorithms ; Brain/physiology/*physiopathology ; Canada ; Electroencephalography/methods ; Feedback ; Foundations/organization &amp; administration/trends ; Humans ; Middle Aged ; Nervous System Diseases/*rehabilitation ; Research/trends ; *Research Design/*trends ; Software/trends ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {The Neil Squire Foundation (NSF) is a Canadian nonprofit organization whose purpose is to create opportunities for independence for individuals who have significant physical disabilities. Over the last ten years, our team in partnership with researchers at the Electrical and Computer Engineering Department, the University of British Columbia, has been working to develop a direct brain-controlled switch for individuals with significant physical disabilities. The NSF Brain Interface Project primarily focuses on the development of brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments. That is, technologies that will work when the User intends control but also remains in a stable off state when there is no intent to control. A prototype of such a switch has successfully been developed. This switch has demonstrated classification accuracies greater than 94%. The initial results are promising, but further research is required to improve switch accuracies and reliability and to test these switch technologies over a larger population of users and operating conditions. This paper provides an overview of the NSF brain-switch technologies and details our approach to future work in this area.},
}
@article {pmid12899250,
year = {2003},
author = {Bianchi, L and Babiloni, F and Cincotti, F and Arrivas, M and Bollero, P and Marciani, MG},
title = {Developing wearable bio-feedback systems: a general-purpose platform.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {117-119},
doi = {10.1109/TNSRE.2003.814452},
pmid = {12899250},
issn = {1534-4320},
mesh = {Biofeedback, Psychology/*instrumentation/methods ; Brain/physiology ; Computer Systems ; Electroencephalography/*methods ; Electronics ; Equipment Design ; Evoked Potentials/*physiology ; Humans ; Miniaturization ; Monitoring, Ambulatory/*instrumentation/methods ; Signal Processing, Computer-Assisted/instrumentation ; Software ; Software Design ; *User-Computer Interface ; },
abstract = {Microprocessors, even those in PocketPCs, have adequate power for many real-time biofeedback applications for disabled people. This power allows design of portable or wearable devices that are smaller and lighter, and that have longer battery life compared to notebook-based systems. In this paper, we discuss a general-purpose hardware/software solution based on industrial or consumer devices and a C++ framework. Its flexibility and modularity make it adaptable to a wide range of situations. Moreover, its design minimizes system requirements and programming effort, thus allowing efficient systems to be built quickly and easily. Our design has been used to build two brain computer interface systems that were easily ported from the Win32 platform.},
}
@article {pmid12899249,
year = {2003},
author = {Bayliss, JD},
title = {Use of the evoked potential P3 component for control in a virtual apartment.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {113-116},
doi = {10.1109/TNSRE.2003.814438},
pmid = {12899249},
issn = {1534-4320},
support = {1-P41-RR09283/RR/NCRR NIH HHS/United States ; },
mesh = {*Activities of Daily Living ; *Computer Graphics ; *Computer Simulation ; Electroencephalography/methods ; *Environment ; Event-Related Potentials, P300/*physiology ; Evoked Potentials, Visual/physiology ; Humans ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Task Performance and Analysis ; *User-Computer Interface ; Visual Perception/physiology ; },
abstract = {Virtual reality (VR) may prove useful for training individuals to use a brain-computer interface (BCI). It could provide complex and controllable experimental environments during BCI research and development as well as increase user motivation. In the study reported here, we examined the robustness of the evoked potential P3 component in virtual and nonvirtual environments. We asked subjects to control several objects or commands in a virtual apartment. Our results indicate that there are no significant differences in the P3 signal between subjects performing a task while immersed in VR versus subjects looking at a computer monitor. This indicates the robustness of the P3 signal over different environments. For an online control task, the performance in a VR environment was not significantly different from performance when looking at a computer monitor. There was, however, a more significant result when the subject's head view of the virtual world was fixed (p < 0.05) when compared with looking at a computer monitor. We also found that subjects' self-reported qualitative experiences did not necessarily match their objective performance. Six out of nine subjects liked the VR environment better, but only one of these subjects performed the best in this environment. The possible ramifications of this, as well as plans for future work, are discussed.},
}
@article {pmid12899248,
year = {2003},
author = {Allison, BZ and Pineda, JA},
title = {ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {110-113},
doi = {10.1109/TNSRE.2003.814448},
pmid = {12899248},
issn = {1534-4320},
mesh = {Adolescent ; Adult ; Brain/*physiology ; Electroencephalography/*methods ; Event-Related Potentials, P300/*physiology ; Evoked Potentials/physiology ; Evoked Potentials, Visual/*physiology ; Female ; Humans ; Male ; Pattern Recognition, Visual/physiology ; Photic Stimulation/methods ; *User-Computer Interface ; Visual Perception/*physiology ; },
abstract = {A brain-computer interface (BCI) system may allow a user to communicate by selecting one of many options. These options may be presented in a matrix. Larger matrices allow a larger vocabulary, but require more time for each selection. In this study, subjects were asked to perform a target detection task using matrices appropriate for a BCI. The study sought to explore the relationship between matrix size and EEG measures, target detection accuracy, and user preferences. Results indicated that larger matrices evoked a larger P300 amplitude, and that matrix size did not significantly affect performance or preferences.},
}
@article {pmid12899247,
year = {2003},
author = {Vaughan, TM and Heetderks, WJ and Trejo, LJ and Rymer, WZ and Weinrich, M and Moore, MM and Kübler, A and Dobkin, BH and Birbaumer, N and Donchin, E and Wolpaw, EW and Wolpaw, JR},
title = {Brain-computer interface technology: a review of the Second International Meeting.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {2},
pages = {94-109},
doi = {10.1109/tnsre.2003.814799},
pmid = {12899247},
issn = {1534-4320},
support = {HD41991-01/HD/NICHD NIH HHS/United States ; },
mesh = {*Algorithms ; Artificial Limbs ; Brain/physiology/*physiopathology ; Brain Mapping/methods ; Cerebral Cortex/physiology/physiopathology ; *Communication Devices for People with Disabilities ; Computer Systems ; Persons with Disabilities/rehabilitation ; Electroencephalography/instrumentation/*methods ; Evoked Potentials ; Feedback ; Humans ; Models, Neurological ; Neuromuscular Diseases/physiopathology/rehabilitation ; Prostheses and Implants ; Robotics/instrumentation/methods ; Self-Help Devices ; Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users.},
}
@article {pmid12880789,
year = {2003},
author = {Weiskopf, N and Veit, R and Erb, M and Mathiak, K and Grodd, W and Goebel, R and Birbaumer, N},
title = {Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data.},
journal = {NeuroImage},
volume = {19},
number = {3},
pages = {577-586},
doi = {10.1016/s1053-8119(03)00145-9},
pmid = {12880789},
issn = {1053-8119},
mesh = {Adult ; Arousal/physiology ; *Biofeedback, Psychology ; Brain/*anatomy &amp; histology/*physiology ; Brain Chemistry ; *Brain Mapping ; Cerebral Cortex/physiology ; Emotions/physiology ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging/*methods ; Male ; Online Systems ; Oxygen/blood ; },
abstract = {A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.},
}
@article {pmid12879040,
year = {2003},
author = {Rudolph, A},
title = {Military: brain machine could benefit millions.},
journal = {Nature},
volume = {424},
number = {6947},
pages = {369},
doi = {10.1038/424369b},
pmid = {12879040},
issn = {1476-4687},
mesh = {Brain/*physiology ; Humans ; *Man-Machine Systems ; Military Science/economics/*ethics ; Neurosciences/economics/*ethics ; Research Support as Topic ; },
}
@article {pmid12876247,
year = {2003},
author = {Neumann, N and Birbaumer, N},
title = {Predictors of successful self control during brain-computer communication.},
journal = {Journal of neurology, neurosurgery, and psychiatry},
volume = {74},
number = {8},
pages = {1117-1121},
pmid = {12876247},
issn = {0022-3050},
mesh = {Adult ; Aged ; Biofeedback, Psychology/*physiology ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Conditioning, Operant/physiology ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Male ; Microcomputers ; Middle Aged ; Motor Neuron Disease/physiopathology/*rehabilitation ; Paraplegia/physiopathology/*rehabilitation ; Prognosis ; Quadriplegia/physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {OBJECTIVES: Direct brain-computer communication uses self regulation of brain potentials to select letters, words, or symbols from a computer menu to re-establish communication in severely paralysed patients. However, not all healthy subjects, or all paralysed patients acquire the skill to self regulate their brain potentials, and predictors of successful learning have not been found yet. Predictors are particularly important, because only successful self regulation will in the end lead to efficient brain-computer communication. This study investigates the question whether initial performance in the self regulation of slow cortical potentials of the brain (SCPs) may be positively correlated to later performance and could thus be used as a predictor.<br><br>METHODS: Five severely paralysed patients diagnosed with amyotrophic lateral sclerosis were trained to produce SCP amplitudes of negative and positive polarity by means of visual feedback and operant conditioning strategies. Performance was measured as percentage of correct SCP amplitude shifts. To determine the relation between initial and later performance in SCP self regulation, Spearman's rank correlations were calculated between maximum and mean performance at the beginning of training (runs 1-30) and mean performance at two later time points (runs 64-93 and 162-191).<br><br>RESULTS: Spearman's rank correlations revealed a significant relation between maximum and mean performance in runs 1-30 and mean performance in runs 64-93 (r= 0.9 and 1.0) and maximum and mean performance in runs 1-30 and mean performance in runs 162-191 (r=1.0 and 1.0).<br><br>CONCLUSIONS: Initial performance in the self regulation of SCP is positively correlated with later performance in severely paralysed patients, and thus represents a useful predictor for efficient brain-computer communication.},
}
@article {pmid12853169,
year = {2003},
author = {McFarland, DJ and Sarnacki, WA and Wolpaw, JR},
title = {Brain-computer interface (BCI) operation: optimizing information transfer rates.},
journal = {Biological psychology},
volume = {63},
number = {3},
pages = {237-251},
doi = {10.1016/s0301-0511(03)00073-5},
pmid = {12853169},
issn = {0301-0511},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiopathology ; Cerebral Palsy/physiopathology ; Communication ; Electroencephalography/methods ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Spinal Injuries/physiopathology ; Task Performance and Analysis ; Time Factors ; *User-Computer Interface ; },
abstract = {People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50% (2-target task) to 20% (5-target task) of the right edge of the screen. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.},
}
@article {pmid12850045,
year = {2003},
author = {Kim, SP and Sanchez, JC and Erdogmus, D and Rao, YN and Wessberg, J and Principe, JC and Nicolelis, M},
title = {Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {16},
number = {5-6},
pages = {865-871},
doi = {10.1016/S0893-6080(03)00108-4},
pmid = {12850045},
issn = {0893-6080},
mesh = {*Artificial Intelligence ; *Brain/physiology ; *Nonlinear Dynamics ; },
abstract = {This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.},
}
@article {pmid12811534,
year = {2003},
author = {Engelbrecht, BM and Kursar, TA},
title = {Comparative drought-resistance of seedlings of 28 species of co-occurring tropical woody plants.},
journal = {Oecologia},
volume = {136},
number = {3},
pages = {383-393},
pmid = {12811534},
issn = {0029-8549},
mesh = {*Adaptation, Physiological ; *Disasters ; *Plant Development ; Plant Leaves ; Seedlings/growth &amp; development ; Survival Analysis ; Tropical Climate ; },
abstract = {Quantifying plant drought resistance is important for understanding plant species' association to microhabitats with different soil moisture availability and their distribution along rainfall gradients, as well as for understanding the role of underlying morphological and physiological mechanisms. The effect of dry season drought on survival and leaf-area change of first year seedlings of 28 species of co-occurring woody tropical plants was experimentally quantified in the understory of a tropical moist forest. The seedlings were subjected to a drought or an irrigation treatment in the forest for 22 weeks during the dry season. Drought decreased survival and growth (assessed as leaf-area change) in almost all of the species. Both survival and leaf-area change in the dry treatment ranged fairly evenly from 0% to about 100% of that in the irrigated treatment. In 43% of the species the difference between treatments in survival was not significant even after 22 weeks. In contrast, only three species showed no significant effect of drought on leaf-area change. The effects of drought on species' survival and growth were not correlated with each other, reflecting different strategies in response to drought. Seedling size at the onset of the dry season had no significant effect on species' drought response. Our study is the first to comparatively assess seedling drought resistance in the habitat for a large number of tropical species, and underlines the importance of drought for plant population dynamics in tropical forests.},
}
@article {pmid12798603,
year = {2003},
author = {Mussa-Ivaldi, FA and Miller, LE},
title = {Brain-machine interfaces: computational demands and clinical needs meet basic neuroscience.},
journal = {Trends in neurosciences},
volume = {26},
number = {6},
pages = {329-334},
doi = {10.1016/S0166-2236(03)00121-8},
pmid = {12798603},
issn = {0166-2236},
support = {NS36976/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Brain/*physiology ; Electrodes, Implanted/standards/*trends ; Electroencephalography/instrumentation/methods/trends ; Electrophysiology/instrumentation/methods/trends ; Feedback/physiology ; Forecasting ; Humans ; Neurophysiology/*instrumentation ; Prostheses and Implants/standards/*trends ; *User-Computer Interface ; },
abstract = {As long as 150 years ago, when Fritz and Hitzig demonstrated the electrical excitability of the motor cortex, scientists and fiction writers were considering the possibility of interfacing a machine with the human brain. Modern attempts have been driven by concrete technological and clinical goals. The most advanced of these has brought the perception of sound to thousands of deaf individuals by means of electrodes implanted in the cochlea. Similar attempts are underway to provide images to the visual cortex and to allow the brains of paralyzed patients to re-establish control of the external environment via recording electrodes. This review focuses on two challenges: (1) establishing a 'closed loop' between sensory input and motor output and (2) controlling neural plasticity to achieve the desired behavior of the brain-machine system. Meeting these challenges is the key to extending the impact of the brain-machine interface.},
}
@article {pmid12797728,
year = {2003},
author = {Mason, SG and Birch, GE},
title = {A general framework for brain-computer interface design.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {1},
pages = {70-85},
doi = {10.1109/TNSRE.2003.810426},
pmid = {12797728},
issn = {1534-4320},
mesh = {Algorithms ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; *Decision Support Techniques ; Equipment Failure Analysis/methods ; Humans ; Prosthesis Design/*methods ; Technology Assessment, Biomedical/methods ; *User-Computer Interface ; },
abstract = {The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.},
}
@article {pmid12797726,
year = {2003},
author = {Müller, GR and Neuper, C and Pfurtscheller, G},
title = {Implementation of a telemonitoring system for the control of an EEG-based brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {11},
number = {1},
pages = {54-59},
doi = {10.1109/TNSRE.2003.810423},
pmid = {12797726},
issn = {1534-4320},
mesh = {Adult ; Biofeedback, Psychology/*instrumentation ; Brain/physiopathology ; Cerebral Palsy/*rehabilitation ; Communication Devices for People with Disabilities ; Education, Distance/methods ; Electroencephalography/instrumentation/*methods ; Equipment Design ; Feasibility Studies ; Home Nursing/methods ; Humans ; Internet ; Male ; Monitoring, Physiologic/instrumentation ; Patient Education as Topic/methods ; Teaching/methods ; Telemedicine/*instrumentation/methods ; *User-Computer Interface ; },
abstract = {By the use of a brain-computer interface (BCI), it is possible for completely paralyzed patients, who have lost their ability to speak, to have a new possibility to communicate with their environment. The training with such a BCI system can be performed at the patient's home, if there is a responsible person present who is familiar with the system. This person has to adjust different parameters and to adapt the training individually to each patient. Since this function is usually taken over by the developers of the system, the number of patients who can be included in regular BCI training is restricted due to geographical distances. This paper describes the implementation of a telemonitoring system, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work. First experiences with a patient living far away from the developer's lab are reported.},
}
@article {pmid12797612,
year = {2003},
author = {Tyler, DJ and Durand, DM},
title = {Chronic response of the rat sciatic nerve to the flat interface nerve electrode.},
journal = {Annals of biomedical engineering},
volume = {31},
number = {6},
pages = {633-642},
doi = {10.1114/1.1569263},
pmid = {12797612},
issn = {0090-6964},
support = {NS32845/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; Electric Stimulation/*instrumentation/methods ; *Electrodes, Implanted ; Equipment Failure Analysis ; Foot/physiology ; Gait/physiology ; Male ; Nerve Compression Syndromes/prevention &amp; control ; Prosthesis Design ; Rats ; Rats, Sprague-Dawley ; Sciatic Nerve/blood supply/*cytology/*physiology ; Stress, Mechanical ; },
abstract = {The chronic effects of a reshaping nerve electrode, the flat interface nerve electrode (FINE), on sciatic nerve physiology, histology, and blood-nerve barrier (BNB) are presented. The FINE electrode applies a small force to a nerve to reshape the nerve and fascicles into elongated ovals. This increases the interface between the nerve and electrode for selective stimulation and recording of peripheral nerve activity. The hypothesis of this study is that a small force applied noncircumferentially to a nerve can chronically reshape the nerve without effecting nerve physiology, histology, or the blood-nerve barrier permeability. Three FINE electrode designs were implanted on rat sciatic nerves to examine the nerve's response to small, moderate, and high reshaping forces. The chronic reshaping, physiology, and histology of the nerve were examined at 1, 7, and 28 days postimplant. All FINEs significantly reshape both the nerve and the fascicles compared to controls. FINEs that applied high forces caused a neurapraxia type injury characterized by changes in the animal's footprint, nerve histology, and the BNB permeability. The physiological changes were greatest at 7 days and fully recover to normal by 14 days postimplant. The moderate force FINE did not result in changes in the footprint or BNB permeability. Only a minor decrease in axon density without accompanying evidence of axon demyelination or regeneration was observe for the moderate force. The small force FINE does not cause any change in nerve physiology, histology, or BNB permeability compared to the sham treatment. An electrode that applies a small force that results in an estimated intrafascicular pressure of less than 30 mm Hg can reshape the nerve without significant changes in the nerve physiology or histology. These results support the conclusion that a small force chronically applied to the nerve reshapes the nerve without injury.},
}
@article {pmid12769460,
year = {2003},
author = {Gil-da-Costa, R and Palleroni, A and Hauser, MD and Touchton, J and Kelley, JP},
title = {Rapid acquisition of an alarm response by a neotropical primate to a newly introduced avian predator.},
journal = {Proceedings. Biological sciences},
volume = {270},
number = {1515},
pages = {605-610},
pmid = {12769460},
issn = {0962-8452},
mesh = {Alouatta/*physiology ; *Animal Communication ; Animals ; Eagles/*physiology ; Geography ; Male ; Panama ; *Predatory Behavior ; },
abstract = {Predation is an important selective pressure in natural ecosystems. Among non-human primates, relatively little is known about how predators hunt primate prey and how primates acquire adaptive responses to counteract predation. In this study we took advantage of the recent reintroduction of radio-tagged harpy eagles (Harpia harpyja) to Barro Colorado Island (BCI), Panama to explore how mantled howler monkeys (Alouatta palliata), one of their primary prey, acquire anti-predator defences. Based on the observation that harpies follow their prey prior to attack, and often call during this pursuit period, we broadcast harpy eagle calls to howlers on BCI as well as to a nearby control population with no harpy predation. Although harpies have been extinct from this area for 50-100 years, results indicate that BCI howlers rapidly acquired an adaptive anti-predator response to harpy calls, while showing no response to other avian vocalizations; howlers maintained this response several months after the removal of the eagles. These results not only show that non-human primates can rapidly acquire an alarm response to a newly introduced predator, but that they can detect and identify predators on the basis of acoustic cues alone. These findings have significant implications both for the role of learning mechanisms in the evolution of prey defence and for conservation strategies, suggesting that the use of 'probing' approaches, such as auditory playbacks, may highly enhance an a priori assessment of the impact of species reintroduction.},
}
@article {pmid12728268,
year = {2003},
author = {Nicolelis, MA},
title = {Brain-machine interfaces to restore motor function and probe neural circuits.},
journal = {Nature reviews. Neuroscience},
volume = {4},
number = {5},
pages = {417-422},
doi = {10.1038/nrn1105},
pmid = {12728268},
issn = {1471-003X},
mesh = {Animals ; Humans ; Motor Activity/*physiology ; Motor Cortex/pathology/*physiology ; Nerve Net/*physiology ; Neuronal Plasticity/physiology ; Paralysis/therapy ; *Prostheses and Implants ; },
}
@article {pmid12727187,
year = {2003},
author = {Curran, EA and Stokes, MJ},
title = {Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems.},
journal = {Brain and cognition},
volume = {51},
number = {3},
pages = {326-336},
doi = {10.1016/s0278-2626(03)00036-8},
pmid = {12727187},
issn = {0278-2626},
mesh = {Brain/anatomy &amp; histology/*physiology ; *Electroencephalography ; Humans ; Learning/*physiology ; Magnetic Resonance Imaging ; *User-Computer Interface ; },
abstract = {Brain-computer interface (BCI) technology relies on the ability of individuals to voluntarily and reliably produce changes in their electroencephalographic (EEG) activity. The present paper reviews research on cognitive tasks and other methods of generating and controlling specific changes in EEG activity that can be used to drive BCI systems. To date, motor imagery has been the most commonly used task. This paper explores the possibility that other cognitive tasks, including those used in imaging studies, may prove to be more effective. Other factors which influence performance are also considered in relation to selection of tasks, as well as training of subjects.},
}
@article {pmid12717881,
year = {1999},
author = {Balkanov, AS and Stashuk, GA and Poliakov, PIu and Sherman, LA and Bychenkov, OA},
title = {[A comparative analysis of the brain computer tomography in patients with acromegaly before and after gamma-therapy].},
journal = {Vestnik rentgenologii i radiologii},
volume = {},
number = {3},
pages = {41-45},
pmid = {12717881},
issn = {0042-4676},
mesh = {Acromegaly/*diagnostic imaging/*radiotherapy ; Adult ; Aged ; Brain/*radiation effects ; *Brain Neoplasms/diagnostic imaging/metabolism/radiotherapy ; Female ; Growth Hormone/*metabolism ; Humans ; Male ; Middle Aged ; *Prolactinoma/diagnostic imaging/metabolism/radiotherapy ; Time Factors ; *Tomography, X-Ray Computed ; },
abstract = {The data given in the paper suggest that X-ray computed tomography is highly effective in evaluating the characteristics of pituitary adenomas in acromegaly and in revealing the changes caused in the pituitary adenoma by gamma-ray teletherapy. The use of brain computed tomography yielded data on the main X-ray criteria to be used in the follow-up of patients undergone radiation therapy for acromegaly.},
}
@article {pmid12705422,
year = {2003},
author = {Hinterberger, T and Kübler, A and Kaiser, J and Neumann, N and Birbaumer, N},
title = {A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {114},
number = {3},
pages = {416-425},
doi = {10.1016/s1388-2457(02)00411-x},
pmid = {12705422},
issn = {1388-2457},
mesh = {*Communication Devices for People with Disabilities ; Communication Barriers ; Electroencephalography/*classification/*methods ; Humans ; Male ; Middle Aged ; Models, Neurological ; Quadriplegia/*rehabilitation ; *User-Computer Interface ; },
abstract = {OBJECTIVE: The Thought Translation Device (TTD) for brain-computer interaction was developed to enable totally paralyzed patients to communicate. Patients learn to regulate slow cortical potentials (SCPs) voluntarily with feedback training to select letters. This study reports the comparison of different methods of electroencephalographic (EEG) analysis to improve spelling accuracy with the TTD on a data set of 6,650 trials of a severely paralyzed patient.<br><br>METHODS: Selections of letters occurred by exceeding a certain SCP amplitude threshold. To enhance the patient's control of an additional event-related cortical potential, a filter with two filter characteristics ('mixed filter') was developed and applied on-line. To improve performance off-line the criterion for threshold-related decisions was varied. Different types of discriminant analysis were applied to the EEG data set as well as on wavelet transformed EEG data.<br><br>RESULTS: The mixed filter condition increased the patients' performance on-line compared to the SCP filter alone. A threshold, based on the ratio between required selections and rejections, resulted in a further improvement off-line. Discriminant analysis of both time-series SCP data and wavelet transformed data increased the patient's correct response rate off-line.<br><br>CONCLUSIONS: It is possible to communicate with event-related potentials using the mixed filter feedback method. As wavelet transformed data cannot be fed back on-line before the end of a trial, they are applicable only if immediate feedback is not necessary for a brain-computer interface (BCI). For future BCIs, wavelet transformed data should serve for BCIs without immediate feedback. A stepwise wavelet transformation would even allow immediate feedback.},
}
@article {pmid12705420,
year = {2003},
author = {Neuper, C and Müller, GR and Kübler, A and Birbaumer, N and Pfurtscheller, G},
title = {Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {114},
number = {3},
pages = {399-409},
doi = {10.1016/s1388-2457(02)00387-5},
pmid = {12705420},
issn = {1388-2457},
mesh = {Adult ; Biofeedback, Psychology ; Cerebral Palsy/*rehabilitation ; *Communication Devices for People with Disabilities ; Communication Barriers ; Electroencephalography/*methods ; Humans ; Male ; Motor Cortex/physiology ; Paralysis/rehabilitation ; Severity of Illness Index ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication.<br><br>METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS).<br><br>RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min.<br><br>CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.},
}
@article {pmid12699485,
year = {2003},
author = {Rolle, U and Chertin, B and Puri, P},
title = {Effects of benzalkonium chloride treatment on the intramural innervation of the upper urinary tract.},
journal = {BJU international},
volume = {91},
number = {7},
pages = {683-686},
doi = {10.1046/j.1464-410x.2003.03081.x},
pmid = {12699485},
issn = {1464-4096},
mesh = {Acetylcholine ; Animals ; Benzalkonium Compounds/*therapeutic use ; Female ; Immunohistochemistry ; Rabbits ; Ureter/*innervation ; Ureteral Obstruction/drug therapy/*etiology ; Vasodilator Agents ; },
abstract = {OBJECTIVE: To establish a model for investigating the pathophysiology of pelvi-ureteric junction (PUJ) obstruction, using benzalkonium chloride (BCI) treatment of the upper urinary tract of rabbits, and thus further elucidate the pathophysiology of PUJ obstruction, the most common urinary tract obstruction in children.<br><br>MATERIALS AND METHODS: Although various histological abnormalities have been described, PUJ obstruction may be functional. Defective innervation in PUJ has been suggested to be a major factor in the failure to transmit peristaltic waves across the PUJ. Previously established animal models of hydronephrosis deal mostly with surgical obstruction of the PUJ, which does not correlate with human congenital hydronephrosis. BCl has been used to ablate selectively neurones of the gastrointestinal myenteric plexus, which generated spastic segments with impaired peristalsis. Thus 12 rabbits were treated with BCl at the PUJ; the right upper urinary tract was dissected extraperitoneally and treated with a local application of 0.1% or 0.5% BCl (six each) for 15 min. The controls were four sham-operated animals treated with saline. The animals were assessed by intravenous urography (IVU) at 4 and 8 weeks after treatment, after which the animals were killed, the upper urinary tracts removed and whole-mounts prepared. Acetylcholinesterase (AChE) histochemistry, and neurofilament and tyrosine hydroxylase (TH) single-enzyme immunohistochemistry were used to detect the intrinsic innervation.<br><br>RESULTS: None of the animals had hydronephrosis on the IVU or at death. AChE histochemistry, TH and neurofilament immunohistochemistry showed no or very few nerve fibres within the BCl-treated PUJs in both (0.1% and 0.5%) groups. After saline treatment there was normal development of the neuronal plexus within the submucosal, muscular and adventitial layers of the upper urinary tract.<br><br>CONCLUSION: These results suggest that treatment with BCl is useful for ablating the intrinsic innervation in the upper urinary tract. Defective intrinsic innervation of the upper urinary tract did not lead to clinically or radiologically evident hydronephrosis. Further physiological studies using this model are needed to further elucidate the neuronal and myogenic influence on the development of PUJ obstruction.},
}
@article {pmid12695802,
year = {2003},
author = {Bianchi, L and Babiloni, F and Cincotti, F and Salinari, S and Marciani, MG},
title = {Introducing BF++: AC++ framework for cognitive bio-feedback systems design.},
journal = {Methods of information in medicine},
volume = {42},
number = {1},
pages = {104-110},
pmid = {12695802},
issn = {0026-1270},
mesh = {*Cognition ; *Communication Devices for People with Disabilities ; *Feedback ; Humans ; Programming Languages ; *Software Design ; *User-Computer Interface ; },
abstract = {OBJECTIVE: This paper addressed the issue of building-up a framework for the realization of several cognitive bio-feedback (CBF) systems. It minimizes the programming effort and maximizes the efficiency and the cross-platform portability so that it can be used with many platforms (either software or hardware).<br><br>METHODS: A generic CBF system was decomposed into six modules: acquisition, kernel, feedback rule, patient feedback, operator user interface and persistent storage. The way in which these modules interact was defined by immutable software interfaces in a way that allows to completely substitute a module without the need to modify the others.<br><br>RESULTS: Three Brain Computer Interface engines were developed with less than 40 lines of C++ code each. They can also be used under virtually any platform that supports an ANSI C++ compiler.<br><br>CONCLUSION: A framework for the implementation of a wide range of CBF systems was developed. Compared to the other approaches that are described in the literature, the proposed one is the most efficient, the most protable across different platforms, the most generic and the one that allows the realization of the cheapest final systems.},
}
@article {pmid12667538,
year = {2003},
author = {Neumann, N and Kübler, A and Kaiser, J and Hinterberger, T and Birbaumer, N},
title = {Conscious perception of brain states: mental strategies for brain-computer communication.},
journal = {Neuropsychologia},
volume = {41},
number = {8},
pages = {1028-1036},
doi = {10.1016/s0028-3932(02)00298-1},
pmid = {12667538},
issn = {0028-3932},
mesh = {Amyotrophic Lateral Sclerosis/*physiopathology ; Biofeedback, Psychology ; *Brain ; *Communication ; *Computers ; Consciousness ; Contingent Negative Variation/physiology ; Electroencephalography/methods ; Evoked Potentials, Somatosensory ; Humans ; Learning ; Male ; Mental Processes ; Middle Aged ; Neuropsychological Tests ; Paralysis/physiopathology ; *Perception ; Photic Stimulation ; Reaction Time ; Social Control, Informal/methods ; User-Computer Interface ; },
abstract = {Direct brain-computer communication utilises self-regulation of brain potentials to select letters, words or symbols from a computer menu. In this study a completely paralysed (locked-in) patient learnt to produce slow cortical potential (SCP) shifts to operate a binary spelling device. After hundreds of training sessions he gave a detailed description of his mental strategies for self-regulation. His cognitive strategies matched with the electrocortical changes perfectly. Thus he produced a contingent negative variation (CNV) with images of preparation such as an arrow being drawn on a bow. To produce a positive potential shift he imagined the arrow shooting up from the bow. To suppress potential shifts he tried to stop thinking. The study demonstrates that patients become sensitive for their brain states with increasing self-regulation practice. The use of conscious cognitive strategies may, however, be incompatible with the complete automatization of the self-regulation skill.},
}
@article {pmid12662759,
year = {1998},
author = {Peters, BO and Pfurtscheller, G and Flyvbjerg, H},
title = {Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {11},
number = {7-8},
pages = {1429-1433},
doi = {10.1016/s0893-6080(98)00060-4},
pmid = {12662759},
issn = {1879-2782},
abstract = {We have studied 56-channel electroencephalograms (EEG) from three subjects who planned and performed three kinds of movements, left and right index finger, and right foot movement. Using autoregressive modeling of EEG time series and artificial neural nets (ANN), we have developed a classifier that can tell which movement is performed from a segment of the EEG signal from a single trial. The classifier's rate of recognition of EEGs not seen before was 92-99% on the basis of a 1s segment per trial. The recognition rate provides a pragmatic measure of the information content of the EEG signal. This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically locates regions of relevant activity on the skull.},
}
@article {pmid12655847,
year = {2003},
author = {Scherer, R and Graimann, B and Huggins, JE and Levine, SP and Pfurtscheller, G},
title = {Frequency component selection for an ECoG-based brain-computer interface.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {48},
number = {1-2},
pages = {31-36},
doi = {10.1515/bmte.2003.48.1-2.31},
pmid = {12655847},
issn = {0013-5585},
support = {1R01NS040681-01/NS/NINDS NIH HHS/United States ; },
mesh = {Brain Mapping ; Cerebral Cortex/*physiopathology ; *Cortical Synchronization ; Electroencephalography/*instrumentation ; Electromyography/instrumentation ; Epilepsy/physiopathology/surgery ; Evoked Potentials/physiology ; Humans ; Motor Activity/physiology ; Nerve Net/*physiopathology ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.},
}
@article {pmid12611367,
year = {2002},
author = {Tyler, DJ and Durand, DM},
title = {Functionally selective peripheral nerve stimulation with a flat interface nerve electrode.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {10},
number = {4},
pages = {294-303},
doi = {10.1109/TNSRE.2002.806840},
pmid = {12611367},
issn = {1534-4320},
support = {2R01 NS32845/NS/NINDS NIH HHS/United States ; },
mesh = {Anatomy, Cross-Sectional ; Animals ; Ankle Joint/innervation/physiology ; Cats ; Electric Stimulation/*instrumentation/methods ; Electric Stimulation Therapy/instrumentation/methods ; *Electrodes, Implanted ; Equipment Design ; Equipment Failure Analysis ; Muscle, Skeletal/innervation/physiology ; Peripheral Nerves/physiology ; Recruitment, Neurophysiological/physiology ; Reproducibility of Results ; Sciatic Nerve/cytology/*physiology ; Sensitivity and Specificity ; },
abstract = {One of the important goals of peripheral nerve electrode development is to design an electrode for selective recruitment of the different functions of a common nerve trunk. A challenging task is gaining selective access to central axon populations. In this paper, a simple electrode that takes advantage of the neural plasticity to reshape the nerve is presented. The flat interface nerve electrode (FINE) reshapes the nerve into a flat geometry to increase the surface area and move central axon populations close to the surface. The electrode was implanted acutely on the sciatic nerve of eight cats. The FINE can significantly reshape the nerve and fascicles (p < 0.0001) while maintaining the same total nerve cross-sectional area. The stimulation thresholds were 2.89 nC for pulse amplitude modulation and 10.2 nC for pulse-width modulation. Monopolar, square-pulse stimulation with single contacts on the FINE selectively recruited each of the four main branches of the sciatic nerve. Simultaneous stimulation with two contacts produced moments about the ankle joint that were a combination of the moments produced by the individual contacts when stimulated separately.},
}
@article {pmid12611359,
year = {2002},
author = {Birch, GE and Bozorgzadeh, Z and Mason, SG},
title = {Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {10},
number = {4},
pages = {219-224},
doi = {10.1109/TNSRE.2002.806839},
pmid = {12611359},
issn = {1534-4320},
mesh = {Action Potentials ; Adult ; Brain Mapping/*methods ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; False Negative Reactions ; False Positive Reactions ; Fingers/*physiopathology ; Humans ; Imagination/classification ; Male ; Motor Neurons ; Movement/physiology ; Online Systems ; Predictive Value of Tests ; Reproducibility of Results ; Sensitivity and Specificity ; Spinal Cord Injuries/*rehabilitation ; *User-Computer Interface ; Volition/classification ; },
abstract = {Previous research has focused on developing a brain-controlled switch named the low frequency asynchronous switch design (LF-ASD) that is suitable for intermittent control of devices such as environmental control systems, computers, and neural prostheses. On-line implementations of the LF-ASD have shown promising results in response to actual index finger flexions with able-bodied subjects. This paper reports the results of initial on-line evaluations of the LF-ASD brain-controlled switch with both able-bodied subjects and subjects with high-level spinal-cord injuries. This paper has demonstrated that users can activate the LF-ASD switch by imaging movement. In this paper, two able-bodied subjects were able to control the LF-ASD with imagined voluntary movements with hit (true positive) rates above 70% and false positive rates below 3% while two subjects with high-level spinal-cord injuries demonstrated hit rates ranging from 45-48% and false positive rates below 1%.},
}
@article {pmid12572752,
year = {2003},
author = {Yom-Tov, E and Inbar, GF},
title = {Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {41},
number = {1},
pages = {85-93},
pmid = {12572752},
issn = {0140-0118},
mesh = {Adult ; Algorithms ; Communication Devices for People with Disabilities ; *Electroencephalography ; Electrophysiology ; Humans ; Male ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Brain-computer interfaces are devices for enabling patients with severe motor disorders to communicate with the world. One method for operating such devices is to use movement-related potentials that are generated in the brain when the patient moves, or imagines a movement of, one of his limbs. An algorithm for detecting movement-related potentials using a small number of EEG channels was developed. This algorithm is a combination of the matched filter, a non-linear transformation previously developed as part of a similar detector, and a classifier. The algorithm was compared with previous designs of similar detectors by both theoretic analysis and off-line evaluation of performance on data recorded from five subjects. It is shown that the performance of the algorithm was superior to that of previous methods, improving the area under the receiver operating characteristic curve to 87.8%, an improvement of 25% compared with the best previously suggested detection method. Finally, the probable sources for false detections were identified, and possible ways to minimise them are proposed.},
}
@article {pmid12570745,
year = {2003},
author = {Sheweita, SA and Tilmisany, AK},
title = {Cancer and phase II drug-metabolizing enzymes.},
journal = {Current drug metabolism},
volume = {4},
number = {1},
pages = {45-58},
doi = {10.2174/1389200033336919},
pmid = {12570745},
issn = {1389-2002},
mesh = {Animals ; Arylamine N-Acetyltransferase/biosynthesis/metabolism ; DNA Adducts/antagonists &amp; inhibitors/metabolism ; Epoxide Hydrolases/metabolism ; Glutathione Transferase/biosynthesis/*metabolism ; Humans ; Isoenzymes/metabolism ; Neoplasms/diagnosis/*enzymology/etiology ; Pharmaceutical Preparations/*metabolism ; Xenobiotics/metabolism ; },
abstract = {Cancer development results from the interaction between genetic factors, the environment, and dietary factors have been identified as modulators of carcinogenesis process. The formation of DNA adducts is recognized as the initial step in chemical carcinogenesis. Accordingly, blocking DNA adducts formation would be the first line of defense against cancer caused by carcinogens. Glutathione-S-transferases inactivate chemical carcinogens into less toxic or inactive metabolite through reduction of DNA adducts formation. There are many different types of glutathione S-transferase isozymes. For example, GST delta serves as a marker for hepatotoxicity in rodent system, and also plays an important role in carcinogen detoxification. Therefore, inhibition of GST activity might potentiate the deleterious effects of many environmental toxicants and carcinogens. In addition, approximately half of the population lacks GST Mu expression. Epidemiological evidence showed that persons possessing this genotype are predisposed to a number of cancers including breast, prostate, liver and colon cancers. In addition, individual risk of cancer depends on the frequency of mutational events in target oncogenes and tumor suppressor genes which could lead to loss of chromosomal materials and tumor progression. The most frequent genetic alteration in a variety of human malignant tumors is the mutation of the coding sequence of the p53 tumor suppressor gene. O(6)-alkylguanine in DNA leads to very high rates of G:C deltaA:T transitions in p53 gene. These alterations will modulate the expression of p53 gene and consequently change DNA repair, cell division, and cell death by apoptosis. Also, changes in the expression of BcI-2 gene results in extended viability of cells by over-riding programmed cell death (apoptosis) induced under various conditions. The prolonged life-span increases the risk of acquiring genetic changes resulting in malignant transformation. In addition, a huge variety of food ingredients have been shown to affect cell proliferation rates. They, therefore, may either reduce or increase the risk of cancer development and progression. For example, it has been found that a high intake of dietary fat accelerates the development of breast cancer in animal models. Certain diets have been suggested to act as tumor promoters also in other types of cancer such as colon cancer, where high intake of fat and phosphate have been linked to colonic hyper-proliferation and colon cancer development. Different factors such as oncogenes, aromatic amines, alkylating agents, and diet have a significant role in cancer induction. Determination of glutathione S-transferase isozymes in plasma or serum could be used as a biomarker for cancer in different organs and could give an early detection.},
}
@article {pmid12570053,
year = {2002},
author = {Itoh, M and Noutomi, T and Chiba, H and Mizuguchi, J},
title = {BcI-xL antisense treatment sensitizes Bcl-xL-overexpressing squamous cell carcinoma cells to carboplatin.},
journal = {Oral oncology},
volume = {38},
number = {8},
pages = {752-756},
doi = {10.1016/s1368-8375(02)00047-7},
pmid = {12570053},
issn = {1368-8375},
mesh = {Antineoplastic Agents/*therapeutic use ; Carboplatin/*therapeutic use ; Carcinoma, Squamous Cell/*drug therapy/pathology ; Cell Division ; Down-Regulation ; Drug Resistance, Neoplasm ; Drug Therapy, Combination ; Humans ; Mouth Neoplasms/*drug therapy/pathology ; Oligonucleotides, Antisense/*therapeutic use ; Proto-Oncogene Proteins c-bcl-2/genetics/*metabolism ; Treatment Outcome ; Tumor Cells, Cultured ; bcl-X Protein ; },
abstract = {Carboplatin (CBDCA) has been widely used for the treatment of oral squamous cell carcinoma (SCC). The Bcl-2 family member Bcl-xL has been demonstrated to provide resistance to chemotherapeutic agents including CBDCA. Morpholino Bcl-xL antisense oligonucleotides (oligos) were employed to down-regulate Bcl-xL in CBDCA-resistant (MIT8, MIT16) as well as CBDCA-sensitive (MIT7) SCC cell lines. The oligos were delivered to adherent cells using a scrape-load procedure. The Bcl-xL antisense reduced Bcl-xL levels without altering the level of control actin, suggesting the specificity of this agent. The addition of Bcl-xL antisense oligos substantially prevented the cell growth of both CBDCA-sensitive and-resistant cells. The CBDCA-induced partial prevention of cell growth was further augmented by the addition of the Bcl-xL, but not the control, antisense oligos. The morpholino type Bcl-xL antisense oligos may be useful for the treatment of SCC, especially multidrug-resistant tumors with enhanced Bcl-xL levels.},
}
@article {pmid12544898,
year = {2003},
author = {Velmahos, GC and Karaiskakis, M and Salim, A and Toutouzas, KG and Murray, J and Asensio, J and Demetriades, D},
title = {Normal electrocardiography and serum troponin I levels preclude the presence of clinically significant blunt cardiac injury.},
journal = {The Journal of trauma},
volume = {54},
number = {1},
pages = {45-50; discussion 50-1},
doi = {10.1097/00005373-200301000-00006},
pmid = {12544898},
issn = {0022-5282},
mesh = {Adult ; Biomarkers/blood ; Echocardiography/methods/standards ; Electrocardiography/*standards ; Female ; Heart Injuries/blood/*diagnosis/mortality ; Hospital Mortality ; Humans ; Injury Severity Score ; Length of Stay/statistics &amp; numerical data ; Los Angeles/epidemiology ; Male ; Middle Aged ; Predictive Value of Tests ; Prospective Studies ; Risk Factors ; Survival Analysis ; Time Factors ; Trauma Centers ; Triage/methods ; Troponin I/*blood ; Wounds, Nonpenetrating/blood/*diagnosis/mortality ; },
abstract = {BACKGROUND: Uncertainty about the definition and diagnosis of blunt cardiac injury (BCI) leads to unnecessary hospitalization and cost while trying to rule it out. The purpose of this study was to examine whether the combination of two simple tests, electrocardiography (ECG) and serum troponin I (TnI) level, may serve as reliable predictors of BCI or the absence of it.<br><br>METHODS: Over a period of 30 months (September 1999-February 2002), 333 consecutive patients with significant blunt thoracic trauma were followed prospectively. Serial ECG and TnI tests were performed routinely and echocardiography was performed selectively. Clinically significant BCI (SigBCI) was defined as the presence of cardiogenic shock, arrhythmias requiring treatment, or posttraumatic structural deficits.<br><br>RESULTS: SigBCI was diagnosed in 44 patients (13%). Of 80 patients with abnormal ECG and TnI, 27 (34%) developed SigBCI. Of 131 with normal serial ECG and TnI, none developed SigBCI. Of patients with abnormal ECG only or TnI only, 22% and 7%, respectively, developed SigBCI. The positive and negative predictive values were 29% and 98% for ECG, 21% and 94% for TnI, and 34% and 100% for the combination of ECG and TnI. The admission ECG or TnI was abnormal in 43 of 44 patients with SigBCI. Only one patient had initially normal ECG and TnI and developed abnormalities 8 hours after admission. Forty-one patients without other significant injuries stayed 1 to 3 days in the hospital only to rule out SigBCI and could have been discharged earlier. Besides ECG and TnI, other independent risk factors of SigBCI were an Injury Severity Score > 15, the presence of significant skeletal trauma, and history of cardiac disease.<br><br>CONCLUSION: The combination of normal ECG and TnI at admission and 8 hours later rules out the diagnosis of SigBCI. In the absence of other reasons for hospitalization, such patients can be safely discharged.},
}
@article {pmid12519530,
year = {2002},
author = {Wiseman, R and Greening, E},
title = {The mind machine: a mass participation experiment into the possible existence of extra-sensory perception.},
journal = {British journal of psychology (London, England : 1953)},
volume = {93},
number = {Pt 4},
pages = {487-499},
doi = {10.1348/000712602761381367},
pmid = {12519530},
issn = {0007-1269},
mesh = {*Cognition ; Humans ; *Perception ; Random Allocation ; },
abstract = {This paper describes a mass participation experiment examining the possible existence of extra-sensory perception (ESP). The Mind Machine consisted of a specially designed steel cabinet containing a multi-media computer and large touch-screen monitor. The computer presented participants with a series of videoclips that led them through the experiment. During the experiment, participants were asked to complete an ESP task that involved them guessing the outcome of four random electronic coin tosses. All of their data were stored by the computer during an 11-month tour of some of Britain's largest shopping centres, museums, and science festivals. A total of 27,856 participants contributed 110,959 trials, and thus, the final database had the statistical power to detect the possible existence of a very small ESP effect. However, the cumulated outcome of the trials was consistent with chance. The experiment also examined the possible relationship between participants' ESP scores and their gender, belief in psychic ability, and degree of predicted success. The results from all of these analyses were non-significant. Also, scoring on 'clairvoyance' trials (where the target was selected prior to the participant's choice) was not significantly different from 'precognitive' trials (where the target was chosen after the participants had made their choice). Competing interpretations of these findings are discussed, along with suggestions for future research.},
}
@article {pmid12509954,
year = {2003},
author = {Taylor, R and Davis, P and Boyages, J},
title = {Long-term survival of women with breast cancer in New South Wales.},
journal = {European journal of cancer (Oxford, England : 1990)},
volume = {39},
number = {2},
pages = {215-222},
doi = {10.1016/s0959-8049(02)00486-0},
pmid = {12509954},
issn = {0959-8049},
mesh = {Adolescent ; Adult ; Aged ; Breast Neoplasms/*mortality ; Child ; Child, Preschool ; Female ; Follow-Up Studies ; Humans ; Infant ; Infant, Newborn ; Middle Aged ; New South Wales/epidemiology ; Regression Analysis ; Survival Analysis ; Survival Rate ; },
abstract = {Several long-term studies of breast cancer survival have shown continued excess mortality from breast cancer up to 20-40 years following treatment. The purpose of this report was to investigate temporal trends in long-term survival from breast cancer in all New South Wales (NSW) women. Breast cancer cases incident in 1972-1996 (54,228) were derived from the NSW Central Cancer Registry-a population-based registry which began in 1972. All cases of breast cancer not known to be dead were matched against death records. The expected survival for NSW women was derived from published annual life tables. Relative survival analysis compared the survival of cancer cases with the age, sex and period matched mortality of the total population. Cases were considered alive at the end of 1996, except when known to be dead. Proportional hazards regression was employed to model survival on age, period and degree of spread at diagnosis. Survival at 5, 10, 15, 20 and 25 years of follow-up was 76 per cent, 65 per cent, 60 per cent, 57 per cent and 56 per cent. The annual hazard rate for excess mortality was 4.3 per cent in year 1, maximal at 6.5 per cent in year 3, declining to 4.7 per cent in year 5, 2.7 per cent in year 10, 1.4 per cent in year 15, 1.0 per cent for years 16-20, and 0.4 per cent for years 20-25 of follow-up. Relative survival was highest in 40-49 year-olds. Cases diagnosed most recently (1992-1996) had the highest survival, compared with cases diagnosed in previous periods. Five-year survival improved over time, especially from the late 1980s for women in the screening age group (50-69 years). Survival was highest for those with localised cancer at diagnosis: 88.4 per cent, 79.1 per cent, 74.6 per cent, 72.7 per cent and 72.8 per cent at 5, 10, 15, 20 and 25 years follow-up (excluding those aged >or=70 years). There was no significant difference between the survival of the breast cancer cases and the general population at 20-25 years follow-up. Degree of spread was less predictive of survival 5-20 years after diagnosis, compared with 0-5 years after diagnosis, and was not significant at 20-25 years of follow-up. Relative survival from breast cancer in NSW women continues to decrease to 25 years after diagnosis, but there is little excess mortality after 15 years follow-up, especially for those with localised cancer at diagnosis, and the minimal excess mortality at 20-25 years of follow-up is not statistically significant.},
}
@article {pmid12503782,
year = {2002},
author = {Yom-Tov, E and Inbar, GF},
title = {Feature selection for the classification of movements from single movement-related potentials.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {10},
number = {3},
pages = {170-177},
doi = {10.1109/TNSRE.2002.802875},
pmid = {12503782},
issn = {1534-4320},
mesh = {Adult ; *Algorithms ; Brain Mapping/*methods ; Cerebral Cortex/*physiology ; Communication Devices for People with Disabilities ; Electroencephalography/*methods ; Female ; Fingers/*physiology ; Humans ; Male ; Movement/*physiology ; Pattern Recognition, Automated ; Predictive Value of Tests ; Psychomotor Performance/physiology ; Reproducibility of Results ; Sensitivity and Specificity ; Toes/*physiology ; User-Computer Interface ; },
abstract = {Classification of movement-related potentials recorded from the scalp to their corresponding limb is a crucial task in brain-computer interfaces based on such potentials. Many features can be extracted from raw electroencephalographic signals to be used for classification, but the utilization of irrelevant or superfluous features is detrimental to the performance of classification algorithms. It is, therefore, necessary to select a small number of relevant features for the classification task. This paper demonstrates the use of two feature selection methods to choose a small number (10-20) of relevant features from a bank containing upward of 1000 features. One method is based on information theory and the other on the use of genetic algorithms. We show that the former is poorly suited for the aforementioned classification task and discuss the probable reasons for this. However, using a genetic algorithm on data recorded from five subjects we demonstrate that it is possible to differentiate between the movements of two limbs with a classification accuracy of 87% using as little as 10 features without subject training. With the addition of a simple coding scheme, this method can be applied to multiple limb classification and a 63% classification accuracy rate can be reached when attempting to distinguish between three limbs.},
}
@article {pmid12503778,
year = {2002},
author = {Palaniappan, R and Paramesran, R and Nishida, S and Saiwaki, N},
title = {A new brain-computer interface design using fuzzy ARTMAP.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {10},
number = {3},
pages = {140-148},
doi = {10.1109/TNSRE.2002.802854},
pmid = {12503778},
issn = {1534-4320},
mesh = {Brain/physiology ; Brain Mapping/*methods ; *Communication Devices for People with Disabilities ; Electroencephalography/methods ; *Fuzzy Logic ; Humans ; Mental Processes/classification/*physiology ; *Neural Networks, Computer ; Pattern Recognition, Automated ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.},
}
@article {pmid12492117,
year = {2002},
author = {Ling, YH and Liebes, L and Ng, B and Buckley, M and Elliott, PJ and Adams, J and Jiang, JD and Muggia, FM and Perez-Soler, R},
title = {PS-341, a novel proteasome inhibitor, induces Bcl-2 phosphorylation and cleavage in association with G2-M phase arrest and apoptosis.},
journal = {Molecular cancer therapeutics},
volume = {1},
number = {10},
pages = {841-849},
pmid = {12492117},
issn = {1535-7163},
support = {CA50270/CA/NCI NIH HHS/United States ; U01 CA76642/CA/NCI NIH HHS/United States ; },
mesh = {Amino Acid Chloromethyl Ketones/pharmacology ; Antineoplastic Agents/*pharmacology ; *Apoptosis ; Blotting, Western ; Boronic Acids/*pharmacology ; Bortezomib ; Cell Line ; Cysteine Endopeptidases ; DNA Fragmentation ; Flow Cytometry ; G2 Phase ; Humans ; Mitosis ; Models, Biological ; Multienzyme Complexes/*antagonists &amp; inhibitors ; Phosphorylation ; Protease Inhibitors/*pharmacology ; Proteasome Endopeptidase Complex ; Proto-Oncogene Proteins c-bcl-2/*metabolism ; Pyrazines/*pharmacology ; Subcellular Fractions/metabolism ; Time Factors ; Tosylphenylalanyl Chloromethyl Ketone/*analogs &amp; derivatives/pharmacology ; Tumor Cells, Cultured ; },
abstract = {Treatment with the proteasome inhibitor, PS-341 resulted in concentration- and time-dependent effects on Bcl-2 phosphorylation and cleavage in H460 cells that coincided with the PS-341-induced G2-M phase arrest. The observed Bcl-2 cleavage paralleled the degree of PS-341-induced apoptosis but was detected to a similar extent with comparable concentrations of two other proteasome inhibitors (MG-132 and PSI). Calpain inhibitors, ALLM and ALLN, and the caspase inhibitors, Z-VAD and AC-YVAD did not induce BcI-2 phosphorylation and cleavage. Exposure to PS-341 resulted in an additional Mr 25,000 cleavage fragment of Bcl-2, whereas only a Mr 23,000 fragment was observed with other anticancer agents. The formation of the Mr 25,000 fragment was not prevented by caspase inhibitors unlike the Mr 23,000 fragment, which suggests mediation by a caspase-independent pathway. Cell fractionation studies revealed that the Bcl-2 cleaved fragments localize within membrane structures and was an early event (at approximately 12 h, posttreatment), and before the observed cleavage of poly(ADP-ribose) polymerase (PARP), beta-catenin, and DNA fragmentation (at approximately 36 h posttreatment). The Mr 23,000 Bcl-2 cleavage product was inhibited by the pan-caspase inhibitor and the inhibitors of capase-3, -8, -9; but the PARP cleavage was prevented only by the pan-caspase and caspase-3 inhibitors, which suggests that the Mr 23,000 Bcl-2 cleavage occurred at both the initiation and execution stages of apoptosis. The inhibition of the ubiquitin/proteasome pathway by PS-341 leads, at an early stage of apoptosis, to Bcl-2 phosphorylation and a unique proteolytic cleavage product, which are associated with G2-M phase arrest and the induction of apoptosis.},
}
@article {pmid12485784,
year = {2003},
author = {Sinkjaer, T and Haugland, M and Inmann, A and Hansen, M and Nielsen, KD},
title = {Biopotentials as command and feedback signals in functional electrical stimulation systems.},
journal = {Medical engineering &amp; physics},
volume = {25},
number = {1},
pages = {29-40},
doi = {10.1016/s1350-4533(02)00178-9},
pmid = {12485784},
issn = {1350-4533},
mesh = {Action Potentials ; Algorithms ; Central Nervous System Diseases/rehabilitation ; Electric Stimulation Therapy/*methods ; Electrodiagnosis/methods ; Electroencephalography/*methods ; Electromyography/*methods ; *Feedback ; Foot Diseases/physiopathology ; Gait Disorders, Neurologic/rehabilitation ; Hand/physiopathology ; Humans ; Neural Networks, Computer ; Peripheral Nerves/physiopathology ; Quadriplegia/rehabilitation ; Signal Processing, Computer-Assisted ; },
abstract = {Today Functional Electrical Stimulation (FES) is available as a clinical tool in muscle activation used for picking up objects, for standing and walking, for controlling bladder emptying, and for breathing. Despite substantial progress in development and new knowledge, many challenges remain to be resolved to provide a more efficient functionality of FES systems. The most important task of these challenges is to improve control of the activated muscles through open loop or feedback systems. Command and feedback signals can be extracted from biopotentials recorded from muscles (Electromyogram, EMG), nerves (Electroneurogram, ENG), and the brain (Electroencephalogram (EEG) or individual cells). This paper reviews work in which EMG, ENG, and EEG signals in humans have been used as command and feedback signals in systems using electrical stimulation of motor nerves to restore movements after an injury to the Central Nervous System (CNS). It is concluded that the technology is ready to push for more substantial clinical FES investigations in applying muscle and nerve signals. Brain-computer interface systems hold great prospects, but require further development of faster and clinically more acceptable technologies.},
}
@article {pmid12453651,
year = {2002},
author = {Imaizumi, S and Onuma, T and Kameyama, M and Ishii, K},
title = {Symptom changes caused by movement of a calcified lateral ventricular meningioma: case report.},
journal = {Surgical neurology},
volume = {58},
number = {2},
pages = {128-130},
doi = {10.1016/s0090-3019(02)00786-3},
pmid = {12453651},
issn = {0090-3019},
mesh = {Adult ; *Calcinosis ; Female ; Humans ; *Lateral Ventricles/diagnostic imaging/pathology/surgery ; Magnetic Resonance Imaging ; Meningeal Neoplasms/*diagnosis/diagnostic imaging/pathology/*surgery ; Meningioma/*diagnosis/diagnostic imaging/pathology/*surgery ; Tomography, X-Ray Computed ; },
abstract = {Large calcified psammomatous meningioma in the left lateral ventricle with a long silent interval of 16 years was presented. The symptoms varied by its moving not enlargement, which was described by sequential images of the brain computer tomography. Combined approaches of transcallosal and transinferior temporal sulcus routes were superior to prevent injury of the speech center in the dominant hemisphere.},
}
@article {pmid12453249,
year = {2002},
author = {Husband, R and Herre, EA and Turner, SL and Gallery, R and Young, JP},
title = {Molecular diversity of arbuscular mycorrhizal fungi and patterns of host association over time and space in a tropical forest.},
journal = {Molecular ecology},
volume = {11},
number = {12},
pages = {2669-2678},
doi = {10.1046/j.1365-294x.2002.01647.x},
pmid = {12453249},
issn = {0962-1083},
mesh = {Base Sequence ; DNA, Fungal/chemistry/*genetics ; Fungi/*genetics ; Molecular Sequence Data ; Mycorrhizae/*genetics ; Panama ; Phylogeny ; Plant Roots/microbiology ; Polymerase Chain Reaction ; Polymorphism, Restriction Fragment Length ; Sequence Alignment ; Sequence Analysis, DNA ; Trees/*microbiology ; Tropical Climate ; },
abstract = {We have used molecular techniques to investigate the diversity and distribution of the arbuscular mycorrhizal (AM) fungi colonizing tree seedling roots in the tropical forest on Barro Colorado Island (BCI), Republic of Panama. In the first year, we sampled newly emergent seedlings of the understory treelet Faramea occidentalis and the canopy emergent Tetragastris panamensis, from mixed seedling carpets at each of two sites. The following year we sampled surviving seedlings from these cohorts. The roots of 48 plants were analysed using AM fungal-specific primers to amplify and clone partial small subunit (SSU) ribosomal RNA gene sequences. Over 1300 clones were screened for random fragment length polymorphism (RFLP) variation and 7% of these were sequenced. Compared with AM fungal communities sampled from temperate habitats using the same method, the overall diversity was high, with a total of 30 AM fungal types identified. Seventeen of these types have not been recorded previously, with the remainder being similar to types reported from temperate habitats. The tropical mycorrhizal population showed significant spatial heterogeneity and nonrandom associations with the different hosts. Moreover there was a strong shift in the mycorrhizal communities over time. AM fungal types that were dominant in the newly germinated seedlings were almost entirely replaced by previously rare types in the surviving seedlings the following year. The high diversity and huge variation detected across time points, sites and hosts, implies that the AM fungal types are ecologically distinct and thus may have the potential to influence recruitment and host composition in tropical forests.},
}
@article {pmid12425246,
year = {2002},
author = {Cincotti, F and Mattia, D and Babiloni, C and Carducci, F and Bianchi, L and del R Millán, J and Mouriño, J and Salinari, S and Marciani, MG and Babiloni, F},
title = {Classification of EEG mental patterns by using two scalp electrodes and Mahalanobis distance-based classifiers.},
journal = {Methods of information in medicine},
volume = {41},
number = {4},
pages = {337-341},
pmid = {12425246},
issn = {0026-1270},
mesh = {Biomedical Engineering ; Brain/physiology ; Electroencephalography/*classification/instrumentation ; Hand/physiology ; Humans ; *Linear Models ; Mental Processes ; Models, Neurological ; Movement/physiology ; Scalp ; User-Computer Interface ; },
abstract = {OBJECTIVES: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes.<br><br>METHODS: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used.<br><br>RESULTS: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes.<br><br>CONCLUSIONS: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.},
}
@article {pmid12403992,
year = {2002},
author = {Donoghue, JP},
title = {Connecting cortex to machines: recent advances in brain interfaces.},
journal = {Nature neuroscience},
volume = {5 Suppl},
number = {},
pages = {1085-1088},
doi = {10.1038/nn947},
pmid = {12403992},
issn = {1097-6256},
mesh = {Action Potentials/physiology ; Animals ; Electrodes, Implanted/standards/trends ; Electrophysiology/methods/*trends ; Feedback/physiology ; Humans ; Motor Cortex/*physiology ; Movement/physiology ; Paralysis/*rehabilitation ; Prostheses and Implants/standards/*trends ; Psychophysiology/methods/*trends ; *User-Computer Interface ; },
abstract = {Recent technological and scientific advances have generated wide interest in the possibility of creating a brain-machine interface (BMI), particularly as a means to aid paralyzed humans in communication. Advances have been made in detecting neural signals and translating them into command signals that can control devices. We now have systems that use externally derived neural signals as a command source, and faster and potentially more flexible systems that directly use intracortical recording are being tested. Studies in behaving monkeys show that neural output from the motor cortex can be used to control computer cursors almost as effectively as a natural hand would carry out the task. Additional research findings explore the possibility of using computers to return behaviorally useful feedback information to the cortex. Although significant scientific and technological challenges remain, progress in creating useful human BMIs is accelerating.},
}
@article {pmid12377606,
year = {2002},
author = {Mou, SS and Ma, AD and Tu, M and Li, LH and Zhou, CR},
title = {Preparation and biocompatibility of tissue-engineered scaffold materials based on collagen.},
journal = {Di 1 jun yi da xue xue bao = Academic journal of the first medical college of PLA},
volume = {22},
number = {10},
pages = {878-879},
pmid = {12377606},
issn = {1000-2588},
mesh = {Biocompatible Materials/*chemistry ; Blood Coagulation ; Collagen/*chemistry ; Cross-Linking Reagents/*chemistry ; Ethyldimethylaminopropyl Carbodiimide/*chemistry ; Tissue Engineering ; },
abstract = {OBJECTIVE: To prepare a tissue-engineered scaffold material using collagen as the matrices and to study the blood compatibility and tissue biocompatibility of this material.<br><br>METHODS: Physical, chemical and physical/chemical methods were used for the crosslinking of the collagen.<br><br>RESULTS: Dynamic blood clotting tests indicated that the blood clotting index (BCI) of the crosslinked collagen materials prepared by different means decreased as their contact with the blood was prolonged, and the collagen material obtained after crosslink through 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide method showed the highest BCI after contact with the blood within certain length of time. Hemolysis ratios of all the crosslinked collagen materials were shown to be much lower than 5%, well conforming to the requirement of biomaterials. Scanning electron microscopy showed that the platelets attached to the surface of the crosslinked collagen materials, having a fairly small number, were not morphologically distorted.<br><br>CONCLUSION: The collagen materials obtained by the 3 crosslink methods have good blood compatibility. The cells grow well on the surfaces of the materials, indicating their good biocompatibility.},
}
@article {pmid12374343,
year = {2002},
author = {Cheng, M and Gao, X and Gao, S and Xu, D},
title = {Design and implementation of a brain-computer interface with high transfer rates.},
journal = {IEEE transactions on bio-medical engineering},
volume = {49},
number = {10},
pages = {1181-1186},
doi = {10.1109/tbme.2002.803536},
pmid = {12374343},
issn = {0018-9294},
mesh = {Activities of Daily Living ; Adolescent ; Adult ; Brain/*physiology ; Child ; Computing Methodologies ; Data Display ; Electroencephalography/*instrumentation/methods ; Equipment Design ; Evoked Potentials, Visual/*physiology ; False Positive Reactions ; Female ; Humans ; Male ; Reproducibility of Results ; Sensitivity and Specificity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This paper presents a brain-computer interface (BCI) that can help users to input phone numbers. The system is based on the steady-state visual evoked potential (SSVEP). Twelve buttons illuminated at different rates were displayed on a computer monitor. The buttons constituted a virtual telephone keypad, representing the ten digits 0-9, BACKSPACE, and ENTER. Users could input phone number by gazing at these buttons. The frequency-coded SSVEP was used to judge which button the user desired. Eight of the thirteen subjects succeeded in ringing the mobile phone using the system. The average transfer rate over all subjects was 27.15 bits/min. The attractive features of the system are noninvasive signal recording, little training required for use, and high information transfer rate. Approaches to improve the performance of the system are discussed.},
}
@article {pmid12232757,
year = {2002},
author = {Boutou, O and Guizard, AV and Slama, R and Pottier, D and Spira, A},
title = {Population mixing and leukaemia in young people around the La Hague nuclear waste reprocessing plant.},
journal = {British journal of cancer},
volume = {87},
number = {7},
pages = {740-745},
pmid = {12232757},
issn = {0007-0920},
mesh = {Adolescent ; Adult ; Age Factors ; Bayes Theorem ; Child ; Child, Preschool ; Cluster Analysis ; Female ; France ; Humans ; Incidence ; Infant ; Leukemia/*epidemiology/*etiology ; Male ; Netherlands/epidemiology ; Nuclear Reactors ; Poisson Distribution ; Radioactive Waste/*adverse effects ; Retrospective Studies ; Risk Factors ; },
abstract = {In order to investigate for an association between population mixing and the occurrence of leukaemia in young people (less than 25 years), a geographical study was conducted, for the years 1979 to 1998, in Nord Cotentin (France). This area experienced between the years 1978 and 1992 a major influx of workers for the construction of a nuclear power station and a new nuclear waste reprocessing unit. A population mixing index was defined on the basis of the number of workers born outside the French department of 'La Manche' and living in each 'commune', the basic geographical unit under study. The analyses were done with indirect standardisation and Poisson regression model allowing or not for extra-Poisson variation. Urban 'communes' were considered as the reference population. The Incidence Rate Ratio was 2.7 in rural 'communes' belonging to the highest tertile of population mixing (95% Bayesian credible interval, 95%BCI=1.2-5.9). A positive trend was observed among rural strata with increasing population mixing index (IRR for trend=1.4, 95%BCI=1.1-1.8). The risk became stronger for Acute Lymphoblastic Leukaemia in children 1-6 years old in the highest tertile of population mixing (IRR=5.5, 95%BCI=1.4-23.3). These findings provide further support for a possible infective basis of childhood leukaemia.},
}
@article {pmid12204848,
year = {2002},
author = {Cordero, RA and Nilsen, ET},
title = {Effects of summer drought and winter freezing on stem hydraulic conductivity of Rhododendron species from contrasting climates.},
journal = {Tree physiology},
volume = {22},
number = {13},
pages = {919-928},
doi = {10.1093/treephys/22.13.919},
pmid = {12204848},
issn = {0829-318X},
mesh = {Climate ; Dehydration ; Freezing ; North Carolina ; Oregon ; Plant Transpiration/physiology ; Rhododendron/*physiology ; Seasons ; },
abstract = {We studied the limits to maximum water transport in three diffuse-porous evergreen shrubs exposed to frequent winter freeze-thaw events (Rhododendron maximum L. and R. catawbiense Michaux from the Appalachian Mountains) and to a severe summer drought (R. macrophyllum G. Don. from the Oregon Cascades). Percent loss of hydraulic conductivity (PLC), vulnerability curves to xylem embolism and freezing point temperatures of stems were measured over 2 years. Controlled freeze-thaw experiments were also conducted to determine the effect of thaw rate on PLC. During both years, native PLC was significantly higher in winter than in summer for R. macrophyllum. Seasonal changes in PLC were variable in both R. catawbiense and R. maximum. Only R. maximum plants growing in gaps or clearings showed higher PLC than understory plants. A rapid (2-4 day) natural recovery of high native PLC during the winter was observed in both R. maximum and R. macrophyllum. Compared with the bench-dehydration method, vulnerability curves based on the air-injection method consistently had less negative slopes and greater variation. Fifty percent PLC (PLC(50)) obtained from vulnerability curves based on the dehydration method occurred at -1.75, -2.42 and -2.96 MPa for R. catawbiense, R. maximum and R. macrophyllum, respectively. Among the study species, R. macrophyllum, which commonly experiences a summer drought, had the most negative water potential at PLC(50). In all species, stem freezing point temperatures were not consistently lower in winter than in summer. A single fast freeze-thaw event significantly increased PLC, and R. catawbiense had the highest PLC in response to freezing treatments. Recovery to control PLC values occurred if a low positive hydraulic pressure was maintained during thawing. Rhododendron macrophyllum plants, which commonly experience few freeze-thaw events, had large stem diameters, whereas plants of R. catawbiense, which had small stem diameters, suffered high embolism in response to a single freeze-thaw event. Both drought-induced and winter-induced embolism caused a significant reduction in hydraulic conductivity in all species during periods when drought or freeze-thaw events occurred in their native habitats. However, rapid recovery of PLC following freezing or drought maintained the species above their relatively low margins of safety for complete xylem dysfunction.},
}
@article {pmid12197666,
year = {2002},
author = {Raymond, JW and Willett, P},
title = {Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases.},
journal = {Journal of computer-aided molecular design},
volume = {16},
number = {1},
pages = {59-71},
pmid = {12197666},
issn = {0920-654X},
mesh = {Algorithms ; *Computer Simulation ; Databases, Factual ; Drug Design ; Drug Evaluation, Preclinical/*methods/statistics &amp; numerical data ; Molecular Structure ; },
abstract = {This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening.},
}
@article {pmid12117764,
year = {2002},
author = {Rutten, WL},
title = {Selective electrical interfaces with the nervous system.},
journal = {Annual review of biomedical engineering},
volume = {4},
number = {},
pages = {407-452},
doi = {10.1146/annurev.bioeng.4.020702.153427},
pmid = {12117764},
issn = {1523-9829},
mesh = {Action Potentials/physiology ; Electric Stimulation/*instrumentation/*methods ; Electrodes/*trends ; Electrodes, Implanted ; Electrophysiology/*instrumentation/*methods ; Equipment Design ; Membrane Potentials/physiology ; Microelectrodes ; *Models, Neurological ; Nerve Net/physiology ; Neural Conduction ; Neurons/physiology ; Sensitivity and Specificity ; },
abstract = {To achieve selective electrical interfacing to the neural system it is necessary to approach neuronal elements on a scale of micrometers. This necessitates microtechnology fabrication and introduces the interdisciplinary field of neurotechnology, lying at the juncture of neuroscience with microtechnology. The neuroelectronic interface occurs where the membrane of a cell soma or axon meets a metal microelectrode surface. The seal between these may be narrow or may be leaky. In the latter case the surrounding volume conductor becomes part of the interface. Electrode design for successful interfacing, either for stimulation or recording, requires good understanding of membrane phenomena, natural and evoked action potential generation, volume conduction, and electrode behavior. Penetrating multimicroelectrodes have been produced as one-, two-, and three-dimensional arrays, mainly in silicon, glass, and metal microtechnology. Cuff electrodes circumvent a nerve; their selectivity aims at fascicles more than at nerve fibers. Other types of electrodes are regenerating sieves and cone-ingrowth electrodes. The latter may play a role in brain-computer interfaces. Planar substrate-embedded electrode arrays with cultured neural cells on top are used to study the activity and plasticity of developing neural networks. They also serve as substrates for future so-called cultured probes.},
}
@article {pmid12114693,
year = {2000},
author = {Nikiforos, K and Kontogeorgos, G},
title = {Bcl-2 Gene Family in Endocrine Pathology: A Review.},
journal = {Endocrine pathology},
volume = {11},
number = {3},
pages = {205-213},
pmid = {12114693},
issn = {1559-0097},
abstract = {BcI-2 is a member of a large multigene family, which includes genes that can inhibit or promote apoptosis. The regulation of apoptosis is achieved by homo- or heterodimerization of their proteins through four highly conserved domains. Bcl-2 protein is a strong cell death suppressor in a wide range of cell types and under a variety of stimuli. Bcl-2 and the other members of this family are differentially expressed in the endocrine glands and disregulation of their expression seems to contribute to the neoplastic transformation in these organs. The significance of bcl-2 and the related proteins for endocrine pathology at the experimental and clinical level is reviewed in this article.},
}
@article {pmid12048038,
year = {2002},
author = {Wolpaw, JR and Birbaumer, N and McFarland, DJ and Pfurtscheller, G and Vaughan, TM},
title = {Brain-computer interfaces for communication and control.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {113},
number = {6},
pages = {767-791},
doi = {10.1016/s1388-2457(02)00057-3},
pmid = {12048038},
issn = {1388-2457},
mesh = {Brain Diseases/*rehabilitation ; *Communication Devices for People with Disabilities ; *Computer Systems ; Electroencephalography/*instrumentation ; Humans ; User-Computer Interface ; },
abstract = {For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.},
}
@article {pmid12029454,
year = {2002},
author = {Luo, CC and Lin, JN and Jaing, TH and Yang, CP and Hsueh, C},
title = {Malignant rhabdoid tumour of the kidney occurring simultaneously with a brain tumour: a report of two cases and review of the literature.},
journal = {European journal of pediatrics},
volume = {161},
number = {6},
pages = {340-342},
doi = {10.1007/s00431-002-0918-8},
pmid = {12029454},
issn = {0340-6199},
mesh = {Brain Neoplasms/diagnostic imaging/*pathology ; Humans ; Infant ; Kidney Neoplasms/diagnostic imaging/*pathology ; Male ; *Neoplasms, Multiple Primary ; Rhabdoid Tumor/*pathology ; Sarcoma, Clear Cell/diagnostic imaging/*pathology ; Tomography, X-Ray Computed ; },
abstract = {UNLABELLED: Malignant rhabdoid tumour of the kidney (MRTK), an uncommon aggressive neoplasm of children, is now recognised as a separate entity from Wilms' tumour with distinct clinical and pathological features. MRTK is unique in its significant association with primary brain tumours or brain metastases. We report two cases, aged 2 and 6 months, of MRTK occurring concurrently with a brain tumour. Radical nephrectomy and ventriculo-peritoneal shunting were performed. Both patients expired 2 and 6 months later despite receiving aggressive post-operative chemotherapy and radiotherapy.<br><br>CONCLUSION: malignant rhabdoid tumour of the kidney is an uncommon neoplasm of early childhood with a poor prognosis. Due to its significant association with brain tumours or early brain metastases, concurrent brain computer tomographic examination is essential for all patients with this disease.},
}
@article {pmid12027994,
year = {2002},
author = {Boyages, J and Chua, B and Taylor, R and Bilous, M and Salisbury, E and Wilcken, N and Ung, O},
title = {Use of the St Gallen classification for patients with node-negative breast cancer may lead to overuse of adjuvant chemotherapy.},
journal = {The British journal of surgery},
volume = {89},
number = {6},
pages = {789-796},
doi = {10.1046/j.1365-2168.2002.02113.x},
pmid = {12027994},
issn = {0007-1323},
mesh = {Adult ; Aged ; Aged, 80 and over ; Antineoplastic Agents/*therapeutic use ; Breast Neoplasms/*classification/radiotherapy/surgery ; Chemotherapy, Adjuvant/statistics &amp; numerical data ; Female ; Follow-Up Studies ; Humans ; Lymphatic Metastasis ; Middle Aged ; Patient Selection ; Risk Factors ; },
abstract = {BACKGROUND: The 1998 St Gallen classification was devised to guide clinicians in the use of adjuvant systemic therapy for women with early breast cancer. In this study, the classification was applied to a historical group of patients with node-negative breast cancer who were treated without adjuvant therapy.<br><br>METHODS: The St Gallen classification was applied to 421 women with breast cancer treated with conservative surgery and radiotherapy alone between 1979 and 1994. Primary tumour characteristics were reviewed centrally.<br><br>RESULTS: When the most stringent version of the St Gallen classification was applied (grade 2 or 3 tumours classified as "high risk"), only 10 per cent of patients were "low risk", with a 10-year distant relapse-free survival (DRFS) rate of 100 per cent, and 15 per cent were at "intermediate risk" (10-year DRFS rate of 94 per cent). The high-risk group (75 per cent of women) had a 10-year DRFS rate of 77 per cent (P < 0.01). If the St Gallen classification had been applied to all patients in this series who were aged less than 70 years, up to 91 per cent would have been recommended to have chemotherapy.<br><br>CONCLUSION: The St Gallen classification is an inaccurate measure of prognosis for patients with node-negative breast cancer and should be used with caution.},
}
@article {pmid11988655,
year = {2002},
author = {Wahl, WL and Brandt, MM and Thompson, BG and Taheri, PA and Greenfield, LJ},
title = {Antiplatelet therapy: an alternative to heparin for blunt carotid injury.},
journal = {The Journal of trauma},
volume = {52},
number = {5},
pages = {896-901},
doi = {10.1097/00005373-200205000-00012},
pmid = {11988655},
issn = {0022-5282},
mesh = {Adult ; Anticoagulants/*therapeutic use ; Carotid Artery Injuries/complications/*drug therapy/*mortality ; Female ; Heparin/*therapeutic use ; Humans ; Male ; Middle Aged ; Nervous System Diseases/etiology/*mortality ; Outcome Assessment, Health Care ; Platelet Aggregation Inhibitors/*therapeutic use ; Retrospective Studies ; Survival Rate ; Trauma Severity Indices ; Wounds, Nonpenetrating/complications/*drug therapy/*mortality ; },
abstract = {BACKGROUND: Blunt carotid injuries (BCIs) are uncommon. Most single-center studies are small and highlight the use of anticoagulation for treatment. In a retrospective review, we identified 22 patients who presented with BCI and assessed neurologic and survival outcomes on the basis of injury grade and treatment with anticoagulation or antiplatelet therapy.<br><br>METHODS: Patient demographics were identified using the trauma registry at a single Level I trauma center. Chart reviews assessed neurologic function, modalities used for diagnosis, and treatment. Neurologic outcomes were graded good (minimal to no deficit), fair (moderate deficit needing some assistance), poor (requiring institutionalization), and dead.<br><br>RESULTS: Twenty-two adult trauma patients were diagnosed with BCI, for an incidence of 0.45% in the 8-year study period. All BCI patients underwent head computed tomography and four-vessel cerebral arteriography. Eight patients were not anticoagulated, five because of intracranial injuries, two who had surgical CCA repairs, and one with an aortic injury. Full anticoagulation with heparin was attempted in seven patients, with four major bleeding complications requiring cessation of heparin and blood transfusions. Seven patients received antiplatelet therapy. No difference in neurologic outcome was observed between those receiving anticoagulation and those receiving antiplatelet therapy. Bleeding complications from full anticoagulation were higher than with antiplatelet agents (p = 0.05).<br><br>CONCLUSION: Contrary to previous reports, we did not observe improved outcomes with full anticoagulation compared with antiplatelet therapy. Anticoagulation was associated with increased extracranial bleeding complications. The risks and possible benefits, as well as timing, of anticoagulation or antiplatelet therapy for BCI should be carefully weighed by the major care providers of the patient with multiple injuries.},
}
@article {pmid11976049,
year = {2002},
author = {Robert, C and Gaudy, JF and Limoge, A},
title = {Electroencephalogram processing using neural networks.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {113},
number = {5},
pages = {694-701},
doi = {10.1016/s1388-2457(02)00033-0},
pmid = {11976049},
issn = {1388-2457},
mesh = {*Artifacts ; Brain/*physiology ; Electroencephalography/*methods ; Humans ; *Neural Networks, Computer ; },
abstract = {The electroencephalogram (EEG), a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. More than 100 current neural network applications dedicated to EEG processing are presented. Works are categorized according to their objective (sleep analysis, monitoring anesthesia depth, brain-computer interface, EEG artifact detection, EEG source-based localization, etc.). Each application involves a specific approach (long-term analysis or short-term EEG segment analysis, real-time or time delayed processing, single or multiple EEG-channel analysis, etc.), for which neural networks were generally successful. The promising performances observed are demonstrative of the efficiency and efficacy of systems developed. This review can aid researchers, clinicians and implementors to understand up-to-date interest in neural network tools for EEG processing. The extended bibliography provides a database to assist in possible new concepts and idea development.},
}
@article {pmid11921635,
year = {2002},
author = {Schlögl, A and Neuper, C and Pfurtscheller, G},
title = {Estimating the mutual information of an EEG-based Brain-Computer Interface.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {47},
number = {1-2},
pages = {3-8},
doi = {10.1515/bmte.2002.47.1-2.3},
pmid = {11921635},
issn = {0013-5585},
mesh = {Attention/physiology ; Beta Rhythm ; Brain Mapping ; Cerebral Cortex/physiology ; *Communication Devices for People with Disabilities ; Dominance, Cerebral/physiology ; Electroencephalography/*instrumentation ; Entropy ; Humans ; Imagination/physiology ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.},
}
@article {pmid11916990,
year = {2002},
author = {Neustadter, DM and Drushel, RF and Chiel, HJ},
title = {Kinematics of the buccal mass during swallowing based on magnetic resonance imaging in intact, behaving Aplysia californica.},
journal = {The Journal of experimental biology},
volume = {205},
number = {Pt 7},
pages = {939-958},
doi = {10.1242/jeb.205.7.939},
pmid = {11916990},
issn = {0022-0949},
mesh = {Animals ; Aplysia/anatomy &amp; histology/drug effects/*physiology ; Biomechanical Phenomena ; Deglutition/*physiology ; Feeding Behavior/*physiology ; Jaw/physiology ; Magnetic Resonance Imaging/*instrumentation ; Mouth/physiology ; Muscles/physiology ; Plant Extracts/pharmacology ; Seaweed ; },
abstract = {A novel magnetic resonance imaging interface has been developed that makes it possible to image movements in intact, freely moving subjects. We have used this interface to image the internal structures of the feeding apparatus (i.e. the buccal mass) of the marine mollusc Aplysia californica. The temporal and spatial resolution of the resulting images is sufficient to describe the kinematics of specific muscles of the buccal mass and the internal movements of the main structures responsible for grasping food, the radula and the odontophore. These observations suggest that a previously undescribed feature on the anterior margin of the odontophore, a fluid-filled structure that we term the prow, may aid in opening the jaw lumen early in protraction. Radular closing during swallowing occurs near the peak of protraction as the radular stalk is pushed rapidly out of the odontophore. Retraction of the odontophore is enhanced by the closure of the lumen of the jaws on the elongated odontophore, causing the odontophore to rotate rapidly towards the esophagus. Radular opening occurs after the peak of retraction and without the active contraction of the protractor muscle 12 and is due, in part, to the movement of the radular stalk into the odontophore. The large variability between responses also suggests that the great flexibility of swallowing responses may be due to variability in neural control and in the biomechanics of the ingested food and to the inherent flexibility of the buccal mass.},
}
@article {pmid11908842,
year = {2002},
author = {Millán, J and Franzé, M and Mouriño, J and Cincotti, F and Babiloni, F},
title = {Relevant EEG features for the classification of spontaneous motor-related tasks.},
journal = {Biological cybernetics},
volume = {86},
number = {2},
pages = {89-95},
doi = {10.1007/s004220100282},
pmid = {11908842},
issn = {0340-1200},
mesh = {*Brain Mapping ; *Communication Devices for People with Disabilities ; Computer User Training ; Cybernetics ; Electroencephalography/*classification ; Humans ; Mental Processes/physiology ; Motor Activity/*physiology ; },
abstract = {There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motor-related mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular, it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus, the use of just those relevant features increases the efficiency of the brain interfaces and, most importantly, enables a greater level of adaptation of the personal BCI to the individual user.},
}
@article {pmid11830792,
year = {2002},
author = {Pfurtscheller, G and Müller, G and Korisek, G},
title = {[Mental activity hand orthosis control using the EEG: a case study].},
journal = {Die Rehabilitation},
volume = {41},
number = {1},
pages = {48-52},
doi = {10.1055/s-2002-19950},
pmid = {11830792},
issn = {0034-3536},
mesh = {Adult ; Biofeedback, Psychology/*instrumentation ; Cervical Vertebrae/injuries ; Electroencephalography/*instrumentation ; Equipment Design ; Functional Laterality/*physiology ; Humans ; Imagination/*physiology ; Male ; Motor Skills/*physiology ; *Orthotic Devices ; Psychomotor Performance/physiology ; Quadriplegia/physiopathology/*rehabilitation ; Spinal Fractures/physiopathology/rehabilitation ; Therapy, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {A report is given on the realization of a steering mechanism of a hand orthosis for a patient with paraplegia. An EEG-based Brain-Computer Interface (BCI) was used here for the first time, transferring purely mental activity to a control signal. This means that the patient has the capability to open or close the hand orthosis only by imagination of a movement. At this time, after a training period of about four months, the patient is able to move the hand orthosis with a certainty of almost hundred percent. The restored grasp function was verified by a grasp function test. Results are compared to those obtained using a conventional EMG-controlled orthosis.},
}
@article {pmid11811619,
year = {2001},
author = {Sharma, R and Kansal, VK},
title = {Heterogeneity of transport systems for L-glutamine in mouse mammary gland.},
journal = {Indian journal of biochemistry &amp; biophysics},
volume = {38},
number = {4},
pages = {241-248},
pmid = {11811619},
issn = {0301-1208},
mesh = {Amino Acids, Cyclic/pharmacology ; Animals ; Biological Transport/drug effects ; Female ; Glutamine/*metabolism ; Hydrogen-Ion Concentration ; Kinetics ; Lactation/physiology ; Mammary Glands, Animal/drug effects/*metabolism ; Mice ; Organ Culture Techniques ; Sodium/metabolism ; },
abstract = {The characteristics of the transport systems of L-glutamine in lactating mouse mammary gland have been studied. L-glutamine uptake was mediated by three Na+-dependent and one Na+-independent systems. The 2-(methylamino)isobutyric acid-sensitive component of Na+-dependent uptake exhibited the usual characteristics of system A. The other two Na+-dependent systems, which we have named BCI(-)-dependent and BCl(-)-independent, are the new systems identified. These are broad specificity systems and were discriminated on the basis of inhibition analysis, Cl- dependency and the effect of preloading mammary tissue with amino acids. While L-aspargine inhibited the uptake of L-glutamine via both these broad specificity systems, L-homoserine inhibited the uptake of L-glutamine via only BCl(-)-dependent system. The uptake of L-glutamine via the BCl(-)-independent system was upregulated by preloading mammary tissue with L-serine, while BCl(-)-dependent system was unaffected. The Na+-independent uptake of L-glutamine was inhibited by 2-aminobicyclo-(2,2,1)heptane carboxylic acid and other neutral amino acids, and identified as the system L.},
}
@article {pmid11755495,
year = {2002},
author = {Zeigler, BP},
title = {The brain-machine disanalogy revisited.},
journal = {Bio Systems},
volume = {64},
number = {1-3},
pages = {127-140},
doi = {10.1016/s0303-2647(01)00181-2},
pmid = {11755495},
issn = {0303-2647},
mesh = {Behavior ; Brain/*physiology ; Computational Biology/history ; Computer Simulation/history ; History, 20th Century ; Humans ; Intelligence ; Mental Processes ; *Models, Neurological ; },
abstract = {Michael Conrad was a pioneer in investigating biological information processing. He believed that there are fundamental lessons to be learned from the structure and behavior of biological brains that we are far from understanding or have implemented in our computers. Accumulation of advances in several fields have confirmed his views in broad outline but not necessarily in some of the strong forms he had tried to establish. For example, his assertion that programmable computers are intrinsically incapable of the brain's efficient and adaptive behavior has not received much examination. Yet, this is clearly a direction that could afford much insight into fundamental differences between brain and machine. In this paper, we pay tribute to Michael, by examining his pioneering thoughts on the brain-machine disanalogy in some depth and from the hindsight of a decade later. We argue that as long as we stay within the frame of reference of classical computation, it is not possible to confirm that programmability places a fundamental limitation on computing power, although the resources required to implement a programmable interface leave fewer resources for actual problem-solving work. However, if we abandon the classical computational frame and adopt one in which the user interacts with the system (artificial or natural) in real time, it becomes easier to examine the key attributes that Michael believed place biological brains on a higher plane of capability than artificial ones. While we then see some of these positive distinctions confirmed (e.g. the limitations of symbol manipulation systems in addressing real-world perception problems), we also see attributes in which the implementation in bioware constrains the behavior of real brains. We conclude by discussing how new insights are emerging, that look at the time-bound problem-solving constraints under which organisms have had to survive and how their so-called 'fast and frugal' faculties are tuned to the environments that coevolved with them. These directions open new paths for a multifaceted understanding of what biological brains do and what we can learn from them. We close by suggesting how the discrete event modeling and simulation paradigm offers a suitable medium for exploring these paths.},
}
@article {pmid11710481,
year = {2001},
author = {Allum, JH and Carpenter, MG and Adkin, AL},
title = {Balance control analysis as a method for screening and identifying balance deficits.},
journal = {Annals of the New York Academy of Sciences},
volume = {942},
number = {},
pages = {413-427},
doi = {10.1111/j.1749-6632.2001.tb03763.x},
pmid = {11710481},
issn = {0077-8923},
mesh = {Aged ; Aging/physiology ; Electromyography ; Humans ; *Postural Balance ; Vestibule, Labyrinth/physiopathology ; },
abstract = {We propose a two-step clinical evaluation procedure to identify the possible etiology and laterality of a balance deficit. Step 1 employs a minimum clinical test battery, developed in our labs, to screen for the balance deficit by examining changes to trunk sway for standard clinical stance and gait tests. Step 2 characterizes pathophysiological components in balance corrections, as well as deficits brought about by aging, using biomechanical and electromyographic (EMG) responses to multidirectional stance perturbations. This is best accomplished by delivering stance perturbations to patients standing on a support surface that is tipped in four directions: forwards to the left and right, and backwards to the left and right. This review provides an overview of the two procedures and proposes for the screening procedure a minimum clinical test battery with a score, termed the balance control index (BCI).},
}
@article {pmid11689972,
year = {2001},
author = {Kübler, A and Neumann, N and Kaiser, J and Kotchoubey, B and Hinterberger, T and Birbaumer, NP},
title = {Brain-computer communication: self-regulation of slow cortical potentials for verbal communication.},
journal = {Archives of physical medicine and rehabilitation},
volume = {82},
number = {11},
pages = {1533-1539},
doi = {10.1053/apmr.2001.26621},
pmid = {11689972},
issn = {0003-9993},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*physiopathology ; Biofeedback, Psychology ; Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Conditioning, Operant ; Electroencephalography ; Evoked Potentials/*physiology ; Humans ; Male ; Middle Aged ; Paralysis/*physiopathology ; Statistics, Nonparametric ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To test a training procedure designed to enable severely paralyzed patients to communicate by means of self-regulation of slow cortical potentials.<br><br>DESIGN: Application of the Thought Translation Device to evaluate the procedure in patients with late-stage amyotrophic lateral sclerosis (ALS).<br><br>SETTING: Training sessions in the patients' homes.<br><br>PARTICIPANTS: Two male patients with late-stage ALS.<br><br>INTERVENTIONS: Patients learned voluntary control of their slow cortical potentials by means of an interface between the brain and a computer. Training was based on visual feedback of slow cortical potentials shifts and operant learning principles. The learning process was divided into small steps of increasing difficulty.<br><br>MAIN OUTCOME MEASURES: Accuracy of self-control of slow cortical potentials (percentage of correct responses). Learning progress calculated as a function of training session.<br><br>RESULTS: Within 3 to 8 weeks, both patients learned to self-regulate their slow cortical potentials and to use this skill to select letters or words in the Language Support Program.<br><br>CONCLUSIONS: This training schedule is the first to enable severely paralyzed patients to communicate without any voluntary muscle control by using self-regulation of an electroencephalogram potential only. The protocol could be a model for training patients in other brain-computer interface techniques.},
}
@article {pmid11605618,
year = {1999},
author = {Condit, R and Ashton, PS and Manokaran, N and LaFrankie, JV and Hubbell, SP and Foster, RB},
title = {Dynamics of the forest communities at Pasoh and Barro Colorado: comparing two 50-ha plots.},
journal = {Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
volume = {354},
number = {1391},
pages = {1739-1748},
pmid = {11605618},
issn = {0962-8436},
mesh = {Ecosystem ; Malaysia ; Panama ; *Trees/growth &amp; development ; Tropical Climate ; },
abstract = {Dynamics of the Pasoh forest in Peninsular Malaysia were assessed by drawing a comparison with a forest in Panama, Central America, whose dynamics have been thoroughly described. Census plots of 50 ha were established at both sites using standard methods. Tree mortality at Pasoh over an eight-year interval was 1.46% yr(-1) for all stems > or = 10 mm diameter at breast height (dbh), and 1.48% yr(-1) for stems > or = 100 mm dbh. Comparable figures at the Barro Colorado Island site in Panama (BCI) were 2.55% and 2.03%. Growth and recruitment rates were likewise considerably higher at BCI than at Pasoh. For example, in all trees 500-700 mm in dbh, mean BCI growth over the period 1985-1995 was 6 mm yr(-1), whereas mean Pasoh growth was about 3.5 mm yr(-1). Examining growth and mortality rates for individual species showed that the difference between the forests can be attributed to a few light-demanding pioneer species at BCI, which have very high growth and mortality; Pasoh is essentially lacking this guild. The bulk of the species in the two forests are shade-tolerant and have very similar mortality, growth and recruitment. The Pasoh forest is more stable than BCI's in another way as well: few of its tree populations changed much over the eight-year census interval. In contrast, at BCI, over 10% of the species had populations increasing or decreasing at a rate of >0.05 yr(-1) compared to just 2% of the species at Pasoh). The faster species turnover at BCI can probably be attributed to severe droughts that have plagued the forest periodically over the past 30 years; Pasoh has not suffered such extreme events recently. The dearth of pioneer species at Pasoh is associated with low-nutrient soil and slow litter breakdown, but the exact mechanisms behind this association remain poorly understood.},
}
@article {pmid11601376,
year = {2001},
author = {},
title = {New electronic device powers hospital employee background checks.},
journal = {Hospital security and safety management},
volume = {22},
number = {5},
pages = {9-11},
pmid = {11601376},
issn = {0745-1148},
mesh = {Crime/*prevention &amp; control ; *Database Management Systems ; Dermatoglyphics ; Electronics/*instrumentation ; Humans ; Internet ; Ohio ; Personnel Selection/*methods/standards ; Records ; Security Measures/*trends ; Software ; },
abstract = {Ohio has a new electronic system for performing criminal background checks on potential employees. The Internet-based computer program, called WebCheck, was developed through the cooperation of Ohio's Bureau of Criminal Identification and Investigation and Cogent Systems, Inc., South Pasadena, CA. BCI&amp;I initiated the development of WebCheck in response to Ohio law, which requires background checks on anyone applying for a job involving children and the elderly.},
}
@article {pmid11561664,
year = {2001},
author = {Obermaier, B and Neuper, C and Guger, C and Pfurtscheller, G},
title = {Information transfer rate in a five-classes brain-computer interface.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {9},
number = {3},
pages = {283-288},
doi = {10.1109/7333.948456},
pmid = {11561664},
issn = {1534-4320},
mesh = {Adolescent ; Adult ; Brain/*physiopathology ; Brain Mapping ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Functional Laterality/physiology ; Humans ; Imagination/physiology ; Male ; Markov Chains ; Mental Processes/physiology ; Motor Neuron Disease/physiopathology/*rehabilitation ; *User-Computer Interface ; },
abstract = {The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns is based on band power estimates and hidden Markov models (HMMs). We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.},
}
@article {pmid11493789,
year = {2001},
author = {Kerwin, AJ and Bynoe, RP and Murray, J and Hudson, ER and Close, TP and Gifford, RR and Carson, KW and Smith, LP and Bell, RM},
title = {Liberalized screening for blunt carotid and vertebral artery injuries is justified.},
journal = {The Journal of trauma},
volume = {51},
number = {2},
pages = {308-314},
doi = {10.1097/00005373-200108000-00013},
pmid = {11493789},
issn = {0022-5282},
mesh = {Adolescent ; Adult ; Carotid Artery Injuries/diagnosis/*epidemiology ; Cerebral Angiography ; Cross-Sectional Studies ; Female ; Heparin/administration &amp; dosage ; Humans ; Incidence ; Male ; *Mass Screening ; Middle Aged ; Prognosis ; Risk Factors ; Treatment Outcome ; Vertebral Artery/*injuries ; Wounds, Nonpenetrating/diagnosis/*epidemiology ; },
abstract = {BACKGROUND: Current literature suggests that blunt carotid injuries (BCIs) and vertebral artery injuries (BVIs) are more common than once appreciated. Screening criteria have been suggested, but only one previous study has attempted to identify factors that predict the presence of BCI/BVI. This current study was conducted for two reasons. First, we wanted to determine the incidence of BCI/BVI in our institution. Second, we wanted to determine the incidence of abnormal four-vessel cerebral angiograms ordered for injuries and signs believed to be associated with BCI/BVI and thus to determine whether the screening protocol developed was appropriate.<br><br>METHODS: From August 1998, we used liberalized screening criteria for patients who were prospectively identified and suspected to be at high risk for BCI/BVI if any of the following were present: anisocoria, unexplained mono-/hemiparesis, unexplained neurologic exam, basilar skull fracture through or near the carotid canal, fracture through the foramen transversarium, cerebrovascular accident or transient ischemic attack, massive epistaxis, severe flexion or extension cervical spine fracture, massive facial fractures, or neck hematoma. Four-vessel cerebral angiograms were used for screening for BCI/BVI.<br><br>RESULTS: Over the 18-month study period, 48 patients were angiographically screened, with 21 patients (44%) being identified as having a total of 19 BCIs and 10 BVIs. Nine patients had unilateral carotid artery injuries and three patients had bilateral carotid artery injuries. Vertebral artery injuries were unilateral in six patients. One patient had bilateral carotid artery injuries and a unilateral vertebral artery injury. One patient had a unilateral carotid artery injury and a unilateral vertebral artery injury, and one patient had a unilateral carotid artery injury and bilateral vertebral artery injuries. During the same study period, 2,331 trauma patients were admitted, with 1,941 (83%) secondary to blunt trauma. The overall incidence of BCI/BVI was 1.1%. The frequency of abnormal angiograms ordered for cerebrovascular accident or transient ischemic attack, massive epistaxis, or severe cervical spine fractures was 100%. The frequency of abnormal angiograms ordered for the other indications was as follows: fracture through foramen transversarium, 60%; unexplained mono- or hemiparesis, 44%; basilar skull fracture, 42%; unexplained neurologic examination, 38%; anisocoria, 33%; and severe facial fractures, 0%.<br><br>CONCLUSION: The liberalized screening criteria used in this study were appropriate to identify patients with BCI/BVI. This study suggests BCI/BVI to be more common than previously believed and justifies that screening should be liberalized.},
}
@article {pmid11482363,
year = {2001},
author = {Guger, C and Schlögl, A and Neuper, C and Walterspacher, D and Strein, T and Pfurtscheller, G},
title = {Rapid prototyping of an EEG-based brain-computer interface (BCI).},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {9},
number = {1},
pages = {49-58},
doi = {10.1109/7333.918276},
pmid = {11482363},
issn = {1534-4320},
mesh = {Adolescent ; Adult ; Algorithms ; Brain/*physiopathology ; Communication Devices for People with Disabilities ; Computer Systems ; Cortical Synchronization/instrumentation ; Discriminant Analysis ; Electroencephalography/*instrumentation ; Equipment Design/instrumentation ; Humans ; Least-Squares Analysis ; Male ; Neuromuscular Diseases/*physiopathology ; Regression Analysis ; Reproducibility of Results ; Time Factors ; *User-Computer Interface ; },
abstract = {The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).},
}
@article {pmid11480447,
year = {2001},
author = {Taylor, R and Boyages, J},
title = {Estimating risk of breast cancer from population incidence affected by widespread mammographic screening.},
journal = {Journal of medical screening},
volume = {8},
number = {2},
pages = {73-76},
doi = {10.1136/jms.8.2.73},
pmid = {11480447},
issn = {0969-1413},
mesh = {Adult ; Aged ; Aged, 80 and over ; Breast Neoplasms/*diagnostic imaging/*epidemiology ; Female ; Humans ; Incidence ; *Mammography ; *Mass Screening ; Middle Aged ; New South Wales/epidemiology ; Regression Analysis ; },
abstract = {OBJECTIVES: To estimate the absolute risk of breast cancer in women, allowing for the effect on incidence of the introduction of widespread mammographic screening.<br><br>DESIGN: Annual breast cancer incidences were compared with numbers of annual mammograms in the population for 1980-96 to identify periods most likely to be affected by screening. Age specific breast cancer incidences 1972-96 were modelled by Poisson regression with an age, period, and cohort analysis. The 1996 age specific incidence was recalculated with the stable period effect 1972-89, and the age and cohort effects. Age specific incidence was converted to cumulative risk of breast cancer to age 79.<br><br>SETTING: Population based data from all women in New South Wales (NSW), Australia.<br><br>PATIENTS OR PARTICIPANTS: Breast cancer incidence in women 1972-96 obtained from the NSW Central Cancer Registry and female populations derived from successive censuses. Mammographic data from private sector mammograms (1985-96), and the mammographic screening service (1988-96) for NSW women.<br><br>INTERVENTIONS: Introduction of population mammographic screening.<br><br>MAIN OUTCOME MEASURES: Recorded age specific incidence and absolute risk of breast cancer to age 79 was compared with underlying incidence and cumulative absolute risk, adjusted for recent period effects, most likely due to mammographic screening in the population.<br><br>RESULTS: The age, period, and cohort model showed an increasing effect for birth cohorts 1910-44 then a plateau, and prominent period effects in 1991 and 1994-6. Increased incidence of breast cancer coincided with an increase in mammographic examinations in the private sector (1991), and prevalent rounds of mammographic screening in the population (1994-6) after introduction of a statewide mammographic screening service. Recorded incidence produced a breast cancer risk to age 79 of 9.9% (1 in 10) for 1996, whereas estimation of underlying incidence yielded a risk of 8.5% (1 in 12).<br><br>CONCLUSIONS: The introduction of mammographic screening in a population inflates the incidence of breast cancer because of diagnosis of prevalent cases. For the purpose of public and clinical communication, it is more reasonable and responsible to adjust for period effects (due to screening) rather than produce risk estimates based on recorded incidence, which may show an alarming increase in risk of breast cancer over a short period.},
}
@article {pmid11475826,
year = {2001},
author = {Yoshizawa, N and Niwano, S and Moriguchi, M and Kitano, Y and Inuo, K and Saito, J and Izumi, T},
title = {Effect of procainamide on the postrepolarization refractoriness in cardiac muscle: evaluation using the block coupling interval in the artificial isthmus model in the canine right atrium.},
journal = {Pacing and clinical electrophysiology : PACE},
volume = {24},
number = {7},
pages = {1100-1107},
doi = {10.1046/j.1460-9592.2001.01100.x},
pmid = {11475826},
issn = {0147-8389},
mesh = {Animals ; Anti-Arrhythmia Agents/*pharmacology ; Disease Models, Animal ; Dogs ; Electrophysiology ; Heart Block/*physiopathology ; Heart Conduction System/*drug effects/*physiopathology ; Procainamide/*pharmacology ; Refractory Period, Electrophysiological/*drug effects ; },
abstract = {The post-repolarization refractoriness (PRR) is an important factor to determine the conduction block in cardiac muscle. Recently, we proposed the block coupling interval (BCI) as an useful electrophysiological index for evaluating the PRR. In the present study, the effect of procainamide on PRR was evaluated using the BCI and the effective refractory period (ERP). In five beagle dogs, radiofrequency linear ablation was performed on the right atrial surface parallel to the AV groove, forming an artificial isthmus (8-10 mm width and 15-20 mm length). Bipolar recordings were performed in the isthmus at a resolution of 1.2 mm and single extrastimuli with eight basic drive trains were delivered to cause conduction blocks in the isthmus. When a conduction block occurred, the recorded coupling interval at the recording site just proximal to the site of block was defined as BCI. At the site of the block, the ERP and duration of the monophasic action potential (MAP) at each drive cycle length was measured. The PRR was calculated using two different formulas: (1) [ERP-MAP] and (2) [BCI-MAP]. Procainamide was administrated intravenously at a dose of 15 mg/kg after the control study and the whole study protocol was repeated. The site of the block in an individual dog was always the same. BCI, ERP, and MAP were all shortened in accordance with the shortening of the basic drive cycle length, and the BCI was always the longest, ERP the middle, and the MAP was the shortest. The administration of procainamide prolonged each parameter, but the order of BCI > ERP > MAP remained unchanged. The PRR calculated as [BCI-MAP] was prolonged from 15 +/- 10 ms to 29 +/- 8 ms by the administration of procainamide (P = 0.048), but [ERP-MAP] was unchanged (8 +/- 10 ms vs 8 +/- 4 ms). In the conduction block model in the canine right atrium, procainamide prolonged the [BCI-MAP], but did not change the [ERP-MAP]. The procainamide effect of prolonging the PRR might be expressed better by the change in the BCI than the ERP.},
}
@article {pmid11474968,
year = {2001},
author = {Turner, JA and Lee, JS and Martinez, O and Medlin, AL and Schandler, SL and Cohen, MJ},
title = {Somatotopy of the motor cortex after long-term spinal cord injury or amputation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {9},
number = {2},
pages = {154-160},
doi = {10.1109/7333.928575},
pmid = {11474968},
issn = {1534-4320},
mesh = {Adult ; *Amputation, Surgical ; *Artificial Limbs ; Female ; Humans ; Leg/physiology/surgery ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Motor Cortex/pathology/*physiology ; Movement ; *Perception ; Quadriplegia/physiopathology ; Spinal Cord Injuries/*physiopathology ; },
abstract = {Certain brain-computer interface (BCI) methods use intrinsic signals from the motor cortex to control neuroprosthetic devices. The organization of the motor pathways in those populations likely to use neuroprosthetic devices, therefore, needs to be determined; there is evidence that following disease or injury the representation of the body in the motor cortex may change. In this study, functional MRI measures of somatotopy following spinal cord injury (SCI) showed evidence of changes in limb representations in the motor cortex. Subjects with chronic SCI had unusual cortical patterns of activity when attempting to move limbs below their injury; amputees showed a more normal somatotopy. The functional reorganization may affect optimal implanted electrode placements for invasive BCI methods for these different populations.},
}
@article {pmid11435146,
year = {2001},
author = {Babiloni, F and Cincotti, F and Bianchi, L and Pirri, G and del R Millán, J and Mouriño, J and Salinari, S and Marciani, MG},
title = {Recognition of imagined hand movements with low resolution surface Laplacian and linear classifiers.},
journal = {Medical engineering &amp; physics},
volume = {23},
number = {5},
pages = {323-328},
doi = {10.1016/s1350-4533(01)00049-2},
pmid = {11435146},
issn = {1350-4533},
mesh = {Biomedical Engineering ; Brain/physiology ; Electroencephalography/*statistics &amp; numerical data ; Female ; Hand/physiology ; Humans ; *Imagination ; Linear Models ; Male ; Mental Processes ; *Models, Neurological ; Movement/physiology ; User-Computer Interface ; },
abstract = {EEG-based Brain Computer Interfaces (BCIs) require on-line detection of mental states from spontaneous EEG signals. In this framework, it was suggested that EEG patterns can be better detected with EEG data transformed with Surface Laplacian computation (SL) than with the unprocessed raw potentials. However, accurate SL estimates require the use of many EEG electrodes, when local estimation methods are used. Since BCI devices have to use a limited number of electrodes for practical reasons, we investigated the performances of spline methods for SL estimates using a limited number of electrodes (low resolution SL). Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements. Linear classifiers were used including Signal Space Projection (SSP) and Fisher's linear discriminant. Results showed an acceptable average correlation between the waveforms obtained with the low resolution SL and these obtained with the SL computed from 26 electrodes (full resolution SL). More importantly, satisfactorily recognition scores for mental EEG-patterns were obtained with the low-resolution surface Laplacian transformation of the recorded potentials when compared with those obtained by using full resolution SL (82%). These results demonstrated also the utility of linear classifiers for the detection of mental patterns in the BCI field.},
}
@article {pmid11417471,
year = {2001},
author = {Blagosklonny, MV},
title = {Unwinding the loop of Bcl-2 phosphorylation.},
journal = {Leukemia},
volume = {15},
number = {6},
pages = {869-874},
doi = {10.1038/sj.leu.2402134},
pmid = {11417471},
issn = {0887-6924},
mesh = {Apoptosis/drug effects ; Enzyme Activation ; Humans ; Interleukin-3/pharmacology ; Microtubules/drug effects ; Mitosis/drug effects/physiology ; Models, Biological ; Myeloid Cell Leukemia Sequence 1 Protein ; Neoplasm Proteins/metabolism ; Paclitaxel/pharmacology ; Phosphorylation/drug effects ; Protein Kinases/metabolism ; *Protein Processing, Post-Translational/drug effects ; Protein Structure, Tertiary ; Proto-Oncogene Proteins c-bcl-2/antagonists &amp; inhibitors/chemistry/*metabolism ; Sequence Deletion ; Signal Transduction/drug effects ; Structure-Activity Relationship ; bcl-X Protein ; },
abstract = {Recent evidence indicates that anti-apoptotic functions of BcI-2 can be regulated by its phosphorylation. According to the 'mitotic arrest-induced' model, multi-site phosphorylation of the BcI-2 loop domain is followed by cell death. In contrast, in cytokine-dependent cell lines, cytokines mediate phosphorylation of BcI-2 on S70, preventing apoptosis. As discussed in this review, these models are not mutually exclusive but reflect different cellular contexts. During mitotic arrest, signal transduction is unique and is fundamentally different from classical mitogenic signaling, since the nucleus membrane is dissolved, gene expression is reduced, and numerous kinases and regulatory proteins are hyperphosphorylated. Hyperphosphorylation of BcI-2 mediated by paclitaxel and other microtubule-active drugs is strictly dependent on targeting microtubules that in turn cause mitotic arrest. In addition to serine-70 (S70), microtubule-active agents promote phosphorylation of S87 and threonine-69 (T69), inactivating BcI-2. A major obstacle for identification of the mitotic BcI-2 kinase(s) is that inhibition of putative kinase(s) by any means (dominant-negative mutants, antisense oligonucleotides, pharmacological agents) may arrest cycle, preventing mitosis and BcI-2 phosphorylation. The role of BcI-2 phosphorylation in cell death is discussed.},
}
@article {pmid11393301,
year = {2001},
author = {Kübler, A and Kotchoubey, B and Kaiser, J and Wolpaw, JR and Birbaumer, N},
title = {Brain-computer communication: unlocking the locked in.},
journal = {Psychological bulletin},
volume = {127},
number = {3},
pages = {358-375},
doi = {10.1037/0033-2909.127.3.358},
pmid = {11393301},
issn = {0033-2909},
mesh = {Brain/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography ; Humans ; Microcomputers ; Quadriplegia/*physiopathology/rehabilitation ; *User-Computer Interface ; },
abstract = {With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients.},
}
@article {pmid11354964,
year = {2000},
author = {Cordero, RA},
title = {Effect of two natural light regimes and nutrient addition on the forest herb Begonia decandra (Begoniaceae).},
journal = {Revista de biologia tropical},
volume = {48},
number = {2-3},
pages = {579-586},
pmid = {11354964},
issn = {0034-7744},
mesh = {Biomass ; Fertilizers ; Food ; *Light ; Magnoliopsida/*growth &amp; development ; Puerto Rico ; Trees ; },
abstract = {The effect of two natural light-growing conditions (understory versus light gaps) and the interaction with nutrient availability (through fertilization) were studied in the understory herb Begonia decandra, in the Luquillo Experimental Forest in Puerto Rico. Sixteen potted plants obtained from cuttings were randomly chosen and distributed in each of eighth forest environments (four light gaps and four understories), for a total of 128 plants. Fertilizer was applied to half of the plants in each site. After seven months in the two given microenvironments, increased light and fertilization resulted in greater growth and some changes in the biomass allocation patterns. All measured variables responded similarly to reported changes for tree seedlings and saplings from other tropical and subtropical regions. Total growth parameters (height, biomass and leaf area) were very sensitive to increases in the main resource (light). The addition of nutrients was less important in producing changes in the allocation variables (root to shoot ratio, leaf area ratio, and specific leaf mass) under conditions of high light availability. Changes due to nutrient levels were relatively greater on plants grown under under-story conditions. Also, small light differences among sites can cause significant changes in the variables related to total growth. Lastly, plant mortality in the nutrient treatments was found to be independent of mortality in two forest light environments. Some hypotheses about resource acquisition and plant growth are not supported by this data.},
}
@article {pmid11319084,
year = {2001},
author = {Parker, MA},
title = {Case of localized recombination in 23S rRNA genes from divergent bradyrhizobium lineages associated with neotropical legumes.},
journal = {Applied and environmental microbiology},
volume = {67},
number = {5},
pages = {2076-2082},
pmid = {11319084},
issn = {0099-2240},
mesh = {Base Sequence ; Bradyrhizobium/*genetics ; Electrophoresis/methods ; Fabaceae/*microbiology ; Genes, Bacterial ; *Genes, rRNA ; Isoenzymes/genetics ; Molecular Sequence Data ; Nitrogenase/metabolism ; Phylogeny ; *Plants, Medicinal ; RNA, Ribosomal, 16S/genetics ; RNA, Ribosomal, 23S/*genetics ; *Recombination, Genetic ; Sequence Analysis, DNA ; *Tropical Climate ; },
abstract = {Enzyme electrophoresis and rRNA sequencing were used to analyze relationships of Bradyrhizobium sp. nodule bacteria from four papilionoid legumes (Clitoria javitensis, Erythrina costaricensis, Rhynchosia pyramidalis, and Desmodium axillare) growing on Barro Colorado Island (BCI), Panama. Bacteria with identical multilocus allele profiles were commonly found in association with two or more legume genera. Among the 16 multilocus genotypes (electrophoretic types [ETs]) detected, six ETs formed a closely related cluster that included isolates from all four legume taxa. Bacteria from two other BCI legumes (Platypodium and Machaerium) sampled in a previous study were also identical to certain ETs in this group. Isolates from different legume genera that had the same ET had identical nucleotide sequences for both a 5' portion of the 23S rRNA and the nearly full-length 16S rRNA genes. These results suggest that Bradyrhizobium genotypes with low host specificity may be prevalent in this tropical forest. Parsimony analysis of 16S rRNA sequence variation indicated that most isolates were related to Bradyrhizobium japonicum USDA 110, although one ET sampled from C. javitensis had a 16S rRNA gene highly similar to that of Bradyrhizobium elkanii USDA 76. However, this isolate displayed a mosaic structure within the 5' 23S rRNA region: one 84-bp segment was identical to that of BCI isolate Pe1-3 (a close relative of B. japonicum USDA 110, based on 16S rRNA data), while an adjacent 288-bp segment matched that of B. elkanii USDA 76. This mosaic structure is one of the first observations suggesting recombination in nature between Bradyrhizobium isolates related to B. japonicum versus B. elkanii.},
}
@article {pmid11249030,
year = {2000},
author = {Parker, MA},
title = {Divergent Bradyrhizobium symbionts on Tachigali versicolor from Barro Colorado Island, Panama.},
journal = {Systematic and applied microbiology},
volume = {23},
number = {4},
pages = {585-590},
doi = {10.1016/S0723-2020(00)80034-X},
pmid = {11249030},
issn = {0723-2020},
mesh = {Base Sequence ; Bradyrhizobium/*classification/genetics/isolation &amp; purification ; DNA, Bacterial/genetics ; DNA, Ribosomal/genetics ; Fabaceae/genetics/*microbiology ; *Genetic Variation ; Molecular Sequence Data ; Nitrogen Fixation/genetics ; Nitrogenase/metabolism ; Panama ; Phylogeny ; *Plants, Medicinal ; RNA, Ribosomal, 16S/genetics ; RNA, Ribosomal, 23S/genetics ; Sequence Alignment ; Symbiosis ; Trees/*microbiology ; },
abstract = {Relationships of root-nodule bacteria from the tree Tachigali versicolor (legume subfamily Caesalpinioideae) were analyzed for 20 isolates sampled from juvenile plants growing on Barro Colorado Island (BCI), Panama. Bacterial genetic diversity appeared to be low. In the highly polymorphic 5' intervening sequence region of 23S rRNA, all isolates had the same length variant. A 472 bp segment spanning this region was sequenced in four isolates, and all proved to be identical at every nucleotide position. RFLP analysis of a 868 bp fragment of the nitrogenase alpha-subunit gene likewise indicated that all 20 isolates shared an identical set of restriction sites. Phylogenetic analysis of both partial 23S rRNA and nearly full-length 16S rRNA sequences showed that bacterial symbionts of T. versicolor fall into the genus Bradyrhizobium. However, they are divergent from the bradyrhizobia associated with other BCI legumes, as well as from other currently known bacteria in this genus. Inoculation tests with two promiscuously-nodulating legumes showed that bacteria from T. versicolor were unable to form nodules on Vigna unguiculata, but did nodulate Macroptilium atropurpureum, although the nodules lacked nitrogen fixation activity. The association of Tachigali with a divergent lineage of Bradyrhizobium is noteworthy in view of this plant's position within a clade of the mostly non-nodulating "primitive" legume subfamily Caesalpinioideae that gave rise to the predominantly nodulating subfamily Mimosoideae.},
}
@article {pmid11213149,
year = {2001},
author = {Nagy, KK and Krosner, SM and Roberts, RR and Joseph, KT and Smith, RF and Barrett, J},
title = {Determining which patients require evaluation for blunt cardiac injury following blunt chest trauma.},
journal = {World journal of surgery},
volume = {25},
number = {1},
pages = {108-111},
doi = {10.1007/s002680020372},
pmid = {11213149},
issn = {0364-2313},
mesh = {Adult ; Creatine Kinase/blood ; Echocardiography ; Electrocardiography ; Female ; Follow-Up Studies ; Heart Injuries/*diagnosis/etiology ; Hemodynamics ; Humans ; Male ; Monitoring, Physiologic ; Prospective Studies ; Risk Factors ; Thoracic Injuries/complications/diagnosis ; Wounds, Nonpenetrating/*diagnosis/etiology ; },
abstract = {The objective of this study was to determine prospectively which risk factors require cardiac monitoring for blunt cardiac injury (BCI) following blunt chest trauma. All patients who sustained blunt chest trauma had an electrocardiogram (ECG) on admission to our urban level I trauma center. Those with ST segment changes, dysrhythmias, hemodynamic instability, history of cardiac disease, age > 55 years, or a need for general anesthesia within 24 hours (group 1) were admitted to the intensive care unit (ICU) for 24 hours where they were subjected to serial ECGs, creatinine phosphokinase (CPK) assays, and echocardiography (ECHO). Those with only mechanism for BCI, i.e., none of the above risk factors (group 2), were admitted to a nonmonitored bed and had a follow-up ECG 24 hours later. A series of 315 patients were admitted with blunt chest trauma during a 17-month period; 144 patients were in group 1 and 171 in group 2. Overall, 22 patients were diagnosed as BCI (+BCI), defined as evolving ST segment changes, dysrhythmias, a CPK-MB index of > 2.5, or hemodynamic instability. Of the 18 +BCI patients in group 1, all were symptomatic (i.e., none was included solely for a cardiac history, age, or need for general anesthesia). Six of these patients required treatment for dysrhythmias, hypotension, or pulmonary edema; one of whom died. Four patients with +BCI were in group 2 and had ECG changes at 24 hours; none of these four had any sequelae from their +BCI. None of the ECHOs demonstrated abnormal wall motion. Patients who sustain blunt chest trauma with a normal ECG, normal blood pressure, and no dysrhythmias on admission require no further intervention for BCI. Patients with ST segment changes, dysrhythmias, or hypotension following blunt chest trauma should be monitored for 24 hours, as this subgroup occasionally requires further treatment for complications of BCI. ECHO adds nothing as a screening test.},
}
@article {pmid11204036,
year = {2000},
author = {Haselsteiner, E and Pfurtscheller, G},
title = {Using time-dependent neural networks for EEG classification.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {4},
pages = {457-463},
doi = {10.1109/86.895948},
pmid = {11204036},
issn = {1063-6528},
mesh = {Cerebral Cortex/physiology ; *Electroencephalography ; *Image Processing, Computer-Assisted ; *Neural Networks, Computer ; *User-Computer Interface ; },
abstract = {This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.},
}
@article {pmid11204035,
year = {2000},
author = {Guger, C and Ramoser, H and Pfurtscheller, G},
title = {Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI).},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {4},
pages = {447-456},
doi = {10.1109/86.895947},
pmid = {11204035},
issn = {1063-6528},
mesh = {Adolescent ; Adult ; Cerebral Cortex/*physiology ; Electrodes ; *Electroencephalography ; Humans ; *Image Processing, Computer-Assisted ; Male ; *User-Computer Interface ; },
abstract = {Electroencephalogram (EEG) recordings during right and left motor imagery allow one to establish a new communication channel for, e.g., patients with amyotrophic lateral sclerosis. Such an EEG-based brain-computer interface (BCI) can be used to develop a simple binary response for the control of a device. Three subjects participated in a series of on-line sessions to test if it is possible to use common spatial patterns to analyze EEG in real time in order to give feedback to the subjects. Furthermore, the classification accuracy that can be achieved after only three days of training was investigated. The patterns are estimated from a set of multichannel EEG data by the method of common spatial patterns and reflect the specific activation of cortical areas. By construction, common spatial patterns weight each electrode according to its importance to the discrimination task and suppress noise in individual channels by using correlations between neighboring electrodes. Experiments with three subjects resulted in an error rate of 2, 6 and 14% during on-line discrimination of left- and right-hand motor imagery after three days of training and make common spatial patterns a promising method for an EEG-based brain-computer interface.},
}
@article {pmid11204034,
year = {2000},
author = {Ramoser, H and Müller-Gerking, J and Pfurtscheller, G},
title = {Optimal spatial filtering of single trial EEG during imagined hand movement.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {4},
pages = {441-446},
doi = {10.1109/86.895946},
pmid = {11204034},
issn = {1063-6528},
mesh = {Adult ; Cerebral Cortex/*physiology ; *Electroencephalography ; Female ; Hand/physiology ; Humans ; *Image Processing, Computer-Assisted ; Imagination/physiology ; Movement/*physiology ; *Self-Help Devices ; *User-Computer Interface ; },
abstract = {The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.},
}
@article {pmid11167596,
year = {2001},
author = {Ung, OA and Lee, WB and Greenberg, ML and Bilous, M},
title = {Complex sclerosing lesion: the lesion is complex, the management is straightforward.},
journal = {ANZ journal of surgery},
volume = {71},
number = {1},
pages = {35-40},
doi = {10.1046/j.1440-1622.2001.02003.x},
pmid = {11167596},
issn = {1445-1433},
mesh = {Biopsy, Needle ; Breast/*pathology ; Breast Diseases/*diagnosis/pathology ; Breast Neoplasms/diagnosis/pathology ; Cicatrix/*pathology ; Diagnosis, Differential ; Female ; Humans ; *Mammography ; *Ultrasonography, Mammary ; },
abstract = {BACKGROUND: Complex sclerosing lesion (CSL) and its smaller counterpart, the radial scar (RS), are frequently seen pathological entities. They are clinically asymptomatic and, prior to the implementation of mammographic screening, were most commonly found incidentally during pathological examination of other biopsied lesions. Complex sclerosing lesions are being detected regularly on mammograms due to widespread screening; many of their features resemble those of malignancy. Management varies and has been controversial.<br><br>METHODS: Twenty-three cases of CSL detected during the first prevalent round of screening at BreastScreen Western Sydney (from February 1993 until June 1995) are presented and reviewed. Assessment was by a combination of radiological, clinical and cytological work-up prior to surgical biopsy. In addition, 126 spiculated carcinomas detected in the same period were reviewed and compared.<br><br>RESULTS: Fourteen RS/CSL (62%) had lucent centres and nine (38%) had a central mass; three had been diagnosed provisionally as RS/CSL. Spicule lengths ranged from 25 to 90 mm; central masses ranged from 5 to 50 mm; and mass:spicule length ratio ranged from 1.2:1 to 1:10. Calcification (benign or indeterminate) was present in six cases (29%). No RS/CSL contained 'suspicious' calcifications, whereas 120 of 126 carcinomas (95%) had a central mass and six (5%) had a lucent centre (spicule lengths: 10-90 mm; central mass: 5-40 mm; and mass:spicule length ratio: 1.1:1-1:10). Twenty-one spiculated carcinomas (17%) contained microcalcifications (14 benign or indeterminate; seven suspicious). Provisional radiological diagnosis (PRD) after mammogram, with or without ultrasound, for histologically confirmed RS/CSL, was RS/CSL in 18 cases (78%), carcinoma in four cases (17%) and equivocal in one case (5%). For eight (6.5%) spiculate carcinomas the PRD was RS/CSL prior to histological diagnosis. The RS/CSL were detected with equal frequency in right and left breasts, and 22 (96%) lesions occurred in the upper breast. Seven RS/CSL (31%) and 83 spiculated carcinomas (65%) had been described as 'palpable' but most were subtle. Twelve fine-needle aspiration biopsies were performed (six 'palpable' lesions (no radiological guidance); four with ultrasound guidance and two with stereotactic guidance), and five (62.5%) of eight adequate lesions were reported as benign, two (25%) were reported as atypical, and one (12.5%) was reported as suspicious.<br><br>CONCLUSIONS: Definitive mammographic and sonographic differentiation of RS/CSL and stellate-type carcinoma is impossible. For screen-detected lesions that may be RS/CSL, the appropriate surgical procedure is a small but adequate biopsy using guidewire or other localization methods with optimal cosmetic incision.},
}
@article {pmid11153820,
year = {2001},
author = {Niwano, S and Yoshizawa, N and Inuo, K and Saito, J and Moriguchi, M and Kitano, Y and Izumi, T},
title = {Evaluation of post-repolarization refractoriness for conduction block in cardiac muscle: studies in an artificial isthmus in the canine right atrium.},
journal = {Japanese circulation journal},
volume = {65},
number = {1},
pages = {40-45},
doi = {10.1253/jcj.65.40},
pmid = {11153820},
issn = {0047-1828},
mesh = {Action Potentials/physiology ; Animals ; Atrial Premature Complexes/physiopathology ; Disease Models, Animal ; Dogs ; Electrocardiography ; Electrophysiologic Techniques, Cardiac ; Heart Atria/physiopathology ; Heart Block/diagnosis/etiology/*physiopathology ; Heart Conduction System/injuries ; },
abstract = {Post-repolarization refractoriness (PRR) is an important factor in determining conduction block and is the difference between the effective refractory period (ERP) and the duration of the monophasic action potential (MAPD). In the present study, conduction block in an artificial isthmus in the canine atrium was evaluated and the coupling interval of a premature beat, which caused the block, was defined as the block coupling interval (BCI). The usefulness of this value was also evaluated. Radiofrequency linear ablation was performed on the right atrial surface parallel to the atrioventricular groove in 5 mongrel dogs, and an artificial isthmus (8-10mm wide and 25-30mm long) was created. Fourteen simultaneous unipolar recordings were performed in the isthmus with a resolution of 1.2 mm. Single extra-stimuli with basic drive train were delivered to induce conduction block in the isthmus and when it occurred, the coupling interval at the recording site just proximal to the site of the block was defined as the BCI. At the site of the block, the ERP and MAPD at each drive cycle length were measured. The PRR was calculated using 2 different formulae: (1) [ERP-MAPD], and (2) [BCI-MAPD]. It was found that each value was shortened in accordance with the shortening of the basic drive cycle length. In all basic drive trains, BCI>ERP>MAPD, and [ERP-MAPD] was always shorter than [BCI-MAPD]. In the shorter cycle length of basic drives, the difference between [ERP-MAPD] and [BCI-MAPD] was more prominent. In the artificial isthmus model in the canine atrium, BCI was always longer than the ERP measured at the same site as the block. Because the ERP may not directly reflect the block phenomenon, the electrophysiologic evaluation should use the BCI instead, as in the PRR evaluation.},
}
@article {pmid11121767,
year = {2000},
author = {Costa, EJ and Cabral, EF},
title = {EEG-based discrimination between imagination of left and right hand movements using Adaptive Gaussian Representation.},
journal = {Medical engineering &amp; physics},
volume = {22},
number = {5},
pages = {345-348},
doi = {10.1016/s1350-4533(00)00051-5},
pmid = {11121767},
issn = {1350-4533},
mesh = {Adult ; *Electroencephalography ; Female ; *Hand ; Humans ; *Imagination ; Male ; *Movement ; *Neural Networks, Computer ; Normal Distribution ; },
abstract = {This article uses the Adaptive Gaussian Representation (AGR) for human electroencephalogram (EEG) feature extraction aiming the discrimination among mental tasks to be used in a brain computer interface (BCI). It does not focus on the AGR time-frequency representation, but rather on their projection coefficients. Ten volunteers were asked to imagine either right or left hand movement, according to a proper visual stimulus. The features of the resulting EEG signals were characterised by extracting AGR coefficients. Classification was carried out using a Multilayer perceptron (MLP) trained with the classical backpropagation algorithm. Overall results show that AGR coefficients representation is able to reveal a significant EEG discrimination between imagination of right and left hand movement with a mean classification performance of 91%+/-5.8% achieved for female subjects and 87%+/-5.0% achieved for male subjects.},
}
@article {pmid11090763,
year = {2000},
author = {Schalk, G and Wolpaw, JR and McFarland, DJ and Pfurtscheller, G},
title = {EEG-based communication: presence of an error potential.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {111},
number = {12},
pages = {2138-2144},
doi = {10.1016/s1388-2457(00)00457-0},
pmid = {11090763},
issn = {1388-2457},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiology ; Brain Mapping ; *Electroencephalography ; Evoked Potentials/*physiology ; Female ; Humans ; Male ; Middle Aged ; *Research Design ; },
abstract = {BACKGROUND: EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.<br><br>METHODS: In an effort to develop a method for detecting errors, this study compared the EEG immediately after correct target selection to that after incorrect selection.<br><br>RESULTS: The data showed that a mistake is followed by a positive potential centered at the vertex that peaks about 180 ms after the incorrect selection.<br><br>CONCLUSION: The results suggest that this error potential might provide a method for detecting and voiding errors that requires no additional time and could thereby improve the speed and accuracy of EEG-based communication.},
}
@article {pmid11081269,
year = {2000},
author = {Medvedev, AV and Ognev, AE and Trifonov, EG and Valova, OA and Kriuchenkova, TP and Zvenigorodskaia, IuV and Ivanova, AV and Savvateeva, NIu and Odintsova, SA},
title = {[The aged patient's first visit to the outpatient psychiatric clinic].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {100},
number = {10},
pages = {51-56},
pmid = {11081269},
issn = {1997-7298},
mesh = {Aged ; Aged, 80 and over ; Cerebrovascular Disorders/complications/*diagnosis/*epidemiology ; Comorbidity ; Female ; Geriatric Assessment ; Humans ; Male ; Mental Disorders/*diagnosis/*epidemiology/etiology ; Middle Aged ; *Office Visits ; Severity of Illness Index ; Somatoform Disorders/diagnosis/epidemiology ; },
abstract = {160 patients over 60 years of age appealed to gerontologic unit of out-patient psychiatric clinic for the first time. The patients were divided into two main groups: with organic mental disorders (OMD) and with functional mental disorders (FMD) (79 and 81 patients, respectively). In the group of OMD the main form of disturbances were cases with dementia (74.4%) mainly of the Alzheimer's type, and cerebral vascular dementia. In 25.3% of the patients the cognitive disturbances didn't attain the level of dementia. In a group of patients with different forms of dementia a high frequency of comorbid mental pathology was observed (83%)--confusional states, delusions, depressive conditions as well as disturbed behavior (67.7%) that was one of the reasons for consulting a psychiatrist. In FMD group the prevailing pathology were depressions, both of the major (37.1%) and mild (34.6%) forms. The remaining cases were characterized by delusions (10.1%), anxiety-phobic (7.6%) states and somatoform disturbances (5.1%). Among the patients both of OMD and FMD groups it was possible to diagnose approximately 3-4 different somatic diseases; vascular and gastrointestinal disorders were met more frequently. The study of contribution of brain computer tomography (CT) to diagnosis of mental pathology (according to ICD-10), has demonstrated that in 30.8% of the cases it was decisive, in 41% it confirmed the clinical data and in 21.8% CT provide additional data (detecting latent cerebral vascular damage). And only in 6.4% of the cases CT fails to give definite information in diagnostically complicated cases. In 26.6% of the patients with FMD, CT of brain had detected symptoms of mild vascular pathology.},
}
@article {pmid11059164,
year = {2000},
author = {Mason, SG and Birch, GE},
title = {A brain-controlled switch for asynchronous control applications.},
journal = {IEEE transactions on bio-medical engineering},
volume = {47},
number = {10},
pages = {1297-1307},
doi = {10.1109/10.871402},
pmid = {11059164},
issn = {0018-9294},
mesh = {Adult ; Algorithms ; *Communication Devices for People with Disabilities ; Electrodes ; Electroencephalography/*instrumentation ; Equipment Design ; Humans ; Male ; Motor Cortex/*physiology ; ROC Curve ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Asynchronous control applications are an important class of application that has not received much attention from the brain-computer interface (BCI) community. This work provides a design for an asynchronous BCI switch and performs the first extensive evaluation of an asynchronous device in attentive, spontaneous electroencephalographic (EEG). The switch design [named the low-frequency asynchronous switch design (LF-ASD)] is based on a new feature set related to imaginary movements in the 1-4 Hz frequency range. This new feature set was identified from a unique analysis of EEG using a bi-scale wavelet. Offline evaluations of a prototype switch demonstrated hit (true positive) rates in the range of 38%-81% with corresponding false positive rates in the range of 0.3%-11.6%. The performance of the LF-ASD was contrasted with two other ASDs: one based on mu-power features and another based on the outlier processing method (OPM) algorithm. The minimum mean error rates for the LF-ASD were shown to be significantly lower than either of these other two switch designs.},
}
@article {pmid11042541,
year = {2000},
author = {Jones, AL and Degusta, D and Turner, SP and Campbell, CJ and Milton, K},
title = {Craniometric variation in a population of mantled howler monkeys (Alouatta palliata): evidence of size selection in females and growth in dentally mature males.},
journal = {American journal of physical anthropology},
volume = {113},
number = {3},
pages = {411-434},
doi = {10.1002/1096-8644(200011)113:3&lt;411::AID-AJPA10&gt;3.0.CO;2-4},
pmid = {11042541},
issn = {0002-9483},
mesh = {Aging/physiology ; Alouatta ; Analysis of Variance ; Animals ; Cephalometry/*methods ; Female ; Male ; Mortality ; Panama ; *Selection, Genetic ; Sex Characteristics ; Tooth Abrasion ; },
abstract = {A large body of work on monkey cranial metrics (involving conclusions about interspecific variation, sexual dimorphism, and ontogeny) depends on the assumptions that growth effectively ceases with dental maturity and that intraspecific variation is negligible. We test these assumptions by examining variation in 39 measurements of 166 dentally mature Alouatta palliata skulls from animals found dead on Barro Colorado Island (BCI), Panama. We also investigate whether this population is under size-based selection, since our found-dead sample reflects the natural mortality in this population. The sample was divided into three age stages by occlusal wear (A-C, least to most wear). Female stage A means are significantly smaller than female stage B means for three cranial measures. Female stage B means are significantly smaller than female stage C means for five cranial measures. Male stage A means are significantly smaller than male stage B means for 21 cranial measures. Multivariate analyses confirm this trend of expansion between adult age stages. The dental metric and suture closure data suggest that the cranial expansion in females is due to size-based selection, while the cranial expansion in males is due to significant growth after dental maturity. Sexual dimorphism ratios are highly variable across different samples of A. palliata, indicating that dimorphism varies between populations of this species. These results provide insight into the selective forces operating on the BCI howlers and challenge the validity of the many studies which pool subspecies and assume growth ceases with maturity.},
}
@article {pmid11027931,
year = {2000},
author = {Austin, H and Hooper, WC and Lally, C and Dilley, A and Ellingsen, D and Wideman, C and Wenger, NK and Rawlins, P and Silva, V and Evatt, B},
title = {Venous thrombosis in relation to fibrinogen and factor VII genes among African-Americans.},
journal = {Journal of clinical epidemiology},
volume = {53},
number = {10},
pages = {997-1001},
doi = {10.1016/s0895-4356(00)00191-8},
pmid = {11027931},
issn = {0895-4356},
mesh = {Adult ; Aged ; Aged, 80 and over ; Alleles ; Black People/*genetics ; Case-Control Studies ; Factor VII/*genetics ; Female ; Fibrinogen/*genetics ; Genetic Predisposition to Disease ; Genotype ; Humans ; Male ; Middle Aged ; Odds Ratio ; Polymerase Chain Reaction ; Polymorphism, Genetic ; Regression Analysis ; Risk Factors ; United States/epidemiology ; Venous Thrombosis/epidemiology/*genetics ; Black or African American ; },
abstract = {We evaluated the relation between venous thrombosis and plasma fibrinogen levels, the HaeIII and BcI polymorphisms of the beta fibrinogen gene, and the MspI polymorphisms of the factor VII gene in a case-control study of African-Americans. The study included 91 venous thrombosis cases and 185 control subjects obtained from a hospital in Atlanta, Georgia. High plasma fibrinogen was associated with increased risk of venous thrombosis, but the finding was not statistically significant. There was little association between the HaeIII polymorphisms and the BclI polymorphisms and the risk of venous thrombosis. The prevalence of the M2/M2 genotype of the factor VII gene was higher among cases than controls, but the difference was not statistically significant. The prevalence of the HaeIII H2 allele and the BclI B2 allele of the beta fibrinogen gene, both of which have been associated with slightly higher levels of plasma fibrinogen in most studies, is considerably lower among African-Americans in this study than it is among Whites in the United States and among Northern Europeans. The study is limited by its small size. However, despite this limitation, it supports the belief that increased plasma fibrinogen levels are associated with increased venous thrombosis risk. The study also indicated that the HaeIII and the BclI polymorphisms of the beta fibrinogen gene and the MspI polymorphisms of the factor VII gene are not strong determinants of venous thrombosis.},
}
@article {pmid10904215,
year = {2000},
author = {Müller-Gerking, J and Pfurtscheller, G and Flyvbjerg, H},
title = {Classification of movement-related EEG in a memorized delay task experiment.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {111},
number = {8},
pages = {1353-1365},
doi = {10.1016/s1388-2457(00)00345-x},
pmid = {10904215},
issn = {1388-2457},
mesh = {Acoustic Stimulation ; Adult ; Brain/*physiology ; *Electroencephalography ; Evoked Potentials/physiology ; Humans ; Memory/*physiology ; Movement/*physiology ; Photic Stimulation ; Task Performance and Analysis ; Time Factors ; },
abstract = {OBJECTIVES: We studied the activation of cortical motor areas during a memorized delay task with a classification technique.<br><br>METHODS: Multichannel EEG was recorded during the sequence of warning stimulus, visual cue, reaction stimulus, and actual execution of hand or foot movements. Two different approaches are presented: first, we trained a classifier on data from the time segments immediately preceding the actual movements, and analyzed the whole recordings in overlapping segments with this fixed classifier. The classification rates obtained as a function of experimental time reflect the activation of the same cortical areas that are active during the actual movements. In the second approach, we trained classifiers on data segments with the same latency in time as the data tested ('running classifiers'). By this, we checked whether we could detect event-related activity sufficiently marked to allow for correct classification.<br><br>RESULTS: With the fixed classifier approach we found two maxima of classification: one maximum after processing of the visual cue corresponding to an activation of motor cortex without overt movement, and a second maximum at the time of the actual movement. The first maximum relates to a very short-lived brain state, in the order of 300 ms, while the broad second maximum (1.5 s) indicates a very stable and long-lasting activation.<br><br>CONCLUSIONS: With the running classifier approach we found similar maxima as with the fixed classifier, indicating that only the activity of motor areas is relevant for classification. Possible implications of our findings for the development of a brain computer interface (BCI) are discussed.},
}
@article {pmid10896195,
year = {2000},
author = {Perelmouter, J and Birbaumer, N},
title = {A binary spelling interface with random errors.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {227-232},
doi = {10.1109/86.847824},
pmid = {10896195},
issn = {1063-6528},
mesh = {Algorithms ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Humans ; Quadriplegia/physiopathology/rehabilitation ; Software ; *User-Computer Interface ; },
abstract = {An algorithm for design of a spelling interface based on a modified Huffman's algorithm is presented. This algorithm builds a full binary tree that allows to maximize an average probability to reach a leaf where a required character is located when a choice at each node is made with possible errors. A means to correct errors (a delete-function) and an optimization method to build this delete-function into the binary tree are also discussed. Such a spelling interface could be successfully applied to any menu-orientated alternative communication system when a user (typically, a patient with devastating neuromuscular handicap) is not able to express an intended single binary response, either through motor responses or by using of brain-computer interfaces, with an absolute reliability.},
}
@article {pmid10896194,
year = {2000},
author = {Wolpaw, JR and McFarland, DJ and Vaughan, TM},
title = {Brain-computer interface research at the Wadsworth Center.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {222-226},
doi = {10.1109/86.847823},
pmid = {10896194},
issn = {1063-6528},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Brain Mapping ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Motor Cortex/*physiopathology ; Prosthesis Design ; Quadriplegia/physiopathology/*rehabilitation ; Somatosensory Cortex/*physiopathology ; *User-Computer Interface ; },
abstract = {Studies at the Wadsworth Center over the past 14 years have shown that people with or without motor disabilities can learn to control the amplitude of mu or beta rhythms in electroencephalographic (EEG) activity recorded from the scalp over sensorimotor cortex and can use that control to move a cursor on a computer screen in one or two dimensions. This EEG-based brain-computer interface (BCI) could provide a new augmentative communication technology for those who are totally paralyzed or have other severe motor impairments. Present research focuses on improving the speed and accuracy of BCI communication.},
}
@article {pmid10896193,
year = {2000},
author = {Pineda, JA and Allison, BZ and Vankov, A},
title = {The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI).},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {219-222},
doi = {10.1109/86.847822},
pmid = {10896193},
issn = {1063-6528},
mesh = {Adult ; Attention/*physiology ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Contingent Negative Variation/*physiology ; Electroencephalography/*instrumentation ; Female ; Foot/innervation ; Fourier Analysis ; Functional Laterality/physiology ; Hand/innervation ; Humans ; Imagination/*physiology ; Kinesthesis/*physiology ; Male ; Middle Aged ; Neurons/physiology ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {Current movement-based brain-computer interfaces (BCI's) utilize spontaneous electroencephalogram (EEG) rhythms associated with movement, such as the mu rhythm, or responses time-locked to movements that are averaged across multiple trials, such as the readiness potential (RP), as control signals. In one study, we report that the mu rhythm is not only modulated by the expression of self-generated movement but also by the observation and imagination of movement. In another study, we show that simultaneous self-generated multiple limb movements exhibit properties distinct from those of single limb movements. Identification and classification of these signals with pattern recognition techniques provides the basis for the development of a practical BCI.},
}
@article {pmid10896192,
year = {2000},
author = {Pfurtscheller, G and Neuper, C and Guger, C and Harkam, W and Ramoser, H and Schlögl, A and Obermaier, B and Pregenzer, M},
title = {Current trends in Graz Brain-Computer Interface (BCI) research.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {216-219},
doi = {10.1109/86.847821},
pmid = {10896192},
issn = {1063-6528},
mesh = {Alpha Rhythm ; Biofeedback, Psychology/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Humans ; Imagination/*physiology ; Motor Cortex/*physiology ; Neural Networks, Computer ; Signal Processing, Computer-Assisted/instrumentation ; Somatosensory Cortex/physiopathology ; *User-Computer Interface ; },
abstract = {This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.},
}
@article {pmid10896191,
year = {2000},
author = {Penny, WD and Roberts, SJ and Curran, EA and Stokes, MJ},
title = {EEG-based communication: a pattern recognition approach.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {214-215},
doi = {10.1109/86.847820},
pmid = {10896191},
issn = {1063-6528},
mesh = {*Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Fourier Analysis ; Humans ; Imagination/physiology ; Motor Cortex/*physiopathology ; Neural Networks, Computer ; Signal Processing, Computer-Assisted/instrumentation ; Somatosensory Cortex/*physiopathology ; *User-Computer Interface ; },
abstract = {We present an overview of our research into brain-computer interfacing (BCI). This comprises an offline study of the effect of motor imagery on EEG and an online study that uses pattern classifiers incorporating parameter uncertainty and temporal information to discriminate between different cognitive tasks in real-time.},
}
@article {pmid10896190,
year = {2000},
author = {Middendorf, M and McMillan, G and Calhoun, G and Jones, KS},
title = {Brain-computer interfaces based on the steady-state visual-evoked response.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {211-214},
doi = {10.1109/86.847819},
pmid = {10896190},
issn = {1063-6528},
mesh = {Biofeedback, Psychology/physiology ; *Communication Devices for People with Disabilities ; Data Display ; Electroencephalography/*instrumentation ; Evoked Potentials, Visual/*physiology ; Exercise Therapy/instrumentation ; Humans ; Muscle, Skeletal/innervation ; Occipital Lobe/physiopathology ; Signal Processing, Computer-Assisted/instrumentation ; Software ; *User-Computer Interface ; },
abstract = {The Air Force Research Laboratory has implemented and evaluated two brain-computer interfaces (BCI's) that translate the steady-state visual evoked response into a control signal for operating a physical device or computer program. In one approach, operators self-regulate the brain response; the other approach uses multiple evoked responses.},
}
@article {pmid10896187,
year = {2000},
author = {Kostov, A and Polak, M},
title = {Parallel man-machine training in development of EEG-based cursor control.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {203-205},
doi = {10.1109/86.847816},
pmid = {10896187},
issn = {1063-6528},
mesh = {Biofeedback, Psychology/physiology ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Neural Networks, Computer ; Postpoliomyelitis Syndrome/physiopathology/rehabilitation ; Signal Processing, Computer-Assisted/instrumentation ; Software ; *User-Computer Interface ; },
abstract = {A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, our subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with our two subjects.},
}
@article {pmid10896186,
year = {2000},
author = {Kennedy, PR and Bakay, RA and Moore, MM and Adams, K and Goldwaithe, J},
title = {Direct control of a computer from the human central nervous system.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {198-202},
doi = {10.1109/86.847815},
pmid = {10896186},
issn = {1063-6528},
support = {1R43 NS 36913-1A1/NS/NINDS NIH HHS/United States ; },
mesh = {Biofeedback, Psychology/physiology ; *Communication Devices for People with Disabilities ; *Electrodes, Implanted ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Female ; Humans ; Male ; Motor Neuron Disease/physiopathology/rehabilitation ; Neocortex/*physiopathology ; Quadriplegia/physiopathology/*rehabilitation ; Software ; *User-Computer Interface ; },
abstract = {We describe an invasive alternative to externally applied brain-computer interface (BCI) devices. This system requires implantation of a special electrode into the outer layers of the human neocortex. The recorded signals are transmitted to a nearby receiver and processed to drive a cursor on a computer monitor in front of the patient. Our present patient has learned to control the cursor for the production of synthetic speech and typing.},
}
@article {pmid10896185,
year = {2000},
author = {Isaacs, RE and Weber, DJ and Schwartz, AB},
title = {Work toward real-time control of a cortical neural prothesis.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {196-198},
doi = {10.1109/86.847814},
pmid = {10896185},
issn = {1063-6528},
mesh = {Animals ; Brain Mapping/instrumentation ; *Computer Systems ; *Electrodes, Implanted ; Evoked Potentials, Motor/physiology ; Humans ; Macaca mulatta ; Motor Cortex/*physiopathology ; Motor Neurons/*physiology ; Parkinsonian Disorders/physiopathology/*rehabilitation ; Prosthesis Design ; Psychomotor Performance/physiology ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {Implantable devices that interact directly with the human nervous system have been gaining acceptance in the field of medicine since the 1960's. More recently, as is noted by the FDA approval of a deep brain stimulator for movement disorders, interest has shifted toward direct communication with the central nervous system (CNS). Deep brain stimulation (DBS) can have a remarkable effect on the lives of those with certain types of disabilities such as Parkinson's disease, Essential Tremor, and dystonia. To correct for many of the motor impairments not treatable by DBS (e.g. quadriplegia), it would be desirable to extract from the CNS a control signal for movement. A direct interface with motor cortical neurons could provide an optimal signal for restoring movement. In order to accomplish this, a real-time conversion of simultaneously recorded neural activity to an online command for movement is required. A system has been established to isolate the cellular activity of a group of motor neurons and interpret their movement-related information with a minimal delay. The real-time interpretation of cortical activity on a millisecond time scale provides an integral first step in the development of a direct brain-computer interface (BCI).},
}
@article {pmid10896184,
year = {2000},
author = {Birch, GE and Mason, SG},
title = {Brain-computer interface research at the Neil Squire Foundation.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {193-195},
doi = {10.1109/86.847813},
pmid = {10896184},
issn = {1063-6528},
mesh = {Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Evoked Potentials, Motor/physiology ; *Foundations ; Humans ; Online Systems/instrumentation ; Signal Processing, Computer-Assisted/instrumentation ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {The ultimate goal of our research is to utilize voluntary motor-related potentials recorded from the scalp in a direct Brain Computer Interface for asynchronous control applications. This type of interface will allow an individual with a high-level impairment to have effective and sophisticated control of devices such as wheelchairs, robotic assistive appliances, computers, and neural prostheses.},
}
@article {pmid10896182,
year = {2000},
author = {Bayliss, JD and Ballard, DH},
title = {A virtual reality testbed for brain-computer interface research.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {188-190},
doi = {10.1109/86.847811},
pmid = {10896182},
issn = {1063-6528},
support = {1-P41-RR09283/RR/NCRR NIH HHS/United States ; },
mesh = {Adult ; Attention/physiology ; *Automobile Driving ; Biofeedback, Psychology/physiology ; Cerebral Cortex/*physiology ; Color Perception/physiology ; *Communication Devices for People with Disabilities ; Electroencephalography/*instrumentation ; Event-Related Potentials, P300/physiology ; Humans ; Microcomputers ; Reference Values ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {Virtual reality promises to extend the realm of possible brain-computer interface (BCI) prototypes. Most of the work using electroencephalograph (EEG) signals in VR has focussed on brain-body actuated control, where biological signals from the body as well as the brain are used. We show that when subjects are allowed to move and act normally in an immersive virtual environment, cognitive evoked potential signals can still be obtained and used reliably. A single trial accuracy average of 85% for recognizing the differences between evoked potentials at red and yellow stop lights will be presented and future directions discussed.},
}
@article {pmid10896181,
year = {2000},
author = {Babiloni, F and Cincotti, F and Lazzarini, L and Millán, J and Mouriño, J and Varsta, M and Heikkonen, J and Bianchi, L and Marciani, MG},
title = {Linear classification of low-resolution EEG patterns produced by imagined hand movements.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {186-188},
doi = {10.1109/86.847810},
pmid = {10896181},
issn = {1063-6528},
mesh = {Adult ; Brain Mapping/instrumentation ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Motor/physiology ; Female ; Fourier Analysis ; Functional Laterality/physiology ; Humans ; Imagination/*physiology ; Male ; Psychomotor Performance/*physiology ; Signal Processing, Computer-Assisted/*instrumentation ; *User-Computer Interface ; },
abstract = {Electroencephalograph (EEG)-based brain-computer interfaces (BCI's) require on-line detection of mental states from spontaneous EEG signals. In this framework, surface Laplacian (SL) transformation of EEG signals has proved to improve the recognition scores of imagined motor activity. The results we obtained in the first year of an European project named adaptive brain interfaces (ABI) suggest that: 1) the detection of mental imagined activity can be obtained by using the signal space projection (SSP) method as a classifier and 2) a particular type of electrodes can be used in such a BCI device, reconciling the benefits of SL waveforms and the need for the use of few electrodes. Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements.},
}
@article {pmid10896179,
year = {2000},
author = {Donchin, E and Spencer, KM and Wijesinghe, R},
title = {The mental prosthesis: assessing the speed of a P300-based brain-computer interface.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {174-179},
doi = {10.1109/86.847808},
pmid = {10896179},
issn = {1063-6528},
support = {1 R41 MH56319-01/MH/NIMH NIH HHS/United States ; },
mesh = {Adult ; Attention/physiology ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Electrodes ; Electroencephalography/*instrumentation ; Event-Related Potentials, P300/*physiology ; Female ; Humans ; Male ; Paraplegia/physiopathology/rehabilitation ; Quadriplegia/physiopathology/*rehabilitation ; Reaction Time/physiology ; Reference Values ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {We describe a study designed to assess a brain-computer interface (BCI), originally described by Farwell and Donchin [9] in 1988. The system utilizes the fact that the rare events in the oddball paradigm elicit the P300 component of the event-related potential (ERP). The BCI presents the user with a matrix of 6 by 6 cells, each containing one letter of the alphabet. The user focuses attention on the cell containing the letter to be communicated while the rows and the columns of the matrix are intensified. Each intensification is an event in the oddball sequence, the row and the column containing the attended cell are "rare" items and, therefore, only these events elicit a P300. The computer thus detects the transmitted character by determining which row and which column elicited the P300. We report an assessment, using a boot-strapping approach, which indicates that an off line version of the system can communicate at the rate of 7.8 characters a minute and achieve 80% accuracy. The system's performance in real time was also assessed. Our data indicate that a P300-based BCI is feasible and practical. However, these conclusions are based on tests using healthy individuals.},
}
@article {pmid10896178,
year = {2000},
author = {Wolpaw, JR and Birbaumer, N and Heetderks, WJ and McFarland, DJ and Peckham, PH and Schalk, G and Donchin, E and Quatrano, LA and Robinson, CJ and Vaughan, TM},
title = {Brain-computer interface technology: a review of the first international meeting.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {164-173},
doi = {10.1109/tre.2000.847807},
pmid = {10896178},
issn = {1063-6528},
mesh = {Algorithms ; Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Neuromuscular Diseases/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.},
}
@article {pmid10896177,
year = {2000},
author = {Robinson, CJ},
title = {A commentary on brain-computer interfacing and its impact on rehabilitation science and clinical applicability.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {8},
number = {2},
pages = {161-163},
pmid = {10896177},
issn = {1063-6528},
mesh = {Cerebral Cortex/*physiopathology ; *Communication Devices for People with Disabilities ; Persons with Disabilities/*rehabilitation ; Electroencephalography/*instrumentation ; Evoked Potentials/physiology ; Humans ; Quadriplegia/physiopathology/*rehabilitation ; Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
}
@article {pmid10879201,
year = {2000},
author = {Wu, G and Wan, C and Duan, Y and Yue, Y},
title = {[Researches on surface modification for prevention of bacterial adhesion to implanting biomaterials].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {17},
number = {1},
pages = {84-86},
pmid = {10879201},
issn = {1001-5515},
mesh = {*Bacterial Adhesion ; *Biocompatible Materials ; Biomedical Engineering ; Thermodynamics ; },
abstract = {The failure of operation caused by biomaterials centered infections (BCI) has seriously restricted the clinical application of biomaterials. Two mechanisms of bacterial adhesion and the relationship between surface free energy of bimaterials and bacterial adhesion are introduced and discussed in this paper. Increasing the surface free energy can improve (decrease) the adhesion of some kinds of bacteria. At last, some methods of the surface modification are reviewed.},
}
@article {pmid10875614,
year = {2000},
author = {Peters, AA and Coulthart, MB and Oger, JJ and Waters, DJ and Crandall, KA and Baumgartner, AA and Ward, RH and Dekaban, GA},
title = {HTLV type I/II in British Columbia Amerindians: a seroprevalence study and sequence characterization of an HTLV type IIa isolate.},
journal = {AIDS research and human retroviruses},
volume = {16},
number = {9},
pages = {883-892},
doi = {10.1089/08892220050042828},
pmid = {10875614},
issn = {0889-2229},
mesh = {Base Sequence ; British Columbia/epidemiology ; DNA, Viral/genetics ; Evolution, Molecular ; Genes, pX ; HTLV-I Infections/epidemiology/virology ; HTLV-II Infections/epidemiology/virology ; Human T-lymphotropic virus 1/*isolation &amp; purification ; Human T-lymphotropic virus 2/classification/*genetics/*isolation &amp; purification ; Humans ; Indians, North American ; Male ; Middle Aged ; Models, Genetic ; Molecular Sequence Data ; Phylogeny ; Sequence Homology, Nucleic Acid ; Seroepidemiologic Studies ; Terminal Repeat Sequences ; },
abstract = {It has been established that the human T cell lymphotropic viruses type I and II (HTLV-I and HTLV-II) are both present in some indigenous peoples of the Americas. While HTLV-I has been identified in coastal British Columbia Indians (BCIs), HTLV-II has not been previously reported in the BCIs or other Canadian Amerindians. The prevalence of HTLV-I and HTLV-II in these populations has not been extensively studied. In this article, we examine a group of BCIs from Vancouver Island who belong to the Nuu-Chah-Nulth and are known to have an increased incidence of rheumatic disease. In 494 serum samples from this tribe, the levels of prevalence of HTLV-I and HTLV-II were 2.8 and 1.6%, respectively. No association could be made between arthropathy and HTLV-I infection. In addition, we characterized an HTLV-II isolate of a BCI from the coastal mainland of British Columbia and with a history of intravenous drug abuse. This case represents the first molecular characterization of a Canadian Amerindian HTLV-II isolate: a subtype IIa virus with phylogenetic affinity for intravenous drug user isolates and containing an extended form of the Tax protein. These results are consistent either with this strain having been sampled from a polymorphic ancestral pool of HTLV-II that gave rise to the current epidemic spread of this virus by intravenous drug use and sexual transmission, or with its being "back-transmitted" into the BC Amerindian population in association with intravenous drug use.},
}
@article {pmid10829392,
year = {2000},
author = {Müller, T and Ball, T and Kristeva-Feige, R and Mergner, T and Timmer, J},
title = {Selecting relevant electrode positions for classification tasks based on the electro-encephalogram.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {38},
number = {1},
pages = {62-67},
pmid = {10829392},
issn = {0140-0118},
mesh = {Electrodes ; Electroencephalography/*methods ; Fingers/physiology ; Humans ; Movement/physiology ; Signal Processing, Computer-Assisted ; },
abstract = {The aim is to describe a general approach to determining important electrode positions when measured electro-encephalogram signals are used for classification. The approach is exemplified in the frame of the brain-computer interface, which crucially depends on the classification of different brain states. To classify two brain states, e.g. planning of movement of right and left index fingers, three different approaches are compared: classification using a physiologically motivated set of four electrodes, a set determined by principal component analysis and electrodes determined by spatial pattern analysis. Spatial pattern analysis enhances the classification rate significantly from 61.3 +/- 1.8% (with four electrodes) to 71.8 +/- 1.4%, whereas the classification rate using principal component analysis is significantly lower (65.2 +/- 1.4%). Most of the 61 electrodes used have no influence on the classification rate, so that, in future experiments, the setup can be simplified drastically to six to eight electrodes without loss of information.},
}
@article {pmid10829391,
year = {2000},
author = {Roberts, SJ and Penny, WD},
title = {Real-time brain-computer interfacing: a preliminary study using Bayesian learning.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {38},
number = {1},
pages = {56-61},
pmid = {10829391},
issn = {0140-0118},
mesh = {Bayes Theorem ; Brain/*physiology ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {Preliminary results from real-time 'brain-computer interface' experiments are presented. The analysis is based on autoregressive modelling of a single EEG channel coupled with classification and temporal smoothing under a Bayesian paradigm. It is shown that uncertainty in decisions is taken into account under such a formalism and that this may be used to reject uncertain samples, thus dramatically improving system performance. Using the strictest rejection method, a classification performance of 86.5 +/- 6.9% is achieved over a set of seven subjects in two-way cursor movement experiments.},
}
@article {pmid10813721,
year = {2000},
author = {Coffey, RN and Watson, RW and Hegarty, NJ and O'Neill, A and Gibbons, N and Brady, HR and Fitzpatrick, JM},
title = {Thiol-mediated apoptosis in prostate carcinoma cells.},
journal = {Cancer},
volume = {88},
number = {9},
pages = {2092-2104},
doi = {10.1002/(sici)1097-0142(20000501)88:9&lt;2092::aid-cncr15&gt;3.0.co;2-9},
pmid = {10813721},
issn = {0008-543X},
mesh = {Annexin A5/drug effects ; Anti-Bacterial Agents/pharmacology ; Apoptosis/*drug effects ; Bongkrekic Acid/pharmacology ; Carcinoma/*pathology ; Caspase 3 ; Caspase 8 ; Caspase 9 ; Caspases/analysis ; Coloring Agents ; DNA, Neoplasm/analysis ; Diamide/pharmacology ; Enzyme Inhibitors/metabolism ; Enzyme Precursors/analysis ; Glutathione/*drug effects/metabolism ; Humans ; Male ; Maleates/*pharmacology ; Mitochondria/drug effects ; Oxidation-Reduction ; Propidium ; Prostatic Neoplasms/*pathology ; Proto-Oncogene Proteins c-bcl-2/analysis ; Reactive Oxygen Species/metabolism ; Receptors, Androgen/drug effects ; Sulfhydryl Reagents/pharmacology ; Tumor Cells, Cultured ; bcl-X Protein ; },
abstract = {BACKGROUND: Glutathione (GSH) maintains an optimum cellular redox potential. Chemical depletion, physical efflux from the cell, or intracellular redistribution of this thiol antioxidant is associated with the onset of apoptosis. The aim of this study was to determine the effects of a thiol-depleting agent, diethylmaleate (DEM), on androgen sensitive and insensitive prostate carcinoma cells.<br><br>METHODS: LNCaP and PC-3 cell lines were induced to undergo apoptosis by DEM and diamide. Apoptosis was quantified by annexin V binding and propidium iodide incorporation using flow cytometry and was confirmed by DNA gel electrophoresis. Intracellular GSH was quantified using a thiol quantitation kit and the generation of reactive oxygen intermediates was measured using dihydrorhodamine 123. Western blot assessed caspase-3, caspase-8, Bcl-2, and Bcl-XL protein expression. Mitochondrial permeability was measured using DiOC6 and stabilized using bongkrekic acid.<br><br>RESULTS: DEM and diamide induced apoptosis in both androgen sensitive and insensitive cells. Apoptosis was also induced in an LNCaP transfectant cell line overexpressing Bcl-2. Apoptosis was caspase-3 dependent and caspase-8 independent. Bongkrekic acid partially prevented the effects of DEM on mitochondrial permeability but was unable to prevent the induction of apoptosis. Decreased Bcl-2 and Bci-XL protein expression was observed at the time of initial caspase-3 activation.<br><br>CONCLUSIONS: This study demonstrates that thiol depletion can be used as an effective means of activating caspase-3 in both androgen sensitive and insensitive prostate carcinoma cells. Direct activation of this effector caspase may serve as a useful strategy for inducing apoptosis in prostate carcinoma cells.},
}
@article {pmid10778992,
year = {2000},
author = {Yang, HL and Dong, YB and Elliott, MJ and Liu, TJ and McMasters, KM},
title = {Caspase activation and changes in Bcl-2 family member protein expression associated with E2F-1-mediated apoptosis in human esophageal cancer cells.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {6},
number = {4},
pages = {1579-1589},
pmid = {10778992},
issn = {1078-0432},
mesh = {Adenoviridae/genetics ; *Apoptosis ; *Carrier Proteins ; Caspases/*metabolism ; Cell Cycle ; *Cell Cycle Proteins ; Cell Death ; Cell Division ; Cell Survival ; DNA, Recombinant/genetics ; *DNA-Binding Proteins ; E2F Transcription Factors ; E2F1 Transcription Factor ; Enzyme Activation ; Esophageal Neoplasms/genetics/metabolism/*pathology ; Gene Expression Regulation, Neoplastic ; Genetic Vectors ; Humans ; Myeloid Cell Leukemia Sequence 1 Protein ; Neoplasm Proteins/biosynthesis ; Proto-Oncogene Proteins c-bcl-2/*biosynthesis ; Retinoblastoma Protein/biosynthesis ; Retinoblastoma-Binding Protein 1 ; Transcription Factor DP1 ; Transcription Factors/genetics/*physiology ; Tumor Cells, Cultured ; Tumor Suppressor Protein p53/genetics ; bcl-X Protein ; },
abstract = {The prognosis for patients with esophageal cancer remains poor, prompting the search for new treatment strategies. Overexpression of E2F-1 has been shown to induce apoptosis in several cancer cell types. In the present study, the effect of adenovirus-mediated E2F-1 overexpression on human esophageal cancer cell lines Yes-4 and Yes-6 was evaluated. Cells were treated by mock infection, infection with an adenoviral vector expressing beta-galactosidase (Ad5CMV-LacZ), or E2F-1 (Ad5CMVE2F-1). Western blot analysis confirmed marked overexpression of E2F-1 in Ad5CMVE2F-1-infected cells. Overexpression of E2F-1 resulted in marked growth inhibition and rapid loss of cell viability due to apoptosis, although Yes-6 cells were somewhat more resistant to E2F-1-mediated growth inhibition than Yes-4 cells. Cell cycle analysis revealed that overexpression of E2F-1 led to G2 arrest, followed by apoptotic cell death. p53 expression remained undetectable in both cell lines after E2F-1 overexpression. The apoptosis inhibitor proteins of the Bcl-2 gene family, Bcl-2, Mcl-1, and BcI-XL, decreased at 48 h after infection in Yes-4 cells, but remained unchanged in Yes-6 cells. Levels of retinoblastoma gene product (pRb) declined at 48 h after E2F-1 infection in Yes-4 cells, at which apoptosis predominated, whereas pRb expression remained constant in Yes-6 cells. Expression of p14ARF did not change after E2F-1 infection in either cell line. Involvement of caspase 3 and caspase 6 in E2F-1-mediated apoptosis was demonstrated by cleavage of caspase 3/CPP32 and poly-ADP-ribose polymerase, as well as fragmentation of the caspase 6 substrate, lamin B. These results indicate that the sensitivity of esophageal cancer cells to E2F-1-mediated apoptosis may be related to differential expression of Bcl-2 family member proteins and suggest that the adenovirus-mediated E2F-1 gene therapy may be a promising treatment strategy for the treatment of this disease.},
}
@article {pmid10661562,
year = {2000},
author = {Wild, DJ and Blankley, CJ},
title = {Comparison of 2D fingerprint types and hierarchy level selection methods for structural grouping using Ward's clustering.},
journal = {Journal of chemical information and computer sciences},
volume = {40},
number = {1},
pages = {155-162},
doi = {10.1021/ci990086j},
pmid = {10661562},
issn = {0095-2338},
abstract = {Four different two-dimensional fingerprint types (MACCS, Unity, BCI, and Daylight) and nine methods of selecting optimal cluster levels from the output of a hierarchical clustering algorithm were evaluated for their ability to select clusters that represent chemical series present in some typical examples of chemical compound data sets. The methods were evaluated using a Ward's clustering algorithm on subsets of the publicly available National Cancer Institute HIV data set, as well as with compounds from our corporate data set. We make a number of observations and recommendations about the choice of fingerprint type and cluster level selection methods for use in this type of clustering},
}
@article {pmid10609628,
year = {1999},
author = {Pregenzer, M and Pfurtscheller, G},
title = {Frequency component selection for an EEG-based brain to computer interface.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {7},
number = {4},
pages = {413-419},
doi = {10.1109/86.808944},
pmid = {10609628},
issn = {1063-6528},
mesh = {Adult ; Algorithms ; Bias ; Biofeedback, Psychology/*methods ; Brain/*physiology ; Classification ; *Communication Devices for People with Disabilities ; Communication Disorders/etiology/physiopathology/*rehabilitation ; Discriminant Analysis ; Electroencephalography/instrumentation/*methods ; Female ; Humans ; Male ; Neural Networks, Computer ; Online Systems/*organization &amp; administration ; Reproducibility of Results ; *Signal Processing, Computer-Assisted/instrumentation ; *User-Computer Interface ; },
abstract = {A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: 1) on the training sessions to select optimal frequency bands for the feedback sessions and 2) on the feedback sessions to monitor changes.},
}
@article {pmid10568710,
year = {1999},
author = {Biffl, WL and Moore, EE and Offner, PJ and Brega, KE and Franciose, RJ and Burch, JM},
title = {Blunt carotid arterial injuries: implications of a new grading scale.},
journal = {The Journal of trauma},
volume = {47},
number = {5},
pages = {845-853},
doi = {10.1097/00005373-199911000-00004},
pmid = {10568710},
issn = {0022-5282},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Angioplasty, Balloon ; Carotid Artery Injuries/*classification/diagnostic imaging/therapy ; Carotid Artery, Internal, Dissection/diagnostic imaging/therapy ; Cerebral Angiography ; Child ; Female ; Glasgow Coma Scale ; Humans ; Male ; Middle Aged ; Stents ; *Trauma Severity Indices ; Treatment Outcome ; },
abstract = {BACKGROUND: Blunt carotid arterial injuries (BCI) have the potential for devastating outcomes. A paucity of literature and the absence of a formal BCI grading scale have been major impediments to the formulation of sound practice guidelines. We reviewed our experience with 109 BCI and developed a grading scale with prognostic and therapeutic implications.<br><br>METHODS: Patients admitted to a Level I trauma center were evaluated with cerebral arteriography if they exhibited signs or symptoms of BCI or met criteria for screening. Patients with BCI were treated with heparin unless they had contraindications, and follow-up arteriography was performed at 7 to 10 days. Endovascular stents were deployed selectively. A prospective database was used to track the patients.<br><br>RESULTS: A total of 76 patients were diagnosed with 109 BCI. Two-thirds of mild intimal injuries (grade I) healed, regardless of therapy. Dissections or hematomas with luminal stenosis (grade II) progressed, despite heparin therapy in 70% of cases. Only 8% of pseudoaneurysms (grade III) healed with heparin, but 89% resolved after endovascular stent placement. Occlusions (grade IV) did not recanalize in the early postinjury period. Grade V injuries (transections) were lethal and refractory to intervention. Stroke risk increased with injury grade. Severe head injuries (Glasgow Coma Scale score < or =6) were found in 46% of patients and confounded evaluation of neurologic outcomes.<br><br>CONCLUSION: This BCI grading scale has prognostic and therapeutic implications. Nonoperative treatment options for grade I BCI should be evaluated in prospective, randomized trials. Accessible grade II, III, IV, and V lesions should be surgically repaired. Inaccessible grade II, III, and IV injuries should be treated with systemic anticoagulation. Endovascular techniques may be the only recourse in high grade V injuries and warrant controlled evaluation in the treatment of grade III BCI.},
}
@article {pmid10478710,
year = {1999},
author = {Neuper, C and Schlögl, A and Pfurtscheller, G},
title = {Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery.},
journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society},
volume = {16},
number = {4},
pages = {373-382},
doi = {10.1097/00004691-199907000-00010},
pmid = {10478710},
issn = {0736-0258},
mesh = {Adult ; Dominance, Cerebral/*physiology ; *Electroencephalography ; Feedback ; Female ; Humans ; Imagination/*physiology ; Male ; Motor Cortex/*physiology ; Movement/*physiology ; Somatosensory Cortex/*physiology ; Time Factors ; },
abstract = {EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback.},
}
@article {pmid10473734,
year = {1999},
author = {Lemon, RN},
title = {Neural control of dexterity: what has been achieved?.},
journal = {Experimental brain research},
volume = {128},
number = {1-2},
pages = {6-12},
doi = {10.1007/s002210050811},
pmid = {10473734},
issn = {0014-4819},
mesh = {Animals ; Biomechanical Phenomena ; Brain Mapping ; Hand/*innervation ; Humans ; Motor Cortex/physiology ; Motor Skills/*physiology ; Neurons, Afferent/physiology ; Psychomotor Performance/*physiology ; },
abstract = {This chapter reviews progress made in our understanding of the neural control of dexterity. It stresses the increasing benefit derived by uniting the different disciplines concerned with the study of the hand. It highlights the study of natural movements and of the importance of tackling the function of the interface between the neural control system and the biomechanical apparatus of the hand and arm. It also highlights the distributed nature of the control system, its utilisation of complex spatio-temporal representations and its dependence on sensory input. It concludes by pointing out the lessons that have been learned from two fields of work: the development of motor skill and the comparative study of dexterity in different primate species},
}
@article {pmid10468235,
year = {1999},
author = {Hsieh, AS and Winet, H and Bao, JY and Stevanovic, M},
title = {Model for intravital microscopic evaluation of the effects of arterial occlusion-caused ischemia in bone.},
journal = {Annals of biomedical engineering},
volume = {27},
number = {4},
pages = {508-516},
doi = {10.1114/1.194},
pmid = {10468235},
issn = {0090-6964},
mesh = {Animals ; Arterial Occlusive Diseases/*complications ; Bone Remodeling ; Bone Resorption/etiology/pathology/physiopathology ; Bone and Bones/*blood supply/*pathology ; Cell Adhesion ; Endothelium, Vascular/pathology ; Female ; Iliac Artery/pathology ; Ischemia/etiology/pathology ; Leukocytes/metabolism/pathology ; Microcirculation ; Neovascularization, Pathologic/pathology ; Osteonecrosis/*etiology/*pathology/physiopathology ; Rabbits ; Reperfusion Injury/complications/pathology ; },
abstract = {An in vivo model has been developed for chronic observation of the effects of ischemia on cortical bone remodeling and perfused vascularity. Diaphragm occluders were implanted around the right common iliac artery of four rabbits and inflated to produce 10 h of ischemia to the limb. Microcirculation was monitored with intravital microscopy of injected fluorescent microspheres and FITC-Dextran 70 through a bone window, the tibial bone chamber implant (BCI). Bone resorption and apposition in the BCI were indicated with mineralization dyes. Between 2 and 12 h following release of the occluder, secondary ischemia/no-reflow and other evidence of reperfusion injury were observed. Vessel damage was suggested by abnormally high leakage of FITC-D70 from the few vessels perfused during secondary ischemia. In the weeks following occluder release perfused vasculature increased beyond pre-occlusion levels. Net bone resorption reached a maximum when vascularity passed normal levels. In order to further validate the arterial occlusion model for osteonecrosis, techniques for (1) confirming bone death and (2) detecting increased leukocyte adherence to endothelial cells were added. The dead cell stain Ethidium homodimer-1 was used to tag dead osteocytes immediately after occlusion and produced a measure designated "osteonecrosis index." To detect leukocytes adhering to vessel walls, carboxyfluorescein diacetate, succinimidyl ester was injected at occluder release. An increase in the number of adherent leukocytes was detected.},
}
@article {pmid10464391,
year = {1999},
author = {Chaves, ML and Ilha, D and Maia, AL and Motta, E and Lehmen, R and Oliveira, LM},
title = {Diagnosing dementia and normal aging: clinical relevance of brain ratios and cognitive performance in a Brazilian sample.},
journal = {Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas},
volume = {32},
number = {9},
pages = {1133-1143},
doi = {10.1590/s0100-879x1999000900013},
pmid = {10464391},
issn = {0100-879X},
mesh = {Age Factors ; Aged ; Aged, 80 and over ; Aging/*physiology ; Alzheimer Disease/diagnosis ; Analysis of Variance ; Brain/*diagnostic imaging ; Cognition/*physiology ; Dementia/*diagnosis/diagnostic imaging ; Dementia, Vascular/diagnosis ; Educational Status ; Female ; Health Status ; Humans ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Radiography ; Sensitivity and Specificity ; Social Class ; },
abstract = {The main objective of the present study was to evaluate the diagnostic value (clinical application) of brain measures and cognitive function. Alzheimer and multi-infarct patients (N = 30) and normal subjects over the age of 50 (N = 40) were submitted to a medical, neurological and cognitive investigation. The cognitive tests applied were Mini-Mental, word span, digit span, logical memory, spatial recognition span, Boston naming test, praxis, and calculation tests. The brain ratios calculated were the ventricle-brain, bifrontal, bicaudate, third ventricle, and suprasellar cistern measures. These data were obtained from a brain computer tomography scan, and the cutoff values from receiver operating characteristic curves. We analyzed the diagnostic parameters provided by these ratios and compared them to those obtained by cognitive evaluation. The sensitivity and specificity of cognitive tests were higher than brain measures, although dementia patients presented higher ratios, showing poorer cognitive performances than normal individuals. Normal controls over the age of 70 presented higher measures than younger groups, but similar cognitive performance. We found diffuse losses of tissue from the central nervous system related to distribution of cerebrospinal fluid in dementia patients. The likelihood of case identification by functional impairment was higher than when changes of the structure of the central nervous system were used. Cognitive evaluation still seems to be the best method to screen individuals from the community, especially for developing countries, where the cost of brain imaging precludes its use for screening and initial assessment of dementia.},
}
@article {pmid10461070,
year = {1999},
author = {Lew, SM and Frumiento, C and Wald, SL},
title = {Pediatric blunt carotid injury: a review of the National Pediatric Trauma Registry.},
journal = {Pediatric neurosurgery},
volume = {30},
number = {5},
pages = {239-244},
doi = {10.1159/000028804},
pmid = {10461070},
issn = {1016-2291},
mesh = {Adolescent ; Adult ; *Carotid Artery Injuries/diagnosis/epidemiology/etiology/therapy ; Child ; Child, Preschool ; Databases as Topic ; Female ; Humans ; Incidence ; Infant ; Male ; Pediatrics/statistics &amp; numerical data ; *Registries ; United States/epidemiology ; *Wounds, Nonpenetrating/diagnosis/epidemiology/etiology/therapy ; },
abstract = {Blunt carotid injury (BCI) is an uncommon yet potentially devastating entity which has received little attention in the pediatric literature. In an attempt to better characterize pediatric BCI, a review of the National Pediatric Trauma Registry was performed. Records were obtained from all children diagnosed with internal or common carotid injury associated with blunt trauma. The incidence of BCI was 0.03% (15 of 57,659 blunt trauma patients). Variables examined included: age, gender, mechanism of injury, associated injuries, various injury severity scores, and outcome. Various injuries were associated with an increase in BCI incidence including chest trauma (4-fold), combined head and chest trauma (6-fold), basilar skull fractures (4-fold), intracranial hemorrhage (6-fold), and clavicle fractures (8-fold). Thirty-three percent of the patients diagnosed with BCI suffered neurological complications directly attributable to their carotid injuries. Current practices regarding screening, diagnosis, and treatment are reviewed.},
}
@article {pmid10457617,
year = {1999},
author = {Wills, C and Condit, R},
title = {Similar non-random processes maintain diversity in two tropical rainforests.},
journal = {Proceedings. Biological sciences},
volume = {266},
number = {1427},
pages = {1445-1452},
pmid = {10457617},
issn = {0962-8452},
mesh = {*Ecosystem ; Forestry/methods ; Stochastic Processes ; Trees/*physiology ; Tropical Climate ; },
abstract = {Quadrat-based analysis of two rainforest plots of area 50 ha, one in Panama (Barro Colorado Island, BCI) and the other in Malaysia (Pasoh), shows that in both plots recruitment is in general negatively correlated with both numbers and biomass of adult trees of the same species in the same quadrat. At BCI, this effect is not significantly influenced by treefall gaps. In both plots, recruitment of individual species is negatively correlated with the numbers of trees of all species in the quadrats, but not with overall biomass. These observations suggest, but do not prove, widespread frequency-dependent effects produced by pathogens and seed-predators that act most effectively in quadrats crowded with trees. Within-species correlations of mortality with numbers or biomass are not found in either plot, indicating that most frequency-dependent mortality takes place before the trees reach 1 cm in diameter. Stochastic effects caused by BCI's more rapid tree turnover may contribute to a larger variance in diversity from quadrat to quadrat at BCI, although they are not sufficient to explain why BCI has fewer than half as many tree species as Pasoh. Finally, in both plots quadrats with low diversity show a significant increase in diversity over time, and this increase is stronger at BCI. This process, like the frequency-dependence, will tend to maintain diversity over time. In general, these non-random forces that should lead to the maintenance of diversity are slightly stronger at BCI, even though the BCI plot is less diverse than the Pasoh plot.},
}
@article {pmid10430086,
year = {1999},
author = {Yang, X and Hao, Y and Ding, Z and Pater, A and Tang, SC},
title = {Differential expression of antiapoptotic gene BAG-1 in human breast normal and cancer cell lines and tissues.},
journal = {Clinical cancer research : an official journal of the American Association for Cancer Research},
volume = {5},
number = {7},
pages = {1816-1822},
pmid = {10430086},
issn = {1078-0432},
mesh = {Adenocarcinoma/metabolism ; Biomarkers, Tumor/biosynthesis ; Breast/*metabolism ; Breast Neoplasms/*metabolism ; Carcinoma, Ductal, Breast/metabolism ; Carrier Proteins/*biosynthesis ; Cells, Cultured ; DNA-Binding Proteins ; Humans ; Protein Isoforms/biosynthesis ; Proto-Oncogene Proteins c-bcl-2/metabolism ; RNA, Messenger/biosynthesis ; Transcription Factors ; Tumor Cells, Cultured ; bcl-X Protein ; },
abstract = {BAG-1 is an antiapoptotic protein that binds to and enhances the antiapoptotic activity of Bcl-2. It binds several growth factor and hormone receptors and modulates their function. BAG-1 was also shown recently to be expressed as four protein isoforms, p50, p46, p33, and p29, through alternative translation initiation. Although many apoptosis-associated genes have been linked to oncogenesis of human breast cancer, the role of BAG-1 has not been fully elucidated. In this study, we examined the expression of BAG-1 RNA or protein isoforms and its interacting antiapoptotic proteins, Bcl-2 and BcI-X(L), in breast normal and tumor cell lines and tissues by Northern or Western blot analysis. We provide convincing evidence that both BAG-1 RNA and protein are overexpressed in human breast cancer cell lines. More importantly, we found that the expression of two isoforms of BAG-1, p46 and p33, was also much higher in breast primary tumors. The expression of Bcl-2 and Bcl-X(L) correlated with that of BAG-1 in breast normal and carcinoma cell lines but not tissues. Our study suggests that BAG-1 isoforms may serve as a molecular marker, independent of Bcl-2 and Bcl-X(L), for human breast cancer.},
}
@article {pmid10427911,
year = {1999},
author = {Obermaier, B and Guger, C and Pfurtscheller, G},
title = {Hidden Markov models used for the offline classification of EEG data.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {44},
number = {6},
pages = {158-162},
doi = {10.1515/bmte.1999.44.6.158},
pmid = {10427911},
issn = {0013-5585},
mesh = {Biofeedback, Psychology ; Electroencephalography/*classification/*methods/statistics &amp; numerical data ; Humans ; Imagination ; *Markov Chains ; *Models, Statistical ; },
abstract = {Hidden Markov models (HMM) are introduced for the offline classification of single-trail EEG data in a brain-computer-interface (BCI). The HMMs are used to classify Hjorth parameters calculated from bipolar EEG data, recorded during the imagination of a left or right hand movement. The effects of different types of HMMs on the recognition rate are discussed. Furthermore a comparison of the results achieved with the linear discriminant (LD) and the HMM, is presented.},
}
@article {pmid10400191,
year = {1999},
author = {Müller-Gerking, J and Pfurtscheller, G and Flyvbjerg, H},
title = {Designing optimal spatial filters for single-trial EEG classification in a movement task.},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
volume = {110},
number = {5},
pages = {787-798},
doi = {10.1016/s1388-2457(98)00038-8},
pmid = {10400191},
issn = {1388-2457},
mesh = {Brain/physiology ; Brain Mapping ; Electroencephalography ; Humans ; Movement/*physiology ; Psychomotor Performance/*physiology ; Reaction Time/physiology ; Space Perception/physiology ; },
abstract = {We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.},
}
@article {pmid10396848,
year = {1999},
author = {Roberts, SJ and Penny, W and Rezek, I},
title = {Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {37},
number = {1},
pages = {93-98},
pmid = {10396848},
issn = {0140-0118},
mesh = {Computer User Training ; *Electroencephalography ; Humans ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {There has been much interest recently in the concept of using information from the motor cortex region of the brain, recorded using non-invasive scalp electrodes, to construct a crude interface with a computer. It is known that movements of the limbs, for example, are accompanied by desynchronisations and synchronisations within the scalp-recorded electroencephalogram (EEG). These event-related desynchronisations and synchronisations (ERD and ERS), however, appear to be present when volition to move a limb occurs, even when actual movement of the limb does not in fact take place. The determination and classification of the ERD/S offers many exciting possibilities for the control of peripheral devices via computer analysis. To date most effort has concentrated on the analysis of the changes in absolute frequency content of signals recorded from the motor cortex. The authors present results which tackle the issues of both the interpretation of changes in signals with time and across channels with simple methods which monitor the temporal and spatial 'complexity' of the data. Results are shown on synthetic and real data sets.},
}
@article {pmid10394346,
year = {1999},
author = {Biffl, WL and Moore, EE and Elliott, JP and Brega, KE and Burch, JM},
title = {Blunt cerebrovascular injuries.},
journal = {Current problems in surgery},
volume = {36},
number = {7},
pages = {505-599},
doi = {10.1016/s0011-3840(99)80807-7},
pmid = {10394346},
issn = {0011-3840},
mesh = {*Brain Injuries/diagnosis/epidemiology/therapy ; Cerebral Angiography ; Cerebral Arteries/*injuries ; Cerebral Veins/*injuries ; Colorado/epidemiology ; Female ; Humans ; Incidence ; Injury Severity Score ; Middle Aged ; Tomography, X-Ray Computed ; *Wounds, Nonpenetrating/diagnosis/epidemiology/therapy ; },
abstract = {On the basis of our experience and the available literature, we submit that aggressive screening for BCI based on injury patterns is warranted. However, several important clinical issues remain unresolved. The precise injury patterns and relative cerebrovascular risks remain to be defined. Furthermore, the optimal diagnostic screening test remains to be identified, with consideration of the relative risk-benefit profile. Finally, we must determine the best methods for the treatment of BCI. Although the definitive study has yet to be completed, the use of heparin was associated with a trend toward improved outcomes in symptomatic patients. In addition, no asymptomatic patient experienced the development of new neurologic deficits during heparin therapy. Therefore we believe that the early institution of heparin therapy is indicated. The role of endovascular stenting, however, remains unclear.},
}
@article {pmid10194880,
year = {1999},
author = {Guger, C and Schlögl, A and Walterspacher, D and Pfurtscheller, G},
title = {Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {44},
number = {1-2},
pages = {12-16},
doi = {10.1515/bmte.1999.44.1-2.12},
pmid = {10194880},
issn = {0013-5585},
mesh = {Amyotrophic Lateral Sclerosis/physiopathology/rehabilitation ; Brain/*physiology ; *Communication Devices for People with Disabilities ; Computer Systems ; Electroencephalography/*instrumentation ; Humans ; Microcomputers ; Psychomotor Performance/physiology ; *Software ; Thinking/physiology ; *User-Computer Interface ; },
abstract = {An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.},
}
@article {pmid10028303,
year = {1998},
author = {Howarth, A and Bowen, DJ},
title = {Linkage analysis in haemophilia A: simultaneous genotyping of two polymorphisms of the human factor VIII gene using induced heteroduplex formation.},
journal = {Haemophilia : the official journal of the World Federation of Hemophilia},
volume = {4},
number = {6},
pages = {812-819},
doi = {10.1046/j.1365-2516.1998.00202.x},
pmid = {10028303},
issn = {1351-8216},
mesh = {*Alleles ; Factor VIII/*genetics ; Female ; Gene Frequency ; *Genetic Linkage ; Hemophilia A/*genetics ; Humans ; Male ; Polymorphism, Genetic ; United Kingdom ; },
abstract = {Linkage analysis is currently the most widely used approach to genetic testing in families affected by haemophilia A. Among the polymorphisms of the factor VIII gene which can be used in such studies, a T/A polymorphism affecting a Bc/I restriction site in intron 18 and a G/A polymorphism in intron 7 are potentially useful. Both may be analysed by polymerase chain reaction (PCR) amplification followed by restriction endonuclease digestion and gel electrophoresis (the intron 7 polymorphism does not directly affect a restriction site but a minor sequence change in one PCR primer introduces an AlwNI site if the 'G' allele is present). We have developed a novel approach for the analysis of these two polymorphisms which uses induced heteroduplex formation to distinguish their allelic forms. Heteroduplex analysis eliminates the restriction endonuclease step and reduces the analysis for both loci to PCR followed directly by gel electrophoresis. Additionally, the new test has been designed to permit both loci to be analysed in the same PCR (multiplex heteroduplex analysis). Multiplex analysis was used to determine the allele frequencies of these polymorphisms in 105 factor VIII genes in the local (South Wales) population: BclI 'T'/BclI 'A' = 0.80/0.20 +/- 0.08 (95% confidence interval), intron 7 'G'/intron 7 'A' = 0.88/0.12 +/- 0.06 (95% confidence interval). The polymorphisms were found to be in strong linkage disequilibrium. The utility of multiplex heteroduplex analysis for linkage studies in haemophilia A was demonstrated by its application to carrier status investigation for an at risk female in a haemophilia A family.},
}
@article {pmid9928845,
year = {1999},
author = {Kübler, A and Kotchoubey, B and Hinterberger, T and Ghanayim, N and Perelmouter, J and Schauer, M and Fritsch, C and Taub, E and Birbaumer, N},
title = {The thought translation device: a neurophysiological approach to communication in total motor paralysis.},
journal = {Experimental brain research},
volume = {124},
number = {2},
pages = {223-232},
doi = {10.1007/s002210050617},
pmid = {9928845},
issn = {0014-4819},
mesh = {Adult ; Amyotrophic Lateral Sclerosis/*rehabilitation ; Biofeedback, Psychology/physiology ; Cerebral Cortex/*physiology ; *Communication Devices for People with Disabilities ; *Communication Barriers ; Computer User Training ; Electroencephalography ; Electrooculography ; Female ; Humans ; Male ; Middle Aged ; Polyneuropathies/rehabilitation ; Quadriplegia/rehabilitation ; *User-Computer Interface ; },
abstract = {A thought translation device (TTD) for brain-computer communication is described. Three patients diagnosed with amyotrophic lateral sclerosis (ALS), with total motor paralysis, were trained for several months. In order to enable such patients to communicate without any motor activity, a technique was developed where subjects learn to control their slow cortical potentials (SCP) in a 2-s rhythm, producing either cortical negativity or positivity according to the task requirement. SCP differences between a baseline interval and an active control interval are transformed into vertical or horizontal cursor movements on a computer screen. Learning SCP self regulation followed an operant-conditioning paradigm with individualized shaping procedures. After prolonged training over more than 100 sessions, all patients achieved self-control, leading to a 70-80% accuracy for two patients. The learned cortical skill enabled the patients to select letters or words in a language-supporting program (LSP) developed for inter-personal communication. The results demonstrate that the fast and stable SCP self-control can be achieved with operant training and without mediation of any muscle activity. The acquired skill allows communication even in total locked-in states.},
}
@article {pmid9891294,
year = {1998},
author = {Znamenskaia, LV and Morozova, OV and Vershinina, VI and Krasnov, SI and Shul'ga, AA and Leshchinskaia, IB},
title = {[Biosynthesis of extracellular guanyl-specific ribonuclease from Bacillus circulans].},
journal = {Mikrobiologiia},
volume = {67},
number = {5},
pages = {619-625},
pmid = {9891294},
issn = {0026-3656},
mesh = {Bacillus/*enzymology/genetics/growth &amp; development ; Base Sequence ; Culture Media ; DNA, Bacterial ; Glucose/metabolism ; Molecular Sequence Data ; Ribonuclease T1/*biosynthesis/genetics ; Sequence Homology, Nucleic Acid ; },
abstract = {Biosynthesis of extracellular alkaline guanyl-specific RNase by Bacillus circulans (RNase Bci) was studied. Synthesis of the enzyme by the culture started in the late exponential phase and was inhibited by inorganic phosphate and glucose, in contrast to the biosynthesis of its structural and functional homologue, RNase Ba (barnase) of B. amyloliquefaciens. It is suggested that differences in the regulation of the biosynthesis of RNase Bci and Ba are related to different structures of their gene promoters.},
}
@article {pmid9796924,
year = {1998},
author = {Eray, M and Liwszyc, GE and Paasinen-Sohns, A and Ståhls, A and Kaartinen, M and Andersson, LC},
title = {p72syk protein tyrosine kinase: an early transducer of sIgG-triggered apoptotic signalling in human follicular lymphoma cells.},
journal = {International immunology},
volume = {10},
number = {10},
pages = {1573-1581},
doi = {10.1093/intimm/10.10.1573},
pmid = {9796924},
issn = {0953-8178},
mesh = {Apoptosis/drug effects/physiology ; Enzyme Precursors/metabolism/*physiology ; Genes, myc/genetics ; Humans ; Immunoglobulin G/metabolism/pharmacology ; Intracellular Signaling Peptides and Proteins ; Lymphoma, Follicular/pathology ; Phosphorylation/drug effects ; Protein-Tyrosine Kinases/metabolism/*physiology ; Proteins/chemistry ; Proto-Oncogene Proteins/biosynthesis ; *Proto-Oncogene Proteins c-bcl-2 ; RNA, Messenger/biosynthesis ; Signal Transduction ; Solubility ; Syk Kinase ; Tumor Cells, Cultured/chemistry/drug effects/physiology ; Tyrosine/metabolism ; bcl-2-Associated X Protein ; src-Family Kinases/metabolism ; },
abstract = {Cross-linking of B cell antigen receptor (sIg) elicits different biological responses, including cell activation, proliferation, differentiation, anergy and cell death depending on the maturational stage of the cell. We established the tumor cell lines HF-1.3.4 and HF-4-9 from two patients with follicular lymphoma. Both cell lines carry the characteristic t(14;18) chromosomal translocation and display constitutively overexpressed Bcl-2. HF-1.3.4 represents a mature B cell with sIgG and several somatic hypermutations in its Ig genes, while HF-4-9 is a less mature B cell, expressing sIgM and only a few mutations in its Ig genes. Cross-linking of sIg with antibodies leads to apoptosis in HF-1.3.4 cells but not in HF-4-9 cells. Triggering of sIg induced, within seconds, identical tyrosine phosphorylation of p53/56lyn protein tyrosine kinase (PTK) and p55blk PTK in both of the cell lines; however, a prominent tyrosine phosphorylation and activation of p72syk PTK only in HF-1.3.4 cells. We conclude that p72syk PTK is of importance in relaying apoptotic signalling upon sIg cross-linking in the HF-1.3.4 cell line. Given the mature phenotype of the HF-1.3.4 cell line it serves as a model for the late negative selection during B cell ontogeny. Moreover, our results question the current concept that a constitutive overexpression of BcI-2 confers resistance to sIg ligation-induced apoptosis in lymphoma cells.},
}
@article {pmid9790336,
year = {1998},
author = {Biffl, WL and Moore, EE and Ryu, RK and Offner, PJ and Novak, Z and Coldwell, DM and Franciose, RJ and Burch, JM},
title = {The unrecognized epidemic of blunt carotid arterial injuries: early diagnosis improves neurologic outcome.},
journal = {Annals of surgery},
volume = {228},
number = {4},
pages = {462-470},
pmid = {9790336},
issn = {0003-4932},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; *Carotid Artery Injuries ; Child ; Clinical Protocols ; Decision Trees ; Female ; Humans ; Incidence ; Male ; Middle Aged ; Nervous System Diseases/etiology/prevention &amp; control ; Time Factors ; Treatment Outcome ; Wounds, Nonpenetrating/complications/*diagnosis/epidemiology/therapy ; },
abstract = {OBJECTIVE: To determine the benefit of screening for blunt carotid arterial injuries (BCI) in patients who are asymptomatic.<br><br>SUMMARY BACKGROUND DATA: Blunt carotid arterial injuries have the potential for devastating complications. Published studies report 23% to 28% mortality rates, with 48% to 58% of survivors having permanent severe neurologic deficits. Most patients have neurologic deficits when the injury is diagnosed. The authors hypothesized that screening patients who are asymptomatic and instituting early therapy would improve neurologic outcome.<br><br>METHODS: The Trauma Registry of the author's Level I Trauma Center identified patients with BCI from 1990 through 1997. Beginning in August 1996, the authors implemented a screening for BCI. Arteriography was used for diagnosis. Patients without specific contraindications were anticoagulated. Endovascular stents were deployed in the setting of pseudoaneurysms.<br><br>RESULTS: Thirty-seven patients with BCI were identified among 15,331 blunt-trauma victims (0.24%). During the screening period, 25 patients were diagnosed with BCI among 2902 admissions (0.86%); 13 (52%) were asymptomatic. Overall, eight patients died, and seven of the survivors had permanent severe neurologic deficits. Excluding those dying of massive brain injury and patients admitted with coma and brain injury, mortality associated with BCI was 15%, with severe neurologic morbidity in 16% of survivors. The patients who were asymptomatic at diagnosis had a better neurologic outcome than those who were symptomatic. Symptomatic patients who were anticoagulated showed a trend toward greater neurologic improvement at the time of discharge than those who were not anticoagulated.<br><br>CONCLUSIONS: Screening allows the identification of asymptomatic BCI and thereby facilitates early systemic anticoagulation, which is associated with improved neurologic outcome. The role of endovascular stents in the treatment of blunt traumatic pseudoaneurysms remains to be defined.},
}
@article {pmid9749909,
year = {1998},
author = {Pfurtscheller, G and Neuper, C and Schlögl, A and Lugger, K},
title = {Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters.},
journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {6},
number = {3},
pages = {316-325},
doi = {10.1109/86.712230},
pmid = {9749909},
issn = {1063-6528},
mesh = {Adult ; Cerebral Cortex/*physiology ; Cortical Synchronization ; Cues ; Discriminant Analysis ; *Electroencephalography ; Electromyography ; Feedback/physiology ; Female ; Humans ; Imagination/*physiology ; Male ; Movement/*physiology ; *Signal Processing, Computer-Assisted ; },
abstract = {Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.},
}
@article {pmid9749678,
year = {1998},
author = {Miner, LA and McFarland, DJ and Wolpaw, JR},
title = {Answering questions with an electroencephalogram-based brain-computer interface.},
journal = {Archives of physical medicine and rehabilitation},
volume = {79},
number = {9},
pages = {1029-1033},
doi = {10.1016/s0003-9993(98)90165-4},
pmid = {9749678},
issn = {0003-9993},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Attention/*physiology ; Biofeedback, Psychology/*physiology ; Brain Mapping/instrumentation ; *Communication Devices for People with Disabilities ; Computer Systems ; Persons with Disabilities/*rehabilitation ; Electrodes ; Electroencephalography/*instrumentation ; Female ; Humans ; Male ; Middle Aged ; Software ; Somatosensory Cortex/*physiopathology ; *User-Computer Interface ; },
abstract = {OBJECTIVE: To demonstrate that humans can learn to control selected electroencephalographic components and use that control to answer simple questions.<br><br>METHODS: Four adults (one with amyotrophic lateral sclerosis) learned to use electroencephalogram (EEG) mu rhythm (8 to 12Hz) or beta rhythm (18 to 25Hz) activity over sensorimotor cortex to control vertical cursor movement to targets at the top or bottom edge of a video screen. In subsequent sessions, the targets were replaced with the words YES and NO, and individuals used the cursor to answer spoken YES/NO questions from single- or multiple-topic question sets. They confirmed their answers through the response verification (RV) procedure, in which the word positions were switched and the question was answered again.<br><br>RESULTS: For 5 consecutive sessions after initial question training, individuals were asked an average of 4.0 to 4.6 questions per minute; 64% to 87% of their answers were confirmed by the RV procedure and 93% to 99% of these answers were correct. Performances for single- and multiple-topic question sets did not differ significantly.<br><br>CONCLUSIONS: The results indicate that (1) EEG-based cursor control can be used to answer simple questions with a high degree of accuracy, (2) attention to auditory queries and formulation of answers does not interfere with EEG-based cursor control, (3) question complexity (at least as represented by single versus multiple-topic question sets) does not noticeably affect performance, and (4) the RV procedure improves accuracy as expected. Several options for increasing the speed of communication appear promising. An EEG-based brain-computer interface could provide a new communication and control modality for people with severe motor disabilities.},
}
@article {pmid9734574,
year = {1998},
author = {Rickard, MT and Taylor, RJ and Fazli, MA and El Hassan, N},
title = {Interval breast cancers in an Australian mammographic screening program.},
journal = {The Medical journal of Australia},
volume = {169},
number = {4},
pages = {184-187},
doi = {10.5694/j.1326-5377.1998.tb140217.x},
pmid = {9734574},
issn = {0025-729X},
mesh = {Adult ; Aged ; Benchmarking ; Breast Neoplasms/*mortality ; Certification ; Cross-Cultural Comparison ; Cross-Sectional Studies ; Female ; Humans ; Incidence ; Mammography/*statistics &amp; numerical data ; Mass Screening/*statistics &amp; numerical data ; Middle Aged ; New South Wales ; Outcome and Process Assessment, Health Care/*statistics &amp; numerical data ; Quality Assurance, Health Care/*statistics &amp; numerical data ; Survival Analysis ; },
abstract = {OBJECTIVE: To determine the incidence of interval cancers which occurred in the first 12 months after mammographic screening at a mammographic screening service.<br><br>DESIGN: Retrospective analysis of data obtained by crossmatching the screening Service and the New South Wales Central Cancer Registry databases.<br><br>SETTING: The Central &amp; Eastern Sydney Service of BreastScreen NSW.<br><br>PARTICIPANTS: Women aged 40-69 years at first screen, who attended for their first or second screen between 1 March 1988 and 31 December 1992.<br><br>MAIN OUTCOME MEASURES: Interval-cancer rates per 10000 screens and as a proportion of the underlying incidence of breast cancer (as estimated by the underlying rate in the total NSW population).<br><br>RESULTS: The 12-month interval-cancer incidence per 10000 screens was 4.17 for the 40-49 years age group (95% confidence interval [CI], 1.35-9.73) and 4.64 for the 50-69 years age group (95% CI, 2.47-7.94). Proportional incidence rates were 30.1% for the 40-49 years age group (95% CI, 9.8-70.3) and 22% for the 50-69 years age group (95% CI, 11.7-37.7). There was no significant difference between the proportional incidence rate for the 50-69 years age group for the Central &amp; Eastern Sydney Service and those of major successful overseas screening trials.<br><br>CONCLUSION: Screening quality was acceptable and should result in a significant mortality reduction in the screened population. Given the small number of cancers involved, comparison of interval-cancer statistics of mammographic screening programs with trials requires age-specific or age-adjusted data, and consideration of confidence intervals of both program and trial data.},
}
@article {pmid9715129,
year = {1998},
author = {Boyages, J and Langlands, A},
title = {Postmastectomy radiation therapy: better late than never.},
journal = {The Australian and New Zealand journal of surgery},
volume = {68},
number = {8},
pages = {550-553},
doi = {10.1111/j.1445-2197.1998.tb02098.x},
pmid = {9715129},
issn = {0004-8682},
mesh = {Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Breast Neoplasms/drug therapy/*radiotherapy/surgery ; Cisplatin/administration &amp; dosage ; Clinical Trials as Topic ; Combined Modality Therapy ; Female ; Fluorouracil/administration &amp; dosage ; Humans ; Lymph Node Excision ; *Mastectomy ; Methotrexate/administration &amp; dosage ; Middle Aged ; Radiotherapy, Adjuvant ; Treatment Outcome ; },
}
@article {pmid9670722,
year = {1998},
author = {Ol'shanetskiĭ, AA and Vysotskiĭ, AA and Frolov, VM and Zelenyĭ, II},
title = {[Laboratory methods of prognostication in suppurative complications of erysipelas inflammation].},
journal = {Klinichna khirurhiia},
volume = {},
number = {3},
pages = {25-26},
pmid = {9670722},
issn = {0023-2130},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Erysipelas/*complications/diagnosis/pathology ; Female ; Humans ; Inflammation ; Leukocyte Count ; Male ; Middle Aged ; Models, Theoretical ; Prognosis ; Suppuration ; },
abstract = {The dynamics of the leukocyte index of intoxication (LII) and the blood-cell index (BCI) was studied in 2756 patients with erysipelas. Both indexes, raised at the height of the disease, are lowering under the therapy influence. At the purulent-inflammatory complications beginning the LII and BCI level remains high or raises 2-3 days before occurrence of clinical signs of complication. The application of above-mentioned indexes for prognostication and diagnosis is possible.},
}
@article {pmid9623460,
year = {1998},
author = {Rickard, M and Donnellan, M},
title = {Diagnosis of small sized invasive breast cancer by an Australian mammography screening service: surrogate end-points for mortality reduction.},
journal = {The Australian and New Zealand journal of surgery},
volume = {68},
number = {6},
pages = {415-418},
doi = {10.1111/j.1445-2197.1998.tb04789.x},
pmid = {9623460},
issn = {0004-8682},
mesh = {Adult ; Aged ; Australia ; Breast Neoplasms/epidemiology/mortality/*prevention &amp; control ; Carcinoma in Situ/epidemiology/mortality/*prevention &amp; control ; Female ; Humans ; Lymph Nodes/pathology ; Lymphatic Metastasis ; *Mammography ; *Mass Screening ; Middle Aged ; National Health Programs ; Prevalence ; },
abstract = {BACKGROUND: The percentage of screen-detected infiltrating cancers that are small, the percentage that are node-negative and the percentage of those that are grade 3 and small, are surrogate end-points for the main object of breast screening, namely the reduction in mortality from breast cancer in the screened population.<br><br>METHODS: This study reports these end-points; that is, the prognostic features of invasive cancers, as detected by the Central and Eastern Sydney Service of BreastScreen NSW. The data reported were collected by the Service on women who attended for screening between March 1988 and December 1994.<br><br>RESULTS: Breast cancer detection rates for women aged 50-69 years were 78 per 10,000 for prevalent screens and 37 per 10,000 for incident screens with 36 and 37%, respectively, of the invasive cancers being < or = 10 mm in size and 64 and 70%, respectively, being < or = 15 mm in size. Seventeen per cent of invasive cancers were grade 3 and of these 45% were < or = 15 mm in size. Axillary nodal dissection was carried out in 86% of invasive cancers and 69% of these were node-negative, the rates of node negativity being higher for small-sized cancers.<br><br>CONCLUSIONS: Breast cancer detection rates, small invasive cancer detection rates, the percentage of small grade 3 cancers and the node negativity rates indicate that the potential long-term mortality benefits of mammography screening should be achieved. These rates could be used to underpin a review of the Australian Accreditation Standards.},
}
@article {pmid9600062,
year = {1998},
author = {Kanzaki, T and Shiina, R and Saito, Y and Oohashi, H and Morisaki, N},
title = {Role of latent TGF-beta 1 binding protein in vascular remodeling.},
journal = {Biochemical and biophysical research communications},
volume = {246},
number = {1},
pages = {26-30},
doi = {10.1006/bbrc.1998.8559},
pmid = {9600062},
issn = {0006-291X},
mesh = {Animals ; Carotid Artery Injuries ; Carotid Artery, Common/metabolism/pathology ; Carrier Proteins/pharmacology/*physiology ; Catheterization/adverse effects ; Cell Division/drug effects ; Cell Movement/drug effects/physiology ; Cells, Cultured ; Chemotaxis/drug effects/physiology ; Immunohistochemistry ; *Intracellular Signaling Peptides and Proteins ; Latent TGF-beta Binding Proteins ; Male ; Muscle, Smooth, Vascular/*cytology/drug effects/*physiology ; Rats ; Rats, Wistar ; Transforming Growth Factor beta/*physiology ; },
abstract = {Transforming growth factor-beta (TGF-beta) is secreted as a latent, high molecular weight complex, which is composed of TGF-beta, a latency associated peptide (LAP) and a latent TGF-beta binding protein (LTBP). In this study, we report on the role of LTBP in vascular remodeling. 0.01-5 ng/ml of LTBP stimulated the migration activities of cultured rat arterial smooth muscle cells (SMC) about 4-7 fold compared with control in vitro. The maximal activity of SMC migration by LTBP was 75% of that by 10 ng/ml of PDGF-BB. A checker board analysis showed that the migration by LTBP was chemotactic, not chemokinetic. By cross-linking experiment, LTBP associated with 80-120 kd cell surface protein of SMC, suggesting that a part of LTBP can bind with SMC. Furthermore, LTBP was more strongly expressed in the intimal layer than in the medial layer of BCI artery. These results suggest that LTBP plays an important role in the initial stage of arterial intimal thickening through the acceleration of SMC migration from the medial to intimal layer and is one of the essential factors influencing vascular remodeling.},
}
@article {pmid9599033,
year = {1998},
author = {Winet, H and Bao, JY},
title = {Fibroblast growth factor-2 alters the effect of eroding polylactide-polyglycolide on osteogenesis in the bone chamber.},
journal = {Journal of biomedical materials research},
volume = {40},
number = {4},
pages = {567-576},
doi = {10.1002/(sici)1097-4636(19980615)40:4&lt;567::aid-jbm8&gt;3.0.co;2-d},
pmid = {9599033},
issn = {0021-9304},
support = {DE 10167/DE/NIDCR NIH HHS/United States ; },
mesh = {Animals ; Biocompatible Materials ; Bone Regeneration/*drug effects ; Drug Carriers ; Female ; Fibroblast Growth Factor 2/*pharmacology ; Implants, Experimental ; Osteogenesis/*drug effects ; Polyesters/*pharmacology ; Polyglycolic Acid/*pharmacology ; Rabbits ; },
abstract = {The effects of recombinant human fibroblast growth factor-2 (rhFGF-2) in the presence of eroding 50:50 poly(DL-lactide-co-glycolide) (PDLLG) on acute bone healing were studied in the optical bone chamber (BCI). BCIs were loaded with disks of PDLLG surrounded by one of four rhFGF-2 doses. Fifty-two female rabbit right tibias were implanted. Commencing the third week post implantation (W3) healing in the BCI compartment was observed weekly, using intravital microscopy, until W8. The doses were: unloaded, loaded with polymer only, and polymer plus 0.5, 1.0, and 10 microg rhFGF-2. Videotaped and photographed bone images were measured and analyzed using a frame-grabber digitizing system. Comparison with controls revealed that ossification rates were significantly above normal in rabbits loaded with polymer plus any of the rhFGF-2 doses. Comparison with polymer-only BCIs showed that PDLLG plus any of the three rhFGF-2 doses was linked with ossification rates significantly higher than baseline. The results indicated that FGF-2 in the dose range studied effectively can overcome the retarding effects of eroding polymer on ossification that has been reported by this laboratory. Interpretation of the retarding effects of the polymer disks, although consistent with previously studied washer-shaped devices of the same material, was complicated by a difference in erosion rate. This result supports the notion that erodible device geometry is a major factor in determining biocompatibility and must be considered in the design of carriers. Accordingly, programming of dose specificity for delivering a given polypeptide cytokine to a given host site must allow for the inhibitory effects of an eroding carrier and the influence of device geometry on these effects and erosion.},
}
@article {pmid9526983,
year = {1998},
author = {Prall, JA and Brega, KE and Coldwell, DM and Breeze, RE},
title = {Incidence of unsuspected blunt carotid artery injury.},
journal = {Neurosurgery},
volume = {42},
number = {3},
pages = {495-8; discussion 498-9},
doi = {10.1097/00006123-199803000-00012},
pmid = {9526983},
issn = {0148-396X},
mesh = {Adult ; Aorta, Thoracic/diagnostic imaging ; Aortography ; Carotid Arteries/diagnostic imaging ; *Carotid Artery Injuries ; Cerebral Angiography ; Female ; Humans ; Incidence ; Male ; Prospective Studies ; Wounds, Nonpenetrating/diagnostic imaging/*epidemiology ; },
abstract = {OBJECTIVE: This study attempts to document the incidence of unsuspected blunt carotid artery injury (BCI) in a prospective series of consecutive blunt trauma patients undergoing angiographic evaluation of the aorta. Previous studies have included mainly patients who became symptomatic from BCI, thus documenting a "detected incidence."<br><br>METHODS: During a 22-month period, all patients undergoing angiographic evaluation of the aorta after blunt trauma who were not felt to be at increased risk for BCI were included in the screening protocol. All patients initially suspected of BCI were studied outside the protocol. Angiographic evaluation of the carotid arteries was performed using nonselective contrast injections after aortic injury had been ruled out.<br><br>RESULTS: The incidence of BCI among those patients screened under the protocol (n = 119) was 2.5% (3 of 119). Among all patients undergoing aortic evaluation at presentation (n = 171), the detected incidence of BCI was 3.5% (6 of 171). The detected incidence of BCI among all patients during the study period was 0.32% (10 of 3174). No risk factors for BCI were identified beyond the severity of trauma that led to aortic evaluation.<br><br>CONCLUSION: The incidence of BCI found in those patients screened in this study, nearly 10 times the incidence of BCI in our blunt trauma population overall, suggests that these patients represent a subgroup on which to focus screening efforts, regardless of the diagnostic tools employed. The similarity between the angiographic incidence and the detected incidence of BCI in this study argues that few BCIs remain asymptomatic. All blunt trauma patients injured sufficiently to prompt aortic evaluation at presentation should be screened in some manner for BCI.},
}
@article {pmid9514059,
year = {1998},
author = {van Slooten, HJ and van de Vijver, MJ and van de Velde, CJ and van Dierendonck, JH},
title = {Loss of Bcl-2 in invasive breast cancer is associated with high rates of cell death, but also with increased proliferative activity.},
journal = {British journal of cancer},
volume = {77},
number = {5},
pages = {789-796},
pmid = {9514059},
issn = {0007-0920},
mesh = {Apoptosis ; Breast Neoplasms/*genetics/metabolism/pathology ; Cell Division ; Female ; *Gene Expression Regulation, Neoplastic ; *Genes, bcl-2 ; Humans ; Ki-67 Antigen/analysis ; Necrosis ; Neoplasm Proteins/analysis/biosynthesis/*genetics ; Premenopause ; Proto-Oncogene Proteins/analysis ; Proto-Oncogene Proteins c-bcl-2/analysis/biosynthesis ; Receptors, Estrogen/analysis ; bcl-2-Associated X Protein ; bcl-X Protein ; },
abstract = {Bcl-2 has been demonstrated to inhibit apoptosis in breast cancer cells in vitro, and the ratio between Bcl-2 and its proapoptotic homologue Bax seems to be an important determinant of cellular sensitivity to induction of apoptosis. However, little information is available on the relationship between Bcl-2 and the rate of apoptotic and necrotic cell death in breast tumours. From a series of 441 premenopausal, lymphnode-negative breast cancer patients, a subset of 49 tumours was selected in which immunostaining for the 26-kDa isoform of Bcl-2 was either absent (n = 23) or very high (n = 26). High expression of Bcl-2 was found to be strongly associated with low rates of apoptotic (P < 0.001) and necrotic cell death (P < 0.001). The mean value of the apoptotic index was 2.69%+/-1.40% in Bcl-2-negative tumours and 0.68%+/-1.00% in Bcl-2-positive tumours. Expression of the proapoptotic protein Bax correlated neither with Bcl-2 nor with the frequency of apoptotic cells. Immunostaining for the antiapoptotic Bcl-2 homologue BcI-X(L) correlated with Bcl-2 expression (P < 0.001) but not with apoptosis. High proliferation rate and high tumour grade (Bloom-Richardson) were strongly associated with absence of Bcl-2 expression (P< 0.001). p53 accumulation was associated with absence of Bcl-2 expression and increased apoptotic activity. Loss of Bcl-2 expression was strongly correlated with increased apoptotic and necrotic cell death, high proliferation rate and high tumour grade, supporting a model in which Bcl-2 not only mediates cell death, but also cell division in breast cancer tissue, and in which regulation of cell division and cell death are tightly linked.},
}
@article {pmid9448429,
year = {1998},
author = {Lerner, EJ},
title = {Brain computer communication: reading your mind.},
journal = {New Jersey medicine : the journal of the Medical Society of New Jersey},
volume = {95},
number = {1},
pages = {59-60},
pmid = {9448429},
issn = {0885-842X},
mesh = {*Artificial Intelligence ; *Communication Devices for People with Disabilities ; *Electroencephalography ; *Evoked Potentials, Visual ; Humans ; },
}
@article {pmid9386005,
year = {1997},
author = {Burstedt, MK and Edin, BB and Johansson, RS},
title = {Coordination of fingertip forces during human manipulation can emerge from independent neural networks controlling each engaged digit.},
journal = {Experimental brain research},
volume = {117},
number = {1},
pages = {67-79},
doi = {10.1007/s002210050200},
pmid = {9386005},
issn = {0014-4819},
mesh = {Adolescent ; Adult ; Female ; Fingers/innervation/*physiology ; Friction ; Hand Strength/physiology ; Humans ; Male ; Movement/*physiology ; Nerve Net/*physiology ; Surface Properties ; Thumb/physiology ; Weight Perception/physiology ; },
abstract = {We investigated the coordination of fingertip forces in subjects who lifted an object (i) using the index finger and thumb of their right hand, (ii) using their left and right index fingers, and (iii) cooperatively with another subject using the right index finger. The forces applied normal and tangential to the two parallel grip surfaces of the test object and the vertical movement of the object were recorded. The friction between the object and the digits was varied independently at each surface between blocks of trials by changing the materials covering the grip surfaces. The object's weight and surface materials were held constant across consecutive trials. The performance was remarkably similar whether the task was shared by two subjects or carried out unimanually or bimanually by a single subject. The local friction was the main factor determining the normal:tangential force ratio employed at each digit-object interface. Irrespective of grasp configuration, the subjects adapted the force ratios to the local frictional conditions such that they maintained adequate safety margins against slips at each of the engaged digits during the various phases of the lifting task. Importantly, the observed force adjustments were not obligatory mechanical consequences of the task. In all three grasp configurations an incidental slip at one of the digits elicited a normal force increase at both engaged digits such that the normal:tangential force ratio was restored at the non-slipping digit and increased at the slipping digit. The initial development of the fingertip forces prior to object lift-off revealed that the subjects employed digit-specific anticipatory mechanisms using weight and frictional experiences in the previous trial. Because grasp stability was accomplished in a similar manner whether the task was carried out by one subject or cooperatively by two subjects, it was concluded that anticipatory adjustments of the fingertip forces can emerge from the action of anatomically independent neural networks controlling each engaged digit. In contrast, important aspects of the temporal coordination of the digits was organized by a "higher level" sensory-based control that influenced both digits. In lifts by single subjects this control was mast probably based on tactile and visual input and on communication between neural control mechanisms associated with each digit. In the two-subject grasp configuration this synchronization information was based on auditory and visual cues.},
}
@article {pmid9393397,
year = {1997},
author = {Punjabi, AP and Plaisier, BR and Haug, RH and Malangoni, MA},
title = {Diagnosis and management of blunt carotid artery injury in oral and maxillofacial surgery.},
journal = {Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons},
volume = {55},
number = {12},
pages = {1388-95; discussion 1396},
doi = {10.1016/s0278-2391(97)90634-0},
pmid = {9393397},
issn = {0278-2391},
mesh = {Adolescent ; Adult ; Anticoagulants/therapeutic use ; Arterial Occlusive Diseases/diagnosis/drug therapy/etiology/surgery ; Arthroplasty, Replacement/adverse effects ; Carotid Artery Diseases/diagnosis/drug therapy/etiology/surgery ; *Carotid Artery Injuries ; Cause of Death ; Child ; Cranial Nerve Diseases/diagnosis/etiology ; Craniocerebral Trauma/diagnosis ; Female ; Glasgow Coma Scale ; Hemiplegia/diagnosis/etiology ; Humans ; Injury Severity Score ; Male ; Middle Aged ; Molar, Third/surgery ; Multiple Trauma ; Neurologic Examination ; Paralysis/diagnosis/etiology ; Perceptual Disorders/diagnosis/etiology ; Postoperative Complications ; Retrospective Studies ; Survival Rate ; Temporomandibular Joint/surgery ; Tooth Extraction/adverse effects ; Treatment Outcome ; Wounds, Nonpenetrating/*diagnosis/drug therapy/surgery ; },
abstract = {PURPOSE: Traumatic occlusion of the internal carotid artery (ICA) is a rare complication of maxillofacial trauma or surgery. This investigation evaluated patient demographics, diagnostic methods, and effective therapeutic modalities associated with blunt carotid injury (BCI).<br><br>PATIENTS AND METHODS: This was a retrospective analysis of patient records with an ICD-9-CM diagnosis of carotid injury conducted at MetroHealth Medical Center during the 24-month period between August 1993 and July 1995. Carotid injuries attributable to penetrating trauma were excluded. Age, gender, cause of injury, Glasgow Coma Scale score, Injury Severity Score, type and location of injury, concomitant injury, diagnostic methods, treatment modalities, and outcome were identified, recorded, and analyzed.<br><br>RESULTS: During the 24-month period, 12 patients (seven males and five females) suffered BCI. These patients were divided into two groups based on cause of the problem. In group I, there were 3,214 blunt trauma patients admitted during the 2-year study, of which 10 patients had BCI, representing 0.31% of blunt trauma patients, and 1.2% of patients with head injuries. Seven patients presented with hemiplegia, two with cranial nerve palsy, and one with perceptual neglect. Ninety percent of the patients had associated injuries. Two patients had surgical intervention, three received anticoagulation, and five had only supportive care. Four of the 10 patients died, four had moderate neurologic deficits, and two survived with only minor neurologic deficits. In group II, two patients developed BCI after surgery. A 52-year-old woman had a carotid injury after right total temporomandibular joint replacement, and a 48-year-old man who underwent surgical removal of a third molar became hemiplegic postoperatively. The first patient recovered after anticoagulation, whereas the second patient, who received only supportive care, has severe neurologic deficits.<br><br>CONCLUSIONS: BCI is an uncommon entity. It is usually recognized when a patient develops an unexplained neurologic deficit, most often hemiplegia, subsequent to trauma or surgery of the head, face, or neck. In the early stages, the diagnosis can be missed by carotid ultrasound or computed tomography. The injury is unrelated to Glasgow Coma Scale score. Symptoms may not develop for days after injury in 50% of patients. Anticoagulation appears to be the most beneficial therapeutic modality.},
}
@article {pmid9301228,
year = {1997},
author = {Funayama, M and Mimasaka, S and Iwashiro, K and Azumi, J and Morita, M},
title = {Spontaneous bilateral dissections of the intracranial vertebral arteries: a case report.},
journal = {Nihon hoigaku zasshi = The Japanese journal of legal medicine},
volume = {51},
number = {3},
pages = {220-225},
pmid = {9301228},
issn = {0047-1887},
mesh = {Adult ; Aortic Dissection/diagnosis/*pathology ; Fatal Outcome ; Female ; Humans ; Intracranial Aneurysm/diagnosis/*pathology ; Subarachnoid Hemorrhage/etiology ; Tomography, X-Ray Computed ; Vertebral Artery/*pathology ; },
abstract = {A 41-year-old woman died of subarachnoid hemorrhage. She had had severe headaches for 10 days, but no abnormalities were detected on the brain computer tomography (CT) taken about a half day prior to her death. At autopsy, bilateral dissecting aneurysms were found in the intracranial vertebral arteries. Headaches related to dissection are considered to be due to distension of the artery, and the dissection may have occurred 10 days before her death. In considering the brain CT and autopsy findings, subaracnoid hemorrhage may have occurred within several hours of death. Although multivessel dissections suggest the possibility of underlying angiopathy, the present case had no clear finding of angiopathy in any of the brain vessels. When one sees subarachnoid hemorrhage in the basal area of the brain and finds "black" or "bluish black" discoloration(s) in the circle of Willis, one should suspect a dissecting aneurysm(s).},
}
@article {pmid9130867,
year = {1997},
author = {Suhail, K and Cochrane, R},
title = {Seasonal changes in affective state in samples of Asian and white women.},
journal = {Social psychiatry and psychiatric epidemiology},
volume = {32},
number = {3},
pages = {149-157},
pmid = {9130867},
issn = {0933-7954},
mesh = {Adult ; Analysis of Variance ; Asia, Western/ethnology ; Disease Susceptibility ; *Emigration and Immigration ; Female ; Humans ; *Photoperiod ; Prospective Studies ; Regression Analysis ; Sampling Studies ; Seasonal Affective Disorder/*epidemiology/etiology ; United Kingdom/epidemiology ; },
abstract = {Seasonality of the affective state has been reported to vary in direct proportion to latitude in temperate regions. The frequency of seasonal affective disorder (SAD) and the severity of the symptoms associated with it have been reported to be greater in higher than in lower latitudes. In addition, recent research has suggested a genetic loading for SAD. Most of the research on the seasonality of affect has been done in high latitude areas, seasonal mood cycles have been infrequently investigated in tropical areas, and no study has so far measured and compared seasonal changes in affect and behaviour in indigenous and populations non-indigenous to high latitudes. To rule out the biases associated with retrospective designs, a prospective longitudinal study was designed to investigate seasonal mood variations in indigenous white and non-indigenous Asian populations. Since previous research has indicated the excessive vulnerability of women to winter depression, it was decided to measure seasonality of the affective state only in women. To examine the relative effects of genetic predispositions and physical environment, the Asian group was further divided into "Asian" and "Asian-British". The former group comprised women who were living in England but who had been born and had spent considerably more time in their country of origin, while the latter group consisted of women who were born in England and who had lived there all their lives. The three groups of 25 women each were matched for age and socio-economic status, and were interviewed every month for 1 year using the Hospital Anxiety and Depression Scale (HAD), a Behavioural Change Inventory (BCI), the Ladder Scale of General Well-being (LSW) and a Monthly Stress Inventory (MSI). One retrospective scale was administered at the end of the study year to compare the extent of seasonal change in affect with that on the HAD-depression subscale. The results showed that seasonal depression peaked in winter in all three groups, with the incidence of winter depression being highest in the Asian group. Seasonal changes on several dimensions of behaviour were in the direction of winter depression for all three groups. States other than depression (anxiety and general well-being) did not show any seasonal variation. Hours of daylight was found to be the best predictor of seasonal variation in mood among environmental and psychosocial variables. There was no evidence to support a genetic hypothesis for SAD.},
}
@article {pmid9103393,
year = {1997},
author = {Malmström, H},
title = {Fine-needle aspiration cytology versus core biopsies in the evaluation of recurrent gynecologic malignancies.},
journal = {Gynecologic oncology},
volume = {65},
number = {1},
pages = {69-73},
doi = {10.1006/gyno.1996.4606},
pmid = {9103393},
issn = {0090-8258},
mesh = {Adult ; Aged ; Aged, 80 and over ; Biopsy/*methods/*standards ; Biopsy, Needle/*standards ; False Negative Reactions ; Female ; Genital Neoplasms, Female/diagnosis/*pathology ; Humans ; Middle Aged ; Neoplasm Recurrence, Local/diagnosis/*pathology ; Predictive Value of Tests ; Sensitivity and Specificity ; },
abstract = {Fine-needle aspiration (FNA) cytology for the diagnosis of malignant lesions has been used in gynecologic oncology for a long time. Core biopsies have also been used for the same purpose for many years but there are, to my knowledge, no reports in the literature of the use of core biopsies in the diagnosis of gynecologic lesions. The purpose of this study was to evaluate the accuracy of these two methods in gynecologic cancer. This study comprises 85 patients examined from 1986 through 1995. The histology and cytology of gynecologic lesions were investigated by the use of an automatic biopsy instrument (Biopty) with a specially designed needle guide. Concomitantly all patients underwent FNA for cytology. Three hundred thirty-nine FNA and 141 biopsies using the Biopty core instrument (BCI) were obtained from patients with persistent, recurrent, or metastatic disease. Correct diagnosis was made with FNA cytology in 67/85 (79%) and with BCI in 62/85 (73%) of the cases (P = 0.08). Insufficient material for evaluation was recorded for FNA in 12/85 (14%) compared to 10/85 (12%) for the BCI (P = 0.29). False-negative diagnoses occurred in 5% of the cases with FNA compared to 15% with BCI (NS). The sensitivity of FNA was 92% and that of BCI 73% (P = 0.01) and the specificities 92 and 100% (NS), respectively. The predictive values of positive results for the two methods were 96 and 100%, respectively. The complication rate was negligible. In conclusion, FNA in combination with BCI in gynecologic lesions is a simple and safe operation using needle guides. In comparison with FNA cytology the sensitivity for BCI is lower but the specificity is higher. No significant differences were found in accuracy between the two methods. BCI biopsy should be considered in the subset of patients where additional information about the tumor is desired for planning the treatment of recurrent disease.},
}
@article {pmid9129578,
year = {1997},
author = {Maynard, EM and Nordhausen, CT and Normann, RA},
title = {The Utah intracortical Electrode Array: a recording structure for potential brain-computer interfaces.},
journal = {Electroencephalography and clinical neurophysiology},
volume = {102},
number = {3},
pages = {228-239},
doi = {10.1016/s0013-4694(96)95176-0},
pmid = {9129578},
issn = {0013-4694},
mesh = {Action Potentials/*physiology ; Animals ; Brain/*physiology ; Cats ; *Computers ; *Microelectrodes ; Neurons/*physiology ; },
abstract = {We investigated the potential of the Utah Intracortical Electrode Array (UIEA) to provide signals for a brain-computer interface (BCI). The UIEA records from small populations of neurons which have an average signal-to-noise ratio (SNR) of 6:1. We provide specific examples that show the activities of these populations of neurons contain sufficient information to perform control tasks. Results from a simple stimulus detection task using these signals as inputs confirm that the number of neurons present in a recording is significant in determining task performance. Increasing the number of units in a recording decreases the sensitivity of the response to the stimulus; decreasing the number of units in the recording, however, increases the variability of the response to the stimulus. We conclude that recordings from small populations of neurons, not single units, provide a reliable source of sufficiently stimulus selective signals which should be suitable for a BCI. In addition, the potential for simultaneous and proportional control of a large number of external devices may be realized through the ability of an array of microelectrodes such as the UIEA to record both spatial and temporal patterns of neuronal activation.},
}
@article {pmid9478085,
year = {1997},
author = {Pach, J and Winnik, L and Kuśmiderski, J and Pach, D and Groszek, B},
title = {The results of the brain computer tomography and clinical picture in acute cholinesterase inhibitors poisoning.},
journal = {Przeglad lekarski},
volume = {54},
number = {10},
pages = {677-683},
pmid = {9478085},
issn = {0033-2240},
mesh = {Acetylcholinesterase/blood ; Adolescent ; Adult ; Aged ; Atrophy ; Brain/*diagnostic imaging/drug effects/pathology ; Brain Diseases/*chemically induced/diagnosis ; Cholinesterase Inhibitors/*poisoning ; Erythrocytes/enzymology ; Female ; Humans ; Male ; Middle Aged ; Neurologic Examination ; Suicide, Attempted/statistics &amp; numerical data ; *Tomography, X-Ray Computed ; },
abstract = {The aim of this study was to evaluate a morphological and functional status of the CNS in acute cholinesterase inhibitors (ChI) poisonings using the brain computer tomography (CT) and complex psychiatric examination. Under examination there were 59 cholinesterase inhibitors orally poisoned patients, treated at the Department of Clinical Toxicology in years 1984-1997, aged from 14 to 68 (mean 34.7 +/- 12.8) years. The examined group comprised 9 women (15.3%) and 50 men (84.7%). Between the 3rd and 7th day of hospitalisation a complex psychiatric examination was performed. The CNS damage was diagnosed when the point score from complex psychiatric examination was minimum 5 points. CT was performed between the 3rd and 10th day after the intoxication. Incorrect CT scans were found in 78% of poisoned patients. The most common lesion was generalised cortex atrophy and subcortex atrophy of the brain (73.9%), followed by isolated cortex (17.4%) and subcortex atrophy with simultaneous areas of low density in the subcortical nuclei (8.7%). The frequency of incorrect CT scans was statistically higher (p < 0.01) in the group of organophosphorous compounds poisoned patients compared to those poisoned with carbamates. The complex psychiatric examination revealed in 24 patients (40.7%) the differently intensified alterations, but the point score was not higher than 4. Unquestionable damage of the CNS was recognised in 34 patients (57.6%) of the ChI poisoned patients. The frequency of CNS changes detected in complex psychiatric examination was statistically higher (p < 0.01) in the group of the severely poisoned patients. The significantly higher frequency of pathological changes revealed by the brain CT was found in the group of patients with higher than 5 point score obtained from the complex psychiatric evaluation compared to those with score lower than 5 (p < 0.001).},
}
@article {pmid9195331,
year = {1997},
author = {Winet, H and Bao, JY},
title = {Comparative bone healing near eroding polylactide-polyglycolide implants of differing crystallinity in rabbit tibial bone chambers.},
journal = {Journal of biomaterials science. Polymer edition},
volume = {8},
number = {7},
pages = {517-532},
doi = {10.1163/156856297x00425},
pmid = {9195331},
issn = {0920-5063},
support = {DE10167/DE/NIDCR NIH HHS/United States ; },
mesh = {Analysis of Variance ; Animals ; Biodegradation, Environmental ; Crystallization ; Fracture Healing/*physiology ; Materials Testing ; Neovascularization, Physiologic ; Osteogenesis/*physiology ; *Polyesters ; *Polyglycolic Acid ; *Prostheses and Implants ; Rabbits ; Tibial Fractures/*physiopathology ; },
abstract = {Eroding poly(DL-lactide-co-glycolide) (PDLLG) washers and poly(L-lactide-co-glycolide) (PLLG) threads were observed chronically in vivo following loading in a bone chamber tibial implant (BCI). Images were recorded using intravital microscopy of the implanted rabbit. Erosion and bone healing, as represented by angiogenesis and osteogenesis, was determined from changes in projected area of observed polymer, vessels and bone, respectively. Erosion rates of the two polymers were significantly different. Healing adjacent to both polymers differed significantly from controls. Healing response to each polymer was also different, with the faster eroding PDLLG causing more deviation from normal osteogenesis and angiogenesis than did PLLG. It was speculated that the faster eroding polymer released macrophage-stimulating fragments earlier in the healing process, thus altering the normal macrophage-endothelial cell interaction which in turn affected angiogenesis-linked components of osteogenesis.},
}
@article {pmid9184765,
year = {1997},
author = {Parisi, J and Rössler, OE},
title = {Some remarks on the experimental realization of a mind machine.},
journal = {Bio Systems},
volume = {42},
number = {2-3},
pages = {207-208},
doi = {10.1016/s0303-2647(97)01707-3},
pmid = {9184765},
issn = {0303-2647},
mesh = {Brain/*physiology ; Humans ; *Models, Psychological ; Psychophysics ; Quantum Theory ; },
abstract = {The brain not only makes use of measuring apparatuses, but perhaps has the potential to serve as one itself. Since Einstein-Podolsky-Rosen correlations must be absent between observer and object in order for a quantum state to become reducible, it is tempting to perturb measurements by changing the quantum state of the brain. The latter would then be part of the measurement. What kind of effects would one expect? It appears that a new psychophysical problem has been opened up, since any observable consequences would be confined to the subjectivity of the observer.},
}
@article {pmid8982478,
year = {1996},
author = {Martin, C and Winet, H and Bao, JY},
title = {Acidity near eroding polylactide-polyglycolide in vitro and in vivo in rabbit tibial bone chambers.},
journal = {Biomaterials},
volume = {17},
number = {24},
pages = {2373-2380},
doi = {10.1016/s0142-9612(96)00075-0},
pmid = {8982478},
issn = {0142-9612},
support = {DE10167/DE/NIDCR NIH HHS/United States ; },
mesh = {Animals ; Biocompatible Materials/*metabolism ; Bone and Bones/blood supply/*metabolism ; Electrodes ; Hydrogen-Ion Concentration ; *Lactic Acid ; *Polyglycolic Acid ; Polylactic Acid-Polyglycolic Acid Copolymer ; Polymers/*metabolism ; Prostheses and Implants ; Prosthesis Failure ; Rabbits ; Tibia ; },
abstract = {Eroding poly(DL-lactide-co-glycolide)(PDLLG) washers were observed chronically in vitro and in vivo following loading in a bone chamber tibial implant (BCI). Images were recorded using intravital microscopy of the implanted rabbit and direct pH measurements were obtained of the tissue in the bone chamber using a combination probe-reference microelectrode. While statistically significant pH differences were found between the control (unloaded) and experimental (PDLLG-bearing) BCIs, they were only of the order of 0.2 pH units. This value proved to be physiologically insignificant as no statistically significant difference in bone defect healing, as indicated by angiogenesis, was detected. It was shown that the measured small pH changes during PDLLG washers erosion would result whether the buffer was phosphate-buffered saline replaced weekly or interstitial fluid subject to vascular exchanges.},
}
@article {pmid8943845,
year = {1996},
author = {Adachi, M and Sekiya, M and Torigoe, T and Takayama, S and Reed, JC and Miyazaki, T and Minami, Y and Taniguchi, T and Imai, K},
title = {Interleukin-2 (IL-2) upregulates BAG-1 gene expression through serine-rich region within IL-2 receptor beta c chain.},
journal = {Blood},
volume = {88},
number = {11},
pages = {4118-4123},
pmid = {8943845},
issn = {0006-4971},
support = {CA-67329/CA/NCI NIH HHS/United States ; },
mesh = {1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine/analogs &amp; derivatives/pharmacology ; Animals ; Apoptosis/drug effects/genetics ; Carrier Proteins/*biosynthesis/genetics ; Cell Line ; DNA-Binding Proteins ; Enzyme Inhibitors/pharmacology ; Gene Expression Regulation/*drug effects ; Genistein ; Hematopoietic Stem Cells/*drug effects/metabolism ; Humans ; Interleukin-2/*pharmacology ; Isoflavones/pharmacology ; Janus Kinase 3 ; Lymphocytes/drug effects/metabolism ; Mice ; Phosphorylation ; Phytohemagglutinins/pharmacology ; Polyenes/pharmacology ; Protein Processing, Post-Translational ; Protein-Tyrosine Kinases/antagonists &amp; inhibitors/physiology ; Proto-Oncogene Proteins c-bcl-2/biosynthesis/genetics ; RNA, Messenger/biosynthesis/genetics ; Receptors, Interleukin-2/*chemistry/drug effects ; *Serine ; Signal Transduction/*drug effects ; Sirolimus ; Stimulation, Chemical ; Tacrolimus/pharmacology ; Transcription Factors ; Transfection ; },
abstract = {BAG-1 is a Bci-2-binding protein which functions in protection from apoptotic cell death. Here we provide evidence for interleukin-2 (IL-2)-mediated upregulation of BAG-1 expression. In hematopoietic cell line BAF-B03 F7 cells, gene transfer mediated expression of the IL-2R beta c chain is sufficient to confer proliferation and cell survival responses to IL-2. In these IL-2R beta c-expressing cells, BAG-1 mRNA was dramatically induced by IL-2. The IL-2-mediated induction of BAG-1 expression required the activation of tyrosine kinase(s) and was sensitive to rapamycin as the induction of bcl-2 expression was. Analysis of the transfectants which express mutant IL-2R beta c chains or mutant Janus family protein tyrosine kinase Jak3 lacking the kinase domain showed that the IL-2-mediated BAG-1 gene expression required the serinerich region within the IL-2R beta c chain, but Jak3 activation was dispensable. The signaling pathway for BAG-1 gene expression thus highly resembles that for bcl-2 gene expression, strongly suggesting that their induction shares the same signaling pathway. In addition, deletion of the serine-rich region led to loss of IL-2-mediated protection from apoptotic cell death. Taken together, these studies demonstrate that the serine-rich region of the IL-2R beta c chain mediates the coordinated expression of bcl-2 and BAG-1 genes, thereby contributing to suppression of apoptosis.},
}
@article {pmid8941921,
year = {1996},
author = {Croker, BP and Clapp, WL and Abu Shamat, AR and Kone, BC and Peterson, JC},
title = {Macrophages and chronic renal allograft nephropathy.},
journal = {Kidney international. Supplement},
volume = {57},
number = {},
pages = {S42-9},
pmid = {8941921},
issn = {0098-6577},
mesh = {Biopsy ; Chronic Disease ; Graft Rejection/enzymology/*immunology/pathology ; Graft Survival ; Humans ; Kidney/enzymology/immunology/pathology ; *Kidney Transplantation/pathology ; Macrophages/enzymology/*immunology ; },
abstract = {In a previous study we demonstrated that macrophage infiltrates stained for thromboxane A synthase (TxAS) correlated inversely with renal function six months after biopsy. We propose that macrophage based inflammation is a cofactor leading to chronic allograft nephropathy. For this study we compared four indices of renal allograft nephropathy with renal survival. The Banff Score of Inflammatory Changes (BSI) is an index of acute inflammation. The Banff Chronic Index (BCI) and Chronic Allograft Damage Index (CADI) are indexes of chronic disease. The Macrophage Index (MI) is the same as the BSI applied only to macrophages. These indices were determined on renal allograft biopsies obtained because of delayed graft function within the first week of transplantation, and for increasing plasma creatinine levels after stable function. All four indices predicted renal survival in the post-biopsy interval. MI predicted renal survival for the entire transplant period. In addition, the presence of TxAS transcripts in the renal allografts was determined using a reverse transcription-polymerase chain reaction-based assay. This confirms previous observations of TxAS in the grafts. This study supports the hypothesis that macrophage derived inflammation is a cofactor for chronic allograft nephropathy.},
}
@article {pmid9020800,
year = {1996},
author = {Pfurtscheller, G and Kalcher, J and Neuper, C and Flotzinger, D and Pregenzer, M},
title = {On-line EEG classification during externally-paced hand movements using a neural network-based classifier.},
journal = {Electroencephalography and clinical neurophysiology},
volume = {99},
number = {5},
pages = {416-425},
doi = {10.1016/s0013-4694(96)95689-8},
pmid = {9020800},
issn = {0013-4694},
mesh = {Adult ; Electroencephalography ; Female ; Hand/*physiology ; Humans ; Male ; Movement/*physiology ; *Neural Networks, Computer ; },
abstract = {EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89-100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.},
}
@article {pmid8939180,
year = {1996},
author = {Barnes, PJ},
title = {Neuroeffector mechanisms: the interface between inflammation and neuronal responses.},
journal = {The Journal of allergy and clinical immunology},
volume = {98},
number = {5 Pt 2},
pages = {S73-81; discussion S81-3},
pmid = {8939180},
issn = {0091-6749},
mesh = {Animals ; Autonomic Nervous System ; Bronchi/*innervation ; Bronchial Hyperreactivity/*physiopathology ; Humans ; Inflammation Mediators/pharmacology ; Neurotransmitter Agents/pharmacology ; },
abstract = {There is a complex relation between inflammation and neural control of the airways. Cholinergic neurotransmission may be enhanced by inflammatory mediators; cholinergic nerves are the dominant neural pathway for bronchoconstriction in humans. Anticholinergic drugs are more effective in acute severe asthma than in chronic asthma, suggesting that cholinergic mechanisms may be important in exacerbations. Several possible abnormalities in adrenergic control in asthma have been proposed and may be caused by the inflammatory process. Adrenergic nerves do not have direct control of airway smooth muscle but may influence bronchomotor tone in several ways, such as adrenergic neural control of the bronchial vasculature or a secondary effect on cholinergic neurotransmission. Nonadrenergic noncholinergic (NANC) mechanisms mediate both bronchoconstriction and bronchodilation, and a defect in NANC bronchodilatation has been suggested to operate in severe asthma. Relatively little is known about the properties of airway sensory (afferent) nerves in human beings. They are thought to be involved in symptoms of cough and chest tightness, and the threshold for their activation is lowered in conditions of chronic inflammation. In addition, retrograde activation of sensory nerves by a local axon reflex, resulting in the release of peptides, may contribute to inflammation of the airways. Neurogenic inflammation is probably not relevant to mild asthma, however, but it may be more important in severe disease such as brittle asthma.},
}
@article {pmid9293546,
year = {1996},
author = {Duke, BJ and Partington, MD},
title = {Blunt carotid injury in children.},
journal = {Pediatric neurosurgery},
volume = {25},
number = {4},
pages = {188-193},
doi = {10.1159/000121122},
pmid = {9293546},
issn = {1016-2291},
mesh = {Adolescent ; Adult ; Brain Ischemia/etiology/pathology/surgery ; Carotid Arteries/diagnostic imaging ; *Carotid Artery Injuries ; Cerebral Angiography ; Child ; Child, Preschool ; Female ; Glasgow Coma Scale ; Humans ; Hypertension/etiology ; Infant ; Intracranial Pressure ; Male ; Temporal Lobe/pathology/surgery ; Wounds, Nonpenetrating/complications/*diagnosis ; },
abstract = {Blunt carotid injury (BCI) is a rare entity which can have devastating neurologic consequences. Little has been reported on the mechanism of injury, presentation or management of these injuries in children. We present a series of 5 children with BCI. One patient died at presentation while the remainder developed delayed infarctions. Three surviving patients developed intracranial hypertension and required intracranial pressure (ICP) monitoring. Surgical resection of infarcted tissue was required to control ICP in 2 patients. All four surviving patients are impaired but ambulatory. We propose an aggressive management strategy for BCI aimed at early detection of deficit, early angiography, anticoagulation if appropriate, and active management of ischemia including hemodynamic treatment, ICP monitoring, and active use of medical and surgical means to monitor and control intracranial hypertension.},
}
@article {pmid9125702,
year = {1996},
author = {Forrest, DV},
title = {Mind, brain, machine: language.},
journal = {The Journal of the American Academy of Psychoanalysis},
volume = {24},
number = {3},
pages = {409-430},
doi = {10.1521/jaap.1.1996.24.3.409},
pmid = {9125702},
issn = {0090-3604},
mesh = {Brain/*physiology ; *Computer Simulation ; Diagnostic Imaging ; Humans ; *Language ; *Psychoanalytic Theory ; *Psychoanalytic Therapy ; Psycholinguistics ; Software ; },
}
@article {pmid8992910,
year = {1996},
author = {Biedler, A and Wilhelm, W and Grüness, V and Kleinschmidt, S and Berg, K and Mertzlufft, F},
title = {[Accuracy of measurement and overestimation of CO2 of two capnometers intended for potential use in emergency medicine].},
journal = {Der Anaesthesist},
volume = {45},
number = {10},
pages = {957-964},
doi = {10.1007/s001010050330},
pmid = {8992910},
issn = {0003-2417},
mesh = {Blood Gas Analysis/*instrumentation ; Carbon Dioxide/*blood ; Emergency Medicine/*instrumentation ; Evaluation Studies as Topic ; Humans ; Respiration, Artificial ; },
abstract = {UNLABELLED: Capnometry, the noninvasive measurement of end-expiratory CO2 concentration (cCO2, vol%) or calculation of its respective partial pressure (pCO2; mmHg) is an established method. However, for prehospital settings, capnometry is still used very restrictively, mainly owing to the respective devices used. The prerequisite for their use is sufficient accuracy (+/-2 mmHg) and easy handling. Two special capnometers (STAT CAP. Nellcor: mainstream, semiquantitative estimation; Capnocheck 8200, BCI: sidestream, quantitative measurement, numeric display), developed recently for potential use in emergency medicine, are said to fit these criteria. Therefore, the objective of the present investigation was to assess the accuracy and precision of both devices, comparing methods under standardized in vitro (reference gases) and in vivo (intubated and ventilated patients) conditions.<br><br>METHODS: Both devices ("STAT CAP": pCO2 range, light bars; "Capnocheck 8200") were evaluated regarding the accuracy of pCO2 (Capnocheck) and the precision of the CO2 range (STAT CAP). Tests were performed with four dry gas mixtures (STPD) of defined composition and during ventilation of 20 intubated patients (BTPS). All measurements were compared with the alveolar gas monitor "AGM 1304" (Br&#xfc;el &amp; Kjaer, Denmark) as a reference method with a proven +/- 1 mmHg accuracy of pCO2 measurement.<br><br>RESULTS: The "Capnocheck" (BCI) presented an accuracy of the pregiven pCO2 of 0.7-1.4 mmHg (dry gas mixtures, STPD) and an overestimation of 0.2 +/- 4.1 mmHg (BTPS) during ventilation with pure oxygen; inaccuracy during ventilation with 70% N2O in O2 proved to be + 1.2 +/- 1.7 mmHg (BTPS). Nellcor's "STAT CAP" failed to reach the target value in 10% of analyses, as shown by the respective segment bar of the display.<br><br>CONCLUSION: Evaluation of the accuracy of capnometers must focus on the necessary pH2O correction and the possible effects exercised by O2 (and N2O) as well as the possible dependence on barometric pressure (if pCO2, mmHg, is the desired value). The "Capnocheck" showed an accuracy of more than 2 mmHg in dry gas mixtures as well as in humidified air. Concerning the practical use during constant artificial ventilation, the digital display and accuracy of the sidestream capnometer allow for reliable conclusions on patients' ventilation and circulation (CO2 elimination). The 90% accuracy of the segment bar display of Nellcor's "STAT CAP", per se covering only a rather broad range of 20 mmHg, obviously does not provide more than a rough overview. Therefore, the STAT CAP cannot be recommended for prehospital capnometry in the field. However, both the accuracy of the BCI capnometer (Capnocheck) and its numeric display and easy handling strongly recommend this device also for clinical use.},
}
@article {pmid8857813,
year = {1996},
author = {Petroianu, GA and Junker, HM and Maleck, WH and Rüfer, R},
title = {A portable quantitative capnometer in test.},
journal = {The American journal of emergency medicine},
volume = {14},
number = {6},
pages = {586-587},
doi = {10.1016/S0735-6757(96)90107-2},
pmid = {8857813},
issn = {0735-6757},
mesh = {Animals ; Capnography/*instrumentation ; Evaluation Studies as Topic ; Materials Testing ; Swine ; *Swine, Miniature ; },
abstract = {A new hand-held quantitative capnometer (BCI Capnocheck) was tested in the animal lab setting. The end-tidal CO2 values, as measured with this device, showed good agreement with arterial (Paco2) values. This device seems suited for quantitative capnometry in the prehospital setting. It must be noted, however, that this device has no alarms.},
}
@article {pmid9109984,
year = {1996},
author = {Gomes, I and Melo, A and Lucena, R and Cunha-Nascimento, MH and Ferreira, A and Góes, J and Barreto, I and Jones, N and Gaspari, V and Embiruçu, EK and Veiga, M},
title = {Prognosis of bacterial meningitis in children.},
journal = {Arquivos de neuro-psiquiatria},
volume = {54},
number = {3},
pages = {407-411},
doi = {10.1590/s0004-282x1996000300008},
pmid = {9109984},
issn = {0004-282X},
mesh = {Child ; Child, Preschool ; Female ; Humans ; Incidence ; Infant ; Male ; *Meningitis, Bacterial/complications/epidemiology ; Nervous System Diseases ; Prognosis ; Prospective Studies ; },
abstract = {We studied the incidence and prognosis of acute neurologic complications in 281 children under 13 years of age with a diagnosis of acute bacterial meningitis. All the patients were examined daily by the same group of neurologists, using a standardized neurological examination. Patients with signs of encephalic lesions, unsatisfactory response to antibiotics or decreased level of consciousness were submitted to brain computer tomography. The overall lethality rate was 20.3% and cases whose causative agent was identified presented a higher lethality rate (23.7%) than those in which the agent was not found. The most important neurological abnormalities were meningeal signs (88.3%) followed by decreased consciousness (47.7%), irritability (35.2%), seizures (22.4%), fontanel bulging (20.6%) and cranial nerve palsy (14.2%). Seizures, cranial nerve palsy and the absence of meningeal signs were related to higher rates of lethality. Diminished consciousness, seizures, subdural effusion, abscess and hydrocephalus were the most important complications, respectively. We can conclude that acute bacterial meningitis continues to be an important health problem in developing countries and that public health measures will be necessary to minimize the impact of sequelae and reduce the mortality rate in children with that pathology.},
}
@article {pmid8992306,
year = {1996},
author = {Dement'ev, AA and Kirpichnikov, MP and Mirgorodskiĭ, OA and Moiseev, GP and Iakovlev, GI and Shliapnikov, SV},
title = {[Isolation, structural characteristics and functional properties of extracellular ribonuclease from Bacillus polymyxa].},
journal = {Molekuliarnaia biologiia},
volume = {30},
number = {5},
pages = {1193-1202},
pmid = {8992306},
issn = {0026-8984},
mesh = {Amino Acid Sequence ; Bacillus/*enzymology ; Bacterial Proteins/pharmacology ; Chromatography, Ion Exchange ; Endoribonucleases/antagonists &amp; inhibitors/*isolation &amp; purification/metabolism ; Enzyme Inhibitors/pharmacology ; Kinetics ; Mass Spectrometry ; Molecular Sequence Data ; Ribonucleases/antagonists &amp; inhibitors/isolation &amp; purification/metabolism ; },
}
@article {pmid8945865,
year = {1996},
author = {Kalcher, J and Flotzinger, D and Neuper, C and Gölly, S and Pfurtscheller, G},
title = {Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns.},
journal = {Medical &amp; biological engineering &amp; computing},
volume = {34},
number = {5},
pages = {382-388},
pmid = {8945865},
issn = {0140-0118},
mesh = {*Communication ; Communication Devices for People with Disabilities ; *Electroencephalography ; Humans ; Imagination/physiology ; Movement ; *User-Computer Interface ; },
abstract = {The paper describes work on the brain--computer interface (BCI). The BCI is designed to help patients with severe motor impairment (e.g. amyotropic lateral sclerosis) to communicate with their environment through wilful modification of their EEG. To establish such a communication channel, two major prerequisites have to be fulfilled: features that reliably describe several distinctive brain states have to be available, and these features must be classified on-line, i.e. on a single-trial basis. The prototype Graz BCI II, which is based on the distinction of three different types of EEG pattern, is described, and results of online and offline classification performance of four subjects are reported. The online results suggest that, in the best case, a classification accuracy of about 60% is reached after only three training sessions. The online results show how selection of specific frequency bands influences the classification performance in single-trial data.},
}
@article {pmid8942027,
year = {1996},
author = {Yang, MT and Chen, CH and Chi, CS and Mak, SC},
title = {Cerebellar dysgenesis in infants and children: an experience of 22 cases.},
journal = {Zhonghua Minguo xiao er ke yi xue hui za zhi [Journal]. Zhonghua Minguo xiao er ke yi xue hui},
volume = {37},
number = {5},
pages = {342-348},
pmid = {8942027},
issn = {0001-6578},
mesh = {Cerebellum/*abnormalities ; Child, Preschool ; Dandy-Walker Syndrome/diagnosis ; Female ; Humans ; Infant ; Infant, Newborn ; Magnetic Resonance Imaging ; Male ; },
abstract = {There were a total of 22 cases of cerebellar dysgenesis documented by brain sonogram, and/or brain computer-tomography scan, and/or brain magnetic resonance imaging (MRI) in our department over the past 10 years. There were ten males and twelve females. The mean age at diagnosis was 5.79 months. The follow-up period ranged from 2 days to 132 months. Seven cases were suspected upon prenatal examination. Three cases presented with isolated cerebellar hypoplasia, one with Dandy- Walker malformation and three with Joubert syndrome. Seven cases presented with cerebellar dysgenesis complicated with supratentorial brain dysgenesis. Among them, three had vermis hypoplasia with hypoplasia of the corpus callosum, 1 had vermis hypoplasia with holoprosencephaly, 1 had cerebellar hypoplasia with lissencephaly and hypoplasia of corpus callosum, 1 had vermis hypoplasia, agenesis of the corpus callosum and pachygyria, and 1 had cerebellar hypoplasia, hypoplasia of corpus callosum and midline cystic malformation. They all showed severe psychomotor retardation. Six cases showed chromosome anomalies. The neurological outcome for cases with isolated cerebellar hypoplasia was better than the outcome of the complicated cases. MRI is recommended for patients with microcephaly to check for the possibility of combined supratentorial brain dysgenesis. When performing MRI, a median sagittal view should be included. A classification for clinical approach was presented at the same time. In this retrospective study, this classification seemed to have benefits in prediction of clinical outcomes.},
}
@article {pmid8704235,
year = {1996},
author = {Weber-Nordt, RM and Egen, C and Wehinger, J and Ludwig, W and Gouilleux-Gruart, V and Mertelsmann, R and Finke, J},
title = {Constitutive activation of STAT proteins in primary lymphoid and myeloid leukemia cells and in Epstein-Barr virus (EBV)-related lymphoma cell lines.},
journal = {Blood},
volume = {88},
number = {3},
pages = {809-816},
pmid = {8704235},
issn = {0006-4971},
mesh = {Acute Disease ; Base Sequence ; Burkitt Lymphoma/*genetics/pathology/virology ; Cell Nucleus/metabolism ; DNA, Neoplasm/genetics/metabolism ; DNA-Binding Proteins/*metabolism ; *Gene Expression Regulation, Leukemic ; *Gene Expression Regulation, Viral ; Herpesviridae Infections/*genetics/virology ; Herpesvirus 4, Human/*physiology ; Humans ; Interleukin-10/pharmacology ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive/*genetics/pathology ; Leukemia, Myeloid/*genetics/pathology ; *Milk Proteins ; Molecular Sequence Data ; Neoplasm Proteins/genetics/*metabolism ; Phosphorylation/drug effects ; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/*genetics/pathology ; Protein Processing, Post-Translational/drug effects ; Proto-Oncogene Proteins/metabolism ; Proto-Oncogene Proteins c-bcl-2 ; STAT1 Transcription Factor ; STAT3 Transcription Factor ; STAT5 Transcription Factor ; Signal Transduction/*physiology ; Trans-Activators/*metabolism ; *Transcription, Genetic ; Tumor Virus Infections/*genetics/virology ; },
abstract = {Although various molecular mechanisms of STAT protein (signal transducers and activators of transcription) activation have been identified, little is known about the functional role of STAT-dependent transcriptional activation. Herein we report the constitutive nuclear localization, phosphorylation, and DNA-binding activity of STAT proteins in leukemia cells and lymphoma cell lines. With the use of oligonucleotide probes derived from the Fc gamma RI promoter, the beta-casein promoter and a STAT-binding element in the promoter of the Bci-2 gene constitutive activation of STAT proteins was detected in untreated acute T- and C/B-leukemia cells (3 of 5 and 12 of 19 patients, respectively). Supershift analyses using Stats 1-6 specific antisera showed the constitutive DNA binding activity of Stat5 in these cells. Confocal microscopy revealed the nuclear localization of Stat5 and Western blot analyses showed tyrosine phosphorylation of Stat5 in nuclear extracts of acute leukemia cells. In contrast, peripheral blood mononuclear cells did not display constitutive STAT-DNA interaction. Further studies were performed on freshly isolated acute myeloid leukemia cells as well as on cell line derived K562, lymphoblastoid cells (LCL), and Burkitt's lymphoma cells (BL). Fluorescence microscopy, gelshift, and supershift experiments showed the nuclear localization and constitutive DNA-binding activity of Stat5 in K562 cells. Stat1 and Stat3 were constitutively activated in freshly isolated AML cells (10 of 14 patients) and in Epstein Barr virus-positive or interleukin-10 expressing permanent LCL and BL cells. Thus, these data indicate a differential pattern of STAT protein activation in lymphoid or myeloid leukemia and in lymphoma cells.},
}
@article {pmid8766621,
year = {1996},
author = {Laemmel, K},
title = {[Interview in medicine].},
journal = {Praxis},
volume = {85},
number = {27-28},
pages = {863-869},
pmid = {8766621},
issn = {1661-8157},
mesh = {Communication ; Empathy ; Humans ; *Medical History Taking ; Patient Compliance ; Physician's Role ; *Physician-Patient Relations ; },
abstract = {The very earliest myths relating to the art of healing give great weight to the vital importance of human dialogue. Today's technology, however, supplies the physician with so many diagnostic and therapeutic tools that the one-on-one encounter with the patient is steadily losing its significance. And yet, even today the dialogue is still one of the most important components of the healing process. The dialogue between physician and patient has three generally acknowledged objectives: gaining information about the illness, getting a clear understanding of the patient as a person, and the therapeutic effect. Taking the patient's history largely serves the first objective. At this stage, the physician--depending on his orientation--already chooses from a wide spectrum of approaches: from conducting a cursory, purpose-oriented interview to engaging in an open compassionate dialogue. The last approach, anchored in a spirit attuned to the patient's psyche and mind-set, will not only yield important clues as to the systemic interrelationships underlying the disease, but it also provides the foundation for the therapeutic dimension of the dialogue. By engaging in a dialogue and by his very readiness to do so, the physician unconsciously reveals a lot about his own person. In order to achieve a true dialogue, it is necessary that he abandon the role-playing so common to physician-patient relationships and that he meet his patient on a person-to-person basis. In the process he will reveal his self-perception, his relationship to fellow human beings and, not least, his idea of what it means to be a healer. This idea depends completely on his perception of human beings in general, on whether he sees people as complex machines controlled by the brain computer or as spiritual beings whose bodies function as the screen onto which their thoughts, ideas and convictions are projected. A true dialogue between physician and patient provides the foundation for healing to take place. The complete trust of the patient, his willingness to cooperate, his compliance, and, in the end, the very healing process itself depend on the physician's ability to engage in dialogue. The art of meaningful communication can be taught. Medical students, the physicians-to-be, can learn a lot from the example set by their mentors. When university professors, chiefs of service and supervising attending physicians are appointed, their ability to demonstrate not only a gift of dialogue, but also the awareness of its vital importance in a doctor-physician relationship should be a decisive factor.},
}
@article {pmid8711455,
year = {1996},
author = {Kaldestad, E},
title = {The empirical relationships between standardized measures of religiosity and personality/mental health.},
journal = {Scandinavian journal of psychology},
volume = {37},
number = {2},
pages = {205-220},
doi = {10.1111/j.1467-9450.1996.tb00652.x},
pmid = {8711455},
issn = {0036-5564},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Mental Disorders/diagnosis/*psychology ; Middle Aged ; Norway ; Personality Disorders/diagnosis/*psychology ; Personality Inventory/*statistics &amp; numerical data ; Psychometrics ; *Religion ; *Religion and Psychology ; Reproducibility of Results ; },
abstract = {The aim of this study was to investigate the relationships between standardized, factor-based measures of religiosity and personality/mental health. In a sample of 471 self-identified Christian subjects, 303 females and 168 males, 79 non-psychotic psychiatric in-patients and 392 non-patients, personal extrinsicness was partially positively correlated with the BCI Obsessive score. In multiple regression analyses some of the factor-based religious orientation indices related differently to the BCI Oral, Obsessive and Hysterical Scales and the SCL-90 Global Symptom Index as dependent variables. The religious orientations explained 8.8% of the variance of the BCI Oral Score, 4.2% of the BCI Obsessive score, 3.3% of the BCI Hysterical score, and 12.3% of the SCL-90 Global Symptom Index score. Of the doctrinal belief and morality indices only with Moral conservatism was significantly related to the BCI Hysterical score, and then negatively.},
}
@article {pmid8651742,
year = {1996},
author = {Fabian, TC and Patton, JH and Croce, MA and Minard, G and Kudsk, KA and Pritchard, FE},
title = {Blunt carotid injury. Importance of early diagnosis and anticoagulant therapy.},
journal = {Annals of surgery},
volume = {223},
number = {5},
pages = {513-22; discussion 522-5},
pmid = {8651742},
issn = {0003-4932},
mesh = {Adolescent ; Adult ; Aged ; Anticoagulants/*therapeutic use ; Carotid Arteries/diagnostic imaging ; *Carotid Artery Injuries ; Chi-Square Distribution ; Female ; Humans ; Incidence ; Logistic Models ; Male ; Middle Aged ; Survival Rate ; Tennessee/epidemiology ; Time Factors ; Tomography, X-Ray Computed ; Trauma Severity Indices ; Treatment Outcome ; Wounds, Nonpenetrating/*diagnostic imaging/*drug therapy/epidemiology ; },
abstract = {OBJECTIVE: The incidence, associated injury pattern, diagnostic factors, risk for adverse outcome, and efficacy of anticoagulant therapy in the setting of blunt and carotid injury (BCI) were evaluated.<br><br>SUMMARY BACKGROUND DATA: Blunt carotid injury is considered uncommon. The authors believe that it is underdiagnosed. Outcome is thought to be compromised by diagnostic delay. If delay in diagnosis is important, it is implied that therapy is effective. Although anticoagulation is the most frequently used therapy, efficacy has not been proven.<br><br>METHODS: Patients with BCI were identified from the registry of a level I trauma center during an 11-year period (ending September 1995). Neurologic examinations and outcomes, brain computed tomography (CT) results, angiographic findings, risk factors, and heparin therapy were evaluated.<br><br>RESULTS: Sixty-seven patients with 87 BCIs were treated. Thirty-four percent were diagnosed by incompatible neurologic and CT findings, 43% by new onset of neurologic deficits, and 23% by physical examination (neck injury, Horner's syndrome). There were 54 intimal dissections, 11 pseudoaneurysms, 17 thromboses, 4 carotid cavernous fistulas, and 1 transected internal carotid artery. Thirty-nine patients had follow-up angiograms. Mortality rate was 31%. Of 46 survivors, 63% had good neurologic outcomes, 17% moderate, and 20% bad. Logistic regression analysis demonstrated heparin therapy to be associated independently with survival (p < 0.02) and improvement in neurologic outcome (p < 0.01).<br><br>CONCLUSIONS: Blunt carotid injury is more common than appreciated, seen in 0.67% of patients admitted after motor vehicle accidents. Therapy with heparin is highly efficacious, significantly reducing neurologic morbidity and mortality. Heparin therapy, when instituted before onset of symptoms, ameliorates neurologic deterioration. Liberal screening, leading to earlier diagnosis, would improve outcome.},
}
@article {pmid9147414,
year = {1996},
author = {Socci, DJ and Arendash, GW},
title = {Chronic nicotine treatment prevents neuronal loss in neocortex resulting from nucleus basalis lesions in young adult and aged rats.},
journal = {Molecular and chemical neuropathology},
volume = {27},
number = {3},
pages = {285-305},
doi = {10.1007/BF02815110},
pmid = {9147414},
issn = {1044-7393},
mesh = {Aging/*physiology ; Analysis of Variance ; Animals ; Cerebral Cortex/*cytology/drug effects/growth &amp; development ; Ibotenic Acid ; Male ; Neurons/*cytology/drug effects/physiology ; *Neuroprotective Agents ; Nicotine/*pharmacology ; Rats ; Rats, Sprague-Dawley ; Substantia Innominata/drug effects/pathology/*physiology ; },
abstract = {In both young adult and aged rats, we tested the ability of chronically administered nicotine to rescue neocortical neurons from transneuronal degeneration resulting 5 mo after ibotenic acid (IBO) lesioning of the nucleus basalis magnocellularis (NBM). Young adult (2-3 mo-old) and aged (20-22-mo-old) rats were given unilateral infusions of IBO (5 mu g/1 mu L) at two sites within the NBM. Following surgery, animals began receiving either daily ip injections of nicotine (0.2 mg/kg) or saline vehicle. Treatment continued for 5 mo, at which time all animals were sacrificed and their brains processed histologically. For each brain, computer-assisted image analysis was then used to analyze the unlesioned (left) and lesioned (right) side of five non-consecutive brain sections from parietal cortex Layers II-IV and V. NBM lesioning in both young adult and aged vehicle-treated rats resulted in a significant 16-21% neuronal loss ipsilateral to NBM lesioning in neocortical Layers II-IV. Aged NBM-lesioned rats also exhibited a significant 12% neuronal loss in neocortical Layer V ipsilaterally. By contrast, those NBM-lesioned young adult and aged rats that received daily nicotine treatment postsurgery did not show any ipsilateral neuronal loss in the same parietal cortex areas, indicating that chronic nicotine treatment prevented the transneuronal degeneration of neocortical neurons resulting 5 mo afer NBM lesioning.},
}
@article {pmid8886669,
year = {1996},
author = {Berlot, G and Nicolazzi, G and Viviani, M and Silvestri, L and Tomasini, A and Gullo, A and Cloffi, V and Bussani, R},
title = {Traumatic blunt carotid injury: clinical experience and review of the literature.},
journal = {European journal of emergency medicine : official journal of the European Society for Emergency Medicine},
volume = {3},
number = {1},
pages = {36-42},
doi = {10.1097/00063110-199603000-00007},
pmid = {8886669},
issn = {0969-9546},
mesh = {Adolescent ; Adult ; Angiography, Digital Subtraction ; *Carotid Artery Injuries ; Fatal Outcome ; Female ; Glasgow Coma Scale ; Hemiplegia/etiology ; Humans ; Injury Severity Score ; Male ; Outcome Assessment, Health Care ; Retrospective Studies ; Tomography, X-Ray Computed ; Wounds, Nonpenetrating/complications/*diagnostic imaging/therapy ; },
abstract = {To evaluate the symptoms, the associated lesions, the treatment and the outcome of patients with blunt carotid injury (BCI), we reviewed the records of all patients admitted to our intensive care unit with head trauma between May 1991 and May 1995. A patient's assessment included the commonly used severity scores and cranial computed tomography (CT). Other diagnostic investigations were performed according to the clinical setting. Four patients (2 males, 2 females, age 29 +/- 13 years) out of 145 were diagnosed to have BCI. At admission, the Glasgow Coma Scale (GCS) was > or = 12 in all patients, and was associated with hemiparesis in three of them; the fourth became paretic 48 hours later. No pathological elements were demonstrated at the initial CT scan, whilst subsequent examinations showed signs of ischaemia after a variable interval from admission. In every patient the radiologic investigations demonstrated a thrombotic obstruction of the internal carotid artery (ICA), associated with an intimal dissection in two cases. Three patients were discharged with only minor neurologic symptoms. The fourth patient was referred to our ICU after the development of a massive hemispheric infarction, and died 3 days after admission.},
}
@article {pmid8577001,
year = {1996},
author = {Dowd, MD and Krug, S},
title = {Pediatric blunt cardiac injury: epidemiology, clinical features, and diagnosis. Pediatric Emergency Medicine Collaborative Research Committee: Working Group on Blunt Cardiac Injury.},
journal = {The Journal of trauma},
volume = {40},
number = {1},
pages = {61-67},
doi = {10.1097/00005373-199601000-00012},
pmid = {8577001},
issn = {0022-5282},
support = {T32 PE10002/PE/BHP HRSA HHS/United States ; },
mesh = {Accidents, Traffic ; Adolescent ; Child ; Child, Preschool ; Female ; *Heart Injuries/complications/diagnosis/epidemiology ; Humans ; Infant ; Infant, Newborn ; Injury Severity Score ; Male ; Prognosis ; Retrospective Studies ; Survival Rate ; Treatment Outcome ; *Wounds, Nonpenetrating/complications/diagnosis/epidemiology ; },
abstract = {AIM: The goal of this study was to describe the epidemiology, clinical presentation, diagnostic methods, and outcome in a large series of children with blunt cardiac injury (BCI).<br><br>METHODS: A multicenter retrospective review of all individuals less than 18 years of age diagnosed with a BCI from 1983 to 1993 was conducted. Cases included all those with a discharge diagnosis of myocardial contusion, concussion, ventricular disruption, or unspecified BCI.<br><br>RESULTS: A total of 184 cases of BCI were identified in 16 participating centers. The median age was 7.4 years, and 73% were male. Myocardial contusions accounted for 95% of the diagnoses. The leading mechanisms were motor vehicle crashes involving a pedestrian (39.7%) or passenger (31.0%). The majority (87%) had multiple system trauma, with a mean Injury Severity Score of 27.2 (SD +/- 14.4). Pulmonary contusions were present in 50.5% and rib fractures in 23.0%. The most common diagnostic test performed was a 12-lead electrocardiogram (EKG) (82%), followed by a MB band of creatine phosphokinase (CPK-MB) (69%) and echocardiogram (65%). All three tests were performed in 50%. In these patients, agreement among various diagnostic test pairs was fair (echocardiogram vs. EKG, kappa = 0.27) to poor (echocardiogram vs. CPK-MB, kappa = 0.07 and EKG vs. CPK-MB, kappa = 0.08). No hemodynamically stable patient who presented with a normal sinus rhythm subsequently developed a cardiac arrhythmia or cardiac failure. There were 25 deaths (13.6%), 3 of which were caused by acute pump failure secondary to massive cardiac injury. The remainder died of head or abdominal injuries. Of the 159 (86.4%) patients surviving, 8 (5% of survivors) had significant cardiac sequela, most commonly mitral or tricuspid insufficiency or ventricular septal defect.<br><br>CONCLUSIONS: Pediatric BCI is usually diagnosed in the context of severe multiple system trauma and is less commonly an isolated event. Because of the lack of a standard, various diagnostic tests are used in the diagnosis of BCI, and these tests rarely agree. In hospitalized pediatric patients with BCI, unanticipated complications are rare. Significant sequela, although uncommon, do occur and follow-up of children with BCI should be ensured.},
}
@article {pmid7583576,
year = {1995},
author = {Kanzaki, T and Tamura, K and Takahashi, K and Saito, Y and Akikusa, B and Oohashi, H and Kasayuki, N and Ueda, M and Morisaki, N},
title = {In vivo effect of TGF- beta 1. Enhanced intimal thickening by administration of TGF- beta 1 in rabbit arteries injured with a balloon catheter.},
journal = {Arteriosclerosis, thrombosis, and vascular biology},
volume = {15},
number = {11},
pages = {1951-1957},
doi = {10.1161/01.atv.15.11.1951},
pmid = {7583576},
issn = {1079-5642},
mesh = {Animals ; Carotid Artery, Common/drug effects/metabolism/pathology ; Carotid Stenosis/*metabolism/pathology ; Catheterization/adverse effects ; Cell Count/drug effects ; Cell Size/drug effects ; Extracellular Matrix/*drug effects/metabolism ; Fibronectins/biosynthesis ; Male ; RNA, Messenger/biosynthesis ; Rabbits ; Receptors, Transforming Growth Factor beta/biosynthesis ; Transforming Growth Factor beta/metabolism/*pharmacology ; },
abstract = {The in vivo effect of transforming growth factor-beta 1 (TGF-beta 1) was studied in a model system in which arterial intimal thickening was induced by injury of rabbit arteries with a balloon catheter (BCI). Intimal area and its ratio to medial area in carotid arteries after BCI were significantly higher in rabbits treated with 10 micrograms/kg TGF-beta 1 and 10 mg/kg aspirin i.v. QD (TGF-beta 1 group) than in those treated with 10 mg/kg aspirin i.v. QD only (control group). Intimal cell numbers in the TGF-beta 1 and control groups were not significantly different from each other, but matrix volume in the intimal layer was significantly higher in the TGF-beta 1 group. By immunohistochemical and Northern blot analyses, the fibronectin content in carotid intimal and medial layers was greater in the TGF-beta 1 group compared with that in the control group. Thus, in intimal thickenings induced by BCI. TGF-beta 1 mainly enhanced the formation of matrix containing fibronectin. Moreover, the mRNAs of TGF-beta 1 and type II receptors were detected in carotid arteries 7 and 14 days after, but not before, BCI. Thus, TGF-beta 1 influences the process of intimal thickening induced by BCI through a receptor-mediated mechanism in vivo. The significance of this fact is discussed in relation to the development of atherosclerosis.},
}
@article {pmid7474147,
year = {1995},
author = {Picard, FJ and Coulthart, MB and Oger, J and King, EE and Kim, S and Arp, J and Rice, GP and Dekaban, GA},
title = {Human T-lymphotropic virus type 1 in coastal natives of British Columbia: phylogenetic affinities and possible origins.},
journal = {Journal of virology},
volume = {69},
number = {11},
pages = {7248-7256},
pmid = {7474147},
issn = {0022-538X},
mesh = {Base Sequence ; British Columbia/epidemiology ; DNA Primers ; Demography ; HTLV-I Infections/epidemiology/*virology ; Human T-lymphotropic virus 1/*classification/*genetics/isolation &amp; purification ; Humans ; Indians, North American ; Molecular Sequence Data ; *Phylogeny ; Polymerase Chain Reaction ; *Polymorphism, Restriction Fragment Length ; Repetitive Sequences, Nucleic Acid ; Species Specificity ; },
abstract = {Human T-lymphotropic virus type 1 (HTLV-1) infection has been discovered recently in people of Amerindian descent living in coastal areas of British Columbia, Canada. DNA sequencing combined with phylogenetic analysis and restriction fragment length polymorphism (RFLP) typing of HTLV-1 strains recovered from these British Columbia Indians (BCI) was conducted. Sequence-based phylogenetic trees distributed the BCI isolates among the Japanese subcluster (subcluster B) and the geographically widely distributed subcluster (subcluster A) of the large HTLV-1 cosmopolitan cluster. Long terminal repeat (LTR) RFLP typing revealed three distinct, equally frequent LTR cleavage patterns, two of which were of previously recognized Japanese and widely dispersed cosmopolitan types. A third, new cleavage pattern was detected which may have arisen by recombination between two other HTLV-1 genotypes. Our results suggest multiple origins for HTLV-1 in BCI, which are equally consistent with (i) a cluster of recent sporadic infections, (ii) ancient endemic vertical transmission through Amerindian lineages, or (iii) both.},
}
@article {pmid7472401,
year = {1995},
author = {Krajewski, S and Mai, JK and Krajewska, M and Sikorska, M and Mossakowski, MJ and Reed, JC},
title = {Upregulation of bax protein levels in neurons following cerebral ischemia.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {15},
number = {10},
pages = {6364-6376},
doi = {10.1523/JNEUROSCI.15-10-06364.1995},
pmid = {7472401},
issn = {0270-6474},
mesh = {Animals ; Brain/metabolism/pathology ; DNA/metabolism ; Female ; Immunoblotting ; Immunohistochemistry ; Ischemic Attack, Transient/*metabolism/pathology ; Neurons/*metabolism ; Proto-Oncogene Proteins/*metabolism ; Proto-Oncogene Proteins c-bcl-2 ; Rats ; Rats, Wistar ; bcl-2-Associated X Protein ; bcl-X Protein ; },
abstract = {The patterns of expression of the bcl-2, bax, and bci-X genes were examined immunohistochemically in neurons of the adult rat brain before and after 10 min of global ischemia induced by transient cardiac arrest. High levels of the cell death promoting protein Bax and concomitant low levels of the apoptosis-blocking protein Bcl-2 were found in some populations of neurons that are particularly sensitive to cell death induced by transient global ischemia, such as the CA1 sector of the hippocampus and the Purkinje cells of the cerebellum. Moreover, within 0.5 to 3 hr after an ischemic episode, immunostaining for Bax was markedly increased within neurons with morphological features of degeneration in many regions of the brain. Use of a two-color staining method for simultaneous analysis of Bax protein and in situ detection of DNA-strand breaks revealed high levels of Bax immunoreactivity in many neurons undergoing apoptosis. Postischemic elevations in Bax protein levels in the hippocampus, cortex, and cerebellum were also demonstrated by immunoblotting. At early times after transient ischemia, regulation of Bcl-2 and Bcl-x protein levels varied among neuronal subpopulations, but from 3 hr on, those neurons with morphological evidence of degeneration uniformly contained reduced levels of Bci-2 and particularly Bci-X immunoreactivity. The findings suggest that differential expression of some members of the bcl-2 gene family may play an important role in determining the relative sensitivity of neuronal subpopulations to ischemia and that postischemic alterations in the expression of bax, bcl-2, and bcl-x may contribute to the delayed neuronal cell death that occurs during the repurfusion phase after a transient ischemic episode.},
}
@article {pmid7554006,
year = {1995},
author = {Lauber, R and Seeberger, B and Zbinden, AM},
title = {Carbon dioxide analysers: accuracy, alarm limits and effects of interfering gases.},
journal = {Canadian journal of anaesthesia = Journal canadien d'anesthesie},
volume = {42},
number = {7},
pages = {643-656},
pmid = {7554006},
issn = {0832-610X},
mesh = {Blood Gas Analysis/*instrumentation ; Carbon Dioxide/*blood ; Equipment Design ; Equipment Failure ; Humans ; Nitrous Oxide/*blood ; Oxygen/*blood ; Predictive Value of Tests ; },
abstract = {Six mainstream and twelve sidestream infrared carbon dioxide (CO2) analysers were tested for accuracy of the CO2 display value, alarm activation and the effects of nitrous oxide (N2O), oxygen (O2) and water vapour according to the ISO Draft International Standard (DIS)#9918. Mainstream analysers (M-type): Novametrix Capnogard 1265; Hewlett Packard HP M1166A (CO2-module HP M1016A); Datascope Passport; Marquette Tramscope 12; Nellcor Ultra Cap N-6000; Hellige Vicom-sm SMU 611/612 ETC. Sidestream analysers: Brüel &amp; Kjaer Type 1304; Datex Capnomac II; Marquette MGA-AS; Datascope Multinex; Ohmeda 4700 OxiCap (all type S1: respiratory cycles not demanded); Biochem BCI 9000; Bruker BCI 9100; Dräger Capnodig and PM 8020; Criticare Poet II; Hellige Vicom-sm SMU 611/612 A-GAS (all type S2: respiratory cycles demanded). The investigations were performed with premixed test gases (2.5, 5, 10 vol%, error < or = 1% rel.). Humidification (37 degrees C) of gases were generated by a Dräger Aquapor. Respiratory cycles were simulated by manually activated valves. All monitors complied with the tolerated accuracy bias in CO2 reading (< or = 12% or 4 mmHg of actual test gas value) for wet and dry test gases at all concentrations, except that the Marquette MGA-AS exceeded this accuracy limit with wet gases at 5 and 10 vol% CO2. Water condensed in the metal airway adapter of the HP M1166A at 37 degrees C gas temperature but not at 30 degrees C. The Servomex 2500 (nonclinical reference monitor), Passport (M-type), Multinex (S1-type) and Poet II (S2-type) showed the least bias for dry and wet gases. Nitrous oxide and O2 had practically no effect on the Capnodig and the errors in the others were max. 3.4 mmHg, still within the tolerated bias in the DIS (same as above). The difference between the display reading at alarm activation and the set point was in all monitors (except in the Capnodig: bias 1.75 mmHg at 5 vol% CO2) below the tolerated limit of the DIS (difference < or = 0.2 vol%). The authors conclude that the tested monitors are safe for clinical used (except those failing the DIS limits). The accuracy of the CO2-reading (average of mean absolute bias) is better in the M-type than in the S1- or S2-type analysers although no statistical (nor clinical) significant differences could be detected. Most manufacturers work with stricter limits than those proposed by the DIS.},
}
@article {pmid7725078,
year = {1995},
author = {Kaldestad, E},
title = {The empirical relationships of the religious orientations to personality.},
journal = {Scandinavian journal of psychology},
volume = {36},
number = {1},
pages = {95-108},
doi = {10.1111/j.1467-9450.1995.tb00971.x},
pmid = {7725078},
issn = {0036-5564},
mesh = {Adolescent ; Adult ; Aged ; Aged, 80 and over ; Character ; Female ; Humans ; Internal-External Control ; Male ; Mental Disorders/psychology ; Middle Aged ; *Personality Development ; Personality Inventory ; *Psychoanalytic Theory ; *Religion and Psychology ; },
abstract = {The aim of this study was to investigate the relationships between the religious orientations and the psychoanalytic character types, as assessed by the Basic Character Inventory (BCI). A person with an intrinsic religious orientation is sincere and integrated in his religiousness. A person with an extrinsic religious orientation uses his religion to promote his personal, social and economic goals. A person with a quest religious orientation is seeking, doubting and changeable in his religiousness. In a sample of 471 subjects, 168 men and 303 women, the level of intrinsic religious orientation was not related to the BCI scores. The levels of extrinsic and quest religious orientations were positively related to the BCI Oral score. Since the oral persons are dependent and craving, it seems likely that they may be extrinsically oriented. And as they also may be insecure, vague and indecisive, it is not unexpected that they also are quest oriented. The changeable quest orientation was also positively related to the changeable BCI Hysterical character.},
}
@article {pmid7721604,
year = {1995},
author = {Oldenburg, B and Owen, N and Parle, M and Gomel, M},
title = {An economic evaluation of four work site based cardiovascular risk factor interventions.},
journal = {Health education quarterly},
volume = {22},
number = {1},
pages = {9-19},
doi = {10.1177/109019819502200103},
pmid = {7721604},
issn = {0195-8402},
mesh = {Adult ; Cardiovascular Diseases/*economics/*prevention &amp; control ; Cost-Benefit Analysis ; Female ; Follow-Up Studies ; Health Education/*economics ; Humans ; Life Style ; Male ; Occupational Health Services/*economics ; Outcome Assessment, Health Care ; Program Evaluation ; Risk Factors ; },
abstract = {We used outcome data from a randomized work site intervention trial to examine the cost-effectiveness of four cardiovascular disease (CVD) risk reduction programs: health risk assessment (HRA), risk factor education (RFE), behavioral counseling (BC), and behavioral counseling plus incentives (BCI). Composite CVD risk scores were derived from measures of serum total cholesterol, blood pressure, number of cigarettes smoked, body mass index, and aerobic capacity. The economic evaluation of the programs focused on the subset of costs most sensitive to the differences between the interventions, and a sensitivity analysis examined some of the relevant cost variations. At the 6-month follow-up (i.e., the "action" or initiation stage of lifestyle change), the RFE, BC, and BCI interventions produced a significant reduction in cardiovascular risk. Incremental analyses demonstrated RFE to be more cost-effective, but not as clinically effective as BC; BC was more cost-effective than RFE when assessment costs were included, and BCI was judged to be the least cost-effective. At the 12-month follow-up (i.e., the "maintenance" stage of lifestyle of change), BC was the only program found to produce a significant reduction in CVD risk. Individualized behavioral counseling was found to be a cost-effective strategy for the initiation and maintenance of CVD risk factor reduction.},
}
@article {pmid8776708,
year = {1995},
author = {Pfurtscheller, G and Flotzinger, D and Pregenzer, M and Wolpaw, JR and McFarland, D},
title = {EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.},
journal = {Medical progress through technology},
volume = {21},
number = {3},
pages = {111-121},
pmid = {8776708},
issn = {0047-6552},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Aged ; Brain Mapping/instrumentation ; *Communication Devices for People with Disabilities ; Cortical Synchronization ; Electrodes ; Electroencephalography/*instrumentation ; Female ; Fourier Analysis ; Humans ; Male ; Middle Aged ; Motor Cortex/*physiology ; Online Systems/instrumentation ; Signal Processing, Computer-Assisted/instrumentation ; Somatosensory Cortex/*physiology ; *User-Computer Interface ; },
abstract = {Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.},
}
@article {pmid8768250,
year = {1995},
author = {Malumbres, M and Mateos, LM and Guerrero, C and Martín, JF},
title = {Molecular cloning of the hom-thrC-thrB cluster from Bacillus sp. ULM1: expression of the thrC gene in Escherichia coli and corynebacteria, and evolutionary relationships of the threonine genes.},
journal = {Folia microbiologica},
volume = {40},
number = {6},
pages = {595-606},
pmid = {8768250},
issn = {0015-5632},
mesh = {Amino Acid Sequence ; Bacillus/*genetics ; Bacteria/classification/genetics ; Bacterial Proteins/*genetics ; Base Sequence ; *Carbon-Oxygen Lyases ; Cloning, Molecular ; Corynebacterium/*genetics ; Escherichia coli/*genetics ; Evolution, Molecular ; *Genes, Bacterial ; Homoserine Dehydrogenase/*genetics ; Lyases/*genetics ; Molecular Sequence Data ; Phosphotransferases (Alcohol Group Acceptor)/*genetics ; Promoter Regions, Genetic ; Species Specificity ; },
abstract = {A 6.5 kb DNA fragment containing the gene (thrC) encoding threonine synthase, the last enzyme of the threonine biosynthetic pathway, has been cloned from the DNA of Bacillus sp. ULM1 by complementation of Escherichia coli and Brevibacterium lactofermentum thrC auxotrophs. Complementation studies showed that the thrB gene (encoding homoserine kinase) is found downstream from the thrC gene, and analysis of nucleotide sequences indicated that the hom gene (encoding homoserine dehydrogenase) is located upstream of the thrC gene. The organization of this cluster of genes is similar to the Bacillus subtilis threonine operon (hom-thrC-thrB). An 1.9 kb BclI fragment from the Bacillus sp. ULM1 DNA insert 351 amino acids was found corresponding to a protein of 37462 Da. The thrC gene showed a low G + C content (39.4%) and the encoded threonine synthase is very similar to the B. subtilis enzyme. Expression of the 1.9 kb BcI DNA fragment in E. coli minicells resulted in the formation of a 37 kDa protein. The upstream region of this gene shows promoter activity in E. coli but not in corynebacteria. A peptide sequence, including a lysine that is known to bind the pyridoxal phosphate cofactor, is conserved in all threonine synthase sequences and also in the threonine and serine dehydratase genes. Amino acid comparison of nine threonine synthases revealed evolutionary relationships between different groups of bacteria.},
}
@article {pmid7889620,
year = {1994},
author = {Vilar, L and Burke, CW},
title = {Quinagolide efficacy and tolerability in hyperprolactinaemic patients who are resistant to or intolerant of bromocriptine.},
journal = {Clinical endocrinology},
volume = {41},
number = {6},
pages = {821-826},
doi = {10.1111/j.1365-2265.1994.tb02799.x},
pmid = {7889620},
issn = {0300-0664},
mesh = {Adult ; Aminoquinolines/*therapeutic use ; Bromocriptine/therapeutic use ; Dopamine Agents/*therapeutic use ; Drug Tolerance ; Female ; Humans ; Hyperprolactinemia/*drug therapy ; Male ; Middle Aged ; Prospective Studies ; },
abstract = {OBJECTIVE: To audit the efficacy of quinagolide (CV205-502, Norprolac, Sandoz) in lowering prolactin, and its tolerability, in patients with bromocriptine resistance (BCR) or bromocriptine intolerance (BCI), in view of the paucity of results published in patients specifically with BCR or BCI, by collating results in our own patients with the reports in the literature.<br><br>DESIGN: Open prospective, uncontrolled administration of quinagolide in patients with BCR (defined for this report as failure to attain normal prolactin levels after 4 months of bromocriptine at maximum tolerated doses), or BCI (defined as a patient request to cease bromocriptine treatment because of side-effects at doses that were required, or failed, to normalize PRL levels).<br><br>MEASUREMENTS: Prolactin levels, menses or pregnancy, and side-effects.<br><br>PATIENTS: Six with BCR, and six with BCI (microprolactinoma in 7, macroprolactinoma in 5), treated with quinagolide 75 micrograms nightly increasing incrementally to a maximum of 450 micrograms. One patient who had taken part in a multicentre study of quinagolide in macroprolactinomas had BCI, and 11 further patients in the endocrine clinic who had BCR or BCI were offered quinagolide therapy under named-patient compassionate arrangements.<br><br>RESULTS: Normal prolactin in 4/5 with BCR (3/6 with side-effects, none of them quinagolide intolerant), and normal prolactin in 2/6 with BCI (4/6 with side-effects, two of them quinagolide intolerant).<br><br>CONCLUSIONS: Results in our 12 patients are broadly in line with those in 51 patients with bromocriptine resistance and 39 with bromocriptine intolerance extracted from various published reports, which together suggest that prolactin can be normalized in 16% of patients with bromocriptine resistance by quinagolide in doses of 225 micrograms or less, and in a further 20% by higher doses up to 600 micrograms. In bromocriptine intolerance, prolactin was normalized by quinagolide in doses of 225 micrograms or less in 58% of published cases and in 3 more patients by higher doses up to 1050 micrograms. About half the patients with bromocriptine resistance or bromocriptine intolerance who are treated with quinagolide experience side-effects, and around 7% are quinagolide intolerant. Doses need not exceed 225 micrograms, until failure to respond at this dose level is demonstrated.},
}
@article {pmid7525495,
year = {1994},
author = {Castellani, P and Viale, G and Dorcaratto, A and Nicolo, G and Kaczmarek, J and Querze, G and Zardi, L},
title = {The fibronectin isoform containing the ED-B oncofetal domain: a marker of angiogenesis.},
journal = {International journal of cancer},
volume = {59},
number = {5},
pages = {612-618},
doi = {10.1002/ijc.2910590507},
pmid = {7525495},
issn = {0020-7136},
mesh = {Alternative Splicing ; Antibodies, Monoclonal ; Astrocytoma/blood supply ; Endometrium/blood supply/chemistry ; Endothelium, Vascular/pathology ; Factor VIII/immunology ; Female ; Fetus/metabolism ; Fibronectins/*analysis/genetics ; Glioblastoma/blood supply ; Humans ; Hyperplasia ; Immunoenzyme Techniques ; Meningioma/blood supply ; Neoplasms/*blood supply/chemistry/pathology ; *Neovascularization, Pathologic ; Tissue Distribution ; },
abstract = {Different fibronectin (FN) isoforms are generated by the alternative splicing of 3 regions (ED-A, ED-B and IIICS) of the primary transcript. The FN isoform containing the ED-B sequence, a complete type-III-homology repeat, while having extremely restricted distribution in normal adult tissues, reveals high expression in fetal and tumor tissues. Using the monoclonal antibody (MAb) BC-I, specific for the FN isoform containing the ED-B sequence (B+.FN), we demonstrated here, using immunohistochemical techniques, that while this FN isoform is undetectable in mature vessels, it is highly expressed during angiogenesis both in neoplastic and in normal tissues, as in the case of the functional layer of endometrium during the proliferative phase. B+.FN is thus a marker for the formation of new vessels, and the BC-I MAb may be a useful reagent for evaluating the level of the angiogenetic process in different neoplasms.},
}
@article {pmid7940159,
year = {1994},
author = {Malangoni, MA and McHenry, CR and Jacobs, DG},
title = {Outcome of serious blunt cardiac injury.},
journal = {Surgery},
volume = {116},
number = {4},
pages = {628-32; discussion 632-3},
pmid = {7940159},
issn = {0039-6060},
mesh = {Adolescent ; Adult ; Aged ; Child ; Creatine Kinase/blood ; Echocardiography ; Female ; Heart Injuries/complications/diagnosis/*surgery ; Humans ; Hypotension/etiology ; Isoenzymes ; Male ; Middle Aged ; Wounds, Nonpenetrating/complications/diagnosis/*surgery ; },
abstract = {BACKGROUND: Although serious blunt cardiac injury (BCI) is usually fatal, patients who reach the hospital alive can have a spectrum of abnormalities. We attempted to define the clinical features that helped identify serious BCI and to evaluate outcome.<br><br>METHODS: Patients with serious BCI at a level I trauma center were identified during a 3-year period.<br><br>RESULTS: Twelve patients had serious BCI. Six patients had cardiac arrest, and six had unexplained hypotension. Specific injuries included acute myocardial rupture (two patients); valvular disruption (two); myocardial contusion associated with either cardiac failure (two), complex ventricular arrhythmias (two), or delayed myocardial rupture (one), or present at autopsy (two); and coronary artery thrombosis (one). Seven of eight patients who did not have associated fatal injuries survived. Electrocardiography suggested cardiac injury in all nine patients in whom it was done, and echocardiography was useful to establish the diagnosis in four of five patients. Creatine phosphokinase isoenzyme levels did not distinguish serious injuries.<br><br>CONCLUSIONS: The outcome of serious blunt cardiac injury can be favorable if patients have signs of life on arrival at the hospital, the signs of injury are recognized promptly, and other injuries do not supervene.},
}
@article {pmid7927891,
year = {1994},
author = {Kaczmarek, J and Castellani, P and Nicolo, G and Spina, B and Allemanni, G and Zardi, L},
title = {Distribution of oncofetal fibronectin isoforms in normal, hyperplastic and neoplastic human breast tissues.},
journal = {International journal of cancer},
volume = {59},
number = {1},
pages = {11-16},
doi = {10.1002/ijc.2910590104},
pmid = {7927891},
issn = {0020-7136},
mesh = {Adenocarcinoma, Mucinous/chemistry ; Antibodies, Monoclonal ; Breast/*chemistry/pathology ; Breast Neoplasms/*chemistry ; Carcinoma, Ductal, Breast/chemistry ; Carcinoma, Lobular/chemistry ; Female ; Fibroadenoma/chemistry ; Fibrocystic Breast Disease/metabolism ; Fibronectins/*analysis/genetics ; Glycosylation ; Humans ; Hyperplasia ; Immunohistochemistry ; RNA Splicing ; Tissue Distribution ; },
abstract = {Two different oncofetal fibronectins (FN) have been reported: one, generated by O-glycosylation in the splicing region IIICS that is recognized by monoclonal antibody (MAb) FDC-6, and another, recognized by MAb BC-I, generated by the alternative splicing of the FN pre-mRNA which includes an extra type-III repeat called ED-B. Using these and 2 other MAbs (IST-4 which recognizes all different FN isoforms and IST-6 which recognizes only the FN molecules that do not include the ED-B sequence) we have immunohistochemically studied 171 normal, hyperplastic and neoplastic breast-tissue specimens. Although all normal specimens reacted strongly with MAbs IST-4 and IST-6, they did not show the presence of oncofetal FNs as established by the use of BC-I and FDC-6. In contrast, out of the 97 cases of invasive ductal carcinomas studied, 90 (93%) and 96 (99%) reacted positively with BC-I and FDC-6, respectively, the reaction being observed in the tumoral stroma connective tissue and in tumoral vessels. Furthermore, invasive lobular carcinoma showed less intense and less frequent staining with BC-1 and FDC-6 (10 and 11 out of 14, respectively). We found differences in the distribution of the 2 oncofetal fibronectin isoforms within the same specimens. The most remarkable difference was observed in the tumoral vessels: in invasive ductal carcinoma MAb BC-1 revealed a positive reaction with vessels in 78% of cases while FDC-6 showed such a reaction in only 59% of cases.},
}
@article {pmid7811910,
year = {1994},
author = {Pregenzer, M and Pfurtscheller, G and Flotzinger, D},
title = {Selection of electrode positions for an EEG-based brain computer interface (BCI).},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {39},
number = {10},
pages = {264-269},
doi = {10.1515/bmte.1994.39.10.264},
pmid = {7811910},
issn = {0013-5585},
mesh = {Adult ; Brain Mapping/instrumentation ; Dominance, Cerebral/physiology ; Electrodes ; Electroencephalography/*instrumentation ; Evoked Potentials, Somatosensory/physiology ; Humans ; Motor Activity/physiology ; Motor Cortex/physiology ; Reference Values ; Signal Processing, Computer-Assisted/*instrumentation ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {One major question in designing an EEG-based Brain Computer Interface to bypass the normal motor pathways is the selection of proper electrode positions. This study investigates electrode selection with a Distinction Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an extended Learning Vector Quantizer (LVQ) which employs a weighted distance function for dynamical scaling and feature selection. The data analysed and classified were 56-channel EEG recordings over sensorimotor areas during preparation for discrete left or right index finger flexions. Data from 3 subjects are reported. It was found by DSLVQ that the most important electrode positions for differentiation between planning of left and right finger movement overlie cortical finger/hand areas over both hemispheres.},
}
@article {pmid7800125,
year = {1994},
author = {Kinney, HC and Karthigasan, J and Borenshteyn, NI and Flax, JD and Kirschner, DA},
title = {Myelination in the developing human brain: biochemical correlates.},
journal = {Neurochemical research},
volume = {19},
number = {8},
pages = {983-996},
pmid = {7800125},
issn = {0364-3190},
support = {P30-HD18655/HD/NICHD NIH HHS/United States ; R01-HD20991/HD/NICHD NIH HHS/United States ; R01-NS20824/NS/NINDS NIH HHS/United States ; },
mesh = {Brain/embryology/growth &amp; development/*physiology ; Central Nervous System Diseases/metabolism ; Embryonic and Fetal Development/physiology ; Female ; Fetal Diseases/physiopathology ; Humans ; Infant ; Infant, Newborn ; Lipids/analysis ; Male ; Maple Syrup Urine Disease/metabolism ; Myelin Proteins/analysis ; Myelin Sheath/*physiology ; },
abstract = {To delineate the biochemical sequences of myelination in the human brain, we analyzed the protein and lipid composition of white matter in 18 baseline cases ranging in age from midgestation through infancy, the critical period in human myelination when the most rapid changes occur. Three adult cases were used as indices of maturity, and 4 cases with major disorders of CNS myelination (maple syrup urine disease, severe periventricular leukomalacia, idiopathic central hypomyelination, and metachromatic leukodystrophy) were analyzed. Brain samples were obtained < or = 24 hours after death. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and high performance thin-layer chromatography were used to separate and identify proteins and polar and neutral lipids in an average of 10 sites/brain; computer-based densitometry was used to quantify polar lipids. Biochemical sequences, as manifested by the appearance of the myelin-associated lipids and myelin-specific proteins, closely followed previously described anatomic sequences both temporally and by region, and were identical in all sites sampled: sphingomyelin was followed simultaneously by cerebrosides, MBP, PLP, and nonhydroxy-sulfatide, followed by hydroxy-sulfatide. The onset and tempo of the expression of individual constituents, however, were quite variable among sites, suggesting a wide differential in vulnerable periods to insult in biochemically-specific pathways in early life. Cholesterol ester was transiently elevated during late gestation and early infancy, prior to and around the time of the appearance of cerebrosides, sulfatides, PLP, and MBP. Distinctive lipid and protein abnormalities were detected in idiopathic central hypomyelination and metachromatic leukodystrophy. This study underscores the feasibility of the combined biochemical approaches in pediatric brains and provides guidelines for the assessment of disorders of myelination in early human life.},
}
@article {pmid7515787,
year = {1994},
author = {Wolpaw, JR and McFarland, DJ},
title = {Multichannel EEG-based brain-computer communication.},
journal = {Electroencephalography and clinical neurophysiology},
volume = {90},
number = {6},
pages = {444-449},
doi = {10.1016/0013-4694(94)90135-x},
pmid = {7515787},
issn = {0013-4694},
support = {HD30146/HD/NICHD NIH HHS/United States ; },
mesh = {Adult ; Brain/*physiopathology ; Communication Devices for People with Disabilities ; *Electroencephalography ; Female ; Humans ; Male ; Movement Disorders/physiopathology ; *User-Computer Interface ; },
abstract = {Individuals who are paralyzed or have other severe movement disorders often need alternative means for communicating with and controlling their environments. In this study, human subjects learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen. Amplitudes of 8-12 Hz activity in the EEG recorded from the scalp across right and left central sulci were determined by fast Fourier transform and combined to control vertical and horizontal cursor movements simultaneously. This independent control of two separate EEG channels cannot be attributed to a non-specific change in brain activity and appeared to be specific to the mu rhythm frequency range. With further development, multichannel EEG-based communication may prove of significant value to those with severe motor disabilities.},
}
@article {pmid8194742,
year = {1994},
author = {Bonnet, X and Naulleau, G and Mauget, R},
title = {The influence of body condition on 17-beta estradiol levels in relation to vitellogenesis in female Vipera aspis (Reptilia, Viperidae).},
journal = {General and comparative endocrinology},
volume = {93},
number = {3},
pages = {424-437},
doi = {10.1006/gcen.1994.1047},
pmid = {8194742},
issn = {0016-6480},
mesh = {Animals ; Blood Proteins/metabolism ; Calcium/blood ; Cholesterol/blood ; Estradiol/administration &amp; dosage/*blood/pharmacology ; Female ; Phospholipids/blood ; Phosphorus/blood ; Reproduction ; Seasons ; Triglycerides/blood ; Viperidae/*physiology ; *Vitellogenesis ; },
abstract = {Seventy-six wild Vipera aspis females were caught over 3 years and placed in outdoor enclosures; 39 reproduced and 37 did not. Almost all the reproductive females had a body condition index (BCI) greater than 0.70 when vitellogenesis began. Monthly blood samples were taken by cardiac puncture. The main plasma parameters of vitellogenesis were measured by spectrophotometry: total plasma calcium, phosphorus, phospholipids, cholesterol, triglycerides, proteins, and albumin. Plasma 17-beta estradiol levels were determined by RIA. Vitellogenesis started soon after hibernation in reproductive females with very high 17-beta estradiol concentrations (average of 4.00 ng/ml) and there was a marked mobilization of maternal reserves (fat bodies, liver, and vertebral bone) associated with very high values of plasma calcium, phosphorus, phospholipids, cholesterol, triglycerides, and proteins. The kinetics of the main plasma components were described throughout the vitellogenesis period (from March to early June), when all plasma parameters differed markedly between reproductive and nonreproductive females. After ovulation, the differences between the two groups of females disappeared except in the case of albumin, which remained at a very low level in reproductive females for 6 months. All nonreproductive females had low 17-beta estradiol plasma levels during vitellogenesis (average of 0.08 ng/ml) and there was no suggestion of mobilization of maternal reserves. After vitellogenesis plasma concentrations of estradiol were low in reproductive (an average of 0.08 ng/ml) and in nonreproductive animals (0.06 ng/ml). Five nonreproductive females kept in the laboratory were estrogenized by 17-beta estradiol silastic implants. The 17-beta estradiol concentrations were close to those measured in reproductive females during vitellogenesis. Maternal reserves were mobilized, with almost all metabolic parameters exhibiting the vitellogenic pattern. When the silastic implants were removed, 17-beta estradiol concentrations dropped sharply to a basal level, but the other components were maintained near the vitellogenic values for several months. In contrast to previous studies on viviparous snakes, these results suggest that in V. aspis 17-beta estradiol levels are linked strictly to vitellogenesis.},
}
@article {pmid7987689,
year = {1994},
author = {Bonnet, X and Naulleau, G},
title = {[Use of a body condition index (BCI) for the study of the reproduction in snakes].},
journal = {Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie},
volume = {317},
number = {1},
pages = {34-41},
pmid = {7987689},
issn = {0764-4469},
mesh = {Animals ; Colubridae/*anatomy &amp; histology ; Elapidae/*anatomy &amp; histology ; Fat Body/anatomy &amp; histology ; Female ; Litter Size ; Liver/anatomy &amp; histology ; *Reproduction ; Viperidae/*anatomy &amp; histology ; },
abstract = {A body condition index (BCI = actual body mass/optimal theoritical body mass of the studied animal) was estimated in females of 3 species of snake. From dissections of 88 Vipera aspis and 18 Coluber viridiflavus, strong relationships between body reserves (such as mass of fat bodies and liver) and BCI were found. Furthermore, BCI explained most of the variance in the mass of the fat bodies and in the mass of the liver; however BCI and body length together increased the percentage of variation explained. Thus, a satisfactory estimation of body reserves in relation to body length is possible in living snakes. We have studied reproductive parameters (clutch size and litter size) in Vipera aspis (Viperidae) and Elaphe longissima (Colubridae) during a 3 year period. In the two species positive relationships between maternal body length and number of offspring were found. At the beginning of vitellogenesis, litter size is related to the BCI level in V. aspis but not in E. longissima. In both species, BCI must exceed a threshold value for reproduction to take place. However this threshold value is much lower in E. longissima (0.55) than in V. aspis (0.70). This indicates that in the latter species, large body reserves are necessary for the induction of vitellogenesis. In E. longissima, maternal body length is an important determinant of reproductive success, body reserves playing a minor role. In contrast, in female V. aspis the reproductive success is related to BCI and to a lesser extent to body length.(ABSTRACT TRUNCATED AT 250 WORDS)},
}
@article {pmid7803700,
year = {1994},
author = {Rambidi, NG},
title = {Biomolecular computing: from the brain-machine disanalogy to the brain-machine analogy.},
journal = {Bio Systems},
volume = {33},
number = {1},
pages = {45-54},
doi = {10.1016/0303-2647(94)90060-4},
pmid = {7803700},
issn = {0303-2647},
mesh = {Brain/*physiology ; *Computers ; Fuzzy Logic ; Humans ; Mental Processes/*physiology ; *Models, Neurological ; Nonlinear Dynamics ; },
abstract = {The analogy between the main information features of the brain and molecular non-discrete information processing devices based on non-linear dynamic mechanisms is considered. These information processing mechanisms predetermine the character of basic primitive operations of these devices, which seem to be capable of solving problems of a rather high computational complexity. Non-linear dynamic processing mechanisms open the way to elaboration of devices embodying, in a natural way, the fuzziness of information features that is typical of information processing inherent in soft 'humanistic' systems.},
}
@article {pmid7537630,
year = {1994},
author = {Kairemo, KJ and Ljunggren, K and Wahlström, T and Stigbrand, T and Strand, SE},
title = {Correlation of beta-camera imaging and immunohistochemistry in radioimmunotherapy using 90Y-labeled monoclonal antibodies in ovarian cancer animal models.},
journal = {Cell biophysics},
volume = {24-25},
number = {},
pages = {293-300},
pmid = {7537630},
issn = {0163-4992},
mesh = {Animals ; Antibodies, Monoclonal ; Biomarkers, Tumor/immunology ; Carcinoembryonic Antigen/immunology ; Disease Models, Animal ; Female ; Fibrin/immunology ; Immunohistochemistry ; Keratins/immunology ; Mice ; Mice, Nude ; Ovarian Neoplasms/chemistry/diagnostic imaging/*radiotherapy ; Proteins/immunology ; *Radioimmunotherapy ; Radionuclide Imaging ; Statistics as Topic ; *Yttrium Radioisotopes ; },
abstract = {Tumor stroma contains much fibrin and monoclonal antifibrin antibody targeting is possible in tumors. In this study, nude mouse human ovarian carcinoma xenograft specimens were investigated after treatment with 90Y-labeled monoclonal antifibrin antibody Fab fragment or with 90Y-labeled OC125-monoclonal antibody F(ab')2 fragments. The mice received the radioimmunotherapy activity either intratumorally, intraperitoneally, or intravenously. Beta-camera imaging (BCI) is a novel device for studying activity distribution in tissue specimens and, together with immunohistochemistry (IHC) with OC125, antifibrin, anticarcinoembryonic antigen, anti-cytokeratin, and anti-placental alkaline phosphatase antibodies, was used for correlation of activity distribution of tissue specimens. These results were in concordance: Antigen distribution measured with IHC and radioactivity distribution were similar with the same antibodies, antifibrin, and OC125: However, these antigens demonstrated rather different distribution. Tissue studies revealed that activity was concentrated also in the necrotic tumor tissue, indicating that cell death was also caused by radiation. Differences in the tumor cell morphology were observed using different routes of administration. With BCI, it is possible to quantitate activities in frozen sections (microdosimetry), and these results were in concordance with absolute activities as measured by tissue sampling and well-counting. Three-dimensional reconstruction of tissue slices combined with radioactivity distribution measured with BCI allows estimation of total absorbed radiation dose in tumor after an appropriate dose planning.},
}
@article {pmid8224254,
year = {1993},
author = {Dementiev, AA and Moiseyev, GP and Shlyapnikov, SV},
title = {Primary structure and catalytic properties of extracellular ribonuclease of Bacillus circulans.},
journal = {FEBS letters},
volume = {334},
number = {2},
pages = {247-249},
doi = {10.1016/0014-5793(93)81721-b},
pmid = {8224254},
issn = {0014-5793},
mesh = {Amino Acid Sequence ; Bacillus/*enzymology ; Chromatography, High Pressure Liquid ; Kinetics ; Mass Spectrometry ; Molecular Sequence Data ; Molecular Weight ; Peptide Fragments/chemistry/isolation &amp; purification ; Ribonucleases/*chemistry/isolation &amp; purification/*metabolism ; Substrate Specificity ; },
abstract = {A complete amino acid sequence of extracellular Bacillus circulans RNase was established and compared with a structure of B. amyloliquefaciens RNase. Gln15, Gly65 and Gln104 in B. amyloliquefaciens RNase were found to be replaced by Leu, Ala and Lys, respectively, in B. circulans RNase. Catalytic properties of B. circulans RNase were studied.},
}
@article {pmid8399775,
year = {1993},
author = {Dement'ev, AA and Golyshin, PN and Riabchenko, NF and Pustobaev, VN and Shliapnikov, SV},
title = {[Two forms of extracellular low molecular weight Bacillus sp. BCF 247 ribonuclease. Isolation and characteristics of the protein].},
journal = {Biokhimiia (Moscow, Russia)},
volume = {58},
number = {8},
pages = {1258-1265},
pmid = {8399775},
issn = {0320-9725},
mesh = {Amino Acid Sequence ; Bacillus/*enzymology ; Chromatography, High Pressure Liquid ; Electrophoresis, Polyacrylamide Gel ; Hydrolysis ; Isoenzymes/chemistry/*isolation &amp; purification ; Mass Spectrometry ; Molecular Sequence Data ; Molecular Weight ; Ribonucleases/chemistry/*isolation &amp; purification ; },
abstract = {Two homogeneous samples of low molecular mass RNAase (RNAases Bci I and Bci II) were obtained from cultural filtrates of spore-forming bacteria strain Bacillus sp. BCF 247 isolated from permafrost soils. The yields of RNAases Bci I and Bci II were 17% and 16% at the 17388- and 15376-fold degree of purification, respectively. Both enzymes have a close specific activity which is equal to approximately 4.7 x 10(-5) activity units per mg of protein. The relative molecular masses of the isolated proteins were determined and their N-terminal amino acid sequences identified. It was shown that the higher molecular mass sample of the enzyme is a pro-RNAase which, in contrast with the mature protein, contains an additional decapeptide segment in the N-terminal part of its molecule. The structure of RNAase Bci was compared with that of RNAases obtained from other Bacillus species; its ability to interact with a natural intracellular inhibitor of B. amyloliquefaciens RNAase was demonstrated.},
}
@article {pmid8105563,
year = {1993},
author = {Malpezzi, EL and de Freitas, JC and Muramoto, K and Kamiya, H},
title = {Characterization of peptides in sea anemone venom collected by a novel procedure.},
journal = {Toxicon : official journal of the International Society on Toxinology},
volume = {31},
number = {7},
pages = {853-864},
doi = {10.1016/0041-0101(93)90220-d},
pmid = {8105563},
issn = {0041-0101},
mesh = {Action Potentials ; Amino Acid Sequence ; Animals ; Brachyura ; Chromatography, Gel ; Chromatography, High Pressure Liquid ; Cnidarian Venoms/chemistry/*isolation &amp; purification/toxicity ; Electric Stimulation ; In Vitro Techniques ; Molecular Sequence Data ; Neurons, Afferent/drug effects ; Neurotoxins/chemistry/*isolation &amp; purification/toxicity ; Peptides/chemistry/*isolation &amp; purification/toxicity ; Sea Anemones/*chemistry ; Sequence Homology, Amino Acid ; },
abstract = {Peptide neurotoxins were isolated from the venom obtained by electrical stimulation of the sea anemone Bunodosoma caissarum. This technique allows almost pure venom to be collected, and the animals to survive. Three neurotoxins (assayed on crustacean nerves) were isolated by gel filtration and reversed-phase high performance liquid chromatography. Hemolysins were also detected in the venom. The amino acid sequence of a major neurotoxin BcIII was determined. BcIII has 48 amino acid residues with six half-cystine residues. This sequence has homology with the type 1 long sea anemone neurotoxins. Two minor toxins (BcI and II) have similar amino acid composition and amino-terminal sequences to BcIII.},
}
@article {pmid10148343,
year = {1993},
author = {Nishimura, N and Taguchi, Y and Yamamuro, T and Nakamura, T and Kokubo, T and Yoshihara, S},
title = {A study of the bioactive bone cement--bone interface: quantitative and histological evaluation.},
journal = {Journal of applied biomaterials : an official journal of the Society for Biomaterials},
volume = {4},
number = {1},
pages = {29-38},
doi = {10.1002/jab.770040104},
pmid = {10148343},
issn = {1045-4861},
mesh = {Animals ; Bone Cements/adverse effects/*chemistry ; Contraindications ; Dogs ; Femur ; Glass/*chemistry ; Hip Prosthesis ; Male ; Materials Testing ; Methylmethacrylates/adverse effects/chemistry ; Phosphates/*chemistry ; Prosthesis Failure ; },
abstract = {The interface between bone and a bioactive glass cement--a mixture of bioactive glass powder and ammonium phosphate solution, previously reported on by the authors--was evaluated quantitatively and histologically. The materials tested were (1) the original bioactive glass cement (BCI cement); (2) an improved type of bioactive glass cement (BCII cement); (3) polymethylmethacrylate (PMMA) bone cement; and (4) a bioactive, apatite-wollastonite-containing, glass ceramic (A-WGC). Hardened cylindrical specimens of each cement were inserted loosely into canine femora and the interfacial shear strengths were measured using a push-out test. The interfacial strength values of the bioactive glass cements increased with prolonged implantation time. At each postimplantation time studied (8, 12, and 24 weeks), the interfacial strength value of BCI cement did not differ significantly from that of A-WGC. BCII cement interfacial strength was greater than that of BCI cement, whereas the interfacial strength of PMMA bone cement remained at a very low level throughout the study. Histological examinations revealed that direct bonding of both bioactive glass cements to bone had occurred without pathologic degradation. After 24 weeks, the defects between the bone and the bioactive glass cements had been filled with mature lamellar bone. Because the bioactive glass cement system developed by the authors, especially BCII cement, shows excellent osteoconductivity and bonds to bone tightly, we consider it to be a promising material for fixing prostheses into bone.},
}
@article {pmid14969931,
year = {1993},
author = {Liu, GE and Côté, B},
title = {Neutralization and buffering capacity of leaves of sugar maple, largetooth aspen, paper birch and balsam fir.},
journal = {Tree physiology},
volume = {12},
number = {1},
pages = {15-21},
doi = {10.1093/treephys/12.1.15},
pmid = {14969931},
issn = {1758-4469},
abstract = {We compared the acidity, the external acid neutralizing capacity and the buffering capacity of leaves of four commercially important tree species, largetooth aspen (Populus grandidentata Michx.), sugar maple (Acer saccharum Marsh.), paper birch (Betula papyrifera Marsh.) and balsam fir (Abies balsamea (L.) Mill), at two sites of contrasting soil fertility in southern Quebec. External acid neutralizing capacity (ENC) of leaves was determined by measuring the change in pH induced by soaking fresh leaves in an acidic solution (pH 4.0) for two hours. The ENC was highest for largetooth aspen (14.3 micro equiv H(+) g(-1)), and lowest for sugar maple and balsam fir (< 5 micro equiv H(+) g(-1)). The buffering capacity index (BCI) was determined by measuring the amount of acid necessary to produce a change of 5 micro equiv H(+) in the leaf homogenate. The BCI ranged from 883 micro equiv H(+) g(-1) for largetooth aspen to less than 105 micro equiv H(+) g(-1) for sugar maple and balsam fir. Leaves of sugar maple and balsam fir had a lower internal pH and a higher percentage of ENC over BCI than paper birch and largetooth aspen. Overall, ENC was correlated with the concentration of all leaf nutrients except Ca, and BCI was correlated with Mg, N and Ca. The site effect was relatively unimportant for all variables.},
}
@article {pmid8480445,
year = {1993},
author = {Köller, W},
title = {[Bone/cement interface reactions following several years of implantation and consequences for its fixation].},
journal = {Zeitschrift fur Orthopadie und ihre Grenzgebiete},
volume = {131},
number = {1},
pages = {75-82},
doi = {10.1055/s-2008-1039908},
pmid = {8480445},
issn = {0044-3220},
mesh = {Adaptation, Physiological ; *Bone Cements ; Calcification, Physiologic ; Connective Tissue Cells ; Femur/*cytology/physiology ; *Hip Prosthesis ; Humans ; Osseointegration ; Osteogenesis ; },
abstract = {Retrieved femora with fixed cemented hip arthroplasties were sectioned horizontally. Bone sections were prepared of the whole cross-sectional area to study the bone/cement-interface (bci) histologically and morphometrically. The interface between bone and cement mainly consists of a thin (< 25 microns) connective tissue layer. Demineralized zones were in parts found, this tendency seems to slightly increase in time. The bone newly formed at the bci is structured like thick trabecular bone or like dense cortical bone. A strong fixation seems possible even if a thin connective tissue layer exists, and a complete adaptation of the implant to the bone is not necessary, as the bone adapts as well.},
}
@article {pmid8372481,
year = {1993},
author = {Brauchli, P},
title = {[Comparative study of the psychophysiologic relaxation effects of an optic-acoustic mind machine with relaxation music].},
journal = {Zeitschrift fur experimentelle und angewandte Psychologie},
volume = {40},
number = {2},
pages = {179-193},
pmid = {8372481},
issn = {0044-2712},
mesh = {Acoustic Stimulation/*instrumentation ; Adult ; Arousal/*physiology ; Female ; Humans ; Male ; Middle Aged ; *Music ; Photic Stimulation/*instrumentation ; Psychophysiology ; Relaxation Therapy/*instrumentation ; },
abstract = {The present study was designed to test the effectiveness of an optical-acoustic mind machine (brain machine) in inducing relaxation. The mind machine used in this study stimulates the user with flickers of light and pulsating sounds. During the treatment the stimulation decreases from 10 to 2 hertz and increases again at half time. No other relaxation inducing effects were used. Sixteen subjects received two or three sessions of instruction with the mind machine. Afterwards the parameters listed below were continuously recorded during one session with the mind machine and one session with the presentation of relaxing environmental sounds, which was conducted one week later: Frontal EMG, SCL on the left hand, heart rate. Pre- and posttreatment samples of saliva were collected and assessed for salivary IgA (S-IgA) and salivary cortisol (S-cortisol). Changes in the subjects' self-report were measured with a bipolar adjective list. ANCOVA with repeated measures revealed a decrease for all electrophysiological parameters during the mind machine session. S-cortisol concentration decreases as S-IgA increases. The mind machine made the subjects feel warmer and calmer. The results of this with in design revealed no reliable differences between the mind machine and the relaxing sounds of nature on the physiological and self-esteem parameters. The significantly greater decrease of SCL during the mind machine session was due to the elevated baseline of this parameter. The results lead to the conclusion that the mind machine seems to be useful in inducing relaxation, but is no more effective than the relaxing nature sounds used in this study.},
}
@article {pmid7691116,
year = {1993},
author = {Groen, HJ and Smit, EF and Haaxma-Reiche, H and Postmus, PE},
title = {Carboplatin as second line treatment for recurrent or progressive brain metastases from small cell lung cancer.},
journal = {European journal of cancer (Oxford, England : 1990)},
volume = {29A},
number = {12},
pages = {1696-1699},
doi = {10.1016/0959-8049(93)90107-q},
pmid = {7691116},
issn = {0959-8049},
mesh = {Adult ; Aged ; Aged, 80 and over ; Brain Neoplasms/*drug therapy/mortality/*secondary ; Carboplatin/*therapeutic use ; *Carcinoma, Non-Small-Cell Lung ; Female ; Humans ; *Lung Neoplasms ; Male ; Middle Aged ; Palliative Care ; Prospective Studies ; Time Factors ; },
abstract = {Patients with brain metastases from small cell lung cancer (SCLC) have a poor prognosis. Although most patients die from metastatic disease outside the central nervous system, this disabling metastatic site often needs treatment to mitigate the signs and symptoms of intracranial disease. The effect of carboplatin (400 mg/m2 every 4 weeks) as second line treatment for recurrent or progressive brain metastases was studied in 20 SCLC patients. 19 patients could be evaluated: 16 by contrast enhanced brain computer tomography (CT) scan (2 patients had complete response, 6 partial response, 4 stable disease and 4 progressive disease) and 3 patients clinically, who had progressive disease. The objective response rate in the brain was 40% (95% CI:22-61%). The median response duration was 8 weeks (range 2-29). The median survival was 15 weeks (range 1-44). Previous cranial irradiation appeared to be beneficial for survival. There was only mild haematological and gastrointestinal toxicity. Carboplatin has activity against brain metastases and gives palliation in responding patients.},
}
@article {pmid1286147,
year = {1992},
author = {Flotzinger, D and Kalcher, J and Pfurtscheller, G},
title = {EEG classification by learning vector quantization.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {37},
number = {12},
pages = {303-309},
doi = {10.1515/bmte.1992.37.12.303},
pmid = {1286147},
issn = {0013-5585},
mesh = {Algorithms ; Dominance, Cerebral/physiology ; Electroencephalography/*classification/instrumentation ; Humans ; Motor Cortex/physiology ; Neural Networks, Computer ; Signal Processing, Computer-Assisted/*instrumentation ; Somatosensory Cortex/physiology ; *User-Computer Interface ; },
abstract = {EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface (BCI) built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp. The method of classification with LVQ is described in detail along with first results on a subject who participated in four on-line cursor control sessions. Using this data, extensive off-line experiments were performed to show the influence of the various parameters of the classifier and the extracted features of the EEG on the classification results.},
}
@article {pmid1522266,
year = {1992},
author = {van der Knaap, MS and Bakker, CJ and Faber, JA and Valk, J and Mali, WP and Willemse, J and Gooskens, RH},
title = {Comparison of skull circumference and linear measurements with CSF volume MR measurements in hydrocephalus.},
journal = {Journal of computer assisted tomography},
volume = {16},
number = {5},
pages = {737-743},
doi = {10.1097/00004728-199209000-00013},
pmid = {1522266},
issn = {0363-8715},
mesh = {Cerebrospinal Fluid/*physiology ; Child ; Child, Preschool ; Female ; Humans ; Hydrocephalus/*diagnosis/pathology/physiopathology ; Infant ; Infant, Newborn ; Magnetic Resonance Imaging ; Male ; Skull/*pathology ; },
abstract = {In children with hydrocephalus, accurate and reproducible estimation of the presence, severity, and course of the condition is of paramount importance for both clinical and scientific purposes. In this study, 30 hydrocephalic patients were assessed with a number of commonly used methods, such as occipitofrontal skull circumference (SC) measurements, Evans ratio (ER), and bicaudate index (BCI), as well as, for comparison, another ratio of linear measurements [ventricle-skull ratio (VSR)] and MR measurements of total intracranial CSF volume. In repeated CSF volume measurements in healthy volunteers, the MR method appeared to be accurate and reproducible. This technique was simpler and easier in application, requiring less interaction than comparable MR techniques described by others. The variation coefficients were within the same range. In increased CSF volumes, our technique can be recommended; in very small CSF volumes, another technique is more adequate. Direct assessment of CSF volume as a measure of hydrocephalus was preferable over derived estimations for scientific purposes and may function as a gold standard against which to evaluate other techniques that are easier to apply clinically. In comparison, SC measurements were poor; CSF volume changes were not reflected in SC changes. VSR was preferable over ER and BCI, because it correlated more closely with CSF volume.},
}
@article {pmid1865901,
year = {1991},
author = {Maddox, J},
title = {Towards the brain-computer's code?.},
journal = {Nature},
volume = {352},
number = {6335},
pages = {469},
doi = {10.1038/352469a0},
pmid = {1865901},
issn = {0028-0836},
mesh = {Animals ; Brain/*physiology ; *Computer Simulation ; Humans ; *Models, Neurological ; Nerve Fibers/physiology ; Neurons/physiology ; },
}
@article {pmid1897555,
year = {1991},
author = {Karacostas, D and Artemis, N and Papadopoulou, M and Christakis, J},
title = {Case report: epidural and bilateral retroorbital hematomas complicating sickle cell anemia.},
journal = {The American journal of the medical sciences},
volume = {302},
number = {2},
pages = {107-109},
doi = {10.1097/00000441-199108000-00008},
pmid = {1897555},
issn = {0002-9629},
mesh = {Adult ; Anemia, Sickle Cell/*complications ; Hematoma/diagnostic imaging/*etiology ; Hematoma, Epidural, Cranial/diagnostic imaging/etiology ; Humans ; Male ; Tomography, X-Ray Computed ; },
abstract = {Early in the course of a painful crisis, a 19-year-old man with known sickle cell anemia (SCA) developed a clinical picture that resembled either early cavernous sinus thrombosis or retroorbital and bifrontal microinfarcts. A brain computer tomography scan demonstrated bilateral retroorbital hemorrhages along with a left frontal epidural hematoma. In the absence of trauma, thrombocytopenia, or any other detectable hemostatic defect, this type of hemorrhagic manifestation in the setting of SCA has not, to our knowledge, been previously reported in the literature.},
}
@article {pmid1896159,
year = {1991},
author = {Mitchell, JL},
title = {The portable mind machine.},
journal = {Occupational health; a journal for occupational health nurses},
volume = {43},
number = {8},
pages = {228, 230},
pmid = {1896159},
issn = {0029-7917},
mesh = {Electric Stimulation Therapy/instrumentation ; Humans ; Relaxation Therapy/*instrumentation ; },
}
@article {pmid1850709,
year = {1991},
author = {Weilguny, D and Praetorius, M and Carr, A and Egel, R and Nielsen, O},
title = {New vectors in fission yeast: application for cloning the his2 gene.},
journal = {Gene},
volume = {99},
number = {1},
pages = {47-54},
doi = {10.1016/0378-1119(91)90032-7},
pmid = {1850709},
issn = {0378-1119},
mesh = {Cloning, Molecular/*methods ; DNA Transposable Elements ; Escherichia coli/genetics ; *Genes, Fungal ; *Genetic Vectors ; Genomic Library ; Plasmids ; Promoter Regions, Genetic ; Restriction Mapping ; Saccharomyces cerevisiae/*genetics/physiology ; Schizosaccharomyces/*genetics/physiology ; },
abstract = {We describe a new Escherichia coli vector (pON5) that allows positive selection for recombinant clones. In this plasmid, the bla gene from pBR322 is permanently active, whereas the neo gene from transposon Tn5 is repressed by the cI-encoded lambda repressor. When DNA is inserted into the Bc/I or HindIII restriction sites situated within the cI gene, the neo gene becomes transcribed from the lambda pR promoter. We have also made a Schizosaccharomyces pombe derivative of pON5 (= pON163) by introducing the fission yeast ars1 and ura4+ sequences. We show that this plasmid is capable of transforming Sc. pombe ura4 strains, as well as ura 3 strains of the distantly related budding yeast Saccharomyces cerevisiae. We have used pON163 for the construction of two fission yeast genomic libraries. From these gene banks clones were isolated that were able to complement fission yeast his2 mutants. Such plasmids could also rescue his4C mutants of Sa. cerevisiae, defective in the histidinol dehydrogenase activity of the multifunctional HIS4 gene product. Finally, we describe the plasmid pDW232 which is useful for functional analysis of fission yeast genes. It is a pGEM3 derivative adapted to fission yeast, carrying multiple cloning sites between the T7 and SP6 promoters, together with ars1 and ura4+ from Sc. pombe.},
}
@article {pmid1707798,
year = {1991},
author = {Wolpaw, JR and McFarland, DJ and Neat, GW and Forneris, CA},
title = {An EEG-based brain-computer interface for cursor control.},
journal = {Electroencephalography and clinical neurophysiology},
volume = {78},
number = {3},
pages = {252-259},
doi = {10.1016/0013-4694(91)90040-b},
pmid = {1707798},
issn = {0013-4694},
mesh = {Adult ; Brain/*physiology ; Electroencephalography/*methods ; Female ; Humans ; Male ; Motor Activity ; *Signal Processing, Computer-Assisted ; *User-Computer Interface ; },
abstract = {This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.},
}
@article {pmid1647608,
year = {1991},
author = {Mukhamedzhanov, NZ and Tumanova, AA and Shcherbakova, EIa},
title = {[Roentgeno-radiological diagnosis of rhinosinusogenic brain abscesses].},
journal = {Zhurnal nevropatologii i psikhiatrii imeni S.S. Korsakova (Moscow, Russia : 1952)},
volume = {91},
number = {2},
pages = {120-123},
pmid = {1647608},
issn = {0044-4588},
mesh = {Adolescent ; Adult ; Aged ; Brain Abscess/*diagnosis/diagnostic imaging/etiology ; Child ; False Negative Reactions ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Radionuclide Imaging ; Sinusitis/*complications ; Sodium Pertechnetate Tc 99m ; Tomography, X-Ray Computed ; },
abstract = {Analysis is made of the data obtained in the use of a complex of diagnostic ++roentgeno-radiological methods in 26 patients with rhinosinusogenous abscesses of the brain. There are convincing examples that the complex (cerebral angiography, gamma-topography of the brain, computer-aided tomography, NMR tomography) enables the identification of the reliable and informative sings of the localization, size, spreading, multiplicity and multicompartmental nature of brain abscesses.},
}
@article {pmid2369688,
year = {1990},
author = {Winet, H and Bao, JY and Moffat, R},
title = {Neo-osteogenesis of haversian trabeculae through a bone chamber implanted in a rabbit tibial cortex: a control model.},
journal = {Calcified tissue international},
volume = {47},
number = {1},
pages = {24-34},
pmid = {2369688},
issn = {0171-967X},
support = {AR35473/AR/NIAMS NIH HHS/United States ; },
mesh = {Air Pressure ; Animals ; Bone and Bones/cytology/*physiology ; Female ; Image Processing, Computer-Assisted ; Osteogenesis/*physiology ; *Prostheses and Implants ; Rabbits ; Statistics as Topic ; Tibia/cytology/physiology/*surgery ; },
abstract = {Neo-osteogenesis of cortical bone trabecular was observed as they regenerated into a bone chamber implant by appositional growth. Measures of change in bone area were obtained from 13 rabbits each week starting the third and ending the eighth week postimplantation. Observations were made using intravital microscopy and were analyzed using digital image processing. Images were computer-captured video frames equivalent to 78 (6 x 13) separate observations. They were measured by tracing the trabecular outlines with a digitizing crosshair each week and comparing changes in area as a percent of the circular field-of-view ("slit-gap") filled. Data supported the hypothesis that trabecular regeneration week 3 to week 8 followed the logistic growth curve regression: A% 100%/1 + 9.47e-0.7747(t-3) where A% is the percent of slit-gap area covered by bone and t is time, at a very high significance level. Nevertheless, a highly significant linear regression fit the data. Statistical analysis showed that the regression line could be fit to bone area measurements from weeks 3 to 8 (W3-W8) postimplantation, giving a constant neo-osteogenesis rate of 7.42 +/- 0.67 X 10(4) microns 2/day and a decreasing linear neo-osteogenesis rate from 73 microns/day at W4 to 21 microns/day at W8; the latter is based on a circle-segment approximation of trabeculum shape. This range approximated a bridge between ranges for cortical gap defect healing and porous ingrowth healing reported by other workers and supported the hypothesis that the BCI control model was a cross between gap healing and porous ingrowth.},
}
@article {pmid15092181,
year = {1990},
author = {Bender, J and Manderscheid, R and Jäger, HJ},
title = {Analyses of enzyme activities and other metabolic criteria after five years of fumigation.},
journal = {Environmental pollution (Barking, Essex : 1987)},
volume = {68},
number = {3-4},
pages = {331-343},
doi = {10.1016/0269-7491(90)90035-b},
pmid = {15092181},
issn = {0269-7491},
abstract = {Enzymatic activity (peroxidase, glutamate dehydrogenase, glutamine synthetase), foliage buffering capacity, soluble protein and nitrogen content were measured in current and previous year needles from young spruce (Picea abies) and fir (Abies alba). The trees were exposed to low levels of SO(2) and/or O(3) and simulated acidic precipitation (pH 4.0) in open-top chambers from 1983 through 1988. Needle samples were taken during March 1988 at the end of the five-year fumigation period. Exposure to SO(2) substantially increased sulphur content in both needle age classes of spruce and fir, and concomitantly reduced the foliage buffering capacity index (BCI), whereas the combined fumigation with SO(2) and O(3) had no effect on BCI. Peroxidase activity was markedly higher in year-old needles compared to current-year needles. However, trees from the SO(2) and SO(2) + O(3) treatments exhibited statistically significant stimulated peroxidase activities. Similarly, changes in the activities of the nitrogen-metabolizing enzymes indicated an altered cellular function of the trees after the long-term pollution stress. Levels of activity of both glutamate dehydrogenase and glutamine synthetase were increased by exposure to SO(2), especially in spruce. Although glutamate dehydrogenase in spruce was affected by all treatments, such changes in activity were found in fir only with the SO(2) treatment. The highest activity of glutamine synthetase, however, occurred in the older needles of trees exposed to SO(2) + O(3). Total nitrogen concentration was either unaffected by the pollutant treatments or decreased in spruce compared to the controls. No statistically significant changes due to the fumigation were found in soluble protein concentrations. Results indicated that chronic exposure to air pollutants lead to alterations in metabolic processes in conifer needles, detectable either by changes in typical stress indicating values or by increases in ammonium assimilation capacity.},
}
@article {pmid2353596,
year = {1990},
author = {Evangelista, AT},
title = {The clinical impact of automated susceptibility reporting using a computer interface.},
journal = {Advances in experimental medicine and biology},
volume = {263},
number = {},
pages = {131-142},
doi = {10.1007/978-1-4613-0601-6_12},
pmid = {2353596},
issn = {0065-2598},
mesh = {Automation ; Bacteria/growth &amp; development/isolation &amp; purification ; Bacterial Infections/diagnosis ; Computer Systems ; Humans ; Microbial Sensitivity Tests/economics/*methods ; Software ; Time Factors ; },
abstract = {The rapid automated reporting capability of the Vitek-Sunquest interface was shown to have a direct clinical and monetary impact on patient management. The audit performed on autofiled positive culture results at Cooper Hospital/University Medical Center demonstrated an average savings of $243 per day in antimicrobial utilization. A further enhancement in the computer software for the Vitek and Sunquest systems was the development of the Bidirectional Computer Interface (BCI). The BCI upgrade allows the Microbiology laboratory to utilize both the Sunquest and Vitek IMS programs to generate a wide variety of epidemiology reports with a minimum of keyboard entry time.},
}
@article {pmid2183679,
year = {1990},
author = {Sparks, DL and Mays, LE},
title = {Signal transformations required for the generation of saccadic eye movements.},
journal = {Annual review of neuroscience},
volume = {13},
number = {},
pages = {309-336},
doi = {10.1146/annurev.ne.13.030190.001521},
pmid = {2183679},
issn = {0147-006X},
support = {P30-EY03039/EY/NEI NIH HHS/United States ; R01-EY01189/EY/NEI NIH HHS/United States ; R01-EY03463/EY/NEI NIH HHS/United States ; },
mesh = {Animals ; *Eye Movements ; *Saccades ; Superior Colliculi/*physiology ; Visual Pathways/physiology ; },
abstract = {Chronic unit recording experiments conducted over the past two decades have identified many functional classes of neurons with saccade-related activity that reside in a host of brainstem nuclei. Older models of the saccadic system were based upon the properties of only a few of these functional types of neurons. They described the putative flow of signals through the brainstem circuitry and specified some, but not all, of the signal transformations to be performed. How the necessary computations were performed by neurons was not always explicit. Recent experiments investigating the neural control of saccadic eye movements and modifications of the original models are designed to fill in the details of the broad sketch of saccadic circuitry originally available. This review suggests one strategy for proceeding with this effort. Saccadic command signals observed in the SC require transformation to interface with the burst generators and motoneuron pools innervating the extraocular muscles. Specifying the signal transformations required for this interface should facilitate the design of experiments directed toward an understanding of the functional properties of cells located in nuclei intervening between the SC and the pulse/step circuitry, subsets of neurons that often have no role in models of the saccadic system. In this review, we hypothesize that neurons residing in various tectorecipient brainstem nuclei participate in one or more of the required signal transformations. The pathway from SC to cMRF and PPRF may be involved in the extraction of information about the amplitude and/or velocity of the horizontal component of oblique saccades. The pathway from SC to NRTP and cerebellar vermis may act selectively to generate signals compensating for the presaccadic orbital position. Finally, the activity of LLBNs and MLBs discharging maximally before oblique saccades may form the basis of computations required to match component velocity with overall saccade direction and amplitude. Although the data supporting these speculations are meager at present, such conjectures do form the basis of working hypotheses that can be tested experimentally. We also considered the implications of kinematic constraints, especially Donders' and Listing's laws, for future investigations. Tweed &amp; Vilis (1987, 1990) proposed models specifically designed to handle these constraints. In their models, eye position is represented on four oculomotor channels: three coding the vector components of eye position, and one carrying a signal inversely related to gaze eccentricity and torsion. Yet, other evidence suggest that simpler computations may suffice for the implementation of laws that are only approximately obeyed.(ABSTRACT TRUNCATED AT 400 WORDS)},
}
@article {pmid1689926,
year = {1990},
author = {Winet, H and Bao, JY and Moffat, R},
title = {A control model for tibial cortex neovascularization in the bone chamber.},
journal = {Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research},
volume = {5},
number = {1},
pages = {19-30},
doi = {10.1002/jbmr.5650050106},
pmid = {1689926},
issn = {0884-0431},
support = {AR35473/AR/NIAMS NIH HHS/United States ; },
mesh = {Animals ; Diffusion Chambers, Culture ; Female ; *Models, Cardiovascular ; Neovascularization, Pathologic/*physiopathology ; Rabbits ; Tibia/*blood supply ; Video Recording ; },
abstract = {Neovascularization across a gap defect in a rabbit tibial cortex was monitored using the optical bone chamber implant (BCI). Cortical bone growing by apposition as trabeculae was observed weekly as it penetrated a slit into a tissue space in vivo and in situ. Each rabbit was viewed weekly with an intravital microscope from 3 to 8 weeks postimplantation. The constant field of view was the slit-gap tissue space, which was 100 microns thick and 2 mm in diameter. Vessels were imaged with epi-illuminated fluorescence microscopy as they carried FITC-dextran 70 that had been injected into an aural vein. Observations were videotaped and photographed. Videotape frames were analyzed with a digital image processing system to obtain measures of vessel length per unit volume (L/V) of fibroblastic granular tissue and trabeculae, caliber C, and flow velocity u, all as functions of time. Observations supported the conclusions that (1) neovascularization precedes neo-osteogenesis, (2) major vessels tend to align with the tibial axis, (3) bone apposition-generated destruction of fibrous granular tissue vessels stimulates fibrous granular tissue angiogenesis, which keeps its L/V constant, (4) L/V in trabeculae increases with time, and (5) blood supply (Q) and nutrient exchange in healing trabeculae are not positively correlated. Thus, O2 supply to the trabeculum cannot be predicted from Q alone because the nutrient exchange area is not constant. It was noted that an increase in the potential nutrient exchange area occurred in both fibrous granular tissue and osseous vessels and the volume fraction of blood decreased in the fibrous granular tissue and remained constant in the trabeculae.},
}
@article {pmid2650754,
year = {1989},
author = {Conrad, M},
title = {The brain-machine disanalogy.},
journal = {Bio Systems},
volume = {22},
number = {3},
pages = {197-213},
doi = {10.1016/0303-2647(89)90061-0},
pmid = {2650754},
issn = {0303-2647},
mesh = {Algorithms ; *Artificial Intelligence ; Biological Evolution ; Brain/*physiology ; *Computer Simulation ; Computers ; Humans ; *Models, Neurological ; Software ; },
abstract = {The comparative study of information processing in brains and machines leads to a picture in which disanalogies are more fundamental than analogies. The major dichotomy is between evolvability and programmability. Brain models, to be tenable, must pass an extended Turing test in which the capacity to self organize through the Darwinian mechanism of variation and selection is a key element. Programmable machines that simulate the type of structure-function relations that allow evolution to occur are, however, too inefficient in their use of resources for problem solving to support cognitive abilities comparable to those of biological organisms. Furthermore, real evolutionary systems are open in that it is always possible for them to tap previously unexploited physical interactions for computing. Nevertheless, computer simulation provides a powerful tool for studying brain function; and non-programmable designs that exploit the high efficiency, high adaptability domain of computing are in principle possible.},
}
@article {pmid3398793,
year = {1988},
author = {Olson, CB},
title = {A possible cure for death.},
journal = {Medical hypotheses},
volume = {26},
number = {1},
pages = {77-84},
doi = {10.1016/0306-9877(88)90118-1},
pmid = {3398793},
issn = {0306-9877},
mesh = {Animals ; *Brain ; *Death ; Freezing ; Humans ; Longevity ; Organ Preservation/*methods ; },
abstract = {Chemical preservation of the brain may prevent death. Life for an individual human being is inextricably linked to the existence of his or her mind. It is widely accepted that the mind is a product of the functioning of the brain, which, according to this view, is nothing more and nothing less than a fantastically complicated machine. Chemical preservation of the brain (promptly after the cessation of vital functions) preserves not only the neuronal configuration but also a great deal of molecular structure. Thus, it is plausible that a chemopreserved brain contains within it the information of the design of the "brain machine". If so, then technology of the distant future may be able to extract that information and construct a new functionally identical brain machine (as well as a body), thereby allowing the corresponding individual to wake up and live again. It is argued that one's identity is defined by what the brain does rather than how it does it or what it does it with, and therefore that replacement of one's brain with a functionally identical machine does not affect one's identity. Some advantages of chemopreservation relative to cryopreservation as a possible means of preventing death are discussed.},
}
@article {pmid3111562,
year = {1987},
author = {Pecorara, M and Casarino, L and Mori, PG and Morfini, M and Mancuso, G and Scrivano, AM and Boeri, E and Molinari, AC and De Biasi, R and Ciavarella, N},
title = {Hemophilia A: carrier detection and prenatal diagnosis by DNA analysis.},
journal = {Blood},
volume = {70},
number = {2},
pages = {531-535},
pmid = {3111562},
issn = {0006-4971},
mesh = {DNA/*analysis ; Factor VIII/genetics ; Female ; *Genetic Carrier Screening ; Genetic Linkage ; Hemophilia A/diagnosis/*genetics ; Humans ; Polymorphism, Genetic ; Pregnancy ; *Prenatal Diagnosis ; },
abstract = {In this study, we used DNA polymorphisms for carrier detection and prenatal diagnosis of hemophilia A in a large group of Italian families. The restriction fragment length polymorphisms (RFLPs) investigated were the intragenic polymorphic Bc/I site within the factor VIII gene; the extragenic multiallelic Taq I system at the St14 locus; and the extragenic Bg/II site at the DX13 locus. The factor VIII probe was informative in 30%, St14 in 82%, and DX13 in 60% of obligate carriers. The combination of factor VIII-Bc/I and St14-Taq I showed that 91% of obligate carriers were heterozygotes for one or both; with all three probes, only 4% of obligate carriers were noninformative. In families clearly segregating for hemophilia A, RFLP analysis allowed us to define the carrier status for the hemophilia A gene in all 27 women tested. RFLP analysis allowed us to exclude the carrier status in 39 of 45 female relatives of sporadic patients. The combination of RFLP analysis and biological assay of factor VIII allowed us to identify a de novo mutation in the maternal grandfather in 7 of 12 of the families with sporadic cases, for which members of three generations were available for study. Nine of 10 couples requesting prenatal diagnosis provided informative RFLP DNA pattern. Carrier status was excluded in two women, two fetuses were shown to be female, and prenatal diagnosis was carried out in five pregnancies by DNA analysis. Prenatal testing was successful in three instances and failed in two because a sufficient amount of chorionic villous DNA was not obtained for the analysis.},
}
@article {pmid3754464,
year = {1986},
author = {Miller, IR and Chapman, D and Drake, AF},
title = {Circular dichroism spectra of aqueous dispersions of sphingolipids.},
journal = {Biochimica et biophysica acta},
volume = {856},
number = {3},
pages = {654-660},
doi = {10.1016/0005-2736(86)90161-6},
pmid = {3754464},
issn = {0006-3002},
mesh = {Animals ; Cattle ; Cerebrosides ; Cholesterol/pharmacology ; Circular Dichroism ; Enkephalin, Leucine/pharmacology ; Humans ; Molecular Conformation ; Pulmonary Surfactants/pharmacology ; *Sphingolipids ; Sphingomyelins ; Temperature ; X-Ray Diffraction ; },
abstract = {The circular dichroism (CD) spectra of a number of sphingolipids dispersed in water have been studied. The lipids include cerebrosides such as palmitoyl cerebroside, glucocerebroside from the spleen of Gaucher patients, bovine brain galactocerebrosides type I and type II, (BCI and BCII, respectively) and also sphingomyelins such as egg sphingomyelin and bovine brain sphingomyelin. Changes in the CD spectra of the lipids which occur upon heating and cooling and the effects of cholesterol, phosphatidylcholine and the opiate leucine enkephalin were studied.},
}
@article {pmid3161457,
year = {1985},
author = {McCoy, JP and Schade, WJ and Siegle, RJ and Waldinger, TP and Vanderveen, EE and Swanson, NA},
title = {Characterization of the humoral immune response to bovine collagen implants.},
journal = {Archives of dermatology},
volume = {121},
number = {8},
pages = {990-994},
pmid = {3161457},
issn = {0003-987X},
mesh = {Animals ; Cattle ; Collagen/adverse effects/*immunology ; Drug Eruptions/etiology/*immunology ; Drug Implants ; Enzyme-Linked Immunosorbent Assay ; Humans ; Immunoglobulin A/biosynthesis ; Immunoglobulin G/biosynthesis ; Immunoglobulin M/biosynthesis ; Immunoglobulins/*biosynthesis ; },
abstract = {The use of bovine collagen implants (BCIs) for the correction of dermal contour deformities is becoming widespread. A small percentage of patients receiving treatment with BCIs suffer adverse reactions that appear to be of an immune nature. Circulating antibodies to BCIs are found in all patients suffering adverse treatment reactions and in small numbers of normal individuals and BCI-treated patients not suffering adverse reactions. These antibodies are always IgG, although quite often IgA is also present. The anti-BCI antibodies are quite stable, suffering virtually no loss of activity after storage at room temperature for 54 days. Immunoblotting studies indicate that no singular component of the BCI collagen is the prime antigenic component; multiple regions of the collagen molecule are recognized by patients' antibodies.},
}
@article {pmid4046895,
year = {1985},
author = {Joseph, AB},
title = {Design considerations for the brain-machine interface.},
journal = {Medical hypotheses},
volume = {17},
number = {3},
pages = {191-195},
doi = {10.1016/0306-9877(85)90124-0},
pmid = {4046895},
issn = {0306-9877},
mesh = {Biomedical Engineering ; *Brain ; Electronics ; Humans ; Prostheses and Implants ; *Prosthesis Design ; },
abstract = {Implantation of prosthetic devices designed to complement the function of the human brain is a rare but well recognized innovative treatment for some patients. If this technique is to become clinically useful special attention will have to be paid to the bio-engineering requirements of the prosthesis-brain interface.},
}
@article {pmid4076772,
year = {1985},
author = {Prats, H and Martin, B and Pognonec, P and Burger, AC and Claverys, JP},
title = {A plasmid vector allowing positive selection of recombinant plasmids in Streptococcus pneumoniae.},
journal = {Gene},
volume = {39},
number = {1},
pages = {41-48},
doi = {10.1016/0378-1119(85)90105-2},
pmid = {4076772},
issn = {0378-1119},
mesh = {Chromosome Mapping ; Cloning, Molecular ; DNA, Bacterial/genetics ; *Genetic Vectors ; *Plasmids ; *Recombination, Genetic ; Repetitive Sequences, Nucleic Acid ; Streptococcus pneumoniae/*genetics ; },
abstract = {A new plasmid, pSP2, was constructed as a cloning vector for use in Streptococcus pneumoniae. It allows direct selection of recombinant plasmids, even for DNA fragments not homologous to the S. pneumoniae chromosome, as based on the failure to maintain long inverted repeats (LIRs) hyphen-free in bacterial plasmids. Plasmid pSP2 contains a 1.4-kb BamHI fragment ("hyphen") flanked by 1.9-kb LIRs. The removal of the 1.4-kb BamHI fragment followed by ligation creates a plasmid containing a 1.9-kb insert-free LIR; plasmids with such non-hyphenated LIRs were not established when transferred into S. pneumoniae. Replacement of the original 1.4-kb insert by other restriction fragments restored plasmid viability. Investigation of plasmid transfer by transformation suggests that intrastrand synapsis between the LIRs could occur, thus facilitating plasmid establishment (a process we call self-facilitation). Such an intrastrand synapsis could also account for rare occurrences of insert-inversion noticed upon transfer as well as for the formation of palindrome-deleted derivatives at low frequency. Plasmid pSP2 carries two selectable genes, tet and ermC, and can be used for cloning of fragments produced by a variety of restriction enzymes (BamHI, Bg/II, Bc/I or Sau3A, and Sa/I or XhoI).},
}
@article {pmid2984981,
year = {1985},
author = {Tenover, FC and Williams, S and Gordon, KP and Nolan, C and Plorde, JJ},
title = {Survey of plasmids and resistance factors in Campylobacter jejuni and Campylobacter coli.},
journal = {Antimicrobial agents and chemotherapy},
volume = {27},
number = {1},
pages = {37-41},
pmid = {2984981},
issn = {0066-4804},
support = {223-81-7041//PHS HHS/United States ; },
mesh = {Anti-Bacterial Agents/pharmacology ; Campylobacter/drug effects/*genetics ; Conjugation, Genetic ; DNA Restriction Enzymes ; DNA, Bacterial/analysis ; Enterobacteriaceae/drug effects ; Hybridization, Genetic ; Microbial Sensitivity Tests ; *Plasmids ; *R Factors ; Tetracycline/pharmacology ; },
abstract = {A total of 688 isolates of Campylobacter jejuni and Campylobacter coli were screened for the presence of plasmid DNA by agarose gel electrophoresis and were tested for susceptibility to ampicillin, chloramphenicol, erythromycin, streptomycin, and tetracycline. Of the isolates examined, 32% were noted to harbor plasmid DNA, ranging in size from 2.0 to 162 kilobases. Only tetracycline resistance was noted to correlate with the presence of plasmids. Plasmids capable of transferring tetracycline resistance via conjugation ranged in size from 42 to 100 kilobases. The Bg/II and Bc/I restriction endonuclease profiles of 31 plasmids examined showed marked diversity in their banding patterns. Although a high degree of DNA-DNA homology was noted among the Campylobacter spp. plasmids, no homology was noted between these plasmids and tetracycline R factors commonly found in the family Enterobacteriaceae.},
}
@article {pmid6726349,
year = {1984},
author = {DeRiemer, SA and Kaczmarek, LK and Lai, Y and McGuinness, TL and Greengard, P},
title = {Calcium/calmodulin-dependent protein phosphorylation in the nervous system of Aplysia.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {4},
number = {6},
pages = {1618-1625},
doi = {10.1523/JNEUROSCI.04-06-01618.1984},
pmid = {6726349},
issn = {0270-6474},
support = {MH-17387/MH/NIMH NIH HHS/United States ; NS-08440/NS/NINDS NIH HHS/United States ; NS-18492/NS/NINDS NIH HHS/United States ; },
mesh = {Animals ; *Aplysia ; Calcium/*metabolism ; Calmodulin/*metabolism ; Nerve Tissue Proteins/*metabolism ; Neurons/metabolism ; Phosphorylation ; },
abstract = {An afterdischarge in the bag cell neurons of Aplysia was previously shown to be associated with calcium entry into these cells and with changes in the phosphorylation state of at least two bag cell proteins (BC-I and BC-II). We have now investigated the role of calcium plus calmodulin (Ca/CaM) in the control of phosphorylation of Aplysia nervous system proteins, including those of the bag cell neurons. In cell-free preparations of Aplysia CNS, we demonstrated Ca/CaM-stimulated protein phosphorylation that could be inhibited by the calmodulin-blocking drugs R24571 , trifluoperazine, chlorpromazine, and W7 . A number of substrate proteins for Ca/CaM-dependent protein phosphorylation with Mr values from 17,000 to 310,000 were consistently observed in homogenates of the Aplysia CNS. In the bag cells, we found that a major substrate for Ca/CaM-dependent protein phosphorylation was the bag cell-specific, Mr = 21,000 protein (BC-II). BC-I (Mr = 33,000), on the other hand, appeared not to be a substrate for a Ca/CaM-dependent protein kinase. We found that there are a minimum of two Ca/CaM-dependent protein kinases in the Aplysia nervous system. These enzymes were distinguished on the basis of their subcellular distribution and their ability to phosphorylate distinct sites on synapsin I, an exogenous neuronal protein from vertebrates. Phosphorylation by one of these kinases (calmodulin kinase I) was on a site recovered in an Mr = 10,000 proteolytic fragment of synapsin I, and phosphorylation by the other (calmodulin kinase II) was on a site recovered in an Mr = 30,000 fragment. The predominant enzyme in the Aplysia CNS, as in the mammalian nervous system, was calmodulin kinase II.(ABSTRACT TRUNCATED AT 250 WORDS)},
}
@article {pmid6306273,
year = {1983},
author = {Putterman, DG and Gryczan, TJ and Dubnau, D and Day, LA},
title = {Cloning of Pf3, a filamentous bacteriophage of Pseudomonas aeruginosa, into the pBD214 vector of Bacillus subtilis.},
journal = {Journal of virology},
volume = {47},
number = {1},
pages = {221-223},
pmid = {6306273},
issn = {0022-538X},
mesh = {Bacillus subtilis/*genetics ; Bacteriophages/*genetics ; DNA Replication ; DNA, Viral ; Electrophoresis, Agar Gel ; Genes, Bacterial ; *Genetic Vectors ; Phenotype ; Plasmids ; Pseudomonas aeruginosa/*genetics ; },
abstract = {The genome of Pf3, a filamentous single-stranded DNA bacteriophage of Pseudomonas aeruginosa (a gram-negative organism) was cloned into pBD214, a plasmid cloning vector of Bacillus subtilis (a gram-positive organism). Cloning in the gram-positive organism was done to avoid anticipated lethal effects. The entire Pf3 genome was inserted in each orientation at a unique Bc/I site within a thymidylate synthetase gene (from B. subtilis phage beta 22) on the plasmid. Additional clones were made by inserting EcoRI fragments of Pf3 DNA into a unique EcoRI site within this gene.},
}
@article {pmid7095799,
year = {1982},
author = {Azevêdo, ES and Fortuna, CM and Silva, KM and Sousa, MG and Machado, MA and Lima, AM and Aguiar, ME and Abé, K and Eulálio, MC and Conceição, MM and Silva, MC and Santos, MG},
title = {Spread and diversity of human populations in Bahia, Brazil.},
journal = {Human biology},
volume = {54},
number = {2},
pages = {329-341},
pmid = {7095799},
issn = {0018-7143},
mesh = {Black or African American ; Black People ; Brazil ; *Ethnicity ; Humans ; Indians, South American ; *Population ; },
}
@article {pmid6922456,
year = {1982},
author = {Barsevick, A and Llewellyn, J},
title = {A comparison of the anxiety-reducing potential of two techniques of bathing.},
journal = {Nursing research},
volume = {31},
number = {1},
pages = {22-27},
pmid = {6922456},
issn = {0029-6562},
mesh = {Adult ; Aged ; Anxiety/*nursing ; *Baths ; Female ; Humans ; Male ; Methods ; Middle Aged ; Pain/nursing/psychology ; Patients/*psychology ; Psychological Tests ; Sampling Studies ; },
abstract = {This study compared the effects of the towel bath and the conventional bed bath on patient anxiety. The sample of 105 patients were divided into two groups--those who would be having invasive procedures and those with unrelieved pain. Anxiety was measured using the State-Trait Anxiety Inventory (STAI), the Palmar Sweat Index (PSI), and the Behavioral Cues Index (BCI). The scores from the STAI A-State subscale supported the hypothesis that the towel bath resulted in a significant decrease in anxiety for the sample as a whole and for the invasive procedure subsample. The hypothesis was rejected for the unrelieved pain subsample. Scores from the Palmar Sweat Index and the Behavioral Cues Index did not support any of the hypotheses. Based on the findings of this study, bathing is recommended for its anxiety-reducing effects.},
}
@article {pmid6165887,
year = {1981},
author = {Bonin, AM and Farquharson, JB and Baker, RS},
title = {Mutagenicity of arylmethane dyes in Salmonella.},
journal = {Mutation research},
volume = {89},
number = {1},
pages = {21-34},
doi = {10.1016/0165-1218(81)90127-0},
pmid = {6165887},
issn = {0027-5107},
mesh = {Animals ; Biotransformation ; Coloring Agents/*adverse effects ; Food Coloring Agents/*adverse effects ; Male ; Microsomes, Liver/metabolism ; Mutagenicity Tests ; *Mutagens ; Rats ; Salmonella typhimurium/genetics ; Staining and Labeling/adverse effects ; },
abstract = {22 arylmethane dyes which have been used as food colours, commercial dyes, laboratory stains and pH indicators were tested in the Salmonella/mammalian microsome mutagenicity assay. 8 mutagenic dyes were identified, including 5 food colours and 3 common laboratory strains; none of the 11 indicator dyes tested was mutagenic. The commercial and laboratory dyes Methyl Violet 2B C.I. 42535 and Crystal Violet C.I. 42555 were mutagenic in base-pair substitution mutation detector strain TA1535 in the absence of metabolic activation. By contrast, the food colours Benzyl Violet 4B C.I. 42640, Guinea Green B.C.I. 42085, Light Green SF C.I. 42095, Lissamine Green B C.I. 44090 and Violet BNP C.I. 42581 and the bacteriological stain, Basic Fuchsin C.I. 42500-42510, were all mutagenic in frameshift mutation detector strains TA98 and/or TA1538 and required metabolic activation. Most of these compounds gave weak mutagenic responses with Salmonella and were positive only within narrow dose ranges. Since conflicting results were obtained using dyes from different sources, minor dye components may have been responsible for their mutagenicity. This suggests the need to improve knowledge about impurities in arylmethane colours still used in food and to review the toxicological role of such impurities.},
}
@article {pmid6264085,
year = {1981},
author = {Landowne, D and Scruggs, V},
title = {Effects of internal and external sodium on the sodium current-voltage relationship in the Squid giant axon.},
journal = {The Journal of membrane biology},
volume = {59},
number = {2},
pages = {79-89},
pmid = {6264085},
issn = {0022-2631},
mesh = {Animals ; Axons/*physiology ; Cations, Monovalent/pharmacology ; Cell Membrane Permeability ; Decapodiformes ; Electric Conductivity ; Ion Channels/*drug effects ; Membrane Potentials/*drug effects ; Sodium/*pharmacology ; Temperature ; },
abstract = {The early transient current-voltage relationship was measured in internally perfused voltage clamped squid giant axons with various concentrations of sodium on the two sides of the membrane. In the absence of sodium on either side there is an outward transient current which is blocked by tetrodotoxin and varies with internal potassium concentration. The current increases linearly with voltage for positive potentials. Adding sodium ions internally increases the slope of the current-voltage relationship. Adding sodium ions externally also increases the slope between +10 and +80 mV. Adding sodium to both sides produces the sum of the two effects. The current-voltage relationships were fit by straight lines between +10 and +80 mV. Plotting the extrapolated intercepts with the current axis against the differences in sodium concentrations gave a straight line, Io = -P (Co-Ci)F. P, the Fickian permeability, is about 10(-4) cm/sec. Plotting the slopes in three dimensions against the two sodium concentrations gave a plane g = go + (aNao + bNai)F. a is about 10(-6) cm/mV-sec and b about 3 x 10(-6) cm/mV-sec. Thus the current-voltage relationship for the sodium current is well described by I = -P(Co-Ci)F+ (aco + bci)FV for positive potentials. This is the linear sum of Fick's Law and Ohm's Law. P/(a + b) = 25 +/- 1 mV (N = 6) and did not vary with the absolute magnitude of the currents. Within experimental error this is equal to kT/e or RT/F. Increasing temperature increased P, a and b proportionately. Adding external calcium, lithium, or Tris selectively decreased P and a without changing b. In the absence of sodium, altering internal and external potassium while observing the early transient currents suggests this channel is more asymmetric in its response to potassium than to sodium.},
}
@article {pmid6261957,
year = {1981},
author = {Fujimura, FK and Deininger, PL and Friedmann, T and Linney, E},
title = {Mutation near the polyoma DNA replication origin permits productive infection of F9 embryonal carcinoma cells.},
journal = {Cell},
volume = {23},
number = {3},
pages = {809-814},
doi = {10.1016/0092-8674(81)90445-1},
pmid = {6261957},
issn = {0092-8674},
mesh = {Animals ; Antigens, Neoplasm/genetics ; Antigens, Viral/genetics ; Antigens, Viral, Tumor ; Base Sequence ; Cells, Cultured ; DNA, Viral/genetics ; Gene Expression Regulation ; Mice ; Mutation ; Polyomavirus/*genetics ; Teratoma/*microbiology ; *Virus Replication ; },
abstract = {F9 mouse embryonal carcinoma cells are resistant to productive infection by wild-type polyoma virus. Continued passage of F9 cells initially infected with wild-type polyoma virus eventually leads to the selection of polyoma virus mutants that are capable of productive infection of undifferentiated F9 cells. Three mutants, PyF101, PyF111 and PyF441, have been plaque-purified and examined. All three PyF mutant DNAs are altered from the wild-type sequence in the Pvu II-4 fragment that spans 67.6 to 70.2 map units on the polyoma genome. PyF441 has a single base change of A to G at 69.6 map units. PyF101 and PyF111 DNAs also contain this point mutation at 69.6 map units. In addition, PyF101 and PyF111 DNAs have exact tandem duplications of 54 and 31 bp, respectively, of sequences encompassing the point mutation, and both copies of the tandem duplication have the point mutation. Other than these changes, no difference exists in the nucleotide sequences of wild-type and PyF mutant DNAs from the BcI I site at 65.6 map units clockwise through the origin of viral DNA replication to the BgI I site at 72.2 map units. DNA infections of F9 cells with wild-type-mutant hybrid DNAs formed by ligation of heterologous combinations of the small and large DNA fragments generated by double digestion with the restriction enzymes BcII and BGI I show that the DNA sequence changes described above are responsible for the ability of the PyF mutants to infect F9 cells.},
}
@article {pmid7332095,
year = {1981},
author = {Petitdidier, M and Margarot, M and Blanchet, P and Roquefeuil, B},
title = {[Nurseling and children general anaesthesia in brain neuroradiology: from gaz tomoencephalography to brain computer tomography (author's transl)].},
journal = {Anesthesie, analgesie, reanimation},
volume = {38},
number = {9-10},
pages = {469-473},
pmid = {7332095},
issn = {0003-3014},
mesh = {Adolescent ; Anesthesia, General/*methods ; Anesthetics/*administration &amp; dosage ; Child ; Child, Preschool ; Drug Evaluation ; Female ; Humans ; Hydroxybutyrates/*administration &amp; dosage ; Infant ; Infant, Newborn ; Male ; *Pneumoencephalography ; Sodium Oxybate ; *Tomography, X-Ray Computed ; },
abstract = {The authors give their experiences in nurseling and children brain neuroradiology anaesthesia. Sodium gammahydroxybutyrate has been definitively adopted after multiples anaesthesial protocoles for the gaz tomoencephalographic exam, known for its technical risks. The gamma OH gives a perfect cardiac and pulmonary stability in difficult conditions, with normal intracranial pression, even in children anaesthesia with Halothane (0.5%) for complementary analgesic effect or with fractionate injections of dextromoramide. Pneumoencephalography has been releguated in second place by the even of brain computer tomography except some particular indications. But this exam qualified as painless is usually indicated in fragile and deficient childrens. Though the intravenous iodated contrasted substance injection can improve the scan image quality and may induce secondary effects at 2 cm3/kg dose. It's again gamma OH after correct premedication that gives stable, perfect immobility, cardiac and pulmonary stability in an ideal anaesthesia for non ventilated patients. The only critical aspect of this method consists on a prolonged and imprevisible delay to awake so that it cannot be an ambulatory anaesthesial method. Therefore it appears that gamma OH in spite of brain computer tomographic event, is an interesting anaesthesic drug but non definitive in brain neuroradiological exam for childrens.},
}
@article {pmid7213285,
year = {1981},
author = {Sem-Jacobsen, CW},
title = {Brain/computer communication to reduce human error: a perspective.},
journal = {Aviation, space, and environmental medicine},
volume = {52},
number = {1},
pages = {33-37},
pmid = {7213285},
issn = {0095-6562},
mesh = {Accidents, Aviation/*prevention &amp; control ; Brain/*physiology ; Communication ; *Computers ; Electrocardiography ; Evoked Potentials, Auditory ; Humans ; Reaction Time ; },
abstract = {Recent developments in physiology and neurophysiology, biomedical monitoring, and micro-processors has made it possible to give the pilot improved electronic support and to increase flight safety. Direct brain/computer communication is a new way to combat human error. Monitoring ECG will give information on the operator's physical and mental load/overload situation as well as on impending cardiac failure. Information presented to the operator will elicit different biological patterns whether the operator is alert and takes the information into his stream of thoughts or not. Together with the reaction time, this wil give needed information about the operators alertness and responsiveness. In the future, with this approach, the computer may know on-line to what extent the operator perceives all the information given, as well as the operator's physical and mental load/overload situation and health. Brain/computer communication should be developed to support key-operating personnel and to reduce human error.},
}
@article {pmid6260582,
year = {1980},
author = {Roberts, TM and Swanberg, SL and Poteete, A and Riedel, G and Backman, K},
title = {A plasmid cloning vehicle allowing a positive selection for inserted fragments.},
journal = {Gene},
volume = {12},
number = {1-2},
pages = {123-127},
doi = {10.1016/0378-1119(80)90022-0},
pmid = {6260582},
issn = {0378-1119},
support = {5 R01 GM22526/GM/NIGMS NIH HHS/United States ; },
mesh = {DNA Restriction Enzymes/metabolism ; DNA Transposable Elements ; DNA, Recombinant ; *Drug Resistance, Microbial ; Escherichia coli/*genetics ; *Genes ; *Genetic Vectors ; Phenotype ; *Plasmids ; Tetracycline/*pharmacology ; },
abstract = {We describe a plasmid cloning vehicle, pTR262, which allows a strong positive selection (resistance to tetracycline) for transformants bearing plasmids which have DNA insertions. pTR262 is derived from plasmid pBR322 and contains the cI gene and adjacent regulator region oRpR or the bacteriophage lambda. The expression of the tetracycline resistance (tet-r) gene(s) in pTR262 requires transcription from pR and is repressed by the cI gene product, lambda repressor. Insertion of a DNA fragment into the HindIII or Bc/I sites in pTR262 inactivates the cI gene and allows expression of the tet-r gene(s) in the host bacterium. A 100-fold increase in the number of tetracycline-resistant transformants is obtained when HindIII- or Bc/I-generated fragments are added to a ligation mixture containing HindIII- or Bc/I-digested pTR 262 DNA.},
}
@article {pmid509626,
year = {1979},
author = {Heger, L and Nettl, S and Smoranc, P and Steinhart, L and Volejník, V},
title = {[Use of brain computer tomography in neurologic practice. Preliminary report].},
journal = {Ceskoslovenska neurologie a neurochirurgie},
volume = {42},
number = {4},
pages = {258-262},
pmid = {509626},
issn = {0301-0597},
mesh = {Adult ; Brain/*diagnostic imaging ; Brain Diseases/diagnostic imaging ; Humans ; Male ; Middle Aged ; *Tomography, X-Ray Computed ; },
}
@article {pmid1267208,
year = {1976},
author = {Aidinis, SJ and Zimmerman, RA and Shapiro, HM and Bilanuick, LT and Broennle, AM},
title = {Anesthesia for brain computer tomography.},
journal = {Anesthesiology},
volume = {44},
number = {5},
pages = {420-425},
doi = {10.1097/00000542-197605000-00012},
pmid = {1267208},
issn = {0003-3022},
mesh = {*Anesthesia, General ; Anesthesia, Inhalation ; Brain Diseases/*diagnostic imaging ; *Computers ; Humans ; Infant ; Nitrous Oxide ; Oxygen ; Radiation Dosage ; *Tomography, X-Ray/methods ; },
}
@article {pmid4583653,
year = {1973},
author = {Vidal, JJ},
title = {Toward direct brain-computer communication.},
journal = {Annual review of biophysics and bioengineering},
volume = {2},
number = {},
pages = {157-180},
doi = {10.1146/annurev.bb.02.060173.001105},
pmid = {4583653},
issn = {0084-6589},
mesh = {Brain/physiology ; *Communication ; *Computers ; Conditioning, Operant ; *Electroencephalography ; Evoked Potentials ; Humans ; Neurophysiology ; },
}
@article {pmid5501987,
year = {1970},
author = {Jensen, RE},
title = {Brain-computer relationships.},
journal = {Progress in brain research},
volume = {33},
number = {},
pages = {1-8},
doi = {10.1016/S0079-6123(08)62439-5},
pmid = {5501987},
issn = {0079-6123},
mesh = {Brain/*physiology ; *Computers ; *Computers, Analog ; Humans ; },
}